[ { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: Results of fine-tuned models on Balanced COPA. Easy: instances with superficial cues, Hard: instances without superficial cues.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Training on B-COPA reduces the performance gap between Easy and Hard subsets and improves performance on the Hard subset, indicating that models rely less on superficial cues.The results are shown in Table 5. The smaller performance gap between Easy and Hard subsets indicates that training on BCOPA encourages BERT and RoBERTa to rely less on superficial cues. Moreover, training on B-COPA improves performance on the Hard subset, both when training with all 1000 instances in B-COPA, and when matching the training size of the original COPA (500 instances, B-COPA 50%). Note that training on B-COPA 50% exposes the model to lexically less diverse training instances than the original COPA due to the high overlap between mirrored alternatives. These results show that once superficial cues. are removed, the models are able to learn the task to a high degree." } ], "images": [ "scigen/train/0.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results of fine-tuned models on Balanced COPA. Easy: instances with superficial cues, Hard: instances without superficial cues." ], "reasoning": "Training on B-COPA reduces the performance gap between Easy and Hard subsets and improves performance on the Hard subset, indicating that models rely less on superficial cues.", "description": "The results are shown in Table 5. The smaller performance gap between Easy and Hard subsets indicates that training on BCOPA encourages BERT and RoBERTa to rely less on superficial cues. Moreover, training on B-COPA improves performance on the Hard subset, both when training with all 1000 instances in B-COPA, and when matching the training size of the original COPA (500 instances, B-COPA 50%). Note that training on B-COPA 50% exposes the model to lexically less diverse training instances than the original COPA due to the high overlap between mirrored alternatives. These results show that once superficial cues. are removed, the models are able to learn the task to a high degree.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 5: Results of fine-tuned models on Balanced COPA. Easy: instances with superficial cues, Hard: instances without superficial cues.'. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model and training data combination achieved the highest performance on the Hard subset?\n" }, { "role": "assistant", "content": "By examining the 'Hard' column, we see that RoBERTa-large-FT trained on B-COPA achieved the highest performance with a score of 89.0. So, the answer is {\"answer\": \"RoBERTa-large-FT with B-COPA\"}." } ], "images": [ "scigen/train/0.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results of fine-tuned models on Balanced COPA. Easy: instances with superficial cues, Hard: instances without superficial cues." ], "reasoning": "By examining the 'Hard' column, we see that RoBERTa-large-FT trained on B-COPA achieved the highest performance with a score of 89.0.", "question": "Which model and training data combination achieved the highest performance on the Hard subset?", "answer": "So, the answer is {\"answer\": \"RoBERTa-large-FT with B-COPA\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nTraining on B-COPA 50% results in higher performance on the Hard subset compared to training on COPA for BERT-large-FT.\n" }, { "role": "assistant", "content": "Comparing the 'Hard' scores for BERT-large-FT, training on B-COPA 50% results in a score of 72.8, while training on COPA results in a score of 71.9. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/0.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results of fine-tuned models on Balanced COPA. Easy: instances with superficial cues, Hard: instances without superficial cues." ], "reasoning": "Comparing the 'Hard' scores for BERT-large-FT, training on B-COPA 50% results in a score of 72.8, while training on COPA results in a score of 71.9. Therefore, the statement is supported.", "statement": "Training on B-COPA 50% results in higher performance on the Hard subset compared to training on COPA for BERT-large-FT.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Reported results on COPA. With the exception of Wang et al. (2019), BERT-large and RoBERTa-large yields substantial improvements over prior approaches. See §2 for model details. * indicates our replication experiments.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Recent studies indicate that BERT and RoBERTa models show significant improvements in accuracy on the COPA dataset.Recent studies show that BERT and RoBERTa achieve considerable improvements on COPA (see Table 1)." } ], "images": [ "scigen/train/1.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Reported results on COPA. With the exception of Wang et al. (2019), BERT-large and RoBERTa-large yields substantial improvements over prior approaches. See §2 for model details. * indicates our replication experiments." ], "reasoning": "Recent studies indicate that BERT and RoBERTa models show significant improvements in accuracy on the COPA dataset.", "description": "Recent studies show that BERT and RoBERTa achieve considerable improvements on COPA (see Table 1).", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: Reported results on COPA. With the exception of Wang et al. (2019), BERT-large and RoBERTa-large yields substantial improvements over prior approaches. See §2 for model details. * indicates our replication experiments.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model achieved the highest accuracy on the COPA dataset?\n" }, { "role": "assistant", "content": "By examining the table, we see that the RoBERTa-large (finetuned)* model achieved the highest accuracy of 87.7 ± 0.9. So, the answer is {\"answer\": \"RoBERTa-large (finetuned)*\"}." } ], "images": [ "scigen/train/1.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Reported results on COPA. With the exception of Wang et al. (2019), BERT-large and RoBERTa-large yields substantial improvements over prior approaches. See §2 for model details. * indicates our replication experiments." ], "reasoning": "By examining the table, we see that the RoBERTa-large (finetuned)* model achieved the highest accuracy of 87.7 ± 0.9.", "question": "Which model achieved the highest accuracy on the COPA dataset?", "answer": "So, the answer is {\"answer\": \"RoBERTa-large (finetuned)*\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The BERT-large model by Wang et al. (2019) achieved an accuracy of 70.5 on the COPA dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe BERT-large model by Wang et al. (2019) achieved an accuracy of 70.5 on the COPA dataset.\n" }, { "role": "assistant", "content": "The table shows that the BERT-large model by Wang et al. (2019) indeed achieved an accuracy of 70.5. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Reported results on COPA. With the exception of Wang et al. (2019), BERT-large and RoBERTa-large yields substantial improvements over prior approaches. See §2 for model details. * indicates our replication experiments." ], "reasoning": "The table shows that the BERT-large model by Wang et al. (2019) indeed achieved an accuracy of 70.5.", "statement": "The BERT-large model by Wang et al. (2019) achieved an accuracy of 70.5 on the COPA dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Applicability (App.), Productivity (Prod.) and Coverage (Cov.) of the various words in the alternatives of the COPA dev set.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The token 'a' has the highest coverage at 21.2% and a productivity of 57.5%, indicating it appears in correct alternatives 7.5% more often than by random chance.Table 2 shows the five tokens with highest coverage. For example, a is the token with the highest coverage and appears in either a correct alternative or wrong alternative in 21.2% of COPA training instances. Its productivity of 57.5% expresses that it appears in in correct alternatives 7.5% more often than expected by random chance. This suggests that a model could rely on such unbalanced distributions of tokens to predict answers based only on alternatives without understanding the task." } ], "images": [ "scigen/train/2.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Applicability (App.), Productivity (Prod.) and Coverage (Cov.) of the various words in the alternatives of the COPA dev set." ], "reasoning": "The token 'a' has the highest coverage at 21.2% and a productivity of 57.5%, indicating it appears in correct alternatives 7.5% more often than by random chance.", "description": "Table 2 shows the five tokens with highest coverage. For example, a is the token with the highest coverage and appears in either a correct alternative or wrong alternative in 21.2% of COPA training instances. Its productivity of 57.5% expresses that it appears in in correct alternatives 7.5% more often than expected by random chance. This suggests that a model could rely on such unbalanced distributions of tokens to predict answers based only on alternatives without understanding the task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Applicability (App.), Productivity (Prod.) and Coverage (Cov.) of the various words in the alternatives of the COPA dev set.', and sequentially solve the question, writing out each step of your reasoning process. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich token has the highest productivity and what is its coverage?\n" }, { "role": "assistant", "content": "The token 'was' has the highest productivity at 61.8%, and its coverage is 11.0%. So, the answer is {\"answer\": \"was, 11.0%\"}" } ], "images": [ "scigen/train/2.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Applicability (App.), Productivity (Prod.) and Coverage (Cov.) of the various words in the alternatives of the COPA dev set." ], "reasoning": "The token 'was' has the highest productivity at 61.8%, and its coverage is 11.0%.", "question": "Which token has the highest productivity and what is its coverage?", "answer": "So, the answer is {\"answer\": \"was, 11.0%\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe token 'to' has a higher coverage than 'the'.\n" }, { "role": "assistant", "content": "The table shows that the token 'to' has a coverage of 16.4%, while 'the' has a coverage of 17.0%, indicating that 'to' has a lower coverage than 'the'. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Applicability (App.), Productivity (Prod.) and Coverage (Cov.) of the various words in the alternatives of the COPA dev set." ], "reasoning": "The table shows that the token 'to' has a coverage of 16.4%, while 'the' has a coverage of 17.0%, indicating that 'to' has a lower coverage than 'the'.", "statement": "The token 'to' has a higher coverage than 'the'.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Results of human performance evaluation of the original COPA and Balanced COPA.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The human evaluation shows that the mirrored instances in the Balanced COPA dataset have a slightly lower accuracy but are still comparable in difficulty to the original COPA dataset.The human evaluation shows that our mirrored instances are comparable in difficulty to the original ones (see Table 3)." } ], "images": [ "scigen/train/3.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of human performance evaluation of the original COPA and Balanced COPA." ], "reasoning": "The human evaluation shows that the mirrored instances in the Balanced COPA dataset have a slightly lower accuracy but are still comparable in difficulty to the original COPA dataset.", "description": "The human evaluation shows that our mirrored instances are comparable in difficulty to the original ones (see Table 3).", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: Results of human performance evaluation of the original COPA and Balanced COPA.' in mind, break down and resolve the question below step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the difference in Fleiss' kappa between the Original COPA and Balanced COPA datasets?\n" }, { "role": "assistant", "content": "To find the difference in Fleiss' kappa, subtract the Fleiss' kappa value of the Balanced COPA dataset from that of the Original COPA dataset: 0.973 - 0.798. So, the answer is {\"answer\": \"0.175\"}." } ], "images": [ "scigen/train/3.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of human performance evaluation of the original COPA and Balanced COPA." ], "reasoning": "To find the difference in Fleiss' kappa, subtract the Fleiss' kappa value of the Balanced COPA dataset from that of the Original COPA dataset: 0.973 - 0.798.", "question": "What is the difference in Fleiss' kappa between the Original COPA and Balanced COPA datasets?", "answer": "So, the answer is {\"answer\": \"0.175\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Balanced COPA dataset has a higher accuracy than the Original COPA dataset.\n" }, { "role": "assistant", "content": "The table shows that the accuracy of the Original COPA dataset is 100.0, while the accuracy of the Balanced COPA dataset is 97.0, indicating that the statement is false. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of human performance evaluation of the original COPA and Balanced COPA." ], "reasoning": "The table shows that the accuracy of the Original COPA dataset is 100.0, while the accuracy of the Balanced COPA dataset is 97.0, indicating that the statement is false.", "statement": "The Balanced COPA dataset has a higher accuracy than the Original COPA dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Model performance on the COPA test set (Overall), on Easy instances with superficial cues, and on Hard instances without superficial cues. p-values according to Approximate Randomization Tests Noreen (1989), with ∗ indicating a significant difference between performance on Easy and Hard p<5%. Methods are pointwise mutual information (PMI), word frequency provided by the wordfreq package Speer et al. (2018), pretrained language model (LM), and next-sentence prediction (NSP).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "BERT and RoBERTa outperform previous models, with BERT showing significant improvement on the Easy subset and RoBERTa showing consistent performance across both subsets.We then compare BERT and RoBERTa with previous models on the Easy and Hard subsets.7 As Table 4 shows, previous models perform similarly on both subsets, with the exception of Sasaki et al. (2017).8 Overall both BERT (76.5%) and. RoBERTa (87.7%) considerably outperform the best previous model (71.4%). However, BERT's improvements over previous work can be almost entirely attributed to high accuracy on the Easy subset: on this subset, finetuned BERT-large improves 8.6 percent over the model by (Sasaki et al., 2017) (83.9% vs. 75.3%), but on the Hard subset, the improvement is only 2.9 percent (71.9% vs. 69.0%). This indicates that BERT relies on superficial cues. The difference between accuracy on Easy and Hard is less pronounced for RoBERTa, but still suggests some reliance on superficial cues." } ], "images": [ "scigen/train/4.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Model performance on the COPA test set (Overall), on Easy instances with superficial cues, and on Hard instances without superficial cues. p-values according to Approximate Randomization Tests Noreen (1989), with ∗ indicating a significant difference between performance on Easy and Hard p<5%. Methods are pointwise mutual information (PMI), word frequency provided by the wordfreq package Speer et al. (2018), pretrained language model (LM), and next-sentence prediction (NSP)." ], "reasoning": "BERT and RoBERTa outperform previous models, with BERT showing significant improvement on the Easy subset and RoBERTa showing consistent performance across both subsets.", "description": "We then compare BERT and RoBERTa with previous models on the Easy and Hard subsets.7 As Table 4 shows, previous models perform similarly on both subsets, with the exception of Sasaki et al. (2017).8 Overall both BERT (76.5%) and. RoBERTa (87.7%) considerably outperform the best previous model (71.4%). However, BERT's improvements over previous work can be almost entirely attributed to high accuracy on the Easy subset: on this subset, finetuned BERT-large improves 8.6 percent over the model by (Sasaki et al., 2017) (83.9% vs. 75.3%), but on the Hard subset, the improvement is only 2.9 percent (71.9% vs. 69.0%). This indicates that BERT relies on superficial cues. The difference between accuracy on Easy and Hard is less pronounced for RoBERTa, but still suggests some reliance on superficial cues.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 4: Model performance on the COPA test set (Overall), on Easy instances with superficial cues, and on Hard instances without superficial cues. p-values according to Approximate Randomization Tests Noreen (1989), with ∗ indicating a significant difference between performance on Easy and Hard p<5%. Methods are pointwise mutual information (PMI), word frequency provided by the wordfreq package Speer et al. (2018), pretrained language model (LM), and next-sentence prediction (NSP).', unfold the steps to solve the question given next. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nQuestion: How much does BERT-large-FT improve over the model by Sasaki et al. (2017) on the Easy subset?\n" }, { "role": "assistant", "content": "The table shows that BERT-large-FT has an accuracy of 83.9% on the Easy subset, while the model by Sasaki et al. (2017) has an accuracy of 75.3%. The improvement is calculated as 83.9% - 75.3%. So, the answer is {\"answer\": \"8.6%\"}." } ], "images": [ "scigen/train/4.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Model performance on the COPA test set (Overall), on Easy instances with superficial cues, and on Hard instances without superficial cues. p-values according to Approximate Randomization Tests Noreen (1989), with ∗ indicating a significant difference between performance on Easy and Hard p<5%. Methods are pointwise mutual information (PMI), word frequency provided by the wordfreq package Speer et al. (2018), pretrained language model (LM), and next-sentence prediction (NSP)." ], "reasoning": "The table shows that BERT-large-FT has an accuracy of 83.9% on the Easy subset, while the model by Sasaki et al. (2017) has an accuracy of 75.3%. The improvement is calculated as 83.9% - 75.3%.", "question": "How much does BERT-large-FT improve over the model by Sasaki et al. (2017) on the Easy subset?", "answer": "So, the answer is {\"answer\": \"8.6%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence RoBERTa-large-FT has a higher overall accuracy than BERT-large-FT. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nRoBERTa-large-FT has a higher overall accuracy than BERT-large-FT.\n" }, { "role": "assistant", "content": "The table shows that RoBERTa-large-FT has an overall accuracy of 87.7%, while BERT-large-FT has an overall accuracy of 76.5%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/4.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Model performance on the COPA test set (Overall), on Easy instances with superficial cues, and on Hard instances without superficial cues. p-values according to Approximate Randomization Tests Noreen (1989), with ∗ indicating a significant difference between performance on Easy and Hard p<5%. Methods are pointwise mutual information (PMI), word frequency provided by the wordfreq package Speer et al. (2018), pretrained language model (LM), and next-sentence prediction (NSP)." ], "reasoning": "The table shows that RoBERTa-large-FT has an overall accuracy of 87.7%, while BERT-large-FT has an overall accuracy of 76.5%.", "statement": "RoBERTa-large-FT has a higher overall accuracy than BERT-large-FT.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 6: Results of non-fine-tuned models on Balanced COPA. Easy: instances with superficial cues, Hard: instances without superficial cues.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The high accuracies of BERT-large, RoBERTa-large, and BERT-*-NSP models indicate their strong performance on the task without additional training.The relatively high accuracies of BERT-large, RoBERTa-large and BERT-*-NSP show that these pretrained models are already well-equipped to perform this task \"out-of-the-box\"." } ], "images": [ "scigen/train/5.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results of non-fine-tuned models on Balanced COPA. Easy: instances with superficial cues, Hard: instances without superficial cues." ], "reasoning": "The high accuracies of BERT-large, RoBERTa-large, and BERT-*-NSP models indicate their strong performance on the task without additional training.", "description": "The relatively high accuracies of BERT-large, RoBERTa-large and BERT-*-NSP show that these pretrained models are already well-equipped to perform this task \"out-of-the-box\".", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: Results of non-fine-tuned models on Balanced COPA. Easy: instances with superficial cues, Hard: instances without superficial cues.\nQuestion: Which model has the highest accuracy on the 'Hard' category?\nSolve the above question based on the table information. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest accuracy on the 'Hard' category, compare the 'Hard' column values for all models. RoBERTa-large trained on B-COPA has the highest accuracy of 78.8. So, the answer is {\"answer\": \"RoBERTa-large (B-COPA)\"}." } ], "images": [ "scigen/train/5.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results of non-fine-tuned models on Balanced COPA. Easy: instances with superficial cues, Hard: instances without superficial cues." ], "reasoning": "To find the model with the highest accuracy on the 'Hard' category, compare the 'Hard' column values for all models. RoBERTa-large trained on B-COPA has the highest accuracy of 78.8.", "question": "Which model has the highest accuracy on the 'Hard' category?", "answer": "So, the answer is {\"answer\": \"RoBERTa-large (B-COPA)\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nBERT-large trained on COPA has the highest accuracy in the 'Easy' category.\n" }, { "role": "assistant", "content": "The table shows that BERT-large trained on COPA has an accuracy of 80.5 in the 'Easy' category, which is the highest among all models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/5.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results of non-fine-tuned models on Balanced COPA. Easy: instances with superficial cues, Hard: instances without superficial cues." ], "reasoning": "The table shows that BERT-large trained on COPA has an accuracy of 80.5 in the 'Easy' category, which is the highest among all models.", "statement": "BERT-large trained on COPA has the highest accuracy in the 'Easy' category.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 7: Sensitivity of BERT-large to superficial cues identified in §2 (unit: 10−2). Cues with top-5 reduction are shown. SCOPA,SB_COPA indicate the mean contributions of BERT-large trained on COPA, and BERT-large trained on B-COPA, respectively.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "BERT trained on Balanced COPA shows reduced sensitivity to highly productive cues compared to BERT trained on original COPA, with a decrease in sensitivity from 0.7 to 0.9.We observe that BERT trained on Balanced COPA is less sensitive to a few highly productive superficial cues than BERT trained on original COPA. Note the decrease in the sensitivity for cues of productivity from 0.7 to 0.9. These cues are shown in Table 7. However, for cues with lower productivity, the picture is less clear, in case of RoBERTa, there are no noticeable trends in the change of sensitivity." } ], "images": [ "scigen/train/6.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Sensitivity of BERT-large to superficial cues identified in §2 (unit: 10−2). Cues with top-5 reduction are shown. SCOPA,SB_COPA indicate the mean contributions of BERT-large trained on COPA, and BERT-large trained on B-COPA, respectively." ], "reasoning": "BERT trained on Balanced COPA shows reduced sensitivity to highly productive cues compared to BERT trained on original COPA, with a decrease in sensitivity from 0.7 to 0.9.", "description": "We observe that BERT trained on Balanced COPA is less sensitive to a few highly productive superficial cues than BERT trained on original COPA. Note the decrease in the sensitivity for cues of productivity from 0.7 to 0.9. These cues are shown in Table 7. However, for cues with lower productivity, the picture is less clear, in case of RoBERTa, there are no noticeable trends in the change of sensitivity.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 7: Sensitivity of BERT-large to superficial cues identified in §2 (unit: 10−2). Cues with top-5 reduction are shown. SCOPA,SB_COPA indicate the mean contributions of BERT-large trained on COPA, and BERT-large trained on B-COPA, respectively.', and sequentially solve the question, writing out each step of your reasoning process. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the difference in sensitivity for the cue 'woman' between SCOPA and SB_COPA?\n" }, { "role": "assistant", "content": "To find the difference in sensitivity for the cue 'woman', look at the 'Diff.' column for the row corresponding to 'woman'. The value is -3.14. So, the answer is {\"answer\": \"-3.14\"}." } ], "images": [ "scigen/train/6.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Sensitivity of BERT-large to superficial cues identified in §2 (unit: 10−2). Cues with top-5 reduction are shown. SCOPA,SB_COPA indicate the mean contributions of BERT-large trained on COPA, and BERT-large trained on B-COPA, respectively." ], "reasoning": "To find the difference in sensitivity for the cue 'woman', look at the 'Diff.' column for the row corresponding to 'woman'. The value is -3.14.", "question": "What is the difference in sensitivity for the cue 'woman' between SCOPA and SB_COPA?", "answer": "So, the answer is {\"answer\": \"-3.14\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe productivity of the cue 'mother' is higher than that of the cue 'went'.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Prod.' values for 'mother' and 'went'. The productivity for 'mother' is 0.75, and for 'went' it is 0.73. Since 0.75 is higher than 0.73, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/6.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Sensitivity of BERT-large to superficial cues identified in §2 (unit: 10−2). Cues with top-5 reduction are shown. SCOPA,SB_COPA indicate the mean contributions of BERT-large trained on COPA, and BERT-large trained on B-COPA, respectively." ], "reasoning": "To verify this statement, compare the 'Prod.' values for 'mother' and 'went'. The productivity for 'mother' is 0.75, and for 'went' it is 0.73. Since 0.75 is higher than 0.73, the statement is supported.", "statement": "The productivity of the cue 'mother' is higher than that of the cue 'went'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 8: Sentiment classification evaluation, using different classifiers on the test set.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The CNN-LSTM-Our-neg-Ant model improves F1 scores for both positive and negative sentiments compared to the CNN-LSTM-w/o neg. baseline.show that the antonym based learned representations are more useful for sentiment task as compared to prefixing with NOT_. The proposed CNN-LSTMOur-neg-Ant improves upon the simple CNNLSTM-w/o neg. baseline with F1 scores improving from 0.72 to 0.78 for positive sentiment and from 0.83 to 0.87 for negative sentiment." } ], "images": [ "scigen/train/7.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Sentiment classification evaluation, using different classifiers on the test set." ], "reasoning": "The CNN-LSTM-Our-neg-Ant model improves F1 scores for both positive and negative sentiments compared to the CNN-LSTM-w/o neg. baseline.", "description": "show that the antonym based learned representations are more useful for sentiment task as compared to prefixing with NOT_. The proposed CNN-LSTMOur-neg-Ant improves upon the simple CNNLSTM-w/o neg. baseline with F1 scores improving from 0.72 to 0.78 for positive sentiment and from 0.83 to 0.87 for negative sentiment.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 8: Sentiment classification evaluation, using different classifiers on the test set.'.\n\nQuestion: What is the improvement in F1 score for positive sentiment when using CNN-LSTM-Our-neg-Ant compared to CNN?\n" }, { "role": "assistant", "content": "The F1 score for positive sentiment using CNN is 0.72, and using CNN-LSTM-Our-neg-Ant is 0.78. The improvement is 0.78 - 0.72. So, the answer is {\"answer\": \"0.06\"}." } ], "images": [ "scigen/train/7.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Sentiment classification evaluation, using different classifiers on the test set." ], "reasoning": "The F1 score for positive sentiment using CNN is 0.72, and using CNN-LSTM-Our-neg-Ant is 0.78. The improvement is 0.78 - 0.72.", "question": "What is the improvement in F1 score for positive sentiment when using CNN-LSTM-Our-neg-Ant compared to CNN?", "answer": "So, the answer is {\"answer\": \"0.06\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe CNN-LSTM-Our-neg-Ant model has the highest F1 score for negative sentiment.\n" }, { "role": "assistant", "content": "The table shows that the CNN-LSTM-Our-neg-Ant model has an F1 score of 0.87 for negative sentiment, which is the highest among all models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/7.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Sentiment classification evaluation, using different classifiers on the test set." ], "reasoning": "The table shows that the CNN-LSTM-Our-neg-Ant model has an F1 score of 0.87 for negative sentiment, which is the highest among all models listed.", "statement": "The CNN-LSTM-Our-neg-Ant model has the highest F1 score for negative sentiment.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 7: Negation classifier performance for scope detection with gold cues and scope.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The proposed method achieves an F-score of 0.85 for in-scope tokens and 0.97 for out-of-scope tokens, which is comparable to the BiLSTM model.The results in Table 7 show that the method is comparable to state of the art BiLSTM model from (Fancellu et al., 2016) on gold negation cues for scope prediction.. report the F-score for both in-scope and out-ofscope tokens." } ], "images": [ "scigen/train/8.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Negation classifier performance for scope detection with gold cues and scope." ], "reasoning": "The proposed method achieves an F-score of 0.85 for in-scope tokens and 0.97 for out-of-scope tokens, which is comparable to the BiLSTM model.", "description": "The results in Table 7 show that the method is comparable to state of the art BiLSTM model from (Fancellu et al., 2016) on gold negation cues for scope prediction.. report the F-score for both in-scope and out-ofscope tokens.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 7: Negation classifier performance for scope detection with gold cues and scope.' and methodically tackle the question that follows. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the difference in F-score between the BiLSTM model and the proposed method for in-scope tokens?\n" }, { "role": "assistant", "content": "The F-score for in-scope tokens is 0.88 for the BiLSTM model and 0.85 for the proposed method. The difference is 0.88 - 0.85. So, the answer is {\"answer\": \"0.03\"}." } ], "images": [ "scigen/train/8.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Negation classifier performance for scope detection with gold cues and scope." ], "reasoning": "The F-score for in-scope tokens is 0.88 for the BiLSTM model and 0.85 for the proposed method. The difference is 0.88 - 0.85.", "question": "What is the difference in F-score between the BiLSTM model and the proposed method for in-scope tokens?", "answer": "So, the answer is {\"answer\": \"0.03\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The proposed method has a higher F-score for out-of-scope tokens compared to the BiLSTM model., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe proposed method has a higher F-score for out-of-scope tokens compared to the BiLSTM model.\n" }, { "role": "assistant", "content": "The table shows that both the BiLSTM model and the proposed method have an F-score of 0.97 for out-of-scope tokens. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/8.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Negation classifier performance for scope detection with gold cues and scope." ], "reasoning": "The table shows that both the BiLSTM model and the proposed method have an F-score of 0.97 for out-of-scope tokens.", "statement": "The proposed method has a higher F-score for out-of-scope tokens compared to the BiLSTM model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Cue and token distribution in the conversational negation corpus.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The corpus statistics indicate that the average number of tokens per tweet is 22.3, per sentence is 13.6, and the average scope length is 2.9.Corpus statistics are shown in Table 3. The average number of tokens per tweet is 22.3, per sentence is 13.6 and average scope length is 2.9." } ], "images": [ "scigen/train/9.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Cue and token distribution in the conversational negation corpus." ], "reasoning": "The corpus statistics indicate that the average number of tokens per tweet is 22.3, per sentence is 13.6, and the average scope length is 2.9.", "description": "Corpus statistics are shown in Table 3. The average number of tokens per tweet is 22.3, per sentence is 13.6 and average scope length is 2.9.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: Cue and token distribution in the conversational negation corpus.'. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the total number of negation cues in the corpus?\n" }, { "role": "assistant", "content": "The table shows the total number of negation cues as 2921. So, the answer is {\"answer\": \"2921\"}." } ], "images": [ "scigen/train/9.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Cue and token distribution in the conversational negation corpus." ], "reasoning": "The table shows the total number of negation cues as 2921.", "question": "What is the total number of negation cues in the corpus?", "answer": "So, the answer is {\"answer\": \"2921\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe corpus has more true negation cues than false negation cues.\n" }, { "role": "assistant", "content": "The table shows that there are 2674 true negation cues and 247 false negation cues, indicating that true negation cues are more numerous. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/9.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Cue and token distribution in the conversational negation corpus." ], "reasoning": "The table shows that there are 2674 true negation cues and 247 false negation cues, indicating that true negation cues are more numerous.", "statement": "The corpus has more true negation cues than false negation cues.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: Cue classification on the test set.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The proposed method improves the F-score for false cues from 0.61 to 0.68 on a test set with 47 false cues and 557 actual cues.improves the F-score of false negation from a 0.61 baseline to 0.68 on a test set containing 47 false and 557 actual negation cues." } ], "images": [ "scigen/train/10.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Cue classification on the test set." ], "reasoning": "The proposed method improves the F-score for false cues from 0.61 to 0.68 on a test set with 47 false cues and 557 actual cues.", "description": "improves the F-score of false negation from a 0.61 baseline to 0.68 on a test set containing 47 false and 557 actual negation cues.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 4: Cue classification on the test set.', solve the subsequent question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the improvement in the F-score for actual cues using the proposed method?\n" }, { "role": "assistant", "content": "The table shows that the F-score for actual cues improved from 0.97 to 0.98 using the proposed method. So, the answer is {\"answer\": \"0.01\"}." } ], "images": [ "scigen/train/10.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Cue classification on the test set." ], "reasoning": "The table shows that the F-score for actual cues improved from 0.97 to 0.98 using the proposed method.", "question": "What is the improvement in the F-score for actual cues using the proposed method?", "answer": "So, the answer is {\"answer\": \"0.01\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe proposed method improves the F-score for false cues by 0.07.\n" }, { "role": "assistant", "content": "The table shows that the F-score for false cues improved from 0.61 to 0.68, which is an improvement of 0.07. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/10.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Cue classification on the test set." ], "reasoning": "The table shows that the F-score for false cues improved from 0.61 to 0.68, which is an improvement of 0.07.", "statement": "The proposed method improves the F-score for false cues by 0.07.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 5: Human evaluation results. Models with data augmentation are noted as (+). App denotes the average appropriateness score.'.\n" }, { "role": "assistant", "content": "With data augmentation, the DAMD model shows significant improvement in diversity and achieves the best average appropriateness score, although it has a slightly increased invalid response percentage.The results are shown in Table 5.. We report the average value of diversity and appropriateness, and the percentage of responses scored for each appropriateness level.. With data augmentation, our model obtains a significant improvement in diversity score and achieves the best average appropriateness score as well.. However, the slightly increased invalid response percentage. We also observe our DAMD model outperforms HDSA in both diversity and appropriateness scores." } ], "images": [ "scigen/train/11.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Human evaluation results. Models with data augmentation are noted as (+). App denotes the average appropriateness score." ], "reasoning": "With data augmentation, the DAMD model shows significant improvement in diversity and achieves the best average appropriateness score, although it has a slightly increased invalid response percentage.", "description": "The results are shown in Table 5.. We report the average value of diversity and appropriateness, and the percentage of responses scored for each appropriateness level.. With data augmentation, our model obtains a significant improvement in diversity score and achieves the best average appropriateness score as well.. However, the slightly increased invalid response percentage. We also observe our DAMD model outperforms HDSA in both diversity and appropriateness scores.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 5: Human evaluation results. Models with data augmentation are noted as (+). App denotes the average appropriateness score.', unfold the steps to solve the question given next. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nQuestion: Which model has the highest percentage of 'Good%' responses?\n" }, { "role": "assistant", "content": "By comparing the 'Good%' column for all models, DAMD (+) has the highest percentage of 'Good%' responses at 63.0%. So, the answer is {\"answer\": \"DAMD (+)\"}." } ], "images": [ "scigen/train/11.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Human evaluation results. Models with data augmentation are noted as (+). App denotes the average appropriateness score." ], "reasoning": "By comparing the 'Good%' column for all models, DAMD (+) has the highest percentage of 'Good%' responses at 63.0%.", "question": "Which model has the highest percentage of 'Good%' responses?", "answer": "So, the answer is {\"answer\": \"DAMD (+)\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe HDSA model has the highest diversity score.\n" }, { "role": "assistant", "content": "The table shows that the HDSA model has a diversity score of 2.14, which is lower than both DAMD and DAMD (+). Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/11.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Human evaluation results. Models with data augmentation are noted as (+). App denotes the average appropriateness score." ], "reasoning": "The table shows that the HDSA model has a diversity score of 2.14, which is lower than both DAMD and DAMD (+). Therefore, the statement is refuted.", "statement": "The HDSA model has the highest diversity score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Multi-action evaluation results. The “w” and “w/o” column denote with and without data augmentation respectively, and the better score between them is in bold. We report the average performance over 5 runs.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The proposed domain-aware multi-decoder network consistently improves both action and slot diversity, outperforming HDSA which benefits less from data augmentation.The results are shown in Table 1.. After applying our data augmentation, both the action and slot diversity are improved consistently,. HDSA has the worse performance and benefits less from data augmentation comparing to our proposed domain-aware multi-decoder network," } ], "images": [ "scigen/train/12.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Multi-action evaluation results. The “w” and “w/o” column denote with and without data augmentation respectively, and the better score between them is in bold. We report the average performance over 5 runs." ], "reasoning": "The proposed domain-aware multi-decoder network consistently improves both action and slot diversity, outperforming HDSA which benefits less from data augmentation.", "description": "The results are shown in Table 1.. After applying our data augmentation, both the action and slot diversity are improved consistently,. HDSA has the worse performance and benefits less from data augmentation comparing to our proposed domain-aware multi-decoder network,", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1: Multi-action evaluation results. The “w” and “w/o” column denote with and without data augmentation respectively, and the better score between them is in bold. We report the average performance over 5 runs.', solve the subsequent question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich decoding scheme shows the highest slot diversity improvement after data augmentation?\n" }, { "role": "assistant", "content": "By comparing the 'Slot # w/' column, the highest value is 6.17, which corresponds to the 'DAMD + top-p sampling' scheme under the '10-Action Generation' model. So, the answer is {\"answer\": \"DAMD + top-p sampling\"}" } ], "images": [ "scigen/train/12.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Multi-action evaluation results. The “w” and “w/o” column denote with and without data augmentation respectively, and the better score between them is in bold. We report the average performance over 5 runs." ], "reasoning": "By comparing the 'Slot # w/' column, the highest value is 6.17, which corresponds to the 'DAMD + top-p sampling' scheme under the '10-Action Generation' model.", "question": "Which decoding scheme shows the highest slot diversity improvement after data augmentation?", "answer": "So, the answer is {\"answer\": \"DAMD + top-p sampling\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nHDSA with a fixed threshold has the lowest slot diversity improvement after data augmentation.\n" }, { "role": "assistant", "content": "The 'Slot # w/' value for 'HDSA + fixed threshold' is 2.40, which is the lowest among all listed schemes. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/12.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Multi-action evaluation results. The “w” and “w/o” column denote with and without data augmentation respectively, and the better score between them is in bold. We report the average performance over 5 runs." ], "reasoning": "The 'Slot # w/' value for 'HDSA + fixed threshold' is 2.40, which is the lowest among all listed schemes.", "statement": "HDSA with a fixed threshold has the lowest slot diversity improvement after data augmentation.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Comparison of response generation results on MultiWOZ. The oracle/generated denotes either using ground truth or generated results. The results are grouped according to whether and how system action is modeled.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The DAMD model with multi-action data augmentation achieves the highest combined score, significantly outperforming other models in inform and success rates.Results are shown in Table 2.. The first group shows that after applying our domain-adaptive delexcalization and domain-aware belief span modeling, the task completion ability of seq2seq models becomes better.. The relative lower BLEU score. Our DAMD model significantly outperforms other models with different system action forms in terms of inform and success rates,. While we find applying our data augmentation achieves a limited improvement on combined score (6 vs 7),. Moreover, if a model has access to ground truth system action, the model further improves its task performance." } ], "images": [ "scigen/train/13.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of response generation results on MultiWOZ. The oracle/generated denotes either using ground truth or generated results. The results are grouped according to whether and how system action is modeled." ], "reasoning": "The DAMD model with multi-action data augmentation achieves the highest combined score, significantly outperforming other models in inform and success rates.", "description": "Results are shown in Table 2.. The first group shows that after applying our domain-adaptive delexcalization and domain-aware belief span modeling, the task completion ability of seq2seq models becomes better.. The relative lower BLEU score. Our DAMD model significantly outperforms other models with different system action forms in terms of inform and success rates,. While we find applying our data augmentation achieves a limited improvement on combined score (6 vs 7),. Moreover, if a model has access to ground truth system action, the model further improves its task performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Comparison of response generation results on MultiWOZ. The oracle/generated denotes either using ground truth or generated results. The results are grouped according to whether and how system action is modeled.', answer the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model achieves the highest combined score?\n" }, { "role": "assistant", "content": "By examining the 'Combined Score' column, the model 'DAMD + multi-action data augmentation' with oracle belief state type and oracle system action type achieves the highest combined score of 118.5. So, the answer is {\"answer\": \"DAMD + multi-action data augmentation\"}." } ], "images": [ "scigen/train/13.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of response generation results on MultiWOZ. The oracle/generated denotes either using ground truth or generated results. The results are grouped according to whether and how system action is modeled." ], "reasoning": "By examining the 'Combined Score' column, the model 'DAMD + multi-action data augmentation' with oracle belief state type and oracle system action type achieves the highest combined score of 118.5.", "question": "Which model achieves the highest combined score?", "answer": "So, the answer is {\"answer\": \"DAMD + multi-action data augmentation\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The DAMD model with multi-action data augmentation achieves the highest BLEU score. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe DAMD model with multi-action data augmentation achieves the highest BLEU score.\n" }, { "role": "assistant", "content": "The table shows that the highest BLEU score is 30.4, achieved by the HDSA model with oracle belief state type and oracle system action type, not the DAMD model with multi-action data augmentation. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/13.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of response generation results on MultiWOZ. The oracle/generated denotes either using ground truth or generated results. The results are grouped according to whether and how system action is modeled." ], "reasoning": "The table shows that the highest BLEU score is 30.4, achieved by the HDSA model with oracle belief state type and oracle system action type, not the DAMD model with multi-action data augmentation.", "statement": "The DAMD model with multi-action data augmentation achieves the highest BLEU score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Impact of using coverage for improving generalization across the datasets of similar tasks. Both models are trained on the SQuAD training data.'.\n" }, { "role": "assistant", "content": "Incorporating coverage improves model performance in both in-domain and out-of-domain evaluations for reading comprehension and QA-SRL tasks.Table 3 shows the impact of coverage for improving generalization across these two datasets that belong to the two similar tasks of reading comprehension and QA-SRL.. The models are evaluated using Exact Match (EM) and F1 measures,. As the results show, incorporating coverage improves the model's performance in the in-domain evaluation as well as the out-of-domain evaluation in QASRL." } ], "images": [ "scigen/train/14.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Impact of using coverage for improving generalization across the datasets of similar tasks. Both models are trained on the SQuAD training data." ], "reasoning": "Incorporating coverage improves model performance in both in-domain and out-of-domain evaluations for reading comprehension and QA-SRL tasks.", "description": "Table 3 shows the impact of coverage for improving generalization across these two datasets that belong to the two similar tasks of reading comprehension and QA-SRL.. The models are evaluated using Exact Match (EM) and F1 measures,. As the results show, incorporating coverage improves the model's performance in the in-domain evaluation as well as the out-of-domain evaluation in QASRL.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: Impact of using coverage for improving generalization across the datasets of similar tasks. Both models are trained on the SQuAD training data.', unfold the steps to solve the question given next. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nQuestion: What is the Exact Match (EM) score for BIDAF (ELMO) with coverage in the out-of-domain QA-SRL evaluation?\n" }, { "role": "assistant", "content": "To find the Exact Match (EM) score for BIDAF (ELMO) with coverage in the out-of-domain QA-SRL evaluation, locate the row for BIDAF (ELMO) +coverage and the column for out-of-domain QA-SRL EM. So, the answer is {\"answer\": \"30.58\"}." } ], "images": [ "scigen/train/14.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Impact of using coverage for improving generalization across the datasets of similar tasks. Both models are trained on the SQuAD training data." ], "reasoning": "To find the Exact Match (EM) score for BIDAF (ELMO) with coverage in the out-of-domain QA-SRL evaluation, locate the row for BIDAF (ELMO) +coverage and the column for out-of-domain QA-SRL EM.", "question": "What is the Exact Match (EM) score for BIDAF (ELMO) with coverage in the out-of-domain QA-SRL evaluation?", "answer": "So, the answer is {\"answer\": \"30.58\"}." }, { "messages": [ { "role": "user", "content": "The F1 score for MQAN with coverage in the in-domain SQuAD evaluation is higher than 76. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe F1 score for MQAN with coverage in the in-domain SQuAD evaluation is higher than 76.\n" }, { "role": "assistant", "content": "To verify this statement, locate the row for MQAN +coverage and the column for in-domain SQuAD F1. The F1 score is 76.83, which is higher than 76. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/14.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Impact of using coverage for improving generalization across the datasets of similar tasks. Both models are trained on the SQuAD training data." ], "reasoning": "To verify this statement, locate the row for MQAN +coverage and the column for in-domain SQuAD F1. The F1 score is 76.83, which is higher than 76.", "statement": "The F1 score for MQAN with coverage in the in-domain SQuAD evaluation is higher than 76.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Impact of using coverage for improving generalization across different datasets of the same task (NLI). All models are trained on MultiNLI.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Coverage information improves the generalization of both MQAN and ESIM (ELMO) models across various NLI datasets, with larger improvements on SNLI and Glockner datasets compared to the SICK dataset.Table 2 shows the performance for both systems for in-domain (the MultiNLI development set) as well as out-of-domain evaluations on SNLI, Glockner, and SICK datasets.. The results show that coverage information considerably improves the generalization of both examined models across various NLI datasets. The resulting cross-dataset improvements on the SNLI and Glockner datasets are larger than those on the SICK dataset." } ], "images": [ "scigen/train/15.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Impact of using coverage for improving generalization across different datasets of the same task (NLI). All models are trained on MultiNLI." ], "reasoning": "Coverage information improves the generalization of both MQAN and ESIM (ELMO) models across various NLI datasets, with larger improvements on SNLI and Glockner datasets compared to the SICK dataset.", "description": "Table 2 shows the performance for both systems for in-domain (the MultiNLI development set) as well as out-of-domain evaluations on SNLI, Glockner, and SICK datasets.. The results show that coverage information considerably improves the generalization of both examined models across various NLI datasets. The resulting cross-dataset improvements on the SNLI and Glockner datasets are larger than those on the SICK dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Impact of using coverage for improving generalization across different datasets of the same task (NLI). All models are trained on MultiNLI.\nQuestion: Which model showed the highest improvement on the out-of-domain SNLI dataset with coverage information?\nSolve the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the model with the highest improvement on the out-of-domain SNLI dataset, compare the scores of MQAN and ESIM (ELMO) with and without coverage information. MQAN improved from 60.91 to 65.38, and ESIM (ELMO) improved from 68.70 to 70.05. The highest improvement is for MQAN. So, the answer is {\"answer\": \"MQAN\"}." } ], "images": [ "scigen/train/15.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Impact of using coverage for improving generalization across different datasets of the same task (NLI). All models are trained on MultiNLI." ], "reasoning": "To find the model with the highest improvement on the out-of-domain SNLI dataset, compare the scores of MQAN and ESIM (ELMO) with and without coverage information. MQAN improved from 60.91 to 65.38, and ESIM (ELMO) improved from 68.70 to 70.05. The highest improvement is for MQAN.", "question": "Which model showed the highest improvement on the out-of-domain SNLI dataset with coverage information?", "answer": "So, the answer is {\"answer\": \"MQAN\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe ESIM (ELMO) model showed a higher improvement on the out-of-domain Glockner dataset compared to the out-of-domain SNLI dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the improvement in scores for ESIM (ELMO) on the Glockner and SNLI datasets. For Glockner, the score improved from 60.21 to 67.47, an improvement of 7.26. For SNLI, the score improved from 68.70 to 70.05, an improvement of 1.35. The improvement on Glockner is higher. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/15.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Impact of using coverage for improving generalization across different datasets of the same task (NLI). All models are trained on MultiNLI." ], "reasoning": "To verify this statement, compare the improvement in scores for ESIM (ELMO) on the Glockner and SNLI datasets. For Glockner, the score improved from 60.21 to 67.47, an improvement of 7.26. For SNLI, the score improved from 68.70 to 70.05, an improvement of 1.35. The improvement on Glockner is higher.", "statement": "The ESIM (ELMO) model showed a higher improvement on the out-of-domain Glockner dataset compared to the out-of-domain SNLI dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: KL-divergence between different dialog policy and the human dialog KL(πturns||pturns), where πturns denotes the discrete distribution over the number of dialog turns of simulated sessions between the policy π and the agenda-based user simulator, and pturns for the real human-human dialog.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "GDPL has the smallest KL-divergence to the human on the number of dialog turns, indicating it behaves more like a human compared to other baselines.Table 4 shows that GDPL has the smallest KL-divergence to the human on the number of dialog turns over the baselines, which implies that GDPL behaves more like the human." } ], "images": [ "scigen/train/16.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: KL-divergence between different dialog policy and the human dialog KL(πturns||pturns), where πturns denotes the discrete distribution over the number of dialog turns of simulated sessions between the policy π and the agenda-based user simulator, and pturns for the real human-human dialog." ], "reasoning": "GDPL has the smallest KL-divergence to the human on the number of dialog turns, indicating it behaves more like a human compared to other baselines.", "description": "Table 4 shows that GDPL has the smallest KL-divergence to the human on the number of dialog turns over the baselines, which implies that GDPL behaves more like the human.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: KL-divergence between different dialog policy and the human dialog KL(πturns||pturns), where πturns denotes the discrete distribution over the number of dialog turns of simulated sessions between the policy π and the agenda-based user simulator, and pturns for the real human-human dialog.', answer the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model has the smallest KL-divergence to the human on the number of dialog turns?\n" }, { "role": "assistant", "content": "The table shows the KL-divergence values for different models. GDPL has the smallest value of 0.238, indicating it has the smallest KL-divergence to the human. So, the answer is {\"answer\": \"GDPL\"}." } ], "images": [ "scigen/train/16.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: KL-divergence between different dialog policy and the human dialog KL(πturns||pturns), where πturns denotes the discrete distribution over the number of dialog turns of simulated sessions between the policy π and the agenda-based user simulator, and pturns for the real human-human dialog." ], "reasoning": "The table shows the KL-divergence values for different models. GDPL has the smallest value of 0.238, indicating it has the smallest KL-divergence to the human.", "question": "Which model has the smallest KL-divergence to the human on the number of dialog turns?", "answer": "So, the answer is {\"answer\": \"GDPL\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nGDPL has the highest KL-divergence to the human on the number of dialog turns.\n" }, { "role": "assistant", "content": "The table shows that GDPL has the smallest KL-divergence value of 0.238, not the highest. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/16.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: KL-divergence between different dialog policy and the human dialog KL(πturns||pturns), where πturns denotes the discrete distribution over the number of dialog turns of simulated sessions between the policy π and the agenda-based user simulator, and pturns for the real human-human dialog." ], "reasoning": "The table shows that GDPL has the smallest KL-divergence value of 0.238, not the highest.", "statement": "GDPL has the highest KL-divergence to the human on the number of dialog turns.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: Performance of different dialog agents on the multi-domain dialog corpus by interacting with the agenda-based user simulator. All the results except “dialog turns” are shown in percentage terms. Real human-human performance computed from the test set (i.e. the last row) serves as the upper bounds.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "GDPL achieves the highest task success rate and inform F1 score, outperforming other methods including humans.The performance of each approach that interacts with the agenda-based user simulator is shown in. Table 3. GDPL achieves extremely high performance in the task success on account of the substantial improvement in inform F1 and match rate over the baselines.. Surprisingly, GDPL even outperforms human in completing the task, and its average dialog turns are close to those of humans, though GDPL is inferior in terms of match rate.. ACER and PPO obtain high performance in inform F1 and match rate as well.. Though ALDM obtains a lower inform F1 and match rate than PPO, it gets a slight improvement. on task success. Ablation test is investigated in Table 3.. It is perceptible that GDPL has better performance than GDPL-sess on the task success and is comparable regarding the dialog turns,. GDPL also outperforms GDPL-discr" } ], "images": [ "scigen/train/17.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of different dialog agents on the multi-domain dialog corpus by interacting with the agenda-based user simulator. All the results except “dialog turns” are shown in percentage terms. Real human-human performance computed from the test set (i.e. the last row) serves as the upper bounds." ], "reasoning": "GDPL achieves the highest task success rate and inform F1 score, outperforming other methods including humans.", "description": "The performance of each approach that interacts with the agenda-based user simulator is shown in. Table 3. GDPL achieves extremely high performance in the task success on account of the substantial improvement in inform F1 and match rate over the baselines.. Surprisingly, GDPL even outperforms human in completing the task, and its average dialog turns are close to those of humans, though GDPL is inferior in terms of match rate.. ACER and PPO obtain high performance in inform F1 and match rate as well.. Though ALDM obtains a lower inform F1 and match rate than PPO, it gets a slight improvement. on task success. Ablation test is investigated in Table 3.. It is perceptible that GDPL has better performance than GDPL-sess on the task success and is comparable regarding the dialog turns,. GDPL also outperforms GDPL-discr", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Performance of different dialog agents on the multi-domain dialog corpus by interacting with the agenda-based user simulator. All the results except “dialog turns” are shown in percentage terms. Real human-human performance computed from the test set (i.e. the last row) serves as the upper bounds.', solve the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method has the highest Agenda Success rate and what is its Agenda Inform score?\n" }, { "role": "assistant", "content": "GDPL has the highest Agenda Success rate of 86.5%, and its Agenda Inform score is 94.97. So, the answer is {\"answer\": \"GDPL with an Agenda Inform score of 94.97\"}" } ], "images": [ "scigen/train/17.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of different dialog agents on the multi-domain dialog corpus by interacting with the agenda-based user simulator. All the results except “dialog turns” are shown in percentage terms. Real human-human performance computed from the test set (i.e. the last row) serves as the upper bounds." ], "reasoning": "GDPL has the highest Agenda Success rate of 86.5%, and its Agenda Inform score is 94.97.", "question": "Which method has the highest Agenda Success rate and what is its Agenda Inform score?", "answer": "So, the answer is {\"answer\": \"GDPL with an Agenda Inform score of 94.97\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nGDPL-sess has a higher Agenda Success rate than GDPL-discr.\n" }, { "role": "assistant", "content": "The table shows that GDPL-sess has an Agenda Success rate of 76.4%, while GDPL-discr has a higher rate of 80.5%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/17.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of different dialog agents on the multi-domain dialog corpus by interacting with the agenda-based user simulator. All the results except “dialog turns” are shown in percentage terms. Real human-human performance computed from the test set (i.e. the last row) serves as the upper bounds." ], "reasoning": "The table shows that GDPL-sess has an Agenda Success rate of 76.4%, while GDPL-discr has a higher rate of 80.5%.", "statement": "GDPL-sess has a higher Agenda Success rate than GDPL-discr.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5: Performance of different agents on the neural user simulator.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "All methods show a significant drop in performance when interacting with VHUS. ALDM performs worse than ACER and PPO, while GDPL is comparable to ACER and PPO, achieving a better match rate and higher task success.the agent The performance that interacts with VHUS is presented in Table 5.. All the methods cause a significant drop in performance when interacting with VHUS. ALDM even gets worse performance than ACER and PPO. In comparison, GDPL is still comparable with ACER and PPO, obtains a better match rate, and even achieves higher task success." } ], "images": [ "scigen/train/18.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Performance of different agents on the neural user simulator." ], "reasoning": "All methods show a significant drop in performance when interacting with VHUS. ALDM performs worse than ACER and PPO, while GDPL is comparable to ACER and PPO, achieving a better match rate and higher task success.", "description": "the agent The performance that interacts with VHUS is presented in Table 5.. All the methods cause a significant drop in performance when interacting with VHUS. ALDM even gets worse performance than ACER and PPO. In comparison, GDPL is still comparable with ACER and PPO, obtains a better match rate, and even achieves higher task success.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 5: Performance of different agents on the neural user simulator.', answer the question below by showing each progressive step toward the answer. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich method achieves the highest VHUS Match rate?\n" }, { "role": "assistant", "content": "By comparing the VHUS Match rates of all methods in the table, GDPL has the highest VHUS Match rate of 36.21. So, the answer is {\"answer\": \"GDPL\"}" } ], "images": [ "scigen/train/18.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Performance of different agents on the neural user simulator." ], "reasoning": "By comparing the VHUS Match rates of all methods in the table, GDPL has the highest VHUS Match rate of 36.21.", "question": "Which method achieves the highest VHUS Match rate?", "answer": "So, the answer is {\"answer\": \"GDPL\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nALDM achieves the highest VHUS Success rate.\n" }, { "role": "assistant", "content": "The table shows that ALDM has a VHUS Success rate of 16.4, which is lower than the VHUS Success rates of ACER (18.6), PPO (18.3), and GDPL (19.7). Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/18.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Performance of different agents on the neural user simulator." ], "reasoning": "The table shows that ALDM has a VHUS Success rate of 16.4, which is lower than the VHUS Success rates of ACER (18.6), PPO (18.3), and GDPL (19.7). Therefore, the statement is refuted.", "statement": "ALDM achieves the highest VHUS Success rate.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: The count of human preference on dialog session pairs that GDPL wins (W), draws with (D) or loses to (L) other methods based on different criteria. One method wins the other if the majority prefer the former one.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "GDPL significantly outperforms three baselines in all aspects except for quality compared with ACER, and obtains the most preference against PPO.Table 6 presents the results of human evaluation. GDPL outperforms three baselines significantly in all aspects (sign test, p-value < 0.01) except for the quality compared with ACER. Among all the baselines, GDPL obtains the most preference against PPO." } ], "images": [ "scigen/train/19.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: The count of human preference on dialog session pairs that GDPL wins (W), draws with (D) or loses to (L) other methods based on different criteria. One method wins the other if the majority prefer the former one." ], "reasoning": "GDPL significantly outperforms three baselines in all aspects except for quality compared with ACER, and obtains the most preference against PPO.", "description": "Table 6 presents the results of human evaluation. GDPL outperforms three baselines significantly in all aspects (sign test, p-value < 0.01) except for the quality compared with ACER. Among all the baselines, GDPL obtains the most preference against PPO.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nTable title: Table 6: The count of human preference on dialog session pairs that GDPL wins (W), draws with (D) or loses to (L) other methods based on different criteria. One method wins the other if the majority prefer the former one.\n\nQuestion: Against which baseline does GDPL have the highest number of wins in the Success category?\n" }, { "role": "assistant", "content": "To find the baseline against which GDPL has the highest number of wins in the Success category, compare the 'Success W' values for ACER, PPO, and ALDM. The highest value is 61 for ALDM. So, the answer is {\"answer\": \"ALDM\"}." } ], "images": [ "scigen/train/19.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: The count of human preference on dialog session pairs that GDPL wins (W), draws with (D) or loses to (L) other methods based on different criteria. One method wins the other if the majority prefer the former one." ], "reasoning": "To find the baseline against which GDPL has the highest number of wins in the Success category, compare the 'Success W' values for ACER, PPO, and ALDM. The highest value is 61 for ALDM.", "question": "Against which baseline does GDPL have the highest number of wins in the Success category?", "answer": "So, the answer is {\"answer\": \"ALDM\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nGDPL has more wins in Efficiency compared to ACER.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Efficiency W' values for GDPL and ACER. GDPL has 55 wins, which is more than ACER's 20 wins. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/19.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: The count of human preference on dialog session pairs that GDPL wins (W), draws with (D) or loses to (L) other methods based on different criteria. One method wins the other if the majority prefer the former one." ], "reasoning": "To verify this statement, compare the 'Efficiency W' values for GDPL and ACER. GDPL has 55 wins, which is more than ACER's 20 wins.", "statement": "GDPL has more wins in Efficiency compared to ACER.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 7: Return distribution of GDPL on each metric. The first row counts the dialog sessions that get the full score of the corresponding metric, and the results of the rest sessions are included in the second row.'.\n" }, { "role": "assistant", "content": "The learned reward function shows positive rewards for full scores on each metric and negative rewards otherwise.Table 7 provides a quantitative evaluation on the learned rewards by showing the distribution of the return R = t γtrt according to each metric.. It can be observed that the learned reward function has good interpretability in that the reward is positive when the dialog gets a full score on each metric, and negative otherwise." } ], "images": [ "scigen/train/20.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Return distribution of GDPL on each metric. The first row counts the dialog sessions that get the full score of the corresponding metric, and the results of the rest sessions are included in the second row." ], "reasoning": "The learned reward function shows positive rewards for full scores on each metric and negative rewards otherwise.", "description": "Table 7 provides a quantitative evaluation on the learned rewards by showing the distribution of the return R = t γtrt according to each metric.. It can be observed that the learned reward function has good interpretability in that the reward is positive when the dialog gets a full score on each metric, and negative otherwise.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 7: Return distribution of GDPL on each metric. The first row counts the dialog sessions that get the full score of the corresponding metric, and the results of the rest sessions are included in the second row.\nQuestion: What is the Success Mean for the 'Full' type?\nSolve the above question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows the Success Mean for different types. For the 'Full' type, the Success Mean is listed in the corresponding row and column. So, the answer is {\"answer\": \"11.18\"}." } ], "images": [ "scigen/train/20.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Return distribution of GDPL on each metric. The first row counts the dialog sessions that get the full score of the corresponding metric, and the results of the rest sessions are included in the second row." ], "reasoning": "The table shows the Success Mean for different types. For the 'Full' type, the Success Mean is listed in the corresponding row and column.", "question": "What is the Success Mean for the 'Full' type?", "answer": "So, the answer is {\"answer\": \"11.18\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The 'Other' type has a higher Inform Mean than the 'Full' type. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe 'Other' type has a higher Inform Mean than the 'Full' type.\n" }, { "role": "assistant", "content": "The table shows the Inform Mean for both 'Full' and 'Other' types. The 'Full' type has an Inform Mean of 8.413, while the 'Other' type has an Inform Mean of -99.95, which is lower. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/20.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Return distribution of GDPL on each metric. The first row counts the dialog sessions that get the full score of the corresponding metric, and the results of the rest sessions are included in the second row." ], "reasoning": "The table shows the Inform Mean for both 'Full' and 'Other' types. The 'Full' type has an Inform Mean of 8.413, while the 'Other' type has an Inform Mean of -99.95, which is lower.", "statement": "The 'Other' type has a higher Inform Mean than the 'Full' type.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'TABLE VII: Precision scores for the Analogy Test'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The proposed method has comparable performance with GloVe, outperforming it in semantic analogy tests and overall results, while GloVe performs slightly better in syntactic tests.We present precision scores for the word analogy tests in Table VII. It can be seen that the alternative approaches that aim to improve interpretability, have poor performance on the word analogy tests. However, our proposed method has comparable performance with the original GloVe embeddings. Our proposed method outperforms GloVe in semantic analogy test set and in overall results, while GloVe performs slightly better in syntactic test set." } ], "images": [ "scigen/train/21.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VII: Precision scores for the Analogy Test" ], "reasoning": "The proposed method has comparable performance with GloVe, outperforming it in semantic analogy tests and overall results, while GloVe performs slightly better in syntactic tests.", "description": "We present precision scores for the word analogy tests in Table VII. It can be seen that the alternative approaches that aim to improve interpretability, have poor performance on the word analogy tests. However, our proposed method has comparable performance with the original GloVe embeddings. Our proposed method outperforms GloVe in semantic analogy test set and in overall results, while GloVe performs slightly better in syntactic test set.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'TABLE VII: Precision scores for the Analogy Test' in mind, break down and resolve the question below step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhich method has the highest total score in the word analogy tests?\n" }, { "role": "assistant", "content": "To find the method with the highest total score, compare the 'Total' scores of all methods. Word2Vec has the highest total score of 73.03. So, the answer is {\"answer\": \"Word2Vec\"}." } ], "images": [ "scigen/train/21.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VII: Precision scores for the Analogy Test" ], "reasoning": "To find the method with the highest total score, compare the 'Total' scores of all methods. Word2Vec has the highest total score of 73.03.", "question": "Which method has the highest total score in the word analogy tests?", "answer": "So, the answer is {\"answer\": \"Word2Vec\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe proposed method has the highest score in the semantic analogy test set.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Analogy (sem)' scores of all methods. The proposed method has a score of 79.96, which is not the highest as Word2Vec has a higher score of 81.03. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/21.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VII: Precision scores for the Analogy Test" ], "reasoning": "To verify this statement, compare the 'Analogy (sem)' scores of all methods. The proposed method has a score of 79.96, which is not the highest as Word2Vec has a higher score of 81.03.", "statement": "The proposed method has the highest score in the semantic analogy test set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'TABLE V: Word Intrusion Test Results: Correct Answers out of 300 Questions'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The proposed method significantly improves interpretability by increasing the average true answer percentage from approximately 28% for the baseline to approximately 71% for the new method.We apply the test on five participants. Results tabulated at Table V shows that our proposed method significantly improves the interpretability by increasing the average true answer percentage from ∼ 28% for baseline to ∼ 71% for our method." } ], "images": [ "scigen/train/22.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Word Intrusion Test Results: Correct Answers out of 300 Questions" ], "reasoning": "The proposed method significantly improves interpretability by increasing the average true answer percentage from approximately 28% for the baseline to approximately 71% for the new method.", "description": "We apply the test on five participants. Results tabulated at Table V shows that our proposed method significantly improves the interpretability by increasing the average true answer percentage from ∼ 28% for baseline to ∼ 71% for our method.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: TABLE V: Word Intrusion Test Results: Correct Answers out of 300 Questions\nQuestion: What is the standard deviation of the scores for the GloVe method?\nSolve the above question based on the table information. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the mean and standard deviation for both methods. The standard deviation for the GloVe method is listed as 6.9. So, the answer is {\"answer\": \"6.9\"}" } ], "images": [ "scigen/train/22.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Word Intrusion Test Results: Correct Answers out of 300 Questions" ], "reasoning": "The table shows the mean and standard deviation for both methods. The standard deviation for the GloVe method is listed as 6.9.", "question": "What is the standard deviation of the scores for the GloVe method?", "answer": "So, the answer is {\"answer\": \"6.9\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe mean score for the Imparted method is higher than the mean score for the GloVe method.\n" }, { "role": "assistant", "content": "The table shows that the mean score for the Imparted method is 212, while the mean score for the GloVe method is 85. Therefore, the mean score for the Imparted method is indeed higher. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/22.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Word Intrusion Test Results: Correct Answers out of 300 Questions" ], "reasoning": "The table shows that the mean score for the Imparted method is 212, while the mean score for the GloVe method is 85. Therefore, the mean score for the Imparted method is indeed higher.", "statement": "The mean score for the Imparted method is higher than the mean score for the GloVe method.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'TABLE VI: Correlations for Word Similarity Tests'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The proposed algorithm shows an almost uniform improvement in correlation values, outperforming all alternatives except Word2Vec on average.The correlation scores for 13 different similarity test sets and their averages are reported in Table VI. We observe that, let alone a reduction in performance, the obtained scores indicate an almost uniform improvement in the correlation values for the proposed algorithm, outperforming all the alternatives except Word2Vec baseline on average. Although Word2Sense performed slightly better on some of the test sets, it should be noted that it is trained on a significantly larger corpus. Categories from Roget's thesaurus are groupings of words that are similar in some sense which the original embedding algorithm may fail to capture. These test results signify that the semantic information injected into the algorithm by the additional cost term is significant enough to result in a measurable improvement. It should also be noted that scores obtained by SPINE is unacceptably low on almost all tests indicating that it has achieved its interpretability performance at the cost of losing its semantic functions." } ], "images": [ "scigen/train/23.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VI: Correlations for Word Similarity Tests" ], "reasoning": "The proposed algorithm shows an almost uniform improvement in correlation values, outperforming all alternatives except Word2Vec on average.", "description": "The correlation scores for 13 different similarity test sets and their averages are reported in Table VI. We observe that, let alone a reduction in performance, the obtained scores indicate an almost uniform improvement in the correlation values for the proposed algorithm, outperforming all the alternatives except Word2Vec baseline on average. Although Word2Sense performed slightly better on some of the test sets, it should be noted that it is trained on a significantly larger corpus. Categories from Roget's thesaurus are groupings of words that are similar in some sense which the original embedding algorithm may fail to capture. These test results signify that the semantic information injected into the algorithm by the additional cost term is significant enough to result in a measurable improvement. It should also be noted that scores obtained by SPINE is unacceptably low on almost all tests indicating that it has achieved its interpretability performance at the cost of losing its semantic functions.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'TABLE VI: Correlations for Word Similarity Tests' in mind, break down and resolve the question below step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich algorithm has the highest average correlation score?\n" }, { "role": "assistant", "content": "By examining the 'Average' row in the table, we see that Word2Vec has the highest average correlation score of 0.6141. So, the answer is {\"answer\": \"Word2Vec\"}." } ], "images": [ "scigen/train/23.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VI: Correlations for Word Similarity Tests" ], "reasoning": "By examining the 'Average' row in the table, we see that Word2Vec has the highest average correlation score of 0.6141.", "question": "Which algorithm has the highest average correlation score?", "answer": "So, the answer is {\"answer\": \"Word2Vec\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe proposed algorithm outperforms SPINE on all test sets.\n" }, { "role": "assistant", "content": "By comparing the 'Proposed' and 'SPINE' columns for each test set, we observe that the proposed algorithm has higher scores than SPINE on all test sets. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/23.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VI: Correlations for Word Similarity Tests" ], "reasoning": "By comparing the 'Proposed' and 'SPINE' columns for each test set, we observe that the proposed algorithm has higher scores than SPINE on all test sets.", "statement": "The proposed algorithm outperforms SPINE on all test sets.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'TABLE VIII: Precision scores for the Semantic Analogy Test'.\n" }, { "role": "assistant", "content": "The proposed algorithm improves precision scores across all scenarios, with the greatest improvement seen when questions consist entirely of concept words.To investigate the effect of the additional cost term on the performance improvement in the semantic analogy test, we. present Table VIII. In particular, we present results for the cases where i) all questions in the dataset are considered, ii) only the questions that contains at least one concept word are considered, iii) only the questions that consist entirely of concept words are considered.. We observe that for all three scenarios, our proposed algorithm results in an improvement in the precision scores. However, the greatest performance increase is seen for the last scenario, which underscores the extent to which the semantic features captured by embeddings can be improved with a reasonable selection of the lexical resource from which the concept wordgroups were derived." } ], "images": [ "scigen/train/24.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VIII: Precision scores for the Semantic Analogy Test" ], "reasoning": "The proposed algorithm improves precision scores across all scenarios, with the greatest improvement seen when questions consist entirely of concept words.", "description": "To investigate the effect of the additional cost term on the performance improvement in the semantic analogy test, we. present Table VIII. In particular, we present results for the cases where i) all questions in the dataset are considered, ii) only the questions that contains at least one concept word are considered, iii) only the questions that consist entirely of concept words are considered.. We observe that for all three scenarios, our proposed algorithm results in an improvement in the precision scores. However, the greatest performance increase is seen for the last scenario, which underscores the extent to which the semantic features captured by embeddings can be improved with a reasonable selection of the lexical resource from which the concept wordgroups were derived.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. The question is related to the given table titled 'TABLE VIII: Precision scores for the Semantic Analogy Test'.\n\nQuestion: Which scenario shows the greatest performance increase in precision scores for the proposed algorithm?\n" }, { "role": "assistant", "content": "The table shows precision scores for different scenarios. The greatest performance increase for the proposed algorithm is seen in the 'All concept words' scenario with a precision score of 83.64. So, the answer is {\"answer\": \"All concept words\"}." } ], "images": [ "scigen/train/24.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VIII: Precision scores for the Semantic Analogy Test" ], "reasoning": "The table shows precision scores for different scenarios. The greatest performance increase for the proposed algorithm is seen in the 'All concept words' scenario with a precision score of 83.64.", "question": "Which scenario shows the greatest performance increase in precision scores for the proposed algorithm?", "answer": "So, the answer is {\"answer\": \"All concept words\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe proposed algorithm performs better than Word2Vec for questions that consist entirely of concept words.\n" }, { "role": "assistant", "content": "The table shows that for the 'All concept words' scenario, the proposed algorithm has a precision score of 83.64, which is lower than Word2Vec's score of 89.09. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/24.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VIII: Precision scores for the Semantic Analogy Test" ], "reasoning": "The table shows that for the 'All concept words' scenario, the proposed algorithm has a precision score of 83.64, which is lower than Word2Vec's score of 89.09.", "statement": "The proposed algorithm performs better than Word2Vec for questions that consist entirely of concept words.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'TABLE IX: Accuracies (%) for Sentiment Classification Task', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The proposed method achieves a classification accuracy of 78.26, outperforming the original embeddings and performing on par with SOV, while Word2Sense embeddings achieve the highest accuracy of 81.21.Classification accuracies are presented in Table IX. The proposed method outperforms the original embeddings and performs on par with the SOV. Pretrained Word2Sense embeddings outperform our method, however it has the advantage of training on a larger corpus. This result along with the intrinsic evaluations show that the proposed imparting method can significantly improve interpretability without a drop in performance." } ], "images": [ "scigen/train/25.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IX: Accuracies (%) for Sentiment Classification Task" ], "reasoning": "The proposed method achieves a classification accuracy of 78.26, outperforming the original embeddings and performing on par with SOV, while Word2Sense embeddings achieve the highest accuracy of 81.21.", "description": "Classification accuracies are presented in Table IX. The proposed method outperforms the original embeddings and performs on par with the SOV. Pretrained Word2Sense embeddings outperform our method, however it has the advantage of training on a larger corpus. This result along with the intrinsic evaluations show that the proposed imparting method can significantly improve interpretability without a drop in performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'TABLE IX: Accuracies (%) for Sentiment Classification Task' to sequentially solve the question presented below. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich method achieves the highest classification accuracy, and how does the proposed method compare to it?\n" }, { "role": "assistant", "content": "The table shows that Word2Sense achieves the highest classification accuracy of 81.21. The proposed method achieves an accuracy of 78.26, which is lower than Word2Sense. So, the answer is {\"answer\": \"Word2Sense\"}" } ], "images": [ "scigen/train/25.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IX: Accuracies (%) for Sentiment Classification Task" ], "reasoning": "The table shows that Word2Sense achieves the highest classification accuracy of 81.21. The proposed method achieves an accuracy of 78.26, which is lower than Word2Sense.", "question": "Which method achieves the highest classification accuracy, and how does the proposed method compare to it?", "answer": "So, the answer is {\"answer\": \"Word2Sense\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe proposed method achieves the highest classification accuracy.\n" }, { "role": "assistant", "content": "The table shows that Word2Sense achieves the highest classification accuracy of 81.21, while the proposed method achieves an accuracy of 78.26, which is lower. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/25.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IX: Accuracies (%) for Sentiment Classification Task" ], "reasoning": "The table shows that Word2Sense achieves the highest classification accuracy of 81.21, while the proposed method achieves an accuracy of 78.26, which is lower.", "statement": "The proposed method achieves the highest classification accuracy.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Combined within- and cross-document entity coreference results on the ECB+ test set.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The joint model outperforms the lemma baseline by 3.8 points in CoNLL F1 score, indicating improved performance in entity coreference.Table 2 presents the performance of our method with respect to entity coreference. Our joint model improves upon the strong lemma baseline by 3.8 points in CoNLL F1 score." } ], "images": [ "scigen/train/26.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Combined within- and cross-document entity coreference results on the ECB+ test set." ], "reasoning": "The joint model outperforms the lemma baseline by 3.8 points in CoNLL F1 score, indicating improved performance in entity coreference.", "description": "Table 2 presents the performance of our method with respect to entity coreference. Our joint model improves upon the strong lemma baseline by 3.8 points in CoNLL F1 score.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Combined within- and cross-document entity coreference results on the ECB+ test set.', answer the question below by showing each progressive step toward the answer. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the CoNLL F1 score difference between the Cluster+Lemma model and the Joint model?\n" }, { "role": "assistant", "content": "The CoNLL F1 score for the Cluster+Lemma model is 67.4, and for the Joint model, it is 71.2. The difference is 71.2 - 67.4 = 3.8 points. So, the answer is {\"answer\": \"3.8\"}" } ], "images": [ "scigen/train/26.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Combined within- and cross-document entity coreference results on the ECB+ test set." ], "reasoning": "The CoNLL F1 score for the Cluster+Lemma model is 67.4, and for the Joint model, it is 71.2. The difference is 71.2 - 67.4 = 3.8 points.", "question": "What is the CoNLL F1 score difference between the Cluster+Lemma model and the Joint model?", "answer": "So, the answer is {\"answer\": \"3.8\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Joint model has a higher CoNLL F1 score than the Disjoint model.\n" }, { "role": "assistant", "content": "The table shows that the CoNLL F1 score for the Joint model is 71.2, while for the Disjoint model, it is 70. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/26.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Combined within- and cross-document entity coreference results on the ECB+ test set." ], "reasoning": "The table shows that the CoNLL F1 score for the Joint model is 71.2, while for the Disjoint model, it is 70. Therefore, the statement is supported.", "statement": "The Joint model has a higher CoNLL F1 score than the Disjoint model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: Combined within- and cross-document event coreference results on the ECB+ test set.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The joint model outperforms all baselines by 10.5 CoNLL F1 points and surpasses the lemma baseline by 3 points, demonstrating the effectiveness of joint modeling and topic clustering.Table 3 presents the results on event coreference. Our joint model outperforms all the base. lines with a gap of 10.5 CoNLL F1 points from the last published results (KCP), while surpassing our strong lemma baseline by 3 points.. The results reconfirm that the lemma baseline, when combined with effective topic clustering, is a strong baseline for CD event coreference resolution on the ECB+ corpus (Upadhyay et al., 2016).. The results of CLUSTER+KCP again indicate that pre-clustering of documents to topics is beneficial, improving upon the KCP performance by 4.6 points, though still performing substantially worse than our joint model.. To test the contribution of joint modeling, we compare our joint model to its disjoint variant. We observe that the joint model performs better on both event and entity coreference. The performance gap is modest but significant with bootstrapping and permutation tests (p < 0.001)." } ], "images": [ "scigen/train/27.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Combined within- and cross-document event coreference results on the ECB+ test set." ], "reasoning": "The joint model outperforms all baselines by 10.5 CoNLL F1 points and surpasses the lemma baseline by 3 points, demonstrating the effectiveness of joint modeling and topic clustering.", "description": "Table 3 presents the results on event coreference. Our joint model outperforms all the base. lines with a gap of 10.5 CoNLL F1 points from the last published results (KCP), while surpassing our strong lemma baseline by 3 points.. The results reconfirm that the lemma baseline, when combined with effective topic clustering, is a strong baseline for CD event coreference resolution on the ECB+ corpus (Upadhyay et al., 2016).. The results of CLUSTER+KCP again indicate that pre-clustering of documents to topics is beneficial, improving upon the KCP performance by 4.6 points, though still performing substantially worse than our joint model.. To test the contribution of joint modeling, we compare our joint model to its disjoint variant. We observe that the joint model performs better on both event and entity coreference. The performance gap is modest but significant with bootstrapping and permutation tests (p < 0.001).", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: Combined within- and cross-document event coreference results on the ECB+ test set.'. Solve the following question based on the table.\n\nQuestion: How much does the joint model outperform the KCP Kenyon-Dean et al. (2018) baseline in terms of CoNLL F1 points?\nLet's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The joint model has a CoNLL F1 score of 79.5, while the KCP Kenyon-Dean et al. (2018) baseline has a CoNLL F1 score of 69. The difference is 79.5 - 69 = 10.5 points. So, the answer is {\"answer\": \"10.5\"}" } ], "images": [ "scigen/train/27.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Combined within- and cross-document event coreference results on the ECB+ test set." ], "reasoning": "The joint model has a CoNLL F1 score of 79.5, while the KCP Kenyon-Dean et al. (2018) baseline has a CoNLL F1 score of 69. The difference is 79.5 - 69 = 10.5 points.", "question": "How much does the joint model outperform the KCP Kenyon-Dean et al. (2018) baseline in terms of CoNLL F1 points?", "answer": "So, the answer is {\"answer\": \"10.5\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The joint model performs better than the disjoint model in terms of CoNLL F1 points., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe joint model performs better than the disjoint model in terms of CoNLL F1 points.\n" }, { "role": "assistant", "content": "The joint model has a CoNLL F1 score of 79.5, while the disjoint model has a CoNLL F1 score of 78.5, indicating that the joint model performs better. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/27.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Combined within- and cross-document event coreference results on the ECB+ test set." ], "reasoning": "The joint model has a CoNLL F1 score of 79.5, while the disjoint model has a CoNLL F1 score of 78.5, indicating that the joint model performs better.", "statement": "The joint model performs better than the disjoint model in terms of CoNLL F1 points.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Combined within- and cross-document entity coreference results on the ECB+ test set.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The joint model achieves a CoNLL F1 score of 71.2, which is 3.8 points higher than the Cluster+Lemma baseline.Table 2 presents the performance of our method with respect to entity coreference. Our joint model improves upon the strong lemma baseline by 3.8 points in CoNLL F1 score." } ], "images": [ "scigen/train/28.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Combined within- and cross-document entity coreference results on the ECB+ test set." ], "reasoning": "The joint model achieves a CoNLL F1 score of 71.2, which is 3.8 points higher than the Cluster+Lemma baseline.", "description": "Table 2 presents the performance of our method with respect to entity coreference. Our joint model improves upon the strong lemma baseline by 3.8 points in CoNLL F1 score.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 2: Combined within- and cross-document entity coreference results on the ECB+ test set.'. Solve the following question based on the table.\n\nQuestion: What is the improvement in CoNLL F1 score of the joint model over the disjoint model?\nLet's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The CoNLL F1 score for the joint model is 71.2, and for the disjoint model, it is 70. The improvement is calculated as 71.2 - 70. So, the answer is {\"answer\": \"1.2\"}" } ], "images": [ "scigen/train/28.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Combined within- and cross-document entity coreference results on the ECB+ test set." ], "reasoning": "The CoNLL F1 score for the joint model is 71.2, and for the disjoint model, it is 70. The improvement is calculated as 71.2 - 70.", "question": "What is the improvement in CoNLL F1 score of the joint model over the disjoint model?", "answer": "So, the answer is {\"answer\": \"1.2\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The joint model has the highest CEAF-e F1 score among all models., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe joint model has the highest CEAF-e F1 score among all models.\n" }, { "role": "assistant", "content": "The CEAF-e F1 score for the joint model is 63.3, which is higher than the Cluster+Lemma model (60) and the disjoint model (61.6). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/28.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Combined within- and cross-document entity coreference results on the ECB+ test set." ], "reasoning": "The CEAF-e F1 score for the joint model is 63.3, which is higher than the Cluster+Lemma model (60) and the disjoint model (61.6).", "statement": "The joint model has the highest CEAF-e F1 score among all models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Combined within- and cross-document event coreference results on the ECB+ test set.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The joint model achieves state-of-the-art results, outperforming previous models by 10.5 CoNLL F1 points on events, and surpasses the strong lemma baseline by 3 points.lines with a gap of 10.5 CoNLL F1 points from the last published results (KCP), while surpassing our strong lemma baseline by 3 points. Table 3 presents the results on event coreference. Our joint model outperforms all the base. The results reconfirm that the lemma baseline, when combined with effective topic clustering, is a strong baseline for CD event coreference resolution on the ECB+ corpus (Upadhyay et al., 2016).. The results of CLUSTER+KCP again indicate that pre-clustering of documents to topics is beneficial, improving upon the KCP performance by 4.6 points, though still performing substantially worse than our joint model.. Our model achieves state-of-the-art results, outperforming previous models by 10.5 CoNLL F1 points on events," } ], "images": [ "scigen/train/29.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Combined within- and cross-document event coreference results on the ECB+ test set." ], "reasoning": "The joint model achieves state-of-the-art results, outperforming previous models by 10.5 CoNLL F1 points on events, and surpasses the strong lemma baseline by 3 points.", "description": "lines with a gap of 10.5 CoNLL F1 points from the last published results (KCP), while surpassing our strong lemma baseline by 3 points. Table 3 presents the results on event coreference. Our joint model outperforms all the base. The results reconfirm that the lemma baseline, when combined with effective topic clustering, is a strong baseline for CD event coreference resolution on the ECB+ corpus (Upadhyay et al., 2016).. The results of CLUSTER+KCP again indicate that pre-clustering of documents to topics is beneficial, improving upon the KCP performance by 4.6 points, though still performing substantially worse than our joint model.. Our model achieves state-of-the-art results, outperforming previous models by 10.5 CoNLL F1 points on events,", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Combined within- and cross-document event coreference results on the ECB+ test set.', unfold the question presented below, detailing every step of your calculation. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nHow much does the joint model outperform the KCP Kenyon-Dean et al. (2018) model in terms of CoNLL F1 points?\n" }, { "role": "assistant", "content": "The CoNLL F1 score for the joint model is 79.5, while the CoNLL F1 score for the KCP Kenyon-Dean et al. (2018) model is 69. The difference is 79.5 - 69 = 10.5 points. So, the answer is {\"answer\": \"10.5\"}" } ], "images": [ "scigen/train/29.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Combined within- and cross-document event coreference results on the ECB+ test set." ], "reasoning": "The CoNLL F1 score for the joint model is 79.5, while the CoNLL F1 score for the KCP Kenyon-Dean et al. (2018) model is 69. The difference is 79.5 - 69 = 10.5 points.", "question": "How much does the joint model outperform the KCP Kenyon-Dean et al. (2018) model in terms of CoNLL F1 points?", "answer": "So, the answer is {\"answer\": \"10.5\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Cluster+KCP model has a higher CoNLL F1 score than the CV Cybulska and Vossen (2015a) model. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Cluster+KCP model has a higher CoNLL F1 score than the CV Cybulska and Vossen (2015a) model.\n" }, { "role": "assistant", "content": "The CoNLL F1 score for the Cluster+KCP model is 73.6, while the CoNLL F1 score for the CV Cybulska and Vossen (2015a) model is 73. Therefore, the Cluster+KCP model has a higher CoNLL F1 score. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/29.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Combined within- and cross-document event coreference results on the ECB+ test set." ], "reasoning": "The CoNLL F1 score for the Cluster+KCP model is 73.6, while the CoNLL F1 score for the CV Cybulska and Vossen (2015a) model is 73. Therefore, the Cluster+KCP model has a higher CoNLL F1 score.", "statement": "The Cluster+KCP model has a higher CoNLL F1 score than the CV Cybulska and Vossen (2015a) model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Precisions on the NYT dataset.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our model generally leads to better precision as shown by higher precision numbers at various recalls and the highest AUC.We also show the precision numbers for some particular recalls as well as the AUC in Table 1, where our model generally leads to better precision." } ], "images": [ "scigen/train/30.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Precisions on the NYT dataset." ], "reasoning": "Our model generally leads to better precision as shown by higher precision numbers at various recalls and the highest AUC.", "description": "We also show the precision numbers for some particular recalls as well as the AUC in Table 1, where our model generally leads to better precision.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: Precisions on the NYT dataset.'. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest precision at a recall of 0.2?\n" }, { "role": "assistant", "content": "To find the model with the highest precision at a recall of 0.2, compare the precision values in the 0.2 column for all models. Our Model has the highest precision of 0.743. So, the answer is {\"answer\": \"Our Model\"}." } ], "images": [ "scigen/train/30.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Precisions on the NYT dataset." ], "reasoning": "To find the model with the highest precision at a recall of 0.2, compare the precision values in the 0.2 column for all models. Our Model has the highest precision of 0.743.", "question": "Which model has the highest precision at a recall of 0.2?", "answer": "So, the answer is {\"answer\": \"Our Model\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nRank+ExATT has a higher AUC than PCNN+ATT.\n" }, { "role": "assistant", "content": "To verify this statement, compare the AUC values for Rank+ExATT and PCNN+ATT. Rank+ExATT has an AUC of 0.395, which is higher than PCNN+ATT's AUC of 0.323. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/30.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Precisions on the NYT dataset." ], "reasoning": "To verify this statement, compare the AUC values for Rank+ExATT and PCNN+ATT. Rank+ExATT has an AUC of 0.395, which is higher than PCNN+ATT's AUC of 0.323.", "statement": "Rank+ExATT has a higher AUC than PCNN+ATT.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Precisions on the Wikidata dataset.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Our model outperforms other models in terms of AUC and shows competitive precision at various recall levels.We show the precision numbers for some particular recalls as well as the AUC in Table 2, where PCNN+ATT (1) refers to train sentences with two entities and one relation label, PCNN+ATT (m) refers to train sentences with four entities7 and two relation labels. We observe that our model exhibits the best performances." } ], "images": [ "scigen/train/31.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Precisions on the Wikidata dataset." ], "reasoning": "Our model outperforms other models in terms of AUC and shows competitive precision at various recall levels.", "description": "We show the precision numbers for some particular recalls as well as the AUC in Table 2, where PCNN+ATT (1) refers to train sentences with two entities and one relation label, PCNN+ATT (m) refers to train sentences with four entities7 and two relation labels. We observe that our model exhibits the best performances.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: Precisions on the Wikidata dataset.' and methodically tackle the question that follows. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model has the highest AUC value?\n" }, { "role": "assistant", "content": "By comparing the AUC values in the table, we see that 'Our Model' has the highest AUC value of 0.405. So, the answer is {\"answer\": \"Our Model\"}." } ], "images": [ "scigen/train/31.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Precisions on the Wikidata dataset." ], "reasoning": "By comparing the AUC values in the table, we see that 'Our Model' has the highest AUC value of 0.405.", "question": "Which model has the highest AUC value?", "answer": "So, the answer is {\"answer\": \"Our Model\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim PCNN+ATT (m) has the lowest precision at a recall of 0.3., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nPCNN+ATT (m) has the lowest precision at a recall of 0.3.\n" }, { "role": "assistant", "content": "By examining the precision values at a recall of 0.3, we see that PCNN+ATT (m) has a precision of 0.213, which is the lowest among the listed models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/31.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Precisions on the Wikidata dataset." ], "reasoning": "By examining the precision values at a recall of 0.3, we see that PCNN+ATT (m) has a precision of 0.213, which is the lowest among the listed models.", "statement": "PCNN+ATT (m) has the lowest precision at a recall of 0.3.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: Ablation study of capsule net and word-level attention on Wikidata dataset.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The table shows that the precision drops when word-level attention is removed, indicating its usefulness.The experimental results on Wikidata dataset are summarized in Table 3. The results of \"-Word-ATT\" row refers to the results without word-level attention. According to the table, the drop of precision demonstrates that the word-level attention is quite useful." } ], "images": [ "scigen/train/32.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation study of capsule net and word-level attention on Wikidata dataset." ], "reasoning": "The table shows that the precision drops when word-level attention is removed, indicating its usefulness.", "description": "The experimental results on Wikidata dataset are summarized in Table 3. The results of \"-Word-ATT\" row refers to the results without word-level attention. According to the table, the drop of precision demonstrates that the word-level attention is quite useful.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: Ablation study of capsule net and word-level attention on Wikidata dataset.', unfold the steps to solve the question given next. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nQuestion: What is the AUC value for the model without word-level attention?\n" }, { "role": "assistant", "content": "The AUC value for the model without word-level attention is found in the '-Word-ATT' row under the AUC column. So, the answer is {\"answer\": \"0.389\"}" } ], "images": [ "scigen/train/32.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation study of capsule net and word-level attention on Wikidata dataset." ], "reasoning": "The AUC value for the model without word-level attention is found in the '-Word-ATT' row under the AUC column.", "question": "What is the AUC value for the model without word-level attention?", "answer": "So, the answer is {\"answer\": \"0.389\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The model with word-level attention has the highest AUC value., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model with word-level attention has the highest AUC value.\n" }, { "role": "assistant", "content": "The table shows that 'Our Model' has the highest AUC value of 0.405, which includes word-level attention. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/32.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation study of capsule net and word-level attention on Wikidata dataset." ], "reasoning": "The table shows that 'Our Model' has the highest AUC value of 0.405, which includes word-level attention.", "statement": "The model with word-level attention has the highest AUC value.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: Precisions on the Wikidata dataset with different choice of d.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "As the dimension d increases, the training time also increases.As the table 4 depicts, the training time increases with the growth of d." } ], "images": [ "scigen/train/33.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Precisions on the Wikidata dataset with different choice of d." ], "reasoning": "As the dimension d increases, the training time also increases.", "description": "As the table 4 depicts, the training time increases with the growth of d.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. The question is related to the given table titled 'Table 4: Precisions on the Wikidata dataset with different choice of d.'.\n\nQuestion: What is the training time for d=16?\n" }, { "role": "assistant", "content": "The table shows that for d=16, the training time is listed in the 'Time' column. So, the answer is {\"answer\": \"20h\"}." } ], "images": [ "scigen/train/33.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Precisions on the Wikidata dataset with different choice of d." ], "reasoning": "The table shows that for d=16, the training time is listed in the 'Time' column.", "question": "What is the training time for d=16?", "answer": "So, the answer is {\"answer\": \"20h\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe training time for d=32 is 10 hours.\n" }, { "role": "assistant", "content": "The table does not provide the training time for d=32, as it is marked with a dash. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/33.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Precisions on the Wikidata dataset with different choice of d." ], "reasoning": "The table does not provide the training time for d=32, as it is marked with a dash.", "statement": "The training time for d=32 is 10 hours.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: Precisions on the Wikidata dataset with different number of dynamic routing iterations.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The performance metrics improve with iterations, peaking at iteration 3 before declining.Table 5 shows the comparison of 1 - 5 iterations. We find that the performance reach the best when iteration is set to 3." } ], "images": [ "scigen/train/34.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Precisions on the Wikidata dataset with different number of dynamic routing iterations." ], "reasoning": "The performance metrics improve with iterations, peaking at iteration 3 before declining.", "description": "Table 5 shows the comparison of 1 - 5 iterations. We find that the performance reach the best when iteration is set to 3.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: 'Table 5: Precisions on the Wikidata dataset with different number of dynamic routing iterations.'\nQuestion: At which iteration does the AUC value reach its highest point?\n" }, { "role": "assistant", "content": "By comparing the AUC values across all iterations, we find that the highest AUC value is 0.405, which occurs at iteration 3. So, the answer is {\"answer\": \"3\"}" } ], "images": [ "scigen/train/34.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Precisions on the Wikidata dataset with different number of dynamic routing iterations." ], "reasoning": "By comparing the AUC values across all iterations, we find that the highest AUC value is 0.405, which occurs at iteration 3.", "question": "At which iteration does the AUC value reach its highest point?", "answer": "So, the answer is {\"answer\": \"3\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The recall value at 0.1 is highest at iteration 3., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe recall value at 0.1 is highest at iteration 3.\n" }, { "role": "assistant", "content": "By examining the recall values at 0.1 across all iterations, we see that the highest recall value of 0.650 occurs at iteration 3. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/34.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Precisions on the Wikidata dataset with different number of dynamic routing iterations." ], "reasoning": "By examining the recall values at 0.1 across all iterations, we see that the highest recall value of 0.650 occurs at iteration 3.", "statement": "The recall value at 0.1 is highest at iteration 3.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Full-length ROUGE F-scores of some recent RL-based (upper) and supervised (middle) extractive summarisation systems, as well as our system with learned rewards (bottom). R-1/2/L stands for ROUGE-1/2/L. Our system maximises the learned reward instead of ROUGE, hence receives lower ROUGE scores.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The NeuralTD+LearnedRewards system has lower ROUGE scores compared to most recent systems because it is optimized for high correlation with human judgment rather than ROUGE metrics.Table 3 presents the ROUGE scores of our system (NeuralTD+LearnedRewards) and multiple stateof-the-art systems. The summaries generated by our system receive decent ROUGE metrics, but are lower than most of the recent systems, because our learned reward is optimised towards high correlation with human judgement instead of ROUGE metrics." } ], "images": [ "scigen/train/35.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Full-length ROUGE F-scores of some recent RL-based (upper) and supervised (middle) extractive summarisation systems, as well as our system with learned rewards (bottom). R-1/2/L stands for ROUGE-1/2/L. Our system maximises the learned reward instead of ROUGE, hence receives lower ROUGE scores." ], "reasoning": "The NeuralTD+LearnedRewards system has lower ROUGE scores compared to most recent systems because it is optimized for high correlation with human judgment rather than ROUGE metrics.", "description": "Table 3 presents the ROUGE scores of our system (NeuralTD+LearnedRewards) and multiple stateof-the-art systems. The summaries generated by our system receive decent ROUGE metrics, but are lower than most of the recent systems, because our learned reward is optimised towards high correlation with human judgement instead of ROUGE metrics.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 3: Full-length ROUGE F-scores of some recent RL-based (upper) and supervised (middle) extractive summarisation systems, as well as our system with learned rewards (bottom). R-1/2/L stands for ROUGE-1/2/L. Our system maximises the learned reward instead of ROUGE, hence receives lower ROUGE scores.', solve the following question step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich system has the highest ROUGE-1 score and what is the score?\n" }, { "role": "assistant", "content": "To find the system with the highest ROUGE-1 score, we need to compare the R-1 scores of all systems listed in the table. The highest R-1 score is 41.6, achieved by Zhou et al. (2018). So, the answer is {\"answer\": \"Zhou et al. (2018)\"}." } ], "images": [ "scigen/train/35.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Full-length ROUGE F-scores of some recent RL-based (upper) and supervised (middle) extractive summarisation systems, as well as our system with learned rewards (bottom). R-1/2/L stands for ROUGE-1/2/L. Our system maximises the learned reward instead of ROUGE, hence receives lower ROUGE scores." ], "reasoning": "To find the system with the highest ROUGE-1 score, we need to compare the R-1 scores of all systems listed in the table. The highest R-1 score is 41.6, achieved by Zhou et al. (2018).", "question": "Which system has the highest ROUGE-1 score and what is the score?", "answer": "So, the answer is {\"answer\": \"Zhou et al. (2018)\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe NeuralTD+LearnedRewards system has the highest ROUGE-L score.\n" }, { "role": "assistant", "content": "The table shows that the NeuralTD+LearnedRewards system has an R-L score of 36.5, which is lower than the R-L scores of several other systems, such as Zhou et al. (2018) with an R-L score of 38.0. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/35.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Full-length ROUGE F-scores of some recent RL-based (upper) and supervised (middle) extractive summarisation systems, as well as our system with learned rewards (bottom). R-1/2/L stands for ROUGE-1/2/L. Our system maximises the learned reward instead of ROUGE, hence receives lower ROUGE scores." ], "reasoning": "The table shows that the NeuralTD+LearnedRewards system has an R-L score of 36.5, which is lower than the R-L scores of several other systems, such as Zhou et al. (2018) with an R-L score of 38.0.", "statement": "The NeuralTD+LearnedRewards system has the highest ROUGE-L score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Quality of reward metrics. G-Pre and G-Rec are the precision and recall rate of the “good” summaries identified by the metrics, resp. All metrics here require reference summaries. We perform stemming and stop words removal as preprosessing, as they help increase the correlation. For InferSent, the embeddings of the reference/system summaries are obtained by averaging the embeddings of the sentences therein.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "All metrics have low correlation with human judgment, and their G-Pre and G-Rec scores are below .50, indicating that more than half of the good summaries identified by the metrics are not actually good.From Table 1, we find that all metrics we consider have low correlation with the human judgement. More importantly, their G-Pre and G-Rec scores are all below .50, which means that more than half of the good summaries identified by the metrics are actually not good, and more than 50%" } ], "images": [ "scigen/train/36.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Quality of reward metrics. G-Pre and G-Rec are the precision and recall rate of the “good” summaries identified by the metrics, resp. All metrics here require reference summaries. We perform stemming and stop words removal as preprosessing, as they help increase the correlation. For InferSent, the embeddings of the reference/system summaries are obtained by averaging the embeddings of the sentences therein." ], "reasoning": "All metrics have low correlation with human judgment, and their G-Pre and G-Rec scores are below .50, indicating that more than half of the good summaries identified by the metrics are not actually good.", "description": "From Table 1, we find that all metrics we consider have low correlation with the human judgement. More importantly, their G-Pre and G-Rec scores are all below .50, which means that more than half of the good summaries identified by the metrics are actually not good, and more than 50%", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: Quality of reward metrics. G-Pre and G-Rec are the precision and recall rate of the “good” summaries identified by the metrics, resp. All metrics here require reference summaries. We perform stemming and stop words removal as preprosessing, as they help increase the correlation. For InferSent, the embeddings of the reference/system summaries are obtained by averaging the embeddings of the sentences therein.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich metric has the highest G-Rec score?\n" }, { "role": "assistant", "content": "By examining the G-Rec column, we find that the highest score is .484, which corresponds to the BERT-Cosine metric. So, the answer is {\"answer\": \"BERT-Cosine\"}." } ], "images": [ "scigen/train/36.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Quality of reward metrics. G-Pre and G-Rec are the precision and recall rate of the “good” summaries identified by the metrics, resp. All metrics here require reference summaries. We perform stemming and stop words removal as preprosessing, as they help increase the correlation. For InferSent, the embeddings of the reference/system summaries are obtained by averaging the embeddings of the sentences therein." ], "reasoning": "By examining the G-Rec column, we find that the highest score is .484, which corresponds to the BERT-Cosine metric.", "question": "Which metric has the highest G-Rec score?", "answer": "So, the answer is {\"answer\": \"BERT-Cosine\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe BLEU-5 metric has a G-Pre score above .50.\n" }, { "role": "assistant", "content": "The table shows that the G-Pre score for BLEU-5 is .420, which is below .50. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/36.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Quality of reward metrics. G-Pre and G-Rec are the precision and recall rate of the “good” summaries identified by the metrics, resp. All metrics here require reference summaries. We perform stemming and stop words removal as preprosessing, as they help increase the correlation. For InferSent, the embeddings of the reference/system summaries are obtained by averaging the embeddings of the sentences therein." ], "reasoning": "The table shows that the G-Pre score for BLEU-5 is .420, which is below .50.", "statement": "The BLEU-5 metric has a G-Pre score above .50.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Summary-level correlation of learned reward functions. All results are averaged over 5-fold cross validations. Unlike the metrics in Table 1, all rewards in this table do not require reference summaries.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "MLP with BERT as encoder has the best overall performance, significantly outperforming other models that do not use BERT+MLP.Table 2 shows the quality of different reward learning models. As a baseline, we also consider the feature-rich reward learning method proposed by Peyrard and Gurevych (see §2). MLP with BERT as en(2018) coder has the best overall performance. Specifically, BERT+MLP+Pref significantly outperforms (p < 0.05) all the other models that do not use BERT+MLP," } ], "images": [ "scigen/train/37.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Summary-level correlation of learned reward functions. All results are averaged over 5-fold cross validations. Unlike the metrics in Table 1, all rewards in this table do not require reference summaries." ], "reasoning": "MLP with BERT as encoder has the best overall performance, significantly outperforming other models that do not use BERT+MLP.", "description": "Table 2 shows the quality of different reward learning models. As a baseline, we also consider the feature-rich reward learning method proposed by Peyrard and Gurevych (see §2). MLP with BERT as en(2018) coder has the best overall performance. Specifically, BERT+MLP+Pref significantly outperforms (p < 0.05) all the other models that do not use BERT+MLP,", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Summary-level correlation of learned reward functions. All results are averaged over 5-fold cross validations. Unlike the metrics in Table 1, all rewards in this table do not require reference summaries.', answer the following question. Give detailed solution consisting of each step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model and encoder combination has the highest performance in Pref. loss (Eq. (3)) G-G-Rec?\n" }, { "role": "assistant", "content": "By examining the table, the highest value in the Pref. loss (Eq. (3)) G-G-Rec column is 0.608, which corresponds to the MLP model with BERT encoder. So, the answer is {\"answer\": \"MLP with BERT\"}" } ], "images": [ "scigen/train/37.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Summary-level correlation of learned reward functions. All results are averaged over 5-fold cross validations. Unlike the metrics in Table 1, all rewards in this table do not require reference summaries." ], "reasoning": "By examining the table, the highest value in the Pref. loss (Eq. (3)) G-G-Rec column is 0.608, which corresponds to the MLP model with BERT encoder.", "question": "Which model and encoder combination has the highest performance in Pref. loss (Eq. (3)) G-G-Rec?", "answer": "So, the answer is {\"answer\": \"MLP with BERT\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nSimRed with BERT encoder has the highest performance in Reg. loss (Eq. (1)) G-Rec.\n" }, { "role": "assistant", "content": "The table shows that the highest value in the Reg. loss (Eq. (1)) G-Rec column is 0.597, which corresponds to the MLP model with BERT encoder, not SimRed with BERT encoder. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/37.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Summary-level correlation of learned reward functions. All results are averaged over 5-fold cross validations. Unlike the metrics in Table 1, all rewards in this table do not require reference summaries." ], "reasoning": "The table shows that the highest value in the Reg. loss (Eq. (1)) G-Rec column is 0.597, which corresponds to the MLP model with BERT encoder, not SimRed with BERT encoder.", "statement": "SimRed with BERT encoder has the highest performance in Reg. loss (Eq. (1)) G-Rec.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: Human evaluation on extractive summaries. Our system receives significantly higher human ratings on average. “Best%”: in how many percentage of documents a system receives the highest human rating.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "NeuralTD receives significantly higher human evaluation scores than Refresh and ExtAbsRL, and Refresh scores higher than ExtAbsRL.Table 4 presents the human evaluation results. summaries generated by NeuralTD receives significantly higher human evaluation scores than those by Refresh (p = 0.0088, double-tailed ttest) and ExtAbsRL (p (cid:28) 0.01). Also, the average human rating for Refresh is significantly higher (p (cid:28) 0.01) than ExtAbsRL," } ], "images": [ "scigen/train/38.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Human evaluation on extractive summaries. Our system receives significantly higher human ratings on average. “Best%”: in how many percentage of documents a system receives the highest human rating." ], "reasoning": "NeuralTD receives significantly higher human evaluation scores than Refresh and ExtAbsRL, and Refresh scores higher than ExtAbsRL.", "description": "Table 4 presents the human evaluation results. summaries generated by NeuralTD receives significantly higher human evaluation scores than those by Refresh (p = 0.0088, double-tailed ttest) and ExtAbsRL (p (cid:28) 0.01). Also, the average human rating for Refresh is significantly higher (p (cid:28) 0.01) than ExtAbsRL,", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 4: Human evaluation on extractive summaries. Our system receives significantly higher human ratings on average. “Best%”: in how many percentage of documents a system receives the highest human rating.\nQuestion: Which method received the highest average human rating?\n" }, { "role": "assistant", "content": "The table shows the average human ratings for Ours, Refresh, and ExtAbsRL. Ours has the highest rating of 2.52. So, the answer is {\"answer\": \"Ours\"}." } ], "images": [ "scigen/train/38.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Human evaluation on extractive summaries. Our system receives significantly higher human ratings on average. “Best%”: in how many percentage of documents a system receives the highest human rating." ], "reasoning": "The table shows the average human ratings for Ours, Refresh, and ExtAbsRL. Ours has the highest rating of 2.52.", "question": "Which method received the highest average human rating?", "answer": "So, the answer is {\"answer\": \"Ours\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Refresh received a higher average human rating than ExtAbsRL. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRefresh received a higher average human rating than ExtAbsRL.\n" }, { "role": "assistant", "content": "The table shows that Refresh has an average human rating of 2.27, which is higher than ExtAbsRL's rating of 1.66. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/38.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Human evaluation on extractive summaries. Our system receives significantly higher human ratings on average. “Best%”: in how many percentage of documents a system receives the highest human rating." ], "reasoning": "The table shows that Refresh has an average human rating of 2.27, which is higher than ExtAbsRL's rating of 1.66.", "statement": "Refresh received a higher average human rating than ExtAbsRL.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Performance of ExtAbsRL with different reward functions, measured in terms of ROUGE (center) and human judgements (right). Using our learned reward yields significantly (p=0.0057) higher average human rating. “Pref%”: in how many percentage of documents a system receives the higher human rating.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Using the learned reward in ExtAbsRL results in significantly higher human ratings and preference percentages compared to using ROUGE as the reward.Table 5 compares the ROUGE scores of using different rewards to train the extractor in ExtAbsRL (the abstractor is pre-trained, and is applied to rephrase the extracted sentences). Again, when ROUGE is used as rewards, the generated summaries have higher ROUGE scores.. It is clear from Table 5 that using the learned reward helps the RL-based system generate summaries with significantly higher human ratings." } ], "images": [ "scigen/train/39.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Performance of ExtAbsRL with different reward functions, measured in terms of ROUGE (center) and human judgements (right). Using our learned reward yields significantly (p=0.0057) higher average human rating. “Pref%”: in how many percentage of documents a system receives the higher human rating." ], "reasoning": "Using the learned reward in ExtAbsRL results in significantly higher human ratings and preference percentages compared to using ROUGE as the reward.", "description": "Table 5 compares the ROUGE scores of using different rewards to train the extractor in ExtAbsRL (the abstractor is pre-trained, and is applied to rephrase the extracted sentences). Again, when ROUGE is used as rewards, the generated summaries have higher ROUGE scores.. It is clear from Table 5 that using the learned reward helps the RL-based system generate summaries with significantly higher human ratings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: Performance of ExtAbsRL with different reward functions, measured in terms of ROUGE (center) and human judgements (right). Using our learned reward yields significantly (p=0.0057) higher average human rating. “Pref%”: in how many percentage of documents a system receives the higher human rating.'.\n\nQuestion: What is the human rating for the summaries generated using the learned reward?\n" }, { "role": "assistant", "content": "The table shows that the human rating for the summaries generated using the learned reward is 2.20. So, the answer is {\"answer\": \"2.20\"}." } ], "images": [ "scigen/train/39.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Performance of ExtAbsRL with different reward functions, measured in terms of ROUGE (center) and human judgements (right). Using our learned reward yields significantly (p=0.0057) higher average human rating. “Pref%”: in how many percentage of documents a system receives the higher human rating." ], "reasoning": "The table shows that the human rating for the summaries generated using the learned reward is 2.20.", "question": "What is the human rating for the summaries generated using the learned reward?", "answer": "So, the answer is {\"answer\": \"2.20\"}." }, { "messages": [ { "role": "user", "content": "The learned reward results in a higher preference percentage compared to the original ROUGE reward. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe learned reward results in a higher preference percentage compared to the original ROUGE reward.\n" }, { "role": "assistant", "content": "The table shows that the preference percentage for the learned reward is 75%, while for the original ROUGE reward it is 15%, indicating that the learned reward results in a higher preference percentage. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/39.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Performance of ExtAbsRL with different reward functions, measured in terms of ROUGE (center) and human judgements (right). Using our learned reward yields significantly (p=0.0057) higher average human rating. “Pref%”: in how many percentage of documents a system receives the higher human rating." ], "reasoning": "The table shows that the preference percentage for the learned reward is 75%, while for the original ROUGE reward it is 15%, indicating that the learned reward results in a higher preference percentage.", "statement": "The learned reward results in a higher preference percentage compared to the original ROUGE reward.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 9: An ablation study showing the effect of different model architectures and training regimes on performance on the proprietary help desk dataset.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The training set size, number of negative responses, and loss function are crucial for model performance, with hinge loss performing worse than cross-entropy loss.The results are shown in Table 9,. As Table 9 shows, the training set size and the number of negative responses for each positive response are the most important factors in model performance. The model performs significantly worse when trained with hinge loss instead of cross-entropy loss, indicating the importance of the loss function.. We observed no advantage to using a hierachical encoder,. Finally, we see that a 2 layer LSTM performs similarly to either a 4 layer or a 2 layer SRU with a comparable number of parameters.. Table 9 shows the results of an ablation study we performed to identify the most important components of our model architecture and training regime." } ], "images": [ "scigen/train/40.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: An ablation study showing the effect of different model architectures and training regimes on performance on the proprietary help desk dataset." ], "reasoning": "The training set size, number of negative responses, and loss function are crucial for model performance, with hinge loss performing worse than cross-entropy loss.", "description": "The results are shown in Table 9,. As Table 9 shows, the training set size and the number of negative responses for each positive response are the most important factors in model performance. The model performs significantly worse when trained with hinge loss instead of cross-entropy loss, indicating the importance of the loss function.. We observed no advantage to using a hierachical encoder,. Finally, we see that a 2 layer LSTM performs similarly to either a 4 layer or a 2 layer SRU with a comparable number of parameters.. Table 9 shows the results of an ablation study we performed to identify the most important components of our model architecture and training regime.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 9: An ablation study showing the effect of different model architectures and training regimes on performance on the proprietary help desk dataset.', answer the following question. Give detailed solution consisting of each step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model configuration shows the highest Test AUC@0.05?\n" }, { "role": "assistant", "content": "By examining the Test AUC@0.05 column, the highest value is 0.829, which is achieved by both the '4L SRU → 2L LSTM' and '4L SRU → 2L SRU' configurations. So, the answer is {\"answer\": \"4L SRU → 2L LSTM and 4L SRU → 2L SRU\"}." } ], "images": [ "scigen/train/40.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: An ablation study showing the effect of different model architectures and training regimes on performance on the proprietary help desk dataset." ], "reasoning": "By examining the Test AUC@0.05 column, the highest value is 0.829, which is achieved by both the '4L SRU → 2L LSTM' and '4L SRU → 2L SRU' configurations.", "question": "Which model configuration shows the highest Test AUC@0.05?", "answer": "So, the answer is {\"answer\": \"4L SRU → 2L LSTM and 4L SRU → 2L SRU\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe model performs better with a hierarchical encoder compared to a flat encoder.\n" }, { "role": "assistant", "content": "The table shows that the 'Flat → hierarchical' configuration has a Validation AUC@0.05 of 0.825 and a Test AUC@0.05 of 0.559, which are lower than the base model's 0.871 and 0.816 respectively, indicating that the hierarchical encoder does not perform better. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/40.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: An ablation study showing the effect of different model architectures and training regimes on performance on the proprietary help desk dataset." ], "reasoning": "The table shows that the 'Flat → hierarchical' configuration has a Validation AUC@0.05 of 0.825 and a Test AUC@0.05 of 0.559, which are lower than the base model's 0.871 and 0.816 respectively, indicating that the hierarchical encoder does not perform better.", "statement": "The model performs better with a hierarchical encoder compared to a flat encoder.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 8: Inference time (milliseconds) of our model to encode a context using an SRU or an LSTM encoder on a single CPU core. The last row shows the extra time needed to compare the response encoding to 10,000 cached candidate response encodings in order to find the best response.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The SRU encoder is over 4x faster than the LSTM encoder with a similar number of parameters, making it more suitable for production use.The model makes use of a fast recurrent network implementation (Lei et al., 2018) and multiheaded attention (Lin et al., 2017) and achieves over a 4.1x inference speedup over traditional encoders such as LSTM (Hochreiter and Schmidhuber, 1997).. SRU also exhibits a significant speedup in inference time compared to LSTM (by a factor of 4.1x in our experiments),. As seen in Table 8, an SRU encoder is over 4x faster than an LSTM encoder with a similar number of parameters, making it more suitable for production use.. Table 8 also highlights the scalability of using a dual encoder architecture.. Retrieving the best candidate once the context is encoded takes a negligible amount of time compared to the time to encode the context.. Since the SRU is more than 4x faster at inference time with the same level of performance," } ], "images": [ "scigen/train/41.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Inference time (milliseconds) of our model to encode a context using an SRU or an LSTM encoder on a single CPU core. The last row shows the extra time needed to compare the response encoding to 10,000 cached candidate response encodings in order to find the best response." ], "reasoning": "The SRU encoder is over 4x faster than the LSTM encoder with a similar number of parameters, making it more suitable for production use.", "description": "The model makes use of a fast recurrent network implementation (Lei et al., 2018) and multiheaded attention (Lin et al., 2017) and achieves over a 4.1x inference speedup over traditional encoders such as LSTM (Hochreiter and Schmidhuber, 1997).. SRU also exhibits a significant speedup in inference time compared to LSTM (by a factor of 4.1x in our experiments),. As seen in Table 8, an SRU encoder is over 4x faster than an LSTM encoder with a similar number of parameters, making it more suitable for production use.. Table 8 also highlights the scalability of using a dual encoder architecture.. Retrieving the best candidate once the context is encoded takes a negligible amount of time compared to the time to encode the context.. Since the SRU is more than 4x faster at inference time with the same level of performance,", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 8: Inference time (milliseconds) of our model to encode a context using an SRU or an LSTM encoder on a single CPU core. The last row shows the extra time needed to compare the response encoding to 10,000 cached candidate response encodings in order to find the best response.' and methodically tackle the question that follows. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nHow much faster is the SRU encoder with 4 layers compared to the LSTM encoder with 4 layers?\n" }, { "role": "assistant", "content": "The SRU encoder with 4 layers has a time of 21.9, while the LSTM encoder with 4 layers has a time of 174.8. The speedup factor is calculated by dividing the LSTM time by the SRU time. So, the answer is {\"answer\": \"7.98x\"}" } ], "images": [ "scigen/train/41.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Inference time (milliseconds) of our model to encode a context using an SRU or an LSTM encoder on a single CPU core. The last row shows the extra time needed to compare the response encoding to 10,000 cached candidate response encodings in order to find the best response." ], "reasoning": "The SRU encoder with 4 layers has a time of 21.9, while the LSTM encoder with 4 layers has a time of 174.8. The speedup factor is calculated by dividing the LSTM time by the SRU time.", "question": "How much faster is the SRU encoder with 4 layers compared to the LSTM encoder with 4 layers?", "answer": "So, the answer is {\"answer\": \"7.98x\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe SRU encoder with 2 layers is faster than the LSTM encoder with 2 layers.\n" }, { "role": "assistant", "content": "The table shows that the SRU encoder with 2 layers has a time of 14.7, while the LSTM encoder with 2 layers has a time of 90.9, indicating that the SRU encoder is faster. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/41.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Inference time (milliseconds) of our model to encode a context using an SRU or an LSTM encoder on a single CPU core. The last row shows the extra time needed to compare the response encoding to 10,000 cached candidate response encodings in order to find the best response." ], "reasoning": "The table shows that the SRU encoder with 2 layers has a time of 14.7, while the LSTM encoder with 2 layers has a time of 90.9, indicating that the SRU encoder is faster.", "statement": "The SRU encoder with 2 layers is faster than the LSTM encoder with 2 layers.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: AUC and AUC@p of our model on the propriety help desk dataset.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The model shows high AUC values, indicating strong performance in distinguishing true responses from negative ones, even under stringent conditions of low false positive rates.The performance of our model according to these AUC metrics can be seen in Table 3. The high AUC indicates that our model can easily distinguish between the true response and negative responses. Furthermore, the AUC@p numbers show that the model has a relatively high true positive rate even under the difficult requirement of a low false positive rate." } ], "images": [ "scigen/train/42.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: AUC and AUC@p of our model on the propriety help desk dataset." ], "reasoning": "The model shows high AUC values, indicating strong performance in distinguishing true responses from negative ones, even under stringent conditions of low false positive rates.", "description": "The performance of our model according to these AUC metrics can be seen in Table 3. The high AUC indicates that our model can easily distinguish between the true response and negative responses. Furthermore, the AUC@p numbers show that the model has a relatively high true positive rate even under the difficult requirement of a low false positive rate.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: 'Table 3: AUC and AUC@p of our model on the propriety help desk dataset.'\nQuestion: What is the AUC@0.05 value for the test set?\n" }, { "role": "assistant", "content": "To find the AUC@0.05 value for the test set, locate the row labeled 'AUC@0.05' and then find the corresponding value under the 'Test' column. So, the answer is {\"answer\": \"0.816\"}." } ], "images": [ "scigen/train/42.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: AUC and AUC@p of our model on the propriety help desk dataset." ], "reasoning": "To find the AUC@0.05 value for the test set, locate the row labeled 'AUC@0.05' and then find the corresponding value under the 'Test' column.", "question": "What is the AUC@0.05 value for the test set?", "answer": "So, the answer is {\"answer\": \"0.816\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe AUC value for the validation set is higher than for the test set.\n" }, { "role": "assistant", "content": "The table shows that the AUC value for the validation set is 0.991, while the AUC value for the test set is 0.977. Since 0.991 is greater than 0.977, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/42.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: AUC and AUC@p of our model on the propriety help desk dataset." ], "reasoning": "The table shows that the AUC value for the validation set is 0.991, while the AUC value for the test set is 0.977. Since 0.991 is greater than 0.977, the statement is supported.", "statement": "The AUC value for the validation set is higher than for the test set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: Recall@k from n response candidates for different values of n using random whitelists. Each random whitelist includes the correct response along with n−1 randomly selected responses.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The table shows that recall significantly decreases as the number of candidates increases, indicating that prior evaluations may overestimate model performance.Table 4 shows Rn@k on the test set for different values of n and k when using a random. Table 4 shows that recall drops significantly as n grows, meaning that the R10@k evaluation performed by prior work may significantly overstate model performance in a production setting." } ], "images": [ "scigen/train/43.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Recall@k from n response candidates for different values of n using random whitelists. Each random whitelist includes the correct response along with n−1 randomly selected responses." ], "reasoning": "The table shows that recall significantly decreases as the number of candidates increases, indicating that prior evaluations may overestimate model performance.", "description": "Table 4 shows Rn@k on the test set for different values of n and k when using a random. Table 4 shows that recall drops significantly as n grows, meaning that the R10@k evaluation performed by prior work may significantly overstate model performance in a production setting.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 4: Recall@k from n response candidates for different values of n using random whitelists. Each random whitelist includes the correct response along with n−1 randomly selected responses.' in mind, break down and resolve the question below step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhat is the recall at 1 for 1,000 candidates?\n" }, { "role": "assistant", "content": "To find the recall at 1 for 1,000 candidates, locate the row with 1,000 candidates and then find the value under the R@1 column. So, the answer is {\"answer\": \"0.449\"}." } ], "images": [ "scigen/train/43.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Recall@k from n response candidates for different values of n using random whitelists. Each random whitelist includes the correct response along with n−1 randomly selected responses." ], "reasoning": "To find the recall at 1 for 1,000 candidates, locate the row with 1,000 candidates and then find the value under the R@1 column.", "question": "What is the recall at 1 for 1,000 candidates?", "answer": "So, the answer is {\"answer\": \"0.449\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe recall at 10 for 10 candidates is 1.\n" }, { "role": "assistant", "content": "The table shows that for 10 candidates, the recall at 10 (R@10) is indeed 1. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/43.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Recall@k from n response candidates for different values of n using random whitelists. Each random whitelist includes the correct response along with n−1 randomly selected responses." ], "reasoning": "The table shows that for 10 candidates, the recall at 10 (R@10) is indeed 1.", "statement": "The recall at 10 for 10 candidates is 1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5: Recall@k for random, frequency, and clustering whitelists of different sizes. The “+” indicates that the true response is added to the whitelist.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The clustering-based whitelists show superior recall performance compared to frequency-based whitelists when considering responses already in the whitelist, but this does not account for the frequency of such cases.The results in Table 5 show that the three types of whitelists perform comparably to each other when the true response is added. However, in the more realistic second case, when recall is only computed on examples with a response already in the whitelist, performance on the frequency and clustering whitelists drops significantly.. The BLEU scores computed with the frequency and clustering whitelists are slightly higher than those computed with random whitelists.. The recall results in Table 5 seem to indicate that the clustering-based whitelists are strictly superior to the frequency-based whitelists in the realistic case when we only consider responses that are already contained in the whitelist, but this analysis fails to account for the frequency with which this is the case." } ], "images": [ "scigen/train/44.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Recall@k for random, frequency, and clustering whitelists of different sizes. The “+” indicates that the true response is added to the whitelist." ], "reasoning": "The clustering-based whitelists show superior recall performance compared to frequency-based whitelists when considering responses already in the whitelist, but this does not account for the frequency of such cases.", "description": "The results in Table 5 show that the three types of whitelists perform comparably to each other when the true response is added. However, in the more realistic second case, when recall is only computed on examples with a response already in the whitelist, performance on the frequency and clustering whitelists drops significantly.. The BLEU scores computed with the frequency and clustering whitelists are slightly higher than those computed with random whitelists.. The recall results in Table 5 seem to indicate that the clustering-based whitelists are strictly superior to the frequency-based whitelists in the realistic case when we only consider responses that are already contained in the whitelist, but this analysis fails to account for the frequency with which this is the case.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 5: Recall@k for random, frequency, and clustering whitelists of different sizes. The “+” indicates that the true response is added to the whitelist.', solve the subsequent question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhich whitelist has the highest BLEU score when considering responses already in the whitelist?\n" }, { "role": "assistant", "content": "To find the highest BLEU score, we need to look at the BLEU column and identify the highest value. The highest BLEU score is 67.05, which corresponds to the Frequency 1K+ whitelist. So, the answer is {\"answer\": \"Frequency 1K+\"}." } ], "images": [ "scigen/train/44.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Recall@k for random, frequency, and clustering whitelists of different sizes. The “+” indicates that the true response is added to the whitelist." ], "reasoning": "To find the highest BLEU score, we need to look at the BLEU column and identify the highest value. The highest BLEU score is 67.05, which corresponds to the Frequency 1K+ whitelist.", "question": "Which whitelist has the highest BLEU score when considering responses already in the whitelist?", "answer": "So, the answer is {\"answer\": \"Frequency 1K+\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The clustering-based whitelists have higher recall than frequency-based whitelists in the realistic case., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe clustering-based whitelists have higher recall than frequency-based whitelists in the realistic case.\n" }, { "role": "assistant", "content": "The table shows that in the realistic case (when considering responses already in the whitelist), the clustering-based whitelists have higher recall values (R@1, R@3, R@5, R@10) compared to frequency-based whitelists. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/44.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Recall@k for random, frequency, and clustering whitelists of different sizes. The “+” indicates that the true response is added to the whitelist." ], "reasoning": "The table shows that in the realistic case (when considering responses already in the whitelist), the clustering-based whitelists have higher recall values (R@1, R@3, R@5, R@10) compared to frequency-based whitelists.", "statement": "The clustering-based whitelists have higher recall than frequency-based whitelists in the realistic case.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6: Recall@1 versus coverage for frequency and clustering whitelists.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The clustering whitelists have higher recall (R@1) compared to the frequency whitelists, but the frequency whitelists have higher coverage.Table 6 shows R@1 and coverage for the frequency and clustering whitelists. While the clustering whitelists have higher recall, the frequency whitelists have higher coverage." } ], "images": [ "scigen/train/45.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Recall@1 versus coverage for frequency and clustering whitelists." ], "reasoning": "The clustering whitelists have higher recall (R@1) compared to the frequency whitelists, but the frequency whitelists have higher coverage.", "description": "Table 6 shows R@1 and coverage for the frequency and clustering whitelists. While the clustering whitelists have higher recall, the frequency whitelists have higher coverage.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 6: Recall@1 versus coverage for frequency and clustering whitelists.' in mind, break down and resolve the question below step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhich whitelist has the highest recall (R@1) and what is its coverage?\n" }, { "role": "assistant", "content": "The clustering whitelist with 1K has the highest recall (R@1) of 0.331, and its coverage is 23.28%. So, the answer is {\"answer\": \"Clustering 1K with 23.28% coverage\"}." } ], "images": [ "scigen/train/45.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Recall@1 versus coverage for frequency and clustering whitelists." ], "reasoning": "The clustering whitelist with 1K has the highest recall (R@1) of 0.331, and its coverage is 23.28%.", "question": "Which whitelist has the highest recall (R@1) and what is its coverage?", "answer": "So, the answer is {\"answer\": \"Clustering 1K with 23.28% coverage\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The frequency whitelist with 10K has the highest coverage., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe frequency whitelist with 10K has the highest coverage.\n" }, { "role": "assistant", "content": "The table shows that the frequency whitelist with 10K has a coverage of 45.04%, which is the highest among all whitelists. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/45.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Recall@1 versus coverage for frequency and clustering whitelists." ], "reasoning": "The table shows that the frequency whitelist with 10K has a coverage of 45.04%, which is the highest among all whitelists.", "statement": "The frequency whitelist with 10K has the highest coverage.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 7: Results of the human evaluation of the responses produced by our model. A response is acceptable if it is either good or great. Note: Numbers may not add up to 100% due to rounding.'.\n" }, { "role": "assistant", "content": "The proposed system selects acceptable responses about 80% of the time and great responses more than 50% of the time, with little effect from the size and type of whitelist.The results of the human evaluation are in Table 7. Our proposed system works well, selecting acceptable (i.e. good or great) responses about 80% of the time and selecting great responses more than 50% of the time. Interestingly, the size and type of whitelist seem to have little effect on performance, indicating that all the whitelists contain responses appropriate to a variety of conversational contexts." } ], "images": [ "scigen/train/46.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Results of the human evaluation of the responses produced by our model. A response is acceptable if it is either good or great. Note: Numbers may not add up to 100% due to rounding." ], "reasoning": "The proposed system selects acceptable responses about 80% of the time and great responses more than 50% of the time, with little effect from the size and type of whitelist.", "description": "The results of the human evaluation are in Table 7. Our proposed system works well, selecting acceptable (i.e. good or great) responses about 80% of the time and selecting great responses more than 50% of the time. Interestingly, the size and type of whitelist seem to have little effect on performance, indicating that all the whitelists contain responses appropriate to a variety of conversational contexts.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 7: Results of the human evaluation of the responses produced by our model. A response is acceptable if it is either good or great. Note: Numbers may not add up to 100% due to rounding.'. Solve the following question based on the table.\n\nQuestion: What is the percentage of great responses selected by the system using the Cluster.10K whitelist?\nLet's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the percentage of great responses for each whitelist. For the Cluster.10K whitelist, the percentage of great responses is 57%. So, the answer is {\"answer\": \"57%\"}." } ], "images": [ "scigen/train/46.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Results of the human evaluation of the responses produced by our model. A response is acceptable if it is either good or great. Note: Numbers may not add up to 100% due to rounding." ], "reasoning": "The table shows the percentage of great responses for each whitelist. For the Cluster.10K whitelist, the percentage of great responses is 57%.", "question": "What is the percentage of great responses selected by the system using the Cluster.10K whitelist?", "answer": "So, the answer is {\"answer\": \"57%\"}." }, { "messages": [ { "role": "user", "content": "The system selects acceptable responses 80% of the time using the Freq.10K whitelist. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe system selects acceptable responses 80% of the time using the Freq.10K whitelist.\n" }, { "role": "assistant", "content": "The table shows that the percentage of acceptable responses for the Freq.10K whitelist is 80%, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/46.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Results of the human evaluation of the responses produced by our model. A response is acceptable if it is either good or great. Note: Numbers may not add up to 100% due to rounding." ], "reasoning": "The table shows that the percentage of acceptable responses for the Freq.10K whitelist is 80%, which matches the statement.", "statement": "The system selects acceptable responses 80% of the time using the Freq.10K whitelist.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 6: Performance of our baselines on the development set. Parallelism+URL tests the page-context setting; all other test the snippet-context setting. Bold indicates best performance in each setting.'.\n" }, { "role": "assistant", "content": "Both cues, Parallelism and Parallelism+URL, yield strong baselines comparable to the strongest OntoNotes-trained systems, with Parallelism+URL showing a 2% overall performance gain compared to Parallelism.Both cues yield strong baselines comparable to the strongest OntoNotes-trained systems (cf. Table 4). In fact, Lee et al. (2017) and PARALLELISM produce remarkably similar output: of the 2000 example pairs in the development set, the two have completely opposing predictions (i.e. Name A vs. Name B) on only 325 examples.. Further, the cues are markedly gender-neutral, improving the Bias metric by 9% in the standard task formulation and to parity in the gold-two-mention case.. The heuristic gives a performance gain of 2% overall compared to PARALLELISM.. TRANSFORMER-MULTI is stronger than TRANSFORMER-SINGLE. .2% overall improvement over TRANSFORMER-SINGLE for the goldtwo-mention task." } ], "images": [ "scigen/train/47.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Performance of our baselines on the development set. Parallelism+URL tests the page-context setting; all other test the snippet-context setting. Bold indicates best performance in each setting." ], "reasoning": "Both cues, Parallelism and Parallelism+URL, yield strong baselines comparable to the strongest OntoNotes-trained systems, with Parallelism+URL showing a 2% overall performance gain compared to Parallelism.", "description": "Both cues yield strong baselines comparable to the strongest OntoNotes-trained systems (cf. Table 4). In fact, Lee et al. (2017) and PARALLELISM produce remarkably similar output: of the 2000 example pairs in the development set, the two have completely opposing predictions (i.e. Name A vs. Name B) on only 325 examples.. Further, the cues are markedly gender-neutral, improving the Bias metric by 9% in the standard task formulation and to parity in the gold-two-mention case.. The heuristic gives a performance gain of 2% overall compared to PARALLELISM.. TRANSFORMER-MULTI is stronger than TRANSFORMER-SINGLE. .2% overall improvement over TRANSFORMER-SINGLE for the goldtwo-mention task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 6: Performance of our baselines on the development set. Parallelism+URL tests the page-context setting; all other test the snippet-context setting. Bold indicates best performance in each setting.'. The final result should be presented in the JSON format of {\"answer\": \"\"}. \n\nQuestion: Which method shows the highest overall performance in the table?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the highest overall performance, compare the 'O' column values for all methods. Parallelism+URL has the highest value of 69.0. So, the answer is {\"answer\": \"Parallelism+URL\"}." } ], "images": [ "scigen/train/47.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Performance of our baselines on the development set. Parallelism+URL tests the page-context setting; all other test the snippet-context setting. Bold indicates best performance in each setting." ], "reasoning": "To find the highest overall performance, compare the 'O' column values for all methods. Parallelism+URL has the highest value of 69.0.", "question": "Which method shows the highest overall performance in the table?", "answer": "So, the answer is {\"answer\": \"Parallelism+URL\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Transformer-Multi performs better than Transformer-Single in the table. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTransformer-Multi performs better than Transformer-Single in the table.\n" }, { "role": "assistant", "content": "Compare the 'O' column values for Transformer-Multi and Transformer-Single. Transformer-Multi has a value of 56.2, which is higher than Transformer-Single's value of 55.0. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/47.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Performance of our baselines on the development set. Parallelism+URL tests the page-context setting; all other test the snippet-context setting. Bold indicates best performance in each setting." ], "reasoning": "Compare the 'O' column values for Transformer-Multi and Transformer-Single. Transformer-Multi has a value of 56.2, which is higher than Transformer-Single's value of 55.0.", "statement": "Transformer-Multi performs better than Transformer-Single in the table.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Performance of off-the-shelf resolvers on the GAP development set, split by Masculine and Feminine (Bias shows F/M), and Overall. Bold indicates best performance.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The table shows a significant performance difference between genders, with the strongest systems yielding comparable baselines.We note particularly the large difference in performance between genders,. Both cues yield strong baselines comparable to the strongest OntoNotes-trained systems (cf. Table 4). In fact, Lee et al. (2017) and PARALLELISM produce remarkably similar output: of the 2000 example pairs in the development set, the two have completely opposing predictions (i.e. Name A vs. Name B) on only 325 examples." } ], "images": [ "scigen/train/48.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance of off-the-shelf resolvers on the GAP development set, split by Masculine and Feminine (Bias shows F/M), and Overall. Bold indicates best performance." ], "reasoning": "The table shows a significant performance difference between genders, with the strongest systems yielding comparable baselines.", "description": "We note particularly the large difference in performance between genders,. Both cues yield strong baselines comparable to the strongest OntoNotes-trained systems (cf. Table 4). In fact, Lee et al. (2017) and PARALLELISM produce remarkably similar output: of the 2000 example pairs in the development set, the two have completely opposing predictions (i.e. Name A vs. Name B) on only 325 examples.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 4: Performance of off-the-shelf resolvers on the GAP development set, split by Masculine and Feminine (Bias shows F/M), and Overall. Bold indicates best performance.' and methodically tackle the question that follows. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich system has the highest overall performance (O) score?\n" }, { "role": "assistant", "content": "To find the system with the highest overall performance score, we compare the O scores of all systems listed in the table. The highest O score is 64.7, achieved by Lee et al. (2017). So, the answer is {\"answer\": \"Lee et al. (2017)\"}" } ], "images": [ "scigen/train/48.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance of off-the-shelf resolvers on the GAP development set, split by Masculine and Feminine (Bias shows F/M), and Overall. Bold indicates best performance." ], "reasoning": "To find the system with the highest overall performance score, we compare the O scores of all systems listed in the table. The highest O score is 64.7, achieved by Lee et al. (2017).", "question": "Which system has the highest overall performance (O) score?", "answer": "So, the answer is {\"answer\": \"Lee et al. (2017)\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Wiseman et al. has the highest performance for females., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nWiseman et al. has the highest performance for females.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the F scores of all systems listed in the table. The highest F score is 62.2, achieved by Lee et al. (2017), not Wiseman et al. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/48.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance of off-the-shelf resolvers on the GAP development set, split by Masculine and Feminine (Bias shows F/M), and Overall. Bold indicates best performance." ], "reasoning": "To verify this statement, we compare the F scores of all systems listed in the table. The highest F score is 62.2, achieved by Lee et al. (2017), not Wiseman et al.", "statement": "Wiseman et al. has the highest performance for females.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Performance of our baselines on the development set in the gold-two-mention task (access to the two candidate name spans). Parallelism+URL tests the page-context setting; all other test the snippet-context setting. Bold indicates best performance in each setting.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Random is closer to the expected 50%, while Token Distance and Topical Entity show only weak improvements above Random. Parallelism+URL shows the highest improvement in the gold-two-mention task.RANDOM is indeed closer here to the expected 50% and other baselines are closer to gender-parity.. TOKEN DISTANCE and TOPICAL ENTITY are only weak improvements above RANDOM,. Further, the cues are markedly gender-neutral, improving the Bias metric by 9% in the standard task formulation and to parity in the gold-two-mention case.. .2% overall improvement over TRANSFORMER-SINGLE for the goldtwo-mention task." } ], "images": [ "scigen/train/49.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Performance of our baselines on the development set in the gold-two-mention task (access to the two candidate name spans). Parallelism+URL tests the page-context setting; all other test the snippet-context setting. Bold indicates best performance in each setting." ], "reasoning": "Random is closer to the expected 50%, while Token Distance and Topical Entity show only weak improvements above Random. Parallelism+URL shows the highest improvement in the gold-two-mention task.", "description": "RANDOM is indeed closer here to the expected 50% and other baselines are closer to gender-parity.. TOKEN DISTANCE and TOPICAL ENTITY are only weak improvements above RANDOM,. Further, the cues are markedly gender-neutral, improving the Bias metric by 9% in the standard task formulation and to parity in the gold-two-mention case.. .2% overall improvement over TRANSFORMER-SINGLE for the goldtwo-mention task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 7: Performance of our baselines on the development set in the gold-two-mention task (access to the two candidate name spans). Parallelism+URL tests the page-context setting; all other test the snippet-context setting. Bold indicates best performance in each setting.', answer the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich method shows the highest overall score (O) in the table?\n" }, { "role": "assistant", "content": "To find the highest overall score, compare the values in the 'O' column. Parallelism+URL has the highest score of 72.9. So, the answer is {\"answer\": \"Parallelism+URL\"}." } ], "images": [ "scigen/train/49.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Performance of our baselines on the development set in the gold-two-mention task (access to the two candidate name spans). Parallelism+URL tests the page-context setting; all other test the snippet-context setting. Bold indicates best performance in each setting." ], "reasoning": "To find the highest overall score, compare the values in the 'O' column. Parallelism+URL has the highest score of 72.9.", "question": "Which method shows the highest overall score (O) in the table?", "answer": "So, the answer is {\"answer\": \"Parallelism+URL\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Token Distance has a higher overall score than Topical Entity., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nToken Distance has a higher overall score than Topical Entity.\n" }, { "role": "assistant", "content": "The overall score (O) for Token Distance is 49.1, while for Topical Entity it is 48.8. Therefore, Token Distance has a higher overall score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/49.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Performance of our baselines on the development set in the gold-two-mention task (access to the two candidate name spans). Parallelism+URL tests the page-context setting; all other test the snippet-context setting. Bold indicates best performance in each setting." ], "reasoning": "The overall score (O) for Token Distance is 49.1, while for Topical Entity it is 48.8. Therefore, Token Distance has a higher overall score.", "statement": "Token Distance has a higher overall score than Topical Entity.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 8: Coreference signal of a Transformer model on the validation dataset, by encoder attention layer and head.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Coreference signals are localized on specific heads, particularly in the deep layers of the network, such as L3H7.Consistent with the observations by Vaswani et al. (2017), we observe that the coreference signal is localized on specific heads and that these heads are in the deep layers of the network (e.g. L3H7)." } ], "images": [ "scigen/train/50.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Coreference signal of a Transformer model on the validation dataset, by encoder attention layer and head." ], "reasoning": "Coreference signals are localized on specific heads, particularly in the deep layers of the network, such as L3H7.", "description": "Consistent with the observations by Vaswani et al. (2017), we observe that the coreference signal is localized on specific heads and that these heads are in the deep layers of the network (e.g. L3H7).", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 8: Coreference signal of a Transformer model on the validation dataset, by encoder attention layer and head.', answer the question below by showing each progressive step toward the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich head and layer combination has the highest coreference signal?\n" }, { "role": "assistant", "content": "To find the highest coreference signal, we need to look at all the values in the table and identify the maximum value. The highest value is 8.6, which appears in L3H3 and L3H0. So, the answer is {\"answer\": \"L3H3\"}." } ], "images": [ "scigen/train/50.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Coreference signal of a Transformer model on the validation dataset, by encoder attention layer and head." ], "reasoning": "To find the highest coreference signal, we need to look at all the values in the table and identify the maximum value. The highest value is 8.6, which appears in L3H3 and L3H0.", "question": "Which head and layer combination has the highest coreference signal?", "answer": "So, the answer is {\"answer\": \"L3H3\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe coreference signal is highest in layer 3, head 3.\n" }, { "role": "assistant", "content": "The table shows that the highest coreference signal value is 8.6, which is found in layer 3, head 3. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/50.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Coreference signal of a Transformer model on the validation dataset, by encoder attention layer and head." ], "reasoning": "The table shows that the highest coreference signal value is 8.6, which is found in layer 3, head 3.", "statement": "The coreference signal is highest in layer 3, head 3.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 9: Comparison of the predictions of the Parallelism and Transformer-Single heuristics over the GAP development dataset.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "TRANSFORMERSINGLE handles coreference substantially better when parallelism is correct, achieving 48.7% accuracy.we find that the instances of coreference that TRANSFORMERSINGLE can handle is substantially" } ], "images": [ "scigen/train/51.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Comparison of the predictions of the Parallelism and Transformer-Single heuristics over the GAP development dataset." ], "reasoning": "TRANSFORMERSINGLE handles coreference substantially better when parallelism is correct, achieving 48.7% accuracy.", "description": "we find that the instances of coreference that TRANSFORMERSINGLE can handle is substantially", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 9: Comparison of the predictions of the Parallelism and Transformer-Single heuristics over the GAP development dataset.', unfold the steps to solve the question given next. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nQuestion: What is the percentage of correct parallelism when TRANSFORMERSINGLE is incorrect?\n" }, { "role": "assistant", "content": "To find the percentage of correct parallelism when TRANSFORMERSINGLE is incorrect, look at the row labeled 'Transf. Incorrect' and the column labeled 'Parallelism Correct'. The value is 21.6%. So, the answer is {\"answer\": \"21.6%\"}." } ], "images": [ "scigen/train/51.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Comparison of the predictions of the Parallelism and Transformer-Single heuristics over the GAP development dataset." ], "reasoning": "To find the percentage of correct parallelism when TRANSFORMERSINGLE is incorrect, look at the row labeled 'Transf. Incorrect' and the column labeled 'Parallelism Correct'. The value is 21.6%.", "question": "What is the percentage of correct parallelism when TRANSFORMERSINGLE is incorrect?", "answer": "So, the answer is {\"answer\": \"21.6%\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nTRANSFORMERSINGLE has a higher percentage of correct parallelism when it is correct compared to when it is incorrect.\n" }, { "role": "assistant", "content": "The table shows that the percentage of correct parallelism is 48.7% when TRANSFORMERSINGLE is correct and 21.6% when it is incorrect. Since 48.7% is higher than 21.6%, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/51.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Comparison of the predictions of the Parallelism and Transformer-Single heuristics over the GAP development dataset." ], "reasoning": "The table shows that the percentage of correct parallelism is 48.7% when TRANSFORMERSINGLE is correct and 21.6% when it is incorrect. Since 48.7% is higher than 21.6%, the statement is supported.", "statement": "TRANSFORMERSINGLE has a higher percentage of correct parallelism when it is correct compared to when it is incorrect.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Performance comparison of our model with different values of m on the two datasets.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The optimal multi-factor count (m) varies by dataset, with m = 1 achieving the highest F1 score for NYT10 and m = 4 for NYT11.We investigate the effects of the multi-factor count (m) in our final model on the test datasets in Table 3. We observe that for the NYT10 dataset, m = {1, 2, 3} gives good performance with m = 1 achieving the highest F1 score. On the NYT11 dataset, m = 4 gives the best performance. These experiments show that the number of factors giving the best performance may vary depending on the underlying data distribution." } ], "images": [ "scigen/train/52.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance comparison of our model with different values of m on the two datasets." ], "reasoning": "The optimal multi-factor count (m) varies by dataset, with m = 1 achieving the highest F1 score for NYT10 and m = 4 for NYT11.", "description": "We investigate the effects of the multi-factor count (m) in our final model on the test datasets in Table 3. We observe that for the NYT10 dataset, m = {1, 2, 3} gives good performance with m = 1 achieving the highest F1 score. On the NYT11 dataset, m = 4 gives the best performance. These experiments show that the number of factors giving the best performance may vary depending on the underlying data distribution.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: Performance comparison of our model with different values of m on the two datasets.' to sequentially solve the question presented below. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the highest F1 score achieved for the NYT11 dataset and at what multi-factor count (m)?\n" }, { "role": "assistant", "content": "To find the highest F1 score for the NYT11 dataset, look at the 'NYT11 F1' column and identify the highest value, which is 0.571 at m = 4. So, the answer is {\"answer\": \"0.571 at m = 4\"}." } ], "images": [ "scigen/train/52.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance comparison of our model with different values of m on the two datasets." ], "reasoning": "To find the highest F1 score for the NYT11 dataset, look at the 'NYT11 F1' column and identify the highest value, which is 0.571 at m = 4.", "question": "What is the highest F1 score achieved for the NYT11 dataset and at what multi-factor count (m)?", "answer": "So, the answer is {\"answer\": \"0.571 at m = 4\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe highest F1 score for the NYT10 dataset is achieved at m = 2.\n" }, { "role": "assistant", "content": "The table shows that the highest F1 score for the NYT10 dataset is 0.566, which is achieved at m = 1, not m = 2. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/52.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance comparison of our model with different values of m on the two datasets." ], "reasoning": "The table shows that the highest F1 score for the NYT10 dataset is 0.566, which is achieved at m = 1, not m = 2.", "statement": "The highest F1 score for the NYT10 dataset is achieved at m = 2.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Performance comparison of different models on the two datasets. * denotes a statistically significant improvement over the previous best state-of-the-art model with p<0.01 under the bootstrap paired t-test. † denotes the previous best state-of-the-art model.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Our model achieves the highest F1 scores on both the NYT10 and NYT11 datasets, outperforming previous state-of-the-art models EA and PCNN by 4.3% and 3.3%, respectively.We present the results of our final model on the relation extraction task on the two datasets in Table 2. Our model outperforms the previous stateof-the-art models on both datasets in terms of F1 score. On the NYT10 dataset, it achieves 4.3% higher F1 score compared to the previous best state-of-the-art model EA. Similarly, it achieves 3.3% higher F1 score compared to the previous best state-of-the-model PCNN on the NYT11 dataset. Our model improves the precision scores on both datasets with good recall scores." } ], "images": [ "scigen/train/53.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance comparison of different models on the two datasets. * denotes a statistically significant improvement over the previous best state-of-the-art model with p<0.01 under the bootstrap paired t-test. † denotes the previous best state-of-the-art model." ], "reasoning": "Our model achieves the highest F1 scores on both the NYT10 and NYT11 datasets, outperforming previous state-of-the-art models EA and PCNN by 4.3% and 3.3%, respectively.", "description": "We present the results of our final model on the relation extraction task on the two datasets in Table 2. Our model outperforms the previous stateof-the-art models on both datasets in terms of F1 score. On the NYT10 dataset, it achieves 4.3% higher F1 score compared to the previous best state-of-the-art model EA. Similarly, it achieves 3.3% higher F1 score compared to the previous best state-of-the-model PCNN on the NYT11 dataset. Our model improves the precision scores on both datasets with good recall scores.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Performance comparison of different models on the two datasets. * denotes a statistically significant improvement over the previous best state-of-the-art model with p<0.01 under the bootstrap paired t-test. † denotes the previous best state-of-the-art model.', answer the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the F1 score improvement of our model over the previous best model on the NYT10 dataset?\n" }, { "role": "assistant", "content": "The table shows that the previous best model on the NYT10 dataset is EA with an F1 score of 0.523. Our model achieves an F1 score of 0.566 on the same dataset. The improvement is calculated as 0.566 - 0.523 = 0.043, which is 4.3%. So, the answer is {\"answer\": \"4.3%\"}." } ], "images": [ "scigen/train/53.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance comparison of different models on the two datasets. * denotes a statistically significant improvement over the previous best state-of-the-art model with p<0.01 under the bootstrap paired t-test. † denotes the previous best state-of-the-art model." ], "reasoning": "The table shows that the previous best model on the NYT10 dataset is EA with an F1 score of 0.523. Our model achieves an F1 score of 0.566 on the same dataset. The improvement is calculated as 0.566 - 0.523 = 0.043, which is 4.3%.", "question": "What is the F1 score improvement of our model over the previous best model on the NYT10 dataset?", "answer": "So, the answer is {\"answer\": \"4.3%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nOur model has the highest precision score on the NYT11 dataset.\n" }, { "role": "assistant", "content": "The table shows that our model has a precision score of 0.507 on the NYT11 dataset. However, the highest precision score on the NYT11 dataset is 0.652, which also belongs to our model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/53.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance comparison of different models on the two datasets. * denotes a statistically significant improvement over the previous best state-of-the-art model with p<0.01 under the bootstrap paired t-test. † denotes the previous best state-of-the-art model." ], "reasoning": "The table shows that our model has a precision score of 0.507 on the NYT11 dataset. However, the highest precision score on the NYT11 dataset is 0.652, which also belongs to our model.", "statement": "Our model has the highest precision score on the NYT11 dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: Effectiveness of model components (m=4) on the NYT11 dataset.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Adding multi-factor attention to the baseline BiLSTM-CNN model improves the F1 score by 0.8%, and further adding a dependency weight factor with a window size of 5 improves it by 3.2%.We include the ablation results on the NYT11 dataset in Table 4. When we add multi-factor attention to the baseline BiLSTM-CNN model without the dependency distance-based weight factor in the attention mechanism, we get 0.8% F1 score improvement (A2−A1). Adding the dependency weight factor with a window size of 5 improves. the F1 score by 3.2% (A3−A2). Increasing the window size to 10 reduces the F1 score marginally (A3−A4). Replacing the attention normalizing function with softmax operation also reduces the F1 score marginally (A3−A5). In our model, we concatenate the features extracted by each attention layer. Rather than concatenating them, we can apply max-pooling operation across the multiple attention scores to compute the final attention scores. These max-pooled attention scores are used to obtain the weighted average vector of Bi-LSTM hidden vectors. This affects the model performance negatively and F1 score of the model decreases by 3.0% (A3−A6)." } ], "images": [ "scigen/train/54.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Effectiveness of model components (m=4) on the NYT11 dataset." ], "reasoning": "Adding multi-factor attention to the baseline BiLSTM-CNN model improves the F1 score by 0.8%, and further adding a dependency weight factor with a window size of 5 improves it by 3.2%.", "description": "We include the ablation results on the NYT11 dataset in Table 4. When we add multi-factor attention to the baseline BiLSTM-CNN model without the dependency distance-based weight factor in the attention mechanism, we get 0.8% F1 score improvement (A2−A1). Adding the dependency weight factor with a window size of 5 improves. the F1 score by 3.2% (A3−A2). Increasing the window size to 10 reduces the F1 score marginally (A3−A4). Replacing the attention normalizing function with softmax operation also reduces the F1 score marginally (A3−A5). In our model, we concatenate the features extracted by each attention layer. Rather than concatenating them, we can apply max-pooling operation across the multiple attention scores to compute the final attention scores. These max-pooled attention scores are used to obtain the weighted average vector of Bi-LSTM hidden vectors. This affects the model performance negatively and F1 score of the model decreases by 3.0% (A3−A6).", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 4: Effectiveness of model components (m=4) on the NYT11 dataset.'. Solve the following question based on the table.\n\nQuestion: What is the F1 score improvement when adding the dependency weight factor with a window size of 5 to the model with standard attention?\nLet's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The F1 score for the model with standard attention (A2) is 0.539, and the F1 score for the model with a window size of 5 (A3) is 0.571. The improvement is 0.571 - 0.539 = 0.032 or 3.2%. So, the answer is {\"answer\": \"3.2%\"}." } ], "images": [ "scigen/train/54.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Effectiveness of model components (m=4) on the NYT11 dataset." ], "reasoning": "The F1 score for the model with standard attention (A2) is 0.539, and the F1 score for the model with a window size of 5 (A3) is 0.571. The improvement is 0.571 - 0.539 = 0.032 or 3.2%.", "question": "What is the F1 score improvement when adding the dependency weight factor with a window size of 5 to the model with standard attention?", "answer": "So, the answer is {\"answer\": \"3.2%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nIncreasing the window size to 10 improves the F1 score.\n" }, { "role": "assistant", "content": "The F1 score for the model with a window size of 5 (A3) is 0.571, and the F1 score for the model with a window size of 10 (A4) is 0.568. Increasing the window size to 10 reduces the F1 score marginally. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/54.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Effectiveness of model components (m=4) on the NYT11 dataset." ], "reasoning": "The F1 score for the model with a window size of 5 (A3) is 0.571, and the F1 score for the model with a window size of 10 (A4) is 0.568. Increasing the window size to 10 reduces the F1 score marginally.", "statement": "Increasing the window size to 10 improves the F1 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: Category-wise performance with the default split of Flickr30k Entities.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Models pretrained on Pascal-VOC perform better on common classes like 'animals', 'people', and 'vehicles', while ZSGNet excels in 'clothing' and 'bodyparts' due to their association with the 'people' category.For Flickr30k we also note entity-wise accuracy in Table 3 and compare against [7, 34].. As these models use object detectors pretrained on Pascal-VOC , they have somewhat higher performance on classes that are common to both Flickr30k and Pascal-VOC (\"animals\", \"people\" and \"vehicles\"). However, on the classes like \"clothing\" and \"bodyparts\" our model ZSGNet shows much better performance; likely because both \"clothing\" and \"bodyparts\" are present along with \"people\" category and so the other methods choose the \"people\" category." } ], "images": [ "scigen/train/55.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Category-wise performance with the default split of Flickr30k Entities." ], "reasoning": "Models pretrained on Pascal-VOC perform better on common classes like 'animals', 'people', and 'vehicles', while ZSGNet excels in 'clothing' and 'bodyparts' due to their association with the 'people' category.", "description": "For Flickr30k we also note entity-wise accuracy in Table 3 and compare against [7, 34].. As these models use object detectors pretrained on Pascal-VOC , they have somewhat higher performance on classes that are common to both Flickr30k and Pascal-VOC (\"animals\", \"people\" and \"vehicles\"). However, on the classes like \"clothing\" and \"bodyparts\" our model ZSGNet shows much better performance; likely because both \"clothing\" and \"bodyparts\" are present along with \"people\" category and so the other methods choose the \"people\" category.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: Category-wise performance with the default split of Flickr30k Entities.', and sequentially solve the question, writing out each step of your reasoning process. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model has the highest accuracy for the 'clothing' category?\n" }, { "role": "assistant", "content": "By comparing the 'clothing' column across all models, ZSGNet - Res50 (cls) has the highest accuracy at 66.18. So, the answer is {\"answer\": \"ZSGNet - Res50 (cls)\"}." } ], "images": [ "scigen/train/55.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Category-wise performance with the default split of Flickr30k Entities." ], "reasoning": "By comparing the 'clothing' column across all models, ZSGNet - Res50 (cls) has the highest accuracy at 66.18.", "question": "Which model has the highest accuracy for the 'clothing' category?", "answer": "So, the answer is {\"answer\": \"ZSGNet - Res50 (cls)\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nCITE - VGG(det) has the highest accuracy for the 'animals' category.\n" }, { "role": "assistant", "content": "By comparing the 'animals' column, CITE - VGG(det) has the highest accuracy at 77.03. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/55.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Category-wise performance with the default split of Flickr30k Entities." ], "reasoning": "By comparing the 'animals' column, CITE - VGG(det) has the highest accuracy at 77.03.", "statement": "CITE - VGG(det) has the highest accuracy for the 'animals' category.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Comparison of our model with other state of the art methods. We denote those networks which use classification weights from ImageNet [41] using “cls” and those networks which use detection weights from Pascal VOC [12] using “det”. The reported numbers are all Accuracy@IoU=0.5 or equivalently Recall@1. Models marked with “*” fine-tune their detection network on the entities in the Flickr30k.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "ZSGNet outperforms prior methods on both Flickr30k Entities and ReferIt datasets, with a notable performance increase on ReferIt due to the lack of fixed entities.Table 2 compares ZSGNet with prior works on Flickr30k Entities and ReferIt . We use \"det\" and \"cls\" to denote models using Pascal VOC detection weights and ImageNet [10, 41] classification weights. Networks marked. with \"*\" fine-tune their object detector pretrained on PascalVOC on the fixed entities of Flickr30k .. However, such information is not available in ReferIt dataset which explains ∼ 9% increase in performance of ZSGNet over other methods." } ], "images": [ "scigen/train/56.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of our model with other state of the art methods. We denote those networks which use classification weights from ImageNet [41] using “cls” and those networks which use detection weights from Pascal VOC [12] using “det”. The reported numbers are all Accuracy@IoU=0.5 or equivalently Recall@1. Models marked with “*” fine-tune their detection network on the entities in the Flickr30k." ], "reasoning": "ZSGNet outperforms prior methods on both Flickr30k Entities and ReferIt datasets, with a notable performance increase on ReferIt due to the lack of fixed entities.", "description": "Table 2 compares ZSGNet with prior works on Flickr30k Entities and ReferIt . We use \"det\" and \"cls\" to denote models using Pascal VOC detection weights and ImageNet [10, 41] classification weights. Networks marked. with \"*\" fine-tune their object detector pretrained on PascalVOC on the fixed entities of Flickr30k .. However, such information is not available in ReferIt dataset which explains ∼ 9% increase in performance of ZSGNet over other methods.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 2: Comparison of our model with other state of the art methods. We denote those networks which use classification weights from ImageNet [41] using “cls” and those networks which use detection weights from Pascal VOC [12] using “det”. The reported numbers are all Accuracy@IoU=0.5 or equivalently Recall@1. Models marked with “*” fine-tune their detection network on the entities in the Flickr30k.', solve the following question step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich method has the highest performance on the ReferIt dataset?\n" }, { "role": "assistant", "content": "By comparing the performance scores on the ReferIt dataset, ZSGNet (cls) with Res50 has the highest score of 58.63. So, the answer is {\"answer\": \"ZSGNet (cls) with Res50\"}." } ], "images": [ "scigen/train/56.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of our model with other state of the art methods. We denote those networks which use classification weights from ImageNet [41] using “cls” and those networks which use detection weights from Pascal VOC [12] using “det”. The reported numbers are all Accuracy@IoU=0.5 or equivalently Recall@1. Models marked with “*” fine-tune their detection network on the entities in the Flickr30k." ], "reasoning": "By comparing the performance scores on the ReferIt dataset, ZSGNet (cls) with Res50 has the highest score of 58.63.", "question": "Which method has the highest performance on the ReferIt dataset?", "answer": "So, the answer is {\"answer\": \"ZSGNet (cls) with Res50\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence ZSGNet (cls) with VGG has the highest performance on the Flickr30k Entities dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nZSGNet (cls) with VGG has the highest performance on the Flickr30k Entities dataset.\n" }, { "role": "assistant", "content": "By comparing the performance scores on the Flickr30k Entities dataset, ZSGNet (cls) with Res50 has the highest score of 63.39, not ZSGNet (cls) with VGG. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/56.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of our model with other state of the art methods. We denote those networks which use classification weights from ImageNet [41] using “cls” and those networks which use detection weights from Pascal VOC [12] using “det”. The reported numbers are all Accuracy@IoU=0.5 or equivalently Recall@1. Models marked with “*” fine-tune their detection network on the entities in the Flickr30k." ], "reasoning": "By comparing the performance scores on the Flickr30k Entities dataset, ZSGNet (cls) with Res50 has the highest score of 63.39, not ZSGNet (cls) with VGG.", "statement": "ZSGNet (cls) with VGG has the highest performance on the Flickr30k Entities dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Accuracy across various unseen splits. For Flickr-Split-0,1 we use Accuracy with IoU threshold of 0.5. Since Visual Genome annotations are noisy we additionally report Accuracy with IoU threshold of 0.3. The second row denotes the IoU threshold at which the Accuracy is calculated. “B” and “UB” denote the balanced and unbalanced sets.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "ZSGNet consistently outperforms QRG by 4-8% across all splits, with higher accuracy on Flickr-Split-0 and 1 compared to VG-splits, and maintains performance across balanced and unbalanced sets.Table 4 shows the performance of our ZSGNet model compared to QRG on the four unseen splits. Across all splits, ZSGNet shows 4−8% higher performance than QRG even though the latter has seen more data. we observe that the accuracy obtained on Flickr-Split-0,1 are higher than the VG-split likely due to larger variation of the referred objects in Visual Genome.. Finally, the accuracy remains the same across the balanced and unbalanced sets indicating the model performs well across all clusters as our training set is balanced." } ], "images": [ "scigen/train/57.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Accuracy across various unseen splits. For Flickr-Split-0,1 we use Accuracy with IoU threshold of 0.5. Since Visual Genome annotations are noisy we additionally report Accuracy with IoU threshold of 0.3. The second row denotes the IoU threshold at which the Accuracy is calculated. “B” and “UB” denote the balanced and unbalanced sets." ], "reasoning": "ZSGNet consistently outperforms QRG by 4-8% across all splits, with higher accuracy on Flickr-Split-0 and 1 compared to VG-splits, and maintains performance across balanced and unbalanced sets.", "description": "Table 4 shows the performance of our ZSGNet model compared to QRG on the four unseen splits. Across all splits, ZSGNet shows 4−8% higher performance than QRG even though the latter has seen more data. we observe that the accuracy obtained on Flickr-Split-0,1 are higher than the VG-split likely due to larger variation of the referred objects in Visual Genome.. Finally, the accuracy remains the same across the balanced and unbalanced sets indicating the model performs well across all clusters as our training set is balanced.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 4: Accuracy across various unseen splits. For Flickr-Split-0,1 we use Accuracy with IoU threshold of 0.5. Since Visual Genome annotations are noisy we additionally report Accuracy with IoU threshold of 0.3. The second row denotes the IoU threshold at which the Accuracy is calculated. “B” and “UB” denote the balanced and unbalanced sets.' and proceed to solve the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the accuracy of ZSGNet with Res50 on the VG-3B 0.3 split?\n" }, { "role": "assistant", "content": "To find the accuracy of ZSGNet with Res50 on the VG-3B 0.3 split, locate the row for ZSGNet with Res50 and the column for VG-3B 0.3. The value is 20.77. So, the answer is {\"answer\": \"20.77\"}" } ], "images": [ "scigen/train/57.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Accuracy across various unseen splits. For Flickr-Split-0,1 we use Accuracy with IoU threshold of 0.5. Since Visual Genome annotations are noisy we additionally report Accuracy with IoU threshold of 0.3. The second row denotes the IoU threshold at which the Accuracy is calculated. “B” and “UB” denote the balanced and unbalanced sets." ], "reasoning": "To find the accuracy of ZSGNet with Res50 on the VG-3B 0.3 split, locate the row for ZSGNet with Res50 and the column for VG-3B 0.3. The value is 20.77.", "question": "What is the accuracy of ZSGNet with Res50 on the VG-3B 0.3 split?", "answer": "So, the answer is {\"answer\": \"20.77\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nZSGNet with VGG has higher accuracy on Flickr-Split-0 than QRG with VGG.\n" }, { "role": "assistant", "content": "Comparing the accuracy of ZSGNet with VGG and QRG with VGG on Flickr-Split-0, ZSGNet has an accuracy of 39.32 while QRG has 35.62, indicating ZSGNet performs better. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/57.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Accuracy across various unseen splits. For Flickr-Split-0,1 we use Accuracy with IoU threshold of 0.5. Since Visual Genome annotations are noisy we additionally report Accuracy with IoU threshold of 0.3. The second row denotes the IoU threshold at which the Accuracy is calculated. “B” and “UB” denote the balanced and unbalanced sets." ], "reasoning": "Comparing the accuracy of ZSGNet with VGG and QRG with VGG on Flickr-Split-0, ZSGNet has an accuracy of 39.32 while QRG has 35.62, indicating ZSGNet performs better.", "statement": "ZSGNet with VGG has higher accuracy on Flickr-Split-0 than QRG with VGG.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Ablation study: BM=Base Model, softmax means we classify only one candidate box as foreground, BCE = Binary Cross Entropy means we classify each candidate box as the foreground or background, FL = Focal Loss, Img-Resize: use images of dimension 600×600'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Using softmax loss alone places the model higher than previous methods, while adding Binary Cross Entropy Loss and Focal loss boosts performance by 7%, and image resizing adds another 4%.We show the performance of our model with different loss functions using the base model of ZSGNet on the validation set of ReferIt in Table 6.. Note that using softmax loss by itself places us higher than the previous methods.. Further using Binary Cross Entropy Loss and Focal loss give a significant (7%) performance boost which is expected in a single shot framework.. Finally, image resizing gives another 4% increase." } ], "images": [ "scigen/train/58.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Ablation study: BM=Base Model, softmax means we classify only one candidate box as foreground, BCE = Binary Cross Entropy means we classify each candidate box as the foreground or background, FL = Focal Loss, Img-Resize: use images of dimension 600×600" ], "reasoning": "Using softmax loss alone places the model higher than previous methods, while adding Binary Cross Entropy Loss and Focal loss boosts performance by 7%, and image resizing adds another 4%.", "description": "We show the performance of our model with different loss functions using the base model of ZSGNet on the validation set of ReferIt in Table 6.. Note that using softmax loss by itself places us higher than the previous methods.. Further using Binary Cross Entropy Loss and Focal loss give a significant (7%) performance boost which is expected in a single shot framework.. Finally, image resizing gives another 4% increase.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 6: Ablation study: BM=Base Model, softmax means we classify only one candidate box as foreground, BCE = Binary Cross Entropy means we classify each candidate box as the foreground or background, FL = Focal Loss, Img-Resize: use images of dimension 600×600'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the combined performance boost percentage when using Binary Cross Entropy Loss, Focal loss, and image resizing?\n" }, { "role": "assistant", "content": "Using Binary Cross Entropy Loss and Focal loss gives a 7% performance boost, and image resizing adds another 4%, resulting in a combined performance boost of 11%. So, the answer is {\"answer\": \"11%\"}" } ], "images": [ "scigen/train/58.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Ablation study: BM=Base Model, softmax means we classify only one candidate box as foreground, BCE = Binary Cross Entropy means we classify each candidate box as the foreground or background, FL = Focal Loss, Img-Resize: use images of dimension 600×600" ], "reasoning": "Using Binary Cross Entropy Loss and Focal loss gives a 7% performance boost, and image resizing adds another 4%, resulting in a combined performance boost of 11%.", "question": "What is the combined performance boost percentage when using Binary Cross Entropy Loss, Focal loss, and image resizing?", "answer": "So, the answer is {\"answer\": \"11%\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Using Binary Cross Entropy Loss and Focal loss together gives a 7% performance boost., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nUsing Binary Cross Entropy Loss and Focal loss together gives a 7% performance boost.\n" }, { "role": "assistant", "content": "The description states that using Binary Cross Entropy Loss and Focal loss together gives a significant 7% performance boost, which is supported by the table. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/58.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Ablation study: BM=Base Model, softmax means we classify only one candidate box as foreground, BCE = Binary Cross Entropy means we classify each candidate box as the foreground or background, FL = Focal Loss, Img-Resize: use images of dimension 600×600" ], "reasoning": "The description states that using Binary Cross Entropy Loss and Focal loss together gives a significant 7% performance boost, which is supported by the table.", "statement": "Using Binary Cross Entropy Loss and Focal loss together gives a 7% performance boost.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Test BLEU for en-de adaptive training, with sequential adaptation to a third task. EWC-tuned models give the best performance on each domain.'.\n" }, { "role": "assistant", "content": "EWC not only reduces forgetting but also improves performance on News and IT tasks, while fine-tuning alone leads to rapid forgetting.In the en-de News/TED task (Table 4), all fine-tuning schemes give similar improvements on TED. However, EWC outperforms no-reg and L2 on News, not only reducing forgetting but giving 0.5 BLEU improvement over the baseline News model.. The IT task is very small: training on IT data alone results in over-fitting, with a 17 BLEU improvement under fine-tuning.. fine-tuning rapidly forgets previous tasks. EWC reduces forgetting on two previous tasks while further improving on the target domain." } ], "images": [ "scigen/train/59.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Test BLEU for en-de adaptive training, with sequential adaptation to a third task. EWC-tuned models give the best performance on each domain." ], "reasoning": "EWC not only reduces forgetting but also improves performance on News and IT tasks, while fine-tuning alone leads to rapid forgetting.", "description": "In the en-de News/TED task (Table 4), all fine-tuning schemes give similar improvements on TED. However, EWC outperforms no-reg and L2 on News, not only reducing forgetting but giving 0.5 BLEU improvement over the baseline News model.. The IT task is very small: training on IT data alone results in over-fitting, with a 17 BLEU improvement under fine-tuning.. fine-tuning rapidly forgets previous tasks. EWC reduces forgetting on two previous tasks while further improving on the target domain.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 4: Test BLEU for en-de adaptive training, with sequential adaptation to a third task. EWC-tuned models give the best performance on each domain.', unfold the steps to solve the question given next. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nQuestion: Which training scheme provides the highest BLEU score on the IT task?\n" }, { "role": "assistant", "content": "By examining the IT column, the highest BLEU score is 57.0, achieved by the '7 then IT, EWC' training scheme. So, the answer is {\"answer\": \"7 then IT, EWC\"}." } ], "images": [ "scigen/train/59.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Test BLEU for en-de adaptive training, with sequential adaptation to a third task. EWC-tuned models give the best performance on each domain." ], "reasoning": "By examining the IT column, the highest BLEU score is 57.0, achieved by the '7 then IT, EWC' training scheme.", "question": "Which training scheme provides the highest BLEU score on the IT task?", "answer": "So, the answer is {\"answer\": \"7 then IT, EWC\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nEWC improves the BLEU score on the News task compared to no-reg and L2.\n" }, { "role": "assistant", "content": "The table shows that '1 then TED, EWC' has a BLEU score of 38.3 on News, which is higher than '1 then TED, No-reg' with 30.6 and '1 then TED, L2' with 37.9. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/59.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Test BLEU for en-de adaptive training, with sequential adaptation to a third task. EWC-tuned models give the best performance on each domain." ], "reasoning": "The table shows that '1 then TED, EWC' has a BLEU score of 38.3 on News, which is higher than '1 then TED, No-reg' with 30.6 and '1 then TED, L2' with 37.9.", "statement": "EWC improves the BLEU score on the News task compared to no-reg and L2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Test BLEU for es-en adaptive training. EWC reduces forgetting compared to other fine-tuning methods, while offering the greatest improvement on the new domain.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Regularization methods like EWC reduce catastrophic forgetting and improve Bio scores over noreg fine-tuning, with EWC outperforming L2.For es-en, the Health and Bio tasks overlap, but catastrophic forgetting still occurs under noreg (Table 3). Regularization reduces forgetting and allows further improvements on Bio over noreg fine-tuning. We find EWC outperforms the L2 approach" } ], "images": [ "scigen/train/60.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Test BLEU for es-en adaptive training. EWC reduces forgetting compared to other fine-tuning methods, while offering the greatest improvement on the new domain." ], "reasoning": "Regularization methods like EWC reduce catastrophic forgetting and improve Bio scores over noreg fine-tuning, with EWC outperforming L2.", "description": "For es-en, the Health and Bio tasks overlap, but catastrophic forgetting still occurs under noreg (Table 3). Regularization reduces forgetting and allows further improvements on Bio over noreg fine-tuning. We find EWC outperforms the L2 approach", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: Test BLEU for es-en adaptive training. EWC reduces forgetting compared to other fine-tuning methods, while offering the greatest improvement on the new domain.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich training scheme achieved the highest Bio score?\n" }, { "role": "assistant", "content": "By examining the Bio scores in the table, the highest score is 37.8, achieved by the '1 then Bio, EWC' training scheme. So, the answer is {\"answer\": \"1 then Bio, EWC\"}" } ], "images": [ "scigen/train/60.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Test BLEU for es-en adaptive training. EWC reduces forgetting compared to other fine-tuning methods, while offering the greatest improvement on the new domain." ], "reasoning": "By examining the Bio scores in the table, the highest score is 37.8, achieved by the '1 then Bio, EWC' training scheme.", "question": "Which training scheme achieved the highest Bio score?", "answer": "So, the answer is {\"answer\": \"1 then Bio, EWC\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Health and Bio training scheme achieved the highest Bio score.\n" }, { "role": "assistant", "content": "The table shows that the highest Bio score is 37.8, achieved by the '1 then Bio, EWC' training scheme, not the 'Health and Bio' scheme. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/60.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Test BLEU for es-en adaptive training. EWC reduces forgetting compared to other fine-tuning methods, while offering the greatest improvement on the new domain." ], "reasoning": "The table shows that the highest Bio score is 37.8, achieved by the '1 then Bio, EWC' training scheme, not the 'Health and Bio' scheme.", "statement": "The Health and Bio training scheme achieved the highest Bio score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: Test BLEU for 2-model es-en and 3-model en-de unadapted model ensembling, compared to oracle unadapted model chosen if test domain is known. Uniform ensembling generally underperforms the oracle, while BI+IS outperforms the oracle.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Adaptive decoding schemes with unadapted models trained on one domain outperform uniform ensembling, especially on general domains, and BI with λ as in Eq. 10 shows further improvements.Table 5 shows improvements on data without domain labelling using our adaptive decoding schemes with unadapted models trained only on one domain. Uniform ensembling under-performs all oracle models except es-en Bio, especially on general domains. Identity-BI strongly improves over uniform ensembling, and BI with λ as in Eq. 10 improves further for all but es-en Bio. BI and IS both individually outperform the oracle for all but IS-News,. With adaptive decoding, we do not need to assume whether a uniform ensemble or a single model might perform better for some potentially unknown domain. We highlight this in Table 7 by reporting results with the ensembles of Tables 5 and 6 over concatenated test sets, to mimic the realistic scenario of unlabelled test data. We additionally include the uniform no-reg ensembling" } ], "images": [ "scigen/train/61.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Test BLEU for 2-model es-en and 3-model en-de unadapted model ensembling, compared to oracle unadapted model chosen if test domain is known. Uniform ensembling generally underperforms the oracle, while BI+IS outperforms the oracle." ], "reasoning": "Adaptive decoding schemes with unadapted models trained on one domain outperform uniform ensembling, especially on general domains, and BI with λ as in Eq. 10 shows further improvements.", "description": "Table 5 shows improvements on data without domain labelling using our adaptive decoding schemes with unadapted models trained only on one domain. Uniform ensembling under-performs all oracle models except es-en Bio, especially on general domains. Identity-BI strongly improves over uniform ensembling, and BI with λ as in Eq. 10 improves further for all but es-en Bio. BI and IS both individually outperform the oracle for all but IS-News,. With adaptive decoding, we do not need to assume whether a uniform ensemble or a single model might perform better for some potentially unknown domain. We highlight this in Table 7 by reporting results with the ensembles of Tables 5 and 6 over concatenated test sets, to mimic the realistic scenario of unlabelled test data. We additionally include the uniform no-reg ensembling", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 5: Test BLEU for 2-model es-en and 3-model en-de unadapted model ensembling, compared to oracle unadapted model chosen if test domain is known. Uniform ensembling generally underperforms the oracle, while BI+IS outperforms the oracle.', solve the following question step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich decoder configuration achieves the highest score for the en-de IT domain?\n" }, { "role": "assistant", "content": "To find the highest score for the en-de IT domain, we need to look at the en-de IT column and identify the highest value. The highest score in this column is 44.7, achieved by both BI and BI + IS configurations. So, the answer is {\"answer\": \"BI and BI + IS\"}." } ], "images": [ "scigen/train/61.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Test BLEU for 2-model es-en and 3-model en-de unadapted model ensembling, compared to oracle unadapted model chosen if test domain is known. Uniform ensembling generally underperforms the oracle, while BI+IS outperforms the oracle." ], "reasoning": "To find the highest score for the en-de IT domain, we need to look at the en-de IT column and identify the highest value. The highest score in this column is 44.7, achieved by both BI and BI + IS configurations.", "question": "Which decoder configuration achieves the highest score for the en-de IT domain?", "answer": "So, the answer is {\"answer\": \"BI and BI + IS\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Identity-BI configuration outperforms the Oracle model in the es-en Bio domain.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the scores of Identity-BI and Oracle model in the es-en Bio domain. The Identity-BI score is 36.6, which is higher than the Oracle model score of 36.1. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/61.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Test BLEU for 2-model es-en and 3-model en-de unadapted model ensembling, compared to oracle unadapted model chosen if test domain is known. Uniform ensembling generally underperforms the oracle, while BI+IS outperforms the oracle." ], "reasoning": "To verify this statement, we compare the scores of Identity-BI and Oracle model in the es-en Bio domain. The Identity-BI score is 36.6, which is higher than the Oracle model score of 36.1.", "statement": "The Identity-BI configuration outperforms the Oracle model in the es-en Bio domain.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 6: Test BLEU for 2-model es-en and 3-model en-de model ensembling for models adapted with EWC, compared to oracle model last trained on each domain, chosen if test domain is known. BI+IS outperforms uniform ensembling and in some cases outperforms the oracle.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Adaptive decoding with BI+IS improves performance over uniform ensembling and the oracle model in most cases, even for EWC models that perform well across multiple domains.In Table 6 we apply the best adaptive decoding scheme, BI+IS, to models fine-tuned with EWC.. EWC models perform well over multiple domains, so the improvement over uniform ensembling is less striking than for unadapted models. Nevertheless adaptive decoding improves over both uniform ensembling and the oracle model in most cases." } ], "images": [ "scigen/train/62.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Test BLEU for 2-model es-en and 3-model en-de model ensembling for models adapted with EWC, compared to oracle model last trained on each domain, chosen if test domain is known. BI+IS outperforms uniform ensembling and in some cases outperforms the oracle." ], "reasoning": "Adaptive decoding with BI+IS improves performance over uniform ensembling and the oracle model in most cases, even for EWC models that perform well across multiple domains.", "description": "In Table 6 we apply the best adaptive decoding scheme, BI+IS, to models fine-tuned with EWC.. EWC models perform well over multiple domains, so the improvement over uniform ensembling is less striking than for unadapted models. Nevertheless adaptive decoding improves over both uniform ensembling and the oracle model in most cases.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 6: Test BLEU for 2-model es-en and 3-model en-de model ensembling for models adapted with EWC, compared to oracle model last trained on each domain, chosen if test domain is known. BI+IS outperforms uniform ensembling and in some cases outperforms the oracle.' to sequentially solve the question presented below. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhich decoder configuration achieves the highest score for the en-de IT domain?\n" }, { "role": "assistant", "content": "By examining the en-de IT column, the highest score is achieved by the Oracle model with a score of 57.0. So, the answer is {\"answer\": \"Oracle model\"}." } ], "images": [ "scigen/train/62.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Test BLEU for 2-model es-en and 3-model en-de model ensembling for models adapted with EWC, compared to oracle model last trained on each domain, chosen if test domain is known. BI+IS outperforms uniform ensembling and in some cases outperforms the oracle." ], "reasoning": "By examining the en-de IT column, the highest score is achieved by the Oracle model with a score of 57.0.", "question": "Which decoder configuration achieves the highest score for the en-de IT domain?", "answer": "So, the answer is {\"answer\": \"Oracle model\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe BI+IS decoder configuration achieves the highest score for the es-en Health domain.\n" }, { "role": "assistant", "content": "By examining the es-en Health column, the highest score is achieved by the BI+IS decoder configuration with a score of 36.2. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/62.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Test BLEU for 2-model es-en and 3-model en-de model ensembling for models adapted with EWC, compared to oracle model last trained on each domain, chosen if test domain is known. BI+IS outperforms uniform ensembling and in some cases outperforms the oracle." ], "reasoning": "By examining the es-en Health column, the highest score is achieved by the BI+IS decoder configuration with a score of 36.2.", "statement": "The BI+IS decoder configuration achieves the highest score for the es-en Health domain.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 7: Total BLEU for test data concatenated across domains. Results from 2-model es-en and 3-model en-de ensembles, compared to oracle model chosen if test domain is known. No-reg uniform corresponds to the approach of Freitag and Al-Onaizan (2016). BI+IS performs similarly to strong oracles with no test domain labeling.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "BI+IS decoding with EWC-adapted models achieves the highest BLEU scores, outperforming both uniform ensembling and single-domain models.Uniform no-reg ensembling outperforms unadapted uniform ensembling, since fine-tuning gives better in-domain performance.. BI+IS decoding with single-domain trained models achieves gains over both the naive uniform approach and over oracle single-domain models. BI+IS with EWC-adapted models gives a 0.9 / 3.4 BLEU gain over the strong uniform EWC ensemble, and a 2.4 / 10.2 overall BLEU gain over the approach described in Freitag and Al-Onaizan (2016)." } ], "images": [ "scigen/train/63.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Total BLEU for test data concatenated across domains. Results from 2-model es-en and 3-model en-de ensembles, compared to oracle model chosen if test domain is known. No-reg uniform corresponds to the approach of Freitag and Al-Onaizan (2016). BI+IS performs similarly to strong oracles with no test domain labeling." ], "reasoning": "BI+IS decoding with EWC-adapted models achieves the highest BLEU scores, outperforming both uniform ensembling and single-domain models.", "description": "Uniform no-reg ensembling outperforms unadapted uniform ensembling, since fine-tuning gives better in-domain performance.. BI+IS decoding with single-domain trained models achieves gains over both the naive uniform approach and over oracle single-domain models. BI+IS with EWC-adapted models gives a 0.9 / 3.4 BLEU gain over the strong uniform EWC ensemble, and a 2.4 / 10.2 overall BLEU gain over the approach described in Freitag and Al-Onaizan (2016).", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 7: Total BLEU for test data concatenated across domains. Results from 2-model es-en and 3-model en-de ensembles, compared to oracle model chosen if test domain is known. No-reg uniform corresponds to the approach of Freitag and Al-Onaizan (2016). BI+IS performs similarly to strong oracles with no test domain labeling.', solve the subsequent question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhat is the BLEU score for the en-de language pair using the EWC model type with BI+IS decoder configuration?\n" }, { "role": "assistant", "content": "To find the BLEU score for the en-de language pair using the EWC model type with BI+IS decoder configuration, locate the en-de row and EWC column, then find the corresponding BI+IS decoder configuration score. So, the answer is {\"answer\": \"42.0\"}." } ], "images": [ "scigen/train/63.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Total BLEU for test data concatenated across domains. Results from 2-model es-en and 3-model en-de ensembles, compared to oracle model chosen if test domain is known. No-reg uniform corresponds to the approach of Freitag and Al-Onaizan (2016). BI+IS performs similarly to strong oracles with no test domain labeling." ], "reasoning": "To find the BLEU score for the en-de language pair using the EWC model type with BI+IS decoder configuration, locate the en-de row and EWC column, then find the corresponding BI+IS decoder configuration score.", "question": "What is the BLEU score for the en-de language pair using the EWC model type with BI+IS decoder configuration?", "answer": "So, the answer is {\"answer\": \"42.0\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe EWC model type with BI+IS decoder configuration achieves the highest BLEU score for the es-en language pair.\n" }, { "role": "assistant", "content": "The table shows that for the es-en language pair, the EWC model type with BI+IS decoder configuration has a BLEU score of 37.2, which is higher than the scores for other model types and configurations. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/63.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Total BLEU for test data concatenated across domains. Results from 2-model es-en and 3-model en-de ensembles, compared to oracle model chosen if test domain is known. No-reg uniform corresponds to the approach of Freitag and Al-Onaizan (2016). BI+IS performs similarly to strong oracles with no test domain labeling." ], "reasoning": "The table shows that for the es-en language pair, the EWC model type with BI+IS decoder configuration has a BLEU score of 37.2, which is higher than the scores for other model types and configurations.", "statement": "The EWC model type with BI+IS decoder configuration achieves the highest BLEU score for the es-en language pair.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 8: Semantic role labeling results. Hybrid-EL-CMP1 represents rule-based model and Hybrid-EL-CMP2 represents probability-based model.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The table shows that using original utterances with ellipsis results in high precision but low recall.Our results in Table 8 show that when only using original utterances with ellipsis, precision is relatively high while recall is low." } ], "images": [ "scigen/train/64.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Semantic role labeling results. Hybrid-EL-CMP1 represents rule-based model and Hybrid-EL-CMP2 represents probability-based model." ], "reasoning": "The table shows that using original utterances with ellipsis results in high precision but low recall.", "description": "Our results in Table 8 show that when only using original utterances with ellipsis, precision is relatively high while recall is low.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 8: Semantic role labeling results. Hybrid-EL-CMP1 represents rule-based model and Hybrid-EL-CMP2 represents probability-based model.' and methodically tackle the question that follows. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model has the highest precision and what is its recall?\n" }, { "role": "assistant", "content": "By examining the table, the model with the highest precision is Hybrid-EL-CMP1 with a precision of 97.42%. Its recall is 84.70%. So, the answer is {\"answer\": \"84.70%\"}." } ], "images": [ "scigen/train/64.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Semantic role labeling results. Hybrid-EL-CMP1 represents rule-based model and Hybrid-EL-CMP2 represents probability-based model." ], "reasoning": "By examining the table, the model with the highest precision is Hybrid-EL-CMP1 with a precision of 97.42%. Its recall is 84.70%.", "question": "Which model has the highest precision and what is its recall?", "answer": "So, the answer is {\"answer\": \"84.70%\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe CMP model has the highest recall.\n" }, { "role": "assistant", "content": "By examining the table, the CMP model has a recall of 88.64%, which is the highest recall among all models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/64.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Semantic role labeling results. Hybrid-EL-CMP1 represents rule-based model and Hybrid-EL-CMP2 represents probability-based model." ], "reasoning": "By examining the table, the CMP model has a recall of 88.64%, which is the highest recall among all models listed.", "statement": "The CMP model has the highest recall.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 6: Dialog act prediction performance using different selection methods.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Adding logits from two models after classifiers achieves the highest F1 score, precision, and recall.We can see from Table 6 that empirically adding logits from two models after classifiers performs the best." } ], "images": [ "scigen/train/65.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Dialog act prediction performance using different selection methods." ], "reasoning": "Adding logits from two models after classifiers achieves the highest F1 score, precision, and recall.", "description": "We can see from Table 6 that empirically adding logits from two models after classifiers performs the best.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 6: Dialog act prediction performance using different selection methods.', solve the subsequent question in a stepwise manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich selection method achieves the highest F1 score?\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all selection methods, 'Add Logits+Expert' achieves the highest F1 score of 80.90%. So, the answer is {\"answer\": \"Add Logits+Expert\"}." } ], "images": [ "scigen/train/65.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Dialog act prediction performance using different selection methods." ], "reasoning": "By comparing the F1 scores of all selection methods, 'Add Logits+Expert' achieves the highest F1 score of 80.90%.", "question": "Which selection method achieves the highest F1 score?", "answer": "So, the answer is {\"answer\": \"Add Logits+Expert\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The 'Concat Hidden' method achieves the highest precision., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe 'Concat Hidden' method achieves the highest precision.\n" }, { "role": "assistant", "content": "The table shows that 'Add Logits' and 'Add Logits+Expert' both achieve the highest precision of 81.30%, not 'Concat Hidden'. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/65.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Dialog act prediction performance using different selection methods." ], "reasoning": "The table shows that 'Add Logits' and 'Add Logits+Expert' both achieve the highest precision of 81.30%, not 'Concat Hidden'.", "statement": "The 'Concat Hidden' method achieves the highest precision.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 6: Experimental results on TREC QA dataset.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The NLP-Capsule approach achieves the best performance on the MAP metric, outperforming traditional neural models like CNN, LSTM, and NTNLSTM.In Table 6, the best performance on MAP metric is achieved by our approach, which verifies the effectiveness of our model. We also observe that our approach exceeds traditional neural models like CNN, LSTM and NTNLSTM by a noticeable margin." } ], "images": [ "scigen/train/66.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Experimental results on TREC QA dataset." ], "reasoning": "The NLP-Capsule approach achieves the best performance on the MAP metric, outperforming traditional neural models like CNN, LSTM, and NTNLSTM.", "description": "In Table 6, the best performance on MAP metric is achieved by our approach, which verifies the effectiveness of our model. We also observe that our approach exceeds traditional neural models like CNN, LSTM and NTNLSTM by a noticeable margin.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 6: Experimental results on TREC QA dataset.' and proceed to solve the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich method achieves the highest MAP score and how does it compare to the MAP score of the CNN method?\n" }, { "role": "assistant", "content": "The NLP-Capsule method achieves the highest MAP score of 77.73. The CNN method has a MAP score of 66.91. Therefore, the NLP-Capsule method outperforms the CNN method by 10.82 points. So, the answer is {\"answer\": \"NLP-Capsule\"}" } ], "images": [ "scigen/train/66.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Experimental results on TREC QA dataset." ], "reasoning": "The NLP-Capsule method achieves the highest MAP score of 77.73. The CNN method has a MAP score of 66.91. Therefore, the NLP-Capsule method outperforms the CNN method by 10.82 points.", "question": "Which method achieves the highest MAP score and how does it compare to the MAP score of the CNN method?", "answer": "So, the answer is {\"answer\": \"NLP-Capsule\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The CNN method achieves a higher MAP score than the LSTM method., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe CNN method achieves a higher MAP score than the LSTM method.\n" }, { "role": "assistant", "content": "The CNN method has a MAP score of 66.91, while the LSTM method has a MAP score of 59.75. Therefore, the CNN method achieves a higher MAP score than the LSTM method. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/66.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Experimental results on TREC QA dataset." ], "reasoning": "The CNN method has a MAP score of 66.91, while the LSTM method has a MAP score of 59.75. Therefore, the CNN method achieves a higher MAP score than the LSTM method.", "statement": "The CNN method achieves a higher MAP score than the LSTM method.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Comparisons of our NLP-Cap approach and baselines on two text classification benchmarks, where ’-’ denotes methods that failed to scale due to memory issues.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The capsule-based approach shows a noticeable improvement over strong baselines on EUR-Lex and competitive results on RCV1, indicating superior generalization ability on datasets with fewer training examples.In Table 2, we can see a noticeable margin brought by our capsule-based approach over the strong baselines on EUR-Lex, and competitive results on RCV1. These results appear to indicate that our approach has superior generalization ability on datasets with fewer training examples, i.e., RCV1 has 729.67 examples per label while EUR-Lex has 15.59 examples." } ], "images": [ "scigen/train/67.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparisons of our NLP-Cap approach and baselines on two text classification benchmarks, where ’-’ denotes methods that failed to scale due to memory issues." ], "reasoning": "The capsule-based approach shows a noticeable improvement over strong baselines on EUR-Lex and competitive results on RCV1, indicating superior generalization ability on datasets with fewer training examples.", "description": "In Table 2, we can see a noticeable margin brought by our capsule-based approach over the strong baselines on EUR-Lex, and competitive results on RCV1. These results appear to indicate that our approach has superior generalization ability on datasets with fewer training examples, i.e., RCV1 has 729.67 examples per label while EUR-Lex has 15.59 examples.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 2: Comparisons of our NLP-Cap approach and baselines on two text classification benchmarks, where ’-’ denotes methods that failed to scale due to memory issues.' and proceed to solve the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich dataset shows the highest improvement margin using the capsule-based approach?\n" }, { "role": "assistant", "content": "By comparing the 'Impv' column for both datasets, EUR-Lex shows a higher improvement margin of +6.75% compared to RCV1's +0.72%. So, the answer is {\"answer\": \"EUR-Lex\"}" } ], "images": [ "scigen/train/67.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparisons of our NLP-Cap approach and baselines on two text classification benchmarks, where ’-’ denotes methods that failed to scale due to memory issues." ], "reasoning": "By comparing the 'Impv' column for both datasets, EUR-Lex shows a higher improvement margin of +6.75% compared to RCV1's +0.72%.", "question": "Which dataset shows the highest improvement margin using the capsule-based approach?", "answer": "So, the answer is {\"answer\": \"EUR-Lex\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The capsule-based approach has a higher improvement margin on EUR-Lex compared to RCV1. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe capsule-based approach has a higher improvement margin on EUR-Lex compared to RCV1.\n" }, { "role": "assistant", "content": "The 'Impv' column shows that EUR-Lex has an improvement margin of +6.75%, while RCV1 has +0.72%, supporting the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/67.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparisons of our NLP-Cap approach and baselines on two text classification benchmarks, where ’-’ denotes methods that failed to scale due to memory issues." ], "reasoning": "The 'Impv' column shows that EUR-Lex has an improvement margin of +6.75%, while RCV1 has +0.72%, supporting the statement.", "statement": "The capsule-based approach has a higher improvement margin on EUR-Lex compared to RCV1.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1: Dataset Distribution for Sub Task A - Task 9: Suggestion Mining from Online Reviews.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows a significant imbalance in the distribution of training instances between suggestions and non-suggestions, with non-suggestions being much more prevalent.As evident from Table 1, there is a significant imbalance in the distribution of training instances that are suggestions and non-suggestions, 2https://www.uservoice.com/. For Sub Task A, the organizers shared a training and a validation dataset whose label distribution (suggestion or a non-suggestion) is presented in Table 1." } ], "images": [ "scigen/train/68.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Dataset Distribution for Sub Task A - Task 9: Suggestion Mining from Online Reviews." ], "reasoning": "The table shows a significant imbalance in the distribution of training instances between suggestions and non-suggestions, with non-suggestions being much more prevalent.", "description": "As evident from Table 1, there is a significant imbalance in the distribution of training instances that are suggestions and non-suggestions, 2https://www.uservoice.com/. For Sub Task A, the organizers shared a training and a validation dataset whose label distribution (suggestion or a non-suggestion) is presented in Table 1.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Dataset Distribution for Sub Task A - Task 9: Suggestion Mining from Online Reviews.', solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the total number of training instances for both suggestions and non-suggestions?\n" }, { "role": "assistant", "content": "To find the total number of training instances, add the number of suggestions (2085) and non-suggestions (6415) in the training dataset. So, the answer is {\"answer\": \"8500\"}." } ], "images": [ "scigen/train/68.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Dataset Distribution for Sub Task A - Task 9: Suggestion Mining from Online Reviews." ], "reasoning": "To find the total number of training instances, add the number of suggestions (2085) and non-suggestions (6415) in the training dataset.", "question": "What is the total number of training instances for both suggestions and non-suggestions?", "answer": "So, the answer is {\"answer\": \"8500\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe number of trial instances for suggestions and non-suggestions is equal.\n" }, { "role": "assistant", "content": "The table shows that both suggestions and non-suggestions have 296 trial instances each. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/68.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Dataset Distribution for Sub Task A - Task 9: Suggestion Mining from Online Reviews." ], "reasoning": "The table shows that both suggestions and non-suggestions have 296 trial instances each.", "statement": "The number of trial instances for suggestions and non-suggestions is equal.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Performance of different models on the provided train and test dataset for Sub Task A.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The ULMFiT model outperformed other models with the highest F1-scores on both the training and test datasets.Table 3, shows the performances of all the models that we trained on the provided training dataset.. The ULMFiT model achieved the best results with a F1-score of 0.861 on the training dataset and a F1-score of 0.701 on the test dataset." } ], "images": [ "scigen/train/69.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of different models on the provided train and test dataset for Sub Task A." ], "reasoning": "The ULMFiT model outperformed other models with the highest F1-scores on both the training and test datasets.", "description": "Table 3, shows the performances of all the models that we trained on the provided training dataset.. The ULMFiT model achieved the best results with a F1-score of 0.861 on the training dataset and a F1-score of 0.701 on the test dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Performance of different models on the provided train and test dataset for Sub Task A.\nQuestion: Which model had the second highest F1-score on the test dataset?\nSolve the above question based on the table information. Let's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the F1-scores on the test dataset, the LSTM model had the second highest F1-score of 0.591. So, the answer is {\"answer\": \"LSTM\"}." } ], "images": [ "scigen/train/69.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of different models on the provided train and test dataset for Sub Task A." ], "reasoning": "By comparing the F1-scores on the test dataset, the LSTM model had the second highest F1-score of 0.591.", "question": "Which model had the second highest F1-score on the test dataset?", "answer": "So, the answer is {\"answer\": \"LSTM\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Logistic Regression model had a higher F1-score on the test dataset than the SVM model.\n" }, { "role": "assistant", "content": "The table shows that the Logistic Regression model had an F1-score of 0.572 on the test dataset, while the SVM model had an F1-score of 0.576 on the test dataset. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/69.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of different models on the provided train and test dataset for Sub Task A." ], "reasoning": "The table shows that the Logistic Regression model had an F1-score of 0.572 on the test dataset, while the SVM model had an F1-score of 0.576 on the test dataset.", "statement": "The Logistic Regression model had a higher F1-score on the test dataset than the SVM model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Best performing models for SemEval Task 9: Sub Task A.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Table 4 displays the top 5 models' performance for Sub Task A of SemEval 2019 Task 9, with our team ranking 10th out of 34 participants.Table 4 shows the performance of the top 5 models for Sub Task A of SemEval 2019 Task 9. Our team ranked 10th out of 34 participants." } ], "images": [ "scigen/train/70.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Best performing models for SemEval Task 9: Sub Task A." ], "reasoning": "Table 4 displays the top 5 models' performance for Sub Task A of SemEval 2019 Task 9, with our team ranking 10th out of 34 participants.", "description": "Table 4 shows the performance of the top 5 models for Sub Task A of SemEval 2019 Task 9. Our team ranked 10th out of 34 participants.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 4: Best performing models for SemEval Task 9: Sub Task A.' and proceed to solve the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the F1 performance score of the team that ranked 3rd?\n" }, { "role": "assistant", "content": "To find the F1 performance score of the team that ranked 3rd, look at the row with the ranking of 3. The team name is 'm_y' and their performance score is 0.7761. So, the answer is {\"answer\": \"0.7761\"}." } ], "images": [ "scigen/train/70.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Best performing models for SemEval Task 9: Sub Task A." ], "reasoning": "To find the F1 performance score of the team that ranked 3rd, look at the row with the ranking of 3. The team name is 'm_y' and their performance score is 0.7761.", "question": "What is the F1 performance score of the team that ranked 3rd?", "answer": "So, the answer is {\"answer\": \"0.7761\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The team that ranked 2nd had a higher performance score than the team that ranked 4th., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe team that ranked 2nd had a higher performance score than the team that ranked 4th.\n" }, { "role": "assistant", "content": "The team that ranked 2nd, ThisIsCompetition, had a performance score of 0.7778, while the team that ranked 4th, yimmon, had a performance score of 0.7629. Since 0.7778 is higher than 0.7629, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/70.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Best performing models for SemEval Task 9: Sub Task A." ], "reasoning": "The team that ranked 2nd, ThisIsCompetition, had a performance score of 0.7778, while the team that ranked 4th, yimmon, had a performance score of 0.7629. Since 0.7778 is higher than 0.7629, the statement is supported.", "statement": "The team that ranked 2nd had a higher performance score than the team that ranked 4th.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'TABLE III: WERs (%) of obtained using different training data of CHiME-4', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "AAS achieves the best WER (24.7%) on the real test set when trained with both simulated and real datasets, while FSEGAN (29.6%) does not generalize well with simulated data.Table III shows the WERs on the simulated and real test sets when AAS is trained with different training data. With the simulated dataset as the training data, FSEGAN (29.6%) does not generalize well compared to AAS (25.2%) in terms of WER. With the real dataset as the training data, AAS shows severe overfitting since the size of training data is small. When AAS is trained with simulated and real datasets, it achieves the best result (24.7%) on the real test set." } ], "images": [ "scigen/train/71.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: WERs (%) of obtained using different training data of CHiME-4" ], "reasoning": "AAS achieves the best WER (24.7%) on the real test set when trained with both simulated and real datasets, while FSEGAN (29.6%) does not generalize well with simulated data.", "description": "Table III shows the WERs on the simulated and real test sets when AAS is trained with different training data. With the simulated dataset as the training data, FSEGAN (29.6%) does not generalize well compared to AAS (25.2%) in terms of WER. With the real dataset as the training data, AAS shows severe overfitting since the size of training data is small. When AAS is trained with simulated and real datasets, it achieves the best result (24.7%) on the real test set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'TABLE III: WERs (%) of obtained using different training data of CHiME-4', and tackle the question below in a sequential manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the WER on the real test set when AAS is trained with simulated and real datasets?\n" }, { "role": "assistant", "content": "The table shows that when AAS is trained with simulated and real datasets, it achieves a WER of 24.7% on the real test set. So, the answer is {\"answer\": \"24.7\"}." } ], "images": [ "scigen/train/71.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: WERs (%) of obtained using different training data of CHiME-4" ], "reasoning": "The table shows that when AAS is trained with simulated and real datasets, it achieves a WER of 24.7% on the real test set.", "question": "What is the WER on the real test set when AAS is trained with simulated and real datasets?", "answer": "So, the answer is {\"answer\": \"24.7\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis AAS shows severe overfitting when trained with the real dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nAAS shows severe overfitting when trained with the real dataset.\n" }, { "role": "assistant", "content": "The description states that AAS shows severe overfitting when trained with the real dataset due to the small size of the training data, which is supported by the high WER (35.2%) on the real test set. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/71.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: WERs (%) of obtained using different training data of CHiME-4" ], "reasoning": "The description states that AAS shows severe overfitting when trained with the real dataset due to the small size of the training data, which is supported by the high WER (35.2%) on the real test set.", "statement": "AAS shows severe overfitting when trained with the real dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'TABLE I: WERs (%) and DCE of different speech enhancement methods on Librispeech + DEMAND test set'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The Wiener filtering method shows lower DCE but higher WER than no enhancement. Acoustic supervision and multi-task learning achieve lower WER than minimizing DCE and FSEGAN.Table. and II show the WER and DCE (normalized by the number of frames) on the test set of Librispeech + DEMAND, and CHiME-4. The Wiener filtering method shows lower DCE, but higher WER than no enhancement. We conjecture that Wiener filter remove some fraction of noise, however, remaining speech is distorted as well. The adversarial supervision (i.e., wAC = 0, wAD > 0) consistently shows very high WER (i.e., > 90%), because the enhanced sample tends to have less correlation with noisy speech, as shown in Fig. 3.. In Librispeech + DEMAND, acoustic supervision (15.6%) and multi-task learning (14.4%) achieves a lower WER than minimizing DCE (15.8%) and FSEGAN (14.9%)." } ], "images": [ "scigen/train/72.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: WERs (%) and DCE of different speech enhancement methods on Librispeech + DEMAND test set" ], "reasoning": "The Wiener filtering method shows lower DCE but higher WER than no enhancement. Acoustic supervision and multi-task learning achieve lower WER than minimizing DCE and FSEGAN.", "description": "Table. and II show the WER and DCE (normalized by the number of frames) on the test set of Librispeech + DEMAND, and CHiME-4. The Wiener filtering method shows lower DCE, but higher WER than no enhancement. We conjecture that Wiener filter remove some fraction of noise, however, remaining speech is distorted as well. The adversarial supervision (i.e., wAC = 0, wAD > 0) consistently shows very high WER (i.e., > 90%), because the enhanced sample tends to have less correlation with noisy speech, as shown in Fig. 3.. In Librispeech + DEMAND, acoustic supervision (15.6%) and multi-task learning (14.4%) achieves a lower WER than minimizing DCE (15.8%) and FSEGAN (14.9%).", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nTable title: TABLE I: WERs (%) and DCE of different speech enhancement methods on Librispeech + DEMAND test set\n\nQuestion: Which method achieves the lowest WER among the enhancement methods?\n" }, { "role": "assistant", "content": "By comparing the WER values in the table, the method 'AAS (wAC=1, wAD=105)' achieves the lowest WER of 14.4% among the enhancement methods. So, the answer is {\"answer\": \"AAS (wAC=1, wAD=105)\"}." } ], "images": [ "scigen/train/72.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: WERs (%) and DCE of different speech enhancement methods on Librispeech + DEMAND test set" ], "reasoning": "By comparing the WER values in the table, the method 'AAS (wAC=1, wAD=105)' achieves the lowest WER of 14.4% among the enhancement methods.", "question": "Which method achieves the lowest WER among the enhancement methods?", "answer": "So, the answer is {\"answer\": \"AAS (wAC=1, wAD=105)\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Wiener filtering method achieves the lowest DCE.\n" }, { "role": "assistant", "content": "The table shows that the Wiener filtering method has a DCE of 0.722, while the method 'Minimizing DCE' has a lower DCE of 0.269. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/72.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: WERs (%) and DCE of different speech enhancement methods on Librispeech + DEMAND test set" ], "reasoning": "The table shows that the Wiener filtering method has a DCE of 0.722, while the method 'Minimizing DCE' has a lower DCE of 0.269. Therefore, the statement is refuted.", "statement": "The Wiener filtering method achieves the lowest DCE.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'TABLE II: WERs (%) and DCE of different speech enhancement methods on CHiME4-simulated test set'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The Wiener filtering method shows lower DCE but higher WER than no enhancement, while adversarial supervision consistently shows very high WER.Table. and II show the WER and DCE (normalized by the number of frames) on the test set of Librispeech + DEMAND, and CHiME-4. The Wiener filtering method shows lower DCE, but higher WER than no enhancement. We conjecture that Wiener filter remove some fraction of noise, however, remaining speech is distorted as well. The adversarial supervision (i.e., wAC = 0, wAD > 0) consistently shows very high WER (i.e., > 90%), because the enhanced sample tends to have less correlation with noisy speech, as shown in Fig. 3.. The same tendency is observed in CHiME-4 (i.e. acoustic supervision (27.7%) and multi-task learning (26.1%) show lower WER than minimizing DCE (31.1%) and FSEGAN (29.1%))." } ], "images": [ "scigen/train/73.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: WERs (%) and DCE of different speech enhancement methods on CHiME4-simulated test set" ], "reasoning": "The Wiener filtering method shows lower DCE but higher WER than no enhancement, while adversarial supervision consistently shows very high WER.", "description": "Table. and II show the WER and DCE (normalized by the number of frames) on the test set of Librispeech + DEMAND, and CHiME-4. The Wiener filtering method shows lower DCE, but higher WER than no enhancement. We conjecture that Wiener filter remove some fraction of noise, however, remaining speech is distorted as well. The adversarial supervision (i.e., wAC = 0, wAD > 0) consistently shows very high WER (i.e., > 90%), because the enhanced sample tends to have less correlation with noisy speech, as shown in Fig. 3.. The same tendency is observed in CHiME-4 (i.e. acoustic supervision (27.7%) and multi-task learning (26.1%) show lower WER than minimizing DCE (31.1%) and FSEGAN (29.1%)).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'TABLE II: WERs (%) and DCE of different speech enhancement methods on CHiME4-simulated test set', answer the following question. Give detailed solution consisting of each step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method has the lowest WER among the enhancement methods?\n" }, { "role": "assistant", "content": "To find the method with the lowest WER among the enhancement methods, compare the WER values for all methods except 'No enhancement' and 'Clean speech'. The method 'AAS (wAC=1, wAD=105)' has the lowest WER of 26.1%. So, the answer is {\"answer\": \"AAS (wAC=1, wAD=105)\"}." } ], "images": [ "scigen/train/73.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: WERs (%) and DCE of different speech enhancement methods on CHiME4-simulated test set" ], "reasoning": "To find the method with the lowest WER among the enhancement methods, compare the WER values for all methods except 'No enhancement' and 'Clean speech'. The method 'AAS (wAC=1, wAD=105)' has the lowest WER of 26.1%.", "question": "Which method has the lowest WER among the enhancement methods?", "answer": "So, the answer is {\"answer\": \"AAS (wAC=1, wAD=105)\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Wiener filter method has the lowest DCE.\n" }, { "role": "assistant", "content": "The table shows that the 'Minimizing DCE' method has the lowest DCE of 0.392, while the Wiener filter method has a DCE of 0.775. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/73.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: WERs (%) and DCE of different speech enhancement methods on CHiME4-simulated test set" ], "reasoning": "The table shows that the 'Minimizing DCE' method has the lowest DCE of 0.392, while the Wiener filter method has a DCE of 0.775.", "statement": "The Wiener filter method has the lowest DCE.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'TABLE IV: Results investigating RQ1 on the Nepal and Kerala datasets. (b) Kerala', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Transfer learning experiments show that source domain embedding models can improve target model performance, with upsampling generally having a positive effect. Our approach shows slight improvements over the upsampling baseline in two scenarios by 2-2.7% on the F-Measure metric.Concerning transfer learning experiments (RQ2), we note that source domain embedding model can improve the performance for target model, and upsampling has a generally positive effect (Tables V-VIII). As expected, transfer learning. Table VII, a result not found to be significant even at the 90% level). Our approach shows a slight improvement over the upsampling baseline on two of the four scenarios (Tables V and VII) by 2-2.7% on the F-Measure metric, which shows the diminishing returns from mixing source and target labeled training data. Further improving performance by high margins" } ], "images": [ "scigen/train/74.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Results investigating RQ1 on the Nepal and Kerala datasets. (b) Kerala" ], "reasoning": "Transfer learning experiments show that source domain embedding models can improve target model performance, with upsampling generally having a positive effect. Our approach shows slight improvements over the upsampling baseline in two scenarios by 2-2.7% on the F-Measure metric.", "description": "Concerning transfer learning experiments (RQ2), we note that source domain embedding model can improve the performance for target model, and upsampling has a generally positive effect (Tables V-VIII). As expected, transfer learning. Table VII, a result not found to be significant even at the 90% level). Our approach shows a slight improvement over the upsampling baseline on two of the four scenarios (Tables V and VII) by 2-2.7% on the F-Measure metric, which shows the diminishing returns from mixing source and target labeled training data. Further improving performance by high margins", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'TABLE IV: Results investigating RQ1 on the Nepal and Kerala datasets. (b) Kerala' and proceed to solve the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich system has the highest F-Measure and what is its value?\n" }, { "role": "assistant", "content": "To find the system with the highest F-Measure, we need to compare the F-Measure values of all systems listed in the table. The highest F-Measure value is 52.20%, achieved by the Wiki-Manual system. So, the answer is {\"answer\": \"Wiki-Manual\"}." } ], "images": [ "scigen/train/74.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Results investigating RQ1 on the Nepal and Kerala datasets. (b) Kerala" ], "reasoning": "To find the system with the highest F-Measure, we need to compare the F-Measure values of all systems listed in the table. The highest F-Measure value is 52.20%, achieved by the Wiki-Manual system.", "question": "Which system has the highest F-Measure and what is its value?", "answer": "So, the answer is {\"answer\": \"Wiki-Manual\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Wiki-Manual system has the highest accuracy among all systems.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the accuracy values of all systems listed in the table. The highest accuracy value is 68.75%, achieved by the Wiki-Manual system. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/74.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Results investigating RQ1 on the Nepal and Kerala datasets. (b) Kerala" ], "reasoning": "To verify this statement, we need to compare the accuracy values of all systems listed in the table. The highest accuracy value is 68.75%, achieved by the Wiki-Manual system.", "statement": "The Wiki-Manual system has the highest accuracy among all systems.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'TABLE II: Details on datasets used for experiments.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The Macedonia dataset is fully labeled with 205 messages, while Nepal and Kerala have both labeled and unlabeled messages, with Nepal being roughly balanced and Kerala being imbalanced in terms of urgent and non-urgent messages.For evaluating the approaches laid out in Section IV, we consider three real-world datasets described in Table II.. Originally, all the raw messages for the datasets described in Table II were unlabeled, in that their urgency status was. unknown. Since the Macedonia dataset only contains 205 messages, and is a small but information-dense dataset, we labeled all messages in Macedonia as urgent or non-urgent (hence, there are no unlabeled messages in Macedonia per Table II). For the two other Twitter-based datasets, we used. terms of urgent and non-urgent messages. Table II shows that Nepal is roughly balanced, while Kerala is imbalanced. We" } ], "images": [ "scigen/train/75.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Details on datasets used for experiments." ], "reasoning": "The Macedonia dataset is fully labeled with 205 messages, while Nepal and Kerala have both labeled and unlabeled messages, with Nepal being roughly balanced and Kerala being imbalanced in terms of urgent and non-urgent messages.", "description": "For evaluating the approaches laid out in Section IV, we consider three real-world datasets described in Table II.. Originally, all the raw messages for the datasets described in Table II were unlabeled, in that their urgency status was. unknown. Since the Macedonia dataset only contains 205 messages, and is a small but information-dense dataset, we labeled all messages in Macedonia as urgent or non-urgent (hence, there are no unlabeled messages in Macedonia per Table II). For the two other Twitter-based datasets, we used. terms of urgent and non-urgent messages. Table II shows that Nepal is roughly balanced, while Kerala is imbalanced. We", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'TABLE II: Details on datasets used for experiments.', unfold the question presented below, detailing every step of your calculation. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich dataset has the highest number of unique tokens and what is the average number of tokens per message in that dataset?\n" }, { "role": "assistant", "content": "From the table, Kerala has the highest number of unique tokens (19,393). The average number of tokens per message in Kerala is 15. So, the answer is {\"answer\": \"15\"}." } ], "images": [ "scigen/train/75.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Details on datasets used for experiments." ], "reasoning": "From the table, Kerala has the highest number of unique tokens (19,393). The average number of tokens per message in Kerala is 15.", "question": "Which dataset has the highest number of unique tokens and what is the average number of tokens per message in that dataset?", "answer": "So, the answer is {\"answer\": \"15\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Nepal dataset has more labeled messages than the Macedonia dataset.\n" }, { "role": "assistant", "content": "The table shows that Nepal has 400 labeled messages, while Macedonia has 205 labeled messages. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/75.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Details on datasets used for experiments." ], "reasoning": "The table shows that Nepal has 400 labeled messages, while Macedonia has 205 labeled messages.", "statement": "The Nepal dataset has more labeled messages than the Macedonia dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'TABLE IV: Results investigating RQ1 on the Nepal and Kerala datasets. (a) Nepal'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The results show that our approach achieves the highest F-Measure of 69.44% on the Nepal dataset, with different feature sets impacting various metrics differently.Table IV illustrate the result for RQ1 on the Nepal and Kerala datasets. The results illustrate the viability of urgency detection in low-supervision settings (with our approach yielding 69.44% F-Measure on Nepal, at 99% significance compared to the Local baseline), with different feature sets contributing differently to the four metrics. While the local embedding model can reduce precision, for example, it can help the system to improve and accuracy and recall. Similarly, manual features reduce recall, but help the system to improve accuracy and precision (sometimes considerably)." } ], "images": [ "scigen/train/76.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Results investigating RQ1 on the Nepal and Kerala datasets. (a) Nepal" ], "reasoning": "The results show that our approach achieves the highest F-Measure of 69.44% on the Nepal dataset, with different feature sets impacting various metrics differently.", "description": "Table IV illustrate the result for RQ1 on the Nepal and Kerala datasets. The results illustrate the viability of urgency detection in low-supervision settings (with our approach yielding 69.44% F-Measure on Nepal, at 99% significance compared to the Local baseline), with different feature sets contributing differently to the four metrics. While the local embedding model can reduce precision, for example, it can help the system to improve and accuracy and recall. Similarly, manual features reduce recall, but help the system to improve accuracy and precision (sometimes considerably).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'TABLE IV: Results investigating RQ1 on the Nepal and Kerala datasets. (a) Nepal', answer the following question. Give detailed solution consisting of each step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich system has the highest precision and what is its value?\n" }, { "role": "assistant", "content": "By examining the Precision column, the Manual system has the highest precision value of 70.84%. So, the answer is {\"answer\": \"Manual with 70.84%\"}." } ], "images": [ "scigen/train/76.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Results investigating RQ1 on the Nepal and Kerala datasets. (a) Nepal" ], "reasoning": "By examining the Precision column, the Manual system has the highest precision value of 70.84%.", "question": "Which system has the highest precision and what is its value?", "answer": "So, the answer is {\"answer\": \"Manual with 70.84%\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Local-Manual system has the highest recall.\n" }, { "role": "assistant", "content": "By examining the Recall column, the Local-Manual system has a recall of 59.50%, which is not the highest. The highest recall is 70.50% achieved by Our Approach. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/76.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Results investigating RQ1 on the Nepal and Kerala datasets. (a) Nepal" ], "reasoning": "By examining the Recall column, the Local-Manual system has a recall of 59.50%, which is not the highest. The highest recall is 70.50% achieved by Our Approach.", "statement": "The Local-Manual system has the highest recall.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'TABLE V: Results investigating RQ2 using the Nepal dataset as source and Macedonia dataset as target.'.\n" }, { "role": "assistant", "content": "The source domain embedding model and upsampling generally improve the performance of the target model.Concerning transfer learning experiments (RQ2), we note that source domain embedding model can improve the performance for target model, and upsampling has a generally positive effect (Tables V-VIII)." } ], "images": [ "scigen/train/77.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Results investigating RQ2 using the Nepal dataset as source and Macedonia dataset as target." ], "reasoning": "The source domain embedding model and upsampling generally improve the performance of the target model.", "description": "Concerning transfer learning experiments (RQ2), we note that source domain embedding model can improve the performance for target model, and upsampling has a generally positive effect (Tables V-VIII).", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'TABLE V: Results investigating RQ2 using the Nepal dataset as source and Macedonia dataset as target.', solve the following question step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich system has the highest accuracy and what is its value?\n" }, { "role": "assistant", "content": "By examining the 'Accuracy' column, we see that 'Our Approach' has the highest accuracy at 61.79%. So, the answer is {\"answer\": \"61.79%\"}." } ], "images": [ "scigen/train/77.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Results investigating RQ2 using the Nepal dataset as source and Macedonia dataset as target." ], "reasoning": "By examining the 'Accuracy' column, we see that 'Our Approach' has the highest accuracy at 61.79%.", "question": "Which system has the highest accuracy and what is its value?", "answer": "So, the answer is {\"answer\": \"61.79%\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The Transform system has the highest recall., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Transform system has the highest recall.\n" }, { "role": "assistant", "content": "By examining the 'Recall' column, we see that the Transform system has the highest recall at 60.32%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/77.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Results investigating RQ2 using the Nepal dataset as source and Macedonia dataset as target." ], "reasoning": "By examining the 'Recall' column, we see that the Transform system has the highest recall at 60.32%.", "statement": "The Transform system has the highest recall.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'TABLE VI: Results investigating RQ2 using the Kerala dataset as source and Macedonia dataset as target.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Transfer learning methods generally outperform the Local baseline, with upsampling showing a positive effect and our approach slightly improving over the upsampling baseline in some scenarios.uncertain in low-supervision settings. Concerning transfer learning experiments (RQ2), we note that source domain embedding model can improve the performance for target model, and upsampling has a generally positive effect (Tables V-VIII). As expected, transfer learning. supervision urgency detection on a single dataset(RQ1). Note that at least one of the transfer learning methods always bests the Local baseline on all metrics (except precision in Table VII, a result not found to be significant even at the. Table VII, a result not found to be significant even at the 90% level). Our approach shows a slight improvement over the upsampling baseline on two of the four scenarios (Tables V and VII) by 2-2.7% on the F-Measure metric, which shows the diminishing returns from mixing source and target labeled training data. Further improving performance by high margins" } ], "images": [ "scigen/train/78.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VI: Results investigating RQ2 using the Kerala dataset as source and Macedonia dataset as target." ], "reasoning": "Transfer learning methods generally outperform the Local baseline, with upsampling showing a positive effect and our approach slightly improving over the upsampling baseline in some scenarios.", "description": "uncertain in low-supervision settings. Concerning transfer learning experiments (RQ2), we note that source domain embedding model can improve the performance for target model, and upsampling has a generally positive effect (Tables V-VIII). As expected, transfer learning. supervision urgency detection on a single dataset(RQ1). Note that at least one of the transfer learning methods always bests the Local baseline on all metrics (except precision in Table VII, a result not found to be significant even at the. Table VII, a result not found to be significant even at the 90% level). Our approach shows a slight improvement over the upsampling baseline on two of the four scenarios (Tables V and VII) by 2-2.7% on the F-Measure metric, which shows the diminishing returns from mixing source and target labeled training data. Further improving performance by high margins", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'TABLE VI: Results investigating RQ2 using the Kerala dataset as source and Macedonia dataset as target.', solve the following question step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich system has the highest F-Measure?\n" }, { "role": "assistant", "content": "To determine the system with the highest F-Measure, compare the F-Measure values of all systems. Upsample has the highest F-Measure at 61.30%. So, the answer is {\"answer\": \"Upsample\"}." } ], "images": [ "scigen/train/78.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VI: Results investigating RQ2 using the Kerala dataset as source and Macedonia dataset as target." ], "reasoning": "To determine the system with the highest F-Measure, compare the F-Measure values of all systems. Upsample has the highest F-Measure at 61.30%.", "question": "Which system has the highest F-Measure?", "answer": "So, the answer is {\"answer\": \"Upsample\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Transform system has the highest accuracy., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Transform system has the highest accuracy.\n" }, { "role": "assistant", "content": "To verify this statement, compare the accuracy values of all systems. The Upsample system has the highest accuracy at 64.90%, not the Transform system. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/78.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VI: Results investigating RQ2 using the Kerala dataset as source and Macedonia dataset as target." ], "reasoning": "To verify this statement, compare the accuracy values of all systems. The Upsample system has the highest accuracy at 64.90%, not the Transform system.", "statement": "The Transform system has the highest accuracy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'TABLE VII: Results investigating RQ2 using the Nepal dataset as source and Kerala dataset as target.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Transfer learning and upsampling generally improve performance, with the best F-Measure achieved by 'Our Approach' at 43.03%.uncertain in low-supervision settings. Concerning transfer learning experiments (RQ2), we note that source domain embedding model can improve the performance for target model, and upsampling has a generally positive effect (Tables V-VIII). As expected, transfer learning. 0The best F-Measure achieved on Nepal in Table IV was more than 69%, but when using Kerala as source, only 62.5% F-Measure could be achieved (Table VIII)." } ], "images": [ "scigen/train/79.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VII: Results investigating RQ2 using the Nepal dataset as source and Kerala dataset as target." ], "reasoning": "Transfer learning and upsampling generally improve performance, with the best F-Measure achieved by 'Our Approach' at 43.03%.", "description": "uncertain in low-supervision settings. Concerning transfer learning experiments (RQ2), we note that source domain embedding model can improve the performance for target model, and upsampling has a generally positive effect (Tables V-VIII). As expected, transfer learning. 0The best F-Measure achieved on Nepal in Table IV was more than 69%, but when using Kerala as source, only 62.5% F-Measure could be achieved (Table VIII).", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'TABLE VII: Results investigating RQ2 using the Nepal dataset as source and Kerala dataset as target.' and proceed to solve the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich system achieved the highest F-Measure?\n" }, { "role": "assistant", "content": "By comparing the F-Measure values in the table, 'Our Approach' achieved the highest F-Measure at 43.03%. So, the answer is {\"answer\": \"Our Approach\"}." } ], "images": [ "scigen/train/79.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VII: Results investigating RQ2 using the Nepal dataset as source and Kerala dataset as target." ], "reasoning": "By comparing the F-Measure values in the table, 'Our Approach' achieved the highest F-Measure at 43.03%.", "question": "Which system achieved the highest F-Measure?", "answer": "So, the answer is {\"answer\": \"Our Approach\"}." }, { "messages": [ { "role": "user", "content": "The Transform system achieved the highest Recall. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Transform system achieved the highest Recall.\n" }, { "role": "assistant", "content": "The table shows that the Transform system has the highest Recall value at 57.47%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/79.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VII: Results investigating RQ2 using the Nepal dataset as source and Kerala dataset as target." ], "reasoning": "The table shows that the Transform system has the highest Recall value at 57.47%.", "statement": "The Transform system achieved the highest Recall.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Average synchronic performance'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "When evaluated synchronically, both Giga and NOW algorithms showed consistent trends in precision, recall, and F1 scores.As a sanity check, we also evaluated it synchronically, that is when Tn and rn are tested on the locations from the same year (including peaceful ones). In this easier setup, we observed exactly the same trends (Table 4)." } ], "images": [ "scigen/train/80.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Average synchronic performance" ], "reasoning": "When evaluated synchronically, both Giga and NOW algorithms showed consistent trends in precision, recall, and F1 scores.", "description": "As a sanity check, we also evaluated it synchronically, that is when Tn and rn are tested on the locations from the same year (including peaceful ones). In this easier setup, we observed exactly the same trends (Table 4).", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nTable title: Table 4: Average synchronic performance\n\nQuestion: Which algorithm and setup combination achieved the highest F1 score?\n" }, { "role": "assistant", "content": "To find the highest F1 score, compare the F1 scores of all algorithm and setup combinations. The highest F1 score is 0.63, achieved by the Giga Threshold setup. So, the answer is {\"answer\": \"Giga Threshold\"}." } ], "images": [ "scigen/train/80.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Average synchronic performance" ], "reasoning": "To find the highest F1 score, compare the F1 scores of all algorithm and setup combinations. The highest F1 score is 0.63, achieved by the Giga Threshold setup.", "question": "Which algorithm and setup combination achieved the highest F1 score?", "answer": "So, the answer is {\"answer\": \"Giga Threshold\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe NOW Baseline setup achieved a higher recall than the Giga Threshold setup.\n" }, { "role": "assistant", "content": "Compare the recall values of NOW Baseline (0.88) and Giga Threshold (0.69). The recall for NOW Baseline is indeed higher. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/80.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Average synchronic performance" ], "reasoning": "Compare the recall values of NOW Baseline (0.88) and Giga Threshold (0.69). The recall for NOW Baseline is indeed higher.", "statement": "The NOW Baseline setup achieved a higher recall than the Giga Threshold setup.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Average recall of diachronic analogy inference'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The replication experiment measures recall @k for armed group names in conflict areas, showing similar performance on both Gigaword and NOW datasets.A replication experiment In Table 2 we replicate the experiments from (Kutuzov et al., 2017) on both sets. It follows their evaluation scheme, where only the presence of the correct armed group name in the k nearest neighbours of the ˆi mattered, and only conflict areas were present in the yearly test sets. Essentially, it measures the recall @k, without penalizing the models for yielding incorrect answers along with the correct ones, and never asking questions having no correct answer at all (e.g., peaceful locations). The performance is very similar on both sets," } ], "images": [ "scigen/train/81.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Average recall of diachronic analogy inference" ], "reasoning": "The replication experiment measures recall @k for armed group names in conflict areas, showing similar performance on both Gigaword and NOW datasets.", "description": "A replication experiment In Table 2 we replicate the experiments from (Kutuzov et al., 2017) on both sets. It follows their evaluation scheme, where only the presence of the correct armed group name in the k nearest neighbours of the ˆi mattered, and only conflict areas were present in the yearly test sets. Essentially, it measures the recall @k, without penalizing the models for yielding incorrect answers along with the correct ones, and never asking questions having no correct answer at all (e.g., peaceful locations). The performance is very similar on both sets,", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: Average recall of diachronic analogy inference', solve the subsequent question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich dataset has a higher recall @1?\n" }, { "role": "assistant", "content": "Compare the recall @1 values for both datasets. Gigaword has a recall @1 of 0.356, while NOW has a recall @1 of 0.442. So, the answer is {\"answer\": \"NOW\"}." } ], "images": [ "scigen/train/81.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Average recall of diachronic analogy inference" ], "reasoning": "Compare the recall @1 values for both datasets. Gigaword has a recall @1 of 0.356, while NOW has a recall @1 of 0.442.", "question": "Which dataset has a higher recall @1?", "answer": "So, the answer is {\"answer\": \"NOW\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe recall @5 is higher for the NOW dataset compared to the Gigaword dataset.\n" }, { "role": "assistant", "content": "Compare the recall @5 values for both datasets. Gigaword has a recall @5 of 0.555, while NOW has a recall @5 of 0.557. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/81.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Average recall of diachronic analogy inference" ], "reasoning": "Compare the recall @5 values for both datasets. Gigaword has a recall @5 of 0.555, while NOW has a recall @5 of 0.557.", "statement": "The recall @5 is higher for the NOW dataset compared to the Gigaword dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Average diachronic performance', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Applying the cosine-based threshold decreases recall and increases precision for both Gigaword and NOW datasets, leading to a consistent improvement in F1 scores.Table 3 shows the diachronic performance of our system in the setup when the matrix Tn and the threshold rn are applied to the year n + 1. For both Gigaword and NOW datasets (and the corresponding embeddings), using the cosinebased threshold decreases recall and increases precision (differences are statistically significant with t-test, p < 0.05). At the same time, the integral. metrics of F1 consistently improves (p < 0.01). Thus, the thresholding reduces prediction noise in the one-to-X analogy task without sacrificing too many correct answers. In our particular case, this helps to more precisely detect events of armed conflicts termination (where no insurgents should be predicted for a location), not only their start." } ], "images": [ "scigen/train/82.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Average diachronic performance" ], "reasoning": "Applying the cosine-based threshold decreases recall and increases precision for both Gigaword and NOW datasets, leading to a consistent improvement in F1 scores.", "description": "Table 3 shows the diachronic performance of our system in the setup when the matrix Tn and the threshold rn are applied to the year n + 1. For both Gigaword and NOW datasets (and the corresponding embeddings), using the cosinebased threshold decreases recall and increases precision (differences are statistically significant with t-test, p < 0.05). At the same time, the integral. metrics of F1 consistently improves (p < 0.01). Thus, the thresholding reduces prediction noise in the one-to-X analogy task without sacrificing too many correct answers. In our particular case, this helps to more precisely detect events of armed conflicts termination (where no insurgents should be predicted for a location), not only their start.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Average diachronic performance\nQuestion: What is the F1 score for the NOW dataset when the threshold is applied?\nSolve the above question based on the table information. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the F1 score for the NOW dataset under the threshold condition as 0.41. So, the answer is {\"answer\": \"0.41\"}." } ], "images": [ "scigen/train/82.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Average diachronic performance" ], "reasoning": "The table shows the F1 score for the NOW dataset under the threshold condition as 0.41.", "question": "What is the F1 score for the NOW dataset when the threshold is applied?", "answer": "So, the answer is {\"answer\": \"0.41\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The precision for the Gigaword dataset increases when the threshold is applied. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe precision for the Gigaword dataset increases when the threshold is applied.\n" }, { "role": "assistant", "content": "The table shows that the precision for the Gigaword dataset increases from 0.19 to 0.46 when the threshold is applied. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/82.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Average diachronic performance" ], "reasoning": "The table shows that the precision for the Gigaword dataset increases from 0.19 to 0.46 when the threshold is applied.", "statement": "The precision for the Gigaword dataset increases when the threshold is applied.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: The ablation study on the WoZ2.0 dataset with the joint goal accuracy on the test set. For “- Hierachical-Attn”, we remove the residual connections between the attention modules in the CMR decoders and all the attention memory access are based on the output from the LSTM. For “- MLP”, we further replace the MLP with a single linear layer with the non-linear activation.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The hierarchical attention design improves joint goal accuracy by 1.95% compared to the model without it.Table 4: The ablation study on the WoZ2.0 dataset with the joint goal accuracy on the test set.. The effectiveness of our hierarchical attention design is proved by an accuracy drop of 1.95% after removing residual connections and the hierarchical stack of our attention modules." } ], "images": [ "scigen/train/83.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The ablation study on the WoZ2.0 dataset with the joint goal accuracy on the test set. For “- Hierachical-Attn”, we remove the residual connections between the attention modules in the CMR decoders and all the attention memory access are based on the output from the LSTM. For “- MLP”, we further replace the MLP with a single linear layer with the non-linear activation." ], "reasoning": "The hierarchical attention design improves joint goal accuracy by 1.95% compared to the model without it.", "description": "Table 4: The ablation study on the WoZ2.0 dataset with the joint goal accuracy on the test set.. The effectiveness of our hierarchical attention design is proved by an accuracy drop of 1.95% after removing residual connections and the hierarchical stack of our attention modules.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 4: The ablation study on the WoZ2.0 dataset with the joint goal accuracy on the test set. For “- Hierachical-Attn”, we remove the residual connections between the attention modules in the CMR decoders and all the attention memory access are based on the output from the LSTM. For “- MLP”, we further replace the MLP with a single linear layer with the non-linear activation.', solve the following question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the joint goal accuracy of the model without the hierarchical attention design?\n" }, { "role": "assistant", "content": "The table shows that the joint goal accuracy drops to 86.69% when the hierarchical attention design is removed. So, the answer is {\"answer\": \"86.69%\"}." } ], "images": [ "scigen/train/83.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The ablation study on the WoZ2.0 dataset with the joint goal accuracy on the test set. For “- Hierachical-Attn”, we remove the residual connections between the attention modules in the CMR decoders and all the attention memory access are based on the output from the LSTM. For “- MLP”, we further replace the MLP with a single linear layer with the non-linear activation." ], "reasoning": "The table shows that the joint goal accuracy drops to 86.69% when the hierarchical attention design is removed.", "question": "What is the joint goal accuracy of the model without the hierarchical attention design?", "answer": "So, the answer is {\"answer\": \"86.69%\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The model with the hierarchical attention design has a joint goal accuracy of 88.64%., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model with the hierarchical attention design has a joint goal accuracy of 88.64%.\n" }, { "role": "assistant", "content": "The table indicates that the COMER model, which includes the hierarchical attention design, has a joint goal accuracy of 88.64%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/83.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The ablation study on the WoZ2.0 dataset with the joint goal accuracy on the test set. For “- Hierachical-Attn”, we remove the residual connections between the attention modules in the CMR decoders and all the attention memory access are based on the output from the LSTM. For “- MLP”, we further replace the MLP with a single linear layer with the non-linear activation." ], "reasoning": "The table indicates that the COMER model, which includes the hierarchical attention design, has a joint goal accuracy of 88.64%.", "statement": "The model with the hierarchical attention design has a joint goal accuracy of 88.64%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: The joint goal accuracy of the DST models on the WoZ2.0 test set and the MultiWoZ test set. We also include the Inference Time Complexity (ITC) for each model as a metric for scalability. The baseline accuracy for the WoZ2.0 dataset is the Delexicalisation-Based (DB) Model Mrksic et al. (2017), while the baseline for the MultiWoZ dataset is taken from the official website of MultiWoZ Budzianowski et al. (2018).', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our model achieves a joint goal accuracy of 48.79% on the MultiWoZ dataset, marginally outperforming the previous state-of-the-art, and maintains performance on the WoZ2.0 dataset with a slight drop of 0.3%.Table 3: The joint goal accuracy of the DST models on the WoZ2.0 test set and the MultiWoZ test set. We also include the Inference Time Complexity (ITC) for each model as a metric for scalability. Table 3 compares our model with the previous state-of-the-art on both the WoZ2.0 test set and the MultiWoZ test set. For the WoZ2.0 dataset, we maintain performance at the level of the state-ofthe-art, with a marginal drop of 0.3% compared with previous work. Considering the fact that WoZ2.0 is a relatively small dataset, this small difference does not represent a significant big performance drop. On the muli-domain dataset, MultiWoZ, our model achieves a joint goal accuracy of 48.79%, which marginally outperforms the previous state-of-the-art." } ], "images": [ "scigen/train/84.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The joint goal accuracy of the DST models on the WoZ2.0 test set and the MultiWoZ test set. We also include the Inference Time Complexity (ITC) for each model as a metric for scalability. The baseline accuracy for the WoZ2.0 dataset is the Delexicalisation-Based (DB) Model Mrksic et al. (2017), while the baseline for the MultiWoZ dataset is taken from the official website of MultiWoZ Budzianowski et al. (2018)." ], "reasoning": "Our model achieves a joint goal accuracy of 48.79% on the MultiWoZ dataset, marginally outperforming the previous state-of-the-art, and maintains performance on the WoZ2.0 dataset with a slight drop of 0.3%.", "description": "Table 3: The joint goal accuracy of the DST models on the WoZ2.0 test set and the MultiWoZ test set. We also include the Inference Time Complexity (ITC) for each model as a metric for scalability. Table 3 compares our model with the previous state-of-the-art on both the WoZ2.0 test set and the MultiWoZ test set. For the WoZ2.0 dataset, we maintain performance at the level of the state-ofthe-art, with a marginal drop of 0.3% compared with previous work. Considering the fact that WoZ2.0 is a relatively small dataset, this small difference does not represent a significant big performance drop. On the muli-domain dataset, MultiWoZ, our model achieves a joint goal accuracy of 48.79%, which marginally outperforms the previous state-of-the-art.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: The joint goal accuracy of the DST models on the WoZ2.0 test set and the MultiWoZ test set. We also include the Inference Time Complexity (ITC) for each model as a metric for scalability. The baseline accuracy for the WoZ2.0 dataset is the Delexicalisation-Based (DB) Model Mrksic et al. (2017), while the baseline for the MultiWoZ dataset is taken from the official website of MultiWoZ Budzianowski et al. (2018).', unfold the steps to solve the question given next. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nQuestion: What is the joint goal accuracy of the model with the lowest inference time complexity on the MultiWoZ dataset?\n" }, { "role": "assistant", "content": "The table shows that the COMER model has the lowest inference time complexity (O(1)) and achieves a joint goal accuracy of 48.79% on the MultiWoZ dataset. So, the answer is {\"answer\": \"48.79%\"}." } ], "images": [ "scigen/train/84.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The joint goal accuracy of the DST models on the WoZ2.0 test set and the MultiWoZ test set. We also include the Inference Time Complexity (ITC) for each model as a metric for scalability. The baseline accuracy for the WoZ2.0 dataset is the Delexicalisation-Based (DB) Model Mrksic et al. (2017), while the baseline for the MultiWoZ dataset is taken from the official website of MultiWoZ Budzianowski et al. (2018)." ], "reasoning": "The table shows that the COMER model has the lowest inference time complexity (O(1)) and achieves a joint goal accuracy of 48.79% on the MultiWoZ dataset.", "question": "What is the joint goal accuracy of the model with the lowest inference time complexity on the MultiWoZ dataset?", "answer": "So, the answer is {\"answer\": \"48.79%\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The COMER model has the highest joint goal accuracy on the WoZ2.0 dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe COMER model has the highest joint goal accuracy on the WoZ2.0 dataset.\n" }, { "role": "assistant", "content": "The table shows that the StateNet_PSI model has the highest joint goal accuracy on the WoZ2.0 dataset with 88.9%, while the COMER model has 88.6%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/84.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The joint goal accuracy of the DST models on the WoZ2.0 test set and the MultiWoZ test set. We also include the Inference Time Complexity (ITC) for each model as a metric for scalability. The baseline accuracy for the WoZ2.0 dataset is the Delexicalisation-Based (DB) Model Mrksic et al. (2017), while the baseline for the MultiWoZ dataset is taken from the official website of MultiWoZ Budzianowski et al. (2018)." ], "reasoning": "The table shows that the StateNet_PSI model has the highest joint goal accuracy on the WoZ2.0 dataset with 88.9%, while the COMER model has 88.6%.", "statement": "The COMER model has the highest joint goal accuracy on the WoZ2.0 dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: The ablation study on the MultiWoZ dataset with the joint domain accuracy (JD Acc.), joint domain-slot accuracy (JDS Acc.) and joint goal accuracy (JG Acc.) on the test set. For “- moveDrop”, we move the dropout layer to be in front of the final linear layer before the Softmax. For “- postprocess”, we further fix the decoder embedding layer and remove the post-processing during model evaluation. For “- ShareParam”, we further remove the parameter sharing mechanism on the encoders and the attention modules. For “- Order”, we further arrange the order of the slots according to its global frequencies in the training set instead of the local frequencies given the domain it belongs to. For “- Nested”, we do not generate domain sequences but generate combined slot sequences which combines the domain and the slot together. For “- BlockGrad”, we further remove the gradient blocking mechanism in the CMR decoder.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "COMER excels in domain prediction (95.52%) and value prediction (87.42%), but its slot prediction accuracy given the correct domain is only 58.43%. Switching to nested tuple representation boosts JDS Acc. by 5.48%.Table 5: The ablation study on the MultiWoZ dataset with the joint domain accuracy (JD Acc.), joint domain-slot accuracy (JDS Acc.) and joint goal accuracy (JG Acc.) on the test set.. From Table 5, We can further calculate that given the correct slot prediction, COMER has 87.42% chance to make the correct value prediction. While COMER has done great job on domain prediction (95.52%) and value prediction (87.42%), the accuracy of the slot prediction given the correct domain is only 58.43%.. We can also see that the JDS Acc. has an absolute boost of 5.48% when we switch from the combined slot representation to the nested tuple representation." } ], "images": [ "scigen/train/85.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The ablation study on the MultiWoZ dataset with the joint domain accuracy (JD Acc.), joint domain-slot accuracy (JDS Acc.) and joint goal accuracy (JG Acc.) on the test set. For “- moveDrop”, we move the dropout layer to be in front of the final linear layer before the Softmax. For “- postprocess”, we further fix the decoder embedding layer and remove the post-processing during model evaluation. For “- ShareParam”, we further remove the parameter sharing mechanism on the encoders and the attention modules. For “- Order”, we further arrange the order of the slots according to its global frequencies in the training set instead of the local frequencies given the domain it belongs to. For “- Nested”, we do not generate domain sequences but generate combined slot sequences which combines the domain and the slot together. For “- BlockGrad”, we further remove the gradient blocking mechanism in the CMR decoder." ], "reasoning": "COMER excels in domain prediction (95.52%) and value prediction (87.42%), but its slot prediction accuracy given the correct domain is only 58.43%. Switching to nested tuple representation boosts JDS Acc. by 5.48%.", "description": "Table 5: The ablation study on the MultiWoZ dataset with the joint domain accuracy (JD Acc.), joint domain-slot accuracy (JDS Acc.) and joint goal accuracy (JG Acc.) on the test set.. From Table 5, We can further calculate that given the correct slot prediction, COMER has 87.42% chance to make the correct value prediction. While COMER has done great job on domain prediction (95.52%) and value prediction (87.42%), the accuracy of the slot prediction given the correct domain is only 58.43%.. We can also see that the JDS Acc. has an absolute boost of 5.48% when we switch from the combined slot representation to the nested tuple representation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: The ablation study on the MultiWoZ dataset with the joint domain accuracy (JD Acc.), joint domain-slot accuracy (JDS Acc.) and joint goal accuracy (JG Acc.) on the test set. For “- moveDrop”, we move the dropout layer to be in front of the final linear layer before the Softmax. For “- postprocess”, we further fix the decoder embedding layer and remove the post-processing during model evaluation. For “- ShareParam”, we further remove the parameter sharing mechanism on the encoders and the attention modules. For “- Order”, we further arrange the order of the slots according to its global frequencies in the training set instead of the local frequencies given the domain it belongs to. For “- Nested”, we do not generate domain sequences but generate combined slot sequences which combines the domain and the slot together. For “- BlockGrad”, we further remove the gradient blocking mechanism in the CMR decoder.'.\n\nQuestion: What is the joint goal accuracy (JG Acc.) of the model with the highest joint domain accuracy (JD Acc.)?\n" }, { "role": "assistant", "content": "The model with the highest JD Acc. is 'postprocess' with 95.53%. The JG Acc. for 'postprocess' is 45.72%. So, the answer is {\"answer\": \"45.72%\"}." } ], "images": [ "scigen/train/85.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The ablation study on the MultiWoZ dataset with the joint domain accuracy (JD Acc.), joint domain-slot accuracy (JDS Acc.) and joint goal accuracy (JG Acc.) on the test set. For “- moveDrop”, we move the dropout layer to be in front of the final linear layer before the Softmax. For “- postprocess”, we further fix the decoder embedding layer and remove the post-processing during model evaluation. For “- ShareParam”, we further remove the parameter sharing mechanism on the encoders and the attention modules. For “- Order”, we further arrange the order of the slots according to its global frequencies in the training set instead of the local frequencies given the domain it belongs to. For “- Nested”, we do not generate domain sequences but generate combined slot sequences which combines the domain and the slot together. For “- BlockGrad”, we further remove the gradient blocking mechanism in the CMR decoder." ], "reasoning": "The model with the highest JD Acc. is 'postprocess' with 95.53%. The JG Acc. for 'postprocess' is 45.72%.", "question": "What is the joint goal accuracy (JG Acc.) of the model with the highest joint domain accuracy (JD Acc.)?", "answer": "So, the answer is {\"answer\": \"45.72%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The nested tuple representation has a higher JDS Acc. than the combined slot representation. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe nested tuple representation has a higher JDS Acc. than the combined slot representation.\n" }, { "role": "assistant", "content": "The nested tuple representation has a JDS Acc. of 49.58%, while the combined slot representation (COMER) has a JDS Acc. of 55.81%. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/85.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The ablation study on the MultiWoZ dataset with the joint domain accuracy (JD Acc.), joint domain-slot accuracy (JDS Acc.) and joint goal accuracy (JG Acc.) on the test set. For “- moveDrop”, we move the dropout layer to be in front of the final linear layer before the Softmax. For “- postprocess”, we further fix the decoder embedding layer and remove the post-processing during model evaluation. For “- ShareParam”, we further remove the parameter sharing mechanism on the encoders and the attention modules. For “- Order”, we further arrange the order of the slots according to its global frequencies in the training set instead of the local frequencies given the domain it belongs to. For “- Nested”, we do not generate domain sequences but generate combined slot sequences which combines the domain and the slot together. For “- BlockGrad”, we further remove the gradient blocking mechanism in the CMR decoder." ], "reasoning": "The nested tuple representation has a JDS Acc. of 49.58%, while the combined slot representation (COMER) has a JDS Acc. of 55.81%. Therefore, the statement is refuted.", "statement": "The nested tuple representation has a higher JDS Acc. than the combined slot representation.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Accuracy of transferring between domains. Models with † use labeled data from source domains and unlabeled data from the target domain. Models with ‡ use human rationales on the target task.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Our model (OURS) shows significant improvement in performance across all target tasks, with an average error reduction of 15.08% over the best baseline.Table 4 presents the results of domain transfer using 200 training examples. We use the three aspects of the beer review data together as our source tasks while use the three aspects of hotel review data as the target. Our model (OURS) shows marked performance improvement. The error reduction over the best baseline is 15.08% on average." } ], "images": [ "scigen/train/86.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Accuracy of transferring between domains. Models with † use labeled data from source domains and unlabeled data from the target domain. Models with ‡ use human rationales on the target task." ], "reasoning": "Our model (OURS) shows significant improvement in performance across all target tasks, with an average error reduction of 15.08% over the best baseline.", "description": "Table 4 presents the results of domain transfer using 200 training examples. We use the three aspects of the beer review data together as our source tasks while use the three aspects of hotel review data as the target. Our model (OURS) shows marked performance improvement. The error reduction over the best baseline is 15.08% on average.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 4: Accuracy of transferring between domains. Models with † use labeled data from source domains and unlabeled data from the target domain. Models with ‡ use human rationales on the target task.' to sequentially solve the question presented below. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the performance improvement of the OURS model over the best baseline for the hotel cleanliness task?\n" }, { "role": "assistant", "content": "The best baseline for the hotel cleanliness task is Ra-Trans with a score of 87.15. The OURS model has a score of 90.66. The performance improvement is 90.66 - 87.15. So, the answer is {\"answer\": \"3.51\"}." } ], "images": [ "scigen/train/86.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Accuracy of transferring between domains. Models with † use labeled data from source domains and unlabeled data from the target domain. Models with ‡ use human rationales on the target task." ], "reasoning": "The best baseline for the hotel cleanliness task is Ra-Trans with a score of 87.15. The OURS model has a score of 90.66. The performance improvement is 90.66 - 87.15.", "question": "What is the performance improvement of the OURS model over the best baseline for the hotel cleanliness task?", "answer": "So, the answer is {\"answer\": \"3.51\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The OURS model outperforms the Oracle model in the hotel location task. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe OURS model outperforms the Oracle model in the hotel location task.\n" }, { "role": "assistant", "content": "The OURS model has a score of 84.52 for the hotel location task, while the Oracle model has a score of 85.43. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/86.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Accuracy of transferring between domains. Models with † use labeled data from source domains and unlabeled data from the target domain. Models with ‡ use human rationales on the target task." ], "reasoning": "The OURS model has a score of 84.52 for the hotel location task, while the Oracle model has a score of 85.43. Therefore, the statement is refuted.", "statement": "The OURS model outperforms the Oracle model in the hotel location task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Accuracy of transferring between aspects. Models with † use labeled data from source aspects. Models with ‡ use human rationales on the target aspect.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our model (OURS) achieves significant accuracy improvements over baselines for all target aspects and closely matches ORACLE's performance with only a 0.40% difference.Table 3 summarizes the results of aspect transfer on the beer review dataset. Our model (OURS) obtains substantial gains in accuracy over the baselines across all three target aspects. It closely matches the performance of ORACLE with only 0.40% absolute difference.. Specifically, all rationale-augmented methods (RA-SVM, RA-TRANS and OURS) outperform their rationale-free counterparts on average. This confirms the value of human rationales in the low-resource settings. We observe that the transfer baseline that directly uses rationale as augmented supervision (RA-TRANS) underperforms ORACLE by a large margin. This validates our hypothesis that human rationales and attention are different." } ], "images": [ "scigen/train/87.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracy of transferring between aspects. Models with † use labeled data from source aspects. Models with ‡ use human rationales on the target aspect." ], "reasoning": "Our model (OURS) achieves significant accuracy improvements over baselines for all target aspects and closely matches ORACLE's performance with only a 0.40% difference.", "description": "Table 3 summarizes the results of aspect transfer on the beer review dataset. Our model (OURS) obtains substantial gains in accuracy over the baselines across all three target aspects. It closely matches the performance of ORACLE with only 0.40% absolute difference.. Specifically, all rationale-augmented methods (RA-SVM, RA-TRANS and OURS) outperform their rationale-free counterparts on average. This confirms the value of human rationales in the low-resource settings. We observe that the transfer baseline that directly uses rationale as augmented supervision (RA-TRANS) underperforms ORACLE by a large margin. This validates our hypothesis that human rationales and attention are different.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: Accuracy of transferring between aspects. Models with † use labeled data from source aspects. Models with ‡ use human rationales on the target aspect.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the accuracy difference between OURS and ORACLE for the 'Beer look' target aspect?\n" }, { "role": "assistant", "content": "The table shows that for the 'Beer look' target aspect, OURS has an accuracy of 79.53 and ORACLE has an accuracy of 80.29. The difference is 80.29 - 79.53 = 0.76. So, the answer is {\"answer\": \"0.76\"}" } ], "images": [ "scigen/train/87.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracy of transferring between aspects. Models with † use labeled data from source aspects. Models with ‡ use human rationales on the target aspect." ], "reasoning": "The table shows that for the 'Beer look' target aspect, OURS has an accuracy of 79.53 and ORACLE has an accuracy of 80.29. The difference is 80.29 - 79.53 = 0.76.", "question": "What is the accuracy difference between OURS and ORACLE for the 'Beer look' target aspect?", "answer": "So, the answer is {\"answer\": \"0.76\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence RA-TRANS outperforms ORACLE in the 'Beer aroma' target aspect. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nRA-TRANS outperforms ORACLE in the 'Beer aroma' target aspect.\n" }, { "role": "assistant", "content": "The table shows that for the 'Beer aroma' target aspect, RA-TRANS has an accuracy of 76.45, while ORACLE has an accuracy of 78.11. Therefore, RA-TRANS does not outperform ORACLE. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/87.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracy of transferring between aspects. Models with † use labeled data from source aspects. Models with ‡ use human rationales on the target aspect." ], "reasoning": "The table shows that for the 'Beer aroma' target aspect, RA-TRANS has an accuracy of 76.45, while ORACLE has an accuracy of 78.11. Therefore, RA-TRANS does not outperform ORACLE.", "statement": "RA-TRANS outperforms ORACLE in the 'Beer aroma' target aspect.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: Ablation study on domain transfer from beer to hotel.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Both the language modeling objective and the Wasserstein distance contribute similarly to the task, with the Wasserstein distance having a bigger impact.Table 5 presents the results of an ablation study of our model in the setting of domain transfer. As this table indicates, both the language modeling objective and the Wasserstein. distance contribute similarly to the task, with the Wasserstein distance having a bigger impact." } ], "images": [ "scigen/train/88.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ablation study on domain transfer from beer to hotel." ], "reasoning": "Both the language modeling objective and the Wasserstein distance contribute similarly to the task, with the Wasserstein distance having a bigger impact.", "description": "Table 5 presents the results of an ablation study of our model in the setting of domain transfer. As this table indicates, both the language modeling objective and the Wasserstein. distance contribute similarly to the task, with the Wasserstein distance having a bigger impact.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 5: Ablation study on domain transfer from beer to hotel.' to sequentially solve the question presented below. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich model component has a bigger impact on hotel location scores?\n" }, { "role": "assistant", "content": "Comparing the scores of 'w/o L wd' and 'w/o L lm' to 'Ours', the absence of the Wasserstein distance (w/o L wd) results in a larger drop in the hotel location score. So, the answer is {\"answer\": \"Wasserstein distance\"}" } ], "images": [ "scigen/train/88.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ablation study on domain transfer from beer to hotel." ], "reasoning": "Comparing the scores of 'w/o L wd' and 'w/o L lm' to 'Ours', the absence of the Wasserstein distance (w/o L wd) results in a larger drop in the hotel location score.", "question": "Which model component has a bigger impact on hotel location scores?", "answer": "So, the answer is {\"answer\": \"Wasserstein distance\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The language modeling objective has a bigger impact on hotel cleanliness scores. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe language modeling objective has a bigger impact on hotel cleanliness scores.\n" }, { "role": "assistant", "content": "The table shows that removing the language modeling objective (w/o L lm) results in a smaller drop in hotel cleanliness scores compared to removing the Wasserstein distance (w/o L wd). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/88.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ablation study on domain transfer from beer to hotel." ], "reasoning": "The table shows that removing the language modeling objective (w/o L lm) results in a smaller drop in hotel cleanliness scores compared to removing the Wasserstein distance (w/o L wd).", "statement": "The language modeling objective has a bigger impact on hotel cleanliness scores.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: Extractive and Abstractive Keyphrase Metrics', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our proposed GAN model outperforms the catSeq and catSeq-RL models in generating abstractive keyphrases on three out of four datasets, while it performs better than the pre-trained catSeq model but slightly worse than catSeq-RL for extractive keyphrases except on Krapivin.We compare our proposed approach against 2 baseline models - catSeq (Yuan et al. 2018), RL-based catSeq Model (Chan et al. 2019) in terms of F1 scores as explained in (Yuan et al. 2018). The results, summarized in Table 1, are broken down in terms of performance on extractive and abstractive keyphrases. For extractive keyphrases, our proposed model performs better than the pre-trained catSeq model on all datasets but is slightly worse than catSeq-RL except for on Krapivin where it obtains the best F1@M of 0.37. On the other hand, for abstractive keyphrases, our model performs better than the other two baselines on three of four datasets suggesting that GAN models are more effective in generation of keyphrases." } ], "images": [ "scigen/train/89.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Extractive and Abstractive Keyphrase Metrics" ], "reasoning": "Our proposed GAN model outperforms the catSeq and catSeq-RL models in generating abstractive keyphrases on three out of four datasets, while it performs better than the pre-trained catSeq model but slightly worse than catSeq-RL for extractive keyphrases except on Krapivin.", "description": "We compare our proposed approach against 2 baseline models - catSeq (Yuan et al. 2018), RL-based catSeq Model (Chan et al. 2019) in terms of F1 scores as explained in (Yuan et al. 2018). The results, summarized in Table 1, are broken down in terms of performance on extractive and abstractive keyphrases. For extractive keyphrases, our proposed model performs better than the pre-trained catSeq model on all datasets but is slightly worse than catSeq-RL except for on Krapivin where it obtains the best F1@M of 0.37. On the other hand, for abstractive keyphrases, our model performs better than the other two baselines on three of four datasets suggesting that GAN models are more effective in generation of keyphrases.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 1: Extractive and Abstractive Keyphrase Metrics' in mind, break down and resolve the question below step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich model achieved the highest F1@M score for extractive keyphrases on the Krapivin dataset?\n" }, { "role": "assistant", "content": "To find the highest F1@M score for extractive keyphrases on the Krapivin dataset, compare the F1@M scores of the catSeq, catSeq-RL, and GAN models. The GAN model has the highest F1@M score of 0.370. So, the answer is {\"answer\": \"GAN\"}" } ], "images": [ "scigen/train/89.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Extractive and Abstractive Keyphrase Metrics" ], "reasoning": "To find the highest F1@M score for extractive keyphrases on the Krapivin dataset, compare the F1@M scores of the catSeq, catSeq-RL, and GAN models. The GAN model has the highest F1@M score of 0.370.", "question": "Which model achieved the highest F1@M score for extractive keyphrases on the Krapivin dataset?", "answer": "So, the answer is {\"answer\": \"GAN\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe GAN model achieved the highest F1@M score for abstractive keyphrases on the Inspec dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the F1@M scores for abstractive keyphrases on the Inspec dataset. The GAN model has an F1@M score of 0.0190, which is higher than the catSeq and catSeq-RL models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/89.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Extractive and Abstractive Keyphrase Metrics" ], "reasoning": "To verify this statement, compare the F1@M scores for abstractive keyphrases on the Inspec dataset. The GAN model has an F1@M score of 0.0190, which is higher than the catSeq and catSeq-RL models.", "statement": "The GAN model achieved the highest F1@M score for abstractive keyphrases on the Inspec dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: α-nDCG@5 metrics'.\n" }, { "role": "assistant", "content": "Our model, GAN, achieves the highest α-nDCG@5 performance on three out of four datasets, with a notable 5% improvement on the largest dataset, KP20k.We also evaluated the models in terms of α-nDCG@5 (Clarke et al. 2008). The results are summarized in Table 2. Our model obtains the best performance on three out of the four datasets. The difference is most prevalent in KP20k, the largest of the four datasets, where our GAN model (at 0.85) is nearly 5% better than both the other baseline models." } ], "images": [ "scigen/train/90.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: α-nDCG@5 metrics" ], "reasoning": "Our model, GAN, achieves the highest α-nDCG@5 performance on three out of four datasets, with a notable 5% improvement on the largest dataset, KP20k.", "description": "We also evaluated the models in terms of α-nDCG@5 (Clarke et al. 2008). The results are summarized in Table 2. Our model obtains the best performance on three out of the four datasets. The difference is most prevalent in KP20k, the largest of the four datasets, where our GAN model (at 0.85) is nearly 5% better than both the other baseline models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model has the highest α-nDCG@5 score on the NUS dataset?\nSolve the above question based on the table titled 'Table 2: α-nDCG@5 metrics'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, the GAN model has the highest α-nDCG@5 score on the NUS dataset with a value of 0.853. So, the answer is {\"answer\": \"GAN\"}" } ], "images": [ "scigen/train/90.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: α-nDCG@5 metrics" ], "reasoning": "By examining the table, the GAN model has the highest α-nDCG@5 score on the NUS dataset with a value of 0.853.", "question": "Which model has the highest α-nDCG@5 score on the NUS dataset?", "answer": "So, the answer is {\"answer\": \"GAN\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Catseq-RL model has the highest α-nDCG@5 score on the Krapivin dataset.\n" }, { "role": "assistant", "content": "The table shows that the Catseq-RL model has an α-nDCG@5 score of 0.786 on the Krapivin dataset, which is indeed the highest among the models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/90.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: α-nDCG@5 metrics" ], "reasoning": "The table shows that the Catseq-RL model has an α-nDCG@5 score of 0.786 on the Krapivin dataset, which is indeed the highest among the models listed.", "statement": "The Catseq-RL model has the highest α-nDCG@5 score on the Krapivin dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: Percentage of female, male and neutral gender pronouns obtained for each of the merged occupation category, averaged over all occupations in said category and tested languages detailed in Table'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The dataset focuses on job positions to study gender bias, using a list of professional occupations from the Bureau of Labor Statistics, filtered for specificity and gender neutrality, and summarized in Table 7 by broader occupation categories.To simplify our dataset, we have decided to focus our work on job positions – which, we believe, are an interesting window into the nature of gender bias –, and were able to obtain a comprehensive list of professional occupations from the Bureau of Labor Statistics' detailed occupations table , from the United States Department of Labor. The values inside, however, had to be expanded since each line contained multiple occupations and sometimes very specific ones. Fortunately this table also provided a percentage of women participation in the jobs shown, for those that had more than 50 thousand workers. We filtered some of these because they were too generic ( \"Computer occupations, all other\", and others) or because they had gender specific words for the profession (\"host/hostess\", \"waiter/waitress\").. and Table 7 summarizes it even further by coalescing occupation categories into broader groups to ease interpretation." } ], "images": [ "scigen/train/91.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Percentage of female, male and neutral gender pronouns obtained for each of the merged occupation category, averaged over all occupations in said category and tested languages detailed in Table" ], "reasoning": "The dataset focuses on job positions to study gender bias, using a list of professional occupations from the Bureau of Labor Statistics, filtered for specificity and gender neutrality, and summarized in Table 7 by broader occupation categories.", "description": "To simplify our dataset, we have decided to focus our work on job positions – which, we believe, are an interesting window into the nature of gender bias –, and were able to obtain a comprehensive list of professional occupations from the Bureau of Labor Statistics' detailed occupations table , from the United States Department of Labor. The values inside, however, had to be expanded since each line contained multiple occupations and sometimes very specific ones. Fortunately this table also provided a percentage of women participation in the jobs shown, for those that had more than 50 thousand workers. We filtered some of these because they were too generic ( \"Computer occupations, all other\", and others) or because they had gender specific words for the profession (\"host/hostess\", \"waiter/waitress\").. and Table 7 summarizes it even further by coalescing occupation categories into broader groups to ease interpretation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 7: Percentage of female, male and neutral gender pronouns obtained for each of the merged occupation category, averaged over all occupations in said category and tested languages detailed in Table', unfold the steps to solve the question given next. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which occupation category has the highest percentage of female participation?\n" }, { "role": "assistant", "content": "To find the occupation category with the highest percentage of female participation, look at the 'Female (%)' column and identify the highest value. So, the answer is {\"answer\": \"Education\"}." } ], "images": [ "scigen/train/91.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Percentage of female, male and neutral gender pronouns obtained for each of the merged occupation category, averaged over all occupations in said category and tested languages detailed in Table" ], "reasoning": "To find the occupation category with the highest percentage of female participation, look at the 'Female (%)' column and identify the highest value.", "question": "Which occupation category has the highest percentage of female participation?", "answer": "So, the answer is {\"answer\": \"Education\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The STEM category has the lowest percentage of female participation., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe STEM category has the lowest percentage of female participation.\n" }, { "role": "assistant", "content": "To verify this statement, compare the percentage of female participation in the STEM category with other categories in the 'Female (%)' column. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/91.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Percentage of female, male and neutral gender pronouns obtained for each of the merged occupation category, averaged over all occupations in said category and tested languages detailed in Table" ], "reasoning": "To verify this statement, compare the percentage of female participation in the STEM category with other categories in the 'Female (%)' column.", "statement": "The STEM category has the lowest percentage of female participation.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 6: Percentage of female, male and neutral gender pronouns obtained for each BLS occupation category, averaged over all occupations in said category and tested languages detailed in Table'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Google Translate shows a higher probability of translating sentences with male pronouns, especially in fields with male stereotypes like life and physical sciences, architecture, engineering, computer science, and mathematics.What we have found is that Google Translate does indeed translate sentences with male pronouns with greater probability than it does either with female or gender-neutral pronouns, in general. Furthermore, this bias is seemingly aggravated for fields suggested to be troubled by male stereotypes, such as life and physical sciences, architecture, engineering, computer science and mathematics . Table 6 summarizes these data," } ], "images": [ "scigen/train/92.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Percentage of female, male and neutral gender pronouns obtained for each BLS occupation category, averaged over all occupations in said category and tested languages detailed in Table" ], "reasoning": "Google Translate shows a higher probability of translating sentences with male pronouns, especially in fields with male stereotypes like life and physical sciences, architecture, engineering, computer science, and mathematics.", "description": "What we have found is that Google Translate does indeed translate sentences with male pronouns with greater probability than it does either with female or gender-neutral pronouns, in general. Furthermore, this bias is seemingly aggravated for fields suggested to be troubled by male stereotypes, such as life and physical sciences, architecture, engineering, computer science and mathematics . Table 6 summarizes these data,", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. The question is related to the given table titled 'Table 6: Percentage of female, male and neutral gender pronouns obtained for each BLS occupation category, averaged over all occupations in said category and tested languages detailed in Table'.\n\nQuestion: Which category has the highest percentage of male pronouns?\n" }, { "role": "assistant", "content": "To find the category with the highest percentage of male pronouns, we need to compare the 'Male (%)' values across all categories. The highest value is 73.284% in the 'Life, physical, and social science' category. So, the answer is {\"answer\": \"Life, physical, and social science\"}." } ], "images": [ "scigen/train/92.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Percentage of female, male and neutral gender pronouns obtained for each BLS occupation category, averaged over all occupations in said category and tested languages detailed in Table" ], "reasoning": "To find the category with the highest percentage of male pronouns, we need to compare the 'Male (%)' values across all categories. The highest value is 73.284% in the 'Life, physical, and social science' category.", "question": "Which category has the highest percentage of male pronouns?", "answer": "So, the answer is {\"answer\": \"Life, physical, and social science\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The category with the lowest percentage of female pronouns is Architecture and engineering., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe category with the lowest percentage of female pronouns is Architecture and engineering.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the 'Female (%)' values across all categories. The lowest value is 2.299% in the 'Architecture and engineering' category. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/92.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Percentage of female, male and neutral gender pronouns obtained for each BLS occupation category, averaged over all occupations in said category and tested languages detailed in Table" ], "reasoning": "To verify this statement, we need to compare the 'Female (%)' values across all categories. The lowest value is 2.299% in the 'Architecture and engineering' category.", "statement": "The category with the lowest percentage of female pronouns is Architecture and engineering.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: Experiment 2, t= “b*tch”'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Classifiers trained on Waseem and Hovy (2016) and Waseem (2016) data are more likely to flag black-aligned tweets as sexism, with the Waseem and Hovy (2016) classifier being particularly sensitive to the word 'b*tch'. Davidson et al. (2017) and Golbeck et al. (2017) classifiers show racial disparities in classifying tweets as offensive and harassment respectively, while Founta et al. (2018) classifier shows a substantial racial disparity in classifying tweets as hate speech.The results for the second variation of Experiment 2 where we conditioned on the word \"b*tch\" are shown in Table 4.. We see similar results for Waseem and Hovy (2016) and Waseem (2016). In both cases the classifiers trained upon their data are still more likely to flag black-aligned tweets as sexism. The Waseem and Hovy (2016) classifier is particularly sensitive to the word \"b*tch\" with 96% of black-aligned and 94% of white-aligned. tweets predicted to belong to this class. For Davidson et al. (2017) almost all of these tweets are classified as offensive, however those in the blackaligned corpus are 1.15 times as frequently classified as hate speech. We see a very similar result for Golbeck et al. (2017) compared to the previous experiment, with black-aligned tweets flagged as harassment at 1.1 times the rate of those in the white-aligned corpus. Finally, for the Founta et al. (2018) classifier we see a substantial racial disparity, with black-aligned tweets classified as hate speech at 2.7 times the rate of white aligned ones, a higher rate than in Experiment 1." } ], "images": [ "scigen/train/93.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Experiment 2, t= “b*tch”" ], "reasoning": "Classifiers trained on Waseem and Hovy (2016) and Waseem (2016) data are more likely to flag black-aligned tweets as sexism, with the Waseem and Hovy (2016) classifier being particularly sensitive to the word 'b*tch'. Davidson et al. (2017) and Golbeck et al. (2017) classifiers show racial disparities in classifying tweets as offensive and harassment respectively, while Founta et al. (2018) classifier shows a substantial racial disparity in classifying tweets as hate speech.", "description": "The results for the second variation of Experiment 2 where we conditioned on the word \"b*tch\" are shown in Table 4.. We see similar results for Waseem and Hovy (2016) and Waseem (2016). In both cases the classifiers trained upon their data are still more likely to flag black-aligned tweets as sexism. The Waseem and Hovy (2016) classifier is particularly sensitive to the word \"b*tch\" with 96% of black-aligned and 94% of white-aligned. tweets predicted to belong to this class. For Davidson et al. (2017) almost all of these tweets are classified as offensive, however those in the blackaligned corpus are 1.15 times as frequently classified as hate speech. We see a very similar result for Golbeck et al. (2017) compared to the previous experiment, with black-aligned tweets flagged as harassment at 1.1 times the rate of those in the white-aligned corpus. Finally, for the Founta et al. (2018) classifier we see a substantial racial disparity, with black-aligned tweets classified as hate speech at 2.7 times the rate of white aligned ones, a higher rate than in Experiment 1.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: Experiment 2, t= “b*tch”', answer the following question. Give detailed solution consisting of each step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich classifier shows the highest racial disparity in classifying tweets as hate speech?\n" }, { "role": "assistant", "content": "By comparing the values of piblack^/piwhite^ for the 'Hate' class across all datasets, we see that the Founta et al. (2018) classifier has the highest value of 2.728, indicating the highest racial disparity. So, the answer is {\"answer\": \"Founta et al. (2018)\"}." } ], "images": [ "scigen/train/93.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Experiment 2, t= “b*tch”" ], "reasoning": "By comparing the values of piblack^/piwhite^ for the 'Hate' class across all datasets, we see that the Founta et al. (2018) classifier has the highest value of 2.728, indicating the highest racial disparity.", "question": "Which classifier shows the highest racial disparity in classifying tweets as hate speech?", "answer": "So, the answer is {\"answer\": \"Founta et al. (2018)\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Waseem and Hovy (2016) classifier is more likely to flag black-aligned tweets as racism., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Waseem and Hovy (2016) classifier is more likely to flag black-aligned tweets as racism.\n" }, { "role": "assistant", "content": "The table shows that for the 'Racism' class in the Waseem and Hovy (2016) dataset, the values of piblack^ and piwhite^ are both 0.010, indicating no racial disparity in flagging tweets as racism. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/93.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Experiment 2, t= “b*tch”" ], "reasoning": "The table shows that for the 'Racism' class in the Waseem and Hovy (2016) dataset, the values of piblack^ and piwhite^ are both 0.010, indicating no racial disparity in flagging tweets as racism.", "statement": "The Waseem and Hovy (2016) classifier is more likely to flag black-aligned tweets as racism.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Classifier performance'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table shows varying performance of classifiers on different datasets, with hate speech and harassment being particularly difficult to detect.The performance of these models on the 20% held-out validation data is reported in Table 1.. Overall we see varying performance across the classifiers, with some performing much. better out-of-sample than others. In particular, we see that hate speech and harassment are particularly difficult to detect. Since we are primarily interested in within classifier, between corpora performance, any variation between classifiers should not impact our results." } ], "images": [ "scigen/train/94.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Classifier performance" ], "reasoning": "The table shows varying performance of classifiers on different datasets, with hate speech and harassment being particularly difficult to detect.", "description": "The performance of these models on the 20% held-out validation data is reported in Table 1.. Overall we see varying performance across the classifiers, with some performing much. better out-of-sample than others. In particular, we see that hate speech and harassment are particularly difficult to detect. Since we are primarily interested in within classifier, between corpora performance, any variation between classifiers should not impact our results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 1: Classifier performance' and proceed to solve the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich class has the highest F1 score across all datasets?\n" }, { "role": "assistant", "content": "By examining the F1 scores in the table, the highest F1 score is found in the 'Neither' class of the 'D. et al.' dataset with a score of 0.95. So, the answer is {\"answer\": \"0.95\"}." } ], "images": [ "scigen/train/94.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Classifier performance" ], "reasoning": "By examining the F1 scores in the table, the highest F1 score is found in the 'Neither' class of the 'D. et al.' dataset with a score of 0.95.", "question": "Which class has the highest F1 score across all datasets?", "answer": "So, the answer is {\"answer\": \"0.95\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe 'Harass.' class in the 'G. et al.' dataset has the lowest precision score.\n" }, { "role": "assistant", "content": "By comparing the precision scores across all classes and datasets, the 'Harass.' class in the 'G. et al.' dataset has a precision score of 0.41, which is not the lowest. The 'Hate' class in the 'D. et al.' dataset has the lowest precision score of 0.32. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/94.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Classifier performance" ], "reasoning": "By comparing the precision scores across all classes and datasets, the 'Harass.' class in the 'G. et al.' dataset has a precision score of 0.41, which is not the lowest. The 'Hate' class in the 'D. et al.' dataset has the lowest precision score of 0.32.", "statement": "The 'Harass.' class in the 'G. et al.' dataset has the lowest precision score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Experiment 1'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The classifiers show significant racial disparities, with black-aligned tweets often being assigned negative labels more frequently than white-aligned tweets, except in a few cases.The results of Experiment 1 are shown in Table 2.. We observe substantial racial disparities in the performance of all classifiers. In all but one of the comparisons, there are statistically significant (p < 0.001) differences and in all but one of these we see that tweets in the black-aligned corpus are assigned negative labels more frequently than those by whites. The only case where blackaligned tweets are classified into a negative class less frequently than white-aligned tweets is the racism class in the Waseem and Hovy (2016) classifier. Note, however, the extremely low rate at which tweets are predicted to belong to this class for both groups. On the other hand, this classifier is 1.7 times more likely to classify tweets in the black-aligned corpus as sexist. For Waseem (2016) we see that there is no significant difference in the estimated rates at which tweets are clas. sified as racist across groups, although the rates remain low. Tweets in the black-aligned corpus are classified as containing sexism almost twice as frequently and 1.1 times as frequently classified as containing racism and sexism compared to those in the white-aligned corpus. Moving onto Davidson et al. (2017), we find large disparities, with around 5% of tweets in the black-aligned corpus classified as hate speech compared to 2% of those in the white-aligned set. Similarly, 17% of black-aligned tweets are predicted to contain offensive language compared to 6.5% of whitealigned tweets. The classifier trained on the Golbeck et al. (2017) dataset predicts black-aligned tweets to be harassment 1.4 times as frequently as white-aligned tweets. The Founta et al. (2018) classifier labels around 11% of tweets in the blackaligned corpus as hate speech and almost 18% as abusive, compared to 6% and 8% of white-aligned tweets respectively. It also classifies black-aligned tweets as spam 1.8 times as frequently. The results of Experiment 2 are consistent with the previous results, although there are some notable differences. In most cases the racial disparities persist, although they are generally smaller in magnitude and in some cases the direction even changes." } ], "images": [ "scigen/train/95.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Experiment 1" ], "reasoning": "The classifiers show significant racial disparities, with black-aligned tweets often being assigned negative labels more frequently than white-aligned tweets, except in a few cases.", "description": "The results of Experiment 1 are shown in Table 2.. We observe substantial racial disparities in the performance of all classifiers. In all but one of the comparisons, there are statistically significant (p < 0.001) differences and in all but one of these we see that tweets in the black-aligned corpus are assigned negative labels more frequently than those by whites. The only case where blackaligned tweets are classified into a negative class less frequently than white-aligned tweets is the racism class in the Waseem and Hovy (2016) classifier. Note, however, the extremely low rate at which tweets are predicted to belong to this class for both groups. On the other hand, this classifier is 1.7 times more likely to classify tweets in the black-aligned corpus as sexist. For Waseem (2016) we see that there is no significant difference in the estimated rates at which tweets are clas. sified as racist across groups, although the rates remain low. Tweets in the black-aligned corpus are classified as containing sexism almost twice as frequently and 1.1 times as frequently classified as containing racism and sexism compared to those in the white-aligned corpus. Moving onto Davidson et al. (2017), we find large disparities, with around 5% of tweets in the black-aligned corpus classified as hate speech compared to 2% of those in the white-aligned set. Similarly, 17% of black-aligned tweets are predicted to contain offensive language compared to 6.5% of whitealigned tweets. The classifier trained on the Golbeck et al. (2017) dataset predicts black-aligned tweets to be harassment 1.4 times as frequently as white-aligned tweets. The Founta et al. (2018) classifier labels around 11% of tweets in the blackaligned corpus as hate speech and almost 18% as abusive, compared to 6% and 8% of white-aligned tweets respectively. It also classifies black-aligned tweets as spam 1.8 times as frequently. The results of Experiment 2 are consistent with the previous results, although there are some notable differences. In most cases the racial disparities persist, although they are generally smaller in magnitude and in some cases the direction even changes.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: Experiment 1' and methodically tackle the question that follows. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich classifier shows the highest disparity in classifying black-aligned tweets as offensive compared to white-aligned tweets?\n" }, { "role": "assistant", "content": "By examining the 'piblack/piwhite' column for the 'Offensive' class, the Davidson et al. (2017) classifier shows the highest disparity with a value of 2.653. So, the answer is {\"answer\": \"Davidson et al. (2017)\"}" } ], "images": [ "scigen/train/95.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Experiment 1" ], "reasoning": "By examining the 'piblack/piwhite' column for the 'Offensive' class, the Davidson et al. (2017) classifier shows the highest disparity with a value of 2.653.", "question": "Which classifier shows the highest disparity in classifying black-aligned tweets as offensive compared to white-aligned tweets?", "answer": "So, the answer is {\"answer\": \"Davidson et al. (2017)\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Waseem and Hovy (2016) classifier shows a higher rate of classifying black-aligned tweets as racist compared to white-aligned tweets. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Waseem and Hovy (2016) classifier shows a higher rate of classifying black-aligned tweets as racist compared to white-aligned tweets.\n" }, { "role": "assistant", "content": "The table shows that the 'piblack/piwhite' value for the 'Racism' class in the Waseem and Hovy (2016) classifier is 0.505, indicating that black-aligned tweets are classified as racist less frequently than white-aligned tweets. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/95.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Experiment 1" ], "reasoning": "The table shows that the 'piblack/piwhite' value for the 'Racism' class in the Waseem and Hovy (2016) classifier is 0.505, indicating that black-aligned tweets are classified as racist less frequently than white-aligned tweets.", "statement": "The Waseem and Hovy (2016) classifier shows a higher rate of classifying black-aligned tweets as racist compared to white-aligned tweets.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Experiment 2, t= “n*gga”'.\n" }, { "role": "assistant", "content": "Classifiers trained on Waseem and Hovy (2016) and Waseem (2016) predict black-aligned tweets to be instances of sexism more frequently than white-aligned tweets, while Davidson et al. (2017) classifier is less likely to classify black-aligned tweets as hate speech but more likely to classify them as offensive.Table 3 shows that for tweets containing the word \"n*gga\", classifiers trained on Waseem and Hovy (2016) and Waseem (2016) are both predict black-aligned tweets to be instances of sexism approximately 1.5 times as often as white-aligned tweets. The classifier trained on the Davidson et al. (2017) data is significantly less likely to classify black-aligned tweets as hate speech, although it is more likely to classify them as offensive. Golbeck et al. (2017) classifies black-aligned tweets as harassment at a higher rate for both groups than in the previous experiment, although the disparity is narrower. For the Founta et al. (2018) classifier we see that black-aligned tweets are slightly less frequently considered to be hate speech but are much more frequently classified as abusive." } ], "images": [ "scigen/train/96.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Experiment 2, t= “n*gga”" ], "reasoning": "Classifiers trained on Waseem and Hovy (2016) and Waseem (2016) predict black-aligned tweets to be instances of sexism more frequently than white-aligned tweets, while Davidson et al. (2017) classifier is less likely to classify black-aligned tweets as hate speech but more likely to classify them as offensive.", "description": "Table 3 shows that for tweets containing the word \"n*gga\", classifiers trained on Waseem and Hovy (2016) and Waseem (2016) are both predict black-aligned tweets to be instances of sexism approximately 1.5 times as often as white-aligned tweets. The classifier trained on the Davidson et al. (2017) data is significantly less likely to classify black-aligned tweets as hate speech, although it is more likely to classify them as offensive. Golbeck et al. (2017) classifies black-aligned tweets as harassment at a higher rate for both groups than in the previous experiment, although the disparity is narrower. For the Founta et al. (2018) classifier we see that black-aligned tweets are slightly less frequently considered to be hate speech but are much more frequently classified as abusive.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Experiment 2, t= “n*gga”'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: Which classifier is significantly less likely to classify black-aligned tweets as hate speech?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The description states that the classifier trained on Davidson et al. (2017) is significantly less likely to classify black-aligned tweets as hate speech. So, the answer is {\"answer\": \"Davidson et al. (2017)\"}." } ], "images": [ "scigen/train/96.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Experiment 2, t= “n*gga”" ], "reasoning": "The description states that the classifier trained on Davidson et al. (2017) is significantly less likely to classify black-aligned tweets as hate speech.", "question": "Which classifier is significantly less likely to classify black-aligned tweets as hate speech?", "answer": "So, the answer is {\"answer\": \"Davidson et al. (2017)\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The classifier trained on Waseem and Hovy (2016) predicts black-aligned tweets to be instances of racism more frequently than white-aligned tweets. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe classifier trained on Waseem and Hovy (2016) predicts black-aligned tweets to be instances of racism more frequently than white-aligned tweets.\n" }, { "role": "assistant", "content": "The table shows that for the Waseem and Hovy (2016) classifier, the probability of black-aligned tweets being classified as racism (0.010) is slightly less than that for white-aligned tweets (0.011). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/96.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Experiment 2, t= “n*gga”" ], "reasoning": "The table shows that for the Waseem and Hovy (2016) classifier, the probability of black-aligned tweets being classified as racism (0.010) is slightly less than that for white-aligned tweets (0.011).", "statement": "The classifier trained on Waseem and Hovy (2016) predicts black-aligned tweets to be instances of racism more frequently than white-aligned tweets.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: Automatic evaluation of caption generation on MSCOCO and Flickr30k.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Sparsemax and TVMAX achieve better results overall compared to softmax, with TVMAX slightly worse than sparsemax but still superior to softmax on MSCOCO and similar on Flickr30k. Selective attention mechanisms like sparsemax and TVMAX reduce repetition as measured by the REP metric.As can be seen in Table 1, sparsemax and TVMAX achieve better results overall when compared with softmax, indicating that the use of selective attention leads to better captions.. Moreover, for TVMAX, automatic metrics results are slightly worse than sparsemax but still superior to softmax on MSCOCO and similar on Flickr30k.. Selective attention mechanisms like sparsemax and especially TVMAX reduce repetition, as measured by the REP metric reported in Table 1." } ], "images": [ "scigen/train/97.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Automatic evaluation of caption generation on MSCOCO and Flickr30k." ], "reasoning": "Sparsemax and TVMAX achieve better results overall compared to softmax, with TVMAX slightly worse than sparsemax but still superior to softmax on MSCOCO and similar on Flickr30k. Selective attention mechanisms like sparsemax and TVMAX reduce repetition as measured by the REP metric.", "description": "As can be seen in Table 1, sparsemax and TVMAX achieve better results overall when compared with softmax, indicating that the use of selective attention leads to better captions.. Moreover, for TVMAX, automatic metrics results are slightly worse than sparsemax but still superior to softmax on MSCOCO and similar on Flickr30k.. Selective attention mechanisms like sparsemax and especially TVMAX reduce repetition, as measured by the REP metric reported in Table 1.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: Automatic evaluation of caption generation on MSCOCO and Flickr30k.', answer the question below by showing each progressive step toward the answer. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich attention mechanism achieves the lowest repetition score on MSCOCO?\n" }, { "role": "assistant", "content": "To find the attention mechanism with the lowest repetition score on MSCOCO, look at the 'MSCOCO rep' column. TVMAX has the lowest score of 3.17. So, the answer is {\"answer\": \"TVMAX\"}." } ], "images": [ "scigen/train/97.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Automatic evaluation of caption generation on MSCOCO and Flickr30k." ], "reasoning": "To find the attention mechanism with the lowest repetition score on MSCOCO, look at the 'MSCOCO rep' column. TVMAX has the lowest score of 3.17.", "question": "Which attention mechanism achieves the lowest repetition score on MSCOCO?", "answer": "So, the answer is {\"answer\": \"TVMAX\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSparsemax achieves the highest cider score on MSCOCO.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'MSCOCO cider' column. Sparsemax has the highest score of 0.990, which is higher than both softmax and TVMAX. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/97.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Automatic evaluation of caption generation on MSCOCO and Flickr30k." ], "reasoning": "To verify this statement, look at the 'MSCOCO cider' column. Sparsemax has the highest score of 0.990, which is higher than both softmax and TVMAX.", "statement": "Sparsemax achieves the highest cider score on MSCOCO.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Human evaluation results on MSCOCO.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "TVMAX outperforms sparsemax and softmax in both caption and attention relevance human evaluations, indicating better feature selection and interpretability.Despite performing slightly worse than sparsemax under automatic metrics, TVMAX outperforms sparsemax and softmax in the caption human evaluation and the attention relevance human evaluation, reported in Table 2. The superior score on attention relevance shows that TVMAX is better at selecting the relevant features and its output is more interpretable. Additionally, the better caption evaluation results demonstrate that the ability to select compact regions induces the generation of better captions.. With these scores, we computed the mean of the captions evaluation scores and the mean of the attention relevance evaluation scores. The results are reported in Table 2." } ], "images": [ "scigen/train/98.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Human evaluation results on MSCOCO." ], "reasoning": "TVMAX outperforms sparsemax and softmax in both caption and attention relevance human evaluations, indicating better feature selection and interpretability.", "description": "Despite performing slightly worse than sparsemax under automatic metrics, TVMAX outperforms sparsemax and softmax in the caption human evaluation and the attention relevance human evaluation, reported in Table 2. The superior score on attention relevance shows that TVMAX is better at selecting the relevant features and its output is more interpretable. Additionally, the better caption evaluation results demonstrate that the ability to select compact regions induces the generation of better captions.. With these scores, we computed the mean of the captions evaluation scores and the mean of the attention relevance evaluation scores. The results are reported in Table 2.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 2: Human evaluation results on MSCOCO.\n\nQuestion: What is the mean score of TVMAX in both caption and attention relevance evaluations?\n" }, { "role": "assistant", "content": "To find the mean score of TVMAX, add the caption score (3.87) and the attention relevance score (4.10), then divide by 2. So, the answer is {\"answer\": \"3.985\"}." } ], "images": [ "scigen/train/98.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Human evaluation results on MSCOCO." ], "reasoning": "To find the mean score of TVMAX, add the caption score (3.87) and the attention relevance score (4.10), then divide by 2.", "question": "What is the mean score of TVMAX in both caption and attention relevance evaluations?", "answer": "So, the answer is {\"answer\": \"3.985\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nTVMAX has the highest score in both caption and attention relevance evaluations.\n" }, { "role": "assistant", "content": "The table shows that TVMAX has a caption score of 3.87 and an attention relevance score of 4.10, both of which are higher than the scores of softmax and sparsemax. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/98.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Human evaluation results on MSCOCO." ], "reasoning": "The table shows that TVMAX has a caption score of 3.87 and an attention relevance score of 4.10, both of which are higher than the scores of softmax and sparsemax.", "statement": "TVMAX has the highest score in both caption and attention relevance evaluations.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: Automatic evaluation of VQA on VQA-2.0. Sparse-TVmax and soft-TVmax correspond to using sparsemax or softmax on the image self-attention and TVmax on the output attention. Other models use softmax or sparsemax on self-attention and output attention.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Models using TVMAX in the output attention layer outperform those using softmax and sparsemax, with the highest accuracy achieved when combining TVMAX with bounding box features.As can be seen in the results presented in Table 3 the models using TVMAX in the output attention layer outperform the models using softmax and sparsemax. Moreover, the results are slightly superior when the sparsemax transformation is used in the self-attention layers of the decoder. It can also be observed that, when using sparsemax both in the self-attention layers of the decoder and in the output attention mechanism, the accuracy is superior than when using softmax. Thus, having sparse attention mechanisms in the self-attention layers is beneficial, but the biggest improvement is obtained when using TVMAX in the output attention. This corroborates our intuition that selecting the features of contiguous regions of the image leads to a better understanding of the image, and, consequently, better accuracy. Additionally, when using bounding box features, sparsemax outperforms softmax, showing that selecting only the bounding boxes of the relevant objects leads to a better answering capability. We can also see that combining ResNet features with bounding box features improves results. Moreover, the model using TVMAX in the final attention layer achieves the highest accuracy, showing that features obtained using the TVMAX transformation are a better complement to bounding box features." } ], "images": [ "scigen/train/99.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Automatic evaluation of VQA on VQA-2.0. Sparse-TVmax and soft-TVmax correspond to using sparsemax or softmax on the image self-attention and TVmax on the output attention. Other models use softmax or sparsemax on self-attention and output attention." ], "reasoning": "Models using TVMAX in the output attention layer outperform those using softmax and sparsemax, with the highest accuracy achieved when combining TVMAX with bounding box features.", "description": "As can be seen in the results presented in Table 3 the models using TVMAX in the output attention layer outperform the models using softmax and sparsemax. Moreover, the results are slightly superior when the sparsemax transformation is used in the self-attention layers of the decoder. It can also be observed that, when using sparsemax both in the self-attention layers of the decoder and in the output attention mechanism, the accuracy is superior than when using softmax. Thus, having sparse attention mechanisms in the self-attention layers is beneficial, but the biggest improvement is obtained when using TVMAX in the output attention. This corroborates our intuition that selecting the features of contiguous regions of the image leads to a better understanding of the image, and, consequently, better accuracy. Additionally, when using bounding box features, sparsemax outperforms softmax, showing that selecting only the bounding boxes of the relevant objects leads to a better answering capability. We can also see that combining ResNet features with bounding box features improves results. Moreover, the model using TVMAX in the final attention layer achieves the highest accuracy, showing that features obtained using the TVMAX transformation are a better complement to bounding box features.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: Automatic evaluation of VQA on VQA-2.0. Sparse-TVmax and soft-TVmax correspond to using sparsemax or softmax on the image self-attention and TVmax on the output attention. Other models use softmax or sparsemax on self-attention and output attention.', answer the following question. Give detailed solution consisting of each step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model configuration achieves the highest accuracy in the Test-Standard Overall category?\n" }, { "role": "assistant", "content": "The table shows that the model using sparse-TVmax with attention to bounding boxes achieves the highest accuracy in the Test-Standard Overall category with a score of 69.28. So, the answer is {\"answer\": \"sparse-TVmax with attention to bounding boxes\"}" } ], "images": [ "scigen/train/99.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Automatic evaluation of VQA on VQA-2.0. Sparse-TVmax and soft-TVmax correspond to using sparsemax or softmax on the image self-attention and TVmax on the output attention. Other models use softmax or sparsemax on self-attention and output attention." ], "reasoning": "The table shows that the model using sparse-TVmax with attention to bounding boxes achieves the highest accuracy in the Test-Standard Overall category with a score of 69.28.", "question": "Which model configuration achieves the highest accuracy in the Test-Standard Overall category?", "answer": "So, the answer is {\"answer\": \"sparse-TVmax with attention to bounding boxes\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The model using sparsemax in the output attention layer achieves the highest accuracy in the Test-Standard Overall category., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe model using sparsemax in the output attention layer achieves the highest accuracy in the Test-Standard Overall category.\n" }, { "role": "assistant", "content": "The table shows that the model using sparse-TVmax with attention to bounding boxes achieves the highest accuracy in the Test-Standard Overall category, not the model using sparsemax. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/99.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Automatic evaluation of VQA on VQA-2.0. Sparse-TVmax and soft-TVmax correspond to using sparsemax or softmax on the image self-attention and TVmax on the output attention. Other models use softmax or sparsemax on self-attention and output attention." ], "reasoning": "The table shows that the model using sparse-TVmax with attention to bounding boxes achieves the highest accuracy in the Test-Standard Overall category, not the model using sparsemax.", "statement": "The model using sparsemax in the output attention layer achieves the highest accuracy in the Test-Standard Overall category.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'TABLE IV: Synthetic Data Performance on three error generation algorithm'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows the system's performance on different error generation algorithms, including Random Character, Character Bigrams, and Characters Swap, for various languages at edit distances 1 and 2.Table IV presents the system's performance on each error generation algorithm. We included only P@1 and P@10 to show trend on all languages. \"Random Character\" and \"Character Bigrams\" includes data for edit distance 1 and 2 whereas \"Characters Swap\" includes data for edit distance 2." } ], "images": [ "scigen/train/100.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Synthetic Data Performance on three error generation algorithm" ], "reasoning": "The table shows the system's performance on different error generation algorithms, including Random Character, Character Bigrams, and Characters Swap, for various languages at edit distances 1 and 2.", "description": "Table IV presents the system's performance on each error generation algorithm. We included only P@1 and P@10 to show trend on all languages. \"Random Character\" and \"Character Bigrams\" includes data for edit distance 1 and 2 whereas \"Characters Swap\" includes data for edit distance 2.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'TABLE IV: Synthetic Data Performance on three error generation algorithm', answer the following question. Give detailed solution consisting of each step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich language has the highest P@1 score for the Characters Swap algorithm?\n" }, { "role": "assistant", "content": "To find the highest P@1 score for the Characters Swap algorithm, look at the 'Characters Swap P@1' column and identify the highest value. The highest value is 97.989 for Thai. So, the answer is {\"answer\": \"Thai\"}." } ], "images": [ "scigen/train/100.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Synthetic Data Performance on three error generation algorithm" ], "reasoning": "To find the highest P@1 score for the Characters Swap algorithm, look at the 'Characters Swap P@1' column and identify the highest value. The highest value is 97.989 for Thai.", "question": "Which language has the highest P@1 score for the Characters Swap algorithm?", "answer": "So, the answer is {\"answer\": \"Thai\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe system's performance for Random Character P@1 is above 90 for all languages.\n" }, { "role": "assistant", "content": "To verify this statement, check the 'Random Character P@1' column for all languages. The value for Hindi is 81.610, which is below 90. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/100.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Synthetic Data Performance on three error generation algorithm" ], "reasoning": "To verify this statement, check the 'Random Character P@1' column for all languages. The value for Hindi is 81.610, which is below 90.", "statement": "The system's performance for Random Character P@1 is above 90 for all languages.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'TABLE I: Average Time taken by suggestion generation algorithms (Edit Distance = 2) (in millisecond)'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The table compares the performance of Trie, DAWGs, and SDA algorithms for edit distance 2, excluding BK trees due to their slower performance.We considered four approaches — Trie data structure, Burkhard-Keller Tree (BK Tree) , Directed Acyclic Word Graphs (DAWGs) and Symmetric Delete algorithm (SDA)6. In Table I, we represent the performance of algorithms for edit distance 2 without adding results for BK trees because its performance was in range of couple of seconds." } ], "images": [ "scigen/train/101.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Average Time taken by suggestion generation algorithms (Edit Distance = 2) (in millisecond)" ], "reasoning": "The table compares the performance of Trie, DAWGs, and SDA algorithms for edit distance 2, excluding BK trees due to their slower performance.", "description": "We considered four approaches — Trie data structure, Burkhard-Keller Tree (BK Tree) , Directed Acyclic Word Graphs (DAWGs) and Symmetric Delete algorithm (SDA)6. In Table I, we represent the performance of algorithms for edit distance 2 without adding results for BK trees because its performance was in range of couple of seconds.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'TABLE I: Average Time taken by suggestion generation algorithms (Edit Distance = 2) (in millisecond)' and methodically tackle the question that follows. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich algorithm has the lowest performance time for 10 tokens?\n" }, { "role": "assistant", "content": "To find the algorithm with the lowest performance time for 10 tokens, compare the values in the row corresponding to 10 tokens. Trie has 379.99, DAWGs has 386.04, and SDA has 1.26. SDA has the lowest performance time. So, the answer is {\"answer\": \"SDA\"}." } ], "images": [ "scigen/train/101.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Average Time taken by suggestion generation algorithms (Edit Distance = 2) (in millisecond)" ], "reasoning": "To find the algorithm with the lowest performance time for 10 tokens, compare the values in the row corresponding to 10 tokens. Trie has 379.99, DAWGs has 386.04, and SDA has 1.26. SDA has the lowest performance time.", "question": "Which algorithm has the lowest performance time for 10 tokens?", "answer": "So, the answer is {\"answer\": \"SDA\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nTrie has the highest performance time for 16 tokens.\n" }, { "role": "assistant", "content": "To verify this statement, compare the performance times for 16 tokens. Trie has 580.44, DAWGs has 584.99, and SDA has 0.37. Trie does not have the highest performance time; DAWGs does. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/101.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Average Time taken by suggestion generation algorithms (Edit Distance = 2) (in millisecond)" ], "reasoning": "To verify this statement, compare the performance times for 16 tokens. Trie has 580.44, DAWGs has 584.99, and SDA has 0.37. Trie does not have the highest performance time; DAWGs does.", "statement": "Trie has the highest performance time for 16 tokens.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'TABLE II: Synthetic Data Performance results', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The system performs well on the synthetic dataset, achieving a minimum of 80% P@1 and 98% P@10 across various languages.The best performances for each language is reported in Table II. We present Precision@k9 for k ∈ 1, 3, 5, 10 and mean reciprocal rank (MRR). The system performs well on synthetic dataset with a minimum of 80% P@1 and 98% P@10." } ], "images": [ "scigen/train/102.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Synthetic Data Performance results" ], "reasoning": "The system performs well on the synthetic dataset, achieving a minimum of 80% P@1 and 98% P@10 across various languages.", "description": "The best performances for each language is reported in Table II. We present Precision@k9 for k ∈ 1, 3, 5, 10 and mean reciprocal rank (MRR). The system performs well on synthetic dataset with a minimum of 80% P@1 and 98% P@10.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'TABLE II: Synthetic Data Performance results', and sequentially solve the question, writing out each step of your reasoning process. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich language has the highest P@1 score?\n" }, { "role": "assistant", "content": "To find the language with the highest P@1 score, we need to compare the P@1 values for all languages. The highest P@1 value is 98.73, which corresponds to the Thai language. So, the answer is {\"answer\": \"Thai\"}" } ], "images": [ "scigen/train/102.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Synthetic Data Performance results" ], "reasoning": "To find the language with the highest P@1 score, we need to compare the P@1 values for all languages. The highest P@1 value is 98.73, which corresponds to the Thai language.", "question": "Which language has the highest P@1 score?", "answer": "So, the answer is {\"answer\": \"Thai\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The system achieved a P@10 score of at least 98% for all languages. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe system achieved a P@10 score of at least 98% for all languages.\n" }, { "role": "assistant", "content": "To verify this statement, we need to check the P@10 scores for all languages. All languages have a P@10 score of at least 98%, confirming the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/102.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Synthetic Data Performance results" ], "reasoning": "To verify this statement, we need to check the P@10 scores for all languages. All languages have a P@10 score of at least 98%, confirming the statement.", "statement": "The system achieved a P@10 score of at least 98% for all languages.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'TABLE III: Synthetic Data Time Performance results', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table reports the average time taken for detection, suggestion, and ranking sub-steps for various languages, weighted by different factors.The system is able to do each sub-step in real-time; the average time taken to perform for each sub-step is reported in Table III. All the sentences used for this analysis had exactly one error according to our system. Detection time is the average time weighted over number of tokens in query sentence, suggestion time is weighted over misspelling character length and ranking time is weighted over length of suggestions generated." } ], "images": [ "scigen/train/103.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Synthetic Data Time Performance results" ], "reasoning": "The table reports the average time taken for detection, suggestion, and ranking sub-steps for various languages, weighted by different factors.", "description": "The system is able to do each sub-step in real-time; the average time taken to perform for each sub-step is reported in Table III. All the sentences used for this analysis had exactly one error according to our system. Detection time is the average time weighted over number of tokens in query sentence, suggestion time is weighted over misspelling character length and ranking time is weighted over length of suggestions generated.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: TABLE III: Synthetic Data Time Performance results\nQuestion: Which language has the highest detection time and what is the value?\n" }, { "role": "assistant", "content": "By examining the 'Detection Time (μs)' column, Hebrew has the highest detection time of 22.35 μs. So, the answer is {\"answer\": \"Hebrew\"}" } ], "images": [ "scigen/train/103.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Synthetic Data Time Performance results" ], "reasoning": "By examining the 'Detection Time (μs)' column, Hebrew has the highest detection time of 22.35 μs.", "question": "Which language has the highest detection time and what is the value?", "answer": "So, the answer is {\"answer\": \"Hebrew\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The suggestion time for ED=2 in English is higher than in Dutch. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe suggestion time for ED=2 in English is higher than in Dutch.\n" }, { "role": "assistant", "content": "The table shows that the suggestion time for ED=2 in English is 39.36 ms, while in Dutch it is 30.44 ms, confirming the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/103.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Synthetic Data Time Performance results" ], "reasoning": "The table shows that the suggestion time for ED=2 in English is 39.36 ms, while in Dutch it is 30.44 ms, confirming the statement.", "statement": "The suggestion time for ED=2 in English is higher than in Dutch.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'TABLE VI: Public dataset comparison results'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table compares the performance of GNU Aspell, Hunspell, and our system using unigram probabilities for ranking. Our system outperforms both GNU Aspell and Hunspell in most metrics.Comparison of most popular spell checkers for English (GNU Aspell and Hunspell12) on this data is presented in Table VI. Since these tools only work on word-error level, we used only unigram probabilities for ranking. Our system outperforms both the systems." } ], "images": [ "scigen/train/104.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VI: Public dataset comparison results" ], "reasoning": "The table compares the performance of GNU Aspell, Hunspell, and our system using unigram probabilities for ranking. Our system outperforms both GNU Aspell and Hunspell in most metrics.", "description": "Comparison of most popular spell checkers for English (GNU Aspell and Hunspell12) on this data is presented in Table VI. Since these tools only work on word-error level, we used only unigram probabilities for ranking. Our system outperforms both the systems.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'TABLE VI: Public dataset comparison results'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich spell checker has the highest P@1 score?\n" }, { "role": "assistant", "content": "The table shows the P@1 scores for Aspell, Hunspell, and our system. Our system has the highest P@1 score of 68.99. So, the answer is {\"answer\": \"Ours\"}." } ], "images": [ "scigen/train/104.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VI: Public dataset comparison results" ], "reasoning": "The table shows the P@1 scores for Aspell, Hunspell, and our system. Our system has the highest P@1 score of 68.99.", "question": "Which spell checker has the highest P@1 score?", "answer": "So, the answer is {\"answer\": \"Ours\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nHunspell has a higher P@3 score than Aspell.\n" }, { "role": "assistant", "content": "The table shows that Hunspell has a P@3 score of 77.86, while Aspell has a P@3 score of 80.81. Therefore, Hunspell does not have a higher P@3 score than Aspell. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/104.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VI: Public dataset comparison results" ], "reasoning": "The table shows that Hunspell has a P@3 score of 77.86, while Aspell has a P@3 score of 80.81. Therefore, Hunspell does not have a higher P@3 score than Aspell.", "statement": "Hunspell has a higher P@3 score than Aspell.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'TABLE VII: False Positive Experiment Results', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Most words in each language were detected as known, with a minor percentage detected as errors.As shown in Table VII, most of the words for each language were detected as known but still there was a minor percentage of words which were detected as errors." } ], "images": [ "scigen/train/105.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VII: False Positive Experiment Results" ], "reasoning": "Most words in each language were detected as known, with a minor percentage detected as errors.", "description": "As shown in Table VII, most of the words for each language were detected as known but still there was a minor percentage of words which were detected as errors.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'TABLE VII: False Positive Experiment Results'. Solve the following question based on the table.\n\nQuestion: Which language had the lowest percentage of words detected as known?\nLet's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the language with the lowest percentage of words detected as known, we need to look at the '%' column and identify the lowest value. So, the answer is {\"answer\": \"Thai\"}" } ], "images": [ "scigen/train/105.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VII: False Positive Experiment Results" ], "reasoning": "To find the language with the lowest percentage of words detected as known, we need to look at the '%' column and identify the lowest value.", "question": "Which language had the lowest percentage of words detected as known?", "answer": "So, the answer is {\"answer\": \"Thai\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe percentage of words detected as known for French is higher than for Finnish.\n" }, { "role": "assistant", "content": "The table shows that the percentage of words detected as known for French is 98.25%, while for Finnish it is 92.02%. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/105.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VII: False Positive Experiment Results" ], "reasoning": "The table shows that the percentage of words detected as known for French is 98.25%, while for Finnish it is 92.02%. Therefore, the statement is supported.", "statement": "The percentage of words detected as known for French is higher than for Finnish.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 9: Performance of models trained with tweets from one domain and tested on other domains. All results are reported in ROC AUC. The All line displays results on training on all categories except the category in testing.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Models trained on tweets from all domains except the one tested show consistent performance, with 'Food & Beverage' performing lower and 'Other' performing higher.Finally, Table 9 presents the results of models trained on tweets from one domain and tested on all tweets from other domains, with additional models trained on tweets from all domains except the one that the model is tested on. We observe that predictive performance is relatively consistent across all domains with two exceptions ('Food & Beverage' consistently shows lower performance, while 'Other' achieves higher performance) when using all the data available from the other domains." } ], "images": [ "scigen/train/106.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Performance of models trained with tweets from one domain and tested on other domains. All results are reported in ROC AUC. The All line displays results on training on all categories except the category in testing." ], "reasoning": "Models trained on tweets from all domains except the one tested show consistent performance, with 'Food & Beverage' performing lower and 'Other' performing higher.", "description": "Finally, Table 9 presents the results of models trained on tweets from one domain and tested on all tweets from other domains, with additional models trained on tweets from all domains except the one that the model is tested on. We observe that predictive performance is relatively consistent across all domains with two exceptions ('Food & Beverage' consistently shows lower performance, while 'Other' achieves higher performance) when using all the data available from the other domains.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 9: Performance of models trained with tweets from one domain and tested on other domains. All results are reported in ROC AUC. The All line displays results on training on all categories except the category in testing.', and sequentially solve the question, writing out each step of your reasoning process. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich domain shows the highest performance when tested on tweets from all other domains?\n" }, { "role": "assistant", "content": "By comparing the performance of models trained on all domains except the one tested, 'Other' achieves the highest performance with a score of 88.2. So, the answer is {\"answer\": \"Other\"}" } ], "images": [ "scigen/train/106.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Performance of models trained with tweets from one domain and tested on other domains. All results are reported in ROC AUC. The All line displays results on training on all categories except the category in testing." ], "reasoning": "By comparing the performance of models trained on all domains except the one tested, 'Other' achieves the highest performance with a score of 88.2.", "question": "Which domain shows the highest performance when tested on tweets from all other domains?", "answer": "So, the answer is {\"answer\": \"Other\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe 'Food & Beverage' domain shows the highest performance when tested on tweets from all other domains.\n" }, { "role": "assistant", "content": "The table shows that 'Food & Beverage' has the lowest performance (70.3) when tested on tweets from all other domains, not the highest. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/106.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Performance of models trained with tweets from one domain and tested on other domains. All results are reported in ROC AUC. The All line displays results on training on all categories except the category in testing." ], "reasoning": "The table shows that 'Food & Beverage' has the lowest performance (70.3) when tested on tweets from all other domains, not the highest.", "statement": "The 'Food & Beverage' domain shows the highest performance when tested on tweets from all other domains.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Number of tweets annotated as complaints across the nine domains.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The majority of tweets are complaints, with 1,232 complaints making up 62.4% of the total, while 739 tweets are not complaints, accounting for 37.6%.In total, 1,232 tweets (62.4%) are complaints and 739 are not complaints (37.6%). The statistics for each category is in Table 3." } ], "images": [ "scigen/train/107.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Number of tweets annotated as complaints across the nine domains." ], "reasoning": "The majority of tweets are complaints, with 1,232 complaints making up 62.4% of the total, while 739 tweets are not complaints, accounting for 37.6%.", "description": "In total, 1,232 tweets (62.4%) are complaints and 739 are not complaints (37.6%). The statistics for each category is in Table 3.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Number of tweets annotated as complaints across the nine domains.', answer the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich category has the highest number of complaints and how many complaints does it have?\n" }, { "role": "assistant", "content": "To find the category with the highest number of complaints, look at the 'Complaints' column and identify the highest value. The highest value is 207 in the 'Services' category. So, the answer is {\"answer\": \"Services, 207\"}." } ], "images": [ "scigen/train/107.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Number of tweets annotated as complaints across the nine domains." ], "reasoning": "To find the category with the highest number of complaints, look at the 'Complaints' column and identify the highest value. The highest value is 207 in the 'Services' category.", "question": "Which category has the highest number of complaints and how many complaints does it have?", "answer": "So, the answer is {\"answer\": \"Services, 207\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The 'Retail' category has more complaints than the 'Apparel' category., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe 'Retail' category has more complaints than the 'Apparel' category.\n" }, { "role": "assistant", "content": "According to the table, the 'Retail' category has 124 complaints, while the 'Apparel' category has 141 complaints. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/107.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Number of tweets annotated as complaints across the nine domains." ], "reasoning": "According to the table, the 'Retail' category has 124 complaints, while the 'Apparel' category has 141 complaints. Therefore, the statement is incorrect.", "statement": "The 'Retail' category has more complaints than the 'Apparel' category.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Features associated with complaint and non-complaint tweets, sorted by Pearson correlation (r) computed between the normalized frequency of each feature and the complaint label across all tweets. All correlations are significant at p'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Complaints are characterized by possessive pronouns, past participle verbs, and negations, while non-complaints feature positive sentiment words and expressions of gratitude.Top unigrams and part-of-speech features specific of complaints and non-complaints are presented in Table 4.. All correlations shown in these tables are statistically significant at p < .01, with Simes correction for multiple comparisons.. Negations are uncovered through unigrams (not, no, won't). Several unigrams (error, issue, working, fix). However, words regularly describing negative sentiment or emotions are not one of the most distinctive features for complaints.. On the other hand, the presence of terms that show positive sentiment or emotions (good, great, win, POSEMO, AFFECT, ASSENT) are among the top most distinctive features for a tweet not being labeled as a complaint.. In addition, other words and clusters expressing positive states such as gratitude (thank, great, love) or laughter (lol) are also distinctive for tweets that are not complaints.. Across unigrams, part-of-speech patterns and word clusters, we see a distinctive pattern emerging around pronoun usage.. Complaints use more possessive pronouns, indicating that the user is describing personal experiences.. A distinctive part-of-speech pattern common in complaints is possessive pronouns followed by nouns (PRP$ NN) which refer to items of services possessed by the complainer (e.g., my account, my order).. Question marks are distinctive of complaints, as many complaints are formulated as questions to the responsible party (e.g., why is this not working?, when will. get my response?).. Mentions of time are specific of complaints (been, still, on, days, Temporal References cluster).. In addition, the presence of verbs in past participle (VBN) is the most distinctive part-of-speech pattern of complaints. These are used to describe actions completed in the past (e.g., i've bought, have come) in order to provide context for the complaint.. Several part-of-speech patterns distinctive of complaints involve present verbs in third person singular (VBZ).. Verbs in gerund or present participle are used as a complaint strategy to describe things that just happened to a user (e.g., got an email saying my service will be terminated)." } ], "images": [ "scigen/train/108.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Features associated with complaint and non-complaint tweets, sorted by Pearson correlation (r) computed between the normalized frequency of each feature and the complaint label across all tweets. All correlations are significant at p" ], "reasoning": "Complaints are characterized by possessive pronouns, past participle verbs, and negations, while non-complaints feature positive sentiment words and expressions of gratitude.", "description": "Top unigrams and part-of-speech features specific of complaints and non-complaints are presented in Table 4.. All correlations shown in these tables are statistically significant at p < .01, with Simes correction for multiple comparisons.. Negations are uncovered through unigrams (not, no, won't). Several unigrams (error, issue, working, fix). However, words regularly describing negative sentiment or emotions are not one of the most distinctive features for complaints.. On the other hand, the presence of terms that show positive sentiment or emotions (good, great, win, POSEMO, AFFECT, ASSENT) are among the top most distinctive features for a tweet not being labeled as a complaint.. In addition, other words and clusters expressing positive states such as gratitude (thank, great, love) or laughter (lol) are also distinctive for tweets that are not complaints.. Across unigrams, part-of-speech patterns and word clusters, we see a distinctive pattern emerging around pronoun usage.. Complaints use more possessive pronouns, indicating that the user is describing personal experiences.. A distinctive part-of-speech pattern common in complaints is possessive pronouns followed by nouns (PRP$ NN) which refer to items of services possessed by the complainer (e.g., my account, my order).. Question marks are distinctive of complaints, as many complaints are formulated as questions to the responsible party (e.g., why is this not working?, when will. get my response?).. Mentions of time are specific of complaints (been, still, on, days, Temporal References cluster).. In addition, the presence of verbs in past participle (VBN) is the most distinctive part-of-speech pattern of complaints. These are used to describe actions completed in the past (e.g., i've bought, have come) in order to provide context for the complaint.. Several part-of-speech patterns distinctive of complaints involve present verbs in third person singular (VBZ).. Verbs in gerund or present participle are used as a complaint strategy to describe things that just happened to a user (e.g., got an email saying my service will be terminated).", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: Features associated with complaint and non-complaint tweets, sorted by Pearson correlation (r) computed between the normalized frequency of each feature and the complaint label across all tweets. All correlations are significant at p', solve this question and give detail solutions. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhat are the top three unigrams specific to complaints and their correlation values?\n" }, { "role": "assistant", "content": "The top three unigrams specific to complaints are 'not', 'my', and 'working' with correlation values of .154, .131, and .124 respectively. So, the answer is {\"answer\": \"not: .154, my: .131, working: .124\"}." } ], "images": [ "scigen/train/108.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Features associated with complaint and non-complaint tweets, sorted by Pearson correlation (r) computed between the normalized frequency of each feature and the complaint label across all tweets. All correlations are significant at p" ], "reasoning": "The top three unigrams specific to complaints are 'not', 'my', and 'working' with correlation values of .154, .131, and .124 respectively.", "question": "What are the top three unigrams specific to complaints and their correlation values?", "answer": "So, the answer is {\"answer\": \"not: .154, my: .131, working: .124\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The unigram 'thank' is a distinctive feature of complaints., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe unigram 'thank' is a distinctive feature of complaints.\n" }, { "role": "assistant", "content": "The table shows that 'thank' is listed under 'Not Complaints Feature' with a correlation value of .067, indicating it is distinctive of non-complaints. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/108.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Features associated with complaint and non-complaint tweets, sorted by Pearson correlation (r) computed between the normalized frequency of each feature and the complaint label across all tweets. All correlations are significant at p" ], "reasoning": "The table shows that 'thank' is listed under 'Not Complaints Feature' with a correlation value of .067, indicating it is distinctive of non-complaints.", "statement": "The unigram 'thank' is a distinctive feature of complaints.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: Group text features associated with tweets that are complaints and not complaints. Features are sorted by Pearson correlation (r) between their each feature’s normalized frequency and the outcome. We restrict to only the top six categories for each feature type. All correlations are significant at p', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Complaints typically lack personal pronouns and exclamation marks, focusing on the self and the addressed party, with common topics including assistance, orders, access, and tech issues.The top features for the LIWC categories and Word2Vec topics are presented in Table 5.. the top LIWC category (NEGATE).. a cluster (Issues) contain words referring to issues or errors.. Complaints tend to not contain personal pronouns (he, she, it, him, you, SHEHE, MALE, FEMALE), as the focus on expressing the complaint is on the self and the party the complaint is addressed to and not other third parties.. Complaints are not usually accompanied by exclamation marks.. General topics typical of complaint tweets include requiring assistance or customer support. Several groups of words are much more likely to appear in a complaint, although not used to express complaints per se: about orders or deliveries (in the retail domain), about access (in complaints to service providers) and about parts of tech products (in tech)." } ], "images": [ "scigen/train/109.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Group text features associated with tweets that are complaints and not complaints. Features are sorted by Pearson correlation (r) between their each feature’s normalized frequency and the outcome. We restrict to only the top six categories for each feature type. All correlations are significant at p" ], "reasoning": "Complaints typically lack personal pronouns and exclamation marks, focusing on the self and the addressed party, with common topics including assistance, orders, access, and tech issues.", "description": "The top features for the LIWC categories and Word2Vec topics are presented in Table 5.. the top LIWC category (NEGATE).. a cluster (Issues) contain words referring to issues or errors.. Complaints tend to not contain personal pronouns (he, she, it, him, you, SHEHE, MALE, FEMALE), as the focus on expressing the complaint is on the self and the party the complaint is addressed to and not other third parties.. Complaints are not usually accompanied by exclamation marks.. General topics typical of complaint tweets include requiring assistance or customer support. Several groups of words are much more likely to appear in a complaint, although not used to express complaints per se: about orders or deliveries (in the retail domain), about access (in complaints to service providers) and about parts of tech products (in tech).", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 5: Group text features associated with tweets that are complaints and not complaints. Features are sorted by Pearson correlation (r) between their each feature’s normalized frequency and the outcome. We restrict to only the top six categories for each feature type. All correlations are significant at p\nQuestion: Which LIWC category has the highest correlation with complaints?\n" }, { "role": "assistant", "content": "By examining the 'Complaints r' column, the highest correlation value is .271, which corresponds to the LIWC category 'NEGATE'. So, the answer is {\"answer\": \"NEGATE\"}." } ], "images": [ "scigen/train/109.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Group text features associated with tweets that are complaints and not complaints. Features are sorted by Pearson correlation (r) between their each feature’s normalized frequency and the outcome. We restrict to only the top six categories for each feature type. All correlations are significant at p" ], "reasoning": "By examining the 'Complaints r' column, the highest correlation value is .271, which corresponds to the LIWC category 'NEGATE'.", "question": "Which LIWC category has the highest correlation with complaints?", "answer": "So, the answer is {\"answer\": \"NEGATE\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Complaints about tech parts are more likely to include words related to pets., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nComplaints about tech parts are more likely to include words related to pets.\n" }, { "role": "assistant", "content": "The table shows that complaints about tech parts include words like 'battery', 'laptop', 'screen', 'warranty', 'desktop', and 'printer', while words related to pets are found in the 'Not Complaints' Word2Vec cluster. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/109.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Group text features associated with tweets that are complaints and not complaints. Features are sorted by Pearson correlation (r) between their each feature’s normalized frequency and the outcome. We restrict to only the top six categories for each feature type. All correlations are significant at p" ], "reasoning": "The table shows that complaints about tech parts include words like 'battery', 'laptop', 'screen', 'warranty', 'desktop', and 'printer', while words related to pets are found in the 'Not Complaints' Word2Vec cluster.", "statement": "Complaints about tech parts are more likely to include words related to pets.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6: Complaint prediction results using logistic regression (with different types of linguistic features), neural network approaches and the most frequent class baseline. Best results are in bold.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The best predictive performance is obtained using bag-of-word features, reaching an F1 of up to 77.5 and AUC of 0.866, while combining all features boosts predictive accuracy to 78 F1 and 0.873 AUC.Results are presented in Table 6. Most sentiment analysis models show accuracy above chance in predicting complaints. The best results are obtained by the Volkova & Bachrach model (Sentiment – V&B) which achieves 60 F1. However, models trained using linguistic features on the training data obtain significantly higher predictive accuracy. Complaint specific features are predictive of complaints, but to a smaller extent than sentiment, reaching an overall 55.2 F1. From this group of features, the most predictive groups are intensifiers and downgraders. Syntactic part-ofspeech features alone obtain higher performance than any sentiment or complaint feature group, showing the syntactic patterns discussed in the previous section hold high predictive accuracy for the task. The topical features such as the LIWC dictionaries (which combine syntactic and semantic information) and Word2Vec topics perform in the same range as the part of speech tags. However, best predictive performance is obtained using bag-of-word features, reaching an F1 of up to 77.5 and AUC of 0.866. Further, combining all features boosts predictive accuracy to 78 F1 and 0.864 AUC. We notice that neural network approaches are comparable, but do not outperform. the best performing feature-based model, likely in part due to the training data size." } ], "images": [ "scigen/train/110.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Complaint prediction results using logistic regression (with different types of linguistic features), neural network approaches and the most frequent class baseline. Best results are in bold." ], "reasoning": "The best predictive performance is obtained using bag-of-word features, reaching an F1 of up to 77.5 and AUC of 0.866, while combining all features boosts predictive accuracy to 78 F1 and 0.873 AUC.", "description": "Results are presented in Table 6. Most sentiment analysis models show accuracy above chance in predicting complaints. The best results are obtained by the Volkova & Bachrach model (Sentiment – V&B) which achieves 60 F1. However, models trained using linguistic features on the training data obtain significantly higher predictive accuracy. Complaint specific features are predictive of complaints, but to a smaller extent than sentiment, reaching an overall 55.2 F1. From this group of features, the most predictive groups are intensifiers and downgraders. Syntactic part-ofspeech features alone obtain higher performance than any sentiment or complaint feature group, showing the syntactic patterns discussed in the previous section hold high predictive accuracy for the task. The topical features such as the LIWC dictionaries (which combine syntactic and semantic information) and Word2Vec topics perform in the same range as the part of speech tags. However, best predictive performance is obtained using bag-of-word features, reaching an F1 of up to 77.5 and AUC of 0.866. Further, combining all features boosts predictive accuracy to 78 F1 and 0.864 AUC. We notice that neural network approaches are comparable, but do not outperform. the best performing feature-based model, likely in part due to the training data size.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 6: Complaint prediction results using logistic regression (with different types of linguistic features), neural network approaches and the most frequent class baseline. Best results are in bold.', answer the following question. Give detailed solution consisting of each step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model achieves the highest AUC score and what is the value?\n" }, { "role": "assistant", "content": "The table shows that the 'All Features' model achieves the highest AUC score of 0.873. So, the answer is {\"answer\": \"All Features, 0.873\"}." } ], "images": [ "scigen/train/110.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Complaint prediction results using logistic regression (with different types of linguistic features), neural network approaches and the most frequent class baseline. Best results are in bold." ], "reasoning": "The table shows that the 'All Features' model achieves the highest AUC score of 0.873.", "question": "Which model achieves the highest AUC score and what is the value?", "answer": "So, the answer is {\"answer\": \"All Features, 0.873\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Sentiment – V&B model achieves the highest F1 score among sentiment analysis models.\n" }, { "role": "assistant", "content": "The table shows that the Sentiment – V&B model achieves an F1 score of 60, which is the highest among the sentiment analysis models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/110.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Complaint prediction results using logistic regression (with different types of linguistic features), neural network approaches and the most frequent class baseline. Best results are in bold." ], "reasoning": "The table shows that the Sentiment – V&B model achieves an F1 score of 60, which is the highest among the sentiment analysis models listed.", "statement": "The Sentiment – V&B model achieves the highest F1 score among sentiment analysis models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Complaint prediction results using the original data set and distantly supervised data. All models are based on logistic regression with bag-of-word and Part-of-Speech tag features.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The domain adaptation approach improves the F1 score by 1 point to 79 and the ROC AUC by 0.012, while pooling the data decreases the F1 score by more than 2 points.Results presented in Table 7 show that the domain adaptation approach further boosts F1 by 1 point to 79 (t-test, p<0.5) and ROC AUC by 0.012.. However, simply pooling the data actually hurts predictive performance leading to a drop of more than 2 points in F1." } ], "images": [ "scigen/train/111.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Complaint prediction results using the original data set and distantly supervised data. All models are based on logistic regression with bag-of-word and Part-of-Speech tag features." ], "reasoning": "The domain adaptation approach improves the F1 score by 1 point to 79 and the ROC AUC by 0.012, while pooling the data decreases the F1 score by more than 2 points.", "description": "Results presented in Table 7 show that the domain adaptation approach further boosts F1 by 1 point to 79 (t-test, p<0.5) and ROC AUC by 0.012.. However, simply pooling the data actually hurts predictive performance leading to a drop of more than 2 points in F1.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: 'Table 7: Complaint prediction results using the original data set and distantly supervised data. All models are based on logistic regression with bag-of-word and Part-of-Speech tag features.'\nQuestion: What is the F1 score for the Dist. Supervision + EasyAdapt model?\n" }, { "role": "assistant", "content": "The table shows the F1 score for the Dist. Supervision + EasyAdapt model as 79.0. So, the answer is {\"answer\": \"79.0\"}" } ], "images": [ "scigen/train/111.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Complaint prediction results using the original data set and distantly supervised data. All models are based on logistic regression with bag-of-word and Part-of-Speech tag features." ], "reasoning": "The table shows the F1 score for the Dist. Supervision + EasyAdapt model as 79.0.", "question": "What is the F1 score for the Dist. Supervision + EasyAdapt model?", "answer": "So, the answer is {\"answer\": \"79.0\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Dist. Supervision + Pooling model has a higher AUC than the LR-All Features – Original Data model.\n" }, { "role": "assistant", "content": "The table shows that the AUC for the Dist. Supervision + Pooling model is 0.853, which is lower than the AUC for the LR-All Features – Original Data model, which is 0.873. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/111.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Complaint prediction results using the original data set and distantly supervised data. All models are based on logistic regression with bag-of-word and Part-of-Speech tag features." ], "reasoning": "The table shows that the AUC for the Dist. Supervision + Pooling model is 0.853, which is lower than the AUC for the LR-All Features – Original Data model, which is 0.873.", "statement": "The Dist. Supervision + Pooling model has a higher AUC than the LR-All Features – Original Data model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 8: Performance of models in Macro F1 on tweets from each domain.'.\n" }, { "role": "assistant", "content": "Adding out-of-domain data generally improves predictive performance across most domains, except for transport, where performance decreases.Table 8 shows the model performance in macro-averaged F1 using the best performing feature set. Results show that, in all but one case, adding out-of-domain data helps predictive performance. The apparel domain is qualitatively very different from the others as a large number of complaints are about returns or the company not stocking items, hence leading to different features being important for prediction. Domain adaptation is beneficial the majority of domains, lowering performance on a single domain compared to data pooling. This highlights the differences in expressing complaints across domains. Overall, predictive performance is high across all domains, with the exception of transport." } ], "images": [ "scigen/train/112.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Performance of models in Macro F1 on tweets from each domain." ], "reasoning": "Adding out-of-domain data generally improves predictive performance across most domains, except for transport, where performance decreases.", "description": "Table 8 shows the model performance in macro-averaged F1 using the best performing feature set. Results show that, in all but one case, adding out-of-domain data helps predictive performance. The apparel domain is qualitatively very different from the others as a large number of complaints are about returns or the company not stocking items, hence leading to different features being important for prediction. Domain adaptation is beneficial the majority of domains, lowering performance on a single domain compared to data pooling. This highlights the differences in expressing complaints across domains. Overall, predictive performance is high across all domains, with the exception of transport.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 8: Performance of models in Macro F1 on tweets from each domain.'.\n\nQuestion: Which domain shows the highest performance using the EasyAdapt method?\n" }, { "role": "assistant", "content": "By comparing the EasyAdapt column values, the highest performance is in the Food & Beverage domain with a score of 83.1. So, the answer is {\"answer\": \"Food & Beverage\"}." } ], "images": [ "scigen/train/112.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Performance of models in Macro F1 on tweets from each domain." ], "reasoning": "By comparing the EasyAdapt column values, the highest performance is in the Food & Beverage domain with a score of 83.1.", "question": "Which domain shows the highest performance using the EasyAdapt method?", "answer": "So, the answer is {\"answer\": \"Food & Beverage\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe apparel domain has the highest in-domain performance.\n" }, { "role": "assistant", "content": "By comparing the In-Domain column values, the highest performance is in the Apparel domain with a score of 76.2. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/112.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Performance of models in Macro F1 on tweets from each domain." ], "reasoning": "By comparing the In-Domain column values, the highest performance is in the Apparel domain with a score of 76.2.", "statement": "The apparel domain has the highest in-domain performance.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Consolidated experiment results. The first section shows finetuning results for base transfer learning methods and the baseline siamese network. The second section shows results for ULMFiT without Language Model Finetuning. The last section shows finetuning results for transformer methods augmented with multitasking heads. BERT and GPT-2 were finetuned for three epochs in all cases and ULMFiT was finetuned for 5 during classifier finetuning.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "BERT achieved a final accuracy of 91.20%, which is comparable to ULMFiT's performance. GPT-2 achieved a final accuracy of 96.28%, which is a 4.69% improvement over ULMFiT.BERT achieved a final accuracy of 91.20%, now marginally comparable to ULMFiT's full performance. GPT-2, on the other hand, finetuned to a final accuracy of 96.28%, a full 4.69% improvement over the performance of ULMFiT.. Rersults for this experiment are outlined in Table 4." } ], "images": [ "scigen/train/113.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Consolidated experiment results. The first section shows finetuning results for base transfer learning methods and the baseline siamese network. The second section shows results for ULMFiT without Language Model Finetuning. The last section shows finetuning results for transformer methods augmented with multitasking heads. BERT and GPT-2 were finetuned for three epochs in all cases and ULMFiT was finetuned for 5 during classifier finetuning." ], "reasoning": "BERT achieved a final accuracy of 91.20%, which is comparable to ULMFiT's performance. GPT-2 achieved a final accuracy of 96.28%, which is a 4.69% improvement over ULMFiT.", "description": "BERT achieved a final accuracy of 91.20%, now marginally comparable to ULMFiT's full performance. GPT-2, on the other hand, finetuned to a final accuracy of 96.28%, a full 4.69% improvement over the performance of ULMFiT.. Rersults for this experiment are outlined in Table 4.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Consolidated experiment results. The first section shows finetuning results for base transfer learning methods and the baseline siamese network. The second section shows results for ULMFiT without Language Model Finetuning. The last section shows finetuning results for transformer methods augmented with multitasking heads. BERT and GPT-2 were finetuned for three epochs in all cases and ULMFiT was finetuned for 5 during classifier finetuning.\nQuestion: Which model had the highest validation accuracy and how much higher was it compared to BERT's validation accuracy?\nSolve the above question based on the table information. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "From the table, GPT-2 + Multitasking had the highest validation accuracy of 96.28%. BERT's validation accuracy was 91.20%. The difference is 96.28% - 91.20% = 5.08%. So, the answer is {\"answer\": \"5.08%\"}." } ], "images": [ "scigen/train/113.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Consolidated experiment results. The first section shows finetuning results for base transfer learning methods and the baseline siamese network. The second section shows results for ULMFiT without Language Model Finetuning. The last section shows finetuning results for transformer methods augmented with multitasking heads. BERT and GPT-2 were finetuned for three epochs in all cases and ULMFiT was finetuned for 5 during classifier finetuning." ], "reasoning": "From the table, GPT-2 + Multitasking had the highest validation accuracy of 96.28%. BERT's validation accuracy was 91.20%. The difference is 96.28% - 91.20% = 5.08%.", "question": "Which model had the highest validation accuracy and how much higher was it compared to BERT's validation accuracy?", "answer": "So, the answer is {\"answer\": \"5.08%\"}." }, { "messages": [ { "role": "user", "content": "ULMFiT had a higher validation accuracy than BERT. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nULMFiT had a higher validation accuracy than BERT.\n" }, { "role": "assistant", "content": "From the table, ULMFiT had a validation accuracy of 91.59%, while BERT had a validation accuracy of 91.20%. Therefore, ULMFiT had a higher validation accuracy than BERT. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/113.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Consolidated experiment results. The first section shows finetuning results for base transfer learning methods and the baseline siamese network. The second section shows results for ULMFiT without Language Model Finetuning. The last section shows finetuning results for transformer methods augmented with multitasking heads. BERT and GPT-2 were finetuned for three epochs in all cases and ULMFiT was finetuned for 5 during classifier finetuning." ], "reasoning": "From the table, ULMFiT had a validation accuracy of 91.59%, while BERT had a validation accuracy of 91.20%. Therefore, ULMFiT had a higher validation accuracy than BERT.", "statement": "ULMFiT had a higher validation accuracy than BERT.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: An ablation study on the effects of pretraining for multitasking-based and standard GPT-2 finetuning. Results show that pretraining greatly accounts for almost half of performance on both finetuning techniques. “Acc. Inc.” refers to the boost in performance contributed by the pretraining step. “% of Perf.” refers to the percentage of the total performance that the pretraining step contributes.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Generative pretraining significantly boosts performance, contributing 44.32% in multitasking and 43.93% in standard finetuning setups.In Table 5, it can be seen that generative pretraining via language modeling does account for a considerable amount of performance, constituting 44.32% of the overall performance (a boost of 42.67% in accuracy) in the multitasking setup, and constituting 43.93% of the overall performance (a boost of 39.97%) in the standard finetuning setup." } ], "images": [ "scigen/train/114.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: An ablation study on the effects of pretraining for multitasking-based and standard GPT-2 finetuning. Results show that pretraining greatly accounts for almost half of performance on both finetuning techniques. “Acc. Inc.” refers to the boost in performance contributed by the pretraining step. “% of Perf.” refers to the percentage of the total performance that the pretraining step contributes." ], "reasoning": "Generative pretraining significantly boosts performance, contributing 44.32% in multitasking and 43.93% in standard finetuning setups.", "description": "In Table 5, it can be seen that generative pretraining via language modeling does account for a considerable amount of performance, constituting 44.32% of the overall performance (a boost of 42.67% in accuracy) in the multitasking setup, and constituting 43.93% of the overall performance (a boost of 39.97%) in the standard finetuning setup.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 5: An ablation study on the effects of pretraining for multitasking-based and standard GPT-2 finetuning. Results show that pretraining greatly accounts for almost half of performance on both finetuning techniques. “Acc. Inc.” refers to the boost in performance contributed by the pretraining step. “% of Perf.” refers to the percentage of the total performance that the pretraining step contributes.', answer the question below by showing each progressive step toward the answer. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the accuracy increase in the multitasking setup when generative pretraining is applied?\n" }, { "role": "assistant", "content": "The table shows that the accuracy increase in the multitasking setup with generative pretraining is 42.67%. So, the answer is {\"answer\": \"+42.67%\"}." } ], "images": [ "scigen/train/114.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: An ablation study on the effects of pretraining for multitasking-based and standard GPT-2 finetuning. Results show that pretraining greatly accounts for almost half of performance on both finetuning techniques. “Acc. Inc.” refers to the boost in performance contributed by the pretraining step. “% of Perf.” refers to the percentage of the total performance that the pretraining step contributes." ], "reasoning": "The table shows that the accuracy increase in the multitasking setup with generative pretraining is 42.67%.", "question": "What is the accuracy increase in the multitasking setup when generative pretraining is applied?", "answer": "So, the answer is {\"answer\": \"+42.67%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Generative pretraining contributes more to the overall performance in the multitasking setup than in the standard finetuning setup. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nGenerative pretraining contributes more to the overall performance in the multitasking setup than in the standard finetuning setup.\n" }, { "role": "assistant", "content": "The table shows that generative pretraining contributes 44.32% to the overall performance in the multitasking setup and 43.93% in the standard finetuning setup, indicating a higher contribution in the multitasking setup. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/114.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: An ablation study on the effects of pretraining for multitasking-based and standard GPT-2 finetuning. Results show that pretraining greatly accounts for almost half of performance on both finetuning techniques. “Acc. Inc.” refers to the boost in performance contributed by the pretraining step. “% of Perf.” refers to the percentage of the total performance that the pretraining step contributes." ], "reasoning": "The table shows that generative pretraining contributes 44.32% to the overall performance in the multitasking setup and 43.93% in the standard finetuning setup, indicating a higher contribution in the multitasking setup.", "statement": "Generative pretraining contributes more to the overall performance in the multitasking setup than in the standard finetuning setup.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: An ablation study on the effect of multiple heads in the attention mechanisms. The results show that increasing the number of heads improves performance, though this plateaus at 10 attention heads. All ablations use the multitask-based finetuning method. “Effect” refers to the increase or decrease of accuracy as the heads are removed. Note that 10 heads is the default used throughout the study.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Increasing the number of attention heads improves multitasking performance, with 16 heads achieving the highest accuracy and lowest validation loss.As shown in Table 6, reducing the number of attention heads severely decreases multitasking performance. Using only one attention head, thereby attending to only one context position at once, degrades the performance to less than the performance of 10 heads using the standard finetuning scheme. This shows that more attention heads, thereby attending to multiple different contexts at once, is important to boosting performance to state-of-the-art results." } ], "images": [ "scigen/train/115.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: An ablation study on the effect of multiple heads in the attention mechanisms. The results show that increasing the number of heads improves performance, though this plateaus at 10 attention heads. All ablations use the multitask-based finetuning method. “Effect” refers to the increase or decrease of accuracy as the heads are removed. Note that 10 heads is the default used throughout the study." ], "reasoning": "Increasing the number of attention heads improves multitasking performance, with 16 heads achieving the highest accuracy and lowest validation loss.", "description": "As shown in Table 6, reducing the number of attention heads severely decreases multitasking performance. Using only one attention head, thereby attending to only one context position at once, degrades the performance to less than the performance of 10 heads using the standard finetuning scheme. This shows that more attention heads, thereby attending to multiple different contexts at once, is important to boosting performance to state-of-the-art results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. The question is related to the given table titled 'Table 6: An ablation study on the effect of multiple heads in the attention mechanisms. The results show that increasing the number of heads improves performance, though this plateaus at 10 attention heads. All ablations use the multitask-based finetuning method. “Effect” refers to the increase or decrease of accuracy as the heads are removed. Note that 10 heads is the default used throughout the study.'.\n\nQuestion: What is the accuracy when using 8 attention heads?\n" }, { "role": "assistant", "content": "The table shows the accuracy for different numbers of attention heads. For 8 heads, the accuracy is listed. So, the answer is {\"answer\": \"96.02%\"}." } ], "images": [ "scigen/train/115.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: An ablation study on the effect of multiple heads in the attention mechanisms. The results show that increasing the number of heads improves performance, though this plateaus at 10 attention heads. All ablations use the multitask-based finetuning method. “Effect” refers to the increase or decrease of accuracy as the heads are removed. Note that 10 heads is the default used throughout the study." ], "reasoning": "The table shows the accuracy for different numbers of attention heads. For 8 heads, the accuracy is listed.", "question": "What is the accuracy when using 8 attention heads?", "answer": "So, the answer is {\"answer\": \"96.02%\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nUsing 4 attention heads results in an accuracy of over 93%.\n" }, { "role": "assistant", "content": "The table shows that using 4 attention heads results in an accuracy of 93.85%, which is over 93%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/115.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: An ablation study on the effect of multiple heads in the attention mechanisms. The results show that increasing the number of heads improves performance, though this plateaus at 10 attention heads. All ablations use the multitask-based finetuning method. “Effect” refers to the increase or decrease of accuracy as the heads are removed. Note that 10 heads is the default used throughout the study." ], "reasoning": "The table shows that using 4 attention heads results in an accuracy of 93.85%, which is over 93%.", "statement": "Using 4 attention heads results in an accuracy of over 93%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 6: Error analysis of HGN model. For ‘Multi-hop’ errors, the model jumps to the wrong film (“Tommy (1975 film)”) instead of the correct one (“Quadrophenia (film)”) from the starting entity “rock opera 5:15”. The supporting fact for the ‘MRC’ example is “Childe Byron is a 1977 play by Romulus Linney about the strained relationship between the poet, Lord Byron, and his daughter, Ada Lovelace”.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Using paragraph selection and entity prediction loss improves the joint F1 score by 0.31 points.Table 6 shows the ablation study results on paragraph selection loss Lpara and entity prediction loss Lentity.. As shown in the table, using paragraph selection and entity prediction loss can further improve the joint F1 by 0.31 points," } ], "images": [ "scigen/train/116.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Error analysis of HGN model. For ‘Multi-hop’ errors, the model jumps to the wrong film (“Tommy (1975 film)”) instead of the correct one (“Quadrophenia (film)”) from the starting entity “rock opera 5:15”. The supporting fact for the ‘MRC’ example is “Childe Byron is a 1977 play by Romulus Linney about the strained relationship between the poet, Lord Byron, and his daughter, Ada Lovelace”." ], "reasoning": "Using paragraph selection and entity prediction loss improves the joint F1 score by 0.31 points.", "description": "Table 6 shows the ablation study results on paragraph selection loss Lpara and entity prediction loss Lentity.. As shown in the table, using paragraph selection and entity prediction loss can further improve the joint F1 by 0.31 points,", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 6: Error analysis of HGN model. For ‘Multi-hop’ errors, the model jumps to the wrong film (“Tommy (1975 film)”) instead of the correct one (“Quadrophenia (film)”) from the starting entity “rock opera 5:15”. The supporting fact for the ‘MRC’ example is “Childe Byron is a 1977 play by Romulus Linney about the strained relationship between the poet, Lord Byron, and his daughter, Ada Lovelace”.\nQuestion: What is the percentage of correct predictions for the category with the question 'Who directed the film based on the rock opera 5:15 appeared in?'?\n" }, { "role": "assistant", "content": "The category for the question 'Who directed the film based on the rock opera 5:15 appeared in?' is Multi-hop. The percentage of correct predictions for Multi-hop is 16%. So, the answer is {\"answer\": \"16%\"}" } ], "images": [ "scigen/train/116.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Error analysis of HGN model. For ‘Multi-hop’ errors, the model jumps to the wrong film (“Tommy (1975 film)”) instead of the correct one (“Quadrophenia (film)”) from the starting entity “rock opera 5:15”. The supporting fact for the ‘MRC’ example is “Childe Byron is a 1977 play by Romulus Linney about the strained relationship between the poet, Lord Byron, and his daughter, Ada Lovelace”." ], "reasoning": "The category for the question 'Who directed the film based on the rock opera 5:15 appeared in?' is Multi-hop. The percentage of correct predictions for Multi-hop is 16%.", "question": "What is the percentage of correct predictions for the category with the question 'Who directed the film based on the rock opera 5:15 appeared in?'?", "answer": "So, the answer is {\"answer\": \"16%\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe category with the highest percentage of correct predictions is MRC.\n" }, { "role": "assistant", "content": "The table shows that the category MRC has a percentage of 20%, which is the highest among all categories listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/116.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Error analysis of HGN model. For ‘Multi-hop’ errors, the model jumps to the wrong film (“Tommy (1975 film)”) instead of the correct one (“Quadrophenia (film)”) from the starting entity “rock opera 5:15”. The supporting fact for the ‘MRC’ example is “Childe Byron is a 1977 play by Romulus Linney about the strained relationship between the poet, Lord Byron, and his daughter, Ada Lovelace”." ], "reasoning": "The table shows that the category MRC has a percentage of 20%, which is the highest among all categories listed.", "statement": "The category with the highest percentage of correct predictions is MRC.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Results on the test set of HotpotQA in the Distractor setting. HGN achieves state-of-the-art results at the time of submission (Dec. 1, 2019). (†) indicates unpublished work. RoBERTa-large is used for context encoding.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The HGN model achieves the highest scores across all metrics in both the Distractor and Fullwiki settings, significantly outperforming previous models.Table 1 and Table 2 summarize our results on the hidden test set of HotpotQA in the Distractor and Fullwiki setting, respectively. The proposed HGN outperforms both published and unpublished work on every metric by a significant margin. For example, HGN achieves a Joint EM/F1 score of 43.57/71.03 and 35.63/59.86 on the Distractor and Fullwiki setting, respectively, with an absolute improvement of 2.36/0.38 and 6.45/4.55 points over the previous state of the art." } ], "images": [ "scigen/train/117.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results on the test set of HotpotQA in the Distractor setting. HGN achieves state-of-the-art results at the time of submission (Dec. 1, 2019). (†) indicates unpublished work. RoBERTa-large is used for context encoding." ], "reasoning": "The HGN model achieves the highest scores across all metrics in both the Distractor and Fullwiki settings, significantly outperforming previous models.", "description": "Table 1 and Table 2 summarize our results on the hidden test set of HotpotQA in the Distractor and Fullwiki setting, respectively. The proposed HGN outperforms both published and unpublished work on every metric by a significant margin. For example, HGN achieves a Joint EM/F1 score of 43.57/71.03 and 35.63/59.86 on the Distractor and Fullwiki setting, respectively, with an absolute improvement of 2.36/0.38 and 6.45/4.55 points over the previous state of the art.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Results on the test set of HotpotQA in the Distractor setting. HGN achieves state-of-the-art results at the time of submission (Dec. 1, 2019). (†) indicates unpublished work. RoBERTa-large is used for context encoding.', answer the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the Joint EM score of the HGN model and how does it compare to the previous state of the art?\n" }, { "role": "assistant", "content": "The Joint EM score of the HGN model is 47.11, which is an improvement of 2.36 points over the previous state of the art. So, the answer is {\"answer\": \"47.11\"}." } ], "images": [ "scigen/train/117.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results on the test set of HotpotQA in the Distractor setting. HGN achieves state-of-the-art results at the time of submission (Dec. 1, 2019). (†) indicates unpublished work. RoBERTa-large is used for context encoding." ], "reasoning": "The Joint EM score of the HGN model is 47.11, which is an improvement of 2.36 points over the previous state of the art.", "question": "What is the Joint EM score of the HGN model and how does it compare to the previous state of the art?", "answer": "So, the answer is {\"answer\": \"47.11\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe HGN model has the highest Ans EM score among all models.\n" }, { "role": "assistant", "content": "The table shows that the HGN model has an Ans EM score of 69.22, which is higher than any other model listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/117.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results on the test set of HotpotQA in the Distractor setting. HGN achieves state-of-the-art results at the time of submission (Dec. 1, 2019). (†) indicates unpublished work. RoBERTa-large is used for context encoding." ], "reasoning": "The table shows that the HGN model has an Ans EM score of 69.22, which is higher than any other model listed.", "statement": "The HGN model has the highest Ans EM score among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Results on the test set of HotpotQA in the Fullwiki setting. HGN achieves close to state-of-the-art results at the time of submission (Dec. 1, 2019). (†) indicates unpublished work. RoBERTa-large is used for context encoding, and SemanticRetrievalMRS is used for retrieval. Leaderboard: https://hotpotqa.github.io/.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "HGN achieves the highest Joint EM/F1 scores in both the Distractor and Fullwiki settings, outperforming previous state-of-the-art models.Table 1 and Table 2 summarize our results on the hidden test set of HotpotQA in the Distractor and Fullwiki setting, respectively. The proposed HGN outperforms both published and unpublished work on every metric by a significant margin. For example, HGN achieves a Joint EM/F1 score of 43.57/71.03 and 35.63/59.86 on the Distractor and Fullwiki setting, respectively, with an absolute improvement of 2.36/0.38 and 6.45/4.55 points over the previous state of the art." } ], "images": [ "scigen/train/118.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on the test set of HotpotQA in the Fullwiki setting. HGN achieves close to state-of-the-art results at the time of submission (Dec. 1, 2019). (†) indicates unpublished work. RoBERTa-large is used for context encoding, and SemanticRetrievalMRS is used for retrieval. Leaderboard: https://hotpotqa.github.io/." ], "reasoning": "HGN achieves the highest Joint EM/F1 scores in both the Distractor and Fullwiki settings, outperforming previous state-of-the-art models.", "description": "Table 1 and Table 2 summarize our results on the hidden test set of HotpotQA in the Distractor and Fullwiki setting, respectively. The proposed HGN outperforms both published and unpublished work on every metric by a significant margin. For example, HGN achieves a Joint EM/F1 score of 43.57/71.03 and 35.63/59.86 on the Distractor and Fullwiki setting, respectively, with an absolute improvement of 2.36/0.38 and 6.45/4.55 points over the previous state of the art.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Results on the test set of HotpotQA in the Fullwiki setting. HGN achieves close to state-of-the-art results at the time of submission (Dec. 1, 2019). (†) indicates unpublished work. RoBERTa-large is used for context encoding, and SemanticRetrievalMRS is used for retrieval. Leaderboard: https://hotpotqa.github.io/.\nQuestion: What is the Joint F1 score achieved by HGN in the Fullwiki setting?\nGive a detailed response to the above question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the Joint F1 scores for various models in both the Distractor and Fullwiki settings. HGN achieves a Joint F1 score of 59.86 in the Fullwiki setting. So, the answer is {\"answer\": \"59.86\"}." } ], "images": [ "scigen/train/118.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on the test set of HotpotQA in the Fullwiki setting. HGN achieves close to state-of-the-art results at the time of submission (Dec. 1, 2019). (†) indicates unpublished work. RoBERTa-large is used for context encoding, and SemanticRetrievalMRS is used for retrieval. Leaderboard: https://hotpotqa.github.io/." ], "reasoning": "The table shows the Joint F1 scores for various models in both the Distractor and Fullwiki settings. HGN achieves a Joint F1 score of 59.86 in the Fullwiki setting.", "question": "What is the Joint F1 score achieved by HGN in the Fullwiki setting?", "answer": "So, the answer is {\"answer\": \"59.86\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nHGN achieves a Joint EM score of 35.63 in the Fullwiki setting.\n" }, { "role": "assistant", "content": "The table shows that HGN achieves a Joint EM score of 35.63 in the Fullwiki setting, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/118.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on the test set of HotpotQA in the Fullwiki setting. HGN achieves close to state-of-the-art results at the time of submission (Dec. 1, 2019). (†) indicates unpublished work. RoBERTa-large is used for context encoding, and SemanticRetrievalMRS is used for retrieval. Leaderboard: https://hotpotqa.github.io/." ], "reasoning": "The table shows that HGN achieves a Joint EM score of 35.63 in the Fullwiki setting, which matches the statement.", "statement": "HGN achieves a Joint EM score of 35.63 in the Fullwiki setting.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: Ablation study on the effectiveness of the hierarchical graph on the dev set in the Distractor setting. RoBERTa-large is used for context encoding.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The two-step paragraph selection process achieves higher precision and recall compared to threshold-based and pure TopN-based methods.Table 3 shows the performance of paragraph selection on the dev set of HotpotQA. In DFGN, paragraphs are selected based on a threshold to maintain high recall (98.27%), leading to a low precision (60.28%). Compared to both threshold-based and pure TopN -based paragraph selection, our two-step paragraph selection process is more accurate, achieving 94.53% precision and 94.53% recall." } ], "images": [ "scigen/train/119.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation study on the effectiveness of the hierarchical graph on the dev set in the Distractor setting. RoBERTa-large is used for context encoding." ], "reasoning": "The two-step paragraph selection process achieves higher precision and recall compared to threshold-based and pure TopN-based methods.", "description": "Table 3 shows the performance of paragraph selection on the dev set of HotpotQA. In DFGN, paragraphs are selected based on a threshold to maintain high recall (98.27%), leading to a low precision (60.28%). Compared to both threshold-based and pure TopN -based paragraph selection, our two-step paragraph selection process is more accurate, achieving 94.53% precision and 94.53% recall.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model achieves the highest Joint F1 score?\nSolve the above question based on the table titled 'Table 3: Ablation study on the effectiveness of the hierarchical graph on the dev set in the Distractor setting. RoBERTa-large is used for context encoding.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the Joint F1 scores of all models in the table, the Hier. Graph model has the highest Joint F1 score of 74.37. So, the answer is {\"answer\": \"Hier. Graph\"}" } ], "images": [ "scigen/train/119.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation study on the effectiveness of the hierarchical graph on the dev set in the Distractor setting. RoBERTa-large is used for context encoding." ], "reasoning": "By comparing the Joint F1 scores of all models in the table, the Hier. Graph model has the highest Joint F1 score of 74.37.", "question": "Which model achieves the highest Joint F1 score?", "answer": "So, the answer is {\"answer\": \"Hier. Graph\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe PS Graph model has the highest Sup F1 score.\n" }, { "role": "assistant", "content": "The table shows that the Hier. Graph model has the highest Sup F1 score of 88.58, not the PS Graph model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/119.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation study on the effectiveness of the hierarchical graph on the dev set in the Distractor setting. RoBERTa-large is used for context encoding." ], "reasoning": "The table shows that the Hier. Graph model has the highest Sup F1 score of 88.58, not the PS Graph model.", "statement": "The PS Graph model has the highest Sup F1 score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 5: Results with different pre-trained language models on the dev set in the Distractor setting. (†) is unpublished work with results on the test set, using BERT whole word masking (wwm).'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The PS Graph improves the joint F1 score of the RoBERTa model by 1.59 points, and adding entity nodes further increases it by 0.18 points.As shown in Table 5, the use of PS Graph improves the joint F1 score over the plain RoBERTa model by 1.59 points. By further adding entity nodes, the Joint F1 increases by 0.18 points." } ], "images": [ "scigen/train/120.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results with different pre-trained language models on the dev set in the Distractor setting. (†) is unpublished work with results on the test set, using BERT whole word masking (wwm)." ], "reasoning": "The PS Graph improves the joint F1 score of the RoBERTa model by 1.59 points, and adding entity nodes further increases it by 0.18 points.", "description": "As shown in Table 5, the use of PS Graph improves the joint F1 score over the plain RoBERTa model by 1.59 points. By further adding entity nodes, the Joint F1 increases by 0.18 points.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 5: Results with different pre-trained language models on the dev set in the Distractor setting. (†) is unpublished work with results on the test set, using BERT whole word masking (wwm).', solve this question and give detail solutions. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nHow much does the joint F1 score increase when using PS Graph and adding entity nodes to the RoBERTa model?\n" }, { "role": "assistant", "content": "The joint F1 score of the RoBERTa model increases by 1.59 points with the use of PS Graph and by an additional 0.18 points when entity nodes are added. So, the answer is {\"answer\": \"1.77\"}." } ], "images": [ "scigen/train/120.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results with different pre-trained language models on the dev set in the Distractor setting. (†) is unpublished work with results on the test set, using BERT whole word masking (wwm)." ], "reasoning": "The joint F1 score of the RoBERTa model increases by 1.59 points with the use of PS Graph and by an additional 0.18 points when entity nodes are added.", "question": "How much does the joint F1 score increase when using PS Graph and adding entity nodes to the RoBERTa model?", "answer": "So, the answer is {\"answer\": \"1.77\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe use of PS Graph and entity nodes improves the joint F1 score of the RoBERTa model by 1.77 points.\n" }, { "role": "assistant", "content": "The table shows that the joint F1 score of the RoBERTa model increases by 1.59 points with PS Graph and by an additional 0.18 points with entity nodes, totaling 1.77 points. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/120.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results with different pre-trained language models on the dev set in the Distractor setting. (†) is unpublished work with results on the test set, using BERT whole word masking (wwm)." ], "reasoning": "The table shows that the joint F1 score of the RoBERTa model increases by 1.59 points with PS Graph and by an additional 0.18 points with entity nodes, totaling 1.77 points.", "statement": "The use of PS Graph and entity nodes improves the joint F1 score of the RoBERTa model by 1.77 points.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 7: Results of HGN for different reasoning types.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The HGN variants outperform DFGN and EPS, as indicated by higher F1 scores in Table 7.Results in Table 7 show that our HGN variants outperform DFGN and EPS, indicating that the performance gain comes from a better model design." } ], "images": [ "scigen/train/121.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Results of HGN for different reasoning types." ], "reasoning": "The HGN variants outperform DFGN and EPS, as indicated by higher F1 scores in Table 7.", "description": "Results in Table 7 show that our HGN variants outperform DFGN and EPS, indicating that the performance gain comes from a better model design.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 7: Results of HGN for different reasoning types.' and proceed to solve the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich question type has the highest percentage (Pct) and what is its Joint F1 score?\n" }, { "role": "assistant", "content": "By examining the table, the 'bridge' question type has the highest percentage (Pct) of 79.91%. The Joint F1 score for 'bridge' is 73.31. So, the answer is {\"answer\": \"73.31\"}." } ], "images": [ "scigen/train/121.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Results of HGN for different reasoning types." ], "reasoning": "By examining the table, the 'bridge' question type has the highest percentage (Pct) of 79.91%. The Joint F1 score for 'bridge' is 73.31.", "question": "Which question type has the highest percentage (Pct) and what is its Joint F1 score?", "answer": "So, the answer is {\"answer\": \"73.31\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe 'comp-yn' question type has the highest Ans F1 score.\n" }, { "role": "assistant", "content": "By looking at the table, the 'comp-yn' question type has an Ans F1 score of 93.45, which is higher than the other question types. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/121.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Results of HGN for different reasoning types." ], "reasoning": "By looking at the table, the 'comp-yn' question type has an Ans F1 score of 93.45, which is higher than the other question types.", "statement": "The 'comp-yn' question type has the highest Ans F1 score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: BLEU scores of Dual2seq on the little prince data, when gold or automatic AMRs are available.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The Dual2seq model shows a significant improvement in BLEU score from 16.8 with automatic AMR to 17.5 with gold AMR, indicating better translation quality with more accurate AMR parsing.Table 4 shows the BLEU scores of our Dual2seq model taking gold or automatic AMRs as inputs.. The improvement from automatic AMR to gold AMR (+0.7 BLEU) is significant, which shows that the translation quality of our model can be further improved with an increase of AMR parsing accuracy." } ], "images": [ "scigen/train/122.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: BLEU scores of Dual2seq on the little prince data, when gold or automatic AMRs are available." ], "reasoning": "The Dual2seq model shows a significant improvement in BLEU score from 16.8 with automatic AMR to 17.5 with gold AMR, indicating better translation quality with more accurate AMR parsing.", "description": "Table 4 shows the BLEU scores of our Dual2seq model taking gold or automatic AMRs as inputs.. The improvement from automatic AMR to gold AMR (+0.7 BLEU) is significant, which shows that the translation quality of our model can be further improved with an increase of AMR parsing accuracy.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: BLEU scores of Dual2seq on the little prince data, when gold or automatic AMRs are available.', answer the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the BLEU score improvement when using gold AMR annotations instead of automatic AMR annotations?\n" }, { "role": "assistant", "content": "The BLEU score with automatic AMR annotations is 16.8, and with gold AMR annotations, it is 17.5. The improvement is calculated as 17.5 - 16.8. So, the answer is {\"answer\": \"0.7\"}" } ], "images": [ "scigen/train/122.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: BLEU scores of Dual2seq on the little prince data, when gold or automatic AMRs are available." ], "reasoning": "The BLEU score with automatic AMR annotations is 16.8, and with gold AMR annotations, it is 17.5. The improvement is calculated as 17.5 - 16.8.", "question": "What is the BLEU score improvement when using gold AMR annotations instead of automatic AMR annotations?", "answer": "So, the answer is {\"answer\": \"0.7\"}" }, { "messages": [ { "role": "user", "content": "The BLEU score of the Dual2seq model with gold AMR annotations is 17.5. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe BLEU score of the Dual2seq model with gold AMR annotations is 17.5.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score with gold AMR annotations is indeed 17.5. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/122.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: BLEU scores of Dual2seq on the little prince data, when gold or automatic AMRs are available." ], "reasoning": "The table shows that the BLEU score with gold AMR annotations is indeed 17.5.", "statement": "The BLEU score of the Dual2seq model with gold AMR annotations is 17.5.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Test performance. NC-v11 represents training only with the NC-v11 data, while Full means using the full training data. * represents significant Koehn (2004) result (p<0.01) over Seq2seq. ↓ indicates the lower the better.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Dual2seq consistently outperforms other systems in BLEU, TER, and Meteor scores, with a significant improvement over Seq2seq, especially in the small-scale setting.Table 3 shows the TEST BLEU, TER and Meteor scores of all systems trained on the smallscale News Commentary v11 subset or the largescale full set. Dual2seq is consistently better than the other systems under all three metrics,. Dual2seq is better than both OpenNMT-tf and Transformer-tf .. Dual2seq is signifi. cantly better than Seq2seq in both settings,. In particular, the improvement is much larger under the small-scale setting (+3.2 BLEU) than that under the large-scale setting (+1.7 BLEU).. When trained on the NC-v11 subset, the gap between Seq2seq and Dual2seq under Meteor (around 5 points) is greater than that under BLEU (around 3 points).. As shown in the second group of Table 3, we further compare our model with other methods of leveraging syntactic or semantic information. Dual2seq-LinAMR shows much worse performance than our model and only slightly outperforms the Seq2seq baseline.. Encoding dependency trees instead of AMRs, Dual2seq-Dep shows a larger performance gap with our model (17.8 vs 19.2) on small-scale training data than on large-scale training data (25.0 vs 25.5).. Dual2seq-SRL is less effective than our model," } ], "images": [ "scigen/train/123.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Test performance. NC-v11 represents training only with the NC-v11 data, while Full means using the full training data. * represents significant Koehn (2004) result (p<0.01) over Seq2seq. ↓ indicates the lower the better." ], "reasoning": "Dual2seq consistently outperforms other systems in BLEU, TER, and Meteor scores, with a significant improvement over Seq2seq, especially in the small-scale setting.", "description": "Table 3 shows the TEST BLEU, TER and Meteor scores of all systems trained on the smallscale News Commentary v11 subset or the largescale full set. Dual2seq is consistently better than the other systems under all three metrics,. Dual2seq is better than both OpenNMT-tf and Transformer-tf .. Dual2seq is signifi. cantly better than Seq2seq in both settings,. In particular, the improvement is much larger under the small-scale setting (+3.2 BLEU) than that under the large-scale setting (+1.7 BLEU).. When trained on the NC-v11 subset, the gap between Seq2seq and Dual2seq under Meteor (around 5 points) is greater than that under BLEU (around 3 points).. As shown in the second group of Table 3, we further compare our model with other methods of leveraging syntactic or semantic information. Dual2seq-LinAMR shows much worse performance than our model and only slightly outperforms the Seq2seq baseline.. Encoding dependency trees instead of AMRs, Dual2seq-Dep shows a larger performance gap with our model (17.8 vs 19.2) on small-scale training data than on large-scale training data (25.0 vs 25.5).. Dual2seq-SRL is less effective than our model,", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 3: Test performance. NC-v11 represents training only with the NC-v11 data, while Full means using the full training data. * represents significant Koehn (2004) result (p<0.01) over Seq2seq. ↓ indicates the lower the better.' and proceed to solve the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the BLEU score difference between Dual2seq and Seq2seq in the small-scale setting?\n" }, { "role": "assistant", "content": "The BLEU score for Dual2seq in the NC-v11 subset is 19.2, while for Seq2seq it is 16.0. The difference is 19.2 - 16.0 = 3.2. So, the answer is {\"answer\": \"3.2\"}." } ], "images": [ "scigen/train/123.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Test performance. NC-v11 represents training only with the NC-v11 data, while Full means using the full training data. * represents significant Koehn (2004) result (p<0.01) over Seq2seq. ↓ indicates the lower the better." ], "reasoning": "The BLEU score for Dual2seq in the NC-v11 subset is 19.2, while for Seq2seq it is 16.0. The difference is 19.2 - 16.0 = 3.2.", "question": "What is the BLEU score difference between Dual2seq and Seq2seq in the small-scale setting?", "answer": "So, the answer is {\"answer\": \"3.2\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Dual2seq-Dep performs better than Dual2seq in the large-scale setting., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nDual2seq-Dep performs better than Dual2seq in the large-scale setting.\n" }, { "role": "assistant", "content": "The table shows that in the large-scale setting, Dual2seq has a BLEU score of 25.5, while Dual2seq-Dep has a BLEU score of 25.0. Therefore, Dual2seq performs better than Dual2seq-Dep in the large-scale setting. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/123.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Test performance. NC-v11 represents training only with the NC-v11 data, while Full means using the full training data. * represents significant Koehn (2004) result (p<0.01) over Seq2seq. ↓ indicates the lower the better." ], "reasoning": "The table shows that in the large-scale setting, Dual2seq has a BLEU score of 25.5, while Dual2seq-Dep has a BLEU score of 25.0. Therefore, Dual2seq performs better than Dual2seq-Dep in the large-scale setting.", "statement": "Dual2seq-Dep performs better than Dual2seq in the large-scale setting.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Comparison of exact-match accuracy achieved on selected benchmarks by a random or majority-choice baseline, various neural contextual embedding models, and humans. ELMo refers to the highest-performing listed approach using ELMo embeddings. Best system performance on each benchmark in bold. Information extracted from leaderboards (linked to in the first column) at time of writing (October 2019), and original papers for benchmarks introduced in Section 2.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Table 2 compares the performance of models like ELMO, GPT, BERT, and XLNET on various benchmarks, showing that smaller tweaks have led to marginal improvements.The most representative models are ELMO, GPT, BERT and its variants, and XLNET. Next, we give a brief overview of these models and summarize their performance on the selected benchmark tasks. Table 2 quantitatively compares the performance of these models on various benchmarks.. smaller tweaks to various aspects of the model have resulted in hundreds of entries on leaderboards (e.g., those linked to in Section 4.3.3 and Table 2) leading only to marginal improvements." } ], "images": [ "scigen/train/124.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of exact-match accuracy achieved on selected benchmarks by a random or majority-choice baseline, various neural contextual embedding models, and humans. ELMo refers to the highest-performing listed approach using ELMo embeddings. Best system performance on each benchmark in bold. Information extracted from leaderboards (linked to in the first column) at time of writing (October 2019), and original papers for benchmarks introduced in Section 2." ], "reasoning": "Table 2 compares the performance of models like ELMO, GPT, BERT, and XLNET on various benchmarks, showing that smaller tweaks have led to marginal improvements.", "description": "The most representative models are ELMO, GPT, BERT and its variants, and XLNET. Next, we give a brief overview of these models and summarize their performance on the selected benchmark tasks. Table 2 quantitatively compares the performance of these models on various benchmarks.. smaller tweaks to various aspects of the model have resulted in hundreds of entries on leaderboards (e.g., those linked to in Section 4.3.3 and Table 2) leading only to marginal improvements.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: Table 2: Comparison of exact-match accuracy achieved on selected benchmarks by a random or majority-choice baseline, various neural contextual embedding models, and humans. ELMo refers to the highest-performing listed approach using ELMo embeddings. Best system performance on each benchmark in bold. Information extracted from leaderboards (linked to in the first column) at time of writing (October 2019), and original papers for benchmarks introduced in Section 2.\nQuestion: Which model has the highest performance on the SQuAD 1.1 benchmark?\n" }, { "role": "assistant", "content": "By examining the SQuAD 1.1 row in the table, we see that ALBERT has the highest score of 89.9. So, the answer is {\"answer\": \"ALBERT\"}." } ], "images": [ "scigen/train/124.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of exact-match accuracy achieved on selected benchmarks by a random or majority-choice baseline, various neural contextual embedding models, and humans. ELMo refers to the highest-performing listed approach using ELMo embeddings. Best system performance on each benchmark in bold. Information extracted from leaderboards (linked to in the first column) at time of writing (October 2019), and original papers for benchmarks introduced in Section 2." ], "reasoning": "By examining the SQuAD 1.1 row in the table, we see that ALBERT has the highest score of 89.9.", "question": "Which model has the highest performance on the SQuAD 1.1 benchmark?", "answer": "So, the answer is {\"answer\": \"ALBERT\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence GPT outperforms ELMo on the RACE benchmark. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nGPT outperforms ELMo on the RACE benchmark.\n" }, { "role": "assistant", "content": "The table shows that GPT has a score of 72.0 on the RACE benchmark, while ELMo has a score of 24.9, indicating that GPT outperforms ELMo. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/124.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of exact-match accuracy achieved on selected benchmarks by a random or majority-choice baseline, various neural contextual embedding models, and humans. ELMo refers to the highest-performing listed approach using ELMo embeddings. Best system performance on each benchmark in bold. Information extracted from leaderboards (linked to in the first column) at time of writing (October 2019), and original papers for benchmarks introduced in Section 2." ], "reasoning": "The table shows that GPT has a score of 72.0 on the RACE benchmark, while ELMo has a score of 24.9, indicating that GPT outperforms ELMo.", "statement": "GPT outperforms ELMo on the RACE benchmark.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: F1 scores on NER.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Coreference propagation (CorefProp) enhances named entity recognition performance in all domains, with the most significant improvement observed in SciERC.Table 2 shows that Coreference propagation (CorefProp) improves named entity recognition performance across all three domains. The largest gains are on the computer science research abstracts of SciERC," } ], "images": [ "scigen/train/125.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: F1 scores on NER." ], "reasoning": "Coreference propagation (CorefProp) enhances named entity recognition performance in all domains, with the most significant improvement observed in SciERC.", "description": "Table 2 shows that Coreference propagation (CorefProp) improves named entity recognition performance across all three domains. The largest gains are on the computer science research abstracts of SciERC,", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2: F1 scores on NER.', unfold the steps to solve the question given next. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which domain shows the highest improvement in named entity recognition performance with Coreference propagation (CorefProp) when using BERT + LSTM?\n" }, { "role": "assistant", "content": "Compare the performance of BERT + LSTM with and without Coreference propagation (CorefProp) across all domains. The largest improvement is observed in SciERC, where the score increases from 69.9 to 72.0. So, the answer is {\"answer\": \"SciERC\"}." } ], "images": [ "scigen/train/125.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: F1 scores on NER." ], "reasoning": "Compare the performance of BERT + LSTM with and without Coreference propagation (CorefProp) across all domains. The largest improvement is observed in SciERC, where the score increases from 69.9 to 72.0.", "question": "Which domain shows the highest improvement in named entity recognition performance with Coreference propagation (CorefProp) when using BERT + LSTM?", "answer": "So, the answer is {\"answer\": \"SciERC\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Coreference propagation (CorefProp) improves named entity recognition performance in the GENIA domain when using BERT Finetune. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nCoreference propagation (CorefProp) improves named entity recognition performance in the GENIA domain when using BERT Finetune.\n" }, { "role": "assistant", "content": "Check the performance of BERT Finetune with and without Coreference propagation (CorefProp) in the GENIA domain. The score increases from 78.3 to 79.5, indicating an improvement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/125.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: F1 scores on NER." ], "reasoning": "Check the performance of BERT Finetune with and without Coreference propagation (CorefProp) in the GENIA domain. The score increases from 78.3 to 79.5, indicating an improvement.", "statement": "Coreference propagation (CorefProp) improves named entity recognition performance in the GENIA domain when using BERT Finetune.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: DyGIE++ achieves state-of-the-art results. Test set F1 scores of best model, on all tasks and datasets. We define the following notations for events: Trig: Trigger, Arg: argument, ID: Identification, C: Classification. * indicates the use of a 4-model ensemble for trigger detection. See Appendix E for details. The results of the single model are reported in Table 2 (c). We ran significance tests on a subset of results in Appendix D. All were statistically significant except Arg-C and Arg-ID on ACE05-Event.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our framework achieves new state-of-the-art results on entity, relation, and event extraction tasks, except for event argument identification.Table 1 shows test set F1 on the entity, relation and event extraction tasks. Our framework establishes a new state-of-the-art on all three high-level tasks, and on all subtasks except event argument identification. Relative error reductions range from 0.2 - 27.9% over previous state of the art models." } ], "images": [ "scigen/train/126.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: DyGIE++ achieves state-of-the-art results. Test set F1 scores of best model, on all tasks and datasets. We define the following notations for events: Trig: Trigger, Arg: argument, ID: Identification, C: Classification. * indicates the use of a 4-model ensemble for trigger detection. See Appendix E for details. The results of the single model are reported in Table 2 (c). We ran significance tests on a subset of results in Appendix D. All were statistically significant except Arg-C and Arg-ID on ACE05-Event." ], "reasoning": "Our framework achieves new state-of-the-art results on entity, relation, and event extraction tasks, except for event argument identification.", "description": "Table 1 shows test set F1 on the entity, relation and event extraction tasks. Our framework establishes a new state-of-the-art on all three high-level tasks, and on all subtasks except event argument identification. Relative error reductions range from 0.2 - 27.9% over previous state of the art models.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 1: DyGIE++ achieves state-of-the-art results. Test set F1 scores of best model, on all tasks and datasets. We define the following notations for events: Trig: Trigger, Arg: argument, ID: Identification, C: Classification. * indicates the use of a 4-model ensemble for trigger detection. See Appendix E for details. The results of the single model are reported in Table 2 (c). We ran significance tests on a subset of results in Appendix D. All were statistically significant except Arg-C and Arg-ID on ACE05-Event.'. Solve the following question based on the table.\n\nQuestion: What is the relative error reduction for the entity extraction task on the ACE05-Event dataset?\nLet's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the F1 score for entity extraction on the ACE05-Event dataset improved from 87.1 to 90.7, resulting in a relative error reduction of 27.9%. So, the answer is {\"answer\": \"27.9%\"}." } ], "images": [ "scigen/train/126.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: DyGIE++ achieves state-of-the-art results. Test set F1 scores of best model, on all tasks and datasets. We define the following notations for events: Trig: Trigger, Arg: argument, ID: Identification, C: Classification. * indicates the use of a 4-model ensemble for trigger detection. See Appendix E for details. The results of the single model are reported in Table 2 (c). We ran significance tests on a subset of results in Appendix D. All were statistically significant except Arg-C and Arg-ID on ACE05-Event." ], "reasoning": "The table shows that the F1 score for entity extraction on the ACE05-Event dataset improved from 87.1 to 90.7, resulting in a relative error reduction of 27.9%.", "question": "What is the relative error reduction for the entity extraction task on the ACE05-Event dataset?", "answer": "So, the answer is {\"answer\": \"27.9%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The new framework improved the F1 score for relation extraction on the SciERC dataset by more than 10%. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe new framework improved the F1 score for relation extraction on the SciERC dataset by more than 10%.\n" }, { "role": "assistant", "content": "The table shows that the F1 score for relation extraction on the SciERC dataset improved from 41.6 to 48.4, which is an improvement of 11.6%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/126.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: DyGIE++ achieves state-of-the-art results. Test set F1 scores of best model, on all tasks and datasets. We define the following notations for events: Trig: Trigger, Arg: argument, ID: Identification, C: Classification. * indicates the use of a 4-model ensemble for trigger detection. See Appendix E for details. The results of the single model are reported in Table 2 (c). We ran significance tests on a subset of results in Appendix D. All were statistically significant except Arg-C and Arg-ID on ACE05-Event." ], "reasoning": "The table shows that the F1 score for relation extraction on the SciERC dataset improved from 41.6 to 48.4, which is an improvement of 11.6%.", "statement": "The new framework improved the F1 score for relation extraction on the SciERC dataset by more than 10%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: F1 scores on Relation.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "CorefProp improves relation extraction on SciERC, while RelProp improves performance over pretrained BERT but not fine-tuned BERT.CorefProp also improves relation extraction on SciERC.. Relation propagation (RelProp) improves relation extraction performance over pretrained BERT, but does not improve fine-tuned BERT." } ], "images": [ "scigen/train/127.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: F1 scores on Relation." ], "reasoning": "CorefProp improves relation extraction on SciERC, while RelProp improves performance over pretrained BERT but not fine-tuned BERT.", "description": "CorefProp also improves relation extraction on SciERC.. Relation propagation (RelProp) improves relation extraction performance over pretrained BERT, but does not improve fine-tuned BERT.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich method shows the highest improvement in relation extraction on SciERC when applied to BERT FineTune?\nSolve the above question based on the table titled 'Table 3: F1 scores on Relation.'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest improvement in relation extraction on SciERC when applied to BERT FineTune, compare the scores of +RelProp and +CorefProp. +CorefProp has a score of 45.3, which is higher than +RelProp's score of 43.0. So, the answer is {\"answer\": \"CorefProp\"}." } ], "images": [ "scigen/train/127.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: F1 scores on Relation." ], "reasoning": "To find the highest improvement in relation extraction on SciERC when applied to BERT FineTune, compare the scores of +RelProp and +CorefProp. +CorefProp has a score of 45.3, which is higher than +RelProp's score of 43.0.", "question": "Which method shows the highest improvement in relation extraction on SciERC when applied to BERT FineTune?", "answer": "So, the answer is {\"answer\": \"CorefProp\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence RelProp improves relation extraction performance on WLPC over BERT FineTune. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nRelProp improves relation extraction performance on WLPC over BERT FineTune.\n" }, { "role": "assistant", "content": "The table shows that RelProp applied to BERT FineTune has a score of 65.5 on WLPC, which is higher than the BERT FineTune score of 65.4. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/127.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: F1 scores on Relation." ], "reasoning": "The table shows that RelProp applied to BERT FineTune has a score of 65.5 on WLPC, which is higher than the BERT FineTune score of 65.4.", "statement": "RelProp improves relation extraction performance on WLPC over BERT FineTune.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: In-domain pre-training: SciBERT vs. BERT'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "SciBERT outperforms BERT in scientific datasets, showing higher scores in SciERC Entity, SciERC Relation, and GENIA Entity.Table 7 compares the results of BERT and SciBERT with the best-performing model configurations. SciBERT significantly boosts performance for scientific datasets including SciERC and GENIA." } ], "images": [ "scigen/train/128.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: In-domain pre-training: SciBERT vs. BERT" ], "reasoning": "SciBERT outperforms BERT in scientific datasets, showing higher scores in SciERC Entity, SciERC Relation, and GENIA Entity.", "description": "Table 7 compares the results of BERT and SciBERT with the best-performing model configurations. SciBERT significantly boosts performance for scientific datasets including SciERC and GENIA.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 7: In-domain pre-training: SciBERT vs. BERT', answer the question below by showing each progressive step toward the answer. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the improvement in the SciERC Relation score when using SciBERT compared to BERT?\n" }, { "role": "assistant", "content": "The SciERC Relation score for BERT is 41.9, while for SciBERT it is 45.3. The improvement is calculated as 45.3 - 41.9. So, the answer is {\"answer\": \"3.4\"}" } ], "images": [ "scigen/train/128.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: In-domain pre-training: SciBERT vs. BERT" ], "reasoning": "The SciERC Relation score for BERT is 41.9, while for SciBERT it is 45.3. The improvement is calculated as 45.3 - 41.9.", "question": "What is the improvement in the SciERC Relation score when using SciBERT compared to BERT?", "answer": "So, the answer is {\"answer\": \"3.4\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nSciBERT performs better than BERT in the GENIA Entity dataset.\n" }, { "role": "assistant", "content": "The table shows that the GENIA Entity score for BERT is 78.4, while for SciBERT it is 79.5, indicating that SciBERT performs better. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/128.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: In-domain pre-training: SciBERT vs. BERT" ], "reasoning": "The table shows that the GENIA Entity score for BERT is 78.4, while for SciBERT it is 79.5, indicating that SciBERT performs better.", "statement": "SciBERT performs better than BERT in the GENIA Entity dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 6: Effect of BERT cross-sentence context. F1 score of relation F1 on ACE05 dev set and entity, arg, trigger extraction F1 on ACE05-E test set, as a function of the BERT context window size.'.\n" }, { "role": "assistant", "content": "Both variations of the BERT model show improved performance with wider context windows, achieving the best results with a 3-sentence window across all tasks.Table 6 shows that both variations of our BERT model benefit from wider context windows. Our model achieves the best performance with a 3sentence window across all relation and event extraction tasks." } ], "images": [ "scigen/train/129.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Effect of BERT cross-sentence context. F1 score of relation F1 on ACE05 dev set and entity, arg, trigger extraction F1 on ACE05-E test set, as a function of the BERT context window size." ], "reasoning": "Both variations of the BERT model show improved performance with wider context windows, achieving the best results with a 3-sentence window across all tasks.", "description": "Table 6 shows that both variations of our BERT model benefit from wider context windows. Our model achieves the best performance with a 3sentence window across all relation and event extraction tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. The question is related to the given table titled 'Table 6: Effect of BERT cross-sentence context. F1 score of relation F1 on ACE05 dev set and entity, arg, trigger extraction F1 on ACE05-E test set, as a function of the BERT context window size.'.\n\nQuestion: Which task and variation achieved the highest performance with a 3-sentence window?\n" }, { "role": "assistant", "content": "By examining the table, the highest performance with a 3-sentence window is found in the Entity task with the BERT+LSTM variation, achieving a score of 90.5. So, the answer is {\"answer\": \"Entity task with BERT+LSTM\"}" } ], "images": [ "scigen/train/129.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Effect of BERT cross-sentence context. F1 score of relation F1 on ACE05 dev set and entity, arg, trigger extraction F1 on ACE05-E test set, as a function of the BERT context window size." ], "reasoning": "By examining the table, the highest performance with a 3-sentence window is found in the Entity task with the BERT+LSTM variation, achieving a score of 90.5.", "question": "Which task and variation achieved the highest performance with a 3-sentence window?", "answer": "So, the answer is {\"answer\": \"Entity task with BERT+LSTM\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The BERT+LSTM variation achieved the highest performance in the Arg Class task with a 3-sentence window. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe BERT+LSTM variation achieved the highest performance in the Arg Class task with a 3-sentence window.\n" }, { "role": "assistant", "content": "The table shows that in the Arg Class task, the BERT+LSTM variation achieved a score of 51.4 with a 3-sentence window, which is not the highest performance across all tasks. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/129.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Effect of BERT cross-sentence context. F1 score of relation F1 on ACE05 dev set and entity, arg, trigger extraction F1 on ACE05-E test set, as a function of the BERT context window size." ], "reasoning": "The table shows that in the Arg Class task, the BERT+LSTM variation achieved a score of 51.4 with a 3-sentence window, which is not the highest performance across all tasks.", "statement": "The BERT+LSTM variation achieved the highest performance in the Arg Class task with a 3-sentence window.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Bilingual Experiment P@1. Numbers are based on 10 runs of each method. The En→De, En→Fi and En→Es improvements are significant at p<0.05 according to ANOVA on the different runs.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The noise-aware model generally outperforms the previous model in both best and average precision@1 scores while requiring fewer iterations to converge.In Table 1 we report the best and average precision@1 scores and the average number of iterations among 10 experiments, for different language translations. Our model improves the results in the translation tasks. In most setups our average case is better than the former best case. In addition, the noise-aware model is more stable and therefore requires fewer iterations to converge. The accuracy improvements are small but consistent, and we note that we consider them as a lower-bound on the actual improvements as the current test set comes from the same distribution of the training set, and also contains similarly noisy pairs." } ], "images": [ "scigen/train/130.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Bilingual Experiment P@1. Numbers are based on 10 runs of each method. The En→De, En→Fi and En→Es improvements are significant at p<0.05 according to ANOVA on the different runs." ], "reasoning": "The noise-aware model generally outperforms the previous model in both best and average precision@1 scores while requiring fewer iterations to converge.", "description": "In Table 1 we report the best and average precision@1 scores and the average number of iterations among 10 experiments, for different language translations. Our model improves the results in the translation tasks. In most setups our average case is better than the former best case. In addition, the noise-aware model is more stable and therefore requires fewer iterations to converge. The accuracy improvements are small but consistent, and we note that we consider them as a lower-bound on the actual improvements as the current test set comes from the same distribution of the training set, and also contains similarly noisy pairs.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 1: Bilingual Experiment P@1. Numbers are based on 10 runs of each method. The En→De, En→Fi and En→Es improvements are significant at p<0.05 according to ANOVA on the different runs.' in mind, break down and resolve the question below step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich method has the highest average precision@1 score for the En→Es translation task?\n" }, { "role": "assistant", "content": "By comparing the average precision@1 scores for the En→Es translation task, the noise-aware model has a higher average score of 37.79 compared to the previous model's 37.33. So, the answer is {\"answer\": \"Noise-aware Alignment\"}." } ], "images": [ "scigen/train/130.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Bilingual Experiment P@1. Numbers are based on 10 runs of each method. The En→De, En→Fi and En→Es improvements are significant at p<0.05 according to ANOVA on the different runs." ], "reasoning": "By comparing the average precision@1 scores for the En→Es translation task, the noise-aware model has a higher average score of 37.79 compared to the previous model's 37.33.", "question": "Which method has the highest average precision@1 score for the En→Es translation task?", "answer": "So, the answer is {\"answer\": \"Noise-aware Alignment\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The noise-aware model requires fewer iterations to converge for the En→De translation task. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe noise-aware model requires fewer iterations to converge for the En→De translation task.\n" }, { "role": "assistant", "content": "The table shows that the noise-aware model requires 568 iterations for the En→De task, whereas the previous model requires 773 iterations. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/130.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Bilingual Experiment P@1. Numbers are based on 10 runs of each method. The En→De, En→Fi and En→Es improvements are significant at p<0.05 according to ANOVA on the different runs." ], "reasoning": "The table shows that the noise-aware model requires 568 iterations for the En→De task, whereas the previous model requires 773 iterations.", "statement": "The noise-aware model requires fewer iterations to converge for the En→De translation task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Discourse structure prediction results; tested on RST-DTtest and Instr-DTtest. Subscripts in inter-domain evaluation sub-table indicate the training set. Best performance in the category is bold. Consistently best model for inter-domain discourse structure prediction is underlined'.\n" }, { "role": "assistant", "content": "The hierarchical right/left branching baselines perform better than the completely right/left branching ones, but still worse than any discourse parser. The Two-Stage approach by Wang et al. (2017) achieves the best intra-domain performance on the RST-DT dataset, while the CODRA parser by Joty et al. (2015) performs best on the Instructional dataset.We further analyze our findings with respect to baselines and existing discourse parsers. The first set of results in Table 3 shows that the hierarchical right/left branching baselines dominate the completely right/left branching ones. However, their performance is still significantly worse than any discourse parser (intra- and interdomain).. The second set of results show the performance of existing discourse parsers when trained and tested on the same dataset (intra-domain).. The Two-Stage approach by Wang et al. (2017) achieves the best performance with 86% on the structure prediction using the RST-DT dataset. On the Instructional dataset, the CODRA discourse parser by Joty et al. (2015) achieves the highest score with 82.88%.. The third set in the table shows the key results from Phase 1 on the inter-domain performance.. While the avg attention-aggregation function achieves the most consistent performance the max function on both evaluation corpora, should not be dismissed, as it performs better on the larger RST-DT dataset, which is arguably more related to the Yelp'13 corpus than the sentimentneutral Instr-DT." } ], "images": [ "scigen/train/131.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Discourse structure prediction results; tested on RST-DTtest and Instr-DTtest. Subscripts in inter-domain evaluation sub-table indicate the training set. Best performance in the category is bold. Consistently best model for inter-domain discourse structure prediction is underlined" ], "reasoning": "The hierarchical right/left branching baselines perform better than the completely right/left branching ones, but still worse than any discourse parser. The Two-Stage approach by Wang et al. (2017) achieves the best intra-domain performance on the RST-DT dataset, while the CODRA parser by Joty et al. (2015) performs best on the Instructional dataset.", "description": "We further analyze our findings with respect to baselines and existing discourse parsers. The first set of results in Table 3 shows that the hierarchical right/left branching baselines dominate the completely right/left branching ones. However, their performance is still significantly worse than any discourse parser (intra- and interdomain).. The second set of results show the performance of existing discourse parsers when trained and tested on the same dataset (intra-domain).. The Two-Stage approach by Wang et al. (2017) achieves the best performance with 86% on the structure prediction using the RST-DT dataset. On the Instructional dataset, the CODRA discourse parser by Joty et al. (2015) achieves the highest score with 82.88%.. The third set in the table shows the key results from Phase 1 on the inter-domain performance.. While the avg attention-aggregation function achieves the most consistent performance the max function on both evaluation corpora, should not be dismissed, as it performs better on the larger RST-DT dataset, which is arguably more related to the Yelp'13 corpus than the sentimentneutral Instr-DT.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 3: Discourse structure prediction results; tested on RST-DTtest and Instr-DTtest. Subscripts in inter-domain evaluation sub-table indicate the training set. Best performance in the category is bold. Consistently best model for inter-domain discourse structure prediction is underlined', answer the question below by showing each progressive step toward the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich approach achieves the highest intra-domain performance on the RST-DT dataset?\n" }, { "role": "assistant", "content": "The table shows that the Two-Stage approach by Wang et al. (2017) achieves the highest intra-domain performance on the RST-DT dataset with a score of 86%. So, the answer is {\"answer\": \"Two-StageWang et al. (2017)\"}." } ], "images": [ "scigen/train/131.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Discourse structure prediction results; tested on RST-DTtest and Instr-DTtest. Subscripts in inter-domain evaluation sub-table indicate the training set. Best performance in the category is bold. Consistently best model for inter-domain discourse structure prediction is underlined" ], "reasoning": "The table shows that the Two-Stage approach by Wang et al. (2017) achieves the highest intra-domain performance on the RST-DT dataset with a score of 86%.", "question": "Which approach achieves the highest intra-domain performance on the RST-DT dataset?", "answer": "So, the answer is {\"answer\": \"Two-StageWang et al. (2017)\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The Two-Stage approach by Wang et al. (2017) achieves the highest intra-domain performance on the Instructional dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Two-Stage approach by Wang et al. (2017) achieves the highest intra-domain performance on the Instructional dataset.\n" }, { "role": "assistant", "content": "The table shows that the Two-Stage approach by Wang et al. (2017) achieves a score of 77.28% on the Instructional dataset, but the highest score on this dataset is achieved by the CODRA parser by Joty et al. (2015) with 82.88%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/131.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Discourse structure prediction results; tested on RST-DTtest and Instr-DTtest. Subscripts in inter-domain evaluation sub-table indicate the training set. Best performance in the category is bold. Consistently best model for inter-domain discourse structure prediction is underlined" ], "reasoning": "The table shows that the Two-Stage approach by Wang et al. (2017) achieves a score of 77.28% on the Instructional dataset, but the highest score on this dataset is achieved by the CODRA parser by Joty et al. (2015) with 82.88%.", "statement": "The Two-Stage approach by Wang et al. (2017) achieves the highest intra-domain performance on the Instructional dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Results of Self-Play Evaluation.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The DKRN system achieves the highest success rate in both TGPC and CWC datasets, with a slightly higher average number of turns compared to Kernel.Figure 2: A conversation example between human (H) and agents (A) with the same target and starting utterance in CWC dataset.Keywords predicted by the agents or mentioned by human are highlighted in bold. The target achieved at the end of a conversation is underlined. judge whether the current conversation context contains the end target. We set a maximum number of turns as 8 to prevent from an endless conversation that can not reach the target. We use the success rate of reaching the targets (Succ.) and the average number of turns taken to achieve the target (#Turns) as our evaluation criterion. Table 4 shows the results of 500 simulations for each of the comparison systems. Although the average number of turns of our approach is slightly more than Kernel, our system obtains the highest success rate, significantly improving over other approaches. We conduct user study for a more thorough on our CWC dataset. We use the DialCrowd e et al. 2018) to set up user study interfaces and kinds of user studies as below. The first study sure system performance in terms of two key amely transition smoothness and target achieveonstruct 50 test cases, each of which has a target g utterance. In each case, a user will chat with a lected agent. If the agent thinks the conversation the given target or the maximum number of conrns, the agent will inform and show the target to e DialCrowd will ask the user to judge whether reached and rate the transition smoothness of tion with a smoothness score. The smoothness s from 1 (very bad) to 5 (very good). All agents ur DKRN agent outpe Table 6 shows the results of the second study outperforms the comparison agents with a large example, compared to Kernel, 62% users deter agent is better on CWC dataset. This superior confirms the effectiveness of our approach. Qualitative Study To more intuitively demonstrate the superior per our agent, two conversations in user study deriv kernel agent and our DKRN agent respe Figure 2. Both conversations have the same targ starting utterance. From Figure 2, we can see th than Kernel, a achieve the target. The complete conversations o line agents and our DKRN agent are presented plemental material. Conclusions In this work, we move forward to develop an effe guided open-domain conversation system that a chat with user target. Spec routing net semantic knowledge relations among candidat into turn-level keyword prediction for the smoot sition. We also propose a simple but effective dua" } ], "images": [ "scigen/train/132.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of Self-Play Evaluation." ], "reasoning": "The DKRN system achieves the highest success rate in both TGPC and CWC datasets, with a slightly higher average number of turns compared to Kernel.", "description": "Figure 2: A conversation example between human (H) and agents (A) with the same target and starting utterance in CWC dataset.Keywords predicted by the agents or mentioned by human are highlighted in bold. The target achieved at the end of a conversation is underlined. judge whether the current conversation context contains the end target. We set a maximum number of turns as 8 to prevent from an endless conversation that can not reach the target. We use the success rate of reaching the targets (Succ.) and the average number of turns taken to achieve the target (#Turns) as our evaluation criterion. Table 4 shows the results of 500 simulations for each of the comparison systems. Although the average number of turns of our approach is slightly more than Kernel, our system obtains the highest success rate, significantly improving over other approaches. We conduct user study for a more thorough on our CWC dataset. We use the DialCrowd e et al. 2018) to set up user study interfaces and kinds of user studies as below. The first study sure system performance in terms of two key amely transition smoothness and target achieveonstruct 50 test cases, each of which has a target g utterance. In each case, a user will chat with a lected agent. If the agent thinks the conversation the given target or the maximum number of conrns, the agent will inform and show the target to e DialCrowd will ask the user to judge whether reached and rate the transition smoothness of tion with a smoothness score. The smoothness s from 1 (very bad) to 5 (very good). All agents ur DKRN agent outpe Table 6 shows the results of the second study outperforms the comparison agents with a large example, compared to Kernel, 62% users deter agent is better on CWC dataset. This superior confirms the effectiveness of our approach. Qualitative Study To more intuitively demonstrate the superior per our agent, two conversations in user study deriv kernel agent and our DKRN agent respe Figure 2. Both conversations have the same targ starting utterance. From Figure 2, we can see th than Kernel, a achieve the target. The complete conversations o line agents and our DKRN agent are presented plemental material. Conclusions In this work, we move forward to develop an effe guided open-domain conversation system that a chat with user target. Spec routing net semantic knowledge relations among candidat into turn-level keyword prediction for the smoot sition. We also propose a simple but effective dua", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 4: Results of Self-Play Evaluation.' to sequentially solve the question presented below. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich system has the highest success rate in the CWC dataset?\n" }, { "role": "assistant", "content": "By comparing the success rates in the CWC dataset column, DKRN has the highest success rate of 84.4%. So, the answer is {\"answer\": \"DKRN\"}" } ], "images": [ "scigen/train/132.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of Self-Play Evaluation." ], "reasoning": "By comparing the success rates in the CWC dataset column, DKRN has the highest success rate of 84.4%.", "question": "Which system has the highest success rate in the CWC dataset?", "answer": "So, the answer is {\"answer\": \"DKRN\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Kernel system has the highest success rate in the TGPC dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Kernel system has the highest success rate in the TGPC dataset.\n" }, { "role": "assistant", "content": "By comparing the success rates in the TGPC dataset column, DKRN has the highest success rate of 89%, not Kernel. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/132.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of Self-Play Evaluation." ], "reasoning": "By comparing the success rates in the TGPC dataset column, DKRN has the highest success rate of 89%, not Kernel.", "statement": "The Kernel system has the highest success rate in the TGPC dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Results of Turn-level Evaluation.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "DKRN achieves the highest scores across all metrics for both TGPC and CWC datasets, outperforming other state-of-the-art methods.Table 3 shows the turn-level evaluation results. Our approach DKRN outperforms all state-of-the-art methods in terms of all metrics on both datasets with two tasks." } ], "images": [ "scigen/train/133.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of Turn-level Evaluation." ], "reasoning": "DKRN achieves the highest scores across all metrics for both TGPC and CWC datasets, outperforming other state-of-the-art methods.", "description": "Table 3 shows the turn-level evaluation results. Our approach DKRN outperforms all state-of-the-art methods in terms of all metrics on both datasets with two tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Results of Turn-level Evaluation.\nQuestion: Which system has the highest Response Retrieval R20@5 score on the CWC dataset?\nSolve the above question based on the table information. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the Response Retrieval R20@5 scores for all systems on the CWC dataset, DKRN has the highest score of 0.9183. So, the answer is {\"answer\": \"DKRN\"}" } ], "images": [ "scigen/train/133.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of Turn-level Evaluation." ], "reasoning": "By comparing the Response Retrieval R20@5 scores for all systems on the CWC dataset, DKRN has the highest score of 0.9183.", "question": "Which system has the highest Response Retrieval R20@5 score on the CWC dataset?", "answer": "So, the answer is {\"answer\": \"DKRN\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim DKRN has the lowest Keyword Prediction Rw@1 score on the TGPC dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nDKRN has the lowest Keyword Prediction Rw@1 score on the TGPC dataset.\n" }, { "role": "assistant", "content": "The table shows that DKRN has a Keyword Prediction Rw@1 score of 0.0909 on the TGPC dataset, which is higher than the scores of PMI, Neural, and Kernel systems. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/133.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of Turn-level Evaluation." ], "reasoning": "The table shows that DKRN has a Keyword Prediction Rw@1 score of 0.0909 on the TGPC dataset, which is higher than the scores of PMI, Neural, and Kernel systems.", "statement": "DKRN has the lowest Keyword Prediction Rw@1 score on the TGPC dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Results of the Human Rating on CWC.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The DKRN agent has the highest success rate and smoothness score compared to other agents.Table 5 shows the evaluation results. Our DKRN agent outperforms all other agents with a large margin." } ], "images": [ "scigen/train/134.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results of the Human Rating on CWC." ], "reasoning": "The DKRN agent has the highest success rate and smoothness score compared to other agents.", "description": "Table 5 shows the evaluation results. Our DKRN agent outperforms all other agents with a large margin.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Results of the Human Rating on CWC.\nQuestion: Which agent has the highest smoothness score and what is that score?\nSolve the above question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the smoothness scores of all agents, DKRN has the highest smoothness score of 3.22. So, the answer is {\"answer\": \"3.22\"}." } ], "images": [ "scigen/train/134.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results of the Human Rating on CWC." ], "reasoning": "By comparing the smoothness scores of all agents, DKRN has the highest smoothness score of 3.22.", "question": "Which agent has the highest smoothness score and what is that score?", "answer": "So, the answer is {\"answer\": \"3.22\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The DKRN agent has a success rate of 88.0%. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe DKRN agent has a success rate of 88.0%.\n" }, { "role": "assistant", "content": "The table shows that the DKRN agent has a success rate of 88.0%, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/134.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results of the Human Rating on CWC." ], "reasoning": "The table shows that the DKRN agent has a success rate of 88.0%, which matches the statement.", "statement": "The DKRN agent has a success rate of 88.0%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 6: Results of the Human Rating on CWC.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our agent consistently outperforms comparison agents across all strategies with a significant margin.Table 6 shows the results of the second study. Our agent outperforms the comparison agents with a large margin." } ], "images": [ "scigen/train/135.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results of the Human Rating on CWC." ], "reasoning": "Our agent consistently outperforms comparison agents across all strategies with a significant margin.", "description": "Table 6 shows the results of the second study. Our agent outperforms the comparison agents with a large margin.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 6: Results of the Human Rating on CWC.', solve the following question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich strategy shows the highest percentage of 'Ours Better' and what is that percentage?\n" }, { "role": "assistant", "content": "By comparing the 'Ours Better' percentages across all strategies, the highest percentage is found in both the Retrieval-Stgy and Kernel strategies, each with 62%. So, the answer is {\"answer\": \"62\"}." } ], "images": [ "scigen/train/135.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results of the Human Rating on CWC." ], "reasoning": "By comparing the 'Ours Better' percentages across all strategies, the highest percentage is found in both the Retrieval-Stgy and Kernel strategies, each with 62%.", "question": "Which strategy shows the highest percentage of 'Ours Better' and what is that percentage?", "answer": "So, the answer is {\"answer\": \"62\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Our agent performs worse than the comparison agents in the Kernel strategy. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nOur agent performs worse than the comparison agents in the Kernel strategy.\n" }, { "role": "assistant", "content": "The table shows that in the Kernel strategy, 'Ours Worse' is only 12%, which is the lowest among all strategies, indicating that our agent does not perform worse than the comparison agents in this strategy. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/135.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results of the Human Rating on CWC." ], "reasoning": "The table shows that in the Kernel strategy, 'Ours Worse' is only 12%, which is the lowest among all strategies, indicating that our agent does not perform worse than the comparison agents in this strategy.", "statement": "Our agent performs worse than the comparison agents in the Kernel strategy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Experiments run on a ResNet-34. Numbers following S (stages) and B (blocks) indicate where SA (self-attention) modules are put. Parameters count concerns only SA and are in millions (M).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Adding self-attention modules in different ResNet blocks improves the VQA task, with the most efficient improvement observed in the 3rd stage.guistic input a CNN augmented with self-attentionWe show encouraging relative improvements for future research in this direction.. We empirically found that self-attention was the most efficient in the 3rd stage.. We notice small improvements relative to the baseline showing that self-attention alone does improve the VQA task.. We showed that it is possible to improve the feature extraction procedure for the VQA task by adding self-attention modules in the different ResNet blocks." } ], "images": [ "scigen/train/136.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Experiments run on a ResNet-34. Numbers following S (stages) and B (blocks) indicate where SA (self-attention) modules are put. Parameters count concerns only SA and are in millions (M)." ], "reasoning": "Adding self-attention modules in different ResNet blocks improves the VQA task, with the most efficient improvement observed in the 3rd stage.", "description": "guistic input a CNN augmented with self-attentionWe show encouraging relative improvements for future research in this direction.. We empirically found that self-attention was the most efficient in the 3rd stage.. We notice small improvements relative to the baseline showing that self-attention alone does improve the VQA task.. We showed that it is possible to improve the feature extraction procedure for the VQA task by adding self-attention modules in the different ResNet blocks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Experiments run on a ResNet-34. Numbers following S (stages) and B (blocks) indicate where SA (self-attention) modules are put. Parameters count concerns only SA and are in millions (M).', answer the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich configuration of self-attention modules in ResNet-34 showed the highest evaluation set percentage?\n" }, { "role": "assistant", "content": "The table shows different configurations of self-attention (SA) modules in ResNet-34 and their corresponding evaluation set percentages. The highest evaluation set percentage is 55.27%, achieved by the configuration with self-attention in stages 1, 2, and 3, block 3. So, the answer is {\"answer\": \"SA (S: 1,2,3 - B: 3)\"}." } ], "images": [ "scigen/train/136.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Experiments run on a ResNet-34. Numbers following S (stages) and B (blocks) indicate where SA (self-attention) modules are put. Parameters count concerns only SA and are in millions (M)." ], "reasoning": "The table shows different configurations of self-attention (SA) modules in ResNet-34 and their corresponding evaluation set percentages. The highest evaluation set percentage is 55.27%, achieved by the configuration with self-attention in stages 1, 2, and 3, block 3.", "question": "Which configuration of self-attention modules in ResNet-34 showed the highest evaluation set percentage?", "answer": "So, the answer is {\"answer\": \"SA (S: 1,2,3 - B: 3)\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The baseline configuration without self-attention modules has the lowest evaluation set percentage., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe baseline configuration without self-attention modules has the lowest evaluation set percentage.\n" }, { "role": "assistant", "content": "The table shows that the baseline configuration without self-attention modules has an evaluation set percentage of 55.00%, which is lower than all configurations with self-attention modules. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/136.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Experiments run on a ResNet-34. Numbers following S (stages) and B (blocks) indicate where SA (self-attention) modules are put. Parameters count concerns only SA and are in millions (M)." ], "reasoning": "The table shows that the baseline configuration without self-attention modules has an evaluation set percentage of 55.00%, which is lower than all configurations with self-attention modules.", "statement": "The baseline configuration without self-attention modules has the lowest evaluation set percentage.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: Experiments run on a ResNet-34. Numbers following S (stages) and B (blocks) indicate where SA (self-attention) modules are put. Parameters count concerns only SA and are in millions (M).'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows various configurations of ResNet-34 with different evaluation set percentages and parameter counts. The description mentions an improvement with β modulation using ResNet-152, suggesting ongoing research in visual modulation.However, we managed to show improvements with the β modulation with a ResNet-152. Though the improvement is slim, it is encouraging to continue researching into visual modulation" } ], "images": [ "scigen/train/137.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Experiments run on a ResNet-34. Numbers following S (stages) and B (blocks) indicate where SA (self-attention) modules are put. Parameters count concerns only SA and are in millions (M)." ], "reasoning": "The table shows various configurations of ResNet-34 with different evaluation set percentages and parameter counts. The description mentions an improvement with β modulation using ResNet-152, suggesting ongoing research in visual modulation.", "description": "However, we managed to show improvements with the β modulation with a ResNet-152. Though the improvement is slim, it is encouraging to continue researching into visual modulation", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. The question is related to the given table titled 'Table 1: Experiments run on a ResNet-34. Numbers following S (stages) and B (blocks) indicate where SA (self-attention) modules are put. Parameters count concerns only SA and are in millions (M).'.\n\nQuestion: Which configuration of ResNet-34 has the highest evaluation set percentage?\n" }, { "role": "assistant", "content": "To find the highest evaluation set percentage, we need to compare the 'Eval set %' values for all configurations. The highest value is 55.56%. So, the answer is {\"answer\": \"55.56%\"}." } ], "images": [ "scigen/train/137.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Experiments run on a ResNet-34. Numbers following S (stages) and B (blocks) indicate where SA (self-attention) modules are put. Parameters count concerns only SA and are in millions (M)." ], "reasoning": "To find the highest evaluation set percentage, we need to compare the 'Eval set %' values for all configurations. The highest value is 55.56%.", "question": "Which configuration of ResNet-34 has the highest evaluation set percentage?", "answer": "So, the answer is {\"answer\": \"55.56%\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe configuration with the highest evaluation set percentage has the most parameters.\n" }, { "role": "assistant", "content": "The configuration with the highest evaluation set percentage (55.56%) has 0.245M parameters, which is indeed the highest parameter count in the table. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/137.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Experiments run on a ResNet-34. Numbers following S (stages) and B (blocks) indicate where SA (self-attention) modules are put. Parameters count concerns only SA and are in millions (M)." ], "reasoning": "The configuration with the highest evaluation set percentage (55.56%) has 0.245M parameters, which is indeed the highest parameter count in the table.", "statement": "The configuration with the highest evaluation set percentage has the most parameters.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Results of thread summarization. ‘HAN’ models are our proposed approaches adapted from the hierarchical attention networks [Yang et al.2016]. The models can be pretrained using unlabeled threads from TripAdvisor (‘T’) and Ubuntuforum (‘U’). r indicates a redundancy removal step is applied. We report the variance of F-scores across all threads (‘±’). A redundancy removal step improves recall scores (shown in gray) of the HAN models and boosts performance.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "HAN models outperform LogReg and SVM in precision and ROUGE scores, except for MEAD in ROUGE, but excel in sentence prediction.The experimental results of all models are shown in Table 1.. First, HAN models appear to be more appealing than SVM and LogReg because there is less variation in program implementation, hence less effort is required to reproduce the results. HAN models outperform both LogReg and SVM using the current set of features. They yield higher precision scores than traditional models.. With respect to ROUGE scores, the HAN models outperform all supervised and unsupervised baselines except MEAD. MEAD has been shown to perform well in previous studies (Luo et al. 2016) and it appears to handle redundancy removal exceptionally well. The HAN models outperform MEAD in terms of sentence prediction.. Pretraining the HAN models, although intuitively promising, yields only comparable results with those without. We suspect that there are not enough data to pretrain the models and that the thread classification task used to pretrain the HAN models may not be sophisticated enough to learn effective thread vectors.. We observe that the redundancy removal step is crucial for the HAN models to achieve outstanding results. It helps improve the recall scores of both ROUGE and sentence prediction. When redundancy removal was applied to LogReg, it produces only marginal improvement. This suggests that future work may need to consider principled ways of redundancy removal." } ], "images": [ "scigen/train/138.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results of thread summarization. ‘HAN’ models are our proposed approaches adapted from the hierarchical attention networks [Yang et al.2016]. The models can be pretrained using unlabeled threads from TripAdvisor (‘T’) and Ubuntuforum (‘U’). r indicates a redundancy removal step is applied. We report the variance of F-scores across all threads (‘±’). A redundancy removal step improves recall scores (shown in gray) of the HAN models and boosts performance." ], "reasoning": "HAN models outperform LogReg and SVM in precision and ROUGE scores, except for MEAD in ROUGE, but excel in sentence prediction.", "description": "The experimental results of all models are shown in Table 1.. First, HAN models appear to be more appealing than SVM and LogReg because there is less variation in program implementation, hence less effort is required to reproduce the results. HAN models outperform both LogReg and SVM using the current set of features. They yield higher precision scores than traditional models.. With respect to ROUGE scores, the HAN models outperform all supervised and unsupervised baselines except MEAD. MEAD has been shown to perform well in previous studies (Luo et al. 2016) and it appears to handle redundancy removal exceptionally well. The HAN models outperform MEAD in terms of sentence prediction.. Pretraining the HAN models, although intuitively promising, yields only comparable results with those without. We suspect that there are not enough data to pretrain the models and that the thread classification task used to pretrain the HAN models may not be sophisticated enough to learn effective thread vectors.. We observe that the redundancy removal step is crucial for the HAN models to achieve outstanding results. It helps improve the recall scores of both ROUGE and sentence prediction. When redundancy removal was applied to LogReg, it produces only marginal improvement. This suggests that future work may need to consider principled ways of redundancy removal.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: 'Table 1: Results of thread summarization. ‘HAN’ models are our proposed approaches adapted from the hierarchical attention networks [Yang et al.2016]. The models can be pretrained using unlabeled threads from TripAdvisor (‘T’) and Ubuntuforum (‘U’). r indicates a redundancy removal step is applied. We report the variance of F-scores across all threads (‘±’). A redundancy removal step improves recall scores (shown in gray) of the HAN models and boosts performance.'\nQuestion: Which model achieves the highest Sentence-Level Recall (R) score?\n" }, { "role": "assistant", "content": "By examining the Sentence-Level Recall (R) column, the highest score is 33.9% achieved by HAN+pretrainU r. So, the answer is {\"answer\": \"HAN+pretrainU r\"}." } ], "images": [ "scigen/train/138.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results of thread summarization. ‘HAN’ models are our proposed approaches adapted from the hierarchical attention networks [Yang et al.2016]. The models can be pretrained using unlabeled threads from TripAdvisor (‘T’) and Ubuntuforum (‘U’). r indicates a redundancy removal step is applied. We report the variance of F-scores across all threads (‘±’). A redundancy removal step improves recall scores (shown in gray) of the HAN models and boosts performance." ], "reasoning": "By examining the Sentence-Level Recall (R) column, the highest score is 33.9% achieved by HAN+pretrainU r.", "question": "Which model achieves the highest Sentence-Level Recall (R) score?", "answer": "So, the answer is {\"answer\": \"HAN+pretrainU r\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nHAN models achieve the highest ROUGE-1 Precision (P) score.\n" }, { "role": "assistant", "content": "The highest ROUGE-1 Precision (P) score is 48.8%, achieved by SVM, not HAN models. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/138.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results of thread summarization. ‘HAN’ models are our proposed approaches adapted from the hierarchical attention networks [Yang et al.2016]. The models can be pretrained using unlabeled threads from TripAdvisor (‘T’) and Ubuntuforum (‘U’). r indicates a redundancy removal step is applied. We report the variance of F-scores across all threads (‘±’). A redundancy removal step improves recall scores (shown in gray) of the HAN models and boosts performance." ], "reasoning": "The highest ROUGE-1 Precision (P) score is 48.8%, achieved by SVM, not HAN models.", "statement": "HAN models achieve the highest ROUGE-1 Precision (P) score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: Joint goal accuracy on DSTC2 and WOZ 2.0 test set vs. various approaches as reported in the literature.'.\n" }, { "role": "assistant", "content": "StateNet PSI achieves the highest joint goal accuracy on both DSTC2 and WOZ 2.0 test sets, outperforming StateNet and StateNet PS.The results in Table 1 show the effectiveness of parameter sharing and initialization. StateNet PS outperforms StateNet, and StateNet PSI performs best among all 3 models.. Besides, StateNet PSI beats all the mod. Table 1: Joint goal accuracy on DSTC2 and WOZ 2.0 test set vs. various approaches as reported in the literature.. els reported in the previous literature, whether the model with delexicalisation (Henderson et al., 2014b,c; Rastogi et al., 2017) or not (Mrkˇsi´c et al., 2017; Perez and Liu, 2017; Xu and Hu, 2018; Ramadan et al., 2018; Zhong et al., 2018)." } ], "images": [ "scigen/train/139.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Joint goal accuracy on DSTC2 and WOZ 2.0 test set vs. various approaches as reported in the literature." ], "reasoning": "StateNet PSI achieves the highest joint goal accuracy on both DSTC2 and WOZ 2.0 test sets, outperforming StateNet and StateNet PS.", "description": "The results in Table 1 show the effectiveness of parameter sharing and initialization. StateNet PS outperforms StateNet, and StateNet PSI performs best among all 3 models.. Besides, StateNet PSI beats all the mod. Table 1: Joint goal accuracy on DSTC2 and WOZ 2.0 test set vs. various approaches as reported in the literature.. els reported in the previous literature, whether the model with delexicalisation (Henderson et al., 2014b,c; Rastogi et al., 2017) or not (Mrkˇsi´c et al., 2017; Perez and Liu, 2017; Xu and Hu, 2018; Ramadan et al., 2018; Zhong et al., 2018).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Joint goal accuracy on DSTC2 and WOZ 2.0 test set vs. various approaches as reported in the literature.\nQuestion: Which model has the highest joint goal accuracy on the DSTC2 test set?\nSolve the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the joint goal accuracy values for the DSTC2 test set, StateNet PSI has the highest accuracy with a value of 75.5. So, the answer is {\"answer\": \"StateNet PSI\"}." } ], "images": [ "scigen/train/139.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Joint goal accuracy on DSTC2 and WOZ 2.0 test set vs. various approaches as reported in the literature." ], "reasoning": "By comparing the joint goal accuracy values for the DSTC2 test set, StateNet PSI has the highest accuracy with a value of 75.5.", "question": "Which model has the highest joint goal accuracy on the DSTC2 test set?", "answer": "So, the answer is {\"answer\": \"StateNet PSI\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim StateNet PS has a higher joint goal accuracy on the WOZ 2.0 test set than the Neural Belief Tracker: NBT-CNN model., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nStateNet PS has a higher joint goal accuracy on the WOZ 2.0 test set than the Neural Belief Tracker: NBT-CNN model.\n" }, { "role": "assistant", "content": "The joint goal accuracy of StateNet PS on the WOZ 2.0 test set is 88.2, while the Neural Belief Tracker: NBT-CNN model has an accuracy of 84.2. Therefore, StateNet PS has a higher accuracy. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/139.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Joint goal accuracy on DSTC2 and WOZ 2.0 test set vs. various approaches as reported in the literature." ], "reasoning": "The joint goal accuracy of StateNet PS on the WOZ 2.0 test set is 88.2, while the Neural Belief Tracker: NBT-CNN model has an accuracy of 84.2. Therefore, StateNet PS has a higher accuracy.", "statement": "StateNet PS has a higher joint goal accuracy on the WOZ 2.0 test set than the Neural Belief Tracker: NBT-CNN model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Joint goal accuracy on DSTC2 and WOZ 2.0 of StateNet_PSI using different pre-trained models based on different single slot.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The food initialization achieves the highest joint accuracy on both DSTC2 and WOZ 2.0, validating the choice of the slot with the worst performance for pre-training.Table 2: Joint goal accuracy on DSTC2 and WOZ 2.0 of StateNet PSI using different pre-trained models based on different single slot.. We also test StateNet PSI with different pre-trained models, as shown in Table 2. The fact that the food initialization has the best performance verifies our selection of the slot with the worst performance for pre-training." } ], "images": [ "scigen/train/140.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Joint goal accuracy on DSTC2 and WOZ 2.0 of StateNet_PSI using different pre-trained models based on different single slot." ], "reasoning": "The food initialization achieves the highest joint accuracy on both DSTC2 and WOZ 2.0, validating the choice of the slot with the worst performance for pre-training.", "description": "Table 2: Joint goal accuracy on DSTC2 and WOZ 2.0 of StateNet PSI using different pre-trained models based on different single slot.. We also test StateNet PSI with different pre-trained models, as shown in Table 2. The fact that the food initialization has the best performance verifies our selection of the slot with the worst performance for pre-training.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 2: Joint goal accuracy on DSTC2 and WOZ 2.0 of StateNet_PSI using different pre-trained models based on different single slot.' in mind, break down and resolve the question below step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich initialization has the highest joint accuracy on DSTC2?\n" }, { "role": "assistant", "content": "By comparing the joint accuracies on DSTC2 for all initializations, the food initialization has the highest value of 75.5. So, the answer is {\"answer\": \"food\"}" } ], "images": [ "scigen/train/140.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Joint goal accuracy on DSTC2 and WOZ 2.0 of StateNet_PSI using different pre-trained models based on different single slot." ], "reasoning": "By comparing the joint accuracies on DSTC2 for all initializations, the food initialization has the highest value of 75.5.", "question": "Which initialization has the highest joint accuracy on DSTC2?", "answer": "So, the answer is {\"answer\": \"food\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe pricerange initialization has a higher joint accuracy on WOZ 2.0 than the area initialization.\n" }, { "role": "assistant", "content": "The table shows that the pricerange initialization has a joint accuracy of 88.2 on WOZ 2.0, while the area initialization has a joint accuracy of 87.8, indicating that the statement is correct. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/140.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Joint goal accuracy on DSTC2 and WOZ 2.0 of StateNet_PSI using different pre-trained models based on different single slot." ], "reasoning": "The table shows that the pricerange initialization has a joint accuracy of 88.2 on WOZ 2.0, while the area initialization has a joint accuracy of 87.8, indicating that the statement is correct.", "statement": "The pricerange initialization has a higher joint accuracy on WOZ 2.0 than the area initialization.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Textual similarity scores (asymmetric, Multi30k).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "AME outperforms FME in all metrics on the Multi30k dataset, highlighting the importance of word embeddings adaptation.Table 5, we show the performance on Multi30k dataset in asymmetric mode. AME outperforms the FME model, confirming the importance of word embeddings adaptation." } ], "images": [ "scigen/train/141.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Textual similarity scores (asymmetric, Multi30k)." ], "reasoning": "AME outperforms FME in all metrics on the Multi30k dataset, highlighting the importance of word embeddings adaptation.", "description": "Table 5, we show the performance on Multi30k dataset in asymmetric mode. AME outperforms the FME model, confirming the importance of word embeddings adaptation.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 5: Textual similarity scores (asymmetric, Multi30k).\nQuestion: What is the R@10 score for the AME model when translating from EN to DE?\n" }, { "role": "assistant", "content": "To find the R@10 score for the AME model when translating from EN to DE, locate the column labeled 'EN → DE R@10' and find the corresponding value for the AME row. So, the answer is {\"answer\": \"85.1\"}." } ], "images": [ "scigen/train/141.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Textual similarity scores (asymmetric, Multi30k)." ], "reasoning": "To find the R@10 score for the AME model when translating from EN to DE, locate the column labeled 'EN → DE R@10' and find the corresponding value for the AME row.", "question": "What is the R@10 score for the AME model when translating from EN to DE?", "answer": "So, the answer is {\"answer\": \"85.1\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The AME model has a higher R@1 score than the FME model when translating from DE to EN., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe AME model has a higher R@1 score than the FME model when translating from DE to EN.\n" }, { "role": "assistant", "content": "To verify this statement, compare the R@1 scores for the AME and FME models in the 'DE → EN R@1' column. The AME model has a score of 49.1, which is higher than the FME model's score of 46.9. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/141.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Textual similarity scores (asymmetric, Multi30k)." ], "reasoning": "To verify this statement, compare the R@1 scores for the AME and FME models in the 'DE → EN R@1' column. The AME model has a score of 49.1, which is higher than the FME model's score of 46.9.", "statement": "The AME model has a higher R@1 score than the FME model when translating from DE to EN.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Image-caption ranking results for English (Multi30k)', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "AME model shows significant improvement over Kiros et al. (2014) and Mono due to more training data and multilingual text encoder, and it performs better than FME in both symmetric and asymmetric modes.we show the results for English and German captions. For English captions, we see 21.28% improvement on average compared to Kiros et al. (2014). There is a 1.8% boost on average compared to Mono due to more training data and multilingual text encoder. AME performs better than FME model on both symmetric and asymmetric modes, which shows the advantage of finetuning word embeddings during training. We have 25.26% boost on average compared to Kiros et al. (2014) in asymmetric mode." } ], "images": [ "scigen/train/142.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Image-caption ranking results for English (Multi30k)" ], "reasoning": "AME model shows significant improvement over Kiros et al. (2014) and Mono due to more training data and multilingual text encoder, and it performs better than FME in both symmetric and asymmetric modes.", "description": "we show the results for English and German captions. For English captions, we see 21.28% improvement on average compared to Kiros et al. (2014). There is a 1.8% boost on average compared to Mono due to more training data and multilingual text encoder. AME performs better than FME model on both symmetric and asymmetric modes, which shows the advantage of finetuning word embeddings during training. We have 25.26% boost on average compared to Kiros et al. (2014) in asymmetric mode.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 1: Image-caption ranking results for English (Multi30k)', unfold the steps to solve the question given next. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nQuestion: What is the average improvement percentage of AME over Kiros et al. (2014) in asymmetric mode?\n" }, { "role": "assistant", "content": "The description states that there is a 25.26% boost on average compared to Kiros et al. (2014) in asymmetric mode. So, the answer is {\"answer\": \"25.26%\"}" } ], "images": [ "scigen/train/142.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Image-caption ranking results for English (Multi30k)" ], "reasoning": "The description states that there is a 25.26% boost on average compared to Kiros et al. (2014) in asymmetric mode.", "question": "What is the average improvement percentage of AME over Kiros et al. (2014) in asymmetric mode?", "answer": "So, the answer is {\"answer\": \"25.26%\"}" }, { "messages": [ { "role": "user", "content": "AME model has a 21.28% improvement on average compared to Kiros et al. (2014) for English captions. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nAME model has a 21.28% improvement on average compared to Kiros et al. (2014) for English captions.\n" }, { "role": "assistant", "content": "The description explicitly states that for English captions, there is a 21.28% improvement on average compared to Kiros et al. (2014). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/142.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Image-caption ranking results for English (Multi30k)" ], "reasoning": "The description explicitly states that for English captions, there is a 21.28% improvement on average compared to Kiros et al. (2014).", "statement": "AME model has a 21.28% improvement on average compared to Kiros et al. (2014) for English captions.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Image-caption ranking results for German (Multi30k)'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "AME achieves the highest scores in both symmetric and asymmetric modes for German descriptions, outperforming the FME model.For German descriptions, The results are 11.05% better on average compared to (Gella et al., 2017) in symmetric mode. AME also achieves competitive or better results than FME model in German descriptions too." } ], "images": [ "scigen/train/143.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Image-caption ranking results for German (Multi30k)" ], "reasoning": "AME achieves the highest scores in both symmetric and asymmetric modes for German descriptions, outperforming the FME model.", "description": "For German descriptions, The results are 11.05% better on average compared to (Gella et al., 2017) in symmetric mode. AME also achieves competitive or better results than FME model in German descriptions too.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Image-caption ranking results for German (Multi30k)' to sequentially solve the question presented below. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhat is the Image to Text R@10 score for the AME model in symmetric mode?\n" }, { "role": "assistant", "content": "To find the Image to Text R@10 score for the AME model in symmetric mode, locate the 'symmetric' section and find the 'AME' row. The Image to Text R@10 score is 82.7. So, the answer is {\"answer\": \"82.7\"}." } ], "images": [ "scigen/train/143.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Image-caption ranking results for German (Multi30k)" ], "reasoning": "To find the Image to Text R@10 score for the AME model in symmetric mode, locate the 'symmetric' section and find the 'AME' row. The Image to Text R@10 score is 82.7.", "question": "What is the Image to Text R@10 score for the AME model in symmetric mode?", "answer": "So, the answer is {\"answer\": \"82.7\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe AME model has a higher Text to Image R@5 score in asymmetric mode compared to symmetric mode.\n" }, { "role": "assistant", "content": "In the table, the Text to Image R@5 score for the AME model in asymmetric mode is 60.5, while in symmetric mode it is 58.0. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/143.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Image-caption ranking results for German (Multi30k)" ], "reasoning": "In the table, the Text to Image R@5 score for the AME model in asymmetric mode is 60.5, while in symmetric mode it is 58.0. Therefore, the statement is supported.", "statement": "The AME model has a higher Text to Image R@5 score in asymmetric mode compared to symmetric mode.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Image-caption ranking results for English (MS-COCO)', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The AME model significantly outperforms the FME model in both symmetric and asymmetric manners, achieving a 10.42% improvement on average compared to Kiros et al. (2014).We achieve 10.42% improvement on aver. age compared to Kiros et al. (2014) in the symmetric manner. We show that adapting the word embedding for the task at hand, boosts the general performance, since AME model significantly outperforms FME model in both languages." } ], "images": [ "scigen/train/144.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Image-caption ranking results for English (MS-COCO)" ], "reasoning": "The AME model significantly outperforms the FME model in both symmetric and asymmetric manners, achieving a 10.42% improvement on average compared to Kiros et al. (2014).", "description": "We achieve 10.42% improvement on aver. age compared to Kiros et al. (2014) in the symmetric manner. We show that adapting the word embedding for the task at hand, boosts the general performance, since AME model significantly outperforms FME model in both languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Image-caption ranking results for English (MS-COCO)', unfold the question presented below, detailing every step of your calculation. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the average alignment score for the AME model in the symmetric manner?\n" }, { "role": "assistant", "content": "The table shows the alignment scores for different models. The alignment score for the AME model in the symmetric manner is 82.54%. So, the answer is {\"answer\": \"82.54%\"}" } ], "images": [ "scigen/train/144.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Image-caption ranking results for English (MS-COCO)" ], "reasoning": "The table shows the alignment scores for different models. The alignment score for the AME model in the symmetric manner is 82.54%.", "question": "What is the average alignment score for the AME model in the symmetric manner?", "answer": "So, the answer is {\"answer\": \"82.54%\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe AME model achieves the highest Image to Text R@10 score in the asymmetric manner.\n" }, { "role": "assistant", "content": "The table shows that the AME model has an Image to Text R@10 score of 96.2 in the asymmetric manner, which is the highest among all models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/144.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Image-caption ranking results for English (MS-COCO)" ], "reasoning": "The table shows that the AME model has an Image to Text R@10 score of 96.2 in the asymmetric manner, which is the highest among all models listed.", "statement": "The AME model achieves the highest Image to Text R@10 score in the asymmetric manner.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Image-caption ranking results for Japanese (MS-COCO)'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "AME outperforms the monolingual model by 6.25% in symmetric mode and 3.66% in asymmetric mode for Japanese captions.For the Japanese captions, AME reaches 6.25% and 3.66% better results on average compared to monolingual model in symmetric and asymmetric modes, respectively." } ], "images": [ "scigen/train/145.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Image-caption ranking results for Japanese (MS-COCO)" ], "reasoning": "AME outperforms the monolingual model by 6.25% in symmetric mode and 3.66% in asymmetric mode for Japanese captions.", "description": "For the Japanese captions, AME reaches 6.25% and 3.66% better results on average compared to monolingual model in symmetric and asymmetric modes, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Image-caption ranking results for Japanese (MS-COCO)\nQuestion: What is the average improvement of AME over the monolingual model in symmetric and asymmetric modes for Japanese captions?\nSolve the above question based on the table information. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that AME achieves 82.54% alignment in symmetric mode and 84.99% in asymmetric mode, while the monolingual model does not have alignment data. The description states that AME improves by 6.25% and 3.66% over the monolingual model in symmetric and asymmetric modes, respectively. So, the answer is {\"answer\": \"4.955%\"}." } ], "images": [ "scigen/train/145.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Image-caption ranking results for Japanese (MS-COCO)" ], "reasoning": "The table shows that AME achieves 82.54% alignment in symmetric mode and 84.99% in asymmetric mode, while the monolingual model does not have alignment data. The description states that AME improves by 6.25% and 3.66% over the monolingual model in symmetric and asymmetric modes, respectively.", "question": "What is the average improvement of AME over the monolingual model in symmetric and asymmetric modes for Japanese captions?", "answer": "So, the answer is {\"answer\": \"4.955%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence AME achieves better results than the monolingual model in both symmetric and asymmetric modes for Japanese captions. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nAME achieves better results than the monolingual model in both symmetric and asymmetric modes for Japanese captions.\n" }, { "role": "assistant", "content": "The table shows that AME achieves higher scores in both symmetric and asymmetric modes compared to the monolingual model, and the description confirms the improvement percentages. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/145.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Image-caption ranking results for Japanese (MS-COCO)" ], "reasoning": "The table shows that AME achieves higher scores in both symmetric and asymmetric modes compared to the monolingual model, and the description confirms the improvement percentages.", "statement": "AME achieves better results than the monolingual model in both symmetric and asymmetric modes for Japanese captions.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Averages of similarities of pairs with same vs. different gender in Italian and German compared to English. The last row is the difference between the averages of the two sets. “Reduction” stands for gap reduction when removing gender signals from the context.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The debiased embeddings significantly reduce the gender bias gap in both Italian and German compared to English, with a 91.67% reduction in Italian and a 100% reduction in German.Table 4 shows the results for Italian and German, compared to English, both for the original and the debiased embeddings (for each language we show the results of the best performing debiased embeddings).. As expected, in both languages, the difference between the average of the two sets with the debiased embeddings is much lower. In Italian, we get a reduction of 91.67% of the gap with respect to English. In German, we get a reduction of 100%." } ], "images": [ "scigen/train/146.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Averages of similarities of pairs with same vs. different gender in Italian and German compared to English. The last row is the difference between the averages of the two sets. “Reduction” stands for gap reduction when removing gender signals from the context." ], "reasoning": "The debiased embeddings significantly reduce the gender bias gap in both Italian and German compared to English, with a 91.67% reduction in Italian and a 100% reduction in German.", "description": "Table 4 shows the results for Italian and German, compared to English, both for the original and the debiased embeddings (for each language we show the results of the best performing debiased embeddings).. As expected, in both languages, the difference between the average of the two sets with the debiased embeddings is much lower. In Italian, we get a reduction of 91.67% of the gap with respect to English. In German, we get a reduction of 100%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Averages of similarities of pairs with same vs. different gender in Italian and German compared to English. The last row is the difference between the averages of the two sets. “Reduction” stands for gap reduction when removing gender signals from the context.\nQuestion: What is the difference in the gender bias gap for German between the original and debiased embeddings?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the difference in the gender bias gap for German is 0.076 for the original embeddings and 0.043 for the debiased embeddings. So, the answer is {\"answer\": \"0.033\"}." } ], "images": [ "scigen/train/146.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Averages of similarities of pairs with same vs. different gender in Italian and German compared to English. The last row is the difference between the averages of the two sets. “Reduction” stands for gap reduction when removing gender signals from the context." ], "reasoning": "The table shows that the difference in the gender bias gap for German is 0.076 for the original embeddings and 0.043 for the debiased embeddings.", "question": "What is the difference in the gender bias gap for German between the original and debiased embeddings?", "answer": "So, the answer is {\"answer\": \"0.033\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe reduction in the gender bias gap for Italian is greater than that for German.\n" }, { "role": "assistant", "content": "The table shows that the reduction in the gender bias gap for Italian is 91.67%, while for German it is 100%. Therefore, the reduction for German is greater. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/146.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Averages of similarities of pairs with same vs. different gender in Italian and German compared to English. The last row is the difference between the averages of the two sets. “Reduction” stands for gap reduction when removing gender signals from the context." ], "reasoning": "The table shows that the reduction in the gender bias gap for Italian is 91.67%, while for German it is 100%. Therefore, the reduction for German is greater.", "statement": "The reduction in the gender bias gap for Italian is greater than that for German.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Averages of rankings of the words in same-gender pairs vs. different-gender pairs for Italian and German, along with their differences. Og stands for the original embeddings, Db for the debiased embeddings, and En for English. Each row presents the averages of pairs with the respective scores in SimLex-999 (0–4, 4–7, 7–10).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The average ranking of same-gender pairs is significantly lower than that of different-gender pairs for both German and Italian, while the difference is smaller for English. The difference between the average ranking of the two sets drops significantly for both languages after debiasing.Table 2 shows the results for Italian and German, compared to English. As expected, the average ranking of samegender pairs is significantly lower than that of different-gender pairs, both for German and Italian, while the difference between the sets in English is much smaller.. Table 2 shows the results for Italian and German, both for the original and the debiased embeddings.. As we expect, the difference between the average ranking of the two sets drops significantly for both languages." } ], "images": [ "scigen/train/147.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Averages of rankings of the words in same-gender pairs vs. different-gender pairs for Italian and German, along with their differences. Og stands for the original embeddings, Db for the debiased embeddings, and En for English. Each row presents the averages of pairs with the respective scores in SimLex-999 (0–4, 4–7, 7–10)." ], "reasoning": "The average ranking of same-gender pairs is significantly lower than that of different-gender pairs for both German and Italian, while the difference is smaller for English. The difference between the average ranking of the two sets drops significantly for both languages after debiasing.", "description": "Table 2 shows the results for Italian and German, compared to English. As expected, the average ranking of samegender pairs is significantly lower than that of different-gender pairs, both for German and Italian, while the difference between the sets in English is much smaller.. Table 2 shows the results for Italian and German, both for the original and the debiased embeddings.. As we expect, the difference between the average ranking of the two sets drops significantly for both languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Averages of rankings of the words in same-gender pairs vs. different-gender pairs for Italian and German, along with their differences. Og stands for the original embeddings, Db for the debiased embeddings, and En for English. Each row presents the averages of pairs with the respective scores in SimLex-999 (0–4, 4–7, 7–10).\nQuestion: What is the difference in the average ranking of same-gender pairs for German before and after debiasing in the 7-10 range?\nGive a detailed response to the above question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the difference in the average ranking of same-gender pairs for German before and after debiasing in the 7-10 range, look at the values under 'German Same-gender' for 'Og' and 'Db' in the 7-10 range. The values are 5925 (Og) and 7653 (Db). So, the answer is {\"answer\": \"1728\"}." } ], "images": [ "scigen/train/147.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Averages of rankings of the words in same-gender pairs vs. different-gender pairs for Italian and German, along with their differences. Og stands for the original embeddings, Db for the debiased embeddings, and En for English. Each row presents the averages of pairs with the respective scores in SimLex-999 (0–4, 4–7, 7–10)." ], "reasoning": "To find the difference in the average ranking of same-gender pairs for German before and after debiasing in the 7-10 range, look at the values under 'German Same-gender' for 'Og' and 'Db' in the 7-10 range. The values are 5925 (Og) and 7653 (Db).", "question": "What is the difference in the average ranking of same-gender pairs for German before and after debiasing in the 7-10 range?", "answer": "So, the answer is {\"answer\": \"1728\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe debiased difference for Italian in the 0-4 range is higher than the original difference.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Italian difference' values for 'Og' and 'Db' in the 0-4 range. The values are -4225 (Og) and 53053 (Db). The debiased difference is indeed higher than the original difference. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/147.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Averages of rankings of the words in same-gender pairs vs. different-gender pairs for Italian and German, along with their differences. Og stands for the original embeddings, Db for the debiased embeddings, and En for English. Each row presents the averages of pairs with the respective scores in SimLex-999 (0–4, 4–7, 7–10)." ], "reasoning": "To verify this statement, compare the 'Italian difference' values for 'Og' and 'Db' in the 0-4 range. The values are -4225 (Og) and 53053 (Db). The debiased difference is indeed higher than the original difference.", "statement": "The debiased difference for Italian in the 0-4 range is higher than the original difference.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 6: Results on SimLex-999 and WordSim-353, in Italian and German, before and after debiasing.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The new embeddings for both Italian and German outperform the original embeddings in both datasets, SimLex and WordSim.Table 6 shows the results for Italian and German for both datasets, compared to the original embeddings. In both cases, the new embeddings perform better than the original ones." } ], "images": [ "scigen/train/148.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results on SimLex-999 and WordSim-353, in Italian and German, before and after debiasing." ], "reasoning": "The new embeddings for both Italian and German outperform the original embeddings in both datasets, SimLex and WordSim.", "description": "Table 6 shows the results for Italian and German for both datasets, compared to the original embeddings. In both cases, the new embeddings perform better than the original ones.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 6: Results on SimLex-999 and WordSim-353, in Italian and German, before and after debiasing.'. Solve the following question based on the table.\n\nQuestion: Which language and dataset combination shows the highest improvement in performance with the new embeddings?\nLet's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest improvement, compare the differences between the original and debiased scores for both languages and datasets. The highest improvement is for German in the SimLex dataset, with an increase from 0.343 to 0.356. So, the answer is {\"answer\": \"German SimLex\"}." } ], "images": [ "scigen/train/148.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results on SimLex-999 and WordSim-353, in Italian and German, before and after debiasing." ], "reasoning": "To find the highest improvement, compare the differences between the original and debiased scores for both languages and datasets. The highest improvement is for German in the SimLex dataset, with an increase from 0.343 to 0.356.", "question": "Which language and dataset combination shows the highest improvement in performance with the new embeddings?", "answer": "So, the answer is {\"answer\": \"German SimLex\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The new embeddings for Italian in the WordSim dataset perform worse than the original embeddings., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe new embeddings for Italian in the WordSim dataset perform worse than the original embeddings.\n" }, { "role": "assistant", "content": "The table shows that the new embeddings for Italian in the WordSim dataset have a score of 0.577, which is higher than the original embeddings score of 0.548. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/148.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results on SimLex-999 and WordSim-353, in Italian and German, before and after debiasing." ], "reasoning": "The table shows that the new embeddings for Italian in the WordSim dataset have a score of 0.577, which is higher than the original embeddings score of 0.548. Therefore, the statement is refuted.", "statement": "The new embeddings for Italian in the WordSim dataset perform worse than the original embeddings.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 7: Cross-lingual embedding alignment in Italian and in German, before and after debiasing.'.\n" }, { "role": "assistant", "content": "The precision on BDI increases for both German and Italian embeddings when the effect of grammatical gender is reduced, allowing better alignment with debiased embeddings.The results reported in Table 7 show that precision on BDI indeed increases as a result of the reduced effect of grammatical gender on the embeddings for German and Italian, i.e. that the embeddings spaces can be aligned better with the debiased embeddings." } ], "images": [ "scigen/train/149.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Cross-lingual embedding alignment in Italian and in German, before and after debiasing." ], "reasoning": "The precision on BDI increases for both German and Italian embeddings when the effect of grammatical gender is reduced, allowing better alignment with debiased embeddings.", "description": "The results reported in Table 7 show that precision on BDI indeed increases as a result of the reduced effect of grammatical gender on the embeddings for German and Italian, i.e. that the embeddings spaces can be aligned better with the debiased embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 7: Cross-lingual embedding alignment in Italian and in German, before and after debiasing.\nQuestion: What is the precision increase for Italian to English embeddings after debiasing?\n" }, { "role": "assistant", "content": "The precision for Italian to English embeddings increases from 58.73 to 60.03 after debiasing, showing an improvement. So, the answer is {\"answer\": \"1.3\"}." } ], "images": [ "scigen/train/149.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Cross-lingual embedding alignment in Italian and in German, before and after debiasing." ], "reasoning": "The precision for Italian to English embeddings increases from 58.73 to 60.03 after debiasing, showing an improvement.", "question": "What is the precision increase for Italian to English embeddings after debiasing?", "answer": "So, the answer is {\"answer\": \"1.3\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nDebiasing improves the precision for German to English embeddings.\n" }, { "role": "assistant", "content": "The precision for German to English embeddings increases from 50.48 to 51.76 after debiasing, indicating an improvement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/149.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Cross-lingual embedding alignment in Italian and in German, before and after debiasing." ], "reasoning": "The precision for German to English embeddings increases from 50.48 to 51.76 after debiasing, indicating an improvement.", "statement": "Debiasing improves the precision for German to English embeddings.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Performance comparison of the distance measures on all 18 datasets. The semantic distance in opinion distance (OD) measure is computed via cosine distance over either Word2vec (OD-w2v with semantic distance threshold 0.6) or Doc2vec (OD-d2v with distance threshold 0.3) embeddings. Sil. refers to Silhouette Coefficient. The second best result is italicized and underlined. The ARI and Silhouette coefficients scores of both OD methods (OD-d2v and OD-w2v) are statistically significant (paired t-test) with respect to baselines at significance level 0.005.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Opinion distance methods (OD-d2v and OD-w2v) generally outperform baselines on ARI and Silhouette coefficients, except for the 'Hydroelectric Dams' dataset where TF-IDF performs better.The semantic threshold for OD-d2v is set at 0.3 while for OD-w2v is set at 0.6.. We evaluate our distance measures in the unsupervised setting, specifically, evaluating the clustering quality using the Adjusted Rand Index (ARI) and Silhouette coefficient. We benchmark against the following baselines: WMD (which relies on word2vec embeddings), Doc2vec and TF-IDF. The results are shown in Table 6. The ARI and Silhouette coefficients scores of both OD methods (OD-d2v and OD-w2v) are statistically significant (paired t-test) with respect to baselines at significance level 0.005.. Opinion distance methods generally outperform the competition on both ARI and Silhouette coefficient.. This is reflected in the average ARI and average Silhouette coefficients of the baseline distance measures.. In the exceptional case of \"Hydroelectric Dams\" dataset, the opinion distance OD performs particularly bad compared to TF-IDF" } ], "images": [ "scigen/train/150.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Performance comparison of the distance measures on all 18 datasets. The semantic distance in opinion distance (OD) measure is computed via cosine distance over either Word2vec (OD-w2v with semantic distance threshold 0.6) or Doc2vec (OD-d2v with distance threshold 0.3) embeddings. Sil. refers to Silhouette Coefficient. The second best result is italicized and underlined. The ARI and Silhouette coefficients scores of both OD methods (OD-d2v and OD-w2v) are statistically significant (paired t-test) with respect to baselines at significance level 0.005." ], "reasoning": "Opinion distance methods (OD-d2v and OD-w2v) generally outperform baselines on ARI and Silhouette coefficients, except for the 'Hydroelectric Dams' dataset where TF-IDF performs better.", "description": "The semantic threshold for OD-d2v is set at 0.3 while for OD-w2v is set at 0.6.. We evaluate our distance measures in the unsupervised setting, specifically, evaluating the clustering quality using the Adjusted Rand Index (ARI) and Silhouette coefficient. We benchmark against the following baselines: WMD (which relies on word2vec embeddings), Doc2vec and TF-IDF. The results are shown in Table 6. The ARI and Silhouette coefficients scores of both OD methods (OD-d2v and OD-w2v) are statistically significant (paired t-test) with respect to baselines at significance level 0.005.. Opinion distance methods generally outperform the competition on both ARI and Silhouette coefficient.. This is reflected in the average ARI and average Silhouette coefficients of the baseline distance measures.. In the exceptional case of \"Hydroelectric Dams\" dataset, the opinion distance OD performs particularly bad compared to TF-IDF", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 6: Performance comparison of the distance measures on all 18 datasets. The semantic distance in opinion distance (OD) measure is computed via cosine distance over either Word2vec (OD-w2v with semantic distance threshold 0.6) or Doc2vec (OD-d2v with distance threshold 0.3) embeddings. Sil. refers to Silhouette Coefficient. The second best result is italicized and underlined. The ARI and Silhouette coefficients scores of both OD methods (OD-d2v and OD-w2v) are statistically significant (paired t-test) with respect to baselines at significance level 0.005.' and methodically tackle the question that follows. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich method has the highest ARI score for the 'Trades Unions' topic?\n" }, { "role": "assistant", "content": "The table shows ARI scores for various methods on different topics. For the 'Trades Unions' topic, the highest ARI score is 0.60, which is achieved by Sent2vec. So, the answer is {\"answer\": \"Sent2vec\"}." } ], "images": [ "scigen/train/150.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Performance comparison of the distance measures on all 18 datasets. The semantic distance in opinion distance (OD) measure is computed via cosine distance over either Word2vec (OD-w2v with semantic distance threshold 0.6) or Doc2vec (OD-d2v with distance threshold 0.3) embeddings. Sil. refers to Silhouette Coefficient. The second best result is italicized and underlined. The ARI and Silhouette coefficients scores of both OD methods (OD-d2v and OD-w2v) are statistically significant (paired t-test) with respect to baselines at significance level 0.005." ], "reasoning": "The table shows ARI scores for various methods on different topics. For the 'Trades Unions' topic, the highest ARI score is 0.60, which is achieved by Sent2vec.", "question": "Which method has the highest ARI score for the 'Trades Unions' topic?", "answer": "So, the answer is {\"answer\": \"Sent2vec\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: OD-w2v has the highest Silhouette coefficient for the 'Pornography' topic., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nOD-w2v has the highest Silhouette coefficient for the 'Pornography' topic.\n" }, { "role": "assistant", "content": "The table shows Silhouette coefficients for various methods on different topics. For the 'Pornography' topic, OD-w2v has a Silhouette coefficient of 0.47, which is the highest among the listed methods. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/150.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Performance comparison of the distance measures on all 18 datasets. The semantic distance in opinion distance (OD) measure is computed via cosine distance over either Word2vec (OD-w2v with semantic distance threshold 0.6) or Doc2vec (OD-d2v with distance threshold 0.3) embeddings. Sil. refers to Silhouette Coefficient. The second best result is italicized and underlined. The ARI and Silhouette coefficients scores of both OD methods (OD-d2v and OD-w2v) are statistically significant (paired t-test) with respect to baselines at significance level 0.005." ], "reasoning": "The table shows Silhouette coefficients for various methods on different topics. For the 'Pornography' topic, OD-w2v has a Silhouette coefficient of 0.47, which is the highest among the listed methods.", "statement": "OD-w2v has the highest Silhouette coefficient for the 'Pornography' topic.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: ARI and Silhouette coefficient scores.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "OD significantly outperforms baseline methods and the OD-parse variant, achieving high ARI and Sil scores across datasets.among opinions: We see that OD significantly outperforms the baseline methods and the OD-parse variant. OD achieves high ARI and Sil scores,. From the above table, we observe that the text-similarity based baselines, such as TF-IDF, WMD and Doc2vec achieving ARI and Silhouette coefficient scores of close to zero on the \"Video Games\" and \"Pornography\" datasets (barely providing a performance improvement over random clustering, i.e., a zero ARI score).. A notable exception is the \"Seanad Abolition\" dataset, where TF-IDF performs relatively better than WMD, Sent2vec and Doc2vec." } ], "images": [ "scigen/train/151.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: ARI and Silhouette coefficient scores." ], "reasoning": "OD significantly outperforms baseline methods and the OD-parse variant, achieving high ARI and Sil scores across datasets.", "description": "among opinions: We see that OD significantly outperforms the baseline methods and the OD-parse variant. OD achieves high ARI and Sil scores,. From the above table, we observe that the text-similarity based baselines, such as TF-IDF, WMD and Doc2vec achieving ARI and Silhouette coefficient scores of close to zero on the \"Video Games\" and \"Pornography\" datasets (barely providing a performance improvement over random clustering, i.e., a zero ARI score).. A notable exception is the \"Seanad Abolition\" dataset, where TF-IDF performs relatively better than WMD, Sent2vec and Doc2vec.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 3: ARI and Silhouette coefficient scores.\nQuestion: Which method achieves the highest ARI score on the 'Video Games' dataset?\n" }, { "role": "assistant", "content": "By comparing the ARI scores for the 'Video Games' dataset, we see that OD achieves the highest ARI score of 0.56. So, the answer is {\"answer\": \"OD\"}." } ], "images": [ "scigen/train/151.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: ARI and Silhouette coefficient scores." ], "reasoning": "By comparing the ARI scores for the 'Video Games' dataset, we see that OD achieves the highest ARI score of 0.56.", "question": "Which method achieves the highest ARI score on the 'Video Games' dataset?", "answer": "So, the answer is {\"answer\": \"OD\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis TF-IDF performs better than WMD on the 'Seanad Abolition' dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nTF-IDF performs better than WMD on the 'Seanad Abolition' dataset.\n" }, { "role": "assistant", "content": "By comparing the ARI and Sil scores for TF-IDF and WMD on the 'Seanad Abolition' dataset, we see that TF-IDF has higher ARI (0.23 vs 0.09) and Sil (0.02 vs 0.01) scores. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/151.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: ARI and Silhouette coefficient scores." ], "reasoning": "By comparing the ARI and Sil scores for TF-IDF and WMD on the 'Seanad Abolition' dataset, we see that TF-IDF has higher ARI (0.23 vs 0.09) and Sil (0.02 vs 0.01) scores.", "statement": "TF-IDF performs better than WMD on the 'Seanad Abolition' dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: The quality of opinion distance when leveraged as a feature for multi-class classification. Each entry in + X feature should be treated independently. The second best result is italicized and underlined.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "SVM with OD features outperforms many baselines, especially on the 'Video Games' and 'Pornography' datasets. For the 'Seanad Abolition' dataset, SVM with OD and bigrams achieves the best performance.For completeness, here we also compare against unigram or n-gram based classifiers. The classification performance of the baselines is reported in Table 4.. SVM with only OD features outperforms many baselines: We see that on \"Video Games\" and \"Pornography\" datasets, the classification performance based on SVM with only OD is significantly better than the SVM with any other combination of features excluding OD. For the \"Seanad Abolition\" dataset, there is one exception: SVM with unigrams and LSA features performs slightly better than OD.. nating word \"democracy\". SVM with OD and baseline features further improves classification performance: We see that SVM with OD and bigrams achieves the best multi-class classification performance on the \"Seanad Abolition\" dataset. On the \"Pornography\" dataset, we observe SVM with OD + LSA to improve classification performance by nearly 2%." } ], "images": [ "scigen/train/152.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The quality of opinion distance when leveraged as a feature for multi-class classification. Each entry in + X feature should be treated independently. The second best result is italicized and underlined." ], "reasoning": "SVM with OD features outperforms many baselines, especially on the 'Video Games' and 'Pornography' datasets. For the 'Seanad Abolition' dataset, SVM with OD and bigrams achieves the best performance.", "description": "For completeness, here we also compare against unigram or n-gram based classifiers. The classification performance of the baselines is reported in Table 4.. SVM with only OD features outperforms many baselines: We see that on \"Video Games\" and \"Pornography\" datasets, the classification performance based on SVM with only OD is significantly better than the SVM with any other combination of features excluding OD. For the \"Seanad Abolition\" dataset, there is one exception: SVM with unigrams and LSA features performs slightly better than OD.. nating word \"democracy\". SVM with OD and baseline features further improves classification performance: We see that SVM with OD and bigrams achieves the best multi-class classification performance on the \"Seanad Abolition\" dataset. On the \"Pornography\" dataset, we observe SVM with OD + LSA to improve classification performance by nearly 2%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 4: The quality of opinion distance when leveraged as a feature for multi-class classification. Each entry in + X feature should be treated independently. The second best result is italicized and underlined.' and proceed to solve the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich combination of features achieves the highest classification performance on the 'Pornography' dataset?\n" }, { "role": "assistant", "content": "By examining the table, the highest classification performance on the 'Pornography' dataset is achieved by SVM with OD + LSA, which has a score of 0.90. So, the answer is {\"answer\": \"OD + LSA\"}." } ], "images": [ "scigen/train/152.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The quality of opinion distance when leveraged as a feature for multi-class classification. Each entry in + X feature should be treated independently. The second best result is italicized and underlined." ], "reasoning": "By examining the table, the highest classification performance on the 'Pornography' dataset is achieved by SVM with OD + LSA, which has a score of 0.90.", "question": "Which combination of features achieves the highest classification performance on the 'Pornography' dataset?", "answer": "So, the answer is {\"answer\": \"OD + LSA\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSVM with OD features achieves the highest classification performance on the 'Video Games' dataset.\n" }, { "role": "assistant", "content": "The table shows that SVM with OD features alone achieves a score of 0.88 on the 'Video Games' dataset, which is the highest score among all combinations. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/152.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The quality of opinion distance when leveraged as a feature for multi-class classification. Each entry in + X feature should be treated independently. The second best result is italicized and underlined." ], "reasoning": "The table shows that SVM with OD features alone achieves a score of 0.88 on the 'Video Games' dataset, which is the highest score among all combinations.", "statement": "SVM with OD features achieves the highest classification performance on the 'Video Games' dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: We compare the quality of variants of Opinion Distance measures on opinion clustering task with ARI.'.\n" }, { "role": "assistant", "content": "OD significantly outperforms OD-parse and Jensen-Shannon divergence on all three datasets, achieving higher weighted F1 scores. Sentiment polarity shifters have a high impact on clustering performance, especially for 'Video games' and 'Pornography' datasets.distribuThe results of different variants are shown in Table 5.. OD significantly outperforms OD-parse: We observe that compared to OD-parse, OD is much more accurate. On the three datasets, OD achieves an average weighted F1 score of 0.54, 0.56 and 0.41 respectively compared to the scores of 0.01, -0.01 and 0.07 by OD-parse.. Table 5 shows that OD is significantly better than Jensen-Shannon divergence on all the three datasets.. Sentiment polarity shifters have a high impact on clustering performance of opinion distance: We find that not utilizing the sentiment polarity shifters, especially in case of datasets \"Video games\" and \"Pornography\" hurts the Opinion Representation phase, and thereby leads to incorrect computation of opinion distance. This is evident from the significant drop in ARI score from OD to OD (no polarity shifters) since the only change in those variants is of sentiment polarity shifters." } ], "images": [ "scigen/train/153.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: We compare the quality of variants of Opinion Distance measures on opinion clustering task with ARI." ], "reasoning": "OD significantly outperforms OD-parse and Jensen-Shannon divergence on all three datasets, achieving higher weighted F1 scores. Sentiment polarity shifters have a high impact on clustering performance, especially for 'Video games' and 'Pornography' datasets.", "description": "distribuThe results of different variants are shown in Table 5.. OD significantly outperforms OD-parse: We observe that compared to OD-parse, OD is much more accurate. On the three datasets, OD achieves an average weighted F1 score of 0.54, 0.56 and 0.41 respectively compared to the scores of 0.01, -0.01 and 0.07 by OD-parse.. Table 5 shows that OD is significantly better than Jensen-Shannon divergence on all the three datasets.. Sentiment polarity shifters have a high impact on clustering performance of opinion distance: We find that not utilizing the sentiment polarity shifters, especially in case of datasets \"Video games\" and \"Pornography\" hurts the Opinion Representation phase, and thereby leads to incorrect computation of opinion distance. This is evident from the significant drop in ARI score from OD to OD (no polarity shifters) since the only change in those variants is of sentiment polarity shifters.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 5: We compare the quality of variants of Opinion Distance measures on opinion clustering task with ARI.', and sequentially solve the question, writing out each step of your reasoning process. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the average weighted F1 score of OD on the 'Video Games' dataset?\n" }, { "role": "assistant", "content": "The table shows that OD achieves a weighted F1 score of 0.56 on the 'Video Games' dataset. So, the answer is {\"answer\": \"0.56\"}." } ], "images": [ "scigen/train/153.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: We compare the quality of variants of Opinion Distance measures on opinion clustering task with ARI." ], "reasoning": "The table shows that OD achieves a weighted F1 score of 0.56 on the 'Video Games' dataset.", "question": "What is the average weighted F1 score of OD on the 'Video Games' dataset?", "answer": "So, the answer is {\"answer\": \"0.56\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nOD achieves a higher weighted F1 score than OD-parse on the 'Pornography' dataset.\n" }, { "role": "assistant", "content": "The table shows that OD achieves a weighted F1 score of 0.41 on the 'Pornography' dataset, while OD-parse achieves 0.07. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/153.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: We compare the quality of variants of Opinion Distance measures on opinion clustering task with ARI." ], "reasoning": "The table shows that OD achieves a weighted F1 score of 0.41 on the 'Pornography' dataset, while OD-parse achieves 0.07.", "statement": "OD achieves a higher weighted F1 score than OD-parse on the 'Pornography' dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Results on the development and testing sets. Accuracy and F1 scores: macro-averaged and per class (S: supporting, D: denying, Q: querying, C: commenting).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The model shows minimal difference in accuracy between the testing and development sets, but a significant difference in Macro-F score due to class balance.The performance of our model on the testing and development set is shown in Table 3.. The difference in accuracy between testing and development set is minimal, however we see significant difference in Macro-F score due to different class balance in these sets.. The branch-LSTM model predicts commenting, the majority class well, however it is unable to pick out any denying, the mostchallenging under-represented class." } ], "images": [ "scigen/train/154.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on the development and testing sets. Accuracy and F1 scores: macro-averaged and per class (S: supporting, D: denying, Q: querying, C: commenting)." ], "reasoning": "The model shows minimal difference in accuracy between the testing and development sets, but a significant difference in Macro-F score due to class balance.", "description": "The performance of our model on the testing and development set is shown in Table 3.. The difference in accuracy between testing and development set is minimal, however we see significant difference in Macro-F score due to different class balance in these sets.. The branch-LSTM model predicts commenting, the majority class well, however it is unable to pick out any denying, the mostchallenging under-represented class.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the difference in Macro-F score between the testing and development sets?\nSolve the above question based on the table titled 'Table 3: Results on the development and testing sets. Accuracy and F1 scores: macro-averaged and per class (S: supporting, D: denying, Q: querying, C: commenting).'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The Macro-F score for the development set is 0.561 and for the testing set is 0.434. The difference is calculated as 0.561 - 0.434. So, the answer is {\"answer\": \"0.127\"}" } ], "images": [ "scigen/train/154.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on the development and testing sets. Accuracy and F1 scores: macro-averaged and per class (S: supporting, D: denying, Q: querying, C: commenting)." ], "reasoning": "The Macro-F score for the development set is 0.561 and for the testing set is 0.434. The difference is calculated as 0.561 - 0.434.", "question": "What is the difference in Macro-F score between the testing and development sets?", "answer": "So, the answer is {\"answer\": \"0.127\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe branch-LSTM model predicts the denying class well in the testing set.\n" }, { "role": "assistant", "content": "The table shows that the Macro-F score for the denying class (D) is 0.000 in both the development and testing sets, indicating that the model does not predict the denying class well. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/154.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on the development and testing sets. Accuracy and F1 scores: macro-averaged and per class (S: supporting, D: denying, Q: querying, C: commenting)." ], "reasoning": "The table shows that the Macro-F score for the denying class (D) is 0.000 in both the development and testing sets, indicating that the model does not predict the denying class well.", "statement": "The branch-LSTM model predicts the denying class well in the testing set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: Confusion matrix for testing set predictions'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Most instances labeled as denying are misclassified as commenting, as indicated by the high number of denying instances predicted as commenting.Most denying instances get misclassified as commenting (see Table 5)," } ], "images": [ "scigen/train/155.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Confusion matrix for testing set predictions" ], "reasoning": "Most instances labeled as denying are misclassified as commenting, as indicated by the high number of denying instances predicted as commenting.", "description": "Most denying instances get misclassified as commenting (see Table 5),", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: Confusion matrix for testing set predictions'.\n\nQuestion: How many denying instances are misclassified as commenting?\n" }, { "role": "assistant", "content": "To find the number of denying instances misclassified as commenting, look at the 'Denying' row and the 'Commenting' column. The value is 68. So, the answer is {\"answer\": \"68\"}." } ], "images": [ "scigen/train/155.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Confusion matrix for testing set predictions" ], "reasoning": "To find the number of denying instances misclassified as commenting, look at the 'Denying' row and the 'Commenting' column. The value is 68.", "question": "How many denying instances are misclassified as commenting?", "answer": "So, the answer is {\"answer\": \"68\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nMost denying instances are correctly classified.\n" }, { "role": "assistant", "content": "The table shows that only 1 denying instance is correctly classified, while 68 are misclassified as commenting. Therefore, most denying instances are not correctly classified. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/155.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Confusion matrix for testing set predictions" ], "reasoning": "The table shows that only 1 denying instance is correctly classified, while 68 are misclassified as commenting. Therefore, most denying instances are not correctly classified.", "statement": "Most denying instances are correctly classified.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: ABSA SemEval 2014-2016 datasets for the restaurant domain. B-target indicates the number of opinion targets in each set; I-target refers to the number of multiword targets.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table displays the number of tokens, targets, and multiword targets for training and test sets in various languages for ABSA datasets from the restaurants domain.Table 1 shows the ABSA datasets from the restaurants domain for English, Spanish, French, Dutch, Russian and Turkish. From left to right each row displays the number of tokens, number of targets and the number of multiword targets for each training and test set." } ], "images": [ "scigen/train/156.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: ABSA SemEval 2014-2016 datasets for the restaurant domain. B-target indicates the number of opinion targets in each set; I-target refers to the number of multiword targets." ], "reasoning": "The table displays the number of tokens, targets, and multiword targets for training and test sets in various languages for ABSA datasets from the restaurants domain.", "description": "Table 1 shows the ABSA datasets from the restaurants domain for English, Spanish, French, Dutch, Russian and Turkish. From left to right each row displays the number of tokens, number of targets and the number of multiword targets for each training and test set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: 'Table 1: ABSA SemEval 2014-2016 datasets for the restaurant domain. B-target indicates the number of opinion targets in each set; I-target refers to the number of multiword targets.'\nQuestion: Which language had the highest number of tokens in the training set in 2016?\n" }, { "role": "assistant", "content": "To find the language with the highest number of tokens in the training set for 2016, compare the 'No. of Tokens and Opinion Targets Train' column for each language in 2016. So, the answer is {\"answer\": \"ru\"}" } ], "images": [ "scigen/train/156.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: ABSA SemEval 2014-2016 datasets for the restaurant domain. B-target indicates the number of opinion targets in each set; I-target refers to the number of multiword targets." ], "reasoning": "To find the language with the highest number of tokens in the training set for 2016, compare the 'No. of Tokens and Opinion Targets Train' column for each language in 2016.", "question": "Which language had the highest number of tokens in the training set in 2016?", "answer": "So, the answer is {\"answer\": \"ru\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The French dataset in 2016 had more than 500 multiword targets in the training set., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe French dataset in 2016 had more than 500 multiword targets in the training set.\n" }, { "role": "assistant", "content": "The table shows that the French dataset in 2016 had 443 multiword targets in the training set, which is less than 500. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/156.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: ABSA SemEval 2014-2016 datasets for the restaurant domain. B-target indicates the number of opinion targets in each set; I-target refers to the number of multiword targets." ], "reasoning": "The table shows that the French dataset in 2016 had 443 multiword targets in the training set, which is less than 500.", "statement": "The French dataset in 2016 had more than 500 multiword targets in the training set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: ABSA SemEval 2014-2016 English results. BY: Brown Yelp 1000 classes; CYF100-CYR200: Clark Yelp Food 100 classes and Clark Yelp Reviews 200 classes; W2VW400: Word2vec Wikipedia 400 classes; ALL: BY+CYF100-CYR200+W2VW400.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Table 3 shows the results of the Opinion Target Extraction task for English, highlighting the best model (ALL) chosen via 5-fold CV on the training data, along with the best models using Brown, Clark, and Word2vec clustering features.Table 3 provides detailed results on the Opinion Target Extraction (OTE) task for English. We show in bold our best model (ALL) chosen via 5-fold CV on the training data. Moreover, we also. show the results of the best models using only one type of clustering feature, namely, the best Brown, Clark and Word2vec models, respectively." } ], "images": [ "scigen/train/157.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: ABSA SemEval 2014-2016 English results. BY: Brown Yelp 1000 classes; CYF100-CYR200: Clark Yelp Food 100 classes and Clark Yelp Reviews 200 classes; W2VW400: Word2vec Wikipedia 400 classes; ALL: BY+CYF100-CYR200+W2VW400." ], "reasoning": "Table 3 shows the results of the Opinion Target Extraction task for English, highlighting the best model (ALL) chosen via 5-fold CV on the training data, along with the best models using Brown, Clark, and Word2vec clustering features.", "description": "Table 3 provides detailed results on the Opinion Target Extraction (OTE) task for English. We show in bold our best model (ALL) chosen via 5-fold CV on the training data. Moreover, we also. show the results of the best models using only one type of clustering feature, namely, the best Brown, Clark and Word2vec models, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: ABSA SemEval 2014-2016 English results. BY: Brown Yelp 1000 classes; CYF100-CYR200: Clark Yelp Food 100 classes and Clark Yelp Reviews 200 classes; W2VW400: Word2vec Wikipedia 400 classes; ALL: BY+CYF100-CYR200+W2VW400.', unfold the steps to solve the question given next. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nQuestion: What is the highest F1 score achieved in 2014 and which model achieved it?\n" }, { "role": "assistant", "content": "To find the highest F1 score in 2014, we need to compare the F1 scores for all models in the 2014 F1 column. The highest F1 score is 84.11, achieved by the L + ALL model. So, the answer is {\"answer\": \"L + ALL\"}." } ], "images": [ "scigen/train/157.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: ABSA SemEval 2014-2016 English results. BY: Brown Yelp 1000 classes; CYF100-CYR200: Clark Yelp Food 100 classes and Clark Yelp Reviews 200 classes; W2VW400: Word2vec Wikipedia 400 classes; ALL: BY+CYF100-CYR200+W2VW400." ], "reasoning": "To find the highest F1 score in 2014, we need to compare the F1 scores for all models in the 2014 F1 column. The highest F1 score is 84.11, achieved by the L + ALL model.", "question": "What is the highest F1 score achieved in 2014 and which model achieved it?", "answer": "So, the answer is {\"answer\": \"L + ALL\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe L + BY model achieved the highest precision score in 2014.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the precision scores for all models in the 2014 P column. The highest precision score in 2014 is 82.91, achieved by the L + CYF100-CYR200 model, not the L + BY model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/157.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: ABSA SemEval 2014-2016 English results. BY: Brown Yelp 1000 classes; CYF100-CYR200: Clark Yelp Food 100 classes and Clark Yelp Reviews 200 classes; W2VW400: Word2vec Wikipedia 400 classes; ALL: BY+CYF100-CYR200+W2VW400." ], "reasoning": "To verify this statement, we need to compare the precision scores for all models in the 2014 P column. The highest precision score in 2014 is 82.91, achieved by the L + CYF100-CYR200 model, not the L + BY model.", "statement": "The L + BY model achieved the highest precision score in 2014.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 6: ABSA SemEval 2016: Comparison of multilingual results in terms of F1 scores.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The table demonstrates that the system 'L + CW600 + W2VW300' achieves the highest F1 score in Spanish, outperforming other systems across five languages.Table 6 shows that our system outperforms the best previous approaches across the five languages." } ], "images": [ "scigen/train/158.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: ABSA SemEval 2016: Comparison of multilingual results in terms of F1 scores." ], "reasoning": "The table demonstrates that the system 'L + CW600 + W2VW300' achieves the highest F1 score in Spanish, outperforming other systems across five languages.", "description": "Table 6 shows that our system outperforms the best previous approaches across the five languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: ABSA SemEval 2016: Comparison of multilingual results in terms of F1 scores.\nQuestion: Which system has the highest F1 score in French, and how does it compare to the baseline?\nGive a detailed response to the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest F1 score in French, look at the 'fr' rows and identify the system with the highest F1 score. Compare this score to the baseline F1 score for French. So, the answer is {\"answer\": \"L + CW100\"}." } ], "images": [ "scigen/train/158.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: ABSA SemEval 2016: Comparison of multilingual results in terms of F1 scores." ], "reasoning": "To find the highest F1 score in French, look at the 'fr' rows and identify the system with the highest F1 score. Compare this score to the baseline F1 score for French.", "question": "Which system has the highest F1 score in French, and how does it compare to the baseline?", "answer": "So, the answer is {\"answer\": \"L + CW100\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe system 'L + W2VW400' has the highest F1 score in Dutch.\n" }, { "role": "assistant", "content": "Check the rows for Dutch ('nl') and compare the F1 scores of all systems. 'L + W2VW400' has the highest F1 score among them. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/158.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: ABSA SemEval 2016: Comparison of multilingual results in terms of F1 scores." ], "reasoning": "Check the rows for Dutch ('nl') and compare the F1 scores of all systems. 'L + W2VW400' has the highest F1 score among them.", "statement": "The system 'L + W2VW400' has the highest F1 score in Dutch.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: False Positives and Negatives for every ABSA 2014-2016 setting.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table shows that false negatives (FN) are higher than false positives (FP) in most cases, indicating that most errors in the system are due to false negatives.most errors in our system are caused by false negatives [FN], as it can be seen in Table 7." } ], "images": [ "scigen/train/159.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: False Positives and Negatives for every ABSA 2014-2016 setting." ], "reasoning": "The table shows that false negatives (FN) are higher than false positives (FP) in most cases, indicating that most errors in the system are due to false negatives.", "description": "most errors in our system are caused by false negatives [FN], as it can be seen in Table 7.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 7: False Positives and Negatives for every ABSA 2014-2016 setting.', unfold the steps to solve the question given next. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nQuestion: Which year and language combination had the highest number of false negatives (FN)?\n" }, { "role": "assistant", "content": "To find the highest number of false negatives, we need to compare the FN values across all years and languages. The highest FN value is 312, which corresponds to the year 2016 and the language 'ru'. So, the answer is {\"answer\": \"2016 ru\"}." } ], "images": [ "scigen/train/159.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: False Positives and Negatives for every ABSA 2014-2016 setting." ], "reasoning": "To find the highest number of false negatives, we need to compare the FN values across all years and languages. The highest FN value is 312, which corresponds to the year 2016 and the language 'ru'.", "question": "Which year and language combination had the highest number of false negatives (FN)?", "answer": "So, the answer is {\"answer\": \"2016 ru\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe number of false positives (FP) in 2016 for the 'ru' language is higher than the number of false negatives (FN) for the same year and language.\n" }, { "role": "assistant", "content": "The table shows that in 2016 for the 'ru' language, the number of false positives (FP) is 390, while the number of false negatives (FN) is 312. Since 390 is greater than 312, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/159.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: False Positives and Negatives for every ABSA 2014-2016 setting." ], "reasoning": "The table shows that in 2016 for the 'ru' language, the number of false positives (FP) is 390, while the number of false negatives (FN) is 312. Since 390 is greater than 312, the statement is supported.", "statement": "The number of false positives (FP) in 2016 for the 'ru' language is higher than the number of false negatives (FN) for the same year and language.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 8: Summary for 3CosMul and top-1 nearest vectors.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The worst performance was in the currency country category, while adjectives antonyms and performer action had the highest out-of-vocabulary terms, leading to lower performance.We noticed that the sub-category in which most models had the worst performance was currency country category,. Sub-categories as adjectives antonyms and performer action had the highest percentage of out-of-vocabulary terms, so we observed lower performance in these categories for all models." } ], "images": [ "scigen/train/160.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Summary for 3CosMul and top-1 nearest vectors." ], "reasoning": "The worst performance was in the currency country category, while adjectives antonyms and performer action had the highest out-of-vocabulary terms, leading to lower performance.", "description": "We noticed that the sub-category in which most models had the worst performance was currency country category,. Sub-categories as adjectives antonyms and performer action had the highest percentage of out-of-vocabulary terms, so we observed lower performance in these categories for all models.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 8: Summary for 3CosMul and top-1 nearest vectors.'. Solve the following question based on the table.\n\nQuestion: Which sub-category had the highest percentage of out-of-vocabulary terms and how did it affect the performance?\nLet's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "Adjectives antonyms and performer action had the highest percentage of out-of-vocabulary terms, leading to lower performance in these categories for all models. So, the answer is {\"answer\": \"adjectives antonyms and performer action\"}" } ], "images": [ "scigen/train/160.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Summary for 3CosMul and top-1 nearest vectors." ], "reasoning": "Adjectives antonyms and performer action had the highest percentage of out-of-vocabulary terms, leading to lower performance in these categories for all models.", "question": "Which sub-category had the highest percentage of out-of-vocabulary terms and how did it affect the performance?", "answer": "So, the answer is {\"answer\": \"adjectives antonyms and performer action\"}" }, { "messages": [ { "role": "user", "content": "The currency country category had the worst performance among all sub-categories. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe currency country category had the worst performance among all sub-categories.\n" }, { "role": "assistant", "content": "The description states that the currency country category had the worst performance, which is supported by the table showing lower scores in this category. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/160.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Summary for 3CosMul and top-1 nearest vectors." ], "reasoning": "The description states that the currency country category had the worst performance, which is supported by the table showing lower scores in this category.", "statement": "The currency country category had the worst performance among all sub-categories.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 1: The Greek word analogy test set.'.\n" }, { "role": "assistant", "content": "The Greek analogy test set contains 39,174 questions, divided into 13,650 semantic questions across 9 categories and 25,524 syntactic questions across 15 categories.Our Greek analogy test set contains 39,174 questions divided into semantic and syntactic analogy questions.. Semantic questions are divided into 9 categories and include 13,650 questions in total.. Syntactic questions are divided into 15 categories, which are mostly language specific. They include 25,524 questions. We show the full Greek word analogy dataset in Table 1." } ], "images": [ "scigen/train/161.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The Greek word analogy test set." ], "reasoning": "The Greek analogy test set contains 39,174 questions, divided into 13,650 semantic questions across 9 categories and 25,524 syntactic questions across 15 categories.", "description": "Our Greek analogy test set contains 39,174 questions divided into semantic and syntactic analogy questions.. Semantic questions are divided into 9 categories and include 13,650 questions in total.. Syntactic questions are divided into 15 categories, which are mostly language specific. They include 25,524 questions. We show the full Greek word analogy dataset in Table 1.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 1: The Greek word analogy test set.\n\nQuestion: How many categories of syntactic questions are there in the Greek analogy test set?\n" }, { "role": "assistant", "content": "The description states that syntactic questions are divided into 15 categories, which is confirmed by the table showing 15 different syntactic relations. So, the answer is {\"answer\": \"15\"}" } ], "images": [ "scigen/train/161.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The Greek word analogy test set." ], "reasoning": "The description states that syntactic questions are divided into 15 categories, which is confirmed by the table showing 15 different syntactic relations.", "question": "How many categories of syntactic questions are there in the Greek analogy test set?", "answer": "So, the answer is {\"answer\": \"15\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Greek analogy test set includes 13,650 semantic questions.\n" }, { "role": "assistant", "content": "The description states that there are 13,650 semantic questions, which is supported by the table showing a total of 13,650 tuples for semantic questions. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/161.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The Greek word analogy test set." ], "reasoning": "The description states that there are 13,650 semantic questions, which is supported by the table showing a total of 13,650 tuples for semantic questions.", "statement": "The Greek analogy test set includes 13,650 semantic questions.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Summary for 3CosAdd and top-1 nearest vectors.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Model cc.el.300 outperformed all other models in most categories except for the Syntactic category with oov terms, where gr def had the best performance.We compared these models in word analogy task. Due to space limitations, we show summarized results only for 3CosAdd in Table 3 and move the rest in supplementary material. Considering the two aggregated categories of syntactic and semantic word analogies respectively and both 3CosAdd and 3CosMul metrics, model cc.el.300 has outperformed all the other models apart from the case of the Syntactic category when we included the out-of-vocabulary (oov) terms. where the model gr def had the best performance. Model cc.el.300 was the only one that was trained with CBOW and position-weights. Model wiki.el, trained only on Wikipedia, was the worst almost in every category (and sub-category). The five models that were trained on the large scale web content (Outsios et al., 2018) had lower percentage of oov terms in comparison with the other two. In some cases where oov terms were considered, they outperformed model cc.el.300 or had a better ranking considering the accuracy rate in most categories or sub-categories. In the basic categories, syntactic and semantic, model gr cbow def was the only one that performed much worse in the semantic category than in the syntactic one. All the other models did not have large differences in performance between semantic and syntactic categories." } ], "images": [ "scigen/train/162.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Summary for 3CosAdd and top-1 nearest vectors." ], "reasoning": "Model cc.el.300 outperformed all other models in most categories except for the Syntactic category with oov terms, where gr def had the best performance.", "description": "We compared these models in word analogy task. Due to space limitations, we show summarized results only for 3CosAdd in Table 3 and move the rest in supplementary material. Considering the two aggregated categories of syntactic and semantic word analogies respectively and both 3CosAdd and 3CosMul metrics, model cc.el.300 has outperformed all the other models apart from the case of the Syntactic category when we included the out-of-vocabulary (oov) terms. where the model gr def had the best performance. Model cc.el.300 was the only one that was trained with CBOW and position-weights. Model wiki.el, trained only on Wikipedia, was the worst almost in every category (and sub-category). The five models that were trained on the large scale web content (Outsios et al., 2018) had lower percentage of oov terms in comparison with the other two. In some cases where oov terms were considered, they outperformed model cc.el.300 or had a better ranking considering the accuracy rate in most categories or sub-categories. In the basic categories, syntactic and semantic, model gr cbow def was the only one that performed much worse in the semantic category than in the syntactic one. All the other models did not have large differences in performance between semantic and syntactic categories.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Summary for 3CosAdd and top-1 nearest vectors.', unfold the question presented below, detailing every step of your calculation. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model had the best performance in the Syntactic category with out-of-vocabulary (oov) terms?\n" }, { "role": "assistant", "content": "The table shows that in the Syntactic category with oov terms, the model gr def had the highest accuracy rate of 53.95%, outperforming all other models. So, the answer is {\"answer\": \"gr def\"}." } ], "images": [ "scigen/train/162.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Summary for 3CosAdd and top-1 nearest vectors." ], "reasoning": "The table shows that in the Syntactic category with oov terms, the model gr def had the highest accuracy rate of 53.95%, outperforming all other models.", "question": "Which model had the best performance in the Syntactic category with out-of-vocabulary (oov) terms?", "answer": "So, the answer is {\"answer\": \"gr def\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Model cc.el.300 had the best performance in the Syntactic category with out-of-vocabulary (oov) terms. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nModel cc.el.300 had the best performance in the Syntactic category with out-of-vocabulary (oov) terms.\n" }, { "role": "assistant", "content": "The table shows that in the Syntactic category with oov terms, the model gr def had the highest accuracy rate of 53.95%, not cc.el.300. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/162.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Summary for 3CosAdd and top-1 nearest vectors." ], "reasoning": "The table shows that in the Syntactic category with oov terms, the model gr def had the highest accuracy rate of 53.95%, not cc.el.300.", "statement": "Model cc.el.300 had the best performance in the Syntactic category with out-of-vocabulary (oov) terms.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 7: Summary for 3CosMul and top-5 nearest vectors.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The worst performance was in the currency country category, while adjectives antonyms and performer action had the highest out-of-vocabulary terms, leading to lower performance.We noticed that the sub-category in which most models had the worst performance was currency country category,. Sub-categories as adjectives antonyms and performer action had the highest percentage of out-of-vocabulary terms, so we observed lower performance in these categories for all models." } ], "images": [ "scigen/train/163.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Summary for 3CosMul and top-5 nearest vectors." ], "reasoning": "The worst performance was in the currency country category, while adjectives antonyms and performer action had the highest out-of-vocabulary terms, leading to lower performance.", "description": "We noticed that the sub-category in which most models had the worst performance was currency country category,. Sub-categories as adjectives antonyms and performer action had the highest percentage of out-of-vocabulary terms, so we observed lower performance in these categories for all models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 7: Summary for 3CosMul and top-5 nearest vectors.', answer the following question. Give detailed solution consisting of each step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich sub-category had the worst performance and which had the highest percentage of out-of-vocabulary terms?\n" }, { "role": "assistant", "content": "The worst performance was in the currency country category, and the highest percentage of out-of-vocabulary terms was in adjectives antonyms and performer action. So, the answer is {\"answer\": \"currency country category and adjectives antonyms and performer action\"}." } ], "images": [ "scigen/train/163.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Summary for 3CosMul and top-5 nearest vectors." ], "reasoning": "The worst performance was in the currency country category, and the highest percentage of out-of-vocabulary terms was in adjectives antonyms and performer action.", "question": "Which sub-category had the worst performance and which had the highest percentage of out-of-vocabulary terms?", "answer": "So, the answer is {\"answer\": \"currency country category and adjectives antonyms and performer action\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The category with the highest percentage of out-of-vocabulary terms had the worst performance., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe category with the highest percentage of out-of-vocabulary terms had the worst performance.\n" }, { "role": "assistant", "content": "The table shows that the worst performance was in the currency country category, while the highest percentage of out-of-vocabulary terms was in adjectives antonyms and performer action, indicating different categories. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/163.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Summary for 3CosMul and top-5 nearest vectors." ], "reasoning": "The table shows that the worst performance was in the currency country category, while the highest percentage of out-of-vocabulary terms was in adjectives antonyms and performer action, indicating different categories.", "statement": "The category with the highest percentage of out-of-vocabulary terms had the worst performance.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Word similarity.'.\n" }, { "role": "assistant", "content": "The gr_def model had the highest Pearson correlation with human ratings of similarity, indicating it was the most accurate.According to Pearson correlation, gr def model had the highest correlation with human ratings of similarity." } ], "images": [ "scigen/train/164.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Word similarity." ], "reasoning": "The gr_def model had the highest Pearson correlation with human ratings of similarity, indicating it was the most accurate.", "description": "According to Pearson correlation, gr def model had the highest correlation with human ratings of similarity.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: Table 4: Word similarity.\nQuestion: Which model had the lowest p-value and what was its Pearson correlation?\n" }, { "role": "assistant", "content": "To find the model with the lowest p-value, we compare the p-values of all models. The gr_def model has the lowest p-value of 3.1E-35. Its Pearson correlation is 0.6042. So, the answer is {\"answer\": \"gr_def with Pearson correlation 0.6042\"}." } ], "images": [ "scigen/train/164.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Word similarity." ], "reasoning": "To find the model with the lowest p-value, we compare the p-values of all models. The gr_def model has the lowest p-value of 3.1E-35. Its Pearson correlation is 0.6042.", "question": "Which model had the lowest p-value and what was its Pearson correlation?", "answer": "So, the answer is {\"answer\": \"gr_def with Pearson correlation 0.6042\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The gr_neg10 model had a higher Pearson correlation than the wiki.el model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe gr_neg10 model had a higher Pearson correlation than the wiki.el model.\n" }, { "role": "assistant", "content": "The table shows that the Pearson correlation for gr_neg10 is 0.5973, while for wiki.el it is 0.5812. Therefore, the statement is correct. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/164.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Word similarity." ], "reasoning": "The table shows that the Pearson correlation for gr_neg10 is 0.5973, while for wiki.el it is 0.5812. Therefore, the statement is correct.", "statement": "The gr_neg10 model had a higher Pearson correlation than the wiki.el model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 7: In-domain and out-of-domain evaluations for the pt and wb genres of the CoNLL test set. The highest scores are boldfaced.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "+EPM generalizes best and significantly outperforms the ensemble model of e2e-coref in out-of-domain evaluations.\"+EPM\" generalizes best, and in out-ofdomain evaluations, it considerably outperforms the ensemble model of e2e-coref," } ], "images": [ "scigen/train/165.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: In-domain and out-of-domain evaluations for the pt and wb genres of the CoNLL test set. The highest scores are boldfaced." ], "reasoning": "+EPM generalizes best and significantly outperforms the ensemble model of e2e-coref in out-of-domain evaluations.", "description": "\"+EPM\" generalizes best, and in out-ofdomain evaluations, it considerably outperforms the ensemble model of e2e-coref,", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nTable title: Table 7: In-domain and out-of-domain evaluations for the pt and wb genres of the CoNLL test set. The highest scores are boldfaced.\n\nQuestion: Which model has the highest score in in-domain CoNLL for pt (Bible)?\n" }, { "role": "assistant", "content": "To find the highest score in in-domain CoNLL for pt (Bible), compare the scores of all models in that column. The highest score is 78.80 by the e2e-coref ensemble model. So, the answer is {\"answer\": \"78.80\"}" } ], "images": [ "scigen/train/165.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: In-domain and out-of-domain evaluations for the pt and wb genres of the CoNLL test set. The highest scores are boldfaced." ], "reasoning": "To find the highest score in in-domain CoNLL for pt (Bible), compare the scores of all models in that column. The highest score is 78.80 by the e2e-coref ensemble model.", "question": "Which model has the highest score in in-domain CoNLL for pt (Bible)?", "answer": "So, the answer is {\"answer\": \"78.80\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\n+EPM outperforms the ranking model in out-of-domain LEA for wb (weblog).\n" }, { "role": "assistant", "content": "In the out-of-domain LEA for wb (weblog) column, the +EPM model has a score of 53.78, while the ranking model has a score of 48.74. Therefore, +EPM outperforms the ranking model. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/165.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: In-domain and out-of-domain evaluations for the pt and wb genres of the CoNLL test set. The highest scores are boldfaced." ], "reasoning": "In the out-of-domain LEA for wb (weblog) column, the +EPM model has a score of 53.78, while the ranking model has a score of 48.74. Therefore, +EPM outperforms the ranking model.", "statement": "+EPM outperforms the ranking model in out-of-domain LEA for wb (weblog).", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: Impact of different EPM feature groups on the CoNLL development set.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Pairwise features have a significant impact when combined with other EPM feature-values, while POS and named entity tags have the least effect.The POS and named entity tags have the least and the pairwise features have the most significant effect.. The results of \"-pairwise\" compared to \"+pairwise\" show that pairwise feature-values have a significant impact, but only when they are considered in combination with other EPM feature-values." } ], "images": [ "scigen/train/166.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Impact of different EPM feature groups on the CoNLL development set." ], "reasoning": "Pairwise features have a significant impact when combined with other EPM feature-values, while POS and named entity tags have the least effect.", "description": "The POS and named entity tags have the least and the pairwise features have the most significant effect.. The results of \"-pairwise\" compared to \"+pairwise\" show that pairwise feature-values have a significant impact, but only when they are considered in combination with other EPM feature-values.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 5: Impact of different EPM feature groups on the CoNLL development set.' in mind, break down and resolve the question below step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhat is the effect of removing pairwise features on the B3 metric?\n" }, { "role": "assistant", "content": "By comparing the B3 values for '+EPM' and '-pairwise', we see that removing pairwise features decreases the B3 value from 65.03 to 64.55. So, the answer is {\"answer\": \"64.55\"}" } ], "images": [ "scigen/train/166.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Impact of different EPM feature groups on the CoNLL development set." ], "reasoning": "By comparing the B3 values for '+EPM' and '-pairwise', we see that removing pairwise features decreases the B3 value from 65.03 to 64.55.", "question": "What is the effect of removing pairwise features on the B3 metric?", "answer": "So, the answer is {\"answer\": \"64.55\"}" }, { "messages": [ { "role": "user", "content": "Removing POS features has the least impact on the CoNLL metric. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nRemoving POS features has the least impact on the CoNLL metric.\n" }, { "role": "assistant", "content": "The CoNLL value for '-POS' is 66.89, which is higher than the values for '-pairwise', '-type', '-dep', and '-NER', indicating that removing POS features does not have the least impact. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/166.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Impact of different EPM feature groups on the CoNLL development set." ], "reasoning": "The CoNLL value for '-POS' is 66.89, which is higher than the values for '-pairwise', '-type', '-dep', and '-NER', indicating that removing POS features does not have the least impact.", "statement": "Removing POS features has the least impact on the CoNLL metric.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Out-of-domain evaluation on the WikiCoref dataset. The highest F1 scores are boldfaced.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Incorporating EPM feature-values improves performance in three points and achieves on-par performance with G&L.Incorporating EPM feature-values improves the three points. performance by about. it achieves onpar performance with that of \"G&L\"." } ], "images": [ "scigen/train/167.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Out-of-domain evaluation on the WikiCoref dataset. The highest F1 scores are boldfaced." ], "reasoning": "Incorporating EPM feature-values improves performance in three points and achieves on-par performance with G&L.", "description": "Incorporating EPM feature-values improves the three points. performance by about. it achieves onpar performance with that of \"G&L\".", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 6: Out-of-domain evaluation on the WikiCoref dataset. The highest F1 scores are boldfaced.' and methodically tackle the question that follows. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the CoNLL score for deep-coref with EPM feature-values?\n" }, { "role": "assistant", "content": "To find the CoNLL score for deep-coref with EPM feature-values, locate the row labeled '+EPM' under 'deep-coref' and find the value in the 'CoNLL' column. So, the answer is {\"answer\": \"55.30\"}" } ], "images": [ "scigen/train/167.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Out-of-domain evaluation on the WikiCoref dataset. The highest F1 scores are boldfaced." ], "reasoning": "To find the CoNLL score for deep-coref with EPM feature-values, locate the row labeled '+EPM' under 'deep-coref' and find the value in the 'CoNLL' column.", "question": "What is the CoNLL score for deep-coref with EPM feature-values?", "answer": "So, the answer is {\"answer\": \"55.30\"}" }, { "messages": [ { "role": "user", "content": "The deep-coref model with EPM feature-values has a higher CEAF_e F1 score than the e2e ensemble model. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe deep-coref model with EPM feature-values has a higher CEAF_e F1 score than the e2e ensemble model.\n" }, { "role": "assistant", "content": "Compare the CEAF_e F1 scores of the deep-coref model with EPM feature-values and the e2e ensemble model. The deep-coref model with EPM feature-values has a CEAF_e F1 score of 49.05, while the e2e ensemble model has a CEAF_e F1 score of 44.35. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/167.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Out-of-domain evaluation on the WikiCoref dataset. The highest F1 scores are boldfaced." ], "reasoning": "Compare the CEAF_e F1 scores of the deep-coref model with EPM feature-values and the e2e ensemble model. The deep-coref model with EPM feature-values has a CEAF_e F1 score of 49.05, while the e2e ensemble model has a CEAF_e F1 score of 44.35.", "statement": "The deep-coref model with EPM feature-values has a higher CEAF_e F1 score than the e2e ensemble model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Impact of linguistic features on deep-coref models on the CoNLL development set.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Incorporating linguistic features narrows the performance gap between 'top-pairs' and 'ranking', but does not significantly improve over 'ranking'.We observe that incorporating all the linguistic features bridges the gap between the performance of \"top-pairs\" and \"ranking\".. However, it does not improve significantly over \"ranking\"." } ], "images": [ "scigen/train/168.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Impact of linguistic features on deep-coref models on the CoNLL development set." ], "reasoning": "Incorporating linguistic features narrows the performance gap between 'top-pairs' and 'ranking', but does not significantly improve over 'ranking'.", "description": "We observe that incorporating all the linguistic features bridges the gap between the performance of \"top-pairs\" and \"ranking\".. However, it does not improve significantly over \"ranking\".", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 1: Impact of linguistic features on deep-coref models on the CoNLL development set.', unfold the steps to solve the question given next. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nQuestion: What is the CoNLL score for 'ranking' with linguistic features?\n" }, { "role": "assistant", "content": "To find the CoNLL score for 'ranking' with linguistic features, look at the row labeled '+linguistic' under 'ranking' and find the value in the CoNLL column. So, the answer is {\"answer\": \"66.17\"}." } ], "images": [ "scigen/train/168.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Impact of linguistic features on deep-coref models on the CoNLL development set." ], "reasoning": "To find the CoNLL score for 'ranking' with linguistic features, look at the row labeled '+linguistic' under 'ranking' and find the value in the CoNLL column.", "question": "What is the CoNLL score for 'ranking' with linguistic features?", "answer": "So, the answer is {\"answer\": \"66.17\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe CEAF_e score for 'top-pairs' with linguistic features is higher than for 'ranking' without linguistic features.\n" }, { "role": "assistant", "content": "To verify this statement, compare the CEAF_e score for 'top-pairs' with linguistic features (60.19) with the CEAF_e score for 'ranking' without linguistic features (59.73). The score for 'top-pairs' with linguistic features is indeed higher. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/168.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Impact of linguistic features on deep-coref models on the CoNLL development set." ], "reasoning": "To verify this statement, compare the CEAF_e score for 'top-pairs' with linguistic features (60.19) with the CEAF_e score for 'ranking' without linguistic features (59.73). The score for 'top-pairs' with linguistic features is indeed higher.", "statement": "The CEAF_e score for 'top-pairs' with linguistic features is higher than for 'ranking' without linguistic features.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Out-of-domain evaluation of deep-coref models on the WikiCoref dataset.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The CoNLL score improves by 0.5 percentage points when comparing the 'ranking' method to the '+linguistic' method, indicating a slight impact on generalization.We observe that the impact on generalization is also not notable, i.e. the CoNLL score improves only by 0.5pp over \"ranking\"." } ], "images": [ "scigen/train/169.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Out-of-domain evaluation of deep-coref models on the WikiCoref dataset." ], "reasoning": "The CoNLL score improves by 0.5 percentage points when comparing the 'ranking' method to the '+linguistic' method, indicating a slight impact on generalization.", "description": "We observe that the impact on generalization is also not notable, i.e. the CoNLL score improves only by 0.5pp over \"ranking\".", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 2: Out-of-domain evaluation of deep-coref models on the WikiCoref dataset.', unfold the question presented below, detailing every step of your calculation. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the difference in the LEA score between the 'ranking' and '+linguistic' methods?\n" }, { "role": "assistant", "content": "To find the difference in the LEA score between the 'ranking' and '+linguistic' methods, subtract the LEA score of 'ranking' (44.40) from the LEA score of '+linguistic' (45.66). So, the answer is {\"answer\": \"1.26\"}." } ], "images": [ "scigen/train/169.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Out-of-domain evaluation of deep-coref models on the WikiCoref dataset." ], "reasoning": "To find the difference in the LEA score between the 'ranking' and '+linguistic' methods, subtract the LEA score of 'ranking' (44.40) from the LEA score of '+linguistic' (45.66).", "question": "What is the difference in the LEA score between the 'ranking' and '+linguistic' methods?", "answer": "So, the answer is {\"answer\": \"1.26\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe B3 score for the 'top-pairs' method is higher than the 'ranking' method.\n" }, { "role": "assistant", "content": "The B3 score for the 'top-pairs' method is 48.42, which is slightly lower than the B3 score for the 'ranking' method, which is 48.43. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/169.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Out-of-domain evaluation of deep-coref models on the WikiCoref dataset." ], "reasoning": "The B3 score for the 'top-pairs' method is 48.42, which is slightly lower than the B3 score for the 'ranking' method, which is 48.43.", "statement": "The B3 score for the 'top-pairs' method is higher than the 'ranking' method.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4: Comparisons on the CoNLL test set. The F1 gains that are statistically significant: (1) “+EPM” compared to “top-pairs”, “ranking” and “JIM”, (2) “+EPM” compared to “reinforce” based on MUC, B3 and LEA, (3) “single” compared to “+EPM” based on MUC and B3, and (4) “ensemble” compared to other systems. Significance is measured based on the approximate randomization test (p<0.05) Noreen (1989).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The +EPM model outperforms recent state-of-the-art coreference models on the CoNLL test set, achieving better performance with fewer feature-values compared to JIM.The performance of the \"+EPM\" model compared to recent state-of-the-art coreference models on the CoNLL test set is presented in Table 4.. EPM feature-values result in significantly better performance than those of JIM while the number of EPM feature-values is considerably less than JIM." } ], "images": [ "scigen/train/170.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparisons on the CoNLL test set. The F1 gains that are statistically significant: (1) “+EPM” compared to “top-pairs”, “ranking” and “JIM”, (2) “+EPM” compared to “reinforce” based on MUC, B3 and LEA, (3) “single” compared to “+EPM” based on MUC and B3, and (4) “ensemble” compared to other systems. Significance is measured based on the approximate randomization test (p<0.05) Noreen (1989)." ], "reasoning": "The +EPM model outperforms recent state-of-the-art coreference models on the CoNLL test set, achieving better performance with fewer feature-values compared to JIM.", "description": "The performance of the \"+EPM\" model compared to recent state-of-the-art coreference models on the CoNLL test set is presented in Table 4.. EPM feature-values result in significantly better performance than those of JIM while the number of EPM feature-values is considerably less than JIM.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 4: Comparisons on the CoNLL test set. The F1 gains that are statistically significant: (1) “+EPM” compared to “top-pairs”, “ranking” and “JIM”, (2) “+EPM” compared to “reinforce” based on MUC, B3 and LEA, (3) “single” compared to “+EPM” based on MUC and B3, and (4) “ensemble” compared to other systems. Significance is measured based on the approximate randomization test (p<0.05) Noreen (1989).', and tackle the question below in a sequential manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest CoNLL score?\n" }, { "role": "assistant", "content": "To find the model with the highest CoNLL score, compare the CoNLL scores of all models listed in the table. The ensemble model has the highest CoNLL score of 68.83. So, the answer is {\"answer\": \"ensemble\"}." } ], "images": [ "scigen/train/170.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparisons on the CoNLL test set. The F1 gains that are statistically significant: (1) “+EPM” compared to “top-pairs”, “ranking” and “JIM”, (2) “+EPM” compared to “reinforce” based on MUC, B3 and LEA, (3) “single” compared to “+EPM” based on MUC and B3, and (4) “ensemble” compared to other systems. Significance is measured based on the approximate randomization test (p<0.05) Noreen (1989)." ], "reasoning": "To find the model with the highest CoNLL score, compare the CoNLL scores of all models listed in the table. The ensemble model has the highest CoNLL score of 68.83.", "question": "Which model has the highest CoNLL score?", "answer": "So, the answer is {\"answer\": \"ensemble\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe +EPM model has a higher B3 F1 score than the ranking model.\n" }, { "role": "assistant", "content": "The table shows that the +EPM model has a B3 F1 score of 69.70, while the ranking model has a B3 F1 score of 69.26. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/170.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparisons on the CoNLL test set. The F1 gains that are statistically significant: (1) “+EPM” compared to “top-pairs”, “ranking” and “JIM”, (2) “+EPM” compared to “reinforce” based on MUC, B3 and LEA, (3) “single” compared to “+EPM” based on MUC and B3, and (4) “ensemble” compared to other systems. Significance is measured based on the approximate randomization test (p<0.05) Noreen (1989)." ], "reasoning": "The table shows that the +EPM model has a B3 F1 score of 69.70, while the ranking model has a B3 F1 score of 69.26. Therefore, the statement is supported.", "statement": "The +EPM model has a higher B3 F1 score than the ranking model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Lexicon member coverage (%)'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Lemmatization improves coverage by allowing more targets to exceed the frequency threshold, while POS-disambiguation reduces coverage by fragmenting the vocabulary.We first analyze the coverage of the VSMs in question with respect to the lexica at hand, see Table 4. For brevity we only report coverage on w2 contexts. lemmatization allows more targets to exceed the SGNS frequency threshold, which results in consistently better coverage. POS-disambiguation, in turn, fragments the vocabulary and consistently reduces the coverage with the effect being less pronounced for lemmatized targets. WN-N shows low coverage containing many low-frequency members." } ], "images": [ "scigen/train/171.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Lexicon member coverage (%)" ], "reasoning": "Lemmatization improves coverage by allowing more targets to exceed the frequency threshold, while POS-disambiguation reduces coverage by fragmenting the vocabulary.", "description": "We first analyze the coverage of the VSMs in question with respect to the lexica at hand, see Table 4. For brevity we only report coverage on w2 contexts. lemmatization allows more targets to exceed the SGNS frequency threshold, which results in consistently better coverage. POS-disambiguation, in turn, fragments the vocabulary and consistently reduces the coverage with the effect being less pronounced for lemmatized targets. WN-N shows low coverage containing many low-frequency members.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 4: Lexicon member coverage (%)'. Solve the following question based on the table.\n\nQuestion: Which target has the highest coverage in the VN lexicon?\nLet's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the target with the highest coverage in the VN lexicon, we look at the VN column and identify the highest value, which is 88 for the lemma target. So, the answer is {\"answer\": \"lemma\"}." } ], "images": [ "scigen/train/171.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Lexicon member coverage (%)" ], "reasoning": "To find the target with the highest coverage in the VN lexicon, we look at the VN column and identify the highest value, which is 88 for the lemma target.", "question": "Which target has the highest coverage in the VN lexicon?", "answer": "So, the answer is {\"answer\": \"lemma\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe shared target has the lowest coverage in the WN-V lexicon.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the values in the WN-V column. The shared target has a coverage of 39, which is the lowest value in that column. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/171.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Lexicon member coverage (%)" ], "reasoning": "To verify this statement, we compare the values in the WN-V column. The shared target has a coverage of 39, which is the lowest value in that column.", "statement": "The shared target has the lowest coverage in the WN-V lexicon.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: Benchmark performance, Spearman’s ρ. SGNS results with * taken from [morphfit]. Best results per column (benchmark) annotated for our setup only.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Lemmatized targets generally perform better, especially on SimVerb, and adding POS information benefits SimVerb and SimLex verb performance. Using dep-W contexts is beneficial for both datasets, and Morph-Fitting, particularly the Repel stage, significantly improves performance on SimLex.Table 1 summarizes the performance of the VSMs in question on similarity benchmarks.. Lemmatized targets generally perform better, with the boost being more pronounced on SimVerb.. Adding POS information benefits the SimVerb and SimLex verb performance,. the type.POS targets show a considerable performance drop on SimVerb and SimLex verbs.. Using dep-W contexts proves beneficial for both datasets. We provide the Morph-Fitting scores (Vuli´c et al., 2017b) as a state-of-the-art reference;. This approach uses word type-based VSMs specialized via Morph-Fitting (MFit), which can be seen as an alternative to lemmatization. Morph-Fitting consists of two stages: the Attract (A) stage brings word forms of the same word closer in the VSM, while the Repel (R) stage sets the derivational antonyms further apart. Lemma grouping is similar to the Attract stage.. as the comparison of MFit-A and -AR shows, a major part of the Morph-Fitting performance gain on SimLex comes from the derivational Repel stage" } ], "images": [ "scigen/train/172.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Benchmark performance, Spearman’s ρ. SGNS results with * taken from [morphfit]. Best results per column (benchmark) annotated for our setup only." ], "reasoning": "Lemmatized targets generally perform better, especially on SimVerb, and adding POS information benefits SimVerb and SimLex verb performance. Using dep-W contexts is beneficial for both datasets, and Morph-Fitting, particularly the Repel stage, significantly improves performance on SimLex.", "description": "Table 1 summarizes the performance of the VSMs in question on similarity benchmarks.. Lemmatized targets generally perform better, with the boost being more pronounced on SimVerb.. Adding POS information benefits the SimVerb and SimLex verb performance,. the type.POS targets show a considerable performance drop on SimVerb and SimLex verbs.. Using dep-W contexts proves beneficial for both datasets. We provide the Morph-Fitting scores (Vuli´c et al., 2017b) as a state-of-the-art reference;. This approach uses word type-based VSMs specialized via Morph-Fitting (MFit), which can be seen as an alternative to lemmatization. Morph-Fitting consists of two stages: the Attract (A) stage brings word forms of the same word closer in the VSM, while the Repel (R) stage sets the derivational antonyms further apart. Lemma grouping is similar to the Attract stage.. as the comparison of MFit-A and -AR shows, a major part of the Morph-Fitting performance gain on SimLex comes from the derivational Repel stage", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 1: Benchmark performance, Spearman’s ρ. SGNS results with * taken from [morphfit]. Best results per column (benchmark) annotated for our setup only.'. Solve the following question based on the table.\n\nQuestion: Which target type shows the highest performance on SimLex verbs when using dep-W contexts?\nLet's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest performance on SimLex verbs using dep-W contexts, look at the 'Context: dep-W' column for 'w2 SimLex V'. The highest value is 0.436, which corresponds to '* type MFit-AR'. So, the answer is {\"answer\": \"* type MFit-AR\"}." } ], "images": [ "scigen/train/172.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Benchmark performance, Spearman’s ρ. SGNS results with * taken from [morphfit]. Best results per column (benchmark) annotated for our setup only." ], "reasoning": "To find the highest performance on SimLex verbs using dep-W contexts, look at the 'Context: dep-W' column for 'w2 SimLex V'. The highest value is 0.436, which corresponds to '* type MFit-AR'.", "question": "Which target type shows the highest performance on SimLex verbs when using dep-W contexts?", "answer": "So, the answer is {\"answer\": \"* type MFit-AR\"}." }, { "messages": [ { "role": "user", "content": "Lemmatized targets perform better than type targets on SimVerb when using w2 contexts. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nLemmatized targets perform better than type targets on SimVerb when using w2 contexts.\n" }, { "role": "assistant", "content": "To verify this statement, compare the performance of 'lemma' and 'type' targets on SimVerb using w2 contexts. The 'lemma' target has a score of 0.400, while the 'type' target has a score of 0.307. Since 0.400 is greater than 0.307, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/172.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Benchmark performance, Spearman’s ρ. SGNS results with * taken from [morphfit]. Best results per column (benchmark) annotated for our setup only." ], "reasoning": "To verify this statement, compare the performance of 'lemma' and 'type' targets on SimVerb using w2 contexts. The 'lemma' target has a score of 0.400, while the 'type' target has a score of 0.307. Since 0.400 is greater than 0.307, the statement is supported.", "statement": "Lemmatized targets perform better than type targets on SimVerb when using w2 contexts.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: WCS performance, shared vocabulary, k=1. Best results across VSMs in bold.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Lemma-based targets significantly outperform type-based targets in terms of F-measure in all cases, with POS disambiguation yielding better F scores on lemmatized targets for VN and borderline significance for WN-N and WN-V.Table 5 provides exact scores for reference.. Note that the shared vocabulary setup puts the type and type.POS VSMs at advantage since it eliminates the effect of low coverage. Still, lemma-based targets significantly7 (p ≤ .005) outperform type-based targets in terms of F-measure in all cases. For window-based w2 contexts POS disambiguation yields significantly better F scores on lemmatized targets for VN (p ≤ .005) with borderline significance for WN-N and WN-V (p ≈ .05). When dependencybased dep contexts are used, the effect of POS disambiguation is only statistically significant on type targets for VN (p ≤ .005) and on lemma-based targets for WN-V (p ≤ .005).. Lemma-based targets without POS disambiguation perform best on WN-N when dependency-based contexts are used; however, the difference to lemmatized and disambiguated targets is not statistically significant (p > .1)." } ], "images": [ "scigen/train/173.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: WCS performance, shared vocabulary, k=1. Best results across VSMs in bold." ], "reasoning": "Lemma-based targets significantly outperform type-based targets in terms of F-measure in all cases, with POS disambiguation yielding better F scores on lemmatized targets for VN and borderline significance for WN-N and WN-V.", "description": "Table 5 provides exact scores for reference.. Note that the shared vocabulary setup puts the type and type.POS VSMs at advantage since it eliminates the effect of low coverage. Still, lemma-based targets significantly7 (p ≤ .005) outperform type-based targets in terms of F-measure in all cases. For window-based w2 contexts POS disambiguation yields significantly better F scores on lemmatized targets for VN (p ≤ .005) with borderline significance for WN-N and WN-V (p ≈ .05). When dependencybased dep contexts are used, the effect of POS disambiguation is only statistically significant on type targets for VN (p ≤ .005) and on lemma-based targets for WN-V (p ≤ .005).. Lemma-based targets without POS disambiguation perform best on WN-N when dependency-based contexts are used; however, the difference to lemmatized and disambiguated targets is not statistically significant (p > .1).", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: WCS performance, shared vocabulary, k=1. Best results across VSMs in bold.'.\n\nQuestion: Which context and target combination yields the highest F-measure for WN-V?\n" }, { "role": "assistant", "content": "To find the highest F-measure for WN-V, we need to compare the F-measure values for WN-V across all context and target combinations. The highest value is 0.565 for the combination of dependency-based context and lemma+POS target. So, the answer is {\"answer\": \"0.565\"}." } ], "images": [ "scigen/train/173.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: WCS performance, shared vocabulary, k=1. Best results across VSMs in bold." ], "reasoning": "To find the highest F-measure for WN-V, we need to compare the F-measure values for WN-V across all context and target combinations. The highest value is 0.565 for the combination of dependency-based context and lemma+POS target.", "question": "Which context and target combination yields the highest F-measure for WN-V?", "answer": "So, the answer is {\"answer\": \"0.565\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nLemma-based targets without POS disambiguation perform best on WN-N when dependency-based contexts are used.\n" }, { "role": "assistant", "content": "The table shows that for WN-N with dependency-based contexts, lemma-based targets without POS disambiguation have an F-measure of 0.691, which is higher than the other target types. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/173.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: WCS performance, shared vocabulary, k=1. Best results across VSMs in bold." ], "reasoning": "The table shows that for WN-N with dependency-based contexts, lemma-based targets without POS disambiguation have an F-measure of 0.691, which is higher than the other target types.", "statement": "Lemma-based targets without POS disambiguation perform best on WN-N when dependency-based contexts are used.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Performance of supervised learning models with different features.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Word embeddings derived from Wiki-PubMed-PMC and BoC features outperform GloVe-based embeddings and BoW features, with Lin-SVM being the best classifier.Word embeddings derived from Wiki-PubMed-PMC outperform GloVe-based embeddings (Table 1). The models using BoC outperform models using BoW as well as ASM features.. Wikipedia-PubMed-PMC embeddings (Moen and Ananiadou, 2013) outperforms GloVe (Mikolov et al., 2013a) in the extraction of most relation types (Table 1). the combination feature of BoC and sentence embeddings outperforms sentence embeddings alone, but do not exceed the upper boundary of BoC feature, in which again demonstrating the competitiveness of BoC feature.. Lin-SVM outperforms other classifiers in extracting most relations. The feed-forward ANN displays significant over-fitting across all relation types, as the performance decreases when increasing the training epochs." } ], "images": [ "scigen/train/174.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance of supervised learning models with different features." ], "reasoning": "Word embeddings derived from Wiki-PubMed-PMC and BoC features outperform GloVe-based embeddings and BoW features, with Lin-SVM being the best classifier.", "description": "Word embeddings derived from Wiki-PubMed-PMC outperform GloVe-based embeddings (Table 1). The models using BoC outperform models using BoW as well as ASM features.. Wikipedia-PubMed-PMC embeddings (Moen and Ananiadou, 2013) outperforms GloVe (Mikolov et al., 2013a) in the extraction of most relation types (Table 1). the combination feature of BoC and sentence embeddings outperforms sentence embeddings alone, but do not exceed the upper boundary of BoC feature, in which again demonstrating the competitiveness of BoC feature.. Lin-SVM outperforms other classifiers in extracting most relations. The feed-forward ANN displays significant over-fitting across all relation types, as the performance decreases when increasing the training epochs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 1: Performance of supervised learning models with different features.', unfold the question presented below, detailing every step of your calculation. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich feature and classifier combination achieves the highest F1 score?\n" }, { "role": "assistant", "content": "By examining the table, the highest F1 score is achieved by the combination of BoC (Wiki-PubMed-PMC) feature and SVM classifier, with an F1 score of 0.93. So, the answer is {\"answer\": \"BoC (Wiki-PubMed-PMC) and SVM\"}." } ], "images": [ "scigen/train/174.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance of supervised learning models with different features." ], "reasoning": "By examining the table, the highest F1 score is achieved by the combination of BoC (Wiki-PubMed-PMC) feature and SVM classifier, with an F1 score of 0.93.", "question": "Which feature and classifier combination achieves the highest F1 score?", "answer": "So, the answer is {\"answer\": \"BoC (Wiki-PubMed-PMC) and SVM\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe combination of BoC (Wiki-PubMed-PMC) and SVM achieves the highest F1 score.\n" }, { "role": "assistant", "content": "The table shows that the combination of BoC (Wiki-PubMed-PMC) and SVM achieves an F1 score of 0.93, which is the highest among all combinations. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/174.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance of supervised learning models with different features." ], "reasoning": "The table shows that the combination of BoC (Wiki-PubMed-PMC) and SVM achieves an F1 score of 0.93, which is the highest among all combinations.", "statement": "The combination of BoC (Wiki-PubMed-PMC) and SVM achieves the highest F1 score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: F1 score results per relation type of the best performing models.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The intra-sentential co-occurrence baseline (ρ = 0) achieves the highest F1 score for RecurLink, indicating strong concentration of semantic relations within sentence boundaries.intra-sentential cooccurrence baseline outperforms other approaches which allow boundary expansion.. As the results of applying the co-occurrence baseline (ρ = 0) shows (Table 2), the semantic relations in this data are strongly concentrated within a sentence boundary, especially for the relation of RecurLink, with an F1 of 1.0. The machine learning approaches based on BoC lexical features effectively complement the deficiency of cross-sentence relation extraction." } ], "images": [ "scigen/train/175.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: F1 score results per relation type of the best performing models." ], "reasoning": "The intra-sentential co-occurrence baseline (ρ = 0) achieves the highest F1 score for RecurLink, indicating strong concentration of semantic relations within sentence boundaries.", "description": "intra-sentential cooccurrence baseline outperforms other approaches which allow boundary expansion.. As the results of applying the co-occurrence baseline (ρ = 0) shows (Table 2), the semantic relations in this data are strongly concentrated within a sentence boundary, especially for the relation of RecurLink, with an F1 of 1.0. The machine learning approaches based on BoC lexical features effectively complement the deficiency of cross-sentence relation extraction.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: F1 score results per relation type of the best performing models.' to sequentially solve the question presented below. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich relation type has the highest F1 score using the intra-sentential co-occurrence baseline with ρ = 0?\n" }, { "role": "assistant", "content": "By examining the table, the relation type RecurLink (YR, YR/CF) has the highest F1 score of 1.0 using the intra-sentential co-occurrence baseline with ρ = 0. So, the answer is {\"answer\": \"RecurLink\"}" } ], "images": [ "scigen/train/175.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: F1 score results per relation type of the best performing models." ], "reasoning": "By examining the table, the relation type RecurLink (YR, YR/CF) has the highest F1 score of 1.0 using the intra-sentential co-occurrence baseline with ρ = 0.", "question": "Which relation type has the highest F1 score using the intra-sentential co-occurrence baseline with ρ = 0?", "answer": "So, the answer is {\"answer\": \"RecurLink\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe intra-sentential co-occurrence baseline with ρ = 0 achieves an F1 score of 0.90 for the relation type NextReview.\n" }, { "role": "assistant", "content": "The table shows that the intra-sentential co-occurrence baseline with ρ = 0 achieves an F1 score of 0.90 for the relation type NextReview. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/175.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: F1 score results per relation type of the best performing models." ], "reasoning": "The table shows that the intra-sentential co-occurrence baseline with ρ = 0 achieves an F1 score of 0.90 for the relation type NextReview.", "statement": "The intra-sentential co-occurrence baseline with ρ = 0 achieves an F1 score of 0.90 for the relation type NextReview.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Comparison of our Multi-News dataset to other MDS datasets as well as an SDS dataset used as training data for MDS (CNNDM). Training, validation and testing size splits (article(s) to summary) are provided when applicable. Statistics for multi-document inputs are calculated on the concatenation of all input sources.'.\n" }, { "role": "assistant", "content": "The Multi-News dataset has a significantly larger number of examples compared to other MDS datasets, and its summaries are notably longer, averaging about 260 words.Table 3 compares Multi-News to other news datasets used in experiments below. We choose to compare Multi-News with DUC data from 2003 and 2004 and TAC 2011 data, which are typically used in multi-document settings. Additionally, we compare to the single-document CNNDM dataset, as this has been recently used in work which adapts SDS to MDS (Lebanoff et al., 2018). The number of examples in our Multi-News dataset is two orders of magnitude larger than previous MDS news data. The total number of words in the concatenated inputs is shorter than other MDS datasets, as those consist of 10 input documents, but larger than SDS datasets, as expected. Our summaries are notably longer than in other works, about 260 words on average." } ], "images": [ "scigen/train/176.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of our Multi-News dataset to other MDS datasets as well as an SDS dataset used as training data for MDS (CNNDM). Training, validation and testing size splits (article(s) to summary) are provided when applicable. Statistics for multi-document inputs are calculated on the concatenation of all input sources." ], "reasoning": "The Multi-News dataset has a significantly larger number of examples compared to other MDS datasets, and its summaries are notably longer, averaging about 260 words.", "description": "Table 3 compares Multi-News to other news datasets used in experiments below. We choose to compare Multi-News with DUC data from 2003 and 2004 and TAC 2011 data, which are typically used in multi-document settings. Additionally, we compare to the single-document CNNDM dataset, as this has been recently used in work which adapts SDS to MDS (Lebanoff et al., 2018). The number of examples in our Multi-News dataset is two orders of magnitude larger than previous MDS news data. The total number of words in the concatenated inputs is shorter than other MDS datasets, as those consist of 10 input documents, but larger than SDS datasets, as expected. Our summaries are notably longer than in other works, about 260 words on average.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich dataset has the highest average number of words per summary?\nSolve the above question based on the table titled 'Table 3: Comparison of our Multi-News dataset to other MDS datasets as well as an SDS dataset used as training data for MDS (CNNDM). Training, validation and testing size splits (article(s) to summary) are provided when applicable. Statistics for multi-document inputs are calculated on the concatenation of all input sources.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the '# words (summary)' column for each dataset, we see that Multi-News has the highest average number of words per summary at 263.66. So, the answer is {\"answer\": \"Multi-News\"}" } ], "images": [ "scigen/train/176.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of our Multi-News dataset to other MDS datasets as well as an SDS dataset used as training data for MDS (CNNDM). Training, validation and testing size splits (article(s) to summary) are provided when applicable. Statistics for multi-document inputs are calculated on the concatenation of all input sources." ], "reasoning": "By comparing the '# words (summary)' column for each dataset, we see that Multi-News has the highest average number of words per summary at 263.66.", "question": "Which dataset has the highest average number of words per summary?", "answer": "So, the answer is {\"answer\": \"Multi-News\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The CNNDM dataset has the smallest vocabulary size among the datasets listed. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe CNNDM dataset has the smallest vocabulary size among the datasets listed.\n" }, { "role": "assistant", "content": "By comparing the 'vocab size' column for each dataset, we see that CNNDM has a vocabulary size of 717,951, which is not the smallest. The smallest vocabulary size is for DUC03+04 at 19,734. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/176.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of our Multi-News dataset to other MDS datasets as well as an SDS dataset used as training data for MDS (CNNDM). Training, validation and testing size splits (article(s) to summary) are provided when applicable. Statistics for multi-document inputs are calculated on the concatenation of all input sources." ], "reasoning": "By comparing the 'vocab size' column for each dataset, we see that CNNDM has a vocabulary size of 717,951, which is not the smallest. The smallest vocabulary size is for DUC03+04 at 19,734.", "statement": "The CNNDM dataset has the smallest vocabulary size among the datasets listed.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Percentage of n-grams in summaries which do not appear in the input documents , a measure of the abstractiveness, in relevant datasets.'.\n" }, { "role": "assistant", "content": "The table shows that smaller MDS datasets are more abstractive, with Multi-News being comparable to SDS datasets in terms of abstractiveness.We report the percentage of n-grams in the gold summaries which do not appear in the input documents as a measure of how abstractive our summaries are in Table 4. As the table shows, the smaller MDS datasets tend to be more abstractive, but Multi-News is comparable and similar to the abstractiveness of SDS datasets. Grusky et al. (2018) additionally define three measures of the extractive nature of a dataset, which we use here for a comparison." } ], "images": [ "scigen/train/177.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Percentage of n-grams in summaries which do not appear in the input documents , a measure of the abstractiveness, in relevant datasets." ], "reasoning": "The table shows that smaller MDS datasets are more abstractive, with Multi-News being comparable to SDS datasets in terms of abstractiveness.", "description": "We report the percentage of n-grams in the gold summaries which do not appear in the input documents as a measure of how abstractive our summaries are in Table 4. As the table shows, the smaller MDS datasets tend to be more abstractive, but Multi-News is comparable and similar to the abstractiveness of SDS datasets. Grusky et al. (2018) additionally define three measures of the extractive nature of a dataset, which we use here for a comparison.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Percentage of n-grams in summaries which do not appear in the input documents , a measure of the abstractiveness, in relevant datasets.\nQuestion: Which dataset has the highest percentage of novel 4-grams?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the dataset with the highest percentage of novel 4-grams, look at the 4-grams row and identify the highest value. The highest value is 96.18, which corresponds to the DUC03+04 dataset. So, the answer is {\"answer\": \"DUC03+04\"}." } ], "images": [ "scigen/train/177.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Percentage of n-grams in summaries which do not appear in the input documents , a measure of the abstractiveness, in relevant datasets." ], "reasoning": "To find the dataset with the highest percentage of novel 4-grams, look at the 4-grams row and identify the highest value. The highest value is 96.18, which corresponds to the DUC03+04 dataset.", "question": "Which dataset has the highest percentage of novel 4-grams?", "answer": "So, the answer is {\"answer\": \"DUC03+04\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The TAC11 dataset has a higher percentage of novel unigrams than the Multi-News dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe TAC11 dataset has a higher percentage of novel unigrams than the Multi-News dataset.\n" }, { "role": "assistant", "content": "The table shows that the percentage of novel unigrams for TAC11 is 16.65, while for Multi-News it is 17.76. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/177.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Percentage of n-grams in summaries which do not appear in the input documents , a measure of the abstractiveness, in relevant datasets." ], "reasoning": "The table shows that the percentage of novel unigrams for TAC11 is 16.65, while for Multi-News it is 17.76. Therefore, the statement is refuted.", "statement": "The TAC11 dataset has a higher percentage of novel unigrams than the Multi-News dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 6: ROUGE scores for models trained and tested on the Multi-News dataset.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Our model, Hi-MAP, outperforms PG-MMR on the Multi-News dataset and achieves the best scores in R-2 and R-SU, while the Transformer performs best in R-1.Our model outperforms PG-MMR when trained and tested on the Multi-News dataset. The Transformer performs best in terms of R-1 while Hi-MAP outperforms it on R-2 and R-SU. Also, we notice a drop in performance between PG-original, and PG-MMR (which takes the pre-trained PG-original and applies MMR on top of the model)." } ], "images": [ "scigen/train/178.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: ROUGE scores for models trained and tested on the Multi-News dataset." ], "reasoning": "Our model, Hi-MAP, outperforms PG-MMR on the Multi-News dataset and achieves the best scores in R-2 and R-SU, while the Transformer performs best in R-1.", "description": "Our model outperforms PG-MMR when trained and tested on the Multi-News dataset. The Transformer performs best in terms of R-1 while Hi-MAP outperforms it on R-2 and R-SU. Also, we notice a drop in performance between PG-original, and PG-MMR (which takes the pre-trained PG-original and applies MMR on top of the model).", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 6: ROUGE scores for models trained and tested on the Multi-News dataset.', unfold the question presented below, detailing every step of your calculation. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich method performs best in terms of R-1, and how does it compare to Hi-MAP in R-2 and R-SU?\n" }, { "role": "assistant", "content": "The CopyTransformer performs best in terms of R-1 with a score of 43.57. Comparing it to Hi-MAP, Hi-MAP scores higher in R-2 (14.89 vs. 14.03) and R-SU (17.41 vs. 17.37). So, the answer is {\"answer\": \"CopyTransformer\"}." } ], "images": [ "scigen/train/178.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: ROUGE scores for models trained and tested on the Multi-News dataset." ], "reasoning": "The CopyTransformer performs best in terms of R-1 with a score of 43.57. Comparing it to Hi-MAP, Hi-MAP scores higher in R-2 (14.89 vs. 14.03) and R-SU (17.41 vs. 17.37).", "question": "Which method performs best in terms of R-1, and how does it compare to Hi-MAP in R-2 and R-SU?", "answer": "So, the answer is {\"answer\": \"CopyTransformer\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nHi-MAP outperforms PG-MMR in all metrics.\n" }, { "role": "assistant", "content": "Hi-MAP has higher scores than PG-MMR in R-2 (14.89 vs. 12.36) and R-SU (17.41 vs. 15.87), but PG-MMR has a higher score in R-1 (40.55 vs. 43.47). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/178.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: ROUGE scores for models trained and tested on the Multi-News dataset." ], "reasoning": "Hi-MAP has higher scores than PG-MMR in R-2 (14.89 vs. 12.36) and R-SU (17.41 vs. 15.87), but PG-MMR has a higher score in R-1 (40.55 vs. 43.47).", "statement": "Hi-MAP outperforms PG-MMR in all metrics.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: Performance breakdown of the PRKGC+NS model. Derivation Precision denotes ROUGE-L F1 of generated NLDs.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "As the number of required derivation steps increases, the PRKGC+NS model's performance in predicting answer entities and generating correct NLDs decreases.As shown in Table 5, as the required derivation step increases, the PRKGC+NS model suffers from predicting answer entities and generating correct NLDs.. This indicates that the challenge of RC-QEDE is in how to extract relevant information from supporting documents and synthesize these multiple facts to derive an answer." } ], "images": [ "scigen/train/179.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Performance breakdown of the PRKGC+NS model. Derivation Precision denotes ROUGE-L F1 of generated NLDs." ], "reasoning": "As the number of required derivation steps increases, the PRKGC+NS model's performance in predicting answer entities and generating correct NLDs decreases.", "description": "As shown in Table 5, as the required derivation step increases, the PRKGC+NS model suffers from predicting answer entities and generating correct NLDs.. This indicates that the challenge of RC-QEDE is in how to extract relevant information from supporting documents and synthesize these multiple facts to derive an answer.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: Performance breakdown of the PRKGC+NS model. Derivation Precision denotes ROUGE-L F1 of generated NLDs.'.\n\nQuestion: What is the Answer Precision for 2 gold NLD steps?\n" }, { "role": "assistant", "content": "The table shows the Answer Precision for different numbers of gold NLD steps. For 2 gold NLD steps, the Answer Precision is 64.4. So, the answer is {\"answer\": \"64.4\"}" } ], "images": [ "scigen/train/179.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Performance breakdown of the PRKGC+NS model. Derivation Precision denotes ROUGE-L F1 of generated NLDs." ], "reasoning": "The table shows the Answer Precision for different numbers of gold NLD steps. For 2 gold NLD steps, the Answer Precision is 64.4.", "question": "What is the Answer Precision for 2 gold NLD steps?", "answer": "So, the answer is {\"answer\": \"64.4\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Derivation Precision for 3 gold NLD steps is higher than for 1 gold NLD step.\n" }, { "role": "assistant", "content": "The table shows that the Derivation Precision for 3 gold NLD steps is 41.3, which is higher than the 38.4 for 1 gold NLD step. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/179.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Performance breakdown of the PRKGC+NS model. Derivation Precision denotes ROUGE-L F1 of generated NLDs." ], "reasoning": "The table shows that the Derivation Precision for 3 gold NLD steps is 41.3, which is higher than the 38.4 for 1 gold NLD step.", "statement": "The Derivation Precision for 3 gold NLD steps is higher than for 1 gold NLD step.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Ratings of annotated NLDs by human judges.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The evaluation results show that while the annotated NLDs are of high quality in terms of Reachability and Derivability, the quality of 3-step NLDs is relatively lower, with 45.3% of them missing steps.The evaluation results shown in Table 2 indicate that the annotated NLDs are of high quality (Reachability), and each NLD is properly derived from supporting documents (Derivability).. On the other hand, we found the quality of 3-step NLDs is relatively lower than the others.. Crowdworkers found that 45.3% of 294 (out of 900) 3-step NLDs has missing steps to derive a statement." } ], "images": [ "scigen/train/180.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ratings of annotated NLDs by human judges." ], "reasoning": "The evaluation results show that while the annotated NLDs are of high quality in terms of Reachability and Derivability, the quality of 3-step NLDs is relatively lower, with 45.3% of them missing steps.", "description": "The evaluation results shown in Table 2 indicate that the annotated NLDs are of high quality (Reachability), and each NLD is properly derived from supporting documents (Derivability).. On the other hand, we found the quality of 3-step NLDs is relatively lower than the others.. Crowdworkers found that 45.3% of 294 (out of 900) 3-step NLDs has missing steps to derive a statement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Ratings of annotated NLDs by human judges.', answer the question below by showing each progressive step toward the answer. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the Reachability score for 3-step NLDs and how does it compare to the Reachability score for 1-step NLDs?\n" }, { "role": "assistant", "content": "The Reachability score for 3-step NLDs is 2.3, which is lower than the Reachability score for 1-step NLDs, which is 3.0. So, the answer is {\"answer\": \"2.3\"}" } ], "images": [ "scigen/train/180.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ratings of annotated NLDs by human judges." ], "reasoning": "The Reachability score for 3-step NLDs is 2.3, which is lower than the Reachability score for 1-step NLDs, which is 3.0.", "question": "What is the Reachability score for 3-step NLDs and how does it compare to the Reachability score for 1-step NLDs?", "answer": "So, the answer is {\"answer\": \"2.3\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Reachability score for 2-step NLDs is higher than that for 3-step NLDs.\n" }, { "role": "assistant", "content": "The table shows that the Reachability score for 2-step NLDs is 2.8, which is higher than the Reachability score for 3-step NLDs, which is 2.3. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/180.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ratings of annotated NLDs by human judges." ], "reasoning": "The table shows that the Reachability score for 2-step NLDs is 2.8, which is higher than the Reachability score for 3-step NLDs, which is 2.3.", "statement": "The Reachability score for 2-step NLDs is higher than that for 3-step NLDs.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4: Performance of RC-QEDE of our baseline models (see Section 2.1 for further details of each evaluation metrics). “NS” indicates the use of annotated NLDs as supervision (i.e. using Ld during training).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The PRKGC+NS model improves human interpretability and generalization ability of question answering compared to the PRKGC model.As shown in Table 4, the PRKGC models learned to reason over more than simple shortest paths.. Yet, the PRKGC model do not give considerably good results, which indicates the non-triviality of RC-QEDE.. Although the PRKGC model do not receive supervision about human-generated NLDs, paths with the maximum score match human-generated NLDs to some extent.. Supervising path attentions (the PRKGC+NS model) is indeed effective for improving the human interpretability of generated NLDs.. It also improves the generalization ability of question answering." } ], "images": [ "scigen/train/181.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance of RC-QEDE of our baseline models (see Section 2.1 for further details of each evaluation metrics). “NS” indicates the use of annotated NLDs as supervision (i.e. using Ld during training)." ], "reasoning": "The PRKGC+NS model improves human interpretability and generalization ability of question answering compared to the PRKGC model.", "description": "As shown in Table 4, the PRKGC models learned to reason over more than simple shortest paths.. Yet, the PRKGC model do not give considerably good results, which indicates the non-triviality of RC-QEDE.. Although the PRKGC model do not receive supervision about human-generated NLDs, paths with the maximum score match human-generated NLDs to some extent.. Supervising path attentions (the PRKGC+NS model) is indeed effective for improving the human interpretability of generated NLDs.. It also improves the generalization ability of question answering.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 4: Performance of RC-QEDE of our baseline models (see Section 2.1 for further details of each evaluation metrics). “NS” indicates the use of annotated NLDs as supervision (i.e. using Ld during training).', and sequentially solve the question, writing out each step of your reasoning process. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest answer precision and how does it compare to the shortest path model?\n" }, { "role": "assistant", "content": "The PRKGC model has the highest answer precision at 45.2, which is significantly higher than the shortest path model's answer precision of 3.6. So, the answer is {\"answer\": \"PRKGC\"}." } ], "images": [ "scigen/train/181.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance of RC-QEDE of our baseline models (see Section 2.1 for further details of each evaluation metrics). “NS” indicates the use of annotated NLDs as supervision (i.e. using Ld during training)." ], "reasoning": "The PRKGC model has the highest answer precision at 45.2, which is significantly higher than the shortest path model's answer precision of 3.6.", "question": "Which model has the highest answer precision and how does it compare to the shortest path model?", "answer": "So, the answer is {\"answer\": \"PRKGC\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe PRKGC+NS model has a higher derivation precision (RG-L) recall than the PRKGC model.\n" }, { "role": "assistant", "content": "The table shows that the PRKGC+NS model has a derivation precision (RG-L) recall of 61.6, which is higher than the PRKGC model's recall of 60.7. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/181.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance of RC-QEDE of our baseline models (see Section 2.1 for further details of each evaluation metrics). “NS” indicates the use of annotated NLDs as supervision (i.e. using Ld during training)." ], "reasoning": "The table shows that the PRKGC+NS model has a derivation precision (RG-L) recall of 61.6, which is higher than the PRKGC model's recall of 60.7.", "statement": "The PRKGC+NS model has a higher derivation precision (RG-L) recall than the PRKGC model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 7: Accuracy of our baseline models and previous work on WikiHop Welbl2017a’s development set. Note that our baseline models are explainable, whereas the others are not. “NS” indicates the use of annotated NLDs as supervision. Accuracies of existing models are taken from the papers.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The PRKGC models, including PRKGC+NS, achieve accuracy scores of 51.4 and 52.7, which are comparable to other sophisticated neural models like CorefGRU and MHPGM+NOIC.As shown in Table 7, the PRKGC models achieve a comparable performance to other sophisticated neural models." } ], "images": [ "scigen/train/182.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Accuracy of our baseline models and previous work on WikiHop Welbl2017a’s development set. Note that our baseline models are explainable, whereas the others are not. “NS” indicates the use of annotated NLDs as supervision. Accuracies of existing models are taken from the papers." ], "reasoning": "The PRKGC models, including PRKGC+NS, achieve accuracy scores of 51.4 and 52.7, which are comparable to other sophisticated neural models like CorefGRU and MHPGM+NOIC.", "description": "As shown in Table 7, the PRKGC models achieve a comparable performance to other sophisticated neural models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 7: Accuracy of our baseline models and previous work on WikiHop Welbl2017a’s development set. Note that our baseline models are explainable, whereas the others are not. “NS” indicates the use of annotated NLDs as supervision. Accuracies of existing models are taken from the papers.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest accuracy and what is its value?\n" }, { "role": "assistant", "content": "To find the model with the highest accuracy, look at the 'Accuracy' column and identify the highest value. The highest accuracy is 66.4, achieved by the CFC model. So, the answer is {\"answer\": \"CFC\"}." } ], "images": [ "scigen/train/182.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Accuracy of our baseline models and previous work on WikiHop Welbl2017a’s development set. Note that our baseline models are explainable, whereas the others are not. “NS” indicates the use of annotated NLDs as supervision. Accuracies of existing models are taken from the papers." ], "reasoning": "To find the model with the highest accuracy, look at the 'Accuracy' column and identify the highest value. The highest accuracy is 66.4, achieved by the CFC model.", "question": "Which model has the highest accuracy and what is its value?", "answer": "So, the answer is {\"answer\": \"CFC\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe PRKGC+NS model has a higher accuracy than the CorefGRU model.\n" }, { "role": "assistant", "content": "The accuracy of the PRKGC+NS model is 52.7, while the accuracy of the CorefGRU model is 56.0. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/182.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Accuracy of our baseline models and previous work on WikiHop Welbl2017a’s development set. Note that our baseline models are explainable, whereas the others are not. “NS” indicates the use of annotated NLDs as supervision. Accuracies of existing models are taken from the papers." ], "reasoning": "The accuracy of the PRKGC+NS model is 52.7, while the accuracy of the CorefGRU model is 56.0. Therefore, the statement is refuted.", "statement": "The PRKGC+NS model has a higher accuracy than the CorefGRU model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: The estimation of impact of various design choices on the final result. The entries are sorted by the out-of-fold scores from CV. The SUBMISSION here uses score from ep_1 run for the single model and ep_2 for the ensemble performance.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The traditional LSTM layout outperformed the alternating one chosen for submission, and some ablated models regained accuracy when ensembled.The results are presented in Table 3.. Perhaps the most striking thing about the ablation results is that the 'traditional' LSTM layout outsperformed the 'alternating' one we chose for our submission.. Apart of the flipped results of the LSTM-800 and the LSTM-400, small differences in CV score are sometimes associated with large discrepancies in test set performance. This is mostly due to small size of the data set (low. Some ablated models that perform poorly in the singlemodel scenario (e.g. NO-OVERLAPS, LSTM-400DROPOUT) are able to regain a lot of accuracy when ensembled.. Also, our data augmentation technique (NO-TRANSLATIONS) seem to have far smaller impact on the final score then we expected." } ], "images": [ "scigen/train/183.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The estimation of impact of various design choices on the final result. The entries are sorted by the out-of-fold scores from CV. The SUBMISSION here uses score from ep_1 run for the single model and ep_2 for the ensemble performance." ], "reasoning": "The traditional LSTM layout outperformed the alternating one chosen for submission, and some ablated models regained accuracy when ensembled.", "description": "The results are presented in Table 3.. Perhaps the most striking thing about the ablation results is that the 'traditional' LSTM layout outsperformed the 'alternating' one we chose for our submission.. Apart of the flipped results of the LSTM-800 and the LSTM-400, small differences in CV score are sometimes associated with large discrepancies in test set performance. This is mostly due to small size of the data set (low. Some ablated models that perform poorly in the singlemodel scenario (e.g. NO-OVERLAPS, LSTM-400DROPOUT) are able to regain a lot of accuracy when ensembled.. Also, our data augmentation technique (NO-TRANSLATIONS) seem to have far smaller impact on the final score then we expected.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: The estimation of impact of various design choices on the final result. The entries are sorted by the out-of-fold scores from CV. The SUBMISSION here uses score from ep_1 run for the single model and ep_2 for the ensemble performance.', answer the following question. Give detailed solution consisting of each step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model had the highest ensemble score and what was its value?\n" }, { "role": "assistant", "content": "By examining the 'Ensemble' column, the model with the highest score is LSTM-400 with a value of 68.00. So, the answer is {\"answer\": \"LSTM-400 with 68.00\"}." } ], "images": [ "scigen/train/183.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The estimation of impact of various design choices on the final result. The entries are sorted by the out-of-fold scores from CV. The SUBMISSION here uses score from ep_1 run for the single model and ep_2 for the ensemble performance." ], "reasoning": "By examining the 'Ensemble' column, the model with the highest score is LSTM-400 with a value of 68.00.", "question": "Which model had the highest ensemble score and what was its value?", "answer": "So, the answer is {\"answer\": \"LSTM-400 with 68.00\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The NO-TRANSLATIONS model had a higher single model score than the SUBMISSION model. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe NO-TRANSLATIONS model had a higher single model score than the SUBMISSION model.\n" }, { "role": "assistant", "content": "The table shows that the NO-TRANSLATIONS model had a single model score of 65.92, which is lower than the SUBMISSION model's score of 66.76. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/183.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The estimation of impact of various design choices on the final result. The entries are sorted by the out-of-fold scores from CV. The SUBMISSION here uses score from ep_1 run for the single model and ep_2 for the ensemble performance." ], "reasoning": "The table shows that the NO-TRANSLATIONS model had a single model score of 65.92, which is lower than the SUBMISSION model's score of 66.76.", "statement": "The NO-TRANSLATIONS model had a higher single model score than the SUBMISSION model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: The scores of our three submitted runs for similarity threshold 50%.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The official score for the system was 60.9%, and after re-scoring with corrections, the score increased to 67.35%.The system's official score was 60.9% (micro-F1). af. Therefore, we report both the official score (from our second submission) and the result of re-scoring our second submission after replacing these 10 files with the ones from our first submission. The results are presented in Tables 1 and 2." } ], "images": [ "scigen/train/184.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The scores of our three submitted runs for similarity threshold 50%." ], "reasoning": "The official score for the system was 60.9%, and after re-scoring with corrections, the score increased to 67.35%.", "description": "The system's official score was 60.9% (micro-F1). af. Therefore, we report both the official score (from our second submission) and the result of re-scoring our second submission after replacing these 10 files with the ones from our first submission. The results are presented in Tables 1 and 2.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 1: The scores of our three submitted runs for similarity threshold 50%.'. Solve the following question based on the table.\n\nQuestion: What was the increase in the score for ep_2 after correction?\nLet's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The official score for ep_2 was 60.90, and the score with correction was 67.35. The increase is calculated by subtracting the official score from the corrected score: 67.35 - 60.90. So, the answer is {\"answer\": \"6.45\"}" } ], "images": [ "scigen/train/184.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The scores of our three submitted runs for similarity threshold 50%." ], "reasoning": "The official score for ep_2 was 60.90, and the score with correction was 67.35. The increase is calculated by subtracting the official score from the corrected score: 67.35 - 60.90.", "question": "What was the increase in the score for ep_2 after correction?", "answer": "So, the answer is {\"answer\": \"6.45\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The official score for ep_1 was higher than 60.5%. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe official score for ep_1 was higher than 60.5%.\n" }, { "role": "assistant", "content": "The table shows that the official score for ep_1 was 60.29%, which is lower than 60.5%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/184.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The scores of our three submitted runs for similarity threshold 50%." ], "reasoning": "The table shows that the official score for ep_1 was 60.29%, which is lower than 60.5%.", "statement": "The official score for ep_1 was higher than 60.5%.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Detailed results of our best run (after correcting the submission format), along with numbers of mentions in the training set.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table presents the number of examples and F1 scores for various mention classes from two submissions.Therefore, we report both the official score (from our second submission) and the result of re-scoring our second submission after replacing these 10 files with the ones from our first submission. The results are presented in Tables 1 and 2." } ], "images": [ "scigen/train/185.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Detailed results of our best run (after correcting the submission format), along with numbers of mentions in the training set." ], "reasoning": "The table presents the number of examples and F1 scores for various mention classes from two submissions.", "description": "Therefore, we report both the official score (from our second submission) and the result of re-scoring our second submission after replacing these 10 files with the ones from our first submission. The results are presented in Tables 1 and 2.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Detailed results of our best run (after correcting the submission format), along with numbers of mentions in the training set.', answer the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the F1 score for the 'Sex' mention class in the test submission?\n" }, { "role": "assistant", "content": "To find the F1 score for the 'Sex' mention class in the test submission, locate the row labeled 'Sex' and check the value under the 'F1 (Test)' column. So, the answer is {\"answer\": \"98.36\"}" } ], "images": [ "scigen/train/185.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Detailed results of our best run (after correcting the submission format), along with numbers of mentions in the training set." ], "reasoning": "To find the F1 score for the 'Sex' mention class in the test submission, locate the row labeled 'Sex' and check the value under the 'F1 (Test)' column.", "question": "What is the F1 score for the 'Sex' mention class in the test submission?", "answer": "So, the answer is {\"answer\": \"98.36\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe 'Dose' mention class has a higher F1 score in the test submission than in the 5-CV submission.\n" }, { "role": "assistant", "content": "To verify this statement, compare the F1 scores for the 'Dose' mention class in both the 'F1 (5-CV)' and 'F1 (Test)' columns. The F1 score for 'Dose' in the 5-CV submission is 78.47, while in the test submission it is 75.85. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/185.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Detailed results of our best run (after correcting the submission format), along with numbers of mentions in the training set." ], "reasoning": "To verify this statement, compare the F1 scores for the 'Dose' mention class in both the 'F1 (5-CV)' and 'F1 (Test)' columns. The F1 score for 'Dose' in the 5-CV submission is 78.47, while in the test submission it is 75.85.", "statement": "The 'Dose' mention class has a higher F1 score in the test submission than in the 5-CV submission.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Results of non-RL (top), cross-input (DeepTD) and input-specific (REAPER) RL approaches (middle) compared with RELIS.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "RELIS outperforms other RL-based systems and performs on par with neural-based TCSum and SRSum while requiring less data and time to train.In Table 3, we compare RELIS with non-RL-based and RL-based summarisation systems. For non-RL-based systems, we report ICSI [Gillick and Favre, 2009] maximising. the bigram overlap of summary and input using integer linear programming, PriorSum [Cao et al., 2015] learning sentence quality with CNNs, TCSum [Cao et al., 2017] employing text classification of the input documents, the variant of TCSum without the text classification pre-training (TCSum(cid:0)) and SRSum [Ren et al., 2018], which learns sentence relations with both word- and sentence-level attentive neural networks to estimate salience.. RELIS significantly outperforms the other RL-based systems. Note that RELIS and REAPER use the identical RL algorithm for input-specific policy learning; hence the improvement of RELIS is due to the higher quality of the L2R-learnt reward ^(cid:27)U x . RELIS outperforms DeepTD because training cross-input policies requires much more data than available in the DUC datasets. At the same time, RELIS performs on par with neural-based TCSum and SRSum, while it requires significantly less data and time to train, as shown next." } ], "images": [ "scigen/train/186.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of non-RL (top), cross-input (DeepTD) and input-specific (REAPER) RL approaches (middle) compared with RELIS." ], "reasoning": "RELIS outperforms other RL-based systems and performs on par with neural-based TCSum and SRSum while requiring less data and time to train.", "description": "In Table 3, we compare RELIS with non-RL-based and RL-based summarisation systems. For non-RL-based systems, we report ICSI [Gillick and Favre, 2009] maximising. the bigram overlap of summary and input using integer linear programming, PriorSum [Cao et al., 2015] learning sentence quality with CNNs, TCSum [Cao et al., 2017] employing text classification of the input documents, the variant of TCSum without the text classification pre-training (TCSum(cid:0)) and SRSum [Ren et al., 2018], which learns sentence relations with both word- and sentence-level attentive neural networks to estimate salience.. RELIS significantly outperforms the other RL-based systems. Note that RELIS and REAPER use the identical RL algorithm for input-specific policy learning; hence the improvement of RELIS is due to the higher quality of the L2R-learnt reward ^(cid:27)U x . RELIS outperforms DeepTD because training cross-input policies requires much more data than available in the DUC datasets. At the same time, RELIS performs on par with neural-based TCSum and SRSum, while it requires significantly less data and time to train, as shown next.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: Results of non-RL (top), cross-input (DeepTD) and input-specific (REAPER) RL approaches (middle) compared with RELIS.' in mind, break down and resolve the question below step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich system has the highest R1 score for DUC'04?\n" }, { "role": "assistant", "content": "To find the system with the highest R1 score for DUC'04, compare the R1 scores of all systems listed under DUC'04. The highest R1 score is 39.39, achieved by SRSum. So, the answer is {\"answer\": \"SRSum\"}" } ], "images": [ "scigen/train/186.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of non-RL (top), cross-input (DeepTD) and input-specific (REAPER) RL approaches (middle) compared with RELIS." ], "reasoning": "To find the system with the highest R1 score for DUC'04, compare the R1 scores of all systems listed under DUC'04. The highest R1 score is 39.39, achieved by SRSum.", "question": "Which system has the highest R1 score for DUC'04?", "answer": "So, the answer is {\"answer\": \"SRSum\"}" }, { "messages": [ { "role": "user", "content": "RELIS has the highest R2 score for DUC'02. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nRELIS has the highest R2 score for DUC'02.\n" }, { "role": "assistant", "content": "To verify this statement, compare the R2 scores of all systems listed under DUC'02. The highest R2 score for DUC'02 is 10.29, achieved by SRSum, not RELIS. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/186.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of non-RL (top), cross-input (DeepTD) and input-specific (REAPER) RL approaches (middle) compared with RELIS." ], "reasoning": "To verify this statement, compare the R2 scores of all systems listed under DUC'02. The highest R2 score for DUC'02 is 10.29, achieved by SRSum, not RELIS.", "statement": "RELIS has the highest R2 score for DUC'02.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: The correlation of approximated and ground-truth ranking. ^σUx has significantly higher correlation over all other approaches.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our ^(cid:27)U x method shows significantly higher correlation to the ground-truth ranking compared to other widely used rewards for input-specific RL, confirming its superiority.Table 2 compares the quality of our ^(cid:27)U x with other widely used rewards for input-specific RL (see x4). ^(cid:27)U x has significantly higher correlation to the ground-truth ranking compared with all other approaches, confirming that our proposed L2R method yields a superior reward oracle." } ], "images": [ "scigen/train/187.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The correlation of approximated and ground-truth ranking. ^σUx has significantly higher correlation over all other approaches." ], "reasoning": "Our ^(cid:27)U x method shows significantly higher correlation to the ground-truth ranking compared to other widely used rewards for input-specific RL, confirming its superiority.", "description": "Table 2 compares the quality of our ^(cid:27)U x with other widely used rewards for input-specific RL (see x4). ^(cid:27)U x has significantly higher correlation to the ground-truth ranking compared with all other approaches, confirming that our proposed L2R method yields a superior reward oracle.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: The correlation of approximated and ground-truth ranking. ^σUx has significantly higher correlation over all other approaches.', answer the following question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method has the highest correlation (ρ) in DUC'04?\n" }, { "role": "assistant", "content": "To find the method with the highest correlation in DUC'04, compare the ρ values in the DUC'04 column. Our ^(cid:27)U x has the highest ρ value of .617. So, the answer is {\"answer\": \".617\"}." } ], "images": [ "scigen/train/187.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The correlation of approximated and ground-truth ranking. ^σUx has significantly higher correlation over all other approaches." ], "reasoning": "To find the method with the highest correlation in DUC'04, compare the ρ values in the DUC'04 column. Our ^(cid:27)U x has the highest ρ value of .617.", "question": "Which method has the highest correlation (ρ) in DUC'04?", "answer": "So, the answer is {\"answer\": \".617\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim ASRL has the lowest correlation (ρ) in DUC'01., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nASRL has the lowest correlation (ρ) in DUC'01.\n" }, { "role": "assistant", "content": "To verify this statement, compare the ρ values in the DUC'01 column. ASRL has the lowest ρ value of .176. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/187.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The correlation of approximated and ground-truth ranking. ^σUx has significantly higher correlation over all other approaches." ], "reasoning": "To verify this statement, compare the ρ values in the DUC'01 column. ASRL has the lowest ρ value of .176.", "statement": "ASRL has the lowest correlation (ρ) in DUC'01.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 6: Task B results with polarity features'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Adding polarity features improved the results for all models except those using SIF embeddings.We furthermore trained models on additional polarity features for Task B as mentioned before. Adding the polarity features improved the results for all models except for those using SIF embeddings (Table6)." } ], "images": [ "scigen/train/188.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Task B results with polarity features" ], "reasoning": "Adding polarity features improved the results for all models except those using SIF embeddings.", "description": "We furthermore trained models on additional polarity features for Task B as mentioned before. Adding the polarity features improved the results for all models except for those using SIF embeddings (Table6).", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 6: Task B results with polarity features'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. \n\nQuestion: Which model had the highest Micro F1 score after adding polarity features?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The description states that adding polarity features improved the results for all models except those using SIF embeddings. The table shows that the highest Micro F1 score is 0.757 for the SIF (DE-EN) model. So, the answer is {\"answer\": \"SIF (DE-EN)\"}." } ], "images": [ "scigen/train/188.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Task B results with polarity features" ], "reasoning": "The description states that adding polarity features improved the results for all models except those using SIF embeddings. The table shows that the highest Micro F1 score is 0.757 for the SIF (DE-EN) model.", "question": "Which model had the highest Micro F1 score after adding polarity features?", "answer": "So, the answer is {\"answer\": \"SIF (DE-EN)\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The S2V model had a higher Micro F1 score than the baseline after adding polarity features., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe S2V model had a higher Micro F1 score than the baseline after adding polarity features.\n" }, { "role": "assistant", "content": "The description mentions that adding polarity features improved the results for all models except those using SIF embeddings. The table shows that the S2V model has a Micro F1 score of 0.748, which is higher than the baseline score of 0.709. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/188.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Task B results with polarity features" ], "reasoning": "The description mentions that adding polarity features improved the results for all models except those using SIF embeddings. The table shows that the S2V model has a Micro F1 score of 0.748, which is higher than the baseline score of 0.709.", "statement": "The S2V model had a higher Micro F1 score than the baseline after adding polarity features.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Task A results'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "All models slightly outperform the baseline in Task A, with the highest Micro F1 score being 0.897 by W2V (d=500).The results of the models better than the baseline are reported in Tables 4. As can be seen, all models only slightly outperform the baseline in Task A." } ], "images": [ "scigen/train/189.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Task A results" ], "reasoning": "All models slightly outperform the baseline in Task A, with the highest Micro F1 score being 0.897 by W2V (d=500).", "description": "The results of the models better than the baseline are reported in Tables 4. As can be seen, all models only slightly outperform the baseline in Task A.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 4: Task A results' and methodically tackle the question that follows. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model achieved the highest Micro F1 score?\n" }, { "role": "assistant", "content": "By comparing the Micro F1 scores of all models listed in the table, W2V (d=500) has the highest score of 0.897. So, the answer is {\"answer\": \"W2V (d=500)\"}." } ], "images": [ "scigen/train/189.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Task A results" ], "reasoning": "By comparing the Micro F1 scores of all models listed in the table, W2V (d=500) has the highest score of 0.897.", "question": "Which model achieved the highest Micro F1 score?", "answer": "So, the answer is {\"answer\": \"W2V (d=500)\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe S2V model achieved a Micro F1 score higher than the baseline.\n" }, { "role": "assistant", "content": "The table shows that the S2V model has a Micro F1 score of 0.885, which is higher than the baseline score of 0.882. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/189.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Task A results" ], "reasoning": "The table shows that the S2V model has a Micro F1 score of 0.885, which is higher than the baseline score of 0.882.", "statement": "The S2V model achieved a Micro F1 score higher than the baseline.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Task B results', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "All models trained on the stacked learner outperform the baseline, with SIF (DE-EN) achieving the highest Micro F1 score.For Task B, all models trained on the stacked learner beat the baseline substantially even when using only plain averaged word embeddings." } ], "images": [ "scigen/train/190.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Task B results" ], "reasoning": "All models trained on the stacked learner outperform the baseline, with SIF (DE-EN) achieving the highest Micro F1 score.", "description": "For Task B, all models trained on the stacked learner beat the baseline substantially even when using only plain averaged word embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model achieved the highest Micro F1 score and how does it compare to the baseline?\nSolve the above question based on the table titled 'Table 5: Task B results'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The model SIF (DE-EN) achieved the highest Micro F1 score of 0.765, which is higher than the baseline score of 0.709. So, the answer is {\"answer\": \"SIF (DE-EN)\"}." } ], "images": [ "scigen/train/190.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Task B results" ], "reasoning": "The model SIF (DE-EN) achieved the highest Micro F1 score of 0.765, which is higher than the baseline score of 0.709.", "question": "Which model achieved the highest Micro F1 score and how does it compare to the baseline?", "answer": "So, the answer is {\"answer\": \"SIF (DE-EN)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe S2V model achieved a higher Micro F1 score than the W2V (d=500) model.\n" }, { "role": "assistant", "content": "The S2V model has a Micro F1 score of 0.748, while the W2V (d=500) model has a higher Micro F1 score of 0.753. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/190.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Task B results" ], "reasoning": "The S2V model has a Micro F1 score of 0.748, while the W2V (d=500) model has a higher Micro F1 score of 0.753.", "statement": "The S2V model achieved a higher Micro F1 score than the W2V (d=500) model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Ablation study results.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The ELMo layer provided the greatest performance boost of 3.71%, while using emoji also contributed significantly. Concatenation of different BiLSTM states and greater BiLSTM hidden size negatively impacted performance.We performed an ablation study on a single model having obtained 69.23% accuracy on the validation set. Results are summarized in Table 2.. We can observe that the architectural choice that had the greatest impact on our model was the ELMo layer, providing a 3.71% boost in performance as compared to using GloVe pre-trained word embeddings.. We can further see that emoji also contributed significantly to the model's performance.. Additionally, we tried using the concatenation of the max-pooled, average-pooled and last hidden states of the BiLSTM as the sentence representation, following Howard and Ruder (2018), but found out that this impacted performance negatively.. Using a greater BiLSTM hidden size did not help the model,. We found that using 50-dimensional part-ofspeech embeddings slightly improved results,. Regarding optimization strategies, we also tried using SGD with different learning rates and a stepwise learning rate schedule as described by Conneau et al. (2018), but we found that doing this did not improve performance." } ], "images": [ "scigen/train/191.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ablation study results." ], "reasoning": "The ELMo layer provided the greatest performance boost of 3.71%, while using emoji also contributed significantly. Concatenation of different BiLSTM states and greater BiLSTM hidden size negatively impacted performance.", "description": "We performed an ablation study on a single model having obtained 69.23% accuracy on the validation set. Results are summarized in Table 2.. We can observe that the architectural choice that had the greatest impact on our model was the ELMo layer, providing a 3.71% boost in performance as compared to using GloVe pre-trained word embeddings.. We can further see that emoji also contributed significantly to the model's performance.. Additionally, we tried using the concatenation of the max-pooled, average-pooled and last hidden states of the BiLSTM as the sentence representation, following Howard and Ruder (2018), but found out that this impacted performance negatively.. Using a greater BiLSTM hidden size did not help the model,. We found that using 50-dimensional part-ofspeech embeddings slightly improved results,. Regarding optimization strategies, we also tried using SGD with different learning rates and a stepwise learning rate schedule as described by Conneau et al. (2018), but we found that doing this did not improve performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: Ablation study results.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. \n\nQuestion: Which architectural choice had the greatest negative impact on the model's performance?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows that using SGD optimization with a learning rate of 0.001 resulted in the largest negative impact on performance, reducing accuracy by 38.74%. So, the answer is {\"answer\": \"SGD optim lr=0.001\"}" } ], "images": [ "scigen/train/191.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ablation study results." ], "reasoning": "The table shows that using SGD optimization with a learning rate of 0.001 resulted in the largest negative impact on performance, reducing accuracy by 38.74%.", "question": "Which architectural choice had the greatest negative impact on the model's performance?", "answer": "So, the answer is {\"answer\": \"SGD optim lr=0.001\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nUsing 50-dimensional part-of-speech embeddings improved the model's performance.\n" }, { "role": "assistant", "content": "The table shows that using 50-dimensional part-of-speech embeddings resulted in a slight improvement in accuracy by 0.10%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/191.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ablation study results." ], "reasoning": "The table shows that using 50-dimensional part-of-speech embeddings resulted in a slight improvement in accuracy by 0.10%.", "statement": "Using 50-dimensional part-of-speech embeddings improved the model's performance.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Classification Report (Test Set).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The classification report shows that anger and surprise are the most difficult classes to predict, while joy, fear, and disgust have better performance.Table 3 the. corresponding classification report.. In general, we confirm what Klinger et al. (2018) report: anger was the most difficult class to predict, followed by surprise, whereas joy, fear, and disgust are the better performing ones." } ], "images": [ "scigen/train/192.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Classification Report (Test Set)." ], "reasoning": "The classification report shows that anger and surprise are the most difficult classes to predict, while joy, fear, and disgust have better performance.", "description": "Table 3 the. corresponding classification report.. In general, we confirm what Klinger et al. (2018) report: anger was the most difficult class to predict, followed by surprise, whereas joy, fear, and disgust are the better performing ones.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: Classification Report (Test Set).' and methodically tackle the question that follows. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich class has the highest F1-score and what is its value?\n" }, { "role": "assistant", "content": "To find the class with the highest F1-score, compare the F1-scores of all classes. Joy has the highest F1-score of 0.783. So, the answer is {\"answer\": \"joy\"}." } ], "images": [ "scigen/train/192.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Classification Report (Test Set)." ], "reasoning": "To find the class with the highest F1-score, compare the F1-scores of all classes. Joy has the highest F1-score of 0.783.", "question": "Which class has the highest F1-score and what is its value?", "answer": "So, the answer is {\"answer\": \"joy\"}." }, { "messages": [ { "role": "user", "content": "The class 'sad' has a higher precision than 'disgust'. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe class 'sad' has a higher precision than 'disgust'.\n" }, { "role": "assistant", "content": "The precision for 'sad' is 0.685, while the precision for 'disgust' is 0.703. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/192.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Classification Report (Test Set)." ], "reasoning": "The precision for 'sad' is 0.685, while the precision for 'disgust' is 0.703. Therefore, the statement is incorrect.", "statement": "The class 'sad' has a higher precision than 'disgust'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4: Number of tweets on the test set with and without emoji and hashtags. The number between parentheses is the proportion of tweets classified correctly.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Tweets with emoji are easier for the model to classify than those without, and hashtags also improve classification performance but to a lesser extent.Table 4 shows the overall effect of hashtags and emoji on classification performance.. Tweets containing emoji seem to be easier for the model to classify than those without.. Hashtags also have a. positive effect on classification performance, however it is less significant." } ], "images": [ "scigen/train/193.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Number of tweets on the test set with and without emoji and hashtags. The number between parentheses is the proportion of tweets classified correctly." ], "reasoning": "Tweets with emoji are easier for the model to classify than those without, and hashtags also improve classification performance but to a lesser extent.", "description": "Table 4 shows the overall effect of hashtags and emoji on classification performance.. Tweets containing emoji seem to be easier for the model to classify than those without.. Hashtags also have a. positive effect on classification performance, however it is less significant.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 4: Number of tweets on the test set with and without emoji and hashtags. The number between parentheses is the proportion of tweets classified correctly.', answer the question below by showing each progressive step toward the answer. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the classification performance percentage for tweets with hashtags?\n" }, { "role": "assistant", "content": "The table shows that tweets with hashtags have a classification performance of 70.5%. So, the answer is {\"answer\": \"70.5%\"}." } ], "images": [ "scigen/train/193.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Number of tweets on the test set with and without emoji and hashtags. The number between parentheses is the proportion of tweets classified correctly." ], "reasoning": "The table shows that tweets with hashtags have a classification performance of 70.5%.", "question": "What is the classification performance percentage for tweets with hashtags?", "answer": "So, the answer is {\"answer\": \"70.5%\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nTweets without emoji have a classification performance of 68.0%.\n" }, { "role": "assistant", "content": "The table indicates that tweets without emoji have a classification performance of 68.0%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/193.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Number of tweets on the test set with and without emoji and hashtags. The number between parentheses is the proportion of tweets classified correctly." ], "reasoning": "The table indicates that tweets without emoji have a classification performance of 68.0%.", "statement": "Tweets without emoji have a classification performance of 68.0%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: Fine grained performance on tweets containing emoji, and the effect of removing them.'.\n" }, { "role": "assistant", "content": "Certain emojis like mask, rage, and cry significantly improve classification accuracy, while others like sweat smile and confused decrease it.Table 5 shows the effect specific emoji have on classification performance.. It is clear some emoji strongly contribute to improving prediction quality.. The most interesting ones are mask, rage, and cry, which significantly increase accuracy.. Further, contrary to intuition, the sob emoji contributes less than cry, despite representing a stronger emotion.. Finally, not all emoji are beneficial for this task.. When removing sweat smile and confused accuracy increased," } ], "images": [ "scigen/train/194.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Fine grained performance on tweets containing emoji, and the effect of removing them." ], "reasoning": "Certain emojis like mask, rage, and cry significantly improve classification accuracy, while others like sweat smile and confused decrease it.", "description": "Table 5 shows the effect specific emoji have on classification performance.. It is clear some emoji strongly contribute to improving prediction quality.. The most interesting ones are mask, rage, and cry, which significantly increase accuracy.. Further, contrary to intuition, the sob emoji contributes less than cry, despite representing a stronger emotion.. Finally, not all emoji are beneficial for this task.. When removing sweat smile and confused accuracy increased,", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 5: Fine grained performance on tweets containing emoji, and the effect of removing them.', solve the subsequent question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich emoji has the highest positive impact on classification accuracy?\n" }, { "role": "assistant", "content": "By comparing the Δ% column, the rage emoji has the highest positive impact with an increase of 13.04%. So, the answer is {\"answer\": \"rage\"}." } ], "images": [ "scigen/train/194.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Fine grained performance on tweets containing emoji, and the effect of removing them." ], "reasoning": "By comparing the Δ% column, the rage emoji has the highest positive impact with an increase of 13.04%.", "question": "Which emoji has the highest positive impact on classification accuracy?", "answer": "So, the answer is {\"answer\": \"rage\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe sob emoji contributes more to classification accuracy than the cry emoji.\n" }, { "role": "assistant", "content": "The table shows that the cry emoji has a Δ% of 12.07, while the sob emoji has a Δ% of 3.67, indicating that the cry emoji contributes more. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/194.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Fine grained performance on tweets containing emoji, and the effect of removing them." ], "reasoning": "The table shows that the cry emoji has a Δ% of 12.07, while the sob emoji has a Δ% of 3.67, indicating that the cry emoji contributes more.", "statement": "The sob emoji contributes more to classification accuracy than the cry emoji.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Performance results on Winograd and WinoCoref datasets. All our three systems are trained on WinoCoref, and we evaluate the predictions on both datasets. Our systems improve over the baselines by over than 20% on Winograd and over 15% on WinoCoref.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "KnowComb outperforms other systems on both Winograd and WinoCoref datasets, showing significant improvements in precision and antecedent prediction respectively.Performance results on Winograd and WinoCoref datasets are shown in Table 7. The best performing system is KnowComb. It improves by over 20% over a state-of-art general coreference system on Winograd and also outperforms Rahman and Ng (2012) by a margin of 3.3%. On the WinoCoref dataset, it improves by 15%. These results show significant performance improvement by using Predicate Schemas knowledge on hard coreference problems. Note that the system developed in Rahman and Ng (2012) cannot be used on the WinoCoref dataset. The results also show that it is better to compile knowledge into constraints when the knowledge quality is high than add them as features." } ], "images": [ "scigen/train/195.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Performance results on Winograd and WinoCoref datasets. All our three systems are trained on WinoCoref, and we evaluate the predictions on both datasets. Our systems improve over the baselines by over than 20% on Winograd and over 15% on WinoCoref." ], "reasoning": "KnowComb outperforms other systems on both Winograd and WinoCoref datasets, showing significant improvements in precision and antecedent prediction respectively.", "description": "Performance results on Winograd and WinoCoref datasets are shown in Table 7. The best performing system is KnowComb. It improves by over 20% over a state-of-art general coreference system on Winograd and also outperforms Rahman and Ng (2012) by a margin of 3.3%. On the WinoCoref dataset, it improves by 15%. These results show significant performance improvement by using Predicate Schemas knowledge on hard coreference problems. Note that the system developed in Rahman and Ng (2012) cannot be used on the WinoCoref dataset. The results also show that it is better to compile knowledge into constraints when the knowledge quality is high than add them as features.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 7: Performance results on Winograd and WinoCoref datasets. All our three systems are trained on WinoCoref, and we evaluate the predictions on both datasets. Our systems improve over the baselines by over than 20% on Winograd and over 15% on WinoCoref.', solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nBy how much does KnowComb outperform Rahman and Ng (2012) on the Winograd dataset?\n" }, { "role": "assistant", "content": "KnowComb achieves a precision of 76.41 on the Winograd dataset, while Rahman and Ng (2012) achieve 73.05. The difference is 76.41 - 73.05 = 3.36. So, the answer is {\"answer\": \"3.36\"}" } ], "images": [ "scigen/train/195.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Performance results on Winograd and WinoCoref datasets. All our three systems are trained on WinoCoref, and we evaluate the predictions on both datasets. Our systems improve over the baselines by over than 20% on Winograd and over 15% on WinoCoref." ], "reasoning": "KnowComb achieves a precision of 76.41 on the Winograd dataset, while Rahman and Ng (2012) achieve 73.05. The difference is 76.41 - 73.05 = 3.36.", "question": "By how much does KnowComb outperform Rahman and Ng (2012) on the Winograd dataset?", "answer": "So, the answer is {\"answer\": \"3.36\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nKnowComb achieves the highest precision on the Winograd dataset.\n" }, { "role": "assistant", "content": "The table shows that KnowComb has a precision of 76.41 on the Winograd dataset, which is higher than all other systems listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/195.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Performance results on Winograd and WinoCoref datasets. All our three systems are trained on WinoCoref, and we evaluate the predictions on both datasets. Our systems improve over the baselines by over than 20% on Winograd and over 15% on WinoCoref." ], "reasoning": "The table shows that KnowComb has a precision of 76.41 on the Winograd dataset, which is higher than all other systems listed.", "statement": "KnowComb achieves the highest precision on the Winograd dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 8: Performance results on ACE and OntoNotes datasets. Our system gets the same level of performance compared to a state-of-art general coreference system.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "KnowComb achieves comparable performance to the state-of-the-art coreference system, with no significant improvement due to the rarity of hard coreference problems in standard datasets.Performance results on standard ACE and OntoNotes datasets are shown in Table 8. Our KnowComb system achieves the same level of performance as does the state-of-art general coreference system we base it on. As hard coreference problems are rare in standard coreference datasets, we do not have significant performance improvement. However, these results show that our additional Predicate Schemas do not harm the predictions for regular mentions." } ], "images": [ "scigen/train/196.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Performance results on ACE and OntoNotes datasets. Our system gets the same level of performance compared to a state-of-art general coreference system." ], "reasoning": "KnowComb achieves comparable performance to the state-of-the-art coreference system, with no significant improvement due to the rarity of hard coreference problems in standard datasets.", "description": "Performance results on standard ACE and OntoNotes datasets are shown in Table 8. Our KnowComb system achieves the same level of performance as does the state-of-art general coreference system we base it on. As hard coreference problems are rare in standard coreference datasets, we do not have significant performance improvement. However, these results show that our additional Predicate Schemas do not harm the predictions for regular mentions.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 8: Performance results on ACE and OntoNotes datasets. Our system gets the same level of performance compared to a state-of-art general coreference system.' in mind, break down and resolve the question below step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhat is the average performance score of KnowComb on the ACE dataset?\n" }, { "role": "assistant", "content": "To find the average performance score of KnowComb on the ACE dataset, look at the AVG column for KnowComb in the ACE row, which shows 78.97. So, the answer is {\"answer\": \"78.97\"}" } ], "images": [ "scigen/train/196.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Performance results on ACE and OntoNotes datasets. Our system gets the same level of performance compared to a state-of-art general coreference system." ], "reasoning": "To find the average performance score of KnowComb on the ACE dataset, look at the AVG column for KnowComb in the ACE row, which shows 78.97.", "question": "What is the average performance score of KnowComb on the ACE dataset?", "answer": "So, the answer is {\"answer\": \"78.97\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nKnowComb outperforms IlliCons in the MUC metric on the OntoNotes dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the MUC scores of KnowComb and IlliCons in the OntoNotes dataset. KnowComb has a score of 84.33, while IlliCons has a score of 84.10, so KnowComb does outperform IlliCons. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/196.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Performance results on ACE and OntoNotes datasets. Our system gets the same level of performance compared to a state-of-art general coreference system." ], "reasoning": "To verify this statement, compare the MUC scores of KnowComb and IlliCons in the OntoNotes dataset. KnowComb has a score of 84.33, while IlliCons has a score of 84.10, so KnowComb does outperform IlliCons.", "statement": "KnowComb outperforms IlliCons in the MUC metric on the OntoNotes dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 9: Distribution of instances in Winograd dataset of each category. Cat1/Cat2 is the subset of instances that require Type 1/Type 2 schema knowledge, respectively. All other instances are put into Cat3. Cat1 and Cat2 instances can be covered by our proposed Predicate Schemas.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The Predicate Schemas cover 73% of the instances in Winograd, with the distribution of instances shown in Table 9.Detailed Analysis To study the coverage of our Predicate Schemas knowledge, we label the instances in Winograd (which also applies to WinoCoref) with the type of Predicate Schemas knowledge required. The distribution of the instances is shown in Table 9. Our proposed Predicate Schemas cover 73% of the instances." } ], "images": [ "scigen/train/197.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Distribution of instances in Winograd dataset of each category. Cat1/Cat2 is the subset of instances that require Type 1/Type 2 schema knowledge, respectively. All other instances are put into Cat3. Cat1 and Cat2 instances can be covered by our proposed Predicate Schemas." ], "reasoning": "The Predicate Schemas cover 73% of the instances in Winograd, with the distribution of instances shown in Table 9.", "description": "Detailed Analysis To study the coverage of our Predicate Schemas knowledge, we label the instances in Winograd (which also applies to WinoCoref) with the type of Predicate Schemas knowledge required. The distribution of the instances is shown in Table 9. Our proposed Predicate Schemas cover 73% of the instances.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the total number of instances in the Winograd dataset?\nSolve the above question based on the table titled 'Table 9: Distribution of instances in Winograd dataset of each category. Cat1/Cat2 is the subset of instances that require Type 1/Type 2 schema knowledge, respectively. All other instances are put into Cat3. Cat1 and Cat2 instances can be covered by our proposed Predicate Schemas.'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the total number of instances, sum the sizes of Cat1, Cat2, and Cat3: 317 + 1060 + 509. So, the answer is {\"answer\": \"1886\"}." } ], "images": [ "scigen/train/197.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Distribution of instances in Winograd dataset of each category. Cat1/Cat2 is the subset of instances that require Type 1/Type 2 schema knowledge, respectively. All other instances are put into Cat3. Cat1 and Cat2 instances can be covered by our proposed Predicate Schemas." ], "reasoning": "To find the total number of instances, sum the sizes of Cat1, Cat2, and Cat3: 317 + 1060 + 509.", "question": "What is the total number of instances in the Winograd dataset?", "answer": "So, the answer is {\"answer\": \"1886\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nCat2 has the highest portion of instances in the Winograd dataset.\n" }, { "role": "assistant", "content": "The table shows that Cat2 has a portion of 56.2%, which is higher than Cat1's 16.8% and Cat3's 27.0%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/197.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Distribution of instances in Winograd dataset of each category. Cat1/Cat2 is the subset of instances that require Type 1/Type 2 schema knowledge, respectively. All other instances are put into Cat3. Cat1 and Cat2 instances can be covered by our proposed Predicate Schemas." ], "reasoning": "The table shows that Cat2 has a portion of 56.2%, which is higher than Cat1's 16.8% and Cat3's 27.0%.", "statement": "Cat2 has the highest portion of instances in the Winograd dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 10: Ablation Study of Knowledge Schemas on WinoCoref. The first line specifies the preformance for KnowComb with only Type 1 schema knowledge tested on all data while the third line specifies the preformance using the same model but tested on Cat1 data. The second line specifies the preformance results for KnowComb system with only Type 2 schema knowledge on all data while the fourth line specifies the preformance using the same model but tested on Cat2 data.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Type 1 and Type 2 schema knowledge have higher precision on Category 1 and Category 2 data instances, respectively, compared to full data, indicating a need for more complex knowledge schemas.We also provide an ablation study on the WinoCoref dataset in Table 10. These results use the best performing KnowComb system. They showthat both Type 1 and Type 2 schema knowledge havehigher precision on Category 1 and Category 2 datainstances, respectively, compared to that on full data. Type 1 and Type 2 knowledge have similiar performance on full data, but the results show that it is harder to solve instances in category 2 than those in category 1. Also, the performance drop between Cat1/Cat2 and full data indicates that there is a need to design more complicated knowledge schemas and to refine the knowledge acquisition for further performance improvement." } ], "images": [ "scigen/train/198.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Ablation Study of Knowledge Schemas on WinoCoref. The first line specifies the preformance for KnowComb with only Type 1 schema knowledge tested on all data while the third line specifies the preformance using the same model but tested on Cat1 data. The second line specifies the preformance results for KnowComb system with only Type 2 schema knowledge on all data while the fourth line specifies the preformance using the same model but tested on Cat2 data." ], "reasoning": "Type 1 and Type 2 schema knowledge have higher precision on Category 1 and Category 2 data instances, respectively, compared to full data, indicating a need for more complex knowledge schemas.", "description": "We also provide an ablation study on the WinoCoref dataset in Table 10. These results use the best performing KnowComb system. They showthat both Type 1 and Type 2 schema knowledge havehigher precision on Category 1 and Category 2 datainstances, respectively, compared to that on full data. Type 1 and Type 2 knowledge have similiar performance on full data, but the results show that it is harder to solve instances in category 2 than those in category 1. Also, the performance drop between Cat1/Cat2 and full data indicates that there is a need to design more complicated knowledge schemas and to refine the knowledge acquisition for further performance improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 10: Ablation Study of Knowledge Schemas on WinoCoref. The first line specifies the preformance for KnowComb with only Type 1 schema knowledge tested on all data while the third line specifies the preformance using the same model but tested on Cat1 data. The second line specifies the preformance results for KnowComb system with only Type 2 schema knowledge on all data while the fourth line specifies the preformance using the same model but tested on Cat2 data.', solve this question and give detail solutions. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhat is the precision of Type 1 schema on Category 1 data during testing?\n" }, { "role": "assistant", "content": "To find the precision of Type 1 schema on Category 1 data during testing, look at the row labeled 'Type 1 (Cat1)' and the column labeled 'AntePre(Test)'. So, the answer is {\"answer\": \"90.26\"}." } ], "images": [ "scigen/train/198.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Ablation Study of Knowledge Schemas on WinoCoref. The first line specifies the preformance for KnowComb with only Type 1 schema knowledge tested on all data while the third line specifies the preformance using the same model but tested on Cat1 data. The second line specifies the preformance results for KnowComb system with only Type 2 schema knowledge on all data while the fourth line specifies the preformance using the same model but tested on Cat2 data." ], "reasoning": "To find the precision of Type 1 schema on Category 1 data during testing, look at the row labeled 'Type 1 (Cat1)' and the column labeled 'AntePre(Test)'.", "question": "What is the precision of Type 1 schema on Category 1 data during testing?", "answer": "So, the answer is {\"answer\": \"90.26\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Type 2 schema has higher precision on Category 2 data during training compared to full data., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nType 2 schema has higher precision on Category 2 data during training compared to full data.\n" }, { "role": "assistant", "content": "Compare the precision of Type 2 schema on Category 2 data during training (92.49) with the precision on full data during training (88.86). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/198.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Ablation Study of Knowledge Schemas on WinoCoref. The first line specifies the preformance for KnowComb with only Type 1 schema knowledge tested on all data while the third line specifies the preformance using the same model but tested on Cat1 data. The second line specifies the preformance results for KnowComb system with only Type 2 schema knowledge on all data while the fourth line specifies the preformance using the same model but tested on Cat2 data." ], "reasoning": "Compare the precision of Type 2 schema on Category 2 data during training (92.49) with the precision on full data during training (88.86).", "statement": "Type 2 schema has higher precision on Category 2 data during training compared to full data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Accuracy obtained by training an standalone classifier, applying the API and the proposed wrapper for each domain'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table shows that applying the rejection method improves the accuracy and quality of predictions when using a pre-trained black-box classifier on a new domain.Table 1 shows the numerical results obtained during the experiments for the four combinations tested.. In general terms, the results displayed in table 1 show that the rejection method can reduce the error of the output predictions when applying a pre-trained black-box classification system to a new domain.. Table 1: Accuracy obtained by training an standalone classifier, applying the API and the proposed wrapper for each domain" } ], "images": [ "scigen/train/199.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Accuracy obtained by training an standalone classifier, applying the API and the proposed wrapper for each domain" ], "reasoning": "The table shows that applying the rejection method improves the accuracy and quality of predictions when using a pre-trained black-box classifier on a new domain.", "description": "Table 1 shows the numerical results obtained during the experiments for the four combinations tested.. In general terms, the results displayed in table 1 show that the rejection method can reduce the error of the output predictions when applying a pre-trained black-box classification system to a new domain.. Table 1: Accuracy obtained by training an standalone classifier, applying the API and the proposed wrapper for each domain", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 1: Accuracy obtained by training an standalone classifier, applying the API and the proposed wrapper for each domain' to sequentially solve the question presented below. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich combination achieved the highest non-reject accuracy at 30%?\n" }, { "role": "assistant", "content": "To find the highest non-reject accuracy at 30%, we need to compare the values in the non-reject accuracy column for 30% across all combinations. The highest value is 97.26±0.31% for applying Electronics BB to Music. So, the answer is {\"answer\": \"97.26±0.31%\"}." } ], "images": [ "scigen/train/199.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Accuracy obtained by training an standalone classifier, applying the API and the proposed wrapper for each domain" ], "reasoning": "To find the highest non-reject accuracy at 30%, we need to compare the values in the non-reject accuracy column for 30% across all combinations. The highest value is 97.26±0.31% for applying Electronics BB to Music.", "question": "Which combination achieved the highest non-reject accuracy at 30%?", "answer": "So, the answer is {\"answer\": \"97.26±0.31%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Applying SST-2 BB to Yelp achieved a reject quality of 5.72±0.7% at 30%. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nApplying SST-2 BB to Yelp achieved a reject quality of 5.72±0.7% at 30%.\n" }, { "role": "assistant", "content": "The table shows that applying SST-2 BB to Yelp achieved a reject quality of 5.72±0.7% at 30%, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/199.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Accuracy obtained by training an standalone classifier, applying the API and the proposed wrapper for each domain" ], "reasoning": "The table shows that applying SST-2 BB to Yelp achieved a reject quality of 5.72±0.7% at 30%, which matches the statement.", "statement": "Applying SST-2 BB to Yelp achieved a reject quality of 5.72±0.7% at 30%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 6: WER for adaptation of the TED-LIUM model without i-vectors and the Somali model using best path as a supervision with varying fractions of the adaptation data.'.\n" }, { "role": "assistant", "content": "Filtering utterances improves results when using best path supervision, with the biggest improvement at 50% adaptation data, but lattice supervision achieves better overall performance.This filtering can be done by using a hard threshold, or by using only the fraction of utterances with the highest confidences. Either way one extra hyper-parameter is introduced. We experiment with the TED-LIUM model without i-vectors, and the Somali model. As can be seen from the table, filtering utterances improves results when using best path supervision. The biggest improvement can be achieved when using only 50% of the adaptation data. Even then the TED-LIUM model does not obtain similar performance as when adapted using lattices for supervision. Furthermore, adaptation of the Somali model using best path supervision only barely matches the unadapted baseline. This is probably due to the fact that the WER of the initial Somali model is high and that the lattice provides much more information than a combination of best path supervision and corresponding confidences. We also performed the same filtering experiment with lattices as supervision. We found that using a threshold of 75% – 100% achieves the best results. Overall, adaptation using lattice supervision does not benefit from filtering utterances as much as adaptation using best path supervision." } ], "images": [ "scigen/train/200.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: WER for adaptation of the TED-LIUM model without i-vectors and the Somali model using best path as a supervision with varying fractions of the adaptation data." ], "reasoning": "Filtering utterances improves results when using best path supervision, with the biggest improvement at 50% adaptation data, but lattice supervision achieves better overall performance.", "description": "This filtering can be done by using a hard threshold, or by using only the fraction of utterances with the highest confidences. Either way one extra hyper-parameter is introduced. We experiment with the TED-LIUM model without i-vectors, and the Somali model. As can be seen from the table, filtering utterances improves results when using best path supervision. The biggest improvement can be achieved when using only 50% of the adaptation data. Even then the TED-LIUM model does not obtain similar performance as when adapted using lattices for supervision. Furthermore, adaptation of the Somali model using best path supervision only barely matches the unadapted baseline. This is probably due to the fact that the WER of the initial Somali model is high and that the lattice provides much more information than a combination of best path supervision and corresponding confidences. We also performed the same filtering experiment with lattices as supervision. We found that using a threshold of 75% – 100% achieves the best results. Overall, adaptation using lattice supervision does not benefit from filtering utterances as much as adaptation using best path supervision.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 6: WER for adaptation of the TED-LIUM model without i-vectors and the Somali model using best path as a supervision with varying fractions of the adaptation data.', solve this question and give detail solutions. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhat is the WER for the TED-LIUM dev model when using best path supervision with 50% of the adaptation data?\n" }, { "role": "assistant", "content": "To find the WER for the TED-LIUM dev model using best path supervision with 50% of the adaptation data, look at the row labeled 'ALL-BP 50%' under the column 'TED-LIUM dev'. The value is 9.4. So, the answer is {\"answer\": \"9.4\"}." } ], "images": [ "scigen/train/200.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: WER for adaptation of the TED-LIUM model without i-vectors and the Somali model using best path as a supervision with varying fractions of the adaptation data." ], "reasoning": "To find the WER for the TED-LIUM dev model using best path supervision with 50% of the adaptation data, look at the row labeled 'ALL-BP 50%' under the column 'TED-LIUM dev'. The value is 9.4.", "question": "What is the WER for the TED-LIUM dev model when using best path supervision with 50% of the adaptation data?", "answer": "So, the answer is {\"answer\": \"9.4\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Somali model achieves better WER with lattice supervision at 75% adaptation data compared to best path supervision at 100%. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Somali model achieves better WER with lattice supervision at 75% adaptation data compared to best path supervision at 100%.\n" }, { "role": "assistant", "content": "To verify this, compare the WER for the Somali NB model using lattice supervision at 75% (8.8) with best path supervision at 100% (10.6). The WER is lower with lattice supervision at 75%, supporting the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/200.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: WER for adaptation of the TED-LIUM model without i-vectors and the Somali model using best path as a supervision with varying fractions of the adaptation data." ], "reasoning": "To verify this, compare the WER for the Somali NB model using lattice supervision at 75% (8.8) with best path supervision at 100% (10.6). The WER is lower with lattice supervision at 75%, supporting the statement.", "statement": "The Somali model achieves better WER with lattice supervision at 75% adaptation data compared to best path supervision at 100%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: WER for adaptation of the MGB model to episodes in the longitudinal eval data.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Adapting all parameters with lattice supervision yields almost no gains, while adapting a subset with LHUC is more stable but less effective.This provides more adaptation data ( 30-45 minutes per episode), but perhaps at the cost of losing finer granularity for adaptation. Using the best path with all parameters yields almost no gains (∼1%). When only adapting a subset of the parameters with LHUC the results are more stable, but does not perform as well as all parameters with lattice supervision." } ], "images": [ "scigen/train/201.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: WER for adaptation of the MGB model to episodes in the longitudinal eval data." ], "reasoning": "Adapting all parameters with lattice supervision yields almost no gains, while adapting a subset with LHUC is more stable but less effective.", "description": "This provides more adaptation data ( 30-45 minutes per episode), but perhaps at the cost of losing finer granularity for adaptation. Using the best path with all parameters yields almost no gains (∼1%). When only adapting a subset of the parameters with LHUC the results are more stable, but does not perform as well as all parameters with lattice supervision.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: WER for adaptation of the MGB model to episodes in the longitudinal eval data.', answer the following question. Give detailed solution consisting of each step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the evaluation score when adapting all parameters with lattice supervision?\n" }, { "role": "assistant", "content": "The table shows the evaluation scores for different adaptation methods. The score for adapting all parameters with lattice supervision (ALL-LAT) is 19.2. So, the answer is {\"answer\": \"19.2\"}." } ], "images": [ "scigen/train/201.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: WER for adaptation of the MGB model to episodes in the longitudinal eval data." ], "reasoning": "The table shows the evaluation scores for different adaptation methods. The score for adapting all parameters with lattice supervision (ALL-LAT) is 19.2.", "question": "What is the evaluation score when adapting all parameters with lattice supervision?", "answer": "So, the answer is {\"answer\": \"19.2\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Adapting all parameters with the best path yields an evaluation score of 19.7. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nAdapting all parameters with the best path yields an evaluation score of 19.7.\n" }, { "role": "assistant", "content": "The table shows that the evaluation score for adapting all parameters with the best path (ALL-BP) is indeed 19.7. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/201.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: WER for adaptation of the MGB model to episodes in the longitudinal eval data." ], "reasoning": "The table shows that the evaluation score for adapting all parameters with the best path (ALL-BP) is indeed 19.7.", "statement": "Adapting all parameters with the best path yields an evaluation score of 19.7.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Percent accuracies for humans (PEB) and models (the bigger the better). GMM is for DPGMM, DS for DeepSpeech. BEnM, BEnT and BMu are (in order) for monophone English, triphone English and multilingual bottleneck models. Art is for articulatory reconstruction.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Listeners' discrimination accuracy decreases for non-English stimuli, and models trained on English also capture this effect, though some models exaggerate it.This implies that, to the extent that any of these models accurately captures listeners’ perceived discriminability, listeners’ behaviour on the task, unsurprisingly, cannot correspond to a hard decision at the optimal decision threshhold. The results also indicate, as expected, a small native language effect—a decrease in listeners’ discrimination accuracy for the non-English stimuli. Such an effect is also captured by all the models trained on English. We observe that some models show native language effects numerically much larger than human listeners, a point we return to below." } ], "images": [ "scigen/train/202.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Percent accuracies for humans (PEB) and models (the bigger the better). GMM is for DPGMM, DS for DeepSpeech. BEnM, BEnT and BMu are (in order) for monophone English, triphone English and multilingual bottleneck models. Art is for articulatory reconstruction." ], "reasoning": "Listeners' discrimination accuracy decreases for non-English stimuli, and models trained on English also capture this effect, though some models exaggerate it.", "description": "This implies that, to the extent that any of these models accurately captures listeners’ perceived discriminability, listeners’ behaviour on the task, unsurprisingly, cannot correspond to a hard decision at the optimal decision threshhold. The results also indicate, as expected, a small native language effect—a decrease in listeners’ discrimination accuracy for the non-English stimuli. Such an effect is also captured by all the models trained on English. We observe that some models show native language effects numerically much larger than human listeners, a point we return to below.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nTable title: Table 1: Percent accuracies for humans (PEB) and models (the bigger the better). GMM is for DPGMM, DS for DeepSpeech. BEnM, BEnT and BMu are (in order) for monophone English, triphone English and multilingual bottleneck models. Art is for articulatory reconstruction.\n\nQuestion: Which model shows the largest decrease in discrimination accuracy for French stimuli compared to English stimuli?\n" }, { "role": "assistant", "content": "To find the model with the largest decrease in discrimination accuracy for French stimuli compared to English stimuli, we need to calculate the difference between the English and French scores for each model and identify the largest difference. So, the answer is {\"answer\": \"Art\"}." } ], "images": [ "scigen/train/202.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Percent accuracies for humans (PEB) and models (the bigger the better). GMM is for DPGMM, DS for DeepSpeech. BEnM, BEnT and BMu are (in order) for monophone English, triphone English and multilingual bottleneck models. Art is for articulatory reconstruction." ], "reasoning": "To find the model with the largest decrease in discrimination accuracy for French stimuli compared to English stimuli, we need to calculate the difference between the English and French scores for each model and identify the largest difference.", "question": "Which model shows the largest decrease in discrimination accuracy for French stimuli compared to English stimuli?", "answer": "So, the answer is {\"answer\": \"Art\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe DS model shows a smaller native language effect than the GMM model.\n" }, { "role": "assistant", "content": "The native language effect can be measured by the difference in scores between English and French stimuli. For DS, the difference is 89.5 - 80.2 = 9.3. For GMM, the difference is 88.3 - 82.0 = 6.3. Since 9.3 is larger than 6.3, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/202.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Percent accuracies for humans (PEB) and models (the bigger the better). GMM is for DPGMM, DS for DeepSpeech. BEnM, BEnT and BMu are (in order) for monophone English, triphone English and multilingual bottleneck models. Art is for articulatory reconstruction." ], "reasoning": "The native language effect can be measured by the difference in scores between English and French stimuli. For DS, the difference is 89.5 - 80.2 = 9.3. For GMM, the difference is 88.3 - 82.0 = 6.3. Since 9.3 is larger than 6.3, the statement is refuted.", "statement": "The DS model shows a smaller native language effect than the GMM model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Quantitative evaluation on the Twitter dataset.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "We compared different models on the Twitter dataset using CNN as the encoder and evaluated them on 2k test data, focusing on relevance and diversity metrics.We further compared our methods on the Twitter dataset. We treated all dialog history before the last response in a multi-turn conversation session as a source sentence, and use the last response as the target to form our dataset. We employed CNN as our encoder because a CNN-based encoder is presumably advantageous in tracking long dialog history comparing to an LSTM encoder. We truncated the vocabulary to contain only 20k most frequent words due to limited flash memory capacity. We evaluated each methods on 2k test data." } ], "images": [ "scigen/train/203.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Quantitative evaluation on the Twitter dataset." ], "reasoning": "We compared different models on the Twitter dataset using CNN as the encoder and evaluated them on 2k test data, focusing on relevance and diversity metrics.", "description": "We further compared our methods on the Twitter dataset. We treated all dialog history before the last response in a multi-turn conversation session as a source sentence, and use the last response as the target to form our dataset. We employed CNN as our encoder because a CNN-based encoder is presumably advantageous in tracking long dialog history comparing to an LSTM encoder. We truncated the vocabulary to contain only 20k most frequent words due to limited flash memory capacity. We evaluated each methods on 2k test data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 4: Quantitative evaluation on the Twitter dataset.', solve the following question step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model achieved the highest Relevance Greedy score?\n" }, { "role": "assistant", "content": "By examining the Relevance Greedy column, AIM achieved the highest score of 1.960. So, the answer is {\"answer\": \"AIM\"}." } ], "images": [ "scigen/train/203.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Quantitative evaluation on the Twitter dataset." ], "reasoning": "By examining the Relevance Greedy column, AIM achieved the highest score of 1.960.", "question": "Which model achieved the highest Relevance Greedy score?", "answer": "So, the answer is {\"answer\": \"AIM\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe seq2seq model has the highest Diversity Dist-1 score.\n" }, { "role": "assistant", "content": "The table shows that the seq2seq model has a Diversity Dist-1 score of 0.020, which is not the highest. DAIM has the highest score of 0.032. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/203.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Quantitative evaluation on the Twitter dataset." ], "reasoning": "The table shows that the seq2seq model has a Diversity Dist-1 score of 0.020, which is not the highest. DAIM has the highest score of 0.032.", "statement": "The seq2seq model has the highest Diversity Dist-1 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Quantitative evaluation on the Reddit dataset. (∗ is implemented based on [5].)'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Incorporating adversarial loss improves the diversity of generated responses, and relevance increases slightly under most metrics except for BLEU.Quantitative evaluation We first evaluated our methods on the Reddit dataset using the relevance and diversity metrics. We truncated the vocabulary to contain only the most frequent 20,000 words. We observe that by incorporating the adversarial loss the diversity of generated responses is improved (cGAN vs. seq2seq). The relevance under most metrics (except for BLEU), increases by a small amount." } ], "images": [ "scigen/train/204.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Quantitative evaluation on the Reddit dataset. (∗ is implemented based on [5].)" ], "reasoning": "Incorporating adversarial loss improves the diversity of generated responses, and relevance increases slightly under most metrics except for BLEU.", "description": "Quantitative evaluation We first evaluated our methods on the Reddit dataset using the relevance and diversity metrics. We truncated the vocabulary to contain only the most frequent 20,000 words. We observe that by incorporating the adversarial loss the diversity of generated responses is improved (cGAN vs. seq2seq). The relevance under most metrics (except for BLEU), increases by a small amount.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Quantitative evaluation on the Reddit dataset. (∗ is implemented based on [5].)', solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model has the highest Relevance Average score?\n" }, { "role": "assistant", "content": "To find the model with the highest Relevance Average score, compare the Relevance Average scores of all models listed in the table. AIM has the highest Relevance Average score of 0.645. So, the answer is {\"answer\": \"AIM\"}" } ], "images": [ "scigen/train/204.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Quantitative evaluation on the Reddit dataset. (∗ is implemented based on [5].)" ], "reasoning": "To find the model with the highest Relevance Average score, compare the Relevance Average scores of all models listed in the table. AIM has the highest Relevance Average score of 0.645.", "question": "Which model has the highest Relevance Average score?", "answer": "So, the answer is {\"answer\": \"AIM\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The cGAN model has a higher Diversity Dist-2 score than the seq2seq model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe cGAN model has a higher Diversity Dist-2 score than the seq2seq model.\n" }, { "role": "assistant", "content": "The table shows that the Diversity Dist-2 score for cGAN is 0.199, while for seq2seq it is 0.153. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/204.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Quantitative evaluation on the Reddit dataset. (∗ is implemented based on [5].)" ], "reasoning": "The table shows that the Diversity Dist-2 score for cGAN is 0.199, while for seq2seq it is 0.153. Therefore, the statement is supported.", "statement": "The cGAN model has a higher Diversity Dist-2 score than the seq2seq model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: Results of sentence selection on the dev set of SQuAD and NewsQA. (Top) We compare different models and training methods. We report Top 1 accuracy (Top 1) and Mean Average Precision (MAP). Our selector outperforms the previous state-of-the-art Tan et al. (2018). (Bottom) We compare different selection methods. We report the number of selected sentences (N sent) and the accuracy of sentence selection (Acc). ‘T’, ‘M’ and ‘N’ are training techniques described in Section 3.2 (weight transfer, data modification and score normalization, respectively).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The model is trained using three techniques: transferring weights from a QA model, modifying training data based on F1 scores, and using a selector that outperforms previous methods by up to 2.9% MAP.We introduce 3 techniques to train the model. (i) As the encoder module of our model is identical to that of S-Reader, we transfer the weights to the encoder module from the QA model trained on the single oracle sentence (Oracle). (ii) We modify the training data by treating a sentence as a wrong sentence if the QA model gets 0 F1, even if the sentence is the oracle sentence. (iii) First, our selector outperforms TF-IDF method and the previous state-of-the-art by large margin (up to 2.9% MAP)." } ], "images": [ "scigen/train/205.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of sentence selection on the dev set of SQuAD and NewsQA. (Top) We compare different models and training methods. We report Top 1 accuracy (Top 1) and Mean Average Precision (MAP). Our selector outperforms the previous state-of-the-art Tan et al. (2018). (Bottom) We compare different selection methods. We report the number of selected sentences (N sent) and the accuracy of sentence selection (Acc). ‘T’, ‘M’ and ‘N’ are training techniques described in Section 3.2 (weight transfer, data modification and score normalization, respectively)." ], "reasoning": "The model is trained using three techniques: transferring weights from a QA model, modifying training data based on F1 scores, and using a selector that outperforms previous methods by up to 2.9% MAP.", "description": "We introduce 3 techniques to train the model. (i) As the encoder module of our model is identical to that of S-Reader, we transfer the weights to the encoder module from the QA model trained on the single oracle sentence (Oracle). (ii) We modify the training data by treating a sentence as a wrong sentence if the QA model gets 0 F1, even if the sentence is the oracle sentence. (iii) First, our selector outperforms TF-IDF method and the previous state-of-the-art by large margin (up to 2.9% MAP).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: Results of sentence selection on the dev set of SQuAD and NewsQA. (Top) We compare different models and training methods. We report Top 1 accuracy (Top 1) and Mean Average Precision (MAP). Our selector outperforms the previous state-of-the-art Tan et al. (2018). (Bottom) We compare different selection methods. We report the number of selected sentences (N sent) and the accuracy of sentence selection (Acc). ‘T’, ‘M’ and ‘N’ are training techniques described in Section 3.2 (weight transfer, data modification and score normalization, respectively).', answer the following question. Give detailed solution consisting of each step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich selection method and configuration achieved the highest accuracy on the SQuAD dataset?\n" }, { "role": "assistant", "content": "To find the highest accuracy on the SQuAD dataset, we look at the 'SQuAD Acc' column and identify the highest value, which is 99.3. This accuracy was achieved using the 'Dyn (T+M+N)' selection method with 1.9 sentences. So, the answer is {\"answer\": \"Dyn (T+M+N) with 1.9 sentences\"}." } ], "images": [ "scigen/train/205.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of sentence selection on the dev set of SQuAD and NewsQA. (Top) We compare different models and training methods. We report Top 1 accuracy (Top 1) and Mean Average Precision (MAP). Our selector outperforms the previous state-of-the-art Tan et al. (2018). (Bottom) We compare different selection methods. We report the number of selected sentences (N sent) and the accuracy of sentence selection (Acc). ‘T’, ‘M’ and ‘N’ are training techniques described in Section 3.2 (weight transfer, data modification and score normalization, respectively)." ], "reasoning": "To find the highest accuracy on the SQuAD dataset, we look at the 'SQuAD Acc' column and identify the highest value, which is 99.3. This accuracy was achieved using the 'Dyn (T+M+N)' selection method with 1.9 sentences.", "question": "Which selection method and configuration achieved the highest accuracy on the SQuAD dataset?", "answer": "So, the answer is {\"answer\": \"Dyn (T+M+N) with 1.9 sentences\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Top k (T+M) method with 3 sentences achieved the highest accuracy on the NewsQA dataset.\n" }, { "role": "assistant", "content": "The table shows that the highest accuracy on the NewsQA dataset is 94.6, achieved by the 'Dyn (T+M+N)' method with 1.9 sentences. The Top k (T+M) method with 3 sentences achieved an accuracy of 92.5, which is not the highest. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/205.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of sentence selection on the dev set of SQuAD and NewsQA. (Top) We compare different models and training methods. We report Top 1 accuracy (Top 1) and Mean Average Precision (MAP). Our selector outperforms the previous state-of-the-art Tan et al. (2018). (Bottom) We compare different selection methods. We report the number of selected sentences (N sent) and the accuracy of sentence selection (Acc). ‘T’, ‘M’ and ‘N’ are training techniques described in Section 3.2 (weight transfer, data modification and score normalization, respectively)." ], "reasoning": "The table shows that the highest accuracy on the NewsQA dataset is 94.6, achieved by the 'Dyn (T+M+N)' method with 1.9 sentences. The Top k (T+M) method with 3 sentences achieved an accuracy of 92.5, which is not the highest.", "statement": "The Top k (T+M) method with 3 sentences achieved the highest accuracy on the NewsQA dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Results of sentence selection on the dev set of SQuAD and NewsQA. (Top) We compare different models and training methods. We report Top 1 accuracy (Top 1) and Mean Average Precision (MAP). Our selector outperforms the previous state-of-the-art Tan et al. (2018). (Bottom) We compare different selection methods. We report the number of selected sentences (N sent) and the accuracy of sentence selection (Acc). ‘T’, ‘M’ and ‘N’ are training techniques described in Section 3.2 (weight transfer, data modification and score normalization, respectively).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The model is trained using three techniques: transferring weights from a QA model, modifying training data based on F1 scores, and using a selector that outperforms TF-IDF and previous state-of-the-art methods.We introduce 3 techniques to train the model. (i) As the encoder module of our model is identical to that of S-Reader, we transfer the weights to the encoder module from the QA model trained on the single oracle sentence (Oracle). (ii) We modify the training data by treating a sentence as a wrong sentence if the QA model gets 0 F1, even if the sentence is the oracle sentence. (iii) First, our selector outperforms TF-IDF method and the previous state-of-the-art by large margin (up to 2.9% MAP)." } ], "images": [ "scigen/train/206.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of sentence selection on the dev set of SQuAD and NewsQA. (Top) We compare different models and training methods. We report Top 1 accuracy (Top 1) and Mean Average Precision (MAP). Our selector outperforms the previous state-of-the-art Tan et al. (2018). (Bottom) We compare different selection methods. We report the number of selected sentences (N sent) and the accuracy of sentence selection (Acc). ‘T’, ‘M’ and ‘N’ are training techniques described in Section 3.2 (weight transfer, data modification and score normalization, respectively)." ], "reasoning": "The model is trained using three techniques: transferring weights from a QA model, modifying training data based on F1 scores, and using a selector that outperforms TF-IDF and previous state-of-the-art methods.", "description": "We introduce 3 techniques to train the model. (i) As the encoder module of our model is identical to that of S-Reader, we transfer the weights to the encoder module from the QA model trained on the single oracle sentence (Oracle). (ii) We modify the training data by treating a sentence as a wrong sentence if the QA model gets 0 F1, even if the sentence is the oracle sentence. (iii) First, our selector outperforms TF-IDF method and the previous state-of-the-art by large margin (up to 2.9% MAP).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 4: Results of sentence selection on the dev set of SQuAD and NewsQA. (Top) We compare different models and training methods. We report Top 1 accuracy (Top 1) and Mean Average Precision (MAP). Our selector outperforms the previous state-of-the-art Tan et al. (2018). (Bottom) We compare different selection methods. We report the number of selected sentences (N sent) and the accuracy of sentence selection (Acc). ‘T’, ‘M’ and ‘N’ are training techniques described in Section 3.2 (weight transfer, data modification and score normalization, respectively).', unfold the steps to solve the question given next. Output the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which model achieved the highest SQuAD MAP score?\n" }, { "role": "assistant", "content": "By comparing the SQuAD MAP scores in the table, 'Our selector (T+M, T+M+N)' achieved the highest score of 95.0. So, the answer is {\"answer\": \"Our selector (T+M, T+M+N)\"}." } ], "images": [ "scigen/train/206.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of sentence selection on the dev set of SQuAD and NewsQA. (Top) We compare different models and training methods. We report Top 1 accuracy (Top 1) and Mean Average Precision (MAP). Our selector outperforms the previous state-of-the-art Tan et al. (2018). (Bottom) We compare different selection methods. We report the number of selected sentences (N sent) and the accuracy of sentence selection (Acc). ‘T’, ‘M’ and ‘N’ are training techniques described in Section 3.2 (weight transfer, data modification and score normalization, respectively)." ], "reasoning": "By comparing the SQuAD MAP scores in the table, 'Our selector (T+M, T+M+N)' achieved the highest score of 95.0.", "question": "Which model achieved the highest SQuAD MAP score?", "answer": "So, the answer is {\"answer\": \"Our selector (T+M, T+M+N)\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Our selector (T) outperforms TF-IDF in NewsQA Top 1., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nOur selector (T) outperforms TF-IDF in NewsQA Top 1.\n" }, { "role": "assistant", "content": "The table shows that 'Our selector (T)' has a NewsQA Top 1 score of 67.1, which is higher than the TF-IDF score of 49.8. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/206.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of sentence selection on the dev set of SQuAD and NewsQA. (Top) We compare different models and training methods. We report Top 1 accuracy (Top 1) and Mean Average Precision (MAP). Our selector outperforms the previous state-of-the-art Tan et al. (2018). (Bottom) We compare different selection methods. We report the number of selected sentences (N sent) and the accuracy of sentence selection (Acc). ‘T’, ‘M’ and ‘N’ are training techniques described in Section 3.2 (weight transfer, data modification and score normalization, respectively)." ], "reasoning": "The table shows that 'Our selector (T)' has a NewsQA Top 1 score of 67.1, which is higher than the TF-IDF score of 49.8.", "statement": "Our selector (T) outperforms TF-IDF in NewsQA Top 1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 8: Results on the dev-full set of TriviaQA (Wikipedia) and the dev set of SQuAD-Open. Full results (including the dev-verified set on TriviaQA) are in Appendix C. For training Full and Minimal on TriviaQA, we use 10 paragraphs and 20 sentences, respectively. For training Full and Minimal on SQuAD-Open, we use 20 paragraphs and 20 sentences, respectively. For evaluating Full and Minimal, we use 40 paragraphs and 5-20 sentences, respectively. ‘n sent’ indicates the number of sentences used during inference. ‘Acc’ indicates accuracy of whether answer text is contained in selected context. ‘Sp’ indicates inference speed. We compare with the results from the sentences selected by TF-IDF method and our selector (Dyn). We also compare with published Rank1-3 models. For TriviaQA(Wikipedia), they are Neural Casecades Swayamdipta et al. (2018), Reading Twice for Natural Language Understanding Weissenborn (2017) and Mnemonic Reader Hu et al. (2017). For SQuAD-Open, they are DrQA Chen et al. (2017) (Multitask), R3 Wang et al. (2018) and DrQA (Plain).'.\n" }, { "role": "assistant", "content": "The Minimal model with our sentence selector achieves higher F1 and EM scores compared to the TF-IDF selector, and outperforms the state-of-the-art on both datasets.First, Minimal obtains higher F1 and EM over Full, with the inference speedup of up to 13.8×. Second, the model with our sentence selector with Dyn achieves higher F1 and EM over the model with TF-IDF selector. For example, on the development-full set, with 5 sentences per question on average, the model with Dyn achieves 59.5 F1 while the model with TF-IDF method achieves 51.9 F1. Third, we outperforms the published state-of-the-art on both dataset." } ], "images": [ "scigen/train/207.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Results on the dev-full set of TriviaQA (Wikipedia) and the dev set of SQuAD-Open. Full results (including the dev-verified set on TriviaQA) are in Appendix C. For training Full and Minimal on TriviaQA, we use 10 paragraphs and 20 sentences, respectively. For training Full and Minimal on SQuAD-Open, we use 20 paragraphs and 20 sentences, respectively. For evaluating Full and Minimal, we use 40 paragraphs and 5-20 sentences, respectively. ‘n sent’ indicates the number of sentences used during inference. ‘Acc’ indicates accuracy of whether answer text is contained in selected context. ‘Sp’ indicates inference speed. We compare with the results from the sentences selected by TF-IDF method and our selector (Dyn). We also compare with published Rank1-3 models. For TriviaQA(Wikipedia), they are Neural Casecades Swayamdipta et al. (2018), Reading Twice for Natural Language Understanding Weissenborn (2017) and Mnemonic Reader Hu et al. (2017). For SQuAD-Open, they are DrQA Chen et al. (2017) (Multitask), R3 Wang et al. (2018) and DrQA (Plain)." ], "reasoning": "The Minimal model with our sentence selector achieves higher F1 and EM scores compared to the TF-IDF selector, and outperforms the state-of-the-art on both datasets.", "description": "First, Minimal obtains higher F1 and EM over Full, with the inference speedup of up to 13.8×. Second, the model with our sentence selector with Dyn achieves higher F1 and EM over the model with TF-IDF selector. For example, on the development-full set, with 5 sentences per question on average, the model with Dyn achieves 59.5 F1 while the model with TF-IDF method achieves 51.9 F1. Third, we outperforms the published state-of-the-art on both dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 8: Results on the dev-full set of TriviaQA (Wikipedia) and the dev set of SQuAD-Open. Full results (including the dev-verified set on TriviaQA) are in Appendix C. For training Full and Minimal on TriviaQA, we use 10 paragraphs and 20 sentences, respectively. For training Full and Minimal on SQuAD-Open, we use 20 paragraphs and 20 sentences, respectively. For evaluating Full and Minimal, we use 40 paragraphs and 5-20 sentences, respectively. ‘n sent’ indicates the number of sentences used during inference. ‘Acc’ indicates accuracy of whether answer text is contained in selected context. ‘Sp’ indicates inference speed. We compare with the results from the sentences selected by TF-IDF method and our selector (Dyn). We also compare with published Rank1-3 models. For TriviaQA(Wikipedia), they are Neural Casecades Swayamdipta et al. (2018), Reading Twice for Natural Language Understanding Weissenborn (2017) and Mnemonic Reader Hu et al. (2017). For SQuAD-Open, they are DrQA Chen et al. (2017) (Multitask), R3 Wang et al. (2018) and DrQA (Plain).', answer the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the F1 score of the Minimal model with our sentence selector on the TriviaQA (Wikipedia) dataset?\n" }, { "role": "assistant", "content": "The table shows that the Minimal model with our sentence selector achieves an F1 score of 59.5 on the TriviaQA (Wikipedia) dataset. So, the answer is {\"answer\": \"59.5\"}" } ], "images": [ "scigen/train/207.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Results on the dev-full set of TriviaQA (Wikipedia) and the dev set of SQuAD-Open. Full results (including the dev-verified set on TriviaQA) are in Appendix C. For training Full and Minimal on TriviaQA, we use 10 paragraphs and 20 sentences, respectively. For training Full and Minimal on SQuAD-Open, we use 20 paragraphs and 20 sentences, respectively. For evaluating Full and Minimal, we use 40 paragraphs and 5-20 sentences, respectively. ‘n sent’ indicates the number of sentences used during inference. ‘Acc’ indicates accuracy of whether answer text is contained in selected context. ‘Sp’ indicates inference speed. We compare with the results from the sentences selected by TF-IDF method and our selector (Dyn). We also compare with published Rank1-3 models. For TriviaQA(Wikipedia), they are Neural Casecades Swayamdipta et al. (2018), Reading Twice for Natural Language Understanding Weissenborn (2017) and Mnemonic Reader Hu et al. (2017). For SQuAD-Open, they are DrQA Chen et al. (2017) (Multitask), R3 Wang et al. (2018) and DrQA (Plain)." ], "reasoning": "The table shows that the Minimal model with our sentence selector achieves an F1 score of 59.5 on the TriviaQA (Wikipedia) dataset.", "question": "What is the F1 score of the Minimal model with our sentence selector on the TriviaQA (Wikipedia) dataset?", "answer": "So, the answer is {\"answer\": \"59.5\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Minimal model with our sentence selector achieves a higher F1 score than the Full model on the TriviaQA (Wikipedia) dataset.\n" }, { "role": "assistant", "content": "The table shows that the Minimal model with our sentence selector has an F1 score of 59.5, which is higher than the Full model's F1 score of 59.6 on the TriviaQA (Wikipedia) dataset. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/207.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Results on the dev-full set of TriviaQA (Wikipedia) and the dev set of SQuAD-Open. Full results (including the dev-verified set on TriviaQA) are in Appendix C. For training Full and Minimal on TriviaQA, we use 10 paragraphs and 20 sentences, respectively. For training Full and Minimal on SQuAD-Open, we use 20 paragraphs and 20 sentences, respectively. For evaluating Full and Minimal, we use 40 paragraphs and 5-20 sentences, respectively. ‘n sent’ indicates the number of sentences used during inference. ‘Acc’ indicates accuracy of whether answer text is contained in selected context. ‘Sp’ indicates inference speed. We compare with the results from the sentences selected by TF-IDF method and our selector (Dyn). We also compare with published Rank1-3 models. For TriviaQA(Wikipedia), they are Neural Casecades Swayamdipta et al. (2018), Reading Twice for Natural Language Understanding Weissenborn (2017) and Mnemonic Reader Hu et al. (2017). For SQuAD-Open, they are DrQA Chen et al. (2017) (Multitask), R3 Wang et al. (2018) and DrQA (Plain)." ], "reasoning": "The table shows that the Minimal model with our sentence selector has an F1 score of 59.5, which is higher than the Full model's F1 score of 59.6 on the TriviaQA (Wikipedia) dataset.", "statement": "The Minimal model with our sentence selector achieves a higher F1 score than the Full model on the TriviaQA (Wikipedia) dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 7: Robustness checks on dev sets of the corresponding datasets.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The model's performance is generally robust to changes in the number of blocks and encoder layers, with optimal performance often achieved with 3 blocks or 3 encoder layers, except on the WikiQA dataset where high variance is observed.The number of blocks is tuned in a range from 1 to 3. The number of layers of the convolutional encoder is tuned from 1 to 3. we validate with up to 5 blocks and layers, in all other experiments we deliberately limit the maximum number of blocks and number of layers to 3 to control the size of the model. The initial learning rate is tuned from 0.0001 to 0.003. The batch size is tuned from 64 to 512. The threshold for gradient clipping is set to 5. For all the experiments except for the comparison of ensemble models, we report the average score and the standard deviation of 10 runs. Robustness checks. To check whether our proposed method is robust to different variants of structural hyperparameters, we experiment with (1) the number of blocks varying from 1 to 5 with the number of encoder layers set to 2; (2) the number of encoder layers varying from 1 to 5 with the number of blocks set to 2. Robustness checks are performed on the development set of SNLI, Quora and Scitail. We can see in the table that fewer blocks or layers may not be sufficient but adding more blocks or layers than necessary hardly harms the performance. On WikiQA dataset, our method does not seem to be robust to structural hyperparameter changes. We leave the further investigation of the high variance on the WikiQA dataset for further work." } ], "images": [ "scigen/train/208.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Robustness checks on dev sets of the corresponding datasets." ], "reasoning": "The model's performance is generally robust to changes in the number of blocks and encoder layers, with optimal performance often achieved with 3 blocks or 3 encoder layers, except on the WikiQA dataset where high variance is observed.", "description": "The number of blocks is tuned in a range from 1 to 3. The number of layers of the convolutional encoder is tuned from 1 to 3. we validate with up to 5 blocks and layers, in all other experiments we deliberately limit the maximum number of blocks and number of layers to 3 to control the size of the model. The initial learning rate is tuned from 0.0001 to 0.003. The batch size is tuned from 64 to 512. The threshold for gradient clipping is set to 5. For all the experiments except for the comparison of ensemble models, we report the average score and the standard deviation of 10 runs. Robustness checks. To check whether our proposed method is robust to different variants of structural hyperparameters, we experiment with (1) the number of blocks varying from 1 to 5 with the number of encoder layers set to 2; (2) the number of encoder layers varying from 1 to 5 with the number of blocks set to 2. Robustness checks are performed on the development set of SNLI, Quora and Scitail. We can see in the table that fewer blocks or layers may not be sufficient but adding more blocks or layers than necessary hardly harms the performance. On WikiQA dataset, our method does not seem to be robust to structural hyperparameter changes. We leave the further investigation of the high variance on the WikiQA dataset for further work.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: Table 7: Robustness checks on dev sets of the corresponding datasets.\nQuestion: What is the highest average score achieved on the Scitail dataset and with how many encoder layers?\n" }, { "role": "assistant", "content": "To find the highest average score on the Scitail dataset, look at the Scitail column and identify the highest value. The highest score is 89.1, achieved with 5 encoder layers. So, the answer is {\"answer\": \"89.1 with 5 encoder layers\"}." } ], "images": [ "scigen/train/208.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Robustness checks on dev sets of the corresponding datasets." ], "reasoning": "To find the highest average score on the Scitail dataset, look at the Scitail column and identify the highest value. The highest score is 89.1, achieved with 5 encoder layers.", "question": "What is the highest average score achieved on the Scitail dataset and with how many encoder layers?", "answer": "So, the answer is {\"answer\": \"89.1 with 5 encoder layers\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model achieves its highest average score on the Quora dataset with 4 blocks.\n" }, { "role": "assistant", "content": "To verify this statement, look at the Quora column and identify the highest average score. The highest score is 89.5, which is achieved with 4 blocks. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/208.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Robustness checks on dev sets of the corresponding datasets." ], "reasoning": "To verify this statement, look at the Quora column and identify the highest average score. The highest score is 89.5, which is achieved with 4 blocks.", "statement": "The model achieves its highest average score on the Quora dataset with 4 blocks.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 6: Ablation study on dev sets of the corresponding datasets.'.\n" }, { "role": "assistant", "content": "The ablation studies show that richer features and stacked blocks are crucial for better performance, while simpler fusion layers degrade performance.The first ablation baseline shows that without richer features as the alignment input, the performance on all datasets degrades significantly. This is the key component in the whole model. The results of the second baseline show that vanilla residual connections without direct access to the original point-wise features are not enough to model the relations in many text matching tasks. The simpler implementation of the fusion layer leads to evidently worse performance, indicating that the fusion layer cannot be further simplified. On the other hand, the alignment layer and the prediction layer can be simplified on some of the datasets. In the last ablation study, we can see that parallel blocks perform worse than stacked blocks, which supports the preference for deeper models over wider ones." } ], "images": [ "scigen/train/209.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Ablation study on dev sets of the corresponding datasets." ], "reasoning": "The ablation studies show that richer features and stacked blocks are crucial for better performance, while simpler fusion layers degrade performance.", "description": "The first ablation baseline shows that without richer features as the alignment input, the performance on all datasets degrades significantly. This is the key component in the whole model. The results of the second baseline show that vanilla residual connections without direct access to the original point-wise features are not enough to model the relations in many text matching tasks. The simpler implementation of the fusion layer leads to evidently worse performance, indicating that the fusion layer cannot be further simplified. On the other hand, the alignment layer and the prediction layer can be simplified on some of the datasets. In the last ablation study, we can see that parallel blocks perform worse than stacked blocks, which supports the preference for deeper models over wider ones.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: 'Table 6: Ablation study on dev sets of the corresponding datasets.'\nQuestion: Which baseline shows the worst performance on the Scitail dataset?\n" }, { "role": "assistant", "content": "The table shows that the 'w/o enc-in' baseline has the lowest score of 78.1 on the Scitail dataset, indicating the worst performance. So, the answer is {\"answer\": \"w/o enc-in\"}." } ], "images": [ "scigen/train/209.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Ablation study on dev sets of the corresponding datasets." ], "reasoning": "The table shows that the 'w/o enc-in' baseline has the lowest score of 78.1 on the Scitail dataset, indicating the worst performance.", "question": "Which baseline shows the worst performance on the Scitail dataset?", "answer": "So, the answer is {\"answer\": \"w/o enc-in\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe alignment layer can be simplified on the WikiQA dataset without significant performance loss.\n" }, { "role": "assistant", "content": "The table shows that the 'alignment alt.' baseline has a score of 0.7702 on the WikiQA dataset, which is close to the original score of 0.7740, supporting the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/209.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Ablation study on dev sets of the corresponding datasets." ], "reasoning": "The table shows that the 'alignment alt.' baseline has a score of 0.7702 on the WikiQA dataset, which is close to the original score of 0.7740, supporting the statement.", "statement": "The alignment layer can be simplified on the WikiQA dataset without significant performance loss.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: Comparison of normalizing query and key in N-SAN.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Normalizing both Q and K yields similar performance to normalizing Q alone, both outperforming SAN, while normalizing K alone is less effective.What if we normalize the keys in addition to the queries? We have the following observations. 1) Normalizing either of Q and K could increase the performance. 2) The performances of normalizing both Q and K and normalizing Q alone are very similar, and are both significantly higher than that of SAN. 3) Normalizing K alone is inferior to normalizing Q alone. The reason is that normalizing K is equivalent to normalizing Θ in Eqn." } ], "images": [ "scigen/train/210.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of normalizing query and key in N-SAN." ], "reasoning": "Normalizing both Q and K yields similar performance to normalizing Q alone, both outperforming SAN, while normalizing K alone is less effective.", "description": "What if we normalize the keys in addition to the queries? We have the following observations. 1) Normalizing either of Q and K could increase the performance. 2) The performances of normalizing both Q and K and normalizing Q alone are very similar, and are both significantly higher than that of SAN. 3) Normalizing K alone is inferior to normalizing Q alone. The reason is that normalizing K is equivalent to normalizing Θ in Eqn.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: Comparison of normalizing query and key in N-SAN.'. Solve the following question based on the table.\n\nQuestion: What is the B@4 score when both Query and Key are normalized?\nLet's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the B@4 score when both Query and Key are normalized, look at the row where both Query and Key are marked with a check (√). The B@4 score in this row is 39.4. So, the answer is {\"answer\": \"39.4\"}" } ], "images": [ "scigen/train/210.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of normalizing query and key in N-SAN." ], "reasoning": "To find the B@4 score when both Query and Key are normalized, look at the row where both Query and Key are marked with a check (√). The B@4 score in this row is 39.4.", "question": "What is the B@4 score when both Query and Key are normalized?", "answer": "So, the answer is {\"answer\": \"39.4\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nNormalizing both Query and Key results in the highest M score.\n" }, { "role": "assistant", "content": "The table shows that the M score is 29.1 when both Query and Key are normalized, which is the highest M score compared to other configurations. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/210.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of normalizing query and key in N-SAN." ], "reasoning": "The table shows that the M score is 29.1 when both Query and Key are normalized, which is the highest M score compared to other configurations.", "statement": "Normalizing both Query and Key results in the highest M score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Comparison of using various normalization methods in NSA.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "IN and IN w/o γ,β outperform SAN and other normalization methods, with extra affine transformations being unnecessary.Since we introduced IN into the NSA module for normalization, an intuitive question to ask is whether we can replace IN with other normalization methods. We have the following observations. 1) Using LN slightly decreases the performance. We conjecture that is because LN normalizes activations of all channels with the same normalization terms (μ and σ), thus limiting the expression capacity of each channel when calculating attention weights. 2) IN and IN w/o γ,β significantly outperform SAN and all the other normalization methods. Meanwhile, the extra affine transformations (γ and β) are not necessary. 3) Applying BN outperforms SAN but is inferior to adopting IN. BN has a similar effect as IN to reduce the internal covariate shift by fixing the distribution of the queries. However, as is described in Sec. ." } ], "images": [ "scigen/train/211.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of using various normalization methods in NSA." ], "reasoning": "IN and IN w/o γ,β outperform SAN and other normalization methods, with extra affine transformations being unnecessary.", "description": "Since we introduced IN into the NSA module for normalization, an intuitive question to ask is whether we can replace IN with other normalization methods. We have the following observations. 1) Using LN slightly decreases the performance. We conjecture that is because LN normalizes activations of all channels with the same normalization terms (μ and σ), thus limiting the expression capacity of each channel when calculating attention weights. 2) IN and IN w/o γ,β significantly outperform SAN and all the other normalization methods. Meanwhile, the extra affine transformations (γ and β) are not necessary. 3) Applying BN outperforms SAN but is inferior to adopting IN. BN has a similar effect as IN to reduce the internal covariate shift by fixing the distribution of the queries. However, as is described in Sec. .", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 2: Comparison of using various normalization methods in NSA.' in mind, break down and resolve the question below step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich normalization method achieves the highest score in the C metric?\n" }, { "role": "assistant", "content": "By comparing the C metric scores in the table, IN w/o γ,β achieves the highest score of 130.8. So, the answer is {\"answer\": \"IN w/o γ,β\"}." } ], "images": [ "scigen/train/211.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of using various normalization methods in NSA." ], "reasoning": "By comparing the C metric scores in the table, IN w/o γ,β achieves the highest score of 130.8.", "question": "Which normalization method achieves the highest score in the C metric?", "answer": "So, the answer is {\"answer\": \"IN w/o γ,β\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nLN achieves the highest score in the S metric.\n" }, { "role": "assistant", "content": "The table shows that IN and IN w/o γ,β both achieve the highest score of 23.0 in the S metric, not LN. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/211.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of using various normalization methods in NSA." ], "reasoning": "The table shows that IN and IN w/o γ,β both achieve the highest score of 23.0 in the S metric, not LN.", "statement": "LN achieves the highest score in the S metric.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: Comparison of various variants of GSA.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The query-dependent approach outperforms other variants by incorporating content information of the associated query, highlighting the effect of relative geometry information.‘+absolute\" denotes adding absolute geometry information of each individual object to their input representations at the bottoms of the encoder. We have the following findings. 1) Adding the absolute geometry information (‘‘absolute\") is not beneficial to the performance. That is probably because it is too complex for SA to infer the 2D layout of objects from their absolute geometry information. 2) All the proposed variants of GSA can improve the performance of SAN, showing the advantages of using relative geometry information. 3) ‘‘query-dependent\" brings the best performance and outperforms the content-independent variant, proving that incorporating the content information of the associated query can help infer a better geometric bias. 4) ‘‘key-dependent\" is inferior to ‘‘query-dependent\". That is because when using key-dependent geometric bias, the scores ϕ3ij= K′j⊤Gij condition on different keys K′j, thus the differences in Gij may be overwhelmed by the differences in K′j when performing softmax on the keys’ dimension. In comparision, when using query-dependent geometric bias, the effect of Gij could be highlighted since the scores condition on a common query Q′i when performing softmax. We did not observe further improvement when combing these variants into ϕ in Eq." } ], "images": [ "scigen/train/212.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of various variants of GSA." ], "reasoning": "The query-dependent approach outperforms other variants by incorporating content information of the associated query, highlighting the effect of relative geometry information.", "description": "‘+absolute\" denotes adding absolute geometry information of each individual object to their input representations at the bottoms of the encoder. We have the following findings. 1) Adding the absolute geometry information (‘‘absolute\") is not beneficial to the performance. That is probably because it is too complex for SA to infer the 2D layout of objects from their absolute geometry information. 2) All the proposed variants of GSA can improve the performance of SAN, showing the advantages of using relative geometry information. 3) ‘‘query-dependent\" brings the best performance and outperforms the content-independent variant, proving that incorporating the content information of the associated query can help infer a better geometric bias. 4) ‘‘key-dependent\" is inferior to ‘‘query-dependent\". That is because when using key-dependent geometric bias, the scores ϕ3ij= K′j⊤Gij condition on different keys K′j, thus the differences in Gij may be overwhelmed by the differences in K′j when performing softmax on the keys’ dimension. In comparision, when using query-dependent geometric bias, the effect of Gij could be highlighted since the scores condition on a common query Q′i when performing softmax. We did not observe further improvement when combing these variants into ϕ in Eq.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich approach has the highest score in the S metric and why?\nSolve the above question based on the table titled 'Table 4: Comparison of various variants of GSA.'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The query-dependent approach has the highest score in the S metric with a value of 23.0. This is because incorporating the content information of the associated query helps infer a better geometric bias, highlighting the effect of Gij. So, the answer is {\"answer\": \"query-dependent\"}" } ], "images": [ "scigen/train/212.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of various variants of GSA." ], "reasoning": "The query-dependent approach has the highest score in the S metric with a value of 23.0. This is because incorporating the content information of the associated query helps infer a better geometric bias, highlighting the effect of Gij.", "question": "Which approach has the highest score in the S metric and why?", "answer": "So, the answer is {\"answer\": \"query-dependent\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe absolute approach has the highest score in the B@4 metric.\n" }, { "role": "assistant", "content": "The table shows that the query-dependent approach has the highest score in the B@4 metric with a value of 39.3, not the absolute approach. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/212.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of various variants of GSA." ], "reasoning": "The table shows that the query-dependent approach has the highest score in the B@4 metric with a value of 39.3, not the absolute approach.", "statement": "The absolute approach has the highest score in the B@4 metric.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 7: Video captioning results on VATEX dataset.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The Transformer+NSA method outperforms both the Transformer and VATEX models on all metrics, with a notable improvement in the CIDEr score by 3.7 points over Transformer and 11.4 points over VATEX.We see that the performance of Transformer strongly exceeds that of VATEX, which adopts an LSTM-based architecture. Our Transformer+NSA method consistently improves over Transformer on all metrics. Particularly, our method improves the CIDEr score by 3.7 points when compared to Transformer, and significantly improves the CIDEr score by 11.4 points when compared to VATEX baseline." } ], "images": [ "scigen/train/213.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Video captioning results on VATEX dataset." ], "reasoning": "The Transformer+NSA method outperforms both the Transformer and VATEX models on all metrics, with a notable improvement in the CIDEr score by 3.7 points over Transformer and 11.4 points over VATEX.", "description": "We see that the performance of Transformer strongly exceeds that of VATEX, which adopts an LSTM-based architecture. Our Transformer+NSA method consistently improves over Transformer on all metrics. Particularly, our method improves the CIDEr score by 3.7 points when compared to Transformer, and significantly improves the CIDEr score by 11.4 points when compared to VATEX baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nTable title: Table 7: Video captioning results on VATEX dataset.\n\nQuestion: How much does the Transformer+NSA method improve the CIDEr score compared to the VATEX model?\n" }, { "role": "assistant", "content": "The table shows that the CIDEr score for VATEX is 45.7 and for Transformer+NSA is 57.1. The improvement is calculated as 57.1 - 45.7 = 11.4 points. So, the answer is {\"answer\": \"11.4\"}" } ], "images": [ "scigen/train/213.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Video captioning results on VATEX dataset." ], "reasoning": "The table shows that the CIDEr score for VATEX is 45.7 and for Transformer+NSA is 57.1. The improvement is calculated as 57.1 - 45.7 = 11.4 points.", "question": "How much does the Transformer+NSA method improve the CIDEr score compared to the VATEX model?", "answer": "So, the answer is {\"answer\": \"11.4\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Transformer+NSA method improves the CIDEr score by 3.7 points compared to the Transformer model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Transformer+NSA method improves the CIDEr score by 3.7 points compared to the Transformer model.\n" }, { "role": "assistant", "content": "The table shows that the CIDEr score for the Transformer model is 53.4 and for Transformer+NSA is 57.1. The improvement is calculated as 57.1 - 53.4 = 3.7 points. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/213.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Video captioning results on VATEX dataset." ], "reasoning": "The table shows that the CIDEr score for the Transformer model is 53.4 and for Transformer+NSA is 57.1. The improvement is calculated as 57.1 - 53.4 = 3.7 points.", "statement": "The Transformer+NSA method improves the CIDEr score by 3.7 points compared to the Transformer model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 1: The same BiLSTM-CRF approach was evaluated twice under Evaluation 1. The threshold column depicts the average difference in percentage points F1-score for statistical significance with 0.04'.\n" }, { "role": "assistant", "content": "For the ACE 2005 - Events task, 34.48% of the cases show a significant difference between models A(j)i and ~A(j)i, while for other tasks, the percentage of statistically significant cases ranges from 10.72% to 33.20%.For the ACE 2005 - Events task, we observe in 34.48% of the cases a significant difference between the models A(j)i and ~A(j)i. For the other tasks, we observe similar results and between 10.72% and 33.20% of the cases are statistically significant." } ], "images": [ "scigen/train/214.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The same BiLSTM-CRF approach was evaluated twice under Evaluation 1. The threshold column depicts the average difference in percentage points F1-score for statistical significance with 0.04" ], "reasoning": "For the ACE 2005 - Events task, 34.48% of the cases show a significant difference between models A(j)i and ~A(j)i, while for other tasks, the percentage of statistically significant cases ranges from 10.72% to 33.20%.", "description": "For the ACE 2005 - Events task, we observe in 34.48% of the cases a significant difference between the models A(j)i and ~A(j)i. For the other tasks, we observe similar results and between 10.72% and 33.20% of the cases are statistically significant.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 1: The same BiLSTM-CRF approach was evaluated twice under Evaluation 1. The threshold column depicts the average difference in percentage points F1-score for statistical significance with 0.04' in mind, break down and resolve the question below step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich task has the highest percentage of statistically significant cases?\n" }, { "role": "assistant", "content": "By examining the '% significant' column, we see that the ACE 2005 - Events task has the highest percentage of statistically significant cases at 34.48%. So, the answer is {\"answer\": \"ACE 2005 - Events\"}." } ], "images": [ "scigen/train/214.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The same BiLSTM-CRF approach was evaluated twice under Evaluation 1. The threshold column depicts the average difference in percentage points F1-score for statistical significance with 0.04" ], "reasoning": "By examining the '% significant' column, we see that the ACE 2005 - Events task has the highest percentage of statistically significant cases at 34.48%.", "question": "Which task has the highest percentage of statistically significant cases?", "answer": "So, the answer is {\"answer\": \"ACE 2005 - Events\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe CoNLL 2003 - NER-En task has a higher percentage of statistically significant cases than the GermEval 2014 - NER-De task.\n" }, { "role": "assistant", "content": "The table shows that the CoNLL 2003 - NER-En task has 31.02% significant cases, while the GermEval 2014 - NER-De task has 26.80% significant cases, indicating that the statement is true. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/214.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The same BiLSTM-CRF approach was evaluated twice under Evaluation 1. The threshold column depicts the average difference in percentage points F1-score for statistical significance with 0.04" ], "reasoning": "The table shows that the CoNLL 2003 - NER-En task has 31.02% significant cases, while the GermEval 2014 - NER-De task has 26.80% significant cases, indicating that the statement is true.", "statement": "The CoNLL 2003 - NER-En task has a higher percentage of statistically significant cases than the GermEval 2014 - NER-De task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: The same BiLSTM-CRF approach was evaluated twice under Evaluation 2. The threshold column depicts the average difference in percentage points F1-score for statistical significance with 0.04', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The low Spearman’s rank correlation ρ between development and test scores indicates that high development scores do not guarantee high test scores, with the ACE 2005 - Events task showing a test performance variance of up to 3.68 percentage points F1-score.For all tasks, we observe small Spearman’s rank correlation ρ between the development and the test score. The low correlation indicates that a run with high development score doesn’t have to yield a high test score. The value 3.68 for the ACE 2005 - Events tasks indicates that, given two models with the same performance on the development set, the test performance can vary up to 3.68 percentage points F1-score (95% interval)." } ], "images": [ "scigen/train/215.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The same BiLSTM-CRF approach was evaluated twice under Evaluation 2. The threshold column depicts the average difference in percentage points F1-score for statistical significance with 0.04" ], "reasoning": "The low Spearman’s rank correlation ρ between development and test scores indicates that high development scores do not guarantee high test scores, with the ACE 2005 - Events task showing a test performance variance of up to 3.68 percentage points F1-score.", "description": "For all tasks, we observe small Spearman’s rank correlation ρ between the development and the test score. The low correlation indicates that a run with high development score doesn’t have to yield a high test score. The value 3.68 for the ACE 2005 - Events tasks indicates that, given two models with the same performance on the development set, the test performance can vary up to 3.68 percentage points F1-score (95% interval).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2: The same BiLSTM-CRF approach was evaluated twice under Evaluation 2. The threshold column depicts the average difference in percentage points F1-score for statistical significance with 0.04', unfold the steps to solve the question given next. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nQuestion: Which task has the highest maximum test performance variance?\n" }, { "role": "assistant", "content": "To find the task with the highest maximum test performance variance, look at the column labeled 'Δ(test) Max' and identify the highest value. The highest value is 7.98, which corresponds to the ACE 2005 - Events task. So, the answer is {\"answer\": \"ACE 2005 - Events\"}." } ], "images": [ "scigen/train/215.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The same BiLSTM-CRF approach was evaluated twice under Evaluation 2. The threshold column depicts the average difference in percentage points F1-score for statistical significance with 0.04" ], "reasoning": "To find the task with the highest maximum test performance variance, look at the column labeled 'Δ(test) Max' and identify the highest value. The highest value is 7.98, which corresponds to the ACE 2005 - Events task.", "question": "Which task has the highest maximum test performance variance?", "answer": "So, the answer is {\"answer\": \"ACE 2005 - Events\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe CoNLL 2003 - NER-En task has a higher Spearman’s rank correlation ρ than the CoNLL 2000 - Chunking task.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Spearman’s rank correlation ρ values for the CoNLL 2003 - NER-En task and the CoNLL 2000 - Chunking task. The CoNLL 2003 - NER-En task has a ρ value of 0.234, while the CoNLL 2000 - Chunking task has a ρ value of 0.262. Since 0.234 is less than 0.262, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/215.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The same BiLSTM-CRF approach was evaluated twice under Evaluation 2. The threshold column depicts the average difference in percentage points F1-score for statistical significance with 0.04" ], "reasoning": "To verify this statement, compare the Spearman’s rank correlation ρ values for the CoNLL 2003 - NER-En task and the CoNLL 2000 - Chunking task. The CoNLL 2003 - NER-En task has a ρ value of 0.234, while the CoNLL 2000 - Chunking task has a ρ value of 0.262. Since 0.234 is less than 0.262, the statement is refuted.", "statement": "The CoNLL 2003 - NER-En task has a higher Spearman’s rank correlation ρ than the CoNLL 2000 - Chunking task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: 95% percentile of Δ(test) after averaging.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "As n increases, the value Δ(test)95 decreases, indicating that the mean score becomes more stable. However, for the CoNLL 2003 NER-En task, there is still a difference of 0.26 percentage points F1-score for n=10, and for the ACE 2005 Events dataset, the difference is 1.39 percentage points F1-score.For increasing n the value Δ(test)95 decreases, i.e. the mean score becomes more stable. However, for the CoNLL 2003 NER- En task we still observe a difference of 0.26 percentage points F1-score between the mean scores for n=10. For the ACE 2005 Events dataset, the value is even at 1.39 percentage points F1-score." } ], "images": [ "scigen/train/216.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: 95% percentile of Δ(test) after averaging." ], "reasoning": "As n increases, the value Δ(test)95 decreases, indicating that the mean score becomes more stable. However, for the CoNLL 2003 NER-En task, there is still a difference of 0.26 percentage points F1-score for n=10, and for the ACE 2005 Events dataset, the difference is 1.39 percentage points F1-score.", "description": "For increasing n the value Δ(test)95 decreases, i.e. the mean score becomes more stable. However, for the CoNLL 2003 NER- En task we still observe a difference of 0.26 percentage points F1-score between the mean scores for n=10. For the ACE 2005 Events dataset, the value is even at 1.39 percentage points F1-score.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 5: 95% percentile of Δ(test) after averaging.' and proceed to solve the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the Δ(test)95 value for n=10 scores for the ACE-Ent. task?\n" }, { "role": "assistant", "content": "To find the Δ(test)95 value for n=10 scores for the ACE-Ent. task, locate the row corresponding to the ACE-Ent. task and then find the value under the column for n=10 scores. So, the answer is {\"answer\": \"0.38\"}." } ], "images": [ "scigen/train/216.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: 95% percentile of Δ(test) after averaging." ], "reasoning": "To find the Δ(test)95 value for n=10 scores for the ACE-Ent. task, locate the row corresponding to the ACE-Ent. task and then find the value under the column for n=10 scores.", "question": "What is the Δ(test)95 value for n=10 scores for the ACE-Ent. task?", "answer": "So, the answer is {\"answer\": \"0.38\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Δ(test)95 value for n=5 scores for the Chk. task is 0.16., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Δ(test)95 value for n=5 scores for the Chk. task is 0.16.\n" }, { "role": "assistant", "content": "To verify this statement, locate the row corresponding to the Chk. task and then find the value under the column for n=5 scores. The value is indeed 0.16. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/216.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: 95% percentile of Δ(test) after averaging." ], "reasoning": "To verify this statement, locate the row corresponding to the Chk. task and then find the value under the column for n=5 scores. The value is indeed 0.16.", "statement": "The Δ(test)95 value for n=5 scores for the Chk. task is 0.16.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: Different choices of optimal loss functions and the predictive performances over three data sets Subset1, Subset2 and FB15K, where fr(h,t)=∥h+r−t∥22, (h,r,t) is a triple in knowledge graph, and (h′,r,t′) is incorrect triple.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Different subsets of the FB15K knowledge graph require different margin values in the optimal margin-based loss function to account for the locality of knowledge graphs.To verify this, we construct knowledge graphs with different locality. We simply partition a knowledge graph into different subgraphs in a uniform manner. Each subgraph contains different types of relations and their corresponding entities. Moreover, different subgraphs have the identical number of relations for the sake of balance of the number of entities. We claim that over different subgraphs, the optimal margin-based loss function may be different in terms of the margin. To validate this point, we perform the embedding method And we partition FB15K into five subsets with equal size of relations. For example, one subset, named Subset1, contains 13,666 entities and 269 relations. Another subset, named Subset2, has 13603 entities and 269 relations. More precisely, they take different values of margin as 3 and 2, respectively. This suggests that knowledge embedding of different knowledge graphs with a global setting of loss function can not well represent the locality of knowledge graphs, and it is indispensable to propose a locality sensitive loss function with different margins." } ], "images": [ "scigen/train/217.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Different choices of optimal loss functions and the predictive performances over three data sets Subset1, Subset2 and FB15K, where fr(h,t)=∥h+r−t∥22, (h,r,t) is a triple in knowledge graph, and (h′,r,t′) is incorrect triple." ], "reasoning": "Different subsets of the FB15K knowledge graph require different margin values in the optimal margin-based loss function to account for the locality of knowledge graphs.", "description": "To verify this, we construct knowledge graphs with different locality. We simply partition a knowledge graph into different subgraphs in a uniform manner. Each subgraph contains different types of relations and their corresponding entities. Moreover, different subgraphs have the identical number of relations for the sake of balance of the number of entities. We claim that over different subgraphs, the optimal margin-based loss function may be different in terms of the margin. To validate this point, we perform the embedding method And we partition FB15K into five subsets with equal size of relations. For example, one subset, named Subset1, contains 13,666 entities and 269 relations. Another subset, named Subset2, has 13603 entities and 269 relations. More precisely, they take different values of margin as 3 and 2, respectively. This suggests that knowledge embedding of different knowledge graphs with a global setting of loss function can not well represent the locality of knowledge graphs, and it is indispensable to propose a locality sensitive loss function with different margins.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the mean rank raw for Subset2?\nSolve the above question based on the table titled 'Table 1: Different choices of optimal loss functions and the predictive performances over three data sets Subset1, Subset2 and FB15K, where fr(h,t)=∥h+r−t∥22, (h,r,t) is a triple in knowledge graph, and (h′,r,t′) is incorrect triple.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the mean rank raw for Subset2 as 500. So, the answer is {\"answer\": \"500\"}" } ], "images": [ "scigen/train/217.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Different choices of optimal loss functions and the predictive performances over three data sets Subset1, Subset2 and FB15K, where fr(h,t)=∥h+r−t∥22, (h,r,t) is a triple in knowledge graph, and (h′,r,t′) is incorrect triple." ], "reasoning": "The table shows the mean rank raw for Subset2 as 500.", "question": "What is the mean rank raw for Subset2?", "answer": "So, the answer is {\"answer\": \"500\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSubset1 has a higher mean rank filter than FB15K.\n" }, { "role": "assistant", "content": "The table shows that Subset1 has a mean rank filter of 240, while FB15K has a mean rank filter of 125, indicating that Subset1 has a higher mean rank filter. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/217.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Different choices of optimal loss functions and the predictive performances over three data sets Subset1, Subset2 and FB15K, where fr(h,t)=∥h+r−t∥22, (h,r,t) is a triple in knowledge graph, and (h′,r,t′) is incorrect triple." ], "reasoning": "The table shows that Subset1 has a mean rank filter of 240, while FB15K has a mean rank filter of 125, indicating that Subset1 has a higher mean rank filter.", "statement": "Subset1 has a higher mean rank filter than FB15K.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Evaluation results on link prediction.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "TransA achieves the lowest mean rank on both WN18 and FB15K datasets, significantly outperforming other baselines.All parameters are determined on the validation set. It can be seen that on both data sets, TransA obtains the lowest mean rank. Furthermore, on WN18, among the baselines, Unstructured and TransH(unif) perform the best, but TransA decreases the mean rank by about 150 compared with both of them. On FB15K, among the baselines, TransH(unif) is the best baseline. TransA decreases its mean rank by 30∼50. Notice that the decreases on WN18 and FB15K are different, because the number of relations in WN18 is quite small and the relation-specific margin is very small too. In this case, the optimal margin is almost equal to the entity-specific margin. While on FB15K, the number of relations is 1345, and the optimal margin is the combination of the entity-specific margin and the relation-specific margin." } ], "images": [ "scigen/train/218.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Evaluation results on link prediction." ], "reasoning": "TransA achieves the lowest mean rank on both WN18 and FB15K datasets, significantly outperforming other baselines.", "description": "All parameters are determined on the validation set. It can be seen that on both data sets, TransA obtains the lowest mean rank. Furthermore, on WN18, among the baselines, Unstructured and TransH(unif) perform the best, but TransA decreases the mean rank by about 150 compared with both of them. On FB15K, among the baselines, TransH(unif) is the best baseline. TransA decreases its mean rank by 30∼50. Notice that the decreases on WN18 and FB15K are different, because the number of relations in WN18 is quite small and the relation-specific margin is very small too. In this case, the optimal margin is almost equal to the entity-specific margin. While on FB15K, the number of relations is 1345, and the optimal margin is the combination of the entity-specific margin and the relation-specific margin.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Evaluation results on link prediction.\nQuestion: Which metric has the lowest mean rank on both WN18 and FB15K datasets?\nSolve the above question based on the table information. Let's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, we see that TransA has the lowest mean rank for both WN18 (Raw: 165, Filter: 153) and FB15K (Raw: 164, Filter: 58). So, the answer is {\"answer\": \"TransA\"}" } ], "images": [ "scigen/train/218.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Evaluation results on link prediction." ], "reasoning": "By examining the table, we see that TransA has the lowest mean rank for both WN18 (Raw: 165, Filter: 153) and FB15K (Raw: 164, Filter: 58).", "question": "Which metric has the lowest mean rank on both WN18 and FB15K datasets?", "answer": "So, the answer is {\"answer\": \"TransA\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis TransH(unif) has the lowest mean rank on the FB15K dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTransH(unif) has the lowest mean rank on the FB15K dataset.\n" }, { "role": "assistant", "content": "The table shows that TransA, not TransH(unif), has the lowest mean rank on the FB15K dataset (Raw: 164, Filter: 58). TransH(unif) has a mean rank of 303 (Raw) and 211 (Filter). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/218.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Evaluation results on link prediction." ], "reasoning": "The table shows that TransA, not TransH(unif), has the lowest mean rank on the FB15K dataset (Raw: 164, Filter: 58). TransH(unif) has a mean rank of 303 (Raw) and 211 (Filter).", "statement": "TransH(unif) has the lowest mean rank on the FB15K dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Evaluation results of triple classification. (%)'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "TransA performs best on WN11 and FB15K, while NTN is more powerful on FB13 due to its density.All parameters are determined on the validation set. The optimal setting are: λ=0.001, d=220, B=120, μ=0.5 and taking L1 as dissimilarity on WN11; λ=0.001, d=50, B=480, μ=0.5 and taking L1 as dissimilarity on FB13. On WN11, TransA outperforms the other methods. On FB13, the method NTN is shown more powerful. This is consistent with the results in previous literature On FB15K, TransA also performs the best. Since FB13 is much denser to FB15K, NTN is more expressive on dense graph. On sparse graph, TransA is superior to other state-of-the-art embedding methods." } ], "images": [ "scigen/train/219.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Evaluation results of triple classification. (%)" ], "reasoning": "TransA performs best on WN11 and FB15K, while NTN is more powerful on FB13 due to its density.", "description": "All parameters are determined on the validation set. The optimal setting are: λ=0.001, d=220, B=120, μ=0.5 and taking L1 as dissimilarity on WN11; λ=0.001, d=50, B=480, μ=0.5 and taking L1 as dissimilarity on FB13. On WN11, TransA outperforms the other methods. On FB13, the method NTN is shown more powerful. This is consistent with the results in previous literature On FB15K, TransA also performs the best. Since FB13 is much denser to FB15K, NTN is more expressive on dense graph. On sparse graph, TransA is superior to other state-of-the-art embedding methods.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: Evaluation results of triple classification. (%)', solve this question and give detail solutions. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhich method performs best on the WN11 dataset?\n" }, { "role": "assistant", "content": "The table shows the performance of various methods on different datasets. On the WN11 dataset, TransA has the highest score of 93.2. So, the answer is {\"answer\": \"TransA\"}" } ], "images": [ "scigen/train/219.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Evaluation results of triple classification. (%)" ], "reasoning": "The table shows the performance of various methods on different datasets. On the WN11 dataset, TransA has the highest score of 93.2.", "question": "Which method performs best on the WN11 dataset?", "answer": "So, the answer is {\"answer\": \"TransA\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: TransH(unif) outperforms TransH(bern) on the FB13 dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nTransH(unif) outperforms TransH(bern) on the FB13 dataset.\n" }, { "role": "assistant", "content": "The table shows that TransH(unif) has a score of 76.5 on FB13, while TransH(bern) has a score of 83.3. Therefore, TransH(bern) outperforms TransH(unif) on FB13. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/219.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Evaluation results of triple classification. (%)" ], "reasoning": "The table shows that TransH(unif) has a score of 76.5 on FB13, while TransH(bern) has a score of 83.3. Therefore, TransH(bern) outperforms TransH(unif) on FB13.", "statement": "TransH(unif) outperforms TransH(bern) on the FB13 dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Correlations between the ground-truth system ranking and the rankings by automatic evaluation.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Human rankings showed a high correlation of 0.87, while existing automatic metrics like BLEU and ROUGE-L had poor correlations, with CHOSEN showing a reasonable correlation above 0.4.First, we yielded the human upper bound. we evaluated the correlation between the rankings made by different annotators (HUMAN). We randomly divided human evaluation into two groups and made two rankings. The correlation coefficient between the two rankings was 0.87. Second, we found that the rankings made using existing automatic evaluation metrics correlate poorly with ground-truth ranking. BLEU, often used to evaluate generation systems, does not correlate with human evaluation at all. One exception is ROUGE-L. However, its correlation coefficient is lower than 0.4, which means reasonable correlation. Third, we found that the ranking made by using our test set reasonably correlates with the ground-truth ranking compared with other metrics, and the correlation coefficient (CHOSEN) is higher than 0.4." } ], "images": [ "scigen/train/220.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Correlations between the ground-truth system ranking and the rankings by automatic evaluation." ], "reasoning": "Human rankings showed a high correlation of 0.87, while existing automatic metrics like BLEU and ROUGE-L had poor correlations, with CHOSEN showing a reasonable correlation above 0.4.", "description": "First, we yielded the human upper bound. we evaluated the correlation between the rankings made by different annotators (HUMAN). We randomly divided human evaluation into two groups and made two rankings. The correlation coefficient between the two rankings was 0.87. Second, we found that the rankings made using existing automatic evaluation metrics correlate poorly with ground-truth ranking. BLEU, often used to evaluate generation systems, does not correlate with human evaluation at all. One exception is ROUGE-L. However, its correlation coefficient is lower than 0.4, which means reasonable correlation. Third, we found that the ranking made by using our test set reasonably correlates with the ground-truth ranking compared with other metrics, and the correlation coefficient (CHOSEN) is higher than 0.4.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Correlations between the ground-truth system ranking and the rankings by automatic evaluation.', solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich metric had the highest correlation coefficient with ground-truth ranking?\n" }, { "role": "assistant", "content": "The table shows the Spearman correlation coefficients for various metrics. The highest correlation coefficient is 0.87, which corresponds to the HUMAN metric. So, the answer is {\"answer\": \"HUMAN\"}" } ], "images": [ "scigen/train/220.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Correlations between the ground-truth system ranking and the rankings by automatic evaluation." ], "reasoning": "The table shows the Spearman correlation coefficients for various metrics. The highest correlation coefficient is 0.87, which corresponds to the HUMAN metric.", "question": "Which metric had the highest correlation coefficient with ground-truth ranking?", "answer": "So, the answer is {\"answer\": \"HUMAN\"}" }, { "messages": [ { "role": "user", "content": "The CHOSEN metric had a higher correlation coefficient than the RANDOM metric. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe CHOSEN metric had a higher correlation coefficient than the RANDOM metric.\n" }, { "role": "assistant", "content": "The table shows that the CHOSEN metric has a Spearman correlation coefficient of 0.48, while the RANDOM metric has a coefficient of 0.43. Therefore, the CHOSEN metric had a higher correlation coefficient than the RANDOM metric. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/220.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Correlations between the ground-truth system ranking and the rankings by automatic evaluation." ], "reasoning": "The table shows that the CHOSEN metric has a Spearman correlation coefficient of 0.48, while the RANDOM metric has a coefficient of 0.43. Therefore, the CHOSEN metric had a higher correlation coefficient than the RANDOM metric.", "statement": "The CHOSEN metric had a higher correlation coefficient than the RANDOM metric.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: ρ for SGNS+OP+CD (L/P, win=2, k=1, t=None) before (ORG) and after time-shuffling (SHF) and downampling them to the same frequency (+DWN).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Shuffling sentences reduces the mean degree of LSC and the correlation of model predictions with the gold rank, especially for DURel, while downsampling frequencies further reduces correlations.As we saw, dispersion measures are sensitive to frequency. In order to test for this influence within our datasets we follow Dubossarsky et al. For each target word we merge all sentences from the two corpora Ca and Cb containing it, shuffle them, split them again into two sets while holding their frequencies from the original corpora approximately stable and merge them again with the original corpora. This reduces the target words’ mean degree of LSC between Ca and Cb significantly. Accordingly, the mean degree of LSC predicted by the models should reduce significantly if the models measure LSC (and not some other controlled property of the dataset such as frequency). We find that the mean prediction on a result sample (L/P, win=2) indeed reduces from 0.5 to 0.36 on DURel and from 0.53 to 0.44 on SURel. Moreover, shuffling should reduce the correlation of individual model predictions with the gold rank, as many items in the gold rank have a high degree of LSC, supposedly being canceled out by the shuffling and hence randomizing the ranking. Testing this on a result sample (SGNS+OP+CD, L/P, win=2, k=1, t (ORG) to 0.180 on the shuffled (SHF) corpora, but not for SURel where the correlation remains stable (0.767 vs. 0.763). We hypothesize that the latter may be due to SURel’s frequency properties and find that downsampling all target words to approximately the same frequency in both corpora (≈50) reduces the correlation (+DWN). However, there is still a rather high correlation left (0.576). Presumably, other factors play a role: (i) Time-shuffling may not totally randomize the rankings because words with a high change still end up having slightly different meaning distributions in the two corpora than words with no change at all. Combined with the fact that the SURel rank is less uniformly distributed than DURel this may lead to a rough preservation of the SURel rank after shuffling. (ii) For words with a strong change the shuffling creates two equally polysemous sets of word uses from two monosemous sets. The models may be sensitive to the different variances in these sets, and hence predict stronger change for more polysemous sets of uses. Overall, our findings demonstrate that much more work has to be done to understand the effects of time-shuffling as well as sensitivity effects of LSC detection models to frequency and polysemy." } ], "images": [ "scigen/train/221.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: ρ for SGNS+OP+CD (L/P, win=2, k=1, t=None) before (ORG) and after time-shuffling (SHF) and downampling them to the same frequency (+DWN)." ], "reasoning": "Shuffling sentences reduces the mean degree of LSC and the correlation of model predictions with the gold rank, especially for DURel, while downsampling frequencies further reduces correlations.", "description": "As we saw, dispersion measures are sensitive to frequency. In order to test for this influence within our datasets we follow Dubossarsky et al. For each target word we merge all sentences from the two corpora Ca and Cb containing it, shuffle them, split them again into two sets while holding their frequencies from the original corpora approximately stable and merge them again with the original corpora. This reduces the target words’ mean degree of LSC between Ca and Cb significantly. Accordingly, the mean degree of LSC predicted by the models should reduce significantly if the models measure LSC (and not some other controlled property of the dataset such as frequency). We find that the mean prediction on a result sample (L/P, win=2) indeed reduces from 0.5 to 0.36 on DURel and from 0.53 to 0.44 on SURel. Moreover, shuffling should reduce the correlation of individual model predictions with the gold rank, as many items in the gold rank have a high degree of LSC, supposedly being canceled out by the shuffling and hence randomizing the ranking. Testing this on a result sample (SGNS+OP+CD, L/P, win=2, k=1, t (ORG) to 0.180 on the shuffled (SHF) corpora, but not for SURel where the correlation remains stable (0.767 vs. 0.763). We hypothesize that the latter may be due to SURel’s frequency properties and find that downsampling all target words to approximately the same frequency in both corpora (≈50) reduces the correlation (+DWN). However, there is still a rather high correlation left (0.576). Presumably, other factors play a role: (i) Time-shuffling may not totally randomize the rankings because words with a high change still end up having slightly different meaning distributions in the two corpora than words with no change at all. Combined with the fact that the SURel rank is less uniformly distributed than DURel this may lead to a rough preservation of the SURel rank after shuffling. (ii) For words with a strong change the shuffling creates two equally polysemous sets of word uses from two monosemous sets. The models may be sensitive to the different variances in these sets, and hence predict stronger change for more polysemous sets of uses. Overall, our findings demonstrate that much more work has to be done to understand the effects of time-shuffling as well as sensitivity effects of LSC detection models to frequency and polysemy.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 5: ρ for SGNS+OP+CD (L/P, win=2, k=1, t=None) before (ORG) and after time-shuffling (SHF) and downampling them to the same frequency (+DWN).', answer the question below by showing each progressive step toward the answer. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the correlation of the model predictions with the gold rank for DURel after shuffling and downsampling?\n" }, { "role": "assistant", "content": "The table shows the correlation values for DURel and SURel under different conditions. For DURel, the correlation after shuffling (SHF) is 0.180, and after downsampling (+DWN) it is 0.372. So, the answer is {\"answer\": \"0.372\"}." } ], "images": [ "scigen/train/221.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: ρ for SGNS+OP+CD (L/P, win=2, k=1, t=None) before (ORG) and after time-shuffling (SHF) and downampling them to the same frequency (+DWN)." ], "reasoning": "The table shows the correlation values for DURel and SURel under different conditions. For DURel, the correlation after shuffling (SHF) is 0.180, and after downsampling (+DWN) it is 0.372.", "question": "What is the correlation of the model predictions with the gold rank for DURel after shuffling and downsampling?", "answer": "So, the answer is {\"answer\": \"0.372\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Shuffling reduces the correlation of model predictions with the gold rank for DURel more than for SURel. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nShuffling reduces the correlation of model predictions with the gold rank for DURel more than for SURel.\n" }, { "role": "assistant", "content": "The table shows that the correlation for DURel reduces from 0.816 (ORG) to 0.180 (SHF), while for SURel it reduces from 0.767 (ORG) to 0.763 (SHF), indicating a greater reduction for DURel. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/221.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: ρ for SGNS+OP+CD (L/P, win=2, k=1, t=None) before (ORG) and after time-shuffling (SHF) and downampling them to the same frequency (+DWN)." ], "reasoning": "The table shows that the correlation for DURel reduces from 0.816 (ORG) to 0.180 (SHF), while for SURel it reduces from 0.767 (ORG) to 0.763 (SHF), indicating a greater reduction for DURel.", "statement": "Shuffling reduces the correlation of model predictions with the gold rank for DURel more than for SURel.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Best and mean ρ scores across similarity measures (CD, LND, JSD) on semantic representations.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "SGNS is the best vector space model overall, while PPMI and SVD are the best among count models.SGNS is clearly the best vector space model, even though its mean performance does not outperform other representations as clearly as its best performance. Regarding count models, PPMI and SVD show the best results." } ], "images": [ "scigen/train/222.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Best and mean ρ scores across similarity measures (CD, LND, JSD) on semantic representations." ], "reasoning": "SGNS is the best vector space model overall, while PPMI and SVD are the best among count models.", "description": "SGNS is clearly the best vector space model, even though its mean performance does not outperform other representations as clearly as its best performance. Regarding count models, PPMI and SVD show the best results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: Best and mean ρ scores across similarity measures (CD, LND, JSD) on semantic representations.'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich representation has the highest mean performance in the SURel dataset?\n" }, { "role": "assistant", "content": "To find the representation with the highest mean performance in the SURel dataset, look at the 'mean' column for SURel. SGNS has the highest mean value of 0.520. So, the answer is {\"answer\": \"SGNS\"}" } ], "images": [ "scigen/train/222.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Best and mean ρ scores across similarity measures (CD, LND, JSD) on semantic representations." ], "reasoning": "To find the representation with the highest mean performance in the SURel dataset, look at the 'mean' column for SURel. SGNS has the highest mean value of 0.520.", "question": "Which representation has the highest mean performance in the SURel dataset?", "answer": "So, the answer is {\"answer\": \"SGNS\"}" }, { "messages": [ { "role": "user", "content": "PPMI has the highest best performance in the DURel dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nPPMI has the highest best performance in the DURel dataset.\n" }, { "role": "assistant", "content": "To verify this statement, check the 'best' column for the DURel dataset. SGNS has the highest best performance with a value of 0.866, not PPMI. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/222.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Best and mean ρ scores across similarity measures (CD, LND, JSD) on semantic representations." ], "reasoning": "To verify this statement, check the 'best' column for the DURel dataset. SGNS has the highest best performance with a value of 0.866, not PPMI.", "statement": "PPMI has the highest best performance in the DURel dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Mean ρ scores for CD across the alignments. Applies only to RI, SVD and SGNS.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "OP+ has the best mean performance on DURel but performs poorly on SURel, and the non-orthogonal whitening transformation is identified as the main reason for a performance drop of ≈20%.OP+ has the best mean performance on DURel, but performs poorly on SURel. Artetxe et al. show that the additional pre- and post-processing steps of OP+ can be harmful in certain conditions. We tested the influence of the different steps and identified the non-orthogonal whitening transformation as the main reason for a performance drop of ≈20%. As expected, the mean performance drops considerably. However, it remains positive, which suggests that the spaces learned in the models are not random but rather slightly rotated variants. Especially interesting is the comparison of Word Injection (WI) where one common vector space is learned against the OP-models where two separately learned vector spaces are aligned. We found that OP profits from mean-centering in the pre-processing step: applying mean-centering to WI matrices improves the performance by 3% on WI+SGNS+CD." } ], "images": [ "scigen/train/223.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Mean ρ scores for CD across the alignments. Applies only to RI, SVD and SGNS." ], "reasoning": "OP+ has the best mean performance on DURel but performs poorly on SURel, and the non-orthogonal whitening transformation is identified as the main reason for a performance drop of ≈20%.", "description": "OP+ has the best mean performance on DURel, but performs poorly on SURel. Artetxe et al. show that the additional pre- and post-processing steps of OP+ can be harmful in certain conditions. We tested the influence of the different steps and identified the non-orthogonal whitening transformation as the main reason for a performance drop of ≈20%. As expected, the mean performance drops considerably. However, it remains positive, which suggests that the spaces learned in the models are not random but rather slightly rotated variants. Especially interesting is the comparison of Word Injection (WI) where one common vector space is learned against the OP-models where two separately learned vector spaces are aligned. We found that OP profits from mean-centering in the pre-processing step: applying mean-centering to WI matrices improves the performance by 3% on WI+SGNS+CD.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: Mean ρ scores for CD across the alignments. Applies only to RI, SVD and SGNS.', answer the following question. Give detailed solution consisting of each step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model has the highest performance on DURel and what is its score?\n" }, { "role": "assistant", "content": "By examining the table, we see that OP+ has the highest score on DURel with a value of 0.621. So, the answer is {\"answer\": \"OP+ with a score of 0.621\"}" } ], "images": [ "scigen/train/223.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Mean ρ scores for CD across the alignments. Applies only to RI, SVD and SGNS." ], "reasoning": "By examining the table, we see that OP+ has the highest score on DURel with a value of 0.621.", "question": "Which model has the highest performance on DURel and what is its score?", "answer": "So, the answer is {\"answer\": \"OP+ with a score of 0.621\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe OP model has a higher performance on SURel than on DURel.\n" }, { "role": "assistant", "content": "According to the table, the OP model has a score of 0.590 on SURel and 0.618 on DURel, indicating that its performance on SURel is lower than on DURel. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/223.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Mean ρ scores for CD across the alignments. Applies only to RI, SVD and SGNS." ], "reasoning": "According to the table, the OP model has a score of 0.590 on SURel and 0.618 on DURel, indicating that its performance on SURel is lower than on DURel.", "statement": "The OP model has a higher performance on SURel than on DURel.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Absolute performance changes for including PPDB information while training LBL vectors. Spearman’s correlation (3 left columns) and accuracy (3 right columns) on different tasks. Bold indicates greatest improvement.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "For lazy, γ=0.01 performs best, while for periodic, k=50M performs best. Retrofitting is competitive and sometimes better.S6SS2SSS0Px1 Results. For lazy, γ=0.01 performs best, but the method is in most cases not highly sensitive to γ’s value. For periodic, which overall leads to greater improvements over the baseline than lazy, k=50M performs best, although all other values of k also outperform the the baseline. Retrofitting, which can be applied to any word vectors, regardless of how they are trained, is competitive and sometimes better." } ], "images": [ "scigen/train/224.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Absolute performance changes for including PPDB information while training LBL vectors. Spearman’s correlation (3 left columns) and accuracy (3 right columns) on different tasks. Bold indicates greatest improvement." ], "reasoning": "For lazy, γ=0.01 performs best, while for periodic, k=50M performs best. Retrofitting is competitive and sometimes better.", "description": "S6SS2SSS0Px1 Results. For lazy, γ=0.01 performs best, but the method is in most cases not highly sensitive to γ’s value. For periodic, which overall leads to greater improvements over the baseline than lazy, k=50M performs best, although all other values of k also outperform the the baseline. Retrofitting, which can be applied to any word vectors, regardless of how they are trained, is competitive and sometimes better.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: Table 3: Absolute performance changes for including PPDB information while training LBL vectors. Spearman’s correlation (3 left columns) and accuracy (3 right columns) on different tasks. Bold indicates greatest improvement.\nQuestion: Which method and parameter combination achieves the highest score in the SYN-REL column?\n" }, { "role": "assistant", "content": "By examining the SYN-REL column, the highest score is 14.7, achieved by the LBL + Retrofitting method. So, the answer is {\"answer\": \"LBL + Retrofitting\"}." } ], "images": [ "scigen/train/224.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Absolute performance changes for including PPDB information while training LBL vectors. Spearman’s correlation (3 left columns) and accuracy (3 right columns) on different tasks. Bold indicates greatest improvement." ], "reasoning": "By examining the SYN-REL column, the highest score is 14.7, achieved by the LBL + Retrofitting method.", "question": "Which method and parameter combination achieves the highest score in the SYN-REL column?", "answer": "So, the answer is {\"answer\": \"LBL + Retrofitting\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: LBL + Periodic with k=50M achieves the highest score in the RG-65 column., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nLBL + Periodic with k=50M achieves the highest score in the RG-65 column.\n" }, { "role": "assistant", "content": "By examining the RG-65 column, the highest score is 19.5, which is indeed achieved by LBL + Periodic with k=50M. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/224.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Absolute performance changes for including PPDB information while training LBL vectors. Spearman’s correlation (3 left columns) and accuracy (3 right columns) on different tasks. Bold indicates greatest improvement." ], "reasoning": "By examining the RG-65 column, the highest score is 19.5, which is indeed achieved by LBL + Periodic with k=50M.", "statement": "LBL + Periodic with k=50M achieves the highest score in the RG-65 column.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Absolute performance changes with retrofitting. Spearman’s correlation (3 left columns) and accuracy (3 right columns) on different tasks. Higher scores are always better. Bold indicates greatest improvement for a vector type.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "All lexicons show high improvements on word similarity tasks, with significant gains in TOEFL accuracy except for FrameNet. Sentiment analysis task shows improvements with all lexicons, with Multi vectors gaining 1.4% in accuracy.All of the lexicons offer high improvements on the word similarity tasks (the first three columns). On the TOEFL task, we observe large improvements of the order of 10 absolute points in accuracy for all lexicons except for FrameNet. FrameNet’s performance is weaker, in some cases leading to worse performance (e.g., with Glove and SG vectors). For the extrinsic sentiment analysis task, we observe improvements using all the lexicons and gain 1.4% (absolute) in accuracy for the Multi vectors over the baseline. This increase is statistically significant (p<0.01, McNemar)." } ], "images": [ "scigen/train/225.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Absolute performance changes with retrofitting. Spearman’s correlation (3 left columns) and accuracy (3 right columns) on different tasks. Higher scores are always better. Bold indicates greatest improvement for a vector type." ], "reasoning": "All lexicons show high improvements on word similarity tasks, with significant gains in TOEFL accuracy except for FrameNet. Sentiment analysis task shows improvements with all lexicons, with Multi vectors gaining 1.4% in accuracy.", "description": "All of the lexicons offer high improvements on the word similarity tasks (the first three columns). On the TOEFL task, we observe large improvements of the order of 10 absolute points in accuracy for all lexicons except for FrameNet. FrameNet’s performance is weaker, in some cases leading to worse performance (e.g., with Glove and SG vectors). For the extrinsic sentiment analysis task, we observe improvements using all the lexicons and gain 1.4% (absolute) in accuracy for the Multi vectors over the baseline. This increase is statistically significant (p<0.01, McNemar).", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: Absolute performance changes with retrofitting. Spearman’s correlation (3 left columns) and accuracy (3 right columns) on different tasks. Higher scores are always better. Bold indicates greatest improvement for a vector type.' and methodically tackle the question that follows. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich lexicon shows the highest improvement in the TOEFL task?\n" }, { "role": "assistant", "content": "By examining the TOEFL column, the highest improvement is shown by GC +PPDB with an improvement of 13.1. So, the answer is {\"answer\": \"GC +PPDB\"}" } ], "images": [ "scigen/train/225.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Absolute performance changes with retrofitting. Spearman’s correlation (3 left columns) and accuracy (3 right columns) on different tasks. Higher scores are always better. Bold indicates greatest improvement for a vector type." ], "reasoning": "By examining the TOEFL column, the highest improvement is shown by GC +PPDB with an improvement of 13.1.", "question": "Which lexicon shows the highest improvement in the TOEFL task?", "answer": "So, the answer is {\"answer\": \"GC +PPDB\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nFrameNet shows the highest improvement in the sentiment analysis task.\n" }, { "role": "assistant", "content": "The sentiment analysis (SA) column shows that FrameNet (+FN) has an improvement of 0.2, which is not the highest. The highest improvement is shown by Multi +PPDB with 1.4. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/225.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Absolute performance changes with retrofitting. Spearman’s correlation (3 left columns) and accuracy (3 right columns) on different tasks. Higher scores are always better. Bold indicates greatest improvement for a vector type." ], "reasoning": "The sentiment analysis (SA) column shows that FrameNet (+FN) has an improvement of 0.2, which is not the highest. The highest improvement is shown by Multi +PPDB with 1.4.", "statement": "FrameNet shows the highest improvement in the sentiment analysis task.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Results of different architectures with different pre-trained knowledge on CNN/DailyMail, where Enc. and Dec. represent document encoder and decoder respectively.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The model CNN-LSTM-Pointer achieved a new state-of-the-art with 42.11 on R-1 when equipped with BERT, significantly surpassing existing models.As shown in Tab. However, when the models are equipped with BERT, we are excited to observe that the performances of all types of architectures are improved by a large margin. Specifically, the model CNN-LSTM-Pointer has achieved a new state-of-the-art with 42.11 on R-1, surpassing existing models dramatically." } ], "images": [ "scigen/train/226.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results of different architectures with different pre-trained knowledge on CNN/DailyMail, where Enc. and Dec. represent document encoder and decoder respectively." ], "reasoning": "The model CNN-LSTM-Pointer achieved a new state-of-the-art with 42.11 on R-1 when equipped with BERT, significantly surpassing existing models.", "description": "As shown in Tab. However, when the models are equipped with BERT, we are excited to observe that the performances of all types of architectures are improved by a large margin. Specifically, the model CNN-LSTM-Pointer has achieved a new state-of-the-art with 42.11 on R-1, surpassing existing models dramatically.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 5: Results of different architectures with different pre-trained knowledge on CNN/DailyMail, where Enc. and Dec. represent document encoder and decoder respectively.\nQuestion: What is the R-1 score for the Pointer LSTM model when equipped with BERT?\n" }, { "role": "assistant", "content": "The table shows that the Pointer LSTM model with BERT achieved an R-1 score of 42.39. So, the answer is {\"answer\": \"42.39\"}" } ], "images": [ "scigen/train/226.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results of different architectures with different pre-trained knowledge on CNN/DailyMail, where Enc. and Dec. represent document encoder and decoder respectively." ], "reasoning": "The table shows that the Pointer LSTM model with BERT achieved an R-1 score of 42.39.", "question": "What is the R-1 score for the Pointer LSTM model when equipped with BERT?", "answer": "So, the answer is {\"answer\": \"42.39\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The SeqLab Transformer model with BERT achieved an R-1 score of 42.28., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe SeqLab Transformer model with BERT achieved an R-1 score of 42.28.\n" }, { "role": "assistant", "content": "The table indicates that the SeqLab Transformer model with BERT achieved an R-1 score of 42.28. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/226.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results of different architectures with different pre-trained knowledge on CNN/DailyMail, where Enc. and Dec. represent document encoder and decoder respectively." ], "reasoning": "The table indicates that the SeqLab Transformer model with BERT achieved an R-1 score of 42.28.", "statement": "The SeqLab Transformer model with BERT achieved an R-1 score of 42.28.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Performance of evaluated models.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The proposed model outperforms the state-of-the-art C-LSTM in all metrics, achieving the highest performance in Accuracy, Precision, Recall, F1, and AUC.As is demonstrated, our proposed model achieves the highest performance in Accuracy, Precision, Recall, F1, and AUC. It outperforms the state of the art (C-LSTM) by 5.2%, 7.1%, 4.9%, 2.6%, and 3.7%, respectively." } ], "images": [ "scigen/train/227.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of evaluated models." ], "reasoning": "The proposed model outperforms the state-of-the-art C-LSTM in all metrics, achieving the highest performance in Accuracy, Precision, Recall, F1, and AUC.", "description": "As is demonstrated, our proposed model achieves the highest performance in Accuracy, Precision, Recall, F1, and AUC. It outperforms the state of the art (C-LSTM) by 5.2%, 7.1%, 4.9%, 2.6%, and 3.7%, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 3: Performance of evaluated models.', solve this question and give detail solutions. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nBy how much does the proposed model (OURS-Att-w) outperform the state-of-the-art C-LSTM in terms of Precision?\n" }, { "role": "assistant", "content": "The Precision of the proposed model (OURS-Att-w) is 0.9312, while the Precision of C-LSTM is 0.8683. The difference is 0.9312 - 0.8683 = 0.0629. So, the answer is {\"answer\": \"0.0629\"}" } ], "images": [ "scigen/train/227.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of evaluated models." ], "reasoning": "The Precision of the proposed model (OURS-Att-w) is 0.9312, while the Precision of C-LSTM is 0.8683. The difference is 0.9312 - 0.8683 = 0.0629.", "question": "By how much does the proposed model (OURS-Att-w) outperform the state-of-the-art C-LSTM in terms of Precision?", "answer": "So, the answer is {\"answer\": \"0.0629\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The proposed model (OURS-Att-w) has the highest Recall among all models., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe proposed model (OURS-Att-w) has the highest Recall among all models.\n" }, { "role": "assistant", "content": "The Recall of the proposed model (OURS-Att-w) is 0.9298, which is lower than the Recall of the OURS model (0.9965) and OURS-Att (0.9895). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/227.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of evaluated models." ], "reasoning": "The Recall of the proposed model (OURS-Att-w) is 0.9298, which is lower than the Recall of the OURS model (0.9965) and OURS-Att (0.9895).", "statement": "The proposed model (OURS-Att-w) has the highest Recall among all models.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Model performance in terms of RMS deviation and macro-averaged F1 score, with best results in bold font.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The LSTM-CRF model had a higher F1 score but a worse RMS score compared to the LSTM-SIG model.While the LSTM-CRF had a better (higher) F-score on the classification task, its RMS score was worse (higher) than the LSTM-SIG model, which may be due to the limitation of the model as discussed in Section 5." } ], "images": [ "scigen/train/228.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Model performance in terms of RMS deviation and macro-averaged F1 score, with best results in bold font." ], "reasoning": "The LSTM-CRF model had a higher F1 score but a worse RMS score compared to the LSTM-SIG model.", "description": "While the LSTM-CRF had a better (higher) F-score on the classification task, its RMS score was worse (higher) than the LSTM-SIG model, which may be due to the limitation of the model as discussed in Section 5.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: Model performance in terms of RMS deviation and macro-averaged F1 score, with best results in bold font.', solve the subsequent question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model had a better F1 score but a worse RMS score?\n" }, { "role": "assistant", "content": "The table shows that the LSTM-CRF model had a higher F1 score (0.60) compared to the LSTM-SIG model (0.519), but a worse RMS score (0.154) compared to the LSTM-SIG model (0.120). So, the answer is {\"answer\": \"LSTM-CRF\"}" } ], "images": [ "scigen/train/228.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Model performance in terms of RMS deviation and macro-averaged F1 score, with best results in bold font." ], "reasoning": "The table shows that the LSTM-CRF model had a higher F1 score (0.60) compared to the LSTM-SIG model (0.519), but a worse RMS score (0.154) compared to the LSTM-SIG model (0.120).", "question": "Which model had a better F1 score but a worse RMS score?", "answer": "So, the answer is {\"answer\": \"LSTM-CRF\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The LSTM-SIG model had a better RMS score but a worse F1 score compared to the LSTM-CRF model., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe LSTM-SIG model had a better RMS score but a worse F1 score compared to the LSTM-CRF model.\n" }, { "role": "assistant", "content": "The table shows that the LSTM-SIG model had a better RMS score (0.120) compared to the LSTM-CRF model (0.154), but a worse F1 score (0.519) compared to the LSTM-CRF model (0.60). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/228.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Model performance in terms of RMS deviation and macro-averaged F1 score, with best results in bold font." ], "reasoning": "The table shows that the LSTM-SIG model had a better RMS score (0.120) compared to the LSTM-CRF model (0.154), but a worse F1 score (0.519) compared to the LSTM-CRF model (0.60).", "statement": "The LSTM-SIG model had a better RMS score but a worse F1 score compared to the LSTM-CRF model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: Comparison with other methods on SQuAD dataset. We demonstrate automatic evaluation results on BLEU 1-4, ROUGE-L, METEOR metrics. The best performance for each column is highlighted in boldface. The WeGen without pre-training means the pipeline of Answer-Related Encoder and Transferred Interaction module are not used and the control gate is abandoned. Refer to Section 4.4 for more details.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The copy mechanism significantly improves results by using attentive read from word embedding and selective read from location-aware hidden states, while the Transformer structure performs poorly, suggesting the need for better modeling of local features and semantic relations.The experimental results reveal a number of interesting points. The copy mechanism improve the results significantly. It uses attentive read from word embedding of sequence in encoder and selective read from location-aware hidden states to enhance the capability of decoder and proves effectiveness of the repeat pattern in human communication. Transformer structure performs badly and just achieves better results than Vanilla Seq2Seq. This suggests that the pure attention-based models are not sufficient for question generation and the local features of sequences and the variant semantic relations should be modelled more effectively." } ], "images": [ "scigen/train/229.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison with other methods on SQuAD dataset. We demonstrate automatic evaluation results on BLEU 1-4, ROUGE-L, METEOR metrics. The best performance for each column is highlighted in boldface. The WeGen without pre-training means the pipeline of Answer-Related Encoder and Transferred Interaction module are not used and the control gate is abandoned. Refer to Section 4.4 for more details." ], "reasoning": "The copy mechanism significantly improves results by using attentive read from word embedding and selective read from location-aware hidden states, while the Transformer structure performs poorly, suggesting the need for better modeling of local features and semantic relations.", "description": "The experimental results reveal a number of interesting points. The copy mechanism improve the results significantly. It uses attentive read from word embedding of sequence in encoder and selective read from location-aware hidden states to enhance the capability of decoder and proves effectiveness of the repeat pattern in human communication. Transformer structure performs badly and just achieves better results than Vanilla Seq2Seq. This suggests that the pure attention-based models are not sufficient for question generation and the local features of sequences and the variant semantic relations should be modelled more effectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Comparison with other methods on SQuAD dataset. We demonstrate automatic evaluation results on BLEU 1-4, ROUGE-L, METEOR metrics. The best performance for each column is highlighted in boldface. The WeGen without pre-training means the pipeline of Answer-Related Encoder and Transferred Interaction module are not used and the control gate is abandoned. Refer to Section 4.4 for more details.\nQuestion: Which model achieves the highest BLEU-1 score and what does this suggest about the effectiveness of the model?\nSolve the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows that WeGen achieves the highest BLEU-1 score of 32.65. This suggests that WeGen is highly effective in generating questions, likely due to its advanced mechanisms and pre-training. So, the answer is {\"answer\": \"WeGen\"}." } ], "images": [ "scigen/train/229.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison with other methods on SQuAD dataset. We demonstrate automatic evaluation results on BLEU 1-4, ROUGE-L, METEOR metrics. The best performance for each column is highlighted in boldface. The WeGen without pre-training means the pipeline of Answer-Related Encoder and Transferred Interaction module are not used and the control gate is abandoned. Refer to Section 4.4 for more details." ], "reasoning": "The table shows that WeGen achieves the highest BLEU-1 score of 32.65. This suggests that WeGen is highly effective in generating questions, likely due to its advanced mechanisms and pre-training.", "question": "Which model achieves the highest BLEU-1 score and what does this suggest about the effectiveness of the model?", "answer": "So, the answer is {\"answer\": \"WeGen\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nSeq2Seq+Attention+Copy achieves the highest ROUGE-L score.\n" }, { "role": "assistant", "content": "The table shows that WeGen achieves the highest ROUGE-L score of 32.36, not Seq2Seq+Attention+Copy, which has a ROUGE-L score of 28.97. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/229.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison with other methods on SQuAD dataset. We demonstrate automatic evaluation results on BLEU 1-4, ROUGE-L, METEOR metrics. The best performance for each column is highlighted in boldface. The WeGen without pre-training means the pipeline of Answer-Related Encoder and Transferred Interaction module are not used and the control gate is abandoned. Refer to Section 4.4 for more details." ], "reasoning": "The table shows that WeGen achieves the highest ROUGE-L score of 32.36, not Seq2Seq+Attention+Copy, which has a ROUGE-L score of 28.97.", "statement": "Seq2Seq+Attention+Copy achieves the highest ROUGE-L score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5: Comparison of word error rates for different language models.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The new n-gram LM combined with the best acoustic model improved WER by 0.5% for SWB and 0.3% for CallHome, partly due to a larger beam for decoding.This new n-gram LM was used in combination with our best acoustic model to decode and generate word lattices for further LM rescoring experiments. The WER improved by 0.5% for SWB and 0.3% for CallHome. Part of this improvement (0.1-0.2%) was due to also using a larger beam for decoding. We built a model M LM on each corpus and interpolated the models, together with the 37M n-gram LM. We built two NNLMs for interpolation. One was trained on just the most relevant data: the 24M word corpus (Switchboard/Fisher/CallHome acoustic transcripts)." } ], "images": [ "scigen/train/230.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison of word error rates for different language models." ], "reasoning": "The new n-gram LM combined with the best acoustic model improved WER by 0.5% for SWB and 0.3% for CallHome, partly due to a larger beam for decoding.", "description": "This new n-gram LM was used in combination with our best acoustic model to decode and generate word lattices for further LM rescoring experiments. The WER improved by 0.5% for SWB and 0.3% for CallHome. Part of this improvement (0.1-0.2%) was due to also using a larger beam for decoding. We built a model M LM on each corpus and interpolated the models, together with the 37M n-gram LM. We built two NNLMs for interpolation. One was trained on just the most relevant data: the 24M word corpus (Switchboard/Fisher/CallHome acoustic transcripts).", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 5: Comparison of word error rates for different language models.', solve the subsequent question in a stepwise manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhat is the WER for SWB when using the n-gram + model M + NNLM?\n" }, { "role": "assistant", "content": "The table shows the WER for different LMs. For the n-gram + model M + NNLM, the WER for SWB is listed. So, the answer is {\"answer\": \"8.0\"}." } ], "images": [ "scigen/train/230.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison of word error rates for different language models." ], "reasoning": "The table shows the WER for different LMs. For the n-gram + model M + NNLM, the WER for SWB is listed.", "question": "What is the WER for SWB when using the n-gram + model M + NNLM?", "answer": "So, the answer is {\"answer\": \"8.0\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The WER for CallHome improved by 1.5% when using the n-gram + model M., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe WER for CallHome improved by 1.5% when using the n-gram + model M.\n" }, { "role": "assistant", "content": "The table shows the WER for CallHome using different LMs. The WER for CallHome improved from 15.6% to 14.3% when using the n-gram + model M, which is an improvement of 1.3%, not 1.5%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/230.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison of word error rates for different language models." ], "reasoning": "The table shows the WER for CallHome using different LMs. The WER for CallHome improved from 15.6% to 14.3% when using the n-gram + model M, which is an improvement of 1.3%, not 1.5%.", "statement": "The WER for CallHome improved by 1.5% when using the n-gram + model M.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1: Word error rates of sigmoid vs. Maxout networks trained with annealed dropout (Maxout-AD) for ST CNNs, DNNs and score fusion on Hub5’00 SWB. Note that all networks are trained only on the SWB-1 data (262 hours).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Maxout networks trained with AD show a clear advantage over traditional networks, with lower WER SWB (ST) values compared to sigmoid-based DNN/CNN counterparts.All Maxout networks utilize 2 filters per hidden unit, and the same number of layers and roughly the same number of parameters per layer as the sigmoid-based DNN/CNN counterparts. Parameter equalization is achieved by having a factor of √2 more neurons per hidden layer for the maxout nets since the maxout operation reduces the number of outputs by a factor of 2. Note that ReLU networks, in our experience, perform on-par with sigmoid-based DNNs in this data regime. Maxout networks trained with AD (Maxout-AD), on the other hand, show a clear advantage over our traditional networks. Also, note that the convolutional layers of the Maxout-AD CNN have only 128 and 256 feature map outputs, whereas those of the sigmoid CNN has 512/512 outputs. Training of the Maxout-AD CNN with a 512/512 filter configuration is in progress." } ], "images": [ "scigen/train/231.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Word error rates of sigmoid vs. Maxout networks trained with annealed dropout (Maxout-AD) for ST CNNs, DNNs and score fusion on Hub5’00 SWB. Note that all networks are trained only on the SWB-1 data (262 hours)." ], "reasoning": "Maxout networks trained with AD show a clear advantage over traditional networks, with lower WER SWB (ST) values compared to sigmoid-based DNN/CNN counterparts.", "description": "All Maxout networks utilize 2 filters per hidden unit, and the same number of layers and roughly the same number of parameters per layer as the sigmoid-based DNN/CNN counterparts. Parameter equalization is achieved by having a factor of √2 more neurons per hidden layer for the maxout nets since the maxout operation reduces the number of outputs by a factor of 2. Note that ReLU networks, in our experience, perform on-par with sigmoid-based DNNs in this data regime. Maxout networks trained with AD (Maxout-AD), on the other hand, show a clear advantage over our traditional networks. Also, note that the convolutional layers of the Maxout-AD CNN have only 128 and 256 feature map outputs, whereas those of the sigmoid CNN has 512/512 outputs. Training of the Maxout-AD CNN with a 512/512 filter configuration is in progress.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: Word error rates of sigmoid vs. Maxout networks trained with annealed dropout (Maxout-AD) for ST CNNs, DNNs and score fusion on Hub5’00 SWB. Note that all networks are trained only on the SWB-1 data (262 hours).'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the WER SWB (ST) value for the DNN+CNN model using Maxout-AD?\n" }, { "role": "assistant", "content": "The table shows the WER SWB (ST) values for different models using sigmoid and Maxout-AD. For the DNN+CNN model, the value under Maxout-AD is 10.2. So, the answer is {\"answer\": \"10.2\"}." } ], "images": [ "scigen/train/231.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Word error rates of sigmoid vs. Maxout networks trained with annealed dropout (Maxout-AD) for ST CNNs, DNNs and score fusion on Hub5’00 SWB. Note that all networks are trained only on the SWB-1 data (262 hours)." ], "reasoning": "The table shows the WER SWB (ST) values for different models using sigmoid and Maxout-AD. For the DNN+CNN model, the value under Maxout-AD is 10.2.", "question": "What is the WER SWB (ST) value for the DNN+CNN model using Maxout-AD?", "answer": "So, the answer is {\"answer\": \"10.2\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The CNN model using Maxout-AD has a higher WER SWB (ST) value than the DNN model using sigmoid. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe CNN model using Maxout-AD has a higher WER SWB (ST) value than the DNN model using sigmoid.\n" }, { "role": "assistant", "content": "The table shows that the CNN model using Maxout-AD has a WER SWB (ST) value of 11.6, while the DNN model using sigmoid has a value of 11.9. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/231.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Word error rates of sigmoid vs. Maxout networks trained with annealed dropout (Maxout-AD) for ST CNNs, DNNs and score fusion on Hub5’00 SWB. Note that all networks are trained only on the SWB-1 data (262 hours)." ], "reasoning": "The table shows that the CNN model using Maxout-AD has a WER SWB (ST) value of 11.6, while the DNN model using sigmoid has a value of 11.9. Therefore, the statement is refuted.", "statement": "The CNN model using Maxout-AD has a higher WER SWB (ST) value than the DNN model using sigmoid.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Comparison of word error rates for CE-trained DNNs with different number of outputs and phonetic context size on Hub5’00 SWB.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Increasing the number of context-dependent HMM output targets and phonetic context can reduce WER, as shown by the lower WER values for higher output targets and increased phonetic context.When training on 2000 hours of data, we found it beneficial to increase the number of context-dependent HMM output targets to values that are far larger than commonly reported. We conjecture that this is because GMMs are a distributed model and require more data for each state to reliably estimate the mixture components, whereas the DNN output layer is shared between states. This allows DNNs to have a much richer target space. Additionally, we experimented with growing acoustic decision trees where the phonetic context is increased to heptaphones (±3 phones within words and ±2 phones across words)." } ], "images": [ "scigen/train/232.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of word error rates for CE-trained DNNs with different number of outputs and phonetic context size on Hub5’00 SWB." ], "reasoning": "Increasing the number of context-dependent HMM output targets and phonetic context can reduce WER, as shown by the lower WER values for higher output targets and increased phonetic context.", "description": "When training on 2000 hours of data, we found it beneficial to increase the number of context-dependent HMM output targets to values that are far larger than commonly reported. We conjecture that this is because GMMs are a distributed model and require more data for each state to reliably estimate the mixture components, whereas the DNN output layer is shared between states. This allows DNNs to have a much richer target space. Additionally, we experimented with growing acoustic decision trees where the phonetic context is increased to heptaphones (±3 phones within words and ±2 phones across words).", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 2: Comparison of word error rates for CE-trained DNNs with different number of outputs and phonetic context size on Hub5’00 SWB.\n\nQuestion: What is the WER when the number of outputs is 32000 and the phonetic context is ±2?\n" }, { "role": "assistant", "content": "The table shows that for 32000 outputs and a phonetic context of ±2, the WER is 11.7. So, the answer is {\"answer\": \"11.7\"}." } ], "images": [ "scigen/train/232.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of word error rates for CE-trained DNNs with different number of outputs and phonetic context size on Hub5’00 SWB." ], "reasoning": "The table shows that for 32000 outputs and a phonetic context of ±2, the WER is 11.7.", "question": "What is the WER when the number of outputs is 32000 and the phonetic context is ±2?", "answer": "So, the answer is {\"answer\": \"11.7\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nIncreasing the number of context-dependent HMM output targets to 64000 results in the lowest WER.\n" }, { "role": "assistant", "content": "The table shows that the lowest WER (11.7) is achieved with 32000 outputs and a phonetic context of ±2, not with 64000 outputs. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/232.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of word error rates for CE-trained DNNs with different number of outputs and phonetic context size on Hub5’00 SWB." ], "reasoning": "The table shows that the lowest WER (11.7) is achieved with 32000 outputs and a phonetic context of ±2, not with 64000 outputs.", "statement": "Increasing the number of context-dependent HMM output targets to 64000 results in the lowest WER.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: Comparison of word error rates for CE and ST CNN, DNN, RNN and various score fusions on Hub5’00.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "All models are trained with 10-15 passes of cross-entropy on 2000 hours of audio and 30 iterations of sequence, and score fusion is done by decoding with a frame-level sum of the outputs of the nets prior to the softmax with uniform weights.All nets are trained with 10-15 passes of cross-entropy on 2000 hours of audio and 30 iterations of sequence For score fusion, we decode with a frame-level sum of the outputs of the nets prior to the softmax with uniform weights." } ], "images": [ "scigen/train/233.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of word error rates for CE and ST CNN, DNN, RNN and various score fusions on Hub5’00." ], "reasoning": "All models are trained with 10-15 passes of cross-entropy on 2000 hours of audio and 30 iterations of sequence, and score fusion is done by decoding with a frame-level sum of the outputs of the nets prior to the softmax with uniform weights.", "description": "All nets are trained with 10-15 passes of cross-entropy on 2000 hours of audio and 30 iterations of sequence For score fusion, we decode with a frame-level sum of the outputs of the nets prior to the softmax with uniform weights.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Comparison of word error rates for CE and ST CNN, DNN, RNN and various score fusions on Hub5’00.', unfold the question presented below, detailing every step of your calculation. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the lowest WER for SWB ST?\n" }, { "role": "assistant", "content": "To find the model with the lowest WER for SWB ST, compare the WER SWB ST values for all models. The lowest value is 9.4, which corresponds to the RNN+CNN model. So, the answer is {\"answer\": \"RNN+CNN\"}." } ], "images": [ "scigen/train/233.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of word error rates for CE and ST CNN, DNN, RNN and various score fusions on Hub5’00." ], "reasoning": "To find the model with the lowest WER for SWB ST, compare the WER SWB ST values for all models. The lowest value is 9.4, which corresponds to the RNN+CNN model.", "question": "Which model has the lowest WER for SWB ST?", "answer": "So, the answer is {\"answer\": \"RNN+CNN\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe DNN model has a higher WER for CH CE than the CNN model.\n" }, { "role": "assistant", "content": "To verify this statement, compare the WER CH CE values for the DNN and CNN models. The DNN model has a WER of 18.5, while the CNN model has a WER of 18.4. Since 18.5 is higher than 18.4, the statement is correct. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/233.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of word error rates for CE and ST CNN, DNN, RNN and various score fusions on Hub5’00." ], "reasoning": "To verify this statement, compare the WER CH CE values for the DNN and CNN models. The DNN model has a WER of 18.5, while the CNN model has a WER of 18.4. Since 18.5 is higher than 18.4, the statement is correct.", "statement": "The DNN model has a higher WER for CH CE than the CNN model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Comparison of word error rates for CE and sequence trained unfolded RNN and DNN with score fusion and joint modeling on Hub5’00. The WERs for the joint models are after sequence training.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The joint model initialized with the fusion of cross-entropy trained RNN and CNN has a WER of 9.3 for SWB and 15.6 for CH, while the joint model using ST models as the starting point has a WER of 9.4 for SWB and 15.7 for CH.Two experimental scenarios were considered. The first is where the joint model was initialized with the fusion of the cross-entropy trained RNN and CNN whereas the second uses ST models as the starting point." } ], "images": [ "scigen/train/234.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of word error rates for CE and sequence trained unfolded RNN and DNN with score fusion and joint modeling on Hub5’00. The WERs for the joint models are after sequence training." ], "reasoning": "The joint model initialized with the fusion of cross-entropy trained RNN and CNN has a WER of 9.3 for SWB and 15.6 for CH, while the joint model using ST models as the starting point has a WER of 9.4 for SWB and 15.7 for CH.", "description": "Two experimental scenarios were considered. The first is where the joint model was initialized with the fusion of the cross-entropy trained RNN and CNN whereas the second uses ST models as the starting point.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 4: Comparison of word error rates for CE and sequence trained unfolded RNN and DNN with score fusion and joint modeling on Hub5’00. The WERs for the joint models are after sequence training.', unfold the question presented below, detailing every step of your calculation. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the WER for the joint model from CE models (ST) in the CH scenario?\n" }, { "role": "assistant", "content": "The table shows the WER for different RNN/CNN combinations. The joint model from CE models (ST) has a WER of 15.6 in the CH scenario. So, the answer is {\"answer\": \"15.6\"}." } ], "images": [ "scigen/train/234.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of word error rates for CE and sequence trained unfolded RNN and DNN with score fusion and joint modeling on Hub5’00. The WERs for the joint models are after sequence training." ], "reasoning": "The table shows the WER for different RNN/CNN combinations. The joint model from CE models (ST) has a WER of 15.6 in the CH scenario.", "question": "What is the WER for the joint model from CE models (ST) in the CH scenario?", "answer": "So, the answer is {\"answer\": \"15.6\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The score fusion of CE models has a higher WER in the SWB scenario compared to the score fusion of ST models., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe score fusion of CE models has a higher WER in the SWB scenario compared to the score fusion of ST models.\n" }, { "role": "assistant", "content": "The table shows that the score fusion of CE models has a WER of 11.2 in the SWB scenario, while the score fusion of ST models has a WER of 9.4 in the same scenario. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/234.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of word error rates for CE and sequence trained unfolded RNN and DNN with score fusion and joint modeling on Hub5’00. The WERs for the joint models are after sequence training." ], "reasoning": "The table shows that the score fusion of CE models has a WER of 11.2 in the SWB scenario, while the score fusion of ST models has a WER of 9.4 in the same scenario. Therefore, the statement is supported.", "statement": "The score fusion of CE models has a higher WER in the SWB scenario compared to the score fusion of ST models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Comparison of word error rates on Hub5’00 (SWB and CH) for existing systems (∗ note that the 19.1% CallHome WER is not reported in [13]).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows the Word Error Rate (WER) for different systems using various types of acoustic modeling training data on the Switchboard (SWB) and CallHome (CH) datasets.Since Switchboard is such a well-studied corpus, we thought we would take a step back and reflect on how far we have come in terms of speech recognition technology. At the height of technological development for GMM-based systems, the winning IBM submission scored 15.2% WER during the 2004 DARPA EARS For clarity, we also specify the type of training data that was used for acoustic modeling in each case." } ], "images": [ "scigen/train/235.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Comparison of word error rates on Hub5’00 (SWB and CH) for existing systems (∗ note that the 19.1% CallHome WER is not reported in [13])." ], "reasoning": "The table shows the Word Error Rate (WER) for different systems using various types of acoustic modeling training data on the Switchboard (SWB) and CallHome (CH) datasets.", "description": "Since Switchboard is such a well-studied corpus, we thought we would take a step back and reflect on how far we have come in terms of speech recognition technology. At the height of technological development for GMM-based systems, the winning IBM submission scored 15.2% WER during the 2004 DARPA EARS For clarity, we also specify the type of training data that was used for acoustic modeling in each case.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 6: Comparison of word error rates on Hub5’00 (SWB and CH) for existing systems (∗ note that the 19.1% CallHome WER is not reported in [13]).' in mind, break down and resolve the question below step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhich system achieved the lowest WER on the Switchboard dataset and what training data was used?\n" }, { "role": "assistant", "content": "To find the system with the lowest WER on the Switchboard dataset, look at the SWB column and identify the lowest value. The lowest WER is 8.0, achieved by 'This system' which used SWB+Fisher+CH for training. So, the answer is {\"answer\": \"This system with SWB+Fisher+CH\"}" } ], "images": [ "scigen/train/235.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Comparison of word error rates on Hub5’00 (SWB and CH) for existing systems (∗ note that the 19.1% CallHome WER is not reported in [13])." ], "reasoning": "To find the system with the lowest WER on the Switchboard dataset, look at the SWB column and identify the lowest value. The lowest WER is 8.0, achieved by 'This system' which used SWB+Fisher+CH for training.", "question": "Which system achieved the lowest WER on the Switchboard dataset and what training data was used?", "answer": "So, the answer is {\"answer\": \"This system with SWB+Fisher+CH\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The system by Maas et al. using SWB+Fisher achieved a WER of 23.0 on the CallHome dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe system by Maas et al. using SWB+Fisher achieved a WER of 23.0 on the CallHome dataset.\n" }, { "role": "assistant", "content": "The table shows that the system by Maas et al. using SWB+Fisher achieved a WER of 23.0 on the CallHome (CH) dataset. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/235.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Comparison of word error rates on Hub5’00 (SWB and CH) for existing systems (∗ note that the 19.1% CallHome WER is not reported in [13])." ], "reasoning": "The table shows that the system by Maas et al. using SWB+Fisher achieved a WER of 23.0 on the CallHome (CH) dataset.", "statement": "The system by Maas et al. using SWB+Fisher achieved a WER of 23.0 on the CallHome dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Evaluation results (ordered by decreasing precision)'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The precision varies slightly between 74.36% and 75.015% for nonzero values of λ1 or λ2, and the number of support vectors is highest when both λ1 and λ2 are at their maximum values.The results show a significant improvement over the standard ESA version (that corresponds to λi=0 for all i. This confirms our approach. On Fig. the reader can see the precision obtained as function of the two first parameters λ1 and λ2, as well the number of support vectors used. We notice that the precision varies slightly (between 74.36% and 75.015%, that is less than 1%) as long as λ1 or λ2 are nonzero, and abruptly goes down to 65.58% when they are both zero. For nonzero values of λi the variation of precision follows no recognizable pattern. On the other hand, the number of support vectors shows a pattern: it is clearly correlated with λ1 and λ2, the highest value being 995, number of support vectors used when both λ1 and λ2 take their highest values. Since CPU time is roughly proportional to the number of support vectors, it is most interesting to take small (but nonzero) values of λi so that, at the same time, precision is high and the number of support vectors (and hence CPU time) is kept small." } ], "images": [ "scigen/train/236.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Evaluation results (ordered by decreasing precision)" ], "reasoning": "The precision varies slightly between 74.36% and 75.015% for nonzero values of λ1 or λ2, and the number of support vectors is highest when both λ1 and λ2 are at their maximum values.", "description": "The results show a significant improvement over the standard ESA version (that corresponds to λi=0 for all i. This confirms our approach. On Fig. the reader can see the precision obtained as function of the two first parameters λ1 and λ2, as well the number of support vectors used. We notice that the precision varies slightly (between 74.36% and 75.015%, that is less than 1%) as long as λ1 or λ2 are nonzero, and abruptly goes down to 65.58% when they are both zero. For nonzero values of λi the variation of precision follows no recognizable pattern. On the other hand, the number of support vectors shows a pattern: it is clearly correlated with λ1 and λ2, the highest value being 995, number of support vectors used when both λ1 and λ2 take their highest values. Since CPU time is roughly proportional to the number of support vectors, it is most interesting to take small (but nonzero) values of λi so that, at the same time, precision is high and the number of support vectors (and hence CPU time) is kept small.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Evaluation results (ordered by decreasing precision)' to sequentially solve the question presented below. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhat is the precision when both λ1 and λ2 are at their highest values?\n" }, { "role": "assistant", "content": "From the table, the highest values for λ1 and λ2 are 1.5 and 1 respectively. The corresponding precision is 75.015%. So, the answer is {\"answer\": \"75.015%\"}." } ], "images": [ "scigen/train/236.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Evaluation results (ordered by decreasing precision)" ], "reasoning": "From the table, the highest values for λ1 and λ2 are 1.5 and 1 respectively. The corresponding precision is 75.015%.", "question": "What is the precision when both λ1 and λ2 are at their highest values?", "answer": "So, the answer is {\"answer\": \"75.015%\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe precision is highest when both λ1 and λ2 are at their maximum values.\n" }, { "role": "assistant", "content": "The table shows that the highest precision of 75.015% is achieved when λ1 is 1.5 and λ2 is 0, not when both are at their maximum values. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/236.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Evaluation results (ordered by decreasing precision)" ], "reasoning": "The table shows that the highest precision of 75.015% is achieved when λ1 is 1.5 and λ2 is 0, not when both are at their maximum values.", "statement": "The precision is highest when both λ1 and λ2 are at their maximum values.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Evaluation results (ordered by decreasing precision)'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The precision varies slightly between 74.36% and 75.015% for nonzero values of λ1 or λ2, but drops to 65.58% when both are zero, while the number of support vectors is highest (995) when both λ1 and λ2 are at their maximum values.The results show a significant improvement over the standard ESA version (that corresponds to λi=0 for all i. This confirms our approach. On Fig. the reader can see the precision obtained as function of the two first parameters λ1 and λ2, as well the number of support vectors used. We notice that the precision varies slightly (between 74.36% and 75.015%, that is less than 1%) as long as λ1 or λ2 are nonzero, and abruptly goes down to 65.58% when they are both zero. For nonzero values of λi the variation of precision follows no recognizable pattern. On the other hand, the number of support vectors shows a pattern: it is clearly correlated with λ1 and λ2, the highest value being 995, number of support vectors used when both λ1 and λ2 take their highest values. Since CPU time is roughly proportional to the number of support vectors, it is most interesting to take small (but nonzero) values of λi so that, at the same time, precision is high and the number of support vectors (and hence CPU time) is kept small." } ], "images": [ "scigen/train/237.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Evaluation results (ordered by decreasing precision)" ], "reasoning": "The precision varies slightly between 74.36% and 75.015% for nonzero values of λ1 or λ2, but drops to 65.58% when both are zero, while the number of support vectors is highest (995) when both λ1 and λ2 are at their maximum values.", "description": "The results show a significant improvement over the standard ESA version (that corresponds to λi=0 for all i. This confirms our approach. On Fig. the reader can see the precision obtained as function of the two first parameters λ1 and λ2, as well the number of support vectors used. We notice that the precision varies slightly (between 74.36% and 75.015%, that is less than 1%) as long as λ1 or λ2 are nonzero, and abruptly goes down to 65.58% when they are both zero. For nonzero values of λi the variation of precision follows no recognizable pattern. On the other hand, the number of support vectors shows a pattern: it is clearly correlated with λ1 and λ2, the highest value being 995, number of support vectors used when both λ1 and λ2 take their highest values. Since CPU time is roughly proportional to the number of support vectors, it is most interesting to take small (but nonzero) values of λi so that, at the same time, precision is high and the number of support vectors (and hence CPU time) is kept small.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Evaluation results (ordered by decreasing precision)', answer the following question. Give detailed solution consisting of each step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the precision when both λ1 and λ2 are at their highest values?\n" }, { "role": "assistant", "content": "To find the precision when both λ1 and λ2 are at their highest values, look at the row where λ1 and λ2 are both 2. The precision in this row is 74.36%. So, the answer is {\"answer\": \"74.36%\"}." } ], "images": [ "scigen/train/237.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Evaluation results (ordered by decreasing precision)" ], "reasoning": "To find the precision when both λ1 and λ2 are at their highest values, look at the row where λ1 and λ2 are both 2. The precision in this row is 74.36%.", "question": "What is the precision when both λ1 and λ2 are at their highest values?", "answer": "So, the answer is {\"answer\": \"74.36%\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe precision is highest when both λ1 and λ2 are at their maximum values.\n" }, { "role": "assistant", "content": "The table shows that when both λ1 and λ2 are at their maximum values (2), the precision is 74.36%. However, there are higher precision values (e.g., 74.716%) for other combinations of λ1 and λ2. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/237.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Evaluation results (ordered by decreasing precision)" ], "reasoning": "The table shows that when both λ1 and λ2 are at their maximum values (2), the precision is 74.36%. However, there are higher precision values (e.g., 74.716%) for other combinations of λ1 and λ2. Therefore, the statement is refuted.", "statement": "The precision is highest when both λ1 and λ2 are at their maximum values.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Word error rates of different models trained with a subset of the Switchboard data.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The TDNN-LSTM model performed better than the TDNN model, and the teacher model, which combines TDNN and TDNN-LSTM, outperformed both individual models.A subset consisting 25% of the training data from the Switchboard data set was used to quickly evaluate the effectiveness of the proposed method and to tune some hyperparameters. As can be seen, the TDNN-LSTM performed better than the TDNN model. The teacher model, which is a fusion of a TDNN model and a TDNN-LSTM model, significantly outperformed any individual model." } ], "images": [ "scigen/train/238.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Word error rates of different models trained with a subset of the Switchboard data." ], "reasoning": "The TDNN-LSTM model performed better than the TDNN model, and the teacher model, which combines TDNN and TDNN-LSTM, outperformed both individual models.", "description": "A subset consisting 25% of the training data from the Switchboard data set was used to quickly evaluate the effectiveness of the proposed method and to tune some hyperparameters. As can be seen, the TDNN-LSTM performed better than the TDNN model. The teacher model, which is a fusion of a TDNN model and a TDNN-LSTM model, significantly outperformed any individual model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Word error rates of different models trained with a subset of the Switchboard data.', answer the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model had the lowest total score?\n" }, { "role": "assistant", "content": "By comparing the 'TOTAL' column for all models, the TDNN-LSTM+TDNN (teacher) model has the lowest total score of 19.3. So, the answer is {\"answer\": \"TDNN-LSTM+TDNN (teacher)\"}." } ], "images": [ "scigen/train/238.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Word error rates of different models trained with a subset of the Switchboard data." ], "reasoning": "By comparing the 'TOTAL' column for all models, the TDNN-LSTM+TDNN (teacher) model has the lowest total score of 19.3.", "question": "Which model had the lowest total score?", "answer": "So, the answer is {\"answer\": \"TDNN-LSTM+TDNN (teacher)\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe TDNN model had a higher total score than the TDNN-LSTM model.\n" }, { "role": "assistant", "content": "The table shows that the TDNN model had a total score of 20.3, while the TDNN-LSTM model had a total score of 20.2, indicating that the TDNN model had a higher total score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/238.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Word error rates of different models trained with a subset of the Switchboard data." ], "reasoning": "The table shows that the TDNN model had a total score of 20.3, while the TDNN-LSTM model had a total score of 20.2, indicating that the TDNN model had a higher total score.", "statement": "The TDNN model had a higher total score than the TDNN-LSTM model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: DSTC2 and WOZ 2.0 test set performance (joint goals and requests) of the NBT-CNN model making use of three different word vector collections. The asterisk indicates statistically significant improvement over the baseline xavier (random) word vectors (paired t-test; p<0.05).'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Paragram-SL999 vectors outperform GloVe and xavier vectors in goal tracking, especially in noisy DSTC2 data, while GloVe vectors do not improve over random initialization in the noise-free WOZ 2.0 dataset.The NBT models use the semantic relations embedded in the pre-trained word vectors to handle semantic variation and produce high-quality intermediate representations. , trained using co-occurrence information in large textual corpora; and 3) semantically specialised Paragram-SL999 vectors Wieting et al. Paragram-SL999 vectors (significantly) outperformed GloVe and xavier vectors for goal tracking on both datasets. The gains are particularly robust for noisy DSTC2 data, where both collections of pre-trained vectors consistently outperformed random initialisation. The gains are weaker for the noise-free WOZ 2.0 dataset, which seems to be large (and clean) enough for the NBT model to learn task-specific rephrasings and compensate for the lack of semantic content in the word vectors. For this dataset, GloVe vectors do not improve over the randomly initialised ones. We believe this happens because distributional models keep related, yet antonymous words close together (e.g. north and south, expensive and inexpensive), offsetting the useful semantic content embedded in this vector spaces. The NBT-DNN model showed the same trends." } ], "images": [ "scigen/train/239.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: DSTC2 and WOZ 2.0 test set performance (joint goals and requests) of the NBT-CNN model making use of three different word vector collections. The asterisk indicates statistically significant improvement over the baseline xavier (random) word vectors (paired t-test; p<0.05)." ], "reasoning": "Paragram-SL999 vectors outperform GloVe and xavier vectors in goal tracking, especially in noisy DSTC2 data, while GloVe vectors do not improve over random initialization in the noise-free WOZ 2.0 dataset.", "description": "The NBT models use the semantic relations embedded in the pre-trained word vectors to handle semantic variation and produce high-quality intermediate representations. , trained using co-occurrence information in large textual corpora; and 3) semantically specialised Paragram-SL999 vectors Wieting et al. Paragram-SL999 vectors (significantly) outperformed GloVe and xavier vectors for goal tracking on both datasets. The gains are particularly robust for noisy DSTC2 data, where both collections of pre-trained vectors consistently outperformed random initialisation. The gains are weaker for the noise-free WOZ 2.0 dataset, which seems to be large (and clean) enough for the NBT model to learn task-specific rephrasings and compensate for the lack of semantic content in the word vectors. For this dataset, GloVe vectors do not improve over the randomly initialised ones. We believe this happens because distributional models keep related, yet antonymous words close together (e.g. north and south, expensive and inexpensive), offsetting the useful semantic content embedded in this vector spaces. The NBT-DNN model showed the same trends.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 2: DSTC2 and WOZ 2.0 test set performance (joint goals and requests) of the NBT-CNN model making use of three different word vector collections. The asterisk indicates statistically significant improvement over the baseline xavier (random) word vectors (paired t-test; p<0.05).'\nQuestion: Which word vector showed the highest performance for DSTC2 Goals?\n" }, { "role": "assistant", "content": "By comparing the values in the 'DSTC2 Goals' column, Paragram-SL999 has the highest score of 73.4. So, the answer is {\"answer\": \"Paragram-SL999\"}" } ], "images": [ "scigen/train/239.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: DSTC2 and WOZ 2.0 test set performance (joint goals and requests) of the NBT-CNN model making use of three different word vector collections. The asterisk indicates statistically significant improvement over the baseline xavier (random) word vectors (paired t-test; p<0.05)." ], "reasoning": "By comparing the values in the 'DSTC2 Goals' column, Paragram-SL999 has the highest score of 73.4.", "question": "Which word vector showed the highest performance for DSTC2 Goals?", "answer": "So, the answer is {\"answer\": \"Paragram-SL999\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nGloVe vectors showed the highest performance for WOZ 2.0 Requests.\n" }, { "role": "assistant", "content": "By comparing the values in the 'WOZ 2.0 Requests' column, Paragram-SL999 has a score of 91.6, which is higher than GloVe's score of 91.4. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/239.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: DSTC2 and WOZ 2.0 test set performance (joint goals and requests) of the NBT-CNN model making use of three different word vector collections. The asterisk indicates statistically significant improvement over the baseline xavier (random) word vectors (paired t-test; p<0.05)." ], "reasoning": "By comparing the values in the 'WOZ 2.0 Requests' column, Paragram-SL999 has a score of 91.6, which is higher than GloVe's score of 91.4.", "statement": "GloVe vectors showed the highest performance for WOZ 2.0 Requests.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: DSTC2 and WOZ 2.0 test set accuracies for: a) joint goals; and b) turn-level requests. The asterisk indicates statistically significant improvement over the baseline trackers (paired t-test; p<0.05).'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The NBT models significantly outperformed the baseline models in joint goal and request accuracies, with no significant difference between NBT and lexicon-supplemented models.The NBT models outperformed the baseline models in terms of both joint goal and request accuracies. For goals, the gains are always statistically significant (paired t-test, p<0.05). Moreover, there was no statistically significant variation between the NBT and the lexicon-supplemented models, showing that the NBT can handle semantic relations which otherwise had to be explicitly encoded in semantic dictionaries." } ], "images": [ "scigen/train/240.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: DSTC2 and WOZ 2.0 test set accuracies for: a) joint goals; and b) turn-level requests. The asterisk indicates statistically significant improvement over the baseline trackers (paired t-test; p<0.05)." ], "reasoning": "The NBT models significantly outperformed the baseline models in joint goal and request accuracies, with no significant difference between NBT and lexicon-supplemented models.", "description": "The NBT models outperformed the baseline models in terms of both joint goal and request accuracies. For goals, the gains are always statistically significant (paired t-test, p<0.05). Moreover, there was no statistically significant variation between the NBT and the lexicon-supplemented models, showing that the NBT can handle semantic relations which otherwise had to be explicitly encoded in semantic dictionaries.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: DSTC2 and WOZ 2.0 test set accuracies for: a) joint goals; and b) turn-level requests. The asterisk indicates statistically significant improvement over the baseline trackers (paired t-test; p<0.05).', answer the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model achieved the highest accuracy for WOZ 2.0 Goals?\n" }, { "role": "assistant", "content": "By comparing the WOZ 2.0 Goals column, the Neural Belief Tracker: NBT-DNN model achieved the highest accuracy with a score of 84.4. So, the answer is {\"answer\": \"Neural Belief Tracker: NBT-DNN\"}." } ], "images": [ "scigen/train/240.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: DSTC2 and WOZ 2.0 test set accuracies for: a) joint goals; and b) turn-level requests. The asterisk indicates statistically significant improvement over the baseline trackers (paired t-test; p<0.05)." ], "reasoning": "By comparing the WOZ 2.0 Goals column, the Neural Belief Tracker: NBT-DNN model achieved the highest accuracy with a score of 84.4.", "question": "Which model achieved the highest accuracy for WOZ 2.0 Goals?", "answer": "So, the answer is {\"answer\": \"Neural Belief Tracker: NBT-DNN\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Delexicalisation-Based Model achieved the highest accuracy for DSTC2 Goals.\n" }, { "role": "assistant", "content": "By comparing the DSTC2 Goals column, the Delexicalisation-Based Model did not achieve the highest accuracy. The highest accuracy was achieved by the Neural Belief Tracker: NBT-CNN model with a score of 73.4. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/240.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: DSTC2 and WOZ 2.0 test set accuracies for: a) joint goals; and b) turn-level requests. The asterisk indicates statistically significant improvement over the baseline trackers (paired t-test; p<0.05)." ], "reasoning": "By comparing the DSTC2 Goals column, the Delexicalisation-Based Model did not achieve the highest accuracy. The highest accuracy was achieved by the Neural Belief Tracker: NBT-CNN model with a score of 73.4.", "statement": "The Delexicalisation-Based Model achieved the highest accuracy for DSTC2 Goals.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title '(b) Con vs Other', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "VarNDRR achieves the best F1 score on EXP/Com among the three models, with improvements mainly due to higher recall values.Because the development and test sets are imbalanced in terms of the ratio of positive and negative instances, we chose the widely-used F1 score as our major evaluation metric. In addition, we also provide the precision, recall and accuracy for further analysis. according to their F1 scores. Although it fails on Con, VarNDRR achieves the best result on EXP/Com among these three models. Overall, VarNDRR is competitive in comparison with these two baselines. With respect to the accuracy, our model does not yield substantial improvements over the two baselines. This may be because that we used the F1 score rather than the accuracy, as our selection criterion on the development set. With respect to the precision and recall, our model tends to produce relatively lower precisions but higher recalls. This suggests that the improvements of VarNDRR in terms of F1 scores mostly benefits from the recall values." } ], "images": [ "scigen/train/241.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) Con vs Other" ], "reasoning": "VarNDRR achieves the best F1 score on EXP/Com among the three models, with improvements mainly due to higher recall values.", "description": "Because the development and test sets are imbalanced in terms of the ratio of positive and negative instances, we chose the widely-used F1 score as our major evaluation metric. In addition, we also provide the precision, recall and accuracy for further analysis. according to their F1 scores. Although it fails on Con, VarNDRR achieves the best result on EXP/Com among these three models. Overall, VarNDRR is competitive in comparison with these two baselines. With respect to the accuracy, our model does not yield substantial improvements over the two baselines. This may be because that we used the F1 score rather than the accuracy, as our selection criterion on the development set. With respect to the precision and recall, our model tends to produce relatively lower precisions but higher recalls. This suggests that the improvements of VarNDRR in terms of F1 scores mostly benefits from the recall values.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to '(b) Con vs Other', and sequentially solve the question, writing out each step of your reasoning process. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest recall value and what is its F1 score?\n" }, { "role": "assistant", "content": "By examining the table, VarNDRR has the highest recall value of 88.53, and its F1 score is 50.56. So, the answer is {\"answer\": \"VarNDRR, 50.56\"}" } ], "images": [ "scigen/train/241.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) Con vs Other" ], "reasoning": "By examining the table, VarNDRR has the highest recall value of 88.53, and its F1 score is 50.56.", "question": "Which model has the highest recall value and what is its F1 score?", "answer": "So, the answer is {\"answer\": \"VarNDRR, 50.56\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nVarNDRR has the highest precision among the three models.\n" }, { "role": "assistant", "content": "The table shows that VarNDRR has a precision of 35.39, which is lower than the precision values of SVM (39.14) and SCNN (39.80). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/241.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) Con vs Other" ], "reasoning": "The table shows that VarNDRR has a precision of 35.39, which is lower than the precision values of SVM (39.14) and SCNN (39.80).", "statement": "VarNDRR has the highest precision among the three models.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title '(a) Com vs Other', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "VarNDRR achieves the best F1 score on EXP/Com among the three models and shows higher recall but lower precision.Because the development and test sets are imbalanced in terms of the ratio of positive and negative instances, we chose the widely-used F1 score as our major evaluation metric. In addition, we also provide the precision, recall and accuracy for further analysis. according to their F1 scores. Although it fails on Con, VarNDRR achieves the best result on EXP/Com among these three models. Overall, VarNDRR is competitive in comparison with these two baselines. With respect to the accuracy, our model does not yield substantial improvements over the two baselines. This may be because that we used the F1 score rather than the accuracy, as our selection criterion on the development set. With respect to the precision and recall, our model tends to produce relatively lower precisions but higher recalls. This suggests that the improvements of VarNDRR in terms of F1 scores mostly benefits from the recall values." } ], "images": [ "scigen/train/242.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) Com vs Other" ], "reasoning": "VarNDRR achieves the best F1 score on EXP/Com among the three models and shows higher recall but lower precision.", "description": "Because the development and test sets are imbalanced in terms of the ratio of positive and negative instances, we chose the widely-used F1 score as our major evaluation metric. In addition, we also provide the precision, recall and accuracy for further analysis. according to their F1 scores. Although it fails on Con, VarNDRR achieves the best result on EXP/Com among these three models. Overall, VarNDRR is competitive in comparison with these two baselines. With respect to the accuracy, our model does not yield substantial improvements over the two baselines. This may be because that we used the F1 score rather than the accuracy, as our selection criterion on the development set. With respect to the precision and recall, our model tends to produce relatively lower precisions but higher recalls. This suggests that the improvements of VarNDRR in terms of F1 scores mostly benefits from the recall values.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of '(a) Com vs Other', answer the question below by showing each progressive step toward the answer. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest recall value?\n" }, { "role": "assistant", "content": "By comparing the recall (R) values of the models listed in the table, VarNDRR has the highest recall value of 71.05. So, the answer is {\"answer\": \"VarNDRR\"}" } ], "images": [ "scigen/train/242.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) Com vs Other" ], "reasoning": "By comparing the recall (R) values of the models listed in the table, VarNDRR has the highest recall value of 71.05.", "question": "Which model has the highest recall value?", "answer": "So, the answer is {\"answer\": \"VarNDRR\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nVarNDRR has the highest accuracy among the models.\n" }, { "role": "assistant", "content": "The table shows that J & E TACL536 has the highest accuracy (Acc) of 70.27, while VarNDRR has an accuracy of 63.30. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/242.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) Com vs Other" ], "reasoning": "The table shows that J & E TACL536 has the highest accuracy (Acc) of 70.27, while VarNDRR has an accuracy of 63.30.", "statement": "VarNDRR has the highest accuracy among the models.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named '(c) Exp vs Other'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "VarNDRR achieves the best F1 score on EXP/Com among the three models but does not yield substantial improvements in accuracy.Because the development and test sets are imbalanced in terms of the ratio of positive and negative instances, we chose the widely-used F1 score as our major evaluation metric. In addition, we also provide the precision, recall and accuracy for further analysis. according to their F1 scores. Although it fails on Con, VarNDRR achieves the best result on EXP/Com among these three models. Overall, VarNDRR is competitive in comparison with these two baselines. With respect to the accuracy, our model does not yield substantial improvements over the two baselines. This may be because that we used the F1 score rather than the accuracy, as our selection criterion on the development set. With respect to the precision and recall, our model tends to produce relatively lower precisions but higher recalls. This suggests that the improvements of VarNDRR in terms of F1 scores mostly benefits from the recall values." } ], "images": [ "scigen/train/243.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(c) Exp vs Other" ], "reasoning": "VarNDRR achieves the best F1 score on EXP/Com among the three models but does not yield substantial improvements in accuracy.", "description": "Because the development and test sets are imbalanced in terms of the ratio of positive and negative instances, we chose the widely-used F1 score as our major evaluation metric. In addition, we also provide the precision, recall and accuracy for further analysis. according to their F1 scores. Although it fails on Con, VarNDRR achieves the best result on EXP/Com among these three models. Overall, VarNDRR is competitive in comparison with these two baselines. With respect to the accuracy, our model does not yield substantial improvements over the two baselines. This may be because that we used the F1 score rather than the accuracy, as our selection criterion on the development set. With respect to the precision and recall, our model tends to produce relatively lower precisions but higher recalls. This suggests that the improvements of VarNDRR in terms of F1 scores mostly benefits from the recall values.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: (c) Exp vs Other\nQuestion: Which model has the highest recall (R) value?\nSolve the above question based on the table information. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the recall (R) values of the models listed in the table, VarNDRR has the highest recall value of 97.39. So, the answer is {\"answer\": \"VarNDRR\"}" } ], "images": [ "scigen/train/243.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(c) Exp vs Other" ], "reasoning": "By comparing the recall (R) values of the models listed in the table, VarNDRR has the highest recall value of 97.39.", "question": "Which model has the highest recall (R) value?", "answer": "So, the answer is {\"answer\": \"VarNDRR\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nVarNDRR has the highest precision (P) value among the models.\n" }, { "role": "assistant", "content": "The table shows that VarNDRR has a precision (P) value of 56.46, which is lower than the precision values of SVM (65.89) and SCNN (56.29). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/243.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(c) Exp vs Other" ], "reasoning": "The table shows that VarNDRR has a precision (P) value of 56.46, which is lower than the precision values of SVM (65.89) and SCNN (56.29).", "statement": "VarNDRR has the highest precision (P) value among the models.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title '(d) Tem vs Other', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "VarNDRR achieves the best F1 score on EXP/Com among the three models, despite not yielding substantial improvements in accuracy.Because the development and test sets are imbalanced in terms of the ratio of positive and negative instances, we chose the widely-used F1 score as our major evaluation metric. In addition, we also provide the precision, recall and accuracy for further analysis. according to their F1 scores. Although it fails on Con, VarNDRR achieves the best result on EXP/Com among these three models. Overall, VarNDRR is competitive in comparison with these two baselines. With respect to the accuracy, our model does not yield substantial improvements over the two baselines. This may be because that we used the F1 score rather than the accuracy, as our selection criterion on the development set. With respect to the precision and recall, our model tends to produce relatively lower precisions but higher recalls. This suggests that the improvements of VarNDRR in terms of F1 scores mostly benefits from the recall values." } ], "images": [ "scigen/train/244.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(d) Tem vs Other" ], "reasoning": "VarNDRR achieves the best F1 score on EXP/Com among the three models, despite not yielding substantial improvements in accuracy.", "description": "Because the development and test sets are imbalanced in terms of the ratio of positive and negative instances, we chose the widely-used F1 score as our major evaluation metric. In addition, we also provide the precision, recall and accuracy for further analysis. according to their F1 scores. Although it fails on Con, VarNDRR achieves the best result on EXP/Com among these three models. Overall, VarNDRR is competitive in comparison with these two baselines. With respect to the accuracy, our model does not yield substantial improvements over the two baselines. This may be because that we used the F1 score rather than the accuracy, as our selection criterion on the development set. With respect to the precision and recall, our model tends to produce relatively lower precisions but higher recalls. This suggests that the improvements of VarNDRR in terms of F1 scores mostly benefits from the recall values.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of '(d) Tem vs Other', unfold the question presented below, detailing every step of your calculation. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest recall (R) value?\n" }, { "role": "assistant", "content": "By examining the recall (R) values in the table, VarNDRR has the highest recall value of 97.65. So, the answer is {\"answer\": \"VarNDRR\"}" } ], "images": [ "scigen/train/244.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(d) Tem vs Other" ], "reasoning": "By examining the recall (R) values in the table, VarNDRR has the highest recall value of 97.65.", "question": "Which model has the highest recall (R) value?", "answer": "So, the answer is {\"answer\": \"VarNDRR\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim VarNDRR has the highest precision (P) value among the models., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nVarNDRR has the highest precision (P) value among the models.\n" }, { "role": "assistant", "content": "The table shows that VarNDRR has a precision (P) value of 17.40, which is lower than SCNN's precision value of 20.22. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/244.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(d) Tem vs Other" ], "reasoning": "The table shows that VarNDRR has a precision (P) value of 17.40, which is lower than SCNN's precision value of 20.22.", "statement": "VarNDRR has the highest precision (P) value among the models.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: The performance of GCBiA with negative sampling augmentation method conditioned on over 0.999 on-topic recall.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The new negative sampling method increased the training data size to 1.67M and improved the average off-topic response recall for both seen and unseen prompts.To augment training data and strengthen the generalization of the off-topic response detection model for unseen prompts, we proposed a new and effective negative sampling method for off-topic response detection task. Comparing with the previous method of generating only one negative sample for each positive one, we generated two. The first one is chosen randomly as before, and the second one consists of words shuffled from the first one. This method contributes to the diversity of negative samples of training data. The size of our training data reaches 1.67M, compared with 1.12M in the previous negative sampling method. To make training data balanced, we gave the weight of positive and negative samples: 1 and 0.5, respectively. Our model GCBiA equipped with negative sampling augmentation can achieve 88.2% and 69.1% average off-topic response recall on seen and unseen prompts, conditioned on 0.999 on-topic recall." } ], "images": [ "scigen/train/245.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The performance of GCBiA with negative sampling augmentation method conditioned on over 0.999 on-topic recall." ], "reasoning": "The new negative sampling method increased the training data size to 1.67M and improved the average off-topic response recall for both seen and unseen prompts.", "description": "To augment training data and strengthen the generalization of the off-topic response detection model for unseen prompts, we proposed a new and effective negative sampling method for off-topic response detection task. Comparing with the previous method of generating only one negative sample for each positive one, we generated two. The first one is chosen randomly as before, and the second one consists of words shuffled from the first one. This method contributes to the diversity of negative samples of training data. The size of our training data reaches 1.67M, compared with 1.12M in the previous negative sampling method. To make training data balanced, we gave the weight of positive and negative samples: 1 and 0.5, respectively. Our model GCBiA equipped with negative sampling augmentation can achieve 88.2% and 69.1% average off-topic response recall on seen and unseen prompts, conditioned on 0.999 on-topic recall.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: 'Table 5: The performance of GCBiA with negative sampling augmentation method conditioned on over 0.999 on-topic recall.'\nQuestion: What is the improvement in the average off-topic response recall for unseen prompts when using the new negative sampling method?\n" }, { "role": "assistant", "content": "The table shows that the average off-topic response recall for unseen prompts improved from 45.0 to 69.1 when using the new negative sampling method. So, the answer is {\"answer\": \"24.1\"}" } ], "images": [ "scigen/train/245.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The performance of GCBiA with negative sampling augmentation method conditioned on over 0.999 on-topic recall." ], "reasoning": "The table shows that the average off-topic response recall for unseen prompts improved from 45.0 to 69.1 when using the new negative sampling method.", "question": "What is the improvement in the average off-topic response recall for unseen prompts when using the new negative sampling method?", "answer": "So, the answer is {\"answer\": \"24.1\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The new negative sampling method improved the seen PPR3 score., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe new negative sampling method improved the seen PPR3 score.\n" }, { "role": "assistant", "content": "The table shows that the seen PPR3 score increased from 93.6 to 94.2 when using the new negative sampling method. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/245.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The performance of GCBiA with negative sampling augmentation method conditioned on over 0.999 on-topic recall." ], "reasoning": "The table shows that the seen PPR3 score increased from 93.6 to 94.2 when using the new negative sampling method.", "statement": "The new negative sampling method improved the seen PPR3 score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: The comparison of different models based on over 0.999 on-topic recall on seen and unseen benchmarks. AOR means Average Off-topic Recall (%) and PRR3 means Prompt Ratio over off-topic Recall 0.3 (%).', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our baseline model G-Att-RNN, which includes residual connections and a gated unit, significantly improves performance on both seen and unseen benchmarks compared to Att-RNN.As is shown in To make the evaluation more convincing, we built a stronger baseline model G-Att-RNN based on Att-RNN by adding residual connections with each layer. Additionally, we add a gated unit as the relevance layer for our baseline model G-Att-RNN. Compared with Att-RNN, our baseline model G-Att-RNN achieved significant improvements on both seen (by +3.2 PPR3 points and +4.6 AOR points) and unseen benchmark (by +22.0 PPR3 points and +17.1 AOR). +24.0 AOR points on the unseen benchmark, as well as +9.0 PPR3 points and +7.0 AOR points on the seen benchmark. Meanwhile, our approach significantly outperforms G-Att-RNN by +14.0 PPR3 points and + 6.9 AOR points on the unseen benchmark, as well as +5.8 PPR3 points and +2.4 AOR points on the seen benchmark." } ], "images": [ "scigen/train/246.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The comparison of different models based on over 0.999 on-topic recall on seen and unseen benchmarks. AOR means Average Off-topic Recall (%) and PRR3 means Prompt Ratio over off-topic Recall 0.3 (%)." ], "reasoning": "Our baseline model G-Att-RNN, which includes residual connections and a gated unit, significantly improves performance on both seen and unseen benchmarks compared to Att-RNN.", "description": "As is shown in To make the evaluation more convincing, we built a stronger baseline model G-Att-RNN based on Att-RNN by adding residual connections with each layer. Additionally, we add a gated unit as the relevance layer for our baseline model G-Att-RNN. Compared with Att-RNN, our baseline model G-Att-RNN achieved significant improvements on both seen (by +3.2 PPR3 points and +4.6 AOR points) and unseen benchmark (by +22.0 PPR3 points and +17.1 AOR). +24.0 AOR points on the unseen benchmark, as well as +9.0 PPR3 points and +7.0 AOR points on the seen benchmark. Meanwhile, our approach significantly outperforms G-Att-RNN by +14.0 PPR3 points and + 6.9 AOR points on the unseen benchmark, as well as +5.8 PPR3 points and +2.4 AOR points on the seen benchmark.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 4: The comparison of different models based on over 0.999 on-topic recall on seen and unseen benchmarks. AOR means Average Off-topic Recall (%) and PRR3 means Prompt Ratio over off-topic Recall 0.3 (%).'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nHow much did the G-Att-RNN model improve in Seen PPR3 compared to the Att-RNN model?\n" }, { "role": "assistant", "content": "The Seen PPR3 for Att-RNN is 84.6, and for G-Att-RNN it is 87.8. The improvement is calculated as 87.8 - 84.6. So, the answer is {\"answer\": \"3.2\"}" } ], "images": [ "scigen/train/246.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The comparison of different models based on over 0.999 on-topic recall on seen and unseen benchmarks. AOR means Average Off-topic Recall (%) and PRR3 means Prompt Ratio over off-topic Recall 0.3 (%)." ], "reasoning": "The Seen PPR3 for Att-RNN is 84.6, and for G-Att-RNN it is 87.8. The improvement is calculated as 87.8 - 84.6.", "question": "How much did the G-Att-RNN model improve in Seen PPR3 compared to the Att-RNN model?", "answer": "So, the answer is {\"answer\": \"3.2\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe G-Att-RNN model achieved a Seen AOR score of 76.8.\n" }, { "role": "assistant", "content": "The table shows that the Seen AOR score for the G-Att-RNN model is indeed 76.8. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/246.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The comparison of different models based on over 0.999 on-topic recall on seen and unseen benchmarks. AOR means Average Off-topic Recall (%) and PRR3 means Prompt Ratio over off-topic Recall 0.3 (%)." ], "reasoning": "The table shows that the Seen AOR score for the G-Att-RNN model is indeed 76.8.", "statement": "The G-Att-RNN model achieved a Seen AOR score of 76.8.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: SER (%), WER (%), and SA-WER (%) for baseline systems and proposed method. The number of profiles per test audio was 8. Each profile was extracted by using 2 utterances (15 sec on average). For random speaker assignment experiment (3rd row), averages of 10 trials were computed. No LM was used in the evaluation.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The SOT-ASR system significantly improved the WER for all evaluation settings compared to the conventional single-speaker ASR based on AED, especially for overlapped speech.Baseline results The first row corresponds the conventional single-speaker ASR based on AED. As expected, the WER was significantly degraded for overlapped speech. The second row shows the result of the SOT-ASR system that was used for initializing the proposed method in training. SOT-ASR significantly improved the WER for all evaluation settings." } ], "images": [ "scigen/train/247.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: SER (%), WER (%), and SA-WER (%) for baseline systems and proposed method. The number of profiles per test audio was 8. Each profile was extracted by using 2 utterances (15 sec on average). For random speaker assignment experiment (3rd row), averages of 10 trials were computed. No LM was used in the evaluation." ], "reasoning": "The SOT-ASR system significantly improved the WER for all evaluation settings compared to the conventional single-speaker ASR based on AED, especially for overlapped speech.", "description": "Baseline results The first row corresponds the conventional single-speaker ASR based on AED. As expected, the WER was significantly degraded for overlapped speech. The second row shows the result of the SOT-ASR system that was used for initializing the proposed method in training. SOT-ASR significantly improved the WER for all evaluation settings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 1: SER (%), WER (%), and SA-WER (%) for baseline systems and proposed method. The number of profiles per test audio was 8. Each profile was extracted by using 2 utterances (15 sec on average). For random speaker assignment experiment (3rd row), averages of 10 trials were computed. No LM was used in the evaluation.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. \n\nQuestion: What is the Total WER for the SOT-ASR system?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The Total WER for the SOT-ASR system can be found in the row labeled 'SOT-ASR' under the column 'Total WER'. So, the answer is {\"answer\": \"13.9\"}." } ], "images": [ "scigen/train/247.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: SER (%), WER (%), and SA-WER (%) for baseline systems and proposed method. The number of profiles per test audio was 8. Each profile was extracted by using 2 utterances (15 sec on average). For random speaker assignment experiment (3rd row), averages of 10 trials were computed. No LM was used in the evaluation." ], "reasoning": "The Total WER for the SOT-ASR system can be found in the row labeled 'SOT-ASR' under the column 'Total WER'.", "question": "What is the Total WER for the SOT-ASR system?", "answer": "So, the answer is {\"answer\": \"13.9\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Proposed Model with SOT-ASR + Spk-Enc + Inv-Attn has a lower Total SA-WER than the SOT-ASR + d-vec speaker identification.\n" }, { "role": "assistant", "content": "The Total SA-WER for the Proposed Model with SOT-ASR + Spk-Enc + Inv-Attn is 18.2, while the Total SA-WER for the SOT-ASR + d-vec speaker identification is 22.2. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/247.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: SER (%), WER (%), and SA-WER (%) for baseline systems and proposed method. The number of profiles per test audio was 8. Each profile was extracted by using 2 utterances (15 sec on average). For random speaker assignment experiment (3rd row), averages of 10 trials were computed. No LM was used in the evaluation." ], "reasoning": "The Total SA-WER for the Proposed Model with SOT-ASR + Spk-Enc + Inv-Attn is 18.2, while the Total SA-WER for the SOT-ASR + d-vec speaker identification is 22.2.", "statement": "The Proposed Model with SOT-ASR + Spk-Enc + Inv-Attn has a lower Total SA-WER than the SOT-ASR + d-vec speaker identification.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title '(a)'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The ASG criterion implemented in C with SSE instructions and OpenMP parallel for is faster on long sequences compared to Baidu's GPU CTC implementation, and achieves competitive WER on the LibriSpeech corpus.Our ASG criterion is implemented in C (CPU only), leveraging SSE instructions when possible. Our batching is done with an OpenMP parallel for. Both criteria lead to the same LER. For comparing the speed, we report performance for sequence sizes as reported initially by Baidu, but also for longer sequence sizes, which corresponds to our average use case. ASG appears faster on long sequences, even though it is running on CPU only. Baidu’s GPU CTC implementation seems more aimed at larger vocabularies (e.g. 5000 Chinese characters). We have introduced a simple end-to-end automatic speech recognition system, which combines a standard 1D convolutional neural network, a sequence criterion which can infer the segmentation, and a simple beam-search decoder. The decoding results are competitive on the LibriSpeech corpus with MFCC features (7.2% WER), and promising with power spectrum and raw speech (9.4% WER and 10.1% WER respectively). (on average, one LibriSpeech sentence is processed in less than 60ms by our ConvNet, and the decoder runs at 8.6x on a single thread)." } ], "images": [ "scigen/train/248.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a)" ], "reasoning": "The ASG criterion implemented in C with SSE instructions and OpenMP parallel for is faster on long sequences compared to Baidu's GPU CTC implementation, and achieves competitive WER on the LibriSpeech corpus.", "description": "Our ASG criterion is implemented in C (CPU only), leveraging SSE instructions when possible. Our batching is done with an OpenMP parallel for. Both criteria lead to the same LER. For comparing the speed, we report performance for sequence sizes as reported initially by Baidu, but also for longer sequence sizes, which corresponds to our average use case. ASG appears faster on long sequences, even though it is running on CPU only. Baidu’s GPU CTC implementation seems more aimed at larger vocabularies (e.g. 5000 Chinese characters). We have introduced a simple end-to-end automatic speech recognition system, which combines a standard 1D convolutional neural network, a sequence criterion which can infer the segmentation, and a simple beam-search decoder. The decoding results are competitive on the LibriSpeech corpus with MFCC features (7.2% WER), and promising with power spectrum and raw speech (9.4% WER and 10.1% WER respectively). (on average, one LibriSpeech sentence is processed in less than 60ms by our ConvNet, and the decoder runs at 8.6x on a single thread).", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of '(a)' and proceed to solve the following question in a stepwise manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the WER for ASG on the test-clean set?\n" }, { "role": "assistant", "content": "The table shows the WER for ASG and CTC on both the dev-clean and test-clean sets. For the test-clean set, the WER for ASG is 10.1. So, the answer is {\"answer\": \"10.1\"}." } ], "images": [ "scigen/train/248.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a)" ], "reasoning": "The table shows the WER for ASG and CTC on both the dev-clean and test-clean sets. For the test-clean set, the WER for ASG is 10.1.", "question": "What is the WER for ASG on the test-clean set?", "answer": "So, the answer is {\"answer\": \"10.1\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim ASG has a lower WER than CTC on the dev-clean set., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nASG has a lower WER than CTC on the dev-clean set.\n" }, { "role": "assistant", "content": "The table shows that the WER for ASG on the dev-clean set is 10.4, while the WER for CTC on the same set is 10.7. Therefore, ASG has a lower WER than CTC on the dev-clean set. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/248.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a)" ], "reasoning": "The table shows that the WER for ASG on the dev-clean set is 10.4, while the WER for CTC on the same set is 10.7. Therefore, ASG has a lower WER than CTC on the dev-clean set.", "statement": "ASG has a lower WER than CTC on the dev-clean set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title '(b)', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "ASG implemented in C with SSE instructions and OpenMP parallel for is faster on long sequences compared to Baidu's GPU CTC, and achieves competitive decoding results on the LibriSpeech corpus.Our ASG criterion is implemented in C (CPU only), leveraging SSE instructions when possible. Our batching is done with an OpenMP parallel for. Both criteria lead to the same LER. For comparing the speed, we report performance for sequence sizes as reported initially by Baidu, but also for longer sequence sizes, which corresponds to our average use case. ASG appears faster on long sequences, even though it is running on CPU only. Baidu’s GPU CTC implementation seems more aimed at larger vocabularies (e.g. 5000 Chinese characters). We have introduced a simple end-to-end automatic speech recognition system, which combines a standard 1D convolutional neural network, a sequence criterion which can infer the segmentation, and a simple beam-search decoder. The decoding results are competitive on the LibriSpeech corpus with MFCC features (7.2% WER), and promising with power spectrum and raw speech (9.4% WER and 10.1% WER respectively). (on average, one LibriSpeech sentence is processed in less than 60ms by our ConvNet, and the decoder runs at 8.6x on a single thread)." } ], "images": [ "scigen/train/249.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b)" ], "reasoning": "ASG implemented in C with SSE instructions and OpenMP parallel for is faster on long sequences compared to Baidu's GPU CTC, and achieves competitive decoding results on the LibriSpeech corpus.", "description": "Our ASG criterion is implemented in C (CPU only), leveraging SSE instructions when possible. Our batching is done with an OpenMP parallel for. Both criteria lead to the same LER. For comparing the speed, we report performance for sequence sizes as reported initially by Baidu, but also for longer sequence sizes, which corresponds to our average use case. ASG appears faster on long sequences, even though it is running on CPU only. Baidu’s GPU CTC implementation seems more aimed at larger vocabularies (e.g. 5000 Chinese characters). We have introduced a simple end-to-end automatic speech recognition system, which combines a standard 1D convolutional neural network, a sequence criterion which can infer the segmentation, and a simple beam-search decoder. The decoding results are competitive on the LibriSpeech corpus with MFCC features (7.2% WER), and promising with power spectrum and raw speech (9.4% WER and 10.1% WER respectively). (on average, one LibriSpeech sentence is processed in less than 60ms by our ConvNet, and the decoder runs at 8.6x on a single thread).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: (b)\nQuestion: What is the processing time for ASG CPU at batch size 4?\nSolve the above question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "From the table, locate the row with batch size 4 and find the corresponding value under the ASG CPU column. So, the answer is {\"answer\": \"2.8\"}" } ], "images": [ "scigen/train/249.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b)" ], "reasoning": "From the table, locate the row with batch size 4 and find the corresponding value under the ASG CPU column.", "question": "What is the processing time for ASG CPU at batch size 4?", "answer": "So, the answer is {\"answer\": \"2.8\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nCTC GPU is faster than ASG CPU for batch size 1.\n" }, { "role": "assistant", "content": "From the table, compare the values for CTC GPU and ASG CPU at batch size 1. CTC GPU has a value of 5.9, while ASG CPU has a value of 2.5, indicating that CTC GPU is slower. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/249.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b)" ], "reasoning": "From the table, compare the values for CTC GPU and ASG CPU at batch size 1. CTC GPU has a value of 5.9, while ASG CPU has a value of 2.5, indicating that CTC GPU is slower.", "statement": "CTC GPU is faster than ASG CPU for batch size 1.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled '(c)'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The ASG criterion implemented in C with SSE instructions and OpenMP parallel for is faster on long sequences compared to Baidu's GPU CTC implementation, which is more suited for larger vocabularies.Our ASG criterion is implemented in C (CPU only), leveraging SSE instructions when possible. Our batching is done with an OpenMP parallel for. Both criteria lead to the same LER. For comparing the speed, we report performance for sequence sizes as reported initially by Baidu, but also for longer sequence sizes, which corresponds to our average use case. ASG appears faster on long sequences, even though it is running on CPU only. Baidu’s GPU CTC implementation seems more aimed at larger vocabularies (e.g. 5000 Chinese characters). We have introduced a simple end-to-end automatic speech recognition system, which combines a standard 1D convolutional neural network, a sequence criterion which can infer the segmentation, and a simple beam-search decoder. The decoding results are competitive on the LibriSpeech corpus with MFCC features (7.2% WER), and promising with power spectrum and raw speech (9.4% WER and 10.1% WER respectively). (on average, one LibriSpeech sentence is processed in less than 60ms by our ConvNet, and the decoder runs at 8.6x on a single thread)." } ], "images": [ "scigen/train/250.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(c)" ], "reasoning": "The ASG criterion implemented in C with SSE instructions and OpenMP parallel for is faster on long sequences compared to Baidu's GPU CTC implementation, which is more suited for larger vocabularies.", "description": "Our ASG criterion is implemented in C (CPU only), leveraging SSE instructions when possible. Our batching is done with an OpenMP parallel for. Both criteria lead to the same LER. For comparing the speed, we report performance for sequence sizes as reported initially by Baidu, but also for longer sequence sizes, which corresponds to our average use case. ASG appears faster on long sequences, even though it is running on CPU only. Baidu’s GPU CTC implementation seems more aimed at larger vocabularies (e.g. 5000 Chinese characters). We have introduced a simple end-to-end automatic speech recognition system, which combines a standard 1D convolutional neural network, a sequence criterion which can infer the segmentation, and a simple beam-search decoder. The decoding results are competitive on the LibriSpeech corpus with MFCC features (7.2% WER), and promising with power spectrum and raw speech (9.4% WER and 10.1% WER respectively). (on average, one LibriSpeech sentence is processed in less than 60ms by our ConvNet, and the decoder runs at 8.6x on a single thread).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: (c)\nQuestion: What is the performance of ASG CPU for a batch size of 8?\nSolve the above question based on the table information. Let's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "From the table, locate the row with batch size 8 and find the corresponding value under the ASG CPU column. So, the answer is {\"answer\": \"19.2\"}" } ], "images": [ "scigen/train/250.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(c)" ], "reasoning": "From the table, locate the row with batch size 8 and find the corresponding value under the ASG CPU column.", "question": "What is the performance of ASG CPU for a batch size of 8?", "answer": "So, the answer is {\"answer\": \"19.2\"}" }, { "messages": [ { "role": "user", "content": "CTC GPU has a performance of 100.3 for a batch size of 4. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nCTC GPU has a performance of 100.3 for a batch size of 4.\n" }, { "role": "assistant", "content": "From the table, locate the row with batch size 4 and find the corresponding value under the CTC GPU column. The value is 99.6, not 100.3. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/250.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(c)" ], "reasoning": "From the table, locate the row with batch size 4 and find the corresponding value under the CTC GPU column. The value is 99.6, not 100.3.", "statement": "CTC GPU has a performance of 100.3 for a batch size of 4.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: LER/WER of the best sets of hyper-parameters for each feature types.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Refining the precision of the output by shifting the input sequence and feeding it to the network several times can improve performance by about 1%. MFCCs perform better than both power spectrum and raw features.every 20 ms. We found that one could squeeze out about 1% in performance by refining the precision of the output. This is efficiently achieved by shifting the input sequence, and feeding it to the network several times. Both power spectrum and raw features are performing slightly worse than MFCCs. the gap would vanish." } ], "images": [ "scigen/train/251.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: LER/WER of the best sets of hyper-parameters for each feature types." ], "reasoning": "Refining the precision of the output by shifting the input sequence and feeding it to the network several times can improve performance by about 1%. MFCCs perform better than both power spectrum and raw features.", "description": "every 20 ms. We found that one could squeeze out about 1% in performance by refining the precision of the output. This is efficiently achieved by shifting the input sequence, and feeding it to the network several times. Both power spectrum and raw features are performing slightly worse than MFCCs. the gap would vanish.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 2: LER/WER of the best sets of hyper-parameters for each feature types.', unfold the question presented below, detailing every step of your calculation. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the difference in WER between MFCC and Raw features for the test-clean dataset?\n" }, { "role": "assistant", "content": "To find the difference in WER between MFCC and Raw features for the test-clean dataset, subtract the MFCC WER (7.2) from the Raw WER (10.1). So, the answer is {\"answer\": \"2.9\"}." } ], "images": [ "scigen/train/251.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: LER/WER of the best sets of hyper-parameters for each feature types." ], "reasoning": "To find the difference in WER between MFCC and Raw features for the test-clean dataset, subtract the MFCC WER (7.2) from the Raw WER (10.1).", "question": "What is the difference in WER between MFCC and Raw features for the test-clean dataset?", "answer": "So, the answer is {\"answer\": \"2.9\"}." }, { "messages": [ { "role": "user", "content": "MFCC LER is the same for both dev-clean and test-clean datasets. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nMFCC LER is the same for both dev-clean and test-clean datasets.\n" }, { "role": "assistant", "content": "The table shows that the MFCC LER for both dev-clean and test-clean datasets is 6.9. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/251.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: LER/WER of the best sets of hyper-parameters for each feature types." ], "reasoning": "The table shows that the MFCC LER for both dev-clean and test-clean datasets is 6.9.", "statement": "MFCC LER is the same for both dev-clean and test-clean datasets.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Evaluating EARL’s Relation Linking performance'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The accuracy of relation-linking systems varies significantly between LC-QuAD and QALD datasets, with LC-QuAD being more challenging due to a higher percentage of questions with multiple relations.Aim: Given a question, the task is to the perform relation linking in the question. This also evaluates our hypothesis H3. we could run on LC-QuAD and QALD. The large difference in accuracy of relation-linking over LC-QuAD over QALD, is due to the face that LC-QuAD has 82% questions with more than one relation, thus detecting relation phrases in the question was difficult." } ], "images": [ "scigen/train/252.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Evaluating EARL’s Relation Linking performance" ], "reasoning": "The accuracy of relation-linking systems varies significantly between LC-QuAD and QALD datasets, with LC-QuAD being more challenging due to a higher percentage of questions with multiple relations.", "description": "Aim: Given a question, the task is to the perform relation linking in the question. This also evaluates our hypothesis H3. we could run on LC-QuAD and QALD. The large difference in accuracy of relation-linking over LC-QuAD over QALD, is due to the face that LC-QuAD has 82% questions with more than one relation, thus detecting relation phrases in the question was difficult.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 6: Evaluating EARL’s Relation Linking performance', unfold the steps to solve the question given next. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nQuestion: Which system has the highest accuracy on the QALD dataset?\n" }, { "role": "assistant", "content": "To find the system with the highest accuracy on the QALD dataset, compare the accuracy values in the 'Accuracy - QALD' column for all systems listed. So, the answer is {\"answer\": \"EARL with adaptive learning\"}" } ], "images": [ "scigen/train/252.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Evaluating EARL’s Relation Linking performance" ], "reasoning": "To find the system with the highest accuracy on the QALD dataset, compare the accuracy values in the 'Accuracy - QALD' column for all systems listed.", "question": "Which system has the highest accuracy on the QALD dataset?", "answer": "So, the answer is {\"answer\": \"EARL with adaptive learning\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nReMatch has a higher accuracy on the LC-QuAD dataset than RelMatch.\n" }, { "role": "assistant", "content": "Compare the accuracy values of ReMatch and RelMatch in the 'Accuracy LC-QuAD' column. ReMatch has an accuracy of 0.12, while RelMatch has an accuracy of 0.15. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/252.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Evaluating EARL’s Relation Linking performance" ], "reasoning": "Compare the accuracy values of ReMatch and RelMatch in the 'Accuracy LC-QuAD' column. ReMatch has an accuracy of 0.12, while RelMatch has an accuracy of 0.15.", "statement": "ReMatch has a higher accuracy on the LC-QuAD dataset than RelMatch.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Empirical comparison of Connection Density and GTSP: n = number of nodes in graph; L = number of clusters in graph; N = number of nodes per cluster; top K results retrieved from ElasticSearch.'.\n" }, { "role": "assistant", "content": "Connection density has better accuracy but worse time complexity compared to LKH-GTSP, and is useful for generating a ranked list of possible solutions.Aim: We evaluate hypotheses (H1 and H2) that the connection density and GTSP can be used for joint linking task. We also evaluate the LKH approximation solution of GTSP for doing this task. We compare the time complexity of the three different approaches. Results: Connection density has worse time complexity than approximate GTSP solver LKH if we assume the best case of equal cluster sizes for LKH. However, it provides a better accuracy. Moreover, the average time taken in EARL using connection density (including the candidate generation step) is 0.42 seconds per question. The approximate solution LKH has polynomial run time, but its accuracy drops compared to the brute force GTSP solution. Moreover, from a question answering perspective the ranked list offered by the Connection Density approach is useful since it can be presented to the user as a list of possible correct solutions or used by subsequent processing steps of a QA system. Hence, for further experiments in this section we used the connection density approach." } ], "images": [ "scigen/train/253.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Empirical comparison of Connection Density and GTSP: n = number of nodes in graph; L = number of clusters in graph; N = number of nodes per cluster; top K results retrieved from ElasticSearch." ], "reasoning": "Connection density has better accuracy but worse time complexity compared to LKH-GTSP, and is useful for generating a ranked list of possible solutions.", "description": "Aim: We evaluate hypotheses (H1 and H2) that the connection density and GTSP can be used for joint linking task. We also evaluate the LKH approximation solution of GTSP for doing this task. We compare the time complexity of the three different approaches. Results: Connection density has worse time complexity than approximate GTSP solver LKH if we assume the best case of equal cluster sizes for LKH. However, it provides a better accuracy. Moreover, the average time taken in EARL using connection density (including the candidate generation step) is 0.42 seconds per question. The approximate solution LKH has polynomial run time, but its accuracy drops compared to the brute force GTSP solution. Moreover, from a question answering perspective the ranked list offered by the Connection Density approach is useful since it can be presented to the user as a list of possible correct solutions or used by subsequent processing steps of a QA system. Hence, for further experiments in this section we used the connection density approach.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: Table 3: Empirical comparison of Connection Density and GTSP: n = number of nodes in graph; L = number of clusters in graph; N = number of nodes per cluster; top K results retrieved from ElasticSearch.\nQuestion: Which approach has the best accuracy for K=10?\n" }, { "role": "assistant", "content": "From the table, the accuracies for K=10 are 0.62 for Brute Force GTSP, 0.58 for LKH-GTSP, and 0.62 for Connection Density. The best accuracy is 0.62. So, the answer is {\"answer\": \"Brute Force GTSP and Connection Density\"}." } ], "images": [ "scigen/train/253.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Empirical comparison of Connection Density and GTSP: n = number of nodes in graph; L = number of clusters in graph; N = number of nodes per cluster; top K results retrieved from ElasticSearch." ], "reasoning": "From the table, the accuracies for K=10 are 0.62 for Brute Force GTSP, 0.58 for LKH-GTSP, and 0.62 for Connection Density. The best accuracy is 0.62.", "question": "Which approach has the best accuracy for K=10?", "answer": "So, the answer is {\"answer\": \"Brute Force GTSP and Connection Density\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The LKH-GTSP approach has the worst accuracy for K=30. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe LKH-GTSP approach has the worst accuracy for K=30.\n" }, { "role": "assistant", "content": "From the table, the accuracies for K=30 are 0.61 for Brute Force GTSP, 0.59 for LKH-GTSP, and 0.61 for Connection Density. The worst accuracy is 0.59. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/253.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Empirical comparison of Connection Density and GTSP: n = number of nodes in graph; L = number of clusters in graph; N = number of nodes per cluster; top K results retrieved from ElasticSearch." ], "reasoning": "From the table, the accuracies for K=30 are 0.61 for Brute Force GTSP, 0.59 for LKH-GTSP, and 0.61 for Connection Density. The worst accuracy is 0.59.", "statement": "The LKH-GTSP approach has the worst accuracy for K=30.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: Evaluation of joint linking performance'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The mean reciprocal rank (MRR) increased from 0.568 to 0.905 when correct URIs were artificially inserted as the last candidate in the list.Metrics: We use the mean reciprocal rank of the correct candidate ¯ci for each entity/relation in the query. From the probable candidate list generation step, we fetch a list of top candidates for each identified phrase in a query with a k value of 10, 30, 50 and 100, where k is the number of results from text search for each keyword spotted. To evaluate the robustness of our classifier and features we perform two tests. i) ii) On the bottom half of Table 4 we artificially insert the correct candidate into each list to purely test re-ranking abilities of our system (this portion of the table contains k∗ as the number of items in each candidate list). We inject the correct uris at the lowest rank (see k∗), if it was not retrieved in the top k results from previous step. Results: In case correct URIs were missing in the candidate list, we inserted URIs artificially as the last candidate . The MRR then increased from 0.568 to 0.905." } ], "images": [ "scigen/train/254.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Evaluation of joint linking performance" ], "reasoning": "The mean reciprocal rank (MRR) increased from 0.568 to 0.905 when correct URIs were artificially inserted as the last candidate in the list.", "description": "Metrics: We use the mean reciprocal rank of the correct candidate ¯ci for each entity/relation in the query. From the probable candidate list generation step, we fetch a list of top candidates for each identified phrase in a query with a k value of 10, 30, 50 and 100, where k is the number of results from text search for each keyword spotted. To evaluate the robustness of our classifier and features we perform two tests. i) ii) On the bottom half of Table 4 we artificially insert the correct candidate into each list to purely test re-ranking abilities of our system (this portion of the table contains k∗ as the number of items in each candidate list). We inject the correct uris at the lowest rank (see k∗), if it was not retrieved in the top k results from previous step. Results: In case correct URIs were missing in the candidate list, we inserted URIs artificially as the last candidate . The MRR then increased from 0.568 to 0.905.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 4: Evaluation of joint linking performance'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. \n\nQuestion: What was the MRR when k was 100 and the correct candidate was artificially inserted?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows that when k was 100 and the correct candidate was artificially inserted, the MRR was 0.905. So, the answer is {\"answer\": \"0.905\"}" } ], "images": [ "scigen/train/254.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Evaluation of joint linking performance" ], "reasoning": "The table shows that when k was 100 and the correct candidate was artificially inserted, the MRR was 0.905.", "question": "What was the MRR when k was 100 and the correct candidate was artificially inserted?", "answer": "So, the answer is {\"answer\": \"0.905\"}" }, { "messages": [ { "role": "user", "content": "The MRR was highest when k was 10 and the correct candidate was artificially inserted. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe MRR was highest when k was 10 and the correct candidate was artificially inserted.\n" }, { "role": "assistant", "content": "The table shows that the highest MRR value is 0.905, which occurs when k was 10 and the correct candidate was artificially inserted. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/254.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Evaluation of joint linking performance" ], "reasoning": "The table shows that the highest MRR value is 0.905, which occurs when k was 10 and the correct candidate was artificially inserted.", "statement": "The MRR was highest when k was 10 and the correct candidate was artificially inserted.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: Evaluating EARL’s Entity Linking performance'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "EARL implements an adaptive approach to reduce errors in pre-processing modules, particularly in shallow parsing, to improve entity linking accuracy.EARL uses a series of sequential modules with little to no feedback across them. Hence, the errors in one module propagate down the line. To trammel this, we implement an adaptive approach especially for curbing the errors made in the pre-processing modules. While conducting experiments, it was observed that most of the errors are in the shallow parsing phase, mainly because of grammatical errors in LC-QuAD which directly affects the consecutive E/R prediction and candidate selection steps. If the E/R prediction is erroneous, it will search in a wrong Elasticsearch index for probable candidate list generation. In such a case none of the candidates ∈ ci for a keyword would contain ¯ci as is reflected by the probabilities assigned to ci by the re-ranker module. If the maximum probability assigned to ci is less than a very small threshold value, empirically chosen as 0.01, we re-do the steps from ER prediction after altering the original prediction. Aim: To evaluate the performance of EARL with other state-of-the-art systems on the entity linking task. This also evaluates our hypothesis H3. Metrics: We are reporting the performance on accuracy. Accuracy is defined by the ratio of the correctly identified entities over the total number of entities present. Result: The value of k is set to 30 while re-ranking and fetching the most probable entity." } ], "images": [ "scigen/train/255.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Evaluating EARL’s Entity Linking performance" ], "reasoning": "EARL implements an adaptive approach to reduce errors in pre-processing modules, particularly in shallow parsing, to improve entity linking accuracy.", "description": "EARL uses a series of sequential modules with little to no feedback across them. Hence, the errors in one module propagate down the line. To trammel this, we implement an adaptive approach especially for curbing the errors made in the pre-processing modules. While conducting experiments, it was observed that most of the errors are in the shallow parsing phase, mainly because of grammatical errors in LC-QuAD which directly affects the consecutive E/R prediction and candidate selection steps. If the E/R prediction is erroneous, it will search in a wrong Elasticsearch index for probable candidate list generation. In such a case none of the candidates ∈ ci for a keyword would contain ¯ci as is reflected by the probabilities assigned to ci by the re-ranker module. If the maximum probability assigned to ci is less than a very small threshold value, empirically chosen as 0.01, we re-do the steps from ER prediction after altering the original prediction. Aim: To evaluate the performance of EARL with other state-of-the-art systems on the entity linking task. This also evaluates our hypothesis H3. Metrics: We are reporting the performance on accuracy. Accuracy is defined by the ratio of the correctly identified entities over the total number of entities present. Result: The value of k is set to 30 while re-ranking and fetching the most probable entity.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: Evaluating EARL’s Entity Linking performance', answer the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich system has the highest accuracy on the LC-QuAD dataset?\n" }, { "role": "assistant", "content": "To find the system with the highest accuracy on the LC-QuAD dataset, compare the accuracy values in the 'Accuracy LC-QuAD' column for all systems. EARL with adaptive learning has the highest accuracy of 0.65. So, the answer is {\"answer\": \"EARL with adaptive learning\"}." } ], "images": [ "scigen/train/255.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Evaluating EARL’s Entity Linking performance" ], "reasoning": "To find the system with the highest accuracy on the LC-QuAD dataset, compare the accuracy values in the 'Accuracy LC-QuAD' column for all systems. EARL with adaptive learning has the highest accuracy of 0.65.", "question": "Which system has the highest accuracy on the LC-QuAD dataset?", "answer": "So, the answer is {\"answer\": \"EARL with adaptive learning\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis EARL without adaptive learning has a higher accuracy on the QALD dataset than TextRazor. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nEARL without adaptive learning has a higher accuracy on the QALD dataset than TextRazor.\n" }, { "role": "assistant", "content": "To verify this statement, compare the accuracy values in the 'Accuracy - QALD' column for EARL without adaptive learning and TextRazor. EARL without adaptive learning has an accuracy of 0.55, while TextRazor has an accuracy of 0.53. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/255.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Evaluating EARL’s Entity Linking performance" ], "reasoning": "To verify this statement, compare the accuracy values in the 'Accuracy - QALD' column for EARL without adaptive learning and TextRazor. EARL without adaptive learning has an accuracy of 0.55, while TextRazor has an accuracy of 0.53.", "statement": "EARL without adaptive learning has a higher accuracy on the QALD dataset than TextRazor.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: Games played by DM with MaxQ=10, and the baseline with 5 fixed questions. Percentages of games (among all games and only decided games) where the DM models ask either fewer or more questions than the baseline. For the decided games, percentages of games where asking fewer/more questions helps (+ Change), hurts (– Change) or does not have an impact on task success w.r.t. the baseline result (No Change).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "DM2 significantly reduces the number of questions asked in decided games compared to the baseline, leading to more efficient interactions.When considering all the games, we see that the DM models ask many more questions (64.43% DM1 and 85.14% DM2) than the baseline. Zooming into decided games thus allows for a more appropriate comparison. helps (+ Change), hurts (– Change) or does not have an impact on task success (No Change) with respect to the baseline results. We observe that DM2 dramatically decreases the number of questions: in 95.17% of decided games, it asks fewer questions than the baseline; interestingly, in only 13.98% of cases where it asks fewer questions its performance is worse than the baseline — in all the other cases, either it achieves the same success (56.18%) or even improves on the baseline results (25.01%). On the other hand, DM1 does not seem to reduce the number of unnecessary questions in a significant way. Our analyses show that using a decision making component produces dialogues with fewer repeated questions and can reduce the number of unnecessary questions, thus potentially leading to more efficient and less unnatural interactions. Indeed, for some games not correctly resolved by the baseline system, our model is able to guess the right target object by asking fewer questions. By being restricted to a fixed number of questions, the baseline system often introduces noise or apparently forgets about important information that was obtained with the initial questions. Qualitative error analysis, however, also shows cases where the DM makes a premature decision to stop asking questions before obtaining enough information. Yet in other occasions, the DM seems to have made a sensible decision, but the inaccuracy of the Oracle or the Guesser components lead to task failure. Further examples are available in Appendix D." } ], "images": [ "scigen/train/256.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Games played by DM with MaxQ=10, and the baseline with 5 fixed questions. Percentages of games (among all games and only decided games) where the DM models ask either fewer or more questions than the baseline. For the decided games, percentages of games where asking fewer/more questions helps (+ Change), hurts (– Change) or does not have an impact on task success w.r.t. the baseline result (No Change)." ], "reasoning": "DM2 significantly reduces the number of questions asked in decided games compared to the baseline, leading to more efficient interactions.", "description": "When considering all the games, we see that the DM models ask many more questions (64.43% DM1 and 85.14% DM2) than the baseline. Zooming into decided games thus allows for a more appropriate comparison. helps (+ Change), hurts (– Change) or does not have an impact on task success (No Change) with respect to the baseline results. We observe that DM2 dramatically decreases the number of questions: in 95.17% of decided games, it asks fewer questions than the baseline; interestingly, in only 13.98% of cases where it asks fewer questions its performance is worse than the baseline — in all the other cases, either it achieves the same success (56.18%) or even improves on the baseline results (25.01%). On the other hand, DM1 does not seem to reduce the number of unnecessary questions in a significant way. Our analyses show that using a decision making component produces dialogues with fewer repeated questions and can reduce the number of unnecessary questions, thus potentially leading to more efficient and less unnatural interactions. Indeed, for some games not correctly resolved by the baseline system, our model is able to guess the right target object by asking fewer questions. By being restricted to a fixed number of questions, the baseline system often introduces noise or apparently forgets about important information that was obtained with the initial questions. Qualitative error analysis, however, also shows cases where the DM makes a premature decision to stop asking questions before obtaining enough information. Yet in other occasions, the DM seems to have made a sensible decision, but the inaccuracy of the Oracle or the Guesser components lead to task failure. Further examples are available in Appendix D.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 4: Games played by DM with MaxQ=10, and the baseline with 5 fixed questions. Percentages of games (among all games and only decided games) where the DM models ask either fewer or more questions than the baseline. For the decided games, percentages of games where asking fewer/more questions helps (+ Change), hurts (– Change) or does not have an impact on task success w.r.t. the baseline result (No Change).'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat percentage of decided games does DM2 ask fewer questions than the baseline?\n" }, { "role": "assistant", "content": "The table shows that in 95.17% of decided games, DM2 asks fewer questions than the baseline. So, the answer is {\"answer\": \"95.17\"}" } ], "images": [ "scigen/train/256.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Games played by DM with MaxQ=10, and the baseline with 5 fixed questions. Percentages of games (among all games and only decided games) where the DM models ask either fewer or more questions than the baseline. For the decided games, percentages of games where asking fewer/more questions helps (+ Change), hurts (– Change) or does not have an impact on task success w.r.t. the baseline result (No Change)." ], "reasoning": "The table shows that in 95.17% of decided games, DM2 asks fewer questions than the baseline.", "question": "What percentage of decided games does DM2 ask fewer questions than the baseline?", "answer": "So, the answer is {\"answer\": \"95.17\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nDM1 asks more questions than the baseline in all games.\n" }, { "role": "assistant", "content": "The table shows that DM1 asks more questions in 64.43% of all games, which is more than the baseline. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/256.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Games played by DM with MaxQ=10, and the baseline with 5 fixed questions. Percentages of games (among all games and only decided games) where the DM models ask either fewer or more questions than the baseline. For the decided games, percentages of games where asking fewer/more questions helps (+ Change), hurts (– Change) or does not have an impact on task success w.r.t. the baseline result (No Change)." ], "reasoning": "The table shows that DM1 asks more questions in 64.43% of all games, which is more than the baseline.", "statement": "DM1 asks more questions than the baseline in all games.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Perplexities on PTB for various LMs.'.\n" }, { "role": "assistant", "content": "The 2nd-order FOFE-FNNLM achieves the lowest perplexity of 108, outperforming all other models including higher-order FNN-LMs.We have first evaluated the performance of the traditional FNN-LMs, taking the previous several words as input, denoted as n-gram FNN-LMs here. We have trained neural networks with a linear projection layer (of 200 hidden nodes) and two hidden layers (of 400 nodes per layer). All hidden units in networks use the rectified linear activation function, i.e., f(x)=max(0,x). We use SGD with a mini-batch size of 200 and an initial learning rate of 0.4. The learning rate is kept fixed as long as the perplexity on the validation set decreases by at least 1. After that, we continue six more epochs of training, where the learning rate is halved after each epoch. The proposed FOFE-FNNLMs can significantly outperform the baseline FNN-LMs using the same architecture. For example, the perplexity of the baseline bigram FNNLM is 176, while the FOFE-FNNLM can improve to 116. It indicates FOFE-FNNLMs can effectively model the long-term dependency in language without using any recurrent feedback. At last, the 2nd-order FOFE-FNNLM can provide further improvement, yielding the perplexity of 108 on PTB. It also outperforms all higher-order FNN-LMs (4-gram, 5-gram and 6-gram), which are bigger in model size. To our knowledge, this is one of the best reported results on PTB without model combination." } ], "images": [ "scigen/train/257.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Perplexities on PTB for various LMs." ], "reasoning": "The 2nd-order FOFE-FNNLM achieves the lowest perplexity of 108, outperforming all other models including higher-order FNN-LMs.", "description": "We have first evaluated the performance of the traditional FNN-LMs, taking the previous several words as input, denoted as n-gram FNN-LMs here. We have trained neural networks with a linear projection layer (of 200 hidden nodes) and two hidden layers (of 400 nodes per layer). All hidden units in networks use the rectified linear activation function, i.e., f(x)=max(0,x). We use SGD with a mini-batch size of 200 and an initial learning rate of 0.4. The learning rate is kept fixed as long as the perplexity on the validation set decreases by at least 1. After that, we continue six more epochs of training, where the learning rate is halved after each epoch. The proposed FOFE-FNNLMs can significantly outperform the baseline FNN-LMs using the same architecture. For example, the perplexity of the baseline bigram FNNLM is 176, while the FOFE-FNNLM can improve to 116. It indicates FOFE-FNNLMs can effectively model the long-term dependency in language without using any recurrent feedback. At last, the 2nd-order FOFE-FNNLM can provide further improvement, yielding the perplexity of 108 on PTB. It also outperforms all higher-order FNN-LMs (4-gram, 5-gram and 6-gram), which are bigger in model size. To our knowledge, this is one of the best reported results on PTB without model combination.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: Perplexities on PTB for various LMs.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. \n\nQuestion: Which model achieves the lowest perplexity according to the table?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table lists various models and their corresponding perplexity values. The 2nd-order FOFE-FNNLM has the lowest perplexity value of 108. So, the answer is {\"answer\": \"2nd-order FOFE-FNNLM\"}" } ], "images": [ "scigen/train/257.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Perplexities on PTB for various LMs." ], "reasoning": "The table lists various models and their corresponding perplexity values. The 2nd-order FOFE-FNNLM has the lowest perplexity value of 108.", "question": "Which model achieves the lowest perplexity according to the table?", "answer": "So, the answer is {\"answer\": \"2nd-order FOFE-FNNLM\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe bigram FNNLM has a lower perplexity than the 4-gram FNNLM.\n" }, { "role": "assistant", "content": "According to the table, the bigram FNNLM has a perplexity of 176, while the 4-gram FNNLM has a perplexity of 118. Therefore, the statement is incorrect. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/257.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Perplexities on PTB for various LMs." ], "reasoning": "According to the table, the bigram FNNLM has a perplexity of 176, while the 4-gram FNNLM has a perplexity of 118. Therefore, the statement is incorrect.", "statement": "The bigram FNNLM has a lower perplexity than the 4-gram FNNLM.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2. Results for detecting the psychosocial codes: aggression, loss and substance use. For each code we report precision (P), recall (R), F1-scores (F1) and average precision (AP). Numbers shown are mean values of 5-fold cross validation performances. The highest performance (based on AP) for each code is marked with an asterisk. In bold and red we highlight all performances not significantly worse than the highest one (based on statistical testing with 95% confidence intervals).'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Text features are more effective for detecting loss, while image features are better for detecting substance use and aggression.Our results indicate that image and text features play different roles in detecting different psychosocial codes. Textual information clearly dominates the detection of code loss. We hypothesize that loss is better conveyed textually whereas substance use and aggression are easier to express visually. Qualitatively, the linguistic features with the highest magnitude weights (averaged over all training splits) in a linear SVM bear this out, with the top five features for loss being i) free, ii) miss, iii) bro, iv) love v) you; the top five features for substance use being i ) smoke, ii) cup, iii) drank, iv) @mention v) purple; and the top five features for aggression being i) Middle Finger Emoji, ii) Syringe Emoji, iii) opps, iv) pipe v) 2017. The loss features are obviously related to the death or incarceration of a loved one (e.g. miss and free are often used in phrases wishing someone was freed from prison). The top features for aggression and substance use are either emojis which are themselves pictographic representations, i.e. not a purely textual expression of the code, or words that reference physical objects (e.g. pipe, smoke, cup) which are relatively easy to picture." } ], "images": [ "scigen/train/258.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2. Results for detecting the psychosocial codes: aggression, loss and substance use. For each code we report precision (P), recall (R), F1-scores (F1) and average precision (AP). Numbers shown are mean values of 5-fold cross validation performances. The highest performance (based on AP) for each code is marked with an asterisk. In bold and red we highlight all performances not significantly worse than the highest one (based on statistical testing with 95% confidence intervals)." ], "reasoning": "Text features are more effective for detecting loss, while image features are better for detecting substance use and aggression.", "description": "Our results indicate that image and text features play different roles in detecting different psychosocial codes. Textual information clearly dominates the detection of code loss. We hypothesize that loss is better conveyed textually whereas substance use and aggression are easier to express visually. Qualitatively, the linguistic features with the highest magnitude weights (averaged over all training splits) in a linear SVM bear this out, with the top five features for loss being i) free, ii) miss, iii) bro, iv) love v) you; the top five features for substance use being i ) smoke, ii) cup, iii) drank, iv) @mention v) purple; and the top five features for aggression being i) Middle Finger Emoji, ii) Syringe Emoji, iii) opps, iv) pipe v) 2017. The loss features are obviously related to the death or incarceration of a loved one (e.g. miss and free are often used in phrases wishing someone was freed from prison). The top features for aggression and substance use are either emojis which are themselves pictographic representations, i.e. not a purely textual expression of the code, or words that reference physical objects (e.g. pipe, smoke, cup) which are relatively easy to picture.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2. Results for detecting the psychosocial codes: aggression, loss and substance use. For each code we report precision (P), recall (R), F1-scores (F1) and average precision (AP). Numbers shown are mean values of 5-fold cross validation performances. The highest performance (based on AP) for each code is marked with an asterisk. In bold and red we highlight all performances not significantly worse than the highest one (based on statistical testing with 95% confidence intervals).' and methodically tackle the question that follows. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich modality and fusion method combination achieves the highest F1 score for detecting loss?\n" }, { "role": "assistant", "content": "From the table, the highest F1 score for detecting loss is 0.81, achieved by the 'text' modality with 'early' fusion method. So, the answer is {\"answer\": \"text with early fusion\"}." } ], "images": [ "scigen/train/258.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2. Results for detecting the psychosocial codes: aggression, loss and substance use. For each code we report precision (P), recall (R), F1-scores (F1) and average precision (AP). Numbers shown are mean values of 5-fold cross validation performances. The highest performance (based on AP) for each code is marked with an asterisk. In bold and red we highlight all performances not significantly worse than the highest one (based on statistical testing with 95% confidence intervals)." ], "reasoning": "From the table, the highest F1 score for detecting loss is 0.81, achieved by the 'text' modality with 'early' fusion method.", "question": "Which modality and fusion method combination achieves the highest F1 score for detecting loss?", "answer": "So, the answer is {\"answer\": \"text with early fusion\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The highest mAP score is achieved by using image features with early fusion., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe highest mAP score is achieved by using image features with early fusion.\n" }, { "role": "assistant", "content": "The table shows that the highest mAP score is 0.60, achieved by the 'image+text' modality with 'early' fusion method, not by using image features with early fusion. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/258.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2. Results for detecting the psychosocial codes: aggression, loss and substance use. For each code we report precision (P), recall (R), F1-scores (F1) and average precision (AP). Numbers shown are mean values of 5-fold cross validation performances. The highest performance (based on AP) for each code is marked with an asterisk. In bold and red we highlight all performances not significantly worse than the highest one (based on statistical testing with 95% confidence intervals)." ], "reasoning": "The table shows that the highest mAP score is 0.60, achieved by the 'image+text' modality with 'early' fusion method, not by using image features with early fusion.", "statement": "The highest mAP score is achieved by using image features with early fusion.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1. Numbers of instances for the different visual concepts and psychosocial codes in our dataset. For the different codes, the first number indicates for how many tweets at least one annotator assigned the corresponding code, numbers in parentheses are based on per-tweet majority votes.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The domain gap between Twitter and Tumblr images affects the performance of concept detection, necessitating additional Twitter images for improved accuracy.Note that in order to ensure sufficient quality of the annotations, but also due to the nature of the data, we relied on a special annotation process and kept the total size of the dataset comparatively small. However, crawling images from Tumblr targeting keywords related to those concepts lead us to gather images where the target concept is the main subject in the image, while in our Twitter images they appear in the image but are rarely the main element in the picture. Further manually analyzing the images crawled from Twitter and Tumblr, we have confirmed this “domain gap\" between the two sources of data that can explain the difference of performance. This puts in light the challenges associated with detecting these concepts in our Twitter data. We believe the only solution is therefore to gather additional images from Twitter from similar users. This will be part of the future work of this research." } ], "images": [ "scigen/train/259.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1. Numbers of instances for the different visual concepts and psychosocial codes in our dataset. For the different codes, the first number indicates for how many tweets at least one annotator assigned the corresponding code, numbers in parentheses are based on per-tweet majority votes." ], "reasoning": "The domain gap between Twitter and Tumblr images affects the performance of concept detection, necessitating additional Twitter images for improved accuracy.", "description": "Note that in order to ensure sufficient quality of the annotations, but also due to the nature of the data, we relied on a special annotation process and kept the total size of the dataset comparatively small. However, crawling images from Tumblr targeting keywords related to those concepts lead us to gather images where the target concept is the main subject in the image, while in our Twitter images they appear in the image but are rarely the main element in the picture. Further manually analyzing the images crawled from Twitter and Tumblr, we have confirmed this “domain gap\" between the two sources of data that can explain the difference of performance. This puts in light the challenges associated with detecting these concepts in our Twitter data. We believe the only solution is therefore to gather additional images from Twitter from similar users. This will be part of the future work of this research.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1. Numbers of instances for the different visual concepts and psychosocial codes in our dataset. For the different codes, the first number indicates for how many tweets at least one annotator assigned the corresponding code, numbers in parentheses are based on per-tweet majority votes.', answer the question below by showing each progressive step toward the answer. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich concept has the highest total count across both Twitter and Tumblr?\n" }, { "role": "assistant", "content": "To find the concept with the highest total count, compare the 'Total' column values for each concept. 'Person' has the highest total count of 1442. So, the answer is {\"answer\": \"person\"}." } ], "images": [ "scigen/train/259.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1. Numbers of instances for the different visual concepts and psychosocial codes in our dataset. For the different codes, the first number indicates for how many tweets at least one annotator assigned the corresponding code, numbers in parentheses are based on per-tweet majority votes." ], "reasoning": "To find the concept with the highest total count, compare the 'Total' column values for each concept. 'Person' has the highest total count of 1442.", "question": "Which concept has the highest total count across both Twitter and Tumblr?", "answer": "So, the answer is {\"answer\": \"person\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe concept of 'tattoo' has a higher count on Twitter than on Tumblr.\n" }, { "role": "assistant", "content": "The table shows that 'tattoo' has a count of 227 on Twitter and 33 on Tumblr, indicating a higher count on Twitter. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/259.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1. Numbers of instances for the different visual concepts and psychosocial codes in our dataset. For the different codes, the first number indicates for how many tweets at least one annotator assigned the corresponding code, numbers in parentheses are based on per-tweet majority votes." ], "reasoning": "The table shows that 'tattoo' has a count of 227 on Twitter and 33 on Tumblr, indicating a higher count on Twitter.", "statement": "The concept of 'tattoo' has a higher count on Twitter than on Tumblr.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Average scores for the six different responses, on the six quality: Understandable, Natural, Maintains Context, Interesting, Uses Knowledge and Overall Quality.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The new human generated response outperforms all other response types, including the original ground truth, likely because it was optimized for turn-level evaluation.Across both datasets and all qualities, the new human generated response strongly outperforms all other response types, even the original ground truth. This may be because the new human generated response was written with this quality annotation in mind, and as such is optimized for turn-level evaluation. On the other hand, the workers who produced the original ground-truth response, were more concerned with the quality of the overall dialog than with the quality of each individual response." } ], "images": [ "scigen/train/260.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Average scores for the six different responses, on the six quality: Understandable, Natural, Maintains Context, Interesting, Uses Knowledge and Overall Quality." ], "reasoning": "The new human generated response outperforms all other response types, including the original ground truth, likely because it was optimized for turn-level evaluation.", "description": "Across both datasets and all qualities, the new human generated response strongly outperforms all other response types, even the original ground truth. This may be because the new human generated response was written with this quality annotation in mind, and as such is optimized for turn-level evaluation. On the other hand, the workers who produced the original ground-truth response, were more concerned with the quality of the overall dialog than with the quality of each individual response.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: 'Table 2: Average scores for the six different responses, on the six quality: Understandable, Natural, Maintains Context, Interesting, Uses Knowledge and Overall Quality.'\nQuestion: Which system achieved the highest overall quality (OQ) score in Topical-Chat?\n" }, { "role": "assistant", "content": "By comparing the OQ scores in the Topical-Chat column, the new human generated response has the highest score of 4.80. So, the answer is {\"answer\": \"New Human Generated\"}" } ], "images": [ "scigen/train/260.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Average scores for the six different responses, on the six quality: Understandable, Natural, Maintains Context, Interesting, Uses Knowledge and Overall Quality." ], "reasoning": "By comparing the OQ scores in the Topical-Chat column, the new human generated response has the highest score of 4.80.", "question": "Which system achieved the highest overall quality (OQ) score in Topical-Chat?", "answer": "So, the answer is {\"answer\": \"New Human Generated\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe new human generated response achieved the highest naturalness (Nat) score in PersonaChat.\n" }, { "role": "assistant", "content": "By comparing the Nat scores in the PersonaChat column, the new human generated response has the highest score of 2.97. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/260.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Average scores for the six different responses, on the six quality: Understandable, Natural, Maintains Context, Interesting, Uses Knowledge and Overall Quality." ], "reasoning": "By comparing the Nat scores in the PersonaChat column, the new human generated response has the highest score of 2.97.", "statement": "The new human generated response achieved the highest naturalness (Nat) score in PersonaChat.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Inter-annotator agreement for all the metrics. For all the correlations presented in this table, p<0.01.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Most inter-annotator correlations are above 0.4, indicating moderate to strong agreement, with lower agreement for Understandable on PersonaChat due to differing background knowledge.The correlation between each pair of annotations is computed and the average correlation over all the pairs is reported. Correlation is used instead of Cohen’s Kappa in order to better account for the ordinal nature of the ratings (i.e., 4 should correlate better with 5 than 1), and to maintain consistency with the evaluation of the automatic metrics. Most inter-annotator correlations are above 0.4, which indicates moderate to strong agreement. The low agreement for Understandable on PersonaChat is likely a consequence of the simple language in the dataset. Most responses are understandable, except for those requiring background knowledge (e.g., that ‘cod’ is an acronym for ‘Call of Duty’). Since the annotators have differing background knowledge, the few occasions where they fail to understand an utterance will differ, hence the lower agreement. The agreement for Overall Quality is relatively high (0.71 for Topical-Chat and 0.66 for PersonaChat) which suggests that any ambiguity in the specific dialog qualities is mitigated when the annotator is asked for an overall impression." } ], "images": [ "scigen/train/261.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Inter-annotator agreement for all the metrics. For all the correlations presented in this table, p<0.01." ], "reasoning": "Most inter-annotator correlations are above 0.4, indicating moderate to strong agreement, with lower agreement for Understandable on PersonaChat due to differing background knowledge.", "description": "The correlation between each pair of annotations is computed and the average correlation over all the pairs is reported. Correlation is used instead of Cohen’s Kappa in order to better account for the ordinal nature of the ratings (i.e., 4 should correlate better with 5 than 1), and to maintain consistency with the evaluation of the automatic metrics. Most inter-annotator correlations are above 0.4, which indicates moderate to strong agreement. The low agreement for Understandable on PersonaChat is likely a consequence of the simple language in the dataset. Most responses are understandable, except for those requiring background knowledge (e.g., that ‘cod’ is an acronym for ‘Call of Duty’). Since the annotators have differing background knowledge, the few occasions where they fail to understand an utterance will differ, hence the lower agreement. The agreement for Overall Quality is relatively high (0.71 for Topical-Chat and 0.66 for PersonaChat) which suggests that any ambiguity in the specific dialog qualities is mitigated when the annotator is asked for an overall impression.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 1: Inter-annotator agreement for all the metrics. For all the correlations presented in this table, p<0.01.' in mind, break down and resolve the question below step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhat is the Spearman correlation for the 'Uses Knowledge' metric in PersonaChat?\n" }, { "role": "assistant", "content": "To find the Spearman correlation for the 'Uses Knowledge' metric in PersonaChat, locate the 'Uses Knowledge' row under the PersonaChat section and read the value under the Spearman column. So, the answer is {\"answer\": \"0.8115\"}." } ], "images": [ "scigen/train/261.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Inter-annotator agreement for all the metrics. For all the correlations presented in this table, p<0.01." ], "reasoning": "To find the Spearman correlation for the 'Uses Knowledge' metric in PersonaChat, locate the 'Uses Knowledge' row under the PersonaChat section and read the value under the Spearman column.", "question": "What is the Spearman correlation for the 'Uses Knowledge' metric in PersonaChat?", "answer": "So, the answer is {\"answer\": \"0.8115\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The Pearson correlation for 'Overall Quality' in Topical-Chat is higher than in PersonaChat., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Pearson correlation for 'Overall Quality' in Topical-Chat is higher than in PersonaChat.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Pearson correlation values for 'Overall Quality' in Topical-Chat and PersonaChat. The value for Topical-Chat is 0.7096, and for PersonaChat, it is 0.6603. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/261.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Inter-annotator agreement for all the metrics. For all the correlations presented in this table, p<0.01." ], "reasoning": "To verify this statement, compare the Pearson correlation values for 'Overall Quality' in Topical-Chat and PersonaChat. The value for Topical-Chat is 0.7096, and for PersonaChat, it is 0.6603.", "statement": "The Pearson correlation for 'Overall Quality' in Topical-Chat is higher than in PersonaChat.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Turn-level correlations on Topical-Chat. We show: (1) best non-USR metric, (2) best USR sub-metric and (3) USR metric. All measures in this table are statistically significant to p<0.01.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "USR consistently outperforms other metrics across all dialog qualities, with METEOR and BERTScore being the best non-USR metrics. USR's overall performance is enhanced by leveraging information from sub-metrics.USR is shown to strongly outperform both word-overlap and embedding-based metrics across all of the dialog qualities. Interestingly, the best non-USR metric is consistently either METEOR or BERTScore – possibly because both methods are adept at comparing synonyms during evaluation. For some dialog qualities, the overall USR metric outperforms the best sub-metric. For example, USR does better for Maintains Context than USR-DR. This is likely because the information from the other sub-metrics (e.g., Uses Knowledge) is valuable and effectively leveraged by USR." } ], "images": [ "scigen/train/262.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Turn-level correlations on Topical-Chat. We show: (1) best non-USR metric, (2) best USR sub-metric and (3) USR metric. All measures in this table are statistically significant to p<0.01." ], "reasoning": "USR consistently outperforms other metrics across all dialog qualities, with METEOR and BERTScore being the best non-USR metrics. USR's overall performance is enhanced by leveraging information from sub-metrics.", "description": "USR is shown to strongly outperform both word-overlap and embedding-based metrics across all of the dialog qualities. Interestingly, the best non-USR metric is consistently either METEOR or BERTScore – possibly because both methods are adept at comparing synonyms during evaluation. For some dialog qualities, the overall USR metric outperforms the best sub-metric. For example, USR does better for Maintains Context than USR-DR. This is likely because the information from the other sub-metrics (e.g., Uses Knowledge) is valuable and effectively leveraged by USR.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Turn-level correlations on Topical-Chat. We show: (1) best non-USR metric, (2) best USR sub-metric and (3) USR metric. All measures in this table are statistically significant to p<0.01.', unfold the question presented below, detailing every step of your calculation. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich metric has the highest Pearson correlation for the 'Interesting' dialog quality?\n" }, { "role": "assistant", "content": "To find the highest Pearson correlation for the 'Interesting' dialog quality, look at the Pearson column under 'Interesting'. The highest value is 0.4555 for USR. So, the answer is {\"answer\": \"USR\"}" } ], "images": [ "scigen/train/262.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Turn-level correlations on Topical-Chat. We show: (1) best non-USR metric, (2) best USR sub-metric and (3) USR metric. All measures in this table are statistically significant to p<0.01." ], "reasoning": "To find the highest Pearson correlation for the 'Interesting' dialog quality, look at the Pearson column under 'Interesting'. The highest value is 0.4555 for USR.", "question": "Which metric has the highest Pearson correlation for the 'Interesting' dialog quality?", "answer": "So, the answer is {\"answer\": \"USR\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nUSR-DR (x = c) has the highest Spearman correlation for the 'Interesting' dialog quality.\n" }, { "role": "assistant", "content": "To verify this statement, check the Spearman column under 'Interesting'. The highest value is 0.4877 for USR-DR (x = c), which supports the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/262.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Turn-level correlations on Topical-Chat. We show: (1) best non-USR metric, (2) best USR sub-metric and (3) USR metric. All measures in this table are statistically significant to p<0.01." ], "reasoning": "To verify this statement, check the Spearman column under 'Interesting'. The highest value is 0.4877 for USR-DR (x = c), which supports the statement.", "statement": "USR-DR (x = c) has the highest Spearman correlation for the 'Interesting' dialog quality.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Turn-level correlations between all automatic metrics and the Overall Quality ratings for the Topical-Chat corpus. All values with p>0.05 are italicized.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "USR outperforms other methods due to its ability to accurately quantify qualities without a reference response and effectively combine MLM and DR.USR shows a strong improvement over all other methods. This strong performance can be attributed to two factors: (1) the ability of MLM and DR to accurately quantify qualities of a generated response without a reference response, and (2) the ability of USR to effectively combine MLM and DR into a better correlated overall metric." } ], "images": [ "scigen/train/263.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Turn-level correlations between all automatic metrics and the Overall Quality ratings for the Topical-Chat corpus. All values with p>0.05 are italicized." ], "reasoning": "USR outperforms other methods due to its ability to accurately quantify qualities without a reference response and effectively combine MLM and DR.", "description": "USR shows a strong improvement over all other methods. This strong performance can be attributed to two factors: (1) the ability of MLM and DR to accurately quantify qualities of a generated response without a reference response, and (2) the ability of USR to effectively combine MLM and DR into a better correlated overall metric.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 5: Turn-level correlations between all automatic metrics and the Overall Quality ratings for the Topical-Chat corpus. All values with p>0.05 are italicized.', solve the following question step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the Spearman correlation value for USR?\n" }, { "role": "assistant", "content": "The table shows the Spearman correlation values for various metrics. The Spearman correlation value for USR is the highest among all listed metrics. So, the answer is {\"answer\": \"0.4192\"}." } ], "images": [ "scigen/train/263.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Turn-level correlations between all automatic metrics and the Overall Quality ratings for the Topical-Chat corpus. All values with p>0.05 are italicized." ], "reasoning": "The table shows the Spearman correlation values for various metrics. The Spearman correlation value for USR is the highest among all listed metrics.", "question": "What is the Spearman correlation value for USR?", "answer": "So, the answer is {\"answer\": \"0.4192\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nUSR has the highest Pearson correlation value among all metrics.\n" }, { "role": "assistant", "content": "The table lists the Pearson correlation values for various metrics. USR has the highest Pearson correlation value of 0.4220, which is higher than all other metrics listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/263.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Turn-level correlations between all automatic metrics and the Overall Quality ratings for the Topical-Chat corpus. All values with p>0.05 are italicized." ], "reasoning": "The table lists the Pearson correlation values for various metrics. USR has the highest Pearson correlation value of 0.4220, which is higher than all other metrics listed.", "statement": "USR has the highest Pearson correlation value among all metrics.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Speech recognition model performance in WER.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The ASR seq2seq model performs better on the Fisher task due to its formal speech compared to the informal speech in Callhome.We construct a baseline cascade of a Spanish ASR seq2seq model whose output is passed into a Spanish to English NMT model. Performance on the Fisher task is significantly better than on Callhome since it contains more formal speech, consisting of conversations between strangers while Callhome conversations were often between family members." } ], "images": [ "scigen/train/264.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Speech recognition model performance in WER." ], "reasoning": "The ASR seq2seq model performs better on the Fisher task due to its formal speech compared to the informal speech in Callhome.", "description": "We construct a baseline cascade of a Spanish ASR seq2seq model whose output is passed into a Spanish to English NMT model. Performance on the Fisher task is significantly better than on Callhome since it contains more formal speech, consisting of conversations between strangers while Callhome conversations were often between family members.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: Speech recognition model performance in WER.' in mind, break down and resolve the question below step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich model has the highest performance on the Fisher devtest set?\n" }, { "role": "assistant", "content": "To find the highest performance on the Fisher devtest set, compare the scores in the Fisher devtest column for all models. Post et al. has the highest score of 41.3. So, the answer is {\"answer\": \"Post et al.\"}." } ], "images": [ "scigen/train/264.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Speech recognition model performance in WER." ], "reasoning": "To find the highest performance on the Fisher devtest set, compare the scores in the Fisher devtest column for all models. Post et al. has the highest score of 41.3.", "question": "Which model has the highest performance on the Fisher devtest set?", "answer": "So, the answer is {\"answer\": \"Post et al.\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Kumar et al. has the lowest performance on the Fisher test set. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nKumar et al. has the lowest performance on the Fisher test set.\n" }, { "role": "assistant", "content": "To verify this statement, compare the scores in the Fisher test column for all models. Kumar et al. has a score of 25.3, which is higher than 'Ours' with a score of 23.2. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/264.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Speech recognition model performance in WER." ], "reasoning": "To verify this statement, compare the scores in the Fisher test column for all models. Kumar et al. has a score of 25.3, which is higher than 'Ours' with a score of 23.2.", "statement": "Kumar et al. has the lowest performance on the Fisher test set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: Varying number of decoder layers in the speech translation model. BLEU score on the Fisher/dev set.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Seq2seq NMT models use deeper decoders to handle the complexity of translation, similar to how traditional ASR systems use acoustic and language models.In contrast, seq2seq NMT models often use much deeper decoders, In analogy to a traditional ASR system, one may think of the seq2seq encoder behaving as the acoustic model while the decoder acts as the language model. The additional complexity of the translation task when compared to monolingual language modeling motivates the use of a higher capacity decoder network." } ], "images": [ "scigen/train/265.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Varying number of decoder layers in the speech translation model. BLEU score on the Fisher/dev set." ], "reasoning": "Seq2seq NMT models use deeper decoders to handle the complexity of translation, similar to how traditional ASR systems use acoustic and language models.", "description": "In contrast, seq2seq NMT models often use much deeper decoders, In analogy to a traditional ASR system, one may think of the seq2seq encoder behaving as the acoustic model while the decoder acts as the language model. The additional complexity of the translation task when compared to monolingual language modeling motivates the use of a higher capacity decoder network.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 1: Varying number of decoder layers in the speech translation model. BLEU score on the Fisher/dev set.', and tackle the question below in a sequential manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the BLEU score for a seq2seq NMT model with 4 decoder layers?\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for different numbers of decoder layers. For 4 decoder layers, the BLEU score is 45.5. So, the answer is {\"answer\": \"45.5\"}." } ], "images": [ "scigen/train/265.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Varying number of decoder layers in the speech translation model. BLEU score on the Fisher/dev set." ], "reasoning": "The table shows the BLEU scores for different numbers of decoder layers. For 4 decoder layers, the BLEU score is 45.5.", "question": "What is the BLEU score for a seq2seq NMT model with 4 decoder layers?", "answer": "So, the answer is {\"answer\": \"45.5\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence A seq2seq NMT model with 3 decoder layers has a BLEU score higher than 45.5. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nA seq2seq NMT model with 3 decoder layers has a BLEU score higher than 45.5.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for a seq2seq NMT model with 3 decoder layers is 45.2, which is lower than 45.5. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/265.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Varying number of decoder layers in the speech translation model. BLEU score on the Fisher/dev set." ], "reasoning": "The table shows that the BLEU score for a seq2seq NMT model with 3 decoder layers is 45.2, which is lower than 45.5.", "statement": "A seq2seq NMT model with 3 decoder layers has a BLEU score higher than 45.5.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Speech translation model performance in BLEU score.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The multi-task ST/ASR model outperforms the end-to-end ST model by 1.4 BLEU points on all Fisher datasets.Despite not having access to source language transcripts at any stage of the training, the end-to-end model outperforms the baseline cascade, which passes the 1-best Spanish ASR output into the NMT model, by about 1.8 BLEU points on the Fisher/test set. We obtain an additional improvement of 1.4 BLEU points or more on all Fisher datasets in the multi-task configuration, in which the Spanish transcripts are used for additional supervision by sharing a single encoder sub-network across independent ASR and ST decoders. The ASR model converged after four days of training (1.5m steps), while the ST and multitask models continued to improve, with the final 1.2 BLEU point improvement taking two more weeks." } ], "images": [ "scigen/train/266.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Speech translation model performance in BLEU score." ], "reasoning": "The multi-task ST/ASR model outperforms the end-to-end ST model by 1.4 BLEU points on all Fisher datasets.", "description": "Despite not having access to source language transcripts at any stage of the training, the end-to-end model outperforms the baseline cascade, which passes the 1-best Spanish ASR output into the NMT model, by about 1.8 BLEU points on the Fisher/test set. We obtain an additional improvement of 1.4 BLEU points or more on all Fisher datasets in the multi-task configuration, in which the Spanish transcripts are used for additional supervision by sharing a single encoder sub-network across independent ASR and ST decoders. The ASR model converged after four days of training (1.5m steps), while the ST and multitask models continued to improve, with the final 1.2 BLEU point improvement taking two more weeks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Speech translation model performance in BLEU score.\nQuestion: Which model achieved the highest BLEU score on the Fisher dev2 dataset?\nSolve the above question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the BLEU scores on the Fisher dev2 dataset, the Multi-task ST/ASR model achieved the highest score of 49.1. So, the answer is {\"answer\": \"Multi-task ST/ASR 3\"}." } ], "images": [ "scigen/train/266.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Speech translation model performance in BLEU score." ], "reasoning": "By comparing the BLEU scores on the Fisher dev2 dataset, the Multi-task ST/ASR model achieved the highest score of 49.1.", "question": "Which model achieved the highest BLEU score on the Fisher dev2 dataset?", "answer": "So, the answer is {\"answer\": \"Multi-task ST/ASR 3\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe ASR→NMT cascade model achieved a higher BLEU score on the Fisher test set than the End-to-end ST model.\n" }, { "role": "assistant", "content": "The table shows that the ASR→NMT cascade model has a BLEU score of 45.5 on the Fisher test set, while the End-to-end ST model has a BLEU score of 47.3, which is higher. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/266.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Speech translation model performance in BLEU score." ], "reasoning": "The table shows that the ASR→NMT cascade model has a BLEU score of 45.5 on the Fisher test set, while the End-to-end ST model has a BLEU score of 47.3, which is higher.", "statement": "The ASR→NMT cascade model achieved a higher BLEU score on the Fisher test set than the End-to-end ST model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1. In-Topic'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Using context information from transfer learning improves classification performance by 10% for two-class and 20% for three-class problems.In this setting we do not expect a large improvement by providing topic information since the models have already been trained with arguments of the same topics as in the training set. However, we see a relative increase of about 10% for the two-classes and 20% for the three-classes classification problem by using context information from transfer learning. Therefore, we conclude that contextual information about potential arguments is important and since the topics are diverse, the model is able to learn argument structure for each topic." } ], "images": [ "scigen/train/267.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1. In-Topic" ], "reasoning": "Using context information from transfer learning improves classification performance by 10% for two-class and 20% for three-class problems.", "description": "In this setting we do not expect a large improvement by providing topic information since the models have already been trained with arguments of the same topics as in the training set. However, we see a relative increase of about 10% for the two-classes and 20% for the three-classes classification problem by using context information from transfer learning. Therefore, we conclude that contextual information about potential arguments is important and since the topics are diverse, the model is able to learn argument structure for each topic.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 1. In-Topic', solve the following question step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich method achieved the highest score for the three-class classification problem?\n" }, { "role": "assistant", "content": "By examining the table, the highest score for the three-class classification problem is 0.69, achieved by the TACAM-BERT Large method. So, the answer is {\"answer\": \"TACAM-BERT Large\"}" } ], "images": [ "scigen/train/267.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1. In-Topic" ], "reasoning": "By examining the table, the highest score for the three-class classification problem is 0.69, achieved by the TACAM-BERT Large method.", "question": "Which method achieved the highest score for the three-class classification problem?", "answer": "So, the answer is {\"answer\": \"TACAM-BERT Large\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe CAM-BERT Base method achieved the highest score for the two-class classification problem.\n" }, { "role": "assistant", "content": "The table shows that the highest score for the two-class classification problem is 0.81, achieved by both TACAM-BERT Base and TACAM-BERT Large methods, not CAM-BERT Base. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/267.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1. In-Topic" ], "reasoning": "The table shows that the highest score for the two-class classification problem is 0.81, achieved by both TACAM-BERT Base and TACAM-BERT Large methods, not CAM-BERT Base.", "statement": "The CAM-BERT Base method achieved the highest score for the two-class classification problem.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2. Cross-Topic'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Topic information and context significantly improve classification performance in both two-classes and three-classes scenarios, with attention-based models showing the most notable improvements.In this experiment, which reflects a real-life argument search scenario, we want to prove our two hypotheses: When classifying potential arguments, it is advantageous to take information about the topic into account. The context of an argument and topic context are important for the classification decision. On the whole, we can see that our two hypotheses are confirmed. In the two-classes scenario the recurrent model improves if topic information is provided by knowledge graph embeddings. By using attention-based models with pre-trained weights we can observe a significant performance boost of eleven score points in average when considering topic information. However, the same model without topic information performs only slightly better than the recurrent models. Therefore, we conclude that both, topic information together with contexts of topic and argument, are important for the correct decision about a potential argument. We observe similar effects in the three-classes scenario. Although in average different contexts for the recurrent model have a similar effect, we can clearly observe that taking topic information into account improves classification results by one score points. The combination of transfer learning for context and topic information again outperforms all other approaches by far. At the same time, the pre-trained model without topic information achieves a macro-f1 score of 0.61 which is 3 points lower than with topic information." } ], "images": [ "scigen/train/268.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2. Cross-Topic" ], "reasoning": "Topic information and context significantly improve classification performance in both two-classes and three-classes scenarios, with attention-based models showing the most notable improvements.", "description": "In this experiment, which reflects a real-life argument search scenario, we want to prove our two hypotheses: When classifying potential arguments, it is advantageous to take information about the topic into account. The context of an argument and topic context are important for the classification decision. On the whole, we can see that our two hypotheses are confirmed. In the two-classes scenario the recurrent model improves if topic information is provided by knowledge graph embeddings. By using attention-based models with pre-trained weights we can observe a significant performance boost of eleven score points in average when considering topic information. However, the same model without topic information performs only slightly better than the recurrent models. Therefore, we conclude that both, topic information together with contexts of topic and argument, are important for the correct decision about a potential argument. We observe similar effects in the three-classes scenario. Although in average different contexts for the recurrent model have a similar effect, we can clearly observe that taking topic information into account improves classification results by one score points. The combination of transfer learning for context and topic information again outperforms all other approaches by far. At the same time, the pre-trained model without topic information achieves a macro-f1 score of 0.61 which is 3 points lower than with topic information.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 2. Cross-Topic' in mind, break down and resolve the question below step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich method achieved the highest score for the topic 'Minimum wage' in the two-classes scenario?\n" }, { "role": "assistant", "content": "In the two-classes scenario, the highest score for the topic 'Minimum wage' is 0.84, achieved by the TACAM-BERT Large method. So, the answer is {\"answer\": \"TACAM-BERT Large\"}" } ], "images": [ "scigen/train/268.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2. Cross-Topic" ], "reasoning": "In the two-classes scenario, the highest score for the topic 'Minimum wage' is 0.84, achieved by the TACAM-BERT Large method.", "question": "Which method achieved the highest score for the topic 'Minimum wage' in the two-classes scenario?", "answer": "So, the answer is {\"answer\": \"TACAM-BERT Large\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe CAM-BERT Base method achieved a higher score for 'Marijuana legalization' in the three-classes scenario than in the two-classes scenario.\n" }, { "role": "assistant", "content": "In the three-classes scenario, the CAM-BERT Base method scored 0.54 for 'Marijuana legalization', while in the two-classes scenario, it scored 0.74. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/268.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2. Cross-Topic" ], "reasoning": "In the three-classes scenario, the CAM-BERT Base method scored 0.54 for 'Marijuana legalization', while in the two-classes scenario, it scored 0.74. Therefore, the statement is refuted.", "statement": "The CAM-BERT Base method achieved a higher score for 'Marijuana legalization' in the three-classes scenario than in the two-classes scenario.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4. Topic dependent cross-topic classification results'.\n" }, { "role": "assistant", "content": "TACAM-BERT Base shows robustness with moderate performance drops and significant gaps to models without topic information, outperforming BiCLSTM and BiLSTM in both two-classes and three-classes scenarios.For the two-classes problem we observe a massive performance drop of ten points in macro-f1 score for the BiCLSTM model. Nonetheless, the model still makes use of topic information and outperforms the standard BiLSTM by two macro-f1 score points. Our approach TACAM-BERT Base is more robust, the performance falls by moderate four score points and the gap to the counterpart model without topic information is incredible 17 score points large. We observe a similar behaviour in the three-classes scenario. Our TACAM-BERT Base approach achieves the same average score as in the original cross topic task. In contrast the performance of the BiCLSTM model drops by 11 score points and it even performs worse than the same model without topic information on this more complex task. Thus we conclude that unlike previous models our approaches are indeed able to grasp the context of the argument and topic and are able to relate them with each other." } ], "images": [ "scigen/train/269.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4. Topic dependent cross-topic classification results" ], "reasoning": "TACAM-BERT Base shows robustness with moderate performance drops and significant gaps to models without topic information, outperforming BiCLSTM and BiLSTM in both two-classes and three-classes scenarios.", "description": "For the two-classes problem we observe a massive performance drop of ten points in macro-f1 score for the BiCLSTM model. Nonetheless, the model still makes use of topic information and outperforms the standard BiLSTM by two macro-f1 score points. Our approach TACAM-BERT Base is more robust, the performance falls by moderate four score points and the gap to the counterpart model without topic information is incredible 17 score points large. We observe a similar behaviour in the three-classes scenario. Our TACAM-BERT Base approach achieves the same average score as in the original cross topic task. In contrast the performance of the BiCLSTM model drops by 11 score points and it even performs worse than the same model without topic information on this more complex task. Thus we conclude that unlike previous models our approaches are indeed able to grasp the context of the argument and topic and are able to relate them with each other.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4. Topic dependent cross-topic classification results\nQuestion: What is the macro-f1 score difference between TACAM-BERT Base and BiCLSTM for the 'Topics Marij. legal.' in the two-classes scenario?\nSolve the above question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "In the two-classes scenario, TACAM-BERT Base has a macro-f1 score of 0.82 for 'Topics Marij. legal.', while BiCLSTM has a score of 0.76. The difference is 0.82 - 0.76 = 0.06. So, the answer is {\"answer\": \"0.06\"}" } ], "images": [ "scigen/train/269.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4. Topic dependent cross-topic classification results" ], "reasoning": "In the two-classes scenario, TACAM-BERT Base has a macro-f1 score of 0.82 for 'Topics Marij. legal.', while BiCLSTM has a score of 0.76. The difference is 0.82 - 0.76 = 0.06.", "question": "What is the macro-f1 score difference between TACAM-BERT Base and BiCLSTM for the 'Topics Marij. legal.' in the two-classes scenario?", "answer": "So, the answer is {\"answer\": \"0.06\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nTACAM-BERT Base outperforms BiCLSTM in the 'Topics Death penalty' for the three-classes scenario.\n" }, { "role": "assistant", "content": "In the three-classes scenario, TACAM-BERT Base has a macro-f1 score of 0.52 for 'Topics Death penalty', while BiCLSTM has a score of 0.35. Therefore, TACAM-BERT Base outperforms BiCLSTM. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/269.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4. Topic dependent cross-topic classification results" ], "reasoning": "In the three-classes scenario, TACAM-BERT Base has a macro-f1 score of 0.52 for 'Topics Death penalty', while BiCLSTM has a score of 0.35. Therefore, TACAM-BERT Base outperforms BiCLSTM.", "statement": "TACAM-BERT Base outperforms BiCLSTM in the 'Topics Death penalty' for the three-classes scenario.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 6: Language modelling test results. Above, training and testing on WSJ. Below, training semi-supervised and testing on WMT.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The perplexities reported are upper bounds because summing over all possible parses is intractable, so an approximation is used by summing over the final beam after decoding.We note that the perplexities reported are upper bounds on the true perplexity of the model, as it is intractable to sum over all possible parses of a sentence to compute the marginal probability of the words. As an approximation we sum over the final beam after decoding." } ], "images": [ "scigen/train/270.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Language modelling test results. Above, training and testing on WSJ. Below, training semi-supervised and testing on WMT." ], "reasoning": "The perplexities reported are upper bounds because summing over all possible parses is intractable, so an approximation is used by summing over the final beam after decoding.", "description": "We note that the perplexities reported are upper bounds on the true perplexity of the model, as it is intractable to sum over all possible parses of a sentence to compute the marginal probability of the words. As an approximation we sum over the final beam after decoding.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 6: Language modelling test results. Above, training and testing on WSJ. Below, training semi-supervised and testing on WMT.', unfold the steps to solve the question given next. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nQuestion: Which model has the lowest perplexity?\n" }, { "role": "assistant", "content": "To find the model with the lowest perplexity, compare the perplexity values of all models listed in the table. The model with the lowest value is EmamiJ05 with a perplexity of 131.3. So, the answer is {\"answer\": \"EmamiJ05\"}." } ], "images": [ "scigen/train/270.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Language modelling test results. Above, training and testing on WSJ. Below, training semi-supervised and testing on WMT." ], "reasoning": "To find the model with the lowest perplexity, compare the perplexity values of all models listed in the table. The model with the lowest value is EmamiJ05 with a perplexity of 131.3.", "question": "Which model has the lowest perplexity?", "answer": "So, the answer is {\"answer\": \"EmamiJ05\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe HPYP 5-gram model has a perplexity of 178.13.\n" }, { "role": "assistant", "content": "The table lists the perplexity values for different models. The HPYP 5-gram model is shown with a perplexity of 178.13. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/270.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Language modelling test results. Above, training and testing on WSJ. Below, training semi-supervised and testing on WMT." ], "reasoning": "The table lists the perplexity values for different models. The HPYP 5-gram model is shown with a perplexity of 178.13.", "statement": "The HPYP 5-gram model has a perplexity of 178.13.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Effect of including elements in the model conditioning contexts. Results are given on the YM development set.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The selection and ordering of elements in the conditioning contexts of HPYP priors are crucial, with the first two words on the stack being most important. However, due to sparsity, word types are often dropped, limiting the inclusion of third-order dependencies.The first modelling choice is the selection and ordering of elements in the conditioning contexts of the HPYP priors. The first two words on the stack are the most important, but insufficient – second-order dependencies and further elements on the stack should also be included in the contexts. The challenge is that the back-off structure of each HPYP specifies an ordering of the elements based on their importance in the prediction. We are therefore much more restricted than classifiers with large, sparse feature-sets which are commonly used in transition-based parsers. Due to sparsity, the word types are the first elements to be dropped in the back-off structure, and elements such as third-order dependencies, which have been shown to improve parsing performance, cannot be included successfully in our model." } ], "images": [ "scigen/train/271.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Effect of including elements in the model conditioning contexts. Results are given on the YM development set." ], "reasoning": "The selection and ordering of elements in the conditioning contexts of HPYP priors are crucial, with the first two words on the stack being most important. However, due to sparsity, word types are often dropped, limiting the inclusion of third-order dependencies.", "description": "The first modelling choice is the selection and ordering of elements in the conditioning contexts of the HPYP priors. The first two words on the stack are the most important, but insufficient – second-order dependencies and further elements on the stack should also be included in the contexts. The challenge is that the back-off structure of each HPYP specifies an ordering of the elements based on their importance in the prediction. We are therefore much more restricted than classifiers with large, sparse feature-sets which are commonly used in transition-based parsers. Due to sparsity, the word types are the first elements to be dropped in the back-off structure, and elements such as third-order dependencies, which have been shown to improve parsing performance, cannot be included successfully in our model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: Effect of including elements in the model conditioning contexts. Results are given on the YM development set.', unfold the steps to solve the question given next. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: What is the UAS score when the context elements include + rc1(σ2).t?\n" }, { "role": "assistant", "content": "To find the UAS score for the context elements including + rc1(σ2).t, locate the row with this context element in the table. The UAS score for this context is 87.95. So, the answer is {\"answer\": \"87.95\"}." } ], "images": [ "scigen/train/271.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Effect of including elements in the model conditioning contexts. Results are given on the YM development set." ], "reasoning": "To find the UAS score for the context elements including + rc1(σ2).t, locate the row with this context element in the table. The UAS score for this context is 87.95.", "question": "What is the UAS score when the context elements include + rc1(σ2).t?", "answer": "So, the answer is {\"answer\": \"87.95\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe UAS score is highest when the context elements include + o2.w.\n" }, { "role": "assistant", "content": "To verify this statement, compare the UAS scores for all context elements in the table. The highest UAS score is 88.93, which corresponds to the context elements + o2.w. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/271.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Effect of including elements in the model conditioning contexts. Results are given on the YM development set." ], "reasoning": "To verify this statement, compare the UAS scores for all context elements in the table. The highest UAS score is 88.93, which corresponds to the context elements + o2.w.", "statement": "The UAS score is highest when the context elements include + o2.w.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5: Parsing accuracies on the YM test set. compared against previous published results. TitovH07 was retrained to enable direct comparison.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The HPYP model performs better than Eisner’s generative model and WallachSM08's Bayesian model, and is only 0.8 UAS below TitovH07's generative model.Our HPYP model performs much better than Eisner’s generative model as well as the Bayesian version of that model proposed by \\newciteWallachSM08 (the result for Eisner’s model is given as reported by \\newciteWallachSM08 on the WSJ). The accuracy of our model is only 0.8 UAS below the generative model of \\newciteTitovH07, despite that model being much more powerful. The Titov and Henderson model takes 3 days to train, and its decoding speed is around 1 sentence per second." } ], "images": [ "scigen/train/272.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Parsing accuracies on the YM test set. compared against previous published results. TitovH07 was retrained to enable direct comparison." ], "reasoning": "The HPYP model performs better than Eisner’s generative model and WallachSM08's Bayesian model, and is only 0.8 UAS below TitovH07's generative model.", "description": "Our HPYP model performs much better than Eisner’s generative model as well as the Bayesian version of that model proposed by \\newciteWallachSM08 (the result for Eisner’s model is given as reported by \\newciteWallachSM08 on the WSJ). The accuracy of our model is only 0.8 UAS below the generative model of \\newciteTitovH07, despite that model being much more powerful. The Titov and Henderson model takes 3 days to train, and its decoding speed is around 1 sentence per second.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 5: Parsing accuracies on the YM test set. compared against previous published results. TitovH07 was retrained to enable direct comparison.' in mind, break down and resolve the question below step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich model has the highest UAS score and how does it compare to the HPYP model?\n" }, { "role": "assistant", "content": "The table shows that the model with the highest UAS score is ChoiM13 with a UAS of 92.96. The HPYP model has a UAS of 88.47, which is 4.49 points lower than ChoiM13. So, the answer is {\"answer\": \"ChoiM13\"}." } ], "images": [ "scigen/train/272.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Parsing accuracies on the YM test set. compared against previous published results. TitovH07 was retrained to enable direct comparison." ], "reasoning": "The table shows that the model with the highest UAS score is ChoiM13 with a UAS of 92.96. The HPYP model has a UAS of 88.47, which is 4.49 points lower than ChoiM13.", "question": "Which model has the highest UAS score and how does it compare to the HPYP model?", "answer": "So, the answer is {\"answer\": \"ChoiM13\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe HPYP model has a higher UAS score than the MaltParser model.\n" }, { "role": "assistant", "content": "The table shows that the HPYP model has a UAS score of 88.47, while the MaltParser model has a UAS score of 88.88. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/272.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Parsing accuracies on the YM test set. compared against previous published results. TitovH07 was retrained to enable direct comparison." ], "reasoning": "The table shows that the HPYP model has a UAS score of 88.47, while the MaltParser model has a UAS score of 88.88. Therefore, the statement is incorrect.", "statement": "The HPYP model has a higher UAS score than the MaltParser model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Comparisons (CER, WER, and training time) of the WSJ task with other end-to-end ASR systems.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "ESPnet shows reasonable performance compared to prior studies, with improvements from deeper encoder networks, character-based LSTMLM, and joint CTC/attention decoding.The use of a deeper encoder network, integration of character-based LSTMLM, and joint CTC/attention decoding steadily improved the performance. But we can state that ESPnet provides reasonable performance by comparing with these prior studies." } ], "images": [ "scigen/train/273.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparisons (CER, WER, and training time) of the WSJ task with other end-to-end ASR systems." ], "reasoning": "ESPnet shows reasonable performance compared to prior studies, with improvements from deeper encoder networks, character-based LSTMLM, and joint CTC/attention decoding.", "description": "The use of a deeper encoder network, integration of character-based LSTMLM, and joint CTC/attention decoding steadily improved the performance. But we can state that ESPnet provides reasonable performance by comparing with these prior studies.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich method has the shortest wall clock time and how many GPUs does it use?\nSolve the above question based on the table titled 'Table 2: Comparisons (CER, WER, and training time) of the WSJ task with other end-to-end ASR systems.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the wall clock times, ESPnet (PyTorch) has the shortest time of 5 hours and uses 1 GPU. So, the answer is {\"answer\": \"ESPnet (PyTorch) with 1 GPU\"}." } ], "images": [ "scigen/train/273.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparisons (CER, WER, and training time) of the WSJ task with other end-to-end ASR systems." ], "reasoning": "By comparing the wall clock times, ESPnet (PyTorch) has the shortest time of 5 hours and uses 1 GPU.", "question": "Which method has the shortest wall clock time and how many GPUs does it use?", "answer": "So, the answer is {\"answer\": \"ESPnet (PyTorch) with 1 GPU\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nESPnet (Chainer) uses more GPUs than seq2seq + CNN.\n" }, { "role": "assistant", "content": "The table shows that ESPnet (Chainer) uses 1 GPU, while seq2seq + CNN uses 10 GPUs, which refutes the statement. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/273.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparisons (CER, WER, and training time) of the WSJ task with other end-to-end ASR systems." ], "reasoning": "The table shows that ESPnet (Chainer) uses 1 GPU, while seq2seq + CNN uses 10 GPUs, which refutes the statement.", "statement": "ESPnet (Chainer) uses more GPUs than seq2seq + CNN.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Comparisons (CER, WER, and training time) of the WSJ task with other end-to-end ASR systems.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The integration of character-based LSTMLM and joint CTC/attention decoding in ESPnet significantly improved CER and WER performance across dev93 and eval92 datasets.The use of a deeper encoder network, integration of character-based LSTMLM, and joint CTC/attention decoding steadily improved the performance. But we can state that ESPnet provides reasonable performance by comparing with these prior studies." } ], "images": [ "scigen/train/274.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparisons (CER, WER, and training time) of the WSJ task with other end-to-end ASR systems." ], "reasoning": "The integration of character-based LSTMLM and joint CTC/attention decoding in ESPnet significantly improved CER and WER performance across dev93 and eval92 datasets.", "description": "The use of a deeper encoder network, integration of character-based LSTMLM, and joint CTC/attention decoding steadily improved the performance. But we can state that ESPnet provides reasonable performance by comparing with these prior studies.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Comparisons (CER, WER, and training time) of the WSJ task with other end-to-end ASR systems.', solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the CER for ESPnet with joint decoding on the eval92 dataset?\n" }, { "role": "assistant", "content": "To find the CER for ESPnet with joint decoding on the eval92 dataset, locate the row labeled '+ joint decoding' under the 'Method' column and then find the corresponding value under the 'eval92' column. So, the answer is {\"answer\": \"3.8\"}." } ], "images": [ "scigen/train/274.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparisons (CER, WER, and training time) of the WSJ task with other end-to-end ASR systems." ], "reasoning": "To find the CER for ESPnet with joint decoding on the eval92 dataset, locate the row labeled '+ joint decoding' under the 'Method' column and then find the corresponding value under the 'eval92' column.", "question": "What is the CER for ESPnet with joint decoding on the eval92 dataset?", "answer": "So, the answer is {\"answer\": \"3.8\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nESPnet with label smoothing has a lower CER on eval92 than ESPnet with joint decoding.\n" }, { "role": "assistant", "content": "Compare the CER values for ESPnet with label smoothing and ESPnet with joint decoding on the eval92 dataset. ESPnet with label smoothing has a CER of 3.6, while ESPnet with joint decoding has a CER of 3.8. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/274.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparisons (CER, WER, and training time) of the WSJ task with other end-to-end ASR systems." ], "reasoning": "Compare the CER values for ESPnet with label smoothing and ESPnet with joint decoding on the eval92 dataset. ESPnet with label smoothing has a CER of 3.6, while ESPnet with joint decoding has a CER of 3.8.", "statement": "ESPnet with label smoothing has a lower CER on eval92 than ESPnet with joint decoding.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Comparison of uniform data processing, random sampling of web data by 20 %, and weighted parameter updates from web data by a factor of 0.4, in NNLM training. The models were trained using normal softmax. Includes development set perplexity, word error rate (%), and word error rate after interpolation with the n-gram model.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Sampling web data during training improves perplexity but not word error rate, and speeds up training, making it preferable when computational resources are limited.Optimizing the weights for neural network training is more difficult than for the n-gram mixture models. As we do not have a computational method for optimizing the weights, we tried a few values, observing the development set perplexity during training. Sampling 20 % of the web data on each iteration, or weighting the web data by a factor of 0.4 seemed to work reasonably well. We used a slightly higher learning rate when weighting the web data to compensate for the fact that the updates are smaller on average. Uniform means that the web data is processed just like other data sets , sampling means that a subset of web data is randomly sampled before each epoch, and weighting means that the parameter updates are given a smaller weight when the mini-batch contains web sentences. Sampling seems to improve perplexity, but not word error rate. Because sampling usually speeds up training considerably and our computational resources were limited, the rest of the experiments were done using sampling." } ], "images": [ "scigen/train/275.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Comparison of uniform data processing, random sampling of web data by 20 %, and weighted parameter updates from web data by a factor of 0.4, in NNLM training. The models were trained using normal softmax. Includes development set perplexity, word error rate (%), and word error rate after interpolation with the n-gram model." ], "reasoning": "Sampling web data during training improves perplexity but not word error rate, and speeds up training, making it preferable when computational resources are limited.", "description": "Optimizing the weights for neural network training is more difficult than for the n-gram mixture models. As we do not have a computational method for optimizing the weights, we tried a few values, observing the development set perplexity during training. Sampling 20 % of the web data on each iteration, or weighting the web data by a factor of 0.4 seemed to work reasonably well. We used a slightly higher learning rate when weighting the web data to compensate for the fact that the updates are smaller on average. Uniform means that the web data is processed just like other data sets , sampling means that a subset of web data is randomly sampled before each epoch, and weighting means that the parameter updates are given a smaller weight when the mini-batch contains web sentences. Sampling seems to improve perplexity, but not word error rate. Because sampling usually speeds up training considerably and our computational resources were limited, the rest of the experiments were done using sampling.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: TABLE III: Comparison of uniform data processing, random sampling of web data by 20 %, and weighted parameter updates from web data by a factor of 0.4, in NNLM training. The models were trained using normal softmax. Includes development set perplexity, word error rate (%), and word error rate after interpolation with the n-gram model.\nQuestion: What is the perplexity for Finnish, 42.5k subwords when using sampling?\n" }, { "role": "assistant", "content": "To find the perplexity for Finnish, 42.5k subwords with sampling, locate the row with 'Finnish, 42.5k subwords' and 'Sampling' under 'Subset Processing', then find the corresponding 'Perplexity' value. So, the answer is {\"answer\": \"671\"}." } ], "images": [ "scigen/train/275.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Comparison of uniform data processing, random sampling of web data by 20 %, and weighted parameter updates from web data by a factor of 0.4, in NNLM training. The models were trained using normal softmax. Includes development set perplexity, word error rate (%), and word error rate after interpolation with the n-gram model." ], "reasoning": "To find the perplexity for Finnish, 42.5k subwords with sampling, locate the row with 'Finnish, 42.5k subwords' and 'Sampling' under 'Subset Processing', then find the corresponding 'Perplexity' value.", "question": "What is the perplexity for Finnish, 42.5k subwords when using sampling?", "answer": "So, the answer is {\"answer\": \"671\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWeighting web data for Estonian, 212k subwords results in a perplexity of 409.\n" }, { "role": "assistant", "content": "To verify the statement, locate the row with 'Estonian, 212k subwords' and 'Weighting' under 'Subset Processing', then check the corresponding 'Perplexity' value. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/275.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Comparison of uniform data processing, random sampling of web data by 20 %, and weighted parameter updates from web data by a factor of 0.4, in NNLM training. The models were trained using normal softmax. Includes development set perplexity, word error rate (%), and word error rate after interpolation with the n-gram model." ], "reasoning": "To verify the statement, locate the row with 'Estonian, 212k subwords' and 'Weighting' under 'Subset Processing', then check the corresponding 'Perplexity' value.", "statement": "Weighting web data for Estonian, 212k subwords results in a perplexity of 409.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: Results for CLTC1, CLTC2, CLTC3 and UCLTC'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Adding more source languages to the training set improves the performance of the testing set in multiple target languages.We tested all combinations of source and target languages for all the CLTC setups. We can see similar patterns to those shown above. With every source language added to the training set, the performance of the testing set (now in two or three target languages) improves." } ], "images": [ "scigen/train/276.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results for CLTC1, CLTC2, CLTC3 and UCLTC" ], "reasoning": "Adding more source languages to the training set improves the performance of the testing set in multiple target languages.", "description": "We tested all combinations of source and target languages for all the CLTC setups. We can see similar patterns to those shown above. With every source language added to the training set, the performance of the testing set (now in two or three target languages) improves.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 4: Results for CLTC1, CLTC2, CLTC3 and UCLTC' and methodically tackle the question that follows. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the LIFG (%) for the CLTC3 setup with F, E, G as source languages and F, E, G as target languages with 450 examples?\n" }, { "role": "assistant", "content": "To find the LIFG (%) for the specified setup, locate the row where Setup is CLTC3, Source is F, E, G, Target is F, E, G, and Examples is 450. The corresponding LIFG (%) is 78.40. So, the answer is {\"answer\": \"78.40\"}" } ], "images": [ "scigen/train/276.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results for CLTC1, CLTC2, CLTC3 and UCLTC" ], "reasoning": "To find the LIFG (%) for the specified setup, locate the row where Setup is CLTC3, Source is F, E, G, Target is F, E, G, and Examples is 450. The corresponding LIFG (%) is 78.40.", "question": "What is the LIFG (%) for the CLTC3 setup with F, E, G as source languages and F, E, G as target languages with 450 examples?", "answer": "So, the answer is {\"answer\": \"78.40\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The LIFG (%) for the UCLTC setup with F, E, G as source languages and F, E, G as target languages with 450 examples is 77.90. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe LIFG (%) for the UCLTC setup with F, E, G as source languages and F, E, G as target languages with 450 examples is 77.90.\n" }, { "role": "assistant", "content": "To verify the statement, locate the row where Setup is UCLTC, Source is F, E, G, Target is F, E, G, and Examples is 450. The corresponding LIFG (%) is 77.90, which matches the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/276.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results for CLTC1, CLTC2, CLTC3 and UCLTC" ], "reasoning": "To verify the statement, locate the row where Setup is UCLTC, Source is F, E, G, Target is F, E, G, and Examples is 450. The corresponding LIFG (%) is 77.90, which matches the statement.", "statement": "The LIFG (%) for the UCLTC setup with F, E, G as source languages and F, E, G as target languages with 450 examples is 77.90.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: CLTC Results on the Webis-CLS-10C Dataset'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The accuracy was reported for all baselines except for Inverted, which reported F1 scores.As the baselines did, we reported the accuracy achieved, except when we compare with Inverted, which reported F1, and so did we." } ], "images": [ "scigen/train/277.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: CLTC Results on the Webis-CLS-10C Dataset" ], "reasoning": "The accuracy was reported for all baselines except for Inverted, which reported F1 scores.", "description": "As the baselines did, we reported the accuracy achieved, except when we compare with Inverted, which reported F1, and so did we.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 1: CLTC Results on the Webis-CLS-10C Dataset'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}. \n\nQuestion: What is the highest accuracy achieved among the baselines?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the highest accuracy, compare the 'Baseline Results' values for all baselines. The highest value is 83.80. So, the answer is {\"answer\": \"83.80\"}." } ], "images": [ "scigen/train/277.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: CLTC Results on the Webis-CLS-10C Dataset" ], "reasoning": "To find the highest accuracy, compare the 'Baseline Results' values for all baselines. The highest value is 83.80.", "question": "What is the highest accuracy achieved among the baselines?", "answer": "So, the answer is {\"answer\": \"83.80\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe LIFG score for SHFR-ECOC with target G is higher than that for DCI with target F.\n" }, { "role": "assistant", "content": "The LIFG score for SHFR-ECOC with target G is 91.29, and for DCI with target F is 90.38. Since 91.29 is greater than 90.38, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/277.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: CLTC Results on the Webis-CLS-10C Dataset" ], "reasoning": "The LIFG score for SHFR-ECOC with target G is 91.29, and for DCI with target F is 90.38. Since 91.29 is greater than 90.38, the statement is supported.", "statement": "The LIFG score for SHFR-ECOC with target G is higher than that for DCI with target F.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: CLTC Results on Reuters RCV1/RCV2 Dataset'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Accuracy was reported for all baselines except for Inverted, which reported F1.As the baselines did, we reported the accuracy achieved, except when we compare with Inverted, which reported F1, and so did we." } ], "images": [ "scigen/train/278.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: CLTC Results on Reuters RCV1/RCV2 Dataset" ], "reasoning": "Accuracy was reported for all baselines except for Inverted, which reported F1.", "description": "As the baselines did, we reported the accuracy achieved, except when we compare with Inverted, which reported F1, and so did we.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: CLTC Results on Reuters RCV1/RCV2 Dataset' to sequentially solve the question presented below. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the highest LIFG score achieved by the BRAVE baseline?\n" }, { "role": "assistant", "content": "To find the highest LIFG score achieved by the BRAVE baseline, look at all rows where the Baseline is BRAVE and identify the maximum value in the LIFG column. So, the answer is {\"answer\": \"89.39\"}." } ], "images": [ "scigen/train/278.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: CLTC Results on Reuters RCV1/RCV2 Dataset" ], "reasoning": "To find the highest LIFG score achieved by the BRAVE baseline, look at all rows where the Baseline is BRAVE and identify the maximum value in the LIFG column.", "question": "What is the highest LIFG score achieved by the BRAVE baseline?", "answer": "So, the answer is {\"answer\": \"89.39\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe SHFR-ECOC baseline achieved a higher Baseline Result than the SHFA baseline when the Source was E and the Target was S.\n" }, { "role": "assistant", "content": "Compare the Baseline Results for SHFR-ECOC and SHFA when the Source is E and the Target is S. SHFR-ECOC has a Baseline Result of 72.79, while SHFA has a Baseline Result of 76.40. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/278.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: CLTC Results on Reuters RCV1/RCV2 Dataset" ], "reasoning": "Compare the Baseline Results for SHFR-ECOC and SHFA when the Source is E and the Target is S. SHFR-ECOC has a Baseline Result of 72.79, while SHFA has a Baseline Result of 76.40.", "statement": "The SHFR-ECOC baseline achieved a higher Baseline Result than the SHFA baseline when the Source was E and the Target was S.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: The Effect of Hierarchical Feature Generation', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The table shows that using CMeta improves LIFG scores by about 10% on average across different source-target pairs.As can be seen, the result improvement is significant – about 10% on average. Clearly, abstract features significantly contributes to performance, and should, therefore, be used when available." } ], "images": [ "scigen/train/279.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The Effect of Hierarchical Feature Generation" ], "reasoning": "The table shows that using CMeta improves LIFG scores by about 10% on average across different source-target pairs.", "description": "As can be seen, the result improvement is significant – about 10% on average. Clearly, abstract features significantly contributes to performance, and should, therefore, be used when available.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: The Effect of Hierarchical Feature Generation', and sequentially solve the question, writing out each step of your reasoning process. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the average improvement in LIFG scores when using CMeta across all source-target pairs?\n" }, { "role": "assistant", "content": "To find the average improvement, calculate the difference between LIFG w/ CMeta and LIFG w/o CMeta for each pair, sum these differences, and then divide by the number of pairs. So, the answer is {\"answer\": \"9.63\"}" } ], "images": [ "scigen/train/279.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The Effect of Hierarchical Feature Generation" ], "reasoning": "To find the average improvement, calculate the difference between LIFG w/ CMeta and LIFG w/o CMeta for each pair, sum these differences, and then divide by the number of pairs.", "question": "What is the average improvement in LIFG scores when using CMeta across all source-target pairs?", "answer": "So, the answer is {\"answer\": \"9.63\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nUsing CMeta improves LIFG scores for the source-target pair E-G by more than 8 points.\n" }, { "role": "assistant", "content": "The LIFG score for E-G without CMeta is 55.19, and with CMeta, it is 63.34. The improvement is 63.34 - 55.19 = 8.15 points, which is more than 8 points. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/279.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The Effect of Hierarchical Feature Generation" ], "reasoning": "The LIFG score for E-G without CMeta is 55.19, and with CMeta, it is 63.34. The improvement is 63.34 - 55.19 = 8.15 points, which is more than 8 points.", "statement": "Using CMeta improves LIFG scores for the source-target pair E-G by more than 8 points.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Figure 3: (top) bAbI QA dataset (Weston et al., 2016) visualization of update and reset gates in QRN ‘2r’ model (bottom two) bAbI dialog and DSTC2 dialog dataset (Bordes and Weston, 2016) visualization of update and reset gates in QRN ‘2r’ model. Note that the stories can have as many as 800+ sentences; we only show part of them here. More visualizations are shown in Figure 4 (bAbI QA) and Figure 5 (dialog datasets).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The model identifies high update gate values in the first layer for facts about possession and in the second layer for facts about location, while low reset gate values indicate changes in possession.In QA Task 2 example (top left), we observe high update gate values in the first layer on facts that state who has the apple, and in the second layer, the high update gate values are on those that inform where that person went to. We also observe that the forward reset gate at t=2 in the first layer (→r12) is low, which is signifying that apple no more belongs to Sandra. In dialog Task 3 (bottom left), the model is able to infer that three restaurants are already recommended so that it can recommend another one. In dialog Task 6 (bottom), the model focuses on the sentences containing Spanish, and does not concentrate much on other facts such as I don’t care." } ], "images": [ "scigen/train/280.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 3: (top) bAbI QA dataset (Weston et al., 2016) visualization of update and reset gates in QRN ‘2r’ model (bottom two) bAbI dialog and DSTC2 dialog dataset (Bordes and Weston, 2016) visualization of update and reset gates in QRN ‘2r’ model. Note that the stories can have as many as 800+ sentences; we only show part of them here. More visualizations are shown in Figure 4 (bAbI QA) and Figure 5 (dialog datasets)." ], "reasoning": "The model identifies high update gate values in the first layer for facts about possession and in the second layer for facts about location, while low reset gate values indicate changes in possession.", "description": "In QA Task 2 example (top left), we observe high update gate values in the first layer on facts that state who has the apple, and in the second layer, the high update gate values are on those that inform where that person went to. We also observe that the forward reset gate at t=2 in the first layer (→r12) is low, which is signifying that apple no more belongs to Sandra. In dialog Task 3 (bottom left), the model is able to infer that three restaurants are already recommended so that it can recommend another one. In dialog Task 6 (bottom), the model focuses on the sentences containing Spanish, and does not concentrate much on other facts such as I don’t care.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Figure 3: (top) bAbI QA dataset (Weston et al., 2016) visualization of update and reset gates in QRN ‘2r’ model (bottom two) bAbI dialog and DSTC2 dialog dataset (Bordes and Weston, 2016) visualization of update and reset gates in QRN ‘2r’ model. Note that the stories can have as many as 800+ sentences; we only show part of them here. More visualizations are shown in Figure 4 (bAbI QA) and Figure 5 (dialog datasets).', answer the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nHow does the model infer that Sandra no longer has the apple?\n" }, { "role": "assistant", "content": "The model observes a low forward reset gate value at t=2 in the first layer, indicating that the apple no longer belongs to Sandra. So, the answer is {\"answer\": \"low forward reset gate value at t=2 in the first layer\"}." } ], "images": [ "scigen/train/280.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 3: (top) bAbI QA dataset (Weston et al., 2016) visualization of update and reset gates in QRN ‘2r’ model (bottom two) bAbI dialog and DSTC2 dialog dataset (Bordes and Weston, 2016) visualization of update and reset gates in QRN ‘2r’ model. Note that the stories can have as many as 800+ sentences; we only show part of them here. More visualizations are shown in Figure 4 (bAbI QA) and Figure 5 (dialog datasets)." ], "reasoning": "The model observes a low forward reset gate value at t=2 in the first layer, indicating that the apple no longer belongs to Sandra.", "question": "How does the model infer that Sandra no longer has the apple?", "answer": "So, the answer is {\"answer\": \"low forward reset gate value at t=2 in the first layer\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe model focuses on sentences containing Spanish in dialog Task 6.\n" }, { "role": "assistant", "content": "The description states that in dialog Task 6, the model focuses on sentences containing Spanish and does not concentrate much on other facts. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/280.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 3: (top) bAbI QA dataset (Weston et al., 2016) visualization of update and reset gates in QRN ‘2r’ model (bottom two) bAbI dialog and DSTC2 dialog dataset (Bordes and Weston, 2016) visualization of update and reset gates in QRN ‘2r’ model. Note that the stories can have as many as 800+ sentences; we only show part of them here. More visualizations are shown in Figure 4 (bAbI QA) and Figure 5 (dialog datasets)." ], "reasoning": "The description states that in dialog Task 6, the model focuses on sentences containing Spanish and does not concentrate much on other facts.", "statement": "The model focuses on sentences containing Spanish in dialog Task 6.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Figure 3: (top) bAbI QA dataset (Weston et al., 2016) visualization of update and reset gates in QRN ‘2r’ model (bottom two) bAbI dialog and DSTC2 dialog dataset (Bordes and Weston, 2016) visualization of update and reset gates in QRN ‘2r’ model. Note that the stories can have as many as 800+ sentences; we only show part of them here. More visualizations are shown in Figure 4 (bAbI QA) and Figure 5 (dialog datasets).', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The model uses high update gate values in the first layer to identify who has the apple and in the second layer to determine where that person went.In QA Task 2 example (top left), we observe high update gate values in the first layer on facts that state who has the apple, and in the second layer, the high update gate values are on those that inform where that person went to. We also observe that the forward reset gate at t=2 in the first layer (→r12) is low, which is signifying that apple no more belongs to Sandra. In dialog Task 3 (bottom left), the model is able to infer that three restaurants are already recommended so that it can recommend another one. In dialog Task 6 (bottom), the model focuses on the sentences containing Spanish, and does not concentrate much on other facts such as I don’t care." } ], "images": [ "scigen/train/281.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 3: (top) bAbI QA dataset (Weston et al., 2016) visualization of update and reset gates in QRN ‘2r’ model (bottom two) bAbI dialog and DSTC2 dialog dataset (Bordes and Weston, 2016) visualization of update and reset gates in QRN ‘2r’ model. Note that the stories can have as many as 800+ sentences; we only show part of them here. More visualizations are shown in Figure 4 (bAbI QA) and Figure 5 (dialog datasets)." ], "reasoning": "The model uses high update gate values in the first layer to identify who has the apple and in the second layer to determine where that person went.", "description": "In QA Task 2 example (top left), we observe high update gate values in the first layer on facts that state who has the apple, and in the second layer, the high update gate values are on those that inform where that person went to. We also observe that the forward reset gate at t=2 in the first layer (→r12) is low, which is signifying that apple no more belongs to Sandra. In dialog Task 3 (bottom left), the model is able to infer that three restaurants are already recommended so that it can recommend another one. In dialog Task 6 (bottom), the model focuses on the sentences containing Spanish, and does not concentrate much on other facts such as I don’t care.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Figure 3: (top) bAbI QA dataset (Weston et al., 2016) visualization of update and reset gates in QRN ‘2r’ model (bottom two) bAbI dialog and DSTC2 dialog dataset (Bordes and Weston, 2016) visualization of update and reset gates in QRN ‘2r’ model. Note that the stories can have as many as 800+ sentences; we only show part of them here. More visualizations are shown in Figure 4 (bAbI QA) and Figure 5 (dialog datasets).\nQuestion: Who has the apple and where did they go?\n" }, { "role": "assistant", "content": "The model identifies that Daniel grabbed the apple with high update gate values in the first layer and then tracks his movement to the hallway with high update gate values in the second layer. So, the answer is {\"answer\": \"Daniel, hallway\"}" } ], "images": [ "scigen/train/281.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 3: (top) bAbI QA dataset (Weston et al., 2016) visualization of update and reset gates in QRN ‘2r’ model (bottom two) bAbI dialog and DSTC2 dialog dataset (Bordes and Weston, 2016) visualization of update and reset gates in QRN ‘2r’ model. Note that the stories can have as many as 800+ sentences; we only show part of them here. More visualizations are shown in Figure 4 (bAbI QA) and Figure 5 (dialog datasets)." ], "reasoning": "The model identifies that Daniel grabbed the apple with high update gate values in the first layer and then tracks his movement to the hallway with high update gate values in the second layer.", "question": "Who has the apple and where did they go?", "answer": "So, the answer is {\"answer\": \"Daniel, hallway\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nSandra dropped the apple and then went to the hallway.\n" }, { "role": "assistant", "content": "The table shows that Sandra dropped the apple with a high update gate value in the first layer, but it does not show her going to the hallway. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/281.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 3: (top) bAbI QA dataset (Weston et al., 2016) visualization of update and reset gates in QRN ‘2r’ model (bottom two) bAbI dialog and DSTC2 dialog dataset (Bordes and Weston, 2016) visualization of update and reset gates in QRN ‘2r’ model. Note that the stories can have as many as 800+ sentences; we only show part of them here. More visualizations are shown in Figure 4 (bAbI QA) and Figure 5 (dialog datasets)." ], "reasoning": "The table shows that Sandra dropped the apple with a high update gate value in the first layer, but it does not show her going to the hallway.", "statement": "Sandra dropped the apple and then went to the hallway.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Figure 3: (top) bAbI QA dataset (Weston et al., 2016) visualization of update and reset gates in QRN ‘2r’ model (bottom two) bAbI dialog and DSTC2 dialog dataset (Bordes and Weston, 2016) visualization of update and reset gates in QRN ‘2r’ model. Note that the stories can have as many as 800+ sentences; we only show part of them here. More visualizations are shown in Figure 4 (bAbI QA) and Figure 5 (dialog datasets).', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The model uses high update gate values in the first layer to identify who has the apple and in the second layer to determine where that person went.In QA Task 2 example (top left), we observe high update gate values in the first layer on facts that state who has the apple, and in the second layer, the high update gate values are on those that inform where that person went to. We also observe that the forward reset gate at t=2 in the first layer (→r12) is low, which is signifying that apple no more belongs to Sandra. In dialog Task 3 (bottom left), the model is able to infer that three restaurants are already recommended so that it can recommend another one. In dialog Task 6 (bottom), the model focuses on the sentences containing Spanish, and does not concentrate much on other facts such as I don’t care." } ], "images": [ "scigen/train/282.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 3: (top) bAbI QA dataset (Weston et al., 2016) visualization of update and reset gates in QRN ‘2r’ model (bottom two) bAbI dialog and DSTC2 dialog dataset (Bordes and Weston, 2016) visualization of update and reset gates in QRN ‘2r’ model. Note that the stories can have as many as 800+ sentences; we only show part of them here. More visualizations are shown in Figure 4 (bAbI QA) and Figure 5 (dialog datasets)." ], "reasoning": "The model uses high update gate values in the first layer to identify who has the apple and in the second layer to determine where that person went.", "description": "In QA Task 2 example (top left), we observe high update gate values in the first layer on facts that state who has the apple, and in the second layer, the high update gate values are on those that inform where that person went to. We also observe that the forward reset gate at t=2 in the first layer (→r12) is low, which is signifying that apple no more belongs to Sandra. In dialog Task 3 (bottom left), the model is able to infer that three restaurants are already recommended so that it can recommend another one. In dialog Task 6 (bottom), the model focuses on the sentences containing Spanish, and does not concentrate much on other facts such as I don’t care.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Figure 3: (top) bAbI QA dataset (Weston et al., 2016) visualization of update and reset gates in QRN ‘2r’ model (bottom two) bAbI dialog and DSTC2 dialog dataset (Bordes and Weston, 2016) visualization of update and reset gates in QRN ‘2r’ model. Note that the stories can have as many as 800+ sentences; we only show part of them here. More visualizations are shown in Figure 4 (bAbI QA) and Figure 5 (dialog datasets).'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: What is the significance of the low forward reset gate value at t=2 in the first layer?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The low forward reset gate value at t=2 in the first layer indicates that the apple no longer belongs to Sandra. So, the answer is {\"answer\": \"the apple no longer belongs to Sandra\"}." } ], "images": [ "scigen/train/282.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 3: (top) bAbI QA dataset (Weston et al., 2016) visualization of update and reset gates in QRN ‘2r’ model (bottom two) bAbI dialog and DSTC2 dialog dataset (Bordes and Weston, 2016) visualization of update and reset gates in QRN ‘2r’ model. Note that the stories can have as many as 800+ sentences; we only show part of them here. More visualizations are shown in Figure 4 (bAbI QA) and Figure 5 (dialog datasets)." ], "reasoning": "The low forward reset gate value at t=2 in the first layer indicates that the apple no longer belongs to Sandra.", "question": "What is the significance of the low forward reset gate value at t=2 in the first layer?", "answer": "So, the answer is {\"answer\": \"the apple no longer belongs to Sandra\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model focuses on sentences containing Spanish in dialog Task 6.\n" }, { "role": "assistant", "content": "The description states that in dialog Task 6, the model focuses on sentences containing Spanish and does not concentrate much on other facts. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/282.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 3: (top) bAbI QA dataset (Weston et al., 2016) visualization of update and reset gates in QRN ‘2r’ model (bottom two) bAbI dialog and DSTC2 dialog dataset (Bordes and Weston, 2016) visualization of update and reset gates in QRN ‘2r’ model. Note that the stories can have as many as 800+ sentences; we only show part of them here. More visualizations are shown in Figure 4 (bAbI QA) and Figure 5 (dialog datasets)." ], "reasoning": "The description states that in dialog Task 6, the model focuses on sentences containing Spanish and does not concentrate much on other facts.", "statement": "The model focuses on sentences containing Spanish in dialog Task 6.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Figure 3: (top) bAbI QA dataset (Weston et al., 2016) visualization of update and reset gates in QRN ‘2r’ model (bottom two) bAbI dialog and DSTC2 dialog dataset (Bordes and Weston, 2016) visualization of update and reset gates in QRN ‘2r’ model. Note that the stories can have as many as 800+ sentences; we only show part of them here. More visualizations are shown in Figure 4 (bAbI QA) and Figure 5 (dialog datasets).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The model identifies key information about Spanish restaurants and disregards irrelevant details like 'I don't care.'In QA Task 2 example (top left), we observe high update gate values in the first layer on facts that state who has the apple, and in the second layer, the high update gate values are on those that inform where that person went to. We also observe that the forward reset gate at t=2 in the first layer (→r12) is low, which is signifying that apple no more belongs to Sandra. In dialog Task 3 (bottom left), the model is able to infer that three restaurants are already recommended so that it can recommend another one. In dialog Task 6 (bottom), the model focuses on the sentences containing Spanish, and does not concentrate much on other facts such as I don’t care." } ], "images": [ "scigen/train/283.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 3: (top) bAbI QA dataset (Weston et al., 2016) visualization of update and reset gates in QRN ‘2r’ model (bottom two) bAbI dialog and DSTC2 dialog dataset (Bordes and Weston, 2016) visualization of update and reset gates in QRN ‘2r’ model. Note that the stories can have as many as 800+ sentences; we only show part of them here. More visualizations are shown in Figure 4 (bAbI QA) and Figure 5 (dialog datasets)." ], "reasoning": "The model identifies key information about Spanish restaurants and disregards irrelevant details like 'I don't care.'", "description": "In QA Task 2 example (top left), we observe high update gate values in the first layer on facts that state who has the apple, and in the second layer, the high update gate values are on those that inform where that person went to. We also observe that the forward reset gate at t=2 in the first layer (→r12) is low, which is signifying that apple no more belongs to Sandra. In dialog Task 3 (bottom left), the model is able to infer that three restaurants are already recommended so that it can recommend another one. In dialog Task 6 (bottom), the model focuses on the sentences containing Spanish, and does not concentrate much on other facts such as I don’t care.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Figure 3: (top) bAbI QA dataset (Weston et al., 2016) visualization of update and reset gates in QRN ‘2r’ model (bottom two) bAbI dialog and DSTC2 dialog dataset (Bordes and Weston, 2016) visualization of update and reset gates in QRN ‘2r’ model. Note that the stories can have as many as 800+ sentences; we only show part of them here. More visualizations are shown in Figure 4 (bAbI QA) and Figure 5 (dialog datasets).'\nQuestion: What does the model focus on when the user mentions 'Spanish food'?\n" }, { "role": "assistant", "content": "The model shows high update gate values in Layer 1 z1 and Layer 2 z2 for 'Spanish food,' indicating it focuses on this information. So, the answer is {\"answer\": \"Spanish food\"}" } ], "images": [ "scigen/train/283.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 3: (top) bAbI QA dataset (Weston et al., 2016) visualization of update and reset gates in QRN ‘2r’ model (bottom two) bAbI dialog and DSTC2 dialog dataset (Bordes and Weston, 2016) visualization of update and reset gates in QRN ‘2r’ model. Note that the stories can have as many as 800+ sentences; we only show part of them here. More visualizations are shown in Figure 4 (bAbI QA) and Figure 5 (dialog datasets)." ], "reasoning": "The model shows high update gate values in Layer 1 z1 and Layer 2 z2 for 'Spanish food,' indicating it focuses on this information.", "question": "What does the model focus on when the user mentions 'Spanish food'?", "answer": "So, the answer is {\"answer\": \"Spanish food\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model disregards the user's indifference about the location.\n" }, { "role": "assistant", "content": "The table shows low values in Layer 1 z1 and Layer 2 z2 for 'I don't care,' indicating the model disregards this information. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/283.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 3: (top) bAbI QA dataset (Weston et al., 2016) visualization of update and reset gates in QRN ‘2r’ model (bottom two) bAbI dialog and DSTC2 dialog dataset (Bordes and Weston, 2016) visualization of update and reset gates in QRN ‘2r’ model. Note that the stories can have as many as 800+ sentences; we only show part of them here. More visualizations are shown in Figure 4 (bAbI QA) and Figure 5 (dialog datasets)." ], "reasoning": "The table shows low values in Layer 1 z1 and Layer 2 z2 for 'I don't care,' indicating the model disregards this information.", "statement": "The model disregards the user's indifference about the location.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: bAbI QA dataset [Weston et al., 2016] error rates (%) of QRN and previous work: LSTM [Weston et al., 2016], End-to-end Memory Networks (N2N) [Sukhbaatar et al., 2015], Dynamic Memory Networks (DMN+) [Xiong et al., 2016], Gated End-to-end Memory Networks(GMemN2N) [Perez and Liu, 2016]. Results within each task of Differentiable Neural Computer(DNC) were not provided in its paper Graves et al. [2016]). For QRN, a number in the front (1, 2, 3, 6) indicates the number of layers. A number in the back (200) indicates the dimension of hidden vector, while the default value is 50. ‘r’ indicates that the reset gate is used, and ‘v’ indicates that the gates were vectorized. ‘*’ indicates joint training.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "QRN's '2r' model outperforms all other models by a large margin (2.8+%) in the 1k dataset, and QRN's '6r200' model achieves a nearly perfect score of 99.7% in the 10k dataset, outperforming previous models by a large margin (2.5+%).In 1k data, QRN’s ‘2r’ (2 layers + reset gate + d=50) outperforms all other models by a large margin (2.8+%). In 10k dataset, the average accuracy of QRN’s ‘6r200’ (6 layers + reset gate + d=200) model outperforms all previous models by a large margin (2.5+%), achieving a nearly perfect score of 99.7%." } ], "images": [ "scigen/train/284.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: bAbI QA dataset [Weston et al., 2016] error rates (%) of QRN and previous work: LSTM [Weston et al., 2016], End-to-end Memory Networks (N2N) [Sukhbaatar et al., 2015], Dynamic Memory Networks (DMN+) [Xiong et al., 2016], Gated End-to-end Memory Networks(GMemN2N) [Perez and Liu, 2016]. Results within each task of Differentiable Neural Computer(DNC) were not provided in its paper Graves et al. [2016]). For QRN, a number in the front (1, 2, 3, 6) indicates the number of layers. A number in the back (200) indicates the dimension of hidden vector, while the default value is 50. ‘r’ indicates that the reset gate is used, and ‘v’ indicates that the gates were vectorized. ‘*’ indicates joint training." ], "reasoning": "QRN's '2r' model outperforms all other models by a large margin (2.8+%) in the 1k dataset, and QRN's '6r200' model achieves a nearly perfect score of 99.7% in the 10k dataset, outperforming previous models by a large margin (2.5+%).", "description": "In 1k data, QRN’s ‘2r’ (2 layers + reset gate + d=50) outperforms all other models by a large margin (2.8+%). In 10k dataset, the average accuracy of QRN’s ‘6r200’ (6 layers + reset gate + d=200) model outperforms all previous models by a large margin (2.5+%), achieving a nearly perfect score of 99.7%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: bAbI QA dataset [Weston et al., 2016] error rates (%) of QRN and previous work: LSTM [Weston et al., 2016], End-to-end Memory Networks (N2N) [Sukhbaatar et al., 2015], Dynamic Memory Networks (DMN+) [Xiong et al., 2016], Gated End-to-end Memory Networks(GMemN2N) [Perez and Liu, 2016]. Results within each task of Differentiable Neural Computer(DNC) were not provided in its paper Graves et al. [2016]). For QRN, a number in the front (1, 2, 3, 6) indicates the number of layers. A number in the back (200) indicates the dimension of hidden vector, while the default value is 50. ‘r’ indicates that the reset gate is used, and ‘v’ indicates that the gates were vectorized. ‘*’ indicates joint training.', answer the following question. Give detailed solution consisting of each step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the average error rate of QRN's '6r200' model on the 10k dataset?\n" }, { "role": "assistant", "content": "The table shows the average error rates for different models. For QRN's '6r200' model on the 10k dataset, the average error rate is 0.3%. So, the answer is {\"answer\": \"0.3%\"}." } ], "images": [ "scigen/train/284.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: bAbI QA dataset [Weston et al., 2016] error rates (%) of QRN and previous work: LSTM [Weston et al., 2016], End-to-end Memory Networks (N2N) [Sukhbaatar et al., 2015], Dynamic Memory Networks (DMN+) [Xiong et al., 2016], Gated End-to-end Memory Networks(GMemN2N) [Perez and Liu, 2016]. Results within each task of Differentiable Neural Computer(DNC) were not provided in its paper Graves et al. [2016]). For QRN, a number in the front (1, 2, 3, 6) indicates the number of layers. A number in the back (200) indicates the dimension of hidden vector, while the default value is 50. ‘r’ indicates that the reset gate is used, and ‘v’ indicates that the gates were vectorized. ‘*’ indicates joint training." ], "reasoning": "The table shows the average error rates for different models. For QRN's '6r200' model on the 10k dataset, the average error rate is 0.3%.", "question": "What is the average error rate of QRN's '6r200' model on the 10k dataset?", "answer": "So, the answer is {\"answer\": \"0.3%\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nQRN's '2r' model has an average error rate of 11.7% on the 1k dataset.\n" }, { "role": "assistant", "content": "The table shows that QRN's '2r' model has an average error rate of 11.7% on the 1k dataset. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/284.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: bAbI QA dataset [Weston et al., 2016] error rates (%) of QRN and previous work: LSTM [Weston et al., 2016], End-to-end Memory Networks (N2N) [Sukhbaatar et al., 2015], Dynamic Memory Networks (DMN+) [Xiong et al., 2016], Gated End-to-end Memory Networks(GMemN2N) [Perez and Liu, 2016]. Results within each task of Differentiable Neural Computer(DNC) were not provided in its paper Graves et al. [2016]). For QRN, a number in the front (1, 2, 3, 6) indicates the number of layers. A number in the back (200) indicates the dimension of hidden vector, while the default value is 50. ‘r’ indicates that the reset gate is used, and ‘v’ indicates that the gates were vectorized. ‘*’ indicates joint training." ], "reasoning": "The table shows that QRN's '2r' model has an average error rate of 11.7% on the 1k dataset.", "statement": "QRN's '2r' model has an average error rate of 11.7% on the 1k dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Evaluation of VOGNet in GT5 setting by training (first column) and testing (top row) on SVSQ, TEMP, SPAT respectively'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Models trained on SVSQ perform poorly on TEMP and SPAT, while models trained on SPAT perform well on TEMP, indicating an asymmetry in performance due to multi-modal transformers applied to individual frames.However, the reverse is not true i.e. models trained on SVSQ fail miserably in SPAT and TEMP (accuracy is <3%). This suggests that both TEMP and SPAT moderately counter the bias caused by having a single object instance in a video. Interestingly, while VOGNet trained on TEMP doesn’t perform well on SPAT (performance is worse than VidGrnd trained on SPAT), when VOGNet is trained on SPAT and tested on TEMP it significantly outperforms VidGrnd trained in TEMP. This asymmetry is possibly because the multi-modal transformer is applied to individual frames." } ], "images": [ "scigen/train/285.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Evaluation of VOGNet in GT5 setting by training (first column) and testing (top row) on SVSQ, TEMP, SPAT respectively" ], "reasoning": "Models trained on SVSQ perform poorly on TEMP and SPAT, while models trained on SPAT perform well on TEMP, indicating an asymmetry in performance due to multi-modal transformers applied to individual frames.", "description": "However, the reverse is not true i.e. models trained on SVSQ fail miserably in SPAT and TEMP (accuracy is <3%). This suggests that both TEMP and SPAT moderately counter the bias caused by having a single object instance in a video. Interestingly, while VOGNet trained on TEMP doesn’t perform well on SPAT (performance is worse than VidGrnd trained on SPAT), when VOGNet is trained on SPAT and tested on TEMP it significantly outperforms VidGrnd trained in TEMP. This asymmetry is possibly because the multi-modal transformer is applied to individual frames.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 4: Evaluation of VOGNet in GT5 setting by training (first column) and testing (top row) on SVSQ, TEMP, SPAT respectively', solve the subsequent question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich model has the highest accuracy when trained on SPAT and tested on TEMP?\n" }, { "role": "assistant", "content": "By examining the table, we see that the SPAT-trained model tested on TEMP has an accuracy of 22.6, which is higher than the TEMP-trained model tested on TEMP with an accuracy of 23.07. So, the answer is {\"answer\": \"SPAT\"}." } ], "images": [ "scigen/train/285.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Evaluation of VOGNet in GT5 setting by training (first column) and testing (top row) on SVSQ, TEMP, SPAT respectively" ], "reasoning": "By examining the table, we see that the SPAT-trained model tested on TEMP has an accuracy of 22.6, which is higher than the TEMP-trained model tested on TEMP with an accuracy of 23.07.", "question": "Which model has the highest accuracy when trained on SPAT and tested on TEMP?", "answer": "So, the answer is {\"answer\": \"SPAT\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nModels trained on SVSQ perform well on TEMP.\n" }, { "role": "assistant", "content": "The table shows that the SVSQ-trained model has an accuracy of 1.7 on TEMP, which is very low, indicating poor performance. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/285.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Evaluation of VOGNet in GT5 setting by training (first column) and testing (top row) on SVSQ, TEMP, SPAT respectively" ], "reasoning": "The table shows that the SVSQ-trained model has an accuracy of 1.7 on TEMP, which is very low, indicating poor performance.", "statement": "Models trained on SVSQ perform well on TEMP.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Comparison of VOGNet against ImgGrnd and VidGrnd. GT5 and P100 use 5 and 100 proposals per frame. Here, Acc: Grounding Accuracy, VAcc: Video accuracy, Cons: Consistency, SAcc: Strict Accuracy (see Section 4.3 for details). On the challenging evaluation metrics of TEMP and SPAT, VOGNet (ours) shows significant improvement over competitive image and video grounding baselines.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "SPAT and TEMP strategies are effective for obtaining naturally occurring contrastive examples from ActivityNet videos, using GT5 and P100 models with different proposal boxes per frame.across GT5 (5 proposal boxes per frame) and P100 (100 proposal boxes per frame). In practice, SPAT and TEMP strategies when applied to contrastive videos from ActivityNet are effective proxies to obtaining naturally occurring contrastive examples from the web." } ], "images": [ "scigen/train/286.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of VOGNet against ImgGrnd and VidGrnd. GT5 and P100 use 5 and 100 proposals per frame. Here, Acc: Grounding Accuracy, VAcc: Video accuracy, Cons: Consistency, SAcc: Strict Accuracy (see Section 4.3 for details). On the challenging evaluation metrics of TEMP and SPAT, VOGNet (ours) shows significant improvement over competitive image and video grounding baselines." ], "reasoning": "SPAT and TEMP strategies are effective for obtaining naturally occurring contrastive examples from ActivityNet videos, using GT5 and P100 models with different proposal boxes per frame.", "description": "across GT5 (5 proposal boxes per frame) and P100 (100 proposal boxes per frame). In practice, SPAT and TEMP strategies when applied to contrastive videos from ActivityNet are effective proxies to obtaining naturally occurring contrastive examples from the web.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 3: Comparison of VOGNet against ImgGrnd and VidGrnd. GT5 and P100 use 5 and 100 proposals per frame. Here, Acc: Grounding Accuracy, VAcc: Video accuracy, Cons: Consistency, SAcc: Strict Accuracy (see Section 4.3 for details). On the challenging evaluation metrics of TEMP and SPAT, VOGNet (ours) shows significant improvement over competitive image and video grounding baselines.\nQuestion: Which model and strategy combination has the highest SPAT VAcc?\n" }, { "role": "assistant", "content": "To find the highest SPAT VAcc, compare the SPAT VAcc values across all model and strategy combinations. The highest value is 57.26 for GT5 VOGNet. So, the answer is {\"answer\": \"GT5 VOGNet\"}." } ], "images": [ "scigen/train/286.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of VOGNet against ImgGrnd and VidGrnd. GT5 and P100 use 5 and 100 proposals per frame. Here, Acc: Grounding Accuracy, VAcc: Video accuracy, Cons: Consistency, SAcc: Strict Accuracy (see Section 4.3 for details). On the challenging evaluation metrics of TEMP and SPAT, VOGNet (ours) shows significant improvement over competitive image and video grounding baselines." ], "reasoning": "To find the highest SPAT VAcc, compare the SPAT VAcc values across all model and strategy combinations. The highest value is 57.26 for GT5 VOGNet.", "question": "Which model and strategy combination has the highest SPAT VAcc?", "answer": "So, the answer is {\"answer\": \"GT5 VOGNet\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe P100 VOGNet model has the highest TEMP Cons value.\n" }, { "role": "assistant", "content": "To verify this statement, compare the TEMP Cons values for the P100 VOGNet model with other models. The highest TEMP Cons value is 34.73 for GT5 ImgGrnd, not P100 VOGNet. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/286.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of VOGNet against ImgGrnd and VidGrnd. GT5 and P100 use 5 and 100 proposals per frame. Here, Acc: Grounding Accuracy, VAcc: Video accuracy, Cons: Consistency, SAcc: Strict Accuracy (see Section 4.3 for details). On the challenging evaluation metrics of TEMP and SPAT, VOGNet (ours) shows significant improvement over competitive image and video grounding baselines." ], "reasoning": "To verify this statement, compare the TEMP Cons values for the P100 VOGNet model with other models. The highest TEMP Cons value is 34.73 for GT5 ImgGrnd, not P100 VOGNet.", "statement": "The P100 VOGNet model has the highest TEMP Cons value.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 7: Ablative study comparing gains from Multi-Modal Transformer (MTx) and Object Transformer (OTx) and Relative Position Encoding (RPE). L: Number of Layers, H: Number of Heads in the Transformer. Note that VOGNet = ImgGrnd +MTx(1L,3H) +OTx(1L,3H) + RPE'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Combining object and multi-modal transformers with more layers and heads yields the best performance, while relative position encoding improves accuracy for both transformers.Ablation Study: We observe: ( i) self-attention via object is an effective way to encode object relations across frames (ii) multi-modal transformer applied on individual frames gives modest gains but falls short of object transformer due to lack of temporal information (iii) relative position encoding (RPE) boosts strict accuracy for both transformers (iv) object transformer with 3 layers and 6 heads performs worse than using a single multi-modal transformer i.e. adding more layers and attention heads to object transformer is not enough (v) using both object and multi-modal transformers with more layers and more heads gives the best performing model." } ], "images": [ "scigen/train/287.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Ablative study comparing gains from Multi-Modal Transformer (MTx) and Object Transformer (OTx) and Relative Position Encoding (RPE). L: Number of Layers, H: Number of Heads in the Transformer. Note that VOGNet = ImgGrnd +MTx(1L,3H) +OTx(1L,3H) + RPE" ], "reasoning": "Combining object and multi-modal transformers with more layers and heads yields the best performance, while relative position encoding improves accuracy for both transformers.", "description": "Ablation Study: We observe: ( i) self-attention via object is an effective way to encode object relations across frames (ii) multi-modal transformer applied on individual frames gives modest gains but falls short of object transformer due to lack of temporal information (iii) relative position encoding (RPE) boosts strict accuracy for both transformers (iv) object transformer with 3 layers and 6 heads performs worse than using a single multi-modal transformer i.e. adding more layers and attention heads to object transformer is not enough (v) using both object and multi-modal transformers with more layers and more heads gives the best performing model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 7: Ablative study comparing gains from Multi-Modal Transformer (MTx) and Object Transformer (OTx) and Relative Position Encoding (RPE). L: Number of Layers, H: Number of Heads in the Transformer. Note that VOGNet = ImgGrnd +MTx(1L,3H) +OTx(1L,3H) + RPE\nQuestion: What is the strict accuracy (SAcc) of the model that uses both object and multi-modal transformers with relative position encoding (RPE)?\nGive a detailed response to the above question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the model using both object and multi-modal transformers with RPE has a strict accuracy (SAcc) of 11.58. So, the answer is {\"answer\": \"11.58\"}." } ], "images": [ "scigen/train/287.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Ablative study comparing gains from Multi-Modal Transformer (MTx) and Object Transformer (OTx) and Relative Position Encoding (RPE). L: Number of Layers, H: Number of Heads in the Transformer. Note that VOGNet = ImgGrnd +MTx(1L,3H) +OTx(1L,3H) + RPE" ], "reasoning": "The table shows that the model using both object and multi-modal transformers with RPE has a strict accuracy (SAcc) of 11.58.", "question": "What is the strict accuracy (SAcc) of the model that uses both object and multi-modal transformers with relative position encoding (RPE)?", "answer": "So, the answer is {\"answer\": \"11.58\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model with object transformer (3 layers, 6 heads) has a higher accuracy than the model with multi-modal transformer (3 layers, 6 heads).\n" }, { "role": "assistant", "content": "The table shows that the model with object transformer (3 layers, 6 heads) has an accuracy (Acc) of 21.14, while the model with multi-modal transformer (3 layers, 6 heads) has an accuracy (Acc) of 24.24. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/287.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Ablative study comparing gains from Multi-Modal Transformer (MTx) and Object Transformer (OTx) and Relative Position Encoding (RPE). L: Number of Layers, H: Number of Heads in the Transformer. Note that VOGNet = ImgGrnd +MTx(1L,3H) +OTx(1L,3H) + RPE" ], "reasoning": "The table shows that the model with object transformer (3 layers, 6 heads) has an accuracy (Acc) of 21.14, while the model with multi-modal transformer (3 layers, 6 heads) has an accuracy (Acc) of 24.24.", "statement": "The model with object transformer (3 layers, 6 heads) has a higher accuracy than the model with multi-modal transformer (3 layers, 6 heads).", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Total number of lemmatized words (with at least 20 occurrence) in the train set of ASRL.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table shows that verbs are more frequent than any argument roles, and Arg0 is highly unbalanced with fewer instances compared to Arg1 and Arg2, indicating a potential gender bias and dominance of 'person-person' interactions.Verb Figure Arg1 Figure Arg2 The higher number of verbs In comparison, Arg0 is highly unbalanced as agents are mostly restricted to “people”. We also observe that “man” appears much more often than “woman”/“she”. This indicates gender bias in video curation or video description. Another interesting observation is that “person” class dominates in each of argument roles which suggest “person-person” interactions are more commonly described than “person-object” interactions." } ], "images": [ "scigen/train/288.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Total number of lemmatized words (with at least 20 occurrence) in the train set of ASRL." ], "reasoning": "The table shows that verbs are more frequent than any argument roles, and Arg0 is highly unbalanced with fewer instances compared to Arg1 and Arg2, indicating a potential gender bias and dominance of 'person-person' interactions.", "description": "Verb Figure Arg1 Figure Arg2 The higher number of verbs In comparison, Arg0 is highly unbalanced as agents are mostly restricted to “people”. We also observe that “man” appears much more often than “woman”/“she”. This indicates gender bias in video curation or video description. Another interesting observation is that “person” class dominates in each of argument roles which suggest “person-person” interactions are more commonly described than “person-object” interactions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Total number of lemmatized words (with at least 20 occurrence) in the train set of ASRL.\nQuestion: What is the total number of instances for argument roles excluding verbs?\nGive a detailed response to the above question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the total number of instances for argument roles excluding verbs, sum the values for Arg0, Arg1, Arg2, and ArgM-LOC. So, the answer is {\"answer\": \"547\"}." } ], "images": [ "scigen/train/288.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Total number of lemmatized words (with at least 20 occurrence) in the train set of ASRL." ], "reasoning": "To find the total number of instances for argument roles excluding verbs, sum the values for Arg0, Arg1, Arg2, and ArgM-LOC.", "question": "What is the total number of instances for argument roles excluding verbs?", "answer": "So, the answer is {\"answer\": \"547\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe number of verbs is higher than the combined total of Arg0 and Arg2.\n" }, { "role": "assistant", "content": "The table shows 338 instances for verbs, while the combined total of Arg0 and Arg2 is 93 + 114 = 207, which is less than 338. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/288.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Total number of lemmatized words (with at least 20 occurrence) in the train set of ASRL." ], "reasoning": "The table shows 338 instances for verbs, while the combined total of Arg0 and Arg2 is 93 + 114 = 207, which is less than 338.", "statement": "The number of verbs is higher than the combined total of Arg0 and Arg2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: Comparing models trained with GT5 and P100. All models are tested in P100 setting.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "In the P100 setting, the threshold for TEMP and SPAT models trained in GT5 is set to 0.5, which is higher than the 0.2 threshold used in GT5 testing, to reduce false positives.GT5 models in P100 setting: While testing in P100, for TEMP and SPAT, we set the threshold for models trained in GT5 as 0.5 which is higher than the threshold used when testing in GT5 (0.2). This is expected as a lower threshold would imply a higher chance of a false positive." } ], "images": [ "scigen/train/289.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparing models trained with GT5 and P100. All models are tested in P100 setting." ], "reasoning": "In the P100 setting, the threshold for TEMP and SPAT models trained in GT5 is set to 0.5, which is higher than the 0.2 threshold used in GT5 testing, to reduce false positives.", "description": "GT5 models in P100 setting: While testing in P100, for TEMP and SPAT, we set the threshold for models trained in GT5 as 0.5 which is higher than the threshold used when testing in GT5 (0.2). This is expected as a lower threshold would imply a higher chance of a false positive.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Comparing models trained with GT5 and P100. All models are tested in P100 setting.\nQuestion: What is the TEMP Acc for the VOGNet model trained in P100?\nSolve the above question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "Locate the row for the VOGNet model trained in P100 and find the value under the TEMP Acc column. So, the answer is {\"answer\": \"12.68\"}" } ], "images": [ "scigen/train/289.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparing models trained with GT5 and P100. All models are tested in P100 setting." ], "reasoning": "Locate the row for the VOGNet model trained in P100 and find the value under the TEMP Acc column.", "question": "What is the TEMP Acc for the VOGNet model trained in P100?", "answer": "So, the answer is {\"answer\": \"12.68\"}" }, { "messages": [ { "role": "user", "content": "The TEMP Cons for the VidGrnd model trained in GT5 is higher than that for the VOGNet model trained in P100. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe TEMP Cons for the VidGrnd model trained in GT5 is higher than that for the VOGNet model trained in P100.\n" }, { "role": "assistant", "content": "Compare the TEMP Cons values for the VidGrnd model trained in GT5 (28.47) and the VOGNet model trained in P100 (25.03). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/289.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparing models trained with GT5 and P100. All models are tested in P100 setting." ], "reasoning": "Compare the TEMP Cons values for the VidGrnd model trained in GT5 (28.47) and the VOGNet model trained in P100 (25.03).", "statement": "The TEMP Cons for the VidGrnd model trained in GT5 is higher than that for the VOGNet model trained in P100.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Ablative study layers and heads of Transformers.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Adding more heads improves the object transformer more than adding layers, but both heads and layers help the multi-modal transformer.Transformer Ablation: It is interesting to note adding more heads better than adding more layers for object transformer, while in the case of multi-modal transformer both number of heads and number of layers help. Finally, we find that simply adding more layers and heads to the object transformer is insufficient, as a multi-modal transformer with 1 layer and 3 heads performs significantly better than the object transformer with 3 layers and 6 heads." } ], "images": [ "scigen/train/290.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ablative study layers and heads of Transformers." ], "reasoning": "Adding more heads improves the object transformer more than adding layers, but both heads and layers help the multi-modal transformer.", "description": "Transformer Ablation: It is interesting to note adding more heads better than adding more layers for object transformer, while in the case of multi-modal transformer both number of heads and number of layers help. Finally, we find that simply adding more layers and heads to the object transformer is insufficient, as a multi-modal transformer with 1 layer and 3 heads performs significantly better than the object transformer with 3 layers and 6 heads.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: Ablative study layers and heads of Transformers.', answer the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich configuration performs better: a multi-modal transformer with 1 layer and 3 heads or an object transformer with 3 layers and 6 heads?\n" }, { "role": "assistant", "content": "The multi-modal transformer with 1 layer and 3 heads has an Acc of 23.38, while the object transformer with 3 layers and 6 heads has an Acc of 21.34. So, the answer is {\"answer\": \"multi-modal transformer with 1 layer and 3 heads\"}." } ], "images": [ "scigen/train/290.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ablative study layers and heads of Transformers." ], "reasoning": "The multi-modal transformer with 1 layer and 3 heads has an Acc of 23.38, while the object transformer with 3 layers and 6 heads has an Acc of 21.34.", "question": "Which configuration performs better: a multi-modal transformer with 1 layer and 3 heads or an object transformer with 3 layers and 6 heads?", "answer": "So, the answer is {\"answer\": \"multi-modal transformer with 1 layer and 3 heads\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence A multi-modal transformer with 1 layer and 3 heads performs better than an object transformer with 3 layers and 6 heads. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nA multi-modal transformer with 1 layer and 3 heads performs better than an object transformer with 3 layers and 6 heads.\n" }, { "role": "assistant", "content": "The multi-modal transformer with 1 layer and 3 heads has an Acc of 23.38, while the object transformer with 3 layers and 6 heads has an Acc of 21.34. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/290.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ablative study layers and heads of Transformers." ], "reasoning": "The multi-modal transformer with 1 layer and 3 heads has an Acc of 23.38, while the object transformer with 3 layers and 6 heads has an Acc of 21.34.", "statement": "A multi-modal transformer with 1 layer and 3 heads performs better than an object transformer with 3 layers and 6 heads.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title '(e) Transposition'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Disagreements in article placement by DocuToads are due to random indexing of new articles, causing negative serial correlation.The most common source of disagreement in our sample of articles can be traced primarily to the addition of new articles. In such cases, new text was added between two previously existing articles in the reference document. DocuToads, however, has to record the additions at an index position in the reference text which will correspond to either the preceding or following article. There is in other words a random component which may cause DocuToads to disagree with human coders who can implement any single rule on the placement of new articles. This constitutes a form of negative serial correlation, a common concern found in time-series analysis. Users of DocuToads are advised to consider this issue if correlating article-level results with other article-level variables assigned by human coders. This source of disagreement has no effect on the document-level results." } ], "images": [ "scigen/train/291.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(e) Transposition" ], "reasoning": "Disagreements in article placement by DocuToads are due to random indexing of new articles, causing negative serial correlation.", "description": "The most common source of disagreement in our sample of articles can be traced primarily to the addition of new articles. In such cases, new text was added between two previously existing articles in the reference document. DocuToads, however, has to record the additions at an index position in the reference text which will correspond to either the preceding or following article. There is in other words a random component which may cause DocuToads to disagree with human coders who can implement any single rule on the placement of new articles. This constitutes a form of negative serial correlation, a common concern found in time-series analysis. Users of DocuToads are advised to consider this issue if correlating article-level results with other article-level variables assigned by human coders. This source of disagreement has no effect on the document-level results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of '(e) Transposition', answer the question below by showing each progressive step toward the answer. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the edit distance between 'edit' and 'algorithm'?\n" }, { "role": "assistant", "content": "To find the edit distance between 'edit' and 'algorithm', locate the intersection of the 'edit' row and 'algorithm' column in the table, which shows a value of 0. So, the answer is {\"answer\": \"0\"}." } ], "images": [ "scigen/train/291.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(e) Transposition" ], "reasoning": "To find the edit distance between 'edit' and 'algorithm', locate the intersection of the 'edit' row and 'algorithm' column in the table, which shows a value of 0.", "question": "What is the edit distance between 'edit' and 'algorithm'?", "answer": "So, the answer is {\"answer\": \"0\"}." }, { "messages": [ { "role": "user", "content": "The edit distance between 'minimum' and 'edit' is 1. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe edit distance between 'minimum' and 'edit' is 1.\n" }, { "role": "assistant", "content": "To verify this statement, locate the intersection of the 'minimum' row and 'edit' column in the table, which shows a value of 0. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/291.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(e) Transposition" ], "reasoning": "To verify this statement, locate the intersection of the 'minimum' row and 'edit' column in the table, which shows a value of 0. Therefore, the statement is incorrect.", "statement": "The edit distance between 'minimum' and 'edit' is 1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title '(a) Identical text sequences', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Disagreements in article placement by DocuToads are due to random indexing of new articles, causing negative serial correlation.The most common source of disagreement in our sample of articles can be traced primarily to the addition of new articles. In such cases, new text was added between two previously existing articles in the reference document. DocuToads, however, has to record the additions at an index position in the reference text which will correspond to either the preceding or following article. There is in other words a random component which may cause DocuToads to disagree with human coders who can implement any single rule on the placement of new articles. This constitutes a form of negative serial correlation, a common concern found in time-series analysis. Users of DocuToads are advised to consider this issue if correlating article-level results with other article-level variables assigned by human coders. This source of disagreement has no effect on the document-level results." } ], "images": [ "scigen/train/292.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) Identical text sequences" ], "reasoning": "Disagreements in article placement by DocuToads are due to random indexing of new articles, causing negative serial correlation.", "description": "The most common source of disagreement in our sample of articles can be traced primarily to the addition of new articles. In such cases, new text was added between two previously existing articles in the reference document. DocuToads, however, has to record the additions at an index position in the reference text which will correspond to either the preceding or following article. There is in other words a random component which may cause DocuToads to disagree with human coders who can implement any single rule on the placement of new articles. This constitutes a form of negative serial correlation, a common concern found in time-series analysis. Users of DocuToads are advised to consider this issue if correlating article-level results with other article-level variables assigned by human coders. This source of disagreement has no effect on the document-level results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to '(a) Identical text sequences', and sequentially solve the question, writing out each step of your reasoning process. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the value at the intersection of 'edit' and 'distance' in the table?\n" }, { "role": "assistant", "content": "Locate the row labeled 'edit' and the column labeled 'distance' to find the intersecting value. So, the answer is {\"answer\": \"0\"}." } ], "images": [ "scigen/train/292.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) Identical text sequences" ], "reasoning": "Locate the row labeled 'edit' and the column labeled 'distance' to find the intersecting value.", "question": "What is the value at the intersection of 'edit' and 'distance' in the table?", "answer": "So, the answer is {\"answer\": \"0\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The table shows that the value at the intersection of 'simple' and 'minimum' is 2., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe table shows that the value at the intersection of 'simple' and 'minimum' is 2.\n" }, { "role": "assistant", "content": "Check the intersection of the row labeled 'simple' and the column labeled 'minimum' to verify the value. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/292.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) Identical text sequences" ], "reasoning": "Check the intersection of the row labeled 'simple' and the column labeled 'minimum' to verify the value.", "statement": "The table shows that the value at the intersection of 'simple' and 'minimum' is 2.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled '(b) Deletion'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Disagreements in article placement by DocuToads are due to random indexing of new articles, causing negative serial correlation.The most common source of disagreement in our sample of articles can be traced primarily to the addition of new articles. In such cases, new text was added between two previously existing articles in the reference document. DocuToads, however, has to record the additions at an index position in the reference text which will correspond to either the preceding or following article. There is in other words a random component which may cause DocuToads to disagree with human coders who can implement any single rule on the placement of new articles. This constitutes a form of negative serial correlation, a common concern found in time-series analysis. Users of DocuToads are advised to consider this issue if correlating article-level results with other article-level variables assigned by human coders. This source of disagreement has no effect on the document-level results." } ], "images": [ "scigen/train/293.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) Deletion" ], "reasoning": "Disagreements in article placement by DocuToads are due to random indexing of new articles, causing negative serial correlation.", "description": "The most common source of disagreement in our sample of articles can be traced primarily to the addition of new articles. In such cases, new text was added between two previously existing articles in the reference document. DocuToads, however, has to record the additions at an index position in the reference text which will correspond to either the preceding or following article. There is in other words a random component which may cause DocuToads to disagree with human coders who can implement any single rule on the placement of new articles. This constitutes a form of negative serial correlation, a common concern found in time-series analysis. Users of DocuToads are advised to consider this issue if correlating article-level results with other article-level variables assigned by human coders. This source of disagreement has no effect on the document-level results.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: (b) Deletion\nQuestion: What is the edit distance between the words 'edit' and 'distance'?\n" }, { "role": "assistant", "content": "To find the edit distance between 'edit' and 'distance', locate the intersection of the 'edit' row and 'distance' column in the table, which shows the value 0. So, the answer is {\"answer\": \"0\"}." } ], "images": [ "scigen/train/293.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) Deletion" ], "reasoning": "To find the edit distance between 'edit' and 'distance', locate the intersection of the 'edit' row and 'distance' column in the table, which shows the value 0.", "question": "What is the edit distance between the words 'edit' and 'distance'?", "answer": "So, the answer is {\"answer\": \"0\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe edit distance between 'A' and 'algorithm' is 4.\n" }, { "role": "assistant", "content": "To verify the statement, locate the intersection of the 'A' row and 'algorithm' column in the table, which shows the value 0, not 4. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/293.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) Deletion" ], "reasoning": "To verify the statement, locate the intersection of the 'A' row and 'algorithm' column in the table, which shows the value 0, not 4.", "statement": "The edit distance between 'A' and 'algorithm' is 4.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled '(c) Addition', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Disagreements in article placement arise due to the random indexing of new articles by DocuToads, which may differ from human coders' placements.The most common source of disagreement in our sample of articles can be traced primarily to the addition of new articles. In such cases, new text was added between two previously existing articles in the reference document. DocuToads, however, has to record the additions at an index position in the reference text which will correspond to either the preceding or following article. There is in other words a random component which may cause DocuToads to disagree with human coders who can implement any single rule on the placement of new articles. This constitutes a form of negative serial correlation, a common concern found in time-series analysis. Users of DocuToads are advised to consider this issue if correlating article-level results with other article-level variables assigned by human coders. This source of disagreement has no effect on the document-level results." } ], "images": [ "scigen/train/294.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(c) Addition" ], "reasoning": "Disagreements in article placement arise due to the random indexing of new articles by DocuToads, which may differ from human coders' placements.", "description": "The most common source of disagreement in our sample of articles can be traced primarily to the addition of new articles. In such cases, new text was added between two previously existing articles in the reference document. DocuToads, however, has to record the additions at an index position in the reference text which will correspond to either the preceding or following article. There is in other words a random component which may cause DocuToads to disagree with human coders who can implement any single rule on the placement of new articles. This constitutes a form of negative serial correlation, a common concern found in time-series analysis. Users of DocuToads are advised to consider this issue if correlating article-level results with other article-level variables assigned by human coders. This source of disagreement has no effect on the document-level results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the '(c) Addition', solve the subsequent question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhat is the edit distance between the words 'edit' and 'distance'?\n" }, { "role": "assistant", "content": "To find the edit distance between 'edit' and 'distance', locate the intersection of the 'edit' row and the 'distance' column in the table. The value is 0. So, the answer is {\"answer\": \"0\"}." } ], "images": [ "scigen/train/294.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(c) Addition" ], "reasoning": "To find the edit distance between 'edit' and 'distance', locate the intersection of the 'edit' row and the 'distance' column in the table. The value is 0.", "question": "What is the edit distance between the words 'edit' and 'distance'?", "answer": "So, the answer is {\"answer\": \"0\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe edit distance between 'simple' and 'minimum' is 0.\n" }, { "role": "assistant", "content": "To verify this statement, locate the intersection of the 'simple' row and the 'minimum' column in the table. The value is 0, which supports the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/294.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(c) Addition" ], "reasoning": "To verify this statement, locate the intersection of the 'simple' row and the 'minimum' column in the table. The value is 0, which supports the statement.", "statement": "The edit distance between 'simple' and 'minimum' is 0.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is '(d) Substitution'.\n" }, { "role": "assistant", "content": "Disagreements in article placement arise due to random indexing of new articles by DocuToads, which differs from human coders' consistent rules.The most common source of disagreement in our sample of articles can be traced primarily to the addition of new articles. In such cases, new text was added between two previously existing articles in the reference document. DocuToads, however, has to record the additions at an index position in the reference text which will correspond to either the preceding or following article. There is in other words a random component which may cause DocuToads to disagree with human coders who can implement any single rule on the placement of new articles. This constitutes a form of negative serial correlation, a common concern found in time-series analysis. Users of DocuToads are advised to consider this issue if correlating article-level results with other article-level variables assigned by human coders. This source of disagreement has no effect on the document-level results." } ], "images": [ "scigen/train/295.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(d) Substitution" ], "reasoning": "Disagreements in article placement arise due to random indexing of new articles by DocuToads, which differs from human coders' consistent rules.", "description": "The most common source of disagreement in our sample of articles can be traced primarily to the addition of new articles. In such cases, new text was added between two previously existing articles in the reference document. DocuToads, however, has to record the additions at an index position in the reference text which will correspond to either the preceding or following article. There is in other words a random component which may cause DocuToads to disagree with human coders who can implement any single rule on the placement of new articles. This constitutes a form of negative serial correlation, a common concern found in time-series analysis. Users of DocuToads are advised to consider this issue if correlating article-level results with other article-level variables assigned by human coders. This source of disagreement has no effect on the document-level results.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of '(d) Substitution'. Solve the following question based on the table.\n\nQuestion: What is the edit distance between 'edit' and 'algorithm'?\nLet's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the edit distance between 'edit' and 'algorithm', locate the intersection of the 'edit' row and the 'algorithm' column, which shows a value of 4. So, the answer is {\"answer\": \"4\"}." } ], "images": [ "scigen/train/295.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(d) Substitution" ], "reasoning": "To find the edit distance between 'edit' and 'algorithm', locate the intersection of the 'edit' row and the 'algorithm' column, which shows a value of 4.", "question": "What is the edit distance between 'edit' and 'algorithm'?", "answer": "So, the answer is {\"answer\": \"4\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe edit distance between 'simple' and 'minimum' is 0.\n" }, { "role": "assistant", "content": "To verify this, locate the intersection of the 'simple' row and the 'minimum' column, which shows a value of 0. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/295.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(d) Substitution" ], "reasoning": "To verify this, locate the intersection of the 'simple' row and the 'minimum' column, which shows a value of 0.", "statement": "The edit distance between 'simple' and 'minimum' is 0.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 10: Results of End2End compared to our model (with GS and ST variants) on hypernym extraction.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The ST estimator significantly improves accuracy in the hypernym extraction task, while the Gumbel-softmax trick has a positive effect without ST but little effect with ST.We report results on the synthetic hypernym extraction task with and without the Gumbel-softmax trick and the ST estimator. We observe that the ST estimator is crucial even on such a simple task, which aligns with prior observations havrylov2017emergence that ST helps overcome the discrepancy between training time and test time. GS improved results without ST, but had little effect with ST." } ], "images": [ "scigen/train/296.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Results of End2End compared to our model (with GS and ST variants) on hypernym extraction." ], "reasoning": "The ST estimator significantly improves accuracy in the hypernym extraction task, while the Gumbel-softmax trick has a positive effect without ST but little effect with ST.", "description": "We report results on the synthetic hypernym extraction task with and without the Gumbel-softmax trick and the ST estimator. We observe that the ST estimator is crucial even on such a simple task, which aligns with prior observations havrylov2017emergence that ST helps overcome the discrepancy between training time and test time. GS improved results without ST, but had little effect with ST.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 10: Results of End2End compared to our model (with GS and ST variants) on hypernym extraction.' and methodically tackle the question that follows. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model configuration achieved the highest accuracy in the hypernym extraction task?\n" }, { "role": "assistant", "content": "By comparing the accuracy values of all model configurations in the table, the End2End model achieved the highest accuracy with a value of 86.5. So, the answer is {\"answer\": \"End2End\"}." } ], "images": [ "scigen/train/296.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Results of End2End compared to our model (with GS and ST variants) on hypernym extraction." ], "reasoning": "By comparing the accuracy values of all model configurations in the table, the End2End model achieved the highest accuracy with a value of 86.5.", "question": "Which model configuration achieved the highest accuracy in the hypernym extraction task?", "answer": "So, the answer is {\"answer\": \"End2End\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The model with both GS and ST achieved the highest accuracy., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe model with both GS and ST achieved the highest accuracy.\n" }, { "role": "assistant", "content": "The table shows that the End2End model achieved the highest accuracy with 86.5, while the model with both GS and ST achieved an accuracy of 84.0. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/296.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Results of End2End compared to our model (with GS and ST variants) on hypernym extraction." ], "reasoning": "The table shows that the End2End model achieved the highest accuracy with 86.5, while the model with both GS and ST achieved an accuracy of 84.0.", "statement": "The model with both GS and ST achieved the highest accuracy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Human-evaluation results for how reasonable hypotheses are (CSQA development set). Each rater determined whether a hypothesis is reasonable (1 point), somewhat reasonable (0.5 point) or not reasonable (0 points). The score is the average rating across raters and examples.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Top-K=5 ST achieved the highest score of 0.74, while SupGen models, despite producing more natural texts, were judged less reasonable.Top-K=5 ST achieved the highest score of 0.74. While SupGen models produce more natural texts, they are judged to be less reasonable in the context of the question." } ], "images": [ "scigen/train/297.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Human-evaluation results for how reasonable hypotheses are (CSQA development set). Each rater determined whether a hypothesis is reasonable (1 point), somewhat reasonable (0.5 point) or not reasonable (0 points). The score is the average rating across raters and examples." ], "reasoning": "Top-K=5 ST achieved the highest score of 0.74, while SupGen models, despite producing more natural texts, were judged less reasonable.", "description": "Top-K=5 ST achieved the highest score of 0.74. While SupGen models produce more natural texts, they are judged to be less reasonable in the context of the question.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 3: Human-evaluation results for how reasonable hypotheses are (CSQA development set). Each rater determined whether a hypothesis is reasonable (1 point), somewhat reasonable (0.5 point) or not reasonable (0 points). The score is the average rating across raters and examples.', solve the following question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieved the highest score and what was the score?\n" }, { "role": "assistant", "content": "The table shows the scores of different models. Top-K=5 ST achieved the highest score of 0.74. So, the answer is {\"answer\": \"Top-K=5 ST with a score of 0.74\"}." } ], "images": [ "scigen/train/297.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Human-evaluation results for how reasonable hypotheses are (CSQA development set). Each rater determined whether a hypothesis is reasonable (1 point), somewhat reasonable (0.5 point) or not reasonable (0 points). The score is the average rating across raters and examples." ], "reasoning": "The table shows the scores of different models. Top-K=5 ST achieved the highest score of 0.74.", "question": "Which model achieved the highest score and what was the score?", "answer": "So, the answer is {\"answer\": \"Top-K=5 ST with a score of 0.74\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nSupGen | cl=3 achieved a score higher than 0.60.\n" }, { "role": "assistant", "content": "The table shows that SupGen | cl=3 achieved a score of exactly 0.60, not higher. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/297.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Human-evaluation results for how reasonable hypotheses are (CSQA development set). Each rater determined whether a hypothesis is reasonable (1 point), somewhat reasonable (0.5 point) or not reasonable (0 points). The score is the average rating across raters and examples." ], "reasoning": "The table shows that SupGen | cl=3 achieved a score of exactly 0.60, not higher.", "statement": "SupGen | cl=3 achieved a score higher than 0.60.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Effect in encoder and decoder.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The ablation study shows that the modified decoder with capsule routing SAN outperforms the baseline, even without the vertical routing part, as evidenced by the highest BLEU score in row 4.S4SS3SSS0Px1 Effect on Transformer Componets To evaluate the effect of capsule routing SAN in encoder and decoder , we perform an ablation study. Especially the modified decoder still outperforms the baseline even we have removed the vertical routing part, which demonstrates the effectiveness of our model. The row 4 proves the complementarity of the encoder and decoder with capsule routing SAN." } ], "images": [ "scigen/train/298.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Effect in encoder and decoder." ], "reasoning": "The ablation study shows that the modified decoder with capsule routing SAN outperforms the baseline, even without the vertical routing part, as evidenced by the highest BLEU score in row 4.", "description": "S4SS3SSS0Px1 Effect on Transformer Componets To evaluate the effect of capsule routing SAN in encoder and decoder , we perform an ablation study. Especially the modified decoder still outperforms the baseline even we have removed the vertical routing part, which demonstrates the effectiveness of our model. The row 4 proves the complementarity of the encoder and decoder with capsule routing SAN.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: Effect in encoder and decoder.' and methodically tackle the question that follows. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich configuration of layers achieved the highest BLEU score?\n" }, { "role": "assistant", "content": "The table shows different configurations of layers and their corresponding BLEU scores. The highest BLEU score is 24.87, which corresponds to the configuration of layers 1-6. So, the answer is {\"answer\": \"1-6\"}." } ], "images": [ "scigen/train/298.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Effect in encoder and decoder." ], "reasoning": "The table shows different configurations of layers and their corresponding BLEU scores. The highest BLEU score is 24.87, which corresponds to the configuration of layers 1-6.", "question": "Which configuration of layers achieved the highest BLEU score?", "answer": "So, the answer is {\"answer\": \"1-6\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe configuration of layers 4-6 achieved the highest BLEU score.\n" }, { "role": "assistant", "content": "The table shows that the highest BLEU score is 24.87, which corresponds to the configuration of layers 1-6, not 4-6. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/298.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Effect in encoder and decoder." ], "reasoning": "The table shows that the highest BLEU score is 24.87, which corresponds to the configuration of layers 1-6, not 4-6.", "statement": "The configuration of layers 4-6 achieved the highest BLEU score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Comparing with existing NMT systems on WMT17 Chinese-to-English (Zh-En) and WMT14 English-to-German (En-De) tasks.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The capsule-Transformer model consistently improves performance across language pairs and model variations, demonstrating its effectiveness and generalization ability.As shown in the table, our capsule-Transformer model consistently improves the performance across both language pairs and model variations, which shows the effectiveness and generalization ability of our approach. For WMT17 Zh-En task, our model outperforms all the models listed above, especially only the capsule-Transformer-Base model could achieve a score higher even than the other Big model. For WMT14 En-De task, our model outperforms the corresponding baseline while inferior to the Big model proposed by Li et al. Considering their model introduces over 33M new parameters (while for our Big model, this number is 1.6K) and uses a much larger batch size than ours (4096 vs. 1024) in the training process, it is reasonable for us to believe that our model would achieve a more promising score if the condition was the same." } ], "images": [ "scigen/train/299.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparing with existing NMT systems on WMT17 Chinese-to-English (Zh-En) and WMT14 English-to-German (En-De) tasks." ], "reasoning": "The capsule-Transformer model consistently improves performance across language pairs and model variations, demonstrating its effectiveness and generalization ability.", "description": "As shown in the table, our capsule-Transformer model consistently improves the performance across both language pairs and model variations, which shows the effectiveness and generalization ability of our approach. For WMT17 Zh-En task, our model outperforms all the models listed above, especially only the capsule-Transformer-Base model could achieve a score higher even than the other Big model. For WMT14 En-De task, our model outperforms the corresponding baseline while inferior to the Big model proposed by Li et al. Considering their model introduces over 33M new parameters (while for our Big model, this number is 1.6K) and uses a much larger batch size than ours (4096 vs. 1024) in the training process, it is reasonable for us to believe that our model would achieve a more promising score if the condition was the same.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 1: Comparing with existing NMT systems on WMT17 Chinese-to-English (Zh-En) and WMT14 English-to-German (En-De) tasks.', answer the following question. Give detailed solution consisting of each step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model achieved the highest score for the WMT17 Zh-En task?\n" }, { "role": "assistant", "content": "By comparing the scores for the WMT17 Zh-En task, the capsule-Transformer-Big model achieved the highest score of 25.14. So, the answer is {\"answer\": \"capsule-Transformer-Big\"}." } ], "images": [ "scigen/train/299.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparing with existing NMT systems on WMT17 Chinese-to-English (Zh-En) and WMT14 English-to-German (En-De) tasks." ], "reasoning": "By comparing the scores for the WMT17 Zh-En task, the capsule-Transformer-Big model achieved the highest score of 25.14.", "question": "Which model achieved the highest score for the WMT17 Zh-En task?", "answer": "So, the answer is {\"answer\": \"capsule-Transformer-Big\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe capsule-Transformer-Base model achieved a higher score than the Transformer-Big model for the WMT14 En-De task.\n" }, { "role": "assistant", "content": "The table shows that the capsule-Transformer-Base model scored 28.04, while the Transformer-Big model by Vaswani et al. (2017) scored 28.40 for the WMT14 En-De task. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/299.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparing with existing NMT systems on WMT17 Chinese-to-English (Zh-En) and WMT14 English-to-German (En-De) tasks." ], "reasoning": "The table shows that the capsule-Transformer-Base model scored 28.04, while the Transformer-Big model by Vaswani et al. (2017) scored 28.40 for the WMT14 En-De task.", "statement": "The capsule-Transformer-Base model achieved a higher score than the Transformer-Big model for the WMT14 En-De task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Command Accuracy (%) for different commands at different SERs.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The ASR-based tuning consistently outperforms the POLQA-based tuning across all SER levels and commands.4.2.2 Recording of Real-World Commands We used eight subjects (male/female, native/non-native) who uttered the command list at a distance of around 1m from the microphone of the Beats Pill™ portable speaker while music was playing. We used four different music tracks in the echo path, where the starting point of the track was chosen randomly. The playback level for the experiments was set to three different levels of 95 dB SPL, 90 dB SPL, and 85 dB SPL. We estimated the range of SER for the different setups to be approximately equal to −35 to −30 dB, −30 to −25 dB, and −25 to −20 dB for the three levels, respectively. The estimation of the SERs were made possible thanks to a lavalier microphone that recorded the near-end speech. Note that the SERs in the experiments are lower than the SERs used in the simulation, which validates the generalization of the tuning methodology. The results clearly show that our proposed tuning based on ASR maximization outperforms the POLQA-based tuning. The difference in performance seems to derive from the POLQA optimization being less aggressive on noise in order to preserve speech quality. More noise in the processed files translates into worse performance of the speech recognizer and the VAD. As a reference, when our speech enhancement front end was not used, the average recognition rate was 25% over all commands (coin toss) in the lowest SER setup." } ], "images": [ "scigen/train/300.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Command Accuracy (%) for different commands at different SERs." ], "reasoning": "The ASR-based tuning consistently outperforms the POLQA-based tuning across all SER levels and commands.", "description": "4.2.2 Recording of Real-World Commands We used eight subjects (male/female, native/non-native) who uttered the command list at a distance of around 1m from the microphone of the Beats Pill™ portable speaker while music was playing. We used four different music tracks in the echo path, where the starting point of the track was chosen randomly. The playback level for the experiments was set to three different levels of 95 dB SPL, 90 dB SPL, and 85 dB SPL. We estimated the range of SER for the different setups to be approximately equal to −35 to −30 dB, −30 to −25 dB, and −25 to −20 dB for the three levels, respectively. The estimation of the SERs were made possible thanks to a lavalier microphone that recorded the near-end speech. Note that the SERs in the experiments are lower than the SERs used in the simulation, which validates the generalization of the tuning methodology. The results clearly show that our proposed tuning based on ASR maximization outperforms the POLQA-based tuning. The difference in performance seems to derive from the POLQA optimization being less aggressive on noise in order to preserve speech quality. More noise in the processed files translates into worse performance of the speech recognizer and the VAD. As a reference, when our speech enhancement front end was not used, the average recognition rate was 25% over all commands (coin toss) in the lowest SER setup.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 2: Command Accuracy (%) for different commands at different SERs.'. Solve the following question based on the table.\n\nQuestion: What is the ASR recognition rate for the 'PLAY' command at the SER level of -30 to -25 dB?\nLet's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the ASR recognition rate for the 'PLAY' command at the SER level of -30 to -25 dB, locate the 'PLAY' row and the ASR column under the -30 to -25 dB SER level. So, the answer is {\"answer\": \"94\"}." } ], "images": [ "scigen/train/300.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Command Accuracy (%) for different commands at different SERs." ], "reasoning": "To find the ASR recognition rate for the 'PLAY' command at the SER level of -30 to -25 dB, locate the 'PLAY' row and the ASR column under the -30 to -25 dB SER level.", "question": "What is the ASR recognition rate for the 'PLAY' command at the SER level of -30 to -25 dB?", "answer": "So, the answer is {\"answer\": \"94\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe POLQA recognition rate for the 'NEXT' command at the SER level of -25 to -20 dB is 63.\n" }, { "role": "assistant", "content": "To verify this statement, locate the 'NEXT' row and the POLQA column under the -25 to -20 dB SER level. The value is 63, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/300.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Command Accuracy (%) for different commands at different SERs." ], "reasoning": "To verify this statement, locate the 'NEXT' row and the POLQA column under the -25 to -20 dB SER level. The value is 63, which matches the statement.", "statement": "The POLQA recognition rate for the 'NEXT' command at the SER level of -25 to -20 dB is 63.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: Phone Accuracy (%) for the noisy TIMIT database .'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The parameters were tuned to maximize the MOS using POLQA on a noisy TIMIT database, and performance was assessed on a second simulated noisy TIMIT database with different conditions.In order to provide a fair comparison, we also tuned the parameters to maximize the mean opinion score (MOS) using the Perceptual Objective Listening Quality Assessment (POLQA) , through the same GA setup and the same noisy TIMIT database. To assess the performance of our tuning method, we tested on data not used in the training by creating a second simulated noisy TIMIT database with different conditions. The SER and SNR were again chosen from uniform distributions ranging from −15 dB to −10 dB and from −10 dB to 10 dB, respectively. The “mix” noise was picked randomly from the babble, music, or factory noise. In the case of music, noisy files were generated from a set of tracks from different genres at different start points. When the front-end speech enhancer was not used, the PAR dropped to 10.1% (unigram) and 15.7% (bigram) for the noisy signal." } ], "images": [ "scigen/train/301.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Phone Accuracy (%) for the noisy TIMIT database ." ], "reasoning": "The parameters were tuned to maximize the MOS using POLQA on a noisy TIMIT database, and performance was assessed on a second simulated noisy TIMIT database with different conditions.", "description": "In order to provide a fair comparison, we also tuned the parameters to maximize the mean opinion score (MOS) using the Perceptual Objective Listening Quality Assessment (POLQA) , through the same GA setup and the same noisy TIMIT database. To assess the performance of our tuning method, we tested on data not used in the training by creating a second simulated noisy TIMIT database with different conditions. The SER and SNR were again chosen from uniform distributions ranging from −15 dB to −10 dB and from −10 dB to 10 dB, respectively. The “mix” noise was picked randomly from the babble, music, or factory noise. In the case of music, noisy files were generated from a set of tracks from different genres at different start points. When the front-end speech enhancer was not used, the PAR dropped to 10.1% (unigram) and 15.7% (bigram) for the noisy signal.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 1: Phone Accuracy (%) for the noisy TIMIT database .' in mind, break down and resolve the question below step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhat is the ASR score for the bigram model with babble noise?\n" }, { "role": "assistant", "content": "To find the ASR score for the bigram model with babble noise, look at the intersection of the 'babble' column and the 'ASR' row under the 'bi.' sub-column. Thus, the answer is {\"answer\": \"37.0\"}." } ], "images": [ "scigen/train/301.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Phone Accuracy (%) for the noisy TIMIT database ." ], "reasoning": "To find the ASR score for the bigram model with babble noise, look at the intersection of the 'babble' column and the 'ASR' row under the 'bi.' sub-column.", "question": "What is the ASR score for the bigram model with babble noise?", "answer": "Thus, the answer is {\"answer\": \"37.0\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe POLQA score for the unigram model with factory noise is higher than the ASR score for the same model.\n" }, { "role": "assistant", "content": "To verify this statement, compare the POLQA score and the ASR score for the unigram model with factory noise. The POLQA score is 21.4, and the ASR score is also 21.6. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/301.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Phone Accuracy (%) for the noisy TIMIT database ." ], "reasoning": "To verify this statement, compare the POLQA score and the ASR score for the unigram model with factory noise. The POLQA score is 21.4, and the ASR score is also 21.6.", "statement": "The POLQA score for the unigram model with factory noise is higher than the ASR score for the same model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Per-grid-point FLOPs of the finite-difference TTI wave-equation stencil with different spatial discretization orders.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Optimizations significantly reduce the number of floating-point operations needed for the weighted rotated Laplacian, making the anisotropic wave-equation more efficient to implement.Owing to the very high number of floating-point operations (FLOPs) needed per grid point for the weighted rotated Laplacian, this anisotropic wave-equation is extremely challenging to implement. The version without FLOP-reducing optimizations is a direct translation of the discretized operators into stencil expressions (see tti-so8-unoptimized.c). The version with optimizations employs transformations such as common sub-expressions elimination, factorization, and cross-iteration redundancy elimination – the latter being key in removing redundancies introduced by mixed derivatives. Implementing all of these techniques manually is inherently difficult and laborious. Further, to obtain the desired performance improvements it is necessary to orchestrate them with aggressive loop fusion (for data locality), tiling (for data locality and tensor temporaries), and potentially ad-hoc vectorization strategies (if rotating registers are used). what is emphasized here is that users can easily take full advantage of these optimizations without needed to concern themselves with the details." } ], "images": [ "scigen/train/302.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Per-grid-point FLOPs of the finite-difference TTI wave-equation stencil with different spatial discretization orders." ], "reasoning": "Optimizations significantly reduce the number of floating-point operations needed for the weighted rotated Laplacian, making the anisotropic wave-equation more efficient to implement.", "description": "Owing to the very high number of floating-point operations (FLOPs) needed per grid point for the weighted rotated Laplacian, this anisotropic wave-equation is extremely challenging to implement. The version without FLOP-reducing optimizations is a direct translation of the discretized operators into stencil expressions (see tti-so8-unoptimized.c). The version with optimizations employs transformations such as common sub-expressions elimination, factorization, and cross-iteration redundancy elimination – the latter being key in removing redundancies introduced by mixed derivatives. Implementing all of these techniques manually is inherently difficult and laborious. Further, to obtain the desired performance improvements it is necessary to orchestrate them with aggressive loop fusion (for data locality), tiling (for data locality and tensor temporaries), and potentially ad-hoc vectorization strategies (if rotating registers are used). what is emphasized here is that users can easily take full advantage of these optimizations without needed to concern themselves with the details.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'TABLE I: Per-grid-point FLOPs of the finite-difference TTI wave-equation stencil with different spatial discretization orders.', answer the following question. Give detailed solution consisting of each step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the reduction in floating-point operations for a spatial order of 16 when optimizations are applied?\n" }, { "role": "assistant", "content": "For a spatial order of 16, the number of floating-point operations without optimizations is 5489, and with optimizations, it is 276. The reduction is 5489 - 276. So, the answer is {\"answer\": \"5213\"}." } ], "images": [ "scigen/train/302.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Per-grid-point FLOPs of the finite-difference TTI wave-equation stencil with different spatial discretization orders." ], "reasoning": "For a spatial order of 16, the number of floating-point operations without optimizations is 5489, and with optimizations, it is 276. The reduction is 5489 - 276.", "question": "What is the reduction in floating-point operations for a spatial order of 16 when optimizations are applied?", "answer": "So, the answer is {\"answer\": \"5213\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nOptimizations reduce the floating-point operations by more than 90% for a spatial order of 4.\n" }, { "role": "assistant", "content": "For a spatial order of 4, the number of floating-point operations without optimizations is 501, and with optimizations, it is 95. The reduction percentage is ((501 - 95) / 501) * 100. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/302.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Per-grid-point FLOPs of the finite-difference TTI wave-equation stencil with different spatial discretization orders." ], "reasoning": "For a spatial order of 4, the number of floating-point operations without optimizations is 501, and with optimizations, it is 95. The reduction percentage is ((501 - 95) / 501) * 100.", "statement": "Optimizations reduce the floating-point operations by more than 90% for a spatial order of 4.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 6: Accuracy scores of the German–English models on the ContraWSD and MuCoW test suites.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The model with 6 decoder layers and fixed attentive patterns (7Ftoken+1L) achieves higher accuracy than the model with all learnable attention heads (8L), while the 1-layer decoder models show the opposite effect.Overall, the model with 6 decoder layers and fixed attentive patterns (7Ftoken+1L) achieves higher accuracy than the model with all learnable attention heads (8L), while the 1-layer decoder models show the opposite effect. It appears that having 6 decoder layers can effectively cope with WSD despite having only one learnable attention head. Interestingly enough, when we disable the learnable attention head (7Ftoken H8 disabled), performance drops consistently in both test suites, showing that the learnable head plays a key role for WSD, specializing in semantic feature extraction." } ], "images": [ "scigen/train/303.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Accuracy scores of the German–English models on the ContraWSD and MuCoW test suites." ], "reasoning": "The model with 6 decoder layers and fixed attentive patterns (7Ftoken+1L) achieves higher accuracy than the model with all learnable attention heads (8L), while the 1-layer decoder models show the opposite effect.", "description": "Overall, the model with 6 decoder layers and fixed attentive patterns (7Ftoken+1L) achieves higher accuracy than the model with all learnable attention heads (8L), while the 1-layer decoder models show the opposite effect. It appears that having 6 decoder layers can effectively cope with WSD despite having only one learnable attention head. Interestingly enough, when we disable the learnable attention head (7Ftoken H8 disabled), performance drops consistently in both test suites, showing that the learnable head plays a key role for WSD, specializing in semantic feature extraction.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nTable title: Table 6: Accuracy scores of the German–English models on the ContraWSD and MuCoW test suites.\n\nQuestion: Which model configuration achieves the highest accuracy on the ContraWSD 6+6 test suite?\n" }, { "role": "assistant", "content": "By comparing the accuracy values in the ContraWSD 6+6 column, the highest accuracy is achieved by the model with 7Ftoken+1L configuration, which is 0.834. So, the answer is {\"answer\": \"7Ftoken+1L\"}" } ], "images": [ "scigen/train/303.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Accuracy scores of the German–English models on the ContraWSD and MuCoW test suites." ], "reasoning": "By comparing the accuracy values in the ContraWSD 6+6 column, the highest accuracy is achieved by the model with 7Ftoken+1L configuration, which is 0.834.", "question": "Which model configuration achieves the highest accuracy on the ContraWSD 6+6 test suite?", "answer": "So, the answer is {\"answer\": \"7Ftoken+1L\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The model with 7Ftoken+1L configuration achieves the highest accuracy on the MuCoW 6+6 test suite., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model with 7Ftoken+1L configuration achieves the highest accuracy on the MuCoW 6+6 test suite.\n" }, { "role": "assistant", "content": "By comparing the accuracy values in the MuCoW 6+6 column, the highest accuracy is achieved by the model with 7Ftoken+1L configuration, which is 0.772. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/303.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Accuracy scores of the German–English models on the ContraWSD and MuCoW test suites." ], "reasoning": "By comparing the accuracy values in the MuCoW 6+6 column, the highest accuracy is achieved by the model with 7Ftoken+1L configuration, which is 0.772.", "statement": "The model with 7Ftoken+1L configuration achieves the highest accuracy on the MuCoW 6+6 test suite.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3. Average ratings of volunteers for original and generated memes (baseline and ours) across themes. ARO represents average ratings for original memes, ARG represents average ratings for generated memes (our model), and ARB represents average ratings for generated memes (baseline model).'.\n" }, { "role": "assistant", "content": "The study shows that the quality of memes generated by our model is almost on par with original memes and outperforms the baseline model across all themes.To evaluate user satisfaction levels, we conducted a rating study. We generated a batch of 100 memes for each theme by randomly picking classes within each theme, individually for the baseline model and our model. For each theme, we showed a set of 5 generated memes from our model, 5 generated memes from the baseline model, and 5 original memes, in a mixed order (without revealing the identity of the original and generated memes), to each volunteer. We asked them to rate the generated and original memes from a range of 1 (lowest rating) to 5 (highest rating) based on caption-content, humour and originality. We observe that the quality of our generated memes is almost on par with the original memes. We also notice that our model outperforms the baseline model, across all themes. Therefore, we maintain this on a holistic level; all of the volunteers were satisfied with the quality of the generated memes as well." } ], "images": [ "scigen/train/304.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. Average ratings of volunteers for original and generated memes (baseline and ours) across themes. ARO represents average ratings for original memes, ARG represents average ratings for generated memes (our model), and ARB represents average ratings for generated memes (baseline model)." ], "reasoning": "The study shows that the quality of memes generated by our model is almost on par with original memes and outperforms the baseline model across all themes.", "description": "To evaluate user satisfaction levels, we conducted a rating study. We generated a batch of 100 memes for each theme by randomly picking classes within each theme, individually for the baseline model and our model. For each theme, we showed a set of 5 generated memes from our model, 5 generated memes from the baseline model, and 5 original memes, in a mixed order (without revealing the identity of the original and generated memes), to each volunteer. We asked them to rate the generated and original memes from a range of 1 (lowest rating) to 5 (highest rating) based on caption-content, humour and originality. We observe that the quality of our generated memes is almost on par with the original memes. We also notice that our model outperforms the baseline model, across all themes. Therefore, we maintain this on a holistic level; all of the volunteers were satisfied with the quality of the generated memes as well.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3. Average ratings of volunteers for original and generated memes (baseline and ours) across themes. ARO represents average ratings for original memes, ARG represents average ratings for generated memes (our model), and ARB represents average ratings for generated memes (baseline model).' to sequentially solve the question presented below. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the overall rating for the memes generated by the baseline model?\n" }, { "role": "assistant", "content": "The table provides the overall ratings for original memes (ARO), our generated memes (ARG), and baseline model generated memes (ARB). The overall rating for the baseline model generated memes is listed under ARB. So, the answer is {\"answer\": \"2.98\"}." } ], "images": [ "scigen/train/304.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. Average ratings of volunteers for original and generated memes (baseline and ours) across themes. ARO represents average ratings for original memes, ARG represents average ratings for generated memes (our model), and ARB represents average ratings for generated memes (baseline model)." ], "reasoning": "The table provides the overall ratings for original memes (ARO), our generated memes (ARG), and baseline model generated memes (ARB). The overall rating for the baseline model generated memes is listed under ARB.", "question": "What is the overall rating for the memes generated by the baseline model?", "answer": "So, the answer is {\"answer\": \"2.98\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe baseline model received the highest rating for the Savage theme.\n" }, { "role": "assistant", "content": "The table shows the ratings for each theme. For the Savage theme, the baseline model (ARB) received a rating of 3.4, which is the highest rating among all themes for the baseline model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/304.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. Average ratings of volunteers for original and generated memes (baseline and ours) across themes. ARO represents average ratings for original memes, ARG represents average ratings for generated memes (our model), and ARB represents average ratings for generated memes (baseline model)." ], "reasoning": "The table shows the ratings for each theme. For the Savage theme, the baseline model (ARB) received a rating of 3.4, which is the highest rating among all themes for the baseline model.", "statement": "The baseline model received the highest rating for the Savage theme.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Performance of discriminative models on VisDial v1.0 test-std. Higher is better for MRR and recall@k, while lower is better for mean rank and NDCG. (*) denotes use of external knowledge.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our submission improved all metrics except for NDCG by using an ensemble of 5 trained Factor Graph Attention models.Our submission to the challenge significantly improved all metrics except for NDCG. We report our results in Tab. While the challenge did allow use of any external resources to improve the model, we only changed our approach to use an ensemble of 5 trained Factor Graph Attention models which were initialized randomly. These features are expensive to extract, and use external detector information." } ], "images": [ "scigen/train/305.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of discriminative models on VisDial v1.0 test-std. Higher is better for MRR and recall@k, while lower is better for mean rank and NDCG. (*) denotes use of external knowledge." ], "reasoning": "Our submission improved all metrics except for NDCG by using an ensemble of 5 trained Factor Graph Attention models.", "description": "Our submission to the challenge significantly improved all metrics except for NDCG. We report our results in Tab. While the challenge did allow use of any external resources to improve the model, we only changed our approach to use an ensemble of 5 trained Factor Graph Attention models which were initialized randomly. These features are expensive to extract, and use external detector information.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 3: Performance of discriminative models on VisDial v1.0 test-std. Higher is better for MRR and recall@k, while lower is better for mean rank and NDCG. (*) denotes use of external knowledge.\nQuestion: Which model achieved the highest Mean score and what was the score?\n" }, { "role": "assistant", "content": "By examining the table, the model '5×FGA (F-RCNNx101)' achieved the highest Mean score, which is 3.14. So, the answer is {\"answer\": \"5×FGA (F-RCNNx101) with a score of 3.14\"}" } ], "images": [ "scigen/train/305.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of discriminative models on VisDial v1.0 test-std. Higher is better for MRR and recall@k, while lower is better for mean rank and NDCG. (*) denotes use of external knowledge." ], "reasoning": "By examining the table, the model '5×FGA (F-RCNNx101)' achieved the highest Mean score, which is 3.14.", "question": "Which model achieved the highest Mean score and what was the score?", "answer": "So, the answer is {\"answer\": \"5×FGA (F-RCNNx101) with a score of 3.14\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model '5×FGA (F-RCNNx101)' achieved the highest NDCG score.\n" }, { "role": "assistant", "content": "The table shows that the model 'NMN (ResNet-152)' achieved the highest NDCG score of 0.581, not '5×FGA (F-RCNNx101)'. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/305.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of discriminative models on VisDial v1.0 test-std. Higher is better for MRR and recall@k, while lower is better for mean rank and NDCG. (*) denotes use of external knowledge." ], "reasoning": "The table shows that the model 'NMN (ResNet-152)' achieved the highest NDCG score of 0.581, not '5×FGA (F-RCNNx101)'.", "statement": "The model '5×FGA (F-RCNNx101)' achieved the highest NDCG score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: Performance of discriminative models on VisDial v0.9. Higher is better for MRR and recall@k, while lower is better for mean rank. (*) denotes use of external knowledge.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Our model outperforms others by using a general attention mechanism, allowing it to attend to the entire set of cues in the dataset.Visual question answering comparison: We first compare against a variety of baselines (see Tab. Note that almost all of the baselines (except LF, HRE and MN and SF-QIH-se-2) use attention, i.e., attention is an important element in any model. Note that our model uses the entire set of answers to predict each answer ’s score, i.e., we use p(ui|A,I,Q,C,H) This is in contrast to SF-QIH-se-2, which doesn’t use attention and models p(ui|^ui,I,Q,C,H). Because CoAtt-GAN uses a hierarchical approach, the ability to further improve the reasoning system is challenging and manual work. In contrast, our general attention mechanism allows to attend to the entire set of cues in the dataset, letting the model automatically choose the more relevant cues. We refer the readers to the appendix for analysis of utility-importance via importance score. As can be seen from Tab. We also report an ensemble of 9 models which differ only by the initial seed. We emphasize that our approach only uses VGG16. Lastly, some baselines report to use GloVe to initialize the word embeddings, while we didn’t use any pre-trained embedding weights." } ], "images": [ "scigen/train/306.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance of discriminative models on VisDial v0.9. Higher is better for MRR and recall@k, while lower is better for mean rank. (*) denotes use of external knowledge." ], "reasoning": "Our model outperforms others by using a general attention mechanism, allowing it to attend to the entire set of cues in the dataset.", "description": "Visual question answering comparison: We first compare against a variety of baselines (see Tab. Note that almost all of the baselines (except LF, HRE and MN and SF-QIH-se-2) use attention, i.e., attention is an important element in any model. Note that our model uses the entire set of answers to predict each answer ’s score, i.e., we use p(ui|A,I,Q,C,H) This is in contrast to SF-QIH-se-2, which doesn’t use attention and models p(ui|^ui,I,Q,C,H). Because CoAtt-GAN uses a hierarchical approach, the ability to further improve the reasoning system is challenging and manual work. In contrast, our general attention mechanism allows to attend to the entire set of cues in the dataset, letting the model automatically choose the more relevant cues. We refer the readers to the appendix for analysis of utility-importance via importance score. As can be seen from Tab. We also report an ensemble of 9 models which differ only by the initial seed. We emphasize that our approach only uses VGG16. Lastly, some baselines report to use GloVe to initialize the word embeddings, while we didn’t use any pre-trained embedding weights.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: Performance of discriminative models on VisDial v0.9. Higher is better for MRR and recall@k, while lower is better for mean rank. (*) denotes use of external knowledge.', answer the question below by showing each progressive step toward the answer. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest Mean Reciprocal Rank (MRR) and what is its value?\n" }, { "role": "assistant", "content": "To find the model with the highest MRR, we look at the 'MRR' column and identify the highest value, which is 0.6892 for the model '9×FGA (VGG)'. So, the answer is {\"answer\": \"9×FGA (VGG) with MRR of 0.6892\"}" } ], "images": [ "scigen/train/306.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance of discriminative models on VisDial v0.9. Higher is better for MRR and recall@k, while lower is better for mean rank. (*) denotes use of external knowledge." ], "reasoning": "To find the model with the highest MRR, we look at the 'MRR' column and identify the highest value, which is 0.6892 for the model '9×FGA (VGG)'.", "question": "Which model has the highest Mean Reciprocal Rank (MRR) and what is its value?", "answer": "So, the answer is {\"answer\": \"9×FGA (VGG) with MRR of 0.6892\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe model CoAtt-GAN w/ R inte-TF has the highest R@10 score.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the R@10 scores of all models. The highest R@10 score is 92.95, which belongs to the model '9×FGA (VGG)', not 'CoAtt-GAN w/ R inte-TF'. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/306.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance of discriminative models on VisDial v0.9. Higher is better for MRR and recall@k, while lower is better for mean rank. (*) denotes use of external knowledge." ], "reasoning": "To verify this statement, we compare the R@10 scores of all models. The highest R@10 score is 92.95, which belongs to the model '9×FGA (VGG)', not 'CoAtt-GAN w/ R inte-TF'.", "statement": "The model CoAtt-GAN w/ R inte-TF has the highest R@10 score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Performance on the question generation task. Higher is better for MRR and recall@k, while lower is better for mean rank.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The FGA model outperforms the SF-QIH-se-2 model in MRR, R@1, R@5, and R@10, but has a slightly lower Mean score.We adapted to this task, by changing the input utilities to the previous interaction (Q+A)t−1 instead of the current question Qt. Our model also improves previous state-of-the-art results (see Tab." } ], "images": [ "scigen/train/307.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance on the question generation task. Higher is better for MRR and recall@k, while lower is better for mean rank." ], "reasoning": "The FGA model outperforms the SF-QIH-se-2 model in MRR, R@1, R@5, and R@10, but has a slightly lower Mean score.", "description": "We adapted to this task, by changing the input utilities to the previous interaction (Q+A)t−1 instead of the current question Qt. Our model also improves previous state-of-the-art results (see Tab.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: Performance on the question generation task. Higher is better for MRR and recall@k, while lower is better for mean rank.', and tackle the question below in a sequential manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model has the highest R@5 score?\n" }, { "role": "assistant", "content": "To determine which model has the highest R@5 score, compare the R@5 values for both models. The FGA model has an R@5 score of 56.33, while the SF-QIH-se-2 model has an R@5 score of 55.17. Therefore, the FGA model has the highest R@5 score. So, the answer is {\"answer\": \"FGA\"}." } ], "images": [ "scigen/train/307.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance on the question generation task. Higher is better for MRR and recall@k, while lower is better for mean rank." ], "reasoning": "To determine which model has the highest R@5 score, compare the R@5 values for both models. The FGA model has an R@5 score of 56.33, while the SF-QIH-se-2 model has an R@5 score of 55.17. Therefore, the FGA model has the highest R@5 score.", "question": "Which model has the highest R@5 score?", "answer": "So, the answer is {\"answer\": \"FGA\"}." }, { "messages": [ { "role": "user", "content": "The SF-QIH-se-2 model has a higher MRR score than the FGA model. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe SF-QIH-se-2 model has a higher MRR score than the FGA model.\n" }, { "role": "assistant", "content": "To verify this statement, compare the MRR scores of both models. The SF-QIH-se-2 model has an MRR score of 0.4060, while the FGA model has an MRR score of 0.4138. Since 0.4138 is greater than 0.4060, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/307.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance on the question generation task. Higher is better for MRR and recall@k, while lower is better for mean rank." ], "reasoning": "To verify this statement, compare the MRR scores of both models. The SF-QIH-se-2 model has an MRR score of 0.4060, while the FGA model has an MRR score of 0.4138. Since 0.4138 is greater than 0.4060, the statement is incorrect.", "statement": "The SF-QIH-se-2 model has a higher MRR score than the FGA model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Case-insensitive BLEU scores (%) for Chinese-to-English translation on NIST datasets. “+CSH” denotes model only trained under the supervision of child attentional adjacency matrix (β = 0). “+PSH” denotes model only trained under the supervision of parent attentional adjacency matrix (α = 0). “+CSH+PSH” is trained under the supervision of both.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The Transformer model outperforms all RNN-based methods, and incorporating child and parent dependencies further improves its performance.We report on case-insensitive BLEU here since English words are lowercased. From the table we can see that syntax-aware RNN models always outperform the RNNsearch baseline. However, the performance of the Transformer is much higher than that of all RNN-based methods. In Transformer+CSH, we use only the child attentional adjacency matrix to guide the encoder. Being aware of child dependencies, Transformer+CSH gains 1.0 BLEU point improvement over the Transformer baseline on the average. Transformer+PSH, in which only the parent attentional adjacency matrix is used as supervision, also achieves about 1.0 BLEU point improvement. After combination, the new Transformer+CSH+PSH can further improve BLEU by about 0.5 point on the average, which significantly outperforms the baseline and other source syntax-based methods in all test sets. This demonstrates that both child dependencies and parent dependencies benefit the Transformer model and their effects can be accumulated." } ], "images": [ "scigen/train/308.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Case-insensitive BLEU scores (%) for Chinese-to-English translation on NIST datasets. “+CSH” denotes model only trained under the supervision of child attentional adjacency matrix (β = 0). “+PSH” denotes model only trained under the supervision of parent attentional adjacency matrix (α = 0). “+CSH+PSH” is trained under the supervision of both." ], "reasoning": "The Transformer model outperforms all RNN-based methods, and incorporating child and parent dependencies further improves its performance.", "description": "We report on case-insensitive BLEU here since English words are lowercased. From the table we can see that syntax-aware RNN models always outperform the RNNsearch baseline. However, the performance of the Transformer is much higher than that of all RNN-based methods. In Transformer+CSH, we use only the child attentional adjacency matrix to guide the encoder. Being aware of child dependencies, Transformer+CSH gains 1.0 BLEU point improvement over the Transformer baseline on the average. Transformer+PSH, in which only the parent attentional adjacency matrix is used as supervision, also achieves about 1.0 BLEU point improvement. After combination, the new Transformer+CSH+PSH can further improve BLEU by about 0.5 point on the average, which significantly outperforms the baseline and other source syntax-based methods in all test sets. This demonstrates that both child dependencies and parent dependencies benefit the Transformer model and their effects can be accumulated.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Case-insensitive BLEU scores (%) for Chinese-to-English translation on NIST datasets. “+CSH” denotes model only trained under the supervision of child attentional adjacency matrix (β = 0). “+PSH” denotes model only trained under the supervision of parent attentional adjacency matrix (α = 0). “+CSH+PSH” is trained under the supervision of both.\nQuestion: What is the average BLEU score improvement of Transformer+CSH+PSH over the Transformer baseline?\nGive a detailed response to the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The average BLEU score for Transformer is 37.10, and for Transformer+CSH+PSH it is 38.63. The improvement is 38.63 - 37.10 = 1.53. So, the answer is {\"answer\": \"1.53\"}" } ], "images": [ "scigen/train/308.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Case-insensitive BLEU scores (%) for Chinese-to-English translation on NIST datasets. “+CSH” denotes model only trained under the supervision of child attentional adjacency matrix (β = 0). “+PSH” denotes model only trained under the supervision of parent attentional adjacency matrix (α = 0). “+CSH+PSH” is trained under the supervision of both." ], "reasoning": "The average BLEU score for Transformer is 37.10, and for Transformer+CSH+PSH it is 38.63. The improvement is 38.63 - 37.10 = 1.53.", "question": "What is the average BLEU score improvement of Transformer+CSH+PSH over the Transformer baseline?", "answer": "So, the answer is {\"answer\": \"1.53\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Transformer+CSH model achieves a higher average BLEU score than the SE-NMT model.\n" }, { "role": "assistant", "content": "The average BLEU score for Transformer+CSH is 38.14, while for SE-NMT it is 33.63. Therefore, Transformer+CSH has a higher average BLEU score. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/308.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Case-insensitive BLEU scores (%) for Chinese-to-English translation on NIST datasets. “+CSH” denotes model only trained under the supervision of child attentional adjacency matrix (β = 0). “+PSH” denotes model only trained under the supervision of parent attentional adjacency matrix (α = 0). “+CSH+PSH” is trained under the supervision of both." ], "reasoning": "The average BLEU score for Transformer+CSH is 38.14, while for SE-NMT it is 33.63. Therefore, Transformer+CSH has a higher average BLEU score.", "statement": "The Transformer+CSH model achieves a higher average BLEU score than the SE-NMT model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Evaluation results on the English-to-Japanese translation task.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Transformer+CSH and Transformer+PSH outperform Transformer and other NMT models in both BLEU and RIBES scores.We conduct experiments on the WAT2016 English-to-Japanese translation task in this section. Our baseline systems include RNNsearch, a tree2seq attentional NMT model using tree-LSTM proposed by Eriguchi et al. (2016) and Transformer. According to the table, our Transformer+CSH and Transformer+PSH outperform Transformer and the other existing NMT models in terms of both BLEU and RIBES." } ], "images": [ "scigen/train/309.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Evaluation results on the English-to-Japanese translation task." ], "reasoning": "Transformer+CSH and Transformer+PSH outperform Transformer and other NMT models in both BLEU and RIBES scores.", "description": "We conduct experiments on the WAT2016 English-to-Japanese translation task in this section. Our baseline systems include RNNsearch, a tree2seq attentional NMT model using tree-LSTM proposed by Eriguchi et al. (2016) and Transformer. According to the table, our Transformer+CSH and Transformer+PSH outperform Transformer and the other existing NMT models in terms of both BLEU and RIBES.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: Evaluation results on the English-to-Japanese translation task.'.\n\nQuestion: Which system has the highest BLEU score and what is the score?\n" }, { "role": "assistant", "content": "By examining the BLEU scores in the table, the system with the highest BLEU score is Transformer+CSH+PSH with a score of 37.22. So, the answer is {\"answer\": \"37.22\"}." } ], "images": [ "scigen/train/309.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Evaluation results on the English-to-Japanese translation task." ], "reasoning": "By examining the BLEU scores in the table, the system with the highest BLEU score is Transformer+CSH+PSH with a score of 37.22.", "question": "Which system has the highest BLEU score and what is the score?", "answer": "So, the answer is {\"answer\": \"37.22\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Transformer model has a higher RIBES score than the RNNsearch model.\n" }, { "role": "assistant", "content": "The table shows that the RIBES score for the Transformer model is 81.90, while the RNNsearch model has a RIBES score of 80.92. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/309.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Evaluation results on the English-to-Japanese translation task." ], "reasoning": "The table shows that the RIBES score for the Transformer model is 81.90, while the RNNsearch model has a RIBES score of 80.92. Therefore, the statement is supported.", "statement": "The Transformer model has a higher RIBES score than the RNNsearch model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: BLEU scores (%) for Chinese-to-English (Zh-En), English-to-Chinese (En-Zh) translation on WMT2017 datasets and English-to-German (En-De) task. Both char-level BLEU (CBLEU) and word-level BLEU (WBLEU) are used as metrics for the En-Zh task.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The proposed method improves BLEU scores across multiple translation tasks, with the highest gains in the English-to-Chinese task.To verify the effect of syntax knowledge on large-scale translation tasks, we further conduct three experiments on the WMT2017 bidirectional English-Chinese tasks and WMT2014 English-to-German. For the Chinese-to-English, our proposed method outperforms baseline by 0.86 BLEU score. For the English-to-Chinese task, the Transformer+CSH+PSH gains 0.91 and 1.05 improvements on char-level BLEU and word-level BLEU respectively. For En-De task, the improvement is 0.6 which is not as much as the other two. We speculate that as the grammars of English and German are very similar, the original model can capture the syntactic knowledge well. Even though, the improvement still illustrates the effectiveness of our method." } ], "images": [ "scigen/train/310.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: BLEU scores (%) for Chinese-to-English (Zh-En), English-to-Chinese (En-Zh) translation on WMT2017 datasets and English-to-German (En-De) task. Both char-level BLEU (CBLEU) and word-level BLEU (WBLEU) are used as metrics for the En-Zh task." ], "reasoning": "The proposed method improves BLEU scores across multiple translation tasks, with the highest gains in the English-to-Chinese task.", "description": "To verify the effect of syntax knowledge on large-scale translation tasks, we further conduct three experiments on the WMT2017 bidirectional English-Chinese tasks and WMT2014 English-to-German. For the Chinese-to-English, our proposed method outperforms baseline by 0.86 BLEU score. For the English-to-Chinese task, the Transformer+CSH+PSH gains 0.91 and 1.05 improvements on char-level BLEU and word-level BLEU respectively. For En-De task, the improvement is 0.6 which is not as much as the other two. We speculate that as the grammars of English and German are very similar, the original model can capture the syntactic knowledge well. Even though, the improvement still illustrates the effectiveness of our method.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: BLEU scores (%) for Chinese-to-English (Zh-En), English-to-Chinese (En-Zh) translation on WMT2017 datasets and English-to-German (En-De) task. Both char-level BLEU (CBLEU) and word-level BLEU (WBLEU) are used as metrics for the En-Zh task.', answer the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the BLEU score improvement for the Chinese-to-English task using the proposed method?\n" }, { "role": "assistant", "content": "The BLEU score for the Chinese-to-English task using the Transformer is 21.29, and with the proposed method (+CSH+PSH), it is 22.15. The improvement is 22.15 - 21.29 = 0.86. So, the answer is {\"answer\": \"0.86\"}" } ], "images": [ "scigen/train/310.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: BLEU scores (%) for Chinese-to-English (Zh-En), English-to-Chinese (En-Zh) translation on WMT2017 datasets and English-to-German (En-De) task. Both char-level BLEU (CBLEU) and word-level BLEU (WBLEU) are used as metrics for the En-Zh task." ], "reasoning": "The BLEU score for the Chinese-to-English task using the Transformer is 21.29, and with the proposed method (+CSH+PSH), it is 22.15. The improvement is 22.15 - 21.29 = 0.86.", "question": "What is the BLEU score improvement for the Chinese-to-English task using the proposed method?", "answer": "So, the answer is {\"answer\": \"0.86\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe proposed method improves the English-to-German BLEU score by 0.6.\n" }, { "role": "assistant", "content": "The BLEU score for the English-to-German task using the Transformer is 25.71, and with the proposed method (+CSH+PSH), it is 26.31. The improvement is 26.31 - 25.71 = 0.6. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/310.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: BLEU scores (%) for Chinese-to-English (Zh-En), English-to-Chinese (En-Zh) translation on WMT2017 datasets and English-to-German (En-De) task. Both char-level BLEU (CBLEU) and word-level BLEU (WBLEU) are used as metrics for the En-Zh task." ], "reasoning": "The BLEU score for the English-to-German task using the Transformer is 25.71, and with the proposed method (+CSH+PSH), it is 26.31. The improvement is 26.31 - 25.71 = 0.6.", "statement": "The proposed method improves the English-to-German BLEU score by 0.6.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: Results of various models on FewRel and TACRED (%).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Pre-training models like BERT and ERNIE significantly improve F1 scores over CNN, with ERNIE achieving the highest scores in both FewRel and TACRED datasets.As FewRel does not have any null instance where there is not any relation between entities, we adopt macro averaged metrics to present the model performances. Since FewRel is built by checking whether the sentences contain facts in Wikidata, we drop the related facts in KGs before pre-training for fair comparison. As the training data does not have enough instances to train the CNN encoder from scratch, CNN just achieves an F1 score of 69.35%. However, the pre-training models including BERT and ERNIE increase the F1 score by at least 15%. (2) ERNIE achieves an absolute F1 increase of 3.4% over BERT, which means fusing external knowledge is very effective. In TACRED, there are nearly 80% null instances so that we follow the previous work Zhang et al. The results of CNN, PA-LSTM, and C-GCN come from the paper by \\newcitezhang2018graph, which are the best results of CNN, RNN, and GCN respectively. The entity mask strategy refers to replacing each subject (and object similarly) entity with a special NER token, which is similar to our proposed pre-training task dEA. (2) ERNIE achieves the best recall and F1 scores, and increases the F1 of BERT by nearly 2.0%, which proves the effectiveness of the knowledgeable module for relation classification." } ], "images": [ "scigen/train/311.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results of various models on FewRel and TACRED (%)." ], "reasoning": "Pre-training models like BERT and ERNIE significantly improve F1 scores over CNN, with ERNIE achieving the highest scores in both FewRel and TACRED datasets.", "description": "As FewRel does not have any null instance where there is not any relation between entities, we adopt macro averaged metrics to present the model performances. Since FewRel is built by checking whether the sentences contain facts in Wikidata, we drop the related facts in KGs before pre-training for fair comparison. As the training data does not have enough instances to train the CNN encoder from scratch, CNN just achieves an F1 score of 69.35%. However, the pre-training models including BERT and ERNIE increase the F1 score by at least 15%. (2) ERNIE achieves an absolute F1 increase of 3.4% over BERT, which means fusing external knowledge is very effective. In TACRED, there are nearly 80% null instances so that we follow the previous work Zhang et al. The results of CNN, PA-LSTM, and C-GCN come from the paper by \\newcitezhang2018graph, which are the best results of CNN, RNN, and GCN respectively. The entity mask strategy refers to replacing each subject (and object similarly) entity with a special NER token, which is similar to our proposed pre-training task dEA. (2) ERNIE achieves the best recall and F1 scores, and increases the F1 of BERT by nearly 2.0%, which proves the effectiveness of the knowledgeable module for relation classification.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 5: Results of various models on FewRel and TACRED (%).' and proceed to solve the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model achieves the highest F1 score in the TACRED dataset?\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all models in the TACRED dataset, ERNIE achieves the highest F1 score of 67.97. So, the answer is {\"answer\": \"ERNIE\"}" } ], "images": [ "scigen/train/311.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results of various models on FewRel and TACRED (%)." ], "reasoning": "By comparing the F1 scores of all models in the TACRED dataset, ERNIE achieves the highest F1 score of 67.97.", "question": "Which model achieves the highest F1 score in the TACRED dataset?", "answer": "So, the answer is {\"answer\": \"ERNIE\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nBERT achieves a higher F1 score than ERNIE in the FewRel dataset.\n" }, { "role": "assistant", "content": "The table shows that ERNIE has an F1 score of 88.32 in the FewRel dataset, which is higher than BERT's F1 score of 84.89. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/311.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results of various models on FewRel and TACRED (%)." ], "reasoning": "The table shows that ERNIE has an F1 score of 88.32 in the FewRel dataset, which is higher than BERT's F1 score of 84.89.", "statement": "BERT achieves a higher F1 score than ERNIE in the FewRel dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Results of various models on FIGER (%).', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "BERT achieves comparable macro and micro metrics with NFGEC but has lower accuracy, while ERNIE significantly improves strict accuracy over BERT, indicating better handling of noisy labels.From the results, we observe that: (1) BERT achieves comparable results with NFGEC on the macro and micro metrics. However, BERT has lower accuracy than the best NFGEC model. As strict accuracy is the ratio of instances whose predictions are identical to human annotations, it illustrates some wrong labels from distant supervision are learned by BERT due to its powerful fitting ability. (2) Compared with BERT, ERNIE significantly improves the strict accuracy, indicating the external knowledge regularizes ERNIE to avoid fitting the noisy labels and accordingly benefits entity typing." } ], "images": [ "scigen/train/312.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of various models on FIGER (%)." ], "reasoning": "BERT achieves comparable macro and micro metrics with NFGEC but has lower accuracy, while ERNIE significantly improves strict accuracy over BERT, indicating better handling of noisy labels.", "description": "From the results, we observe that: (1) BERT achieves comparable results with NFGEC on the macro and micro metrics. However, BERT has lower accuracy than the best NFGEC model. As strict accuracy is the ratio of instances whose predictions are identical to human annotations, it illustrates some wrong labels from distant supervision are learned by BERT due to its powerful fitting ability. (2) Compared with BERT, ERNIE significantly improves the strict accuracy, indicating the external knowledge regularizes ERNIE to avoid fitting the noisy labels and accordingly benefits entity typing.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Results of various models on FIGER (%).', and sequentially solve the question, writing out each step of your reasoning process. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model has the highest strict accuracy and what does this indicate about its performance?\n" }, { "role": "assistant", "content": "ERNIE has the highest strict accuracy at 57.19, indicating that it better avoids fitting noisy labels compared to other models. So, the answer is {\"answer\": \"ERNIE\"}" } ], "images": [ "scigen/train/312.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of various models on FIGER (%)." ], "reasoning": "ERNIE has the highest strict accuracy at 57.19, indicating that it better avoids fitting noisy labels compared to other models.", "question": "Which model has the highest strict accuracy and what does this indicate about its performance?", "answer": "So, the answer is {\"answer\": \"ERNIE\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nBERT has the highest macro metric score.\n" }, { "role": "assistant", "content": "The table shows that ERNIE has the highest macro metric score at 76.51, not BERT. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/312.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of various models on FIGER (%)." ], "reasoning": "The table shows that ERNIE has the highest macro metric score at 76.51, not BERT.", "statement": "BERT has the highest macro metric score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Results of various models on Open Entity (%).'.\n" }, { "role": "assistant", "content": "ERNIE outperforms BERT by 2% in both precision and recall, indicating that informative entities enhance ERNIE's label prediction accuracy.From the table, we observe that: (1) BERT and ERNIE achieve much higher recall scores than the previous entity typing models, which means pre-training language models make full use of both the unsupervised pre-training and manually-annotated training data for better entity typing. (2) Compared to BERT, ERNIE improves the precision by 2% and the recall by 2%, which means the informative entities help ERNIE predict the labels more precisely." } ], "images": [ "scigen/train/313.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of various models on Open Entity (%)." ], "reasoning": "ERNIE outperforms BERT by 2% in both precision and recall, indicating that informative entities enhance ERNIE's label prediction accuracy.", "description": "From the table, we observe that: (1) BERT and ERNIE achieve much higher recall scores than the previous entity typing models, which means pre-training language models make full use of both the unsupervised pre-training and manually-annotated training data for better entity typing. (2) Compared to BERT, ERNIE improves the precision by 2% and the recall by 2%, which means the informative entities help ERNIE predict the labels more precisely.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: Results of various models on Open Entity (%).', answer the following question. Give detailed solution consisting of each step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model achieves the highest F1 score and by how much does it surpass the second highest model?\n" }, { "role": "assistant", "content": "From the table, ERNIE has the highest F1 score of 75.56. The second highest F1 score is 73.56 by BERT. The difference is 75.56 - 73.56 = 2.00. So, the answer is {\"answer\": \"ERNIE by 2.00\"}" } ], "images": [ "scigen/train/313.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of various models on Open Entity (%)." ], "reasoning": "From the table, ERNIE has the highest F1 score of 75.56. The second highest F1 score is 73.56 by BERT. The difference is 75.56 - 73.56 = 2.00.", "question": "Which model achieves the highest F1 score and by how much does it surpass the second highest model?", "answer": "So, the answer is {\"answer\": \"ERNIE by 2.00\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nBERT has a higher recall score than UFET.\n" }, { "role": "assistant", "content": "According to the table, BERT has a recall score of 70.96, while UFET has a recall score of 60.60. Therefore, BERT has a higher recall score than UFET. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/313.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of various models on Open Entity (%)." ], "reasoning": "According to the table, BERT has a recall score of 70.96, while UFET has a recall score of 60.60. Therefore, BERT has a higher recall score than UFET.", "statement": "BERT has a higher recall score than UFET.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 6: Results of BERT and ERNIE on different tasks of GLUE (%).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "ERNIE performs similarly to BERTBASE on large datasets but shows better performance on CoLA and RTE, and worse on STS-B and MRPC.We notice that ERNIE is consistent with BERTBASE on big datasets like MNLI, QQP, QNLI, and SST-2. The results become more unstable on small datasets, that is, ERNIE is better on CoLA and RTE, but worse on STS-B and MRPC." } ], "images": [ "scigen/train/314.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results of BERT and ERNIE on different tasks of GLUE (%)." ], "reasoning": "ERNIE performs similarly to BERTBASE on large datasets but shows better performance on CoLA and RTE, and worse on STS-B and MRPC.", "description": "We notice that ERNIE is consistent with BERTBASE on big datasets like MNLI, QQP, QNLI, and SST-2. The results become more unstable on small datasets, that is, ERNIE is better on CoLA and RTE, but worse on STS-B and MRPC.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: Results of BERT and ERNIE on different tasks of GLUE (%).\nQuestion: Which model performs better on the RTE dataset?\nGive a detailed response to the above question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the RTE scores of both models, ERNIE has a score of 68.8 while BERTBASE has a score of 66.4, indicating ERNIE performs better. So, the answer is {\"answer\": \"ERNIE\"}." } ], "images": [ "scigen/train/314.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results of BERT and ERNIE on different tasks of GLUE (%)." ], "reasoning": "By comparing the RTE scores of both models, ERNIE has a score of 68.8 while BERTBASE has a score of 66.4, indicating ERNIE performs better.", "question": "Which model performs better on the RTE dataset?", "answer": "So, the answer is {\"answer\": \"ERNIE\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nERNIE outperforms BERTBASE on the CoLA dataset.\n" }, { "role": "assistant", "content": "The table shows that ERNIE has a score of 52.3 on CoLA, while BERTBASE has a score of 52.1, indicating ERNIE performs better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/314.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results of BERT and ERNIE on different tasks of GLUE (%)." ], "reasoning": "The table shows that ERNIE has a score of 52.3 on CoLA, while BERTBASE has a score of 52.1, indicating ERNIE performs better.", "statement": "ERNIE outperforms BERTBASE on the CoLA dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Ablation study on FewRel (%).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "ERNIE with informative entities and dEA pre-training task increases the F1 score compared to BERT and ERNIE without these features.In this subsection, we explore the effects of the informative entities and the knowledgeable pre-training task (dEA) for ERNIE using FewRel dataset. w/o entities and w/o dEA refer to fine-tuning ERNIE without entity sequence input and the pre-training task dEA respectively. still injects knowledge information into language representation during pre-training, which increases the F1 score of BERT by 0.9%. (2) Although the informative entities bring much knowledge information which intuitively benefits relation classification, ERNIE without dEA takes little advantage of this, leading to the F1 increase of 0.7%." } ], "images": [ "scigen/train/315.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Ablation study on FewRel (%)." ], "reasoning": "ERNIE with informative entities and dEA pre-training task increases the F1 score compared to BERT and ERNIE without these features.", "description": "In this subsection, we explore the effects of the informative entities and the knowledgeable pre-training task (dEA) for ERNIE using FewRel dataset. w/o entities and w/o dEA refer to fine-tuning ERNIE without entity sequence input and the pre-training task dEA respectively. still injects knowledge information into language representation during pre-training, which increases the F1 score of BERT by 0.9%. (2) Although the informative entities bring much knowledge information which intuitively benefits relation classification, ERNIE without dEA takes little advantage of this, leading to the F1 increase of 0.7%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 7: Ablation study on FewRel (%).', unfold the steps to solve the question given next. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nQuestion: What is the F1 score increase of ERNIE compared to BERT?\n" }, { "role": "assistant", "content": "The F1 score of BERT is 84.89, and the F1 score of ERNIE is 88.32. The increase is 88.32 - 84.89. So, the answer is {\"answer\": \"3.43\"}" } ], "images": [ "scigen/train/315.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Ablation study on FewRel (%)." ], "reasoning": "The F1 score of BERT is 84.89, and the F1 score of ERNIE is 88.32. The increase is 88.32 - 84.89.", "question": "What is the F1 score increase of ERNIE compared to BERT?", "answer": "So, the answer is {\"answer\": \"3.43\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nERNIE without dEA has a higher F1 score than BERT.\n" }, { "role": "assistant", "content": "The F1 score of ERNIE without dEA is 85.62, which is higher than BERT's F1 score of 84.89. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/315.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Ablation study on FewRel (%)." ], "reasoning": "The F1 score of ERNIE without dEA is 85.62, which is higher than BERT's F1 score of 84.89.", "statement": "ERNIE without dEA has a higher F1 score than BERT.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: Fluency of reviews (in MOS). Bold font indicates highest score.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Fine-tuning existing models can improve fluency and effectiveness, with sentiment modeling achieving the highest scores for Amazon reviews, while original reviews scored higher for Yelp.The fine-tuning improved the fluency compared with that of the reviews generated by the pre-trained GPT-2. This suggests that an attack can be made more effective by simply fine-tuning existing models. For the Amazon dataset, the reviews generated by explicitly modeling the sentiment (sentiment modeling) had the highest overall score, followed by those generated by the fine-tuned GPT-2 model. Interestingly, the scores for all fake review were higher than that for the original review. This observation is similar to that of Yao et al. This observation does not hold for the Yelp database — the score for the original reviews is higher than those for the fake ones. Among the fake review generation models, the fine-tuned GPT-2 model had the highest score (3.30)." } ], "images": [ "scigen/train/316.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Fluency of reviews (in MOS). Bold font indicates highest score." ], "reasoning": "Fine-tuning existing models can improve fluency and effectiveness, with sentiment modeling achieving the highest scores for Amazon reviews, while original reviews scored higher for Yelp.", "description": "The fine-tuning improved the fluency compared with that of the reviews generated by the pre-trained GPT-2. This suggests that an attack can be made more effective by simply fine-tuning existing models. For the Amazon dataset, the reviews generated by explicitly modeling the sentiment (sentiment modeling) had the highest overall score, followed by those generated by the fine-tuned GPT-2 model. Interestingly, the scores for all fake review were higher than that for the original review. This observation is similar to that of Yao et al. This observation does not hold for the Yelp database — the score for the original reviews is higher than those for the fake ones. Among the fake review generation models, the fine-tuned GPT-2 model had the highest score (3.30).", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 5: Fluency of reviews (in MOS). Bold font indicates highest score.' and methodically tackle the question that follows. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model had the highest overall score for Amazon reviews?\n" }, { "role": "assistant", "content": "The table shows the overall scores for Amazon reviews. Sentiment modeling had the highest overall score of 3.47. So, the answer is {\"answer\": \"Sentiment modeling\"}" } ], "images": [ "scigen/train/316.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Fluency of reviews (in MOS). Bold font indicates highest score." ], "reasoning": "The table shows the overall scores for Amazon reviews. Sentiment modeling had the highest overall score of 3.47.", "question": "Which model had the highest overall score for Amazon reviews?", "answer": "So, the answer is {\"answer\": \"Sentiment modeling\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe fine-tuned GPT-2 model had the highest overall score for Yelp reviews.\n" }, { "role": "assistant", "content": "The table shows the overall scores for Yelp reviews. The fine-tuned GPT-2 model had an overall score of 3.30, which is not the highest. The original review had the highest overall score of 3.49. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/316.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Fluency of reviews (in MOS). Bold font indicates highest score." ], "reasoning": "The table shows the overall scores for Yelp reviews. The fine-tuned GPT-2 model had an overall score of 3.30, which is not the highest. The original review had the highest overall score of 3.49.", "statement": "The fine-tuned GPT-2 model had the highest overall score for Yelp reviews.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Rate (in %) and standard error of fake reviews preserving sentiment of original review.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Sentiment modeling had the highest rate for Amazon reviews, while mLSTM performed best for Yelp reviews, indicating the importance of explicitly modeling sentiment and preserving context.This means that a large number of fake reviews can be efficiently generated with a desired sentiment by just fine-tuning an LM. The sentiment modeling method had the highest rate for the Amazon database. This was because explicitly modeling sentiment benefits from the additional sentiment information given before the fake reviews are generated. This indicates that explicitly modeling sentiment could be a more efficient way to generate desired sentiment reviews. For the Yelp reviews, fine-tuned GPT-2 was also clearly better than the pretrained GPT-2 and the mLSTM had the highest rate. Further analysis revealed that the mLSTM model performs very well only for food and restaurant reviews but it did not generalize well to other domains or it generates reviews completely outside the context of the original review. This suggests that we need to further explicitly preserve context. (We leave this for future work.)" } ], "images": [ "scigen/train/317.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Rate (in %) and standard error of fake reviews preserving sentiment of original review." ], "reasoning": "Sentiment modeling had the highest rate for Amazon reviews, while mLSTM performed best for Yelp reviews, indicating the importance of explicitly modeling sentiment and preserving context.", "description": "This means that a large number of fake reviews can be efficiently generated with a desired sentiment by just fine-tuning an LM. The sentiment modeling method had the highest rate for the Amazon database. This was because explicitly modeling sentiment benefits from the additional sentiment information given before the fake reviews are generated. This indicates that explicitly modeling sentiment could be a more efficient way to generate desired sentiment reviews. For the Yelp reviews, fine-tuned GPT-2 was also clearly better than the pretrained GPT-2 and the mLSTM had the highest rate. Further analysis revealed that the mLSTM model performs very well only for food and restaurant reviews but it did not generalize well to other domains or it generates reviews completely outside the context of the original review. This suggests that we need to further explicitly preserve context. (We leave this for future work.)", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: Table 3: Rate (in %) and standard error of fake reviews preserving sentiment of original review.\nQuestion: Which language model had the highest rate for Amazon reviews and why?\n" }, { "role": "assistant", "content": "The sentiment modeling method had the highest rate for the Amazon database because explicitly modeling sentiment benefits from the additional sentiment information given before the fake reviews are generated. So, the answer is {\"answer\": \"Sentiment modeling\"}." } ], "images": [ "scigen/train/317.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Rate (in %) and standard error of fake reviews preserving sentiment of original review." ], "reasoning": "The sentiment modeling method had the highest rate for the Amazon database because explicitly modeling sentiment benefits from the additional sentiment information given before the fake reviews are generated.", "question": "Which language model had the highest rate for Amazon reviews and why?", "answer": "So, the answer is {\"answer\": \"Sentiment modeling\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nFine-tuned GPT-2 had the highest rate for Yelp reviews.\n" }, { "role": "assistant", "content": "The table shows that mLSTM had the highest rate for Yelp reviews, not fine-tuned GPT-2. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/317.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Rate (in %) and standard error of fake reviews preserving sentiment of original review." ], "reasoning": "The table shows that mLSTM had the highest rate for Yelp reviews, not fine-tuned GPT-2.", "statement": "Fine-tuned GPT-2 had the highest rate for Yelp reviews.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: Word error rates of monolingual sgmm and 10-lingual TDNN ASR system evaluated on the development sets.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The multilingual model shows small but consistent improvements for all languages except Vietnamese, and the focus is on generalizing to zero-resource applications on unseen languages.The multilingual model shows small but consistent improvements for all languages except Vietnamese. Ultimately though, we are not so much interested in the performance on typical \\glsasr tasks, but in whether \\glsbnfs from this model also generalize to zero-resource applications on unseen languages." } ], "images": [ "scigen/train/318.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Word error rates of monolingual sgmm and 10-lingual TDNN ASR system evaluated on the development sets." ], "reasoning": "The multilingual model shows small but consistent improvements for all languages except Vietnamese, and the focus is on generalizing to zero-resource applications on unseen languages.", "description": "The multilingual model shows small but consistent improvements for all languages except Vietnamese. Ultimately though, we are not so much interested in the performance on typical \\glsasr tasks, but in whether \\glsbnfs from this model also generalize to zero-resource applications on unseen languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: Word error rates of monolingual sgmm and 10-lingual TDNN ASR system evaluated on the development sets.'. Solve the following question based on the table.\n\nQuestion: Which language shows the smallest improvement in the multilingual model compared to the monolingual model?\nLet's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the Mono and Multi columns, we see that DE (German) has the smallest improvement, with Mono at 9.6 and Multi at 9.3, a difference of 0.3. So, the answer is {\"answer\": \"DE\"}." } ], "images": [ "scigen/train/318.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Word error rates of monolingual sgmm and 10-lingual TDNN ASR system evaluated on the development sets." ], "reasoning": "By comparing the Mono and Multi columns, we see that DE (German) has the smallest improvement, with Mono at 9.6 and Multi at 9.3, a difference of 0.3.", "question": "Which language shows the smallest improvement in the multilingual model compared to the monolingual model?", "answer": "So, the answer is {\"answer\": \"DE\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe multilingual model shows the largest improvement for the French language.\n" }, { "role": "assistant", "content": "By comparing the Mono and Multi columns, we see that the French language (FR) has a difference of 0.5 (24.5 - 24.0), which is not the largest improvement. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/318.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Word error rates of monolingual sgmm and 10-lingual TDNN ASR system evaluated on the development sets." ], "reasoning": "By comparing the Mono and Multi columns, we see that the French language (FR) has a difference of 0.5 (24.5 - 24.0), which is not the largest improvement. Therefore, the statement is refuted.", "statement": "The multilingual model shows the largest improvement for the French language.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Average precision scores on the same-different task (dev sets), showing the effects of applying vtln to the input features for the utd and/or cae systems. cae input is either MFCC or MFCC+VTLN. Topline results (rows 5-6) train cAE on gold standard pairs, rather than UTD output. Baseline results (final rows) directly evaluate acoustic features without UTD/cAE training. Best unsupervised result in bold.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Applying VTLN to both UTD and cAE inputs improves performance, suggesting that VTLN and cAE training abstract different aspects of the speech signal.We find that \\glscae features as trained previously are slightly better than MFCC+VTLN, but can be improved considerably by applying \\glsvtln to the input of both \\glsutd and \\glscae training—indeed, even using gold pairs as \\glscae input applying VTLN is beneficial. This suggests that \\glscae training and VTLN abstract over different aspects of the speech signal, and that both should be used when only target language data is available." } ], "images": [ "scigen/train/319.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Average precision scores on the same-different task (dev sets), showing the effects of applying vtln to the input features for the utd and/or cae systems. cae input is either MFCC or MFCC+VTLN. Topline results (rows 5-6) train cAE on gold standard pairs, rather than UTD output. Baseline results (final rows) directly evaluate acoustic features without UTD/cAE training. Best unsupervised result in bold." ], "reasoning": "Applying VTLN to both UTD and cAE inputs improves performance, suggesting that VTLN and cAE training abstract different aspects of the speech signal.", "description": "We find that \\glscae features as trained previously are slightly better than MFCC+VTLN, but can be improved considerably by applying \\glsvtln to the input of both \\glsutd and \\glscae training—indeed, even using gold pairs as \\glscae input applying VTLN is beneficial. This suggests that \\glscae training and VTLN abstract over different aspects of the speech signal, and that both should be used when only target language data is available.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 2: Average precision scores on the same-different task (dev sets), showing the effects of applying vtln to the input features for the utd and/or cae systems. cae input is either MFCC or MFCC+VTLN. Topline results (rows 5-6) train cAE on gold standard pairs, rather than UTD output. Baseline results (final rows) directly evaluate acoustic features without UTD/cAE training. Best unsupervised result in bold.', solve the following question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the highest score achieved in the ZH column and under which UTD and cAE input combination?\n" }, { "role": "assistant", "content": "To find the highest score in the ZH column, we look at all the values in the ZH column and identify the highest one, which is 69.5. This score is achieved under the 'Gold pairs + VTLN' combination. So, the answer is {\"answer\": \"69.5\"}." } ], "images": [ "scigen/train/319.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Average precision scores on the same-different task (dev sets), showing the effects of applying vtln to the input features for the utd and/or cae systems. cae input is either MFCC or MFCC+VTLN. Topline results (rows 5-6) train cAE on gold standard pairs, rather than UTD output. Baseline results (final rows) directly evaluate acoustic features without UTD/cAE training. Best unsupervised result in bold." ], "reasoning": "To find the highest score in the ZH column, we look at all the values in the ZH column and identify the highest one, which is 69.5. This score is achieved under the 'Gold pairs + VTLN' combination.", "question": "What is the highest score achieved in the ZH column and under which UTD and cAE input combination?", "answer": "So, the answer is {\"answer\": \"69.5\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe combination of PLP+VTLN for UTD input and +VTLN for cAE input achieves the highest score in the ES column.\n" }, { "role": "assistant", "content": "The highest score in the ES column is 68.9, which is achieved under the 'Gold pairs + VTLN' combination, not the 'PLP+VTLN + VTLN' combination. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/319.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Average precision scores on the same-different task (dev sets), showing the effects of applying vtln to the input features for the utd and/or cae systems. cae input is either MFCC or MFCC+VTLN. Topline results (rows 5-6) train cAE on gold standard pairs, rather than UTD output. Baseline results (final rows) directly evaluate acoustic features without UTD/cAE training. Best unsupervised result in bold." ], "reasoning": "The highest score in the ES column is 68.9, which is achieved under the 'Gold pairs + VTLN' combination, not the 'PLP+VTLN + VTLN' combination.", "statement": "The combination of PLP+VTLN for UTD input and +VTLN for cAE input achieves the highest score in the ES column.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Word error rates of monolingual sgmm and 10-lingual TDNN ASR system evaluated on the development sets.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The multilingual model shows consistent improvements for all languages except Vietnamese, where the performance slightly decreases.The multilingual model shows small but consistent improvements for all languages except Vietnamese. Ultimately though, we are not so much interested in the performance on typical \\glsasr tasks, but in whether \\glsbnfs from this model also generalize to zero-resource applications on unseen languages." } ], "images": [ "scigen/train/320.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Word error rates of monolingual sgmm and 10-lingual TDNN ASR system evaluated on the development sets." ], "reasoning": "The multilingual model shows consistent improvements for all languages except Vietnamese, where the performance slightly decreases.", "description": "The multilingual model shows small but consistent improvements for all languages except Vietnamese. Ultimately though, we are not so much interested in the performance on typical \\glsasr tasks, but in whether \\glsbnfs from this model also generalize to zero-resource applications on unseen languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Word error rates of monolingual sgmm and 10-lingual TDNN ASR system evaluated on the development sets.', solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich language shows the smallest improvement in performance from Mono to Multi?\n" }, { "role": "assistant", "content": "By comparing the Mono and Multi columns, Vietnamese shows a slight decrease from 11.3 to 11.6, indicating the smallest improvement. So, the answer is {\"answer\": \"VI\"}" } ], "images": [ "scigen/train/320.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Word error rates of monolingual sgmm and 10-lingual TDNN ASR system evaluated on the development sets." ], "reasoning": "By comparing the Mono and Multi columns, Vietnamese shows a slight decrease from 11.3 to 11.6, indicating the smallest improvement.", "question": "Which language shows the smallest improvement in performance from Mono to Multi?", "answer": "So, the answer is {\"answer\": \"VI\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The multilingual model shows improvement for all languages. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe multilingual model shows improvement for all languages.\n" }, { "role": "assistant", "content": "The table shows that the performance for Vietnamese slightly decreases from 11.3 to 11.6, refuting the statement. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/320.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Word error rates of monolingual sgmm and 10-lingual TDNN ASR system evaluated on the development sets." ], "reasoning": "The table shows that the performance for Vietnamese slightly decreases from 11.3 to 11.6, refuting the statement.", "statement": "The multilingual model shows improvement for all languages.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: ap on the same-different task when training cae on the 10-lingual bnfs from above (cAE-BNF) with UTD and gold standard word pairs (test set results). Baselines are MFCC+VTLN and the cAE models from rows 4 and 6 of Table 2 that use MFCC+VTLN as input features. Best result without target language supervision in bold.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Replacing VTLN-adapted MFCCs with 10-lingual BNFs as input features improves the cAE features in all languages when using gold standard pairs.We trained the \\glscae with the same sets of same-word pairs as before, but replaced VTLN-adapted MFCCs with the 10-lingual \\glsbnfs as input features without any other changes in the training procedure. The limiting factor appears to be the quality of the \\glsutd pairs. With gold standard pairs, the \\glscae features improve in all languages." } ], "images": [ "scigen/train/321.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: ap on the same-different task when training cae on the 10-lingual bnfs from above (cAE-BNF) with UTD and gold standard word pairs (test set results). Baselines are MFCC+VTLN and the cAE models from rows 4 and 6 of Table 2 that use MFCC+VTLN as input features. Best result without target language supervision in bold." ], "reasoning": "Replacing VTLN-adapted MFCCs with 10-lingual BNFs as input features improves the cAE features in all languages when using gold standard pairs.", "description": "We trained the \\glscae with the same sets of same-word pairs as before, but replaced VTLN-adapted MFCCs with the 10-lingual \\glsbnfs as input features without any other changes in the training procedure. The limiting factor appears to be the quality of the \\glsutd pairs. With gold standard pairs, the \\glscae features improve in all languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: ap on the same-different task when training cae on the 10-lingual bnfs from above (cAE-BNF) with UTD and gold standard word pairs (test set results). Baselines are MFCC+VTLN and the cAE models from rows 4 and 6 of Table 2 that use MFCC+VTLN as input features. Best result without target language supervision in bold.\nQuestion: What is the highest score achieved using cAE-BNF UTD features?\nSolve the above question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the highest score achieved using cAE-BNF UTD features, look at the row labeled 'cAE-BNF UTD' and identify the highest value in that row. So, the answer is {\"answer\": \"78.8\"}." } ], "images": [ "scigen/train/321.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: ap on the same-different task when training cae on the 10-lingual bnfs from above (cAE-BNF) with UTD and gold standard word pairs (test set results). Baselines are MFCC+VTLN and the cAE models from rows 4 and 6 of Table 2 that use MFCC+VTLN as input features. Best result without target language supervision in bold." ], "reasoning": "To find the highest score achieved using cAE-BNF UTD features, look at the row labeled 'cAE-BNF UTD' and identify the highest value in that row.", "question": "What is the highest score achieved using cAE-BNF UTD features?", "answer": "So, the answer is {\"answer\": \"78.8\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe cAE gold features achieved a higher score in ZH than the cAE-BNF gold features.\n" }, { "role": "assistant", "content": "To verify this statement, compare the scores for ZH in the 'cAE gold' row and the 'cAE-BNF gold' row. The cAE gold score for ZH is 70.8, and the cAE-BNF gold score for ZH is 81.6. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/321.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: ap on the same-different task when training cae on the 10-lingual bnfs from above (cAE-BNF) with UTD and gold standard word pairs (test set results). Baselines are MFCC+VTLN and the cAE models from rows 4 and 6 of Table 2 that use MFCC+VTLN as input features. Best result without target language supervision in bold." ], "reasoning": "To verify this statement, compare the scores for ZH in the 'cAE gold' row and the 'cAE-BNF gold' row. The cAE gold score for ZH is 70.8, and the cAE-BNF gold score for ZH is 81.6.", "statement": "The cAE gold features achieved a higher score in ZH than the cAE-BNF gold features.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Development and test set results for shift-reduce dependency parser on Penn Treebank using only (s1, s0, q0) positional features.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Despite minimal design and few iterations, the parser matched state-of-the-art incremental parsers and slightly outperformed the state-of-the-art greedy parser.Despite the minimally designed feature representation, relatively few training iterations, and lack of pre-computed embeddings, the parser performed on par with state-of-the-art incremental dependency parsers, and slightly outperformed the state-of-the-art greedy parser." } ], "images": [ "scigen/train/322.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Development and test set results for shift-reduce dependency parser on Penn Treebank using only (s1, s0, q0) positional features." ], "reasoning": "Despite minimal design and few iterations, the parser matched state-of-the-art incremental parsers and slightly outperformed the state-of-the-art greedy parser.", "description": "Despite the minimally designed feature representation, relatively few training iterations, and lack of pre-computed embeddings, the parser performed on par with state-of-the-art incremental dependency parsers, and slightly outperformed the state-of-the-art greedy parser.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: 'Table 2: Development and test set results for shift-reduce dependency parser on Penn Treebank using only (s1, s0, q0) positional features.'\nQuestion: Which parser has the highest Test UAS score?\n" }, { "role": "assistant", "content": "To find the parser with the highest Test UAS score, compare the Test UAS scores of all parsers listed in the table. The highest Test UAS score is 93.99, achieved by Weiss et al. 2015 + Percept./Beam. So, the answer is {\"answer\": \"Weiss et al. 2015 + Percept./Beam\"}." } ], "images": [ "scigen/train/322.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Development and test set results for shift-reduce dependency parser on Penn Treebank using only (s1, s0, q0) positional features." ], "reasoning": "To find the parser with the highest Test UAS score, compare the Test UAS scores of all parsers listed in the table. The highest Test UAS score is 93.99, achieved by Weiss et al. 2015 + Percept./Beam.", "question": "Which parser has the highest Test UAS score?", "answer": "So, the answer is {\"answer\": \"Weiss et al. 2015 + Percept./Beam\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Bi-LSTM parser has a higher Dev UAS score than the Dyer et al. 2015 parser. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Bi-LSTM parser has a higher Dev UAS score than the Dyer et al. 2015 parser.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Dev UAS scores of the Bi-LSTM parser and the Dyer et al. 2015 parser. The Bi-LSTM parser has a Dev UAS score of 93.31, which is higher than the Dyer et al. 2015 parser's score of 93.2. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/322.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Development and test set results for shift-reduce dependency parser on Penn Treebank using only (s1, s0, q0) positional features." ], "reasoning": "To verify this statement, compare the Dev UAS scores of the Bi-LSTM parser and the Dyer et al. 2015 parser. The Bi-LSTM parser has a Dev UAS score of 93.31, which is higher than the Dyer et al. 2015 parser's score of 93.2.", "statement": "The Bi-LSTM parser has a higher Dev UAS score than the Dyer et al. 2015 parser.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Ablation studies on PTB dev set (wsj 22). Forward and backward context, and part-of-speech input were all critical to strong performace.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Performance improved by separating structural action decisions and arc label assignments, using separate classifiers for each.We found that performance could be improved, however, by factoring out the decision over structural actions (i.e., shift, left-reduce, or right-reduce) and the decision of which arc label to assign upon a reduce. We therefore use separate classifiers for those decisions, each with its own fully-connected hidden and output layers but sharing the underlying recurrent architecture. Using only word forms and no part-of-speech input similarly degraded performance." } ], "images": [ "scigen/train/323.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation studies on PTB dev set (wsj 22). Forward and backward context, and part-of-speech input were all critical to strong performace." ], "reasoning": "Performance improved by separating structural action decisions and arc label assignments, using separate classifiers for each.", "description": "We found that performance could be improved, however, by factoring out the decision over structural actions (i.e., shift, left-reduce, or right-reduce) and the decision of which arc label to assign upon a reduce. We therefore use separate classifiers for those decisions, each with its own fully-connected hidden and output layers but sharing the underlying recurrent architecture. Using only word forms and no part-of-speech input similarly degraded performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 3: Ablation studies on PTB dev set (wsj 22). Forward and backward context, and part-of-speech input were all critical to strong performace.', and tackle the question below in a sequential manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich parser has the highest UAS score?\n" }, { "role": "assistant", "content": "To find the parser with the highest UAS score, compare the UAS scores of all parsers listed in the table. The parser with the highest UAS score is 'Bi-LSTM Hierarchical' with a score of 93.31. So, the answer is {\"answer\": \"Bi-LSTM Hierarchical\"}." } ], "images": [ "scigen/train/323.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation studies on PTB dev set (wsj 22). Forward and backward context, and part-of-speech input were all critical to strong performace." ], "reasoning": "To find the parser with the highest UAS score, compare the UAS scores of all parsers listed in the table. The parser with the highest UAS score is 'Bi-LSTM Hierarchical' with a score of 93.31.", "question": "Which parser has the highest UAS score?", "answer": "So, the answer is {\"answer\": \"Bi-LSTM Hierarchical\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The parser with hierarchical actions has a higher LAS score than the parser with tag embeddings. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe parser with hierarchical actions has a higher LAS score than the parser with tag embeddings.\n" }, { "role": "assistant", "content": "The table shows that the parser with hierarchical actions has an LAS score of 90.96, while the parser with tag embeddings has an LAS score of 89.81. Since 90.96 is greater than 89.81, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/323.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation studies on PTB dev set (wsj 22). Forward and backward context, and part-of-speech input were all critical to strong performace." ], "reasoning": "The table shows that the parser with hierarchical actions has an LAS score of 90.96, while the parser with tag embeddings has an LAS score of 89.81. Since 90.96 is greater than 89.81, the statement is supported.", "statement": "The parser with hierarchical actions has a higher LAS score than the parser with tag embeddings.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: Test F-scores for constituency parsing on Penn Treebank and CTB-5.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The 2-Layer Bi-LSTM parser achieves the highest accuracy among greedy parsers for both English and Chinese.Although our work are definitely less accurate than those beam-search parsers, we achieve the highest accuracy among greedy parsers, for both English and Chinese." } ], "images": [ "scigen/train/324.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Test F-scores for constituency parsing on Penn Treebank and CTB-5." ], "reasoning": "The 2-Layer Bi-LSTM parser achieves the highest accuracy among greedy parsers for both English and Chinese.", "description": "Although our work are definitely less accurate than those beam-search parsers, we achieve the highest accuracy among greedy parsers, for both English and Chinese.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 5: Test F-scores for constituency parsing on Penn Treebank and CTB-5.', and sequentially solve the question, writing out each step of your reasoning process. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich parser achieves the highest accuracy among greedy parsers for both English and Chinese?\n" }, { "role": "assistant", "content": "By comparing the accuracy of all greedy parsers for both English and Chinese, the 2-Layer Bi-LSTM parser has the highest accuracy with 89.95 for English and 80.13 for Chinese. So, the answer is {\"answer\": \"2-Layer Bi-LSTM\"}" } ], "images": [ "scigen/train/324.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Test F-scores for constituency parsing on Penn Treebank and CTB-5." ], "reasoning": "By comparing the accuracy of all greedy parsers for both English and Chinese, the 2-Layer Bi-LSTM parser has the highest accuracy with 89.95 for English and 80.13 for Chinese.", "question": "Which parser achieves the highest accuracy among greedy parsers for both English and Chinese?", "answer": "So, the answer is {\"answer\": \"2-Layer Bi-LSTM\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Bi-LSTM parser achieves the highest accuracy among greedy parsers for both English and Chinese. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Bi-LSTM parser achieves the highest accuracy among greedy parsers for both English and Chinese.\n" }, { "role": "assistant", "content": "The table shows that the 2-Layer Bi-LSTM parser, not the Bi-LSTM parser, achieves the highest accuracy among greedy parsers for both English and Chinese. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/324.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Test F-scores for constituency parsing on Penn Treebank and CTB-5." ], "reasoning": "The table shows that the 2-Layer Bi-LSTM parser, not the Bi-LSTM parser, achieves the highest accuracy among greedy parsers for both English and Chinese.", "statement": "The Bi-LSTM parser achieves the highest accuracy among greedy parsers for both English and Chinese.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 6: Hyperparameters and training settings.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "All experiments used minimal hyperparameter tuning and applied dropout with p=0.5 to each LSTM layer's output.All experiments were conducted with minimal hyperparameter tuning. We also applied dropout (with p=0.5) to the output of each LSTM layer (separately for each connection in the case of the two-layer network)." } ], "images": [ "scigen/train/325.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Hyperparameters and training settings." ], "reasoning": "All experiments used minimal hyperparameter tuning and applied dropout with p=0.5 to each LSTM layer's output.", "description": "All experiments were conducted with minimal hyperparameter tuning. We also applied dropout (with p=0.5) to the output of each LSTM layer (separately for each connection in the case of the two-layer network).", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 6: Hyperparameters and training settings.', answer the question below by showing each progressive step toward the answer. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the dimensionality of the word embeddings used in the Dependency model?\n" }, { "role": "assistant", "content": "The table shows that the word embeddings for the Dependency model have a dimensionality of 50. So, the answer is {\"answer\": \"50\"}" } ], "images": [ "scigen/train/325.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Hyperparameters and training settings." ], "reasoning": "The table shows that the word embeddings for the Dependency model have a dimensionality of 50.", "question": "What is the dimensionality of the word embeddings used in the Dependency model?", "answer": "So, the answer is {\"answer\": \"50\"}" }, { "messages": [ { "role": "user", "content": "The Constituency model used a dropout rate of 0.5 for the LSTM output. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Constituency model used a dropout rate of 0.5 for the LSTM output.\n" }, { "role": "assistant", "content": "The table indicates that the Constituency model applied a dropout rate of 0.5 to the LSTM output. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/325.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Hyperparameters and training settings." ], "reasoning": "The table indicates that the Constituency model applied a dropout rate of 0.5 to the LSTM output.", "statement": "The Constituency model used a dropout rate of 0.5 for the LSTM output.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 6: Experimental results on dropped pronoun recovery. ∗ indicate that our approach is statistical significant over all the baselines (within 0.95 confidence interval using the t-test).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The NRM model outperforms other models in both DPI and DPG tasks on the Baidu Zhidao dataset, while the SOTA model performs well on the DPG task on the OntoNotes 4.0 dataset due to its smaller scale.We compare the performance of the proposed NRM and the baselines in both of the OntoNotes 4.0 and Baidu Zhidao datasets. the Baidu Zhidao dataset for DPG task. To compare the results of the SVMSL and SVMDL, we can see that the dense representation of the dropped hypothesis has better impact on the fine grained task, namely DPG rather than the DPI task. To see the results on the two datasets, we can see that the results on Baidu Zhidao dataset are better than those on OntoNotes 4.0 dataset. It may be caused by the different scales of the two datasets and the different category numbers. We also note that the SOTA, which is a feature engineering approach, performs good when the data scale is small, such as the experiment results of DPG on OntoNotes 4.0 dataset. It may illustrates that the feature engineering approach can fit the data better when the data scale is small. However, the feature engineering is a highly empirical process and also a complicated task." } ], "images": [ "scigen/train/326.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Experimental results on dropped pronoun recovery. ∗ indicate that our approach is statistical significant over all the baselines (within 0.95 confidence interval using the t-test)." ], "reasoning": "The NRM model outperforms other models in both DPI and DPG tasks on the Baidu Zhidao dataset, while the SOTA model performs well on the DPG task on the OntoNotes 4.0 dataset due to its smaller scale.", "description": "We compare the performance of the proposed NRM and the baselines in both of the OntoNotes 4.0 and Baidu Zhidao datasets. the Baidu Zhidao dataset for DPG task. To compare the results of the SVMSL and SVMDL, we can see that the dense representation of the dropped hypothesis has better impact on the fine grained task, namely DPG rather than the DPI task. To see the results on the two datasets, we can see that the results on Baidu Zhidao dataset are better than those on OntoNotes 4.0 dataset. It may be caused by the different scales of the two datasets and the different category numbers. We also note that the SOTA, which is a feature engineering approach, performs good when the data scale is small, such as the experiment results of DPG on OntoNotes 4.0 dataset. It may illustrates that the feature engineering approach can fit the data better when the data scale is small. However, the feature engineering is a highly empirical process and also a complicated task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 6: Experimental results on dropped pronoun recovery. ∗ indicate that our approach is statistical significant over all the baselines (within 0.95 confidence interval using the t-test).', solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest accuracy for the DPI task on the Baidu Zhidao dataset?\n" }, { "role": "assistant", "content": "By comparing the accuracy values for the DPI task on the Baidu Zhidao dataset, we see that the NRM model has the highest accuracy of 0.879. So, the answer is {\"answer\": \"NRM\"}." } ], "images": [ "scigen/train/326.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Experimental results on dropped pronoun recovery. ∗ indicate that our approach is statistical significant over all the baselines (within 0.95 confidence interval using the t-test)." ], "reasoning": "By comparing the accuracy values for the DPI task on the Baidu Zhidao dataset, we see that the NRM model has the highest accuracy of 0.879.", "question": "Which model has the highest accuracy for the DPI task on the Baidu Zhidao dataset?", "answer": "So, the answer is {\"answer\": \"NRM\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe SOTA model has the highest accuracy for the DPG task on the OntoNotes 4.0 dataset.\n" }, { "role": "assistant", "content": "By examining the accuracy values for the DPG task on the OntoNotes 4.0 dataset, we see that the SOTA model has an accuracy of 0.191, which is not the highest. The highest accuracy is 0.21 by the SVMDL model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/326.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Experimental results on dropped pronoun recovery. ∗ indicate that our approach is statistical significant over all the baselines (within 0.95 confidence interval using the t-test)." ], "reasoning": "By examining the accuracy values for the DPG task on the OntoNotes 4.0 dataset, we see that the SOTA model has an accuracy of 0.191, which is not the highest. The highest accuracy is 0.21 by the SVMDL model.", "statement": "The SOTA model has the highest accuracy for the DPG task on the OntoNotes 4.0 dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 7: Experimental results on zero pronoun resolution. ∗ indicate that our approach is statistical significant over all the baselines (within 0.95 confidence interval using the t-test).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The ZPSNN+NRM approach significantly outperforms the SOTA approach in overall F-score statistics.on zero pronoun resolution task. Meanwhile, the ZPSNN+NRM significantly outperforms the SOTA approach in statistics. For further analysis, we will show the case study on the following section." } ], "images": [ "scigen/train/327.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Experimental results on zero pronoun resolution. ∗ indicate that our approach is statistical significant over all the baselines (within 0.95 confidence interval using the t-test)." ], "reasoning": "The ZPSNN+NRM approach significantly outperforms the SOTA approach in overall F-score statistics.", "description": "on zero pronoun resolution task. Meanwhile, the ZPSNN+NRM significantly outperforms the SOTA approach in statistics. For further analysis, we will show the case study on the following section.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 7: Experimental results on zero pronoun resolution. ∗ indicate that our approach is statistical significant over all the baselines (within 0.95 confidence interval using the t-test).' and methodically tackle the question that follows. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the highest F-score achieved by the ZPSNN+NRM approach in any category?\n" }, { "role": "assistant", "content": "To find the highest F-score achieved by the ZPSNN+NRM approach, we need to look at the F column under the ZPSNN+NRM approach for each category and identify the maximum value. So, the answer is {\"answer\": \"39.9\"}." } ], "images": [ "scigen/train/327.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Experimental results on zero pronoun resolution. ∗ indicate that our approach is statistical significant over all the baselines (within 0.95 confidence interval using the t-test)." ], "reasoning": "To find the highest F-score achieved by the ZPSNN+NRM approach, we need to look at the F column under the ZPSNN+NRM approach for each category and identify the maximum value.", "question": "What is the highest F-score achieved by the ZPSNN+NRM approach in any category?", "answer": "So, the answer is {\"answer\": \"39.9\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe ZPSNN+NRM approach achieved an F-score of 39.9 in the TC category.\n" }, { "role": "assistant", "content": "The table shows that under the ZPSNN+NRM approach, the F-score for the TC category is 39.9, which supports the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/327.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Experimental results on zero pronoun resolution. ∗ indicate that our approach is statistical significant over all the baselines (within 0.95 confidence interval using the t-test)." ], "reasoning": "The table shows that under the ZPSNN+NRM approach, the F-score for the TC category is 39.9, which supports the statement.", "statement": "The ZPSNN+NRM approach achieved an F-score of 39.9 in the TC category.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: BLUE and ROUGE score'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The average BLEU score is calculated only on BLEU assessable sentences, while the average ROUGE score is calculated on sentences whose structure can be recognized and encoded by the system.Note that the average BLUE score is calculated only on BLEU assessable sentences, while average ROUGE score is calculated on the sentences whose structure can be recognized and encoded by our system. We note that the BLEU or ROUGE score might not be sufficiently high for a good quality translation. We believe that two reasons contribute to this low score. First, the present system uses fairly simple sentence structures. Second, it does not consider the use of relative clauses to enrich the sentences. This feature will be added to the next version of the system." } ], "images": [ "scigen/train/328.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: BLUE and ROUGE score" ], "reasoning": "The average BLEU score is calculated only on BLEU assessable sentences, while the average ROUGE score is calculated on sentences whose structure can be recognized and encoded by the system.", "description": "Note that the average BLUE score is calculated only on BLEU assessable sentences, while average ROUGE score is calculated on the sentences whose structure can be recognized and encoded by our system. We note that the BLEU or ROUGE score might not be sufficiently high for a good quality translation. We believe that two reasons contribute to this low score. First, the present system uses fairly simple sentence structures. Second, it does not consider the use of relative clauses to enrich the sentences. This feature will be added to the next version of the system.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 7: BLUE and ROUGE score', and tackle the question below in a sequential manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the highest BLEU score among the categories?\n" }, { "role": "assistant", "content": "To find the highest BLEU score, compare the BLEU scores for People, Mathematics, and Food & drink. The highest score is 52.2 in the Food & drink category. So, the answer is {\"answer\": \"52.2\"}." } ], "images": [ "scigen/train/328.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: BLUE and ROUGE score" ], "reasoning": "To find the highest BLEU score, compare the BLEU scores for People, Mathematics, and Food & drink. The highest score is 52.2 in the Food & drink category.", "question": "What is the highest BLEU score among the categories?", "answer": "So, the answer is {\"answer\": \"52.2\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe ROUGE-1 score for Mathematics is higher than the ROUGE-1 score for Food & drink.\n" }, { "role": "assistant", "content": "The ROUGE-1 score for Mathematics is 17.9, while the ROUGE-1 score for Food & drink is 14.6. Since 17.9 is higher than 14.6, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/328.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: BLUE and ROUGE score" ], "reasoning": "The ROUGE-1 score for Mathematics is 17.9, while the ROUGE-1 score for Food & drink is 14.6. Since 17.9 is higher than 14.6, the statement is supported.", "statement": "The ROUGE-1 score for Mathematics is higher than the ROUGE-1 score for Food & drink.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 6: BLEU assessable sentences', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The system effectively recognizes sentence structures in most cases, with only 2 out of 62 sentences in Mathematics being too complex to determine.With the small set of rules introducing in this paper to recognize sentence structure, there would be very limited 4-gram in the generated text appearing in original Wikipedia corpus. Therefore, we use BLEU-3 with equal weight distribution instead of BLEU-4 to assess the generated content. Out of 62 sentences from 3 portals, the system cannot determine the structure 2 sentences in Mathematics due to their complexity. This low number of failure shows that our 5 proposed sentence structures effectively act as a lower bound on sentence recognition module." } ], "images": [ "scigen/train/329.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: BLEU assessable sentences" ], "reasoning": "The system effectively recognizes sentence structures in most cases, with only 2 out of 62 sentences in Mathematics being too complex to determine.", "description": "With the small set of rules introducing in this paper to recognize sentence structure, there would be very limited 4-gram in the generated text appearing in original Wikipedia corpus. Therefore, we use BLEU-3 with equal weight distribution instead of BLEU-4 to assess the generated content. Out of 62 sentences from 3 portals, the system cannot determine the structure 2 sentences in Mathematics due to their complexity. This low number of failure shows that our 5 proposed sentence structures effectively act as a lower bound on sentence recognition module.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 6: BLEU assessable sentences' and proceed to solve the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nHow many sentences in the Mathematics portal were recognized by the system?\n" }, { "role": "assistant", "content": "The table shows that out of 24 sentences in the Mathematics portal, 22 were recognized by the system. So, the answer is {\"answer\": \"22\"}" } ], "images": [ "scigen/train/329.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: BLEU assessable sentences" ], "reasoning": "The table shows that out of 24 sentences in the Mathematics portal, 22 were recognized by the system.", "question": "How many sentences in the Mathematics portal were recognized by the system?", "answer": "So, the answer is {\"answer\": \"22\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The system recognized all sentences in the People portal. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe system recognized all sentences in the People portal.\n" }, { "role": "assistant", "content": "The table indicates that all 15 sentences in the People portal were recognized by the system. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/329.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: BLEU assessable sentences" ], "reasoning": "The table indicates that all 15 sentences in the People portal were recognized by the system.", "statement": "The system recognized all sentences in the People portal.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 9: Comparison to other systems for full diacritization', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Our system achieved a WER of 6.0% for MSA, outperforming the state-of-the-art system with a WER of 12.2%. For CA, our best system achieved a WER of 4.3%, significantly better than using our best MSA system for CA, which had a WER of 14.7%.As the results show for MSA, our overall diacritization WER is 6.0% while the state of the art system has a WER of 12.2%. As for CA, our best system produced an error rate of 4.3%, which is significantly better than using our best MSA system to diacritize CA." } ], "images": [ "scigen/train/330.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Comparison to other systems for full diacritization" ], "reasoning": "Our system achieved a WER of 6.0% for MSA, outperforming the state-of-the-art system with a WER of 12.2%. For CA, our best system achieved a WER of 4.3%, significantly better than using our best MSA system for CA, which had a WER of 14.7%.", "description": "As the results show for MSA, our overall diacritization WER is 6.0% while the state of the art system has a WER of 12.2%. As for CA, our best system produced an error rate of 4.3%, which is significantly better than using our best MSA system to diacritize CA.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 9: Comparison to other systems for full diacritization', solve the subsequent question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the WER of the state-of-the-art system for MSA, and how does it compare to our system's WER for MSA?\n" }, { "role": "assistant", "content": "The table shows that the state-of-the-art system (Microsoft ATKS) has a WER of 12.2% for MSA, while our system has a WER of 6.0% for MSA, indicating that our system performs better. So, the answer is {\"answer\": \"12.2%\"}" } ], "images": [ "scigen/train/330.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Comparison to other systems for full diacritization" ], "reasoning": "The table shows that the state-of-the-art system (Microsoft ATKS) has a WER of 12.2% for MSA, while our system has a WER of 6.0% for MSA, indicating that our system performs better.", "question": "What is the WER of the state-of-the-art system for MSA, and how does it compare to our system's WER for MSA?", "answer": "So, the answer is {\"answer\": \"12.2%\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Our best MSA system has a lower WER for CA than our best system for CA., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nOur best MSA system has a lower WER for CA than our best system for CA.\n" }, { "role": "assistant", "content": "The table shows that our best MSA system has a WER of 14.7% for CA, while our best system for CA has a WER of 4.3%, indicating that the statement is false. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/330.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Comparison to other systems for full diacritization" ], "reasoning": "The table shows that our best MSA system has a WER of 14.7% for CA, while our best system for CA has a WER of 4.3%, indicating that the statement is false.", "statement": "Our best MSA system has a lower WER for CA than our best system for CA.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Error analysis: Core word error types for MSA'.\n" }, { "role": "assistant", "content": "The most common error type in diacritization is the selection of a valid diacritized form that does not match the context (40.8%), followed by transliterated foreign words (23.5%) and Arabic named entities (10.6%).For error analysis, we analyzed all the errors (527 errors). The most prominent error type arises from the selection of a valid diacritized form that does not match the context (40.8%). Perhaps, including POS tags as a feature or augmenting the PRIOR feature with POS tag information and a bigram language model may reduce the error rate further. The second most common error is due to transliterated foreign words including foreign named entities (23.5%). Such words were not observed during training. Further, Arabic Named entities account for 10.6% of the errors, where they were either not seen in training or they share identical non-diacritized forms with other words. Perhaps, building larger gazetteers of diacritized named entities may resolve NE related errors. In 10.8% of the cases, the diacritizer produced in completely incorrect diacritized forms. In some the cases (9.1%), though the diacritizer produced a form that is different from the reference, both forms were in fact correct. Most of these cases were due to variations in diacritization conventions (ex. “bare alef” (A) at start of a word receiving a diacritic or not). Other cases include foreign words and some words where both diacritized forms are equally valid." } ], "images": [ "scigen/train/331.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Error analysis: Core word error types for MSA" ], "reasoning": "The most common error type in diacritization is the selection of a valid diacritized form that does not match the context (40.8%), followed by transliterated foreign words (23.5%) and Arabic named entities (10.6%).", "description": "For error analysis, we analyzed all the errors (527 errors). The most prominent error type arises from the selection of a valid diacritized form that does not match the context (40.8%). Perhaps, including POS tags as a feature or augmenting the PRIOR feature with POS tag information and a bigram language model may reduce the error rate further. The second most common error is due to transliterated foreign words including foreign named entities (23.5%). Such words were not observed during training. Further, Arabic Named entities account for 10.6% of the errors, where they were either not seen in training or they share identical non-diacritized forms with other words. Perhaps, building larger gazetteers of diacritized named entities may resolve NE related errors. In 10.8% of the cases, the diacritizer produced in completely incorrect diacritized forms. In some the cases (9.1%), though the diacritizer produced a form that is different from the reference, both forms were in fact correct. Most of these cases were due to variations in diacritization conventions (ex. “bare alef” (A) at start of a word receiving a diacritic or not). Other cases include foreign words and some words where both diacritized forms are equally valid.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. The question is related to the given table titled 'Table 3: Error analysis: Core word error types for MSA'.\n\nQuestion: What percentage of errors are due to both correct forms being produced?\n" }, { "role": "assistant", "content": "The table shows that 9.1% of the errors are due to cases where the diacritizer produced a form that is different from the reference, but both forms were correct. So, the answer is {\"answer\": \"9.1%\"}." } ], "images": [ "scigen/train/331.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Error analysis: Core word error types for MSA" ], "reasoning": "The table shows that 9.1% of the errors are due to cases where the diacritizer produced a form that is different from the reference, but both forms were correct.", "question": "What percentage of errors are due to both correct forms being produced?", "answer": "So, the answer is {\"answer\": \"9.1%\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe most common error type is due to transliterated foreign words.\n" }, { "role": "assistant", "content": "The table indicates that the most common error type is the selection of a valid diacritized form that does not match the context (40.8%), while transliterated foreign words account for 23.5% of the errors. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/331.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Error analysis: Core word error types for MSA" ], "reasoning": "The table indicates that the most common error type is the selection of a valid diacritized form that does not match the context (40.8%), while transliterated foreign words account for 23.5% of the errors.", "statement": "The most common error type is due to transliterated foreign words.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: MSA case errors accounting from more than 1% of errors'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The most common error type involves guessing a fatHa (a) instead of damma (u) or vice versa (19.3%), often due to POS errors and confusion between subject and object.For example, the most common error type involves guessing a fatHa (a) instead of damma (u) or vice versa (19.3%). The most common reasons for this error type, based on inspecting the errors, were due to: POS errors (ex. a word is tagged as a verb instead of a noun); and a noun is treated as a subject instead of an object or vice versa. The table details the rest of the error types. Overall, some of the errors are potentially fixable using better POS tagging, improved detection of non-Arabized foreign names, and detection of indeclinability. However, some errors are more difficult and require greater understanding of semantics such as improper attachment, incorrect idafa, and confusion between subject and object. Perhaps, such semantic errors can be resolved using parsing." } ], "images": [ "scigen/train/332.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: MSA case errors accounting from more than 1% of errors" ], "reasoning": "The most common error type involves guessing a fatHa (a) instead of damma (u) or vice versa (19.3%), often due to POS errors and confusion between subject and object.", "description": "For example, the most common error type involves guessing a fatHa (a) instead of damma (u) or vice versa (19.3%). The most common reasons for this error type, based on inspecting the errors, were due to: POS errors (ex. a word is tagged as a verb instead of a noun); and a noun is treated as a subject instead of an object or vice versa. The table details the rest of the error types. Overall, some of the errors are potentially fixable using better POS tagging, improved detection of non-Arabized foreign names, and detection of indeclinability. However, some errors are more difficult and require greater understanding of semantics such as improper attachment, incorrect idafa, and confusion between subject and object. Perhaps, such semantic errors can be resolved using parsing.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: MSA case errors accounting from more than 1% of errors\nQuestion: What is the percentage of errors caused by guessing a kasra (i) instead of a fatHa (a) or vice versa?\nSolve the above question based on the table information. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the percentage of errors caused by guessing a kasra (i) instead of a fatHa (a) or vice versa is 18.9%. So, the answer is {\"answer\": \"18.9%\"}." } ], "images": [ "scigen/train/332.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: MSA case errors accounting from more than 1% of errors" ], "reasoning": "The table shows that the percentage of errors caused by guessing a kasra (i) instead of a fatHa (a) or vice versa is 18.9%.", "question": "What is the percentage of errors caused by guessing a kasra (i) instead of a fatHa (a) or vice versa?", "answer": "So, the answer is {\"answer\": \"18.9%\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The least common error type involves guessing a fatHa (a) instead of a kasra (i) or vice versa., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe least common error type involves guessing a fatHa (a) instead of a kasra (i) or vice versa.\n" }, { "role": "assistant", "content": "The table shows that the least common error type involves guessing a fatHa (a) instead of a kasra (i) or vice versa, which has a percentage of 1.2%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/332.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: MSA case errors accounting from more than 1% of errors" ], "reasoning": "The table shows that the least common error type involves guessing a fatHa (a) instead of a kasra (i) or vice versa, which has a percentage of 1.2%.", "statement": "The least common error type involves guessing a fatHa (a) instead of a kasra (i) or vice versa.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: CA case errors accounting from more than 1% of errors'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Errors in MSA are often syntactic and morphological, requiring better POS tagging and parsing, with some errors due to reference inaccuracies.The error types are similar to those observed for MSA. Some errors are more syntactic and morphological in nature and can be addressed using better POS tagging and identification of indeclinability, particularly as they relate to named entities and nouns with feminine markers. Other errors such as incorrect attachment, incorrect idafa, false subject, and confusion between subject and object can perhaps benefit from the use of parsing. As with the core-word errors for CA, the reference has some errors (ex. {a,i,o} ⇒ #), and extra rounds of reviews of the reference are in order." } ], "images": [ "scigen/train/333.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: CA case errors accounting from more than 1% of errors" ], "reasoning": "Errors in MSA are often syntactic and morphological, requiring better POS tagging and parsing, with some errors due to reference inaccuracies.", "description": "The error types are similar to those observed for MSA. Some errors are more syntactic and morphological in nature and can be addressed using better POS tagging and identification of indeclinability, particularly as they relate to named entities and nouns with feminine markers. Other errors such as incorrect attachment, incorrect idafa, false subject, and confusion between subject and object can perhaps benefit from the use of parsing. As with the core-word errors for CA, the reference has some errors (ex. {a,i,o} ⇒ #), and extra rounds of reviews of the reference are in order.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: CA case errors accounting from more than 1% of errors', answer the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the most common error type and its percentage?\n" }, { "role": "assistant", "content": "The table lists error types along with their counts and percentages. The most common error type is 'a ⇔ u' with a count of 2,907 and a percentage of 28.4%. So, the answer is {\"answer\": \"a ⇔ u, 28.4%\"}." } ], "images": [ "scigen/train/333.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: CA case errors accounting from more than 1% of errors" ], "reasoning": "The table lists error types along with their counts and percentages. The most common error type is 'a ⇔ u' with a count of 2,907 and a percentage of 28.4%.", "question": "What is the most common error type and its percentage?", "answer": "So, the answer is {\"answer\": \"a ⇔ u, 28.4%\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe error type 'i ⇔ u' has a higher count than 'a ⇔ #'.\n" }, { "role": "assistant", "content": "The table shows that 'i ⇔ u' has a count of 1,316, while 'a ⇔ #' has a count of 480. Therefore, 'i ⇔ u' has a higher count than 'a ⇔ #'. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/333.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: CA case errors accounting from more than 1% of errors" ], "reasoning": "The table shows that 'i ⇔ u' has a count of 1,316, while 'a ⇔ #' has a count of 480. Therefore, 'i ⇔ u' has a higher count than 'a ⇔ #'.", "statement": "The error type 'i ⇔ u' has a higher count than 'a ⇔ #'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 6: Error analysis: Core word error types for CA'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The most common errors in the system are producing completely correct diacritized forms (38.8%) and correct forms that don’t match the context (31.4%). Errors related to diacritics on attached suffixes account for 13.2%, while named entities errors are 8.8%.We randomly selected and analyzed 500 errors (5.2% of the errors). The two most common errors involve the system producing completely correct diacritized forms (38.8%) or correct forms that don’t match the context (31.4%). The relatively higher percentage of completely incorrect guesses, compared to MSA, may point to the higher lexical diversity of classical Arabic. As for MSA, we suspect that adding additional POS information and employing a word bigram to constrain the PRIOR feature may help reduce selection errors. Another prominent error is related to the diacritics that appear on attached suffixes, particularly pronouns, which depend on the choice of case ending (13.2%). Errors due to named entities are slightly fewer than those seen for MSA (8.8%). A noticeable number of mismatches between the guess and the reference are due to partial diacritization of the reference (4.4%). We plan to conduct an extra round of checks on the test set." } ], "images": [ "scigen/train/334.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Error analysis: Core word error types for CA" ], "reasoning": "The most common errors in the system are producing completely correct diacritized forms (38.8%) and correct forms that don’t match the context (31.4%). Errors related to diacritics on attached suffixes account for 13.2%, while named entities errors are 8.8%.", "description": "We randomly selected and analyzed 500 errors (5.2% of the errors). The two most common errors involve the system producing completely correct diacritized forms (38.8%) or correct forms that don’t match the context (31.4%). The relatively higher percentage of completely incorrect guesses, compared to MSA, may point to the higher lexical diversity of classical Arabic. As for MSA, we suspect that adding additional POS information and employing a word bigram to constrain the PRIOR feature may help reduce selection errors. Another prominent error is related to the diacritics that appear on attached suffixes, particularly pronouns, which depend on the choice of case ending (13.2%). Errors due to named entities are slightly fewer than those seen for MSA (8.8%). A noticeable number of mismatches between the guess and the reference are due to partial diacritization of the reference (4.4%). We plan to conduct an extra round of checks on the test set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 6: Error analysis: Core word error types for CA', unfold the question presented below, detailing every step of your calculation. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat percentage of errors are due to named entities and problems with reference combined?\n" }, { "role": "assistant", "content": "The percentage of errors due to named entities is 8.8%, and the percentage of errors due to problems with reference is 4.4%. Adding these together gives 8.8% + 4.4% = 13.2%. So, the answer is {\"answer\": \"13.2%\"}" } ], "images": [ "scigen/train/334.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Error analysis: Core word error types for CA" ], "reasoning": "The percentage of errors due to named entities is 8.8%, and the percentage of errors due to problems with reference is 4.4%. Adding these together gives 8.8% + 4.4% = 13.2%.", "question": "What percentage of errors are due to named entities and problems with reference combined?", "answer": "So, the answer is {\"answer\": \"13.2%\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe most common error involves the system producing completely correct diacritized forms.\n" }, { "role": "assistant", "content": "The table shows that the most common error is 'Invalid diacritized form' with a frequency of 195, which is 38.8% of the errors. This matches the description that the most common error involves the system producing completely correct diacritized forms. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/334.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Error analysis: Core word error types for CA" ], "reasoning": "The table shows that the most common error is 'Invalid diacritized form' with a frequency of 195, which is 38.8% of the errors. This matches the description that the most common error involves the system producing completely correct diacritized forms.", "statement": "The most common error involves the system producing completely correct diacritized forms.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 7: Comparing our system to state-of-the-art systems – Core word diacritics', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our system outperforms five other systems in both WER and DER error rates for MSA.Moreover, post correction improved results overall. We compare our results to five other systems, namely Farasa (darwish2017arabic), MADAMIRA (pasha2014madamira), RDI (Rashwan et al., 2015), MIT (Belinkow and Glass, 2015), and Microsoft ATKS (microsoft2013diac). As the results show, our results beat the current state-of-the-art." } ], "images": [ "scigen/train/335.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Comparing our system to state-of-the-art systems – Core word diacritics" ], "reasoning": "Our system outperforms five other systems in both WER and DER error rates for MSA.", "description": "Moreover, post correction improved results overall. We compare our results to five other systems, namely Farasa (darwish2017arabic), MADAMIRA (pasha2014madamira), RDI (Rashwan et al., 2015), MIT (Belinkow and Glass, 2015), and Microsoft ATKS (microsoft2013diac). As the results show, our results beat the current state-of-the-art.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 7: Comparing our system to state-of-the-art systems – Core word diacritics\nQuestion: Which system has the lowest WER error rate for MSA?\n" }, { "role": "assistant", "content": "By comparing the WER error rates for MSA in the table, our system has the lowest WER error rate of 2.9. So, the answer is {\"answer\": \"Our system\"}." } ], "images": [ "scigen/train/335.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Comparing our system to state-of-the-art systems – Core word diacritics" ], "reasoning": "By comparing the WER error rates for MSA in the table, our system has the lowest WER error rate of 2.9.", "question": "Which system has the lowest WER error rate for MSA?", "answer": "So, the answer is {\"answer\": \"Our system\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nOur system has a lower DER error rate for MSA than Farasa.\n" }, { "role": "assistant", "content": "The table shows that our system has a DER error rate of 0.9 for MSA, while Farasa has a DER error rate of 1.1, indicating that our system's DER error rate is indeed lower. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/335.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Comparing our system to state-of-the-art systems – Core word diacritics" ], "reasoning": "The table shows that our system has a DER error rate of 0.9 for MSA, while Farasa has a DER error rate of 1.1, indicating that our system's DER error rate is indeed lower.", "statement": "Our system has a lower DER error rate for MSA than Farasa.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1. Number of test documents |D|, unique entities T∗ in all articles, and mean amount of unique entities ¯¯¯¯T in articles containing a given entity type (for context this is the mean amount of nouns as explained in Section 3.1.2) for TamperedNews (left) and News400 (right). Valid image-text relations for News400 were first manually verified according to Section 4.1.3.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The TamperedNews dataset includes documents with various contexts such as persons, locations, and events, and provides specific counts and average values for each category.They were both manipulated to perform experiments for cross-modal consistency verification. Experiments and comparisons to related work (jaiswal2017multimedia; sabir2018deep) on datasets such as MEIR (sabir2018deep) are not reasonable since 1) they do not contain public persons or events, and 2) rely on pre-defined reference or training data for given entities. These restrictions severely limit the application in practice. We propose an automated solution for real-world scenarios that works for public personalities and entities represented in a knowledge base." } ], "images": [ "scigen/train/336.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1. Number of test documents |D|, unique entities T∗ in all articles, and mean amount of unique entities ¯¯¯¯T in articles containing a given entity type (for context this is the mean amount of nouns as explained in Section 3.1.2) for TamperedNews (left) and News400 (right). Valid image-text relations for News400 were first manually verified according to Section 4.1.3." ], "reasoning": "The TamperedNews dataset includes documents with various contexts such as persons, locations, and events, and provides specific counts and average values for each category.", "description": "They were both manipulated to perform experiments for cross-modal consistency verification. Experiments and comparisons to related work (jaiswal2017multimedia; sabir2018deep) on datasets such as MEIR (sabir2018deep) are not reasonable since 1) they do not contain public persons or events, and 2) rely on pre-defined reference or training data for given entities. These restrictions severely limit the application in practice. We propose an automated solution for real-world scenarios that works for public personalities and entities represented in a knowledge base.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 1. Number of test documents |D|, unique entities T∗ in all articles, and mean amount of unique entities ¯¯¯¯T in articles containing a given entity type (for context this is the mean amount of nouns as explained in Section 3.1.2) for TamperedNews (left) and News400 (right). Valid image-text relations for News400 were first manually verified according to Section 4.1.3.', solve the following question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the average value of the TamperedNews dataset with locations?\n" }, { "role": "assistant", "content": "To find the average value of the TamperedNews dataset with locations, look at the row labeled 'With locations' and find the value under the column 'TamperedNews dataset T'. So, the answer is {\"answer\": \"4.90\"}." } ], "images": [ "scigen/train/336.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1. Number of test documents |D|, unique entities T∗ in all articles, and mean amount of unique entities ¯¯¯¯T in articles containing a given entity type (for context this is the mean amount of nouns as explained in Section 3.1.2) for TamperedNews (left) and News400 (right). Valid image-text relations for News400 were first manually verified according to Section 4.1.3." ], "reasoning": "To find the average value of the TamperedNews dataset with locations, look at the row labeled 'With locations' and find the value under the column 'TamperedNews dataset T'.", "question": "What is the average value of the TamperedNews dataset with locations?", "answer": "So, the answer is {\"answer\": \"4.90\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The TamperedNews dataset contains more documents with persons than with locations., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe TamperedNews dataset contains more documents with persons than with locations.\n" }, { "role": "assistant", "content": "The table shows that the TamperedNews dataset contains 34,051 documents with persons and 67,148 documents with locations. Since 34,051 is less than 67,148, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/336.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1. Number of test documents |D|, unique entities T∗ in all articles, and mean amount of unique entities ¯¯¯¯T in articles containing a given entity type (for context this is the mean amount of nouns as explained in Section 3.1.2) for TamperedNews (left) and News400 (right). Valid image-text relations for News400 were first manually verified according to Section 4.1.3." ], "reasoning": "The table shows that the TamperedNews dataset contains 34,051 documents with persons and 67,148 documents with locations. Since 34,051 is less than 67,148, the statement is incorrect.", "statement": "The TamperedNews dataset contains more documents with persons than with locations.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1. Number of test documents |D|, unique entities T∗ in all articles, and mean amount of unique entities ¯¯¯¯T in articles containing a given entity type (for context this is the mean amount of nouns as explained in Section 3.1.2) for TamperedNews (left) and News400 (right). Valid image-text relations for News400 were first manually verified according to Section 4.1.3.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The proposed solution addresses limitations in existing datasets by automating verification for public personalities and entities without relying on pre-defined reference data.They were both manipulated to perform experiments for cross-modal consistency verification. Experiments and comparisons to related work (jaiswal2017multimedia; sabir2018deep) on datasets such as MEIR (sabir2018deep) are not reasonable since 1) they do not contain public persons or events, and 2) rely on pre-defined reference or training data for given entities. These restrictions severely limit the application in practice. We propose an automated solution for real-world scenarios that works for public personalities and entities represented in a knowledge base." } ], "images": [ "scigen/train/337.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1. Number of test documents |D|, unique entities T∗ in all articles, and mean amount of unique entities ¯¯¯¯T in articles containing a given entity type (for context this is the mean amount of nouns as explained in Section 3.1.2) for TamperedNews (left) and News400 (right). Valid image-text relations for News400 were first manually verified according to Section 4.1.3." ], "reasoning": "The proposed solution addresses limitations in existing datasets by automating verification for public personalities and entities without relying on pre-defined reference data.", "description": "They were both manipulated to perform experiments for cross-modal consistency verification. Experiments and comparisons to related work (jaiswal2017multimedia; sabir2018deep) on datasets such as MEIR (sabir2018deep) are not reasonable since 1) they do not contain public persons or events, and 2) rely on pre-defined reference or training data for given entities. These restrictions severely limit the application in practice. We propose an automated solution for real-world scenarios that works for public personalities and entities represented in a knowledge base.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1. Number of test documents |D|, unique entities T∗ in all articles, and mean amount of unique entities ¯¯¯¯T in articles containing a given entity type (for context this is the mean amount of nouns as explained in Section 3.1.2) for TamperedNews (left) and News400 (right). Valid image-text relations for News400 were first manually verified according to Section 4.1.3.\nQuestion: What is the total number of verified documents with persons and locations in the News400 dataset?\nSolve the above question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the total number of verified documents with persons and locations, add the number of documents with persons (322) and the number of documents with locations (389). So, the answer is {\"answer\": \"711\"}." } ], "images": [ "scigen/train/337.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1. Number of test documents |D|, unique entities T∗ in all articles, and mean amount of unique entities ¯¯¯¯T in articles containing a given entity type (for context this is the mean amount of nouns as explained in Section 3.1.2) for TamperedNews (left) and News400 (right). Valid image-text relations for News400 were first manually verified according to Section 4.1.3." ], "reasoning": "To find the total number of verified documents with persons and locations, add the number of documents with persons (322) and the number of documents with locations (389).", "question": "What is the total number of verified documents with persons and locations in the News400 dataset?", "answer": "So, the answer is {\"answer\": \"711\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe News400 dataset has more verified documents with events than with locations.\n" }, { "role": "assistant", "content": "The table shows that the News400 dataset has 170 verified documents with events and 389 verified documents with locations, indicating that there are more verified documents with locations than with events. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/337.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1. Number of test documents |D|, unique entities T∗ in all articles, and mean amount of unique entities ¯¯¯¯T in articles containing a given entity type (for context this is the mean amount of nouns as explained in Section 3.1.2) for TamperedNews (left) and News400 (right). Valid image-text relations for News400 were first manually verified according to Section 4.1.3." ], "reasoning": "The table shows that the News400 dataset has 170 verified documents with events and 389 verified documents with locations, indicating that there are more verified documents with locations than with events.", "statement": "The News400 dataset has more verified documents with events than with locations.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5. Results for document verification (DV) and collection retrieval for the the News400 dataset. Results are reported for all available and verified documents |D|.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Performance for context verification is worse compared to TamperedNews due to less powerful word embedding for the German language.Since the number of documents is rather limited and cross-modal mutual presence of entities was manually verified, results for News400 are reported for all documents with verified relations. However, results while retrieving tampered documents are noticeable worse. This is mainly caused by the fact that some untampered entities in the documents that are depicted in both image and text, can be either unspecific (e.g. mentioning of a country) or the retrieved images for visual verification do not fit the document’s image content. this problem was bypassed. We have verified the same behavior for News400 when experimenting on these subsets. In addition, performance for context verification is worse compared to TamperedNews. We assume that this is due to the less powerful word embedding for the German language." } ], "images": [ "scigen/train/338.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5. Results for document verification (DV) and collection retrieval for the the News400 dataset. Results are reported for all available and verified documents |D|." ], "reasoning": "Performance for context verification is worse compared to TamperedNews due to less powerful word embedding for the German language.", "description": "Since the number of documents is rather limited and cross-modal mutual presence of entities was manually verified, results for News400 are reported for all documents with verified relations. However, results while retrieving tampered documents are noticeable worse. This is mainly caused by the fact that some untampered entities in the documents that are depicted in both image and text, can be either unspecific (e.g. mentioning of a country) or the retrieved images for visual verification do not fit the document’s image content. this problem was bypassed. We have verified the same behavior for News400 when experimenting on these subsets. In addition, performance for context verification is worse compared to TamperedNews. We assume that this is due to the less powerful word embedding for the German language.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5. Results for document verification (DV) and collection retrieval for the the News400 dataset. Results are reported for all available and verified documents |D|.', answer the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the Collection Retrieval AP-tampered @25% for the Random subset in the Persons category?\n" }, { "role": "assistant", "content": "To find the Collection Retrieval AP-tampered @25% for the Random subset in the Persons category, locate the row labeled 'Random' under the 'Persons' category and find the value under the 'Collection Retrieval AP-tampered @25%' column. So, the answer is {\"answer\": \"87.68\"}" } ], "images": [ "scigen/train/338.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5. Results for document verification (DV) and collection retrieval for the the News400 dataset. Results are reported for all available and verified documents |D|." ], "reasoning": "To find the Collection Retrieval AP-tampered @25% for the Random subset in the Persons category, locate the row labeled 'Random' under the 'Persons' category and find the value under the 'Collection Retrieval AP-tampered @25%' column.", "question": "What is the Collection Retrieval AP-tampered @25% for the Random subset in the Persons category?", "answer": "So, the answer is {\"answer\": \"87.68\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Collection Retrieval AP-clean @100% for the Random subset in the Events category is 83.37.\n" }, { "role": "assistant", "content": "To verify this statement, locate the row labeled 'Random' under the 'Events' category and find the value under the 'Collection Retrieval AP-clean @100%' column. The value is 89.10, not 83.37. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/338.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5. Results for document verification (DV) and collection retrieval for the the News400 dataset. Results are reported for all available and verified documents |D|." ], "reasoning": "To verify this statement, locate the row labeled 'Random' under the 'Events' category and find the value under the 'Collection Retrieval AP-clean @100%' column. The value is 89.10, not 83.37.", "statement": "The Collection Retrieval AP-clean @100% for the Random subset in the Events category is 83.37.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5: Impact of diversity regularization on performance of retrieval. The lower the diversity loss, the more diverse the local regions focused by the multiple attention maps.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Diversity regularization improves performance in sentence and image retrieval tasks while reducing diversity loss.In this subsection, we experiment to observe the effect of diversity regularization in the loss. We conjecture that diversity regularization encourages encoded vectors to contain various semantic meanings in an image, reducing the redundancy of the encoded vectors and attention weight vectors. Visualization. We qualitatively present the multiple attention maps focusing on more distinct local regions by introducing the diversity regularization. Therefore, the image embedding encodes more distinct components in an image, resulting in an improvement of performance on the image-text retrieval tasks." } ], "images": [ "scigen/train/339.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Impact of diversity regularization on performance of retrieval. The lower the diversity loss, the more diverse the local regions focused by the multiple attention maps." ], "reasoning": "Diversity regularization improves performance in sentence and image retrieval tasks while reducing diversity loss.", "description": "In this subsection, we experiment to observe the effect of diversity regularization in the loss. We conjecture that diversity regularization encourages encoded vectors to contain various semantic meanings in an image, reducing the redundancy of the encoded vectors and attention weight vectors. Visualization. We qualitatively present the multiple attention maps focusing on more distinct local regions by introducing the diversity regularization. Therefore, the image embedding encodes more distinct components in an image, resulting in an improvement of performance on the image-text retrieval tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 5: Impact of diversity regularization on performance of retrieval. The lower the diversity loss, the more diverse the local regions focused by the multiple attention maps.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. \n\nQuestion: How does diversity regularization affect the diversity loss and image retrieval performance?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows that with diversity regularization, the diversity loss is reduced to 5.59 and the image retrieval performance improves to 59.1, compared to without diversity regularization where the diversity loss is 7.36 and image retrieval performance is 58.4. So, the answer is {\"answer\": \"Diversity loss is reduced and image retrieval performance improves.\"}." } ], "images": [ "scigen/train/339.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Impact of diversity regularization on performance of retrieval. The lower the diversity loss, the more diverse the local regions focused by the multiple attention maps." ], "reasoning": "The table shows that with diversity regularization, the diversity loss is reduced to 5.59 and the image retrieval performance improves to 59.1, compared to without diversity regularization where the diversity loss is 7.36 and image retrieval performance is 58.4.", "question": "How does diversity regularization affect the diversity loss and image retrieval performance?", "answer": "So, the answer is {\"answer\": \"Diversity loss is reduced and image retrieval performance improves.\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Diversity regularization decreases the diversity loss., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nDiversity regularization decreases the diversity loss.\n" }, { "role": "assistant", "content": "The table indicates that with diversity regularization, the diversity loss is 5.59, which is lower than the 7.36 diversity loss without diversity regularization. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/339.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Impact of diversity regularization on performance of retrieval. The lower the diversity loss, the more diverse the local regions focused by the multiple attention maps." ], "reasoning": "The table indicates that with diversity regularization, the diversity loss is 5.59, which is lower than the 7.36 diversity loss without diversity regularization.", "statement": "Diversity regularization decreases the diversity loss.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Effect of self-attention (1-head) in each encoder. We denote the image encoder using selective spatial pooling and the text encoder using the last hidden state which are used in Engilberg [3] as W/O attention. We report only R@1 for effective comparison.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Applying single-head self-attention to each encoder separately shows that it captures missing parts in selective spatial pooling, making the model more suitable for visual semantic embedding.For this experiment, we use the MS-COCO dataset. To see the effect of the single-head self-attention mechanism at each encoder, we apply the single-head attention to each encoder separately. Visualization. We observe that self-attention with a single-head captures the missing parts on the selective spatial pooling, Through the visualization, we demonstrate that our model is more suitable for visual semantic embedding, where the encoding of rich details is necessary." } ], "images": [ "scigen/train/340.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Effect of self-attention (1-head) in each encoder. We denote the image encoder using selective spatial pooling and the text encoder using the last hidden state which are used in Engilberg [3] as W/O attention. We report only R@1 for effective comparison." ], "reasoning": "Applying single-head self-attention to each encoder separately shows that it captures missing parts in selective spatial pooling, making the model more suitable for visual semantic embedding.", "description": "For this experiment, we use the MS-COCO dataset. To see the effect of the single-head self-attention mechanism at each encoder, we apply the single-head attention to each encoder separately. Visualization. We observe that self-attention with a single-head captures the missing parts on the selective spatial pooling, Through the visualization, we demonstrate that our model is more suitable for visual semantic embedding, where the encoding of rich details is necessary.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Effect of self-attention (1-head) in each encoder. We denote the image encoder using selective spatial pooling and the text encoder using the last hidden state which are used in Engilberg [3] as W/O attention. We report only R@1 for effective comparison.\nQuestion: Which combination of image and text encoders achieved the highest score in Sentence Retrieval (R@1)?\nGive a detailed response to the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the Sentence Retrieval (R@1) column, the highest score is 70.1, achieved by the combination of 'Our image encoder (1-head)' and 'W/O attention' text encoder. So, the answer is {\"answer\": \"Our image encoder (1-head) and W/O attention\"}" } ], "images": [ "scigen/train/340.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Effect of self-attention (1-head) in each encoder. We denote the image encoder using selective spatial pooling and the text encoder using the last hidden state which are used in Engilberg [3] as W/O attention. We report only R@1 for effective comparison." ], "reasoning": "By examining the Sentence Retrieval (R@1) column, the highest score is 70.1, achieved by the combination of 'Our image encoder (1-head)' and 'W/O attention' text encoder.", "question": "Which combination of image and text encoders achieved the highest score in Sentence Retrieval (R@1)?", "answer": "So, the answer is {\"answer\": \"Our image encoder (1-head) and W/O attention\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe combination of 'Our text encoder (1-head)' and 'W/O attention' image encoder achieved the highest score in Image Retrieval (R@1).\n" }, { "role": "assistant", "content": "By examining the Image Retrieval (R@1) column, the highest score is 56.2, achieved by the combination of 'W/O attention' image encoder and 'Our text encoder (1-head)'. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/340.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Effect of self-attention (1-head) in each encoder. We denote the image encoder using selective spatial pooling and the text encoder using the last hidden state which are used in Engilberg [3] as W/O attention. We report only R@1 for effective comparison." ], "reasoning": "By examining the Image Retrieval (R@1) column, the highest score is 56.2, achieved by the combination of 'W/O attention' image encoder and 'Our text encoder (1-head)'.", "statement": "The combination of 'Our text encoder (1-head)' and 'W/O attention' image encoder achieved the highest score in Image Retrieval (R@1).", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 13: WER on Broadcast News, 400 hrs', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The proposed 512-hybrid CNN-based feature system improved over the old CNN, but performance slightly deteriorates after feature extraction. The 5,999-hybrid CNN offers significant relative improvements over both the DNN hybrid system and the old CNN-based features systems.While the proposed 512-hybrid CNN-based feature system did improve (14.1 WER) over the old CNN (14.8 WER), performance slightly deteriorates after CNN-based features are extracted from the network. However, the 5,999-hybrid CNN offers between a 13-16% relative improvement over the DNN hybrid system, and between a 4-5% relative improvement over the old CNN-based features systems. This helps to strengthen the hypothesis that hybrid CNNs have more potential for improvement, and the proposed fMLLR and ReLU+dropout techniques provide substantial improvements over DNNs and CNNs with a sigmoid non-linearity and VTLN-warped log-mel features." } ], "images": [ "scigen/train/341.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 13: WER on Broadcast News, 400 hrs" ], "reasoning": "The proposed 512-hybrid CNN-based feature system improved over the old CNN, but performance slightly deteriorates after feature extraction. The 5,999-hybrid CNN offers significant relative improvements over both the DNN hybrid system and the old CNN-based features systems.", "description": "While the proposed 512-hybrid CNN-based feature system did improve (14.1 WER) over the old CNN (14.8 WER), performance slightly deteriorates after CNN-based features are extracted from the network. However, the 5,999-hybrid CNN offers between a 13-16% relative improvement over the DNN hybrid system, and between a 4-5% relative improvement over the old CNN-based features systems. This helps to strengthen the hypothesis that hybrid CNNs have more potential for improvement, and the proposed fMLLR and ReLU+dropout techniques provide substantial improvements over DNNs and CNNs with a sigmoid non-linearity and VTLN-warped log-mel features.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 13: WER on Broadcast News, 400 hrs', answer the following question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the relative improvement in WER of the Proposed Hybrid CNN over the Hybrid DNN on the rt04 dataset?\n" }, { "role": "assistant", "content": "The WER for the Hybrid DNN on the rt04 dataset is 13.4, and for the Proposed Hybrid CNN, it is 11.7. The relative improvement is calculated as (13.4 - 11.7) / 13.4 * 100%. So, the answer is {\"answer\": \"12.69%\"}." } ], "images": [ "scigen/train/341.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 13: WER on Broadcast News, 400 hrs" ], "reasoning": "The WER for the Hybrid DNN on the rt04 dataset is 13.4, and for the Proposed Hybrid CNN, it is 11.7. The relative improvement is calculated as (13.4 - 11.7) / 13.4 * 100%.", "question": "What is the relative improvement in WER of the Proposed Hybrid CNN over the Hybrid DNN on the rt04 dataset?", "answer": "So, the answer is {\"answer\": \"12.69%\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Proposed Hybrid CNN has the lowest WER on both dev04f and rt04 datasets., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Proposed Hybrid CNN has the lowest WER on both dev04f and rt04 datasets.\n" }, { "role": "assistant", "content": "The table shows that the Proposed Hybrid CNN has WER values of 12.7 on dev04f and 11.7 on rt04, which are the lowest compared to other models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/341.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 13: WER on Broadcast News, 400 hrs" ], "reasoning": "The table shows that the Proposed Hybrid CNN has WER values of 12.7 on dev04f and 11.7 on rt04, which are the lowest compared to other models listed.", "statement": "The Proposed Hybrid CNN has the lowest WER on both dev04f and rt04 datasets.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: WER as a Function of # of Convolutional Layers', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Increasing the number of convolutional layers up to 2 improves performance, but further increases lead to deterioration. CNNs outperform DNNs for the same input feature set.Note that for each experiment, the number of parameters in the network is kept the same. The table shows that increasing the number of convolutional layers up to 2 helps, and then performance starts to deteriorate. Furthermore, we can see from the table that CNNs offer improvements over DNNs for the same input feature set." } ], "images": [ "scigen/train/342.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: WER as a Function of # of Convolutional Layers" ], "reasoning": "Increasing the number of convolutional layers up to 2 improves performance, but further increases lead to deterioration. CNNs outperform DNNs for the same input feature set.", "description": "Note that for each experiment, the number of parameters in the network is kept the same. The table shows that increasing the number of convolutional layers up to 2 helps, and then performance starts to deteriorate. Furthermore, we can see from the table that CNNs offer improvements over DNNs for the same input feature set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: WER as a Function of # of Convolutional Layers\nQuestion: What is the WER when using 2 convolutional layers and 4 fully connected layers?\nSolve the above question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows the WER for different configurations of convolutional and fully connected layers. For 2 convolutional layers and 4 fully connected layers, the WER is listed as 22.1. So, the answer is {\"answer\": \"22.1\"}." } ], "images": [ "scigen/train/342.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: WER as a Function of # of Convolutional Layers" ], "reasoning": "The table shows the WER for different configurations of convolutional and fully connected layers. For 2 convolutional layers and 4 fully connected layers, the WER is listed as 22.1.", "question": "What is the WER when using 2 convolutional layers and 4 fully connected layers?", "answer": "So, the answer is {\"answer\": \"22.1\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nUsing 3 convolutional layers results in the best performance.\n" }, { "role": "assistant", "content": "The table shows that the WER for 3 convolutional layers is 22.4, which is higher than the WER for 2 convolutional layers (22.1). Therefore, using 3 convolutional layers does not result in the best performance. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/342.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: WER as a Function of # of Convolutional Layers" ], "reasoning": "The table shows that the WER for 3 convolutional layers is 22.4, which is higher than the WER for 2 convolutional layers (22.1). Therefore, using 3 convolutional layers does not result in the best performance.", "statement": "Using 3 convolutional layers results in the best performance.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: WER as a function of # of hidden units'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Increasing the number of hidden units up to 220 decreases WER, but reducing the number of hidden units in fully connected layers below 1,024 increases WER. Using 128 and 256 hidden units in the first and second convolutional layers respectively yields a slight improvement.Again the total number of parameters in the network is kept constant for all experiments. We can observe that as we increase the number of hidden units up to 220, the WER steadily decreases. We do not increase the number of hidden units past 220 as this would require us to reduce the number of hidden units in the fully connected layers to be less than 1,024 in order to keep the total number of network parameters constant. We have observed that reducing the number of hidden units from 1,024 results in an increase in WER. We were able to obtain a slight improvement by using 128 hidden units for the first convolutional layer, and 256 for the second layer. This is more hidden units in the convolutional layers than are typically used for vision tasks as many hidden units are needed to capture the locality differences between different frequency regions in speech." } ], "images": [ "scigen/train/343.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: WER as a function of # of hidden units" ], "reasoning": "Increasing the number of hidden units up to 220 decreases WER, but reducing the number of hidden units in fully connected layers below 1,024 increases WER. Using 128 and 256 hidden units in the first and second convolutional layers respectively yields a slight improvement.", "description": "Again the total number of parameters in the network is kept constant for all experiments. We can observe that as we increase the number of hidden units up to 220, the WER steadily decreases. We do not increase the number of hidden units past 220 as this would require us to reduce the number of hidden units in the fully connected layers to be less than 1,024 in order to keep the total number of network parameters constant. We have observed that reducing the number of hidden units from 1,024 results in an increase in WER. We were able to obtain a slight improvement by using 128 hidden units for the first convolutional layer, and 256 for the second layer. This is more hidden units in the convolutional layers than are typically used for vision tasks as many hidden units are needed to capture the locality differences between different frequency regions in speech.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: WER as a function of # of hidden units', answer the following question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the WER when using 128 hidden units for the first convolutional layer and 256 for the second layer?\n" }, { "role": "assistant", "content": "The table shows the WER for different configurations of hidden units. The configuration with 128 hidden units for the first convolutional layer and 256 for the second layer has a WER of 21.9. So, the answer is {\"answer\": \"21.9\"}." } ], "images": [ "scigen/train/343.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: WER as a function of # of hidden units" ], "reasoning": "The table shows the WER for different configurations of hidden units. The configuration with 128 hidden units for the first convolutional layer and 256 for the second layer has a WER of 21.9.", "question": "What is the WER when using 128 hidden units for the first convolutional layer and 256 for the second layer?", "answer": "So, the answer is {\"answer\": \"21.9\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nUsing 220 hidden units results in the lowest WER.\n" }, { "role": "assistant", "content": "The table shows that the WER for 220 hidden units is 22.1, while the WER for 128/256 hidden units is 21.9, which is lower. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/343.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: WER as a function of # of hidden units" ], "reasoning": "The table shows that the WER for 220 hidden units is 22.1, while the WER for 128/256 hidden units is 21.9, which is lower.", "statement": "Using 220 hidden units results in the lowest WER.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 6: Results with Different Pooling Types'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Stochastic pooling provides slight improvements over max and lp pooling in LVCSR tasks, but generalization methods do not offer great improvements over max pooling in speech recognition tasks with more data.Given the success of lp and stochastic pooling, we compare both of these strategies to max-pooling on an LVCSR task. Stochastic pooling seems to provide improvements over max and lp pooling, though the gains are slight. Unlike vision tasks, in appears that in tasks such as speech recognition which have a lot more data and thus better model estimates, generalization methods such as lp and stochastic pooling do not offer great improvements over max pooling." } ], "images": [ "scigen/train/344.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results with Different Pooling Types" ], "reasoning": "Stochastic pooling provides slight improvements over max and lp pooling in LVCSR tasks, but generalization methods do not offer great improvements over max pooling in speech recognition tasks with more data.", "description": "Given the success of lp and stochastic pooling, we compare both of these strategies to max-pooling on an LVCSR task. Stochastic pooling seems to provide improvements over max and lp pooling, though the gains are slight. Unlike vision tasks, in appears that in tasks such as speech recognition which have a lot more data and thus better model estimates, generalization methods such as lp and stochastic pooling do not offer great improvements over max pooling.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 6: Results with Different Pooling Types' to sequentially solve the question presented below. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich pooling method has the lowest WER in the LVCSR task?\n" }, { "role": "assistant", "content": "The table shows the WER for different pooling methods. Stochastic pooling has a WER of 18.8, which is lower than the WERs of max pooling and lp pooling, both of which are 18.9. So, the answer is {\"answer\": \"Stochastic Pooling\"}." } ], "images": [ "scigen/train/344.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results with Different Pooling Types" ], "reasoning": "The table shows the WER for different pooling methods. Stochastic pooling has a WER of 18.8, which is lower than the WERs of max pooling and lp pooling, both of which are 18.9.", "question": "Which pooling method has the lowest WER in the LVCSR task?", "answer": "So, the answer is {\"answer\": \"Stochastic Pooling\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Stochastic pooling has a higher WER than max pooling in the LVCSR task., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nStochastic pooling has a higher WER than max pooling in the LVCSR task.\n" }, { "role": "assistant", "content": "The table shows that stochastic pooling has a WER of 18.8, while max pooling has a WER of 18.9. Therefore, stochastic pooling has a lower WER than max pooling. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/344.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results with Different Pooling Types" ], "reasoning": "The table shows that stochastic pooling has a WER of 18.8, while max pooling has a WER of 18.9. Therefore, stochastic pooling has a lower WER than max pooling.", "statement": "Stochastic pooling has a higher WER than max pooling in the LVCSR task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 8: Pooling in Time', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Pooling in time, whether stochastic or lp, shows slight improvements in WER compared to the baseline, but the gains are minimal and may diminish with sequence training.We see that pooling in time helps slightly with stochastic and lp pooling. However, the gains are not large, and are likely to be diminished after sequence training. It appears that for large tasks with more data, regularizations such as pooling in time are not helpful, similar to other regularization schemes such as lp/stochastic pooling and pooling with overlap in frequency." } ], "images": [ "scigen/train/345.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Pooling in Time" ], "reasoning": "Pooling in time, whether stochastic or lp, shows slight improvements in WER compared to the baseline, but the gains are minimal and may diminish with sequence training.", "description": "We see that pooling in time helps slightly with stochastic and lp pooling. However, the gains are not large, and are likely to be diminished after sequence training. It appears that for large tasks with more data, regularizations such as pooling in time are not helpful, similar to other regularization schemes such as lp/stochastic pooling and pooling with overlap in frequency.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 8: Pooling in Time', and sequentially solve the question, writing out each step of your reasoning process. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich pooling method shows the same WER as the baseline?\n" }, { "role": "assistant", "content": "The table shows that the 'Pooling in Time, Max' method has the same WER of 18.9 as the baseline. So, the answer is {\"answer\": \"Pooling in Time, Max\"}." } ], "images": [ "scigen/train/345.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Pooling in Time" ], "reasoning": "The table shows that the 'Pooling in Time, Max' method has the same WER of 18.9 as the baseline.", "question": "Which pooling method shows the same WER as the baseline?", "answer": "So, the answer is {\"answer\": \"Pooling in Time, Max\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Pooling in Time, Stochastic and Pooling in Time, lp have the same WER., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nPooling in Time, Stochastic and Pooling in Time, lp have the same WER.\n" }, { "role": "assistant", "content": "The table shows that both 'Pooling in Time, Stochastic' and 'Pooling in Time, lp' have a WER of 18.8. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/345.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Pooling in Time" ], "reasoning": "The table shows that both 'Pooling in Time, Stochastic' and 'Pooling in Time, lp' have a WER of 18.8.", "statement": "Pooling in Time, Stochastic and Pooling in Time, lp have the same WER.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 9: WER With Improved fMLLR Features', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Applying fMLLR in a decorrelated space improves the WER by 0.5% over the baseline VTLN-warped log-mel system.Notice that by applying fMLLR in a decorrelated space, we can achieve a 0.5% improvement over the baseline VTLN-warped log-mel system." } ], "images": [ "scigen/train/346.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: WER With Improved fMLLR Features" ], "reasoning": "Applying fMLLR in a decorrelated space improves the WER by 0.5% over the baseline VTLN-warped log-mel system.", "description": "Notice that by applying fMLLR in a decorrelated space, we can achieve a 0.5% improvement over the baseline VTLN-warped log-mel system.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 9: WER With Improved fMLLR Features\nQuestion: What is the WER improvement when applying fMLLR in a decorrelated space over the baseline VTLN-warped log-mel system?\nSolve the above question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The baseline VTLN-warped log-mel system has a WER of 18.8%, while the proposed fMLLR + VTLN-warped log-mel system has a WER of 18.3%. The improvement is 18.8% - 18.3% = 0.5%. So, the answer is {\"answer\": \"0.5%\"}" } ], "images": [ "scigen/train/346.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: WER With Improved fMLLR Features" ], "reasoning": "The baseline VTLN-warped log-mel system has a WER of 18.8%, while the proposed fMLLR + VTLN-warped log-mel system has a WER of 18.3%. The improvement is 18.8% - 18.3% = 0.5%.", "question": "What is the WER improvement when applying fMLLR in a decorrelated space over the baseline VTLN-warped log-mel system?", "answer": "So, the answer is {\"answer\": \"0.5%\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe proposed fMLLR + VTLN-warped log-mel system has a WER of 18.3%.\n" }, { "role": "assistant", "content": "The table shows that the WER for the proposed fMLLR + VTLN-warped log-mel system is 18.3%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/346.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: WER With Improved fMLLR Features" ], "reasoning": "The table shows that the WER for the proposed fMLLR + VTLN-warped log-mel system is 18.3%.", "statement": "The proposed fMLLR + VTLN-warped log-mel system has a WER of 18.3%.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 10: WER of HF Sequence Training + Dropout'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Fixing the dropout mask per utterance across all CG iterations results in a 0.6% improvement in WER compared to varying the dropout mask per CG training iteration.By using dropout but fixing the dropout mask per utterance across all CG iterations, we can achieve a 0.6% improvement in WER. Finally, if we compare this to varying the dropout mask per CG training iteration, the WER increases. This shows experimental evidence that if the dropout mask is not fixed, we cannot guarantee that CG iterations produce conjugate search directions for the loss function." } ], "images": [ "scigen/train/347.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: WER of HF Sequence Training + Dropout" ], "reasoning": "Fixing the dropout mask per utterance across all CG iterations results in a 0.6% improvement in WER compared to varying the dropout mask per CG training iteration.", "description": "By using dropout but fixing the dropout mask per utterance across all CG iterations, we can achieve a 0.6% improvement in WER. Finally, if we compare this to varying the dropout mask per CG training iteration, the WER increases. This shows experimental evidence that if the dropout mask is not fixed, we cannot guarantee that CG iterations produce conjugate search directions for the loss function.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 10: WER of HF Sequence Training + Dropout'. Solve the following question based on the table.\n\nQuestion: What is the WER when using ReLU with dropout fixed for CG iterations?\nLet's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that using ReLU with dropout fixed for CG iterations results in a WER of 15.0. So, the answer is {\"answer\": \"15.0\"}" } ], "images": [ "scigen/train/347.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: WER of HF Sequence Training + Dropout" ], "reasoning": "The table shows that using ReLU with dropout fixed for CG iterations results in a WER of 15.0.", "question": "What is the WER when using ReLU with dropout fixed for CG iterations?", "answer": "So, the answer is {\"answer\": \"15.0\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nUsing ReLU with dropout fixed for CG iterations results in the lowest WER.\n" }, { "role": "assistant", "content": "The table shows that the WER for ReLU with dropout fixed for CG iterations is 15.0, which is the lowest compared to other configurations. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/347.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: WER of HF Sequence Training + Dropout" ], "reasoning": "The table shows that the WER for ReLU with dropout fixed for CG iterations is 15.0, which is the lowest compared to other configurations.", "statement": "Using ReLU with dropout fixed for CG iterations results in the lowest WER.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 11: HF Seq. Training WER Per CE Iteration'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "By reducing the number of CE iterations and annealing twice, we can achieve the same WER after HF training as with more CE iterations, indicating that prolonged CE training is unnecessary.Finally, we explore if we can reduce the number of CE iterations before moving to sequence training. A main advantage of sequence training is that it is more closely linked to the speech recognition objective function compared to cross-entropy. Using this fact, we explore how many iterations of CE are actually necessary before moving to HF training. Note that HF training is started and lattices are dumped using the CE weight that is stopped at. Notice that just by annealing two times, we can achieve the same WER after HF training, compared to having the CE weights converge. This points to the fact that spending too much time in CE is unnecessary. Once the weights are in a relatively decent space, it is better to just jump to HF sequence training which is more closely matched to the speech objective function." } ], "images": [ "scigen/train/348.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: HF Seq. Training WER Per CE Iteration" ], "reasoning": "By reducing the number of CE iterations and annealing twice, we can achieve the same WER after HF training as with more CE iterations, indicating that prolonged CE training is unnecessary.", "description": "Finally, we explore if we can reduce the number of CE iterations before moving to sequence training. A main advantage of sequence training is that it is more closely linked to the speech recognition objective function compared to cross-entropy. Using this fact, we explore how many iterations of CE are actually necessary before moving to HF training. Note that HF training is started and lattices are dumped using the CE weight that is stopped at. Notice that just by annealing two times, we can achieve the same WER after HF training, compared to having the CE weights converge. This points to the fact that spending too much time in CE is unnecessary. Once the weights are in a relatively decent space, it is better to just jump to HF sequence training which is more closely matched to the speech objective function.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 11: HF Seq. Training WER Per CE Iteration', answer the following question. Give detailed solution consisting of each step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nHow many times was annealing performed when the CE WER was 19.8?\n" }, { "role": "assistant", "content": "The table shows that when the CE WER was 19.8, the number of times annealed was 2. So, the answer is {\"answer\": \"2\"}." } ], "images": [ "scigen/train/348.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: HF Seq. Training WER Per CE Iteration" ], "reasoning": "The table shows that when the CE WER was 19.8, the number of times annealed was 2.", "question": "How many times was annealing performed when the CE WER was 19.8?", "answer": "So, the answer is {\"answer\": \"2\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Annealing three times results in a CE WER of 19.4. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nAnnealing three times results in a CE WER of 19.4.\n" }, { "role": "assistant", "content": "The table indicates that when annealing was performed three times, the CE WER was indeed 19.4. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/348.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: HF Seq. Training WER Per CE Iteration" ], "reasoning": "The table indicates that when annealing was performed three times, the CE WER was indeed 19.4.", "statement": "Annealing three times results in a CE WER of 19.4.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 12: WER on Broadcast News, 50 hours'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The proposed CNN hybrid system shows significant improvements over both the DNN hybrid and the old CNN hybrid systems, while the feature-based systems show only modest improvements.The proposed CNN hybrid system offers between a 6-7% relative improvement over the DNN hybrid, and a 2-3% relative improvement over the old CNN hybrid system. While the proposed CNN-based feature system offers a modest 1% improvement over the old CNN-based feature system, this slight improvements with feature-based system is not surprising all. We have observed huge relative improvements in WER (10-12%) on a hybrid sequence trained DNN with 512 output targets, compared to a hybrid CE-trained DNN. Feature-based systems use the neural network to learn a feature transformation, and seem to saturate in performance even when the hybrid system used to extract the features improves. Thus, as the table shows, there is more potential to improve a hybrid system as opposed to a feature-based system." } ], "images": [ "scigen/train/349.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 12: WER on Broadcast News, 50 hours" ], "reasoning": "The proposed CNN hybrid system shows significant improvements over both the DNN hybrid and the old CNN hybrid systems, while the feature-based systems show only modest improvements.", "description": "The proposed CNN hybrid system offers between a 6-7% relative improvement over the DNN hybrid, and a 2-3% relative improvement over the old CNN hybrid system. While the proposed CNN-based feature system offers a modest 1% improvement over the old CNN-based feature system, this slight improvements with feature-based system is not surprising all. We have observed huge relative improvements in WER (10-12%) on a hybrid sequence trained DNN with 512 output targets, compared to a hybrid CE-trained DNN. Feature-based systems use the neural network to learn a feature transformation, and seem to saturate in performance even when the hybrid system used to extract the features improves. Thus, as the table shows, there is more potential to improve a hybrid system as opposed to a feature-based system.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 12: WER on Broadcast News, 50 hours\nQuestion: What is the relative improvement in WER of the proposed Hybrid CNN over the Old Hybrid CNN on rt04?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The WER for the Old Hybrid CNN on rt04 is 15.0, and for the Proposed Hybrid CNN, it is 14.7. The relative improvement is calculated as (15.0 - 14.7) / 15.0 * 100%, which is 2%. So, the answer is {\"answer\": \"2%\"}." } ], "images": [ "scigen/train/349.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 12: WER on Broadcast News, 50 hours" ], "reasoning": "The WER for the Old Hybrid CNN on rt04 is 15.0, and for the Proposed Hybrid CNN, it is 14.7. The relative improvement is calculated as (15.0 - 14.7) / 15.0 * 100%, which is 2%.", "question": "What is the relative improvement in WER of the proposed Hybrid CNN over the Old Hybrid CNN on rt04?", "answer": "So, the answer is {\"answer\": \"2%\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The proposed CNN-based feature system offers a 1% improvement over the old CNN-based feature system on dev04f., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe proposed CNN-based feature system offers a 1% improvement over the old CNN-based feature system on dev04f.\n" }, { "role": "assistant", "content": "The WER for the Old CNN-based Features on dev04f is 15.5, and for the Proposed CNN-based Features, it is 15.3. The relative improvement is calculated as (15.5 - 15.3) / 15.5 * 100%, which is approximately 1%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/349.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 12: WER on Broadcast News, 50 hours" ], "reasoning": "The WER for the Old CNN-based Features on dev04f is 15.5, and for the Proposed CNN-based Features, it is 15.3. The relative improvement is calculated as (15.5 - 15.3) / 15.5 * 100%, which is approximately 1%.", "statement": "The proposed CNN-based feature system offers a 1% improvement over the old CNN-based feature system on dev04f.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4: Mean and variance of the average BLEU score for the Diverse group. The models trained with MultiDDS-S perform better and have less variance.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "MultiDDS-S has a smaller variance in both M2O and O2M mean scores compared to MultiDDS.MultiDDS -S also results in smaller variance in the final model performance. We run MultiDDS and MultiDDS-S with 4 different random seeds, and record the mean and variance of the average BLEU score." } ], "images": [ "scigen/train/350.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Mean and variance of the average BLEU score for the Diverse group. The models trained with MultiDDS-S perform better and have less variance." ], "reasoning": "MultiDDS-S has a smaller variance in both M2O and O2M mean scores compared to MultiDDS.", "description": "MultiDDS -S also results in smaller variance in the final model performance. We run MultiDDS and MultiDDS-S with 4 different random seeds, and record the mean and variance of the average BLEU score.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 4: Mean and variance of the average BLEU score for the Diverse group. The models trained with MultiDDS-S perform better and have less variance.\n\nQuestion: Which method has the smallest variance in the O2M mean score?\n" }, { "role": "assistant", "content": "By comparing the O2M variance values for both methods, MultiDDS has a variance of 0.05, while MultiDDS-S has a variance of 0.02. Therefore, MultiDDS-S has the smallest variance in the O2M mean score. So, the answer is {\"answer\": \"MultiDDS-S\"}" } ], "images": [ "scigen/train/350.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Mean and variance of the average BLEU score for the Diverse group. The models trained with MultiDDS-S perform better and have less variance." ], "reasoning": "By comparing the O2M variance values for both methods, MultiDDS has a variance of 0.05, while MultiDDS-S has a variance of 0.02. Therefore, MultiDDS-S has the smallest variance in the O2M mean score.", "question": "Which method has the smallest variance in the O2M mean score?", "answer": "So, the answer is {\"answer\": \"MultiDDS-S\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis MultiDDS-S has a higher M2O mean score than MultiDDS. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nMultiDDS-S has a higher M2O mean score than MultiDDS.\n" }, { "role": "assistant", "content": "The table shows that the M2O mean score for MultiDDS-S is 26.94, while for MultiDDS it is 26.85. Therefore, MultiDDS-S has a higher M2O mean score than MultiDDS. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/350.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Mean and variance of the average BLEU score for the Diverse group. The models trained with MultiDDS-S perform better and have less variance." ], "reasoning": "The table shows that the M2O mean score for MultiDDS-S is 26.94, while for MultiDDS it is 26.85. Therefore, MultiDDS-S has a higher M2O mean score than MultiDDS.", "statement": "MultiDDS-S has a higher M2O mean score than MultiDDS.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Average BLEU for the baselines and our methods. Bold indicates the highest value.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "MultiDDS-S consistently outperforms the best baseline and MultiDDS in three out of four settings, providing a stable strategy for training multilingual systems.First, comparing the baselines, we can see that there is no consistently strong strategy for setting the sampling ratio, with proportional sampling being best in the M2O setting, but worst in the O2M setting. Next, we can see that MultiDDS outperforms the best baseline in three of the four settings and is comparable to proportional sampling in the last M2O-Diverse setting. With the stabilized reward, MultiDDS-S consistently delivers better overall performance than the best baseline, and outperforms MultiDDS in three settings. From these results, we can conclude that MultiDDS-S provides a stable strategy to train multilingual systems over a variety of settings." } ], "images": [ "scigen/train/351.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Average BLEU for the baselines and our methods. Bold indicates the highest value." ], "reasoning": "MultiDDS-S consistently outperforms the best baseline and MultiDDS in three out of four settings, providing a stable strategy for training multilingual systems.", "description": "First, comparing the baselines, we can see that there is no consistently strong strategy for setting the sampling ratio, with proportional sampling being best in the M2O setting, but worst in the O2M setting. Next, we can see that MultiDDS outperforms the best baseline in three of the four settings and is comparable to proportional sampling in the last M2O-Diverse setting. With the stabilized reward, MultiDDS-S consistently delivers better overall performance than the best baseline, and outperforms MultiDDS in three settings. From these results, we can conclude that MultiDDS-S provides a stable strategy to train multilingual systems over a variety of settings.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 1: Average BLEU for the baselines and our methods. Bold indicates the highest value.'. Solve the following question based on the table.\n\nQuestion: Which method delivers the best performance in the M2O-Diverse setting?\nLet's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the values in the M2O-Diverse column, MultiDDS-S has the highest score of 27.00, indicating the best performance. So, the answer is {\"answer\": \"MultiDDS-S\"}." } ], "images": [ "scigen/train/351.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Average BLEU for the baselines and our methods. Bold indicates the highest value." ], "reasoning": "By comparing the values in the M2O-Diverse column, MultiDDS-S has the highest score of 27.00, indicating the best performance.", "question": "Which method delivers the best performance in the M2O-Diverse setting?", "answer": "So, the answer is {\"answer\": \"MultiDDS-S\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Proportional sampling is the best strategy in the O2M-Diverse setting., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nProportional sampling is the best strategy in the O2M-Diverse setting.\n" }, { "role": "assistant", "content": "The table shows that proportional sampling has a score of 16.79 in the O2M-Diverse setting, which is lower than both MultiDDS and MultiDDS-S, refuting the statement. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/351.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Average BLEU for the baselines and our methods. Bold indicates the highest value." ], "reasoning": "The table shows that proportional sampling has a score of 16.79 in the O2M-Diverse setting, which is lower than both MultiDDS and MultiDDS-S, refuting the statement.", "statement": "Proportional sampling is the best strategy in the O2M-Diverse setting.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Average BLEU of the best baseline and three MultiDDS-S settings for the Diverse group. MultiDDS-S always outperform the baseline.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "MultiDDS prioritizes different predefined objectives, with Low performing better on low-performing languages and High achieving the best performance on high-performing languages, especially in the O2M setting.We performed experiments with these aggregation methods on the Diverse group, mainly because there is more performance trade-off among these languages. The languages are ordered on the x-axis from left to right in decreasing perplexity. Low generally performs better on the low-performing languages on the left, while High generally achieves the best performance on the high-performing languages on the right, with results most consistent in the O2M setting. This indicates that MultiDDS is able to prioritize different predefined objectives." } ], "images": [ "scigen/train/352.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Average BLEU of the best baseline and three MultiDDS-S settings for the Diverse group. MultiDDS-S always outperform the baseline." ], "reasoning": "MultiDDS prioritizes different predefined objectives, with Low performing better on low-performing languages and High achieving the best performance on high-performing languages, especially in the O2M setting.", "description": "We performed experiments with these aggregation methods on the Diverse group, mainly because there is more performance trade-off among these languages. The languages are ordered on the x-axis from left to right in decreasing perplexity. Low generally performs better on the low-performing languages on the left, while High generally achieves the best performance on the high-performing languages on the right, with results most consistent in the O2M setting. This indicates that MultiDDS is able to prioritize different predefined objectives.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: Average BLEU of the best baseline and three MultiDDS-S settings for the Diverse group. MultiDDS-S always outperform the baseline.', answer the following question. Give detailed solution consisting of each step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich aggregation method achieves the highest performance in the O2M setting?\n" }, { "role": "assistant", "content": "In the O2M setting, MultiDDS-S High achieves the highest performance with a value of 18.55, compared to other methods. So, the answer is {\"answer\": \"MultiDDS-S High\"}." } ], "images": [ "scigen/train/352.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Average BLEU of the best baseline and three MultiDDS-S settings for the Diverse group. MultiDDS-S always outperform the baseline." ], "reasoning": "In the O2M setting, MultiDDS-S High achieves the highest performance with a value of 18.55, compared to other methods.", "question": "Which aggregation method achieves the highest performance in the O2M setting?", "answer": "So, the answer is {\"answer\": \"MultiDDS-S High\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nMultiDDS-S Low performs better than the Baseline in the M2O setting.\n" }, { "role": "assistant", "content": "In the M2O setting, MultiDDS-S Low has a value of 26.97, which is higher than the Baseline value of 26.68. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/352.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Average BLEU of the best baseline and three MultiDDS-S settings for the Diverse group. MultiDDS-S always outperform the baseline." ], "reasoning": "In the M2O setting, MultiDDS-S Low has a value of 26.97, which is higher than the Baseline value of 26.68.", "statement": "MultiDDS-S Low performs better than the Baseline in the M2O setting.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Super Sense Tagging (SST) accuracy for Simple Projection, RNN and their combination.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The best performance on Italian is achieved using MSC-IT-1 clean corpus, while noisy training corpus degrades SST performance. The combination of simple projection and RNN yields the best results, confirming their complementary nature.SRNN-POS-X and BRNN-POS-X refer to our RNN variants: In means input layer, H1 means first hidden layer and H2 means second hidden layer. We achieve the best performance on Italian using MSC-IT-1 clean corpus while noisy training corpus degrades SST performance. The best results are obtained with combination of simple projection and RNN which confirms (as for POS tagging) that both approaches are complementary." } ], "images": [ "scigen/train/353.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Super Sense Tagging (SST) accuracy for Simple Projection, RNN and their combination." ], "reasoning": "The best performance on Italian is achieved using MSC-IT-1 clean corpus, while noisy training corpus degrades SST performance. The combination of simple projection and RNN yields the best results, confirming their complementary nature.", "description": "SRNN-POS-X and BRNN-POS-X refer to our RNN variants: In means input layer, H1 means first hidden layer and H2 means second hidden layer. We achieve the best performance on Italian using MSC-IT-1 clean corpus while noisy training corpus degrades SST performance. The best results are obtained with combination of simple projection and RNN which confirms (as for POS tagging) that both approaches are complementary.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: Super Sense Tagging (SST) accuracy for Simple Projection, RNN and their combination.', and tackle the question below in a sequential manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model achieved the highest score for Italian MSC-IT-1 trans. auto?\n" }, { "role": "assistant", "content": "To find the highest score for Italian MSC-IT-1 trans. auto, look at the column 'Italian MSC-IT-1 trans. auto' and identify the highest value. The highest score is 64.7, achieved by the 'Combination Projection + BRNN-POS-H2 OOV' model. So, the answer is {\"answer\": \"Combination Projection + BRNN-POS-H2 OOV\"}." } ], "images": [ "scigen/train/353.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Super Sense Tagging (SST) accuracy for Simple Projection, RNN and their combination." ], "reasoning": "To find the highest score for Italian MSC-IT-1 trans. auto, look at the column 'Italian MSC-IT-1 trans. auto' and identify the highest value. The highest score is 64.7, achieved by the 'Combination Projection + BRNN-POS-H2 OOV' model.", "question": "Which model achieved the highest score for Italian MSC-IT-1 trans. auto?", "answer": "So, the answer is {\"answer\": \"Combination Projection + BRNN-POS-H2 OOV\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The GETALP model achieved the highest score for French MSC-FR-2 trans. auto., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe GETALP model achieved the highest score for French MSC-FR-2 trans. auto.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'French MSC-FR-2 trans. auto' column. The highest score in this column is 52.4, achieved by the 'S-E MFS Semeval 2013' model, not the GETALP model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/353.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Super Sense Tagging (SST) accuracy for Simple Projection, RNN and their combination." ], "reasoning": "To verify this statement, look at the 'French MSC-FR-2 trans. auto' column. The highest score in this column is 52.4, achieved by the 'S-E MFS Semeval 2013' model, not the GETALP model.", "statement": "The GETALP model achieved the highest score for French MSC-FR-2 trans. auto.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1: Token-level POS tagging accuracy for Simple Projection, SRNN using MultiVec bilingual word embeddings as input, RNN444For RNN models, only one (same) system is used to tag German, Greek and Spanish, Projection+RNN and methods of Das & Petrov (2011), Duong et al (2013) and Gouws & Søgaard (2015).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The BRNN model outperforms the SRNN model, indicating that using both past and future contexts improves tagging accuracy. Replacing OOVs with the closest words using CBOW further enhances performance.We note that the POS tagger based on bidirectional RNN (BRNN) has better performance than simple RNN (SRNN), which means that both past and future contexts help select the correct tag. It is shown that after replacing OOVs by the closest words using CBOW, the tagging accuracy significantly increases." } ], "images": [ "scigen/train/354.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Token-level POS tagging accuracy for Simple Projection, SRNN using MultiVec bilingual word embeddings as input, RNN444For RNN models, only one (same) system is used to tag German, Greek and Spanish, Projection+RNN and methods of Das & Petrov (2011), Duong et al (2013) and Gouws & Søgaard (2015)." ], "reasoning": "The BRNN model outperforms the SRNN model, indicating that using both past and future contexts improves tagging accuracy. Replacing OOVs with the closest words using CBOW further enhances performance.", "description": "We note that the POS tagger based on bidirectional RNN (BRNN) has better performance than simple RNN (SRNN), which means that both past and future contexts help select the correct tag. It is shown that after replacing OOVs by the closest words using CBOW, the tagging accuracy significantly increases.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 1: Token-level POS tagging accuracy for Simple Projection, SRNN using MultiVec bilingual word embeddings as input, RNN444For RNN models, only one (same) system is used to tag German, Greek and Spanish, Projection+RNN and methods of Das & Petrov (2011), Duong et al (2013) and Gouws & Søgaard (2015).', unfold the steps to solve the question given next. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nQuestion: Which model and method combination achieves the highest accuracy for German OOV words?\n" }, { "role": "assistant", "content": "To find the highest accuracy for German OOV words, we look at the German OOV column and identify the highest value. The highest accuracy is 82.5, achieved by the model and method combination from (Das, 2011). So, the answer is {\"answer\": \"Das, 2011\"}" } ], "images": [ "scigen/train/354.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Token-level POS tagging accuracy for Simple Projection, SRNN using MultiVec bilingual word embeddings as input, RNN444For RNN models, only one (same) system is used to tag German, Greek and Spanish, Projection+RNN and methods of Das & Petrov (2011), Duong et al (2013) and Gouws & Søgaard (2015)." ], "reasoning": "To find the highest accuracy for German OOV words, we look at the German OOV column and identify the highest value. The highest accuracy is 82.5, achieved by the model and method combination from (Das, 2011).", "question": "Which model and method combination achieves the highest accuracy for German OOV words?", "answer": "So, the answer is {\"answer\": \"Das, 2011\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe BRNN model achieves higher accuracy than the SRNN model for French OOV words.\n" }, { "role": "assistant", "content": "Comparing the French OOV column, the BRNN model has an accuracy of 77.8, while the SRNN model has an accuracy of 70.9. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/354.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Token-level POS tagging accuracy for Simple Projection, SRNN using MultiVec bilingual word embeddings as input, RNN444For RNN models, only one (same) system is used to tag German, Greek and Spanish, Projection+RNN and methods of Das & Petrov (2011), Duong et al (2013) and Gouws & Søgaard (2015)." ], "reasoning": "Comparing the French OOV column, the BRNN model has an accuracy of 77.8, while the SRNN model has an accuracy of 70.9. Therefore, the statement is supported.", "statement": "The BRNN model achieves higher accuracy than the SRNN model for French OOV words.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Test perplexities on three document modeling tasks: 20-NewGroup (20-NG), Reuters corpus (RCV1) and CADE12 (CADE). Perplexities were calculated using 10 samples to estimate the variational lower-bound. The H-NVDM models perform best across all three datasets.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Integrating piecewise variables in H-NVDM models improves perplexity on 20-NG, RCV1, and CADE datasets, outperforming G-NVDM.Next, we observe that integrating our proposed piecewise variables yields even better results in our document modeling experiments, substantially improving over the baselines. More importantly, in the 20-NG and Reuters datasets, increasing the number of pieces from 3 to 5 further reduces perplexity. Thus, we have achieved a new state-of-the-art perplexity on 20 News-Groups task and — to the best of our knowledge – better perplexities on the CADE12 and RCV1 tasks compared to using a state-of-the-art model like the G-NVDM. We also evaluated the converged models using an non-parametric inference procedure, where a separate approximate posterior is learned for each test example in order to tighten the variational lower-bound. H-NVDM also performed best in this evaluation across all three datasets, which confirms that the performance improvement is due to the piecewise components. See appendix for details." } ], "images": [ "scigen/train/355.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Test perplexities on three document modeling tasks: 20-NewGroup (20-NG), Reuters corpus (RCV1) and CADE12 (CADE). Perplexities were calculated using 10 samples to estimate the variational lower-bound. The H-NVDM models perform best across all three datasets." ], "reasoning": "Integrating piecewise variables in H-NVDM models improves perplexity on 20-NG, RCV1, and CADE datasets, outperforming G-NVDM.", "description": "Next, we observe that integrating our proposed piecewise variables yields even better results in our document modeling experiments, substantially improving over the baselines. More importantly, in the 20-NG and Reuters datasets, increasing the number of pieces from 3 to 5 further reduces perplexity. Thus, we have achieved a new state-of-the-art perplexity on 20 News-Groups task and — to the best of our knowledge – better perplexities on the CADE12 and RCV1 tasks compared to using a state-of-the-art model like the G-NVDM. We also evaluated the converged models using an non-parametric inference procedure, where a separate approximate posterior is learned for each test example in order to tighten the variational lower-bound. H-NVDM also performed best in this evaluation across all three datasets, which confirms that the performance improvement is due to the piecewise components. See appendix for details.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 1: Test perplexities on three document modeling tasks: 20-NewGroup (20-NG), Reuters corpus (RCV1) and CADE12 (CADE). Perplexities were calculated using 10 samples to estimate the variational lower-bound. The H-NVDM models perform best across all three datasets.\nQuestion: Which model achieved the lowest perplexity on the 20-NG dataset?\n" }, { "role": "assistant", "content": "The table shows the perplexity scores for different models on the 20-NG dataset. The lowest score is 566, achieved by the H-NVDM-5 model. So, the answer is {\"answer\": \"H-NVDM-5\"}." } ], "images": [ "scigen/train/355.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Test perplexities on three document modeling tasks: 20-NewGroup (20-NG), Reuters corpus (RCV1) and CADE12 (CADE). Perplexities were calculated using 10 samples to estimate the variational lower-bound. The H-NVDM models perform best across all three datasets." ], "reasoning": "The table shows the perplexity scores for different models on the 20-NG dataset. The lowest score is 566, achieved by the H-NVDM-5 model.", "question": "Which model achieved the lowest perplexity on the 20-NG dataset?", "answer": "So, the answer is {\"answer\": \"H-NVDM-5\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nH-NVDM-3 outperformed G-NVDM on the CADE dataset.\n" }, { "role": "assistant", "content": "The table shows that H-NVDM-3 has a perplexity score of 258 on the CADE dataset, which is lower than the G-NVDM score of 339, indicating better performance. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/355.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Test perplexities on three document modeling tasks: 20-NewGroup (20-NG), Reuters corpus (RCV1) and CADE12 (CADE). Perplexities were calculated using 10 samples to estimate the variational lower-bound. The H-NVDM models perform best across all three datasets." ], "reasoning": "The table shows that H-NVDM-3 has a perplexity score of 258 on the CADE dataset, which is lower than the G-NVDM score of 339, indicating better performance.", "statement": "H-NVDM-3 outperformed G-NVDM on the CADE dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Ubuntu evaluation using F1 metrics w.r.t. activities and entities. G-VHRED, P-VHRED and H-VHRED all outperform the baseline HRED. G-VHRED performs best w.r.t. activities and H-VHRED performs best w.r.t. entities.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Latent variable models outperform HRED in both activities and entities, with G-VHRED excelling in frequent activities and H-VHRED in rare entities.All latent variable models outperform HRED w.r.t. both activities and entities. This strongly suggests that the high-level concepts represented by the latent variables help generate meaningful, goal-directed responses. Furthermore, each type of latent variable appears to help with a different aspects of the generation task. G-VHRED performs best w.r.t. activities (e.g. download, install and so on), which occur frequently in the dataset. This suggests that the Gaussian latent variables learn useful latent representations for frequent actions. On the other hand, H-VHRED performs best w.r.t. entities (e.g. Firefox, GNOME), which are often much rarer and mutually exclusive in the dataset. This suggests that the combination of Gaussian and piecewise latent variables help learn useful representations for entities, which could not be learned by Gaussian latent variables alone. We further conducted a qualitative analysis of the model responses, which supports these conclusions." } ], "images": [ "scigen/train/356.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ubuntu evaluation using F1 metrics w.r.t. activities and entities. G-VHRED, P-VHRED and H-VHRED all outperform the baseline HRED. G-VHRED performs best w.r.t. activities and H-VHRED performs best w.r.t. entities." ], "reasoning": "Latent variable models outperform HRED in both activities and entities, with G-VHRED excelling in frequent activities and H-VHRED in rare entities.", "description": "All latent variable models outperform HRED w.r.t. both activities and entities. This strongly suggests that the high-level concepts represented by the latent variables help generate meaningful, goal-directed responses. Furthermore, each type of latent variable appears to help with a different aspects of the generation task. G-VHRED performs best w.r.t. activities (e.g. download, install and so on), which occur frequently in the dataset. This suggests that the Gaussian latent variables learn useful latent representations for frequent actions. On the other hand, H-VHRED performs best w.r.t. entities (e.g. Firefox, GNOME), which are often much rarer and mutually exclusive in the dataset. This suggests that the combination of Gaussian and piecewise latent variables help learn useful representations for entities, which could not be learned by Gaussian latent variables alone. We further conducted a qualitative analysis of the model responses, which supports these conclusions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 3: Ubuntu evaluation using F1 metrics w.r.t. activities and entities. G-VHRED, P-VHRED and H-VHRED all outperform the baseline HRED. G-VHRED performs best w.r.t. activities and H-VHRED performs best w.r.t. entities.\nQuestion: Which model performs best in terms of activities and which in terms of entities?\n" }, { "role": "assistant", "content": "The table shows that G-VHRED has the highest score for activities (9.24) and H-VHRED has the highest score for entities (3.72). So, the answer is {\"answer\": \"G-VHRED for activities and H-VHRED for entities\"}." } ], "images": [ "scigen/train/356.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ubuntu evaluation using F1 metrics w.r.t. activities and entities. G-VHRED, P-VHRED and H-VHRED all outperform the baseline HRED. G-VHRED performs best w.r.t. activities and H-VHRED performs best w.r.t. entities." ], "reasoning": "The table shows that G-VHRED has the highest score for activities (9.24) and H-VHRED has the highest score for entities (3.72).", "question": "Which model performs best in terms of activities and which in terms of entities?", "answer": "So, the answer is {\"answer\": \"G-VHRED for activities and H-VHRED for entities\"}." }, { "messages": [ { "role": "user", "content": "H-VHRED performs best in terms of activities. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nH-VHRED performs best in terms of activities.\n" }, { "role": "assistant", "content": "The table shows that G-VHRED has the highest score for activities (9.24), not H-VHRED. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/356.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ubuntu evaluation using F1 metrics w.r.t. activities and entities. G-VHRED, P-VHRED and H-VHRED all outperform the baseline HRED. G-VHRED performs best w.r.t. activities and H-VHRED performs best w.r.t. entities." ], "reasoning": "The table shows that G-VHRED has the highest score for activities (9.24), not H-VHRED.", "statement": "H-VHRED performs best in terms of activities.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 6: Approximate posterior word encodings (20-NG). For P-KL, we bold every case where piecewise variables showed greater word sensitivity than Gaussian variables w/in the same hybrid model.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The hybrid model shifts the responsibility of capturing temporal words from Gaussian variables to piecewise variables, which are better equipped to handle certain latent factors, especially nationality-based adjectives.The Gaussian variables were originally were sensitive to some of the words in the table. However, in the hybrid model, nearly all of the temporal words that the Gaussian variables were once more sensitive to now more strongly affect the piecewise variables, which themselves also capture all of the words that were originally missed This shift in responsibility indicates that the piecewise constant variables are better equipped to handle certain latent factors. This effect appears to be particularly strong in the case of certain nationality-based adjectives (e.g., “american”, “israeli”, etc.). While the G-NVDM could model multi-modality in the data to some degree, this work would be primarily done in the model’s decoder. In the H-NVDM, the piecewise variables provide an explicit mechanism for capturing modes in the unknown target distribution, so it makes sense that the model would learn to use the piecewise variables instead, thus freeing up the Gaussian variables to capture other aspects of the data, as we found was the case with names (e.g., “jesus”, “kent”, etc.)." } ], "images": [ "scigen/train/357.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Approximate posterior word encodings (20-NG). For P-KL, we bold every case where piecewise variables showed greater word sensitivity than Gaussian variables w/in the same hybrid model." ], "reasoning": "The hybrid model shifts the responsibility of capturing temporal words from Gaussian variables to piecewise variables, which are better equipped to handle certain latent factors, especially nationality-based adjectives.", "description": "The Gaussian variables were originally were sensitive to some of the words in the table. However, in the hybrid model, nearly all of the temporal words that the Gaussian variables were once more sensitive to now more strongly affect the piecewise variables, which themselves also capture all of the words that were originally missed This shift in responsibility indicates that the piecewise constant variables are better equipped to handle certain latent factors. This effect appears to be particularly strong in the case of certain nationality-based adjectives (e.g., “american”, “israeli”, etc.). While the G-NVDM could model multi-modality in the data to some degree, this work would be primarily done in the model’s decoder. In the H-NVDM, the piecewise variables provide an explicit mechanism for capturing modes in the unknown target distribution, so it makes sense that the model would learn to use the piecewise variables instead, thus freeing up the Gaussian variables to capture other aspects of the data, as we found was the case with names (e.g., “jesus”, “kent”, etc.).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 6: Approximate posterior word encodings (20-NG). For P-KL, we bold every case where piecewise variables showed greater word sensitivity than Gaussian variables w/in the same hybrid model.', answer the following question. Give detailed solution consisting of each step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest P-KL value for the word 'week'?\n" }, { "role": "assistant", "content": "The table shows the P-KL values for different words under the H-NVDM-5 model. For the word 'week', the P-KL value is 58, which is the highest among the models. So, the answer is {\"answer\": \"H-NVDM-5\"}." } ], "images": [ "scigen/train/357.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Approximate posterior word encodings (20-NG). For P-KL, we bold every case where piecewise variables showed greater word sensitivity than Gaussian variables w/in the same hybrid model." ], "reasoning": "The table shows the P-KL values for different words under the H-NVDM-5 model. For the word 'week', the P-KL value is 58, which is the highest among the models.", "question": "Which model has the highest P-KL value for the word 'week'?", "answer": "So, the answer is {\"answer\": \"H-NVDM-5\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe G-NVDM model has a higher G-KL value for the word 'time' compared to the H-NVDM-5 model.\n" }, { "role": "assistant", "content": "The table shows that the G-KL value for 'time' under the G-NVDM model is 55, while under the H-NVDM-5 model, it is 22. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/357.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Approximate posterior word encodings (20-NG). For P-KL, we bold every case where piecewise variables showed greater word sensitivity than Gaussian variables w/in the same hybrid model." ], "reasoning": "The table shows that the G-KL value for 'time' under the G-NVDM model is 55, while under the H-NVDM-5 model, it is 22. Therefore, the statement is supported.", "statement": "The G-NVDM model has a higher G-KL value for the word 'time' compared to the H-NVDM-5 model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 6: Approximate posterior word encodings (20-NG). For P-KL, we bold every case where piecewise variables showed greater word sensitivity than Gaussian variables w/in the same hybrid model.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The hybrid model shifts the responsibility of capturing temporal words from Gaussian variables to piecewise variables, which are better equipped to handle certain latent factors, especially nationality-based adjectives.The Gaussian variables were originally were sensitive to some of the words in the table. However, in the hybrid model, nearly all of the temporal words that the Gaussian variables were once more sensitive to now more strongly affect the piecewise variables, which themselves also capture all of the words that were originally missed This shift in responsibility indicates that the piecewise constant variables are better equipped to handle certain latent factors. This effect appears to be particularly strong in the case of certain nationality-based adjectives (e.g., “american”, “israeli”, etc.). While the G-NVDM could model multi-modality in the data to some degree, this work would be primarily done in the model’s decoder. In the H-NVDM, the piecewise variables provide an explicit mechanism for capturing modes in the unknown target distribution, so it makes sense that the model would learn to use the piecewise variables instead, thus freeing up the Gaussian variables to capture other aspects of the data, as we found was the case with names (e.g., “jesus”, “kent”, etc.)." } ], "images": [ "scigen/train/358.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Approximate posterior word encodings (20-NG). For P-KL, we bold every case where piecewise variables showed greater word sensitivity than Gaussian variables w/in the same hybrid model." ], "reasoning": "The hybrid model shifts the responsibility of capturing temporal words from Gaussian variables to piecewise variables, which are better equipped to handle certain latent factors, especially nationality-based adjectives.", "description": "The Gaussian variables were originally were sensitive to some of the words in the table. However, in the hybrid model, nearly all of the temporal words that the Gaussian variables were once more sensitive to now more strongly affect the piecewise variables, which themselves also capture all of the words that were originally missed This shift in responsibility indicates that the piecewise constant variables are better equipped to handle certain latent factors. This effect appears to be particularly strong in the case of certain nationality-based adjectives (e.g., “american”, “israeli”, etc.). While the G-NVDM could model multi-modality in the data to some degree, this work would be primarily done in the model’s decoder. In the H-NVDM, the piecewise variables provide an explicit mechanism for capturing modes in the unknown target distribution, so it makes sense that the model would learn to use the piecewise variables instead, thus freeing up the Gaussian variables to capture other aspects of the data, as we found was the case with names (e.g., “jesus”, “kent”, etc.).", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 6: Approximate posterior word encodings (20-NG). For P-KL, we bold every case where piecewise variables showed greater word sensitivity than Gaussian variables w/in the same hybrid model.', answer the question below by showing each progressive step toward the answer. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has a higher G-KL value for the word 'jesus'?\n" }, { "role": "assistant", "content": "The table shows the G-KL values for both G-NVDM and H-NVDM-5 models. For the word 'jesus', the G-KL value for G-NVDM is 28 and for H-NVDM-5 is 87. Therefore, the H-NVDM-5 model has a higher G-KL value for the word 'jesus'. So, the answer is {\"answer\": \"H-NVDM-5\"}." } ], "images": [ "scigen/train/358.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Approximate posterior word encodings (20-NG). For P-KL, we bold every case where piecewise variables showed greater word sensitivity than Gaussian variables w/in the same hybrid model." ], "reasoning": "The table shows the G-KL values for both G-NVDM and H-NVDM-5 models. For the word 'jesus', the G-KL value for G-NVDM is 28 and for H-NVDM-5 is 87. Therefore, the H-NVDM-5 model has a higher G-KL value for the word 'jesus'.", "question": "Which model has a higher G-KL value for the word 'jesus'?", "answer": "So, the answer is {\"answer\": \"H-NVDM-5\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The word 'american' has a higher P-KL value in the H-NVDM-5 model than the word 'israeli'. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe word 'american' has a higher P-KL value in the H-NVDM-5 model than the word 'israeli'.\n" }, { "role": "assistant", "content": "The table shows the P-KL values for the H-NVDM-5 model. The P-KL value for 'american' is 40 and for 'israeli' is 17. Therefore, the statement is supported by the table. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/358.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Approximate posterior word encodings (20-NG). For P-KL, we bold every case where piecewise variables showed greater word sensitivity than Gaussian variables w/in the same hybrid model." ], "reasoning": "The table shows the P-KL values for the H-NVDM-5 model. The P-KL value for 'american' is 40 and for 'israeli' is 17. Therefore, the statement is supported by the table.", "statement": "The word 'american' has a higher P-KL value in the H-NVDM-5 model than the word 'israeli'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 6: Bootstrapping results on MLDoc with and without pretraining, trained on 1k/10k LASER labels.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Pretraining with MultiFiT improves performance and robustness to noise, outperforming a randomly initialized model on Spanish MLDoc with up to 65% noisy examples.S6SS0SSS0Px3 Robustness to noise We suspect that MultiFiT is able to outperform its teacher as the information from pretraining makes it robust to label noise. To test this hypothesis, we train MultiFiT and a randomly initialized model with the same architecture on 1k and 10k examples of the Spanish MLDoc. The pretrained MultiFiT is able to partially ignore the noise, up to 65% of noisy training examples. Without pretraining, the model does not exceed the theoretical baseline (the percentage of correct examples). Pretraining enables MultiFiT to achieve much better performance compared to a randomly initialised model. Both results together suggest a) that pretraining increases robustness to noise and b) that information from monolingual and cross-lingual language models is complementary." } ], "images": [ "scigen/train/359.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Bootstrapping results on MLDoc with and without pretraining, trained on 1k/10k LASER labels." ], "reasoning": "Pretraining with MultiFiT improves performance and robustness to noise, outperforming a randomly initialized model on Spanish MLDoc with up to 65% noisy examples.", "description": "S6SS0SSS0Px3 Robustness to noise We suspect that MultiFiT is able to outperform its teacher as the information from pretraining makes it robust to label noise. To test this hypothesis, we train MultiFiT and a randomly initialized model with the same architecture on 1k and 10k examples of the Spanish MLDoc. The pretrained MultiFiT is able to partially ignore the noise, up to 65% of noisy training examples. Without pretraining, the model does not exceed the theoretical baseline (the percentage of correct examples). Pretraining enables MultiFiT to achieve much better performance compared to a randomly initialised model. Both results together suggest a) that pretraining increases robustness to noise and b) that information from monolingual and cross-lingual language models is complementary.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 6: Bootstrapping results on MLDoc with and without pretraining, trained on 1k/10k LASER labels.'.\n\nQuestion: What is the performance of MultiFiT on the Spanish MLDoc dataset with 1k examples?\n" }, { "role": "assistant", "content": "The table shows the performance of MultiFiT on various datasets. For the Spanish (es) MLDoc dataset with 1k examples, MultiFiT achieves a score of 78.92. So, the answer is {\"answer\": \"78.92\"}." } ], "images": [ "scigen/train/359.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Bootstrapping results on MLDoc with and without pretraining, trained on 1k/10k LASER labels." ], "reasoning": "The table shows the performance of MultiFiT on various datasets. For the Spanish (es) MLDoc dataset with 1k examples, MultiFiT achieves a score of 78.92.", "question": "What is the performance of MultiFiT on the Spanish MLDoc dataset with 1k examples?", "answer": "So, the answer is {\"answer\": \"78.92\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis MultiFiT outperforms the randomly initialized model on the Spanish MLDoc dataset with 1k examples. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nMultiFiT outperforms the randomly initialized model on the Spanish MLDoc dataset with 1k examples.\n" }, { "role": "assistant", "content": "The table shows that MultiFiT achieves a score of 78.92 on the Spanish MLDoc dataset with 1k examples, while the randomly initialized model achieves a score of 70.50. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/359.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Bootstrapping results on MLDoc with and without pretraining, trained on 1k/10k LASER labels." ], "reasoning": "The table shows that MultiFiT achieves a score of 78.92 on the Spanish MLDoc dataset with 1k examples, while the randomly initialized model achieves a score of 70.50.", "statement": "MultiFiT outperforms the randomly initialized model on the Spanish MLDoc dataset with 1k examples.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Comparison of zero-shot and supervised methods on MLDoc.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Fine-tuning with even a small number of target examples yields superior performance compared to zero-shot approaches, with MultiFiT outperforming all methods when fine-tuned with 1,000 target examples.In the zero-shot setting, MultiBERT underperforms the comparison methods as the shared embedding space between many languages is overly restrictive. Our monolingual LMs outperform their cross-lingual teacher LASER in almost every setting. When fine-tuned with only 100 target language examples, they are able to outperform all zero-shot approaches except MultiFiT on de and fr. This calls into question the need for zero-shot approaches, as fine-tuning with even a small number of target examples is able to yield superior performance. When fine-tuning with 1,000 target examples, MultiFiT—even without pretraining—outperforms all comparison methods, including monolingual BERT." } ], "images": [ "scigen/train/360.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of zero-shot and supervised methods on MLDoc." ], "reasoning": "Fine-tuning with even a small number of target examples yields superior performance compared to zero-shot approaches, with MultiFiT outperforming all methods when fine-tuned with 1,000 target examples.", "description": "In the zero-shot setting, MultiBERT underperforms the comparison methods as the shared embedding space between many languages is overly restrictive. Our monolingual LMs outperform their cross-lingual teacher LASER in almost every setting. When fine-tuned with only 100 target language examples, they are able to outperform all zero-shot approaches except MultiFiT on de and fr. This calls into question the need for zero-shot approaches, as fine-tuning with even a small number of target examples is able to yield superior performance. When fine-tuning with 1,000 target examples, MultiFiT—even without pretraining—outperforms all comparison methods, including monolingual BERT.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Comparison of zero-shot and supervised methods on MLDoc.', and sequentially solve the question, writing out each step of your reasoning process. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method outperforms all others when fine-tuned with 1,000 target examples?\n" }, { "role": "assistant", "content": "The table shows that MultiFiT, when fine-tuned with 1,000 target examples, has the highest scores across all languages compared to other methods. So, the answer is {\"answer\": \"MultiFiT\"}" } ], "images": [ "scigen/train/360.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of zero-shot and supervised methods on MLDoc." ], "reasoning": "The table shows that MultiFiT, when fine-tuned with 1,000 target examples, has the highest scores across all languages compared to other methods.", "question": "Which method outperforms all others when fine-tuned with 1,000 target examples?", "answer": "So, the answer is {\"answer\": \"MultiFiT\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim MultiBERT outperforms all other methods when fine-tuned with 1,000 target examples., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nMultiBERT outperforms all other methods when fine-tuned with 1,000 target examples.\n" }, { "role": "assistant", "content": "The table shows that MultiFiT, not MultiBERT, has the highest scores when fine-tuned with 1,000 target examples across all languages. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/360.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of zero-shot and supervised methods on MLDoc." ], "reasoning": "The table shows that MultiFiT, not MultiBERT, has the highest scores when fine-tuned with 1,000 target examples across all languages.", "statement": "MultiBERT outperforms all other methods when fine-tuned with 1,000 target examples.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: Comparison of zero-shot, translation-based and supervised methods (with 2k training examples) on all domains of CLS. MT-BOW and CL-SCL results are from Zhou et al. (2016).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "MultiFiT outperforms its zero-shot teacher LASER and other sophisticated models across almost all domains, and in the supervised setting, it also outperforms multilingual BERT.Cls MultiFiT is able to outperform its zero-shot teacher LASER across all domains. Importantly, the bootstrapped monolingual model also outperforms more sophisticated models that are trained on translations across almost all domains. In the supervised setting, MultiFiT similarly outperforms multilingual BERT. For both datasets, our methods that have been pretrained on 100 million tokens outperform both multilingual BERT and LASER, models that have been trained with orders of magnitude more data and compute." } ], "images": [ "scigen/train/361.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of zero-shot, translation-based and supervised methods (with 2k training examples) on all domains of CLS. MT-BOW and CL-SCL results are from Zhou et al. (2016)." ], "reasoning": "MultiFiT outperforms its zero-shot teacher LASER and other sophisticated models across almost all domains, and in the supervised setting, it also outperforms multilingual BERT.", "description": "Cls MultiFiT is able to outperform its zero-shot teacher LASER across all domains. Importantly, the bootstrapped monolingual model also outperforms more sophisticated models that are trained on translations across almost all domains. In the supervised setting, MultiFiT similarly outperforms multilingual BERT. For both datasets, our methods that have been pretrained on 100 million tokens outperform both multilingual BERT and LASER, models that have been trained with orders of magnitude more data and compute.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Comparison of zero-shot, translation-based and supervised methods (with 2k training examples) on all domains of CLS. MT-BOW and CL-SCL results are from Zhou et al. (2016).\nQuestion: Which model has the highest score in the 'de Books' domain?\nGive a detailed response to the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the 'de Books' column, the highest score is 93.19, which is achieved by the Supervised MultiFiT model. So, the answer is {\"answer\": \"Supervised MultiFiT\"}" } ], "images": [ "scigen/train/361.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of zero-shot, translation-based and supervised methods (with 2k training examples) on all domains of CLS. MT-BOW and CL-SCL results are from Zhou et al. (2016)." ], "reasoning": "By examining the 'de Books' column, the highest score is 93.19, which is achieved by the Supervised MultiFiT model.", "question": "Which model has the highest score in the 'de Books' domain?", "answer": "So, the answer is {\"answer\": \"Supervised MultiFiT\"}" }, { "messages": [ { "role": "user", "content": "MultiFiT outperforms multilingual BERT in the 'fr Music' domain. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nMultiFiT outperforms multilingual BERT in the 'fr Music' domain.\n" }, { "role": "assistant", "content": "In the 'fr Music' domain, MultiFiT (Supervised) has a score of 89.55, while multilingual BERT (Supervised) has a score of 86.90, indicating that MultiFiT outperforms multilingual BERT. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/361.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of zero-shot, translation-based and supervised methods (with 2k training examples) on all domains of CLS. MT-BOW and CL-SCL results are from Zhou et al. (2016)." ], "reasoning": "In the 'fr Music' domain, MultiFiT (Supervised) has a score of 89.55, while multilingual BERT (Supervised) has a score of 86.90, indicating that MultiFiT outperforms multilingual BERT.", "statement": "MultiFiT outperforms multilingual BERT in the 'fr Music' domain.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: Comparison of MultiFiT results with different pretraining corpora and ULMFiT, fine-tuned with 1k labels on MLDoc.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "MultiFiT significantly outperforms ULMFiT, especially in Chinese, due to better tokenization.Pretraining on more data generally helps. MultiFiT outperforms ULMFiT significantly; the performance improvement is particularly pronounced in Chinese where ULMFiT’s word-based tokenization underperformed." } ], "images": [ "scigen/train/362.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison of MultiFiT results with different pretraining corpora and ULMFiT, fine-tuned with 1k labels on MLDoc." ], "reasoning": "MultiFiT significantly outperforms ULMFiT, especially in Chinese, due to better tokenization.", "description": "Pretraining on more data generally helps. MultiFiT outperforms ULMFiT significantly; the performance improvement is particularly pronounced in Chinese where ULMFiT’s word-based tokenization underperformed.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 5: Comparison of MultiFiT results with different pretraining corpora and ULMFiT, fine-tuned with 1k labels on MLDoc.'. Solve the following question based on the table.\n\nQuestion: What is the performance improvement of MultiFiT over ULMFiT in Chinese?\nLet's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the performance improvement, compare the scores of MultiFiT and ULMFiT in the 'zh' column. MultiFiT has a score of 92.52, while ULMFiT has a score of 66.82. The improvement is 92.52 - 66.82. So, the answer is {\"answer\": \"25.7\"}." } ], "images": [ "scigen/train/362.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison of MultiFiT results with different pretraining corpora and ULMFiT, fine-tuned with 1k labels on MLDoc." ], "reasoning": "To find the performance improvement, compare the scores of MultiFiT and ULMFiT in the 'zh' column. MultiFiT has a score of 92.52, while ULMFiT has a score of 66.82. The improvement is 92.52 - 66.82.", "question": "What is the performance improvement of MultiFiT over ULMFiT in Chinese?", "answer": "So, the answer is {\"answer\": \"25.7\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nMultiFiT performs better than ULMFiT in all languages listed.\n" }, { "role": "assistant", "content": "By comparing the scores of MultiFiT and ULMFiT in the 'de', 'es', and 'zh' columns, it is evident that MultiFiT has higher scores in all three languages. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/362.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison of MultiFiT results with different pretraining corpora and ULMFiT, fine-tuned with 1k labels on MLDoc." ], "reasoning": "By comparing the scores of MultiFiT and ULMFiT in the 'de', 'es', and 'zh' columns, it is evident that MultiFiT has higher scores in all three languages.", "statement": "MultiFiT performs better than ULMFiT in all languages listed.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 7: Comparison of different tokenization strategies for different languages on MLDoc.'.\n" }, { "role": "assistant", "content": "Subword tokenization, with a smaller vocabulary size of 15k, outperforms word-based tokenization with a vocabulary size of 60k in most languages, making it faster to train.Tokenization Subword tokenization has been found useful for language modeling with morphologically rich languages Czapla et al. We train models with the best performing vocabulary sizes for subword (15k) and regular word-based tokenization (60k) with the Moses tokenizer Koehn et al. Subword tokenization outperforms word-based tokenization on most languages, while being faster to train due to the smaller vocabulary size." } ], "images": [ "scigen/train/363.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Comparison of different tokenization strategies for different languages on MLDoc." ], "reasoning": "Subword tokenization, with a smaller vocabulary size of 15k, outperforms word-based tokenization with a vocabulary size of 60k in most languages, making it faster to train.", "description": "Tokenization Subword tokenization has been found useful for language modeling with morphologically rich languages Czapla et al. We train models with the best performing vocabulary sizes for subword (15k) and regular word-based tokenization (60k) with the Moses tokenizer Koehn et al. Subword tokenization outperforms word-based tokenization on most languages, while being faster to train due to the smaller vocabulary size.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 7: Comparison of different tokenization strategies for different languages on MLDoc.', and tackle the question below in a sequential manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich tokenization method performs better for the Italian language?\n" }, { "role": "assistant", "content": "By comparing the scores for Italian (it) in the table, subword tokenization has a score of 94.75, while word-based tokenization has a score of 89.97. Therefore, subword tokenization performs better. So, the answer is {\"answer\": \"Subword\"}" } ], "images": [ "scigen/train/363.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Comparison of different tokenization strategies for different languages on MLDoc." ], "reasoning": "By comparing the scores for Italian (it) in the table, subword tokenization has a score of 94.75, while word-based tokenization has a score of 89.97. Therefore, subword tokenization performs better.", "question": "Which tokenization method performs better for the Italian language?", "answer": "So, the answer is {\"answer\": \"Subword\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSubword tokenization performs better than word-based tokenization for the Russian language.\n" }, { "role": "assistant", "content": "By comparing the scores for Russian (ru) in the table, subword tokenization has a score of 87.65, while word-based tokenization has a score of 88.02. Therefore, word-based tokenization performs better. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/363.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Comparison of different tokenization strategies for different languages on MLDoc." ], "reasoning": "By comparing the scores for Russian (ru) in the table, subword tokenization has a score of 87.65, while word-based tokenization has a score of 88.02. Therefore, word-based tokenization performs better.", "statement": "Subword tokenization performs better than word-based tokenization for the Russian language.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 7: Confusion Matrix of the POS tags assigned by SpeedRead over the words of sections 22-24 of PTB. O represents all the other not mentioned tags.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Proper nouns are often misclassified due to capitalization, and functional words like adverbs and prepositions require deeper contextual understanding for accurate tagging.Proper nouns are the second source of errors as most of the capitalized words will be mistakenly tagged as proper nouns while they are either adjectives or nouns. Such errors are the result of the weak logic implemented in the backoff tagger in SpeedRead, where regular expressions are applied in sequence returning the first match. Other types of errors are adverbs (RB) and propositions (IN). These errors are mainly because of the ambiguity of the functional words. Functional words need deeper understanding of discourse, semantic and syntactic nature of the text. Taking into consideration the contexts around the words improves the accuracy of tagging. However, trigrams are still small to be considered sufficient context for resolving all the ambiguities." } ], "images": [ "scigen/train/364.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Confusion Matrix of the POS tags assigned by SpeedRead over the words of sections 22-24 of PTB. O represents all the other not mentioned tags." ], "reasoning": "Proper nouns are often misclassified due to capitalization, and functional words like adverbs and prepositions require deeper contextual understanding for accurate tagging.", "description": "Proper nouns are the second source of errors as most of the capitalized words will be mistakenly tagged as proper nouns while they are either adjectives or nouns. Such errors are the result of the weak logic implemented in the backoff tagger in SpeedRead, where regular expressions are applied in sequence returning the first match. Other types of errors are adverbs (RB) and propositions (IN). These errors are mainly because of the ambiguity of the functional words. Functional words need deeper understanding of discourse, semantic and syntactic nature of the text. Taking into consideration the contexts around the words improves the accuracy of tagging. However, trigrams are still small to be considered sufficient context for resolving all the ambiguities.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. The question is related to the given table titled 'Table 7: Confusion Matrix of the POS tags assigned by SpeedRead over the words of sections 22-24 of PTB. O represents all the other not mentioned tags.'.\n\nQuestion: What is the total number of errors for the tag 'RB'?\n" }, { "role": "assistant", "content": "To find the total number of errors for the tag 'RB', sum the values in the 'RB' column. So, the answer is {\"answer\": \"4810\"}." } ], "images": [ "scigen/train/364.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Confusion Matrix of the POS tags assigned by SpeedRead over the words of sections 22-24 of PTB. O represents all the other not mentioned tags." ], "reasoning": "To find the total number of errors for the tag 'RB', sum the values in the 'RB' column.", "question": "What is the total number of errors for the tag 'RB'?", "answer": "So, the answer is {\"answer\": \"4810\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe tag 'NN' has the highest number of errors in the table.\n" }, { "role": "assistant", "content": "By comparing the total errors for each tag, 'NN' has the highest number of errors with a value of 17196. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/364.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Confusion Matrix of the POS tags assigned by SpeedRead over the words of sections 22-24 of PTB. O represents all the other not mentioned tags." ], "reasoning": "By comparing the total errors for each tag, 'NN' has the highest number of errors with a value of 17196.", "statement": "The tag 'NN' has the highest number of errors in the table.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 8: F1 scores of the chunking phase using different POS tags. F1 score is calculated over tokens and not entities.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "SpeedRead POS tagging is sufficient for chunking, and SENNA and SpeedRead POS taggers are more aggressive and better for detection.This score is calculated over the chunking tags of the words. I and B tags are considered as one class while O is left as it is. that using better POS taggers does not necessarily produce better results. The quality of SpeedRead POS tagging is sufficient for the chunking stage. SENNA and SpeedRead POS taggers work better for the detection phase because they are more aggressive, assigning the NNP tag to any capitalized word. On the other hand, Stanford tagger prefers to assign the tag of the lowered case shape of the word, if it is a common word." } ], "images": [ "scigen/train/365.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: F1 scores of the chunking phase using different POS tags. F1 score is calculated over tokens and not entities." ], "reasoning": "SpeedRead POS tagging is sufficient for chunking, and SENNA and SpeedRead POS taggers are more aggressive and better for detection.", "description": "This score is calculated over the chunking tags of the words. I and B tags are considered as one class while O is left as it is. that using better POS taggers does not necessarily produce better results. The quality of SpeedRead POS tagging is sufficient for the chunking stage. SENNA and SpeedRead POS taggers work better for the detection phase because they are more aggressive, assigning the NNP tag to any capitalized word. On the other hand, Stanford tagger prefers to assign the tag of the lowered case shape of the word, if it is a common word.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 8: F1 scores of the chunking phase using different POS tags. F1 score is calculated over tokens and not entities.' to sequentially solve the question presented below. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich POS tagger combination achieved the highest test score?\n" }, { "role": "assistant", "content": "By comparing the test scores of all POS tagger combinations, the highest test score is 93.80 achieved by SR+SENNA POS. So, the answer is {\"answer\": \"SR+SENNA POS\"}" } ], "images": [ "scigen/train/365.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: F1 scores of the chunking phase using different POS tags. F1 score is calculated over tokens and not entities." ], "reasoning": "By comparing the test scores of all POS tagger combinations, the highest test score is 93.80 achieved by SR+SENNA POS.", "question": "Which POS tagger combination achieved the highest test score?", "answer": "So, the answer is {\"answer\": \"SR+SENNA POS\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nSR+Stanford POS L3W achieved the highest development score.\n" }, { "role": "assistant", "content": "By comparing the development scores of all POS tagger combinations, SR+SENNA POS achieved the highest development score of 95.07, not SR+Stanford POS L3W. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/365.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: F1 scores of the chunking phase using different POS tags. F1 score is calculated over tokens and not entities." ], "reasoning": "By comparing the development scores of all POS tagger combinations, SR+SENNA POS achieved the highest development score of 95.07, not SR+Stanford POS L3W.", "statement": "SR+Stanford POS L3W achieved the highest development score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 10: F1 scores calculated using conlleval.pl script for NER taggers. The table shows that SpeedRead F1 score is 10% below the sate-of-art achieved by SENNA.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The classification phase achieves close scores to state-of-the-art classifiers with chunked input, but scores drop by around 9.5% in F1 scores with SpeedRead chunks.First row shows that, given chunked input, the classification phase is able to achieve close scores to the state-of- art classifiers. However, given the chunks generated by SpeedRead, the scores drop around 9.5% in F1 scores." } ], "images": [ "scigen/train/366.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: F1 scores calculated using conlleval.pl script for NER taggers. The table shows that SpeedRead F1 score is 10% below the sate-of-art achieved by SENNA." ], "reasoning": "The classification phase achieves close scores to state-of-the-art classifiers with chunked input, but scores drop by around 9.5% in F1 scores with SpeedRead chunks.", "description": "First row shows that, given chunked input, the classification phase is able to achieve close scores to the state-of- art classifiers. However, given the chunks generated by SpeedRead, the scores drop around 9.5% in F1 scores.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 10: F1 scores calculated using conlleval.pl script for NER taggers. The table shows that SpeedRead F1 score is 10% below the sate-of-art achieved by SENNA.', solve the following question step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the difference in test scores between SR+Gold Chunks and SpeedRead?\n" }, { "role": "assistant", "content": "The test score for SR+Gold Chunks is 87.87, and for SpeedRead, it is 78.28. The difference is 87.87 - 78.28. So, the answer is {\"answer\": \"9.59\"}" } ], "images": [ "scigen/train/366.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: F1 scores calculated using conlleval.pl script for NER taggers. The table shows that SpeedRead F1 score is 10% below the sate-of-art achieved by SENNA." ], "reasoning": "The test score for SR+Gold Chunks is 87.87, and for SpeedRead, it is 78.28. The difference is 87.87 - 78.28.", "question": "What is the difference in test scores between SR+Gold Chunks and SpeedRead?", "answer": "So, the answer is {\"answer\": \"9.59\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Stanford has the highest training score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nStanford has the highest training score.\n" }, { "role": "assistant", "content": "The training scores are 90.80 for SR+Gold Chunks, 82.05 for SpeedRead, 99.28 for Stanford, and 96.75 for SENNA. Stanford has the highest training score of 99.28. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/366.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: F1 scores calculated using conlleval.pl script for NER taggers. The table shows that SpeedRead F1 score is 10% below the sate-of-art achieved by SENNA." ], "reasoning": "The training scores are 90.80 for SR+Gold Chunks, 82.05 for SpeedRead, 99.28 for Stanford, and 96.75 for SENNA. Stanford has the highest training score of 99.28.", "statement": "Stanford has the highest training score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 12: Speed of different NER taggers. SpeedRead is faster by 13.9 times using half the memory consumed by Stanford.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "SENNA achieves close accuracy with twice the speed and less memory usage compared to Stanford, while SpeedRead focuses on speed, achieving a 13.9x speedup and half the memory usage of Stanford.SENNA achieves close accuracy with twice the speed and less memory usage. SpeedRead takes another approach by focusing on speed. We are able to speed up the pipeline to the factor of 13. SpeedRead’s memory footprint is half the memory consumed by the Stanford pipeline. Even though SpeedRead’s accuracy is not close to the state-of-art, it still achieves 18% increase over the CONLL 2003 baseline. Moreover, adapting the pipeline to new domains could be easily done by integrating other knowledge base sources as freebase or Wikipedia. SENNA and SpeedRead are able to calculate POS tags at the end of the NER phase without extra computation while that is not true of Stanford pipeline standalone NER application. Using Stanford corenlp pipeline does not guarantee better execution time." } ], "images": [ "scigen/train/367.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 12: Speed of different NER taggers. SpeedRead is faster by 13.9 times using half the memory consumed by Stanford." ], "reasoning": "SENNA achieves close accuracy with twice the speed and less memory usage compared to Stanford, while SpeedRead focuses on speed, achieving a 13.9x speedup and half the memory usage of Stanford.", "description": "SENNA achieves close accuracy with twice the speed and less memory usage. SpeedRead takes another approach by focusing on speed. We are able to speed up the pipeline to the factor of 13. SpeedRead’s memory footprint is half the memory consumed by the Stanford pipeline. Even though SpeedRead’s accuracy is not close to the state-of-art, it still achieves 18% increase over the CONLL 2003 baseline. Moreover, adapting the pipeline to new domains could be easily done by integrating other knowledge base sources as freebase or Wikipedia. SENNA and SpeedRead are able to calculate POS tags at the end of the NER phase without extra computation while that is not true of Stanford pipeline standalone NER application. Using Stanford corenlp pipeline does not guarantee better execution time.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 12: Speed of different NER taggers. SpeedRead is faster by 13.9 times using half the memory consumed by Stanford.\nQuestion: Which NER tagger has the highest token processing speed and what is its memory usage?\n" }, { "role": "assistant", "content": "From the table, SpeedRead has the highest token processing speed at 153,194 tokens/sec. Its memory usage is 950 MiB. So, the answer is {\"answer\": \"SpeedRead, 950 MiB\"}" } ], "images": [ "scigen/train/367.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 12: Speed of different NER taggers. SpeedRead is faster by 13.9 times using half the memory consumed by Stanford." ], "reasoning": "From the table, SpeedRead has the highest token processing speed at 153,194 tokens/sec. Its memory usage is 950 MiB.", "question": "Which NER tagger has the highest token processing speed and what is its memory usage?", "answer": "So, the answer is {\"answer\": \"SpeedRead, 950 MiB\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nSpeedRead uses less memory than SENNA.\n" }, { "role": "assistant", "content": "The table shows that SpeedRead uses 950 MiB of memory, while SENNA uses 150 MiB of memory. Therefore, SpeedRead does not use less memory than SENNA. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/367.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 12: Speed of different NER taggers. SpeedRead is faster by 13.9 times using half the memory consumed by Stanford." ], "reasoning": "The table shows that SpeedRead uses 950 MiB of memory, while SENNA uses 150 MiB of memory. Therefore, SpeedRead does not use less memory than SENNA.", "statement": "SpeedRead uses less memory than SENNA.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Blind speech inpainting (section 3.2). STOI & PESQ were computed between the recovered and the actual speech segments from validation set and averaged. The best scores were denoted in bold. PC - informed (partial conv.), FC - blind (full conv.) inpainting.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The informed inpainting framework (PC-gaps) generally outperforms the blind counterpart (FC-gaps, -noise) in recovering missing or degraded speech parts, except when high-amplitude noise is mixed with the input speech, where the blind setup (FC-additive) performs best for larger intrusions.All of the considered framework configurations successfully recovered missing or degraded parts of the input speech resulting in improved STOI and PESQ scores. The framework for the informed inpainting (PC-gaps) yielded better results, as compared to its blind counterpart, when the masked parts of the input were set to either zeros or random noise (FC -gaps, -noise, respectively). Notably, when input speech was mixed with, not replaced by, the high-amplitude noise, the framework operating in the blind setup (FC-additive) led to the best results for larger intrusions of all the considered shapes. These results suggest that the framework for the blind speech inpainting takes advantage of the original information underlying the noisy intrusions, even when the transient SNR is very low (below -10 dB)." } ], "images": [ "scigen/train/368.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Blind speech inpainting (section 3.2). STOI & PESQ were computed between the recovered and the actual speech segments from validation set and averaged. The best scores were denoted in bold. PC - informed (partial conv.), FC - blind (full conv.) inpainting." ], "reasoning": "The informed inpainting framework (PC-gaps) generally outperforms the blind counterpart (FC-gaps, -noise) in recovering missing or degraded speech parts, except when high-amplitude noise is mixed with the input speech, where the blind setup (FC-additive) performs best for larger intrusions.", "description": "All of the considered framework configurations successfully recovered missing or degraded parts of the input speech resulting in improved STOI and PESQ scores. The framework for the informed inpainting (PC-gaps) yielded better results, as compared to its blind counterpart, when the masked parts of the input were set to either zeros or random noise (FC -gaps, -noise, respectively). Notably, when input speech was mixed with, not replaced by, the high-amplitude noise, the framework operating in the blind setup (FC-additive) led to the best results for larger intrusions of all the considered shapes. These results suggest that the framework for the blind speech inpainting takes advantage of the original information underlying the noisy intrusions, even when the transient SNR is very low (below -10 dB).", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 3: Blind speech inpainting (section 3.2). STOI & PESQ were computed between the recovered and the actual speech segments from validation set and averaged. The best scores were denoted in bold. PC - informed (partial conv.), FC - blind (full conv.) inpainting.' and proceed to solve the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich framework configuration yielded the highest PESQ score for a 10% intrusion size with random noise?\n" }, { "role": "assistant", "content": "To find the highest PESQ score for a 10% intrusion size with random noise, look at the 'Random' intrusion type and '10%' size row. Compare the PESQ scores for 'Gaps PESQ', 'Noise PESQ', 'PC-gaps PESQ', 'FC-gaps PESQ', 'FC-noise PESQ', and 'FC-additive PESQ'. The highest score is 3.496 under 'PC-gaps PESQ'. So, the answer is {\"answer\": \"3.496\"}." } ], "images": [ "scigen/train/368.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Blind speech inpainting (section 3.2). STOI & PESQ were computed between the recovered and the actual speech segments from validation set and averaged. The best scores were denoted in bold. PC - informed (partial conv.), FC - blind (full conv.) inpainting." ], "reasoning": "To find the highest PESQ score for a 10% intrusion size with random noise, look at the 'Random' intrusion type and '10%' size row. Compare the PESQ scores for 'Gaps PESQ', 'Noise PESQ', 'PC-gaps PESQ', 'FC-gaps PESQ', 'FC-noise PESQ', and 'FC-additive PESQ'. The highest score is 3.496 under 'PC-gaps PESQ'.", "question": "Which framework configuration yielded the highest PESQ score for a 10% intrusion size with random noise?", "answer": "So, the answer is {\"answer\": \"3.496\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe FC-additive framework yielded the best STOI score for a 40% intrusion size with time and frequency intrusion.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'Time + Freq.' intrusion type and '40%' size row. Compare the STOI scores for 'Gaps STOI', 'Noise STOI', 'PC-gaps STOI', 'FC-gaps STOI', 'FC-noise STOI', and 'FC-additive STOI'. The highest score is 0.685 under 'PC-gaps STOI', not 'FC-additive STOI'. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/368.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Blind speech inpainting (section 3.2). STOI & PESQ were computed between the recovered and the actual speech segments from validation set and averaged. The best scores were denoted in bold. PC - informed (partial conv.), FC - blind (full conv.) inpainting." ], "reasoning": "To verify this statement, look at the 'Time + Freq.' intrusion type and '40%' size row. Compare the STOI scores for 'Gaps STOI', 'Noise STOI', 'PC-gaps STOI', 'FC-gaps STOI', 'FC-noise STOI', and 'FC-additive STOI'. The highest score is 0.685 under 'PC-gaps STOI', not 'FC-additive STOI'.", "statement": "The FC-additive framework yielded the best STOI score for a 40% intrusion size with time and frequency intrusion.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: Composition of MERLIN Corpus', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The MERLIN corpus contains 2286 texts graded on the CEFR scale for German, Italian, and Czech, with certain low-frequency combinations removed.To test our hypotheses, we need corpora graded with CEFR scale for multiple languages. One such multi-lingual corpus is the freely available MERLIN Boyd et al. This corpus consists of 2286 manually graded texts written by second language learners of German (DE), Italian (IT), and Czech (CZ) as a part of written examinations at authorized test institutions. The aim of these examinations is to test the knowledge of the learners on the CEFR scale which consists of six categories – A1, A2, B1, B2, C1, C2 – which indicate improving language abilities. The writing tasks primarily consisted of writing formal/informal letters/emails and essays. MERLIN corpus has a multi-dimensional annotation of language proficiency covering aspects such as grammatical accuracy, vocabulary range, socio-linguistic awareness etc., and we used the “Overall CEFR rating” as the label for our experiments in this paper. Other information provided about the authors included- age, gender, and native language, and information about the task such as topic, and the CEFR level of the test itself. We did not use these information in the experiments reported in this paper. Further, we removed all Language-CEFR Category combinations that had less than 10 examples in the corpus (German had 5 examples for level C2 and Italian had 2 examples for B2 which were removed from the data). We also removed all the unrated texts from the original corpus." } ], "images": [ "scigen/train/369.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Composition of MERLIN Corpus" ], "reasoning": "The MERLIN corpus contains 2286 texts graded on the CEFR scale for German, Italian, and Czech, with certain low-frequency combinations removed.", "description": "To test our hypotheses, we need corpora graded with CEFR scale for multiple languages. One such multi-lingual corpus is the freely available MERLIN Boyd et al. This corpus consists of 2286 manually graded texts written by second language learners of German (DE), Italian (IT), and Czech (CZ) as a part of written examinations at authorized test institutions. The aim of these examinations is to test the knowledge of the learners on the CEFR scale which consists of six categories – A1, A2, B1, B2, C1, C2 – which indicate improving language abilities. The writing tasks primarily consisted of writing formal/informal letters/emails and essays. MERLIN corpus has a multi-dimensional annotation of language proficiency covering aspects such as grammatical accuracy, vocabulary range, socio-linguistic awareness etc., and we used the “Overall CEFR rating” as the label for our experiments in this paper. Other information provided about the authors included- age, gender, and native language, and information about the task such as topic, and the CEFR level of the test itself. We did not use these information in the experiments reported in this paper. Further, we removed all Language-CEFR Category combinations that had less than 10 examples in the corpus (German had 5 examples for level C2 and Italian had 2 examples for B2 which were removed from the data). We also removed all the unrated texts from the original corpus.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1: Composition of MERLIN Corpus', solve the subsequent question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nHow many texts in the MERLIN corpus were written by second language learners of German at the B1 level?\n" }, { "role": "assistant", "content": "To find the number of B1 level texts written by German learners, look at the B1 row under the DE column in the table. So, the answer is {\"answer\": \"331\"}." } ], "images": [ "scigen/train/369.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Composition of MERLIN Corpus" ], "reasoning": "To find the number of B1 level texts written by German learners, look at the B1 row under the DE column in the table.", "question": "How many texts in the MERLIN corpus were written by second language learners of German at the B1 level?", "answer": "So, the answer is {\"answer\": \"331\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThere are more A2 level texts written by Italian learners than by German learners.\n" }, { "role": "assistant", "content": "Compare the number of A2 level texts for Italian (381) and German (306) in the table. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/369.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Composition of MERLIN Corpus" ], "reasoning": "Compare the number of A2 level texts for Italian (381) and German (306) in the table.", "statement": "There are more A2 level texts written by Italian learners than by German learners.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 7: Task 2: Results of our best system (submission 1) on the test set when one of the feature groups is removed.'.\n" }, { "role": "assistant", "content": "General textual features significantly impact performance, with their removal causing a notable drop in F-measure, while domain-specific n-grams are the most useful among domain-specific features.In this task, the general textual features (row b) played a bigger role in the overall performance than the domain-specific (row c) or sentiment lexicon (row d) features. Removing this group of features results in more than 2.5 percentage points drop in the F-measure affecting both precision and recall (row b). However, removing any one feature subgroup in this group (e.g., general n-grams, general clusters, general embeddings, etc.) results only in slight drop or even increase in the performance (rows b.1–b.4). This indicates that the features in this group capture similar information. Among the domain-specific features, the n-grams generalized over domain terms are the most useful. The model trained without these n-grams features performs almost one percentage point worse than the model that uses all the features (row c.1). The sentiment lexicon features were not helpful (row d)." } ], "images": [ "scigen/train/370.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Task 2: Results of our best system (submission 1) on the test set when one of the feature groups is removed." ], "reasoning": "General textual features significantly impact performance, with their removal causing a notable drop in F-measure, while domain-specific n-grams are the most useful among domain-specific features.", "description": "In this task, the general textual features (row b) played a bigger role in the overall performance than the domain-specific (row c) or sentiment lexicon (row d) features. Removing this group of features results in more than 2.5 percentage points drop in the F-measure affecting both precision and recall (row b). However, removing any one feature subgroup in this group (e.g., general n-grams, general clusters, general embeddings, etc.) results only in slight drop or even increase in the performance (rows b.1–b.4). This indicates that the features in this group capture similar information. Among the domain-specific features, the n-grams generalized over domain terms are the most useful. The model trained without these n-grams features performs almost one percentage point worse than the model that uses all the features (row c.1). The sentiment lexicon features were not helpful (row d).", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 7: Task 2: Results of our best system (submission 1) on the test set when one of the feature groups is removed.', and sequentially solve the question, writing out each step of your reasoning process. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich feature group, when removed, results in the largest drop in F-measure?\n" }, { "role": "assistant", "content": "The table shows that removing general textual features (row b) results in a drop in F-measure from 0.673 to 0.647, which is more than 2.5 percentage points. So, the answer is {\"answer\": \"general textual features\"}" } ], "images": [ "scigen/train/370.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Task 2: Results of our best system (submission 1) on the test set when one of the feature groups is removed." ], "reasoning": "The table shows that removing general textual features (row b) results in a drop in F-measure from 0.673 to 0.647, which is more than 2.5 percentage points.", "question": "Which feature group, when removed, results in the largest drop in F-measure?", "answer": "So, the answer is {\"answer\": \"general textual features\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence Removing sentiment lexicon features improves the model's performance. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRemoving sentiment lexicon features improves the model's performance.\n" }, { "role": "assistant", "content": "The table shows that removing sentiment lexicon features (row d) results in an F-measure of 0.679, which is higher than the F-measure of 0.673 when all features are used (row a). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/370.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Task 2: Results of our best system (submission 1) on the test set when one of the feature groups is removed." ], "reasoning": "The table shows that removing sentiment lexicon features (row d) results in an F-measure of 0.679, which is higher than the F-measure of 0.673 when all features are used (row a).", "statement": "Removing sentiment lexicon features improves the model's performance.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: Task 1: Results for our three official submissions, baselines, and top three teams. Evaluation measures for Task 1 are precision (P), recall (R), and F1-measure (F) for class 1 (ADR).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Submission 1 achieved the best results in Fclass1, while submission 2 had the lowest results. Larger ensembles outperformed the best single-classifier model by over one percentage point.The best results in Fclass1 were obtained with submission 1 (row c.1). The results for submission 2 are the lowest, with F-measure being 3.5 percentage points lower than the result for submission 1 (row c.2). The ensemble classifier (submission 3) shows a slightly worse performance than the best result. However, in the post-competition experiments, we found that larger ensembles (with 7–11 classifiers, each trained on a random sub-sample of the majority class to reduce class imbalance to 1:2) outperform our best single-classifier model by over one percentage point with Fclass1 reaching up to 0.446 (row d). Our best submission is ranked first among the nine teams participated in this task (rows b.1–b.3). The first baseline is a classifier that assigns class 1 (ADR) to all instances (row a.1). The performance of this baseline is very low (Fclass1=0.143) due to the small proportion of class 1 instances in the test set. The second baseline is an SVM classifier trained only on the unigram features (row a.2). Its performance is much higher than the performance of the first baseline, but substantially lower than that of our system. By adding a variety of textual and domain-specific features as well as applying under-sampling, we are able to improve the classification performance by almost ten percentage points in F-measure." } ], "images": [ "scigen/train/371.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Task 1: Results for our three official submissions, baselines, and top three teams. Evaluation measures for Task 1 are precision (P), recall (R), and F1-measure (F) for class 1 (ADR)." ], "reasoning": "Submission 1 achieved the best results in Fclass1, while submission 2 had the lowest results. Larger ensembles outperformed the best single-classifier model by over one percentage point.", "description": "The best results in Fclass1 were obtained with submission 1 (row c.1). The results for submission 2 are the lowest, with F-measure being 3.5 percentage points lower than the result for submission 1 (row c.2). The ensemble classifier (submission 3) shows a slightly worse performance than the best result. However, in the post-competition experiments, we found that larger ensembles (with 7–11 classifiers, each trained on a random sub-sample of the majority class to reduce class imbalance to 1:2) outperform our best single-classifier model by over one percentage point with Fclass1 reaching up to 0.446 (row d). Our best submission is ranked first among the nine teams participated in this task (rows b.1–b.3). The first baseline is a classifier that assigns class 1 (ADR) to all instances (row a.1). The performance of this baseline is very low (Fclass1=0.143) due to the small proportion of class 1 instances in the test set. The second baseline is an SVM classifier trained only on the unigram features (row a.2). Its performance is much higher than the performance of the first baseline, but substantially lower than that of our system. By adding a variety of textual and domain-specific features as well as applying under-sampling, we are able to improve the classification performance by almost ten percentage points in F-measure.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 4: Task 1: Results for our three official submissions, baselines, and top three teams. Evaluation measures for Task 1 are precision (P), recall (R), and F1-measure (F) for class 1 (ADR).' in mind, break down and resolve the question below step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhat is the Fclass1 score for the ensemble classifier (submission 3) compared to the best single-classifier model?\n" }, { "role": "assistant", "content": "The ensemble classifier (submission 3) has an Fclass1 score of 0.427, which is slightly worse than the best single-classifier model's Fclass1 score of 0.435. So, the answer is {\"answer\": \"0.427\"}" } ], "images": [ "scigen/train/371.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Task 1: Results for our three official submissions, baselines, and top three teams. Evaluation measures for Task 1 are precision (P), recall (R), and F1-measure (F) for class 1 (ADR)." ], "reasoning": "The ensemble classifier (submission 3) has an Fclass1 score of 0.427, which is slightly worse than the best single-classifier model's Fclass1 score of 0.435.", "question": "What is the Fclass1 score for the ensemble classifier (submission 3) compared to the best single-classifier model?", "answer": "So, the answer is {\"answer\": \"0.427\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe SVM classifier trained only on unigram features performed better than the classifier that assigns class 1 (ADR) to all instances.\n" }, { "role": "assistant", "content": "The SVM classifier (row a.2) has an Fclass1 score of 0.339, which is higher than the Fclass1 score of 0.143 for the classifier that assigns class 1 (ADR) to all instances (row a.1). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/371.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Task 1: Results for our three official submissions, baselines, and top three teams. Evaluation measures for Task 1 are precision (P), recall (R), and F1-measure (F) for class 1 (ADR)." ], "reasoning": "The SVM classifier (row a.2) has an Fclass1 score of 0.339, which is higher than the Fclass1 score of 0.143 for the classifier that assigns class 1 (ADR) to all instances (row a.1).", "statement": "The SVM classifier trained only on unigram features performed better than the classifier that assigns class 1 (ADR) to all instances.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 5: Task 1: Results of our best system (submission 1) on the test set when one of the feature groups is removed.'.\n" }, { "role": "assistant", "content": "Removing general textual features or domain-specific features results in a two percentage points drop in Fclass1, with general textual features affecting recall and domain-specific features impacting precision.To investigate the impact of each feature group on the overall performance, we conduct ablation experiments where we repeat the same classification process but remove one feature group at a time. Comparing the two major groups of features, general textual features (row b) and domain-specific features (row c), we observe that they both have a substantial impact on the performance. Removing one of these groups leads to a two percentage points drop in Fclass1. The general textual features mostly affect recall of the ADR class (row b) while the domain-specific features impact precision (row c). Among the general textual features, the most influential feature is general-domain word embeddings (row b.2). Among the domain-specific features, n-grams generalized over domain terms (row c.1) and domain word embeddings (row c.3) provide noticeable contribution to the overall performance. In the Appendix, we provide a list of top 25 n-gram features (including n-grams generalized over domain terms) ranked by their importance in separating the two classes." } ], "images": [ "scigen/train/372.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Task 1: Results of our best system (submission 1) on the test set when one of the feature groups is removed." ], "reasoning": "Removing general textual features or domain-specific features results in a two percentage points drop in Fclass1, with general textual features affecting recall and domain-specific features impacting precision.", "description": "To investigate the impact of each feature group on the overall performance, we conduct ablation experiments where we repeat the same classification process but remove one feature group at a time. Comparing the two major groups of features, general textual features (row b) and domain-specific features (row c), we observe that they both have a substantial impact on the performance. Removing one of these groups leads to a two percentage points drop in Fclass1. The general textual features mostly affect recall of the ADR class (row b) while the domain-specific features impact precision (row c). Among the general textual features, the most influential feature is general-domain word embeddings (row b.2). Among the domain-specific features, n-grams generalized over domain terms (row c.1) and domain word embeddings (row c.3) provide noticeable contribution to the overall performance. In the Appendix, we provide a list of top 25 n-gram features (including n-grams generalized over domain terms) ranked by their importance in separating the two classes.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: 'Table 5: Task 1: Results of our best system (submission 1) on the test set when one of the feature groups is removed.'\nQuestion: Which feature group has the highest recall for the ADR class?\n" }, { "role": "assistant", "content": "From the table, the highest recall for the ADR class (Rclass1) is observed in row c (all domain-specific features) with a value of 0.523. So, the answer is {\"answer\": \"all domain-specific features\"}." } ], "images": [ "scigen/train/372.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Task 1: Results of our best system (submission 1) on the test set when one of the feature groups is removed." ], "reasoning": "From the table, the highest recall for the ADR class (Rclass1) is observed in row c (all domain-specific features) with a value of 0.523.", "question": "Which feature group has the highest recall for the ADR class?", "answer": "So, the answer is {\"answer\": \"all domain-specific features\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nGeneral-domain word embeddings have the highest precision among general textual features.\n" }, { "role": "assistant", "content": "From the table, the precision (Pclass1) for general-domain word embeddings (row b.2) is 0.365, which is lower than the precision for general n-grams (row b.1) at 0.397. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/372.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Task 1: Results of our best system (submission 1) on the test set when one of the feature groups is removed." ], "reasoning": "From the table, the precision (Pclass1) for general-domain word embeddings (row b.2) is 0.365, which is lower than the precision for general n-grams (row b.1) at 0.397.", "statement": "General-domain word embeddings have the highest precision among general textual features.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 6: Task 2: Results for our three official submissions, baselines, and top three teams. Evaluation measures for Task 2 are micro-averaged P, R, and F1-score for class 1 (intake) and class 2 (possible intake).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Submission 1 achieved the best results in Fclass1+class2, while the addition of ADR and Pronoun lexicons did not improve performance. The best system ranked third among nine teams.The best results in Fclass1+class2 are achieved with submission 1 (row c.1). The results for the other two submissions, submission 2 and submission 3, are quite similar to the results of submission 1 in both precision and recall (rows c.2–c.3). Adding the features from the ADR lexicon and the Pronoun lexicon did not result in performance improvement on the test set. Our best system is ranked third among the nine teams participated in this task (rows b.1–b.3). The first baseline is a classifier that assigns class 2 (possible medication intake) to all instances (row a.1). Class 2 is the majority class among the two positive classes, class 1 and class 2, in the training set. The performance of this baseline is quite low (Fclass1+class2=0.452) since class 2 covers only 36% of the instances in the test set. The second baseline is an SVM classifier trained only on the unigram features (row a.2). The performance of such a simple model is surprisingly high (Fclass1+class2=0.646), only 4.7 percentage points below the top result in the competition." } ], "images": [ "scigen/train/373.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Task 2: Results for our three official submissions, baselines, and top three teams. Evaluation measures for Task 2 are micro-averaged P, R, and F1-score for class 1 (intake) and class 2 (possible intake)." ], "reasoning": "Submission 1 achieved the best results in Fclass1+class2, while the addition of ADR and Pronoun lexicons did not improve performance. The best system ranked third among nine teams.", "description": "The best results in Fclass1+class2 are achieved with submission 1 (row c.1). The results for the other two submissions, submission 2 and submission 3, are quite similar to the results of submission 1 in both precision and recall (rows c.2–c.3). Adding the features from the ADR lexicon and the Pronoun lexicon did not result in performance improvement on the test set. Our best system is ranked third among the nine teams participated in this task (rows b.1–b.3). The first baseline is a classifier that assigns class 2 (possible medication intake) to all instances (row a.1). Class 2 is the majority class among the two positive classes, class 1 and class 2, in the training set. The performance of this baseline is quite low (Fclass1+class2=0.452) since class 2 covers only 36% of the instances in the test set. The second baseline is an SVM classifier trained only on the unigram features (row a.2). The performance of such a simple model is surprisingly high (Fclass1+class2=0.646), only 4.7 percentage points below the top result in the competition.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: Table 6: Task 2: Results for our three official submissions, baselines, and top three teams. Evaluation measures for Task 2 are micro-averaged P, R, and F1-score for class 1 (intake) and class 2 (possible intake).\nQuestion: Which submission had the highest Fclass1+class2 score among the NRC-Canada official submissions?\n" }, { "role": "assistant", "content": "Among the NRC-Canada official submissions (rows c.1 to c.3), submission 1 (row c.1) had the highest Fclass1+class2 score of 0.673. So, the answer is {\"answer\": \"submission 1\"}." } ], "images": [ "scigen/train/373.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Task 2: Results for our three official submissions, baselines, and top three teams. Evaluation measures for Task 2 are micro-averaged P, R, and F1-score for class 1 (intake) and class 2 (possible intake)." ], "reasoning": "Among the NRC-Canada official submissions (rows c.1 to c.3), submission 1 (row c.1) had the highest Fclass1+class2 score of 0.673.", "question": "Which submission had the highest Fclass1+class2 score among the NRC-Canada official submissions?", "answer": "So, the answer is {\"answer\": \"submission 1\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe SVM-unigrams baseline had a higher Fclass1+class2 score than the NRC-Canada official submission 1.\n" }, { "role": "assistant", "content": "The SVM-unigrams baseline (row a.2) had an Fclass1+class2 score of 0.646, which is lower than the NRC-Canada official submission 1 (row c.1) with a score of 0.673. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/373.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Task 2: Results for our three official submissions, baselines, and top three teams. Evaluation measures for Task 2 are micro-averaged P, R, and F1-score for class 1 (intake) and class 2 (possible intake)." ], "reasoning": "The SVM-unigrams baseline (row a.2) had an Fclass1+class2 score of 0.646, which is lower than the NRC-Canada official submission 1 (row c.1) with a score of 0.673.", "statement": "The SVM-unigrams baseline had a higher Fclass1+class2 score than the NRC-Canada official submission 1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Feature ablation results. The five features that lead to largest drop in performance are displayed.'.\n" }, { "role": "assistant", "content": "TF-IDF is the most impactful feature, causing a significant 12-point drop in performance, highlighting the importance of web redundancy for QA systems.Note that TF-IDF is by far the most impactful feature, leading to a large drop of 12 points in performance. This shows the importance of using the redundancy of the web for our QA system." } ], "images": [ "scigen/train/374.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Feature ablation results. The five features that lead to largest drop in performance are displayed." ], "reasoning": "TF-IDF is the most impactful feature, causing a significant 12-point drop in performance, highlighting the importance of web redundancy for QA systems.", "description": "Note that TF-IDF is by far the most impactful feature, leading to a large drop of 12 points in performance. This shows the importance of using the redundancy of the web for our QA system.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich feature causes the largest drop in performance when removed?\nSolve the above question based on the table titled 'Table 4: Feature ablation results. The five features that lead to largest drop in performance are displayed.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the F1 scores and the corresponding drop (Δ) in performance for each feature. The largest drop is 12 points, which corresponds to the removal of the TF-IDF feature. So, the answer is {\"answer\": \"TF-IDF\"}." } ], "images": [ "scigen/train/374.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Feature ablation results. The five features that lead to largest drop in performance are displayed." ], "reasoning": "The table shows the F1 scores and the corresponding drop (Δ) in performance for each feature. The largest drop is 12 points, which corresponds to the removal of the TF-IDF feature.", "question": "Which feature causes the largest drop in performance when removed?", "answer": "So, the answer is {\"answer\": \"TF-IDF\"}." }, { "messages": [ { "role": "user", "content": "Removing the Google Rank feature causes the largest drop in performance. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nRemoving the Google Rank feature causes the largest drop in performance.\n" }, { "role": "assistant", "content": "The table shows that removing the TF-IDF feature causes the largest drop in performance (12 points), while removing the Google Rank feature causes a 2.2-point drop. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/374.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Feature ablation results. The five features that lead to largest drop in performance are displayed." ], "reasoning": "The table shows that removing the TF-IDF feature causes the largest drop in performance (12 points), while removing the Google Rank feature causes a 2.2-point drop.", "statement": "Removing the Google Rank feature causes the largest drop in performance.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Results on development (average over random splits) and test set. Middle: results on all examples. Bottom: results on the subset where candidate extraction succeeded.'.\n" }, { "role": "assistant", "content": "WebQA's performance is hindered by string matching issues, but normalizing entities improves its F1 score significantly.WebQA obtained 32.6 F1 (33.5 p@1, 42.4 MRR) compared to 40.9 F1 of CompQ. Our candidate extraction step finds the correct answer in the top-K candidates in 65.9% of development examples and 62.7% of test examples. Thus, our test F1 on examples for which candidate extraction succeeded (WebQA-Subset) is 51.9 (53.4 p@1, 67.5 MRR). Not using a KB, results in a considerable disadvantage for WebQA. KB entities have normalized descriptions, and the answers have been annotated according to those descriptions. We, conversely, find answers on the web and often predict a correct answer, but get penalized due to small string differences. E.g., for “what is the longest river in China?” we answer “yangtze river”, while the gold answer is “yangtze”. To quantify this effect we manually annotated all 258 examples in the first random development set split, and determined whether string matching failed, and we actually returned the gold answer. This improved performance from 53.6 F1 to 56.6 F1 (on examples that passed candidate extraction). Further normalizing gold and predicted entities, such that “Hillary Clinton” and “Hillary Rodham Clinton” are unified, improved F1 to 57.3 F1. Extrapolating this to the test set would result in an F1 of 34.4" } ], "images": [ "scigen/train/375.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on development (average over random splits) and test set. Middle: results on all examples. Bottom: results on the subset where candidate extraction succeeded." ], "reasoning": "WebQA's performance is hindered by string matching issues, but normalizing entities improves its F1 score significantly.", "description": "WebQA obtained 32.6 F1 (33.5 p@1, 42.4 MRR) compared to 40.9 F1 of CompQ. Our candidate extraction step finds the correct answer in the top-K candidates in 65.9% of development examples and 62.7% of test examples. Thus, our test F1 on examples for which candidate extraction succeeded (WebQA-Subset) is 51.9 (53.4 p@1, 67.5 MRR). Not using a KB, results in a considerable disadvantage for WebQA. KB entities have normalized descriptions, and the answers have been annotated according to those descriptions. We, conversely, find answers on the web and often predict a correct answer, but get penalized due to small string differences. E.g., for “what is the longest river in China?” we answer “yangtze river”, while the gold answer is “yangtze”. To quantify this effect we manually annotated all 258 examples in the first random development set split, and determined whether string matching failed, and we actually returned the gold answer. This improved performance from 53.6 F1 to 56.6 F1 (on examples that passed candidate extraction). Further normalizing gold and predicted entities, such that “Hillary Clinton” and “Hillary Rodham Clinton” are unified, improved F1 to 57.3 F1. Extrapolating this to the test set would result in an F1 of 34.4", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Results on development (average over random splits) and test set. Middle: results on all examples. Bottom: results on the subset where candidate extraction succeeded.\nQuestion: What is the Test F1 score for WebQA-Subset?\nGive a detailed response to the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table lists the Test F1 scores for various systems. The Test F1 score for WebQA-Subset is found in the corresponding row and column. So, the answer is {\"answer\": \"51.9\"}" } ], "images": [ "scigen/train/375.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on development (average over random splits) and test set. Middle: results on all examples. Bottom: results on the subset where candidate extraction succeeded." ], "reasoning": "The table lists the Test F1 scores for various systems. The Test F1 score for WebQA-Subset is found in the corresponding row and column.", "question": "What is the Test F1 score for WebQA-Subset?", "answer": "So, the answer is {\"answer\": \"51.9\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nCompQ has the highest Test F1 score among all systems.\n" }, { "role": "assistant", "content": "The table shows the Test F1 scores for different systems. CompQ has a Test F1 score of 40.9, which is lower than WebQA-Subset's Test F1 score of 51.9. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/375.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on development (average over random splits) and test set. Middle: results on all examples. Bottom: results on the subset where candidate extraction succeeded." ], "reasoning": "The table shows the Test F1 scores for different systems. CompQ has a Test F1 score of 40.9, which is lower than WebQA-Subset's Test F1 score of 51.9.", "statement": "CompQ has the highest Test F1 score among all systems.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Performance of various methods over all questions (question match) and all interactions (interaction match).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Both models perform worse on CoSQL DST than on SParC, indicating CoSQL DST's higher difficulty due to diverse question generation and complex intent switches.We use the same evaluation metrics used by the SParC dataset Yu et al. The two models achieve less than 16% question-level accuracy and less than 3% on interaction-level accuracy. Since the two models have been benchmarked on both CoSQL and SParC, we cross-compare their performance on these two datasets. Both models perform significantly worse on CoSQL DST than on SParC. This indicates that CoSQL DST is more difficult than SParC. The possible reasons is that the questions in CoSQL are generated by a more diverse pool of users (crowd workers instead of SQL experts), the task includes ambiguous questions and the context contains more complex intent switches." } ], "images": [ "scigen/train/376.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Performance of various methods over all questions (question match) and all interactions (interaction match)." ], "reasoning": "Both models perform worse on CoSQL DST than on SParC, indicating CoSQL DST's higher difficulty due to diverse question generation and complex intent switches.", "description": "We use the same evaluation metrics used by the SParC dataset Yu et al. The two models achieve less than 16% question-level accuracy and less than 3% on interaction-level accuracy. Since the two models have been benchmarked on both CoSQL and SParC, we cross-compare their performance on these two datasets. Both models perform significantly worse on CoSQL DST than on SParC. This indicates that CoSQL DST is more difficult than SParC. The possible reasons is that the questions in CoSQL are generated by a more diverse pool of users (crowd workers instead of SQL experts), the task includes ambiguous questions and the context contains more complex intent switches.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 5: Performance of various methods over all questions (question match) and all interactions (interaction match).', unfold the steps to solve the question given next. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nQuestion: Which model has a higher Question Match accuracy on the Test set?\n" }, { "role": "assistant", "content": "Compare the Question Match accuracy on the Test set for both models. CD-Seq2Seq has 13.9, while SyntaxSQL-con has 14.1. So, the answer is {\"answer\": \"SyntaxSQL-con\"}" } ], "images": [ "scigen/train/376.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Performance of various methods over all questions (question match) and all interactions (interaction match)." ], "reasoning": "Compare the Question Match accuracy on the Test set for both models. CD-Seq2Seq has 13.9, while SyntaxSQL-con has 14.1.", "question": "Which model has a higher Question Match accuracy on the Test set?", "answer": "So, the answer is {\"answer\": \"SyntaxSQL-con\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nCD-Seq2Seq has a higher Interaction Match accuracy on the Test set than SyntaxSQL-con.\n" }, { "role": "assistant", "content": "Compare the Interaction Match accuracy on the Test set for both models. CD-Seq2Seq has 2.6, while SyntaxSQL-con has 2.2. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/376.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Performance of various methods over all questions (question match) and all interactions (interaction match)." ], "reasoning": "Compare the Interaction Match accuracy on the Test set for both models. CD-Seq2Seq has 2.6, while SyntaxSQL-con has 2.2.", "statement": "CD-Seq2Seq has a higher Interaction Match accuracy on the Test set than SyntaxSQL-con.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 6: BLEU scores on the development and test sets, and human evaluations of logic correctness rate (LCR) and grammar check on the 100 examples randomly sampled from the test set.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The Pointer-generator model has the highest BLEU scores for both Dev and Test sets, while the Template model has the highest LCR and Grammar scores.et al. To compute LCR and grammar score, we randomly sampled 100 descriptions generated by each model. Three students proficient in English participated in the evaluation, They were asked to choose a score 0 or 1 for LCR, and 1 to 5 for grammar check (the larger, the better). For LCR, the final score was decided by majority vote. We computed the average grammar score." } ], "images": [ "scigen/train/377.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: BLEU scores on the development and test sets, and human evaluations of logic correctness rate (LCR) and grammar check on the 100 examples randomly sampled from the test set." ], "reasoning": "The Pointer-generator model has the highest BLEU scores for both Dev and Test sets, while the Template model has the highest LCR and Grammar scores.", "description": "et al. To compute LCR and grammar score, we randomly sampled 100 descriptions generated by each model. Three students proficient in English participated in the evaluation, They were asked to choose a score 0 or 1 for LCR, and 1 to 5 for grammar check (the larger, the better). For LCR, the final score was decided by majority vote. We computed the average grammar score.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 6: BLEU scores on the development and test sets, and human evaluations of logic correctness rate (LCR) and grammar check on the 100 examples randomly sampled from the test set.' and methodically tackle the question that follows. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model has the highest LCR score?\n" }, { "role": "assistant", "content": "To find the model with the highest LCR score, compare the LCR scores of all models. The Template model has the highest LCR score of 41.0%. So, the answer is {\"answer\": \"Template\"}." } ], "images": [ "scigen/train/377.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: BLEU scores on the development and test sets, and human evaluations of logic correctness rate (LCR) and grammar check on the 100 examples randomly sampled from the test set." ], "reasoning": "To find the model with the highest LCR score, compare the LCR scores of all models. The Template model has the highest LCR score of 41.0%.", "question": "Which model has the highest LCR score?", "answer": "So, the answer is {\"answer\": \"Template\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Seq2Seq model has the highest grammar score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the grammar scores of all models. The Seq2Seq model has a grammar score of 3.5, which is lower than the Template model's score of 4.0 and the Pointer-generator model's score of 3.6. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/377.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: BLEU scores on the development and test sets, and human evaluations of logic correctness rate (LCR) and grammar check on the 100 examples randomly sampled from the test set." ], "reasoning": "To verify this statement, compare the grammar scores of all models. The Seq2Seq model has a grammar score of 3.5, which is lower than the Template model's score of 4.0 and the Pointer-generator model's score of 3.6.", "statement": "The Seq2Seq model has the highest grammar score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 7: Accuracy of user dialog act prediction on the development and test sets.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The Majority model indicates that 40% of user questions cannot be directly converted into SQL queries, while TBCNN can predict around 85% of user intents correctly, but struggles with more complex dialog acts.The result of Majority indicates that about 40% of user questions cannot be directly converted into SQL queries. This confirms the necessity of considering a larger set of dialogue actions for building a practical NLIDB system. Even though TBCNN can predict around 85% of user intents correctly, most of the correct predictions are for simple classes such as INFORM_SQL, THANK_YOU, and GOODBYE etc. The F-scores for more interesting and important dialog acts such as INFER_SQL and AMBIGUOUS are around 10%. This indicates that improving the accuracy on user DAT prediction is still important." } ], "images": [ "scigen/train/378.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Accuracy of user dialog act prediction on the development and test sets." ], "reasoning": "The Majority model indicates that 40% of user questions cannot be directly converted into SQL queries, while TBCNN can predict around 85% of user intents correctly, but struggles with more complex dialog acts.", "description": "The result of Majority indicates that about 40% of user questions cannot be directly converted into SQL queries. This confirms the necessity of considering a larger set of dialogue actions for building a practical NLIDB system. Even though TBCNN can predict around 85% of user intents correctly, most of the correct predictions are for simple classes such as INFORM_SQL, THANK_YOU, and GOODBYE etc. The F-scores for more interesting and important dialog acts such as INFER_SQL and AMBIGUOUS are around 10%. This indicates that improving the accuracy on user DAT prediction is still important.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 7: Accuracy of user dialog act prediction on the development and test sets.', solve the following question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the difference in the test accuracy between the Majority model and the TBCNN-pair model?\n" }, { "role": "assistant", "content": "The test accuracy for the Majority model is 62.8, and for the TBCNN-pair model, it is 83.9. The difference is 83.9 - 62.8. So, the answer is {\"answer\": \"21.1\"}" } ], "images": [ "scigen/train/378.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Accuracy of user dialog act prediction on the development and test sets." ], "reasoning": "The test accuracy for the Majority model is 62.8, and for the TBCNN-pair model, it is 83.9. The difference is 83.9 - 62.8.", "question": "What is the difference in the test accuracy between the Majority model and the TBCNN-pair model?", "answer": "So, the answer is {\"answer\": \"21.1\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The TBCNN-pair model has a higher development accuracy than the Majority model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe TBCNN-pair model has a higher development accuracy than the Majority model.\n" }, { "role": "assistant", "content": "The table shows that the development accuracy for the TBCNN-pair model is 84.2, while for the Majority model, it is 63.3. Therefore, the TBCNN-pair model has a higher development accuracy. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/378.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Accuracy of user dialog act prediction on the development and test sets." ], "reasoning": "The table shows that the development accuracy for the TBCNN-pair model is 84.2, while for the Majority model, it is 63.3. Therefore, the TBCNN-pair model has a higher development accuracy.", "statement": "The TBCNN-pair model has a higher development accuracy than the Majority model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Ablation results of HMNet on AMI’s test set. “+role text” means the role vector is not used, but the role name is prepended to each turn’s transcript.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Removing the role vector or hierarchy structure from HMNet significantly reduces the ROUGE-1 score, indicating the importance of these components.Ablation Study. We conduct an ablation study of HMNet to verify the effectiveness of its various components. When the role vector is removed, the ROUGE-1 score drops 4.3 points. The “+role text” setting removes the role vector. Instead, it prepends the role name to each turn’s transcript. Its performance is higher than that of “-role vector” setting in ROUGE-2 and ROUGE-SU4, but still falls behind HMNet. This indicates the effectiveness of the vectorized representation of speaker roles. When HMNet is without the hierarchy structure, i.e. the turn-level transformer is removed and role vectors are appended to word-level embeddings, the ROUGE-1 score drops as much as 7.0 points. Thus, all these components we propose both play an important role in the summarization capability of HMNet." } ], "images": [ "scigen/train/379.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation results of HMNet on AMI’s test set. “+role text” means the role vector is not used, but the role name is prepended to each turn’s transcript." ], "reasoning": "Removing the role vector or hierarchy structure from HMNet significantly reduces the ROUGE-1 score, indicating the importance of these components.", "description": "Ablation Study. We conduct an ablation study of HMNet to verify the effectiveness of its various components. When the role vector is removed, the ROUGE-1 score drops 4.3 points. The “+role text” setting removes the role vector. Instead, it prepends the role name to each turn’s transcript. Its performance is higher than that of “-role vector” setting in ROUGE-2 and ROUGE-SU4, but still falls behind HMNet. This indicates the effectiveness of the vectorized representation of speaker roles. When HMNet is without the hierarchy structure, i.e. the turn-level transformer is removed and role vectors are appended to word-level embeddings, the ROUGE-1 score drops as much as 7.0 points. Thus, all these components we propose both play an important role in the summarization capability of HMNet.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: Ablation results of HMNet on AMI’s test set. “+role text” means the role vector is not used, but the role name is prepended to each turn’s transcript.' and methodically tackle the question that follows. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the ROUGE-1 score when the hierarchy structure is removed from HMNet?\n" }, { "role": "assistant", "content": "The table shows that the ROUGE-1 score drops to 45.1 when the hierarchy structure is removed from HMNet. So, the answer is {\"answer\": \"45.1\"}." } ], "images": [ "scigen/train/379.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation results of HMNet on AMI’s test set. “+role text” means the role vector is not used, but the role name is prepended to each turn’s transcript." ], "reasoning": "The table shows that the ROUGE-1 score drops to 45.1 when the hierarchy structure is removed from HMNet.", "question": "What is the ROUGE-1 score when the hierarchy structure is removed from HMNet?", "answer": "So, the answer is {\"answer\": \"45.1\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe ROUGE-2 score for the '+role text' setting is higher than that of the '-role vector' setting.\n" }, { "role": "assistant", "content": "The table shows that the ROUGE-2 score for the '+role text' setting is 18.8, which is higher than the 17.2 score for the '-role vector' setting. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/379.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation results of HMNet on AMI’s test set. “+role text” means the role vector is not used, but the role name is prepended to each turn’s transcript." ], "reasoning": "The table shows that the ROUGE-2 score for the '+role text' setting is 18.8, which is higher than the 17.2 score for the '-role vector' setting.", "statement": "The ROUGE-2 score for the '+role text' setting is higher than that of the '-role vector' setting.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: ROUGE-1, ROUGE-2, ROUGE-SU4 scores of generated summary in AMI and ICSI datasets. Numbers in bold are the overall best result. Numbers with underscore are the best result from previous literature. ∗ The two baseline MM models require additional human annotations of topic segmentation and visual signals from cameras. ∗∗ Results are statistically significant at level 0.05.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "HMNet outperforms all baseline models in all metrics except for ROUGE-1 in AMI, and the results are statistically significant at level 0.05.As shown, except for ROUGE-1 in AMI, HMNet outperforms all baseline models in all metrics, and the result is statistically significant at level 0.05, under paired t-test with the best baseline results. On ICSI dataset, HMNet achieves 7.51, 3.06 and 5.44 higher ROUGE points than previous best results." } ], "images": [ "scigen/train/380.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: ROUGE-1, ROUGE-2, ROUGE-SU4 scores of generated summary in AMI and ICSI datasets. Numbers in bold are the overall best result. Numbers with underscore are the best result from previous literature. ∗ The two baseline MM models require additional human annotations of topic segmentation and visual signals from cameras. ∗∗ Results are statistically significant at level 0.05." ], "reasoning": "HMNet outperforms all baseline models in all metrics except for ROUGE-1 in AMI, and the results are statistically significant at level 0.05.", "description": "As shown, except for ROUGE-1 in AMI, HMNet outperforms all baseline models in all metrics, and the result is statistically significant at level 0.05, under paired t-test with the best baseline results. On ICSI dataset, HMNet achieves 7.51, 3.06 and 5.44 higher ROUGE points than previous best results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 2: ROUGE-1, ROUGE-2, ROUGE-SU4 scores of generated summary in AMI and ICSI datasets. Numbers in bold are the overall best result. Numbers with underscore are the best result from previous literature. ∗ The two baseline MM models require additional human annotations of topic segmentation and visual signals from cameras. ∗∗ Results are statistically significant at level 0.05.\nQuestion: What is the highest ROUGE-1 score achieved on the AMI dataset and by which model?\n" }, { "role": "assistant", "content": "The highest ROUGE-1 score on the AMI dataset is 53.29, achieved by the MM (TopicSeg+VFOA) model. So, the answer is {\"answer\": \"53.29\"}." } ], "images": [ "scigen/train/380.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: ROUGE-1, ROUGE-2, ROUGE-SU4 scores of generated summary in AMI and ICSI datasets. Numbers in bold are the overall best result. Numbers with underscore are the best result from previous literature. ∗ The two baseline MM models require additional human annotations of topic segmentation and visual signals from cameras. ∗∗ Results are statistically significant at level 0.05." ], "reasoning": "The highest ROUGE-1 score on the AMI dataset is 53.29, achieved by the MM (TopicSeg+VFOA) model.", "question": "What is the highest ROUGE-1 score achieved on the AMI dataset and by which model?", "answer": "So, the answer is {\"answer\": \"53.29\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nHMNet achieved the highest ROUGE-1 score on the AMI dataset.\n" }, { "role": "assistant", "content": "The table shows that the highest ROUGE-1 score on the AMI dataset is 53.29, achieved by the MM (TopicSeg+VFOA) model, not HMNet. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/380.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: ROUGE-1, ROUGE-2, ROUGE-SU4 scores of generated summary in AMI and ICSI datasets. Numbers in bold are the overall best result. Numbers with underscore are the best result from previous literature. ∗ The two baseline MM models require additional human annotations of topic segmentation and visual signals from cameras. ∗∗ Results are statistically significant at level 0.05." ], "reasoning": "The table shows that the highest ROUGE-1 score on the AMI dataset is 53.29, achieved by the MM (TopicSeg+VFOA) model, not HMNet.", "statement": "HMNet achieved the highest ROUGE-1 score on the AMI dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: In each cell, the top value shows the result of the potential threshold and the bottom reports the optimal value (shown as - when equal to our threshold value). † indicates a significant difference to the baseline. There is no significance difference between the results of the optimal value and our threshold.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The table shows the MAP and NDCG values for different collections across various dimensions, with significant differences marked by the † symbol.For each dimension our threshold and its confidence interval are shown with vertical lines. Significant differences of the results to the baseline are marked on the plots using the † symbol." } ], "images": [ "scigen/train/381.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: In each cell, the top value shows the result of the potential threshold and the bottom reports the optimal value (shown as - when equal to our threshold value). † indicates a significant difference to the baseline. There is no significance difference between the results of the optimal value and our threshold." ], "reasoning": "The table shows the MAP and NDCG values for different collections across various dimensions, with significant differences marked by the † symbol.", "description": "For each dimension our threshold and its confidence interval are shown with vertical lines. Significant differences of the results to the baseline are marked on the plots using the † symbol.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: In each cell, the top value shows the result of the potential threshold and the bottom reports the optimal value (shown as - when equal to our threshold value). † indicates a significant difference to the baseline. There is no significance difference between the results of the optimal value and our threshold.', and sequentially solve the question, writing out each step of your reasoning process. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich collection has the highest NDCG value at dimension 300?\n" }, { "role": "assistant", "content": "To find the highest NDCG value at dimension 300, look at the NDCG column for dimension 300 and compare the values across all collections. The highest value is 0.461 for TREC-8. So, the answer is {\"answer\": \"TREC-8\"}." } ], "images": [ "scigen/train/381.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: In each cell, the top value shows the result of the potential threshold and the bottom reports the optimal value (shown as - when equal to our threshold value). † indicates a significant difference to the baseline. There is no significance difference between the results of the optimal value and our threshold." ], "reasoning": "To find the highest NDCG value at dimension 300, look at the NDCG column for dimension 300 and compare the values across all collections. The highest value is 0.461 for TREC-8.", "question": "Which collection has the highest NDCG value at dimension 300?", "answer": "So, the answer is {\"answer\": \"TREC-8\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe HARD collection has a significant difference in MAP value at dimension 100.\n" }, { "role": "assistant", "content": "The table shows that the HARD collection has a MAP value of 0.257 at dimension 100, marked with the † symbol, indicating a significant difference. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/381.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: In each cell, the top value shows the result of the potential threshold and the bottom reports the optimal value (shown as - when equal to our threshold value). † indicates a significant difference to the baseline. There is no significance difference between the results of the optimal value and our threshold." ], "reasoning": "The table shows that the HARD collection has a MAP value of 0.257 at dimension 100, marked with the † symbol, indicating a significant difference.", "statement": "The HARD collection has a significant difference in MAP value at dimension 100.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: Potential thresholds'.\n" }, { "role": "assistant", "content": "The threshold boundaries are determined by the points where the confidence intervals intersect the approximated mean.We also consider an upper and lower bound for this threshold based on the points that the confident intervals cross the approximated mean." } ], "images": [ "scigen/train/382.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Potential thresholds" ], "reasoning": "The threshold boundaries are determined by the points where the confidence intervals intersect the approximated mean.", "description": "We also consider an upper and lower bound for this threshold based on the points that the confident intervals cross the approximated mean.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Potential thresholds\nQuestion: What is the difference between the upper and lower threshold boundaries for a dimensionality of 300?\nSolve the above question based on the table information. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the difference, subtract the lower threshold boundary (0.692) from the upper threshold boundary (0.726) for a dimensionality of 300. So, the answer is {\"answer\": \"0.034\"}." } ], "images": [ "scigen/train/382.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Potential thresholds" ], "reasoning": "To find the difference, subtract the lower threshold boundary (0.692) from the upper threshold boundary (0.726) for a dimensionality of 300.", "question": "What is the difference between the upper and lower threshold boundaries for a dimensionality of 300?", "answer": "So, the answer is {\"answer\": \"0.034\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The main threshold boundary for a dimensionality of 200 is higher than that for a dimensionality of 100. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe main threshold boundary for a dimensionality of 200 is higher than that for a dimensionality of 100.\n" }, { "role": "assistant", "content": "The main threshold boundary for a dimensionality of 200 is 0.756, which is lower than the main threshold boundary for a dimensionality of 100, which is 0.818. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/382.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Potential thresholds" ], "reasoning": "The main threshold boundary for a dimensionality of 200 is 0.756, which is lower than the main threshold boundary for a dimensionality of 100, which is 0.818.", "statement": "The main threshold boundary for a dimensionality of 200 is higher than that for a dimensionality of 100.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: ASR WER results with varying amounts of training data randomly sampled from si284. Feature extractors pre-trained with just 10 hours of LibriSpeech audio are denoted with a subscript 10.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "R-APC and T-APC consistently outperform log Mel across all training sizes, with T-APC even surpassing log Mel's performance when using only half the training data.For example, 1/16 means that we take only 72×1/16=4.5 hours from si284 for training. We find that for all input features, there is a significant increase in WER whenever the training size is reduced by half. When comparing R-APC and T-APC with log Mel, we see the former two always outperform the latter across all proportions, and the gap becomes larger as training size decreases. Note that when using only half of si284 for training, R-APC already matches the performance of log Mel trained on the full set (18.3), and T-APC even outperforms it (16.4 vs. 18.3). In particular, we observe that T-APC always outperforms log Mel by using half of the training data log Mel uses. This observation aligns with the findings in recent NLP literature Finally, we see that most of the time APC outperforms CPC and PASE. In some cases PASE is slightly better than T-APC10 (e.g., when only 1/8 or less of si284 is available), but is still worse than R-APC10." } ], "images": [ "scigen/train/383.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: ASR WER results with varying amounts of training data randomly sampled from si284. Feature extractors pre-trained with just 10 hours of LibriSpeech audio are denoted with a subscript 10." ], "reasoning": "R-APC and T-APC consistently outperform log Mel across all training sizes, with T-APC even surpassing log Mel's performance when using only half the training data.", "description": "For example, 1/16 means that we take only 72×1/16=4.5 hours from si284 for training. We find that for all input features, there is a significant increase in WER whenever the training size is reduced by half. When comparing R-APC and T-APC with log Mel, we see the former two always outperform the latter across all proportions, and the gap becomes larger as training size decreases. Note that when using only half of si284 for training, R-APC already matches the performance of log Mel trained on the full set (18.3), and T-APC even outperforms it (16.4 vs. 18.3). In particular, we observe that T-APC always outperforms log Mel by using half of the training data log Mel uses. This observation aligns with the findings in recent NLP literature Finally, we see that most of the time APC outperforms CPC and PASE. In some cases PASE is slightly better than T-APC10 (e.g., when only 1/8 or less of si284 is available), but is still worse than R-APC10.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: ASR WER results with varying amounts of training data randomly sampled from si284. Feature extractors pre-trained with just 10 hours of LibriSpeech audio are denoted with a subscript 10.', and tackle the question below in a sequential manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the WER for T-APC when using 1/16 of si284 for training?\n" }, { "role": "assistant", "content": "To find the WER for T-APC when using 1/16 of si284 for training, locate the T-APC row and the column corresponding to 1/16 of si284. The value is 43.0. So, the answer is {\"answer\": \"43.0\"}." } ], "images": [ "scigen/train/383.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: ASR WER results with varying amounts of training data randomly sampled from si284. Feature extractors pre-trained with just 10 hours of LibriSpeech audio are denoted with a subscript 10." ], "reasoning": "To find the WER for T-APC when using 1/16 of si284 for training, locate the T-APC row and the column corresponding to 1/16 of si284. The value is 43.0.", "question": "What is the WER for T-APC when using 1/16 of si284 for training?", "answer": "So, the answer is {\"answer\": \"43.0\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nR-APC10 has a lower WER than log Mel when using 1/2 of si284 for training.\n" }, { "role": "assistant", "content": "To verify this statement, compare the WER values for R-APC10 and log Mel in the column for 1/2 of si284. R-APC10 has a WER of 22.7, while log Mel has a WER of 24.1, confirming that R-APC10 has a lower WER. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/383.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: ASR WER results with varying amounts of training data randomly sampled from si284. Feature extractors pre-trained with just 10 hours of LibriSpeech audio are denoted with a subscript 10." ], "reasoning": "To verify this statement, compare the WER values for R-APC10 and log Mel in the column for 1/2 of si284. R-APC10 has a WER of 22.7, while log Mel has a WER of 24.1, confirming that R-APC10 has a lower WER.", "statement": "R-APC10 has a lower WER than log Mel when using 1/2 of si284 for training.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: ASR results (WER ↓) of APC with varying n during pre-training and different transfer learning approaches (Frozen vs. Finetuned). log Mel is the baseline that uses log Mel spectrograms as input features. The best transfer learning result is marked in bold.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "APC transfer learning significantly reduces WER, with the best n for R-APC being 3 and for T-APC being 5, and frozen APC weights outperforming fine-tuned ones.We also include the case where APC is randomly initialized and trained from scratch along with the seq2seq model. We think this is because for a small n, APC can exploit local smoothness in the spectrograms for predicting the target future frame (since xk can be very similar to xk+n when n is small) and thus does not need to learn to encode information useful for inferring more global structures; an overly large n, on the other hand, makes the prediction task too challenging such that APC is unable to generalize across the training set. The best n for R-APC is 3 and for T-APC it is 5. We also find that for all n, keeping pre-trained APC weights fixed (*-APC Frozen), surprisingly, works better than fine-tuning them (*-APC Finetuned), while the latter still outperforms the baseline. Furthermore, we see that training APC from scratch along with the seq2seq model (*-APC Scratch) always performs the worst—even worse than the baseline. With APC transfer learning, WER is reduced by more than 25% from 18.3 to 13.7." } ], "images": [ "scigen/train/384.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: ASR results (WER ↓) of APC with varying n during pre-training and different transfer learning approaches (Frozen vs. Finetuned). log Mel is the baseline that uses log Mel spectrograms as input features. The best transfer learning result is marked in bold." ], "reasoning": "APC transfer learning significantly reduces WER, with the best n for R-APC being 3 and for T-APC being 5, and frozen APC weights outperforming fine-tuned ones.", "description": "We also include the case where APC is randomly initialized and trained from scratch along with the seq2seq model. We think this is because for a small n, APC can exploit local smoothness in the spectrograms for predicting the target future frame (since xk can be very similar to xk+n when n is small) and thus does not need to learn to encode information useful for inferring more global structures; an overly large n, on the other hand, makes the prediction task too challenging such that APC is unable to generalize across the training set. The best n for R-APC is 3 and for T-APC it is 5. We also find that for all n, keeping pre-trained APC weights fixed (*-APC Frozen), surprisingly, works better than fine-tuning them (*-APC Finetuned), while the latter still outperforms the baseline. Furthermore, we see that training APC from scratch along with the seq2seq model (*-APC Scratch) always performs the worst—even worse than the baseline. With APC transfer learning, WER is reduced by more than 25% from 18.3 to 13.7.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 1: ASR results (WER ↓) of APC with varying n during pre-training and different transfer learning approaches (Frozen vs. Finetuned). log Mel is the baseline that uses log Mel spectrograms as input features. The best transfer learning result is marked in bold.'. Solve the following question based on the table.\n\nQuestion: What is the WER for T-APC Frozen when n is 5, and how does it compare to the baseline?\nLet's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The WER for T-APC Frozen when n is 5 is 13.7. The baseline WER (log Mel) is 18.3. Therefore, T-APC Frozen reduces WER by 4.6 points compared to the baseline. So, the answer is {\"answer\": \"13.7\"}" } ], "images": [ "scigen/train/384.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: ASR results (WER ↓) of APC with varying n during pre-training and different transfer learning approaches (Frozen vs. Finetuned). log Mel is the baseline that uses log Mel spectrograms as input features. The best transfer learning result is marked in bold." ], "reasoning": "The WER for T-APC Frozen when n is 5 is 13.7. The baseline WER (log Mel) is 18.3. Therefore, T-APC Frozen reduces WER by 4.6 points compared to the baseline.", "question": "What is the WER for T-APC Frozen when n is 5, and how does it compare to the baseline?", "answer": "So, the answer is {\"answer\": \"13.7\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence Training APC from scratch along with the seq2seq model always performs the worst. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nTraining APC from scratch along with the seq2seq model always performs the worst.\n" }, { "role": "assistant", "content": "The table shows that R-APC Scratch and T-APC Scratch have the highest WER values across all n, even higher than the baseline (log Mel), confirming that training APC from scratch performs the worst. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/384.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: ASR results (WER ↓) of APC with varying n during pre-training and different transfer learning approaches (Frozen vs. Finetuned). log Mel is the baseline that uses log Mel spectrograms as input features. The best transfer learning result is marked in bold." ], "reasoning": "The table shows that R-APC Scratch and T-APC Scratch have the highest WER values across all n, even higher than the baseline (log Mel), confirming that training APC from scratch performs the worst.", "statement": "Training APC from scratch along with the seq2seq model always performs the worst.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: ASR WER results using different numbers of GRU layers for the encoder in the ASR seq2seq model.'.\n" }, { "role": "assistant", "content": "APC transfer learning is effective in reducing downstream model size, with T-APC using 2 layers performing similarly to log Mel using 4 layers.The next aspect we examine is to what extent can we reduce the downstream model size with transfer learning. We see that when using the same number of layers, * -APC and *-APC10 always outperform other features. It is noteworthy that T-APC with just 2 layers performs similar to log Mel using 4 layers (18.6 vs. 18.3), which demonstrates the effectiveness of APC transfer learning for reducing downstream model size." } ], "images": [ "scigen/train/385.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: ASR WER results using different numbers of GRU layers for the encoder in the ASR seq2seq model." ], "reasoning": "APC transfer learning is effective in reducing downstream model size, with T-APC using 2 layers performing similarly to log Mel using 4 layers.", "description": "The next aspect we examine is to what extent can we reduce the downstream model size with transfer learning. We see that when using the same number of layers, * -APC and *-APC10 always outperform other features. It is noteworthy that T-APC with just 2 layers performs similar to log Mel using 4 layers (18.6 vs. 18.3), which demonstrates the effectiveness of APC transfer learning for reducing downstream model size.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: ASR WER results using different numbers of GRU layers for the encoder in the ASR seq2seq model.\nQuestion: Which feature performs best with 3 encoder layers?\nSolve the above question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the values in the 'Number of encoder layers 3' column, we see that T-APC has the lowest value of 15.8, indicating the best performance. So, the answer is {\"answer\": \"T-APC\"}." } ], "images": [ "scigen/train/385.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: ASR WER results using different numbers of GRU layers for the encoder in the ASR seq2seq model." ], "reasoning": "By comparing the values in the 'Number of encoder layers 3' column, we see that T-APC has the lowest value of 15.8, indicating the best performance.", "question": "Which feature performs best with 3 encoder layers?", "answer": "So, the answer is {\"answer\": \"T-APC\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: T-APC with 2 layers performs better than log Mel with 4 layers., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nT-APC with 2 layers performs better than log Mel with 4 layers.\n" }, { "role": "assistant", "content": "The table shows that T-APC with 2 layers has a value of 18.6, while log Mel with 4 layers has a value of 18.3. Therefore, T-APC with 2 layers does not perform better than log Mel with 4 layers. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/385.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: ASR WER results using different numbers of GRU layers for the encoder in the ASR seq2seq model." ], "reasoning": "The table shows that T-APC with 2 layers has a value of 18.6, while log Mel with 4 layers has a value of 18.3. Therefore, T-APC with 2 layers does not perform better than log Mel with 4 layers.", "statement": "T-APC with 2 layers performs better than log Mel with 4 layers.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: Speech translation results. BLEU scores (↑) are reported. We also include the results of the cascaded system (ASR + MT) reported in [28] and the S-Transformer model reported in [29]. Only the results on the test set are available for these two approaches.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The RNN-based model with T-APC features (14.3) outperforms S-Transformer (13.8) and is comparable to the cascaded system (14.6).Besides, our RNN-based model with T-APC features (14.3) outperforms S-Transformer (13.8), and is comparable with the cascaded system (14.6)." } ], "images": [ "scigen/train/386.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Speech translation results. BLEU scores (↑) are reported. We also include the results of the cascaded system (ASR + MT) reported in [28] and the S-Transformer model reported in [29]. Only the results on the test set are available for these two approaches." ], "reasoning": "The RNN-based model with T-APC features (14.3) outperforms S-Transformer (13.8) and is comparable to the cascaded system (14.6).", "description": "Besides, our RNN-based model with T-APC features (14.3) outperforms S-Transformer (13.8), and is comparable with the cascaded system (14.6).", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 4: Speech translation results. BLEU scores (↑) are reported. We also include the results of the cascaded system (ASR + MT) reported in [28] and the S-Transformer model reported in [29]. Only the results on the test set are available for these two approaches.'\nQuestion: Which method has the highest test score among the listed methods?\n" }, { "role": "assistant", "content": "By comparing the test scores of all methods, the cascaded system has the highest test score of 14.6. So, the answer is {\"answer\": \"Cascaded\"}" } ], "images": [ "scigen/train/386.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Speech translation results. BLEU scores (↑) are reported. We also include the results of the cascaded system (ASR + MT) reported in [28] and the S-Transformer model reported in [29]. Only the results on the test set are available for these two approaches." ], "reasoning": "By comparing the test scores of all methods, the cascaded system has the highest test score of 14.6.", "question": "Which method has the highest test score among the listed methods?", "answer": "So, the answer is {\"answer\": \"Cascaded\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe RNN-based model with T-APC features has a higher test score than the S-Transformer.\n" }, { "role": "assistant", "content": "The table shows that the RNN-based model with T-APC features has a test score of 14.3, which is higher than the S-Transformer's test score of 13.8. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/386.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Speech translation results. BLEU scores (↑) are reported. We also include the results of the cascaded system (ASR + MT) reported in [28] and the S-Transformer model reported in [29]. Only the results on the test set are available for these two approaches." ], "reasoning": "The table shows that the RNN-based model with T-APC features has a test score of 14.3, which is higher than the S-Transformer's test score of 13.8.", "statement": "The RNN-based model with T-APC features has a higher test score than the S-Transformer.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Results of all proposed causal models, using stratified 10-fold cross-validation. The combined system is a sieve-based architecture that applies the models in decreasing order of their precision. The combined system significantly outperforms the best single model, SVM with L1 regularization, according to a bootstrap resampling test (p = 0.022).'.\n" }, { "role": "assistant", "content": "The models were evaluated using micro precision, recall, and F1 scores, with training and testing performed using stratified 10-fold cross validation.We report results using micro precision, recall, and F1 scores for each model. With fewer than 200 instances of causal precedence occurring in 1000 annotations, training and testing for both the feature-based classifiers and latent feature models was performed using stratified 10-fold cross validation. Weight updates were made on batches of 32 examples and all folds completed in fewer than 50 epochs." } ], "images": [ "scigen/train/387.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of all proposed causal models, using stratified 10-fold cross-validation. The combined system is a sieve-based architecture that applies the models in decreasing order of their precision. The combined system significantly outperforms the best single model, SVM with L1 regularization, according to a bootstrap resampling test (p = 0.022)." ], "reasoning": "The models were evaluated using micro precision, recall, and F1 scores, with training and testing performed using stratified 10-fold cross validation.", "description": "We report results using micro precision, recall, and F1 scores for each model. With fewer than 200 instances of causal precedence occurring in 1000 annotations, training and testing for both the feature-based classifiers and latent feature models was performed using stratified 10-fold cross validation. Weight updates were made on batches of 32 examples and all folds completed in fewer than 50 epochs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 4: Results of all proposed causal models, using stratified 10-fold cross-validation. The combined system is a sieve-based architecture that applies the models in decreasing order of their precision. The combined system significantly outperforms the best single model, SVM with L1 regularization, according to a bootstrap resampling test (p = 0.022).'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model achieved the highest F1 score?\n" }, { "role": "assistant", "content": "To find the model with the highest F1 score, look at the 'f1' column and identify the highest value. The highest F1 score is 0.46, achieved by the Combined model. So, the answer is {\"answer\": \"Combined\"}." } ], "images": [ "scigen/train/387.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of all proposed causal models, using stratified 10-fold cross-validation. The combined system is a sieve-based architecture that applies the models in decreasing order of their precision. The combined system significantly outperforms the best single model, SVM with L1 regularization, according to a bootstrap resampling test (p = 0.022)." ], "reasoning": "To find the model with the highest F1 score, look at the 'f1' column and identify the highest value. The highest F1 score is 0.46, achieved by the Combined model.", "question": "Which model achieved the highest F1 score?", "answer": "So, the answer is {\"answer\": \"Combined\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe SVM+L1 model achieved an F1 score higher than 0.40.\n" }, { "role": "assistant", "content": "To verify this statement, check the F1 score of the SVM+L1 model in the table. The F1 score for SVM+L1 is 0.43, which is higher than 0.40. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/387.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of all proposed causal models, using stratified 10-fold cross-validation. The combined system is a sieve-based architecture that applies the models in decreasing order of their precision. The combined system significantly outperforms the best single model, SVM with L1 regularization, according to a bootstrap resampling test (p = 0.022)." ], "reasoning": "To verify this statement, check the F1 score of the SVM+L1 model in the table. The F1 score for SVM+L1 is 0.43, which is higher than 0.40.", "statement": "The SVM+L1 model achieved an F1 score higher than 0.40.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: Comparing our models with several state-of-the-art systems on the CoNLL 2003 English NER dataset.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The best-performing model for NER in English is LSTM-CRF-TI(g) with a score of 91.68, outperforming all other models.A3SS0SSS0Px2 Results on NER For Dutch and Spanish, we used cross-lingual embedding as a way to exploit lexical information. The results are shown in Tab. Our best-performing model outperform all the competing systems." } ], "images": [ "scigen/train/388.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparing our models with several state-of-the-art systems on the CoNLL 2003 English NER dataset." ], "reasoning": "The best-performing model for NER in English is LSTM-CRF-TI(g) with a score of 91.68, outperforming all other models.", "description": "A3SS0SSS0Px2 Results on NER For Dutch and Spanish, we used cross-lingual embedding as a way to exploit lexical information. The results are shown in Tab. Our best-performing model outperform all the competing systems.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 5: Comparing our models with several state-of-the-art systems on the CoNLL 2003 English NER dataset.', solve the following question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model achieved the highest score for NER in English?\n" }, { "role": "assistant", "content": "By comparing the scores of all models listed in the table, the LSTM-CRF-TI(g) model has the highest score of 91.68. So, the answer is {\"answer\": \"LSTM-CRF-TI(g)\"}." } ], "images": [ "scigen/train/388.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparing our models with several state-of-the-art systems on the CoNLL 2003 English NER dataset." ], "reasoning": "By comparing the scores of all models listed in the table, the LSTM-CRF-TI(g) model has the highest score of 91.68.", "question": "Which model achieved the highest score for NER in English?", "answer": "So, the answer is {\"answer\": \"LSTM-CRF-TI(g)\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe LSTM-CRF-T model achieved a score higher than 91.00 for NER in English.\n" }, { "role": "assistant", "content": "The table shows that the LSTM-CRF-T model achieved a score of 90.8, which is lower than 91.00. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/388.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparing our models with several state-of-the-art systems on the CoNLL 2003 English NER dataset." ], "reasoning": "The table shows that the LSTM-CRF-T model achieved a score of 90.8, which is lower than 91.00.", "statement": "The LSTM-CRF-T model achieved a score higher than 91.00 for NER in English.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Comparing our models with recent results on the Aspect Sentiment datasets.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Our modular approach, which includes aspect position identification, aspect category prediction, and sentiment polarity prediction, outperforms monolithic approaches across four languages.S6SS2SSS0Px2 Aspect Based Sentiment We evaluated our models on two tasks: The first uses two modules, for identifying the position of the aspect in the text (i.e., chunking) and the aspect category prediction (denoted E+A). The second adds a third module that predicts the sentiment polarity associated with the aspect (denoted E+A+S). I.e., for a given sentence, label its entity span, the aspect category of the entity and the sentiment polarity of the entity at the same time. The results over four languages are summarized in Tab. In all cases, our modular approach outperforms all monolithic approaches." } ], "images": [ "scigen/train/389.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparing our models with recent results on the Aspect Sentiment datasets." ], "reasoning": "Our modular approach, which includes aspect position identification, aspect category prediction, and sentiment polarity prediction, outperforms monolithic approaches across four languages.", "description": "S6SS2SSS0Px2 Aspect Based Sentiment We evaluated our models on two tasks: The first uses two modules, for identifying the position of the aspect in the text (i.e., chunking) and the aspect category prediction (denoted E+A). The second adds a third module that predicts the sentiment polarity associated with the aspect (denoted E+A+S). I.e., for a given sentence, label its entity span, the aspect category of the entity and the sentiment polarity of the entity at the same time. The results over four languages are summarized in Tab. In all cases, our modular approach outperforms all monolithic approaches.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Comparing our models with recent results on the Aspect Sentiment datasets.' to sequentially solve the question presented below. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model achieved the highest score for English E+A+S?\n" }, { "role": "assistant", "content": "To find the highest score for English E+A+S, compare the scores in the English E+A+S column. The highest score is 51.24, achieved by the LSTM-CRF-TI(g) model. So, the answer is {\"answer\": \"LSTM-CRF-TI(g)\"}." } ], "images": [ "scigen/train/389.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparing our models with recent results on the Aspect Sentiment datasets." ], "reasoning": "To find the highest score for English E+A+S, compare the scores in the English E+A+S column. The highest score is 51.24, achieved by the LSTM-CRF-TI(g) model.", "question": "Which model achieved the highest score for English E+A+S?", "answer": "So, the answer is {\"answer\": \"LSTM-CRF-TI(g)\"}." }, { "messages": [ { "role": "user", "content": "The LSTM-CRF-TI(g) model achieved the highest score for Russian E+A. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe LSTM-CRF-TI(g) model achieved the highest score for Russian E+A.\n" }, { "role": "assistant", "content": "To verify this statement, compare the scores in the Russian E+A column. The highest score is 65.64, achieved by the LSTM-CRF-TI(g) model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/389.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparing our models with recent results on the Aspect Sentiment datasets." ], "reasoning": "To verify this statement, compare the scores in the Russian E+A column. The highest score is 65.64, achieved by the LSTM-CRF-TI(g) model.", "statement": "The LSTM-CRF-TI(g) model achieved the highest score for Russian E+A.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Performance on the target sentiment task'.\n" }, { "role": "assistant", "content": "The LSTM-CRF-TI(g) model achieves the highest Precision, Recall, and F1 scores in both English and Spanish compared to other models.The complete results of our experiments on the target sentiment task are summarized in Tab. Our LSTM-CRF-TI(g) model outperforms all the other competing models in Precision, Recall and the F1 score." } ], "images": [ "scigen/train/390.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance on the target sentiment task" ], "reasoning": "The LSTM-CRF-TI(g) model achieves the highest Precision, Recall, and F1 scores in both English and Spanish compared to other models.", "description": "The complete results of our experiments on the target sentiment task are summarized in Tab. Our LSTM-CRF-TI(g) model outperforms all the other competing models in Precision, Recall and the F1 score.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 4: Performance on the target sentiment task' to sequentially solve the question presented below. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model has the highest F1 score for Spanish?\n" }, { "role": "assistant", "content": "By comparing the F1 scores for Spanish across all models, the LSTM-CRF-TI(g) model has the highest F1 score of 50.50. So, the answer is {\"answer\": \"LSTM-CRF-TI(g)\"}." } ], "images": [ "scigen/train/390.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance on the target sentiment task" ], "reasoning": "By comparing the F1 scores for Spanish across all models, the LSTM-CRF-TI(g) model has the highest F1 score of 50.50.", "question": "Which model has the highest F1 score for Spanish?", "answer": "So, the answer is {\"answer\": \"LSTM-CRF-TI(g)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe LSTM-CRF-TI(g) model has the highest Recall score for English.\n" }, { "role": "assistant", "content": "By comparing the Recall scores for English across all models, the LSTM-CRF-TI(g) model has the highest Recall score of 49.36. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/390.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance on the target sentiment task" ], "reasoning": "By comparing the Recall scores for English across all models, the LSTM-CRF-TI(g) model has the highest Recall score of 49.36.", "statement": "The LSTM-CRF-TI(g) model has the highest Recall score for English.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 6: Comparing our models with recent results on the 2002 CoNLL Dutch and Spanish NER datasets.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The best-performing model, LSTM-CRF-TI(g), achieved the highest scores in both Dutch and Spanish NER tasks, outperforming all competing systems.A3SS0SSS0Px2 Results on NER For Dutch and Spanish, we used cross-lingual embedding as a way to exploit lexical information. The results are shown in Tab. Our best-performing model outperform all the competing systems." } ], "images": [ "scigen/train/391.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Comparing our models with recent results on the 2002 CoNLL Dutch and Spanish NER datasets." ], "reasoning": "The best-performing model, LSTM-CRF-TI(g), achieved the highest scores in both Dutch and Spanish NER tasks, outperforming all competing systems.", "description": "A3SS0SSS0Px2 Results on NER For Dutch and Spanish, we used cross-lingual embedding as a way to exploit lexical information. The results are shown in Tab. Our best-performing model outperform all the competing systems.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 6: Comparing our models with recent results on the 2002 CoNLL Dutch and Spanish NER datasets.', unfold the question presented below, detailing every step of your calculation. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieved the highest score in the Spanish NER task?\n" }, { "role": "assistant", "content": "By examining the table, the highest score in the Spanish column is 85.92, which corresponds to the LSTM-CRF-TI(g) model. So, the answer is {\"answer\": \"LSTM-CRF-TI(g)\"}." } ], "images": [ "scigen/train/391.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Comparing our models with recent results on the 2002 CoNLL Dutch and Spanish NER datasets." ], "reasoning": "By examining the table, the highest score in the Spanish column is 85.92, which corresponds to the LSTM-CRF-TI(g) model.", "question": "Which model achieved the highest score in the Spanish NER task?", "answer": "So, the answer is {\"answer\": \"LSTM-CRF-TI(g)\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe LSTM-CRF-TI model achieved the highest score in the Dutch NER task.\n" }, { "role": "assistant", "content": "The table shows that the highest score in the Dutch column is 84.51, which corresponds to the LSTM-CRF-TI(g) model, not the LSTM-CRF-TI model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/391.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Comparing our models with recent results on the 2002 CoNLL Dutch and Spanish NER datasets." ], "reasoning": "The table shows that the highest score in the Dutch column is 84.51, which corresponds to the LSTM-CRF-TI(g) model, not the LSTM-CRF-TI model.", "statement": "The LSTM-CRF-TI model achieved the highest score in the Dutch NER task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 11: Results of classification of argument components in the cross-domain scenario. Macro-F1 scores reported, bold numbers denote the best results. HS – homeschooling, MS – mainstreaming, PIS – prayer in schools, PPS – private vs. public schools, RS – redshirting, SSE – single sex education. Results in the aggregated row are computed from an aggregated confusion matrix over all domains. The differences between the best feature set combination (4) and others are statistically significant (p<0.001; paired exact Liddell’s test).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Using only feature set 4, the system performance increases rapidly and is comparable to in-domain scenarios, indicating that embedding features generalize well across domains.However, using only feature set 4 (embeddings), the system performance increases rapidly, so it is even comparable to numbers achieved in the in-domain scenario. These results indicate that embedding features generalize well across domains in our task of argument component identification." } ], "images": [ "scigen/train/392.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Results of classification of argument components in the cross-domain scenario. Macro-F1 scores reported, bold numbers denote the best results. HS – homeschooling, MS – mainstreaming, PIS – prayer in schools, PPS – private vs. public schools, RS – redshirting, SSE – single sex education. Results in the aggregated row are computed from an aggregated confusion matrix over all domains. The differences between the best feature set combination (4) and others are statistically significant (p<0.001; paired exact Liddell’s test)." ], "reasoning": "Using only feature set 4, the system performance increases rapidly and is comparable to in-domain scenarios, indicating that embedding features generalize well across domains.", "description": "However, using only feature set 4 (embeddings), the system performance increases rapidly, so it is even comparable to numbers achieved in the in-domain scenario. These results indicate that embedding features generalize well across domains in our task of argument component identification.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 11: Results of classification of argument components in the cross-domain scenario. Macro-F1 scores reported, bold numbers denote the best results. HS – homeschooling, MS – mainstreaming, PIS – prayer in schools, PPS – private vs. public schools, RS – redshirting, SSE – single sex education. Results in the aggregated row are computed from an aggregated confusion matrix over all domains. The differences between the best feature set combination (4) and others are statistically significant (p<0.001; paired exact Liddell’s test).', solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich domain shows the highest performance using feature set 4?\n" }, { "role": "assistant", "content": "By examining the table, the highest value in the 'Feature set combinations 4' column is 0.257, which corresponds to the RS domain. So, the answer is {\"answer\": \"RS\"}" } ], "images": [ "scigen/train/392.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Results of classification of argument components in the cross-domain scenario. Macro-F1 scores reported, bold numbers denote the best results. HS – homeschooling, MS – mainstreaming, PIS – prayer in schools, PPS – private vs. public schools, RS – redshirting, SSE – single sex education. Results in the aggregated row are computed from an aggregated confusion matrix over all domains. The differences between the best feature set combination (4) and others are statistically significant (p<0.001; paired exact Liddell’s test)." ], "reasoning": "By examining the table, the highest value in the 'Feature set combinations 4' column is 0.257, which corresponds to the RS domain.", "question": "Which domain shows the highest performance using feature set 4?", "answer": "So, the answer is {\"answer\": \"RS\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe performance using feature set 4 is higher than using feature set combinations 01234 in the HS domain.\n" }, { "role": "assistant", "content": "In the HS domain, the performance using feature set 4 is 0.197, while using feature set combinations 01234 is 0.075. Since 0.197 is higher than 0.075, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/392.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Results of classification of argument components in the cross-domain scenario. Macro-F1 scores reported, bold numbers denote the best results. HS – homeschooling, MS – mainstreaming, PIS – prayer in schools, PPS – private vs. public schools, RS – redshirting, SSE – single sex education. Results in the aggregated row are computed from an aggregated confusion matrix over all domains. The differences between the best feature set combination (4) and others are statistically significant (p<0.001; paired exact Liddell’s test)." ], "reasoning": "In the HS domain, the performance using feature set 4 is 0.197, while using feature set combinations 01234 is 0.075. Since 0.197 is higher than 0.075, the statement is supported.", "statement": "The performance using feature set 4 is higher than using feature set combinations 01234 in the HS domain.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Correlations between αU and various measures on different data sub-sets. SC – full sentence coverage; DL – document length; APL – average paragraph length; ASL = average sentence length; ARI, C-L (Coleman-Liau), Flesch, LIX – readability measures. Bold numbers denote statistically significant correlation (p<0.05).'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Longer document and paragraph lengths, as well as higher readability scores, negatively correlate with agreement in comments, blog posts, and public vs. private school domains respectively.We observed the following statistically significant (p<0.05) correlations. First, document length negatively correlates with agreement in comments. The longer the comment was the lower the agreement was. Second, average paragraph length negatively correlates with agreement in blog posts. The longer the paragraphs in blogs were, the lower agreement was reached. Third, all readability scores negatively correlate with agreement in the public vs. private school domain, meaning that the more complicated the text in terms of readability is, the lower agreement was reached. We observed no significant correlation in sentence coverage and average sentence length measures. We cannot draw any general conclusion from these results, but we can state that some registers and topics, given their properties, are more challenging to annotate than others." } ], "images": [ "scigen/train/393.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Correlations between αU and various measures on different data sub-sets. SC – full sentence coverage; DL – document length; APL – average paragraph length; ASL = average sentence length; ARI, C-L (Coleman-Liau), Flesch, LIX – readability measures. Bold numbers denote statistically significant correlation (p<0.05)." ], "reasoning": "Longer document and paragraph lengths, as well as higher readability scores, negatively correlate with agreement in comments, blog posts, and public vs. private school domains respectively.", "description": "We observed the following statistically significant (p<0.05) correlations. First, document length negatively correlates with agreement in comments. The longer the comment was the lower the agreement was. Second, average paragraph length negatively correlates with agreement in blog posts. The longer the paragraphs in blogs were, the lower agreement was reached. Third, all readability scores negatively correlate with agreement in the public vs. private school domain, meaning that the more complicated the text in terms of readability is, the lower agreement was reached. We observed no significant correlation in sentence coverage and average sentence length measures. We cannot draw any general conclusion from these results, but we can state that some registers and topics, given their properties, are more challenging to annotate than others.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 5: Correlations between αU and various measures on different data sub-sets. SC – full sentence coverage; DL – document length; APL – average paragraph length; ASL = average sentence length; ARI, C-L (Coleman-Liau), Flesch, LIX – readability measures. Bold numbers denote statistically significant correlation (p<0.05).', solve this question and give detail solutions. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhich readability score has the highest negative correlation with agreement in the public vs. private school domain?\n" }, { "role": "assistant", "content": "The description states that all readability scores negatively correlate with agreement in the public vs. private school domain. By examining the table, the highest negative correlation in the public vs. private school domain is with the LIX score (-0.56). So, the answer is {\"answer\": \"LIX\"}." } ], "images": [ "scigen/train/393.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Correlations between αU and various measures on different data sub-sets. SC – full sentence coverage; DL – document length; APL – average paragraph length; ASL = average sentence length; ARI, C-L (Coleman-Liau), Flesch, LIX – readability measures. Bold numbers denote statistically significant correlation (p<0.05)." ], "reasoning": "The description states that all readability scores negatively correlate with agreement in the public vs. private school domain. By examining the table, the highest negative correlation in the public vs. private school domain is with the LIX score (-0.56).", "question": "Which readability score has the highest negative correlation with agreement in the public vs. private school domain?", "answer": "So, the answer is {\"answer\": \"LIX\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nAverage paragraph length negatively correlates with agreement in blog posts.\n" }, { "role": "assistant", "content": "The description states that average paragraph length negatively correlates with agreement in blog posts. The table shows a negative correlation of -0.81 for average paragraph length (APL) in blog posts, supporting the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/393.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Correlations between αU and various measures on different data sub-sets. SC – full sentence coverage; DL – document length; APL – average paragraph length; ASL = average sentence length; ARI, C-L (Coleman-Liau), Flesch, LIX – readability measures. Bold numbers denote statistically significant correlation (p<0.05)." ], "reasoning": "The description states that average paragraph length negatively correlates with agreement in blog posts. The table shows a negative correlation of -0.81 for average paragraph length (APL) in blog posts, supporting the statement.", "statement": "Average paragraph length negatively correlates with agreement in blog posts.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 8: Class distribution of the gold data Toulmin corpus approximated to the sentence level boundaries.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The dataset is highly unbalanced with the 'O' class dominating at 56.8%, while rebuttal and refutation classes are very low, accounting for only 3.4% combined.The little presence of rebuttal and refutation (4 classes account only for 3.4% of the data) makes this dataset very unbalanced. The overall best performance (Macro-F1 0.251) was achieved using the rich feature sets (01234 and 234) and significantly outperformed the baseline as well as other feature sets. Classification of non-argumentative text (the \"O\" class) yields about 0.7 F1 score even in the baseline setting. The boundaries of claims (Cla-B), premises (Pre-B), and backing (Bac-B) reach in average lower scores then their respective inside tags (Cla-I, Pre-I, Bac-I). It can be interpreted such that the system is able to classify that a certain sentence belongs to a certain argument component, but the distinction whether it is a beginning of the argument component is harder. The very low numbers for rebuttal and refutation have two reasons." } ], "images": [ "scigen/train/394.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Class distribution of the gold data Toulmin corpus approximated to the sentence level boundaries." ], "reasoning": "The dataset is highly unbalanced with the 'O' class dominating at 56.8%, while rebuttal and refutation classes are very low, accounting for only 3.4% combined.", "description": "The little presence of rebuttal and refutation (4 classes account only for 3.4% of the data) makes this dataset very unbalanced. The overall best performance (Macro-F1 0.251) was achieved using the rich feature sets (01234 and 234) and significantly outperformed the baseline as well as other feature sets. Classification of non-argumentative text (the \"O\" class) yields about 0.7 F1 score even in the baseline setting. The boundaries of claims (Cla-B), premises (Pre-B), and backing (Bac-B) reach in average lower scores then their respective inside tags (Cla-I, Pre-I, Bac-I). It can be interpreted such that the system is able to classify that a certain sentence belongs to a certain argument component, but the distinction whether it is a beginning of the argument component is harder. The very low numbers for rebuttal and refutation have two reasons.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 8: Class distribution of the gold data Toulmin corpus approximated to the sentence level boundaries.', and tackle the question below in a sequential manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the total number of sentences classified as Backing-B and Backing-I?\n" }, { "role": "assistant", "content": "To find the total number of sentences classified as Backing-B and Backing-I, add the absolute counts of Backing-B (220) and Backing-I (281). So, the answer is {\"answer\": \"501\"}." } ], "images": [ "scigen/train/394.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Class distribution of the gold data Toulmin corpus approximated to the sentence level boundaries." ], "reasoning": "To find the total number of sentences classified as Backing-B and Backing-I, add the absolute counts of Backing-B (220) and Backing-I (281).", "question": "What is the total number of sentences classified as Backing-B and Backing-I?", "answer": "So, the answer is {\"answer\": \"501\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The dataset contains more sentences classified as Premise-I than Premise-B., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe dataset contains more sentences classified as Premise-I than Premise-B.\n" }, { "role": "assistant", "content": "The table shows that Premise-I has 336 sentences, while Premise-B has 530 sentences. Therefore, Premise-B has more sentences than Premise-I. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/394.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Class distribution of the gold data Toulmin corpus approximated to the sentence level boundaries." ], "reasoning": "The table shows that Premise-I has 336 sentences, while Premise-B has 530 sentences. Therefore, Premise-B has more sentences than Premise-I.", "statement": "The dataset contains more sentences classified as Premise-I than Premise-B.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: (Left) Comparison against the best prior work for NMT on the IWSLT 2014 German-English test set. (Upper Right) Comparison of inference alternatives of variational attention on IWSLT 2014. (Lower Right) Comparison of different models in terms of implied discrete entropy (lower = more certain alignment).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Variational attention performs comparably to log marginal likelihood optimization on the IWSLT 2014 task and shows superior performance on the WMT 2017 task, while Gumbel-Softmax is a viable alternative with sensitivity to temperature.For NMT, on the IWSLT 2014 German-English task, variational attention with enumeration and sampling performs comparably to optimizing the log marginal likelihood, despite the fact that it is optimizing a lower bound. We believe that this is due to the use of q(z), which conditions on the entire source/target and therefore potentially provides better training signal to p(z|x,~x) through the KL term. Note that it is also possible to have q(z) Even with sampling, our system improves on the state-of-the-art. On the larger WMT 2017 English-German task, the superior performance of variational attention persists: our baseline soft attention reaches 24.10 BLEU score, while variational attention reaches 24.98. Note that this only reflects a reasonable setting without exhaustive tuning, yet we show that we can train variational attention at scale. For VQA the trend is largely similar, and results for NLL with variational attention improve on soft attention and hard attention. However the task-specific evaluation metrics are slightly worse. Note that hard attention has very low entropy (high certainty) whereas soft attention is quite high. The variational attention model falls in between. RWS reaches a comparable performance as REINFORCE, but at a higher memory cost as it requires multiple samples. Gumbel-Softmax reaches nearly the same performance and seems like a viable alternative; although we found its performance is sensitive to its temperature parameter. We also trained a non-amortized SVI model, but found that at similar runtime it was not able to produce satisfactory results, likely due to insufficient updates of the local variational parameters. Despite extensive experiments, we found that variational relaxed attention performed worse than other methods. In particular we found that when training with a Dirichlet KL, it is hard to reach low-entropy regions of the simplex, and the attentions are more uniform than either soft or variational categorical attention." } ], "images": [ "scigen/train/395.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: (Left) Comparison against the best prior work for NMT on the IWSLT 2014 German-English test set. (Upper Right) Comparison of inference alternatives of variational attention on IWSLT 2014. (Lower Right) Comparison of different models in terms of implied discrete entropy (lower = more certain alignment)." ], "reasoning": "Variational attention performs comparably to log marginal likelihood optimization on the IWSLT 2014 task and shows superior performance on the WMT 2017 task, while Gumbel-Softmax is a viable alternative with sensitivity to temperature.", "description": "For NMT, on the IWSLT 2014 German-English task, variational attention with enumeration and sampling performs comparably to optimizing the log marginal likelihood, despite the fact that it is optimizing a lower bound. We believe that this is due to the use of q(z), which conditions on the entire source/target and therefore potentially provides better training signal to p(z|x,~x) through the KL term. Note that it is also possible to have q(z) Even with sampling, our system improves on the state-of-the-art. On the larger WMT 2017 English-German task, the superior performance of variational attention persists: our baseline soft attention reaches 24.10 BLEU score, while variational attention reaches 24.98. Note that this only reflects a reasonable setting without exhaustive tuning, yet we show that we can train variational attention at scale. For VQA the trend is largely similar, and results for NLL with variational attention improve on soft attention and hard attention. However the task-specific evaluation metrics are slightly worse. Note that hard attention has very low entropy (high certainty) whereas soft attention is quite high. The variational attention model falls in between. RWS reaches a comparable performance as REINFORCE, but at a higher memory cost as it requires multiple samples. Gumbel-Softmax reaches nearly the same performance and seems like a viable alternative; although we found its performance is sensitive to its temperature parameter. We also trained a non-amortized SVI model, but found that at similar runtime it was not able to produce satisfactory results, likely due to insufficient updates of the local variational parameters. Despite extensive experiments, we found that variational relaxed attention performed worse than other methods. In particular we found that when training with a Dirichlet KL, it is hard to reach low-entropy regions of the simplex, and the attentions are more uniform than either soft or variational categorical attention.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: (Left) Comparison against the best prior work for NMT on the IWSLT 2014 German-English test set. (Upper Right) Comparison of inference alternatives of variational attention on IWSLT 2014. (Lower Right) Comparison of different models in terms of implied discrete entropy (lower = more certain alignment).' and methodically tackle the question that follows. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich inference method has the highest BLEU score and how many samples does it use?\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for different inference methods. REINFORCE has the highest BLEU score of 33.30 and uses 1 sample. So, the answer is {\"answer\": \"REINFORCE with 1 sample\"}." } ], "images": [ "scigen/train/395.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: (Left) Comparison against the best prior work for NMT on the IWSLT 2014 German-English test set. (Upper Right) Comparison of inference alternatives of variational attention on IWSLT 2014. (Lower Right) Comparison of different models in terms of implied discrete entropy (lower = more certain alignment)." ], "reasoning": "The table shows the BLEU scores for different inference methods. REINFORCE has the highest BLEU score of 33.30 and uses 1 sample.", "question": "Which inference method has the highest BLEU score and how many samples does it use?", "answer": "So, the answer is {\"answer\": \"REINFORCE with 1 sample\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence RWS has the highest BLEU score among the inference methods. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nRWS has the highest BLEU score among the inference methods.\n" }, { "role": "assistant", "content": "The table shows that RWS has a BLEU score of 32.96, which is lower than both REINFORCE (33.30) and Gumbel-Softmax (33.08). Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/395.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: (Left) Comparison against the best prior work for NMT on the IWSLT 2014 German-English test set. (Upper Right) Comparison of inference alternatives of variational attention on IWSLT 2014. (Lower Right) Comparison of different models in terms of implied discrete entropy (lower = more certain alignment)." ], "reasoning": "The table shows that RWS has a BLEU score of 32.96, which is lower than both REINFORCE (33.30) and Gumbel-Softmax (33.08). Therefore, the statement is refuted.", "statement": "RWS has the highest BLEU score among the inference methods.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: Evaluation on NMT and VQA for the various models. E column indicates whether the expectation is calculated via enumeration (Enum) or a single sample (Sample) during training. For NMT we evaluate intrinsically on perplexity (PPL) (lower is better) and extrinsically on BLEU (higher is better), where for BLEU we perform beam search with beam size 10 and length penalty (see Appendix B for further details). For VQA we evaluate intrinsically on negative log-likelihood (NLL) (lower is better) and extrinsically on VQA evaluation metric (higher is better). All results except for relaxed attention use enumeration at test time.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Soft attention underperforms hard attention, but exact marginal likelihood outperforms soft attention in both experiments.We first note that hard attention underperforms soft attention, even when its expectation is enumerated. This indicates that Jensen’s inequality alone is a poor bound. On the other hand, on both experiments, exact marginal likelihood outperforms soft attention, indicating that when possible it is better to have latent alignments." } ], "images": [ "scigen/train/396.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Evaluation on NMT and VQA for the various models. E column indicates whether the expectation is calculated via enumeration (Enum) or a single sample (Sample) during training. For NMT we evaluate intrinsically on perplexity (PPL) (lower is better) and extrinsically on BLEU (higher is better), where for BLEU we perform beam search with beam size 10 and length penalty (see Appendix B for further details). For VQA we evaluate intrinsically on negative log-likelihood (NLL) (lower is better) and extrinsically on VQA evaluation metric (higher is better). All results except for relaxed attention use enumeration at test time." ], "reasoning": "Soft attention underperforms hard attention, but exact marginal likelihood outperforms soft attention in both experiments.", "description": "We first note that hard attention underperforms soft attention, even when its expectation is enumerated. This indicates that Jensen’s inequality alone is a poor bound. On the other hand, on both experiments, exact marginal likelihood outperforms soft attention, indicating that when possible it is better to have latent alignments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Evaluation on NMT and VQA for the various models. E column indicates whether the expectation is calculated via enumeration (Enum) or a single sample (Sample) during training. For NMT we evaluate intrinsically on perplexity (PPL) (lower is better) and extrinsically on BLEU (higher is better), where for BLEU we perform beam search with beam size 10 and length penalty (see Appendix B for further details). For VQA we evaluate intrinsically on negative log-likelihood (NLL) (lower is better) and extrinsically on VQA evaluation metric (higher is better). All results except for relaxed attention use enumeration at test time.', answer the following question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest VQA Eval score?\n" }, { "role": "assistant", "content": "By examining the VQA Eval column, the highest score is 60.33, which corresponds to the Marginal Likelihood model. So, the answer is {\"answer\": \"Marginal Likelihood\"}." } ], "images": [ "scigen/train/396.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Evaluation on NMT and VQA for the various models. E column indicates whether the expectation is calculated via enumeration (Enum) or a single sample (Sample) during training. For NMT we evaluate intrinsically on perplexity (PPL) (lower is better) and extrinsically on BLEU (higher is better), where for BLEU we perform beam search with beam size 10 and length penalty (see Appendix B for further details). For VQA we evaluate intrinsically on negative log-likelihood (NLL) (lower is better) and extrinsically on VQA evaluation metric (higher is better). All results except for relaxed attention use enumeration at test time." ], "reasoning": "By examining the VQA Eval column, the highest score is 60.33, which corresponds to the Marginal Likelihood model.", "question": "Which model has the highest VQA Eval score?", "answer": "So, the answer is {\"answer\": \"Marginal Likelihood\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Variational Relaxed Attention has the highest NMT BLEU score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nVariational Relaxed Attention has the highest NMT BLEU score.\n" }, { "role": "assistant", "content": "By looking at the NMT BLEU column, the highest score is 33.68, which corresponds to the Variational Attention model, not the Variational Relaxed Attention model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/396.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Evaluation on NMT and VQA for the various models. E column indicates whether the expectation is calculated via enumeration (Enum) or a single sample (Sample) during training. For NMT we evaluate intrinsically on perplexity (PPL) (lower is better) and extrinsically on BLEU (higher is better), where for BLEU we perform beam search with beam size 10 and length penalty (see Appendix B for further details). For VQA we evaluate intrinsically on negative log-likelihood (NLL) (lower is better) and extrinsically on VQA evaluation metric (higher is better). All results except for relaxed attention use enumeration at test time." ], "reasoning": "By looking at the NMT BLEU column, the highest score is 33.68, which corresponds to the Variational Attention model, not the Variational Relaxed Attention model.", "statement": "Variational Relaxed Attention has the highest NMT BLEU score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: (Left) Performance change on NMT from exact decoding to K-Max decoding with K=5. (see section 5 for definition of K-max decoding). (Right) Test perplexity of different approaches while varying K to estimate Ez[p(y|x,~x)]. Dotted lines compare soft baseline and variational with full enumeration.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Exact enumeration methods outperform others, but K-max is a reasonable approximation. Training with latent alignments and testing with soft attention performed poorly.For all methods exact enumeration is better, however K-max is a reasonable approximation. This possibly indicates that soft attention models are approximating latent alignment models. On the other hand, training with latent alignments and testing with soft attention performed badly." } ], "images": [ "scigen/train/397.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: (Left) Performance change on NMT from exact decoding to K-Max decoding with K=5. (see section 5 for definition of K-max decoding). (Right) Test perplexity of different approaches while varying K to estimate Ez[p(y|x,~x)]. Dotted lines compare soft baseline and variational with full enumeration." ], "reasoning": "Exact enumeration methods outperform others, but K-max is a reasonable approximation. Training with latent alignments and testing with soft attention performed poorly.", "description": "For all methods exact enumeration is better, however K-max is a reasonable approximation. This possibly indicates that soft attention models are approximating latent alignment models. On the other hand, training with latent alignments and testing with soft attention performed badly.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nTable title: Table 2: (Left) Performance change on NMT from exact decoding to K-Max decoding with K=5. (see section 5 for definition of K-max decoding). (Right) Test perplexity of different approaches while varying K to estimate Ez[p(y|x,~x)]. Dotted lines compare soft baseline and variational with full enumeration.\n\nQuestion: Which model has the lowest PPL Exact score?\n" }, { "role": "assistant", "content": "To find the model with the lowest PPL Exact score, compare the PPL Exact values for all models. The model with the lowest value is 'Variational + Enum' with a score of 6.08. So, the answer is {\"answer\": \"Variational + Enum\"}." } ], "images": [ "scigen/train/397.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: (Left) Performance change on NMT from exact decoding to K-Max decoding with K=5. (see section 5 for definition of K-max decoding). (Right) Test perplexity of different approaches while varying K to estimate Ez[p(y|x,~x)]. Dotted lines compare soft baseline and variational with full enumeration." ], "reasoning": "To find the model with the lowest PPL Exact score, compare the PPL Exact values for all models. The model with the lowest value is 'Variational + Enum' with a score of 6.08.", "question": "Which model has the lowest PPL Exact score?", "answer": "So, the answer is {\"answer\": \"Variational + Enum\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The BLEU K-Max score for 'Hard + Enum' is higher than that for 'Variational + Sample'., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe BLEU K-Max score for 'Hard + Enum' is higher than that for 'Variational + Sample'.\n" }, { "role": "assistant", "content": "To verify this statement, compare the BLEU K-Max scores for 'Hard + Enum' and 'Variational + Sample'. 'Hard + Enum' has a BLEU K-Max score of 31.37, while 'Variational + Sample' has a score of 33.27. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/397.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: (Left) Performance change on NMT from exact decoding to K-Max decoding with K=5. (see section 5 for definition of K-max decoding). (Right) Test perplexity of different approaches while varying K to estimate Ez[p(y|x,~x)]. Dotted lines compare soft baseline and variational with full enumeration." ], "reasoning": "To verify this statement, compare the BLEU K-Max scores for 'Hard + Enum' and 'Variational + Sample'. 'Hard + Enum' has a BLEU K-Max score of 31.37, while 'Variational + Sample' has a score of 33.27. Therefore, the statement is refuted.", "statement": "The BLEU K-Max score for 'Hard + Enum' is higher than that for 'Variational + Sample'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: (Left) Comparison against the best prior work for NMT on the IWSLT 2014 German-English test set. (Upper Right) Comparison of inference alternatives of variational attention on IWSLT 2014. (Lower Right) Comparison of different models in terms of implied discrete entropy (lower = more certain alignment).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Variational attention with enumeration and sampling performs comparably to optimizing the log marginal likelihood on the IWSLT 2014 German-English task, and outperforms soft attention on the WMT 2017 English-German task.For NMT, on the IWSLT 2014 German-English task, variational attention with enumeration and sampling performs comparably to optimizing the log marginal likelihood, despite the fact that it is optimizing a lower bound. We believe that this is due to the use of q(z), which conditions on the entire source/target and therefore potentially provides better training signal to p(z|x,~x) through the KL term. Note that it is also possible to have q(z) Even with sampling, our system improves on the state-of-the-art. On the larger WMT 2017 English-German task, the superior performance of variational attention persists: our baseline soft attention reaches 24.10 BLEU score, while variational attention reaches 24.98. Note that this only reflects a reasonable setting without exhaustive tuning, yet we show that we can train variational attention at scale. For VQA the trend is largely similar, and results for NLL with variational attention improve on soft attention and hard attention. However the task-specific evaluation metrics are slightly worse. Note that hard attention has very low entropy (high certainty) whereas soft attention is quite high. The variational attention model falls in between. RWS reaches a comparable performance as REINFORCE, but at a higher memory cost as it requires multiple samples. Gumbel-Softmax reaches nearly the same performance and seems like a viable alternative; although we found its performance is sensitive to its temperature parameter. We also trained a non-amortized SVI model, but found that at similar runtime it was not able to produce satisfactory results, likely due to insufficient updates of the local variational parameters. Despite extensive experiments, we found that variational relaxed attention performed worse than other methods. In particular we found that when training with a Dirichlet KL, it is hard to reach low-entropy regions of the simplex, and the attentions are more uniform than either soft or variational categorical attention." } ], "images": [ "scigen/train/398.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: (Left) Comparison against the best prior work for NMT on the IWSLT 2014 German-English test set. (Upper Right) Comparison of inference alternatives of variational attention on IWSLT 2014. (Lower Right) Comparison of different models in terms of implied discrete entropy (lower = more certain alignment)." ], "reasoning": "Variational attention with enumeration and sampling performs comparably to optimizing the log marginal likelihood on the IWSLT 2014 German-English task, and outperforms soft attention on the WMT 2017 English-German task.", "description": "For NMT, on the IWSLT 2014 German-English task, variational attention with enumeration and sampling performs comparably to optimizing the log marginal likelihood, despite the fact that it is optimizing a lower bound. We believe that this is due to the use of q(z), which conditions on the entire source/target and therefore potentially provides better training signal to p(z|x,~x) through the KL term. Note that it is also possible to have q(z) Even with sampling, our system improves on the state-of-the-art. On the larger WMT 2017 English-German task, the superior performance of variational attention persists: our baseline soft attention reaches 24.10 BLEU score, while variational attention reaches 24.98. Note that this only reflects a reasonable setting without exhaustive tuning, yet we show that we can train variational attention at scale. For VQA the trend is largely similar, and results for NLL with variational attention improve on soft attention and hard attention. However the task-specific evaluation metrics are slightly worse. Note that hard attention has very low entropy (high certainty) whereas soft attention is quite high. The variational attention model falls in between. RWS reaches a comparable performance as REINFORCE, but at a higher memory cost as it requires multiple samples. Gumbel-Softmax reaches nearly the same performance and seems like a viable alternative; although we found its performance is sensitive to its temperature parameter. We also trained a non-amortized SVI model, but found that at similar runtime it was not able to produce satisfactory results, likely due to insufficient updates of the local variational parameters. Despite extensive experiments, we found that variational relaxed attention performed worse than other methods. In particular we found that when training with a Dirichlet KL, it is hard to reach low-entropy regions of the simplex, and the attentions are more uniform than either soft or variational categorical attention.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: (Left) Comparison against the best prior work for NMT on the IWSLT 2014 German-English test set. (Upper Right) Comparison of inference alternatives of variational attention on IWSLT 2014. (Lower Right) Comparison of different models in terms of implied discrete entropy (lower = more certain alignment).'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. \n\nQuestion: Which model achieved the highest BLEU score on the IWSLT task?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the model with the highest BLEU score, we need to compare the BLEU scores of all models listed in the table. The model with the highest BLEU score is 'Variational Attention + Enum' with a score of 33.69. So, the answer is {\"answer\": \"Variational Attention + Enum\"}." } ], "images": [ "scigen/train/398.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: (Left) Comparison against the best prior work for NMT on the IWSLT 2014 German-English test set. (Upper Right) Comparison of inference alternatives of variational attention on IWSLT 2014. (Lower Right) Comparison of different models in terms of implied discrete entropy (lower = more certain alignment)." ], "reasoning": "To find the model with the highest BLEU score, we need to compare the BLEU scores of all models listed in the table. The model with the highest BLEU score is 'Variational Attention + Enum' with a score of 33.69.", "question": "Which model achieved the highest BLEU score on the IWSLT task?", "answer": "So, the answer is {\"answer\": \"Variational Attention + Enum\"}." }, { "messages": [ { "role": "user", "content": "Variational Relaxed Attention achieved a BLEU score higher than 30 on the IWSLT task. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nVariational Relaxed Attention achieved a BLEU score higher than 30 on the IWSLT task.\n" }, { "role": "assistant", "content": "The table shows that Variational Relaxed Attention achieved a BLEU score of 30.05, which is higher than 30. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/398.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: (Left) Comparison against the best prior work for NMT on the IWSLT 2014 German-English test set. (Upper Right) Comparison of inference alternatives of variational attention on IWSLT 2014. (Lower Right) Comparison of different models in terms of implied discrete entropy (lower = more certain alignment)." ], "reasoning": "The table shows that Variational Relaxed Attention achieved a BLEU score of 30.05, which is higher than 30.", "statement": "Variational Relaxed Attention achieved a BLEU score higher than 30 on the IWSLT task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: (Left) Comparison against the best prior work for NMT on the IWSLT 2014 German-English test set. (Upper Right) Comparison of inference alternatives of variational attention on IWSLT 2014. (Lower Right) Comparison of different models in terms of implied discrete entropy (lower = more certain alignment).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Variational attention models generally perform better than soft and hard attention models in NMT and VQA tasks, with variational attention + sample achieving the lowest entropy in NMT.For NMT, on the IWSLT 2014 German-English task, variational attention with enumeration and sampling performs comparably to optimizing the log marginal likelihood, despite the fact that it is optimizing a lower bound. We believe that this is due to the use of q(z), which conditions on the entire source/target and therefore potentially provides better training signal to p(z|x,~x) through the KL term. Note that it is also possible to have q(z) Even with sampling, our system improves on the state-of-the-art. On the larger WMT 2017 English-German task, the superior performance of variational attention persists: our baseline soft attention reaches 24.10 BLEU score, while variational attention reaches 24.98. Note that this only reflects a reasonable setting without exhaustive tuning, yet we show that we can train variational attention at scale. For VQA the trend is largely similar, and results for NLL with variational attention improve on soft attention and hard attention. However the task-specific evaluation metrics are slightly worse. Note that hard attention has very low entropy (high certainty) whereas soft attention is quite high. The variational attention model falls in between. RWS reaches a comparable performance as REINFORCE, but at a higher memory cost as it requires multiple samples. Gumbel-Softmax reaches nearly the same performance and seems like a viable alternative; although we found its performance is sensitive to its temperature parameter. We also trained a non-amortized SVI model, but found that at similar runtime it was not able to produce satisfactory results, likely due to insufficient updates of the local variational parameters. Despite extensive experiments, we found that variational relaxed attention performed worse than other methods. In particular we found that when training with a Dirichlet KL, it is hard to reach low-entropy regions of the simplex, and the attentions are more uniform than either soft or variational categorical attention." } ], "images": [ "scigen/train/399.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: (Left) Comparison against the best prior work for NMT on the IWSLT 2014 German-English test set. (Upper Right) Comparison of inference alternatives of variational attention on IWSLT 2014. (Lower Right) Comparison of different models in terms of implied discrete entropy (lower = more certain alignment)." ], "reasoning": "Variational attention models generally perform better than soft and hard attention models in NMT and VQA tasks, with variational attention + sample achieving the lowest entropy in NMT.", "description": "For NMT, on the IWSLT 2014 German-English task, variational attention with enumeration and sampling performs comparably to optimizing the log marginal likelihood, despite the fact that it is optimizing a lower bound. We believe that this is due to the use of q(z), which conditions on the entire source/target and therefore potentially provides better training signal to p(z|x,~x) through the KL term. Note that it is also possible to have q(z) Even with sampling, our system improves on the state-of-the-art. On the larger WMT 2017 English-German task, the superior performance of variational attention persists: our baseline soft attention reaches 24.10 BLEU score, while variational attention reaches 24.98. Note that this only reflects a reasonable setting without exhaustive tuning, yet we show that we can train variational attention at scale. For VQA the trend is largely similar, and results for NLL with variational attention improve on soft attention and hard attention. However the task-specific evaluation metrics are slightly worse. Note that hard attention has very low entropy (high certainty) whereas soft attention is quite high. The variational attention model falls in between. RWS reaches a comparable performance as REINFORCE, but at a higher memory cost as it requires multiple samples. Gumbel-Softmax reaches nearly the same performance and seems like a viable alternative; although we found its performance is sensitive to its temperature parameter. We also trained a non-amortized SVI model, but found that at similar runtime it was not able to produce satisfactory results, likely due to insufficient updates of the local variational parameters. Despite extensive experiments, we found that variational relaxed attention performed worse than other methods. In particular we found that when training with a Dirichlet KL, it is hard to reach low-entropy regions of the simplex, and the attentions are more uniform than either soft or variational categorical attention.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: (Left) Comparison against the best prior work for NMT on the IWSLT 2014 German-English test set. (Upper Right) Comparison of inference alternatives of variational attention on IWSLT 2014. (Lower Right) Comparison of different models in terms of implied discrete entropy (lower = more certain alignment).', and sequentially solve the question, writing out each step of your reasoning process. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model has the lowest entropy in the NMT task?\n" }, { "role": "assistant", "content": "To find the model with the lowest entropy in the NMT task, we look at the 'Entropy NMT' column and identify the model with the smallest value. The lowest entropy value is 0.05, which corresponds to the 'Hard Attention + Enum' model. So, the answer is {\"answer\": \"Hard Attention + Enum\"}." } ], "images": [ "scigen/train/399.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: (Left) Comparison against the best prior work for NMT on the IWSLT 2014 German-English test set. (Upper Right) Comparison of inference alternatives of variational attention on IWSLT 2014. (Lower Right) Comparison of different models in terms of implied discrete entropy (lower = more certain alignment)." ], "reasoning": "To find the model with the lowest entropy in the NMT task, we look at the 'Entropy NMT' column and identify the model with the smallest value. The lowest entropy value is 0.05, which corresponds to the 'Hard Attention + Enum' model.", "question": "Which model has the lowest entropy in the NMT task?", "answer": "So, the answer is {\"answer\": \"Hard Attention + Enum\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Variational attention + sample has the highest entropy in the VQA task. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nVariational attention + sample has the highest entropy in the VQA task.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the 'Entropy VQA' column and compare the values. The highest entropy value in the VQA task is 2.70, which corresponds to the 'Soft Attention' model, not the 'Variational Attention + Sample' model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/399.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: (Left) Comparison against the best prior work for NMT on the IWSLT 2014 German-English test set. (Upper Right) Comparison of inference alternatives of variational attention on IWSLT 2014. (Lower Right) Comparison of different models in terms of implied discrete entropy (lower = more certain alignment)." ], "reasoning": "To verify this statement, we look at the 'Entropy VQA' column and compare the values. The highest entropy value in the VQA task is 2.70, which corresponds to the 'Soft Attention' model, not the 'Variational Attention + Sample' model.", "statement": "Variational attention + sample has the highest entropy in the VQA task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: Official entity discovery performance of our methods on KBP2016 trilingual EDL track.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "After re-training models using KBP2015 and iFLYTEK data, the overall trilingual F1 score improved to 0.754 in the KBP2016 EDL2 evaluation, with a high recall rate of 0.735.After fixing some system bugs, we have used both the KBP2015 data and iFLYTEK data to re-train our models for three languages and finally submitted three systems to the final KBP2016 EDL2 evaluation. In our systems, we treat all nominal mentions as special types of named entities and both named and nominal entities are recognized using one model. Here we have broken down the system performance according to different languages and categories of entities (named or nominal). In RUN1, we have submitted our best NER system, achieving about 0.718 in F1 score in the KBP2016 trilingual EDL track. This is a very strong performance among all KBP2016 participating teams. The overall trilingual F1 score is improved to 0.754. It is worth to note that we have obtained a pretty high recall rate, about 0.735, after the system combination because the NER methods used by these two systems are quite complementary." } ], "images": [ "scigen/train/400.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Official entity discovery performance of our methods on KBP2016 trilingual EDL track." ], "reasoning": "After re-training models using KBP2015 and iFLYTEK data, the overall trilingual F1 score improved to 0.754 in the KBP2016 EDL2 evaluation, with a high recall rate of 0.735.", "description": "After fixing some system bugs, we have used both the KBP2015 data and iFLYTEK data to re-train our models for three languages and finally submitted three systems to the final KBP2016 EDL2 evaluation. In our systems, we treat all nominal mentions as special types of named entities and both named and nominal entities are recognized using one model. Here we have broken down the system performance according to different languages and categories of entities (named or nominal). In RUN1, we have submitted our best NER system, achieving about 0.718 in F1 score in the KBP2016 trilingual EDL track. This is a very strong performance among all KBP2016 participating teams. The overall trilingual F1 score is improved to 0.754. It is worth to note that we have obtained a pretty high recall rate, about 0.735, after the system combination because the NER methods used by these two systems are quite complementary.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 5: Official entity discovery performance of our methods on KBP2016 trilingual EDL track.' and methodically tackle the question that follows. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat was the overall trilingual F1 score achieved in RUN3 after system fusion?\n" }, { "role": "assistant", "content": "The description mentions that the overall trilingual F1 score improved to 0.754 after system fusion in RUN3. The table confirms this score under the 'OVERALL F1' column for 'ALL' languages in RUN3. So, the answer is {\"answer\": \"0.754\"}." } ], "images": [ "scigen/train/400.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Official entity discovery performance of our methods on KBP2016 trilingual EDL track." ], "reasoning": "The description mentions that the overall trilingual F1 score improved to 0.754 after system fusion in RUN3. The table confirms this score under the 'OVERALL F1' column for 'ALL' languages in RUN3.", "question": "What was the overall trilingual F1 score achieved in RUN3 after system fusion?", "answer": "So, the answer is {\"answer\": \"0.754\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The recall rate for nominal entities in Spanish was 0.000 in RUN1., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe recall rate for nominal entities in Spanish was 0.000 in RUN1.\n" }, { "role": "assistant", "content": "The table shows that the recall rate for nominal entities in Spanish (SPA) was indeed 0.000 in RUN1 under the 'NOMINAL R' column. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/400.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Official entity discovery performance of our methods on KBP2016 trilingual EDL track." ], "reasoning": "The table shows that the recall rate for nominal entities in Spanish (SPA) was indeed 0.000 in RUN1 under the 'NOMINAL R' column.", "statement": "The recall rate for nominal entities in Spanish was 0.000 in RUN1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Entity Discovery Performance of our method on the KBP2015 EDL evaluation data, with comparison to the best system in KBP2015 official evaluation.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The trilingual entity discovery performance of the current system is slightly better than the best system in the KBP2015 evaluation, with F1 scores of 73.9 vs. 72.4.The overall trilingual entity discovery performance is slightly better than the best system participated in the official KBP2015 evaluation, with 73.9 vs. 72.4 as measured by F1 scores." } ], "images": [ "scigen/train/401.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Entity Discovery Performance of our method on the KBP2015 EDL evaluation data, with comparison to the best system in KBP2015 official evaluation." ], "reasoning": "The trilingual entity discovery performance of the current system is slightly better than the best system in the KBP2015 evaluation, with F1 scores of 73.9 vs. 72.4.", "description": "The overall trilingual entity discovery performance is slightly better than the best system participated in the official KBP2015 evaluation, with 73.9 vs. 72.4 as measured by F1 scores.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Entity Discovery Performance of our method on the KBP2015 EDL evaluation data, with comparison to the best system in KBP2015 official evaluation.', answer the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the F1 score difference between the trilingual performance of the current system and the best system in the KBP2015 evaluation?\n" }, { "role": "assistant", "content": "The F1 score for the trilingual performance of the current system is 73.9, while the best system in the KBP2015 evaluation has an F1 score of 72.4. The difference is calculated as 73.9 - 72.4. So, the answer is {\"answer\": \"1.5\"}" } ], "images": [ "scigen/train/401.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Entity Discovery Performance of our method on the KBP2015 EDL evaluation data, with comparison to the best system in KBP2015 official evaluation." ], "reasoning": "The F1 score for the trilingual performance of the current system is 73.9, while the best system in the KBP2015 evaluation has an F1 score of 72.4. The difference is calculated as 73.9 - 72.4.", "question": "What is the F1 score difference between the trilingual performance of the current system and the best system in the KBP2015 evaluation?", "answer": "So, the answer is {\"answer\": \"1.5\"}" }, { "messages": [ { "role": "user", "content": "The current system's trilingual F1 score is higher than the best system in the KBP2015 evaluation. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe current system's trilingual F1 score is higher than the best system in the KBP2015 evaluation.\n" }, { "role": "assistant", "content": "The table shows that the current system's trilingual F1 score is 73.9, which is higher than the best system's F1 score of 72.4 in the KBP2015 evaluation. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/401.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Entity Discovery Performance of our method on the KBP2015 EDL evaluation data, with comparison to the best system in KBP2015 official evaluation." ], "reasoning": "The table shows that the current system's trilingual F1 score is 73.9, which is higher than the best system's F1 score of 72.4 in the KBP2015 evaluation.", "statement": "The current system's trilingual F1 score is higher than the best system in the KBP2015 evaluation.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Entity discovery performance (English only) in KBP2016 EDL1 evaluation window is shown as a comparison of three models trained by different combinations of training data sets.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Using KBP2015 data alone yields an F1 score of 0.693, adding WIKI data improves it to 0.707, and combining KBP2015 with iFLYTEK data achieves the highest F1 score of 0.731.In our first set of experiments, we investigate the effect of using different training data sets on the final entity discovery performance. Different training runs are conducted on different combinations of the aforementioned data sources. The first system, using only the KBP2015 data to train the model, has achieved 0.693 in F1 score in the official KBP2016 English evaluation data. After adding the weakly labelled data, WIKI, we can see the entity discovery performance is improved to 0.707 in F1 score. Finally, we can see that it yields the best performance by using the KBP2015 data and the iFLYTEK in-house data sets to train our models, giving 0.731 in F1 score." } ], "images": [ "scigen/train/402.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Entity discovery performance (English only) in KBP2016 EDL1 evaluation window is shown as a comparison of three models trained by different combinations of training data sets." ], "reasoning": "Using KBP2015 data alone yields an F1 score of 0.693, adding WIKI data improves it to 0.707, and combining KBP2015 with iFLYTEK data achieves the highest F1 score of 0.731.", "description": "In our first set of experiments, we investigate the effect of using different training data sets on the final entity discovery performance. Different training runs are conducted on different combinations of the aforementioned data sources. The first system, using only the KBP2015 data to train the model, has achieved 0.693 in F1 score in the official KBP2016 English evaluation data. After adding the weakly labelled data, WIKI, we can see the entity discovery performance is improved to 0.707 in F1 score. Finally, we can see that it yields the best performance by using the KBP2015 data and the iFLYTEK in-house data sets to train our models, giving 0.731 in F1 score.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nTable title: Table 4: Entity discovery performance (English only) in KBP2016 EDL1 evaluation window is shown as a comparison of three models trained by different combinations of training data sets.\n\nQuestion: What is the F1 score when using KBP2015 data combined with WIKI data?\n" }, { "role": "assistant", "content": "The table shows that the F1 score for the combination of KBP2015 and WIKI data is 0.707. So, the answer is {\"answer\": \"0.707\"}." } ], "images": [ "scigen/train/402.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Entity discovery performance (English only) in KBP2016 EDL1 evaluation window is shown as a comparison of three models trained by different combinations of training data sets." ], "reasoning": "The table shows that the F1 score for the combination of KBP2015 and WIKI data is 0.707.", "question": "What is the F1 score when using KBP2015 data combined with WIKI data?", "answer": "So, the answer is {\"answer\": \"0.707\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The highest F1 score is achieved by using KBP2015 data alone., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe highest F1 score is achieved by using KBP2015 data alone.\n" }, { "role": "assistant", "content": "The table shows that the highest F1 score of 0.731 is achieved by using KBP2015 data combined with iFLYTEK data, not by using KBP2015 data alone. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/402.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Entity discovery performance (English only) in KBP2016 EDL1 evaluation window is shown as a comparison of three models trained by different combinations of training data sets." ], "reasoning": "The table shows that the highest F1 score of 0.731 is achieved by using KBP2015 data combined with iFLYTEK data, not by using KBP2015 data alone.", "statement": "The highest F1 score is achieved by using KBP2015 data alone.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Experiment results on Relation Extraction and Sentiment Analysis. Average and standard deviation of F1 scores (%) over multiple runs are reported (5 runs for RE and 10 runs for SA). LF(E) denotes directly applying logical forms onto explanations. Bracket behind each method illustrates corresponding data used in the method. S denotes training data without labels, E denotes explanations, R denotes surface pattern rules transformed from explanations; Sa denotes labeled data annotated with explanations, Su denotes the remaining unlabeled data. Sl denotes labeled data annotated using same time as creating explanations E, Slu denotes remaining unlabeled data corresponding to Sl.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "NExT outperforms all baseline models in low-resource settings, achieving significant F1 improvements on TACRED and SemEval, validating its effectiveness in expanding rule coverage and providing useful information for classifier training.We observe that our proposed NExT consistently outperforms all baseline models in low-resource setting. Also, we found that, (1) directly applying logical forms to unlabeled data results in poor performance. We notice that this method achieves high precision but low recall. Based on our observation of the collected dataset, this is because people tend to use detailed and specific constraints in an NL explanation to ensure they cover all aspects of the instance. As a result, those instances that satisfy the constraints are correctly labeled in most cases, and thus the precision is high. Meanwhile, generalization ability is compromised, and thus the recall is low. (2) Compared to its downstream classifier baseline (BiLSTM+ATT with Sa), NExT achieves 4.2% F1 improvement in absolute value on TACRED, and 5.5% on SemEval. This validates that the expansion of rule coverage by NExT is effective and is providing useful information to classifier training. (3) Performance gap further widens when we take annotation efforts into account. The annotation time for E and Sl are equivalent; but the performance of BiLSTM+ATT significantly degrades with fewer instances in Sl. (4) Results of semi-supervised methods are unsatisfactory. This may be explained with difference between underlying data distribution of Sa and Su." } ], "images": [ "scigen/train/403.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Experiment results on Relation Extraction and Sentiment Analysis. Average and standard deviation of F1 scores (%) over multiple runs are reported (5 runs for RE and 10 runs for SA). LF(E) denotes directly applying logical forms onto explanations. Bracket behind each method illustrates corresponding data used in the method. S denotes training data without labels, E denotes explanations, R denotes surface pattern rules transformed from explanations; Sa denotes labeled data annotated with explanations, Su denotes the remaining unlabeled data. Sl denotes labeled data annotated using same time as creating explanations E, Slu denotes remaining unlabeled data corresponding to Sl." ], "reasoning": "NExT outperforms all baseline models in low-resource settings, achieving significant F1 improvements on TACRED and SemEval, validating its effectiveness in expanding rule coverage and providing useful information for classifier training.", "description": "We observe that our proposed NExT consistently outperforms all baseline models in low-resource setting. Also, we found that, (1) directly applying logical forms to unlabeled data results in poor performance. We notice that this method achieves high precision but low recall. Based on our observation of the collected dataset, this is because people tend to use detailed and specific constraints in an NL explanation to ensure they cover all aspects of the instance. As a result, those instances that satisfy the constraints are correctly labeled in most cases, and thus the precision is high. Meanwhile, generalization ability is compromised, and thus the recall is low. (2) Compared to its downstream classifier baseline (BiLSTM+ATT with Sa), NExT achieves 4.2% F1 improvement in absolute value on TACRED, and 5.5% on SemEval. This validates that the expansion of rule coverage by NExT is effective and is providing useful information to classifier training. (3) Performance gap further widens when we take annotation efforts into account. The annotation time for E and Sl are equivalent; but the performance of BiLSTM+ATT significantly degrades with fewer instances in Sl. (4) Results of semi-supervised methods are unsatisfactory. This may be explained with difference between underlying data distribution of Sa and Su.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: Experiment results on Relation Extraction and Sentiment Analysis. Average and standard deviation of F1 scores (%) over multiple runs are reported (5 runs for RE and 10 runs for SA). LF(E) denotes directly applying logical forms onto explanations. Bracket behind each method illustrates corresponding data used in the method. S denotes training data without labels, E denotes explanations, R denotes surface pattern rules transformed from explanations; Sa denotes labeled data annotated with explanations, Su denotes the remaining unlabeled data. Sl denotes labeled data annotated using same time as creating explanations E, Slu denotes remaining unlabeled data corresponding to Sl.'. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the F1 improvement percentage of NExT over BiLSTM+ATT (S a) on SemEval?\n" }, { "role": "assistant", "content": "NExT achieves an F1 score of 63.5 on SemEval, while BiLSTM+ATT (S a) achieves 58.0. The improvement is calculated as 63.5 - 58.0 = 5.5%. So, the answer is {\"answer\": \"5.5%\"}" } ], "images": [ "scigen/train/403.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Experiment results on Relation Extraction and Sentiment Analysis. Average and standard deviation of F1 scores (%) over multiple runs are reported (5 runs for RE and 10 runs for SA). LF(E) denotes directly applying logical forms onto explanations. Bracket behind each method illustrates corresponding data used in the method. S denotes training data without labels, E denotes explanations, R denotes surface pattern rules transformed from explanations; Sa denotes labeled data annotated with explanations, Su denotes the remaining unlabeled data. Sl denotes labeled data annotated using same time as creating explanations E, Slu denotes remaining unlabeled data corresponding to Sl." ], "reasoning": "NExT achieves an F1 score of 63.5 on SemEval, while BiLSTM+ATT (S a) achieves 58.0. The improvement is calculated as 63.5 - 58.0 = 5.5%.", "question": "What is the F1 improvement percentage of NExT over BiLSTM+ATT (S a) on SemEval?", "answer": "So, the answer is {\"answer\": \"5.5%\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nNExT achieves the highest F1 score on TACRED.\n" }, { "role": "assistant", "content": "The table shows that NExT achieves an F1 score of 45.6 on TACRED, which is higher than all other models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/403.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Experiment results on Relation Extraction and Sentiment Analysis. Average and standard deviation of F1 scores (%) over multiple runs are reported (5 runs for RE and 10 runs for SA). LF(E) denotes directly applying logical forms onto explanations. Bracket behind each method illustrates corresponding data used in the method. S denotes training data without labels, E denotes explanations, R denotes surface pattern rules transformed from explanations; Sa denotes labeled data annotated with explanations, Su denotes the remaining unlabeled data. Sl denotes labeled data annotated using same time as creating explanations E, Slu denotes remaining unlabeled data corresponding to Sl." ], "reasoning": "The table shows that NExT achieves an F1 score of 45.6 on TACRED, which is higher than all other models listed.", "statement": "NExT achieves the highest F1 score on TACRED.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Experiment results on Relation Extraction and Sentiment Analysis. Average and standard deviation of F1 scores (%) over multiple runs are reported (5 runs for RE and 10 runs for SA). LF(E) denotes directly applying logical forms onto explanations. Bracket behind each method illustrates corresponding data used in the method. S denotes training data without labels, E denotes explanations, R denotes surface pattern rules transformed from explanations; Sa denotes labeled data annotated with explanations, Su denotes the remaining unlabeled data. Sl denotes labeled data annotated using same time as creating explanations E, Slu denotes remaining unlabeled data corresponding to Sl.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "NExT outperforms all baseline models in low-resource settings, achieving significant F1 improvements on TACRED and SemEval, and maintains high precision but low recall due to detailed constraints in NL explanations.We observe that our proposed NExT consistently outperforms all baseline models in low-resource setting. Also, we found that, (1) directly applying logical forms to unlabeled data results in poor performance. We notice that this method achieves high precision but low recall. Based on our observation of the collected dataset, this is because people tend to use detailed and specific constraints in an NL explanation to ensure they cover all aspects of the instance. As a result, those instances that satisfy the constraints are correctly labeled in most cases, and thus the precision is high. Meanwhile, generalization ability is compromised, and thus the recall is low. (2) Compared to its downstream classifier baseline (BiLSTM+ATT with Sa), NExT achieves 4.2% F1 improvement in absolute value on TACRED, and 5.5% on SemEval. This validates that the expansion of rule coverage by NExT is effective and is providing useful information to classifier training. (3) Performance gap further widens when we take annotation efforts into account. The annotation time for E and Sl are equivalent; but the performance of BiLSTM+ATT significantly degrades with fewer instances in Sl. (4) Results of semi-supervised methods are unsatisfactory. This may be explained with difference between underlying data distribution of Sa and Su." } ], "images": [ "scigen/train/404.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Experiment results on Relation Extraction and Sentiment Analysis. Average and standard deviation of F1 scores (%) over multiple runs are reported (5 runs for RE and 10 runs for SA). LF(E) denotes directly applying logical forms onto explanations. Bracket behind each method illustrates corresponding data used in the method. S denotes training data without labels, E denotes explanations, R denotes surface pattern rules transformed from explanations; Sa denotes labeled data annotated with explanations, Su denotes the remaining unlabeled data. Sl denotes labeled data annotated using same time as creating explanations E, Slu denotes remaining unlabeled data corresponding to Sl." ], "reasoning": "NExT outperforms all baseline models in low-resource settings, achieving significant F1 improvements on TACRED and SemEval, and maintains high precision but low recall due to detailed constraints in NL explanations.", "description": "We observe that our proposed NExT consistently outperforms all baseline models in low-resource setting. Also, we found that, (1) directly applying logical forms to unlabeled data results in poor performance. We notice that this method achieves high precision but low recall. Based on our observation of the collected dataset, this is because people tend to use detailed and specific constraints in an NL explanation to ensure they cover all aspects of the instance. As a result, those instances that satisfy the constraints are correctly labeled in most cases, and thus the precision is high. Meanwhile, generalization ability is compromised, and thus the recall is low. (2) Compared to its downstream classifier baseline (BiLSTM+ATT with Sa), NExT achieves 4.2% F1 improvement in absolute value on TACRED, and 5.5% on SemEval. This validates that the expansion of rule coverage by NExT is effective and is providing useful information to classifier training. (3) Performance gap further widens when we take annotation efforts into account. The annotation time for E and Sl are equivalent; but the performance of BiLSTM+ATT significantly degrades with fewer instances in Sl. (4) Results of semi-supervised methods are unsatisfactory. This may be explained with difference between underlying data distribution of Sa and Su.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Experiment results on Relation Extraction and Sentiment Analysis. Average and standard deviation of F1 scores (%) over multiple runs are reported (5 runs for RE and 10 runs for SA). LF(E) denotes directly applying logical forms onto explanations. Bracket behind each method illustrates corresponding data used in the method. S denotes training data without labels, E denotes explanations, R denotes surface pattern rules transformed from explanations; Sa denotes labeled data annotated with explanations, Su denotes the remaining unlabeled data. Sl denotes labeled data annotated using same time as creating explanations E, Slu denotes remaining unlabeled data corresponding to Sl.\nQuestion: What is the F1 improvement percentage of NExT over its downstream classifier baseline on SemEval?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The description states that NExT achieves a 5.5% F1 improvement in absolute value on SemEval compared to its downstream classifier baseline (BiLSTM+ATT with Sa). So, the answer is {\"answer\": \"5.5%\"}" } ], "images": [ "scigen/train/404.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Experiment results on Relation Extraction and Sentiment Analysis. Average and standard deviation of F1 scores (%) over multiple runs are reported (5 runs for RE and 10 runs for SA). LF(E) denotes directly applying logical forms onto explanations. Bracket behind each method illustrates corresponding data used in the method. S denotes training data without labels, E denotes explanations, R denotes surface pattern rules transformed from explanations; Sa denotes labeled data annotated with explanations, Su denotes the remaining unlabeled data. Sl denotes labeled data annotated using same time as creating explanations E, Slu denotes remaining unlabeled data corresponding to Sl." ], "reasoning": "The description states that NExT achieves a 5.5% F1 improvement in absolute value on SemEval compared to its downstream classifier baseline (BiLSTM+ATT with Sa).", "question": "What is the F1 improvement percentage of NExT over its downstream classifier baseline on SemEval?", "answer": "So, the answer is {\"answer\": \"5.5%\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nNExT achieves the highest performance on both Restaurant and Laptop datasets.\n" }, { "role": "assistant", "content": "The table shows that NExT has the highest scores for both Restaurant (75.8±0.8) and Laptop (62.8±1.9) datasets compared to other models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/404.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Experiment results on Relation Extraction and Sentiment Analysis. Average and standard deviation of F1 scores (%) over multiple runs are reported (5 runs for RE and 10 runs for SA). LF(E) denotes directly applying logical forms onto explanations. Bracket behind each method illustrates corresponding data used in the method. S denotes training data without labels, E denotes explanations, R denotes surface pattern rules transformed from explanations; Sa denotes labeled data annotated with explanations, Su denotes the remaining unlabeled data. Sl denotes labeled data annotated using same time as creating explanations E, Slu denotes remaining unlabeled data corresponding to Sl." ], "reasoning": "The table shows that NExT has the highest scores for both Restaurant (75.8±0.8) and Laptop (62.8±1.9) datasets compared to other models.", "statement": "NExT achieves the highest performance on both Restaurant and Laptop datasets.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Performance of NLProlog when extracted facts are used as input. Average accuracy over 3 runs is reported. NLProlog empowered by 21 natural language explanations and 5 hand-written rules achieves 1% gain in accuracy.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Adding 103 strictly-matched facts alone does not significantly improve accuracy, but incorporating NExT with 21 explanations and 5 rules increases accuracy by 1 point.From the result we observe that simply adding the 103 strictly-matched facts is not making notable improvement. However, with the help of NExT, a larger number of structured facts are recognized from support sentences, so that external knowledge from only 21 explanations and 5 rules improve the accuracy by 1 point. This observation validates NExT’s capability in low resource setting and highlight its potential when applied to downstream tasks." } ], "images": [ "scigen/train/405.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance of NLProlog when extracted facts are used as input. Average accuracy over 3 runs is reported. NLProlog empowered by 21 natural language explanations and 5 hand-written rules achieves 1% gain in accuracy." ], "reasoning": "Adding 103 strictly-matched facts alone does not significantly improve accuracy, but incorporating NExT with 21 explanations and 5 rules increases accuracy by 1 point.", "description": "From the result we observe that simply adding the 103 strictly-matched facts is not making notable improvement. However, with the help of NExT, a larger number of structured facts are recognized from support sentences, so that external knowledge from only 21 explanations and 5 rules improve the accuracy by 1 point. This observation validates NExT’s capability in low resource setting and highlight its potential when applied to downstream tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 4: Performance of NLProlog when extracted facts are used as input. Average accuracy over 3 runs is reported. NLProlog empowered by 21 natural language explanations and 5 hand-written rules achieves 1% gain in accuracy.\nQuestion: What is the accuracy when 103 strictly-matched facts and 832 structured facts with confidence >0.1 are added?\n" }, { "role": "assistant", "content": "The table shows that when 103 strictly-matched facts and 832 structured facts with confidence >0.1 are added, the accuracy is 75.60. So, the answer is {\"answer\": \"75.60\"}" } ], "images": [ "scigen/train/405.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance of NLProlog when extracted facts are used as input. Average accuracy over 3 runs is reported. NLProlog empowered by 21 natural language explanations and 5 hand-written rules achieves 1% gain in accuracy." ], "reasoning": "The table shows that when 103 strictly-matched facts and 832 structured facts with confidence >0.1 are added, the accuracy is 75.60.", "question": "What is the accuracy when 103 strictly-matched facts and 832 structured facts with confidence >0.1 are added?", "answer": "So, the answer is {\"answer\": \"75.60\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nAdding 103 strictly-matched facts alone improves the accuracy to 74.40.\n" }, { "role": "assistant", "content": "The table shows that adding 103 strictly-matched facts alone results in an accuracy of 74.40. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/405.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance of NLProlog when extracted facts are used as input. Average accuracy over 3 runs is reported. NLProlog empowered by 21 natural language explanations and 5 hand-written rules achieves 1% gain in accuracy." ], "reasoning": "The table shows that adding 103 strictly-matched facts alone results in an accuracy of 74.40.", "statement": "Adding 103 strictly-matched facts alone improves the accuracy to 74.40.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 8: TACRED results on 130 explanations and 100 explanations', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The NEXT (E+S) model achieves the highest performance in TACRED 130 F1 and TACRED 100 F1 compared to other baseline methods.As a supplement to Fig. Results show that our model achieves best performance compared with baseline methods." } ], "images": [ "scigen/train/406.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: TACRED results on 130 explanations and 100 explanations" ], "reasoning": "The NEXT (E+S) model achieves the highest performance in TACRED 130 F1 and TACRED 100 F1 compared to other baseline methods.", "description": "As a supplement to Fig. Results show that our model achieves best performance compared with baseline methods.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 8: TACRED results on 130 explanations and 100 explanations', answer the following question. Give detailed solution consisting of each step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model achieves the highest TACRED 130 F1 score and what is the value?\n" }, { "role": "assistant", "content": "By examining the TACRED 130 F1 column, the NEXT (E+S) model has the highest score of 42.0±1.1. So, the answer is {\"answer\": \"NEXT (E+S) with 42.0±1.1\"}." } ], "images": [ "scigen/train/406.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: TACRED results on 130 explanations and 100 explanations" ], "reasoning": "By examining the TACRED 130 F1 column, the NEXT (E+S) model has the highest score of 42.0±1.1.", "question": "Which model achieves the highest TACRED 130 F1 score and what is the value?", "answer": "So, the answer is {\"answer\": \"NEXT (E+S) with 42.0±1.1\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe PA-LSTM (S a) model achieves the highest TACRED 100 Precision score.\n" }, { "role": "assistant", "content": "By examining the TACRED 100 Precision column, the LF (E) model has the highest score of 85.2, not the PA-LSTM (S a) model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/406.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: TACRED results on 130 explanations and 100 explanations" ], "reasoning": "By examining the TACRED 100 Precision column, the LF (E) model has the highest score of 85.2, not the PA-LSTM (S a) model.", "statement": "The PA-LSTM (S a) model achieves the highest TACRED 100 Precision score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 9: SemEval results on 150 explanations and 100 explanations', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The NEXT model achieves the best performance in SemEval 150 Recall and SemEval 100 F1 compared to other baseline methods.As a supplement to Fig. Results show that our model achieves best performance compared with baseline methods." } ], "images": [ "scigen/train/407.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: SemEval results on 150 explanations and 100 explanations" ], "reasoning": "The NEXT model achieves the best performance in SemEval 150 Recall and SemEval 100 F1 compared to other baseline methods.", "description": "As a supplement to Fig. Results show that our model achieves best performance compared with baseline methods.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 9: SemEval results on 150 explanations and 100 explanations' in mind, break down and resolve the question below step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich model achieves the highest SemEval 150 Recall and what is the value?\n" }, { "role": "assistant", "content": "By examining the SemEval 150 Recall column, the NEXT model achieves the highest value of 60.0±1.7. So, the answer is {\"answer\": \"NEXT, 60.0±1.7\"}." } ], "images": [ "scigen/train/407.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: SemEval results on 150 explanations and 100 explanations" ], "reasoning": "By examining the SemEval 150 Recall column, the NEXT model achieves the highest value of 60.0±1.7.", "question": "Which model achieves the highest SemEval 150 Recall and what is the value?", "answer": "So, the answer is {\"answer\": \"NEXT, 60.0±1.7\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The LF model achieves the highest SemEval 100 Precision., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe LF model achieves the highest SemEval 100 Precision.\n" }, { "role": "assistant", "content": "By examining the SemEval 100 Precision column, the LF model has the highest value of 90.7. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/407.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: SemEval results on 150 explanations and 100 explanations" ], "reasoning": "By examining the SemEval 100 Precision column, the LF model has the highest value of 90.7.", "statement": "The LF model achieves the highest SemEval 100 Precision.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 10: Laptop results on 55 explanations and 70 explanations'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The NEXT (E+S) model achieves the highest performance in terms of F1 scores for both Laptop 55 and Laptop 70 datasets compared to other baseline methods.As a supplement to Fig. Results show that our model achieves best performance compared with baseline methods." } ], "images": [ "scigen/train/408.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Laptop results on 55 explanations and 70 explanations" ], "reasoning": "The NEXT (E+S) model achieves the highest performance in terms of F1 scores for both Laptop 55 and Laptop 70 datasets compared to other baseline methods.", "description": "As a supplement to Fig. Results show that our model achieves best performance compared with baseline methods.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 10: Laptop results on 55 explanations and 70 explanations', solve the following question step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model achieves the highest F1 score for the Laptop 70 dataset?\n" }, { "role": "assistant", "content": "By examining the F1 scores for the Laptop 70 dataset, the NEXT (E+S) model has the highest F1 score of 65.5. So, the answer is {\"answer\": \"NEXT (E+S)\"}." } ], "images": [ "scigen/train/408.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Laptop results on 55 explanations and 70 explanations" ], "reasoning": "By examining the F1 scores for the Laptop 70 dataset, the NEXT (E+S) model has the highest F1 score of 65.5.", "question": "Which model achieves the highest F1 score for the Laptop 70 dataset?", "answer": "So, the answer is {\"answer\": \"NEXT (E+S)\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The LF (E) model has the highest precision for the Laptop 55 dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe LF (E) model has the highest precision for the Laptop 55 dataset.\n" }, { "role": "assistant", "content": "By checking the precision values for the Laptop 55 dataset, the LF (E) model has the highest precision of 90.8. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/408.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Laptop results on 55 explanations and 70 explanations" ], "reasoning": "By checking the precision values for the Laptop 55 dataset, the LF (E) model has the highest precision of 90.8.", "statement": "The LF (E) model has the highest precision for the Laptop 55 dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 11: Restaurant results on 60 explanations and 75 explanations'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The NEXT (E+S) model achieves the highest performance in Restaurant 60 Recall, Restaurant 75 Recall, and Restaurant 75 F1 compared to other baseline methods.As a supplement to Fig. Results show that our model achieves best performance compared with baseline methods." } ], "images": [ "scigen/train/409.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Restaurant results on 60 explanations and 75 explanations" ], "reasoning": "The NEXT (E+S) model achieves the highest performance in Restaurant 60 Recall, Restaurant 75 Recall, and Restaurant 75 F1 compared to other baseline methods.", "description": "As a supplement to Fig. Results show that our model achieves best performance compared with baseline methods.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 11: Restaurant results on 60 explanations and 75 explanations', and tackle the question below in a sequential manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model achieves the highest Restaurant 75 Recall and what is the value?\n" }, { "role": "assistant", "content": "By examining the Restaurant 75 Recall column, the NEXT (E+S) model achieves the highest value of 82.8±1.9. So, the answer is {\"answer\": \"NEXT (E+S)\"}." } ], "images": [ "scigen/train/409.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Restaurant results on 60 explanations and 75 explanations" ], "reasoning": "By examining the Restaurant 75 Recall column, the NEXT (E+S) model achieves the highest value of 82.8±1.9.", "question": "Which model achieves the highest Restaurant 75 Recall and what is the value?", "answer": "So, the answer is {\"answer\": \"NEXT (E+S)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe LF (E) model achieves the highest Restaurant 60 Precision.\n" }, { "role": "assistant", "content": "By examining the Restaurant 60 Precision column, the LF (E) model achieves the highest value of 86.0. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/409.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Restaurant results on 60 explanations and 75 explanations" ], "reasoning": "By examining the Restaurant 60 Precision column, the LF (E) model achieves the highest value of 86.0.", "statement": "The LF (E) model achieves the highest Restaurant 60 Precision.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 12: BERT experiments on Restaurant dataset using 45 and 75 explanations', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Our model, NExT, outperforms baseline methods and approaches the upper bound of 85% with only 75 explanations, demonstrating its annotation efficiency.Our framework is model-agnostic as it can be integrated with any downstream classifier. Results show that our model still outperforms baseline methods when BERT is incorporated. We observe the performance of NExT is approaching the upper bound 85% (by feeding all data to BERT), with only 75 explanations, which again demonstrates the annotation efficiency of NExT." } ], "images": [ "scigen/train/410.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 12: BERT experiments on Restaurant dataset using 45 and 75 explanations" ], "reasoning": "Our model, NExT, outperforms baseline methods and approaches the upper bound of 85% with only 75 explanations, demonstrating its annotation efficiency.", "description": "Our framework is model-agnostic as it can be integrated with any downstream classifier. Results show that our model still outperforms baseline methods when BERT is incorporated. We observe the performance of NExT is approaching the upper bound 85% (by feeding all data to BERT), with only 75 explanations, which again demonstrates the annotation efficiency of NExT.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 12: BERT experiments on Restaurant dataset using 45 and 75 explanations', and tackle the question below in a sequential manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the performance of NExT with 75 explanations?\n" }, { "role": "assistant", "content": "The table shows the performance of NExT with 75 explanations is 82.0. So, the answer is {\"answer\": \"82.0\"}." } ], "images": [ "scigen/train/410.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 12: BERT experiments on Restaurant dataset using 45 and 75 explanations" ], "reasoning": "The table shows the performance of NExT with 75 explanations is 82.0.", "question": "What is the performance of NExT with 75 explanations?", "answer": "So, the answer is {\"answer\": \"82.0\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: NExT achieves the highest performance with 75 explanations., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nNExT achieves the highest performance with 75 explanations.\n" }, { "role": "assistant", "content": "The table shows that NExT has a performance of 82.0 with 75 explanations, which is higher than all other methods listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/410.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 12: BERT experiments on Restaurant dataset using 45 and 75 explanations" ], "reasoning": "The table shows that NExT has a performance of 82.0 with 75 explanations, which is higher than all other methods listed.", "statement": "NExT achieves the highest performance with 75 explanations.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Ablation study for our TAPA model reporting F1 scores on test sets.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Removing topics consistently reduces F1 scores across all datasets, while the effect of ELMo representations varies; deleting ELMo improves Quora performance but drastically reduces PAWS performance.Removing topics consistently reduces F1 scores on all datasets, while the effect of ELMo representations is dataset dependent. Deleting ELMo improves performance on Quora, but leads to a massive performance drop on PAWS. The large impact on PAWS can be explained by the fact that this dataset was automatically constructed to have high textual overlap between questions and differences between paraphrases are chiefly due to variations in syntax. Our full TAPA model uses early fusion as this was the best setting during hyperparameter tuning. We conclude that topics contribute consistently to the performance of our proposed model, but that early topic-embedding fusion is crucial." } ], "images": [ "scigen/train/411.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation study for our TAPA model reporting F1 scores on test sets." ], "reasoning": "Removing topics consistently reduces F1 scores across all datasets, while the effect of ELMo representations varies; deleting ELMo improves Quora performance but drastically reduces PAWS performance.", "description": "Removing topics consistently reduces F1 scores on all datasets, while the effect of ELMo representations is dataset dependent. Deleting ELMo improves performance on Quora, but leads to a massive performance drop on PAWS. The large impact on PAWS can be explained by the fact that this dataset was automatically constructed to have high textual overlap between questions and differences between paraphrases are chiefly due to variations in syntax. Our full TAPA model uses early fusion as this was the best setting during hyperparameter tuning. We conclude that topics contribute consistently to the performance of our proposed model, but that early topic-embedding fusion is crucial.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: Ablation study for our TAPA model reporting F1 scores on test sets.' and methodically tackle the question that follows. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the impact of removing ELMo representations on the PAWS dataset?\n" }, { "role": "assistant", "content": "The table shows that removing ELMo representations leads to a significant drop in F1 score for the PAWS dataset, from 42.2 to 26.9. So, the answer is {\"answer\": \"26.9\"}" } ], "images": [ "scigen/train/411.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation study for our TAPA model reporting F1 scores on test sets." ], "reasoning": "The table shows that removing ELMo representations leads to a significant drop in F1 score for the PAWS dataset, from 42.2 to 26.9.", "question": "What is the impact of removing ELMo representations on the PAWS dataset?", "answer": "So, the answer is {\"answer\": \"26.9\"}" }, { "messages": [ { "role": "user", "content": "Removing topics improves performance on the Quora dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRemoving topics improves performance on the Quora dataset.\n" }, { "role": "assistant", "content": "The table shows that removing topics slightly reduces the F1 score on the Quora dataset from 84.1 to 83.9. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/411.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation study for our TAPA model reporting F1 scores on test sets." ], "reasoning": "The table shows that removing topics slightly reduces the F1 score on the Quora dataset from 84.1 to 83.9.", "statement": "Removing topics improves performance on the Quora dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Summarization results when using the full training set. Our scores are averaged over three models trained with different random seeds. *Other abstractive summarization model scores are provided to contextualize performance on this task but are not directly comparable to our models.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Pre-training improves ROUGE scores, but pre-training only the decoder worsens performance, while the Transformer LM performs best despite fewer parameters.We find that pre-training improves performance by about 2 ROUGE points, on average. Surprisingly, when only the decoder is pre-trained, ROUGE gets substantially worse. We speculate this is because the model starting out with a well-trained decoder and poor encoder learns to overly rely on its language modeling abilities and not adequately incorporate information from the encoder. The Transformer LM outperforms corresponding models both with and without pre-training, despite having almost half as many parameters. Our best model performs competitively with existing models on the CNN/Daily Mail abstractive summarization task, despite the absence of model augmentations such as a copy mechanism and reinforcement learning objectives." } ], "images": [ "scigen/train/412.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Summarization results when using the full training set. Our scores are averaged over three models trained with different random seeds. *Other abstractive summarization model scores are provided to contextualize performance on this task but are not directly comparable to our models." ], "reasoning": "Pre-training improves ROUGE scores, but pre-training only the decoder worsens performance, while the Transformer LM performs best despite fewer parameters.", "description": "We find that pre-training improves performance by about 2 ROUGE points, on average. Surprisingly, when only the decoder is pre-trained, ROUGE gets substantially worse. We speculate this is because the model starting out with a well-trained decoder and poor encoder learns to overly rely on its language modeling abilities and not adequately incorporate information from the encoder. The Transformer LM outperforms corresponding models both with and without pre-training, despite having almost half as many parameters. Our best model performs competitively with existing models on the CNN/Daily Mail abstractive summarization task, despite the absence of model augmentations such as a copy mechanism and reinforcement learning objectives.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 1: Summarization results when using the full training set. Our scores are averaged over three models trained with different random seeds. *Other abstractive summarization model scores are provided to contextualize performance on this task but are not directly comparable to our models.\nQuestion: Which model has the highest ROUGE-L score and how does it compare to the model with no pre-training?\n" }, { "role": "assistant", "content": "The Transformer LM with pre-training has the highest ROUGE-L score of 36.85, compared to the model with no pre-training which has a ROUGE-L score of 34.62. So, the answer is {\"answer\": \"Transformer LM with pre-training\"}" } ], "images": [ "scigen/train/412.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Summarization results when using the full training set. Our scores are averaged over three models trained with different random seeds. *Other abstractive summarization model scores are provided to contextualize performance on this task but are not directly comparable to our models." ], "reasoning": "The Transformer LM with pre-training has the highest ROUGE-L score of 36.85, compared to the model with no pre-training which has a ROUGE-L score of 34.62.", "question": "Which model has the highest ROUGE-L score and how does it compare to the model with no pre-training?", "answer": "So, the answer is {\"answer\": \"Transformer LM with pre-training\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe model with pre-trained encoder only has a higher ROUGE-1 score than the model with no pre-training.\n" }, { "role": "assistant", "content": "The model with pre-trained encoder only has a ROUGE-1 score of 36.05, which is lower than the ROUGE-1 score of 37.72 for the model with no pre-training. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/412.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Summarization results when using the full training set. Our scores are averaged over three models trained with different random seeds. *Other abstractive summarization model scores are provided to contextualize performance on this task but are not directly comparable to our models." ], "reasoning": "The model with pre-trained encoder only has a ROUGE-1 score of 36.05, which is lower than the ROUGE-1 score of 37.72 for the model with no pre-training.", "statement": "The model with pre-trained encoder only has a higher ROUGE-1 score than the model with no pre-training.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: Document classification results, reporting number of parameters and accuracy.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The optimized model uses significantly fewer parameters than both the recurrent and convolutional baselines, with only a slight drop in accuracy compared to the recurrent baseline, and achieves better latency metrics.Our optimized representation is significantly smaller than the baselines, using 5x and 27x less parameters than the recurrent and convolutional baselines respectively. Even with far less capacity, the optimized model’s accuracy is comparable to the baselines. Specifically, it only drops by 1% in comparison to the recurrent baseline. In addition, each successively compressed convolutional model achieves an accuracy better than the baseline. This suggests that the original convolutional model is over-parameterized and the reduction in capacity helps during optimization. Our optimized model shows gains on the file size and latency metrics; most notably, its latency is an order of magnitude lower than the recurrent baseline’s. However, the CNNs all use slightly more memory than the recurrent model, presenting a clear trade-off between latency and memory requirements." } ], "images": [ "scigen/train/413.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Document classification results, reporting number of parameters and accuracy." ], "reasoning": "The optimized model uses significantly fewer parameters than both the recurrent and convolutional baselines, with only a slight drop in accuracy compared to the recurrent baseline, and achieves better latency metrics.", "description": "Our optimized representation is significantly smaller than the baselines, using 5x and 27x less parameters than the recurrent and convolutional baselines respectively. Even with far less capacity, the optimized model’s accuracy is comparable to the baselines. Specifically, it only drops by 1% in comparison to the recurrent baseline. In addition, each successively compressed convolutional model achieves an accuracy better than the baseline. This suggests that the original convolutional model is over-parameterized and the reduction in capacity helps during optimization. Our optimized model shows gains on the file size and latency metrics; most notably, its latency is an order of magnitude lower than the recurrent baseline’s. However, the CNNs all use slightly more memory than the recurrent model, presenting a clear trade-off between latency and memory requirements.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: Document classification results, reporting number of parameters and accuracy.'.\n\nQuestion: How many parameters does the optimized model with bottlenecks use, and what is its accuracy?\n" }, { "role": "assistant", "content": "The table shows that the optimized model with bottlenecks uses 92.5K parameters and has an accuracy of 86.4. So, the answer is {\"answer\": \"92.5K parameters and 86.4 accuracy\"}." } ], "images": [ "scigen/train/413.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Document classification results, reporting number of parameters and accuracy." ], "reasoning": "The table shows that the optimized model with bottlenecks uses 92.5K parameters and has an accuracy of 86.4.", "question": "How many parameters does the optimized model with bottlenecks use, and what is its accuracy?", "answer": "So, the answer is {\"answer\": \"92.5K parameters and 86.4 accuracy\"}." }, { "messages": [ { "role": "user", "content": "The optimized model with separability has fewer parameters than the recurrent baseline. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe optimized model with separability has fewer parameters than the recurrent baseline.\n" }, { "role": "assistant", "content": "The table shows that the optimized model with separability has 234.4K parameters, which is fewer than the recurrent baseline's 509.6K parameters. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/413.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Document classification results, reporting number of parameters and accuracy." ], "reasoning": "The table shows that the optimized model with separability has 234.4K parameters, which is fewer than the recurrent baseline's 509.6K parameters.", "statement": "The optimized model with separability has fewer parameters than the recurrent baseline.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Experiments Results of generation-based approaches.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Attention-based approaches generally outperform vanilla Seq2Seq models, with HREDs achieving the highest BLEU scores due to considering history information.Intention and Emotion-enhanced To utilize the intention and emotion labels, we follow Zhou et al. The intention and emotion labels are characterized as one-hot vectors. (last four columns), we can see that in general attention-based approaches are better than vanilla Seq2Seq model. Among the three compared approaches, HREDs achieve highest BLEU scores because they take history information into consideration. Furthermore, label information is effective even though we utilize them in the simplest way. These findings are consistent with previous work Sordoni et al. ; Serban et al. OpenSubtitle converge faster, achieving lower Perplexity (PPL) but poorer BLEU scores. We conjecture it as a result of domain difference. OpenSubtitle dataset is constructed by movie lines, whereas our datasets are daily dialogues. Moreover, OpenSubtitles has approximately 1000+ speaker turns in one “conversation”, while our dataset has in average 8 turns. To pretrain a model by corpus from different domain will harm its performance on the target domain. Hence, it is less optimal to simply pretrain models with large-scale datasets such as OpenSubtitle, which is domain different from the evaluation datasets. We further examine this issue by comparing the generated answers by models trained solely on DailyDialog with and without pre-training." } ], "images": [ "scigen/train/414.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Experiments Results of generation-based approaches." ], "reasoning": "Attention-based approaches generally outperform vanilla Seq2Seq models, with HREDs achieving the highest BLEU scores due to considering history information.", "description": "Intention and Emotion-enhanced To utilize the intention and emotion labels, we follow Zhou et al. The intention and emotion labels are characterized as one-hot vectors. (last four columns), we can see that in general attention-based approaches are better than vanilla Seq2Seq model. Among the three compared approaches, HREDs achieve highest BLEU scores because they take history information into consideration. Furthermore, label information is effective even though we utilize them in the simplest way. These findings are consistent with previous work Sordoni et al. ; Serban et al. OpenSubtitle converge faster, achieving lower Perplexity (PPL) but poorer BLEU scores. We conjecture it as a result of domain difference. OpenSubtitle dataset is constructed by movie lines, whereas our datasets are daily dialogues. Moreover, OpenSubtitles has approximately 1000+ speaker turns in one “conversation”, while our dataset has in average 8 turns. To pretrain a model by corpus from different domain will harm its performance on the target domain. Hence, it is less optimal to simply pretrain models with large-scale datasets such as OpenSubtitle, which is domain different from the evaluation datasets. We further examine this issue by comparing the generated answers by models trained solely on DailyDialog with and without pre-training.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Experiments Results of generation-based approaches.\nQuestion: Which model achieves the highest BLEU-1 score and what is its PPL?\nGive a detailed response to the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the BLEU-1 column, the L+Attn-Seq2Seq model achieves the highest BLEU-1 score of 0.464. The corresponding PPL for this model is 50.03. So, the answer is {\"answer\": \"L+Attn-Seq2Seq with PPL 50.03\"}." } ], "images": [ "scigen/train/414.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Experiments Results of generation-based approaches." ], "reasoning": "By examining the BLEU-1 column, the L+Attn-Seq2Seq model achieves the highest BLEU-1 score of 0.464. The corresponding PPL for this model is 50.03.", "question": "Which model achieves the highest BLEU-1 score and what is its PPL?", "answer": "So, the answer is {\"answer\": \"L+Attn-Seq2Seq with PPL 50.03\"}." }, { "messages": [ { "role": "user", "content": "The Pre+HRED model has the lowest PPL among all models. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Pre+HRED model has the lowest PPL among all models.\n" }, { "role": "assistant", "content": "By comparing the PPL values in the table, the Pre+HRED model has a PPL of 37.65, which is indeed the lowest among all models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/414.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Experiments Results of generation-based approaches." ], "reasoning": "By comparing the PPL values in the table, the Pre+HRED model has a PPL of 37.65, which is indeed the lowest among all models listed.", "statement": "The Pre+HRED model has the lowest PPL among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: BLEU scores of retrieval-based approaches.'.\n" }, { "role": "assistant", "content": "Since the groundtruth responses in the test set are not seen in the training set, ranking-like metrics such as Recall-k cannot be used for evaluation.Because the groundtruth responses in the test set are not seen in the training set, we can not evaluate the performance using ranking-like metrics such as Recall-k." } ], "images": [ "scigen/train/415.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: BLEU scores of retrieval-based approaches." ], "reasoning": "Since the groundtruth responses in the test set are not seen in the training set, ranking-like metrics such as Recall-k cannot be used for evaluation.", "description": "Because the groundtruth responses in the test set are not seen in the training set, we can not evaluate the performance using ranking-like metrics such as Recall-k.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 5: BLEU scores of retrieval-based approaches.' and methodically tackle the question that follows. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich method has the highest BLEU-2 score and what is that score?\n" }, { "role": "assistant", "content": "To find the method with the highest BLEU-2 score, compare the BLEU-2 scores of all methods listed in the table. The highest BLEU-2 score is 0.258, which corresponds to the 'Feature' method. So, the answer is {\"answer\": \"Feature with 0.258\"}." } ], "images": [ "scigen/train/415.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: BLEU scores of retrieval-based approaches." ], "reasoning": "To find the method with the highest BLEU-2 score, compare the BLEU-2 scores of all methods listed in the table. The highest BLEU-2 score is 0.258, which corresponds to the 'Feature' method.", "question": "Which method has the highest BLEU-2 score and what is that score?", "answer": "So, the answer is {\"answer\": \"Feature with 0.258\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe I-Rerank method has a higher BLEU-4 score than the Embedding method.\n" }, { "role": "assistant", "content": "To verify this statement, compare the BLEU-4 scores of the I-Rerank method and the Embedding method. The BLEU-4 score for I-Rerank is 0.181, while the BLEU-4 score for Embedding is 0.150. Since 0.181 is greater than 0.150, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/415.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: BLEU scores of retrieval-based approaches." ], "reasoning": "To verify this statement, compare the BLEU-4 scores of the I-Rerank method and the Embedding method. The BLEU-4 score for I-Rerank is 0.181, while the BLEU-4 score for Embedding is 0.150. Since 0.181 is greater than 0.150, the statement is supported.", "statement": "The I-Rerank method has a higher BLEU-4 score than the Embedding method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 6: “Equivalence” percentage (%) of retrieval-based approaches.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The equivalence percentage between the labels of retrieved responses and groundtruth responses shows subtle improvements when using labels, but it is not a strong evaluation metric.We also evaluate them by calculating the “Equivalence” percentage between the labels (i.e., intention, emotion) of the retrieved responses and those of the groundtruth responses. Though subtle improvements can be seen when using labels, we speculate it as not a very strong evaluation metric. It is unsafe to conclude that the higher the “equivalence” percentage is, the better (more coherent, more suitable) the retrieved response will be." } ], "images": [ "scigen/train/416.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: “Equivalence” percentage (%) of retrieval-based approaches." ], "reasoning": "The equivalence percentage between the labels of retrieved responses and groundtruth responses shows subtle improvements when using labels, but it is not a strong evaluation metric.", "description": "We also evaluate them by calculating the “Equivalence” percentage between the labels (i.e., intention, emotion) of the retrieved responses and those of the groundtruth responses. Though subtle improvements can be seen when using labels, we speculate it as not a very strong evaluation metric. It is unsafe to conclude that the higher the “equivalence” percentage is, the better (more coherent, more suitable) the retrieved response will be.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nTable title: Table 6: “Equivalence” percentage (%) of retrieval-based approaches.\n\nQuestion: What is the equivalence percentage for Emotion when using +I-E-Rerank?\n" }, { "role": "assistant", "content": "To find the equivalence percentage for Emotion when using +I-E-Rerank, look at the row labeled 'Emotion' and the column labeled '+I-E-Rerank'. So, the answer is {\"answer\": \"74.3\"}." } ], "images": [ "scigen/train/416.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: “Equivalence” percentage (%) of retrieval-based approaches." ], "reasoning": "To find the equivalence percentage for Emotion when using +I-E-Rerank, look at the row labeled 'Emotion' and the column labeled '+I-E-Rerank'.", "question": "What is the equivalence percentage for Emotion when using +I-E-Rerank?", "answer": "So, the answer is {\"answer\": \"74.3\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe equivalence percentage for Intention is higher when using +I-Rerank compared to +I-E-Rerank.\n" }, { "role": "assistant", "content": "Compare the equivalence percentages for Intention in the +I-Rerank and +I-E-Rerank columns. The percentage for +I-Rerank is 47.3, which is higher than the 46.7 for +I-E-Rerank. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/416.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: “Equivalence” percentage (%) of retrieval-based approaches." ], "reasoning": "Compare the equivalence percentages for Intention in the +I-Rerank and +I-E-Rerank columns. The percentage for +I-Rerank is 47.3, which is higher than the 46.7 for +I-E-Rerank.", "statement": "The equivalence percentage for Intention is higher when using +I-Rerank compared to +I-E-Rerank.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 9: Comparison of the 3 models across all tasks'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "BertPT and AlbertPT performed equivalently in most comparisons, with AlbertPT having a slight advantage in wins and being less expensive to train.The different tasks and settings under which BertPT and AlbertPT were compared to Multilingual BERT and published baselines yield 38 possible pairwise comparisons. We analyzed in how many times the performance of each model was better than, worse than or equivalent to the performance of the others. If the proportional difference in scores was below 5%, then the two models were considered equivalent. We can see that no single method is always better than the others. The baselines have fewer wins, which means that BERT-based models tend to yield better results. BertPT and AlbertPT achieved equivalent performances most of the time (28 out of 38). AlbertPT has an advantage (6 wins versus 4 losses), and since its training is less expensive, then it would be the model of choice." } ], "images": [ "scigen/train/417.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Comparison of the 3 models across all tasks" ], "reasoning": "BertPT and AlbertPT performed equivalently in most comparisons, with AlbertPT having a slight advantage in wins and being less expensive to train.", "description": "The different tasks and settings under which BertPT and AlbertPT were compared to Multilingual BERT and published baselines yield 38 possible pairwise comparisons. We analyzed in how many times the performance of each model was better than, worse than or equivalent to the performance of the others. If the proportional difference in scores was below 5%, then the two models were considered equivalent. We can see that no single method is always better than the others. The baselines have fewer wins, which means that BERT-based models tend to yield better results. BertPT and AlbertPT achieved equivalent performances most of the time (28 out of 38). AlbertPT has an advantage (6 wins versus 4 losses), and since its training is less expensive, then it would be the model of choice.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 9: Comparison of the 3 models across all tasks', and tackle the question below in a sequential manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nHow many times did BertPT perform better than AlbertPT?\n" }, { "role": "assistant", "content": "From the table, we can see that BertPT performed better than AlbertPT 4 times. So, the answer is {\"answer\": \"4\"}" } ], "images": [ "scigen/train/417.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Comparison of the 3 models across all tasks" ], "reasoning": "From the table, we can see that BertPT performed better than AlbertPT 4 times.", "question": "How many times did BertPT perform better than AlbertPT?", "answer": "So, the answer is {\"answer\": \"4\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nAlbertPT performed better than Multilingual BERT 1 time.\n" }, { "role": "assistant", "content": "The table shows that AlbertPT performed better than Multilingual BERT exactly 1 time. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/417.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Comparison of the 3 models across all tasks" ], "reasoning": "The table shows that AlbertPT performed better than Multilingual BERT exactly 1 time.", "statement": "AlbertPT performed better than Multilingual BERT 1 time.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: RTE and STS evaluated on Test Sets.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "BERT-based models consistently outperformed previous baselines in both RTE and STS tasks, with the multilingual model showing the highest performance.Next, we applied our fine-tuned model to the entire training set and ran the evaluation over the test set. The first observation is that the BP setting is difficult for all models. The EP+BP * * in the last rows indicates that the model was trained using both EP and BP training sets, but they were evaluated only using the EP test set. The results of this evaluation can be compared to the first rows, in which the models were trained and evaluated only on EP test set. Although it makes sense to think that more training data would help the model generalize, we reached the same conclusion as FialhoMarquesMartinsCoheurQuaresma2016 that the improvement, if any, in this dataset is meaningless. We could not find a published baseline for the case where the model was trained using EP+BP and evaluated using both test sets. Again, as expected, BERT models were superior to previous baselines for all combinations. Analysing 10-fold evaluation and using only the test set, we can conclude that BERT-based models were superior to previous baselines. The goal of Semantic Textual Similarity is to quantify the degree of similarity between two sentences. In the ASSIN dataset, similarity is a number ranging from 1 (no similarity) to 5 (high similarity). This is a linear regression problem as the output is a similarity score. The evaluation measures how far the predicted score is from the ground truth using two metrics: ( i) Pearson Correlation which measures the correlation with the ground truth, so the higher, the better, and (ii) and the Mean Squared Error which measures the square of the difference between the prediction and the ground truth, so the lower the better. Following the same procedure of Textual Entailment, we used 3,000 pairs for training. Since STS is related to RTE, again, all models obtained better results than the baselines. Also, the multilingual model was again superior to our pre-trained Portuguese models." } ], "images": [ "scigen/train/418.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: RTE and STS evaluated on Test Sets." ], "reasoning": "BERT-based models consistently outperformed previous baselines in both RTE and STS tasks, with the multilingual model showing the highest performance.", "description": "Next, we applied our fine-tuned model to the entire training set and ran the evaluation over the test set. The first observation is that the BP setting is difficult for all models. The EP+BP * * in the last rows indicates that the model was trained using both EP and BP training sets, but they were evaluated only using the EP test set. The results of this evaluation can be compared to the first rows, in which the models were trained and evaluated only on EP test set. Although it makes sense to think that more training data would help the model generalize, we reached the same conclusion as FialhoMarquesMartinsCoheurQuaresma2016 that the improvement, if any, in this dataset is meaningless. We could not find a published baseline for the case where the model was trained using EP+BP and evaluated using both test sets. Again, as expected, BERT models were superior to previous baselines for all combinations. Analysing 10-fold evaluation and using only the test set, we can conclude that BERT-based models were superior to previous baselines. The goal of Semantic Textual Similarity is to quantify the degree of similarity between two sentences. In the ASSIN dataset, similarity is a number ranging from 1 (no similarity) to 5 (high similarity). This is a linear regression problem as the output is a similarity score. The evaluation measures how far the predicted score is from the ground truth using two metrics: ( i) Pearson Correlation which measures the correlation with the ground truth, so the higher, the better, and (ii) and the Mean Squared Error which measures the square of the difference between the prediction and the ground truth, so the lower the better. Following the same procedure of Textual Entailment, we used 3,000 pairs for training. Since STS is related to RTE, again, all models obtained better results than the baselines. Also, the multilingual model was again superior to our pre-trained Portuguese models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: RTE and STS evaluated on Test Sets.' to sequentially solve the question presented below. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich model achieved the highest STS Pearson score when trained on the EP dataset?\n" }, { "role": "assistant", "content": "By examining the STS Pearson column for models trained on the EP dataset, the Multilingual model achieved the highest score of 0.84. So, the answer is {\"answer\": \"Multilingual\"}." } ], "images": [ "scigen/train/418.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: RTE and STS evaluated on Test Sets." ], "reasoning": "By examining the STS Pearson column for models trained on the EP dataset, the Multilingual model achieved the highest score of 0.84.", "question": "Which model achieved the highest STS Pearson score when trained on the EP dataset?", "answer": "So, the answer is {\"answer\": \"Multilingual\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Multilingual model achieved the highest RTE Accuracy when trained on the EP+BP dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Multilingual model achieved the highest RTE Accuracy when trained on the EP+BP dataset.\n" }, { "role": "assistant", "content": "By examining the RTE Accuracy column for models trained on the EP+BP dataset, the Multilingual model achieved the highest score of 0.91. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/418.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: RTE and STS evaluated on Test Sets." ], "reasoning": "By examining the RTE Accuracy column for models trained on the EP+BP dataset, the Multilingual model achieved the highest score of 0.91.", "statement": "The Multilingual model achieved the highest RTE Accuracy when trained on the EP+BP dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5: Sentiment Polarity Classification'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The corpus contains emoticons and out-of-vocabulary expressions, making it challenging for models not trained with similar vocabulary, so only positive and negative samples are used for comparability.In order keep the results comparable, we use only the positive and negative samples. The fact that this corpus has several emoticons and out-of-vocabulary expressions makes it hard for the models that were not trained using a similar vocabulary." } ], "images": [ "scigen/train/419.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Sentiment Polarity Classification" ], "reasoning": "The corpus contains emoticons and out-of-vocabulary expressions, making it challenging for models not trained with similar vocabulary, so only positive and negative samples are used for comparability.", "description": "In order keep the results comparable, we use only the positive and negative samples. The fact that this corpus has several emoticons and out-of-vocabulary expressions makes it hard for the models that were not trained using a similar vocabulary.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 5: Sentiment Polarity Classification' and methodically tackle the question that follows. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest accuracy and what is its F1-W score?\n" }, { "role": "assistant", "content": "By examining the table, we see that AlbertPT has the highest accuracy of 0.79. Its corresponding F1-W score is 0.78. So, the answer is {\"answer\": \"0.78\"}." } ], "images": [ "scigen/train/419.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Sentiment Polarity Classification" ], "reasoning": "By examining the table, we see that AlbertPT has the highest accuracy of 0.79. Its corresponding F1-W score is 0.78.", "question": "Which model has the highest accuracy and what is its F1-W score?", "answer": "So, the answer is {\"answer\": \"0.78\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis AlbertPT has the highest F1-W score among the models. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nAlbertPT has the highest F1-W score among the models.\n" }, { "role": "assistant", "content": "By examining the F1-W scores in the table, we see that AlbertPT has the highest F1-W score of 0.78. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/419.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Sentiment Polarity Classification" ], "reasoning": "By examining the F1-W scores in the table, we see that AlbertPT has the highest F1-W score of 0.78.", "statement": "AlbertPT has the highest F1-W score among the models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 8: Emotion Classification'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "BertPT achieved a Binary F1-W score of 0.83, which is similar to but does not surpass the baseline score of 0.84.The baseline for the binary version reaches up to 0.84 of F-measure. In this experiment, BertPT has a result similar to the baseline, but it was not able to overcome it." } ], "images": [ "scigen/train/420.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Emotion Classification" ], "reasoning": "BertPT achieved a Binary F1-W score of 0.83, which is similar to but does not surpass the baseline score of 0.84.", "description": "The baseline for the binary version reaches up to 0.84 of F-measure. In this experiment, BertPT has a result similar to the baseline, but it was not able to overcome it.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: 'Table 8: Emotion Classification'\nQuestion: Which model achieved the highest Binary F1-W score?\n" }, { "role": "assistant", "content": "The table shows the Binary F1-W scores for different models. The highest score is 0.84, achieved by Becker:2017:MEC:3063600.3063706. So, the answer is {\"answer\": \"Becker:2017:MEC:3063600.3063706\"}" } ], "images": [ "scigen/train/420.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Emotion Classification" ], "reasoning": "The table shows the Binary F1-W scores for different models. The highest score is 0.84, achieved by Becker:2017:MEC:3063600.3063706.", "question": "Which model achieved the highest Binary F1-W score?", "answer": "So, the answer is {\"answer\": \"Becker:2017:MEC:3063600.3063706\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nAlbertPT achieved a Binary F1-W score higher than 0.80.\n" }, { "role": "assistant", "content": "The table shows that AlbertPT achieved a Binary F1-W score of 0.81, which is higher than 0.80. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/420.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Emotion Classification" ], "reasoning": "The table shows that AlbertPT achieved a Binary F1-W score of 0.81, which is higher than 0.80.", "statement": "AlbertPT achieved a Binary F1-W score higher than 0.80.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Evaluation on balanced test set. All accuracies are calculated using the VQA [2] evaluation metric.'.\n" }, { "role": "assistant", "content": "Models trained on balanced data perform better than those trained on unbalanced data, likely due to the need to extract visual information rather than relying on language biases.We also evaluate all models trained on the train splits of both the unbalanced and balanced datasets, by testing on the balanced test set. Training on balanced is better. both language+vision models trained on balanced data perform better than the models trained on unbalanced data. This may be because the models trained on balanced data have to learn to extract visual information to answer the question correctly, since they are no longer able to exploit language biases in the training set. Where as models trained on the unbalanced set are blindsided into learning strong language priors, which are then not available at test time." } ], "images": [ "scigen/train/421.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Evaluation on balanced test set. All accuracies are calculated using the VQA [2] evaluation metric." ], "reasoning": "Models trained on balanced data perform better than those trained on unbalanced data, likely due to the need to extract visual information rather than relying on language biases.", "description": "We also evaluate all models trained on the train splits of both the unbalanced and balanced datasets, by testing on the balanced test set. Training on balanced is better. both language+vision models trained on balanced data perform better than the models trained on unbalanced data. This may be because the models trained on balanced data have to learn to extract visual information to answer the question correctly, since they are no longer able to exploit language biases in the training set. Where as models trained on the unbalanced set are blindsided into learning strong language priors, which are then not available at test time.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: Evaluation on balanced test set. All accuracies are calculated using the VQA [2] evaluation metric.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. \n\nQuestion: Which model shows the highest performance when trained on the balanced dataset?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By comparing the performance of all models trained on the balanced dataset, the model 'Ours Q+Tuple+A-IMG' has the highest score of 74.65. So, the answer is {\"answer\": \"Ours Q+Tuple+A-IMG\"}." } ], "images": [ "scigen/train/421.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Evaluation on balanced test set. All accuracies are calculated using the VQA [2] evaluation metric." ], "reasoning": "By comparing the performance of all models trained on the balanced dataset, the model 'Ours Q+Tuple+A-IMG' has the highest score of 74.65.", "question": "Which model shows the highest performance when trained on the balanced dataset?", "answer": "So, the answer is {\"answer\": \"Ours Q+Tuple+A-IMG\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Blind-Q+Tuple model performs better when trained on the unbalanced dataset compared to the balanced dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Blind-Q+Tuple model performs better when trained on the unbalanced dataset compared to the balanced dataset.\n" }, { "role": "assistant", "content": "The table shows that the Blind-Q+Tuple model has a score of 65.98 when trained on the unbalanced dataset and 63.33 when trained on the balanced dataset, indicating better performance on the unbalanced dataset. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/421.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Evaluation on balanced test set. All accuracies are calculated using the VQA [2] evaluation metric." ], "reasoning": "The table shows that the Blind-Q+Tuple model has a score of 65.98 when trained on the unbalanced dataset and 63.33 when trained on the balanced dataset, indicating better performance on the unbalanced dataset.", "statement": "The Blind-Q+Tuple model performs better when trained on the unbalanced dataset compared to the balanced dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Classifying a pair of complementary scenes. All accuracies are percentage of test pairs that have been predicted correctly.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The model Q+Tuple+H-IMG performs best on the balanced dataset without modeling attention.We observe that our model trained on the balanced dataset performs the best. (Q+Tuple+H-IMG) that does not model attention." } ], "images": [ "scigen/train/422.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Classifying a pair of complementary scenes. All accuracies are percentage of test pairs that have been predicted correctly." ], "reasoning": "The model Q+Tuple+H-IMG performs best on the balanced dataset without modeling attention.", "description": "We observe that our model trained on the balanced dataset performs the best. (Q+Tuple+H-IMG) that does not model attention.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Classifying a pair of complementary scenes. All accuracies are percentage of test pairs that have been predicted correctly.', unfold the question presented below, detailing every step of your calculation. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model performs best on the balanced dataset?\n" }, { "role": "assistant", "content": "By comparing the performance of all models on the balanced dataset, we see that Q+Tuple+A-IMG has the highest score of 34.73. So, the answer is {\"answer\": \"Q+Tuple+A-IMG\"}." } ], "images": [ "scigen/train/422.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Classifying a pair of complementary scenes. All accuracies are percentage of test pairs that have been predicted correctly." ], "reasoning": "By comparing the performance of all models on the balanced dataset, we see that Q+Tuple+A-IMG has the highest score of 34.73.", "question": "Which model performs best on the balanced dataset?", "answer": "So, the answer is {\"answer\": \"Q+Tuple+A-IMG\"}." }, { "messages": [ { "role": "user", "content": "The Q+Tuple+H-IMG model performs better on the balanced dataset than on the unbalanced dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Q+Tuple+H-IMG model performs better on the balanced dataset than on the unbalanced dataset.\n" }, { "role": "assistant", "content": "The table shows that the Q+Tuple+H-IMG model has a score of 23.13 on the balanced dataset and 3.20 on the unbalanced dataset, indicating better performance on the balanced dataset. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/422.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Classifying a pair of complementary scenes. All accuracies are percentage of test pairs that have been predicted correctly." ], "reasoning": "The table shows that the Q+Tuple+H-IMG model has a score of 23.13 on the balanced dataset and 3.20 on the unbalanced dataset, indicating better performance on the balanced dataset.", "statement": "The Q+Tuple+H-IMG model performs better on the balanced dataset than on the unbalanced dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Ablation Study for 4 Aspects of ROTS. Bold values indicates the best in the row'.\n" }, { "role": "assistant", "content": "The dependency tree's score is higher than the binary tree's score, and it slightly decreases after the third level. Dependency Tree consistently outperformed uSIF and ParaMNT, making it the best combination.The dependency tree’s score is higher than that from binary tree, while it slightly decrease after 3-th level. In the first four cases that we could see the results by changing the word vectors and tree structures. We conclude that almost at every case our approach consistently outperformed uSIF and ParaMNT and Dependency Tree is the best combination. In STS-Benchmark test set, ROTS outperformed uSIF about 1.4 in average of those four different cases. Without any of those, the results is much worse than uSIF." } ], "images": [ "scigen/train/423.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation Study for 4 Aspects of ROTS. Bold values indicates the best in the row" ], "reasoning": "The dependency tree's score is higher than the binary tree's score, and it slightly decreases after the third level. Dependency Tree consistently outperformed uSIF and ParaMNT, making it the best combination.", "description": "The dependency tree’s score is higher than that from binary tree, while it slightly decrease after 3-th level. In the first four cases that we could see the results by changing the word vectors and tree structures. We conclude that almost at every case our approach consistently outperformed uSIF and ParaMNT and Dependency Tree is the best combination. In STS-Benchmark test set, ROTS outperformed uSIF about 1.4 in average of those four different cases. Without any of those, the results is much worse than uSIF.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: Ablation Study for 4 Aspects of ROTS. Bold values indicates the best in the row', and sequentially solve the question, writing out each step of your reasoning process. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the average score difference between ROTS and uSIF in the STSB test set across all cases?\n" }, { "role": "assistant", "content": "To find the average score difference, calculate the difference between ROTS and uSIF for each case in the STSB test set and then find the average of these differences. The differences are: 1.1, 0.7, 2.7, 1.2, 1.1, 1.1, 1.1, 1.1. The average difference is (1.1 + 0.7 + 2.7 + 1.2 + 1.1 + 1.1 + 1.1 + 1.1) / 8 = 1.2625. So, the answer is {\"answer\": \"1.2625\"}" } ], "images": [ "scigen/train/423.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation Study for 4 Aspects of ROTS. Bold values indicates the best in the row" ], "reasoning": "To find the average score difference, calculate the difference between ROTS and uSIF for each case in the STSB test set and then find the average of these differences. The differences are: 1.1, 0.7, 2.7, 1.2, 1.1, 1.1, 1.1, 1.1. The average difference is (1.1 + 0.7 + 2.7 + 1.2 + 1.1 + 1.1 + 1.1 + 1.1) / 8 = 1.2625.", "question": "What is the average score difference between ROTS and uSIF in the STSB test set across all cases?", "answer": "So, the answer is {\"answer\": \"1.2625\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nROTS outperformed uSIF by 1.4 on average in the STSB test set.\n" }, { "role": "assistant", "content": "The table shows the differences between ROTS and uSIF in the STSB test set for each case. The average difference is calculated as (1.1 + 0.7 + 2.7 + 1.2 + 1.1 + 1.1 + 1.1 + 1.1) / 8 = 1.2625, which is not equal to 1.4. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/423.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation Study for 4 Aspects of ROTS. Bold values indicates the best in the row" ], "reasoning": "The table shows the differences between ROTS and uSIF in the STSB test set for each case. The average difference is calculated as (1.1 + 0.7 + 2.7 + 1.2 + 1.1 + 1.1 + 1.1 + 1.1) / 8 = 1.2625, which is not equal to 1.4.", "statement": "ROTS outperformed uSIF by 1.4 on average in the STSB test set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Weakly Supervised Model Results on STS-Benchmark Dataset.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The ROTS model with dependency parsing tree achieved the highest test score among the models proposed in 2019, surpassing the previous state-of-the-art uSIF results by about one point.The benchmark comparisons are focused on weakly supervised models with no parameters to train but only hyper-parameters to select. The results that we compared are gathered from either the leaderboard in STS-Benchmark website or from directly the best reported models in the paper The ROTS model gets much more improvement compared to other models proposed in year 2019. ROTS with dependency parsing tree increased about one point than previous state-of-the-art uSIF results. This means that hierarchical prior takes significant effects." } ], "images": [ "scigen/train/424.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Weakly Supervised Model Results on STS-Benchmark Dataset." ], "reasoning": "The ROTS model with dependency parsing tree achieved the highest test score among the models proposed in 2019, surpassing the previous state-of-the-art uSIF results by about one point.", "description": "The benchmark comparisons are focused on weakly supervised models with no parameters to train but only hyper-parameters to select. The results that we compared are gathered from either the leaderboard in STS-Benchmark website or from directly the best reported models in the paper The ROTS model gets much more improvement compared to other models proposed in year 2019. ROTS with dependency parsing tree increased about one point than previous state-of-the-art uSIF results. This means that hierarchical prior takes significant effects.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Weakly Supervised Model Results on STS-Benchmark Dataset.', solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model achieved the highest test score in 2019?\n" }, { "role": "assistant", "content": "By examining the table, the ROTS+ dependency tree model achieved the highest test score of 80.6 in 2019. So, the answer is {\"answer\": \"ROTS+ dependency tree\"}" } ], "images": [ "scigen/train/424.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Weakly Supervised Model Results on STS-Benchmark Dataset." ], "reasoning": "By examining the table, the ROTS+ dependency tree model achieved the highest test score of 80.6 in 2019.", "question": "Which model achieved the highest test score in 2019?", "answer": "So, the answer is {\"answer\": \"ROTS+ dependency tree\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe ROTS+ binary tree model achieved a higher test score than the GEM model.\n" }, { "role": "assistant", "content": "The table shows that the ROTS+ binary tree model has a test score of 80.0, which is higher than the GEM model's test score of 78.4. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/424.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Weakly Supervised Model Results on STS-Benchmark Dataset." ], "reasoning": "The table shows that the ROTS+ binary tree model has a test score of 80.0, which is higher than the GEM model's test score of 78.4.", "statement": "The ROTS+ binary tree model achieved a higher test score than the GEM model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Detailed Comparisons with Similar Unsupervised Approaches on 20 STS Datasets'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The ROTS models consistently outperform uSIF and DynaMax models, showing clear improvement in average scores.While our HOT approaches almost steadily outperformed uSIF and has best average scores. Notebly, best DynaMax model also employs the same uSIF weights as well as ParaMNT word vectors. So the improvement of ROTS over uSIF and DynaMax is fair and clear." } ], "images": [ "scigen/train/425.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Detailed Comparisons with Similar Unsupervised Approaches on 20 STS Datasets" ], "reasoning": "The ROTS models consistently outperform uSIF and DynaMax models, showing clear improvement in average scores.", "description": "While our HOT approaches almost steadily outperformed uSIF and has best average scores. Notebly, best DynaMax model also employs the same uSIF weights as well as ParaMNT word vectors. So the improvement of ROTS over uSIF and DynaMax is fair and clear.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 2: Detailed Comparisons with Similar Unsupervised Approaches on 20 STS Datasets\nQuestion: Which model has the highest average score in the table?\n" }, { "role": "assistant", "content": "By comparing the 'AVE' column, the ROTS + dependency tree model has the highest average score of 73.2. So, the answer is {\"answer\": \"ROTS + dependency tree\"}." } ], "images": [ "scigen/train/425.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Detailed Comparisons with Similar Unsupervised Approaches on 20 STS Datasets" ], "reasoning": "By comparing the 'AVE' column, the ROTS + dependency tree model has the highest average score of 73.2.", "question": "Which model has the highest average score in the table?", "answer": "So, the answer is {\"answer\": \"ROTS + dependency tree\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The DynaMax model has the highest score in STS15. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe DynaMax model has the highest score in STS15.\n" }, { "role": "assistant", "content": "By examining the 'STS15' column, the DynaMax model has the highest score of 80.1. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/425.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Detailed Comparisons with Similar Unsupervised Approaches on 20 STS Datasets" ], "reasoning": "By examining the 'STS15' column, the DynaMax model has the highest score of 80.1.", "statement": "The DynaMax model has the highest score in STS15.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Human evaluation results. Models marked with * are evaluated only for the Description, Quality, and Comparison categories. The underlined results are significantly worse than those of SCC-mult(*) in a two-tail T-test (p<0.01).'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "SCC-mult and SCC-strong outperform SCC-weak in Accuracy and Overall scores, demonstrating the efficacy of the models with better internal engines.The human annotators are required to be good at playing chess. That is to say, they are the true audiences of the commentator researches and applications. By introducing human evaluations, we further reveal the performances in the perspective of the audiences. We further demonstrate the efficacy of our models with significantly better overall performances than the retrieval-based model and previous state-of-the-art ones. It is worth noting that the evaluations about Accuracy and Insights show that our models can produce more precise and thorough analysis owing to the internal chess engine. SCC-mult and SCC-strong perform better than SCC-weak in Accuracy and Overall scores. It also supports the points that the our commentary model can be improved with better internal engine." } ], "images": [ "scigen/train/426.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Human evaluation results. Models marked with * are evaluated only for the Description, Quality, and Comparison categories. The underlined results are significantly worse than those of SCC-mult(*) in a two-tail T-test (p<0.01)." ], "reasoning": "SCC-mult and SCC-strong outperform SCC-weak in Accuracy and Overall scores, demonstrating the efficacy of the models with better internal engines.", "description": "The human annotators are required to be good at playing chess. That is to say, they are the true audiences of the commentator researches and applications. By introducing human evaluations, we further reveal the performances in the perspective of the audiences. We further demonstrate the efficacy of our models with significantly better overall performances than the retrieval-based model and previous state-of-the-art ones. It is worth noting that the evaluations about Accuracy and Insights show that our models can produce more precise and thorough analysis owing to the internal chess engine. SCC-mult and SCC-strong perform better than SCC-weak in Accuracy and Overall scores. It also supports the points that the our commentary model can be improved with better internal engine.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Human evaluation results. Models marked with * are evaluated only for the Description, Quality, and Comparison categories. The underlined results are significantly worse than those of SCC-mult(*) in a two-tail T-test (p<0.01).', and sequentially solve the question, writing out each step of your reasoning process. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model has the highest Accuracy score?\n" }, { "role": "assistant", "content": "By examining the Accuracy column in the table, the model with the highest score is SCC-mult with a score of 3.91. So, the answer is {\"answer\": \"SCC-mult\"}." } ], "images": [ "scigen/train/426.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Human evaluation results. Models marked with * are evaluated only for the Description, Quality, and Comparison categories. The underlined results are significantly worse than those of SCC-mult(*) in a two-tail T-test (p<0.01)." ], "reasoning": "By examining the Accuracy column in the table, the model with the highest score is SCC-mult with a score of 3.91.", "question": "Which model has the highest Accuracy score?", "answer": "So, the answer is {\"answer\": \"SCC-mult\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nSCC-weak has a higher Overall score than SCC-strong.\n" }, { "role": "assistant", "content": "By comparing the Overall scores of SCC-weak and SCC-strong in the table, SCC-weak has a score of 3.30, while SCC-strong has a score of 3.49. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/426.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Human evaluation results. Models marked with * are evaluated only for the Description, Quality, and Comparison categories. The underlined results are significantly worse than those of SCC-mult(*) in a two-tail T-test (p<0.01)." ], "reasoning": "By comparing the Overall scores of SCC-weak and SCC-strong in the table, SCC-weak has a score of 3.30, while SCC-strong has a score of 3.49.", "statement": "SCC-weak has a higher Overall score than SCC-strong.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Automatic evaluation results.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "SCC models outperform all baselines and previous state-of-the-art models, with SCC-strong being better than SCC-weak, and SCC-mult achieving state-of-the-art performances despite a slight overall performance drop due to multi-task learning.Our SCC models outperform all of the baselines and previous state-of-the-art models. Temp is limited by the variety of templates. It is competitive with the neural models on Description and Quality due to limited expressions in these tasks. But when coming to Comparison, Planning and Contexts, Temp shows really bad performances. Re keeps flexibility by copying the sentences from training set. But it does not perform well, either. KWG and GAC provide competitive results. With the help of external information from powerful chess engines, GAC shows good performances on Quality and Comparison. Although our internal chess engine is no match for the external engines that GAC uses at playing chess, it turns out that our models with directly internal information can better bridge the semantic spaces of chess game and comment language. As for the comparisons within our models, SCC-strong turns to be better than SCC-weak, which supports our assumption that better skills enable more precise predictions, resulting in better comments. Training with multi-task learning seems to hurt the overall performances a little. But SCC-mult still has the state-of-the-art performances. And more important, it can react to all sub-tasks as a whole." } ], "images": [ "scigen/train/427.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Automatic evaluation results." ], "reasoning": "SCC models outperform all baselines and previous state-of-the-art models, with SCC-strong being better than SCC-weak, and SCC-mult achieving state-of-the-art performances despite a slight overall performance drop due to multi-task learning.", "description": "Our SCC models outperform all of the baselines and previous state-of-the-art models. Temp is limited by the variety of templates. It is competitive with the neural models on Description and Quality due to limited expressions in these tasks. But when coming to Comparison, Planning and Contexts, Temp shows really bad performances. Re keeps flexibility by copying the sentences from training set. But it does not perform well, either. KWG and GAC provide competitive results. With the help of external information from powerful chess engines, GAC shows good performances on Quality and Comparison. Although our internal chess engine is no match for the external engines that GAC uses at playing chess, it turns out that our models with directly internal information can better bridge the semantic spaces of chess game and comment language. As for the comparisons within our models, SCC-strong turns to be better than SCC-weak, which supports our assumption that better skills enable more precise predictions, resulting in better comments. Training with multi-task learning seems to hurt the overall performances a little. But SCC-mult still has the state-of-the-art performances. And more important, it can react to all sub-tasks as a whole.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: Table 1: Automatic evaluation results.\nQuestion: Which model has the highest BLEU-4 score for Quality?\n" }, { "role": "assistant", "content": "To find the model with the highest BLEU-4 score for Quality, we look at the Quality row under the BLEU-4 (%) column. The highest score is 20.06, which belongs to SCC-mult. So, the answer is {\"answer\": \"SCC-mult\"}." } ], "images": [ "scigen/train/427.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Automatic evaluation results." ], "reasoning": "To find the model with the highest BLEU-4 score for Quality, we look at the Quality row under the BLEU-4 (%) column. The highest score is 20.06, which belongs to SCC-mult.", "question": "Which model has the highest BLEU-4 score for Quality?", "answer": "So, the answer is {\"answer\": \"SCC-mult\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis SCC-strong has the highest METEOR score for Contexts. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nSCC-strong has the highest METEOR score for Contexts.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the Contexts row under the METEOR (%) column. The highest score is 11.76, which belongs to SCC-strong. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/427.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Automatic evaluation results." ], "reasoning": "To verify this statement, we look at the Contexts row under the METEOR (%) column. The highest score is 11.76, which belongs to SCC-strong.", "statement": "SCC-strong has the highest METEOR score for Contexts.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: Distribution of positive and negative references in different sections of publications.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Positive references are common in the proposed method section, while negative references are frequent in the evaluation section.This analysis presents a general behavior for this research area based on the dataset. This shows that the negative references are often in the evaluation section of a paper because the methods, in general, get outperformed by the proposed systems. In contrast to this, the positive references occur frequently in the proposed method section because authors adopt approaches which work well." } ], "images": [ "scigen/train/428.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Distribution of positive and negative references in different sections of publications." ], "reasoning": "Positive references are common in the proposed method section, while negative references are frequent in the evaluation section.", "description": "This analysis presents a general behavior for this research area based on the dataset. This shows that the negative references are often in the evaluation section of a paper because the methods, in general, get outperformed by the proposed systems. In contrast to this, the positive references occur frequently in the proposed method section because authors adopt approaches which work well.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nTable title: Table 3: Distribution of positive and negative references in different sections of publications.\n\nQuestion: Which section has the highest frequency of negative references?\n" }, { "role": "assistant", "content": "By examining the table, the Evaluation / Experiments section has the highest negative reference frequency of 0.5. So, the answer is {\"answer\": \"Evaluation / Experiments\"}." } ], "images": [ "scigen/train/428.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Distribution of positive and negative references in different sections of publications." ], "reasoning": "By examining the table, the Evaluation / Experiments section has the highest negative reference frequency of 0.5.", "question": "Which section has the highest frequency of negative references?", "answer": "So, the answer is {\"answer\": \"Evaluation / Experiments\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Approach / Method section has the highest frequency of positive references., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Approach / Method section has the highest frequency of positive references.\n" }, { "role": "assistant", "content": "The table shows that the Approach / Method section has a positive reference frequency of 0.3, which is the highest among all sections. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/428.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Distribution of positive and negative references in different sections of publications." ], "reasoning": "The table shows that the Approach / Method section has a positive reference frequency of 0.3, which is the highest among all sections.", "statement": "The Approach / Method section has the highest frequency of positive references.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: SentiCiteDB.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "SentiCiteDB contains 2100 citations with sentiments distributed as 210 positive, 1805 neutral, and 85 negative. The training set includes 50 citations from each sentiment class to ensure balanced learning.SentiCiteDB is a dataset of publication sentiment analysis which is created using Scientific publications from International Conference on Document Analysis and Recognition (ICDAR) 2013. Sentences with citation are manually extracted from the publication and their manual ground truth is created. In total, SentiCiteDB contains about 2100 citations with 210 positive, 1805 neutral and 85 negative sentiment scores. Out of these, 50 citations from each of the three classes of sentiment are used for training of SentiCite. This dataset includes references from different sections of the document to make sure that the proposed method does not learn the sentiment classification for one specific section. The training set was selected with the same number of references for all classes to ensure that the classifier learns the classes in a sufficient way. Providing a real-world distribution a training set resulted in bad results for negative and positive labels." } ], "images": [ "scigen/train/429.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: SentiCiteDB." ], "reasoning": "SentiCiteDB contains 2100 citations with sentiments distributed as 210 positive, 1805 neutral, and 85 negative. The training set includes 50 citations from each sentiment class to ensure balanced learning.", "description": "SentiCiteDB is a dataset of publication sentiment analysis which is created using Scientific publications from International Conference on Document Analysis and Recognition (ICDAR) 2013. Sentences with citation are manually extracted from the publication and their manual ground truth is created. In total, SentiCiteDB contains about 2100 citations with 210 positive, 1805 neutral and 85 negative sentiment scores. Out of these, 50 citations from each of the three classes of sentiment are used for training of SentiCite. This dataset includes references from different sections of the document to make sure that the proposed method does not learn the sentiment classification for one specific section. The training set was selected with the same number of references for all classes to ensure that the classifier learns the classes in a sufficient way. Providing a real-world distribution a training set resulted in bad results for negative and positive labels.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: SentiCiteDB.', solve the subsequent question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nHow many citations are in the test set for the neutral sentiment class?\n" }, { "role": "assistant", "content": "The table shows the distribution of citations in the test set for each sentiment class. For the neutral sentiment class, the test set contains 1755 citations. So, the answer is {\"answer\": \"1755\"}" } ], "images": [ "scigen/train/429.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: SentiCiteDB." ], "reasoning": "The table shows the distribution of citations in the test set for each sentiment class. For the neutral sentiment class, the test set contains 1755 citations.", "question": "How many citations are in the test set for the neutral sentiment class?", "answer": "So, the answer is {\"answer\": \"1755\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe training set for SentiCiteDB contains an equal number of citations from each sentiment class.\n" }, { "role": "assistant", "content": "The table indicates that the training set includes 50 citations each for positive, neutral, and negative sentiment classes, confirming equal distribution. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/429.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: SentiCiteDB." ], "reasoning": "The table indicates that the training set includes 50 citations each for positive, neutral, and negative sentiment classes, confirming equal distribution.", "statement": "The training set for SentiCiteDB contains an equal number of citations from each sentiment class.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 5: Evaluation of different features for SentiCite (SC).'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Feature extraction involves using POS tagging, token type, and stemming for training classifiers, with additional features like n-grams tested but found insufficient.The purpose of the feature extraction step is to get a meaningful set of features to train the classifiers. Besides the string of the tokens, three additional feature extraction modules were used. The POS tagger assigns different features e.g., type of the token, length or capitalization. The type of the token helps to indicate the importance of the token e.g., an adjective is more relevant than an article or nouns. The last module is a stemming tool for a more generalized representation of the different tokens. This procedure was done for the sentiment and the nature of citation. Furthermore, it has to be mentioned that features like n-gram, hyponyms, hypernyms and others were tested but the performance of these features was not sufficient. It is important to note here, that, a classifier can often perform well on the neutral classes and bad on the positive and negative classes but, it can still result in a good overall accuracy because of the disproportionate ratio of neutral and positive or negative references in a scientific document. For more meaningful results further experiments were performed." } ], "images": [ "scigen/train/430.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Evaluation of different features for SentiCite (SC)." ], "reasoning": "Feature extraction involves using POS tagging, token type, and stemming for training classifiers, with additional features like n-grams tested but found insufficient.", "description": "The purpose of the feature extraction step is to get a meaningful set of features to train the classifiers. Besides the string of the tokens, three additional feature extraction modules were used. The POS tagger assigns different features e.g., type of the token, length or capitalization. The type of the token helps to indicate the importance of the token e.g., an adjective is more relevant than an article or nouns. The last module is a stemming tool for a more generalized representation of the different tokens. This procedure was done for the sentiment and the nature of citation. Furthermore, it has to be mentioned that features like n-gram, hyponyms, hypernyms and others were tested but the performance of these features was not sufficient. It is important to note here, that, a classifier can often perform well on the neutral classes and bad on the positive and negative classes but, it can still result in a good overall accuracy because of the disproportionate ratio of neutral and positive or negative references in a scientific document. For more meaningful results further experiments were performed.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Evaluation of different features for SentiCite (SC).\nQuestion: Which classifier performed better with the combination of features?\nSolve the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows the performance of SC-SVM and SC-Paum classifiers with different feature sets. For the combination of features, SC-Paum has a higher value (0.8154) compared to SC-SVM (0.7260). So, the answer is {\"answer\": \"SC-Paum\"}." } ], "images": [ "scigen/train/430.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Evaluation of different features for SentiCite (SC)." ], "reasoning": "The table shows the performance of SC-SVM and SC-Paum classifiers with different feature sets. For the combination of features, SC-Paum has a higher value (0.8154) compared to SC-SVM (0.7260).", "question": "Which classifier performed better with the combination of features?", "answer": "So, the answer is {\"answer\": \"SC-Paum\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe SC-SVM classifier performed better with only POS features than with the combination of features.\n" }, { "role": "assistant", "content": "The table shows that SC-SVM has a performance of 0.7241 with only POS features and 0.7260 with the combination of features, indicating that it performed better with the combination of features. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/430.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Evaluation of different features for SentiCite (SC)." ], "reasoning": "The table shows that SC-SVM has a performance of 0.7241 with only POS features and 0.7260 with the combination of features, indicating that it performed better with the combination of features.", "statement": "The SC-SVM classifier performed better with only POS features than with the combination of features.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6: Evaluation of different test corpus size for SentiCite (SC).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The SVM's f-score remains consistent regardless of corpus size, while the perceptron's performance improves initially.In the next experiment, the impact of the corpus size was tested. The results show that for the SVM the f-score is almost the same for each run and that the corpus size has no impact. In addition, it shows that the perceptron performance increased in the beginning." } ], "images": [ "scigen/train/431.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Evaluation of different test corpus size for SentiCite (SC)." ], "reasoning": "The SVM's f-score remains consistent regardless of corpus size, while the perceptron's performance improves initially.", "description": "In the next experiment, the impact of the corpus size was tested. The results show that for the SVM the f-score is almost the same for each run and that the corpus size has no impact. In addition, it shows that the perceptron performance increased in the beginning.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: Evaluation of different test corpus size for SentiCite (SC).\nQuestion: How does the performance of SC-Paum change as the corpus size increases?\nSolve the above question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows that the f-score for SC-Paum increases from 0.5727 with 5 docs to 0.8221 with 30 docs, indicating an improvement in performance. So, the answer is {\"answer\": \"improves\"}." } ], "images": [ "scigen/train/431.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Evaluation of different test corpus size for SentiCite (SC)." ], "reasoning": "The table shows that the f-score for SC-Paum increases from 0.5727 with 5 docs to 0.8221 with 30 docs, indicating an improvement in performance.", "question": "How does the performance of SC-Paum change as the corpus size increases?", "answer": "So, the answer is {\"answer\": \"improves\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe SC-SVM's performance is significantly impacted by the corpus size.\n" }, { "role": "assistant", "content": "The table shows that the f-score for SC-SVM remains almost the same across different corpus sizes, indicating that the corpus size has no significant impact. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/431.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Evaluation of different test corpus size for SentiCite (SC)." ], "reasoning": "The table shows that the f-score for SC-SVM remains almost the same across different corpus sizes, indicating that the corpus size has no significant impact.", "statement": "The SC-SVM's performance is significantly impacted by the corpus size.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 9: Ten run k-fold cross-fold for SentiCite (SC).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Cross-validation was performed 10 times with different splits of the training corpus, leading to decreased performance due to less training data.Finally, to validate that the results of the approach are correct a cross validation was done. The cross validation was done with the training corpus to have a balanced number of all classes. Therefore, the classifier was trained with a subset of the training corpus and evaluated on the remaining documents. This procedure as repeated 10 times with different splits. Also, this was the reason why the performance decreased because there was less training data. This was done because the training corpus was very large and the test corpus has for some labels only a few examples which was the case because of the distribution of the documents. The cross-validation was done exclusively with the training set which was split into two sets one for training and one for testing." } ], "images": [ "scigen/train/432.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Ten run k-fold cross-fold for SentiCite (SC)." ], "reasoning": "Cross-validation was performed 10 times with different splits of the training corpus, leading to decreased performance due to less training data.", "description": "Finally, to validate that the results of the approach are correct a cross validation was done. The cross validation was done with the training corpus to have a balanced number of all classes. Therefore, the classifier was trained with a subset of the training corpus and evaluated on the remaining documents. This procedure as repeated 10 times with different splits. Also, this was the reason why the performance decreased because there was less training data. This was done because the training corpus was very large and the test corpus has for some labels only a few examples which was the case because of the distribution of the documents. The cross-validation was done exclusively with the training set which was split into two sets one for training and one for testing.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 9: Ten run k-fold cross-fold for SentiCite (SC).', answer the following question. Give detailed solution consisting of each step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich algorithm had the highest F1 score in the cross-validation?\n" }, { "role": "assistant", "content": "By examining the table, we see that the highest F1 score is 0.67, achieved by both SC-SVM and SC-Paum in F4 and F5 respectively. So, the answer is {\"answer\": \"SC-SVM and SC-Paum\"}." } ], "images": [ "scigen/train/432.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Ten run k-fold cross-fold for SentiCite (SC)." ], "reasoning": "By examining the table, we see that the highest F1 score is 0.67, achieved by both SC-SVM and SC-Paum in F4 and F5 respectively.", "question": "Which algorithm had the highest F1 score in the cross-validation?", "answer": "So, the answer is {\"answer\": \"SC-SVM and SC-Paum\"}." }, { "messages": [ { "role": "user", "content": "The SC-SVM algorithm had a higher overall performance than the SC-Paum algorithm. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe SC-SVM algorithm had a higher overall performance than the SC-Paum algorithm.\n" }, { "role": "assistant", "content": "The table shows that both SC-SVM and SC-Paum have the same overall performance score of 0.53. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/432.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Ten run k-fold cross-fold for SentiCite (SC)." ], "reasoning": "The table shows that both SC-SVM and SC-Paum have the same overall performance score of 0.53.", "statement": "The SC-SVM algorithm had a higher overall performance than the SC-Paum algorithm.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 10: Different nature of citation classes.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The classifiers perform differently on various labels, with important labels like 'Reference' achieving high scores up to 1.0, while 'Dataset' scores around 0.667.As the numbers indicate, the classification of the different nature of references is not equally hard for the classifiers. The important labels have a good performance with scores around 0.667 for the datasets and up to 1.0 for the references." } ], "images": [ "scigen/train/433.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Different nature of citation classes." ], "reasoning": "The classifiers perform differently on various labels, with important labels like 'Reference' achieving high scores up to 1.0, while 'Dataset' scores around 0.667.", "description": "As the numbers indicate, the classification of the different nature of references is not equally hard for the classifiers. The important labels have a good performance with scores around 0.667 for the datasets and up to 1.0 for the references.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 10: Different nature of citation classes.', solve the following question step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich label has the highest SC-SVM score and how many references does it have?\n" }, { "role": "assistant", "content": "By examining the SC-SVM column, the highest score is 1.0 for the 'Reference' label. The number of references for 'Reference' is 110. So, the answer is {\"answer\": \"110\"}." } ], "images": [ "scigen/train/433.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Different nature of citation classes." ], "reasoning": "By examining the SC-SVM column, the highest score is 1.0 for the 'Reference' label. The number of references for 'Reference' is 110.", "question": "Which label has the highest SC-SVM score and how many references does it have?", "answer": "So, the answer is {\"answer\": \"110\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe 'Usage' label has the lowest SC-Paum score.\n" }, { "role": "assistant", "content": "By examining the SC-Paum column, the lowest score is 0.4602 for the 'Rest' label, not 'Usage'. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/433.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Different nature of citation classes." ], "reasoning": "By examining the SC-Paum column, the lowest score is 0.4602 for the 'Rest' label, not 'Usage'.", "statement": "The 'Usage' label has the lowest SC-Paum score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5: Performance improvement of the two-stage APR-based UV.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The two-stage APR-based UV shows minimal improvement over the pure APR-based UV, compensating for a few errors.We finally investigate the effect of the proposed two-stage APR-based UV. Although the improvement is minimal, the two-stage APR-based UV compensates for a few errors of the pure APR-based UV." } ], "images": [ "scigen/train/434.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Performance improvement of the two-stage APR-based UV." ], "reasoning": "The two-stage APR-based UV shows minimal improvement over the pure APR-based UV, compensating for a few errors.", "description": "We finally investigate the effect of the proposed two-stage APR-based UV. Although the improvement is minimal, the two-stage APR-based UV compensates for a few errors of the pure APR-based UV.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: Performance improvement of the two-stage APR-based UV.'.\n\nQuestion: What is the improvement in the APR2-stage over APR for the BNS-2 test set?\n" }, { "role": "assistant", "content": "The table shows the APR and APR2-stage values for the BNS-2 test set. The improvement (Δ) is given as +0.0006. So, the answer is {\"answer\": \"+0.0006\"}." } ], "images": [ "scigen/train/434.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Performance improvement of the two-stage APR-based UV." ], "reasoning": "The table shows the APR and APR2-stage values for the BNS-2 test set. The improvement (Δ) is given as +0.0006.", "question": "What is the improvement in the APR2-stage over APR for the BNS-2 test set?", "answer": "So, the answer is {\"answer\": \"+0.0006\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe APR2-stage shows a significant improvement over APR for the BNS-1 test set.\n" }, { "role": "assistant", "content": "The table shows that the improvement (Δ) for the BNS-1 test set is +0.0002, which is minimal and not significant. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/434.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Performance improvement of the two-stage APR-based UV." ], "reasoning": "The table shows that the improvement (Δ) for the BNS-1 test set is +0.0002, which is minimal and not significant.", "statement": "The APR2-stage shows a significant improvement over APR for the BNS-1 test set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Excerpted test sets from a speech database and an MMORPG'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Three test sets (DICT01, BNS-1, and BNS-2) are created to detect mismatches between text scripts and voice-overs, with DICT01 containing 1,600 read-style utterances and BNS-2 comprising 483 voice-overs with various tones and effects.The second type of test set is used to detect a mismatch between text script and voice-over. We create three test sets (DICT01, BNS-1, and BNS-2). DICT01 is the 1,600 read-style utterances excerpted from a Korean speech DB (DICT01) BNS-2 comprises 483 voice-overs with various tones and sound effects. To create the mismatched pairs, we assign an arbitrary text script to a voice-over similar to the real-world scenario." } ], "images": [ "scigen/train/435.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Excerpted test sets from a speech database and an MMORPG" ], "reasoning": "Three test sets (DICT01, BNS-1, and BNS-2) are created to detect mismatches between text scripts and voice-overs, with DICT01 containing 1,600 read-style utterances and BNS-2 comprising 483 voice-overs with various tones and effects.", "description": "The second type of test set is used to detect a mismatch between text script and voice-over. We create three test sets (DICT01, BNS-1, and BNS-2). DICT01 is the 1,600 read-style utterances excerpted from a Korean speech DB (DICT01) BNS-2 comprises 483 voice-overs with various tones and sound effects. To create the mismatched pairs, we assign an arbitrary text script to a voice-over similar to the real-world scenario.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 1: Excerpted test sets from a speech database and an MMORPG', and sequentially solve the question, writing out each step of your reasoning process. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich test set has the least number of utterances?\n" }, { "role": "assistant", "content": "By comparing the number of utterances in each test set, DICT01 and BNS-1 both have 1,600 utterances, while BNS-2 has 483 utterances, which is the least. So, the answer is {\"answer\": \"BNS-2\"}" } ], "images": [ "scigen/train/435.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Excerpted test sets from a speech database and an MMORPG" ], "reasoning": "By comparing the number of utterances in each test set, DICT01 and BNS-1 both have 1,600 utterances, while BNS-2 has 483 utterances, which is the least.", "question": "Which test set has the least number of utterances?", "answer": "So, the answer is {\"answer\": \"BNS-2\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim BNS-1 has more incorrect utterances than DICT01., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nBNS-1 has more incorrect utterances than DICT01.\n" }, { "role": "assistant", "content": "The table shows that both BNS-1 and DICT01 have the same number of incorrect utterances, which is 1,600. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/435.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Excerpted test sets from a speech database and an MMORPG" ], "reasoning": "The table shows that both BNS-1 and DICT01 have the same number of incorrect utterances, which is 1,600.", "statement": "BNS-1 has more incorrect utterances than DICT01.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Comparison between the proposed APR-based UV and the conventional LRT-based UV with the optimized thresholds.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The APR-based UV method outperforms the LRT-based UV method in all test sets, showing higher accuracy and improvement percentages.In the test sets of text scripts and voice-overs, we perform an experiment for comparing the performances of the proposed APR-based UV and the conventional LRT-based UV. APR is an alternative to the log-likelihood ratio (LLR) of the LRT-based UV. For the evaluation, we apply the optimized thresholds (i.e., τ and θ) that show the best performances for test sets to each method." } ], "images": [ "scigen/train/436.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison between the proposed APR-based UV and the conventional LRT-based UV with the optimized thresholds." ], "reasoning": "The APR-based UV method outperforms the LRT-based UV method in all test sets, showing higher accuracy and improvement percentages.", "description": "In the test sets of text scripts and voice-overs, we perform an experiment for comparing the performances of the proposed APR-based UV and the conventional LRT-based UV. APR is an alternative to the log-likelihood ratio (LLR) of the LRT-based UV. For the evaluation, we apply the optimized thresholds (i.e., τ and θ) that show the best performances for test sets to each method.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Comparison between the proposed APR-based UV and the conventional LRT-based UV with the optimized thresholds.\nQuestion: What is the accuracy difference between APR-based UV and LRT-based UV for the BNS-2 test set?\nSolve the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows the accuracy for LRT-based UV (0.901) and APR-based UV (0.959) for the BNS-2 test set. The difference is 0.959 - 0.901. So, the answer is {\"answer\": \"0.058\"}." } ], "images": [ "scigen/train/436.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison between the proposed APR-based UV and the conventional LRT-based UV with the optimized thresholds." ], "reasoning": "The table shows the accuracy for LRT-based UV (0.901) and APR-based UV (0.959) for the BNS-2 test set. The difference is 0.959 - 0.901.", "question": "What is the accuracy difference between APR-based UV and LRT-based UV for the BNS-2 test set?", "answer": "So, the answer is {\"answer\": \"0.058\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe APR-based UV method has a higher accuracy than the LRT-based UV method for the DICT01 test set.\n" }, { "role": "assistant", "content": "The table shows that the APR-based UV method has an accuracy of 0.998, while the LRT-based UV method has an accuracy of 0.992 for the DICT01 test set. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/436.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison between the proposed APR-based UV and the conventional LRT-based UV with the optimized thresholds." ], "reasoning": "The table shows that the APR-based UV method has an accuracy of 0.998, while the LRT-based UV method has an accuracy of 0.992 for the DICT01 test set.", "statement": "The APR-based UV method has a higher accuracy than the LRT-based UV method for the DICT01 test set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4: Performance degradation in the exaggerated voice-overs, when applying the optimized thresholds of the read-speech utterances.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The APR-based UV shows significantly lower values in both absolute and percentage terms compared to the LRT-based UV.(-14.4%) and -0.228 (-33.8%), respectively. However, those in the case of the APR-based UV are -0.016 (-1.7%) and -0.059 (-6.6%), respectively, which are remarkably lower than that of the LRT-based UV." } ], "images": [ "scigen/train/437.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance degradation in the exaggerated voice-overs, when applying the optimized thresholds of the read-speech utterances." ], "reasoning": "The APR-based UV shows significantly lower values in both absolute and percentage terms compared to the LRT-based UV.", "description": "(-14.4%) and -0.228 (-33.8%), respectively. However, those in the case of the APR-based UV are -0.016 (-1.7%) and -0.059 (-6.6%), respectively, which are remarkably lower than that of the LRT-based UV.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 4: Performance degradation in the exaggerated voice-overs, when applying the optimized thresholds of the read-speech utterances.' in mind, break down and resolve the question below step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the difference in LRT Δ and APR Δ for the BNS-2 test set?\n" }, { "role": "assistant", "content": "For the BNS-2 test set, the LRT Δ is -0.228 (-33.8%) and the APR Δ is -0.059 (-6.6%). The difference in absolute terms is -0.228 - (-0.059) = -0.169, and in percentage terms, it is -33.8% - (-6.6%) = -27.2%. So, the answer is {\"answer\": \"-0.169 and -27.2%\"}" } ], "images": [ "scigen/train/437.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance degradation in the exaggerated voice-overs, when applying the optimized thresholds of the read-speech utterances." ], "reasoning": "For the BNS-2 test set, the LRT Δ is -0.228 (-33.8%) and the APR Δ is -0.059 (-6.6%). The difference in absolute terms is -0.228 - (-0.059) = -0.169, and in percentage terms, it is -33.8% - (-6.6%) = -27.2%.", "question": "What is the difference in LRT Δ and APR Δ for the BNS-2 test set?", "answer": "So, the answer is {\"answer\": \"-0.169 and -27.2%\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe APR-based UV for the BNS-1 test set has a lower Δ value than the LRT-based UV.\n" }, { "role": "assistant", "content": "For the BNS-1 test set, the LRT Δ is -0.117 (-14.4%) and the APR Δ is -0.016 (-1.7%). Both the absolute and percentage values of APR Δ are lower than those of LRT Δ. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/437.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance degradation in the exaggerated voice-overs, when applying the optimized thresholds of the read-speech utterances." ], "reasoning": "For the BNS-1 test set, the LRT Δ is -0.117 (-14.4%) and the APR Δ is -0.016 (-1.7%). Both the absolute and percentage values of APR Δ are lower than those of LRT Δ.", "statement": "The APR-based UV for the BNS-1 test set has a lower Δ value than the LRT-based UV.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: Average inter-annotator agreement per section, ordered from highest to lowest, the corresponding average F1 of the neural segmenter, and number of tokens (there are 2 documents per section, except 1 for Summary).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Annotators find the Introduction section easy to segment but the neural segmenter struggles due to citations, while the Discussion section is challenging for both annotators and the segmenter due to complex discourse.Here we compare the level of annotator agreement with the performance of the neural segmenter. However, high agreement does not always translate to good performance. The Introduction section is straightforward for the annotators to segment, but this is also where most citations occur, causing the segmenter to perform more poorly. Earlier, we had noted the Discussion section was the hardest for annotators to label because of the more complex discourse. These more ambiguous syntactic constructions also pose a challenge for the segmenter, with lower performance than most other sections." } ], "images": [ "scigen/train/438.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Average inter-annotator agreement per section, ordered from highest to lowest, the corresponding average F1 of the neural segmenter, and number of tokens (there are 2 documents per section, except 1 for Summary)." ], "reasoning": "Annotators find the Introduction section easy to segment but the neural segmenter struggles due to citations, while the Discussion section is challenging for both annotators and the segmenter due to complex discourse.", "description": "Here we compare the level of annotator agreement with the performance of the neural segmenter. However, high agreement does not always translate to good performance. The Introduction section is straightforward for the annotators to segment, but this is also where most citations occur, causing the segmenter to perform more poorly. Earlier, we had noted the Discussion section was the hardest for annotators to label because of the more complex discourse. These more ambiguous syntactic constructions also pose a challenge for the segmenter, with lower performance than most other sections.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nTable title: Table 5: Average inter-annotator agreement per section, ordered from highest to lowest, the corresponding average F1 of the neural segmenter, and number of tokens (there are 2 documents per section, except 1 for Summary).\n\nQuestion: Which section has the highest annotator agreement but the neural segmenter performs poorly?\n" }, { "role": "assistant", "content": "The Summary section has the highest Kappa value (1.00) indicating high annotator agreement, but the Introduction section, despite having a high Kappa value (0.96), has a lower F1 score (86.58) due to the presence of citations. So, the answer is {\"answer\": \"Introduction\"}." } ], "images": [ "scigen/train/438.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Average inter-annotator agreement per section, ordered from highest to lowest, the corresponding average F1 of the neural segmenter, and number of tokens (there are 2 documents per section, except 1 for Summary)." ], "reasoning": "The Summary section has the highest Kappa value (1.00) indicating high annotator agreement, but the Introduction section, despite having a high Kappa value (0.96), has a lower F1 score (86.58) due to the presence of citations.", "question": "Which section has the highest annotator agreement but the neural segmenter performs poorly?", "answer": "So, the answer is {\"answer\": \"Introduction\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Discussion section has the lowest performance by the neural segmenter. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Discussion section has the lowest performance by the neural segmenter.\n" }, { "role": "assistant", "content": "The Discussion section has the lowest F1 score (89.03) among all sections, indicating the lowest performance by the neural segmenter. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/438.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Average inter-annotator agreement per section, ordered from highest to lowest, the corresponding average F1 of the neural segmenter, and number of tokens (there are 2 documents per section, except 1 for Summary)." ], "reasoning": "The Discussion section has the lowest F1 score (89.03) among all sections, indicating the lowest performance by the neural segmenter.", "statement": "The Discussion section has the lowest performance by the neural segmenter.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: F1, precision (P) and recall (R) of RST discourse segmenters on two domains (best numbers for News are underlined, for Medical are bolded).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The neural segmenter achieves the best performance on News and reduces the gap on Medical, with a 5.64 F1 difference.As expected, the News domain outperforms the Medical domain, regardless of which segmenter is used. In the case of the DPLP segmenter, the gap between the two domains is about 7.4 F1 points. Note that the performance of DPLP on News lags considerably behind the state of the art (-14.76 F1 points). When switching to the two-pass segmenter, the performance on News increases dramatically (+13 F1 points). However, the performance on Medical increases by only 3.75 F1 points. Thus, large gains in News translate into only a small gain in Medical. The neural segmenter achieves the best performance on News and is also able to more successfully close the gap on Medical, with only a 5.64 F1 difference, largely attributable to lower recall." } ], "images": [ "scigen/train/439.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: F1, precision (P) and recall (R) of RST discourse segmenters on two domains (best numbers for News are underlined, for Medical are bolded)." ], "reasoning": "The neural segmenter achieves the best performance on News and reduces the gap on Medical, with a 5.64 F1 difference.", "description": "As expected, the News domain outperforms the Medical domain, regardless of which segmenter is used. In the case of the DPLP segmenter, the gap between the two domains is about 7.4 F1 points. Note that the performance of DPLP on News lags considerably behind the state of the art (-14.76 F1 points). When switching to the two-pass segmenter, the performance on News increases dramatically (+13 F1 points). However, the performance on Medical increases by only 3.75 F1 points. Thus, large gains in News translate into only a small gain in Medical. The neural segmenter achieves the best performance on News and is also able to more successfully close the gap on Medical, with only a 5.64 F1 difference, largely attributable to lower recall.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 3: F1, precision (P) and recall (R) of RST discourse segmenters on two domains (best numbers for News are underlined, for Medical are bolded).', solve this question and give detail solutions. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the F1 score difference between the DPLP and two-pass segmenters for the News domain?\n" }, { "role": "assistant", "content": "The F1 score for DPLP on News is 82.56, and for the two-pass segmenter on News, it is 95.72. The difference is 95.72 - 82.56. So, the answer is {\"answer\": \"13.16\"}" } ], "images": [ "scigen/train/439.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: F1, precision (P) and recall (R) of RST discourse segmenters on two domains (best numbers for News are underlined, for Medical are bolded)." ], "reasoning": "The F1 score for DPLP on News is 82.56, and for the two-pass segmenter on News, it is 95.72. The difference is 95.72 - 82.56.", "question": "What is the F1 score difference between the DPLP and two-pass segmenters for the News domain?", "answer": "So, the answer is {\"answer\": \"13.16\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe two-pass segmenter improves the F1 score for the Medical domain by 3.75 points compared to the DPLP segmenter.\n" }, { "role": "assistant", "content": "The F1 score for DPLP on Medical is 75.29, and for the two-pass segmenter on Medical, it is 84.69. The difference is 84.69 - 75.29, which is 9.4 points, not 3.75. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/439.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: F1, precision (P) and recall (R) of RST discourse segmenters on two domains (best numbers for News are underlined, for Medical are bolded)." ], "reasoning": "The F1 score for DPLP on Medical is 75.29, and for the two-pass segmenter on Medical, it is 84.69. The difference is 84.69 - 75.29, which is 9.4 points, not 3.75.", "statement": "The two-pass segmenter improves the F1 score for the Medical domain by 3.75 points compared to the DPLP segmenter.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title '(b) Negative PageRank'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Idioms perform best in negative PageRank results due to their higher synonymy with other idioms and higher probability of acting as sentiment words, but they perform poorly in positive PageRank.In negative pagerank result, idioms gets the best result. After checking the final ranking result, we find that idioms have more synonymies with other idioms and they have higher probability to act as sentiment word. In addition, the performance in positive PageRank is poor." } ], "images": [ "scigen/train/440.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) Negative PageRank" ], "reasoning": "Idioms perform best in negative PageRank results due to their higher synonymy with other idioms and higher probability of acting as sentiment words, but they perform poorly in positive PageRank.", "description": "In negative pagerank result, idioms gets the best result. After checking the final ranking result, we find that idioms have more synonymies with other idioms and they have higher probability to act as sentiment word. In addition, the performance in positive PageRank is poor.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: (b) Negative PageRank\nQuestion: Which postag has the highest precision at P@50?\n" }, { "role": "assistant", "content": "By examining the table, the postag 'i' has the highest precision at P@50 with a value of 0.980. So, the answer is {\"answer\": \"i\"}." } ], "images": [ "scigen/train/440.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) Negative PageRank" ], "reasoning": "By examining the table, the postag 'i' has the highest precision at P@50 with a value of 0.980.", "question": "Which postag has the highest precision at P@50?", "answer": "So, the answer is {\"answer\": \"i\"}." }, { "messages": [ { "role": "user", "content": "The postag 'a' has a higher precision at P@100 than at P@50. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe postag 'a' has a higher precision at P@100 than at P@50.\n" }, { "role": "assistant", "content": "The table shows that the precision for postag 'a' at P@100 is 0.200, which is lower than the precision at P@50, which is 0.260. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/440.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) Negative PageRank" ], "reasoning": "The table shows that the precision for postag 'a' at P@100 is 0.200, which is lower than the precision at P@50, which is 0.260.", "statement": "The postag 'a' has a higher precision at P@100 than at P@50.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title '(a) Positive PageRank'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Idioms have the best result in negative PageRank due to their higher synonymy with other idioms and higher probability to act as sentiment words.In negative pagerank result, idioms gets the best result. After checking the final ranking result, we find that idioms have more synonymies with other idioms and they have higher probability to act as sentiment word. In addition, the performance in positive PageRank is poor." } ], "images": [ "scigen/train/441.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) Positive PageRank" ], "reasoning": "Idioms have the best result in negative PageRank due to their higher synonymy with other idioms and higher probability to act as sentiment words.", "description": "In negative pagerank result, idioms gets the best result. After checking the final ranking result, we find that idioms have more synonymies with other idioms and they have higher probability to act as sentiment word. In addition, the performance in positive PageRank is poor.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about '(a) Positive PageRank', and tackle the question below in a sequential manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich postag has the highest P@1000 value?\n" }, { "role": "assistant", "content": "By comparing the P@1000 values for each postag, we see that 'a' has the highest value of 0.385. So, the answer is {\"answer\": \"a\"}." } ], "images": [ "scigen/train/441.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) Positive PageRank" ], "reasoning": "By comparing the P@1000 values for each postag, we see that 'a' has the highest value of 0.385.", "question": "Which postag has the highest P@1000 value?", "answer": "So, the answer is {\"answer\": \"a\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Adjectives have the highest P@500 value. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nAdjectives have the highest P@500 value.\n" }, { "role": "assistant", "content": "By comparing the P@500 values for each postag, we see that 'a' (adjectives) has the highest value of 0.370. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/441.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) Positive PageRank" ], "reasoning": "By comparing the P@500 values for each postag, we see that 'a' (adjectives) has the highest value of 0.370.", "statement": "Adjectives have the highest P@500 value.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 6: Experimental results on DSSW extraction'.\n" }, { "role": "assistant", "content": "Our precision improved significantly by 9.4% in the finance domain, while recall improved slightly due to some sentiment words not co-occurring with target words.Our precision(P) improves significantly, especially in finance domain with 9.4% improvement. Our recall(R) improves slightly because there are still some sentiment words don’t co-occur with target words. Problem with hidden target words will be studied in future work." } ], "images": [ "scigen/train/442.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Experimental results on DSSW extraction" ], "reasoning": "Our precision improved significantly by 9.4% in the finance domain, while recall improved slightly due to some sentiment words not co-occurring with target words.", "description": "Our precision(P) improves significantly, especially in finance domain with 9.4% improvement. Our recall(R) improves slightly because there are still some sentiment words don’t co-occur with target words. Problem with hidden target words will be studied in future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 6: Experimental results on DSSW extraction'. Solve the following question based on the table.\n\nQuestion: What is the precision improvement in the finance domain according to the table?\nLet's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the precision (P) for finance improved from 0.5423 (Hu04) to 0.6347 (Our), which is a 9.24% improvement. So, the answer is {\"answer\": \"9.24%\"}" } ], "images": [ "scigen/train/442.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Experimental results on DSSW extraction" ], "reasoning": "The table shows that the precision (P) for finance improved from 0.5423 (Hu04) to 0.6347 (Our), which is a 9.24% improvement.", "question": "What is the precision improvement in the finance domain according to the table?", "answer": "So, the answer is {\"answer\": \"9.24%\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Our recall in the finance domain is higher than in the digital domain., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nOur recall in the finance domain is higher than in the digital domain.\n" }, { "role": "assistant", "content": "The table shows that the recall (R) for finance is 0.3411, while for digital it is 0.3457. Therefore, the recall in the finance domain is not higher than in the digital domain. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/442.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Experimental results on DSSW extraction" ], "reasoning": "The table shows that the recall (R) for finance is 0.3411, while for digital it is 0.3457. Therefore, the recall in the finance domain is not higher than in the digital domain.", "statement": "Our recall in the finance domain is higher than in the digital domain.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Acoustic model performance by senone set, model architecture, and for various frame-level combinations, using an N-gram LM. The “puhpum” senone sets use an alternate dictionary with special phones for filled pauses.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Expanding the vocabulary size from 30,500 to 165,000 words reduced the out-of-vocabulary rate on the eval2002 devset from 0.29% to 0.06%.The results are based on N-gram language models, and all combinations are equal-weighted. In the past we had used a relatively small vocabulary of 30,500 words drawn only from in-domain (Switchboard and Fisher corpus) training data. While this yields an out-of-vocabulary (OOV) rate well below 1%, our error rates have reached levels where even small absolute reductions in OOVs could potentially have a significant impact on overall accuracy. We supplemented the in-domain vocabulary with the most frequent words in the out-of-domain sources also used for language model training: the LDC Broadcast News corpus and the UW Conversational Web corpus. Boosting the vocabulary size to 165k reduced the OOV rate (excluding word fragments) on the eval2002 devset from 0.29% to 0.06%." } ], "images": [ "scigen/train/443.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Acoustic model performance by senone set, model architecture, and for various frame-level combinations, using an N-gram LM. The “puhpum” senone sets use an alternate dictionary with special phones for filled pauses." ], "reasoning": "Expanding the vocabulary size from 30,500 to 165,000 words reduced the out-of-vocabulary rate on the eval2002 devset from 0.29% to 0.06%.", "description": "The results are based on N-gram language models, and all combinations are equal-weighted. In the past we had used a relatively small vocabulary of 30,500 words drawn only from in-domain (Switchboard and Fisher corpus) training data. While this yields an out-of-vocabulary (OOV) rate well below 1%, our error rates have reached levels where even small absolute reductions in OOVs could potentially have a significant impact on overall accuracy. We supplemented the in-domain vocabulary with the most frequent words in the out-of-domain sources also used for language model training: the LDC Broadcast News corpus and the UW Conversational Web corpus. Boosting the vocabulary size to 165k reduced the OOV rate (excluding word fragments) on the eval2002 devset from 0.29% to 0.06%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: Acoustic model performance by senone set, model architecture, and for various frame-level combinations, using an N-gram LM. The “puhpum” senone sets use an alternate dictionary with special phones for filled pauses.' and methodically tackle the question that follows. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich architecture and senone set combination achieved the lowest test WER?\n" }, { "role": "assistant", "content": "By examining the table, the combination of 'BLSTM+ResNet+LACE+CNN-BLSTM' architecture with the '9k' senone set achieved the lowest test WER of 7.2. So, the answer is {\"answer\": \"BLSTM+ResNet+LACE+CNN-BLSTM with 9k senone set\"}" } ], "images": [ "scigen/train/443.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Acoustic model performance by senone set, model architecture, and for various frame-level combinations, using an N-gram LM. The “puhpum” senone sets use an alternate dictionary with special phones for filled pauses." ], "reasoning": "By examining the table, the combination of 'BLSTM+ResNet+LACE+CNN-BLSTM' architecture with the '9k' senone set achieved the lowest test WER of 7.2.", "question": "Which architecture and senone set combination achieved the lowest test WER?", "answer": "So, the answer is {\"answer\": \"BLSTM+ResNet+LACE+CNN-BLSTM with 9k senone set\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe BLSTM architecture with the 27k senone set achieved a devset WER of 11.4.\n" }, { "role": "assistant", "content": "The table shows that the BLSTM architecture with the 27k senone set indeed achieved a devset WER of 11.4. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/443.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Acoustic model performance by senone set, model architecture, and for various frame-level combinations, using an N-gram LM. The “puhpum” senone sets use an alternate dictionary with special phones for filled pauses." ], "reasoning": "The table shows that the BLSTM architecture with the 27k senone set indeed achieved a devset WER of 11.4.", "statement": "The BLSTM architecture with the 27k senone set achieved a devset WER of 11.4.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: Perplexities and word errors with session-based LSTM-LMs (forward direction only). The last line reflects the use of 1-best recognition output for words in preceding utterances.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Conditioning on the previous word context reduces perplexity by 21%, with smaller reductions from adding speaker change and overlap information. The use of 1-best recognition output for conversation history slightly degrades perplexity by 7-8%.There is a large perplexity reduction of 21% by conditioning on the previous word context, with smaller incremental reductions from adding speaker change and overlap information. The table also compares the word error rate with the full session-based model to the baseline, within-utterance LSTM-LM. As shown in the last row of the table, some of the perplexity gain over the baseline is negated by the use of 1-best recognition output for the conversation history. However, the perplexity degrades by only 7-8% relative due to the noisy history." } ], "images": [ "scigen/train/444.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Perplexities and word errors with session-based LSTM-LMs (forward direction only). The last line reflects the use of 1-best recognition output for words in preceding utterances." ], "reasoning": "Conditioning on the previous word context reduces perplexity by 21%, with smaller reductions from adding speaker change and overlap information. The use of 1-best recognition output for conversation history slightly degrades perplexity by 7-8%.", "description": "There is a large perplexity reduction of 21% by conditioning on the previous word context, with smaller incremental reductions from adding speaker change and overlap information. The table also compares the word error rate with the full session-based model to the baseline, within-utterance LSTM-LM. As shown in the last row of the table, some of the perplexity gain over the baseline is negated by the use of 1-best recognition output for the conversation history. However, the perplexity degrades by only 7-8% relative due to the noisy history.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 4: Perplexities and word errors with session-based LSTM-LMs (forward direction only). The last line reflects the use of 1-best recognition output for words in preceding utterances.' and methodically tackle the question that follows. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the perplexity reduction when adding session history words compared to the baseline?\n" }, { "role": "assistant", "content": "The baseline perplexity on the dev set is 50.76, and it reduces to 39.69 when adding session history words. The reduction is 50.76 - 39.69 = 11.07. So, the answer is {\"answer\": \"11.07\"}." } ], "images": [ "scigen/train/444.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Perplexities and word errors with session-based LSTM-LMs (forward direction only). The last line reflects the use of 1-best recognition output for words in preceding utterances." ], "reasoning": "The baseline perplexity on the dev set is 50.76, and it reduces to 39.69 when adding session history words. The reduction is 50.76 - 39.69 = 11.07.", "question": "What is the perplexity reduction when adding session history words compared to the baseline?", "answer": "So, the answer is {\"answer\": \"11.07\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nAdding speaker change information reduces the perplexity on the test set to 35.48.\n" }, { "role": "assistant", "content": "The table shows that adding speaker change information reduces the perplexity on the test set to 35.48. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/444.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Perplexities and word errors with session-based LSTM-LMs (forward direction only). The last line reflects the use of 1-best recognition output for words in preceding utterances." ], "reasoning": "The table shows that adding speaker change information reduces the perplexity on the test set to 35.48.", "statement": "Adding speaker change information reduces the perplexity on the test set to 35.48.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Results for LSTM-LM rescoring on systems selected for combination, the combined system, and confusion network rescoring'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The LSTM-LMs consistently reduce relative error by 22 to 25% compared to the N-gram LM, and system combination further reduces error by 4%, with CN rescoring improving another 2-3%.The collection of LSTM-LMs (which includes the session-based LMs) gives a very consistent 22 to 25% relative error reduction on individual systems, compared to the N-gram LM. The system combination reduces error by 4% relative over the best individual systems, and the CN rescoring improves another 2-3% relative." } ], "images": [ "scigen/train/445.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results for LSTM-LM rescoring on systems selected for combination, the combined system, and confusion network rescoring" ], "reasoning": "The LSTM-LMs consistently reduce relative error by 22 to 25% compared to the N-gram LM, and system combination further reduces error by 4%, with CN rescoring improving another 2-3%.", "description": "The collection of LSTM-LMs (which includes the session-based LMs) gives a very consistent 22 to 25% relative error reduction on individual systems, compared to the N-gram LM. The system combination reduces error by 4% relative over the best individual systems, and the CN rescoring improves another 2-3% relative.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 5: Results for LSTM-LM rescoring on systems selected for combination, the combined system, and confusion network rescoring', and sequentially solve the question, writing out each step of your reasoning process. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the WER test score for the 27k-puhupum senone set using BLSTM+ResNet+LACE+CNN-BLSTM with LSTM-LMs?\n" }, { "role": "assistant", "content": "Identify the row corresponding to the 27k-puhupum senone set using BLSTM+ResNet+LACE+CNN-BLSTM, then find the WER test score under the LSTM-LMs column. So, the answer is {\"answer\": \"5.1\"}." } ], "images": [ "scigen/train/445.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results for LSTM-LM rescoring on systems selected for combination, the combined system, and confusion network rescoring" ], "reasoning": "Identify the row corresponding to the 27k-puhupum senone set using BLSTM+ResNet+LACE+CNN-BLSTM, then find the WER test score under the LSTM-LMs column.", "question": "What is the WER test score for the 27k-puhupum senone set using BLSTM+ResNet+LACE+CNN-BLSTM with LSTM-LMs?", "answer": "So, the answer is {\"answer\": \"5.1\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The confusion network combination with LSTM rescoring has a WER devset score of 7.3. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe confusion network combination with LSTM rescoring has a WER devset score of 7.3.\n" }, { "role": "assistant", "content": "Locate the row for the confusion network combination with LSTM rescoring and check the WER devset score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/445.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results for LSTM-LM rescoring on systems selected for combination, the combined system, and confusion network rescoring" ], "reasoning": "Locate the row for the confusion network combination with LSTM rescoring and check the WER devset score.", "statement": "The confusion network combination with LSTM rescoring has a WER devset score of 7.3.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: F0.5 results on the CoNLL-13 set of main model architectures, on different segments of the set according to whether the input contains OOVs.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The nested attention hybrid model improves performance in the OOV segment but slightly degrades in the non-OOV segment compared to the basic hybrid model.We present a comparative performance analysis of models on the CoNLL-13 development set. First, we divide the set into two segments: OOV and NonOOV, based on whether there is at least one OOV word in the given source input. The additional nested character-level attention of our hybrid model brings a sizable improvement over the basic hybrid model in the OOV segment and a small degradation in the non-OOV segment. We should note that in future work character-level attention can be added for non-OOV source words in the nested attention model, which could improve performance on this segment as well." } ], "images": [ "scigen/train/446.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: F0.5 results on the CoNLL-13 set of main model architectures, on different segments of the set according to whether the input contains OOVs." ], "reasoning": "The nested attention hybrid model improves performance in the OOV segment but slightly degrades in the non-OOV segment compared to the basic hybrid model.", "description": "We present a comparative performance analysis of models on the CoNLL-13 development set. First, we divide the set into two segments: OOV and NonOOV, based on whether there is at least one OOV word in the given source input. The additional nested character-level attention of our hybrid model brings a sizable improvement over the basic hybrid model in the OOV segment and a small degradation in the non-OOV segment. We should note that in future work character-level attention can be added for non-OOV source words in the nested attention model, which could improve performance on this segment as well.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 5: F0.5 results on the CoNLL-13 set of main model architectures, on different segments of the set according to whether the input contains OOVs.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest performance in the OOV segment?\n" }, { "role": "assistant", "content": "By comparing the OOV segment scores, the Nested Attention Hybrid Model has the highest score of 27.39. So, the answer is {\"answer\": \"Nested Attention Hybrid Model\"}." } ], "images": [ "scigen/train/446.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: F0.5 results on the CoNLL-13 set of main model architectures, on different segments of the set according to whether the input contains OOVs." ], "reasoning": "By comparing the OOV segment scores, the Nested Attention Hybrid Model has the highest score of 27.39.", "question": "Which model has the highest performance in the OOV segment?", "answer": "So, the answer is {\"answer\": \"Nested Attention Hybrid Model\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Hybrid model performs better than the Word NMT + UNK replacement model in both NonOOV and OOV segments.\n" }, { "role": "assistant", "content": "The table shows that the Hybrid model has higher scores in both NonOOV (29.36 vs. 27.61) and OOV (25.92 vs. 21.57) segments compared to the Word NMT + UNK replacement model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/446.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: F0.5 results on the CoNLL-13 set of main model architectures, on different segments of the set according to whether the input contains OOVs." ], "reasoning": "The table shows that the Hybrid model has higher scores in both NonOOV (29.36 vs. 27.61) and OOV (25.92 vs. 21.57) segments compared to the Word NMT + UNK replacement model.", "statement": "The Hybrid model performs better than the Word NMT + UNK replacement model in both NonOOV and OOV segments.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Overview of the datasets used.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "We evaluate model performance using the CoNLL-14 test set and analyze it on the CoNLL-13 development set, reporting results in F0.5-measure using the m2scorer.We evaluate the performance of the models on the standard sets from the CoNLL-14 shared task Ng et al. We report final performance on the CoNLL-14 test set without alternatives, and analyze model performance on the CoNLL-13 development set Dahlmeier et al. We use the development and validation sets for model selection. We report performance in F0.5-measure, as calculated by the m2scorer—the official implementation of the scoring metric in the shared task. Given system outputs and gold-standard edits, m2scorer computes the F0.5 measure of a set of system edits against a set of gold-standard edits." } ], "images": [ "scigen/train/447.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Overview of the datasets used." ], "reasoning": "We evaluate model performance using the CoNLL-14 test set and analyze it on the CoNLL-13 development set, reporting results in F0.5-measure using the m2scorer.", "description": "We evaluate the performance of the models on the standard sets from the CoNLL-14 shared task Ng et al. We report final performance on the CoNLL-14 test set without alternatives, and analyze model performance on the CoNLL-13 development set Dahlmeier et al. We use the development and validation sets for model selection. We report performance in F0.5-measure, as calculated by the m2scorer—the official implementation of the scoring metric in the shared task. Given system outputs and gold-standard edits, m2scorer computes the F0.5 measure of a set of system edits against a set of gold-standard edits.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 1: Overview of the datasets used.\nQuestion: What is the total number of sentence pairs used for the development and test sets combined?\n" }, { "role": "assistant", "content": "To find the total number of sentence pairs for the development and test sets, add the number of sentence pairs in the development set (1,381) to the number of sentence pairs in the test set (1,312). So, the answer is {\"answer\": \"2693\"}" } ], "images": [ "scigen/train/447.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Overview of the datasets used." ], "reasoning": "To find the total number of sentence pairs for the development and test sets, add the number of sentence pairs in the development set (1,381) to the number of sentence pairs in the test set (1,312).", "question": "What is the total number of sentence pairs used for the development and test sets combined?", "answer": "So, the answer is {\"answer\": \"2693\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe validation set contains more sentence pairs than the development set.\n" }, { "role": "assistant", "content": "The table shows that the validation set contains 4,771 sentence pairs, while the development set contains 1,381 sentence pairs. Since 4,771 is greater than 1,381, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/447.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Overview of the datasets used." ], "reasoning": "The table shows that the validation set contains 4,771 sentence pairs, while the development set contains 1,381 sentence pairs. Since 4,771 is greater than 1,381, the statement is supported.", "statement": "The validation set contains more sentence pairs than the development set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: F0.5 results on the CoNLL-13 and CoNLL-14 test sets of main model architectures.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The nested attention hybrid model significantly outperforms the baseline and hybrid models, showing the effectiveness of nested attention in improving performance on the GEC task.In addition to the word-level baseline, we include the performance of a hybrid model with a single level of attention, which follows the work of \\newciteluong-manning:2016:P16-1 for machine translation, and is the first application of a hybrid word/character-level model to grammatical error correction. Based on hyper-parameter selection, the character-level component weight of the loss is α=1 for the basic hybrid model. N16-1 (38.77 versus 39.9). We attribute the difference to differences in the training set and the word-alignment methods used. Our reimplementation serves to provide a controlled experimental evaluation of the impact of hybrid models and nested attention on the GEC task. As seen, our nested attention hybrid model substantially improves upon the baseline, achieving a gain of close to 3 points on the test set. The hybrid word/character model with a single level of attention brings a large improvement as well, showing the importance of character-level information for this task. We delve deeper into the impact of nested attention for the hybrid model in Section 5." } ], "images": [ "scigen/train/448.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: F0.5 results on the CoNLL-13 and CoNLL-14 test sets of main model architectures." ], "reasoning": "The nested attention hybrid model significantly outperforms the baseline and hybrid models, showing the effectiveness of nested attention in improving performance on the GEC task.", "description": "In addition to the word-level baseline, we include the performance of a hybrid model with a single level of attention, which follows the work of \\newciteluong-manning:2016:P16-1 for machine translation, and is the first application of a hybrid word/character-level model to grammatical error correction. Based on hyper-parameter selection, the character-level component weight of the loss is α=1 for the basic hybrid model. N16-1 (38.77 versus 39.9). We attribute the difference to differences in the training set and the word-alignment methods used. Our reimplementation serves to provide a controlled experimental evaluation of the impact of hybrid models and nested attention on the GEC task. As seen, our nested attention hybrid model substantially improves upon the baseline, achieving a gain of close to 3 points on the test set. The hybrid word/character model with a single level of attention brings a large improvement as well, showing the importance of character-level information for this task. We delve deeper into the impact of nested attention for the hybrid model in Section 5.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: 'Table 3: F0.5 results on the CoNLL-13 and CoNLL-14 test sets of main model architectures.'\nQuestion: What is the performance improvement on the test set when using the Nested Attention Hybrid Model compared to the Word NMT + UNK replacement model?\n" }, { "role": "assistant", "content": "The performance of the Word NMT + UNK replacement model on the test set is 38.77, while the Nested Attention Hybrid Model achieves 41.53. The improvement is calculated as 41.53 - 38.77. So, the answer is {\"answer\": \"2.76\"}." } ], "images": [ "scigen/train/448.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: F0.5 results on the CoNLL-13 and CoNLL-14 test sets of main model architectures." ], "reasoning": "The performance of the Word NMT + UNK replacement model on the test set is 38.77, while the Nested Attention Hybrid Model achieves 41.53. The improvement is calculated as 41.53 - 38.77.", "question": "What is the performance improvement on the test set when using the Nested Attention Hybrid Model compared to the Word NMT + UNK replacement model?", "answer": "So, the answer is {\"answer\": \"2.76\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Hybrid model performs better than the Word NMT + UNK replacement model on the test set. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Hybrid model performs better than the Word NMT + UNK replacement model on the test set.\n" }, { "role": "assistant", "content": "The table shows that the Hybrid model has a performance of 40.44 on the test set, while the Word NMT + UNK replacement model has a performance of 38.77. Therefore, the Hybrid model performs better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/448.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: F0.5 results on the CoNLL-13 and CoNLL-14 test sets of main model architectures." ], "reasoning": "The table shows that the Hybrid model has a performance of 40.44 on the test set, while the Word NMT + UNK replacement model has a performance of 38.77. Therefore, the Hybrid model performs better.", "statement": "The Hybrid model performs better than the Word NMT + UNK replacement model on the test set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 7: Precision, Recall and F0.5 results on CoNLL-13,on the ”small changes” and “large changes” portions of the OOV segment.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The additional character-level attention layer significantly improves performance in the 'small changes' portion, while its impact on the 'large changes' portion is minimal.Our hypothesis is that the additional character-level attention layer is particularly useful to model edits among orthographically similar words. We can see that the gains in the “small changes” portion are indeed quite large, indicating that the fine-grained character-level attention empowers the model to more accurately correct confusions among phrases with high character-level similarity. The impact in the “large changes” portion is slightly positive in precision and slightly negative in recall. Thus most of the benefit of the additional character-level attention stems from improvements in the “small changes” portion." } ], "images": [ "scigen/train/449.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Precision, Recall and F0.5 results on CoNLL-13,on the ”small changes” and “large changes” portions of the OOV segment." ], "reasoning": "The additional character-level attention layer significantly improves performance in the 'small changes' portion, while its impact on the 'large changes' portion is minimal.", "description": "Our hypothesis is that the additional character-level attention layer is particularly useful to model edits among orthographically similar words. We can see that the gains in the “small changes” portion are indeed quite large, indicating that the fine-grained character-level attention empowers the model to more accurately correct confusions among phrases with high character-level similarity. The impact in the “large changes” portion is slightly positive in precision and slightly negative in recall. Thus most of the benefit of the additional character-level attention stems from improvements in the “small changes” portion.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 7: Precision, Recall and F0.5 results on CoNLL-13,on the ”small changes” and “large changes” portions of the OOV segment.' in mind, break down and resolve the question below step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the precision performance of the Nested Attention Hybrid Model in the small changes portion?\n" }, { "role": "assistant", "content": "The table shows the precision performance (Performance P) for the Nested Attention Hybrid Model in the small changes portion as 48.25. So, the answer is {\"answer\": \"48.25\"}." } ], "images": [ "scigen/train/449.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Precision, Recall and F0.5 results on CoNLL-13,on the ”small changes” and “large changes” portions of the OOV segment." ], "reasoning": "The table shows the precision performance (Performance P) for the Nested Attention Hybrid Model in the small changes portion as 48.25.", "question": "What is the precision performance of the Nested Attention Hybrid Model in the small changes portion?", "answer": "So, the answer is {\"answer\": \"48.25\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Nested Attention Hybrid Model has a higher recall performance in the large changes portion compared to the Hybrid model.\n" }, { "role": "assistant", "content": "The table shows that the recall performance (Performance R) for the Nested Attention Hybrid Model in the large changes portion is 8.11, which is lower than the Hybrid model's recall performance of 8.32. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/449.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Precision, Recall and F0.5 results on CoNLL-13,on the ”small changes” and “large changes” portions of the OOV segment." ], "reasoning": "The table shows that the recall performance (Performance R) for the Nested Attention Hybrid Model in the large changes portion is 8.11, which is lower than the Hybrid model's recall performance of 8.32.", "statement": "The Nested Attention Hybrid Model has a higher recall performance in the large changes portion compared to the Hybrid model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: WER of LSTMLM lattice rescoring algorithms'.\n" }, { "role": "assistant", "content": "Increasing the LSTM hypotheses per lattice node (k) improves WER, with 2-gram lattices showing more benefit from lattice expansion compared to 5-gram lattices.While the WER reduced by 2% relative for the 2-gram lattices, it showed no variation for the 5-gram lattices. The push-forward algorithm with k=1 results in a more severe approximation for the 2-gram lattices. Hence, increasing the LSTM hypotheses per lattice node (k) results in a better WER. We next expanded the lattice to different N-gram orders, up to a maximum of 6, prior to applying the push-forward algorithm with k=1. While 2-gram lattices benefitted considerably from lattice expansion (3% relative), there was less gain for 5-gram lattices which already contained unique 5-gram histories for most lattice nodes prior to expansion. LSTM state pooling" } ], "images": [ "scigen/train/450.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: WER of LSTMLM lattice rescoring algorithms" ], "reasoning": "Increasing the LSTM hypotheses per lattice node (k) improves WER, with 2-gram lattices showing more benefit from lattice expansion compared to 5-gram lattices.", "description": "While the WER reduced by 2% relative for the 2-gram lattices, it showed no variation for the 5-gram lattices. The push-forward algorithm with k=1 results in a more severe approximation for the 2-gram lattices. Hence, increasing the LSTM hypotheses per lattice node (k) results in a better WER. We next expanded the lattice to different N-gram orders, up to a maximum of 6, prior to applying the push-forward algorithm with k=1. While 2-gram lattices benefitted considerably from lattice expansion (3% relative), there was less gain for 5-gram lattices which already contained unique 5-gram histories for most lattice nodes prior to expansion. LSTM state pooling", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 3: WER of LSTMLM lattice rescoring algorithms', solve the following question step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the WER for the 2-gram lattice when using the push-forward algorithm with k=50?\n" }, { "role": "assistant", "content": "The table shows the WER for different values of k using the push-forward algorithm for 2-gram lattices. For k=50, the WER is 12.6%. So, the answer is {\"answer\": \"12.6\"}." } ], "images": [ "scigen/train/450.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: WER of LSTMLM lattice rescoring algorithms" ], "reasoning": "The table shows the WER for different values of k using the push-forward algorithm for 2-gram lattices. For k=50, the WER is 12.6%.", "question": "What is the WER for the 2-gram lattice when using the push-forward algorithm with k=50?", "answer": "So, the answer is {\"answer\": \"12.6\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe WER for the 5-gram lattice using the default setting is 12.5%.\n" }, { "role": "assistant", "content": "The table lists the WER for the default setting under the 5-gram lattice column as 12.5%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/450.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: WER of LSTMLM lattice rescoring algorithms" ], "reasoning": "The table lists the WER for the default setting under the 5-gram lattice column as 12.5%.", "statement": "The WER for the 5-gram lattice using the default setting is 12.5%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Fisher speaker recognition task results'.\n" }, { "role": "assistant", "content": "The self-attentive pooling (SAP) layer in the x-vector architecture results in a 13.2% relative EER reduction compared to regular statistics pooling. Training on contextual frame representations from the ASR model further improves performance over the x-vector and multitasking approaches.The results of i-vector and x-vector from the original work are first presented. To ablate the merit of the SAP layer, we train a similar x-vector architecture with self-attentive instead of regular statistics pooling to get a 13.2% relative EER reduction. Training on contextual frame representations induced from the ASR model leads to better performance than the x-vector approach and improves performance even over the multitasking approach (where the phonetic extractor is learned jointly between ASR and SR)" } ], "images": [ "scigen/train/451.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Fisher speaker recognition task results" ], "reasoning": "The self-attentive pooling (SAP) layer in the x-vector architecture results in a 13.2% relative EER reduction compared to regular statistics pooling. Training on contextual frame representations from the ASR model further improves performance over the x-vector and multitasking approaches.", "description": "The results of i-vector and x-vector from the original work are first presented. To ablate the merit of the SAP layer, we train a similar x-vector architecture with self-attentive instead of regular statistics pooling to get a 13.2% relative EER reduction. Training on contextual frame representations induced from the ASR model leads to better performance than the x-vector approach and improves performance even over the multitasking approach (where the phonetic extractor is learned jointly between ASR and SR)", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 2: Fisher speaker recognition task results'. Solve the following question based on the table.\n\nQuestion: What is the EER reduction percentage when using the SAP layer in the x-vector architecture compared to regular statistics pooling?\nLet's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The EER for x-vector with regular statistics pooling is 1.73, and the EER for x-vector with SAP is 1.50. The reduction in EER is calculated as (1.73 - 1.50) / 1.73 * 100%. So, the answer is {\"answer\": \"13.29%\"}." } ], "images": [ "scigen/train/451.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Fisher speaker recognition task results" ], "reasoning": "The EER for x-vector with regular statistics pooling is 1.73, and the EER for x-vector with SAP is 1.50. The reduction in EER is calculated as (1.73 - 1.50) / 1.73 * 100%.", "question": "What is the EER reduction percentage when using the SAP layer in the x-vector architecture compared to regular statistics pooling?", "answer": "So, the answer is {\"answer\": \"13.29%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe pretrain + CNN + SAP system has the lowest EER among all systems.\n" }, { "role": "assistant", "content": "The table shows that the EER for the pretrain + CNN + SAP system is 1.07, which is the lowest value compared to other systems listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/451.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Fisher speaker recognition task results" ], "reasoning": "The table shows that the EER for the pretrain + CNN + SAP system is 1.07, which is the lowest value compared to other systems listed.", "statement": "The pretrain + CNN + SAP system has the lowest EER among all systems.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: CER (%) on HKUST datasets compared to previous works.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The character-based model with the Transformer achieves a 13.4% relative reduction in CER compared to the deep multidimensional residual learning with 9 LSTM layers, demonstrating the superiority of the sequence-to-sequence attention-based model.We can observe that the best CER 26.64% of character based model with the Transformer on HKUST datasets achieves a 13.4% relative reduction compared to the best CER of 30.79% by the deep multidimensional residual learning with 9 LSTM layers. It shows the superiority of the sequence-to-sequence attention-based model compared to the hybrid LSTM-HMM system." } ], "images": [ "scigen/train/452.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: CER (%) on HKUST datasets compared to previous works." ], "reasoning": "The character-based model with the Transformer achieves a 13.4% relative reduction in CER compared to the deep multidimensional residual learning with 9 LSTM layers, demonstrating the superiority of the sequence-to-sequence attention-based model.", "description": "We can observe that the best CER 26.64% of character based model with the Transformer on HKUST datasets achieves a 13.4% relative reduction compared to the best CER of 30.79% by the deep multidimensional residual learning with 9 LSTM layers. It shows the superiority of the sequence-to-sequence attention-based model compared to the hybrid LSTM-HMM system.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 6: CER (%) on HKUST datasets compared to previous works.' and proceed to solve the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the relative reduction in CER achieved by the character-based model with the Transformer compared to the deep multidimensional residual learning with 9 LSTM layers?\n" }, { "role": "assistant", "content": "The character-based model with the Transformer achieves a CER of 26.64%, while the deep multidimensional residual learning with 9 LSTM layers achieves a CER of 30.79%. The relative reduction is calculated as (30.79 - 26.64) / 30.79 * 100%, which equals 13.4%. So, the answer is {\"answer\": \"13.4%\"}" } ], "images": [ "scigen/train/452.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: CER (%) on HKUST datasets compared to previous works." ], "reasoning": "The character-based model with the Transformer achieves a CER of 26.64%, while the deep multidimensional residual learning with 9 LSTM layers achieves a CER of 30.79%. The relative reduction is calculated as (30.79 - 26.64) / 30.79 * 100%, which equals 13.4%.", "question": "What is the relative reduction in CER achieved by the character-based model with the Transformer compared to the deep multidimensional residual learning with 9 LSTM layers?", "answer": "So, the answer is {\"answer\": \"13.4%\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The character-based model with the Transformer achieves a CER of 26.64%. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe character-based model with the Transformer achieves a CER of 26.64%.\n" }, { "role": "assistant", "content": "The table shows that the character-based model with the Transformer achieves a CER of 26.64%, which supports the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/452.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: CER (%) on HKUST datasets compared to previous works." ], "reasoning": "The table shows that the character-based model with the Transformer achieves a CER of 26.64%, which supports the statement.", "statement": "The character-based model with the Transformer achieves a CER of 26.64%.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Same-vs-Different classification. P, R, and F1 are calculated with respect to Same.'.\n" }, { "role": "assistant", "content": "MaxEnt-2C performs better than MaxEnt-3C in terms of F1 and accuracy, despite being skewed towards the majority class.We can see that the two-class MaxEnt-2C classifier works better than the three-class MaxEnt-3C. MaxEnt-3C has more balanced P and R, but loses in both F1 and accuracy. MaxEnt-2C is very skewed towards the majority class, but performs better due to the class imbalance. Overall, it seems very difficult to learn with the current features, and both methods only outperform the majority-class baseline by a small margin. Yet, while the overall accuracy is low, note that the graph-cut/ILP inference allows us to recover from some errors, because if nearby utterances are clustered correctly, the wrong decisions should be outvoted by correct ones." } ], "images": [ "scigen/train/453.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Same-vs-Different classification. P, R, and F1 are calculated with respect to Same." ], "reasoning": "MaxEnt-2C performs better than MaxEnt-3C in terms of F1 and accuracy, despite being skewed towards the majority class.", "description": "We can see that the two-class MaxEnt-2C classifier works better than the three-class MaxEnt-3C. MaxEnt-3C has more balanced P and R, but loses in both F1 and accuracy. MaxEnt-2C is very skewed towards the majority class, but performs better due to the class imbalance. Overall, it seems very difficult to learn with the current features, and both methods only outperform the majority-class baseline by a small margin. Yet, while the overall accuracy is low, note that the graph-cut/ILP inference allows us to recover from some errors, because if nearby utterances are clustered correctly, the wrong decisions should be outvoted by correct ones.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Same-vs-Different classification. P, R, and F1 are calculated with respect to Same.', answer the question below by showing each progressive step toward the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich classifier has the highest F1 score?\n" }, { "role": "assistant", "content": "By comparing the F1 scores of the classifiers, MaxEnt-2C has an F1 score of 81.59, which is higher than MaxEnt-3C's F1 score of 78.75. So, the answer is {\"answer\": \"MaxEnt-2C\"}" } ], "images": [ "scigen/train/453.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Same-vs-Different classification. P, R, and F1 are calculated with respect to Same." ], "reasoning": "By comparing the F1 scores of the classifiers, MaxEnt-2C has an F1 score of 81.59, which is higher than MaxEnt-3C's F1 score of 78.75.", "question": "Which classifier has the highest F1 score?", "answer": "So, the answer is {\"answer\": \"MaxEnt-2C\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nMaxEnt-3C has a higher accuracy than MaxEnt-2C.\n" }, { "role": "assistant", "content": "The table shows that MaxEnt-2C has an accuracy of 71.56, while MaxEnt-3C has an accuracy of 69.94. Therefore, MaxEnt-3C does not have a higher accuracy than MaxEnt-2C. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/453.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Same-vs-Different classification. P, R, and F1 are calculated with respect to Same." ], "reasoning": "The table shows that MaxEnt-2C has an accuracy of 71.56, while MaxEnt-3C has an accuracy of 69.94. Therefore, MaxEnt-3C does not have a higher accuracy than MaxEnt-2C.", "statement": "MaxEnt-3C has a higher accuracy than MaxEnt-2C.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Good-vs-Bad classification. ‡ and † mark statistically significant differences in accuracy compared to the baseline MaxEnt classifier with confidence levels of 99% and 95%, respectively (randomized test).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The MaxEnt classifier shows higher accuracy than two of the top systems at SemEval-2015 Task 3 and has the highest F1 score overall.On the top are the best systems at SemEval-2015 Task 3. We can see that our MaxEnt classifier is competitive: it shows higher accuracy than two of them, and the highest F1 overall." } ], "images": [ "scigen/train/454.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Good-vs-Bad classification. ‡ and † mark statistically significant differences in accuracy compared to the baseline MaxEnt classifier with confidence levels of 99% and 95%, respectively (randomized test)." ], "reasoning": "The MaxEnt classifier shows higher accuracy than two of the top systems at SemEval-2015 Task 3 and has the highest F1 score overall.", "description": "On the top are the best systems at SemEval-2015 Task 3. We can see that our MaxEnt classifier is competitive: it shows higher accuracy than two of them, and the highest F1 overall.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: Good-vs-Bad classification. ‡ and † mark statistically significant differences in accuracy compared to the baseline MaxEnt classifier with confidence levels of 99% and 95%, respectively (randomized test).'. Solve the following question based on the table.\n\nQuestion: Which system has the highest F1 score and what is its accuracy?\nLet's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The MaxEnt classifier has the highest F1 score of 79.77 and an accuracy of 78.43. So, the answer is {\"answer\": \"MaxEnt with an accuracy of 78.43\"}" } ], "images": [ "scigen/train/454.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Good-vs-Bad classification. ‡ and † mark statistically significant differences in accuracy compared to the baseline MaxEnt classifier with confidence levels of 99% and 95%, respectively (randomized test)." ], "reasoning": "The MaxEnt classifier has the highest F1 score of 79.77 and an accuracy of 78.43.", "question": "Which system has the highest F1 score and what is its accuracy?", "answer": "So, the answer is {\"answer\": \"MaxEnt with an accuracy of 78.43\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe ILP system has the highest accuracy among all systems.\n" }, { "role": "assistant", "content": "The table shows that the ILP system has an accuracy of 79.14, which is the highest among all listed systems. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/454.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Good-vs-Bad classification. ‡ and † mark statistically significant differences in accuracy compared to the baseline MaxEnt classifier with confidence levels of 99% and 95%, respectively (randomized test)." ], "reasoning": "The table shows that the ILP system has an accuracy of 79.14, which is the highest among all listed systems.", "statement": "The ILP system has the highest accuracy among all systems.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'TABLE VIII: Effects of varying the fraction of tweets used for training and testing on classification accuracy in the state-prediction task, using All Words and LDA topics.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Performance improves from 11.76% to 64.7% as the training fraction increases from 0.2 to 1.0, with a larger positive effect from increasing the training set size.Performance varies from 11.76% when using 20% of tweets in the training set and 20% in the testing set, to 64.7% when using all available tweets. Increasing the number of tweets in the training set has a larger positive effect on accuracy than increasing the number of tweets in the testing set. As in the city prediction task, performance continues to increase as we add more data, suggesting that the performance ceiling has not been reached." } ], "images": [ "scigen/train/455.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VIII: Effects of varying the fraction of tweets used for training and testing on classification accuracy in the state-prediction task, using All Words and LDA topics." ], "reasoning": "Performance improves from 11.76% to 64.7% as the training fraction increases from 0.2 to 1.0, with a larger positive effect from increasing the training set size.", "description": "Performance varies from 11.76% when using 20% of tweets in the training set and 20% in the testing set, to 64.7% when using all available tweets. Increasing the number of tweets in the training set has a larger positive effect on accuracy than increasing the number of tweets in the testing set. As in the city prediction task, performance continues to increase as we add more data, suggesting that the performance ceiling has not been reached.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'TABLE VIII: Effects of varying the fraction of tweets used for training and testing on classification accuracy in the state-prediction task, using All Words and LDA topics.', answer the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the accuracy when using 60% of tweets in the training set and 80% in the testing set?\n" }, { "role": "assistant", "content": "To find the accuracy, locate the row for a 0.6 training fraction and the column for a 0.8 testing fraction. The intersection shows an accuracy of 41.17%. So, the answer is {\"answer\": \"41.17\"}" } ], "images": [ "scigen/train/455.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VIII: Effects of varying the fraction of tweets used for training and testing on classification accuracy in the state-prediction task, using All Words and LDA topics." ], "reasoning": "To find the accuracy, locate the row for a 0.6 training fraction and the column for a 0.8 testing fraction. The intersection shows an accuracy of 41.17%.", "question": "What is the accuracy when using 60% of tweets in the training set and 80% in the testing set?", "answer": "So, the answer is {\"answer\": \"41.17\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nUsing 100% of tweets in the training set and 60% in the testing set results in an accuracy of 54.90%.\n" }, { "role": "assistant", "content": "The table shows that with a 1.0 training fraction and a 0.6 testing fraction, the accuracy is indeed 54.90%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/455.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VIII: Effects of varying the fraction of tweets used for training and testing on classification accuracy in the state-prediction task, using All Words and LDA topics." ], "reasoning": "The table shows that with a 1.0 training fraction and a 0.6 testing fraction, the accuracy is indeed 54.90%.", "statement": "Using 100% of tweets in the training set and 60% in the testing set results in an accuracy of 54.90%.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'TABLE V: Effects of varying the fraction of tweets used for training and testing on classification accuracy in the city-prediction task, using All Words and LDA topics.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Using only 20% of the training set, models achieve the baseline score of 6.67%. Performance improves with more data, indicating no performance ceiling yet.Indeed, when only 20% of the training set is used, the models achieve the same score as the baseline classifier (6.67%). Performance continues to increase as we add more data, suggesting that we have not reached a performance ceiling yet." } ], "images": [ "scigen/train/456.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Effects of varying the fraction of tweets used for training and testing on classification accuracy in the city-prediction task, using All Words and LDA topics." ], "reasoning": "Using only 20% of the training set, models achieve the baseline score of 6.67%. Performance improves with more data, indicating no performance ceiling yet.", "description": "Indeed, when only 20% of the training set is used, the models achieve the same score as the baseline classifier (6.67%). Performance continues to increase as we add more data, suggesting that we have not reached a performance ceiling yet.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'TABLE V: Effects of varying the fraction of tweets used for training and testing on classification accuracy in the city-prediction task, using All Words and LDA topics.' and methodically tackle the question that follows. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the performance score when using 60% of the training set and 100% of the testing fraction?\n" }, { "role": "assistant", "content": "From the table, locate the row for 60% training fraction and the column for 100% testing fraction. The intersection shows the performance score. So, the answer is {\"answer\": \"40\"}." } ], "images": [ "scigen/train/456.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Effects of varying the fraction of tweets used for training and testing on classification accuracy in the city-prediction task, using All Words and LDA topics." ], "reasoning": "From the table, locate the row for 60% training fraction and the column for 100% testing fraction. The intersection shows the performance score.", "question": "What is the performance score when using 60% of the training set and 100% of the testing fraction?", "answer": "So, the answer is {\"answer\": \"40\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The performance score is 33.33% when using 80% of the training set and 60% of the testing fraction., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe performance score is 33.33% when using 80% of the training set and 60% of the testing fraction.\n" }, { "role": "assistant", "content": "Check the table for the row corresponding to 80% training fraction and the column for 60% testing fraction. The value at the intersection is 33.33%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/456.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Effects of varying the fraction of tweets used for training and testing on classification accuracy in the city-prediction task, using All Words and LDA topics." ], "reasoning": "Check the table for the row corresponding to 80% training fraction and the column for 60% testing fraction. The value at the intersection is 33.33%.", "statement": "The performance score is 33.33% when using 80% of the training set and 60% of the testing fraction.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Document-level evaluation of three approaches in two scientific areas: computer science (CS) and biomedical (Bio).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The statistical approach has the highest precision but lowest yield, while the hybrid approach has the highest yield but lower precision. Off-the-shelf libraries show varying results in precision and yield across domains.In both domains, the statistical approach gives the highest precision and the lowest yield. The hybrid approach consistently gives the highest yield, but sacrifices precision. The TagMe off-the-shelf library used for the CS domain gives surprisingly good results, with precision within 1 point from the statistical models. However, the MetaMap Lite off-the-shelf library we used for the biomedical domain suffered a huge loss in precision. Our error analysis showed that each of the approaches is able to predict entities not predicted by the other approaches so we decided to pool their outputs in our deployed system, which gives significantly higher yield than any individual approach while maintaining reasonably high precision." } ], "images": [ "scigen/train/457.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Document-level evaluation of three approaches in two scientific areas: computer science (CS) and biomedical (Bio)." ], "reasoning": "The statistical approach has the highest precision but lowest yield, while the hybrid approach has the highest yield but lower precision. Off-the-shelf libraries show varying results in precision and yield across domains.", "description": "In both domains, the statistical approach gives the highest precision and the lowest yield. The hybrid approach consistently gives the highest yield, but sacrifices precision. The TagMe off-the-shelf library used for the CS domain gives surprisingly good results, with precision within 1 point from the statistical models. However, the MetaMap Lite off-the-shelf library we used for the biomedical domain suffered a huge loss in precision. Our error analysis showed that each of the approaches is able to predict entities not predicted by the other approaches so we decided to pool their outputs in our deployed system, which gives significantly higher yield than any individual approach while maintaining reasonably high precision.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: Document-level evaluation of three approaches in two scientific areas: computer science (CS) and biomedical (Bio).', and tackle the question below in a sequential manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich approach has the highest yield in the biomedical domain and what is its precision?\n" }, { "role": "assistant", "content": "The table shows that the hybrid approach has the highest yield in the biomedical domain with a yield of 3126. Its precision is 92.1. So, the answer is {\"answer\": \"92.1\"}." } ], "images": [ "scigen/train/457.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Document-level evaluation of three approaches in two scientific areas: computer science (CS) and biomedical (Bio)." ], "reasoning": "The table shows that the hybrid approach has the highest yield in the biomedical domain with a yield of 3126. Its precision is 92.1.", "question": "Which approach has the highest yield in the biomedical domain and what is its precision?", "answer": "So, the answer is {\"answer\": \"92.1\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe off-the-shelf approach has the highest precision in the CS domain.\n" }, { "role": "assistant", "content": "The table shows that the statistical approach has the highest precision in the CS domain with a precision of 98.4, not the off-the-shelf approach. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/457.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Document-level evaluation of three approaches in two scientific areas: computer science (CS) and biomedical (Bio)." ], "reasoning": "The table shows that the statistical approach has the highest precision in the CS domain with a precision of 98.4, not the off-the-shelf approach.", "statement": "The off-the-shelf approach has the highest precision in the CS domain.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: Results of the entity extraction model on the development set of SemEval-2017 task 10.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Including LM embeddings improves the F1 score by 4.2 points, and an ensemble of 15 models with LM embeddings further improves it by 1.1 points.We use the standard data splits of the SemEval-2017 Task 10 on entity (and relation) extraction from scientific papers Augenstein et al. The first line omits the LM embeddings lmk, while the second line is the full model (including LM embeddings) showing a large improvement of 4.2 F1 points. The third line shows that creating an ensemble of 15 models further improves the results by 1.1 F1 points." } ], "images": [ "scigen/train/458.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of the entity extraction model on the development set of SemEval-2017 task 10." ], "reasoning": "Including LM embeddings improves the F1 score by 4.2 points, and an ensemble of 15 models with LM embeddings further improves it by 1.1 points.", "description": "We use the standard data splits of the SemEval-2017 Task 10 on entity (and relation) extraction from scientific papers Augenstein et al. The first line omits the LM embeddings lmk, while the second line is the full model (including LM embeddings) showing a large improvement of 4.2 F1 points. The third line shows that creating an ensemble of 15 models further improves the results by 1.1 F1 points.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: Table 3: Results of the entity extraction model on the development set of SemEval-2017 task 10.\nQuestion: How much does the F1 score improve when using an ensemble of 15 models with LM embeddings compared to without LM embeddings?\n" }, { "role": "assistant", "content": "The F1 score without LM embeddings is 49.9. Using an ensemble of 15 models with LM embeddings, the F1 score is 55.2. The improvement is 55.2 - 49.9. So, the answer is {\"answer\": \"5.3\"}" } ], "images": [ "scigen/train/458.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of the entity extraction model on the development set of SemEval-2017 task 10." ], "reasoning": "The F1 score without LM embeddings is 49.9. Using an ensemble of 15 models with LM embeddings, the F1 score is 55.2. The improvement is 55.2 - 49.9.", "question": "How much does the F1 score improve when using an ensemble of 15 models with LM embeddings compared to without LM embeddings?", "answer": "So, the answer is {\"answer\": \"5.3\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nUsing LM embeddings alone improves the F1 score by more than 4 points.\n" }, { "role": "assistant", "content": "The F1 score without LM embeddings is 49.9, and with LM embeddings, it is 54.1. The improvement is 54.1 - 49.9, which is 4.2 points. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/458.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of the entity extraction model on the development set of SemEval-2017 task 10." ], "reasoning": "The F1 score without LM embeddings is 49.9, and with LM embeddings, it is 54.1. The improvement is 54.1 - 49.9, which is 4.2 points.", "statement": "Using LM embeddings alone improves the F1 score by more than 4 points.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: The Bag of Concepts F1 score of the baseline and neural model on the two curated datasets.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "An index is built to map tokens to entity IDs and their frequencies, which is used to find candidate entities for mentions during training and testing.Candidate selection. In a preprocessing step, we build an index which maps any token used in a labeled mention or an entity name in the KB to associated entity IDs, along with the frequency this token is associated with that entity. This is similar to the index used in previous entity linking systems At train and test time, we use this index to find candidate entities for a given mention by looking up the tokens in the mention." } ], "images": [ "scigen/train/459.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The Bag of Concepts F1 score of the baseline and neural model on the two curated datasets." ], "reasoning": "An index is built to map tokens to entity IDs and their frequencies, which is used to find candidate entities for mentions during training and testing.", "description": "Candidate selection. In a preprocessing step, we build an index which maps any token used in a labeled mention or an entity name in the KB to associated entity IDs, along with the frequency this token is associated with that entity. This is similar to the index used in previous entity linking systems At train and test time, we use this index to find candidate entities for a given mention by looking up the tokens in the mention.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nTable title: Table 4: The Bag of Concepts F1 score of the baseline and neural model on the two curated datasets.\n\nQuestion: Which model performed better in the Bio category?\n" }, { "role": "assistant", "content": "To determine which model performed better in the Bio category, compare the Bio scores of the Baseline and Neural models. The Baseline model scored 54.2, while the Neural model scored 85.8. So, the answer is {\"answer\": \"Neural\"}." } ], "images": [ "scigen/train/459.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The Bag of Concepts F1 score of the baseline and neural model on the two curated datasets." ], "reasoning": "To determine which model performed better in the Bio category, compare the Bio scores of the Baseline and Neural models. The Baseline model scored 54.2, while the Neural model scored 85.8.", "question": "Which model performed better in the Bio category?", "answer": "So, the answer is {\"answer\": \"Neural\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Baseline model performed better in the CS category.\n" }, { "role": "assistant", "content": "To verify this statement, compare the CS scores of the Baseline and Neural models. The Baseline model scored 84.2, while the Neural model scored 84.6. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/459.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The Bag of Concepts F1 score of the baseline and neural model on the two curated datasets." ], "reasoning": "To verify this statement, compare the CS scores of the Baseline and Neural models. The Baseline model scored 84.2, while the Neural model scored 84.6.", "statement": "The Baseline model performed better in the CS category.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Precision, Recall, and F1 on Chinese GALE test data. BPE indicates “with BPE”, and NER denotes restriction to NER spans.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Our model, DiscAlign, achieves the highest F1 scores across all experimental conditions, outperforming FastAlign and average attention methods.Our model outperforms both baselines for all languages and experimental conditions. By increasing the threshold α above which p(ai,j|s,t) is considered an alignment we can obtain high-precision alignments, exceeding FastAlign’s precision and recall. The best threshold values on the development set were α=0.13 and α=0.14 for average attention on data with and without BPE (respectively) and α=0.15 for the discriminative settings (α=0.5 was used for the high-precision “+prec” setting)." } ], "images": [ "scigen/train/460.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Precision, Recall, and F1 on Chinese GALE test data. BPE indicates “with BPE”, and NER denotes restriction to NER spans." ], "reasoning": "Our model, DiscAlign, achieves the highest F1 scores across all experimental conditions, outperforming FastAlign and average attention methods.", "description": "Our model outperforms both baselines for all languages and experimental conditions. By increasing the threshold α above which p(ai,j|s,t) is considered an alignment we can obtain high-precision alignments, exceeding FastAlign’s precision and recall. The best threshold values on the development set were α=0.13 and α=0.14 for average attention on data with and without BPE (respectively) and α=0.15 for the discriminative settings (α=0.5 was used for the high-precision “+prec” setting).", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 2: Precision, Recall, and F1 on Chinese GALE test data. BPE indicates “with BPE”, and NER denotes restriction to NER spans.\nQuestion: Which method achieved the highest F1 score in the experiments?\n" }, { "role": "assistant", "content": "By examining the F1 scores in the table, DiscAlign (NER) achieved the highest F1 score of 75.78. So, the answer is {\"answer\": \"75.78\"}." } ], "images": [ "scigen/train/460.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Precision, Recall, and F1 on Chinese GALE test data. BPE indicates “with BPE”, and NER denotes restriction to NER spans." ], "reasoning": "By examining the F1 scores in the table, DiscAlign (NER) achieved the highest F1 score of 75.78.", "question": "Which method achieved the highest F1 score in the experiments?", "answer": "So, the answer is {\"answer\": \"75.78\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nFastAlign (NER) achieved the highest precision among all methods.\n" }, { "role": "assistant", "content": "The table shows that FastAlign (NER) has a precision of 83.70, but DiscAlign (NER) +prec. has a higher precision of 84.69. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/460.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Precision, Recall, and F1 on Chinese GALE test data. BPE indicates “with BPE”, and NER denotes restriction to NER spans." ], "reasoning": "The table shows that FastAlign (NER) has a precision of 83.70, but DiscAlign (NER) +prec. has a higher precision of 84.69.", "statement": "FastAlign (NER) achieved the highest precision among all methods.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Precision, Recall, and F1 on Arabic GALE test data.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The discriminative aligner shows high precision and F1 scores, especially compared to average attention methods, which perform poorly in Arabic.Furthermore, we see that average attention performs abysmally in Arabic. The best thresholds for Arabic differed substantially from those for Chinese: for average attention (with and without BPE) the values were α=0.05 and α=0.1, while for the discriminative aligner they were α=0.94 and α=0.99 (compared to 0.15 for Chinese). This is reflected in the high precision of the discriminative aligner." } ], "images": [ "scigen/train/461.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Precision, Recall, and F1 on Arabic GALE test data." ], "reasoning": "The discriminative aligner shows high precision and F1 scores, especially compared to average attention methods, which perform poorly in Arabic.", "description": "Furthermore, we see that average attention performs abysmally in Arabic. The best thresholds for Arabic differed substantially from those for Chinese: for average attention (with and without BPE) the values were α=0.05 and α=0.1, while for the discriminative aligner they were α=0.94 and α=0.99 (compared to 0.15 for Chinese). This is reflected in the high precision of the discriminative aligner.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Precision, Recall, and F1 on Arabic GALE test data.\nQuestion: What is the F1 score for the discriminative aligner without BPE?\nGive a detailed response to the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the F1 score for various methods. The discriminative aligner without BPE has an F1 score of 82.74. So, the answer is {\"answer\": \"82.74\"}." } ], "images": [ "scigen/train/461.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Precision, Recall, and F1 on Arabic GALE test data." ], "reasoning": "The table shows the F1 score for various methods. The discriminative aligner without BPE has an F1 score of 82.74.", "question": "What is the F1 score for the discriminative aligner without BPE?", "answer": "So, the answer is {\"answer\": \"82.74\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The average attention method without BPE has a higher precision than the average attention method with BPE. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe average attention method without BPE has a higher precision than the average attention method with BPE.\n" }, { "role": "assistant", "content": "The table shows that the precision (P) for average attention without BPE is 8.46, while with BPE it is 10.11. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/461.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Precision, Recall, and F1 on Arabic GALE test data." ], "reasoning": "The table shows that the precision (P) for average attention without BPE is 8.46, while with BPE it is 10.11. Therefore, the statement is refuted.", "statement": "The average attention method without BPE has a higher precision than the average attention method with BPE.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: F1 results on OntoNotes test for systems trained on data projected via FastAlign and DiscAlign.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Discriminative alignments outperform unsupervised baselines in different languages, leading to significant NER improvements with minimal labeled data from L2 speakers.Based on our findings that: [topsep=0.0pt,itemsep=-1ex,partopsep=1ex,parsep=1ex] discriminative alignments outperform common unsupervised baselines in two typologically divergent languages this performance boost leads to major downstream improvements on NER only a small amount of labelled data is needed to realize these improvements and that these labelled examples can be obtained from L2 speakers with minimal training we conclude with a call for additional annotation efforts in a wider variety of languages. While multilingual datasets directly annotated for a given task typically lead to the highest-performing systems (see these datasets are task-specific and not robust to ontology changes. In contrast, the framework of projection via discriminatively-trained alignments which we present is task-agnostic (any token-level annotations can be projected) and requires only one source-side dataset to be annotated." } ], "images": [ "scigen/train/462.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: F1 results on OntoNotes test for systems trained on data projected via FastAlign and DiscAlign." ], "reasoning": "Discriminative alignments outperform unsupervised baselines in different languages, leading to significant NER improvements with minimal labeled data from L2 speakers.", "description": "Based on our findings that: [topsep=0.0pt,itemsep=-1ex,partopsep=1ex,parsep=1ex] discriminative alignments outperform common unsupervised baselines in two typologically divergent languages this performance boost leads to major downstream improvements on NER only a small amount of labelled data is needed to realize these improvements and that these labelled examples can be obtained from L2 speakers with minimal training we conclude with a call for additional annotation efforts in a wider variety of languages. While multilingual datasets directly annotated for a given task typically lead to the highest-performing systems (see these datasets are task-specific and not robust to ontology changes. In contrast, the framework of projection via discriminatively-trained alignments which we present is task-agnostic (any token-level annotations can be projected) and requires only one source-side dataset to be annotated.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 4: F1 results on OntoNotes test for systems trained on data projected via FastAlign and DiscAlign.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich method achieved the highest F1 score with 36K training examples?\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all methods with 36K training examples, Zh Gold has the highest F1 score of 77.81. So, the answer is {\"answer\": \"Zh Gold\"}." } ], "images": [ "scigen/train/462.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: F1 results on OntoNotes test for systems trained on data projected via FastAlign and DiscAlign." ], "reasoning": "By comparing the F1 scores of all methods with 36K training examples, Zh Gold has the highest F1 score of 77.81.", "question": "Which method achieved the highest F1 score with 36K training examples?", "answer": "So, the answer is {\"answer\": \"Zh Gold\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nFastAlign with 53K training examples has a higher precision than DiscAlign with 36K training examples.\n" }, { "role": "assistant", "content": "FastAlign with 53K training examples has a precision of 39.46, while DiscAlign with 36K training examples has a precision of 51.94. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/462.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: F1 results on OntoNotes test for systems trained on data projected via FastAlign and DiscAlign." ], "reasoning": "FastAlign with 53K training examples has a precision of 39.46, while DiscAlign with 36K training examples has a precision of 51.94. Therefore, the statement is refuted.", "statement": "FastAlign with 53K training examples has a higher precision than DiscAlign with 36K training examples.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 7: Sentences per minute and average scores against gold-labelled data for sentences annotated for alignment by human annotators (Hu.), compared to DiscAlign (DA) on the same sentences.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Human annotators have high precision but low recall for alignment, and their F1 score improves significantly for NER spans, outperforming DiscAlign.The annotators achieve high overall precision for alignment, but fall short on recall. When only alignments of NER spans are considered, their F1 score improves considerably. Additionally, human annotators outperform DiscAlign when evaluated on the same sentences." } ], "images": [ "scigen/train/463.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Sentences per minute and average scores against gold-labelled data for sentences annotated for alignment by human annotators (Hu.), compared to DiscAlign (DA) on the same sentences." ], "reasoning": "Human annotators have high precision but low recall for alignment, and their F1 score improves significantly for NER spans, outperforming DiscAlign.", "description": "The annotators achieve high overall precision for alignment, but fall short on recall. When only alignments of NER spans are considered, their F1 score improves considerably. Additionally, human annotators outperform DiscAlign when evaluated on the same sentences.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the F1 score for human annotators when only alignments of NER spans are considered?\nSolve the above question based on the table titled 'Table 7: Sentences per minute and average scores against gold-labelled data for sentences annotated for alignment by human annotators (Hu.), compared to DiscAlign (DA) on the same sentences.'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the F1 score for human annotators (Hu.) when only alignments of NER spans are considered is 78.24. So, the answer is {\"answer\": \"78.24\"}" } ], "images": [ "scigen/train/463.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Sentences per minute and average scores against gold-labelled data for sentences annotated for alignment by human annotators (Hu.), compared to DiscAlign (DA) on the same sentences." ], "reasoning": "The table shows that the F1 score for human annotators (Hu.) when only alignments of NER spans are considered is 78.24.", "question": "What is the F1 score for human annotators when only alignments of NER spans are considered?", "answer": "So, the answer is {\"answer\": \"78.24\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nDiscAlign has a higher precision than human annotators for NER spans.\n" }, { "role": "assistant", "content": "The table shows that the precision (P) for DiscAlign (DA) for NER spans is 77.37, which is lower than the precision for human annotators (Hu.) for NER spans, which is 87.73. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/463.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Sentences per minute and average scores against gold-labelled data for sentences annotated for alignment by human annotators (Hu.), compared to DiscAlign (DA) on the same sentences." ], "reasoning": "The table shows that the precision (P) for DiscAlign (DA) for NER spans is 77.37, which is lower than the precision for human annotators (Hu.) for NER spans, which is 87.73.", "statement": "DiscAlign has a higher precision than human annotators for NER spans.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 11: Effects of applying the coverage regularizer and the reference beam search to structural models, evaluated on test-1951. Combining both yields the highest scores.'.\n" }, { "role": "assistant", "content": "Using a larger output vocabulary size can improve system performance, as shown by the increase in R-2 F1-score from 17.25 to 17.62 when moving from a 5K to a 10K vocabulary, despite a longer training time.Coverage and reference beam. The coverage regularizer is applied in a second training stage, where the system is trained for an extra 5 epochs with coverage and the model yielding the lowest validation loss is selected. Both coverage and ref_beam can improve the system performance. Our observation suggests that ref_beam is an effective addition to shorten the gap between different systems. Output vocabulary size. All our models by default use an output vocabulary of 5K words in order to make the results comparable to state-of-the-art-systems. However, we observe that there is a potential to further boost the system performance (17.25→17.62 R-2 F1-score, w/o coverage or ref_beam) if we had chosen to use a larger vocabulary (10K) and can endure a slightly longer training time (1.2x). The gap between the two conditions shortens as the size of the output vocabulary is increased." } ], "images": [ "scigen/train/464.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Effects of applying the coverage regularizer and the reference beam search to structural models, evaluated on test-1951. Combining both yields the highest scores." ], "reasoning": "Using a larger output vocabulary size can improve system performance, as shown by the increase in R-2 F1-score from 17.25 to 17.62 when moving from a 5K to a 10K vocabulary, despite a longer training time.", "description": "Coverage and reference beam. The coverage regularizer is applied in a second training stage, where the system is trained for an extra 5 epochs with coverage and the model yielding the lowest validation loss is selected. Both coverage and ref_beam can improve the system performance. Our observation suggests that ref_beam is an effective addition to shorten the gap between different systems. Output vocabulary size. All our models by default use an output vocabulary of 5K words in order to make the results comparable to state-of-the-art-systems. However, we observe that there is a potential to further boost the system performance (17.25→17.62 R-2 F1-score, w/o coverage or ref_beam) if we had chosen to use a larger vocabulary (10K) and can endure a slightly longer training time (1.2x). The gap between the two conditions shortens as the size of the output vocabulary is increased.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 11: Effects of applying the coverage regularizer and the reference beam search to structural models, evaluated on test-1951. Combining both yields the highest scores.\nQuestion: What is the R-2 F1-score for a model with a 2K output vocabulary size and how does it compare to a 1K vocabulary size?\nSolve the above question based on the table information. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the R-2 F1-score for a 2K vocabulary size is 15.35, which is higher than the 13.99 score for a 1K vocabulary size. So, the answer is {\"answer\": \"15.35\"}." } ], "images": [ "scigen/train/464.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Effects of applying the coverage regularizer and the reference beam search to structural models, evaluated on test-1951. Combining both yields the highest scores." ], "reasoning": "The table shows that the R-2 F1-score for a 2K vocabulary size is 15.35, which is higher than the 13.99 score for a 1K vocabulary size.", "question": "What is the R-2 F1-score for a model with a 2K output vocabulary size and how does it compare to a 1K vocabulary size?", "answer": "So, the answer is {\"answer\": \"15.35\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nA model with a 10K output vocabulary size has a training speed of 3.8h/epoch.\n" }, { "role": "assistant", "content": "The table indicates that the training speed for a model with a 10K output vocabulary size is indeed 3.8h/epoch. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/464.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Effects of applying the coverage regularizer and the reference beam search to structural models, evaluated on test-1951. Combining both yields the highest scores." ], "reasoning": "The table indicates that the training speed for a model with a 10K output vocabulary size is indeed 3.8h/epoch.", "statement": "A model with a 10K output vocabulary size has a training speed of 3.8h/epoch.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 9: Informativeness, fluency, and faithfulness scores of summaries. They are rated by Amazon turkers on a Likert scale of 1 (worst) to 5 (best). We choose to evaluate Struct+2Way+Relation (as oppose to 2Way+Word) because it focuses on preserving source relations in the summaries.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Struct+2Way+Relation outperforms Struct+Input on all criteria and compares favorably to ground-truth summaries on fluency and faithfulness.Linguistic quality. fluency (is the summary grammatical and well-formed?), informativeness (to what extent is the meaning of the original sentence preserved in the summary?), and faithfulness (is the summary accurate and faithful to the original?). We found that “Struct+2Way+Relation” outperforms “Struct+Input” on all three criteria. It also compares favorably to ground-truth summaries on “fluency” and “faithfulness.” On the other hand, the ground-truth summaries, corresponding to article titles, are judged as less satisfying according to human raters." } ], "images": [ "scigen/train/465.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Informativeness, fluency, and faithfulness scores of summaries. They are rated by Amazon turkers on a Likert scale of 1 (worst) to 5 (best). We choose to evaluate Struct+2Way+Relation (as oppose to 2Way+Word) because it focuses on preserving source relations in the summaries." ], "reasoning": "Struct+2Way+Relation outperforms Struct+Input on all criteria and compares favorably to ground-truth summaries on fluency and faithfulness.", "description": "Linguistic quality. fluency (is the summary grammatical and well-formed?), informativeness (to what extent is the meaning of the original sentence preserved in the summary?), and faithfulness (is the summary accurate and faithful to the original?). We found that “Struct+2Way+Relation” outperforms “Struct+Input” on all three criteria. It also compares favorably to ground-truth summaries on “fluency” and “faithfulness.” On the other hand, the ground-truth summaries, corresponding to article titles, are judged as less satisfying according to human raters.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 9: Informativeness, fluency, and faithfulness scores of summaries. They are rated by Amazon turkers on a Likert scale of 1 (worst) to 5 (best). We choose to evaluate Struct+2Way+Relation (as oppose to 2Way+Word) because it focuses on preserving source relations in the summaries.', solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich system has the highest score for informativeness?\n" }, { "role": "assistant", "content": "By comparing the informativeness scores of all systems, Ground-truth Summ. has the highest score of 3.2. So, the answer is {\"answer\": \"Ground-truth Summ.\"}." } ], "images": [ "scigen/train/465.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Informativeness, fluency, and faithfulness scores of summaries. They are rated by Amazon turkers on a Likert scale of 1 (worst) to 5 (best). We choose to evaluate Struct+2Way+Relation (as oppose to 2Way+Word) because it focuses on preserving source relations in the summaries." ], "reasoning": "By comparing the informativeness scores of all systems, Ground-truth Summ. has the highest score of 3.2.", "question": "Which system has the highest score for informativeness?", "answer": "So, the answer is {\"answer\": \"Ground-truth Summ.\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Struct+2Way+Relation has a higher fluency score than Struct+Input. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nStruct+2Way+Relation has a higher fluency score than Struct+Input.\n" }, { "role": "assistant", "content": "The table shows that Struct+2Way+Relation has a fluency score of 3.4, which is higher than Struct+Input's score of 3.3. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/465.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Informativeness, fluency, and faithfulness scores of summaries. They are rated by Amazon turkers on a Likert scale of 1 (worst) to 5 (best). We choose to evaluate Struct+2Way+Relation (as oppose to 2Way+Word) because it focuses on preserving source relations in the summaries." ], "reasoning": "The table shows that Struct+2Way+Relation has a fluency score of 3.4, which is higher than Struct+Input's score of 3.3.", "statement": "Struct+2Way+Relation has a higher fluency score than Struct+Input.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 10: Percentages of source dependency relations (of various types) preserved in the system summaries.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Models with structure-infused copy mechanisms, such as 'Struct+2Way+Word', are more effective at preserving important dependency relations like nsubj, dobj, amod, nmod, and nmod:poss in summaries.Dependency relations. A source relation is considered preserved if both its words appear in the summary. We observe that the models implementing structure-infused copy mechanisms (e.g., “Struct+2Way+Word”) are more likely to preserve important dependency relations in the summaries, including nsubj, dobj, amod, nmod, and nmod:poss. Dependency relations that are less important (mark, case, conj, cc, det) are less likely to be preserved. These results show that our structure-infused copy mechanisms can learn to recognize the importance of dependency relations and selectively preserve them in the summaries." } ], "images": [ "scigen/train/466.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Percentages of source dependency relations (of various types) preserved in the system summaries." ], "reasoning": "Models with structure-infused copy mechanisms, such as 'Struct+2Way+Word', are more effective at preserving important dependency relations like nsubj, dobj, amod, nmod, and nmod:poss in summaries.", "description": "Dependency relations. A source relation is considered preserved if both its words appear in the summary. We observe that the models implementing structure-infused copy mechanisms (e.g., “Struct+2Way+Word”) are more likely to preserve important dependency relations in the summaries, including nsubj, dobj, amod, nmod, and nmod:poss. Dependency relations that are less important (mark, case, conj, cc, det) are less likely to be preserved. These results show that our structure-infused copy mechanisms can learn to recognize the importance of dependency relations and selectively preserve them in the summaries.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 10: Percentages of source dependency relations (of various types) preserved in the system summaries.\n\nQuestion: Which model has the highest preservation rate for the 'nmod:poss' dependency relation?\n" }, { "role": "assistant", "content": "By comparing the values in the 'nmod:poss' column, the 'Struct+Hidden' model has the highest preservation rate at 14.86. So, the answer is {\"answer\": \"Struct+Hidden\"}." } ], "images": [ "scigen/train/466.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Percentages of source dependency relations (of various types) preserved in the system summaries." ], "reasoning": "By comparing the values in the 'nmod:poss' column, the 'Struct+Hidden' model has the highest preservation rate at 14.86.", "question": "Which model has the highest preservation rate for the 'nmod:poss' dependency relation?", "answer": "So, the answer is {\"answer\": \"Struct+Hidden\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe 'Struct+2Way+Word' model has a higher preservation rate for 'amod' than the 'Baseline' model.\n" }, { "role": "assistant", "content": "The table shows that the 'Struct+2Way+Word' model has a preservation rate of 20.59 for 'amod', which is higher than the 'Baseline' model's rate of 20.45. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/466.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Percentages of source dependency relations (of various types) preserved in the system summaries." ], "reasoning": "The table shows that the 'Struct+2Way+Word' model has a preservation rate of 20.59 for 'amod', which is higher than the 'Baseline' model's rate of 20.45.", "statement": "The 'Struct+2Way+Word' model has a higher preservation rate for 'amod' than the 'Baseline' model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Automatic evaluation results of all the models on Ubuntu and modified dailydialog test dataset (The table above is the results of ubuntu dataset, and the below is dailydialog). Baseline HRED cannot control the timing of talking and the accuracy and macro-F1 of it is empty. All the best results are shown in bold.'.\n" }, { "role": "assistant", "content": "The proposed gated neural network outperforms simple graph neural networks like GCN and GAT in decision performance, and although it has lower BLEU scores compared to HRED-CF, it shows better diversity in generation.For all the models, we adopt the early stopping to avoid the overfitting. Generation Quality: The strong baseline HRED-CF which explicity models the timing of talking is much better than HRED on almost all the metrics such as PPL and BLEU. Especially in terms of BLEU, HRED-CF exceeds HRED by up to 1.5% on Embedding Average points. The experimental results demonstrate that controlling the timing of talking can significantly improve the quality the dialogue generation. The other two modified datasets shows the same conclusion which can be found in appendix. Compared with the simple graph neural networks such as GCN and GAT (third and forth rows), our proposed gated neural network is much better. It indicates that the simply appling the graph neural networks is not enough and the gated mechanism is very important in modeling timing of talking. Compared with the strong baseline HRED-CF, the response quality of our proposed graph neural networks is weak. We attribute this phenomenon to the negative transfer problem Liu et al. We leave this problem to the future research. But the quality of responses is still much better than HRED. It can be shown that although the BLEU scores, PPL and embedding average scores of GNN-based models are lower than HRED-CF, the diversity of the generation is better than strong baseline HRED-CF (distinct-1 and distinc-2). In order to test the effectiveness of the gated mechanism, we also replace the first gated mechanism with the self-attention attention mechanism and propose another strong baseline W2T-GGAT (fifth row). Except for PPL and accuracy, W2T-GGAT ’s performance is far worse than our proposed double-gated graph neural network. Decision Performance: It can be shown that our proposed graph neural network models significantly outperforms the strong baseline HRED-CF by up to 5.77% Accuracy and 2.07% macro-F1. Furthermore, compared with the graph neural networks without gated mechanism, it can be found that out proposed model can achieve better performance on accuracy and macro-F1. It demonstrates that the gated mechanism is very important in modeling timing of talking." } ], "images": [ "scigen/train/467.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Automatic evaluation results of all the models on Ubuntu and modified dailydialog test dataset (The table above is the results of ubuntu dataset, and the below is dailydialog). Baseline HRED cannot control the timing of talking and the accuracy and macro-F1 of it is empty. All the best results are shown in bold." ], "reasoning": "The proposed gated neural network outperforms simple graph neural networks like GCN and GAT in decision performance, and although it has lower BLEU scores compared to HRED-CF, it shows better diversity in generation.", "description": "For all the models, we adopt the early stopping to avoid the overfitting. Generation Quality: The strong baseline HRED-CF which explicity models the timing of talking is much better than HRED on almost all the metrics such as PPL and BLEU. Especially in terms of BLEU, HRED-CF exceeds HRED by up to 1.5% on Embedding Average points. The experimental results demonstrate that controlling the timing of talking can significantly improve the quality the dialogue generation. The other two modified datasets shows the same conclusion which can be found in appendix. Compared with the simple graph neural networks such as GCN and GAT (third and forth rows), our proposed gated neural network is much better. It indicates that the simply appling the graph neural networks is not enough and the gated mechanism is very important in modeling timing of talking. Compared with the strong baseline HRED-CF, the response quality of our proposed graph neural networks is weak. We attribute this phenomenon to the negative transfer problem Liu et al. We leave this problem to the future research. But the quality of responses is still much better than HRED. It can be shown that although the BLEU scores, PPL and embedding average scores of GNN-based models are lower than HRED-CF, the diversity of the generation is better than strong baseline HRED-CF (distinct-1 and distinc-2). In order to test the effectiveness of the gated mechanism, we also replace the first gated mechanism with the self-attention attention mechanism and propose another strong baseline W2T-GGAT (fifth row). Except for PPL and accuracy, W2T-GGAT ’s performance is far worse than our proposed double-gated graph neural network. Decision Performance: It can be shown that our proposed graph neural network models significantly outperforms the strong baseline HRED-CF by up to 5.77% Accuracy and 2.07% macro-F1. Furthermore, compared with the graph neural networks without gated mechanism, it can be found that out proposed model can achieve better performance on accuracy and macro-F1. It demonstrates that the gated mechanism is very important in modeling timing of talking.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Automatic evaluation results of all the models on Ubuntu and modified dailydialog test dataset (The table above is the results of ubuntu dataset, and the below is dailydialog). Baseline HRED cannot control the timing of talking and the accuracy and macro-F1 of it is empty. All the best results are shown in bold.\nQuestion: Which model has the highest accuracy and how does it compare to HRED-CF in terms of BLEU2?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The W2T-DGGNN model has the highest accuracy at 0.7192. Comparing BLEU2 scores, W2T-DGGNN has a BLEU2 score of 0.0548, while HRED-CF has a BLEU2 score of 0.0605. So, the answer is {\"answer\": \"W2T-DGGNN\"}" } ], "images": [ "scigen/train/467.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Automatic evaluation results of all the models on Ubuntu and modified dailydialog test dataset (The table above is the results of ubuntu dataset, and the below is dailydialog). Baseline HRED cannot control the timing of talking and the accuracy and macro-F1 of it is empty. All the best results are shown in bold." ], "reasoning": "The W2T-DGGNN model has the highest accuracy at 0.7192. Comparing BLEU2 scores, W2T-DGGNN has a BLEU2 score of 0.0548, while HRED-CF has a BLEU2 score of 0.0605.", "question": "Which model has the highest accuracy and how does it compare to HRED-CF in terms of BLEU2?", "answer": "So, the answer is {\"answer\": \"W2T-DGGNN\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe W2T-GGAT model has a higher BERTscore than the HRED-CF model.\n" }, { "role": "assistant", "content": "The table shows that the W2T-GGAT model has a BERTscore of 0.8344, while the HRED-CF model has a BERTscore of 0.8343. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/467.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Automatic evaluation results of all the models on Ubuntu and modified dailydialog test dataset (The table above is the results of ubuntu dataset, and the below is dailydialog). Baseline HRED cannot control the timing of talking and the accuracy and macro-F1 of it is empty. All the best results are shown in bold." ], "reasoning": "The table shows that the W2T-GGAT model has a BERTscore of 0.8344, while the HRED-CF model has a BERTscore of 0.8343.", "statement": "The W2T-GGAT model has a higher BERTscore than the HRED-CF model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: Automatic evaluation results of all the models on Ubuntu and modified dailydialog test dataset (The table above is the results of ubuntu dataset, and the below is dailydialog). Baseline HRED cannot control the timing of talking and the accuracy and macro-F1 of it is empty. All the best results are shown in bold.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The proposed gated neural network outperforms simple graph neural networks like GCN and GAT in decision performance, and although it has lower BLEU scores compared to HRED-CF, it shows better diversity in generation.For all the models, we adopt the early stopping to avoid the overfitting. Generation Quality: The strong baseline HRED-CF which explicity models the timing of talking is much better than HRED on almost all the metrics such as PPL and BLEU. Especially in terms of BLEU, HRED-CF exceeds HRED by up to 1.5% on Embedding Average points. The experimental results demonstrate that controlling the timing of talking can significantly improve the quality the dialogue generation. The other two modified datasets shows the same conclusion which can be found in appendix. Compared with the simple graph neural networks such as GCN and GAT (third and forth rows), our proposed gated neural network is much better. It indicates that the simply appling the graph neural networks is not enough and the gated mechanism is very important in modeling timing of talking. Compared with the strong baseline HRED-CF, the response quality of our proposed graph neural networks is weak. We attribute this phenomenon to the negative transfer problem Liu et al. We leave this problem to the future research. But the quality of responses is still much better than HRED. It can be shown that although the BLEU scores, PPL and embedding average scores of GNN-based models are lower than HRED-CF, the diversity of the generation is better than strong baseline HRED-CF (distinct-1 and distinc-2). In order to test the effectiveness of the gated mechanism, we also replace the first gated mechanism with the self-attention attention mechanism and propose another strong baseline W2T-GGAT (fifth row). Except for PPL and accuracy, W2T-GGAT ’s performance is far worse than our proposed double-gated graph neural network. Decision Performance: It can be shown that our proposed graph neural network models significantly outperforms the strong baseline HRED-CF by up to 5.77% Accuracy and 2.07% macro-F1. Furthermore, compared with the graph neural networks without gated mechanism, it can be found that out proposed model can achieve better performance on accuracy and macro-F1. It demonstrates that the gated mechanism is very important in modeling timing of talking." } ], "images": [ "scigen/train/468.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Automatic evaluation results of all the models on Ubuntu and modified dailydialog test dataset (The table above is the results of ubuntu dataset, and the below is dailydialog). Baseline HRED cannot control the timing of talking and the accuracy and macro-F1 of it is empty. All the best results are shown in bold." ], "reasoning": "The proposed gated neural network outperforms simple graph neural networks like GCN and GAT in decision performance, and although it has lower BLEU scores compared to HRED-CF, it shows better diversity in generation.", "description": "For all the models, we adopt the early stopping to avoid the overfitting. Generation Quality: The strong baseline HRED-CF which explicity models the timing of talking is much better than HRED on almost all the metrics such as PPL and BLEU. Especially in terms of BLEU, HRED-CF exceeds HRED by up to 1.5% on Embedding Average points. The experimental results demonstrate that controlling the timing of talking can significantly improve the quality the dialogue generation. The other two modified datasets shows the same conclusion which can be found in appendix. Compared with the simple graph neural networks such as GCN and GAT (third and forth rows), our proposed gated neural network is much better. It indicates that the simply appling the graph neural networks is not enough and the gated mechanism is very important in modeling timing of talking. Compared with the strong baseline HRED-CF, the response quality of our proposed graph neural networks is weak. We attribute this phenomenon to the negative transfer problem Liu et al. We leave this problem to the future research. But the quality of responses is still much better than HRED. It can be shown that although the BLEU scores, PPL and embedding average scores of GNN-based models are lower than HRED-CF, the diversity of the generation is better than strong baseline HRED-CF (distinct-1 and distinc-2). In order to test the effectiveness of the gated mechanism, we also replace the first gated mechanism with the self-attention attention mechanism and propose another strong baseline W2T-GGAT (fifth row). Except for PPL and accuracy, W2T-GGAT ’s performance is far worse than our proposed double-gated graph neural network. Decision Performance: It can be shown that our proposed graph neural network models significantly outperforms the strong baseline HRED-CF by up to 5.77% Accuracy and 2.07% macro-F1. Furthermore, compared with the graph neural networks without gated mechanism, it can be found that out proposed model can achieve better performance on accuracy and macro-F1. It demonstrates that the gated mechanism is very important in modeling timing of talking.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 4: Automatic evaluation results of all the models on Ubuntu and modified dailydialog test dataset (The table above is the results of ubuntu dataset, and the below is dailydialog). Baseline HRED cannot control the timing of talking and the accuracy and macro-F1 of it is empty. All the best results are shown in bold.', solve the following question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest accuracy and how does it compare to HRED-CF in terms of BLEU1 score?\n" }, { "role": "assistant", "content": "The table shows that W2T-DGGNN has the highest accuracy at 0.8369. Comparing BLEU1 scores, W2T-DGGNN has a BLEU1 score of 0.1944, which is slightly lower than HRED-CF's BLEU1 score of 0.1950. So, the answer is {\"answer\": \"W2T-DGGNN\"}" } ], "images": [ "scigen/train/468.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Automatic evaluation results of all the models on Ubuntu and modified dailydialog test dataset (The table above is the results of ubuntu dataset, and the below is dailydialog). Baseline HRED cannot control the timing of talking and the accuracy and macro-F1 of it is empty. All the best results are shown in bold." ], "reasoning": "The table shows that W2T-DGGNN has the highest accuracy at 0.8369. Comparing BLEU1 scores, W2T-DGGNN has a BLEU1 score of 0.1944, which is slightly lower than HRED-CF's BLEU1 score of 0.1950.", "question": "Which model has the highest accuracy and how does it compare to HRED-CF in terms of BLEU1 score?", "answer": "So, the answer is {\"answer\": \"W2T-DGGNN\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe W2T-GGAT model has the highest Dist-1 score among all models.\n" }, { "role": "assistant", "content": "The table shows that W2T-GGAT has a Dist-1 score of 0.0698, which is indeed the highest among all the models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/468.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Automatic evaluation results of all the models on Ubuntu and modified dailydialog test dataset (The table above is the results of ubuntu dataset, and the below is dailydialog). Baseline HRED cannot control the timing of talking and the accuracy and macro-F1 of it is empty. All the best results are shown in bold." ], "reasoning": "The table shows that W2T-GGAT has a Dist-1 score of 0.0698, which is indeed the highest among all the models listed.", "statement": "The W2T-GGAT model has the highest Dist-1 score among all models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: Results (accuracy) of SeaReader and other approaches on MedQA task'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "SeaReader and its ensemble version outperform baseline models and the human passing score in both the valid and test sets.Quantitative Results We evaluated model performance by accuracy of choosing the best candidate. Our SeaReader clearly outperforms baseline models by a large margin. We also include the human passing score as a reference. As MedQA is not a commonsense question answering task, human performance relies heavily on individual skill and knowledge. A passing score is the minimum score required to get certified as a Licensed Physician and should reflect a decent expertise in medicine." } ], "images": [ "scigen/train/469.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results (accuracy) of SeaReader and other approaches on MedQA task" ], "reasoning": "SeaReader and its ensemble version outperform baseline models and the human passing score in both the valid and test sets.", "description": "Quantitative Results We evaluated model performance by accuracy of choosing the best candidate. Our SeaReader clearly outperforms baseline models by a large margin. We also include the human passing score as a reference. As MedQA is not a commonsense question answering task, human performance relies heavily on individual skill and knowledge. A passing score is the minimum score required to get certified as a Licensed Physician and should reflect a decent expertise in medicine.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Results (accuracy) of SeaReader and other approaches on MedQA task\nQuestion: What is the accuracy of SeaReader on the valid set and how does it compare to the human passing score?\nSolve the above question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "SeaReader has an accuracy of 73.9 on the valid set, which is higher than the human passing score of 60.0. So, the answer is {\"answer\": \"73.9\"}." } ], "images": [ "scigen/train/469.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results (accuracy) of SeaReader and other approaches on MedQA task" ], "reasoning": "SeaReader has an accuracy of 73.9 on the valid set, which is higher than the human passing score of 60.0.", "question": "What is the accuracy of SeaReader on the valid set and how does it compare to the human passing score?", "answer": "So, the answer is {\"answer\": \"73.9\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence SeaReader ensemble has the highest accuracy on the test set. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSeaReader ensemble has the highest accuracy on the test set.\n" }, { "role": "assistant", "content": "The table shows that SeaReader ensemble has an accuracy of 75.3 on the test set, which is higher than all other models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/469.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results (accuracy) of SeaReader and other approaches on MedQA task" ], "reasoning": "The table shows that SeaReader ensemble has an accuracy of 75.3 on the test set, which is higher than all other models listed.", "statement": "SeaReader ensemble has the highest accuracy on the test set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Test performance with different number of documents given per candidate answer', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "SeaReader maintains high accuracy even with a low ratio of relevant documents, demonstrating its ability to extract useful information from large-scale text.To discover the general relevancy of documents returned by our retrieval system, we hand-labeled the relevancy of 1000 retrieved documents for 100 problems. We notice that there is still performance gain using as many as 20 documents per candidate answer, while the ratio of relevant documents is already low. This illustrates SeaReader’s ability to discern useful information from large-scale text and integrate them in reasoning and decision-making." } ], "images": [ "scigen/train/470.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Test performance with different number of documents given per candidate answer" ], "reasoning": "SeaReader maintains high accuracy even with a low ratio of relevant documents, demonstrating its ability to extract useful information from large-scale text.", "description": "To discover the general relevancy of documents returned by our retrieval system, we hand-labeled the relevancy of 1000 retrieved documents for 100 problems. We notice that there is still performance gain using as many as 20 documents per candidate answer, while the ratio of relevant documents is already low. This illustrates SeaReader’s ability to discern useful information from large-scale text and integrate them in reasoning and decision-making.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 4: Test performance with different number of documents given per candidate answer' in mind, break down and resolve the question below step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the accuracy of SeaReader when using the top 10 documents?\n" }, { "role": "assistant", "content": "The table shows SeaReader's accuracy for different numbers of top documents. For the top 10 documents, the accuracy is listed as 73.6. So, the answer is {\"answer\": \"73.6\"}." } ], "images": [ "scigen/train/470.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Test performance with different number of documents given per candidate answer" ], "reasoning": "The table shows SeaReader's accuracy for different numbers of top documents. For the top 10 documents, the accuracy is listed as 73.6.", "question": "What is the accuracy of SeaReader when using the top 10 documents?", "answer": "So, the answer is {\"answer\": \"73.6\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSeaReader's accuracy decreases as the number of documents increases.\n" }, { "role": "assistant", "content": "The table shows that SeaReader's accuracy increases from 57.8 to 74.4 as the number of documents increases from 1 to 20. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/470.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Test performance with different number of documents given per candidate answer" ], "reasoning": "The table shows that SeaReader's accuracy increases from 57.8 to 74.4 as the number of documents increases from 1 to 20.", "statement": "SeaReader's accuracy decreases as the number of documents increases.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Comparing recent language model results on lm1b.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Increasing the model capacity from 0.2B to 0.8B parameters significantly improves the performance of tokenizer-free models, making them competitive with word-based models.We observe that tokenizer-free LM performance improves significantly (40.6 to 23.0) when the model capacity is increased from 0.2B to 0.8B parameters. With sufficient capacity our byte-level LM is competitive with word based models (ranging from 21.8 to 28.0). Note, our model is able to achieve comparable performance without any explicit signal of word boundaries." } ], "images": [ "scigen/train/471.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparing recent language model results on lm1b." ], "reasoning": "Increasing the model capacity from 0.2B to 0.8B parameters significantly improves the performance of tokenizer-free models, making them competitive with word-based models.", "description": "We observe that tokenizer-free LM performance improves significantly (40.6 to 23.0) when the model capacity is increased from 0.2B to 0.8B parameters. With sufficient capacity our byte-level LM is competitive with word based models (ranging from 21.8 to 28.0). Note, our model is able to achieve comparable performance without any explicit signal of word boundaries.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: 'Table 2: Comparing recent language model results on lm1b.'\nQuestion: What is the perplexity of the byte-level model with 0.8B parameters?\n" }, { "role": "assistant", "content": "The table shows that the byte-level model with 0.8B parameters has a perplexity of 23.0. So, the answer is {\"answer\": \"23.0\"}." } ], "images": [ "scigen/train/471.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparing recent language model results on lm1b." ], "reasoning": "The table shows that the byte-level model with 0.8B parameters has a perplexity of 23.0.", "question": "What is the perplexity of the byte-level model with 0.8B parameters?", "answer": "So, the answer is {\"answer\": \"23.0\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The byte-level model with 0.2B parameters has a higher perplexity than the word-based model with 0.8B parameters., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe byte-level model with 0.2B parameters has a higher perplexity than the word-based model with 0.8B parameters.\n" }, { "role": "assistant", "content": "The table shows that the byte-level model with 0.2B parameters has a perplexity of 40.6, which is higher than the perplexity of 21.8 for the word-based model with 0.8B parameters. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/471.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparing recent language model results on lm1b." ], "reasoning": "The table shows that the byte-level model with 0.2B parameters has a perplexity of 40.6, which is higher than the perplexity of 21.8 for the word-based model with 0.8B parameters.", "statement": "The byte-level model with 0.2B parameters has a higher perplexity than the word-based model with 0.8B parameters.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Comparison to Beam Search Optimization. We report the best likelihood (MLE) and BSO results from Wiseman and Rush (2016), as well as results from our MLE reimplementation and training with Risk. Results based on unnormalized beam search (k=5).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Risk improves BLEU by +2.75, which is better than the +2.33 improvement from Beam Search Optimization, showing classical objectives are competitive.Risk significantly improves BLEU compared to our baseline at +2.75 BLEU, which is slightly better than the +2.33 BLEU improvement reported for Beam Search Optimization (cf. This shows that classical objectives for structured prediction are still very competitive." } ], "images": [ "scigen/train/472.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison to Beam Search Optimization. We report the best likelihood (MLE) and BSO results from Wiseman and Rush (2016), as well as results from our MLE reimplementation and training with Risk. Results based on unnormalized beam search (k=5)." ], "reasoning": "Risk improves BLEU by +2.75, which is better than the +2.33 improvement from Beam Search Optimization, showing classical objectives are competitive.", "description": "Risk significantly improves BLEU compared to our baseline at +2.75 BLEU, which is slightly better than the +2.33 BLEU improvement reported for Beam Search Optimization (cf. This shows that classical objectives for structured prediction are still very competitive.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 5: Comparison to Beam Search Optimization. We report the best likelihood (MLE) and BSO results from Wiseman and Rush (2016), as well as results from our MLE reimplementation and training with Risk. Results based on unnormalized beam search (k=5).', and sequentially solve the question, writing out each step of your reasoning process. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the BLEU score improvement for Risk compared to the baseline?\n" }, { "role": "assistant", "content": "The table shows that the BLEU score improvement for Risk is +2.75 compared to the baseline. So, the answer is {\"answer\": \"+2.75\"}." } ], "images": [ "scigen/train/472.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison to Beam Search Optimization. We report the best likelihood (MLE) and BSO results from Wiseman and Rush (2016), as well as results from our MLE reimplementation and training with Risk. Results based on unnormalized beam search (k=5)." ], "reasoning": "The table shows that the BLEU score improvement for Risk is +2.75 compared to the baseline.", "question": "What is the BLEU score improvement for Risk compared to the baseline?", "answer": "So, the answer is {\"answer\": \"+2.75\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Risk improves BLEU score by +2.75 compared to the baseline. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nRisk improves BLEU score by +2.75 compared to the baseline.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score improvement for Risk is +2.75 compared to the baseline. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/472.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison to Beam Search Optimization. We report the best likelihood (MLE) and BSO results from Wiseman and Rush (2016), as well as results from our MLE reimplementation and training with Risk. Results based on unnormalized beam search (k=5)." ], "reasoning": "The table shows that the BLEU score improvement for Risk is +2.75 compared to the baseline.", "statement": "Risk improves BLEU score by +2.75 compared to the baseline.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: Test accuracy in terms of BLEU on IWSLT’14 German-English translation with various loss functions cf. Figure 1. W & R (2016) refers to Wiseman and Rush (2016), B (2016) to Bahdanau et al. (2016), [S] indicates sequence level-training and [T] token-level training. We report averages and standard deviations over five runs with different random initialization.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Our baseline token-level results outperform previous figures in the literature, and Risk training further enhances these results by up to 0.61 BLEU.Our baseline token-level results are several points above other figures in the literature and we further improve these results by up to 0.61 BLEU with Risk training." } ], "images": [ "scigen/train/473.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Test accuracy in terms of BLEU on IWSLT’14 German-English translation with various loss functions cf. Figure 1. W & R (2016) refers to Wiseman and Rush (2016), B (2016) to Bahdanau et al. (2016), [S] indicates sequence level-training and [T] token-level training. We report averages and standard deviations over five runs with different random initialization." ], "reasoning": "Our baseline token-level results outperform previous figures in the literature, and Risk training further enhances these results by up to 0.61 BLEU.", "description": "Our baseline token-level results are several points above other figures in the literature and we further improve these results by up to 0.61 BLEU with Risk training.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 1: Test accuracy in terms of BLEU on IWSLT’14 German-English translation with various loss functions cf. Figure 1. W & R (2016) refers to Wiseman and Rush (2016), B (2016) to Bahdanau et al. (2016), [S] indicates sequence level-training and [T] token-level training. We report averages and standard deviations over five runs with different random initialization.', solve this question and give detail solutions. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhat is the highest test score achieved in the table, and which method achieved it?\n" }, { "role": "assistant", "content": "To find the highest test score, look at the 'test' column and identify the highest value. The highest test score is 32.84, achieved by the Risk method. So, the answer is {\"answer\": \"Risk\"}." } ], "images": [ "scigen/train/473.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Test accuracy in terms of BLEU on IWSLT’14 German-English translation with various loss functions cf. Figure 1. W & R (2016) refers to Wiseman and Rush (2016), B (2016) to Bahdanau et al. (2016), [S] indicates sequence level-training and [T] token-level training. We report averages and standard deviations over five runs with different random initialization." ], "reasoning": "To find the highest test score, look at the 'test' column and identify the highest value. The highest test score is 32.84, achieved by the Risk method.", "question": "What is the highest test score achieved in the table, and which method achieved it?", "answer": "So, the answer is {\"answer\": \"Risk\"}." }, { "messages": [ { "role": "user", "content": "The TokNLL method achieved a test score of 31.78. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe TokNLL method achieved a test score of 31.78.\n" }, { "role": "assistant", "content": "The table shows that the TokNLL method has a test score of 31.78, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/473.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Test accuracy in terms of BLEU on IWSLT’14 German-English translation with various loss functions cf. Figure 1. W & R (2016) refers to Wiseman and Rush (2016), B (2016) to Bahdanau et al. (2016), [S] indicates sequence level-training and [T] token-level training. We report averages and standard deviations over five runs with different random initialization." ], "reasoning": "The table shows that the TokNLL method has a test score of 31.78, which matches the statement.", "statement": "The TokNLL method achieved a test score of 31.78.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Validation and test BLEU for loss combination strategies. We either use token-level TokLS and sequence-level Riskindividually or combine them as a weighted combination, a constrained combination, a random choice for each sample, cf. §3.3.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The weighted strategy outperforms other strategies in both validation and test scores, while the random strategy underperforms compared to the constrained strategy.Next, we compare various strategies to combine sequence-level and token-level objectives (cf. For these experiments we use 5 candidate sequences per training example for faster experimental turnaround. We consider Risk as sequence-level loss and label smoothing as token-level loss. We also compare to randomly choosing between token-level and sequence-level updates and find it underperforms the more principled constrained strategy. In the remaining experiments we use the weighted strategy." } ], "images": [ "scigen/train/474.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Validation and test BLEU for loss combination strategies. We either use token-level TokLS and sequence-level Riskindividually or combine them as a weighted combination, a constrained combination, a random choice for each sample, cf. §3.3." ], "reasoning": "The weighted strategy outperforms other strategies in both validation and test scores, while the random strategy underperforms compared to the constrained strategy.", "description": "Next, we compare various strategies to combine sequence-level and token-level objectives (cf. For these experiments we use 5 candidate sequences per training example for faster experimental turnaround. We consider Risk as sequence-level loss and label smoothing as token-level loss. We also compare to randomly choosing between token-level and sequence-level updates and find it underperforms the more principled constrained strategy. In the remaining experiments we use the weighted strategy.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2: Validation and test BLEU for loss combination strategies. We either use token-level TokLS and sequence-level Riskindividually or combine them as a weighted combination, a constrained combination, a random choice for each sample, cf. §3.3.', unfold the steps to solve the question given next. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nQuestion: Which strategy achieved the highest validation score and what was the score?\n" }, { "role": "assistant", "content": "By examining the table, the weighted strategy achieved the highest validation score of 33.91. So, the answer is {\"answer\": \"Weighted, 33.91\"}" } ], "images": [ "scigen/train/474.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Validation and test BLEU for loss combination strategies. We either use token-level TokLS and sequence-level Riskindividually or combine them as a weighted combination, a constrained combination, a random choice for each sample, cf. §3.3." ], "reasoning": "By examining the table, the weighted strategy achieved the highest validation score of 33.91.", "question": "Which strategy achieved the highest validation score and what was the score?", "answer": "So, the answer is {\"answer\": \"Weighted, 33.91\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The constrained strategy achieved a higher test score than the random strategy., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe constrained strategy achieved a higher test score than the random strategy.\n" }, { "role": "assistant", "content": "The table shows that the constrained strategy has a test score of 32.79, which is higher than the random strategy's test score of 32.61. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/474.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Validation and test BLEU for loss combination strategies. We either use token-level TokLS and sequence-level Riskindividually or combine them as a weighted combination, a constrained combination, a random choice for each sample, cf. §3.3." ], "reasoning": "The table shows that the constrained strategy has a test score of 32.79, which is higher than the random strategy's test score of 32.61.", "statement": "The constrained strategy achieved a higher test score than the random strategy.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Effect of initializing sequence-level training (Risk) with parameters from token-level likelihood (TokNLL) or label smoothing (TokLS).'.\n" }, { "role": "assistant", "content": "Initializing sequence-level models with TokLS parameters improves BLEU scores more significantly than with TokNLL parameters, likely due to the regularization effect of label smoothing.So far we initialized sequence-level models with parameters from a token-level model trained with label smoothing. The improvement of initializing with TokNLL is only 0.3 BLEU with respect to the TokNLL baseline, whereas, the improvement from initializing with TokLS is 0.6-0.8 BLEU. We believe that the regularization provided by label smoothing leads to models with less sharp distributions that are a better starting point for sequence-level training." } ], "images": [ "scigen/train/475.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Effect of initializing sequence-level training (Risk) with parameters from token-level likelihood (TokNLL) or label smoothing (TokLS)." ], "reasoning": "Initializing sequence-level models with TokLS parameters improves BLEU scores more significantly than with TokNLL parameters, likely due to the regularization effect of label smoothing.", "description": "So far we initialized sequence-level models with parameters from a token-level model trained with label smoothing. The improvement of initializing with TokNLL is only 0.3 BLEU with respect to the TokNLL baseline, whereas, the improvement from initializing with TokLS is 0.6-0.8 BLEU. We believe that the regularization provided by label smoothing leads to models with less sharp distributions that are a better starting point for sequence-level training.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 3: Effect of initializing sequence-level training (Risk) with parameters from token-level likelihood (TokNLL) or label smoothing (TokLS).', answer the question below by showing each progressive step toward the answer. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the improvement in BLEU score on the test set when initializing with TokLS compared to TokNLL?\n" }, { "role": "assistant", "content": "The table shows that the improvement in BLEU score on the test set when initializing with TokLS is +0.64, while the improvement with TokNLL is +0.33. The difference between these improvements is 0.64 - 0.33. So, the answer is {\"answer\": \"0.31\"}" } ], "images": [ "scigen/train/475.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Effect of initializing sequence-level training (Risk) with parameters from token-level likelihood (TokNLL) or label smoothing (TokLS)." ], "reasoning": "The table shows that the improvement in BLEU score on the test set when initializing with TokLS is +0.64, while the improvement with TokNLL is +0.33. The difference between these improvements is 0.64 - 0.33.", "question": "What is the improvement in BLEU score on the test set when initializing with TokLS compared to TokNLL?", "answer": "So, the answer is {\"answer\": \"0.31\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Initializing with TokLS improves the BLEU score on the valid set by 0.8. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nInitializing with TokLS improves the BLEU score on the valid set by 0.8.\n" }, { "role": "assistant", "content": "The table shows that the improvement in BLEU score on the valid set when initializing with TokLS is +0.8. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/475.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Effect of initializing sequence-level training (Risk) with parameters from token-level likelihood (TokNLL) or label smoothing (TokLS)." ], "reasoning": "The table shows that the improvement in BLEU score on the valid set when initializing with TokLS is +0.8.", "statement": "Initializing with TokLS improves the BLEU score on the valid set by 0.8.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Generating candidates online or offline.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Online generation results in slightly better accuracy compared to offline generation, but it is much slower due to the need for incremental inference.Next, we consider the question if refreshing the candidate subset at every training step (online) results in better accuracy compared to generating candidates before training and keeping the set static throughout training (offline). However the online setting is much slower, since regenerating the candidate set requires incremental (left to right) inference with our model which is very slow compared to efficient forward/backward over large batches of pre-generated hypothesis." } ], "images": [ "scigen/train/476.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Generating candidates online or offline." ], "reasoning": "Online generation results in slightly better accuracy compared to offline generation, but it is much slower due to the need for incremental inference.", "description": "Next, we consider the question if refreshing the candidate subset at every training step (online) results in better accuracy compared to generating candidates before training and keeping the set static throughout training (offline). However the online setting is much slower, since regenerating the candidate set requires incremental (left to right) inference with our model which is very slow compared to efficient forward/backward over large batches of pre-generated hypothesis.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Generating candidates online or offline.', solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich generation method results in better accuracy on the validation set?\n" }, { "role": "assistant", "content": "The table shows that online generation has a validation accuracy of 33.91, while offline generation has a validation accuracy of 33.52, indicating that online generation results in better accuracy. So, the answer is {\"answer\": \"Online generation\"}." } ], "images": [ "scigen/train/476.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Generating candidates online or offline." ], "reasoning": "The table shows that online generation has a validation accuracy of 33.91, while offline generation has a validation accuracy of 33.52, indicating that online generation results in better accuracy.", "question": "Which generation method results in better accuracy on the validation set?", "answer": "So, the answer is {\"answer\": \"Online generation\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Offline generation has better accuracy on the test set compared to online generation., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nOffline generation has better accuracy on the test set compared to online generation.\n" }, { "role": "assistant", "content": "The table shows that offline generation has a test accuracy of 32.44, while online generation has a test accuracy of 32.85, indicating that online generation has better accuracy on the test set. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/476.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Generating candidates online or offline." ], "reasoning": "The table shows that offline generation has a test accuracy of 32.44, while online generation has a test accuracy of 32.85, indicating that online generation has better accuracy on the test set.", "statement": "Offline generation has better accuracy on the test set compared to online generation.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 6: Accuracy on Gigaword abstractive summarization in terms of F-measure Rouge-1 (RG-1), Rouge-2 (RG-2), and Rouge-L (RG-L) for token-level label smoothing, and Risk optimization of all three ROUGE F1 metrics. [T] indicates a token-level objective and [S] indicates a sequence level objectives. ABS+ refers to Rush et al. (2015), RNN MLE/MRT (Ayana et al., 2016), WFE (Suzuki and Nagata, 2017), SEASS (Zhou et al., 2017), DRGD (Li et al., 2017).'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Risk optimization improves the baseline for all three ROUGE metrics, but the accuracy on the validation set is generally lower compared to the weighted approach.We optimize all three ROUGE metrics separately and find that Risk can further improve our strong baseline. but accuracy was generally lower on the validation set: RG-1 (36.59 Risk only vs. 36.67 Weighted), RG-2 (17.34 vs. 18.05), and RG-L (33.66 vs. 33.98)." } ], "images": [ "scigen/train/477.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Accuracy on Gigaword abstractive summarization in terms of F-measure Rouge-1 (RG-1), Rouge-2 (RG-2), and Rouge-L (RG-L) for token-level label smoothing, and Risk optimization of all three ROUGE F1 metrics. [T] indicates a token-level objective and [S] indicates a sequence level objectives. ABS+ refers to Rush et al. (2015), RNN MLE/MRT (Ayana et al., 2016), WFE (Suzuki and Nagata, 2017), SEASS (Zhou et al., 2017), DRGD (Li et al., 2017)." ], "reasoning": "Risk optimization improves the baseline for all three ROUGE metrics, but the accuracy on the validation set is generally lower compared to the weighted approach.", "description": "We optimize all three ROUGE metrics separately and find that Risk can further improve our strong baseline. but accuracy was generally lower on the validation set: RG-1 (36.59 Risk only vs. 36.67 Weighted), RG-2 (17.34 vs. 18.05), and RG-L (33.66 vs. 33.98).", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 6: Accuracy on Gigaword abstractive summarization in terms of F-measure Rouge-1 (RG-1), Rouge-2 (RG-2), and Rouge-L (RG-L) for token-level label smoothing, and Risk optimization of all three ROUGE F1 metrics. [T] indicates a token-level objective and [S] indicates a sequence level objectives. ABS+ refers to Rush et al. (2015), RNN MLE/MRT (Ayana et al., 2016), WFE (Suzuki and Nagata, 2017), SEASS (Zhou et al., 2017), DRGD (Li et al., 2017).', solve this question and give detail solutions. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich method achieved the highest RG-L score after applying Risk optimization?\n" }, { "role": "assistant", "content": "By examining the table, we see that the highest RG-L score after applying Risk optimization is 34.29, achieved by + Risk RG-L. So, the answer is {\"answer\": \"34.29\"}." } ], "images": [ "scigen/train/477.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Accuracy on Gigaword abstractive summarization in terms of F-measure Rouge-1 (RG-1), Rouge-2 (RG-2), and Rouge-L (RG-L) for token-level label smoothing, and Risk optimization of all three ROUGE F1 metrics. [T] indicates a token-level objective and [S] indicates a sequence level objectives. ABS+ refers to Rush et al. (2015), RNN MLE/MRT (Ayana et al., 2016), WFE (Suzuki and Nagata, 2017), SEASS (Zhou et al., 2017), DRGD (Li et al., 2017)." ], "reasoning": "By examining the table, we see that the highest RG-L score after applying Risk optimization is 34.29, achieved by + Risk RG-L.", "question": "Which method achieved the highest RG-L score after applying Risk optimization?", "answer": "So, the answer is {\"answer\": \"34.29\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe RNN MRT [S] method achieved the highest RG-2 score before applying Risk optimization.\n" }, { "role": "assistant", "content": "The table shows that the highest RG-2 score before applying Risk optimization is 18.10, achieved by TokLS, not RNN MRT [S]. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/477.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Accuracy on Gigaword abstractive summarization in terms of F-measure Rouge-1 (RG-1), Rouge-2 (RG-2), and Rouge-L (RG-L) for token-level label smoothing, and Risk optimization of all three ROUGE F1 metrics. [T] indicates a token-level objective and [S] indicates a sequence level objectives. ABS+ refers to Rush et al. (2015), RNN MLE/MRT (Ayana et al., 2016), WFE (Suzuki and Nagata, 2017), SEASS (Zhou et al., 2017), DRGD (Li et al., 2017)." ], "reasoning": "The table shows that the highest RG-2 score before applying Risk optimization is 18.10, achieved by TokLS, not RNN MRT [S].", "statement": "The RNN MRT [S] method achieved the highest RG-2 score before applying Risk optimization.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'TABLE IV: Class-wise Performance (in Terms of F{}_{1}-score) of Our RCN Model and Baseline Methods'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The RCN model achieves the highest F1-scores in three relations and the second highest in one relation, with scores above 90.00 except for r:percentage(e1,e2) due to its low frequency in the training set.Furthermore, we compare class-wise performance of our RCN model with baseline methods. Firstly, we clearly observe that our model achieves No.1 in three relations (i.e. r:modifier[e1,e2], r:negative[e2,e1] and r:percentage[e2,e1]) and No.2 in one relation (i.e. r:percentage[e1,e2]) among all the five relations. The F{}_{1}-scores are higher than 90.00 except for r:percentage(e1,e2) relation because of its low frequency (only 70 instances) in the training set. Secondly, we also obtain the same observation on the proposed entity type features as mentioned above that they can help improve performance of the original word embedding features." } ], "images": [ "scigen/train/478.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Class-wise Performance (in Terms of F{}_{1}-score) of Our RCN Model and Baseline Methods" ], "reasoning": "The RCN model achieves the highest F1-scores in three relations and the second highest in one relation, with scores above 90.00 except for r:percentage(e1,e2) due to its low frequency in the training set.", "description": "Furthermore, we compare class-wise performance of our RCN model with baseline methods. Firstly, we clearly observe that our model achieves No.1 in three relations (i.e. r:modifier[e1,e2], r:negative[e2,e1] and r:percentage[e2,e1]) and No.2 in one relation (i.e. r:percentage[e1,e2]) among all the five relations. The F{}_{1}-scores are higher than 90.00 except for r:percentage(e1,e2) relation because of its low frequency (only 70 instances) in the training set. Secondly, we also obtain the same observation on the proposed entity type features as mentioned above that they can help improve performance of the original word embedding features.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'TABLE IV: Class-wise Performance (in Terms of F{}_{1}-score) of Our RCN Model and Baseline Methods', unfold the question presented below, detailing every step of your calculation. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich relation has the lowest F1-score for the RCN model with Word + Entity Type input?\n" }, { "role": "assistant", "content": "From the table, the RCN model with Word + Entity Type input has the lowest F1-score for the relation r:percentage(e1,e2), which is 82.35. So, the answer is {\"answer\": \"r:percentage(e1,e2)\"}" } ], "images": [ "scigen/train/478.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Class-wise Performance (in Terms of F{}_{1}-score) of Our RCN Model and Baseline Methods" ], "reasoning": "From the table, the RCN model with Word + Entity Type input has the lowest F1-score for the relation r:percentage(e1,e2), which is 82.35.", "question": "Which relation has the lowest F1-score for the RCN model with Word + Entity Type input?", "answer": "So, the answer is {\"answer\": \"r:percentage(e1,e2)\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe RCN model achieves the highest F1-score in the r:negative(e2,e1) relation.\n" }, { "role": "assistant", "content": "The table shows that the RCN model with Word + Entity Type input achieves an F1-score of 99.54 in the r:negative(e2,e1) relation, which is the highest among all methods. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/478.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Class-wise Performance (in Terms of F{}_{1}-score) of Our RCN Model and Baseline Methods" ], "reasoning": "The table shows that the RCN model with Word + Entity Type input achieves an F1-score of 99.54 in the r:negative(e2,e1) relation, which is the highest among all methods.", "statement": "The RCN model achieves the highest F1-score in the r:negative(e2,e1) relation.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'TABLE III: Comparative Results of Our RCN Model and Baseline Methods', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our model with entity type features outperforms reference algorithms in Precision, Recall, and F1-score, and the entity type features improve the performance of original baselines.First of all, we can observe that our model with entity type features outperforms these reference algorithms, with 95.59% in Precision, 97.45% in Recall and 96.51% in F{}_{1}-score. The improvements compared with the original baselines without entity type features are 1.5, 1.5, 1.05, 0.45 and 1.2 points in Recall, 0.54, 1.64, 2.53, 1.63 and 1.77 points in F{}_{1}-score, respectively. Without entity type features, our model also outperforms the reference algorithms without entity type features. Both the Recall and F{}_{1}-score of our model achieve the best ones, and the Precision of our model is just below the CNN + MaxPooling method, which use an extra position feature, while our model does not. Secondly, the entity type features proposed by us can help improve performance of the original baselines. The benefits in F{}_{1}-score brought by the entity type features are 0.27, 0.87, 2.28, 0.69 and 1.22 points, respectively. However, the improved performance is still worse than our model. Thirdly, among the baselines, it is interesting to note that without entity type features, attention-based pooling technique performs worse than conventional max-pooling strategy, which has also been observed earlier by Sahu and Anand [sahu2017drug] and Raj et al. [raj2017learning], while with entity type features, attention-based pooling technique performs better than conventional max-pooling strategy." } ], "images": [ "scigen/train/479.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Comparative Results of Our RCN Model and Baseline Methods" ], "reasoning": "Our model with entity type features outperforms reference algorithms in Precision, Recall, and F1-score, and the entity type features improve the performance of original baselines.", "description": "First of all, we can observe that our model with entity type features outperforms these reference algorithms, with 95.59% in Precision, 97.45% in Recall and 96.51% in F{}_{1}-score. The improvements compared with the original baselines without entity type features are 1.5, 1.5, 1.05, 0.45 and 1.2 points in Recall, 0.54, 1.64, 2.53, 1.63 and 1.77 points in F{}_{1}-score, respectively. Without entity type features, our model also outperforms the reference algorithms without entity type features. Both the Recall and F{}_{1}-score of our model achieve the best ones, and the Precision of our model is just below the CNN + MaxPooling method, which use an extra position feature, while our model does not. Secondly, the entity type features proposed by us can help improve performance of the original baselines. The benefits in F{}_{1}-score brought by the entity type features are 0.27, 0.87, 2.28, 0.69 and 1.22 points, respectively. However, the improved performance is still worse than our model. Thirdly, among the baselines, it is interesting to note that without entity type features, attention-based pooling technique performs worse than conventional max-pooling strategy, which has also been observed earlier by Sahu and Anand [sahu2017drug] and Raj et al. [raj2017learning], while with entity type features, attention-based pooling technique performs better than conventional max-pooling strategy.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'TABLE III: Comparative Results of Our RCN Model and Baseline Methods', unfold the steps to solve the question given next. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: What is the Precision of the CNN + MaxPooling method with Word + Position input?\n" }, { "role": "assistant", "content": "The table shows the Precision for CNN + MaxPooling with Word + Position input as 95.54. So, the answer is {\"answer\": \"95.54\"}" } ], "images": [ "scigen/train/479.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Comparative Results of Our RCN Model and Baseline Methods" ], "reasoning": "The table shows the Precision for CNN + MaxPooling with Word + Position input as 95.54.", "question": "What is the Precision of the CNN + MaxPooling method with Word + Position input?", "answer": "So, the answer is {\"answer\": \"95.54\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The CRNN + Attention method with Word + Entity Type input has a higher Recall than the BiLSTM + Attention method with Word + Entity Type input., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe CRNN + Attention method with Word + Entity Type input has a higher Recall than the BiLSTM + Attention method with Word + Entity Type input.\n" }, { "role": "assistant", "content": "The table shows that the Recall for CRNN + Attention with Word + Entity Type input is 96.25, while for BiLSTM + Attention with Word + Entity Type input, it is 96.40. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/479.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Comparative Results of Our RCN Model and Baseline Methods" ], "reasoning": "The table shows that the Recall for CRNN + Attention with Word + Entity Type input is 96.25, while for BiLSTM + Attention with Word + Entity Type input, it is 96.40.", "statement": "The CRNN + Attention method with Word + Entity Type input has a higher Recall than the BiLSTM + Attention method with Word + Entity Type input.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'TABLE V: Performance of Our Automatic Severity Classification Method'.\n" }, { "role": "assistant", "content": "The proposed severity classification method achieves an overall accuracy of 97.00%, with high precision, recall, and F1-score for mild stenosis, and acceptable performance for moderate and severe stenosis.To evaluate the effectiveness of our proposed severity classification method, we randomly select 200 coronary arteriography texts for evaluation. First of all, our method obtains an overall Accuracy of 97.00%. Only six texts are classified into the wrong level. Secondly, most of the coronary arteriography texts (72.5%) belongs to mild stenosis in practice. Our method achieves an relative high performance in terms of Precision (100%), Recall (98.62%) and F{}_{1}-score (99.31%). Thirdly, our method is a little confused with moderate stenosis and severe stenosis. The precision, recall and F{}_{1}-score of moderate stenosis are all 93.33%. It is still acceptable. As to severe stenosis, which appears rarely (5%) in practice, though the precision is merely 75.00%, but the recall is 90%. That is to say, only one text of severe stenosis is not recognized by our method." } ], "images": [ "scigen/train/480.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Performance of Our Automatic Severity Classification Method" ], "reasoning": "The proposed severity classification method achieves an overall accuracy of 97.00%, with high precision, recall, and F1-score for mild stenosis, and acceptable performance for moderate and severe stenosis.", "description": "To evaluate the effectiveness of our proposed severity classification method, we randomly select 200 coronary arteriography texts for evaluation. First of all, our method obtains an overall Accuracy of 97.00%. Only six texts are classified into the wrong level. Secondly, most of the coronary arteriography texts (72.5%) belongs to mild stenosis in practice. Our method achieves an relative high performance in terms of Precision (100%), Recall (98.62%) and F{}_{1}-score (99.31%). Thirdly, our method is a little confused with moderate stenosis and severe stenosis. The precision, recall and F{}_{1}-score of moderate stenosis are all 93.33%. It is still acceptable. As to severe stenosis, which appears rarely (5%) in practice, though the precision is merely 75.00%, but the recall is 90%. That is to say, only one text of severe stenosis is not recognized by our method.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'TABLE V: Performance of Our Automatic Severity Classification Method'. Solve the following question based on the table.\n\nQuestion: What is the F1-score for severe stenosis, and how does it compare to the F1-score for moderate stenosis?\nLet's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The F1-score for severe stenosis is 81.82%, while the F1-score for moderate stenosis is 93.33%. So, the answer is {\"answer\": \"81.82%\"}" } ], "images": [ "scigen/train/480.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Performance of Our Automatic Severity Classification Method" ], "reasoning": "The F1-score for severe stenosis is 81.82%, while the F1-score for moderate stenosis is 93.33%.", "question": "What is the F1-score for severe stenosis, and how does it compare to the F1-score for moderate stenosis?", "answer": "So, the answer is {\"answer\": \"81.82%\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe classification method has a recall of 90% for severe stenosis.\n" }, { "role": "assistant", "content": "The table shows that the recall for severe stenosis is indeed 90%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/480.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Performance of Our Automatic Severity Classification Method" ], "reasoning": "The table shows that the recall for severe stenosis is indeed 90%.", "statement": "The classification method has a recall of 90% for severe stenosis.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'TABLE VI: Comparisons between Different Input Features with Different Routing Iterations', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The model's performance improves with entity type features, and the best F1-score is achieved with r=4 when both words and entity types are used.To study the effect of the input features and the routing iterations in our model, we experimentally compare the performance between different input features with different routing iterations. The Precision, Recall and F{}_{1}-score are 95.59%, 97.45% and 96.51%, respectively. Comparing the model inputs, we can observe that the performance of our model with entity type features is better than that without entity type features. The benefits are 0.66 in Precision, 0.63 in Recall and 0.65 in F{}_{1}-score on average. Comparing the five lines in the table, we can observe that whatever inputs are used, the performance (in terms of F{}_{1}-score) first grows then drops down with the increase of iterative number r. When words are only used, our model achieves its best performance with r=2. And when words and entity types are both used, our model achieves its best performance with r=4." } ], "images": [ "scigen/train/481.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VI: Comparisons between Different Input Features with Different Routing Iterations" ], "reasoning": "The model's performance improves with entity type features, and the best F1-score is achieved with r=4 when both words and entity types are used.", "description": "To study the effect of the input features and the routing iterations in our model, we experimentally compare the performance between different input features with different routing iterations. The Precision, Recall and F{}_{1}-score are 95.59%, 97.45% and 96.51%, respectively. Comparing the model inputs, we can observe that the performance of our model with entity type features is better than that without entity type features. The benefits are 0.66 in Precision, 0.63 in Recall and 0.65 in F{}_{1}-score on average. Comparing the five lines in the table, we can observe that whatever inputs are used, the performance (in terms of F{}_{1}-score) first grows then drops down with the increase of iterative number r. When words are only used, our model achieves its best performance with r=2. And when words and entity types are both used, our model achieves its best performance with r=4.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'TABLE VI: Comparisons between Different Input Features with Different Routing Iterations', answer the following question. Give detailed solution consisting of each step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the best F1-score achieved when both words and entity types are used, and at which iteration r?\n" }, { "role": "assistant", "content": "By comparing the F1-scores in the 'Word + Entity Type' column, the highest F1-score is 96.51, which occurs at r=4. So, the answer is {\"answer\": \"96.51\"}" } ], "images": [ "scigen/train/481.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VI: Comparisons between Different Input Features with Different Routing Iterations" ], "reasoning": "By comparing the F1-scores in the 'Word + Entity Type' column, the highest F1-score is 96.51, which occurs at r=4.", "question": "What is the best F1-score achieved when both words and entity types are used, and at which iteration r?", "answer": "So, the answer is {\"answer\": \"96.51\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The model achieves its best performance with words only at r=2. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model achieves its best performance with words only at r=2.\n" }, { "role": "assistant", "content": "The highest F1-score in the 'Word Only' column is 95.99, which occurs at r=2, confirming the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/481.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VI: Comparisons between Different Input Features with Different Routing Iterations" ], "reasoning": "The highest F1-score in the 'Word Only' column is 95.99, which occurs at r=2, confirming the statement.", "statement": "The model achieves its best performance with words only at r=2.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'TABLE VII: Comparison of the Interest of Uni/Bi-LSTMs and Capsules'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The F1-score of the Uni/Bi-LSTMs and Capsule layer model is higher than both the All Bi-LSTMs model and the Softmax layer model, indicating its effectiveness.To analyze the interest of Uni/Bi-LSTMs and capsules, we compare our model with that all using Bi-LSTMs or replacing the capsule layer by a fully-connected layer with a softmax function. From the table, we can observe that the F{}_{1}-score of our model is higher than that all using Bi-LSTMs by 0.29\\%, and higher than that using a softmax layer by 0.35 point. It indicates the interest of the proposed Uni/Bi-LSTMs and capsules." } ], "images": [ "scigen/train/482.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VII: Comparison of the Interest of Uni/Bi-LSTMs and Capsules" ], "reasoning": "The F1-score of the Uni/Bi-LSTMs and Capsule layer model is higher than both the All Bi-LSTMs model and the Softmax layer model, indicating its effectiveness.", "description": "To analyze the interest of Uni/Bi-LSTMs and capsules, we compare our model with that all using Bi-LSTMs or replacing the capsule layer by a fully-connected layer with a softmax function. From the table, we can observe that the F{}_{1}-score of our model is higher than that all using Bi-LSTMs by 0.29\\%, and higher than that using a softmax layer by 0.35 point. It indicates the interest of the proposed Uni/Bi-LSTMs and capsules.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}. The question is related to the given table titled 'TABLE VII: Comparison of the Interest of Uni/Bi-LSTMs and Capsules'.\n\nQuestion: How much higher is the F1-score of the Uni/Bi-LSTMs and Capsule layer model compared to the All Bi-LSTMs model?\n" }, { "role": "assistant", "content": "The F1-score of the Uni/Bi-LSTMs and Capsule layer model is 96.51, while the F1-score of the All Bi-LSTMs model is 96.16. The difference is 96.51 - 96.16 = 0.35. So, the answer is {\"answer\": \"0.35\"}." } ], "images": [ "scigen/train/482.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VII: Comparison of the Interest of Uni/Bi-LSTMs and Capsules" ], "reasoning": "The F1-score of the Uni/Bi-LSTMs and Capsule layer model is 96.51, while the F1-score of the All Bi-LSTMs model is 96.16. The difference is 96.51 - 96.16 = 0.35.", "question": "How much higher is the F1-score of the Uni/Bi-LSTMs and Capsule layer model compared to the All Bi-LSTMs model?", "answer": "So, the answer is {\"answer\": \"0.35\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe F1-score of the Softmax layer model is higher than the All Bi-LSTMs model.\n" }, { "role": "assistant", "content": "The F1-score of the Softmax layer model is 96.22, while the F1-score of the All Bi-LSTMs model is 96.16. Since 96.22 is greater than 96.16, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/482.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VII: Comparison of the Interest of Uni/Bi-LSTMs and Capsules" ], "reasoning": "The F1-score of the Softmax layer model is 96.22, while the F1-score of the All Bi-LSTMs model is 96.16. Since 96.22 is greater than 96.16, the statement is supported.", "statement": "The F1-score of the Softmax layer model is higher than the All Bi-LSTMs model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Across data set experiments', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The CNN model's performance is sometimes worse than random chance, indicating non-generalization issues.First, it is noteworthy that in some cases the performance of the CNN is worse than mere chance. This inferior performance substantiates the existence of the non-generalization problems we are targeting." } ], "images": [ "scigen/train/483.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Across data set experiments" ], "reasoning": "The CNN model's performance is sometimes worse than random chance, indicating non-generalization issues.", "description": "First, it is noteworthy that in some cases the performance of the CNN is worse than mere chance. This inferior performance substantiates the existence of the non-generalization problems we are targeting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nTable title: Table 2: Across data set experiments\n\nQuestion: Which CNN model has the lowest performance on the Verbal modality?\n" }, { "role": "assistant", "content": "To find the lowest performance on the Verbal modality, compare the values in the Verbal row across all CNN models. The lowest value is 0.522 for MOUD CNN. So, the answer is {\"answer\": \"0.522\"}." } ], "images": [ "scigen/train/483.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Across data set experiments" ], "reasoning": "To find the lowest performance on the Verbal modality, compare the values in the Verbal row across all CNN models. The lowest value is 0.522 for MOUD CNN.", "question": "Which CNN model has the lowest performance on the Verbal modality?", "answer": "So, the answer is {\"answer\": \"0.522\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Youtube SAL-CNN model performs better than the MOUD CNN model on the Acoustic modality., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Youtube SAL-CNN model performs better than the MOUD CNN model on the Acoustic modality.\n" }, { "role": "assistant", "content": "Compare the performance of Youtube SAL-CNN and MOUD CNN on the Acoustic modality. Youtube SAL-CNN has a performance of 0.564, while MOUD CNN has a performance of 0.455. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/483.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Across data set experiments" ], "reasoning": "Compare the performance of Youtube SAL-CNN and MOUD CNN on the Acoustic modality. Youtube SAL-CNN has a performance of 0.564, while MOUD CNN has a performance of 0.455. Therefore, the statement is supported.", "statement": "The Youtube SAL-CNN model performs better than the MOUD CNN model on the Acoustic modality.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Results on three word similarity datasets.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The method outperforms the baseline on all datasets, with a notable improvement of 5.4 points on the rare word dataset RW.Word Similarity From the table, we can see that our method consistently outperforms the baseline on all datasets. In particular, we outperform the baseline for about 5.4 points on the rare word dataset RW. This result shows that our method improves the representation of words, especially the rare words." } ], "images": [ "scigen/train/484.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results on three word similarity datasets." ], "reasoning": "The method outperforms the baseline on all datasets, with a notable improvement of 5.4 points on the rare word dataset RW.", "description": "Word Similarity From the table, we can see that our method consistently outperforms the baseline on all datasets. In particular, we outperform the baseline for about 5.4 points on the rare word dataset RW. This result shows that our method improves the representation of words, especially the rare words.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 1: Results on three word similarity datasets.' and proceed to solve the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nHow much does the method improve the score on the WS dataset compared to the original?\n" }, { "role": "assistant", "content": "The original WS score is 66.74, and the score with the method is 69.35. The improvement is 69.35 - 66.74 = 2.61 points. So, the answer is {\"answer\": \"2.61\"}" } ], "images": [ "scigen/train/484.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results on three word similarity datasets." ], "reasoning": "The original WS score is 66.74, and the score with the method is 69.35. The improvement is 69.35 - 66.74 = 2.61 points.", "question": "How much does the method improve the score on the WS dataset compared to the original?", "answer": "So, the answer is {\"answer\": \"2.61\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe method improves the score on the RG65 dataset by more than 3 points.\n" }, { "role": "assistant", "content": "The original RG65 score is 75.63, and the score with the method is 78.78. The improvement is 78.78 - 75.63 = 3.15 points, which is more than 3 points. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/484.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results on three word similarity datasets." ], "reasoning": "The original RG65 score is 75.63, and the score with the method is 78.78. The improvement is 78.78 - 75.63 = 3.15 points, which is more than 3 points.", "statement": "The method improves the score on the RG65 dataset by more than 3 points.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Perplexity on validation and test sets on Penn Treebank and WikiText2. Smaller the perplexity, better the result. Baseline results are obtained from DBLP:journals/corr/abs-1708-02182 ; DBLP:journals/corr/abs-1711-03953 . “Paras” denotes the number of model parameters.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Our method outperforms AWD-LSTM and AWD-LSTM-MoS baselines on both PTB and WT2 datasets, showing larger improvements on the WT2 dataset.Language Modeling In all these settings, our method outperforms the two baselines. On PTB dataset, our method improves the AWD-LSTM and AWD-LSTM-MoS baseline by 0.8/1.2/1.0 and 0.76/1.13/1.15 points in test set at different checkpoints. On WT2 dataset, which contains more rare words, our method achieves larger improvements. We improve the results of AWD-LSTM and AWD-LSTM-MoS by 2.3/2.4/2.7 and 1.15/1.72/1.54 in terms of test perplexity, respectively." } ], "images": [ "scigen/train/485.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Perplexity on validation and test sets on Penn Treebank and WikiText2. Smaller the perplexity, better the result. Baseline results are obtained from DBLP:journals/corr/abs-1708-02182 ; DBLP:journals/corr/abs-1711-03953 . “Paras” denotes the number of model parameters." ], "reasoning": "Our method outperforms AWD-LSTM and AWD-LSTM-MoS baselines on both PTB and WT2 datasets, showing larger improvements on the WT2 dataset.", "description": "Language Modeling In all these settings, our method outperforms the two baselines. On PTB dataset, our method improves the AWD-LSTM and AWD-LSTM-MoS baseline by 0.8/1.2/1.0 and 0.76/1.13/1.15 points in test set at different checkpoints. On WT2 dataset, which contains more rare words, our method achieves larger improvements. We improve the results of AWD-LSTM and AWD-LSTM-MoS by 2.3/2.4/2.7 and 1.15/1.72/1.54 in terms of test perplexity, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 2: Perplexity on validation and test sets on Penn Treebank and WikiText2. Smaller the perplexity, better the result. Baseline results are obtained from DBLP:journals/corr/abs-1708-02182 ; DBLP:journals/corr/abs-1711-03953 . “Paras” denotes the number of model parameters.' and proceed to solve the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the test perplexity improvement of AWD-LSTM-MoS with dynamic evaluation on the PTB dataset using the new method?\n" }, { "role": "assistant", "content": "The original test perplexity of AWD-LSTM-MoS with dynamic evaluation on the PTB dataset is 47.69. With the new method, the test perplexity is 46.54. The improvement is 47.69 - 46.54 = 1.15. So, the answer is {\"answer\": \"1.15\"}." } ], "images": [ "scigen/train/485.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Perplexity on validation and test sets on Penn Treebank and WikiText2. Smaller the perplexity, better the result. Baseline results are obtained from DBLP:journals/corr/abs-1708-02182 ; DBLP:journals/corr/abs-1711-03953 . “Paras” denotes the number of model parameters." ], "reasoning": "The original test perplexity of AWD-LSTM-MoS with dynamic evaluation on the PTB dataset is 47.69. With the new method, the test perplexity is 46.54. The improvement is 47.69 - 46.54 = 1.15.", "question": "What is the test perplexity improvement of AWD-LSTM-MoS with dynamic evaluation on the PTB dataset using the new method?", "answer": "So, the answer is {\"answer\": \"1.15\"}." }, { "messages": [ { "role": "user", "content": "The new method improves the test perplexity of AWD-LSTM on the WT2 dataset by more than 2 points. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe new method improves the test perplexity of AWD-LSTM on the WT2 dataset by more than 2 points.\n" }, { "role": "assistant", "content": "The original test perplexity of AWD-LSTM on the WT2 dataset is 65.8. With the new method, the test perplexity is 63.4. The improvement is 65.8 - 63.4 = 2.4, which is more than 2 points. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/485.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Perplexity on validation and test sets on Penn Treebank and WikiText2. Smaller the perplexity, better the result. Baseline results are obtained from DBLP:journals/corr/abs-1708-02182 ; DBLP:journals/corr/abs-1711-03953 . “Paras” denotes the number of model parameters." ], "reasoning": "The original test perplexity of AWD-LSTM on the WT2 dataset is 65.8. With the new method, the test perplexity is 63.4. The improvement is 65.8 - 63.4 = 2.4, which is more than 2 points.", "statement": "The new method improves the test perplexity of AWD-LSTM on the WT2 dataset by more than 2 points.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: BLEU scores on test set on WMT2014 English-German and IWSLT German-English tasks.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The model using adversarial training outperforms the original model in both WMT14 English-German and IWSLT14 German-English tasks, showing that improving word embeddings can lead to better results.Machine Translation We outperform the baselines for 1.06/0.71 in the term of BLEU in transformer_base and transformer_big settings in WMT14 English-German task, respectively. The model learned from adversarial training also outperforms original one in IWSLT14 German-English task by 0.85. These results show improving word embeddings can achieve better results in more complicated tasks and larger datasets." } ], "images": [ "scigen/train/486.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: BLEU scores on test set on WMT2014 English-German and IWSLT German-English tasks." ], "reasoning": "The model using adversarial training outperforms the original model in both WMT14 English-German and IWSLT14 German-English tasks, showing that improving word embeddings can lead to better results.", "description": "Machine Translation We outperform the baselines for 1.06/0.71 in the term of BLEU in transformer_base and transformer_big settings in WMT14 English-German task, respectively. The model learned from adversarial training also outperforms original one in IWSLT14 German-English task by 0.85. These results show improving word embeddings can achieve better results in more complicated tasks and larger datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: BLEU scores on test set on WMT2014 English-German and IWSLT German-English tasks.' and methodically tackle the question that follows. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich method achieved the highest BLEU score in the IWSLT De→En task?\n" }, { "role": "assistant", "content": "By examining the IWSLT De→En BLEU column, the highest score is 33.97, achieved by the 'Transformer with FRAGE' method. So, the answer is {\"answer\": \"Transformer with FRAGE\"}." } ], "images": [ "scigen/train/486.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: BLEU scores on test set on WMT2014 English-German and IWSLT German-English tasks." ], "reasoning": "By examining the IWSLT De→En BLEU column, the highest score is 33.97, achieved by the 'Transformer with FRAGE' method.", "question": "Which method achieved the highest BLEU score in the IWSLT De→En task?", "answer": "So, the answer is {\"answer\": \"Transformer with FRAGE\"}." }, { "messages": [ { "role": "user", "content": "The Transformer Base with FRAGE method outperforms the Transformer Big method in the WMT En→De task. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Transformer Base with FRAGE method outperforms the Transformer Big method in the WMT En→De task.\n" }, { "role": "assistant", "content": "In the WMT En→De BLEU column, the Transformer Base with FRAGE has a score of 28.36, while the Transformer Big has a score of 28.40. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/486.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: BLEU scores on test set on WMT2014 English-German and IWSLT German-English tasks." ], "reasoning": "In the WMT En→De BLEU column, the Transformer Base with FRAGE has a score of 28.36, while the Transformer Big has a score of 28.40. Therefore, the statement is refuted.", "statement": "The Transformer Base with FRAGE method outperforms the Transformer Big method in the WMT En→De task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 10: BLEU scores on test set of the WMT14 English-German task and IWSLT14 German-English task. Our method is denoted as “FRAGE”, “Reweighting” denotes reweighting the loss of each word by reciprocal of its frequency, and “Weight Decay” denotes putting weight decay rate (0.2) on embeddings.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Simple methods like reweighting and weight decay negatively affect machine translation tasks, while the FRAGE method improves performance.We compare some other simple methods with ours on machine translation tasks, which include reweighting method and l2 regularization (weight decay). We notice that those simple methods do not work for the tasks, even have negative effects." } ], "images": [ "scigen/train/487.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: BLEU scores on test set of the WMT14 English-German task and IWSLT14 German-English task. Our method is denoted as “FRAGE”, “Reweighting” denotes reweighting the loss of each word by reciprocal of its frequency, and “Weight Decay” denotes putting weight decay rate (0.2) on embeddings." ], "reasoning": "Simple methods like reweighting and weight decay negatively affect machine translation tasks, while the FRAGE method improves performance.", "description": "We compare some other simple methods with ours on machine translation tasks, which include reweighting method and l2 regularization (weight decay). We notice that those simple methods do not work for the tasks, even have negative effects.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 10: BLEU scores on test set of the WMT14 English-German task and IWSLT14 German-English task. Our method is denoted as “FRAGE”, “Reweighting” denotes reweighting the loss of each word by reciprocal of its frequency, and “Weight Decay” denotes putting weight decay rate (0.2) on embeddings.', solve the following question step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich method achieved the highest BLEU score for the IWSLT De→En task?\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for different methods on various tasks. For the IWSLT De→En task, the highest BLEU score is 33.97, achieved by the Transformer with FRAGE method. So, the answer is {\"answer\": \"Transformer with FRAGE\"}." } ], "images": [ "scigen/train/487.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: BLEU scores on test set of the WMT14 English-German task and IWSLT14 German-English task. Our method is denoted as “FRAGE”, “Reweighting” denotes reweighting the loss of each word by reciprocal of its frequency, and “Weight Decay” denotes putting weight decay rate (0.2) on embeddings." ], "reasoning": "The table shows the BLEU scores for different methods on various tasks. For the IWSLT De→En task, the highest BLEU score is 33.97, achieved by the Transformer with FRAGE method.", "question": "Which method achieved the highest BLEU score for the IWSLT De→En task?", "answer": "So, the answer is {\"answer\": \"Transformer with FRAGE\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Transformer Base method achieved the highest BLEU score for the WMT En→De task.\n" }, { "role": "assistant", "content": "The table shows that the highest BLEU score for the WMT En→De task is 28.36, achieved by the Transformer Base with FRAGE method, not the Transformer Base method. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/487.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: BLEU scores on test set of the WMT14 English-German task and IWSLT14 German-English task. Our method is denoted as “FRAGE”, “Reweighting” denotes reweighting the loss of each word by reciprocal of its frequency, and “Weight Decay” denotes putting weight decay rate (0.2) on embeddings." ], "reasoning": "The table shows that the highest BLEU score for the WMT En→De task is 28.36, achieved by the Transformer Base with FRAGE method, not the Transformer Base method.", "statement": "The Transformer Base method achieved the highest BLEU score for the WMT En→De task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 6: F1 for different model variants on the binary WSJ validation set with included punctuation. The binary trees are as-is (∅) or modified according to the post-processing heuristic (+PP). The mean F1 is shown across three random seeds.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "MLP consistently outperforms TreeLSTM, benefits from Softmax loss, and achieves the best performance with shared parameters.We see that MLP composition consistently performs better than with TreeLSTM, that MLP benefits from the Softmax loss, and that the best performance comes from sharing parameters. All other experimental results use this highly performant setting unless otherwise specified." } ], "images": [ "scigen/train/488.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: F1 for different model variants on the binary WSJ validation set with included punctuation. The binary trees are as-is (∅) or modified according to the post-processing heuristic (+PP). The mean F1 is shown across three random seeds." ], "reasoning": "MLP consistently outperforms TreeLSTM, benefits from Softmax loss, and achieves the best performance with shared parameters.", "description": "We see that MLP composition consistently performs better than with TreeLSTM, that MLP benefits from the Softmax loss, and that the best performance comes from sharing parameters. All other experimental results use this highly performant setting unless otherwise specified.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 6: F1 for different model variants on the binary WSJ validation set with included punctuation. The binary trees are as-is (∅) or modified according to the post-processing heuristic (+PP). The mean F1 is shown across three random seeds.' and methodically tackle the question that follows. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich composition and loss combination achieves the highest F1 score with post-processing?\n" }, { "role": "assistant", "content": "To find the highest F1 score with post-processing, look at the 'F1 μ +PP' column. The highest value is 56.7, which corresponds to the MLPShared composition with Softmax loss. So, the answer is {\"answer\": \"MLPShared with Softmax\"}" } ], "images": [ "scigen/train/488.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: F1 for different model variants on the binary WSJ validation set with included punctuation. The binary trees are as-is (∅) or modified according to the post-processing heuristic (+PP). The mean F1 is shown across three random seeds." ], "reasoning": "To find the highest F1 score with post-processing, look at the 'F1 μ +PP' column. The highest value is 56.7, which corresponds to the MLPShared composition with Softmax loss.", "question": "Which composition and loss combination achieves the highest F1 score with post-processing?", "answer": "So, the answer is {\"answer\": \"MLPShared with Softmax\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: MLP with Margin loss achieves a higher F1 score than TreeLSTM with Softmax loss., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nMLP with Margin loss achieves a higher F1 score than TreeLSTM with Softmax loss.\n" }, { "role": "assistant", "content": "Comparing the F1 scores in the 'F1 μ' column, MLP with Margin loss has a score of 49.7, while TreeLSTM with Softmax loss has a score of 52.0. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/488.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: F1 for different model variants on the binary WSJ validation set with included punctuation. The binary trees are as-is (∅) or modified according to the post-processing heuristic (+PP). The mean F1 is shown across three random seeds." ], "reasoning": "Comparing the F1 scores in the 'F1 μ' column, MLP with Margin loss has a score of 49.7, while TreeLSTM with Softmax loss has a score of 52.0. Therefore, the statement is refuted.", "statement": "MLP with Margin loss achieves a higher F1 score than TreeLSTM with Softmax loss.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 1: Full WSJ (test set) unsupervised unlabeled binary constituency parsing including punctuation. † indicates trained to optimize NLI task. Mean and max are calculated over five random restarts. PRPN F1 was calculated using the parse trees and results provided by Htut et al. (2018). The depth (δ) is the average tree height. +PP refers to post-processing heuristic that attaches trailing punctuation to the root of the tree. The top F1 value in each column is bolded.'.\n" }, { "role": "assistant", "content": "DIORA achieves a mean F1 score 7 points higher than ON-LSTM and shows less variance between random seeds. PRPN-UP and DIORA benefit more from the +PP heuristic than PRPN-LM.This model achieves a mean F1 7 points higher than ON-LSTM and an increase of over 6.5 max F1 points. We also see that DIORA exhibits much less variance between random seeds than ON-LSTM. Additionally, we find that PRPN-UP and DIORA benefit much more from the +PP heuristic than PRPN-LM. This is consistent with qualitative analysis showing that DIORA and PRPN-UP incorrectly attach trailing punctuation much more often than PRPN-LM." } ], "images": [ "scigen/train/489.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Full WSJ (test set) unsupervised unlabeled binary constituency parsing including punctuation. † indicates trained to optimize NLI task. Mean and max are calculated over five random restarts. PRPN F1 was calculated using the parse trees and results provided by Htut et al. (2018). The depth (δ) is the average tree height. +PP refers to post-processing heuristic that attaches trailing punctuation to the root of the tree. The top F1 value in each column is bolded." ], "reasoning": "DIORA achieves a mean F1 score 7 points higher than ON-LSTM and shows less variance between random seeds. PRPN-UP and DIORA benefit more from the +PP heuristic than PRPN-LM.", "description": "This model achieves a mean F1 7 points higher than ON-LSTM and an increase of over 6.5 max F1 points. We also see that DIORA exhibits much less variance between random seeds than ON-LSTM. Additionally, we find that PRPN-UP and DIORA benefit much more from the +PP heuristic than PRPN-LM. This is consistent with qualitative analysis showing that DIORA and PRPN-UP incorrectly attach trailing punctuation much more often than PRPN-LM.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 1: Full WSJ (test set) unsupervised unlabeled binary constituency parsing including punctuation. † indicates trained to optimize NLI task. Mean and max are calculated over five random restarts. PRPN F1 was calculated using the parse trees and results provided by Htut et al. (2018). The depth (δ) is the average tree height. +PP refers to post-processing heuristic that attaches trailing punctuation to the root of the tree. The top F1 value in each column is bolded.', answer the following question. Give detailed solution consisting of each step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model benefits the most from the +PP heuristic in terms of max F1 score?\n" }, { "role": "assistant", "content": "By comparing the max F1 scores of models with and without the +PP heuristic, DIORA+PP has the highest max F1 score of 56.2. So, the answer is {\"answer\": \"DIORA+PP\"}" } ], "images": [ "scigen/train/489.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Full WSJ (test set) unsupervised unlabeled binary constituency parsing including punctuation. † indicates trained to optimize NLI task. Mean and max are calculated over five random restarts. PRPN F1 was calculated using the parse trees and results provided by Htut et al. (2018). The depth (δ) is the average tree height. +PP refers to post-processing heuristic that attaches trailing punctuation to the root of the tree. The top F1 value in each column is bolded." ], "reasoning": "By comparing the max F1 scores of models with and without the +PP heuristic, DIORA+PP has the highest max F1 score of 56.2.", "question": "Which model benefits the most from the +PP heuristic in terms of max F1 score?", "answer": "So, the answer is {\"answer\": \"DIORA+PP\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nDIORA exhibits much less variance between random seeds than ON-LSTM.\n" }, { "role": "assistant", "content": "The table shows that DIORA has a variance (δ) of 0.5, while ON-LSTM has a variance of 6.6, supporting the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/489.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Full WSJ (test set) unsupervised unlabeled binary constituency parsing including punctuation. † indicates trained to optimize NLI task. Mean and max are calculated over five random restarts. PRPN F1 was calculated using the parse trees and results provided by Htut et al. (2018). The depth (δ) is the average tree height. +PP refers to post-processing heuristic that attaches trailing punctuation to the root of the tree. The top F1 value in each column is bolded." ], "reasoning": "The table shows that DIORA has a variance (δ) of 0.5, while ON-LSTM has a variance of 6.6, supporting the statement.", "statement": "DIORA exhibits much less variance between random seeds than ON-LSTM.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: NLI unsupervised unlabeled binary constituency parsing comparing to CoreNLP predicted parses. PRPN F1 was calculated using the parse trees and results provided by Htut et al. (2018). F1 median and max are calculated over five random seeds and the top F1 value in each column is bolded. Note that we use median rather than mean in order to compare with previous work.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "DIORA and DIORA+PP outperform both PRPN-UP and PRPN-LM in F1 median and F1 max scores.Using the heuristic, DIORA greatly surpasses both variants of PRPN. A second caveat is that SNLI Bowman et al. Syntactic parsers often suffer significant performance drops when predicting outside of the newswire domain that the models were trained on." } ], "images": [ "scigen/train/490.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: NLI unsupervised unlabeled binary constituency parsing comparing to CoreNLP predicted parses. PRPN F1 was calculated using the parse trees and results provided by Htut et al. (2018). F1 median and max are calculated over five random seeds and the top F1 value in each column is bolded. Note that we use median rather than mean in order to compare with previous work." ], "reasoning": "DIORA and DIORA+PP outperform both PRPN-UP and PRPN-LM in F1 median and F1 max scores.", "description": "Using the heuristic, DIORA greatly surpasses both variants of PRPN. A second caveat is that SNLI Bowman et al. Syntactic parsers often suffer significant performance drops when predicting outside of the newswire domain that the models were trained on.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: NLI unsupervised unlabeled binary constituency parsing comparing to CoreNLP predicted parses. PRPN F1 was calculated using the parse trees and results provided by Htut et al. (2018). F1 median and max are calculated over five random seeds and the top F1 value in each column is bolded. Note that we use median rather than mean in order to compare with previous work.\nQuestion: Which model has the highest F1 max score and how does it compare to PRPN-UP?\nSolve the above question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows that DIORA+PP has the highest F1 max score of 59.1, which is higher than PRPN-UP's F1 median score of 48.6. So, the answer is {\"answer\": \"DIORA+PP\"}." } ], "images": [ "scigen/train/490.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: NLI unsupervised unlabeled binary constituency parsing comparing to CoreNLP predicted parses. PRPN F1 was calculated using the parse trees and results provided by Htut et al. (2018). F1 median and max are calculated over five random seeds and the top F1 value in each column is bolded. Note that we use median rather than mean in order to compare with previous work." ], "reasoning": "The table shows that DIORA+PP has the highest F1 max score of 59.1, which is higher than PRPN-UP's F1 median score of 48.6.", "question": "Which model has the highest F1 max score and how does it compare to PRPN-UP?", "answer": "So, the answer is {\"answer\": \"DIORA+PP\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nDIORA+PP has a higher F1 max score than PRPN-LM+PP.\n" }, { "role": "assistant", "content": "The table shows that DIORA+PP has an F1 max score of 59.1, while PRPN-LM+PP has an F1 max score of 50.4. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/490.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: NLI unsupervised unlabeled binary constituency parsing comparing to CoreNLP predicted parses. PRPN F1 was calculated using the parse trees and results provided by Htut et al. (2018). F1 median and max are calculated over five random seeds and the top F1 value in each column is bolded. Note that we use median rather than mean in order to compare with previous work." ], "reasoning": "The table shows that DIORA+PP has an F1 max score of 59.1, while PRPN-LM+PP has an F1 max score of 50.4.", "statement": "DIORA+PP has a higher F1 max score than PRPN-LM+PP.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: WSJ-10 and WSJ-40 unsupervised non-binary unlabeled constituency parsing with punctuation removed. † indicates that the model predicts a full, non-binary parse with additional resources. ‡ indicates model was trained on WSJ data and PRPNNLI was trained on MultiNLI data. CCM uses predicted POS tags while CCMgold uses gold POS tags. PRPN F1 was calculated using the parse trees and results provided by Htut et al. (2018). LB and RB are the left and right-branching baselines. UB is the upper bound attainable by a model that produces binary trees.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "DIORA performs better on longer sequences in the WSJ-40 dataset compared to WSJ-10, where it struggles along with other latent tree parsers.We also compare our models to two subsets of the WSJ dataset that were used in previous unsupervised parsing evaluations. WSJ-10 and WSJ-40 contain sentences up to length 10 and 40 respectively after punctuation removal. The WSJ-10 split has been difficult for latent tree parsers such as DIORA, PRPN, and ON-LSTM, none of which (including our model) are able to improve upon previous non-neural methods. However, when we compare trends between WSJ-10 and WSJ-40, we see that DIORA does a better job at extending to longer sequences." } ], "images": [ "scigen/train/491.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: WSJ-10 and WSJ-40 unsupervised non-binary unlabeled constituency parsing with punctuation removed. † indicates that the model predicts a full, non-binary parse with additional resources. ‡ indicates model was trained on WSJ data and PRPNNLI was trained on MultiNLI data. CCM uses predicted POS tags while CCMgold uses gold POS tags. PRPN F1 was calculated using the parse trees and results provided by Htut et al. (2018). LB and RB are the left and right-branching baselines. UB is the upper bound attainable by a model that produces binary trees." ], "reasoning": "DIORA performs better on longer sequences in the WSJ-40 dataset compared to WSJ-10, where it struggles along with other latent tree parsers.", "description": "We also compare our models to two subsets of the WSJ dataset that were used in previous unsupervised parsing evaluations. WSJ-10 and WSJ-40 contain sentences up to length 10 and 40 respectively after punctuation removal. The WSJ-10 split has been difficult for latent tree parsers such as DIORA, PRPN, and ON-LSTM, none of which (including our model) are able to improve upon previous non-neural methods. However, when we compare trends between WSJ-10 and WSJ-40, we see that DIORA does a better job at extending to longer sequences.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: WSJ-10 and WSJ-40 unsupervised non-binary unlabeled constituency parsing with punctuation removed. † indicates that the model predicts a full, non-binary parse with additional resources. ‡ indicates model was trained on WSJ data and PRPNNLI was trained on MultiNLI data. CCM uses predicted POS tags while CCMgold uses gold POS tags. PRPN F1 was calculated using the parse trees and results provided by Htut et al. (2018). LB and RB are the left and right-branching baselines. UB is the upper bound attainable by a model that produces binary trees.', solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model has the highest WSJ-10 F1 max score?\n" }, { "role": "assistant", "content": "By examining the WSJ-10 F1 max column, we see that the highest score is 72.1, achieved by the PRLG model. So, the answer is {\"answer\": \"PRLG\"}." } ], "images": [ "scigen/train/491.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: WSJ-10 and WSJ-40 unsupervised non-binary unlabeled constituency parsing with punctuation removed. † indicates that the model predicts a full, non-binary parse with additional resources. ‡ indicates model was trained on WSJ data and PRPNNLI was trained on MultiNLI data. CCM uses predicted POS tags while CCMgold uses gold POS tags. PRPN F1 was calculated using the parse trees and results provided by Htut et al. (2018). LB and RB are the left and right-branching baselines. UB is the upper bound attainable by a model that produces binary trees." ], "reasoning": "By examining the WSJ-10 F1 max column, we see that the highest score is 72.1, achieved by the PRLG model.", "question": "Which model has the highest WSJ-10 F1 max score?", "answer": "So, the answer is {\"answer\": \"PRLG\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim DIORA has the highest WSJ-40 F1 max score., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nDIORA has the highest WSJ-40 F1 max score.\n" }, { "role": "assistant", "content": "By examining the WSJ-40 F1 max column, we see that DIORA has a score of 60.9, but the highest score is 85.7 achieved by UB. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/491.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: WSJ-10 and WSJ-40 unsupervised non-binary unlabeled constituency parsing with punctuation removed. † indicates that the model predicts a full, non-binary parse with additional resources. ‡ indicates model was trained on WSJ data and PRPNNLI was trained on MultiNLI data. CCM uses predicted POS tags while CCMgold uses gold POS tags. PRPN F1 was calculated using the parse trees and results provided by Htut et al. (2018). LB and RB are the left and right-branching baselines. UB is the upper bound attainable by a model that produces binary trees." ], "reasoning": "By examining the WSJ-40 F1 max column, we see that DIORA has a score of 60.9, but the highest score is 85.7 achieved by UB.", "statement": "DIORA has the highest WSJ-40 F1 max score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: Segment recall from WSJ separated by phrase type. The 10 most frequent phrase types are shown above, and the highest value in each row is bolded. P-UP=PRNP-UP, P-LM=PRPN-LM', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "DIORA achieves the highest recall across most types, including verb-phrases, but performs worse than PRPN-LM on prepositional phrases.In many scenarios, one is only concerned with extracting particular constituent phrases rather than a full parse. Common use cases would be identifying entities, noun phrases, or verb phrases for downstream analysis. To get an idea of how well our model can perform on phrase segmentation, we consider the maximum recall of spans in our predicted parse tree. We leave methods for cutting the tree to future work and instead consider the maximum recall of our model which serves as an upper bound on its performance. Recall here is the percentage of labeled constituents that appear in our predicted tree relative to the total number of constituents in the gold tree. DIORA achieves the highest recall across the most types and is the only model to perform effectively on verb-phrases. Interestingly, DIORA performs worse than PRPN-LM at prepositional phrases." } ], "images": [ "scigen/train/492.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Segment recall from WSJ separated by phrase type. The 10 most frequent phrase types are shown above, and the highest value in each row is bolded. P-UP=PRNP-UP, P-LM=PRPN-LM" ], "reasoning": "DIORA achieves the highest recall across most types, including verb-phrases, but performs worse than PRPN-LM on prepositional phrases.", "description": "In many scenarios, one is only concerned with extracting particular constituent phrases rather than a full parse. Common use cases would be identifying entities, noun phrases, or verb phrases for downstream analysis. To get an idea of how well our model can perform on phrase segmentation, we consider the maximum recall of spans in our predicted parse tree. We leave methods for cutting the tree to future work and instead consider the maximum recall of our model which serves as an upper bound on its performance. Recall here is the percentage of labeled constituents that appear in our predicted tree relative to the total number of constituents in the gold tree. DIORA achieves the highest recall across the most types and is the only model to perform effectively on verb-phrases. Interestingly, DIORA performs worse than PRPN-LM at prepositional phrases.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Segment recall from WSJ separated by phrase type. The 10 most frequent phrase types are shown above, and the highest value in each row is bolded. P-UP=PRNP-UP, P-LM=PRPN-LM\nQuestion: Which model performs best on prepositional phrases (PP) and what is its recall value?\nSolve the above question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the best-performing model on prepositional phrases, look at the 'PP' row and compare the recall values of DIORA, P-UP, and P-LM. The highest value is 0.602 under P-LM. So, the answer is {\"answer\": \"P-LM with a recall value of 0.602\"}." } ], "images": [ "scigen/train/492.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Segment recall from WSJ separated by phrase type. The 10 most frequent phrase types are shown above, and the highest value in each row is bolded. P-UP=PRNP-UP, P-LM=PRPN-LM" ], "reasoning": "To find the best-performing model on prepositional phrases, look at the 'PP' row and compare the recall values of DIORA, P-UP, and P-LM. The highest value is 0.602 under P-LM.", "question": "Which model performs best on prepositional phrases (PP) and what is its recall value?", "answer": "So, the answer is {\"answer\": \"P-LM with a recall value of 0.602\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nDIORA has the highest recall for noun phrases (NP).\n" }, { "role": "assistant", "content": "To verify this statement, check the 'NP' row and compare the recall values of DIORA, P-UP, and P-LM. DIORA has the highest recall value of 0.767. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/492.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Segment recall from WSJ separated by phrase type. The 10 most frequent phrase types are shown above, and the highest value in each row is bolded. P-UP=PRNP-UP, P-LM=PRPN-LM" ], "reasoning": "To verify this statement, check the 'NP' row and compare the recall values of DIORA, P-UP, and P-LM. DIORA has the highest recall value of 0.767.", "statement": "DIORA has the highest recall for noun phrases (NP).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5: P@1, P@10, and P@100 for labeled chunks from CoNLL-2000 and CoNLL 2012 datasets. For all metrics, higher is better. The top value in each column is bolded. Diora uses the concatenation of the inside and outside vector at each cell which performed better than either in isolation.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "We generate phrase representations for labeled spans longer than one, calculate cosine similarity with other spans, and check if the labels match for the K most similar spans.For each of the labeled spans with length greater than one, we first generate its phrase representation. We then calculate its cosine similarity to all other labeled spans. We then calculate if the label for that query span matches the labels for each of the K most similar other spans in the dataset." } ], "images": [ "scigen/train/493.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: P@1, P@10, and P@100 for labeled chunks from CoNLL-2000 and CoNLL 2012 datasets. For all metrics, higher is better. The top value in each column is bolded. Diora uses the concatenation of the inside and outside vector at each cell which performed better than either in isolation." ], "reasoning": "We generate phrase representations for labeled spans longer than one, calculate cosine similarity with other spans, and check if the labels match for the K most similar spans.", "description": "For each of the labeled spans with length greater than one, we first generate its phrase representation. We then calculate its cosine similarity to all other labeled spans. We then calculate if the label for that query span matches the labels for each of the K most similar other spans in the dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 5: P@1, P@10, and P@100 for labeled chunks from CoNLL-2000 and CoNLL 2012 datasets. For all metrics, higher is better. The top value in each column is bolded. Diora uses the concatenation of the inside and outside vector at each cell which performed better than either in isolation.', and sequentially solve the question, writing out each step of your reasoning process. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model has the highest CoNLL 2000 P@1 score?\n" }, { "role": "assistant", "content": "To find the model with the highest CoNLL 2000 P@1 score, compare the values in the CoNLL 2000 P@1 column. The highest value is 0.990, which corresponds to the DIORA In/Out model. So, the answer is {\"answer\": \"DIORA In/Out\"}." } ], "images": [ "scigen/train/493.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: P@1, P@10, and P@100 for labeled chunks from CoNLL-2000 and CoNLL 2012 datasets. For all metrics, higher is better. The top value in each column is bolded. Diora uses the concatenation of the inside and outside vector at each cell which performed better than either in isolation." ], "reasoning": "To find the model with the highest CoNLL 2000 P@1 score, compare the values in the CoNLL 2000 P@1 column. The highest value is 0.990, which corresponds to the DIORA In/Out model.", "question": "Which model has the highest CoNLL 2000 P@1 score?", "answer": "So, the answer is {\"answer\": \"DIORA In/Out\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nELMo CI has a higher CoNLL 2012 P@1 score than ELMo SI.\n" }, { "role": "assistant", "content": "To verify this statement, compare the CoNLL 2012 P@1 scores for ELMo CI and ELMo SI. ELMo CI has a score of 0.708, while ELMo SI has a score of 0.660. Therefore, ELMo CI has a higher score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/493.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: P@1, P@10, and P@100 for labeled chunks from CoNLL-2000 and CoNLL 2012 datasets. For all metrics, higher is better. The top value in each column is bolded. Diora uses the concatenation of the inside and outside vector at each cell which performed better than either in isolation." ], "reasoning": "To verify this statement, compare the CoNLL 2012 P@1 scores for ELMo CI and ELMo SI. ELMo CI has a score of 0.708, while ELMo SI has a score of 0.660. Therefore, ELMo CI has a higher score.", "statement": "ELMo CI has a higher CoNLL 2012 P@1 score than ELMo SI.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: The number of articles, paragraphs and questions for FQuAD1.1'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The FQuAD1.1 dataset consists of 271 training articles, 30 development articles, and 25 test articles, with a total of 62003 questions.The guidelines for writing question and answer pairs for each paragraph are the same as for SQuAD1.1 [rajpurkar-etal-2016-squad]. First, the paragraph is presented to the student on the platform and the student reads it. Second, the student thinks of a question whose answer is a span of text within the context. Third, the student selects the smallest span in the paragraph which contains the answer. The process is then repeated until 3 to 5 questions are generated and correctly answered. The students were asked to spend on average 1 minute on each question and answer pair. This amounts to an average of 3-5 minutes per annotated paragraph. The total number of questions amounts to 62003. The FQuAD1.1 training, development and test sets are then respectively composed of 271 articles (83%), 30 (9%) and 25 (8%). The difference with the first annotation process is that the workers were specifically asked to come up with complex questions by varying style and question types in order to increase difficulty. The additional answer collection process remains the same." } ], "images": [ "scigen/train/494.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The number of articles, paragraphs and questions for FQuAD1.1" ], "reasoning": "The FQuAD1.1 dataset consists of 271 training articles, 30 development articles, and 25 test articles, with a total of 62003 questions.", "description": "The guidelines for writing question and answer pairs for each paragraph are the same as for SQuAD1.1 [rajpurkar-etal-2016-squad]. First, the paragraph is presented to the student on the platform and the student reads it. Second, the student thinks of a question whose answer is a span of text within the context. Third, the student selects the smallest span in the paragraph which contains the answer. The process is then repeated until 3 to 5 questions are generated and correctly answered. The students were asked to spend on average 1 minute on each question and answer pair. This amounts to an average of 3-5 minutes per annotated paragraph. The total number of questions amounts to 62003. The FQuAD1.1 training, development and test sets are then respectively composed of 271 articles (83%), 30 (9%) and 25 (8%). The difference with the first annotation process is that the workers were specifically asked to come up with complex questions by varying style and question types in order to increase difficulty. The additional answer collection process remains the same.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: The number of articles, paragraphs and questions for FQuAD1.1' to sequentially solve the question presented below. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nHow many questions are there in the development and test sets combined?\n" }, { "role": "assistant", "content": "To find the total number of questions in the development and test sets, add the number of questions in the development set (5668) to the number of questions in the test set (5594). So, the answer is {\"answer\": \"11262\"}" } ], "images": [ "scigen/train/494.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The number of articles, paragraphs and questions for FQuAD1.1" ], "reasoning": "To find the total number of questions in the development and test sets, add the number of questions in the development set (5668) to the number of questions in the test set (5594).", "question": "How many questions are there in the development and test sets combined?", "answer": "So, the answer is {\"answer\": \"11262\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe training set has more paragraphs than the development and test sets combined.\n" }, { "role": "assistant", "content": "The training set has 12123 paragraphs. The development set has 1387 paragraphs and the test set has 1398 paragraphs. Adding the paragraphs in the development and test sets gives 2785, which is less than 12123. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/494.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The number of articles, paragraphs and questions for FQuAD1.1" ], "reasoning": "The training set has 12123 paragraphs. The development set has 1387 paragraphs and the test set has 1398 paragraphs. Adding the paragraphs in the development and test sets gives 2785, which is less than 12123.", "statement": "The training set has more paragraphs than the development and test sets combined.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: Human Performance on FQuAD'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Human performance on FQuAD1.1 is lower than on FQuAD1.0, indicating that FQuAD1.1 is more challenging.The human score on FQuAD1.0 reaches 92.1% F1 and 78.4% EM on the test set and 92.6% and 79.5% on the development set. On FQuAD1.1, it reaches 91.2% F1 and 75.9% EM on the test set and 92.1% and 78.3% on the development set. We observe that there is a noticeable gap between the human performance on FQuAD1.0 test dataset and the human performance on the new samples of FQuAD1.1 with 78.4% EM score on the 2189 questions of FQuAD1.0 test set and 74.1% EM score on the 3405 new questions of FQuAD1.1 test set. This gap in human performance constitutes for us a proof that answering to FQuAD1.1 new questions is globally more difficult than answering to FQuAD1.0 questions, hence making the final FQuAD1.1 dataset even more challenging." } ], "images": [ "scigen/train/495.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Human Performance on FQuAD" ], "reasoning": "Human performance on FQuAD1.1 is lower than on FQuAD1.0, indicating that FQuAD1.1 is more challenging.", "description": "The human score on FQuAD1.0 reaches 92.1% F1 and 78.4% EM on the test set and 92.6% and 79.5% on the development set. On FQuAD1.1, it reaches 91.2% F1 and 75.9% EM on the test set and 92.1% and 78.3% on the development set. We observe that there is a noticeable gap between the human performance on FQuAD1.0 test dataset and the human performance on the new samples of FQuAD1.1 with 78.4% EM score on the 2189 questions of FQuAD1.0 test set and 74.1% EM score on the 3405 new questions of FQuAD1.1 test set. This gap in human performance constitutes for us a proof that answering to FQuAD1.1 new questions is globally more difficult than answering to FQuAD1.0 questions, hence making the final FQuAD1.1 dataset even more challenging.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 7: Human Performance on FQuAD\nQuestion: What is the EM score difference between FQuAD1.0-test and FQuAD1.1-test new samples?\nSolve the above question based on the table information. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The EM score for FQuAD1.0-test is 78.4%, and for FQuAD1.1-test new samples, it is 74.1%. The difference is 78.4% - 74.1% = 4.3%. So, the answer is {\"answer\": \"4.3%\"}" } ], "images": [ "scigen/train/495.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Human Performance on FQuAD" ], "reasoning": "The EM score for FQuAD1.0-test is 78.4%, and for FQuAD1.1-test new samples, it is 74.1%. The difference is 78.4% - 74.1% = 4.3%.", "question": "What is the EM score difference between FQuAD1.0-test and FQuAD1.1-test new samples?", "answer": "So, the answer is {\"answer\": \"4.3%\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe F1 score for FQuAD1.1-dev is higher than for FQuAD1.0-dev.\n" }, { "role": "assistant", "content": "The F1 score for FQuAD1.1-dev is 92.1%, while for FQuAD1.0-dev, it is 92.6%. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/495.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Human Performance on FQuAD" ], "reasoning": "The F1 score for FQuAD1.1-dev is 92.1%, while for FQuAD1.0-dev, it is 92.6%. Therefore, the statement is refuted.", "statement": "The F1 score for FQuAD1.1-dev is higher than for FQuAD1.0-dev.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 8: Answer type comparison for the development sets of FQuAD1.1 and SQuAD1.1'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Common Noun is the most represented answer type in both datasets, while Adjective and Other are less represented, with SQuAD1.1 having higher proportions for these categories.S5SS3SSS0Px1 Answer type distribution For both datasets, the most represented answer type is Common Noun with FQuAD1.1 scoring 26.6% and SQuAD1.1 scoring 31.8%. The less represented ones are Adjective and Other which have a noticeable higher proportion for SQuAD1.1 than FQuAD1.1 Compared to SQuAD1.1, a significant difference exists on structured entities such as Person, Location, and Other Numeric where FQuAD1.1 consistently scores above SQuAD1.1 with the exception of the Date category where FQuAD scores less. Based on these observations, it is difficult to understand the difference in human score between the two datasets." } ], "images": [ "scigen/train/496.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Answer type comparison for the development sets of FQuAD1.1 and SQuAD1.1" ], "reasoning": "Common Noun is the most represented answer type in both datasets, while Adjective and Other are less represented, with SQuAD1.1 having higher proportions for these categories.", "description": "S5SS3SSS0Px1 Answer type distribution For both datasets, the most represented answer type is Common Noun with FQuAD1.1 scoring 26.6% and SQuAD1.1 scoring 31.8%. The less represented ones are Adjective and Other which have a noticeable higher proportion for SQuAD1.1 than FQuAD1.1 Compared to SQuAD1.1, a significant difference exists on structured entities such as Person, Location, and Other Numeric where FQuAD1.1 consistently scores above SQuAD1.1 with the exception of the Date category where FQuAD scores less. Based on these observations, it is difficult to understand the difference in human score between the two datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 8: Answer type comparison for the development sets of FQuAD1.1 and SQuAD1.1', answer the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich dataset has a higher percentage of answers classified as Location?\n" }, { "role": "assistant", "content": "To determine which dataset has a higher percentage of answers classified as Location, compare the percentages for the Location category in both FQuAD1.1 and SQuAD1.1. So, the answer is {\"answer\": \"FQuAD1.1\"}" } ], "images": [ "scigen/train/496.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Answer type comparison for the development sets of FQuAD1.1 and SQuAD1.1" ], "reasoning": "To determine which dataset has a higher percentage of answers classified as Location, compare the percentages for the Location category in both FQuAD1.1 and SQuAD1.1.", "question": "Which dataset has a higher percentage of answers classified as Location?", "answer": "So, the answer is {\"answer\": \"FQuAD1.1\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nSQuAD1.1 has a higher percentage of answers classified as Date compared to FQuAD1.1.\n" }, { "role": "assistant", "content": "The table shows that SQuAD1.1 has 8.9% of answers classified as Date, while FQuAD1.1 has 7.3%, indicating that SQuAD1.1 has a higher percentage for this category. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/496.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Answer type comparison for the development sets of FQuAD1.1 and SQuAD1.1" ], "reasoning": "The table shows that SQuAD1.1 has 8.9% of answers classified as Date, while FQuAD1.1 has 7.3%, indicating that SQuAD1.1 has a higher percentage for this category.", "statement": "SQuAD1.1 has a higher percentage of answers classified as Date compared to FQuAD1.1.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Test accuracy of sentiment classification on Stanford Sentiment Treebank. Bold font indicates the best performance.'.\n" }, { "role": "assistant", "content": "Pipelined semantic dependency predictions improve classification accuracy by 0.9%, but spigot outperforms all baselines, while ste performs slightly worse than a fixed pre-trained pipeline.Pipelined semantic dependency predictions brings 0.9% absolute improvement in classification accuracy, and spigot outperforms all baselines. In this task ste achieves slightly worse performance than a fixed pre-trained pipeline." } ], "images": [ "scigen/train/497.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Test accuracy of sentiment classification on Stanford Sentiment Treebank. Bold font indicates the best performance." ], "reasoning": "Pipelined semantic dependency predictions improve classification accuracy by 0.9%, but spigot outperforms all baselines, while ste performs slightly worse than a fixed pre-trained pipeline.", "description": "Pipelined semantic dependency predictions brings 0.9% absolute improvement in classification accuracy, and spigot outperforms all baselines. In this task ste achieves slightly worse performance than a fixed pre-trained pipeline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: Table 2: Test accuracy of sentiment classification on Stanford Sentiment Treebank. Bold font indicates the best performance.\nQuestion: Which model has the highest classification accuracy and by how much does it outperform the BiLSTM model?\n" }, { "role": "assistant", "content": "The spigot model has the highest classification accuracy at 86.3%. The BiLSTM model has an accuracy of 84.8%. The difference in accuracy between spigot and BiLSTM is 86.3% - 84.8% = 1.5%. So, the answer is {\"answer\": \"1.5%\"}." } ], "images": [ "scigen/train/497.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Test accuracy of sentiment classification on Stanford Sentiment Treebank. Bold font indicates the best performance." ], "reasoning": "The spigot model has the highest classification accuracy at 86.3%. The BiLSTM model has an accuracy of 84.8%. The difference in accuracy between spigot and BiLSTM is 86.3% - 84.8% = 1.5%.", "question": "Which model has the highest classification accuracy and by how much does it outperform the BiLSTM model?", "answer": "So, the answer is {\"answer\": \"1.5%\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe ste model outperforms the pipeline model in classification accuracy.\n" }, { "role": "assistant", "content": "The table shows that the ste model has an accuracy of 85.4%, while the pipeline model has an accuracy of 85.7%. Therefore, the ste model does not outperform the pipeline model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/497.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Test accuracy of sentiment classification on Stanford Sentiment Treebank. Bold font indicates the best performance." ], "reasoning": "The table shows that the ste model has an accuracy of 85.4%, while the pipeline model has an accuracy of 85.7%. Therefore, the ste model does not outperform the pipeline model.", "statement": "The ste model outperforms the pipeline model in classification accuracy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Syntactic parsing performance (in unlabeled attachment score, UAS) and DM semantic parsing performance (in labeled F1) on different groups of the development data. Both systems predict the same syntactic parses for instances from Same, and they disagree on instances from Diff (§5).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Spigot reduces syntactic parsing accuracy but improves semantic parsing, with a smaller negative impact on intermediate parsing compared to ste.We consider the development set instances where both syntactic and semantic annotations are available, and partition them based on whether the two systems’ syntactic predictions agree (Same), or not (Diff). The second group includes sentences with much lower syntactic parsing accuracy (91.3 vs. 97.4 UAS), and spigot further reduces this to 89.6. Even though these changes hurt syntactic parsing accuracy, they lead to a 1.1% absolute gain in labeled F1 for semantic parsing. Furthermore, spigot has an overall less detrimental effect on the intermediate parser than ste: using spigot, intermediate dev. parsing UAS drops to 92.5 from the 92.9 pipelined performance, while ste reduces it to 91.8." } ], "images": [ "scigen/train/498.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Syntactic parsing performance (in unlabeled attachment score, UAS) and DM semantic parsing performance (in labeled F1) on different groups of the development data. Both systems predict the same syntactic parses for instances from Same, and they disagree on instances from Diff (§5)." ], "reasoning": "Spigot reduces syntactic parsing accuracy but improves semantic parsing, with a smaller negative impact on intermediate parsing compared to ste.", "description": "We consider the development set instances where both syntactic and semantic annotations are available, and partition them based on whether the two systems’ syntactic predictions agree (Same), or not (Diff). The second group includes sentences with much lower syntactic parsing accuracy (91.3 vs. 97.4 UAS), and spigot further reduces this to 89.6. Even though these changes hurt syntactic parsing accuracy, they lead to a 1.1% absolute gain in labeled F1 for semantic parsing. Furthermore, spigot has an overall less detrimental effect on the intermediate parser than ste: using spigot, intermediate dev. parsing UAS drops to 92.5 from the 92.9 pipelined performance, while ste reduces it to 91.8.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Syntactic parsing performance (in unlabeled attachment score, UAS) and DM semantic parsing performance (in labeled F1) on different groups of the development data. Both systems predict the same syntactic parses for instances from Same, and they disagree on instances from Diff (§5).', solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the UAS for the Diff split using the spigot model?\n" }, { "role": "assistant", "content": "The table shows the UAS for different splits and models. For the Diff split using the spigot model, the UAS is 89.6. So, the answer is {\"answer\": \"89.6\"}" } ], "images": [ "scigen/train/498.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Syntactic parsing performance (in unlabeled attachment score, UAS) and DM semantic parsing performance (in labeled F1) on different groups of the development data. Both systems predict the same syntactic parses for instances from Same, and they disagree on instances from Diff (§5)." ], "reasoning": "The table shows the UAS for different splits and models. For the Diff split using the spigot model, the UAS is 89.6.", "question": "What is the UAS for the Diff split using the spigot model?", "answer": "So, the answer is {\"answer\": \"89.6\"}" }, { "messages": [ { "role": "user", "content": "The spigot model has a higher UAS for the Diff split compared to the pipeline model. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe spigot model has a higher UAS for the Diff split compared to the pipeline model.\n" }, { "role": "assistant", "content": "The table shows that for the Diff split, the UAS for the spigot model is 89.6, which is lower than the UAS for the pipeline model, which is 91.3. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/498.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Syntactic parsing performance (in unlabeled attachment score, UAS) and DM semantic parsing performance (in labeled F1) on different groups of the development data. Both systems predict the same syntactic parses for instances from Same, and they disagree on instances from Diff (§5)." ], "reasoning": "The table shows that for the Diff split, the UAS for the spigot model is 89.6, which is lower than the UAS for the pipeline model, which is 91.3.", "statement": "The spigot model has a higher UAS for the Diff split compared to the pipeline model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 7: Accuracy of Semantic Analogy'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Entity embeddings perform better with a vector size of 50, but as the vector size increases, GloVe word embeddings outperform entity embeddings, especially at sizes 200 and higher.In comparison, it also shows the accuracy from GloVe word embeddings with vector sizes of 50, 100, 200, and 300. Entity embeddings have better performance with vector size of 50. As we increase vector size, word embeddings perform significantly better and outperform entity embeddings when the vector size is 200 or higher. The degraded performance of entity embeddings may due to less training data, since our entity embeddings were obtained from 2.2B tokens, where GloVe’s word embeddings were obtained from 6B tokens." } ], "images": [ "scigen/train/499.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Accuracy of Semantic Analogy" ], "reasoning": "Entity embeddings perform better with a vector size of 50, but as the vector size increases, GloVe word embeddings outperform entity embeddings, especially at sizes 200 and higher.", "description": "In comparison, it also shows the accuracy from GloVe word embeddings with vector sizes of 50, 100, 200, and 300. Entity embeddings have better performance with vector size of 50. As we increase vector size, word embeddings perform significantly better and outperform entity embeddings when the vector size is 200 or higher. The degraded performance of entity embeddings may due to less training data, since our entity embeddings were obtained from 2.2B tokens, where GloVe’s word embeddings were obtained from 6B tokens.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 7: Accuracy of Semantic Analogy' and proceed to solve the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nAt which vector size do GloVe word embeddings start to outperform DAWT entity embeddings in terms of average accuracy?\n" }, { "role": "assistant", "content": "By comparing the average accuracy of GloVe word embeddings and DAWT entity embeddings, we see that GloVe word embeddings start to outperform DAWT entity embeddings at a vector size of 200. So, the answer is {\"answer\": \"200\"}." } ], "images": [ "scigen/train/499.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Accuracy of Semantic Analogy" ], "reasoning": "By comparing the average accuracy of GloVe word embeddings and DAWT entity embeddings, we see that GloVe word embeddings start to outperform DAWT entity embeddings at a vector size of 200.", "question": "At which vector size do GloVe word embeddings start to outperform DAWT entity embeddings in terms of average accuracy?", "answer": "So, the answer is {\"answer\": \"200\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nDAWT entity embeddings have the highest accuracy at a vector size of 50.\n" }, { "role": "assistant", "content": "By examining the table, we see that DAWT entity embeddings have the highest accuracy at a vector size of 50 for the 'Currency' relation, but not overall. The highest average accuracy for DAWT entity embeddings is at a vector size of 300. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/499.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Accuracy of Semantic Analogy" ], "reasoning": "By examining the table, we see that DAWT entity embeddings have the highest accuracy at a vector size of 50 for the 'Currency' relation, but not overall. The highest average accuracy for DAWT entity embeddings is at a vector size of 300.", "statement": "DAWT entity embeddings have the highest accuracy at a vector size of 50.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Accuracy of P3 when trained and evaluated with labeled logical forms, food webs, or both.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Providing gold food webs as input improves accuracy more than providing gold logical forms, indicating that the execution model contributes more to errors than semantic parsing.Our third experiment analyses sources of error by training and evaluating P3 while providing the gold logical form, food web, or both as input. The final entry shows the maximum accuracy possible given our domain theory and answer selection. The larger accuracy improvement with gold food webs suggests that the execution model is responsible for more error than semantic parsing, though both components contribute." } ], "images": [ "scigen/train/500.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracy of P3 when trained and evaluated with labeled logical forms, food webs, or both." ], "reasoning": "Providing gold food webs as input improves accuracy more than providing gold logical forms, indicating that the execution model contributes more to errors than semantic parsing.", "description": "Our third experiment analyses sources of error by training and evaluating P3 while providing the gold logical form, food web, or both as input. The final entry shows the maximum accuracy possible given our domain theory and answer selection. The larger accuracy improvement with gold food webs suggests that the execution model is responsible for more error than semantic parsing, though both components contribute.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Accuracy of P3 when trained and evaluated with labeled logical forms, food webs, or both.\nQuestion: What is the accuracy improvement when both gold logical form and gold food web are provided as input?\nSolve the above question based on the table information. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the accuracy improvement for different inputs. When both gold logical form and gold food web are provided, the accuracy improvement is +22.5. So, the answer is {\"answer\": \"+22.5\"}" } ], "images": [ "scigen/train/500.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracy of P3 when trained and evaluated with labeled logical forms, food webs, or both." ], "reasoning": "The table shows the accuracy improvement for different inputs. When both gold logical form and gold food web are provided, the accuracy improvement is +22.5.", "question": "What is the accuracy improvement when both gold logical form and gold food web are provided as input?", "answer": "So, the answer is {\"answer\": \"+22.5\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The accuracy of P3 with gold logical form is higher than with gold food web. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe accuracy of P3 with gold logical form is higher than with gold food web.\n" }, { "role": "assistant", "content": "The table shows that the accuracy of P3 with gold logical form is 75.1, while with gold food web it is 82.3. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/500.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracy of P3 when trained and evaluated with labeled logical forms, food webs, or both." ], "reasoning": "The table shows that the accuracy of P3 with gold logical form is 75.1, while with gold food web it is 82.3. Therefore, the statement is refuted.", "statement": "The accuracy of P3 with gold logical form is higher than with gold food web.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Accuracy on the scene data set. KK2013 results are from Krishnamurthy and Kollar (2013).'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "P3 outperforms KK2013 in the QA condition and also when trained with labeled environments.The evaluation metric is exact match accuracy between the predicted and labeled sets of objects. We consider three supervision conditions: QA trains with question/answer pairs, QA+E further includes labeled environments, and QA+E+LF further includes labeled logical forms. We trained P3 in the first two conditions, while prior work trained in the first and third conditions. KK2013 is a possible worlds model with a max-margin training objective. P3 slightly outperforms in the QA condition and P3 trained with labeled environments outperforms prior work trained with additional logical form labels." } ], "images": [ "scigen/train/501.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Accuracy on the scene data set. KK2013 results are from Krishnamurthy and Kollar (2013)." ], "reasoning": "P3 outperforms KK2013 in the QA condition and also when trained with labeled environments.", "description": "The evaluation metric is exact match accuracy between the predicted and labeled sets of objects. We consider three supervision conditions: QA trains with question/answer pairs, QA+E further includes labeled environments, and QA+E+LF further includes labeled logical forms. We trained P3 in the first two conditions, while prior work trained in the first and third conditions. KK2013 is a possible worlds model with a max-margin training objective. P3 slightly outperforms in the QA condition and P3 trained with labeled environments outperforms prior work trained with additional logical form labels.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: Table 4: Accuracy on the scene data set. KK2013 results are from Krishnamurthy and Kollar (2013).\nQuestion: Which model performs better in the QA condition?\n" }, { "role": "assistant", "content": "The table shows that P3 has a score of 68 in the QA condition, while KK2013 has a score of 67. So, the answer is {\"answer\": \"P3\"}" } ], "images": [ "scigen/train/501.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Accuracy on the scene data set. KK2013 results are from Krishnamurthy and Kollar (2013)." ], "reasoning": "The table shows that P3 has a score of 68 in the QA condition, while KK2013 has a score of 67.", "question": "Which model performs better in the QA condition?", "answer": "So, the answer is {\"answer\": \"P3\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nP3 performs better than KK2013 when trained with labeled environments.\n" }, { "role": "assistant", "content": "The table shows that P3 has a score of 75 in the QA+E condition, while KK2013 does not have a score in this condition. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/501.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Accuracy on the scene data set. KK2013 results are from Krishnamurthy and Kollar (2013)." ], "reasoning": "The table shows that P3 has a score of 75 in the QA+E condition, while KK2013 does not have a score in this condition.", "statement": "P3 performs better than KK2013 when trained with labeled environments.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Classification performance of the different representations. The upper part of the table presents one-hot-encoding methods, while the bottom part methods that depend on the dimensionality of the distributed representations. We report the best performance obtained when the size N of the training data N∈{1,50,100,150,200}×103. The best achieved performance per classification problem is shown in bold. We have performed statistical significance tests to test if improvements obtained compared to tf-idf representations are statistically important, which is noted by putting a dagger (†) to the accuracy scores.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "TF-IDF representations perform better than x_conc and hash, and combining distributed document representations with one-hot-encoding representations improves classification performance.We first discuss the performance when single representations are used: x_conc, tf-idf and hash. Notice that tf-idf performs better than both x_conc and hash, with the latter achieving the lowest performance. The performance of the three representations on PubMed1,000 is comparable, but in the bigger classification problems tf-idf and performs considerably better. We, thus, consider the tf-idf representations as our baseline model, and we examine how the models with concatenated representations behave compared to it. We investigate now whether the fusion of distributed document representations with the one-hot-encoding representations benefits the classification performance. In the experiments, both hash and tf-idf consistently achieve better performance when combined with the distributed representations. For instance, for PubMed10,000 and D=400 the tf-idf (resp. hash) representations improve in absolute numbers by 2 (resp. 3.5) F1 points. We have performed two-sided student’s t-tests (p<0.01) to compare whether the improvements obtained for each classification problem are statistically significant compared to using tf-idf representations. In addition to the important improvements, note than in the fused representations, the effect of D diminishes. For instance in PubMed1,000, one can obtain the optimal performance using embedding dimensions with D<400 and similar observations can be made in the rest of the datasets." } ], "images": [ "scigen/train/502.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Classification performance of the different representations. The upper part of the table presents one-hot-encoding methods, while the bottom part methods that depend on the dimensionality of the distributed representations. We report the best performance obtained when the size N of the training data N∈{1,50,100,150,200}×103. The best achieved performance per classification problem is shown in bold. We have performed statistical significance tests to test if improvements obtained compared to tf-idf representations are statistically important, which is noted by putting a dagger (†) to the accuracy scores." ], "reasoning": "TF-IDF representations perform better than x_conc and hash, and combining distributed document representations with one-hot-encoding representations improves classification performance.", "description": "We first discuss the performance when single representations are used: x_conc, tf-idf and hash. Notice that tf-idf performs better than both x_conc and hash, with the latter achieving the lowest performance. The performance of the three representations on PubMed1,000 is comparable, but in the bigger classification problems tf-idf and performs considerably better. We, thus, consider the tf-idf representations as our baseline model, and we examine how the models with concatenated representations behave compared to it. We investigate now whether the fusion of distributed document representations with the one-hot-encoding representations benefits the classification performance. In the experiments, both hash and tf-idf consistently achieve better performance when combined with the distributed representations. For instance, for PubMed10,000 and D=400 the tf-idf (resp. hash) representations improve in absolute numbers by 2 (resp. 3.5) F1 points. We have performed two-sided student’s t-tests (p<0.01) to compare whether the improvements obtained for each classification problem are statistically significant compared to using tf-idf representations. In addition to the important improvements, note than in the fused representations, the effect of D diminishes. For instance in PubMed1,000, one can obtain the optimal performance using embedding dimensions with D<400 and similar observations can be made in the rest of the datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: Classification performance of the different representations. The upper part of the table presents one-hot-encoding methods, while the bottom part methods that depend on the dimensionality of the distributed representations. We report the best performance obtained when the size N of the training data N∈{1,50,100,150,200}×103. The best achieved performance per classification problem is shown in bold. We have performed statistical significance tests to test if improvements obtained compared to tf-idf representations are statistically important, which is noted by putting a dagger (†) to the accuracy scores.' and methodically tackle the question that follows. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the F1 score improvement for tf-idf representations when combined with x_conc for PubMed10,000 with D=400?\n" }, { "role": "assistant", "content": "The table shows that the F1 score for tf-idf alone on PubMed10,000 with D=400 is 0.492, and when combined with x_conc, it improves to 0.512. The improvement is 0.512 - 0.492 = 0.02. So, the answer is {\"answer\": \"0.02\"}." } ], "images": [ "scigen/train/502.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Classification performance of the different representations. The upper part of the table presents one-hot-encoding methods, while the bottom part methods that depend on the dimensionality of the distributed representations. We report the best performance obtained when the size N of the training data N∈{1,50,100,150,200}×103. The best achieved performance per classification problem is shown in bold. We have performed statistical significance tests to test if improvements obtained compared to tf-idf representations are statistically important, which is noted by putting a dagger (†) to the accuracy scores." ], "reasoning": "The table shows that the F1 score for tf-idf alone on PubMed10,000 with D=400 is 0.492, and when combined with x_conc, it improves to 0.512. The improvement is 0.512 - 0.492 = 0.02.", "question": "What is the F1 score improvement for tf-idf representations when combined with x_conc for PubMed10,000 with D=400?", "answer": "So, the answer is {\"answer\": \"0.02\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The tf-idf representations achieve the highest performance on PubMed1,000. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe tf-idf representations achieve the highest performance on PubMed1,000.\n" }, { "role": "assistant", "content": "The table shows that the highest performance on PubMed1,000 is achieved by tfidf+x_conc with a score of 0.66, not tf-idf alone. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/502.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Classification performance of the different representations. The upper part of the table presents one-hot-encoding methods, while the bottom part methods that depend on the dimensionality of the distributed representations. We report the best performance obtained when the size N of the training data N∈{1,50,100,150,200}×103. The best achieved performance per classification problem is shown in bold. We have performed statistical significance tests to test if improvements obtained compared to tf-idf representations are statistically important, which is noted by putting a dagger (†) to the accuracy scores." ], "reasoning": "The table shows that the highest performance on PubMed1,000 is achieved by tfidf+x_conc with a score of 0.66, not tf-idf alone.", "statement": "The tf-idf representations achieve the highest performance on PubMed1,000.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Rouge-N limited-length recall on DUC2004. Size denotes the size of decoder vocabulary in a model.'.\n" }, { "role": "assistant", "content": "The evaluation on DUC2004 uses ROUGE limited-length recall with summaries capped at 75 characters. The neural model is trained on the Gigaword training set and generates summaries with 15 words using a beam-size of 10.Evaluation on DUC2004: DUC 2004 (Over et al. Each article is paired with 4 different human-generated reference summaries, capped at 75 characters. This dataset is evaluation-only. Similar to Rush et al. , we train our neural model on the Gigaword training set, and show the models’ performances on DUC2004. Following the convention, we also use ROUGE limited-length recall as our evaluation metric, and set the capping length to 75 characters. We generate summaries with 15 words using beam-size of 10. Furthermore, our model only uses 15k decoder vocabulary, while previous methods use 69k or 200k." } ], "images": [ "scigen/train/503.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Rouge-N limited-length recall on DUC2004. Size denotes the size of decoder vocabulary in a model." ], "reasoning": "The evaluation on DUC2004 uses ROUGE limited-length recall with summaries capped at 75 characters. The neural model is trained on the Gigaword training set and generates summaries with 15 words using a beam-size of 10.", "description": "Evaluation on DUC2004: DUC 2004 (Over et al. Each article is paired with 4 different human-generated reference summaries, capped at 75 characters. This dataset is evaluation-only. Similar to Rush et al. , we train our neural model on the Gigaword training set, and show the models’ performances on DUC2004. Following the convention, we also use ROUGE limited-length recall as our evaluation metric, and set the capping length to 75 characters. We generate summaries with 15 words using beam-size of 10. Furthermore, our model only uses 15k decoder vocabulary, while previous methods use 69k or 200k.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 2: Rouge-N limited-length recall on DUC2004. Size denotes the size of decoder vocabulary in a model.' in mind, break down and resolve the question below step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich model achieved the highest Rouge-2 score and what was the score?\n" }, { "role": "assistant", "content": "By examining the Rouge-2 column, the highest score is 9.47, achieved by the Ours-LSTM (C) model. So, the answer is {\"answer\": \"Ours-LSTM (C) with 9.47\"}" } ], "images": [ "scigen/train/503.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Rouge-N limited-length recall on DUC2004. Size denotes the size of decoder vocabulary in a model." ], "reasoning": "By examining the Rouge-2 column, the highest score is 9.47, achieved by the Ours-LSTM (C) model.", "question": "Which model achieved the highest Rouge-2 score and what was the score?", "answer": "So, the answer is {\"answer\": \"Ours-LSTM (C) with 9.47\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Ours-Opt-2 (C) model has the highest Rouge-L score.\n" }, { "role": "assistant", "content": "By examining the Rouge-L column, the highest score is 25.94, achieved by the Ours-Opt-2 (C) model. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/503.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Rouge-N limited-length recall on DUC2004. Size denotes the size of decoder vocabulary in a model." ], "reasoning": "By examining the Rouge-L column, the highest score is 25.94, achieved by the Ours-Opt-2 (C) model.", "statement": "The Ours-Opt-2 (C) model has the highest Rouge-L score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: Different Read-Again Model. Ours denotes Read-Again models. C denotes copy mechanism. Ours-Opt-1 and Ours-Opt-2 are the models described in section 3.1.3. Size denotes the size of decoder vocabulary in a model.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The Multiple-Sentences model with copy mechanism achieves the highest ROUGE scores despite having a smaller decoder vocabulary size compared to other models.Results on Gigaword : We compare the performances of different architectures and report ROUGE scores in Tab. Our baselines include the ABS model of Rush et al. We allow the decoder to generate variable length summaries. As shown in Tab. We also observe that adding the copy mechanism further helps to improve performance: Even though the decoder vocabulary size of our approach with copy (15K) is much smaller than ABS (69K) and GRU (69K), it achieves a higher ROUGE score. Besides, our Multiple-Sentences model achieves the best performance." } ], "images": [ "scigen/train/504.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Different Read-Again Model. Ours denotes Read-Again models. C denotes copy mechanism. Ours-Opt-1 and Ours-Opt-2 are the models described in section 3.1.3. Size denotes the size of decoder vocabulary in a model." ], "reasoning": "The Multiple-Sentences model with copy mechanism achieves the highest ROUGE scores despite having a smaller decoder vocabulary size compared to other models.", "description": "Results on Gigaword : We compare the performances of different architectures and report ROUGE scores in Tab. Our baselines include the ABS model of Rush et al. We allow the decoder to generate variable length summaries. As shown in Tab. We also observe that adding the copy mechanism further helps to improve performance: Even though the decoder vocabulary size of our approach with copy (15K) is much smaller than ABS (69K) and GRU (69K), it achieves a higher ROUGE score. Besides, our Multiple-Sentences model achieves the best performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Different Read-Again Model. Ours denotes Read-Again models. C denotes copy mechanism. Ours-Opt-1 and Ours-Opt-2 are the models described in section 3.1.3. Size denotes the size of decoder vocabulary in a model.', answer the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model achieves the highest ROUGE-1 score and what is its decoder vocabulary size?\n" }, { "role": "assistant", "content": "The table shows that the 'Ours-Opt-2 (C)' model achieves the highest ROUGE-1 score of 27.96, and its decoder vocabulary size is 15K. So, the answer is {\"answer\": \"Ours-Opt-2 (C), 15K\"}" } ], "images": [ "scigen/train/504.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Different Read-Again Model. Ours denotes Read-Again models. C denotes copy mechanism. Ours-Opt-1 and Ours-Opt-2 are the models described in section 3.1.3. Size denotes the size of decoder vocabulary in a model." ], "reasoning": "The table shows that the 'Ours-Opt-2 (C)' model achieves the highest ROUGE-1 score of 27.96, and its decoder vocabulary size is 15K.", "question": "Which model achieves the highest ROUGE-1 score and what is its decoder vocabulary size?", "answer": "So, the answer is {\"answer\": \"Ours-Opt-2 (C), 15K\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The ABS model achieves the highest ROUGE-1 score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe ABS model achieves the highest ROUGE-1 score.\n" }, { "role": "assistant", "content": "The table shows that the ABS model has a ROUGE-1 score of 24.12, which is not the highest. The highest ROUGE-1 score is 27.96 achieved by the 'Ours-Opt-2 (C)' model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/504.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Different Read-Again Model. Ours denotes Read-Again models. C denotes copy mechanism. Ours-Opt-1 and Ours-Opt-2 are the models described in section 3.1.3. Size denotes the size of decoder vocabulary in a model." ], "reasoning": "The table shows that the ABS model has a ROUGE-1 score of 24.12, which is not the highest. The highest ROUGE-1 score is 27.96 achieved by the 'Ours-Opt-2 (C)' model.", "statement": "The ABS model achieves the highest ROUGE-1 score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Parsing accuracies of Multilingual (Univ→Univ). Significance tests with MaltEval yield p-values < 0.01 for (MTL vs. Sup) on all languages.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Cas yields better performance than Sup, especially for SV, indicating better parameter initialization. SMTL outperforms Cas overall, implying joint training of treebanks helps.Cas yields slightly better performance than Sup, especially for SV (+1.52% UAS and +2.04% LAS), indicating that pre-training with EN training data indeed provides a better initialization of the parameters for cascaded training. SMTL in turn outperforms Cas overall (comparable for IT), which implies that training two treebanks jointly helps even with an unique model." } ], "images": [ "scigen/train/505.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Parsing accuracies of Multilingual (Univ→Univ). Significance tests with MaltEval yield p-values < 0.01 for (MTL vs. Sup) on all languages." ], "reasoning": "Cas yields better performance than Sup, especially for SV, indicating better parameter initialization. SMTL outperforms Cas overall, implying joint training of treebanks helps.", "description": "Cas yields slightly better performance than Sup, especially for SV (+1.52% UAS and +2.04% LAS), indicating that pre-training with EN training data indeed provides a better initialization of the parameters for cascaded training. SMTL in turn outperforms Cas overall (comparable for IT), which implies that training two treebanks jointly helps even with an unique model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: Parsing accuracies of Multilingual (Univ→Univ). Significance tests with MaltEval yield p-values < 0.01 for (MTL vs. Sup) on all languages.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model shows the highest UAS score for PT?\n" }, { "role": "assistant", "content": "To find the highest UAS score for PT, compare the UAS scores across all models for PT. The highest UAS score for PT is 89.24 under the SMTLEN model. So, the answer is {\"answer\": \"89.24\"}." } ], "images": [ "scigen/train/505.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Parsing accuracies of Multilingual (Univ→Univ). Significance tests with MaltEval yield p-values < 0.01 for (MTL vs. Sup) on all languages." ], "reasoning": "To find the highest UAS score for PT, compare the UAS scores across all models for PT. The highest UAS score for PT is 89.24 under the SMTLEN model.", "question": "Which model shows the highest UAS score for PT?", "answer": "So, the answer is {\"answer\": \"89.24\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe SMTLEN model has the highest LAS score for IT.\n" }, { "role": "assistant", "content": "To verify this statement, compare the LAS scores for IT across all models. The highest LAS score for IT is 84.27 under the SMTLEN model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/505.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Parsing accuracies of Multilingual (Univ→Univ). Significance tests with MaltEval yield p-values < 0.01 for (MTL vs. Sup) on all languages." ], "reasoning": "To verify this statement, compare the LAS scores for IT across all models. The highest LAS score for IT is 84.27 under the SMTLEN model.", "statement": "The SMTLEN model has the highest LAS score for IT.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: SMTL for Swedish without sharing BiLSTM(chars).'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Tests on SMTL without sharing Char-BiLSTMs show that MTL can achieve higher performance through language-specific parameter tuning.To verify the first issue, we conduct tests on SMTL without sharing Char-BiLSTMs. This observation also indicates that MTL has the potential to reach higher performances through language-specific tuning of parameter sharing strategies." } ], "images": [ "scigen/train/506.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: SMTL for Swedish without sharing BiLSTM(chars)." ], "reasoning": "Tests on SMTL without sharing Char-BiLSTMs show that MTL can achieve higher performance through language-specific parameter tuning.", "description": "To verify the first issue, we conduct tests on SMTL without sharing Char-BiLSTMs. This observation also indicates that MTL has the potential to reach higher performances through language-specific tuning of parameter sharing strategies.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 4: SMTL for Swedish without sharing BiLSTM(chars).', and tackle the question below in a sequential manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the UAS score for SMTL without sharing Char-BiLSTMs?\n" }, { "role": "assistant", "content": "The table shows the UAS score for SMTL without sharing Char-BiLSTMs as 86.06. So, the answer is {\"answer\": \"86.06\"}." } ], "images": [ "scigen/train/506.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: SMTL for Swedish without sharing BiLSTM(chars)." ], "reasoning": "The table shows the UAS score for SMTL without sharing Char-BiLSTMs as 86.06.", "question": "What is the UAS score for SMTL without sharing Char-BiLSTMs?", "answer": "So, the answer is {\"answer\": \"86.06\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe LAS score for SMTL without sharing Char-BiLSTMs is higher than the LAS score for SMTL with shared Char-BiLSTMs.\n" }, { "role": "assistant", "content": "The table shows that the LAS score for SMTL without sharing Char-BiLSTMs is 81.50, which is lower than the LAS score for SMTL with shared Char-BiLSTMs, which is 82.31. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/506.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: SMTL for Swedish without sharing BiLSTM(chars)." ], "reasoning": "The table shows that the LAS score for SMTL without sharing Char-BiLSTMs is 81.50, which is lower than the LAS score for SMTL with shared Char-BiLSTMs, which is 82.31.", "statement": "The LAS score for SMTL without sharing Char-BiLSTMs is higher than the LAS score for SMTL with shared Char-BiLSTMs.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: Low resource setup (3K tokens), evaluated with LAS.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "In low resource settings, SMTL and MTL models can be significantly improved through weighted sampling of tasks during training, encouraging the tasks to converge at a similar rate.To verify the second issue, we consider a low resource setup following \\newciteduong-EtAl:2015:EMNLP, where the target language has a small treebank (3K tokens). We train our models on identical sampled dataset shared by \\newciteduong-EtAl:2015:EMNLP on DE, ES and FR. Although not the primary focus of this work, we find that SMTL and MTL can be significantly improved in low resource setting through weighted sampling of tasks during training. In this way, the two tasks are encouraged to converge at a similar rate." } ], "images": [ "scigen/train/507.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Low resource setup (3K tokens), evaluated with LAS." ], "reasoning": "In low resource settings, SMTL and MTL models can be significantly improved through weighted sampling of tasks during training, encouraging the tasks to converge at a similar rate.", "description": "To verify the second issue, we consider a low resource setup following \\newciteduong-EtAl:2015:EMNLP, where the target language has a small treebank (3K tokens). We train our models on identical sampled dataset shared by \\newciteduong-EtAl:2015:EMNLP on DE, ES and FR. Although not the primary focus of this work, we find that SMTL and MTL can be significantly improved in low resource setting through weighted sampling of tasks during training. In this way, the two tasks are encouraged to converge at a similar rate.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 5: Low resource setup (3K tokens), evaluated with LAS.', unfold the question presented below, detailing every step of your calculation. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the highest score achieved in the ES column using the Cas method?\n" }, { "role": "assistant", "content": "To find the highest score in the ES column using the Cas method, locate the Cas row and identify the value in the ES column. So, the answer is {\"answer\": \"70.45\"}." } ], "images": [ "scigen/train/507.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Low resource setup (3K tokens), evaluated with LAS." ], "reasoning": "To find the highest score in the ES column using the Cas method, locate the Cas row and identify the value in the ES column.", "question": "What is the highest score achieved in the ES column using the Cas method?", "answer": "So, the answer is {\"answer\": \"70.45\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The SMTL method with weighted sampling achieves a higher score in DE than the MTL method with weighted sampling. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe SMTL method with weighted sampling achieves a higher score in DE than the MTL method with weighted sampling.\n" }, { "role": "assistant", "content": "Compare the scores of SMTL with weighted sampling and MTL with weighted sampling in the DE column. SMTL with weighted sampling has a score of 63.50, while MTL with weighted sampling has a score of 64.22. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/507.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Low resource setup (3K tokens), evaluated with LAS." ], "reasoning": "Compare the scores of SMTL with weighted sampling and MTL with weighted sampling in the DE column. SMTL with weighted sampling has a score of 63.50, while MTL with weighted sampling has a score of 64.22.", "statement": "The SMTL method with weighted sampling achieves a higher score in DE than the MTL method with weighted sampling.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 6: Monolingual (Conll↔Univ) performance. SV∗ is used for computing the Avg values.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "MTL systems outperform supervised baselines significantly in both conditions, demonstrating the mutual benefits of UDT and CoNLL-X treebanks.Overall MTL systems outperforms the supervised baselines by significant margins in both conditions, showing the mutual benefits of UDT and CoNLL-X treebanks." } ], "images": [ "scigen/train/508.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Monolingual (Conll↔Univ) performance. SV∗ is used for computing the Avg values." ], "reasoning": "MTL systems outperform supervised baselines significantly in both conditions, demonstrating the mutual benefits of UDT and CoNLL-X treebanks.", "description": "Overall MTL systems outperforms the supervised baselines by significant margins in both conditions, showing the mutual benefits of UDT and CoNLL-X treebanks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 6: Monolingual (Conll↔Univ) performance. SV∗ is used for computing the Avg values.'.\n\nQuestion: What is the average MTL LAS score for Monolingual (Conll→Univ) across all languages?\n" }, { "role": "assistant", "content": "To find the average MTL LAS score for Monolingual (Conll→Univ), look at the MTL LAS column under Monolingual (Conll→Univ) and calculate the average of the scores for DE, ES, PT, SV, and SV*. The scores are 80.64, 81.45, 87.60, 83.52, and 82.73 respectively. The average is (80.64 + 81.45 + 87.60 + 83.52 + 82.73) / 5 = 83.19. So, the answer is {\"answer\": \"83.19\"}" } ], "images": [ "scigen/train/508.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Monolingual (Conll↔Univ) performance. SV∗ is used for computing the Avg values." ], "reasoning": "To find the average MTL LAS score for Monolingual (Conll→Univ), look at the MTL LAS column under Monolingual (Conll→Univ) and calculate the average of the scores for DE, ES, PT, SV, and SV*. The scores are 80.64, 81.45, 87.60, 83.52, and 82.73 respectively. The average is (80.64 + 81.45 + 87.60 + 83.52 + 82.73) / 5 = 83.19.", "question": "What is the average MTL LAS score for Monolingual (Conll→Univ) across all languages?", "answer": "So, the answer is {\"answer\": \"83.19\"}" }, { "messages": [ { "role": "user", "content": "The MTL UAS score for PT in Monolingual (Univ→Conll) is higher than 90. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe MTL UAS score for PT in Monolingual (Univ→Conll) is higher than 90.\n" }, { "role": "assistant", "content": "To verify this statement, look at the MTL UAS score for PT under Monolingual (Univ→Conll). The score is 91.60, which is higher than 90. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/508.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Monolingual (Conll↔Univ) performance. SV∗ is used for computing the Avg values." ], "reasoning": "To verify this statement, look at the MTL UAS score for PT under Monolingual (Univ→Conll). The score is 91.60, which is higher than 90.", "statement": "The MTL UAS score for PT in Monolingual (Univ→Conll) is higher than 90.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Automatic evaluation: BLEU. Significant differences at p<0.01 are in bold.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The context encoder using the previous sentence achieves the highest BLEU score, outperforming both the baseline and the concatenation model.We use the traditional automatic metric BLEU on a general test set to get an estimate of the overall performance of the discourse-aware model, before turning to more targeted evaluation in the next section. The ‘baseline’ is the discourse-agnostic version of the Transformer. As another baseline we use the standard Transformer applied to the concatenation of the previous and source sentences, as proposed by \\newcitetiedemann_neural_2017. \\newcitetiedemann_neural_2017 only used a special symbol to mark where the context sentence ends and the source sentence begins. This technique performed badly with the non-recurrent Transformer architecture in preliminary experiments, resulting in a substantial degradation of performance (over 1 BLEU). Instead, we use a binary flag at every word position in our concatenation baseline telling the encoder whether the word belongs to the context sentence or to the source sentence." } ], "images": [ "scigen/train/509.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Automatic evaluation: BLEU. Significant differences at p<0.01 are in bold." ], "reasoning": "The context encoder using the previous sentence achieves the highest BLEU score, outperforming both the baseline and the concatenation model.", "description": "We use the traditional automatic metric BLEU on a general test set to get an estimate of the overall performance of the discourse-aware model, before turning to more targeted evaluation in the next section. The ‘baseline’ is the discourse-agnostic version of the Transformer. As another baseline we use the standard Transformer applied to the concatenation of the previous and source sentences, as proposed by \\newcitetiedemann_neural_2017. \\newcitetiedemann_neural_2017 only used a special symbol to mark where the context sentence ends and the source sentence begins. This technique performed badly with the non-recurrent Transformer architecture in preliminary experiments, resulting in a substantial degradation of performance (over 1 BLEU). Instead, we use a binary flag at every word position in our concatenation baseline telling the encoder whether the word belongs to the context sentence or to the source sentence.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 1: Automatic evaluation: BLEU. Significant differences at p<0.01 are in bold.', unfold the question presented below, detailing every step of your calculation. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieved the highest BLEU score?\n" }, { "role": "assistant", "content": "By comparing the BLEU scores of all models listed in the table, the context encoder (previous sentence) has the highest score of 30.14. So, the answer is {\"answer\": \"context encoder (previous sentence)\"}." } ], "images": [ "scigen/train/509.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Automatic evaluation: BLEU. Significant differences at p<0.01 are in bold." ], "reasoning": "By comparing the BLEU scores of all models listed in the table, the context encoder (previous sentence) has the highest score of 30.14.", "question": "Which model achieved the highest BLEU score?", "answer": "So, the answer is {\"answer\": \"context encoder (previous sentence)\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe baseline model achieved the highest BLEU score.\n" }, { "role": "assistant", "content": "The table shows that the context encoder (previous sentence) achieved the highest BLEU score of 30.14, while the baseline model achieved a BLEU score of 29.46. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/509.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Automatic evaluation: BLEU. Significant differences at p<0.01 are in bold." ], "reasoning": "The table shows that the context encoder (previous sentence) achieved the highest BLEU score of 30.14, while the baseline model achieved a BLEU score of 29.46.", "statement": "The baseline model achieved the highest BLEU score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 9: Performance of CoreNLP and our model’s attention mechanism compared to human assessment (%). Examples with ≥1 noun in context sentence.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Our model's agreement with the ground truth is 72%, which is 5% below the coreference system but 18% higher than the best heuristic, indicating effective latent anaphora resolution.The agreement between our model and the ground truth is 72%. Though 5% below the coreference system, this is a lot higher than the best heuristic (+18%). This confirms our conclusion that our model performs latent anaphora resolution. Nevertheless, there is room for improvement, and improving the attention component is likely to boost translation performance." } ], "images": [ "scigen/train/510.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Performance of CoreNLP and our model’s attention mechanism compared to human assessment (%). Examples with ≥1 noun in context sentence." ], "reasoning": "Our model's agreement with the ground truth is 72%, which is 5% below the coreference system but 18% higher than the best heuristic, indicating effective latent anaphora resolution.", "description": "The agreement between our model and the ground truth is 72%. Though 5% below the coreference system, this is a lot higher than the best heuristic (+18%). This confirms our conclusion that our model performs latent anaphora resolution. Nevertheless, there is room for improvement, and improving the attention component is likely to boost translation performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 9: Performance of CoreNLP and our model’s attention mechanism compared to human assessment (%). Examples with ≥1 noun in context sentence.\nQuestion: What is the total number of cases where CoreNLP was right?\n" }, { "role": "assistant", "content": "To find the total number of cases where CoreNLP was right, sum the values in the 'CoreNLP right' column: 53 (attn right) + 24 (attn wrong) = 77. So, the answer is {\"answer\": \"77\"}." } ], "images": [ "scigen/train/510.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Performance of CoreNLP and our model’s attention mechanism compared to human assessment (%). Examples with ≥1 noun in context sentence." ], "reasoning": "To find the total number of cases where CoreNLP was right, sum the values in the 'CoreNLP right' column: 53 (attn right) + 24 (attn wrong) = 77.", "question": "What is the total number of cases where CoreNLP was right?", "answer": "So, the answer is {\"answer\": \"77\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The model's agreement with the ground truth is higher than the best heuristic by 18%. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model's agreement with the ground truth is higher than the best heuristic by 18%.\n" }, { "role": "assistant", "content": "The description states that the model's agreement with the ground truth is 72%, which is 18% higher than the best heuristic. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/510.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Performance of CoreNLP and our model’s attention mechanism compared to human assessment (%). Examples with ≥1 noun in context sentence." ], "reasoning": "The description states that the model's agreement with the ground truth is 72%, which is 18% higher than the best heuristic.", "statement": "The model's agreement with the ground truth is higher than the best heuristic by 18%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Human evaluation ratings for (on a scale from 1 to 5) for FeHED, FeHED−FST-DA, FeHED−FST-S, and HED. We conducted third-person rating and second-person rating. Sale price is normalized.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "FeHED outperforms other models in persuasiveness, coherence, and naturalness in third-person ratings, and in second-person ratings, it excels in all metrics except naturalness where HED+RNN is better.For third-person rating, we asked an expert to generate 20 dialogs by negotiating with FeHED, FeHED−FST-DA, FedHED−S, HED+RNN and HED respectively (5 dialogs each). We then recruited 50 people on AMT to rate these generated dialogs. Result shows that FeHED is more persuasive, coherent and natural than all the baselines. For second-person rating, we asked 50 participants on AMT to play the role of buyer to negotiate with FeHED, FeHED−FST-DA, FeHED−FST-S, HED+RNN and HED respectively. Results show that FeHED outperforms RNN and other models on all the metrics except naturalness. It is likely because RNN is trained jointly with HED, but it is not good at explicitly tracking and preserving the history. Although FST is learned separately, it forces the model to learn the history through a list of traversed states. We analyze the generated dialogs for human evaluation and find that baselines are more likely to accept unfair offers and apply inappropriate strategies." } ], "images": [ "scigen/train/511.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Human evaluation ratings for (on a scale from 1 to 5) for FeHED, FeHED−FST-DA, FeHED−FST-S, and HED. We conducted third-person rating and second-person rating. Sale price is normalized." ], "reasoning": "FeHED outperforms other models in persuasiveness, coherence, and naturalness in third-person ratings, and in second-person ratings, it excels in all metrics except naturalness where HED+RNN is better.", "description": "For third-person rating, we asked an expert to generate 20 dialogs by negotiating with FeHED, FeHED−FST-DA, FedHED−S, HED+RNN and HED respectively (5 dialogs each). We then recruited 50 people on AMT to rate these generated dialogs. Result shows that FeHED is more persuasive, coherent and natural than all the baselines. For second-person rating, we asked 50 participants on AMT to play the role of buyer to negotiate with FeHED, FeHED−FST-DA, FeHED−FST-S, HED+RNN and HED respectively. Results show that FeHED outperforms RNN and other models on all the metrics except naturalness. It is likely because RNN is trained jointly with HED, but it is not good at explicitly tracking and preserving the history. Although FST is learned separately, it forces the model to learn the history through a list of traversed states. We analyze the generated dialogs for human evaluation and find that baselines are more likely to accept unfair offers and apply inappropriate strategies.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Human evaluation ratings for (on a scale from 1 to 5) for FeHED, FeHED−FST-DA, FeHED−FST-S, and HED. We conducted third-person rating and second-person rating. Sale price is normalized.\nQuestion: Which model has the highest third-person rating for coherence?\nGive a detailed response to the above question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the 'Third-person Rating Coherent' column, FeHED has the highest rating of 3.9. So, the answer is {\"answer\": \"FeHED\"}." } ], "images": [ "scigen/train/511.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Human evaluation ratings for (on a scale from 1 to 5) for FeHED, FeHED−FST-DA, FeHED−FST-S, and HED. We conducted third-person rating and second-person rating. Sale price is normalized." ], "reasoning": "By examining the 'Third-person Rating Coherent' column, FeHED has the highest rating of 3.9.", "question": "Which model has the highest third-person rating for coherence?", "answer": "So, the answer is {\"answer\": \"FeHED\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence FeHED has the highest second-person rating for naturalness. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nFeHED has the highest second-person rating for naturalness.\n" }, { "role": "assistant", "content": "The table shows that FeHED has a second-person rating of 3.0 for naturalness, which is higher than the other models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/511.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Human evaluation ratings for (on a scale from 1 to 5) for FeHED, FeHED−FST-DA, FeHED−FST-S, and HED. We conducted third-person rating and second-person rating. Sale price is normalized." ], "reasoning": "The table shows that FeHED has a second-person rating of 3.0 for naturalness, which is higher than the other models.", "statement": "FeHED has the highest second-person rating for naturalness.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Results for all test languages on the official test sets for Task 2.'.\n" }, { "role": "assistant", "content": "NYU-CUBoulder-2, the vanilla transformer ensemble, performed slightly better overall with an average best-match accuracy of 18.02%, and for Basque, it achieved the highest result with a best-match accuracy of 0.07%.All three systems produce relatively similar results. NYU-CUBoulder-2, our vanilla transformer ensemble, performed slightly better overall with an average best-match accuracy of 18.02%. Since our system is close to the baseline models, it performs similarly, achieving slightly worse results. For Basque, our all-round ensemble NYU-CUBoulder-2 outperformed both baselines with a best-match accuracy of 00.07%, achieving the highest result in the shared task." } ], "images": [ "scigen/train/512.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results for all test languages on the official test sets for Task 2." ], "reasoning": "NYU-CUBoulder-2, the vanilla transformer ensemble, performed slightly better overall with an average best-match accuracy of 18.02%, and for Basque, it achieved the highest result with a best-match accuracy of 0.07%.", "description": "All three systems produce relatively similar results. NYU-CUBoulder-2, our vanilla transformer ensemble, performed slightly better overall with an average best-match accuracy of 18.02%. Since our system is close to the baseline models, it performs similarly, achieving slightly worse results. For Basque, our all-round ensemble NYU-CUBoulder-2 outperformed both baselines with a best-match accuracy of 00.07%, achieving the highest result in the shared task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Results for all test languages on the official test sets for Task 2.'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. \n\nQuestion: Which system achieved the highest best-match accuracy for Basque?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The description states that NYU-CUBoulder-2 outperformed both baselines for Basque with a best-match accuracy of 0.07%, which is confirmed by the table showing 0.0007 for Sub-3 Test Set. So, the answer is {\"answer\": \"NYU-CUBoulder-2\"}." } ], "images": [ "scigen/train/512.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results for all test languages on the official test sets for Task 2." ], "reasoning": "The description states that NYU-CUBoulder-2 outperformed both baselines for Basque with a best-match accuracy of 0.07%, which is confirmed by the table showing 0.0007 for Sub-3 Test Set.", "question": "Which system achieved the highest best-match accuracy for Basque?", "answer": "So, the answer is {\"answer\": \"NYU-CUBoulder-2\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nNYU-CUBoulder-2 achieved the highest best-match accuracy for Basque.\n" }, { "role": "assistant", "content": "The description and the table both indicate that NYU-CUBoulder-2 achieved the highest best-match accuracy for Basque with a value of 0.07%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/512.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results for all test languages on the official test sets for Task 2." ], "reasoning": "The description and the table both indicate that NYU-CUBoulder-2 achieved the highest best-match accuracy for Basque with a value of 0.07%.", "statement": "NYU-CUBoulder-2 achieved the highest best-match accuracy for Basque.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: The hyperparameters used in our inflection models for both Task 0 and Task 2.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "NYU-CUBoulder-1 uses six pointer-generator transformers, NYU-CUBoulder-2 uses six vanilla transformers, and NYU-CUBoulder-3 combines all twelve models.For Task 2, we use ensembling for all submissions. NYU-CUBoulder-1 is an ensemble of six pointer-generator transformers, NYU-CUBoulder-2 is an ensemble of six vanilla transformers, and NYU-CUBoulder-3 is an ensemble of all twelve models. For all models in both tasks, we use the hyperparameters described in Table" } ], "images": [ "scigen/train/513.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The hyperparameters used in our inflection models for both Task 0 and Task 2." ], "reasoning": "NYU-CUBoulder-1 uses six pointer-generator transformers, NYU-CUBoulder-2 uses six vanilla transformers, and NYU-CUBoulder-3 combines all twelve models.", "description": "For Task 2, we use ensembling for all submissions. NYU-CUBoulder-1 is an ensemble of six pointer-generator transformers, NYU-CUBoulder-2 is an ensemble of six vanilla transformers, and NYU-CUBoulder-3 is an ensemble of all twelve models. For all models in both tasks, we use the hyperparameters described in Table", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: The hyperparameters used in our inflection models for both Task 0 and Task 2.', answer the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the total number of encoder layers used in the NYU-CUBoulder-3 ensemble?\n" }, { "role": "assistant", "content": "NYU-CUBoulder-3 is an ensemble of all twelve models, each having 4 encoder layers. Therefore, the total number of encoder layers is 12 models * 4 layers = 48 layers. So, the answer is {\"answer\": \"48\"}" } ], "images": [ "scigen/train/513.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The hyperparameters used in our inflection models for both Task 0 and Task 2." ], "reasoning": "NYU-CUBoulder-3 is an ensemble of all twelve models, each having 4 encoder layers. Therefore, the total number of encoder layers is 12 models * 4 layers = 48 layers.", "question": "What is the total number of encoder layers used in the NYU-CUBoulder-3 ensemble?", "answer": "So, the answer is {\"answer\": \"48\"}" }, { "messages": [ { "role": "user", "content": "Each model in the NYU-CUBoulder-1 ensemble has 4 decoder layers. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nEach model in the NYU-CUBoulder-1 ensemble has 4 decoder layers.\n" }, { "role": "assistant", "content": "The table shows that each model has 4 decoder layers, and NYU-CUBoulder-1 is an ensemble of six pointer-generator transformers, each having 4 decoder layers. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/513.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The hyperparameters used in our inflection models for both Task 0 and Task 2." ], "reasoning": "The table shows that each model has 4 decoder layers, and NYU-CUBoulder-1 is an ensemble of six pointer-generator transformers, each having 4 decoder layers.", "statement": "Each model in the NYU-CUBoulder-1 ensemble has 4 decoder layers.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Macro-averaged results over all languages on the official development and test sets for Task 0. Low=languages with less than 1000 train instances, Other=all other languages, All=all languages.'.\n" }, { "role": "assistant", "content": "NYU-CUBoulder-3, a five-model ensemble, achieves the highest overall accuracy of 88.8%, with pointer-generator systems showing an advantage in low-resource settings and NYU-CUBoulder-4 performing best in high-resource settings.All four systems produce relatively similar results. NYU-CUBoulder-3, our five-model ensemble, performs best overall with 88.8% accuracy on average. We further look at the results for low-resource (< 1000 training examples) and high-resource (>= 1000 training examples) languages separately. This way, we are able to see the advantage of the pointer-generator transformer in the low-resource setting, where all pointer-generator systems achieve an at least 0.9% higher accuracy than the vanilla transformer model. However, in the setting where training data is abundant, the effect of the copy mechanism vanishes, as NYU-CUBoulder-4 – our only vanilla transformer – achieved the best results for our high-resource languages." } ], "images": [ "scigen/train/514.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Macro-averaged results over all languages on the official development and test sets for Task 0. Low=languages with less than 1000 train instances, Other=all other languages, All=all languages." ], "reasoning": "NYU-CUBoulder-3, a five-model ensemble, achieves the highest overall accuracy of 88.8%, with pointer-generator systems showing an advantage in low-resource settings and NYU-CUBoulder-4 performing best in high-resource settings.", "description": "All four systems produce relatively similar results. NYU-CUBoulder-3, our five-model ensemble, performs best overall with 88.8% accuracy on average. We further look at the results for low-resource (< 1000 training examples) and high-resource (>= 1000 training examples) languages separately. This way, we are able to see the advantage of the pointer-generator transformer in the low-resource setting, where all pointer-generator systems achieve an at least 0.9% higher accuracy than the vanilla transformer model. However, in the setting where training data is abundant, the effect of the copy mechanism vanishes, as NYU-CUBoulder-4 – our only vanilla transformer – achieved the best results for our high-resource languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Macro-averaged results over all languages on the official development and test sets for Task 0. Low=languages with less than 1000 train instances, Other=all other languages, All=all languages.\nQuestion: Which model achieved the highest accuracy in the 'Other' category of the Development Set?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the 'Other' category in the Development Set, NYU-CUBoulder-4 has the highest accuracy of 90.94%. So, the answer is {\"answer\": \"90.94\"}." } ], "images": [ "scigen/train/514.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Macro-averaged results over all languages on the official development and test sets for Task 0. Low=languages with less than 1000 train instances, Other=all other languages, All=all languages." ], "reasoning": "By examining the 'Other' category in the Development Set, NYU-CUBoulder-4 has the highest accuracy of 90.94%.", "question": "Which model achieved the highest accuracy in the 'Other' category of the Development Set?", "answer": "So, the answer is {\"answer\": \"90.94\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nNYU-CUBoulder-4 achieved the highest accuracy in the 'Low' category of the Test Set.\n" }, { "role": "assistant", "content": "By examining the 'Low' category in the Test Set, the Base model achieved the highest accuracy of 89.77%, not NYU-CUBoulder-4. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/514.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Macro-averaged results over all languages on the official development and test sets for Task 0. Low=languages with less than 1000 train instances, Other=all other languages, All=all languages." ], "reasoning": "By examining the 'Low' category in the Test Set, the Base model achieved the highest accuracy of 89.77%, not NYU-CUBoulder-4.", "statement": "NYU-CUBoulder-4 achieved the highest accuracy in the 'Low' category of the Test Set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: Results on the official development data for our low-resource experiment. Trm=Vanilla transformer, Trm-PG=Pointer-generator transformer, Baseline=neural transducer by Makarov and Clematide (2018).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The neural transducer achieves a higher accuracy, but our model performs only 2.45% worse than this state-of-the-art model, and the copy mechanism is used for OOV characters in some languages.S5SS3SSS0Px4 Results. For some languages, such as Chichicapan Zapotec, the difference is up to 14%. While the neural transducer achieves a higher accuracy, our model performs only 2.45% worse than this state-of-the-art model. We are also able to observe the use of the copy mechanism for copying of OOV characters in the test sets of some languages." } ], "images": [ "scigen/train/515.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on the official development data for our low-resource experiment. Trm=Vanilla transformer, Trm-PG=Pointer-generator transformer, Baseline=neural transducer by Makarov and Clematide (2018)." ], "reasoning": "The neural transducer achieves a higher accuracy, but our model performs only 2.45% worse than this state-of-the-art model, and the copy mechanism is used for OOV characters in some languages.", "description": "S5SS3SSS0Px4 Results. For some languages, such as Chichicapan Zapotec, the difference is up to 14%. While the neural transducer achieves a higher accuracy, our model performs only 2.45% worse than this state-of-the-art model. We are also able to observe the use of the copy mechanism for copying of OOV characters in the test sets of some languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Results on the official development data for our low-resource experiment. Trm=Vanilla transformer, Trm-PG=Pointer-generator transformer, Baseline=neural transducer by Makarov and Clematide (2018).\nQuestion: What is the accuracy difference between the Trm-PG model and the Baseline model?\nSolve the above question based on the table information. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The Baseline model has an accuracy of 70.06%, while the Trm-PG model has an accuracy of 67.61%. The difference is calculated as 70.06% - 67.61% = 2.45%. So, the answer is {\"answer\": \"2.45%\"}" } ], "images": [ "scigen/train/515.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on the official development data for our low-resource experiment. Trm=Vanilla transformer, Trm-PG=Pointer-generator transformer, Baseline=neural transducer by Makarov and Clematide (2018)." ], "reasoning": "The Baseline model has an accuracy of 70.06%, while the Trm-PG model has an accuracy of 67.61%. The difference is calculated as 70.06% - 67.61% = 2.45%.", "question": "What is the accuracy difference between the Trm-PG model and the Baseline model?", "answer": "So, the answer is {\"answer\": \"2.45%\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Trm model has a higher accuracy than the Baseline model. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Trm model has a higher accuracy than the Baseline model.\n" }, { "role": "assistant", "content": "The table shows that the Trm model has an accuracy of 63.06%, while the Baseline model has an accuracy of 70.06%. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/515.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on the official development data for our low-resource experiment. Trm=Vanilla transformer, Trm-PG=Pointer-generator transformer, Baseline=neural transducer by Makarov and Clematide (2018)." ], "reasoning": "The table shows that the Trm model has an accuracy of 63.06%, while the Baseline model has an accuracy of 70.06%. Therefore, the statement is refuted.", "statement": "The Trm model has a higher accuracy than the Baseline model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: F1-scores on the NER target domain (CBS SciTech News).'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Template regularization is less effective in NER tasks due to the open nature of the text, making it difficult to capture templates for each label type.However, we observe that template regularization loses its effectiveness in this task, since the text in NER is relatively more open, which makes it hard to capture the templates for each label type." } ], "images": [ "scigen/train/516.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: F1-scores on the NER target domain (CBS SciTech News)." ], "reasoning": "Template regularization is less effective in NER tasks due to the open nature of the text, making it difficult to capture templates for each label type.", "description": "However, we observe that template regularization loses its effectiveness in this task, since the text in NER is relatively more open, which makes it hard to capture the templates for each label type.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 3: F1-scores on the NER target domain (CBS SciTech News).\nQuestion: Which method achieved the highest score with 50 target samples?\n" }, { "role": "assistant", "content": "To find the highest score with 50 target samples, compare the values in the '50' column. The highest score is 68.35, achieved by the 'Coach' method. So, the answer is {\"answer\": \"Coach\"}" } ], "images": [ "scigen/train/516.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: F1-scores on the NER target domain (CBS SciTech News)." ], "reasoning": "To find the highest score with 50 target samples, compare the values in the '50' column. The highest score is 68.35, achieved by the 'Coach' method.", "question": "Which method achieved the highest score with 50 target samples?", "answer": "So, the answer is {\"answer\": \"Coach\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe BiLSTM-CRF method achieved a higher score with 50 target samples than the RZT method.\n" }, { "role": "assistant", "content": "Compare the scores of BiLSTM-CRF and RZT with 50 target samples. BiLSTM-CRF scored 66.57, while RZT scored 65.21, indicating that BiLSTM-CRF achieved a higher score. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/516.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: F1-scores on the NER target domain (CBS SciTech News)." ], "reasoning": "Compare the scores of BiLSTM-CRF and RZT with 50 target samples. BiLSTM-CRF scored 66.57, while RZT scored 65.21, indicating that BiLSTM-CRF achieved a higher score.", "statement": "The BiLSTM-CRF method achieved a higher score with 50 target samples than the RZT method.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Slot F1-scores based on standard BIO structure for SNIPS. Scores in each row represents the performance of the leftmost target domain, and TR denotes template regularization.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The CT framework struggles with capturing whole slot entities, while the proposed framework improves recognition by parameter sharing across slot types, leading to better performance and robustness.The CT framework suffers from the difficulty of capturing the whole slot entity, while our framework is able to recognize the slot entity tokens by sharing its parameters across all slot types. Based on the CT framework, the performance of RZT is still limited, and Coach outperforms RZT by a ∼3% F1-score in the zero-shot setting. Additionally, template regularization further improves the adaptation robustness by helping the model cluster the utterance representations into a similar vector space based on their corresponding template representations." } ], "images": [ "scigen/train/517.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Slot F1-scores based on standard BIO structure for SNIPS. Scores in each row represents the performance of the leftmost target domain, and TR denotes template regularization." ], "reasoning": "The CT framework struggles with capturing whole slot entities, while the proposed framework improves recognition by parameter sharing across slot types, leading to better performance and robustness.", "description": "The CT framework suffers from the difficulty of capturing the whole slot entity, while our framework is able to recognize the slot entity tokens by sharing its parameters across all slot types. Based on the CT framework, the performance of RZT is still limited, and Coach outperforms RZT by a ∼3% F1-score in the zero-shot setting. Additionally, template regularization further improves the adaptation robustness by helping the model cluster the utterance representations into a similar vector space based on their corresponding template representations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Slot F1-scores based on standard BIO structure for SNIPS. Scores in each row represents the performance of the leftmost target domain, and TR denotes template regularization.\nQuestion: What is the average F1-score improvement of Coach over RZT in the zero-shot setting?\nSolve the above question based on the table information. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the average F1-score improvement of Coach over RZT in the zero-shot setting, subtract the average F1-score of RZT (32.85) from that of Coach (35.82). So, the answer is {\"answer\": \"2.97\"}" } ], "images": [ "scigen/train/517.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Slot F1-scores based on standard BIO structure for SNIPS. Scores in each row represents the performance of the leftmost target domain, and TR denotes template regularization." ], "reasoning": "To find the average F1-score improvement of Coach over RZT in the zero-shot setting, subtract the average F1-score of RZT (32.85) from that of Coach (35.82).", "question": "What is the average F1-score improvement of Coach over RZT in the zero-shot setting?", "answer": "So, the answer is {\"answer\": \"2.97\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nCoach outperforms RZT by approximately 3% F1-score in the zero-shot setting.\n" }, { "role": "assistant", "content": "The table shows that in the zero-shot setting, Coach has an average F1-score of 35.82, while RZT has 32.85. The difference is 2.97, which is approximately 3%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/517.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Slot F1-scores based on standard BIO structure for SNIPS. Scores in each row represents the performance of the leftmost target domain, and TR denotes template regularization." ], "reasoning": "The table shows that in the zero-shot setting, Coach has an average F1-score of 35.82, while RZT has 32.85. The difference is 2.97, which is approximately 3%.", "statement": "Coach outperforms RZT by approximately 3% F1-score in the zero-shot setting.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Averaged F1-scores for seen and unseen slots over all target domains. ‡ represent the number of training samples utilized for the target domain.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Our models improve performance on both unseen and seen slots compared to baseline models, with template regularization further boosting adaptation ability.We take a further step to test the models on seen and unseen slots in target domains to analyze the effectiveness of our approaches. To test the performance, we split the test set into “unseen” and “seen” parts. An utterance is categorized into the “unseen” part as long as there is an unseen slot (i.e., the slot does not exist in the remaining six source domains) in it. Otherwise we categorize it into the “seen” part. We observe that our approaches generally improve on both unseen and seen slot types compared to the baseline models. For the improvements in the unseen slots, our models are better able to capture the unseen slots since they explicitly learn the general pattern of slot entities. Interestingly, our models also bring large improvements in the seen slot types. We conjecture that it is also challenging to adapt models to seen slots due to the large variance between the source and target domains. For example, slot entities belonging to the “object type” in the “RateBook” domain are different from those in the “SearchCreativeWork” domain. Hence, the baseline models might fail to recognize these seen slots in the target domain, while our approaches can adapt to the seen slot types more quickly in comparison. In addition, we observe that template regularization improves performance in both seen and unseen slots, which illustrates that clustering representations based on templates can boost the adaptation ability." } ], "images": [ "scigen/train/518.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Averaged F1-scores for seen and unseen slots over all target domains. ‡ represent the number of training samples utilized for the target domain." ], "reasoning": "Our models improve performance on both unseen and seen slots compared to baseline models, with template regularization further boosting adaptation ability.", "description": "We take a further step to test the models on seen and unseen slots in target domains to analyze the effectiveness of our approaches. To test the performance, we split the test set into “unseen” and “seen” parts. An utterance is categorized into the “unseen” part as long as there is an unseen slot (i.e., the slot does not exist in the remaining six source domains) in it. Otherwise we categorize it into the “seen” part. We observe that our approaches generally improve on both unseen and seen slot types compared to the baseline models. For the improvements in the unseen slots, our models are better able to capture the unseen slots since they explicitly learn the general pattern of slot entities. Interestingly, our models also bring large improvements in the seen slot types. We conjecture that it is also challenging to adapt models to seen slots due to the large variance between the source and target domains. For example, slot entities belonging to the “object type” in the “RateBook” domain are different from those in the “SearchCreativeWork” domain. Hence, the baseline models might fail to recognize these seen slots in the target domain, while our approaches can adapt to the seen slot types more quickly in comparison. In addition, we observe that template regularization improves performance in both seen and unseen slots, which illustrates that clustering representations based on templates can boost the adaptation ability.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: Averaged F1-scores for seen and unseen slots over all target domains. ‡ represent the number of training samples utilized for the target domain.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. \n\nQuestion: What is the performance of Coach+TR on 50 unseen samples?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the performance of Coach+TR on 50 unseen samples, look at the row labeled 'Coach+TR' and the column labeled '50 samples unseen'. The value is 76.49. So, the answer is {\"answer\": \"76.49\"}." } ], "images": [ "scigen/train/518.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Averaged F1-scores for seen and unseen slots over all target domains. ‡ represent the number of training samples utilized for the target domain." ], "reasoning": "To find the performance of Coach+TR on 50 unseen samples, look at the row labeled 'Coach+TR' and the column labeled '50 samples unseen'. The value is 76.49.", "question": "What is the performance of Coach+TR on 50 unseen samples?", "answer": "So, the answer is {\"answer\": \"76.49\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nCoach+TR performs better than Coach on 20 seen samples.\n" }, { "role": "assistant", "content": "To verify this statement, compare the performance values of Coach+TR and Coach on 20 seen samples. Coach+TR has a value of 73.85, while Coach has a value of 73.78. Since 73.85 is greater than 73.78, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/518.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Averaged F1-scores for seen and unseen slots over all target domains. ‡ represent the number of training samples utilized for the target domain." ], "reasoning": "To verify this statement, compare the performance values of Coach+TR and Coach on 20 seen samples. Coach+TR has a value of 73.85, while Coach has a value of 73.78. Since 73.85 is greater than 73.78, the statement is supported.", "statement": "Coach+TR performs better than Coach on 20 seen samples.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: PPL and relative WERRs (%) with varying floor interpolation weights for the translation component in the 180 hour setup.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Adjusting the floor weight parameter affects the translation component's effectiveness, with low weights making it ineffective and high weights undermining the transcription component.The purpose of this investigation is to observe the effect of changing the floor weight parameter for the translation component, which provides a lever to override its relative importance in the interpolated LM. However, WERR demonstrates fluctuation with varying floor weights: a low weight renders the translation component ineffective whereas a high value undermines the transcription component. Floor weight sweep can provide empirical guidance for adjusting this parameter." } ], "images": [ "scigen/train/519.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: PPL and relative WERRs (%) with varying floor interpolation weights for the translation component in the 180 hour setup." ], "reasoning": "Adjusting the floor weight parameter affects the translation component's effectiveness, with low weights making it ineffective and high weights undermining the transcription component.", "description": "The purpose of this investigation is to observe the effect of changing the floor weight parameter for the translation component, which provides a lever to override its relative importance in the interpolated LM. However, WERR demonstrates fluctuation with varying floor weights: a low weight renders the translation component ineffective whereas a high value undermines the transcription component. Floor weight sweep can provide empirical guidance for adjusting this parameter.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 4: PPL and relative WERRs (%) with varying floor interpolation weights for the translation component in the 180 hour setup.', and sequentially solve the question, writing out each step of your reasoning process. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the WERR % when the floor weight is 0.25?\n" }, { "role": "assistant", "content": "To find the WERR % for a floor weight of 0.25, locate the row where the floor weight is 0.25 and read the corresponding WERR % value. So, the answer is {\"answer\": \"7.86\"}." } ], "images": [ "scigen/train/519.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: PPL and relative WERRs (%) with varying floor interpolation weights for the translation component in the 180 hour setup." ], "reasoning": "To find the WERR % for a floor weight of 0.25, locate the row where the floor weight is 0.25 and read the corresponding WERR % value.", "question": "What is the WERR % when the floor weight is 0.25?", "answer": "So, the answer is {\"answer\": \"7.86\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe WERR % is highest when the floor weight is 0.25.\n" }, { "role": "assistant", "content": "To verify this statement, compare the WERR % values for all floor weights. The highest WERR % value is 7.86, which corresponds to a floor weight of 0.25. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/519.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: PPL and relative WERRs (%) with varying floor interpolation weights for the translation component in the 180 hour setup." ], "reasoning": "To verify this statement, compare the WERR % values for all floor weights. The highest WERR % value is 7.86, which corresponds to a floor weight of 0.25.", "statement": "The WERR % is highest when the floor weight is 0.25.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: Relative WERRs (%) with different post-editing techniques. Perplexity (PPL) is evaluated on a held-out in-domain dataset. Relative WERR captures the WER reduction w.r.t baseline trained on transcribed data only.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Introducing post-editing techniques like NE copy-over, NE resampling, and code mixing significantly reduces perplexity and improves WERR, with the best result being a 5.83% WERR.The approach of ingesting raw translations without any postprocessing yields a high perplexity translation component, resulting in negative WERR. We observe consistent improvements by introducing attention weight based post-editing. NE copy-over alone reduces the perplexity significantly. This, coupled with NE resampling and code mixing, results in a 5.83% WERR." } ], "images": [ "scigen/train/520.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Relative WERRs (%) with different post-editing techniques. Perplexity (PPL) is evaluated on a held-out in-domain dataset. Relative WERR captures the WER reduction w.r.t baseline trained on transcribed data only." ], "reasoning": "Introducing post-editing techniques like NE copy-over, NE resampling, and code mixing significantly reduces perplexity and improves WERR, with the best result being a 5.83% WERR.", "description": "The approach of ingesting raw translations without any postprocessing yields a high perplexity translation component, resulting in negative WERR. We observe consistent improvements by introducing attention weight based post-editing. NE copy-over alone reduces the perplexity significantly. This, coupled with NE resampling and code mixing, results in a 5.83% WERR.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Relative WERRs (%) with different post-editing techniques. Perplexity (PPL) is evaluated on a held-out in-domain dataset. Relative WERR captures the WER reduction w.r.t baseline trained on transcribed data only.', answer the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the PPL value when using NE resampling as the post-editing approach?\n" }, { "role": "assistant", "content": "To find the PPL value for NE resampling, look at the row corresponding to NE resampling under the Post-editing section. So, the answer is {\"answer\": \"1241.52\"}." } ], "images": [ "scigen/train/520.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Relative WERRs (%) with different post-editing techniques. Perplexity (PPL) is evaluated on a held-out in-domain dataset. Relative WERR captures the WER reduction w.r.t baseline trained on transcribed data only." ], "reasoning": "To find the PPL value for NE resampling, look at the row corresponding to NE resampling under the Post-editing section.", "question": "What is the PPL value when using NE resampling as the post-editing approach?", "answer": "So, the answer is {\"answer\": \"1241.52\"}." }, { "messages": [ { "role": "user", "content": "The raw translations approach yields the highest perplexity. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe raw translations approach yields the highest perplexity.\n" }, { "role": "assistant", "content": "The table shows that the PPL value for raw translations is 11941.08, which is the highest among all approaches listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/520.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Relative WERRs (%) with different post-editing techniques. Perplexity (PPL) is evaluated on a held-out in-domain dataset. Relative WERR captures the WER reduction w.r.t baseline trained on transcribed data only." ], "reasoning": "The table shows that the PPL value for raw translations is 11941.08, which is the highest among all approaches listed.", "statement": "The raw translations approach yields the highest perplexity.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Relative WERRs (%) with different NMT adaptation strategies. Note that these results include the effect of NE resampling and code mixing techniques.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "SIF embeddings achieve the highest WERR of 7.23%, while LASER and SIF outperform unweighted averaging in BLEU score despite the latter achieving the lowest perplexity.For MT training data selection, we retain only top-25% (out of 8.4M) sentences w.r.t. their relative similarity with in-domain data. This reduction in training data impacts the BLEU score adversely. Amongst the sentence representation techniques, LASER and SIF embeddings outperform the unweighted averaging approach in terms of BLEU score. Interestingly, while the unweighted averaging achieves lowest perplexity on held-out in-domain dataset, the gains don’t carry over while measuring overall ASR performance. SIF embedding based selection achieves the highest WERR of 7.23%, followed closely by LASER encoder representation." } ], "images": [ "scigen/train/521.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Relative WERRs (%) with different NMT adaptation strategies. Note that these results include the effect of NE resampling and code mixing techniques." ], "reasoning": "SIF embeddings achieve the highest WERR of 7.23%, while LASER and SIF outperform unweighted averaging in BLEU score despite the latter achieving the lowest perplexity.", "description": "For MT training data selection, we retain only top-25% (out of 8.4M) sentences w.r.t. their relative similarity with in-domain data. This reduction in training data impacts the BLEU score adversely. Amongst the sentence representation techniques, LASER and SIF embeddings outperform the unweighted averaging approach in terms of BLEU score. Interestingly, while the unweighted averaging achieves lowest perplexity on held-out in-domain dataset, the gains don’t carry over while measuring overall ASR performance. SIF embedding based selection achieves the highest WERR of 7.23%, followed closely by LASER encoder representation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 2: Relative WERRs (%) with different NMT adaptation strategies. Note that these results include the effect of NE resampling and code mixing techniques.' and proceed to solve the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich approach achieves the highest WERR and what is its BLEU score?\n" }, { "role": "assistant", "content": "The table shows that the SIF embedding based selection achieves the highest WERR of 7.23%. The BLEU score for SIF is 37.8. So, the answer is {\"answer\": \"SIF embedding based selection with BLEU score of 37.8\"}" } ], "images": [ "scigen/train/521.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Relative WERRs (%) with different NMT adaptation strategies. Note that these results include the effect of NE resampling and code mixing techniques." ], "reasoning": "The table shows that the SIF embedding based selection achieves the highest WERR of 7.23%. The BLEU score for SIF is 37.8.", "question": "Which approach achieves the highest WERR and what is its BLEU score?", "answer": "So, the answer is {\"answer\": \"SIF embedding based selection with BLEU score of 37.8\"}" }, { "messages": [ { "role": "user", "content": "The unweighted averaging approach achieves the highest WERR. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe unweighted averaging approach achieves the highest WERR.\n" }, { "role": "assistant", "content": "The table shows that the unweighted averaging approach has a WERR of 6.94%, which is not the highest. The highest WERR is achieved by SIF embedding based selection with 7.23%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/521.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Relative WERRs (%) with different NMT adaptation strategies. Note that these results include the effect of NE resampling and code mixing techniques." ], "reasoning": "The table shows that the unweighted averaging approach has a WERR of 6.94%, which is not the highest. The highest WERR is achieved by SIF embedding based selection with 7.23%.", "statement": "The unweighted averaging approach achieves the highest WERR.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Relative WERR % by interaction scenarios captured along with the extent of coverage in transcribed collections. Named entity proportion in the utterances is given by NE %. Adaptation contribution % captures the relative contribution of adaptation towards WERR. For e.g., with a 5.75% post-editing WERR, adaptation yields an additional 1.51% WERR towards a combined WERR of 7.26%, i.e. 20.80%.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The table analyzes WERR for different interaction scenarios, showing post-editing, combined WERR, adaptation contribution, and named entity proportions.Cleo prompts cover multiple interaction use cases. In order to derive fine grained insights into the effect of translations, we study WERR on test utterances manually categorized into scenarios. In order to isolate the gains obtained from post-editing and adaptation, we study both the post-editing and combined WERR. We also analyse the proportion of named entities for utterances belonging to each of these scenarios. Following observations can be made from this analysis." } ], "images": [ "scigen/train/522.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Relative WERR % by interaction scenarios captured along with the extent of coverage in transcribed collections. Named entity proportion in the utterances is given by NE %. Adaptation contribution % captures the relative contribution of adaptation towards WERR. For e.g., with a 5.75% post-editing WERR, adaptation yields an additional 1.51% WERR towards a combined WERR of 7.26%, i.e. 20.80%." ], "reasoning": "The table analyzes WERR for different interaction scenarios, showing post-editing, combined WERR, adaptation contribution, and named entity proportions.", "description": "Cleo prompts cover multiple interaction use cases. In order to derive fine grained insights into the effect of translations, we study WERR on test utterances manually categorized into scenarios. In order to isolate the gains obtained from post-editing and adaptation, we study both the post-editing and combined WERR. We also analyse the proportion of named entities for utterances belonging to each of these scenarios. Following observations can be made from this analysis.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 3: Relative WERR % by interaction scenarios captured along with the extent of coverage in transcribed collections. Named entity proportion in the utterances is given by NE %. Adaptation contribution % captures the relative contribution of adaptation towards WERR. For e.g., with a 5.75% post-editing WERR, adaptation yields an additional 1.51% WERR towards a combined WERR of 7.26%, i.e. 20.80%.', solve the subsequent question in a stepwise manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich interaction scenario has the highest adaptation contribution percentage?\n" }, { "role": "assistant", "content": "To find the interaction scenario with the highest adaptation contribution percentage, compare the 'Adaptation contribution %' values for all scenarios. So, the answer is {\"answer\": \"Weather\"}." } ], "images": [ "scigen/train/522.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Relative WERR % by interaction scenarios captured along with the extent of coverage in transcribed collections. Named entity proportion in the utterances is given by NE %. Adaptation contribution % captures the relative contribution of adaptation towards WERR. For e.g., with a 5.75% post-editing WERR, adaptation yields an additional 1.51% WERR towards a combined WERR of 7.26%, i.e. 20.80%." ], "reasoning": "To find the interaction scenario with the highest adaptation contribution percentage, compare the 'Adaptation contribution %' values for all scenarios.", "question": "Which interaction scenario has the highest adaptation contribution percentage?", "answer": "So, the answer is {\"answer\": \"Weather\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Shopping scenario has a higher post-editing WERR % than the Video scenario. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Shopping scenario has a higher post-editing WERR % than the Video scenario.\n" }, { "role": "assistant", "content": "Compare the post-editing WERR % values for the Shopping and Video scenarios. Shopping has 7.86% while Video has 6.44%, so the statement is true. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/522.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Relative WERR % by interaction scenarios captured along with the extent of coverage in transcribed collections. Named entity proportion in the utterances is given by NE %. Adaptation contribution % captures the relative contribution of adaptation towards WERR. For e.g., with a 5.75% post-editing WERR, adaptation yields an additional 1.51% WERR towards a combined WERR of 7.26%, i.e. 20.80%." ], "reasoning": "Compare the post-editing WERR % values for the Shopping and Video scenarios. Shopping has 7.86% while Video has 6.44%, so the statement is true.", "statement": "The Shopping scenario has a higher post-editing WERR % than the Video scenario.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 5: Relative WERRs (%) with varying levels of in-domain transcribed data.'.\n" }, { "role": "assistant", "content": "The WERR increases from 7.86% to 15.65% as the amount of in-domain transcribed utterances decreases from 200K to 10K, indicating higher relative gains from translation data with less in-domain data.We now attempt to address the following question: what are the relative gains provided by the translation data during different phases of bootstrapping? In particular, we measure the WERR between the baseline and translation-augmented LMs, by varying the in-domain transcribed utterances from 10K to 200K. We observe that the combined WERR after post-editing and adaptation increases from 7.86% to 15.65% as the amount of in-domain data reduces. Note that in this experiment, we use the same AM trained on 180 hours data, in order to precisely study the effect of data augmentation for LM. The WERR we report is hence an underestimate, and will probably be much higher, if the AM was trained using similar levels of transcribed data." } ], "images": [ "scigen/train/523.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Relative WERRs (%) with varying levels of in-domain transcribed data." ], "reasoning": "The WERR increases from 7.86% to 15.65% as the amount of in-domain transcribed utterances decreases from 200K to 10K, indicating higher relative gains from translation data with less in-domain data.", "description": "We now attempt to address the following question: what are the relative gains provided by the translation data during different phases of bootstrapping? In particular, we measure the WERR between the baseline and translation-augmented LMs, by varying the in-domain transcribed utterances from 10K to 200K. We observe that the combined WERR after post-editing and adaptation increases from 7.86% to 15.65% as the amount of in-domain data reduces. Note that in this experiment, we use the same AM trained on 180 hours data, in order to precisely study the effect of data augmentation for LM. The WERR we report is hence an underestimate, and will probably be much higher, if the AM was trained using similar levels of transcribed data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 5: Relative WERRs (%) with varying levels of in-domain transcribed data.', solve the subsequent question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the WERR when the transcribed volume is 50K?\n" }, { "role": "assistant", "content": "The table shows the WERR for different volumes of transcribed data. For 50K transcribed utterances, the WERR is listed as 9.42%. So, the answer is {\"answer\": \"9.42%\"}" } ], "images": [ "scigen/train/523.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Relative WERRs (%) with varying levels of in-domain transcribed data." ], "reasoning": "The table shows the WERR for different volumes of transcribed data. For 50K transcribed utterances, the WERR is listed as 9.42%.", "question": "What is the WERR when the transcribed volume is 50K?", "answer": "So, the answer is {\"answer\": \"9.42%\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe WERR is highest when the transcribed volume is 10K.\n" }, { "role": "assistant", "content": "The table indicates that the WERR is 15.65% for 10K transcribed utterances, which is the highest value compared to other volumes. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/523.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Relative WERRs (%) with varying levels of in-domain transcribed data." ], "reasoning": "The table indicates that the WERR is 15.65% for 10K transcribed utterances, which is the highest value compared to other volumes.", "statement": "The WERR is highest when the transcribed volume is 10K.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Mapping of FCE to essay scores', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The neural network processes hidden states by concatenating and averaging them before feeding into the AES output layer, which uses squared error loss on the FCE dataset containing 1244 scripts.In particular, we modified the neural network such that the output of the hidden states in each direction are concatenated and averaged over all time-steps before being fed into the AES output layer. The AES output layer maps this concatenated vector to a single value, feeds this value into a sigmoid function and scales it appropriately. The AES loss used is the squared error between the predicted score and the gold standard essay score. We use the public FCE dataset for our experiments. The dataset contains short essays written by ESL learners for the First Certificate in English (FCE) (an upper intermediate level exam) To our knowledge, this is the only public dataset that has annotations for both GED and AES on the same texts. The dataset contains 1244 scripts where each script contains two essays. Our final training, development, and test set contains 2059, 198, and 194 essays respectively." } ], "images": [ "scigen/train/524.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Mapping of FCE to essay scores" ], "reasoning": "The neural network processes hidden states by concatenating and averaging them before feeding into the AES output layer, which uses squared error loss on the FCE dataset containing 1244 scripts.", "description": "In particular, we modified the neural network such that the output of the hidden states in each direction are concatenated and averaged over all time-steps before being fed into the AES output layer. The AES output layer maps this concatenated vector to a single value, feeds this value into a sigmoid function and scales it appropriately. The AES loss used is the squared error between the predicted score and the gold standard essay score. We use the public FCE dataset for our experiments. The dataset contains short essays written by ESL learners for the First Certificate in English (FCE) (an upper intermediate level exam) To our knowledge, this is the only public dataset that has annotations for both GED and AES on the same texts. The dataset contains 1244 scripts where each script contains two essays. Our final training, development, and test set contains 2059, 198, and 194 essays respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 1: Mapping of FCE to essay scores', solve the following question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the essay score for an exam score of 2.3?\n" }, { "role": "assistant", "content": "To find the essay score for an exam score of 2.3, locate the row with the exam score 2.3 in the table and read the corresponding essay score. So, the answer is {\"answer\": \"11\"}" } ], "images": [ "scigen/train/524.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Mapping of FCE to essay scores" ], "reasoning": "To find the essay score for an exam score of 2.3, locate the row with the exam score 2.3 in the table and read the corresponding essay score.", "question": "What is the essay score for an exam score of 2.3?", "answer": "So, the answer is {\"answer\": \"11\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The dataset contains 1244 scripts, each with two essays. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe dataset contains 1244 scripts, each with two essays.\n" }, { "role": "assistant", "content": "The description states that the dataset contains 1244 scripts, and each script contains two essays. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/524.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Mapping of FCE to essay scores" ], "reasoning": "The description states that the dataset contains 1244 scripts, and each script contains two essays.", "statement": "The dataset contains 1244 scripts, each with two essays.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Performance of BiLSTM model on public-FCE test set for GED with multi-task training objectives (cumulatively) for entire essay contexts.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Using the AES training objective results in a small but insignificant increase in performance (F0.5).(+ Eaes) for the GED task. We see a small but insignificant increase in performance (F0.5) when using the AES training objective." } ], "images": [ "scigen/train/525.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of BiLSTM model on public-FCE test set for GED with multi-task training objectives (cumulatively) for entire essay contexts." ], "reasoning": "Using the AES training objective results in a small but insignificant increase in performance (F0.5).", "description": "(+ Eaes) for the GED task. We see a small but insignificant increase in performance (F0.5) when using the AES training objective.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: Performance of BiLSTM model on public-FCE test set for GED with multi-task training objectives (cumulatively) for entire essay contexts.'.\n\nQuestion: What is the Error Detection Precision when using the AES training objective?\n" }, { "role": "assistant", "content": "The table shows that the Error Detection Precision for '+ Eaes' is 0.543. So, the answer is {\"answer\": \"0.543\"}" } ], "images": [ "scigen/train/525.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of BiLSTM model on public-FCE test set for GED with multi-task training objectives (cumulatively) for entire essay contexts." ], "reasoning": "The table shows that the Error Detection Precision for '+ Eaes' is 0.543.", "question": "What is the Error Detection Precision when using the AES training objective?", "answer": "So, the answer is {\"answer\": \"0.543\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Error Detection Recall is highest when using the AES training objective., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Error Detection Recall is highest when using the AES training objective.\n" }, { "role": "assistant", "content": "The table shows that the Error Detection Recall for '+ Eaes' is 0.265, which is higher than the other methods listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/525.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of BiLSTM model on public-FCE test set for GED with multi-task training objectives (cumulatively) for entire essay contexts." ], "reasoning": "The table shows that the Error Detection Recall for '+ Eaes' is 0.265, which is higher than the other methods listed.", "statement": "The Error Detection Recall is highest when using the AES training objective.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Performance of BiLSTM AES task on public-FCE test set with and without multi-task training objectives (cumulatively) for entire essay context. * indicates that the results are statistically significant when compared to + Elm'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The addition of the GED objective significantly improves the model's performance on the AES task, indicating the model's ability to utilize multiple signals effectively.In particular, we see that the semi-supervised language modelling objective (Elm) improves the performance of the model. However, more strikingly we see that when the GED objective is added it increases the performance of our model on the AES task substantially. This is an encouraging result as it means that a neural model can utilise multiple signals in an end-to-end manner to improve AES." } ], "images": [ "scigen/train/526.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of BiLSTM AES task on public-FCE test set with and without multi-task training objectives (cumulatively) for entire essay context. * indicates that the results are statistically significant when compared to + Elm" ], "reasoning": "The addition of the GED objective significantly improves the model's performance on the AES task, indicating the model's ability to utilize multiple signals effectively.", "description": "In particular, we see that the semi-supervised language modelling objective (Elm) improves the performance of the model. However, more strikingly we see that when the GED objective is added it increases the performance of our model on the AES task substantially. This is an encouraging result as it means that a neural model can utilise multiple signals in an end-to-end manner to improve AES.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 3: Performance of BiLSTM AES task on public-FCE test set with and without multi-task training objectives (cumulatively) for entire essay context. * indicates that the results are statistically significant when compared to + Elm', answer the question below by showing each progressive step toward the answer. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the improvement in Automated Essay Scoring QWK when the GED objective is added?\n" }, { "role": "assistant", "content": "The table shows that the Automated Essay Scoring QWK increases from 0.347 with Elm to 0.459 with GED, indicating a substantial improvement. So, the answer is {\"answer\": \"0.112\"}" } ], "images": [ "scigen/train/526.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of BiLSTM AES task on public-FCE test set with and without multi-task training objectives (cumulatively) for entire essay context. * indicates that the results are statistically significant when compared to + Elm" ], "reasoning": "The table shows that the Automated Essay Scoring QWK increases from 0.347 with Elm to 0.459 with GED, indicating a substantial improvement.", "question": "What is the improvement in Automated Essay Scoring QWK when the GED objective is added?", "answer": "So, the answer is {\"answer\": \"0.112\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe addition of the GED objective improves the Automated Essay Scoring Spearman to 0.537.\n" }, { "role": "assistant", "content": "The table indicates that the Automated Essay Scoring Spearman increases to 0.537 when the GED objective is added. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/526.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of BiLSTM AES task on public-FCE test set with and without multi-task training objectives (cumulatively) for entire essay context. * indicates that the results are statistically significant when compared to + Elm" ], "reasoning": "The table indicates that the Automated Essay Scoring Spearman increases to 0.537 when the GED objective is added.", "statement": "The addition of the GED objective improves the Automated Essay Scoring Spearman to 0.537.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Results of the control experiment on zero-shot performance on the Amazon German test set.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The third row shows a 7% improvement over random, indicating that shared sub-word vocabulary is necessary but not sufficient without training on parallel data.The third row in the table shows the small deviation of 7% over random, which is likely obtained from common sub-words having similar meaning across languages. This control experiment suggests that although having a shared sub-word vocabulary is necessary, we still need to train the NMT system on parallel data from the language of interest so that the system can perform zero-shot classification." } ], "images": [ "scigen/train/527.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of the control experiment on zero-shot performance on the Amazon German test set." ], "reasoning": "The third row shows a 7% improvement over random, indicating that shared sub-word vocabulary is necessary but not sufficient without training on parallel data.", "description": "The third row in the table shows the small deviation of 7% over random, which is likely obtained from common sub-words having similar meaning across languages. This control experiment suggests that although having a shared sub-word vocabulary is necessary, we still need to train the NMT system on parallel data from the language of interest so that the system can perform zero-shot classification.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 4: Results of the control experiment on zero-shot performance on the Amazon German test set.', and tackle the question below in a sequential manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the score difference between the Zero-shot Encoder-Classifier and the model with the frozen encoder?\n" }, { "role": "assistant", "content": "The Zero-shot Encoder-Classifier has a score of 52.33, and the model with the frozen encoder has a score of 57.72. The difference is 57.72 - 52.33. So, the answer is {\"answer\": \"5.39\"}" } ], "images": [ "scigen/train/527.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of the control experiment on zero-shot performance on the Amazon German test set." ], "reasoning": "The Zero-shot Encoder-Classifier has a score of 52.33, and the model with the frozen encoder has a score of 57.72. The difference is 57.72 - 52.33.", "question": "What is the score difference between the Zero-shot Encoder-Classifier and the model with the frozen encoder?", "answer": "So, the answer is {\"answer\": \"5.39\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The model with the pre-trained encoder has a score of 52.98. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe model with the pre-trained encoder has a score of 52.98.\n" }, { "role": "assistant", "content": "The table shows that the model with the pre-trained encoder has a score of 52.98 under the Amazon (De) column. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/527.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of the control experiment on zero-shot performance on the Amazon German test set." ], "reasoning": "The table shows that the model with the pre-trained encoder has a score of 52.98 under the Amazon (De) column.", "statement": "The model with the pre-trained encoder has a score of 52.98.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: Transfer learning results of the classification accuracy on all the datasets. Amazon (En) and Amazon (Fr) are the English and French versions of the task, training the models on the data for each language. The state-of-the-art results are cited from ijcai2018-802 ijcai2018-802 for both Amazon Reviews tasks and NIPS2017_7209 NIPS2017_7209 for SST and SNLI.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The pre-trained multilingual NMT encoder significantly improves accuracy across all tasks, with further improvements seen when the encoder is frozen for document-level classification tasks like Amazon Reviews.The first row in the table shows the baseline accuracy of our system for all four datasets. The second row shows the result from initializing with a pre-trained multilingual NMT encoder. It can be seen that this provides a significant improvement in accuracy, an average of 4.63%, across all the tasks. This illustrates that the multilingual NMT encoder has successfully learned transferable contextualized representations that are leveraged by the classifier component of our proposed system. These results are in line with the results in \\citeauthorNIPS2017_7209 \\shortciteNIPS2017_7209 where the authors used the representations from the top NMT encoder layer as an additional input to the task-specific system. However, in our setup we reused all of the layers of the encoder as a single pre-trained component in the task-specific system. The third row shows the results from freezing the pre-trained encoder after initialization and only training the classifier component. For the Amazon English and French tasks, freezing the encoder after initialization significantly improves the performance further. We hypothesize that since the Amazon dataset is a document level classification task, the long input sequences are very different from the short sequences consumed by the NMT system and hence freezing the encoder seems to have a positive effect. This hypothesis is also supported by the SNLI and SST results, which contain sentence-level input sequences, where we did not find any significant difference between freezing and not freezing the encoder. In this section, we explore the zero-shot classification task in French for our systems. We assume that we do not have any French training data for all the three tasks and test how well our proposed method can generalize to the unseen French language without any further training. A reasonable upper bound to which zero-shot performance should be compared to is bridging - translating a French test text to English and then applying the English classifier on the translated text. If we assume the translation to be perfect, we should expect this approach to perform as well as the English classifier. The Amazon Reviews and SNLI tasks have a French test set available, and we evaluate the performance of the bridged and zero-shot systems on each French set. -S4SS1SSS0Px3 is used to evaluate the zero-shot performance. We do this since translating the ‘pseudo French’ back to English will result in two distinct translation steps and hence more errors." } ], "images": [ "scigen/train/528.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Transfer learning results of the classification accuracy on all the datasets. Amazon (En) and Amazon (Fr) are the English and French versions of the task, training the models on the data for each language. The state-of-the-art results are cited from ijcai2018-802 ijcai2018-802 for both Amazon Reviews tasks and NIPS2017_7209 NIPS2017_7209 for SST and SNLI." ], "reasoning": "The pre-trained multilingual NMT encoder significantly improves accuracy across all tasks, with further improvements seen when the encoder is frozen for document-level classification tasks like Amazon Reviews.", "description": "The first row in the table shows the baseline accuracy of our system for all four datasets. The second row shows the result from initializing with a pre-trained multilingual NMT encoder. It can be seen that this provides a significant improvement in accuracy, an average of 4.63%, across all the tasks. This illustrates that the multilingual NMT encoder has successfully learned transferable contextualized representations that are leveraged by the classifier component of our proposed system. These results are in line with the results in \\citeauthorNIPS2017_7209 \\shortciteNIPS2017_7209 where the authors used the representations from the top NMT encoder layer as an additional input to the task-specific system. However, in our setup we reused all of the layers of the encoder as a single pre-trained component in the task-specific system. The third row shows the results from freezing the pre-trained encoder after initialization and only training the classifier component. For the Amazon English and French tasks, freezing the encoder after initialization significantly improves the performance further. We hypothesize that since the Amazon dataset is a document level classification task, the long input sequences are very different from the short sequences consumed by the NMT system and hence freezing the encoder seems to have a positive effect. This hypothesis is also supported by the SNLI and SST results, which contain sentence-level input sequences, where we did not find any significant difference between freezing and not freezing the encoder. In this section, we explore the zero-shot classification task in French for our systems. We assume that we do not have any French training data for all the three tasks and test how well our proposed method can generalize to the unseen French language without any further training. A reasonable upper bound to which zero-shot performance should be compared to is bridging - translating a French test text to English and then applying the English classifier on the translated text. If we assume the translation to be perfect, we should expect this approach to perform as well as the English classifier. The Amazon Reviews and SNLI tasks have a French test set available, and we evaluate the performance of the bridged and zero-shot systems on each French set. -S4SS1SSS0Px3 is used to evaluate the zero-shot performance. We do this since translating the ‘pseudo French’ back to English will result in two distinct translation steps and hence more errors.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: Table 1: Transfer learning results of the classification accuracy on all the datasets. Amazon (En) and Amazon (Fr) are the English and French versions of the task, training the models on the data for each language. The state-of-the-art results are cited from ijcai2018-802 ijcai2018-802 for both Amazon Reviews tasks and NIPS2017_7209 NIPS2017_7209 for SST and SNLI.\nQuestion: What is the accuracy improvement for the Amazon (En) task when using the pre-trained encoder compared to the baseline?\n" }, { "role": "assistant", "content": "The baseline accuracy for Amazon (En) is 76.60. With the pre-trained encoder, the accuracy is 80.70. The improvement is 80.70 - 76.60 = 4.10. So, the answer is {\"answer\": \"4.10\"}." } ], "images": [ "scigen/train/528.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Transfer learning results of the classification accuracy on all the datasets. Amazon (En) and Amazon (Fr) are the English and French versions of the task, training the models on the data for each language. The state-of-the-art results are cited from ijcai2018-802 ijcai2018-802 for both Amazon Reviews tasks and NIPS2017_7209 NIPS2017_7209 for SST and SNLI." ], "reasoning": "The baseline accuracy for Amazon (En) is 76.60. With the pre-trained encoder, the accuracy is 80.70. The improvement is 80.70 - 76.60 = 4.10.", "question": "What is the accuracy improvement for the Amazon (En) task when using the pre-trained encoder compared to the baseline?", "answer": "So, the answer is {\"answer\": \"4.10\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nFreezing the pre-trained encoder improves the performance for the Amazon (Fr) task.\n" }, { "role": "assistant", "content": "The accuracy for Amazon (Fr) with the pre-trained encoder is 83.18, and with the frozen encoder, it is 85.65. This shows an improvement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/528.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Transfer learning results of the classification accuracy on all the datasets. Amazon (En) and Amazon (Fr) are the English and French versions of the task, training the models on the data for each language. The state-of-the-art results are cited from ijcai2018-802 ijcai2018-802 for both Amazon Reviews tasks and NIPS2017_7209 NIPS2017_7209 for SST and SNLI." ], "reasoning": "The accuracy for Amazon (Fr) with the pre-trained encoder is 83.18, and with the frozen encoder, it is 85.65. This shows an improvement.", "statement": "Freezing the pre-trained encoder improves the performance for the Amazon (Fr) task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Zero-Shot performance on all French test sets. ∗Note that we use the English accuracy in the bridged column for SST.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Using the pre-trained NMT encoder significantly improves zero-shot performance, and freezing the encoder further enhances it, bringing it close to the bridged system's performance.It can be seen that just by using the pre-trained NMT encoder, the zero-shot performance increases drastically from almost random to within 10% of the bridged system. Freezing the encoder further pushes this performance closer to the bridged system. On the Amazon Review task, our zero-shot system is within 2% of the best bridged system. On the SST task, our zero-shot system obtains an accuracy of 83.14% which is within 1.5% of the bridged equivalent (in this case the English system)." } ], "images": [ "scigen/train/529.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Zero-Shot performance on all French test sets. ∗Note that we use the English accuracy in the bridged column for SST." ], "reasoning": "Using the pre-trained NMT encoder significantly improves zero-shot performance, and freezing the encoder further enhances it, bringing it close to the bridged system's performance.", "description": "It can be seen that just by using the pre-trained NMT encoder, the zero-shot performance increases drastically from almost random to within 10% of the bridged system. Freezing the encoder further pushes this performance closer to the bridged system. On the Amazon Review task, our zero-shot system is within 2% of the best bridged system. On the SST task, our zero-shot system obtains an accuracy of 83.14% which is within 1.5% of the bridged equivalent (in this case the English system).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Zero-Shot performance on all French test sets. ∗Note that we use the English accuracy in the bridged column for SST.', answer the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the accuracy of the zero-shot system on the SST task after freezing the encoder?\n" }, { "role": "assistant", "content": "The table shows that freezing the encoder results in an SST (Fr) Zero-Shot accuracy of 83.14%, which is within 1.5% of the bridged system. So, the answer is {\"answer\": \"83.14%\"}." } ], "images": [ "scigen/train/529.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Zero-Shot performance on all French test sets. ∗Note that we use the English accuracy in the bridged column for SST." ], "reasoning": "The table shows that freezing the encoder results in an SST (Fr) Zero-Shot accuracy of 83.14%, which is within 1.5% of the bridged system.", "question": "What is the accuracy of the zero-shot system on the SST task after freezing the encoder?", "answer": "So, the answer is {\"answer\": \"83.14%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Freezing the encoder results in the highest zero-shot performance on the Amazon Review task. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nFreezing the encoder results in the highest zero-shot performance on the Amazon Review task.\n" }, { "role": "assistant", "content": "The table shows that freezing the encoder results in an Amazon (Fr) Zero-Shot accuracy of 81.32%, which is the highest among the zero-shot performances listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/529.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Zero-Shot performance on all French test sets. ∗Note that we use the English accuracy in the bridged column for SST." ], "reasoning": "The table shows that freezing the encoder results in an Amazon (Fr) Zero-Shot accuracy of 81.32%, which is the highest among the zero-shot performances listed.", "statement": "Freezing the encoder results in the highest zero-shot performance on the Amazon Review task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Comparison of our best zero-shot result on the French SNLI test set to other baselines. See text for details.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our best zero-shot system outperforms all listed methods on the French SNLI test set by 10.66% to 15.24%, demonstrating its effectiveness.Finally, on SNLI, we compare our best zero-shot system with bilingual and multilingual embedding based methods evaluated on the same French test set in \\citeauthorlrec2018 \\shortcitelrec2018. INVERT BiCVM Our system significantly outperforms all methods listed in the second column by 10.66% to 15.24% and demonstrates the effectiveness of our proposed approach." } ], "images": [ "scigen/train/530.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of our best zero-shot result on the French SNLI test set to other baselines. See text for details." ], "reasoning": "Our best zero-shot system outperforms all listed methods on the French SNLI test set by 10.66% to 15.24%, demonstrating its effectiveness.", "description": "Finally, on SNLI, we compare our best zero-shot system with bilingual and multilingual embedding based methods evaluated on the same French test set in \\citeauthorlrec2018 \\shortcitelrec2018. INVERT BiCVM Our system significantly outperforms all methods listed in the second column by 10.66% to 15.24% and demonstrates the effectiveness of our proposed approach.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: Comparison of our best zero-shot result on the French SNLI test set to other baselines. See text for details.' in mind, break down and resolve the question below step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nBy how much does our best zero-shot system outperform the BiCVM method on the French SNLI test set?\n" }, { "role": "assistant", "content": "Our best zero-shot system scored 73.88, while BiCVM scored 59.03. The difference is 73.88 - 59.03. So, the answer is {\"answer\": \"14.85\"}" } ], "images": [ "scigen/train/530.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of our best zero-shot result on the French SNLI test set to other baselines. See text for details." ], "reasoning": "Our best zero-shot system scored 73.88, while BiCVM scored 59.03. The difference is 73.88 - 59.03.", "question": "By how much does our best zero-shot system outperform the BiCVM method on the French SNLI test set?", "answer": "So, the answer is {\"answer\": \"14.85\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The INVERT method scored higher than the RANDOM method on the French SNLI test set., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe INVERT method scored higher than the RANDOM method on the French SNLI test set.\n" }, { "role": "assistant", "content": "According to the table, the INVERT method scored 62.60, while the RANDOM method scored 63.21. Therefore, INVERT did not score higher than RANDOM. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/530.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of our best zero-shot result on the French SNLI test set to other baselines. See text for details." ], "reasoning": "According to the table, the INVERT method scored 62.60, while the RANDOM method scored 63.21. Therefore, INVERT did not score higher than RANDOM.", "statement": "The INVERT method scored higher than the RANDOM method on the French SNLI test set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Effect of machine translation data over our proposed Encoder-Classifier on the SNLI tasks. The results of SNLI (Fr) shows the zero-shot performance of our system.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Both symmetric and asymmetric data perform comparably in the multilingual NMT system, indicating effective interlingua learning without symmetric data.We explore two dimensions that could affect zero-shot performance related to our training data in the multilingual NMT model. First, we investigate the effect of using symmetric training data to train both directions in the multilingual NMT system. We conduct an experiment where we take half of the sentences from the En→Fr training set and use the swapped version of the other half of the sentences for training the model. Second, we want to see the effect of training data size, so we run an experiment where we use only half of the training set in a symmetric fashion. However, both the symmetric and asymmetric versions of the data perform comparably on both tasks. This shows that the multilingual NMT system is able to learn an effective interlingua even without the need of symmetric data across the language pairs involved." } ], "images": [ "scigen/train/531.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Effect of machine translation data over our proposed Encoder-Classifier on the SNLI tasks. The results of SNLI (Fr) shows the zero-shot performance of our system." ], "reasoning": "Both symmetric and asymmetric data perform comparably in the multilingual NMT system, indicating effective interlingua learning without symmetric data.", "description": "We explore two dimensions that could affect zero-shot performance related to our training data in the multilingual NMT model. First, we investigate the effect of using symmetric training data to train both directions in the multilingual NMT system. We conduct an experiment where we take half of the sentences from the En→Fr training set and use the swapped version of the other half of the sentences for training the model. Second, we want to see the effect of training data size, so we run an experiment where we use only half of the training set in a symmetric fashion. However, both the symmetric and asymmetric versions of the data perform comparably on both tasks. This shows that the multilingual NMT system is able to learn an effective interlingua even without the need of symmetric data across the language pairs involved.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 5: Effect of machine translation data over our proposed Encoder-Classifier on the SNLI tasks. The results of SNLI (Fr) shows the zero-shot performance of our system.', unfold the steps to solve the question given next. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nQuestion: What is the SNLI (Fr) score for the asymmetric data (half) experiment?\n" }, { "role": "assistant", "content": "The table shows the SNLI (Fr) score for the asymmetric data (half) experiment as 67.63. So, the answer is {\"answer\": \"67.63\"}" } ], "images": [ "scigen/train/531.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Effect of machine translation data over our proposed Encoder-Classifier on the SNLI tasks. The results of SNLI (Fr) shows the zero-shot performance of our system." ], "reasoning": "The table shows the SNLI (Fr) score for the asymmetric data (half) experiment as 67.63.", "question": "What is the SNLI (Fr) score for the asymmetric data (half) experiment?", "answer": "So, the answer is {\"answer\": \"67.63\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe SNLI (En) score for symmetric data (half) is higher than for asymmetric data (half).\n" }, { "role": "assistant", "content": "The table shows that the SNLI (En) score for symmetric data (half) is 80.79, which is lower than the 81.15 score for asymmetric data (half). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/531.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Effect of machine translation data over our proposed Encoder-Classifier on the SNLI tasks. The results of SNLI (Fr) shows the zero-shot performance of our system." ], "reasoning": "The table shows that the SNLI (En) score for symmetric data (half) is 80.79, which is lower than the 81.15 score for asymmetric data (half).", "statement": "The SNLI (En) score for symmetric data (half) is higher than for asymmetric data (half).", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Zero-shot analyses of classifier network model capacity. The SNLI (Fr) results report the zero-shot performance.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Increasing the complexity of the classifier and selectively reusing parts of the encoder improves zero-shot performance significantly, especially with a complex classifier and up to layer 3 of the encoder.S7SS0SSS0Px3 Effect of Encoder/Classifier Capacity We study the effect of the capacity of the two parts of our model on the final accuracies. Next, we experimented with only reusing different parts of the multilingual encoder in a bottom-up fashion. It can be seen that, as expected going from a simple linear classifier to a complex classifier significantly improves both English and zero-shot French performance on the SNLI tasks. However, even a simple linear classifier can achieve significant zero-shot performance when provided with rich enough encodings (49.66 to 61.61 accuracy). However, changing the encoder capacity tells an interesting story. As we selectively reuse parts of the encoder from the embedding layer to the top, we notice that the English performance only increases by about 2% whereas the zero-shot performance increases by about 18% at most in the complex classifier. This means that the additional layers in the encoder are essential for the proposed system to model a language agnostic representation (interlingua) which enables it to perform better zero-shot classification. Moreover, it should be noted that best zero-shot performance is obtained by using the complex classifier and up to layer 3 of the encoder. Although this gap is not big enough to be significant, we hypothesize that top layer of the encoder could be very specific to the MT task and hence might not be best suited for zero-shot classification." } ], "images": [ "scigen/train/532.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Zero-shot analyses of classifier network model capacity. The SNLI (Fr) results report the zero-shot performance." ], "reasoning": "Increasing the complexity of the classifier and selectively reusing parts of the encoder improves zero-shot performance significantly, especially with a complex classifier and up to layer 3 of the encoder.", "description": "S7SS0SSS0Px3 Effect of Encoder/Classifier Capacity We study the effect of the capacity of the two parts of our model on the final accuracies. Next, we experimented with only reusing different parts of the multilingual encoder in a bottom-up fashion. It can be seen that, as expected going from a simple linear classifier to a complex classifier significantly improves both English and zero-shot French performance on the SNLI tasks. However, even a simple linear classifier can achieve significant zero-shot performance when provided with rich enough encodings (49.66 to 61.61 accuracy). However, changing the encoder capacity tells an interesting story. As we selectively reuse parts of the encoder from the embedding layer to the top, we notice that the English performance only increases by about 2% whereas the zero-shot performance increases by about 18% at most in the complex classifier. This means that the additional layers in the encoder are essential for the proposed system to model a language agnostic representation (interlingua) which enables it to perform better zero-shot classification. Moreover, it should be noted that best zero-shot performance is obtained by using the complex classifier and up to layer 3 of the encoder. Although this gap is not big enough to be significant, we hypothesize that top layer of the encoder could be very specific to the MT task and hence might not be best suited for zero-shot classification.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 6: Zero-shot analyses of classifier network model capacity. The SNLI (Fr) results report the zero-shot performance.'.\n\nQuestion: What is the zero-shot French performance when using a complex classifier and up to layer 3 of the encoder?\n" }, { "role": "assistant", "content": "To find the zero-shot French performance with a complex classifier and up to layer 3 of the encoder, locate the row '+ layer 3' and the column 'Complex classifier SNLI (Fr)' in the table. Thus, the answer is {\"answer\": \"74.33\"}." } ], "images": [ "scigen/train/532.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Zero-shot analyses of classifier network model capacity. The SNLI (Fr) results report the zero-shot performance." ], "reasoning": "To find the zero-shot French performance with a complex classifier and up to layer 3 of the encoder, locate the row '+ layer 3' and the column 'Complex classifier SNLI (Fr)' in the table.", "question": "What is the zero-shot French performance when using a complex classifier and up to layer 3 of the encoder?", "answer": "Thus, the answer is {\"answer\": \"74.33\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe best zero-shot French performance is achieved with a complex classifier and up to layer 3 of the encoder.\n" }, { "role": "assistant", "content": "The table shows that the highest zero-shot French performance (74.33) is achieved with a complex classifier and up to layer 3 of the encoder. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/532.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Zero-shot analyses of classifier network model capacity. The SNLI (Fr) results report the zero-shot performance." ], "reasoning": "The table shows that the highest zero-shot French performance (74.33) is achieved with a complex classifier and up to layer 3 of the encoder.", "statement": "The best zero-shot French performance is achieved with a complex classifier and up to layer 3 of the encoder.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Effect of parameter smoothing on the English SNLI test set and zero-shot performance on the French test set.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Averaging multiple steps from the training run for inference aims to improve generalization and reduce over-fitting, potentially enhancing zero-shot classification performance.is a technique which aims to smooth point estimates of the learned parameters by averaging n steps from the training run and using it for inference. This is aimed at improving generalization and being less susceptible to the effects of over-fitting at inference. We hypothesize that a system with enhanced generalization might be better suited for zero-shot classification since it is a measure of the ability of the model to generalize to a new task." } ], "images": [ "scigen/train/533.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Effect of parameter smoothing on the English SNLI test set and zero-shot performance on the French test set." ], "reasoning": "Averaging multiple steps from the training run for inference aims to improve generalization and reduce over-fitting, potentially enhancing zero-shot classification performance.", "description": "is a technique which aims to smooth point estimates of the learned parameters by averaging n steps from the training run and using it for inference. This is aimed at improving generalization and being less susceptible to the effects of over-fitting at inference. We hypothesize that a system with enhanced generalization might be better suited for zero-shot classification since it is a measure of the ability of the model to generalize to a new task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 7: Effect of parameter smoothing on the English SNLI test set and zero-shot performance on the French test set.' and methodically tackle the question that follows. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the SNLI (Fr) score when the smoothing range is 20K steps?\n" }, { "role": "assistant", "content": "To find the SNLI (Fr) score for a smoothing range of 20K steps, locate the row with 20K in the 'Smoothing Range (steps)' column and read the corresponding value in the 'SNLI (Fr)' column. So, the answer is {\"answer\": \"75.93\"}." } ], "images": [ "scigen/train/533.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Effect of parameter smoothing on the English SNLI test set and zero-shot performance on the French test set." ], "reasoning": "To find the SNLI (Fr) score for a smoothing range of 20K steps, locate the row with 20K in the 'Smoothing Range (steps)' column and read the corresponding value in the 'SNLI (Fr)' column.", "question": "What is the SNLI (Fr) score when the smoothing range is 20K steps?", "answer": "So, the answer is {\"answer\": \"75.93\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe SNLI (En) score is highest when the smoothing range is 1K steps.\n" }, { "role": "assistant", "content": "To verify this statement, compare the SNLI (En) scores across all smoothing ranges. The highest score is 84.67, which corresponds to the 1K steps range. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/533.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Effect of parameter smoothing on the English SNLI test set and zero-shot performance on the French test set." ], "reasoning": "To verify this statement, compare the SNLI (En) scores across all smoothing ranges. The highest score is 84.67, which corresponds to the 1K steps range.", "statement": "The SNLI (En) score is highest when the smoothing range is 1K steps.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Confusion Matrix of Empirical Test'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Participants were asked to distinguish between human-written and RevGAN-generated reviews and assess their helpfulness, with a chi-test showing no significant difference.Besides the statistical and semantical metrics, we also design an empirical study to test the personalized performance of our generated reviews. We randomly select 15 reviews to include in each questionnaire, 5 from the original dataset, 5 from RevGAN generated results with personalization and 5 from RevGAN generated results without personalization, and ask participants to analyze which ones are generated by the machine and which ones are really created by humans. Besides, they are also asked to assess the helpfulness of each review by choosing the helpfulness score scale 1-5 for each review. To test that whether the RevGAN generated reviews are indeed statistically indistinguishable from original ones, we run chi-test for significance testing: χ2=0.012" } ], "images": [ "scigen/train/534.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Confusion Matrix of Empirical Test" ], "reasoning": "Participants were asked to distinguish between human-written and RevGAN-generated reviews and assess their helpfulness, with a chi-test showing no significant difference.", "description": "Besides the statistical and semantical metrics, we also design an empirical study to test the personalized performance of our generated reviews. We randomly select 15 reviews to include in each questionnaire, 5 from the original dataset, 5 from RevGAN generated results with personalization and 5 from RevGAN generated results without personalization, and ask participants to analyze which ones are generated by the machine and which ones are really created by humans. Besides, they are also asked to assess the helpfulness of each review by choosing the helpfulness score scale 1-5 for each review. To test that whether the RevGAN generated reviews are indeed statistically indistinguishable from original ones, we run chi-test for significance testing: χ2=0.012", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: Confusion Matrix of Empirical Test', answer the following question. Give detailed solution consisting of each step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nHow many human-written reviews were correctly identified by humans in the empirical test?\n" }, { "role": "assistant", "content": "The table shows that humans correctly identified 119 human-written reviews. So, the answer is {\"answer\": \"119\"}." } ], "images": [ "scigen/train/534.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Confusion Matrix of Empirical Test" ], "reasoning": "The table shows that humans correctly identified 119 human-written reviews.", "question": "How many human-written reviews were correctly identified by humans in the empirical test?", "answer": "So, the answer is {\"answer\": \"119\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nParticipants found it easier to identify machine-generated reviews than human-written ones.\n" }, { "role": "assistant", "content": "The table shows that humans correctly identified 119 human-written reviews and 61 RevGAN-generated reviews, indicating that human-written reviews were more accurately identified. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/534.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Confusion Matrix of Empirical Test" ], "reasoning": "The table shows that humans correctly identified 119 human-written reviews and 61 RevGAN-generated reviews, indicating that human-written reviews were more accurately identified.", "statement": "Participants found it easier to identify machine-generated reviews than human-written ones.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: Comparison of Experimental Results on Amazon Review Dataset (** stands for significance under 99% confidence, * stands for 95% confidence)', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The RevGAN model, when combined with all three novel components (CD, SA, PD), achieves the best performance across multiple metrics, demonstrating significant improvements in WMD, BLEU, and ROUGE scores.To illustrate the superiority and generalizability of our RevGAN model, we implement our model on three different domains of the Amazon Review Dataset including musical instruments, automotive and patio products. On average, we could witness a 5% increase in Word Mover Distance (WMD), 80% improvement in BLEU and 10% rising in ROUGE. Besides, the comparison between different variations of RevGAN model verifies that indeed the combination of all three novel components gives the best generation performance. By deploying bootstrap re-sampling techniques introduced in the previous section, we conduct hypothesis tests where all the tests confirm the significant improvement of our RevGAN model. In that sense, we claim that our model achieved the state-of-the-art results on review generation. We also showcase some generated reviews at the end of this section." } ], "images": [ "scigen/train/535.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of Experimental Results on Amazon Review Dataset (** stands for significance under 99% confidence, * stands for 95% confidence)" ], "reasoning": "The RevGAN model, when combined with all three novel components (CD, SA, PD), achieves the best performance across multiple metrics, demonstrating significant improvements in WMD, BLEU, and ROUGE scores.", "description": "To illustrate the superiority and generalizability of our RevGAN model, we implement our model on three different domains of the Amazon Review Dataset including musical instruments, automotive and patio products. On average, we could witness a 5% increase in Word Mover Distance (WMD), 80% improvement in BLEU and 10% rising in ROUGE. Besides, the comparison between different variations of RevGAN model verifies that indeed the combination of all three novel components gives the best generation performance. By deploying bootstrap re-sampling techniques introduced in the previous section, we conduct hypothesis tests where all the tests confirm the significant improvement of our RevGAN model. In that sense, we claim that our model achieved the state-of-the-art results on review generation. We also showcase some generated reviews at the end of this section.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Comparison of Experimental Results on Amazon Review Dataset (** stands for significance under 99% confidence, * stands for 95% confidence)', answer the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model shows the highest BLEU-4(%) score?\n" }, { "role": "assistant", "content": "By comparing the BLEU-4(%) scores of all models listed in the table, the RevGAN+CD+SA+PD model has the highest BLEU-4(%) score of 27.16. So, the answer is {\"answer\": \"RevGAN+CD+SA+PD\"}" } ], "images": [ "scigen/train/535.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of Experimental Results on Amazon Review Dataset (** stands for significance under 99% confidence, * stands for 95% confidence)" ], "reasoning": "By comparing the BLEU-4(%) scores of all models listed in the table, the RevGAN+CD+SA+PD model has the highest BLEU-4(%) score of 27.16.", "question": "Which model shows the highest BLEU-4(%) score?", "answer": "So, the answer is {\"answer\": \"RevGAN+CD+SA+PD\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe RevGAN+CD+SA+PD model has the lowest WMD score.\n" }, { "role": "assistant", "content": "By comparing the WMD scores of all models listed in the table, the RevGAN+CD+SA+PD model has the lowest WMD score of 1.762. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/535.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of Experimental Results on Amazon Review Dataset (** stands for significance under 99% confidence, * stands for 95% confidence)" ], "reasoning": "By comparing the WMD scores of all models listed in the table, the RevGAN+CD+SA+PD model has the lowest WMD score of 1.762.", "statement": "The RevGAN+CD+SA+PD model has the lowest WMD score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Confusion Matrix of Empirical Test'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Participants were asked to distinguish between human-written and machine-generated reviews and assess their helpfulness, with a chi-test showing no significant difference between them.Besides the statistical and semantical metrics, we also design an empirical study to test the personalized performance of our generated reviews. We randomly select 15 reviews to include in each questionnaire, 5 from the original dataset, 5 from RevGAN generated results with personalization and 5 from RevGAN generated results without personalization, and ask participants to analyze which ones are generated by the machine and which ones are really created by humans. Besides, they are also asked to assess the helpfulness of each review by choosing the helpfulness score scale 1-5 for each review. To test that whether the RevGAN generated reviews are indeed statistically indistinguishable from original ones, we run chi-test for significance testing: χ2=0.012" } ], "images": [ "scigen/train/536.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Confusion Matrix of Empirical Test" ], "reasoning": "Participants were asked to distinguish between human-written and machine-generated reviews and assess their helpfulness, with a chi-test showing no significant difference between them.", "description": "Besides the statistical and semantical metrics, we also design an empirical study to test the personalized performance of our generated reviews. We randomly select 15 reviews to include in each questionnaire, 5 from the original dataset, 5 from RevGAN generated results with personalization and 5 from RevGAN generated results without personalization, and ask participants to analyze which ones are generated by the machine and which ones are really created by humans. Besides, they are also asked to assess the helpfulness of each review by choosing the helpfulness score scale 1-5 for each review. To test that whether the RevGAN generated reviews are indeed statistically indistinguishable from original ones, we run chi-test for significance testing: χ2=0.012", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 3: Confusion Matrix of Empirical Test\nQuestion: How many human-written reviews were identified as machine-generated?\n" }, { "role": "assistant", "content": "The table shows the empirical test results where 118 human-written reviews were identified as machine-generated. So, the answer is {\"answer\": \"118\"}." } ], "images": [ "scigen/train/536.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Confusion Matrix of Empirical Test" ], "reasoning": "The table shows the empirical test results where 118 human-written reviews were identified as machine-generated.", "question": "How many human-written reviews were identified as machine-generated?", "answer": "So, the answer is {\"answer\": \"118\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nParticipants identified more RevGAN+PD generated reviews as human-written than actual human-written reviews.\n" }, { "role": "assistant", "content": "The table shows that 62 RevGAN+PD generated reviews were identified as human-written, while 119 actual human-written reviews were identified as human-written. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/536.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Confusion Matrix of Empirical Test" ], "reasoning": "The table shows that 62 RevGAN+PD generated reviews were identified as human-written, while 119 actual human-written reviews were identified as human-written.", "statement": "Participants identified more RevGAN+PD generated reviews as human-written than actual human-written reviews.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Results comparison with average and standard deviation of five runs. “ANS”, “SUP” and “JOINT” indicate the jointly trained models’ performance in terms of measurements on answer span prediction, supporting sentence prediction and joint tasks.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Joint training improves supporting sentence prediction but decreases answer span prediction, leading to better overall performance.Compared to the baseline model, more improvement is attained than models trained only on one task, especially for the supporting sentence prediction task. With joint training, the performance on answer span prediction drops while the performance on supporting sentence prediction increases. Actually we observed better joint EM and F1 scores with joint training compared to separate training for both baseline models and GSN-based models. Thus, joint training still boosts the performance overall, because the joint trained models find the correct answer and supporting sentences of a question simultaneously." } ], "images": [ "scigen/train/537.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results comparison with average and standard deviation of five runs. “ANS”, “SUP” and “JOINT” indicate the jointly trained models’ performance in terms of measurements on answer span prediction, supporting sentence prediction and joint tasks." ], "reasoning": "Joint training improves supporting sentence prediction but decreases answer span prediction, leading to better overall performance.", "description": "Compared to the baseline model, more improvement is attained than models trained only on one task, especially for the supporting sentence prediction task. With joint training, the performance on answer span prediction drops while the performance on supporting sentence prediction increases. Actually we observed better joint EM and F1 scores with joint training compared to separate training for both baseline models and GSN-based models. Thus, joint training still boosts the performance overall, because the joint trained models find the correct answer and supporting sentences of a question simultaneously.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Results comparison with average and standard deviation of five runs. “ANS”, “SUP” and “JOINT” indicate the jointly trained models’ performance in terms of measurements on answer span prediction, supporting sentence prediction and joint tasks.\nQuestion: What is the difference in SUP F1 scores between the baseline and GSN models?\nSolve the above question based on the table information. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The SUP F1 score for the baseline model is 88.73±0.09, while for the GSN model it is 89.35±0.04. The difference is calculated by subtracting the baseline score from the GSN score. So, the answer is {\"answer\": \"0.62\"}" } ], "images": [ "scigen/train/537.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results comparison with average and standard deviation of five runs. “ANS”, “SUP” and “JOINT” indicate the jointly trained models’ performance in terms of measurements on answer span prediction, supporting sentence prediction and joint tasks." ], "reasoning": "The SUP F1 score for the baseline model is 88.73±0.09, while for the GSN model it is 89.35±0.04. The difference is calculated by subtracting the baseline score from the GSN score.", "question": "What is the difference in SUP F1 scores between the baseline and GSN models?", "answer": "So, the answer is {\"answer\": \"0.62\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe GSN model has a higher ANS EM score than the baseline model.\n" }, { "role": "assistant", "content": "The table shows that the ANS EM score for the baseline model is 62.99±0.16, while for the GSN model it is 63.56±0.31. Since 63.56 is higher than 62.99, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/537.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results comparison with average and standard deviation of five runs. “ANS”, “SUP” and “JOINT” indicate the jointly trained models’ performance in terms of measurements on answer span prediction, supporting sentence prediction and joint tasks." ], "reasoning": "The table shows that the ANS EM score for the baseline model is 62.99±0.16, while for the GSN model it is 63.56±0.31. Since 63.56 is higher than 62.99, the statement is supported.", "statement": "The GSN model has a higher ANS EM score than the baseline model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Results comparison with average and standard deviation of five runs. “ANS-only” and “SUP-only” indicate the model is only trained on two separate tasks.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The GSN-based models outperform the baseline GCN-based models in both tasks, with a more significant improvement in EM scores, indicating better performance in finding complete answer spans or supporting sentences.4.2.2 Results We report the results using the best hyperparameters for each experimental setting. The results show that the proposed GSN-based models perform better on both tasks with strong statistical significance compared to the baseline GCN-based model. The improvement on EM score is slightly more significant, indicating GSN-based models are better at finding complete answer span or supporting sentences than the baseline models. When jointly training both tasks, the 2-layer GSN gives the best performance. This pattern is reasonable because the powerful BERT based encoder possibly learns good contextual representation on token level. This learned representation can benefit the answer prediction task which is also on token level, thus less graph based reasoning is required. Similarly, some recent studies Min et al. However, it is not the case for supporting sentence prediction as it requires the model to find sentences that could be far from each other in the context. On the contrast, our proposed GSN is suitable to model the relational information among sentences, thus we observe that more layers give better results for supporting sentence prediction." } ], "images": [ "scigen/train/538.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results comparison with average and standard deviation of five runs. “ANS-only” and “SUP-only” indicate the model is only trained on two separate tasks." ], "reasoning": "The GSN-based models outperform the baseline GCN-based models in both tasks, with a more significant improvement in EM scores, indicating better performance in finding complete answer spans or supporting sentences.", "description": "4.2.2 Results We report the results using the best hyperparameters for each experimental setting. The results show that the proposed GSN-based models perform better on both tasks with strong statistical significance compared to the baseline GCN-based model. The improvement on EM score is slightly more significant, indicating GSN-based models are better at finding complete answer span or supporting sentences than the baseline models. When jointly training both tasks, the 2-layer GSN gives the best performance. This pattern is reasonable because the powerful BERT based encoder possibly learns good contextual representation on token level. This learned representation can benefit the answer prediction task which is also on token level, thus less graph based reasoning is required. Similarly, some recent studies Min et al. However, it is not the case for supporting sentence prediction as it requires the model to find sentences that could be far from each other in the context. On the contrast, our proposed GSN is suitable to model the relational information among sentences, thus we observe that more layers give better results for supporting sentence prediction.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 1: Results comparison with average and standard deviation of five runs. “ANS-only” and “SUP-only” indicate the model is only trained on two separate tasks.', unfold the question presented below, detailing every step of your calculation. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model has a higher ANS-only EM score and by how much?\n" }, { "role": "assistant", "content": "The ANS-only EM score for the baseline model is 63.87, while for the GSN model it is 64.39. The difference is 64.39 - 63.87 = 0.52. So, the answer is {\"answer\": \"GSN by 0.52\"}" } ], "images": [ "scigen/train/538.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results comparison with average and standard deviation of five runs. “ANS-only” and “SUP-only” indicate the model is only trained on two separate tasks." ], "reasoning": "The ANS-only EM score for the baseline model is 63.87, while for the GSN model it is 64.39. The difference is 64.39 - 63.87 = 0.52.", "question": "Which model has a higher ANS-only EM score and by how much?", "answer": "So, the answer is {\"answer\": \"GSN by 0.52\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The GSN model has a higher SUP-only F1 score than the baseline model., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe GSN model has a higher SUP-only F1 score than the baseline model.\n" }, { "role": "assistant", "content": "The SUP-only F1 score for the baseline model is 88.94, while for the GSN model it is 89.29. Since 89.29 is greater than 88.94, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/538.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results comparison with average and standard deviation of five runs. “ANS-only” and “SUP-only” indicate the model is only trained on two separate tasks." ], "reasoning": "The SUP-only F1 score for the baseline model is 88.94, while for the GSN model it is 89.29. Since 89.29 is greater than 88.94, the statement is supported.", "statement": "The GSN model has a higher SUP-only F1 score than the baseline model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5: Results on FEVER development and test sets.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Our re-implementation of the baseline system slightly outperforms Zhou et al., and the GSN based system further improves both ACC and FEVER scores by more than 1%.Our re-implementation of the baseline system gets slightly better numbers than those reported by Zhou et al. And our proposed GSN based system improves over the baseline system by more than 1% in terms of both ACC and FEVER score. Since the fact verification task can be regarded as a graph classification task, we further prove the proposed GSN is able to achieve better performance than GNN based models when using sequences on graph nodes." } ], "images": [ "scigen/train/539.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on FEVER development and test sets." ], "reasoning": "Our re-implementation of the baseline system slightly outperforms Zhou et al., and the GSN based system further improves both ACC and FEVER scores by more than 1%.", "description": "Our re-implementation of the baseline system gets slightly better numbers than those reported by Zhou et al. And our proposed GSN based system improves over the baseline system by more than 1% in terms of both ACC and FEVER score. Since the fact verification task can be regarded as a graph classification task, we further prove the proposed GSN is able to achieve better performance than GNN based models when using sequences on graph nodes.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 5: Results on FEVER development and test sets.', and sequentially solve the question, writing out each step of your reasoning process. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the improvement in test FEVER score from the baseline (ours) to the GSN based system?\n" }, { "role": "assistant", "content": "The test FEVER score for the baseline (ours) is 65.88, and for the GSN based system, it is 67.13. The improvement is 67.13 - 65.88 = 1.25. So, the answer is {\"answer\": \"1.25\"}" } ], "images": [ "scigen/train/539.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on FEVER development and test sets." ], "reasoning": "The test FEVER score for the baseline (ours) is 65.88, and for the GSN based system, it is 67.13. The improvement is 67.13 - 65.88 = 1.25.", "question": "What is the improvement in test FEVER score from the baseline (ours) to the GSN based system?", "answer": "So, the answer is {\"answer\": \"1.25\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe GSN based system achieves a test ACC of 72.00.\n" }, { "role": "assistant", "content": "The table shows that the test ACC for the GSN based system is indeed 72.00. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/539.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on FEVER development and test sets." ], "reasoning": "The table shows that the test ACC for the GSN based system is indeed 72.00.", "statement": "The GSN based system achieves a test ACC of 72.00.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Token accuracy (T) and sentence accuracy (S) for POS tagging on the testing data.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "GM-LVeGs outperform LVGs in both token and sentence accuracy across all datasets, with GM-LVeG-D excelling in sentence accuracy and GM-LVeG-S in token accuracy.It can be seen that, on all the testing data, GM-LVeGs consistently surpass LVGs in terms of both token accuracy and sentence accuracy. GM-LVeG-D is slightly better than GM-LVeG-S in sentence accuracy, producing the best sentence accuracy on 5 of the 9 testing datasets. GM-LVeG-S performs slightly better than GM-LVeG-D in token accuracy on 5 of the 9 datasets. Overall, there is not significant difference between GM-LVeG-D and GM-LVeG-S. However, GM-LVeG-S admits more efficient learning than GM-LVeG-D in practice since it has fewer parameters." } ], "images": [ "scigen/train/540.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Token accuracy (T) and sentence accuracy (S) for POS tagging on the testing data." ], "reasoning": "GM-LVeGs outperform LVGs in both token and sentence accuracy across all datasets, with GM-LVeG-D excelling in sentence accuracy and GM-LVeG-S in token accuracy.", "description": "It can be seen that, on all the testing data, GM-LVeGs consistently surpass LVGs in terms of both token accuracy and sentence accuracy. GM-LVeG-D is slightly better than GM-LVeG-S in sentence accuracy, producing the best sentence accuracy on 5 of the 9 testing datasets. GM-LVeG-S performs slightly better than GM-LVeG-D in token accuracy on 5 of the 9 datasets. Overall, there is not significant difference between GM-LVeG-D and GM-LVeG-S. However, GM-LVeG-S admits more efficient learning than GM-LVeG-D in practice since it has fewer parameters.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Token accuracy (T) and sentence accuracy (S) for POS tagging on the testing data.\nQuestion: Which model has the highest sentence accuracy in the French dataset?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, GM-LVeG-S has the highest sentence accuracy in the French dataset with a score of 39.26. So, the answer is {\"answer\": \"GM-LVeG-S\"}." } ], "images": [ "scigen/train/540.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Token accuracy (T) and sentence accuracy (S) for POS tagging on the testing data." ], "reasoning": "By examining the table, GM-LVeG-S has the highest sentence accuracy in the French dataset with a score of 39.26.", "question": "Which model has the highest sentence accuracy in the French dataset?", "answer": "So, the answer is {\"answer\": \"GM-LVeG-S\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim GM-LVeG-D has the highest token accuracy in the WSJ dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nGM-LVeG-D has the highest token accuracy in the WSJ dataset.\n" }, { "role": "assistant", "content": "The table shows that GM-LVeG-S has the highest token accuracy in the WSJ dataset with a score of 97.00, not GM-LVeG-D. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/540.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Token accuracy (T) and sentence accuracy (S) for POS tagging on the testing data." ], "reasoning": "The table shows that GM-LVeG-S has the highest token accuracy in the WSJ dataset with a score of 97.00, not GM-LVeG-D.", "statement": "GM-LVeG-D has the highest token accuracy in the WSJ dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Parsing accuracy on the testing data of WSJ. EX indicates the exact match score.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "GM-LVeG-S achieves the highest F1 scores on both development and testing data, surpassing the Berkeley parser by 0.92% in F1 score on the testing data, and has a slightly lower exact match score on the testing data compared to LVG-D-16.It can be seen that GM-LVeG-S produces the best F1 scores on both the development data and the testing data. It surpasses the Berkeley parser by 0.92% in F1 score on the testing data. Its exact match score on the testing data is only slightly lower than that of LVG-D-16." } ], "images": [ "scigen/train/541.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Parsing accuracy on the testing data of WSJ. EX indicates the exact match score." ], "reasoning": "GM-LVeG-S achieves the highest F1 scores on both development and testing data, surpassing the Berkeley parser by 0.92% in F1 score on the testing data, and has a slightly lower exact match score on the testing data compared to LVG-D-16.", "description": "It can be seen that GM-LVeG-S produces the best F1 scores on both the development data and the testing data. It surpasses the Berkeley parser by 0.92% in F1 score on the testing data. Its exact match score on the testing data is only slightly lower than that of LVG-D-16.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 3: Parsing accuracy on the testing data of WSJ. EX indicates the exact match score.', solve this question and give detail solutions. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhich model has the highest exact match score on the testing data?\n" }, { "role": "assistant", "content": "By comparing the 'test (all) EX' column, LVG-D-16 has the highest exact match score of 39.40. So, the answer is {\"answer\": \"LVG-D-16\"}." } ], "images": [ "scigen/train/541.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Parsing accuracy on the testing data of WSJ. EX indicates the exact match score." ], "reasoning": "By comparing the 'test (all) EX' column, LVG-D-16 has the highest exact match score of 39.40.", "question": "Which model has the highest exact match score on the testing data?", "answer": "So, the answer is {\"answer\": \"LVG-D-16\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: GM-LVeG-S has the highest F1 score on the development data., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nGM-LVeG-S has the highest F1 score on the development data.\n" }, { "role": "assistant", "content": "The table shows that GM-LVeG-S has an F1 score of 91.24 on the development data, which is the highest among all models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/541.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Parsing accuracy on the testing data of WSJ. EX indicates the exact match score." ], "reasoning": "The table shows that GM-LVeG-S has an F1 score of 91.24 on the development data, which is the highest among all models listed.", "statement": "GM-LVeG-S has the highest F1 score on the development data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 7: Token accuracy (T) and sentence accuracy (S) for POS tagging on the testing data. The numerical postfix of each LVG model indicates the number of nonterminal subtypes, and hence LVG-G-1 denotes HMM.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table includes tagging results of LVGs with 1, 2, 4, 8 subtypes for each nonterminal, in addition to the results shown in the paper.In addition to the results shown in the paper, this table includes the tagging results of LVGs with 1, 2, 4, 8 subtypes for each nonterminal." } ], "images": [ "scigen/train/542.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Token accuracy (T) and sentence accuracy (S) for POS tagging on the testing data. The numerical postfix of each LVG model indicates the number of nonterminal subtypes, and hence LVG-G-1 denotes HMM." ], "reasoning": "The table includes tagging results of LVGs with 1, 2, 4, 8 subtypes for each nonterminal, in addition to the results shown in the paper.", "description": "In addition to the results shown in the paper, this table includes the tagging results of LVGs with 1, 2, 4, 8 subtypes for each nonterminal.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 7: Token accuracy (T) and sentence accuracy (S) for POS tagging on the testing data. The numerical postfix of each LVG model indicates the number of nonterminal subtypes, and hence LVG-G-1 denotes HMM.', solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model has the highest score for the English T category?\n" }, { "role": "assistant", "content": "To find the model with the highest score for the English T category, look at the English T column and identify the highest value. The highest value is 93.66, which corresponds to the GM-LVeG-D model. So, the answer is {\"answer\": \"GM-LVeG-D\"}." } ], "images": [ "scigen/train/542.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Token accuracy (T) and sentence accuracy (S) for POS tagging on the testing data. The numerical postfix of each LVG model indicates the number of nonterminal subtypes, and hence LVG-G-1 denotes HMM." ], "reasoning": "To find the model with the highest score for the English T category, look at the English T column and identify the highest value. The highest value is 93.66, which corresponds to the GM-LVeG-D model.", "question": "Which model has the highest score for the English T category?", "answer": "So, the answer is {\"answer\": \"GM-LVeG-D\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The LVG-D-16 model has the highest score for the French S category. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe LVG-D-16 model has the highest score for the French S category.\n" }, { "role": "assistant", "content": "To verify this statement, look at the French S column and compare the scores. The highest score in the French S category is 39.60, which corresponds to the GM-LVeG-D model, not the LVG-D-16 model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/542.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Token accuracy (T) and sentence accuracy (S) for POS tagging on the testing data. The numerical postfix of each LVG model indicates the number of nonterminal subtypes, and hence LVG-G-1 denotes HMM." ], "reasoning": "To verify this statement, look at the French S column and compare the scores. The highest score in the French S category is 39.60, which corresponds to the GM-LVeG-D model, not the LVG-D-16 model.", "statement": "The LVG-D-16 model has the highest score for the French S category.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Distribution of sentences over classes', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Out of 22,281 sentences, 65.68% were labeled as NFS, 23.87% as CFS, and 10.45% as UFS.We collected 88,313 labels among which 62,404 (70.6%) are from top-quality participants. There are 22,281 (99.02%) sentences which satisfy the above stopping condition. The remaining 220 sentences, though, received many responses from top-quality participants, the labeling agreement did not satisfy the stopping condition. We assign each sentence the label with the majority count." } ], "images": [ "scigen/train/543.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Distribution of sentences over classes" ], "reasoning": "Out of 22,281 sentences, 65.68% were labeled as NFS, 23.87% as CFS, and 10.45% as UFS.", "description": "We collected 88,313 labels among which 62,404 (70.6%) are from top-quality participants. There are 22,281 (99.02%) sentences which satisfy the above stopping condition. The remaining 220 sentences, though, received many responses from top-quality participants, the labeling agreement did not satisfy the stopping condition. We assign each sentence the label with the majority count.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 3: Distribution of sentences over classes', answer the question below by showing each progressive step toward the answer. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat percentage of sentences were labeled as CFS?\n" }, { "role": "assistant", "content": "The table shows that 5,318 sentences were labeled as CFS, which is 23.87% of the total 22,281 sentences. So, the answer is {\"answer\": \"23.87%\"}" } ], "images": [ "scigen/train/543.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Distribution of sentences over classes" ], "reasoning": "The table shows that 5,318 sentences were labeled as CFS, which is 23.87% of the total 22,281 sentences.", "question": "What percentage of sentences were labeled as CFS?", "answer": "So, the answer is {\"answer\": \"23.87%\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe majority of sentences were labeled as UFS.\n" }, { "role": "assistant", "content": "The table shows that 14,635 sentences (65.68%) were labeled as NFS, which is the majority, not UFS. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/543.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Distribution of sentences over classes" ], "reasoning": "The table shows that 14,635 sentences (65.68%) were labeled as NFS, which is the majority, not UFS.", "statement": "The majority of sentences were labeled as UFS.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 9: Validation and test perplexity on CC-News and Toronto Book Corpus. * denotes models initialized with RoBERTa trained on additional data. The joint model perplexity ranges are estimated using 100,000 samples, see Eq. 5. The number of parameters of each model is shown in parentheses.'.\n" }, { "role": "assistant", "content": "Joint BiT-Base and Joint BiT-Large models achieve better performance than joint UniT and the baselines, with Joint BiT-Large* reaching the highest performance.We can see that on both datasets, residual EBMs with causal attention joint UniT outperforms the baseline RALM with approximately the same number of parameters. The non-residual baseline BALM performs similarly to joint UniT, which might be due to the limitation that Pϕ is not trained jointly with the residual model in both joint UniT and RALM. However, by using our EBM approach, we can remove the causal attention mask and use bi-directional models, which achieves better performance than both baselines and joint UniT: without external data, joint BiT-Base reaches a higher performance than joint UniT with fewer parameters. By initializing from the state-of-the-art pretrained bi-directional transformers RoBERTa-Base and RoBERTa-Large, joint BiT-Base* and Joint BiT-Large* reach even better performance than joint BiT-Base." } ], "images": [ "scigen/train/544.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Validation and test perplexity on CC-News and Toronto Book Corpus. * denotes models initialized with RoBERTa trained on additional data. The joint model perplexity ranges are estimated using 100,000 samples, see Eq. 5. The number of parameters of each model is shown in parentheses." ], "reasoning": "Joint BiT-Base and Joint BiT-Large models achieve better performance than joint UniT and the baselines, with Joint BiT-Large* reaching the highest performance.", "description": "We can see that on both datasets, residual EBMs with causal attention joint UniT outperforms the baseline RALM with approximately the same number of parameters. The non-residual baseline BALM performs similarly to joint UniT, which might be due to the limitation that Pϕ is not trained jointly with the residual model in both joint UniT and RALM. However, by using our EBM approach, we can remove the causal attention mask and use bi-directional models, which achieves better performance than both baselines and joint UniT: without external data, joint BiT-Base reaches a higher performance than joint UniT with fewer parameters. By initializing from the state-of-the-art pretrained bi-directional transformers RoBERTa-Base and RoBERTa-Large, joint BiT-Base* and Joint BiT-Large* reach even better performance than joint BiT-Base.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 9: Validation and test perplexity on CC-News and Toronto Book Corpus. * denotes models initialized with RoBERTa trained on additional data. The joint model perplexity ranges are estimated using 100,000 samples, see Eq. 5. The number of parameters of each model is shown in parentheses.' in mind, break down and resolve the question below step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhich model achieves the highest performance on the Toronto Book Corpus Test?\n" }, { "role": "assistant", "content": "By examining the Toronto Book Corpus Test column, the model with the lowest value indicates the highest performance. Joint BiT-Large* (LM+355M) achieves the highest performance with a score of 15.33-15.36. So, the answer is {\"answer\": \"Joint BiT-Large* (LM+355M)\"}." } ], "images": [ "scigen/train/544.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Validation and test perplexity on CC-News and Toronto Book Corpus. * denotes models initialized with RoBERTa trained on additional data. The joint model perplexity ranges are estimated using 100,000 samples, see Eq. 5. The number of parameters of each model is shown in parentheses." ], "reasoning": "By examining the Toronto Book Corpus Test column, the model with the lowest value indicates the highest performance. Joint BiT-Large* (LM+355M) achieves the highest performance with a score of 15.33-15.36.", "question": "Which model achieves the highest performance on the Toronto Book Corpus Test?", "answer": "So, the answer is {\"answer\": \"Joint BiT-Large* (LM+355M)\"}." }, { "messages": [ { "role": "user", "content": "Joint BiT-Base* (LM+125M) achieves better performance than joint UniT (LM+203M) on the CC-News Test. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nJoint BiT-Base* (LM+125M) achieves better performance than joint UniT (LM+203M) on the CC-News Test.\n" }, { "role": "assistant", "content": "By comparing the CC-News Test scores, joint BiT-Base* (LM+125M) has a score of 14.42-14.75, which is lower (better) than joint UniT (LM+203M) with a score of 15.58-15.74. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/544.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Validation and test perplexity on CC-News and Toronto Book Corpus. * denotes models initialized with RoBERTa trained on additional data. The joint model perplexity ranges are estimated using 100,000 samples, see Eq. 5. The number of parameters of each model is shown in parentheses." ], "reasoning": "By comparing the CC-News Test scores, joint BiT-Base* (LM+125M) has a score of 14.42-14.75, which is lower (better) than joint UniT (LM+203M) with a score of 15.58-15.74.", "statement": "Joint BiT-Base* (LM+125M) achieves better performance than joint UniT (LM+203M) on the CC-News Test.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Number of parameters in millions for the discriminator. The computational cost is directly related to the number of parameters in other layers than the input embedding layer (second row).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The Wikitext dataset has lower accuracy due to the discriminator overfitting to the smaller dataset, despite using multi-GPU training with up to 24 nodes, each with 8 Nvidia V100 GPUs.We use data-parallel synchronous multi-GPU training with up to 24 nodes, each with 8 Nvidia V100 GPUs. The Wikitext dataset has lower accuracy because the discriminator overfits to such smaller dataset." } ], "images": [ "scigen/train/545.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Number of parameters in millions for the discriminator. The computational cost is directly related to the number of parameters in other layers than the input embedding layer (second row)." ], "reasoning": "The Wikitext dataset has lower accuracy due to the discriminator overfitting to the smaller dataset, despite using multi-GPU training with up to 24 nodes, each with 8 Nvidia V100 GPUs.", "description": "We use data-parallel synchronous multi-GPU training with up to 24 nodes, each with 8 Nvidia V100 GPUs. The Wikitext dataset has lower accuracy because the discriminator overfits to such smaller dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 3: Number of parameters in millions for the discriminator. The computational cost is directly related to the number of parameters in other layers than the input embedding layer (second row).', solve the following question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich discriminator has the highest total value?\n" }, { "role": "assistant", "content": "To find the discriminator with the highest total value, compare the 'total' row values for each discriminator. The highest value is 355 under the Discriminators BiT column. So, the answer is {\"answer\": \"Discriminators BiT\"}" } ], "images": [ "scigen/train/545.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Number of parameters in millions for the discriminator. The computational cost is directly related to the number of parameters in other layers than the input embedding layer (second row)." ], "reasoning": "To find the discriminator with the highest total value, compare the 'total' row values for each discriminator. The highest value is 355 under the Discriminators BiT column.", "question": "Which discriminator has the highest total value?", "answer": "So, the answer is {\"answer\": \"Discriminators BiT\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Discriminators BiLSTM Big has a total value of 129. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Discriminators BiLSTM Big has a total value of 129.\n" }, { "role": "assistant", "content": "The table shows that the total value for Discriminators BiLSTM Big is indeed 129. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/545.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Number of parameters in millions for the discriminator. The computational cost is directly related to the number of parameters in other layers than the input embedding layer (second row)." ], "reasoning": "The table shows that the total value for Discriminators BiLSTM Big is indeed 129.", "statement": "The Discriminators BiLSTM Big has a total value of 129.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Cross-corpora generalization accuracy using TransfBig generator and UniT discriminator (except for the last row which used a bidirectional transformer). Each row specifies the corpora used at training time, Ctrain. Each column shows the corpus used at test time, Ctest.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Models trained on the union of two corpora perform better on a third corpus compared to models trained on a single corpus.We observe that models generalize less well across corpora; for instance, when testing on Wikitext a discriminator trained with either Books or CCNews, the accuracy is 59.1% and 65.5%, respectively. However, training on the union of two of the corpora gives a large benefit over training on just one or the other when testing on the third. To answer this question and to provide an automatic way to assess generation quality, we have tested the false positive rate, that is the fraction of machine generated samples that are deemed human generated text, using as discriminator the BiT model. We found that the baseline language model Pϕ (Base LM) has a false positive rate of 17.8% while the joint language model Pθ (Joint BiT-med) has a much higher false positive rate of 31.8%." } ], "images": [ "scigen/train/546.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Cross-corpora generalization accuracy using TransfBig generator and UniT discriminator (except for the last row which used a bidirectional transformer). Each row specifies the corpora used at training time, Ctrain. Each column shows the corpus used at test time, Ctest." ], "reasoning": "Models trained on the union of two corpora perform better on a third corpus compared to models trained on a single corpus.", "description": "We observe that models generalize less well across corpora; for instance, when testing on Wikitext a discriminator trained with either Books or CCNews, the accuracy is 59.1% and 65.5%, respectively. However, training on the union of two of the corpora gives a large benefit over training on just one or the other when testing on the third. To answer this question and to provide an automatic way to assess generation quality, we have tested the false positive rate, that is the fraction of machine generated samples that are deemed human generated text, using as discriminator the BiT model. We found that the baseline language model Pϕ (Base LM) has a false positive rate of 17.8% while the joint language model Pθ (Joint BiT-med) has a much higher false positive rate of 31.8%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: Table 7: Cross-corpora generalization accuracy using TransfBig generator and UniT discriminator (except for the last row which used a bidirectional transformer). Each row specifies the corpora used at training time, Ctrain. Each column shows the corpus used at test time, Ctest.\nQuestion: What is the accuracy when testing on Wiki with a model trained on the union of CCNews and Wiki?\n" }, { "role": "assistant", "content": "To find the accuracy when testing on Wiki with a model trained on the union of CCNews and Wiki, look at the row labeled 'CCNews + Wiki' and the column labeled 'test corpora Wiki'. The value is 81.0%. So, the answer is {\"answer\": \"81.0%\"}" } ], "images": [ "scigen/train/546.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Cross-corpora generalization accuracy using TransfBig generator and UniT discriminator (except for the last row which used a bidirectional transformer). Each row specifies the corpora used at training time, Ctrain. Each column shows the corpus used at test time, Ctest." ], "reasoning": "To find the accuracy when testing on Wiki with a model trained on the union of CCNews and Wiki, look at the row labeled 'CCNews + Wiki' and the column labeled 'test corpora Wiki'. The value is 81.0%.", "question": "What is the accuracy when testing on Wiki with a model trained on the union of CCNews and Wiki?", "answer": "So, the answer is {\"answer\": \"81.0%\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The accuracy when testing on CCNews with a model trained on Books is higher than when trained on Wiki. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe accuracy when testing on CCNews with a model trained on Books is higher than when trained on Wiki.\n" }, { "role": "assistant", "content": "To verify this statement, compare the accuracy values in the 'test corpora CCNews' column for the 'Books' and 'Wiki' rows. The accuracy for 'Books' is 63.5%, and for 'Wiki' it is 73.6%. Since 63.5% is not higher than 73.6%, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/546.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Cross-corpora generalization accuracy using TransfBig generator and UniT discriminator (except for the last row which used a bidirectional transformer). Each row specifies the corpora used at training time, Ctrain. Each column shows the corpus used at test time, Ctest." ], "reasoning": "To verify this statement, compare the accuracy values in the 'test corpora CCNews' column for the 'Books' and 'Wiki' rows. The accuracy for 'Books' is 63.5%, and for 'Wiki' it is 73.6%. Since 63.5% is not higher than 73.6%, the statement is refuted.", "statement": "The accuracy when testing on CCNews with a model trained on Books is higher than when trained on Wiki.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 8: Generalization in the wild of the discriminator to unconditional generation from various GPT2 models (model size in parentheses, followed by sampling method used). Each row contains the accuracy on the corresponding test set. TF-IDF results are taken from Radford and Wu (2019). Results in parentheses are taken from https://openai.com/blog/gpt-2-1-5b-release/.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "BiT discriminator outperforms TF-IDF in-domain baseline with over 95% accuracy, but matching the training set domain is crucial.In this case, we finetune the discriminator on the training set of each of the datasets, following the same protocol used by the provided TF-IDF baseline. We notice that BiT discriminator has consistently superior performance, with an accuracy greater than 95%. While the discriminator still works much better than a random predictor, it lags behind the simple (in-domain) linear baseline. That suggests that matching the domain of the training set is more important than matching the model complexity." } ], "images": [ "scigen/train/547.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Generalization in the wild of the discriminator to unconditional generation from various GPT2 models (model size in parentheses, followed by sampling method used). Each row contains the accuracy on the corresponding test set. TF-IDF results are taken from Radford and Wu (2019). Results in parentheses are taken from https://openai.com/blog/gpt-2-1-5b-release/." ], "reasoning": "BiT discriminator outperforms TF-IDF in-domain baseline with over 95% accuracy, but matching the training set domain is crucial.", "description": "In this case, we finetune the discriminator on the training set of each of the datasets, following the same protocol used by the provided TF-IDF baseline. We notice that BiT discriminator has consistently superior performance, with an accuracy greater than 95%. While the discriminator still works much better than a random predictor, it lags behind the simple (in-domain) linear baseline. That suggests that matching the domain of the training set is more important than matching the model complexity.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 8: Generalization in the wild of the discriminator to unconditional generation from various GPT2 models (model size in parentheses, followed by sampling method used). Each row contains the accuracy on the corresponding test set. TF-IDF results are taken from Radford and Wu (2019). Results in parentheses are taken from https://openai.com/blog/gpt-2-1-5b-release/.', unfold the steps to solve the question given next. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nQuestion: What is the accuracy of the BiT in-domain discriminator for the Huge (1542) top-k test setting?\n" }, { "role": "assistant", "content": "The table shows the accuracy of the BiT in-domain discriminator for various test settings. For the Huge (1542) top-k test setting, the accuracy is listed. So, the answer is {\"answer\": \"95.01\"}" } ], "images": [ "scigen/train/547.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Generalization in the wild of the discriminator to unconditional generation from various GPT2 models (model size in parentheses, followed by sampling method used). Each row contains the accuracy on the corresponding test set. TF-IDF results are taken from Radford and Wu (2019). Results in parentheses are taken from https://openai.com/blog/gpt-2-1-5b-release/." ], "reasoning": "The table shows the accuracy of the BiT in-domain discriminator for various test settings. For the Huge (1542) top-k test setting, the accuracy is listed.", "question": "What is the accuracy of the BiT in-domain discriminator for the Huge (1542) top-k test setting?", "answer": "So, the answer is {\"answer\": \"95.01\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The BiT cross-architecture discriminator has an accuracy of 97.37% for the Med (380) top-k test setting., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe BiT cross-architecture discriminator has an accuracy of 97.37% for the Med (380) top-k test setting.\n" }, { "role": "assistant", "content": "The table lists the accuracy of the BiT cross-architecture discriminator for the Med (380) top-k test setting as 97.37%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/547.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Generalization in the wild of the discriminator to unconditional generation from various GPT2 models (model size in parentheses, followed by sampling method used). Each row contains the accuracy on the corresponding test set. TF-IDF results are taken from Radford and Wu (2019). Results in parentheses are taken from https://openai.com/blog/gpt-2-1-5b-release/." ], "reasoning": "The table lists the accuracy of the BiT cross-architecture discriminator for the Med (380) top-k test setting as 97.37%.", "statement": "The BiT cross-architecture discriminator has an accuracy of 97.37% for the Med (380) top-k test setting.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Performance of our automatic feature selection vs prior work. In general our local model with automatic feature selection is a few points behind joint inference models but matches or exceeds other local inference models. Results for FrameNet are on top, Propbank below.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Our method performs comparably to expert-designed features for SRL, but other systems using global information achieve better performance.Overall, our method seems to work about as well as experts manually designing features for SRL. Other systems achieve better performance, but these models all use global information, an orthogonal issue to the local feature set." } ], "images": [ "scigen/train/548.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance of our automatic feature selection vs prior work. In general our local model with automatic feature selection is a few points behind joint inference models but matches or exceeds other local inference models. Results for FrameNet are on top, Propbank below." ], "reasoning": "Our method performs comparably to expert-designed features for SRL, but other systems using global information achieve better performance.", "description": "Overall, our method seems to work about as well as experts manually designing features for SRL. Other systems achieve better performance, but these models all use global information, an orthogonal issue to the local feature set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 1: Performance of our automatic feature selection vs prior work. In general our local model with automatic feature selection is a few points behind joint inference models but matches or exceeds other local inference models. Results for FrameNet are on top, Propbank below.', and tackle the question below in a sequential manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the highest F1 score achieved by a system that does not use global information?\n" }, { "role": "assistant", "content": "Identify the rows where the 'Global' column is marked with '✗' and find the highest value in the 'F1' column among these rows. So, the answer is {\"answer\": \"77.2\"}" } ], "images": [ "scigen/train/548.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance of our automatic feature selection vs prior work. In general our local model with automatic feature selection is a few points behind joint inference models but matches or exceeds other local inference models. Results for FrameNet are on top, Propbank below." ], "reasoning": "Identify the rows where the 'Global' column is marked with '✗' and find the highest value in the 'F1' column among these rows.", "question": "What is the highest F1 score achieved by a system that does not use global information?", "answer": "So, the answer is {\"answer\": \"77.2\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe highest precision score achieved by a system using global information is 87.5.\n" }, { "role": "assistant", "content": "Check the rows where the 'Global' column is marked with '✓' and find the highest value in the 'P' column. The highest precision score for systems using global information is 81.3, not 87.5. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/548.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance of our automatic feature selection vs prior work. In general our local model with automatic feature selection is a few points behind joint inference models but matches or exceeds other local inference models. Results for FrameNet are on top, Propbank below." ], "reasoning": "Check the rows where the 'Global' column is marked with '✓' and find the highest value in the 'P' column. The highest precision score for systems using global information is 81.3, not 87.5.", "statement": "The highest precision score achieved by a system using global information is 87.5.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Columns are the dataset used for feature selection and rows are the dataset used for training and testing.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The performance on the diagonal is considerably higher, indicating that there likely isn’t one 'SRL feature set'. The average increase in error due to domain shift is 7.9%.but it is another question of whether this matters towards system performance. It could be that there are many different types of feature sets which lead to good performance on either task/dataset, and only one is needed (possibly created manually). The performance on the diagonal is considerably higher, indicating empirically that there likely isn’t one “SRL feature set”. If you weight both equally, the average increase in error due to domain shift is 7.9%. This is even the case for feature selection with FrameNet, where you might expect that selecting features on Propbank, a much larger resource, could yield gains because of much lower variance without much bias." } ], "images": [ "scigen/train/549.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Columns are the dataset used for feature selection and rows are the dataset used for training and testing." ], "reasoning": "The performance on the diagonal is considerably higher, indicating that there likely isn’t one 'SRL feature set'. The average increase in error due to domain shift is 7.9%.", "description": "but it is another question of whether this matters towards system performance. It could be that there are many different types of feature sets which lead to good performance on either task/dataset, and only one is needed (possibly created manually). The performance on the diagonal is considerably higher, indicating empirically that there likely isn’t one “SRL feature set”. If you weight both equally, the average increase in error due to domain shift is 7.9%. This is even the case for feature selection with FrameNet, where you might expect that selecting features on Propbank, a much larger resource, could yield gains because of much lower variance without much bias.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 2: Columns are the dataset used for feature selection and rows are the dataset used for training and testing.' and proceed to solve the following question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the performance increase when using PB features on FN compared to using FN features on FN?\n" }, { "role": "assistant", "content": "The performance using PB features on FN is 74.8, while using FN features on FN is 63.6. The increase is 74.8 - 63.6 = 11.2. So, the answer is {\"answer\": \"11.2\"}." } ], "images": [ "scigen/train/549.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Columns are the dataset used for feature selection and rows are the dataset used for training and testing." ], "reasoning": "The performance using PB features on FN is 74.8, while using FN features on FN is 63.6. The increase is 74.8 - 63.6 = 11.2.", "question": "What is the performance increase when using PB features on FN compared to using FN features on FN?", "answer": "So, the answer is {\"answer\": \"11.2\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Using PB features on PB yields the highest performance., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nUsing PB features on PB yields the highest performance.\n" }, { "role": "assistant", "content": "The table shows that using PB features on PB yields a performance of 77.2, which is the highest value in the table. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/549.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Columns are the dataset used for feature selection and rows are the dataset used for training and testing." ], "reasoning": "The table shows that using PB features on PB yields a performance of 77.2, which is the highest value in the table.", "statement": "Using PB features on PB yields the highest performance.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Columns how many features were used for argument identification and rows how many features were used for role classification. FrameNet (FN) is on top β=10, Propbank (PB) is below β=0.01.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Automatically generating feature sets helps balance prediction time cost with performance, reducing labor and errors.Given that we can automatically generate feature sets, we can easily determine how adding or removing features from each stage will affect performance. This is useful for choosing a feature set which balances the cost of prediction time with performance, which is labor intensive and error prone when done manually. id features than role classification ones." } ], "images": [ "scigen/train/550.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Columns how many features were used for argument identification and rows how many features were used for role classification. FrameNet (FN) is on top β=10, Propbank (PB) is below β=0.01." ], "reasoning": "Automatically generating feature sets helps balance prediction time cost with performance, reducing labor and errors.", "description": "Given that we can automatically generate feature sets, we can easily determine how adding or removing features from each stage will affect performance. This is useful for choosing a feature set which balances the cost of prediction time with performance, which is labor intensive and error prone when done manually. id features than role classification ones.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: Columns how many features were used for argument identification and rows how many features were used for role classification. FrameNet (FN) is on top β=10, Propbank (PB) is below β=0.01.', unfold the steps to solve the question given next. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nQuestion: What is the performance score for FN 640 and PB 1280?\n" }, { "role": "assistant", "content": "To find the performance score for FN 640 and PB 1280, locate the row for FN 640 and the column for PB 1280. The intersection gives the score. So, the answer is {\"answer\": \"75.3\"}" } ], "images": [ "scigen/train/550.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Columns how many features were used for argument identification and rows how many features were used for role classification. FrameNet (FN) is on top β=10, Propbank (PB) is below β=0.01." ], "reasoning": "To find the performance score for FN 640 and PB 1280, locate the row for FN 640 and the column for PB 1280. The intersection gives the score.", "question": "What is the performance score for FN 640 and PB 1280?", "answer": "So, the answer is {\"answer\": \"75.3\"}" }, { "messages": [ { "role": "user", "content": "The performance score for FN 1280 and PB 640 is 75.1. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe performance score for FN 1280 and PB 640 is 75.1.\n" }, { "role": "assistant", "content": "To verify this statement, locate the row for FN 1280 and the column for PB 640. The intersection shows the score is 75.1. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/550.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Columns how many features were used for argument identification and rows how many features were used for role classification. FrameNet (FN) is on top β=10, Propbank (PB) is below β=0.01." ], "reasoning": "To verify this statement, locate the row for FN 1280 and the column for PB 640. The intersection shows the score is 75.1.", "statement": "The performance score for FN 1280 and PB 640 is 75.1.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Fine-grained entity typing performance. The performance of “Ours (DirectTrain)” on BBN is omitted since this dataset does not have fine-grained types for person.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Our approach achieves the highest scores across all metrics on both FIGER (GOLD) and BBN datasets, outperforming existing methods.As we can see, our approach performs much better than existing approaches on both datasets." } ], "images": [ "scigen/train/551.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Fine-grained entity typing performance. The performance of “Ours (DirectTrain)” on BBN is omitted since this dataset does not have fine-grained types for person." ], "reasoning": "Our approach achieves the highest scores across all metrics on both FIGER (GOLD) and BBN datasets, outperforming existing methods.", "description": "As we can see, our approach performs much better than existing approaches on both datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 1: Fine-grained entity typing performance. The performance of “Ours (DirectTrain)” on BBN is omitted since this dataset does not have fine-grained types for person.', unfold the steps to solve the question given next. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nQuestion: Which approach has the highest BBN Macro F1 score?\n" }, { "role": "assistant", "content": "By comparing the BBN Macro F1 scores of all approaches, we see that 'Ours (Full)' has the highest score of 89.2. So, the answer is {\"answer\": \"Ours (Full)\"}" } ], "images": [ "scigen/train/551.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Fine-grained entity typing performance. The performance of “Ours (DirectTrain)” on BBN is omitted since this dataset does not have fine-grained types for person." ], "reasoning": "By comparing the BBN Macro F1 scores of all approaches, we see that 'Ours (Full)' has the highest score of 89.2.", "question": "Which approach has the highest BBN Macro F1 score?", "answer": "So, the answer is {\"answer\": \"Ours (Full)\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe AAA approach has the highest FIGER (GOLD) Accuracy.\n" }, { "role": "assistant", "content": "The table shows that the 'Ours (Full)' approach has the highest FIGER (GOLD) Accuracy of 75.5, while the AAA approach has a lower accuracy of 65.8. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/551.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Fine-grained entity typing performance. The performance of “Ours (DirectTrain)” on BBN is omitted since this dataset does not have fine-grained types for person." ], "reasoning": "The table shows that the 'Ours (Full)' approach has the highest FIGER (GOLD) Accuracy of 75.5, while the AAA approach has a lower accuracy of 65.8.", "statement": "The AAA approach has the highest FIGER (GOLD) Accuracy.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Effect of activation functions. Perplexity after 1 epoch (10 sub-epochs in our setup) for (24, 2048, 512, 8).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The largest number of heads tried in this setup is 16, which gives the best performance. Despite the limited impact of activation functions on perplexity, all other models use ReLU.16 heads which is the largest number we try in this setup give the best performance. As opposed to previous work on feed-forward language models using GLUs [ As we observe that the impact of choice of activation functions on the perplexity is overall limited, all our other models use the standard ReLU." } ], "images": [ "scigen/train/552.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Effect of activation functions. Perplexity after 1 epoch (10 sub-epochs in our setup) for (24, 2048, 512, 8)." ], "reasoning": "The largest number of heads tried in this setup is 16, which gives the best performance. Despite the limited impact of activation functions on perplexity, all other models use ReLU.", "description": "16 heads which is the largest number we try in this setup give the best performance. As opposed to previous work on feed-forward language models using GLUs [ As we observe that the impact of choice of activation functions on the perplexity is overall limited, all our other models use the standard ReLU.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 4: Effect of activation functions. Perplexity after 1 epoch (10 sub-epochs in our setup) for (24, 2048, 512, 8).', unfold the steps to solve the question given next. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nQuestion: Which activation function has the lowest perplexity on the development set?\n" }, { "role": "assistant", "content": "To find the activation function with the lowest perplexity on the development set, we compare the 'Perplexity Dev' values for ReLU, GLU, and GELU. GELU has the lowest perplexity at 72.2. So, the answer is {\"answer\": \"GELU\"}." } ], "images": [ "scigen/train/552.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Effect of activation functions. Perplexity after 1 epoch (10 sub-epochs in our setup) for (24, 2048, 512, 8)." ], "reasoning": "To find the activation function with the lowest perplexity on the development set, we compare the 'Perplexity Dev' values for ReLU, GLU, and GELU. GELU has the lowest perplexity at 72.2.", "question": "Which activation function has the lowest perplexity on the development set?", "answer": "So, the answer is {\"answer\": \"GELU\"}." }, { "messages": [ { "role": "user", "content": "GLU has the highest perplexity on the training set. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nGLU has the highest perplexity on the training set.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the 'Perplexity Train' values for ReLU, GLU, and GELU. GLU has the highest perplexity at 76.5. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/552.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Effect of activation functions. Perplexity after 1 epoch (10 sub-epochs in our setup) for (24, 2048, 512, 8)." ], "reasoning": "To verify this statement, we compare the 'Perplexity Train' values for ReLU, GLU, and GELU. GLU has the highest perplexity at 76.5.", "statement": "GLU has the highest perplexity on the training set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Effect of activation functions. Perplexity after 1 epoch (10 sub-epochs in our setup) for (24, 2048, 512, 8).'.\n" }, { "role": "assistant", "content": "Using 16 heads results in the best performance with the lowest perplexity in both training and development sets.16 heads which is the largest number we try in this setup give the best performance. As opposed to previous work on feed-forward language models using GLUs [ As we observe that the impact of choice of activation functions on the perplexity is overall limited, all our other models use the standard ReLU." } ], "images": [ "scigen/train/553.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Effect of activation functions. Perplexity after 1 epoch (10 sub-epochs in our setup) for (24, 2048, 512, 8)." ], "reasoning": "Using 16 heads results in the best performance with the lowest perplexity in both training and development sets.", "description": "16 heads which is the largest number we try in this setup give the best performance. As opposed to previous work on feed-forward language models using GLUs [ As we observe that the impact of choice of activation functions on the perplexity is overall limited, all our other models use the standard ReLU.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nTable title: Table 4: Effect of activation functions. Perplexity after 1 epoch (10 sub-epochs in our setup) for (24, 2048, 512, 8).\n\nQuestion: What is the perplexity on the development set when using 16 heads?\n" }, { "role": "assistant", "content": "The table shows the perplexity values for different numbers of heads. For 16 heads, the perplexity on the development set is the lowest. So, the answer is {\"answer\": \"66.6\"}." } ], "images": [ "scigen/train/553.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Effect of activation functions. Perplexity after 1 epoch (10 sub-epochs in our setup) for (24, 2048, 512, 8)." ], "reasoning": "The table shows the perplexity values for different numbers of heads. For 16 heads, the perplexity on the development set is the lowest.", "question": "What is the perplexity on the development set when using 16 heads?", "answer": "So, the answer is {\"answer\": \"66.6\"}." }, { "messages": [ { "role": "user", "content": "Using 8 heads results in the highest perplexity on the training set. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nUsing 8 heads results in the highest perplexity on the training set.\n" }, { "role": "assistant", "content": "The table shows that using 8 heads results in a perplexity of 67.6 on the training set, which is not the highest. The highest perplexity on the training set is 71.9 when using 1 head. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/553.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Effect of activation functions. Perplexity after 1 epoch (10 sub-epochs in our setup) for (24, 2048, 512, 8)." ], "reasoning": "The table shows that using 8 heads results in a perplexity of 67.6 on the training set, which is not the highest. The highest perplexity on the training set is 71.9 when using 1 head.", "statement": "Using 8 heads results in the highest perplexity on the training set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: WERs (%) for hybrid systems on the LibriSpeech 960hr. 4-gram model is used in the first pass to generates lattices for rescoring. The row ”Lattice” shows oracle WERs of the lattices.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Applying word-level Transformer language models to hybrid speech recognition by lattice rescoring consistently improves WER over LSTM baselines.We apply our word-level Transformer language models to conventional hybrid speech recognition by lattice rescoring. We obtain consistent improvements in terms of WER over the LSTM baselines." } ], "images": [ "scigen/train/554.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: WERs (%) for hybrid systems on the LibriSpeech 960hr. 4-gram model is used in the first pass to generates lattices for rescoring. The row ”Lattice” shows oracle WERs of the lattices." ], "reasoning": "Applying word-level Transformer language models to hybrid speech recognition by lattice rescoring consistently improves WER over LSTM baselines.", "description": "We apply our word-level Transformer language models to conventional hybrid speech recognition by lattice rescoring. We obtain consistent improvements in terms of WER over the LSTM baselines.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 7: WERs (%) for hybrid systems on the LibriSpeech 960hr. 4-gram model is used in the first pass to generates lattices for rescoring. The row ”Lattice” shows oracle WERs of the lattices.' in mind, break down and resolve the question below step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the WER for the Transformer model with 96 layers on the test clean dataset?\n" }, { "role": "assistant", "content": "To find the WER for the Transformer model with 96 layers on the test clean dataset, locate the row for the Transformer model with 96 layers and then find the WER value under the 'test clean' column. So, the answer is {\"answer\": \"2.5\"}." } ], "images": [ "scigen/train/554.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: WERs (%) for hybrid systems on the LibriSpeech 960hr. 4-gram model is used in the first pass to generates lattices for rescoring. The row ”Lattice” shows oracle WERs of the lattices." ], "reasoning": "To find the WER for the Transformer model with 96 layers on the test clean dataset, locate the row for the Transformer model with 96 layers and then find the WER value under the 'test clean' column.", "question": "What is the WER for the Transformer model with 96 layers on the test clean dataset?", "answer": "So, the answer is {\"answer\": \"2.5\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The LSTM model with 2 layers has a higher WER on the dev clean dataset compared to the Transformer model with 24 layers., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe LSTM model with 2 layers has a higher WER on the dev clean dataset compared to the Transformer model with 24 layers.\n" }, { "role": "assistant", "content": "To verify this statement, compare the WER values for the LSTM model with 2 layers and the Transformer model with 24 layers on the dev clean dataset. The LSTM model with 2 layers has a WER of 3.3, while the Transformer model with 24 layers has a WER of 2.2. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/554.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: WERs (%) for hybrid systems on the LibriSpeech 960hr. 4-gram model is used in the first pass to generates lattices for rescoring. The row ”Lattice” shows oracle WERs of the lattices." ], "reasoning": "To verify this statement, compare the WER values for the LSTM model with 2 layers and the Transformer model with 24 layers on the dev clean dataset. The LSTM model with 2 layers has a WER of 3.3, while the Transformer model with 24 layers has a WER of 2.2.", "statement": "The LSTM model with 2 layers has a higher WER on the dev clean dataset compared to the Transformer model with 24 layers.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 8: WERs (%) for attention-based models on LibriSpeech 960hr dataset. Perplexities are on the 10K BPE level.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The Transformer model with 64 beams shows consistent improvements over the LSTM baseline in terms of WER and PPL across both dev and test sets.The 10K BPE level training data has a longer average length of 24 tokens per sentence with the longest sentence length of 1343, which is still manageable without any truncation for self-attention. We use the Transformer architecture of (24, 4096, 1024, 8). The LSTM model has 4 layers with 2048 nodes. Again, we obtain consistent improvements over the LSTM baseline. These results are better than previously reported WERs" } ], "images": [ "scigen/train/555.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: WERs (%) for attention-based models on LibriSpeech 960hr dataset. Perplexities are on the 10K BPE level." ], "reasoning": "The Transformer model with 64 beams shows consistent improvements over the LSTM baseline in terms of WER and PPL across both dev and test sets.", "description": "The 10K BPE level training data has a longer average length of 24 tokens per sentence with the longest sentence length of 1343, which is still manageable without any truncation for self-attention. We use the Transformer architecture of (24, 4096, 1024, 8). The LSTM model has 4 layers with 2048 nodes. Again, we obtain consistent improvements over the LSTM baseline. These results are better than previously reported WERs", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 8: WERs (%) for attention-based models on LibriSpeech 960hr dataset. Perplexities are on the 10K BPE level.', answer the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the perplexity (PPL) of the Transformer model on the dev clean set?\n" }, { "role": "assistant", "content": "The table shows the PPL values for different models and sets. For the Transformer model on the dev clean set, the PPL is 35.9. So, the answer is {\"answer\": \"35.9\"}." } ], "images": [ "scigen/train/555.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: WERs (%) for attention-based models on LibriSpeech 960hr dataset. Perplexities are on the 10K BPE level." ], "reasoning": "The table shows the PPL values for different models and sets. For the Transformer model on the dev clean set, the PPL is 35.9.", "question": "What is the perplexity (PPL) of the Transformer model on the dev clean set?", "answer": "So, the answer is {\"answer\": \"35.9\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The LSTM model has a lower WER on the test clean set compared to the Transformer model., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe LSTM model has a lower WER on the test clean set compared to the Transformer model.\n" }, { "role": "assistant", "content": "The table shows that the WER for the LSTM model on the test clean set is 3.2, while for the Transformer model it is 2.8. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/555.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: WERs (%) for attention-based models on LibriSpeech 960hr dataset. Perplexities are on the 10K BPE level." ], "reasoning": "The table shows that the WER for the LSTM model on the test clean set is 3.2, while for the Transformer model it is 2.8. Therefore, the statement is refuted.", "statement": "The LSTM model has a lower WER on the test clean set compared to the Transformer model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 9: Effect of sinusoidal positional encoding. Perplexity after 5 epochs (13M updates) for (L, 2048, 512, 8) models.'.\n" }, { "role": "assistant", "content": "Models trained without positional encoding show lower perplexity values in both training and testing phases compared to those with sinusoidal encoding.In the autoregressive problem where a new token is provided to the model at each time step, the amount of information the model has access to strictly increases from left to right at the lowest level of the network; the deeper layers should be able to recognize this structure which should provide the model with some positional information by its own. To check this hypothesis, we train models without any positional encoding." } ], "images": [ "scigen/train/556.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Effect of sinusoidal positional encoding. Perplexity after 5 epochs (13M updates) for (L, 2048, 512, 8) models." ], "reasoning": "Models trained without positional encoding show lower perplexity values in both training and testing phases compared to those with sinusoidal encoding.", "description": "In the autoregressive problem where a new token is provided to the model at each time step, the amount of information the model has access to strictly increases from left to right at the lowest level of the network; the deeper layers should be able to recognize this structure which should provide the model with some positional information by its own. To check this hypothesis, we train models without any positional encoding.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 9: Effect of sinusoidal positional encoding. Perplexity after 5 epochs (13M updates) for (L, 2048, 512, 8) models.' and proceed to solve the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model configuration achieved the lowest perplexity on the Dev set?\n" }, { "role": "assistant", "content": "To find the model configuration with the lowest perplexity on the Dev set, we need to compare the 'Perplexity Dev' values across all configurations. The lowest value is 54.2, which corresponds to the model with L=42, Params in M.=338, and no positional encoding. So, the answer is {\"answer\": \"L=42, Params in M.=338, None\"}." } ], "images": [ "scigen/train/556.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Effect of sinusoidal positional encoding. Perplexity after 5 epochs (13M updates) for (L, 2048, 512, 8) models." ], "reasoning": "To find the model configuration with the lowest perplexity on the Dev set, we need to compare the 'Perplexity Dev' values across all configurations. The lowest value is 54.2, which corresponds to the model with L=42, Params in M.=338, and no positional encoding.", "question": "Which model configuration achieved the lowest perplexity on the Dev set?", "answer": "So, the answer is {\"answer\": \"L=42, Params in M.=338, None\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe model with 24 layers and no positional encoding achieved a lower perplexity on the Test set than the model with 42 layers and sinusoidal encoding.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the 'Perplexity Test' values for the model with 24 layers and no positional encoding (59.2) and the model with 42 layers and sinusoidal encoding (57.7). Since 59.2 is higher than 57.7, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/556.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Effect of sinusoidal positional encoding. Perplexity after 5 epochs (13M updates) for (L, 2048, 512, 8) models." ], "reasoning": "To verify this statement, we compare the 'Perplexity Test' values for the model with 24 layers and no positional encoding (59.2) and the model with 42 layers and sinusoidal encoding (57.7). Since 59.2 is higher than 57.7, the statement is refuted.", "statement": "The model with 24 layers and no positional encoding achieved a lower perplexity on the Test set than the model with 42 layers and sinusoidal encoding.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Test set performance for different training regimes and feature sets. The models use the same decoders for testing and training. For each setting, the average and standard deviation across 5 runs with different random initializations are reported. Boldface: best (averaged) result per dataset/measure.'.\n" }, { "role": "assistant", "content": "All models use the same decoder for testing as during training, and the edge-factored model, which is trained globally, uses a maximum spanning tree algorithm to find the tree with the highest sum of edge scores.All models use the same decoder for testing as during the training process. Though no global decoder for the arc-standard system has been explored in this paper, its local models are listed for comparison. We also include an edge-factored graph-based model, which is conventionally trained globally. The edge-factored model scores bi-LSTM features for each head-modifier pair; a maximum spanning tree algorithm is used to find the tree with the highest sum of edge scores." } ], "images": [ "scigen/train/557.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Test set performance for different training regimes and feature sets. The models use the same decoders for testing and training. For each setting, the average and standard deviation across 5 runs with different random initializations are reported. Boldface: best (averaged) result per dataset/measure." ], "reasoning": "All models use the same decoder for testing as during training, and the edge-factored model, which is trained globally, uses a maximum spanning tree algorithm to find the tree with the highest sum of edge scores.", "description": "All models use the same decoder for testing as during the training process. Though no global decoder for the arc-standard system has been explored in this paper, its local models are listed for comparison. We also include an edge-factored graph-based model, which is conventionally trained globally. The edge-factored model scores bi-LSTM features for each head-modifier pair; a maximum spanning tree algorithm is used to find the tree with the highest sum of edge scores.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: Test set performance for different training regimes and feature sets. The models use the same decoders for testing and training. For each setting, the average and standard deviation across 5 runs with different random initializations are reported. Boldface: best (averaged) result per dataset/measure.' and methodically tackle the question that follows. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model has the highest PTB UAS score and what is its training type?\n" }, { "role": "assistant", "content": "By examining the PTB UAS column, the highest score is 94.53% achieved by the Arc-eager model with Global training. So, the answer is {\"answer\": \"Arc-eager with Global training\"}" } ], "images": [ "scigen/train/557.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Test set performance for different training regimes and feature sets. The models use the same decoders for testing and training. For each setting, the average and standard deviation across 5 runs with different random initializations are reported. Boldface: best (averaged) result per dataset/measure." ], "reasoning": "By examining the PTB UAS column, the highest score is 94.53% achieved by the Arc-eager model with Global training.", "question": "Which model has the highest PTB UAS score and what is its training type?", "answer": "So, the answer is {\"answer\": \"Arc-eager with Global training\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Arc-hybrid model trained globally has a higher CTB UAS score than the Arc-eager model trained locally., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Arc-hybrid model trained globally has a higher CTB UAS score than the Arc-eager model trained locally.\n" }, { "role": "assistant", "content": "The Arc-hybrid model trained globally has a CTB UAS score of 88.38%, while the Arc-eager model trained locally has a CTB UAS score of 87.49%. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/557.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Test set performance for different training regimes and feature sets. The models use the same decoders for testing and training. For each setting, the average and standard deviation across 5 runs with different random initializations are reported. Boldface: best (averaged) result per dataset/measure." ], "reasoning": "The Arc-hybrid model trained globally has a CTB UAS score of 88.38%, while the Arc-eager model trained locally has a CTB UAS score of 87.49%. Therefore, the statement is supported.", "statement": "The Arc-hybrid model trained globally has a higher CTB UAS score than the Arc-eager model trained locally.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Results in F1 scores for FG-NER (We run each setting five times and report the average F1 scores.)'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Hierarchical-shared models achieve the best performance in FG-NER systems, with ELMo outperforming GloVe in both NER (CoNLL) and NER (Ontonotes) tasks.Deep Contextualized Word Representations In the first experiment, we investigate the effectiveness of contextualized word representations (ELMo) compared to uncontextualized word representations (GloVe) when incorporating in our FG-NER systems (Base Model (GloVe) vs. Base Model (ELMo)). In both cases, hierarchical-shared model gives the best performances. In particular, it achieves an F1 score of 82.14% when learning with NER (CoNLL) and an F1 score of 83.04% when learning with NER (Ontonotes) compared to F1 scores of 81.51% and 82.74% of base model in GloVe and ELMo settings respectively. For same-level-shared models, they also achieve better results compared to base model but the differences are not very large. These results indicate that learning FG-NER with other sequence labeling tasks at different parameter sharing schemes helps to improve the performances of FG-NER system. Also, in most cases, it is more beneficial when learning the auxiliary and the main tasks at different levels (hierarchical-shared model) compared to learning at the same level (RNN-shared and embedding-shared models)." } ], "images": [ "scigen/train/558.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results in F1 scores for FG-NER (We run each setting five times and report the average F1 scores.)" ], "reasoning": "Hierarchical-shared models achieve the best performance in FG-NER systems, with ELMo outperforming GloVe in both NER (CoNLL) and NER (Ontonotes) tasks.", "description": "Deep Contextualized Word Representations In the first experiment, we investigate the effectiveness of contextualized word representations (ELMo) compared to uncontextualized word representations (GloVe) when incorporating in our FG-NER systems (Base Model (GloVe) vs. Base Model (ELMo)). In both cases, hierarchical-shared model gives the best performances. In particular, it achieves an F1 score of 82.14% when learning with NER (CoNLL) and an F1 score of 83.04% when learning with NER (Ontonotes) compared to F1 scores of 81.51% and 82.74% of base model in GloVe and ELMo settings respectively. For same-level-shared models, they also achieve better results compared to base model but the differences are not very large. These results indicate that learning FG-NER with other sequence labeling tasks at different parameter sharing schemes helps to improve the performances of FG-NER system. Also, in most cases, it is more beneficial when learning the auxiliary and the main tasks at different levels (hierarchical-shared model) compared to learning at the same level (RNN-shared and embedding-shared models).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Results in F1 scores for FG-NER (We run each setting five times and report the average F1 scores.)', solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model achieved the highest F1 score when learning with NER (CoNLL)?\n" }, { "role": "assistant", "content": "To find the model with the highest F1 score for NER (CoNLL), compare the F1 scores in the +NER (CoNLL) column. The Hierarchical-Shared Model (ELMo) achieved the highest score of 83.04%. So, the answer is {\"answer\": \"Hierarchical-Shared Model (ELMo)\"}." } ], "images": [ "scigen/train/558.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results in F1 scores for FG-NER (We run each setting five times and report the average F1 scores.)" ], "reasoning": "To find the model with the highest F1 score for NER (CoNLL), compare the F1 scores in the +NER (CoNLL) column. The Hierarchical-Shared Model (ELMo) achieved the highest score of 83.04%.", "question": "Which model achieved the highest F1 score when learning with NER (CoNLL)?", "answer": "So, the answer is {\"answer\": \"Hierarchical-Shared Model (ELMo)\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Base Model (GloVe) achieved an F1 score of 81.51% in FG-NER.\n" }, { "role": "assistant", "content": "The table shows that the Base Model (GloVe) achieved an F1 score of 81.51% in FG-NER, which supports the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/558.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results in F1 scores for FG-NER (We run each setting five times and report the average F1 scores.)" ], "reasoning": "The table shows that the Base Model (GloVe) achieved an F1 score of 81.51% in FG-NER, which supports the statement.", "statement": "The Base Model (GloVe) achieved an F1 score of 81.51% in FG-NER.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Hyper-parameters used in our systems.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The training procedure involves sampling a task, then a batch of examples, and updating gradients using stochastic gradient descent with a decay rate of 0.05.S3SS2SSS0Px1 The training procedure for multi-task sequence labeling models is as follows. For same-level-shared models, at each iteration, we first sample a task (main or auxiliary tasks) by Bernoulli trial based on sizes of datasets. Next, we sample a batch of training examples from the given task and then update gradients for both the shared parameters and the task-specific parameters according to the loss function of the given task. We use stochastic gradient descent algorithm with decay rate 0.05." } ], "images": [ "scigen/train/559.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Hyper-parameters used in our systems." ], "reasoning": "The training procedure involves sampling a task, then a batch of examples, and updating gradients using stochastic gradient descent with a decay rate of 0.05.", "description": "S3SS2SSS0Px1 The training procedure for multi-task sequence labeling models is as follows. For same-level-shared models, at each iteration, we first sample a task (main or auxiliary tasks) by Bernoulli trial based on sizes of datasets. Next, we sample a batch of training examples from the given task and then update gradients for both the shared parameters and the task-specific parameters according to the loss function of the given task. We use stochastic gradient descent algorithm with decay rate 0.05.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 2: Hyper-parameters used in our systems.\nQuestion: What is the initial learning rate used in the training procedure?\n" }, { "role": "assistant", "content": "The table lists various hyper-parameters and their values. The initial learning rate is specified under the 'Training' category. So, the answer is {\"answer\": \"0.01\"}" } ], "images": [ "scigen/train/559.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Hyper-parameters used in our systems." ], "reasoning": "The table lists various hyper-parameters and their values. The initial learning rate is specified under the 'Training' category.", "question": "What is the initial learning rate used in the training procedure?", "answer": "So, the answer is {\"answer\": \"0.01\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe dropout rate for input dropout is 0.5.\n" }, { "role": "assistant", "content": "The table lists the dropout rates for different types of dropout. The input dropout rate is specified as 0.33, not 0.5. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/559.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Hyper-parameters used in our systems." ], "reasoning": "The table lists the dropout rates for different types of dropout. The input dropout rate is specified as 0.33, not 0.5.", "statement": "The dropout rate for input dropout is 0.5.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: 5 most improved NE types when using ELMo.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "ELMo improves the performance of identifying long sequence NEs compared to GloVe, as it learns dependencies through both embedding and BLSTM layers.While the average token length of NEs in our dataset is 1.9, the average token lengths of these NE types are much longer. It shows that ELMo helps to improve the performance of our system when identifying NEs which are long sequences. This result is understandable because Base Model (GloVe) relies on only BLSTM layer to learn the dependencies among words in sequence to predict NE labels while Base Model (ELMo) learns these dependencies by both embedding and BLSTM layers. Unlike NER, NE types in FG-NER are often more complex and longer so using only BLSTM layer is not sufficient to capture these dependencies." } ], "images": [ "scigen/train/560.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: 5 most improved NE types when using ELMo." ], "reasoning": "ELMo improves the performance of identifying long sequence NEs compared to GloVe, as it learns dependencies through both embedding and BLSTM layers.", "description": "While the average token length of NEs in our dataset is 1.9, the average token lengths of these NE types are much longer. It shows that ELMo helps to improve the performance of our system when identifying NEs which are long sequences. This result is understandable because Base Model (GloVe) relies on only BLSTM layer to learn the dependencies among words in sequence to predict NE labels while Base Model (ELMo) learns these dependencies by both embedding and BLSTM layers. Unlike NER, NE types in FG-NER are often more complex and longer so using only BLSTM layer is not sufficient to capture these dependencies.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 4: 5 most improved NE types when using ELMo.\nQuestion: Which Named Entity type has the highest token length and what is its ELMo score?\n" }, { "role": "assistant", "content": "To find the Named Entity type with the highest token length, look at the 'Token Length' column and identify the maximum value. Then, find the corresponding ELMo score for that Named Entity type. So, the answer is {\"answer\": \"Public Institution, 95.00\"}." } ], "images": [ "scigen/train/560.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: 5 most improved NE types when using ELMo." ], "reasoning": "To find the Named Entity type with the highest token length, look at the 'Token Length' column and identify the maximum value. Then, find the corresponding ELMo score for that Named Entity type.", "question": "Which Named Entity type has the highest token length and what is its ELMo score?", "answer": "So, the answer is {\"answer\": \"Public Institution, 95.00\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: ELMo has a higher score than GloVe for all Named Entity types., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nELMo has a higher score than GloVe for all Named Entity types.\n" }, { "role": "assistant", "content": "Compare the ELMo and GloVe scores for each Named Entity type in the table. ELMo scores are higher than GloVe scores for all Named Entity types listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/560.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: 5 most improved NE types when using ELMo." ], "reasoning": "Compare the ELMo and GloVe scores for each Named Entity type in the table. ELMo scores are higher than GloVe scores for all Named Entity types listed.", "statement": "ELMo has a higher score than GloVe for all Named Entity types.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 6: Impact of changing the target language on POS tagging accuracy. Self = German/Czech in rows 1/2 respectively.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Layer 1 outperforms layers 0 and 2, and char-based representations are superior to word-based for learning morphology.We report here results that were omitted from the paper due to the space limit. As noted in the paper, all the results consistently show that i) layer 1 performs better than layers 0 and 2; and ii) char-based representations are better than word-based for learning morphology." } ], "images": [ "scigen/train/561.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Impact of changing the target language on POS tagging accuracy. Self = German/Czech in rows 1/2 respectively." ], "reasoning": "Layer 1 outperforms layers 0 and 2, and char-based representations are superior to word-based for learning morphology.", "description": "We report here results that were omitted from the paper due to the space limit. As noted in the paper, all the results consistently show that i) layer 1 performs better than layers 0 and 2; and ii) char-based representations are better than word-based for learning morphology.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: Impact of changing the target language on POS tagging accuracy. Self = German/Czech in rows 1/2 respectively.\nQuestion: Which language has the highest score in the English column?\nSolve the above question based on the table information. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest score in the English column, compare the scores for German and Czech. German has a score of 93.5, and Czech has a score of 75.7. Therefore, German has the highest score. So, the answer is {\"answer\": \"German\"}." } ], "images": [ "scigen/train/561.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Impact of changing the target language on POS tagging accuracy. Self = German/Czech in rows 1/2 respectively." ], "reasoning": "To find the highest score in the English column, compare the scores for German and Czech. German has a score of 93.5, and Czech has a score of 75.7. Therefore, German has the highest score.", "question": "Which language has the highest score in the English column?", "answer": "So, the answer is {\"answer\": \"German\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nCzech has a higher score in Arabic than in Self.\n" }, { "role": "assistant", "content": "To verify this statement, compare the scores for Czech in the Arabic and Self columns. Czech has a score of 75.2 in Arabic and 71.8 in Self. Since 75.2 is greater than 71.8, the statement is true. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/561.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Impact of changing the target language on POS tagging accuracy. Self = German/Czech in rows 1/2 respectively." ], "reasoning": "To verify this statement, compare the scores for Czech in the Arabic and Self columns. Czech has a score of 75.2 in Arabic and 71.8 in Self. Since 75.2 is greater than 71.8, the statement is true.", "statement": "Czech has a higher score in Arabic than in Self.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: POS accuracy on gold and predicted tags using word-based and character-based representations, as well as corresponding BLEU scores.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Char-based models generate better representations for POS tagging, especially in morphologically-richer languages like Arabic and Czech, and this pattern is consistent in morphological tagging tasks.Char-based models always generate better representations for POS tagging, especially in the case of morphologically-richer languages like Arabic and Czech. We observed a similar pattern in the full morphological tagging task. For example, we obtain morphological tagging accuracy of 65.2/79.66 and 67.66/81.66 using word/char-based representations from the Arabic-Hebrew and Arabic-English encoders, respectively. The inherent difficulty in translating Arabic to Hebrew/German may affect the ability to learn good representations of word structure. To probe this more, we trained an Arabic-Arabic autoencoder on the same training data. However, its word representations are actually inferior for the purpose of POS/morphological tagging. This implies that higher BLEU does not necessarily entail better morphological representations. In other words, a better translation model learns more informative representations, but only when it is actually learning to translate rather than merely memorizing the data as in the autoencoder case. We found this to be consistently true also for char-based experiments, and in other language pairs." } ], "images": [ "scigen/train/562.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: POS accuracy on gold and predicted tags using word-based and character-based representations, as well as corresponding BLEU scores." ], "reasoning": "Char-based models generate better representations for POS tagging, especially in morphologically-richer languages like Arabic and Czech, and this pattern is consistent in morphological tagging tasks.", "description": "Char-based models always generate better representations for POS tagging, especially in the case of morphologically-richer languages like Arabic and Czech. We observed a similar pattern in the full morphological tagging task. For example, we obtain morphological tagging accuracy of 65.2/79.66 and 67.66/81.66 using word/char-based representations from the Arabic-Hebrew and Arabic-English encoders, respectively. The inherent difficulty in translating Arabic to Hebrew/German may affect the ability to learn good representations of word structure. To probe this more, we trained an Arabic-Arabic autoencoder on the same training data. However, its word representations are actually inferior for the purpose of POS/morphological tagging. This implies that higher BLEU does not necessarily entail better morphological representations. In other words, a better translation model learns more informative representations, but only when it is actually learning to translate rather than merely memorizing the data as in the autoencoder case. We found this to be consistently true also for char-based experiments, and in other language pairs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: POS accuracy on gold and predicted tags using word-based and character-based representations, as well as corresponding BLEU scores.'.\n\nQuestion: What is the BLEU score for the Arabic-English char-based model?\n" }, { "role": "assistant", "content": "The table shows BLEU scores for different language pairs and models. For the Arabic-English char-based model, the BLEU score is listed under the BLEU column in the Word/Char section. So, the answer is {\"answer\": \"28.4\"}" } ], "images": [ "scigen/train/562.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: POS accuracy on gold and predicted tags using word-based and character-based representations, as well as corresponding BLEU scores." ], "reasoning": "The table shows BLEU scores for different language pairs and models. For the Arabic-English char-based model, the BLEU score is listed under the BLEU column in the Word/Char section.", "question": "What is the BLEU score for the Arabic-English char-based model?", "answer": "So, the answer is {\"answer\": \"28.4\"}" }, { "messages": [ { "role": "user", "content": "The Arabic-Hebrew char-based model has a higher BLEU score than the Arabic-English char-based model. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Arabic-Hebrew char-based model has a higher BLEU score than the Arabic-English char-based model.\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for the Arabic-Hebrew char-based model as 10.7 and the Arabic-English char-based model as 28.4. Since 10.7 is less than 28.4, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/562.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: POS accuracy on gold and predicted tags using word-based and character-based representations, as well as corresponding BLEU scores." ], "reasoning": "The table shows the BLEU scores for the Arabic-Hebrew char-based model as 10.7 and the Arabic-English char-based model as 28.4. Since 10.7 is less than 28.4, the statement is refuted.", "statement": "The Arabic-Hebrew char-based model has a higher BLEU score than the Arabic-English char-based model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: POS tagging accuracy using encoder and decoder representations with/without attention.'.\n" }, { "role": "assistant", "content": "The decoder's representation quality drops modestly without attention, which may correlate with lower BLEU scores for English to Arabic translation compared to Arabic to English.There is a modest drop in representation quality with the decoder. This drop may be correlated with lower BLEU scores when translating English to Arabic vs. Arabic to English. To test this hypothesis, we train NMT models with and without attention and compare the quality of their learned representations. It seems that the decoder does not rely on the attention mechanism to obtain good target word representations, contrary to our hypothesis." } ], "images": [ "scigen/train/563.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: POS tagging accuracy using encoder and decoder representations with/without attention." ], "reasoning": "The decoder's representation quality drops modestly without attention, which may correlate with lower BLEU scores for English to Arabic translation compared to Arabic to English.", "description": "There is a modest drop in representation quality with the decoder. This drop may be correlated with lower BLEU scores when translating English to Arabic vs. Arabic to English. To test this hypothesis, we train NMT models with and without attention and compare the quality of their learned representations. It seems that the decoder does not rely on the attention mechanism to obtain good target word representations, contrary to our hypothesis.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: POS tagging accuracy using encoder and decoder representations with/without attention.' to sequentially solve the question presented below. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the POS accuracy of the encoder with attention?\n" }, { "role": "assistant", "content": "The table shows the POS accuracy for both the encoder and decoder with and without attention. The POS accuracy of the encoder with attention is listed in the first row under 'POS Accuracy ENC'. So, the answer is {\"answer\": \"89.62\"}." } ], "images": [ "scigen/train/563.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: POS tagging accuracy using encoder and decoder representations with/without attention." ], "reasoning": "The table shows the POS accuracy for both the encoder and decoder with and without attention. The POS accuracy of the encoder with attention is listed in the first row under 'POS Accuracy ENC'.", "question": "What is the POS accuracy of the encoder with attention?", "answer": "So, the answer is {\"answer\": \"89.62\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The BLEU score for Arabic to English translation is higher with attention. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe BLEU score for Arabic to English translation is higher with attention.\n" }, { "role": "assistant", "content": "The table shows BLEU scores for both Arabic to English and English to Arabic translations with and without attention. The BLEU score for Arabic to English translation with attention is 24.69, which is higher than the score without attention, 11.88. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/563.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: POS tagging accuracy using encoder and decoder representations with/without attention." ], "reasoning": "The table shows BLEU scores for both Arabic to English and English to Arabic translations with and without attention. The BLEU score for Arabic to English translation with attention is 24.69, which is higher than the score without attention, 11.88.", "statement": "The BLEU score for Arabic to English translation is higher with attention.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: POS tagging accuracy using word-based and char-based encoder/decoder representations.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Char-based models outperform word-based models in POS accuracy for both encoder and decoder, and in BLEU scores for Arabic-to-English translation, but not for English-to-Arabic translation.In both bases, char-based representations perform better. BLEU scores behave differently: the char-based model leads to better translations in Arabic-to-English, but not in English-to-Arabic. A possible explanation for this phenomenon is that the decoder’s predictions are still done at word level even with the char-based model (which encodes the target input but not the output). In practice, this can lead to generating unknown words. Indeed, in Arabic-to-English the char-based model reduces the number of generated unknown words in the MT test set by 25%, while in English-to-Arabic the number of unknown words remains roughly the same between word-based and char-based models." } ], "images": [ "scigen/train/564.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: POS tagging accuracy using word-based and char-based encoder/decoder representations." ], "reasoning": "Char-based models outperform word-based models in POS accuracy for both encoder and decoder, and in BLEU scores for Arabic-to-English translation, but not for English-to-Arabic translation.", "description": "In both bases, char-based representations perform better. BLEU scores behave differently: the char-based model leads to better translations in Arabic-to-English, but not in English-to-Arabic. A possible explanation for this phenomenon is that the decoder’s predictions are still done at word level even with the char-based model (which encodes the target input but not the output). In practice, this can lead to generating unknown words. Indeed, in Arabic-to-English the char-based model reduces the number of generated unknown words in the MT test set by 25%, while in English-to-Arabic the number of unknown words remains roughly the same between word-based and char-based models.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 4: POS tagging accuracy using word-based and char-based encoder/decoder representations.'. Solve the following question based on the table.\n\nQuestion: Which model has higher POS accuracy for the decoder?\nLet's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the char-based model has a POS accuracy of 91.11 for the decoder, while the word-based model has a POS accuracy of 86.71. So, the answer is {\"answer\": \"Char-based model\"}." } ], "images": [ "scigen/train/564.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: POS tagging accuracy using word-based and char-based encoder/decoder representations." ], "reasoning": "The table shows that the char-based model has a POS accuracy of 91.11 for the decoder, while the word-based model has a POS accuracy of 86.71.", "question": "Which model has higher POS accuracy for the decoder?", "answer": "So, the answer is {\"answer\": \"Char-based model\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The char-based model has a higher BLEU score for English-to-Arabic translation compared to the word-based model. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe char-based model has a higher BLEU score for English-to-Arabic translation compared to the word-based model.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for the char-based model in English-to-Arabic translation is 13.00, which is lower than the word-based model's BLEU score of 13.37. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/564.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: POS tagging accuracy using word-based and char-based encoder/decoder representations." ], "reasoning": "The table shows that the BLEU score for the char-based model in English-to-Arabic translation is 13.00, which is lower than the word-based model's BLEU score of 13.37.", "statement": "The char-based model has a higher BLEU score for English-to-Arabic translation compared to the word-based model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5: POS and morphology accuracy on predicted tags using word- and char-based representations from different layers of *-to-En systems.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Layer 1 consistently outperforms layers 0 and 2, and char-based representations are better than word-based for learning morphology.We report here results that were omitted from the paper due to the space limit. As noted in the paper, all the results consistently show that i) layer 1 performs better than layers 0 and 2; and ii) char-based representations are better than word-based for learning morphology." } ], "images": [ "scigen/train/565.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: POS and morphology accuracy on predicted tags using word- and char-based representations from different layers of *-to-En systems." ], "reasoning": "Layer 1 consistently outperforms layers 0 and 2, and char-based representations are better than word-based for learning morphology.", "description": "We report here results that were omitted from the paper due to the space limit. As noted in the paper, all the results consistently show that i) layer 1 performs better than layers 0 and 2; and ii) char-based representations are better than word-based for learning morphology.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 5: POS and morphology accuracy on predicted tags using word- and char-based representations from different layers of *-to-En systems.', solve the following question step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich layer and representation type combination performs best for POS tagging in French?\n" }, { "role": "assistant", "content": "To determine the best performance for POS tagging in French, compare the scores for each layer and representation type. Layer 1 with char-based representation has the highest score of 95.9. So, the answer is {\"answer\": \"Layer 1 with char-based representation\"}." } ], "images": [ "scigen/train/565.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: POS and morphology accuracy on predicted tags using word- and char-based representations from different layers of *-to-En systems." ], "reasoning": "To determine the best performance for POS tagging in French, compare the scores for each layer and representation type. Layer 1 with char-based representation has the highest score of 95.9.", "question": "Which layer and representation type combination performs best for POS tagging in French?", "answer": "So, the answer is {\"answer\": \"Layer 1 with char-based representation\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Layer 0 performs better than Layer 2 for morphology in German., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nLayer 0 performs better than Layer 2 for morphology in German.\n" }, { "role": "assistant", "content": "For morphology in German, Layer 0 has scores of 87.6/88.8, while Layer 2 has scores of 88.7/90.5. Layer 2 performs better than Layer 0. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/565.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: POS and morphology accuracy on predicted tags using word- and char-based representations from different layers of *-to-En systems." ], "reasoning": "For morphology in German, Layer 0 has scores of 87.6/88.8, while Layer 2 has scores of 88.7/90.5. Layer 2 performs better than Layer 0.", "statement": "Layer 0 performs better than Layer 2 for morphology in German.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 7: POS accuracy and BLEU using decoder representations from different language pairs.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The table shows that layer 1 performs better than layers 0 and 2, and char-based representations outperform word-based ones for learning morphology.There is a modest drop in representation quality with the decoder. This drop may be correlated with lower BLEU scores when translating English to Arabic vs. Arabic to English. We report here results that were omitted from the paper due to the space limit. As noted in the paper, all the results consistently show that i) layer 1 performs better than layers 0 and 2; and ii) char-based representations are better than word-based for learning morphology." } ], "images": [ "scigen/train/566.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: POS accuracy and BLEU using decoder representations from different language pairs." ], "reasoning": "The table shows that layer 1 performs better than layers 0 and 2, and char-based representations outperform word-based ones for learning morphology.", "description": "There is a modest drop in representation quality with the decoder. This drop may be correlated with lower BLEU scores when translating English to Arabic vs. Arabic to English. We report here results that were omitted from the paper due to the space limit. As noted in the paper, all the results consistently show that i) layer 1 performs better than layers 0 and 2; and ii) char-based representations are better than word-based for learning morphology.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 7: POS accuracy and BLEU using decoder representations from different language pairs.', and sequentially solve the question, writing out each step of your reasoning process. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich language pair has the highest BLEU score?\n" }, { "role": "assistant", "content": "To find the highest BLEU score, compare the BLEU scores for each language pair in the table. The highest BLEU score is 37.8 for Fr-En. So, the answer is {\"answer\": \"Fr-En\"}." } ], "images": [ "scigen/train/566.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: POS accuracy and BLEU using decoder representations from different language pairs." ], "reasoning": "To find the highest BLEU score, compare the BLEU scores for each language pair in the table. The highest BLEU score is 37.8 for Fr-En.", "question": "Which language pair has the highest BLEU score?", "answer": "So, the answer is {\"answer\": \"Fr-En\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The BLEU score for En-De is higher than for En-Cz., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe BLEU score for En-De is higher than for En-Cz.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for En-De is 23.4, which is higher than the BLEU score for En-Cz, which is 13.9. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/566.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: POS accuracy and BLEU using decoder representations from different language pairs." ], "reasoning": "The table shows that the BLEU score for En-De is 23.4, which is higher than the BLEU score for En-Cz, which is 13.9.", "statement": "The BLEU score for En-De is higher than for En-Cz.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Transformer encoder parameter setup.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The Transformer Transducer model has 18 audio and 2 label encoder layers, with each layer having an input feature size of 512, and it trains faster than an LSTM-based RNN-T model.Our Transformer Transducer model architecture has 18 audio and 2 label encoder layers. Every layer is identical for both audio and label encoders. All the models for experiments presented in this paper are trained on 8x8 TPU with a per-core batch size of 16 (effective batch size of 2048). The learning rate schedule is ramped up linearly from 0 to 2.5e−4 during first 4K steps, it is then held constant till 30K steps and then decays exponentially to 2.5e−6 till 200K steps. We train this model to output grapheme units in all our experiments. We found that the Transformer Transducer models trained much faster (≈1 day) compared to the an LSTM-based RNN-T model (≈3.5 days), with a similar number of parameters." } ], "images": [ "scigen/train/567.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Transformer encoder parameter setup." ], "reasoning": "The Transformer Transducer model has 18 audio and 2 label encoder layers, with each layer having an input feature size of 512, and it trains faster than an LSTM-based RNN-T model.", "description": "Our Transformer Transducer model architecture has 18 audio and 2 label encoder layers. Every layer is identical for both audio and label encoders. All the models for experiments presented in this paper are trained on 8x8 TPU with a per-core batch size of 16 (effective batch size of 2048). The learning rate schedule is ramped up linearly from 0 to 2.5e−4 during first 4K steps, it is then held constant till 30K steps and then decays exponentially to 2.5e−6 till 200K steps. We train this model to output grapheme units in all our experiments. We found that the Transformer Transducer models trained much faster (≈1 day) compared to the an LSTM-based RNN-T model (≈3.5 days), with a similar number of parameters.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nTable title: Table 1: Transformer encoder parameter setup.\n\nQuestion: What is the total number of dense layer units in the Transformer Transducer model?\n" }, { "role": "assistant", "content": "The table shows that Dense layer 1 has 2048 units and Dense layer 2 has 1024 units. Summing these gives the total number of dense layer units: 2048 + 1024 = 3072. So, the answer is {\"answer\": \"3072\"}." } ], "images": [ "scigen/train/567.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Transformer encoder parameter setup." ], "reasoning": "The table shows that Dense layer 1 has 2048 units and Dense layer 2 has 1024 units. Summing these gives the total number of dense layer units: 2048 + 1024 = 3072.", "question": "What is the total number of dense layer units in the Transformer Transducer model?", "answer": "So, the answer is {\"answer\": \"3072\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Transformer Transducer model uses 10 attention heads.\n" }, { "role": "assistant", "content": "The table indicates that the number of attention heads is 8, not 10. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/567.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Transformer encoder parameter setup." ], "reasoning": "The table indicates that the number of attention heads is 8, not 10.", "statement": "The Transformer Transducer model uses 10 attention heads.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Comparison of WERs for Hybrid (streamable), LAS (e2e), RNN-T (e2e & streamable) and Transformer Transducer models (e2e & streamable) on LibriSpeech test sets.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The FullAttn T-T model with 139M parameters achieved competitive recognition accuracy with a WER of 2.0% on the clean set when using a language model.We first compared the performance of Transformer Transducer (T-T) models with full attention on audio to an RNN-T model using a bidirectional LSTM audio encoder. We also observed that T-T models can achieve competitive recognition accuracy with existing wordpiece-based end-to-end models with similar model size. This Transformer LM (6 layers; 57M parameters) had a perplexity of 2.49 on the dev-clean set; the use of dropout, and of larger models, did not improve either perplexity or WER. Shallow fusion was then performed using that LM and both the trained T-T system and the trained bidirectional LSTM-based RNN-T baseline, with scaling factors on the LM output and on the non-blank symbol sequence length tuned on the LibriSpeech dev sets. The shallow fusion result for the T-T system is competitive with corresponding results for top-performing existing systems." } ], "images": [ "scigen/train/568.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of WERs for Hybrid (streamable), LAS (e2e), RNN-T (e2e & streamable) and Transformer Transducer models (e2e & streamable) on LibriSpeech test sets." ], "reasoning": "The FullAttn T-T model with 139M parameters achieved competitive recognition accuracy with a WER of 2.0% on the clean set when using a language model.", "description": "We first compared the performance of Transformer Transducer (T-T) models with full attention on audio to an RNN-T model using a bidirectional LSTM audio encoder. We also observed that T-T models can achieve competitive recognition accuracy with existing wordpiece-based end-to-end models with similar model size. This Transformer LM (6 layers; 57M parameters) had a perplexity of 2.49 on the dev-clean set; the use of dropout, and of larger models, did not improve either perplexity or WER. Shallow fusion was then performed using that LM and both the trained T-T system and the trained bidirectional LSTM-based RNN-T baseline, with scaling factors on the LM output and on the non-blank symbol sequence length tuned on the LibriSpeech dev sets. The shallow fusion result for the T-T system is competitive with corresponding results for top-performing existing systems.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Comparison of WERs for Hybrid (streamable), LAS (e2e), RNN-T (e2e & streamable) and Transformer Transducer models (e2e & streamable) on LibriSpeech test sets.', answer the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the WER for the FullAttn T-T model on the clean set without using a language model?\n" }, { "role": "assistant", "content": "The table shows the WER for the FullAttn T-T model on the clean set without using a language model as 2.4%. So, the answer is {\"answer\": \"2.4%\"}" } ], "images": [ "scigen/train/568.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of WERs for Hybrid (streamable), LAS (e2e), RNN-T (e2e & streamable) and Transformer Transducer models (e2e & streamable) on LibriSpeech test sets." ], "reasoning": "The table shows the WER for the FullAttn T-T model on the clean set without using a language model as 2.4%.", "question": "What is the WER for the FullAttn T-T model on the clean set without using a language model?", "answer": "So, the answer is {\"answer\": \"2.4%\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe BiLSTM RNN-T model has a higher WER on the clean set without using a language model compared to the FullAttn T-T model.\n" }, { "role": "assistant", "content": "The table shows that the BiLSTM RNN-T model has a WER of 3.2% on the clean set without using a language model, while the FullAttn T-T model has a WER of 2.4% on the clean set without using a language model. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/568.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of WERs for Hybrid (streamable), LAS (e2e), RNN-T (e2e & streamable) and Transformer Transducer models (e2e & streamable) on LibriSpeech test sets." ], "reasoning": "The table shows that the BiLSTM RNN-T model has a WER of 3.2% on the clean set without using a language model, while the FullAttn T-T model has a WER of 2.4% on the clean set without using a language model.", "statement": "The BiLSTM RNN-T model has a higher WER on the clean set without using a language model compared to the FullAttn T-T model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Limited left context per layer for audio encoder.'.\n" }, { "role": "assistant", "content": "Limiting the attention window of the AudioEncoder to a fixed number of previous frames reduces WER, with a left context of up to 10 frames per layer achieving the lowest WER.Next, we ran training and decoding experiments using T-T models with limited attention windows over audio and text, with a view to building online streaming speech recognition systems with low latency. Similarly to the use of unidirectional RNN audio encoders in online models, where activations for time t are computed with conditioning only on audio frames before t, here we constrain the AudioEncoder to attend to the left of the current frame by masking the attention scores to the right of the current frame. In order to make one-step inference for AudioEncoder tractable (i.e. to have constant time complexity), we further limit the attention for AudioEncoder to a fixed window of previous states by again masking the attention score. Due to limited computation resources, we used the same mask for different Transformer layers, but the use of different contexts (masks) for different layers is worth exploring. As we can see, using more audio history gives the lower WER, but considering a streamable model with reasonable time complexity for inference, we experimented with a left context of up to 10 frames per layer." } ], "images": [ "scigen/train/569.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Limited left context per layer for audio encoder." ], "reasoning": "Limiting the attention window of the AudioEncoder to a fixed number of previous frames reduces WER, with a left context of up to 10 frames per layer achieving the lowest WER.", "description": "Next, we ran training and decoding experiments using T-T models with limited attention windows over audio and text, with a view to building online streaming speech recognition systems with low latency. Similarly to the use of unidirectional RNN audio encoders in online models, where activations for time t are computed with conditioning only on audio frames before t, here we constrain the AudioEncoder to attend to the left of the current frame by masking the attention scores to the right of the current frame. In order to make one-step inference for AudioEncoder tractable (i.e. to have constant time complexity), we further limit the attention for AudioEncoder to a fixed window of previous states by again masking the attention score. Due to limited computation resources, we used the same mask for different Transformer layers, but the use of different contexts (masks) for different layers is worth exploring. As we can see, using more audio history gives the lower WER, but considering a streamable model with reasonable time complexity for inference, we experimented with a left context of up to 10 frames per layer.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 3: Limited left context per layer for audio encoder.', solve this question and give detail solutions. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the WER (%) for the 'Test-other' set when the Audio Mask left is set to 6?\n" }, { "role": "assistant", "content": "The table shows the WER (%) for different Audio Mask left values. When the Audio Mask left is 6, the WER (%) for the 'Test-other' set is 11.8. So, the answer is {\"answer\": \"11.8\"}" } ], "images": [ "scigen/train/569.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Limited left context per layer for audio encoder." ], "reasoning": "The table shows the WER (%) for different Audio Mask left values. When the Audio Mask left is 6, the WER (%) for the 'Test-other' set is 11.8.", "question": "What is the WER (%) for the 'Test-other' set when the Audio Mask left is set to 6?", "answer": "So, the answer is {\"answer\": \"11.8\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nUsing an Audio Mask left of 2 results in the highest WER (%) for the 'Test-other' set.\n" }, { "role": "assistant", "content": "The table shows that an Audio Mask left of 2 results in a WER (%) of 14.5 for the 'Test-other' set, which is the highest among the given values. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/569.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Limited left context per layer for audio encoder." ], "reasoning": "The table shows that an Audio Mask left of 2 results in a WER (%) of 14.5 for the 'Test-other' set, which is the highest among the given values.", "statement": "Using an Audio Mask left of 2 results in the highest WER (%) for the 'Test-other' set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Limited right context per layer for audio encoder.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Using a right context of 2 frames per layer with a fixed 512 frames left context improves WER by around 30% compared to a streamable T-T model with no right context.Similarly, we explored the use of limited right context to allow the model to see some future audio frames, in the hope of bridging the gap between a streamable T-T model (left = 10, right = 0) and a full attention T-T model (left = 512, right = 512). Since we apply the same mask for every layer, the latency introduced by using right context is aggregated over all the layers. To explore the right context impact for modeling, we did comparisons with fixed 512 frames left context per layer to compared with full attention T-T model. Compared with streamable T-T model, 2 frames right context per layer (around 1 sec of latency) brings around 30% improvements." } ], "images": [ "scigen/train/570.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Limited right context per layer for audio encoder." ], "reasoning": "Using a right context of 2 frames per layer with a fixed 512 frames left context improves WER by around 30% compared to a streamable T-T model with no right context.", "description": "Similarly, we explored the use of limited right context to allow the model to see some future audio frames, in the hope of bridging the gap between a streamable T-T model (left = 10, right = 0) and a full attention T-T model (left = 512, right = 512). Since we apply the same mask for every layer, the latency introduced by using right context is aggregated over all the layers. To explore the right context impact for modeling, we did comparisons with fixed 512 frames left context per layer to compared with full attention T-T model. Compared with streamable T-T model, 2 frames right context per layer (around 1 sec of latency) brings around 30% improvements.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 4: Limited right context per layer for audio encoder.'\nQuestion: What is the WER (%) on the Test-clean dataset when using an audio mask with 512 frames left context and 10 frames right context?\n" }, { "role": "assistant", "content": "The table shows the WER (%) for different audio mask configurations. For an audio mask with 512 frames left context and 10 frames right context, the WER (%) on the Test-clean dataset is 2.7. So, the answer is {\"answer\": \"2.7\"}." } ], "images": [ "scigen/train/570.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Limited right context per layer for audio encoder." ], "reasoning": "The table shows the WER (%) for different audio mask configurations. For an audio mask with 512 frames left context and 10 frames right context, the WER (%) on the Test-clean dataset is 2.7.", "question": "What is the WER (%) on the Test-clean dataset when using an audio mask with 512 frames left context and 10 frames right context?", "answer": "So, the answer is {\"answer\": \"2.7\"}." }, { "messages": [ { "role": "user", "content": "Using an audio mask with 512 frames left context and 6 frames right context results in a WER (%) of 6.9 on the Test-other dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nUsing an audio mask with 512 frames left context and 6 frames right context results in a WER (%) of 6.9 on the Test-other dataset.\n" }, { "role": "assistant", "content": "The table shows that for an audio mask with 512 frames left context and 6 frames right context, the WER (%) on the Test-other dataset is indeed 6.9. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/570.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Limited right context per layer for audio encoder." ], "reasoning": "The table shows that for an audio mask with 512 frames left context and 6 frames right context, the WER (%) on the Test-other dataset is indeed 6.9.", "statement": "Using an audio mask with 512 frames left context and 6 frames right context results in a WER (%) of 6.9 on the Test-other dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 5: Limited left context per layer for label encoder.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Limiting the left context for the label encoder shows minimal impact on the T-T model's performance, as indicated by the WER percentages.In addition, we evaluated how the left context used in the T-T LabelEncoder affects performance. It shows very limited left context for label encoder is good engough for T-T model. We see a similar trend when limiting left label states while using a full attention T-T audio encoder." } ], "images": [ "scigen/train/571.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Limited left context per layer for label encoder." ], "reasoning": "Limiting the left context for the label encoder shows minimal impact on the T-T model's performance, as indicated by the WER percentages.", "description": "In addition, we evaluated how the left context used in the T-T LabelEncoder affects performance. It shows very limited left context for label encoder is good engough for T-T model. We see a similar trend when limiting left label states while using a full attention T-T audio encoder.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 5: Limited left context per layer for label encoder.', unfold the question presented below, detailing every step of your calculation. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the WER (%) for Test-other when the Label Mask left is 1?\n" }, { "role": "assistant", "content": "To find the WER (%) for Test-other when the Label Mask left is 1, locate the row where Label Mask left is 1 and read the corresponding WER (%) Test-other value. So, the answer is {\"answer\": \"12\"}." } ], "images": [ "scigen/train/571.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Limited left context per layer for label encoder." ], "reasoning": "To find the WER (%) for Test-other when the Label Mask left is 1, locate the row where Label Mask left is 1 and read the corresponding WER (%) Test-other value.", "question": "What is the WER (%) for Test-other when the Label Mask left is 1?", "answer": "So, the answer is {\"answer\": \"12\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe WER (%) for Test-clean remains constant when the Label Mask left is 4 or 3.\n" }, { "role": "assistant", "content": "Check the WER (%) Test-clean values for the rows where Label Mask left is 4 and 3. Both values are 4.2, indicating that the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/571.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Limited left context per layer for label encoder." ], "reasoning": "Check the WER (%) Test-clean values for the rows where Label Mask left is 4 and 3. Both values are 4.2, indicating that the statement is supported.", "statement": "The WER (%) for Test-clean remains constant when the Label Mask left is 4 or 3.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Span prediction and IR performance of the query generator models for Hop 1 (G1) and Hop 2 (G2) evaluated separately on the HotpotQA dev set.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Generated queries perform slightly better on d1 but significantly better on d2, especially for non-comparison questions, improving recall from 27.88% to 53.23%.To evaluate the query generators, we begin by determining how well they emulate the oracles. We evaluate them using Exact Match (EM) and F1 on the span prediction task, as well as compare their queries’ retrieval performance against the oracle queries. When we combine them into a pipeline, the generated queries perform only slightly better on d1 when a total of 10 documents are retrieved (89.91% vs 87.85%), but are significantly more effective for d2 (61.01% vs 36.91%). If we further zoom in on the retrieval performance on non-comparison questions for which finding the two entities involved is less trivial, we can see that the recall on d2 improves from 27.88% to 53.23%, almost doubling the number of questions we have the complete gold context to answer. We note that the IR performance we report on the full pipeline is different to that when we evaluate the query generators separately. We attribute this difference to the fact that the generated queries sometimes retrieve both gold documents in one step." } ], "images": [ "scigen/train/572.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Span prediction and IR performance of the query generator models for Hop 1 (G1) and Hop 2 (G2) evaluated separately on the HotpotQA dev set." ], "reasoning": "Generated queries perform slightly better on d1 but significantly better on d2, especially for non-comparison questions, improving recall from 27.88% to 53.23%.", "description": "To evaluate the query generators, we begin by determining how well they emulate the oracles. We evaluate them using Exact Match (EM) and F1 on the span prediction task, as well as compare their queries’ retrieval performance against the oracle queries. When we combine them into a pipeline, the generated queries perform only slightly better on d1 when a total of 10 documents are retrieved (89.91% vs 87.85%), but are significantly more effective for d2 (61.01% vs 36.91%). If we further zoom in on the retrieval performance on non-comparison questions for which finding the two entities involved is less trivial, we can see that the recall on d2 improves from 27.88% to 53.23%, almost doubling the number of questions we have the complete gold context to answer. We note that the IR performance we report on the full pipeline is different to that when we evaluate the query generators separately. We attribute this difference to the fact that the generated queries sometimes retrieve both gold documents in one step.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 6: Span prediction and IR performance of the query generator models for Hop 1 (G1) and Hop 2 (G2) evaluated separately on the HotpotQA dev set.' and methodically tackle the question that follows. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model has a higher Span EM score?\n" }, { "role": "assistant", "content": "By comparing the Span EM scores of G1 and G2, we see that G2 has a higher Span EM score of 52.29 compared to G1's 51.40. So, the answer is {\"answer\": \"G2\"}" } ], "images": [ "scigen/train/572.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Span prediction and IR performance of the query generator models for Hop 1 (G1) and Hop 2 (G2) evaluated separately on the HotpotQA dev set." ], "reasoning": "By comparing the Span EM scores of G1 and G2, we see that G2 has a higher Span EM score of 52.29 compared to G1's 51.40.", "question": "Which model has a higher Span EM score?", "answer": "So, the answer is {\"answer\": \"G2\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nG1 has a higher Span F1 score than G2.\n" }, { "role": "assistant", "content": "The table shows that G1 has a Span F1 score of 78.75, which is higher than G2's Span F1 score of 63.07. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/572.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Span prediction and IR performance of the query generator models for Hop 1 (G1) and Hop 2 (G2) evaluated separately on the HotpotQA dev set." ], "reasoning": "The table shows that G1 has a Span F1 score of 78.75, which is higher than G2's Span F1 score of 63.07.", "statement": "G1 has a higher Span F1 score than G2.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: End-to-end QA performance of baselines and our GoldEn Retriever model on the HotpotQA fullwiki test set. Among systems that were not published at the time of submission of this paper, “BERT pip.” was submitted to the official HotpotQA leaderboard on May 15th (thus contemporaneous), while “Entity-centric BERT Pipeline” and “PR-Bert” were submitted after the paper submission deadline.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "GoldEn Retriever outperforms several QA systems on the HotpotQA dataset, despite not using pretrained contextualization models.We compare the end-to-end performance of GoldEn Retriever against several QA systems on the HotpotQA dataset: (1) the baseline presented in Yang et al. , (2) CogQA Ding et al. However, the QA performance is handicapped because we do not make use of pretrained contextualization models (e.g., BERT) that these systems use. We expect a boost in QA performance from adopting these more powerful question answering models, especially ones that are tailored to perform few-document multi-hop reasoning. We leave this to future work." } ], "images": [ "scigen/train/573.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: End-to-end QA performance of baselines and our GoldEn Retriever model on the HotpotQA fullwiki test set. Among systems that were not published at the time of submission of this paper, “BERT pip.” was submitted to the official HotpotQA leaderboard on May 15th (thus contemporaneous), while “Entity-centric BERT Pipeline” and “PR-Bert” were submitted after the paper submission deadline." ], "reasoning": "GoldEn Retriever outperforms several QA systems on the HotpotQA dataset, despite not using pretrained contextualization models.", "description": "We compare the end-to-end performance of GoldEn Retriever against several QA systems on the HotpotQA dataset: (1) the baseline presented in Yang et al. , (2) CogQA Ding et al. However, the QA performance is handicapped because we do not make use of pretrained contextualization models (e.g., BERT) that these systems use. We expect a boost in QA performance from adopting these more powerful question answering models, especially ones that are tailored to perform few-document multi-hop reasoning. We leave this to future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2: End-to-end QA performance of baselines and our GoldEn Retriever model on the HotpotQA fullwiki test set. Among systems that were not published at the time of submission of this paper, “BERT pip.” was submitted to the official HotpotQA leaderboard on May 15th (thus contemporaneous), while “Entity-centric BERT Pipeline” and “PR-Bert” were submitted after the paper submission deadline.', unfold the steps to solve the question given next. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nQuestion: Which QA system has the highest Answer EM score?\n" }, { "role": "assistant", "content": "To find the QA system with the highest Answer EM score, compare the Answer EM scores of all systems listed in the table. The highest score is 45.32, achieved by BERT pip. (contemporaneous). So, the answer is {\"answer\": \"BERT pip. (contemporaneous)\"}" } ], "images": [ "scigen/train/573.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: End-to-end QA performance of baselines and our GoldEn Retriever model on the HotpotQA fullwiki test set. Among systems that were not published at the time of submission of this paper, “BERT pip.” was submitted to the official HotpotQA leaderboard on May 15th (thus contemporaneous), while “Entity-centric BERT Pipeline” and “PR-Bert” were submitted after the paper submission deadline." ], "reasoning": "To find the QA system with the highest Answer EM score, compare the Answer EM scores of all systems listed in the table. The highest score is 45.32, achieved by BERT pip. (contemporaneous).", "question": "Which QA system has the highest Answer EM score?", "answer": "So, the answer is {\"answer\": \"BERT pip. (contemporaneous)\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nGoldEn Retriever has the highest Sup Fact EM score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Sup Fact EM scores of all systems listed in the table. The highest Sup Fact EM score is 38.67, achieved by BERT pip. (contemporaneous), not GoldEn Retriever. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/573.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: End-to-end QA performance of baselines and our GoldEn Retriever model on the HotpotQA fullwiki test set. Among systems that were not published at the time of submission of this paper, “BERT pip.” was submitted to the official HotpotQA leaderboard on May 15th (thus contemporaneous), while “Entity-centric BERT Pipeline” and “PR-Bert” were submitted after the paper submission deadline." ], "reasoning": "To verify this statement, compare the Sup Fact EM scores of all systems listed in the table. The highest Sup Fact EM score is 38.67, achieved by BERT pip. (contemporaneous), not GoldEn Retriever.", "statement": "GoldEn Retriever has the highest Sup Fact EM score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Question answering and IR performance amongst different IR settings on the dev set. We observe that although improving the IR engine is helpful, most of the performance gain results from the iterative retrieve-and-read strategy of GoldEn Retriever. (*: for GoldEn Retriever, the 10 paragraphs are combined from both hops, 5 from each hop.)'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "GoldEn Retriever significantly improves recall and support F1 scores compared to single-hop query and HotpotQA IR.In all cases, we use the QA component in GoldEn Retriever for the final question answering step. Further inspection reveals that despite Elasticsearch improving overall recall of gold documents, it is only able to retrieve both gold documents for 36.91% of the dev set questions, in comparison to 28.21% from the IR engine in Yang et al. In contrast, GoldEn Retriever improves this percentage to 61.01%, almost doubling the recall over the single-hop baseline, providing the QA component a much better set of context documents to predict answers from." } ], "images": [ "scigen/train/574.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Question answering and IR performance amongst different IR settings on the dev set. We observe that although improving the IR engine is helpful, most of the performance gain results from the iterative retrieve-and-read strategy of GoldEn Retriever. (*: for GoldEn Retriever, the 10 paragraphs are combined from both hops, 5 from each hop.)" ], "reasoning": "GoldEn Retriever significantly improves recall and support F1 scores compared to single-hop query and HotpotQA IR.", "description": "In all cases, we use the QA component in GoldEn Retriever for the final question answering step. Further inspection reveals that despite Elasticsearch improving overall recall of gold documents, it is only able to retrieve both gold documents for 36.91% of the dev set questions, in comparison to 28.21% from the IR engine in Yang et al. In contrast, GoldEn Retriever improves this percentage to 61.01%, almost doubling the recall over the single-hop baseline, providing the QA component a much better set of context documents to predict answers from.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 3: Question answering and IR performance amongst different IR settings on the dev set. We observe that although improving the IR engine is helpful, most of the performance gain results from the iterative retrieve-and-read strategy of GoldEn Retriever. (*: for GoldEn Retriever, the 10 paragraphs are combined from both hops, 5 from each hop.)' and proceed to solve the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich setting has the highest R@10 score?\n" }, { "role": "assistant", "content": "By comparing the R@10 scores in the table, GoldEn Retriever has the highest R@10 score of 75.46. So, the answer is {\"answer\": \"GoldEn Retriever\"}" } ], "images": [ "scigen/train/574.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Question answering and IR performance amongst different IR settings on the dev set. We observe that although improving the IR engine is helpful, most of the performance gain results from the iterative retrieve-and-read strategy of GoldEn Retriever. (*: for GoldEn Retriever, the 10 paragraphs are combined from both hops, 5 from each hop.)" ], "reasoning": "By comparing the R@10 scores in the table, GoldEn Retriever has the highest R@10 score of 75.46.", "question": "Which setting has the highest R@10 score?", "answer": "So, the answer is {\"answer\": \"GoldEn Retriever\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nSingle-hop query has a higher Ans F1 score than HotpotQA IR.\n" }, { "role": "assistant", "content": "The table shows that Single-hop query has an Ans F1 score of 38.19, which is higher than HotpotQA IR's Ans F1 score of 36.34. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/574.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Question answering and IR performance amongst different IR settings on the dev set. We observe that although improving the IR engine is helpful, most of the performance gain results from the iterative retrieve-and-read strategy of GoldEn Retriever. (*: for GoldEn Retriever, the 10 paragraphs are combined from both hops, 5 from each hop.)" ], "reasoning": "The table shows that Single-hop query has an Ans F1 score of 38.19, which is higher than HotpotQA IR's Ans F1 score of 36.34.", "statement": "Single-hop query has a higher Ans F1 score than HotpotQA IR.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Pipeline ablative analysis of GoldEn Retriever end-to-end QA performance by replacing each query generator with a query oracle.'.\n" }, { "role": "assistant", "content": "Replacing G2 with the oracle significantly improves end-to-end performance, indicating that G2's performance loss is due to solving a harder span selection problem.Lastly, we perform an ablation study in which we replace our query generator models with our query oracles and observe the effect on end-to-end performance. only slightly improves end-to-end performance, but further substituting G2 with the oracle yields a significant improvement. This illustrates that the performance loss is largely attributed to G2 rather than G1, because G2 solves a harder span selection problem from a longer retrieval context. In the next section, we examine the query generation models more closely by evaluating their performance without the QA component." } ], "images": [ "scigen/train/575.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Pipeline ablative analysis of GoldEn Retriever end-to-end QA performance by replacing each query generator with a query oracle." ], "reasoning": "Replacing G2 with the oracle significantly improves end-to-end performance, indicating that G2's performance loss is due to solving a harder span selection problem.", "description": "Lastly, we perform an ablation study in which we replace our query generator models with our query oracles and observe the effect on end-to-end performance. only slightly improves end-to-end performance, but further substituting G2 with the oracle yields a significant improvement. This illustrates that the performance loss is largely attributed to G2 rather than G1, because G2 solves a harder span selection problem from a longer retrieval context. In the next section, we examine the query generation models more closely by evaluating their performance without the QA component.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Pipeline ablative analysis of GoldEn Retriever end-to-end QA performance by replacing each query generator with a query oracle.', answer the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the Joint F1 score when both Hop 1 and Hop 2 oracles are used?\n" }, { "role": "assistant", "content": "The table shows the Joint F1 scores for different systems. The Joint F1 score for the system with both Hop 1 and Hop 2 oracles is 52.18. So, the answer is {\"answer\": \"52.18\"}." } ], "images": [ "scigen/train/575.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Pipeline ablative analysis of GoldEn Retriever end-to-end QA performance by replacing each query generator with a query oracle." ], "reasoning": "The table shows the Joint F1 scores for different systems. The Joint F1 score for the system with both Hop 1 and Hop 2 oracles is 52.18.", "question": "What is the Joint F1 score when both Hop 1 and Hop 2 oracles are used?", "answer": "So, the answer is {\"answer\": \"52.18\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nUsing Hop 1 oracle alone improves the Joint F1 score by more than 2 points.\n" }, { "role": "assistant", "content": "The table shows that the Joint F1 score for the GoldEn Retriever is 40.21, and for the system with Hop 1 oracle, it is 42.68. The improvement is 42.68 - 40.21 = 2.47 points, which is more than 2 points. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/575.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Pipeline ablative analysis of GoldEn Retriever end-to-end QA performance by replacing each query generator with a query oracle." ], "reasoning": "The table shows that the Joint F1 score for the GoldEn Retriever is 40.21, and for the system with Hop 1 oracle, it is 42.68. The improvement is 42.68 - 40.21 = 2.47 points, which is more than 2 points.", "statement": "Using Hop 1 oracle alone improves the Joint F1 score by more than 2 points.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 7: IR performance (recall in percentages) of various Elasticsearch setups on the HotpotQA dev set using the original question.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The proposed heuristic improves query results by boosting scores of documents with titles matching the query, leading to better rankings.To this end, we propose to rerank these query results with a simple but effective heuristic that alleviates this issue. We would first retrieve at least 50 candidate documents for each query for consideration, and boost the query scores of documents whose title exactly matches the search query, or is a substring of the search query. Specifically, we multiply the document score by a heuristic constant between 1.05 and 1.5, depending on how well the document title matches the query, before reranking all search results. This results in a significant improvement in these cases. For the query “George W. Bush”, the page for the former US president is ranked at the top after reranking." } ], "images": [ "scigen/train/576.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: IR performance (recall in percentages) of various Elasticsearch setups on the HotpotQA dev set using the original question." ], "reasoning": "The proposed heuristic improves query results by boosting scores of documents with titles matching the query, leading to better rankings.", "description": "To this end, we propose to rerank these query results with a simple but effective heuristic that alleviates this issue. We would first retrieve at least 50 candidate documents for each query for consideration, and boost the query scores of documents whose title exactly matches the search query, or is a substring of the search query. Specifically, we multiply the document score by a heuristic constant between 1.05 and 1.5, depending on how well the document title matches the query, before reranking all search results. This results in a significant improvement in these cases. For the query “George W. Bush”, the page for the former US president is ranked at the top after reranking.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 7: IR performance (recall in percentages) of various Elasticsearch setups on the HotpotQA dev set using the original question.', answer the following question. Give detailed solution consisting of each step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the R@10 for d2 when both title boosting and reranking are not used?\n" }, { "role": "assistant", "content": "The table shows the R@10 for d2 under different conditions. When both title boosting and reranking are not used, the R@10 for d2 is 29.55. So, the answer is {\"answer\": \"29.55\"}" } ], "images": [ "scigen/train/576.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: IR performance (recall in percentages) of various Elasticsearch setups on the HotpotQA dev set using the original question." ], "reasoning": "The table shows the R@10 for d2 under different conditions. When both title boosting and reranking are not used, the R@10 for d2 is 29.55.", "question": "What is the R@10 for d2 when both title boosting and reranking are not used?", "answer": "So, the answer is {\"answer\": \"29.55\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The final system has the highest R@10 for d1., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe final system has the highest R@10 for d1.\n" }, { "role": "assistant", "content": "The table shows that the final system has an R@10 for d1 of 87.85, which is higher than any other system listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/576.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: IR performance (recall in percentages) of various Elasticsearch setups on the HotpotQA dev set using the original question." ], "reasoning": "The table shows that the final system has an R@10 for d1 of 87.85, which is higher than any other system listed.", "statement": "The final system has the highest R@10 for d1.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Document retrieval evaluation on dev set (%). (’-’ denotes a missing value)', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our model's OFEVER score is slightly lower than the score of the model proposed by Hanselowski et al. (2018).We use the OFEVER metric to evaluate the document retrieval component. After running the same model proposed by \\newcitehanselowski2018ukp, we find our OFEVER score is slightly lower, which may due to the random factors." } ], "images": [ "scigen/train/577.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Document retrieval evaluation on dev set (%). (’-’ denotes a missing value)" ], "reasoning": "Our model's OFEVER score is slightly lower than the score of the model proposed by Hanselowski et al. (2018).", "description": "We use the OFEVER metric to evaluate the document retrieval component. After running the same model proposed by \\newcitehanselowski2018ukp, we find our OFEVER score is slightly lower, which may due to the random factors.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 3: Document retrieval evaluation on dev set (%). (’-’ denotes a missing value)', answer the question below by showing each progressive step toward the answer. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the OFEVER score of the model proposed by Hanselowski et al. (2018)?\n" }, { "role": "assistant", "content": "The description mentions that the model proposed by Hanselowski et al. (2018) was used for comparison, and the table shows the OFEVER score for Athene, which is 93.55. So, the answer is {\"answer\": \"93.55\"}" } ], "images": [ "scigen/train/577.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Document retrieval evaluation on dev set (%). (’-’ denotes a missing value)" ], "reasoning": "The description mentions that the model proposed by Hanselowski et al. (2018) was used for comparison, and the table shows the OFEVER score for Athene, which is 93.55.", "question": "What is the OFEVER score of the model proposed by Hanselowski et al. (2018)?", "answer": "So, the answer is {\"answer\": \"93.55\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence Our model has a higher OFEVER score than UNC NLP. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nOur model has a higher OFEVER score than UNC NLP.\n" }, { "role": "assistant", "content": "The table shows that the OFEVER score for Our Model is 93.33, while the score for UNC NLP is 92.82, indicating that Our Model has a higher score. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/577.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Document retrieval evaluation on dev set (%). (’-’ denotes a missing value)" ], "reasoning": "The table shows that the OFEVER score for Our Model is 93.33, while the score for UNC NLP is 92.82, indicating that Our Model has a higher score.", "statement": "Our model has a higher OFEVER score than UNC NLP.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Sentence selection evaluation and average label accuracy of GEAR with different thresholds on dev set (%).'.\n" }, { "role": "assistant", "content": "As the threshold increases, recall and OFEVER scores decrease, while precision and F1 scores increase.We find the model with threshold 0 achieves the highest recall and OFEVER score. When the threshold increases, the recall value and the OFEVER score drop gradually while the precision and F1 score increase. The results are consistent with our intuition. If we do not filter out evidence, more claims could be provided with the full evidence set. If we increase the value of the threshold, more pieces of noisy evidence are filtered out, which contributes to the increase of precision and F1." } ], "images": [ "scigen/train/578.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Sentence selection evaluation and average label accuracy of GEAR with different thresholds on dev set (%)." ], "reasoning": "As the threshold increases, recall and OFEVER scores decrease, while precision and F1 scores increase.", "description": "We find the model with threshold 0 achieves the highest recall and OFEVER score. When the threshold increases, the recall value and the OFEVER score drop gradually while the precision and F1 score increase. The results are consistent with our intuition. If we do not filter out evidence, more claims could be provided with the full evidence set. If we increase the value of the threshold, more pieces of noisy evidence are filtered out, which contributes to the increase of precision and F1.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Sentence selection evaluation and average label accuracy of GEAR with different thresholds on dev set (%).\nQuestion: What is the precision value when the threshold is 10^-2?\nSolve the above question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the precision value for the threshold 10^-2, locate the row corresponding to 10^-2 and then find the precision value in that row. So, the answer is {\"answer\": \"53.12\"}" } ], "images": [ "scigen/train/578.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Sentence selection evaluation and average label accuracy of GEAR with different thresholds on dev set (%)." ], "reasoning": "To find the precision value for the threshold 10^-2, locate the row corresponding to 10^-2 and then find the precision value in that row.", "question": "What is the precision value when the threshold is 10^-2?", "answer": "So, the answer is {\"answer\": \"53.12\"}" }, { "messages": [ { "role": "user", "content": "The highest F1 score is achieved at the threshold 10^-1. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe highest F1 score is achieved at the threshold 10^-1.\n" }, { "role": "assistant", "content": "To verify this statement, compare the F1 scores across all thresholds. The highest F1 score is 75.72, which occurs at the threshold 10^-1. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/578.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Sentence selection evaluation and average label accuracy of GEAR with different thresholds on dev set (%)." ], "reasoning": "To verify this statement, compare the F1 scores across all thresholds. The highest F1 score is 75.72, which occurs at the threshold 10^-1.", "statement": "The highest F1 score is achieved at the threshold 10^-1.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 5: Label accuracy on the difficult dev set with different ERNet layers and evidence aggregators (%).'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Models with 2 ERNet layers achieve the best results, indicating the need for multi-step evidence propagation.We find our models with ERNet perform better than models without ERNet and the minimal improvement between them is 1.27%. We can also discover from the table that models with 2 ERNet layers achieve the best results, which indicates that claims from the difficult subset require multi-step evidence propagation. This result demonstrates the ability of our framework to deal with claims which need multiple evidence." } ], "images": [ "scigen/train/579.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Label accuracy on the difficult dev set with different ERNet layers and evidence aggregators (%)." ], "reasoning": "Models with 2 ERNet layers achieve the best results, indicating the need for multi-step evidence propagation.", "description": "We find our models with ERNet perform better than models without ERNet and the minimal improvement between them is 1.27%. We can also discover from the table that models with 2 ERNet layers achieve the best results, which indicates that claims from the difficult subset require multi-step evidence propagation. This result demonstrates the ability of our framework to deal with claims which need multiple evidence.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 5: Label accuracy on the difficult dev set with different ERNet layers and evidence aggregators (%).', solve this question and give detail solutions. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhat is the minimal improvement in percentage between models with and without ERNet?\n" }, { "role": "assistant", "content": "The table shows that models with ERNet layers have higher scores than those without. The minimal improvement is calculated by comparing the lowest score with ERNet (66.17) to the highest score without ERNet (65.36), resulting in an improvement of 1.27%. So, the answer is {\"answer\": \"1.27%\"}" } ], "images": [ "scigen/train/579.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Label accuracy on the difficult dev set with different ERNet layers and evidence aggregators (%)." ], "reasoning": "The table shows that models with ERNet layers have higher scores than those without. The minimal improvement is calculated by comparing the lowest score with ERNet (66.17) to the highest score without ERNet (65.36), resulting in an improvement of 1.27%.", "question": "What is the minimal improvement in percentage between models with and without ERNet?", "answer": "So, the answer is {\"answer\": \"1.27%\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Models with 2 ERNet layers achieve the best results in all aggregators., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nModels with 2 ERNet layers achieve the best results in all aggregators.\n" }, { "role": "assistant", "content": "The table shows that models with 2 ERNet layers have the highest scores in all three aggregators: Attention (67.44), Max (67.24), and Mean (67.56). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/579.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Label accuracy on the difficult dev set with different ERNet layers and evidence aggregators (%)." ], "reasoning": "The table shows that models with 2 ERNet layers have the highest scores in all three aggregators: Attention (67.44), Max (67.24), and Mean (67.56).", "statement": "Models with 2 ERNet layers achieve the best results in all aggregators.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Results of three types of acoustic features.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The experiments using LSTMs with 250 dimensional hidden states showed that multitask learning improved recognition accuracy, especially for FBANK features.We only show results of using LSTMs with 250 dimensional hidden states. The interpolation weight was set to be 0.5. In our experiments, tuning the interpolation weight did not further improve the recognition accuracy. The improvement for FBANK features is much larger than fMLLR features. In particular, with multitask learning, the recognition accuracy of our CTC system with best path decoding is comparable to the results obtained by Graves et al." } ], "images": [ "scigen/train/580.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of three types of acoustic features." ], "reasoning": "The experiments using LSTMs with 250 dimensional hidden states showed that multitask learning improved recognition accuracy, especially for FBANK features.", "description": "We only show results of using LSTMs with 250 dimensional hidden states. The interpolation weight was set to be 0.5. In our experiments, tuning the interpolation weight did not further improve the recognition accuracy. The improvement for FBANK features is much larger than fMLLR features. In particular, with multitask learning, the recognition accuracy of our CTC system with best path decoding is comparable to the results obtained by Graves et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: Results of three types of acoustic features.' and methodically tackle the question that follows. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the evaluation accuracy for the CTC model with fMLLR features?\n" }, { "role": "assistant", "content": "The table shows the evaluation accuracy for different models and features. For the CTC model with fMLLR features, the evaluation accuracy is listed in the 'eval' column. So, the answer is {\"answer\": \"17.8\"}." } ], "images": [ "scigen/train/580.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of three types of acoustic features." ], "reasoning": "The table shows the evaluation accuracy for different models and features. For the CTC model with fMLLR features, the evaluation accuracy is listed in the 'eval' column.", "question": "What is the evaluation accuracy for the CTC model with fMLLR features?", "answer": "So, the answer is {\"answer\": \"17.8\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe SRNN model with FBANK features has a higher evaluation accuracy than the CTC model with FBANK features.\n" }, { "role": "assistant", "content": "The table shows the evaluation accuracy for different models and features. The SRNN model with FBANK features has an evaluation accuracy of 20.0, while the CTC model with FBANK features has an evaluation accuracy of 19.9. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/580.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of three types of acoustic features." ], "reasoning": "The table shows the evaluation accuracy for different models and features. The SRNN model with FBANK features has an evaluation accuracy of 20.0, while the CTC model with FBANK features has an evaluation accuracy of 19.9.", "statement": "The SRNN model with FBANK features has a higher evaluation accuracy than the CTC model with FBANK features.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Phone error rates of baseline CTC and SRNN models.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The FBANK features are 120-dimensional with delta and delta-delta coefficients, while the fMLLR features are 40-dimensional. Both SRNN and CTC models used 3-layer bidirectional LSTMs and achieved comparable phone error rates.The FBANK features are 120-dimensional with delta and delta-delta coefficients, and the fMLLR features are 40-dimensional, which were obtained from a Kaldi baseline system. We used a 3-layer bidirectional LSTMs for feature extraction, and we used the greedy best path decoding algorithm for both models. Our SRNN and CTC achieved comparable phone error rate (PER) for both kinds of features. However, for the CTC system, Graves et al. Apart from the implementation difference of using different code bases, Graves et al." } ], "images": [ "scigen/train/581.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Phone error rates of baseline CTC and SRNN models." ], "reasoning": "The FBANK features are 120-dimensional with delta and delta-delta coefficients, while the fMLLR features are 40-dimensional. Both SRNN and CTC models used 3-layer bidirectional LSTMs and achieved comparable phone error rates.", "description": "The FBANK features are 120-dimensional with delta and delta-delta coefficients, and the fMLLR features are 40-dimensional, which were obtained from a Kaldi baseline system. We used a 3-layer bidirectional LSTMs for feature extraction, and we used the greedy best path decoding algorithm for both models. Our SRNN and CTC achieved comparable phone error rate (PER) for both kinds of features. However, for the CTC system, Graves et al. Apart from the implementation difference of using different code bases, Graves et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nTable title: Table 1: Phone error rates of baseline CTC and SRNN models.\n\nQuestion: What is the evaluation score for the SRNN model with 250-dimensional FBANK features?\n" }, { "role": "assistant", "content": "To find the evaluation score for the SRNN model with 250-dimensional FBANK features, locate the row where the model is SRNN, the features are FBANK, and the dimension is 250. The evaluation score in this row is 20.0. So, the answer is {\"answer\": \"20.0\"}." } ], "images": [ "scigen/train/581.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Phone error rates of baseline CTC and SRNN models." ], "reasoning": "To find the evaluation score for the SRNN model with 250-dimensional FBANK features, locate the row where the model is SRNN, the features are FBANK, and the dimension is 250. The evaluation score in this row is 20.0.", "question": "What is the evaluation score for the SRNN model with 250-dimensional FBANK features?", "answer": "So, the answer is {\"answer\": \"20.0\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe CTC model with 128-dimensional fMLLR features has a development score of 17.7.\n" }, { "role": "assistant", "content": "To verify this statement, locate the row where the model is CTC, the features are fMLLR, and the dimension is 128. The development score in this row is 17.7. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/581.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Phone error rates of baseline CTC and SRNN models." ], "reasoning": "To verify this statement, locate the row where the model is CTC, the features are fMLLR, and the dimension is 128. The development score in this row is 17.7.", "statement": "The CTC model with 128-dimensional fMLLR features has a development score of 17.7.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: Quantitative results of the models in terms of BLEU score and average decoding times per sentence in milliseconds. Results on WMT14 English-German translation and results without back-translation are in the Appendix.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Beam search significantly enhances translation quality over CTC-based non-autoregressive models, and adjusting the beam size allows control over the speed/quality trade-off.We observe that the beam search greatly improves the translation quality over the CTC-based nAR models (“Non-autoregressive” vs. “Ours”). Additionally, we have control over the speed/quality trade-off by either lowering or increasing the beam size." } ], "images": [ "scigen/train/582.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Quantitative results of the models in terms of BLEU score and average decoding times per sentence in milliseconds. Results on WMT14 English-German translation and results without back-translation are in the Appendix." ], "reasoning": "Beam search significantly enhances translation quality over CTC-based non-autoregressive models, and adjusting the beam size allows control over the speed/quality trade-off.", "description": "We observe that the beam search greatly improves the translation quality over the CTC-based nAR models (“Non-autoregressive” vs. “Ours”). Additionally, we have control over the speed/quality trade-off by either lowering or increasing the beam size.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1: Quantitative results of the models in terms of BLEU score and average decoding times per sentence in milliseconds. Results on WMT14 English-German translation and results without back-translation are in the Appendix.', solve the subsequent question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the highest translation quality score for Romanian WMT16 en → ro using the 'Ours' method?\n" }, { "role": "assistant", "content": "To find the highest translation quality score for Romanian WMT16 en → ro using the 'Ours' method, compare the scores for 'Ours' with different beam sizes. The scores are 29.65, 33.01, 33.29, and 33.51. The highest score is 33.51. So, the answer is {\"answer\": \"33.51\"}." } ], "images": [ "scigen/train/582.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Quantitative results of the models in terms of BLEU score and average decoding times per sentence in milliseconds. Results on WMT14 English-German translation and results without back-translation are in the Appendix." ], "reasoning": "To find the highest translation quality score for Romanian WMT16 en → ro using the 'Ours' method, compare the scores for 'Ours' with different beam sizes. The scores are 29.65, 33.01, 33.29, and 33.51. The highest score is 33.51.", "question": "What is the highest translation quality score for Romanian WMT16 en → ro using the 'Ours' method?", "answer": "So, the answer is {\"answer\": \"33.51\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe 'Ours' method with beam size 20 has the highest translation quality score for German WMT15 en → de.\n" }, { "role": "assistant", "content": "To verify this statement, compare the translation quality scores for German WMT15 en → de using the 'Ours' method with different beam sizes. The scores are 22.68, 25.50, 25.93, and 26.03. The highest score is 26.03, which corresponds to beam size 20. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/582.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Quantitative results of the models in terms of BLEU score and average decoding times per sentence in milliseconds. Results on WMT14 English-German translation and results without back-translation are in the Appendix." ], "reasoning": "To verify this statement, compare the translation quality scores for German WMT15 en → de using the 'Ours' method with different beam sizes. The scores are 22.68, 25.50, 25.93, and 26.03. The highest score is 26.03, which corresponds to beam size 20.", "statement": "The 'Ours' method with beam size 20 has the highest translation quality score for German WMT15 en → de.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: BLEU scores for English-to-German translation for different beam sizes and feature sets: CTC score (c), language model (l), ratio of the blank symbols (r), and the number of trailing blank symbols (t).', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Combining features leads to significant improvements, especially with larger beam sizes.We can see that combining the features is beneficial and that the improvement is substantial with larger beam sizes. The feature weights were trained separately for each beam size." } ], "images": [ "scigen/train/583.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: BLEU scores for English-to-German translation for different beam sizes and feature sets: CTC score (c), language model (l), ratio of the blank symbols (r), and the number of trailing blank symbols (t)." ], "reasoning": "Combining features leads to significant improvements, especially with larger beam sizes.", "description": "We can see that combining the features is beneficial and that the improvement is substantial with larger beam sizes. The feature weights were trained separately for each beam size.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: BLEU scores for English-to-German translation for different beam sizes and feature sets: CTC score (c), language model (l), ratio of the blank symbols (r), and the number of trailing blank symbols (t).', solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the score for the combination of c+l+r+t at beam size 20?\n" }, { "role": "assistant", "content": "To find the score for the combination of c+l+r+t at beam size 20, locate the row labeled 'c+l+r+t' and the column labeled '20'. So, the answer is {\"answer\": \"26.03\"}" } ], "images": [ "scigen/train/583.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: BLEU scores for English-to-German translation for different beam sizes and feature sets: CTC score (c), language model (l), ratio of the blank symbols (r), and the number of trailing blank symbols (t)." ], "reasoning": "To find the score for the combination of c+l+r+t at beam size 20, locate the row labeled 'c+l+r+t' and the column labeled '20'.", "question": "What is the score for the combination of c+l+r+t at beam size 20?", "answer": "So, the answer is {\"answer\": \"26.03\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe score for c+l at beam size 10 is higher than the score for c+l+r at beam size 5.\n" }, { "role": "assistant", "content": "The score for c+l at beam size 10 is 24.77, and the score for c+l+r at beam size 5 is 24.92. Since 24.77 is less than 24.92, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/583.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: BLEU scores for English-to-German translation for different beam sizes and feature sets: CTC score (c), language model (l), ratio of the blank symbols (r), and the number of trailing blank symbols (t)." ], "reasoning": "The score for c+l at beam size 10 is 24.77, and the score for c+l+r at beam size 5 is 24.92. Since 24.77 is less than 24.92, the statement is refuted.", "statement": "The score for c+l at beam size 10 is higher than the score for c+l+r at beam size 5.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Target word coverage ratio (%) on the test set.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The CoRe model effectively combines copying and restricted generative decoders, achieving high coverage and quality with a significantly smaller vocabulary size compared to common Seq2Seq models.In this paper, we develop a novel Seq2Seq model called CoRe, which captures the two core writing modes in paraphrase, i.e., Copying and Rewriting. CoRe fuses a copying decoder and a restricted generative decoder. Therefore, the weights learned by the attention mechanism have the explicit meanings in the copying mode. Meanwhile, the generative decoder produces the words restricted in the source-specific vocabulary. This vocabulary is composed of a source-target word alignment table and a small set of frequent words. The alignment table is trained in advance, and many frequent rewriting patterns are included in it. It seems better to update the alignment table according to the learned attention weights. However, with the supplement of a few frequent words, experiments (see Table: While the output dimension of our generative decoder is just one tenth of the output dimension used by the common Seq2Seq models, it is able to generate highly relevant words concerning rewriting. To combine the two decoders and determine the final output, we develop a predictor to predict the writing mode of copying or rewriting. Since we know the actual mode at each output position in a training instance, we introduce a binary sequence labeling task to guide the learning of this predictor, which takes advantages of the supervision derived from the writing modes. It appears that both datasets hold a high copying ratio. When we restrict the generative decoder to produce the source alignments, more than 85% target words can be covered. When combined with 2000 frequent words, the coverage ratio of our model is already close to that using the vocabulary of 30000 words. We conduct text quality evaluation from several points of view. The lower PPL usually means higher readability. We also perform the statistical analysis on the average length of the target text, UNK ratio and copy ratio." } ], "images": [ "scigen/train/584.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Target word coverage ratio (%) on the test set." ], "reasoning": "The CoRe model effectively combines copying and restricted generative decoders, achieving high coverage and quality with a significantly smaller vocabulary size compared to common Seq2Seq models.", "description": "In this paper, we develop a novel Seq2Seq model called CoRe, which captures the two core writing modes in paraphrase, i.e., Copying and Rewriting. CoRe fuses a copying decoder and a restricted generative decoder. Therefore, the weights learned by the attention mechanism have the explicit meanings in the copying mode. Meanwhile, the generative decoder produces the words restricted in the source-specific vocabulary. This vocabulary is composed of a source-target word alignment table and a small set of frequent words. The alignment table is trained in advance, and many frequent rewriting patterns are included in it. It seems better to update the alignment table according to the learned attention weights. However, with the supplement of a few frequent words, experiments (see Table: While the output dimension of our generative decoder is just one tenth of the output dimension used by the common Seq2Seq models, it is able to generate highly relevant words concerning rewriting. To combine the two decoders and determine the final output, we develop a predictor to predict the writing mode of copying or rewriting. Since we know the actual mode at each output position in a training instance, we introduce a binary sequence labeling task to guide the learning of this predictor, which takes advantages of the supervision derived from the writing modes. It appears that both datasets hold a high copying ratio. When we restrict the generative decoder to produce the source alignments, more than 85% target words can be covered. When combined with 2000 frequent words, the coverage ratio of our model is already close to that using the vocabulary of 30000 words. We conduct text quality evaluation from several points of view. The lower PPL usually means higher readability. We also perform the statistical analysis on the average length of the target text, UNK ratio and copy ratio.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the summarization score when the vocabulary is Xu A(X) ∪ U?\nSolve the above question based on the table titled 'Table 2: Target word coverage ratio (%) on the test set.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the summarization score for different vocabularies. For Xu A(X) ∪ U, the summarization score is 95.3. So, the answer is {\"answer\": \"95.3\"}" } ], "images": [ "scigen/train/584.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Target word coverage ratio (%) on the test set." ], "reasoning": "The table shows the summarization score for different vocabularies. For Xu A(X) ∪ U, the summarization score is 95.3.", "question": "What is the summarization score when the vocabulary is Xu A(X) ∪ U?", "answer": "So, the answer is {\"answer\": \"95.3\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe CoRe model achieves a simplification score of 96.0 with the vocabulary Xu A(X) ∪ U.\n" }, { "role": "assistant", "content": "The table indicates that the simplification score for the vocabulary Xu A(X) ∪ U is indeed 96.0. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/584.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Target word coverage ratio (%) on the test set." ], "reasoning": "The table indicates that the simplification score for the vocabulary Xu A(X) ∪ U is indeed 96.0.", "statement": "The CoRe model achieves a simplification score of 96.0 with the vocabulary Xu A(X) ∪ U.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Performance of different models. ∗Moses simply ignore the unknown words.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "CoRe achieves the highest ROUGE scores in both summarization and text simplification, and also produces sentences that resemble the target language the most according to PPL.In this table, the metrics that measure informativeness and text quality are separated. Let’s look at the informativeness performance first. As can be seen, CoRe achieves the highest ROUGE scores on both summarization and text simplification. In contrast, the standard attentive Seq2Seq model ABS is slightly inferior to Moses. It even performs worse than the simple baseline LEAD in terms of ROUGE-2 in summarization. Apparently, introducing the copying and restricted generation mechanisms is critical for the paraphrase-oriented tasks. Then, we check the quality of the generated sentences. According to PPL, the sentences produced by CoRe resemble the target language the most. It is interesting that LEAD extracts human-written text in the source. Nevertheless, its PPL is considerably higher than CoRe on both datasets. It seems that CoRe indeed captures some characteristics of the target language, such as the diction. We also find that the PPL of Moses is the largest, and its generated length reaches the length of the source text. Moses seems to conduct word-to-word translation. This practice is acceptable in text simplification, but totally offends the summarization requirement. Although not manually controlled, the lengths of the outputs in ABS and CoRe are both similar to the actual one, which demonstrates the learning ability of Seq2Seq models. The former verifies the power of our two decoders, while the latter may be attributed to the supplement of the supervision of writing modes." } ], "images": [ "scigen/train/585.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of different models. ∗Moses simply ignore the unknown words." ], "reasoning": "CoRe achieves the highest ROUGE scores in both summarization and text simplification, and also produces sentences that resemble the target language the most according to PPL.", "description": "In this table, the metrics that measure informativeness and text quality are separated. Let’s look at the informativeness performance first. As can be seen, CoRe achieves the highest ROUGE scores on both summarization and text simplification. In contrast, the standard attentive Seq2Seq model ABS is slightly inferior to Moses. It even performs worse than the simple baseline LEAD in terms of ROUGE-2 in summarization. Apparently, introducing the copying and restricted generation mechanisms is critical for the paraphrase-oriented tasks. Then, we check the quality of the generated sentences. According to PPL, the sentences produced by CoRe resemble the target language the most. It is interesting that LEAD extracts human-written text in the source. Nevertheless, its PPL is considerably higher than CoRe on both datasets. It seems that CoRe indeed captures some characteristics of the target language, such as the diction. We also find that the PPL of Moses is the largest, and its generated length reaches the length of the source text. Moses seems to conduct word-to-word translation. This practice is acceptable in text simplification, but totally offends the summarization requirement. Although not manually controlled, the lengths of the outputs in ABS and CoRe are both similar to the actual one, which demonstrates the learning ability of Seq2Seq models. The former verifies the power of our two decoders, while the latter may be attributed to the supplement of the supervision of writing modes.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Performance of different models. ∗Moses simply ignore the unknown words.\nQuestion: Which model has the lowest PPL score for text simplification?\nSolve the above question based on the table information. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the PPL scores for text simplification, CoRe has the lowest PPL score of 60.9. So, the answer is {\"answer\": \"CoRe\"}" } ], "images": [ "scigen/train/585.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of different models. ∗Moses simply ignore the unknown words." ], "reasoning": "By examining the PPL scores for text simplification, CoRe has the lowest PPL score of 60.9.", "question": "Which model has the lowest PPL score for text simplification?", "answer": "So, the answer is {\"answer\": \"CoRe\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nABS performs worse than Moses in terms of ROUGE-2 for summarization.\n" }, { "role": "assistant", "content": "The table shows that ABS has a ROUGE-2 score of 12.4 for summarization, while Moses has a ROUGE-2 score of 14.1, indicating that ABS performs worse than Moses. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/585.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of different models. ∗Moses simply ignore the unknown words." ], "reasoning": "The table shows that ABS has a ROUGE-2 score of 12.4 for summarization, while Moses has a ROUGE-2 score of 14.1, indicating that ABS performs worse than Moses.", "statement": "ABS performs worse than Moses in terms of ROUGE-2 for summarization.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: PDP and PCFP results for all languages and models, averaged over 4 runs. Metrics are defined in § 3.3. An refers to the Analogy metric and LE to the Lexicon Expansion metric.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The benchmark performs similarly to the gold k in German, Latin, and Russian, but overestimates the number of cells in English and underestimates in Arabic.For reference, we also report a supervised benchmark, sup, which assumes a gold grid as input, then solves the PCFP exactly as the benchmark does. In terms of the PDP, clustering assigns lexicon forms to paradigms (46–82%) more accurately than to cells (26–80%). Results are high for English, which has the fewest gold cells, and lower elsewhere. In German, Latin, and Russian, our benchmark proposes nearly as many cells as gold k, thus performing similarly. For English, it overestimates the true number and performs worse. For Arabic, it severely underestimates k but performs better, likely due to the orthography: without diacritics, the three case distinctions become obscured in almost all instances. In general, fixing the true number of cells can be unhelpful because syncretism and the Zipfian distribution of cells creates situations where certain gold cells are too difficult to detect. Allowing the system to choose its own number of cells lets it focus on distinctions for which there is sufficient distributional evidence." } ], "images": [ "scigen/train/586.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: PDP and PCFP results for all languages and models, averaged over 4 runs. Metrics are defined in § 3.3. An refers to the Analogy metric and LE to the Lexicon Expansion metric." ], "reasoning": "The benchmark performs similarly to the gold k in German, Latin, and Russian, but overestimates the number of cells in English and underestimates in Arabic.", "description": "For reference, we also report a supervised benchmark, sup, which assumes a gold grid as input, then solves the PCFP exactly as the benchmark does. In terms of the PDP, clustering assigns lexicon forms to paradigms (46–82%) more accurately than to cells (26–80%). Results are high for English, which has the fewest gold cells, and lower elsewhere. In German, Latin, and Russian, our benchmark proposes nearly as many cells as gold k, thus performing similarly. For English, it overestimates the true number and performs worse. For Arabic, it severely underestimates k but performs better, likely due to the orthography: without diacritics, the three case distinctions become obscured in almost all instances. In general, fixing the true number of cells can be unhelpful because syncretism and the Zipfian distribution of cells creates situations where certain gold cells are too difficult to detect. Allowing the system to choose its own number of cells lets it focus on distinctions for which there is sufficient distributional evidence.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 4: PDP and PCFP results for all languages and models, averaged over 4 runs. Metrics are defined in § 3.3. An refers to the Analogy metric and LE to the Lexicon Expansion metric.'. Solve the following question based on the table.\n\nQuestion: Which language has the highest PCFP An score in the benchmark?\nLet's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the PCFP An column for the benchmark row, Russian has the highest score of 31.9. So, the answer is {\"answer\": \"Russian\"}." } ], "images": [ "scigen/train/586.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: PDP and PCFP results for all languages and models, averaged over 4 runs. Metrics are defined in § 3.3. An refers to the Analogy metric and LE to the Lexicon Expansion metric." ], "reasoning": "By examining the PCFP An column for the benchmark row, Russian has the highest score of 31.9.", "question": "Which language has the highest PCFP An score in the benchmark?", "answer": "So, the answer is {\"answer\": \"Russian\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The benchmark performs better than the gold k for English verbs in terms of PDP F_{\\mathrm{par}}. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe benchmark performs better than the gold k for English verbs in terms of PDP F_{\\mathrm{par}}.\n" }, { "role": "assistant", "content": "The table shows that for English verbs, the benchmark has a PDP F_{\\mathrm{par}} score of 64.0, while the gold k has a score of 79.6, indicating that the benchmark performs worse. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/586.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: PDP and PCFP results for all languages and models, averaged over 4 runs. Metrics are defined in § 3.3. An refers to the Analogy metric and LE to the Lexicon Expansion metric." ], "reasoning": "The table shows that for English verbs, the benchmark has a PDP F_{\\mathrm{par}} score of 64.0, while the gold k has a score of 79.6, indicating that the benchmark performs worse.", "statement": "The benchmark performs better than the gold k for English verbs in terms of PDP F_{\\mathrm{par}}.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 7: Benchmark variations demonstrating the effects of various factors, averaged over 4 runs.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The table compares different configurations for Arabic and Latin nouns across various metrics, including corpus size, bias settings, and source selection.We consider augmenting and shrinking the corpus. We also reset the FastText hyperparameters used to achieve a morphosyntactic inductive bias to their default values (no affix/window bias) and consider two constant exponent penalty weights (\\omega(x_{f},c)=1 and \\omega(x_{f},c)=0) Finally, we consider selecting random sources for PCFP reinflection instead of identifying reliable sources. For all variants, the number of cells is fixed to the ground truth." } ], "images": [ "scigen/train/587.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Benchmark variations demonstrating the effects of various factors, averaged over 4 runs." ], "reasoning": "The table compares different configurations for Arabic and Latin nouns across various metrics, including corpus size, bias settings, and source selection.", "description": "We consider augmenting and shrinking the corpus. We also reset the FastText hyperparameters used to achieve a morphosyntactic inductive bias to their default values (no affix/window bias) and consider two constant exponent penalty weights (\\omega(x_{f},c)=1 and \\omega(x_{f},c)=0) Finally, we consider selecting random sources for PCFP reinflection instead of identifying reliable sources. For all variants, the number of cells is fixed to the ground truth.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 7: Benchmark variations demonstrating the effects of various factors, averaged over 4 runs.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the PCFP An score for Arabic nouns with no window bias?\n" }, { "role": "assistant", "content": "To find the PCFP An score for Arabic nouns with no window bias, locate the row labeled 'no window bias' under the 'Arabic nouns – 27 cells' section and find the corresponding value in the 'PCFP An' column. So, the answer is {\"answer\": \"17.6\"}" } ], "images": [ "scigen/train/587.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Benchmark variations demonstrating the effects of various factors, averaged over 4 runs." ], "reasoning": "To find the PCFP An score for Arabic nouns with no window bias, locate the row labeled 'no window bias' under the 'Arabic nouns – 27 cells' section and find the corresponding value in the 'PCFP An' column.", "question": "What is the PCFP An score for Arabic nouns with no window bias?", "answer": "So, the answer is {\"answer\": \"17.6\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe PDP F_{\\mathrm{par}} score for Latin nouns with a larger corpus is higher than that for Arabic nouns with a larger corpus.\n" }, { "role": "assistant", "content": "Compare the PDP F_{\\mathrm{par}} scores for Latin nouns and Arabic nouns with a larger corpus. The score for Latin nouns is 79.1, while the score for Arabic nouns is 37.5. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/587.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Benchmark variations demonstrating the effects of various factors, averaged over 4 runs." ], "reasoning": "Compare the PDP F_{\\mathrm{par}} scores for Latin nouns and Arabic nouns with a larger corpus. The score for Latin nouns is 79.1, while the score for Arabic nouns is 37.5.", "statement": "The PDP F_{\\mathrm{par}} score for Latin nouns with a larger corpus is higher than that for Arabic nouns with a larger corpus.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Number of reference chains, dialogue segments, and image types (targets and non-targets) in each data split.'.\n" }, { "role": "assistant", "content": "The dataset was divided into training, validation, and testing sets with 70%, 15%, and 15% of the data respectively, ensuring equal distribution of target image domains.The automatically extracted co-reference chains per target image were split into three disjoint sets for training (70%), validation (15%) and testing (15%), aiming at an equal distribution of target image domains in all three sets. The results show that the resolution capabilities of our model are well above the baseline. The History model achieves higher recall and F-score than the No-History model, while precision is comparable across these two conditions." } ], "images": [ "scigen/train/588.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Number of reference chains, dialogue segments, and image types (targets and non-targets) in each data split." ], "reasoning": "The dataset was divided into training, validation, and testing sets with 70%, 15%, and 15% of the data respectively, ensuring equal distribution of target image domains.", "description": "The automatically extracted co-reference chains per target image were split into three disjoint sets for training (70%), validation (15%) and testing (15%), aiming at an equal distribution of target image domains in all three sets. The results show that the resolution capabilities of our model are well above the baseline. The History model achieves higher recall and F-score than the No-History model, while precision is comparable across these two conditions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: Number of reference chains, dialogue segments, and image types (targets and non-targets) in each data split.' and methodically tackle the question that follows. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the total number of chains used in the validation and testing sets combined?\n" }, { "role": "assistant", "content": "To find the total number of chains in the validation and testing sets, add the number of chains in the validation set (2,811) to the number of chains in the testing set (2,816). So, the answer is {\"answer\": \"5627\"}." } ], "images": [ "scigen/train/588.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Number of reference chains, dialogue segments, and image types (targets and non-targets) in each data split." ], "reasoning": "To find the total number of chains in the validation and testing sets, add the number of chains in the validation set (2,811) to the number of chains in the testing set (2,816).", "question": "What is the total number of chains used in the validation and testing sets combined?", "answer": "So, the answer is {\"answer\": \"5627\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe training set contains more targets than the validation and testing sets combined.\n" }, { "role": "assistant", "content": "The training set has 40,898 targets. The validation set has 9,070 targets and the testing set has 9,025 targets. Combined, the validation and testing sets have 18,095 targets, which is less than the training set. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/588.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Number of reference chains, dialogue segments, and image types (targets and non-targets) in each data split." ], "reasoning": "The training set has 40,898 targets. The validation set has 9,070 targets and the testing set has 9,025 targets. Combined, the validation and testing sets have 18,095 targets, which is less than the training set.", "statement": "The training set contains more targets than the validation and testing sets combined.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Avg. token counts in COCO captions and the first and last descriptions in PhotoBook, plus their cosine distance to the caption’s cluster mean vector. The distance between first and last descriptions is 0.083.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The study compares the length and content of first and last referring expressions to COCO captions, finding that last descriptions are significantly shorter and have a different word class distribution.In a small-scale pilot study, Ilinykh et al. We argue that in the PhotoBook task referring expressions are not only adapted based on the goal-oriented nature of the interaction but also by incorporating the developing common ground between the participants. This effect becomes most apparent when collecting all referring expressions for a specific target image produced during the different rounds of a game in its coreference chain. The following excerpt displays such a coreference chain extracted from the PhotoBook dataset: [itemsep=-1pt,leftmargin=14pt] A: Do you have a boy with a teal coloured shirt with yellow holding a bear with a red shirt? B: Boy with teal shirt and bear with red shirt? A: Teal shirt boy? To quantify the effect of referring expression refinement, we compare participants’ first and last descriptions to a given target image with the image’s captions provided in the MS COCO dataset. For this purpose we manually annotated the first and last expressions referring to a set of six target images across ten random games in the PhotoBook dataset. Before filtering, first referring expressions do not significantly differ in length from the COCO captions. Last descriptions however are significantly shorter than both the COCO captions and first descriptions. After filtering for content words, no significant differences remain. We also calculate the cosine distance between the three different descriptions based on their average word vectors. mean word vector, which is confirmed in our results. Comparing the distribution of word classes in the captions and referring expressions finally revealed a similar distribution in first referring expressions and COCO captions, and a significantly different distribution in last referring expressions, among other things doubling the relative frequency of nouns." } ], "images": [ "scigen/train/589.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Avg. token counts in COCO captions and the first and last descriptions in PhotoBook, plus their cosine distance to the caption’s cluster mean vector. The distance between first and last descriptions is 0.083." ], "reasoning": "The study compares the length and content of first and last referring expressions to COCO captions, finding that last descriptions are significantly shorter and have a different word class distribution.", "description": "In a small-scale pilot study, Ilinykh et al. We argue that in the PhotoBook task referring expressions are not only adapted based on the goal-oriented nature of the interaction but also by incorporating the developing common ground between the participants. This effect becomes most apparent when collecting all referring expressions for a specific target image produced during the different rounds of a game in its coreference chain. The following excerpt displays such a coreference chain extracted from the PhotoBook dataset: [itemsep=-1pt,leftmargin=14pt] A: Do you have a boy with a teal coloured shirt with yellow holding a bear with a red shirt? B: Boy with teal shirt and bear with red shirt? A: Teal shirt boy? To quantify the effect of referring expression refinement, we compare participants’ first and last descriptions to a given target image with the image’s captions provided in the MS COCO dataset. For this purpose we manually annotated the first and last expressions referring to a set of six target images across ten random games in the PhotoBook dataset. Before filtering, first referring expressions do not significantly differ in length from the COCO captions. Last descriptions however are significantly shorter than both the COCO captions and first descriptions. After filtering for content words, no significant differences remain. We also calculate the cosine distance between the three different descriptions based on their average word vectors. mean word vector, which is confirmed in our results. Comparing the distribution of word classes in the captions and referring expressions finally revealed a similar distribution in first referring expressions and COCO captions, and a significantly different distribution in last referring expressions, among other things doubling the relative frequency of nouns.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Avg. token counts in COCO captions and the first and last descriptions in PhotoBook, plus their cosine distance to the caption’s cluster mean vector. The distance between first and last descriptions is 0.083.\nQuestion: What is the cosine distance between the first and last descriptions?\nGive a detailed response to the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the cosine distance between the first description and the last description as 0.156. So, the answer is {\"answer\": \"0.156\"}" } ], "images": [ "scigen/train/589.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Avg. token counts in COCO captions and the first and last descriptions in PhotoBook, plus their cosine distance to the caption’s cluster mean vector. The distance between first and last descriptions is 0.083." ], "reasoning": "The table shows the cosine distance between the first description and the last description as 0.156.", "question": "What is the cosine distance between the first and last descriptions?", "answer": "So, the answer is {\"answer\": \"0.156\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe number of content words in the first description is higher than in the COCO captions.\n" }, { "role": "assistant", "content": "The table shows that the number of content words in the first description is 5.185, which is less than the 5.255 content words in the COCO captions. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/589.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Avg. token counts in COCO captions and the first and last descriptions in PhotoBook, plus their cosine distance to the caption’s cluster mean vector. The distance between first and last descriptions is 0.083." ], "reasoning": "The table shows that the number of content words in the first description is 5.185, which is less than the 5.255 content words in the COCO captions.", "statement": "The number of content words in the first description is higher than in the COCO captions.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Results for the target images in the test set.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Random baseline scores are obtained by averaging 10 runs with a model that predicts targets and non-targets randomly for the test set images.Every candidate image contributes individually to the scores, i.e., the task is not treated as multi-label for evaluation purposes. Random baseline scores are obtained by taking the average of 10 runs with a model that predicts targets and non-targets randomly for the images in the test set." } ], "images": [ "scigen/train/590.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results for the target images in the test set." ], "reasoning": "Random baseline scores are obtained by averaging 10 runs with a model that predicts targets and non-targets randomly for the test set images.", "description": "Every candidate image contributes individually to the scores, i.e., the task is not treated as multi-label for evaluation purposes. Random baseline scores are obtained by taking the average of 10 runs with a model that predicts targets and non-targets randomly for the images in the test set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: 'Table 3: Results for the target images in the test set.'\nQuestion: Which model has the highest F1 score?\n" }, { "role": "assistant", "content": "To find the model with the highest F1 score, compare the F1 scores of all models listed in the table. The model with the highest F1 score is 'History' with an F1 score of 65.43. So, the answer is {\"answer\": \"History\"}." } ], "images": [ "scigen/train/590.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results for the target images in the test set." ], "reasoning": "To find the model with the highest F1 score, compare the F1 scores of all models listed in the table. The model with the highest F1 score is 'History' with an F1 score of 65.43.", "question": "Which model has the highest F1 score?", "answer": "So, the answer is {\"answer\": \"History\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe 'No-History' model has a higher recall than the 'History / No image' model.\n" }, { "role": "assistant", "content": "To verify this statement, compare the recall values of the 'No-History' model and the 'History / No image' model. The 'No-History' model has a recall of 75.86, which is higher than the 'History / No image' model's recall of 63.18. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/590.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results for the target images in the test set." ], "reasoning": "To verify this statement, compare the recall values of the 'No-History' model and the 'History / No image' model. The 'No-History' model has a recall of 75.86, which is higher than the 'History / No image' model's recall of 63.18.", "statement": "The 'No-History' model has a higher recall than the 'History / No image' model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 6: Results for target images in the validation set.'.\n" }, { "role": "assistant", "content": "The model with history has the highest recall and F1 score, while the no image model has the lowest precision, recall, and F1 score.The latter constitute the large majority of candidate images, and thus results are substantially higher for this class." } ], "images": [ "scigen/train/591.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results for target images in the validation set." ], "reasoning": "The model with history has the highest recall and F1 score, while the no image model has the lowest precision, recall, and F1 score.", "description": "The latter constitute the large majority of candidate images, and thus results are substantially higher for this class.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 6: Results for target images in the validation set.', and tackle the question below in a sequential manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest F1 score and what is its recall?\n" }, { "role": "assistant", "content": "The model with the highest F1 score is the History model with an F1 score of 65.45. Its recall is 78.10. So, the answer is {\"answer\": \"78.10\"}." } ], "images": [ "scigen/train/591.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results for target images in the validation set." ], "reasoning": "The model with the highest F1 score is the History model with an F1 score of 65.45. Its recall is 78.10.", "question": "Which model has the highest F1 score and what is its recall?", "answer": "So, the answer is {\"answer\": \"78.10\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The model with no history has the highest precision. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model with no history has the highest precision.\n" }, { "role": "assistant", "content": "The table shows that the model with no history has a precision of 56.37, which is higher than the precision of the history model (56.32) and the no image model (34.61). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/591.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results for target images in the validation set." ], "reasoning": "The table shows that the model with no history has a precision of 56.37, which is higher than the precision of the history model (56.32) and the no image model (34.61).", "statement": "The model with no history has the highest precision.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Results on the Weibo NER dataset'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The LEMON-2 model achieved the highest F1-score of 62.19%, outperforming other models on all four datasets, including the challenging Weibo NER dataset.The LEMON-2 achieved state-of-the-art results on all the four datasets. Our model also achieved the highest F1-score Note that the Weibo NER data is extracted from the social media, it is full of non-standard expressions and only contains about 1.4k samples. The problems of out-of-vocabulary words and ambiguity of word boundaries become more serious for NER on this dataset." } ], "images": [ "scigen/train/592.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Results on the Weibo NER dataset" ], "reasoning": "The LEMON-2 model achieved the highest F1-score of 62.19%, outperforming other models on all four datasets, including the challenging Weibo NER dataset.", "description": "The LEMON-2 achieved state-of-the-art results on all the four datasets. Our model also achieved the highest F1-score Note that the Weibo NER data is extracted from the social media, it is full of non-standard expressions and only contains about 1.4k samples. The problems of out-of-vocabulary words and ambiguity of word boundaries become more serious for NER on this dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 7: Results on the Weibo NER dataset', answer the following question. Give detailed solution consisting of each step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model achieved the highest F1-score on the datasets?\n" }, { "role": "assistant", "content": "The table shows the F1-scores of different models. The LEMON model has the highest F1-score of 62.19%. So, the answer is {\"answer\": \"LEMON\"}." } ], "images": [ "scigen/train/592.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Results on the Weibo NER dataset" ], "reasoning": "The table shows the F1-scores of different models. The LEMON model has the highest F1-score of 62.19%.", "question": "Which model achieved the highest F1-score on the datasets?", "answer": "So, the answer is {\"answer\": \"LEMON\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The LEMON model achieved the highest precision (P) score., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe LEMON model achieved the highest precision (P) score.\n" }, { "role": "assistant", "content": "The table shows that the LEMON model has a precision (P) score of 70.86%, which is the only precision score listed and thus the highest. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/592.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Results on the Weibo NER dataset" ], "reasoning": "The table shows that the LEMON model has a precision (P) score of 70.86%, which is the only precision score listed and thus the highest.", "statement": "The LEMON model achieved the highest precision (P) score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Results on OntoNotes-4 development set with different model architectures.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The NER performance decreases by approximately 4% in F1-score when using automatically generated results by THULAC toolkit instead of the ground truth, indicating significant error propagation from upstream tasks.The performances of all models will decrease of approximately 4\\% in F1-score if we used the results of word segmentation and POS-tagging automatically generated by THULAC toolkit instead of the ground truth. It shows that the NER performance is significantly influenced by the results of the upstream tasks through the error propagation." } ], "images": [ "scigen/train/593.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on OntoNotes-4 development set with different model architectures." ], "reasoning": "The NER performance decreases by approximately 4% in F1-score when using automatically generated results by THULAC toolkit instead of the ground truth, indicating significant error propagation from upstream tasks.", "description": "The performances of all models will decrease of approximately 4\\% in F1-score if we used the results of word segmentation and POS-tagging automatically generated by THULAC toolkit instead of the ground truth. It shows that the NER performance is significantly influenced by the results of the upstream tasks through the error propagation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: Results on OntoNotes-4 development set with different model architectures.'.\n\nQuestion: What is the F1-score for the Bi-RNN + Lex model using the automatically generated results by THULAC toolkit under the Baseline setting?\n" }, { "role": "assistant", "content": "To find the F1-score for the Bi-RNN + Lex model using the automatically generated results by THULAC toolkit under the Baseline setting, locate the row labeled 'Auto' and the column labeled 'Bi-RNN + Lex' under the 'F1 (%) Baseline' section. The value is 71.34. So, the answer is {\"answer\": \"71.34\"}" } ], "images": [ "scigen/train/593.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on OntoNotes-4 development set with different model architectures." ], "reasoning": "To find the F1-score for the Bi-RNN + Lex model using the automatically generated results by THULAC toolkit under the Baseline setting, locate the row labeled 'Auto' and the column labeled 'Bi-RNN + Lex' under the 'F1 (%) Baseline' section. The value is 71.34.", "question": "What is the F1-score for the Bi-RNN + Lex model using the automatically generated results by THULAC toolkit under the Baseline setting?", "answer": "So, the answer is {\"answer\": \"71.34\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe F1-score for the FOFE + Lex model using the automatically generated results by THULAC toolkit under the Bi-RNN setting is higher than 70%.\n" }, { "role": "assistant", "content": "To verify this statement, locate the row labeled 'Auto' and the column labeled 'FOFE + Lex' under the 'F1 (%) Bi-RNN' section. The value is 68.76, which is lower than 70%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/593.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on OntoNotes-4 development set with different model architectures." ], "reasoning": "To verify this statement, locate the row labeled 'Auto' and the column labeled 'FOFE + Lex' under the 'F1 (%) Bi-RNN' section. The value is 68.76, which is lower than 70%.", "statement": "The F1-score for the FOFE + Lex model using the automatically generated results by THULAC toolkit under the Bi-RNN setting is higher than 70%.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: Results on the OntoNotes-4 development set with different features'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The LSTM-CRF model trained with features derived from word segmentation and POS-tagging shows improved performance across all models, regardless of whether the labels are human or automatically generated.We also trained a LSTM-CRF model as a traditional approach for comparison by NCRF++, an open source neural sequence labeling toolkit [yang2018ncrf]. The experimental results demonstrate that the features derived from the word segmentation and POS-tagging always benefit to all the models no matter they are labeled by human or produced by automatic toolkit." } ], "images": [ "scigen/train/594.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on the OntoNotes-4 development set with different features" ], "reasoning": "The LSTM-CRF model trained with features derived from word segmentation and POS-tagging shows improved performance across all models, regardless of whether the labels are human or automatically generated.", "description": "We also trained a LSTM-CRF model as a traditional approach for comparison by NCRF++, an open source neural sequence labeling toolkit [yang2018ncrf]. The experimental results demonstrate that the features derived from the word segmentation and POS-tagging always benefit to all the models no matter they are labeled by human or produced by automatic toolkit.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 3: Results on the OntoNotes-4 development set with different features', solve the subsequent question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model and feature combination achieved the highest F1 score with automatically labelled data?\n" }, { "role": "assistant", "content": "By examining the table, the highest F1 score with automatically labelled data is 72.30%, achieved by the Lex model with char + seg + pos features. So, the answer is {\"answer\": \"Lex model with char + seg + pos features\"}" } ], "images": [ "scigen/train/594.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on the OntoNotes-4 development set with different features" ], "reasoning": "By examining the table, the highest F1 score with automatically labelled data is 72.30%, achieved by the Lex model with char + seg + pos features.", "question": "Which model and feature combination achieved the highest F1 score with automatically labelled data?", "answer": "So, the answer is {\"answer\": \"Lex model with char + seg + pos features\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe NCRF model with char + pos features achieved the highest precision with ground truth data.\n" }, { "role": "assistant", "content": "The table shows that the highest precision with ground truth data is 78.70%, achieved by the Lex model with char + seg + pos features, not the NCRF model with char + pos features. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/594.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on the OntoNotes-4 development set with different features" ], "reasoning": "The table shows that the highest precision with ground truth data is 78.70%, achieved by the Lex model with char + seg + pos features, not the NCRF model with char + pos features.", "statement": "The NCRF model with char + pos features achieved the highest precision with ground truth data.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Results on the MSRA dataset', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The LEMON model achieved the highest F1-score among all models, demonstrating state-of-the-art performance on all four datasets.The LEMON-2 achieved state-of-the-art results on all the four datasets. Our model also achieved the highest F1-score Note that the Weibo NER data is extracted from the social media, it is full of non-standard expressions and only contains about 1.4k samples. The problems of out-of-vocabulary words and ambiguity of word boundaries become more serious for NER on this dataset." } ], "images": [ "scigen/train/595.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on the MSRA dataset" ], "reasoning": "The LEMON model achieved the highest F1-score among all models, demonstrating state-of-the-art performance on all four datasets.", "description": "The LEMON-2 achieved state-of-the-art results on all the four datasets. Our model also achieved the highest F1-score Note that the Weibo NER data is extracted from the social media, it is full of non-standard expressions and only contains about 1.4k samples. The problems of out-of-vocabulary words and ambiguity of word boundaries become more serious for NER on this dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nTable title: Table 4: Results on the MSRA dataset\n\nQuestion: Which model achieved the highest precision (P) score?\n" }, { "role": "assistant", "content": "By examining the table, the LEMON model has the highest precision score of 95.39%. So, the answer is {\"answer\": \"LEMON\"}" } ], "images": [ "scigen/train/595.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on the MSRA dataset" ], "reasoning": "By examining the table, the LEMON model has the highest precision score of 95.39%.", "question": "Which model achieved the highest precision (P) score?", "answer": "So, the answer is {\"answer\": \"LEMON\"}" }, { "messages": [ { "role": "user", "content": "The LEMON model achieved the highest recall (R) score. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe LEMON model achieved the highest recall (R) score.\n" }, { "role": "assistant", "content": "The table shows that the highest recall score is 92.79%, achieved by the zhang2018lattice model, not the LEMON model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/595.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on the MSRA dataset" ], "reasoning": "The table shows that the highest recall score is 92.79%, achieved by the zhang2018lattice model, not the LEMON model.", "statement": "The LEMON model achieved the highest recall (R) score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Results on the Resume NER dataset'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The LEMON-2 model achieved state-of-the-art results on all four datasets, including the highest F1-score, despite the challenges posed by the Weibo NER dataset.The LEMON-2 achieved state-of-the-art results on all the four datasets. Our model also achieved the highest F1-score Note that the Weibo NER data is extracted from the social media, it is full of non-standard expressions and only contains about 1.4k samples. The problems of out-of-vocabulary words and ambiguity of word boundaries become more serious for NER on this dataset." } ], "images": [ "scigen/train/596.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on the Resume NER dataset" ], "reasoning": "The LEMON-2 model achieved state-of-the-art results on all four datasets, including the highest F1-score, despite the challenges posed by the Weibo NER dataset.", "description": "The LEMON-2 achieved state-of-the-art results on all the four datasets. Our model also achieved the highest F1-score Note that the Weibo NER data is extracted from the social media, it is full of non-standard expressions and only contains about 1.4k samples. The problems of out-of-vocabulary words and ambiguity of word boundaries become more serious for NER on this dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 5: Results on the Resume NER dataset', and tackle the question below in a sequential manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model achieved the highest F1-score and what was the value?\n" }, { "role": "assistant", "content": "The table shows the F1-scores for various models. The LEMON model has the highest F1-score of 94.82%. So, the answer is {\"answer\": \"LEMON with 94.82%\"}" } ], "images": [ "scigen/train/596.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on the Resume NER dataset" ], "reasoning": "The table shows the F1-scores for various models. The LEMON model has the highest F1-score of 94.82%.", "question": "Which model achieved the highest F1-score and what was the value?", "answer": "So, the answer is {\"answer\": \"LEMON with 94.82%\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe char+bichar+softword model achieved the highest recall.\n" }, { "role": "assistant", "content": "The table shows that the char+bichar+softword model has a recall of 94.29%, which is not the highest. The LEMON model has the highest recall of 94.07%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/596.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on the Resume NER dataset" ], "reasoning": "The table shows that the char+bichar+softword model has a recall of 94.29%, which is not the highest. The LEMON model has the highest recall of 94.07%.", "statement": "The char+bichar+softword model achieved the highest recall.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Results on the OntoNotes-4 dataset'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The LEMON-2 model achieved state-of-the-art results on all four datasets, including the highest F1-score, despite the challenges posed by the Weibo NER dataset.The LEMON-2 achieved state-of-the-art results on all the four datasets. Our model also achieved the highest F1-score Note that the Weibo NER data is extracted from the social media, it is full of non-standard expressions and only contains about 1.4k samples. The problems of out-of-vocabulary words and ambiguity of word boundaries become more serious for NER on this dataset." } ], "images": [ "scigen/train/597.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results on the OntoNotes-4 dataset" ], "reasoning": "The LEMON-2 model achieved state-of-the-art results on all four datasets, including the highest F1-score, despite the challenges posed by the Weibo NER dataset.", "description": "The LEMON-2 achieved state-of-the-art results on all the four datasets. Our model also achieved the highest F1-score Note that the Weibo NER data is extracted from the social media, it is full of non-standard expressions and only contains about 1.4k samples. The problems of out-of-vocabulary words and ambiguity of word boundaries become more serious for NER on this dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 6: Results on the OntoNotes-4 dataset', answer the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model achieved the highest F1-score and what was the score?\n" }, { "role": "assistant", "content": "The table shows the F1-scores for different models. The highest F1-score is 78.78%, achieved by the LEMON {\\dagger} model. So, the answer is {\"answer\": \"LEMON {\\dagger}, 78.78%\"}." } ], "images": [ "scigen/train/597.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results on the OntoNotes-4 dataset" ], "reasoning": "The table shows the F1-scores for different models. The highest F1-score is 78.78%, achieved by the LEMON {\\dagger} model.", "question": "Which model achieved the highest F1-score and what was the score?", "answer": "So, the answer is {\"answer\": \"LEMON {\\dagger}, 78.78%\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe LEMON model achieved the highest precision score.\n" }, { "role": "assistant", "content": "The table shows that the LEMON model has the highest precision score of 80.61%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/597.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results on the OntoNotes-4 dataset" ], "reasoning": "The table shows that the LEMON model has the highest precision score of 80.61%.", "statement": "The LEMON model achieved the highest precision score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1. Comparison between our method, our Joint Neural Embedding (JNE)(Marín et al., 2018) and AdaMine (Carvalho et al., 2018) re-implementation. For all models we were using selected matching pairs generated by reducing noisy instruction sentences as described above. Recall rates are averaged over the evaluation batches.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The model's performance is evaluated by ranking image embeddings against text embeddings using cosine distance, estimating median rank, and recall percentage at top K percent.One sample of these subsets is composed of text embedding and image embedding in the shared latent space. Since our interest lies in the recipe retrieval task, we optimized and evaluated our model by using each image embedding in the subsets as query against all text embeddings. By ranking the query and the candidate embeddings according to their cosine distance, we estimate the median rank. The model’s performance is best, if the matching text embedding is found at the first rank. Further, we estimate the recall percentage at the top K percent over all queries. The recall percentage describes the quantity of queries ranked amid the top K closest results." } ], "images": [ "scigen/train/598.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1. Comparison between our method, our Joint Neural Embedding (JNE)(Marín et al., 2018) and AdaMine (Carvalho et al., 2018) re-implementation. For all models we were using selected matching pairs generated by reducing noisy instruction sentences as described above. Recall rates are averaged over the evaluation batches." ], "reasoning": "The model's performance is evaluated by ranking image embeddings against text embeddings using cosine distance, estimating median rank, and recall percentage at top K percent.", "description": "One sample of these subsets is composed of text embedding and image embedding in the shared latent space. Since our interest lies in the recipe retrieval task, we optimized and evaluated our model by using each image embedding in the subsets as query against all text embeddings. By ranking the query and the candidate embeddings according to their cosine distance, we estimate the median rank. The model’s performance is best, if the matching text embedding is found at the first rank. Further, we estimate the recall percentage at the top K percent over all queries. The recall percentage describes the quantity of queries ranked amid the top K closest results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1. Comparison between our method, our Joint Neural Embedding (JNE)(Marín et al., 2018) and AdaMine (Carvalho et al., 2018) re-implementation. For all models we were using selected matching pairs generated by reducing noisy instruction sentences as described above. Recall rates are averaged over the evaluation batches.', solve the subsequent question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhich model has the lowest median rank (MedR) for the Image to Recipe task?\n" }, { "role": "assistant", "content": "To find the model with the lowest median rank, compare the 'Image to Recipe MedR' values for all models. The IA model has the lowest median rank of 2.9±0.3. So, the answer is {\"answer\": \"IA\"}." } ], "images": [ "scigen/train/598.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1. Comparison between our method, our Joint Neural Embedding (JNE)(Marín et al., 2018) and AdaMine (Carvalho et al., 2018) re-implementation. For all models we were using selected matching pairs generated by reducing noisy instruction sentences as described above. Recall rates are averaged over the evaluation batches." ], "reasoning": "To find the model with the lowest median rank, compare the 'Image to Recipe MedR' values for all models. The IA model has the lowest median rank of 2.9±0.3.", "question": "Which model has the lowest median rank (MedR) for the Image to Recipe task?", "answer": "So, the answer is {\"answer\": \"IA\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Random model has the highest recall percentage at top 10 percent (R@10) for the Image to Recipe task. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Random model has the highest recall percentage at top 10 percent (R@10) for the Image to Recipe task.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Image to Recipe R@10' values for all models. The Random model has an R@10 value of 0.01, which is the lowest, not the highest. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/598.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1. Comparison between our method, our Joint Neural Embedding (JNE)(Marín et al., 2018) and AdaMine (Carvalho et al., 2018) re-implementation. For all models we were using selected matching pairs generated by reducing noisy instruction sentences as described above. Recall rates are averaged over the evaluation batches." ], "reasoning": "To verify this statement, compare the 'Image to Recipe R@10' values for all models. The Random model has an R@10 value of 0.01, which is the lowest, not the highest.", "statement": "The Random model has the highest recall percentage at top 10 percent (R@10) for the Image to Recipe task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Distribution of Assertion classes in the data.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The model's performance is influenced by the quantity of training data and the distribution of scope lengths, with the best performance on the present class and the least on the AWSE class.The annotations were done using BRAT tool Rules for annotation were generated after consulting with the Radiologist supervising the annotators. Other Radiologists were consulted to annotate any mentions that were previously unseen or ambiguous and also for the final review. For a fair comparison with the baseline, the box predictions from our model are converted to a sequence of labels per token. On first impressions, the performance seem to be affected by the quantity of data available for training with the best performance on present class and least performance on AWSE class. After further analysis, it appears that the scope lengths found in the training set is also a crucial factor. As shown, model performance for the present class declines with scope lengths 7, 10, and 20, which reflect sparsity of this class for these scopes in the training set. In contrast, the model performs well on the hypothetical class with scope length 7, reflective of the better distribution of this class for this scope relative to other scopes." } ], "images": [ "scigen/train/599.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Distribution of Assertion classes in the data." ], "reasoning": "The model's performance is influenced by the quantity of training data and the distribution of scope lengths, with the best performance on the present class and the least on the AWSE class.", "description": "The annotations were done using BRAT tool Rules for annotation were generated after consulting with the Radiologist supervising the annotators. Other Radiologists were consulted to annotate any mentions that were previously unseen or ambiguous and also for the final review. For a fair comparison with the baseline, the box predictions from our model are converted to a sequence of labels per token. On first impressions, the performance seem to be affected by the quantity of data available for training with the best performance on present class and least performance on AWSE class. After further analysis, it appears that the scope lengths found in the training set is also a crucial factor. As shown, model performance for the present class declines with scope lengths 7, 10, and 20, which reflect sparsity of this class for these scopes in the training set. In contrast, the model performs well on the hypothetical class with scope length 7, reflective of the better distribution of this class for this scope relative to other scopes.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 2: Distribution of Assertion classes in the data.'. Solve the following question based on the table.\n\nQuestion: Which class has the least amount of training data in Dataset-I?\nLet's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the 'Dataset-I Train' column, the AWSE class has the least amount of training data with only 15 instances. So, the answer is {\"answer\": \"AWSE\"}." } ], "images": [ "scigen/train/599.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Distribution of Assertion classes in the data." ], "reasoning": "By examining the 'Dataset-I Train' column, the AWSE class has the least amount of training data with only 15 instances.", "question": "Which class has the least amount of training data in Dataset-I?", "answer": "So, the answer is {\"answer\": \"AWSE\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The hypothetical class has more training data in Dataset-II than in Dataset-I. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe hypothetical class has more training data in Dataset-II than in Dataset-I.\n" }, { "role": "assistant", "content": "The table shows that the hypothetical class has 69 instances in Dataset-II Train and 147 instances in Dataset-I Train, indicating that Dataset-I has more training data for the hypothetical class. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/599.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Distribution of Assertion classes in the data." ], "reasoning": "The table shows that the hypothetical class has 69 instances in Dataset-II Train and 147 instances in Dataset-I Train, indicating that Dataset-I has more training data for the hypothetical class.", "statement": "The hypothetical class has more training data in Dataset-II than in Dataset-I.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Distribution of Assertion classes in the data.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The model's performance is influenced by the quantity of training data and the distribution of scope lengths, with the best performance on the present class and the least on the AWSE class.The annotations were done using BRAT tool Rules for annotation were generated after consulting with the Radiologist supervising the annotators. Other Radiologists were consulted to annotate any mentions that were previously unseen or ambiguous and also for the final review. For a fair comparison with the baseline, the box predictions from our model are converted to a sequence of labels per token. On first impressions, the performance seem to be affected by the quantity of data available for training with the best performance on present class and least performance on AWSE class. After further analysis, it appears that the scope lengths found in the training set is also a crucial factor. As shown, model performance for the present class declines with scope lengths 7, 10, and 20, which reflect sparsity of this class for these scopes in the training set. In contrast, the model performs well on the hypothetical class with scope length 7, reflective of the better distribution of this class for this scope relative to other scopes." } ], "images": [ "scigen/train/600.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Distribution of Assertion classes in the data." ], "reasoning": "The model's performance is influenced by the quantity of training data and the distribution of scope lengths, with the best performance on the present class and the least on the AWSE class.", "description": "The annotations were done using BRAT tool Rules for annotation were generated after consulting with the Radiologist supervising the annotators. Other Radiologists were consulted to annotate any mentions that were previously unseen or ambiguous and also for the final review. For a fair comparison with the baseline, the box predictions from our model are converted to a sequence of labels per token. On first impressions, the performance seem to be affected by the quantity of data available for training with the best performance on present class and least performance on AWSE class. After further analysis, it appears that the scope lengths found in the training set is also a crucial factor. As shown, model performance for the present class declines with scope lengths 7, 10, and 20, which reflect sparsity of this class for these scopes in the training set. In contrast, the model performs well on the hypothetical class with scope length 7, reflective of the better distribution of this class for this scope relative to other scopes.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: Distribution of Assertion classes in the data.', solve the subsequent question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the mean value of Dataset-II in the validation split?\n" }, { "role": "assistant", "content": "To find the mean value of Dataset-II in the validation split, look at the 'val' row and the 'Dataset-II Mean' column. So, the answer is {\"answer\": \"630\"}." } ], "images": [ "scigen/train/600.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Distribution of Assertion classes in the data." ], "reasoning": "To find the mean value of Dataset-II in the validation split, look at the 'val' row and the 'Dataset-II Mean' column.", "question": "What is the mean value of Dataset-II in the validation split?", "answer": "So, the answer is {\"answer\": \"630\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe minimum value of Dataset-I in the training split is higher than in the test split.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Dataset-I Min' values in the 'train' and 'test' rows. The training split has a minimum value of 19, while the test split has a minimum value of 228. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/600.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Distribution of Assertion classes in the data." ], "reasoning": "To verify this statement, compare the 'Dataset-I Min' values in the 'train' and 'test' rows. The training split has a minimum value of 19, while the test split has a minimum value of 228.", "statement": "The minimum value of Dataset-I in the training split is higher than in the test split.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Distribution of Assertion classes in the data.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The model's performance is influenced by the quantity of training data and the distribution of scope lengths, with the best performance on the present class and the least on the AWSE class.The annotations were done using BRAT tool Rules for annotation were generated after consulting with the Radiologist supervising the annotators. Other Radiologists were consulted to annotate any mentions that were previously unseen or ambiguous and also for the final review. For a fair comparison with the baseline, the box predictions from our model are converted to a sequence of labels per token. On first impressions, the performance seem to be affected by the quantity of data available for training with the best performance on present class and least performance on AWSE class. After further analysis, it appears that the scope lengths found in the training set is also a crucial factor. As shown, model performance for the present class declines with scope lengths 7, 10, and 20, which reflect sparsity of this class for these scopes in the training set. In contrast, the model performs well on the hypothetical class with scope length 7, reflective of the better distribution of this class for this scope relative to other scopes." } ], "images": [ "scigen/train/601.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Distribution of Assertion classes in the data." ], "reasoning": "The model's performance is influenced by the quantity of training data and the distribution of scope lengths, with the best performance on the present class and the least on the AWSE class.", "description": "The annotations were done using BRAT tool Rules for annotation were generated after consulting with the Radiologist supervising the annotators. Other Radiologists were consulted to annotate any mentions that were previously unseen or ambiguous and also for the final review. For a fair comparison with the baseline, the box predictions from our model are converted to a sequence of labels per token. On first impressions, the performance seem to be affected by the quantity of data available for training with the best performance on present class and least performance on AWSE class. After further analysis, it appears that the scope lengths found in the training set is also a crucial factor. As shown, model performance for the present class declines with scope lengths 7, 10, and 20, which reflect sparsity of this class for these scopes in the training set. In contrast, the model performs well on the hypothetical class with scope length 7, reflective of the better distribution of this class for this scope relative to other scopes.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Distribution of Assertion classes in the data.\nQuestion: Which class had the highest mean score in Dataset-II Train?\nGive a detailed response to the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the class with the highest mean score in Dataset-II Train, compare the mean scores across all classes in the Dataset-II Train column. Class 1 has the highest mean score of 3.48. So, the answer is {\"answer\": \"1\"}." } ], "images": [ "scigen/train/601.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Distribution of Assertion classes in the data." ], "reasoning": "To find the class with the highest mean score in Dataset-II Train, compare the mean scores across all classes in the Dataset-II Train column. Class 1 has the highest mean score of 3.48.", "question": "Which class had the highest mean score in Dataset-II Train?", "answer": "So, the answer is {\"answer\": \"1\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Class 4 had the highest mean score in Dataset-I Train. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nClass 4 had the highest mean score in Dataset-I Train.\n" }, { "role": "assistant", "content": "To verify this statement, compare the mean scores in the Dataset-I Train column. Class 4 has the highest mean score of 5.05. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/601.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Distribution of Assertion classes in the data." ], "reasoning": "To verify this statement, compare the mean scores in the Dataset-I Train column. Class 4 has the highest mean score of 5.05.", "statement": "Class 4 had the highest mean score in Dataset-I Train.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Distribution of Assertion classes in the data.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The model's performance varies by class and dataset, with the best performance on the present class and the least on the AWSE class, influenced by the quantity of training data and scope lengths.The annotations were done using BRAT tool Rules for annotation were generated after consulting with the Radiologist supervising the annotators. Other Radiologists were consulted to annotate any mentions that were previously unseen or ambiguous and also for the final review. For a fair comparison with the baseline, the box predictions from our model are converted to a sequence of labels per token. On first impressions, the performance seem to be affected by the quantity of data available for training with the best performance on present class and least performance on AWSE class. After further analysis, it appears that the scope lengths found in the training set is also a crucial factor. As shown, model performance for the present class declines with scope lengths 7, 10, and 20, which reflect sparsity of this class for these scopes in the training set. In contrast, the model performs well on the hypothetical class with scope length 7, reflective of the better distribution of this class for this scope relative to other scopes." } ], "images": [ "scigen/train/602.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Distribution of Assertion classes in the data." ], "reasoning": "The model's performance varies by class and dataset, with the best performance on the present class and the least on the AWSE class, influenced by the quantity of training data and scope lengths.", "description": "The annotations were done using BRAT tool Rules for annotation were generated after consulting with the Radiologist supervising the annotators. Other Radiologists were consulted to annotate any mentions that were previously unseen or ambiguous and also for the final review. For a fair comparison with the baseline, the box predictions from our model are converted to a sequence of labels per token. On first impressions, the performance seem to be affected by the quantity of data available for training with the best performance on present class and least performance on AWSE class. After further analysis, it appears that the scope lengths found in the training set is also a crucial factor. As shown, model performance for the present class declines with scope lengths 7, 10, and 20, which reflect sparsity of this class for these scopes in the training set. In contrast, the model performs well on the hypothetical class with scope length 7, reflective of the better distribution of this class for this scope relative to other scopes.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Distribution of Assertion classes in the data.', and sequentially solve the question, writing out each step of your reasoning process. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich class had the highest performance in the Model Scope Localization model for Dataset-II?\n" }, { "role": "assistant", "content": "To determine the highest performance in the Model Scope Localization model for Dataset-II, compare the values in the last column. The highest value is 0.95 for the None class. So, the answer is {\"answer\": \"None\"}." } ], "images": [ "scigen/train/602.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Distribution of Assertion classes in the data." ], "reasoning": "To determine the highest performance in the Model Scope Localization model for Dataset-II, compare the values in the last column. The highest value is 0.95 for the None class.", "question": "Which class had the highest performance in the Model Scope Localization model for Dataset-II?", "answer": "So, the answer is {\"answer\": \"None\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe model performed better on the hypothetical class in Dataset-I compared to Dataset-II.\n" }, { "role": "assistant", "content": "The table shows that the Model Scope Localization model's performance for the hypothetical class is 0.87 in Dataset-I and 0.75 in Dataset-II, indicating better performance in Dataset-I. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/602.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Distribution of Assertion classes in the data." ], "reasoning": "The table shows that the Model Scope Localization model's performance for the hypothetical class is 0.87 in Dataset-I and 0.75 in Dataset-II, indicating better performance in Dataset-I.", "statement": "The model performed better on the hypothetical class in Dataset-I compared to Dataset-II.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 9: Ngram Bag-of-words baseline sentence embeddings performance on DisSent training task: test recall / precision for each discourse marker on the classification task, and overall accuracy. Average metric reports the weighted average of all classes.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The DisSent classifier outperforms both unigram, bigram, and trigram bag of words and Arora et al.'s baseline sentence representations in accuracy across all tasks.As a reference point for training task performance we present baseline performance. Note that a model which simply chose the most common class would perform with 21.79% accuracy on the ALL task, 28.35% on the Books 8 task, and 31.87% on the Books 5 task. Using either unigram, bigram and trigram bag of words or Arora et al. ’s baseline sentence representations as features to a logistic regression results in much lower performance than our DisSent classifier." } ], "images": [ "scigen/train/603.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Ngram Bag-of-words baseline sentence embeddings performance on DisSent training task: test recall / precision for each discourse marker on the classification task, and overall accuracy. Average metric reports the weighted average of all classes." ], "reasoning": "The DisSent classifier outperforms both unigram, bigram, and trigram bag of words and Arora et al.'s baseline sentence representations in accuracy across all tasks.", "description": "As a reference point for training task performance we present baseline performance. Note that a model which simply chose the most common class would perform with 21.79% accuracy on the ALL task, 28.35% on the Books 8 task, and 31.87% on the Books 5 task. Using either unigram, bigram and trigram bag of words or Arora et al. ’s baseline sentence representations as features to a logistic regression results in much lower performance than our DisSent classifier.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: Table 9: Ngram Bag-of-words baseline sentence embeddings performance on DisSent training task: test recall / precision for each discourse marker on the classification task, and overall accuracy. Average metric reports the weighted average of all classes.\nQuestion: What is the precision and recall for the marker 'and' in the Books 8 task?\n" }, { "role": "assistant", "content": "To find the precision and recall for the marker 'and' in the Books 8 task, look at the 'Books 8 Prec' and 'Books 8 Rec' columns in the row corresponding to 'and'. The precision is 65.6 and the recall is 70.1. So, the answer is {\"answer\": \"Precision: 65.6, Recall: 70.1\"}." } ], "images": [ "scigen/train/603.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Ngram Bag-of-words baseline sentence embeddings performance on DisSent training task: test recall / precision for each discourse marker on the classification task, and overall accuracy. Average metric reports the weighted average of all classes." ], "reasoning": "To find the precision and recall for the marker 'and' in the Books 8 task, look at the 'Books 8 Prec' and 'Books 8 Rec' columns in the row corresponding to 'and'. The precision is 65.6 and the recall is 70.1.", "question": "What is the precision and recall for the marker 'and' in the Books 8 task?", "answer": "So, the answer is {\"answer\": \"Precision: 65.6, Recall: 70.1\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The marker 'if' has a higher recall than precision in the Books 5 task., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe marker 'if' has a higher recall than precision in the Books 5 task.\n" }, { "role": "assistant", "content": "To verify this statement, compare the precision and recall values for the marker 'if' in the Books 5 task. The precision is 56.4 and the recall is 64.4, indicating that the recall is indeed higher than the precision. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/603.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Ngram Bag-of-words baseline sentence embeddings performance on DisSent training task: test recall / precision for each discourse marker on the classification task, and overall accuracy. Average metric reports the weighted average of all classes." ], "reasoning": "To verify this statement, compare the precision and recall values for the marker 'if' in the Books 5 task. The precision is 56.4 and the recall is 64.4, indicating that the recall is indeed higher than the precision.", "statement": "The marker 'if' has a higher recall than precision in the Books 5 task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Discourse classification task performance: Unweighted average F1 across discourse markers on the test set, and overall accuracy. Ngram-bow is a bag-of-words model built on mixture of ngram features. GloVe-bow averages word embedding with correction to frequency Arora et al. (2017). BiLSTM is the DisSent sentence encoder model. BERT is finetuned on all of the DisSent tasks.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "As the number of discourse markers increases, the task becomes more difficult, leading to lower test accuracy due to increased ambiguity between markers., we achieve high levels of test performance for all discourse markers. (Though it is interesting that because, perhaps the conceptually deepest relation, is also systematically the hardest for our model.) The larger the set of discourse markers, the more difficult the task becomes, and we therefore see lower test accuracy despite larger dataset size. We conjecture that as we increase the number of discourse markers, we also increase the ambiguity between them (semantic overlap in discourse markers’ meanings), which may further explain the drop in performance. We provide per-discourse-marker performance in the Appendix." } ], "images": [ "scigen/train/604.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Discourse classification task performance: Unweighted average F1 across discourse markers on the test set, and overall accuracy. Ngram-bow is a bag-of-words model built on mixture of ngram features. GloVe-bow averages word embedding with correction to frequency Arora et al. (2017). BiLSTM is the DisSent sentence encoder model. BERT is finetuned on all of the DisSent tasks." ], "reasoning": "As the number of discourse markers increases, the task becomes more difficult, leading to lower test accuracy due to increased ambiguity between markers.", "description": ", we achieve high levels of test performance for all discourse markers. (Though it is interesting that because, perhaps the conceptually deepest relation, is also systematically the hardest for our model.) The larger the set of discourse markers, the more difficult the task becomes, and we therefore see lower test accuracy despite larger dataset size. We conjecture that as we increase the number of discourse markers, we also increase the ambiguity between them (semantic overlap in discourse markers’ meanings), which may further explain the drop in performance. We provide per-discourse-marker performance in the Appendix.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: Table 5: Discourse classification task performance: Unweighted average F1 across discourse markers on the test set, and overall accuracy. Ngram-bow is a bag-of-words model built on mixture of ngram features. GloVe-bow averages word embedding with correction to frequency Arora et al. (2017). BiLSTM is the DisSent sentence encoder model. BERT is finetuned on all of the DisSent tasks.\nQuestion: Which model has the highest accuracy for Books 5?\n" }, { "role": "assistant", "content": "To find the model with the highest accuracy for Books 5, compare the 'Books 5 Acc' values for all models. BERT has the highest accuracy with 86.1. So, the answer is {\"answer\": \"BERT\"}" } ], "images": [ "scigen/train/604.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Discourse classification task performance: Unweighted average F1 across discourse markers on the test set, and overall accuracy. Ngram-bow is a bag-of-words model built on mixture of ngram features. GloVe-bow averages word embedding with correction to frequency Arora et al. (2017). BiLSTM is the DisSent sentence encoder model. BERT is finetuned on all of the DisSent tasks." ], "reasoning": "To find the model with the highest accuracy for Books 5, compare the 'Books 5 Acc' values for all models. BERT has the highest accuracy with 86.1.", "question": "Which model has the highest accuracy for Books 5?", "answer": "So, the answer is {\"answer\": \"BERT\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nBiLSTM has the highest F1 score for Books 8.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Books 8 F1' values for all models. BERT has the highest F1 score for Books 8 with 76.2, not BiLSTM. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/604.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Discourse classification task performance: Unweighted average F1 across discourse markers on the test set, and overall accuracy. Ngram-bow is a bag-of-words model built on mixture of ngram features. GloVe-bow averages word embedding with correction to frequency Arora et al. (2017). BiLSTM is the DisSent sentence encoder model. BERT is finetuned on all of the DisSent tasks." ], "reasoning": "To verify this statement, compare the 'Books 8 F1' values for all models. BERT has the highest F1 score for Books 8 with 76.2, not BiLSTM.", "statement": "BiLSTM has the highest F1 score for Books 8.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 6: SentEval Task Results Using Fixed Sentence Encoder. We report the best results for generalization tasks. † indicates models that we trained. FastSent, FastSent + AE Hill et al. (2016), SkipThought Kiros et al. (2015), SkipThought-LN, DictRep (bow), and InferSent are reported from Conneau et al. (2017). LSMTL is reported from Subramanian et al. (2018). Globally best results are shown in bold, best DisSent results are underlined.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Training on 5, 8, and all 15 discourse markers yielded similar generalization performance.Similar generalization performance was achieved when training on 5, 8, and all 15 discourse markers." } ], "images": [ "scigen/train/605.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: SentEval Task Results Using Fixed Sentence Encoder. We report the best results for generalization tasks. † indicates models that we trained. FastSent, FastSent + AE Hill et al. (2016), SkipThought Kiros et al. (2015), SkipThought-LN, DictRep (bow), and InferSent are reported from Conneau et al. (2017). LSMTL is reported from Subramanian et al. (2018). Globally best results are shown in bold, best DisSent results are underlined." ], "reasoning": "Training on 5, 8, and all 15 discourse markers yielded similar generalization performance.", "description": "Similar generalization performance was achieved when training on 5, 8, and all 15 discourse markers.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 6: SentEval Task Results Using Fixed Sentence Encoder. We report the best results for generalization tasks. † indicates models that we trained. FastSent, FastSent + AE Hill et al. (2016), SkipThought Kiros et al. (2015), SkipThought-LN, DictRep (bow), and InferSent are reported from Conneau et al. (2017). LSMTL is reported from Subramanian et al. (2018). Globally best results are shown in bold, best DisSent results are underlined.\nQuestion: Which model achieved the highest score in the TREC task?\n" }, { "role": "assistant", "content": "By examining the TREC column, the highest score is 93.6, achieved by DisSent Books ALL†. So, the answer is {\"answer\": \"DisSent Books ALL†\"}" } ], "images": [ "scigen/train/605.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: SentEval Task Results Using Fixed Sentence Encoder. We report the best results for generalization tasks. † indicates models that we trained. FastSent, FastSent + AE Hill et al. (2016), SkipThought Kiros et al. (2015), SkipThought-LN, DictRep (bow), and InferSent are reported from Conneau et al. (2017). LSMTL is reported from Subramanian et al. (2018). Globally best results are shown in bold, best DisSent results are underlined." ], "reasoning": "By examining the TREC column, the highest score is 93.6, achieved by DisSent Books ALL†.", "question": "Which model achieved the highest score in the TREC task?", "answer": "So, the answer is {\"answer\": \"DisSent Books ALL†\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis InferSent achieved the highest score in the SST task. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nInferSent achieved the highest score in the SST task.\n" }, { "role": "assistant", "content": "By examining the SST column, the highest score is 84.6, achieved by InferSent. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/605.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: SentEval Task Results Using Fixed Sentence Encoder. We report the best results for generalization tasks. † indicates models that we trained. FastSent, FastSent + AE Hill et al. (2016), SkipThought Kiros et al. (2015), SkipThought-LN, DictRep (bow), and InferSent are reported from Conneau et al. (2017). LSMTL is reported from Subramanian et al. (2018). Globally best results are shown in bold, best DisSent results are underlined." ], "reasoning": "By examining the SST column, the highest score is 84.6, achieved by InferSent.", "statement": "InferSent achieved the highest score in the SST task.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Discourse Generalization Tasks using PDTB: We report test accuracy for sentence embedding and state-of-the-art models.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Despite expectations, fine-tuned BERT models do not outperform the BiLSTM sentence encoder model, warranting further investigation.Much to our surprise, fine-tuned BERT models are not able to perform better than the BiLSTM sentence encoder model. We leave explorations of this phenomenon to future work." } ], "images": [ "scigen/train/606.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Discourse Generalization Tasks using PDTB: We report test accuracy for sentence embedding and state-of-the-art models." ], "reasoning": "Despite expectations, fine-tuned BERT models do not outperform the BiLSTM sentence encoder model, warranting further investigation.", "description": "Much to our surprise, fine-tuned BERT models are not able to perform better than the BiLSTM sentence encoder model. We leave explorations of this phenomenon to future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 7: Discourse Generalization Tasks using PDTB: We report test accuracy for sentence embedding and state-of-the-art models.', and sequentially solve the question, writing out each step of your reasoning process. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model has the highest IMP score among the Sentence Encoder Models?\n" }, { "role": "assistant", "content": "To find the highest IMP score among the Sentence Encoder Models, we look at the IMP column under Sentence Encoder Models. The highest score is 42.9 by DisSent Books ALL. So, the answer is {\"answer\": \"DisSent Books ALL\"}." } ], "images": [ "scigen/train/606.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Discourse Generalization Tasks using PDTB: We report test accuracy for sentence embedding and state-of-the-art models." ], "reasoning": "To find the highest IMP score among the Sentence Encoder Models, we look at the IMP column under Sentence Encoder Models. The highest score is 42.9 by DisSent Books ALL.", "question": "Which model has the highest IMP score among the Sentence Encoder Models?", "answer": "So, the answer is {\"answer\": \"DisSent Books ALL\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe BERT + DisSent Books 5 model has the highest IMP score among all models.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the IMP scores of all models. The highest IMP score is 54.7 by BERT + DisSent Books 5, which supports the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/606.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Discourse Generalization Tasks using PDTB: We report test accuracy for sentence embedding and state-of-the-art models." ], "reasoning": "To verify this statement, we compare the IMP scores of all models. The highest IMP score is 54.7 by BERT + DisSent Books 5, which supports the statement.", "statement": "The BERT + DisSent Books 5 model has the highest IMP score among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 10: Corrected GloVe Bag-of-words sentence embeddings performance on DisSent training task: test recall / precision for each discourse marker on the classification task, and overall accuracy. Average metric reports the weighted average of all classes.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The DisSent classifier outperforms both unigram, bigram, and trigram bag of words and Arora et al.'s baseline sentence representations in terms of accuracy across different tasks.As a reference point for training task performance we present baseline performance. Note that a model which simply chose the most common class would perform with 21.79% accuracy on the ALL task, 28.35% on the Books 8 task, and 31.87% on the Books 5 task. Using either unigram, bigram and trigram bag of words or Arora et al. ’s baseline sentence representations as features to a logistic regression results in much lower performance than our DisSent classifier." } ], "images": [ "scigen/train/607.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Corrected GloVe Bag-of-words sentence embeddings performance on DisSent training task: test recall / precision for each discourse marker on the classification task, and overall accuracy. Average metric reports the weighted average of all classes." ], "reasoning": "The DisSent classifier outperforms both unigram, bigram, and trigram bag of words and Arora et al.'s baseline sentence representations in terms of accuracy across different tasks.", "description": "As a reference point for training task performance we present baseline performance. Note that a model which simply chose the most common class would perform with 21.79% accuracy on the ALL task, 28.35% on the Books 8 task, and 31.87% on the Books 5 task. Using either unigram, bigram and trigram bag of words or Arora et al. ’s baseline sentence representations as features to a logistic regression results in much lower performance than our DisSent classifier.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 10: Corrected GloVe Bag-of-words sentence embeddings performance on DisSent training task: test recall / precision for each discourse marker on the classification task, and overall accuracy. Average metric reports the weighted average of all classes.' and proceed to solve the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the average precision for the 'Books 8' task?\n" }, { "role": "assistant", "content": "To find the average precision for the 'Books 8' task, look at the 'Books 8 Prec' column and find the average value listed at the bottom of the column. So, the answer is {\"answer\": \"47.3\"}" } ], "images": [ "scigen/train/607.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Corrected GloVe Bag-of-words sentence embeddings performance on DisSent training task: test recall / precision for each discourse marker on the classification task, and overall accuracy. Average metric reports the weighted average of all classes." ], "reasoning": "To find the average precision for the 'Books 8' task, look at the 'Books 8 Prec' column and find the average value listed at the bottom of the column.", "question": "What is the average precision for the 'Books 8' task?", "answer": "So, the answer is {\"answer\": \"47.3\"}" }, { "messages": [ { "role": "user", "content": "The precision for the marker 'if' in the 'Books 5' task is higher than in the 'Books 8' task. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe precision for the marker 'if' in the 'Books 5' task is higher than in the 'Books 8' task.\n" }, { "role": "assistant", "content": "The precision for the marker 'if' in the 'Books 5' task is 50.5, while in the 'Books 8' task it is 46.2. Since 50.5 is higher than 46.2, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/607.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Corrected GloVe Bag-of-words sentence embeddings performance on DisSent training task: test recall / precision for each discourse marker on the classification task, and overall accuracy. Average metric reports the weighted average of all classes." ], "reasoning": "The precision for the marker 'if' in the 'Books 5' task is 50.5, while in the 'Books 8' task it is 46.2. Since 50.5 is higher than 46.2, the statement is supported.", "statement": "The precision for the marker 'if' in the 'Books 5' task is higher than in the 'Books 8' task.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Results of deeper Transformer models. L denotes the model depth.'.\n" }, { "role": "assistant", "content": "Increasing the depth of the Transformer model to 24 layers results in a more considerable gain on the dev set compared to increasing the hidden state dimension, but the gain on the eval set remains small.We observe similar trend on the eval set. We also investigated the tradeoff between increasing the dimension of hidden state in self-attention layers and increasing the depth the model. For the offline 12-layer Transformer, we increased dk to 960, which resulted in a model with 97 million parameters. However, we only achieved very marginal improvement on the dev set. The gain from increasing the model depth to 24 layers is more considerable on the dev set, but the gain on the eval set is still small. It is possible that the model is overfitted, and increasing the dropout ratio may result in more gains. As for Transformer-XL, we can obtain gains in terms of accuracy by increasing the depth up to 48 layers. However, the gains are not as large as we have expected, and regularizing the deep Transformers may result in further improvements." } ], "images": [ "scigen/train/608.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of deeper Transformer models. L denotes the model depth." ], "reasoning": "Increasing the depth of the Transformer model to 24 layers results in a more considerable gain on the dev set compared to increasing the hidden state dimension, but the gain on the eval set remains small.", "description": "We observe similar trend on the eval set. We also investigated the tradeoff between increasing the dimension of hidden state in self-attention layers and increasing the depth the model. For the offline 12-layer Transformer, we increased dk to 960, which resulted in a model with 97 million parameters. However, we only achieved very marginal improvement on the dev set. The gain from increasing the model depth to 24 layers is more considerable on the dev set, but the gain on the eval set is still small. It is possible that the model is overfitted, and increasing the dropout ratio may result in more gains. As for Transformer-XL, we can obtain gains in terms of accuracy by increasing the depth up to 48 layers. However, the gains are not as large as we have expected, and regularizing the deep Transformers may result in further improvements.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Results of deeper Transformer models. L denotes the model depth.\nQuestion: What is the dev set score for the 24-layer Transformer model with a hidden state dimension of 960?\nGive a detailed response to the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the 24-layer Transformer model with a hidden state dimension of 960 has a dev set score of 18.3. So, the answer is {\"answer\": \"18.3\"}" } ], "images": [ "scigen/train/608.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of deeper Transformer models. L denotes the model depth." ], "reasoning": "The table shows that the 24-layer Transformer model with a hidden state dimension of 960 has a dev set score of 18.3.", "question": "What is the dev set score for the 24-layer Transformer model with a hidden state dimension of 960?", "answer": "So, the answer is {\"answer\": \"18.3\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe 48-layer Transformer-XL model has a higher eval set score than the 24-layer Transformer model.\n" }, { "role": "assistant", "content": "The table shows that the 48-layer Transformer-XL model has an eval set score of 12.2, while the 24-layer Transformer model has an eval set score of 11.7. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/608.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of deeper Transformer models. L denotes the model depth." ], "reasoning": "The table shows that the 48-layer Transformer-XL model has an eval set score of 12.2, while the 24-layer Transformer model has an eval set score of 11.7. Therefore, the statement is refuted.", "statement": "The 48-layer Transformer-XL model has a higher eval set score than the 24-layer Transformer model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Results of the Transformers with convolution layers in the offline mode. The number of heads is 4, and the number of layers is 12. IC stands for interleaved 1D convolution. All models have around 50 million (M) model parameters.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Interleaved convolution and VGG net improve Transformer model accuracy, with VGG net benefiting from increased model dimension of self-attention layers when interleaved convolutions are removed.The self-attention operation cannot maintain the monotonicity of input sequence, which is particularly harmful for time-synchronous acoustic model such as the hybrid model studies in this paper. The kernel size for the 1D convolution is 3, while the VGG net has 4 layers of 2D convolutions with 3x3 filters. When the VGG encoder was applied, we used features of frame rate as 10ms, and employed a max-pooling layer to down-sample the features by a factor of 2. Both interleaved convolution and VGG net can significant improve the accuracy of Transformer models. In addition, when applying a VGG as the encoder, it is more beneficial to remove the interleaved convolutions but increase the model dimension of self-attention layers if the model size is constrained to be the same." } ], "images": [ "scigen/train/609.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results of the Transformers with convolution layers in the offline mode. The number of heads is 4, and the number of layers is 12. IC stands for interleaved 1D convolution. All models have around 50 million (M) model parameters." ], "reasoning": "Interleaved convolution and VGG net improve Transformer model accuracy, with VGG net benefiting from increased model dimension of self-attention layers when interleaved convolutions are removed.", "description": "The self-attention operation cannot maintain the monotonicity of input sequence, which is particularly harmful for time-synchronous acoustic model such as the hybrid model studies in this paper. The kernel size for the 1D convolution is 3, while the VGG net has 4 layers of 2D convolutions with 3x3 filters. When the VGG encoder was applied, we used features of frame rate as 10ms, and employed a max-pooling layer to down-sample the features by a factor of 2. Both interleaved convolution and VGG net can significant improve the accuracy of Transformer models. In addition, when applying a VGG as the encoder, it is more beneficial to remove the interleaved convolutions but increase the model dimension of self-attention layers if the model size is constrained to be the same.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 1: Results of the Transformers with convolution layers in the offline mode. The number of heads is 4, and the number of layers is 12. IC stands for interleaved 1D convolution. All models have around 50 million (M) model parameters.', unfold the question presented below, detailing every step of your calculation. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the dev score for the Transformer model with VGG encoder and no interleaved convolution?\n" }, { "role": "assistant", "content": "From the table, the Transformer model with VGG encoder and no interleaved convolution (IC: x) has a dev score of 19.4. So, the answer is {\"answer\": \"19.4\"}" } ], "images": [ "scigen/train/609.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results of the Transformers with convolution layers in the offline mode. The number of heads is 4, and the number of layers is 12. IC stands for interleaved 1D convolution. All models have around 50 million (M) model parameters." ], "reasoning": "From the table, the Transformer model with VGG encoder and no interleaved convolution (IC: x) has a dev score of 19.4.", "question": "What is the dev score for the Transformer model with VGG encoder and no interleaved convolution?", "answer": "So, the answer is {\"answer\": \"19.4\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Transformer model with VGG encoder and interleaved convolution has a dev score of 20.2. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Transformer model with VGG encoder and interleaved convolution has a dev score of 20.2.\n" }, { "role": "assistant", "content": "The table shows that the Transformer model with VGG encoder and interleaved convolution (IC: ✓) has a dev score of 19.6, not 20.2. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/609.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results of the Transformers with convolution layers in the offline mode. The number of heads is 4, and the number of layers is 12. IC stands for interleaved 1D convolution. All models have around 50 million (M) model parameters." ], "reasoning": "The table shows that the Transformer model with VGG encoder and interleaved convolution (IC: ✓) has a dev score of 19.6, not 20.2.", "statement": "The Transformer model with VGG encoder and interleaved convolution has a dev score of 20.2.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Results of the 12-layer Transformer model with different number of attention heads. VGG net was used as the encoding layer for all the Transformers. N denotes the number of attention heads, and dk is the model dimension as in Eq(1).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The Transformer with 16 attention heads and interleaved convolution achieved the lowest WER, while the vanilla Transformer with 8 attention heads was sufficient, both outperforming the BLSTM in terms of WER reduction with similar model sizes.With the interleaved convolution, the Transformer with 16 attention heads achieved the lowest WER, while for the vanilla Transformer, 8 attention heads are sufficient. We did not further increase the number of the attention heads in our experiments due to the memory constraint. Compared to the BLSTM with around 55 million model parameters, the Transformer can achieve around 6% relative WER reduction with a similar model size." } ], "images": [ "scigen/train/610.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of the 12-layer Transformer model with different number of attention heads. VGG net was used as the encoding layer for all the Transformers. N denotes the number of attention heads, and dk is the model dimension as in Eq(1)." ], "reasoning": "The Transformer with 16 attention heads and interleaved convolution achieved the lowest WER, while the vanilla Transformer with 8 attention heads was sufficient, both outperforming the BLSTM in terms of WER reduction with similar model sizes.", "description": "With the interleaved convolution, the Transformer with 16 attention heads achieved the lowest WER, while for the vanilla Transformer, 8 attention heads are sufficient. We did not further increase the number of the attention heads in our experiments due to the memory constraint. Compared to the BLSTM with around 55 million model parameters, the Transformer can achieve around 6% relative WER reduction with a similar model size.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Results of the 12-layer Transformer model with different number of attention heads. VGG net was used as the encoding layer for all the Transformers. N denotes the number of attention heads, and dk is the model dimension as in Eq(1).', answer the question below by showing each progressive step toward the answer. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model configuration achieved the lowest WER and what was its dev value?\n" }, { "role": "assistant", "content": "The table shows that the Transformer with interleaved convolution (IC) and 16 attention heads achieved the lowest WER with a dev value of 18.8. So, the answer is {\"answer\": \"18.8\"}" } ], "images": [ "scigen/train/610.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of the 12-layer Transformer model with different number of attention heads. VGG net was used as the encoding layer for all the Transformers. N denotes the number of attention heads, and dk is the model dimension as in Eq(1)." ], "reasoning": "The table shows that the Transformer with interleaved convolution (IC) and 16 attention heads achieved the lowest WER with a dev value of 18.8.", "question": "Which model configuration achieved the lowest WER and what was its dev value?", "answer": "So, the answer is {\"answer\": \"18.8\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Transformer with 8 attention heads and interleaved convolution achieved a lower WER than the BLSTM.\n" }, { "role": "assistant", "content": "The table shows that the Transformer with 8 attention heads and interleaved convolution (IC) has a dev value of 19.7, which is higher than the BLSTM's dev value of 19.5. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/610.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of the 12-layer Transformer model with different number of attention heads. VGG net was used as the encoding layer for all the Transformers. N denotes the number of attention heads, and dk is the model dimension as in Eq(1)." ], "reasoning": "The table shows that the Transformer with 8 attention heads and interleaved convolution (IC) has a dev value of 19.7, which is higher than the BLSTM's dev value of 19.5.", "statement": "The Transformer with 8 attention heads and interleaved convolution achieved a lower WER than the BLSTM.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Results of streaming Transformer models. The number of layers is 12.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Streaming Transformers with interleaved convolution layers have smaller accuracy loss compared to offline models, and Transformer-XL lags behind the vanilla Transformer in low latency conditions.The previous experiments focused on the offline scenario. In this section, we evaluate the accuracy of Transformers in the steaming condition. For instance, [−∞,40] corresponds to looking ahead 3 frames for each self-attention layer in a 12-layer Transformer without interleaved convolution, with an additional 4 frames latency from the VGG encoder. Since our Transformers operated at the 20 ms frame rate, 40 frames correspond to 800 ms latency. For the attention mask based Transformer, we did not limit the left context, so it is marked as −∞, while the context window as [−∞,∞] refers to the offline system. For Transformer-XL, we set the chunk size as 40, and since the model takes the hidden states from the previous chunk as feature, we denote the context size as [−40,40]. Note that there are not overlaps in the chunks during both training and inference, and it emits 40 outputs each time during inference. For LC-BLSTM, the chunk size is also 40, and because it takes the previous hidden state as the representation of history, arguably, we denote the context size as [−1,40]. The chunks in LC-BLSTM are overlapped by 20 frames, so it only emits 20 outputs each time during inference. First, without the interleaved convolution, the streaming Transformers based on attention masks degrade the recognition accuracy by 18% - 25% compared to the offline baseline model in the low latency condition. With the interleaved convolution, the accuracy loss is much smaller. When the latency constraint is less tight, the effect of interleaved convolution layers is diminishing. Second, Transformer-XL still lags behind the vanilla Transformer with the context window of [−∞,40]. This is not surprising as in Transformer-XL, the previous chunk is only an approximation of the full history. Third, the gap between the streaming Transformer (or Transformer-XL) and its offline model is larger than that between LC-BLSTM and BLSTM. The offline Transformer outperforms BLSTM by a considerable margin, while Transformer-XL is only comparable with LC-BLSTM in terms of WERs. Though the Transformer with attention mask as [−∞,40] can outperform LC-BLSTM, it is not computationally feasible during inference. This observation may align well with the argument that Transformers are more powerful to capture the long-term correlations in sequential signals. In a scenario with limited feature context, however, Transformers are hindered to release their modeling power." } ], "images": [ "scigen/train/611.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of streaming Transformer models. The number of layers is 12." ], "reasoning": "Streaming Transformers with interleaved convolution layers have smaller accuracy loss compared to offline models, and Transformer-XL lags behind the vanilla Transformer in low latency conditions.", "description": "The previous experiments focused on the offline scenario. In this section, we evaluate the accuracy of Transformers in the steaming condition. For instance, [−∞,40] corresponds to looking ahead 3 frames for each self-attention layer in a 12-layer Transformer without interleaved convolution, with an additional 4 frames latency from the VGG encoder. Since our Transformers operated at the 20 ms frame rate, 40 frames correspond to 800 ms latency. For the attention mask based Transformer, we did not limit the left context, so it is marked as −∞, while the context window as [−∞,∞] refers to the offline system. For Transformer-XL, we set the chunk size as 40, and since the model takes the hidden states from the previous chunk as feature, we denote the context size as [−40,40]. Note that there are not overlaps in the chunks during both training and inference, and it emits 40 outputs each time during inference. For LC-BLSTM, the chunk size is also 40, and because it takes the previous hidden state as the representation of history, arguably, we denote the context size as [−1,40]. The chunks in LC-BLSTM are overlapped by 20 frames, so it only emits 20 outputs each time during inference. First, without the interleaved convolution, the streaming Transformers based on attention masks degrade the recognition accuracy by 18% - 25% compared to the offline baseline model in the low latency condition. With the interleaved convolution, the accuracy loss is much smaller. When the latency constraint is less tight, the effect of interleaved convolution layers is diminishing. Second, Transformer-XL still lags behind the vanilla Transformer with the context window of [−∞,40]. This is not surprising as in Transformer-XL, the previous chunk is only an approximation of the full history. Third, the gap between the streaming Transformer (or Transformer-XL) and its offline model is larger than that between LC-BLSTM and BLSTM. The offline Transformer outperforms BLSTM by a considerable margin, while Transformer-XL is only comparable with LC-BLSTM in terms of WERs. Though the Transformer with attention mask as [−∞,40] can outperform LC-BLSTM, it is not computationally feasible during inference. This observation may align well with the argument that Transformers are more powerful to capture the long-term correlations in sequential signals. In a scenario with limited feature context, however, Transformers are hindered to release their modeling power.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the dev score for the Transformer model with interleaved convolution and a context window of [−∞,16]?\nSolve the above question based on the table titled 'Table 3: Results of streaming Transformer models. The number of layers is 12.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the Transformer model with interleaved convolution (IC) and a context window of [−∞,16] has a dev score of 20.6. So, the answer is {\"answer\": \"20.6\"}" } ], "images": [ "scigen/train/611.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of streaming Transformer models. The number of layers is 12." ], "reasoning": "The table shows that the Transformer model with interleaved convolution (IC) and a context window of [−∞,16] has a dev score of 20.6.", "question": "What is the dev score for the Transformer model with interleaved convolution and a context window of [−∞,16]?", "answer": "So, the answer is {\"answer\": \"20.6\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Transformer-XL model with interleaved convolution has a dev score of 20.4. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Transformer-XL model with interleaved convolution has a dev score of 20.4.\n" }, { "role": "assistant", "content": "The table shows that the Transformer-XL model with interleaved convolution (IC) has a dev score of 20.4. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/611.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of streaming Transformer models. The number of layers is 12." ], "reasoning": "The table shows that the Transformer-XL model with interleaved convolution (IC) has a dev score of 20.4.", "statement": "The Transformer-XL model with interleaved convolution has a dev score of 20.4.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Ablation tests of different architecture choices using MTMSN\\textscLARGE.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Removing question and passage vectors, CLS token representation, and gating mechanism during span prediction all lead to performance drops, with the worst drop observed when removing the CLS token representation.S4SS3SSS0Px2 Architecture ablation First, we investigate the effects of some “global vectors” used in our model. Specifically, we find that removing the question and passage vectors from all involved computation leads to 1.3 % drop on F1. Ablating the representation of [CLS] token leads to even worse results. We also try to use the last hidden representation (denoted as M3) to calculate question and passage vectors, but find that does not work. Next, we remove the gating mechanism used during span prediction, and observe a nearly 0.8% decline on both metrics. Finally, we share parameters between the arithmetic expression component and the negation component, and find the performance drops by 1.1% on F1." } ], "images": [ "scigen/train/612.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation tests of different architecture choices using MTMSN\\textscLARGE." ], "reasoning": "Removing question and passage vectors, CLS token representation, and gating mechanism during span prediction all lead to performance drops, with the worst drop observed when removing the CLS token representation.", "description": "S4SS3SSS0Px2 Architecture ablation First, we investigate the effects of some “global vectors” used in our model. Specifically, we find that removing the question and passage vectors from all involved computation leads to 1.3 % drop on F1. Ablating the representation of [CLS] token leads to even worse results. We also try to use the last hidden representation (denoted as M3) to calculate question and passage vectors, but find that does not work. Next, we remove the gating mechanism used during span prediction, and observe a nearly 0.8% decline on both metrics. Finally, we share parameters between the arithmetic expression component and the negation component, and find the performance drops by 1.1% on F1.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Ablation tests of different architecture choices using MTMSN\\textscLARGE.\nQuestion: What is the effect on F1 score when the CLS token representation is ablated?\nGive a detailed response to the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that removing the CLS token representation results in an F1 score of 78.4, which is a drop from the original 80.5. Thus, the answer is {\"answer\": \"78.4\"}." } ], "images": [ "scigen/train/612.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation tests of different architecture choices using MTMSN\\textscLARGE." ], "reasoning": "The table shows that removing the CLS token representation results in an F1 score of 78.4, which is a drop from the original 80.5.", "question": "What is the effect on F1 score when the CLS token representation is ablated?", "answer": "Thus, the answer is {\"answer\": \"78.4\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nRemoving the gating mechanism during span prediction leads to a 0.8% decline in F1 score.\n" }, { "role": "assistant", "content": "The table shows that removing the gating mechanism results in an F1 score of 79.7, which is a 0.8% decline from the original 80.5. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/612.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation tests of different architecture choices using MTMSN\\textscLARGE." ], "reasoning": "The table shows that removing the gating mechanism results in an F1 score of 79.7, which is a 0.8% decline from the original 80.5.", "statement": "Removing the gating mechanism during span prediction leads to a 0.8% decline in F1 score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: The performance of MTMSN and other competing approaches on DROP dev and test set.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "MTMSN\textscLARGE achieves state-of-the-art results with an EM score of 75.85 and an F1 score of 79.88, outperforming the NABERT\textscLARGE baseline by 12.07 in EM and 13.19 in F1.MTMSN outperforms all existing approaches by a large margin, and creates new state-of-the-art results by achieving an EM score of 75.85 and a F1 score of 79.88 on the test set. Since our best model utilizes BERT\\textscLARGE as encoder, we therefore compare MTMSN\\textscLARGE with the NABERT\\textscLARGE baseline. As we can see, our model obtains 12.07/13.19 absolute gain of EM/F1 over the baseline, demonstrating the effectiveness of our approach. However, as the human achieves 95.98 F1 on the test set, our results suggest that there is still room for improvement." } ], "images": [ "scigen/train/613.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The performance of MTMSN and other competing approaches on DROP dev and test set." ], "reasoning": "MTMSN\textscLARGE achieves state-of-the-art results with an EM score of 75.85 and an F1 score of 79.88, outperforming the NABERT\textscLARGE baseline by 12.07 in EM and 13.19 in F1.", "description": "MTMSN outperforms all existing approaches by a large margin, and creates new state-of-the-art results by achieving an EM score of 75.85 and a F1 score of 79.88 on the test set. Since our best model utilizes BERT\\textscLARGE as encoder, we therefore compare MTMSN\\textscLARGE with the NABERT\\textscLARGE baseline. As we can see, our model obtains 12.07/13.19 absolute gain of EM/F1 over the baseline, demonstrating the effectiveness of our approach. However, as the human achieves 95.98 F1 on the test set, our results suggest that there is still room for improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: The performance of MTMSN and other competing approaches on DROP dev and test set.', answer the question below by showing each progressive step toward the answer. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the absolute gain in EM and F1 scores of MTMSN\textscLARGE over NABERT\textscLARGE?\n" }, { "role": "assistant", "content": "MTMSN\textscLARGE has an EM score of 75.85 and an F1 score of 79.88, while NABERT\textscLARGE has an EM score of 63.78 and an F1 score of 66.69. The absolute gain in EM is 75.85 - 63.78 = 12.07, and the absolute gain in F1 is 79.88 - 66.69 = 13.19. So, the answer is {\"answer\": \"12.07 in EM and 13.19 in F1\"}" } ], "images": [ "scigen/train/613.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The performance of MTMSN and other competing approaches on DROP dev and test set." ], "reasoning": "MTMSN\textscLARGE has an EM score of 75.85 and an F1 score of 79.88, while NABERT\textscLARGE has an EM score of 63.78 and an F1 score of 66.69. The absolute gain in EM is 75.85 - 63.78 = 12.07, and the absolute gain in F1 is 79.88 - 66.69 = 13.19.", "question": "What is the absolute gain in EM and F1 scores of MTMSN\textscLARGE over NABERT\textscLARGE?", "answer": "So, the answer is {\"answer\": \"12.07 in EM and 13.19 in F1\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nMTMSN\textscLARGE achieves higher EM and F1 scores than NABERT\textscLARGE.\n" }, { "role": "assistant", "content": "The table shows that MTMSN\textscLARGE has an EM score of 75.85 and an F1 score of 79.88, while NABERT\textscLARGE has an EM score of 63.78 and an F1 score of 66.69. Therefore, MTMSN\textscLARGE achieves higher scores in both metrics. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/613.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The performance of MTMSN and other competing approaches on DROP dev and test set." ], "reasoning": "The table shows that MTMSN\textscLARGE has an EM score of 75.85 and an F1 score of 79.88, while NABERT\textscLARGE has an EM score of 63.78 and an F1 score of 66.69. Therefore, MTMSN\textscLARGE achieves higher scores in both metrics.", "statement": "MTMSN\textscLARGE achieves higher EM and F1 scores than NABERT\textscLARGE.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Ablation tests of base and large models on the DROP dev set.'.\n" }, { "role": "assistant", "content": "The ablation study shows that predicting count numbers and enhancing the model with negation type significantly improve the performance metrics.S4SS3SSS0Px1 Component ablation To analyze the effect of the proposed components, we conduct ablation studies on the development set. Predicting count numbers is also an important component that contributes nearly 5% gain on both metrics. Moreover, enhancing the model with the negation type significantly increases the F1 by roughly 9 percent on both models. In brief, the above results show that multi-type answer prediction is vitally important for handling different forms of answers, especially in cases where discrete reasoning abilities are required." } ], "images": [ "scigen/train/614.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ablation tests of base and large models on the DROP dev set." ], "reasoning": "The ablation study shows that predicting count numbers and enhancing the model with negation type significantly improve the performance metrics.", "description": "S4SS3SSS0Px1 Component ablation To analyze the effect of the proposed components, we conduct ablation studies on the development set. Predicting count numbers is also an important component that contributes nearly 5% gain on both metrics. Moreover, enhancing the model with the negation type significantly increases the F1 by roughly 9 percent on both models. In brief, the above results show that multi-type answer prediction is vitally important for handling different forms of answers, especially in cases where discrete reasoning abilities are required.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: Ablation tests of base and large models on the DROP dev set.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. \n\nQuestion: What is the impact on BASE F1 when the model is enhanced with negation type?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The BASE F1 score without negation is 63.6, while the BASE F1 score with negation (MTMSN) is 72.8, indicating an increase of 9.2 points. So, the answer is {\"answer\": \"9.2\"}" } ], "images": [ "scigen/train/614.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ablation tests of base and large models on the DROP dev set." ], "reasoning": "The BASE F1 score without negation is 63.6, while the BASE F1 score with negation (MTMSN) is 72.8, indicating an increase of 9.2 points.", "question": "What is the impact on BASE F1 when the model is enhanced with negation type?", "answer": "So, the answer is {\"answer\": \"9.2\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Enhancing the model with the negation type increases the LARGE F1 by roughly 9 percent., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nEnhancing the model with the negation type increases the LARGE F1 by roughly 9 percent.\n" }, { "role": "assistant", "content": "The LARGE F1 score without negation is 70.9, while the LARGE F1 score with negation (MTMSN) is 80.5, indicating an increase of 9.6 points, which is roughly 9 percent. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/614.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ablation tests of base and large models on the DROP dev set." ], "reasoning": "The LARGE F1 score without negation is 70.9, while the LARGE F1 score with negation (MTMSN) is 80.5, indicating an increase of 9.6 points, which is roughly 9 percent.", "statement": "Enhancing the model with the negation type increases the LARGE F1 by roughly 9 percent.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Performance breakdown of NABERT\\textscLARGE and MTMSN\\textscLARGE by gold answer types.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The multi-span category saw significant improvements with the F1 score increasing by more than 40 points, validating the multi-span extraction method.Performance breakdown We now provide a quantitative analysis by showing performance breakdown on the development set. Moreover, significant improvements are also obtained in the multi-span category, where the F1 score increases by more than 40 points. This result further proves the validity of our multi-span extraction method." } ], "images": [ "scigen/train/615.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance breakdown of NABERT\\textscLARGE and MTMSN\\textscLARGE by gold answer types." ], "reasoning": "The multi-span category saw significant improvements with the F1 score increasing by more than 40 points, validating the multi-span extraction method.", "description": "Performance breakdown We now provide a quantitative analysis by showing performance breakdown on the development set. Moreover, significant improvements are also obtained in the multi-span category, where the F1 score increases by more than 40 points. This result further proves the validity of our multi-span extraction method.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the F1 score improvement for the multi-span category from NABERT to MTMSN?\nSolve the above question based on the table titled 'Table 4: Performance breakdown of NABERT\\textscLARGE and MTMSN\\textscLARGE by gold answer types.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The F1 score for the multi-span category increased from 22.7 in NABERT to 62.8 in MTMSN, which is an improvement of 40.1 points. So, the answer is {\"answer\": \"40.1\"}" } ], "images": [ "scigen/train/615.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance breakdown of NABERT\\textscLARGE and MTMSN\\textscLARGE by gold answer types." ], "reasoning": "The F1 score for the multi-span category increased from 22.7 in NABERT to 62.8 in MTMSN, which is an improvement of 40.1 points.", "question": "What is the F1 score improvement for the multi-span category from NABERT to MTMSN?", "answer": "So, the answer is {\"answer\": \"40.1\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The EM score for the number category is higher in MTMSN than in NABERT., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe EM score for the number category is higher in MTMSN than in NABERT.\n" }, { "role": "assistant", "content": "The EM score for the number category in NABERT is 63.8, while in MTMSN it is 80.9, indicating that the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/615.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance breakdown of NABERT\\textscLARGE and MTMSN\\textscLARGE by gold answer types." ], "reasoning": "The EM score for the number category in NABERT is 63.8, while in MTMSN it is 80.9, indicating that the statement is supported.", "statement": "The EM score for the number category is higher in MTMSN than in NABERT.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: Performance breakdown of NABERT\\textscLARGE and MTMSN\\textscLARGE by predicted answer types.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The main improvements are due to the addition/subtraction and negation types, attributed to the expression reranking mechanism and a new inductive bias for logical negation.As shown in the Table, the main improvements are due to the addition/subtraction and negation types. We conjecture that there are two reasons for these improvements. First, our proposed expression reranking mechanism helps validate candidate expressions. Second, a new inductive bias that enables the model to perform logical negation has been introduced. The impressive performance on the negation type confirms our judgement, and suggests that the model is able to find most of negation operations. In addition, we also observe promising gains brought by the span and count types. We think the gains are mainly due to the multi-span extraction method as well as architecture designs." } ], "images": [ "scigen/train/616.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Performance breakdown of NABERT\\textscLARGE and MTMSN\\textscLARGE by predicted answer types." ], "reasoning": "The main improvements are due to the addition/subtraction and negation types, attributed to the expression reranking mechanism and a new inductive bias for logical negation.", "description": "As shown in the Table, the main improvements are due to the addition/subtraction and negation types. We conjecture that there are two reasons for these improvements. First, our proposed expression reranking mechanism helps validate candidate expressions. Second, a new inductive bias that enables the model to perform logical negation has been introduced. The impressive performance on the negation type confirms our judgement, and suggests that the model is able to find most of negation operations. In addition, we also observe promising gains brought by the span and count types. We think the gains are mainly due to the multi-span extraction method as well as architecture designs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 5: Performance breakdown of NABERT\\textscLARGE and MTMSN\\textscLARGE by predicted answer types.', unfold the steps to solve the question given next. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nQuestion: What is the F1 score for the negation type in the MTMSN model?\n" }, { "role": "assistant", "content": "The table shows the F1 score for the negation type under the MTMSN model as 96.3%. So, the answer is {\"answer\": \"96.3\"}." } ], "images": [ "scigen/train/616.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Performance breakdown of NABERT\\textscLARGE and MTMSN\\textscLARGE by predicted answer types." ], "reasoning": "The table shows the F1 score for the negation type under the MTMSN model as 96.3%.", "question": "What is the F1 score for the negation type in the MTMSN model?", "answer": "So, the answer is {\"answer\": \"96.3\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe MTMSN model has a higher EM score for the span type compared to the NABERT model.\n" }, { "role": "assistant", "content": "The table shows that the EM score for the span type is 72.2% for the MTMSN model and 67.9% for the NABERT model, indicating that the MTMSN model has a higher EM score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/616.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Performance breakdown of NABERT\\textscLARGE and MTMSN\\textscLARGE by predicted answer types." ], "reasoning": "The table shows that the EM score for the span type is 72.2% for the MTMSN model and 67.9% for the NABERT model, indicating that the MTMSN model has a higher EM score.", "statement": "The MTMSN model has a higher EM score for the span type compared to the NABERT model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: Comparing the results in Phrase Similarity dataset for common entries between all approaches. Rho is Spearmans’s correlation.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The heuristic-based approach slightly outperforms the base model, indicating that it increases coverage without sacrificing accuracy.We also compared the results with another structural based similarity approach called HitSim The comparable result of our approach to structural based methods is another proof that we could embed the Wikipedia link structure properly. The result of heuristic based approach is slightly better than our base model. This shows that without sacrificing the accuracy, we could increase the coverage. This means that with the proposed heuristic, we have a vector representation of more Wikipedia pages. Results for only anchors version of ConVec This shows it is better to learn Wikipedia’s concepts vector in the context of other words (words that are not anchored) and as a result to have the same vector space for both Concepts and words." } ], "images": [ "scigen/train/617.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparing the results in Phrase Similarity dataset for common entries between all approaches. Rho is Spearmans’s correlation." ], "reasoning": "The heuristic-based approach slightly outperforms the base model, indicating that it increases coverage without sacrificing accuracy.", "description": "We also compared the results with another structural based similarity approach called HitSim The comparable result of our approach to structural based methods is another proof that we could embed the Wikipedia link structure properly. The result of heuristic based approach is slightly better than our base model. This shows that without sacrificing the accuracy, we could increase the coverage. This means that with the proposed heuristic, we have a vector representation of more Wikipedia pages. Results for only anchors version of ConVec This shows it is better to learn Wikipedia’s concepts vector in the context of other words (words that are not anchored) and as a result to have the same vector space for both Concepts and words.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 4: Comparing the results in Phrase Similarity dataset for common entries between all approaches. Rho is Spearmans’s correlation.'.\n\nQuestion: Which dataset has the highest ConVec (Heuristic) Rho value?\n" }, { "role": "assistant", "content": "To find the dataset with the highest ConVec (Heuristic) Rho value, compare the values in the ConVec (Heuristic) Rho column for all datasets. So, the answer is {\"answer\": \"WS-SIM\"}." } ], "images": [ "scigen/train/617.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparing the results in Phrase Similarity dataset for common entries between all approaches. Rho is Spearmans’s correlation." ], "reasoning": "To find the dataset with the highest ConVec (Heuristic) Rho value, compare the values in the ConVec (Heuristic) Rho column for all datasets.", "question": "Which dataset has the highest ConVec (Heuristic) Rho value?", "answer": "So, the answer is {\"answer\": \"WS-SIM\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe ConVec (Only Anchors) Rho value for the WS-411 dataset is higher than the ConVec Rho value for the same dataset.\n" }, { "role": "assistant", "content": "Compare the ConVec (Only Anchors) Rho value and the ConVec Rho value for the WS-411 dataset. The ConVec (Only Anchors) Rho value is 0.7308, which is higher than the ConVec Rho value of 0.7126. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/617.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparing the results in Phrase Similarity dataset for common entries between all approaches. Rho is Spearmans’s correlation." ], "reasoning": "Compare the ConVec (Only Anchors) Rho value and the ConVec Rho value for the WS-411 dataset. The ConVec (Only Anchors) Rho value is 0.7308, which is higher than the ConVec Rho value of 0.7126.", "statement": "The ConVec (Only Anchors) Rho value for the WS-411 dataset is higher than the ConVec Rho value for the same dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Comparing the results of three different versions of ConVec (trained on Wikipedia 2.1B tokens) with Google Freebase pre-trained vectors over Google 100B tokens news dataset in the Phrase Analogy task. The Accuracy (All), shows the coverage and performance of each approach for answering questions. The accuracy for common questions (Accuracy (Commons)), is for fair comparison of each approach. #phrases shows the number of top frequent words of each approach that are used to calculate the accuracy. #found is the number of questions that all 4 words of them are present in the approach dictionary.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The ConVec Heuristic approach improves coverage but does not significantly impact the quality of the base ConVec model, indicating that the quality of the training corpus is more important than its size.The first accuracy is to compare the coverage and performance of each approach over the all questions in the test dataset ( Accuracy All). The second accuracy is to compare the methods over only common questions (Accuracy commons). Only common questions between each method are used to compare the Accuracy for Commons scenario. The result of ConVec Heuristic for common questions, argue that this heuristic does not have a significant impact on the quality of base ConVec model and it just improved the coverage (added more concepts to the list of concept vectors). and consequently, the quality of the training corpus is more important than its size." } ], "images": [ "scigen/train/618.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparing the results of three different versions of ConVec (trained on Wikipedia 2.1B tokens) with Google Freebase pre-trained vectors over Google 100B tokens news dataset in the Phrase Analogy task. The Accuracy (All), shows the coverage and performance of each approach for answering questions. The accuracy for common questions (Accuracy (Commons)), is for fair comparison of each approach. #phrases shows the number of top frequent words of each approach that are used to calculate the accuracy. #found is the number of questions that all 4 words of them are present in the approach dictionary." ], "reasoning": "The ConVec Heuristic approach improves coverage but does not significantly impact the quality of the base ConVec model, indicating that the quality of the training corpus is more important than its size.", "description": "The first accuracy is to compare the coverage and performance of each approach over the all questions in the test dataset ( Accuracy All). The second accuracy is to compare the methods over only common questions (Accuracy commons). Only common questions between each method are used to compare the Accuracy for Commons scenario. The result of ConVec Heuristic for common questions, argue that this heuristic does not have a significant impact on the quality of base ConVec model and it just improved the coverage (added more concepts to the list of concept vectors). and consequently, the quality of the training corpus is more important than its size.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2: Comparing the results of three different versions of ConVec (trained on Wikipedia 2.1B tokens) with Google Freebase pre-trained vectors over Google 100B tokens news dataset in the Phrase Analogy task. The Accuracy (All), shows the coverage and performance of each approach for answering questions. The accuracy for common questions (Accuracy (Commons)), is for fair comparison of each approach. #phrases shows the number of top frequent words of each approach that are used to calculate the accuracy. #found is the number of questions that all 4 words of them are present in the approach dictionary.', unfold the steps to solve the question given next. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: What is the Accuracy (Commons) Accuracy for ConVec (Fine Tuned) with Top 300,000 phrases?\n" }, { "role": "assistant", "content": "To find the Accuracy (Commons) Accuracy for ConVec (Fine Tuned) with Top 300,000 phrases, locate the row for ConVec (Fine Tuned) and Top 300,000 phrases, then find the value under the Accuracy (Commons) Accuracy column. So, the answer is {\"answer\": \"73.0%\"}." } ], "images": [ "scigen/train/618.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparing the results of three different versions of ConVec (trained on Wikipedia 2.1B tokens) with Google Freebase pre-trained vectors over Google 100B tokens news dataset in the Phrase Analogy task. The Accuracy (All), shows the coverage and performance of each approach for answering questions. The accuracy for common questions (Accuracy (Commons)), is for fair comparison of each approach. #phrases shows the number of top frequent words of each approach that are used to calculate the accuracy. #found is the number of questions that all 4 words of them are present in the approach dictionary." ], "reasoning": "To find the Accuracy (Commons) Accuracy for ConVec (Fine Tuned) with Top 300,000 phrases, locate the row for ConVec (Fine Tuned) and Top 300,000 phrases, then find the value under the Accuracy (Commons) Accuracy column.", "question": "What is the Accuracy (Commons) Accuracy for ConVec (Fine Tuned) with Top 300,000 phrases?", "answer": "So, the answer is {\"answer\": \"73.0%\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nConVec Heuristic with Top 300,000 phrases has a higher Accuracy (All) Accuracy than ConVec with Top 300,000 phrases.\n" }, { "role": "assistant", "content": "Compare the Accuracy (All) Accuracy values for ConVec Heuristic and ConVec with Top 300,000 phrases. ConVec Heuristic has 65.6% while ConVec has 68.0%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/618.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparing the results of three different versions of ConVec (trained on Wikipedia 2.1B tokens) with Google Freebase pre-trained vectors over Google 100B tokens news dataset in the Phrase Analogy task. The Accuracy (All), shows the coverage and performance of each approach for answering questions. The accuracy for common questions (Accuracy (Commons)), is for fair comparison of each approach. #phrases shows the number of top frequent words of each approach that are used to calculate the accuracy. #found is the number of questions that all 4 words of them are present in the approach dictionary." ], "reasoning": "Compare the Accuracy (All) Accuracy values for ConVec Heuristic and ConVec with Top 300,000 phrases. ConVec Heuristic has 65.6% while ConVec has 68.0%.", "statement": "ConVec Heuristic with Top 300,000 phrases has a higher Accuracy (All) Accuracy than ConVec with Top 300,000 phrases.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Comparing the results in Phrase Similarity dataset. Rho is Spearman’s correlation to the human evaluators. !Found is the number of pairs not found in each approach dataset.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The heuristic-based approach increases the coverage of Wikipedia concepts despite having a lower average correlation compared to other approaches.In these datasets, each row consists of two words with their relatedness assigned by the human. The Spearman’s correlation is used for comparing the result of different approaches with the human evaluated results. These datasets contain words and not the Wikipedia concepts. We replaced all the words in these datasets with their corresponding Wikipedia pages if their surface form and the Wikipedia concept match. We used the simple but effective most frequent sense disambiguation method to disambiguate words that may correspond to several Wikipedia concept. This method of assigning words to concepts is not error prone but this error is considered for all approaches. The average correlation for the heuristic based approach is less than the other approaches, but average of not-found entries in this approach is much less than the others. It shows that using the heuristic can increase the coverage of the Wikipedia concepts." } ], "images": [ "scigen/train/619.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparing the results in Phrase Similarity dataset. Rho is Spearman’s correlation to the human evaluators. !Found is the number of pairs not found in each approach dataset." ], "reasoning": "The heuristic-based approach increases the coverage of Wikipedia concepts despite having a lower average correlation compared to other approaches.", "description": "In these datasets, each row consists of two words with their relatedness assigned by the human. The Spearman’s correlation is used for comparing the result of different approaches with the human evaluated results. These datasets contain words and not the Wikipedia concepts. We replaced all the words in these datasets with their corresponding Wikipedia pages if their surface form and the Wikipedia concept match. We used the simple but effective most frequent sense disambiguation method to disambiguate words that may correspond to several Wikipedia concept. This method of assigning words to concepts is not error prone but this error is considered for all approaches. The average correlation for the heuristic based approach is less than the other approaches, but average of not-found entries in this approach is much less than the others. It shows that using the heuristic can increase the coverage of the Wikipedia concepts.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: Comparing the results in Phrase Similarity dataset. Rho is Spearman’s correlation to the human evaluators. !Found is the number of pairs not found in each approach dataset.', answer the following question. Give detailed solution consisting of each step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich dataset has the highest ConVec Rho value?\n" }, { "role": "assistant", "content": "To find the dataset with the highest ConVec Rho value, we need to compare the ConVec Rho values across all datasets. The highest value is 0.7524, which corresponds to the WS-SIM dataset. So, the answer is {\"answer\": \"WS-SIM\"}." } ], "images": [ "scigen/train/619.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparing the results in Phrase Similarity dataset. Rho is Spearman’s correlation to the human evaluators. !Found is the number of pairs not found in each approach dataset." ], "reasoning": "To find the dataset with the highest ConVec Rho value, we need to compare the ConVec Rho values across all datasets. The highest value is 0.7524, which corresponds to the WS-SIM dataset.", "question": "Which dataset has the highest ConVec Rho value?", "answer": "So, the answer is {\"answer\": \"WS-SIM\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The heuristic-based approach has the highest average correlation. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe heuristic-based approach has the highest average correlation.\n" }, { "role": "assistant", "content": "The table shows that the average ConVec (Heuristic) Rho is 0.3612, which is lower than the average ConVec Rho of 0.4391 and the Wikipedia Miner Rho of 0.4402. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/619.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparing the results in Phrase Similarity dataset. Rho is Spearman’s correlation to the human evaluators. !Found is the number of pairs not found in each approach dataset." ], "reasoning": "The table shows that the average ConVec (Heuristic) Rho is 0.3612, which is lower than the average ConVec Rho of 0.4391 and the Wikipedia Miner Rho of 0.4402. Therefore, the statement is refuted.", "statement": "The heuristic-based approach has the highest average correlation.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Data Sources and Counts', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The data collection focuses on contemporary contents like books and magazines from Jehova's Witnesses, and also includes audio and non-electronic texts from local media houses.A large chunk of the data is collected from the Jehova’s Though we included the Bible, more contemporary contents (books and magazine e.g. Teta! (Awake!), Ulo Nche! (WatchTower)) were the main focus. This phase is still on-going but we have so far collected and cleaned ≈ It is important to point out that we have also collected data in other formats (e.g. audio, non-electronic texts) from local media houses which we hope to also transcribe and include in our collection." } ], "images": [ "scigen/train/620.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Data Sources and Counts" ], "reasoning": "The data collection focuses on contemporary contents like books and magazines from Jehova's Witnesses, and also includes audio and non-electronic texts from local media houses.", "description": "A large chunk of the data is collected from the Jehova’s Though we included the Bible, more contemporary contents (books and magazine e.g. Teta! (Awake!), Ulo Nche! (WatchTower)) were the main focus. This phase is still on-going but we have so far collected and cleaned ≈ It is important to point out that we have also collected data in other formats (e.g. audio, non-electronic texts) from local media houses which we hope to also transcribe and include in our collection.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 3: Data Sources and Counts' and proceed to solve the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich source has the highest number of unique tokens?\n" }, { "role": "assistant", "content": "To find the source with the highest number of unique tokens, compare the 'UniqToks' column for all sources. The source with the highest value is 'bbc-igbo.txt' with 28,459 unique tokens. So, the answer is {\"answer\": \"bbc-igbo.txt\"}." } ], "images": [ "scigen/train/620.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Data Sources and Counts" ], "reasoning": "To find the source with the highest number of unique tokens, compare the 'UniqToks' column for all sources. The source with the highest value is 'bbc-igbo.txt' with 28,459 unique tokens.", "question": "Which source has the highest number of unique tokens?", "answer": "So, the answer is {\"answer\": \"bbc-igbo.txt\"}." }, { "messages": [ { "role": "user", "content": "The total number of sentences collected is over 400,000. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe total number of sentences collected is over 400,000.\n" }, { "role": "assistant", "content": "The table shows that the total number of sentences collected is 383,449, which is less than 400,000. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/620.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Data Sources and Counts" ], "reasoning": "The table shows that the total number of sentences collected is 383,449, which is less than 400,000.", "statement": "The total number of sentences collected is over 400,000.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Splits of the Benchmark Evaluation Parallel Data'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The task involved collecting, cleaning, and translating over 10,000 sentences in both English and Igbo, with translations and corrections performed by native Igbo speakers.To achieve the objectives above, the task was broken down in the following phases: Phase 1: Raw data collection and pre-processing : This phase is to produce cleaned and pre-processed a minimum 10,000 sentences: 5,000 English and 5,000 Igbo. It involved the collection, cleaning and pre-processing (normalisation, diacritic restoration, spelling correction etc.) of Igbo and English sentences from freely available electronic texts (e.g. Wikipedia, CommonCrawl, local government materials, local TV/Radio stations etc). Phase 2: Translation and correction In this phase, the 10,000 sentence pairs are created manual translation and correction. The key tasks include: Translating English sentences to Igbo (EN-IG) Translating Igbo sentences to English (IG-EN) Correcting the translations 5 Igbo speakers were engaged for the bidirectional of translations while 3 other Igbo speakers, including an Igbo linguist are assisting with the on-going corrections. Chunks (≈ 250 each) of sentences are given to each translator in each direction (i.e. IG-EN and EN-IG)." } ], "images": [ "scigen/train/621.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Splits of the Benchmark Evaluation Parallel Data" ], "reasoning": "The task involved collecting, cleaning, and translating over 10,000 sentences in both English and Igbo, with translations and corrections performed by native Igbo speakers.", "description": "To achieve the objectives above, the task was broken down in the following phases: Phase 1: Raw data collection and pre-processing : This phase is to produce cleaned and pre-processed a minimum 10,000 sentences: 5,000 English and 5,000 Igbo. It involved the collection, cleaning and pre-processing (normalisation, diacritic restoration, spelling correction etc.) of Igbo and English sentences from freely available electronic texts (e.g. Wikipedia, CommonCrawl, local government materials, local TV/Radio stations etc). Phase 2: Translation and correction In this phase, the 10,000 sentence pairs are created manual translation and correction. The key tasks include: Translating English sentences to Igbo (EN-IG) Translating Igbo sentences to English (IG-EN) Correcting the translations 5 Igbo speakers were engaged for the bidirectional of translations while 3 other Igbo speakers, including an Igbo linguist are assisting with the on-going corrections. Chunks (≈ 250 each) of sentences are given to each translator in each direction (i.e. IG-EN and EN-IG).", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: Splits of the Benchmark Evaluation Parallel Data', solve the subsequent question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nHow many sentence pairs were collected from local newspapers?\n" }, { "role": "assistant", "content": "The table shows that 5,748 English-Igbo sentence pairs were collected, and the description mentions that these were mostly from local newspapers. So, the answer is {\"answer\": \"5,748\"}." } ], "images": [ "scigen/train/621.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Splits of the Benchmark Evaluation Parallel Data" ], "reasoning": "The table shows that 5,748 English-Igbo sentence pairs were collected, and the description mentions that these were mostly from local newspapers.", "question": "How many sentence pairs were collected from local newspapers?", "answer": "So, the answer is {\"answer\": \"5,748\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe total number of sentence pairs collected is 11,584.\n" }, { "role": "assistant", "content": "The table shows that the total number of sentence pairs collected is 11,584, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/621.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Splits of the Benchmark Evaluation Parallel Data" ], "reasoning": "The table shows that the total number of sentence pairs collected is 11,584, which matches the statement.", "statement": "The total number of sentence pairs collected is 11,584.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Splits of the Benchmark Evaluation Parallel Data'.\n" }, { "role": "assistant", "content": "The project involved collecting and pre-processing 10,000 sentences, followed by translating and correcting these sentences with the help of Igbo speakers and linguists.To achieve the objectives above, the task was broken down in the following phases: Phase 1: Raw data collection and pre-processing : This phase is to produce cleaned and pre-processed a minimum 10,000 sentences: 5,000 English and 5,000 Igbo. It involved the collection, cleaning and pre-processing (normalisation, diacritic restoration, spelling correction etc.) of Igbo and English sentences from freely available electronic texts (e.g. Wikipedia, CommonCrawl, local government materials, local TV/Radio stations etc). Phase 2: Translation and correction In this phase, the 10,000 sentence pairs are created manual translation and correction. The key tasks include: Translating English sentences to Igbo (EN-IG) Translating Igbo sentences to English (IG-EN) Correcting the translations 5 Igbo speakers were engaged for the bidirectional of translations while 3 other Igbo speakers, including an Igbo linguist are assisting with the on-going corrections. Chunks (≈ 250 each) of sentences are given to each translator in each direction (i.e. IG-EN and EN-IG)." } ], "images": [ "scigen/train/622.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Splits of the Benchmark Evaluation Parallel Data" ], "reasoning": "The project involved collecting and pre-processing 10,000 sentences, followed by translating and correcting these sentences with the help of Igbo speakers and linguists.", "description": "To achieve the objectives above, the task was broken down in the following phases: Phase 1: Raw data collection and pre-processing : This phase is to produce cleaned and pre-processed a minimum 10,000 sentences: 5,000 English and 5,000 Igbo. It involved the collection, cleaning and pre-processing (normalisation, diacritic restoration, spelling correction etc.) of Igbo and English sentences from freely available electronic texts (e.g. Wikipedia, CommonCrawl, local government materials, local TV/Radio stations etc). Phase 2: Translation and correction In this phase, the 10,000 sentence pairs are created manual translation and correction. The key tasks include: Translating English sentences to Igbo (EN-IG) Translating Igbo sentences to English (IG-EN) Correcting the translations 5 Igbo speakers were engaged for the bidirectional of translations while 3 other Igbo speakers, including an Igbo linguist are assisting with the on-going corrections. Chunks (≈ 250 each) of sentences are given to each translator in each direction (i.e. IG-EN and EN-IG).", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Splits of the Benchmark Evaluation Parallel Data', answer the question below by showing each progressive step toward the answer. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nHow many sentences are included in the Hidden Test set for IG-EN and EN-IG combined?\n" }, { "role": "assistant", "content": "The Hidden Test set includes 336 sentences for IG-EN and 248 sentences for EN-IG. Adding these together gives 336 + 248 = 584 sentences. So, the answer is {\"answer\": \"584\"}." } ], "images": [ "scigen/train/622.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Splits of the Benchmark Evaluation Parallel Data" ], "reasoning": "The Hidden Test set includes 336 sentences for IG-EN and 248 sentences for EN-IG. Adding these together gives 336 + 248 = 584 sentences.", "question": "How many sentences are included in the Hidden Test set for IG-EN and EN-IG combined?", "answer": "So, the answer is {\"answer\": \"584\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Development Set contains 10,000 sentences in total.\n" }, { "role": "assistant", "content": "The Development Set contains 5,000 sentences for IG-EN and 5,000 sentences for EN-IG, which sums up to 10,000 sentences in total. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/622.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Splits of the Benchmark Evaluation Parallel Data" ], "reasoning": "The Development Set contains 5,000 sentences for IG-EN and 5,000 sentences for EN-IG, which sums up to 10,000 sentences in total.", "statement": "The Development Set contains 10,000 sentences in total.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Result on SICK. Here, * stands for multimodal NLU models.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The integration of the framework into pre-trained NLU models improves their performance, achieving higher accuracy.Similar with performance on SNLI, we can also see the framework bring improvement to SOTA NLU model, and achieve higher performance after integrate to pre-trained NLU models." } ], "images": [ "scigen/train/623.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Result on SICK. Here, * stands for multimodal NLU models." ], "reasoning": "The integration of the framework into pre-trained NLU models improves their performance, achieving higher accuracy.", "description": "Similar with performance on SNLI, we can also see the framework bring improvement to SOTA NLU model, and achieve higher performance after integrate to pre-trained NLU models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 5: Result on SICK. Here, * stands for multimodal NLU models.', solve the following question step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the accuracy improvement when integrating STVF with BERT?\n" }, { "role": "assistant", "content": "The accuracy of BERT alone is 90.4%, and the accuracy of BERT + STVF is 90.6%. The improvement is calculated by subtracting the accuracy of BERT from the accuracy of BERT + STVF. So, the answer is {\"answer\": \"0.2%\"}" } ], "images": [ "scigen/train/623.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Result on SICK. Here, * stands for multimodal NLU models." ], "reasoning": "The accuracy of BERT alone is 90.4%, and the accuracy of BERT + STVF is 90.6%. The improvement is calculated by subtracting the accuracy of BERT from the accuracy of BERT + STVF.", "question": "What is the accuracy improvement when integrating STVF with BERT?", "answer": "So, the answer is {\"answer\": \"0.2%\"}" }, { "messages": [ { "role": "user", "content": "The Cap2Both model has the lowest accuracy among the listed models. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Cap2Both model has the lowest accuracy among the listed models.\n" }, { "role": "assistant", "content": "The table shows that Cap2Both has an accuracy of 81.7%, which is lower than both BERT (90.4%) and BERT + STVF (90.6%). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/623.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Result on SICK. Here, * stands for multimodal NLU models." ], "reasoning": "The table shows that Cap2Both has an accuracy of 81.7%, which is lower than both BERT (90.4%) and BERT + STVF (90.6%).", "statement": "The Cap2Both model has the lowest accuracy among the listed models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Results on SemEval 2018 Task 11 (MCScript). Here, models with * are ensemble models.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The Integrated TriAN model, especially when combined with ConceptNet, shows significant improvement over previous models on the development set.Integrated Reading Comprehension: Table. After integrating the semi-supervised visual integration framework, the TriAN model outperforms previous SOTA performance both on single and ensemble condition. Because we could not access the test data, we verify the improvement of the framework on develop set and see statistically improvement under the one-tailed paired t-test at the 99% significance level. To show the ability of the semi-supervised visual integration framework, we also compare how much performance gain the framework and ConceptNet conceptnet:2012 give to TriAN base model." } ], "images": [ "scigen/train/624.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results on SemEval 2018 Task 11 (MCScript). Here, models with * are ensemble models." ], "reasoning": "The Integrated TriAN model, especially when combined with ConceptNet, shows significant improvement over previous models on the development set.", "description": "Integrated Reading Comprehension: Table. After integrating the semi-supervised visual integration framework, the TriAN model outperforms previous SOTA performance both on single and ensemble condition. Because we could not access the test data, we verify the improvement of the framework on develop set and see statistically improvement under the one-tailed paired t-test at the 99% significance level. To show the ability of the semi-supervised visual integration framework, we also compare how much performance gain the framework and ConceptNet conceptnet:2012 give to TriAN base model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. The question is related to the given table titled 'Table 1: Results on SemEval 2018 Task 11 (MCScript). Here, models with * are ensemble models.'.\n\nQuestion: Which model achieved the highest performance on the test set?\n" }, { "role": "assistant", "content": "By comparing the test set scores of all models, the *Integrated TriAN + ConceptNet model achieved the highest performance with a score of 84.41. So, the answer is {\"answer\": \"*Integrated TriAN + ConceptNet\"}." } ], "images": [ "scigen/train/624.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results on SemEval 2018 Task 11 (MCScript). Here, models with * are ensemble models." ], "reasoning": "By comparing the test set scores of all models, the *Integrated TriAN + ConceptNet model achieved the highest performance with a score of 84.41.", "question": "Which model achieved the highest performance on the test set?", "answer": "So, the answer is {\"answer\": \"*Integrated TriAN + ConceptNet\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The TriAN model outperforms the Integrated TriAN model on the development set., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe TriAN model outperforms the Integrated TriAN model on the development set.\n" }, { "role": "assistant", "content": "By comparing the development set scores, the TriAN model scored 82.78, while the Integrated TriAN model scored 83.90, indicating that the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/624.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results on SemEval 2018 Task 11 (MCScript). Here, models with * are ensemble models." ], "reasoning": "By comparing the development set scores, the TriAN model scored 82.78, while the Integrated TriAN model scored 83.90, indicating that the statement is incorrect.", "statement": "The TriAN model outperforms the Integrated TriAN model on the development set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Results on six most frequent question types.'.\n" }, { "role": "assistant", "content": "The semi-supervised visual integration framework improves performance on 'what' and 'where' questions but does not help with 'why', 'who', and 'when' questions.Analysis by Question Categories: To figure out how the semi-supervised visual integration framework helps reading comprehension, we perform an analysis of performance change on six most frequent question types. The framework does not help answering abstract why, who and when questions, which supports our hypothesis that the framework mainly gives real world state of objects and scene." } ], "images": [ "scigen/train/625.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on six most frequent question types." ], "reasoning": "The semi-supervised visual integration framework improves performance on 'what' and 'where' questions but does not help with 'why', 'who', and 'when' questions.", "description": "Analysis by Question Categories: To figure out how the semi-supervised visual integration framework helps reading comprehension, we perform an analysis of performance change on six most frequent question types. The framework does not help answering abstract why, who and when questions, which supports our hypothesis that the framework mainly gives real world state of objects and scene.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Results on six most frequent question types.\nQuestion: Which question type shows the most improvement with the Integrated model compared to the TriAN model?\nSolve the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the scores of the TriAN and Integrated models, the 'what' question type shows the most improvement, increasing from 85.4 to 86.3. So, the answer is {\"answer\": \"what\"}" } ], "images": [ "scigen/train/625.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on six most frequent question types." ], "reasoning": "By comparing the scores of the TriAN and Integrated models, the 'what' question type shows the most improvement, increasing from 85.4 to 86.3.", "question": "Which question type shows the most improvement with the Integrated model compared to the TriAN model?", "answer": "So, the answer is {\"answer\": \"what\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Integrated model performs better than the TriAN model on 'who' questions.\n" }, { "role": "assistant", "content": "The table shows that both the TriAN and Integrated models have the same score of 89.7 for 'who' questions, indicating no improvement. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/625.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on six most frequent question types." ], "reasoning": "The table shows that both the TriAN and Integrated models have the same score of 89.7 for 'who' questions, indicating no improvement.", "statement": "The Integrated model performs better than the TriAN model on 'who' questions.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Result of SemEval 2018 Task 11 (MCScript) on different train/test image bases.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The model performs best when trained and tested on the MSCOCO image base, and performance decreases when the test image base is smaller or different.In order to study the effect of image memory base size on experimental results, we alternate image base in train and test process. As shown in Table. When we shrink the test image memory base to the Flicker30k, the integrated model performance reduces, but is still higher than using 30k image both in train and test. More importantly, when the model is trained on the Flicker30k image base, using a bigger one does not contribute to test performance. These results suggest that the framework could work better as the size of the image base in train and test increases." } ], "images": [ "scigen/train/626.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Result of SemEval 2018 Task 11 (MCScript) on different train/test image bases." ], "reasoning": "The model performs best when trained and tested on the MSCOCO image base, and performance decreases when the test image base is smaller or different.", "description": "In order to study the effect of image memory base size on experimental results, we alternate image base in train and test process. As shown in Table. When we shrink the test image memory base to the Flicker30k, the integrated model performance reduces, but is still higher than using 30k image both in train and test. More importantly, when the model is trained on the Flicker30k image base, using a bigger one does not contribute to test performance. These results suggest that the framework could work better as the size of the image base in train and test increases.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Result of SemEval 2018 Task 11 (MCScript) on different train/test image bases.'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}. \n\nQuestion: What is the test performance when the model is trained on MSCOCO and tested on Flicker30k?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows that when the model is trained on MSCOCO and tested on Flicker30k, the test performance is 83.09. So, the answer is {\"answer\": \"83.09\"}." } ], "images": [ "scigen/train/626.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Result of SemEval 2018 Task 11 (MCScript) on different train/test image bases." ], "reasoning": "The table shows that when the model is trained on MSCOCO and tested on Flicker30k, the test performance is 83.09.", "question": "What is the test performance when the model is trained on MSCOCO and tested on Flicker30k?", "answer": "So, the answer is {\"answer\": \"83.09\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe model performs better when trained and tested on the same image base.\n" }, { "role": "assistant", "content": "The table shows that the highest test performance is achieved when the model is trained and tested on the MSCOCO image base (83.12), and the performance is lower when the test base is different from the train base. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/626.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Result of SemEval 2018 Task 11 (MCScript) on different train/test image bases." ], "reasoning": "The table shows that the highest test performance is achieved when the model is trained and tested on the MSCOCO image base (83.12), and the performance is lower when the test base is different from the train base.", "statement": "The model performs better when trained and tested on the same image base.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Result on SNLI. Here, * stands for multimodal NLU models.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The integrated visualization language inference model based on BERT achieved a new state-of-the-art performance of 91.3% on the SNLI dataset, surpassing the previous BERT-large model performance of 91.2%.Integrated NLI Model: In Table we shows the results of integrating the mechanism to language inference model based on BERT. We firstly report the state-of-the-art performance of BERT-large model (91.2%) on the SNLI dataset. Based on the trained BERT-large encoder, we then train the integrated visualization language inference model using three-step training process, and achieve new SOTA performance reported (91.3%) yet on the SNLI dataset. By integrate to pre-trained NLU model, the framework also largely outperforms previous multimodal NLU models." } ], "images": [ "scigen/train/627.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Result on SNLI. Here, * stands for multimodal NLU models." ], "reasoning": "The integrated visualization language inference model based on BERT achieved a new state-of-the-art performance of 91.3% on the SNLI dataset, surpassing the previous BERT-large model performance of 91.2%.", "description": "Integrated NLI Model: In Table we shows the results of integrating the mechanism to language inference model based on BERT. We firstly report the state-of-the-art performance of BERT-large model (91.2%) on the SNLI dataset. Based on the trained BERT-large encoder, we then train the integrated visualization language inference model using three-step training process, and achieve new SOTA performance reported (91.3%) yet on the SNLI dataset. By integrate to pre-trained NLU model, the framework also largely outperforms previous multimodal NLU models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 4: Result on SNLI. Here, * stands for multimodal NLU models.' and proceed to solve the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the performance difference between the BERT-large model and the integrated visualization language inference model on the SNLI dataset?\n" }, { "role": "assistant", "content": "The BERT-large model achieved a performance of 91.2% on the SNLI dataset, while the integrated visualization language inference model achieved 91.3%. The difference is 0.1%. So, the answer is {\"answer\": \"0.1%\"}" } ], "images": [ "scigen/train/627.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Result on SNLI. Here, * stands for multimodal NLU models." ], "reasoning": "The BERT-large model achieved a performance of 91.2% on the SNLI dataset, while the integrated visualization language inference model achieved 91.3%. The difference is 0.1%.", "question": "What is the performance difference between the BERT-large model and the integrated visualization language inference model on the SNLI dataset?", "answer": "So, the answer is {\"answer\": \"0.1%\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe integrated visualization language inference model achieved a performance of 91.3% on the SNLI dataset.\n" }, { "role": "assistant", "content": "The table shows that the BERT + STVF model, which is the integrated visualization language inference model, achieved a performance of 91.3% on the SNLI dataset. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/627.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Result on SNLI. Here, * stands for multimodal NLU models." ], "reasoning": "The table shows that the BERT + STVF model, which is the integrated visualization language inference model, achieved a performance of 91.3% on the SNLI dataset.", "statement": "The integrated visualization language inference model achieved a performance of 91.3% on the SNLI dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: Effect of the magnitude of the shift parameter k in SPPMI on the word similarity tasks. Boldface means best performance per dateset.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "For the Apt model, k=40 generally performs best, except for SimLex-999 where smaller shifts are better, and k=1 is optimal for the untyped vector space model.For the Apt model, a value of k=40 performs best (except for SimLex-999, where smaller shifts give better results), with a performance drop-off for larger shifts. In our experiments we find that a shift of k=1 results in top performance for the untyped vector space model. It appears that shifting the PPMI scores in the Apt model has the effect of cleaning the vectors from noisy PPMI artefacts, which reinforces the predominant sense, while other senses get suppressed. Subsequently, this results in a cleaner neighbourhood around the word vector, dominated by a single sense. This explains why distributional inference slightly degrades performance for smaller values of k." } ], "images": [ "scigen/train/628.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Effect of the magnitude of the shift parameter k in SPPMI on the word similarity tasks. Boldface means best performance per dateset." ], "reasoning": "For the Apt model, k=40 generally performs best, except for SimLex-999 where smaller shifts are better, and k=1 is optimal for the untyped vector space model.", "description": "For the Apt model, a value of k=40 performs best (except for SimLex-999, where smaller shifts give better results), with a performance drop-off for larger shifts. In our experiments we find that a shift of k=1 results in top performance for the untyped vector space model. It appears that shifting the PPMI scores in the Apt model has the effect of cleaning the vectors from noisy PPMI artefacts, which reinforces the predominant sense, while other senses get suppressed. Subsequently, this results in a cleaner neighbourhood around the word vector, dominated by a single sense. This explains why distributional inference slightly degrades performance for smaller values of k.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Effect of the magnitude of the shift parameter k in SPPMI on the word similarity tasks. Boldface means best performance per dateset.\nQuestion: What is the performance of the Apt model with k=40 for the WordSim-353 (rel) dataset with DI?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that for the Apt model with k=40, the performance for the WordSim-353 (rel) dataset with DI is 0.61. So, the answer is {\"answer\": \"0.61\"}." } ], "images": [ "scigen/train/628.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Effect of the magnitude of the shift parameter k in SPPMI on the word similarity tasks. Boldface means best performance per dateset." ], "reasoning": "The table shows that for the Apt model with k=40, the performance for the WordSim-353 (rel) dataset with DI is 0.61.", "question": "What is the performance of the Apt model with k=40 for the WordSim-353 (rel) dataset with DI?", "answer": "So, the answer is {\"answer\": \"0.61\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Apt model with k=1 performs best for the SimLex-999 dataset with DI.\n" }, { "role": "assistant", "content": "The table shows that for the SimLex-999 dataset with DI, the highest performance is 0.36, which is achieved by both k=5 and k=10, not k=1. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/628.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Effect of the magnitude of the shift parameter k in SPPMI on the word similarity tasks. Boldface means best performance per dateset." ], "reasoning": "The table shows that for the SimLex-999 dataset with DI, the highest performance is 0.36, which is achieved by both k=5 and k=10, not k=1.", "statement": "The Apt model with k=1 performs best for the SimLex-999 dataset with DI.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Example feature spaces for the lexemes white and clothes extracted from the dependency tree of Figure 1. Not all features are displayed for space reasons. Offsetting ¯¯¯¯¯¯¯¯¯¯¯amod:shoes by amod results in an empty dependency path, leaving just the word co-occurrence :shoes as feature.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The adjective 'white' interacts with the noun 'clothes' through first-order relations like 'amod' and second-order features align with the first-order features of the noun.For composing the adjective white with the noun clothes via the dependency relation amod we need to consider how the adjective interacts with the noun in the vector space. The distributional features of white describe things that are white via their first order relations such as ¯¯¯¯¯¯¯¯¯¯¯amod, and things that can be done to white things, such as bought via ¯¯¯¯¯¯¯¯¯¯¯amod:¯¯¯¯¯¯¯¯¯¯¯dobj in the example above. However through the inclusion of inverse and higher order dependency paths we can observe that the second order features of the adjective align with the first order features of the noun." } ], "images": [ "scigen/train/629.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Example feature spaces for the lexemes white and clothes extracted from the dependency tree of Figure 1. Not all features are displayed for space reasons. Offsetting ¯¯¯¯¯¯¯¯¯¯¯amod:shoes by amod results in an empty dependency path, leaving just the word co-occurrence :shoes as feature." ], "reasoning": "The adjective 'white' interacts with the noun 'clothes' through first-order relations like 'amod' and second-order features align with the first-order features of the noun.", "description": "For composing the adjective white with the noun clothes via the dependency relation amod we need to consider how the adjective interacts with the noun in the vector space. The distributional features of white describe things that are white via their first order relations such as ¯¯¯¯¯¯¯¯¯¯¯amod, and things that can be done to white things, such as bought via ¯¯¯¯¯¯¯¯¯¯¯amod:¯¯¯¯¯¯¯¯¯¯¯dobj in the example above. However through the inclusion of inverse and higher order dependency paths we can observe that the second order features of the adjective align with the first order features of the noun.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: Example feature spaces for the lexemes white and clothes extracted from the dependency tree of Figure 1. Not all features are displayed for space reasons. Offsetting ¯¯¯¯¯¯¯¯¯¯¯amod:shoes by amod results in an empty dependency path, leaving just the word co-occurrence :shoes as feature.', answer the question below by showing each progressive step toward the answer. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the co-occurrence count for the feature 'folded' in both 'white' and 'clothes' distributional features?\n" }, { "role": "assistant", "content": "The table shows that the co-occurrence count for 'folded' in both 'white' and 'clothes' distributional features is 1. So, the answer is {\"answer\": \"1\"}." } ], "images": [ "scigen/train/629.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Example feature spaces for the lexemes white and clothes extracted from the dependency tree of Figure 1. Not all features are displayed for space reasons. Offsetting ¯¯¯¯¯¯¯¯¯¯¯amod:shoes by amod results in an empty dependency path, leaving just the word co-occurrence :shoes as feature." ], "reasoning": "The table shows that the co-occurrence count for 'folded' in both 'white' and 'clothes' distributional features is 1.", "question": "What is the co-occurrence count for the feature 'folded' in both 'white' and 'clothes' distributional features?", "answer": "So, the answer is {\"answer\": \"1\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The feature 'bought' has a co-occurrence count of 1 in 'white' distributional features., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe feature 'bought' has a co-occurrence count of 1 in 'white' distributional features.\n" }, { "role": "assistant", "content": "The table shows that the feature 'bought' has a co-occurrence count of 1 in 'white' distributional features. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/629.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Example feature spaces for the lexemes white and clothes extracted from the dependency tree of Figure 1. Not all features are displayed for space reasons. Offsetting ¯¯¯¯¯¯¯¯¯¯¯amod:shoes by amod results in an empty dependency path, leaving just the word co-occurrence :shoes as feature." ], "reasoning": "The table shows that the feature 'bought' has a co-occurrence count of 1 in 'white' distributional features.", "statement": "The feature 'bought' has a co-occurrence count of 1 in 'white' distributional features.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Comparison of composition by union and composition by intersection. Not all features are displayed for space reasons.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The composition operation uses pointwise addition for combining features, and due to its syntactic nature, it avoids identical representations for phrases like 'house boat' and 'boat house'.We are then in a position to compose the offset representation of white with the vector for clothes by the union or the intersection of their features. It is worth noting that any arithmetic operation can be used to combine the counts of the aligned features, however for this paper we use pointwise addition for both composition functions. One of the advantages of this approach to composition is that the inherent interpretability of count-based models naturally expands beyond the word level, allowing us to study the distributional semantics of phrases in the same space as words. Due to offsetting one of the constituents, the composition operation is not commutative and hence avoids identical representations for house boat and boat house. However, the typed nature of our vector space results in extreme sparsity, for example while the untyped VSM has 130k dimensions, our Apt model can have more than 3m dimensions. We therefore need to enrich the elementary vector representations with the distributional information of their nearest neighbours to ease the sparsity effect and infer missing information. Due to the syntactic nature of our composition operation it is not straightforward to apply common dimensionality reduction techniques such as SVD, as the type information needs to be preserved." } ], "images": [ "scigen/train/630.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of composition by union and composition by intersection. Not all features are displayed for space reasons." ], "reasoning": "The composition operation uses pointwise addition for combining features, and due to its syntactic nature, it avoids identical representations for phrases like 'house boat' and 'boat house'.", "description": "We are then in a position to compose the offset representation of white with the vector for clothes by the union or the intersection of their features. It is worth noting that any arithmetic operation can be used to combine the counts of the aligned features, however for this paper we use pointwise addition for both composition functions. One of the advantages of this approach to composition is that the inherent interpretability of count-based models naturally expands beyond the word level, allowing us to study the distributional semantics of phrases in the same space as words. Due to offsetting one of the constituents, the composition operation is not commutative and hence avoids identical representations for house boat and boat house. However, the typed nature of our vector space results in extreme sparsity, for example while the untyped VSM has 130k dimensions, our Apt model can have more than 3m dimensions. We therefore need to enrich the elementary vector representations with the distributional information of their nearest neighbours to ease the sparsity effect and infer missing information. Due to the syntactic nature of our composition operation it is not straightforward to apply common dimensionality reduction techniques such as SVD, as the type information needs to be preserved.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: Comparison of composition by union and composition by intersection. Not all features are displayed for space reasons.', and tackle the question below in a sequential manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the co-occurrence count for the feature 'folded' in both union and intersection compositions?\n" }, { "role": "assistant", "content": "The table shows that the co-occurrence count for 'folded' is 2 in both the union and intersection compositions. So, the answer is {\"answer\": \"2\"}." } ], "images": [ "scigen/train/630.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of composition by union and composition by intersection. Not all features are displayed for space reasons." ], "reasoning": "The table shows that the co-occurrence count for 'folded' is 2 in both the union and intersection compositions.", "question": "What is the co-occurrence count for the feature 'folded' in both union and intersection compositions?", "answer": "So, the answer is {\"answer\": \"2\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe feature 'bought' has a co-occurrence count of 1 in the union composition.\n" }, { "role": "assistant", "content": "The table indicates that the feature 'bought' has a co-occurrence count of 1 in the union composition. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/630.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of composition by union and composition by intersection. Not all features are displayed for space reasons." ], "reasoning": "The table indicates that the feature 'bought' has a co-occurrence count of 1 in the union composition.", "statement": "The feature 'bought' has a co-occurrence count of 1 in the union composition.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: Neighbour retrieval function comparison. Boldface means best performance on a dataset per VSM type. *) With 3 significant figures, the density window approach (0.713) is slightly better than the baseline without DI (0.708), static top n (0.710) and WordNet (0.710).', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The Apt model shows larger improvements, indicating it lacks more distributional knowledge. The static top n neighbour retrieval is more consistent, and WordNet performs well on SimLex-999 due to its synonym-only retrieval.The improvements are typically larger for the Apt model, suggesting that it is missing more distributional knowledge in its elementary representations than untyped models. The density window and static top n neighbour retrieval functions perform very similar, however the static approach is more consistent and never underperforms the baseline for either model type on any dataset. The WordNet based neighbour retrieval function performs particularly well on SimLex-999. This can be explained by the fact that antonyms, which frequently happen to be among the nearest neighbours in distributional vector spaces, are regarded as dissimilar in SimLex-999, whereas the WordNet neighbour retrieval function only returns synonyms. The results furthermore confirm the effect that untyped models perform better on datasets modelling relatedness, whereas typed models work better for substitutability tasks" } ], "images": [ "scigen/train/631.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Neighbour retrieval function comparison. Boldface means best performance on a dataset per VSM type. *) With 3 significant figures, the density window approach (0.713) is slightly better than the baseline without DI (0.708), static top n (0.710) and WordNet (0.710)." ], "reasoning": "The Apt model shows larger improvements, indicating it lacks more distributional knowledge. The static top n neighbour retrieval is more consistent, and WordNet performs well on SimLex-999 due to its synonym-only retrieval.", "description": "The improvements are typically larger for the Apt model, suggesting that it is missing more distributional knowledge in its elementary representations than untyped models. The density window and static top n neighbour retrieval functions perform very similar, however the static approach is more consistent and never underperforms the baseline for either model type on any dataset. The WordNet based neighbour retrieval function performs particularly well on SimLex-999. This can be explained by the fact that antonyms, which frequently happen to be among the nearest neighbours in distributional vector spaces, are regarded as dissimilar in SimLex-999, whereas the WordNet neighbour retrieval function only returns synonyms. The results furthermore confirm the effect that untyped models perform better on datasets modelling relatedness, whereas typed models work better for substitutability tasks", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 4: Neighbour retrieval function comparison. Boldface means best performance on a dataset per VSM type. *) With 3 significant figures, the density window approach (0.713) is slightly better than the baseline without DI (0.708), static top n (0.710) and WordNet (0.710).', and sequentially solve the question, writing out each step of your reasoning process. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich neighbour retrieval function performs best on SimLex-999 for the Apt model?\n" }, { "role": "assistant", "content": "The table shows the performance of different neighbour retrieval functions on various datasets. For the Apt model on SimLex-999, the WordNet function has the highest score of 0.38. So, the answer is {\"answer\": \"WordNet\"}." } ], "images": [ "scigen/train/631.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Neighbour retrieval function comparison. Boldface means best performance on a dataset per VSM type. *) With 3 significant figures, the density window approach (0.713) is slightly better than the baseline without DI (0.708), static top n (0.710) and WordNet (0.710)." ], "reasoning": "The table shows the performance of different neighbour retrieval functions on various datasets. For the Apt model on SimLex-999, the WordNet function has the highest score of 0.38.", "question": "Which neighbour retrieval function performs best on SimLex-999 for the Apt model?", "answer": "So, the answer is {\"answer\": \"WordNet\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe static top n neighbour retrieval function never underperforms the baseline for either model type on any dataset.\n" }, { "role": "assistant", "content": "The table shows that the static top n neighbour retrieval function has consistent performance and does not underperform the baseline for either model type on any dataset. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/631.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Neighbour retrieval function comparison. Boldface means best performance on a dataset per VSM type. *) With 3 significant figures, the density window approach (0.713) is slightly better than the baseline without DI (0.708), static top n (0.710) and WordNet (0.710)." ], "reasoning": "The table shows that the static top n neighbour retrieval function has consistent performance and does not underperform the baseline for either model type on any dataset.", "statement": "The static top n neighbour retrieval function never underperforms the baseline for either model type on any dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 6: Neighbour retrieval function. Underlined means best performance per phrase type, boldface means best average performance overall.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The density window retrieval function outperforms static top n for composition by intersection, while static top n outperforms density window for composition by union.For a quantitative analysis of distributional inference for semantic composition, we evaluate our model on the composition dataset of Mitchell and Lapata \\shortciteMitchell_2010, consisting of 108 adjective-noun, 108 noun-noun, and 108 verb-object pairs. The task is to compare the model’s similarity estimates with the human judgements by computing Spearman’s ρ. For comparing the performance of the different neighbour retrieval functions, we choose the same parameter settings as in the word similarity experiments (k=40 and using 30 neighbours for DI). The density window retrieval function outperforms static top n for composition by intersection and vice versa for composition by union. The WordNet approach is competitive for composition by union, but underperfoms the other approaches for composition by intersection significantly. For further experiments we use the static top n approach as it is computationally cheap and easy to interpret due to the fixed number of neighbours." } ], "images": [ "scigen/train/632.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Neighbour retrieval function. Underlined means best performance per phrase type, boldface means best average performance overall." ], "reasoning": "The density window retrieval function outperforms static top n for composition by intersection, while static top n outperforms density window for composition by union.", "description": "For a quantitative analysis of distributional inference for semantic composition, we evaluate our model on the composition dataset of Mitchell and Lapata \\shortciteMitchell_2010, consisting of 108 adjective-noun, 108 noun-noun, and 108 verb-object pairs. The task is to compare the model’s similarity estimates with the human judgements by computing Spearman’s ρ. For comparing the performance of the different neighbour retrieval functions, we choose the same parameter settings as in the word similarity experiments (k=40 and using 30 neighbours for DI). The density window retrieval function outperforms static top n for composition by intersection and vice versa for composition by union. The WordNet approach is competitive for composition by union, but underperfoms the other approaches for composition by intersection significantly. For further experiments we use the static top n approach as it is computationally cheap and easy to interpret due to the fixed number of neighbours.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 6: Neighbour retrieval function. Underlined means best performance per phrase type, boldface means best average performance overall.', unfold the question presented below, detailing every step of your calculation. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich retrieval function has the highest average performance for composition by union?\n" }, { "role": "assistant", "content": "To find the retrieval function with the highest average performance for composition by union, compare the average values in the 'union' columns. The highest average value is 0.40 in the 'No Distributional Inference union' column. So, the answer is {\"answer\": \"No Distributional Inference union\"}." } ], "images": [ "scigen/train/632.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Neighbour retrieval function. Underlined means best performance per phrase type, boldface means best average performance overall." ], "reasoning": "To find the retrieval function with the highest average performance for composition by union, compare the average values in the 'union' columns. The highest average value is 0.40 in the 'No Distributional Inference union' column.", "question": "Which retrieval function has the highest average performance for composition by union?", "answer": "So, the answer is {\"answer\": \"No Distributional Inference union\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe WordNet approach has the highest performance for composition by intersection.\n" }, { "role": "assistant", "content": "To verify this statement, compare the values in the 'WordNet intersection' column with other columns for composition by intersection. The highest value in the 'WordNet intersection' column is 0.25, which is lower than the highest values in other columns for composition by intersection. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/632.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Neighbour retrieval function. Underlined means best performance per phrase type, boldface means best average performance overall." ], "reasoning": "To verify this statement, compare the values in the 'WordNet intersection' column with other columns for composition by intersection. The highest value in the 'WordNet intersection' column is 0.25, which is lower than the highest values in other columns for composition by intersection.", "statement": "The WordNet approach has the highest performance for composition by intersection.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Accuracy scores for the data augmentation and the two dataset extension strategies in comparison to the same FF model without any augmentation or extension.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The augmented models outperform the non-augmented model by 4-6 points in accuracy, reducing relative error by 14%-26%, and compare favorably with previously published results.Our techniques are able to outperform a non-augmented model by 4-6 points in accuracy, representing a relative error reduction of 14%-26%. While the primary objective in this work is to improve an existing model setup with data augmentation, our augmented models compare favourably with previously published results." } ], "images": [ "scigen/train/633.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracy scores for the data augmentation and the two dataset extension strategies in comparison to the same FF model without any augmentation or extension." ], "reasoning": "The augmented models outperform the non-augmented model by 4-6 points in accuracy, reducing relative error by 14%-26%, and compare favorably with previously published results.", "description": "Our techniques are able to outperform a non-augmented model by 4-6 points in accuracy, representing a relative error reduction of 14%-26%. While the primary objective in this work is to improve an existing model setup with data augmentation, our augmented models compare favourably with previously published results.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: Accuracy scores for the data augmentation and the two dataset extension strategies in comparison to the same FF model without any augmentation or extension.', solve this question and give detail solutions. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhich model shows the highest accuracy on the LEDS dataset?\n" }, { "role": "assistant", "content": "To find the model with the highest accuracy on the LEDS dataset, compare the accuracy values in the LEDS column. The highest value is 0.83, achieved by both the Distributional Composition Augmentation and WordNet Extension models. So, the answer is {\"answer\": \"Distributional Composition Augmentation and WordNet Extension\"}." } ], "images": [ "scigen/train/633.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracy scores for the data augmentation and the two dataset extension strategies in comparison to the same FF model without any augmentation or extension." ], "reasoning": "To find the model with the highest accuracy on the LEDS dataset, compare the accuracy values in the LEDS column. The highest value is 0.83, achieved by both the Distributional Composition Augmentation and WordNet Extension models.", "question": "Which model shows the highest accuracy on the LEDS dataset?", "answer": "So, the answer is {\"answer\": \"Distributional Composition Augmentation and WordNet Extension\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe GANDALF Augmentation model has the highest accuracy on the HP4K dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the accuracy values in the HP4K column. The highest value is 0.71, achieved by the GANDALF Augmentation model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/633.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracy scores for the data augmentation and the two dataset extension strategies in comparison to the same FF model without any augmentation or extension." ], "reasoning": "To verify this statement, compare the accuracy values in the HP4K column. The highest value is 0.71, achieved by the GANDALF Augmentation model.", "statement": "The GANDALF Augmentation model has the highest accuracy on the HP4K dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: Accuracy for the hypernym-only and full models on the Weeds dataset with no, DC or GAN augmentation.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The classifier should ideally show weak performance on the hypernym-only model and strong performance on the full model, indicating a focus on specific features in the hyponym-hypernym pair.Ideally, we would hope to see weak performance for the hypernym-only and strong performance on the full model. This would indicate that the classifier does not rely on prototypical features in the hypernym, but is able to focus on specific features in a given hyponym-hypernym pair." } ], "images": [ "scigen/train/634.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Accuracy for the hypernym-only and full models on the Weeds dataset with no, DC or GAN augmentation." ], "reasoning": "The classifier should ideally show weak performance on the hypernym-only model and strong performance on the full model, indicating a focus on specific features in the hyponym-hypernym pair.", "description": "Ideally, we would hope to see weak performance for the hypernym-only and strong performance on the full model. This would indicate that the classifier does not rely on prototypical features in the hypernym, but is able to focus on specific features in a given hyponym-hypernym pair.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 4: Accuracy for the hypernym-only and full models on the Weeds dataset with no, DC or GAN augmentation.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich augmentation method shows the highest performance for the full model?\n" }, { "role": "assistant", "content": "By comparing the Full Model column, the highest performance is 0.75, which corresponds to the GAN (size=500) augmentation method. So, the answer is {\"answer\": \"GAN (size=500)\"}." } ], "images": [ "scigen/train/634.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Accuracy for the hypernym-only and full models on the Weeds dataset with no, DC or GAN augmentation." ], "reasoning": "By comparing the Full Model column, the highest performance is 0.75, which corresponds to the GAN (size=500) augmentation method.", "question": "Which augmentation method shows the highest performance for the full model?", "answer": "So, the answer is {\"answer\": \"GAN (size=500)\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The hypernym-only model performs better with DC (size=100) augmentation than with GAN (size=500) augmentation., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe hypernym-only model performs better with DC (size=100) augmentation than with GAN (size=500) augmentation.\n" }, { "role": "assistant", "content": "The hypernym-only model has a performance of 0.60 with DC (size=100) and 0.58 with GAN (size=500), indicating better performance with DC (size=100). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/634.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Accuracy for the hypernym-only and full models on the Weeds dataset with no, DC or GAN augmentation." ], "reasoning": "The hypernym-only model has a performance of 0.60 with DC (size=100) and 0.58 with GAN (size=500), indicating better performance with DC (size=100).", "statement": "The hypernym-only model performs better with DC (size=100) augmentation than with GAN (size=500) augmentation.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Best-of-100 clustering errors with fewer training examples', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The models trained with reduced training sets achieved perplexities comparable to those trained with the full training set, indicating no overfitting.Note that the perplexities these models achieved are all comparable to their reference models (trained with the full training set), and none of the models overfit the training data." } ], "images": [ "scigen/train/635.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Best-of-100 clustering errors with fewer training examples" ], "reasoning": "The models trained with reduced training sets achieved perplexities comparable to those trained with the full training set, indicating no overfitting.", "description": "Note that the perplexities these models achieved are all comparable to their reference models (trained with the full training set), and none of the models overfit the training data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 3: Best-of-100 clustering errors with fewer training examples', solve the following question step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the perplexity of the R-D-256 model when trained with the full training set?\n" }, { "role": "assistant", "content": "To find the perplexity of the R-D-256 model when trained with the full training set, look at the intersection of the 'Full training set' row and the 'R-D-256' column. So, the answer is {\"answer\": \"29\"}" } ], "images": [ "scigen/train/635.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Best-of-100 clustering errors with fewer training examples" ], "reasoning": "To find the perplexity of the R-D-256 model when trained with the full training set, look at the intersection of the 'Full training set' row and the 'R-D-256' column.", "question": "What is the perplexity of the R-D-256 model when trained with the full training set?", "answer": "So, the answer is {\"answer\": \"29\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe R-D-F-1024 model trained with the 1/3 training set has a perplexity of 19.\n" }, { "role": "assistant", "content": "The table shows that the R-D-F-1024 model trained with the 1/3 training set has a perplexity of 19, which matches the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/635.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Best-of-100 clustering errors with fewer training examples" ], "reasoning": "The table shows that the R-D-F-1024 model trained with the 1/3 training set has a perplexity of 19, which matches the statement.", "statement": "The R-D-F-1024 model trained with the 1/3 training set has a perplexity of 19.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Clustering errors by model'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Sentences are clustered using K-means with K=14, and the clustering error is calculated by summing the misclassified sentences across all clusters.Each sentence prototype is randomly populated by common English words 100 times. The syntax of each sentence in such a category is very similar or identical to all others in the same category, but different from sentences in other categories. These sentences are then fed into our models. We record every resulting representation and pair it with its input sentence. Using K-means clustering with K=14 we cluster the representation-sentence pairs in the representation space. For each resulting cluster, we count how many sentences of each prototype it contains. This yields a list such as this: [30,3,1,7,88,0,0,0,0,0,0,0,0,0], which shows the content of one of 14 clusters: 30 sentences of type one, 3 of type two and so on. Since most sentences in this cluster are of type five, this cluster is assigned to be the cluster of sentence category five. However, 41 sentences of type other than five were “falsely” assigned to this cluster. Therefore, the error of this cluster is 41. The sum of errors of all 14 clusters is the clustering error, which is our quality measure for this experiment. Since K-means clustering is nondeterministic, we run the algorithm 100 times." } ], "images": [ "scigen/train/636.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Clustering errors by model" ], "reasoning": "Sentences are clustered using K-means with K=14, and the clustering error is calculated by summing the misclassified sentences across all clusters.", "description": "Each sentence prototype is randomly populated by common English words 100 times. The syntax of each sentence in such a category is very similar or identical to all others in the same category, but different from sentences in other categories. These sentences are then fed into our models. We record every resulting representation and pair it with its input sentence. Using K-means clustering with K=14 we cluster the representation-sentence pairs in the representation space. For each resulting cluster, we count how many sentences of each prototype it contains. This yields a list such as this: [30,3,1,7,88,0,0,0,0,0,0,0,0,0], which shows the content of one of 14 clusters: 30 sentences of type one, 3 of type two and so on. Since most sentences in this cluster are of type five, this cluster is assigned to be the cluster of sentence category five. However, 41 sentences of type other than five were “falsely” assigned to this cluster. Therefore, the error of this cluster is 41. The sum of errors of all 14 clusters is the clustering error, which is our quality measure for this experiment. Since K-means clustering is nondeterministic, we run the algorithm 100 times.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: Clustering errors by model' and methodically tackle the question that follows. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the lowest clustering error?\n" }, { "role": "assistant", "content": "To find the model with the lowest clustering error, compare the error values for each model in the table. The model with the error value of 0 has the lowest clustering error. So, the answer is {\"answer\": \"REP-DE-POS\"}" } ], "images": [ "scigen/train/636.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Clustering errors by model" ], "reasoning": "To find the model with the lowest clustering error, compare the error values for each model in the table. The model with the error value of 0 has the lowest clustering error.", "question": "Which model has the lowest clustering error?", "answer": "So, the answer is {\"answer\": \"REP-DE-POS\"}" }, { "messages": [ { "role": "user", "content": "The REP model has the highest clustering error. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe REP model has the highest clustering error.\n" }, { "role": "assistant", "content": "To verify this statement, compare the error values for each model in the table. The REP model has an error value of 51, which is the highest among all models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/636.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Clustering errors by model" ], "reasoning": "To verify this statement, compare the error values for each model in the table. The REP model has an error value of 51, which is the highest among all models.", "statement": "The REP model has the highest clustering error.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table S3: Additive noise augmented CPC, ABX errors (Libri-light dev set). Within- and across-speaker phoneme discriminability scores (lower is better) on the Libri-light clean and other dev sets for CPC training as a function of varying types of additive noise augmentation.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The optimal additive noise for data augmentation was found to be in the [80,240] Hz band, which corresponds roughly to the range of human pitch (F0).We discovered accidentally that for additive noise, low frequencies are more effective than high frequencies. We therefore explored systematically the effect of the spectral characteristics of noise by filtering sounds from the MUSAN dataset [musan2015] in successive frequency bands. We selected 5 broad bands, defined by 4 cutoff points by the tripling of the frequency: 80Hz, 240Hz, 720Hz, 2160Hz). We found that the optimal additive noise was obtained by bandpass filtering MUSAN sounds in the [80,240] Here, we explore how frequency filtering affects additive noise data augmentation. We did two experiments: band-pass filtering, and lowpass filtering. For bandpass, here are the frequency bands we applied to the MUSAN dataset: [0,80] Hz, [80,240] Hz, [240,720] Hz, [720,2060] Hz, [2160−8000] Hz. The second band corresponds roughly to the range of human pitch (F0), the third, to the range of the first formant (F1), the fourth to the range of the second formant (F2). The extreme ranges (very low or very high frequencies) do not typically carry much information. An optimal range seems to be [80,240] Hz. For lowpass, we selected sucessive 100Hz bands, starting from zero." } ], "images": [ "scigen/train/637.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table S3: Additive noise augmented CPC, ABX errors (Libri-light dev set). Within- and across-speaker phoneme discriminability scores (lower is better) on the Libri-light clean and other dev sets for CPC training as a function of varying types of additive noise augmentation." ], "reasoning": "The optimal additive noise for data augmentation was found to be in the [80,240] Hz band, which corresponds roughly to the range of human pitch (F0).", "description": "We discovered accidentally that for additive noise, low frequencies are more effective than high frequencies. We therefore explored systematically the effect of the spectral characteristics of noise by filtering sounds from the MUSAN dataset [musan2015] in successive frequency bands. We selected 5 broad bands, defined by 4 cutoff points by the tripling of the frequency: 80Hz, 240Hz, 720Hz, 2160Hz). We found that the optimal additive noise was obtained by bandpass filtering MUSAN sounds in the [80,240] Here, we explore how frequency filtering affects additive noise data augmentation. We did two experiments: band-pass filtering, and lowpass filtering. For bandpass, here are the frequency bands we applied to the MUSAN dataset: [0,80] Hz, [80,240] Hz, [240,720] Hz, [720,2060] Hz, [2160−8000] Hz. The second band corresponds roughly to the range of human pitch (F0), the third, to the range of the first formant (F1), the fourth to the range of the second formant (F2). The extreme ranges (very low or very high frequencies) do not typically carry much information. An optimal range seems to be [80,240] Hz. For lowpass, we selected sucessive 100Hz bands, starting from zero.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table S3: Additive noise augmented CPC, ABX errors (Libri-light dev set). Within- and across-speaker phoneme discriminability scores (lower is better) on the Libri-light clean and other dev sets for CPC training as a function of varying types of additive noise augmentation.' to sequentially solve the question presented below. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhat is the lowest 'Across spk. dev other' score for band-pass filtering with MUSAN past + future?\n" }, { "role": "assistant", "content": "To find the lowest 'Across spk. dev other' score for band-pass filtering with MUSAN past + future, we look at the values in the 'Across spk. dev other' column under the 'Band pass – Musan – past + future' section. The values are 13.5, 12.1, 11.7, 12.9, and 14.2. The lowest value is 11.7. So, the answer is {\"answer\": \"11.7\"}." } ], "images": [ "scigen/train/637.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table S3: Additive noise augmented CPC, ABX errors (Libri-light dev set). Within- and across-speaker phoneme discriminability scores (lower is better) on the Libri-light clean and other dev sets for CPC training as a function of varying types of additive noise augmentation." ], "reasoning": "To find the lowest 'Across spk. dev other' score for band-pass filtering with MUSAN past + future, we look at the values in the 'Across spk. dev other' column under the 'Band pass – Musan – past + future' section. The values are 13.5, 12.1, 11.7, 12.9, and 14.2. The lowest value is 11.7.", "question": "What is the lowest 'Across spk. dev other' score for band-pass filtering with MUSAN past + future?", "answer": "So, the answer is {\"answer\": \"11.7\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The lowest 'Within spk. dev clean' score for band-pass filtering with MUSAN past only is 5.38. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe lowest 'Within spk. dev clean' score for band-pass filtering with MUSAN past only is 5.38.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the 'Within spk. dev clean' scores for band-pass filtering with MUSAN past only. The values are 5.81, 5.55, 5.38, 6.22, and 6.71. The lowest value is indeed 5.38. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/637.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table S3: Additive noise augmented CPC, ABX errors (Libri-light dev set). Within- and across-speaker phoneme discriminability scores (lower is better) on the Libri-light clean and other dev sets for CPC training as a function of varying types of additive noise augmentation." ], "reasoning": "To verify this statement, we look at the 'Within spk. dev clean' scores for band-pass filtering with MUSAN past only. The values are 5.81, 5.55, 5.38, 6.22, and 6.71. The lowest value is indeed 5.38.", "statement": "The lowest 'Within spk. dev clean' score for band-pass filtering with MUSAN past only is 5.38.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: ABX errors on data-augmented CPC features (Libri-light dev set). Within- and across-speaker phoneme discriminability scores (lower is better) on the Libri-light clean and other dev sets for CPC training as a function of types of data augmentation, in isolation or combination (see Section 4.1).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The individual augmentations sorted by average performance are pitch, add, reverb, tdrop, and bandrej. Combinations of augmentations were studied, excluding bandrej due to poor results, focusing on past augmentations for efficiency.The only augmentation performing better on past+future is add. According to their average performance, the individual augmentations can be sorted, from most to least useful: pitch, add, reverb, tdrop, and bandrej. Next, we study the performance of combinations of augmentations. We decided to drop bandrej from consideration due to its poor results. We only consider augmenting past, as this gives roughly the same quality of representations, but requires less computation. As a result, we have 6 possible two-way, 4 three-way, and 1 four-way combination of effects." } ], "images": [ "scigen/train/638.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: ABX errors on data-augmented CPC features (Libri-light dev set). Within- and across-speaker phoneme discriminability scores (lower is better) on the Libri-light clean and other dev sets for CPC training as a function of types of data augmentation, in isolation or combination (see Section 4.1)." ], "reasoning": "The individual augmentations sorted by average performance are pitch, add, reverb, tdrop, and bandrej. Combinations of augmentations were studied, excluding bandrej due to poor results, focusing on past augmentations for efficiency.", "description": "The only augmentation performing better on past+future is add. According to their average performance, the individual augmentations can be sorted, from most to least useful: pitch, add, reverb, tdrop, and bandrej. Next, we study the performance of combinations of augmentations. We decided to drop bandrej from consideration due to its poor results. We only consider augmenting past, as this gives roughly the same quality of representations, but requires less computation. As a result, we have 6 possible two-way, 4 three-way, and 1 four-way combination of effects.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 1: ABX errors on data-augmented CPC features (Libri-light dev set). Within- and across-speaker phoneme discriminability scores (lower is better) on the Libri-light clean and other dev sets for CPC training as a function of types of data augmentation, in isolation or combination (see Section 4.1).', and sequentially solve the question, writing out each step of your reasoning process. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich single augmentation has the best performance within speaker development on clean data?\n" }, { "role": "assistant", "content": "To find the best single augmentation within speaker development on clean data, we look at the 'Within spk. dev clean' column under 'Single augmentations'. The lowest value indicates the best performance. So, the answer is {\"answer\": \"pitch-past\"}." } ], "images": [ "scigen/train/638.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: ABX errors on data-augmented CPC features (Libri-light dev set). Within- and across-speaker phoneme discriminability scores (lower is better) on the Libri-light clean and other dev sets for CPC training as a function of types of data augmentation, in isolation or combination (see Section 4.1)." ], "reasoning": "To find the best single augmentation within speaker development on clean data, we look at the 'Within spk. dev clean' column under 'Single augmentations'. The lowest value indicates the best performance.", "question": "Which single augmentation has the best performance within speaker development on clean data?", "answer": "So, the answer is {\"answer\": \"pitch-past\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe combination of pitch and add has the best performance across speaker development on clean data.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the 'Across spk. dev clean' column under '2-way combinations, past only'. The combination of pitch and add has a value of 6.79, which is the lowest among the 2-way combinations. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/638.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: ABX errors on data-augmented CPC features (Libri-light dev set). Within- and across-speaker phoneme discriminability scores (lower is better) on the Libri-light clean and other dev sets for CPC training as a function of types of data augmentation, in isolation or combination (see Section 4.1)." ], "reasoning": "To verify this statement, we look at the 'Across spk. dev clean' column under '2-way combinations, past only'. The combination of pitch and add has a value of 6.79, which is the lowest among the 2-way combinations.", "statement": "The combination of pitch and add has the best performance across speaker development on clean data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: ABX errors on the ZeroResource Speech Challenge 2017 (120s). Within- (“W.”) and across-speaker (“A.”) phoneme discriminability scores on English, French and Mandarin speech for CPC features with and without data augmentation. For comparison, the best systems plus supervised topline of the ZeroSpeech leaderboard trained on the provided datasets.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Noise augmentation improves scores across all languages, but larger datasets are needed to match or exceed the best performing baselines, especially with larger models.As can be seen, while noise augmentation improves the score on all three languages, we cannot reach the SOTA with the small training datasets provided from the challenge. We can however, be on par with or improve over best performing baseline with our out-of-domain train sets (same languages, larger datasets), in particular with the larger model. This shows that while our technique scales with dataset size, it is still less data efficient than the techniques described in Heck et al. [heck2017] and Choroskwi et al. [chor2019]. Note however, that both studies used speaker adaptation which are outside the scope of what can be done with standard time domain data augmentation techniques." } ], "images": [ "scigen/train/639.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: ABX errors on the ZeroResource Speech Challenge 2017 (120s). Within- (“W.”) and across-speaker (“A.”) phoneme discriminability scores on English, French and Mandarin speech for CPC features with and without data augmentation. For comparison, the best systems plus supervised topline of the ZeroSpeech leaderboard trained on the provided datasets." ], "reasoning": "Noise augmentation improves scores across all languages, but larger datasets are needed to match or exceed the best performing baselines, especially with larger models.", "description": "As can be seen, while noise augmentation improves the score on all three languages, we cannot reach the SOTA with the small training datasets provided from the challenge. We can however, be on par with or improve over best performing baseline with our out-of-domain train sets (same languages, larger datasets), in particular with the larger model. This shows that while our technique scales with dataset size, it is still less data efficient than the techniques described in Heck et al. [heck2017] and Choroskwi et al. [chor2019]. Note however, that both studies used speaker adaptation which are outside the scope of what can be done with standard time domain data augmentation techniques.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: ABX errors on the ZeroResource Speech Challenge 2017 (120s). Within- (“W.”) and across-speaker (“A.”) phoneme discriminability scores on English, French and Mandarin speech for CPC features with and without data augmentation. For comparison, the best systems plus supervised topline of the ZeroSpeech leaderboard trained on the provided datasets.' to sequentially solve the question presented below. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the average score for the CPC2 model trained on ZeroSpeech2017 datasets across all languages?\n" }, { "role": "assistant", "content": "To find the average score for the CPC2 model trained on ZeroSpeech2017 datasets, we need to look at the scores for English W., English A., French W., French A., Mandarin W., and Mandarin A. The scores are 8.6, 12.0, 16.4, 12.0, 14.0, and 12.0 respectively. The average is calculated as (8.6 + 12.0 + 16.4 + 12.0 + 14.0 + 12.0) / 6 = 12.53. So, the answer is {\"answer\": \"12.53\"}" } ], "images": [ "scigen/train/639.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: ABX errors on the ZeroResource Speech Challenge 2017 (120s). Within- (“W.”) and across-speaker (“A.”) phoneme discriminability scores on English, French and Mandarin speech for CPC features with and without data augmentation. For comparison, the best systems plus supervised topline of the ZeroSpeech leaderboard trained on the provided datasets." ], "reasoning": "To find the average score for the CPC2 model trained on ZeroSpeech2017 datasets, we need to look at the scores for English W., English A., French W., French A., Mandarin W., and Mandarin A. The scores are 8.6, 12.0, 16.4, 12.0, 14.0, and 12.0 respectively. The average is calculated as (8.6 + 12.0 + 16.4 + 12.0 + 14.0 + 12.0) / 6 = 12.53.", "question": "What is the average score for the CPC2 model trained on ZeroSpeech2017 datasets across all languages?", "answer": "So, the answer is {\"answer\": \"12.53\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The CPC2+WavAug model trained on out-of-domain datasets has a higher average score than the CPC2 model trained on ZeroSpeech2017 datasets., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe CPC2+WavAug model trained on out-of-domain datasets has a higher average score than the CPC2 model trained on ZeroSpeech2017 datasets.\n" }, { "role": "assistant", "content": "The average score for the CPC2+WavAug model trained on out-of-domain datasets is 7.77, while the average score for the CPC2 model trained on ZeroSpeech2017 datasets is 12.53. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/639.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: ABX errors on the ZeroResource Speech Challenge 2017 (120s). Within- (“W.”) and across-speaker (“A.”) phoneme discriminability scores on English, French and Mandarin speech for CPC features with and without data augmentation. For comparison, the best systems plus supervised topline of the ZeroSpeech leaderboard trained on the provided datasets." ], "reasoning": "The average score for the CPC2+WavAug model trained on out-of-domain datasets is 7.77, while the average score for the CPC2 model trained on ZeroSpeech2017 datasets is 12.53. Therefore, the statement is refuted.", "statement": "The CPC2+WavAug model trained on out-of-domain datasets has a higher average score than the CPC2 model trained on ZeroSpeech2017 datasets.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Phone Error Rate (PER) in the semi-supervised setting. A linear classifier is added on top of Librispeech-100 pretrained CPC2 models and fine tuned with either 10min, 1h or 10h of Libri-light labelled data with a CTC loss. For comparison, reference Libri-light results plus the untrained CPC2 architecture fully fined-tuned with 10 h.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Data augmentation, particularly pitch augmentation, significantly improves performance, allowing models to outperform baselines with substantially less data.For the supervised fine-tuning phase, we found out that we got the best results by using only pitch augmentation. Other methods having low or negative effects in this case. The combined effects of data augmentation on pretraining and fine-tuning adds up to 12-15% relative improvement across the different training sets. Interestingly, we find that with data augmentation we can beat the reference baseline (pretraining on 60k hours plus fine tuning on 10 hours) on frozen features with substantially less data (pretraining on 100 hours, plus fine tuning on 1 hour). Another point worth mentioning is that with data augmentation, 10 minutes of data on frozen features is sufficient to outperform the no-pretraining reference with 10 hours of labels." } ], "images": [ "scigen/train/640.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Phone Error Rate (PER) in the semi-supervised setting. A linear classifier is added on top of Librispeech-100 pretrained CPC2 models and fine tuned with either 10min, 1h or 10h of Libri-light labelled data with a CTC loss. For comparison, reference Libri-light results plus the untrained CPC2 architecture fully fined-tuned with 10 h." ], "reasoning": "Data augmentation, particularly pitch augmentation, significantly improves performance, allowing models to outperform baselines with substantially less data.", "description": "For the supervised fine-tuning phase, we found out that we got the best results by using only pitch augmentation. Other methods having low or negative effects in this case. The combined effects of data augmentation on pretraining and fine-tuning adds up to 12-15% relative improvement across the different training sets. Interestingly, we find that with data augmentation we can beat the reference baseline (pretraining on 60k hours plus fine tuning on 10 hours) on frozen features with substantially less data (pretraining on 100 hours, plus fine tuning on 1 hour). Another point worth mentioning is that with data augmentation, 10 minutes of data on frozen features is sufficient to outperform the no-pretraining reference with 10 hours of labels.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: 'Table 4: Phone Error Rate (PER) in the semi-supervised setting. A linear classifier is added on top of Librispeech-100 pretrained CPC2 models and fine tuned with either 10min, 1h or 10h of Libri-light labelled data with a CTC loss. For comparison, reference Libri-light results plus the untrained CPC2 architecture fully fined-tuned with 10 h.'\nQuestion: What is the test-other score for the system that uses CPC2 with WavAug and full fine-tuning on 10 hours of data?\n" }, { "role": "assistant", "content": "To find the test-other score for the system that uses CPC2 with WavAug and full fine-tuning on 10 hours of data, locate the row labeled 'CPC2+WavAug' under the 'Full fine-tuning, 10h of data' section and find the corresponding value in the 'test-other' column. So, the answer is {\"answer\": \"41.2\"}." } ], "images": [ "scigen/train/640.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Phone Error Rate (PER) in the semi-supervised setting. A linear classifier is added on top of Librispeech-100 pretrained CPC2 models and fine tuned with either 10min, 1h or 10h of Libri-light labelled data with a CTC loss. For comparison, reference Libri-light results plus the untrained CPC2 architecture fully fined-tuned with 10 h." ], "reasoning": "To find the test-other score for the system that uses CPC2 with WavAug and full fine-tuning on 10 hours of data, locate the row labeled 'CPC2+WavAug' under the 'Full fine-tuning, 10h of data' section and find the corresponding value in the 'test-other' column.", "question": "What is the test-other score for the system that uses CPC2 with WavAug and full fine-tuning on 10 hours of data?", "answer": "So, the answer is {\"answer\": \"41.2\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nUsing CPC2 with WavAug and full fine-tuning on 10 hours of data results in a test-clean score of 24.5.\n" }, { "role": "assistant", "content": "To verify this statement, locate the row labeled 'CPC2+WavAug' under the 'Full fine-tuning, 10h of data' section and check the value in the 'test-clean' column. The value is 24.5, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/640.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Phone Error Rate (PER) in the semi-supervised setting. A linear classifier is added on top of Librispeech-100 pretrained CPC2 models and fine tuned with either 10min, 1h or 10h of Libri-light labelled data with a CTC loss. For comparison, reference Libri-light results plus the untrained CPC2 architecture fully fined-tuned with 10 h." ], "reasoning": "To verify this statement, locate the row labeled 'CPC2+WavAug' under the 'Full fine-tuning, 10h of data' section and check the value in the 'test-clean' column. The value is 24.5, which matches the statement.", "statement": "Using CPC2 with WavAug and full fine-tuning on 10 hours of data results in a test-clean score of 24.5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table S1: Architecture ablations, ABX errors (Libri-light dev set). We compare the original CPC model described in [riviere2020multi] with modifications including more LSTM layers and a single Multi-Head prediction model for all time steps (MH). The bottom model is the one we refer to as CPC2 in the paper.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "CPC2 uses a 2-layer LSTM for the context network and a single multi-head transformer for prediction, improving training speed while maintaining accuracy.CPC2 is a modified version of the CPC architecture in [kahn2020, riviere2020multi]. The encoder architecture is unchanged (5 convolutional layers with kernel sizes [10,8,4,4,4], strides [5,4,2,2,2] and hidden dimension 256). For the recurrent context nextwork, we use a 2-layer LSTM, as a tradeoff between feature quality and training speed. In the prediction network, we replace the k independent transformers in [kahn2020, riviere2020multi], each one predicting a specific time-step ahead, to a single multi-head transformer layer with k classifiers at its heads. This has a limited impact on accuracy but dramatically decreases training time. We started from the model described in [riviere2020multi]: the encoder network is composed of 5 convolutional layers with kernel sizes [10,8,4,4,4], strides [5,4,2,2,2] and hidden dimension 256. We worked with ReLU activation and inserted a channel normalization procedure between each convolutional layer. As far as the context network is concerned, we used a 2-layers LSTM. Finally, we used a single layer multihead transformer to do the prediction instead of several single head transformers. We observe that in the cases of 3h and 45h datasets, the architecture with 2 layers of LSTM still perform best. However, with 100h of data, increasing the model depth turns out to be beneficial." } ], "images": [ "scigen/train/641.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table S1: Architecture ablations, ABX errors (Libri-light dev set). We compare the original CPC model described in [riviere2020multi] with modifications including more LSTM layers and a single Multi-Head prediction model for all time steps (MH). The bottom model is the one we refer to as CPC2 in the paper." ], "reasoning": "CPC2 uses a 2-layer LSTM for the context network and a single multi-head transformer for prediction, improving training speed while maintaining accuracy.", "description": "CPC2 is a modified version of the CPC architecture in [kahn2020, riviere2020multi]. The encoder architecture is unchanged (5 convolutional layers with kernel sizes [10,8,4,4,4], strides [5,4,2,2,2] and hidden dimension 256). For the recurrent context nextwork, we use a 2-layer LSTM, as a tradeoff between feature quality and training speed. In the prediction network, we replace the k independent transformers in [kahn2020, riviere2020multi], each one predicting a specific time-step ahead, to a single multi-head transformer layer with k classifiers at its heads. This has a limited impact on accuracy but dramatically decreases training time. We started from the model described in [riviere2020multi]: the encoder network is composed of 5 convolutional layers with kernel sizes [10,8,4,4,4], strides [5,4,2,2,2] and hidden dimension 256. We worked with ReLU activation and inserted a channel normalization procedure between each convolutional layer. As far as the context network is concerned, we used a 2-layers LSTM. Finally, we used a single layer multihead transformer to do the prediction instead of several single head transformers. We observe that in the cases of 3h and 45h datasets, the architecture with 2 layers of LSTM still perform best. However, with 100h of data, increasing the model depth turns out to be beneficial.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table S1: Architecture ablations, ABX errors (Libri-light dev set). We compare the original CPC model described in [riviere2020multi] with modifications including more LSTM layers and a single Multi-Head prediction model for all time steps (MH). The bottom model is the one we refer to as CPC2 in the paper.', and sequentially solve the question, writing out each step of your reasoning process. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich system has the lowest 'Within spk. dev clean' score?\n" }, { "role": "assistant", "content": "To find the system with the lowest 'Within spk. dev clean' score, compare the values in the 'Within spk. dev clean' column. The lowest value is 5.93, which corresponds to the 'CPC + 3 layers LSTM' system. So, the answer is {\"answer\": \"CPC + 3 layers LSTM\"}." } ], "images": [ "scigen/train/641.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table S1: Architecture ablations, ABX errors (Libri-light dev set). We compare the original CPC model described in [riviere2020multi] with modifications including more LSTM layers and a single Multi-Head prediction model for all time steps (MH). The bottom model is the one we refer to as CPC2 in the paper." ], "reasoning": "To find the system with the lowest 'Within spk. dev clean' score, compare the values in the 'Within spk. dev clean' column. The lowest value is 5.93, which corresponds to the 'CPC + 3 layers LSTM' system.", "question": "Which system has the lowest 'Within spk. dev clean' score?", "answer": "So, the answer is {\"answer\": \"CPC + 3 layers LSTM\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe CPC + MH system has the highest 'Across spk. dev other' score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Across spk. dev other' scores for all systems. The highest score is 14.08, which corresponds to the 'CPC + MH' system. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/641.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table S1: Architecture ablations, ABX errors (Libri-light dev set). We compare the original CPC model described in [riviere2020multi] with modifications including more LSTM layers and a single Multi-Head prediction model for all time steps (MH). The bottom model is the one we refer to as CPC2 in the paper." ], "reasoning": "To verify this statement, compare the 'Across spk. dev other' scores for all systems. The highest score is 14.08, which corresponds to the 'CPC + MH' system.", "statement": "The CPC + MH system has the highest 'Across spk. dev other' score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table S2: Architecture ablations, ABX errors (Libri-light dev set). We compare modifications of the CPC architecture across different dataset sizes. In all cases, we apply the best data augmentation reported in the main text.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The experiment tests the effectiveness of a data augmentation technique on speech features using different amounts of training data and LSTM layers, evaluating performance on phone classification tasks.In this experiment, we test whether our data augmentation technique can build better speech features that can be used for downstream tasks. Here, we use the Libri-light limited supervision phone classification task [kahn2020], which contains intentionally small training sets (10 min, 1h or 10 hours of labelled data). We fine-tune a linear phone classifier built on top of the CPC features with a CTC loss (frozen features). On 10 hours of data, we also fine-tune the entire network. Again, we additionally experiment with an architecture that has a 3-layer LSTM (CPC2-L3) In the next experiment, we study the performance of our model in function of the size of the available data and the architecture size (controlled by the number of LSTM layers). We simulate the amounts of data available at ZeroSpeech2017 for Mandarin (3h), French (45h), and English (100h) by sub-sampling from LibriLight (3h and 45h) and using LibriSpeech (100h). In all experiments, we use the best data augmentation found in the main text (pitch+add+reverb-past)." } ], "images": [ "scigen/train/642.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table S2: Architecture ablations, ABX errors (Libri-light dev set). We compare modifications of the CPC architecture across different dataset sizes. In all cases, we apply the best data augmentation reported in the main text." ], "reasoning": "The experiment tests the effectiveness of a data augmentation technique on speech features using different amounts of training data and LSTM layers, evaluating performance on phone classification tasks.", "description": "In this experiment, we test whether our data augmentation technique can build better speech features that can be used for downstream tasks. Here, we use the Libri-light limited supervision phone classification task [kahn2020], which contains intentionally small training sets (10 min, 1h or 10 hours of labelled data). We fine-tune a linear phone classifier built on top of the CPC features with a CTC loss (frozen features). On 10 hours of data, we also fine-tune the entire network. Again, we additionally experiment with an architecture that has a 3-layer LSTM (CPC2-L3) In the next experiment, we study the performance of our model in function of the size of the available data and the architecture size (controlled by the number of LSTM layers). We simulate the amounts of data available at ZeroSpeech2017 for Mandarin (3h), French (45h), and English (100h) by sub-sampling from LibriLight (3h and 45h) and using LibriSpeech (100h). In all experiments, we use the best data augmentation found in the main text (pitch+add+reverb-past).", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table S2: Architecture ablations, ABX errors (Libri-light dev set). We compare modifications of the CPC architecture across different dataset sizes. In all cases, we apply the best data augmentation reported in the main text.' and proceed to solve the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the performance of the CPC2 + 3 layers LSTM system on the 45h Libri-light dataset for the 'Across spk. dev' clean condition?\n" }, { "role": "assistant", "content": "To find the performance, locate the row corresponding to '45h Libri-light' and 'CPC2 + 3 layers LSTM' and then find the value under the 'Across spk. dev' clean column. So, the answer is {\"answer\": \"8.43\"}." } ], "images": [ "scigen/train/642.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table S2: Architecture ablations, ABX errors (Libri-light dev set). We compare modifications of the CPC architecture across different dataset sizes. In all cases, we apply the best data augmentation reported in the main text." ], "reasoning": "To find the performance, locate the row corresponding to '45h Libri-light' and 'CPC2 + 3 layers LSTM' and then find the value under the 'Across spk. dev' clean column.", "question": "What is the performance of the CPC2 + 3 layers LSTM system on the 45h Libri-light dataset for the 'Across spk. dev' clean condition?", "answer": "So, the answer is {\"answer\": \"8.43\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe CPC2 + 2 layers LSTM system performs better on the 100h LibriSpeech dataset than on the 45h Libri-light dataset for the 'Within spk. dev' clean condition.\n" }, { "role": "assistant", "content": "Compare the 'Within spk. dev' clean values for the CPC2 + 2 layers LSTM system on the 100h LibriSpeech dataset and the 45h Libri-light dataset. The values are 4.66 for 100h LibriSpeech and 6.31 for 45h Libri-light, indicating better performance on the 100h LibriSpeech dataset. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/642.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table S2: Architecture ablations, ABX errors (Libri-light dev set). We compare modifications of the CPC architecture across different dataset sizes. In all cases, we apply the best data augmentation reported in the main text." ], "reasoning": "Compare the 'Within spk. dev' clean values for the CPC2 + 2 layers LSTM system on the 100h LibriSpeech dataset and the 45h Libri-light dataset. The values are 4.66 for 100h LibriSpeech and 6.31 for 45h Libri-light, indicating better performance on the 100h LibriSpeech dataset.", "statement": "The CPC2 + 2 layers LSTM system performs better on the 100h LibriSpeech dataset than on the 45h Libri-light dataset for the 'Within spk. dev' clean condition.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Prealigned Data (CzEng 1.0); Experiment: Effectiveness', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The scoring of preliminary alignments significantly improves the exact match ratio from 50.30% to 71.30%, and 93.74% of detected pairs are correct with a recall of 63.02%.In the preliminary alignments of the tail, 50.30% of all the top candidates are exact matches. However, this ratio is more satisfactory in the scored alignments—71.30%. This means that the scoring of the preliminary alignments is an important step in the whole process. Of all the existing pairs of parallel sentences 63.02% were detected, and 93.74% of all the detected pairs were correct." } ], "images": [ "scigen/train/643.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Prealigned Data (CzEng 1.0); Experiment: Effectiveness" ], "reasoning": "The scoring of preliminary alignments significantly improves the exact match ratio from 50.30% to 71.30%, and 93.74% of detected pairs are correct with a recall of 63.02%.", "description": "In the preliminary alignments of the tail, 50.30% of all the top candidates are exact matches. However, this ratio is more satisfactory in the scored alignments—71.30%. This means that the scoring of the preliminary alignments is an important step in the whole process. Of all the existing pairs of parallel sentences 63.02% were detected, and 93.74% of all the detected pairs were correct.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 1: Prealigned Data (CzEng 1.0); Experiment: Effectiveness' to sequentially solve the question presented below. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhat percentage of detected pairs were correct in the scored alignments?\n" }, { "role": "assistant", "content": "The description states that 93.74% of all the detected pairs were correct, which is confirmed by the table showing a precision of 93.74%. So, the answer is {\"answer\": \"93.74\"}." } ], "images": [ "scigen/train/643.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Prealigned Data (CzEng 1.0); Experiment: Effectiveness" ], "reasoning": "The description states that 93.74% of all the detected pairs were correct, which is confirmed by the table showing a precision of 93.74%.", "question": "What percentage of detected pairs were correct in the scored alignments?", "answer": "So, the answer is {\"answer\": \"93.74\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The recall percentage of detected pairs is higher than the precision percentage., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe recall percentage of detected pairs is higher than the precision percentage.\n" }, { "role": "assistant", "content": "The table shows that the recall percentage is 63.02% and the precision percentage is 93.74%, indicating that the recall percentage is lower than the precision percentage. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/643.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Prealigned Data (CzEng 1.0); Experiment: Effectiveness" ], "reasoning": "The table shows that the recall percentage is 63.02% and the precision percentage is 93.74%, indicating that the recall percentage is lower than the precision percentage.", "statement": "The recall percentage of detected pairs is higher than the precision percentage.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Cross-Dataset performance, comparing detector training using baseline to augmented-baseline training sets'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Augmenting the original labeled training sets with automatically generated text sequences resulted in a significant increase in recall and F1 score, while maintaining good levels of precision.In another set of experiments, we re-trained the detector with each one of the corresponding augmented training set, and evaluated on the same cross-dataset combinations of the first experiment. The average metrics of all cross-dataset pairs reveals a consistent increase in accuracy (+6.9%), precision (+3.5%), recall (+182.8%) and F1 (+179.7%). That is, augmenting the original labeled training sets with the automatically generated text sequences resulted in a dramatic increase of recall, while maintaining good levels of precision. Overall, this trend is reflected by a steep increase in the combined measure of F1. These results thus provide another approval of the benefits of augmenting the data with generated sentences and using a broader data distribution by utilizing a pre-trained language model." } ], "images": [ "scigen/train/644.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Cross-Dataset performance, comparing detector training using baseline to augmented-baseline training sets" ], "reasoning": "Augmenting the original labeled training sets with automatically generated text sequences resulted in a significant increase in recall and F1 score, while maintaining good levels of precision.", "description": "In another set of experiments, we re-trained the detector with each one of the corresponding augmented training set, and evaluated on the same cross-dataset combinations of the first experiment. The average metrics of all cross-dataset pairs reveals a consistent increase in accuracy (+6.9%), precision (+3.5%), recall (+182.8%) and F1 (+179.7%). That is, augmenting the original labeled training sets with the automatically generated text sequences resulted in a dramatic increase of recall, while maintaining good levels of precision. Overall, this trend is reflected by a steep increase in the combined measure of F1. These results thus provide another approval of the benefits of augmenting the data with generated sentences and using a broader data distribution by utilizing a pre-trained language model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 4: Cross-Dataset performance, comparing detector training using baseline to augmented-baseline training sets' and methodically tackle the question that follows. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the average increase in recall percentage when the training set is augmented?\n" }, { "role": "assistant", "content": "The table shows the recall percentages for both baseline and augmented training sets. The average increase in recall percentage is highlighted in yellow, showing an increase of +182.81%. So, the answer is {\"answer\": \"+182.81%\"}" } ], "images": [ "scigen/train/644.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Cross-Dataset performance, comparing detector training using baseline to augmented-baseline training sets" ], "reasoning": "The table shows the recall percentages for both baseline and augmented training sets. The average increase in recall percentage is highlighted in yellow, showing an increase of +182.81%.", "question": "What is the average increase in recall percentage when the training set is augmented?", "answer": "So, the answer is {\"answer\": \"+182.81%\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Augmenting the training set resulted in a decrease in precision for the FN-WH dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nAugmenting the training set resulted in a decrease in precision for the FN-WH dataset.\n" }, { "role": "assistant", "content": "The table shows that the precision for the FN-WH dataset decreased from 0.636 to 0.528 when the training set was augmented, which is a decrease of -16.98%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/644.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Cross-Dataset performance, comparing detector training using baseline to augmented-baseline training sets" ], "reasoning": "The table shows that the precision for the FN-WH dataset decreased from 0.636 to 0.528 when the training set was augmented, which is a decrease of -16.98%.", "statement": "Augmenting the training set resulted in a decrease in precision for the FN-WH dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: ROUGE-L scores of data generated by the GPT-2 language model per dataset. These values indicate on low similarity between the generated sequences and the sequences on which the model has been fine-tuned, for each dataset and class.'.\n" }, { "role": "assistant", "content": "The similarity scores for generated sequences are low, indicating effective use of GPT-2 for creating diverse text.We further wished to verify that the generated sequences depart from the seed labeled examples, forming new and diverse language usage, as opposed to duplicating or repeating parts of the input examples. (nothing in common) to 1 (identical sequences). As shown, the similarity scores are low, indicating that the rich language model encoded within GPT-2 was effectively leveraged for generating new and different text sequences." } ], "images": [ "scigen/train/645.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: ROUGE-L scores of data generated by the GPT-2 language model per dataset. These values indicate on low similarity between the generated sequences and the sequences on which the model has been fine-tuned, for each dataset and class." ], "reasoning": "The similarity scores for generated sequences are low, indicating effective use of GPT-2 for creating diverse text.", "description": "We further wished to verify that the generated sequences depart from the seed labeled examples, forming new and diverse language usage, as opposed to duplicating or repeating parts of the input examples. (nothing in common) to 1 (identical sequences). As shown, the similarity scores are low, indicating that the rich language model encoded within GPT-2 was effectively leveraged for generating new and different text sequences.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: ROUGE-L scores of data generated by the GPT-2 language model per dataset. These values indicate on low similarity between the generated sequences and the sequences on which the model has been fine-tuned, for each dataset and class.\nQuestion: Which source dataset has the highest similarity score for generated non-hate sequences?\nSolve the above question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the 'Generated non-hate' column, the highest similarity score is 0.16 from the WH dataset. So, the answer is {\"answer\": \"WH\"}." } ], "images": [ "scigen/train/645.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: ROUGE-L scores of data generated by the GPT-2 language model per dataset. These values indicate on low similarity between the generated sequences and the sequences on which the model has been fine-tuned, for each dataset and class." ], "reasoning": "By comparing the 'Generated non-hate' column, the highest similarity score is 0.16 from the WH dataset.", "question": "Which source dataset has the highest similarity score for generated non-hate sequences?", "answer": "So, the answer is {\"answer\": \"WH\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe SE dataset has the lowest similarity score for generated hate sequences.\n" }, { "role": "assistant", "content": "By comparing the 'Generated hate' column, the SE dataset has the lowest score of 0.05. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/645.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: ROUGE-L scores of data generated by the GPT-2 language model per dataset. These values indicate on low similarity between the generated sequences and the sequences on which the model has been fine-tuned, for each dataset and class." ], "reasoning": "By comparing the 'Generated hate' column, the SE dataset has the lowest score of 0.05.", "statement": "The SE dataset has the lowest similarity score for generated hate sequences.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Intra-Dataset classification results, comparing the baseline and augmented-baseline training sets'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Augmenting training sets with automatically generated examples improves Recall and F1 scores significantly, despite a decrease in Precision.Intra-Dataset Experiments. The results indicate that augmenting the training sets with automatically generated examples leads to improvement in Recall and F1 in most cases, peaking at +73.0% and +33.1%, respectively, for the SE dataset. Precision on the other hand is decreased for most datsets, and Accuracy changes mildly. This is not surprising, as the training and test sets belong to the same distribution and are highly similar in this setup, whereas the generated text sequences introduce language diversity, as well as some label noise. For the combined dataset, which is relatively diverse, there is a yet a boost due to train set augmentation of +48.2% in Recall and +16.1% in F1; Precision decreases by 19.7% and Accuracy almost unchanged. Therefore, by dataset augmentation, a dramatic drop in false-negatives (missed hate speech sequences) is achieved, namely, significantly more hate speech is detected, which is also evident in the improved F1. The decrease in Precision indicates more false-positives (non-hate sequences classified as hate), however, these are less severe than missed hate speech sequences. These curves clearly demonstrates that training data augmentation yields significantly imrpved Recall (and consequently F1) across this range." } ], "images": [ "scigen/train/646.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Intra-Dataset classification results, comparing the baseline and augmented-baseline training sets" ], "reasoning": "Augmenting training sets with automatically generated examples improves Recall and F1 scores significantly, despite a decrease in Precision.", "description": "Intra-Dataset Experiments. The results indicate that augmenting the training sets with automatically generated examples leads to improvement in Recall and F1 in most cases, peaking at +73.0% and +33.1%, respectively, for the SE dataset. Precision on the other hand is decreased for most datsets, and Accuracy changes mildly. This is not surprising, as the training and test sets belong to the same distribution and are highly similar in this setup, whereas the generated text sequences introduce language diversity, as well as some label noise. For the combined dataset, which is relatively diverse, there is a yet a boost due to train set augmentation of +48.2% in Recall and +16.1% in F1; Precision decreases by 19.7% and Accuracy almost unchanged. Therefore, by dataset augmentation, a dramatic drop in false-negatives (missed hate speech sequences) is achieved, namely, significantly more hate speech is detected, which is also evident in the improved F1. The decrease in Precision indicates more false-positives (non-hate sequences classified as hate), however, these are less severe than missed hate speech sequences. These curves clearly demonstrates that training data augmentation yields significantly imrpved Recall (and consequently F1) across this range.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 3: Intra-Dataset classification results, comparing the baseline and augmented-baseline training sets', answer the question below by showing each progressive step toward the answer. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich dataset experienced the highest increase in Recall due to augmentation?\n" }, { "role": "assistant", "content": "By examining the Recall (%) column, the SE dataset shows the highest increase in Recall with +73.02%. So, the answer is {\"answer\": \"SE\"}" } ], "images": [ "scigen/train/646.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Intra-Dataset classification results, comparing the baseline and augmented-baseline training sets" ], "reasoning": "By examining the Recall (%) column, the SE dataset shows the highest increase in Recall with +73.02%.", "question": "Which dataset experienced the highest increase in Recall due to augmentation?", "answer": "So, the answer is {\"answer\": \"SE\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nAugmenting the training set for the WH dataset resulted in a decrease in Precision.\n" }, { "role": "assistant", "content": "The table shows that the Precision for the WH dataset decreased by -30.40% after augmentation. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/646.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Intra-Dataset classification results, comparing the baseline and augmented-baseline training sets" ], "reasoning": "The table shows that the Precision for the WH dataset decreased by -30.40% after augmentation.", "statement": "Augmenting the training set for the WH dataset resulted in a decrease in Precision.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Performance of our model when using one and two layers'.\n" }, { "role": "assistant", "content": "The Bi-LSTM model outperforms the LSTM model in terms of precision, recall, and F1 score across all entities.S4SS3SSS0Px2 Effect of Bidirectional Learning In the second experiment, we examine the benefit of accessing both past and future contexts by comparing the performances of RNN, LSTM and Bi-LSTM models. In this task, RNN model fails because it faces the gradient vanishing/exploding problem when training with long-range dependencies (132 time steps), leading to the unstable value of the cost functions." } ], "images": [ "scigen/train/647.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance of our model when using one and two layers" ], "reasoning": "The Bi-LSTM model outperforms the LSTM model in terms of precision, recall, and F1 score across all entities.", "description": "S4SS3SSS0Px2 Effect of Bidirectional Learning In the second experiment, we examine the benefit of accessing both past and future contexts by comparing the performances of RNN, LSTM and Bi-LSTM models. In this task, RNN model fails because it faces the gradient vanishing/exploding problem when training with long-range dependencies (132 time steps), leading to the unstable value of the cost functions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Performance of our model when using one and two layers', answer the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model has the highest F1 score for the MISC entity?\n" }, { "role": "assistant", "content": "To find the model with the highest F1 score for the MISC entity, compare the F1 scores of Bi-LSTM and LSTM models for the MISC entity. The Bi-LSTM model has an F1 score of 81.07, while the LSTM model has an F1 score of 2.09. So, the answer is {\"answer\": \"Bi-LSTM\"}." } ], "images": [ "scigen/train/647.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance of our model when using one and two layers" ], "reasoning": "To find the model with the highest F1 score for the MISC entity, compare the F1 scores of Bi-LSTM and LSTM models for the MISC entity. The Bi-LSTM model has an F1 score of 81.07, while the LSTM model has an F1 score of 2.09.", "question": "Which model has the highest F1 score for the MISC entity?", "answer": "So, the answer is {\"answer\": \"Bi-LSTM\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The LSTM model has a higher recall than the Bi-LSTM model for the LOC entity. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe LSTM model has a higher recall than the Bi-LSTM model for the LOC entity.\n" }, { "role": "assistant", "content": "To verify this statement, compare the recall values of the LSTM and Bi-LSTM models for the LOC entity. The Bi-LSTM model has a recall of 82.48, while the LSTM model has a recall of 77.38. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/647.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance of our model when using one and two layers" ], "reasoning": "To verify this statement, compare the recall values of the LSTM and Bi-LSTM models for the LOC entity. The Bi-LSTM model has a recall of 82.48, while the LSTM model has a recall of 77.38.", "statement": "The LSTM model has a higher recall than the Bi-LSTM model for the LOC entity.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: Performance of our model when using one and two layers', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Adding a second Bi-LSTM layer improves the overall F1 score from 71.74% to 74.02%.S4SS3SSS0Px3 Number of Bi-LSTM Layers In the third experiment, we investigate the improvement when adding more Bi-LSTM layers. We observe a significant improvement when using two layers of Bi-LSTM. The performance is increased from 71.74% to 74.02%" } ], "images": [ "scigen/train/648.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Performance of our model when using one and two layers" ], "reasoning": "Adding a second Bi-LSTM layer improves the overall F1 score from 71.74% to 74.02%.", "description": "S4SS3SSS0Px3 Number of Bi-LSTM Layers In the third experiment, we investigate the improvement when adding more Bi-LSTM layers. We observe a significant improvement when using two layers of Bi-LSTM. The performance is increased from 71.74% to 74.02%", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Performance of our model when using one and two layers\nQuestion: What is the overall F1 score when using two layers of Bi-LSTM?\nSolve the above question based on the table information. Let's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the F1 scores for different entities with one and two layers of Bi-LSTM. The overall F1 score for two layers is highlighted as 74.02%. So, the answer is {\"answer\": \"74.02\"}." } ], "images": [ "scigen/train/648.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Performance of our model when using one and two layers" ], "reasoning": "The table shows the F1 scores for different entities with one and two layers of Bi-LSTM. The overall F1 score for two layers is highlighted as 74.02%.", "question": "What is the overall F1 score when using two layers of Bi-LSTM?", "answer": "So, the answer is {\"answer\": \"74.02\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The overall precision score improves when using two layers of Bi-LSTM., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe overall precision score improves when using two layers of Bi-LSTM.\n" }, { "role": "assistant", "content": "The table shows that the overall precision score increases from 74.83% with one layer to 75.88% with two layers. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/648.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Performance of our model when using one and two layers" ], "reasoning": "The table shows that the overall precision score increases from 74.83% with one layer to 75.88% with two layers.", "statement": "The overall precision score improves when using two layers of Bi-LSTM.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 7: Performance of our model when adding more features', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The model using only word features achieved an accuracy of 74.02%, which is significantly lower than state-of-the-art systems. Adding more features improved the model's performance.As shown in the previous experiments, using only word features in deep learning models is not enough to achieve the state-of-the-art result. In particular, the accuracy of this model is only 74.02%. This result is far lower in comparison to that of state-of-the-art systems for Vietnamese NER. In the following experiments, we add more useful features to enhance the performance of our deep learning model." } ], "images": [ "scigen/train/649.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Performance of our model when adding more features" ], "reasoning": "The model using only word features achieved an accuracy of 74.02%, which is significantly lower than state-of-the-art systems. Adding more features improved the model's performance.", "description": "As shown in the previous experiments, using only word features in deep learning models is not enough to achieve the state-of-the-art result. In particular, the accuracy of this model is only 74.02%. This result is far lower in comparison to that of state-of-the-art systems for Vietnamese NER. In the following experiments, we add more useful features to enhance the performance of our deep learning model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 7: Performance of our model when adding more features' and methodically tackle the question that follows. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the F1 score when using Word+POS+Chunk+Regex features?\n" }, { "role": "assistant", "content": "The table shows the F1 scores for different feature combinations. The F1 score for Word+POS+Chunk+Regex is 92.05. So, the answer is {\"answer\": \"92.05\"}." } ], "images": [ "scigen/train/649.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Performance of our model when adding more features" ], "reasoning": "The table shows the F1 scores for different feature combinations. The F1 score for Word+POS+Chunk+Regex is 92.05.", "question": "What is the F1 score when using Word+POS+Chunk+Regex features?", "answer": "So, the answer is {\"answer\": \"92.05\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nUsing Word+POS features results in an F1 score higher than 85.\n" }, { "role": "assistant", "content": "The table shows that the F1 score for Word+POS features is 85.90, which is higher than 85. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/649.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Performance of our model when adding more features" ], "reasoning": "The table shows that the F1 score for Word+POS features is 85.90, which is higher than 85.", "statement": "Using Word+POS features results in an F1 score higher than 85.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 8: Pearson (P) and Spearman (S) correlation between human-annotated faithfulness scores and ROUGE scores of content selection (computed between the reference and the output sentence). High content selection scores (typical ROUGE score for summarization) do not necessarily imply faithfulness of the summary.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Content selection metrics like ROUGE scores do not significantly correlate with faithfulness in XSum, suggesting separate evaluation for content selection and faithfulness.Current evaluation metrics for summarization produce a single measure of the overall quality of the summary. Typically, the output summary is compared against the reference summary in terms of n-gram overlap. These metrics mainly evaluate content selection, i.e. whether the content of the output is similar to the content of the reference. In contrast, to evaluate faithfulness, we compare the output summary against the source document. One natural question that follows is whether high content matching sufficient for faithfulness. We compute the correlation coefficients between human-annotated faithfulness scores and ROUGE scores computed from the reference and the output sentence. For XSum, there is no significant correlation between the content selection metrics and faithfulness. This suggests that content selection and faithfulness should be measured separately as opposed to using a unified score." } ], "images": [ "scigen/train/650.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Pearson (P) and Spearman (S) correlation between human-annotated faithfulness scores and ROUGE scores of content selection (computed between the reference and the output sentence). High content selection scores (typical ROUGE score for summarization) do not necessarily imply faithfulness of the summary." ], "reasoning": "Content selection metrics like ROUGE scores do not significantly correlate with faithfulness in XSum, suggesting separate evaluation for content selection and faithfulness.", "description": "Current evaluation metrics for summarization produce a single measure of the overall quality of the summary. Typically, the output summary is compared against the reference summary in terms of n-gram overlap. These metrics mainly evaluate content selection, i.e. whether the content of the output is similar to the content of the reference. In contrast, to evaluate faithfulness, we compare the output summary against the source document. One natural question that follows is whether high content matching sufficient for faithfulness. We compute the correlation coefficients between human-annotated faithfulness scores and ROUGE scores computed from the reference and the output sentence. For XSum, there is no significant correlation between the content selection metrics and faithfulness. This suggests that content selection and faithfulness should be measured separately as opposed to using a unified score.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 8: Pearson (P) and Spearman (S) correlation between human-annotated faithfulness scores and ROUGE scores of content selection (computed between the reference and the output sentence). High content selection scores (typical ROUGE score for summarization) do not necessarily imply faithfulness of the summary.', and tackle the question below in a sequential manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the ROUGE-2 score for XSum P?\n" }, { "role": "assistant", "content": "The table lists the ROUGE-2 score for XSum P as 8.25. So, the answer is {\"answer\": \"8.25\"}." } ], "images": [ "scigen/train/650.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Pearson (P) and Spearman (S) correlation between human-annotated faithfulness scores and ROUGE scores of content selection (computed between the reference and the output sentence). High content selection scores (typical ROUGE score for summarization) do not necessarily imply faithfulness of the summary." ], "reasoning": "The table lists the ROUGE-2 score for XSum P as 8.25.", "question": "What is the ROUGE-2 score for XSum P?", "answer": "So, the answer is {\"answer\": \"8.25\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nROUGE-1 score for CNN/DM P is higher than ROUGE-2 score for XSum P.\n" }, { "role": "assistant", "content": "The table shows that the ROUGE-1 score for CNN/DM P is 15.31, which is higher than the ROUGE-2 score for XSum P, which is 8.25. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/650.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Pearson (P) and Spearman (S) correlation between human-annotated faithfulness scores and ROUGE scores of content selection (computed between the reference and the output sentence). High content selection scores (typical ROUGE score for summarization) do not necessarily imply faithfulness of the summary." ], "reasoning": "The table shows that the ROUGE-1 score for CNN/DM P is 15.31, which is higher than the ROUGE-2 score for XSum P, which is 8.25.", "statement": "ROUGE-1 score for CNN/DM P is higher than ROUGE-2 score for XSum P.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Experimental results for comparative paragraph generation on the proposed dataset. For human captions, mean and standard deviation are given for a one-vs-rest scheme across twenty-five runs. We observed that CIDEr-D scores had little correlation with description quality. The Neural Naturalist model benefits from a strong joint encoding and Transformer-based comparative module, achieving the highest BLEU-4 and ROUGE-L scores.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The Neural Naturalist - Full model shows improvement in BLEU-4 and ROUGE-L scores compared to baselines, but CIDEr-D is influenced by common patterns in the data.We observe improvement across BLEU-4 and ROUGE-L scores compared to baselines. Curiously, we observe that the CIDEr-D metric is susceptible to common patterns in the data; our model, when stopped at its highest CIDEr-D score, outputs a variant of, “these animals appear exactly the same” for 95% of paragraphs, nearly mimicking the behavior of the most frequent paragraph (Freq.) baseline. The corpus-level behavior of CIDEr-D gives these outputs a higher score. We observed anecdotally higher quality outputs correlated with ROUGE-L score, which we verify using a human evaluation (paragraph after next)." } ], "images": [ "scigen/train/651.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Experimental results for comparative paragraph generation on the proposed dataset. For human captions, mean and standard deviation are given for a one-vs-rest scheme across twenty-five runs. We observed that CIDEr-D scores had little correlation with description quality. The Neural Naturalist model benefits from a strong joint encoding and Transformer-based comparative module, achieving the highest BLEU-4 and ROUGE-L scores." ], "reasoning": "The Neural Naturalist - Full model shows improvement in BLEU-4 and ROUGE-L scores compared to baselines, but CIDEr-D is influenced by common patterns in the data.", "description": "We observe improvement across BLEU-4 and ROUGE-L scores compared to baselines. Curiously, we observe that the CIDEr-D metric is susceptible to common patterns in the data; our model, when stopped at its highest CIDEr-D score, outputs a variant of, “these animals appear exactly the same” for 95% of paragraphs, nearly mimicking the behavior of the most frequent paragraph (Freq.) baseline. The corpus-level behavior of CIDEr-D gives these outputs a higher score. We observed anecdotally higher quality outputs correlated with ROUGE-L score, which we verify using a human evaluation (paragraph after next).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Experimental results for comparative paragraph generation on the proposed dataset. For human captions, mean and standard deviation are given for a one-vs-rest scheme across twenty-five runs. We observed that CIDEr-D scores had little correlation with description quality. The Neural Naturalist model benefits from a strong joint encoding and Transformer-based comparative module, achieving the highest BLEU-4 and ROUGE-L scores.\nQuestion: Which model has the highest Dev ROUGE-L score and what is its corresponding Test BLEU-4 score?\nGive a detailed response to the above question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The Neural Naturalist - Full model has the highest Dev ROUGE-L score of 0.46. Its corresponding Test BLEU-4 score is 0.22. So, the answer is {\"answer\": \"0.22\"}." } ], "images": [ "scigen/train/651.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Experimental results for comparative paragraph generation on the proposed dataset. For human captions, mean and standard deviation are given for a one-vs-rest scheme across twenty-five runs. We observed that CIDEr-D scores had little correlation with description quality. The Neural Naturalist model benefits from a strong joint encoding and Transformer-based comparative module, achieving the highest BLEU-4 and ROUGE-L scores." ], "reasoning": "The Neural Naturalist - Full model has the highest Dev ROUGE-L score of 0.46. Its corresponding Test BLEU-4 score is 0.22.", "question": "Which model has the highest Dev ROUGE-L score and what is its corresponding Test BLEU-4 score?", "answer": "So, the answer is {\"answer\": \"0.22\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Neural Naturalist - Full model has the highest Test CIDEr-D score.\n" }, { "role": "assistant", "content": "The table shows that the Most Frequent model has the highest Test CIDEr-D score of 0.43, while the Neural Naturalist - Full model has a Test CIDEr-D score of 0.25. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/651.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Experimental results for comparative paragraph generation on the proposed dataset. For human captions, mean and standard deviation are given for a one-vs-rest scheme across twenty-five runs. We observed that CIDEr-D scores had little correlation with description quality. The Neural Naturalist model benefits from a strong joint encoding and Transformer-based comparative module, achieving the highest BLEU-4 and ROUGE-L scores." ], "reasoning": "The table shows that the Most Frequent model has the highest Test CIDEr-D score of 0.43, while the Neural Naturalist - Full model has a Test CIDEr-D score of 0.25.", "statement": "The Neural Naturalist - Full model has the highest Test CIDEr-D score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Human evaluation results on 120 test set samples, twenty per column. Scale: -1 (perfectly wrong) to 1 (perfectly correct). Columns are ordered left-to-right by increasing distance. Our model outperforms baselines for several distances, though highly similar comparisons still prove difficult.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The frequency and text-only baselines perform poorly, while our model excels in distinguishing visually distinct species but struggles with subsets having high visual similarity.In this measure, we see the frequency and text- only baselines now fall flat, as expected. The frequency baseline never receives any points, and the text-only baseline is often penalized for incorrectly guessing. Our model is successful at making distinctions between visually distinct species (Genus column and ones further right), which is near the challenge level of current fine-grained visual classification tasks. However, it struggles on the two data subsets with highest visual similarity (Visual, Species). The significant gap between all methods and human performance in these columns indicates ultra fine-grained distinctions are still possible for humans to describe, but pose a challenge for current models to capture." } ], "images": [ "scigen/train/652.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Human evaluation results on 120 test set samples, twenty per column. Scale: -1 (perfectly wrong) to 1 (perfectly correct). Columns are ordered left-to-right by increasing distance. Our model outperforms baselines for several distances, though highly similar comparisons still prove difficult." ], "reasoning": "The frequency and text-only baselines perform poorly, while our model excels in distinguishing visually distinct species but struggles with subsets having high visual similarity.", "description": "In this measure, we see the frequency and text- only baselines now fall flat, as expected. The frequency baseline never receives any points, and the text-only baseline is often penalized for incorrectly guessing. Our model is successful at making distinctions between visually distinct species (Genus column and ones further right), which is near the challenge level of current fine-grained visual classification tasks. However, it struggles on the two data subsets with highest visual similarity (Visual, Species). The significant gap between all methods and human performance in these columns indicates ultra fine-grained distinctions are still possible for humans to describe, but pose a challenge for current models to capture.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 4: Human evaluation results on 120 test set samples, twenty per column. Scale: -1 (perfectly wrong) to 1 (perfectly correct). Columns are ordered left-to-right by increasing distance. Our model outperforms baselines for several distances, though highly similar comparisons still prove difficult.' to sequentially solve the question presented below. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich method performs best in distinguishing the 'Family' category?\n" }, { "role": "assistant", "content": "By comparing the scores in the 'Family' column, the highest score is 1.00, achieved by the 'Human' method. So, the answer is {\"answer\": \"Human\"}" } ], "images": [ "scigen/train/652.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Human evaluation results on 120 test set samples, twenty per column. Scale: -1 (perfectly wrong) to 1 (perfectly correct). Columns are ordered left-to-right by increasing distance. Our model outperforms baselines for several distances, though highly similar comparisons still prove difficult." ], "reasoning": "By comparing the scores in the 'Family' column, the highest score is 1.00, achieved by the 'Human' method.", "question": "Which method performs best in distinguishing the 'Family' category?", "answer": "So, the answer is {\"answer\": \"Human\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The Neural Naturalist method outperforms the CNN + LSTM method in the 'Genus' category., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Neural Naturalist method outperforms the CNN + LSTM method in the 'Genus' category.\n" }, { "role": "assistant", "content": "In the 'Genus' category, the Neural Naturalist method has a score of 0.35, while the CNN + LSTM method has a score of 0.15. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/652.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Human evaluation results on 120 test set samples, twenty per column. Scale: -1 (perfectly wrong) to 1 (perfectly correct). Columns are ordered left-to-right by increasing distance. Our model outperforms baselines for several distances, though highly similar comparisons still prove difficult." ], "reasoning": "In the 'Genus' category, the Neural Naturalist method has a score of 0.35, while the CNN + LSTM method has a score of 0.15.", "statement": "The Neural Naturalist method outperforms the CNN + LSTM method in the 'Genus' category.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 8: Model comparison on 100 Billion Word Google News Dataset'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The 68-billion-parameter MoE model has a 39% lower perplexity after 100 billion training words compared to the baseline model, but its computational efficiency is low due to not increasing the training batch size proportionally to the number of GPUs.: We evaluate our model using perplexity on a holdout dataset. Perplexity after 100 billion training words is 39% lower for the 68-billion-parameter MoE model than for the baseline model. It is notable that the measured computational efficiency of the largest model (0.30 TFLOPS/GPU) is very low compared to the other models. This is likely a result of the fact that, for purposes of comparison to the other models, we did not increase the training batch size proportionally to the number of GPUs." } ], "images": [ "scigen/train/653.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Model comparison on 100 Billion Word Google News Dataset" ], "reasoning": "The 68-billion-parameter MoE model has a 39% lower perplexity after 100 billion training words compared to the baseline model, but its computational efficiency is low due to not increasing the training batch size proportionally to the number of GPUs.", "description": ": We evaluate our model using perplexity on a holdout dataset. Perplexity after 100 billion training words is 39% lower for the 68-billion-parameter MoE model than for the baseline model. It is notable that the measured computational efficiency of the largest model (0.30 TFLOPS/GPU) is very low compared to the other models. This is likely a result of the fact that, for purposes of comparison to the other models, we did not increase the training batch size proportionally to the number of GPUs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 8: Model comparison on 100 Billion Word Google News Dataset' to sequentially solve the question presented below. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhat is the test perplexity after 1 epoch for the MoE-65536-h model and how does it compare to the MoE-4096-h model?\n" }, { "role": "assistant", "content": "The test perplexity after 1 epoch for the MoE-65536-h model is 28.9, which is lower than the MoE-4096-h model's test perplexity of 30.9. So, the answer is {\"answer\": \"28.9\"}" } ], "images": [ "scigen/train/653.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Model comparison on 100 Billion Word Google News Dataset" ], "reasoning": "The test perplexity after 1 epoch for the MoE-65536-h model is 28.9, which is lower than the MoE-4096-h model's test perplexity of 30.9.", "question": "What is the test perplexity after 1 epoch for the MoE-65536-h model and how does it compare to the MoE-4096-h model?", "answer": "So, the answer is {\"answer\": \"28.9\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe MoE-131072-h model has the lowest TFLOPS per GPU among all models.\n" }, { "role": "assistant", "content": "The table shows that the MoE-131072-h model has a TFLOPS per GPU of 0.30, which is the lowest compared to all other models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/653.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Model comparison on 100 Billion Word Google News Dataset" ], "reasoning": "The table shows that the MoE-131072-h model has a TFLOPS per GPU of 0.30, which is the lowest compared to all other models listed.", "statement": "The MoE-131072-h model has the lowest TFLOPS per GPU among all models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 6: Experiments with different combinations of losses.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Models with higher wload values had lower loads on the most overloaded expert, and combinations with at least one loss led to similar model quality.All the combinations containing at least one the two losses led to very similar model quality, where having no loss was much worse. Models with higher values of wload had lower loads on the most overloaded expert." } ], "images": [ "scigen/train/654.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Experiments with different combinations of losses." ], "reasoning": "Models with higher wload values had lower loads on the most overloaded expert, and combinations with at least one loss led to similar model quality.", "description": "All the combinations containing at least one the two losses led to very similar model quality, where having no loss was much worse. Models with higher values of wload had lower loads on the most overloaded expert.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 6: Experiments with different combinations of losses.', solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the Test Perplexity for the model with the lowest max(Load(X))/mean(Load(X))?\n" }, { "role": "assistant", "content": "The lowest max(Load(X))/mean(Load(X)) value is 1.07, which corresponds to a wimportance of 1.0 and wload of 1.0. The Test Perplexity for this model is 35.7. So, the answer is {\"answer\": \"35.7\"}." } ], "images": [ "scigen/train/654.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Experiments with different combinations of losses." ], "reasoning": "The lowest max(Load(X))/mean(Load(X)) value is 1.07, which corresponds to a wimportance of 1.0 and wload of 1.0. The Test Perplexity for this model is 35.7.", "question": "What is the Test Perplexity for the model with the lowest max(Load(X))/mean(Load(X))?", "answer": "So, the answer is {\"answer\": \"35.7\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model with wimportance of 0.2 and wload of 0.0 has the highest Test Perplexity.\n" }, { "role": "assistant", "content": "The model with wimportance of 0.2 and wload of 0.0 has a Test Perplexity of 35.6. The highest Test Perplexity in the table is 39.8, which corresponds to a model with wimportance of 0.0 and wload of 0.0. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/654.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Experiments with different combinations of losses." ], "reasoning": "The model with wimportance of 0.2 and wload of 0.0 has a Test Perplexity of 35.6. The highest Test Perplexity in the table is 39.8, which corresponds to a model with wimportance of 0.0 and wload of 0.0.", "statement": "The model with wimportance of 0.2 and wload of 0.0 has the highest Test Perplexity.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 7: Model comparison on 1 Billion Word Language Modeling Benchmark. Models marked with * are from (Jozefowicz et al., 2016).', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The highest-computation MoE model achieved the highest computational efficiency at 1.56 TFLOPS/GPU, while the low-computation MoE models ranged from 0.74-0.90 TFLOPS/GPU, except for the 4-expert model.For our baseline models wtih no MoE, observed computational efficiency ranged from 1.07-1.29 TFLOPS/GPU. For our low-computation MoE models, computation efficiency ranged from 0.74-0.90 TFLOPS/GPU, except for the 4-expert model which did not make full use of the available parallelism. Our highest-computation MoE model was more efficient at 1.56 TFLOPS/GPU, likely due to the larger matrices. These numbers represent a significant fraction of the theoretical maximum of 4.29 TFLOPS/GPU claimed by NVIDIA." } ], "images": [ "scigen/train/655.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Model comparison on 1 Billion Word Language Modeling Benchmark. Models marked with * are from (Jozefowicz et al., 2016)." ], "reasoning": "The highest-computation MoE model achieved the highest computational efficiency at 1.56 TFLOPS/GPU, while the low-computation MoE models ranged from 0.74-0.90 TFLOPS/GPU, except for the 4-expert model.", "description": "For our baseline models wtih no MoE, observed computational efficiency ranged from 1.07-1.29 TFLOPS/GPU. For our low-computation MoE models, computation efficiency ranged from 0.74-0.90 TFLOPS/GPU, except for the 4-expert model which did not make full use of the available parallelism. Our highest-computation MoE model was more efficient at 1.56 TFLOPS/GPU, likely due to the larger matrices. These numbers represent a significant fraction of the theoretical maximum of 4.29 TFLOPS/GPU claimed by NVIDIA.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: 'Table 7: Model comparison on 1 Billion Word Language Modeling Benchmark. Models marked with * are from (Jozefowicz et al., 2016).'\nQuestion: Which model achieved the highest computational efficiency in TFLOPS per GPU?\n" }, { "role": "assistant", "content": "The table shows that the MoE-143M model achieved the highest computational efficiency with 1.56 TFLOPS per GPU. So, the answer is {\"answer\": \"MoE-143M\"}" } ], "images": [ "scigen/train/655.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Model comparison on 1 Billion Word Language Modeling Benchmark. Models marked with * are from (Jozefowicz et al., 2016)." ], "reasoning": "The table shows that the MoE-143M model achieved the highest computational efficiency with 1.56 TFLOPS per GPU.", "question": "Which model achieved the highest computational efficiency in TFLOPS per GPU?", "answer": "So, the answer is {\"answer\": \"MoE-143M\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The MoE-4 model did not make full use of the available parallelism. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe MoE-4 model did not make full use of the available parallelism.\n" }, { "role": "assistant", "content": "The description states that the 4-expert model did not make full use of the available parallelism, and the table shows that the MoE-4 model has the lowest TFLOPS per GPU among the MoE models, supporting this statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/655.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Model comparison on 1 Billion Word Language Modeling Benchmark. Models marked with * are from (Jozefowicz et al., 2016)." ], "reasoning": "The description states that the 4-expert model did not make full use of the available parallelism, and the table shows that the MoE-4 model has the lowest TFLOPS per GPU among the MoE models, supporting this statement.", "statement": "The MoE-4 model did not make full use of the available parallelism.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Performance of the individual groups of hand-crafted features.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Lexical features yield the best results, followed by stylometric features, grammatical features, and lastly embedding features.We can see that, among the hand-crafted features, the lexical features yield the best results, i.e., words are the most indicative features. The good results of the stylometric features indicate that the intricacies of language use are highly discriminative. The next group is the one with the grammatical features, which shows good performance in terms of Precision. The last one are the embedding features, which although having low individual performance, contribute to the overall performance of the system as shown in next paragraph." } ], "images": [ "scigen/train/656.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of the individual groups of hand-crafted features." ], "reasoning": "Lexical features yield the best results, followed by stylometric features, grammatical features, and lastly embedding features.", "description": "We can see that, among the hand-crafted features, the lexical features yield the best results, i.e., words are the most indicative features. The good results of the stylometric features indicate that the intricacies of language use are highly discriminative. The next group is the one with the grammatical features, which shows good performance in terms of Precision. The last one are the embedding features, which although having low individual performance, contribute to the overall performance of the system as shown in next paragraph.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 2: Performance of the individual groups of hand-crafted features.'. Solve the following question based on the table.\n\nQuestion: Which feature group shows the best performance in terms of F1 score?\nLet's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all feature groups, the lexical features have the highest F1 score of 75.02. So, the answer is {\"answer\": \"Lexical\"}" } ], "images": [ "scigen/train/656.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of the individual groups of hand-crafted features." ], "reasoning": "By comparing the F1 scores of all feature groups, the lexical features have the highest F1 score of 75.02.", "question": "Which feature group shows the best performance in terms of F1 score?", "answer": "So, the answer is {\"answer\": \"Lexical\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Grammatical features have the highest Precision among all feature groups., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nGrammatical features have the highest Precision among all feature groups.\n" }, { "role": "assistant", "content": "The table shows that the Precision (P) for grammatical features is 73.23, which is lower than the Precision for lexical features (75.53) and stylometric features (74.35). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/656.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of the individual groups of hand-crafted features." ], "reasoning": "The table shows that the Precision (P) for grammatical features is 73.23, which is lower than the Precision for lexical features (75.53) and stylometric features (74.35).", "statement": "Grammatical features have the highest Precision among all feature groups.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 1: Words most strongly associated with the fake news class.'.\n" }, { "role": "assistant", "content": "Using PMI, terms highly correlated with fake/factual news were identified, including context-independent words like 'mysterious' and 'phenomenon' which are stable across domains.Fact-checking lexicon: Using lexicons of sentiment words has been shown to be very successful for the task of sentiment analysis Mohammad and Turney , and we applied the same idea to extract a fact-checking lexicon. In particular, we use point-wise mutual information (PMI) to find terms (words, word bi-grams, and named entities) that are highly correlated with the fake/factual news class. We calculated the PMI scores for uni-grams, bi-grams and on extracted named entities. We can see in the table some words that grab people attention, but are not very informative by themselves, such as mysterious or phenomenon. These words are largely context-independent and are likely to remain stable in their usage across different domains and even over an extended period of time. Thus, they should be useful beyond this task and this dataset." } ], "images": [ "scigen/train/657.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Words most strongly associated with the fake news class." ], "reasoning": "Using PMI, terms highly correlated with fake/factual news were identified, including context-independent words like 'mysterious' and 'phenomenon' which are stable across domains.", "description": "Fact-checking lexicon: Using lexicons of sentiment words has been shown to be very successful for the task of sentiment analysis Mohammad and Turney , and we applied the same idea to extract a fact-checking lexicon. In particular, we use point-wise mutual information (PMI) to find terms (words, word bi-grams, and named entities) that are highly correlated with the fake/factual news class. We calculated the PMI scores for uni-grams, bi-grams and on extracted named entities. We can see in the table some words that grab people attention, but are not very informative by themselves, such as mysterious or phenomenon. These words are largely context-independent and are likely to remain stable in their usage across different domains and even over an extended period of time. Thus, they should be useful beyond this task and this dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 1: Words most strongly associated with the fake news class.', answer the following question. Give detailed solution consisting of each step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich word has the highest PMI score and what is its translation?\n" }, { "role": "assistant", "content": "To find the word with the highest PMI score, look at the PMI column and identify the highest value, which is 0.97. The corresponding original word is 'следете в' and its translation is 'follow in'. So, the answer is {\"answer\": \"follow in\"}." } ], "images": [ "scigen/train/657.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Words most strongly associated with the fake news class." ], "reasoning": "To find the word with the highest PMI score, look at the PMI column and identify the highest value, which is 0.97. The corresponding original word is 'следете в' and its translation is 'follow in'.", "question": "Which word has the highest PMI score and what is its translation?", "answer": "So, the answer is {\"answer\": \"follow in\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The word 'hidden' has a PMI score of 0.84. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe word 'hidden' has a PMI score of 0.84.\n" }, { "role": "assistant", "content": "Check the PMI score for the word 'hidden' in the table. The table shows that 'hidden' has a PMI score of 0.84. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/657.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Words most strongly associated with the fake news class." ], "reasoning": "Check the PMI score for the word 'hidden' in the table. The table shows that 'hidden' has a PMI score of 0.84.", "statement": "The word 'hidden' has a PMI score of 0.84.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Performance of different models.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The TF.IDF bag-of-words approach sets a high bar for the model score, and the addition of attention mechanism embeddings and hand-crafted features further improves the performance.Evaluating the final model, we set as a baseline the prediction of the majority class, i.e., the fake news class. This baseline has an F1 of 41.59% and accuracy of 71.22%. Another stable baseline, apart from just taking the majority class, is the TF.IDF bag-of-words approach, which sets a high bar for the general model score. We then observe how much the attention mechanism embeddings improve the score (AttNN). Finally, we add the hand-crafted features (Feats), which further improve the performance. From the results, we can conclude that both the attention-based task-specific embeddings and the manual features are important for the task of finding fake news." } ], "images": [ "scigen/train/658.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of different models." ], "reasoning": "The TF.IDF bag-of-words approach sets a high bar for the model score, and the addition of attention mechanism embeddings and hand-crafted features further improves the performance.", "description": "Evaluating the final model, we set as a baseline the prediction of the majority class, i.e., the fake news class. This baseline has an F1 of 41.59% and accuracy of 71.22%. Another stable baseline, apart from just taking the majority class, is the TF.IDF bag-of-words approach, which sets a high bar for the general model score. We then observe how much the attention mechanism embeddings improve the score (AttNN). Finally, we add the hand-crafted features (Feats), which further improve the performance. From the results, we can conclude that both the attention-based task-specific embeddings and the manual features are important for the task of finding fake news.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: Performance of different models.' to sequentially solve the question presented below. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the F1 score improvement from the baseline to the TF.IDF & Feats & AttNN model?\n" }, { "role": "assistant", "content": "The baseline F1 score is 41.59%, and the TF.IDF & Feats & AttNN model has an F1 score of 79.77%. The improvement is calculated as 79.77% - 41.59%. So, the answer is {\"answer\": \"38.18%\"}" } ], "images": [ "scigen/train/658.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of different models." ], "reasoning": "The baseline F1 score is 41.59%, and the TF.IDF & Feats & AttNN model has an F1 score of 79.77%. The improvement is calculated as 79.77% - 41.59%.", "question": "What is the F1 score improvement from the baseline to the TF.IDF & Feats & AttNN model?", "answer": "So, the answer is {\"answer\": \"38.18%\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe TF.IDF & AttNN model has a higher accuracy than the AttNN model.\n" }, { "role": "assistant", "content": "The accuracy of the TF.IDF & AttNN model is 83.44%, while the accuracy of the AttNN model is 81.99%. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/658.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of different models." ], "reasoning": "The accuracy of the TF.IDF & AttNN model is 83.44%, while the accuracy of the AttNN model is 81.99%. Therefore, the statement is supported.", "statement": "The TF.IDF & AttNN model has a higher accuracy than the AttNN model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Example summaries of four LC models. (Note that “gunners” is a nickname of arsenal)'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "LenLInit and LenMC models perform better on short sentence summaries, as observed from the table.It is also observed that LenLInit and LenMC perform better on short sentence summary in this case." } ], "images": [ "scigen/train/659.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Example summaries of four LC models. (Note that “gunners” is a nickname of arsenal)" ], "reasoning": "LenLInit and LenMC models perform better on short sentence summaries, as observed from the table.", "description": "It is also observed that LenLInit and LenMC perform better on short sentence summary in this case.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 1: Example summaries of four LC models. (Note that “gunners” is a nickname of arsenal)', unfold the question presented below, detailing every step of your calculation. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model has the shortest true length summary for the desired length of 25?\n" }, { "role": "assistant", "content": "By examining the table, we see that LenLInit has a true length of 22 for the desired length of 25, which is the shortest among the models listed. So, the answer is {\"answer\": \"LenLInit\"}." } ], "images": [ "scigen/train/659.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Example summaries of four LC models. (Note that “gunners” is a nickname of arsenal)" ], "reasoning": "By examining the table, we see that LenLInit has a true length of 22 for the desired length of 25, which is the shortest among the models listed.", "question": "Which model has the shortest true length summary for the desired length of 25?", "answer": "So, the answer is {\"answer\": \"LenLInit\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nLenEmb has the longest true length summary for the desired length of 65.\n" }, { "role": "assistant", "content": "By examining the table, we see that LenEmb has a true length of 65 for the desired length of 65, which is the longest among the models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/659.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Example summaries of four LC models. (Note that “gunners” is a nickname of arsenal)" ], "reasoning": "By examining the table, we see that LenEmb has a true length of 65 for the desired length of 65, which is the longest among the models listed.", "statement": "LenEmb has the longest true length summary for the desired length of 65.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Results of ML training on standard “test-1951”'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The LenLInit and LenMC models slightly reduce LC capacity but significantly improve evaluation scores compared to other models.Although the evaluation score is not the unique objective in this research, it is of interest that how exactly the score is deprived by LC capacity. After individually comparing two WLI models and two RLI models, we find the two proposed models, LenLInit and LenMC, slightly corrupt LC capacity while improve the scores obviously." } ], "images": [ "scigen/train/660.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of ML training on standard “test-1951”" ], "reasoning": "The LenLInit and LenMC models slightly reduce LC capacity but significantly improve evaluation scores compared to other models.", "description": "Although the evaluation score is not the unique objective in this research, it is of interest that how exactly the score is deprived by LC capacity. After individually comparing two WLI models and two RLI models, we find the two proposed models, LenLInit and LenMC, slightly corrupt LC capacity while improve the scores obviously.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 2: Results of ML training on standard “test-1951”', unfold the question presented below, detailing every step of your calculation. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model has the highest R-1 score among the length-control models?\n" }, { "role": "assistant", "content": "By comparing the R-1 scores of the length-control models, LenLInit has the highest R-1 score of 30.47. So, the answer is {\"answer\": \"LenLInit\"}." } ], "images": [ "scigen/train/660.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of ML training on standard “test-1951”" ], "reasoning": "By comparing the R-1 scores of the length-control models, LenLInit has the highest R-1 score of 30.47.", "question": "Which model has the highest R-1 score among the length-control models?", "answer": "So, the answer is {\"answer\": \"LenLInit\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nLenMC has the lowest svar among the length-control models.\n" }, { "role": "assistant", "content": "By comparing the svar values of the length-control models, LenMC has a svar of 0.87, which is not the lowest. LenEmb has the lowest svar of 0.85. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/660.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of ML training on standard “test-1951”" ], "reasoning": "By comparing the svar values of the length-control models, LenMC has a svar of 0.87, which is not the lowest. LenEmb has the lowest svar of 0.85.", "statement": "LenMC has the lowest svar among the length-control models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Performance of length control RL in “test-4k” (ML results also included for comparison). Obviously highest scores (0.4 larger than the second best) are in bolded font, the scores in italic font are significantly worse score (2 lower than best socre).'.\n" }, { "role": "assistant", "content": "MTS-RL and SCD-RL algorithms can control LC capacity for both WLI and RLI models, preventing length control collapse.We evaluate our models with sentence length of 25, 45 and 65, which represent short, median and long sentences separately. Results may vary after each training process since RL is usually unstable, so we repeat training for multiple times in each model and statistic the results on average. We make experiments of MTS-RL on three LC models, for WLI models LenLInit and LenInit, accuracy and svar both rise under the selected dth increasing, which means hyper-parameter dth in MTS-RL can be used to adjust the LC capacity. However, for the RLI model LenMC, results show there is no obvious distinction in scores when we use different dth. Hence, we adopt SCD-RL training algorithm for LenMC, the results show our SCD-RL algorithm can control LC capacity for RLI model as MTS-RL does for the WLI model. and SCD-RL can also manage the LC capacity for WLI models. Overall, two RL training algorithms prevent the model from length control collapsing, and make this capacity controllable via their own hyper-parameters." } ], "images": [ "scigen/train/661.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of length control RL in “test-4k” (ML results also included for comparison). Obviously highest scores (0.4 larger than the second best) are in bolded font, the scores in italic font are significantly worse score (2 lower than best socre)." ], "reasoning": "MTS-RL and SCD-RL algorithms can control LC capacity for both WLI and RLI models, preventing length control collapse.", "description": "We evaluate our models with sentence length of 25, 45 and 65, which represent short, median and long sentences separately. Results may vary after each training process since RL is usually unstable, so we repeat training for multiple times in each model and statistic the results on average. We make experiments of MTS-RL on three LC models, for WLI models LenLInit and LenInit, accuracy and svar both rise under the selected dth increasing, which means hyper-parameter dth in MTS-RL can be used to adjust the LC capacity. However, for the RLI model LenMC, results show there is no obvious distinction in scores when we use different dth. Hence, we adopt SCD-RL training algorithm for LenMC, the results show our SCD-RL algorithm can control LC capacity for RLI model as MTS-RL does for the WLI model. and SCD-RL can also manage the LC capacity for WLI models. Overall, two RL training algorithms prevent the model from length control collapsing, and make this capacity controllable via their own hyper-parameters.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: Table 3: Performance of length control RL in “test-4k” (ML results also included for comparison). Obviously highest scores (0.4 larger than the second best) are in bolded font, the scores in italic font are significantly worse score (2 lower than best socre).\nQuestion: Which model and parameter combination achieved the highest accuracy for 25 R-1 sentences?\n" }, { "role": "assistant", "content": "By examining the table, the highest accuracy for 25 R-1 sentences is achieved by LenLInit with SCST parameter, scoring 42.90. So, the answer is {\"answer\": \"LenLInit with SCST\"}." } ], "images": [ "scigen/train/661.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of length control RL in “test-4k” (ML results also included for comparison). Obviously highest scores (0.4 larger than the second best) are in bolded font, the scores in italic font are significantly worse score (2 lower than best socre)." ], "reasoning": "By examining the table, the highest accuracy for 25 R-1 sentences is achieved by LenLInit with SCST parameter, scoring 42.90.", "question": "Which model and parameter combination achieved the highest accuracy for 25 R-1 sentences?", "answer": "So, the answer is {\"answer\": \"LenLInit with SCST\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe LenMC model with dth=0 parameter achieved the highest accuracy for 65 R-2 sentences.\n" }, { "role": "assistant", "content": "By examining the table, the highest accuracy for 65 R-2 sentences is achieved by LenLInit with SCST parameter, scoring 42.61, not LenMC with dth=0. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/661.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of length control RL in “test-4k” (ML results also included for comparison). Obviously highest scores (0.4 larger than the second best) are in bolded font, the scores in italic font are significantly worse score (2 lower than best socre)." ], "reasoning": "By examining the table, the highest accuracy for 65 R-2 sentences is achieved by LenLInit with SCST parameter, scoring 42.61, not LenMC with dth=0.", "statement": "The LenMC model with dth=0 parameter achieved the highest accuracy for 65 R-2 sentences.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'TABLE III: Synsets and corresponding shortest path distances from WordNet', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Proper synset selection is crucial for accurate similarity calculation, as it significantly impacts the overall similarity measure.When comparing two sentences, we have many such word pairs which have multiple synsets. Therefore, not considering the proper synset in context of the sentence, could introduce errors at the early stage of similarity calculation. Hence, sense of the word affects significantly on the overall similarity measure. Identifying sense of the word is part of the ‘word sense disambiguation’ research area. We use ‘max similarity’ algorithm, Eq. argmaxsynset(a)(n∑imaxsynset(i)(sim(i,a)) (1)" } ], "images": [ "scigen/train/662.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Synsets and corresponding shortest path distances from WordNet" ], "reasoning": "Proper synset selection is crucial for accurate similarity calculation, as it significantly impacts the overall similarity measure.", "description": "When comparing two sentences, we have many such word pairs which have multiple synsets. Therefore, not considering the proper synset in context of the sentence, could introduce errors at the early stage of similarity calculation. Hence, sense of the word affects significantly on the overall similarity measure. Identifying sense of the word is part of the ‘word sense disambiguation’ research area. We use ‘max similarity’ algorithm, Eq. argmaxsynset(a)(n∑imaxsynset(i)(sim(i,a)) (1)", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. The question is related to the given table titled 'TABLE III: Synsets and corresponding shortest path distances from WordNet'.\n\nQuestion: What is the shortest path distance between Synset('river.n.01') and Synset('bank.n.01')?\n" }, { "role": "assistant", "content": "To find the shortest path distance, look at the table and identify the row where Synset('river.n.01') is paired with Synset('bank.n.01'). The shortest path distance for this pair is 8. So, the answer is {\"answer\": \"8\"}." } ], "images": [ "scigen/train/662.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Synsets and corresponding shortest path distances from WordNet" ], "reasoning": "To find the shortest path distance, look at the table and identify the row where Synset('river.n.01') is paired with Synset('bank.n.01'). The shortest path distance for this pair is 8.", "question": "What is the shortest path distance between Synset('river.n.01') and Synset('bank.n.01')?", "answer": "So, the answer is {\"answer\": \"8\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The shortest path distance between Synset('river.n.01') and Synset('bank.n.09') is 10., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe shortest path distance between Synset('river.n.01') and Synset('bank.n.09') is 10.\n" }, { "role": "assistant", "content": "The table shows that the shortest path distance between Synset('river.n.01') and Synset('bank.n.09') is indeed 10. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/662.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Synsets and corresponding shortest path distances from WordNet" ], "reasoning": "The table shows that the shortest path distance between Synset('river.n.01') and Synset('bank.n.09') is indeed 10.", "statement": "The shortest path distance between Synset('river.n.01') and Synset('bank.n.09') is 10.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: Performance in terms of CRR, WRR for Google OCR', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "CopyNet outperforms PCRF in correcting deletion errors and introduces fewer system-induced errors, particularly in substitution errors., we analyse the reduction in specific error types for PCRF and CopyNet after the alignment of the predicted string with that of the ground truth in terms of insertion, deletion and substitution. We also report the system induced errors, where a correct component at the input (OCR output) is mispredicted to a wrong output by the model. CopyNet outperforms PCRF in correcting the errors and it also introduces lesser number of errors of its own. Both CopyNet and PCRF Schnober et al. Both the systems perform well in handling substitution errors, the type which dominated the strings in OCRTest, though neither of the systems was able to correct the insertion errors. Insertion can be seen as a special case of 1-to-many insertion matches, which both systems are ideally capable of handling. We see that for Sahaśranāma, CopyNet corrects about 17.24 % of the deletion errors as against <5% of the deletion errors corrected by PCRF." } ], "images": [ "scigen/train/663.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance in terms of CRR, WRR for Google OCR" ], "reasoning": "CopyNet outperforms PCRF in correcting deletion errors and introduces fewer system-induced errors, particularly in substitution errors.", "description": ", we analyse the reduction in specific error types for PCRF and CopyNet after the alignment of the predicted string with that of the ground truth in terms of insertion, deletion and substitution. We also report the system induced errors, where a correct component at the input (OCR output) is mispredicted to a wrong output by the model. CopyNet outperforms PCRF in correcting the errors and it also introduces lesser number of errors of its own. Both CopyNet and PCRF Schnober et al. Both the systems perform well in handling substitution errors, the type which dominated the strings in OCRTest, though neither of the systems was able to correct the insertion errors. Insertion can be seen as a special case of 1-to-many insertion matches, which both systems are ideally capable of handling. We see that for Sahaśranāma, CopyNet corrects about 17.24 % of the deletion errors as against <5% of the deletion errors corrected by PCRF.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Performance in terms of CRR, WRR for Google OCR', answer the following question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model corrected the highest percentage of deletion errors for Sahaśranāma?\n" }, { "role": "assistant", "content": "The table shows that CopyNet corrected 576 deletion errors for Sahaśranāma, while PCRF corrected 663 deletion errors. The description states that CopyNet corrected about 17.24% of deletion errors, whereas PCRF corrected less than 5%. So, the answer is {\"answer\": \"CopyNet\"}" } ], "images": [ "scigen/train/663.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance in terms of CRR, WRR for Google OCR" ], "reasoning": "The table shows that CopyNet corrected 576 deletion errors for Sahaśranāma, while PCRF corrected 663 deletion errors. The description states that CopyNet corrected about 17.24% of deletion errors, whereas PCRF corrected less than 5%.", "question": "Which model corrected the highest percentage of deletion errors for Sahaśranāma?", "answer": "So, the answer is {\"answer\": \"CopyNet\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nPCRF corrected more insertion errors than CopyNet for Bhagavad Gītā.\n" }, { "role": "assistant", "content": "The table shows that PCRF corrected 22 insertion errors for Bhagavad Gītā, while CopyNet also corrected 22 insertion errors. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/663.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance in terms of CRR, WRR for Google OCR" ], "reasoning": "The table shows that PCRF corrected 22 insertion errors for Bhagavad Gītā, while CopyNet also corrected 22 insertion errors. Therefore, the statement is refuted.", "statement": "PCRF corrected more insertion errors than CopyNet for Bhagavad Gītā.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: OCR performances for different languages with overall CRR, total Insertion, Deletion and Substitution errors.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "French has the highest grapheme overlap with Sanskrit, and English and French lead in CRR, so English is selected for further experiments.French alphabet has the highest grapheme overlap with that of the Sanskrit alphabet (37 of 50), while all other languages have one less grapheme common with Sanskrit. Hence, we arbitrarily take 5 of the languages in addition to French and perform our analysis. The table also shows the count of error types made by the OCR after alignment Jiampojamarn et al. D’hondt et al. All the languages have a near similar CRR with English and French leading the list. Based on our observations on the OCR performance, we select English for our further experiments." } ], "images": [ "scigen/train/664.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: OCR performances for different languages with overall CRR, total Insertion, Deletion and Substitution errors." ], "reasoning": "French has the highest grapheme overlap with Sanskrit, and English and French lead in CRR, so English is selected for further experiments.", "description": "French alphabet has the highest grapheme overlap with that of the Sanskrit alphabet (37 of 50), while all other languages have one less grapheme common with Sanskrit. Hence, we arbitrarily take 5 of the languages in addition to French and perform our analysis. The table also shows the count of error types made by the OCR after alignment Jiampojamarn et al. D’hondt et al. All the languages have a near similar CRR with English and French leading the list. Based on our observations on the OCR performance, we select English for our further experiments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: OCR performances for different languages with overall CRR, total Insertion, Deletion and Substitution errors.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich language has the highest combined CRR?\n" }, { "role": "assistant", "content": "By comparing the Combined CRR values for all languages, English has the highest combined CRR of 80.08. So, the answer is {\"answer\": \"English\"}" } ], "images": [ "scigen/train/664.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: OCR performances for different languages with overall CRR, total Insertion, Deletion and Substitution errors." ], "reasoning": "By comparing the Combined CRR values for all languages, English has the highest combined CRR of 80.08.", "question": "Which language has the highest combined CRR?", "answer": "So, the answer is {\"answer\": \"English\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nFrench has the highest CRR for the Bhagavad Gita.\n" }, { "role": "assistant", "content": "By comparing the Bhagavad Gita CRR values, English has the highest CRR of 84.92, not French. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/664.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: OCR performances for different languages with overall CRR, total Insertion, Deletion and Substitution errors." ], "reasoning": "By comparing the Bhagavad Gita CRR values, English has the highest CRR of 84.92, not French.", "statement": "French has the highest CRR for the Bhagavad Gita.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: Study on the effect of different smoothing methods for word weights generation. Baseline is the same as before. w.w. without smoothing means the word weights (w.w.) are computed without smoothing in the log domain. w.w. (mean smooth.) indicates smoothing the word scores via using a mean average filter before thresholding and w.w. (gauss. smooth.) indicates using a normal distributed filter before thresholding. The approaches regarding different smoothing methods are described in Section 2.2.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Smoothing methods improve translation quality by increasing BLEU scores and decreasing TER scores compared to not using smoothing.The word weights generated without using smoothing methods, where ^st=st, lead to poor translation quality of 21.38% from 24.37% BLEU and 66.25% from 61.66% TER, respectively. We need to smooth the word scores before thresholding because the values of logPI(yt|yt−1t−n)−logPO(yt|yt−1t−n) are noisy. If there are selected isolated words like ’,’ which have higher scores than the surrounding text, it may cause rare vocabulary problem after training." } ], "images": [ "scigen/train/665.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Study on the effect of different smoothing methods for word weights generation. Baseline is the same as before. w.w. without smoothing means the word weights (w.w.) are computed without smoothing in the log domain. w.w. (mean smooth.) indicates smoothing the word scores via using a mean average filter before thresholding and w.w. (gauss. smooth.) indicates using a normal distributed filter before thresholding. The approaches regarding different smoothing methods are described in Section 2.2." ], "reasoning": "Smoothing methods improve translation quality by increasing BLEU scores and decreasing TER scores compared to not using smoothing.", "description": "The word weights generated without using smoothing methods, where ^st=st, lead to poor translation quality of 21.38% from 24.37% BLEU and 66.25% from 61.66% TER, respectively. We need to smooth the word scores before thresholding because the values of logPI(yt|yt−1t−n)−logPO(yt|yt−1t−n) are noisy. If there are selected isolated words like ’,’ which have higher scores than the surrounding text, it may cause rare vocabulary problem after training.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 4: Study on the effect of different smoothing methods for word weights generation. Baseline is the same as before. w.w. without smoothing means the word weights (w.w.) are computed without smoothing in the log domain. w.w. (mean smooth.) indicates smoothing the word scores via using a mean average filter before thresholding and w.w. (gauss. smooth.) indicates using a normal distributed filter before thresholding. The approaches regarding different smoothing methods are described in Section 2.2.' and proceed to solve the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the BLEU score when using Gaussian smoothing?\n" }, { "role": "assistant", "content": "The table shows different BLEU scores for various systems. The BLEU score for the system using Gaussian smoothing is 26.14%. So, the answer is {\"answer\": \"26.14\"}" } ], "images": [ "scigen/train/665.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Study on the effect of different smoothing methods for word weights generation. Baseline is the same as before. w.w. without smoothing means the word weights (w.w.) are computed without smoothing in the log domain. w.w. (mean smooth.) indicates smoothing the word scores via using a mean average filter before thresholding and w.w. (gauss. smooth.) indicates using a normal distributed filter before thresholding. The approaches regarding different smoothing methods are described in Section 2.2." ], "reasoning": "The table shows different BLEU scores for various systems. The BLEU score for the system using Gaussian smoothing is 26.14%.", "question": "What is the BLEU score when using Gaussian smoothing?", "answer": "So, the answer is {\"answer\": \"26.14\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nUsing mean smoothing results in a higher TER score than the baseline.\n" }, { "role": "assistant", "content": "The table shows that the TER score for the baseline is 61.66%, while the TER score for mean smoothing is 60.70%, which is lower. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/665.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Study on the effect of different smoothing methods for word weights generation. Baseline is the same as before. w.w. without smoothing means the word weights (w.w.) are computed without smoothing in the log domain. w.w. (mean smooth.) indicates smoothing the word scores via using a mean average filter before thresholding and w.w. (gauss. smooth.) indicates using a normal distributed filter before thresholding. The approaches regarding different smoothing methods are described in Section 2.2." ], "reasoning": "The table shows that the TER score for the baseline is 61.66%, while the TER score for mean smoothing is 60.70%, which is lower.", "statement": "Using mean smoothing results in a higher TER score than the baseline.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: E-commerce English → Chinese BLEU results on test set. Baseline is trained on mixed in-domain and out-of-domain data. No. 2 is continuing training from baseline with objective defined as Eq. 1. No. 3 is continuing training from baseline with sentence-level weights and No. 4 is with word weights, as defined in Section 2.2. No. 5 refers to assigning wt using LCW method described in Section 2.3. No. 6 is equivalent to directly fine-tuning on in-domain datasets starting from the baseline model and No. 7 is equivalent to fine-tuning on in-domain datasets after No. 5 is finished.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Fine-tuning on the in-domain dataset improves the model's performance, reducing TER and increasing BLEU scores.First, the baseline trained on mixed in-domain and out-of-domain datasets gives 24.37% BLEU and 61.66% TER, respectively. Directly fine-tuning on in-domain dataset already improves the model due to the bias of the model towards in-domain data." } ], "images": [ "scigen/train/666.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: E-commerce English → Chinese BLEU results on test set. Baseline is trained on mixed in-domain and out-of-domain data. No. 2 is continuing training from baseline with objective defined as Eq. 1. No. 3 is continuing training from baseline with sentence-level weights and No. 4 is with word weights, as defined in Section 2.2. No. 5 refers to assigning wt using LCW method described in Section 2.3. No. 6 is equivalent to directly fine-tuning on in-domain datasets starting from the baseline model and No. 7 is equivalent to fine-tuning on in-domain datasets after No. 5 is finished." ], "reasoning": "Fine-tuning on the in-domain dataset improves the model's performance, reducing TER and increasing BLEU scores.", "description": "First, the baseline trained on mixed in-domain and out-of-domain datasets gives 24.37% BLEU and 61.66% TER, respectively. Directly fine-tuning on in-domain dataset already improves the model due to the bias of the model towards in-domain data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. The question is related to the given table titled 'Table 3: E-commerce English → Chinese BLEU results on test set. Baseline is trained on mixed in-domain and out-of-domain data. No. 2 is continuing training from baseline with objective defined as Eq. 1. No. 3 is continuing training from baseline with sentence-level weights and No. 4 is with word weights, as defined in Section 2.2. No. 5 refers to assigning wt using LCW method described in Section 2.3. No. 6 is equivalent to directly fine-tuning on in-domain datasets starting from the baseline model and No. 7 is equivalent to fine-tuning on in-domain datasets after No. 5 is finished.'.\n\nQuestion: What is the BLEU score after fine-tuning on the in-domain dataset following training with chunk weights?\n" }, { "role": "assistant", "content": "First, identify the row where the system is fine-tuned on the in-domain dataset after training with chunk weights, which is row 7. Then, find the BLEU score in that row, which is 27.30. So, the answer is {\"answer\": \"27.30\"}." } ], "images": [ "scigen/train/666.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: E-commerce English → Chinese BLEU results on test set. Baseline is trained on mixed in-domain and out-of-domain data. No. 2 is continuing training from baseline with objective defined as Eq. 1. No. 3 is continuing training from baseline with sentence-level weights and No. 4 is with word weights, as defined in Section 2.2. No. 5 refers to assigning wt using LCW method described in Section 2.3. No. 6 is equivalent to directly fine-tuning on in-domain datasets starting from the baseline model and No. 7 is equivalent to fine-tuning on in-domain datasets after No. 5 is finished." ], "reasoning": "First, identify the row where the system is fine-tuned on the in-domain dataset after training with chunk weights, which is row 7. Then, find the BLEU score in that row, which is 27.30.", "question": "What is the BLEU score after fine-tuning on the in-domain dataset following training with chunk weights?", "answer": "So, the answer is {\"answer\": \"27.30\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe BLEU score improves when fine-tuning on the in-domain dataset after training with sentence weights.\n" }, { "role": "assistant", "content": "Compare the BLEU score of the system trained with sentence weights (row 3, BLEU: 25.79) with the BLEU score after fine-tuning on the in-domain dataset (row 6, BLEU: 26.06). The BLEU score increases from 25.79 to 26.06. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/666.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: E-commerce English → Chinese BLEU results on test set. Baseline is trained on mixed in-domain and out-of-domain data. No. 2 is continuing training from baseline with objective defined as Eq. 1. No. 3 is continuing training from baseline with sentence-level weights and No. 4 is with word weights, as defined in Section 2.2. No. 5 refers to assigning wt using LCW method described in Section 2.3. No. 6 is equivalent to directly fine-tuning on in-domain datasets starting from the baseline model and No. 7 is equivalent to fine-tuning on in-domain datasets after No. 5 is finished." ], "reasoning": "Compare the BLEU score of the system trained with sentence weights (row 3, BLEU: 25.79) with the BLEU score after fine-tuning on the in-domain dataset (row 6, BLEU: 26.06). The BLEU score increases from 25.79 to 26.06.", "statement": "The BLEU score improves when fine-tuning on the in-domain dataset after training with sentence weights.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Tokens normalized correctly (%) for each dataset. Upper half: results on (A)ll tokens reported by Pettersson et al. (2014) for a hybrid model (apply memorization baseline to seen tokens and an edit-distance-based model to unseen tokens) and two SMT models (which align character unigrams and bigrams, respectively). Lower half: results from our experiments, including accuracy reported separately on (S)een and (U)nseen tokens.'.\n" }, { "role": "assistant", "content": "The hard attention model performs best on unseen tokens across all datasets and provides competitive overall performance, while the soft attention model underperforms the baseline on seen items in English and Hungarian.The split into seen/unseen highlights the fact that neither of the neural models does as well on seen items as the baseline; indeed the soft attention model is considerably worse in English and Hungarian, the two largest datasets. The result is that this model actually underperforms the baseline when applied to all tokens, although a hybrid model (baseline for seen, soft attention for unseen) would outperform the baseline. Nevertheless, the hard attention model performs best on unseen tokens in all cases, often by a wide margin, and also yields competitive overall performance." } ], "images": [ "scigen/train/667.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Tokens normalized correctly (%) for each dataset. Upper half: results on (A)ll tokens reported by Pettersson et al. (2014) for a hybrid model (apply memorization baseline to seen tokens and an edit-distance-based model to unseen tokens) and two SMT models (which align character unigrams and bigrams, respectively). Lower half: results from our experiments, including accuracy reported separately on (S)een and (U)nseen tokens." ], "reasoning": "The hard attention model performs best on unseen tokens across all datasets and provides competitive overall performance, while the soft attention model underperforms the baseline on seen items in English and Hungarian.", "description": "The split into seen/unseen highlights the fact that neither of the neural models does as well on seen items as the baseline; indeed the soft attention model is considerably worse in English and Hungarian, the two largest datasets. The result is that this model actually underperforms the baseline when applied to all tokens, although a hybrid model (baseline for seen, soft attention for unseen) would outperform the baseline. Nevertheless, the hard attention model performs best on unseen tokens in all cases, often by a wide margin, and also yields competitive overall performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Tokens normalized correctly (%) for each dataset. Upper half: results on (A)ll tokens reported by Pettersson et al. (2014) for a hybrid model (apply memorization baseline to seen tokens and an edit-distance-based model to unseen tokens) and two SMT models (which align character unigrams and bigrams, respectively). Lower half: results from our experiments, including accuracy reported separately on (S)een and (U)nseen tokens.\nQuestion: Which model performs best on unseen tokens in the German dataset?\nGive a detailed response to the above question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the performance of different models on seen and unseen tokens across various datasets. For the German dataset, the hard attention model has the highest score for unseen tokens. So, the answer is {\"answer\": \"Hard attention\"}" } ], "images": [ "scigen/train/667.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Tokens normalized correctly (%) for each dataset. Upper half: results on (A)ll tokens reported by Pettersson et al. (2014) for a hybrid model (apply memorization baseline to seen tokens and an edit-distance-based model to unseen tokens) and two SMT models (which align character unigrams and bigrams, respectively). Lower half: results from our experiments, including accuracy reported separately on (S)een and (U)nseen tokens." ], "reasoning": "The table shows the performance of different models on seen and unseen tokens across various datasets. For the German dataset, the hard attention model has the highest score for unseen tokens.", "question": "Which model performs best on unseen tokens in the German dataset?", "answer": "So, the answer is {\"answer\": \"Hard attention\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The soft attention model performs better than the memory baseline on unseen tokens in the Swedish dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe soft attention model performs better than the memory baseline on unseen tokens in the Swedish dataset.\n" }, { "role": "assistant", "content": "The table shows that the soft attention model has a score of 63.8 for unseen tokens in the Swedish dataset, while the memory baseline has a score of 41.4. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/667.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Tokens normalized correctly (%) for each dataset. Upper half: results on (A)ll tokens reported by Pettersson et al. (2014) for a hybrid model (apply memorization baseline to seen tokens and an edit-distance-based model to unseen tokens) and two SMT models (which align character unigrams and bigrams, respectively). Lower half: results from our experiments, including accuracy reported separately on (S)een and (U)nseen tokens." ], "reasoning": "The table shows that the soft attention model has a score of 63.8 for unseen tokens in the Swedish dataset, while the memory baseline has a score of 41.4.", "statement": "The soft attention model performs better than the memory baseline on unseen tokens in the Swedish dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Link prediction results on WN18RR dataset.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "CBR performs competitively with GNTPs and most embedding-based methods except RotatE, which outperforms CBR in all metrics.WN18RR: CBR performs competitively with GNTPs and most embedding based methods except RotatE sun2019rotate. Upon further analysis, we find that for 210 triples in the test set, the entity was not present in the graph and hence no answers were returned for those query entities." } ], "images": [ "scigen/train/668.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Link prediction results on WN18RR dataset." ], "reasoning": "CBR performs competitively with GNTPs and most embedding-based methods except RotatE, which outperforms CBR in all metrics.", "description": "WN18RR: CBR performs competitively with GNTPs and most embedding based methods except RotatE sun2019rotate. Upon further analysis, we find that for 210 triples in the test set, the entity was not present in the graph and hence no answers were returned for those query entities.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich method has the highest hits@10 score?\nSolve the above question based on the table titled 'Table 3: Link prediction results on WN18RR dataset.'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the hits@10 row, we see that RotatE has the highest score of 0.57. So, the answer is {\"answer\": \"RotatE\"}" } ], "images": [ "scigen/train/668.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Link prediction results on WN18RR dataset." ], "reasoning": "By examining the hits@10 row, we see that RotatE has the highest score of 0.57.", "question": "Which method has the highest hits@10 score?", "answer": "So, the answer is {\"answer\": \"RotatE\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nCBR has a higher hits@3 score than GNTP.\n" }, { "role": "assistant", "content": "By comparing the hits@3 scores, CBR has a score of 0.46, which is higher than GNTP's score of 0.44. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/668.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Link prediction results on WN18RR dataset." ], "reasoning": "By comparing the hits@3 scores, CBR has a score of 0.46, which is higher than GNTP's score of 0.44.", "statement": "CBR has a higher hits@3 score than GNTP.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: Link prediction results on the FB122 dataset.'.\n" }, { "role": "assistant", "content": "CBR significantly outperforms GNTPs and most models with hand-coded rules, uncovering correct rules for 87% of query relations.FB122: Next we consider the FB122 dataset by guo2016jointly. Comparing results on FB122 is attractive for a couple of reasons — (a) Firstly, this dataset comes with a set of logical rules hand coded by the authors that can be used for logical inference. It would be interesting to see if our CBR approach is able to automatically uncover the rules from the data. (b) Secondly, there is a recent work on a neural model for logical inference (GNTPs) minervini2019differentiable that scales neural-theorem-provers ntp to this dataset and hence we can directly compare with them. We compare with several baselines. CBR significantly outperforms GNTPs and even outperforms most models which have access to the hand-coded rules during training. We also find that CBR is able to uncover correct rules for 27 out of 31 (87%) query relations." } ], "images": [ "scigen/train/669.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Link prediction results on the FB122 dataset." ], "reasoning": "CBR significantly outperforms GNTPs and most models with hand-coded rules, uncovering correct rules for 87% of query relations.", "description": "FB122: Next we consider the FB122 dataset by guo2016jointly. Comparing results on FB122 is attractive for a couple of reasons — (a) Firstly, this dataset comes with a set of logical rules hand coded by the authors that can be used for logical inference. It would be interesting to see if our CBR approach is able to automatically uncover the rules from the data. (b) Secondly, there is a recent work on a neural model for logical inference (GNTPs) minervini2019differentiable that scales neural-theorem-provers ntp to this dataset and hence we can directly compare with them. We compare with several baselines. CBR significantly outperforms GNTPs and even outperforms most models which have access to the hand-coded rules during training. We also find that CBR is able to uncover correct rules for 27 out of 31 (87%) query relations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 1: Link prediction results on the FB122 dataset.' and proceed to solve the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model has the highest hits@3 score?\n" }, { "role": "assistant", "content": "By comparing the hits@3 scores of all models in the table, we find that CBR has the highest hits@3 score of 0.400. So, the answer is {\"answer\": \"CBR\"}." } ], "images": [ "scigen/train/669.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Link prediction results on the FB122 dataset." ], "reasoning": "By comparing the hits@3 scores of all models in the table, we find that CBR has the highest hits@3 score of 0.400.", "question": "Which model has the highest hits@3 score?", "answer": "So, the answer is {\"answer\": \"CBR\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nCBR has the highest MRR score among all models.\n" }, { "role": "assistant", "content": "By comparing the MRR scores of all models in the table, we find that CBR has the highest MRR score of 0.359. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/669.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Link prediction results on the FB122 dataset." ], "reasoning": "By comparing the MRR scores of all models in the table, we find that CBR has the highest MRR score of 0.359.", "statement": "CBR has the highest MRR score among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: Link prediction results on NELL-995 for few shot relations.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The CBR based approach, which requires no training, is compared with Meta-KGR for few-shot relations and shows better generalization.As mentioned before, our CBR based approach needs no training and gathers reasoning patterns from few similar entities. Therefore, it should ideally perform well for query relations for which we do not have a lot of data. Recently, lv2019adapting studied this problem and propose a meta-learning finn2017model based solution (Meta-KGR) for few-shot relations. We compare with their model to see if CBR based approach will be able to generalize for such few-shot relations." } ], "images": [ "scigen/train/670.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Link prediction results on NELL-995 for few shot relations." ], "reasoning": "The CBR based approach, which requires no training, is compared with Meta-KGR for few-shot relations and shows better generalization.", "description": "As mentioned before, our CBR based approach needs no training and gathers reasoning patterns from few similar entities. Therefore, it should ideally perform well for query relations for which we do not have a lot of data. Recently, lv2019adapting studied this problem and propose a meta-learning finn2017model based solution (Meta-KGR) for few-shot relations. We compare with their model to see if CBR based approach will be able to generalize for such few-shot relations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nTable title: Table 4: Link prediction results on NELL-995 for few shot relations.\n\nQuestion: Which model has the highest hits@10 score?\n" }, { "role": "assistant", "content": "By comparing the hits@10 scores of all models in the table, the CBR (ours) model has the highest hits@10 score of 0.403. So, the answer is {\"answer\": \"CBR (ours)\"}" } ], "images": [ "scigen/train/670.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Link prediction results on NELL-995 for few shot relations." ], "reasoning": "By comparing the hits@10 scores of all models in the table, the CBR (ours) model has the highest hits@10 score of 0.403.", "question": "Which model has the highest hits@10 score?", "answer": "So, the answer is {\"answer\": \"CBR (ours)\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe NTP-λ model has a higher MRR than the MINERVA model.\n" }, { "role": "assistant", "content": "The table shows that the NTP-λ model has an MRR of 0.155, while the MINERVA model has an MRR of 0.201. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/670.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Link prediction results on NELL-995 for few shot relations." ], "reasoning": "The table shows that the NTP-λ model has an MRR of 0.155, while the MINERVA model has an MRR of 0.201. Therefore, the statement is refuted.", "statement": "The NTP-λ model has a higher MRR than the MINERVA model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: Time Usage Comparison'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "HashNet is slower on CPU due to lack of optimized matrix multiplication and overhead from the hash function, while the SE model is faster due to optimized subroutines and dynamic programming.We report the time used on both CPU and GPU. All the computations use 32 bit floating point numbers. On CPU, HashNet is slower than the normal uncompressed model, mainly because of two reasons: 1) The uncompressed model uses optimized matrix multiplication subroutines, 2) The hash function used in HashNet is cheap, but it still has overhead compared with the uncompressed model. The SE model runs faster mainly because it uses matrix multiplication subroutines and has lower time complexity with the help of dynamic programming." } ], "images": [ "scigen/train/671.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Time Usage Comparison" ], "reasoning": "HashNet is slower on CPU due to lack of optimized matrix multiplication and overhead from the hash function, while the SE model is faster due to optimized subroutines and dynamic programming.", "description": "We report the time used on both CPU and GPU. All the computations use 32 bit floating point numbers. On CPU, HashNet is slower than the normal uncompressed model, mainly because of two reasons: 1) The uncompressed model uses optimized matrix multiplication subroutines, 2) The hash function used in HashNet is cheap, but it still has overhead compared with the uncompressed model. The SE model runs faster mainly because it uses matrix multiplication subroutines and has lower time complexity with the help of dynamic programming.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: Time Usage Comparison', answer the following question. Give detailed solution consisting of each step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model has the fastest computation time on CPU and why?\n" }, { "role": "assistant", "content": "The SE model has the fastest computation time on CPU at 0.7 seconds because it uses optimized matrix multiplication subroutines and dynamic programming. So, the answer is {\"answer\": \"SE\"}." } ], "images": [ "scigen/train/671.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Time Usage Comparison" ], "reasoning": "The SE model has the fastest computation time on CPU at 0.7 seconds because it uses optimized matrix multiplication subroutines and dynamic programming.", "question": "Which model has the fastest computation time on CPU and why?", "answer": "So, the answer is {\"answer\": \"SE\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The SE model is faster than the Uncompressed model on GPU. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe SE model is faster than the Uncompressed model on GPU.\n" }, { "role": "assistant", "content": "The table shows that the SE model has a GPU time of 25 milliseconds, while the Uncompressed model has a GPU time of 38 milliseconds, indicating that the SE model is faster. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/671.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Time Usage Comparison" ], "reasoning": "The table shows that the SE model has a GPU time of 25 milliseconds, while the Uncompressed model has a GPU time of 38 milliseconds, indicating that the SE model is faster.", "statement": "The SE model is faster than the Uncompressed model on GPU.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Perplexity results for single models on BillionW. Bold number denotes results on a single GPU.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The three-layer model with IS achieved a perplexity of 38.3 after training for 8 days, making it the most compact recurrent neural language model with a perplexity below 40 on the dataset.In the one billion word experiments, the total memory during training used on the GPU is about 7GB, and is smaller if a larger compression rate is used. We use a fixed smoothed unigram distribution (unigram distribution raised to 0.75) as the noise distribution. For the two layer model, the compression rate for the input layer is 1/32 and the output layer is 1/8, and the total number of parameters is 322 million. For the three layer model, the compression rates for the input and output layer are 1/32 and 1/16, and the total number of parameters is 254 million. Both experiments using NCE take about seven days of training on a GTX 1080 GPU. \\citeauthorjozefowicz2016exploring \\shortcitejozefowicz2016exploring suggests importance sampling (IS) could perform better than the NCE model, so we ran the experiment using IS and we used 4000 noise samples for each mini-batch. The PPL decreased to 38.3 after training for 8 days. As far as we know, the 3 layer model is the most compact recurrent neural language model that has a perplexity below 40 on this dataset." } ], "images": [ "scigen/train/672.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Perplexity results for single models on BillionW. Bold number denotes results on a single GPU." ], "reasoning": "The three-layer model with IS achieved a perplexity of 38.3 after training for 8 days, making it the most compact recurrent neural language model with a perplexity below 40 on the dataset.", "description": "In the one billion word experiments, the total memory during training used on the GPU is about 7GB, and is smaller if a larger compression rate is used. We use a fixed smoothed unigram distribution (unigram distribution raised to 0.75) as the noise distribution. For the two layer model, the compression rate for the input layer is 1/32 and the output layer is 1/8, and the total number of parameters is 322 million. For the three layer model, the compression rates for the input and output layer are 1/32 and 1/16, and the total number of parameters is 254 million. Both experiments using NCE take about seven days of training on a GTX 1080 GPU. \\citeauthorjozefowicz2016exploring \\shortcitejozefowicz2016exploring suggests importance sampling (IS) could perform better than the NCE model, so we ran the experiment using IS and we used 4000 noise samples for each mini-batch. The PPL decreased to 38.3 after training for 8 days. As far as we know, the 3 layer model is the most compact recurrent neural language model that has a perplexity below 40 on this dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 2: Perplexity results for single models on BillionW. Bold number denotes results on a single GPU.' and proceed to solve the following question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the lowest perplexity and how many parameters does it have?\n" }, { "role": "assistant", "content": "The table shows that the model with the lowest perplexity is the MOE model with a perplexity of 28.0. The number of parameters for this model is greater than 4.37 billion. So, the answer is {\"answer\": \">4.37 billion\"}." } ], "images": [ "scigen/train/672.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Perplexity results for single models on BillionW. Bold number denotes results on a single GPU." ], "reasoning": "The table shows that the model with the lowest perplexity is the MOE model with a perplexity of 28.0. The number of parameters for this model is greater than 4.37 billion.", "question": "Which model has the lowest perplexity and how many parameters does it have?", "answer": "So, the answer is {\"answer\": \">4.37 billion\"}." }, { "messages": [ { "role": "user", "content": "The SE (3-layer 2048 LSTM IS) model has a perplexity of 38.3. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe SE (3-layer 2048 LSTM IS) model has a perplexity of 38.3.\n" }, { "role": "assistant", "content": "The table lists the SE (3-layer 2048 LSTM IS) model with a perplexity of 38.3, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/672.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Perplexity results for single models on BillionW. Bold number denotes results on a single GPU." ], "reasoning": "The table lists the SE (3-layer 2048 LSTM IS) model with a perplexity of 38.3, which matches the statement.", "statement": "The SE (3-layer 2048 LSTM IS) model has a perplexity of 38.3.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Performance on the test portion of Karpathy et al. [20]’s splits on COCO dataset. ∗ directly optimizes the CIDEr Metric, † uses better image features, and are thus not directly comparable.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Our method outperforms 4 out of 5 automatic evaluation metrics compared to the state of the art, and NBToracle shows little improvement over NBT due to the absence of explicit ground truth annotation for visual words.Our method outperforms 4 out of 5 automatic evaluation metrics compared to the state of the art Interestingly, the NBToracle has little improvement over NBT. We suspect the reason is that explicit ground truth annotation is absent for visual words. Our model can be further improved with explicit co-reference supervision where the ground truth location annotation of the visual word is provided. Fig. We see that our model learns to correctly identify the visual word, and ground it in image regions even under weak supervision (COCO). Our model is also robust to erroneous detections and produces correct captions (3rd column)." } ], "images": [ "scigen/train/673.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance on the test portion of Karpathy et al. [20]’s splits on COCO dataset. ∗ directly optimizes the CIDEr Metric, † uses better image features, and are thus not directly comparable." ], "reasoning": "Our method outperforms 4 out of 5 automatic evaluation metrics compared to the state of the art, and NBToracle shows little improvement over NBT due to the absence of explicit ground truth annotation for visual words.", "description": "Our method outperforms 4 out of 5 automatic evaluation metrics compared to the state of the art Interestingly, the NBToracle has little improvement over NBT. We suspect the reason is that explicit ground truth annotation is absent for visual words. Our model can be further improved with explicit co-reference supervision where the ground truth location annotation of the visual word is provided. Fig. We see that our model learns to correctly identify the visual word, and ground it in image regions even under weak supervision (COCO). Our model is also robust to erroneous detections and produces correct captions (3rd column).", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 2: Performance on the test portion of Karpathy et al. [20]’s splits on COCO dataset. ∗ directly optimizes the CIDEr Metric, † uses better image features, and are thus not directly comparable.\nQuestion: Which method has the highest score in the CIDEr metric and what is the score?\n" }, { "role": "assistant", "content": "To find the method with the highest CIDEr score, look at the CIDEr column and identify the highest value. The highest CIDEr score is 120.1, which corresponds to the Up-Down† method. So, the answer is {\"answer\": \"Up-Down† with 120.1\"}." } ], "images": [ "scigen/train/673.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance on the test portion of Karpathy et al. [20]’s splits on COCO dataset. ∗ directly optimizes the CIDEr Metric, † uses better image features, and are thus not directly comparable." ], "reasoning": "To find the method with the highest CIDEr score, look at the CIDEr column and identify the highest value. The highest CIDEr score is 120.1, which corresponds to the Up-Down† method.", "question": "Which method has the highest score in the CIDEr metric and what is the score?", "answer": "So, the answer is {\"answer\": \"Up-Down† with 120.1\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nNBToracle has the highest score in the METEOR metric.\n" }, { "role": "assistant", "content": "To verify this statement, look at the METEOR column and compare the scores. The highest METEOR score is 27.7, which belongs to the Up-Down† method, not NBToracle. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/673.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance on the test portion of Karpathy et al. [20]’s splits on COCO dataset. ∗ directly optimizes the CIDEr Metric, † uses better image features, and are thus not directly comparable." ], "reasoning": "To verify this statement, look at the METEOR column and compare the scores. The highest METEOR score is 27.7, which belongs to the Up-Down† method, not NBToracle.", "statement": "NBToracle has the highest score in the METEOR metric.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: Performance on the test portion of Karpathy et al. [20]’s splits on Flickr30k Entities dataset.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "NBToracle significantly outperforms previous methods, improving SPICE by 5.1 points, indicating potential benefits from improved object detectors.When using ground truth proposals, NBToracle significantly outperforms previous methods, improving 5.1 on SPICE, which implies that our method could further benefit from improved object detectors." } ], "images": [ "scigen/train/674.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance on the test portion of Karpathy et al. [20]’s splits on Flickr30k Entities dataset." ], "reasoning": "NBToracle significantly outperforms previous methods, improving SPICE by 5.1 points, indicating potential benefits from improved object detectors.", "description": "When using ground truth proposals, NBToracle significantly outperforms previous methods, improving 5.1 on SPICE, which implies that our method could further benefit from improved object detectors.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 1: Performance on the test portion of Karpathy et al. [20]’s splits on Flickr30k Entities dataset.\nQuestion: What is the SPICE score improvement of NBToracle over NBT?\n" }, { "role": "assistant", "content": "NBToracle has a SPICE score of 19.6, while NBT has a SPICE score of 15.6. The improvement is calculated as 19.6 - 15.6. So, the answer is {\"answer\": \"4.0\"}" } ], "images": [ "scigen/train/674.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance on the test portion of Karpathy et al. [20]’s splits on Flickr30k Entities dataset." ], "reasoning": "NBToracle has a SPICE score of 19.6, while NBT has a SPICE score of 15.6. The improvement is calculated as 19.6 - 15.6.", "question": "What is the SPICE score improvement of NBToracle over NBT?", "answer": "So, the answer is {\"answer\": \"4.0\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim NBToracle has the highest BLEU4 score among all methods., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nNBToracle has the highest BLEU4 score among all methods.\n" }, { "role": "assistant", "content": "The table shows that NBToracle has a BLEU4 score of 28.5, which is higher than the BLEU4 scores of all other methods listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/674.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance on the test portion of Karpathy et al. [20]’s splits on Flickr30k Entities dataset." ], "reasoning": "The table shows that NBToracle has a BLEU4 score of 28.5, which is higher than the BLEU4 scores of all other methods listed.", "statement": "NBToracle has the highest BLEU4 score among all methods.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Performance on the test portion of the robust image captioning split on COCO dataset.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "All models perform worse on the robust-COCO split than on Karpathy’s split by 2-3 points. The NBToracle model shows consistent improvements across all metrics, with a 3.3-point increase on the proposed metric.Results and analysis. As we can see, all models perform worse on the robust-COCO split than the Karpathy’s split by 2∼3 points in general. The oracle setting (NBToracle) has consistent improvements on all metrics, improving 3.3 on the proposed metric." } ], "images": [ "scigen/train/675.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance on the test portion of the robust image captioning split on COCO dataset." ], "reasoning": "All models perform worse on the robust-COCO split than on Karpathy’s split by 2-3 points. The NBToracle model shows consistent improvements across all metrics, with a 3.3-point increase on the proposed metric.", "description": "Results and analysis. As we can see, all models perform worse on the robust-COCO split than the Karpathy’s split by 2∼3 points in general. The oracle setting (NBToracle) has consistent improvements on all metrics, improving 3.3 on the proposed metric.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: Performance on the test portion of the robust image captioning split on COCO dataset.', and sequentially solve the question, writing out each step of your reasoning process. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method has the highest accuracy and what is its value?\n" }, { "role": "assistant", "content": "By comparing the accuracy values of all methods, NBToracle has the highest accuracy with a value of 45.7. So, the answer is {\"answer\": \"NBToracle with 45.7\"}." } ], "images": [ "scigen/train/675.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance on the test portion of the robust image captioning split on COCO dataset." ], "reasoning": "By comparing the accuracy values of all methods, NBToracle has the highest accuracy with a value of 45.7.", "question": "Which method has the highest accuracy and what is its value?", "answer": "So, the answer is {\"answer\": \"NBToracle with 45.7\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe NBT model has the highest BLEU4 score.\n" }, { "role": "assistant", "content": "By comparing the BLEU4 scores of all methods, NBToracle has the highest BLEU4 score of 31.9, not the NBT model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/675.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance on the test portion of the robust image captioning split on COCO dataset." ], "reasoning": "By comparing the BLEU4 scores of all methods, NBToracle has the highest BLEU4 score of 31.9, not the NBT model.", "statement": "The NBT model has the highest BLEU4 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: Evaluation of captions generated using the proposed method. G means greedy decoding, and T1−2 means using constrained beam search [2] with 1−2 top detected concepts. ∗ is the result using VGG-16 [41] and † is the result using ResNet-101.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "NBT†+T2 achieves the highest average F1 score, outperforming C-LSTM by 14.6%, but it is less likely to select small objects like 'bottle' and 'racket' using greedy decoding.Specifically, NBT†+T2 outperforms the previous state-of-art model C-LSTM by 14.6% on average F1 scores. From the category F1 scores, we can see that our model is less likely to select small objects, e.g. “bottle”, “racket” when only using the greedy decoding. Fig. Also see rightmost example in Fig." } ], "images": [ "scigen/train/676.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Evaluation of captions generated using the proposed method. G means greedy decoding, and T1−2 means using constrained beam search [2] with 1−2 top detected concepts. ∗ is the result using VGG-16 [41] and † is the result using ResNet-101." ], "reasoning": "NBT†+T2 achieves the highest average F1 score, outperforming C-LSTM by 14.6%, but it is less likely to select small objects like 'bottle' and 'racket' using greedy decoding.", "description": "Specifically, NBT†+T2 outperforms the previous state-of-art model C-LSTM by 14.6% on average F1 scores. From the category F1 scores, we can see that our model is less likely to select small objects, e.g. “bottle”, “racket” when only using the greedy decoding. Fig. Also see rightmost example in Fig.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 4: Evaluation of captions generated using the proposed method. G means greedy decoding, and T1−2 means using constrained beam search [2] with 1−2 top detected concepts. ∗ is the result using VGG-16 [41] and † is the result using ResNet-101.'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: What is the average F1 score for the NBT†+T2 model on out-of-domain test data?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the average F1 score for the NBT†+T2 model on out-of-domain test data, look at the 'Out-of-Domain Test Data Avg' column for the NBT†+T2 row. So, the answer is {\"answer\": \"67.8\"}" } ], "images": [ "scigen/train/676.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Evaluation of captions generated using the proposed method. G means greedy decoding, and T1−2 means using constrained beam search [2] with 1−2 top detected concepts. ∗ is the result using VGG-16 [41] and † is the result using ResNet-101." ], "reasoning": "To find the average F1 score for the NBT†+T2 model on out-of-domain test data, look at the 'Out-of-Domain Test Data Avg' column for the NBT†+T2 row.", "question": "What is the average F1 score for the NBT†+T2 model on out-of-domain test data?", "answer": "So, the answer is {\"answer\": \"67.8\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The NBT†+T2 model has the highest F1 score for the 'bus' category in out-of-domain test data. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe NBT†+T2 model has the highest F1 score for the 'bus' category in out-of-domain test data.\n" }, { "role": "assistant", "content": "To verify this statement, compare the F1 scores for the 'bus' category in out-of-domain test data across all models. The NBT†+T2 model has the highest score of 80.0. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/676.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Evaluation of captions generated using the proposed method. G means greedy decoding, and T1−2 means using constrained beam search [2] with 1−2 top detected concepts. ∗ is the result using VGG-16 [41] and † is the result using ResNet-101." ], "reasoning": "To verify this statement, compare the F1 scores for the 'bus' category in out-of-domain test data across all models. The NBT†+T2 model has the highest score of 80.0.", "statement": "The NBT†+T2 model has the highest F1 score for the 'bus' category in out-of-domain test data.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 10: The performance (accuracy) of ESIM with our proposed attention enhancement on MultiNLI (Williams et al., 2017) development set. (d is the output hidden size of BiLSTM)'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Fully-aware attention significantly improves performance, and further gains are achieved with multi-level fusion in ESIM, regardless of CoVe embedding.Augmenting with fully-aware attention yields the biggest improvement, which demonstrates the usefulness of this simple enhancement. Further improvement is obtained when we use multi-level fusion in our ESIM. Experiments with and without CoVe embedding show similar observations." } ], "images": [ "scigen/train/677.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: The performance (accuracy) of ESIM with our proposed attention enhancement on MultiNLI (Williams et al., 2017) development set. (d is the output hidden size of BiLSTM)" ], "reasoning": "Fully-aware attention significantly improves performance, and further gains are achieved with multi-level fusion in ESIM, regardless of CoVe embedding.", "description": "Augmenting with fully-aware attention yields the biggest improvement, which demonstrates the usefulness of this simple enhancement. Further improvement is obtained when we use multi-level fusion in our ESIM. Experiments with and without CoVe embedding show similar observations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 10: The performance (accuracy) of ESIM with our proposed attention enhancement on MultiNLI (Williams et al., 2017) development set. (d is the output hidden size of BiLSTM)' and proceed to solve the following question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the highest score achieved in the In-Domain category and with which configuration?\n" }, { "role": "assistant", "content": "The highest score in the In-Domain category is 78.2, achieved with 'Our ESIM + fully-aware + multi-level (d=250)'. So, the answer is {\"answer\": \"78.2\"}." } ], "images": [ "scigen/train/677.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: The performance (accuracy) of ESIM with our proposed attention enhancement on MultiNLI (Williams et al., 2017) development set. (d is the output hidden size of BiLSTM)" ], "reasoning": "The highest score in the In-Domain category is 78.2, achieved with 'Our ESIM + fully-aware + multi-level (d=250)'.", "question": "What is the highest score achieved in the In-Domain category and with which configuration?", "answer": "So, the answer is {\"answer\": \"78.2\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe configuration 'Our ESIM without CoVe + fully-aware (d=250)' achieved the highest score in the Cross-Domain category.\n" }, { "role": "assistant", "content": "The highest score in the Cross-Domain category is 78.4, achieved with 'Our ESIM + fully-aware + multi-level (d=250)', not 'Our ESIM without CoVe + fully-aware (d=250)'. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/677.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: The performance (accuracy) of ESIM with our proposed attention enhancement on MultiNLI (Williams et al., 2017) development set. (d is the output hidden size of BiLSTM)" ], "reasoning": "The highest score in the Cross-Domain category is 78.4, achieved with 'Our ESIM + fully-aware + multi-level (d=250)', not 'Our ESIM without CoVe + fully-aware (d=250)'.", "statement": "The configuration 'Our ESIM without CoVe + fully-aware (d=250)' achieved the highest score in the Cross-Domain category.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Results on the Wiktionary datasets'.\n" }, { "role": "assistant", "content": "The hard attention model outperforms both the encoder-decoder approach and the soft-attention implementation, demonstrating the advantage of using independently learned alignments.This shows the robustness of our model also with large amounts of training examples, and the advantage the hard attention mechanism provides over the encoder-decoder approach of \\newcitefaruquiTND15 which does not employ an attention mechanism. Our model is also significantly more accurate than the model of \\newciteyu2016online, which shows the advantage of using independently learned alignments to guide the network’s attention from the beginning of the training process. While our soft-attention implementation outperformed the models of \\newciteyu2016online and \\newcitedurrettdenero2013, it still performed inferiorly to the hard attention model." } ], "images": [ "scigen/train/678.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on the Wiktionary datasets" ], "reasoning": "The hard attention model outperforms both the encoder-decoder approach and the soft-attention implementation, demonstrating the advantage of using independently learned alignments.", "description": "This shows the robustness of our model also with large amounts of training examples, and the advantage the hard attention mechanism provides over the encoder-decoder approach of \\newcitefaruquiTND15 which does not employ an attention mechanism. Our model is also significantly more accurate than the model of \\newciteyu2016online, which shows the advantage of using independently learned alignments to guide the network’s attention from the beginning of the training process. While our soft-attention implementation outperformed the models of \\newciteyu2016online and \\newcitedurrettdenero2013, it still performed inferiorly to the hard attention model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Results on the Wiktionary datasets\nQuestion: Which model has the highest average accuracy across all languages?\nSolve the above question based on the table information. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest average accuracy, compare the 'Avg.' column for all models. The hard attention model has the highest average accuracy of 96.55. So, the answer is {\"answer\": \"Hard\"}" } ], "images": [ "scigen/train/678.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on the Wiktionary datasets" ], "reasoning": "To find the model with the highest average accuracy, compare the 'Avg.' column for all models. The hard attention model has the highest average accuracy of 96.55.", "question": "Which model has the highest average accuracy across all languages?", "answer": "So, the answer is {\"answer\": \"Hard\"}" }, { "messages": [ { "role": "user", "content": "The soft attention model outperformed the model of yu2016online in DE-V. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe soft attention model outperformed the model of yu2016online in DE-V.\n" }, { "role": "assistant", "content": "By comparing the DE-V scores, the soft attention model scored 95.62, while the yu2016online model scored 92.11, confirming that the soft attention model outperformed yu2016online in DE-V. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/678.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on the Wiktionary datasets" ], "reasoning": "By comparing the DE-V scores, the soft attention model scored 95.62, while the yu2016online model scored 92.11, confirming that the soft attention model outperformed yu2016online in DE-V.", "statement": "The soft attention model outperformed the model of yu2016online in DE-V.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Results on the CELEX dataset', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The hard attention model outperforms the soft attention model in overall accuracy, particularly on the dev-set, due to its ability to harness monotonic alignments and condition on the entire output history.In addition, it significantly outperforms our implementation of the soft attention model (Soft). It is also, to our knowledge, the first model that surpassed in overall accuracy the latent variable model on this dataset. We attribute our advantage over the soft attention models to the ability of the hard attention control mechanism to harness the monotonic alignments found in the data. The advantage over the FST models may be explained by our conditioning on the entire output history which is not available in those models. While both models perform similarly on the train-set (with the soft attention model fitting it slightly faster), the hard attention model performs significantly better on the dev-set. This shows the soft attention model’s tendency to overfit on the small dataset, as it is not enforcing the monotonic assumption of the hard attention model. ; Hulden et al. ; Nicolai et al. Our work was mainly inspired by \\newcitefaruquiTND15 which trained an independent encoder-decoder neural network for every inflection type in the training data, alleviating the need for feature engineering. \\newcitekann2016,kann2016medtl tackled the task with a single soft attention model Bahdanau et al. Cotterell et al. In another closely related work, Rastogi et al. (2016) model the task with a WFST in which the arc weights are learned by optimizing a global loss function over all the possible paths in the state graph, while modeling contextual features with bi-directional LSTMS. This is similar to our approach, where instead of learning to mimic a single greedy alignment as we do, they sum over all possible alignments. Another recent work Kann et al." } ], "images": [ "scigen/train/679.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results on the CELEX dataset" ], "reasoning": "The hard attention model outperforms the soft attention model in overall accuracy, particularly on the dev-set, due to its ability to harness monotonic alignments and condition on the entire output history.", "description": "In addition, it significantly outperforms our implementation of the soft attention model (Soft). It is also, to our knowledge, the first model that surpassed in overall accuracy the latent variable model on this dataset. We attribute our advantage over the soft attention models to the ability of the hard attention control mechanism to harness the monotonic alignments found in the data. The advantage over the FST models may be explained by our conditioning on the entire output history which is not available in those models. While both models perform similarly on the train-set (with the soft attention model fitting it slightly faster), the hard attention model performs significantly better on the dev-set. This shows the soft attention model’s tendency to overfit on the small dataset, as it is not enforcing the monotonic assumption of the hard attention model. ; Hulden et al. ; Nicolai et al. Our work was mainly inspired by \\newcitefaruquiTND15 which trained an independent encoder-decoder neural network for every inflection type in the training data, alleviating the need for feature engineering. \\newcitekann2016,kann2016medtl tackled the task with a single soft attention model Bahdanau et al. Cotterell et al. In another closely related work, Rastogi et al. (2016) model the task with a WFST in which the arc weights are learned by optimizing a global loss function over all the possible paths in the state graph, while modeling contextual features with bi-directional LSTMS. This is similar to our approach, where instead of learning to mimic a single greedy alignment as we do, they sum over all possible alignments. Another recent work Kann et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Results on the CELEX dataset', answer the following question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest average accuracy and what is its value?\n" }, { "role": "assistant", "content": "By examining the 'Avg.' column in the table, the Hard model has the highest average accuracy with a value of 89.44. So, the answer is {\"answer\": \"89.44\"}." } ], "images": [ "scigen/train/679.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results on the CELEX dataset" ], "reasoning": "By examining the 'Avg.' column in the table, the Hard model has the highest average accuracy with a value of 89.44.", "question": "Which model has the highest average accuracy and what is its value?", "answer": "So, the answer is {\"answer\": \"89.44\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe soft attention model has a higher average accuracy than the hard attention model.\n" }, { "role": "assistant", "content": "The table shows that the soft attention model has an average accuracy of 87, while the hard attention model has an average accuracy of 89.44. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/679.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results on the CELEX dataset" ], "reasoning": "The table shows that the soft attention model has an average accuracy of 87, while the hard attention model has an average accuracy of 89.44. Therefore, the statement is refuted.", "statement": "The soft attention model has a higher average accuracy than the hard attention model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Results on the SIGMORPHON 2016 morphological inflection dataset. The text above each language lists the morphological phenomena it includes: circ.=circumfixing, agg.=agglutinative, v.h.=vowel harmony, c.h.=consonant harmony'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The hard attention model performs better than the soft attention model on suffixing+stem-change languages (Russian, German, and Spanish), but the soft attention model performs better on the rest of the languages due to its ability to handle long-range dependencies.As different languages show different morphological phenomena, we also experiment with how our model copes with these various phenomena using the morphological inflection dataset from the SIGMORPHON2016 shared task Cotterell et al. We compare our model to two soft attention baselines on this dataset: med Kann and Schütze dataset our model performs better than both soft-attention baselines for the suffixing+stem-change languages (Russian, German and Spanish) and is slightly less accurate than our implementation of the soft attention model on the rest of the languages, which is now the best performing model on this dataset to our knowledge. We explain this by looking at the languages from a linguistic typology point of view, as detailed in \\newcitecotterell-sigmorphon2016. Since Russian, German and Spanish employ a suffixing morphology with internal stem changes, they are more suitable for monotonic alignment as the transformations they need to model are the addition of suffixes and changing characters in the stem. The rest of the languages in the dataset employ more context sensitive morphological phenomena like vowel harmony and consonant harmony, which require to model long range dependencies in the input sequence which better suits the soft attention mechanism. While our implementation of the soft attention model and med are very similar model-wise, we hypothesize that our soft attention model results are better due to the fact that we trained the model for 100 epochs and picked the best performing model on the development set, while the med system was trained for a fixed amount of 20 epochs (although trained on more data – both train and development sets)." } ], "images": [ "scigen/train/680.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on the SIGMORPHON 2016 morphological inflection dataset. The text above each language lists the morphological phenomena it includes: circ.=circumfixing, agg.=agglutinative, v.h.=vowel harmony, c.h.=consonant harmony" ], "reasoning": "The hard attention model performs better than the soft attention model on suffixing+stem-change languages (Russian, German, and Spanish), but the soft attention model performs better on the rest of the languages due to its ability to handle long-range dependencies.", "description": "As different languages show different morphological phenomena, we also experiment with how our model copes with these various phenomena using the morphological inflection dataset from the SIGMORPHON2016 shared task Cotterell et al. We compare our model to two soft attention baselines on this dataset: med Kann and Schütze dataset our model performs better than both soft-attention baselines for the suffixing+stem-change languages (Russian, German and Spanish) and is slightly less accurate than our implementation of the soft attention model on the rest of the languages, which is now the best performing model on this dataset to our knowledge. We explain this by looking at the languages from a linguistic typology point of view, as detailed in \\newcitecotterell-sigmorphon2016. Since Russian, German and Spanish employ a suffixing morphology with internal stem changes, they are more suitable for monotonic alignment as the transformations they need to model are the addition of suffixes and changing characters in the stem. The rest of the languages in the dataset employ more context sensitive morphological phenomena like vowel harmony and consonant harmony, which require to model long range dependencies in the input sequence which better suits the soft attention mechanism. While our implementation of the soft attention model and med are very similar model-wise, we hypothesize that our soft attention model results are better due to the fact that we trained the model for 100 epochs and picked the best performing model on the development set, while the med system was trained for a fixed amount of 20 epochs (although trained on more data – both train and development sets).", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: Results on the SIGMORPHON 2016 morphological inflection dataset. The text above each language lists the morphological phenomena it includes: circ.=circumfixing, agg.=agglutinative, v.h.=vowel harmony, c.h.=consonant harmony', and sequentially solve the question, writing out each step of your reasoning process. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model performs best on the suffixing+stem-change languages and what is its average accuracy?\n" }, { "role": "assistant", "content": "The hard attention model performs best on the suffixing+stem-change languages (Russian, German, and Spanish) with accuracies of 92.21, 96.58, and 98.92 respectively. The average accuracy for these three languages is (92.21 + 96.58 + 98.92) / 3 = 95.90. So, the answer is {\"answer\": \"95.90\"}." } ], "images": [ "scigen/train/680.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on the SIGMORPHON 2016 morphological inflection dataset. The text above each language lists the morphological phenomena it includes: circ.=circumfixing, agg.=agglutinative, v.h.=vowel harmony, c.h.=consonant harmony" ], "reasoning": "The hard attention model performs best on the suffixing+stem-change languages (Russian, German, and Spanish) with accuracies of 92.21, 96.58, and 98.92 respectively. The average accuracy for these three languages is (92.21 + 96.58 + 98.92) / 3 = 95.90.", "question": "Which model performs best on the suffixing+stem-change languages and what is its average accuracy?", "answer": "So, the answer is {\"answer\": \"95.90\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe soft attention model performs better than the med model on the suffixing+stem-change languages.\n" }, { "role": "assistant", "content": "The soft attention model has accuracies of 92.18, 96.51, and 98.88 on the suffixing+stem-change languages (Russian, German, and Spanish) respectively, while the med model has accuracies of 91.46, 95.8, and 98.84. Therefore, the soft attention model performs better than the med model on these languages. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/680.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on the SIGMORPHON 2016 morphological inflection dataset. The text above each language lists the morphological phenomena it includes: circ.=circumfixing, agg.=agglutinative, v.h.=vowel harmony, c.h.=consonant harmony" ], "reasoning": "The soft attention model has accuracies of 92.18, 96.51, and 98.88 on the suffixing+stem-change languages (Russian, German, and Spanish) respectively, while the med model has accuracies of 91.46, 95.8, and 98.84. Therefore, the soft attention model performs better than the med model on these languages.", "statement": "The soft attention model performs better than the med model on the suffixing+stem-change languages.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: Ablation studies on the dev set.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Matching sentences in both directions improves performance by about 1 percent compared to single direction matching. Eliminating any matching strategy significantly reduces performance.Second, to check the effectiveness of bilateral matching, we build two ablation models to matching sentences in only a single direction: 1) “Only P→Q” which only matches P against Q; 2) Comparing the two ablation models with the “Full Model”, we can observe that single direction matching hurts the performance for about 1 percent. Therefore, matching sentences in two directions is really necessary for acquiring better performance. Third, we evaluate the effectiveness of different matching strategies. To this end, we construct four ablation models (w/o Full-Matching, w/o Maxpooling-Matching, w/o Attentive-Matching, w/o Max-Attentive-Matching) by eliminating a matching strategy at each time. We can see that eliminating any of the matching strategies would hurt the performance significantly." } ], "images": [ "scigen/train/681.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Ablation studies on the dev set." ], "reasoning": "Matching sentences in both directions improves performance by about 1 percent compared to single direction matching. Eliminating any matching strategy significantly reduces performance.", "description": "Second, to check the effectiveness of bilateral matching, we build two ablation models to matching sentences in only a single direction: 1) “Only P→Q” which only matches P against Q; 2) Comparing the two ablation models with the “Full Model”, we can observe that single direction matching hurts the performance for about 1 percent. Therefore, matching sentences in two directions is really necessary for acquiring better performance. Third, we evaluate the effectiveness of different matching strategies. To this end, we construct four ablation models (w/o Full-Matching, w/o Maxpooling-Matching, w/o Attentive-Matching, w/o Max-Attentive-Matching) by eliminating a matching strategy at each time. We can see that eliminating any of the matching strategies would hurt the performance significantly.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the accuracy difference between the Full Model and the model without Maxpooling-Matching?\nSolve the above question based on the table titled 'Table 1: Ablation studies on the dev set.'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The Full Model has an accuracy of 88.69, while the model without Maxpooling-Matching has an accuracy of 87.64. The difference is 88.69 - 87.64. So, the answer is {\"answer\": \"1.05\"}" } ], "images": [ "scigen/train/681.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Ablation studies on the dev set." ], "reasoning": "The Full Model has an accuracy of 88.69, while the model without Maxpooling-Matching has an accuracy of 87.64. The difference is 88.69 - 87.64.", "question": "What is the accuracy difference between the Full Model and the model without Maxpooling-Matching?", "answer": "So, the answer is {\"answer\": \"1.05\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The model without Attentive-Matching has the highest accuracy among the ablation models., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model without Attentive-Matching has the highest accuracy among the ablation models.\n" }, { "role": "assistant", "content": "The model without Attentive-Matching has an accuracy of 87.87, while the model without MaxAttentive-Matching has an accuracy of 87.98, which is higher. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/681.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Ablation studies on the dev set." ], "reasoning": "The model without Attentive-Matching has an accuracy of 87.87, while the model without MaxAttentive-Matching has an accuracy of 87.98, which is higher.", "statement": "The model without Attentive-Matching has the highest accuracy among the ablation models.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Performance for paraphrase identification on the Quora dataset.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The BiMPM model has an accuracy of 88.17%, which is more than two percent higher than the L.D.C. model's accuracy of 85.55%, demonstrating its effectiveness for the paraphrase identification task.Our “BiMPM” model outperforms the “L.D.C.” model by more than two percent. Therefore, our model is very effective for the paraphrase identification task." } ], "images": [ "scigen/train/682.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance for paraphrase identification on the Quora dataset." ], "reasoning": "The BiMPM model has an accuracy of 88.17%, which is more than two percent higher than the L.D.C. model's accuracy of 85.55%, demonstrating its effectiveness for the paraphrase identification task.", "description": "Our “BiMPM” model outperforms the “L.D.C.” model by more than two percent. Therefore, our model is very effective for the paraphrase identification task.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: Performance for paraphrase identification on the Quora dataset.', solve this question and give detail solutions. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhat is the accuracy difference between the BiMPM model and the Siamese-CNN model?\n" }, { "role": "assistant", "content": "The accuracy of the BiMPM model is 88.17%, and the accuracy of the Siamese-CNN model is 79.60%. Subtracting the accuracy of the Siamese-CNN model from the BiMPM model gives the difference. So, the answer is {\"answer\": \"8.57\"}." } ], "images": [ "scigen/train/682.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance for paraphrase identification on the Quora dataset." ], "reasoning": "The accuracy of the BiMPM model is 88.17%, and the accuracy of the Siamese-CNN model is 79.60%. Subtracting the accuracy of the Siamese-CNN model from the BiMPM model gives the difference.", "question": "What is the accuracy difference between the BiMPM model and the Siamese-CNN model?", "answer": "So, the answer is {\"answer\": \"8.57\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The BiMPM model is less accurate than the Multi-Perspective-LSTM model., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe BiMPM model is less accurate than the Multi-Perspective-LSTM model.\n" }, { "role": "assistant", "content": "The table shows that the BiMPM model has an accuracy of 88.17%, while the Multi-Perspective-LSTM model has an accuracy of 83.21%. Therefore, the BiMPM model is more accurate. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/682.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance for paraphrase identification on the Quora dataset." ], "reasoning": "The table shows that the BiMPM model has an accuracy of 88.17%, while the Multi-Perspective-LSTM model has an accuracy of 83.21%. Therefore, the BiMPM model is more accurate.", "statement": "The BiMPM model is less accurate than the Multi-Perspective-LSTM model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Performance for natural language inference on the SNLI dataset.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Matching the hypothesis against the premise is more effective than the other way around, and the BiMPM model further improves performance by matching the premise against the hypothesis.First, we can see that “Only P←Q” works significantly better than “Only P→Q”, which tells us that, for natural language inference, matching the hypothesis against the premise is more effective than the other way around. Second, our “BiMPM” model works much better than “Only P←Q”, which reveals that matching premise against the hypothesis can also bring some benefits. (Ensemble)”. Therefore, our models achieve the state-of-the-art performance in both single and ensemble scenarios for the natural language inference task." } ], "images": [ "scigen/train/683.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance for natural language inference on the SNLI dataset." ], "reasoning": "Matching the hypothesis against the premise is more effective than the other way around, and the BiMPM model further improves performance by matching the premise against the hypothesis.", "description": "First, we can see that “Only P←Q” works significantly better than “Only P→Q”, which tells us that, for natural language inference, matching the hypothesis against the premise is more effective than the other way around. Second, our “BiMPM” model works much better than “Only P←Q”, which reveals that matching premise against the hypothesis can also bring some benefits. (Ensemble)”. Therefore, our models achieve the state-of-the-art performance in both single and ensemble scenarios for the natural language inference task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: Performance for natural language inference on the SNLI dataset.' and methodically tackle the question that follows. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieves the highest accuracy in the table?\n" }, { "role": "assistant", "content": "By comparing the accuracy values of all models listed in the table, the BiMPM (Ensemble) model has the highest accuracy of 88.8. So, the answer is {\"answer\": \"BiMPM (Ensemble)\"}." } ], "images": [ "scigen/train/683.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance for natural language inference on the SNLI dataset." ], "reasoning": "By comparing the accuracy values of all models listed in the table, the BiMPM (Ensemble) model has the highest accuracy of 88.8.", "question": "Which model achieves the highest accuracy in the table?", "answer": "So, the answer is {\"answer\": \"BiMPM (Ensemble)\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe BiMPM model works better than the Only P←Q model.\n" }, { "role": "assistant", "content": "The table shows that the BiMPM model has an accuracy of 86.9, while the Only P←Q model has an accuracy of 86.3, indicating that the BiMPM model performs better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/683.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance for natural language inference on the SNLI dataset." ], "reasoning": "The table shows that the BiMPM model has an accuracy of 86.9, while the Only P←Q model has an accuracy of 86.3, indicating that the BiMPM model performs better.", "statement": "The BiMPM model works better than the Only P←Q model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: Performance for answer sentence selection on TREC-QA and WikiQA datasets.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The model's performance in answer sentence selection tasks is comparable to state-of-the-art models, as indicated by MAP and MRR scores.In this Sub-section, we study the effectiveness of our model for answer sentence selection tasks. The answer sentence selection task is to rank a list of candidate answer sentences based on their similarities to the question, and the performance is measured by the mean average precision (MAP) and mean reciprocal rank (MRR). We can see that the performance from our model is on par with the state-of-the-art models. Therefore, our model is also effective for answer sentence selection tasks." } ], "images": [ "scigen/train/684.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance for answer sentence selection on TREC-QA and WikiQA datasets." ], "reasoning": "The model's performance in answer sentence selection tasks is comparable to state-of-the-art models, as indicated by MAP and MRR scores.", "description": "In this Sub-section, we study the effectiveness of our model for answer sentence selection tasks. The answer sentence selection task is to rank a list of candidate answer sentences based on their similarities to the question, and the performance is measured by the mean average precision (MAP) and mean reciprocal rank (MRR). We can see that the performance from our model is on par with the state-of-the-art models. Therefore, our model is also effective for answer sentence selection tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Performance for answer sentence selection on TREC-QA and WikiQA datasets.', answer the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model achieved the highest TREC-QA MRR score?\n" }, { "role": "assistant", "content": "By comparing the TREC-QA MRR scores in the table, the highest score is 0.877, achieved by the model listed in the row with the score 0.801 for TREC-QA MAP. So, the answer is {\"answer\": \"0.877\"}" } ], "images": [ "scigen/train/684.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance for answer sentence selection on TREC-QA and WikiQA datasets." ], "reasoning": "By comparing the TREC-QA MRR scores in the table, the highest score is 0.877, achieved by the model listed in the row with the score 0.801 for TREC-QA MAP.", "question": "Which model achieved the highest TREC-QA MRR score?", "answer": "So, the answer is {\"answer\": \"0.877\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe BiMPM model achieved the highest WikiQA MAP score.\n" }, { "role": "assistant", "content": "By examining the WikiQA MAP scores in the table, the highest score is 0.743, which is achieved by the BiMPM model. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/684.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance for answer sentence selection on TREC-QA and WikiQA datasets." ], "reasoning": "By examining the WikiQA MAP scores in the table, the highest score is 0.743, which is achieved by the BiMPM model.", "statement": "The BiMPM model achieved the highest WikiQA MAP score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Test accuracy on the text classification datasets. For each dataset, we conduct significance test against the best reproducible model, and * means that the improvement is significant at 0.05 significance level.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The TextNAS model outperforms state-of-the-art methods on all text classification datasets except Sogou, likely due to the language difference in embeddings.The results demonstrate that the TextNAS model outperforms state-of-the-art methods on all text classification datasets except Sogou. One potential reason is that Sogou is a dataset in Chinese language, while the Glove embedding vectors are trained by English corpus. One can improve the performance by adding Chinese-language embeddings or char-embeddings, but we do not add them to keep the solution neat. In addition, we can pay a specific attention to the comparison of TextNAS with 29-layers CNN (Conneau ET AL., 2016) and 24-layers Transformer (VASWANI ET AL., 2017). As shown in the table, the TextNAS network improves two baselines by a large margin, indicating the advantage for mixture of different layers." } ], "images": [ "scigen/train/685.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Test accuracy on the text classification datasets. For each dataset, we conduct significance test against the best reproducible model, and * means that the improvement is significant at 0.05 significance level." ], "reasoning": "The TextNAS model outperforms state-of-the-art methods on all text classification datasets except Sogou, likely due to the language difference in embeddings.", "description": "The results demonstrate that the TextNAS model outperforms state-of-the-art methods on all text classification datasets except Sogou. One potential reason is that Sogou is a dataset in Chinese language, while the Glove embedding vectors are trained by English corpus. One can improve the performance by adding Chinese-language embeddings or char-embeddings, but we do not add them to keep the solution neat. In addition, we can pay a specific attention to the comparison of TextNAS with 29-layers CNN (Conneau ET AL., 2016) and 24-layers Transformer (VASWANI ET AL., 2017). As shown in the table, the TextNAS network improves two baselines by a large margin, indicating the advantage for mixture of different layers.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nTable title: Table 4: Test accuracy on the text classification datasets. For each dataset, we conduct significance test against the best reproducible model, and * means that the improvement is significant at 0.05 significance level.\n\nQuestion: Which model has the highest accuracy on the DBP dataset and how does it compare to the TextNAS model?\n" }, { "role": "assistant", "content": "The table shows that the TextNAS model has an accuracy of 99.01 on the DBP dataset. Comparing this to other models, the highest accuracy on the DBP dataset is also 99.01, achieved by the TextNAS model. So, the answer is {\"answer\": \"TextNAS\"}" } ], "images": [ "scigen/train/685.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Test accuracy on the text classification datasets. For each dataset, we conduct significance test against the best reproducible model, and * means that the improvement is significant at 0.05 significance level." ], "reasoning": "The table shows that the TextNAS model has an accuracy of 99.01 on the DBP dataset. Comparing this to other models, the highest accuracy on the DBP dataset is also 99.01, achieved by the TextNAS model.", "question": "Which model has the highest accuracy on the DBP dataset and how does it compare to the TextNAS model?", "answer": "So, the answer is {\"answer\": \"TextNAS\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The TextNAS model has the highest accuracy on the AG dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe TextNAS model has the highest accuracy on the AG dataset.\n" }, { "role": "assistant", "content": "The table shows that the TextNAS model has an accuracy of 93.14 on the AG dataset. Comparing this to other models, the highest accuracy on the AG dataset is 92.50, achieved by Zhang ET AL., 2015, making the statement incorrect. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/685.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Test accuracy on the text classification datasets. For each dataset, we conduct significance test against the best reproducible model, and * means that the improvement is significant at 0.05 significance level." ], "reasoning": "The table shows that the TextNAS model has an accuracy of 93.14 on the AG dataset. Comparing this to other models, the highest accuracy on the AG dataset is 92.50, achieved by Zhang ET AL., 2015, making the statement incorrect.", "statement": "The TextNAS model has the highest accuracy on the AG dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Results on SST dataset. For each dataset, we conduct significance test against the best reproducible model, and * means that the improvement is significant at 0.05 significance level.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "TextNAS achieves competitive performance compared to state-of-the-art manual architectures and outperforms other automatically discovered architectures on the SST dataset.We can see that the neural architecture discovered by TextNAS achieves competitive performances compared with state-of-the-art manual architectures, including the 24-layers Transformer adopted by BERT. At the same time, it outperforms other network architectures discovered automatically by other search spaces and algorithms. Specifically, the accuracy is improved by 11.7% from ENAS-MICRO and 1.9% from ENAS-MACRO on the SST dataset respectively, which shows the superiority of our novel search space for text representation. It should be noticed that there are other publications that have reported higher accuracies. However, they are not directly comparable to our scenario since they incorporate various kinds of external knowledge" } ], "images": [ "scigen/train/686.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on SST dataset. For each dataset, we conduct significance test against the best reproducible model, and * means that the improvement is significant at 0.05 significance level." ], "reasoning": "TextNAS achieves competitive performance compared to state-of-the-art manual architectures and outperforms other automatically discovered architectures on the SST dataset.", "description": "We can see that the neural architecture discovered by TextNAS achieves competitive performances compared with state-of-the-art manual architectures, including the 24-layers Transformer adopted by BERT. At the same time, it outperforms other network architectures discovered automatically by other search spaces and algorithms. Specifically, the accuracy is improved by 11.7% from ENAS-MICRO and 1.9% from ENAS-MACRO on the SST dataset respectively, which shows the superiority of our novel search space for text representation. It should be noticed that there are other publications that have reported higher accuracies. However, they are not directly comparable to our scenario since they incorporate various kinds of external knowledge", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 3: Results on SST dataset. For each dataset, we conduct significance test against the best reproducible model, and * means that the improvement is significant at 0.05 significance level.' and proceed to solve the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nHow much higher is the accuracy of TextNAS compared to ENAS-MICRO on the SST dataset?\n" }, { "role": "assistant", "content": "TextNAS has an accuracy of 52.51% on the SST dataset, while ENAS-MICRO has an accuracy of 47.00%. The difference is 52.51% - 47.00% = 5.51%. So, the answer is {\"answer\": \"5.51%\"}." } ], "images": [ "scigen/train/686.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on SST dataset. For each dataset, we conduct significance test against the best reproducible model, and * means that the improvement is significant at 0.05 significance level." ], "reasoning": "TextNAS has an accuracy of 52.51% on the SST dataset, while ENAS-MICRO has an accuracy of 47.00%. The difference is 52.51% - 47.00% = 5.51%.", "question": "How much higher is the accuracy of TextNAS compared to ENAS-MICRO on the SST dataset?", "answer": "So, the answer is {\"answer\": \"5.51%\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim TextNAS has the highest accuracy on the SST-B dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nTextNAS has the highest accuracy on the SST-B dataset.\n" }, { "role": "assistant", "content": "The table shows that TextNAS has an accuracy of 90.33% on the SST-B dataset, which is higher than any other model listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/686.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on SST dataset. For each dataset, we conduct significance test against the best reproducible model, and * means that the improvement is significant at 0.05 significance level." ], "reasoning": "The table shows that TextNAS has an accuracy of 90.33% on the SST-B dataset, which is higher than any other model listed.", "statement": "TextNAS has the highest accuracy on the SST-B dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 6: Detailed settings for experiments of text classification.'.\n" }, { "role": "assistant", "content": "Dropout with a ratio of 0.5 is applied to various layers, and a sliding window technique with window_size 64 and stride 32 is used to segment long sentences for certain experiments.In all the experiments, we apply dropout (ratio=0.5) to the embedding layers, final output layers and self-attention layers. In addition, in the bidirectional GRU layers, we apply dropout (ratio=0.5) on the input and output tensors. Besides, for several time-consuming experiments, we employ sliding window trick to accelerate the training procedure. Given a sentence, we utilize a sliding window to segment the long input sentence into several sub-sentences, where window_size and stride are pre-defined hyper-parameters. The sub-sentences are fed separately to the neural network to output fixed-length vector representation for each sub-sentence. Then, a max pooling operator is applied on top to calculate the vector representation for the entire sentence. In all experiments using sliding window, we set window_size as 64 and stride as 32." } ], "images": [ "scigen/train/687.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Detailed settings for experiments of text classification." ], "reasoning": "Dropout with a ratio of 0.5 is applied to various layers, and a sliding window technique with window_size 64 and stride 32 is used to segment long sentences for certain experiments.", "description": "In all the experiments, we apply dropout (ratio=0.5) to the embedding layers, final output layers and self-attention layers. In addition, in the bidirectional GRU layers, we apply dropout (ratio=0.5) on the input and output tensors. Besides, for several time-consuming experiments, we employ sliding window trick to accelerate the training procedure. Given a sentence, we utilize a sliding window to segment the long input sentence into several sub-sentences, where window_size and stride are pre-defined hyper-parameters. The sub-sentences are fed separately to the neural network to output fixed-length vector representation for each sub-sentence. Then, a max pooling operator is applied on top to calculate the vector representation for the entire sentence. In all experiments using sliding window, we set window_size as 64 and stride as 32.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 6: Detailed settings for experiments of text classification.', solve the subsequent question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich experiments use the sliding window technique and what is their hidden size?\n" }, { "role": "assistant", "content": "The experiments Sogou, Yahoo, and Amz-B use the sliding window technique, and their hidden sizes are 32, 32, and 128 respectively. So, the answer is {\"answer\": \"Sogou: 32, Yahoo: 32, Amz-B: 128\"}" } ], "images": [ "scigen/train/687.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Detailed settings for experiments of text classification." ], "reasoning": "The experiments Sogou, Yahoo, and Amz-B use the sliding window technique, and their hidden sizes are 32, 32, and 128 respectively.", "question": "Which experiments use the sliding window technique and what is their hidden size?", "answer": "So, the answer is {\"answer\": \"Sogou: 32, Yahoo: 32, Amz-B: 128\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The AG experiment uses a sliding window technique., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe AG experiment uses a sliding window technique.\n" }, { "role": "assistant", "content": "The table shows that the AG experiment does not use the sliding window technique as indicated by 'no' in the sliding window column. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/687.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Detailed settings for experiments of text classification." ], "reasoning": "The table shows that the AG experiment does not use the sliding window technique as indicated by 'no' in the sliding window column.", "statement": "The AG experiment uses a sliding window technique.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 7: Detailed settings for experiments of natural language inference.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The model is trained with Adam optimizer using cosine decay of learning rate and warm up over the first epoch, with specific learning rates, training epochs, l2 regularization, dropout ratios, and penalization values for different experiments.In NLI experiments, we evaluate the result model of TextNAS by training it from scratch. We set the dimension of hidden units as 512 for all layers in the sentence encoder and 2400 for the three fully-connected layers before softmax output. All 24 layers in the sentence encoder are linearly combined to produce the ultimate sentence embedding vector. All convolutions follow an ordering of ReLU, convolution operation and batch normalization. We also employ layer normalization after the outputs of bidirectional GRU and self-attention layers. Dropout is adopted on the output of each word-embedding, GRU and fully-connected layer. We set batch size as 32 and max input length as 128. Adam optimizer with cosine decay of learning rate and warm up over the first epoch are utilized to train the model. Learning rate: 1×10−4, 2×10−4, 3×10−4 Training epochs: 8, 12, 16, 20, 30 l2 regularization: 1×10−2, 2×10−2, 5×10−2, 1×10−1, 2×10−1, 5×10−1 Dropout ratio: 0.1, 0.2, 0.3 Penalization: 0, 1×10−1, 1×10−2, 1×10−3" } ], "images": [ "scigen/train/688.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Detailed settings for experiments of natural language inference." ], "reasoning": "The model is trained with Adam optimizer using cosine decay of learning rate and warm up over the first epoch, with specific learning rates, training epochs, l2 regularization, dropout ratios, and penalization values for different experiments.", "description": "In NLI experiments, we evaluate the result model of TextNAS by training it from scratch. We set the dimension of hidden units as 512 for all layers in the sentence encoder and 2400 for the three fully-connected layers before softmax output. All 24 layers in the sentence encoder are linearly combined to produce the ultimate sentence embedding vector. All convolutions follow an ordering of ReLU, convolution operation and batch normalization. We also employ layer normalization after the outputs of bidirectional GRU and self-attention layers. Dropout is adopted on the output of each word-embedding, GRU and fully-connected layer. We set batch size as 32 and max input length as 128. Adam optimizer with cosine decay of learning rate and warm up over the first epoch are utilized to train the model. Learning rate: 1×10−4, 2×10−4, 3×10−4 Training epochs: 8, 12, 16, 20, 30 l2 regularization: 1×10−2, 2×10−2, 5×10−2, 1×10−1, 2×10−1, 5×10−1 Dropout ratio: 0.1, 0.2, 0.3 Penalization: 0, 1×10−1, 1×10−2, 1×10−3", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 7: Detailed settings for experiments of natural language inference.', answer the question below by showing each progressive step toward the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the l2 regularization value used for the MNLI experiment?\n" }, { "role": "assistant", "content": "The table lists the l2 regularization values for different experiments. For the MNLI experiment, the l2 regularization value is 1×10−2. So, the answer is {\"answer\": \"1×10−2\"}" } ], "images": [ "scigen/train/688.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Detailed settings for experiments of natural language inference." ], "reasoning": "The table lists the l2 regularization values for different experiments. For the MNLI experiment, the l2 regularization value is 1×10−2.", "question": "What is the l2 regularization value used for the MNLI experiment?", "answer": "So, the answer is {\"answer\": \"1×10−2\"}" }, { "messages": [ { "role": "user", "content": "The SNLI experiment used a dropout rate of 0.3. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe SNLI experiment used a dropout rate of 0.3.\n" }, { "role": "assistant", "content": "The table shows that the dropout rate for the SNLI experiment is 0.2, not 0.3. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/688.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Detailed settings for experiments of natural language inference." ], "reasoning": "The table shows that the dropout rate for the SNLI experiment is 0.2, not 0.3.", "statement": "The SNLI experiment used a dropout rate of 0.3.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: Per-label breakdown of F1 scores for Catalan and Spanish. These numbers reflect labels for each argument; the combination is different from the overall semantic F1, which includes predicate sense disambiguation.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The addition of English data improves performance in common label categories like arg1 and argM, but has inconsistent effects on less common labels.Label-wise results. In both languages, we find a small but consistent improvement in the most common label categories (e.g., arg1 and argM). Less common label categories are sensitive to small changes in performance; they have the largest changes in F1 in absolute value, but without a consistent direction. This could be attributed to the addition of English data, which improves learning of representations that are useful for the most common labels, but is essentially a random perturbation for the rarer ones. This pattern is seen across languages, and consistently results in overall gains from polyglot training." } ], "images": [ "scigen/train/689.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Per-label breakdown of F1 scores for Catalan and Spanish. These numbers reflect labels for each argument; the combination is different from the overall semantic F1, which includes predicate sense disambiguation." ], "reasoning": "The addition of English data improves performance in common label categories like arg1 and argM, but has inconsistent effects on less common labels.", "description": "Label-wise results. In both languages, we find a small but consistent improvement in the most common label categories (e.g., arg1 and argM). Less common label categories are sensitive to small changes in performance; they have the largest changes in F1 in absolute value, but without a consistent direction. This could be attributed to the addition of English data, which improves learning of representations that are useful for the most common labels, but is essentially a random perturbation for the rarer ones. This pattern is seen across languages, and consistently results in overall gains from polyglot training.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Per-label breakdown of F1 scores for Catalan and Spanish. These numbers reflect labels for each argument; the combination is different from the overall semantic F1, which includes predicate sense disambiguation.\nQuestion: What is the F1 improvement for arg3 in the Catalan language after adding English data?\nSolve the above question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows the F1 improvement for arg3 in Catalan as +18.17 after adding English data. So, the answer is {\"answer\": \"+18.17\"}" } ], "images": [ "scigen/train/689.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Per-label breakdown of F1 scores for Catalan and Spanish. These numbers reflect labels for each argument; the combination is different from the overall semantic F1, which includes predicate sense disambiguation." ], "reasoning": "The table shows the F1 improvement for arg3 in Catalan as +18.17 after adding English data.", "question": "What is the F1 improvement for arg3 in the Catalan language after adding English data?", "answer": "So, the answer is {\"answer\": \"+18.17\"}" }, { "messages": [ { "role": "user", "content": "Adding English data improves the F1 score for arg4 in Spanish. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nAdding English data improves the F1 score for arg4 in Spanish.\n" }, { "role": "assistant", "content": "The table shows an F1 improvement of +4.01 for arg4 in Spanish after adding English data. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/689.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Per-label breakdown of F1 scores for Catalan and Spanish. These numbers reflect labels for each argument; the combination is different from the overall semantic F1, which includes predicate sense disambiguation." ], "reasoning": "The table shows an F1 improvement of +4.01 for arg4 in Spanish after adding English data.", "statement": "Adding English data improves the F1 score for arg4 in Spanish.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Semantic F1 scores (including predicate sense disambiguation) on the CoNLL 2009 dataset. State of the art for Catalan and Japanese is from Zhao2009, for German and Spanish from Roth2016-fn, for English and Chinese from marcheggiani2017gcn. Italics indicate use of syntax.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Simple polyglot training improves performance over monolingual training for all languages except Czech, where there is no change.We observe that simple polyglot training improves over monolingual training, with the exception of Czech, where we observe no change in performance." } ], "images": [ "scigen/train/690.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Semantic F1 scores (including predicate sense disambiguation) on the CoNLL 2009 dataset. State of the art for Catalan and Japanese is from Zhao2009, for German and Spanish from Roth2016-fn, for English and Chinese from marcheggiani2017gcn. Italics indicate use of syntax." ], "reasoning": "Simple polyglot training improves performance over monolingual training for all languages except Czech, where there is no change.", "description": "We observe that simple polyglot training improves over monolingual training, with the exception of Czech, where we observe no change in performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2: Semantic F1 scores (including predicate sense disambiguation) on the CoNLL 2009 dataset. State of the art for Catalan and Japanese is from Zhao2009, for German and Spanish from Roth2016-fn, for English and Chinese from marcheggiani2017gcn. Italics indicate use of syntax.', unfold the steps to solve the question given next. Output the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which language shows no change in performance when comparing monolingual training to simple polyglot training?\n" }, { "role": "assistant", "content": "By comparing the monolingual and simple polyglot training rows, we see that Czech (ces) has the same performance score of 84.82 in both cases. So, the answer is {\"answer\": \"Czech\"}" } ], "images": [ "scigen/train/690.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Semantic F1 scores (including predicate sense disambiguation) on the CoNLL 2009 dataset. State of the art for Catalan and Japanese is from Zhao2009, for German and Spanish from Roth2016-fn, for English and Chinese from marcheggiani2017gcn. Italics indicate use of syntax." ], "reasoning": "By comparing the monolingual and simple polyglot training rows, we see that Czech (ces) has the same performance score of 84.82 in both cases.", "question": "Which language shows no change in performance when comparing monolingual training to simple polyglot training?", "answer": "So, the answer is {\"answer\": \"Czech\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nSimple polyglot training improves performance for all languages compared to monolingual training.\n" }, { "role": "assistant", "content": "The table shows that simple polyglot training does not improve performance for Czech, as the score remains the same at 84.82. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/690.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Semantic F1 scores (including predicate sense disambiguation) on the CoNLL 2009 dataset. State of the art for Catalan and Japanese is from Zhao2009, for German and Spanish from Roth2016-fn, for English and Chinese from marcheggiani2017gcn. Italics indicate use of syntax." ], "reasoning": "The table shows that simple polyglot training does not improve performance for Czech, as the score remains the same at 84.82.", "statement": "Simple polyglot training improves performance for all languages compared to monolingual training.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Semantic F1 scores on the English test set for each language pair.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "English SRL improves with polyglot training, with the smallest improvement seen with Czech, suggesting annotation differences rather than dataset size.English SRL consistently benefits from polyglot training, with an increase of 0.25–0.7 absolute F1 points, depending on the language. Surprisingly, Czech provides the smallest improvement, despite the large amount of data added; the absence of crosslingual transfer in both directions for the English-Czech case, breaking the pattern seen in other languages, could therefore be due to differences in annotation rather than questions of dataset size." } ], "images": [ "scigen/train/691.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Semantic F1 scores on the English test set for each language pair." ], "reasoning": "English SRL improves with polyglot training, with the smallest improvement seen with Czech, suggesting annotation differences rather than dataset size.", "description": "English SRL consistently benefits from polyglot training, with an increase of 0.25–0.7 absolute F1 points, depending on the language. Surprisingly, Czech provides the smallest improvement, despite the large amount of data added; the absence of crosslingual transfer in both directions for the English-Czech case, breaking the pattern seen in other languages, could therefore be due to differences in annotation rather than questions of dataset size.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 4: Semantic F1 scores on the English test set for each language pair.' in mind, break down and resolve the question below step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich language provides the smallest improvement in F1 points for English SRL with polyglot training?\n" }, { "role": "assistant", "content": "The table shows the F1 scores for English SRL with different languages added. The smallest improvement is seen with Czech (+ces), which increases the score from 86.54 to 87.07. So, the answer is {\"answer\": \"Czech\"}." } ], "images": [ "scigen/train/691.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Semantic F1 scores on the English test set for each language pair." ], "reasoning": "The table shows the F1 scores for English SRL with different languages added. The smallest improvement is seen with Czech (+ces), which increases the score from 86.54 to 87.07.", "question": "Which language provides the smallest improvement in F1 points for English SRL with polyglot training?", "answer": "So, the answer is {\"answer\": \"Czech\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Adding Spanish data provides the largest improvement in F1 points for English SRL., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nAdding Spanish data provides the largest improvement in F1 points for English SRL.\n" }, { "role": "assistant", "content": "The table shows the F1 scores for English SRL with different languages added. The largest improvement is seen with Spanish (+spa), which increases the score from 86.54 to 87.24. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/691.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Semantic F1 scores on the English test set for each language pair." ], "reasoning": "The table shows the F1 scores for English SRL with different languages added. The largest improvement is seen with Spanish (+spa), which increases the score from 86.54 to 87.24.", "statement": "Adding Spanish data provides the largest improvement in F1 points for English SRL.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: Unlabeled semantic F1 scores on the CoNLL 2009 dataset.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Polyglot training generally improves unlabeled F1 scores, with the largest gains in languages with smaller datasets such as German and Catalan.S4SS0SSS0Px3 Labeled vs. unlabeled F1. As can be seen here, the unlabeled F1 improvements are generally positive but small, indicating that polyglot training can help both in structure prediction and labeling of arguments. The pattern of seeing the largest improvements on the languages with the smallest datasets generally holds here: the largest F1 gains are in German and Catalan, followed by Japanese, with minimal or no improvement elsewhere." } ], "images": [ "scigen/train/692.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Unlabeled semantic F1 scores on the CoNLL 2009 dataset." ], "reasoning": "Polyglot training generally improves unlabeled F1 scores, with the largest gains in languages with smaller datasets such as German and Catalan.", "description": "S4SS0SSS0Px3 Labeled vs. unlabeled F1. As can be seen here, the unlabeled F1 improvements are generally positive but small, indicating that polyglot training can help both in structure prediction and labeling of arguments. The pattern of seeing the largest improvements on the languages with the smallest datasets generally holds here: the largest F1 gains are in German and Catalan, followed by Japanese, with minimal or no improvement elsewhere.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 5: Unlabeled semantic F1 scores on the CoNLL 2009 dataset.'\nQuestion: Which language showed the largest improvement in F1 score with polyglot training?\n" }, { "role": "assistant", "content": "By comparing the F1 scores of the monolingual and polyglot models, the largest improvement is seen in German, increasing from 87.95 to 89.01. So, the answer is {\"answer\": \"German\"}." } ], "images": [ "scigen/train/692.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Unlabeled semantic F1 scores on the CoNLL 2009 dataset." ], "reasoning": "By comparing the F1 scores of the monolingual and polyglot models, the largest improvement is seen in German, increasing from 87.95 to 89.01.", "question": "Which language showed the largest improvement in F1 score with polyglot training?", "answer": "So, the answer is {\"answer\": \"German\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nPolyglot training improved the F1 score for Japanese by more than 1 point.\n" }, { "role": "assistant", "content": "The F1 score for Japanese improved from 85.55 to 86.17, which is an increase of 0.62 points, not more than 1 point. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/692.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Unlabeled semantic F1 scores on the CoNLL 2009 dataset." ], "reasoning": "The F1 score for Japanese improved from 85.55 to 86.17, which is an increase of 0.62 points, not more than 1 point.", "statement": "Polyglot training improved the F1 score for Japanese by more than 1 point.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Comparison of performance of systems with different stack capacities'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Increasing the stack capacity from 1-best to 2-best rmstart hypotheses improves overall accuracy in Frm and Fs but negatively impacts EO and time-to-detection scores TDrm and TDrp.Our experiments showed that different system settings perform better in different metrics, and no individual setting achieved the best result in all of them. ’s measure and STIR achieves 0.736 on the previously unevaluated Fs. The fastest average time to detection is 1 word for TDrp and 2.6 words for TDrm We make a preliminary investigation into the effect of increasing the stack capacity by comparing stacks with 1-best rmstart hypotheses per rpstart and 2-best stacks. Moving to the 2-stack condition results in gain in overall accuracy in Frm and Fs, but at the cost of EO and also time-to-detection scores TDrm and TDrp. The extent to which the stack can be increased without increasing jitter, latency and complexity will be investigated in future work." } ], "images": [ "scigen/train/693.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of performance of systems with different stack capacities" ], "reasoning": "Increasing the stack capacity from 1-best to 2-best rmstart hypotheses improves overall accuracy in Frm and Fs but negatively impacts EO and time-to-detection scores TDrm and TDrp.", "description": "Our experiments showed that different system settings perform better in different metrics, and no individual setting achieved the best result in all of them. ’s measure and STIR achieves 0.736 on the previously unevaluated Fs. The fastest average time to detection is 1 word for TDrp and 2.6 words for TDrm We make a preliminary investigation into the effect of increasing the stack capacity by comparing stacks with 1-best rmstart hypotheses per rpstart and 2-best stacks. Moving to the 2-stack condition results in gain in overall accuracy in Frm and Fs, but at the cost of EO and also time-to-detection scores TDrm and TDrp. The extent to which the stack can be increased without increasing jitter, latency and complexity will be investigated in future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 3: Comparison of performance of systems with different stack capacities\nQuestion: What is the impact on EO when moving from 1-best rmstart to 2-best rmstart?\n" }, { "role": "assistant", "content": "The table shows that EO increases from 3.780 to 4.319 when moving from 1-best rmstart to 2-best rmstart. So, the answer is {\"answer\": \"4.319\"}." } ], "images": [ "scigen/train/693.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of performance of systems with different stack capacities" ], "reasoning": "The table shows that EO increases from 3.780 to 4.319 when moving from 1-best rmstart to 2-best rmstart.", "question": "What is the impact on EO when moving from 1-best rmstart to 2-best rmstart?", "answer": "So, the answer is {\"answer\": \"4.319\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nIncreasing the stack capacity to 2-best rmstart improves the time-to-detection scores TDrm and TDrp.\n" }, { "role": "assistant", "content": "The table shows that increasing the stack capacity to 2-best rmstart actually increases the time-to-detection scores TDrm from 2.6 to 2.7 and TDrp from 1.0 to 1.12, indicating a negative impact. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/693.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of performance of systems with different stack capacities" ], "reasoning": "The table shows that increasing the stack capacity to 2-best rmstart actually increases the time-to-detection scores TDrm from 2.6 to 2.7 and TDrp from 1.0 to 1.12, indicating a negative impact.", "statement": "Increasing the stack capacity to 2-best rmstart improves the time-to-detection scores TDrm and TDrp.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Quantitative comparison of the proposed PRN model against the baselines.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "PRN achieves the best performance on average in single-task training, while in multi-task training, PRN and BIDAF w/ static memory perform comparably and better than BIDAF.In single-task training setting, PRN gives state-of-the-art results compared to other neural models. Moreover, it achieves the best performance on average. These results demonstrate the importance of having a dynamic memory and keeping track of entities extracted from the recipe. In multi-task training setting where a single model is trained to solve all the tasks at once, PRN and BIDAF w/ static memory perform comparably and give much better results than BIDAF. Note that the model performances in the multi-task training setting are worse than single-task performances. We believe that this is due to the nature of the tasks that some are more difficult than the others. We think that the performance could be improved by employing a carefully selected curriculum strategy McCann et al." } ], "images": [ "scigen/train/694.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Quantitative comparison of the proposed PRN model against the baselines." ], "reasoning": "PRN achieves the best performance on average in single-task training, while in multi-task training, PRN and BIDAF w/ static memory perform comparably and better than BIDAF.", "description": "In single-task training setting, PRN gives state-of-the-art results compared to other neural models. Moreover, it achieves the best performance on average. These results demonstrate the importance of having a dynamic memory and keeping track of entities extracted from the recipe. In multi-task training setting where a single model is trained to solve all the tasks at once, PRN and BIDAF w/ static memory perform comparably and give much better results than BIDAF. Note that the model performances in the multi-task training setting are worse than single-task performances. We believe that this is due to the nature of the tasks that some are more difficult than the others. We think that the performance could be improved by employing a carefully selected curriculum strategy McCann et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1: Quantitative comparison of the proposed PRN model against the baselines.', solve the subsequent question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich model achieves the highest average score in single-task training?\n" }, { "role": "assistant", "content": "To find the model with the highest average score in single-task training, compare the 'Single-task Training Average' scores of all models. PRN has the highest score of 57.57. So, the answer is {\"answer\": \"PRN\"}." } ], "images": [ "scigen/train/694.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Quantitative comparison of the proposed PRN model against the baselines." ], "reasoning": "To find the model with the highest average score in single-task training, compare the 'Single-task Training Average' scores of all models. PRN has the highest score of 57.57.", "question": "Which model achieves the highest average score in single-task training?", "answer": "So, the answer is {\"answer\": \"PRN\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nBIDAF w/ static memory performs better than PRN in multi-task training.\n" }, { "role": "assistant", "content": "In multi-task training, compare the 'Multi-task Training All' scores of BIDAF w/ static memory and PRN. BIDAF w/ static memory has a score of 50.59, while PRN has a score of 50.17. Therefore, BIDAF w/ static memory performs better than PRN. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/694.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Quantitative comparison of the proposed PRN model against the baselines." ], "reasoning": "In multi-task training, compare the 'Multi-task Training All' scores of BIDAF w/ static memory and PRN. BIDAF w/ static memory has a score of 50.59, while PRN has a score of 50.17. Therefore, BIDAF w/ static memory performs better than PRN.", "statement": "BIDAF w/ static memory performs better than PRN in multi-task training.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Speed comparison of our systems with other open-sourced systems.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Our segmenter is 6.8x faster than SPADE, and our parser is 3.9x faster than CODRA, making our end-to-end system 5.9x faster than SPADE.As noted earlier, both our segmenter and parser operate in linear time with respect to the number of input units. We test all the systems with the same 100 sentences randomly selected from our test set on our machine (CPU: Intel Xeon W-2133, GPU: NVIDIA GTX 1080Ti). We include the model loading time for all the systems. In addition, CODRA’s DCRF parser has a O(n3) inference time. Our segmenter is 6.8x faster than SPADE. Compared to CODRA (the fastest parser as of yet), our parser is 3.9x faster. Finally, our end-to-end system is 5.9x faster than the fastest system out there (SPADE), making our system not only effective but also highly efficient. Even when tested only on CPU, our model is faster than all the other models." } ], "images": [ "scigen/train/695.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Speed comparison of our systems with other open-sourced systems." ], "reasoning": "Our segmenter is 6.8x faster than SPADE, and our parser is 3.9x faster than CODRA, making our end-to-end system 5.9x faster than SPADE.", "description": "As noted earlier, both our segmenter and parser operate in linear time with respect to the number of input units. We test all the systems with the same 100 sentences randomly selected from our test set on our machine (CPU: Intel Xeon W-2133, GPU: NVIDIA GTX 1080Ti). We include the model loading time for all the systems. In addition, CODRA’s DCRF parser has a O(n3) inference time. Our segmenter is 6.8x faster than SPADE. Compared to CODRA (the fastest parser as of yet), our parser is 3.9x faster. Finally, our end-to-end system is 5.9x faster than the fastest system out there (SPADE), making our system not only effective but also highly efficient. Even when tested only on CPU, our model is faster than all the other models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 4: Speed comparison of our systems with other open-sourced systems.', unfold the question presented below, detailing every step of your calculation. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nHow much faster is our end-to-end system compared to SPADE?\n" }, { "role": "assistant", "content": "The table shows that our end-to-end system (Segmenter → Parser) has a speedup of 9.5x on GPU and 3.9x on CPU compared to SPADE's 1.6x speedup. So, the answer is {\"answer\": \"5.9x\"}." } ], "images": [ "scigen/train/695.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Speed comparison of our systems with other open-sourced systems." ], "reasoning": "The table shows that our end-to-end system (Segmenter → Parser) has a speedup of 9.5x on GPU and 3.9x on CPU compared to SPADE's 1.6x speedup.", "question": "How much faster is our end-to-end system compared to SPADE?", "answer": "So, the answer is {\"answer\": \"5.9x\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Our segmenter is faster than SPADE. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nOur segmenter is faster than SPADE.\n" }, { "role": "assistant", "content": "The table shows that our segmenter has a speedup of 11.6x on GPU and 3.9x on CPU, while SPADE has a speedup of 1.7x. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/695.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Speed comparison of our systems with other open-sourced systems." ], "reasoning": "The table shows that our segmenter has a speedup of 11.6x on GPU and 3.9x on CPU, while SPADE has a speedup of 1.7x.", "statement": "Our segmenter is faster than SPADE.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: Discourse segmentation results. Superscript ⋆ indicates the model is significantly superior to the WLYELMo model with a p-value <0.01.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Our segmenter, using ELMo and joint training, achieves significant improvements in F1, Recall, and Precision, outperforming all baselines and approaching human agreement levels.Using encoder hidden states as decoder inputs and adopting dot product as the attention score function together gives 0.40%-7.29% relative improvement in F1 over the first four baselines. Using ELMo, our segmenter outperforms all the baselines in all three measures. We achieve 2.3%-11.9%, 2.4%-11.3% and 2.3%-12.3% relative improvements in F1, Recall and Precision, respectively. Jointly training with the parser improves this further (95.55 F1). It is worthwhile to mention that our segmenter’s performance of 95.55 F1 is very close to the human agreement of 98.3 F1. ELMo, as a transfer learning method, provides notable improvements. A similar observation was reported in Wang et al. Surprisingly, the results with BERT were not as good. We suspect this is due to BERT’s special tokenization." } ], "images": [ "scigen/train/696.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Discourse segmentation results. Superscript ⋆ indicates the model is significantly superior to the WLYELMo model with a p-value <0.01." ], "reasoning": "Our segmenter, using ELMo and joint training, achieves significant improvements in F1, Recall, and Precision, outperforming all baselines and approaching human agreement levels.", "description": "Using encoder hidden states as decoder inputs and adopting dot product as the attention score function together gives 0.40%-7.29% relative improvement in F1 over the first four baselines. Using ELMo, our segmenter outperforms all the baselines in all three measures. We achieve 2.3%-11.9%, 2.4%-11.3% and 2.3%-12.3% relative improvements in F1, Recall and Precision, respectively. Jointly training with the parser improves this further (95.55 F1). It is worthwhile to mention that our segmenter’s performance of 95.55 F1 is very close to the human agreement of 98.3 F1. ELMo, as a transfer learning method, provides notable improvements. A similar observation was reported in Wang et al. Surprisingly, the results with BERT were not as good. We suspect this is due to BERT’s special tokenization.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 1: Discourse segmentation results. Superscript ⋆ indicates the model is significantly superior to the WLYELMo model with a p-value <0.01.', answer the following question. Give detailed solution consisting of each step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the F1 score of the segmenter when using ELMo and joint training?\n" }, { "role": "assistant", "content": "The table shows that the F1 score of the segmenter using ELMo is 95.35±0.10, and with joint training, it is 95.55±0.13. So, the answer is {\"answer\": \"95.55±0.13\"}" } ], "images": [ "scigen/train/696.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Discourse segmentation results. Superscript ⋆ indicates the model is significantly superior to the WLYELMo model with a p-value <0.01." ], "reasoning": "The table shows that the F1 score of the segmenter using ELMo is 95.35±0.10, and with joint training, it is 95.55±0.13.", "question": "What is the F1 score of the segmenter when using ELMo and joint training?", "answer": "So, the answer is {\"answer\": \"95.55±0.13\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe segmenter using BERT achieves better performance than the one using Glove.\n" }, { "role": "assistant", "content": "The table shows that the segmenter using BERT has an F1 score of 93.51±0.16, while the one using Glove has an F1 score of 91.41±0.21. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/696.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Discourse segmentation results. Superscript ⋆ indicates the model is significantly superior to the WLYELMo model with a p-value <0.01." ], "reasoning": "The table shows that the segmenter using BERT has an F1 score of 93.51±0.16, while the one using Glove has an F1 score of 91.41±0.21. Therefore, the statement is supported.", "statement": "The segmenter using BERT achieves better performance than the one using Glove.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 7: Human evaluations on Yelp Reviews dataset. Each block is a head-to-head comparison of two models on grammatically, consistency, and non-redundancy.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "ml-VAE outperforms flat-VAE and AAE in all evaluation criteria, producing more grammatically-correct and semantically-coherent samples, which aligns with human evaluations.As shown in Even though both models underperform when compared against the ground-truth real reviews, ml-VAE was rated higher in comparison to flat-VAE (raters find ml-VAE closer to human-generated than the flat-VAE) in all the criteria evaluation criteria. When compared against AAE baseline models using the same data preprocessing steps and hyperparameters, ml-VAE again produces more grammatically-correct and semantically-coherent samples. The human evaluations correlate with the automatic metrics, which indicate that our ml-VAE is actually generating more coherent stories than the baseline models. We leave further evaluations using embedding based metrics as a possible extension to our work." } ], "images": [ "scigen/train/697.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Human evaluations on Yelp Reviews dataset. Each block is a head-to-head comparison of two models on grammatically, consistency, and non-redundancy." ], "reasoning": "ml-VAE outperforms flat-VAE and AAE in all evaluation criteria, producing more grammatically-correct and semantically-coherent samples, which aligns with human evaluations.", "description": "As shown in Even though both models underperform when compared against the ground-truth real reviews, ml-VAE was rated higher in comparison to flat-VAE (raters find ml-VAE closer to human-generated than the flat-VAE) in all the criteria evaluation criteria. When compared against AAE baseline models using the same data preprocessing steps and hyperparameters, ml-VAE again produces more grammatically-correct and semantically-coherent samples. The human evaluations correlate with the automatic metrics, which indicate that our ml-VAE is actually generating more coherent stories than the baseline models. We leave further evaluations using embedding based metrics as a possible extension to our work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: 'Table 7: Human evaluations on Yelp Reviews dataset. Each block is a head-to-head comparison of two models on grammatically, consistency, and non-redundancy.'\nQuestion: Which model has the highest overall score in the table?\n" }, { "role": "assistant", "content": "By examining the 'Overall' column, the highest score is 86.0, which corresponds to ml-VAE. So, the answer is {\"answer\": \"ml-VAE\"}." } ], "images": [ "scigen/train/697.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Human evaluations on Yelp Reviews dataset. Each block is a head-to-head comparison of two models on grammatically, consistency, and non-redundancy." ], "reasoning": "By examining the 'Overall' column, the highest score is 86.0, which corresponds to ml-VAE.", "question": "Which model has the highest overall score in the table?", "answer": "So, the answer is {\"answer\": \"ml-VAE\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim ml-VAE has a higher consistency score than flat-VAE., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nml-VAE has a higher consistency score than flat-VAE.\n" }, { "role": "assistant", "content": "The table shows that ml-VAE has consistency scores of 55.0, 86.0, and 26.3, while flat-VAE has consistency scores of 33.0 and 18.7, indicating that ml-VAE consistently scores higher. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/697.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Human evaluations on Yelp Reviews dataset. Each block is a head-to-head comparison of two models on grammatically, consistency, and non-redundancy." ], "reasoning": "The table shows that ml-VAE has consistency scores of 55.0, 86.0, and 26.3, while flat-VAE has consistency scores of 33.0 and 18.7, indicating that ml-VAE consistently scores higher.", "statement": "ml-VAE has a higher consistency score than flat-VAE.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Language modeling results on Yelp and arXiv data. Upper block are baselines, and lower are our models.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The ml-VAE-S model improves PPL from 47.9 to 46.6 compared to ml-LM, indicating better word-level predictions.The flat-VAE model obtains slightly worse NNL and PPL relative to a flat LSTM-based language model. With multi-level LSTM decoder, our ml-VAE-S yields increased KL divergence, demonstrating that the VAE model tends to leverage more information from the latent variable in the decoding stage. The PPL of ml-VAE-S is also decreased from 47.9 to 46.6 (compared to ml-LM), indicating that the sampled latent codes improve word-level predictions." } ], "images": [ "scigen/train/698.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Language modeling results on Yelp and arXiv data. Upper block are baselines, and lower are our models." ], "reasoning": "The ml-VAE-S model improves PPL from 47.9 to 46.6 compared to ml-LM, indicating better word-level predictions.", "description": "The flat-VAE model obtains slightly worse NNL and PPL relative to a flat LSTM-based language model. With multi-level LSTM decoder, our ml-VAE-S yields increased KL divergence, demonstrating that the VAE model tends to leverage more information from the latent variable in the decoding stage. The PPL of ml-VAE-S is also decreased from 47.9 to 46.6 (compared to ml-LM), indicating that the sampled latent codes improve word-level predictions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: Language modeling results on Yelp and arXiv data. Upper block are baselines, and lower are our models.', and tackle the question below in a sequential manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model has the lowest PPL for the Yelp dataset?\n" }, { "role": "assistant", "content": "By examining the PPL values for the Yelp dataset in the table, the ml-VAE-D model has the lowest PPL value of 45.8. So, the answer is {\"answer\": \"ml-VAE-D\"}." } ], "images": [ "scigen/train/698.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Language modeling results on Yelp and arXiv data. Upper block are baselines, and lower are our models." ], "reasoning": "By examining the PPL values for the Yelp dataset in the table, the ml-VAE-D model has the lowest PPL value of 45.8.", "question": "Which model has the lowest PPL for the Yelp dataset?", "answer": "So, the answer is {\"answer\": \"ml-VAE-D\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The flat-VAE model has a higher KL divergence than the ml-VAE-S model for the arXiv dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe flat-VAE model has a higher KL divergence than the ml-VAE-S model for the arXiv dataset.\n" }, { "role": "assistant", "content": "The table shows that the flat-VAE model has a KL divergence of 0.01 for the arXiv dataset, while the ml-VAE-S model has a KL divergence of 5.3, indicating that the statement is false. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/698.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Language modeling results on Yelp and arXiv data. Upper block are baselines, and lower are our models." ], "reasoning": "The table shows that the flat-VAE model has a KL divergence of 0.01 for the arXiv dataset, while the ml-VAE-S model has a KL divergence of 5.3, indicating that the statement is false.", "statement": "The flat-VAE model has a higher KL divergence than the ml-VAE-S model for the arXiv dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Evaluation results for generated sequences by our models and baselines on corpus-level BLEU scores (B-n denotes the corpus-level BLEU-n score.)', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The VAE models, particularly those with multi-level decoders, outperform AAE and ARAE in BLEU scores, with ml-VAE-D showing the best results due to its flexible prior distribution.VAE tends to be a stronger baseline for paragraph generation, exhibiting higher corpus-level BLEU scores than both AAE and ARAE. The VAE with multi-level decoder demonstrates better BLEU scores than the one with a flat decoder, indicating that the plan-ahead mechanism associated with the hierarchical decoding process indeed benefits the sampling quality. ml-VAE-D exhibits slightly better results than ml-VAE-S. We attribute this to the more flexible prior distribution of ml-VAE-D, which improves the ability of inference networks to extract semantic features from a paragraph, yielding more informative latent codes." } ], "images": [ "scigen/train/699.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Evaluation results for generated sequences by our models and baselines on corpus-level BLEU scores (B-n denotes the corpus-level BLEU-n score.)" ], "reasoning": "The VAE models, particularly those with multi-level decoders, outperform AAE and ARAE in BLEU scores, with ml-VAE-D showing the best results due to its flexible prior distribution.", "description": "VAE tends to be a stronger baseline for paragraph generation, exhibiting higher corpus-level BLEU scores than both AAE and ARAE. The VAE with multi-level decoder demonstrates better BLEU scores than the one with a flat decoder, indicating that the plan-ahead mechanism associated with the hierarchical decoding process indeed benefits the sampling quality. ml-VAE-D exhibits slightly better results than ml-VAE-S. We attribute this to the more flexible prior distribution of ml-VAE-D, which improves the ability of inference networks to extract semantic features from a paragraph, yielding more informative latent codes.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Evaluation results for generated sequences by our models and baselines on corpus-level BLEU scores (B-n denotes the corpus-level BLEU-n score.)\nQuestion: Which model demonstrates the highest BLEU score for Yelp B-4?\nGive a detailed response to the above question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, the highest BLEU score for Yelp B-4 is found in the ml-VAE-D model with a score of 0.549. So, the answer is {\"answer\": \"ml-VAE-D\"}." } ], "images": [ "scigen/train/699.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Evaluation results for generated sequences by our models and baselines on corpus-level BLEU scores (B-n denotes the corpus-level BLEU-n score.)" ], "reasoning": "By examining the table, the highest BLEU score for Yelp B-4 is found in the ml-VAE-D model with a score of 0.549.", "question": "Which model demonstrates the highest BLEU score for Yelp B-4?", "answer": "So, the answer is {\"answer\": \"ml-VAE-D\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe flat-VAE model has a higher BLEU score for arXiv B-3 than the ml-VAE-S model.\n" }, { "role": "assistant", "content": "The table shows that the flat-VAE model has a BLEU score of 0.625 for arXiv B-3, while the ml-VAE-S model has a higher score of 0.663. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/699.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Evaluation results for generated sequences by our models and baselines on corpus-level BLEU scores (B-n denotes the corpus-level BLEU-n score.)" ], "reasoning": "The table shows that the flat-VAE model has a BLEU score of 0.625 for arXiv B-3, while the ml-VAE-S model has a higher score of 0.663.", "statement": "The flat-VAE model has a higher BLEU score for arXiv B-3 than the ml-VAE-S model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: Evaluation of diversity of 1000 generated sentences on self-BLEU scores (B-n), unique n-gram percentages (ngr), 2-gram entropy score.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "ml-VAE-D demonstrates the effectiveness of hierarchically structured generative networks with the smallest BLEU score and largest unique n-grams percentage, despite AAE and ARAE having better diversity but worse BLEU scores.A small self-BLEU score together with a large BLEU score can justify the effectiveness of a model, i.e., being able to generate realistic-looking as well as diverse samples. Among all the VAE variants, ml-VAE-D shows the smallest BLEU score and largest unique n-grams percentage, demonstrating the effectiveness of hieararhically structured generative networks as well as latent variables. Even though AAE and ARAE yield better diversity according to both metrics, their corpus-level BLEU scores are much worse relative to ml-VAE-D. We leverage human evaluation for further comparison." } ], "images": [ "scigen/train/700.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Evaluation of diversity of 1000 generated sentences on self-BLEU scores (B-n), unique n-gram percentages (ngr), 2-gram entropy score." ], "reasoning": "ml-VAE-D demonstrates the effectiveness of hierarchically structured generative networks with the smallest BLEU score and largest unique n-grams percentage, despite AAE and ARAE having better diversity but worse BLEU scores.", "description": "A small self-BLEU score together with a large BLEU score can justify the effectiveness of a model, i.e., being able to generate realistic-looking as well as diverse samples. Among all the VAE variants, ml-VAE-D shows the smallest BLEU score and largest unique n-grams percentage, demonstrating the effectiveness of hieararhically structured generative networks as well as latent variables. Even though AAE and ARAE yield better diversity according to both metrics, their corpus-level BLEU scores are much worse relative to ml-VAE-D. We leverage human evaluation for further comparison.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 5: Evaluation of diversity of 1000 generated sentences on self-BLEU scores (B-n), unique n-gram percentages (ngr), 2-gram entropy score.\nQuestion: Which model shows the smallest BLEU score and largest unique n-grams percentage?\n" }, { "role": "assistant", "content": "The description states that ml-VAE-D shows the smallest BLEU score and largest unique n-grams percentage, demonstrating its effectiveness. So, the answer is {\"answer\": \"ml-VAE-D\"}" } ], "images": [ "scigen/train/700.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Evaluation of diversity of 1000 generated sentences on self-BLEU scores (B-n), unique n-gram percentages (ngr), 2-gram entropy score." ], "reasoning": "The description states that ml-VAE-D shows the smallest BLEU score and largest unique n-grams percentage, demonstrating its effectiveness.", "question": "Which model shows the smallest BLEU score and largest unique n-grams percentage?", "answer": "So, the answer is {\"answer\": \"ml-VAE-D\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nml-VAE-D has the highest BLEU score among all VAE variants.\n" }, { "role": "assistant", "content": "The table shows that ml-VAE-D has a BLEU score of 0.579 for Yelp B-4, which is not the highest among all VAE variants. flat-VAE has a higher BLEU score of 0.617. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/700.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Evaluation of diversity of 1000 generated sentences on self-BLEU scores (B-n), unique n-gram percentages (ngr), 2-gram entropy score." ], "reasoning": "The table shows that ml-VAE-D has a BLEU score of 0.579 for Yelp B-4, which is not the highest among all VAE variants. flat-VAE has a higher BLEU score of 0.617.", "statement": "ml-VAE-D has the highest BLEU score among all VAE variants.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: BLEU scores on WMT14 English-to-French for Transformer results.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The positional encoding of the Transformer reduces the model's ability to capture order dependency, which diminishes the effectiveness of reinforcement learning and denoising techniques.However, the improvement from reinforcement learning is quite modest, while the denoising part even declines the scores. We conjecture that this is because the positional encoding of the Transformer reduces the order dependency capture of the model, thus counteracts the effects of these approaches." } ], "images": [ "scigen/train/701.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: BLEU scores on WMT14 English-to-French for Transformer results." ], "reasoning": "The positional encoding of the Transformer reduces the model's ability to capture order dependency, which diminishes the effectiveness of reinforcement learning and denoising techniques.", "description": "However, the improvement from reinforcement learning is quite modest, while the denoising part even declines the scores. We conjecture that this is because the positional encoding of the Transformer reduces the order dependency capture of the model, thus counteracts the effects of these approaches.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: BLEU scores on WMT14 English-to-French for Transformer results.', and sequentially solve the question, writing out each step of your reasoning process. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model achieved the highest score for De→En translation?\n" }, { "role": "assistant", "content": "By examining the De→En column, the highest score is 32.1, achieved by the 'Transformer + AD (RL)' model. So, the answer is {\"answer\": \"Transformer + AD (RL)\"}." } ], "images": [ "scigen/train/701.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: BLEU scores on WMT14 English-to-French for Transformer results." ], "reasoning": "By examining the De→En column, the highest score is 32.1, achieved by the 'Transformer + AD (RL)' model.", "question": "Which model achieved the highest score for De→En translation?", "answer": "So, the answer is {\"answer\": \"Transformer + AD (RL)\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Transformer + AD (Denoising) model achieved the highest score for En→De translation. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Transformer + AD (Denoising) model achieved the highest score for En→De translation.\n" }, { "role": "assistant", "content": "The highest score for En→De translation is 28.1, achieved by both 'Transformer + AD' and 'BiDAN (Transformer)' models, not the 'Transformer + AD (Denoising)' model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/701.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: BLEU scores on WMT14 English-to-French for Transformer results." ], "reasoning": "The highest score for En→De translation is 28.1, achieved by both 'Transformer + AD' and 'BiDAN (Transformer)' models, not the 'Transformer + AD (Denoising)' model.", "statement": "The Transformer + AD (Denoising) model achieved the highest score for En→De translation.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: BLEU scores for NMT models on WMT14 English-German and English-French and IWSLT 2015 English-Vietnamese dataset. “AD” denotes auxiliary decoder, “RL” denotes reinforcement learning.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Adding the auxiliary decoder, denoising, and reinforcement learning improves BLEU scores, but training all modules together leads to the best performance.In the medium part, we conduct an ablation experiment to evaluate the individual contribution of each component of our model. At first, we only add the auxiliary decoder D2 to the baseline model, and the BLEU scores on all the test sets rise about 1.4 point, which shows the effectiveness of our bi-decoder architecture and the significance of the language-independent representation of the text. We then train our model with the process of denoising, in other words, we take out the objective function JRL when optimizing our BiDAN model. We observe that the performance rises around 0.3 point, which proves that focusing on the internal structure of the languages is very helpful to the task. Finally, we use the reinforcement learning with the original loss to train our auxiliary decoder, which means we don’t use the objective function JD. The results show that this leads to about 0.4 point improvement, which indicates that relaxing the grammatical limitation and capturing the keyword information is very useful in our bi-decoder architecture. Finally, instead of first jointly train the parameters of the whole model to the decoder D1 90% convergence and then fix θ2 (parameters of the D2) and train Θ1 using J1 until the model fully converges, we directly training the model until all of its parts converge. The results show that training from a well-trained model may induce the local minimum of the optimization. In other words, we keep λd and λr as 2 and change the ratio of the original objective J2. As we can see, on all of the datasets the model drops sharply and even worse than the baseline model when we set λa as 0, which means we only use the objective functions JD and JRL without the original one. This indicates that totally ignoring the grammatical structure of the input text is not helpful to the task. We also observe that the performance rises with the increase of λa until 5 or 6. Afterwards, the results get worse when we raise the λa, which means the multi-objective learning can improve the performance." } ], "images": [ "scigen/train/702.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: BLEU scores for NMT models on WMT14 English-German and English-French and IWSLT 2015 English-Vietnamese dataset. “AD” denotes auxiliary decoder, “RL” denotes reinforcement learning." ], "reasoning": "Adding the auxiliary decoder, denoising, and reinforcement learning improves BLEU scores, but training all modules together leads to the best performance.", "description": "In the medium part, we conduct an ablation experiment to evaluate the individual contribution of each component of our model. At first, we only add the auxiliary decoder D2 to the baseline model, and the BLEU scores on all the test sets rise about 1.4 point, which shows the effectiveness of our bi-decoder architecture and the significance of the language-independent representation of the text. We then train our model with the process of denoising, in other words, we take out the objective function JRL when optimizing our BiDAN model. We observe that the performance rises around 0.3 point, which proves that focusing on the internal structure of the languages is very helpful to the task. Finally, we use the reinforcement learning with the original loss to train our auxiliary decoder, which means we don’t use the objective function JD. The results show that this leads to about 0.4 point improvement, which indicates that relaxing the grammatical limitation and capturing the keyword information is very useful in our bi-decoder architecture. Finally, instead of first jointly train the parameters of the whole model to the decoder D1 90% convergence and then fix θ2 (parameters of the D2) and train Θ1 using J1 until the model fully converges, we directly training the model until all of its parts converge. The results show that training from a well-trained model may induce the local minimum of the optimization. In other words, we keep λd and λr as 2 and change the ratio of the original objective J2. As we can see, on all of the datasets the model drops sharply and even worse than the baseline model when we set λa as 0, which means we only use the objective functions JD and JRL without the original one. This indicates that totally ignoring the grammatical structure of the input text is not helpful to the task. We also observe that the performance rises with the increase of λa until 5 or 6. Afterwards, the results get worse when we raise the λa, which means the multi-objective learning can improve the performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: BLEU scores for NMT models on WMT14 English-German and English-French and IWSLT 2015 English-Vietnamese dataset. “AD” denotes auxiliary decoder, “RL” denotes reinforcement learning.\nQuestion: What is the BLEU score improvement on the WMT14 En→De dataset when using the BiDAN model compared to the Baseline model?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The BLEU score for the Baseline model on the WMT14 En→De dataset is 22.6, and for the BiDAN model, it is 24.7. The improvement is 24.7 - 22.6 = 2.1 points. So, the answer is {\"answer\": \"2.1\"}" } ], "images": [ "scigen/train/702.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: BLEU scores for NMT models on WMT14 English-German and English-French and IWSLT 2015 English-Vietnamese dataset. “AD” denotes auxiliary decoder, “RL” denotes reinforcement learning." ], "reasoning": "The BLEU score for the Baseline model on the WMT14 En→De dataset is 22.6, and for the BiDAN model, it is 24.7. The improvement is 24.7 - 22.6 = 2.1 points.", "question": "What is the BLEU score improvement on the WMT14 En→De dataset when using the BiDAN model compared to the Baseline model?", "answer": "So, the answer is {\"answer\": \"2.1\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe BiDAN model achieves the highest BLEU score on the WMT14 En→Fr dataset.\n" }, { "role": "assistant", "content": "The table shows that the BiDAN model has a BLEU score of 34.2 on the WMT14 En→Fr dataset, which is the highest score compared to other models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/702.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: BLEU scores for NMT models on WMT14 English-German and English-French and IWSLT 2015 English-Vietnamese dataset. “AD” denotes auxiliary decoder, “RL” denotes reinforcement learning." ], "reasoning": "The table shows that the BiDAN model has a BLEU score of 34.2 on the WMT14 En→Fr dataset, which is the highest score compared to other models.", "statement": "The BiDAN model achieves the highest BLEU score on the WMT14 En→Fr dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: BLEU scores on WMT14 English-to-French for NMT models with different encoders.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Replacing the original encoder with different sources at testing time shows that the BiDAN framework, trained on a different dataset, performs better than random and En→De encoders but still lags behind the original En→Fr encoder.We train the NMT model on WMT14 English-to-French dataset, and then replace the well-trained encoder with different sources at the testing time. We first replace the encoder with random parameters and the results drop a lot, which is not surprising. Then we use the encoder trained on the WMT14 English-to-German dataset to test the performance of cross language performance. The results improve modestly compared to the random encoder but still have a huge gap to the original model. This indicates that the coarse cross language training doesn’t work even though the languages are quite similar. Finally, we replace the original encoder with the encoder from our BiDAN framework, which is also trained on the WMT14 English-to-German dataset. Although the performance is still far away from the level which is achieved by the original encoder, it is much better than the previous two methods. We point out that the only difference among those models are the parameters of the encoder, which determine the text representation. Thus the different performances demonstrate that our model provides a more language-dependent text representation, and this may also explain the improvement of our structure to the general NMT model." } ], "images": [ "scigen/train/703.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: BLEU scores on WMT14 English-to-French for NMT models with different encoders." ], "reasoning": "Replacing the original encoder with different sources at testing time shows that the BiDAN framework, trained on a different dataset, performs better than random and En→De encoders but still lags behind the original En→Fr encoder.", "description": "We train the NMT model on WMT14 English-to-French dataset, and then replace the well-trained encoder with different sources at the testing time. We first replace the encoder with random parameters and the results drop a lot, which is not surprising. Then we use the encoder trained on the WMT14 English-to-German dataset to test the performance of cross language performance. The results improve modestly compared to the random encoder but still have a huge gap to the original model. This indicates that the coarse cross language training doesn’t work even though the languages are quite similar. Finally, we replace the original encoder with the encoder from our BiDAN framework, which is also trained on the WMT14 English-to-German dataset. Although the performance is still far away from the level which is achieved by the original encoder, it is much better than the previous two methods. We point out that the only difference among those models are the parameters of the encoder, which determine the text representation. Thus the different performances demonstrate that our model provides a more language-dependent text representation, and this may also explain the improvement of our structure to the general NMT model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 3: BLEU scores on WMT14 English-to-French for NMT models with different encoders.'\nQuestion: What is the BLEU score for p1 when using the En→De Encoder (BiDAN)?\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for different encoder sources. For the En→De Encoder (BiDAN), the BLEU score for p1 is listed. So, the answer is {\"answer\": \"27.8\"}." } ], "images": [ "scigen/train/703.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: BLEU scores on WMT14 English-to-French for NMT models with different encoders." ], "reasoning": "The table shows the BLEU scores for different encoder sources. For the En→De Encoder (BiDAN), the BLEU score for p1 is listed.", "question": "What is the BLEU score for p1 when using the En→De Encoder (BiDAN)?", "answer": "So, the answer is {\"answer\": \"27.8\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe En→De Encoder (BiDAN) has a higher BLEU score for p2 than the En→De Encoder.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for p2 is 2.5 for the En→De Encoder (BiDAN) and 0.2 for the En→De Encoder, indicating that the statement is correct. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/703.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: BLEU scores on WMT14 English-to-French for NMT models with different encoders." ], "reasoning": "The table shows that the BLEU score for p2 is 2.5 for the En→De Encoder (BiDAN) and 0.2 for the En→De Encoder, indicating that the statement is correct.", "statement": "The En→De Encoder (BiDAN) has a higher BLEU score for p2 than the En→De Encoder.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 8: Ablation of cross-entropy loss vs. cross-entropy+maxmargin loss for our BiDAF-based generative model (on dev set).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Max-margin loss improves the retrieval-based recall@k scores by providing knowledge about the negative samples for the generative model.Max-margin loss provides knowledge about the negative samples for the generative model, hence improves the retrieval-based recall@k scores." } ], "images": [ "scigen/train/704.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Ablation of cross-entropy loss vs. cross-entropy+maxmargin loss for our BiDAF-based generative model (on dev set)." ], "reasoning": "Max-margin loss improves the retrieval-based recall@k scores by providing knowledge about the negative samples for the generative model.", "description": "Max-margin loss provides knowledge about the negative samples for the generative model, hence improves the retrieval-based recall@k scores.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: Table 8: Ablation of cross-entropy loss vs. cross-entropy+maxmargin loss for our BiDAF-based generative model (on dev set).\nQuestion: What is the difference in recall@2 scores between the Cross-entropy (XE) model and the XE+Max-margin model?\n" }, { "role": "assistant", "content": "To find the difference in recall@2 scores, subtract the recall@2 score of the Cross-entropy (XE) model (23.45) from the recall@2 score of the XE+Max-margin model (27.39). So, the answer is {\"answer\": \"3.94\"}." } ], "images": [ "scigen/train/704.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Ablation of cross-entropy loss vs. cross-entropy+maxmargin loss for our BiDAF-based generative model (on dev set)." ], "reasoning": "To find the difference in recall@2 scores, subtract the recall@2 score of the Cross-entropy (XE) model (23.45) from the recall@2 score of the XE+Max-margin model (27.39).", "question": "What is the difference in recall@2 scores between the Cross-entropy (XE) model and the XE+Max-margin model?", "answer": "So, the answer is {\"answer\": \"3.94\"}." }, { "messages": [ { "role": "user", "content": "The XE+Max-margin model has a higher recall@5 score than the Cross-entropy (XE) model. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe XE+Max-margin model has a higher recall@5 score than the Cross-entropy (XE) model.\n" }, { "role": "assistant", "content": "The table shows that the recall@5 score for the XE+Max-margin model is 57.02, which is higher than the recall@5 score for the Cross-entropy (XE) model, which is 54.78. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/704.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Ablation of cross-entropy loss vs. cross-entropy+maxmargin loss for our BiDAF-based generative model (on dev set)." ], "reasoning": "The table shows that the recall@5 score for the XE+Max-margin model is 57.02, which is higher than the recall@5 score for the Cross-entropy (XE) model, which is 54.78.", "statement": "The XE+Max-margin model has a higher recall@5 score than the Cross-entropy (XE) model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Performance of our baselines, discriminative models, and generative models for recall@k metrics on our Twitch-FIFA test set. C and V represent chat and video context, respectively.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The TriDAF model with self-attention outperforms all other models, including the triple encoder model, in recall@k scores.We first discuss results of our simple non-trained and trained baselines (see Sec. The ‘Most-Frequent-Response’ baseline, which just ranks the 10-sized response retrieval list based on their frequency in the training data, gets only around 10% recall@1. Our other non-trained baselines: ‘Chat-Response-Cosine’ and ‘Nearest Neighbor’, which ranks the candidate responses based on (Twitch-trained RNN encoder’s vector) cosine similarity with chat-context and K-best training contexts’ response vectors, respectively, achieves slightly better scores. We also show that our simple trained baselines (logistic regression and nearest neighbor) also achieve relatively low scores, indicating that a simple, shallow model will not work on this challenging dataset. Our dual encoder models are significantly better than random choice and all our simple baselines above, and further show that they have complementary information because using both of them together (in ‘Triple Encoder’) improves the overall performance of the model. Finally, we show that our novel TriDAF model with self-attention performs significantly better than the triple encoder model. Next, we evaluate the performance of our generative models with both retrieval-based recall@k scores and phrase matching-based metrics as discussed in Sec. Starting with a simple sequence-to-sequence attention model with video only, chat only, and both video and chat encoders, the recall@k scores are better than all the simple baselines. Moreover, using both video+chat context is again better than using only one context modality. Finally, we show that the addition of the bidirectional attention flow mechanism improves the performance in all recall@k scores. Das et al." } ], "images": [ "scigen/train/705.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of our baselines, discriminative models, and generative models for recall@k metrics on our Twitch-FIFA test set. C and V represent chat and video context, respectively." ], "reasoning": "The TriDAF model with self-attention outperforms all other models, including the triple encoder model, in recall@k scores.", "description": "We first discuss results of our simple non-trained and trained baselines (see Sec. The ‘Most-Frequent-Response’ baseline, which just ranks the 10-sized response retrieval list based on their frequency in the training data, gets only around 10% recall@1. Our other non-trained baselines: ‘Chat-Response-Cosine’ and ‘Nearest Neighbor’, which ranks the candidate responses based on (Twitch-trained RNN encoder’s vector) cosine similarity with chat-context and K-best training contexts’ response vectors, respectively, achieves slightly better scores. We also show that our simple trained baselines (logistic regression and nearest neighbor) also achieve relatively low scores, indicating that a simple, shallow model will not work on this challenging dataset. Our dual encoder models are significantly better than random choice and all our simple baselines above, and further show that they have complementary information because using both of them together (in ‘Triple Encoder’) improves the overall performance of the model. Finally, we show that our novel TriDAF model with self-attention performs significantly better than the triple encoder model. Next, we evaluate the performance of our generative models with both retrieval-based recall@k scores and phrase matching-based metrics as discussed in Sec. Starting with a simple sequence-to-sequence attention model with video only, chat only, and both video and chat encoders, the recall@k scores are better than all the simple baselines. Moreover, using both video+chat context is again better than using only one context modality. Finally, we show that the addition of the bidirectional attention flow mechanism improves the performance in all recall@k scores. Das et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Performance of our baselines, discriminative models, and generative models for recall@k metrics on our Twitch-FIFA test set. C and V represent chat and video context, respectively.\nQuestion: Which model achieves the highest recall@1 score?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By examining the table, the TriDAF+Self Attn (C+V) model has the highest recall@1 score of 20.7. So, the answer is {\"answer\": \"TriDAF+Self Attn (C+V)\"}" } ], "images": [ "scigen/train/705.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of our baselines, discriminative models, and generative models for recall@k metrics on our Twitch-FIFA test set. C and V represent chat and video context, respectively." ], "reasoning": "By examining the table, the TriDAF+Self Attn (C+V) model has the highest recall@1 score of 20.7.", "question": "Which model achieves the highest recall@1 score?", "answer": "So, the answer is {\"answer\": \"TriDAF+Self Attn (C+V)\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Seq2seq + Attn (C+V) model achieves a recall@5 score of 57.0.\n" }, { "role": "assistant", "content": "According to the table, the Seq2seq + Attn (C+V) model has a recall@5 score of 57.0. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/705.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of our baselines, discriminative models, and generative models for recall@k metrics on our Twitch-FIFA test set. C and V represent chat and video context, respectively." ], "reasoning": "According to the table, the Seq2seq + Attn (C+V) model has a recall@5 score of 57.0.", "statement": "The Seq2seq + Attn (C+V) model achieves a recall@5 score of 57.0.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Performance of our generative models on phrase matching metrics.'.\n" }, { "role": "assistant", "content": "The BiDAF model significantly outperforms the non-BiDAF models on both METEOR and ROUGE-L metrics in a multi-reference setup.Again, our BiDAF model is stat. significantly better than non-BiDAF model on both METEOR (p<0.01) and ROUGE-L (p<0.02) metrics. Since dialogue systems can have several diverse, non-overlapping valid responses, we consider a multi-reference setup where all the utterances in the 10-sec response window are treated as valid responses." } ], "images": [ "scigen/train/706.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance of our generative models on phrase matching metrics." ], "reasoning": "The BiDAF model significantly outperforms the non-BiDAF models on both METEOR and ROUGE-L metrics in a multi-reference setup.", "description": "Again, our BiDAF model is stat. significantly better than non-BiDAF model on both METEOR (p<0.01) and ROUGE-L (p<0.02) metrics. Since dialogue systems can have several diverse, non-overlapping valid responses, we consider a multi-reference setup where all the utterances in the 10-sec response window are treated as valid responses.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 4: Performance of our generative models on phrase matching metrics.' and methodically tackle the question that follows. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model has the highest METEOR score and what is the score?\n" }, { "role": "assistant", "content": "By comparing the METEOR scores of all models, the ⊗ + BiDAF (C+V) model has the highest score of 3.70. So, the answer is {\"answer\": \"⊗ + BiDAF (C+V), 3.70\"}" } ], "images": [ "scigen/train/706.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance of our generative models on phrase matching metrics." ], "reasoning": "By comparing the METEOR scores of all models, the ⊗ + BiDAF (C+V) model has the highest score of 3.70.", "question": "Which model has the highest METEOR score and what is the score?", "answer": "So, the answer is {\"answer\": \"⊗ + BiDAF (C+V), 3.70\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Seq2seq + Atten. (C) model has a higher ROUGE-L score than the Seq2seq + Atten. (V) model.\n" }, { "role": "assistant", "content": "The table shows that the Seq2seq + Atten. (C) model has a ROUGE-L score of 8.44, while the Seq2seq + Atten. (V) model has a ROUGE-L score of 8.34. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/706.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance of our generative models on phrase matching metrics." ], "reasoning": "The table shows that the Seq2seq + Atten. (C) model has a ROUGE-L score of 8.44, while the Seq2seq + Atten. (V) model has a ROUGE-L score of 8.34.", "statement": "The Seq2seq + Atten. (C) model has a higher ROUGE-L score than the Seq2seq + Atten. (V) model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 7: Ablation of classification vs. max-margin loss on our TriDAF discriminative model (on dev set).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Max-margin loss outperforms classification loss in recall metrics, indicating its effectiveness in distinguishing between positive and negative examples.We observe that max-margin loss performs better than the classification loss, which is intuitive because max-margin loss tries to differentiate between positive and negative training example triples." } ], "images": [ "scigen/train/707.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Ablation of classification vs. max-margin loss on our TriDAF discriminative model (on dev set)." ], "reasoning": "Max-margin loss outperforms classification loss in recall metrics, indicating its effectiveness in distinguishing between positive and negative examples.", "description": "We observe that max-margin loss performs better than the classification loss, which is intuitive because max-margin loss tries to differentiate between positive and negative training example triples.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: 'Table 7: Ablation of classification vs. max-margin loss on our TriDAF discriminative model (on dev set).'\nQuestion: Which model has the highest recall@5 score?\n" }, { "role": "assistant", "content": "By comparing the recall@5 scores of both models, we see that the Max-margin loss model has a higher recall@5 score of 68.09 compared to the Classification loss model's 66.60. So, the answer is {\"answer\": \"Max-margin loss\"}." } ], "images": [ "scigen/train/707.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Ablation of classification vs. max-margin loss on our TriDAF discriminative model (on dev set)." ], "reasoning": "By comparing the recall@5 scores of both models, we see that the Max-margin loss model has a higher recall@5 score of 68.09 compared to the Classification loss model's 66.60.", "question": "Which model has the highest recall@5 score?", "answer": "So, the answer is {\"answer\": \"Max-margin loss\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Classification loss has a higher recall@2 score than Max-margin loss., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nClassification loss has a higher recall@2 score than Max-margin loss.\n" }, { "role": "assistant", "content": "The table shows that the recall@2 score for Classification loss is 33.72, while for Max-margin loss it is 35.90. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/707.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Ablation of classification vs. max-margin loss on our TriDAF discriminative model (on dev set)." ], "reasoning": "The table shows that the recall@2 score for Classification loss is 33.72, while for Max-margin loss it is 35.90. Therefore, the statement is incorrect.", "statement": "Classification loss has a higher recall@2 score than Max-margin loss.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: Conpono improves the previous state-of-the-art on four DiscoEval tasks. The average accuracy across all tasks is also a new state-of-the-art, despite a small drop in accuracy for PDTB-E. BERT-Base and BERT-Large numbers are reported from Chen et al. (2019), while the rest were collected for this paper. We report standard deviations by running the evaluations 10 times with different seeds for the same Conpono model weights.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Conpono's model, with the same parameter count as BERT-Base, outperforms BERT-Large in sentence ordering and coherence tasks, indicating the effectiveness of its encoding approach.We report results from versions of Conpono using each of these encoding approaches, labeled isolated to represent separate encoding and uni-encoder to represent joint encoding of the anchor and target without a separate anchor encoding. Results: Our model excels in particular on the sentence ordering and coherence tasks (SP, BSO, and DC). Note that our model parameter count is the same as BERT-Base but it outperforms BERT-Large, which has significantly more parameters and has used much more compute for pretraining. BERT-Base BSO scores tend to fall between those of BERT-Base and our model, implying that the sentence ordering objective is improving the models for this benchmark, but that binary sentence ordering is not sufficient to capture the added benefits of including more fine-grained ordering and negative examples." } ], "images": [ "scigen/train/708.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Conpono improves the previous state-of-the-art on four DiscoEval tasks. The average accuracy across all tasks is also a new state-of-the-art, despite a small drop in accuracy for PDTB-E. BERT-Base and BERT-Large numbers are reported from Chen et al. (2019), while the rest were collected for this paper. We report standard deviations by running the evaluations 10 times with different seeds for the same Conpono model weights." ], "reasoning": "Conpono's model, with the same parameter count as BERT-Base, outperforms BERT-Large in sentence ordering and coherence tasks, indicating the effectiveness of its encoding approach.", "description": "We report results from versions of Conpono using each of these encoding approaches, labeled isolated to represent separate encoding and uni-encoder to represent joint encoding of the anchor and target without a separate anchor encoding. Results: Our model excels in particular on the sentence ordering and coherence tasks (SP, BSO, and DC). Note that our model parameter count is the same as BERT-Base but it outperforms BERT-Large, which has significantly more parameters and has used much more compute for pretraining. BERT-Base BSO scores tend to fall between those of BERT-Base and our model, implying that the sentence ordering objective is improving the models for this benchmark, but that binary sentence ordering is not sufficient to capture the added benefits of including more fine-grained ordering and negative examples.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model has the highest average score and what is that score?\nSolve the above question based on the table titled 'Table 1: Conpono improves the previous state-of-the-art on four DiscoEval tasks. The average accuracy across all tasks is also a new state-of-the-art, despite a small drop in accuracy for PDTB-E. BERT-Base and BERT-Large numbers are reported from Chen et al. (2019), while the rest were collected for this paper. We report standard deviations by running the evaluations 10 times with different seeds for the same Conpono model weights.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the 'avg.' column, we see that Conpono (k=2) has the highest average score of 63.0. So, the answer is {\"answer\": \"Conpono (k=2) with 63.0\"}" } ], "images": [ "scigen/train/708.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Conpono improves the previous state-of-the-art on four DiscoEval tasks. The average accuracy across all tasks is also a new state-of-the-art, despite a small drop in accuracy for PDTB-E. BERT-Base and BERT-Large numbers are reported from Chen et al. (2019), while the rest were collected for this paper. We report standard deviations by running the evaluations 10 times with different seeds for the same Conpono model weights." ], "reasoning": "By examining the 'avg.' column, we see that Conpono (k=2) has the highest average score of 63.0.", "question": "Which model has the highest average score and what is that score?", "answer": "So, the answer is {\"answer\": \"Conpono (k=2) with 63.0\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence Conpono isolated has a higher BSO score than BERT-Base. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nConpono isolated has a higher BSO score than BERT-Base.\n" }, { "role": "assistant", "content": "The BSO score for Conpono isolated is 57.9, which is lower than BERT-Base's BSO score of 68.5. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/708.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Conpono improves the previous state-of-the-art on four DiscoEval tasks. The average accuracy across all tasks is also a new state-of-the-art, despite a small drop in accuracy for PDTB-E. BERT-Base and BERT-Large numbers are reported from Chen et al. (2019), while the rest were collected for this paper. We report standard deviations by running the evaluations 10 times with different seeds for the same Conpono model weights." ], "reasoning": "The BSO score for Conpono isolated is 57.9, which is lower than BERT-Base's BSO score of 68.5.", "statement": "Conpono isolated has a higher BSO score than BERT-Base.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Our model improves accuracy over BERT-Base for RTE and COPA benchmarks. Improvements are comparable to BERT-Large but still lag behind much larger models trained on more data, such as ALBERT. All scores are on the validation set.'.\n" }, { "role": "assistant", "content": "Our model shows improvements over BERT-Base and BERT-Base BSO, demonstrating its effectiveness in both artificial sentence ordering tasks and general language understanding benchmarks.Results: We believe that the coherence and ordering aspects of these evaluation tasks are well fit to demonstrate the how our model can improve on strong baselines such as BERT-Base. Interestingly, we observe improvements over the baseline with BERT-Base BSO, showing that even simple discourse-level objectives could lead to noticeable downstream effects. Though these improvements are modest compared to BERT-Large, they are meant to highlight that our model does not only improve on results for artificial sentence ordering tasks, but also on aspects of benchmarks used to generally evaluate pretrained language models and language understanding." } ], "images": [ "scigen/train/709.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Our model improves accuracy over BERT-Base for RTE and COPA benchmarks. Improvements are comparable to BERT-Large but still lag behind much larger models trained on more data, such as ALBERT. All scores are on the validation set." ], "reasoning": "Our model shows improvements over BERT-Base and BERT-Base BSO, demonstrating its effectiveness in both artificial sentence ordering tasks and general language understanding benchmarks.", "description": "Results: We believe that the coherence and ordering aspects of these evaluation tasks are well fit to demonstrate the how our model can improve on strong baselines such as BERT-Base. Interestingly, we observe improvements over the baseline with BERT-Base BSO, showing that even simple discourse-level objectives could lead to noticeable downstream effects. Though these improvements are modest compared to BERT-Large, they are meant to highlight that our model does not only improve on results for artificial sentence ordering tasks, but also on aspects of benchmarks used to generally evaluate pretrained language models and language understanding.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: Our model improves accuracy over BERT-Base for RTE and COPA benchmarks. Improvements are comparable to BERT-Large but still lag behind much larger models trained on more data, such as ALBERT. All scores are on the validation set.', answer the following question. Give detailed solution consisting of each step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model has the highest RTE score?\n" }, { "role": "assistant", "content": "To find the model with the highest RTE score, compare the RTE scores of all models listed in the table. The highest RTE score is 86.6, which belongs to ALBERT. So, the answer is {\"answer\": \"ALBERT\"}." } ], "images": [ "scigen/train/709.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Our model improves accuracy over BERT-Base for RTE and COPA benchmarks. Improvements are comparable to BERT-Large but still lag behind much larger models trained on more data, such as ALBERT. All scores are on the validation set." ], "reasoning": "To find the model with the highest RTE score, compare the RTE scores of all models listed in the table. The highest RTE score is 86.6, which belongs to ALBERT.", "question": "Which model has the highest RTE score?", "answer": "So, the answer is {\"answer\": \"ALBERT\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nBERT-Base BSO has a higher COPA score than Conpono.\n" }, { "role": "assistant", "content": "To verify this statement, compare the COPA scores of BERT-Base BSO and Conpono. BERT-Base BSO has a COPA score of 67.0, while Conpono has a COPA score of 69.0. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/709.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Our model improves accuracy over BERT-Base for RTE and COPA benchmarks. Improvements are comparable to BERT-Large but still lag behind much larger models trained on more data, such as ALBERT. All scores are on the validation set." ], "reasoning": "To verify this statement, compare the COPA scores of BERT-Base BSO and Conpono. BERT-Base BSO has a COPA score of 67.0, while Conpono has a COPA score of 69.0. Therefore, the statement is incorrect.", "statement": "BERT-Base BSO has a higher COPA score than Conpono.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: Conpono is more effective at classifying the most plausible sentence from the extended context than BERT-Base. We report the BERT-Large exact match score, where the model selects only the target entity from the context, for reference. All scores are on the validation set.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The task evaluates model performance by generating all possible target sentences and selecting the most coherent one, with BERT-Large showing strong results.The task for the ReCoRD dataset is to select the correct entity from those that appear in the context to fill in the blank in the target. Previous models for ReCoRD have used a similar structure to SQuAD Rajpurkar et al. We, instead, generate all possible target sentences by filling the blank with each marked entity and discriminatively choose the sentence most likely to be the true “plausible” sentence from the context. This modified task evaluates how our model compares to BERT-Base choosing the most coherent sentence from a set of nearly identical sentences. The strong results from BERT-Large imply that having a better representation of the text with a large model is able to subsume any improvement from learning plausible contexts for this task." } ], "images": [ "scigen/train/710.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Conpono is more effective at classifying the most plausible sentence from the extended context than BERT-Base. We report the BERT-Large exact match score, where the model selects only the target entity from the context, for reference. All scores are on the validation set." ], "reasoning": "The task evaluates model performance by generating all possible target sentences and selecting the most coherent one, with BERT-Large showing strong results.", "description": "The task for the ReCoRD dataset is to select the correct entity from those that appear in the context to fill in the blank in the target. Previous models for ReCoRD have used a similar structure to SQuAD Rajpurkar et al. We, instead, generate all possible target sentences by filling the blank with each marked entity and discriminatively choose the sentence most likely to be the true “plausible” sentence from the context. This modified task evaluates how our model compares to BERT-Base choosing the most coherent sentence from a set of nearly identical sentences. The strong results from BERT-Large imply that having a better representation of the text with a large model is able to subsume any improvement from learning plausible contexts for this task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Conpono is more effective at classifying the most plausible sentence from the extended context than BERT-Base. We report the BERT-Large exact match score, where the model selects only the target entity from the context, for reference. All scores are on the validation set.\nQuestion: Which model has the highest accuracy according to the table?\nSolve the above question based on the table information. Let's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table lists the accuracies of different models, and BERT-Large has the highest accuracy of 69.8. So, the answer is {\"answer\": \"BERT-Large\"}." } ], "images": [ "scigen/train/710.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Conpono is more effective at classifying the most plausible sentence from the extended context than BERT-Base. We report the BERT-Large exact match score, where the model selects only the target entity from the context, for reference. All scores are on the validation set." ], "reasoning": "The table lists the accuracies of different models, and BERT-Large has the highest accuracy of 69.8.", "question": "Which model has the highest accuracy according to the table?", "answer": "So, the answer is {\"answer\": \"BERT-Large\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nConpono has a higher accuracy than BERT-Base.\n" }, { "role": "assistant", "content": "The table shows that Conpono has an accuracy of 63.2, while BERT-Base has an accuracy of 61.2, indicating that Conpono has a higher accuracy. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/710.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Conpono is more effective at classifying the most plausible sentence from the extended context than BERT-Base. We report the BERT-Large exact match score, where the model selects only the target entity from the context, for reference. All scores are on the validation set." ], "reasoning": "The table shows that Conpono has an accuracy of 63.2, while BERT-Base has an accuracy of 61.2, indicating that Conpono has a higher accuracy.", "statement": "Conpono has a higher accuracy than BERT-Base.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 5: The ablation analysis shows the effects of different k values (ie. window sizes) in our objective, removing the MLM objective during pretraining and training with a small transformer encoder.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Using a window size larger than 1 improves performance, but increasing it beyond 2 does not yield significant gains due to increased ambiguity.We observe that using a window size for our objective that is larger than 1 is key to seeing downstream improvements. We believe that this is due to the objective being harder for the model because there is more variation farther from the anchor. At the same time, increasing the window size beyond 2 seems to result in similar performance. This may be because larger distances from the anchor also lead to more ambiguity." } ], "images": [ "scigen/train/711.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The ablation analysis shows the effects of different k values (ie. window sizes) in our objective, removing the MLM objective during pretraining and training with a small transformer encoder." ], "reasoning": "Using a window size larger than 1 improves performance, but increasing it beyond 2 does not yield significant gains due to increased ambiguity.", "description": "We observe that using a window size for our objective that is larger than 1 is key to seeing downstream improvements. We believe that this is due to the objective being harder for the model because there is more variation farther from the anchor. At the same time, increasing the window size beyond 2 seems to result in similar performance. This may be because larger distances from the anchor also lead to more ambiguity.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 5: The ablation analysis shows the effects of different k values (ie. window sizes) in our objective, removing the MLM objective during pretraining and training with a small transformer encoder.', solve the following question step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the average performance score when the window size is set to 2?\n" }, { "role": "assistant", "content": "To find the average performance score for k=2, look at the 'avg.' column for the row where k=2. The value is 63.07. So, the answer is {\"answer\": \"63.07\"}." } ], "images": [ "scigen/train/711.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The ablation analysis shows the effects of different k values (ie. window sizes) in our objective, removing the MLM objective during pretraining and training with a small transformer encoder." ], "reasoning": "To find the average performance score for k=2, look at the 'avg.' column for the row where k=2. The value is 63.07.", "question": "What is the average performance score when the window size is set to 2?", "answer": "So, the answer is {\"answer\": \"63.07\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The model performs best with a window size of 2. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model performs best with a window size of 2.\n" }, { "role": "assistant", "content": "To determine the best performance, compare the 'avg.' values for different window sizes. The highest average score is 63.07 for k=2. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/711.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The ablation analysis shows the effects of different k values (ie. window sizes) in our objective, removing the MLM objective during pretraining and training with a small transformer encoder." ], "reasoning": "To determine the best performance, compare the 'avg.' values for different window sizes. The highest average score is 63.07 for k=2.", "statement": "The model performs best with a window size of 2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: ROUGE-L of our model and versions thereof with less synthetic data (second block), using only one noising method (third block), and without some modules (fourth block). A more comprehensive table and discussion can be found in the Appendix.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Increasing the size of synthetic data and using segment and document noising improves performance, while human-labeled categories decrease model performance.Our experiments confirm that increasing the size of the synthetic data improves performance, and that both segment and document noising are useful. We also show that explicit denoising, partial copy, and the discriminator help achieve best results. Finally, human-labeled categories (instead of LDA topics) decrease model performance, which suggests that more useful labels can be approximated by automatic means." } ], "images": [ "scigen/train/712.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: ROUGE-L of our model and versions thereof with less synthetic data (second block), using only one noising method (third block), and without some modules (fourth block). A more comprehensive table and discussion can be found in the Appendix." ], "reasoning": "Increasing the size of synthetic data and using segment and document noising improves performance, while human-labeled categories decrease model performance.", "description": "Our experiments confirm that increasing the size of the synthetic data improves performance, and that both segment and document noising are useful. We also show that explicit denoising, partial copy, and the discriminator help achieve best results. Finally, human-labeled categories (instead of LDA topics) decrease model performance, which suggests that more useful labels can be approximated by automatic means.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: ROUGE-L of our model and versions thereof with less synthetic data (second block), using only one noising method (third block), and without some modules (fourth block). A more comprehensive table and discussion can be found in the Appendix.\nQuestion: Which model has the lowest performance on the Yelp dataset?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the model with the lowest performance on the Yelp dataset, compare the Yelp scores for all models. The model 'using human categories' has the lowest score of 15.86. So, the answer is {\"answer\": \"using human categories\"}" } ], "images": [ "scigen/train/712.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: ROUGE-L of our model and versions thereof with less synthetic data (second block), using only one noising method (third block), and without some modules (fourth block). A more comprehensive table and discussion can be found in the Appendix." ], "reasoning": "To find the model with the lowest performance on the Yelp dataset, compare the Yelp scores for all models. The model 'using human categories' has the lowest score of 15.86.", "question": "Which model has the lowest performance on the Yelp dataset?", "answer": "So, the answer is {\"answer\": \"using human categories\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The model with no explicit denoising performs better than the model with no segment noising on the RT dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model with no explicit denoising performs better than the model with no segment noising on the RT dataset.\n" }, { "role": "assistant", "content": "The RT score for the model with no explicit denoising is 16.06, while the RT score for the model with no segment noising is 16.03. Since 16.06 is greater than 16.03, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/712.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: ROUGE-L of our model and versions thereof with less synthetic data (second block), using only one noising method (third block), and without some modules (fourth block). A more comprehensive table and discussion can be found in the Appendix." ], "reasoning": "The RT score for the model with no explicit denoising is 16.06, while the RT score for the model with no segment noising is 16.03. Since 16.06 is greater than 16.03, the statement is refuted.", "statement": "The model with no explicit denoising performs better than the model with no segment noising on the RT dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: ROUGE-1/2/L F1 scores of our model and versions thereof with less synthetic data (second block), using only one noising method (third block), and without some modules (fourth block).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The final model, DenoiseSum, outperforms other versions on all metrics, except when using human-labeled categories, which slightly improves ROUGE-2 on Rotten Tomatoes but worsens other metrics.The final model consistently performs better on all metrics when compared to versions with less synthetic data (second block), versions with only one type of noise (second block), and versions with a module removed (third block). When using human-labeled categories, we see a slight improvement in ROUGE-2 on Rotten Tomatoes, however the model performs substantially worse on other metrics. We believe there are several reasons for this. Firstly, human-labeled categories, at least the ones available, are not fine-grained enough to capture various aspects mentioned in the reviews and their sentiment (e.g., did the actors perform well? was the plot convoluted?). Secondly, the number of business types available on Yelp is very large (i.e., 898 types), which makes the discriminator loss Ldisc hard to optimize. This explains the relatively larger decrease in performance on Yelp." } ], "images": [ "scigen/train/713.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: ROUGE-1/2/L F1 scores of our model and versions thereof with less synthetic data (second block), using only one noising method (third block), and without some modules (fourth block)." ], "reasoning": "The final model, DenoiseSum, outperforms other versions on all metrics, except when using human-labeled categories, which slightly improves ROUGE-2 on Rotten Tomatoes but worsens other metrics.", "description": "The final model consistently performs better on all metrics when compared to versions with less synthetic data (second block), versions with only one type of noise (second block), and versions with a module removed (third block). When using human-labeled categories, we see a slight improvement in ROUGE-2 on Rotten Tomatoes, however the model performs substantially worse on other metrics. We believe there are several reasons for this. Firstly, human-labeled categories, at least the ones available, are not fine-grained enough to capture various aspects mentioned in the reviews and their sentiment (e.g., did the actors perform well? was the plot convoluted?). Secondly, the number of business types available on Yelp is very large (i.e., 898 types), which makes the discriminator loss Ldisc hard to optimize. This explains the relatively larger decrease in performance on Yelp.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 6: ROUGE-1/2/L F1 scores of our model and versions thereof with less synthetic data (second block), using only one noising method (third block), and without some modules (fourth block).', answer the question below by showing each progressive step toward the answer. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest ROUGE-1 score on Yelp?\n" }, { "role": "assistant", "content": "By examining the ROUGE-1 scores for Yelp in the table, the highest score is 30.14, which corresponds to the DenoiseSum model. So, the answer is {\"answer\": \"DenoiseSum\"}" } ], "images": [ "scigen/train/713.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: ROUGE-1/2/L F1 scores of our model and versions thereof with less synthetic data (second block), using only one noising method (third block), and without some modules (fourth block)." ], "reasoning": "By examining the ROUGE-1 scores for Yelp in the table, the highest score is 30.14, which corresponds to the DenoiseSum model.", "question": "Which model has the highest ROUGE-1 score on Yelp?", "answer": "So, the answer is {\"answer\": \"DenoiseSum\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The model with no segment noising performs better than the model with no document noising on ROUGE-2 for Rotten Tomatoes., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe model with no segment noising performs better than the model with no document noising on ROUGE-2 for Rotten Tomatoes.\n" }, { "role": "assistant", "content": "The table shows that the model with no segment noising has a ROUGE-2 score of 4.39, while the model with no document noising has a ROUGE-2 score of 4.38 for Rotten Tomatoes. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/713.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: ROUGE-1/2/L F1 scores of our model and versions thereof with less synthetic data (second block), using only one noising method (third block), and without some modules (fourth block)." ], "reasoning": "The table shows that the model with no segment noising has a ROUGE-2 score of 4.39, while the model with no document noising has a ROUGE-2 score of 4.38 for Rotten Tomatoes. Therefore, the statement is supported.", "statement": "The model with no segment noising performs better than the model with no document noising on ROUGE-2 for Rotten Tomatoes.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Pragmatics communication accuracy using virtual interlocutors.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Agents S and L learn their own models and communicate using pragmatic frameworks, resulting in high but not identical fidelities, which affects pragmatic performance.In classic pragmatic frameworks, it is often assumed that interlocutors know about each other very well. For example, the prior probabilites PS0 and PL0 are common knowledge to each other. However, in practice, game information may be incomplete and one’s assumptions about the other interlocutor may diverge from reality. Each person learns their own language model and reasons about others based on their own mental model. To check how this affects pragmatics, we assume agents S and L first respectively learn to speak and listen as before, so we obtain PS0 and PL0 , S has its own listener model PL′0 and L has its own speaker model PS′0. We then train PS′0 and PL′0 by communicating with and simulating outputs of PS0 and PL0 regarding game instances. In the end, PS′0 is similar to PS0 but not exactly the same, and similarly for the listener model. This amounts to policy reconstruction methods from the perspective of theory of mind and opponent modeling. During testing, S and L try to communicate using pragmatic frameworks, each using their own model as the virtual interlocutor model. “Fide\" here describes the average fidelity when a virtual model simulates the real one, which is computed as the cosine similarity between the output distributions of the models on the same input. We observe that although the fidelities are fairly high after training, their minor differences substantially hamper the pragmatic performance. GameTable is most susceptible to this, while IBR and GameTable-sequential are fairly robust." } ], "images": [ "scigen/train/714.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Pragmatics communication accuracy using virtual interlocutors." ], "reasoning": "Agents S and L learn their own models and communicate using pragmatic frameworks, resulting in high but not identical fidelities, which affects pragmatic performance.", "description": "In classic pragmatic frameworks, it is often assumed that interlocutors know about each other very well. For example, the prior probabilites PS0 and PL0 are common knowledge to each other. However, in practice, game information may be incomplete and one’s assumptions about the other interlocutor may diverge from reality. Each person learns their own language model and reasons about others based on their own mental model. To check how this affects pragmatics, we assume agents S and L first respectively learn to speak and listen as before, so we obtain PS0 and PL0 , S has its own listener model PL′0 and L has its own speaker model PS′0. We then train PS′0 and PL′0 by communicating with and simulating outputs of PS0 and PL0 regarding game instances. In the end, PS′0 is similar to PS0 but not exactly the same, and similarly for the listener model. This amounts to policy reconstruction methods from the perspective of theory of mind and opponent modeling. During testing, S and L try to communicate using pragmatic frameworks, each using their own model as the virtual interlocutor model. “Fide\" here describes the average fidelity when a virtual model simulates the real one, which is computed as the cosine similarity between the output distributions of the models on the same input. We observe that although the fidelities are fairly high after training, their minor differences substantially hamper the pragmatic performance. GameTable is most susceptible to this, while IBR and GameTable-sequential are fairly robust.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: Pragmatics communication accuracy using virtual interlocutors.', solve the subsequent question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhat is the difference in IBR performance between the exact copy and the training 100k rnd virtual opponent?\n" }, { "role": "assistant", "content": "The IBR performance for the exact copy is 80.6±2.7, while for the training 100k rnd it is 68.6±1.6. The difference is 80.6 - 68.6 = 12.0. So, the answer is {\"answer\": \"12.0\"}." } ], "images": [ "scigen/train/714.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Pragmatics communication accuracy using virtual interlocutors." ], "reasoning": "The IBR performance for the exact copy is 80.6±2.7, while for the training 100k rnd it is 68.6±1.6. The difference is 80.6 - 68.6 = 12.0.", "question": "What is the difference in IBR performance between the exact copy and the training 100k rnd virtual opponent?", "answer": "So, the answer is {\"answer\": \"12.0\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe GTs performance is higher for the exact copy than for the training 100k rnd virtual opponent.\n" }, { "role": "assistant", "content": "The GTs performance for the exact copy is 94.0±0.6, while for the training 100k rnd it is 69.9±1.8. Since 94.0 is higher than 69.9, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/714.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Pragmatics communication accuracy using virtual interlocutors." ], "reasoning": "The GTs performance for the exact copy is 94.0±0.6, while for the training 100k rnd it is 69.9±1.8. Since 94.0 is higher than 69.9, the statement is supported.", "statement": "The GTs performance is higher for the exact copy than for the training 100k rnd virtual opponent.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Inflection accuracy on the test languages; best results in bold. Languages listed by language family; from top to bottom: Romance, Slavic, Uralic.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "MAML-trained models outperform other models of the same architecture, and fine-tuning improves performance for both architectures. PG models generally outperform MED models.We make the following observations: For both MED and PG, MAML-trained models outperform all other models of the same architecture: MAML-PG (resp. MAML-MED) obtains a 1.21% (resp. 3.16%) higher accuracy on average over languages than the second best model MulPG+FT (resp. MulMED+FT). This demonstrates that MAML is more effective than multi-task training. For both architectures, models which are obtained by multi-task training and subse1uent fine-tuning outperform models trained exclusively in a multi-task fashion: MulPG+FT (resp. MulMED+FT) obtains a 10.41% (resp. 19.06%) higher accuracy on average than MulPG (resp. MulMED). Since differences in performance are substantial, we conclude that the use of fine-tuning—with or without MAML—is important. All PG models outperform their corresponding MED models. This is in line with the findings of \\citeauthorcotterell-EtAl:2018:K18 -30 \\shortcitecotterell-EtAl:2018:K18-30 that the pointer-generator network is a strong model for morphological inflection in the low-resource setting. Plain PG performs worse than fine-tuned PG models, but better than MulPG. That both MAML-PG and MulPG+FT outperform PG shows the importance of fine-tuning. Multi-task training as suggested by \\citeauthorkann-cotterell-schutze:2017: Long \\shortcitekann-cotterell-schutze:2017: Long seems to not work well in our setup. One possible reason for this is that we include languages from 3 different families as opposed to the original experiments which focussed on one family at a time. Thus, better ways to account for the amount of unrelated examples are probably needed for MulPG. MulPG+FT and MAML-PG perform better than UZH, the state-of-the-art model. We conclude that cross-lingual transfer is a promising direction to improve morphological inflection for resource-poor languages. Looking at individual languages, MAML-PG performs better than the plain PG model in all cases, except for 3 Romance (Ladin, Norman, and Venetian) and 3 Uralic (Hungarian, Karelian, and Livonian) languages. There are no exceptions for the Slavic family. That relatively many monolingual models outperform cross-lingual transfer models for Uralic languages might be due to these languages being less similar than those in the other families. We look into this in more detail in the next section." } ], "images": [ "scigen/train/715.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Inflection accuracy on the test languages; best results in bold. Languages listed by language family; from top to bottom: Romance, Slavic, Uralic." ], "reasoning": "MAML-trained models outperform other models of the same architecture, and fine-tuning improves performance for both architectures. PG models generally outperform MED models.", "description": "We make the following observations: For both MED and PG, MAML-trained models outperform all other models of the same architecture: MAML-PG (resp. MAML-MED) obtains a 1.21% (resp. 3.16%) higher accuracy on average over languages than the second best model MulPG+FT (resp. MulMED+FT). This demonstrates that MAML is more effective than multi-task training. For both architectures, models which are obtained by multi-task training and subse1uent fine-tuning outperform models trained exclusively in a multi-task fashion: MulPG+FT (resp. MulMED+FT) obtains a 10.41% (resp. 19.06%) higher accuracy on average than MulPG (resp. MulMED). Since differences in performance are substantial, we conclude that the use of fine-tuning—with or without MAML—is important. All PG models outperform their corresponding MED models. This is in line with the findings of \\citeauthorcotterell-EtAl:2018:K18 -30 \\shortcitecotterell-EtAl:2018:K18-30 that the pointer-generator network is a strong model for morphological inflection in the low-resource setting. Plain PG performs worse than fine-tuned PG models, but better than MulPG. That both MAML-PG and MulPG+FT outperform PG shows the importance of fine-tuning. Multi-task training as suggested by \\citeauthorkann-cotterell-schutze:2017: Long \\shortcitekann-cotterell-schutze:2017: Long seems to not work well in our setup. One possible reason for this is that we include languages from 3 different families as opposed to the original experiments which focussed on one family at a time. Thus, better ways to account for the amount of unrelated examples are probably needed for MulPG. MulPG+FT and MAML-PG perform better than UZH, the state-of-the-art model. We conclude that cross-lingual transfer is a promising direction to improve morphological inflection for resource-poor languages. Looking at individual languages, MAML-PG performs better than the plain PG model in all cases, except for 3 Romance (Ladin, Norman, and Venetian) and 3 Uralic (Hungarian, Karelian, and Livonian) languages. There are no exceptions for the Slavic family. That relatively many monolingual models outperform cross-lingual transfer models for Uralic languages might be due to these languages being less similar than those in the other families. We look into this in more detail in the next section.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: Inflection accuracy on the test languages; best results in bold. Languages listed by language family; from top to bottom: Romance, Slavic, Uralic.'. The final result should be presented in the JSON format of {\"answer\": \"\"}. \n\nQuestion: Which model has the highest average accuracy across all languages?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the model with the highest average accuracy, we compare the average accuracy values at the bottom of each column. The highest average accuracy is 54.12, which corresponds to the PG MAML-PG model. So, the answer is {\"answer\": \"PG MAML-PG\"}." } ], "images": [ "scigen/train/715.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Inflection accuracy on the test languages; best results in bold. Languages listed by language family; from top to bottom: Romance, Slavic, Uralic." ], "reasoning": "To find the model with the highest average accuracy, we compare the average accuracy values at the bottom of each column. The highest average accuracy is 54.12, which corresponds to the PG MAML-PG model.", "question": "Which model has the highest average accuracy across all languages?", "answer": "So, the answer is {\"answer\": \"PG MAML-PG\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe PG MulPG+FT model outperforms the UZH model for the French language.\n" }, { "role": "assistant", "content": "For the French language, the accuracy of the PG MulPG+FT model is 61.54, while the accuracy of the UZH model is 64.04. Since 61.54 is less than 64.04, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/715.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Inflection accuracy on the test languages; best results in bold. Languages listed by language family; from top to bottom: Romance, Slavic, Uralic." ], "reasoning": "For the French language, the accuracy of the PG MulPG+FT model is 61.54, while the accuracy of the UZH model is 64.04. Since 61.54 is less than 64.04, the statement is refuted.", "statement": "The PG MulPG+FT model outperforms the UZH model for the French language.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'TABLE II: Performance of Each of the Sound-class Detectors Measured in Terms of Equal-error-rates (EERs) for the Development Data. Frequency of Observation in Development Utterances is also Shown for Each Class Type.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Performance varies significantly by class and attack method, with vowels performing best for HMM-based TTS attacks and stops becoming more informative for voice-conversion attacks despite their lower frequency.Performance of each class is significantly different from each other and they change substantially depending on the attack method. Also note that, even though vowel class is observed more than other classes, their performance is better than other systems only for HMM-based TTS attacks. For the voice-conversion attacks, short-duration stop sounds become more informative even though they occur far less frequently than the vowels." } ], "images": [ "scigen/train/716.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Performance of Each of the Sound-class Detectors Measured in Terms of Equal-error-rates (EERs) for the Development Data. Frequency of Observation in Development Utterances is also Shown for Each Class Type." ], "reasoning": "Performance varies significantly by class and attack method, with vowels performing best for HMM-based TTS attacks and stops becoming more informative for voice-conversion attacks despite their lower frequency.", "description": "Performance of each class is significantly different from each other and they change substantially depending on the attack method. Also note that, even though vowel class is observed more than other classes, their performance is better than other systems only for HMM-based TTS attacks. For the voice-conversion attacks, short-duration stop sounds become more informative even though they occur far less frequently than the vowels.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: TABLE II: Performance of Each of the Sound-class Detectors Measured in Terms of Equal-error-rates (EERs) for the Development Data. Frequency of Observation in Development Utterances is also Shown for Each Class Type.\nQuestion: Which class has the highest overall performance for HMM-based TTS attacks?\nSolve the above question based on the table information. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The description states that vowels perform better than other systems for HMM-based TTS attacks. By looking at the 'All' column, vowels have a score of 6.35, which is higher than the other classes. So, the answer is {\"answer\": \"Vowel\"}." } ], "images": [ "scigen/train/716.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Performance of Each of the Sound-class Detectors Measured in Terms of Equal-error-rates (EERs) for the Development Data. Frequency of Observation in Development Utterances is also Shown for Each Class Type." ], "reasoning": "The description states that vowels perform better than other systems for HMM-based TTS attacks. By looking at the 'All' column, vowels have a score of 6.35, which is higher than the other classes.", "question": "Which class has the highest overall performance for HMM-based TTS attacks?", "answer": "So, the answer is {\"answer\": \"Vowel\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nStop sounds have the highest frequency among all classes.\n" }, { "role": "assistant", "content": "The table shows that the frequency of stop sounds is 0.112, which is lower than the frequency of vowels (0.542), nasals (0.156), and glides (0.118). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/716.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Performance of Each of the Sound-class Detectors Measured in Terms of Equal-error-rates (EERs) for the Development Data. Frequency of Observation in Development Utterances is also Shown for Each Class Type." ], "reasoning": "The table shows that the frequency of stop sounds is 0.112, which is lower than the frequency of vowels (0.542), nasals (0.156), and glides (0.118).", "statement": "Stop sounds have the highest frequency among all classes.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Comparison of different readers with fixed answer verifier.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Adding Model-III to DocQA increases no-answer accuracy by 6 points, and adding Model-III to DocQA + ELMo increases it by 5.5 points.We find that the absolute improvements are even larger: the no-answer accuracy roughly increases by 6 points when adding Model-III to DocQA (from 69.1 to 75.2), and 5.5 points when adding Model-III to DocQA + ELMo (from 70.6 to 76.1)." } ], "images": [ "scigen/train/717.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison of different readers with fixed answer verifier." ], "reasoning": "Adding Model-III to DocQA increases no-answer accuracy by 6 points, and adding Model-III to DocQA + ELMo increases it by 5.5 points.", "description": "We find that the absolute improvements are even larger: the no-answer accuracy roughly increases by 6 points when adding Model-III to DocQA (from 69.1 to 75.2), and 5.5 points when adding Model-III to DocQA + ELMo (from 70.6 to 76.1).", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 5: Comparison of different readers with fixed answer verifier.' in mind, break down and resolve the question below step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the increase in no-answer accuracy when adding Model-III to DocQA + ELMo?\n" }, { "role": "assistant", "content": "The no-answer accuracy for DocQA + ELMo is 70.6, and for DocQA + ELMo + Model-III, it is 76.1. The increase is 76.1 - 70.6 = 5.5 points. So, the answer is {\"answer\": \"5.5\"}" } ], "images": [ "scigen/train/717.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison of different readers with fixed answer verifier." ], "reasoning": "The no-answer accuracy for DocQA + ELMo is 70.6, and for DocQA + ELMo + Model-III, it is 76.1. The increase is 76.1 - 70.6 = 5.5 points.", "question": "What is the increase in no-answer accuracy when adding Model-III to DocQA + ELMo?", "answer": "So, the answer is {\"answer\": \"5.5\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nAdding Model-III to DocQA increases the no-answer accuracy by 6 points.\n" }, { "role": "assistant", "content": "The no-answer accuracy for DocQA is 69.1, and for DocQA + Model-III, it is 75.2. The increase is 75.2 - 69.1 = 6.1 points, which supports the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/717.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison of different readers with fixed answer verifier." ], "reasoning": "The no-answer accuracy for DocQA is 69.1, and for DocQA + Model-III, it is 75.2. The increase is 75.2 - 69.1 = 6.1 points, which supports the statement.", "statement": "Adding Model-III to DocQA increases the no-answer accuracy by 6 points.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Comparison of different approaches on the SQuAD 2.0 test set, extracted on Aug 28, 2018: Levy et al. Levy171, Clark et al. Clark182, Liu et al. liu2017stochastic3, Huang et al. Huang17b4 and Wang et al. wang2018multi5. † indicates unpublished works.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Our system, RMR + ELMo + Verifier, achieves state-of-the-art results with an EM score of 71.7 and an F1 score of 74.2 on the test set, comparable to SLQA+.We use Model-III as the default answer verifier, and only report the best result. As we can see, our system obtains state-of-the-art results by achieving an EM score of 71.7 and a F1 score of 74.2 on the test set. Notice that SLQA+ has reached a comparable result compared to our approach." } ], "images": [ "scigen/train/718.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of different approaches on the SQuAD 2.0 test set, extracted on Aug 28, 2018: Levy et al. Levy171, Clark et al. Clark182, Liu et al. liu2017stochastic3, Huang et al. Huang17b4 and Wang et al. wang2018multi5. † indicates unpublished works." ], "reasoning": "Our system, RMR + ELMo + Verifier, achieves state-of-the-art results with an EM score of 71.7 and an F1 score of 74.2 on the test set, comparable to SLQA+.", "description": "We use Model-III as the default answer verifier, and only report the best result. As we can see, our system obtains state-of-the-art results by achieving an EM score of 71.7 and a F1 score of 74.2 on the test set. Notice that SLQA+ has reached a comparable result compared to our approach.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 1: Comparison of different approaches on the SQuAD 2.0 test set, extracted on Aug 28, 2018: Levy et al. Levy171, Clark et al. Clark182, Liu et al. liu2017stochastic3, Huang et al. Huang17b4 and Wang et al. wang2018multi5. † indicates unpublished works.' and methodically tackle the question that follows. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model achieved the highest F1 score on the test set?\n" }, { "role": "assistant", "content": "By comparing the Test F1 scores of all models in the table, we see that the Human model achieved the highest F1 score of 89.5. So, the answer is {\"answer\": \"Human\"}" } ], "images": [ "scigen/train/718.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of different approaches on the SQuAD 2.0 test set, extracted on Aug 28, 2018: Levy et al. Levy171, Clark et al. Clark182, Liu et al. liu2017stochastic3, Huang et al. Huang17b4 and Wang et al. wang2018multi5. † indicates unpublished works." ], "reasoning": "By comparing the Test F1 scores of all models in the table, we see that the Human model achieved the highest F1 score of 89.5.", "question": "Which model achieved the highest F1 score on the test set?", "answer": "So, the answer is {\"answer\": \"Human\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe RMR + ELMo + Verifier model achieved the highest EM score on the test set.\n" }, { "role": "assistant", "content": "By comparing the Test EM scores of all models in the table, we see that the Human model achieved the highest EM score of 86.9, not the RMR + ELMo + Verifier model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/718.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of different approaches on the SQuAD 2.0 test set, extracted on Aug 28, 2018: Levy et al. Levy171, Clark et al. Clark182, Liu et al. liu2017stochastic3, Huang et al. Huang17b4 and Wang et al. wang2018multi5. † indicates unpublished works." ], "reasoning": "By comparing the Test EM scores of all models in the table, we see that the Human model achieved the highest EM score of 86.9, not the RMR + ELMo + Verifier model.", "statement": "The RMR + ELMo + Verifier model achieved the highest EM score on the test set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Comparison of readers with different auxiliary losses.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Removing independent span loss decreases performance on answerable questions, while removing independent no-answer loss significantly reduces no-answer accuracy.Next, we do an ablation study on the SQuAD 2.0 development set to show the effects of our proposed methods for each individual component. Removing the independent span loss (indep-I) results in a performance drop for all answerable questions (HasAns), indicating that this loss helps the model in better identifying the answer boundary. Ablating independent no-answer loss (indep-II), on the other hand, causes little influence on HasAns, but leads to a severe decline on no-answer accuracy (NoAns ACC). This suggests that a confliction between answer extraction and no-answer detection indeed happens. Finally, deleting both of two losses causes a degradation of more than 1.5 points on the overall performance in terms of F1, with or without ELMo embeddings." } ], "images": [ "scigen/train/719.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of readers with different auxiliary losses." ], "reasoning": "Removing independent span loss decreases performance on answerable questions, while removing independent no-answer loss significantly reduces no-answer accuracy.", "description": "Next, we do an ablation study on the SQuAD 2.0 development set to show the effects of our proposed methods for each individual component. Removing the independent span loss (indep-I) results in a performance drop for all answerable questions (HasAns), indicating that this loss helps the model in better identifying the answer boundary. Ablating independent no-answer loss (indep-II), on the other hand, causes little influence on HasAns, but leads to a severe decline on no-answer accuracy (NoAns ACC). This suggests that a confliction between answer extraction and no-answer detection indeed happens. Finally, deleting both of two losses causes a degradation of more than 1.5 points on the overall performance in terms of F1, with or without ELMo embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Comparison of readers with different auxiliary losses.', answer the following question. Give detailed solution consisting of each step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the NoAns ACC when both independent span loss and independent no-answer loss are removed without ELMo embeddings?\n" }, { "role": "assistant", "content": "The table shows that removing both independent span loss and independent no-answer loss without ELMo embeddings results in a NoAns ACC of 71.4. So, the answer is {\"answer\": \"71.4\"}" } ], "images": [ "scigen/train/719.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of readers with different auxiliary losses." ], "reasoning": "The table shows that removing both independent span loss and independent no-answer loss without ELMo embeddings results in a NoAns ACC of 71.4.", "question": "What is the NoAns ACC when both independent span loss and independent no-answer loss are removed without ELMo embeddings?", "answer": "So, the answer is {\"answer\": \"71.4\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nRemoving independent span loss results in a performance drop for all answerable questions.\n" }, { "role": "assistant", "content": "The table shows that removing independent span loss (indep-I) results in a decrease in HasAns EM and HasAns F1 scores, indicating a performance drop for answerable questions. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/719.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of readers with different auxiliary losses." ], "reasoning": "The table shows that removing independent span loss (indep-I) results in a decrease in HasAns EM and HasAns F1 scores, indicating a performance drop for answerable questions.", "statement": "Removing independent span loss results in a performance drop for all answerable questions.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Comparison of different architectures for the answer verifier.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Model-III achieves the highest no-answer accuracy of 76.2, showing that combining two different architectures improves performance, while adding ELMo embeddings does not.Model-III outperforms all of other competitors, achieving a no-answer accuracy of 76.2. This illustrates that the combination of two different architectures can bring in further improvement. Adding ELMo embeddings, however, does not boost the performance." } ], "images": [ "scigen/train/720.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of different architectures for the answer verifier." ], "reasoning": "Model-III achieves the highest no-answer accuracy of 76.2, showing that combining two different architectures improves performance, while adding ELMo embeddings does not.", "description": "Model-III outperforms all of other competitors, achieving a no-answer accuracy of 76.2. This illustrates that the combination of two different architectures can bring in further improvement. Adding ELMo embeddings, however, does not boost the performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 3: Comparison of different architectures for the answer verifier.\nQuestion: Which model configuration achieves the highest no-answer accuracy, and what does this illustrate about combining architectures?\n" }, { "role": "assistant", "content": "Model-III achieves the highest no-answer accuracy of 76.2, illustrating that combining two different architectures can improve performance. So, the answer is {\"answer\": \"Model-III\"}." } ], "images": [ "scigen/train/720.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of different architectures for the answer verifier." ], "reasoning": "Model-III achieves the highest no-answer accuracy of 76.2, illustrating that combining two different architectures can improve performance.", "question": "Which model configuration achieves the highest no-answer accuracy, and what does this illustrate about combining architectures?", "answer": "So, the answer is {\"answer\": \"Model-III\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Adding ELMo embeddings to Model-III boosts its performance. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nAdding ELMo embeddings to Model-III boosts its performance.\n" }, { "role": "assistant", "content": "The table shows that Model-III has a no-answer accuracy of 76.2, while Model-III + ELMo has a slightly lower accuracy of 76.1, indicating that adding ELMo embeddings does not boost performance. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/720.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of different architectures for the answer verifier." ], "reasoning": "The table shows that Model-III has a no-answer accuracy of 76.2, while Model-III + ELMo has a slightly lower accuracy of 76.1, indicating that adding ELMo embeddings does not boost performance.", "statement": "Adding ELMo embeddings to Model-III boosts its performance.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Comparison of readers with different answer verifiers.'.\n" }, { "role": "assistant", "content": "Combining the base reader with Model-III results in the best performance, significantly improving no-answer accuracy from 73.1 to 77.1.The combination of base reader with any answer verifier can always result in considerable performance gains, and combining the reader with Model-III obtains the best result. We find that the improvement on no-answer accuracy is significant. This metric raises from 73.1 to 77.1 after adding Model-III to RMR, increasing by 4 absolute points. Similar observation can be found when ELMo embeddings are used, demonstrating that the gains are consistent and stable." } ], "images": [ "scigen/train/721.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of readers with different answer verifiers." ], "reasoning": "Combining the base reader with Model-III results in the best performance, significantly improving no-answer accuracy from 73.1 to 77.1.", "description": "The combination of base reader with any answer verifier can always result in considerable performance gains, and combining the reader with Model-III obtains the best result. We find that the improvement on no-answer accuracy is significant. This metric raises from 73.1 to 77.1 after adding Model-III to RMR, increasing by 4 absolute points. Similar observation can be found when ELMo embeddings are used, demonstrating that the gains are consistent and stable.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 4: Comparison of readers with different answer verifiers.\nQuestion: What is the no-answer accuracy improvement when Model-III is added to RMR?\n" }, { "role": "assistant", "content": "The no-answer accuracy improves from 73.1 to 77.1 when Model-III is added to RMR, resulting in an increase of 4 absolute points. So, the answer is {\"answer\": \"4\"}" } ], "images": [ "scigen/train/721.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of readers with different answer verifiers." ], "reasoning": "The no-answer accuracy improves from 73.1 to 77.1 when Model-III is added to RMR, resulting in an increase of 4 absolute points.", "question": "What is the no-answer accuracy improvement when Model-III is added to RMR?", "answer": "So, the answer is {\"answer\": \"4\"}" }, { "messages": [ { "role": "user", "content": "Combining the base reader with Model-III results in the highest no-answer accuracy. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nCombining the base reader with Model-III results in the highest no-answer accuracy.\n" }, { "role": "assistant", "content": "The table shows that the highest no-answer accuracy is 78.6, achieved by combining the base reader with Model-III. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/721.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of readers with different answer verifiers." ], "reasoning": "The table shows that the highest no-answer accuracy is 78.6, achieved by combining the base reader with Model-III.", "statement": "Combining the base reader with Model-III results in the highest no-answer accuracy.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: Document-level F1-score calculated by comparing document attention from ELDAN and human coders on 20 CPT codes. #enc is the number of encounters that contains the code. #doc is the number of documents within those encounters. #source is the number of documents being labeled by human coders as the source documents for the code. Attention (from ELDAN) and Chance both report document-level F1-score, and Diff is the difference between them.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "ELDAN achieves a 100% F1-score on 7 out of 19 codes and consistently outperforms the chance baseline, demonstrating its effectiveness in identifying signals from source documents without document-level annotations.for the most frequent 20 encounter-level codes, with surprisingly strong results: 100% F1-score on 7 out of 19 available codes. However, even chance performance could be good if the number of possible documents to assign credit to is very small. Therefore we compare to the chance baseline. ELDAN is consistently better, usually by a large margin. These results support the conclusion that ELDAN’s document attention is effective in identifying signal from “source” documents for the targeted code — crucially, without training on document-level annotations." } ], "images": [ "scigen/train/722.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Document-level F1-score calculated by comparing document attention from ELDAN and human coders on 20 CPT codes. #enc is the number of encounters that contains the code. #doc is the number of documents within those encounters. #source is the number of documents being labeled by human coders as the source documents for the code. Attention (from ELDAN) and Chance both report document-level F1-score, and Diff is the difference between them." ], "reasoning": "ELDAN achieves a 100% F1-score on 7 out of 19 codes and consistently outperforms the chance baseline, demonstrating its effectiveness in identifying signals from source documents without document-level annotations.", "description": "for the most frequent 20 encounter-level codes, with surprisingly strong results: 100% F1-score on 7 out of 19 available codes. However, even chance performance could be good if the number of possible documents to assign credit to is very small. Therefore we compare to the chance baseline. ELDAN is consistently better, usually by a large margin. These results support the conclusion that ELDAN’s document attention is effective in identifying signal from “source” documents for the targeted code — crucially, without training on document-level annotations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 1: Document-level F1-score calculated by comparing document attention from ELDAN and human coders on 20 CPT codes. #enc is the number of encounters that contains the code. #doc is the number of documents within those encounters. #source is the number of documents being labeled by human coders as the source documents for the code. Attention (from ELDAN) and Chance both report document-level F1-score, and Diff is the difference between them.', and tackle the question below in a sequential manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich CPT code has the highest difference between Attention and Chance performance?\n" }, { "role": "assistant", "content": "To find the CPT code with the highest difference, compare the 'Diff' values for all codes. The highest 'Diff' value is 54.37 for CPT code 12001. So, the answer is {\"answer\": \"12001\"}." } ], "images": [ "scigen/train/722.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Document-level F1-score calculated by comparing document attention from ELDAN and human coders on 20 CPT codes. #enc is the number of encounters that contains the code. #doc is the number of documents within those encounters. #source is the number of documents being labeled by human coders as the source documents for the code. Attention (from ELDAN) and Chance both report document-level F1-score, and Diff is the difference between them." ], "reasoning": "To find the CPT code with the highest difference, compare the 'Diff' values for all codes. The highest 'Diff' value is 54.37 for CPT code 12001.", "question": "Which CPT code has the highest difference between Attention and Chance performance?", "answer": "So, the answer is {\"answer\": \"12001\"}." }, { "messages": [ { "role": "user", "content": "CPT code 66984 has a higher Attention score than CPT code 45380. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nCPT code 66984 has a higher Attention score than CPT code 45380.\n" }, { "role": "assistant", "content": "Compare the Attention scores of CPT codes 66984 and 45380. CPT code 66984 has an Attention score of 100.00, while CPT code 45380 has an Attention score of 90.91. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/722.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Document-level F1-score calculated by comparing document attention from ELDAN and human coders on 20 CPT codes. #enc is the number of encounters that contains the code. #doc is the number of documents within those encounters. #source is the number of documents being labeled by human coders as the source documents for the code. Attention (from ELDAN) and Chance both report document-level F1-score, and Diff is the difference between them." ], "reasoning": "Compare the Attention scores of CPT codes 66984 and 45380. CPT code 66984 has an Attention score of 100.00, while CPT code 45380 has an Attention score of 90.91.", "statement": "CPT code 66984 has a higher Attention score than CPT code 45380.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Figure 1: Left: Encounter-level F1-scores of the 20 most frequent CPT codes. #Docs is the average number of documents found in the encounters that contain the code; prevalence is the percentage of all encounters that contain that code. Right: Macro average of encounter-level F1 scores for every 10 codes (from most to least frequent). ΔELDAN =\\textscELDAN+transfer−\\textscELDAN.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "ELDAN with or without transfer learning consistently outperforms ELDN, especially for rare codes, due to the effectiveness of the embedding layer in handling sparse features.To show the trend across the full range of codes we macro-average every 10 codes from most frequent to least frequent ELDAN with or without naïve transfer learning consistently outperforms ELDN, even for extremely rare codes (<0.1%). As codes become rarer, ELDAN+transfer tends toward outperforming ELDAN more substantially; see increasing trend for ΔELDAN. This improvement can be explained by viewing the embedding layer as a vector space model that maps sparse features that are extracted from the document (such as medical concepts, UMLS CUIs) to a dense representation, which can be effective for bootstrapping the training of rare codes. Our 80-10-10 dataset split results in 371,092 encounters for training, 46,387 encounters for development/tuning, and 46,387 encounters for testing. Note that no document-level annotations are available. Since we are in an imbalanced setting (some medical codes can be extremely rare, see Fig. No resampling is done for the development set and test set. These hyperparameters were selected based on our results on the development set." } ], "images": [ "scigen/train/723.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 1: Left: Encounter-level F1-scores of the 20 most frequent CPT codes. #Docs is the average number of documents found in the encounters that contain the code; prevalence is the percentage of all encounters that contain that code. Right: Macro average of encounter-level F1 scores for every 10 codes (from most to least frequent). ΔELDAN =\\textscELDAN+transfer−\\textscELDAN." ], "reasoning": "ELDAN with or without transfer learning consistently outperforms ELDN, especially for rare codes, due to the effectiveness of the embedding layer in handling sparse features.", "description": "To show the trend across the full range of codes we macro-average every 10 codes from most frequent to least frequent ELDAN with or without naïve transfer learning consistently outperforms ELDN, even for extremely rare codes (<0.1%). As codes become rarer, ELDAN+transfer tends toward outperforming ELDAN more substantially; see increasing trend for ΔELDAN. This improvement can be explained by viewing the embedding layer as a vector space model that maps sparse features that are extracted from the document (such as medical concepts, UMLS CUIs) to a dense representation, which can be effective for bootstrapping the training of rare codes. Our 80-10-10 dataset split results in 371,092 encounters for training, 46,387 encounters for development/tuning, and 46,387 encounters for testing. Note that no document-level annotations are available. Since we are in an imbalanced setting (some medical codes can be extremely rare, see Fig. No resampling is done for the development set and test set. These hyperparameters were selected based on our results on the development set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Figure 1: Left: Encounter-level F1-scores of the 20 most frequent CPT codes. #Docs is the average number of documents found in the encounters that contain the code; prevalence is the percentage of all encounters that contain that code. Right: Macro average of encounter-level F1 scores for every 10 codes (from most to least frequent). ΔELDAN =\\textscELDAN+transfer−\\textscELDAN.' and methodically tackle the question that follows. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the average performance of ELDAN+transfer across all codes?\n" }, { "role": "assistant", "content": "To find the average performance of ELDAN+transfer, look at the 'Average' row under the 'ELDAN+transfer' column, which shows the average value. So, the answer is {\"answer\": \"61.26\"}." } ], "images": [ "scigen/train/723.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 1: Left: Encounter-level F1-scores of the 20 most frequent CPT codes. #Docs is the average number of documents found in the encounters that contain the code; prevalence is the percentage of all encounters that contain that code. Right: Macro average of encounter-level F1 scores for every 10 codes (from most to least frequent). ΔELDAN =\\textscELDAN+transfer−\\textscELDAN." ], "reasoning": "To find the average performance of ELDAN+transfer, look at the 'Average' row under the 'ELDAN+transfer' column, which shows the average value.", "question": "What is the average performance of ELDAN+transfer across all codes?", "answer": "So, the answer is {\"answer\": \"61.26\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: ELDAN outperforms ELDN for the code with the highest prevalence., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nELDAN outperforms ELDN for the code with the highest prevalence.\n" }, { "role": "assistant", "content": "The code with the highest prevalence is 43239 with a prevalence of 4.15%. For this code, ELDAN has a score of 86.21, which is higher than ELDN's score of 84.59. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/723.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 1: Left: Encounter-level F1-scores of the 20 most frequent CPT codes. #Docs is the average number of documents found in the encounters that contain the code; prevalence is the percentage of all encounters that contain that code. Right: Macro average of encounter-level F1 scores for every 10 codes (from most to least frequent). ΔELDAN =\\textscELDAN+transfer−\\textscELDAN." ], "reasoning": "The code with the highest prevalence is 43239 with a prevalence of 4.15%. For this code, ELDAN has a score of 86.21, which is higher than ELDN's score of 84.59.", "statement": "ELDAN outperforms ELDN for the code with the highest prevalence.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Figure 1: Left: Encounter-level F1-scores of the 20 most frequent CPT codes. #Docs is the average number of documents found in the encounters that contain the code; prevalence is the percentage of all encounters that contain that code. Right: Macro average of encounter-level F1 scores for every 10 codes (from most to least frequent). ΔELDAN =\\textscELDAN+transfer−\\textscELDAN.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "ELDAN with transfer learning consistently outperforms ELDN, especially for rare codes, due to the effectiveness of the embedding layer in mapping sparse features to dense representations.To show the trend across the full range of codes we macro-average every 10 codes from most frequent to least frequent ELDAN with or without naïve transfer learning consistently outperforms ELDN, even for extremely rare codes (<0.1%). As codes become rarer, ELDAN+transfer tends toward outperforming ELDAN more substantially; see increasing trend for ΔELDAN. This improvement can be explained by viewing the embedding layer as a vector space model that maps sparse features that are extracted from the document (such as medical concepts, UMLS CUIs) to a dense representation, which can be effective for bootstrapping the training of rare codes. Our 80-10-10 dataset split results in 371,092 encounters for training, 46,387 encounters for development/tuning, and 46,387 encounters for testing. Note that no document-level annotations are available. Since we are in an imbalanced setting (some medical codes can be extremely rare, see Fig. No resampling is done for the development set and test set. These hyperparameters were selected based on our results on the development set." } ], "images": [ "scigen/train/724.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 1: Left: Encounter-level F1-scores of the 20 most frequent CPT codes. #Docs is the average number of documents found in the encounters that contain the code; prevalence is the percentage of all encounters that contain that code. Right: Macro average of encounter-level F1 scores for every 10 codes (from most to least frequent). ΔELDAN =\\textscELDAN+transfer−\\textscELDAN." ], "reasoning": "ELDAN with transfer learning consistently outperforms ELDN, especially for rare codes, due to the effectiveness of the embedding layer in mapping sparse features to dense representations.", "description": "To show the trend across the full range of codes we macro-average every 10 codes from most frequent to least frequent ELDAN with or without naïve transfer learning consistently outperforms ELDN, even for extremely rare codes (<0.1%). As codes become rarer, ELDAN+transfer tends toward outperforming ELDAN more substantially; see increasing trend for ΔELDAN. This improvement can be explained by viewing the embedding layer as a vector space model that maps sparse features that are extracted from the document (such as medical concepts, UMLS CUIs) to a dense representation, which can be effective for bootstrapping the training of rare codes. Our 80-10-10 dataset split results in 371,092 encounters for training, 46,387 encounters for development/tuning, and 46,387 encounters for testing. Note that no document-level annotations are available. Since we are in an imbalanced setting (some medical codes can be extremely rare, see Fig. No resampling is done for the development set and test set. These hyperparameters were selected based on our results on the development set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Figure 1: Left: Encounter-level F1-scores of the 20 most frequent CPT codes. #Docs is the average number of documents found in the encounters that contain the code; prevalence is the percentage of all encounters that contain that code. Right: Macro average of encounter-level F1 scores for every 10 codes (from most to least frequent). ΔELDAN =\\textscELDAN+transfer−\\textscELDAN.\nQuestion: What is the ΔELDAN value for the 121st to 130th codes?\n" }, { "role": "assistant", "content": "To find the ΔELDAN value for the 121st to 130th codes, locate the row labeled '121st to 130th' and read the value in the ΔELDAN column. So, the answer is {\"answer\": \"3.99\"}." } ], "images": [ "scigen/train/724.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 1: Left: Encounter-level F1-scores of the 20 most frequent CPT codes. #Docs is the average number of documents found in the encounters that contain the code; prevalence is the percentage of all encounters that contain that code. Right: Macro average of encounter-level F1 scores for every 10 codes (from most to least frequent). ΔELDAN =\\textscELDAN+transfer−\\textscELDAN." ], "reasoning": "To find the ΔELDAN value for the 121st to 130th codes, locate the row labeled '121st to 130th' and read the value in the ΔELDAN column.", "question": "What is the ΔELDAN value for the 121st to 130th codes?", "answer": "So, the answer is {\"answer\": \"3.99\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nELDAN+transfer outperforms ELDAN for the 31st to 40th codes.\n" }, { "role": "assistant", "content": "To verify this statement, compare the ELDAN and ELDAN+transfer values for the 31st to 40th codes. ELDAN+transfer has a value of 55.24, which is higher than ELDAN's value of 51.92. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/724.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 1: Left: Encounter-level F1-scores of the 20 most frequent CPT codes. #Docs is the average number of documents found in the encounters that contain the code; prevalence is the percentage of all encounters that contain that code. Right: Macro average of encounter-level F1 scores for every 10 codes (from most to least frequent). ΔELDAN =\\textscELDAN+transfer−\\textscELDAN." ], "reasoning": "To verify this statement, compare the ELDAN and ELDAN+transfer values for the 31st to 40th codes. ELDAN+transfer has a value of 55.24, which is higher than ELDAN's value of 51.92.", "statement": "ELDAN+transfer outperforms ELDAN for the 31st to 40th codes.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Document-level F1-score calculated by comparing document attention from ELDAN and human coders on 20 CPT codes. #enc is the number of encounters that contains the code. #doc is the number of documents within those encounters. #source is the number of documents being labeled by human coders as the source documents for the code. Attention (from ELDAN) and Chance both report document-level F1-score, and Diff is the difference between them.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "ELDAN achieves a 100% F1-score on 7 out of 19 codes and consistently outperforms the chance baseline, demonstrating effective document attention without training on document-level annotations.for the most frequent 20 encounter-level codes, with surprisingly strong results: 100% F1-score on 7 out of 19 available codes. However, even chance performance could be good if the number of possible documents to assign credit to is very small. Therefore we compare to the chance baseline. ELDAN is consistently better, usually by a large margin. These results support the conclusion that ELDAN’s document attention is effective in identifying signal from “source” documents for the targeted code — crucially, without training on document-level annotations." } ], "images": [ "scigen/train/725.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Document-level F1-score calculated by comparing document attention from ELDAN and human coders on 20 CPT codes. #enc is the number of encounters that contains the code. #doc is the number of documents within those encounters. #source is the number of documents being labeled by human coders as the source documents for the code. Attention (from ELDAN) and Chance both report document-level F1-score, and Diff is the difference between them." ], "reasoning": "ELDAN achieves a 100% F1-score on 7 out of 19 codes and consistently outperforms the chance baseline, demonstrating effective document attention without training on document-level annotations.", "description": "for the most frequent 20 encounter-level codes, with surprisingly strong results: 100% F1-score on 7 out of 19 available codes. However, even chance performance could be good if the number of possible documents to assign credit to is very small. Therefore we compare to the chance baseline. ELDAN is consistently better, usually by a large margin. These results support the conclusion that ELDAN’s document attention is effective in identifying signal from “source” documents for the targeted code — crucially, without training on document-level annotations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Document-level F1-score calculated by comparing document attention from ELDAN and human coders on 20 CPT codes. #enc is the number of encounters that contains the code. #doc is the number of documents within those encounters. #source is the number of documents being labeled by human coders as the source documents for the code. Attention (from ELDAN) and Chance both report document-level F1-score, and Diff is the difference between them.', answer the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich CPT code has the highest difference between Attention and Chance performance?\n" }, { "role": "assistant", "content": "To find the CPT code with the highest difference, compare the 'Diff' column values for all codes. The highest value is 42.38 for CPT code 47562. Thus, the answer is {\"answer\": \"47562\"}." } ], "images": [ "scigen/train/725.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Document-level F1-score calculated by comparing document attention from ELDAN and human coders on 20 CPT codes. #enc is the number of encounters that contains the code. #doc is the number of documents within those encounters. #source is the number of documents being labeled by human coders as the source documents for the code. Attention (from ELDAN) and Chance both report document-level F1-score, and Diff is the difference between them." ], "reasoning": "To find the CPT code with the highest difference, compare the 'Diff' column values for all codes. The highest value is 42.38 for CPT code 47562.", "question": "Which CPT code has the highest difference between Attention and Chance performance?", "answer": "Thus, the answer is {\"answer\": \"47562\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: CPT code 11042 has a higher Attention score than Chance score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nCPT code 11042 has a higher Attention score than Chance score.\n" }, { "role": "assistant", "content": "The Attention score for CPT code 11042 is 58.06, while the Chance score is 64.89. Since 58.06 is less than 64.89, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/725.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Document-level F1-score calculated by comparing document attention from ELDAN and human coders on 20 CPT codes. #enc is the number of encounters that contains the code. #doc is the number of documents within those encounters. #source is the number of documents being labeled by human coders as the source documents for the code. Attention (from ELDAN) and Chance both report document-level F1-score, and Diff is the difference between them." ], "reasoning": "The Attention score for CPT code 11042 is 58.06, while the Chance score is 64.89. Since 58.06 is less than 64.89, the statement is refuted.", "statement": "CPT code 11042 has a higher Attention score than Chance score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: Ablation study of Q-WAAE model', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The ablation study shows that removing the adversarial discriminator and features v affects the BLEU and METEOR scores on the COCO-ambiguous dataset.To understand the success of Q-WAAE on the ambiguous COCO data-set, we perform an ablation study of the model. We first discard the adversarial discriminator so that we only train the reconstruction module with the MSE loss (+ G). We also discard the use of the features v in the translation model for both the ablated model and Q-WAAE (no v)." } ], "images": [ "scigen/train/726.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Ablation study of Q-WAAE model" ], "reasoning": "The ablation study shows that removing the adversarial discriminator and features v affects the BLEU and METEOR scores on the COCO-ambiguous dataset.", "description": "To understand the success of Q-WAAE on the ambiguous COCO data-set, we perform an ablation study of the model. We first discard the adversarial discriminator so that we only train the reconstruction module with the MSE loss (+ G). We also discard the use of the features v in the translation model for both the ablated model and Q-WAAE (no v).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Ablation study of Q-WAAE model\nQuestion: What is the BLEU score for the Q-WAAE model without the features v?\nSolve the above question based on the table information. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the BLEU score for the Q-WAAE model without the features v, look at the row labeled 'Q-WAAE + no v' under the COCO-ambiguous BLEU column. So, the answer is {\"answer\": \"30.57\"}." } ], "images": [ "scigen/train/726.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Ablation study of Q-WAAE model" ], "reasoning": "To find the BLEU score for the Q-WAAE model without the features v, look at the row labeled 'Q-WAAE + no v' under the COCO-ambiguous BLEU column.", "question": "What is the BLEU score for the Q-WAAE model without the features v?", "answer": "So, the answer is {\"answer\": \"30.57\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Q-WAAE model achieves the highest METEOR score on the COCO-ambiguous dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Q-WAAE model achieves the highest METEOR score on the COCO-ambiguous dataset.\n" }, { "role": "assistant", "content": "The table shows that the Q-WAAE model has a METEOR score of 50.95, which is the highest among all the test sets listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/726.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Ablation study of Q-WAAE model" ], "reasoning": "The table shows that the Q-WAAE model has a METEOR score of 50.95, which is the highest among all the test sets listed.", "statement": "The Q-WAAE model achieves the highest METEOR score on the COCO-ambiguous dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Q-WAAE : Impact on the METEOR metric of the reconstruction and adversarial loss coefficient on the ambiguous COCO data-set'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Increasing the importance of auxiliary loss relative to translation loss leads to a decrease in translation quality.The results show that if the auxiliary loss (adversarial and/or reconstruction) is made too important compared to the translation loss, the translation quality is impaired." } ], "images": [ "scigen/train/727.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Q-WAAE : Impact on the METEOR metric of the reconstruction and adversarial loss coefficient on the ambiguous COCO data-set" ], "reasoning": "Increasing the importance of auxiliary loss relative to translation loss leads to a decrease in translation quality.", "description": "The results show that if the auxiliary loss (adversarial and/or reconstruction) is made too important compared to the translation loss, the translation quality is impaired.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: Q-WAAE : Impact on the METEOR metric of the reconstruction and adversarial loss coefficient on the ambiguous COCO data-set'.\n\nQuestion: What is the translation quality score when λa is 0.2 and λr is 0.2?\n" }, { "role": "assistant", "content": "To find the translation quality score, locate the row where λa is 0.2 and the column where λr is 0.2. The intersection of this row and column gives the score. So, the answer is {\"answer\": \"50.95\"}." } ], "images": [ "scigen/train/727.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Q-WAAE : Impact on the METEOR metric of the reconstruction and adversarial loss coefficient on the ambiguous COCO data-set" ], "reasoning": "To find the translation quality score, locate the row where λa is 0.2 and the column where λr is 0.2. The intersection of this row and column gives the score.", "question": "What is the translation quality score when λa is 0.2 and λr is 0.2?", "answer": "So, the answer is {\"answer\": \"50.95\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe translation quality score is highest when λa is 0.2 and λr is 0.2.\n" }, { "role": "assistant", "content": "The table shows that the highest translation quality score is 50.95, which occurs when λa is 0.2 and λr is 0.2. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/727.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Q-WAAE : Impact on the METEOR metric of the reconstruction and adversarial loss coefficient on the ambiguous COCO data-set" ], "reasoning": "The table shows that the highest translation quality score is 50.95, which occurs when λa is 0.2 and λr is 0.2.", "statement": "The translation quality score is highest when λa is 0.2 and λr is 0.2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: G-GWAN : Impact of the noise concatenated to the hidden state of size 512', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The G-WGAN shows improvements over the baseline and achieves similar results to Q-WAAE, but a small discrepancy is noticeable on the COCO-ambiguous dataset.The G-WGAN also shows improvements over the baseline and obtains similar results to Q-WAAE. Nonetheless, a small discrepancy is noticeable on the COCO-ambiguous. We believe that the main advantage of the Q-WAAE loss is the actual presence of a direct mean square error reconstruction loss along the adversarial loss." } ], "images": [ "scigen/train/728.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: G-GWAN : Impact of the noise concatenated to the hidden state of size 512" ], "reasoning": "The G-WGAN shows improvements over the baseline and achieves similar results to Q-WAAE, but a small discrepancy is noticeable on the COCO-ambiguous dataset.", "description": "The G-WGAN also shows improvements over the baseline and obtains similar results to Q-WAAE. Nonetheless, a small discrepancy is noticeable on the COCO-ambiguous. We believe that the main advantage of the Q-WAAE loss is the actual presence of a direct mean square error reconstruction loss along the adversarial loss.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: G-GWAN : Impact of the noise concatenated to the hidden state of size 512'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the highest METEOR score achieved by the model and for which z value?\n" }, { "role": "assistant", "content": "The table shows METEOR scores for different z values. The highest score is 50.43, which corresponds to z=128. So, the answer is {\"answer\": \"50.43 for z=128\"}" } ], "images": [ "scigen/train/728.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: G-GWAN : Impact of the noise concatenated to the hidden state of size 512" ], "reasoning": "The table shows METEOR scores for different z values. The highest score is 50.43, which corresponds to z=128.", "question": "What is the highest METEOR score achieved by the model and for which z value?", "answer": "So, the answer is {\"answer\": \"50.43 for z=128\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe METEOR score for z=256 is higher than for z=64.\n" }, { "role": "assistant", "content": "The table shows that the METEOR score for z=256 is 49.71, which is lower than the score for z=64, which is 50.35. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/728.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: G-GWAN : Impact of the noise concatenated to the hidden state of size 512" ], "reasoning": "The table shows that the METEOR score for z=256 is 49.71, which is lower than the score for z=64, which is 50.35.", "statement": "The METEOR score for z=256 is higher than for z=64.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 6: Effect of different dispersion functions, content coverage, and dissimilarity metrics on our system. [Left] JSD values for different combinations on Yahoo! data, using LDA with 100 topics. All systems are significantly different from each other at significance level α=0.05. Systems using summation of distances for dispersion function (hsum) uniformly outperform the ones using minimum distance (hmin). [Right] ROUGE scores of different choices for TAC 2008 data. All systems use LDA with 40 topics. The parameters of our systems are adopted from the ones tuned on Yahoo! Answers.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Cosine using TFIDF is more effective at measuring content coverage than WordNet-based semantic measurement, and topical-based dissimilarity marginally outperforms other metrics in blog data.Given that the text similarity metrics and dispersion functions play important roles in the framework, we further study the effectiveness of different content coverage functions (Cosine using TFIDF vs. Semantic), dispersion functions (hsum vs. hmin), and dissimilarity metrics used in dispersion functions (Semantic vs. Topical vs. Lexical). Results on Yahoo! Meanwhile, Cosine using TFIDF is better at measuring content coverage than WordNet-based semantic measurement, and this may due to the limited coverage of WordNet on verbs. This is also true for dissimilarity metrics. This indicates that optimal dispersion function varies by genre. Topical-based dissimilarity also marginally outperforms the other two metrics in blog data." } ], "images": [ "scigen/train/729.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Effect of different dispersion functions, content coverage, and dissimilarity metrics on our system. [Left] JSD values for different combinations on Yahoo! data, using LDA with 100 topics. All systems are significantly different from each other at significance level α=0.05. Systems using summation of distances for dispersion function (hsum) uniformly outperform the ones using minimum distance (hmin). [Right] ROUGE scores of different choices for TAC 2008 data. All systems use LDA with 40 topics. The parameters of our systems are adopted from the ones tuned on Yahoo! Answers." ], "reasoning": "Cosine using TFIDF is more effective at measuring content coverage than WordNet-based semantic measurement, and topical-based dissimilarity marginally outperforms other metrics in blog data.", "description": "Given that the text similarity metrics and dispersion functions play important roles in the framework, we further study the effectiveness of different content coverage functions (Cosine using TFIDF vs. Semantic), dispersion functions (hsum vs. hmin), and dissimilarity metrics used in dispersion functions (Semantic vs. Topical vs. Lexical). Results on Yahoo! Meanwhile, Cosine using TFIDF is better at measuring content coverage than WordNet-based semantic measurement, and this may due to the limited coverage of WordNet on verbs. This is also true for dissimilarity metrics. This indicates that optimal dispersion function varies by genre. Topical-based dissimilarity also marginally outperforms the other two metrics in blog data.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 6: Effect of different dispersion functions, content coverage, and dissimilarity metrics on our system. [Left] JSD values for different combinations on Yahoo! data, using LDA with 100 topics. All systems are significantly different from each other at significance level α=0.05. Systems using summation of distances for dispersion function (hsum) uniformly outperform the ones using minimum distance (hmin). [Right] ROUGE scores of different choices for TAC 2008 data. All systems use LDA with 40 topics. The parameters of our systems are adopted from the ones tuned on Yahoo! Answers.' in mind, break down and resolve the question below step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich combination of content coverage function and dispersion function has the highest dissimilarity metric?\n" }, { "role": "assistant", "content": "To find the highest dissimilarity metric, we need to compare all the values in the table. The highest value is 0.3234, which corresponds to the combination of topical-based dissimilarity, content coverage using TFIDF, and dispersion function min. So, the answer is {\"answer\": \"0.3234\"}." } ], "images": [ "scigen/train/729.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Effect of different dispersion functions, content coverage, and dissimilarity metrics on our system. [Left] JSD values for different combinations on Yahoo! data, using LDA with 100 topics. All systems are significantly different from each other at significance level α=0.05. Systems using summation of distances for dispersion function (hsum) uniformly outperform the ones using minimum distance (hmin). [Right] ROUGE scores of different choices for TAC 2008 data. All systems use LDA with 40 topics. The parameters of our systems are adopted from the ones tuned on Yahoo! Answers." ], "reasoning": "To find the highest dissimilarity metric, we need to compare all the values in the table. The highest value is 0.3234, which corresponds to the combination of topical-based dissimilarity, content coverage using TFIDF, and dispersion function min.", "question": "Which combination of content coverage function and dispersion function has the highest dissimilarity metric?", "answer": "So, the answer is {\"answer\": \"0.3234\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe combination of lexical dissimilarity, content coverage using TFIDF, and dispersion function sum has a higher dissimilarity metric than the combination of semantic dissimilarity, content coverage using TFIDF, and dispersion function sum.\n" }, { "role": "assistant", "content": "The dissimilarity metric for lexical dissimilarity, content coverage using TFIDF, and dispersion function sum is 0.2167, while for semantic dissimilarity, content coverage using TFIDF, and dispersion function sum, it is 0.2216. Since 0.2167 is less than 0.2216, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/729.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Effect of different dispersion functions, content coverage, and dissimilarity metrics on our system. [Left] JSD values for different combinations on Yahoo! data, using LDA with 100 topics. All systems are significantly different from each other at significance level α=0.05. Systems using summation of distances for dispersion function (hsum) uniformly outperform the ones using minimum distance (hmin). [Right] ROUGE scores of different choices for TAC 2008 data. All systems use LDA with 40 topics. The parameters of our systems are adopted from the ones tuned on Yahoo! Answers." ], "reasoning": "The dissimilarity metric for lexical dissimilarity, content coverage using TFIDF, and dispersion function sum is 0.2167, while for semantic dissimilarity, content coverage using TFIDF, and dispersion function sum, it is 0.2216. Since 0.2167 is less than 0.2216, the statement is refuted.", "statement": "The combination of lexical dissimilarity, content coverage using TFIDF, and dispersion function sum has a higher dissimilarity metric than the combination of semantic dissimilarity, content coverage using TFIDF, and dispersion function sum.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: [Left] Summaries evaluated by Jensen-Shannon divergence (JSD) on Yahoo Answer for summaries of 100 words and 200 words. The average length of the best answer is 102.70. [Right] Value addition of each component in the objective function. The JSD on each line is statistically significantly lower than the JSD on the previous (α=0.05).'.\n" }, { "role": "assistant", "content": "Our system achieves the lowest JSD scores for both lengths 100 and 200, indicating its effectiveness compared to other systems.The topic number is tuned on the development set, and we find that varying the number of topics does not impact performance too much. Meanwhile, both our system and \\newcitedasgupta-kumar-ravi:2013:ACL2013 produce better JSD scores than the two variants of the \\newciteLin:2011:CSF:2002472.2002537 system, which implies the effectiveness of the dispersion function." } ], "images": [ "scigen/train/730.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: [Left] Summaries evaluated by Jensen-Shannon divergence (JSD) on Yahoo Answer for summaries of 100 words and 200 words. The average length of the best answer is 102.70. [Right] Value addition of each component in the objective function. The JSD on each line is statistically significantly lower than the JSD on the previous (α=0.05)." ], "reasoning": "Our system achieves the lowest JSD scores for both lengths 100 and 200, indicating its effectiveness compared to other systems.", "description": "The topic number is tuned on the development set, and we find that varying the number of topics does not impact performance too much. Meanwhile, both our system and \\newcitedasgupta-kumar-ravi:2013:ACL2013 produce better JSD scores than the two variants of the \\newciteLin:2011:CSF:2002472.2002537 system, which implies the effectiveness of the dispersion function.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: Table 3: [Left] Summaries evaluated by Jensen-Shannon divergence (JSD) on Yahoo Answer for summaries of 100 words and 200 words. The average length of the best answer is 102.70. [Right] Value addition of each component in the objective function. The JSD on each line is statistically significantly lower than the JSD on the previous (α=0.05).\nQuestion: Which system has the lowest JSD score for length 200?\n" }, { "role": "assistant", "content": "By comparing the JSD scores for length 200 in the table, we see that 'Our system' has the lowest score of 0.1758. So, the answer is {\"answer\": \"Our system\"}." } ], "images": [ "scigen/train/730.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: [Left] Summaries evaluated by Jensen-Shannon divergence (JSD) on Yahoo Answer for summaries of 100 words and 200 words. The average length of the best answer is 102.70. [Right] Value addition of each component in the objective function. The JSD on each line is statistically significantly lower than the JSD on the previous (α=0.05)." ], "reasoning": "By comparing the JSD scores for length 200 in the table, we see that 'Our system' has the lowest score of 0.1758.", "question": "Which system has the lowest JSD score for length 200?", "answer": "So, the answer is {\"answer\": \"Our system\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe system by dasgupta-kumar-ravi:2013:ACL2013 has a lower JSD score than Lin:2011:CSF:2002472.2002537 for length 100.\n" }, { "role": "assistant", "content": "The table shows that the JSD score for dasgupta-kumar-ravi:2013:ACL2013 is 0.3316, which is lower than the JSD score of 0.3398 for Lin:2011:CSF:2002472.2002537 for length 100. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/730.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: [Left] Summaries evaluated by Jensen-Shannon divergence (JSD) on Yahoo Answer for summaries of 100 words and 200 words. The average length of the best answer is 102.70. [Right] Value addition of each component in the objective function. The JSD on each line is statistically significantly lower than the JSD on the previous (α=0.05)." ], "reasoning": "The table shows that the JSD score for dasgupta-kumar-ravi:2013:ACL2013 is 0.3316, which is lower than the JSD score of 0.3398 for Lin:2011:CSF:2002472.2002537 for length 100.", "statement": "The system by dasgupta-kumar-ravi:2013:ACL2013 has a lower JSD score than Lin:2011:CSF:2002472.2002537 for length 100.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: [Left] Summaries evaluated by Jensen-Shannon divergence (JSD) on Yahoo Answer for summaries of 100 words and 200 words. The average length of the best answer is 102.70. [Right] Value addition of each component in the objective function. The JSD on each line is statistically significantly lower than the JSD on the previous (α=0.05).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The number of topics does not significantly impact performance, and the system with dispersion function achieves better JSD scores than other systems.The topic number is tuned on the development set, and we find that varying the number of topics does not impact performance too much. Meanwhile, both our system and \\newcitedasgupta-kumar-ravi:2013:ACL2013 produce better JSD scores than the two variants of the \\newciteLin:2011:CSF:2002472.2002537 system, which implies the effectiveness of the dispersion function." } ], "images": [ "scigen/train/731.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: [Left] Summaries evaluated by Jensen-Shannon divergence (JSD) on Yahoo Answer for summaries of 100 words and 200 words. The average length of the best answer is 102.70. [Right] Value addition of each component in the objective function. The JSD on each line is statistically significantly lower than the JSD on the previous (α=0.05)." ], "reasoning": "The number of topics does not significantly impact performance, and the system with dispersion function achieves better JSD scores than other systems.", "description": "The topic number is tuned on the development set, and we find that varying the number of topics does not impact performance too much. Meanwhile, both our system and \\newcitedasgupta-kumar-ravi:2013:ACL2013 produce better JSD scores than the two variants of the \\newciteLin:2011:CSF:2002472.2002537 system, which implies the effectiveness of the dispersion function.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: [Left] Summaries evaluated by Jensen-Shannon divergence (JSD) on Yahoo Answer for summaries of 100 words and 200 words. The average length of the best answer is 102.70. [Right] Value addition of each component in the objective function. The JSD on each line is statistically significantly lower than the JSD on the previous (α=0.05).', solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich system achieves the lowest JSD score for JSD200?\n" }, { "role": "assistant", "content": "By examining the JSD200 column, the system 'Rel + Aut + TM + Pol + Cont + Disp' achieves the lowest JSD score of 0.1758. So, the answer is {\"answer\": \"Rel + Aut + TM + Pol + Cont + Disp\"}" } ], "images": [ "scigen/train/731.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: [Left] Summaries evaluated by Jensen-Shannon divergence (JSD) on Yahoo Answer for summaries of 100 words and 200 words. The average length of the best answer is 102.70. [Right] Value addition of each component in the objective function. The JSD on each line is statistically significantly lower than the JSD on the previous (α=0.05)." ], "reasoning": "By examining the JSD200 column, the system 'Rel + Aut + TM + Pol + Cont + Disp' achieves the lowest JSD score of 0.1758.", "question": "Which system achieves the lowest JSD score for JSD200?", "answer": "So, the answer is {\"answer\": \"Rel + Aut + TM + Pol + Cont + Disp\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The system with the dispersion function achieves the lowest JSD score for JSD100. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe system with the dispersion function achieves the lowest JSD score for JSD100.\n" }, { "role": "assistant", "content": "By examining the JSD100 column, the system 'Rel + Aut + TM + Pol + Cont + Disp' achieves the lowest JSD score of 0.3017, which supports the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/731.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: [Left] Summaries evaluated by Jensen-Shannon divergence (JSD) on Yahoo Answer for summaries of 100 words and 200 words. The average length of the best answer is 102.70. [Right] Value addition of each component in the objective function. The JSD on each line is statistically significantly lower than the JSD on the previous (α=0.05)." ], "reasoning": "By examining the JSD100 column, the system 'Rel + Aut + TM + Pol + Cont + Disp' achieves the lowest JSD score of 0.3017, which supports the statement.", "statement": "The system with the dispersion function achieves the lowest JSD score for JSD100.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 6: Effect of different dispersion functions, content coverage, and dissimilarity metrics on our system. [Left] JSD values for different combinations on Yahoo! data, using LDA with 100 topics. All systems are significantly different from each other at significance level α=0.05. Systems using summation of distances for dispersion function (hsum) uniformly outperform the ones using minimum distance (hmin). [Right] ROUGE scores of different choices for TAC 2008 data. All systems use LDA with 40 topics. The parameters of our systems are adopted from the ones tuned on Yahoo! Answers.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Cosine using TFIDF is more effective for content coverage than WordNet-based semantic measurement, and topical-based dissimilarity marginally outperforms other metrics in blog data.Given that the text similarity metrics and dispersion functions play important roles in the framework, we further study the effectiveness of different content coverage functions (Cosine using TFIDF vs. Semantic), dispersion functions (hsum vs. hmin), and dissimilarity metrics used in dispersion functions (Semantic vs. Topical vs. Lexical). Results on Yahoo! Meanwhile, Cosine using TFIDF is better at measuring content coverage than WordNet-based semantic measurement, and this may due to the limited coverage of WordNet on verbs. This is also true for dissimilarity metrics. This indicates that optimal dispersion function varies by genre. Topical-based dissimilarity also marginally outperforms the other two metrics in blog data." } ], "images": [ "scigen/train/732.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Effect of different dispersion functions, content coverage, and dissimilarity metrics on our system. [Left] JSD values for different combinations on Yahoo! data, using LDA with 100 topics. All systems are significantly different from each other at significance level α=0.05. Systems using summation of distances for dispersion function (hsum) uniformly outperform the ones using minimum distance (hmin). [Right] ROUGE scores of different choices for TAC 2008 data. All systems use LDA with 40 topics. The parameters of our systems are adopted from the ones tuned on Yahoo! Answers." ], "reasoning": "Cosine using TFIDF is more effective for content coverage than WordNet-based semantic measurement, and topical-based dissimilarity marginally outperforms other metrics in blog data.", "description": "Given that the text similarity metrics and dispersion functions play important roles in the framework, we further study the effectiveness of different content coverage functions (Cosine using TFIDF vs. Semantic), dispersion functions (hsum vs. hmin), and dissimilarity metrics used in dispersion functions (Semantic vs. Topical vs. Lexical). Results on Yahoo! Meanwhile, Cosine using TFIDF is better at measuring content coverage than WordNet-based semantic measurement, and this may due to the limited coverage of WordNet on verbs. This is also true for dissimilarity metrics. This indicates that optimal dispersion function varies by genre. Topical-based dissimilarity also marginally outperforms the other two metrics in blog data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich dissimilarity metric has the highest value for Yahoo! Answer Dispersion sum using Cont tfidf?\nSolve the above question based on the table titled 'Table 6: Effect of different dispersion functions, content coverage, and dissimilarity metrics on our system. [Left] JSD values for different combinations on Yahoo! data, using LDA with 100 topics. All systems are significantly different from each other at significance level α=0.05. Systems using summation of distances for dispersion function (hsum) uniformly outperform the ones using minimum distance (hmin). [Right] ROUGE scores of different choices for TAC 2008 data. All systems use LDA with 40 topics. The parameters of our systems are adopted from the ones tuned on Yahoo! Answers.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest value for Yahoo! Answer Dispersion sum using Cont tfidf, look at the 'Yahoo! Answer Dispersion sum' column under 'Cont tfidf'. The highest value is 0.3143 for the Semantic dissimilarity metric. So, the answer is {\"answer\": \"Semantic\"}." } ], "images": [ "scigen/train/732.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Effect of different dispersion functions, content coverage, and dissimilarity metrics on our system. [Left] JSD values for different combinations on Yahoo! data, using LDA with 100 topics. All systems are significantly different from each other at significance level α=0.05. Systems using summation of distances for dispersion function (hsum) uniformly outperform the ones using minimum distance (hmin). [Right] ROUGE scores of different choices for TAC 2008 data. All systems use LDA with 40 topics. The parameters of our systems are adopted from the ones tuned on Yahoo! Answers." ], "reasoning": "To find the highest value for Yahoo! Answer Dispersion sum using Cont tfidf, look at the 'Yahoo! Answer Dispersion sum' column under 'Cont tfidf'. The highest value is 0.3143 for the Semantic dissimilarity metric.", "question": "Which dissimilarity metric has the highest value for Yahoo! Answer Dispersion sum using Cont tfidf?", "answer": "So, the answer is {\"answer\": \"Semantic\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nLexical dissimilarity has the lowest value for Yahoo! Answer Dispersion min using Cont sem.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'Yahoo! Answer Dispersion min' column under 'Cont sem'. The values are 0.3232 for Semantic, 0.3209 for Topical, and 0.3172 for Lexical. Lexical has the lowest value. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/732.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Effect of different dispersion functions, content coverage, and dissimilarity metrics on our system. [Left] JSD values for different combinations on Yahoo! data, using LDA with 100 topics. All systems are significantly different from each other at significance level α=0.05. Systems using summation of distances for dispersion function (hsum) uniformly outperform the ones using minimum distance (hmin). [Right] ROUGE scores of different choices for TAC 2008 data. All systems use LDA with 40 topics. The parameters of our systems are adopted from the ones tuned on Yahoo! Answers." ], "reasoning": "To verify this statement, look at the 'Yahoo! Answer Dispersion min' column under 'Cont sem'. The values are 0.3232 for Semantic, 0.3209 for Topical, and 0.3172 for Lexical. Lexical has the lowest value.", "statement": "Lexical dissimilarity has the lowest value for Yahoo! Answer Dispersion min using Cont sem.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 6: BLEU scores of selective distillation (our method) and distillation all the time during the training process on the Ted talk dataset.'.\n" }, { "role": "assistant", "content": "Selective distillation outperforms distillation all the time in 13 out of 16 languages, showing significant BLEU score improvements.Selective Distillation We list the 16 languages on which the two methods (selective distillation, and distillation all the time) that have difference bigger than 0.5 in terms of BLEU score. It can be seen that selective distillation performs better on 13 out of 16 languages, with large BLEU score improvements, which demonstrates the effectiveness of the selective distillation." } ], "images": [ "scigen/train/733.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: BLEU scores of selective distillation (our method) and distillation all the time during the training process on the Ted talk dataset." ], "reasoning": "Selective distillation outperforms distillation all the time in 13 out of 16 languages, showing significant BLEU score improvements.", "description": "Selective Distillation We list the 16 languages on which the two methods (selective distillation, and distillation all the time) that have difference bigger than 0.5 in terms of BLEU score. It can be seen that selective distillation performs better on 13 out of 16 languages, with large BLEU score improvements, which demonstrates the effectiveness of the selective distillation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 6: BLEU scores of selective distillation (our method) and distillation all the time during the training process on the Ted talk dataset.\nQuestion: Which language shows the highest improvement in BLEU score with selective distillation?\n" }, { "role": "assistant", "content": "By comparing the Δ values for all languages, the highest improvement is observed in Ku with a Δ of +4.88. So, the answer is {\"answer\": \"Ku\"}." } ], "images": [ "scigen/train/733.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: BLEU scores of selective distillation (our method) and distillation all the time during the training process on the Ted talk dataset." ], "reasoning": "By comparing the Δ values for all languages, the highest improvement is observed in Ku with a Δ of +4.88.", "question": "Which language shows the highest improvement in BLEU score with selective distillation?", "answer": "So, the answer is {\"answer\": \"Ku\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Selective distillation performs worse than distillation all the time in the Sk language. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSelective distillation performs worse than distillation all the time in the Sk language.\n" }, { "role": "assistant", "content": "The table shows that for Sk, the Δ value is -0.54, indicating that selective distillation performs worse than distillation all the time. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/733.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: BLEU scores of selective distillation (our method) and distillation all the time during the training process on the Ted talk dataset." ], "reasoning": "The table shows that for Sk, the Δ value is -0.54, indicating that selective distillation performs worse than distillation all the time.", "statement": "Selective distillation performs worse than distillation all the time in the Sk language.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 14: BLEU scores of the individual and multilingual models on the 44 languages→English on the Ted talk dataset.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The multilingual model based on our method nearly matches or outperforms the individual model while handling 44 languages, reducing model parameters size to 1/44 without loss of accuracy.It can be seen that while multilingual baseline performs worse than the individual model, multilingual model based on our method nearly matches and even outperforms the individual model. Note that the multilingual model handles 44 languages in total, which means our method can reduce the model parameters size to 1/44 without loss of accuracy." } ], "images": [ "scigen/train/734.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 14: BLEU scores of the individual and multilingual models on the 44 languages→English on the Ted talk dataset." ], "reasoning": "The multilingual model based on our method nearly matches or outperforms the individual model while handling 44 languages, reducing model parameters size to 1/44 without loss of accuracy.", "description": "It can be seen that while multilingual baseline performs worse than the individual model, multilingual model based on our method nearly matches and even outperforms the individual model. Note that the multilingual model handles 44 languages in total, which means our method can reduce the model parameters size to 1/44 without loss of accuracy.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 14: BLEU scores of the individual and multilingual models on the 44 languages→English on the Ted talk dataset.', solve the following question step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nHow does the performance of the multilingual model based on our method compare to the individual model for the language 'El'?\n" }, { "role": "assistant", "content": "The individual model score for 'El' is 36.69, while the multilingual model based on our method scores 41.43, indicating that the multilingual model outperforms the individual model. So, the answer is {\"answer\": \"41.43\"}" } ], "images": [ "scigen/train/734.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 14: BLEU scores of the individual and multilingual models on the 44 languages→English on the Ted talk dataset." ], "reasoning": "The individual model score for 'El' is 36.69, while the multilingual model based on our method scores 41.43, indicating that the multilingual model outperforms the individual model.", "question": "How does the performance of the multilingual model based on our method compare to the individual model for the language 'El'?", "answer": "So, the answer is {\"answer\": \"41.43\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe multilingual model based on our method performs worse than the individual model for the language 'Pt'.\n" }, { "role": "assistant", "content": "The individual model score for 'Pt' is 44.28, while the multilingual model based on our method scores 44.44, indicating that the multilingual model performs better. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/734.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 14: BLEU scores of the individual and multilingual models on the 44 languages→English on the Ted talk dataset." ], "reasoning": "The individual model score for 'Pt' is 44.28, while the multilingual model based on our method scores 44.44, indicating that the multilingual model performs better.", "statement": "The multilingual model based on our method performs worse than the individual model for the language 'Pt'.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Test set performance. * denotes the models trained on separate datasets while ** indicates those trained on multiple datasets. †specifies the models in Rudinger et al. (2018) that are significantly improved with BERT.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The proposed model for EFP is evaluated using MAE and Pearson Correlation on benchmark datasets, and compared with state-of-the-art models.This section evaluates the effectiveness of the proposed model for EFP on the benchmark datasets. We compare the proposed model with the best reported systems in the literature with linguistic features Lee et al. ; Stanovsky et al. et al. Importantly, to achieve a fair comparison, we obtain the actual implementation of the current state-of-the-art EFP models from Rudinger et al. Following the prior work, we use MAE (Mean Absolute Error), and r (Pearson Correlation) as the performance measures." } ], "images": [ "scigen/train/735.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Test set performance. * denotes the models trained on separate datasets while ** indicates those trained on multiple datasets. †specifies the models in Rudinger et al. (2018) that are significantly improved with BERT." ], "reasoning": "The proposed model for EFP is evaluated using MAE and Pearson Correlation on benchmark datasets, and compared with state-of-the-art models.", "description": "This section evaluates the effectiveness of the proposed model for EFP on the benchmark datasets. We compare the proposed model with the best reported systems in the literature with linguistic features Lee et al. ; Stanovsky et al. et al. Importantly, to achieve a fair comparison, we obtain the actual implementation of the current state-of-the-art EFP models from Rudinger et al. Following the prior work, we use MAE (Mean Absolute Error), and r (Pearson Correlation) as the performance measures.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. The question is related to the given table titled 'Table 1: Test set performance. * denotes the models trained on separate datasets while ** indicates those trained on multiple datasets. †specifies the models in Rudinger et al. (2018) that are significantly improved with BERT.'.\n\nQuestion: Which model has the lowest Meantime MAE?\n" }, { "role": "assistant", "content": "To find the model with the lowest Meantime MAE, look at the 'Meantime MAE' column and identify the smallest value. The lowest Meantime MAE is 0.204, which corresponds to the 'Ours with multiple datasets' model. So, the answer is {\"answer\": \"Ours with multiple datasets\"}." } ], "images": [ "scigen/train/735.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Test set performance. * denotes the models trained on separate datasets while ** indicates those trained on multiple datasets. †specifies the models in Rudinger et al. (2018) that are significantly improved with BERT." ], "reasoning": "To find the model with the lowest Meantime MAE, look at the 'Meantime MAE' column and identify the smallest value. The lowest Meantime MAE is 0.204, which corresponds to the 'Ours with multiple datasets' model.", "question": "Which model has the lowest Meantime MAE?", "answer": "So, the answer is {\"answer\": \"Ours with multiple datasets\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The model 'Ours with multiple datasets' has the highest FactBank r., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model 'Ours with multiple datasets' has the highest FactBank r.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'FactBank r' column and compare the values. The highest FactBank r is 0.903, which corresponds to the 'Ours with multiple datasets' model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/735.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Test set performance. * denotes the models trained on separate datasets while ** indicates those trained on multiple datasets. †specifies the models in Rudinger et al. (2018) that are significantly improved with BERT." ], "reasoning": "To verify this statement, look at the 'FactBank r' column and compare the values. The highest FactBank r is 0.903, which corresponds to the 'Ours with multiple datasets' model.", "statement": "The model 'Ours with multiple datasets' has the highest FactBank r.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Symbol 27', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The TPRN network assigns symbols to word tokens based on cosine similarity, with each symbol representing a specific semantic meaning, such as 'occupation' or 'geopolitical unit'.To interpret the lexical-semantic content of the TPR symbols s(t) learned by the TPRN network: s(t)=SaS(t)∈R10 is calculated for all (120,950) word tokens w(t)in the validation set. The cosine similarity is computed between aS(t) and the embedding vector of each symbol. The symbol with maximum (cosine) similarity is assigned to the corresponding token. For each symbol, all tokens assigned to it are sorted based on their similarity to it; tokens of the same type are removed, and the top tokens from this list are examined to assess by inspection the semantic coherence of the symbol assignments (see The results provide significant support for our hypothesis that each symbol corresponds to a particular meaning, assigned to a cloud of semantically-related word tokens. For example, symbol 27 and symbol 6 can be respectively interpreted as meaning ‘occupation’ and ‘geopolitical unit’. Symbol 11 is assigned to multiple forms of the verb to be, e.g., was (85.8% of occurrences of tokens in the validation set), is, (93.2%) being (100%) and be (98%). Symbol 29 is selected by 10 of the 12 month names (along with other word types; more details in supplementary materials). Other symbols with semantically coherent token sets are reported in the supplementary materials." } ], "images": [ "scigen/train/736.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Symbol 27" ], "reasoning": "The TPRN network assigns symbols to word tokens based on cosine similarity, with each symbol representing a specific semantic meaning, such as 'occupation' or 'geopolitical unit'.", "description": "To interpret the lexical-semantic content of the TPR symbols s(t) learned by the TPRN network: s(t)=SaS(t)∈R10 is calculated for all (120,950) word tokens w(t)in the validation set. The cosine similarity is computed between aS(t) and the embedding vector of each symbol. The symbol with maximum (cosine) similarity is assigned to the corresponding token. For each symbol, all tokens assigned to it are sorted based on their similarity to it; tokens of the same type are removed, and the top tokens from this list are examined to assess by inspection the semantic coherence of the symbol assignments (see The results provide significant support for our hypothesis that each symbol corresponds to a particular meaning, assigned to a cloud of semantically-related word tokens. For example, symbol 27 and symbol 6 can be respectively interpreted as meaning ‘occupation’ and ‘geopolitical unit’. Symbol 11 is assigned to multiple forms of the verb to be, e.g., was (85.8% of occurrences of tokens in the validation set), is, (93.2%) being (100%) and be (98%). Symbol 29 is selected by 10 of the 12 month names (along with other word types; more details in supplementary materials). Other symbols with semantically coherent token sets are reported in the supplementary materials.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Symbol 27', answer the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich token has the highest cosine similarity to its assigned symbol?\n" }, { "role": "assistant", "content": "The table lists tokens and their cosine similarity values. The token with the highest similarity value is 'annexed' with a similarity of 0.836. So, the answer is {\"answer\": \"annexed\"}." } ], "images": [ "scigen/train/736.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Symbol 27" ], "reasoning": "The table lists tokens and their cosine similarity values. The token with the highest similarity value is 'annexed' with a similarity of 0.836.", "question": "Which token has the highest cosine similarity to its assigned symbol?", "answer": "So, the answer is {\"answer\": \"annexed\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The token 'Brisbane' has a higher cosine similarity than 'European'., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe token 'Brisbane' has a higher cosine similarity than 'European'.\n" }, { "role": "assistant", "content": "According to the table, 'Brisbane' has a cosine similarity of 0.8359, while 'European' has a similarity of 0.8341. Therefore, 'Brisbane' has a higher similarity. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/736.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Symbol 27" ], "reasoning": "According to the table, 'Brisbane' has a cosine similarity of 0.8359, while 'European' has a similarity of 0.8341. Therefore, 'Brisbane' has a higher similarity.", "statement": "The token 'Brisbane' has a higher cosine similarity than 'European'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Performance of the proposed TPRN model compared to BiDAF proposed in [Seo et al.2016]'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The TPRN model, with specific settings for symbols, roles, and embedding sizes, performs comparably to the BiDAF model, offering similar results and added interpretability.We compared the performance of single models. In the TPRN model tested here for performance comparison purposes, we set the number of symbols and roles to 600 and 100 respectively and the embedding size of symbols and roles to 15 and 10. Each experiment for the TPRN model took about 13 hours on a single Tesla P100 GPU. Overall, the proposed TPRN gives results comparable to those of the state-of-the-art BiDAF model. Moreover, as we will elaborate in the following sections, our model offers considerable interpretability thanks to the structure built into TPRs." } ], "images": [ "scigen/train/737.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance of the proposed TPRN model compared to BiDAF proposed in [Seo et al.2016]" ], "reasoning": "The TPRN model, with specific settings for symbols, roles, and embedding sizes, performs comparably to the BiDAF model, offering similar results and added interpretability.", "description": "We compared the performance of single models. In the TPRN model tested here for performance comparison purposes, we set the number of symbols and roles to 600 and 100 respectively and the embedding size of symbols and roles to 15 and 10. Each experiment for the TPRN model took about 13 hours on a single Tesla P100 GPU. Overall, the proposed TPRN gives results comparable to those of the state-of-the-art BiDAF model. Moreover, as we will elaborate in the following sections, our model offers considerable interpretability thanks to the structure built into TPRs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Performance of the proposed TPRN model compared to BiDAF proposed in [Seo et al.2016]\nQuestion: What is the F1 score on the test set for the TPRN model?\nSolve the above question based on the table information. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the F1(test) scores for both TPRN and BiDAF models. The F1(test) score for the TPRN model is listed as 76.3. So, the answer is {\"answer\": \"76.3\"}." } ], "images": [ "scigen/train/737.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance of the proposed TPRN model compared to BiDAF proposed in [Seo et al.2016]" ], "reasoning": "The table shows the F1(test) scores for both TPRN and BiDAF models. The F1(test) score for the TPRN model is listed as 76.3.", "question": "What is the F1 score on the test set for the TPRN model?", "answer": "So, the answer is {\"answer\": \"76.3\"}." }, { "messages": [ { "role": "user", "content": "The TPRN model has a higher EM score on the test set compared to the BiDAF model. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe TPRN model has a higher EM score on the test set compared to the BiDAF model.\n" }, { "role": "assistant", "content": "The table shows the EM(test) scores for both TPRN and BiDAF models. The EM(test) score for TPRN is 66.6, while for BiDAF it is 67.1. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/737.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance of the proposed TPRN model compared to BiDAF proposed in [Seo et al.2016]" ], "reasoning": "The table shows the EM(test) scores for both TPRN and BiDAF models. The EM(test) score for TPRN is 66.6, while for BiDAF it is 67.1. Therefore, the statement is refuted.", "statement": "The TPRN model has a higher EM score on the test set compared to the BiDAF model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: Symbol 27'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The TPRN network assigns symbols to word tokens based on cosine similarity, revealing semantic coherence in symbol assignments.To interpret the lexical-semantic content of the TPR symbols s(t) learned by the TPRN network: s(t)=SaS(t)∈R10 is calculated for all (120,950) word tokens w(t)in the validation set. The cosine similarity is computed between aS(t) and the embedding vector of each symbol. The symbol with maximum (cosine) similarity is assigned to the corresponding token. For each symbol, all tokens assigned to it are sorted based on their similarity to it; tokens of the same type are removed, and the top tokens from this list are examined to assess by inspection the semantic coherence of the symbol assignments (see The results provide significant support for our hypothesis that each symbol corresponds to a particular meaning, assigned to a cloud of semantically-related word tokens. For example, symbol 27 and symbol 6 can be respectively interpreted as meaning ‘occupation’ and ‘geopolitical unit’. Symbol 11 is assigned to multiple forms of the verb to be, e.g., was (85.8% of occurrences of tokens in the validation set), is, (93.2%) being (100%) and be (98%). Symbol 29 is selected by 10 of the 12 month names (along with other word types; more details in supplementary materials). Other symbols with semantically coherent token sets are reported in the supplementary materials." } ], "images": [ "scigen/train/738.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Symbol 27" ], "reasoning": "The TPRN network assigns symbols to word tokens based on cosine similarity, revealing semantic coherence in symbol assignments.", "description": "To interpret the lexical-semantic content of the TPR symbols s(t) learned by the TPRN network: s(t)=SaS(t)∈R10 is calculated for all (120,950) word tokens w(t)in the validation set. The cosine similarity is computed between aS(t) and the embedding vector of each symbol. The symbol with maximum (cosine) similarity is assigned to the corresponding token. For each symbol, all tokens assigned to it are sorted based on their similarity to it; tokens of the same type are removed, and the top tokens from this list are examined to assess by inspection the semantic coherence of the symbol assignments (see The results provide significant support for our hypothesis that each symbol corresponds to a particular meaning, assigned to a cloud of semantically-related word tokens. For example, symbol 27 and symbol 6 can be respectively interpreted as meaning ‘occupation’ and ‘geopolitical unit’. Symbol 11 is assigned to multiple forms of the verb to be, e.g., was (85.8% of occurrences of tokens in the validation set), is, (93.2%) being (100%) and be (98%). Symbol 29 is selected by 10 of the 12 month names (along with other word types; more details in supplementary materials). Other symbols with semantically coherent token sets are reported in the supplementary materials.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 3: Symbol 27', and tackle the question below in a sequential manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich token has the highest similarity score?\n" }, { "role": "assistant", "content": "By examining the 'Similarity' column, the token with the highest similarity score is identified. So, the answer is {\"answer\": \"printmaker\"}" } ], "images": [ "scigen/train/738.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Symbol 27" ], "reasoning": "By examining the 'Similarity' column, the token with the highest similarity score is identified.", "question": "Which token has the highest similarity score?", "answer": "So, the answer is {\"answer\": \"printmaker\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe token 'composer' has a similarity score higher than 0.9.\n" }, { "role": "assistant", "content": "The table shows that the similarity score for 'composer' is 0.8992, which is less than 0.9. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/738.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Symbol 27" ], "reasoning": "The table shows that the similarity score for 'composer' is 0.8992, which is less than 0.9.", "statement": "The token 'composer' has a similarity score higher than 0.9.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Symbol 27'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The TPRN network assigns symbols to word tokens based on cosine similarity, revealing semantic coherence among tokens assigned to the same symbol.To interpret the lexical-semantic content of the TPR symbols s(t) learned by the TPRN network: s(t)=SaS(t)∈R10 is calculated for all (120,950) word tokens w(t)in the validation set. The cosine similarity is computed between aS(t) and the embedding vector of each symbol. The symbol with maximum (cosine) similarity is assigned to the corresponding token. For each symbol, all tokens assigned to it are sorted based on their similarity to it; tokens of the same type are removed, and the top tokens from this list are examined to assess by inspection the semantic coherence of the symbol assignments (see The results provide significant support for our hypothesis that each symbol corresponds to a particular meaning, assigned to a cloud of semantically-related word tokens. For example, symbol 27 and symbol 6 can be respectively interpreted as meaning ‘occupation’ and ‘geopolitical unit’. Symbol 11 is assigned to multiple forms of the verb to be, e.g., was (85.8% of occurrences of tokens in the validation set), is, (93.2%) being (100%) and be (98%). Symbol 29 is selected by 10 of the 12 month names (along with other word types; more details in supplementary materials). Other symbols with semantically coherent token sets are reported in the supplementary materials." } ], "images": [ "scigen/train/739.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Symbol 27" ], "reasoning": "The TPRN network assigns symbols to word tokens based on cosine similarity, revealing semantic coherence among tokens assigned to the same symbol.", "description": "To interpret the lexical-semantic content of the TPR symbols s(t) learned by the TPRN network: s(t)=SaS(t)∈R10 is calculated for all (120,950) word tokens w(t)in the validation set. The cosine similarity is computed between aS(t) and the embedding vector of each symbol. The symbol with maximum (cosine) similarity is assigned to the corresponding token. For each symbol, all tokens assigned to it are sorted based on their similarity to it; tokens of the same type are removed, and the top tokens from this list are examined to assess by inspection the semantic coherence of the symbol assignments (see The results provide significant support for our hypothesis that each symbol corresponds to a particular meaning, assigned to a cloud of semantically-related word tokens. For example, symbol 27 and symbol 6 can be respectively interpreted as meaning ‘occupation’ and ‘geopolitical unit’. Symbol 11 is assigned to multiple forms of the verb to be, e.g., was (85.8% of occurrences of tokens in the validation set), is, (93.2%) being (100%) and be (98%). Symbol 29 is selected by 10 of the 12 month names (along with other word types; more details in supplementary materials). Other symbols with semantically coherent token sets are reported in the supplementary materials.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: Symbol 27'. Solve the following question based on the table.\n\nQuestion: Which token has the highest similarity score in the table?\nLet's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the token with the highest similarity score, look at the 'Similarity' column and identify the highest value, then find the corresponding token in the 'Token' column. So, the answer is {\"answer\": \"phrase\"}" } ], "images": [ "scigen/train/739.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Symbol 27" ], "reasoning": "To find the token with the highest similarity score, look at the 'Similarity' column and identify the highest value, then find the corresponding token in the 'Token' column.", "question": "Which token has the highest similarity score in the table?", "answer": "So, the answer is {\"answer\": \"phrase\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The token 'mean' has a similarity score higher than 0.8. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe token 'mean' has a similarity score higher than 0.8.\n" }, { "role": "assistant", "content": "Check the 'Similarity' column for the token 'mean' and verify if its similarity score is higher than 0.8. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/739.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Symbol 27" ], "reasoning": "Check the 'Similarity' column for the token 'mean' and verify if its similarity score is higher than 0.8.", "statement": "The token 'mean' has a similarity score higher than 0.8.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Symbol 27'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The TPRN network assigns symbols to word tokens based on cosine similarity, revealing semantic coherence among tokens assigned to the same symbol.To interpret the lexical-semantic content of the TPR symbols s(t) learned by the TPRN network: s(t)=SaS(t)∈R10 is calculated for all (120,950) word tokens w(t)in the validation set. The cosine similarity is computed between aS(t) and the embedding vector of each symbol. The symbol with maximum (cosine) similarity is assigned to the corresponding token. For each symbol, all tokens assigned to it are sorted based on their similarity to it; tokens of the same type are removed, and the top tokens from this list are examined to assess by inspection the semantic coherence of the symbol assignments (see The results provide significant support for our hypothesis that each symbol corresponds to a particular meaning, assigned to a cloud of semantically-related word tokens. For example, symbol 27 and symbol 6 can be respectively interpreted as meaning ‘occupation’ and ‘geopolitical unit’. Symbol 11 is assigned to multiple forms of the verb to be, e.g., was (85.8% of occurrences of tokens in the validation set), is, (93.2%) being (100%) and be (98%). Symbol 29 is selected by 10 of the 12 month names (along with other word types; more details in supplementary materials). Other symbols with semantically coherent token sets are reported in the supplementary materials." } ], "images": [ "scigen/train/740.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Symbol 27" ], "reasoning": "The TPRN network assigns symbols to word tokens based on cosine similarity, revealing semantic coherence among tokens assigned to the same symbol.", "description": "To interpret the lexical-semantic content of the TPR symbols s(t) learned by the TPRN network: s(t)=SaS(t)∈R10 is calculated for all (120,950) word tokens w(t)in the validation set. The cosine similarity is computed between aS(t) and the embedding vector of each symbol. The symbol with maximum (cosine) similarity is assigned to the corresponding token. For each symbol, all tokens assigned to it are sorted based on their similarity to it; tokens of the same type are removed, and the top tokens from this list are examined to assess by inspection the semantic coherence of the symbol assignments (see The results provide significant support for our hypothesis that each symbol corresponds to a particular meaning, assigned to a cloud of semantically-related word tokens. For example, symbol 27 and symbol 6 can be respectively interpreted as meaning ‘occupation’ and ‘geopolitical unit’. Symbol 11 is assigned to multiple forms of the verb to be, e.g., was (85.8% of occurrences of tokens in the validation set), is, (93.2%) being (100%) and be (98%). Symbol 29 is selected by 10 of the 12 month names (along with other word types; more details in supplementary materials). Other symbols with semantically coherent token sets are reported in the supplementary materials.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: 'Table 3: Symbol 27'\nQuestion: Which token has the highest cosine similarity to its assigned symbol?\n" }, { "role": "assistant", "content": "To find the token with the highest cosine similarity, we look for the token with the highest similarity score in the table. The token 'abolished' has the highest similarity score of 0.8777. So, the answer is {\"answer\": \"abolished\"}." } ], "images": [ "scigen/train/740.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Symbol 27" ], "reasoning": "To find the token with the highest cosine similarity, we look for the token with the highest similarity score in the table. The token 'abolished' has the highest similarity score of 0.8777.", "question": "Which token has the highest cosine similarity to its assigned symbol?", "answer": "So, the answer is {\"answer\": \"abolished\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe token 'Melbourne' has a higher similarity score than 'Newcastle'.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the similarity scores of 'Melbourne' and 'Newcastle'. 'Melbourne' has a similarity score of 0.8558, while 'Newcastle' has a similarity score of 0.8588. Therefore, 'Melbourne' does not have a higher similarity score than 'Newcastle'. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/740.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Symbol 27" ], "reasoning": "To verify this statement, we compare the similarity scores of 'Melbourne' and 'Newcastle'. 'Melbourne' has a similarity score of 0.8558, while 'Newcastle' has a similarity score of 0.8588. Therefore, 'Melbourne' does not have a higher similarity score than 'Newcastle'.", "statement": "The token 'Melbourne' has a higher similarity score than 'Newcastle'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 6: NFH Resolution accuracies on the development and test sets.'.\n" }, { "role": "assistant", "content": "The model with ELMo embeddings significantly outperforms the base model, achieving 77.2% accuracy on the development set and 74.0% accuracy on the test set.The complete model trained on the entire training data achieves 65.6% accuracy on the development set and 60.8% accuracy on the test set. The model with ELMo embeddings Peters et al." } ], "images": [ "scigen/train/741.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: NFH Resolution accuracies on the development and test sets." ], "reasoning": "The model with ELMo embeddings significantly outperforms the base model, achieving 77.2% accuracy on the development set and 74.0% accuracy on the test set.", "description": "The complete model trained on the entire training data achieves 65.6% accuracy on the development set and 60.8% accuracy on the test set. The model with ELMo embeddings Peters et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the difference in test set accuracy between the base model and the model with ELMo embeddings?\nSolve the above question based on the table titled 'Table 6: NFH Resolution accuracies on the development and test sets.'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The base model achieves 60.8% accuracy on the test set, while the model with ELMo embeddings achieves 74.0% accuracy. The difference is 74.0% - 60.8% = 13.2%. So, the answer is {\"answer\": \"13.2%\"}." } ], "images": [ "scigen/train/741.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: NFH Resolution accuracies on the development and test sets." ], "reasoning": "The base model achieves 60.8% accuracy on the test set, while the model with ELMo embeddings achieves 74.0% accuracy. The difference is 74.0% - 60.8% = 13.2%.", "question": "What is the difference in test set accuracy between the base model and the model with ELMo embeddings?", "answer": "So, the answer is {\"answer\": \"13.2%\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The model with ELMo embeddings achieves over 75% accuracy on the development set., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model with ELMo embeddings achieves over 75% accuracy on the development set.\n" }, { "role": "assistant", "content": "The table shows that the model with ELMo embeddings achieves 77.2% accuracy on the development set, which is over 75%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/741.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: NFH Resolution accuracies on the development and test sets." ], "reasoning": "The table shows that the model with ELMo embeddings achieves 77.2% accuracy on the development set, which is over 75%.", "statement": "The model with ELMo embeddings achieves over 75% accuracy on the development set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: NFH Identification corpus summary. The train and dev splits are noisy and the test set are gold annotations.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "We used a rule-based approach to identify NFH, creating a dataset with 79,678 positive and 104,329 negative examples, and split it into 90% train and 10% development sets.We improve the NFH identification using machine learning. We create a large but noisy data set by considering all the numbers in the corpus and treating the NFH identified by the rule-based approach as positive (79,678 examples) and all other numbers as negative (104,329 examples). We randomly split the dataset into train and development sets in a 90%, 10% split." } ], "images": [ "scigen/train/742.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: NFH Identification corpus summary. The train and dev splits are noisy and the test set are gold annotations." ], "reasoning": "We used a rule-based approach to identify NFH, creating a dataset with 79,678 positive and 104,329 negative examples, and split it into 90% train and 10% development sets.", "description": "We improve the NFH identification using machine learning. We create a large but noisy data set by considering all the numbers in the corpus and treating the NFH identified by the rule-based approach as positive (79,678 examples) and all other numbers as negative (104,329 examples). We randomly split the dataset into train and development sets in a 90%, 10% split.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: NFH Identification corpus summary. The train and dev splits are noisy and the test set are gold annotations.', answer the question below by showing each progressive step toward the answer. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nHow many positive examples are in the development set?\n" }, { "role": "assistant", "content": "The table shows the number of positive examples in the 'dev' column under 'pos', which is 7865. So, the answer is {\"answer\": \"7865\"}" } ], "images": [ "scigen/train/742.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: NFH Identification corpus summary. The train and dev splits are noisy and the test set are gold annotations." ], "reasoning": "The table shows the number of positive examples in the 'dev' column under 'pos', which is 7865.", "question": "How many positive examples are in the development set?", "answer": "So, the answer is {\"answer\": \"7865\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe total number of examples in the test set is 500.\n" }, { "role": "assistant", "content": "The table shows the total number of examples in the 'test' column under 'all', which is 500. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/742.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: NFH Identification corpus summary. The train and dev splits are noisy and the test set are gold annotations." ], "reasoning": "The table shows the total number of examples in the 'test' column under 'all', which is 500.", "statement": "The total number of examples in the test set is 500.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Full-match scores of predicted keyphrases by various methods'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Yake! outperforms TopicRank among unsupervised models, while CopyRnn is the best among supervised models, surpassing Maui.From the unsupervised models, we see that Yake! is consistently better than TopicRank. The next two supervised models perform even better, with CopyRnn being discretely superior than Maui." } ], "images": [ "scigen/train/743.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Full-match scores of predicted keyphrases by various methods" ], "reasoning": "Yake! outperforms TopicRank among unsupervised models, while CopyRnn is the best among supervised models, surpassing Maui.", "description": "From the unsupervised models, we see that Yake! is consistently better than TopicRank. The next two supervised models perform even better, with CopyRnn being discretely superior than Maui.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 2: Full-match scores of predicted keyphrases by various methods' in mind, break down and resolve the question below step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhich model has the highest F1@7 score for the Meng (20K) dataset?\n" }, { "role": "assistant", "content": "To find the model with the highest F1@7 score for the Meng (20K) dataset, look at the column labeled 'Meng (20K) F1@7'. The highest value in this column is 33.06, which corresponds to the CopyRnn model. So, the answer is {\"answer\": \"CopyRnn\"}." } ], "images": [ "scigen/train/743.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Full-match scores of predicted keyphrases by various methods" ], "reasoning": "To find the model with the highest F1@7 score for the Meng (20K) dataset, look at the column labeled 'Meng (20K) F1@7'. The highest value in this column is 33.06, which corresponds to the CopyRnn model.", "question": "Which model has the highest F1@7 score for the Meng (20K) dataset?", "answer": "So, the answer is {\"answer\": \"CopyRnn\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nTopicRank has a higher F1@5 score than Yake! for the Hulth (500) dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the F1@5 scores for TopicRank and Yake! in the Hulth (500) dataset column. TopicRank has an F1@5 score of 16.5, while Yake! has an F1@5 score of 19.35. Since 16.5 is less than 19.35, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/743.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Full-match scores of predicted keyphrases by various methods" ], "reasoning": "To verify this statement, compare the F1@5 scores for TopicRank and Yake! in the Hulth (500) dataset column. TopicRank has an F1@5 score of 16.5, while Yake! has an F1@5 score of 19.35. Since 16.5 is less than 19.35, the statement is refuted.", "statement": "TopicRank has a higher F1@5 score than Yake! for the Hulth (500) dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Rouge scores of predicted keyphrases by various methods', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "PointCov is the best text summarizer with clean and concise keyphrase predictions, while CopyRnn is still the best overall. Abs scores are comparable to Maui and Yake!, and TopicRank, Merge, and Inject are the worst.Abs works slightly better reaching scores from 10.24 to 14.75 %. PointCov is the best of the text summarizers producing keyphrase predictions that are usually clean and concise with few repetitions. This is probably the merit of the coverage mechanism. There is still a considerable gap between PointCov and CopyRnn. CopyRnn is still the best but PointCov is close. Abs scores are also comparable to those of Maui and Yake!. TopicRank, Merge and Inject are again the worst." } ], "images": [ "scigen/train/744.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Rouge scores of predicted keyphrases by various methods" ], "reasoning": "PointCov is the best text summarizer with clean and concise keyphrase predictions, while CopyRnn is still the best overall. Abs scores are comparable to Maui and Yake!, and TopicRank, Merge, and Inject are the worst.", "description": "Abs works slightly better reaching scores from 10.24 to 14.75 %. PointCov is the best of the text summarizers producing keyphrase predictions that are usually clean and concise with few repetitions. This is probably the merit of the coverage mechanism. There is still a considerable gap between PointCov and CopyRnn. CopyRnn is still the best but PointCov is close. Abs scores are also comparable to those of Maui and Yake!. TopicRank, Merge and Inject are again the worst.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nTable title: Table 3: Rouge scores of predicted keyphrases by various methods\n\nQuestion: Which method has the highest F1 score for the Meng (20K) dataset?\n" }, { "role": "assistant", "content": "To find the method with the highest F1 score for the Meng (20K) dataset, look at the F1 column under Meng (20K). CopyRnn has the highest F1 score of 42.93. So, the answer is {\"answer\": \"CopyRnn\"}." } ], "images": [ "scigen/train/744.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Rouge scores of predicted keyphrases by various methods" ], "reasoning": "To find the method with the highest F1 score for the Meng (20K) dataset, look at the F1 column under Meng (20K). CopyRnn has the highest F1 score of 42.93.", "question": "Which method has the highest F1 score for the Meng (20K) dataset?", "answer": "So, the answer is {\"answer\": \"CopyRnn\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nAbs has the highest F1 score for the OAGK (100K) dataset.\n" }, { "role": "assistant", "content": "To verify this statement, check the F1 scores for the OAGK (100K) dataset. Abs has an F1 score of 25.77, but CopyRnn has a higher F1 score of 36.09. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/744.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Rouge scores of predicted keyphrases by various methods" ], "reasoning": "To verify this statement, check the F1 scores for the OAGK (100K) dataset. Abs has an F1 score of 25.77, but CopyRnn has a higher F1 score of 36.09.", "statement": "Abs has the highest F1 score for the OAGK (100K) dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: This table shows the average performance of the classifiers with the various timeframes. The values are the arithmetic mean of the classifiers’ results. These are an SVM, a NB classifier and an LM classifier. A bold font highlights the best result considering this metric. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−).', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The 1 day timeframe resulted in the highest positive F1 score and accuracy, despite the positive precision being slightly higher at 13 hours.The figure shows how the values for all metrics peak at the 1 day timeframe and worsen the longer the timeframe becomes. The positive precision value is the only one which was not the highest using a 1 day timeframe. It was 49.81% for 13 hours and 49.29% for 1 day. Nevertheless, this is negligible considering that the 1 day timeframe resulted in the greatest positive F1 score and accuracy. Thus, successive tests were performed using 1 day as timeframe length." } ], "images": [ "scigen/train/745.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: This table shows the average performance of the classifiers with the various timeframes. The values are the arithmetic mean of the classifiers’ results. These are an SVM, a NB classifier and an LM classifier. A bold font highlights the best result considering this metric. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−)." ], "reasoning": "The 1 day timeframe resulted in the highest positive F1 score and accuracy, despite the positive precision being slightly higher at 13 hours.", "description": "The figure shows how the values for all metrics peak at the 1 day timeframe and worsen the longer the timeframe becomes. The positive precision value is the only one which was not the highest using a 1 day timeframe. It was 49.81% for 13 hours and 49.29% for 1 day. Nevertheless, this is negligible considering that the 1 day timeframe resulted in the greatest positive F1 score and accuracy. Thus, successive tests were performed using 1 day as timeframe length.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 2: This table shows the average performance of the classifiers with the various timeframes. The values are the arithmetic mean of the classifiers’ results. These are an SVM, a NB classifier and an LM classifier. A bold font highlights the best result considering this metric. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−).\nQuestion: What is the positive precision value for the 1 day timeframe, and how does it compare to the 13 hours timeframe?\n" }, { "role": "assistant", "content": "The positive precision value for the 1 day timeframe is 67.83%, which is slightly lower than the 13 hours timeframe value of 68.68%. So, the answer is {\"answer\": \"67.83%\"}." } ], "images": [ "scigen/train/745.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: This table shows the average performance of the classifiers with the various timeframes. The values are the arithmetic mean of the classifiers’ results. These are an SVM, a NB classifier and an LM classifier. A bold font highlights the best result considering this metric. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−)." ], "reasoning": "The positive precision value for the 1 day timeframe is 67.83%, which is slightly lower than the 13 hours timeframe value of 68.68%.", "question": "What is the positive precision value for the 1 day timeframe, and how does it compare to the 13 hours timeframe?", "answer": "So, the answer is {\"answer\": \"67.83%\"}." }, { "messages": [ { "role": "user", "content": "The 1 day timeframe resulted in the highest positive F1 score and accuracy. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe 1 day timeframe resulted in the highest positive F1 score and accuracy.\n" }, { "role": "assistant", "content": "The table shows that the positive F1 score for the 1 day timeframe is 67.42, which is the highest among all timeframes. Similarly, the accuracy for the 1 day timeframe is 62.96, also the highest. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/745.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: This table shows the average performance of the classifiers with the various timeframes. The values are the arithmetic mean of the classifiers’ results. These are an SVM, a NB classifier and an LM classifier. A bold font highlights the best result considering this metric. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−)." ], "reasoning": "The table shows that the positive F1 score for the 1 day timeframe is 67.42, which is the highest among all timeframes. Similarly, the accuracy for the 1 day timeframe is 62.96, also the highest.", "statement": "The 1 day timeframe resulted in the highest positive F1 score and accuracy.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: The table shows how commonly a term appears in disruptive or constructive posts. A bold font indicates that the term appears more frequently in that class.'.\n" }, { "role": "assistant", "content": "Swear words are rarely used in disruptive posts, but when they are, they are highly expressive. Terms like 'I' and 'you' are more frequently used in both disruptive and constructive posts, indicating their potential for improving recall in detecting post types.All 3,467,402 posts have been considered for these values. Common swear words like “fucking”, “fuck” and “shit” are hardly used in disruptive posts. With only 6.39‰ of disruptive posts containing any of the three swear words, the few posts that do, contain the terms multiple times. However, when they are used, they are quite expressive. For example, the term “shit” is 9.43 times likelier to appear in a disruptive than in a constructive post. In sum, with these swear words alone, a small recall but a high precision could potentially be achieved. Hence, a collection of swear words would be unlikely to suffice for identifying many disruptive posts. The use of “I”- and “You”-messages can also be investigated without using a classifier. As initially stated, correctly detecting these messages is out of the scope of this thesis. Instead, terms indicative of “I”- and “You”-messages can be counted like “I”, “you” but also “myself”, “yourself” and others. However, they are a lot more frequently used: 36.60% of all disruptive and 26.89% of all constructive posts contain at least one of the terms “I” and “you”. Additionally considering the terms “me”, “my”, “your”, “myself” and “yourself” increases this to 40.13% and 29.37% respectively. 43.93% of all disruptive and 32.11% of all constructive posts contain at least two of the terms. Therefore, if these terms and especially “I” and “you” were strong indicators for constructiveness or disruptiveness, they would improve the recall noticeably." } ], "images": [ "scigen/train/746.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The table shows how commonly a term appears in disruptive or constructive posts. A bold font indicates that the term appears more frequently in that class." ], "reasoning": "Swear words are rarely used in disruptive posts, but when they are, they are highly expressive. Terms like 'I' and 'you' are more frequently used in both disruptive and constructive posts, indicating their potential for improving recall in detecting post types.", "description": "All 3,467,402 posts have been considered for these values. Common swear words like “fucking”, “fuck” and “shit” are hardly used in disruptive posts. With only 6.39‰ of disruptive posts containing any of the three swear words, the few posts that do, contain the terms multiple times. However, when they are used, they are quite expressive. For example, the term “shit” is 9.43 times likelier to appear in a disruptive than in a constructive post. In sum, with these swear words alone, a small recall but a high precision could potentially be achieved. Hence, a collection of swear words would be unlikely to suffice for identifying many disruptive posts. The use of “I”- and “You”-messages can also be investigated without using a classifier. As initially stated, correctly detecting these messages is out of the scope of this thesis. Instead, terms indicative of “I”- and “You”-messages can be counted like “I”, “you” but also “myself”, “yourself” and others. However, they are a lot more frequently used: 36.60% of all disruptive and 26.89% of all constructive posts contain at least one of the terms “I” and “you”. Additionally considering the terms “me”, “my”, “your”, “myself” and “yourself” increases this to 40.13% and 29.37% respectively. 43.93% of all disruptive and 32.11% of all constructive posts contain at least two of the terms. Therefore, if these terms and especially “I” and “you” were strong indicators for constructiveness or disruptiveness, they would improve the recall noticeably.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: The table shows how commonly a term appears in disruptive or constructive posts. A bold font indicates that the term appears more frequently in that class.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the share of the term 'you' in disruptive posts compared to constructive posts?\n" }, { "role": "assistant", "content": "The table shows that the share of the term 'you' in disruptive posts is 10.64‰, while in constructive posts it is 4.52‰. This indicates that 'you' is more frequently used in disruptive posts. So, the answer is {\"answer\": \"10.64‰ in disruptive posts and 4.52‰ in constructive posts\"}." } ], "images": [ "scigen/train/746.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The table shows how commonly a term appears in disruptive or constructive posts. A bold font indicates that the term appears more frequently in that class." ], "reasoning": "The table shows that the share of the term 'you' in disruptive posts is 10.64‰, while in constructive posts it is 4.52‰. This indicates that 'you' is more frequently used in disruptive posts.", "question": "What is the share of the term 'you' in disruptive posts compared to constructive posts?", "answer": "So, the answer is {\"answer\": \"10.64‰ in disruptive posts and 4.52‰ in constructive posts\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe term 'shit' is more frequently used in constructive posts than in disruptive posts.\n" }, { "role": "assistant", "content": "According to the table, the share of the term 'shit' in disruptive posts is 0.09‰, while in constructive posts it is 0.01‰. This shows that 'shit' is more frequently used in disruptive posts. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/746.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The table shows how commonly a term appears in disruptive or constructive posts. A bold font indicates that the term appears more frequently in that class." ], "reasoning": "According to the table, the share of the term 'shit' in disruptive posts is 0.09‰, while in constructive posts it is 0.01‰. This shows that 'shit' is more frequently used in disruptive posts.", "statement": "The term 'shit' is more frequently used in constructive posts than in disruptive posts.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: This table shows the performance of the SVM, NB and LM classifiers using the independent posts approach. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−).', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The SVM classifier performs best overall, with the highest positive recall and AUC, while the NB classifier ranks second and the LM classifier ranks last.Overall, the support vector machine performs best, the naïve Bayes classifier ranks second and the language model ranks last. The SVM is outperformed only in negative recall by the NB classifier. However, due to lower negative precision, the NB classifier results in a worse negative F1 score than the SVM. The SVM is significantly better in positive recall and AUC, whereas the NB classifier and the LM classifier repeatedly perform similarly. When looking at the positive and negative F1 scores, it can be seen that all classifiers are better in predicting constructive posts than disruptive ones. It was expected that the SVM would outperform the NB classifier. Yet, we imagined that the language model would perform better. All in all, the three classifiers perform hardly well enough to draw reliable conclusions from the results. F1, AUC and accuracy scores of about 80% and above would be needed." } ], "images": [ "scigen/train/747.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: This table shows the performance of the SVM, NB and LM classifiers using the independent posts approach. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−)." ], "reasoning": "The SVM classifier performs best overall, with the highest positive recall and AUC, while the NB classifier ranks second and the LM classifier ranks last.", "description": "Overall, the support vector machine performs best, the naïve Bayes classifier ranks second and the language model ranks last. The SVM is outperformed only in negative recall by the NB classifier. However, due to lower negative precision, the NB classifier results in a worse negative F1 score than the SVM. The SVM is significantly better in positive recall and AUC, whereas the NB classifier and the LM classifier repeatedly perform similarly. When looking at the positive and negative F1 scores, it can be seen that all classifiers are better in predicting constructive posts than disruptive ones. It was expected that the SVM would outperform the NB classifier. Yet, we imagined that the language model would perform better. All in all, the three classifiers perform hardly well enough to draw reliable conclusions from the results. F1, AUC and accuracy scores of about 80% and above would be needed.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: Table 3: This table shows the performance of the SVM, NB and LM classifiers using the independent posts approach. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−).\nQuestion: Which classifier has the highest positive recall and AUC?\n" }, { "role": "assistant", "content": "The table shows that the SVM classifier has the highest positive recall (59.91) and AUC (0.750). So, the answer is {\"answer\": \"SVM\"}" } ], "images": [ "scigen/train/747.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: This table shows the performance of the SVM, NB and LM classifiers using the independent posts approach. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−)." ], "reasoning": "The table shows that the SVM classifier has the highest positive recall (59.91) and AUC (0.750).", "question": "Which classifier has the highest positive recall and AUC?", "answer": "So, the answer is {\"answer\": \"SVM\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe NB classifier has the highest negative recall.\n" }, { "role": "assistant", "content": "The table shows that the NB classifier has a negative recall of 80.78, which is the highest among all classifiers. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/747.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: This table shows the performance of the SVM, NB and LM classifiers using the independent posts approach. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−)." ], "reasoning": "The table shows that the NB classifier has a negative recall of 80.78, which is the highest among all classifiers.", "statement": "The NB classifier has the highest negative recall.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: This table shows the performance of the SVM, NB and LM classifiers using the sliding window approach with linear sampling. RapidMiner did not return AUC values for the SVM and NB classifier, so they had to be left out. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−).', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The sliding window algorithm increased the number of constructive and disruptive posts compared to independent posts classifications, but multiple runs showed similar results.The number of constructive and disruptive posts was increased compared to the independent posts classifications. This is conditioned by the sliding window algorithm which regards all merged posts containing a blocked post as disruptive. As this approach considers an editor’s post history, the data is different and the approach was evaluated on newly sampled data. However, multiple runs of classifications using both the independent posts as well as the sliding window approach showed that the results always remained similar." } ], "images": [ "scigen/train/748.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: This table shows the performance of the SVM, NB and LM classifiers using the sliding window approach with linear sampling. RapidMiner did not return AUC values for the SVM and NB classifier, so they had to be left out. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−)." ], "reasoning": "The sliding window algorithm increased the number of constructive and disruptive posts compared to independent posts classifications, but multiple runs showed similar results.", "description": "The number of constructive and disruptive posts was increased compared to the independent posts classifications. This is conditioned by the sliding window algorithm which regards all merged posts containing a blocked post as disruptive. As this approach considers an editor’s post history, the data is different and the approach was evaluated on newly sampled data. However, multiple runs of classifications using both the independent posts as well as the sliding window approach showed that the results always remained similar.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: This table shows the performance of the SVM, NB and LM classifiers using the sliding window approach with linear sampling. RapidMiner did not return AUC values for the SVM and NB classifier, so they had to be left out. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−).', answer the following question. Give detailed solution consisting of each step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich classifier had the highest Recall+ value using the sliding window approach?\n" }, { "role": "assistant", "content": "By comparing the Recall+ values for classifiers using the sliding window approach, NB (FW) had the highest Recall+ value of 70.11. So, the answer is {\"answer\": \"NB (FW)\"}" } ], "images": [ "scigen/train/748.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: This table shows the performance of the SVM, NB and LM classifiers using the sliding window approach with linear sampling. RapidMiner did not return AUC values for the SVM and NB classifier, so they had to be left out. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−)." ], "reasoning": "By comparing the Recall+ values for classifiers using the sliding window approach, NB (FW) had the highest Recall+ value of 70.11.", "question": "Which classifier had the highest Recall+ value using the sliding window approach?", "answer": "So, the answer is {\"answer\": \"NB (FW)\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe LM classifier had the highest Accuracy using the sliding window approach.\n" }, { "role": "assistant", "content": "By comparing the Accuracy values for classifiers using the sliding window approach, NB (FW) had the highest Accuracy of 60.70, not LM (FW) which had 50.78. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/748.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: This table shows the performance of the SVM, NB and LM classifiers using the sliding window approach with linear sampling. RapidMiner did not return AUC values for the SVM and NB classifier, so they had to be left out. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−)." ], "reasoning": "By comparing the Accuracy values for classifiers using the sliding window approach, NB (FW) had the highest Accuracy of 60.70, not LM (FW) which had 50.78.", "statement": "The LM classifier had the highest Accuracy using the sliding window approach.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: This table shows the performance of the SVM, NB and LM classifiers using the independent posts approach on the oldest discussion. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Tests were executed multiple times with newly sampled data sets, confirming consistent performance except for improved results with chronologically sampled early data.Although we only present the data of a single run each, all tests have been executed multiple times with newly sampled data sets. This confirmed that the observed performances were not the result of a sampling bias. The results always remained comparable but for one exception. When operating on a data set chronologically sampled from the earliest \\glspldiscussion, we found that the classifiers yielded significantly improved performance. Chronologically sampling data from more recent \\glspldiscussion could not reproduce these results. However, due to the amount of existing data, we were not able to test all possible partitions." } ], "images": [ "scigen/train/749.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: This table shows the performance of the SVM, NB and LM classifiers using the independent posts approach on the oldest discussion. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−)." ], "reasoning": "Tests were executed multiple times with newly sampled data sets, confirming consistent performance except for improved results with chronologically sampled early data.", "description": "Although we only present the data of a single run each, all tests have been executed multiple times with newly sampled data sets. This confirmed that the observed performances were not the result of a sampling bias. The results always remained comparable but for one exception. When operating on a data set chronologically sampled from the earliest \\glspldiscussion, we found that the classifiers yielded significantly improved performance. Chronologically sampling data from more recent \\glspldiscussion could not reproduce these results. However, due to the amount of existing data, we were not able to test all possible partitions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: This table shows the performance of the SVM, NB and LM classifiers using the independent posts approach on the oldest discussion. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−).', solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich classifier had the highest F1+ score when operating on a data set chronologically sampled from the earliest discussions?\n" }, { "role": "assistant", "content": "The description mentions improved performance with early data, and the table shows the F1+ scores for each classifier. The highest F1+ score is 81.17 for SVM. So, the answer is {\"answer\": \"SVM\"}" } ], "images": [ "scigen/train/749.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: This table shows the performance of the SVM, NB and LM classifiers using the independent posts approach on the oldest discussion. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−)." ], "reasoning": "The description mentions improved performance with early data, and the table shows the F1+ scores for each classifier. The highest F1+ score is 81.17 for SVM.", "question": "Which classifier had the highest F1+ score when operating on a data set chronologically sampled from the earliest discussions?", "answer": "So, the answer is {\"answer\": \"SVM\"}" }, { "messages": [ { "role": "user", "content": "The SVM classifier had the highest accuracy when operating on a data set chronologically sampled from the earliest discussions. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe SVM classifier had the highest accuracy when operating on a data set chronologically sampled from the earliest discussions.\n" }, { "role": "assistant", "content": "The table shows the accuracy for each classifier, and the highest accuracy is 79.12 for SVM. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/749.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: This table shows the performance of the SVM, NB and LM classifiers using the independent posts approach on the oldest discussion. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−)." ], "reasoning": "The table shows the accuracy for each classifier, and the highest accuracy is 79.12 for SVM.", "statement": "The SVM classifier had the highest accuracy when operating on a data set chronologically sampled from the earliest discussions.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 6: This table shows the performance of the SVM and NB classifier using the sliding window approach with stratified sampling. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The SVM classifier performed better when considering the full text compared to only function words, as indicated by higher values in all performance metrics.This bar chart shows the performance of the full text SVM in contrast to it only factoring in function words. The classifier used the sliding window approach with stratified sampling. Thus, it erroneously performed well. The values are in relation to a random classifier with 0% expressing equal performance. The percentages refer to the overall performance, meaning that 50% equates to a perfect result." } ], "images": [ "scigen/train/750.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: This table shows the performance of the SVM and NB classifier using the sliding window approach with stratified sampling. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−)." ], "reasoning": "The SVM classifier performed better when considering the full text compared to only function words, as indicated by higher values in all performance metrics.", "description": "This bar chart shows the performance of the full text SVM in contrast to it only factoring in function words. The classifier used the sliding window approach with stratified sampling. Thus, it erroneously performed well. The values are in relation to a random classifier with 0% expressing equal performance. The percentages refer to the overall performance, meaning that 50% equates to a perfect result.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 6: This table shows the performance of the SVM and NB classifier using the sliding window approach with stratified sampling. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−).', answer the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich classifier had the highest F1+ score and what was the value?\n" }, { "role": "assistant", "content": "By comparing the F1+ scores of all classifiers, the SVM classifier with full text consideration had the highest F1+ score of 87.07. So, the answer is {\"answer\": \"SVM with 87.07\"}." } ], "images": [ "scigen/train/750.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: This table shows the performance of the SVM and NB classifier using the sliding window approach with stratified sampling. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−)." ], "reasoning": "By comparing the F1+ scores of all classifiers, the SVM classifier with full text consideration had the highest F1+ score of 87.07.", "question": "Which classifier had the highest F1+ score and what was the value?", "answer": "So, the answer is {\"answer\": \"SVM with 87.07\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe NB classifier with function words had a higher Recall+ than the SVM classifier with function words.\n" }, { "role": "assistant", "content": "The Recall+ for NB (FW) is 42.27, while the Recall+ for SVM (FW) is 78.73. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/750.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: This table shows the performance of the SVM and NB classifier using the sliding window approach with stratified sampling. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−)." ], "reasoning": "The Recall+ for NB (FW) is 42.27, while the Recall+ for SVM (FW) is 78.73. Therefore, the statement is refuted.", "statement": "The NB classifier with function words had a higher Recall+ than the SVM classifier with function words.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 8: F1 score of char models and their performance on the dev. set for selected languages with different gather strategies, concatenate to gi (Equation 1). DQM shows results for our reimplementation of DBLP:conf/conll/DozatQM17 (cf. §3.2), where we feed in only the characters. The final column shows the number of xpos tags in the training set.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The model concatenates four features and passes them to an MLP, which then predicts tags using a linear classifier with a softmax function.Thus, the proposed model concatenates all four of these and passes it as input to an multilayer perceptron (MLP): gi = concat(F1st(w),Flast(w), (1) B1st(w),Blast(w)) mcharsi = MLP(gi) A tag can then be predicted with a linear classifier that takes as input the output of the MLP mcharsi, applies a softmax function and chooses for each word the tag with highest probability. The Table also contains a column with results for our reimplementation of \\newciteDBLP:conf/conll/DozatQM17. We removed, for all systems, the word model in order to assess each strategy in isolation. The performance is quite different per language. E.g., for Latin, the outputs of the forward and backward LSTMs of the last character scored highest." } ], "images": [ "scigen/train/751.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: F1 score of char models and their performance on the dev. set for selected languages with different gather strategies, concatenate to gi (Equation 1). DQM shows results for our reimplementation of DBLP:conf/conll/DozatQM17 (cf. §3.2), where we feed in only the characters. The final column shows the number of xpos tags in the training set." ], "reasoning": "The model concatenates four features and passes them to an MLP, which then predicts tags using a linear classifier with a softmax function.", "description": "Thus, the proposed model concatenates all four of these and passes it as input to an multilayer perceptron (MLP): gi = concat(F1st(w),Flast(w), (1) B1st(w),Blast(w)) mcharsi = MLP(gi) A tag can then be predicted with a linear classifier that takes as input the output of the MLP mcharsi, applies a softmax function and chooses for each word the tag with highest probability. The Table also contains a column with results for our reimplementation of \\newciteDBLP:conf/conll/DozatQM17. We removed, for all systems, the word model in order to assess each strategy in isolation. The performance is quite different per language. E.g., for Latin, the outputs of the forward and backward LSTMs of the last character scored highest.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 8: F1 score of char models and their performance on the dev. set for selected languages with different gather strategies, concatenate to gi (Equation 1). DQM shows results for our reimplementation of DBLP:conf/conll/DozatQM17 (cf. §3.2), where we feed in only the characters. The final column shows the number of xpos tags in the training set.\nQuestion: Which language had the highest score for the F1st Blast strategy?\n" }, { "role": "assistant", "content": "To find the language with the highest score for the F1st Blast strategy, look at the column labeled 'F1st Blast' and identify the highest value, then find the corresponding language in the 'dev. set. lang.' column. So, the answer is {\"answer\": \"la_ittb\"}" } ], "images": [ "scigen/train/751.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: F1 score of char models and their performance on the dev. set for selected languages with different gather strategies, concatenate to gi (Equation 1). DQM shows results for our reimplementation of DBLP:conf/conll/DozatQM17 (cf. §3.2), where we feed in only the characters. The final column shows the number of xpos tags in the training set." ], "reasoning": "To find the language with the highest score for the F1st Blast strategy, look at the column labeled 'F1st Blast' and identify the highest value, then find the corresponding language in the 'dev. set. lang.' column.", "question": "Which language had the highest score for the F1st Blast strategy?", "answer": "So, the answer is {\"answer\": \"la_ittb\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe language with the highest DQM score is Russian.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'DQM' column and identify the highest value, then check the corresponding language in the 'dev. set. lang.' column. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/751.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: F1 score of char models and their performance on the dev. set for selected languages with different gather strategies, concatenate to gi (Equation 1). DQM shows results for our reimplementation of DBLP:conf/conll/DozatQM17 (cf. §3.2), where we feed in only the characters. The final column shows the number of xpos tags in the training set." ], "reasoning": "To verify this statement, look at the 'DQM' column and identify the highest value, then check the corresponding language in the 'dev. set. lang.' column.", "statement": "The language with the highest DQM score is Russian.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Results on WSJ test set.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Our model achieved an accuracy of 97.96%, outperforming other systems by 0.32% in accuracy, corresponding to a RRIE of 12%.We also performed experiments on the Penn Treebank with the usual split in train, development and test set. Our model significantly outperforms these systems, with an absolute difference of 0.32% in accuracy, which corresponds to a RRIE of 12%." } ], "images": [ "scigen/train/752.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on WSJ test set." ], "reasoning": "Our model achieved an accuracy of 97.96%, outperforming other systems by 0.32% in accuracy, corresponding to a RRIE of 12%.", "description": "We also performed experiments on the Penn Treebank with the usual split in train, development and test set. Our model significantly outperforms these systems, with an absolute difference of 0.32% in accuracy, which corresponds to a RRIE of 12%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: Results on WSJ test set.' to sequentially solve the question presented below. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhat is the accuracy difference between the 'ours' model and the 'choi:16a' model?\n" }, { "role": "assistant", "content": "The 'ours' model has an accuracy of 97.96%, while the 'choi:16a' model has an accuracy of 97.64%. The difference is 97.96% - 97.64% = 0.32%. So, the answer is {\"answer\": \"0.32%\"}" } ], "images": [ "scigen/train/752.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on WSJ test set." ], "reasoning": "The 'ours' model has an accuracy of 97.96%, while the 'choi:16a' model has an accuracy of 97.64%. The difference is 97.96% - 97.64% = 0.32%.", "question": "What is the accuracy difference between the 'ours' model and the 'choi:16a' model?", "answer": "So, the answer is {\"answer\": \"0.32%\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe 'ours' model has the highest accuracy among all listed systems.\n" }, { "role": "assistant", "content": "The table shows that the 'ours' model has an accuracy of 97.96%, which is higher than all other listed systems. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/752.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on WSJ test set." ], "reasoning": "The table shows that the 'ours' model has an accuracy of 97.96%, which is higher than all other listed systems.", "statement": "The 'ours' model has the highest accuracy among all listed systems.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: Comparison of optimization methods: Separate optimization of the word, character and meta model is more accurate on average than full back-propagation using a single loss function.The results are statistically significant with two-tailed paired t-test for xpos with p<0.001 and for morphology with p<0.0001.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Separate optimization leads to better accuracy in both morphological features and xpos tagging compared to joint optimization.We first compare jointly training the three model components (Meta-BiLSTM, character model, word model) to training each separately. Separate optimization leads to better accuracy for 34 out of 40 treebanks for the morphological features task and for 30 out of 39 treebanks for xpos tagging. Separate optimization out-performed joint optimization by up to 2.1 percent absolute, while joint never out-performed separate by more than 0.5% absolute. We hypothesize that separately training the models forces each sub-model (word and character) to be strong enough to make high accuracy predictions and in some sense serves as a regularizer in the same way that dropout does for individual neurons." } ], "images": [ "scigen/train/753.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison of optimization methods: Separate optimization of the word, character and meta model is more accurate on average than full back-propagation using a single loss function.The results are statistically significant with two-tailed paired t-test for xpos with p<0.001 and for morphology with p<0.0001." ], "reasoning": "Separate optimization leads to better accuracy in both morphological features and xpos tagging compared to joint optimization.", "description": "We first compare jointly training the three model components (Meta-BiLSTM, character model, word model) to training each separately. Separate optimization leads to better accuracy for 34 out of 40 treebanks for the morphological features task and for 30 out of 39 treebanks for xpos tagging. Separate optimization out-performed joint optimization by up to 2.1 percent absolute, while joint never out-performed separate by more than 0.5% absolute. We hypothesize that separately training the models forces each sub-model (word and character) to be strong enough to make high accuracy predictions and in some sense serves as a regularizer in the same way that dropout does for individual neurons.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 5: Comparison of optimization methods: Separate optimization of the word, character and meta model is more accurate on average than full back-propagation using a single loss function.The results are statistically significant with two-tailed paired t-test for xpos with p<0.001 and for morphology with p<0.0001.' and methodically tackle the question that follows. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the average F1 score for morphology when using separate optimization?\n" }, { "role": "assistant", "content": "The table shows the average F1 scores for morphology and xpos tagging for both separate and joint optimization. The average F1 score for morphology with separate optimization is listed in the table. So, the answer is {\"answer\": \"94.57\"}" } ], "images": [ "scigen/train/753.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison of optimization methods: Separate optimization of the word, character and meta model is more accurate on average than full back-propagation using a single loss function.The results are statistically significant with two-tailed paired t-test for xpos with p<0.001 and for morphology with p<0.0001." ], "reasoning": "The table shows the average F1 scores for morphology and xpos tagging for both separate and joint optimization. The average F1 score for morphology with separate optimization is listed in the table.", "question": "What is the average F1 score for morphology when using separate optimization?", "answer": "So, the answer is {\"answer\": \"94.57\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Joint optimization out-performed separate optimization by more than 0.5% absolute., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nJoint optimization out-performed separate optimization by more than 0.5% absolute.\n" }, { "role": "assistant", "content": "The description states that separate optimization out-performed joint optimization by up to 2.1% absolute, while joint never out-performed separate by more than 0.5% absolute. The table also shows higher average F1 scores for separate optimization. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/753.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison of optimization methods: Separate optimization of the word, character and meta model is more accurate on average than full back-propagation using a single loss function.The results are statistically significant with two-tailed paired t-test for xpos with p<0.001 and for morphology with p<0.0001." ], "reasoning": "The description states that separate optimization out-performed joint optimization by up to 2.1% absolute, while joint never out-performed separate by more than 0.5% absolute. The table also shows higher average F1 scores for separate optimization.", "statement": "Joint optimization out-performed separate optimization by more than 0.5% absolute.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: F1 score for selected languages on sentence vs. word level character models for the prediction of morphology using late integration.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The sentence-based character model outperforms the word-based character model across all selected morphological rich languages.We compared the setup with sentence-based character context We selected for these experiments a number of morphological rich languages. The accuracy of the word-based character model joint with a word-based model were significantly lower than a sentence-based character model. We conclude also from these results and comparing with results of the reimplementation of DQM that early integration of the word-based character model performs much better as late integration via Meta-BiLSTM for a word-based character model." } ], "images": [ "scigen/train/754.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: F1 score for selected languages on sentence vs. word level character models for the prediction of morphology using late integration." ], "reasoning": "The sentence-based character model outperforms the word-based character model across all selected morphological rich languages.", "description": "We compared the setup with sentence-based character context We selected for these experiments a number of morphological rich languages. The accuracy of the word-based character model joint with a word-based model were significantly lower than a sentence-based character model. We conclude also from these results and comparing with results of the reimplementation of DQM that early integration of the word-based character model performs much better as late integration via Meta-BiLSTM for a word-based character model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 6: F1 score for selected languages on sentence vs. word level character models for the prediction of morphology using late integration.', unfold the question presented below, detailing every step of your calculation. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich language had the highest accuracy for the sentence-based character model?\n" }, { "role": "assistant", "content": "By comparing the accuracies of the sentence-based character model across all languages, we find that 'la_ittb' has the highest accuracy of 95.69. So, the answer is {\"answer\": \"la_ittb\"}." } ], "images": [ "scigen/train/754.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: F1 score for selected languages on sentence vs. word level character models for the prediction of morphology using late integration." ], "reasoning": "By comparing the accuracies of the sentence-based character model across all languages, we find that 'la_ittb' has the highest accuracy of 95.69.", "question": "Which language had the highest accuracy for the sentence-based character model?", "answer": "So, the answer is {\"answer\": \"la_ittb\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe word-based character model had higher accuracy than the sentence-based character model for the 'ru' language.\n" }, { "role": "assistant", "content": "The table shows that for the 'ru' language, the word-based character model has an accuracy of 88.94, while the sentence-based character model has an accuracy of 92.31. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/754.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: F1 score for selected languages on sentence vs. word level character models for the prediction of morphology using late integration." ], "reasoning": "The table shows that for the 'ru' language, the word-based character model has an accuracy of 88.94, while the sentence-based character model has an accuracy of 92.31. Therefore, the statement is refuted.", "statement": "The word-based character model had higher accuracy than the sentence-based character model for the 'ru' language.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 7: F1 score for the character, word and meta models. The standard deviation of 10 random restarts of each model is show in the last three columns. The differences in means are all statistically significant at p<0.001 (paired t-test).', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The combined model (mean meta) consistently shows higher accuracy across all languages compared to the character and word models individually.The examples show that the combined model has significantly higher accuracy compared with either the character and word models individually." } ], "images": [ "scigen/train/755.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: F1 score for the character, word and meta models. The standard deviation of 10 random restarts of each model is show in the last three columns. The differences in means are all statistically significant at p<0.001 (paired t-test)." ], "reasoning": "The combined model (mean meta) consistently shows higher accuracy across all languages compared to the character and word models individually.", "description": "The examples show that the combined model has significantly higher accuracy compared with either the character and word models individually.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 7: F1 score for the character, word and meta models. The standard deviation of 10 random restarts of each model is show in the last three columns. The differences in means are all statistically significant at p<0.001 (paired t-test).' to sequentially solve the question presented below. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich language has the highest mean meta accuracy and what is its value?\n" }, { "role": "assistant", "content": "To find the language with the highest mean meta accuracy, compare the mean meta values for all languages. The highest value is 97.01 for the language 'el'. So, the answer is {\"answer\": \"el\"}" } ], "images": [ "scigen/train/755.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: F1 score for the character, word and meta models. The standard deviation of 10 random restarts of each model is show in the last three columns. The differences in means are all statistically significant at p<0.001 (paired t-test)." ], "reasoning": "To find the language with the highest mean meta accuracy, compare the mean meta values for all languages. The highest value is 97.01 for the language 'el'.", "question": "Which language has the highest mean meta accuracy and what is its value?", "answer": "So, the answer is {\"answer\": \"el\"}" }, { "messages": [ { "role": "user", "content": "The mean meta accuracy for the 'grc' language is higher than the mean word accuracy for the 'el' language. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe mean meta accuracy for the 'grc' language is higher than the mean word accuracy for the 'el' language.\n" }, { "role": "assistant", "content": "The mean meta accuracy for 'grc' is 88.85, while the mean word accuracy for 'el' is 95.36. Since 88.85 is not higher than 95.36, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/755.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: F1 score for the character, word and meta models. The standard deviation of 10 random restarts of each model is show in the last three columns. The differences in means are all statistically significant at p<0.001 (paired t-test)." ], "reasoning": "The mean meta accuracy for 'grc' is 88.85, while the mean word accuracy for 'el' is 95.36. Since 88.85 is not higher than 95.36, the statement is refuted.", "statement": "The mean meta accuracy for the 'grc' language is higher than the mean word accuracy for the 'el' language.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: The results on the standard BLI task and BLI controlled for lexeme for the original Ruder et al. (2018)’s model (✗) and the same model trained with a morphological constraint (✓) (discussed in §4.6).', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The accuracy of BLI tasks is generally high for Slavic languages even in open-vocabulary setups, while Romance languages show a drop in performance due to the complexity of verb paradigms.We report the accuracy on the in-vocabulary pairs as well as all the pairs in the dictionary, including OOVs. As expected, compared to standard BLI this task is much easier for the models—the performance is generally high. For Slavic languages numbers remain high even in the open-vocabulary setup, which suggests that the models can generalize morphologically. On the other hand, for Romance languages we observe a visible drop in performance. We hypothesize that this difference is due to the large quantities of verbs in Romance dictionaries; in both Slavic and Romance languages verbs have substantial paradigms, often of more than 60 forms, which makes identifying the correct form more difficult. In contrast, most words in our Slavic dictionaries are nouns and adjectives with much smaller paradigms. As expected, the BLI results on unrelated languages are generally, but not uniformly, worse than those on related language pairs. The accuracy for Spanish–Polish is particularly low, at 28% (for in vocabulary pairs). We see large variation in performance across morphosyntactic categories and more and less frequent lexemes, similar to that observed for related language pairs. In particular, we observe that 2;imp;pl;v—the category difficult for Polish– Czech BLI is also among the most challenging for Polish–Spanish. However, one of the highest performing categories for Polish–Czech, 3;masc;pl;pst;v, yields much worse accuracy for Polish–Spanish. In our final experiment we demonstrate that improving morphological generalization has the potential to improve BLI results. We show that enforcing a simple, hard morphological constraint at training time can lead to performance improvements at test time—both on the standard BLI task and the controlled for lexeme BLI. We adapt the self-learning models of Artetxe et al. so that at each iteration they can align two words only if they share the same morphosyntactic category. Note that this limits the training data only to word forms present in UniMorph, as those are the only ones for which we have a gold tag. We take this as evidence that properly modelling morphology will have a role to play in BLI." } ], "images": [ "scigen/train/756.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The results on the standard BLI task and BLI controlled for lexeme for the original Ruder et al. (2018)’s model (✗) and the same model trained with a morphological constraint (✓) (discussed in §4.6)." ], "reasoning": "The accuracy of BLI tasks is generally high for Slavic languages even in open-vocabulary setups, while Romance languages show a drop in performance due to the complexity of verb paradigms.", "description": "We report the accuracy on the in-vocabulary pairs as well as all the pairs in the dictionary, including OOVs. As expected, compared to standard BLI this task is much easier for the models—the performance is generally high. For Slavic languages numbers remain high even in the open-vocabulary setup, which suggests that the models can generalize morphologically. On the other hand, for Romance languages we observe a visible drop in performance. We hypothesize that this difference is due to the large quantities of verbs in Romance dictionaries; in both Slavic and Romance languages verbs have substantial paradigms, often of more than 60 forms, which makes identifying the correct form more difficult. In contrast, most words in our Slavic dictionaries are nouns and adjectives with much smaller paradigms. As expected, the BLI results on unrelated languages are generally, but not uniformly, worse than those on related language pairs. The accuracy for Spanish–Polish is particularly low, at 28% (for in vocabulary pairs). We see large variation in performance across morphosyntactic categories and more and less frequent lexemes, similar to that observed for related language pairs. In particular, we observe that 2;imp;pl;v—the category difficult for Polish– Czech BLI is also among the most challenging for Polish–Spanish. However, one of the highest performing categories for Polish–Czech, 3;masc;pl;pst;v, yields much worse accuracy for Polish–Spanish. In our final experiment we demonstrate that improving morphological generalization has the potential to improve BLI results. We show that enforcing a simple, hard morphological constraint at training time can lead to performance improvements at test time—both on the standard BLI task and the controlled for lexeme BLI. We adapt the self-learning models of Artetxe et al. so that at each iteration they can align two words only if they share the same morphosyntactic category. Note that this limits the training data only to word forms present in UniMorph, as those are the only ones for which we have a gold tag. We take this as evidence that properly modelling morphology will have a role to play in BLI.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the accuracy for Polish–Spanish in the Normal +OOVs setup without constraints?\nSolve the above question based on the table titled 'Table 4: The results on the standard BLI task and BLI controlled for lexeme for the original Ruder et al. (2018)’s model (✗) and the same model trained with a morphological constraint (✓) (discussed in §4.6).'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the accuracy for Polish–Spanish in the Normal +OOVs setup without constraints, locate the row for Polish–Spanish and the column for Normal +OOVs under the 'x' constraint. The value is 28.0. So, the answer is {\"answer\": \"28.0\"}." } ], "images": [ "scigen/train/756.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The results on the standard BLI task and BLI controlled for lexeme for the original Ruder et al. (2018)’s model (✗) and the same model trained with a morphological constraint (✓) (discussed in §4.6)." ], "reasoning": "To find the accuracy for Polish–Spanish in the Normal +OOVs setup without constraints, locate the row for Polish–Spanish and the column for Normal +OOVs under the 'x' constraint. The value is 28.0.", "question": "What is the accuracy for Polish–Spanish in the Normal +OOVs setup without constraints?", "answer": "So, the answer is {\"answer\": \"28.0\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The accuracy for Russian–Slovak in the Lexeme +OOVs setup with constraints is higher than 75%. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe accuracy for Russian–Slovak in the Lexeme +OOVs setup with constraints is higher than 75%.\n" }, { "role": "assistant", "content": "To verify this statement, locate the row for Russian–Slovak and the column for Lexeme +OOVs under the '✓' constraint. The value is 76.0, which is higher than 75%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/756.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The results on the standard BLI task and BLI controlled for lexeme for the original Ruder et al. (2018)’s model (✗) and the same model trained with a morphological constraint (✓) (discussed in §4.6)." ], "reasoning": "To verify this statement, locate the row for Russian–Slovak and the column for Lexeme +OOVs under the '✓' constraint. The value is 76.0, which is higher than 75%.", "statement": "The accuracy for Russian–Slovak in the Lexeme +OOVs setup with constraints is higher than 75%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: The sizes of our morphologically complete dictionaries for Slavic and Romance language families. We present the sizes for 20 base dictionaries. We further split those to obtain 40 train, development and test dictionaries—one for each mapping direction to ensure the correct source language lemma separation.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The dictionaries for different language pairs vary in size and POS distribution, with Slavic dictionaries dominated by nouns and adjectives, and Romance dictionaries by verbs.For each language pair (L1,L2) we first generated lemma translation pairs by mapping all L1 lemmata to all L2 lemmata for each synset that appeared in both L1 and L2 WordNets. We then filtered out the pairs which contained lemmata not present in UniMorph and generated inflected entries from the remaining pairs: one entry for each tag that appears in the UniMorph paradigms of both lemmata. The sizes of dictionaries vary across different language pairs and so does the POS distribution. In particular, while Slavic dictionaries are dominated by nouns and adjectives, verbs constitute the majority of pairs in Romance dictionaries. In our split, the train dictionary contains 60% of all lemmata, while the development and test dictionaries each have 20% of the lemmata." } ], "images": [ "scigen/train/757.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The sizes of our morphologically complete dictionaries for Slavic and Romance language families. We present the sizes for 20 base dictionaries. We further split those to obtain 40 train, development and test dictionaries—one for each mapping direction to ensure the correct source language lemma separation." ], "reasoning": "The dictionaries for different language pairs vary in size and POS distribution, with Slavic dictionaries dominated by nouns and adjectives, and Romance dictionaries by verbs.", "description": "For each language pair (L1,L2) we first generated lemma translation pairs by mapping all L1 lemmata to all L2 lemmata for each synset that appeared in both L1 and L2 WordNets. We then filtered out the pairs which contained lemmata not present in UniMorph and generated inflected entries from the remaining pairs: one entry for each tag that appears in the UniMorph paradigms of both lemmata. The sizes of dictionaries vary across different language pairs and so does the POS distribution. In particular, while Slavic dictionaries are dominated by nouns and adjectives, verbs constitute the majority of pairs in Romance dictionaries. In our split, the train dictionary contains 60% of all lemmata, while the development and test dictionaries each have 20% of the lemmata.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 2: The sizes of our morphologically complete dictionaries for Slavic and Romance language families. We present the sizes for 20 base dictionaries. We further split those to obtain 40 train, development and test dictionaries—one for each mapping direction to ensure the correct source language lemma separation.' in mind, break down and resolve the question below step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich language pair has the highest number of lemma translation pairs?\n" }, { "role": "assistant", "content": "By examining the table, the highest number of lemma translation pairs is found in the Polish-Italian pair with 705,800 pairs. So, the answer is {\"answer\": \"Polish-Italian\"}." } ], "images": [ "scigen/train/757.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The sizes of our morphologically complete dictionaries for Slavic and Romance language families. We present the sizes for 20 base dictionaries. We further split those to obtain 40 train, development and test dictionaries—one for each mapping direction to ensure the correct source language lemma separation." ], "reasoning": "By examining the table, the highest number of lemma translation pairs is found in the Polish-Italian pair with 705,800 pairs.", "question": "Which language pair has the highest number of lemma translation pairs?", "answer": "So, the answer is {\"answer\": \"Polish-Italian\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Polish-Czech dictionary has more lemma translation pairs than the Polish-Russian dictionary. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Polish-Czech dictionary has more lemma translation pairs than the Polish-Russian dictionary.\n" }, { "role": "assistant", "content": "The table shows that the Polish-Czech dictionary has 53,353 pairs, while the Polish-Russian dictionary has 128,638 pairs, which is more. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/757.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The sizes of our morphologically complete dictionaries for Slavic and Romance language families. We present the sizes for 20 base dictionaries. We further split those to obtain 40 train, development and test dictionaries—one for each mapping direction to ensure the correct source language lemma separation." ], "reasoning": "The table shows that the Polish-Czech dictionary has 53,353 pairs, while the Polish-Russian dictionary has 128,638 pairs, which is more.", "statement": "The Polish-Czech dictionary has more lemma translation pairs than the Polish-Russian dictionary.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: Performance Comparison of Fake News Detection on Two Datasets.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The DEAN model outperforms all baseline models on both Weibo and Twitter datasets, achieving an accuracy of 87.2% on Weibo and 75.1% on Twitter, demonstrating the effectiveness of incorporating emotion information.In particular, the DEAN model achieves an overall accuracy of 87.2% on Weibo dataset and 75.1% on Twitter dataset, which outperforms all the baseline models on both datasets. The outstanding performance of the proposed model demonstrates that the incorporation of emotion through embedding representation and gated fusion could effectively promote the detecting process on fake news. Besides, compared to the emotion-based model SAME, DEAN earns quite better performance in all aspects. On the one hand, it is because that SAME is mainly designed to deal with heterogeneous multi-modal data. On the other hand, the results demonstrate that only utilizing user comments’ emotion just like SAME is limited, and DEAN is more effective for detecting fake news by exploiting dual emotion from publisher and users corporately. Our method shows its strength on fake news detection in these experiments. And its f1-score is also 3.4% higher than the second one. On Twitter dataset, the improvement is more obvious by boosting the accuracy from 61.3% of feature-based models to 75.1%. Meanwhile, DEAN outperforms the second-best model by nearly 6% in f1-score. These observations demonstrate the importance of incorporating emotion information into models. By the way, comparing between datasets, we can see that performances on Weibo dataset are rather better than Twitter." } ], "images": [ "scigen/train/758.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance Comparison of Fake News Detection on Two Datasets." ], "reasoning": "The DEAN model outperforms all baseline models on both Weibo and Twitter datasets, achieving an accuracy of 87.2% on Weibo and 75.1% on Twitter, demonstrating the effectiveness of incorporating emotion information.", "description": "In particular, the DEAN model achieves an overall accuracy of 87.2% on Weibo dataset and 75.1% on Twitter dataset, which outperforms all the baseline models on both datasets. The outstanding performance of the proposed model demonstrates that the incorporation of emotion through embedding representation and gated fusion could effectively promote the detecting process on fake news. Besides, compared to the emotion-based model SAME, DEAN earns quite better performance in all aspects. On the one hand, it is because that SAME is mainly designed to deal with heterogeneous multi-modal data. On the other hand, the results demonstrate that only utilizing user comments’ emotion just like SAME is limited, and DEAN is more effective for detecting fake news by exploiting dual emotion from publisher and users corporately. Our method shows its strength on fake news detection in these experiments. And its f1-score is also 3.4% higher than the second one. On Twitter dataset, the improvement is more obvious by boosting the accuracy from 61.3% of feature-based models to 75.1%. Meanwhile, DEAN outperforms the second-best model by nearly 6% in f1-score. These observations demonstrate the importance of incorporating emotion information into models. By the way, comparing between datasets, we can see that performances on Weibo dataset are rather better than Twitter.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: Performance Comparison of Fake News Detection on Two Datasets.', answer the following question. Give detailed solution consisting of each step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the difference in F1-score between the DEAN model and the second-best model on the Twitter dataset?\n" }, { "role": "assistant", "content": "The DEAN model has an F1-score of 0.771 on the Twitter dataset. The second-best model on the Twitter dataset is HSA-BLSTM with an F1-score of 0.695. The difference in F1-score is 0.771 - 0.695 = 0.076. So, the answer is {\"answer\": \"0.076\"}." } ], "images": [ "scigen/train/758.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance Comparison of Fake News Detection on Two Datasets." ], "reasoning": "The DEAN model has an F1-score of 0.771 on the Twitter dataset. The second-best model on the Twitter dataset is HSA-BLSTM with an F1-score of 0.695. The difference in F1-score is 0.771 - 0.695 = 0.076.", "question": "What is the difference in F1-score between the DEAN model and the second-best model on the Twitter dataset?", "answer": "So, the answer is {\"answer\": \"0.076\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe DEAN model has the highest recall on the Weibo dataset.\n" }, { "role": "assistant", "content": "The DEAN model has a recall of 0.890 on the Weibo dataset. However, the CSI model has a recall of 0.996, which is higher than DEAN's recall. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/758.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance Comparison of Fake News Detection on Two Datasets." ], "reasoning": "The DEAN model has a recall of 0.890 on the Weibo dataset. However, the CSI model has a recall of 0.996, which is higher than DEAN's recall.", "statement": "The DEAN model has the highest recall on the Weibo dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: 19 Hand-crafted Emotion Features of News Content.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The overall emotion information of news content helps measure the importance of emotion in each word, especially in content expressing intense emotions.Hand-crafted News Emotion Features The overall emotion information of news content is also important to help measure how much signal from the emotion part should be absorbed for each word. For example, the news content which expresses intense emotions could further strengthen the importance of emotion part in each word of the content. News emotion features of pj is denoted as sej." } ], "images": [ "scigen/train/759.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: 19 Hand-crafted Emotion Features of News Content." ], "reasoning": "The overall emotion information of news content helps measure the importance of emotion in each word, especially in content expressing intense emotions.", "description": "Hand-crafted News Emotion Features The overall emotion information of news content is also important to help measure how much signal from the emotion part should be absorbed for each word. For example, the news content which expresses intense emotions could further strengthen the importance of emotion part in each word of the content. News emotion features of pj is denoted as sej.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: 19 Hand-crafted Emotion Features of News Content.', answer the question below by showing each progressive step toward the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the combined amount of fractions for punctuation and pronouns?\n" }, { "role": "assistant", "content": "To find the combined amount, add the fraction of punctuation (5) and the fraction of pronouns (3). So, the answer is {\"answer\": \"8\"}." } ], "images": [ "scigen/train/759.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: 19 Hand-crafted Emotion Features of News Content." ], "reasoning": "To find the combined amount, add the fraction of punctuation (5) and the fraction of pronouns (3).", "question": "What is the combined amount of fractions for punctuation and pronouns?", "answer": "So, the answer is {\"answer\": \"8\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe fraction of negative adverbs is higher than the fraction of adverbs of degree.\n" }, { "role": "assistant", "content": "The table shows that the fraction of negative adverbs is 1 and the fraction of adverbs of degree is also 1, so they are equal. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/759.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: 19 Hand-crafted Emotion Features of News Content." ], "reasoning": "The table shows that the fraction of negative adverbs is 1 and the fraction of adverbs of degree is also 1, so they are equal.", "statement": "The fraction of negative adverbs is higher than the fraction of adverbs of degree.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: Analysis of the Dual Emotion Modeling.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Incorporating emotion embeddings improves performance, with publisher emotion being more impactful than social emotion, and dual emotion enhancing fake news detection., we could make the following observations: 1) in content module, the overall performance rises while using emotion embeddings; especially on Twitter dataset, adding emotion information increases the f1-score by nearly 6%; 2) publisher emotion plays a more important role than social emotion on both datasets. It possibly results from the sparsity of comments data, which limits the effectiveness of social emotion; 3) compared to merely using semantic information, incorporation of emotion from one side or two sides all improve the performance of the whole framework, which demonstrates the importance of dual emotion on fake news detection." } ], "images": [ "scigen/train/760.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Analysis of the Dual Emotion Modeling." ], "reasoning": "Incorporating emotion embeddings improves performance, with publisher emotion being more impactful than social emotion, and dual emotion enhancing fake news detection.", "description": ", we could make the following observations: 1) in content module, the overall performance rises while using emotion embeddings; especially on Twitter dataset, adding emotion information increases the f1-score by nearly 6%; 2) publisher emotion plays a more important role than social emotion on both datasets. It possibly results from the sparsity of comments data, which limits the effectiveness of social emotion; 3) compared to merely using semantic information, incorporation of emotion from one side or two sides all improve the performance of the whole framework, which demonstrates the importance of dual emotion on fake news detection.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Analysis of the Dual Emotion Modeling.', answer the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the highest F1-score achieved on the Twitter dataset, and which method achieves it?\n" }, { "role": "assistant", "content": "By examining the Twitter Dataset F1-Score column, the highest score is 0.776, achieved by the method (WEE(gn)+WEE(gc))(c) under the Content + Comment (Dual Emotion) module. So, the answer is {\"answer\": \"0.776\"}" } ], "images": [ "scigen/train/760.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Analysis of the Dual Emotion Modeling." ], "reasoning": "By examining the Twitter Dataset F1-Score column, the highest score is 0.776, achieved by the method (WEE(gn)+WEE(gc))(c) under the Content + Comment (Dual Emotion) module.", "question": "What is the highest F1-score achieved on the Twitter dataset, and which method achieves it?", "answer": "So, the answer is {\"answer\": \"0.776\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The method WEE(gn) achieves the highest accuracy on the Weibo dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe method WEE(gn) achieves the highest accuracy on the Weibo dataset.\n" }, { "role": "assistant", "content": "By examining the Weibo Dataset Accuracy column, the highest accuracy is 0.872, achieved by the method (WEE(gn)+WEE(gc))(gm) under the Content + Comment (Dual Emotion) module, not WEE(gn). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/760.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Analysis of the Dual Emotion Modeling." ], "reasoning": "By examining the Weibo Dataset Accuracy column, the highest accuracy is 0.872, achieved by the method (WEE(gn)+WEE(gc))(gm) under the Content + Comment (Dual Emotion) module, not WEE(gn).", "statement": "The method WEE(gn) achieves the highest accuracy on the Weibo dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: Accuracy (\\%) of discourse phenomena. * different data and system conditions, only for reference.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The proposed model outperforms the context-agnostic baseline in resolving discourse inconsistencies across multiple test sets, despite being trained on less data.We also want to examine whether the proposed model actually learns to utilize document context to resolve discourse inconsistencies that context-agnostic models cannot handle. We use contrastive test sets for the evaluation of discourse phenomena for English-Russian by \\citeauthorvoita2018context [\\citeyearvoita2018context]. There are four test sets in the suite regarding deixis, lexicon consistency, ellipsis (inflection), and ellipsis (verb phrase). Each testset contains groups of contrastive examples consisting of a positive translation with correct discourse phenomenon and negative translations with incorrect phenomena. The goal is to figure out if a model is more likely to generate a correct translation compared to the incorrect variation. Our model is better at resolving discourse consistencies compared to context-agnostic baseline. \\citeauthorvoita2018context [\\citeyearvoita2018context] use a context-agnostic baseline, trained on 4\\times larger data, to generate first-pass drafts, and perform post-processings, which is not directly comparable, but would be easily incorporated with our model to achieve better results." } ], "images": [ "scigen/train/761.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Accuracy (\\%) of discourse phenomena. * different data and system conditions, only for reference." ], "reasoning": "The proposed model outperforms the context-agnostic baseline in resolving discourse inconsistencies across multiple test sets, despite being trained on less data.", "description": "We also want to examine whether the proposed model actually learns to utilize document context to resolve discourse inconsistencies that context-agnostic models cannot handle. We use contrastive test sets for the evaluation of discourse phenomena for English-Russian by \\citeauthorvoita2018context [\\citeyearvoita2018context]. There are four test sets in the suite regarding deixis, lexicon consistency, ellipsis (inflection), and ellipsis (verb phrase). Each testset contains groups of contrastive examples consisting of a positive translation with correct discourse phenomenon and negative translations with incorrect phenomena. The goal is to figure out if a model is more likely to generate a correct translation compared to the incorrect variation. Our model is better at resolving discourse consistencies compared to context-agnostic baseline. \\citeauthorvoita2018context [\\citeyearvoita2018context] use a context-agnostic baseline, trained on 4\\times larger data, to generate first-pass drafts, and perform post-processings, which is not directly comparable, but would be easily incorporated with our model to achieve better results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 5: Accuracy (\\%) of discourse phenomena. * different data and system conditions, only for reference.', solve the following question step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest score for ellipsis (verb phrase) and what is the score?\n" }, { "role": "assistant", "content": "The table shows the scores for different models across various discourse phenomena. The highest score for ellipsis (verb phrase) is 80.0, achieved by the voita2018context model. So, the answer is {\"answer\": \"voita2018context\"}." } ], "images": [ "scigen/train/761.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Accuracy (\\%) of discourse phenomena. * different data and system conditions, only for reference." ], "reasoning": "The table shows the scores for different models across various discourse phenomena. The highest score for ellipsis (verb phrase) is 80.0, achieved by the voita2018context model.", "question": "Which model has the highest score for ellipsis (verb phrase) and what is the score?", "answer": "So, the answer is {\"answer\": \"voita2018context\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The SentNmt model has the lowest score for deixis. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe SentNmt model has the lowest score for deixis.\n" }, { "role": "assistant", "content": "The table shows that the SentNmt model has a score of 50.0 for deixis, which is lower than the scores of the other models (61.3 for Ours and 81.6 for voita2018context). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/761.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Accuracy (\\%) of discourse phenomena. * different data and system conditions, only for reference." ], "reasoning": "The table shows that the SentNmt model has a score of 50.0 for deixis, which is lower than the scores of the other models (61.3 for Ours and 81.6 for voita2018context).", "statement": "The SentNmt model has the lowest score for deixis.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Experiment results of our model in comparison with several baselines, including increments of the number of parameters over Transformer baseline (\\Delta|{\\bm{\\theta}}|), training/testing speeds (v_{\\rm{train}}/v_{\\rm{test}}, some of them are derived from maruf2019selective [maruf2019selective]), and translation results of the testsets in BLEU score.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The final model achieves new state-of-the-art results on TED Zh-En and Europarl, and the best average performance on English-German benchmarks by at least 0.47 BLEU score over the best previous model.[maruf2019selective, SAN] and Query-guided Capsule Network [yang2019enhancing, QCN]. Among them, our model archives new state-of-the-art results on TED Zh-En and Europarl, showing the superiority of exploiting the whole document context. Though our model is not the best on TED En-De and News tasks, it is still comparable with QCN and HAN and achieves the best average performance on English-German benchmarks by at least 0.47 BLEU score over the best previous model. We suggest this could probably because we did not apply the two-stage training scheme used in \\citeauthormiculicich2018document [\\citeyearmiculicich2018document] or regularizations introduced in \\citeauthoryang2019enhancing [\\citeyearyang2019enhancing]. In addition, while sacrificing training speed, the parameter increment and decoding speed could be manageable." } ], "images": [ "scigen/train/762.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Experiment results of our model in comparison with several baselines, including increments of the number of parameters over Transformer baseline (\\Delta|{\\bm{\\theta}}|), training/testing speeds (v_{\\rm{train}}/v_{\\rm{test}}, some of them are derived from maruf2019selective [maruf2019selective]), and translation results of the testsets in BLEU score." ], "reasoning": "The final model achieves new state-of-the-art results on TED Zh-En and Europarl, and the best average performance on English-German benchmarks by at least 0.47 BLEU score over the best previous model.", "description": "[maruf2019selective, SAN] and Query-guided Capsule Network [yang2019enhancing, QCN]. Among them, our model archives new state-of-the-art results on TED Zh-En and Europarl, showing the superiority of exploiting the whole document context. Though our model is not the best on TED En-De and News tasks, it is still comparable with QCN and HAN and achieves the best average performance on English-German benchmarks by at least 0.47 BLEU score over the best previous model. We suggest this could probably because we did not apply the two-stage training scheme used in \\citeauthormiculicich2018document [\\citeyearmiculicich2018document] or regularizations introduced in \\citeauthoryang2019enhancing [\\citeyearyang2019enhancing]. In addition, while sacrificing training speed, the parameter increment and decoding speed could be manageable.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 1: Experiment results of our model in comparison with several baselines, including increments of the number of parameters over Transformer baseline (\\Delta|{\\bm{\\theta}}|), training/testing speeds (v_{\\rm{train}}/v_{\\rm{test}}, some of them are derived from maruf2019selective [maruf2019selective]), and translation results of the testsets in BLEU score.', and tackle the question below in a sequential manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model achieves the highest BLEU score on the En-De Europarl task?\n" }, { "role": "assistant", "content": "By examining the BLEU scores for the En-De Europarl task, the final model achieves the highest score of 30.40. So, the answer is {\"answer\": \"Final\"}" } ], "images": [ "scigen/train/762.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Experiment results of our model in comparison with several baselines, including increments of the number of parameters over Transformer baseline (\\Delta|{\\bm{\\theta}}|), training/testing speeds (v_{\\rm{train}}/v_{\\rm{test}}, some of them are derived from maruf2019selective [maruf2019selective]), and translation results of the testsets in BLEU score." ], "reasoning": "By examining the BLEU scores for the En-De Europarl task, the final model achieves the highest score of 30.40.", "question": "Which model achieves the highest BLEU score on the En-De Europarl task?", "answer": "So, the answer is {\"answer\": \"Final\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe final model achieves the highest average BLEU score on English-German benchmarks.\n" }, { "role": "assistant", "content": "The table shows that the final model has an average BLEU score of 26.80 on English-German benchmarks, which is higher than any other model listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/762.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Experiment results of our model in comparison with several baselines, including increments of the number of parameters over Transformer baseline (\\Delta|{\\bm{\\theta}}|), training/testing speeds (v_{\\rm{train}}/v_{\\rm{test}}, some of them are derived from maruf2019selective [maruf2019selective]), and translation results of the testsets in BLEU score." ], "reasoning": "The table shows that the final model has an average BLEU score of 26.80 on English-German benchmarks, which is higher than any other model listed.", "statement": "The final model achieves the highest average BLEU score on English-German benchmarks.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Ablation study on modeling context on TED Zh-En development set. ”Doc” means using a entire document as a sequence for input or output. BLEU{}_{\\rm doc} indicates the document-level BLEU score calculated on the concatenation of all output sentences.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Resetting word positions and introducing segment embedding for each sentence improves document translation performance, and combining source and target context modeling yields the best results.First of all, using the entire document as input and output directly cannot even generate document translation with the same number of sentences as source document, which is much worse than sentence-level baseline and our model in terms of document-level BLEU. For source context modeling, only casting the whole source document as an input sequence (Doc2Sent) does not work. Meanwhile, reset word positions and introducing segment embedding for each sentence alleviate this problem, which verifies one of our motivations that we should focus more on local sentences. Moreover, the gains by the segment-level relative attention and gated context fusion mechanism demonstrate retrieving and integrating source global context are useful for document translation. As for target context, employing Transformer-XL decoder to exploit target historically global context also leads to better performance on document translation. This is somewhat contrasted to [zhang2018improving] claiming that using target context leading to error propagation. In the end, by jointly modeling both source and target contexts, our final model can obtain the best performance." } ], "images": [ "scigen/train/763.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation study on modeling context on TED Zh-En development set. ”Doc” means using a entire document as a sequence for input or output. BLEU{}_{\\rm doc} indicates the document-level BLEU score calculated on the concatenation of all output sentences." ], "reasoning": "Resetting word positions and introducing segment embedding for each sentence improves document translation performance, and combining source and target context modeling yields the best results.", "description": "First of all, using the entire document as input and output directly cannot even generate document translation with the same number of sentences as source document, which is much worse than sentence-level baseline and our model in terms of document-level BLEU. For source context modeling, only casting the whole source document as an input sequence (Doc2Sent) does not work. Meanwhile, reset word positions and introducing segment embedding for each sentence alleviate this problem, which verifies one of our motivations that we should focus more on local sentences. Moreover, the gains by the segment-level relative attention and gated context fusion mechanism demonstrate retrieving and integrating source global context are useful for document translation. As for target context, employing Transformer-XL decoder to exploit target historically global context also leads to better performance on document translation. This is somewhat contrasted to [zhang2018improving] claiming that using target context leading to error propagation. In the end, by jointly modeling both source and target contexts, our final model can obtain the best performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: 'Table 3: Ablation study on modeling context on TED Zh-En development set. ”Doc” means using a entire document as a sequence for input or output. BLEU{}_{\\rm doc} indicates the document-level BLEU score calculated on the concatenation of all output sentences.'\nQuestion: What is the BLEU score for the final model when both source and target contexts are jointly modeled?\n" }, { "role": "assistant", "content": "The final model, which jointly models both source and target contexts, achieves a BLEU score of 12.9. So, the answer is {\"answer\": \"12.9\"}" } ], "images": [ "scigen/train/763.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation study on modeling context on TED Zh-En development set. ”Doc” means using a entire document as a sequence for input or output. BLEU{}_{\\rm doc} indicates the document-level BLEU score calculated on the concatenation of all output sentences." ], "reasoning": "The final model, which jointly models both source and target contexts, achieves a BLEU score of 12.9.", "question": "What is the BLEU score for the final model when both source and target contexts are jointly modeled?", "answer": "So, the answer is {\"answer\": \"12.9\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe DocNmt model as input/output has a BLEU score of 17.0.\n" }, { "role": "assistant", "content": "The table shows that the DocNmt model as input/output has a BLEU score of 17.0. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/763.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation study on modeling context on TED Zh-En development set. ”Doc” means using a entire document as a sequence for input or output. BLEU{}_{\\rm doc} indicates the document-level BLEU score calculated on the concatenation of all output sentences." ], "reasoning": "The table shows that the DocNmt model as input/output has a BLEU score of 17.0.", "statement": "The DocNmt model as input/output has a BLEU score of 17.0.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Binary accuracy for different variants of C-MFN and training scenarios outlined in Section 5. The best performance is achieved using all three modalities of text (T), vision (V) and acoustic (A).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "C-MFN outperforms both C-MFN (P) and C-MFN (C), indicating that both context and punchline information are important for humor detection.Results demonstrate that both context and punchline information are important as C-MFN outperforms C-MFN (P) and C-MFN (C) models. Punchline is the most important component for detecting humor as the performance of C-MFN (P) is significantly higher than C-MFN (C). Therefore, UR-FUNNY dataset presents new challenges to the field of NLP, specifically research areas of humor detection and multimodal language analysis." } ], "images": [ "scigen/train/764.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Binary accuracy for different variants of C-MFN and training scenarios outlined in Section 5. The best performance is achieved using all three modalities of text (T), vision (V) and acoustic (A)." ], "reasoning": "C-MFN outperforms both C-MFN (P) and C-MFN (C), indicating that both context and punchline information are important for humor detection.", "description": "Results demonstrate that both context and punchline information are important as C-MFN outperforms C-MFN (P) and C-MFN (C) models. Punchline is the most important component for detecting humor as the performance of C-MFN (P) is significantly higher than C-MFN (C). Therefore, UR-FUNNY dataset presents new challenges to the field of NLP, specifically research areas of humor detection and multimodal language analysis.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 4: Binary accuracy for different variants of C-MFN and training scenarios outlined in Section 5. The best performance is achieved using all three modalities of text (T), vision (V) and acoustic (A).', solve the subsequent question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhich model has the highest performance in the T+A+V modality?\n" }, { "role": "assistant", "content": "By comparing the T+A+V modality scores, C-MFN has the highest performance with a score of 65.23. So, the answer is {\"answer\": \"C-MFN\"}." } ], "images": [ "scigen/train/764.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Binary accuracy for different variants of C-MFN and training scenarios outlined in Section 5. The best performance is achieved using all three modalities of text (T), vision (V) and acoustic (A)." ], "reasoning": "By comparing the T+A+V modality scores, C-MFN has the highest performance with a score of 65.23.", "question": "Which model has the highest performance in the T+A+V modality?", "answer": "So, the answer is {\"answer\": \"C-MFN\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nC-MFN (C) has a higher performance than C-MFN (P) in the T modality.\n" }, { "role": "assistant", "content": "In the T modality, C-MFN (P) has a score of 62.85, while C-MFN (C) has a score of 57.96, indicating that C-MFN (P) performs better. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/764.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Binary accuracy for different variants of C-MFN and training scenarios outlined in Section 5. The best performance is achieved using all three modalities of text (T), vision (V) and acoustic (A)." ], "reasoning": "In the T modality, C-MFN (P) has a score of 62.85, while C-MFN (C) has a score of 57.96, indicating that C-MFN (P) performs better.", "statement": "C-MFN (C) has a higher performance than C-MFN (P) in the T modality.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: Comparison between UR-FUNNY and notable humor detection datasets in the NLP community. Here, ‘pos’, ’neg’ , ‘mod’ and ‘spk’ denote positive, negative, modalities and speaker respectively.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "UR-FUNNY is unique in humor detection as it uses text, vision, and audio modalities and samples negatives from the same videos.Humor Analysis: Humor analysis has been among active areas of research in both natural language processing and affective computing. Pun of the Day” Yang et al. The above datasets have studied humor from different perspectives. For example, “16000 One-Liner” and “Pun of the Day” focus on joke detection (joke vs. not joke binary task), while “Ted Laughter” focuses on punchline detection (whether or not punchline triggers laughter). Similar to “Ted Laughter”, UR-FUNNY focuses on punchline detection. Furthermore, punchline is accompanied by context sentences to properly model the build up of humor. Unlike previous datasets where negative samples were drawn from a different domain, UR-FUNNY uses a challenging negative sampling case where samples are drawn from the same videos. Furthermore, UR-FUNNY is the only humor detection dataset which incorporates all three modalities of text, vision and audio." } ], "images": [ "scigen/train/765.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison between UR-FUNNY and notable humor detection datasets in the NLP community. Here, ‘pos’, ’neg’ , ‘mod’ and ‘spk’ denote positive, negative, modalities and speaker respectively." ], "reasoning": "UR-FUNNY is unique in humor detection as it uses text, vision, and audio modalities and samples negatives from the same videos.", "description": "Humor Analysis: Humor analysis has been among active areas of research in both natural language processing and affective computing. Pun of the Day” Yang et al. The above datasets have studied humor from different perspectives. For example, “16000 One-Liner” and “Pun of the Day” focus on joke detection (joke vs. not joke binary task), while “Ted Laughter” focuses on punchline detection (whether or not punchline triggers laughter). Similar to “Ted Laughter”, UR-FUNNY focuses on punchline detection. Furthermore, punchline is accompanied by context sentences to properly model the build up of humor. Unlike previous datasets where negative samples were drawn from a different domain, UR-FUNNY uses a challenging negative sampling case where samples are drawn from the same videos. Furthermore, UR-FUNNY is the only humor detection dataset which incorporates all three modalities of text, vision and audio.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 1: Comparison between UR-FUNNY and notable humor detection datasets in the NLP community. Here, ‘pos’, ’neg’ , ‘mod’ and ‘spk’ denote positive, negative, modalities and speaker respectively.', solve the following question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich dataset focuses on punchline detection and uses text, vision, and audio modalities?\n" }, { "role": "assistant", "content": "The description mentions that UR-FUNNY focuses on punchline detection and incorporates text, vision, and audio modalities. The table confirms that UR-FUNNY uses {t, a, v} modalities. So, the answer is {\"answer\": \"UR-FUNNY\"}." } ], "images": [ "scigen/train/765.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison between UR-FUNNY and notable humor detection datasets in the NLP community. Here, ‘pos’, ’neg’ , ‘mod’ and ‘spk’ denote positive, negative, modalities and speaker respectively." ], "reasoning": "The description mentions that UR-FUNNY focuses on punchline detection and incorporates text, vision, and audio modalities. The table confirms that UR-FUNNY uses {t, a, v} modalities.", "question": "Which dataset focuses on punchline detection and uses text, vision, and audio modalities?", "answer": "So, the answer is {\"answer\": \"UR-FUNNY\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nUR-FUNNY is the only dataset that uses text, vision, and audio modalities.\n" }, { "role": "assistant", "content": "The description and table both indicate that UR-FUNNY is the only dataset incorporating all three modalities: text, vision, and audio. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/765.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison between UR-FUNNY and notable humor detection datasets in the NLP community. Here, ‘pos’, ’neg’ , ‘mod’ and ‘spk’ denote positive, negative, modalities and speaker respectively." ], "reasoning": "The description and table both indicate that UR-FUNNY is the only dataset incorporating all three modalities: text, vision, and audio.", "statement": "UR-FUNNY is the only dataset that uses text, vision, and audio modalities.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Comparison of validation and test set perplexity for r-RNTNs with f mapping (K=100 for PTB, K=376 for text8) versus s-RNNs and m-RNN. r-RNTNs with the same H as corresponding s-RNNs significantly increase model capacity and performance with no computational cost. The RNTN was not run on text8 due to the number of parameters required.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The r-RNTN model with K as small as 100 approaches the performance of the RNTN with fewer parameters, suggesting that distinct matrices are needed for frequent words, while infrequent words can be represented by a shared matrix and distinct vector embedding.It is remarkable that even with K as small as 100, the r-RNTN approaches the performance of the RNTN with a small fraction of the parameters. This reinforces our hypothesis that complex transformation modeling afforded by distinct matrices is needed for frequent words, but not so much for infrequent words which can be well represented by a shared matrix and a distinct vector embedding. It appears that heuristically allocating increased model capacity as done by the f based r-RNTN is a better way to increase performance than simply increasing hidden layer size, which also incurs a computational penalty. Although m-RNNs have been successfully employed in character-level language models with small vocabularies, they are seldom used in word-level models." } ], "images": [ "scigen/train/766.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of validation and test set perplexity for r-RNTNs with f mapping (K=100 for PTB, K=376 for text8) versus s-RNNs and m-RNN. r-RNTNs with the same H as corresponding s-RNNs significantly increase model capacity and performance with no computational cost. The RNTN was not run on text8 due to the number of parameters required." ], "reasoning": "The r-RNTN model with K as small as 100 approaches the performance of the RNTN with fewer parameters, suggesting that distinct matrices are needed for frequent words, while infrequent words can be represented by a shared matrix and distinct vector embedding.", "description": "It is remarkable that even with K as small as 100, the r-RNTN approaches the performance of the RNTN with a small fraction of the parameters. This reinforces our hypothesis that complex transformation modeling afforded by distinct matrices is needed for frequent words, but not so much for infrequent words which can be well represented by a shared matrix and a distinct vector embedding. It appears that heuristically allocating increased model capacity as done by the f based r-RNTN is a better way to increase performance than simply increasing hidden layer size, which also incurs a computational penalty. Although m-RNNs have been successfully employed in character-level language models with small vocabularies, they are seldom used in word-level models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: Comparison of validation and test set perplexity for r-RNTNs with f mapping (K=100 for PTB, K=376 for text8) versus s-RNNs and m-RNN. r-RNTNs with the same H as corresponding s-RNNs significantly increase model capacity and performance with no computational cost. The RNTN was not run on text8 due to the number of parameters required.', answer the question below by showing each progressive step toward the answer. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the PTB Test PPL for the r-RNTN f model with H=150?\n" }, { "role": "assistant", "content": "From the table, locate the row where the Method is r-RNTN f and H is 150. The PTB Test PPL for this configuration is 126.4. So, the answer is {\"answer\": \"126.4\"}." } ], "images": [ "scigen/train/766.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of validation and test set perplexity for r-RNTNs with f mapping (K=100 for PTB, K=376 for text8) versus s-RNNs and m-RNN. r-RNTNs with the same H as corresponding s-RNNs significantly increase model capacity and performance with no computational cost. The RNTN was not run on text8 due to the number of parameters required." ], "reasoning": "From the table, locate the row where the Method is r-RNTN f and H is 150. The PTB Test PPL for this configuration is 126.4.", "question": "What is the PTB Test PPL for the r-RNTN f model with H=150?", "answer": "So, the answer is {\"answer\": \"126.4\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe r-RNTN f model with H=100 has a lower PTB Test PPL than the s-RNN model with H=100.\n" }, { "role": "assistant", "content": "From the table, the PTB Test PPL for the r-RNTN f model with H=100 is 131.2, and for the s-RNN model with H=100, it is 146.7. Since 131.2 is lower than 146.7, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/766.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of validation and test set perplexity for r-RNTNs with f mapping (K=100 for PTB, K=376 for text8) versus s-RNNs and m-RNN. r-RNTNs with the same H as corresponding s-RNNs significantly increase model capacity and performance with no computational cost. The RNTN was not run on text8 due to the number of parameters required." ], "reasoning": "From the table, the PTB Test PPL for the r-RNTN f model with H=100 is 131.2, and for the s-RNN model with H=100, it is 146.7. Since 131.2 is lower than 146.7, the statement is supported.", "statement": "The r-RNTN f model with H=100 has a lower PTB Test PPL than the s-RNN model with H=100.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Comparison on dialog response generation. BLEU (B) scores 1-4 are used for evaluation. Monotonic (M) and Cyclical (C) schedules are tested on two models.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The cyclical schedule outperforms the monotonic schedule by providing lower reconstruction errors, higher KL values, and higher BLEU values, and it does not overfit.A4SS1SSS0Px2 Results The cyclical schedule outperforms the monotonic schedule in both settings. Under similar ELBO results, the cyclical schedule provide lower reconstruction errors, higher KL values, and higher BLEU values than the monotonic schedule. Interestingly, the monotonic schedule tends to overfit, while the cyclical schedule does not, particularly on reconstruction errors. It means the monotonic schedule can learn better latent codes for VAEs, thus preventing overfitting." } ], "images": [ "scigen/train/767.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison on dialog response generation. BLEU (B) scores 1-4 are used for evaluation. Monotonic (M) and Cyclical (C) schedules are tested on two models." ], "reasoning": "The cyclical schedule outperforms the monotonic schedule by providing lower reconstruction errors, higher KL values, and higher BLEU values, and it does not overfit.", "description": "A4SS1SSS0Px2 Results The cyclical schedule outperforms the monotonic schedule in both settings. Under similar ELBO results, the cyclical schedule provide lower reconstruction errors, higher KL values, and higher BLEU values than the monotonic schedule. Interestingly, the monotonic schedule tends to overfit, while the cyclical schedule does not, particularly on reconstruction errors. It means the monotonic schedule can learn better latent codes for VAEs, thus preventing overfitting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 5: Comparison on dialog response generation. BLEU (B) scores 1-4 are used for evaluation. Monotonic (M) and Cyclical (C) schedules are tested on two models.', and sequentially solve the question, writing out each step of your reasoning process. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich schedule, CVAE M or CVAE C, has the highest precision for B1?\n" }, { "role": "assistant", "content": "To determine which schedule has the highest precision for B1, compare the precision values for CVAE M and CVAE C under B1. CVAE M has a precision of 0.326, while CVAE C has a precision of 0.423. So, the answer is {\"answer\": \"CVAE C\"}." } ], "images": [ "scigen/train/767.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison on dialog response generation. BLEU (B) scores 1-4 are used for evaluation. Monotonic (M) and Cyclical (C) schedules are tested on two models." ], "reasoning": "To determine which schedule has the highest precision for B1, compare the precision values for CVAE M and CVAE C under B1. CVAE M has a precision of 0.326, while CVAE C has a precision of 0.423.", "question": "Which schedule, CVAE M or CVAE C, has the highest precision for B1?", "answer": "So, the answer is {\"answer\": \"CVAE C\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The CVAE+BoW C schedule has a higher recall for B2 than the CVAE+BoW M schedule., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe CVAE+BoW C schedule has a higher recall for B2 than the CVAE+BoW M schedule.\n" }, { "role": "assistant", "content": "To verify this statement, compare the recall values for B2 under CVAE+BoW C and CVAE+BoW M. CVAE+BoW C has a recall of 0.323, while CVAE+BoW M has a recall of 0.312. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/767.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison on dialog response generation. BLEU (B) scores 1-4 are used for evaluation. Monotonic (M) and Cyclical (C) schedules are tested on two models." ], "reasoning": "To verify this statement, compare the recall values for B2 under CVAE+BoW C and CVAE+BoW M. CVAE+BoW C has a recall of 0.323, while CVAE+BoW M has a recall of 0.312.", "statement": "The CVAE+BoW C schedule has a higher recall for B2 than the CVAE+BoW M schedule.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Comparison on dialog response generation. Reconstruction perplexity (Rec-P) and BLEU (B) scores are used for evaluation.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The cyclical schedule applied to CVAE reduces KL vanishing and yields the highest BLEU scores, indicating higher fidelity dialog responses.(i) Smoothed Sentence-level BLEU Chen and Cherry : BLEU is a popular metric that measures the geometric mean of modified n-gram precision with a length penalty. We use BLEU-1 to 4 as our lexical similarity metric and normalize the score to 0 to 1 scale. (ii) Cosine Distance of Bag-of-word Embedding Liu et al. We used Glove embedding and denote the average method as A−bow and extreme method as E−bow. The score is normalized to [0,1]. Higher values indicate more plausible responses. The BoW indeed reduces the KL vanishing issue, as indicated by the increased KL and decreased reconstruction perplexity. When applying the proposed cyclical schedule to CVAE, we also see a reduced KL vanishing issue. Interestingly, it also yields the highest BLEU scores. This suggests that the cyclical schedule can generate dialog responses of higher fidelity with lower cost, as the auxiliary BoW loss is not necessary." } ], "images": [ "scigen/train/768.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison on dialog response generation. Reconstruction perplexity (Rec-P) and BLEU (B) scores are used for evaluation." ], "reasoning": "The cyclical schedule applied to CVAE reduces KL vanishing and yields the highest BLEU scores, indicating higher fidelity dialog responses.", "description": "(i) Smoothed Sentence-level BLEU Chen and Cherry : BLEU is a popular metric that measures the geometric mean of modified n-gram precision with a length penalty. We use BLEU-1 to 4 as our lexical similarity metric and normalize the score to 0 to 1 scale. (ii) Cosine Distance of Bag-of-word Embedding Liu et al. We used Glove embedding and denote the average method as A−bow and extreme method as E−bow. The score is normalized to [0,1]. Higher values indicate more plausible responses. The BoW indeed reduces the KL vanishing issue, as indicated by the increased KL and decreased reconstruction perplexity. When applying the proposed cyclical schedule to CVAE, we also see a reduced KL vanishing issue. Interestingly, it also yields the highest BLEU scores. This suggests that the cyclical schedule can generate dialog responses of higher fidelity with lower cost, as the auxiliary BoW loss is not necessary.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 3: Comparison on dialog response generation. Reconstruction perplexity (Rec-P) and BLEU (B) scores are used for evaluation.' and proceed to solve the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model schedule has the highest B4 precision?\n" }, { "role": "assistant", "content": "By comparing the B4 precision values across all model schedules, the highest value is found in the CVAE+C schedule with a B4 precision of 0.234. So, the answer is {\"answer\": \"CVAE+C\"}" } ], "images": [ "scigen/train/768.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison on dialog response generation. Reconstruction perplexity (Rec-P) and BLEU (B) scores are used for evaluation." ], "reasoning": "By comparing the B4 precision values across all model schedules, the highest value is found in the CVAE+C schedule with a B4 precision of 0.234.", "question": "Which model schedule has the highest B4 precision?", "answer": "So, the answer is {\"answer\": \"CVAE+C\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe CVAE+BoW C model schedule has the highest A-bow precision.\n" }, { "role": "assistant", "content": "By comparing the A-bow precision values across all model schedules, the CVAE+BoW C model schedule has an A-bow precision of 0.961, which is the highest. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/768.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison on dialog response generation. Reconstruction perplexity (Rec-P) and BLEU (B) scores are used for evaluation." ], "reasoning": "By comparing the A-bow precision values across all model schedules, the CVAE+BoW C model schedule has an A-bow precision of 0.961, which is the highest.", "statement": "The CVAE+BoW C model schedule has the highest A-bow precision.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Results on the test set using all subject’s history as a single document, i.e. timeless classification.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "SS3 achieved the highest F1 and Precision measures, indicating its competitive performance in standard evaluation measures, while MNB had a high Recall but poor Precision due to classifying all subjects as depressed.It is interesting to notice that we also performed classification of subjects on the test set using all subject’s writings as if it were a single document (i.e. classical timeless classification); results are shown in SS3 obtained the highest values for F1 (0.61) and Precision (0.63) measures, possibly due to the flexibility that is given by its three hyper-parameters to discover important and discriminative terms. These results provide strong evidence that SS3 also achieves competitive performance when is trained and tested to optimize standard (non-temporal) evaluation measures. Note that the best configuration of MNB obtained after the model selection stage, aiming at overcoming the unbalanced dataset problem, tends to classify all subjects as depressed, that is the reason MNB had a Recall(ρ) close to 1 but a really poor precision (0.25)." } ], "images": [ "scigen/train/769.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on the test set using all subject’s history as a single document, i.e. timeless classification." ], "reasoning": "SS3 achieved the highest F1 and Precision measures, indicating its competitive performance in standard evaluation measures, while MNB had a high Recall but poor Precision due to classifying all subjects as depressed.", "description": "It is interesting to notice that we also performed classification of subjects on the test set using all subject’s writings as if it were a single document (i.e. classical timeless classification); results are shown in SS3 obtained the highest values for F1 (0.61) and Precision (0.63) measures, possibly due to the flexibility that is given by its three hyper-parameters to discover important and discriminative terms. These results provide strong evidence that SS3 also achieves competitive performance when is trained and tested to optimize standard (non-temporal) evaluation measures. Note that the best configuration of MNB obtained after the model selection stage, aiming at overcoming the unbalanced dataset problem, tends to classify all subjects as depressed, that is the reason MNB had a Recall(ρ) close to 1 but a really poor precision (0.25).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Results on the test set using all subject’s history as a single document, i.e. timeless classification.\nQuestion: Which model had the highest Recall (ρ) and what was its Precision (π)?\nGive a detailed response to the above question. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that MNB had the highest Recall (ρ) of 0.96. According to the description, MNB's Precision (π) was 0.25. So, the answer is {\"answer\": \"0.25\"}." } ], "images": [ "scigen/train/769.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on the test set using all subject’s history as a single document, i.e. timeless classification." ], "reasoning": "The table shows that MNB had the highest Recall (ρ) of 0.96. According to the description, MNB's Precision (π) was 0.25.", "question": "Which model had the highest Recall (ρ) and what was its Precision (π)?", "answer": "So, the answer is {\"answer\": \"0.25\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSS3 had the highest Precision (π) among all models.\n" }, { "role": "assistant", "content": "The table shows that SS3 had a Precision (π) of 0.63, which is the highest among all the models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/769.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on the test set using all subject’s history as a single document, i.e. timeless classification." ], "reasoning": "The table shows that SS3 had a Precision (π) of 0.63, which is the highest among all the models listed.", "statement": "SS3 had the highest Precision (π) among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: Effect of dimensionlity: additional results for the test performance of GLOSS-BoW and GLOSS-Pos models with different dimensionality of the latent vectors on unsupervised STS-(12-16, B) and supervised tasks. (∗) Following SentEval, STS-13 does not include the SMT dataset due to licensing issues. Best results for each task are in bold.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "GLOSS-BoW generally performs better than GLOSS-POS on unsupervised tasks, and increasing dimensionality does not necessarily improve accuracy.Test performance on unsupervised STS-(12-16, B) and supervised tasks. (α) indicate results computed by us. (β) PV-DBOW results are taken from Pagliardini et al. (†) The unsupervised results for Skip-thought are taken from Arora et al. (∗) Following SentEval, STS-13 does not include the SMT dataset due to licensing issues. The STS-B scores of other baselines are from the official webpage, except for InferSent which are from Wieting and Gimpel All other results are from the respective publications. We observe that GLOSS-BoW is better than GLOSS-POS for unsupervised tasks (cf. Generally, increasing dimensionality does not improve accuracy on unsupervised tasks. This is in line with Hill et al." } ], "images": [ "scigen/train/770.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Effect of dimensionlity: additional results for the test performance of GLOSS-BoW and GLOSS-Pos models with different dimensionality of the latent vectors on unsupervised STS-(12-16, B) and supervised tasks. (∗) Following SentEval, STS-13 does not include the SMT dataset due to licensing issues. Best results for each task are in bold." ], "reasoning": "GLOSS-BoW generally performs better than GLOSS-POS on unsupervised tasks, and increasing dimensionality does not necessarily improve accuracy.", "description": "Test performance on unsupervised STS-(12-16, B) and supervised tasks. (α) indicate results computed by us. (β) PV-DBOW results are taken from Pagliardini et al. (†) The unsupervised results for Skip-thought are taken from Arora et al. (∗) Following SentEval, STS-13 does not include the SMT dataset due to licensing issues. The STS-B scores of other baselines are from the official webpage, except for InferSent which are from Wieting and Gimpel All other results are from the respective publications. We observe that GLOSS-BoW is better than GLOSS-POS for unsupervised tasks (cf. Generally, increasing dimensionality does not improve accuracy on unsupervised tasks. This is in line with Hill et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Effect of dimensionlity: additional results for the test performance of GLOSS-BoW and GLOSS-Pos models with different dimensionality of the latent vectors on unsupervised STS-(12-16, B) and supervised tasks. (∗) Following SentEval, STS-13 does not include the SMT dataset due to licensing issues. Best results for each task are in bold.', unfold the question presented below, detailing every step of your calculation. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model and configuration achieved the highest average score on supervised tasks?\n" }, { "role": "assistant", "content": "To find the highest average score on supervised tasks, look at the 'Supervised tasks Avg' column. The highest score is 82.2, achieved by GLOSS-POS with a configuration of 1K dimensions. So, the answer is {\"answer\": \"GLOSS-POS with 1K dimensions\"}." } ], "images": [ "scigen/train/770.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Effect of dimensionlity: additional results for the test performance of GLOSS-BoW and GLOSS-Pos models with different dimensionality of the latent vectors on unsupervised STS-(12-16, B) and supervised tasks. (∗) Following SentEval, STS-13 does not include the SMT dataset due to licensing issues. Best results for each task are in bold." ], "reasoning": "To find the highest average score on supervised tasks, look at the 'Supervised tasks Avg' column. The highest score is 82.2, achieved by GLOSS-POS with a configuration of 1K dimensions.", "question": "Which model and configuration achieved the highest average score on supervised tasks?", "answer": "So, the answer is {\"answer\": \"GLOSS-POS with 1K dimensions\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Increasing the dimensionality to 1K improves the performance of GLOSS-BoW on unsupervised tasks. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nIncreasing the dimensionality to 1K improves the performance of GLOSS-BoW on unsupervised tasks.\n" }, { "role": "assistant", "content": "The table shows that increasing the dimensionality to 1K for GLOSS-BoW results in a decrease in the average score for unsupervised tasks from 66.2 (300 dimensions) to 63.9 (1K dimensions). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/770.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Effect of dimensionlity: additional results for the test performance of GLOSS-BoW and GLOSS-Pos models with different dimensionality of the latent vectors on unsupervised STS-(12-16, B) and supervised tasks. (∗) Following SentEval, STS-13 does not include the SMT dataset due to licensing issues. Best results for each task are in bold." ], "reasoning": "The table shows that increasing the dimensionality to 1K for GLOSS-BoW results in a decrease in the average score for unsupervised tasks from 66.2 (300 dimensions) to 63.9 (1K dimensions).", "statement": "Increasing the dimensionality to 1K improves the performance of GLOSS-BoW on unsupervised tasks.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 1: Test performance on unsupervised STS-(12-16, B) and supervised tasks. (α) indicate results computed by us. (β) PV-DBOW results are taken from Pagliardini et al. (2017). (†) The unsupervised results for Skip-thought are taken from Arora et al. (2017) and the supervised ones from Pagliardini et al. (2017). (∗) Following SentEval, STS-13 does not include the SMT dataset due to licensing issues. The STS-B scores of other baselines are from the official webpage, except for InferSent which are from Wieting and Gimpel (2018). All other results are from the respective publications. See Table 3 in supplementary material for results with higher dimensionality.'.\n" }, { "role": "assistant", "content": "GLOSS-BoW outperforms all methods on STS-Benchmark and is competitive with Sent2Vec and InferSent, showing data efficiency.GLOSS-BoW performs particularly well on STS-Benchmark where it outperforms all methods. GLOSS-BoW is competitive to Sent2Vec, even though the latter was trained on 30 times more training data. In fact, it also matches the performance of InferSent which requires labeled training data. This shows that GLOSS is very data efficient which makes it attractive for low-resource languages. Amongst unsupervised methods, GLOSS-POS is slightly behind Sent2Vec which was trained on much more data. Both GLOSS-POS and GLOSS-BoW outperform paragraph vectors (PV-DBOW). Skip-thought and InferSent use much larger vectors and they require corpora with ordered sentences. We expect that increasing the dimensionality of z even further would also help our models (cf. However, we show that strong performance can be achieved with reasonable representation sizes." } ], "images": [ "scigen/train/771.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Test performance on unsupervised STS-(12-16, B) and supervised tasks. (α) indicate results computed by us. (β) PV-DBOW results are taken from Pagliardini et al. (2017). (†) The unsupervised results for Skip-thought are taken from Arora et al. (2017) and the supervised ones from Pagliardini et al. (2017). (∗) Following SentEval, STS-13 does not include the SMT dataset due to licensing issues. The STS-B scores of other baselines are from the official webpage, except for InferSent which are from Wieting and Gimpel (2018). All other results are from the respective publications. See Table 3 in supplementary material for results with higher dimensionality." ], "reasoning": "GLOSS-BoW outperforms all methods on STS-Benchmark and is competitive with Sent2Vec and InferSent, showing data efficiency.", "description": "GLOSS-BoW performs particularly well on STS-Benchmark where it outperforms all methods. GLOSS-BoW is competitive to Sent2Vec, even though the latter was trained on 30 times more training data. In fact, it also matches the performance of InferSent which requires labeled training data. This shows that GLOSS is very data efficient which makes it attractive for low-resource languages. Amongst unsupervised methods, GLOSS-POS is slightly behind Sent2Vec which was trained on much more data. Both GLOSS-POS and GLOSS-BoW outperform paragraph vectors (PV-DBOW). Skip-thought and InferSent use much larger vectors and they require corpora with ordered sentences. We expect that increasing the dimensionality of z even further would also help our models (cf. However, we show that strong performance can be achieved with reasonable representation sizes.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1: Test performance on unsupervised STS-(12-16, B) and supervised tasks. (α) indicate results computed by us. (β) PV-DBOW results are taken from Pagliardini et al. (2017). (†) The unsupervised results for Skip-thought are taken from Arora et al. (2017) and the supervised ones from Pagliardini et al. (2017). (∗) Following SentEval, STS-13 does not include the SMT dataset due to licensing issues. The STS-B scores of other baselines are from the official webpage, except for InferSent which are from Wieting and Gimpel (2018). All other results are from the respective publications. See Table 3 in supplementary material for results with higher dimensionality.', solve the subsequent question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model has the highest average score on supervised tasks?\n" }, { "role": "assistant", "content": "To find the model with the highest average score on supervised tasks, compare the 'Supervised tasks Avg' column for all models. InferSent (AllNLI) has the highest average score of 87.6. So, the answer is {\"answer\": \"InferSent (AllNLI)\"}" } ], "images": [ "scigen/train/771.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Test performance on unsupervised STS-(12-16, B) and supervised tasks. (α) indicate results computed by us. (β) PV-DBOW results are taken from Pagliardini et al. (2017). (†) The unsupervised results for Skip-thought are taken from Arora et al. (2017) and the supervised ones from Pagliardini et al. (2017). (∗) Following SentEval, STS-13 does not include the SMT dataset due to licensing issues. The STS-B scores of other baselines are from the official webpage, except for InferSent which are from Wieting and Gimpel (2018). All other results are from the respective publications. See Table 3 in supplementary material for results with higher dimensionality." ], "reasoning": "To find the model with the highest average score on supervised tasks, compare the 'Supervised tasks Avg' column for all models. InferSent (AllNLI) has the highest average score of 87.6.", "question": "Which model has the highest average score on supervised tasks?", "answer": "So, the answer is {\"answer\": \"InferSent (AllNLI)\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence GLOSS-BoW outperforms PV-DBOW on unsupervised STS tasks. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nGLOSS-BoW outperforms PV-DBOW on unsupervised STS tasks.\n" }, { "role": "assistant", "content": "Comparing the scores of GLOSS-BoW and PV-DBOW on unsupervised STS tasks, GLOSS-BoW consistently has higher scores across all tasks. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/771.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Test performance on unsupervised STS-(12-16, B) and supervised tasks. (α) indicate results computed by us. (β) PV-DBOW results are taken from Pagliardini et al. (2017). (†) The unsupervised results for Skip-thought are taken from Arora et al. (2017) and the supervised ones from Pagliardini et al. (2017). (∗) Following SentEval, STS-13 does not include the SMT dataset due to licensing issues. The STS-B scores of other baselines are from the official webpage, except for InferSent which are from Wieting and Gimpel (2018). All other results are from the respective publications. See Table 3 in supplementary material for results with higher dimensionality." ], "reasoning": "Comparing the scores of GLOSS-BoW and PV-DBOW on unsupervised STS tasks, GLOSS-BoW consistently has higher scores across all tasks.", "statement": "GLOSS-BoW outperforms PV-DBOW on unsupervised STS tasks.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Accuracy results for temporal adaptation in the PPCMBE and the PPCEME of historical English. Percentage error reduction is shown for the best-performing method, Fema-attribute.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Fema outperforms other domain adaptation algorithms, showing a significant increase in accuracy on both PPCMBE and PPCEME test sets.English spelling had become mostly uniform and stable since around 1700 Among the two baseline systems, MEMM performs slightly better than SVM, showing a small benefit to structured prediction. Among the domain adaptation algorithms, Fema clearly outperforms SCL, Brown clustering and word2vec, with an averaged increase of about 0.5% and 1.5% accuracies on the PPCMBE and the PPCEME test sets respectively. The metadata attribute information boosts performance by a small but consistent margin, 0.1-0.2% on average." } ], "images": [ "scigen/train/772.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracy results for temporal adaptation in the PPCMBE and the PPCEME of historical English. Percentage error reduction is shown for the best-performing method, Fema-attribute." ], "reasoning": "Fema outperforms other domain adaptation algorithms, showing a significant increase in accuracy on both PPCMBE and PPCEME test sets.", "description": "English spelling had become mostly uniform and stable since around 1700 Among the two baseline systems, MEMM performs slightly better than SVM, showing a small benefit to structured prediction. Among the domain adaptation algorithms, Fema clearly outperforms SCL, Brown clustering and word2vec, with an averaged increase of about 0.5% and 1.5% accuracies on the PPCMBE and the PPCEME test sets respectively. The metadata attribute information boosts performance by a small but consistent margin, 0.1-0.2% on average.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: Accuracy results for temporal adaptation in the PPCMBE and the PPCEME of historical English. Percentage error reduction is shown for the best-performing method, Fema-attribute.', unfold the steps to solve the question given next. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nQuestion: Which algorithm shows the highest average accuracy for Early Modern English (training from 1640-1710)?\n" }, { "role": "assistant", "content": "To find the highest average accuracy for Early Modern English (training from 1640-1710), compare the average accuracies of all algorithms listed in the table. Fema attribute embeddings show the highest average accuracy of 93.41%. So, the answer is {\"answer\": \"Fema attribute embeddings\"}." } ], "images": [ "scigen/train/772.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracy results for temporal adaptation in the PPCMBE and the PPCEME of historical English. Percentage error reduction is shown for the best-performing method, Fema-attribute." ], "reasoning": "To find the highest average accuracy for Early Modern English (training from 1640-1710), compare the average accuracies of all algorithms listed in the table. Fema attribute embeddings show the highest average accuracy of 93.41%.", "question": "Which algorithm shows the highest average accuracy for Early Modern English (training from 1640-1710)?", "answer": "So, the answer is {\"answer\": \"Fema attribute embeddings\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nFema single embedding has the highest accuracy for the period 1770-1839.\n" }, { "role": "assistant", "content": "The table shows that for the period 1770-1839, Fema single embedding has an accuracy of 96.80, which is lower than Fema attribute embeddings with an accuracy of 96.84. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/772.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracy results for temporal adaptation in the PPCMBE and the PPCEME of historical English. Percentage error reduction is shown for the best-performing method, Fema-attribute." ], "reasoning": "The table shows that for the period 1770-1839, Fema single embedding has an accuracy of 96.80, which is lower than Fema attribute embeddings with an accuracy of 96.84.", "statement": "Fema single embedding has the highest accuracy for the period 1770-1839.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Accuracy results for adapting from the PTB to the PPCMBE and the PPCEME of historical English. ∗Error reduction for the normalized PPCEME is computed against the unnormalized SVM accuracy, showing total error reduction.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Domain adaptation and spelling normalization improve performance, with a combined accuracy increase from 74.2% to 79.1% and a relative error reduction of 19%.Nonetheless, domain adaptation can help: Fema improves performance by 1.3% on the PPCMBE data, and by 3.8% on the unnormalized PPCEME data. Spelling normalization also helps, improving the baseline systems by more than 2.5%. The combination of spelling normalization and domain adaptation gives an overall improvement in accuracy from 74.2% to 79.1%. The relative error reduction is lower than in the temporal adaptation setting: only 19% at best, versus 30% error reduction in temporal adaptation. This is because there are now at least two sources of error — language change and tagset mismatch — and unsupervised domain adaptation cannot address mismatches in the tag set." } ], "images": [ "scigen/train/773.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Accuracy results for adapting from the PTB to the PPCMBE and the PPCEME of historical English. ∗Error reduction for the normalized PPCEME is computed against the unnormalized SVM accuracy, showing total error reduction." ], "reasoning": "Domain adaptation and spelling normalization improve performance, with a combined accuracy increase from 74.2% to 79.1% and a relative error reduction of 19%.", "description": "Nonetheless, domain adaptation can help: Fema improves performance by 1.3% on the PPCMBE data, and by 3.8% on the unnormalized PPCEME data. Spelling normalization also helps, improving the baseline systems by more than 2.5%. The combination of spelling normalization and domain adaptation gives an overall improvement in accuracy from 74.2% to 79.1%. The relative error reduction is lower than in the temporal adaptation setting: only 19% at best, versus 30% error reduction in temporal adaptation. This is because there are now at least two sources of error — language change and tagset mismatch — and unsupervised domain adaptation cannot address mismatches in the tag set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 4: Accuracy results for adapting from the PTB to the PPCMBE and the PPCEME of historical English. ∗Error reduction for the normalized PPCEME is computed against the unnormalized SVM accuracy, showing total error reduction.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}. \n\nQuestion: What is the accuracy improvement when combining spelling normalization and domain adaptation for the normalized PPCEME data?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows that the accuracy for the normalized PPCEME data with Fema attribute embeddings is 79.05%, and the description states the baseline accuracy is 74.2%. The improvement is 79.05% - 74.2%. So, the answer is {\"answer\": \"4.85%\"}" } ], "images": [ "scigen/train/773.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Accuracy results for adapting from the PTB to the PPCMBE and the PPCEME of historical English. ∗Error reduction for the normalized PPCEME is computed against the unnormalized SVM accuracy, showing total error reduction." ], "reasoning": "The table shows that the accuracy for the normalized PPCEME data with Fema attribute embeddings is 79.05%, and the description states the baseline accuracy is 74.2%. The improvement is 79.05% - 74.2%.", "question": "What is the accuracy improvement when combining spelling normalization and domain adaptation for the normalized PPCEME data?", "answer": "So, the answer is {\"answer\": \"4.85%\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe relative error reduction for the normalized PPCEME data is 19%.\n" }, { "role": "assistant", "content": "The table shows that the relative error reduction for the normalized PPCEME data with Fema attribute embeddings is 19%, which matches the description. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/773.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Accuracy results for adapting from the PTB to the PPCMBE and the PPCEME of historical English. ∗Error reduction for the normalized PPCEME is computed against the unnormalized SVM accuracy, showing total error reduction." ], "reasoning": "The table shows that the relative error reduction for the normalized PPCEME data with Fema attribute embeddings is 19%, which matches the description.", "statement": "The relative error reduction for the normalized PPCEME data is 19%.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: Tagging accuracies of adaptation of our baseline SVM tagger from the PTB to the PPCEME in ablation experiments.'.\n" }, { "role": "assistant", "content": "Word context features significantly improve accuracies for both IV and OOV tokens, while suffix features are crucial for OOV tokens. Orthographic features are nearly irrelevant if affix features are present.Word context features are important for obtaining good accuracies on both IV and OOV tokens. Affix features, particularly suffix features, are crucial for the OOV tokens. The orthographic features are shown to be nearly irrelevant, as long as affix features are present. Overall, the high percentage of OOV tokens can be a major source of errors, as the tagging accuracy on OOV tokens is below 50% in our best baseline system. Note that these results are for a classification-based tagger; while the Viterbi-based MEMM tagger performs only marginally better overall (∼0.2% improvement), it is possible that its error distribution might be different due to the advantages of structured prediction." } ], "images": [ "scigen/train/774.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Tagging accuracies of adaptation of our baseline SVM tagger from the PTB to the PPCEME in ablation experiments." ], "reasoning": "Word context features significantly improve accuracies for both IV and OOV tokens, while suffix features are crucial for OOV tokens. Orthographic features are nearly irrelevant if affix features are present.", "description": "Word context features are important for obtaining good accuracies on both IV and OOV tokens. Affix features, particularly suffix features, are crucial for the OOV tokens. The orthographic features are shown to be nearly irrelevant, as long as affix features are present. Overall, the high percentage of OOV tokens can be a major source of errors, as the tagging accuracy on OOV tokens is below 50% in our best baseline system. Note that these results are for a classification-based tagger; while the Viterbi-based MEMM tagger performs only marginally better overall (∼0.2% improvement), it is possible that its error distribution might be different due to the advantages of structured prediction.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Tagging accuracies of adaptation of our baseline SVM tagger from the PTB to the PPCEME in ablation experiments.\nQuestion: What is the tagging accuracy for OOV tokens when suffix features are removed?\nSolve the above question based on the table information. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the tagging accuracy for OOV tokens with different feature sets. When suffix features are removed, the accuracy for OOV tokens is 38.13. So, the answer is {\"answer\": \"38.13\"}." } ], "images": [ "scigen/train/774.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Tagging accuracies of adaptation of our baseline SVM tagger from the PTB to the PPCEME in ablation experiments." ], "reasoning": "The table shows the tagging accuracy for OOV tokens with different feature sets. When suffix features are removed, the accuracy for OOV tokens is 38.13.", "question": "What is the tagging accuracy for OOV tokens when suffix features are removed?", "answer": "So, the answer is {\"answer\": \"38.13\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Removing word context features results in an overall accuracy below 71%., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nRemoving word context features results in an overall accuracy below 71%.\n" }, { "role": "assistant", "content": "The table shows that removing word context features results in an overall accuracy of 70.23%, which is below 71%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/774.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Tagging accuracies of adaptation of our baseline SVM tagger from the PTB to the PPCEME in ablation experiments." ], "reasoning": "The table shows that removing word context features results in an overall accuracy of 70.23%, which is below 71%.", "statement": "Removing word context features results in an overall accuracy below 71%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 7: Tagging accuracies of domain adaptation models from the PTB to the PPCEME.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Fema-attribute achieves the highest accuracy on OOV tokens and performs similarly to Fema-single on IV tokens. Brown clustering slightly outperforms word2vec and SCL on IV tokens.Compared against the baseline tagger, Fema-attribute achieves an absolute improvement of 14% in accuracy on OOV tokens. SCL performs slightly better than Brown clustering and word2vec on IV tokens, but worse on OOV tokens. By incorporating metadata attributes, Fema-attribute performs better than Fema-single on OOV tokens, though the accuracies on IV tokens are similar. Interestingly, the venerable method of Brown clustering (slightly) outperforms both word2vec and SCL." } ], "images": [ "scigen/train/775.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Tagging accuracies of domain adaptation models from the PTB to the PPCEME." ], "reasoning": "Fema-attribute achieves the highest accuracy on OOV tokens and performs similarly to Fema-single on IV tokens. Brown clustering slightly outperforms word2vec and SCL on IV tokens.", "description": "Compared against the baseline tagger, Fema-attribute achieves an absolute improvement of 14% in accuracy on OOV tokens. SCL performs slightly better than Brown clustering and word2vec on IV tokens, but worse on OOV tokens. By incorporating metadata attributes, Fema-attribute performs better than Fema-single on OOV tokens, though the accuracies on IV tokens are similar. Interestingly, the venerable method of Brown clustering (slightly) outperforms both word2vec and SCL.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 7: Tagging accuracies of domain adaptation models from the PTB to the PPCEME.' to sequentially solve the question presented below. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich system achieves the highest accuracy on OOV tokens and how does it compare to Fema-single on IV tokens?\n" }, { "role": "assistant", "content": "Fema-attribute achieves the highest accuracy on OOV tokens with 63.16%. On IV tokens, Fema-attribute performs similarly to Fema-single, with accuracies of 82.34% and 82.30% respectively. So, the answer is {\"answer\": \"Fema-attribute achieves the highest accuracy on OOV tokens and performs similarly to Fema-single on IV tokens.\"}." } ], "images": [ "scigen/train/775.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Tagging accuracies of domain adaptation models from the PTB to the PPCEME." ], "reasoning": "Fema-attribute achieves the highest accuracy on OOV tokens with 63.16%. On IV tokens, Fema-attribute performs similarly to Fema-single, with accuracies of 82.34% and 82.30% respectively.", "question": "Which system achieves the highest accuracy on OOV tokens and how does it compare to Fema-single on IV tokens?", "answer": "So, the answer is {\"answer\": \"Fema-attribute achieves the highest accuracy on OOV tokens and performs similarly to Fema-single on IV tokens.\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: SCL performs better than Brown clustering on OOV tokens., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSCL performs better than Brown clustering on OOV tokens.\n" }, { "role": "assistant", "content": "According to the table, SCL has an accuracy of 55.45% on OOV tokens, while Brown clustering has an accuracy of 56.76% on OOV tokens. Therefore, SCL does not perform better than Brown clustering on OOV tokens. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/775.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Tagging accuracies of domain adaptation models from the PTB to the PPCEME." ], "reasoning": "According to the table, SCL has an accuracy of 55.45% on OOV tokens, while Brown clustering has an accuracy of 56.76% on OOV tokens. Therefore, SCL does not perform better than Brown clustering on OOV tokens.", "statement": "SCL performs better than Brown clustering on OOV tokens.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: WER results'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The proposed method achieved similar performance to language-specific rescoring, with slight degradation in short utterance sets.One pass decoding with the proposed method To experiment that our method can automatically select the best result without having to identify the input language, we did two experiments. The first experiment was to simulate the case of the input language is known in advance. We firstly decoded all 6 testing sets to obtain lattices using AM-2 and LM0. Afterward rescored English sets by LM1 and Vietnamese sets by LM2. In the second experiment, we combined all six testing sets into one set to simulate that the decoder will not know in advance the input language. It could be English or Vietnamese at random. Lattices of this combined set were also obtained by AM-2 and LM0 at the first decoding step. But at the rescoring step, both LM1 and LM2 were used to produce the best paths and applied the proposed method to select final outputs. Finally, we separated the final outputs based on their utterance ID back to the six corresponding testing sets to individually measure WER for each one. Comparing the results of these two experiments shown that our method produced a similar performance without a language identifier for input signals. There was a bit worse on Conversation-test and YouTube-test sets. We found that these sets contain some very short utterances with only one or two words. For these cases the language scores given by LM1 and LM2 could be similar since the output sentences chosen probably have meaning and are made up of homonyms between the two languages." } ], "images": [ "scigen/train/776.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: WER results" ], "reasoning": "The proposed method achieved similar performance to language-specific rescoring, with slight degradation in short utterance sets.", "description": "One pass decoding with the proposed method To experiment that our method can automatically select the best result without having to identify the input language, we did two experiments. The first experiment was to simulate the case of the input language is known in advance. We firstly decoded all 6 testing sets to obtain lattices using AM-2 and LM0. Afterward rescored English sets by LM1 and Vietnamese sets by LM2. In the second experiment, we combined all six testing sets into one set to simulate that the decoder will not know in advance the input language. It could be English or Vietnamese at random. Lattices of this combined set were also obtained by AM-2 and LM0 at the first decoding step. But at the rescoring step, both LM1 and LM2 were used to produce the best paths and applied the proposed method to select final outputs. Finally, we separated the final outputs based on their utterance ID back to the six corresponding testing sets to individually measure WER for each one. Comparing the results of these two experiments shown that our method produced a similar performance without a language identifier for input signals. There was a bit worse on Conversation-test and YouTube-test sets. We found that these sets contain some very short utterances with only one or two words. For these cases the language scores given by LM1 and LM2 could be similar since the output sentences chosen probably have meaning and are made up of homonyms between the two languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: WER results'. Output the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: What was the WER for the YouTube-test set using the proposed method?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows the WER for the YouTube-test set under different methods. The proposed method column shows a WER of 21.83 for the YouTube-test set. So, the answer is {\"answer\": \"21.83\"}." } ], "images": [ "scigen/train/776.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: WER results" ], "reasoning": "The table shows the WER for the YouTube-test set under different methods. The proposed method column shows a WER of 21.83 for the YouTube-test set.", "question": "What was the WER for the YouTube-test set using the proposed method?", "answer": "So, the answer is {\"answer\": \"21.83\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe proposed method performed worse on the Conversation-test set compared to rescoring with LM2.\n" }, { "role": "assistant", "content": "The table shows that the WER for the Conversation-test set was 19.2 using the proposed method and 19.00 using rescoring with LM2, indicating slightly worse performance with the proposed method. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/776.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: WER results" ], "reasoning": "The table shows that the WER for the Conversation-test set was 19.2 using the proposed method and 19.00 using rescoring with LM2, indicating slightly worse performance with the proposed method.", "statement": "The proposed method performed worse on the Conversation-test set compared to rescoring with LM2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 1: Audio datasets'.\n" }, { "role": "assistant", "content": "The YouTube-test set is the most challenging due to its noisy speech and high percentage of foreign words, which is 24.8%.4 testing sets were used to evaluate for Vietnamese performance. They were named Reading-test, Conversation-test, YouTube-test, and VLSP2018. For English evaluation, testing sets were Test-clean and Test-other [librispeech]. Those sets were randomly selected from the same raw data at the beginning to construct training, evaluation, and test sets, excepting the YouTube-test and VLSP2018. The YouTube-test was randomly collected from YouTube. It includes teenage conversations and technique talks with a lot of noises like music, non-human sounds. This was the most challenging test set since it not only is a noisy speech but also contains many foreign words. The percentage of foreign words in the vocabulary of this set is up to 23% with 939 unique words, and most of them are common English words and property names. The accuracy of foreign words recognition will be evaluated only for this set because the ratio in others is not significant. VLSP2018 was developed by research teams involved in Vietnamese language and speech processing [vlsp2018]. All audio files were saved to wave format with sample rate of 16kHz and analog/digital conversion precision of 16 bits." } ], "images": [ "scigen/train/777.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Audio datasets" ], "reasoning": "The YouTube-test set is the most challenging due to its noisy speech and high percentage of foreign words, which is 24.8%.", "description": "4 testing sets were used to evaluate for Vietnamese performance. They were named Reading-test, Conversation-test, YouTube-test, and VLSP2018. For English evaluation, testing sets were Test-clean and Test-other [librispeech]. Those sets were randomly selected from the same raw data at the beginning to construct training, evaluation, and test sets, excepting the YouTube-test and VLSP2018. The YouTube-test was randomly collected from YouTube. It includes teenage conversations and technique talks with a lot of noises like music, non-human sounds. This was the most challenging test set since it not only is a noisy speech but also contains many foreign words. The percentage of foreign words in the vocabulary of this set is up to 23% with 939 unique words, and most of them are common English words and property names. The accuracy of foreign words recognition will be evaluated only for this set because the ratio in others is not significant. VLSP2018 was developed by research teams involved in Vietnamese language and speech processing [vlsp2018]. All audio files were saved to wave format with sample rate of 16kHz and analog/digital conversion precision of 16 bits.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 1: Audio datasets', and tackle the question below in a sequential manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich Vietnamese testing set has the highest percentage of foreign words?\n" }, { "role": "assistant", "content": "The YouTube-test set has the highest percentage of foreign words among the Vietnamese testing sets, with 24.8%. So, the answer is {\"answer\": \"YouTube-test\"}" } ], "images": [ "scigen/train/777.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Audio datasets" ], "reasoning": "The YouTube-test set has the highest percentage of foreign words among the Vietnamese testing sets, with 24.8%.", "question": "Which Vietnamese testing set has the highest percentage of foreign words?", "answer": "So, the answer is {\"answer\": \"YouTube-test\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe VLSP2018 testing set has the highest number of sentences among the Vietnamese testing sets.\n" }, { "role": "assistant", "content": "The table shows that the Reading-test set has 5,358 sentences, Conversation-test has 11,533 sentences, YouTube-test has 5,432 sentences, and VLSP2018 has 796 sentences. Therefore, VLSP2018 does not have the highest number of sentences. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/777.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Audio datasets" ], "reasoning": "The table shows that the Reading-test set has 5,358 sentences, Conversation-test has 11,533 sentences, YouTube-test has 5,432 sentences, and VLSP2018 has 796 sentences. Therefore, VLSP2018 does not have the highest number of sentences.", "statement": "The VLSP2018 testing set has the highest number of sentences among the Vietnamese testing sets.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Language model evaluation'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "LM0 was created by interpolating LM1 and LM2 with α=0.5 and then pruning n-grams with probabilities less than 2E-8, reducing its size from 800MB to 30MB.After building LM1 and LM2 as above, the multilingual model LM0 was created by linearly interpolating LM1 and LM2 with α=0.5. LM0 was further cut off all n-grams that have a probability of less than 2E-8 to reduce its size and speed up the first decoding step. After this step, the size of the unpruned LM0 from 800MB has decreased to 30MB. The perplexities of LM0, LM1, and LM2 on All of these models were built by using the tool SRILM [tedlium]." } ], "images": [ "scigen/train/778.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Language model evaluation" ], "reasoning": "LM0 was created by interpolating LM1 and LM2 with α=0.5 and then pruning n-grams with probabilities less than 2E-8, reducing its size from 800MB to 30MB.", "description": "After building LM1 and LM2 as above, the multilingual model LM0 was created by linearly interpolating LM1 and LM2 with α=0.5. LM0 was further cut off all n-grams that have a probability of less than 2E-8 to reduce its size and speed up the first decoding step. After this step, the size of the unpruned LM0 from 800MB has decreased to 30MB. The perplexities of LM0, LM1, and LM2 on All of these models were built by using the tool SRILM [tedlium].", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 2: Language model evaluation' in mind, break down and resolve the question below step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhat is the perplexity of LM0 on the YouTube-test compared to LM2?\n" }, { "role": "assistant", "content": "The table shows the perplexity of LM0 on the YouTube-test as 199.5 and LM2 as 111.4. Comparing these values shows that LM0 has a higher perplexity. So, the answer is {\"answer\": \"199.5\"}." } ], "images": [ "scigen/train/778.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Language model evaluation" ], "reasoning": "The table shows the perplexity of LM0 on the YouTube-test as 199.5 and LM2 as 111.4. Comparing these values shows that LM0 has a higher perplexity.", "question": "What is the perplexity of LM0 on the YouTube-test compared to LM2?", "answer": "So, the answer is {\"answer\": \"199.5\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nLM0 has a lower perplexity than LM2 on the Reading-test.\n" }, { "role": "assistant", "content": "The table shows that the perplexity of LM0 on the Reading-test is 136.6, while LM2 has a perplexity of 87.2. Therefore, LM0 has a higher perplexity than LM2 on the Reading-test. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/778.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Language model evaluation" ], "reasoning": "The table shows that the perplexity of LM0 on the Reading-test is 136.6, while LM2 has a perplexity of 87.2. Therefore, LM0 has a higher perplexity than LM2 on the Reading-test.", "statement": "LM0 has a lower perplexity than LM2 on the Reading-test.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Intrinsic evaluation on crowdsourced semantic equivalence vs. divergence testsets. We report overall F-score, as well as precision (P), recall (R) and F-score (F) for the equivalent (+) and divergent (-) classes separately. Semantic similarity yields better results across the board, with larger improvements on the divergent class.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The table shows that divergent examples have a significant improvement in F-score, with gains of about 10 points compared to the next-best scores.The break down per class shows that both equivalent and divergent examples are better detected. The improvement is larger for divergent examples with gains of about 10 points for F-score for the divergent class, when compared to the next-best scores." } ], "images": [ "scigen/train/779.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Intrinsic evaluation on crowdsourced semantic equivalence vs. divergence testsets. We report overall F-score, as well as precision (P), recall (R) and F-score (F) for the equivalent (+) and divergent (-) classes separately. Semantic similarity yields better results across the board, with larger improvements on the divergent class." ], "reasoning": "The table shows that divergent examples have a significant improvement in F-score, with gains of about 10 points compared to the next-best scores.", "description": "The break down per class shows that both equivalent and divergent examples are better detected. The improvement is larger for divergent examples with gains of about 10 points for F-score for the divergent class, when compared to the next-best scores.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Intrinsic evaluation on crowdsourced semantic equivalence vs. divergence testsets. We report overall F-score, as well as precision (P), recall (R) and F-score (F) for the equivalent (+) and divergent (-) classes separately. Semantic similarity yields better results across the board, with larger improvements on the divergent class.\nQuestion: What is the F-score for Semantic Dissimilarity in the Common Crawl dataset?\nSolve the above question based on the table information. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the F-score for Semantic Dissimilarity in the Common Crawl dataset, locate the row for Semantic Dissimilarity and the column for Common Crawl Overall F. So, the answer is {\"answer\": \"80\"}." } ], "images": [ "scigen/train/779.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Intrinsic evaluation on crowdsourced semantic equivalence vs. divergence testsets. We report overall F-score, as well as precision (P), recall (R) and F-score (F) for the equivalent (+) and divergent (-) classes separately. Semantic similarity yields better results across the board, with larger improvements on the divergent class." ], "reasoning": "To find the F-score for Semantic Dissimilarity in the Common Crawl dataset, locate the row for Semantic Dissimilarity and the column for Common Crawl Overall F.", "question": "What is the F-score for Semantic Dissimilarity in the Common Crawl dataset?", "answer": "So, the answer is {\"answer\": \"80\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The F-score for Non-parallel in the OpenSubtitles dataset is higher than in the Common Crawl dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe F-score for Non-parallel in the OpenSubtitles dataset is higher than in the Common Crawl dataset.\n" }, { "role": "assistant", "content": "Compare the F-scores for Non-parallel in both datasets. The OpenSubtitles Overall F-score is 66, while the Common Crawl Overall F-score is 58. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/779.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Intrinsic evaluation on crowdsourced semantic equivalence vs. divergence testsets. We report overall F-score, as well as precision (P), recall (R) and F-score (F) for the equivalent (+) and divergent (-) classes separately. Semantic similarity yields better results across the board, with larger improvements on the divergent class." ], "reasoning": "Compare the F-scores for Non-parallel in both datasets. The OpenSubtitles Overall F-score is 66, while the Common Crawl Overall F-score is 58.", "statement": "The F-score for Non-parallel in the OpenSubtitles dataset is higher than in the Common Crawl dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 13: The average value of E[1N∑NipMi(yij|yi'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "High performance gain is achieved when teacher models are well-trained from parallel data, resulting in high confidence and expectation values.Through certain experiments we will discuss later, we observe that our method is able to achieve high performance gain under the following conditions: Both sides of Eqn. This can be realized when the teacher models are well-trained from the parallel data. ,N} and yij=yej∀yj. First, when the constituent models Mi are well-trained (but not overfitted), the confidence of the models is also high. This results in the expectation E[maxykj1N∑NipMi(ykj|y" } ], "images": [ "scigen/train/780.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 13: The average value of E[1N∑NipMi(yij|yi" ], "reasoning": "High performance gain is achieved when teacher models are well-trained from parallel data, resulting in high confidence and expectation values.", "description": "Through certain experiments we will discuss later, we observe that our method is able to achieve high performance gain under the following conditions: Both sides of Eqn. This can be realized when the teacher models are well-trained from the parallel data. ,N} and yij=yej∀yj. First, when the constituent models Mi are well-trained (but not overfitted), the confidence of the models is also high. This results in the expectation E[maxykj1N∑NipMi(ykj|y", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 13: The average value of E[1N∑NipMi(yij|yi' in mind, break down and resolve the question below step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhat is the Test BLEU score for the En-Fr language pair when using Diversified Model ^ M?\n" }, { "role": "assistant", "content": "To find the Test BLEU score for the En-Fr language pair using Diversified Model ^ M, locate the En-Fr column and find the corresponding row for Diversified Model ^ M. So, the answer is {\"answer\": \"45.5\"}." } ], "images": [ "scigen/train/780.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 13: The average value of E[1N∑NipMi(yij|yi" ], "reasoning": "To find the Test BLEU score for the En-Fr language pair using Diversified Model ^ M, locate the En-Fr column and find the corresponding row for Diversified Model ^ M.", "question": "What is the Test BLEU score for the En-Fr language pair when using Diversified Model ^ M?", "answer": "So, the answer is {\"answer\": \"45.5\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Test BLEU score for the De-En language pair using Overfitted Diversified Model ^ M is higher than using Teacher models Mi., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Test BLEU score for the De-En language pair using Overfitted Diversified Model ^ M is higher than using Teacher models Mi.\n" }, { "role": "assistant", "content": "Compare the Test BLEU scores for the De-En language pair using Overfitted Diversified Model ^ M and Teacher models Mi. The score for Overfitted Diversified Model ^ M is 34.8, which is higher than the score for Teacher models Mi, which is 34.7. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/780.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 13: The average value of E[1N∑NipMi(yij|yi" ], "reasoning": "Compare the Test BLEU scores for the De-En language pair using Overfitted Diversified Model ^ M and Teacher models Mi. The score for Overfitted Diversified Model ^ M is 34.8, which is higher than the score for Teacher models Mi, which is 34.7.", "statement": "The Test BLEU score for the De-En language pair using Overfitted Diversified Model ^ M is higher than using Teacher models Mi.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Performances on low-resource translations. As done by flores, the from-English pairs are measured in tokenized BLEU, while to-English are measured in detokenized SacreBLEU.'.\n" }, { "role": "assistant", "content": "The Data Diversification method outperforms the flores baseline in all four low-resource tasks, achieving BLEU improvements of 1.4, 1.3, 2.2, and 1.5 for En-Ne, Ne-En, En-Si, and Si-En tasks, respectively.Specifically, the method achieves 5.7, 8.9, 2.2, and 8.2 BLEU for En-Ne, Ne-En, En-Si and Si-En tasks, respectively. In absolute terms, these are 1.4, 1.3, 2.2 and 1.5 BLEU improvements over the baseline model (flores). Without any monolingual data involved, our method establishes a new state of the art in all four low-resource tasks." } ], "images": [ "scigen/train/781.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performances on low-resource translations. As done by flores, the from-English pairs are measured in tokenized BLEU, while to-English are measured in detokenized SacreBLEU." ], "reasoning": "The Data Diversification method outperforms the flores baseline in all four low-resource tasks, achieving BLEU improvements of 1.4, 1.3, 2.2, and 1.5 for En-Ne, Ne-En, En-Si, and Si-En tasks, respectively.", "description": "Specifically, the method achieves 5.7, 8.9, 2.2, and 8.2 BLEU for En-Ne, Ne-En, En-Si and Si-En tasks, respectively. In absolute terms, these are 1.4, 1.3, 2.2 and 1.5 BLEU improvements over the baseline model (flores). Without any monolingual data involved, our method establishes a new state of the art in all four low-resource tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: 'Table 4: Performances on low-resource translations. As done by flores, the from-English pairs are measured in tokenized BLEU, while to-English are measured in detokenized SacreBLEU.'\nQuestion: What is the BLEU score improvement for the En-Si task using the Data Diversification method compared to the flores baseline?\n" }, { "role": "assistant", "content": "The BLEU score for the En-Si task using the Data Diversification method is 2.2, while the score for the flores baseline is 1.0. The improvement is calculated as 2.2 - 1.0. So, the answer is {\"answer\": \"1.2\"}" } ], "images": [ "scigen/train/781.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performances on low-resource translations. As done by flores, the from-English pairs are measured in tokenized BLEU, while to-English are measured in detokenized SacreBLEU." ], "reasoning": "The BLEU score for the En-Si task using the Data Diversification method is 2.2, while the score for the flores baseline is 1.0. The improvement is calculated as 2.2 - 1.0.", "question": "What is the BLEU score improvement for the En-Si task using the Data Diversification method compared to the flores baseline?", "answer": "So, the answer is {\"answer\": \"1.2\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Data Diversification method achieves a BLEU score of 8.9 for the Ne-En task.\n" }, { "role": "assistant", "content": "The table shows that the Data Diversification method achieves a BLEU score of 8.9 for the Ne-En task. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/781.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performances on low-resource translations. As done by flores, the from-English pairs are measured in tokenized BLEU, while to-English are measured in detokenized SacreBLEU." ], "reasoning": "The table shows that the Data Diversification method achieves a BLEU score of 8.9 for the Ne-En task.", "statement": "The Data Diversification method achieves a BLEU score of 8.9 for the Ne-En task.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 9: BLEU scores for models with and without back-translation (BT) on the IWSLT’14 English-German (En-De), German-English (De-En) and WMT’14 En-De tasks. Column |D| shows the total data used in back-translation compared to the original parallel data.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Our method outperforms the back-translation baseline in all tasks, with an additional +1.0 BLEU score improvement when using monolingual data.Our method is also complementary to back-translation (BT) (backtranslate_sennrich-etal-2016-improving). To demonstrate this, we conducted experiments on the IWSLT’14 En-De and De-En tasks with extra monolingual data extracted from the WMT’14 En-De corpus. In addition, we also compare our method against the back-translation baseline in the WMT’14 En-De task with extra monolingual data from News Crawl 2009. We use the big Transformer as the final model in all our back-translation experiments. Further details of these experiments are provided in the Appendix. However, using our data diversification strategy with such monolingual data boosts the performance further with additional +1.0 BLEU over the back-translation baselines." } ], "images": [ "scigen/train/782.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: BLEU scores for models with and without back-translation (BT) on the IWSLT’14 English-German (En-De), German-English (De-En) and WMT’14 En-De tasks. Column |D| shows the total data used in back-translation compared to the original parallel data." ], "reasoning": "Our method outperforms the back-translation baseline in all tasks, with an additional +1.0 BLEU score improvement when using monolingual data.", "description": "Our method is also complementary to back-translation (BT) (backtranslate_sennrich-etal-2016-improving). To demonstrate this, we conducted experiments on the IWSLT’14 En-De and De-En tasks with extra monolingual data extracted from the WMT’14 En-De corpus. In addition, we also compare our method against the back-translation baseline in the WMT’14 En-De task with extra monolingual data from News Crawl 2009. We use the big Transformer as the final model in all our back-translation experiments. Further details of these experiments are provided in the Appendix. However, using our data diversification strategy with such monolingual data boosts the performance further with additional +1.0 BLEU over the back-translation baselines.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 9: BLEU scores for models with and without back-translation (BT) on the IWSLT’14 English-German (En-De), German-English (De-En) and WMT’14 En-De tasks. Column |D| shows the total data used in back-translation compared to the original parallel data.', unfold the steps to solve the question given next. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nQuestion: What is the BLEU score for the IWSLT'14 De-En task using our method with back-translation?\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for various tasks and methods. For the IWSLT'14 De-En task, the BLEU score using our method with back-translation is 38.5. So, the answer is {\"answer\": \"38.5\"}" } ], "images": [ "scigen/train/782.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: BLEU scores for models with and without back-translation (BT) on the IWSLT’14 English-German (En-De), German-English (De-En) and WMT’14 En-De tasks. Column |D| shows the total data used in back-translation compared to the original parallel data." ], "reasoning": "The table shows the BLEU scores for various tasks and methods. For the IWSLT'14 De-En task, the BLEU score using our method with back-translation is 38.5.", "question": "What is the BLEU score for the IWSLT'14 De-En task using our method with back-translation?", "answer": "So, the answer is {\"answer\": \"38.5\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nOur method achieves a BLEU score of 31.8 in the WMT'14 En-De task with back-translation.\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for various tasks and methods. For the WMT'14 En-De task with back-translation, our method achieves a BLEU score of 31.8. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/782.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: BLEU scores for models with and without back-translation (BT) on the IWSLT’14 English-German (En-De), German-English (De-En) and WMT’14 En-De tasks. Column |D| shows the total data used in back-translation compared to the original parallel data." ], "reasoning": "The table shows the BLEU scores for various tasks and methods. For the WMT'14 En-De task with back-translation, our method achieves a BLEU score of 31.8.", "statement": "Our method achieves a BLEU score of 31.8 in the WMT'14 En-De task with back-translation.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 10: WMT’14 English-German (En-De) and English-French (En-Fr) diversity performances in BLEU and Pairwise-BLEU scores. Lower Pairwise-BLEU means more diversity, higher BLEU means better quality.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our method shows less diversity but higher quality translations compared to the mixture of experts method in both En-De and En-Fr experiments.By just training multiple models with different seeds, their translations from the training set yield only 14% and 22% duplicates for En-De and En-Fr. These results may be surprising as we might expect more duplicates. To evaluate the diversity the teacher models used in data diversification, we compare them with the BLEU/Pairwise-BLEU benchmark proposed by mixture_model_nmt_shen2019. Specifically, we use our forward models trained on WMT’14 English-German and English-French and measure the BLEU and Pairwise-BLEU scores in the provided testset. As it can be seen in En-De experiments, our method is less diverse than the mixture of experts method (hMup) with 57.1 versus 50.2 Pairwise-BLEU. However, translations of our method is of better quality (69.5 BLEU), which is very close to human performance. The same conclusion can be derived from the WMT’14 English-French experiments." } ], "images": [ "scigen/train/783.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: WMT’14 English-German (En-De) and English-French (En-Fr) diversity performances in BLEU and Pairwise-BLEU scores. Lower Pairwise-BLEU means more diversity, higher BLEU means better quality." ], "reasoning": "Our method shows less diversity but higher quality translations compared to the mixture of experts method in both En-De and En-Fr experiments.", "description": "By just training multiple models with different seeds, their translations from the training set yield only 14% and 22% duplicates for En-De and En-Fr. These results may be surprising as we might expect more duplicates. To evaluate the diversity the teacher models used in data diversification, we compare them with the BLEU/Pairwise-BLEU benchmark proposed by mixture_model_nmt_shen2019. Specifically, we use our forward models trained on WMT’14 English-German and English-French and measure the BLEU and Pairwise-BLEU scores in the provided testset. As it can be seen in En-De experiments, our method is less diverse than the mixture of experts method (hMup) with 57.1 versus 50.2 Pairwise-BLEU. However, translations of our method is of better quality (69.5 BLEU), which is very close to human performance. The same conclusion can be derived from the WMT’14 English-French experiments.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 10: WMT’14 English-German (En-De) and English-French (En-Fr) diversity performances in BLEU and Pairwise-BLEU scores. Lower Pairwise-BLEU means more diversity, higher BLEU means better quality.', solve this question and give detail solutions. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhich method has the highest BLEU score for English-French translations?\n" }, { "role": "assistant", "content": "By comparing the BLEU En-Fr scores in the table, the highest score is 79.8, which corresponds to the Beam method. So, the answer is {\"answer\": \"Beam\"}" } ], "images": [ "scigen/train/783.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: WMT’14 English-German (En-De) and English-French (En-Fr) diversity performances in BLEU and Pairwise-BLEU scores. Lower Pairwise-BLEU means more diversity, higher BLEU means better quality." ], "reasoning": "By comparing the BLEU En-Fr scores in the table, the highest score is 79.8, which corresponds to the Beam method.", "question": "Which method has the highest BLEU score for English-French translations?", "answer": "So, the answer is {\"answer\": \"Beam\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Pairwise-BLEU score for En-De using the Beam method is higher than that of the hMup method.\n" }, { "role": "assistant", "content": "The table shows that the Pairwise-BLEU score for En-De using the Beam method is 73.0, which is higher than the 50.2 score for the hMup method. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/783.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: WMT’14 English-German (En-De) and English-French (En-Fr) diversity performances in BLEU and Pairwise-BLEU scores. Lower Pairwise-BLEU means more diversity, higher BLEU means better quality." ], "reasoning": "The table shows that the Pairwise-BLEU score for En-De using the Beam method is 73.0, which is higher than the 50.2 score for the hMup method.", "statement": "The Pairwise-BLEU score for En-De using the Beam method is higher than that of the hMup method.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 11: Improvements of data diversification under conditions with- and without- dropout in the IWSLT’14 English-German and German-English.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Experiments showed that using dropout did not significantly enhance the performance gains of the data diversification method over the baseline in IWSLT'14 English-German and German-English tasks.First, given that parameter initialization affects diversity, it is logical to assume that dropout will magnify the diversification effects. However, our empirical results did not support this. We ran experiments to test whether non-zero dropout magnify the improvements of our method over the baseline. We trained the single-model baseline and our data diversification’s final model in cases of dropout=0.3 and dropout=0, in the IWSLT’14 English-German and German-English. We used factor k=1 in these experiments. However, the gains made by our data diversification with dropout are not particularly higher than the non-dropout counterpart. This suggests that dropout may not contribute to the diversity of the synthetic data." } ], "images": [ "scigen/train/784.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Improvements of data diversification under conditions with- and without- dropout in the IWSLT’14 English-German and German-English." ], "reasoning": "Experiments showed that using dropout did not significantly enhance the performance gains of the data diversification method over the baseline in IWSLT'14 English-German and German-English tasks.", "description": "First, given that parameter initialization affects diversity, it is logical to assume that dropout will magnify the diversification effects. However, our empirical results did not support this. We ran experiments to test whether non-zero dropout magnify the improvements of our method over the baseline. We trained the single-model baseline and our data diversification’s final model in cases of dropout=0.3 and dropout=0, in the IWSLT’14 English-German and German-English. We used factor k=1 in these experiments. However, the gains made by our data diversification with dropout are not particularly higher than the non-dropout counterpart. This suggests that dropout may not contribute to the diversity of the synthetic data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 11: Improvements of data diversification under conditions with- and without- dropout in the IWSLT’14 English-German and German-English.\nQuestion: What was the gain in performance for the En-De task with dropout=0.3 using the data diversification method compared to the baseline?\nGive a detailed response to the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the performance scores for the En-De task with dropout=0.3 for both the baseline and the data diversification method. The gain is calculated as the difference between the two scores. So, the answer is {\"answer\": \"+1.5 (5%)\"}." } ], "images": [ "scigen/train/784.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Improvements of data diversification under conditions with- and without- dropout in the IWSLT’14 English-German and German-English." ], "reasoning": "The table shows the performance scores for the En-De task with dropout=0.3 for both the baseline and the data diversification method. The gain is calculated as the difference between the two scores.", "question": "What was the gain in performance for the En-De task with dropout=0.3 using the data diversification method compared to the baseline?", "answer": "So, the answer is {\"answer\": \"+1.5 (5%)\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe data diversification method showed a higher gain in performance for the De-En task with dropout=0 compared to dropout=0.3.\n" }, { "role": "assistant", "content": "The table shows the gains for the De-En task with dropout=0 and dropout=0.3. Both gains are +1.8 (5%), indicating no higher gain for dropout=0. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/784.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Improvements of data diversification under conditions with- and without- dropout in the IWSLT’14 English-German and German-English." ], "reasoning": "The table shows the gains for the De-En task with dropout=0 and dropout=0.3. Both gains are +1.8 (5%), indicating no higher gain for dropout=0.", "statement": "The data diversification method showed a higher gain in performance for the De-En task with dropout=0 compared to dropout=0.3.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 12: Improvements of data diversification under conditions maximum likelihood and beam search in the IWSLT’14 English-German and German-English.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Beam search with a beam size of 5 generates more diverse synthetic translations and improves performance compared to greedy decoding with a beam size of 1.We hypothesized that beam search will generate more diverse synthetic translations of the original dataset, thus increases the diversity and improves generalization. We tested this hypothesis by using greedy decoding (beam size=1) to generate the synthetic data and compare its performance against beam search (beam size=5) counterparts. We also used the IWSLT’14 English-German and German-English as a testbed. Note that for testing with the final model, we used the same beam search (beam size5) procedure for both cases." } ], "images": [ "scigen/train/785.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 12: Improvements of data diversification under conditions maximum likelihood and beam search in the IWSLT’14 English-German and German-English." ], "reasoning": "Beam search with a beam size of 5 generates more diverse synthetic translations and improves performance compared to greedy decoding with a beam size of 1.", "description": "We hypothesized that beam search will generate more diverse synthetic translations of the original dataset, thus increases the diversity and improves generalization. We tested this hypothesis by using greedy decoding (beam size=1) to generate the synthetic data and compare its performance against beam search (beam size=5) counterparts. We also used the IWSLT’14 English-German and German-English as a testbed. Note that for testing with the final model, we used the same beam search (beam size5) procedure for both cases.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: 'Table 12: Improvements of data diversification under conditions maximum likelihood and beam search in the IWSLT’14 English-German and German-English.'\nQuestion: What is the performance improvement from the baseline to using beam search with a beam size of 5 for the De-En task?\n" }, { "role": "assistant", "content": "The baseline performance for the De-En task is 34.7, and the performance with beam search (beam size=5) is 36.8. The improvement is calculated as 36.8 - 34.7. So, the answer is {\"answer\": \"2.1\"}." } ], "images": [ "scigen/train/785.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 12: Improvements of data diversification under conditions maximum likelihood and beam search in the IWSLT’14 English-German and German-English." ], "reasoning": "The baseline performance for the De-En task is 34.7, and the performance with beam search (beam size=5) is 36.8. The improvement is calculated as 36.8 - 34.7.", "question": "What is the performance improvement from the baseline to using beam search with a beam size of 5 for the De-En task?", "answer": "So, the answer is {\"answer\": \"2.1\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nUsing beam search with a beam size of 5 improves performance more than using greedy decoding with a beam size of 1 for the En-De task.\n" }, { "role": "assistant", "content": "For the En-De task, the performance with greedy decoding (beam size=1) is 30.3, and the performance with beam search (beam size=5) is 30.4. The improvement from greedy decoding to beam search is 30.4 - 30.3, which is 0.1. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/785.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 12: Improvements of data diversification under conditions maximum likelihood and beam search in the IWSLT’14 English-German and German-English." ], "reasoning": "For the En-De task, the performance with greedy decoding (beam size=1) is 30.3, and the performance with beam search (beam size=5) is 30.4. The improvement from greedy decoding to beam search is 30.4 - 30.3, which is 0.1.", "statement": "Using beam search with a beam size of 5 improves performance more than using greedy decoding with a beam size of 1 for the En-De task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Accuracy of LAMBADA with or without label vs. unlabeled data for ATIS dataset with 5 samples per class. Significant improvement for BERT and SVM classifiers (*McNemar, p−value<0.01).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "LAMBADA achieves better accuracy than a simple weak labeling approach using unlabeled data for most classifiers.Our augmentation framework does not require additional unlabeled data. As such, it can be applied when unlabeled data is unavailable or costly. To test the expected LAMBADA performance in such a scenario, we compared it to a semi-supervised approach [ruder2018strong] that uses unlabeled data. To create an unlabeled dataset, we randomly selected samples from the original dataset while ignoring their labels. Next, following a simple weak labeling approach, we classified the samples with one of the classifiers after training it on the labeled dataset. We compared LAMBADA’s classification results with the results we obtained from this classifier. Surprisingly, for most classifiers, LAMBADA achieves better accuracy compared to a simple weak labeling approach. Clearly, the generated dataset contributes more to improving the accuracy of the classifier than the unlabeled samples taken from the original dataset. We further assessed the importance of the ”generated” labels by removing them from LAMBADA’s synthesized dataset. In future work, we plan to use various data balancing approaches on the unlabeled dataset to assess the importance of the second factor above." } ], "images": [ "scigen/train/786.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Accuracy of LAMBADA with or without label vs. unlabeled data for ATIS dataset with 5 samples per class. Significant improvement for BERT and SVM classifiers (*McNemar, p−value<0.01)." ], "reasoning": "LAMBADA achieves better accuracy than a simple weak labeling approach using unlabeled data for most classifiers.", "description": "Our augmentation framework does not require additional unlabeled data. As such, it can be applied when unlabeled data is unavailable or costly. To test the expected LAMBADA performance in such a scenario, we compared it to a semi-supervised approach [ruder2018strong] that uses unlabeled data. To create an unlabeled dataset, we randomly selected samples from the original dataset while ignoring their labels. Next, following a simple weak labeling approach, we classified the samples with one of the classifiers after training it on the labeled dataset. We compared LAMBADA’s classification results with the results we obtained from this classifier. Surprisingly, for most classifiers, LAMBADA achieves better accuracy compared to a simple weak labeling approach. Clearly, the generated dataset contributes more to improving the accuracy of the classifier than the unlabeled samples taken from the original dataset. We further assessed the importance of the ”generated” labels by removing them from LAMBADA’s synthesized dataset. In future work, we plan to use various data balancing approaches on the unlabeled dataset to assess the importance of the second factor above.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 6: Accuracy of LAMBADA with or without label vs. unlabeled data for ATIS dataset with 5 samples per class. Significant improvement for BERT and SVM classifiers (*McNemar, p−value<0.01).', solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich classifier showed the highest improvement in accuracy using LAMBADA compared to its base accuracy?\n" }, { "role": "assistant", "content": "To find the classifier with the highest improvement, we need to compare the base accuracy and LAMBADA accuracy for each classifier. BERT has a base accuracy of 53.3 and LAMBADA accuracy of 75.7, SVM has a base accuracy of 35.6 and LAMBADA accuracy of 56.5, and LSTM has a base accuracy of 29.0 and LAMBADA accuracy of 33.7. The improvement for BERT is 75.7 - 53.3 = 22.4, for SVM is 56.5 - 35.6 = 20.9, and for LSTM is 33.7 - 29.0 = 4.7. Therefore, BERT showed the highest improvement. So, the answer is {\"answer\": \"BERT\"}" } ], "images": [ "scigen/train/786.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Accuracy of LAMBADA with or without label vs. unlabeled data for ATIS dataset with 5 samples per class. Significant improvement for BERT and SVM classifiers (*McNemar, p−value<0.01)." ], "reasoning": "To find the classifier with the highest improvement, we need to compare the base accuracy and LAMBADA accuracy for each classifier. BERT has a base accuracy of 53.3 and LAMBADA accuracy of 75.7, SVM has a base accuracy of 35.6 and LAMBADA accuracy of 56.5, and LSTM has a base accuracy of 29.0 and LAMBADA accuracy of 33.7. The improvement for BERT is 75.7 - 53.3 = 22.4, for SVM is 56.5 - 35.6 = 20.9, and for LSTM is 33.7 - 29.0 = 4.7. Therefore, BERT showed the highest improvement.", "question": "Which classifier showed the highest improvement in accuracy using LAMBADA compared to its base accuracy?", "answer": "So, the answer is {\"answer\": \"BERT\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: LSTM showed the highest accuracy using unlabeled data., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nLSTM showed the highest accuracy using unlabeled data.\n" }, { "role": "assistant", "content": "The table shows the accuracy of LSTM using unlabeled data is 40.1, which is higher than its base accuracy of 29.0 but not the highest among all classifiers. BERT has a higher accuracy of 54.5 using unlabeled data. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/786.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Accuracy of LAMBADA with or without label vs. unlabeled data for ATIS dataset with 5 samples per class. Significant improvement for BERT and SVM classifiers (*McNemar, p−value<0.01)." ], "reasoning": "The table shows the accuracy of LSTM using unlabeled data is 40.1, which is higher than its base accuracy of 29.0 but not the highest among all classifiers. BERT has a higher accuracy of 54.5 using unlabeled data.", "statement": "LSTM showed the highest accuracy using unlabeled data.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5: Accuracy of LAMBADA vs. other generative approaches over all datasets and classifiers. LAMBADA is statistically (* McNemar, p−value<0.01) superior to all models on each classifier and each dataset (on par to EDA with SVM on TREC).', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "In the TREC dataset, BERT outperforms all other methods, while our method is comparable to EDA when using the SVM classifier.S5SSx4SSSx3 Comparison of Generative Models We compared our approach to other leading text generator approaches. In the TREC dataset, the results for BERT are significantly better than all other methods. On the TREC dataset with SVM classifier, our method is on par with EDA." } ], "images": [ "scigen/train/787.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Accuracy of LAMBADA vs. other generative approaches over all datasets and classifiers. LAMBADA is statistically (* McNemar, p−value<0.01) superior to all models on each classifier and each dataset (on par to EDA with SVM on TREC)." ], "reasoning": "In the TREC dataset, BERT outperforms all other methods, while our method is comparable to EDA when using the SVM classifier.", "description": "S5SSx4SSSx3 Comparison of Generative Models We compared our approach to other leading text generator approaches. In the TREC dataset, the results for BERT are significantly better than all other methods. On the TREC dataset with SVM classifier, our method is on par with EDA.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 5: Accuracy of LAMBADA vs. other generative approaches over all datasets and classifiers. LAMBADA is statistically (* McNemar, p−value<0.01) superior to all models on each classifier and each dataset (on par to EDA with SVM on TREC).\nQuestion: Which method has the highest score on the TREC dataset using the BERT model?\n" }, { "role": "assistant", "content": "To find the highest score on the TREC dataset using the BERT model, look at the BERT column for the TREC dataset. The highest score is 64.3 by LAMBADA. So, the answer is {\"answer\": \"LAMBADA\"}." } ], "images": [ "scigen/train/787.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Accuracy of LAMBADA vs. other generative approaches over all datasets and classifiers. LAMBADA is statistically (* McNemar, p−value<0.01) superior to all models on each classifier and each dataset (on par to EDA with SVM on TREC)." ], "reasoning": "To find the highest score on the TREC dataset using the BERT model, look at the BERT column for the TREC dataset. The highest score is 64.3 by LAMBADA.", "question": "Which method has the highest score on the TREC dataset using the BERT model?", "answer": "So, the answer is {\"answer\": \"LAMBADA\"}." }, { "messages": [ { "role": "user", "content": "Our method is on par with EDA on the TREC dataset using the SVM classifier. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nOur method is on par with EDA on the TREC dataset using the SVM classifier.\n" }, { "role": "assistant", "content": "The table shows that on the TREC dataset using the SVM classifier, our method (LAMBADA) has a score of 43.9, while EDA has a score of 44.8. These scores are very close, supporting the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/787.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Accuracy of LAMBADA vs. other generative approaches over all datasets and classifiers. LAMBADA is statistically (* McNemar, p−value<0.01) superior to all models on each classifier and each dataset (on par to EDA with SVM on TREC)." ], "reasoning": "The table shows that on the TREC dataset using the SVM classifier, our method (LAMBADA) has a score of 43.9, while EDA has a score of 44.8. These scores are very close, supporting the statement.", "statement": "Our method is on par with EDA on the TREC dataset using the SVM classifier.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2. Hyper-parameters which were used in experiments for News20, BBC & BBC-Sports datasets', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The experiments were conducted on a Dell Precision Tower 7910 server with a Quadro M5000 GPU, using 10-fold cross-validation and a learning rate that decreases every 10 epochs.All experiments were carried on a Dell Precision Tower 7910 server with Quadro M5000 GPU with 8 GB of memory. The word embeddings were randomly initialized and trained along with the model. For testing purposes the algorithm was tested using 10-fold cross-validation scheme. The learning rate is regulated over the training epochs, it is decreased to 0.3 times its previous value after every 10 epochs." } ], "images": [ "scigen/train/788.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2. Hyper-parameters which were used in experiments for News20, BBC & BBC-Sports datasets" ], "reasoning": "The experiments were conducted on a Dell Precision Tower 7910 server with a Quadro M5000 GPU, using 10-fold cross-validation and a learning rate that decreases every 10 epochs.", "description": "All experiments were carried on a Dell Precision Tower 7910 server with Quadro M5000 GPU with 8 GB of memory. The word embeddings were randomly initialized and trained along with the model. For testing purposes the algorithm was tested using 10-fold cross-validation scheme. The learning rate is regulated over the training epochs, it is decreased to 0.3 times its previous value after every 10 epochs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 2. Hyper-parameters which were used in experiments for News20, BBC & BBC-Sports datasets\nQuestion: What is the batch size used for the BBC dataset?\n" }, { "role": "assistant", "content": "The table lists the batch sizes for different datasets under the 'Batch size' row. The batch size for the BBC dataset is found in the column labeled 'BBC'. So, the answer is {\"answer\": \"32\"}" } ], "images": [ "scigen/train/788.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2. Hyper-parameters which were used in experiments for News20, BBC & BBC-Sports datasets" ], "reasoning": "The table lists the batch sizes for different datasets under the 'Batch size' row. The batch size for the BBC dataset is found in the column labeled 'BBC'.", "question": "What is the batch size used for the BBC dataset?", "answer": "So, the answer is {\"answer\": \"32\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe learning rate for the News20 dataset is 0.01.\n" }, { "role": "assistant", "content": "The table lists the learning rates for different datasets under the 'Learning rate' row. The learning rate for the News20 dataset is 0.01. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/788.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2. Hyper-parameters which were used in experiments for News20, BBC & BBC-Sports datasets" ], "reasoning": "The table lists the learning rates for different datasets under the 'Learning rate' row. The learning rate for the News20 dataset is 0.01.", "statement": "The learning rate for the News20 dataset is 0.01.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1. Dataset Specifications'.\n" }, { "role": "assistant", "content": "The datasets chosen for the experiments include News20, BBC, and BBC Sports, which are divided into 20 and 5 classes respectively, with varying training and test sizes.For our experiments, we have chosen three popular publicly-available news classification datasets. The dataset is partitioned almost evenly across 20 different classes: comp.graphics, comp.os.ms-windows.misc, comp.sys.ibm.pc.hardware, comp.sys.mac. hardware, comp. windows.x, rec.autos, rec.motorcycles, rec.sport. baseball, rec.sport. hockey, sci.crypt, sci.electronics, sci.med, sci.space, misc.forsale, talk.politics.misc, talk.politics.guns, talk. politics.mideast, talk. religion.misc, alt.atheism and soc. religion.christian. The dataset is divided into 5 major classes: athletics, cricket, football, rugby and tennis. The datasets are chosen in such a way that all of them share common domain knowledge and have small number of training examples so that the improvement observed using instance-infusion is significant." } ], "images": [ "scigen/train/789.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1. Dataset Specifications" ], "reasoning": "The datasets chosen for the experiments include News20, BBC, and BBC Sports, which are divided into 20 and 5 classes respectively, with varying training and test sizes.", "description": "For our experiments, we have chosen three popular publicly-available news classification datasets. The dataset is partitioned almost evenly across 20 different classes: comp.graphics, comp.os.ms-windows.misc, comp.sys.ibm.pc.hardware, comp.sys.mac. hardware, comp. windows.x, rec.autos, rec.motorcycles, rec.sport. baseball, rec.sport. hockey, sci.crypt, sci.electronics, sci.med, sci.space, misc.forsale, talk.politics.misc, talk.politics.guns, talk. politics.mideast, talk. religion.misc, alt.atheism and soc. religion.christian. The dataset is divided into 5 major classes: athletics, cricket, football, rugby and tennis. The datasets are chosen in such a way that all of them share common domain knowledge and have small number of training examples so that the improvement observed using instance-infusion is significant.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 1. Dataset Specifications', answer the following question. Give detailed solution consisting of each step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the total number of training examples across all datasets?\n" }, { "role": "assistant", "content": "To find the total number of training examples, sum the training sizes of News20, BBC, and BBC Sports: 18000 + 2000 + 660. So, the answer is {\"answer\": \"20660\"}." } ], "images": [ "scigen/train/789.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1. Dataset Specifications" ], "reasoning": "To find the total number of training examples, sum the training sizes of News20, BBC, and BBC Sports: 18000 + 2000 + 660.", "question": "What is the total number of training examples across all datasets?", "answer": "So, the answer is {\"answer\": \"20660\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe BBC dataset has more classes than the BBC Sports dataset.\n" }, { "role": "assistant", "content": "The table shows that both the BBC and BBC Sports datasets have 5 classes each. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/789.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1. Dataset Specifications" ], "reasoning": "The table shows that both the BBC and BBC Sports datasets have 5 classes each.", "statement": "The BBC dataset has more classes than the BBC Sports dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3. Classification accuracies and F1-Scores for news arcticle classifications for different source and target domains. The first row corresponds to the baseline performance trained on only the target dataset. The following two rows shows the performance of instance-infusion method with and without the usage of penalty function. In all the three cases, our approach outperforms the baselines by a significant margin.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The instance-infused Bi-LSTM with penalty function shows the highest improvement in accuracy and F1-score across all datasets compared to the baseline Bi-LSTM model.The source and target datasets are chosen in a manner such that the source dataset is able to provide relevant information. 20Newsgroups contains news articles from all categories, so a good choice for source dataset is BBC which also encompasses articles from similar broad categories. For the same reason BBC also has 20Newsgroups as its source dataset. BBC Sports focuses on sports articles, BBC is chosen as the source dataset as the news articles share a common domain (articles come from same news media BBC). For the proper functioning of the model, the final layer of the instance-infused model is replaced while the rest of the network is inherited from the pre-trained target only model. For 20Newsgroups the improvement over baseline model is 12%, BBC and BBC Sports datasets show an improvement of around 5%. As mentioned earlier, our approach is independent of the sentence encoder being used. Instead of bi-LSTM any other model can be used. As the proposed approach is independent of the source encoding procedure and the source instance embeddings are kept constant during the training procedure, we can incorporate source instances from multiple datasets simultaneously. In the subsequent experimental setups, we try varying setups to prove the robustness and efficacy of our proposed model." } ], "images": [ "scigen/train/790.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. Classification accuracies and F1-Scores for news arcticle classifications for different source and target domains. The first row corresponds to the baseline performance trained on only the target dataset. The following two rows shows the performance of instance-infusion method with and without the usage of penalty function. In all the three cases, our approach outperforms the baselines by a significant margin." ], "reasoning": "The instance-infused Bi-LSTM with penalty function shows the highest improvement in accuracy and F1-score across all datasets compared to the baseline Bi-LSTM model.", "description": "The source and target datasets are chosen in a manner such that the source dataset is able to provide relevant information. 20Newsgroups contains news articles from all categories, so a good choice for source dataset is BBC which also encompasses articles from similar broad categories. For the same reason BBC also has 20Newsgroups as its source dataset. BBC Sports focuses on sports articles, BBC is chosen as the source dataset as the news articles share a common domain (articles come from same news media BBC). For the proper functioning of the model, the final layer of the instance-infused model is replaced while the rest of the network is inherited from the pre-trained target only model. For 20Newsgroups the improvement over baseline model is 12%, BBC and BBC Sports datasets show an improvement of around 5%. As mentioned earlier, our approach is independent of the sentence encoder being used. Instead of bi-LSTM any other model can be used. As the proposed approach is independent of the source encoding procedure and the source instance embeddings are kept constant during the training procedure, we can incorporate source instances from multiple datasets simultaneously. In the subsequent experimental setups, we try varying setups to prove the robustness and efficacy of our proposed model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3. Classification accuracies and F1-Scores for news arcticle classifications for different source and target domains. The first row corresponds to the baseline performance trained on only the target dataset. The following two rows shows the performance of instance-infusion method with and without the usage of penalty function. In all the three cases, our approach outperforms the baselines by a significant margin.', unfold the question presented below, detailing every step of your calculation. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the accuracy improvement of the Instance-Infused Bi-LSTM with penalty function over the Bi-LSTM (Target Only) for the BBC News20 dataset?\n" }, { "role": "assistant", "content": "The accuracy for the Bi-LSTM (Target Only) on the BBC News20 dataset is 91.33, while the accuracy for the Instance-Infused Bi-LSTM with penalty function is 96.09. The improvement is calculated as 96.09 - 91.33. So, the answer is {\"answer\": \"4.76\"}" } ], "images": [ "scigen/train/790.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. Classification accuracies and F1-Scores for news arcticle classifications for different source and target domains. The first row corresponds to the baseline performance trained on only the target dataset. The following two rows shows the performance of instance-infusion method with and without the usage of penalty function. In all the three cases, our approach outperforms the baselines by a significant margin." ], "reasoning": "The accuracy for the Bi-LSTM (Target Only) on the BBC News20 dataset is 91.33, while the accuracy for the Instance-Infused Bi-LSTM with penalty function is 96.09. The improvement is calculated as 96.09 - 91.33.", "question": "What is the accuracy improvement of the Instance-Infused Bi-LSTM with penalty function over the Bi-LSTM (Target Only) for the BBC News20 dataset?", "answer": "So, the answer is {\"answer\": \"4.76\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Instance-Infused Bi-LSTM with penalty function has the highest F1-score for the BBC Sports BBC dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Instance-Infused Bi-LSTM with penalty function has the highest F1-score for the BBC Sports BBC dataset.\n" }, { "role": "assistant", "content": "The F1-score for the Instance-Infused Bi-LSTM with penalty function on the BBC Sports BBC dataset is 0.9100, which is higher than the F1-scores for the other methods on the same dataset. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/790.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. Classification accuracies and F1-Scores for news arcticle classifications for different source and target domains. The first row corresponds to the baseline performance trained on only the target dataset. The following two rows shows the performance of instance-infusion method with and without the usage of penalty function. In all the three cases, our approach outperforms the baselines by a significant margin." ], "reasoning": "The F1-score for the Instance-Infused Bi-LSTM with penalty function on the BBC Sports BBC dataset is 0.9100, which is higher than the F1-scores for the other methods on the same dataset.", "statement": "The Instance-Infused Bi-LSTM with penalty function has the highest F1-score for the BBC Sports BBC dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 5. Test Accuracy for proposed model using instances from multiple source datasets with 50% target dataset'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Using only 0.5 fraction of the target dataset, the infusion of multiple source datasets improves the performance metrics.Target Dataset Reduction with Multiple Source. In this section, we design an experimental setup in which only 0.5 fraction of the target dataset is utilized and study the influence of multiple source dataset infusion. The results improve as and when more source datasets are used in the infusion process. This can be effectively leveraged for improving the performance of very lean datasets, by heavily deploying large datasets as source. In multiple source experiment setup, for a given target dataset the other two datasets are used as source." } ], "images": [ "scigen/train/791.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5. Test Accuracy for proposed model using instances from multiple source datasets with 50% target dataset" ], "reasoning": "Using only 0.5 fraction of the target dataset, the infusion of multiple source datasets improves the performance metrics.", "description": "Target Dataset Reduction with Multiple Source. In this section, we design an experimental setup in which only 0.5 fraction of the target dataset is utilized and study the influence of multiple source dataset infusion. The results improve as and when more source datasets are used in the infusion process. This can be effectively leveraged for improving the performance of very lean datasets, by heavily deploying large datasets as source. In multiple source experiment setup, for a given target dataset the other two datasets are used as source.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 5. Test Accuracy for proposed model using instances from multiple source datasets with 50% target dataset', and sequentially solve the question, writing out each step of your reasoning process. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the increase in accuracy for the News20 dataset when using multiple sources compared to a single source?\n" }, { "role": "assistant", "content": "The accuracy for the News20 dataset with a single source is 61.72, and with multiple sources, it is 67.32. The increase in accuracy is 67.32 - 61.72. So, the answer is {\"answer\": \"5.6\"}" } ], "images": [ "scigen/train/791.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5. Test Accuracy for proposed model using instances from multiple source datasets with 50% target dataset" ], "reasoning": "The accuracy for the News20 dataset with a single source is 61.72, and with multiple sources, it is 67.32. The increase in accuracy is 67.32 - 61.72.", "question": "What is the increase in accuracy for the News20 dataset when using multiple sources compared to a single source?", "answer": "So, the answer is {\"answer\": \"5.6\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The F1-Score for the BBC dataset improves when multiple sources are used., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe F1-Score for the BBC dataset improves when multiple sources are used.\n" }, { "role": "assistant", "content": "The F1-Score for the BBC dataset with a single source is 0.9108, and with multiple sources, it is 0.9120. The score improves with multiple sources. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/791.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5. Test Accuracy for proposed model using instances from multiple source datasets with 50% target dataset" ], "reasoning": "The F1-Score for the BBC dataset with a single source is 0.9108, and with multiple sources, it is 0.9120. The score improves with multiple sources.", "statement": "The F1-Score for the BBC dataset improves when multiple sources are used.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 6. Comparison of results using other learning schemes on News20, BBC and BBC Sports datasets. The proposed model using a deep learning model as a baseline achieves competitive performance for all the three datasets.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Instance infusion improves the performance of the Bi-LSTM model, making it competitive with conventional models across all datasets and outperforming them on the BBC dataset.Comparative Study. Literature involving these datasets mainly focus on non-deep learning based approaches. Thereby, we compare our results with some popular conventional learning techniques. For the k-NN-ngram experiments, the number of nearest neighbours k was set to 5. For the mentioned datasets, conventional models outperform our baseline Bi-LSTM model, however upon instance infusion the deep learning based model is able to achieve competitive performance across all datasets. Moreover by instance infusion the simple bi-LSTM model approaches the classical models in performance on News20 and BBC Sports dataset, whereas on BBC Dataset the proposed instance infused bi-LSTM model beats all the mentioned models." } ], "images": [ "scigen/train/792.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6. Comparison of results using other learning schemes on News20, BBC and BBC Sports datasets. The proposed model using a deep learning model as a baseline achieves competitive performance for all the three datasets." ], "reasoning": "Instance infusion improves the performance of the Bi-LSTM model, making it competitive with conventional models across all datasets and outperforming them on the BBC dataset.", "description": "Comparative Study. Literature involving these datasets mainly focus on non-deep learning based approaches. Thereby, we compare our results with some popular conventional learning techniques. For the k-NN-ngram experiments, the number of nearest neighbours k was set to 5. For the mentioned datasets, conventional models outperform our baseline Bi-LSTM model, however upon instance infusion the deep learning based model is able to achieve competitive performance across all datasets. Moreover by instance infusion the simple bi-LSTM model approaches the classical models in performance on News20 and BBC Sports dataset, whereas on BBC Dataset the proposed instance infused bi-LSTM model beats all the mentioned models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nTable title: Table 6. Comparison of results using other learning schemes on News20, BBC and BBC Sports datasets. The proposed model using a deep learning model as a baseline achieves competitive performance for all the three datasets.\n\nQuestion: Which model achieved the highest F1-Score on the BBC Sports dataset?\n" }, { "role": "assistant", "content": "By examining the table, the Random Forests-tf-idf model has the highest F1-Score on the BBC Sports dataset with a score of 0.9660. So, the answer is {\"answer\": \"Random Forests-tf-idf\"}." } ], "images": [ "scigen/train/792.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6. Comparison of results using other learning schemes on News20, BBC and BBC Sports datasets. The proposed model using a deep learning model as a baseline achieves competitive performance for all the three datasets." ], "reasoning": "By examining the table, the Random Forests-tf-idf model has the highest F1-Score on the BBC Sports dataset with a score of 0.9660.", "question": "Which model achieved the highest F1-Score on the BBC Sports dataset?", "answer": "So, the answer is {\"answer\": \"Random Forests-tf-idf\"}." }, { "messages": [ { "role": "user", "content": "The Bi-LSTM model outperforms all other models on the BBC dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Bi-LSTM model outperforms all other models on the BBC dataset.\n" }, { "role": "assistant", "content": "The table shows that the Instance-Infused Bi-LSTM model outperforms all other models on the BBC dataset, not the simple Bi-LSTM model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/792.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6. Comparison of results using other learning schemes on News20, BBC and BBC Sports datasets. The proposed model using a deep learning model as a baseline achieves competitive performance for all the three datasets." ], "reasoning": "The table shows that the Instance-Infused Bi-LSTM model outperforms all other models on the BBC dataset, not the simple Bi-LSTM model.", "statement": "The Bi-LSTM model outperforms all other models on the BBC dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: Hyper-parameter settings'.\n" }, { "role": "assistant", "content": "Both encoder and decoder use GloVe, POS tag embeddings, and CharCNN, with dropout applied to each module's outputs.Both encoder and decoder embedding layers have GloVe and POS tag embeddings as well as CharCNN, but their parameters are not tied. We apply dropout (dropout_rate = 0.33) to the outputs of each module." } ], "images": [ "scigen/train/793.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Hyper-parameter settings" ], "reasoning": "Both encoder and decoder use GloVe, POS tag embeddings, and CharCNN, with dropout applied to each module's outputs.", "description": "Both encoder and decoder embedding layers have GloVe and POS tag embeddings as well as CharCNN, but their parameters are not tied. We apply dropout (dropout_rate = 0.33) to the outputs of each module.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 1: Hyper-parameter settings', answer the following question. Give detailed solution consisting of each step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the hidden size of the encoder and decoder?\n" }, { "role": "assistant", "content": "The table shows that the encoder has a hidden size of 512 and the decoder has a hidden size of 1024. So, the answer is {\"answer\": \"Encoder: 512, Decoder: 1024\"}." } ], "images": [ "scigen/train/793.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Hyper-parameter settings" ], "reasoning": "The table shows that the encoder has a hidden size of 512 and the decoder has a hidden size of 1024.", "question": "What is the hidden size of the encoder and decoder?", "answer": "So, the answer is {\"answer\": \"Encoder: 512, Decoder: 1024\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The encoder and decoder have the same number of layers. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe encoder and decoder have the same number of layers.\n" }, { "role": "assistant", "content": "The table indicates that both the encoder and decoder have 2 layers each. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/793.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Hyper-parameter settings" ], "reasoning": "The table indicates that both the encoder and decoder have 2 layers each.", "statement": "The encoder and decoder have the same number of layers.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Fine-grained F1 scores on the AMR 2.0 test set. vN’17 is van Noord and Bos (2017b); L’18 is Lyu and Titov (2018); N’19 is Naseem et al. (2019).'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows significant improvements in various metrics, particularly in reentrancies, wikification, and negation, due to the use of target-side copy, DBpedia Spotlight API, and negation detection rules., we assess the quality of each subtask using the AMR-evaluation tools Damonte et al. We see a notable increase on reentrancies, which we attribute to target-side copy (based on our ablation studies in the next section). Significant increases are also shown on wikification and negation, indicating the benefits of using DBpedia Spotlight API and negation detection rules in post-processing." } ], "images": [ "scigen/train/794.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Fine-grained F1 scores on the AMR 2.0 test set. vN’17 is van Noord and Bos (2017b); L’18 is Lyu and Titov (2018); N’19 is Naseem et al. (2019)." ], "reasoning": "The table shows significant improvements in various metrics, particularly in reentrancies, wikification, and negation, due to the use of target-side copy, DBpedia Spotlight API, and negation detection rules.", "description": ", we assess the quality of each subtask using the AMR-evaluation tools Damonte et al. We see a notable increase on reentrancies, which we attribute to target-side copy (based on our ablation studies in the next section). Significant increases are also shown on wikification and negation, indicating the benefits of using DBpedia Spotlight API and negation detection rules in post-processing.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: Fine-grained F1 scores on the AMR 2.0 test set. vN’17 is van Noord and Bos (2017b); L’18 is Lyu and Titov (2018); N’19 is Naseem et al. (2019).'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the highest score achieved in the Wikification metric?\n" }, { "role": "assistant", "content": "By comparing the Wikification scores across all columns, the highest score is found in the 'Ours' column with a value of 85.8±0.3. So, the answer is {\"answer\": \"85.8±0.3\"}." } ], "images": [ "scigen/train/794.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Fine-grained F1 scores on the AMR 2.0 test set. vN’17 is van Noord and Bos (2017b); L’18 is Lyu and Titov (2018); N’19 is Naseem et al. (2019)." ], "reasoning": "By comparing the Wikification scores across all columns, the highest score is found in the 'Ours' column with a value of 85.8±0.3.", "question": "What is the highest score achieved in the Wikification metric?", "answer": "So, the answer is {\"answer\": \"85.8±0.3\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Ours model achieved the highest score in the Named Ent. metric. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Ours model achieved the highest score in the Named Ent. metric.\n" }, { "role": "assistant", "content": "By comparing the Named Ent. scores across all columns, the highest score is found in the 'Ours' column with a value of 77.9±0.2. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/794.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Fine-grained F1 scores on the AMR 2.0 test set. vN’17 is van Noord and Bos (2017b); L’18 is Lyu and Titov (2018); N’19 is Naseem et al. (2019)." ], "reasoning": "By comparing the Named Ent. scores across all columns, the highest score is found in the 'Ours' column with a value of 77.9±0.2.", "statement": "The Ours model achieved the highest score in the Named Ent. metric.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: Ablation studies on components of our model. (Scores are sorted by the delta from the full model.)'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Removing target-side copy significantly reduces the subtask score of reentrancies, while coverage loss helps avoid unnecessary repetitive nodes.Ablation Study Removing target-side copy also leads to a large drop. Specifically, the subtask score of reentrancies drops down to 38.4% when target-side copy is disabled. Coverage loss is useful with regard to discouraging unnecessary repetitive nodes. In addition, our model benefits from input features such as language representations from BERT, index embeddings, POS tags, anonymization indicators, and character-level features from CharCNN. Beam search, commonly used in machine translation, is also helpful in our model. We provide side-by-side examples in the Appendix to further illustrate the contribution from each component, which are largely intuitive, with the exception of BERT embeddings. There the exact contribution of the component (qualitative, before/after ablation) stands out less: future work might consider a probing analysis with manually constructed examples, in the spirit of Linzen et al. ; Conneau et al." } ], "images": [ "scigen/train/795.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Ablation studies on components of our model. (Scores are sorted by the delta from the full model.)" ], "reasoning": "Removing target-side copy significantly reduces the subtask score of reentrancies, while coverage loss helps avoid unnecessary repetitive nodes.", "description": "Ablation Study Removing target-side copy also leads to a large drop. Specifically, the subtask score of reentrancies drops down to 38.4% when target-side copy is disabled. Coverage loss is useful with regard to discouraging unnecessary repetitive nodes. In addition, our model benefits from input features such as language representations from BERT, index embeddings, POS tags, anonymization indicators, and character-level features from CharCNN. Beam search, commonly used in machine translation, is also helpful in our model. We provide side-by-side examples in the Appendix to further illustrate the contribution from each component, which are largely intuitive, with the exception of BERT embeddings. There the exact contribution of the component (qualitative, before/after ablation) stands out less: future work might consider a probing analysis with manually constructed examples, in the spirit of Linzen et al. ; Conneau et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 4: Ablation studies on components of our model. (Scores are sorted by the delta from the full model.)'\nQuestion: What is the AMR 1.0 score when BERT embeddings are removed?\n" }, { "role": "assistant", "content": "To find the AMR 1.0 score when BERT embeddings are removed, look at the row labeled 'no BERT embeddings' and find the corresponding score under the AMR 1.0 column. So, the answer is {\"answer\": \"68.8\"}" } ], "images": [ "scigen/train/795.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Ablation studies on components of our model. (Scores are sorted by the delta from the full model.)" ], "reasoning": "To find the AMR 1.0 score when BERT embeddings are removed, look at the row labeled 'no BERT embeddings' and find the corresponding score under the AMR 1.0 column.", "question": "What is the AMR 1.0 score when BERT embeddings are removed?", "answer": "So, the answer is {\"answer\": \"68.8\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence Removing target-side copy results in a higher AMR 1.0 score than removing source-side copy. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRemoving target-side copy results in a higher AMR 1.0 score than removing source-side copy.\n" }, { "role": "assistant", "content": "Compare the AMR 1.0 scores for 'no target-side copy' and 'no source-side copy'. The score for 'no target-side copy' is 66.2, which is higher than the score for 'no source-side copy' which is 62.7. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/795.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Ablation studies on components of our model. (Scores are sorted by the delta from the full model.)" ], "reasoning": "Compare the AMR 1.0 scores for 'no target-side copy' and 'no source-side copy'. The score for 'no target-side copy' is 66.2, which is higher than the score for 'no source-side copy' which is 62.7.", "statement": "Removing target-side copy results in a higher AMR 1.0 score than removing source-side copy.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5: Smatch scores of full models trained and tested based on different node linearization strategies.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The linearization strategy using Pre-order + Alphanum yields the best results compared to other strategies.Alignments are created using the tool by Pourdamghani et al. Clearly, our linearization strategy leads to much better results than the two alternates. We also tried other traversal strategies such as combining in-order traversal with alphanumerical sorting or alignment-based sorting, but did not get scores even comparable to the two alternates." } ], "images": [ "scigen/train/796.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Smatch scores of full models trained and tested based on different node linearization strategies." ], "reasoning": "The linearization strategy using Pre-order + Alphanum yields the best results compared to other strategies.", "description": "Alignments are created using the tool by Pourdamghani et al. Clearly, our linearization strategy leads to much better results than the two alternates. We also tried other traversal strategies such as combining in-order traversal with alphanumerical sorting or alignment-based sorting, but did not get scores even comparable to the two alternates.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Smatch scores of full models trained and tested based on different node linearization strategies.\nQuestion: Which node linearization strategy achieved the highest score in AMR 2.0?\nGive a detailed response to the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the scores in the AMR 2.0 column, the highest score is 76.3, which corresponds to the Pre-order + Alphanum strategy. So, the answer is {\"answer\": \"Pre-order + Alphanum\"}." } ], "images": [ "scigen/train/796.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Smatch scores of full models trained and tested based on different node linearization strategies." ], "reasoning": "By comparing the scores in the AMR 2.0 column, the highest score is 76.3, which corresponds to the Pre-order + Alphanum strategy.", "question": "Which node linearization strategy achieved the highest score in AMR 2.0?", "answer": "So, the answer is {\"answer\": \"Pre-order + Alphanum\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nPure Alignment achieved a higher score than Pre-order + Alignment in AMR 1.0.\n" }, { "role": "assistant", "content": "In the AMR 1.0 column, Pure Alignment has a score of 64.3, while Pre-order + Alignment has a score of 61.9, indicating that Pure Alignment achieved a higher score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/796.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Smatch scores of full models trained and tested based on different node linearization strategies." ], "reasoning": "In the AMR 1.0 column, Pure Alignment has a score of 64.3, while Pre-order + Alignment has a score of 61.9, indicating that Pure Alignment achieved a higher score.", "statement": "Pure Alignment achieved a higher score than Pre-order + Alignment in AMR 1.0.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: Results of the synchronic evaluation in Pearson’s r averaged over all three VAD dimensions. The best system for each seed lexicon and those with statistically non-significant differences (p ≥ 0.05) are in bold.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "SVDPPMI embeddings outperform SGNS embeddings for both full and limited seed lexicons, with the highest correlation observed with ParaSimNum using the full seed set.SGNS embeddings are worse than SVDPPMI embeddings for both full and limited seed lexicons. SVDPPMI embeddings seem to be better suited for induction based on the full seed set, leading to the highest observed correlation with ParaSimNum. However, results with other induction algorithms are not significantly different. For the limited seed set, consistent with claims by Hamilton et al. However, all results with the limited seed set are far (and significantly) worse than those with the full seed lexicon. For English, using the full seed lexicons, we achieve performance figures around r=.35. In contrast, using the limited seed lexicon we find that the performance is markedly weaker in each of our six conditions compared to using the full seed lexicon. This observation directly opposes the claims from Hamilton et al." } ], "images": [ "scigen/train/797.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of the synchronic evaluation in Pearson’s r averaged over all three VAD dimensions. The best system for each seed lexicon and those with statistically non-significant differences (p ≥ 0.05) are in bold." ], "reasoning": "SVDPPMI embeddings outperform SGNS embeddings for both full and limited seed lexicons, with the highest correlation observed with ParaSimNum using the full seed set.", "description": "SGNS embeddings are worse than SVDPPMI embeddings for both full and limited seed lexicons. SVDPPMI embeddings seem to be better suited for induction based on the full seed set, leading to the highest observed correlation with ParaSimNum. However, results with other induction algorithms are not significantly different. For the limited seed set, consistent with claims by Hamilton et al. However, all results with the limited seed set are far (and significantly) worse than those with the full seed lexicon. For English, using the full seed lexicons, we achieve performance figures around r=.35. In contrast, using the limited seed lexicon we find that the performance is markedly weaker in each of our six conditions compared to using the full seed lexicon. This observation directly opposes the claims from Hamilton et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Results of the synchronic evaluation in Pearson’s r averaged over all three VAD dimensions. The best system for each seed lexicon and those with statistically non-significant differences (p ≥ 0.05) are in bold.\nQuestion: Which induction method and seed selection combination achieved the highest correlation with SVDPPMI embeddings?\nSolve the above question based on the table information. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, the highest correlation value for SVDPPMI embeddings is 0.557, which is achieved by the ParaSimNum induction method with the full seed selection. So, the answer is {\"answer\": \"ParaSimNum with full seed selection\"}" } ], "images": [ "scigen/train/797.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of the synchronic evaluation in Pearson’s r averaged over all three VAD dimensions. The best system for each seed lexicon and those with statistically non-significant differences (p ≥ 0.05) are in bold." ], "reasoning": "By examining the table, the highest correlation value for SVDPPMI embeddings is 0.557, which is achieved by the ParaSimNum induction method with the full seed selection.", "question": "Which induction method and seed selection combination achieved the highest correlation with SVDPPMI embeddings?", "answer": "So, the answer is {\"answer\": \"ParaSimNum with full seed selection\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nSGNS embeddings perform better than SVDPPMI embeddings for the kNN induction method with the full seed lexicon.\n" }, { "role": "assistant", "content": "The table shows that for the kNN induction method with the full seed lexicon, SVDPPMI embeddings have a correlation of 0.548, while SGNS embeddings have a correlation of 0.487. Therefore, SGNS embeddings do not perform better than SVDPPMI embeddings in this case. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/797.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of the synchronic evaluation in Pearson’s r averaged over all three VAD dimensions. The best system for each seed lexicon and those with statistically non-significant differences (p ≥ 0.05) are in bold." ], "reasoning": "The table shows that for the kNN induction method with the full seed lexicon, SVDPPMI embeddings have a correlation of 0.548, while SGNS embeddings have a correlation of 0.487. Therefore, SGNS embeddings do not perform better than SVDPPMI embeddings in this case.", "statement": "SGNS embeddings perform better than SVDPPMI embeddings for the kNN induction method with the full seed lexicon.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Inter-annotator agreement for our English (goldEN) and German (goldDE) gold standard, as well as the lexicon by Warriner13 for comparision; Averaged standard deviation of ratings for each VAD dimension and mean over all dimensions.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The gold standard lexicon shows higher rating consistency with lower IAA scores for both English and German compared to Warriner's lexicon.We measure inter-annotator agreement (IAA) by calculating the standard deviation (SD) for each word and dimension and averaging these, first, for each dimension alone, and then over these aggregate values, thus constituting an error-based score (the lower the better). In comparison with the lexicon by \\newciteWarriner13, our gold standard displays higher rating consistency. As average over all three VAD dimensions, our lexicon displays an IAA of 1.23 and 1.86 for English and German, respectively, compared to 2.05 as reported by \\newciteWarriner13. This suggests that experts show higher consensus, even when judging word emotions for a historical language period, than crowdworkers for contemporary language. An alternative explanation might be differences in word material, i.e., our random sample of frequent words." } ], "images": [ "scigen/train/798.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Inter-annotator agreement for our English (goldEN) and German (goldDE) gold standard, as well as the lexicon by Warriner13 for comparision; Averaged standard deviation of ratings for each VAD dimension and mean over all dimensions." ], "reasoning": "The gold standard lexicon shows higher rating consistency with lower IAA scores for both English and German compared to Warriner's lexicon.", "description": "We measure inter-annotator agreement (IAA) by calculating the standard deviation (SD) for each word and dimension and averaging these, first, for each dimension alone, and then over these aggregate values, thus constituting an error-based score (the lower the better). In comparison with the lexicon by \\newciteWarriner13, our gold standard displays higher rating consistency. As average over all three VAD dimensions, our lexicon displays an IAA of 1.23 and 1.86 for English and German, respectively, compared to 2.05 as reported by \\newciteWarriner13. This suggests that experts show higher consensus, even when judging word emotions for a historical language period, than crowdworkers for contemporary language. An alternative explanation might be differences in word material, i.e., our random sample of frequent words.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: Inter-annotator agreement for our English (goldEN) and German (goldDE) gold standard, as well as the lexicon by Warriner13 for comparision; Averaged standard deviation of ratings for each VAD dimension and mean over all dimensions.'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the IAA score for the Arousal dimension in Warriner's lexicon?\n" }, { "role": "assistant", "content": "The table lists the IAA scores for Valence, Arousal, and Dominance dimensions for both goldEN, goldDE, and Warriner's lexicon. The Arousal score for Warriner's lexicon is 2.30. So, the answer is {\"answer\": \"2.30\"}" } ], "images": [ "scigen/train/798.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Inter-annotator agreement for our English (goldEN) and German (goldDE) gold standard, as well as the lexicon by Warriner13 for comparision; Averaged standard deviation of ratings for each VAD dimension and mean over all dimensions." ], "reasoning": "The table lists the IAA scores for Valence, Arousal, and Dominance dimensions for both goldEN, goldDE, and Warriner's lexicon. The Arousal score for Warriner's lexicon is 2.30.", "question": "What is the IAA score for the Arousal dimension in Warriner's lexicon?", "answer": "So, the answer is {\"answer\": \"2.30\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe goldEN lexicon has a higher IAA score for the Dominance dimension than the goldDE lexicon.\n" }, { "role": "assistant", "content": "The table shows that the IAA score for the Dominance dimension in the goldEN lexicon is 1.41, while in the goldDE lexicon it is 2.31. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/798.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Inter-annotator agreement for our English (goldEN) and German (goldDE) gold standard, as well as the lexicon by Warriner13 for comparision; Averaged standard deviation of ratings for each VAD dimension and mean over all dimensions." ], "reasoning": "The table shows that the IAA score for the Dominance dimension in the goldEN lexicon is 1.41, while in the goldDE lexicon it is 2.31. Therefore, the statement is refuted.", "statement": "The goldEN lexicon has a higher IAA score for the Dominance dimension than the goldDE lexicon.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 6: Aspect and opinion term extraction performance.'.\n" }, { "role": "assistant", "content": "Using BERT improves performance over word2vec, and RINANTE further enhances performance when combined with BERT.We can see that using BERT yields better performance than using word2vec. RINANTE is still able to further improve the performance when contextual embeddings obtained with BERT are used." } ], "images": [ "scigen/train/799.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Aspect and opinion term extraction performance." ], "reasoning": "Using BERT improves performance over word2vec, and RINANTE further enhances performance when combined with BERT.", "description": "We can see that using BERT yields better performance than using word2vec. RINANTE is still able to further improve the performance when contextual embeddings obtained with BERT are used.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 6: Aspect and opinion term extraction performance.' to sequentially solve the question presented below. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich approach yields the highest performance for SE14-R Opinion?\n" }, { "role": "assistant", "content": "By comparing the SE14-R Opinion column, RINANTE+BERT has the highest score of 86.82. So, the answer is {\"answer\": \"RINANTE+BERT\"}" } ], "images": [ "scigen/train/799.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Aspect and opinion term extraction performance." ], "reasoning": "By comparing the SE14-R Opinion column, RINANTE+BERT has the highest score of 86.82.", "question": "Which approach yields the highest performance for SE14-R Opinion?", "answer": "So, the answer is {\"answer\": \"RINANTE+BERT\"}" }, { "messages": [ { "role": "user", "content": "BERT fine-tuning performs better than BERT feature-based for SE14-L Opinion. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nBERT fine-tuning performs better than BERT feature-based for SE14-L Opinion.\n" }, { "role": "assistant", "content": "The table shows that BERT fine-tuning has a score of 79.75 for SE14-L Opinion, while BERT feature-based has a score of 81.41, indicating that BERT feature-based performs better. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/799.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Aspect and opinion term extraction performance." ], "reasoning": "The table shows that BERT fine-tuning has a score of 79.75 for SE14-L Opinion, while BERT feature-based has a score of 81.41, indicating that BERT feature-based performs better.", "statement": "BERT fine-tuning performs better than BERT feature-based for SE14-L Opinion.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Aspect and opinion term extraction performance of different approaches. F1 score is reported. IHS_RD, DLIREC, Elixa and WDEmb* use manually designed features. For different versions of RINANTE, “Shared” and “Double” means shared BiLSTM model and double BiLSTM model, respectively; “Alt” and “Pre” means the first and the second training method, respectively. RINANTE-Double-Pre†: fine-tune the pre-trained model for only extracting aspect terms, and use the same number of validation samples as DE-CNN Xu et al. (2018). The results of RINANTE-Double-Pre† are obtained after this paper gets accepted and are not included in the ACL version. RINANTE-Double-Pre† achieves the best performance on SE15-R.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The mined rules alone do not perform well, but they improve the performance of neural models when used for automatic labeling, especially on SE14-L and SE15-R.From the results, we can see that the mined rules alone do not perform well. However, by learning from the data automatically labeled by these rules, all four versions of RINANTE achieves better performances than RINANTE (no rule). This verifies that we can indeed use the results of the mined rules to improve the performance of neural models. Moreover, the improvement over RINANTE (no rule) can be especially significant on SE14-L and SE15-R. We think this is because SE14-L is relatively more difficult and SE15-R has much less manually labeled training data. This is because our algorithm is able to mine hundreds of effective rules, while Double Propagation only has eight manually designed rules." } ], "images": [ "scigen/train/800.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Aspect and opinion term extraction performance of different approaches. F1 score is reported. IHS_RD, DLIREC, Elixa and WDEmb* use manually designed features. For different versions of RINANTE, “Shared” and “Double” means shared BiLSTM model and double BiLSTM model, respectively; “Alt” and “Pre” means the first and the second training method, respectively. RINANTE-Double-Pre†: fine-tune the pre-trained model for only extracting aspect terms, and use the same number of validation samples as DE-CNN Xu et al. (2018). The results of RINANTE-Double-Pre† are obtained after this paper gets accepted and are not included in the ACL version. RINANTE-Double-Pre† achieves the best performance on SE15-R." ], "reasoning": "The mined rules alone do not perform well, but they improve the performance of neural models when used for automatic labeling, especially on SE14-L and SE15-R.", "description": "From the results, we can see that the mined rules alone do not perform well. However, by learning from the data automatically labeled by these rules, all four versions of RINANTE achieves better performances than RINANTE (no rule). This verifies that we can indeed use the results of the mined rules to improve the performance of neural models. Moreover, the improvement over RINANTE (no rule) can be especially significant on SE14-L and SE15-R. We think this is because SE14-L is relatively more difficult and SE15-R has much less manually labeled training data. This is because our algorithm is able to mine hundreds of effective rules, while Double Propagation only has eight manually designed rules.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: 'Table 2: Aspect and opinion term extraction performance of different approaches. F1 score is reported. IHS_RD, DLIREC, Elixa and WDEmb* use manually designed features. For different versions of RINANTE, “Shared” and “Double” means shared BiLSTM model and double BiLSTM model, respectively; “Alt” and “Pre” means the first and the second training method, respectively. RINANTE-Double-Pre†: fine-tune the pre-trained model for only extracting aspect terms, and use the same number of validation samples as DE-CNN Xu et al. (2018). The results of RINANTE-Double-Pre† are obtained after this paper gets accepted and are not included in the ACL version. RINANTE-Double-Pre† achieves the best performance on SE15-R.'\nQuestion: Which approach achieved the highest performance on SE14-L Aspect?\n" }, { "role": "assistant", "content": "By examining the SE14-L Aspect column, we see that the highest performance is achieved by RINANTE-Double-Pre with a score of 81.96. So, the answer is {\"answer\": \"RINANTE-Double-Pre\"}" } ], "images": [ "scigen/train/800.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Aspect and opinion term extraction performance of different approaches. F1 score is reported. IHS_RD, DLIREC, Elixa and WDEmb* use manually designed features. For different versions of RINANTE, “Shared” and “Double” means shared BiLSTM model and double BiLSTM model, respectively; “Alt” and “Pre” means the first and the second training method, respectively. RINANTE-Double-Pre†: fine-tune the pre-trained model for only extracting aspect terms, and use the same number of validation samples as DE-CNN Xu et al. (2018). The results of RINANTE-Double-Pre† are obtained after this paper gets accepted and are not included in the ACL version. RINANTE-Double-Pre† achieves the best performance on SE15-R." ], "reasoning": "By examining the SE14-L Aspect column, we see that the highest performance is achieved by RINANTE-Double-Pre with a score of 81.96.", "question": "Which approach achieved the highest performance on SE14-L Aspect?", "answer": "So, the answer is {\"answer\": \"RINANTE-Double-Pre\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nRINANTE-Shared-Alt achieved the highest performance on SE14-R Aspect.\n" }, { "role": "assistant", "content": "By examining the SE14-R Aspect column, we see that the highest performance is achieved by RINANTE-Shared-Alt with a score of 86.76. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/800.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Aspect and opinion term extraction performance of different approaches. F1 score is reported. IHS_RD, DLIREC, Elixa and WDEmb* use manually designed features. For different versions of RINANTE, “Shared” and “Double” means shared BiLSTM model and double BiLSTM model, respectively; “Alt” and “Pre” means the first and the second training method, respectively. RINANTE-Double-Pre†: fine-tune the pre-trained model for only extracting aspect terms, and use the same number of validation samples as DE-CNN Xu et al. (2018). The results of RINANTE-Double-Pre† are obtained after this paper gets accepted and are not included in the ACL version. RINANTE-Double-Pre† achieves the best performance on SE15-R." ], "reasoning": "By examining the SE14-R Aspect column, we see that the highest performance is achieved by RINANTE-Shared-Alt with a score of 86.76.", "statement": "RINANTE-Shared-Alt achieved the highest performance on SE14-R Aspect.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: COCO-QA question type break-down'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "COCO-QA questions are easier for humans to answer than DAQUAR, encouraging models to focus on salient object relations.It should be noted that since we applied the QA pair rejection process, mode-guessing performs very poorly on COCO-QA. However, COCO-QA questions are actually easier to answer than DAQUAR from a human point of view. This encourages the model to exploit salient object relations instead of exhaustively searching all possible relations. COCO-QA dataset can be downloaded at http://www.cs.toronto.edu/~mren/imageqa/data/cocoqa" } ], "images": [ "scigen/train/801.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: COCO-QA question type break-down" ], "reasoning": "COCO-QA questions are easier for humans to answer than DAQUAR, encouraging models to focus on salient object relations.", "description": "It should be noted that since we applied the QA pair rejection process, mode-guessing performs very poorly on COCO-QA. However, COCO-QA questions are actually easier to answer than DAQUAR from a human point of view. This encourages the model to exploit salient object relations instead of exhaustively searching all possible relations. COCO-QA dataset can be downloaded at http://www.cs.toronto.edu/~mren/imageqa/data/cocoqa", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1: COCO-QA question type break-down', solve the subsequent question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the total number of training and testing questions in the COCO-QA dataset?\n" }, { "role": "assistant", "content": "To find the total number of questions, sum the total training questions (78736) and the total testing questions (38948). So, the answer is {\"answer\": \"117684\"}" } ], "images": [ "scigen/train/801.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: COCO-QA question type break-down" ], "reasoning": "To find the total number of questions, sum the total training questions (78736) and the total testing questions (38948).", "question": "What is the total number of training and testing questions in the COCO-QA dataset?", "answer": "So, the answer is {\"answer\": \"117684\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe percentage of color-related questions in the test set is higher than in the train set.\n" }, { "role": "assistant", "content": "The table shows that the percentage of color-related questions in the test set is 16.71%, which is higher than the 16.59% in the train set. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/801.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: COCO-QA question type break-down" ], "reasoning": "The table shows that the percentage of color-related questions in the test set is 16.71%, which is higher than the 16.59% in the train set.", "statement": "The percentage of color-related questions in the test set is higher than in the train set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: DistilBERT retains 97% of BERT performance. Comparison on the dev sets of the GLUE benchmark. ELMo results as reported by the authors. BERT and DistilBERT results are the medians of 5 runs with different seeds.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "DistilBERT consistently outperforms or matches ELMo across all tasks and retains 97% of BERT's performance with 40% fewer parameters.Among the 9 tasks, DistilBERT is always on par or improving over the ELMo baseline (up to 19 points of accuracy on STS-B). DistilBERT also compares surprisingly well to BERT, retaining 97% of the performance with 40% fewer parameters." } ], "images": [ "scigen/train/802.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: DistilBERT retains 97% of BERT performance. Comparison on the dev sets of the GLUE benchmark. ELMo results as reported by the authors. BERT and DistilBERT results are the medians of 5 runs with different seeds." ], "reasoning": "DistilBERT consistently outperforms or matches ELMo across all tasks and retains 97% of BERT's performance with 40% fewer parameters.", "description": "Among the 9 tasks, DistilBERT is always on par or improving over the ELMo baseline (up to 19 points of accuracy on STS-B). DistilBERT also compares surprisingly well to BERT, retaining 97% of the performance with 40% fewer parameters.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 1: DistilBERT retains 97% of BERT performance. Comparison on the dev sets of the GLUE benchmark. ELMo results as reported by the authors. BERT and DistilBERT results are the medians of 5 runs with different seeds.'\nQuestion: Which model has the highest score on the SST-2 task?\n" }, { "role": "assistant", "content": "By comparing the scores for the SST-2 task across all models, BERT-base has the highest score of 92.7. So, the answer is {\"answer\": \"BERT-base\"}" } ], "images": [ "scigen/train/802.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: DistilBERT retains 97% of BERT performance. Comparison on the dev sets of the GLUE benchmark. ELMo results as reported by the authors. BERT and DistilBERT results are the medians of 5 runs with different seeds." ], "reasoning": "By comparing the scores for the SST-2 task across all models, BERT-base has the highest score of 92.7.", "question": "Which model has the highest score on the SST-2 task?", "answer": "So, the answer is {\"answer\": \"BERT-base\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence DistilBERT has a higher score than ELMo on the MRPC task. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nDistilBERT has a higher score than ELMo on the MRPC task.\n" }, { "role": "assistant", "content": "The table shows that DistilBERT has a score of 87.5 on the MRPC task, while ELMo has a score of 76.6. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/802.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: DistilBERT retains 97% of BERT performance. Comparison on the dev sets of the GLUE benchmark. ELMo results as reported by the authors. BERT and DistilBERT results are the medians of 5 runs with different seeds." ], "reasoning": "The table shows that DistilBERT has a score of 87.5 on the MRPC task, while ELMo has a score of 76.6.", "statement": "DistilBERT has a higher score than ELMo on the MRPC task.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: DistilBERT yields to comparable performance on downstream tasks. Comparison on downstream tasks: IMDb (test accuracy) and SQuAD 1.1 (EM/F1 on dev set). D: with a second step of distillation during fine-tuning.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "DistilBERT has 40% fewer parameters and is 60% faster than BERT, while being within 3.9 points of BERT on SQuAD.On SQuAD, DistilBERT is within 3.9 points of the full BERT. the number of parameters of each model along with the inference time needed to do a full pass on the STS-B development set on CPU (Intel Xeon E5-2690 v3 Haswell @2.9GHz) using a batch size of 1. DistilBERT has 40% fewer parameters than BERT and is 60% faster than BERT." } ], "images": [ "scigen/train/803.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: DistilBERT yields to comparable performance on downstream tasks. Comparison on downstream tasks: IMDb (test accuracy) and SQuAD 1.1 (EM/F1 on dev set). D: with a second step of distillation during fine-tuning." ], "reasoning": "DistilBERT has 40% fewer parameters and is 60% faster than BERT, while being within 3.9 points of BERT on SQuAD.", "description": "On SQuAD, DistilBERT is within 3.9 points of the full BERT. the number of parameters of each model along with the inference time needed to do a full pass on the STS-B development set on CPU (Intel Xeon E5-2690 v3 Haswell @2.9GHz) using a batch size of 1. DistilBERT has 40% fewer parameters than BERT and is 60% faster than BERT.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: DistilBERT yields to comparable performance on downstream tasks. Comparison on downstream tasks: IMDb (test accuracy) and SQuAD 1.1 (EM/F1 on dev set). D: with a second step of distillation during fine-tuning.', and sequentially solve the question, writing out each step of your reasoning process. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the F1 score of DistilBERT on SQuAD, and how does it compare to BERT-base?\n" }, { "role": "assistant", "content": "The F1 score of DistilBERT on SQuAD is 85.8, which is 2.7 points lower than BERT-base's F1 score of 88.5. So, the answer is {\"answer\": \"85.8\"}." } ], "images": [ "scigen/train/803.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: DistilBERT yields to comparable performance on downstream tasks. Comparison on downstream tasks: IMDb (test accuracy) and SQuAD 1.1 (EM/F1 on dev set). D: with a second step of distillation during fine-tuning." ], "reasoning": "The F1 score of DistilBERT on SQuAD is 85.8, which is 2.7 points lower than BERT-base's F1 score of 88.5.", "question": "What is the F1 score of DistilBERT on SQuAD, and how does it compare to BERT-base?", "answer": "So, the answer is {\"answer\": \"85.8\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence DistilBERT has a higher IMDb accuracy than BERT-base. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nDistilBERT has a higher IMDb accuracy than BERT-base.\n" }, { "role": "assistant", "content": "The table shows that BERT-base has an IMDb accuracy of 93.46, while DistilBERT has an IMDb accuracy of 92.82, which is lower than BERT-base. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/803.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: DistilBERT yields to comparable performance on downstream tasks. Comparison on downstream tasks: IMDb (test accuracy) and SQuAD 1.1 (EM/F1 on dev set). D: with a second step of distillation during fine-tuning." ], "reasoning": "The table shows that BERT-base has an IMDb accuracy of 93.46, while DistilBERT has an IMDb accuracy of 92.82, which is lower than BERT-base.", "statement": "DistilBERT has a higher IMDb accuracy than BERT-base.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: DistilBERT yields to comparable performance on downstream tasks. Comparison on downstream tasks: IMDb (test accuracy) and SQuAD 1.1 (EM/F1 on dev set). D: with a second step of distillation during fine-tuning.'.\n" }, { "role": "assistant", "content": "DistilBERT has 40% fewer parameters and is 60% faster than BERT-base, with an inference time of 410 seconds compared to BERT-base's 668 seconds.On SQuAD, DistilBERT is within 3.9 points of the full BERT. the number of parameters of each model along with the inference time needed to do a full pass on the STS-B development set on CPU (Intel Xeon E5-2690 v3 Haswell @2.9GHz) using a batch size of 1. DistilBERT has 40% fewer parameters than BERT and is 60% faster than BERT." } ], "images": [ "scigen/train/804.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: DistilBERT yields to comparable performance on downstream tasks. Comparison on downstream tasks: IMDb (test accuracy) and SQuAD 1.1 (EM/F1 on dev set). D: with a second step of distillation during fine-tuning." ], "reasoning": "DistilBERT has 40% fewer parameters and is 60% faster than BERT-base, with an inference time of 410 seconds compared to BERT-base's 668 seconds.", "description": "On SQuAD, DistilBERT is within 3.9 points of the full BERT. the number of parameters of each model along with the inference time needed to do a full pass on the STS-B development set on CPU (Intel Xeon E5-2690 v3 Haswell @2.9GHz) using a batch size of 1. DistilBERT has 40% fewer parameters than BERT and is 60% faster than BERT.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: DistilBERT yields to comparable performance on downstream tasks. Comparison on downstream tasks: IMDb (test accuracy) and SQuAD 1.1 (EM/F1 on dev set). D: with a second step of distillation during fine-tuning.', and tackle the question below in a sequential manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the difference in the number of parameters between ELMo and DistilBERT?\n" }, { "role": "assistant", "content": "ELMo has 180 million parameters and DistilBERT has 66 million parameters. The difference is 180 - 66 = 114 million parameters. So, the answer is {\"answer\": \"114\"}" } ], "images": [ "scigen/train/804.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: DistilBERT yields to comparable performance on downstream tasks. Comparison on downstream tasks: IMDb (test accuracy) and SQuAD 1.1 (EM/F1 on dev set). D: with a second step of distillation during fine-tuning." ], "reasoning": "ELMo has 180 million parameters and DistilBERT has 66 million parameters. The difference is 180 - 66 = 114 million parameters.", "question": "What is the difference in the number of parameters between ELMo and DistilBERT?", "answer": "So, the answer is {\"answer\": \"114\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence DistilBERT has fewer parameters than ELMo and BERT-base. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nDistilBERT has fewer parameters than ELMo and BERT-base.\n" }, { "role": "assistant", "content": "The table shows that DistilBERT has 66 million parameters, which is fewer than both ELMo's 180 million and BERT-base's 110 million parameters. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/804.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: DistilBERT yields to comparable performance on downstream tasks. Comparison on downstream tasks: IMDb (test accuracy) and SQuAD 1.1 (EM/F1 on dev set). D: with a second step of distillation during fine-tuning." ], "reasoning": "The table shows that DistilBERT has 66 million parameters, which is fewer than both ELMo's 180 million and BERT-base's 110 million parameters.", "statement": "DistilBERT has fewer parameters than ELMo and BERT-base.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Comparison between the accuracy of our aligned model (RCSLS+Fine.), the accuracy of models trained on each split separately (Train on …) and two baselines. We observe that a model obtained using our procedure matches the performance of the Vote baseline while not requiring to store two separate models.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Training on individual splits reduces the performance gap compared to the topline, especially on smaller datasets, due to incomplete coverage of discriminative words and n-grams in each split.We report the performance of a model trained on each of the two splits alone, the two baselines, and a topline obtained by training on S0∪S1. First of all, we observe that our approach reduces the gap between training on a single set with the topline. This effect is especially true on small versions of the datasets (3k or 30k). The main reason for this improvement is that each split has an incomplete coverage of the discriminative words and n-grams." } ], "images": [ "scigen/train/805.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison between the accuracy of our aligned model (RCSLS+Fine.), the accuracy of models trained on each split separately (Train on …) and two baselines. We observe that a model obtained using our procedure matches the performance of the Vote baseline while not requiring to store two separate models." ], "reasoning": "Training on individual splits reduces the performance gap compared to the topline, especially on smaller datasets, due to incomplete coverage of discriminative words and n-grams in each split.", "description": "We report the performance of a model trained on each of the two splits alone, the two baselines, and a topline obtained by training on S0∪S1. First of all, we observe that our approach reduces the gap between training on a single set with the topline. This effect is especially true on small versions of the datasets (3k or 30k). The main reason for this improvement is that each split has an incomplete coverage of the discriminative words and n-grams.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Comparison between the accuracy of our aligned model (RCSLS+Fine.), the accuracy of models trained on each split separately (Train on …) and two baselines. We observe that a model obtained using our procedure matches the performance of the Vote baseline while not requiring to store two separate models.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. \n\nQuestion: What is the performance of the model trained on S0 for the Amazon 3k dataset?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the performance of the model trained on S0 for the Amazon 3k dataset, locate the 'Train on S0' row and the 'Amazon 3k' column. The value is 46.1. So, the answer is {\"answer\": \"46.1\"}" } ], "images": [ "scigen/train/805.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison between the accuracy of our aligned model (RCSLS+Fine.), the accuracy of models trained on each split separately (Train on …) and two baselines. We observe that a model obtained using our procedure matches the performance of the Vote baseline while not requiring to store two separate models." ], "reasoning": "To find the performance of the model trained on S0 for the Amazon 3k dataset, locate the 'Train on S0' row and the 'Amazon 3k' column. The value is 46.1.", "question": "What is the performance of the model trained on S0 for the Amazon 3k dataset?", "answer": "So, the answer is {\"answer\": \"46.1\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The model trained on S0 for the Sogou 30k dataset performs better than the model trained on S1 for the same dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model trained on S0 for the Sogou 30k dataset performs better than the model trained on S1 for the same dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the performance values for 'Train on S0' and 'Train on S1' in the 'Sogou 30k' column. 'Train on S0' has a value of 94.5, while 'Train on S1' has a value of 94.4. Since 94.5 is greater than 94.4, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/805.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison between the accuracy of our aligned model (RCSLS+Fine.), the accuracy of models trained on each split separately (Train on …) and two baselines. We observe that a model obtained using our procedure matches the performance of the Vote baseline while not requiring to store two separate models." ], "reasoning": "To verify this statement, compare the performance values for 'Train on S0' and 'Train on S1' in the 'Sogou 30k' column. 'Train on S0' has a value of 94.5, while 'Train on S1' has a value of 94.4. Since 94.5 is greater than 94.4, the statement is supported.", "statement": "The model trained on S0 for the Sogou 30k dataset performs better than the model trained on S1 for the same dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Performance of updated word vectors on the word analogy task. We split the English word analogy datasets (Mikolov et al., 2013a) into Out of vocab and In vocab questions. Out of vocab questions are composed of words that are out of vocabulary for S0, hence the null accuracy.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Fine-tuning X on S1 improves Out of vocab performance but decreases In vocab accuracy by 3.3%.S4SS1SSS0Px4 Results. First, we see that fine-tuning X on S1 leads to decent performance on the Out of vocab questions, but saps the accuracy on In vocab questions. As mentionned before, learning vectors initialized with X on S1 may lead to a loss of important statistics learnt on S0: the total accuracy on In vocab questions drops by 3.3%." } ], "images": [ "scigen/train/806.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance of updated word vectors on the word analogy task. We split the English word analogy datasets (Mikolov et al., 2013a) into Out of vocab and In vocab questions. Out of vocab questions are composed of words that are out of vocabulary for S0, hence the null accuracy." ], "reasoning": "Fine-tuning X on S1 improves Out of vocab performance but decreases In vocab accuracy by 3.3%.", "description": "S4SS1SSS0Px4 Results. First, we see that fine-tuning X on S1 leads to decent performance on the Out of vocab questions, but saps the accuracy on In vocab questions. As mentionned before, learning vectors initialized with X on S1 may lead to a loss of important statistics learnt on S0: the total accuracy on In vocab questions drops by 3.3%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Performance of updated word vectors on the word analogy task. We split the English word analogy datasets (Mikolov et al., 2013a) into Out of vocab and In vocab questions. Out of vocab questions are composed of words that are out of vocabulary for S0, hence the null accuracy.', answer the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the total accuracy on In vocab questions when fine-tuning X on S1?\n" }, { "role": "assistant", "content": "The description mentions that fine-tuning X on S1 leads to a drop in In vocab accuracy by 3.3%. The table shows that the In vocab accuracy for fine-tuning is 66.8%. So, the answer is {\"answer\": \"66.8%\"}" } ], "images": [ "scigen/train/806.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance of updated word vectors on the word analogy task. We split the English word analogy datasets (Mikolov et al., 2013a) into Out of vocab and In vocab questions. Out of vocab questions are composed of words that are out of vocabulary for S0, hence the null accuracy." ], "reasoning": "The description mentions that fine-tuning X on S1 leads to a drop in In vocab accuracy by 3.3%. The table shows that the In vocab accuracy for fine-tuning is 66.8%.", "question": "What is the total accuracy on In vocab questions when fine-tuning X on S1?", "answer": "So, the answer is {\"answer\": \"66.8%\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nFine-tuning X on S1 leads to the highest Out of vocab accuracy.\n" }, { "role": "assistant", "content": "The table shows that the highest Out of vocab accuracy is 68.6% for 'Train on S0∪S1', not for fine-tuning X on S1 which has an accuracy of 67.7%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/806.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance of updated word vectors on the word analogy task. We split the English word analogy datasets (Mikolov et al., 2013a) into Out of vocab and In vocab questions. Out of vocab questions are composed of words that are out of vocabulary for S0, hence the null accuracy." ], "reasoning": "The table shows that the highest Out of vocab accuracy is 68.6% for 'Train on S0∪S1', not for fine-tuning X on S1 which has an accuracy of 67.7%.", "statement": "Fine-tuning X on S1 leads to the highest Out of vocab accuracy.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Classification accuracy on Yelp reviews from 2018. We compare models trained on 1.2M reviews from 2013-2014 (S0) and a those trained on a smaller sample of reviews from 2018 (S1). We vary the size of S1 from 10k to 500k samples, while keeping S0 of fixed size.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The performance of models trained on S0 and S1 depends on the size of S1, with larger S1 sizes leading to better performance. Training on a combination of S0 and S1 or fine-tuning yields the best results.First of all, we observe that the performance of models trained on S0 and S1 strongly depend on the size of S1. When the two datasets are of the same size (500k), the best performing model is the one trained on S1, as in that case there is no train/test distribution discrepancy. However, when S1 is small (10k or 30k), it is better to use a larger yet ill-distributed dataset (70.8% versus 74.9%). We also observe that when training on a concatenation of the two, the model performs at least as well as the best one." } ], "images": [ "scigen/train/807.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Classification accuracy on Yelp reviews from 2018. We compare models trained on 1.2M reviews from 2013-2014 (S0) and a those trained on a smaller sample of reviews from 2018 (S1). We vary the size of S1 from 10k to 500k samples, while keeping S0 of fixed size." ], "reasoning": "The performance of models trained on S0 and S1 depends on the size of S1, with larger S1 sizes leading to better performance. Training on a combination of S0 and S1 or fine-tuning yields the best results.", "description": "First of all, we observe that the performance of models trained on S0 and S1 strongly depend on the size of S1. When the two datasets are of the same size (500k), the best performing model is the one trained on S1, as in that case there is no train/test distribution discrepancy. However, when S1 is small (10k or 30k), it is better to use a larger yet ill-distributed dataset (70.8% versus 74.9%). We also observe that when training on a concatenation of the two, the model performs at least as well as the best one.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Classification accuracy on Yelp reviews from 2018. We compare models trained on 1.2M reviews from 2013-2014 (S0) and a those trained on a smaller sample of reviews from 2018 (S1). We vary the size of S1 from 10k to 500k samples, while keeping S0 of fixed size.', solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the performance of the model when trained on S1 with a size of 100k?\n" }, { "role": "assistant", "content": "To find the performance of the model trained on S1 with a size of 100k, we look at the intersection of the 'Train on S1' row and the 'Size of S1 100k' column. Thus, the answer is {\"answer\": \"74.4\"}." } ], "images": [ "scigen/train/807.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Classification accuracy on Yelp reviews from 2018. We compare models trained on 1.2M reviews from 2013-2014 (S0) and a those trained on a smaller sample of reviews from 2018 (S1). We vary the size of S1 from 10k to 500k samples, while keeping S0 of fixed size." ], "reasoning": "To find the performance of the model trained on S1 with a size of 100k, we look at the intersection of the 'Train on S1' row and the 'Size of S1 100k' column.", "question": "What is the performance of the model when trained on S1 with a size of 100k?", "answer": "Thus, the answer is {\"answer\": \"74.4\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model performs best when trained on S1 with a size of 500k.\n" }, { "role": "assistant", "content": "The table shows that the highest performance for the 'Train on S1' row is 76.2 when the size of S1 is 500k. However, the highest overall performance is 76.3 when fine-tuning or using RCSLS+Fine-tune with the same size. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/807.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Classification accuracy on Yelp reviews from 2018. We compare models trained on 1.2M reviews from 2013-2014 (S0) and a those trained on a smaller sample of reviews from 2018 (S1). We vary the size of S1 from 10k to 500k samples, while keeping S0 of fixed size." ], "reasoning": "The table shows that the highest performance for the 'Train on S1' row is 76.2 when the size of S1 is 500k. However, the highest overall performance is 76.3 when fine-tuning or using RCSLS+Fine-tune with the same size.", "statement": "The model performs best when trained on S1 with a size of 500k.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Results of different methods on NYT and WebNLG datasets. Our re-implementation is marked by *.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "CasRel significantly outperforms all baselines in F1-score on both NYT and WebNLG datasets, demonstrating the effectiveness of the proposed cascade decoder and the importance of pre-trained language models.The CasRel model overwhelmingly outperforms all the baselines in terms of all three evaluation metrics and achieves encouraging 17.5% and 30.2% improvements in F1-score over the best state-of-the-art method zeng2019Learning on NYT and WebNLG datasets respectively. Even without taking advantage of the pre-trained BERT, CasRelrandom and CasRelLSTM are still competitive to existing state-of-the-art models. This validates the utility of the proposed cascade decoder that adopts a novel binary tagging scheme. The performance improvements from CasRelrandom to CasRel highlight the importance of the prior knowledge in a pre-trained language model." } ], "images": [ "scigen/train/808.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of different methods on NYT and WebNLG datasets. Our re-implementation is marked by *." ], "reasoning": "CasRel significantly outperforms all baselines in F1-score on both NYT and WebNLG datasets, demonstrating the effectiveness of the proposed cascade decoder and the importance of pre-trained language models.", "description": "The CasRel model overwhelmingly outperforms all the baselines in terms of all three evaluation metrics and achieves encouraging 17.5% and 30.2% improvements in F1-score over the best state-of-the-art method zeng2019Learning on NYT and WebNLG datasets respectively. Even without taking advantage of the pre-trained BERT, CasRelrandom and CasRelLSTM are still competitive to existing state-of-the-art models. This validates the utility of the proposed cascade decoder that adopts a novel binary tagging scheme. The performance improvements from CasRelrandom to CasRel highlight the importance of the prior knowledge in a pre-trained language model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Results of different methods on NYT and WebNLG datasets. Our re-implementation is marked by *.' to sequentially solve the question presented below. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhat is the F1-score improvement of CasRel over the best state-of-the-art method on the WebNLG dataset?\n" }, { "role": "assistant", "content": "The best state-of-the-art method on the WebNLG dataset is zeng2019Learning with an F1-score of 61.6. CasRel achieves an F1-score of 91.8. The improvement is calculated as 91.8 - 61.6 = 30.2. So, the answer is {\"answer\": \"30.2\"}" } ], "images": [ "scigen/train/808.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of different methods on NYT and WebNLG datasets. Our re-implementation is marked by *." ], "reasoning": "The best state-of-the-art method on the WebNLG dataset is zeng2019Learning with an F1-score of 61.6. CasRel achieves an F1-score of 91.8. The improvement is calculated as 91.8 - 61.6 = 30.2.", "question": "What is the F1-score improvement of CasRel over the best state-of-the-art method on the WebNLG dataset?", "answer": "So, the answer is {\"answer\": \"30.2\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis CasRel achieves the highest F1-score on the NYT dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nCasRel achieves the highest F1-score on the NYT dataset.\n" }, { "role": "assistant", "content": "The table shows that CasRel has an F1-score of 89.6 on the NYT dataset, which is higher than any other method listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/808.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of different methods on NYT and WebNLG datasets. Our re-implementation is marked by *." ], "reasoning": "The table shows that CasRel has an F1-score of 89.6 on the NYT dataset, which is higher than any other method listed.", "statement": "CasRel achieves the highest F1-score on the NYT dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: F1-score of extracting relational triples from sentences with different number (denoted as N) of triples.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The CasRel model consistently outperforms other baseline models across all overlapping patterns and number of relational triples, showing the least performance degradation with increasing complexity.It can be seen that the performance of most baselines on Normal, EPO and SEO presents a decreasing trend, reflecting the increasing difficulty of extracting relational triples from sentences with different overlapping patterns. That is, among the three overlapping patterns, Normal class is the easiest pattern while EPO and SEO classes are the relatively harder ones for baseline models to extract. In contrast, the proposed CasRel model attains consistently strong performance over all three overlapping patterns, especially for those hard patterns. We also validate the CasRel’s capability in extracting relational triples from sentences with different number of triples. Again, the CasRel model achieves excellent performance over all five classes. Though it’s not surprising to find that the performance of most baselines decreases with the increasing number of relational triples that a sentence contains, some patterns still can be observed from the performance changes of different models. Compared to previous works that devote to solving the overlapping problem in relational triple extraction, our model suffers the least from the increasing complexity of the input sentence. Though the CasRel model gain considerable improvements on all five classes compared to the best state-of-the-art method CopyRRL zeng2019Learning, the greatest improvement of F1-score on the two datasets both come from the most difficult class (N≥5), indicating that our model is more suitable for complicated scenarios than the baselines." } ], "images": [ "scigen/train/809.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: F1-score of extracting relational triples from sentences with different number (denoted as N) of triples." ], "reasoning": "The CasRel model consistently outperforms other baseline models across all overlapping patterns and number of relational triples, showing the least performance degradation with increasing complexity.", "description": "It can be seen that the performance of most baselines on Normal, EPO and SEO presents a decreasing trend, reflecting the increasing difficulty of extracting relational triples from sentences with different overlapping patterns. That is, among the three overlapping patterns, Normal class is the easiest pattern while EPO and SEO classes are the relatively harder ones for baseline models to extract. In contrast, the proposed CasRel model attains consistently strong performance over all three overlapping patterns, especially for those hard patterns. We also validate the CasRel’s capability in extracting relational triples from sentences with different number of triples. Again, the CasRel model achieves excellent performance over all five classes. Though it’s not surprising to find that the performance of most baselines decreases with the increasing number of relational triples that a sentence contains, some patterns still can be observed from the performance changes of different models. Compared to previous works that devote to solving the overlapping problem in relational triple extraction, our model suffers the least from the increasing complexity of the input sentence. Though the CasRel model gain considerable improvements on all five classes compared to the best state-of-the-art method CopyRRL zeng2019Learning, the greatest improvement of F1-score on the two datasets both come from the most difficult class (N≥5), indicating that our model is more suitable for complicated scenarios than the baselines.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 3: F1-score of extracting relational triples from sentences with different number (denoted as N) of triples.', solve the subsequent question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhich model shows the greatest improvement in F1-score for the most difficult class (N≥5) on both datasets?\n" }, { "role": "assistant", "content": "The description states that the CasRel model shows the greatest improvement in F1-score for the most difficult class (N≥5) on both datasets, and the table confirms this with CasRel achieving 83.7 on NYT N≥5 and 90.9 on WebNLG N≥5. So, the answer is {\"answer\": \"CasRel\"}" } ], "images": [ "scigen/train/809.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: F1-score of extracting relational triples from sentences with different number (denoted as N) of triples." ], "reasoning": "The description states that the CasRel model shows the greatest improvement in F1-score for the most difficult class (N≥5) on both datasets, and the table confirms this with CasRel achieving 83.7 on NYT N≥5 and 90.9 on WebNLG N≥5.", "question": "Which model shows the greatest improvement in F1-score for the most difficult class (N≥5) on both datasets?", "answer": "So, the answer is {\"answer\": \"CasRel\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe CasRel model achieves the highest F1-score for NYT N=3.\n" }, { "role": "assistant", "content": "The table shows that for NYT N=3, the CasRel model achieves an F1-score of 91.9, which is higher than any other model listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/809.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: F1-score of extracting relational triples from sentences with different number (denoted as N) of triples." ], "reasoning": "The table shows that for NYT N=3, the CasRel model achieves an F1-score of 91.9, which is higher than any other model listed.", "statement": "The CasRel model achieves the highest F1-score for NYT N=3.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Results on relational triple elements.'.\n" }, { "role": "assistant", "content": "The model effectively identifies both subject and object entity mentions, with a small gap between F1-scores on (E1, E2) and (E1, R, E2), but a larger gap between (E1, R, E2) and (E1, R)/(R, E2). This indicates that relation identification is easier than entity identification.For NYT, the performance on E1 and E2 are consistent with that on (E1, R) and (R, E2), demonstrating the effectiveness of our proposed framework in identifying both subject and object entity mentions. We also find that there is only a trivial gap between the F1-score on (E1, E2) and (E1, R, E2), but an obvious gap between (E1, R, E2) and (E1, R)/(R, E2). It reveals that most relations for the entity pairs in extracted triples are correctly identified while some extracted entities fail to form a valid relational triple. In other words, it implies that identifying relations is somehow easier than identifying entities for our model." } ], "images": [ "scigen/train/810.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on relational triple elements." ], "reasoning": "The model effectively identifies both subject and object entity mentions, with a small gap between F1-scores on (E1, E2) and (E1, R, E2), but a larger gap between (E1, R, E2) and (E1, R)/(R, E2). This indicates that relation identification is easier than entity identification.", "description": "For NYT, the performance on E1 and E2 are consistent with that on (E1, R) and (R, E2), demonstrating the effectiveness of our proposed framework in identifying both subject and object entity mentions. We also find that there is only a trivial gap between the F1-score on (E1, E2) and (E1, R, E2), but an obvious gap between (E1, R, E2) and (E1, R)/(R, E2). It reveals that most relations for the entity pairs in extracted triples are correctly identified while some extracted entities fail to form a valid relational triple. In other words, it implies that identifying relations is somehow easier than identifying entities for our model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 4: Results on relational triple elements.'\nQuestion: What is the F1-score for (E1, R, E2) on the NYT dataset?\n" }, { "role": "assistant", "content": "To find the F1-score for (E1, R, E2) on the NYT dataset, look at the row labeled (E1, R, E2) and the column labeled NYT F1. So, the answer is {\"answer\": \"89.6\"}" } ], "images": [ "scigen/train/810.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on relational triple elements." ], "reasoning": "To find the F1-score for (E1, R, E2) on the NYT dataset, look at the row labeled (E1, R, E2) and the column labeled NYT F1.", "question": "What is the F1-score for (E1, R, E2) on the NYT dataset?", "answer": "So, the answer is {\"answer\": \"89.6\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The precision for E1 on the WebNLG dataset is higher than the recall for E2 on the same dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe precision for E1 on the WebNLG dataset is higher than the recall for E2 on the same dataset.\n" }, { "role": "assistant", "content": "The precision for E1 on the WebNLG dataset is 98.7, and the recall for E2 on the WebNLG dataset is 93.0. Since 98.7 is higher than 93.0, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/810.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on relational triple elements." ], "reasoning": "The precision for E1 on the WebNLG dataset is 98.7, and the recall for E2 on the WebNLG dataset is 93.0. Since 98.7 is higher than 93.0, the statement is supported.", "statement": "The precision for E1 on the WebNLG dataset is higher than the recall for E2 on the same dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 8. Market trend prediction using main technical indicators — the baseline model.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "LSTM often favored the positive class over the negative class, producing unbalanced results due to the small dataset size, while SVM yielded balanced and stable results.The F1 scores suggest that LSTM often favoured the positive class over the negative class and produced unbalanced results. The reason could be that the size of the dataset is relatively small: there are 670 data points in the analysed time period 2011-2015. Contrary to LSTM, SVM always yielded balanced and stable results." } ], "images": [ "scigen/train/811.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8. Market trend prediction using main technical indicators — the baseline model." ], "reasoning": "LSTM often favored the positive class over the negative class, producing unbalanced results due to the small dataset size, while SVM yielded balanced and stable results.", "description": "The F1 scores suggest that LSTM often favoured the positive class over the negative class and produced unbalanced results. The reason could be that the size of the dataset is relatively small: there are 670 data points in the analysed time period 2011-2015. Contrary to LSTM, SVM always yielded balanced and stable results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 8. Market trend prediction using main technical indicators — the baseline model.', and sequentially solve the question, writing out each step of your reasoning process. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich method had the highest 5-day ahead Fup1 score for the EUR/USD type?\n" }, { "role": "assistant", "content": "To find the highest 5-day ahead Fup1 score for the EUR/USD type, compare the values in the '5-day ahead Fup1' column for EUR/USD. SVM has a score of 0.740, and LSTM has a score of 0.370. Therefore, SVM has the highest score. So, the answer is {\"answer\": \"SVM\"}." } ], "images": [ "scigen/train/811.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8. Market trend prediction using main technical indicators — the baseline model." ], "reasoning": "To find the highest 5-day ahead Fup1 score for the EUR/USD type, compare the values in the '5-day ahead Fup1' column for EUR/USD. SVM has a score of 0.740, and LSTM has a score of 0.370. Therefore, SVM has the highest score.", "question": "Which method had the highest 5-day ahead Fup1 score for the EUR/USD type?", "answer": "So, the answer is {\"answer\": \"SVM\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nLSTM had a higher 3-day ahead Acc score than SVM for the AAPL type.\n" }, { "role": "assistant", "content": "For the AAPL type, the 3-day ahead Acc score for LSTM is 0.547, while for SVM it is 0.577. Since 0.547 is less than 0.577, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/811.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8. Market trend prediction using main technical indicators — the baseline model." ], "reasoning": "For the AAPL type, the 3-day ahead Acc score for LSTM is 0.547, while for SVM it is 0.577. Since 0.547 is less than 0.577, the statement is refuted.", "statement": "LSTM had a higher 3-day ahead Acc score than SVM for the AAPL type.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2. Sentiment attitudes Granger-causality on the FT I dataset.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Sentiment attitudes generally do not predict stock price movements, but stock price changes often predict sentiment attitudes, with varying results across different stocks and models.In all the experiments, we failed to discover any sign that sentiment attitudes Granger-cause stock price changes, which would suggest that in general sentiment attitudes probably cannot be useful for the prediction of stock price movements. However, in many cases, we found that the opposite was true — stock price changes Granger-cause sentiment attitudes in the news, with the strongest causality found using the temporal sentiment analysis model. The individual stocks also produced mixed results, with each company behaving differently. For the Apple stock, we failed to detect any causality. For the Google stock, we identified that the prices would Granger-cause sentiment attitudes, but only with a two-day lag. For the HP stock, we detected causality only in temporal sentiment and only with a one-day lag. For the JPM stock, we found causality using standard sentiment, but it was absent using temporal sentiment. It is difficult to draw general conclusions from such varying results. According to the Granger-causality test with a one-day or two-day lag, sentiment attitudes do not seem to be useful for predicting stock price movements. However, the opposite seems to be true — the sentiment attitudes should be predicted using stock price movements. It is still possible that the Granger-causality from sentiment attitudes to stock price changes is present at a finer time granularity (e.g., minutes), but we are unable to perform such an analysis using our current datasets." } ], "images": [ "scigen/train/812.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2. Sentiment attitudes Granger-causality on the FT I dataset." ], "reasoning": "Sentiment attitudes generally do not predict stock price movements, but stock price changes often predict sentiment attitudes, with varying results across different stocks and models.", "description": "In all the experiments, we failed to discover any sign that sentiment attitudes Granger-cause stock price changes, which would suggest that in general sentiment attitudes probably cannot be useful for the prediction of stock price movements. However, in many cases, we found that the opposite was true — stock price changes Granger-cause sentiment attitudes in the news, with the strongest causality found using the temporal sentiment analysis model. The individual stocks also produced mixed results, with each company behaving differently. For the Apple stock, we failed to detect any causality. For the Google stock, we identified that the prices would Granger-cause sentiment attitudes, but only with a two-day lag. For the HP stock, we detected causality only in temporal sentiment and only with a one-day lag. For the JPM stock, we found causality using standard sentiment, but it was absent using temporal sentiment. It is difficult to draw general conclusions from such varying results. According to the Granger-causality test with a one-day or two-day lag, sentiment attitudes do not seem to be useful for predicting stock price movements. However, the opposite seems to be true — the sentiment attitudes should be predicted using stock price movements. It is still possible that the Granger-causality from sentiment attitudes to stock price changes is present at a finer time granularity (e.g., minutes), but we are unable to perform such an analysis using our current datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2. Sentiment attitudes Granger-causality on the FT I dataset.', unfold the steps to solve the question given next. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nQuestion: Which stock showed causality from stock price changes to sentiment attitudes using the temporal model with a one-day lag?\n" }, { "role": "assistant", "content": "From the table, we need to find the stock where the 'Price⇒Attitude' value is highlighted under the 'Temporal' model with a lag of 1. The highlighted value for 'Price⇒Attitude' under these conditions is 0.0495 for S&P 500. So, the answer is {\"answer\": \"S&P 500\"}." } ], "images": [ "scigen/train/812.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2. Sentiment attitudes Granger-causality on the FT I dataset." ], "reasoning": "From the table, we need to find the stock where the 'Price⇒Attitude' value is highlighted under the 'Temporal' model with a lag of 1. The highlighted value for 'Price⇒Attitude' under these conditions is 0.0495 for S&P 500.", "question": "Which stock showed causality from stock price changes to sentiment attitudes using the temporal model with a one-day lag?", "answer": "So, the answer is {\"answer\": \"S&P 500\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence For the Google stock, the prices would Granger-cause sentiment attitudes with a one-day lag using the temporal model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nFor the Google stock, the prices would Granger-cause sentiment attitudes with a one-day lag using the temporal model.\n" }, { "role": "assistant", "content": "The table shows that for Google (GOOGL) stock, the 'Price⇒Attitude' value using the temporal model with a one-day lag is 0.5388, which is not highlighted, indicating no significant causality. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/812.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2. Sentiment attitudes Granger-causality on the FT I dataset." ], "reasoning": "The table shows that for Google (GOOGL) stock, the 'Price⇒Attitude' value using the temporal model with a one-day lag is 0.5388, which is not highlighted, indicating no significant causality.", "statement": "For the Google stock, the prices would Granger-cause sentiment attitudes with a one-day lag using the temporal model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 9. Market trend prediction using FT news articles and RWNC headlines (2011-2015).'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Sentiments from FT news articles improved the model's performance for DJIA, JPM, and EUR/USD, but not for AAPL and GBP/USD.Sentiment attitudes and emotions were extracted from the FT news articles and the RWNC headlines in the time period from 2011 to 2015. This is consistent with the previous section, in which no correlation or causality link was established between headlines sentiments and stock prices. It might be explained by the fact that headlines are very short text snippets and therefore provide little chance for us to reliably detect sentiment attitudes and sentiment emotions. The sentiments extracted from FT news articles painted a quite different picture. The sentiment enriched model outperformed the baseline model in half of the scenarios: it demonstrated slightly better results for DJIA, JPM, and EUR/USD, but slightly worse results for AAPL and GBP/USD. These experimental results are consistent with the previous section and confirm again that for some stocks sentiment emotions could be used to improve the baseline model for market trend prediction." } ], "images": [ "scigen/train/813.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9. Market trend prediction using FT news articles and RWNC headlines (2011-2015)." ], "reasoning": "Sentiments from FT news articles improved the model's performance for DJIA, JPM, and EUR/USD, but not for AAPL and GBP/USD.", "description": "Sentiment attitudes and emotions were extracted from the FT news articles and the RWNC headlines in the time period from 2011 to 2015. This is consistent with the previous section, in which no correlation or causality link was established between headlines sentiments and stock prices. It might be explained by the fact that headlines are very short text snippets and therefore provide little chance for us to reliably detect sentiment attitudes and sentiment emotions. The sentiments extracted from FT news articles painted a quite different picture. The sentiment enriched model outperformed the baseline model in half of the scenarios: it demonstrated slightly better results for DJIA, JPM, and EUR/USD, but slightly worse results for AAPL and GBP/USD. These experimental results are consistent with the previous section and confirm again that for some stocks sentiment emotions could be used to improve the baseline model for market trend prediction.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 9. Market trend prediction using FT news articles and RWNC headlines (2011-2015).', unfold the question presented below, detailing every step of your calculation. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich stock had the highest accuracy improvement in the Financial Times Fup1 model compared to the Baseline Fup1 model?\n" }, { "role": "assistant", "content": "To find the stock with the highest accuracy improvement, compare the values in the Financial Times Fup1 column with the Baseline Fup1 column for each stock. The highest improvement is for JPM, with an increase from 0.733 to 0.739. So, the answer is {\"answer\": \"JPM\"}." } ], "images": [ "scigen/train/813.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9. Market trend prediction using FT news articles and RWNC headlines (2011-2015)." ], "reasoning": "To find the stock with the highest accuracy improvement, compare the values in the Financial Times Fup1 column with the Baseline Fup1 column for each stock. The highest improvement is for JPM, with an increase from 0.733 to 0.739.", "question": "Which stock had the highest accuracy improvement in the Financial Times Fup1 model compared to the Baseline Fup1 model?", "answer": "So, the answer is {\"answer\": \"JPM\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Financial Times Fdown1 model performed worse than the Baseline Fdown1 model for all stocks.\n" }, { "role": "assistant", "content": "By comparing the Financial Times Fdown1 values with the Baseline Fdown1 values for each stock, we see that the Financial Times Fdown1 model performed better for GBP/USD (0.708 vs. 0.723) and EUR/USD (0.605 vs. 0.518). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/813.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9. Market trend prediction using FT news articles and RWNC headlines (2011-2015)." ], "reasoning": "By comparing the Financial Times Fdown1 values with the Baseline Fdown1 values for each stock, we see that the Financial Times Fdown1 model performed better for GBP/USD (0.708 vs. 0.723) and EUR/USD (0.605 vs. 0.518).", "statement": "The Financial Times Fdown1 model performed worse than the Baseline Fdown1 model for all stocks.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 10. Market trend prediction using financial tweets from Twitter (01/04/2014 – 01/04/2015).', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The filtering+emotion setting showed noticeable performance improvements for JPM stock, indicating that sentiment emotions can enhance machine learning models for specific stocks.Only for the JPM stock we could see noticeable performance improvements in the “filtering+emotion” setting. Our results have also confirmed that sentiment attitudes on their own are probably not very useful for market trend prediction, but at least for some particular stocks sentiment emotions could be exploited to improve machine learning models like LSTM to get better market trend prediction." } ], "images": [ "scigen/train/814.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10. Market trend prediction using financial tweets from Twitter (01/04/2014 – 01/04/2015)." ], "reasoning": "The filtering+emotion setting showed noticeable performance improvements for JPM stock, indicating that sentiment emotions can enhance machine learning models for specific stocks.", "description": "Only for the JPM stock we could see noticeable performance improvements in the “filtering+emotion” setting. Our results have also confirmed that sentiment attitudes on their own are probably not very useful for market trend prediction, but at least for some particular stocks sentiment emotions could be exploited to improve machine learning models like LSTM to get better market trend prediction.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 10. Market trend prediction using financial tweets from Twitter (01/04/2014 – 01/04/2015).', answer the question below by showing each progressive step toward the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich stock showed noticeable performance improvements in the filtering+emotion setting?\n" }, { "role": "assistant", "content": "The description states that only the JPM stock showed noticeable performance improvements in the filtering+emotion setting. The table confirms this by showing the highest values for JPM in the filtering+emotion columns. So, the answer is {\"answer\": \"JPM\"}." } ], "images": [ "scigen/train/814.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10. Market trend prediction using financial tweets from Twitter (01/04/2014 – 01/04/2015)." ], "reasoning": "The description states that only the JPM stock showed noticeable performance improvements in the filtering+emotion setting. The table confirms this by showing the highest values for JPM in the filtering+emotion columns.", "question": "Which stock showed noticeable performance improvements in the filtering+emotion setting?", "answer": "So, the answer is {\"answer\": \"JPM\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSentiment attitudes alone are useful for market trend prediction.\n" }, { "role": "assistant", "content": "The description states that sentiment attitudes on their own are probably not very useful for market trend prediction. The table does not provide specific data to support or refute this claim. Thus, the answer is {\"answer\": \"not enough info\"}." } ], "images": [ "scigen/train/814.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10. Market trend prediction using financial tweets from Twitter (01/04/2014 – 01/04/2015)." ], "reasoning": "The description states that sentiment attitudes on their own are probably not very useful for market trend prediction. The table does not provide specific data to support or refute this claim.", "statement": "Sentiment attitudes alone are useful for market trend prediction.", "answer": "Thus, the answer is {\"answer\": \"not enough info\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: Effect of objective function'.\n" }, { "role": "assistant", "content": "The L1 distance-based objective function yields slightly better results compared to Cosine and L2 in terms of accuracy on test sets.Overall, these results indicate that the evaluated objective functions behave similarly in this task, however L1 distance based objective function yields slightly better results." } ], "images": [ "scigen/train/815.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Effect of objective function" ], "reasoning": "The L1 distance-based objective function yields slightly better results compared to Cosine and L2 in terms of accuracy on test sets.", "description": "Overall, these results indicate that the evaluated objective functions behave similarly in this task, however L1 distance based objective function yields slightly better results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 5: Effect of objective function', solve the following question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich objective function has the highest accuracy on the MRDA test set?\n" }, { "role": "assistant", "content": "By comparing the accuracy values on the MRDA test set for each objective function, the L1 function has the highest accuracy at 60.39%. So, the answer is {\"answer\": \"L1\"}." } ], "images": [ "scigen/train/815.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Effect of objective function" ], "reasoning": "By comparing the accuracy values on the MRDA test set for each objective function, the L1 function has the highest accuracy at 60.39%.", "question": "Which objective function has the highest accuracy on the MRDA test set?", "answer": "So, the answer is {\"answer\": \"L1\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Cosine objective function has the highest accuracy on the FSC test set.\n" }, { "role": "assistant", "content": "The table shows that the accuracy of the Cosine objective function on the FSC test set is 89.45%, while the L1 objective function has a higher accuracy of 89.98%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/815.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Effect of objective function" ], "reasoning": "The table shows that the accuracy of the Cosine objective function on the FSC test set is 89.45%, while the L1 objective function has a higher accuracy of 89.98%.", "statement": "The Cosine objective function has the highest accuracy on the FSC test set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Effect of layers fine-tuning'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Fine-tuning the top two former ASR encoder layers improves accuracy in downstream tasks, and optimizing the SLU model for smaller output distance from the NLU model generally enhances accuracy.While it is not completely clear how many layers should be fine-tuned, we can conclusively tell that fine-tuning of former ASR encoder layers is more beneficial than former NLU layers. We decide to fine-tune the two top former ASR encoder layers. The results also illustrate that the optimization of SLU model for smaller distance of its output from the output of NLU model is general enough and translates to accuracy improvements in the downstream tasks, although not in all cases." } ], "images": [ "scigen/train/816.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Effect of layers fine-tuning" ], "reasoning": "Fine-tuning the top two former ASR encoder layers improves accuracy in downstream tasks, and optimizing the SLU model for smaller output distance from the NLU model generally enhances accuracy.", "description": "While it is not completely clear how many layers should be fine-tuned, we can conclusively tell that fine-tuning of former ASR encoder layers is more beneficial than former NLU layers. We decide to fine-tune the two top former ASR encoder layers. The results also illustrate that the optimization of SLU model for smaller distance of its output from the output of NLU model is general enough and translates to accuracy improvements in the downstream tasks, although not in all cases.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Effect of layers fine-tuning', unfold the question presented below, detailing every step of your calculation. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the validation loss when fine-tuning two top former ASR encoder layers?\n" }, { "role": "assistant", "content": "The table shows that when 2 ASR layers and 0 NLU layers are fine-tuned, the validation loss is 0.15. So, the answer is {\"answer\": \"0.15\"}." } ], "images": [ "scigen/train/816.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Effect of layers fine-tuning" ], "reasoning": "The table shows that when 2 ASR layers and 0 NLU layers are fine-tuned, the validation loss is 0.15.", "question": "What is the validation loss when fine-tuning two top former ASR encoder layers?", "answer": "So, the answer is {\"answer\": \"0.15\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nFine-tuning three ASR layers results in the highest accuracy on the SwBD test.\n" }, { "role": "assistant", "content": "The table shows that fine-tuning three ASR layers results in an accuracy of 53.81% on the SwBD test, which is the highest among all configurations. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/816.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Effect of layers fine-tuning" ], "reasoning": "The table shows that fine-tuning three ASR layers results in an accuracy of 53.81% on the SwBD test, which is the highest among all configurations.", "statement": "Fine-tuning three ASR layers results in the highest accuracy on the SwBD test.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Effect of learning rate schedule'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Increasing the number of warmup steps positively affects the learning rate ramp-up, but requires increasing the number of epochs from 10 to 20 when warmup steps are close to the total fine-tuning steps.After deciding which layers to fine-tune, we run a series of experiments to determine the best learning rate schedule. When we increase number of warmup steps, we notice positive effect from slower learning rate ramp up. However, as number number of warmup steps becomes close to the total number of fine-tuning steps, we have to increase number of epochs from 10 to 20 in order to see the whole fine-tuning process." } ], "images": [ "scigen/train/817.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Effect of learning rate schedule" ], "reasoning": "Increasing the number of warmup steps positively affects the learning rate ramp-up, but requires increasing the number of epochs from 10 to 20 when warmup steps are close to the total fine-tuning steps.", "description": "After deciding which layers to fine-tune, we run a series of experiments to determine the best learning rate schedule. When we increase number of warmup steps, we notice positive effect from slower learning rate ramp up. However, as number number of warmup steps becomes close to the total number of fine-tuning steps, we have to increase number of epochs from 10 to 20 in order to see the whole fine-tuning process.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: 'Table 4: Effect of learning rate schedule'\nQuestion: What is the validation loss when the number of warmup steps is 700,000 and the learning rate is constant at 50?\n" }, { "role": "assistant", "content": "From the table, when the number of warmup steps is 700,000 and the learning rate is constant at 50, the validation loss is 0.14. So, the answer is {\"answer\": \"0.14\"}." } ], "images": [ "scigen/train/817.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Effect of learning rate schedule" ], "reasoning": "From the table, when the number of warmup steps is 700,000 and the learning rate is constant at 50, the validation loss is 0.14.", "question": "What is the validation loss when the number of warmup steps is 700,000 and the learning rate is constant at 50?", "answer": "So, the answer is {\"answer\": \"0.14\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The highest accuracy on the MRDA test is achieved with 600,000 warmup steps and 20 epochs. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe highest accuracy on the MRDA test is achieved with 600,000 warmup steps and 20 epochs.\n" }, { "role": "assistant", "content": "The table shows that the highest accuracy on the MRDA test is 60.12%, which is achieved with 600,000 warmup steps and 20 epochs. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/817.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Effect of learning rate schedule" ], "reasoning": "The table shows that the highest accuracy on the MRDA test is 60.12%, which is achieved with 600,000 warmup steps and 20 epochs.", "statement": "The highest accuracy on the MRDA test is achieved with 600,000 warmup steps and 20 epochs.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Results for multilingual direct SLT systems with 6 and 8 target languages.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Adding ASR data to the 6 target languages improves performance in most languages, but using all 8 target languages worsens the results due to the model's limited parameters.Number of languages. When training a system with all the 6 target languages (De, Nl, Es, Fr, It, Pt) When adding ASR data to the 6 languages, we observe improvements in most languages, and the new system is worse than the baseline only for Spanish and French, although the gap for French has been reduced to −0.9. However, using all the 8 target languages leads to worse results and adding ASR data contributes to worsen the performance in this case. We think that the reason is related to the relatively low number of parameters of our models (∼33 millions), which reduce their capability to learn and discriminate between a larger number of languages." } ], "images": [ "scigen/train/818.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results for multilingual direct SLT systems with 6 and 8 target languages." ], "reasoning": "Adding ASR data to the 6 target languages improves performance in most languages, but using all 8 target languages worsens the results due to the model's limited parameters.", "description": "Number of languages. When training a system with all the 6 target languages (De, Nl, Es, Fr, It, Pt) When adding ASR data to the 6 languages, we observe improvements in most languages, and the new system is worse than the baseline only for Spanish and French, although the gap for French has been reduced to −0.9. However, using all the 8 target languages leads to worse results and adding ASR data contributes to worsen the performance in this case. We think that the reason is related to the relatively low number of parameters of our models (∼33 millions), which reduce their capability to learn and discriminate between a larger number of languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 3: Results for multilingual direct SLT systems with 6 and 8 target languages.', solve this question and give detail solutions. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich language showed the greatest improvement when ASR data was added to the 6 target languages?\n" }, { "role": "assistant", "content": "Compare the scores of the 6 target languages with and without ASR data. The greatest improvement is seen in the language with the highest positive difference. So, the answer is {\"answer\": \"Nl\"}." } ], "images": [ "scigen/train/818.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results for multilingual direct SLT systems with 6 and 8 target languages." ], "reasoning": "Compare the scores of the 6 target languages with and without ASR data. The greatest improvement is seen in the language with the highest positive difference.", "question": "Which language showed the greatest improvement when ASR data was added to the 6 target languages?", "answer": "So, the answer is {\"answer\": \"Nl\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nAdding ASR data to the 8 target languages improved the performance for Romanian.\n" }, { "role": "assistant", "content": "The table shows that adding ASR data to the 8 target languages resulted in a score of 15.5 for Romanian, which is worse than the baseline score of 16.5. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/818.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results for multilingual direct SLT systems with 6 and 8 target languages." ], "reasoning": "The table shows that adding ASR data to the 8 target languages resulted in a score of 15.5 for Romanian, which is worse than the baseline score of 16.5.", "statement": "Adding ASR data to the 8 target languages improved the performance for Romanian.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Results with concat (C-*) and merge (M-*) target forcing on 6 languages. The baselines are one-to-one systems. All the other results are computed with one multilingual system for En→De,NL and one for En→Es,Fr,It,Pt.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The merge target forcing mechanism, particularly M-Pre, consistently outperforms the concat method and baselines across multiple languages, with notable improvements in En-Pt and En-Nl.Concat vs Merge. Our first experiment consists in comparing the baselines with the multilingual models based on the target forcing mechanism. By looking at the translations, we found that the cause of the degradation is that many sentences are acceptable translations, but in a wrong language. We first hypothesize that the processing performed in the layer preceding the encoder self-attentional stack loses the language information. Thus, we concatenate the language embedding to the representations of the Post and Final positions (see Fig. Our second hypothesis is that the networks are not able to learn the joint probabilities of language embeddings and character sequences because of a combination of factors: character-level translations (instead of sub-words), very long source-side sequences and the source sides in the corpus are highly overlapping between languages. Thus, we assume that our networks can learn to discriminate better among target languages by giving a stronger language signal. For this reason, we introduce the merge target forcing that forces the network to generate target-language dependent encoder representations by translating them in different portions of the space according to the target language. (M- *) is definitely better than concat for all the target languages, and also obtains performance that is on par with or better than the baselines. M-Pre is the system that shows high results more consistently in all languages, and the largest improvement is observed in En-Pt, with over 2.0 BLEU points of gain, followed by +0.7 in En-Nl. The BLEU score slightly degrades for Spanish by 0.2∼0.4, and for French by 0.2∼0.7. Besides the three different language embedding positions in the encoder, we also performed experiments by applying target forcing on the decoder, but they show slightly worse performance. Then, for the following experiments we will continue only with the Pre position that results in the best average performance on all the language directions." } ], "images": [ "scigen/train/819.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results with concat (C-*) and merge (M-*) target forcing on 6 languages. The baselines are one-to-one systems. All the other results are computed with one multilingual system for En→De,NL and one for En→Es,Fr,It,Pt." ], "reasoning": "The merge target forcing mechanism, particularly M-Pre, consistently outperforms the concat method and baselines across multiple languages, with notable improvements in En-Pt and En-Nl.", "description": "Concat vs Merge. Our first experiment consists in comparing the baselines with the multilingual models based on the target forcing mechanism. By looking at the translations, we found that the cause of the degradation is that many sentences are acceptable translations, but in a wrong language. We first hypothesize that the processing performed in the layer preceding the encoder self-attentional stack loses the language information. Thus, we concatenate the language embedding to the representations of the Post and Final positions (see Fig. Our second hypothesis is that the networks are not able to learn the joint probabilities of language embeddings and character sequences because of a combination of factors: character-level translations (instead of sub-words), very long source-side sequences and the source sides in the corpus are highly overlapping between languages. Thus, we assume that our networks can learn to discriminate better among target languages by giving a stronger language signal. For this reason, we introduce the merge target forcing that forces the network to generate target-language dependent encoder representations by translating them in different portions of the space according to the target language. (M- *) is definitely better than concat for all the target languages, and also obtains performance that is on par with or better than the baselines. M-Pre is the system that shows high results more consistently in all languages, and the largest improvement is observed in En-Pt, with over 2.0 BLEU points of gain, followed by +0.7 in En-Nl. The BLEU score slightly degrades for Spanish by 0.2∼0.4, and for French by 0.2∼0.7. Besides the three different language embedding positions in the encoder, we also performed experiments by applying target forcing on the decoder, but they show slightly worse performance. Then, for the following experiments we will continue only with the Pre position that results in the best average performance on all the language directions.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: Results with concat (C-*) and merge (M-*) target forcing on 6 languages. The baselines are one-to-one systems. All the other results are computed with one multilingual system for En→De,NL and one for En→Es,Fr,It,Pt.', solve this question and give detail solutions. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich method shows the highest BLEU score for French?\n" }, { "role": "assistant", "content": "By comparing the BLEU scores for French across all methods, the highest score is found in the Baseline method with a score of 26.9. So, the answer is {\"answer\": \"Baseline\"}." } ], "images": [ "scigen/train/819.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results with concat (C-*) and merge (M-*) target forcing on 6 languages. The baselines are one-to-one systems. All the other results are computed with one multilingual system for En→De,NL and one for En→Es,Fr,It,Pt." ], "reasoning": "By comparing the BLEU scores for French across all methods, the highest score is found in the Baseline method with a score of 26.9.", "question": "Which method shows the highest BLEU score for French?", "answer": "So, the answer is {\"answer\": \"Baseline\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe M-Pre method shows the highest BLEU score for Portuguese.\n" }, { "role": "assistant", "content": "By examining the BLEU scores for Portuguese, the highest score is found in the M-Pre + ASR method with a score of 22.6, not the M-Pre method. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/819.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results with concat (C-*) and merge (M-*) target forcing on 6 languages. The baselines are one-to-one systems. All the other results are computed with one multilingual system for En→De,NL and one for En→Es,Fr,It,Pt." ], "reasoning": "By examining the BLEU scores for Portuguese, the highest score is found in the M-Pre + ASR method with a score of 22.6, not the M-Pre method.", "statement": "The M-Pre method shows the highest BLEU score for Portuguese.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Comparison of the Baseline and the best multilingual system with the single language cascade (BL-Cascade) and the multilingual cascade (M-Cascade)'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The BL-Cascade systems outperform the direct SLT baselines, with differences ranging from −1.0 for French to −3.4 for Dutch. The M-Cascade systems show lower results in 3 out of 4 Romance languages compared to BL-Cascade, except for Portuguese which improved by +1.3.Comparison with cascade. As expected, our direct SLT baselines are significantly worse than the BL-cascade systems with differences that range from −1.0 for French to −3.4 for Dutch. Comparing the BL-Cascade with the M-Cascade systems, we observe not significant variation for the Germanic languages, but lower results in 3 out of 4 Romance languages (−0.6 for French), with the single improvement of +1.3 for Portuguese. Thus, multilingual MT generally affects negatively the performance, being beneficial only for the lowest resourced languages." } ], "images": [ "scigen/train/820.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of the Baseline and the best multilingual system with the single language cascade (BL-Cascade) and the multilingual cascade (M-Cascade)" ], "reasoning": "The BL-Cascade systems outperform the direct SLT baselines, with differences ranging from −1.0 for French to −3.4 for Dutch. The M-Cascade systems show lower results in 3 out of 4 Romance languages compared to BL-Cascade, except for Portuguese which improved by +1.3.", "description": "Comparison with cascade. As expected, our direct SLT baselines are significantly worse than the BL-cascade systems with differences that range from −1.0 for French to −3.4 for Dutch. Comparing the BL-Cascade with the M-Cascade systems, we observe not significant variation for the Germanic languages, but lower results in 3 out of 4 Romance languages (−0.6 for French), with the single improvement of +1.3 for Portuguese. Thus, multilingual MT generally affects negatively the performance, being beneficial only for the lowest resourced languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 4: Comparison of the Baseline and the best multilingual system with the single language cascade (BL-Cascade) and the multilingual cascade (M-Cascade)', and tackle the question below in a sequential manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich language showed the greatest improvement in score when comparing the Baseline to the M-Cascade system?\n" }, { "role": "assistant", "content": "To find the language with the greatest improvement, subtract the Baseline score from the M-Cascade score for each language. The language with the highest positive difference is the one with the greatest improvement. So, the answer is {\"answer\": \"Pt\"}." } ], "images": [ "scigen/train/820.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of the Baseline and the best multilingual system with the single language cascade (BL-Cascade) and the multilingual cascade (M-Cascade)" ], "reasoning": "To find the language with the greatest improvement, subtract the Baseline score from the M-Cascade score for each language. The language with the highest positive difference is the one with the greatest improvement.", "question": "Which language showed the greatest improvement in score when comparing the Baseline to the M-Cascade system?", "answer": "So, the answer is {\"answer\": \"Pt\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The M-Cascade system performed worse than the Baseline for French., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe M-Cascade system performed worse than the Baseline for French.\n" }, { "role": "assistant", "content": "Comparing the scores for French, the Baseline score is 26.9 and the M-Cascade score is 27.3. Since 27.3 is higher than 26.9, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/820.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of the Baseline and the best multilingual system with the single language cascade (BL-Cascade) and the multilingual cascade (M-Cascade)" ], "reasoning": "Comparing the scores for French, the Baseline score is 26.9 and the M-Cascade score is 27.3. Since 27.3 is higher than 26.9, the statement is refuted.", "statement": "The M-Cascade system performed worse than the Baseline for French.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 5: Percentage of sentences in the correct language computed with langdetect.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Using ASR data improves the percentage of correct language in all languages except Portuguese, but this improvement does not correlate with the BLEU score improvement.Language analysis. Then, when using also ASR data, the percentage of correct language increases slightly in all languages except for Portuguese. However, the improvement in the correct language does not correlate with the improvement in BLEU score. This suggests that the improvement in BLEU score of M-PRE + ASR comes from better translations and not from more sentences translated in the correct language." } ], "images": [ "scigen/train/821.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Percentage of sentences in the correct language computed with langdetect." ], "reasoning": "Using ASR data improves the percentage of correct language in all languages except Portuguese, but this improvement does not correlate with the BLEU score improvement.", "description": "Language analysis. Then, when using also ASR data, the percentage of correct language increases slightly in all languages except for Portuguese. However, the improvement in the correct language does not correlate with the improvement in BLEU score. This suggests that the improvement in BLEU score of M-PRE + ASR comes from better translations and not from more sentences translated in the correct language.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 5: Percentage of sentences in the correct language computed with langdetect.', solve the following question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich language showed a decrease in the percentage of correct language when using ASR data?\n" }, { "role": "assistant", "content": "By comparing the percentages of correct language for M-Pre and M-Pre + ASR, it is evident that Portuguese (Pt) is the only language where the percentage decreased from 96.6 to 95.2. So, the answer is {\"answer\": \"Portuguese\"}." } ], "images": [ "scigen/train/821.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Percentage of sentences in the correct language computed with langdetect." ], "reasoning": "By comparing the percentages of correct language for M-Pre and M-Pre + ASR, it is evident that Portuguese (Pt) is the only language where the percentage decreased from 96.6 to 95.2.", "question": "Which language showed a decrease in the percentage of correct language when using ASR data?", "answer": "So, the answer is {\"answer\": \"Portuguese\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe percentage of correct language for French increased when using ASR data.\n" }, { "role": "assistant", "content": "The table shows that the percentage of correct language for French (Fr) increased from 94.6 to 95.3 when using ASR data. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/821.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Percentage of sentences in the correct language computed with langdetect." ], "reasoning": "The table shows that the percentage of correct language for French (Fr) increased from 94.6 to 95.3 when using ASR data.", "statement": "The percentage of correct language for French increased when using ASR data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Quadratic Weighted Kappa (QWK) of different AES models. The CO-ATTN model significantly outperforms the Rubric and SG models, respectively (p<0.05).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The CO-ATTN model has the best performance in terms of QWK, but SG is used in eRevise for its interpretability and features like NPE and SPC.We have developed several AES systems for RTA assessment Our first model (denoted by Rubric) A subsequent model (denoted by SG) Most recently, \\citeauthorzhang2018co \\shortcitezhang2018co developed a neural network model with a co-attention layer (denoted by CO-ATTN) to eliminate human feature engineering. Although the neural CO-ATTN model has the best performance, to select formative feedback messages that address essay weaknesses in terms of rubric constructs, a more interpretable representation of the essay is necessary. Therefore, SG is the AES system used in eRevise. In particular, two of the features used by SG for score prediction, namely Number of Pieces of Evidence (NPE) and Specificity (SPC), form the basis of eRevise’s feedback selection algorithm." } ], "images": [ "scigen/train/822.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Quadratic Weighted Kappa (QWK) of different AES models. The CO-ATTN model significantly outperforms the Rubric and SG models, respectively (p<0.05)." ], "reasoning": "The CO-ATTN model has the best performance in terms of QWK, but SG is used in eRevise for its interpretability and features like NPE and SPC.", "description": "We have developed several AES systems for RTA assessment Our first model (denoted by Rubric) A subsequent model (denoted by SG) Most recently, \\citeauthorzhang2018co \\shortcitezhang2018co developed a neural network model with a co-attention layer (denoted by CO-ATTN) to eliminate human feature engineering. Although the neural CO-ATTN model has the best performance, to select formative feedback messages that address essay weaknesses in terms of rubric constructs, a more interpretable representation of the essay is necessary. Therefore, SG is the AES system used in eRevise. In particular, two of the features used by SG for score prediction, namely Number of Pieces of Evidence (NPE) and Specificity (SPC), form the basis of eRevise’s feedback selection algorithm.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: Quadratic Weighted Kappa (QWK) of different AES models. The CO-ATTN model significantly outperforms the Rubric and SG models, respectively (p<0.05).' in mind, break down and resolve the question below step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich AES model has the highest QWK score and why is it not used in eRevise?\n" }, { "role": "assistant", "content": "The CO-ATTN model has the highest QWK score of 0.697. However, it is not used in eRevise because a more interpretable representation of the essay is necessary for selecting formative feedback messages, which SG provides. So, the answer is {\"answer\": \"CO-ATTN\"}." } ], "images": [ "scigen/train/822.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Quadratic Weighted Kappa (QWK) of different AES models. The CO-ATTN model significantly outperforms the Rubric and SG models, respectively (p<0.05)." ], "reasoning": "The CO-ATTN model has the highest QWK score of 0.697. However, it is not used in eRevise because a more interpretable representation of the essay is necessary for selecting formative feedback messages, which SG provides.", "question": "Which AES model has the highest QWK score and why is it not used in eRevise?", "answer": "So, the answer is {\"answer\": \"CO-ATTN\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nSG is the AES system with the highest QWK score.\n" }, { "role": "assistant", "content": "The table shows that the CO-ATTN model has the highest QWK score of 0.697, while SG has a QWK score of 0.653. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/822.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Quadratic Weighted Kappa (QWK) of different AES models. The CO-ATTN model significantly outperforms the Rubric and SG models, respectively (p<0.05)." ], "reasoning": "The table shows that the CO-ATTN model has the highest QWK score of 0.697, while SG has a QWK score of 0.653.", "statement": "SG is the AES system with the highest QWK score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5: Lookup table for feedback selection.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "eRevise will now include a control condition with generic feedback and will be used for two forms of RTA, with exploration into adding CO-ATTN scores.SPCAWE=3, and SPClmh =M. We are about to begin the next deployment of eRevise, which will extend our work in two ways. First, to better determine the benefit of using AES to adaptively guide revision, we have added a control condition where eRevise will display the same generic feedback message to all students: “MAKE YOUR ESSAY MORE CONVINCING - Help readers understand why you believe the fight against poverty is/isn’t achievable in our lifetime.” This is in contrast to the existing eRevise adaptive feedback, where students receive different messages based on AES. Second, students will use eRevise for two different forms of the RTA (i.e., RTAspace in addition to RTAMVP). While we have already trained a SG model for scoring RTAspace We are also exploring adding CO-ATTN scores to the lookup table." } ], "images": [ "scigen/train/823.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Lookup table for feedback selection." ], "reasoning": "eRevise will now include a control condition with generic feedback and will be used for two forms of RTA, with exploration into adding CO-ATTN scores.", "description": "SPCAWE=3, and SPClmh =M. We are about to begin the next deployment of eRevise, which will extend our work in two ways. First, to better determine the benefit of using AES to adaptively guide revision, we have added a control condition where eRevise will display the same generic feedback message to all students: “MAKE YOUR ESSAY MORE CONVINCING - Help readers understand why you believe the fight against poverty is/isn’t achievable in our lifetime.” This is in contrast to the existing eRevise adaptive feedback, where students receive different messages based on AES. Second, students will use eRevise for two different forms of the RTA (i.e., RTAspace in addition to RTAMVP). While we have already trained a SG model for scoring RTAspace We are also exploring adding CO-ATTN scores to the lookup table.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: Lookup table for feedback selection.'.\n\nQuestion: What feedback messages are given when SPCAWE is 3 and SPClmh is M?\n" }, { "role": "assistant", "content": "From the table, when SPCAWE is 3 and SPClmh is M, the feedback messages are found in the column where Value is 3 and SPClmh is M. So, the answer is {\"answer\": \"2,3\"}." } ], "images": [ "scigen/train/823.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Lookup table for feedback selection." ], "reasoning": "From the table, when SPCAWE is 3 and SPClmh is M, the feedback messages are found in the column where Value is 3 and SPClmh is M.", "question": "What feedback messages are given when SPCAWE is 3 and SPClmh is M?", "answer": "So, the answer is {\"answer\": \"2,3\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe feedback messages for SPCAWE=4 and SPClmh=H are 3,4.\n" }, { "role": "assistant", "content": "From the table, when SPCAWE is 4 and SPClmh is H, the feedback messages are 3,4. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/823.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Lookup table for feedback selection." ], "reasoning": "From the table, when SPCAWE is 4 and SPClmh is H, the feedback messages are 3,4.", "statement": "The feedback messages for SPCAWE=4 and SPClmh=H are 3,4.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Test Results for Event Coreference with the Singleton and Matching baselines.'.\n" }, { "role": "assistant", "content": "Our system outperforms the best system in TAC-KBP 2015 by about 2 points in F-score on average and performs best on B3 and CEAF-E metrics.Comparing to the top 3 coreference systems in TAC-KBP 2015, we outperform the best system by about 2 points absolute F-score on average. Our system is also competitive on individual metrics. Our model performs the best based on B3 and CEAF-E, and is comparable to the top performing systems on MUC and BLANC. Note that while the Matching baseline only links event mentions based on event type and realis status, it is very competitive and performs close to the top systems. This is not surprising since these two attributes are based on the gold standard. To take a closer look, we conduct an ablation study by removing the simple match features one by one. We observe that some features produce mixed results on different metrics: they provide improvements on some metrics but not all. This is partially caused by the different characteristics of different metrics. On the other hand, these features (parsing and frames) are automatically predicted, which make them less stable. Furthermore, the Frame features contain duplicate information to event types, which makes it less useful in this setting." } ], "images": [ "scigen/train/824.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Test Results for Event Coreference with the Singleton and Matching baselines." ], "reasoning": "Our system outperforms the best system in TAC-KBP 2015 by about 2 points in F-score on average and performs best on B3 and CEAF-E metrics.", "description": "Comparing to the top 3 coreference systems in TAC-KBP 2015, we outperform the best system by about 2 points absolute F-score on average. Our system is also competitive on individual metrics. Our model performs the best based on B3 and CEAF-E, and is comparable to the top performing systems on MUC and BLANC. Note that while the Matching baseline only links event mentions based on event type and realis status, it is very competitive and performs close to the top systems. This is not surprising since these two attributes are based on the gold standard. To take a closer look, we conduct an ablation study by removing the simple match features one by one. We observe that some features produce mixed results on different metrics: they provide improvements on some metrics but not all. This is partially caused by the different characteristics of different metrics. On the other hand, these features (parsing and frames) are automatically predicted, which make them less stable. Furthermore, the Frame features contain duplicate information to event types, which makes it less useful in this setting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Test Results for Event Coreference with the Singleton and Matching baselines.', and sequentially solve the question, writing out each step of your reasoning process. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich feature removal results in the highest average F-score?\n" }, { "role": "assistant", "content": "By comparing the average F-scores in the table, the removal of the Frame feature results in the highest average F-score of 77.79. So, the answer is {\"answer\": \"Frame\"}" } ], "images": [ "scigen/train/824.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Test Results for Event Coreference with the Singleton and Matching baselines." ], "reasoning": "By comparing the average F-scores in the table, the removal of the Frame feature results in the highest average F-score of 77.79.", "question": "Which feature removal results in the highest average F-score?", "answer": "So, the answer is {\"answer\": \"Frame\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRemoving the Distance feature results in the highest BLANC score.\n" }, { "role": "assistant", "content": "The table shows that removing the Distance feature results in a BLANC score of 77.55, which is not the highest. The highest BLANC score is 77.74, achieved by removing the Frame feature. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/824.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Test Results for Event Coreference with the Singleton and Matching baselines." ], "reasoning": "The table shows that removing the Distance feature results in a BLANC score of 77.55, which is not the highest. The highest BLANC score is 77.74, achieved by removing the Frame feature.", "statement": "Removing the Distance feature results in the highest BLANC score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Test Results for Event Coreference with the Singleton and Matching baselines.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our system outperforms the best system by about 2 points in average F-score, performs best on B3 and CEAF-E, and is comparable on MUC and BLANC.Comparing to the top 3 coreference systems in TAC-KBP 2015, we outperform the best system by about 2 points absolute F-score on average. Our system is also competitive on individual metrics. Our model performs the best based on B3 and CEAF-E, and is comparable to the top performing systems on MUC and BLANC. Note that while the Matching baseline only links event mentions based on event type and realis status, it is very competitive and performs close to the top systems. This is not surprising since these two attributes are based on the gold standard. To take a closer look, we conduct an ablation study by removing the simple match features one by one. We observe that some features produce mixed results on different metrics: they provide improvements on some metrics but not all. This is partially caused by the different characteristics of different metrics. On the other hand, these features (parsing and frames) are automatically predicted, which make them less stable. Furthermore, the Frame features contain duplicate information to event types, which makes it less useful in this setting." } ], "images": [ "scigen/train/825.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Test Results for Event Coreference with the Singleton and Matching baselines." ], "reasoning": "Our system outperforms the best system by about 2 points in average F-score, performs best on B3 and CEAF-E, and is comparable on MUC and BLANC.", "description": "Comparing to the top 3 coreference systems in TAC-KBP 2015, we outperform the best system by about 2 points absolute F-score on average. Our system is also competitive on individual metrics. Our model performs the best based on B3 and CEAF-E, and is comparable to the top performing systems on MUC and BLANC. Note that while the Matching baseline only links event mentions based on event type and realis status, it is very competitive and performs close to the top systems. This is not surprising since these two attributes are based on the gold standard. To take a closer look, we conduct an ablation study by removing the simple match features one by one. We observe that some features produce mixed results on different metrics: they provide improvements on some metrics but not all. This is partially caused by the different characteristics of different metrics. On the other hand, these features (parsing and frames) are automatically predicted, which make them less stable. Furthermore, the Frame features contain duplicate information to event types, which makes it less useful in this setting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich system has the highest average F-score?\nSolve the above question based on the table titled 'Table 2: Test Results for Event Coreference with the Singleton and Matching baselines.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the 'AVG.' column, 'This work' has the highest average F-score of 77.61. So, the answer is {\"answer\": \"This work\"}" } ], "images": [ "scigen/train/825.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Test Results for Event Coreference with the Singleton and Matching baselines." ], "reasoning": "By comparing the 'AVG.' column, 'This work' has the highest average F-score of 77.61.", "question": "Which system has the highest average F-score?", "answer": "So, the answer is {\"answer\": \"This work\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Matching baseline performs the best on the MUC metric. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Matching baseline performs the best on the MUC metric.\n" }, { "role": "assistant", "content": "The table shows that the Matching baseline has a MUC score of 69.83, which is higher than the scores of other systems. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/825.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Test Results for Event Coreference with the Singleton and Matching baselines." ], "reasoning": "The table shows that the Matching baseline has a MUC score of 69.83, which is higher than the scores of other systems.", "statement": "The Matching baseline performs the best on the MUC metric.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Test Results for event sequencing. The Oracle Cluster+Temporal system is using Caevo’s result on the Oracle Clusters.'.\n" }, { "role": "assistant", "content": "The baseline system has high precision but low recall due to its reliance on oracle script clusters, while our model has a higher recall but lower precision.Because the baseline system has access to the oracle script clusters, it produces high precision. However, the low recall value shows that it fails to produce enough After links. Our analysis shows that a lot of After relations are not indicated by clear temporal clues, but can only be solved with script knowledge. However, it fails to identify that “extradited” is after “arrested”, which requires knowledge about prototypical event sequences." } ], "images": [ "scigen/train/826.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Test Results for event sequencing. The Oracle Cluster+Temporal system is using Caevo’s result on the Oracle Clusters." ], "reasoning": "The baseline system has high precision but low recall due to its reliance on oracle script clusters, while our model has a higher recall but lower precision.", "description": "Because the baseline system has access to the oracle script clusters, it produces high precision. However, the low recall value shows that it fails to produce enough After links. Our analysis shows that a lot of After relations are not indicated by clear temporal clues, but can only be solved with script knowledge. However, it fails to identify that “extradited” is after “arrested”, which requires knowledge about prototypical event sequences.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 4: Test Results for event sequencing. The Oracle Cluster+Temporal system is using Caevo’s result on the Oracle Clusters.' in mind, break down and resolve the question below step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich model has the highest F-Score?\n" }, { "role": "assistant", "content": "The F-Score for the Oracle Cluster+Temporal model is 14.68, while the F-Score for Our Model is 17.57. Therefore, Our Model has the highest F-Score. So, the answer is {\"answer\": \"Our Model\"}" } ], "images": [ "scigen/train/826.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Test Results for event sequencing. The Oracle Cluster+Temporal system is using Caevo’s result on the Oracle Clusters." ], "reasoning": "The F-Score for the Oracle Cluster+Temporal model is 14.68, while the F-Score for Our Model is 17.57. Therefore, Our Model has the highest F-Score.", "question": "Which model has the highest F-Score?", "answer": "So, the answer is {\"answer\": \"Our Model\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Oracle Cluster+Temporal model has a higher recall than Our Model.\n" }, { "role": "assistant", "content": "The recall for the Oracle Cluster+Temporal model is 8.72, while the recall for Our Model is 16.91. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/826.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Test Results for event sequencing. The Oracle Cluster+Temporal system is using Caevo’s result on the Oracle Clusters." ], "reasoning": "The recall for the Oracle Cluster+Temporal model is 8.72, while the recall for Our Model is 16.91. Therefore, the statement is refuted.", "statement": "The Oracle Cluster+Temporal model has a higher recall than Our Model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: Ablation Study for Event Sequencing.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Removing mention or sentence features significantly drops precision and recall, indicating discourse proximity's importance.While most of the features only affect the performance by less than 1 absolute F1 score, the feature sets after removing mention or sentences show a significant drop in both precision and recall. This shows that discourse proximity is the most significant ones among these features. In addition, the mention feature set captures the following explain away intuition: the event mentions A and B are less likely to be related if there are similar mentions in between." } ], "images": [ "scigen/train/827.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ablation Study for Event Sequencing." ], "reasoning": "Removing mention or sentence features significantly drops precision and recall, indicating discourse proximity's importance.", "description": "While most of the features only affect the performance by less than 1 absolute F1 score, the feature sets after removing mention or sentences show a significant drop in both precision and recall. This shows that discourse proximity is the most significant ones among these features. In addition, the mention feature set captures the following explain away intuition: the event mentions A and B are less likely to be related if there are similar mentions in between.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nTable title: Table 5: Ablation Study for Event Sequencing.\n\nQuestion: Which feature removal results in the highest drop in F1 score?\n" }, { "role": "assistant", "content": "By comparing the Δ values in the table, the removal of the Mention Type feature results in the highest drop in F1 score with a Δ of 6.29. So, the answer is {\"answer\": \"Mention Type\"}" } ], "images": [ "scigen/train/827.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ablation Study for Event Sequencing." ], "reasoning": "By comparing the Δ values in the table, the removal of the Mention Type feature results in the highest drop in F1 score with a Δ of 6.29.", "question": "Which feature removal results in the highest drop in F1 score?", "answer": "So, the answer is {\"answer\": \"Mention Type\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRemoving the Temporal feature set results in the highest drop in F1 score.\n" }, { "role": "assistant", "content": "The table shows that removing the Temporal feature set results in a Δ of 0.55, which is not the highest drop. The highest drop is from removing the Mention Type feature set with a Δ of 6.29. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/827.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ablation Study for Event Sequencing." ], "reasoning": "The table shows that removing the Temporal feature set results in a Δ of 0.55, which is not the highest drop. The highest drop is from removing the Mention Type feature set with a Δ of 6.29.", "statement": "Removing the Temporal feature set results in the highest drop in F1 score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'TABLE I: Stopping method results on 20Newsgroups for different batch sizes using various window sizes. The top number in each row shows the number of annotations at the stopping point and the bottom number shows the F-Measure at the stopping point.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "BV2009 with a window size of one stops with fewer annotations than with a window size of three, without significantly losing F-Measure.We considered different batch sizes in our experiments, based on percentages of the entire set of training data. We ran BV2009 with smaller window sizes for each of our different batch sizes. When using a window size of one, BV2009 is able to stop with a smaller number of annotations than when using a window size of three. This is done without losing much F-Measure. The next subsection provides an explanation as to why smaller window sizes are more effective than larger window sizes when larger batch sizes are used." } ], "images": [ "scigen/train/828.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Stopping method results on 20Newsgroups for different batch sizes using various window sizes. The top number in each row shows the number of annotations at the stopping point and the bottom number shows the F-Measure at the stopping point." ], "reasoning": "BV2009 with a window size of one stops with fewer annotations than with a window size of three, without significantly losing F-Measure.", "description": "We considered different batch sizes in our experiments, based on percentages of the entire set of training data. We ran BV2009 with smaller window sizes for each of our different batch sizes. When using a window size of one, BV2009 is able to stop with a smaller number of annotations than when using a window size of three. This is done without losing much F-Measure. The next subsection provides an explanation as to why smaller window sizes are more effective than larger window sizes when larger batch sizes are used.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'TABLE I: Stopping method results on 20Newsgroups for different batch sizes using various window sizes. The top number in each row shows the number of annotations at the stopping point and the bottom number shows the F-Measure at the stopping point.', answer the following question. Give detailed solution consisting of each step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the F-Measure for BV2009 with a window size of 1 at a 10% batch size?\n" }, { "role": "assistant", "content": "To find the F-Measure for BV2009 with a window size of 1 at a 10% batch size, look at the intersection of the row labeled 'BV2009 (Window Size = 1)' and the column labeled '10%'. So, the answer is {\"answer\": \"75.11\"}." } ], "images": [ "scigen/train/828.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Stopping method results on 20Newsgroups for different batch sizes using various window sizes. The top number in each row shows the number of annotations at the stopping point and the bottom number shows the F-Measure at the stopping point." ], "reasoning": "To find the F-Measure for BV2009 with a window size of 1 at a 10% batch size, look at the intersection of the row labeled 'BV2009 (Window Size = 1)' and the column labeled '10%'.", "question": "What is the F-Measure for BV2009 with a window size of 1 at a 10% batch size?", "answer": "So, the answer is {\"answer\": \"75.11\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis BV2009 with a window size of 3 has a higher F-Measure at 5% batch size than the Oracle Method. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nBV2009 with a window size of 3 has a higher F-Measure at 5% batch size than the Oracle Method.\n" }, { "role": "assistant", "content": "Compare the F-Measure values for BV2009 with a window size of 3 and the Oracle Method at a 5% batch size. BV2009 with a window size of 3 has an F-Measure of 75.17, while the Oracle Method has an F-Measure of 75.55. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/828.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Stopping method results on 20Newsgroups for different batch sizes using various window sizes. The top number in each row shows the number of annotations at the stopping point and the bottom number shows the F-Measure at the stopping point." ], "reasoning": "Compare the F-Measure values for BV2009 with a window size of 3 and the Oracle Method at a 5% batch size. BV2009 with a window size of 3 has an F-Measure of 75.17, while the Oracle Method has an F-Measure of 75.55.", "statement": "BV2009 with a window size of 3 has a higher F-Measure at 5% batch size than the Oracle Method.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Analysis of errors by a human annotator of the top ten retrievals on development data. Percentages (%) indicate the absolute drop in P@10 due to that error type.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "XVisionSpeechCNN has a higher percentage of semantically related retrievals marked as errors compared to XBoWCNN, which could be useful depending on the application.Transcriptions of the top English utterances retrieved using XVisionSpeechCNN for a selection of German keywords. Errors from both XVisionSpeechCNN and XBoWCNN were presented to the annotator in shuffled order. For both models, around 10% of the retrievals marked as errors are actually correct. The bulk of errors from XVisionSpeechCNN is due to semantically related retrievals. These retrievals are marked as errors, but could actually be useful depending on the type of retrieval application. If type (1) and type (2) errors are not counted as incorrect, XVisionSpeechCNN and XBoWCNN would achieve a P@10 of 91% and 95%, respectively (but, again, this will depend on the use-case). We leave a larger analysis, which will also measure recall (not only top retrievals), for future work." } ], "images": [ "scigen/train/829.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Analysis of errors by a human annotator of the top ten retrievals on development data. Percentages (%) indicate the absolute drop in P@10 due to that error type." ], "reasoning": "XVisionSpeechCNN has a higher percentage of semantically related retrievals marked as errors compared to XBoWCNN, which could be useful depending on the application.", "description": "Transcriptions of the top English utterances retrieved using XVisionSpeechCNN for a selection of German keywords. Errors from both XVisionSpeechCNN and XBoWCNN were presented to the annotator in shuffled order. For both models, around 10% of the retrievals marked as errors are actually correct. The bulk of errors from XVisionSpeechCNN is due to semantically related retrievals. These retrievals are marked as errors, but could actually be useful depending on the type of retrieval application. If type (1) and type (2) errors are not counted as incorrect, XVisionSpeechCNN and XBoWCNN would achieve a P@10 of 91% and 95%, respectively (but, again, this will depend on the use-case). We leave a larger analysis, which will also measure recall (not only top retrievals), for future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: Analysis of errors by a human annotator of the top ten retrievals on development data. Percentages (%) indicate the absolute drop in P@10 due to that error type.' to sequentially solve the question presented below. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the percentage of semantically related errors for XVisionSpeechCNN?\n" }, { "role": "assistant", "content": "The table shows the percentage of semantically related errors for XVisionSpeechCNN as 22.1%. So, the answer is {\"answer\": \"22.1%\"}." } ], "images": [ "scigen/train/829.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Analysis of errors by a human annotator of the top ten retrievals on development data. Percentages (%) indicate the absolute drop in P@10 due to that error type." ], "reasoning": "The table shows the percentage of semantically related errors for XVisionSpeechCNN as 22.1%.", "question": "What is the percentage of semantically related errors for XVisionSpeechCNN?", "answer": "So, the answer is {\"answer\": \"22.1%\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nXVisionSpeechCNN has a higher percentage of correct exact retrievals than XBoWCNN.\n" }, { "role": "assistant", "content": "The table shows that XVisionSpeechCNN has 8.2% correct exact retrievals, while XBoWCNN has 11.5%, indicating that the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/829.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Analysis of errors by a human annotator of the top ten retrievals on development data. Percentages (%) indicate the absolute drop in P@10 due to that error type." ], "reasoning": "The table shows that XVisionSpeechCNN has 8.2% correct exact retrievals, while XBoWCNN has 11.5%, indicating that the statement is incorrect.", "statement": "XVisionSpeechCNN has a higher percentage of correct exact retrievals than XBoWCNN.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Cross-lingual keyword spotting results (%) on test data.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "XVisionSpeechCNN outperforms DEVisionCNN in all metrics, indicating it captures more information from speech than just visual tags.Without seeing any speech transcriptions or translated text, XVisionSpeechCNN achieves a P@10 of 58%, with XBoWCNN the only model to outperform the visually grounded model. By comparing performance to DETextPrior, we see that XVisionSpeechCNN is not just predicting common German words. Interestingly, XVisionSpeechCNN also outperforms DEVisionCNN over all metrics. If the former were perfectly predicting the German visual tags (which is what it is trained to do), then the performance of these two models would be the same. We see, however, that XVisionSpeechCNN is doing more than simply mapping the acoustics to the visual tags; we speculate that it is therefore picking up information in the speech which cannot be obtained from the corresponding test images." } ], "images": [ "scigen/train/830.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Cross-lingual keyword spotting results (%) on test data." ], "reasoning": "XVisionSpeechCNN outperforms DEVisionCNN in all metrics, indicating it captures more information from speech than just visual tags.", "description": "Without seeing any speech transcriptions or translated text, XVisionSpeechCNN achieves a P@10 of 58%, with XBoWCNN the only model to outperform the visually grounded model. By comparing performance to DETextPrior, we see that XVisionSpeechCNN is not just predicting common German words. Interestingly, XVisionSpeechCNN also outperforms DEVisionCNN over all metrics. If the former were perfectly predicting the German visual tags (which is what it is trained to do), then the performance of these two models would be the same. We see, however, that XVisionSpeechCNN is doing more than simply mapping the acoustics to the visual tags; we speculate that it is therefore picking up information in the speech which cannot be obtained from the corresponding test images.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 2: Cross-lingual keyword spotting results (%) on test data.', solve the following question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model has the highest P@N and how does it compare to XVisionSpeechCNN?\n" }, { "role": "assistant", "content": "XBoWCNN has the highest P@N at 54.3, which is higher than XVisionSpeechCNN's P@N of 40.4. So, the answer is {\"answer\": \"XBoWCNN\"}." } ], "images": [ "scigen/train/830.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Cross-lingual keyword spotting results (%) on test data." ], "reasoning": "XBoWCNN has the highest P@N at 54.3, which is higher than XVisionSpeechCNN's P@N of 40.4.", "question": "Which model has the highest P@N and how does it compare to XVisionSpeechCNN?", "answer": "So, the answer is {\"answer\": \"XBoWCNN\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nXVisionSpeechCNN has a higher EER than DEVisionCNN.\n" }, { "role": "assistant", "content": "The table shows that XVisionSpeechCNN has an EER of 23.5, which is lower than DEVisionCNN's EER of 25.9. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/830.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Cross-lingual keyword spotting results (%) on test data." ], "reasoning": "The table shows that XVisionSpeechCNN has an EER of 23.5, which is lower than DEVisionCNN's EER of 25.9.", "statement": "XVisionSpeechCNN has a higher EER than DEVisionCNN.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Cross-lingual keyword spotting results (%) for different variants of XVisionSpeechCNN on development data.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "XVisionSpeechCNN, trained on soft scores from a visual tagger, performs slightly better on most metrics compared to KeyXVisionSpeechCNN and OracleXVisionSpeechCNN.Variants and ideal supervision. We compare different variants of XVisionSpeechCNN to gain insight into properties of the model. XVisionSpeechCNN produces scores →f(X)∈[0,1]W for all W=1k words in its output vocabulary. But we are actually only interested in those dimensions w corresponding to the test keywords. If we knew the keywords at training time, we could train a model which only tries to predict the visual tags corresponding to these keywords. Performance is similar to that of XVisionSpeechCNN, with the latter being slightly better on most metrics. This effectively regularises our model (improving results). XVisionSpeechCNN is trained on soft scores from a visual tagger. What if we had the true hard assignments from the manual annotations for the training images? OracleXVisionSpeechCNN is trained on such oracle targets. They described this as a student-teacher approach, where the student (in our case the speech network) is trying to distil knowledge from the teacher network (in our case the visual tagger)." } ], "images": [ "scigen/train/831.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Cross-lingual keyword spotting results (%) for different variants of XVisionSpeechCNN on development data." ], "reasoning": "XVisionSpeechCNN, trained on soft scores from a visual tagger, performs slightly better on most metrics compared to KeyXVisionSpeechCNN and OracleXVisionSpeechCNN.", "description": "Variants and ideal supervision. We compare different variants of XVisionSpeechCNN to gain insight into properties of the model. XVisionSpeechCNN produces scores →f(X)∈[0,1]W for all W=1k words in its output vocabulary. But we are actually only interested in those dimensions w corresponding to the test keywords. If we knew the keywords at training time, we could train a model which only tries to predict the visual tags corresponding to these keywords. Performance is similar to that of XVisionSpeechCNN, with the latter being slightly better on most metrics. This effectively regularises our model (improving results). XVisionSpeechCNN is trained on soft scores from a visual tagger. What if we had the true hard assignments from the manual annotations for the training images? OracleXVisionSpeechCNN is trained on such oracle targets. They described this as a student-teacher approach, where the student (in our case the speech network) is trying to distil knowledge from the teacher network (in our case the visual tagger).", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model has the highest P@10 score?\nSolve the above question based on the table titled 'Table 4: Cross-lingual keyword spotting results (%) for different variants of XVisionSpeechCNN on development data.'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the P@10 scores of all models, XVisionSpeechCNN has the highest P@10 score of 60.8. So, the answer is {\"answer\": \"XVisionSpeechCNN\"}." } ], "images": [ "scigen/train/831.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Cross-lingual keyword spotting results (%) for different variants of XVisionSpeechCNN on development data." ], "reasoning": "By comparing the P@10 scores of all models, XVisionSpeechCNN has the highest P@10 score of 60.8.", "question": "Which model has the highest P@10 score?", "answer": "So, the answer is {\"answer\": \"XVisionSpeechCNN\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nOracleXVisionSpeechCNN has the lowest EER.\n" }, { "role": "assistant", "content": "By comparing the EER values of all models, XVisionSpeechCNN has the lowest EER of 23.1, not OracleXVisionSpeechCNN. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/831.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Cross-lingual keyword spotting results (%) for different variants of XVisionSpeechCNN on development data." ], "reasoning": "By comparing the EER values of all models, XVisionSpeechCNN has the lowest EER of 23.1, not OracleXVisionSpeechCNN.", "statement": "OracleXVisionSpeechCNN has the lowest EER.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: Human Evaluation on the MusicConvers dataset.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "GenDS-single achieves the best performance in human evaluation metrics, while GenDS shows the best performance in automatic evaluation metrics.MusicConvers: We compute the mean score of each metric. For automatic evaluation, GenDS shows the best performance on BLEU and entity-accuracy, while GenDS-single achieves the highest entity-recall. Although GenDS does not overwhelm on S2SA in terms of BLEU, it improves entity accuracy and recall by 39% and 14% respectively. This indicates that GenDS can reply the message with more correct information. S2SA cannot respond with correct information, which is mainly due to the lack of grounding into external knowledge. Although GenQA and GenQAD can incorporate KB in response, their performance on entity-accuracy and entity-recall still cannot compete with GenDS. For GenQA, the entity generation probability is fixed during decoding. As a consequence, GenQA cannot generate different entities. For GenQAD, its update mechanism of entity generation probability is less effective than our dynamic knowledge enquirer. BLEU scores of GenQA and GenQAD are lowest among all models. We think that MusicConvers has no enough data for GenQA and GenQAD to learn reliable entity representations. This illustrate thet GenDS can achieve decent performance even on small dataset. GenDS achieves higher BLEU than GenDS-Single, which confirms the benefit of multi-task learning for improving the fluency. For human evaluation, the GenDS-single achieves the best performance in terms of grammar, context relevance and information correctness. Although S2SA can generate fluent responses, these responses contain little correct information and are less semantically relevant with the message. The performance of GenDS is slightly worse than GenDS-single. We infer that is due to the task 2, where GenDS tends to generate fluent response instead of correct information." } ], "images": [ "scigen/train/832.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Human Evaluation on the MusicConvers dataset." ], "reasoning": "GenDS-single achieves the best performance in human evaluation metrics, while GenDS shows the best performance in automatic evaluation metrics.", "description": "MusicConvers: We compute the mean score of each metric. For automatic evaluation, GenDS shows the best performance on BLEU and entity-accuracy, while GenDS-single achieves the highest entity-recall. Although GenDS does not overwhelm on S2SA in terms of BLEU, it improves entity accuracy and recall by 39% and 14% respectively. This indicates that GenDS can reply the message with more correct information. S2SA cannot respond with correct information, which is mainly due to the lack of grounding into external knowledge. Although GenQA and GenQAD can incorporate KB in response, their performance on entity-accuracy and entity-recall still cannot compete with GenDS. For GenQA, the entity generation probability is fixed during decoding. As a consequence, GenQA cannot generate different entities. For GenQAD, its update mechanism of entity generation probability is less effective than our dynamic knowledge enquirer. BLEU scores of GenQA and GenQAD are lowest among all models. We think that MusicConvers has no enough data for GenQA and GenQAD to learn reliable entity representations. This illustrate thet GenDS can achieve decent performance even on small dataset. GenDS achieves higher BLEU than GenDS-Single, which confirms the benefit of multi-task learning for improving the fluency. For human evaluation, the GenDS-single achieves the best performance in terms of grammar, context relevance and information correctness. Although S2SA can generate fluent responses, these responses contain little correct information and are less semantically relevant with the message. The performance of GenDS is slightly worse than GenDS-single. We infer that is due to the task 2, where GenDS tends to generate fluent response instead of correct information.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 4: Human Evaluation on the MusicConvers dataset.'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model achieves the highest score in context relevance and what is the score?\n" }, { "role": "assistant", "content": "From the table, GenDS-Single achieves the highest score in context relevance with a score of 1.67. So, the answer is {\"answer\": \"GenDS-Single with a score of 1.67\"}" } ], "images": [ "scigen/train/832.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Human Evaluation on the MusicConvers dataset." ], "reasoning": "From the table, GenDS-Single achieves the highest score in context relevance with a score of 1.67.", "question": "Which model achieves the highest score in context relevance and what is the score?", "answer": "So, the answer is {\"answer\": \"GenDS-Single with a score of 1.67\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nGenDS-Single has the highest correctness score among all models.\n" }, { "role": "assistant", "content": "The table shows that GenDS-Single has a correctness score of 1.18, which is the highest among all models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/832.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Human Evaluation on the MusicConvers dataset." ], "reasoning": "The table shows that GenDS-Single has a correctness score of 1.18, which is the highest among all models listed.", "statement": "GenDS-Single has the highest correctness score among all models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Automatic Evaluation on the Music dataset'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "GenDS shows the best performance on BLEU and entity-accuracy, while GenDS-single achieves the highest entity-recall. GenDS improves entity accuracy and recall significantly compared to S2SA.MusicConvers: We compute the mean score of each metric. For automatic evaluation, GenDS shows the best performance on BLEU and entity-accuracy, while GenDS-single achieves the highest entity-recall. Although GenDS does not overwhelm on S2SA in terms of BLEU, it improves entity accuracy and recall by 39% and 14% respectively. This indicates that GenDS can reply the message with more correct information. S2SA cannot respond with correct information, which is mainly due to the lack of grounding into external knowledge. Although GenQA and GenQAD can incorporate KB in response, their performance on entity-accuracy and entity-recall still cannot compete with GenDS. For GenQA, the entity generation probability is fixed during decoding. As a consequence, GenQA cannot generate different entities. For GenQAD, its update mechanism of entity generation probability is less effective than our dynamic knowledge enquirer. BLEU scores of GenQA and GenQAD are lowest among all models. We think that MusicConvers has no enough data for GenQA and GenQAD to learn reliable entity representations. This illustrate thet GenDS can achieve decent performance even on small dataset. GenDS achieves higher BLEU than GenDS-Single, which confirms the benefit of multi-task learning for improving the fluency. For human evaluation, the GenDS-single achieves the best performance in terms of grammar, context relevance and information correctness. Although S2SA can generate fluent responses, these responses contain little correct information and are less semantically relevant with the message. The performance of GenDS is slightly worse than GenDS-single. We infer that is due to the task 2, where GenDS tends to generate fluent response instead of correct information." } ], "images": [ "scigen/train/833.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Automatic Evaluation on the Music dataset" ], "reasoning": "GenDS shows the best performance on BLEU and entity-accuracy, while GenDS-single achieves the highest entity-recall. GenDS improves entity accuracy and recall significantly compared to S2SA.", "description": "MusicConvers: We compute the mean score of each metric. For automatic evaluation, GenDS shows the best performance on BLEU and entity-accuracy, while GenDS-single achieves the highest entity-recall. Although GenDS does not overwhelm on S2SA in terms of BLEU, it improves entity accuracy and recall by 39% and 14% respectively. This indicates that GenDS can reply the message with more correct information. S2SA cannot respond with correct information, which is mainly due to the lack of grounding into external knowledge. Although GenQA and GenQAD can incorporate KB in response, their performance on entity-accuracy and entity-recall still cannot compete with GenDS. For GenQA, the entity generation probability is fixed during decoding. As a consequence, GenQA cannot generate different entities. For GenQAD, its update mechanism of entity generation probability is less effective than our dynamic knowledge enquirer. BLEU scores of GenQA and GenQAD are lowest among all models. We think that MusicConvers has no enough data for GenQA and GenQAD to learn reliable entity representations. This illustrate thet GenDS can achieve decent performance even on small dataset. GenDS achieves higher BLEU than GenDS-Single, which confirms the benefit of multi-task learning for improving the fluency. For human evaluation, the GenDS-single achieves the best performance in terms of grammar, context relevance and information correctness. Although S2SA can generate fluent responses, these responses contain little correct information and are less semantically relevant with the message. The performance of GenDS is slightly worse than GenDS-single. We infer that is due to the task 2, where GenDS tends to generate fluent response instead of correct information.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 3: Automatic Evaluation on the Music dataset', answer the question below by showing each progressive step toward the answer. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model achieves the highest precision and what is its value?\n" }, { "role": "assistant", "content": "From the table, the model with the highest precision is GenDS with a value of 0.40±0.25. So, the answer is {\"answer\": \"GenDS\"}" } ], "images": [ "scigen/train/833.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Automatic Evaluation on the Music dataset" ], "reasoning": "From the table, the model with the highest precision is GenDS with a value of 0.40±0.25.", "question": "Which model achieves the highest precision and what is its value?", "answer": "So, the answer is {\"answer\": \"GenDS\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: GenDS-single has the highest recall among all models., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nGenDS-single has the highest recall among all models.\n" }, { "role": "assistant", "content": "According to the table, GenDS-single has a recall of 0.19±0.18, which is the highest among all models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/833.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Automatic Evaluation on the Music dataset" ], "reasoning": "According to the table, GenDS-single has a recall of 0.19±0.18, which is the highest among all models listed.", "statement": "GenDS-single has the highest recall among all models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 5: Automatic Evaluation on the QA dataset'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "MusicQA GenDS outperforms S2SA, GenQA, and GenQAD in BLEU score, entity-accuracy, and entity-recall, demonstrating the effectiveness of the dynamic knowledge enquirer.MusicQA GenDS improves BLEU score, entity-accuracy and entity-recall significantly compared with S2SA, GenQA and GenQAD. GenQA does not obtain comparable performance with the original QA This may be the due to our mitigation of redundancy for the dataset. Unlike MusicConvers, GenDS-static exhibits decent performance on entity-accuracy and entity-recall. 99% questions in MusicQA only contain one entity in answer. Thus, most of messages do not need dynamic knowledge enquirer to generate multiple entities. However, GenDS still achieves higher entity-accuracy and entity-recall than GenDS-static. This verifies that the proposed dynamic knowledge enquirer is useful even when only one entity is generated." } ], "images": [ "scigen/train/834.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Automatic Evaluation on the QA dataset" ], "reasoning": "MusicQA GenDS outperforms S2SA, GenQA, and GenQAD in BLEU score, entity-accuracy, and entity-recall, demonstrating the effectiveness of the dynamic knowledge enquirer.", "description": "MusicQA GenDS improves BLEU score, entity-accuracy and entity-recall significantly compared with S2SA, GenQA and GenQAD. GenQA does not obtain comparable performance with the original QA This may be the due to our mitigation of redundancy for the dataset. Unlike MusicConvers, GenDS-static exhibits decent performance on entity-accuracy and entity-recall. 99% questions in MusicQA only contain one entity in answer. Thus, most of messages do not need dynamic knowledge enquirer to generate multiple entities. However, GenDS still achieves higher entity-accuracy and entity-recall than GenDS-static. This verifies that the proposed dynamic knowledge enquirer is useful even when only one entity is generated.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: Automatic Evaluation on the QA dataset', answer the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model has the highest BLEU score and what is its value?\n" }, { "role": "assistant", "content": "By examining the BLEU score column, we see that GenDS has the highest BLEU score of 0.227. So, the answer is {\"answer\": \"GenDS with 0.227\"}." } ], "images": [ "scigen/train/834.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Automatic Evaluation on the QA dataset" ], "reasoning": "By examining the BLEU score column, we see that GenDS has the highest BLEU score of 0.227.", "question": "Which model has the highest BLEU score and what is its value?", "answer": "So, the answer is {\"answer\": \"GenDS with 0.227\"}." }, { "messages": [ { "role": "user", "content": "GenDS-static has a higher recall than GenDS-Single. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nGenDS-static has a higher recall than GenDS-Single.\n" }, { "role": "assistant", "content": "By comparing the recall values, GenDS-static has a recall of 0.66±0.235, while GenDS-Single has a recall of 0.77±0.21. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/834.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Automatic Evaluation on the QA dataset" ], "reasoning": "By comparing the recall values, GenDS-static has a recall of 0.66±0.235, while GenDS-Single has a recall of 0.77±0.21.", "statement": "GenDS-static has a higher recall than GenDS-Single.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: Training speed and memory consumption on WMT 2017 German→English. Train time is for seeing the full train dataset once. Batch size is in words, such that it almost maximizes the GPU memory consumption. The BLEU score is for the converged models, reported for newstest2015 (dev) and newstest2017. The encoder has one bidirectional LSTM layer and either 3 or 5 unidirectional LSTM layers.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The comparison of toolkits in training and decoding for a recurrent attention model on a GPU shows that Sockeye generally has higher BLEU scores but takes longer training time compared to RETURNN.We want to compare different toolkits in training and decoding for a recurrent attention model in terms of speed on a GPU. Here, we try to maximize the batch size such that it still fits into the GPU memory of our reference GPU card, the Nvidia GTX 1080 Ti with 11 GB of memory. We keep the maximum sequence length in a batch the same, which is 60 words. We always use Adam Kingma and Ba For these speed experiments, we did not tune any of the hyper parameters of RETURNN which explains its worse performance. The aim here is to match Sockeye’s exact architecture for speed and memory comparison. During training, we observed that the learning rate scheduling settings of Sockeye are more pessimistic, i.e. the decrease is slower and it sees the data more often until convergence. This greatly increases the total training time but in our experience also improves the model." } ], "images": [ "scigen/train/835.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Training speed and memory consumption on WMT 2017 German→English. Train time is for seeing the full train dataset once. Batch size is in words, such that it almost maximizes the GPU memory consumption. The BLEU score is for the converged models, reported for newstest2015 (dev) and newstest2017. The encoder has one bidirectional LSTM layer and either 3 or 5 unidirectional LSTM layers." ], "reasoning": "The comparison of toolkits in training and decoding for a recurrent attention model on a GPU shows that Sockeye generally has higher BLEU scores but takes longer training time compared to RETURNN.", "description": "We want to compare different toolkits in training and decoding for a recurrent attention model in terms of speed on a GPU. Here, we try to maximize the batch size such that it still fits into the GPU memory of our reference GPU card, the Nvidia GTX 1080 Ti with 11 GB of memory. We keep the maximum sequence length in a batch the same, which is 60 words. We always use Adam Kingma and Ba For these speed experiments, we did not tune any of the hyper parameters of RETURNN which explains its worse performance. The aim here is to match Sockeye’s exact architecture for speed and memory comparison. During training, we observed that the learning rate scheduling settings of Sockeye are more pessimistic, i.e. the decrease is slower and it sees the data more often until convergence. This greatly increases the total training time but in our experience also improves the model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 1: Training speed and memory consumption on WMT 2017 German→English. Train time is for seeing the full train dataset once. Batch size is in words, such that it almost maximizes the GPU memory consumption. The BLEU score is for the converged models, reported for newstest2015 (dev) and newstest2017. The encoder has one bidirectional LSTM layer and either 3 or 5 unidirectional LSTM layers.' and methodically tackle the question that follows. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich toolkit and encoder configuration achieved the highest BLEU score in 2017?\n" }, { "role": "assistant", "content": "By examining the BLEU scores for 2017 in the table, the highest score is 29.2%, achieved by Sockeye with 4 encoder layers. So, the answer is {\"answer\": \"Sockeye with 4 encoder layers\"}." } ], "images": [ "scigen/train/835.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Training speed and memory consumption on WMT 2017 German→English. Train time is for seeing the full train dataset once. Batch size is in words, such that it almost maximizes the GPU memory consumption. The BLEU score is for the converged models, reported for newstest2015 (dev) and newstest2017. The encoder has one bidirectional LSTM layer and either 3 or 5 unidirectional LSTM layers." ], "reasoning": "By examining the BLEU scores for 2017 in the table, the highest score is 29.2%, achieved by Sockeye with 4 encoder layers.", "question": "Which toolkit and encoder configuration achieved the highest BLEU score in 2017?", "answer": "So, the answer is {\"answer\": \"Sockeye with 4 encoder layers\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis RETURNN with 6 encoder layers has a higher batch size than Sockeye with 4 encoder layers. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nRETURNN with 6 encoder layers has a higher batch size than Sockeye with 4 encoder layers.\n" }, { "role": "assistant", "content": "According to the table, RETURNN with 6 encoder layers has a batch size of 7500, while Sockeye with 4 encoder layers has a batch size of 3000. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/835.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Training speed and memory consumption on WMT 2017 German→English. Train time is for seeing the full train dataset once. Batch size is in words, such that it almost maximizes the GPU memory consumption. The BLEU score is for the converged models, reported for newstest2015 (dev) and newstest2017. The encoder has one bidirectional LSTM layer and either 3 or 5 unidirectional LSTM layers." ], "reasoning": "According to the table, RETURNN with 6 encoder layers has a batch size of 7500, while Sockeye with 4 encoder layers has a batch size of 3000.", "statement": "RETURNN with 6 encoder layers has a higher batch size than Sockeye with 4 encoder layers.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Comparison on German→English.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The best performing Sockeye model, which uses a Transformer network, achieved 32.0% BLEU on newstest2017.We report the best performing Sockeye model we trained, which has 1 bidirectional and 3 unidirectional encoder layers, 1 pre-attention target recurrent layer, and 1 post-attention decoder layer. We trained with a max sequence length of 75, and used the ‘coverage’ RNN attention type. For Sockeye, the final model is an average of the 4 best runs according to the development perplexity. We obtain the best results with Sockeye using a Transformer network model Vaswani et al. , where we achieve 32.0% BLEU on newstest2017." } ], "images": [ "scigen/train/836.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison on German→English." ], "reasoning": "The best performing Sockeye model, which uses a Transformer network, achieved 32.0% BLEU on newstest2017.", "description": "We report the best performing Sockeye model we trained, which has 1 bidirectional and 3 unidirectional encoder layers, 1 pre-attention target recurrent layer, and 1 post-attention decoder layer. We trained with a max sequence length of 75, and used the ‘coverage’ RNN attention type. For Sockeye, the final model is an average of the 4 best runs according to the development perplexity. We obtain the best results with Sockeye using a Transformer network model Vaswani et al. , where we achieve 32.0% BLEU on newstest2017.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Comparison on German→English.\nQuestion: What is the BLEU score achieved by the best performing Sockeye model on newstest2017?\nSolve the above question based on the table information. Let's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The description mentions that the best performing Sockeye model achieved 32.0% BLEU on newstest2017, which is higher than the BLEU scores listed in the table for Sockeye. So, the answer is {\"answer\": \"32.0%\"}" } ], "images": [ "scigen/train/836.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison on German→English." ], "reasoning": "The description mentions that the best performing Sockeye model achieved 32.0% BLEU on newstest2017, which is higher than the BLEU scores listed in the table for Sockeye.", "question": "What is the BLEU score achieved by the best performing Sockeye model on newstest2017?", "answer": "So, the answer is {\"answer\": \"32.0%\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Sockeye model achieved a BLEU score of 32.0% on newstest2017.\n" }, { "role": "assistant", "content": "The description states that the best performing Sockeye model achieved 32.0% BLEU on newstest2017, which is not reflected in the table. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/836.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison on German→English." ], "reasoning": "The description states that the best performing Sockeye model achieved 32.0% BLEU on newstest2017, which is not reflected in the table.", "statement": "The Sockeye model achieved a BLEU score of 32.0% on newstest2017.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: Performance comparison on WMT 2017 English→German. The baseline systems (upper half) are trained on the parallel data of the WMT Enlgish→German 2017 task. We downloaded the hypotheses from here.66footnotemark: 6 The WMT 2017 system hypotheses (lower half) are generated using systems having additional back- translation (bt) data. These hypotheses are downloaded from here.77footnotemark: 7', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "RETURNN achieves a BLEU score of 26.1%, outperforming other toolkits like Marian, which has a BLEU score of 25.6%. However, it performs slightly worse than the best single systems of WMT 2017, which achieve up to 27.0% BLEU.We observe that our toolkit outperforms all other toolkits. The best result obtained by other toolkits is using Marian (25.5% BLEU). In comparison, RETURNN achieves 26.1%. We also compare RETURNN to the best performing single systems of WMT 2017. In comparison to the fine-tuned evaluation systems that also include back-translated data, our model performs worse by only 0.3 to 0.9 BLEU. We did not run experiments with back-translated data, which can potentially boost the performance by several BLEU points." } ], "images": [ "scigen/train/837.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance comparison on WMT 2017 English→German. The baseline systems (upper half) are trained on the parallel data of the WMT Enlgish→German 2017 task. We downloaded the hypotheses from here.66footnotemark: 6 The WMT 2017 system hypotheses (lower half) are generated using systems having additional back- translation (bt) data. These hypotheses are downloaded from here.77footnotemark: 7" ], "reasoning": "RETURNN achieves a BLEU score of 26.1%, outperforming other toolkits like Marian, which has a BLEU score of 25.6%. However, it performs slightly worse than the best single systems of WMT 2017, which achieve up to 27.0% BLEU.", "description": "We observe that our toolkit outperforms all other toolkits. The best result obtained by other toolkits is using Marian (25.5% BLEU). In comparison, RETURNN achieves 26.1%. We also compare RETURNN to the best performing single systems of WMT 2017. In comparison to the fine-tuned evaluation systems that also include back-translated data, our model performs worse by only 0.3 to 0.9 BLEU. We did not run experiments with back-translated data, which can potentially boost the performance by several BLEU points.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 4: Performance comparison on WMT 2017 English→German. The baseline systems (upper half) are trained on the parallel data of the WMT Enlgish→German 2017 task. We downloaded the hypotheses from here.66footnotemark: 6 The WMT 2017 system hypotheses (lower half) are generated using systems having additional back- translation (bt) data. These hypotheses are downloaded from here.77footnotemark: 7'.\n\nQuestion: What is the BLEU score difference between RETURNN and the best performing single system of WMT 2017?\n" }, { "role": "assistant", "content": "RETURNN achieves a BLEU score of 26.1%, while the best performing single system of WMT 2017 achieves 27.0%. The difference is 27.0 - 26.1. So, the answer is {\"answer\": \"0.9\"}" } ], "images": [ "scigen/train/837.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance comparison on WMT 2017 English→German. The baseline systems (upper half) are trained on the parallel data of the WMT Enlgish→German 2017 task. We downloaded the hypotheses from here.66footnotemark: 6 The WMT 2017 system hypotheses (lower half) are generated using systems having additional back- translation (bt) data. These hypotheses are downloaded from here.77footnotemark: 7" ], "reasoning": "RETURNN achieves a BLEU score of 26.1%, while the best performing single system of WMT 2017 achieves 27.0%. The difference is 27.0 - 26.1.", "question": "What is the BLEU score difference between RETURNN and the best performing single system of WMT 2017?", "answer": "So, the answer is {\"answer\": \"0.9\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nRETURNN achieves a higher BLEU score than Marian.\n" }, { "role": "assistant", "content": "The table shows that RETURNN has a BLEU score of 26.1%, while Marian has a BLEU score of 25.6%. Therefore, RETURNN achieves a higher score. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/837.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance comparison on WMT 2017 English→German. The baseline systems (upper half) are trained on the parallel data of the WMT Enlgish→German 2017 task. We downloaded the hypotheses from here.66footnotemark: 6 The WMT 2017 system hypotheses (lower half) are generated using systems having additional back- translation (bt) data. These hypotheses are downloaded from here.77footnotemark: 7" ], "reasoning": "The table shows that RETURNN has a BLEU score of 26.1%, while Marian has a BLEU score of 25.6%. Therefore, RETURNN achieves a higher score.", "statement": "RETURNN achieves a higher BLEU score than Marian.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 5: Performance comparison on Switchboard, trained on 300h. hybrid1 is the IBM 2017 ResNet model Saon et al. (2017). hybrid2 trained with Lattice-free MMI Hadian et al. (2018). CTC3 is the Baidu 2014 DeepSpeech model Hannun et al. (2014). Our attention model does not use any language model.'.\n" }, { "role": "assistant", "content": "The attention model achieves the best reported result for an end-to-end system on Switchboard 300h without using a language model or lexicon, with a WER of 13.5% on the SWB part of Hub5'00.We also have preliminary results with recurrent attention models for speech recognition on the Switchboard task, which we trained on the 300h trainset. We report on both the Switchboard (SWB) and the CallHome (CH) part of Hub5’00 and Hub5’01. We also compare to a conventional frame-wise trained hybrid deep bidirectional LSTM with 6 layers Zeyer et al. The frame-wise trained hybrid model also uses focal loss Lin et al. All the hybrid models use a phonetic lexicon and an external 4-gram language model which was trained on the transcripts of both the Switchboard and the Fisher corpus. The attention model does not use any external language model nor a phonetic lexicon. Its output labels are byte-pair encoded subword units Sennrich et al. It has a 6 layer bidirectional encoder, which also applies max-pooling in the time dimension , i.e. it reduces the input sequence by factor 8. To our knowledge, this is the best reported result for an end-to-end system on Switchboard 300h without using a language model or the lexicon. For comparison, we also selected comparable results from the literature. From these, the Baidu DeepSpeech CTC model is modeled on characters and does not use the lexicon but it does use a language model." } ], "images": [ "scigen/train/838.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Performance comparison on Switchboard, trained on 300h. hybrid1 is the IBM 2017 ResNet model Saon et al. (2017). hybrid2 trained with Lattice-free MMI Hadian et al. (2018). CTC3 is the Baidu 2014 DeepSpeech model Hannun et al. (2014). Our attention model does not use any language model." ], "reasoning": "The attention model achieves the best reported result for an end-to-end system on Switchboard 300h without using a language model or lexicon, with a WER of 13.5% on the SWB part of Hub5'00.", "description": "We also have preliminary results with recurrent attention models for speech recognition on the Switchboard task, which we trained on the 300h trainset. We report on both the Switchboard (SWB) and the CallHome (CH) part of Hub5’00 and Hub5’01. We also compare to a conventional frame-wise trained hybrid deep bidirectional LSTM with 6 layers Zeyer et al. The frame-wise trained hybrid model also uses focal loss Lin et al. All the hybrid models use a phonetic lexicon and an external 4-gram language model which was trained on the transcripts of both the Switchboard and the Fisher corpus. The attention model does not use any external language model nor a phonetic lexicon. Its output labels are byte-pair encoded subword units Sennrich et al. It has a 6 layer bidirectional encoder, which also applies max-pooling in the time dimension , i.e. it reduces the input sequence by factor 8. To our knowledge, this is the best reported result for an end-to-end system on Switchboard 300h without using a language model or the lexicon. For comparison, we also selected comparable results from the literature. From these, the Baidu DeepSpeech CTC model is modeled on characters and does not use the lexicon but it does use a language model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 5: Performance comparison on Switchboard, trained on 300h. hybrid1 is the IBM 2017 ResNet model Saon et al. (2017). hybrid2 trained with Lattice-free MMI Hadian et al. (2018). CTC3 is the Baidu 2014 DeepSpeech model Hannun et al. (2014). Our attention model does not use any language model.' and proceed to solve the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model has the lowest WER on the SWB part of Hub5'00 and what is the value?\n" }, { "role": "assistant", "content": "To find the model with the lowest WER on the SWB part of Hub5'00, look at the column 'WER [%] Hub5'00 SWB' and identify the lowest value. The hybrid model trained frame-wise has the lowest WER of 9.8%. So, the answer is {\"answer\": \"hybrid model trained frame-wise with WER of 9.8%\"}." } ], "images": [ "scigen/train/838.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Performance comparison on Switchboard, trained on 300h. hybrid1 is the IBM 2017 ResNet model Saon et al. (2017). hybrid2 trained with Lattice-free MMI Hadian et al. (2018). CTC3 is the Baidu 2014 DeepSpeech model Hannun et al. (2014). Our attention model does not use any language model." ], "reasoning": "To find the model with the lowest WER on the SWB part of Hub5'00, look at the column 'WER [%] Hub5'00 SWB' and identify the lowest value. The hybrid model trained frame-wise has the lowest WER of 9.8%.", "question": "Which model has the lowest WER on the SWB part of Hub5'00 and what is the value?", "answer": "So, the answer is {\"answer\": \"hybrid model trained frame-wise with WER of 9.8%\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The attention model has the highest WER on the CH part of Hub5'00., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe attention model has the highest WER on the CH part of Hub5'00.\n" }, { "role": "assistant", "content": "To verify this statement, look at the column 'WER [%] Hub5'00 CH' and compare the values. The attention model has a WER of 27.1%, which is the highest among the listed models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/838.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Performance comparison on Switchboard, trained on 300h. hybrid1 is the IBM 2017 ResNet model Saon et al. (2017). hybrid2 trained with Lattice-free MMI Hadian et al. (2018). CTC3 is the Baidu 2014 DeepSpeech model Hannun et al. (2014). Our attention model does not use any language model." ], "reasoning": "To verify this statement, look at the column 'WER [%] Hub5'00 CH' and compare the values. The attention model has a WER of 27.1%, which is the highest among the listed models.", "statement": "The attention model has the highest WER on the CH part of Hub5'00.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 6: Pretraining comparison.'.\n" }, { "role": "assistant", "content": "The pretraining scheme for deep bidirectional LSTMs improves both performance and training speed, as evidenced by higher BLEU scores with pretraining.RETURNN supports very generic and flexible pretraining which iteratively starts with a small model and adds new layers in the process. A similar pretraining scheme for deep bidirectional LSTMs acoustic speech models was presented earlier Zeyer et al. Here, we only study a layer-wise construction of the deep bidirectional LSTM encoder network of an encoder-decoder-attention model for translation on the WMT 2017 German→English task. The observations very clearly match our expectations, that we can both greatly improve the overall performance, and we are able to train deeper models. A minor benefit is faster training speed of the initial pretrain epochs." } ], "images": [ "scigen/train/839.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Pretraining comparison." ], "reasoning": "The pretraining scheme for deep bidirectional LSTMs improves both performance and training speed, as evidenced by higher BLEU scores with pretraining.", "description": "RETURNN supports very generic and flexible pretraining which iteratively starts with a small model and adds new layers in the process. A similar pretraining scheme for deep bidirectional LSTMs acoustic speech models was presented earlier Zeyer et al. Here, we only study a layer-wise construction of the deep bidirectional LSTM encoder network of an encoder-decoder-attention model for translation on the WMT 2017 German→English task. The observations very clearly match our expectations, that we can both greatly improve the overall performance, and we are able to train deeper models. A minor benefit is faster training speed of the initial pretrain epochs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: Pretraining comparison.\nQuestion: What is the BLEU score for a 7-layer encoder with pretraining?\nGive a detailed response to the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the BLEU score for different encoder layers with and without pretraining. For a 7-layer encoder with pretraining, the BLEU score is 30.9. So, the answer is {\"answer\": \"30.9\"}." } ], "images": [ "scigen/train/839.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Pretraining comparison." ], "reasoning": "The table shows the BLEU score for different encoder layers with and without pretraining. For a 7-layer encoder with pretraining, the BLEU score is 30.9.", "question": "What is the BLEU score for a 7-layer encoder with pretraining?", "answer": "So, the answer is {\"answer\": \"30.9\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nPretraining improves the BLEU score for a 4-layer encoder.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for a 4-layer encoder without pretraining is 29.1, while with pretraining it is 30.3, indicating an improvement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/839.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Pretraining comparison." ], "reasoning": "The table shows that the BLEU score for a 4-layer encoder without pretraining is 29.1, while with pretraining it is 30.3, indicating an improvement.", "statement": "Pretraining improves the BLEU score for a 4-layer encoder.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Pearson’s r of EyeScore for different feature sets with MET (training/development set, 88 participants) and TOEFL (all 53 participants). Fixed denotes the Fixed Text regime in which all the participants read the same sentences, and Any denotes the Any Text regime where different readers read different sentences.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "EyeScore's WFC feature set shows the best performance in the Fixed Text regime for both MET and TOEFL, with Pearson’s r of 0.7 and MAE of 3.31 for MET, and MAE of 6.68 for TOEFL.We evaluate the ability of EyeScore to capture language proficiency by comparing it against our two external proficiency tests, MET and TOEFL. Similarly to the EyeScore outcomes, the best performance in the Fixed Text regime is obtained using the WFC feature set, with a Pearson’s r of 0.7 and MAE of 3.31 for MET. This result is highly competitive with correlations between different standardized English proficiency tests. On TOEFL, WFC features obtain the strongest MAE of 6.68, while S-Clusters have a higher r coefficient of 0.55." } ], "images": [ "scigen/train/840.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Pearson’s r of EyeScore for different feature sets with MET (training/development set, 88 participants) and TOEFL (all 53 participants). Fixed denotes the Fixed Text regime in which all the participants read the same sentences, and Any denotes the Any Text regime where different readers read different sentences." ], "reasoning": "EyeScore's WFC feature set shows the best performance in the Fixed Text regime for both MET and TOEFL, with Pearson’s r of 0.7 and MAE of 3.31 for MET, and MAE of 6.68 for TOEFL.", "description": "We evaluate the ability of EyeScore to capture language proficiency by comparing it against our two external proficiency tests, MET and TOEFL. Similarly to the EyeScore outcomes, the best performance in the Fixed Text regime is obtained using the WFC feature set, with a Pearson’s r of 0.7 and MAE of 3.31 for MET. This result is highly competitive with correlations between different standardized English proficiency tests. On TOEFL, WFC features obtain the strongest MAE of 6.68, while S-Clusters have a higher r coefficient of 0.55.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: Pearson’s r of EyeScore for different feature sets with MET (training/development set, 88 participants) and TOEFL (all 53 participants). Fixed denotes the Fixed Text regime in which all the participants read the same sentences, and Any denotes the Any Text regime where different readers read different sentences.', answer the question below by showing each progressive step toward the answer. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich feature set has the highest Pearson's r coefficient for MET Any?\n" }, { "role": "assistant", "content": "By examining the table, the highest Pearson's r coefficient for MET Any is found in the WFC feature set with a value of 0.54. So, the answer is {\"answer\": \"WFC\"}." } ], "images": [ "scigen/train/840.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Pearson’s r of EyeScore for different feature sets with MET (training/development set, 88 participants) and TOEFL (all 53 participants). Fixed denotes the Fixed Text regime in which all the participants read the same sentences, and Any denotes the Any Text regime where different readers read different sentences." ], "reasoning": "By examining the table, the highest Pearson's r coefficient for MET Any is found in the WFC feature set with a value of 0.54.", "question": "Which feature set has the highest Pearson's r coefficient for MET Any?", "answer": "So, the answer is {\"answer\": \"WFC\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe WFC feature set has the highest Pearson's r coefficient for TOEFL Fixed.\n" }, { "role": "assistant", "content": "The table shows that the highest Pearson's r coefficient for TOEFL Fixed is 0.50, which is achieved by the S-Clusters feature set, not WFC. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/840.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Pearson’s r of EyeScore for different feature sets with MET (training/development set, 88 participants) and TOEFL (all 53 participants). Fixed denotes the Fixed Text regime in which all the participants read the same sentences, and Any denotes the Any Text regime where different readers read different sentences." ], "reasoning": "The table shows that the highest Pearson's r coefficient for TOEFL Fixed is 0.50, which is achieved by the S-Clusters feature set, not WFC.", "statement": "The WFC feature set has the highest Pearson's r coefficient for TOEFL Fixed.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Pearson’s r and Mean Absolute Error (MAE) for prediction of MET scores (test set, 57 participants) and TOEFL scores (leave-one-out cross validation, all 53 participants) from eye movement patterns in reading. We consider two baselines which do not use eyetracking information: (1) the average proficiency score in the training set, which yields 4.82 MAE on MET and 8.29 MAE on TOEFL, and (2) the reading speed of the participant.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Eyetracking based features outperform both the average score and reading speed baselines in predicting MET and TOEFL scores.We consider two baselines; the first is assigning all test set participants with the average score of the training participants. This baseline yields an MAE of 4.82 on MET and 8.29 on TOEFL. The second baseline uses reading speed as the sole feature for prediction. In all cases, our eyetracking based features outperform the average score and reading speed baselines." } ], "images": [ "scigen/train/841.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Pearson’s r and Mean Absolute Error (MAE) for prediction of MET scores (test set, 57 participants) and TOEFL scores (leave-one-out cross validation, all 53 participants) from eye movement patterns in reading. We consider two baselines which do not use eyetracking information: (1) the average proficiency score in the training set, which yields 4.82 MAE on MET and 8.29 MAE on TOEFL, and (2) the reading speed of the participant." ], "reasoning": "Eyetracking based features outperform both the average score and reading speed baselines in predicting MET and TOEFL scores.", "description": "We consider two baselines; the first is assigning all test set participants with the average score of the training participants. This baseline yields an MAE of 4.82 on MET and 8.29 on TOEFL. The second baseline uses reading speed as the sole feature for prediction. In all cases, our eyetracking based features outperform the average score and reading speed baselines.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Pearson’s r and Mean Absolute Error (MAE) for prediction of MET scores (test set, 57 participants) and TOEFL scores (leave-one-out cross validation, all 53 participants) from eye movement patterns in reading. We consider two baselines which do not use eyetracking information: (1) the average proficiency score in the training set, which yields 4.82 MAE on MET and 8.29 MAE on TOEFL, and (2) the reading speed of the participant.', answer the question below by showing each progressive step toward the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich feature has the lowest MAE for MET Fixed?\n" }, { "role": "assistant", "content": "To find the feature with the lowest MAE for MET Fixed, compare the MAE values in the MET Fixed column. The lowest MAE value is 3.31, which corresponds to the WFC feature. So, the answer is {\"answer\": \"WFC\"}" } ], "images": [ "scigen/train/841.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Pearson’s r and Mean Absolute Error (MAE) for prediction of MET scores (test set, 57 participants) and TOEFL scores (leave-one-out cross validation, all 53 participants) from eye movement patterns in reading. We consider two baselines which do not use eyetracking information: (1) the average proficiency score in the training set, which yields 4.82 MAE on MET and 8.29 MAE on TOEFL, and (2) the reading speed of the participant." ], "reasoning": "To find the feature with the lowest MAE for MET Fixed, compare the MAE values in the MET Fixed column. The lowest MAE value is 3.31, which corresponds to the WFC feature.", "question": "Which feature has the lowest MAE for MET Fixed?", "answer": "So, the answer is {\"answer\": \"WFC\"}" }, { "messages": [ { "role": "user", "content": "Reading speed has the highest correlation (r) for MET Fixed. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nReading speed has the highest correlation (r) for MET Fixed.\n" }, { "role": "assistant", "content": "To verify this statement, compare the correlation (r) values for MET Fixed. The highest correlation value is 0.70, which corresponds to the WFC feature, not Reading Speed. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/841.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Pearson’s r and Mean Absolute Error (MAE) for prediction of MET scores (test set, 57 participants) and TOEFL scores (leave-one-out cross validation, all 53 participants) from eye movement patterns in reading. We consider two baselines which do not use eyetracking information: (1) the average proficiency score in the training set, which yields 4.82 MAE on MET and 8.29 MAE on TOEFL, and (2) the reading speed of the participant." ], "reasoning": "To verify this statement, compare the correlation (r) values for MET Fixed. The highest correlation value is 0.70, which corresponds to the WFC feature, not Reading Speed.", "statement": "Reading speed has the highest correlation (r) for MET Fixed.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 9: Differences in entropy and repetition of unigrams/trigrams in references and translations. Negative values indicate translations have a higher value, so references show consistently higher entropies and lower repetition rates.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The translated text shows lower n-gram entropies and higher repetition rates, especially in common crawl and French-English 109 datasets.The translated text has lower n-gram entropies and higher rates of repetition. This appears for all datasets, but is strongest for common crawl and French-English 109." } ], "images": [ "scigen/train/842.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Differences in entropy and repetition of unigrams/trigrams in references and translations. Negative values indicate translations have a higher value, so references show consistently higher entropies and lower repetition rates." ], "reasoning": "The translated text shows lower n-gram entropies and higher repetition rates, especially in common crawl and French-English 109 datasets.", "description": "The translated text has lower n-gram entropies and higher rates of repetition. This appears for all datasets, but is strongest for common crawl and French-English 109.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 9: Differences in entropy and repetition of unigrams/trigrams in references and translations. Negative values indicate translations have a higher value, so references show consistently higher entropies and lower repetition rates.'. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich dataset has the highest trigram repetition rate for French?\n" }, { "role": "assistant", "content": "To find the dataset with the highest trigram repetition rate for French, look at the 'Rep. (tri)' column for rows where 'Lang.' is FR. The highest value is 7.74% for the Giga dataset. So, the answer is {\"answer\": \"Giga\"}" } ], "images": [ "scigen/train/842.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Differences in entropy and repetition of unigrams/trigrams in references and translations. Negative values indicate translations have a higher value, so references show consistently higher entropies and lower repetition rates." ], "reasoning": "To find the dataset with the highest trigram repetition rate for French, look at the 'Rep. (tri)' column for rows where 'Lang.' is FR. The highest value is 7.74% for the Giga dataset.", "question": "Which dataset has the highest trigram repetition rate for French?", "answer": "So, the answer is {\"answer\": \"Giga\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe common crawl dataset for French has the highest unigram repetition rate.\n" }, { "role": "assistant", "content": "To verify this statement, check the 'Rep. (uni)' column for rows where 'Lang.' is FR. The common crawl dataset has a unigram repetition rate of 8.50%, which is indeed the highest among the French datasets. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/842.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Differences in entropy and repetition of unigrams/trigrams in references and translations. Negative values indicate translations have a higher value, so references show consistently higher entropies and lower repetition rates." ], "reasoning": "To verify this statement, check the 'Rep. (uni)' column for rows where 'Lang.' is FR. The common crawl dataset has a unigram repetition rate of 8.50%, which is indeed the highest among the French datasets.", "statement": "The common crawl dataset for French has the highest unigram repetition rate.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 5: Test correlations for our models when trained on sentences with particular length ranges (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100).'.\n" }, { "role": "assistant", "content": "Performance drops as sentence length increases for both GRAN and Avg models, especially in NMT data.These results are averages across all language pairs and data sources of training data for each length range shown. We find it best to select NMT data where the translations have between 0 and 10 tokens, with performance dropping as sentence length increases. This is true for both the GRAN and Avg models. We do the same filtering for the SimpWiki data, though the trend is not nearly as strong. This may be because machine translation quality drops as sentence length increases. This trend appears even though the datasets with higher ranges have more tokens of training data, since only the number of training sentence pairs is kept constant across configurations." } ], "images": [ "scigen/train/843.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Test correlations for our models when trained on sentences with particular length ranges (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100)." ], "reasoning": "Performance drops as sentence length increases for both GRAN and Avg models, especially in NMT data.", "description": "These results are averages across all language pairs and data sources of training data for each length range shown. We find it best to select NMT data where the translations have between 0 and 10 tokens, with performance dropping as sentence length increases. This is true for both the GRAN and Avg models. We do the same filtering for the SimpWiki data, though the trend is not nearly as strong. This may be because machine translation quality drops as sentence length increases. This trend appears even though the datasets with higher ranges have more tokens of training data, since only the number of training sentence pairs is kept constant across configurations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: Test correlations for our models when trained on sentences with particular length ranges (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100).', solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the average performance of the GRAN model on NMT data for sentences with 20-30 tokens?\n" }, { "role": "assistant", "content": "To find the average performance of the GRAN model on NMT data for sentences with 20-30 tokens, look at the intersection of the 'NMT' row and 'GRAN' column under the 'Length Range 20-30' heading. So, the answer is {\"answer\": \"66.0\"}" } ], "images": [ "scigen/train/843.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Test correlations for our models when trained on sentences with particular length ranges (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100)." ], "reasoning": "To find the average performance of the GRAN model on NMT data for sentences with 20-30 tokens, look at the intersection of the 'NMT' row and 'GRAN' column under the 'Length Range 20-30' heading.", "question": "What is the average performance of the GRAN model on NMT data for sentences with 20-30 tokens?", "answer": "So, the answer is {\"answer\": \"66.0\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe GRAN model performs better on SimpWiki data than on NMT data for sentences with 0-10 tokens.\n" }, { "role": "assistant", "content": "Compare the performance of the GRAN model on SimpWiki data (67.4) and NMT data (66.6) for sentences with 0-10 tokens. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/843.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Test correlations for our models when trained on sentences with particular length ranges (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100)." ], "reasoning": "Compare the performance of the GRAN model on SimpWiki data (67.4) and NMT data (66.6) for sentences with 0-10 tokens.", "statement": "The GRAN model performs better on SimpWiki data than on NMT data for sentences with 0-10 tokens.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 6: Length filtering test results after tuning length ranges on development data (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100).'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Length-based filtering improves performance for both NMT and SimpWiki compared to random selection, with the best results achieved using short length ranges.We then tune the length range using our development data, considering the following length ranges: [0,10], [0,15], [0,20], [0,30], [0,100], [10,20], [10,30], [10,100], [15,25], [15,30], [15,100], [20,30], [20,100], [30,100]. We tune over ranges as well as language, data source, and stopping epoch, each time training on 24,000 sentence pairs. We compare to a baseline that draws a random set of data, showing that length-based filtering leads to gains of nearly half a point on average across our test sets. The tuned length ranges are short for both NMT and SimpWiki." } ], "images": [ "scigen/train/844.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Length filtering test results after tuning length ranges on development data (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100)." ], "reasoning": "Length-based filtering improves performance for both NMT and SimpWiki compared to random selection, with the best results achieved using short length ranges.", "description": "We then tune the length range using our development data, considering the following length ranges: [0,10], [0,15], [0,20], [0,30], [0,100], [10,20], [10,30], [10,100], [15,25], [15,30], [15,100], [20,30], [20,100], [30,100]. We tune over ranges as well as language, data source, and stopping epoch, each time training on 24,000 sentence pairs. We compare to a baseline that draws a random set of data, showing that length-based filtering leads to gains of nearly half a point on average across our test sets. The tuned length ranges are short for both NMT and SimpWiki.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nTable title: Table 6: Length filtering test results after tuning length ranges on development data (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100).\n\nQuestion: What is the tuned length range for SimpWiki Avg and how does it compare to the NMT Avg score with length filtering?\n" }, { "role": "assistant", "content": "The tuned length range for SimpWiki Avg is [0,15]. The NMT Avg score with length filtering is 66.0. So, the answer is {\"answer\": \"[0,15] and 66.0\"}." } ], "images": [ "scigen/train/844.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Length filtering test results after tuning length ranges on development data (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100)." ], "reasoning": "The tuned length range for SimpWiki Avg is [0,15]. The NMT Avg score with length filtering is 66.0.", "question": "What is the tuned length range for SimpWiki Avg and how does it compare to the NMT Avg score with length filtering?", "answer": "So, the answer is {\"answer\": \"[0,15] and 66.0\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe tuned length range for NMT GRAN is [0,10].\n" }, { "role": "assistant", "content": "The table shows that the tuned length range for NMT GRAN is indeed [0,10]. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/844.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Length filtering test results after tuning length ranges on development data (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100)." ], "reasoning": "The table shows that the tuned length range for NMT GRAN is indeed [0,10].", "statement": "The tuned length range for NMT GRAN is [0,10].", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 7: Quality filtering test results after tuning quality hyperparameters on development data (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The reference classifier slightly outperforms random selection using the stronger GRAN model, while translation cost and language model are not helpful for filtering.The translation cost and language model are not helpful for filtering, as random selection outperforms them. Both methods are outperformed by the reference classifier, which slightly outperforms random selection when using the stronger GRAN model. We now discuss further how we trained the reference classifier and the data characteristics that it reveals. We did not experiment with quality filtering for SimpWiki since it is human-written text." } ], "images": [ "scigen/train/845.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Quality filtering test results after tuning quality hyperparameters on development data (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100)." ], "reasoning": "The reference classifier slightly outperforms random selection using the stronger GRAN model, while translation cost and language model are not helpful for filtering.", "description": "The translation cost and language model are not helpful for filtering, as random selection outperforms them. Both methods are outperformed by the reference classifier, which slightly outperforms random selection when using the stronger GRAN model. We now discuss further how we trained the reference classifier and the data characteristics that it reveals. We did not experiment with quality filtering for SimpWiki since it is human-written text.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 7: Quality filtering test results after tuning quality hyperparameters on development data (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100).'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich filtering method achieved the highest score using the GRAN model?\n" }, { "role": "assistant", "content": "By comparing the scores under the GRAN model column, the reference classification method achieved the highest score of 67.0. So, the answer is {\"answer\": \"Reference Classification\"}" } ], "images": [ "scigen/train/845.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Quality filtering test results after tuning quality hyperparameters on development data (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100)." ], "reasoning": "By comparing the scores under the GRAN model column, the reference classification method achieved the highest score of 67.0.", "question": "Which filtering method achieved the highest score using the GRAN model?", "answer": "So, the answer is {\"answer\": \"Reference Classification\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The language model outperforms random selection using the GRAN model., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe language model outperforms random selection using the GRAN model.\n" }, { "role": "assistant", "content": "The table shows that the language model has a score of 66.7, which is lower than the random selection score of 66.9 using the GRAN model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/845.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Quality filtering test results after tuning quality hyperparameters on development data (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100)." ], "reasoning": "The table shows that the language model has a score of 66.7, which is lower than the random selection score of 66.9 using the GRAN model.", "statement": "The language model outperforms random selection using the GRAN model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 8: Results of reference/translation classification (accuracy×100). The highest score in each column is in boldface. Final two columns show accuracies of positive (reference) and negative classes, respectively.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The LSTM model consistently outperforms the word averaging model across different datasets, and increasing the training data size by 90,000 examples improved results by about 2% on both validation and testing data.While performance varies greatly across data sources, the LSTM always outperforms the word averaging model. For our translation-reference classification, we note that our results can be further improved. We also trained models on 90,000 examples, essentially doubling the amount of data, and the results improved by about 2% absolute on each dataset on both the validation and testing data." } ], "images": [ "scigen/train/846.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Results of reference/translation classification (accuracy×100). The highest score in each column is in boldface. Final two columns show accuracies of positive (reference) and negative classes, respectively." ], "reasoning": "The LSTM model consistently outperforms the word averaging model across different datasets, and increasing the training data size by 90,000 examples improved results by about 2% on both validation and testing data.", "description": "While performance varies greatly across data sources, the LSTM always outperforms the word averaging model. For our translation-reference classification, we note that our results can be further improved. We also trained models on 90,000 examples, essentially doubling the amount of data, and the results improved by about 2% absolute on each dataset on both the validation and testing data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 8: Results of reference/translation classification (accuracy×100). The highest score in each column is in boldface. Final two columns show accuracies of positive (reference) and negative classes, respectively.', and sequentially solve the question, writing out each step of your reasoning process. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the highest test accuracy achieved by the LSTM model?\n" }, { "role": "assistant", "content": "To find the highest test accuracy achieved by the LSTM model, we need to look at the 'Test Acc.' column for the LSTM rows. The highest value is 93.1. So, the answer is {\"answer\": \"93.1\"}" } ], "images": [ "scigen/train/846.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Results of reference/translation classification (accuracy×100). The highest score in each column is in boldface. Final two columns show accuracies of positive (reference) and negative classes, respectively." ], "reasoning": "To find the highest test accuracy achieved by the LSTM model, we need to look at the 'Test Acc.' column for the LSTM rows. The highest value is 93.1.", "question": "What is the highest test accuracy achieved by the LSTM model?", "answer": "So, the answer is {\"answer\": \"93.1\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The word averaging model achieved a test accuracy of 92.5% on the Giga dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe word averaging model achieved a test accuracy of 92.5% on the Giga dataset.\n" }, { "role": "assistant", "content": "The table shows that the word averaging model (Avg) achieved a test accuracy of 92.5% on the Giga dataset. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/846.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Results of reference/translation classification (accuracy×100). The highest score in each column is in boldface. Final two columns show accuracies of positive (reference) and negative classes, respectively." ], "reasoning": "The table shows that the word averaging model (Avg) achieved a test accuracy of 92.5% on the Giga dataset.", "statement": "The word averaging model achieved a test accuracy of 92.5% on the Giga dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 12: Diversity filtering test results after tuning filtering hyperparameters on development data (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Diversity filtering methods improve performance on SimpWiki because they favor data with more differences, addressing the issue of near-duplicate sentence pairs.We find that the diversity filtering methods lead to consistent improvements when training on SimpWiki. We believe this is because many of the sentence pairs in SimpWiki are near-duplicates and these filtering methods favor data with more differences." } ], "images": [ "scigen/train/847.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 12: Diversity filtering test results after tuning filtering hyperparameters on development data (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100)." ], "reasoning": "Diversity filtering methods improve performance on SimpWiki because they favor data with more differences, addressing the issue of near-duplicate sentence pairs.", "description": "We find that the diversity filtering methods lead to consistent improvements when training on SimpWiki. We believe this is because many of the sentence pairs in SimpWiki are near-duplicates and these filtering methods favor data with more differences.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 12: Diversity filtering test results after tuning filtering hyperparameters on development data (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100).', answer the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich filtering method has the highest SimpWiki GRAN score?\n" }, { "role": "assistant", "content": "To find the filtering method with the highest SimpWiki GRAN score, compare the SimpWiki GRAN scores for each filtering method. The highest score is 68.0, which corresponds to the Bigram Overlap method. So, the answer is {\"answer\": \"Bigram Overlap\"}." } ], "images": [ "scigen/train/847.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 12: Diversity filtering test results after tuning filtering hyperparameters on development data (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100)." ], "reasoning": "To find the filtering method with the highest SimpWiki GRAN score, compare the SimpWiki GRAN scores for each filtering method. The highest score is 68.0, which corresponds to the Bigram Overlap method.", "question": "Which filtering method has the highest SimpWiki GRAN score?", "answer": "So, the answer is {\"answer\": \"Bigram Overlap\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The BLEU Score filtering method has a higher SimpWiki Avg score than the Random filtering method. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe BLEU Score filtering method has a higher SimpWiki Avg score than the Random filtering method.\n" }, { "role": "assistant", "content": "To verify this statement, compare the SimpWiki Avg scores for the BLEU Score and Random filtering methods. The BLEU Score has a SimpWiki Avg of 66.5, while Random has a SimpWiki Avg of 65.8. Since 66.5 is greater than 65.8, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/847.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 12: Diversity filtering test results after tuning filtering hyperparameters on development data (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100)." ], "reasoning": "To verify this statement, compare the SimpWiki Avg scores for the BLEU Score and Random filtering methods. The BLEU Score has a SimpWiki Avg of 66.5, while Random has a SimpWiki Avg of 65.8. Since 66.5 is greater than 65.8, the statement is supported.", "statement": "The BLEU Score filtering method has a higher SimpWiki Avg score than the Random filtering method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 13: Test results when using more training data. More data helps both Avg and GRAN, where both models get close to or surpass training on SimpWiki and comfortably surpass the PPDB baseline. The amount of training examples used is in parentheses.'.\n" }, { "role": "assistant", "content": "PPDB is outperformed by both CC-CS and CC-DE data sources, which show significant improvement when trained on larger datasets.PPDB is soundly beaten by both data sources. Moreover, the CC-CS and CC-DE data have greatly improved their performance from training on just 24,000 examples from 66.77 and 66.61 respectively, providing more evidence that this approach does indeed scale." } ], "images": [ "scigen/train/848.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 13: Test results when using more training data. More data helps both Avg and GRAN, where both models get close to or surpass training on SimpWiki and comfortably surpass the PPDB baseline. The amount of training examples used is in parentheses." ], "reasoning": "PPDB is outperformed by both CC-CS and CC-DE data sources, which show significant improvement when trained on larger datasets.", "description": "PPDB is soundly beaten by both data sources. Moreover, the CC-CS and CC-DE data have greatly improved their performance from training on just 24,000 examples from 66.77 and 66.61 respectively, providing more evidence that this approach does indeed scale.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 13: Test results when using more training data. More data helps both Avg and GRAN, where both models get close to or surpass training on SimpWiki and comfortably surpass the PPDB baseline. The amount of training examples used is in parentheses.', and tackle the question below in a sequential manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the GRAN score for CC-CS when trained on 100k examples?\n" }, { "role": "assistant", "content": "The table shows the GRAN score for CC-CS (100k) as 68.5, which is higher than the score for CC-CS (24k). So, the answer is {\"answer\": \"68.5\"}." } ], "images": [ "scigen/train/848.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 13: Test results when using more training data. More data helps both Avg and GRAN, where both models get close to or surpass training on SimpWiki and comfortably surpass the PPDB baseline. The amount of training examples used is in parentheses." ], "reasoning": "The table shows the GRAN score for CC-CS (100k) as 68.5, which is higher than the score for CC-CS (24k).", "question": "What is the GRAN score for CC-CS when trained on 100k examples?", "answer": "So, the answer is {\"answer\": \"68.5\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nCC-DE has a higher average score than PPDB.\n" }, { "role": "assistant", "content": "The table shows that the average score for CC-DE (168k) is 67.6, which is higher than the average score for PPDB, which is 66.3. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/848.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 13: Test results when using more training data. More data helps both Avg and GRAN, where both models get close to or surpass training on SimpWiki and comfortably surpass the PPDB baseline. The amount of training examples used is in parentheses." ], "reasoning": "The table shows that the average score for CC-DE (168k) is 67.6, which is higher than the average score for PPDB, which is 66.3.", "statement": "CC-DE has a higher average score than PPDB.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: Accuracies for the natural language inference task. Shown are our implementations of a system without attention, and with logistic, soft, and sparse attentions.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "SoftAttention and SparseAttention systems perform similarly, with SparseAttention being slightly more accurate on the test set, and both outperform NoAttention and LogisticAttention systems.We observe that the soft and sparse-activated attention systems perform similarly, the latter being slightly more accurate on the test set, and that both outperform the NoAttention and LogisticAttention systems." } ], "images": [ "scigen/train/849.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracies for the natural language inference task. Shown are our implementations of a system without attention, and with logistic, soft, and sparse attentions." ], "reasoning": "SoftAttention and SparseAttention systems perform similarly, with SparseAttention being slightly more accurate on the test set, and both outperform NoAttention and LogisticAttention systems.", "description": "We observe that the soft and sparse-activated attention systems perform similarly, the latter being slightly more accurate on the test set, and that both outperform the NoAttention and LogisticAttention systems.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Accuracies for the natural language inference task. Shown are our implementations of a system without attention, and with logistic, soft, and sparse attentions.', unfold the question presented below, detailing every step of your calculation. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich attention system has the highest test accuracy?\n" }, { "role": "assistant", "content": "By comparing the test accuracies of all the systems, SparseAttention has the highest test accuracy at 82.20. So, the answer is {\"answer\": \"SparseAttention\"}." } ], "images": [ "scigen/train/849.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracies for the natural language inference task. Shown are our implementations of a system without attention, and with logistic, soft, and sparse attentions." ], "reasoning": "By comparing the test accuracies of all the systems, SparseAttention has the highest test accuracy at 82.20.", "question": "Which attention system has the highest test accuracy?", "answer": "So, the answer is {\"answer\": \"SparseAttention\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The NoAttention system has the lowest test accuracy. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe NoAttention system has the lowest test accuracy.\n" }, { "role": "assistant", "content": "By comparing the test accuracies of all the systems, NoAttention has the lowest test accuracy at 80.99. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/849.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracies for the natural language inference task. Shown are our implementations of a system without attention, and with logistic, soft, and sparse attentions." ], "reasoning": "By comparing the test accuracies of all the systems, NoAttention has the lowest test accuracy at 80.99.", "statement": "The NoAttention system has the lowest test accuracy.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Comparison between machine translation and human interpretation. The interpretation reference consists of a collection of interpretations from S, A and B. Our model is trained on the large-scale corpus.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Human interpreters often skip non-primary information, leading to shorter interpretations with high brevity penalties, whereas machine translation models provide more adequate translations.We concatenate the translation of each talk into one big sentence, and then evaluate it by BLEU score. Moreover, the length of interpretations are relatively short, and results in a high length penalty provided by the evaluation script. The result is unsurprising, because human interpreters often deliberately skip non-primary information to keep a reasonable ear-voice span, which may bring a loss of adequacy and yet a shorter lag time, whereas the machine translation model translates the content adequately. We also use human interpreting results as references." } ], "images": [ "scigen/train/850.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison between machine translation and human interpretation. The interpretation reference consists of a collection of interpretations from S, A and B. Our model is trained on the large-scale corpus." ], "reasoning": "Human interpreters often skip non-primary information, leading to shorter interpretations with high brevity penalties, whereas machine translation models provide more adequate translations.", "description": "We concatenate the translation of each talk into one big sentence, and then evaluate it by BLEU score. Moreover, the length of interpretations are relatively short, and results in a high length penalty provided by the evaluation script. The result is unsurprising, because human interpreters often deliberately skip non-primary information to keep a reasonable ear-voice span, which may bring a loss of adequacy and yet a shorter lag time, whereas the machine translation model translates the content adequately. We also use human interpreting results as references.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Comparison between machine translation and human interpretation. The interpretation reference consists of a collection of interpretations from S, A and B. Our model is trained on the large-scale corpus.\nQuestion: Which model has the highest Translation Reference BLEU score?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the Translation Reference BLEU scores of all models, we see that 'Our Model' has the highest score of 20.93. So, the answer is {\"answer\": \"Our Model\"}" } ], "images": [ "scigen/train/850.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison between machine translation and human interpretation. The interpretation reference consists of a collection of interpretations from S, A and B. Our model is trained on the large-scale corpus." ], "reasoning": "By comparing the Translation Reference BLEU scores of all models, we see that 'Our Model' has the highest score of 20.93.", "question": "Which model has the highest Translation Reference BLEU score?", "answer": "So, the answer is {\"answer\": \"Our Model\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Model B has a higher Translation Reference BLEU score than Model S. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nModel B has a higher Translation Reference BLEU score than Model S.\n" }, { "role": "assistant", "content": "The table shows that Model B has a Translation Reference BLEU score of 12.08, while Model S has a score of 16.02. Therefore, Model B does not have a higher score than Model S. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/850.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison between machine translation and human interpretation. The interpretation reference consists of a collection of interpretations from S, A and B. Our model is trained on the large-scale corpus." ], "reasoning": "The table shows that Model B has a Translation Reference BLEU score of 12.08, while Model S has a score of 16.02. Therefore, Model B does not have a higher score than Model S.", "statement": "Model B has a higher Translation Reference BLEU score than Model S.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: The overall results on NIST Chinese-English translation task.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The sub-sentence model shows a 3.08 drop in BLEU score, while the wait-15 model shows a 2.15 drop. The discard 6 tokens model shows an improvement of 1.76 BLEU score.Effectiveness on Translation Quality. On an average the sub-sentence shows weaker performance by a 3.08 drop in BLEU score (40.39 → 37.31). Similarly, the wait-k model also brings an obvious decrease in translation quality, even with the best wait-15 policy, its performance is still worse than the baseline system, with a 2.15 drop, averagely, in BLEU (40.39 → 38.24). For a machine translation product, a large degradation in translation quality will largely affect the use experience even if it has low latency. Lastly, the discard 6 tokens obtains an impressive result, with an average improvement of 1.76 BLEU score (37.96 → 39.72)." } ], "images": [ "scigen/train/851.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The overall results on NIST Chinese-English translation task." ], "reasoning": "The sub-sentence model shows a 3.08 drop in BLEU score, while the wait-15 model shows a 2.15 drop. The discard 6 tokens model shows an improvement of 1.76 BLEU score.", "description": "Effectiveness on Translation Quality. On an average the sub-sentence shows weaker performance by a 3.08 drop in BLEU score (40.39 → 37.31). Similarly, the wait-k model also brings an obvious decrease in translation quality, even with the best wait-15 policy, its performance is still worse than the baseline system, with a 2.15 drop, averagely, in BLEU (40.39 → 38.24). For a machine translation product, a large degradation in translation quality will largely affect the use experience even if it has low latency. Lastly, the discard 6 tokens obtains an impressive result, with an average improvement of 1.76 BLEU score (37.96 → 39.72).", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model shows the highest average BLEU score improvement?\nSolve the above question based on the table titled 'Table 2: The overall results on NIST Chinese-English translation task.'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The discard 6 tokens model shows an improvement of 1.76 BLEU score, which is the highest improvement mentioned in the description. So, the answer is {\"answer\": \"discard 6 tokens\"}" } ], "images": [ "scigen/train/851.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The overall results on NIST Chinese-English translation task." ], "reasoning": "The discard 6 tokens model shows an improvement of 1.76 BLEU score, which is the highest improvement mentioned in the description.", "question": "Which model shows the highest average BLEU score improvement?", "answer": "So, the answer is {\"answer\": \"discard 6 tokens\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe wait-15 model has a better average BLEU score than the baseline system.\n" }, { "role": "assistant", "content": "The wait-15 model has an average BLEU score of 38.24, which is lower than the baseline system's average BLEU score of 40.39. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/851.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The overall results on NIST Chinese-English translation task." ], "reasoning": "The wait-15 model has an average BLEU score of 38.24, which is lower than the baseline system's average BLEU score of 40.39.", "statement": "The wait-15 model has a better average BLEU score than the baseline system.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: The comparison between our sequence detector and previous work. The latency represents the words requiring to make an explicit decision.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The dynamic context-based information unit boundary detector improves model performance, as evidenced by higher precision, recall, and F-score in our model compared to 5-LM and RNN.In our context-sensitive model, the dynamic context based information unit boundary detector is essential to determine the IU boundaries in the steaming input. Both of two contrastive models are trained on approximate 2 million monolingual Chinese sentences. This observation indicates that with bidirectional context, the model can learn better representation to help the downstream tasks. In the next experiments, we will evaluate models given testing data with IU boundaries detected by our detector." } ], "images": [ "scigen/train/852.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The comparison between our sequence detector and previous work. The latency represents the words requiring to make an explicit decision." ], "reasoning": "The dynamic context-based information unit boundary detector improves model performance, as evidenced by higher precision, recall, and F-score in our model compared to 5-LM and RNN.", "description": "In our context-sensitive model, the dynamic context based information unit boundary detector is essential to determine the IU boundaries in the steaming input. Both of two contrastive models are trained on approximate 2 million monolingual Chinese sentences. This observation indicates that with bidirectional context, the model can learn better representation to help the downstream tasks. In the next experiments, we will evaluate models given testing data with IU boundaries detected by our detector.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 3: The comparison between our sequence detector and previous work. The latency represents the words requiring to make an explicit decision.', solve the following question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model has the highest F-score and what is its value?\n" }, { "role": "assistant", "content": "The table shows the F-scores for three models: 5-LM, RNN, and Our model. The highest F-score is 78.26, which belongs to Our model. So, the answer is {\"answer\": \"78.26\"}." } ], "images": [ "scigen/train/852.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The comparison between our sequence detector and previous work. The latency represents the words requiring to make an explicit decision." ], "reasoning": "The table shows the F-scores for three models: 5-LM, RNN, and Our model. The highest F-score is 78.26, which belongs to Our model.", "question": "Which model has the highest F-score and what is its value?", "answer": "So, the answer is {\"answer\": \"78.26\"}." }, { "messages": [ { "role": "user", "content": "Our model has the lowest max latency among the three models. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nOur model has the lowest max latency among the three models.\n" }, { "role": "assistant", "content": "The table lists the max latency for 5-LM, RNN, and Our model. Our model has a max latency of 39, which is lower than 46 for 5-LM and 48 for RNN. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/852.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The comparison between our sequence detector and previous work. The latency represents the words requiring to make an explicit decision." ], "reasoning": "The table lists the max latency for 5-LM, RNN, and Our model. Our model has a max latency of 39, which is lower than 46 for 5-LM and 48 for RNN.", "statement": "Our model has the lowest max latency among the three models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: The overall results on BSTC Chinese-English translation task (Pre-train represents training on the NIST dataset, and fine-tune represents fine-tuning on the BSTC dataset.). Clean input indicates the input is from human annotated transcription, while the ASR input represents the input contains ASR errors. ASR + Auto IU indicates that the sentence boundary as well as sub-sentence is detected by our IU detector. Therefore, this data basically reflects the real environment in practical product.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Fine-tuning on BSTC data improves model performance, and context-aware models outperform wait-15 models in all settings.Due to the relatively lower CER in ASR errors (10.32 %), the distinction between the clean input and the noisy input results in a BLEU score difference smaller than 2 points (15.85 vs. 14.60 for pre-train, and 21.98 vs. 19.91 for fine-tune). Despite the small size of the training data in BSTC, fine-tuning on this data is essential to improve the performance of all models. In all settings, the best system in context-aware model beats the wait-15 model. Pre-trained models are not sensitive to errors from Auto IU, while fine-tuned models are." } ], "images": [ "scigen/train/853.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The overall results on BSTC Chinese-English translation task (Pre-train represents training on the NIST dataset, and fine-tune represents fine-tuning on the BSTC dataset.). Clean input indicates the input is from human annotated transcription, while the ASR input represents the input contains ASR errors. ASR + Auto IU indicates that the sentence boundary as well as sub-sentence is detected by our IU detector. Therefore, this data basically reflects the real environment in practical product." ], "reasoning": "Fine-tuning on BSTC data improves model performance, and context-aware models outperform wait-15 models in all settings.", "description": "Due to the relatively lower CER in ASR errors (10.32 %), the distinction between the clean input and the noisy input results in a BLEU score difference smaller than 2 points (15.85 vs. 14.60 for pre-train, and 21.98 vs. 19.91 for fine-tune). Despite the small size of the training data in BSTC, fine-tuning on this data is essential to improve the performance of all models. In all settings, the best system in context-aware model beats the wait-15 model. Pre-trained models are not sensitive to errors from Auto IU, while fine-tuned models are.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 4: The overall results on BSTC Chinese-English translation task (Pre-train represents training on the NIST dataset, and fine-tune represents fine-tuning on the BSTC dataset.). Clean input indicates the input is from human annotated transcription, while the ASR input represents the input contains ASR errors. ASR + Auto IU indicates that the sentence boundary as well as sub-sentence is detected by our IU detector. Therefore, this data basically reflects the real environment in practical product.', and tackle the question below in a sequential manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the BLEU score difference between clean input pre-train and ASR input pre-train for the baseline model?\n" }, { "role": "assistant", "content": "The BLEU score for clean input pre-train is 15.85 and for ASR input pre-train is 14.60. The difference is 15.85 - 14.60 = 1.25. So, the answer is {\"answer\": \"1.25\"}" } ], "images": [ "scigen/train/853.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The overall results on BSTC Chinese-English translation task (Pre-train represents training on the NIST dataset, and fine-tune represents fine-tuning on the BSTC dataset.). Clean input indicates the input is from human annotated transcription, while the ASR input represents the input contains ASR errors. ASR + Auto IU indicates that the sentence boundary as well as sub-sentence is detected by our IU detector. Therefore, this data basically reflects the real environment in practical product." ], "reasoning": "The BLEU score for clean input pre-train is 15.85 and for ASR input pre-train is 14.60. The difference is 15.85 - 14.60 = 1.25.", "question": "What is the BLEU score difference between clean input pre-train and ASR input pre-train for the baseline model?", "answer": "So, the answer is {\"answer\": \"1.25\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe context-aware model has a higher BLEU score than the wait-15 model in the ASR + Auto IU Fine-tune setting.\n" }, { "role": "assistant", "content": "The BLEU score for the context-aware model in the ASR + Auto IU Fine-tune setting is 16.83, while for the wait-15 model it is 17.21. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/853.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The overall results on BSTC Chinese-English translation task (Pre-train represents training on the NIST dataset, and fine-tune represents fine-tuning on the BSTC dataset.). Clean input indicates the input is from human annotated transcription, while the ASR input represents the input contains ASR errors. ASR + Auto IU indicates that the sentence boundary as well as sub-sentence is detected by our IU detector. Therefore, this data basically reflects the real environment in practical product." ], "reasoning": "The BLEU score for the context-aware model in the ASR + Auto IU Fine-tune setting is 16.83, while for the wait-15 model it is 17.21.", "statement": "The context-aware model has a higher BLEU score than the wait-15 model in the ASR + Auto IU Fine-tune setting.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Results of image retrieval task on Flickr30k.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The higher R@1 score for ViLBERT with augmentation indicates improved image caption quality, aiding better image understanding.The higher relative gain on R@1 also indicates that the generator can produce high-quality image captions which can help the model better understand images." } ], "images": [ "scigen/train/854.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results of image retrieval task on Flickr30k." ], "reasoning": "The higher R@1 score for ViLBERT with augmentation indicates improved image caption quality, aiding better image understanding.", "description": "The higher relative gain on R@1 also indicates that the generator can produce high-quality image captions which can help the model better understand images.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 5: Results of image retrieval task on Flickr30k.' and methodically tackle the question that follows. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the difference in R@1 scores between ViLBERT and ViLBERT with augmentation?\n" }, { "role": "assistant", "content": "To find the difference in R@1 scores, subtract the R@1 score of ViLBERT from the R@1 score of ViLBERT with augmentation: 60.4 - 58.2. So, the answer is {\"answer\": \"2.2\"}." } ], "images": [ "scigen/train/854.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results of image retrieval task on Flickr30k." ], "reasoning": "To find the difference in R@1 scores, subtract the R@1 score of ViLBERT from the R@1 score of ViLBERT with augmentation: 60.4 - 58.2.", "question": "What is the difference in R@1 scores between ViLBERT and ViLBERT with augmentation?", "answer": "So, the answer is {\"answer\": \"2.2\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nViLBERT with augmentation has a higher R@5 score than ViLBERT.\n" }, { "role": "assistant", "content": "The table shows that ViLBERT with augmentation has an R@5 score of 86.4, which is higher than ViLBERT's R@5 score of 84.9. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/854.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results of image retrieval task on Flickr30k." ], "reasoning": "The table shows that ViLBERT with augmentation has an R@5 score of 86.4, which is higher than ViLBERT's R@5 score of 84.9.", "statement": "ViLBERT with augmentation has a higher R@5 score than ViLBERT.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Comparison of two masking methods on COCO Captions.'.\n" }, { "role": "assistant", "content": "Two-stage pre-training with in-domain data improves model adaptation to downstream data, and single [MASK] strategy outperforms multi [MASK] strategy.Comparing with one-stage pre-training, we find that each task combination pre-trained after the second stage (Row 6-8) gains approximately +2 on CIDEr. This indicates that two-stage pre-training with in-domain data enables the model to adapt to the downstream data better than only using out-of-domain pre-training. Combining all three tasks leads to the highest score on all metrics. We use this as the optimal pre-training setting for further experiments. How to Efficiently Mask? We conduct further experiments to compare two masking strategies for IDA: (1) Multi [MASK]: replace tokens in the sampled fragment with exactly the same number of [MASK] tokens, (2) Single [MASK]: replace the fragment with a single [MASK] token. Single [MASK] is obviously better than Multi [MASK]. Multi [MASK] provides the decoder with full position information of masked token, which reduced the difficulty for the decoder to predict correct words, thus, gives a less well performance (-0.3 on CIDEr). Therefore, we use the single [MASK] as the optimal pre-training setting." } ], "images": [ "scigen/train/855.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of two masking methods on COCO Captions." ], "reasoning": "Two-stage pre-training with in-domain data improves model adaptation to downstream data, and single [MASK] strategy outperforms multi [MASK] strategy.", "description": "Comparing with one-stage pre-training, we find that each task combination pre-trained after the second stage (Row 6-8) gains approximately +2 on CIDEr. This indicates that two-stage pre-training with in-domain data enables the model to adapt to the downstream data better than only using out-of-domain pre-training. Combining all three tasks leads to the highest score on all metrics. We use this as the optimal pre-training setting for further experiments. How to Efficiently Mask? We conduct further experiments to compare two masking strategies for IDA: (1) Multi [MASK]: replace tokens in the sampled fragment with exactly the same number of [MASK] tokens, (2) Single [MASK]: replace the fragment with a single [MASK] token. Single [MASK] is obviously better than Multi [MASK]. Multi [MASK] provides the decoder with full position information of masked token, which reduced the difficulty for the decoder to predict correct words, thus, gives a less well performance (-0.3 on CIDEr). Therefore, we use the single [MASK] as the optimal pre-training setting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Comparison of two masking methods on COCO Captions.\nQuestion: What is the CIDEr score difference between multi [MASK] and single [MASK] strategies?\nGive a detailed response to the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The CIDEr score for multi [MASK] is 117.8, and for single [MASK] it is 118.1. The difference is 118.1 - 117.8 = 0.3. So, the answer is {\"answer\": \"0.3\"}" } ], "images": [ "scigen/train/855.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of two masking methods on COCO Captions." ], "reasoning": "The CIDEr score for multi [MASK] is 117.8, and for single [MASK] it is 118.1. The difference is 118.1 - 117.8 = 0.3.", "question": "What is the CIDEr score difference between multi [MASK] and single [MASK] strategies?", "answer": "So, the answer is {\"answer\": \"0.3\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Single [MASK] strategy results in a higher CIDEr score than multi [MASK] strategy. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nSingle [MASK] strategy results in a higher CIDEr score than multi [MASK] strategy.\n" }, { "role": "assistant", "content": "The table shows that the CIDEr score for single [MASK] is 118.1, which is higher than the CIDEr score for multi [MASK], which is 117.8. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/855.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of two masking methods on COCO Captions." ], "reasoning": "The table shows that the CIDEr score for single [MASK] is 118.1, which is higher than the CIDEr score for multi [MASK], which is 117.8.", "statement": "Single [MASK] strategy results in a higher CIDEr score than multi [MASK] strategy.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Distillation-augmented performances for selected high sparsity levels. All pruning methods benefit from distillation signal further enhancing the ratio Performance VS Model Size.'.\n" }, { "role": "assistant", "content": "Soft movement pruning combined with distillation consistently outperforms other pruning methods across all datasets, achieving up to 95% of BERT-base performance with significantly fewer weights.The training objective is a linear combination of the training loss and a knowledge distillation loss on the output distributions. Overall, we observe that the relative comparisons of the pruning methods remain unchanged while the performances are strictly increased. When combined with distillation, soft movement pruning yields the strongest performances across all pruning methods and studied datasets: it reaches 95% of BERT-base with only a fraction of the weights in the encoder (∼5% on SQuAD and MNLI)." } ], "images": [ "scigen/train/856.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Distillation-augmented performances for selected high sparsity levels. All pruning methods benefit from distillation signal further enhancing the ratio Performance VS Model Size." ], "reasoning": "Soft movement pruning combined with distillation consistently outperforms other pruning methods across all datasets, achieving up to 95% of BERT-base performance with significantly fewer weights.", "description": "The training objective is a linear combination of the training loss and a knowledge distillation loss on the output distributions. Overall, we observe that the relative comparisons of the pruning methods remain unchanged while the performances are strictly increased. When combined with distillation, soft movement pruning yields the strongest performances across all pruning methods and studied datasets: it reaches 95% of BERT-base with only a fraction of the weights in the encoder (∼5% on SQuAD and MNLI).", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: Distillation-augmented performances for selected high sparsity levels. All pruning methods benefit from distillation signal further enhancing the ratio Performance VS Model Size.'. Solve the following question based on the table.\n\nQuestion: Which pruning method achieves the highest F1 score on the QQP dataset with 3% remaining weights?\nLet's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "From the table, we observe the F1 scores for QQP with 3% remaining weights. The highest F1 score is achieved by soft movement pruning (soft MvP) with a score of 85.6. So, the answer is {\"answer\": \"soft MvP\"}." } ], "images": [ "scigen/train/856.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Distillation-augmented performances for selected high sparsity levels. All pruning methods benefit from distillation signal further enhancing the ratio Performance VS Model Size." ], "reasoning": "From the table, we observe the F1 scores for QQP with 3% remaining weights. The highest F1 score is achieved by soft movement pruning (soft MvP) with a score of 85.6.", "question": "Which pruning method achieves the highest F1 score on the QQP dataset with 3% remaining weights?", "answer": "So, the answer is {\"answer\": \"soft MvP\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Soft movement pruning achieves the highest performance on the SQuAD dataset with 10% remaining weights., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nSoft movement pruning achieves the highest performance on the SQuAD dataset with 10% remaining weights.\n" }, { "role": "assistant", "content": "The table shows the performance metrics for different pruning methods on the SQuAD dataset with 10% remaining weights. Soft movement pruning (soft MvP) achieves the highest EM/F1 scores of 76.6/84.9. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/856.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Distillation-augmented performances for selected high sparsity levels. All pruning methods benefit from distillation signal further enhancing the ratio Performance VS Model Size." ], "reasoning": "The table shows the performance metrics for different pruning methods on the SQuAD dataset with 10% remaining weights. Soft movement pruning (soft MvP) achieves the highest EM/F1 scores of 76.6/84.9.", "statement": "Soft movement pruning achieves the highest performance on the SQuAD dataset with 10% remaining weights.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Performance at high sparsity levels. (Soft) movement pruning outperforms current state-of-the art pruning methods at different high sparsity levels.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "In high sparsity regimes, movement pruning and soft movement pruning achieve significantly better F1 scores compared to magnitude pruning and L0 regularization.Magnitude pruning on SQuAD achieves 54.5 F1 with 3% of the weights compared to 73.6 F1 with L0 regularization, 76.3 F1 for movement pruning, and 79.9 F1 with soft movement pruning. These experiments indicate that in high sparsity regimes, importance scores derived from the movement accumulated during fine-tuning induce significantly better pruned models compared to absolute values." } ], "images": [ "scigen/train/857.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance at high sparsity levels. (Soft) movement pruning outperforms current state-of-the art pruning methods at different high sparsity levels." ], "reasoning": "In high sparsity regimes, movement pruning and soft movement pruning achieve significantly better F1 scores compared to magnitude pruning and L0 regularization.", "description": "Magnitude pruning on SQuAD achieves 54.5 F1 with 3% of the weights compared to 73.6 F1 with L0 regularization, 76.3 F1 for movement pruning, and 79.9 F1 with soft movement pruning. These experiments indicate that in high sparsity regimes, importance scores derived from the movement accumulated during fine-tuning induce significantly better pruned models compared to absolute values.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: Performance at high sparsity levels. (Soft) movement pruning outperforms current state-of-the art pruning methods at different high sparsity levels.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the F1 score achieved by soft movement pruning on SQuAD with 3% of the weights?\n" }, { "role": "assistant", "content": "The table shows that soft movement pruning on SQuAD with 3% of the weights achieves an F1 score of 79.9. So, the answer is {\"answer\": \"79.9\"}." } ], "images": [ "scigen/train/857.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance at high sparsity levels. (Soft) movement pruning outperforms current state-of-the art pruning methods at different high sparsity levels." ], "reasoning": "The table shows that soft movement pruning on SQuAD with 3% of the weights achieves an F1 score of 79.9.", "question": "What is the F1 score achieved by soft movement pruning on SQuAD with 3% of the weights?", "answer": "So, the answer is {\"answer\": \"79.9\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nMovement pruning achieves a higher F1 score than L0 regularization on SQuAD with 1.3% of the weights.\n" }, { "role": "assistant", "content": "The table shows that movement pruning achieves an F1 score of 76.3 on SQuAD with 1.3% of the weights, while L0 regularization achieves an F1 score of 61.6. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/857.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance at high sparsity levels. (Soft) movement pruning outperforms current state-of-the art pruning methods at different high sparsity levels." ], "reasoning": "The table shows that movement pruning achieves an F1 score of 76.3 on SQuAD with 1.3% of the weights, while L0 regularization achieves an F1 score of 61.6.", "statement": "Movement pruning achieves a higher F1 score than L0 regularization on SQuAD with 1.3% of the weights.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: ROUGE F1 and METEOR scores of sanity check ablations, evaluated on CNN/DM validation set.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "To ensure improvements are due to the closed-book decoder and not model capacity, a variant with two attention-pointer decoders and a pointer-generator baseline with increased parameters were evaluated.Model Capacity: To validate and sanity-check that the improvements are the result of the inclusion of our closed-book decoder and not due to some trivial effects of having two decoders or larger model capacity (more parameters), we train a variant of our model with two duplicated (initialized to be different) attention-pointer decoders. We also evaluate a pointer-generator baseline with 2-layer encoder and decoder (pg-2layer) and increase the LSTM hidden dimension and word embedding dimension of the pointer-generator baseline (pg-big) to exceed the total number of parameters of our 2-decoder model (34.5M versus 34.4M parameters). Moreover, as shown in Sec." } ], "images": [ "scigen/train/858.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: ROUGE F1 and METEOR scores of sanity check ablations, evaluated on CNN/DM validation set." ], "reasoning": "To ensure improvements are due to the closed-book decoder and not model capacity, a variant with two attention-pointer decoders and a pointer-generator baseline with increased parameters were evaluated.", "description": "Model Capacity: To validate and sanity-check that the improvements are the result of the inclusion of our closed-book decoder and not due to some trivial effects of having two decoders or larger model capacity (more parameters), we train a variant of our model with two duplicated (initialized to be different) attention-pointer decoders. We also evaluate a pointer-generator baseline with 2-layer encoder and decoder (pg-2layer) and increase the LSTM hidden dimension and word embedding dimension of the pointer-generator baseline (pg-big) to exceed the total number of parameters of our 2-decoder model (34.5M versus 34.4M parameters). Moreover, as shown in Sec.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 7: ROUGE F1 and METEOR scores of sanity check ablations, evaluated on CNN/DM validation set.', solve the following question step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model achieved the highest ROUGE-1 score?\n" }, { "role": "assistant", "content": "By comparing the ROUGE-1 scores of all models in the table, the 'pg + cbdec' model has the highest score of 38.87. So, the answer is {\"answer\": \"pg + cbdec\"}." } ], "images": [ "scigen/train/858.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: ROUGE F1 and METEOR scores of sanity check ablations, evaluated on CNN/DM validation set." ], "reasoning": "By comparing the ROUGE-1 scores of all models in the table, the 'pg + cbdec' model has the highest score of 38.87.", "question": "Which model achieved the highest ROUGE-1 score?", "answer": "So, the answer is {\"answer\": \"pg + cbdec\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe pg-big model has the highest ROUGE-2 score.\n" }, { "role": "assistant", "content": "The table shows that the 'pg + cbdec' model has the highest ROUGE-2 score of 16.93, not the 'pg-big' model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/858.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: ROUGE F1 and METEOR scores of sanity check ablations, evaluated on CNN/DM validation set." ], "reasoning": "The table shows that the 'pg + cbdec' model has the highest ROUGE-2 score of 16.93, not the 'pg-big' model.", "statement": "The pg-big model has the highest ROUGE-2 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: ROUGE F1 and METEOR scores (non-coverage) on CNN/Daily Mail test set of previous works and our models. ‘pg’ is the pointer-generator baseline, and ‘pg + cbdec’ is our 2-decoder model with closed-book decoder(cbdec). The model marked with ⋆ is trained and evaluated on the anonymized version of the data.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Our 2-decoder model outperforms the pointer-generator baseline in all metrics on the CNN/Daily Mail dataset, with a significant advantage (p<0.001) and a 95% ROUGE-significance interval of at most ±0.25.We first report our evaluation results on CNN/Daily Mail dataset. In the reinforced setting, our 2-decoder model still maintains significant (p<0.001) advantage in all metrics over the pointer-generator baseline. and have a 95% ROUGE-significance interval of at most ±0.25." } ], "images": [ "scigen/train/859.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: ROUGE F1 and METEOR scores (non-coverage) on CNN/Daily Mail test set of previous works and our models. ‘pg’ is the pointer-generator baseline, and ‘pg + cbdec’ is our 2-decoder model with closed-book decoder(cbdec). The model marked with ⋆ is trained and evaluated on the anonymized version of the data." ], "reasoning": "Our 2-decoder model outperforms the pointer-generator baseline in all metrics on the CNN/Daily Mail dataset, with a significant advantage (p<0.001) and a 95% ROUGE-significance interval of at most ±0.25.", "description": "We first report our evaluation results on CNN/Daily Mail dataset. In the reinforced setting, our 2-decoder model still maintains significant (p<0.001) advantage in all metrics over the pointer-generator baseline. and have a 95% ROUGE-significance interval of at most ±0.25.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: ROUGE F1 and METEOR scores (non-coverage) on CNN/Daily Mail test set of previous works and our models. ‘pg’ is the pointer-generator baseline, and ‘pg + cbdec’ is our 2-decoder model with closed-book decoder(cbdec). The model marked with ⋆ is trained and evaluated on the anonymized version of the data.\nQuestion: What is the ROUGE 1 score for the RL + pg + cbdec model?\nSolve the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows the ROUGE 1 score for the RL + pg + cbdec model as 38.58, which is the highest among all models listed. So, the answer is {\"answer\": \"38.58\"}" } ], "images": [ "scigen/train/859.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: ROUGE F1 and METEOR scores (non-coverage) on CNN/Daily Mail test set of previous works and our models. ‘pg’ is the pointer-generator baseline, and ‘pg + cbdec’ is our 2-decoder model with closed-book decoder(cbdec). The model marked with ⋆ is trained and evaluated on the anonymized version of the data." ], "reasoning": "The table shows the ROUGE 1 score for the RL + pg + cbdec model as 38.58, which is the highest among all models listed.", "question": "What is the ROUGE 1 score for the RL + pg + cbdec model?", "answer": "So, the answer is {\"answer\": \"38.58\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The pg (baseline) model has a higher ROUGE 2 score than the pg + cbdec model., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe pg (baseline) model has a higher ROUGE 2 score than the pg + cbdec model.\n" }, { "role": "assistant", "content": "The table shows that the pg (baseline) model has a ROUGE 2 score of 15.71, while the pg + cbdec model has a ROUGE 2 score of 16.45. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/859.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: ROUGE F1 and METEOR scores (non-coverage) on CNN/Daily Mail test set of previous works and our models. ‘pg’ is the pointer-generator baseline, and ‘pg + cbdec’ is our 2-decoder model with closed-book decoder(cbdec). The model marked with ⋆ is trained and evaluated on the anonymized version of the data." ], "reasoning": "The table shows that the pg (baseline) model has a ROUGE 2 score of 15.71, while the pg + cbdec model has a ROUGE 2 score of 16.45. Therefore, the statement is refuted.", "statement": "The pg (baseline) model has a higher ROUGE 2 score than the pg + cbdec model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: ROUGE F1 and METEOR scores (with-coverage) on the CNN/Daily Mail test set. Coverage mechanism See et al. (2017) is used in all models except the RL model Paulus et al. (2018). The model marked with ⋆ is trained and evaluated on the anonymized version of the data.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The 2-decoder model (RL + pg + cbdec) significantly outperforms the strong RL baseline (RL + pg) in all metrics, with a statistical significance of p<0.001.In the reinforced setting, our 2-decoder model (RL + pg + cbdec) outperforms our strong RL baseline (RL + pg) by a considerable margin (stat. significance of p<0.001). Fig." } ], "images": [ "scigen/train/860.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: ROUGE F1 and METEOR scores (with-coverage) on the CNN/Daily Mail test set. Coverage mechanism See et al. (2017) is used in all models except the RL model Paulus et al. (2018). The model marked with ⋆ is trained and evaluated on the anonymized version of the data." ], "reasoning": "The 2-decoder model (RL + pg + cbdec) significantly outperforms the strong RL baseline (RL + pg) in all metrics, with a statistical significance of p<0.001.", "description": "In the reinforced setting, our 2-decoder model (RL + pg + cbdec) outperforms our strong RL baseline (RL + pg) by a considerable margin (stat. significance of p<0.001). Fig.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: ROUGE F1 and METEOR scores (with-coverage) on the CNN/Daily Mail test set. Coverage mechanism See et al. (2017) is used in all models except the RL model Paulus et al. (2018). The model marked with ⋆ is trained and evaluated on the anonymized version of the data.\nQuestion: Which model achieved the highest score in the ROUGE 1 metric?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the ROUGE 1 scores of all models, the model 'RL + pg + cbdec' achieved the highest score of 40.66. So, the answer is {\"answer\": \"RL + pg + cbdec\"}." } ], "images": [ "scigen/train/860.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: ROUGE F1 and METEOR scores (with-coverage) on the CNN/Daily Mail test set. Coverage mechanism See et al. (2017) is used in all models except the RL model Paulus et al. (2018). The model marked with ⋆ is trained and evaluated on the anonymized version of the data." ], "reasoning": "By comparing the ROUGE 1 scores of all models, the model 'RL + pg + cbdec' achieved the highest score of 40.66.", "question": "Which model achieved the highest score in the ROUGE 1 metric?", "answer": "So, the answer is {\"answer\": \"RL + pg + cbdec\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe pg + cbdec model outperforms the RL + pg model in the ROUGE L metric.\n" }, { "role": "assistant", "content": "The table shows that the pg + cbdec model has a ROUGE L score of 36.73, while the RL + pg model has a ROUGE L score of 36.16, indicating that the pg + cbdec model outperforms the RL + pg model in this metric. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/860.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: ROUGE F1 and METEOR scores (with-coverage) on the CNN/Daily Mail test set. Coverage mechanism See et al. (2017) is used in all models except the RL model Paulus et al. (2018). The model marked with ⋆ is trained and evaluated on the anonymized version of the data." ], "reasoning": "The table shows that the pg + cbdec model has a ROUGE L score of 36.73, while the RL + pg model has a ROUGE L score of 36.16, indicating that the pg + cbdec model outperforms the RL + pg model in this metric.", "statement": "The pg + cbdec model outperforms the RL + pg model in the ROUGE L metric.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: ROUGE F1 scores of ablation studies, evaluated on CNN/Daily Mail validation set.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The ablation study shows that the 2-decoder model's encoder outperforms the baseline encoder, and stopping the gradient flow between the encoder and closed-book decoder affects the model's performance.Fixed-Encoder Ablation: Next, we conduct an ablation study in order to prove the qualitative superiority of our 2-decoder model’s encoder to the baseline encoder. To do this, we train two pointer-generators with randomly initialized decoders and word embeddings. For the first model, we restore the pre-trained encoder from our pointer-generator baseline; for the second model, we restore the pre-trained encoder from our 2-decoder model. We then fix the encoder’s parameters for both models during the training, only updating the embeddings and decoders with gradient descent. Since these two models have the exact same structure with only the encoders initialized according to different pre-trained models, the significant improvements in metric scores suggest that our 2-decoder model does have a stronger encoder than the pointer-generator baseline. Gradient-Flow-Cut Ablation: We further design another ablation test to identify how the gradients from the closed-book decoder influence the entire model during training. Fig. As we can see, the closed-book decoder only depends on the word embeddings and encoder. Therefore it can affect the entire model during training by influencing either the encoder or the word-embedding matrix. When we stop the gradient flow between the encoder and closed-book decoder (\\⃝raisebox{-0.9pt}{1} in Fig. This proves that the gradients back-propagated from closed-book decoder to the encoder can strengthen the entire model, and hence verifies the gradient-flow intuition discussed in introduction (Sec." } ], "images": [ "scigen/train/861.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: ROUGE F1 scores of ablation studies, evaluated on CNN/Daily Mail validation set." ], "reasoning": "The ablation study shows that the 2-decoder model's encoder outperforms the baseline encoder, and stopping the gradient flow between the encoder and closed-book decoder affects the model's performance.", "description": "Fixed-Encoder Ablation: Next, we conduct an ablation study in order to prove the qualitative superiority of our 2-decoder model’s encoder to the baseline encoder. To do this, we train two pointer-generators with randomly initialized decoders and word embeddings. For the first model, we restore the pre-trained encoder from our pointer-generator baseline; for the second model, we restore the pre-trained encoder from our 2-decoder model. We then fix the encoder’s parameters for both models during the training, only updating the embeddings and decoders with gradient descent. Since these two models have the exact same structure with only the encoders initialized according to different pre-trained models, the significant improvements in metric scores suggest that our 2-decoder model does have a stronger encoder than the pointer-generator baseline. Gradient-Flow-Cut Ablation: We further design another ablation test to identify how the gradients from the closed-book decoder influence the entire model during training. Fig. As we can see, the closed-book decoder only depends on the word embeddings and encoder. Therefore it can affect the entire model during training by influencing either the encoder or the word-embedding matrix. When we stop the gradient flow between the encoder and closed-book decoder (\\⃝raisebox{-0.9pt}{1} in Fig. This proves that the gradients back-propagated from closed-book decoder to the encoder can strengthen the entire model, and hence verifies the gradient-flow intuition discussed in introduction (Sec.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 6: ROUGE F1 scores of ablation studies, evaluated on CNN/Daily Mail validation set.'\nQuestion: What is the ROUGE 1 score for the 2-decoder's encoder in the Fixed-encoder ablation?\n" }, { "role": "assistant", "content": "The table shows the ROUGE 1 score for the 2-decoder's encoder in the Fixed-encoder ablation as 38.44. So, the answer is {\"answer\": \"38.44\"}" } ], "images": [ "scigen/train/861.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: ROUGE F1 scores of ablation studies, evaluated on CNN/Daily Mail validation set." ], "reasoning": "The table shows the ROUGE 1 score for the 2-decoder's encoder in the Fixed-encoder ablation as 38.44.", "question": "What is the ROUGE 1 score for the 2-decoder's encoder in the Fixed-encoder ablation?", "answer": "So, the answer is {\"answer\": \"38.44\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The pg baseline's encoder has a higher ROUGE 2 score than the 2-decoder's encoder in the Fixed-encoder ablation. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe pg baseline's encoder has a higher ROUGE 2 score than the 2-decoder's encoder in the Fixed-encoder ablation.\n" }, { "role": "assistant", "content": "The table shows that the ROUGE 2 score for the pg baseline's encoder is 16.27, while the ROUGE 2 score for the 2-decoder's encoder is 16.85, indicating that the statement is false. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/861.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: ROUGE F1 scores of ablation studies, evaluated on CNN/Daily Mail validation set." ], "reasoning": "The table shows that the ROUGE 2 score for the pg baseline's encoder is 16.27, while the ROUGE 2 score for the 2-decoder's encoder is 16.85, indicating that the statement is false.", "statement": "The pg baseline's encoder has a higher ROUGE 2 score than the 2-decoder's encoder in the Fixed-encoder ablation.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: The effect of source languages for comparable corpus creation. These News Crawl corpora are as of 2017 version. The number of sentences in each dataset is approximately 20M, respectively. These results are obtained by USMTforward in iter 1.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Finnish data yields the highest F0.5 score, while languages more similar to English result in lower F0.5 scores.We also examine how source languages of machine translation affect performance. The outputs using Finnish data is the best score among various languages; the more similar to English the source-side data is, the lower the F0.5 score of the output." } ], "images": [ "scigen/train/862.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The effect of source languages for comparable corpus creation. These News Crawl corpora are as of 2017 version. The number of sentences in each dataset is approximately 20M, respectively. These results are obtained by USMTforward in iter 1." ], "reasoning": "Finnish data yields the highest F0.5 score, while languages more similar to English result in lower F0.5 scores.", "description": "We also examine how source languages of machine translation affect performance. The outputs using Finnish data is the best score among various languages; the more similar to English the source-side data is, the lower the F0.5 score of the output.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: The effect of source languages for comparable corpus creation. These News Crawl corpora are as of 2017 version. The number of sentences in each dataset is approximately 20M, respectively. These results are obtained by USMTforward in iter 1.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. \n\nQuestion: Which source language has the highest F0.5 score and what is the score?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows the F0.5 scores for different source languages. Finnish has the highest F0.5 score of 29.04. So, the answer is {\"answer\": \"Finnish, 29.04\"}" } ], "images": [ "scigen/train/862.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The effect of source languages for comparable corpus creation. These News Crawl corpora are as of 2017 version. The number of sentences in each dataset is approximately 20M, respectively. These results are obtained by USMTforward in iter 1." ], "reasoning": "The table shows the F0.5 scores for different source languages. Finnish has the highest F0.5 score of 29.04.", "question": "Which source language has the highest F0.5 score and what is the score?", "answer": "So, the answer is {\"answer\": \"Finnish, 29.04\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nFrench data yields the highest recall score.\n" }, { "role": "assistant", "content": "The table shows that the recall score for French data is 30.27, which is the highest among the listed languages. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/862.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The effect of source languages for comparable corpus creation. These News Crawl corpora are as of 2017 version. The number of sentences in each dataset is approximately 20M, respectively. These results are obtained by USMTforward in iter 1." ], "reasoning": "The table shows that the recall score for French data is 30.27, which is the highest among the listed languages.", "statement": "French data yields the highest recall score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: M2 and GLEU results. The bold scores represent the best score in unsupervised SMT. The underlined scores represent the best overall score.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "USMTforward in iter 1 has the highest GLEU score among unsupervised SMT models and its F0.5 score is lower than both supervised SMT and NMT.The F0.5 score for USMTforward in iter 1 is 13.57 points lower than that of supervised SMT and 17.17 points lower than that of supervised NMT. On JFLEG, the highest score was achieved with USMTforward in iter 1 among the unsupervised SMT models; its GLEU scores are 5.28 points and 3.39 points lower than those of supervised SMT and supervised NMT, respectively." } ], "images": [ "scigen/train/863.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: M2 and GLEU results. The bold scores represent the best score in unsupervised SMT. The underlined scores represent the best overall score." ], "reasoning": "USMTforward in iter 1 has the highest GLEU score among unsupervised SMT models and its F0.5 score is lower than both supervised SMT and NMT.", "description": "The F0.5 score for USMTforward in iter 1 is 13.57 points lower than that of supervised SMT and 17.17 points lower than that of supervised NMT. On JFLEG, the highest score was achieved with USMTforward in iter 1 among the unsupervised SMT models; its GLEU scores are 5.28 points and 3.39 points lower than those of supervised SMT and supervised NMT, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: M2 and GLEU results. The bold scores represent the best score in unsupervised SMT. The underlined scores represent the best overall score.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. \n\nQuestion: What is the GLEU score of USMTforward in iter 1 and how does it compare to the GLEU scores of supervised SMT and NMT?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The GLEU score of USMTforward in iter 1 is 50.65. It is 5.28 points lower than the GLEU score of supervised SMT (55.93) and 3.39 points lower than the GLEU score of supervised NMT (54.04). So, the answer is {\"answer\": \"50.65\"}" } ], "images": [ "scigen/train/863.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: M2 and GLEU results. The bold scores represent the best score in unsupervised SMT. The underlined scores represent the best overall score." ], "reasoning": "The GLEU score of USMTforward in iter 1 is 50.65. It is 5.28 points lower than the GLEU score of supervised SMT (55.93) and 3.39 points lower than the GLEU score of supervised NMT (54.04).", "question": "What is the GLEU score of USMTforward in iter 1 and how does it compare to the GLEU scores of supervised SMT and NMT?", "answer": "So, the answer is {\"answer\": \"50.65\"}" }, { "messages": [ { "role": "user", "content": "The F0.5 score of USMTforward in iter 1 is higher than that of supervised SMT. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe F0.5 score of USMTforward in iter 1 is higher than that of supervised SMT.\n" }, { "role": "assistant", "content": "The F0.5 score of USMTforward in iter 1 is 27.04, which is lower than the F0.5 score of supervised SMT, which is 40.61. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/863.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: M2 and GLEU results. The bold scores represent the best score in unsupervised SMT. The underlined scores represent the best overall score." ], "reasoning": "The F0.5 score of USMTforward in iter 1 is 27.04, which is lower than the F0.5 score of supervised SMT, which is 40.61.", "statement": "The F0.5 score of USMTforward in iter 1 is higher than that of supervised SMT.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: GEC results with W&I+LOCNESS test data.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "TMU's F0.5 score is 28.31, placing it eighth among nine teams, and it has the highest number of false positives at 4,314.The F0.5 score for our system (TMU) is 28.31; this score is eighth among the nine teams. In particular, the number of false positives of our system is 4,314; this is the worst result of all." } ], "images": [ "scigen/train/864.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: GEC results with W&I+LOCNESS test data." ], "reasoning": "TMU's F0.5 score is 28.31, placing it eighth among nine teams, and it has the highest number of false positives at 4,314.", "description": "The F0.5 score for our system (TMU) is 28.31; this score is eighth among the nine teams. In particular, the number of false positives of our system is 4,314; this is the worst result of all.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 4: GEC results with W&I+LOCNESS test data.', answer the question below by showing each progressive step toward the answer. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich team has the highest number of false positives and what is their F0.5 score?\n" }, { "role": "assistant", "content": "The table shows that TMU has the highest number of false positives at 4,314 and their F0.5 score is 28.31. So, the answer is {\"answer\": \"TMU with an F0.5 score of 28.31\"}" } ], "images": [ "scigen/train/864.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: GEC results with W&I+LOCNESS test data." ], "reasoning": "The table shows that TMU has the highest number of false positives at 4,314 and their F0.5 score is 28.31.", "question": "Which team has the highest number of false positives and what is their F0.5 score?", "answer": "So, the answer is {\"answer\": \"TMU with an F0.5 score of 28.31\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nTMU has the highest number of false positives among all teams.\n" }, { "role": "assistant", "content": "The table indicates that TMU has 4,314 false positives, which is the highest number among all the teams listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/864.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: GEC results with W&I+LOCNESS test data." ], "reasoning": "The table indicates that TMU has 4,314 false positives, which is the highest number among all the teams listed.", "statement": "TMU has the highest number of false positives among all teams.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4. The hyper-parameter settings in the generation-based baselines and the generation module in the proposed hybrid neural conversation model. These settings are the optimized settings tuned with the validation data.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The Seq2Seq model uses an embedding size of 512, 2 LSTM layers in the encoder/decoder, an LSTM hidden state size of 512, a learning rate of 0.0001, a learning rate decay of 0.5, 10000 steps between validation, a patience of early stopping of 10, and a dropout rate of 0.3.Parameter Settings Hyper-parameters are tuned with the validation data. For the hyper-parameter settings in the hybrid ranking module, we set the window size of the convolution and pooling kernels as (6,6). The number of convolution kernels is 64. The dropout rate is set to 0.5. The margin in the pairwise-ranking hinge loss is 1.0. The distant supervision signals and the number of positive samples per context in the hybrid ranking module are tuned with validation data. The used distant supervision signal is BLEU-1 and we treat top 3 response candidates ranked by BLEU-1 as positive samples. All models are trained on a single Nvidia Titan X GPU by stochastic gradient descent with Adam (DBLP: journals/corr/KingmaB14) algorithm. The initial learning rate is 0.0001. The parameters of Adam, β1 and β2, are 0.9 and 0.999 respectively. The batch size is 500. The maximum conversation context/ response length is 30." } ], "images": [ "scigen/train/865.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4. The hyper-parameter settings in the generation-based baselines and the generation module in the proposed hybrid neural conversation model. These settings are the optimized settings tuned with the validation data." ], "reasoning": "The Seq2Seq model uses an embedding size of 512, 2 LSTM layers in the encoder/decoder, an LSTM hidden state size of 512, a learning rate of 0.0001, a learning rate decay of 0.5, 10000 steps between validation, a patience of early stopping of 10, and a dropout rate of 0.3.", "description": "Parameter Settings Hyper-parameters are tuned with the validation data. For the hyper-parameter settings in the hybrid ranking module, we set the window size of the convolution and pooling kernels as (6,6). The number of convolution kernels is 64. The dropout rate is set to 0.5. The margin in the pairwise-ranking hinge loss is 1.0. The distant supervision signals and the number of positive samples per context in the hybrid ranking module are tuned with validation data. The used distant supervision signal is BLEU-1 and we treat top 3 response candidates ranked by BLEU-1 as positive samples. All models are trained on a single Nvidia Titan X GPU by stochastic gradient descent with Adam (DBLP: journals/corr/KingmaB14) algorithm. The initial learning rate is 0.0001. The parameters of Adam, β1 and β2, are 0.9 and 0.999 respectively. The batch size is 500. The maximum conversation context/ response length is 30.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4. The hyper-parameter settings in the generation-based baselines and the generation module in the proposed hybrid neural conversation model. These settings are the optimized settings tuned with the validation data.', answer the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the learning rate for the Seq2Seq-Facts model?\n" }, { "role": "assistant", "content": "The table shows the learning rate for the Seq2Seq-Facts model as 0.001. So, the answer is {\"answer\": \"0.001\"}" } ], "images": [ "scigen/train/865.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4. The hyper-parameter settings in the generation-based baselines and the generation module in the proposed hybrid neural conversation model. These settings are the optimized settings tuned with the validation data." ], "reasoning": "The table shows the learning rate for the Seq2Seq-Facts model as 0.001.", "question": "What is the learning rate for the Seq2Seq-Facts model?", "answer": "So, the answer is {\"answer\": \"0.001\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The Seq2Seq model has a dropout rate of 0.5., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Seq2Seq model has a dropout rate of 0.5.\n" }, { "role": "assistant", "content": "The table indicates that the Seq2Seq model has a dropout rate of 0.3, not 0.5. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/865.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4. The hyper-parameter settings in the generation-based baselines and the generation module in the proposed hybrid neural conversation model. These settings are the optimized settings tuned with the validation data." ], "reasoning": "The table indicates that the Seq2Seq model has a dropout rate of 0.3, not 0.5.", "statement": "The Seq2Seq model has a dropout rate of 0.5.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 9. The response generation performance when we vary the ratios of positive samples in distant supervision.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Increasing the value of k' improves response generation performance but also introduces the risk of noisy positive training data.We further analyze the impact of the ratios of positive/ negative training samples on the response generation performance. The value of k′ is the number of positive response candidates for each conversation context when we train the hybrid ranking module. When k′=1, we select one positive candidate from the ground truth responses in the training data, which is equivalent to the negative sampling technique. As k′ increases, we construct the positive candidates by selecting one positive sample from the ground truth responses and k′−1 positive samples from the top ranked candidates by distant supervision. We find that larger k′ can improve the response generation performance. This is reasonable since larger k′ means the model can observe more positive training samples and positive/ negative response pairs in the pairwise ranking loss minimization process. However, increasing the value of k′ also adds risks of introducing noisy positive training data. Thus, the value of k′ is a hyper-parameter, and needs to be tweaked via trial and error." } ], "images": [ "scigen/train/866.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9. The response generation performance when we vary the ratios of positive samples in distant supervision." ], "reasoning": "Increasing the value of k' improves response generation performance but also introduces the risk of noisy positive training data.", "description": "We further analyze the impact of the ratios of positive/ negative training samples on the response generation performance. The value of k′ is the number of positive response candidates for each conversation context when we train the hybrid ranking module. When k′=1, we select one positive candidate from the ground truth responses in the training data, which is equivalent to the negative sampling technique. As k′ increases, we construct the positive candidates by selecting one positive sample from the ground truth responses and k′−1 positive samples from the top ranked candidates by distant supervision. We find that larger k′ can improve the response generation performance. This is reasonable since larger k′ means the model can observe more positive training samples and positive/ negative response pairs in the pairwise ranking loss minimization process. However, increasing the value of k′ also adds risks of introducing noisy positive training data. Thus, the value of k′ is a hyper-parameter, and needs to be tweaked via trial and error.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 9. The response generation performance when we vary the ratios of positive samples in distant supervision.', answer the question below by showing each progressive step toward the answer. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the BLEU-1 score for the HybridNCM-RSF model when k' is 3?\n" }, { "role": "assistant", "content": "To find the BLEU-1 score for the HybridNCM-RSF model when k' is 3, locate the row where the model is HybridNCM-RSF and k' is 3, then find the corresponding BLEU-1 score. So, the answer is {\"answer\": \"1.3695\"}" } ], "images": [ "scigen/train/866.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9. The response generation performance when we vary the ratios of positive samples in distant supervision." ], "reasoning": "To find the BLEU-1 score for the HybridNCM-RSF model when k' is 3, locate the row where the model is HybridNCM-RSF and k' is 3, then find the corresponding BLEU-1 score.", "question": "What is the BLEU-1 score for the HybridNCM-RSF model when k' is 3?", "answer": "So, the answer is {\"answer\": \"1.3695\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe HybridNCM-RS model with k' = 3 has the highest BLEU-1 score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the BLEU-1 scores of all models when k' is 3. The HybridNCM-RS model with k' = 3 has a BLEU-1 score of 1.3450, which is not the highest as the HybridNCM-RSF model with k' = 3 has a BLEU-1 score of 1.3695. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/866.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9. The response generation performance when we vary the ratios of positive samples in distant supervision." ], "reasoning": "To verify this statement, compare the BLEU-1 scores of all models when k' is 3. The HybridNCM-RS model with k' = 3 has a BLEU-1 score of 1.3450, which is not the highest as the HybridNCM-RSF model with k' = 3 has a BLEU-1 score of 1.3695.", "statement": "The HybridNCM-RS model with k' = 3 has the highest BLEU-1 score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 10. The response generation performance when we vary different distant supervision signals. This table shows the results for the setting “k’=3”, where there are 3 positive response candidates for each conversation context. “SentBLEU” denotes using sentence-level BLEU scores as distant supervision signals.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "BLEU-1 is effective for training the hybrid ranking module, while sentence-level BLEU is not suitable due to its high variance and small smoothed values in short text pairs.We find that distant supervision signals like BLEU-1 are quite effective for training the hybrid ranking module. The sentence-level BLEU is not a good choice for the distant supervision signal. The reason is that the sentence-level BLEU is computed only based on the n-gram precision statistics for a given sentence pair. This score has a larger variance compared with the corpus-level BLEU. Since sentence-level BLEU scores would become very small smoothed values if there are no 4-gram or trigram matches between two sentences, which may happen frequently in short text pairs." } ], "images": [ "scigen/train/867.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10. The response generation performance when we vary different distant supervision signals. This table shows the results for the setting “k’=3”, where there are 3 positive response candidates for each conversation context. “SentBLEU” denotes using sentence-level BLEU scores as distant supervision signals." ], "reasoning": "BLEU-1 is effective for training the hybrid ranking module, while sentence-level BLEU is not suitable due to its high variance and small smoothed values in short text pairs.", "description": "We find that distant supervision signals like BLEU-1 are quite effective for training the hybrid ranking module. The sentence-level BLEU is not a good choice for the distant supervision signal. The reason is that the sentence-level BLEU is computed only based on the n-gram precision statistics for a given sentence pair. This score has a larger variance compared with the corpus-level BLEU. Since sentence-level BLEU scores would become very small smoothed values if there are no 4-gram or trigram matches between two sentences, which may happen frequently in short text pairs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 10. The response generation performance when we vary different distant supervision signals. This table shows the results for the setting “k’=3”, where there are 3 positive response candidates for each conversation context. “SentBLEU” denotes using sentence-level BLEU scores as distant supervision signals.', answer the following question. Give detailed solution consisting of each step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model supervision has the highest HybridNCM-RSF BLEU score?\n" }, { "role": "assistant", "content": "To find the highest HybridNCM-RSF BLEU score, we compare the values in the HybridNCM-RSF BLEU column. BLEU-1 has the highest score of 1.3695. So, the answer is {\"answer\": \"BLEU-1\"}." } ], "images": [ "scigen/train/867.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10. The response generation performance when we vary different distant supervision signals. This table shows the results for the setting “k’=3”, where there are 3 positive response candidates for each conversation context. “SentBLEU” denotes using sentence-level BLEU scores as distant supervision signals." ], "reasoning": "To find the highest HybridNCM-RSF BLEU score, we compare the values in the HybridNCM-RSF BLEU column. BLEU-1 has the highest score of 1.3695.", "question": "Which model supervision has the highest HybridNCM-RSF BLEU score?", "answer": "So, the answer is {\"answer\": \"BLEU-1\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nBLEU-2 has the highest HybridNCM-RS ROUGE-L score.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the HybridNCM-RS ROUGE-L scores for all model supervisions. BLEU-1 has the highest score of 10.4078, not BLEU-2. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/867.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10. The response generation performance when we vary different distant supervision signals. This table shows the results for the setting “k’=3”, where there are 3 positive response candidates for each conversation context. “SentBLEU” denotes using sentence-level BLEU scores as distant supervision signals." ], "reasoning": "To verify this statement, we compare the HybridNCM-RS ROUGE-L scores for all model supervisions. BLEU-1 has the highest score of 10.4078, not BLEU-2.", "statement": "BLEU-2 has the highest HybridNCM-RS ROUGE-L score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2. Macro-average AUC and APR over test queries with different DAG structures are used to evaluate the performance. All and H-Neg. denote macro-averaged across all query types and query types with hard negative sampling (see Section 3.2.3).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The CGA model consistently outperforms the baseline GQE model in most query types across all datasets, particularly in queries with intersection structures.We use the ROC AUC score and average percentile rank (APR) as two evaluation metrics. All evaluation results are macro-averaged across queries with different DAG structures The hyper-parameters for the baseline models GQE are tuned using grid search and the best ones are selected. Then we follow the practice of Hamilton et al. We use Adam optimizer for model optimization. The overall delta of CGA over GQE reported in Tab. This is because CGA will significantly outperform GQE in query types with intersection structures, e.g., the 9th query type in Fig. Macro-average computation over all query types makes the improvement less obvious. In order to compare the performance of different models on different query structures (different query types), we show the individual AUC and APR scores on each query type in three datasets for all models ( To highlight the difference, we subtract the minimum score from the other scores in each figure. We can see that our model consistently outperforms the baseline models in almost all query types on all datasets except for the sixth and tenth query type (see In both these two query types, GQE+KG[min] has the best performance. The advantage of our attention-based models is more obvious for query types with hard negative sampling strategy. For example, as for the 9th query type (Hard-3-inter) in Fig. Note that this query type has the largest number of neighboring nodes (3 nodes) which shows that our attention mechanism becomes more effective when a query type contains more neighboring nodes in an intersection structure. This indicates that the attention mechanism as well as the original KG training phase are effective in discriminating the correct answer from misleading answers." } ], "images": [ "scigen/train/868.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2. Macro-average AUC and APR over test queries with different DAG structures are used to evaluate the performance. All and H-Neg. denote macro-averaged across all query types and query types with hard negative sampling (see Section 3.2.3)." ], "reasoning": "The CGA model consistently outperforms the baseline GQE model in most query types across all datasets, particularly in queries with intersection structures.", "description": "We use the ROC AUC score and average percentile rank (APR) as two evaluation metrics. All evaluation results are macro-averaged across queries with different DAG structures The hyper-parameters for the baseline models GQE are tuned using grid search and the best ones are selected. Then we follow the practice of Hamilton et al. We use Adam optimizer for model optimization. The overall delta of CGA over GQE reported in Tab. This is because CGA will significantly outperform GQE in query types with intersection structures, e.g., the 9th query type in Fig. Macro-average computation over all query types makes the improvement less obvious. In order to compare the performance of different models on different query structures (different query types), we show the individual AUC and APR scores on each query type in three datasets for all models ( To highlight the difference, we subtract the minimum score from the other scores in each figure. We can see that our model consistently outperforms the baseline models in almost all query types on all datasets except for the sixth and tenth query type (see In both these two query types, GQE+KG[min] has the best performance. The advantage of our attention-based models is more obvious for query types with hard negative sampling strategy. For example, as for the 9th query type (Hard-3-inter) in Fig. Note that this query type has the largest number of neighboring nodes (3 nodes) which shows that our attention mechanism becomes more effective when a query type contains more neighboring nodes in an intersection structure. This indicates that the attention mechanism as well as the original KG training phase are effective in discriminating the correct answer from misleading answers.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2. Macro-average AUC and APR over test queries with different DAG structures are used to evaluate the performance. All and H-Neg. denote macro-averaged across all query types and query types with hard negative sampling (see Section 3.2.3).', answer the following question. Give detailed solution consisting of each step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model has the highest AUC score for the Bio dataset with hard negative sampling?\n" }, { "role": "assistant", "content": "To find the model with the highest AUC score for the Bio dataset with hard negative sampling, we look at the 'Bio AUC H-Neg' column. The highest value in this column is 85.04, which corresponds to the CGA+KG+8[min] model. So, the answer is {\"answer\": \"CGA+KG+8[min]\"}" } ], "images": [ "scigen/train/868.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2. Macro-average AUC and APR over test queries with different DAG structures are used to evaluate the performance. All and H-Neg. denote macro-averaged across all query types and query types with hard negative sampling (see Section 3.2.3)." ], "reasoning": "To find the model with the highest AUC score for the Bio dataset with hard negative sampling, we look at the 'Bio AUC H-Neg' column. The highest value in this column is 85.04, which corresponds to the CGA+KG+8[min] model.", "question": "Which model has the highest AUC score for the Bio dataset with hard negative sampling?", "answer": "So, the answer is {\"answer\": \"CGA+KG+8[min]\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe GQE[mean] model has the highest APR score for the DB18 dataset with hard negative sampling.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the 'DB18 APR H-Neg' column. The highest value in this column is 75.23, which corresponds to the CGA+KG+8[min] model, not the GQE[mean] model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/868.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2. Macro-average AUC and APR over test queries with different DAG structures are used to evaluate the performance. All and H-Neg. denote macro-averaged across all query types and query types with hard negative sampling (see Section 3.2.3)." ], "reasoning": "To verify this statement, we look at the 'DB18 APR H-Neg' column. The highest value in this column is 75.23, which corresponds to the CGA+KG+8[min] model, not the GQE[mean] model.", "statement": "The GQE[mean] model has the highest APR score for the DB18 dataset with hard negative sampling.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Human evaluation of the data collected with each MR (** = p<0.01 and *** = p<0.001 for Pictorial versus Textual conditions). Italics denote averages across all numbers of attributes.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The pictorial modality significantly improves the perceived Informativeness, Naturalness, and Phrasing of utterances compared to the textual/logical modality.Informativeness was defined (on the questionnaires) as whether the utterance “provides enough useful information about the venue”. A two-way ANOVA was conducted to examine the effect of MR modality and the number of attributes on the perceived Informativeness. There was no statistically significant interaction between the effects of modality and the number of attributes in the MR, F(2,1236) = 1.79, p=0.17. A main effects analysis showed that the average Informativeness of utterances elicited through the pictorial method (4.51) was significantly higher than that of utterances elicited using the textual/logical modality (4.28), with p<0.01. This is an increase of 0.23 points on the 6-point scale (=4.6%) in average Informativeness rating for the pictorial condition. Naturalness was defined (on the questionnaires) as whether the utterance “could have been produced by a native speaker”. A two-way ANOVA was conducted to examine the effects of MR modality and the number of attributes on the perceived Naturalness. There was no statistically significant interaction between the effects of modality and the number of attributes in the MR, F(2,1236) = 0.73, p=0.48. A main effects analysis showed that the average Naturalness of utterances elicited using the pictorial modality (4.43) was significantly higher than that of utterances elicited using the textual/logical modality (4.09), with p<0.001. This is an increase of about 0.34 points on the scale (=6.8%) for average Naturalness rating for the pictorial condition. A two-way ANOVA was conducted to examine the effect of MR modality and the number of attributes on the perceived Phrasing. There was no statistically significant interaction between the effects of modality and the number of attributes in MR, F(2,1236) = 0.85 , p=0.43. A main effects analysis showed that the average Phrasing score for the utterances elicited using the pictorial modality was significantly higher than that of the utterances elicited using the textual/logical modality, with p<0.001. This is an increase of +0.39 points (about 7.8%) in average Phrasing rating for the pictorial condition." } ], "images": [ "scigen/train/869.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Human evaluation of the data collected with each MR (** = p<0.01 and *** = p<0.001 for Pictorial versus Textual conditions). Italics denote averages across all numbers of attributes." ], "reasoning": "The pictorial modality significantly improves the perceived Informativeness, Naturalness, and Phrasing of utterances compared to the textual/logical modality.", "description": "Informativeness was defined (on the questionnaires) as whether the utterance “provides enough useful information about the venue”. A two-way ANOVA was conducted to examine the effect of MR modality and the number of attributes on the perceived Informativeness. There was no statistically significant interaction between the effects of modality and the number of attributes in the MR, F(2,1236) = 1.79, p=0.17. A main effects analysis showed that the average Informativeness of utterances elicited through the pictorial method (4.51) was significantly higher than that of utterances elicited using the textual/logical modality (4.28), with p<0.01. This is an increase of 0.23 points on the 6-point scale (=4.6%) in average Informativeness rating for the pictorial condition. Naturalness was defined (on the questionnaires) as whether the utterance “could have been produced by a native speaker”. A two-way ANOVA was conducted to examine the effects of MR modality and the number of attributes on the perceived Naturalness. There was no statistically significant interaction between the effects of modality and the number of attributes in the MR, F(2,1236) = 0.73, p=0.48. A main effects analysis showed that the average Naturalness of utterances elicited using the pictorial modality (4.43) was significantly higher than that of utterances elicited using the textual/logical modality (4.09), with p<0.001. This is an increase of about 0.34 points on the scale (=6.8%) for average Naturalness rating for the pictorial condition. A two-way ANOVA was conducted to examine the effect of MR modality and the number of attributes on the perceived Phrasing. There was no statistically significant interaction between the effects of modality and the number of attributes in MR, F(2,1236) = 0.85 , p=0.43. A main effects analysis showed that the average Phrasing score for the utterances elicited using the pictorial modality was significantly higher than that of the utterances elicited using the textual/logical modality, with p<0.001. This is an increase of +0.39 points (about 7.8%) in average Phrasing rating for the pictorial condition.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 3: Human evaluation of the data collected with each MR (** = p<0.01 and *** = p<0.001 for Pictorial versus Textual conditions). Italics denote averages across all numbers of attributes.' and proceed to solve the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the average Informativeness score for utterances with 8 attributes using the pictorial modality?\n" }, { "role": "assistant", "content": "To find the average Informativeness score for utterances with 8 attributes using the pictorial modality, look at the row labeled '8 attributes' under the 'Informativeness' section and the 'Pictorial MR Mean' column. So, the answer is {\"answer\": \"4.98\"}." } ], "images": [ "scigen/train/869.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Human evaluation of the data collected with each MR (** = p<0.01 and *** = p<0.001 for Pictorial versus Textual conditions). Italics denote averages across all numbers of attributes." ], "reasoning": "To find the average Informativeness score for utterances with 8 attributes using the pictorial modality, look at the row labeled '8 attributes' under the 'Informativeness' section and the 'Pictorial MR Mean' column.", "question": "What is the average Informativeness score for utterances with 8 attributes using the pictorial modality?", "answer": "So, the answer is {\"answer\": \"4.98\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe average Naturalness score for utterances with 5 attributes using the textual modality is higher than that using the pictorial modality.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Naturalness' scores for 5 attributes under both 'Textual MR Mean' and 'Pictorial MR Mean' columns. The textual modality score is 4.07, while the pictorial modality score is 4.41. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/869.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Human evaluation of the data collected with each MR (** = p<0.01 and *** = p<0.001 for Pictorial versus Textual conditions). Italics denote averages across all numbers of attributes." ], "reasoning": "To verify this statement, compare the 'Naturalness' scores for 5 attributes under both 'Textual MR Mean' and 'Pictorial MR Mean' columns. The textual modality score is 4.07, while the pictorial modality score is 4.41.", "statement": "The average Naturalness score for utterances with 5 attributes using the textual modality is higher than that using the pictorial modality.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Nature of the data collected with each MR. Italics denote averages across all numbers of attributes.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The number of attributes significantly affects the average task duration, length of utterance, and number of sentences per utterance, with larger numbers of attributes leading to longer durations and more sentences.A two-way ANOVA was conducted to examine the effect of MR modality and the number of attributes on average task duration. The difference between two modalities was not significant, with p=0.76. There was no statistically significant interaction between the effects of modality and the number of attributes in the MR, on time taken to collect the data. A main effects analysis showed that the average duration of utterance creation was significantly longer for larger numbers of attributes, F(2,1236) = 24.99, p<0.001, as expected. A two-way ANOVA was conducted to examine the effect of MR modality and the number of attributes on the length of utterance. There was a statistically significant interaction between the effects of modality and the number of attributes in the MR, F(2,1236) = 23.74, p<0.001. A main effects analysis showed that the average length of utterance was significantly larger not only for a larger number of attributes, with p<0.001, but also for the utterances created based on a textual/logical MR which had a higher number of attributes, p<0.001. A two-way ANOVA was conducted to examine the effect of MR modality and the number of attributes on the number of sentences per utterance. There was a statistically significant interaction between the effects of modality and the number of attributes in the MR, F(2,1236) = 3.83, p<0.05. A main effects analysis showed that the average number of sentences was significantly larger not only for a larger number of attributes, with p<0.001, but also for the utterances created based on a textual/logical MR which had a higher number of attributes, p<0.001." } ], "images": [ "scigen/train/870.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Nature of the data collected with each MR. Italics denote averages across all numbers of attributes." ], "reasoning": "The number of attributes significantly affects the average task duration, length of utterance, and number of sentences per utterance, with larger numbers of attributes leading to longer durations and more sentences.", "description": "A two-way ANOVA was conducted to examine the effect of MR modality and the number of attributes on average task duration. The difference between two modalities was not significant, with p=0.76. There was no statistically significant interaction between the effects of modality and the number of attributes in the MR, on time taken to collect the data. A main effects analysis showed that the average duration of utterance creation was significantly longer for larger numbers of attributes, F(2,1236) = 24.99, p<0.001, as expected. A two-way ANOVA was conducted to examine the effect of MR modality and the number of attributes on the length of utterance. There was a statistically significant interaction between the effects of modality and the number of attributes in the MR, F(2,1236) = 23.74, p<0.001. A main effects analysis showed that the average length of utterance was significantly larger not only for a larger number of attributes, with p<0.001, but also for the utterances created based on a textual/logical MR which had a higher number of attributes, p<0.001. A two-way ANOVA was conducted to examine the effect of MR modality and the number of attributes on the number of sentences per utterance. There was a statistically significant interaction between the effects of modality and the number of attributes in the MR, F(2,1236) = 3.83, p<0.05. A main effects analysis showed that the average number of sentences was significantly larger not only for a larger number of attributes, with p<0.001, but also for the utterances created based on a textual/logical MR which had a higher number of attributes, p<0.001.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 2: Nature of the data collected with each MR. Italics denote averages across all numbers of attributes.\n\nQuestion: What is the average task duration for 8 attributes in the Pictorial MR modality?\n" }, { "role": "assistant", "content": "To find the average task duration for 8 attributes in the Pictorial MR modality, look at the 'Time, sec' row under 'Pictorial MR Mean' and find the value corresponding to '8 attributes'. So, the answer is {\"answer\": \"405.56\"}." } ], "images": [ "scigen/train/870.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Nature of the data collected with each MR. Italics denote averages across all numbers of attributes." ], "reasoning": "To find the average task duration for 8 attributes in the Pictorial MR modality, look at the 'Time, sec' row under 'Pictorial MR Mean' and find the value corresponding to '8 attributes'.", "question": "What is the average task duration for 8 attributes in the Pictorial MR modality?", "answer": "So, the answer is {\"answer\": \"405.56\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe average length of utterance for 5 attributes is higher in the Textual MR modality than in the Pictorial MR modality.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Length, char' values for 5 attributes under 'Textual MR Mean' and 'Pictorial MR Mean'. The value for Textual MR is 95.18, and for Pictorial MR, it is 91.13. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/870.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Nature of the data collected with each MR. Italics denote averages across all numbers of attributes." ], "reasoning": "To verify this statement, compare the 'Length, char' values for 5 attributes under 'Textual MR Mean' and 'Pictorial MR Mean'. The value for Textual MR is 95.18, and for Pictorial MR, it is 91.13.", "statement": "The average length of utterance for 5 attributes is higher in the Textual MR modality than in the Pictorial MR modality.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 6: Comparative results of the proposed model (sdpLSTM) with different baselines and state-of-the-art systems. Ref. choi2016extraction ∗ and li2015approach ∗ denote the reimplementation of the systems proposed in choi2016extraction and li2015approach with the authors reported experimental setups.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The proposed sdpLSTM model shows significant improvements in F1-score over the baselines for both AiMed and BioInfer datasets, with statistical significance (p-value < 0.05).We perform 10-fold cross validation on both the datasets. With no official development data set available, cross validation seems to be the most reliable method of evaluating our proposed model. To evaluate the performance of our model, we use standard recall, precision, and F1-score. The obtained results clearly show the effectiveness of our proposed sdpLSTM based model over the other models exploring neural network architectures or conventional kernel or SDP based machine learning model. Statistical significance tests verify that improvements over both the baselines are statistically significant as (p-value < 0.05). In our proposed model we obtain the significant F1-score improvement of 19.99 and 16.23 points over the first two baselines for the AiMed dataset, respectively. On BioInfer dataset, our system shows the F1-Score improvement of 7.13 and 4.14 points over these two baselines, respectively. In order to perform the comparative analysis with the existing approaches, we choose the recent approach exploiting neural network model. We observe that sdpLSTM significantly performs better than all the state-of the-art techniques for both the datasets. We further make an interesting observation that incorporating the latent features embedded into the neural network based architecture improves the performance of the system." } ], "images": [ "scigen/train/871.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Comparative results of the proposed model (sdpLSTM) with different baselines and state-of-the-art systems. Ref. choi2016extraction ∗ and li2015approach ∗ denote the reimplementation of the systems proposed in choi2016extraction and li2015approach with the authors reported experimental setups." ], "reasoning": "The proposed sdpLSTM model shows significant improvements in F1-score over the baselines for both AiMed and BioInfer datasets, with statistical significance (p-value < 0.05).", "description": "We perform 10-fold cross validation on both the datasets. With no official development data set available, cross validation seems to be the most reliable method of evaluating our proposed model. To evaluate the performance of our model, we use standard recall, precision, and F1-score. The obtained results clearly show the effectiveness of our proposed sdpLSTM based model over the other models exploring neural network architectures or conventional kernel or SDP based machine learning model. Statistical significance tests verify that improvements over both the baselines are statistically significant as (p-value < 0.05). In our proposed model we obtain the significant F1-score improvement of 19.99 and 16.23 points over the first two baselines for the AiMed dataset, respectively. On BioInfer dataset, our system shows the F1-Score improvement of 7.13 and 4.14 points over these two baselines, respectively. In order to perform the comparative analysis with the existing approaches, we choose the recent approach exploiting neural network model. We observe that sdpLSTM significantly performs better than all the state-of the-art techniques for both the datasets. We further make an interesting observation that incorporating the latent features embedded into the neural network based architecture improves the performance of the system.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. The question is related to the given table titled 'Table 6: Comparative results of the proposed model (sdpLSTM) with different baselines and state-of-the-art systems. Ref. choi2016extraction ∗ and li2015approach ∗ denote the reimplementation of the systems proposed in choi2016extraction and li2015approach with the authors reported experimental setups.'.\n\nQuestion: What is the F1-score improvement of the proposed model over Baseline 1 for the AiMed dataset?\n" }, { "role": "assistant", "content": "The F1-score for Baseline 1 on the AiMed dataset is 66.46, and for the proposed model, it is 86.45. The improvement is calculated as 86.45 - 66.46 = 19.99. So, the answer is {\"answer\": \"19.99\"}" } ], "images": [ "scigen/train/871.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Comparative results of the proposed model (sdpLSTM) with different baselines and state-of-the-art systems. Ref. choi2016extraction ∗ and li2015approach ∗ denote the reimplementation of the systems proposed in choi2016extraction and li2015approach with the authors reported experimental setups." ], "reasoning": "The F1-score for Baseline 1 on the AiMed dataset is 66.46, and for the proposed model, it is 86.45. The improvement is calculated as 86.45 - 66.46 = 19.99.", "question": "What is the F1-score improvement of the proposed model over Baseline 1 for the AiMed dataset?", "answer": "So, the answer is {\"answer\": \"19.99\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe proposed model has the highest F1-score for the BioInfer dataset.\n" }, { "role": "assistant", "content": "The table shows that the proposed model has an F1-score of 77.35 for the BioInfer dataset, which is higher than any other model listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/871.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Comparative results of the proposed model (sdpLSTM) with different baselines and state-of-the-art systems. Ref. choi2016extraction ∗ and li2015approach ∗ denote the reimplementation of the systems proposed in choi2016extraction and li2015approach with the authors reported experimental setups." ], "reasoning": "The table shows that the proposed model has an F1-score of 77.35 for the BioInfer dataset, which is higher than any other model listed.", "statement": "The proposed model has the highest F1-score for the BioInfer dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Comparison of different activation functions with same hyperparameters values'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The Sigmoid activation function outperforms ReLU and tanh in terms of F1-Score on both AiMed and BioInfer datasets.We setup all the experiments by varying the hyper-parameter values and analyze the behaviors of our model. For AiMed dataset, we observed that addition of LSTM units improves the model performance to a certain extent. Thereafter, it keeps on decreasing gradually. We define an optimal value 64 for the same, via cross-validation experiment. It was observed that deep MLP layer helps to improve the overall performance of the model when compared to a shallow MLP layer as shown in Figure 3. However, this improvement was dependent upon the size of the output layer, which increases initially from 20 to 30 and then starts decreasing while the size of the output layer is increased to 50. We also notice that stacking of another MLP layer makes our model complex, thus reducing the performance on a cross-validation setting. In case of BioInfer dataset, we also observe quite a similar trend in performance with the addition of LSTM units, size of MLP output layer and stacking of another MLP layer. We also analyze the performance of our model on the number of epochs for which training was performed on both the datasets. On AIMed dataset, the value of F1-score initially shows a short fall with the increasing number of epochs from 1 to 2 and then shows regular growth with the increasing number of epochs from 2 to 130, and finally a dip on further increasing the number of epochs to 135 and 140. This can be attributed to the fact that training on a very large number of epochs makes our model over-fitted and hence the cross-validation accuracy decreases. There has also been an initial decline in the F1-score in case of BioInfer dataset but then there has been steady increase with the increase in the number of epochs. We achieve the optimum performance with the same number of epochs (130) for both the datasets. To further compare the performance of ReLU with Sigmoid, we have also conducted the experiments considering both ReLU and Sigmoid on both the datasets. On both the datasets, the sigmoid function was found to be superior over ReLU. \\captionof figureEffect of varying MLP layers and their output sizes \\captionof figureEffect of varying epochs on F1-Score" } ], "images": [ "scigen/train/872.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison of different activation functions with same hyperparameters values" ], "reasoning": "The Sigmoid activation function outperforms ReLU and tanh in terms of F1-Score on both AiMed and BioInfer datasets.", "description": "We setup all the experiments by varying the hyper-parameter values and analyze the behaviors of our model. For AiMed dataset, we observed that addition of LSTM units improves the model performance to a certain extent. Thereafter, it keeps on decreasing gradually. We define an optimal value 64 for the same, via cross-validation experiment. It was observed that deep MLP layer helps to improve the overall performance of the model when compared to a shallow MLP layer as shown in Figure 3. However, this improvement was dependent upon the size of the output layer, which increases initially from 20 to 30 and then starts decreasing while the size of the output layer is increased to 50. We also notice that stacking of another MLP layer makes our model complex, thus reducing the performance on a cross-validation setting. In case of BioInfer dataset, we also observe quite a similar trend in performance with the addition of LSTM units, size of MLP output layer and stacking of another MLP layer. We also analyze the performance of our model on the number of epochs for which training was performed on both the datasets. On AIMed dataset, the value of F1-score initially shows a short fall with the increasing number of epochs from 1 to 2 and then shows regular growth with the increasing number of epochs from 2 to 130, and finally a dip on further increasing the number of epochs to 135 and 140. This can be attributed to the fact that training on a very large number of epochs makes our model over-fitted and hence the cross-validation accuracy decreases. There has also been an initial decline in the F1-score in case of BioInfer dataset but then there has been steady increase with the increase in the number of epochs. We achieve the optimum performance with the same number of epochs (130) for both the datasets. To further compare the performance of ReLU with Sigmoid, we have also conducted the experiments considering both ReLU and Sigmoid on both the datasets. On both the datasets, the sigmoid function was found to be superior over ReLU. \\captionof figureEffect of varying MLP layers and their output sizes \\captionof figureEffect of varying epochs on F1-Score", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: Comparison of different activation functions with same hyperparameters values', solve the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich activation function achieved the highest F1-Score on the AiMed dataset?\n" }, { "role": "assistant", "content": "By comparing the F1-Scores for the AiMed dataset in the table, the Sigmoid activation function has the highest F1-Score of 86.45. So, the answer is {\"answer\": \"Sigmoid\"}." } ], "images": [ "scigen/train/872.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison of different activation functions with same hyperparameters values" ], "reasoning": "By comparing the F1-Scores for the AiMed dataset in the table, the Sigmoid activation function has the highest F1-Score of 86.45.", "question": "Which activation function achieved the highest F1-Score on the AiMed dataset?", "answer": "So, the answer is {\"answer\": \"Sigmoid\"}." }, { "messages": [ { "role": "user", "content": "ReLU achieved the highest F1-Score on the BioInfer dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nReLU achieved the highest F1-Score on the BioInfer dataset.\n" }, { "role": "assistant", "content": "The table shows that the Sigmoid activation function achieved the highest F1-Score on the BioInfer dataset with a score of 77.35, while ReLU has a lower score of 76.92. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/872.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison of different activation functions with same hyperparameters values" ], "reasoning": "The table shows that the Sigmoid activation function achieved the highest F1-Score on the BioInfer dataset with a score of 77.35, while ReLU has a lower score of 76.92.", "statement": "ReLU achieved the highest F1-Score on the BioInfer dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Tabelle 3: Comparison of document-level model architectures and complexity.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The multi-encoder approach with parallel attention components in the decoder and context encoding restricted to word embeddings shows comparable performance to a full 6-layer encoder, easing memory-intensive training by reducing model parameters by 22%.Model Architecture The tested methods are equal or closest to: Single-Encoder: \\newciteagrawal2018contextual Integration outside the decoder: \\newcitevoita2018context without sharing the encoder hidden layers over current/context sentences Integration inside the decoder Sequential attention : Decoder integration of \\newcitezhang2018improving with the order of attentions (current/context) switched Parallel attention: Gating version of \\newcitebawden2018evaluating Context encoding without any sequential modeling (the last row) shows indeed comparable performance to using a full 6-layer encoder. This simplified encoding eases the memory-intensive document-level training by having 22% fewer model parameters, which allows us to adopt a larger batch size without accumulating gradients. For the remainder of this paper, we stick to using the multi-encoder approach with parallel attention components in the decoder and restricting the context encoding to only word embeddings." } ], "images": [ "scigen/train/873.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Tabelle 3: Comparison of document-level model architectures and complexity." ], "reasoning": "The multi-encoder approach with parallel attention components in the decoder and context encoding restricted to word embeddings shows comparable performance to a full 6-layer encoder, easing memory-intensive training by reducing model parameters by 22%.", "description": "Model Architecture The tested methods are equal or closest to: Single-Encoder: \\newciteagrawal2018contextual Integration outside the decoder: \\newcitevoita2018context without sharing the encoder hidden layers over current/context sentences Integration inside the decoder Sequential attention : Decoder integration of \\newcitezhang2018improving with the order of attentions (current/context) switched Parallel attention: Gating version of \\newcitebawden2018evaluating Context encoding without any sequential modeling (the last row) shows indeed comparable performance to using a full 6-layer encoder. This simplified encoding eases the memory-intensive document-level training by having 22% fewer model parameters, which allows us to adopt a larger batch size without accumulating gradients. For the remainder of this paper, we stick to using the multi-encoder approach with parallel attention components in the decoder and restricting the context encoding to only word embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Tabelle 3: Comparison of document-level model architectures and complexity.', solve this question and give detail solutions. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich approach has the highest en-it Bleu score and what is its context encoder architecture?\n" }, { "role": "assistant", "content": "The highest en-it Bleu score is 32.7%, which is achieved by the Multi-Encoder (Para.) approach with a Transformer context encoder architecture. So, the answer is {\"answer\": \"Multi-Encoder (Para.) with Transformer\"}." } ], "images": [ "scigen/train/873.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Tabelle 3: Comparison of document-level model architectures and complexity." ], "reasoning": "The highest en-it Bleu score is 32.7%, which is achieved by the Multi-Encoder (Para.) approach with a Transformer context encoder architecture.", "question": "Which approach has the highest en-it Bleu score and what is its context encoder architecture?", "answer": "So, the answer is {\"answer\": \"Multi-Encoder (Para.) with Transformer\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Multi-Encoder (Para.) approach with a Word Embedding context encoder architecture has the highest en-de Ter score.\n" }, { "role": "assistant", "content": "The table shows that the Multi-Encoder (Para.) approach with a Word Embedding context encoder architecture has an en-de Ter score of 30.3%, which is not the highest. The highest en-de Ter score is 30.1% achieved by the Multi-Encoder (Para.) approach with a Transformer context encoder architecture. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/873.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Tabelle 3: Comparison of document-level model architectures and complexity." ], "reasoning": "The table shows that the Multi-Encoder (Para.) approach with a Word Embedding context encoder architecture has an en-de Ter score of 30.3%, which is not the highest. The highest en-de Ter score is 30.1% achieved by the Multi-Encoder (Para.) approach with a Transformer context encoder architecture.", "statement": "The Multi-Encoder (Para.) approach with a Word Embedding context encoder architecture has the highest en-de Ter score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Tabelle 4: Comparison of context word filtering methods.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Filtering methods reduce context input significantly while maintaining performance, with named entities achieving similar improvements using only 13% of tokens.All filtering methods shrink the context input drastically without a significant loss of performance. Each method has its own motivation to retain only useful tokens in the context; the results show that they are all reasonable in practice. In particular, using only named entities as context input, we achieve the same level of improvement with only 13% of tokens in the full context sentences. By filtering words in the context sentences, we can use more examples in each batch for a robust training." } ], "images": [ "scigen/train/874.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Tabelle 4: Comparison of context word filtering methods." ], "reasoning": "Filtering methods reduce context input significantly while maintaining performance, with named entities achieving similar improvements using only 13% of tokens.", "description": "All filtering methods shrink the context input drastically without a significant loss of performance. Each method has its own motivation to retain only useful tokens in the context; the results show that they are all reasonable in practice. In particular, using only named entities as context input, we achieve the same level of improvement with only 13% of tokens in the full context sentences. By filtering words in the context sentences, we can use more examples in each batch for a robust training.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Tabelle 4: Comparison of context word filtering methods.', answer the question below by showing each progressive step toward the answer. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich filtering method retains the least number of tokens while maintaining performance?\n" }, { "role": "assistant", "content": "By comparing the '#tokens' column, the 'Retain only named entities' method retains only 13% of tokens, the least among all methods. So, the answer is {\"answer\": \"Retain only named entities\"}" } ], "images": [ "scigen/train/874.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Tabelle 4: Comparison of context word filtering methods." ], "reasoning": "By comparing the '#tokens' column, the 'Retain only named entities' method retains only 13% of tokens, the least among all methods.", "question": "Which filtering method retains the least number of tokens while maintaining performance?", "answer": "So, the answer is {\"answer\": \"Retain only named entities\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRemoving stopwords retains 63% of tokens.\n" }, { "role": "assistant", "content": "The table shows that the 'Remove stopwords' method retains 63% of tokens, as indicated in the '#tokens' column. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/874.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Tabelle 4: Comparison of context word filtering methods." ], "reasoning": "The table shows that the 'Remove stopwords' method retains 63% of tokens, as indicated in the '#tokens' column.", "statement": "Removing stopwords retains 63% of tokens.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Statistical information of SuicideWatch and mental health related subreddits, i.e., SWMH dataset'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The SWMH dataset from mental health-related subreddits on Reddit.com contains 54,412 posts discussing suicide-related intentions and mental disorders such as depression, anxiety, and bipolar.As severe mental health issues are very likely to lead to suicidal ideation, we also collect another dataset from some mental health related subreddits in Reddit.com to further the study of mental disorders and suicidal ideation. We name this dataset as Reddit SuicideWatch and Mental Health Collection, or SWMH for short, where discussions comprise suicide-related intention and mental disorders like depression, anxiety, and bipolar. This collection contains totally 54,412 posts." } ], "images": [ "scigen/train/875.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Statistical information of SuicideWatch and mental health related subreddits, i.e., SWMH dataset" ], "reasoning": "The SWMH dataset from mental health-related subreddits on Reddit.com contains 54,412 posts discussing suicide-related intentions and mental disorders such as depression, anxiety, and bipolar.", "description": "As severe mental health issues are very likely to lead to suicidal ideation, we also collect another dataset from some mental health related subreddits in Reddit.com to further the study of mental disorders and suicidal ideation. We name this dataset as Reddit SuicideWatch and Mental Health Collection, or SWMH for short, where discussions comprise suicide-related intention and mental disorders like depression, anxiety, and bipolar. This collection contains totally 54,412 posts.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 4: Statistical information of SuicideWatch and mental health related subreddits, i.e., SWMH dataset'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich subreddit has the highest percentage of validation posts?\n" }, { "role": "assistant", "content": "To find the subreddit with the highest percentage of validation posts, compare the '% of valid.' column for each subreddit. The highest percentage is 34.83% for the 'depression' subreddit. So, the answer is {\"answer\": \"depression\"}." } ], "images": [ "scigen/train/875.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Statistical information of SuicideWatch and mental health related subreddits, i.e., SWMH dataset" ], "reasoning": "To find the subreddit with the highest percentage of validation posts, compare the '% of valid.' column for each subreddit. The highest percentage is 34.83% for the 'depression' subreddit.", "question": "Which subreddit has the highest percentage of validation posts?", "answer": "So, the answer is {\"answer\": \"depression\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The 'Anxiety' subreddit has more training posts than the 'offmychest' subreddit., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe 'Anxiety' subreddit has more training posts than the 'offmychest' subreddit.\n" }, { "role": "assistant", "content": "The table shows that the 'Anxiety' subreddit has 6,136 training posts, while the 'offmychest' subreddit has 5,265 training posts. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/875.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Statistical information of SuicideWatch and mental health related subreddits, i.e., SWMH dataset" ], "reasoning": "The table shows that the 'Anxiety' subreddit has 6,136 training posts, while the 'offmychest' subreddit has 5,265 training posts. Therefore, the statement is supported.", "statement": "The 'Anxiety' subreddit has more training posts than the 'offmychest' subreddit.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: Selected linguistic statistical information of UMD dataset extracted by LIWC'.\n" }, { "role": "assistant", "content": "The risk of suicide increases among labels -1, 0, and 1, with negative emotions, anxiety, sadness, family issues, death-related mentions, and swear words being more expressed in posts with high suicide risk, while positive emotions are less expressed.We have a brief exploratory analysis on the data. Some selected linguistic statistical information of UMD dataset extracted by Linguistic Inquiry and Word Count software (LIWC) The risk of suicide increases among labels of -1, 0, and 1. The linguistic inquiry results show that negative emotion, anxiety, and sadness are expressed more in posts with high-level suicide risk. The same trend exists in family issues, death-related mentions and swear words. Naturally, positive emotions are less presented in posts with high suicide risk." } ], "images": [ "scigen/train/876.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Selected linguistic statistical information of UMD dataset extracted by LIWC" ], "reasoning": "The risk of suicide increases among labels -1, 0, and 1, with negative emotions, anxiety, sadness, family issues, death-related mentions, and swear words being more expressed in posts with high suicide risk, while positive emotions are less expressed.", "description": "We have a brief exploratory analysis on the data. Some selected linguistic statistical information of UMD dataset extracted by Linguistic Inquiry and Word Count software (LIWC) The risk of suicide increases among labels of -1, 0, and 1. The linguistic inquiry results show that negative emotion, anxiety, and sadness are expressed more in posts with high-level suicide risk. The same trend exists in family issues, death-related mentions and swear words. Naturally, positive emotions are less presented in posts with high suicide risk.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 5: Selected linguistic statistical information of UMD dataset extracted by LIWC', and tackle the question below in a sequential manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich linguistic clue shows the highest increase in expression from Label -1 to Label 1?\n" }, { "role": "assistant", "content": "To find the linguistic clue with the highest increase in expression from Label -1 to Label 1, we need to calculate the difference for each clue and identify the maximum difference. The differences are: positive emotion (-0.34), negative emotion (1.18), anxiety (0.24), sadness (0.40), family (0.18), friend (0.11), work (-0.74), money (-0.52), death (0.14), swear words (0.17). The highest increase is in negative emotion with a difference of 1.18. So, the answer is {\"answer\": \"negative emotion\"}." } ], "images": [ "scigen/train/876.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Selected linguistic statistical information of UMD dataset extracted by LIWC" ], "reasoning": "To find the linguistic clue with the highest increase in expression from Label -1 to Label 1, we need to calculate the difference for each clue and identify the maximum difference. The differences are: positive emotion (-0.34), negative emotion (1.18), anxiety (0.24), sadness (0.40), family (0.18), friend (0.11), work (-0.74), money (-0.52), death (0.14), swear words (0.17). The highest increase is in negative emotion with a difference of 1.18.", "question": "Which linguistic clue shows the highest increase in expression from Label -1 to Label 1?", "answer": "So, the answer is {\"answer\": \"negative emotion\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nPosts with high suicide risk express more sadness than those with low suicide risk.\n" }, { "role": "assistant", "content": "The table shows that sadness increases from 0.28 in Label -1 to 0.68 in Label 1, indicating that posts with high suicide risk express more sadness than those with low suicide risk. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/876.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Selected linguistic statistical information of UMD dataset extracted by LIWC" ], "reasoning": "The table shows that sadness increases from 0.28 in Label -1 to 0.68 in Label 1, indicating that posts with high suicide risk express more sadness than those with low suicide risk.", "statement": "Posts with high suicide risk express more sadness than those with low suicide risk.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 7: Comparison of different models on Reddit SWMH collection, where precision, recall, and F1 score are weighted average.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The RN model shows the highest performance in terms of accuracy, precision, recall, and F1 score on the Reddit SWMH dataset, indicating its strong relational encoding capacity for mental health-related texts.5.2.2 Reddit SWMH Then, we perform experiments on the Reddit SWMH dataset, which contains both suicidal ideation and mental health issues to study the predictive performance of our model. It is a larger dataset with more instances when compared with the UMD dataset. Experiments on this dataset show the relational encoding capacity of RN for mental health related texts with similar characteristics." } ], "images": [ "scigen/train/877.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Comparison of different models on Reddit SWMH collection, where precision, recall, and F1 score are weighted average." ], "reasoning": "The RN model shows the highest performance in terms of accuracy, precision, recall, and F1 score on the Reddit SWMH dataset, indicating its strong relational encoding capacity for mental health-related texts.", "description": "5.2.2 Reddit SWMH Then, we perform experiments on the Reddit SWMH dataset, which contains both suicidal ideation and mental health issues to study the predictive performance of our model. It is a larger dataset with more instances when compared with the UMD dataset. Experiments on this dataset show the relational encoding capacity of RN for mental health related texts with similar characteristics.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 7: Comparison of different models on Reddit SWMH collection, where precision, recall, and F1 score are weighted average.'. Solve the following question based on the table.\n\nQuestion: Which model has the highest precision and what is its value?\nLet's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest precision, we look at the 'Precision' column in the table. The RN model has the highest precision value of 0.6510. So, the answer is {\"answer\": \"RN\"}." } ], "images": [ "scigen/train/877.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Comparison of different models on Reddit SWMH collection, where precision, recall, and F1 score are weighted average." ], "reasoning": "To find the model with the highest precision, we look at the 'Precision' column in the table. The RN model has the highest precision value of 0.6510.", "question": "Which model has the highest precision and what is its value?", "answer": "So, the answer is {\"answer\": \"RN\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe SSA model has a higher recall than the BiLSTM model.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the recall values of the SSA and BiLSTM models. The recall for SSA is 0.6214, while the recall for BiLSTM is 0.6196. Since 0.6214 is greater than 0.6196, the statement is true. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/877.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Comparison of different models on Reddit SWMH collection, where precision, recall, and F1 score are weighted average." ], "reasoning": "To verify this statement, we compare the recall values of the SSA and BiLSTM models. The recall for SSA is 0.6214, while the recall for BiLSTM is 0.6196. Since 0.6214 is greater than 0.6196, the statement is true.", "statement": "The SSA model has a higher recall than the BiLSTM model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 8: Performance comparison on Twitter dataset, where precision, recall, and F1 score are weighted average.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our model outperforms others on the Twitter dataset, showing a 1.77% and 1.82% improvement in accuracy and F1-score over the second-best BiLSTM model.Twitter Collection Lastly, we conduct experiments on the Twitter dataset with similar settings of previous experiments. Unlike posts in Reddit, tweets in this dataset are short sequences due to the tweet’s length limit of 280 characters. Among these competitive methods, our model gains the best performance on these four metrics, with 1.77% and 1.82% improvement than the second best BiLSTM model in terms of accuracy and F1-score respectively. Our proposed method introduces auxiliary information of lexicon-based sentiment and topics features learnt from corpus, and utilizes RNs for modeling the interaction between LSTM-based text encodings and risk indicators. Richer auxiliary information and efficient relational encoding help our model boost performance in short tweet classification." } ], "images": [ "scigen/train/878.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Performance comparison on Twitter dataset, where precision, recall, and F1 score are weighted average." ], "reasoning": "Our model outperforms others on the Twitter dataset, showing a 1.77% and 1.82% improvement in accuracy and F1-score over the second-best BiLSTM model.", "description": "Twitter Collection Lastly, we conduct experiments on the Twitter dataset with similar settings of previous experiments. Unlike posts in Reddit, tweets in this dataset are short sequences due to the tweet’s length limit of 280 characters. Among these competitive methods, our model gains the best performance on these four metrics, with 1.77% and 1.82% improvement than the second best BiLSTM model in terms of accuracy and F1-score respectively. Our proposed method introduces auxiliary information of lexicon-based sentiment and topics features learnt from corpus, and utilizes RNs for modeling the interaction between LSTM-based text encodings and risk indicators. Richer auxiliary information and efficient relational encoding help our model boost performance in short tweet classification.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 8: Performance comparison on Twitter dataset, where precision, recall, and F1 score are weighted average.\nQuestion: Which model has the highest F1-score and what is its value?\n" }, { "role": "assistant", "content": "By examining the table, the RN model has the highest F1-score with a value of 0.8377. So, the answer is {\"answer\": \"RN, 0.8377\"}" } ], "images": [ "scigen/train/878.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Performance comparison on Twitter dataset, where precision, recall, and F1 score are weighted average." ], "reasoning": "By examining the table, the RN model has the highest F1-score with a value of 0.8377.", "question": "Which model has the highest F1-score and what is its value?", "answer": "So, the answer is {\"answer\": \"RN, 0.8377\"}" }, { "messages": [ { "role": "user", "content": "The BiLSTM model has the highest accuracy among all models. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe BiLSTM model has the highest accuracy among all models.\n" }, { "role": "assistant", "content": "The table shows that the RN model has the highest accuracy (0.8385), not the BiLSTM model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/878.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Performance comparison on Twitter dataset, where precision, recall, and F1 score are weighted average." ], "reasoning": "The table shows that the RN model has the highest accuracy (0.8385), not the BiLSTM model.", "statement": "The BiLSTM model has the highest accuracy among all models.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 9: Performance on each class of UMD suicidality dataset'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The RN-based model excels at predicting posts with high suicide risk but struggles with posts without suicidality. All models perform poorly on posts with low suicide risk.This section studies the performance on each class of UMD dataset. We select two baselines with better performance for comparison. The results are shown in Fig. The proposed RN-based model is poor on predicting post without suicidality, but good at predicting posts with high suicide risk. Unfortunately, all these three models have very poor capacity on predicting posts with low suicide risk. In the UMD dataset with a small volume of instances, these models tend to predict posts as classes with more instances, even though we apply penalty on the objective function." } ], "images": [ "scigen/train/879.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Performance on each class of UMD suicidality dataset" ], "reasoning": "The RN-based model excels at predicting posts with high suicide risk but struggles with posts without suicidality. All models perform poorly on posts with low suicide risk.", "description": "This section studies the performance on each class of UMD dataset. We select two baselines with better performance for comparison. The results are shown in Fig. The proposed RN-based model is poor on predicting post without suicidality, but good at predicting posts with high suicide risk. Unfortunately, all these three models have very poor capacity on predicting posts with low suicide risk. In the UMD dataset with a small volume of instances, these models tend to predict posts as classes with more instances, even though we apply penalty on the objective function.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 9: Performance on each class of UMD suicidality dataset', solve the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest recall for posts without suicidality?\n" }, { "role": "assistant", "content": "To find the model with the highest recall for posts without suicidality, look at the recall values for Label -1. The SSA model has the highest recall value of 0.92. So, the answer is {\"answer\": \"SSA\"}." } ], "images": [ "scigen/train/879.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Performance on each class of UMD suicidality dataset" ], "reasoning": "To find the model with the highest recall for posts without suicidality, look at the recall values for Label -1. The SSA model has the highest recall value of 0.92.", "question": "Which model has the highest recall for posts without suicidality?", "answer": "So, the answer is {\"answer\": \"SSA\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe RN-based model has the highest precision for posts with high suicide risk.\n" }, { "role": "assistant", "content": "To verify this statement, check the precision values for Label 1. The RN-based model has a precision of 0.48, which is lower than the BiLSTM model's precision of 0.51 and SSA's precision of 0.57. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/879.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Performance on each class of UMD suicidality dataset" ], "reasoning": "To verify this statement, check the precision values for Label 1. The RN-based model has a precision of 0.48, which is lower than the BiLSTM model's precision of 0.51 and SSA's precision of 0.57.", "statement": "The RN-based model has the highest precision for posts with high suicide risk.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 10: Performance of different variants of risk indicator injection'.\n" }, { "role": "assistant", "content": "The BiLSTM+RN+sent.+topic model achieves the best performance across all metrics, demonstrating the effectiveness of combining sentiment and topic features.We then conduct ablation study to explore several variants and compare their performance. We compare our complete framework with three different settings of injecting risk indicators. The BiLSTM+concat model concatenates final hidden state with sentiment and topic features. The BiLSTM+RN+sentiment and BiLSTM+RN+topic use single-channel relation network. Relation networks are generally better than BiLSTM with simple feature concatenation, where the performance of the latter model decreased compared to vanilla BiLSTM. The BiLSTM+RN+sentiment model is better than vanilla BiLSTM, while the BiLSTM+RN+topic is slightly worse. But two channels of sentiment and topic couple well, achieving the best performance. This study shows the effectiveness of proposed model." } ], "images": [ "scigen/train/880.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Performance of different variants of risk indicator injection" ], "reasoning": "The BiLSTM+RN+sent.+topic model achieves the best performance across all metrics, demonstrating the effectiveness of combining sentiment and topic features.", "description": "We then conduct ablation study to explore several variants and compare their performance. We compare our complete framework with three different settings of injecting risk indicators. The BiLSTM+concat model concatenates final hidden state with sentiment and topic features. The BiLSTM+RN+sentiment and BiLSTM+RN+topic use single-channel relation network. Relation networks are generally better than BiLSTM with simple feature concatenation, where the performance of the latter model decreased compared to vanilla BiLSTM. The BiLSTM+RN+sentiment model is better than vanilla BiLSTM, while the BiLSTM+RN+topic is slightly worse. But two channels of sentiment and topic couple well, achieving the best performance. This study shows the effectiveness of proposed model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 10: Performance of different variants of risk indicator injection'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model achieves the highest F1 score?\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all models, the BiLSTM+RN+sent.+topic model has the highest F1 score of 0.8377. So, the answer is {\"answer\": \"BiLSTM+RN+sent.+topic\"}" } ], "images": [ "scigen/train/880.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Performance of different variants of risk indicator injection" ], "reasoning": "By comparing the F1 scores of all models, the BiLSTM+RN+sent.+topic model has the highest F1 score of 0.8377.", "question": "Which model achieves the highest F1 score?", "answer": "So, the answer is {\"answer\": \"BiLSTM+RN+sent.+topic\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The BiLSTM+concat model has a higher accuracy than the BiLSTM+RN+sentiment model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe BiLSTM+concat model has a higher accuracy than the BiLSTM+RN+sentiment model.\n" }, { "role": "assistant", "content": "The table shows that the BiLSTM+concat model has an accuracy of 0.8167, while the BiLSTM+RN+sentiment model has an accuracy of 0.8240, indicating that the statement is incorrect. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/880.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Performance of different variants of risk indicator injection" ], "reasoning": "The table shows that the BiLSTM+concat model has an accuracy of 0.8167, while the BiLSTM+RN+sentiment model has an accuracy of 0.8240, indicating that the statement is incorrect.", "statement": "The BiLSTM+concat model has a higher accuracy than the BiLSTM+RN+sentiment model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Comparison between state-of-the-art models Merity et al. (2017); Yang et al. (2017) and a Simple LSTM, and the same models with Alleviated TOI. The comparison highlights how the addition of Alleviated TOI is able to improve state-of-the-art models, as well as a simple model that does not benefit from extensive hyper-parameter optimization.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The MoS model with Alleviated TOI achieves a test perplexity of 54.58 on the PTB dataset, improving the state of the art without fine-tuning.Comparison with State of the Art and Simple LSTM. With the MoS model and an Alleviated TOI, we improve the current state of the art without fine tuning for the PTB dataset with 54.58 perplexity on the test set. AWD-LSTM-MoS Yang et al. We compare the results with the same hyper-parameters used on the original papers with the only exception of the batch size, that must be prime. To ensure fairness, we allocate the same computational resources for the base model as well the model with Alleviated TOI, i.e. we train with the equivalent number of epochs." } ], "images": [ "scigen/train/881.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison between state-of-the-art models Merity et al. (2017); Yang et al. (2017) and a Simple LSTM, and the same models with Alleviated TOI. The comparison highlights how the addition of Alleviated TOI is able to improve state-of-the-art models, as well as a simple model that does not benefit from extensive hyper-parameter optimization." ], "reasoning": "The MoS model with Alleviated TOI achieves a test perplexity of 54.58 on the PTB dataset, improving the state of the art without fine-tuning.", "description": "Comparison with State of the Art and Simple LSTM. With the MoS model and an Alleviated TOI, we improve the current state of the art without fine tuning for the PTB dataset with 54.58 perplexity on the test set. AWD-LSTM-MoS Yang et al. We compare the results with the same hyper-parameters used on the original papers with the only exception of the batch size, that must be prime. To ensure fairness, we allocate the same computational resources for the base model as well the model with Alleviated TOI, i.e. we train with the equivalent number of epochs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 3: Comparison between state-of-the-art models Merity et al. (2017); Yang et al. (2017) and a Simple LSTM, and the same models with Alleviated TOI. The comparison highlights how the addition of Alleviated TOI is able to improve state-of-the-art models, as well as a simple model that does not benefit from extensive hyper-parameter optimization.' and proceed to solve the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the test perplexity of the AWD-LSTM-MoS model with Alleviated TOI?\n" }, { "role": "assistant", "content": "The table shows the test perplexity (ppl) for various models. The AWD-LSTM-MoS model with Alleviated TOI has a test perplexity of 54.58. So, the answer is {\"answer\": \"54.58\"}" } ], "images": [ "scigen/train/881.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison between state-of-the-art models Merity et al. (2017); Yang et al. (2017) and a Simple LSTM, and the same models with Alleviated TOI. The comparison highlights how the addition of Alleviated TOI is able to improve state-of-the-art models, as well as a simple model that does not benefit from extensive hyper-parameter optimization." ], "reasoning": "The table shows the test perplexity (ppl) for various models. The AWD-LSTM-MoS model with Alleviated TOI has a test perplexity of 54.58.", "question": "What is the test perplexity of the AWD-LSTM-MoS model with Alleviated TOI?", "answer": "So, the answer is {\"answer\": \"54.58\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe AWD-LSTM-MoS model with Alleviated TOI has a lower test perplexity than the AWD-LSTM model with Alleviated TOI.\n" }, { "role": "assistant", "content": "The table shows that the AWD-LSTM-MoS model with Alleviated TOI has a test perplexity of 54.58, while the AWD-LSTM model with Alleviated TOI has a test perplexity of 56.46. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/881.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison between state-of-the-art models Merity et al. (2017); Yang et al. (2017) and a Simple LSTM, and the same models with Alleviated TOI. The comparison highlights how the addition of Alleviated TOI is able to improve state-of-the-art models, as well as a simple model that does not benefit from extensive hyper-parameter optimization." ], "reasoning": "The table shows that the AWD-LSTM-MoS model with Alleviated TOI has a test perplexity of 54.58, while the AWD-LSTM model with Alleviated TOI has a test perplexity of 56.46.", "statement": "The AWD-LSTM-MoS model with Alleviated TOI has a lower test perplexity than the AWD-LSTM model with Alleviated TOI.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Data point repetition with period q for batch size K=20 and Alleviated TOI (P).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The worst case occurs when P=10 with 10 repetitions, while the best case occurs when P=7 with only 1 repetition due to the GCD of P and K being 1.The worst case is P=10 with 10 repetitions of the same data point within the same batch and the best case is P=7, which avoids any redundancy because the GCD of P and K is 1." } ], "images": [ "scigen/train/882.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Data point repetition with period q for batch size K=20 and Alleviated TOI (P)." ], "reasoning": "The worst case occurs when P=10 with 10 repetitions, while the best case occurs when P=7 with only 1 repetition due to the GCD of P and K being 1.", "description": "The worst case is P=10 with 10 repetitions of the same data point within the same batch and the best case is P=7, which avoids any redundancy because the GCD of P and K is 1.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 1: Data point repetition with period q for batch size K=20 and Alleviated TOI (P).', solve this question and give detail solutions. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhat is the period q for the worst case scenario?\n" }, { "role": "assistant", "content": "The worst case scenario is when P=10 with 10 repetitions. According to the table, when P=10, the period q is 2. So, the answer is {\"answer\": \"2\"}." } ], "images": [ "scigen/train/882.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Data point repetition with period q for batch size K=20 and Alleviated TOI (P)." ], "reasoning": "The worst case scenario is when P=10 with 10 repetitions. According to the table, when P=10, the period q is 2.", "question": "What is the period q for the worst case scenario?", "answer": "So, the answer is {\"answer\": \"2\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The best case scenario has a period q of 20., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe best case scenario has a period q of 20.\n" }, { "role": "assistant", "content": "The best case scenario is when P=7 with 1 repetition. According to the table, when P=7, the period q is 20. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/882.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Data point repetition with period q for batch size K=20 and Alleviated TOI (P)." ], "reasoning": "The best case scenario is when P=7 with 1 repetition. According to the table, when P=7, the period q is 20.", "statement": "The best case scenario has a period q of 20.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Perplexity score (PPL) comparison of the AWD model, on the three datasets, with batch sizes K=20 (PTB), K=80 (WT2) and K=60 (WT103), with different levels of Token Order Imbalance (TOI). With Alleviated TOI (P), we use a prime batch size of K=19 (PTB), K=79 (WT2) and K=59 (WT103).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Higher values of P in Alleviated TOI experiments lead to better results, as indicated by lower test perplexity scores compared to Standard, Extreme, and Inter-batch TOI.We use the test perplexity after the same equivalent number of epochs. The different Alleviated TOI (P) experiments use a different number of overlapped sequence: An Alleviated TOI (P) means building and concatenating P overlapped sequences. Our results indicate that an Alleviated TOI (P) is better than the Standard TOI, which is better than an Extreme or Inter-batch TOI. We note a tendency that higher values of P lead to better results, which is in accordance with our hypothesis that a higher TOI ratio (P−1)/P improves the results." } ], "images": [ "scigen/train/883.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Perplexity score (PPL) comparison of the AWD model, on the three datasets, with batch sizes K=20 (PTB), K=80 (WT2) and K=60 (WT103), with different levels of Token Order Imbalance (TOI). With Alleviated TOI (P), we use a prime batch size of K=19 (PTB), K=79 (WT2) and K=59 (WT103)." ], "reasoning": "Higher values of P in Alleviated TOI experiments lead to better results, as indicated by lower test perplexity scores compared to Standard, Extreme, and Inter-batch TOI.", "description": "We use the test perplexity after the same equivalent number of epochs. The different Alleviated TOI (P) experiments use a different number of overlapped sequence: An Alleviated TOI (P) means building and concatenating P overlapped sequences. Our results indicate that an Alleviated TOI (P) is better than the Standard TOI, which is better than an Extreme or Inter-batch TOI. We note a tendency that higher values of P lead to better results, which is in accordance with our hypothesis that a higher TOI ratio (P−1)/P improves the results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2: Perplexity score (PPL) comparison of the AWD model, on the three datasets, with batch sizes K=20 (PTB), K=80 (WT2) and K=60 (WT103), with different levels of Token Order Imbalance (TOI). With Alleviated TOI (P), we use a prime batch size of K=19 (PTB), K=79 (WT2) and K=59 (WT103).', unfold the steps to solve the question given next. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nQuestion: Which experiment achieved the lowest test perplexity score on the PTB dataset?\n" }, { "role": "assistant", "content": "To find the experiment with the lowest test perplexity score on the PTB dataset, compare the PTB scores across all experiments. The lowest score is 56.46, achieved by Alleviated TOI 10. So, the answer is {\"answer\": \"Alleviated TOI 10\"}." } ], "images": [ "scigen/train/883.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Perplexity score (PPL) comparison of the AWD model, on the three datasets, with batch sizes K=20 (PTB), K=80 (WT2) and K=60 (WT103), with different levels of Token Order Imbalance (TOI). With Alleviated TOI (P), we use a prime batch size of K=19 (PTB), K=79 (WT2) and K=59 (WT103)." ], "reasoning": "To find the experiment with the lowest test perplexity score on the PTB dataset, compare the PTB scores across all experiments. The lowest score is 56.46, achieved by Alleviated TOI 10.", "question": "Which experiment achieved the lowest test perplexity score on the PTB dataset?", "answer": "So, the answer is {\"answer\": \"Alleviated TOI 10\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nStandard TOI has a lower test perplexity score on the WT2 dataset compared to Alleviated TOI 2.\n" }, { "role": "assistant", "content": "To verify this statement, compare the WT2 scores of Standard TOI and Alleviated TOI 2. Standard TOI has a score of 65.86, while Alleviated TOI 2 has a score of 65.14. Since 65.86 is higher than 65.14, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/883.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Perplexity score (PPL) comparison of the AWD model, on the three datasets, with batch sizes K=20 (PTB), K=80 (WT2) and K=60 (WT103), with different levels of Token Order Imbalance (TOI). With Alleviated TOI (P), we use a prime batch size of K=19 (PTB), K=79 (WT2) and K=59 (WT103)." ], "reasoning": "To verify this statement, compare the WT2 scores of Standard TOI and Alleviated TOI 2. Standard TOI has a score of 65.86, while Alleviated TOI 2 has a score of 65.14. Since 65.86 is higher than 65.14, the statement is refuted.", "statement": "Standard TOI has a lower test perplexity score on the WT2 dataset compared to Alleviated TOI 2.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Perplexity score (PPL) comparison on the PTB dataset and the AWD model. We use two different values for the batch size K --- the original one with K=20, and a prime one with K=19. The results directly corroborate the observation portrayed in Figure 4, where the obtained score is related to the diversity of grayscale values in each row.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The AWD model with Alleviated TOI shows consistent and increasing results with a prime batch size K=19 as P increases, while the original batch size K=20 shows strong performance at P=7 but low performance at P=10.We compare the scores of a prime batch size K=19 with the scores of the original batch size K=20 for the AWD model with Alleviated TOI (P). When using a prime batch size, we observe consistent and increasing results as P increases. With the original batch size K=20, we observe a strong performance for P=7, but a low performance for P=10. The matrix with P=7 shows a good distribution---corresponding to the strong performance---and the matrix with P=10 shows that each row contains a low diversity of data points." } ], "images": [ "scigen/train/884.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Perplexity score (PPL) comparison on the PTB dataset and the AWD model. We use two different values for the batch size K --- the original one with K=20, and a prime one with K=19. The results directly corroborate the observation portrayed in Figure 4, where the obtained score is related to the diversity of grayscale values in each row." ], "reasoning": "The AWD model with Alleviated TOI shows consistent and increasing results with a prime batch size K=19 as P increases, while the original batch size K=20 shows strong performance at P=7 but low performance at P=10.", "description": "We compare the scores of a prime batch size K=19 with the scores of the original batch size K=20 for the AWD model with Alleviated TOI (P). When using a prime batch size, we observe consistent and increasing results as P increases. With the original batch size K=20, we observe a strong performance for P=7, but a low performance for P=10. The matrix with P=7 shows a good distribution---corresponding to the strong performance---and the matrix with P=10 shows that each row contains a low diversity of data points.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 4: Perplexity score (PPL) comparison on the PTB dataset and the AWD model. We use two different values for the batch size K --- the original one with K=20, and a prime one with K=19. The results directly corroborate the observation portrayed in Figure 4, where the obtained score is related to the diversity of grayscale values in each row.', unfold the question presented below, detailing every step of your calculation. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the score for Alleviated TOI 10 with the original batch size K=20?\n" }, { "role": "assistant", "content": "The table shows the scores for different values of P with batch sizes K=20 and K=19. For Alleviated TOI 10 with K=20, the score is 65.88. So, the answer is {\"answer\": \"65.88\"}." } ], "images": [ "scigen/train/884.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Perplexity score (PPL) comparison on the PTB dataset and the AWD model. We use two different values for the batch size K --- the original one with K=20, and a prime one with K=19. The results directly corroborate the observation portrayed in Figure 4, where the obtained score is related to the diversity of grayscale values in each row." ], "reasoning": "The table shows the scores for different values of P with batch sizes K=20 and K=19. For Alleviated TOI 10 with K=20, the score is 65.88.", "question": "What is the score for Alleviated TOI 10 with the original batch size K=20?", "answer": "So, the answer is {\"answer\": \"65.88\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe score for Alleviated TOI 7 with a prime batch size K=19 is higher than with the original batch size K=20.\n" }, { "role": "assistant", "content": "The table shows that the score for Alleviated TOI 7 with K=19 is 57.16, which is higher than the score with K=20, which is 56.70. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/884.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Perplexity score (PPL) comparison on the PTB dataset and the AWD model. We use two different values for the batch size K --- the original one with K=20, and a prime one with K=19. The results directly corroborate the observation portrayed in Figure 4, where the obtained score is related to the diversity of grayscale values in each row." ], "reasoning": "The table shows that the score for Alleviated TOI 7 with K=19 is 57.16, which is higher than the score with K=20, which is 56.70.", "statement": "The score for Alleviated TOI 7 with a prime batch size K=19 is higher than with the original batch size K=20.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Token order imbalance (TOI) comparison for the IEMOCAP dataset on a SER task using angry, happy, neutral and sad classes with a simple LSTM model.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The Extreme TOI approach shows a smaller effect compared to the Standard TOI approach due to the different nature of the text datasets and the IEMOCAP dataset, which consists of shorter utterances.When choosing the Extreme TOI instead of the Standard TOI approach we observe a smaller effect than in text related task: this is due to the different nature of the text datasets (large \"continuous\" corpuses) and the IEMOCAP one (composed of shorter utterances). The fact that we can still observe improvements on a dataset with short utterances is a proof of the robustness of the method." } ], "images": [ "scigen/train/885.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Token order imbalance (TOI) comparison for the IEMOCAP dataset on a SER task using angry, happy, neutral and sad classes with a simple LSTM model." ], "reasoning": "The Extreme TOI approach shows a smaller effect compared to the Standard TOI approach due to the different nature of the text datasets and the IEMOCAP dataset, which consists of shorter utterances.", "description": "When choosing the Extreme TOI instead of the Standard TOI approach we observe a smaller effect than in text related task: this is due to the different nature of the text datasets (large \"continuous\" corpuses) and the IEMOCAP one (composed of shorter utterances). The fact that we can still observe improvements on a dataset with short utterances is a proof of the robustness of the method.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 5: Token order imbalance (TOI) comparison for the IEMOCAP dataset on a SER task using angry, happy, neutral and sad classes with a simple LSTM model.', unfold the steps to solve the question given next. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nQuestion: Which experiment shows the highest WA and UA scores?\n" }, { "role": "assistant", "content": "By comparing the WA and UA scores across all experiments, the Alleviated TOI (60 epochs) shows the highest WA score of 0.591 and the highest UA score of 0.523. So, the answer is {\"answer\": \"Alleviated TOI (60 epochs)\"}." } ], "images": [ "scigen/train/885.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Token order imbalance (TOI) comparison for the IEMOCAP dataset on a SER task using angry, happy, neutral and sad classes with a simple LSTM model." ], "reasoning": "By comparing the WA and UA scores across all experiments, the Alleviated TOI (60 epochs) shows the highest WA score of 0.591 and the highest UA score of 0.523.", "question": "Which experiment shows the highest WA and UA scores?", "answer": "So, the answer is {\"answer\": \"Alleviated TOI (60 epochs)\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Standard TOI approach has a higher WA score than the Inter-batch TOI approach. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Standard TOI approach has a higher WA score than the Inter-batch TOI approach.\n" }, { "role": "assistant", "content": "The table shows that the Standard TOI (15k steps) has a WA score of 0.486, which is higher than the Inter-batch TOI (15k steps) WA score of 0.478. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/885.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Token order imbalance (TOI) comparison for the IEMOCAP dataset on a SER task using angry, happy, neutral and sad classes with a simple LSTM model." ], "reasoning": "The table shows that the Standard TOI (15k steps) has a WA score of 0.486, which is higher than the Inter-batch TOI (15k steps) WA score of 0.478.", "statement": "The Standard TOI approach has a higher WA score than the Inter-batch TOI approach.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 6: Token order imbalance (TOI) comparison for the IEMOCAP dataset on a SER task using angry, happy, neutral and sad classes for 60 epochs using the Transformer model.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The experiments use a smaller Transformer model to reduce computational cost, showing that a higher TOI ratio improves results.The previously described Transformer model is used in these experiments. the standard deviation computed for different P-values of Alleviated TOI (P). We want to highlight the fact that the goal of these experiments is to show the direct contribution of the Alleviated TOI technique for a different model. For this reason we use a smaller version of the Transformer in order to reduce the computational cost. We believe that with a more expressive model and more repetitions, the proposed method may further improve the results. This is in accordance with our hypothesis that a higher TOI ratio (P−1)/P improves the results." } ], "images": [ "scigen/train/886.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Token order imbalance (TOI) comparison for the IEMOCAP dataset on a SER task using angry, happy, neutral and sad classes for 60 epochs using the Transformer model." ], "reasoning": "The experiments use a smaller Transformer model to reduce computational cost, showing that a higher TOI ratio improves results.", "description": "The previously described Transformer model is used in these experiments. the standard deviation computed for different P-values of Alleviated TOI (P). We want to highlight the fact that the goal of these experiments is to show the direct contribution of the Alleviated TOI technique for a different model. For this reason we use a smaller version of the Transformer in order to reduce the computational cost. We believe that with a more expressive model and more repetitions, the proposed method may further improve the results. This is in accordance with our hypothesis that a higher TOI ratio (P−1)/P improves the results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 6: Token order imbalance (TOI) comparison for the IEMOCAP dataset on a SER task using angry, happy, neutral and sad classes for 60 epochs using the Transformer model.', solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the WA score for Alleviated TOI 10 after 60 epochs?\n" }, { "role": "assistant", "content": "The table lists the WA score for Alleviated TOI 10 as 0.617 with a standard deviation of ±0.015 after 60 epochs. So, the answer is {\"answer\": \"0.617\"}." } ], "images": [ "scigen/train/886.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Token order imbalance (TOI) comparison for the IEMOCAP dataset on a SER task using angry, happy, neutral and sad classes for 60 epochs using the Transformer model." ], "reasoning": "The table lists the WA score for Alleviated TOI 10 as 0.617 with a standard deviation of ±0.015 after 60 epochs.", "question": "What is the WA score for Alleviated TOI 10 after 60 epochs?", "answer": "So, the answer is {\"answer\": \"0.617\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nAlleviated TOI 3 has a higher UA score than Alleviated TOI 5 after 60 epochs.\n" }, { "role": "assistant", "content": "The table shows that Alleviated TOI 3 has a UA score of 0.563, while Alleviated TOI 5 has a UA score of 0.562 after 60 epochs. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/886.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Token order imbalance (TOI) comparison for the IEMOCAP dataset on a SER task using angry, happy, neutral and sad classes for 60 epochs using the Transformer model." ], "reasoning": "The table shows that Alleviated TOI 3 has a UA score of 0.563, while Alleviated TOI 5 has a UA score of 0.562 after 60 epochs.", "statement": "Alleviated TOI 3 has a higher UA score than Alleviated TOI 5 after 60 epochs.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: BLEU score on IWSLT 2015 Zh-En'.\n" }, { "role": "assistant", "content": "Using BPT with k=4 and a context length of 32 improves the baseline BLEU score by 0.93, indicating a significant enhancement.BPT with k=4 and context length of 32 could further improve the baseline result by 0.93 in terms of BLEU score, which is a significant margin." } ], "images": [ "scigen/train/887.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: BLEU score on IWSLT 2015 Zh-En" ], "reasoning": "Using BPT with k=4 and a context length of 32 improves the baseline BLEU score by 0.93, indicating a significant enhancement.", "description": "BPT with k=4 and context length of 32 could further improve the baseline result by 0.93 in terms of BLEU score, which is a significant margin.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: BLEU score on IWSLT 2015 Zh-En\nQuestion: What is the BLEU score improvement when using BPT with k=4 and a context length of 32 compared to the Transformer (ours, single sentence) model?\nSolve the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The BLEU score for BPT (k=4, single sentence) is 19.19, and for Transformer (ours, single sentence) it is 18.91. The improvement is 19.19 - 18.91 = 0.28. So, the answer is {\"answer\": \"0.28\"}." } ], "images": [ "scigen/train/887.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: BLEU score on IWSLT 2015 Zh-En" ], "reasoning": "The BLEU score for BPT (k=4, single sentence) is 19.19, and for Transformer (ours, single sentence) it is 18.91. The improvement is 19.19 - 18.91 = 0.28.", "question": "What is the BLEU score improvement when using BPT with k=4 and a context length of 32 compared to the Transformer (ours, single sentence) model?", "answer": "So, the answer is {\"answer\": \"0.28\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: BPT with k=4 and a context length of 32 has a BLEU score of 19.19., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nBPT with k=4 and a context length of 32 has a BLEU score of 19.19.\n" }, { "role": "assistant", "content": "The table shows that BPT (k=4, single sentence) has a BLEU score of 19.19, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/887.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: BLEU score on IWSLT 2015 Zh-En" ], "reasoning": "The table shows that BPT (k=4, single sentence) has a BLEU score of 19.19, which matches the statement.", "statement": "BPT with k=4 and a context length of 32 has a BLEU score of 19.19.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: Test accuracy on SST-5 and IMDB. In BPT, k=2 and k=4 for SST and IMDB respectively. The last model used word embeddings pretrained with translation and additional character-level embeddings.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our model, BPT, outperforms Transformer and LSTM based models on SST-5 and outperforms a bidirectional LSTM with pre-trained character and CoVe embeddings on IMDB.On SST-5, our model outperforms Transformer and LSTM based models. On IMDB, our proposed model outperforms a bidirectional LSTM initialized with pre-trained character embedding and CoVe embedding mccann2017learned." } ], "images": [ "scigen/train/888.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Test accuracy on SST-5 and IMDB. In BPT, k=2 and k=4 for SST and IMDB respectively. The last model used word embeddings pretrained with translation and additional character-level embeddings." ], "reasoning": "Our model, BPT, outperforms Transformer and LSTM based models on SST-5 and outperforms a bidirectional LSTM with pre-trained character and CoVe embeddings on IMDB.", "description": "On SST-5, our model outperforms Transformer and LSTM based models. On IMDB, our proposed model outperforms a bidirectional LSTM initialized with pre-trained character embedding and CoVe embedding mccann2017learned.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Test accuracy on SST-5 and IMDB. In BPT, k=2 and k=4 for SST and IMDB respectively. The last model used word embeddings pretrained with translation and additional character-level embeddings.', answer the following question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest score on the IMDB dataset?\n" }, { "role": "assistant", "content": "By comparing the scores of all models on the IMDB dataset, BPT has the highest score of 92.12. So, the answer is {\"answer\": \"BPT\"}" } ], "images": [ "scigen/train/888.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Test accuracy on SST-5 and IMDB. In BPT, k=2 and k=4 for SST and IMDB respectively. The last model used word embeddings pretrained with translation and additional character-level embeddings." ], "reasoning": "By comparing the scores of all models on the IMDB dataset, BPT has the highest score of 92.12.", "question": "Which model has the highest score on the IMDB dataset?", "answer": "So, the answer is {\"answer\": \"BPT\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe QRNN model has a higher score than the BCN+Char+CoVe model on the IMDB dataset.\n" }, { "role": "assistant", "content": "The table shows that the QRNN model has a score of 91.4 on the IMDB dataset, while the BCN+Char+CoVe model has a score of 91.8. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/888.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Test accuracy on SST-5 and IMDB. In BPT, k=2 and k=4 for SST and IMDB respectively. The last model used word embeddings pretrained with translation and additional character-level embeddings." ], "reasoning": "The table shows that the QRNN model has a score of 91.4 on the IMDB dataset, while the BCN+Char+CoVe model has a score of 91.8. Therefore, the statement is refuted.", "statement": "The QRNN model has a higher score than the BCN+Char+CoVe model on the IMDB dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 6: BLEU score vs context length on different models'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "BPT models maintain performance as context size increases, unlike Transformers which show a decrease.Similar to tu2018learning and miculicich-etal-2018-document , we found a small context length is enough for achieving good performance on IWSLT for Document-Level Translation. However, as we increases context size, the performance of BPT does not get worse as these models and Transformers, suggesting the inductive bias encoded by BPT makes the model less likely to overfit." } ], "images": [ "scigen/train/889.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: BLEU score vs context length on different models" ], "reasoning": "BPT models maintain performance as context size increases, unlike Transformers which show a decrease.", "description": "Similar to tu2018learning and miculicich-etal-2018-document , we found a small context length is enough for achieving good performance on IWSLT for Document-Level Translation. However, as we increases context size, the performance of BPT does not get worse as these models and Transformers, suggesting the inductive bias encoded by BPT makes the model less likely to overfit.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 6: BLEU score vs context length on different models'.\n\nQuestion: What is the performance of the Transformer model at a context length of 128 compared to BPT (k=4) at the same context length?\n" }, { "role": "assistant", "content": "The table shows that the performance of the Transformer model at a context length of 128 is 15.55, while the performance of BPT (k=4) at the same context length is 19.84. So, the answer is {\"answer\": \"15.55\"}." } ], "images": [ "scigen/train/889.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: BLEU score vs context length on different models" ], "reasoning": "The table shows that the performance of the Transformer model at a context length of 128 is 15.55, while the performance of BPT (k=4) at the same context length is 19.84.", "question": "What is the performance of the Transformer model at a context length of 128 compared to BPT (k=4) at the same context length?", "answer": "So, the answer is {\"answer\": \"15.55\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nBPT (k=8) performs better than BPT (k=4) at a context length of 64.\n" }, { "role": "assistant", "content": "The table shows that at a context length of 64, BPT (k=4) has a performance of 19.71, while BPT (k=8) has a performance of 19.78, indicating that BPT (k=8) performs better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/889.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: BLEU score vs context length on different models" ], "reasoning": "The table shows that at a context length of 64, BPT (k=4) has a performance of 19.71, while BPT (k=8) has a performance of 19.78, indicating that BPT (k=8) performs better.", "statement": "BPT (k=8) performs better than BPT (k=4) at a context length of 64.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 7: BLEU score on newstest 2014', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "BPT with k=2 and k=4 achieves higher BLEU scores than the Vanilla Transformer with the same number of parameters and a sparse attention pattern.In the setting of k=2 and k=4, BPT outperforms Vanilla Transformer with the same number of parameters and a sparse attention pattern." } ], "images": [ "scigen/train/890.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: BLEU score on newstest 2014" ], "reasoning": "BPT with k=2 and k=4 achieves higher BLEU scores than the Vanilla Transformer with the same number of parameters and a sparse attention pattern.", "description": "In the setting of k=2 and k=4, BPT outperforms Vanilla Transformer with the same number of parameters and a sparse attention pattern.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 7: BLEU score on newstest 2014\nQuestion: What is the BLEU score of BPT with k=4, and how does it compare to the Vanilla Transformer?\nGive a detailed response to the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The BLEU score of BPT with k=4 is 27.6, which is higher than the BLEU score of the Vanilla Transformer, which is 27.2. So, the answer is {\"answer\": \"27.6\"}." } ], "images": [ "scigen/train/890.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: BLEU score on newstest 2014" ], "reasoning": "The BLEU score of BPT with k=4 is 27.6, which is higher than the BLEU score of the Vanilla Transformer, which is 27.2.", "question": "What is the BLEU score of BPT with k=4, and how does it compare to the Vanilla Transformer?", "answer": "So, the answer is {\"answer\": \"27.6\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim BPT with k=2 has a higher BLEU score than the Vanilla Transformer., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nBPT with k=2 has a higher BLEU score than the Vanilla Transformer.\n" }, { "role": "assistant", "content": "The BLEU score of BPT with k=2 is 27.4, which is higher than the BLEU score of the Vanilla Transformer, which is 27.2. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/890.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: BLEU score on newstest 2014" ], "reasoning": "The BLEU score of BPT with k=2 is 27.4, which is higher than the BLEU score of the Vanilla Transformer, which is 27.2.", "statement": "BPT with k=2 has a higher BLEU score than the Vanilla Transformer.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: WERs (%) of binary weight networks on WSJ1. The number of hidden units is 1024 for experiments in this table.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Training neural networks with binary weights from random initialization usually did not converge, so models were initialized from well-trained networks with real-valued weights, and different settings for input and Softmax layers were explored.In our experiments, training neural networks with binary weights from random initialization usually did not converge, or converged to very poor models. We addressed this problem by initializing our models from a well-trained neural network model with real-valued weights. This approach worked well, and was used in all our experiments. We trained the baseline neural network with an initial learning rate of 0.008, following the nnet1 recipe in Kaldi. This initial learning rate was found to be a good tradeoff between convergence speed and model accuracy. Here, we explored several settings: the weights in the input layer and Softmax layer were binary, real-valued or fixed from initialization. We also did experiment to update those real-valued weights jointly with the binary weights using the same learning rate, but obtained much worse results. The reason may be that the gradients of the real-valued weights and binary weights are in very different ranges, and updating them using the same learning rate is not appropriate. In order to have a complete picture, we have also tried using binary weights for the input layer and the Softmax layer. As expected, we achieved much lower accuracy, confirming that reducing the resolution of the input features and activations for the Softmax classifier are harmful for classification. Applying this step very frequently is harmful to the SGD optimization as it can counteract the SGD update. In our experiments, we set a probability p to control the frequency of this operation. More precisely, after each SGD update, we draw a sample from a uniform distribution between 0 and 1, and if its value is smaller than p, then the semi-stochastic binarization approach will be applied. Therefore, a larger p means more frequent operations and vice versa. Note that, we used the dev93 set to choose the language model score for the eval92 set, and from our observations, the results of development and evaluation sets are usually not well aligned, demonstrating that there may be a slight mismatch between the two evaluation sets. The semi-stochastic binarization approach will be revisited on the AMI dataset." } ], "images": [ "scigen/train/891.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: WERs (%) of binary weight networks on WSJ1. The number of hidden units is 1024 for experiments in this table." ], "reasoning": "Training neural networks with binary weights from random initialization usually did not converge, so models were initialized from well-trained networks with real-valued weights, and different settings for input and Softmax layers were explored.", "description": "In our experiments, training neural networks with binary weights from random initialization usually did not converge, or converged to very poor models. We addressed this problem by initializing our models from a well-trained neural network model with real-valued weights. This approach worked well, and was used in all our experiments. We trained the baseline neural network with an initial learning rate of 0.008, following the nnet1 recipe in Kaldi. This initial learning rate was found to be a good tradeoff between convergence speed and model accuracy. Here, we explored several settings: the weights in the input layer and Softmax layer were binary, real-valued or fixed from initialization. We also did experiment to update those real-valued weights jointly with the binary weights using the same learning rate, but obtained much worse results. The reason may be that the gradients of the real-valued weights and binary weights are in very different ranges, and updating them using the same learning rate is not appropriate. In order to have a complete picture, we have also tried using binary weights for the input layer and the Softmax layer. As expected, we achieved much lower accuracy, confirming that reducing the resolution of the input features and activations for the Softmax classifier are harmful for classification. Applying this step very frequently is harmful to the SGD optimization as it can counteract the SGD update. In our experiments, we set a probability p to control the frequency of this operation. More precisely, after each SGD update, we draw a sample from a uniform distribution between 0 and 1, and if its value is smaller than p, then the semi-stochastic binarization approach will be applied. Therefore, a larger p means more frequent operations and vice versa. Note that, we used the dev93 set to choose the language model score for the eval92 set, and from our observations, the results of development and evaluation sets are usually not well aligned, demonstrating that there may be a slight mismatch between the two evaluation sets. The semi-stochastic binarization approach will be revisited on the AMI dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 1: WERs (%) of binary weight networks on WSJ1. The number of hidden units is 1024 for experiments in this table.', and sequentially solve the question, writing out each step of your reasoning process. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the dev93 score for the model with binary weights and binary input and Softmax layers?\n" }, { "role": "assistant", "content": "The table shows the dev93 score for various models. For the model with binary weights and binary input and Softmax layers, the dev93 score is 19.0. So, the answer is {\"answer\": \"19.0\"}" } ], "images": [ "scigen/train/891.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: WERs (%) of binary weight networks on WSJ1. The number of hidden units is 1024 for experiments in this table." ], "reasoning": "The table shows the dev93 score for various models. For the model with binary weights and binary input and Softmax layers, the dev93 score is 19.0.", "question": "What is the dev93 score for the model with binary weights and binary input and Softmax layers?", "answer": "So, the answer is {\"answer\": \"19.0\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The baseline model achieved a higher eval92 score than the model with binary weights and fixed input and Softmax layers (p=0). and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe baseline model achieved a higher eval92 score than the model with binary weights and fixed input and Softmax layers (p=0).\n" }, { "role": "assistant", "content": "The table shows that the baseline model has an eval92 score of 3.8, while the model with binary weights and fixed input and Softmax layers (p=0) has an eval92 score of 4.8. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/891.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: WERs (%) of binary weight networks on WSJ1. The number of hidden units is 1024 for experiments in this table." ], "reasoning": "The table shows that the baseline model has an eval92 score of 3.8, while the model with binary weights and fixed input and Softmax layers (p=0) has an eval92 score of 4.8. Therefore, the statement is refuted.", "statement": "The baseline model achieved a higher eval92 score than the model with binary weights and fixed input and Softmax layers (p=0).", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: WERs (%) of neural networks with binary weights and activations on the WSJ1 dataset. We set p=0 for the system with binary weights, and k=1 for the system with binary activations.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Training fully binary neural networks is challenging, and using (0,1) for binary activations achieved lower WER compared to (−1,1).For all the experiments in this table, the weights in the input and Softmax layer were fixed, and the first and last hidden layers used Sigmoid activations. Using a larger number of hidden units works slightly better for both binary weight and binary activation systems. However, we only managed to train the network with 1024 hidden units and achieved much inferior accuracies. Training fully binary neural networks is still a challenge from our study. With Sigmoid activations, using (0, 1) for binary weights can cause training divergence as the elements of the activation vector ^hl are always positive. Using (0,1) for binary activations, however, we achieved lower WER. The reason may be that the network was initialized from Sigmoid activations, and (0,1) is much closer to Sigmoid compared to (−1,1). In fact, (0,1) binary function can be viewed as the hard version of Sigmoid. Using (−1,+1) for binary activations may work better with networks initialized from Tanh activations, and that will be investigated in our future works." } ], "images": [ "scigen/train/892.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: WERs (%) of neural networks with binary weights and activations on the WSJ1 dataset. We set p=0 for the system with binary weights, and k=1 for the system with binary activations." ], "reasoning": "Training fully binary neural networks is challenging, and using (0,1) for binary activations achieved lower WER compared to (−1,1).", "description": "For all the experiments in this table, the weights in the input and Softmax layer were fixed, and the first and last hidden layers used Sigmoid activations. Using a larger number of hidden units works slightly better for both binary weight and binary activation systems. However, we only managed to train the network with 1024 hidden units and achieved much inferior accuracies. Training fully binary neural networks is still a challenge from our study. With Sigmoid activations, using (0, 1) for binary weights can cause training divergence as the elements of the activation vector ^hl are always positive. Using (0,1) for binary activations, however, we achieved lower WER. The reason may be that the network was initialized from Sigmoid activations, and (0,1) is much closer to Sigmoid compared to (−1,1). In fact, (0,1) binary function can be viewed as the hard version of Sigmoid. Using (−1,+1) for binary activations may work better with networks initialized from Tanh activations, and that will be investigated in our future works.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: WERs (%) of neural networks with binary weights and activations on the WSJ1 dataset. We set p=0 for the system with binary weights, and k=1 for the system with binary activations.'. Solve the following question based on the table.\n\nQuestion: What was the dev93 accuracy for the Binary activations model with 1024 size and (−1,+1) binary weights?\nLet's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the Binary activations model with 1024 size and (−1,+1) binary weights achieved a dev93 accuracy of 8.2. So, the answer is {\"answer\": \"8.2\"}." } ], "images": [ "scigen/train/892.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: WERs (%) of neural networks with binary weights and activations on the WSJ1 dataset. We set p=0 for the system with binary weights, and k=1 for the system with binary activations." ], "reasoning": "The table shows that the Binary activations model with 1024 size and (−1,+1) binary weights achieved a dev93 accuracy of 8.2.", "question": "What was the dev93 accuracy for the Binary activations model with 1024 size and (−1,+1) binary weights?", "answer": "So, the answer is {\"answer\": \"8.2\"}." }, { "messages": [ { "role": "user", "content": "The Binary neural network model with 2048 size and (−1,+1) binary weights did not converge. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Binary neural network model with 2048 size and (−1,+1) binary weights did not converge.\n" }, { "role": "assistant", "content": "The table shows that the Binary neural network model with 2048 size and (−1,+1) binary weights did not converge. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/892.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: WERs (%) of neural networks with binary weights and activations on the WSJ1 dataset. We set p=0 for the system with binary weights, and k=1 for the system with binary activations." ], "reasoning": "The table shows that the Binary neural network model with 2048 size and (−1,+1) binary weights did not converge.", "statement": "The Binary neural network model with 2048 size and (−1,+1) binary weights did not converge.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: WERs (%) of neural network with binary weights and activations on the AMI dataset. The number of hidden units is 2048, and b denotes binarization.'.\n" }, { "role": "assistant", "content": "Binary activation systems with (0,1) degrade rapidly with increasing k, while those with (−1,+1) are more tolerant, only diverging at k=7.For the binary weight systems, we revisited the semi-stochastic binarization approach. While the convergence curves were similar Since the model was initialized from a Sigmoid network, the binary activation system with (0,1) worked much better than its counterpart with (−1,+1) when k=1. Again, we did experiments by tuning the threshold k for binary activation systems. Unlike the experiments on WSJ1, the models are relatively tolerant to changing k with (−1,+1) for binarization, and we only observed divergence when k=7. This may be because that we used different features in these experiments, causing differences in the distributions of ^hl. However, this is not the case for binarization with (0,1), as the system degrades rapidly when k increases. The reason may be that the binarization function with (0,1) is not symmetric, and the approximation error using an identity function is significant for large k when inputs are negative. Again, we failed to train binary neural networks with 2048 hidden units due to divergence in training." } ], "images": [ "scigen/train/893.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: WERs (%) of neural network with binary weights and activations on the AMI dataset. The number of hidden units is 2048, and b denotes binarization." ], "reasoning": "Binary activation systems with (0,1) degrade rapidly with increasing k, while those with (−1,+1) are more tolerant, only diverging at k=7.", "description": "For the binary weight systems, we revisited the semi-stochastic binarization approach. While the convergence curves were similar Since the model was initialized from a Sigmoid network, the binary activation system with (0,1) worked much better than its counterpart with (−1,+1) when k=1. Again, we did experiments by tuning the threshold k for binary activation systems. Unlike the experiments on WSJ1, the models are relatively tolerant to changing k with (−1,+1) for binarization, and we only observed divergence when k=7. This may be because that we used different features in these experiments, causing differences in the distributions of ^hl. However, this is not the case for binarization with (0,1), as the system degrades rapidly when k increases. The reason may be that the binarization function with (0,1) is not symmetric, and the approximation error using an identity function is significant for large k when inputs are negative. Again, we failed to train binary neural networks with 2048 hidden units due to divergence in training.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: WERs (%) of neural network with binary weights and activations on the AMI dataset. The number of hidden units is 2048, and b denotes binarization.', solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the evaluation score for the binary activation system with (0,1) when k=3?\n" }, { "role": "assistant", "content": "From the table, locate the row for 'Binary activations (0,1)' with k=3 and find the 'eval' column value. So, the answer is {\"answer\": \"32.2\"}." } ], "images": [ "scigen/train/893.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: WERs (%) of neural network with binary weights and activations on the AMI dataset. The number of hidden units is 2048, and b denotes binarization." ], "reasoning": "From the table, locate the row for 'Binary activations (0,1)' with k=3 and find the 'eval' column value.", "question": "What is the evaluation score for the binary activation system with (0,1) when k=3?", "answer": "So, the answer is {\"answer\": \"32.2\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe binary activation system with (0,1) and k=1 has a higher evaluation score than the baseline.\n" }, { "role": "assistant", "content": "The table shows that the evaluation score for 'Binary activations (0,1)' with k=1 is 29.5, which is higher than the baseline evaluation score of 27.5. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/893.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: WERs (%) of neural network with binary weights and activations on the AMI dataset. The number of hidden units is 2048, and b denotes binarization." ], "reasoning": "The table shows that the evaluation score for 'Binary activations (0,1)' with k=1 is 29.5, which is higher than the baseline evaluation score of 27.5.", "statement": "The binary activation system with (0,1) and k=1 has a higher evaluation score than the baseline.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Quantitative analysis of the generated relation embeddings by our generator. These presented relations are from the NELL test relation set. “# Can. Num.” denotes the number of candidates of test relations. For one relation, “# Cos. Sim.” denotes the mean cosine similarity between the corresponding generated relation embedding and the cluster center xrc of the relation triples.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The cosine similarity between generated relation embeddings and their cluster centers is positively correlated with link prediction performance, as seen in the table.Unlike image, our generated data cannot be observed intuitively. Instead, we calculate the cosine similarity between the generated relation embeddings and the cluster center xrc of their corresponding relations. It can be seen that our method indeed generates the plausible relation embeddings for many relations and the link prediction performance is positively correlated with the quality of the relation embeddings." } ], "images": [ "scigen/train/894.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Quantitative analysis of the generated relation embeddings by our generator. These presented relations are from the NELL test relation set. “# Can. Num.” denotes the number of candidates of test relations. For one relation, “# Cos. Sim.” denotes the mean cosine similarity between the corresponding generated relation embedding and the cluster center xrc of the relation triples." ], "reasoning": "The cosine similarity between generated relation embeddings and their cluster centers is positively correlated with link prediction performance, as seen in the table.", "description": "Unlike image, our generated data cannot be observed intuitively. Instead, we calculate the cosine similarity between the generated relation embeddings and the cluster center xrc of their corresponding relations. It can be seen that our method indeed generates the plausible relation embeddings for many relations and the link prediction performance is positively correlated with the quality of the relation embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 3: Quantitative analysis of the generated relation embeddings by our generator. These presented relations are from the NELL test relation set. “# Can. Num.” denotes the number of candidates of test relations. For one relation, “# Cos. Sim.” denotes the mean cosine similarity between the corresponding generated relation embedding and the cluster center xrc of the relation triples.'\nQuestion: Which relation has the highest cosine similarity and what is its Hits@10 ZSGAN KG value?\n" }, { "role": "assistant", "content": "The relation with the highest cosine similarity is 'animalThatFeedOnInsect' with a cosine similarity of 0.8580. Its Hits@10 ZSGAN KG value is 63.4. So, the answer is {\"answer\": \"63.4\"}" } ], "images": [ "scigen/train/894.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Quantitative analysis of the generated relation embeddings by our generator. These presented relations are from the NELL test relation set. “# Can. Num.” denotes the number of candidates of test relations. For one relation, “# Cos. Sim.” denotes the mean cosine similarity between the corresponding generated relation embedding and the cluster center xrc of the relation triples." ], "reasoning": "The relation with the highest cosine similarity is 'animalThatFeedOnInsect' with a cosine similarity of 0.8580. Its Hits@10 ZSGAN KG value is 63.4.", "question": "Which relation has the highest cosine similarity and what is its Hits@10 ZSGAN KG value?", "answer": "So, the answer is {\"answer\": \"63.4\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe relation 'produceBy' has a higher MRR ZSGAN KG value than 'teamCoach'.\n" }, { "role": "assistant", "content": "The MRR ZSGAN KG value for 'produceBy' is 0.467, which is higher than the MRR ZSGAN KG value for 'teamCoach', which is 0.393. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/894.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Quantitative analysis of the generated relation embeddings by our generator. These presented relations are from the NELL test relation set. “# Can. Num.” denotes the number of candidates of test relations. For one relation, “# Cos. Sim.” denotes the mean cosine similarity between the corresponding generated relation embedding and the cluster center xrc of the relation triples." ], "reasoning": "The MRR ZSGAN KG value for 'produceBy' is 0.467, which is higher than the MRR ZSGAN KG value for 'teamCoach', which is 0.393.", "statement": "The relation 'produceBy' has a higher MRR ZSGAN KG value than 'teamCoach'.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Zero-shot link prediction results on the unseen relations. The proposed baselines are shown at the top of the table; Our generative adversarial model is denoted as ZSGANKG and the results are shown at the bottom. Bold numbers denote the best results, and Underline numbers denote the best ones among our ZSGANKG methods.'.\n" }, { "role": "assistant", "content": "The proposed generative method achieves consistent improvements over various baselines on both NELL-ZS and Wiki-ZS datasets, demonstrating its effectiveness in generating plausible relation embeddings for unseen relations.For NELL-ZS dataset, we set the embedding size as 100. For Wiki-ZS, we set the embedding size as 50 for faster training. The proposed generative method uses the pre-trained KG embeddings as input, which are trained on the triples in the training set. For TransE and DistMult, we directly use their 1-D vectors. For feature encoder, the upper limit of the neighbor number is 50, the number of reference triples k in one training step is 30, and the learning rate is 5e−4. For the generative model, the learning rate is 1e−4, and β1, β2 are set as 0.5, 0.9 respectively. When updating the generator one time, the iteration number nd of the discriminator is 5. The dimension of the random vector z is 15, and the number of the generated relation embedding Ntest is 20. Spectral normalization is applied for both generator and discriminator. These hyperparameters are also tuned on the validation set Dvalid. As for word embeddings, we directly use the released word embedding set GoogleNews- Even though NELL-ZS and Wiki-ZS have different scales of triples and relation sets, the proposed generative method still achieves consistent improvements over various baselines on both two zero-shot datasets. It demonstrates that the generator successfully finds the intermediate semantic representation to bridge the gap between seen and unseen relations and generates reasonable relation embeddings for unseen relations merely from their text descriptions. Therefore, once trained, our model can be used to predict arbitrary newly-added relations without fine-tuning, which is significant for real-world knowledge graph completion. Unlike image, our generated data cannot be observed intuitively. Instead, we calculate the cosine similarity between the generated relation embeddings and the cluster center xrc of their corresponding relations. It can be seen that our method indeed generates the plausible relation embeddings for many relations and the link prediction performance is positively correlated with the quality of the relation embeddings." } ], "images": [ "scigen/train/895.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Zero-shot link prediction results on the unseen relations. The proposed baselines are shown at the top of the table; Our generative adversarial model is denoted as ZSGANKG and the results are shown at the bottom. Bold numbers denote the best results, and Underline numbers denote the best ones among our ZSGANKG methods." ], "reasoning": "The proposed generative method achieves consistent improvements over various baselines on both NELL-ZS and Wiki-ZS datasets, demonstrating its effectiveness in generating plausible relation embeddings for unseen relations.", "description": "For NELL-ZS dataset, we set the embedding size as 100. For Wiki-ZS, we set the embedding size as 50 for faster training. The proposed generative method uses the pre-trained KG embeddings as input, which are trained on the triples in the training set. For TransE and DistMult, we directly use their 1-D vectors. For feature encoder, the upper limit of the neighbor number is 50, the number of reference triples k in one training step is 30, and the learning rate is 5e−4. For the generative model, the learning rate is 1e−4, and β1, β2 are set as 0.5, 0.9 respectively. When updating the generator one time, the iteration number nd of the discriminator is 5. The dimension of the random vector z is 15, and the number of the generated relation embedding Ntest is 20. Spectral normalization is applied for both generator and discriminator. These hyperparameters are also tuned on the validation set Dvalid. As for word embeddings, we directly use the released word embedding set GoogleNews- Even though NELL-ZS and Wiki-ZS have different scales of triples and relation sets, the proposed generative method still achieves consistent improvements over various baselines on both two zero-shot datasets. It demonstrates that the generator successfully finds the intermediate semantic representation to bridge the gap between seen and unseen relations and generates reasonable relation embeddings for unseen relations merely from their text descriptions. Therefore, once trained, our model can be used to predict arbitrary newly-added relations without fine-tuning, which is significant for real-world knowledge graph completion. Unlike image, our generated data cannot be observed intuitively. Instead, we calculate the cosine similarity between the generated relation embeddings and the cluster center xrc of their corresponding relations. It can be seen that our method indeed generates the plausible relation embeddings for many relations and the link prediction performance is positively correlated with the quality of the relation embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 2: Zero-shot link prediction results on the unseen relations. The proposed baselines are shown at the top of the table; Our generative adversarial model is denoted as ZSGANKG and the results are shown at the bottom. Bold numbers denote the best results, and Underline numbers denote the best ones among our ZSGANKG methods.\nQuestion: Which model achieves the highest NELL-ZS Hits@1 score?\n" }, { "role": "assistant", "content": "To find the model with the highest NELL-ZS Hits@1 score, we need to compare the values in the NELL-ZS Hits@1 column. The highest value is 19.4, achieved by the ZSGAN KG (DistMult) model. So, the answer is {\"answer\": \"ZSGAN KG (DistMult)\"}" } ], "images": [ "scigen/train/895.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Zero-shot link prediction results on the unseen relations. The proposed baselines are shown at the top of the table; Our generative adversarial model is denoted as ZSGANKG and the results are shown at the bottom. Bold numbers denote the best results, and Underline numbers denote the best ones among our ZSGANKG methods." ], "reasoning": "To find the model with the highest NELL-ZS Hits@1 score, we need to compare the values in the NELL-ZS Hits@1 column. The highest value is 19.4, achieved by the ZSGAN KG (DistMult) model.", "question": "Which model achieves the highest NELL-ZS Hits@1 score?", "answer": "So, the answer is {\"answer\": \"ZSGAN KG (DistMult)\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The ZSGAN KG (TransE) model achieves the highest Wiki-ZS Hits@10 score. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe ZSGAN KG (TransE) model achieves the highest Wiki-ZS Hits@10 score.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the Wiki-ZS Hits@10 scores of all models. The highest score is 29.4, achieved by the ZSGAN KG (DistMult) model, not the ZSGAN KG (TransE) model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/895.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Zero-shot link prediction results on the unseen relations. The proposed baselines are shown at the top of the table; Our generative adversarial model is denoted as ZSGANKG and the results are shown at the bottom. Bold numbers denote the best results, and Underline numbers denote the best ones among our ZSGANKG methods." ], "reasoning": "To verify this statement, we need to compare the Wiki-ZS Hits@10 scores of all models. The highest score is 29.4, achieved by the ZSGAN KG (DistMult) model, not the ZSGAN KG (TransE) model.", "statement": "The ZSGAN KG (TransE) model achieves the highest Wiki-ZS Hits@10 score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled '(i) verb-past-tense'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The table shows translation performance scores between different languages using B-CCA-cu transformation with dictionaries of size 20,000, highlighting each language's strengths and weaknesses.Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses." } ], "images": [ "scigen/train/896.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(i) verb-past-tense" ], "reasoning": "The table shows translation performance scores between different languages using B-CCA-cu transformation with dictionaries of size 20,000, highlighting each language's strengths and weaknesses.", "description": "Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of '(i) verb-past-tense', unfold the question presented below, detailing every step of your calculation. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich language pair has the highest translation performance score?\n" }, { "role": "assistant", "content": "To find the highest translation performance score, we need to look at all the values in the table and identify the maximum value. So, the answer is {\"answer\": \"80.8\"}." } ], "images": [ "scigen/train/896.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(i) verb-past-tense" ], "reasoning": "To find the highest translation performance score, we need to look at all the values in the table and identify the maximum value.", "question": "Which language pair has the highest translation performance score?", "answer": "So, the answer is {\"answer\": \"80.8\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe translation performance score from Croatian to Czech is higher than from Czech to Croatian.\n" }, { "role": "assistant", "content": "The table shows the translation performance score from Croatian to Czech (Hr to Cs) is 73.4 and from Czech to Croatian (Cs to Hr) is 55.6. Since 73.4 is higher than 55.6, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/896.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(i) verb-past-tense" ], "reasoning": "The table shows the translation performance score from Croatian to Czech (Hr to Cs) is 73.4 and from Czech to Croatian (Cs to Hr) is 55.6. Since 73.4 is higher than 55.6, the statement is supported.", "statement": "The translation performance score from Croatian to Czech is higher than from Czech to Croatian.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: Number of word pairs for each language and each analogy type.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The table shows the number of questions for different analogy types across six languages, with each analogy type contributing equally to the final accuracy score.We combine and extend available corpora for monolingual word analogies in English (En ) Mikolov et al. (It) Berardi et al. Czech (Cs) Svoboda and Brychcín Svoboda and Beliga We consider only those analogy types, which exist across all six languages (three semantically oriented and six syntactically oriented analogy types). For all languages, questions composed of single words are taken into account (i.e., no phrases). In the following list we briefly introduce each analogy type and describe the changes and extensions we have made compared with the original corpora: For each analogy type we process all combinations of pairs between languages a and b (e.g., for the category family and the transformation from Czech to German, we have 26×24=624 questions). In the case a=b (i.e., monolingual experiments), we omit the questions composed from two same pairs (e.g., for the category family in Italian, we have 20×19=380 questions). The final accuracy is an average over accuracies for individual categories. By averaging the accuracies each analogy type contributes equally to the final score and the results are comparable across languages. In the following text, Acc@1 denotes the accuracy considering only the most similar word as a correct answer. Acc@5 assumes that the correct answer is in the list of five most similar words. All accuracies are expressed in percentages." } ], "images": [ "scigen/train/897.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Number of word pairs for each language and each analogy type." ], "reasoning": "The table shows the number of questions for different analogy types across six languages, with each analogy type contributing equally to the final accuracy score.", "description": "We combine and extend available corpora for monolingual word analogies in English (En ) Mikolov et al. (It) Berardi et al. Czech (Cs) Svoboda and Brychcín Svoboda and Beliga We consider only those analogy types, which exist across all six languages (three semantically oriented and six syntactically oriented analogy types). For all languages, questions composed of single words are taken into account (i.e., no phrases). In the following list we briefly introduce each analogy type and describe the changes and extensions we have made compared with the original corpora: For each analogy type we process all combinations of pairs between languages a and b (e.g., for the category family and the transformation from Czech to German, we have 26×24=624 questions). In the case a=b (i.e., monolingual experiments), we omit the questions composed from two same pairs (e.g., for the category family in Italian, we have 20×19=380 questions). The final accuracy is an average over accuracies for individual categories. By averaging the accuracies each analogy type contributes equally to the final score and the results are comparable across languages. In the following text, Acc@1 denotes the accuracy considering only the most similar word as a correct answer. Acc@5 assumes that the correct answer is in the list of five most similar words. All accuracies are expressed in percentages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 1: Number of word pairs for each language and each analogy type.', unfold the question presented below, detailing every step of your calculation. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the total number of questions for the 'adjective-comparative' analogy type across all languages?\n" }, { "role": "assistant", "content": "To find the total number of questions for the 'adjective-comparative' analogy type, sum the values across all languages: 23 (En) + 37 (De) + 5 (Es) + 10 (It) + 40 (Cs) + 77 (Hr). So, the answer is {\"answer\": \"192\"}" } ], "images": [ "scigen/train/897.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Number of word pairs for each language and each analogy type." ], "reasoning": "To find the total number of questions for the 'adjective-comparative' analogy type, sum the values across all languages: 23 (En) + 37 (De) + 5 (Es) + 10 (It) + 40 (Cs) + 77 (Hr).", "question": "What is the total number of questions for the 'adjective-comparative' analogy type across all languages?", "answer": "So, the answer is {\"answer\": \"192\"}" }, { "messages": [ { "role": "user", "content": "The 'noun-plural' analogy type has the highest number of questions in English. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe 'noun-plural' analogy type has the highest number of questions in English.\n" }, { "role": "assistant", "content": "By comparing the number of questions for each analogy type in English (En), we see that 'noun-plural' has 112 questions, which is the highest among all listed analogy types. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/897.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Number of word pairs for each language and each analogy type." ], "reasoning": "By comparing the number of questions for each analogy type in English (En), we see that 'noun-plural' has 112 questions, which is the highest among all listed analogy types.", "statement": "The 'noun-plural' analogy type has the highest number of questions in English.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: The average accuracies across all combinations of language pairs for different linear transformations and post-processing techniques. The size of bilingual dictionary was set to n=20,000. No trans. denotes the monolingual experiments without transforming the spaces.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Canonical correlation analysis performs best for bilingual cases, while orthogonal transformation yields better accuracies in multilingual spaces.The columns represent different post-processing techniques and rows different transformations. The upper part of the table shows the monolingual experiments with original spaces without transformation (No trans.) compared with the unified multilingual space for all six languages. The orthogonal transformation provides same results as the original semantic space. Canonical correlation analysis leads to slightly lower accuracies and least squares method is worst. The most interesting is the lower part of the table, i.e., cross-lingual experiments, showing the average accuracies over all language pairs, but where source a and target b languages differ a≠b. We can see that canonical correlation analysis performs best for bilingual cases, while orthogonal transformation yields better accuracies in multilingual spaces. In all cases, the mean centering followed by vector normalization led to the best results." } ], "images": [ "scigen/train/898.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The average accuracies across all combinations of language pairs for different linear transformations and post-processing techniques. The size of bilingual dictionary was set to n=20,000. No trans. denotes the monolingual experiments without transforming the spaces." ], "reasoning": "Canonical correlation analysis performs best for bilingual cases, while orthogonal transformation yields better accuracies in multilingual spaces.", "description": "The columns represent different post-processing techniques and rows different transformations. The upper part of the table shows the monolingual experiments with original spaces without transformation (No trans.) compared with the unified multilingual space for all six languages. The orthogonal transformation provides same results as the original semantic space. Canonical correlation analysis leads to slightly lower accuracies and least squares method is worst. The most interesting is the lower part of the table, i.e., cross-lingual experiments, showing the average accuracies over all language pairs, but where source a and target b languages differ a≠b. We can see that canonical correlation analysis performs best for bilingual cases, while orthogonal transformation yields better accuracies in multilingual spaces. In all cases, the mean centering followed by vector normalization led to the best results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: Table 2: The average accuracies across all combinations of language pairs for different linear transformations and post-processing techniques. The size of bilingual dictionary was set to n=20,000. No trans. denotes the monolingual experiments without transforming the spaces.\nQuestion: Which post-processing technique yields the highest accuracy in cross-lingual experiments for the '-u Acc@1' column?\n" }, { "role": "assistant", "content": "In the cross-lingual experiments section, the highest accuracy in the '-u Acc@1' column is 43.1, achieved by B-CCA. So, the answer is {\"answer\": \"B-CCA\"}." } ], "images": [ "scigen/train/898.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The average accuracies across all combinations of language pairs for different linear transformations and post-processing techniques. The size of bilingual dictionary was set to n=20,000. No trans. denotes the monolingual experiments without transforming the spaces." ], "reasoning": "In the cross-lingual experiments section, the highest accuracy in the '-u Acc@1' column is 43.1, achieved by B-CCA.", "question": "Which post-processing technique yields the highest accuracy in cross-lingual experiments for the '-u Acc@1' column?", "answer": "So, the answer is {\"answer\": \"B-CCA\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Orthogonal transformation yields better accuracies than canonical correlation analysis in monolingual experiments. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nOrthogonal transformation yields better accuracies than canonical correlation analysis in monolingual experiments.\n" }, { "role": "assistant", "content": "In the monolingual experiments section, the accuracies for M-OT are higher than those for M-CCA in all columns except '-c Acc@1' and '-c Acc@5'. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/898.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The average accuracies across all combinations of language pairs for different linear transformations and post-processing techniques. The size of bilingual dictionary was set to n=20,000. No trans. denotes the monolingual experiments without transforming the spaces." ], "reasoning": "In the monolingual experiments section, the accuracies for M-OT are higher than those for M-CCA in all columns except '-c Acc@1' and '-c Acc@5'.", "statement": "Orthogonal transformation yields better accuracies than canonical correlation analysis in monolingual experiments.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled '(a) family', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The table shows translation accuracy between six languages using B-CCA-cu transformation with dictionaries of size 20,000, highlighting each language's strengths and weaknesses.Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses." } ], "images": [ "scigen/train/899.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) family" ], "reasoning": "The table shows translation accuracy between six languages using B-CCA-cu transformation with dictionaries of size 20,000, highlighting each language's strengths and weaknesses.", "description": "Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled '(a) family', solve this question and give detail solutions. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich language pair has the highest translation accuracy?\n" }, { "role": "assistant", "content": "By examining the table, the highest translation accuracy is found in the cell where the source language is Es and the target language is Es, with a value of 86.8. So, the answer is {\"answer\": \"Es to Es\"}." } ], "images": [ "scigen/train/899.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) family" ], "reasoning": "By examining the table, the highest translation accuracy is found in the cell where the source language is Es and the target language is Es, with a value of 86.8.", "question": "Which language pair has the highest translation accuracy?", "answer": "So, the answer is {\"answer\": \"Es to Es\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe translation accuracy from English to Croatian is higher than from Croatian to English.\n" }, { "role": "assistant", "content": "The table shows that the translation accuracy from English to Croatian (En to Hr) is 47.2, while from Croatian to English (Hr to En) is 57.4. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/899.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) family" ], "reasoning": "The table shows that the translation accuracy from English to Croatian (En to Hr) is 47.2, while from Croatian to English (Hr to En) is 57.4. Therefore, the statement is refuted.", "statement": "The translation accuracy from English to Croatian is higher than from Croatian to English.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title '(b) state-currency'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The table shows the performance of B-CCA-cu transformation with dictionaries of size 20,000 for different source and target languages, highlighting varying strengths and weaknesses.Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses." } ], "images": [ "scigen/train/900.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) state-currency" ], "reasoning": "The table shows the performance of B-CCA-cu transformation with dictionaries of size 20,000 for different source and target languages, highlighting varying strengths and weaknesses.", "description": "Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: '(b) state-currency'\nQuestion: Which language pair has the highest score when translating from English?\n" }, { "role": "assistant", "content": "To find the highest score when translating from English, look at the row labeled 'En' and identify the highest value. The highest value in the 'En' row is 11.1. So, the answer is {\"answer\": \"11.1\"}." } ], "images": [ "scigen/train/900.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) state-currency" ], "reasoning": "To find the highest score when translating from English, look at the row labeled 'En' and identify the highest value. The highest value in the 'En' row is 11.1.", "question": "Which language pair has the highest score when translating from English?", "answer": "So, the answer is {\"answer\": \"11.1\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe translation from Croatian to German has a higher score than the translation from German to Croatian.\n" }, { "role": "assistant", "content": "To verify this statement, compare the score for translating from Croatian to German (Hr to De) with the score for translating from German to Croatian (De to Hr). The score for Hr to De is 5.7, and the score for De to Hr is 3.4. Since 5.7 is greater than 3.4, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/900.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) state-currency" ], "reasoning": "To verify this statement, compare the score for translating from Croatian to German (Hr to De) with the score for translating from German to Croatian (De to Hr). The score for Hr to De is 5.7, and the score for De to Hr is 3.4. Since 5.7 is greater than 3.4, the statement is supported.", "statement": "The translation from Croatian to German has a higher score than the translation from German to Croatian.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled '(c) capital-common-countries', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The table shows translation accuracy scores between six languages using B-CCA-cu transformation with dictionaries of size 20,000, highlighting each language's strengths and weaknesses.Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses." } ], "images": [ "scigen/train/901.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(c) capital-common-countries" ], "reasoning": "The table shows translation accuracy scores between six languages using B-CCA-cu transformation with dictionaries of size 20,000, highlighting each language's strengths and weaknesses.", "description": "Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of '(c) capital-common-countries'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich language pair has the highest translation accuracy score?\n" }, { "role": "assistant", "content": "By examining the table, the highest translation accuracy score is found in the En to En pair with a score of 95.3. So, the answer is {\"answer\": \"En to En\"}." } ], "images": [ "scigen/train/901.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(c) capital-common-countries" ], "reasoning": "By examining the table, the highest translation accuracy score is found in the En to En pair with a score of 95.3.", "question": "Which language pair has the highest translation accuracy score?", "answer": "So, the answer is {\"answer\": \"En to En\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe translation accuracy from Croatian to English is higher than from English to Croatian.\n" }, { "role": "assistant", "content": "The table shows that the translation accuracy from Croatian to English (Hr to En) is 71.1, while from English to Croatian (En to Hr) is 53.3. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/901.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(c) capital-common-countries" ], "reasoning": "The table shows that the translation accuracy from Croatian to English (Hr to En) is 71.1, while from English to Croatian (En to Hr) is 53.3. Therefore, the statement is supported.", "statement": "The translation accuracy from Croatian to English is higher than from English to Croatian.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled '(d) state-adjective'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The table shows translation accuracy scores between six languages using B-CCA-cu transformation with dictionaries of size 20,000, highlighting each language's strengths and weaknesses.Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses." } ], "images": [ "scigen/train/902.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(d) state-adjective" ], "reasoning": "The table shows translation accuracy scores between six languages using B-CCA-cu transformation with dictionaries of size 20,000, highlighting each language's strengths and weaknesses.", "description": "Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled '(d) state-adjective' and methodically tackle the question that follows. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich language pair has the highest translation accuracy score?\n" }, { "role": "assistant", "content": "By examining the table, the highest translation accuracy score is found in the Es to It pair with a score of 94.1. So, the answer is {\"answer\": \"Es to It\"}." } ], "images": [ "scigen/train/902.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(d) state-adjective" ], "reasoning": "By examining the table, the highest translation accuracy score is found in the Es to It pair with a score of 94.1.", "question": "Which language pair has the highest translation accuracy score?", "answer": "So, the answer is {\"answer\": \"Es to It\"}." }, { "messages": [ { "role": "user", "content": "The translation accuracy from English to German is higher than from German to English. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe translation accuracy from English to German is higher than from German to English.\n" }, { "role": "assistant", "content": "The table shows that the translation accuracy from English to German is 58.7, while from German to English it is 91.1, indicating that the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/902.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(d) state-adjective" ], "reasoning": "The table shows that the translation accuracy from English to German is 58.7, while from German to English it is 91.1, indicating that the statement is refuted.", "statement": "The translation accuracy from English to German is higher than from German to English.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled '(e) adjective-comparative', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The table shows translation accuracy percentages between different languages using B-CCA-cu transformation with dictionaries of size 20,000, highlighting each language's strengths and weaknesses.Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses." } ], "images": [ "scigen/train/903.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(e) adjective-comparative" ], "reasoning": "The table shows translation accuracy percentages between different languages using B-CCA-cu transformation with dictionaries of size 20,000, highlighting each language's strengths and weaknesses.", "description": "Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: (e) adjective-comparative\nQuestion: Which language pair has the highest translation accuracy?\nSolve the above question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By examining the table, the highest translation accuracy is found in the En to En pair with a score of 78.5. So, the answer is {\"answer\": \"En to En\"}." } ], "images": [ "scigen/train/903.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(e) adjective-comparative" ], "reasoning": "By examining the table, the highest translation accuracy is found in the En to En pair with a score of 78.5.", "question": "Which language pair has the highest translation accuracy?", "answer": "So, the answer is {\"answer\": \"En to En\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe translation accuracy from English to Croatian is higher than from English to Czech.\n" }, { "role": "assistant", "content": "The table shows that the translation accuracy from English to Croatian (31.6) is lower than from English to Czech (34.5). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/903.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(e) adjective-comparative" ], "reasoning": "The table shows that the translation accuracy from English to Croatian (31.6) is lower than from English to Czech (34.5).", "statement": "The translation accuracy from English to Croatian is higher than from English to Czech.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled '(f) adjective-superlative', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The table shows the performance of translations between different languages using B-CCA-cu transformation with dictionaries of size 20,000, highlighting strengths and weaknesses for each language pair.Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses." } ], "images": [ "scigen/train/904.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(f) adjective-superlative" ], "reasoning": "The table shows the performance of translations between different languages using B-CCA-cu transformation with dictionaries of size 20,000, highlighting strengths and weaknesses for each language pair.", "description": "Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about '(f) adjective-superlative', solve the following question step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich language pair has the highest translation performance?\n" }, { "role": "assistant", "content": "To find the highest translation performance, look for the highest value in the table. The highest value is 68.9 for the En to En translation. So, the answer is {\"answer\": \"En to En\"}." } ], "images": [ "scigen/train/904.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(f) adjective-superlative" ], "reasoning": "To find the highest translation performance, look for the highest value in the table. The highest value is 68.9 for the En to En translation.", "question": "Which language pair has the highest translation performance?", "answer": "So, the answer is {\"answer\": \"En to En\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The translation performance from Italian to Croatian is higher than from Croatian to Italian., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe translation performance from Italian to Croatian is higher than from Croatian to Italian.\n" }, { "role": "assistant", "content": "The table shows the translation performance from Italian to Croatian as 0.2 and from Croatian to Italian as 0.2. Both values are the same. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/904.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(f) adjective-superlative" ], "reasoning": "The table shows the translation performance from Italian to Croatian as 0.2 and from Croatian to Italian as 0.2. Both values are the same.", "statement": "The translation performance from Italian to Croatian is higher than from Croatian to Italian.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption '(g) adjective-opposite'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table shows translation accuracy scores between six languages using B-CCA-cu transformation with dictionaries of size 20,000, highlighting each language's strengths and weaknesses.Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses." } ], "images": [ "scigen/train/905.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(g) adjective-opposite" ], "reasoning": "The table shows translation accuracy scores between six languages using B-CCA-cu transformation with dictionaries of size 20,000, highlighting each language's strengths and weaknesses.", "description": "Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: (g) adjective-opposite\n\nQuestion: Which language pair has the highest translation accuracy score?\n" }, { "role": "assistant", "content": "By examining the table, the highest translation accuracy score is found in the cell where the source language is 'Cs' and the target language is 'En', with a score of 79.8. So, the answer is {\"answer\": \"Cs to En\"}." } ], "images": [ "scigen/train/905.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(g) adjective-opposite" ], "reasoning": "By examining the table, the highest translation accuracy score is found in the cell where the source language is 'Cs' and the target language is 'En', with a score of 79.8.", "question": "Which language pair has the highest translation accuracy score?", "answer": "So, the answer is {\"answer\": \"Cs to En\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The translation accuracy from English to Croatian is higher than from Croatian to English. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe translation accuracy from English to Croatian is higher than from Croatian to English.\n" }, { "role": "assistant", "content": "The table shows that the translation accuracy from English to Croatian (En to Hr) is 49.8, while from Croatian to English (Hr to En) is 46.6. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/905.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(g) adjective-opposite" ], "reasoning": "The table shows that the translation accuracy from English to Croatian (En to Hr) is 49.8, while from Croatian to English (Hr to En) is 46.6. Therefore, the statement is supported.", "statement": "The translation accuracy from English to Croatian is higher than from Croatian to English.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled '(h) noun-plural'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table shows translation accuracy scores between six languages using B-CCA-cu transformation with dictionaries of size 20,000, highlighting each language's strengths and weaknesses.Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses." } ], "images": [ "scigen/train/906.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(h) noun-plural" ], "reasoning": "The table shows translation accuracy scores between six languages using B-CCA-cu transformation with dictionaries of size 20,000, highlighting each language's strengths and weaknesses.", "description": "Again, rows represent the source language a and columns the target language b. The results were achieved using B-CCA-cu transformation with dictionaries of size n=20,000. Each language seems to have strengths and weaknesses.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled '(h) noun-plural' and methodically tackle the question that follows. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich language pair has the highest translation accuracy score?\n" }, { "role": "assistant", "content": "To find the highest translation accuracy score, we need to compare all the values in the table and identify the maximum value. So, the answer is {\"answer\": \"It to Es\"}." } ], "images": [ "scigen/train/906.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(h) noun-plural" ], "reasoning": "To find the highest translation accuracy score, we need to compare all the values in the table and identify the maximum value.", "question": "Which language pair has the highest translation accuracy score?", "answer": "So, the answer is {\"answer\": \"It to Es\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe translation accuracy from English to Croatian is higher than from Croatian to English.\n" }, { "role": "assistant", "content": "The table shows that the translation accuracy from English to Croatian (40.7) is lower than from Croatian to English (44.2). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/906.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(h) noun-plural" ], "reasoning": "The table shows that the translation accuracy from English to Croatian (40.7) is lower than from Croatian to English (44.2).", "statement": "The translation accuracy from English to Croatian is higher than from Croatian to English.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4. F1-score, Precision, and Recall with different IVA parameters'.\n" }, { "role": "assistant", "content": "Increasing the number of former neighbors R and keyframes P improves the F1-score, validating the interactive and real-time properties of TSCs.Finally, we change the number of former neighbors R and keyframe P to see their influence on the experiment result in the validation set. We can find the F1-score increase with the number of former neighbors R and keyframe P. This result proves that our application of the interactive and real-time properties of TSCs is correct. When we increase former neighbors, TSCs establish semantics association with more surrounding neighbors. When we increase keyframes, TSCs can be compared with more important plots in the video." } ], "images": [ "scigen/train/907.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4. F1-score, Precision, and Recall with different IVA parameters" ], "reasoning": "Increasing the number of former neighbors R and keyframes P improves the F1-score, validating the interactive and real-time properties of TSCs.", "description": "Finally, we change the number of former neighbors R and keyframe P to see their influence on the experiment result in the validation set. We can find the F1-score increase with the number of former neighbors R and keyframe P. This result proves that our application of the interactive and real-time properties of TSCs is correct. When we increase former neighbors, TSCs establish semantics association with more surrounding neighbors. When we increase keyframes, TSCs can be compared with more important plots in the video.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 4. F1-score, Precision, and Recall with different IVA parameters\nQuestion: What is the F1-score when R=3 and P=5?\n" }, { "role": "assistant", "content": "To find the F1-score when R=3 and P=5, locate the row where P=5 and the column where R=3 F1-score intersect. So, the answer is {\"answer\": \"0.846\"}." } ], "images": [ "scigen/train/907.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4. F1-score, Precision, and Recall with different IVA parameters" ], "reasoning": "To find the F1-score when R=3 and P=5, locate the row where P=5 and the column where R=3 F1-score intersect.", "question": "What is the F1-score when R=3 and P=5?", "answer": "So, the answer is {\"answer\": \"0.846\"}." }, { "messages": [ { "role": "user", "content": "The F1-score for R=4 and P=6 is higher than for R=1 and P=6. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe F1-score for R=4 and P=6 is higher than for R=1 and P=6.\n" }, { "role": "assistant", "content": "Compare the F1-score for R=4 and P=6 (0.844) with the F1-score for R=1 and P=6 (0.773). The F1-score for R=4 and P=6 is indeed higher. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/907.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4. F1-score, Precision, and Recall with different IVA parameters" ], "reasoning": "Compare the F1-score for R=4 and P=6 (0.844) with the F1-score for R=1 and P=6 (0.773). The F1-score for R=4 and P=6 is indeed higher.", "statement": "The F1-score for R=4 and P=6 is higher than for R=1 and P=6.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3. F1-score, Precision, and Recall of each method'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The DN-GAA method proposed by Chang et al. is the state-of-the-art method and achieves the highest performance among the baselines, demonstrating the powerful feature extraction capabilities of neural networks with sufficient data.The experiments of all the models are repeated for 10 times, and we use the average values as the final results. Compared to the-state-of-art DN-GAA method proposed by Chang et al In other baselines, keyword-matching (KM) method achieves high Recall and low Precision, since it treats many non-spoiler TSCs as spoilers. Latent Dirichlet Allocation (LDA) based method does not perform well because the LDA model is not suitable to process short texts like TSC data. LI-NPP is an SVM-based method and has better performance than the unsupervised learning methods. DN-GAA is the state-of-the-art method and has the highest performance among the baselines. The results show that the neural network has powerful feature extraction capabilities when there is sufficient data." } ], "images": [ "scigen/train/908.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. F1-score, Precision, and Recall of each method" ], "reasoning": "The DN-GAA method proposed by Chang et al. is the state-of-the-art method and achieves the highest performance among the baselines, demonstrating the powerful feature extraction capabilities of neural networks with sufficient data.", "description": "The experiments of all the models are repeated for 10 times, and we use the average values as the final results. Compared to the-state-of-art DN-GAA method proposed by Chang et al In other baselines, keyword-matching (KM) method achieves high Recall and low Precision, since it treats many non-spoiler TSCs as spoilers. Latent Dirichlet Allocation (LDA) based method does not perform well because the LDA model is not suitable to process short texts like TSC data. LI-NPP is an SVM-based method and has better performance than the unsupervised learning methods. DN-GAA is the state-of-the-art method and has the highest performance among the baselines. The results show that the neural network has powerful feature extraction capabilities when there is sufficient data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3. F1-score, Precision, and Recall of each method', and sequentially solve the question, writing out each step of your reasoning process. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich method achieves the highest F1-score for TV-series?\n" }, { "role": "assistant", "content": "To find the method with the highest F1-score for TV-series, look at the 'TV-series F1-score' column and identify the highest value. The highest F1-score is 0.850, achieved by the SBN-IVA method. So, the answer is {\"answer\": \"SBN-IVA\"}." } ], "images": [ "scigen/train/908.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. F1-score, Precision, and Recall of each method" ], "reasoning": "To find the method with the highest F1-score for TV-series, look at the 'TV-series F1-score' column and identify the highest value. The highest F1-score is 0.850, achieved by the SBN-IVA method.", "question": "Which method achieves the highest F1-score for TV-series?", "answer": "So, the answer is {\"answer\": \"SBN-IVA\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The KM method achieves the highest Recall for Movies., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe KM method achieves the highest Recall for Movies.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'Movies Recall' column. The highest Recall value is 0.912, which is indeed achieved by the KM method. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/908.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. F1-score, Precision, and Recall of each method" ], "reasoning": "To verify this statement, look at the 'Movies Recall' column. The highest Recall value is 0.912, which is indeed achieved by the KM method.", "statement": "The KM method achieves the highest Recall for Movies.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: Comparison of prediction results based on encoder using NYT122, NYT71, and NYT27 datasets with different KB regularization strategies.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "RegDVAE+D consistently outperforms the original DVAE model across all datasets, showing significant improvements in F1 scores.S4SS3SSS0Px2 Comparison on Different Datasets. We also compare our algorithm on the three datasets with different baseline settings. In order to evaluate our model rigorously, besides the original DVAE model, we compare two additional augmented baseline models with the same hyper-parameter setting: DVAE with TransE embeddings appended to encoder input features (DVAE+E) and DVAE with decoder entity vectors replaced by pre-trained KB embeddings (DVAE+D). For our method, we report RegDVAE with the best setting where we use Euclidean distance based constraints to regularize the encoder. Moreover, we report a setting with fixed embeddings in the decoder as the ones obtained from TransE (RegDVAE+D). This also makes sense since even though the TransE embeddings are not trained with the observation of the same relations as the text corpus, the embeddings already contain much semantic information about entities. Then by fixing the embeddings of entities in the decoder, we can significantly reduce the number of parameters that need to be trained. As we can see that, RegDVAE+D can outperform the original DVAE by 8∼23 points on F1. DVAE+D is also good but may fail when there are a lot of out-of-sample entities in the training corpus." } ], "images": [ "scigen/train/909.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of prediction results based on encoder using NYT122, NYT71, and NYT27 datasets with different KB regularization strategies." ], "reasoning": "RegDVAE+D consistently outperforms the original DVAE model across all datasets, showing significant improvements in F1 scores.", "description": "S4SS3SSS0Px2 Comparison on Different Datasets. We also compare our algorithm on the three datasets with different baseline settings. In order to evaluate our model rigorously, besides the original DVAE model, we compare two additional augmented baseline models with the same hyper-parameter setting: DVAE with TransE embeddings appended to encoder input features (DVAE+E) and DVAE with decoder entity vectors replaced by pre-trained KB embeddings (DVAE+D). For our method, we report RegDVAE with the best setting where we use Euclidean distance based constraints to regularize the encoder. Moreover, we report a setting with fixed embeddings in the decoder as the ones obtained from TransE (RegDVAE+D). This also makes sense since even though the TransE embeddings are not trained with the observation of the same relations as the text corpus, the embeddings already contain much semantic information about entities. Then by fixing the embeddings of entities in the decoder, we can significantly reduce the number of parameters that need to be trained. As we can see that, RegDVAE+D can outperform the original DVAE by 8∼23 points on F1. DVAE+D is also good but may fail when there are a lot of out-of-sample entities in the training corpus.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Comparison of prediction results based on encoder using NYT122, NYT71, and NYT27 datasets with different KB regularization strategies.\nQuestion: Which model achieved the highest mean F1 score on the NYT27 dataset?\nGive a detailed response to the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, we see that RegDVAE+D has the highest mean F1 score of 0.665 on the NYT27 dataset. So, the answer is {\"answer\": \"RegDVAE+D\"}" } ], "images": [ "scigen/train/909.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of prediction results based on encoder using NYT122, NYT71, and NYT27 datasets with different KB regularization strategies." ], "reasoning": "By examining the table, we see that RegDVAE+D has the highest mean F1 score of 0.665 on the NYT27 dataset.", "question": "Which model achieved the highest mean F1 score on the NYT27 dataset?", "answer": "So, the answer is {\"answer\": \"RegDVAE+D\"}" }, { "messages": [ { "role": "user", "content": "DVAE+E achieved a higher mean F1 score than DVAE on the NYT71 dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nDVAE+E achieved a higher mean F1 score than DVAE on the NYT71 dataset.\n" }, { "role": "assistant", "content": "The table shows that DVAE+E has a mean F1 score of 0.418, which is higher than DVAE's mean F1 score of 0.375 on the NYT71 dataset. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/909.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of prediction results based on encoder using NYT122, NYT71, and NYT27 datasets with different KB regularization strategies." ], "reasoning": "The table shows that DVAE+E has a mean F1 score of 0.418, which is higher than DVAE's mean F1 score of 0.375 on the NYT71 dataset.", "statement": "DVAE+E achieved a higher mean F1 score than DVAE on the NYT71 dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Comparison results on NYT122 with different prediction and regularization strategies (using encoder or decoder).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Regularization with Euclidean distance performs best, especially over the encoder, due to constraints on sampled sentences.From the table, we can see that regularization with Euclidean distance performs the best compared to KL and JS. Moreover, the regularization over encoder is better than the regularization over decoder. This may be because the way that we put constraints only over sampled sentences in a batch may hurt the regularization of decoder, since sampled unique pairs may be less than sample sentences. If we look at results comparing original DVAE prediction based on the encoder and the decoder, both result in similar F1 and NMI numbers. Thus, we can only conclude that currently in the way we do sampling, constraining over encoder is a better choice." } ], "images": [ "scigen/train/910.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison results on NYT122 with different prediction and regularization strategies (using encoder or decoder)." ], "reasoning": "Regularization with Euclidean distance performs best, especially over the encoder, due to constraints on sampled sentences.", "description": "From the table, we can see that regularization with Euclidean distance performs the best compared to KL and JS. Moreover, the regularization over encoder is better than the regularization over decoder. This may be because the way that we put constraints only over sampled sentences in a batch may hurt the regularization of decoder, since sampled unique pairs may be less than sample sentences. If we look at results comparing original DVAE prediction based on the encoder and the decoder, both result in similar F1 and NMI numbers. Thus, we can only conclude that currently in the way we do sampling, constraining over encoder is a better choice.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich regularization method and location combination achieves the highest F1 score based on the encoder?\nSolve the above question based on the table titled 'Table 3: Comparison results on NYT122 with different prediction and regularization strategies (using encoder or decoder).'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the F1 scores for different regularization methods and locations, we see that 'RegDVAE (Euclidean at encoder)' has the highest F1 score of 0.469. So, the answer is {\"answer\": \"RegDVAE (Euclidean at encoder)\"}" } ], "images": [ "scigen/train/910.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison results on NYT122 with different prediction and regularization strategies (using encoder or decoder)." ], "reasoning": "By comparing the F1 scores for different regularization methods and locations, we see that 'RegDVAE (Euclidean at encoder)' has the highest F1 score of 0.469.", "question": "Which regularization method and location combination achieves the highest F1 score based on the encoder?", "answer": "So, the answer is {\"answer\": \"RegDVAE (Euclidean at encoder)\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nRegularization with KL at encoder achieves the highest NMI score based on the decoder.\n" }, { "role": "assistant", "content": "By examining the NMI scores based on the decoder, we see that 'RegDVAE (Euclidean at encoder)' has the highest NMI score of 0.384, not 'RegDVAE (KL at encoder)'. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/910.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison results on NYT122 with different prediction and regularization strategies (using encoder or decoder)." ], "reasoning": "By examining the NMI scores based on the decoder, we see that 'RegDVAE (Euclidean at encoder)' has the highest NMI score of 0.384, not 'RegDVAE (KL at encoder)'.", "statement": "Regularization with KL at encoder achieves the highest NMI score based on the decoder.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: F1 scores on CoNLL-2003. † refers to models trained on both training and development datasets.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our model achieves the highest F1 score on all three datasets, including 87.08 on the CoNLL-2002 Spanish dataset, surpassing the previous best by 0.4.Our model surpasses previous state-of-the-art approaches on all the three datasets. On CoNLL-2002 Spanish dataset, our model achieves 87.08 F1 score without external knowledge, which surpasses previous best score by 0.4. Considering that the above two datasets are relatively small, we further conduct experiment on a much more large OntoNotes 5.0 dataset, which also has more entity types. Overall, the comparisons on these three benchmark datasets well demonstrate that our model truly learns and benefits from useful sentence-level and document-level representation without the support from external knowledge." } ], "images": [ "scigen/train/911.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: F1 scores on CoNLL-2003. † refers to models trained on both training and development datasets." ], "reasoning": "Our model achieves the highest F1 score on all three datasets, including 87.08 on the CoNLL-2002 Spanish dataset, surpassing the previous best by 0.4.", "description": "Our model surpasses previous state-of-the-art approaches on all the three datasets. On CoNLL-2002 Spanish dataset, our model achieves 87.08 F1 score without external knowledge, which surpasses previous best score by 0.4. Considering that the above two datasets are relatively small, we further conduct experiment on a much more large OntoNotes 5.0 dataset, which also has more entity types. Overall, the comparisons on these three benchmark datasets well demonstrate that our model truly learns and benefits from useful sentence-level and document-level representation without the support from external knowledge.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: F1 scores on CoNLL-2003. † refers to models trained on both training and development datasets.' and methodically tackle the question that follows. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the F1 score achieved by the model without external knowledge on the CoNLL-2002 Spanish dataset?\n" }, { "role": "assistant", "content": "The description states that the model achieves 87.08 F1 score on the CoNLL-2002 Spanish dataset without external knowledge. So, the answer is {\"answer\": \"87.08\"}" } ], "images": [ "scigen/train/911.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: F1 scores on CoNLL-2003. † refers to models trained on both training and development datasets." ], "reasoning": "The description states that the model achieves 87.08 F1 score on the CoNLL-2002 Spanish dataset without external knowledge.", "question": "What is the F1 score achieved by the model without external knowledge on the CoNLL-2002 Spanish dataset?", "answer": "So, the answer is {\"answer\": \"87.08\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe model achieves the highest F1 score of 93.37 with BERT.\n" }, { "role": "assistant", "content": "The table shows that the highest F1 score achieved is 93.37 when the model is combined with BERT. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/911.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: F1 scores on CoNLL-2003. † refers to models trained on both training and development datasets." ], "reasoning": "The table shows that the highest F1 score achieved is 93.37 when the model is combined with BERT.", "statement": "The model achieves the highest F1 score of 93.37 with BERT.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: F1 scores on CoNLL-2002.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Our model achieves the highest F1 score of 87.08 on the CoNLL-2002 Spanish dataset, surpassing the previous best score by 0.4.Our model surpasses previous state-of-the-art approaches on all the three datasets. On CoNLL-2002 Spanish dataset, our model achieves 87.08 F1 score without external knowledge, which surpasses previous best score by 0.4. Considering that the above two datasets are relatively small, we further conduct experiment on a much more large OntoNotes 5.0 dataset, which also has more entity types. Overall, the comparisons on these three benchmark datasets well demonstrate that our model truly learns and benefits from useful sentence-level and document-level representation without the support from external knowledge." } ], "images": [ "scigen/train/912.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: F1 scores on CoNLL-2002." ], "reasoning": "Our model achieves the highest F1 score of 87.08 on the CoNLL-2002 Spanish dataset, surpassing the previous best score by 0.4.", "description": "Our model surpasses previous state-of-the-art approaches on all the three datasets. On CoNLL-2002 Spanish dataset, our model achieves 87.08 F1 score without external knowledge, which surpasses previous best score by 0.4. Considering that the above two datasets are relatively small, we further conduct experiment on a much more large OntoNotes 5.0 dataset, which also has more entity types. Overall, the comparisons on these three benchmark datasets well demonstrate that our model truly learns and benefits from useful sentence-level and document-level representation without the support from external knowledge.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: F1 scores on CoNLL-2002.', unfold the steps to solve the question given next. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nQuestion: What is the F1 score achieved by the model that surpasses the previous best score by 0.4 on the CoNLL-2002 Spanish dataset?\n" }, { "role": "assistant", "content": "The description states that the model achieves an F1 score of 87.08 on the CoNLL-2002 Spanish dataset, which surpasses the previous best score by 0.4. So, the answer is {\"answer\": \"87.08\"}" } ], "images": [ "scigen/train/912.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: F1 scores on CoNLL-2002." ], "reasoning": "The description states that the model achieves an F1 score of 87.08 on the CoNLL-2002 Spanish dataset, which surpasses the previous best score by 0.4.", "question": "What is the F1 score achieved by the model that surpasses the previous best score by 0.4 on the CoNLL-2002 Spanish dataset?", "answer": "So, the answer is {\"answer\": \"87.08\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model achieves an F1 score of 87.08 on the CoNLL-2002 Spanish dataset.\n" }, { "role": "assistant", "content": "The table shows that the model achieves an F1 score of 87.08, which matches the description. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/912.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: F1 scores on CoNLL-2002." ], "reasoning": "The table shows that the model achieves an F1 score of 87.08, which matches the description.", "statement": "The model achieves an F1 score of 87.08 on the CoNLL-2002 Spanish dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: F1 scores on OntoNotes 5.0.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Our model achieves the highest F1 score of 87.98 on the CoNLL-2002 Spanish dataset without external knowledge, surpassing the previous best score by 0.4.Our model surpasses previous state-of-the-art approaches on all the three datasets. On CoNLL-2002 Spanish dataset, our model achieves 87.08 F1 score without external knowledge, which surpasses previous best score by 0.4. Considering that the above two datasets are relatively small, we further conduct experiment on a much more large OntoNotes 5.0 dataset, which also has more entity types. Overall, the comparisons on these three benchmark datasets well demonstrate that our model truly learns and benefits from useful sentence-level and document-level representation without the support from external knowledge." } ], "images": [ "scigen/train/913.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: F1 scores on OntoNotes 5.0." ], "reasoning": "Our model achieves the highest F1 score of 87.98 on the CoNLL-2002 Spanish dataset without external knowledge, surpassing the previous best score by 0.4.", "description": "Our model surpasses previous state-of-the-art approaches on all the three datasets. On CoNLL-2002 Spanish dataset, our model achieves 87.08 F1 score without external knowledge, which surpasses previous best score by 0.4. Considering that the above two datasets are relatively small, we further conduct experiment on a much more large OntoNotes 5.0 dataset, which also has more entity types. Overall, the comparisons on these three benchmark datasets well demonstrate that our model truly learns and benefits from useful sentence-level and document-level representation without the support from external knowledge.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nTable title: Table 4: F1 scores on OntoNotes 5.0.\n\nQuestion: What is the F1 score achieved by the model without external knowledge on the CoNLL-2002 Spanish dataset?\n" }, { "role": "assistant", "content": "The table shows that the model achieves an F1 score of 87.98 without external knowledge, which is highlighted in the row labeled 'Ours'. So, the answer is {\"answer\": \"87.98\"}" } ], "images": [ "scigen/train/913.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: F1 scores on OntoNotes 5.0." ], "reasoning": "The table shows that the model achieves an F1 score of 87.98 without external knowledge, which is highlighted in the row labeled 'Ours'.", "question": "What is the F1 score achieved by the model without external knowledge on the CoNLL-2002 Spanish dataset?", "answer": "So, the answer is {\"answer\": \"87.98\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe model achieves an F1 score of 87.98 on the CoNLL-2002 Spanish dataset without external knowledge.\n" }, { "role": "assistant", "content": "The table shows that the model achieves an F1 score of 87.98 without external knowledge, which is highlighted in the row labeled 'Ours'. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/913.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: F1 scores on OntoNotes 5.0." ], "reasoning": "The table shows that the model achieves an F1 score of 87.98 without external knowledge, which is highlighted in the row labeled 'Ours'.", "statement": "The model achieves an F1 score of 87.98 on the CoNLL-2002 Spanish dataset without external knowledge.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 5: Ablation study on the three benchmark datasets.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Combining sentence-level and document-level representations results in the highest performance gain across all datasets.In this experiment, we individually adopt two hierarchical contextualized representations to enhance the representation of tokens: sentence-level representation for assigning the sentence state to each token and document-level representation for inference. We discover that both sentence-level and document-level representations enhance the baseline. By combing these two representations together, we get a larger gain of 0.36 / 0.43 / 0.40, respectively." } ], "images": [ "scigen/train/914.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ablation study on the three benchmark datasets." ], "reasoning": "Combining sentence-level and document-level representations results in the highest performance gain across all datasets.", "description": "In this experiment, we individually adopt two hierarchical contextualized representations to enhance the representation of tokens: sentence-level representation for assigning the sentence state to each token and document-level representation for inference. We discover that both sentence-level and document-level representations enhance the baseline. By combing these two representations together, we get a larger gain of 0.36 / 0.43 / 0.40, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 5: Ablation study on the three benchmark datasets.'. Solve the following question based on the table.\n\nQuestion: What is the performance gain on the CoNLL03 dataset when both sentence-level and document-level representations are combined?\nLet's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The base model score for CoNLL03 is 91.60. When both representations are combined, the score is 91.96. The performance gain is 91.96 - 91.60 = 0.36. So, the answer is {\"answer\": \"0.36\"}" } ], "images": [ "scigen/train/914.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ablation study on the three benchmark datasets." ], "reasoning": "The base model score for CoNLL03 is 91.60. When both representations are combined, the score is 91.96. The performance gain is 91.96 - 91.60 = 0.36.", "question": "What is the performance gain on the CoNLL03 dataset when both sentence-level and document-level representations are combined?", "answer": "So, the answer is {\"answer\": \"0.36\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe document-level representation alone provides a higher performance gain than the sentence-level representation on the CoNLL02 dataset.\n" }, { "role": "assistant", "content": "The base model score for CoNLL02 is 86.65. The sentence-level representation score is 86.95, and the document-level representation score is 86.76. The performance gain for sentence-level is 86.95 - 86.65 = 0.30, and for document-level is 86.76 - 86.65 = 0.11. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/914.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ablation study on the three benchmark datasets." ], "reasoning": "The base model score for CoNLL02 is 86.65. The sentence-level representation score is 86.95, and the document-level representation score is 86.76. The performance gain for sentence-level is 86.95 - 86.65 = 0.30, and for document-level is 86.76 - 86.65 = 0.11. Therefore, the statement is refuted.", "statement": "The document-level representation alone provides a higher performance gain than the sentence-level representation on the CoNLL02 dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 6: Comparison of different strategies on CoNLL-2003 dataset. ERR is the relative error rate reduction of our model compared to the baseline.'.\n" }, { "role": "assistant", "content": "Cosine similarity is chosen as the compatibility function because it performs best among the three functions, despite having a higher ERR.We further analyze the two hierarchical representations by adopting different strategies. We further conduct experiments to investigate the three compatibility functions used to employ memorized information. Among the three compatibility functions to compute the weight of query word and memorized slots, cosine similarity performs best, while dot-product performs worst. Cosine similarity calculates the inner product of word vectors with unit length, and can further solve the inconsistency between the embeddings and the similarity measurement. Thus, we eventually adopt cosine similarity as the compatibility function." } ], "images": [ "scigen/train/915.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Comparison of different strategies on CoNLL-2003 dataset. ERR is the relative error rate reduction of our model compared to the baseline." ], "reasoning": "Cosine similarity is chosen as the compatibility function because it performs best among the three functions, despite having a higher ERR.", "description": "We further analyze the two hierarchical representations by adopting different strategies. We further conduct experiments to investigate the three compatibility functions used to employ memorized information. Among the three compatibility functions to compute the weight of query word and memorized slots, cosine similarity performs best, while dot-product performs worst. Cosine similarity calculates the inner product of word vectors with unit length, and can further solve the inconsistency between the embeddings and the similarity measurement. Thus, we eventually adopt cosine similarity as the compatibility function.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 6: Comparison of different strategies on CoNLL-2003 dataset. ERR is the relative error rate reduction of our model compared to the baseline.' in mind, break down and resolve the question below step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich strategy has the highest F1 score and what is its ERR?\n" }, { "role": "assistant", "content": "The table shows that the 'ALL' strategy has the highest F1 score of 91.96, and its ERR is 3.81. So, the answer is {\"answer\": \"91.96 and 3.81\"}" } ], "images": [ "scigen/train/915.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Comparison of different strategies on CoNLL-2003 dataset. ERR is the relative error rate reduction of our model compared to the baseline." ], "reasoning": "The table shows that the 'ALL' strategy has the highest F1 score of 91.96, and its ERR is 3.81.", "question": "Which strategy has the highest F1 score and what is its ERR?", "answer": "So, the answer is {\"answer\": \"91.96 and 3.81\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe document-level strategy using cosine similarity has the highest ERR.\n" }, { "role": "assistant", "content": "The table shows that the document-level strategy using cosine similarity has an ERR of 2.38, which is not the highest. The highest ERR is 3.81 for the 'ALL' strategy. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/915.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Comparison of different strategies on CoNLL-2003 dataset. ERR is the relative error rate reduction of our model compared to the baseline." ], "reasoning": "The table shows that the document-level strategy using cosine similarity has an ERR of 2.38, which is not the highest. The highest ERR is 3.81 for the 'ALL' strategy.", "statement": "The document-level strategy using cosine similarity has the highest ERR.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 7: Detailed results on the CoNLL-2003 dataset for IV, OOTV, OOEV, OOBV.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The model improves the F1 score for IV, OOTV, and OOBV NEs by 0.39, 0.44, and 0.43 respectively, by using memory networks and sentence-level representations.out-of-training-vocabulary words (OOTV), out-of-embedding-vocabulary words (OOEV), and out-of-both-vocabulary words (OOBV) on CoNLL-2003 datset. According to our statistic, 63.40% / 52.43% / 84.68% of the NEs in the test set of CoNLL-2003, CoNLL-2002, and OntoNotes datasets are located in the IV part, respectively. Therefore, it is of great importance to focus on this part. We adopt memory network to memorize and retrieve the global representations and use the memorized training instances directly to participate in inference, which greatly improves both the precision and recall of the NEs in IV part, in which our model outperforms baseline by 0.39 in terms of F1 score. For OOV NEs, sentence-level representation can help these concerned tokens aware of the entire sentence, thus enhance the performance. The improvement is 0.44 / 0.43 F1 score for OOTV NEs and OOBV NEs, respectively." } ], "images": [ "scigen/train/916.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Detailed results on the CoNLL-2003 dataset for IV, OOTV, OOEV, OOBV." ], "reasoning": "The model improves the F1 score for IV, OOTV, and OOBV NEs by 0.39, 0.44, and 0.43 respectively, by using memory networks and sentence-level representations.", "description": "out-of-training-vocabulary words (OOTV), out-of-embedding-vocabulary words (OOEV), and out-of-both-vocabulary words (OOBV) on CoNLL-2003 datset. According to our statistic, 63.40% / 52.43% / 84.68% of the NEs in the test set of CoNLL-2003, CoNLL-2002, and OntoNotes datasets are located in the IV part, respectively. Therefore, it is of great importance to focus on this part. We adopt memory network to memorize and retrieve the global representations and use the memorized training instances directly to participate in inference, which greatly improves both the precision and recall of the NEs in IV part, in which our model outperforms baseline by 0.39 in terms of F1 score. For OOV NEs, sentence-level representation can help these concerned tokens aware of the entire sentence, thus enhance the performance. The improvement is 0.44 / 0.43 F1 score for OOTV NEs and OOBV NEs, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the F1 score improvement for OOTV NEs when using the model compared to the baseline?\nSolve the above question based on the table titled 'Table 7: Detailed results on the CoNLL-2003 dataset for IV, OOTV, OOEV, OOBV.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The baseline F1 score for OOTV NEs is 92.51, and the model's F1 score is 92.95. The improvement is 92.95 - 92.51 = 0.44. So, the answer is {\"answer\": \"0.44\"}" } ], "images": [ "scigen/train/916.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Detailed results on the CoNLL-2003 dataset for IV, OOTV, OOEV, OOBV." ], "reasoning": "The baseline F1 score for OOTV NEs is 92.51, and the model's F1 score is 92.95. The improvement is 92.95 - 92.51 = 0.44.", "question": "What is the F1 score improvement for OOTV NEs when using the model compared to the baseline?", "answer": "So, the answer is {\"answer\": \"0.44\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The model's F1 score for IV NEs is higher than the baseline., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model's F1 score for IV NEs is higher than the baseline.\n" }, { "role": "assistant", "content": "The baseline F1 score for IV NEs is 93.87, while the model's F1 score is 94.26, which is higher. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/916.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Detailed results on the CoNLL-2003 dataset for IV, OOTV, OOEV, OOBV." ], "reasoning": "The baseline F1 score for IV NEs is 93.87, while the model's F1 score is 94.26, which is higher.", "statement": "The model's F1 score for IV NEs is higher than the baseline.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Mean Reciprocal Rank (MRR) and HITS@n scores obtained for the link prediction tasks on the WN18, FB15k, WN18RR, and FB15k-237 datasets. The highest result for each column is shown in bold. The results of TransE and TorusE were reported by Ebisu and Ichise Ebisu and Ichise (2018), the results of RESCAL were reported by Nickel et al. Nickel et al. (2016), the results of DistMult and ComplEx were reported by Trouillon et al. Trouillon et al. (2016), the results of R-GCN and ConvE were reported by Dettmers et al. Dettmers et al. (2018), the results of PRA were reported by Liu et al. Liu et al. (2016), and the results of Node+LinkFeat were reported by Toutanova and Chen Toutanova and Chen (2015).'.\n" }, { "role": "assistant", "content": "GRank with dMAP and fdMAP yielded the best results on FB15k, indicating the importance of considering the multiplicity of matchings and deeper information in knowledge graphs.The Node+LinkFeat model performed well on WN18 and FB15k because these datasets often contain the reverse relations of other relations. In other words, it shows that knowledge graph embedding models failed to capture this redundancy. On the other hand, our proposed models, GPro and GRank, generally yield better results than the knowledge graph embedding models and results which are better than or comparable to Node+LinkFeat, which means that our models can also handle such redundancy. In particular, GRank with dMAP and fdMAP yielded the best results on FB15k. This indicates that taking the multiplicity of matchings and deeper information into account is important for knowledge graphs such as FreeBase that contain miscellaneous relations and are not well curated like WordNet. As a result, GRank performed well. For FB15k-237, the performance of Node+LinkFeat is comparable with most of the other more sophisticated knowledge graph models and GPro does not yield good results because FB15k-237 has less redundancy. GRank also performs better than the most other models for the FB15k-237 dataset for the same reason as the FB15k dataset. However, our models do not utilize the information related to the co-occurrence of entities and relations in triples (node features Toutanova and Chen We also limited the size and the shapes of graph patterns because of the calculation time; we will address these and improve our models further in our future work." } ], "images": [ "scigen/train/917.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Mean Reciprocal Rank (MRR) and HITS@n scores obtained for the link prediction tasks on the WN18, FB15k, WN18RR, and FB15k-237 datasets. The highest result for each column is shown in bold. The results of TransE and TorusE were reported by Ebisu and Ichise Ebisu and Ichise (2018), the results of RESCAL were reported by Nickel et al. Nickel et al. (2016), the results of DistMult and ComplEx were reported by Trouillon et al. Trouillon et al. (2016), the results of R-GCN and ConvE were reported by Dettmers et al. Dettmers et al. (2018), the results of PRA were reported by Liu et al. Liu et al. (2016), and the results of Node+LinkFeat were reported by Toutanova and Chen Toutanova and Chen (2015)." ], "reasoning": "GRank with dMAP and fdMAP yielded the best results on FB15k, indicating the importance of considering the multiplicity of matchings and deeper information in knowledge graphs.", "description": "The Node+LinkFeat model performed well on WN18 and FB15k because these datasets often contain the reverse relations of other relations. In other words, it shows that knowledge graph embedding models failed to capture this redundancy. On the other hand, our proposed models, GPro and GRank, generally yield better results than the knowledge graph embedding models and results which are better than or comparable to Node+LinkFeat, which means that our models can also handle such redundancy. In particular, GRank with dMAP and fdMAP yielded the best results on FB15k. This indicates that taking the multiplicity of matchings and deeper information into account is important for knowledge graphs such as FreeBase that contain miscellaneous relations and are not well curated like WordNet. As a result, GRank performed well. For FB15k-237, the performance of Node+LinkFeat is comparable with most of the other more sophisticated knowledge graph models and GPro does not yield good results because FB15k-237 has less redundancy. GRank also performs better than the most other models for the FB15k-237 dataset for the same reason as the FB15k dataset. However, our models do not utilize the information related to the co-occurrence of entities and relations in triples (node features Toutanova and Chen We also limited the size and the shapes of graph patterns because of the calculation time; we will address these and improve our models further in our future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: Mean Reciprocal Rank (MRR) and HITS@n scores obtained for the link prediction tasks on the WN18, FB15k, WN18RR, and FB15k-237 datasets. The highest result for each column is shown in bold. The results of TransE and TorusE were reported by Ebisu and Ichise Ebisu and Ichise (2018), the results of RESCAL were reported by Nickel et al. Nickel et al. (2016), the results of DistMult and ComplEx were reported by Trouillon et al. Trouillon et al. (2016), the results of R-GCN and ConvE were reported by Dettmers et al. Dettmers et al. (2018), the results of PRA were reported by Liu et al. Liu et al. (2016), and the results of Node+LinkFeat were reported by Toutanova and Chen Toutanova and Chen (2015).', solve the subsequent question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhich model yielded the best results on FB15k in terms of HITS@10?\n" }, { "role": "assistant", "content": "The table shows that GRank(fdMAP) has the highest HITS@10 score of 0.856 for the FB15k dataset, indicating it performed the best. So, the answer is {\"answer\": \"GRank(fdMAP)\"}" } ], "images": [ "scigen/train/917.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Mean Reciprocal Rank (MRR) and HITS@n scores obtained for the link prediction tasks on the WN18, FB15k, WN18RR, and FB15k-237 datasets. The highest result for each column is shown in bold. The results of TransE and TorusE were reported by Ebisu and Ichise Ebisu and Ichise (2018), the results of RESCAL were reported by Nickel et al. Nickel et al. (2016), the results of DistMult and ComplEx were reported by Trouillon et al. Trouillon et al. (2016), the results of R-GCN and ConvE were reported by Dettmers et al. Dettmers et al. (2018), the results of PRA were reported by Liu et al. Liu et al. (2016), and the results of Node+LinkFeat were reported by Toutanova and Chen Toutanova and Chen (2015)." ], "reasoning": "The table shows that GRank(fdMAP) has the highest HITS@10 score of 0.856 for the FB15k dataset, indicating it performed the best.", "question": "Which model yielded the best results on FB15k in terms of HITS@10?", "answer": "So, the answer is {\"answer\": \"GRank(fdMAP)\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nNode+LinkFeat performed better than GPro on the FB15k-237 dataset.\n" }, { "role": "assistant", "content": "The table shows that Node+LinkFeat has a higher MRR (0.229) compared to GPro (0.163) on the FB15k-237 dataset, supporting the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/917.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Mean Reciprocal Rank (MRR) and HITS@n scores obtained for the link prediction tasks on the WN18, FB15k, WN18RR, and FB15k-237 datasets. The highest result for each column is shown in bold. The results of TransE and TorusE were reported by Ebisu and Ichise Ebisu and Ichise (2018), the results of RESCAL were reported by Nickel et al. Nickel et al. (2016), the results of DistMult and ComplEx were reported by Trouillon et al. Trouillon et al. (2016), the results of R-GCN and ConvE were reported by Dettmers et al. Dettmers et al. (2018), the results of PRA were reported by Liu et al. Liu et al. (2016), and the results of Node+LinkFeat were reported by Toutanova and Chen Toutanova and Chen (2015)." ], "reasoning": "The table shows that Node+LinkFeat has a higher MRR (0.229) compared to GPro (0.163) on the FB15k-237 dataset, supporting the statement.", "statement": "Node+LinkFeat performed better than GPro on the FB15k-237 dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: High Resource Results', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The wFST model outperforms the baseline models for high resource languages, as indicated by lower WER and PER values.Having shown that our model exceeds the performance of the wFST-adaptation approach, we next compare it to the baseline models for just high resource languages. The wFST models here are purely monolingual – they do not use data adaptation because there is sufficient training data for each of them. We omit models trained on the Adapted languages because they were not trained on high resource languages with unique writing systems, such as Georgian and Greek, and consequently performed very poorly on them." } ], "images": [ "scigen/train/918.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: High Resource Results" ], "reasoning": "The wFST model outperforms the baseline models for high resource languages, as indicated by lower WER and PER values.", "description": "Having shown that our model exceeds the performance of the wFST-adaptation approach, we next compare it to the baseline models for just high resource languages. The wFST models here are purely monolingual – they do not use data adaptation because there is sufficient training data for each of them. We omit models trained on the Adapted languages because they were not trained on high resource languages with unique writing systems, such as Georgian and Greek, and consequently performed very poorly on them.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: High Resource Results\nQuestion: Which model has the lowest WER 100 value among the high resource languages?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the WER 100 values for the models, the LangID-High model has the lowest WER 100 value of 15.50. So, the answer is {\"answer\": \"LangID-High\"}." } ], "images": [ "scigen/train/918.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: High Resource Results" ], "reasoning": "By comparing the WER 100 values for the models, the LangID-High model has the lowest WER 100 value of 15.50.", "question": "Which model has the lowest WER 100 value among the high resource languages?", "answer": "So, the answer is {\"answer\": \"LangID-High\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe NoLangID-High model has the highest PER value.\n" }, { "role": "assistant", "content": "By comparing the PER values for all models, the NoLangID-All model has the highest PER value of 35.47, not the NoLangID-High model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/918.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: High Resource Results" ], "reasoning": "By comparing the PER values for all models, the NoLangID-All model has the highest PER value of 35.47, not the NoLangID-High model.", "statement": "The NoLangID-High model has the highest PER value.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4: Adapted Results', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The best performance in WER, WER 100, and PER was achieved by the LangID-All model, which was trained on data in all available languages.On the 229 languages for which Deri and Knight The best performance came with the version of our model that was trained on data in all available languages, not just the languages it was tested on. Using a language ID token improves results considerably, but even NoLangID beats the baseline in WER and WER 100." } ], "images": [ "scigen/train/919.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Adapted Results" ], "reasoning": "The best performance in WER, WER 100, and PER was achieved by the LangID-All model, which was trained on data in all available languages.", "description": "On the 229 languages for which Deri and Knight The best performance came with the version of our model that was trained on data in all available languages, not just the languages it was tested on. Using a language ID token improves results considerably, but even NoLangID beats the baseline in WER and WER 100.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Adapted Results', answer the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model achieved the lowest WER 100 score?\n" }, { "role": "assistant", "content": "By examining the WER 100 column, the LangID-All model has the lowest score of 43.23. So, the answer is {\"answer\": \"LangID-All\"}." } ], "images": [ "scigen/train/919.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Adapted Results" ], "reasoning": "By examining the WER 100 column, the LangID-All model has the lowest score of 43.23.", "question": "Which model achieved the lowest WER 100 score?", "answer": "So, the answer is {\"answer\": \"LangID-All\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe NoLangID-High model has a lower WER than the LangID-High model.\n" }, { "role": "assistant", "content": "The WER for NoLangID-High is 82.14, which is higher than the WER for LangID-High at 74.99. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/919.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Adapted Results" ], "reasoning": "The WER for NoLangID-High is 82.14, which is higher than the WER for LangID-High at 74.99.", "statement": "The NoLangID-High model has a lower WER than the LangID-High model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 6: Results on languages not in the training corpus'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Despite not learning embeddings for unseen languages, LangID models generally perform better than NoLangID models in WER and PER, even for WER 100.The unseen languages are any that are present in the test corpus but absent from the training data. Deri and Knight did not report results specifically on these languages. Although the NoLangID models sometimes do better on WER 100, even here the LangID models have a slight advantage in WER and PER. This is somewhat surprising because the LangID models have not learned embeddings for the language ID tokens of unseen languages. Perhaps negative associations are also being learned, driving the model towards predicting more common pronunciations for unseen languages." } ], "images": [ "scigen/train/920.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results on languages not in the training corpus" ], "reasoning": "Despite not learning embeddings for unseen languages, LangID models generally perform better than NoLangID models in WER and PER, even for WER 100.", "description": "The unseen languages are any that are present in the test corpus but absent from the training data. Deri and Knight did not report results specifically on these languages. Although the NoLangID models sometimes do better on WER 100, even here the LangID models have a slight advantage in WER and PER. This is somewhat surprising because the LangID models have not learned embeddings for the language ID tokens of unseen languages. Perhaps negative associations are also being learned, driving the model towards predicting more common pronunciations for unseen languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 6: Results on languages not in the training corpus', unfold the question presented below, detailing every step of your calculation. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the lowest WER 100 and what is its WER?\n" }, { "role": "assistant", "content": "To find the model with the lowest WER 100, look at the WER 100 column and identify the smallest value. The model with the lowest WER 100 is NoLangID-Adapted with a WER 100 of 56.29. The corresponding WER for this model is 91.27. So, the answer is {\"answer\": \"NoLangID-Adapted with WER 91.27\"}" } ], "images": [ "scigen/train/920.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results on languages not in the training corpus" ], "reasoning": "To find the model with the lowest WER 100, look at the WER 100 column and identify the smallest value. The model with the lowest WER 100 is NoLangID-Adapted with a WER 100 of 56.29. The corresponding WER for this model is 91.27.", "question": "Which model has the lowest WER 100 and what is its WER?", "answer": "So, the answer is {\"answer\": \"NoLangID-Adapted with WER 91.27\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim LangID-High has the lowest PER among all models., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nLangID-High has the lowest PER among all models.\n" }, { "role": "assistant", "content": "To verify this statement, look at the PER column and identify the smallest value. The lowest PER is 53.06, which belongs to LangID-High. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/920.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results on languages not in the training corpus" ], "reasoning": "To verify this statement, look at the PER column and identify the smallest value. The lowest PER is 53.06, which belongs to LangID-High.", "statement": "LangID-High has the lowest PER among all models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Performance comparison for the video+subtitle task on MovieQA public validation/test dataset. (–) means that the method does not participate on the task. Baselines include DEMM (Deep embedded memory network), OVQAP (Only video question answer pairs) and VCFSM (Video clip features with simple MLP).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "RWMN achieves the highest performance on both validation and test sets, with 38.67% on validation and 36.25% on the test set, outperforming DEMN which has 29.97% on the test set.Results of VQA task. We observe that RWMN achieves the best performance on both validation and test sets. For example, in the test set, RWMN attains 36.25%, which is significantly better than the runner-up DEMN of 29.97%." } ], "images": [ "scigen/train/921.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance comparison for the video+subtitle task on MovieQA public validation/test dataset. (–) means that the method does not participate on the task. Baselines include DEMM (Deep embedded memory network), OVQAP (Only video question answer pairs) and VCFSM (Video clip features with simple MLP)." ], "reasoning": "RWMN achieves the highest performance on both validation and test sets, with 38.67% on validation and 36.25% on the test set, outperforming DEMN which has 29.97% on the test set.", "description": "Results of VQA task. We observe that RWMN achieves the best performance on both validation and test sets. For example, in the test set, RWMN attains 36.25%, which is significantly better than the runner-up DEMN of 29.97%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: Performance comparison for the video+subtitle task on MovieQA public validation/test dataset. (–) means that the method does not participate on the task. Baselines include DEMM (Deep embedded memory network), OVQAP (Only video question answer pairs) and VCFSM (Video clip features with simple MLP).'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich method has the second highest performance on the Video+Subtitle validation set?\n" }, { "role": "assistant", "content": "By examining the Video+Subtitle validation set column, the highest performance is by RWMN with 38.67%. The second highest performance is by RWMN-ensemble with 38.30%. So, the answer is {\"answer\": \"RWMN-ensemble\"}." } ], "images": [ "scigen/train/921.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance comparison for the video+subtitle task on MovieQA public validation/test dataset. (–) means that the method does not participate on the task. Baselines include DEMM (Deep embedded memory network), OVQAP (Only video question answer pairs) and VCFSM (Video clip features with simple MLP)." ], "reasoning": "By examining the Video+Subtitle validation set column, the highest performance is by RWMN with 38.67%. The second highest performance is by RWMN-ensemble with 38.30%.", "question": "Which method has the second highest performance on the Video+Subtitle validation set?", "answer": "So, the answer is {\"answer\": \"RWMN-ensemble\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nRWMN-noVid outperforms DEMN on the Video+Subtitle test set.\n" }, { "role": "assistant", "content": "RWMN-noVid has a performance of 37.20% on the Video+Subtitle test set, while DEMN has a performance of 29.97%. Therefore, RWMN-noVid outperforms DEMN. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/921.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance comparison for the video+subtitle task on MovieQA public validation/test dataset. (–) means that the method does not participate on the task. Baselines include DEMM (Deep embedded memory network), OVQAP (Only video question answer pairs) and VCFSM (Video clip features with simple MLP)." ], "reasoning": "RWMN-noVid has a performance of 37.20% on the Video+Subtitle test set, while DEMN has a performance of 29.97%. Therefore, RWMN-noVid outperforms DEMN.", "statement": "RWMN-noVid outperforms DEMN on the Video+Subtitle test set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: Performance of the RWMN on the video+subtitle task, according to the structure parameters of write/read networks. νw/r: the number of layers for write/read networks, (fw/rvi,sw/rvi,fw/rci): the height and the stride of convolution filters, and the number of output channels.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The two-layer CNN shows the best training performance, while the one-layer CNN shows the best validation performance, indicating overfitting with more layers.We make several observations from the results. First, as the number of CNN layers in read /write network increases, the capacity of memory interaction may increase as well; yet the performance becomes worsen. Presumably, the main reason may be overfitting due to a relative small dataset size of MovieQA as discussed. It is hinted by our results that the two-layer CNN is the best for training performance, while the one-layer CNN is the best for validation. Second, we observe that there is no absolute magic number of how many memory slots should be read/written as a single chunk and how many strides the memory controller moves. If the stride height is too small or too large compared to the height of a convolution filter, the performance decreases. It means that the performance can be degraded when too much information is read/written as a single abstracted slot, when too much information is overlapped in adjacent reads/writes (due to a small stride), or when the information overlap is too coarse (due to a high stride). We present more ablation results to the supplementary file." } ], "images": [ "scigen/train/922.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance of the RWMN on the video+subtitle task, according to the structure parameters of write/read networks. νw/r: the number of layers for write/read networks, (fw/rvi,sw/rvi,fw/rci): the height and the stride of convolution filters, and the number of output channels." ], "reasoning": "The two-layer CNN shows the best training performance, while the one-layer CNN shows the best validation performance, indicating overfitting with more layers.", "description": "We make several observations from the results. First, as the number of CNN layers in read /write network increases, the capacity of memory interaction may increase as well; yet the performance becomes worsen. Presumably, the main reason may be overfitting due to a relative small dataset size of MovieQA as discussed. It is hinted by our results that the two-layer CNN is the best for training performance, while the one-layer CNN is the best for validation. Second, we observe that there is no absolute magic number of how many memory slots should be read/written as a single chunk and how many strides the memory controller moves. If the stride height is too small or too large compared to the height of a convolution filter, the performance decreases. It means that the performance can be degraded when too much information is read/written as a single abstracted slot, when too much information is overlapped in adjacent reads/writes (due to a small stride), or when the information overlap is too coarse (due to a high stride). We present more ablation results to the supplementary file.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 4: Performance of the RWMN on the video+subtitle task, according to the structure parameters of write/read networks. νw/r: the number of layers for write/read networks, (fw/rvi,sw/rvi,fw/rci): the height and the stride of convolution filters, and the number of output channels.\nQuestion: What is the accuracy of the model with the best validation performance, and how many layers does it have in the read and write networks?\n" }, { "role": "assistant", "content": "The table shows that the model with the highest accuracy (38.6) has 1 layer in both the read and write networks. So, the answer is {\"answer\": \"38.6\"}." } ], "images": [ "scigen/train/922.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance of the RWMN on the video+subtitle task, according to the structure parameters of write/read networks. νw/r: the number of layers for write/read networks, (fw/rvi,sw/rvi,fw/rci): the height and the stride of convolution filters, and the number of output channels." ], "reasoning": "The table shows that the model with the highest accuracy (38.6) has 1 layer in both the read and write networks.", "question": "What is the accuracy of the model with the best validation performance, and how many layers does it have in the read and write networks?", "answer": "So, the answer is {\"answer\": \"38.6\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The model with the highest accuracy has 3 layers in the write network., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model with the highest accuracy has 3 layers in the write network.\n" }, { "role": "assistant", "content": "The table shows that the model with the highest accuracy (38.6) has 1 layer in the write network, not 3 layers. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/922.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance of the RWMN on the video+subtitle task, according to the structure parameters of write/read networks. νw/r: the number of layers for write/read networks, (fw/rvi,sw/rvi,fw/rci): the height and the stride of convolution filters, and the number of output channels." ], "reasoning": "The table shows that the model with the highest accuracy (38.6) has 1 layer in the write network, not 3 layers.", "statement": "The model with the highest accuracy has 3 layers in the write network.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: A comparison of BLEU and NIST scores, RCQ marks in the three possible weightings, and GF success rates at different densities.'.\n" }, { "role": "assistant", "content": "Due to the non-normal distribution of scores, the Kolmogorov–Smirnov test is used instead of Welch’s t-test to report statistical significances between RCQ and GF scores.In view that score distributions are actually very far from normality, the usual significance tests (such as Welch’s t-test) are not applicable; therefore, statistical significances of differences between RCQ and GF scores will be reported throughout using the distribution-agnostic Kolmogorov–Smirnov test. . Jordan-Núñez et al." } ], "images": [ "scigen/train/923.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: A comparison of BLEU and NIST scores, RCQ marks in the three possible weightings, and GF success rates at different densities." ], "reasoning": "Due to the non-normal distribution of scores, the Kolmogorov–Smirnov test is used instead of Welch’s t-test to report statistical significances between RCQ and GF scores.", "description": "In view that score distributions are actually very far from normality, the usual significance tests (such as Welch’s t-test) are not applicable; therefore, statistical significances of differences between RCQ and GF scores will be reported throughout using the distribution-agnostic Kolmogorov–Smirnov test. . Jordan-Núñez et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: A comparison of BLEU and NIST scores, RCQ marks in the three possible weightings, and GF success rates at different densities.\nQuestion: Which system has the highest RCQ scores Simple and what is the value?\nGive a detailed response to the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the system with the highest RCQ scores Simple, compare the values in the RCQ scores Simple column. The highest value is 0.813, which corresponds to the Human system. So, the answer is {\"answer\": \"Human with 0.813\"}." } ], "images": [ "scigen/train/923.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: A comparison of BLEU and NIST scores, RCQ marks in the three possible weightings, and GF success rates at different densities." ], "reasoning": "To find the system with the highest RCQ scores Simple, compare the values in the RCQ scores Simple column. The highest value is 0.813, which corresponds to the Human system.", "question": "Which system has the highest RCQ scores Simple and what is the value?", "answer": "So, the answer is {\"answer\": \"Human with 0.813\"}." }, { "messages": [ { "role": "user", "content": "Google has the highest GF scores 20%. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nGoogle has the highest GF scores 20%.\n" }, { "role": "assistant", "content": "To verify this statement, compare the values in the GF scores 20% column. The highest value is 0.640, which corresponds to Bing, not Google. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/923.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: A comparison of BLEU and NIST scores, RCQ marks in the three possible weightings, and GF success rates at different densities." ], "reasoning": "To verify this statement, compare the values in the GF scores 20% column. The highest value is 0.640, which corresponds to Bing, not Google.", "statement": "Google has the highest GF scores 20%.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Effect in success rates of allowing for synonyms in GF', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Allowing for synonyms in GF success scores maintains the relative ranking of MT systems and reveals additional statistically significant differences.Allowing for synonyms: The GF success scores reported thus far have been computed by giving credit only to exact matches. We have studied giving credit to synonyms observed in informant work, namely to those appearing at least twice (in the work of all informants) that, according to one of the authors, preserved the meaning of the problem sentence, or were trivial spelling or case variations. A total of 124 frequent valid substitutions were considered. The relative ranking of MT systems is maintained; the statistical significance of the homebrew Moses results versus Bing results is maintained, and two additional statistically significant differences appear: Google vs. homebrew Moses and Systran vs. homebrew Moses. The statistical significance of the effect of gap density disappears when allowing for synonyms. This indicates that it would be beneficial to assign credit to synonyms if the necessary language resources are available or if further analysis of actual GF results is feasible." } ], "images": [ "scigen/train/924.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Effect in success rates of allowing for synonyms in GF" ], "reasoning": "Allowing for synonyms in GF success scores maintains the relative ranking of MT systems and reveals additional statistically significant differences.", "description": "Allowing for synonyms: The GF success scores reported thus far have been computed by giving credit only to exact matches. We have studied giving credit to synonyms observed in informant work, namely to those appearing at least twice (in the work of all informants) that, according to one of the authors, preserved the meaning of the problem sentence, or were trivial spelling or case variations. A total of 124 frequent valid substitutions were considered. The relative ranking of MT systems is maintained; the statistical significance of the homebrew Moses results versus Bing results is maintained, and two additional statistically significant differences appear: Google vs. homebrew Moses and Systran vs. homebrew Moses. The statistical significance of the effect of gap density disappears when allowing for synonyms. This indicates that it would be beneficial to assign credit to synonyms if the necessary language resources are available or if further analysis of actual GF results is feasible.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich system has the highest GF score with synonyms at 20%?\nSolve the above question based on the table titled 'Table 2: Effect in success rates of allowing for synonyms in GF'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the system with the highest GF score with synonyms at 20%, look at the 'GF scores with synonyms 20%' column and identify the highest value. So, the answer is {\"answer\": \"Bing\"}." } ], "images": [ "scigen/train/924.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Effect in success rates of allowing for synonyms in GF" ], "reasoning": "To find the system with the highest GF score with synonyms at 20%, look at the 'GF scores with synonyms 20%' column and identify the highest value.", "question": "Which system has the highest GF score with synonyms at 20%?", "answer": "So, the answer is {\"answer\": \"Bing\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Homebrew has a higher GF score without synonyms at 10% than Systran. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nHomebrew has a higher GF score without synonyms at 10% than Systran.\n" }, { "role": "assistant", "content": "Compare the GF scores without synonyms at 10% for Homebrew and Systran. Homebrew has a score of 0.547, while Systran has a score of 0.544. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/924.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Effect in success rates of allowing for synonyms in GF" ], "reasoning": "Compare the GF scores without synonyms at 10% for Homebrew and Systran. Homebrew has a score of 0.547, while Systran has a score of 0.544.", "statement": "Homebrew has a higher GF score without synonyms at 10% than Systran.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Average Monetary gains and Confidence scores (Females).', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Females score higher in decision tasks with NLG or multi-modal presentations compared to graphics-only, with multi-modal presentations also enhancing their confidence.We found that females score significantly higher at the decision task when exposed to either of the NLG output presentations, when compared to the graphics-only presentation (p<0.05, effect = +53.03). In addition, the same group of users scores significantly higher when presented with the multi-modal output as compared to graphics only (p=0.05, effect =60.74%). Interestingly, for this group, the multi-modal presentation adds little more in effectiveness of decision-making than the NLG-only condition, but the multi-modal presentations do enhance their confidence (+15%). We furthermore found that educated (i.e. holding a BSc or higher degree) females, who also correctly answered the risk literacy test, feel significantly more confident when presented with the multi-modal representations than with NLG only (p=0.01, effect = 16.7%)." } ], "images": [ "scigen/train/925.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Average Monetary gains and Confidence scores (Females)." ], "reasoning": "Females score higher in decision tasks with NLG or multi-modal presentations compared to graphics-only, with multi-modal presentations also enhancing their confidence.", "description": "We found that females score significantly higher at the decision task when exposed to either of the NLG output presentations, when compared to the graphics-only presentation (p<0.05, effect = +53.03). In addition, the same group of users scores significantly higher when presented with the multi-modal output as compared to graphics only (p=0.05, effect =60.74%). Interestingly, for this group, the multi-modal presentation adds little more in effectiveness of decision-making than the NLG-only condition, but the multi-modal presentations do enhance their confidence (+15%). We furthermore found that educated (i.e. holding a BSc or higher degree) females, who also correctly answered the risk literacy test, feel significantly more confident when presented with the multi-modal representations than with NLG only (p=0.01, effect = 16.7%).", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 3: Average Monetary gains and Confidence scores (Females).', answer the question below by showing each progressive step toward the answer. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the confidence percentage for females when presented with multi-modal output?\n" }, { "role": "assistant", "content": "The table shows the confidence percentages for different presentation types. The multi-modal output has a confidence percentage of 81.3%. So, the answer is {\"answer\": \"81.3%\"}." } ], "images": [ "scigen/train/925.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Average Monetary gains and Confidence scores (Females)." ], "reasoning": "The table shows the confidence percentages for different presentation types. The multi-modal output has a confidence percentage of 81.3%.", "question": "What is the confidence percentage for females when presented with multi-modal output?", "answer": "So, the answer is {\"answer\": \"81.3%\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nFemales have the highest monetary gains when presented with multi-modal output.\n" }, { "role": "assistant", "content": "The table shows that the monetary gains for multi-modal output are 118.41, which is higher than the gains for graphs-only (60.83) and NLG-only (113.86). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/925.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Average Monetary gains and Confidence scores (Females)." ], "reasoning": "The table shows that the monetary gains for multi-modal output are 118.41, which is higher than the gains for graphs-only (60.83) and NLG-only (113.86).", "statement": "Females have the highest monetary gains when presented with multi-modal output.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Average Monetary gains and Confidence scores (All Adults).'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Multi-modal representation leads to significantly higher game scores and confidence compared to Graphics-only and NLG-only representations.Multi-modal vs. Graphics-only : We found that use of multi-modal representations leads to gaining significantly higher game scores (i.e. better decision-making) than the Graphics-only representation (p=0.03, effect = +36.36). This is a 44% average increase in game score. Multi-modal vs. NLG-only: However, there is no significant difference between the NLG only and the multi-modal representation, for game score. NLG vs. Graphics-only There was no significant difference found between the WMO and NATURAL NLG conditions. Confidence: For confidence, the multi-modal representation is significantly more effective than NLG only (p<0.01, effect = 17.7%)." } ], "images": [ "scigen/train/926.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Average Monetary gains and Confidence scores (All Adults)." ], "reasoning": "Multi-modal representation leads to significantly higher game scores and confidence compared to Graphics-only and NLG-only representations.", "description": "Multi-modal vs. Graphics-only : We found that use of multi-modal representations leads to gaining significantly higher game scores (i.e. better decision-making) than the Graphics-only representation (p=0.03, effect = +36.36). This is a 44% average increase in game score. Multi-modal vs. NLG-only: However, there is no significant difference between the NLG only and the multi-modal representation, for game score. NLG vs. Graphics-only There was no significant difference found between the WMO and NATURAL NLG conditions. Confidence: For confidence, the multi-modal representation is significantly more effective than NLG only (p<0.01, effect = 17.7%).", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: Average Monetary gains and Confidence scores (All Adults).', solve this question and give detail solutions. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich representation leads to the highest confidence?\n" }, { "role": "assistant", "content": "By comparing the confidence percentages in the table, the multi-modal representation has the highest confidence at 83.7%. So, the answer is {\"answer\": \"Multi-modal\"}." } ], "images": [ "scigen/train/926.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Average Monetary gains and Confidence scores (All Adults)." ], "reasoning": "By comparing the confidence percentages in the table, the multi-modal representation has the highest confidence at 83.7%.", "question": "Which representation leads to the highest confidence?", "answer": "So, the answer is {\"answer\": \"Multi-modal\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The NLG-only representation leads to higher monetary gains than the Graphics-only representation., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe NLG-only representation leads to higher monetary gains than the Graphics-only representation.\n" }, { "role": "assistant", "content": "The table shows that the NLG-only representation has monetary gains of 101.33, which is higher than the 81.15 monetary gains of the Graphics-only representation. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/926.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Average Monetary gains and Confidence scores (All Adults)." ], "reasoning": "The table shows that the NLG-only representation has monetary gains of 101.33, which is higher than the 81.15 monetary gains of the Graphics-only representation.", "statement": "The NLG-only representation leads to higher monetary gains than the Graphics-only representation.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: Results, in %, on CLEVR. SAN denotes the SAN [9] implementation of [34]. SAN* denotes the SAN implementation of [25]. Object RN** [55] and Stack-NMNs** [52] report the results only on the validation set, whereas others report on the test set. Overall performance of Stack-NMNs** [52] is measured with the “expert layout” (similar to N2NMN) yielding 96.6 and without it (93.0). DDRprog− [71], PG+EE (700k)− [70], TbD−, and TbD+hres− [50] are trained with a privileged state-description, while others are trained directly from images-questions-answers. TbD+hres [50] uses high-resolution (28x28) spatial tensor, while majority uses either 8x8 or 14x14. HAN+Sum/RN+ denotes a larger relational model, or a different hyper-parameters setup, than the model of [25]. HAN+RN++ denotes HAN+RN+ with larger input images with spatial dimensions 224x224 as opposed to 128x128, and larger image tensors with spatial dimension 14x14 as opposed to 8x8.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Increasing spatial resolution improves performance, and a fraction of input cells is sufficient to cover important objects, resembling a saliency mechanism.(TbD+hres) have noted increasing the spatial resolution definitely helps in achieving better performance. Finally, through a visual inspection, we have observed that the fraction of input cells that we have experimented with (k=16 for 8x8 spatial tensor, and k=64 for 14x14 spatial tensor) is sufficient to cover all the important objects in the image, and thus the mechanism resembles more the saliency mechanism. It is worth noting, the hard-attention mechanism often selects a few cells that correspond to the object as this is sufficient to recognize the object’s properties such as size, material, color, type, and spatial location." } ], "images": [ "scigen/train/927.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results, in %, on CLEVR. SAN denotes the SAN [9] implementation of [34]. SAN* denotes the SAN implementation of [25]. Object RN** [55] and Stack-NMNs** [52] report the results only on the validation set, whereas others report on the test set. Overall performance of Stack-NMNs** [52] is measured with the “expert layout” (similar to N2NMN) yielding 96.6 and without it (93.0). DDRprog− [71], PG+EE (700k)− [70], TbD−, and TbD+hres− [50] are trained with a privileged state-description, while others are trained directly from images-questions-answers. TbD+hres [50] uses high-resolution (28x28) spatial tensor, while majority uses either 8x8 or 14x14. HAN+Sum/RN+ denotes a larger relational model, or a different hyper-parameters setup, than the model of [25]. HAN+RN++ denotes HAN+RN+ with larger input images with spatial dimensions 224x224 as opposed to 128x128, and larger image tensors with spatial dimension 14x14 as opposed to 8x8." ], "reasoning": "Increasing spatial resolution improves performance, and a fraction of input cells is sufficient to cover important objects, resembling a saliency mechanism.", "description": "(TbD+hres) have noted increasing the spatial resolution definitely helps in achieving better performance. Finally, through a visual inspection, we have observed that the fraction of input cells that we have experimented with (k=16 for 8x8 spatial tensor, and k=64 for 14x14 spatial tensor) is sufficient to cover all the important objects in the image, and thus the mechanism resembles more the saliency mechanism. It is worth noting, the hard-attention mechanism often selects a few cells that correspond to the object as this is sufficient to recognize the object’s properties such as size, material, color, type, and spatial location.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 4: Results, in %, on CLEVR. SAN denotes the SAN [9] implementation of [34]. SAN* denotes the SAN implementation of [25]. Object RN** [55] and Stack-NMNs** [52] report the results only on the validation set, whereas others report on the test set. Overall performance of Stack-NMNs** [52] is measured with the “expert layout” (similar to N2NMN) yielding 96.6 and without it (93.0). DDRprog− [71], PG+EE (700k)− [70], TbD−, and TbD+hres− [50] are trained with a privileged state-description, while others are trained directly from images-questions-answers. TbD+hres [50] uses high-resolution (28x28) spatial tensor, while majority uses either 8x8 or 14x14. HAN+Sum/RN+ denotes a larger relational model, or a different hyper-parameters setup, than the model of [25]. HAN+RN++ denotes HAN+RN+ with larger input images with spatial dimensions 224x224 as opposed to 128x128, and larger image tensors with spatial dimension 14x14 as opposed to 8x8.\nQuestion: Which model has the highest overall performance and what is its score?\n" }, { "role": "assistant", "content": "To find the model with the highest overall performance, look at the 'Overall' column and identify the highest score. The model with the highest score is HAN+RN++ (Ours) with a score of 98.8. So, the answer is {\"answer\": \"HAN+RN++ (Ours) with a score of 98.8\"}." } ], "images": [ "scigen/train/927.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results, in %, on CLEVR. SAN denotes the SAN [9] implementation of [34]. SAN* denotes the SAN implementation of [25]. Object RN** [55] and Stack-NMNs** [52] report the results only on the validation set, whereas others report on the test set. Overall performance of Stack-NMNs** [52] is measured with the “expert layout” (similar to N2NMN) yielding 96.6 and without it (93.0). DDRprog− [71], PG+EE (700k)− [70], TbD−, and TbD+hres− [50] are trained with a privileged state-description, while others are trained directly from images-questions-answers. TbD+hres [50] uses high-resolution (28x28) spatial tensor, while majority uses either 8x8 or 14x14. HAN+Sum/RN+ denotes a larger relational model, or a different hyper-parameters setup, than the model of [25]. HAN+RN++ denotes HAN+RN+ with larger input images with spatial dimensions 224x224 as opposed to 128x128, and larger image tensors with spatial dimension 14x14 as opposed to 8x8." ], "reasoning": "To find the model with the highest overall performance, look at the 'Overall' column and identify the highest score. The model with the highest score is HAN+RN++ (Ours) with a score of 98.8.", "question": "Which model has the highest overall performance and what is its score?", "answer": "So, the answer is {\"answer\": \"HAN+RN++ (Ours) with a score of 98.8\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The TbD+hres model has a higher overall performance than the RN model. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe TbD+hres model has a higher overall performance than the RN model.\n" }, { "role": "assistant", "content": "The TbD+hres model has an overall performance score of 99.1, while the RN model has an overall performance score of 95.5. Therefore, the TbD+hres model has a higher overall performance than the RN model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/927.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results, in %, on CLEVR. SAN denotes the SAN [9] implementation of [34]. SAN* denotes the SAN implementation of [25]. Object RN** [55] and Stack-NMNs** [52] report the results only on the validation set, whereas others report on the test set. Overall performance of Stack-NMNs** [52] is measured with the “expert layout” (similar to N2NMN) yielding 96.6 and without it (93.0). DDRprog− [71], PG+EE (700k)− [70], TbD−, and TbD+hres− [50] are trained with a privileged state-description, while others are trained directly from images-questions-answers. TbD+hres [50] uses high-resolution (28x28) spatial tensor, while majority uses either 8x8 or 14x14. HAN+Sum/RN+ denotes a larger relational model, or a different hyper-parameters setup, than the model of [25]. HAN+RN++ denotes HAN+RN+ with larger input images with spatial dimensions 224x224 as opposed to 128x128, and larger image tensors with spatial dimension 14x14 as opposed to 8x8." ], "reasoning": "The TbD+hres model has an overall performance score of 99.1, while the RN model has an overall performance score of 95.5. Therefore, the TbD+hres model has a higher overall performance than the RN model.", "statement": "The TbD+hres model has a higher overall performance than the RN model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Comparison between different number of attended cells (percentage of the whole input), and aggregation operation. We consider a simple summation, and non-local pairwise computations as the aggregation tool.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Hard attention can capture important parts of the image effectively, with performance loss being minimal even when attending to less than half of the units.We begin with the most basic hard attention architecture, which applies hard attention and then does sum pooling over the attended cells, followed by a small MLP. For each experiment, we take the top k cells, out of 100, according to our L2-norm criterion, where k ranges from 16 to 100 (with 100, there is no attention, and the whole image is summed). Considering that the hard attention selects only a subset of the input cells, we might expect that the algorithm would lose important information and be unable to recover. In fact, however, the performance is almost the same with less than half of the units attended. Even with just 16 units, the performance loss is less than 1%, suggesting that hard attention is quite capable of capturing the important parts of the image. The fact that hard attention can work is interesting itself, but it should be especially useful for models that devote significant processing to each attended cell. actually boosts performance over an analogous model without hard attention. Surprisingly, soft attention does not outperform basic sum pooling, even with careful implementation that outperforms the previously reported results with the same method on this dataset; in fact, it performs slightly worse. The non-local pairwise aggregation performs better than SAN on its own, although the best result includes hard attention. SAN’s Our implementation has 2 attention hops, 1024 dimensional multimodal embedding size, a fixed learning rate 0.0001, and ResNet-101. In these experiments we pool the attended representations by weighted average with the attention weights. We use 2 heads, with embedding size 512." } ], "images": [ "scigen/train/928.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison between different number of attended cells (percentage of the whole input), and aggregation operation. We consider a simple summation, and non-local pairwise computations as the aggregation tool." ], "reasoning": "Hard attention can capture important parts of the image effectively, with performance loss being minimal even when attending to less than half of the units.", "description": "We begin with the most basic hard attention architecture, which applies hard attention and then does sum pooling over the attended cells, followed by a small MLP. For each experiment, we take the top k cells, out of 100, according to our L2-norm criterion, where k ranges from 16 to 100 (with 100, there is no attention, and the whole image is summed). Considering that the hard attention selects only a subset of the input cells, we might expect that the algorithm would lose important information and be unable to recover. In fact, however, the performance is almost the same with less than half of the units attended. Even with just 16 units, the performance loss is less than 1%, suggesting that hard attention is quite capable of capturing the important parts of the image. The fact that hard attention can work is interesting itself, but it should be especially useful for models that devote significant processing to each attended cell. actually boosts performance over an analogous model without hard attention. Surprisingly, soft attention does not outperform basic sum pooling, even with careful implementation that outperforms the previously reported results with the same method on this dataset; in fact, it performs slightly worse. The non-local pairwise aggregation performs better than SAN on its own, although the best result includes hard attention. SAN’s Our implementation has 2 attention hops, 1024 dimensional multimodal embedding size, a fixed learning rate 0.0001, and ResNet-101. In these experiments we pool the attended representations by weighted average with the attention weights. We use 2 heads, with embedding size 512.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Comparison between different number of attended cells (percentage of the whole input), and aggregation operation. We consider a simple summation, and non-local pairwise computations as the aggregation tool.\nQuestion: What is the overall performance of the HAN+sum model when 32% of the cells are attended?\nSolve the above question based on the table information. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the overall performance of the HAN+sum model when 32% of the cells are attended, look at the row corresponding to HAN+sum with 32% in the 'Percentage of cells' column and find the value in the 'Overall' column. So, the answer is {\"answer\": \"27.43\"}." } ], "images": [ "scigen/train/928.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison between different number of attended cells (percentage of the whole input), and aggregation operation. We consider a simple summation, and non-local pairwise computations as the aggregation tool." ], "reasoning": "To find the overall performance of the HAN+sum model when 32% of the cells are attended, look at the row corresponding to HAN+sum with 32% in the 'Percentage of cells' column and find the value in the 'Overall' column.", "question": "What is the overall performance of the HAN+sum model when 32% of the cells are attended?", "answer": "So, the answer is {\"answer\": \"27.43\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe GVQA model has the highest overall performance.\n" }, { "role": "assistant", "content": "To verify if the GVQA model has the highest overall performance, compare the 'Overall' performance values of all models listed in the table. The GVQA model has an overall performance of 31.30, which is higher than any other model listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/928.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison between different number of attended cells (percentage of the whole input), and aggregation operation. We consider a simple summation, and non-local pairwise computations as the aggregation tool." ], "reasoning": "To verify if the GVQA model has the highest overall performance, compare the 'Overall' performance values of all models listed in the table. The GVQA model has an overall performance of 31.30, which is higher than any other model listed.", "statement": "The GVQA model has the highest overall performance.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Comparison between different adaptive hard-attention techniques with average number of attended parts, and aggregation operation. We consider a simple summation, and the non-local pairwise aggregation. Since AdaHAN adaptively selects relevant features, based on the fixed threshold 1w∗h, we report here the average number of attended parts.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Adaptive mechanisms use fewer cells on average and can outperform non-adaptive models, suggesting potential for future improvements.Thus far, our experiments have dealt with networks that have a fixed threshold for all images. However, some images and questions may require reasoning about more entities than others. Therefore, we explore a simple adaptive method, where the network chooses how many cells to attend to for each image. We can see that on average, the adaptive mechanism uses surprisingly few cells: 25.66 out of 100 when sum pooling is used, and 32.63 whenever the non-local pairwise aggregation mechanism is used. For sum pooling, this is on-par with a non-adaptive network that uses more cells on average (HAN+sum 32); for the non-local pairwise aggregation mechanism, just 32.63 cells are enough to outperform our best non-adaptive model, which uses roughly 50% more cells. This shows that even very simple methods of adapting hard attention to the image and the question can lead to both computation and performance gains, suggesting that more sophisticated methods will be an important direction for future work." } ], "images": [ "scigen/train/929.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison between different adaptive hard-attention techniques with average number of attended parts, and aggregation operation. We consider a simple summation, and the non-local pairwise aggregation. Since AdaHAN adaptively selects relevant features, based on the fixed threshold 1w∗h, we report here the average number of attended parts." ], "reasoning": "Adaptive mechanisms use fewer cells on average and can outperform non-adaptive models, suggesting potential for future improvements.", "description": "Thus far, our experiments have dealt with networks that have a fixed threshold for all images. However, some images and questions may require reasoning about more entities than others. Therefore, we explore a simple adaptive method, where the network chooses how many cells to attend to for each image. We can see that on average, the adaptive mechanism uses surprisingly few cells: 25.66 out of 100 when sum pooling is used, and 32.63 whenever the non-local pairwise aggregation mechanism is used. For sum pooling, this is on-par with a non-adaptive network that uses more cells on average (HAN+sum 32); for the non-local pairwise aggregation mechanism, just 32.63 cells are enough to outperform our best non-adaptive model, which uses roughly 50% more cells. This shows that even very simple methods of adapting hard attention to the image and the question can lead to both computation and performance gains, suggesting that more sophisticated methods will be an important direction for future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Comparison between different adaptive hard-attention techniques with average number of attended parts, and aggregation operation. We consider a simple summation, and the non-local pairwise aggregation. Since AdaHAN adaptively selects relevant features, based on the fixed threshold 1w∗h, we report here the average number of attended parts.\nQuestion: How many cells does the AdaHAN+pairwise mechanism use on average?\nGive a detailed response to the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the AdaHAN+pairwise mechanism uses 32.63% of cells on average. So, the answer is {\"answer\": \"32.63\"}" } ], "images": [ "scigen/train/929.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison between different adaptive hard-attention techniques with average number of attended parts, and aggregation operation. We consider a simple summation, and the non-local pairwise aggregation. Since AdaHAN adaptively selects relevant features, based on the fixed threshold 1w∗h, we report here the average number of attended parts." ], "reasoning": "The table shows that the AdaHAN+pairwise mechanism uses 32.63% of cells on average.", "question": "How many cells does the AdaHAN+pairwise mechanism use on average?", "answer": "So, the answer is {\"answer\": \"32.63\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The HAN+sum mechanism uses fewer cells than the AdaHAN+sum mechanism., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe HAN+sum mechanism uses fewer cells than the AdaHAN+sum mechanism.\n" }, { "role": "assistant", "content": "The table shows that the HAN+sum mechanism uses 32% of cells, while the AdaHAN+sum mechanism uses 25.66% of cells. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/929.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison between different adaptive hard-attention techniques with average number of attended parts, and aggregation operation. We consider a simple summation, and the non-local pairwise aggregation. Since AdaHAN adaptively selects relevant features, based on the fixed threshold 1w∗h, we report here the average number of attended parts." ], "reasoning": "The table shows that the HAN+sum mechanism uses 32% of cells, while the AdaHAN+sum mechanism uses 25.66% of cells.", "statement": "The HAN+sum mechanism uses fewer cells than the AdaHAN+sum mechanism.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Comparison between different number of the attended cells as the percentage of the whole input. The results are reported on VQA-CP v2. The second column denotes the percentage of the attended input. The third column denotes number of layers of the MLP (Equations 2 and 3).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Adding layers on top of pretrained embeddings improves performance by about 1%, indicating the importance of aligning information before combining question and image representations.In this section, we briefly analyze an important architectural choice: the number of layers used on top of the pretrained embeddings. That is, before the question and image representations are combined, we perform a small amount of processing to “align” the information, so that the embedding can easily tell the relevance of the visual information to the question. We consistently see a drop of about 1% without the layers, suggesting that deciding which cells to attend to requires different information than the classification-tuned ResNet is designed to provide." } ], "images": [ "scigen/train/930.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison between different number of the attended cells as the percentage of the whole input. The results are reported on VQA-CP v2. The second column denotes the percentage of the attended input. The third column denotes number of layers of the MLP (Equations 2 and 3)." ], "reasoning": "Adding layers on top of pretrained embeddings improves performance by about 1%, indicating the importance of aligning information before combining question and image representations.", "description": "In this section, we briefly analyze an important architectural choice: the number of layers used on top of the pretrained embeddings. That is, before the question and image representations are combined, we perform a small amount of processing to “align” the information, so that the embedding can easily tell the relevance of the visual information to the question. We consistently see a drop of about 1% without the layers, suggesting that deciding which cells to attend to requires different information than the classification-tuned ResNet is designed to provide.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the overall performance when 75% of cells are used with 2 layers?\nSolve the above question based on the table titled 'Table 3: Comparison between different number of the attended cells as the percentage of the whole input. The results are reported on VQA-CP v2. The second column denotes the percentage of the attended input. The third column denotes number of layers of the MLP (Equations 2 and 3).'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the overall performance when 75% of cells are used with 2 layers, look at the row where 'Percentage of cells' is 75% and 'Number of layers' is 2. The 'Overall' column in this row shows the performance. So, the answer is {\"answer\": \"27.94\"}" } ], "images": [ "scigen/train/930.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison between different number of the attended cells as the percentage of the whole input. The results are reported on VQA-CP v2. The second column denotes the percentage of the attended input. The third column denotes number of layers of the MLP (Equations 2 and 3)." ], "reasoning": "To find the overall performance when 75% of cells are used with 2 layers, look at the row where 'Percentage of cells' is 75% and 'Number of layers' is 2. The 'Overall' column in this row shows the performance.", "question": "What is the overall performance when 75% of cells are used with 2 layers?", "answer": "So, the answer is {\"answer\": \"27.94\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nUsing 50% of cells with 2 layers results in higher overall performance than using 75% of cells with 0 layers.\n" }, { "role": "assistant", "content": "Compare the 'Overall' performance for 50% of cells with 2 layers (27.43) and 75% of cells with 0 layers (26.82). The performance is higher for 50% of cells with 2 layers. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/930.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison between different number of the attended cells as the percentage of the whole input. The results are reported on VQA-CP v2. The second column denotes the percentage of the attended input. The third column denotes number of layers of the MLP (Equations 2 and 3)." ], "reasoning": "Compare the 'Overall' performance for 50% of cells with 2 layers (27.43) and 75% of cells with 0 layers (26.82). The performance is higher for 50% of cells with 2 layers.", "statement": "Using 50% of cells with 2 layers results in higher overall performance than using 75% of cells with 0 layers.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'TABLE V: Performance of the CRF model for PoS tagging with two feature sets'.\n" }, { "role": "assistant", "content": "Incorporating word embedding features improves the accuracy of the CRF model from 87.62% to 88.97%, but the CRF model's accuracy is lower than LSTMs when word embedding features are used.The table indicates that incorporating word embedding features helps to improves the accuracy of the CRF model 1.35% from 87.62% to 88.97%. The CRF model outperformed LSTMs when we do not use word embedding features. However, its accuracy is lower than that of LSTMs when word embedding features are incorporated." } ], "images": [ "scigen/train/931.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Performance of the CRF model for PoS tagging with two feature sets" ], "reasoning": "Incorporating word embedding features improves the accuracy of the CRF model from 87.62% to 88.97%, but the CRF model's accuracy is lower than LSTMs when word embedding features are used.", "description": "The table indicates that incorporating word embedding features helps to improves the accuracy of the CRF model 1.35% from 87.62% to 88.97%. The CRF model outperformed LSTMs when we do not use word embedding features. However, its accuracy is lower than that of LSTMs when word embedding features are incorporated.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'TABLE V: Performance of the CRF model for PoS tagging with two feature sets', and sequentially solve the question, writing out each step of your reasoning process. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the improvement in test accuracy for the CRF model when word embedding features are added for n≤20?\n" }, { "role": "assistant", "content": "The test accuracy for the CRF model without word embeddings for n≤20 is 87.62%, and with word embeddings, it is 88.97%. The improvement is 88.97% - 87.62%. So, the answer is {\"answer\": \"1.35%\"}." } ], "images": [ "scigen/train/931.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Performance of the CRF model for PoS tagging with two feature sets" ], "reasoning": "The test accuracy for the CRF model without word embeddings for n≤20 is 87.62%, and with word embeddings, it is 88.97%. The improvement is 88.97% - 87.62%.", "question": "What is the improvement in test accuracy for the CRF model when word embedding features are added for n≤20?", "answer": "So, the answer is {\"answer\": \"1.35%\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe CRF model's training accuracy for n≤25 is higher when word embedding features are incorporated.\n" }, { "role": "assistant", "content": "The training accuracy for the CRF model for n≤25 without word embeddings is 91.56%, and with word embeddings, it is 92.29%. The accuracy is higher with word embeddings. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/931.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Performance of the CRF model for PoS tagging with two feature sets" ], "reasoning": "The training accuracy for the CRF model for n≤25 without word embeddings is 91.56%, and with word embeddings, it is 92.29%. The accuracy is higher with word embeddings.", "statement": "The CRF model's training accuracy for n≤25 is higher when word embedding features are incorporated.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'TABLE III: Performance of LSTMs for PoS tagging using word identities and word shapes', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The LSTM tagger achieves the highest test accuracy of 85.98% with 200 hidden units, indicating that more hidden units lead to better performance.We train different LSTMs with varying number hidden units in the range from 32 to 200. We see that the larger number of hidden units is, the better result the tagger can achieve on the test set. The LSTMs tagger achieves 85.98% of accuracy on the test set when the network has 200 hidden units." } ], "images": [ "scigen/train/932.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Performance of LSTMs for PoS tagging using word identities and word shapes" ], "reasoning": "The LSTM tagger achieves the highest test accuracy of 85.98% with 200 hidden units, indicating that more hidden units lead to better performance.", "description": "We train different LSTMs with varying number hidden units in the range from 32 to 200. We see that the larger number of hidden units is, the better result the tagger can achieve on the test set. The LSTMs tagger achieves 85.98% of accuracy on the test set when the network has 200 hidden units.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: TABLE III: Performance of LSTMs for PoS tagging using word identities and word shapes\nQuestion: What is the test accuracy for the LSTM tagger with 150 hidden units on the test set with n≤20?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the test accuracy for the LSTM tagger with 150 hidden units on the test set with n≤20, locate the row with 150 hidden units and the column for Test Acc. n≤20. So, the answer is {\"answer\": \"85.84\"}." } ], "images": [ "scigen/train/932.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Performance of LSTMs for PoS tagging using word identities and word shapes" ], "reasoning": "To find the test accuracy for the LSTM tagger with 150 hidden units on the test set with n≤20, locate the row with 150 hidden units and the column for Test Acc. n≤20.", "question": "What is the test accuracy for the LSTM tagger with 150 hidden units on the test set with n≤20?", "answer": "So, the answer is {\"answer\": \"85.84\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe LSTM tagger with 64 hidden units achieves a higher test accuracy on the test set with n≤25 than the one with 100 hidden units.\n" }, { "role": "assistant", "content": "Compare the test accuracy for 64 hidden units and 100 hidden units in the Test Acc. n≤25 column. The accuracy for 64 hidden units is 85.93%, and for 100 hidden units, it is 85.53%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/932.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Performance of LSTMs for PoS tagging using word identities and word shapes" ], "reasoning": "Compare the test accuracy for 64 hidden units and 100 hidden units in the Test Acc. n≤25 column. The accuracy for 64 hidden units is 85.93%, and for 100 hidden units, it is 85.53%.", "statement": "The LSTM tagger with 64 hidden units achieves a higher test accuracy on the test set with n≤25 than the one with 100 hidden units.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'TABLE X: Performances of NER systems at VLSP 2016'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "In VLSP 2016, various systems for Vietnamese NER were proposed, primarily using CRF and maximum entropy models, which depend heavily on feature engineering. Despite relying only on word features, the models studied showed competitive performance.In VLSP 2016 workshop, several different systems have been proposed for Vietnamese NER. That system used many hand-crafted features to improve the performance of MEMM. Most approaches in VLSP 2016 used the CRF and maximum entropy models, whose performance is heavily dependent on feature engineering. We observe that although the models studied in this work only rely on word features, their performance is very competitive." } ], "images": [ "scigen/train/933.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE X: Performances of NER systems at VLSP 2016" ], "reasoning": "In VLSP 2016, various systems for Vietnamese NER were proposed, primarily using CRF and maximum entropy models, which depend heavily on feature engineering. Despite relying only on word features, the models studied showed competitive performance.", "description": "In VLSP 2016 workshop, several different systems have been proposed for Vietnamese NER. That system used many hand-crafted features to improve the performance of MEMM. Most approaches in VLSP 2016 used the CRF and maximum entropy models, whose performance is heavily dependent on feature engineering. We observe that although the models studied in this work only rely on word features, their performance is very competitive.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: TABLE X: Performances of NER systems at VLSP 2016\nQuestion: Which team achieved the highest performance using the maximum entropy model?\nSolve the above question based on the table information. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table lists the performance of different teams using various models. Le-Hong used the maximum entropy (ME) model and achieved a performance of 88.78, which is the highest among the ME models listed. So, the answer is {\"answer\": \"Le-Hong\"}." } ], "images": [ "scigen/train/933.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE X: Performances of NER systems at VLSP 2016" ], "reasoning": "The table lists the performance of different teams using various models. Le-Hong used the maximum entropy (ME) model and achieved a performance of 88.78, which is the highest among the ME models listed.", "question": "Which team achieved the highest performance using the maximum entropy model?", "answer": "So, the answer is {\"answer\": \"Le-Hong\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nNguyen et al. achieved the highest performance using the CRF model.\n" }, { "role": "assistant", "content": "The table shows that Nguyen et al. did not use the CRF model. The highest performance using the CRF model was achieved by the [Anonymous] team with a performance of 86.62. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/933.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE X: Performances of NER systems at VLSP 2016" ], "reasoning": "The table shows that Nguyen et al. did not use the CRF model. The highest performance using the CRF model was achieved by the [Anonymous] team with a performance of 86.62.", "statement": "Nguyen et al. achieved the highest performance using the CRF model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Experiments with WMT 2017 data, correcting a phrase-base system.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Our dual-source model with soft and hard attention placed second in the WMT2017 APE shared task, right after the FBK team's similar model.During the WMT2017 APE shared task we submitted a dual-source model with soft and hard attention which placed second right after a very similar dual-source model by the FBK team. FBK and WMT17:AMU (ours). An ensemble of four identical models trained with different random initializations strongly improves over last year’s best models on all indicators." } ], "images": [ "scigen/train/934.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Experiments with WMT 2017 data, correcting a phrase-base system." ], "reasoning": "Our dual-source model with soft and hard attention placed second in the WMT2017 APE shared task, right after the FBK team's similar model.", "description": "During the WMT2017 APE shared task we submitted a dual-source model with soft and hard attention which placed second right after a very similar dual-source model by the FBK team. FBK and WMT17:AMU (ours). An ensemble of four identical models trained with different random initializations strongly improves over last year’s best models on all indicators.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: Table 1: Experiments with WMT 2017 data, correcting a phrase-base system.\nQuestion: Which model had the highest test 2017 BLEU score?\n" }, { "role": "assistant", "content": "By examining the test 2017 BLEU scores in the table, the Transformer-base x4 model has the highest score of 70.46. So, the answer is {\"answer\": \"Transformer-base x4\"}" } ], "images": [ "scigen/train/934.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Experiments with WMT 2017 data, correcting a phrase-base system." ], "reasoning": "By examining the test 2017 BLEU scores in the table, the Transformer-base x4 model has the highest score of 70.46.", "question": "Which model had the highest test 2017 BLEU score?", "answer": "So, the answer is {\"answer\": \"Transformer-base x4\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe WMT17: AMU Primary model had a higher test 2017 BLEU score than the Baseline model.\n" }, { "role": "assistant", "content": "The table shows that the WMT17: AMU Primary model had a test 2017 BLEU score of 69.50, while the Baseline model had a score of 68.48. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/934.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Experiments with WMT 2017 data, correcting a phrase-base system." ], "reasoning": "The table shows that the WMT17: AMU Primary model had a test 2017 BLEU score of 69.50, while the Baseline model had a score of 68.48. Therefore, the statement is supported.", "statement": "The WMT17: AMU Primary model had a higher test 2017 BLEU score than the Baseline model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Experiments with WMT 2017+eSCAPE data for SMT system.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "We experimented with different subsets of the eSCAPE corpus for the IT domain and found that adding eSCAPE data was generally helpful, but no clear winner emerged. We used all experimental models in a 4x ensemble for potential benefits.So far, we only trained on data that was available during WMT2017. This year, the task organizers added a new large corpus created for automatic post-editing across many domains. We experimented with domain selection algorithms for this corpus and tried to find subsets that would be better suited to the given IT domain. We trained an 5-gram language model on a 10M words randomly sampled subset of the German IT training data and a similarly size language model on the eSCAPE data. Next we applied cross-entropy filtering Moore and Lewis We sorted eSCAPE by these scores and selected different sizes of subsets. Smaller subsets should be more in-domain. We experimented with 1M, 2M, 4M and all sentences (nearly 8M). Adding eSCAPE to the training data was generally helpful, but we did not see a clear winner across subsets and test sets. In the end we use all the experimental models as components of a 4x ensemble. The different training sets might as well serve as additional randomization factors potentially beneficial for ensembling." } ], "images": [ "scigen/train/935.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Experiments with WMT 2017+eSCAPE data for SMT system." ], "reasoning": "We experimented with different subsets of the eSCAPE corpus for the IT domain and found that adding eSCAPE data was generally helpful, but no clear winner emerged. We used all experimental models in a 4x ensemble for potential benefits.", "description": "So far, we only trained on data that was available during WMT2017. This year, the task organizers added a new large corpus created for automatic post-editing across many domains. We experimented with domain selection algorithms for this corpus and tried to find subsets that would be better suited to the given IT domain. We trained an 5-gram language model on a 10M words randomly sampled subset of the German IT training data and a similarly size language model on the eSCAPE data. Next we applied cross-entropy filtering Moore and Lewis We sorted eSCAPE by these scores and selected different sizes of subsets. Smaller subsets should be more in-domain. We experimented with 1M, 2M, 4M and all sentences (nearly 8M). Adding eSCAPE to the training data was generally helpful, but we did not see a clear winner across subsets and test sets. In the end we use all the experimental models as components of a 4x ensemble. The different training sets might as well serve as additional randomization factors potentially beneficial for ensembling.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: Experiments with WMT 2017+eSCAPE data for SMT system.'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieved the highest BLEU score on the test 2017 dataset?\n" }, { "role": "assistant", "content": "To find the model with the highest BLEU score on the test 2017 dataset, we look at the 'test 2017 BLEU' column and identify the highest value, which is 72.84 achieved by the Transformer x4 (all above) model. So, the answer is {\"answer\": \"Transformer x4 (all above)\"}" } ], "images": [ "scigen/train/935.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Experiments with WMT 2017+eSCAPE data for SMT system." ], "reasoning": "To find the model with the highest BLEU score on the test 2017 dataset, we look at the 'test 2017 BLEU' column and identify the highest value, which is 72.84 achieved by the Transformer x4 (all above) model.", "question": "Which model achieved the highest BLEU score on the test 2017 dataset?", "answer": "So, the answer is {\"answer\": \"Transformer x4 (all above)\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Transformer 1M model had the lowest TER score on the dev 2016 dataset.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the TER scores in the 'dev 2016 TER' column. The lowest TER score is 17.31, achieved by the Transformer x4 (all above) model, not the Transformer 1M model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/935.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Experiments with WMT 2017+eSCAPE data for SMT system." ], "reasoning": "To verify this statement, we compare the TER scores in the 'dev 2016 TER' column. The lowest TER score is 17.31, achieved by the Transformer x4 (all above) model, not the Transformer 1M model.", "statement": "The Transformer 1M model had the lowest TER score on the dev 2016 dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is '(a) PBSMT sub-task'.\n" }, { "role": "assistant", "content": "The MS-UEdin system achieved the best performance with the lowest TER of 18.00 and the highest BLEU score of 72.52, significantly outperforming the baseline and other systems.For full results with information concerning statistical significance see the full shared task description Chatterjee et al. As expected, improvements are quite significant for the SMT-based system, and much smaller for the NMT-based system. Our submissions to the PBSMT sub-task strongly outperforms all submissions by other teams in terms of TER and BLEU and established the new state-of-the-art for the field. The improvements over the PBSMT baseline approach impressive 10 BLEU points." } ], "images": [ "scigen/train/936.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) PBSMT sub-task" ], "reasoning": "The MS-UEdin system achieved the best performance with the lowest TER of 18.00 and the highest BLEU score of 72.52, significantly outperforming the baseline and other systems.", "description": "For full results with information concerning statistical significance see the full shared task description Chatterjee et al. As expected, improvements are quite significant for the SMT-based system, and much smaller for the NMT-based system. Our submissions to the PBSMT sub-task strongly outperforms all submissions by other teams in terms of TER and BLEU and established the new state-of-the-art for the field. The improvements over the PBSMT baseline approach impressive 10 BLEU points.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of '(a) PBSMT sub-task', answer the following question. Give detailed solution consisting of each step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich system achieved the highest BLEU score and what was the score?\n" }, { "role": "assistant", "content": "By examining the BLEU scores in the table, the MS-UEdin system has the highest BLEU score of 72.52. So, the answer is {\"answer\": \"MS-UEdin with 72.52\"}." } ], "images": [ "scigen/train/936.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) PBSMT sub-task" ], "reasoning": "By examining the BLEU scores in the table, the MS-UEdin system has the highest BLEU score of 72.52.", "question": "Which system achieved the highest BLEU score and what was the score?", "answer": "So, the answer is {\"answer\": \"MS-UEdin with 72.52\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The MS-UEdin system achieved a TER score lower than 19. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe MS-UEdin system achieved a TER score lower than 19.\n" }, { "role": "assistant", "content": "The table shows that the MS-UEdin system has a TER score of 18.00, which is indeed lower than 19. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/936.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) PBSMT sub-task" ], "reasoning": "The table shows that the MS-UEdin system has a TER score of 18.00, which is indeed lower than 19.", "statement": "The MS-UEdin system achieved a TER score lower than 19.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled '(b) NMT sub-task', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The PBSMT sub-task submission outperformed all other teams in terms of TER and BLEU, establishing a new state-of-the-art with an impressive 10 BLEU points improvement over the baseline.For full results with information concerning statistical significance see the full shared task description Chatterjee et al. As expected, improvements are quite significant for the SMT-based system, and much smaller for the NMT-based system. Our submissions to the PBSMT sub-task strongly outperforms all submissions by other teams in terms of TER and BLEU and established the new state-of-the-art for the field. The improvements over the PBSMT baseline approach impressive 10 BLEU points." } ], "images": [ "scigen/train/937.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) NMT sub-task" ], "reasoning": "The PBSMT sub-task submission outperformed all other teams in terms of TER and BLEU, establishing a new state-of-the-art with an impressive 10 BLEU points improvement over the baseline.", "description": "For full results with information concerning statistical significance see the full shared task description Chatterjee et al. As expected, improvements are quite significant for the SMT-based system, and much smaller for the NMT-based system. Our submissions to the PBSMT sub-task strongly outperforms all submissions by other teams in terms of TER and BLEU and established the new state-of-the-art for the field. The improvements over the PBSMT baseline approach impressive 10 BLEU points.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: (b) NMT sub-task\nQuestion: Which system had the highest BLEU score and what was it?\nSolve the above question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the system with the highest BLEU score, look at the BLEU column and identify the highest value. The highest BLEU score is 75.53, achieved by the FBK system. So, the answer is {\"answer\": \"FBK with 75.53\"}" } ], "images": [ "scigen/train/937.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) NMT sub-task" ], "reasoning": "To find the system with the highest BLEU score, look at the BLEU column and identify the highest value. The highest BLEU score is 75.53, achieved by the FBK system.", "question": "Which system had the highest BLEU score and what was it?", "answer": "So, the answer is {\"answer\": \"FBK with 75.53\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe MS-UEdin system had the lowest TER score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the TER scores of all systems. The lowest TER score is 16.46, achieved by the FBK system, not the MS-UEdin system. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/937.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) NMT sub-task" ], "reasoning": "To verify this statement, compare the TER scores of all systems. The lowest TER score is 16.46, achieved by the FBK system, not the MS-UEdin system.", "statement": "The MS-UEdin system had the lowest TER score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: Results on IMDB Movie Review Dataset'.\n" }, { "role": "assistant", "content": "The new composite document vector method achieves the highest accuracy, surpassing both BOW and individual document vectors.The main contributor for improvement in results is our new document vector which overcomes the weaknesses of BOW and document vectors taken separately." } ], "images": [ "scigen/train/938.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on IMDB Movie Review Dataset" ], "reasoning": "The new composite document vector method achieves the highest accuracy, surpassing both BOW and individual document vectors.", "description": "The main contributor for improvement in results is our new document vector which overcomes the weaknesses of BOW and document vectors taken separately.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 5: Results on IMDB Movie Review Dataset' to sequentially solve the question presented below. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich method achieved the highest accuracy and what was the accuracy percentage?\n" }, { "role": "assistant", "content": "By examining the table, we see that the Composite Document Vector method has the highest accuracy at 93.91%. So, the answer is {\"answer\": \"Composite Document Vector, 93.91%\"}." } ], "images": [ "scigen/train/938.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on IMDB Movie Review Dataset" ], "reasoning": "By examining the table, we see that the Composite Document Vector method has the highest accuracy at 93.91%.", "question": "Which method achieved the highest accuracy and what was the accuracy percentage?", "answer": "So, the answer is {\"answer\": \"Composite Document Vector, 93.91%\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Paragraph Vector method achieved an accuracy higher than 90%.\n" }, { "role": "assistant", "content": "The table shows that the Paragraph Vector method achieved an accuracy of 92.58%, which is higher than 90%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/938.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on IMDB Movie Review Dataset" ], "reasoning": "The table shows that the Paragraph Vector method achieved an accuracy of 92.58%, which is higher than 90%.", "statement": "The Paragraph Vector method achieved an accuracy higher than 90%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Comparison of accuracies on 3 Datasets (IMDB, Amazon Electronics Review and Hindi Movie Reviews (IITB)) for various types of document composition models. The state of the art for these tasks are: IMDB: 92.58% [Le and Mikolov2014]; Amazon:85.90% [Dredze et al.2008], Hindi:79.0% [Bakliwal et al.2012].'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The weighted average vector method improves sentiment analysis accuracy for Hindi from 80.2% to 85.9%, and further pruning high-frequency stop words increases accuracy by 0.45%.A surprising event in Information Theory has higher information content than an expected event (Shanon, 1948). The same happens when we give weights to word vectors. We give more weight to events which evoke surprise and less weight to events which are expected. In this work we present an early experiment on the possibilities of distributional semantic models (word vectors) for low-resource, highly inflected languages such as Hindi. What is interesting is that our word vector averaging method along with tf-idf results in improvements of accuracy compared to existing state-of-the art methods for sentiment analysis in Hindi (from 80.2% to 90.3% on IITB Movie Review Dataset). The size of the corpus is also small to learn paragraph vectors. Thus, our model overcomes these weaknesses with a better document representation. We observe that pruning high-frequency stop words improves the accuracy by around 0.45%. This is most likely because such words tend to occur in most of the documents and don’t contribute to sentiment. For example, the word \\dnEPSm(Film) occurs in 139/252 documents in Movie Reviews(55.16%) and has little effect on sentiment. Similarly words such as \\dnEs\\388wAT\\0(Siddharth) occur in 2/252 documents in Movie Reviews(0.79%). These words don’t provide much information." } ], "images": [ "scigen/train/939.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of accuracies on 3 Datasets (IMDB, Amazon Electronics Review and Hindi Movie Reviews (IITB)) for various types of document composition models. The state of the art for these tasks are: IMDB: 92.58% [Le and Mikolov2014]; Amazon:85.90% [Dredze et al.2008], Hindi:79.0% [Bakliwal et al.2012]." ], "reasoning": "The weighted average vector method improves sentiment analysis accuracy for Hindi from 80.2% to 85.9%, and further pruning high-frequency stop words increases accuracy by 0.45%.", "description": "A surprising event in Information Theory has higher information content than an expected event (Shanon, 1948). The same happens when we give weights to word vectors. We give more weight to events which evoke surprise and less weight to events which are expected. In this work we present an early experiment on the possibilities of distributional semantic models (word vectors) for low-resource, highly inflected languages such as Hindi. What is interesting is that our word vector averaging method along with tf-idf results in improvements of accuracy compared to existing state-of-the art methods for sentiment analysis in Hindi (from 80.2% to 90.3% on IITB Movie Review Dataset). The size of the corpus is also small to learn paragraph vectors. Thus, our model overcomes these weaknesses with a better document representation. We observe that pruning high-frequency stop words improves the accuracy by around 0.45%. This is most likely because such words tend to occur in most of the documents and don’t contribute to sentiment. For example, the word \\dnEPSm(Film) occurs in 139/252 documents in Movie Reviews(55.16%) and has little effect on sentiment. Similarly words such as \\dnEs\\388wAT\\0(Siddharth) occur in 2/252 documents in Movie Reviews(0.79%). These words don’t provide much information.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Comparison of accuracies on 3 Datasets (IMDB, Amazon Electronics Review and Hindi Movie Reviews (IITB)) for various types of document composition models. The state of the art for these tasks are: IMDB: 92.58% [Le and Mikolov2014]; Amazon:85.90% [Dredze et al.2008], Hindi:79.0% [Bakliwal et al.2012].', solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the accuracy improvement for sentiment analysis in Hindi using the Composite Document Vector method compared to the RNNLM (Baseline) method?\n" }, { "role": "assistant", "content": "The accuracy for the RNNLM (Baseline) method is 78.84%, and for the Composite Document Vector method, it is 90.30%. The improvement is calculated as 90.30% - 78.84%. So, the answer is {\"answer\": \"11.46%\"}" } ], "images": [ "scigen/train/939.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of accuracies on 3 Datasets (IMDB, Amazon Electronics Review and Hindi Movie Reviews (IITB)) for various types of document composition models. The state of the art for these tasks are: IMDB: 92.58% [Le and Mikolov2014]; Amazon:85.90% [Dredze et al.2008], Hindi:79.0% [Bakliwal et al.2012]." ], "reasoning": "The accuracy for the RNNLM (Baseline) method is 78.84%, and for the Composite Document Vector method, it is 90.30%. The improvement is calculated as 90.30% - 78.84%.", "question": "What is the accuracy improvement for sentiment analysis in Hindi using the Composite Document Vector method compared to the RNNLM (Baseline) method?", "answer": "So, the answer is {\"answer\": \"11.46%\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Averaged Vector method achieves higher accuracy than the Paragraph Vector method for sentiment analysis in Hindi.\n" }, { "role": "assistant", "content": "The table shows that the Averaged Vector method has an accuracy of 79.62% for Hindi, while the Paragraph Vector method has an accuracy of 74.57%. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/939.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of accuracies on 3 Datasets (IMDB, Amazon Electronics Review and Hindi Movie Reviews (IITB)) for various types of document composition models. The state of the art for these tasks are: IMDB: 92.58% [Le and Mikolov2014]; Amazon:85.90% [Dredze et al.2008], Hindi:79.0% [Bakliwal et al.2012]." ], "reasoning": "The table shows that the Averaged Vector method has an accuracy of 79.62% for Hindi, while the Paragraph Vector method has an accuracy of 74.57%. Therefore, the statement is supported.", "statement": "The Averaged Vector method achieves higher accuracy than the Paragraph Vector method for sentiment analysis in Hindi.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Results of Vector Composition with different Operations'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The idf graded weighted average method achieves the highest accuracy in vector composition, outperforming both simple average and multiplication methods.We, therefore, adopt both simple and idf weighted average methods in our work. The advantage with addition is that, it doesnot increase the dimension of the vector and captures high level semantics with ease. They also use additive composition to reflect semantic dependencies. queen - king ≈ clearly show that vectors of Neural Language Model and Distributed Model when used with additive composition outperform those with multiplicative composition in Paraphrase Classification task. DM vectors outperform by nearly giving accuracy difference of 6%. They also perform very well on Phrase similarity tasks. We, therefore, propose graded weighting schema for better composition of vectors which is described below." } ], "images": [ "scigen/train/940.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of Vector Composition with different Operations" ], "reasoning": "The idf graded weighted average method achieves the highest accuracy in vector composition, outperforming both simple average and multiplication methods.", "description": "We, therefore, adopt both simple and idf weighted average methods in our work. The advantage with addition is that, it doesnot increase the dimension of the vector and captures high level semantics with ease. They also use additive composition to reflect semantic dependencies. queen - king ≈ clearly show that vectors of Neural Language Model and Distributed Model when used with additive composition outperform those with multiplicative composition in Paraphrase Classification task. DM vectors outperform by nearly giving accuracy difference of 6%. They also perform very well on Phrase similarity tasks. We, therefore, propose graded weighting schema for better composition of vectors which is described below.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 2: Results of Vector Composition with different Operations'. Solve the following question based on the table.\n\nQuestion: Which composition method achieves the highest accuracy according to the table?\nLet's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table lists the accuracy of different composition methods. The idf graded weighted average method has the highest accuracy of 89.56. So, the answer is {\"answer\": \"Idf Graded Weighted Average\"}." } ], "images": [ "scigen/train/940.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of Vector Composition with different Operations" ], "reasoning": "The table lists the accuracy of different composition methods. The idf graded weighted average method has the highest accuracy of 89.56.", "question": "Which composition method achieves the highest accuracy according to the table?", "answer": "So, the answer is {\"answer\": \"Idf Graded Weighted Average\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The average method achieves an accuracy of 88.42. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe average method achieves an accuracy of 88.42.\n" }, { "role": "assistant", "content": "The table shows that the average method has an accuracy of 88.42, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/940.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of Vector Composition with different Operations" ], "reasoning": "The table shows that the average method has an accuracy of 88.42, which matches the statement.", "statement": "The average method achieves an accuracy of 88.42.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: Accuracies on our newly released 700-Movie Review Dataset'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Dimensionality reduction techniques like PCA and ANOVA-F are used to reduce the number of features, but PCA may lose important features, leading to lower accuracy compared to ANOVA-F.Dimensionality Reduction is the process of reducing the number of random variables in such a way that the remaining variables effectively reproduce most of the variability of the dataset. The reason for using such techniques is because of the curse of dimensionality which is a phenomena that occurs in high-dimension but doesn’t occur in low-dimension. With ANOVA-F, we selected around 4k features but with PCA, this number was just 50. So, the low accuracy with PCA can be attributed to the fact that we may have lost some important features in low dimension. Also, PCA cannot work with size of dimension d >size of learning set. This sharp decrease in accuracy in both cases happens because ANOVA-F selects features with larger variance across group and thus reduces noise to a larger extent whereas PCA reduces angular variance which is not effective in this case due to the distribution of data points in high-dimensional space." } ], "images": [ "scigen/train/941.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracies on our newly released 700-Movie Review Dataset" ], "reasoning": "Dimensionality reduction techniques like PCA and ANOVA-F are used to reduce the number of features, but PCA may lose important features, leading to lower accuracy compared to ANOVA-F.", "description": "Dimensionality Reduction is the process of reducing the number of random variables in such a way that the remaining variables effectively reproduce most of the variability of the dataset. The reason for using such techniques is because of the curse of dimensionality which is a phenomena that occurs in high-dimension but doesn’t occur in low-dimension. With ANOVA-F, we selected around 4k features but with PCA, this number was just 50. So, the low accuracy with PCA can be attributed to the fact that we may have lost some important features in low dimension. Also, PCA cannot work with size of dimension d >size of learning set. This sharp decrease in accuracy in both cases happens because ANOVA-F selects features with larger variance across group and thus reduces noise to a larger extent whereas PCA reduces angular variance which is not effective in this case due to the distribution of data points in high-dimensional space.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Accuracies on our newly released 700-Movie Review Dataset', answer the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich feature selection method resulted in the highest accuracy for the Weighted Word Vector + tfidf method?\n" }, { "role": "assistant", "content": "By examining the table, the highest accuracy for the Weighted Word Vector + tfidf method is achieved with ANOVA-F, which has an accuracy of 90.37. So, the answer is {\"answer\": \"ANOVA-F\"}" } ], "images": [ "scigen/train/941.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracies on our newly released 700-Movie Review Dataset" ], "reasoning": "By examining the table, the highest accuracy for the Weighted Word Vector + tfidf method is achieved with ANOVA-F, which has an accuracy of 90.37.", "question": "Which feature selection method resulted in the highest accuracy for the Weighted Word Vector + tfidf method?", "answer": "So, the answer is {\"answer\": \"ANOVA-F\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nPCA resulted in higher accuracy than ANOVA-F for the Document Vector + tfidf method.\n" }, { "role": "assistant", "content": "The table shows that for the Document Vector + tfidf method, PCA resulted in an accuracy of 76.33, while ANOVA-F resulted in a higher accuracy of 88.07. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/941.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracies on our newly released 700-Movie Review Dataset" ], "reasoning": "The table shows that for the Document Vector + tfidf method, PCA resulted in an accuracy of 76.33, while ANOVA-F resulted in a higher accuracy of 88.07.", "statement": "PCA resulted in higher accuracy than ANOVA-F for the Document Vector + tfidf method.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 9: Accuracies for Product Review and Movie Review Datasets.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Removing stop-words slightly improves accuracy, but the major improvement is seen with the weighted averaging technique.We see that there is a slight improvement in accuracy on both datasets once we remove stop-words but the major breakthrough occurs once we used weighted averaging technique for construction of document vectors from word vectors." } ], "images": [ "scigen/train/942.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Accuracies for Product Review and Movie Review Datasets." ], "reasoning": "Removing stop-words slightly improves accuracy, but the major improvement is seen with the weighted averaging technique.", "description": "We see that there is a slight improvement in accuracy on both datasets once we remove stop-words but the major breakthrough occurs once we used weighted averaging technique for construction of document vectors from word vectors.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 9: Accuracies for Product Review and Movie Review Datasets.', solve this question and give detail solutions. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhat is the accuracy on dataset 1 when using the weighted averaging technique for constructing document vectors from word vectors?\n" }, { "role": "assistant", "content": "The table shows that the accuracy on dataset 1 using the weighted averaging technique (Weighted WordVector) is 89.71. So, the answer is {\"answer\": \"89.71\"}" } ], "images": [ "scigen/train/942.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Accuracies for Product Review and Movie Review Datasets." ], "reasoning": "The table shows that the accuracy on dataset 1 using the weighted averaging technique (Weighted WordVector) is 89.71.", "question": "What is the accuracy on dataset 1 when using the weighted averaging technique for constructing document vectors from word vectors?", "answer": "So, the answer is {\"answer\": \"89.71\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The accuracy on dataset 2 is higher when using WordVector Averaging compared to Weighted WordVector., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe accuracy on dataset 2 is higher when using WordVector Averaging compared to Weighted WordVector.\n" }, { "role": "assistant", "content": "The table shows that the accuracy on dataset 2 for WordVector Averaging is 79.62, while for Weighted WordVector it is 85.90. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/942.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Accuracies for Product Review and Movie Review Datasets." ], "reasoning": "The table shows that the accuracy on dataset 2 for WordVector Averaging is 79.62, while for Weighted WordVector it is 85.90. Therefore, the statement is refuted.", "statement": "The accuracy on dataset 2 is higher when using WordVector Averaging compared to Weighted WordVector.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: Accuracy (%) of our models and other methods from the literature. The majority class model predicts the most frequent class. SVM: [Dernoncourt et al.2016]. Graphical model: [Ji and Bilmes2006]. Naive Bayes: [Lendvai and Geertzen2007]. HMM: [Stolcke et al.2000]. Memory-based Learning: [Rotaru2002]. All five models use features derived from transcribed words, as well as previous predicted dialog acts except for Naive Bayes. The interlabeler agreement could be obtained only for SwDA. For the CNN and LSTM models, the presented results are the test set accuracy of the run with the highest accuracy on the validation set.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our model performs competitively without human-engineered features, but comparisons are difficult due to varying preprocessing and training details.Overall, our model shows competitive results, while requiring no human-engineered features. Rigorous comparisons are challenging to draw, as many important details such as text preprocessing and train/valid/test split may vary, and many studies fail to perform several runs despite the randomness in some parts of the training process, such as weight initialization." } ], "images": [ "scigen/train/943.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Accuracy (%) of our models and other methods from the literature. The majority class model predicts the most frequent class. SVM: [Dernoncourt et al.2016]. Graphical model: [Ji and Bilmes2006]. Naive Bayes: [Lendvai and Geertzen2007]. HMM: [Stolcke et al.2000]. Memory-based Learning: [Rotaru2002]. All five models use features derived from transcribed words, as well as previous predicted dialog acts except for Naive Bayes. The interlabeler agreement could be obtained only for SwDA. For the CNN and LSTM models, the presented results are the test set accuracy of the run with the highest accuracy on the validation set." ], "reasoning": "Our model performs competitively without human-engineered features, but comparisons are difficult due to varying preprocessing and training details.", "description": "Overall, our model shows competitive results, while requiring no human-engineered features. Rigorous comparisons are challenging to draw, as many important details such as text preprocessing and train/valid/test split may vary, and many studies fail to perform several runs despite the randomness in some parts of the training process, such as weight initialization.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 4: Accuracy (%) of our models and other methods from the literature. The majority class model predicts the most frequent class. SVM: [Dernoncourt et al.2016]. Graphical model: [Ji and Bilmes2006]. Naive Bayes: [Lendvai and Geertzen2007]. HMM: [Stolcke et al.2000]. Memory-based Learning: [Rotaru2002]. All five models use features derived from transcribed words, as well as previous predicted dialog acts except for Naive Bayes. The interlabeler agreement could be obtained only for SwDA. For the CNN and LSTM models, the presented results are the test set accuracy of the run with the highest accuracy on the validation set.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model has the highest accuracy on the DSTC 4 dataset?\n" }, { "role": "assistant", "content": "To find the model with the highest accuracy on the DSTC 4 dataset, compare the accuracy values in the DSTC 4 column. The highest value is 66.2, achieved by the LSTM model. So, the answer is {\"answer\": \"LSTM\"}." } ], "images": [ "scigen/train/943.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Accuracy (%) of our models and other methods from the literature. The majority class model predicts the most frequent class. SVM: [Dernoncourt et al.2016]. Graphical model: [Ji and Bilmes2006]. Naive Bayes: [Lendvai and Geertzen2007]. HMM: [Stolcke et al.2000]. Memory-based Learning: [Rotaru2002]. All five models use features derived from transcribed words, as well as previous predicted dialog acts except for Naive Bayes. The interlabeler agreement could be obtained only for SwDA. For the CNN and LSTM models, the presented results are the test set accuracy of the run with the highest accuracy on the validation set." ], "reasoning": "To find the model with the highest accuracy on the DSTC 4 dataset, compare the accuracy values in the DSTC 4 column. The highest value is 66.2, achieved by the LSTM model.", "question": "Which model has the highest accuracy on the DSTC 4 dataset?", "answer": "So, the answer is {\"answer\": \"LSTM\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe CNN model has the highest accuracy on the MRDA dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the accuracy values in the MRDA column. The CNN model has an accuracy of 84.6, which is not the highest as the Interlabeler agreement has an accuracy of 84.0. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/943.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Accuracy (%) of our models and other methods from the literature. The majority class model predicts the most frequent class. SVM: [Dernoncourt et al.2016]. Graphical model: [Ji and Bilmes2006]. Naive Bayes: [Lendvai and Geertzen2007]. HMM: [Stolcke et al.2000]. Memory-based Learning: [Rotaru2002]. All five models use features derived from transcribed words, as well as previous predicted dialog acts except for Naive Bayes. The interlabeler agreement could be obtained only for SwDA. For the CNN and LSTM models, the presented results are the test set accuracy of the run with the highest accuracy on the validation set." ], "reasoning": "To verify this statement, compare the accuracy values in the MRDA column. The CNN model has an accuracy of 84.6, which is not the highest as the Interlabeler agreement has an accuracy of 84.0.", "statement": "The CNN model has the highest accuracy on the MRDA dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: WER comparison on CHiME-4 single-channel track evaluation sets with adversarial examples (AdvEx) (ϵ=0.1).'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The proposed approach consistently improved performance across all noise types, achieving relative WER reductions of 5.7% on the et05_real set and 5.3% on the et05_simu set.We see that within a reasonable range (ϵ<0.25) the proposed approach brings consistent gain. Relative WER reductions obtained on et05_real and et05_simu sets were 5.7% and 5.3%, respectively. The proposed approach was able to bring consistent improvements for all types of noises, whether in simulated or real environments." } ], "images": [ "scigen/train/944.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: WER comparison on CHiME-4 single-channel track evaluation sets with adversarial examples (AdvEx) (ϵ=0.1)." ], "reasoning": "The proposed approach consistently improved performance across all noise types, achieving relative WER reductions of 5.7% on the et05_real set and 5.3% on the et05_simu set.", "description": "We see that within a reasonable range (ϵ<0.25) the proposed approach brings consistent gain. Relative WER reductions obtained on et05_real and et05_simu sets were 5.7% and 5.3%, respectively. The proposed approach was able to bring consistent improvements for all types of noises, whether in simulated or real environments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 2: WER comparison on CHiME-4 single-channel track evaluation sets with adversarial examples (AdvEx) (ϵ=0.1).', solve the following question step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the average WER reduction on the et05_real set when using the AdvEx system compared to the Baseline system?\n" }, { "role": "assistant", "content": "To find the average WER reduction on the et05_real set, subtract the average WER of the AdvEx system (28.82) from the average WER of the Baseline system (30.55). The reduction is 30.55 - 28.82 = 1.73. So, the answer is {\"answer\": \"1.73\"}." } ], "images": [ "scigen/train/944.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: WER comparison on CHiME-4 single-channel track evaluation sets with adversarial examples (AdvEx) (ϵ=0.1)." ], "reasoning": "To find the average WER reduction on the et05_real set, subtract the average WER of the AdvEx system (28.82) from the average WER of the Baseline system (30.55). The reduction is 30.55 - 28.82 = 1.73.", "question": "What is the average WER reduction on the et05_real set when using the AdvEx system compared to the Baseline system?", "answer": "So, the answer is {\"answer\": \"1.73\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe AdvEx system achieved a lower WER than the Baseline system on the et05_simu CAF noise type.\n" }, { "role": "assistant", "content": "The table shows that the WER for the AdvEx system on the et05_simu CAF noise type is 29.29, while the WER for the Baseline system is 30.69. Since 29.29 is lower than 30.69, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/944.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: WER comparison on CHiME-4 single-channel track evaluation sets with adversarial examples (AdvEx) (ϵ=0.1)." ], "reasoning": "The table shows that the WER for the AdvEx system on the et05_simu CAF noise type is 29.29, while the WER for the Baseline system is 30.69. Since 29.29 is lower than 30.69, the statement is supported.", "statement": "The AdvEx system achieved a lower WER than the Baseline system on the et05_simu CAF noise type.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: WER comparison on the Aurora-4 evaluation set with adversarial examples (AdvEx) (ϵ=0.3)'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The model trained with ϵ=0.3 achieved a 9.49% WER, a 14.1% relative improvement over the baseline, with the highest WER reduction of 18.6% on test set D.Based on the results, ϵ=0.3 is chosen as the best perturbation weight to train the Aurora-4 model. The model trained on WSJ0m serves as the baseline. With ϵ=0.3, the augmented data training achieves 9.49% WER averaged across the four test sets, a 14.1% relative improvement over the baseline. For the test set with the highest WER on the baseline system, D, in which both noise and channel distortion are present, the proposed method reduced the WER by 18.6% relative. We also experimented with dropout training, and it gave only very small gains over the baseline." } ], "images": [ "scigen/train/945.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: WER comparison on the Aurora-4 evaluation set with adversarial examples (AdvEx) (ϵ=0.3)" ], "reasoning": "The model trained with ϵ=0.3 achieved a 9.49% WER, a 14.1% relative improvement over the baseline, with the highest WER reduction of 18.6% on test set D.", "description": "Based on the results, ϵ=0.3 is chosen as the best perturbation weight to train the Aurora-4 model. The model trained on WSJ0m serves as the baseline. With ϵ=0.3, the augmented data training achieves 9.49% WER averaged across the four test sets, a 14.1% relative improvement over the baseline. For the test set with the highest WER on the baseline system, D, in which both noise and channel distortion are present, the proposed method reduced the WER by 18.6% relative. We also experimented with dropout training, and it gave only very small gains over the baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 1: WER comparison on the Aurora-4 evaluation set with adversarial examples (AdvEx) (ϵ=0.3)'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. \n\nQuestion: What is the WER reduction percentage for test set C when using the proposed method?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows the WER reduction percentages for each test set. For test set C, the WER reduction is 9.7%. So, the answer is {\"answer\": \"9.7\"}" } ], "images": [ "scigen/train/945.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: WER comparison on the Aurora-4 evaluation set with adversarial examples (AdvEx) (ϵ=0.3)" ], "reasoning": "The table shows the WER reduction percentages for each test set. For test set C, the WER reduction is 9.7%.", "question": "What is the WER reduction percentage for test set C when using the proposed method?", "answer": "So, the answer is {\"answer\": \"9.7\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe proposed method achieved a WER reduction of 18.6% on test set D.\n" }, { "role": "assistant", "content": "The table indicates that the WER reduction for test set D using the proposed method is indeed 18.6%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/945.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: WER comparison on the Aurora-4 evaluation set with adversarial examples (AdvEx) (ϵ=0.3)" ], "reasoning": "The table indicates that the WER reduction for test set D using the proposed method is indeed 18.6%.", "statement": "The proposed method achieved a WER reduction of 18.6% on test set D.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Our experiment results based on BTEC Japanese-English speech-to-speech translation.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The best performance was achieved with a codebook size of 128 and a time-reduction factor of 8, resulting in scores of 15.3 for both BLEU and METEOR.We tried several hyperparameters, including codebook size and time-reduction factor. Our best performance was produced by a codebook of 128 and a time-reduction factor 8 with a score of 15.3 BLEU and 15.3 METEOR." } ], "images": [ "scigen/train/946.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Our experiment results based on BTEC Japanese-English speech-to-speech translation." ], "reasoning": "The best performance was achieved with a codebook size of 128 and a time-reduction factor of 8, resulting in scores of 15.3 for both BLEU and METEOR.", "description": "We tried several hyperparameters, including codebook size and time-reduction factor. Our best performance was produced by a codebook of 128 and a time-reduction factor 8 with a score of 15.3 BLEU and 15.3 METEOR.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 2: Our experiment results based on BTEC Japanese-English speech-to-speech translation.'. Solve the following question based on the table.\n\nQuestion: What codebook size and time-reduction factor combination produced the highest BLEU score?\nLet's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, the highest BLEU score of 37.4 was achieved with the Topline (Cascade ASR -> TTS) model. So, the answer is {\"answer\": \"Topline (Cascade ASR -> TTS)\"}" } ], "images": [ "scigen/train/946.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Our experiment results based on BTEC Japanese-English speech-to-speech translation." ], "reasoning": "By examining the table, the highest BLEU score of 37.4 was achieved with the Topline (Cascade ASR -> TTS) model.", "question": "What codebook size and time-reduction factor combination produced the highest BLEU score?", "answer": "So, the answer is {\"answer\": \"Topline (Cascade ASR -> TTS)\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim A codebook size of 64 and a time-reduction factor of 4 produced a BLEU score of 14.8., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nA codebook size of 64 and a time-reduction factor of 4 produced a BLEU score of 14.8.\n" }, { "role": "assistant", "content": "The table shows that a codebook size of 64 and a time-reduction factor of 4 indeed produced a BLEU score of 14.8. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/946.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Our experiment results based on BTEC Japanese-English speech-to-speech translation." ], "reasoning": "The table shows that a codebook size of 64 and a time-reduction factor of 4 indeed produced a BLEU score of 14.8.", "statement": "A codebook size of 64 and a time-reduction factor of 4 produced a BLEU score of 14.8.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Our experiment results based on BTEC French-English speech-to-speech translation:'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The best performance was achieved with a codebook size of 64 and a time-reduction factor of 12, resulting in a BLEU score of 25.0 and a METEOR score of 23.2.We tried several hyperparameters, including codebook size and time-reduction factor. Our best performance was produced by codebook of 64 and a time-reduction factor of 12 with a score of 25.0 BLEU and 23.2 METEOR." } ], "images": [ "scigen/train/947.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Our experiment results based on BTEC French-English speech-to-speech translation:" ], "reasoning": "The best performance was achieved with a codebook size of 64 and a time-reduction factor of 12, resulting in a BLEU score of 25.0 and a METEOR score of 23.2.", "description": "We tried several hyperparameters, including codebook size and time-reduction factor. Our best performance was produced by codebook of 64 and a time-reduction factor of 12 with a score of 25.0 BLEU and 23.2 METEOR.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 1: Our experiment results based on BTEC French-English speech-to-speech translation:', and tackle the question below in a sequential manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat codebook size and time-reduction factor combination resulted in the highest BLEU score?\n" }, { "role": "assistant", "content": "By examining the table, the highest BLEU score of 25.0 was achieved with a codebook size of 64 and a time-reduction factor of 12. So, the answer is {\"answer\": \"64 and 12\"}" } ], "images": [ "scigen/train/947.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Our experiment results based on BTEC French-English speech-to-speech translation:" ], "reasoning": "By examining the table, the highest BLEU score of 25.0 was achieved with a codebook size of 64 and a time-reduction factor of 12.", "question": "What codebook size and time-reduction factor combination resulted in the highest BLEU score?", "answer": "So, the answer is {\"answer\": \"64 and 12\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe combination of a codebook size of 64 and a time-reduction factor of 12 resulted in the highest METEOR score.\n" }, { "role": "assistant", "content": "The table shows that the highest METEOR score of 23.2 was achieved with a codebook size of 64 and a time-reduction factor of 12. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/947.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Our experiment results based on BTEC French-English speech-to-speech translation:" ], "reasoning": "The table shows that the highest METEOR score of 23.2 was achieved with a codebook size of 64 and a time-reduction factor of 12.", "statement": "The combination of a codebook size of 64 and a time-reduction factor of 12 resulted in the highest METEOR score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: Effect of automatically predicting language ID and POS tags with MaLOPa on LAS scores.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The parser's performance decreases by 0.8 LAS points with predicted language IDs and by 5.1 LAS points with predicted POS tags, with an additional 0.2 LAS points lost without block dropout.The macro average language ID prediction accuracy on the test set across sentences is 94.7%. The macro average accuracy of the POS tagger is 93.3%. the four configurations: {gold language ID, predicted language ID} × {gold POS tags, predicted POS tags}. The performance of the parser suffers mildly (–0.8 LAS points) when using predicted language IDs, but more (–5.1 LAS points) when using predicted POS tags. The disparity in parsing results with gold vs. predicted POS tags is an important open problem, and has been previously discussed by \\newcitetiedemann:15. Without using block dropout, we lose an extra 0.2 LAS points in both configurations using predicted POS tags." } ], "images": [ "scigen/train/948.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Effect of automatically predicting language ID and POS tags with MaLOPa on LAS scores." ], "reasoning": "The parser's performance decreases by 0.8 LAS points with predicted language IDs and by 5.1 LAS points with predicted POS tags, with an additional 0.2 LAS points lost without block dropout.", "description": "The macro average language ID prediction accuracy on the test set across sentences is 94.7%. The macro average accuracy of the POS tagger is 93.3%. the four configurations: {gold language ID, predicted language ID} × {gold POS tags, predicted POS tags}. The performance of the parser suffers mildly (–0.8 LAS points) when using predicted language IDs, but more (–5.1 LAS points) when using predicted POS tags. The disparity in parsing results with gold vs. predicted POS tags is an important open problem, and has been previously discussed by \\newcitetiedemann:15. Without using block dropout, we lose an extra 0.2 LAS points in both configurations using predicted POS tags.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 5: Effect of automatically predicting language ID and POS tags with MaLOPa on LAS scores.'. Solve the following question based on the table.\n\nQuestion: What is the average LAS score when using predicted language ID and predicted POS tags?\nLet's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the average LAS score when using predicted language ID and predicted POS tags, look at the last row of the table under the 'average' column. So, the answer is {\"answer\": \"77.2\"}" } ], "images": [ "scigen/train/948.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Effect of automatically predicting language ID and POS tags with MaLOPa on LAS scores." ], "reasoning": "To find the average LAS score when using predicted language ID and predicted POS tags, look at the last row of the table under the 'average' column.", "question": "What is the average LAS score when using predicted language ID and predicted POS tags?", "answer": "So, the answer is {\"answer\": \"77.2\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe parser's performance is better with gold POS tags than with predicted POS tags.\n" }, { "role": "assistant", "content": "Comparing the LAS scores for gold POS tags (first and second rows) with those for predicted POS tags (third and fourth rows), the scores are higher with gold POS tags. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/948.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Effect of automatically predicting language ID and POS tags with MaLOPa on LAS scores." ], "reasoning": "Comparing the LAS scores for gold POS tags (first and second rows) with those for predicted POS tags (third and fourth rows), the scores are higher with gold POS tags.", "statement": "The parser's performance is better with gold POS tags than with predicted POS tags.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 8: Micro and Macro accuracies over all classes and Macro accuracy for the 50% rarest classes.'.\n" }, { "role": "assistant", "content": "WECT outperforms all other methods in micro, macro, and rare classes accuracy, with the highest gains observed in rare classes.We also show the macro accuracy of the 50% rarest classes. We find that our snippet selection method WECT provides the highest gains over the baseline. Further, regularizing using our word stability measure provides more useful embeddings than Yang17’s stability scores. Also, while Src-Tune performs better than regularization-based method, it is still inferior to WECT on all three metrics. The X-axis lists the classes in increasing order of frequency. We find that our method achieves impressive gains in the left side corresponding to low frequency classes. In the first class, Tgt alone gave 50% accuracy, whereas WECT gave 100%. For the second class, Tgt alone got 0% accuracy whereas WECT got 25%." } ], "images": [ "scigen/train/949.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Micro and Macro accuracies over all classes and Macro accuracy for the 50% rarest classes." ], "reasoning": "WECT outperforms all other methods in micro, macro, and rare classes accuracy, with the highest gains observed in rare classes.", "description": "We also show the macro accuracy of the 50% rarest classes. We find that our snippet selection method WECT provides the highest gains over the baseline. Further, regularizing using our word stability measure provides more useful embeddings than Yang17’s stability scores. Also, while Src-Tune performs better than regularization-based method, it is still inferior to WECT on all three metrics. The X-axis lists the classes in increasing order of frequency. We find that our method achieves impressive gains in the left side corresponding to low frequency classes. In the first class, Tgt alone gave 50% accuracy, whereas WECT gave 100%. For the second class, Tgt alone got 0% accuracy whereas WECT got 25%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 8: Micro and Macro accuracies over all classes and Macro accuracy for the 50% rarest classes.', answer the following question. Give detailed solution consisting of each step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method provides the highest macro accuracy?\n" }, { "role": "assistant", "content": "The table shows the macro accuracy for different methods. WECT has the highest macro accuracy at 53.4. So, the answer is {\"answer\": \"WECT\"}." } ], "images": [ "scigen/train/949.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Micro and Macro accuracies over all classes and Macro accuracy for the 50% rarest classes." ], "reasoning": "The table shows the macro accuracy for different methods. WECT has the highest macro accuracy at 53.4.", "question": "Which method provides the highest macro accuracy?", "answer": "So, the answer is {\"answer\": \"WECT\"}." }, { "messages": [ { "role": "user", "content": "Src-Tune performs better than WECT on rare classes. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nSrc-Tune performs better than WECT on rare classes.\n" }, { "role": "assistant", "content": "The table shows that WECT has a rare classes accuracy of 33.8, while Src-Tune has 32.3, indicating that WECT performs better. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/949.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Micro and Macro accuracies over all classes and Macro accuracy for the 50% rarest classes." ], "reasoning": "The table shows that WECT has a rare classes accuracy of 33.8, while Src-Tune has 32.3, indicating that WECT performs better.", "statement": "Src-Tune performs better than WECT on rare classes.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Perplexity of the trained language model on various target domains (lower is better).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The WECT method achieves the lowest perplexity across all domains, outperforming both Src-Tune and Yang17C methods.We observe that perplexity is lowest with our method of jointly training with target and selected source snippets. Surprisingly, the Src-Tune method does not perform as well on the LM task. On two of the four domains Src-Tune increases perplexity beyond the Tgt baseline. Likewise, Yang17C while providing gains over Tgt in the question deduplication task, increases perplexity for the LM task. Using our word stability measure instead of Yang17’s helps significantly reduce perplexity." } ], "images": [ "scigen/train/950.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Perplexity of the trained language model on various target domains (lower is better)." ], "reasoning": "The WECT method achieves the lowest perplexity across all domains, outperforming both Src-Tune and Yang17C methods.", "description": "We observe that perplexity is lowest with our method of jointly training with target and selected source snippets. Surprisingly, the Src-Tune method does not perform as well on the LM task. On two of the four domains Src-Tune increases perplexity beyond the Tgt baseline. Likewise, Yang17C while providing gains over Tgt in the question deduplication task, increases perplexity for the LM task. Using our word stability measure instead of Yang17’s helps significantly reduce perplexity.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: Perplexity of the trained language model on various target domains (lower is better).' and methodically tackle the question that follows. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich method achieves the lowest perplexity in the Physics domain?\n" }, { "role": "assistant", "content": "By comparing the perplexity values in the Physics column, we see that the WECT method has the lowest value of 110.6. So, the answer is {\"answer\": \"WECT\"}." } ], "images": [ "scigen/train/950.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Perplexity of the trained language model on various target domains (lower is better)." ], "reasoning": "By comparing the perplexity values in the Physics column, we see that the WECT method has the lowest value of 110.6.", "question": "Which method achieves the lowest perplexity in the Physics domain?", "answer": "So, the answer is {\"answer\": \"WECT\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe WECT:word method achieves the lowest perplexity in the Unix domain.\n" }, { "role": "assistant", "content": "By examining the Unix column, we see that the WECT method has the lowest perplexity of 121.7, not the WECT:word method which has a perplexity of 121.9. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/950.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Perplexity of the trained language model on various target domains (lower is better)." ], "reasoning": "By examining the Unix column, we see that the WECT method has the lowest perplexity of 121.7, not the WECT:word method which has a perplexity of 121.9.", "statement": "The WECT:word method achieves the lowest perplexity in the Unix domain.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Results for Neural Type Predictor.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The coarse-grained model achieved high accuracy, exceeding 95% for incomplete questions and 98% for full questions, while the fine-grained model had comparable Precision@1 scores but lower accuracy and F1 scores.The coarse-grained model performed very accurately; the accuracies exceeded 95% for incomplete questions and 98% for full questions. The fine-grained model also achieved good results; its Precision@1 scores were comparable to the accuracies of the coarse-grained model. However, the model suffered when it came to predicting all the fine-grained entity types, resulting in the relatively degraded performance in its accuracy and its F1 score." } ], "images": [ "scigen/train/951.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results for Neural Type Predictor." ], "reasoning": "The coarse-grained model achieved high accuracy, exceeding 95% for incomplete questions and 98% for full questions, while the fine-grained model had comparable Precision@1 scores but lower accuracy and F1 scores.", "description": "The coarse-grained model performed very accurately; the accuracies exceeded 95% for incomplete questions and 98% for full questions. The fine-grained model also achieved good results; its Precision@1 scores were comparable to the accuracies of the coarse-grained model. However, the model suffered when it came to predicting all the fine-grained entity types, resulting in the relatively degraded performance in its accuracy and its F1 score.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat was the accuracy of the fine-grained model for Sent 1–3?\nSolve the above question based on the table titled 'Table 2: Results for Neural Type Predictor.'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the accuracy of the fine-grained model for different sentence ranges. For Sent 1–3, the accuracy is listed as 0.69. So, the answer is {\"answer\": \"0.69\"}." } ], "images": [ "scigen/train/951.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results for Neural Type Predictor." ], "reasoning": "The table shows the accuracy of the fine-grained model for different sentence ranges. For Sent 1–3, the accuracy is listed as 0.69.", "question": "What was the accuracy of the fine-grained model for Sent 1–3?", "answer": "So, the answer is {\"answer\": \"0.69\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe fine-grained model had a Precision@1 score of 0.97 for Sent 1–3.\n" }, { "role": "assistant", "content": "The table lists the Precision@1 score of the fine-grained model for Sent 1–3 as 0.96, not 0.97. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/951.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results for Neural Type Predictor." ], "reasoning": "The table lists the Precision@1 score of the fine-grained model for Sent 1–3 as 0.96, not 0.97.", "statement": "The fine-grained model had a Precision@1 score of 0.97 for Sent 1–3.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: Accuracies of our question answering system. NQS and NTP stand for Neural Quiz Solver and Neural Type Predictor, respectively.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The Full model (NQS + NTP + IR) achieved the highest accuracy of 97% when given the full set of sentences, showing the effectiveness of combining sub-models.Here, we tested the performance using Dataset QA, and used the output of the Answer Scorer to predict the answer. Our system performed very accurately; it achieved 56% accuracy when given only a single sentence and 97% accuracy given the full set of sentences. To further evaluate the effectiveness of each sub-model presented above, we added the sub-models incrementally to the Answer Scorer. Note that the features not based on sub-models (e.g., the number of words in a question) were included in all instances. As a result, all of the sub-models effectively contributed to the performance. We also observed that the neural network models (i.e., Neural Quiz Solver and Neural Type Predictor) achieved good performance only for longer questions. Further, the IR models substantially improved the performance, especially for shorter questions." } ], "images": [ "scigen/train/952.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracies of our question answering system. NQS and NTP stand for Neural Quiz Solver and Neural Type Predictor, respectively." ], "reasoning": "The Full model (NQS + NTP + IR) achieved the highest accuracy of 97% when given the full set of sentences, showing the effectiveness of combining sub-models.", "description": "Here, we tested the performance using Dataset QA, and used the output of the Answer Scorer to predict the answer. Our system performed very accurately; it achieved 56% accuracy when given only a single sentence and 97% accuracy given the full set of sentences. To further evaluate the effectiveness of each sub-model presented above, we added the sub-models incrementally to the Answer Scorer. Note that the features not based on sub-models (e.g., the number of words in a question) were included in all instances. As a result, all of the sub-models effectively contributed to the performance. We also observed that the neural network models (i.e., Neural Quiz Solver and Neural Type Predictor) achieved good performance only for longer questions. Further, the IR models substantially improved the performance, especially for shorter questions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nTable title: Table 3: Accuracies of our question answering system. NQS and NTP stand for Neural Quiz Solver and Neural Type Predictor, respectively.\n\nQuestion: Which model achieved the highest accuracy for Sent 1–3?\n" }, { "role": "assistant", "content": "By examining the table, the Full model (NQS + NTP + IR) achieved the highest accuracy of 0.88 for Sent 1–3. So, the answer is {\"answer\": \"Full model (NQS + NTP + IR)\"}" } ], "images": [ "scigen/train/952.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracies of our question answering system. NQS and NTP stand for Neural Quiz Solver and Neural Type Predictor, respectively." ], "reasoning": "By examining the table, the Full model (NQS + NTP + IR) achieved the highest accuracy of 0.88 for Sent 1–3.", "question": "Which model achieved the highest accuracy for Sent 1–3?", "answer": "So, the answer is {\"answer\": \"Full model (NQS + NTP + IR)\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe NQS + NTP + IR-Dataset model achieved higher accuracy than the NQS + NTP + IR-Wikipedia model for Sent 1–2.\n" }, { "role": "assistant", "content": "The table shows that the NQS + NTP + IR-Dataset model achieved an accuracy of 0.73 for Sent 1–2, while the NQS + NTP + IR-Wikipedia model achieved an accuracy of 0.71 for Sent 1–2. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/952.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracies of our question answering system. NQS and NTP stand for Neural Quiz Solver and Neural Type Predictor, respectively." ], "reasoning": "The table shows that the NQS + NTP + IR-Dataset model achieved an accuracy of 0.73 for Sent 1–2, while the NQS + NTP + IR-Wikipedia model achieved an accuracy of 0.71 for Sent 1–2.", "statement": "The NQS + NTP + IR-Dataset model achieved higher accuracy than the NQS + NTP + IR-Wikipedia model for Sent 1–2.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: Accuracies of the top three QA systems submitted in the competition.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Our system achieved the highest accuracy and outperformed Acelove and Lunit.io by wide margins in simulated pairwise matches.Our system achieved the best performance by a wide margin. To further evaluate the actual performance of the systems in the quiz bowl, the competition organizers performed simulated pairwise matches between the systems following the official quiz bowl rules. Our system outperformed the Acelove system (our system: 1220 points; the Acelove system: 60 points) and the Lunit.io system (our system: 1145 points; the Lunit.io system: 105 points) by considerably wide margins." } ], "images": [ "scigen/train/953.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Accuracies of the top three QA systems submitted in the competition." ], "reasoning": "Our system achieved the highest accuracy and outperformed Acelove and Lunit.io by wide margins in simulated pairwise matches.", "description": "Our system achieved the best performance by a wide margin. To further evaluate the actual performance of the systems in the quiz bowl, the competition organizers performed simulated pairwise matches between the systems following the official quiz bowl rules. Our system outperformed the Acelove system (our system: 1220 points; the Acelove system: 60 points) and the Lunit.io system (our system: 1145 points; the Lunit.io system: 105 points) by considerably wide margins.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 4: Accuracies of the top three QA systems submitted in the competition.\nQuestion: Which system had the lowest accuracy and how did it perform in the simulated pairwise matches?\n" }, { "role": "assistant", "content": "The table shows that the Baseline system had the lowest accuracy of 0.55. The description does not provide specific performance details for the Baseline system in the simulated pairwise matches. So, the answer is {\"answer\": \"Baseline\"}." } ], "images": [ "scigen/train/953.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Accuracies of the top three QA systems submitted in the competition." ], "reasoning": "The table shows that the Baseline system had the lowest accuracy of 0.55. The description does not provide specific performance details for the Baseline system in the simulated pairwise matches.", "question": "Which system had the lowest accuracy and how did it perform in the simulated pairwise matches?", "answer": "So, the answer is {\"answer\": \"Baseline\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nOur system had an accuracy of 0.85 and outperformed Acelove and Lunit.io in simulated pairwise matches.\n" }, { "role": "assistant", "content": "The table confirms that our system had an accuracy of 0.85. The description states that our system outperformed Acelove and Lunit.io in simulated pairwise matches. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/953.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Accuracies of the top three QA systems submitted in the competition." ], "reasoning": "The table confirms that our system had an accuracy of 0.85. The description states that our system outperformed Acelove and Lunit.io in simulated pairwise matches.", "statement": "Our system had an accuracy of 0.85 and outperformed Acelove and Lunit.io in simulated pairwise matches.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Performance on the open-domain binary classification dataset of 984 Wikipedia paragraphs.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The discriminative model performs poorly due to sentence-level negative sampling, which covers only a small proportion of the semantic space in an open-domain setting.Contrary to the findings on the domain specific dataset in the previous subsection, the discriminative model does not yield compelling results, performing only slightly better than the entity grid model. We believe the poor performance is due to the sentence-level negative sampling used by the discriminative model. Due to the huge semantic space in the open-domain setting, the sampled instances can only cover a tiny proportion of the possible negative candidates, and therefore don’t cover the space of possible meanings. By contrast the dataset in \\newcitebarzilay2008modeling is very domain-specific, and the semantic space is thus relatively small. By treating all other sentences in the document as negative, the discriminative strategy ’s negative samples form a much larger proportion of the semantic space, leading to good performance." } ], "images": [ "scigen/train/954.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance on the open-domain binary classification dataset of 984 Wikipedia paragraphs." ], "reasoning": "The discriminative model performs poorly due to sentence-level negative sampling, which covers only a small proportion of the semantic space in an open-domain setting.", "description": "Contrary to the findings on the domain specific dataset in the previous subsection, the discriminative model does not yield compelling results, performing only slightly better than the entity grid model. We believe the poor performance is due to the sentence-level negative sampling used by the discriminative model. Due to the huge semantic space in the open-domain setting, the sampled instances can only cover a tiny proportion of the possible negative candidates, and therefore don’t cover the space of possible meanings. By contrast the dataset in \\newcitebarzilay2008modeling is very domain-specific, and the semantic space is thus relatively small. By treating all other sentences in the document as negative, the discriminative strategy ’s negative samples form a much larger proportion of the semantic space, leading to good performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2: Performance on the open-domain binary classification dataset of 984 Wikipedia paragraphs.', unfold the steps to solve the question given next. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nQuestion: What is the accuracy difference between the VLV-GM (MMI) model and the Discriminative Model?\n" }, { "role": "assistant", "content": "The accuracy of the VLV-GM (MMI) model is 0.873, and the accuracy of the Discriminative Model is 0.715. The difference is 0.873 - 0.715 = 0.158. So, the answer is {\"answer\": \"0.158\"}." } ], "images": [ "scigen/train/954.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance on the open-domain binary classification dataset of 984 Wikipedia paragraphs." ], "reasoning": "The accuracy of the VLV-GM (MMI) model is 0.873, and the accuracy of the Discriminative Model is 0.715. The difference is 0.873 - 0.715 = 0.158.", "question": "What is the accuracy difference between the VLV-GM (MMI) model and the Discriminative Model?", "answer": "So, the answer is {\"answer\": \"0.158\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Entity Grid Model has a higher accuracy than the Discriminative Model.\n" }, { "role": "assistant", "content": "The accuracy of the Entity Grid Model is 0.686, while the accuracy of the Discriminative Model is 0.715. Therefore, the Entity Grid Model does not have a higher accuracy. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/954.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance on the open-domain binary classification dataset of 984 Wikipedia paragraphs." ], "reasoning": "The accuracy of the Entity Grid Model is 0.686, while the accuracy of the Discriminative Model is 0.715. Therefore, the Entity Grid Model does not have a higher accuracy.", "statement": "The Entity Grid Model has a higher accuracy than the Discriminative Model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Adversarial Success for different models.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The VLV-GM model generates the most coherent text chunks due to its ability to handle dependencies between neighboring sentences, but performance declines with more turns due to error accumulation and difficulty in modeling long-term dependencies.As can be seen, the latent variable model VLV-GM is able to generate chunk of texts that are most indistinguishable from coherent texts from humans. This is due to its ability to handle the dependency between neighboring sentences. Performance declines as the number of turns increases due to the accumulation of errors and current models’ inability to model long-term sentence-level dependency. All models perform poorly on the adver-3 evaluation metric, with the best adversarial success value being 0.081 (the trained evaluator is able to distinguish between human-generated and machine generated dialogues with greater than 90 percent accuracy for all models)." } ], "images": [ "scigen/train/955.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Adversarial Success for different models." ], "reasoning": "The VLV-GM model generates the most coherent text chunks due to its ability to handle dependencies between neighboring sentences, but performance declines with more turns due to error accumulation and difficulty in modeling long-term dependencies.", "description": "As can be seen, the latent variable model VLV-GM is able to generate chunk of texts that are most indistinguishable from coherent texts from humans. This is due to its ability to handle the dependency between neighboring sentences. Performance declines as the number of turns increases due to the accumulation of errors and current models’ inability to model long-term sentence-level dependency. All models perform poorly on the adver-3 evaluation metric, with the best adversarial success value being 0.081 (the trained evaluator is able to distinguish between human-generated and machine generated dialogues with greater than 90 percent accuracy for all models).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Adversarial Success for different models.', answer the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest adversarial success value for adver-1?\n" }, { "role": "assistant", "content": "By comparing the values in the adver-1 column, the VLV-GM (MMI) model has the highest value of 0.174. So, the answer is {\"answer\": \"VLV-GM (MMI)\"}" } ], "images": [ "scigen/train/955.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Adversarial Success for different models." ], "reasoning": "By comparing the values in the adver-1 column, the VLV-GM (MMI) model has the highest value of 0.174.", "question": "Which model has the highest adversarial success value for adver-1?", "answer": "So, the answer is {\"answer\": \"VLV-GM (MMI)\"}" }, { "messages": [ { "role": "user", "content": "The VLV-GM model has the lowest adversarial success value for adver-3. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe VLV-GM model has the lowest adversarial success value for adver-3.\n" }, { "role": "assistant", "content": "By comparing the values in the adver-3 column, the VLV-GM (MMI) model has a value of 0.054, which is not the lowest. The Seq2Seq (uni) model has the lowest value of 0.024. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/955.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Adversarial Success for different models." ], "reasoning": "By comparing the values in the adver-3 column, the VLV-GM (MMI) model has a value of 0.054, which is not the lowest. The Seq2Seq (uni) model has the lowest value of 0.024.", "statement": "The VLV-GM model has the lowest adversarial success value for adver-3.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 9: Performance comparison of different versions of m-RNN models on the Flickr30K dataset. All the models adopt VggNet as the image representation. See Figure 5 for details of the models.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Three m-RNN models were trained with different configurations of word embedding layers and multimodal layers, and three variants were trained with image representation inputted into the recurrent layer.To validate its efficiency, we train three different m-RNN networks: m-RNN-NoEmbInput, m-RNN-OneLayerEmb, m-RNN-EmbOneInput. “m-RNN-NoEmbInput” denotes the m-RNN model whose connection between the word embedding layer \\@slowromancapii@ and the multimodal layer is cut off. Thus the multimodal layer has only two inputs: the recurrent layer and the image representation. “m-RNN-OneLayerEmb” denotes the m-RNN model whose two word embedding layers are replaced by a single 256 dimensional word-embedding layer. There are much more parameters of the word-embedding layers in the m-RNN-OneLayerEmb than those in the original m-RNN ( 256⋅M v.s. 128⋅M+128⋅256) if the dictionary size M is large. “m-RNN-EmbOneInput” denotes the m-RNN model whose connection between the word embedding layer \\@slowromancapii@ and the multimodal layer is replaced by the connection between the word embedding layer \\@slowromancapi@ and the multimodal layer. It verifies the effectiveness of the two word embedding layers. How to connect the vision and the language part of the model. We train three variants of m-RNN models where the image representation is inputted into the recurrent layer: m-RNN-VisualInRNN, m-RNN-VisualInRNN-both, and m -RNN-VisualInRNN-Both-Shared. For m-RNN-VisualInRNN, we only input the image representation to the word embedding layer \\@slowromancapii@ while for the later two models, we input the image representation to both the multimodal layer and word embedding layer \\@slowromancapii@. The weights of the two connections V(1)I, V(2)I are shared for m-RNN-VisualInRNN-Both-Shared." } ], "images": [ "scigen/train/956.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Performance comparison of different versions of m-RNN models on the Flickr30K dataset. All the models adopt VggNet as the image representation. See Figure 5 for details of the models." ], "reasoning": "Three m-RNN models were trained with different configurations of word embedding layers and multimodal layers, and three variants were trained with image representation inputted into the recurrent layer.", "description": "To validate its efficiency, we train three different m-RNN networks: m-RNN-NoEmbInput, m-RNN-OneLayerEmb, m-RNN-EmbOneInput. “m-RNN-NoEmbInput” denotes the m-RNN model whose connection between the word embedding layer \\@slowromancapii@ and the multimodal layer is cut off. Thus the multimodal layer has only two inputs: the recurrent layer and the image representation. “m-RNN-OneLayerEmb” denotes the m-RNN model whose two word embedding layers are replaced by a single 256 dimensional word-embedding layer. There are much more parameters of the word-embedding layers in the m-RNN-OneLayerEmb than those in the original m-RNN ( 256⋅M v.s. 128⋅M+128⋅256) if the dictionary size M is large. “m-RNN-EmbOneInput” denotes the m-RNN model whose connection between the word embedding layer \\@slowromancapii@ and the multimodal layer is replaced by the connection between the word embedding layer \\@slowromancapi@ and the multimodal layer. It verifies the effectiveness of the two word embedding layers. How to connect the vision and the language part of the model. We train three variants of m-RNN models where the image representation is inputted into the recurrent layer: m-RNN-VisualInRNN, m-RNN-VisualInRNN-both, and m -RNN-VisualInRNN-Both-Shared. For m-RNN-VisualInRNN, we only input the image representation to the word embedding layer \\@slowromancapii@ while for the later two models, we input the image representation to both the multimodal layer and word embedding layer \\@slowromancapii@. The weights of the two connections V(1)I, V(2)I are shared for m-RNN-VisualInRNN-Both-Shared.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. The question is related to the given table titled 'Table 9: Performance comparison of different versions of m-RNN models on the Flickr30K dataset. All the models adopt VggNet as the image representation. See Figure 5 for details of the models.'.\n\nQuestion: Which m-RNN model variant has the highest B-1 score?\n" }, { "role": "assistant", "content": "To find the m-RNN model variant with the highest B-1 score, we compare the B-1 scores of all the models listed in the table. The highest B-1 score is 0.600, which corresponds to the m-RNN model. So, the answer is {\"answer\": \"m-RNN\"}." } ], "images": [ "scigen/train/956.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Performance comparison of different versions of m-RNN models on the Flickr30K dataset. All the models adopt VggNet as the image representation. See Figure 5 for details of the models." ], "reasoning": "To find the m-RNN model variant with the highest B-1 score, we compare the B-1 scores of all the models listed in the table. The highest B-1 score is 0.600, which corresponds to the m-RNN model.", "question": "Which m-RNN model variant has the highest B-1 score?", "answer": "So, the answer is {\"answer\": \"m-RNN\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe m-RNN-OneLayerEmb model has a higher B-2 score than the m-RNN-EmbOneInput model.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the B-2 scores of the m-RNN-OneLayerEmb and m-RNN-EmbOneInput models. The B-2 score for m-RNN-OneLayerEmb is 0.406, and for m-RNN-EmbOneInput, it is also 0.406. Since they are equal, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/956.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Performance comparison of different versions of m-RNN models on the Flickr30K dataset. All the models adopt VggNet as the image representation. See Figure 5 for details of the models." ], "reasoning": "To verify this statement, we compare the B-2 scores of the m-RNN-OneLayerEmb and m-RNN-EmbOneInput models. The B-2 score for m-RNN-OneLayerEmb is 0.406, and for m-RNN-EmbOneInput, it is also 0.406. Since they are equal, the statement is refuted.", "statement": "The m-RNN-OneLayerEmb model has a higher B-2 score than the m-RNN-EmbOneInput model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Results of R@K and median rank (Med r) for Flickr8K dataset. “-AlexNet” denotes the image representation based on AlexNet extracted from the whole image frame. “-RCNN” denotes the image representation extracted from possible objects detected by the RCNN algorithm.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our model outperforms several state-of-the-art methods in both sentence and image retrieval tasks, especially when using the same image representation like AlexNet.This dataset was widely used as a benchmark dataset for image and sentence retrieval. We compare our model with several state-of-the-art methods: SDT-RNN (Socher et al. (Frome et al. , DeepFE ( Karpathy et al. with various image representations. Our model outperforms these methods by a large margin when using the same image representation (e.g. AlexNet). “-avg-RCNN” denotes methods with features of the average CNN activation of all objects above a detection confidence threshold. DeepFE-RCNN Karpathy et al. The results show that using these features improves the performance. Even without the help from the object detection methods, however, our method performs better than these methods in almost all the evaluation metrics. We will develop our framework using better image features based on object detection in the future work." } ], "images": [ "scigen/train/957.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of R@K and median rank (Med r) for Flickr8K dataset. “-AlexNet” denotes the image representation based on AlexNet extracted from the whole image frame. “-RCNN” denotes the image representation extracted from possible objects detected by the RCNN algorithm." ], "reasoning": "Our model outperforms several state-of-the-art methods in both sentence and image retrieval tasks, especially when using the same image representation like AlexNet.", "description": "This dataset was widely used as a benchmark dataset for image and sentence retrieval. We compare our model with several state-of-the-art methods: SDT-RNN (Socher et al. (Frome et al. , DeepFE ( Karpathy et al. with various image representations. Our model outperforms these methods by a large margin when using the same image representation (e.g. AlexNet). “-avg-RCNN” denotes methods with features of the average CNN activation of all objects above a detection confidence threshold. DeepFE-RCNN Karpathy et al. The results show that using these features improves the performance. Even without the help from the object detection methods, however, our method performs better than these methods in almost all the evaluation metrics. We will develop our framework using better image features based on object detection in the future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Results of R@K and median rank (Med r) for Flickr8K dataset. “-AlexNet” denotes the image representation based on AlexNet extracted from the whole image frame. “-RCNN” denotes the image representation extracted from possible objects detected by the RCNN algorithm.', answer the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich method has the lowest median rank for Sentence Retrieval (Image to Text)?\n" }, { "role": "assistant", "content": "To find the method with the lowest median rank for Sentence Retrieval (Image to Text), look at the 'Sentence Retrieval (Image to Text) Med r' column and identify the smallest value. The smallest value is 11, which corresponds to 'Ours-m-RNN-AlexNet'. So, the answer is {\"answer\": \"Ours-m-RNN-AlexNet\"}" } ], "images": [ "scigen/train/957.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of R@K and median rank (Med r) for Flickr8K dataset. “-AlexNet” denotes the image representation based on AlexNet extracted from the whole image frame. “-RCNN” denotes the image representation extracted from possible objects detected by the RCNN algorithm." ], "reasoning": "To find the method with the lowest median rank for Sentence Retrieval (Image to Text), look at the 'Sentence Retrieval (Image to Text) Med r' column and identify the smallest value. The smallest value is 11, which corresponds to 'Ours-m-RNN-AlexNet'.", "question": "Which method has the lowest median rank for Sentence Retrieval (Image to Text)?", "answer": "So, the answer is {\"answer\": \"Ours-m-RNN-AlexNet\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nDeepFE-RCNN has the highest R@10 score for Image Retrieval (Text to Image).\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'Image Retrieval (Text to Image) R@10' column. The highest value is 42.5, which corresponds to 'DeepFE-RCNN'. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/957.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of R@K and median rank (Med r) for Flickr8K dataset. “-AlexNet” denotes the image representation based on AlexNet extracted from the whole image frame. “-RCNN” denotes the image representation extracted from possible objects detected by the RCNN algorithm." ], "reasoning": "To verify this statement, look at the 'Image Retrieval (Text to Image) R@10' column. The highest value is 42.5, which corresponds to 'DeepFE-RCNN'. Therefore, the statement is supported.", "statement": "DeepFE-RCNN has the highest R@10 score for Image Retrieval (Text to Image).", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 8: Results of m-RNN-shared model after applying consensus reranking using nearest neighbors as references (m-RNN-shared-NNref), compared with those of the original m-RNN model on our validation set and MS COCO test server.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "BLEU-based consensus reranking shows an improvement of 3.5 points on the validation set and 3.3 points on the MS COCO test 2014 set, while CIDEr-based consensus reranking shows an improvement of 9.4 points on the validation set and 9.8 points on the MS COCO test 2014 set.For BLEU-based consensus reranking, we get an improvement of 3.5 points on our validation set and 3.3 points on the MS COCO test 2014 set in terms of BLEU4 score. For the CIDEr-based consensus reranking, we get an improvement of 9.4 points on our validation set and 9.8 points on the MS COCO test 2014 set in terms of CIDEr. We also show the oracle performance of the ten hypotheses, which is the upper bound of the consensus reranking. More specifically, for each image in our validation set, we rerank the hypotheses according to the scores (BLEU or CIDEr) w.r.t to the groundtruth captions. (see rows with “-oracle”). The oracle performance is surprisingly high, indicating that there is still room for improvement, both for the m-RNN model itself and the reranking strategy." } ], "images": [ "scigen/train/958.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Results of m-RNN-shared model after applying consensus reranking using nearest neighbors as references (m-RNN-shared-NNref), compared with those of the original m-RNN model on our validation set and MS COCO test server." ], "reasoning": "BLEU-based consensus reranking shows an improvement of 3.5 points on the validation set and 3.3 points on the MS COCO test 2014 set, while CIDEr-based consensus reranking shows an improvement of 9.4 points on the validation set and 9.8 points on the MS COCO test 2014 set.", "description": "For BLEU-based consensus reranking, we get an improvement of 3.5 points on our validation set and 3.3 points on the MS COCO test 2014 set in terms of BLEU4 score. For the CIDEr-based consensus reranking, we get an improvement of 9.4 points on our validation set and 9.8 points on the MS COCO test 2014 set in terms of CIDEr. We also show the oracle performance of the ten hypotheses, which is the upper bound of the consensus reranking. More specifically, for each image in our validation set, we rerank the hypotheses according to the scores (BLEU or CIDEr) w.r.t to the groundtruth captions. (see rows with “-oracle”). The oracle performance is surprisingly high, indicating that there is still room for improvement, both for the m-RNN model itself and the reranking strategy.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: 'Table 8: Results of m-RNN-shared model after applying consensus reranking using nearest neighbors as references (m-RNN-shared-NNref), compared with those of the original m-RNN model on our validation set and MS COCO test server.'\nQuestion: What is the BLEU4 score improvement on the MS COCO test 2014 set for m-RNN-shared-NNref-BLEU?\n" }, { "role": "assistant", "content": "The BLEU4 score improvement on the MS COCO test 2014 set for m-RNN-shared-NNref-BLEU can be found by comparing the BLEU4 scores of m-RNN-shared and m-RNN-shared-NNref-BLEU on the MS COCO 2014 test server. So, the answer is {\"answer\": \"0.302\"}." } ], "images": [ "scigen/train/958.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Results of m-RNN-shared model after applying consensus reranking using nearest neighbors as references (m-RNN-shared-NNref), compared with those of the original m-RNN model on our validation set and MS COCO test server." ], "reasoning": "The BLEU4 score improvement on the MS COCO test 2014 set for m-RNN-shared-NNref-BLEU can be found by comparing the BLEU4 scores of m-RNN-shared and m-RNN-shared-NNref-BLEU on the MS COCO 2014 test server.", "question": "What is the BLEU4 score improvement on the MS COCO test 2014 set for m-RNN-shared-NNref-BLEU?", "answer": "So, the answer is {\"answer\": \"0.302\"}." }, { "messages": [ { "role": "user", "content": "The CIDEr score for m-RNN-shared-NNref-CIDEr on the MS COCO validation set is higher than the BLEU4 score for m-RNN-shared-NNref-BLEU on the same set. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe CIDEr score for m-RNN-shared-NNref-CIDEr on the MS COCO validation set is higher than the BLEU4 score for m-RNN-shared-NNref-BLEU on the same set.\n" }, { "role": "assistant", "content": "The CIDEr score for m-RNN-shared-NNref-CIDEr on the MS COCO validation set is 0.938, while the BLEU4 score for m-RNN-shared-NNref-BLEU on the same set is 0.305. Since 0.938 is higher than 0.305, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/958.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Results of m-RNN-shared model after applying consensus reranking using nearest neighbors as references (m-RNN-shared-NNref), compared with those of the original m-RNN model on our validation set and MS COCO test server." ], "reasoning": "The CIDEr score for m-RNN-shared-NNref-CIDEr on the MS COCO validation set is 0.938, while the BLEU4 score for m-RNN-shared-NNref-BLEU on the same set is 0.305. Since 0.938 is higher than 0.305, the statement is supported.", "statement": "The CIDEr score for m-RNN-shared-NNref-CIDEr on the MS COCO validation set is higher than the BLEU4 score for m-RNN-shared-NNref-BLEU on the same set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Performance on AAPD', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Different models excel in different metrics, with RNN decoder-based models performing well on ACC, BR and BR++ excelling in HA, and OCD outperforming all baselines overall.In the following, we show results of the baseline models and the proposed method on three text datasets. For MTL models, we show the results of the four kinds of different decoding strategies described in section Decoder Integration. For a simple comparison, we also compute averages of the five metrics as a reference. Note that blue an bold texts in Table We see that different models are skilled at different metrics. For example, RNN decoder based models, i.e. Seq2seq in row (f) and Order-free RNN in row (h), perform well on ACC, whereas BR and BR++ have better results in terms of HA but show clear weaknesses in predicting rare labels (cf. especially maF1). However, OCD in row (j) performs better than all the baselines (row (d)–(i))" } ], "images": [ "scigen/train/959.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance on AAPD" ], "reasoning": "Different models excel in different metrics, with RNN decoder-based models performing well on ACC, BR and BR++ excelling in HA, and OCD outperforming all baselines overall.", "description": "In the following, we show results of the baseline models and the proposed method on three text datasets. For MTL models, we show the results of the four kinds of different decoding strategies described in section Decoder Integration. For a simple comparison, we also compute averages of the five metrics as a reference. Note that blue an bold texts in Table We see that different models are skilled at different metrics. For example, RNN decoder based models, i.e. Seq2seq in row (f) and Order-free RNN in row (h), perform well on ACC, whereas BR and BR++ have better results in terms of HA but show clear weaknesses in predicting rare labels (cf. especially maF1). However, OCD in row (j) performs better than all the baselines (row (d)–(i))", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Performance on AAPD', and sequentially solve the question, writing out each step of your reasoning process. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model has the highest average score?\n" }, { "role": "assistant", "content": "By comparing the average scores of all models, the model with the highest average score is the Logistic rescore model with an average of 0.682. So, the answer is {\"answer\": \"Logistic rescore\"}" } ], "images": [ "scigen/train/959.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance on AAPD" ], "reasoning": "By comparing the average scores of all models, the model with the highest average score is the Logistic rescore model with an average of 0.682.", "question": "Which model has the highest average score?", "answer": "So, the answer is {\"answer\": \"Logistic rescore\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Seq2seq model has the highest ACC score among all models.\n" }, { "role": "assistant", "content": "The table shows that the Seq2seq model has an ACC score of 0.421, which is not the highest. The highest ACC score is 0.421, achieved by the Seq2seq model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/959.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance on AAPD" ], "reasoning": "The table shows that the Seq2seq model has an ACC score of 0.421, which is not the highest. The highest ACC score is 0.421, achieved by the Seq2seq model.", "statement": "The Seq2seq model has the highest ACC score among all models.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: Performance comparisons on Reuters-21578'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The results show that OCD combined with the MTL framework consistently outperforms other methods, especially in multi-label classification tasks.These results demonstrate again the superiority of OCD and the performance gains afforded by the MTL framework. Since there are over 80% of test samples only have one label in this corpus, to truly know the effect of proposed approaches to multi-label classification , we also provide results only on test samples with more than one label in the Section Analysis of Reuters-21758 in Appendix." } ], "images": [ "scigen/train/960.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance comparisons on Reuters-21578" ], "reasoning": "The results show that OCD combined with the MTL framework consistently outperforms other methods, especially in multi-label classification tasks.", "description": "These results demonstrate again the superiority of OCD and the performance gains afforded by the MTL framework. Since there are over 80% of test samples only have one label in this corpus, to truly know the effect of proposed approaches to multi-label classification , we also provide results only on test samples with more than one label in the Section Analysis of Reuters-21758 in Appendix.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nTable title: Table 3: Performance comparisons on Reuters-21578\n\nQuestion: Which proposed method achieves the highest maF1 score?\n" }, { "role": "assistant", "content": "To find the highest maF1 score, look at the 'maF1' column under 'Proposed methods'. The highest score is 0.490 achieved by 'OCD + MTL Logistic joint dec.'. So, the answer is {\"answer\": \"OCD + MTL Logistic joint dec.\"}" } ], "images": [ "scigen/train/960.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance comparisons on Reuters-21578" ], "reasoning": "To find the highest maF1 score, look at the 'maF1' column under 'Proposed methods'. The highest score is 0.490 achieved by 'OCD + MTL Logistic joint dec.'.", "question": "Which proposed method achieves the highest maF1 score?", "answer": "So, the answer is {\"answer\": \"OCD + MTL Logistic joint dec.\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The OCD + MTL RNN dec. method achieves the highest ebF1 score. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe OCD + MTL RNN dec. method achieves the highest ebF1 score.\n" }, { "role": "assistant", "content": "To verify this, check the 'ebF1' column under 'Proposed methods'. The highest ebF1 score is 0.905, which is achieved by 'OCD + MTL RNN dec.'. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/960.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance comparisons on Reuters-21578" ], "reasoning": "To verify this, check the 'ebF1' column under 'Proposed methods'. The highest ebF1 score is 0.905, which is achieved by 'OCD + MTL RNN dec.'.", "statement": "The OCD + MTL RNN dec. method achieves the highest ebF1 score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Performance comparisons on Audio set.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "OCD outperforms other models in maF1, miF1, and ebF1, and MTL improves performance overall.In the following, we show results of the baseline models and the proposed method on three text datasets. For MTL models, we show the results of the four kinds of different decoding strategies described in section Decoder Integration. For a simple comparison, we also compute averages of the five metrics as a reference. Note that blue an bold texts in Table In this experiment, all models have similar performance in HA. Surprisingly, BR is a competitive baseline model and performs especially well in miF1. Seq2seq achieves the best performance in terms of ACC, which is the same as the observation on AAPD. Overall, OCD performs better than all the baseline and MTL indeed improves the performance. OCD outperforms other RNN decoder-based models in maF1, miF1 and ebF1 and performs worse than BR only in terms of miF1." } ], "images": [ "scigen/train/961.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance comparisons on Audio set." ], "reasoning": "OCD outperforms other models in maF1, miF1, and ebF1, and MTL improves performance overall.", "description": "In the following, we show results of the baseline models and the proposed method on three text datasets. For MTL models, we show the results of the four kinds of different decoding strategies described in section Decoder Integration. For a simple comparison, we also compute averages of the five metrics as a reference. Note that blue an bold texts in Table In this experiment, all models have similar performance in HA. Surprisingly, BR is a competitive baseline model and performs especially well in miF1. Seq2seq achieves the best performance in terms of ACC, which is the same as the observation on AAPD. Overall, OCD performs better than all the baseline and MTL indeed improves the performance. OCD outperforms other RNN decoder-based models in maF1, miF1 and ebF1 and performs worse than BR only in terms of miF1.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 4: Performance comparisons on Audio set.', answer the question below by showing each progressive step toward the answer. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model achieves the highest miF1 score among the proposed methods?\n" }, { "role": "assistant", "content": "By examining the miF1 column under the Proposed methods section, the highest score is 0.488 achieved by OCD + MTL with Logistic joint dec. So, the answer is {\"answer\": \"OCD + MTL with Logistic joint dec.\"}" } ], "images": [ "scigen/train/961.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance comparisons on Audio set." ], "reasoning": "By examining the miF1 column under the Proposed methods section, the highest score is 0.488 achieved by OCD + MTL with Logistic joint dec.", "question": "Which model achieves the highest miF1 score among the proposed methods?", "answer": "So, the answer is {\"answer\": \"OCD + MTL with Logistic joint dec.\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSeq2seq achieves the highest ACC score among all models.\n" }, { "role": "assistant", "content": "The ACC column shows that the highest score is 0.140, achieved by Seq2seq among the baseline models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/961.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance comparisons on Audio set." ], "reasoning": "The ACC column shows that the highest score is 0.140, achieved by Seq2seq among the baseline models.", "statement": "Seq2seq achieves the highest ACC score among all models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: Performance comparison on resplited AAPD, whose test set contains 2000 samples whose label sets occur in the training set (Seen test set) and 2000 samples are not (Unseen test set). OCD (correct prefix) means we only sample correct labels in the training phase.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Sampling wrong labels from the predicted distribution helps models become more robust, as seen with the improved performance of OCD and SS on the unseen test set.OCD (correct prefix) means we only sample correct labels in the training phase, so this model has not encountered wrong prefix during training. Clearly, all models perform worse on unseen test set. We can see that SS improves the performance significantly on the unseen test set for both seq2seq and order free RNN. Additionally, OCD with correct prefix, which suffers from the exposure bias, performs worse in both case than OCD. They all demonstrate that sampling wrong labels from predicted distribution helps models become more robust when encountering rare situation." } ], "images": [ "scigen/train/962.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Performance comparison on resplited AAPD, whose test set contains 2000 samples whose label sets occur in the training set (Seen test set) and 2000 samples are not (Unseen test set). OCD (correct prefix) means we only sample correct labels in the training phase." ], "reasoning": "Sampling wrong labels from the predicted distribution helps models become more robust, as seen with the improved performance of OCD and SS on the unseen test set.", "description": "OCD (correct prefix) means we only sample correct labels in the training phase, so this model has not encountered wrong prefix during training. Clearly, all models perform worse on unseen test set. We can see that SS improves the performance significantly on the unseen test set for both seq2seq and order free RNN. Additionally, OCD with correct prefix, which suffers from the exposure bias, performs worse in both case than OCD. They all demonstrate that sampling wrong labels from predicted distribution helps models become more robust when encountering rare situation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 5: Performance comparison on resplited AAPD, whose test set contains 2000 samples whose label sets occur in the training set (Seen test set) and 2000 samples are not (Unseen test set). OCD (correct prefix) means we only sample correct labels in the training phase.', answer the question below by showing each progressive step toward the answer. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model performs the best on the unseen test set according to ebF1?\n" }, { "role": "assistant", "content": "By comparing the ebF1 scores for the unseen test set, OCD has the highest score of 0.530. So, the answer is {\"answer\": \"OCD\"}" } ], "images": [ "scigen/train/962.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Performance comparison on resplited AAPD, whose test set contains 2000 samples whose label sets occur in the training set (Seen test set) and 2000 samples are not (Unseen test set). OCD (correct prefix) means we only sample correct labels in the training phase." ], "reasoning": "By comparing the ebF1 scores for the unseen test set, OCD has the highest score of 0.530.", "question": "Which model performs the best on the unseen test set according to ebF1?", "answer": "So, the answer is {\"answer\": \"OCD\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nOrder-free RNN with SS performs better than Seq2seq on the unseen test set.\n" }, { "role": "assistant", "content": "Comparing the unseen test set scores, Order-free RNN with SS has a miF1 of 0.520 and an ebF1 of 0.517, while Seq2seq has a miF1 of 0.508 and an ebF1 of 0.503. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/962.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Performance comparison on resplited AAPD, whose test set contains 2000 samples whose label sets occur in the training set (Seen test set) and 2000 samples are not (Unseen test set). OCD (correct prefix) means we only sample correct labels in the training phase." ], "reasoning": "Comparing the unseen test set scores, Order-free RNN with SS has a miF1 of 0.520 and an ebF1 of 0.517, while Seq2seq has a miF1 of 0.508 and an ebF1 of 0.503.", "statement": "Order-free RNN with SS performs better than Seq2seq on the unseen test set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 12: Performance comparisons on Reuters-21578 with more than one label.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The smaller test set has 405 samples, and the performance gap between baseline models and proposed methods is larger, highlighting the superiority of OCD and MTL.The smaller test set has 405 samples. However, the performance gap between baseline models and proposed methods are larger, which strengthen the superiority of OCD and MTL." } ], "images": [ "scigen/train/963.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 12: Performance comparisons on Reuters-21578 with more than one label." ], "reasoning": "The smaller test set has 405 samples, and the performance gap between baseline models and proposed methods is larger, highlighting the superiority of OCD and MTL.", "description": "The smaller test set has 405 samples. However, the performance gap between baseline models and proposed methods are larger, which strengthen the superiority of OCD and MTL.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 12: Performance comparisons on Reuters-21578 with more than one label.', answer the question below by showing each progressive step toward the answer. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich proposed method has the highest miF1 Baselines score?\n" }, { "role": "assistant", "content": "To find the proposed method with the highest miF1 Baselines score, look at the 'miF1 Baselines' column under 'Proposed methods'. The highest score is 0.750, achieved by 'OCD + MTL Logistic rescore'. So, the answer is {\"answer\": \"OCD + MTL Logistic rescore\"}." } ], "images": [ "scigen/train/963.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 12: Performance comparisons on Reuters-21578 with more than one label." ], "reasoning": "To find the proposed method with the highest miF1 Baselines score, look at the 'miF1 Baselines' column under 'Proposed methods'. The highest score is 0.750, achieved by 'OCD + MTL Logistic rescore'.", "question": "Which proposed method has the highest miF1 Baselines score?", "answer": "So, the answer is {\"answer\": \"OCD + MTL Logistic rescore\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe OCD + MTL Logistic joint dec. method has the highest maF1 Baselines score among the proposed methods.\n" }, { "role": "assistant", "content": "To verify this statement, check the 'maF1 Baselines' column under 'Proposed methods'. The highest score is 0.342, achieved by 'OCD + MTL Logistic joint dec.', which supports the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/963.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 12: Performance comparisons on Reuters-21578 with more than one label." ], "reasoning": "To verify this statement, check the 'maF1 Baselines' column under 'Proposed methods'. The highest score is 0.342, achieved by 'OCD + MTL Logistic joint dec.', which supports the statement.", "statement": "The OCD + MTL Logistic joint dec. method has the highest maF1 Baselines score among the proposed methods.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Performance on Named Entity Recognition and Part-of-Speech Tagging tasks. All methods are evaluated on test data containing OOV words. Results demonstrate that the proposed approach, HiCE + Morph + MAML, improves the downstream model by learning better representations for OOV words.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "HiCE outperforms all baselines in Rare-NER, Bio-NER, and Twitter POS, with the largest improvement in Rare-NER due to its high ratio of OOV words.HiCE outperforms the baselines in all the settings. Compared to the best baseline `a la carte, the relative improvements are 12.4%, 2.9% and 5.1% for Rare-NER, Bio-NER, and Twitter POS, respectively. As aforementioned, the ratio of OOV words in Rare-NER is high. As a result, all the systems perform worse on Rare-NER than Bio-NER, while HiCE reaches the largest improvement than all the other baselines. Besides, our baseline embedding is trained on Wikipedia corpus (WikiText-103), which is quite different from the bio-medical texts and social media domain. The experiment demonstrates that HiCE trained on DT is already able to leverage the general language knowledge which can be transferred through different domains, and adaptation with MAML can further reduce the domain gap and enhance the performance." } ], "images": [ "scigen/train/964.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance on Named Entity Recognition and Part-of-Speech Tagging tasks. All methods are evaluated on test data containing OOV words. Results demonstrate that the proposed approach, HiCE + Morph + MAML, improves the downstream model by learning better representations for OOV words." ], "reasoning": "HiCE outperforms all baselines in Rare-NER, Bio-NER, and Twitter POS, with the largest improvement in Rare-NER due to its high ratio of OOV words.", "description": "HiCE outperforms the baselines in all the settings. Compared to the best baseline `a la carte, the relative improvements are 12.4%, 2.9% and 5.1% for Rare-NER, Bio-NER, and Twitter POS, respectively. As aforementioned, the ratio of OOV words in Rare-NER is high. As a result, all the systems perform worse on Rare-NER than Bio-NER, while HiCE reaches the largest improvement than all the other baselines. Besides, our baseline embedding is trained on Wikipedia corpus (WikiText-103), which is quite different from the bio-medical texts and social media domain. The experiment demonstrates that HiCE trained on DT is already able to leverage the general language knowledge which can be transferred through different domains, and adaptation with MAML can further reduce the domain gap and enhance the performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: Table 2: Performance on Named Entity Recognition and Part-of-Speech Tagging tasks. All methods are evaluated on test data containing OOV words. Results demonstrate that the proposed approach, HiCE + Morph + MAML, improves the downstream model by learning better representations for OOV words.\nQuestion: What is the relative improvement of HiCE + Morph + MAML over the best baseline in Bio-NER?\n" }, { "role": "assistant", "content": "The best baseline for Bio-NER is `a la carte` with an F1-score of 0.7423. HiCE + Morph + MAML has an F1-score of 0.7636. The relative improvement is calculated as ((0.7636 - 0.7423) / 0.7423) * 100 = 2.9%. So, the answer is {\"answer\": \"2.9%\"}." } ], "images": [ "scigen/train/964.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance on Named Entity Recognition and Part-of-Speech Tagging tasks. All methods are evaluated on test data containing OOV words. Results demonstrate that the proposed approach, HiCE + Morph + MAML, improves the downstream model by learning better representations for OOV words." ], "reasoning": "The best baseline for Bio-NER is `a la carte` with an F1-score of 0.7423. HiCE + Morph + MAML has an F1-score of 0.7636. The relative improvement is calculated as ((0.7636 - 0.7423) / 0.7423) * 100 = 2.9%.", "question": "What is the relative improvement of HiCE + Morph + MAML over the best baseline in Bio-NER?", "answer": "So, the answer is {\"answer\": \"2.9%\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nHiCE + Morph + MAML achieves the highest F1-score in Bio-NER.\n" }, { "role": "assistant", "content": "The table shows that HiCE + Morph + MAML has an F1-score of 0.7636 in Bio-NER, which is higher than all other methods listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/964.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance on Named Entity Recognition and Part-of-Speech Tagging tasks. All methods are evaluated on test data containing OOV words. Results demonstrate that the proposed approach, HiCE + Morph + MAML, improves the downstream model by learning better representations for OOV words." ], "reasoning": "The table shows that HiCE + Morph + MAML has an F1-score of 0.7636 in Bio-NER, which is higher than all other methods listed.", "statement": "HiCE + Morph + MAML achieves the highest F1-score in Bio-NER.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: Performance on the Chimera benchmark dataset with different numbers of context sentences, which is measured by Spearman correlation. Baseline results are from the corresponding papers.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "HiCE+Morph+MAML shows significant improvements over the state-of-the-art method 'a la carte' in 2, 4, and 6-shot learning, and its performance is close to the upper bound (Oracle Embedding) when K=6.In particular, our method (HiCE+Morph+MAML) Compared with the current state-of-the-art method, `a la carte, the relative improvements (i.e., the performance difference divided by the baseline performance) of HiCE are 4.0%, 5.4% and 9.3% in terms of 2,4,6-shot learning, respectively. We also compare our results with that of the oracle embedding, which is the embeddings trained from DT, and used as ground-truth to train HiCE. This results can be regarded as an upper bound. As is shown, when the number of context sentences (K) is relatively large (i.e., K=6), the performance of HiCE is on a par with the upper bound (Oracle Embedding) and the relative performance difference is merely 2.7%. This indicates the significance of using an advanced aggregation model." } ], "images": [ "scigen/train/965.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance on the Chimera benchmark dataset with different numbers of context sentences, which is measured by Spearman correlation. Baseline results are from the corresponding papers." ], "reasoning": "HiCE+Morph+MAML shows significant improvements over the state-of-the-art method 'a la carte' in 2, 4, and 6-shot learning, and its performance is close to the upper bound (Oracle Embedding) when K=6.", "description": "In particular, our method (HiCE+Morph+MAML) Compared with the current state-of-the-art method, `a la carte, the relative improvements (i.e., the performance difference divided by the baseline performance) of HiCE are 4.0%, 5.4% and 9.3% in terms of 2,4,6-shot learning, respectively. We also compare our results with that of the oracle embedding, which is the embeddings trained from DT, and used as ground-truth to train HiCE. This results can be regarded as an upper bound. As is shown, when the number of context sentences (K) is relatively large (i.e., K=6), the performance of HiCE is on a par with the upper bound (Oracle Embedding) and the relative performance difference is merely 2.7%. This indicates the significance of using an advanced aggregation model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the relative performance improvement of HiCE+Morph+MAML over 'a la carte' in 4-shot learning?\nSolve the above question based on the table titled 'Table 1: Performance on the Chimera benchmark dataset with different numbers of context sentences, which is measured by Spearman correlation. Baseline results are from the corresponding papers.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The performance of HiCE+Morph+MAML in 4-shot learning is 0.4053, while 'a la carte' is 0.3844. The relative improvement is calculated as (0.4053 - 0.3844) / 0.3844 * 100%. So, the answer is {\"answer\": \"5.4%\"}" } ], "images": [ "scigen/train/965.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance on the Chimera benchmark dataset with different numbers of context sentences, which is measured by Spearman correlation. Baseline results are from the corresponding papers." ], "reasoning": "The performance of HiCE+Morph+MAML in 4-shot learning is 0.4053, while 'a la carte' is 0.3844. The relative improvement is calculated as (0.4053 - 0.3844) / 0.3844 * 100%.", "question": "What is the relative performance improvement of HiCE+Morph+MAML over 'a la carte' in 4-shot learning?", "answer": "So, the answer is {\"answer\": \"5.4%\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: HiCE+Morph+MAML outperforms Oracle Embedding in 6-shot learning., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nHiCE+Morph+MAML outperforms Oracle Embedding in 6-shot learning.\n" }, { "role": "assistant", "content": "The performance of HiCE+Morph+MAML in 6-shot learning is 0.4307, while Oracle Embedding is 0.4427. Since 0.4307 is less than 0.4427, HiCE+Morph+MAML does not outperform Oracle Embedding. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/965.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance on the Chimera benchmark dataset with different numbers of context sentences, which is measured by Spearman correlation. Baseline results are from the corresponding papers." ], "reasoning": "The performance of HiCE+Morph+MAML in 6-shot learning is 0.4307, while Oracle Embedding is 0.4427. Since 0.4307 is less than 0.4427, HiCE+Morph+MAML does not outperform Oracle Embedding.", "statement": "HiCE+Morph+MAML outperforms Oracle Embedding in 6-shot learning.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Effect of different models on canonical/non-canonical words.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Word embeddings and normalization have complementary effects on non-canonical words, with the combined model yielding the highest accuracy overall, despite a slight decrease in performance on canonical words.Word embeddings have a higher impact on standard, canonical tokens. It is interesting to note that word embeddings and normalization both have a similar yet complementary effect on the words to be normalized (non-canonical). The improvements on non-canonical words seem to be complementary. The combined model additionally improves on words which need normalization, whereas it scores almost 1% lower on canonical words. This suggests that both strategies have potential to complement each other. Embeddings work considerably better than normalization, which confirms what we found on the Dev data. The combined approach yields the highest accuracy over all evaluation sets, however, it significantly differs from embeddings only on Test_L. This can be explained by our earlier observation (cf. Notice that Test_L does indeed contain the highest proportion of non-canonical tokens." } ], "images": [ "scigen/train/966.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Effect of different models on canonical/non-canonical words." ], "reasoning": "Word embeddings and normalization have complementary effects on non-canonical words, with the combined model yielding the highest accuracy overall, despite a slight decrease in performance on canonical words.", "description": "Word embeddings have a higher impact on standard, canonical tokens. It is interesting to note that word embeddings and normalization both have a similar yet complementary effect on the words to be normalized (non-canonical). The improvements on non-canonical words seem to be complementary. The combined model additionally improves on words which need normalization, whereas it scores almost 1% lower on canonical words. This suggests that both strategies have potential to complement each other. Embeddings work considerably better than normalization, which confirms what we found on the Dev data. The combined approach yields the highest accuracy over all evaluation sets, however, it significantly differs from embeddings only on Test_L. This can be explained by our earlier observation (cf. Notice that Test_L does indeed contain the highest proportion of non-canonical tokens.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: Effect of different models on canonical/non-canonical words.', unfold the steps to solve the question given next. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nQuestion: What is the accuracy of the combined model on non-canonical words?\n" }, { "role": "assistant", "content": "The table shows the accuracy of different models on canonical and non-canonical words. For the combined model, the accuracy on non-canonical words is listed. So, the answer is {\"answer\": \"78.5\"}." } ], "images": [ "scigen/train/966.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Effect of different models on canonical/non-canonical words." ], "reasoning": "The table shows the accuracy of different models on canonical and non-canonical words. For the combined model, the accuracy on non-canonical words is listed.", "question": "What is the accuracy of the combined model on non-canonical words?", "answer": "So, the answer is {\"answer\": \"78.5\"}." }, { "messages": [ { "role": "user", "content": "The combined model has the highest accuracy on canonical words. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe combined model has the highest accuracy on canonical words.\n" }, { "role": "assistant", "content": "The table shows that the +Vecs model has the highest accuracy on canonical words with 91.2, while the combined model has an accuracy of 90.1. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/966.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Effect of different models on canonical/non-canonical words." ], "reasoning": "The table shows that the +Vecs model has the highest accuracy on canonical words with 91.2, while the combined model has an accuracy of 90.1.", "statement": "The combined model has the highest accuracy on canonical words.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 6: F1 scores of each model for the claims with various context length values.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Models with larger context representations significantly improve F1 scores for claims with varying context lengths, suggesting better representation learning.To understand for what kinds of claims the best performing contextual model is more effective, we evaluate the BERT model with flat context representation for claims with context length values 1, 2, 3 and 4 separately. For the claims with context length 1, adding Contextf(3) and Contextf(4) representation along with the claim achieves significantly better (p<0.05) F1 score than modeling the claim only. Similarly for the claims with context length 3 and 4, Contextf(4) and Contextf(3) perform significantly better than BERT with claim only ((p<0.05) and (p<0.01) respectively). We see that models with larger context are helpful even for claims which have limited context (e.g. Cl=1). This may suggest that when we train the models with larger context, they learn how to represent the claims and their context better." } ], "images": [ "scigen/train/967.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: F1 scores of each model for the claims with various context length values." ], "reasoning": "Models with larger context representations significantly improve F1 scores for claims with varying context lengths, suggesting better representation learning.", "description": "To understand for what kinds of claims the best performing contextual model is more effective, we evaluate the BERT model with flat context representation for claims with context length values 1, 2, 3 and 4 separately. For the claims with context length 1, adding Contextf(3) and Contextf(4) representation along with the claim achieves significantly better (p<0.05) F1 score than modeling the claim only. Similarly for the claims with context length 3 and 4, Contextf(4) and Contextf(3) perform significantly better than BERT with claim only ((p<0.05) and (p<0.01) respectively). We see that models with larger context are helpful even for claims which have limited context (e.g. Cl=1). This may suggest that when we train the models with larger context, they learn how to represent the claims and their context better.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 6: F1 scores of each model for the claims with various context length values.', and tackle the question below in a sequential manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich BERT model achieves the highest F1 score for claims with context length 4?\n" }, { "role": "assistant", "content": "By examining the table, the highest F1 score for claims with context length 4 is achieved by the 'Claim + Context f(3)' model with a score of 58.17±1.89. So, the answer is {\"answer\": \"Claim + Context f(3)\"}." } ], "images": [ "scigen/train/967.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: F1 scores of each model for the claims with various context length values." ], "reasoning": "By examining the table, the highest F1 score for claims with context length 4 is achieved by the 'Claim + Context f(3)' model with a score of 58.17±1.89.", "question": "Which BERT model achieves the highest F1 score for claims with context length 4?", "answer": "So, the answer is {\"answer\": \"Claim + Context f(3)\"}." }, { "messages": [ { "role": "user", "content": "The 'Claim only' model achieves the highest F1 score for claims with context length 1. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe 'Claim only' model achieves the highest F1 score for claims with context length 1.\n" }, { "role": "assistant", "content": "The table shows that the 'Claim only' model has an F1 score of 48.61±3.16 for context length 1, while the 'Claim + Context f(3)' model has a higher score of 54.88±2.49. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/967.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: F1 scores of each model for the claims with various context length values." ], "reasoning": "The table shows that the 'Claim only' model has an F1 score of 48.61±3.16 for context length 1, while the 'Claim + Context f(3)' model has a higher score of 54.88±2.49.", "statement": "The 'Claim only' model achieves the highest F1 score for claims with context length 1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Number of claims for the given range of number of votes. There are 19,512 claims in the dataset with 3 or more votes. Out of the claims with 3 or more votes, majority of them have 5 or more votes.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The majority of claims with 3 or more votes have at least 5 votes, and the study focuses on claims with at least 5 votes for reliable impact labeling.Distribution of impact votes. There are 19,512 claims in total with 3 or more votes. Out of the claims with 3 or more votes, majority of them have 5 or more votes. We limit our study to the claims with at least 5 votes to have a more reliable assignment for the accumulated impact label for each claim." } ], "images": [ "scigen/train/968.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Number of claims for the given range of number of votes. There are 19,512 claims in the dataset with 3 or more votes. Out of the claims with 3 or more votes, majority of them have 5 or more votes." ], "reasoning": "The majority of claims with 3 or more votes have at least 5 votes, and the study focuses on claims with at least 5 votes for reliable impact labeling.", "description": "Distribution of impact votes. There are 19,512 claims in total with 3 or more votes. Out of the claims with 3 or more votes, majority of them have 5 or more votes. We limit our study to the claims with at least 5 votes to have a more reliable assignment for the accumulated impact label for each claim.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 1: Number of claims for the given range of number of votes. There are 19,512 claims in the dataset with 3 or more votes. Out of the claims with 3 or more votes, majority of them have 5 or more votes.'. Solve the following question based on the table.\n\nQuestion: How many claims have between 5 and 15 impact votes?\nLet's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the number of claims with between 5 and 15 impact votes, sum the claims in the [5,10) and [10,15) ranges: 5,405 + 5,338. So, the answer is {\"answer\": \"10743\"}." } ], "images": [ "scigen/train/968.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Number of claims for the given range of number of votes. There are 19,512 claims in the dataset with 3 or more votes. Out of the claims with 3 or more votes, majority of them have 5 or more votes." ], "reasoning": "To find the number of claims with between 5 and 15 impact votes, sum the claims in the [5,10) and [10,15) ranges: 5,405 + 5,338.", "question": "How many claims have between 5 and 15 impact votes?", "answer": "So, the answer is {\"answer\": \"10743\"}." }, { "messages": [ { "role": "user", "content": "There are more claims with 5 to 10 votes than with 3 to 5 votes. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThere are more claims with 5 to 10 votes than with 3 to 5 votes.\n" }, { "role": "assistant", "content": "The table shows that there are 5,405 claims with 5 to 10 votes and 4,495 claims with 3 to 5 votes, indicating that the statement is true. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/968.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Number of claims for the given range of number of votes. There are 19,512 claims in the dataset with 3 or more votes. Out of the claims with 3 or more votes, majority of them have 5 or more votes." ], "reasoning": "The table shows that there are 5,405 claims with 5 to 10 votes and 4,495 claims with 3 to 5 votes, indicating that the statement is true.", "statement": "There are more claims with 5 to 10 votes than with 3 to 5 votes.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Number of claims, with at least 5 votes, above the given threshold of agreement percentage for 3-class and 5-class cases. When we combine the low impact and high impact classes, there are more claims with high agreement score.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Combining low and high impact classes results in more claims with high agreement scores, suggesting difficulty in distinguishing between no impact-low impact and high impact-very high impact classes.We see that when we combine the low impact and high impact classes, there are more claims with high agreement score. This may imply that distinguishing between no impact-low impact and high impact-very high impact classes is difficult. To decrease the sparsity issue, in our experiments, we use 3-class representation for the impact labels. Moreover, to have a more reliable assignment of impact labels, we consider only the claims with have more than 60% agreement." } ], "images": [ "scigen/train/969.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Number of claims, with at least 5 votes, above the given threshold of agreement percentage for 3-class and 5-class cases. When we combine the low impact and high impact classes, there are more claims with high agreement score." ], "reasoning": "Combining low and high impact classes results in more claims with high agreement scores, suggesting difficulty in distinguishing between no impact-low impact and high impact-very high impact classes.", "description": "We see that when we combine the low impact and high impact classes, there are more claims with high agreement score. This may imply that distinguishing between no impact-low impact and high impact-very high impact classes is difficult. To decrease the sparsity issue, in our experiments, we use 3-class representation for the impact labels. Moreover, to have a more reliable assignment of impact labels, we consider only the claims with have more than 60% agreement.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 2: Number of claims, with at least 5 votes, above the given threshold of agreement percentage for 3-class and 5-class cases. When we combine the low impact and high impact classes, there are more claims with high agreement score.'. Solve the following question based on the table.\n\nQuestion: How many claims have more than 60% agreement in the 3-class case?\nLet's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the number of claims with more than 60% agreement in the 3-class case, look at the row labeled '>60%' under the '3-class case' column. So, the answer is {\"answer\": \"7,386\"}." } ], "images": [ "scigen/train/969.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Number of claims, with at least 5 votes, above the given threshold of agreement percentage for 3-class and 5-class cases. When we combine the low impact and high impact classes, there are more claims with high agreement score." ], "reasoning": "To find the number of claims with more than 60% agreement in the 3-class case, look at the row labeled '>60%' under the '3-class case' column.", "question": "How many claims have more than 60% agreement in the 3-class case?", "answer": "So, the answer is {\"answer\": \"7,386\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThere are more claims with over 80% agreement in the 5-class case than in the 3-class case.\n" }, { "role": "assistant", "content": "Compare the number of claims with over 80% agreement in both the 3-class and 5-class cases. The 3-class case has 2,068 claims, while the 5-class case has 840 claims. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/969.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Number of claims, with at least 5 votes, above the given threshold of agreement percentage for 3-class and 5-class cases. When we combine the low impact and high impact classes, there are more claims with high agreement score." ], "reasoning": "Compare the number of claims with over 80% agreement in both the 3-class and 5-class cases. The 3-class case has 2,068 claims, while the 5-class case has 840 claims.", "statement": "There are more claims with over 80% agreement in the 5-class case than in the 3-class case.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Number of votes for the given impact label. There are 241,884 total votes and majority of them belongs to the category medium impact.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The majority of the impact votes belong to the medium impact category, and users assign more high and very high impact votes than low and no impact votes respectively.Impact label statistics. The claims have 241,884 total votes. The majority of the impact votes belong to medium impact category. We observe that users assign more high impact and very high impact votes than low impact and no impact votes respectively." } ], "images": [ "scigen/train/970.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Number of votes for the given impact label. There are 241,884 total votes and majority of them belongs to the category medium impact." ], "reasoning": "The majority of the impact votes belong to the medium impact category, and users assign more high and very high impact votes than low and no impact votes respectively.", "description": "Impact label statistics. The claims have 241,884 total votes. The majority of the impact votes belong to medium impact category. We observe that users assign more high impact and very high impact votes than low impact and no impact votes respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: Number of votes for the given impact label. There are 241,884 total votes and majority of them belongs to the category medium impact.'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the total number of votes for high impact and very high impact categories combined?\n" }, { "role": "assistant", "content": "To find the total number of votes for high impact and very high impact categories combined, add the votes for high impact (52,764) and very high impact (58,846). So, the answer is {\"answer\": \"111,610\"}." } ], "images": [ "scigen/train/970.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Number of votes for the given impact label. There are 241,884 total votes and majority of them belongs to the category medium impact." ], "reasoning": "To find the total number of votes for high impact and very high impact categories combined, add the votes for high impact (52,764) and very high impact (58,846).", "question": "What is the total number of votes for high impact and very high impact categories combined?", "answer": "So, the answer is {\"answer\": \"111,610\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe number of votes for medium impact is greater than the combined votes for low impact and no impact.\n" }, { "role": "assistant", "content": "The number of votes for medium impact is 60,136. The combined votes for low impact (37,457) and no impact (32,681) is 70,138, which is greater than 60,136. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/970.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Number of votes for the given impact label. There are 241,884 total votes and majority of them belongs to the category medium impact." ], "reasoning": "The number of votes for medium impact is 60,136. The combined votes for low impact (37,457) and no impact (32,681) is 70,138, which is greater than 60,136.", "statement": "The number of votes for medium impact is greater than the combined votes for low impact and no impact.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Number of claims for the given range of context length, for claims with more than 5 votes and an agreement score greater than 60%.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "More than half of the claims have a context length of 3 or higher.Context length (Cl) for a particular claim C is defined by number of claims included in the argument path starting from the thesis until the claim C. We observe that more than half of these claims have 3 or higher context length." } ], "images": [ "scigen/train/971.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Number of claims for the given range of context length, for claims with more than 5 votes and an agreement score greater than 60%." ], "reasoning": "More than half of the claims have a context length of 3 or higher.", "description": "Context length (Cl) for a particular claim C is defined by number of claims included in the argument path starting from the thesis until the claim C. We observe that more than half of these claims have 3 or higher context length.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 4: Number of claims for the given range of context length, for claims with more than 5 votes and an agreement score greater than 60%.\nQuestion: What is the total number of claims with a context length of 3 or higher?\n" }, { "role": "assistant", "content": "To find the total number of claims with a context length of 3 or higher, sum the number of claims for context lengths 3, [4,5], (5,10], and >10. So, the answer is {\"answer\": \"3,885\"}." } ], "images": [ "scigen/train/971.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Number of claims for the given range of context length, for claims with more than 5 votes and an agreement score greater than 60%." ], "reasoning": "To find the total number of claims with a context length of 3 or higher, sum the number of claims for context lengths 3, [4,5], (5,10], and >10.", "question": "What is the total number of claims with a context length of 3 or higher?", "answer": "So, the answer is {\"answer\": \"3,885\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe majority of claims have a context length of 2 or less.\n" }, { "role": "assistant", "content": "To determine if the majority of claims have a context length of 2 or less, sum the number of claims for context lengths 1 and 2, and compare it to the total number of claims. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/971.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Number of claims for the given range of context length, for claims with more than 5 votes and an agreement score greater than 60%." ], "reasoning": "To determine if the majority of claims have a context length of 2 or less, sum the number of claims for context lengths 1 and 2, and compare it to the total number of claims.", "statement": "The majority of claims have a context length of 2 or less.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 6: Varying encoders from pretrained Transformers in our sequence labeling system. Training was done on data from training stage II only.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "BERT, RoBERTa, and XLNet encoders outperform GPT-2 and ALBERT, leading to their selection for further experiments.Encoders from pretrained transformers. We fine-tuned BERT Devlin et al. Radford et al. XLNet Yang et al. et al. We also added LSTM with randomly initialized embeddings (dim=300) as a baseline. BERT, RoBERTa and XLNet encoders perform better than GPT-2 and ALBERT, so we used them only in our next experiments. We hypothesize that encoders from Transformers which were pretrained as a part of the entire encoder-decoder pipeline are less useful for GECToR." } ], "images": [ "scigen/train/972.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Varying encoders from pretrained Transformers in our sequence labeling system. Training was done on data from training stage II only." ], "reasoning": "BERT, RoBERTa, and XLNet encoders outperform GPT-2 and ALBERT, leading to their selection for further experiments.", "description": "Encoders from pretrained transformers. We fine-tuned BERT Devlin et al. Radford et al. XLNet Yang et al. et al. We also added LSTM with randomly initialized embeddings (dim=300) as a baseline. BERT, RoBERTa and XLNet encoders perform better than GPT-2 and ALBERT, so we used them only in our next experiments. We hypothesize that encoders from Transformers which were pretrained as a part of the entire encoder-decoder pipeline are less useful for GECToR.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 6: Varying encoders from pretrained Transformers in our sequence labeling system. Training was done on data from training stage II only.', solve the following question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich encoder achieved the highest precision score on the CoNLL-2014 (test) dataset?\n" }, { "role": "assistant", "content": "By examining the CoNLL-2014 (test) P column, RoBERTa has the highest precision score of 67.5. So, the answer is {\"answer\": \"RoBERTa\"}" } ], "images": [ "scigen/train/972.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Varying encoders from pretrained Transformers in our sequence labeling system. Training was done on data from training stage II only." ], "reasoning": "By examining the CoNLL-2014 (test) P column, RoBERTa has the highest precision score of 67.5.", "question": "Which encoder achieved the highest precision score on the CoNLL-2014 (test) dataset?", "answer": "So, the answer is {\"answer\": \"RoBERTa\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRoBERTa achieved the highest F0.5 score on the BEA-2019 (dev) dataset.\n" }, { "role": "assistant", "content": "By examining the BEA-2019 (dev) F0.5 column, RoBERTa has the highest F0.5 score of 44.5. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/972.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Varying encoders from pretrained Transformers in our sequence labeling system. Training was done on data from training stage II only." ], "reasoning": "By examining the BEA-2019 (dev) F0.5 column, RoBERTa has the highest F0.5 score of 44.5.", "statement": "RoBERTa achieved the highest F0.5 score on the BEA-2019 (dev) dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 7: Comparison of single models and ensembles. The M2 score for CoNLL-2014 (test) and ERRANT for the BEA-2019 (test) are reported. In ensembles we simply average output probabilities from single models.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The best single-model, GECToR (XLNet), achieves F0.5 scores of 65.3 on CoNLL-2014 and 72.4 on BEA-2019, while the best ensemble model is GECToR (BERT + RoBERTa + XLNet).Finally, our best single-model, GECToR (XLNet) achieves F0.5 = 65.3 on CoNLL-2014 (test) and F0.5 = 72.4 on BEA-2019 (test). Best ensemble model, GECToR (BERT + RoBERTa + XLNet)" } ], "images": [ "scigen/train/973.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Comparison of single models and ensembles. The M2 score for CoNLL-2014 (test) and ERRANT for the BEA-2019 (test) are reported. In ensembles we simply average output probabilities from single models." ], "reasoning": "The best single-model, GECToR (XLNet), achieves F0.5 scores of 65.3 on CoNLL-2014 and 72.4 on BEA-2019, while the best ensemble model is GECToR (BERT + RoBERTa + XLNet).", "description": "Finally, our best single-model, GECToR (XLNet) achieves F0.5 = 65.3 on CoNLL-2014 (test) and F0.5 = 72.4 on BEA-2019 (test). Best ensemble model, GECToR (BERT + RoBERTa + XLNet)", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 7: Comparison of single models and ensembles. The M2 score for CoNLL-2014 (test) and ERRANT for the BEA-2019 (test) are reported. In ensembles we simply average output probabilities from single models.', unfold the question presented below, detailing every step of your calculation. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model has the highest F0.5 score on BEA-2019 (test)?\n" }, { "role": "assistant", "content": "By examining the F0.5 scores on BEA-2019 (test) in the table, the highest score is 73.7 achieved by GECToR (RoBERTa + XLNet). So, the answer is {\"answer\": \"GECToR (RoBERTa + XLNet)\"}." } ], "images": [ "scigen/train/973.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Comparison of single models and ensembles. The M2 score for CoNLL-2014 (test) and ERRANT for the BEA-2019 (test) are reported. In ensembles we simply average output probabilities from single models." ], "reasoning": "By examining the F0.5 scores on BEA-2019 (test) in the table, the highest score is 73.7 achieved by GECToR (RoBERTa + XLNet).", "question": "Which model has the highest F0.5 score on BEA-2019 (test)?", "answer": "So, the answer is {\"answer\": \"GECToR (RoBERTa + XLNet)\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis GECToR (BERT + RoBERTa + XLNet) has the highest F0.5 score on CoNLL-2014 (test). and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nGECToR (BERT + RoBERTa + XLNet) has the highest F0.5 score on CoNLL-2014 (test).\n" }, { "role": "assistant", "content": "By examining the F0.5 scores on CoNLL-2014 (test) in the table, the highest score is 66.5 achieved by GECToR (BERT + RoBERTa + XLNet). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/973.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Comparison of single models and ensembles. The M2 score for CoNLL-2014 (test) and ERRANT for the BEA-2019 (test) are reported. In ensembles we simply average output probabilities from single models." ], "reasoning": "By examining the F0.5 scores on CoNLL-2014 (test) in the table, the highest score is 66.5 achieved by GECToR (BERT + RoBERTa + XLNet).", "statement": "GECToR (BERT + RoBERTa + XLNet) has the highest F0.5 score on CoNLL-2014 (test).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 8: Inference time for NVIDIA Tesla V100 on CoNLL-2014 (test), single model, batch size=128.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The sequence tagger's inference speed is significantly faster than the state-of-the-art Transformer, with up to 10 times the speed.Speed comparison. We measured the model’s average inference time on NVIDIA Tesla V100 on batch size 128. For sequence tagging we don’t need to predict corrections one-by-one as in autoregressive transformer decoders, so inference is naturally parallelizable and therefore runs many times faster. Our sequence tagger’s inference speed is up to 10 times as fast as the state-of-the-art Transformer from Zhao et al." } ], "images": [ "scigen/train/974.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Inference time for NVIDIA Tesla V100 on CoNLL-2014 (test), single model, batch size=128." ], "reasoning": "The sequence tagger's inference speed is significantly faster than the state-of-the-art Transformer, with up to 10 times the speed.", "description": "Speed comparison. We measured the model’s average inference time on NVIDIA Tesla V100 on batch size 128. For sequence tagging we don’t need to predict corrections one-by-one as in autoregressive transformer decoders, so inference is naturally parallelizable and therefore runs many times faster. Our sequence tagger’s inference speed is up to 10 times as fast as the state-of-the-art Transformer from Zhao et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 8: Inference time for NVIDIA Tesla V100 on CoNLL-2014 (test), single model, batch size=128.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: What is the inference time for GECToR (XLNet) with 1 iteration compared to Transformer-NMT with beam size 12?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The inference time for GECToR (XLNet) with 1 iteration is 0.20 seconds, while for Transformer-NMT with beam size 12, it is 4.35 seconds. Comparing these times shows that GECToR (XLNet) with 1 iteration is significantly faster. So, the answer is {\"answer\": \"0.20 seconds\"}." } ], "images": [ "scigen/train/974.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Inference time for NVIDIA Tesla V100 on CoNLL-2014 (test), single model, batch size=128." ], "reasoning": "The inference time for GECToR (XLNet) with 1 iteration is 0.20 seconds, while for Transformer-NMT with beam size 12, it is 4.35 seconds. Comparing these times shows that GECToR (XLNet) with 1 iteration is significantly faster.", "question": "What is the inference time for GECToR (XLNet) with 1 iteration compared to Transformer-NMT with beam size 12?", "answer": "So, the answer is {\"answer\": \"0.20 seconds\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nGECToR (XLNet) with 5 iterations has a faster inference time than Transformer-NMT with beam size 1.\n" }, { "role": "assistant", "content": "The table shows that GECToR (XLNet) with 5 iterations has an inference time of 0.40 seconds, while Transformer-NMT with beam size 1 has an inference time of 0.71 seconds. Therefore, GECToR (XLNet) with 5 iterations is faster. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/974.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Inference time for NVIDIA Tesla V100 on CoNLL-2014 (test), single model, batch size=128." ], "reasoning": "The table shows that GECToR (XLNet) with 5 iterations has an inference time of 0.40 seconds, while Transformer-NMT with beam size 1 has an inference time of 0.71 seconds. Therefore, GECToR (XLNet) with 5 iterations is faster.", "statement": "GECToR (XLNet) with 5 iterations has a faster inference time than Transformer-NMT with beam size 1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Performance of trained image captioning models. Higher BLEU-4 score means better performance.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The 3-to-1 sparse-to-dense ratio used in language modeling also shows a performance improvement in image captioning, demonstrating generality beyond language model datasets.Compared to the 5.92% performance loss in language modeling, this result demonstrates generality to applications beyond language model datasets. Moreover, the performance gap between the sparse training and the dense training can be mitigated by the dense after sparse approach. Interestingly, the 3-to-1 sparse-to-dense ratio used in the word language model also works well for the image captioning." } ], "images": [ "scigen/train/975.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of trained image captioning models. Higher BLEU-4 score means better performance." ], "reasoning": "The 3-to-1 sparse-to-dense ratio used in language modeling also shows a performance improvement in image captioning, demonstrating generality beyond language model datasets.", "description": "Compared to the 5.92% performance loss in language modeling, this result demonstrates generality to applications beyond language model datasets. Moreover, the performance gap between the sparse training and the dense training can be mitigated by the dense after sparse approach. Interestingly, the 3-to-1 sparse-to-dense ratio used in the word language model also works well for the image captioning.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich training method shows the highest improvement in BLEU-4 score?\nSolve the above question based on the table titled 'Table 3: Performance of trained image captioning models. Higher BLEU-4 score means better performance.'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the 'Improvement' column, the '3/4 fine + 1/4 dense' method shows the highest improvement with +0.32%. So, the answer is {\"answer\": \"3/4 fine + 1/4 dense\"}." } ], "images": [ "scigen/train/975.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of trained image captioning models. Higher BLEU-4 score means better performance." ], "reasoning": "By comparing the 'Improvement' column, the '3/4 fine + 1/4 dense' method shows the highest improvement with +0.32%.", "question": "Which training method shows the highest improvement in BLEU-4 score?", "answer": "So, the answer is {\"answer\": \"3/4 fine + 1/4 dense\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe coarse-grained training method has the highest BLEU-4 score.\n" }, { "role": "assistant", "content": "The table shows that the coarse-grained method has a BLEU-4 score of 30.3, which is not the highest. The dense method has the highest BLEU-4 score of 31.0. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/975.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of trained image captioning models. Higher BLEU-4 score means better performance." ], "reasoning": "The table shows that the coarse-grained method has a BLEU-4 score of 30.3, which is not the highest. The dense method has the highest BLEU-4 score of 31.0.", "statement": "The coarse-grained training method has the highest BLEU-4 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Performance of trained language models by dense after sparse method. Lower perplexity means better performance.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Mixing sparse and dense training methods improves model performance, with a 3-to-1 ratio of sparse to dense steps being sufficient to achieve better results than pure methods.The model is first trained with the coarse-grained method or the fine-grained method, and then trained with the regular dense training model. With 75% steps of sparse training and 25% steps of dense training, the resulted model achieves slightly better performance than the baseline model. The number of total training steps remains constant across the methods. These results demonstrate that mixing the two methods compensates for the quality gap between the pure sparse training and pure dense training methods. A key parameter of applying this mixed training is the ratio of sparse steps to dense steps. Intuitively, the resulting model will perform better with more dense time. Our results show that a 3-to-1 ratio of sparse to dense is sufficient." } ], "images": [ "scigen/train/976.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of trained language models by dense after sparse method. Lower perplexity means better performance." ], "reasoning": "Mixing sparse and dense training methods improves model performance, with a 3-to-1 ratio of sparse to dense steps being sufficient to achieve better results than pure methods.", "description": "The model is first trained with the coarse-grained method or the fine-grained method, and then trained with the regular dense training model. With 75% steps of sparse training and 25% steps of dense training, the resulted model achieves slightly better performance than the baseline model. The number of total training steps remains constant across the methods. These results demonstrate that mixing the two methods compensates for the quality gap between the pure sparse training and pure dense training methods. A key parameter of applying this mixed training is the ratio of sparse steps to dense steps. Intuitively, the resulting model will perform better with more dense time. Our results show that a 3-to-1 ratio of sparse to dense is sufficient.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Performance of trained language models by dense after sparse method. Lower perplexity means better performance.\nQuestion: Which mixed training method achieves the highest improvement in performance?\nSolve the above question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the 'Improvement' column for all mixed training methods, the '1/2 fine + 1/2 dense' method achieves the highest improvement of +4.83%. So, the answer is {\"answer\": \"1/2 fine + 1/2 dense\"}" } ], "images": [ "scigen/train/976.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of trained language models by dense after sparse method. Lower perplexity means better performance." ], "reasoning": "By comparing the 'Improvement' column for all mixed training methods, the '1/2 fine + 1/2 dense' method achieves the highest improvement of +4.83%.", "question": "Which mixed training method achieves the highest improvement in performance?", "answer": "So, the answer is {\"answer\": \"1/2 fine + 1/2 dense\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The 3/4 coarse + 1/4 dense method achieves better performance than the baseline dense method. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe 3/4 coarse + 1/4 dense method achieves better performance than the baseline dense method.\n" }, { "role": "assistant", "content": "The '3/4 coarse + 1/4 dense' method shows an improvement of +1.84%, which is better than the baseline dense method with 0% improvement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/976.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of trained language models by dense after sparse method. Lower perplexity means better performance." ], "reasoning": "The '3/4 coarse + 1/4 dense' method shows an improvement of +1.84%, which is better than the baseline dense method with 0% improvement.", "statement": "The 3/4 coarse + 1/4 dense method achieves better performance than the baseline dense method.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: Performance of trained NMT models. Higher BLEU score means better performance.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The coarse-grain sparsifying method results in a 3.5% decrease in BLEU score compared to dense SGD, while the fine-grain method results in a 2.0% decrease.The BLEU scores are the validation BLEU scores at the end of the training (after 600,000 steps). Although our sparsifying methods enforce 50% sparsity in the gate gradients, the resulting models achieve acceptable BLEU scores. Even compared to the dense SGD, the coarse-grain sparsifying only suffers a 3.5% BLEU score decrease while the fine-grain method is slightly better with just a 2.0% decrease." } ], "images": [ "scigen/train/977.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance of trained NMT models. Higher BLEU score means better performance." ], "reasoning": "The coarse-grain sparsifying method results in a 3.5% decrease in BLEU score compared to dense SGD, while the fine-grain method results in a 2.0% decrease.", "description": "The BLEU scores are the validation BLEU scores at the end of the training (after 600,000 steps). Although our sparsifying methods enforce 50% sparsity in the gate gradients, the resulting models achieve acceptable BLEU scores. Even compared to the dense SGD, the coarse-grain sparsifying only suffers a 3.5% BLEU score decrease while the fine-grain method is slightly better with just a 2.0% decrease.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 4: Performance of trained NMT models. Higher BLEU score means better performance.'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. \n\nQuestion: Which training method achieved the highest BLEU score improvement?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By comparing the 'Improvement' column, the method '3/4 fine + 1/4 dense' achieved the highest BLEU score improvement of +0.64%. So, the answer is {\"answer\": \"3/4 fine + 1/4 dense\"}" } ], "images": [ "scigen/train/977.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance of trained NMT models. Higher BLEU score means better performance." ], "reasoning": "By comparing the 'Improvement' column, the method '3/4 fine + 1/4 dense' achieved the highest BLEU score improvement of +0.64%.", "question": "Which training method achieved the highest BLEU score improvement?", "answer": "So, the answer is {\"answer\": \"3/4 fine + 1/4 dense\"}" }, { "messages": [ { "role": "user", "content": "The fine-grained method achieved a BLEU score improvement of 2.0%. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe fine-grained method achieved a BLEU score improvement of 2.0%.\n" }, { "role": "assistant", "content": "The table shows that the fine-grained method achieved a BLEU score of 19.92, which is a 2.0% decrease, not an improvement. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/977.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance of trained NMT models. Higher BLEU score means better performance." ], "reasoning": "The table shows that the fine-grained method achieved a BLEU score of 19.92, which is a 2.0% decrease, not an improvement.", "statement": "The fine-grained method achieved a BLEU score improvement of 2.0%.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Results of different act generation methods, where BiLSTM, Word-CNN and Transformer are baselines from Chen et al. (2019). MarCo is our act generator trained jointly with the response generator and Transformer (GEN) is that without joint training.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "MarCo, trained jointly with the response generator, outperforms other methods, achieving the highest F1 score.To evaluate the performance of our act generator, we compare it with several baseline methods mentioned in Chen et al. We use MarCo to represent our act generator which is trained jointly with the response generator, and use Transformer (GEN) to denote our act generator without joint training. But after trained jointly with the response generator, MarCo manages to show the best performance, confirming the effect of the co-generation." } ], "images": [ "scigen/train/978.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of different act generation methods, where BiLSTM, Word-CNN and Transformer are baselines from Chen et al. (2019). MarCo is our act generator trained jointly with the response generator and Transformer (GEN) is that without joint training." ], "reasoning": "MarCo, trained jointly with the response generator, outperforms other methods, achieving the highest F1 score.", "description": "To evaluate the performance of our act generator, we compare it with several baseline methods mentioned in Chen et al. We use MarCo to represent our act generator which is trained jointly with the response generator, and use Transformer (GEN) to denote our act generator without joint training. But after trained jointly with the response generator, MarCo manages to show the best performance, confirming the effect of the co-generation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 3: Results of different act generation methods, where BiLSTM, Word-CNN and Transformer are baselines from Chen et al. (2019). MarCo is our act generator trained jointly with the response generator and Transformer (GEN) is that without joint training.', answer the question below by showing each progressive step toward the answer. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich method achieved the highest F1 score?\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all methods listed in the table, MarCo has the highest F1 score of 73.9. So, the answer is {\"answer\": \"MarCo\"}" } ], "images": [ "scigen/train/978.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of different act generation methods, where BiLSTM, Word-CNN and Transformer are baselines from Chen et al. (2019). MarCo is our act generator trained jointly with the response generator and Transformer (GEN) is that without joint training." ], "reasoning": "By comparing the F1 scores of all methods listed in the table, MarCo has the highest F1 score of 73.9.", "question": "Which method achieved the highest F1 score?", "answer": "So, the answer is {\"answer\": \"MarCo\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Transformer (GEN) achieved an F1 score higher than 73.5., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nTransformer (GEN) achieved an F1 score higher than 73.5.\n" }, { "role": "assistant", "content": "The table shows that Transformer (GEN) has an F1 score of 73.2, which is not higher than 73.5. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/978.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of different act generation methods, where BiLSTM, Word-CNN and Transformer are baselines from Chen et al. (2019). MarCo is our act generator trained jointly with the response generator and Transformer (GEN) is that without joint training." ], "reasoning": "The table shows that Transformer (GEN) has an F1 score of 73.2, which is not higher than 73.5.", "statement": "Transformer (GEN) achieved an F1 score higher than 73.5.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Overall results on the MultiWOZ 2.0 dataset.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "MarCo outperforms all baselines in Inform Rate, Request Success, and Combined Score, especially when using BERT.From the table we can notice that our co-generation model (MarCo) outperforms all the baselines in Inform Rate, Request Success, and especially in combined score which is an overall metric. By comparing the two HDSA models, we can find HDSA derives its main performance from the external BERT, which can also be used to improve our MarCo considerably (MarCo (BERT)). These results confirm the success of MarCo by modeling act prediction as a generation problem and training it jointly with response generation." } ], "images": [ "scigen/train/979.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Overall results on the MultiWOZ 2.0 dataset." ], "reasoning": "MarCo outperforms all baselines in Inform Rate, Request Success, and Combined Score, especially when using BERT.", "description": "From the table we can notice that our co-generation model (MarCo) outperforms all the baselines in Inform Rate, Request Success, and especially in combined score which is an overall metric. By comparing the two HDSA models, we can find HDSA derives its main performance from the external BERT, which can also be used to improve our MarCo considerably (MarCo (BERT)). These results confirm the success of MarCo by modeling act prediction as a generation problem and training it jointly with response generation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 1: Overall results on the MultiWOZ 2.0 dataset.', solve the following question step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model has the highest Combined Score and what is its value?\n" }, { "role": "assistant", "content": "By comparing the Combined Score column, MarCo (BERT) has the highest value of 105.47. So, the answer is {\"answer\": \"MarCo (BERT)\"}." } ], "images": [ "scigen/train/979.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Overall results on the MultiWOZ 2.0 dataset." ], "reasoning": "By comparing the Combined Score column, MarCo (BERT) has the highest value of 105.47.", "question": "Which model has the highest Combined Score and what is its value?", "answer": "So, the answer is {\"answer\": \"MarCo (BERT)\"}." }, { "messages": [ { "role": "user", "content": "HDSA (MarCo) has a higher Inform Rate than MarCo. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nHDSA (MarCo) has a higher Inform Rate than MarCo.\n" }, { "role": "assistant", "content": "The Inform Rate for HDSA (MarCo) is 76.50, while for MarCo it is 90.30, indicating that the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/979.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Overall results on the MultiWOZ 2.0 dataset." ], "reasoning": "The Inform Rate for HDSA (MarCo) is 76.50, while for MarCo it is 90.30, indicating that the statement is incorrect.", "statement": "HDSA (MarCo) has a higher Inform Rate than MarCo.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: English→German performance on WMT test sets. Our pretrained model outperforms all other models. Note that the model without pretraining uses the LM objective.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Our best single model outperforms the previous state of the art ensemble of 4 models, and our ensemble of 5 models matches or exceeds the previous best ensemble of 12 models.Equally impressive is the fact that our best single model outperforms the previous state of the art ensemble of 4 models. Our ensemble of 5 models matches or exceeds the previous best ensemble of 12 models." } ], "images": [ "scigen/train/980.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: English→German performance on WMT test sets. Our pretrained model outperforms all other models. Note that the model without pretraining uses the LM objective." ], "reasoning": "Our best single model outperforms the previous state of the art ensemble of 4 models, and our ensemble of 5 models matches or exceeds the previous best ensemble of 12 models.", "description": "Equally impressive is the fact that our best single model outperforms the previous state of the art ensemble of 4 models. Our ensemble of 5 models matches or exceeds the previous best ensemble of 12 models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: English→German performance on WMT test sets. Our pretrained model outperforms all other models. Note that the model without pretraining uses the LM objective.\nQuestion: What is the BLEU score of the best single model on newstest2015?\nGive a detailed response to the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The best single model is the Pretrained seq2seq model with a BLEU score of 27.0 on newstest2015. So, the answer is {\"answer\": \"27.0\"}." } ], "images": [ "scigen/train/980.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: English→German performance on WMT test sets. Our pretrained model outperforms all other models. Note that the model without pretraining uses the LM objective." ], "reasoning": "The best single model is the Pretrained seq2seq model with a BLEU score of 27.0 on newstest2015.", "question": "What is the BLEU score of the best single model on newstest2015?", "answer": "So, the answer is {\"answer\": \"27.0\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe BLEU score of the best single model on newstest2014 is higher than the BLEU score of the previous best ensemble of 4 models.\n" }, { "role": "assistant", "content": "The BLEU score of the best single model (Pretrained seq2seq) on newstest2014 is 24.0, which is higher than the BLEU score of the previous best ensemble of 4 models (Backtranslation) which is 23.8. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/980.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: English→German performance on WMT test sets. Our pretrained model outperforms all other models. Note that the model without pretraining uses the LM objective." ], "reasoning": "The BLEU score of the best single model (Pretrained seq2seq) on newstest2014 is 24.0, which is higher than the BLEU score of the previous best ensemble of 4 models (Backtranslation) which is 23.8.", "statement": "The BLEU score of the best single model on newstest2014 is higher than the BLEU score of the previous best ensemble of 4 models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 5: Different λ’s effect on our model performance. The compared models are trained for 2000 epochs'.\n" }, { "role": "assistant", "content": "As λ increases, both Hits@10 and mean rank increase, indicating improved reasoning capability but higher overall rank of correct entities.We compare how different value of λ would influence our model’s performance in Table. With large λ and higher domain sampling probability, our model’s Hits@10 increases while mean rank also increases. The rise of mean rank is due to higher probability of generating a valid triple as a negative sample causing the energy of a valid triple to increase, which leads to a higher overall rank of a correct entity. However, the reasoning capability is boosted with higher Hits@10 as shown in the table." } ], "images": [ "scigen/train/981.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Different λ’s effect on our model performance. The compared models are trained for 2000 epochs" ], "reasoning": "As λ increases, both Hits@10 and mean rank increase, indicating improved reasoning capability but higher overall rank of correct entities.", "description": "We compare how different value of λ would influence our model’s performance in Table. With large λ and higher domain sampling probability, our model’s Hits@10 increases while mean rank also increases. The rise of mean rank is due to higher probability of generating a valid triple as a negative sample causing the energy of a valid triple to increase, which leads to a higher overall rank of a correct entity. However, the reasoning capability is boosted with higher Hits@10 as shown in the table.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 5: Different λ’s effect on our model performance. The compared models are trained for 2000 epochs'. Solve the following question based on the table.\n\nQuestion: What is the mean rank (MR) for WN18 when λ is 0.003?\nLet's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the mean rank for WN18 when λ is 0.003, look at the row where λ=0.003 and the column under WN18 MR. So, the answer is {\"answer\": \"239\"}." } ], "images": [ "scigen/train/981.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Different λ’s effect on our model performance. The compared models are trained for 2000 epochs" ], "reasoning": "To find the mean rank for WN18 when λ is 0.003, look at the row where λ=0.003 and the column under WN18 MR.", "question": "What is the mean rank (MR) for WN18 when λ is 0.003?", "answer": "So, the answer is {\"answer\": \"239\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Hits@10 for FB15k is highest when λ is 0.003.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Hits@10 values for FB15k across different λ values. The Hits@10 for FB15k is 80.9 when λ is 0.003, which is the highest among the given values. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/981.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Different λ’s effect on our model performance. The compared models are trained for 2000 epochs" ], "reasoning": "To verify this statement, compare the Hits@10 values for FB15k across different λ values. The Hits@10 for FB15k is 80.9 when λ is 0.003, which is the highest among the given values.", "statement": "The Hits@10 for FB15k is highest when λ is 0.003.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Link prediction results on two datasets. Higher Hits@10 or lower Mean Rank indicates better performance. Following Nguyen et al. (2016b) and Shen et al. (2016), we divide the models into two groups. The first group contains intrinsic models without using extra information. The second group make use of additional information. Results in the brackets are another set of results STransE reported.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Our model outperforms previous models on WN18 and FB15k metrics without external information, achieving a better mean rank on WN18 with comparable Hits@10 to IRN.The overall link prediction results Our model consistently outperforms previous models without external information on both the metrics of WN18 and FB15k. On WN18, we even achieve a much better mean rank with comparable Hits@10 than current state-of-the-art model IRN employing external information." } ], "images": [ "scigen/train/982.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Link prediction results on two datasets. Higher Hits@10 or lower Mean Rank indicates better performance. Following Nguyen et al. (2016b) and Shen et al. (2016), we divide the models into two groups. The first group contains intrinsic models without using extra information. The second group make use of additional information. Results in the brackets are another set of results STransE reported." ], "reasoning": "Our model outperforms previous models on WN18 and FB15k metrics without external information, achieving a better mean rank on WN18 with comparable Hits@10 to IRN.", "description": "The overall link prediction results Our model consistently outperforms previous models without external information on both the metrics of WN18 and FB15k. On WN18, we even achieve a much better mean rank with comparable Hits@10 than current state-of-the-art model IRN employing external information.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Link prediction results on two datasets. Higher Hits@10 or lower Mean Rank indicates better performance. Following Nguyen et al. (2016b) and Shen et al. (2016), we divide the models into two groups. The first group contains intrinsic models without using extra information. The second group make use of additional information. Results in the brackets are another set of results STransE reported.', answer the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model achieves the best mean rank on WN18 without using additional information?\n" }, { "role": "assistant", "content": "To find the model with the best mean rank on WN18 without using additional information, we look at the 'WN18 Mean Rank' column for models with 'No' in the 'Additional Information' column. The model with the lowest mean rank is STransE with a mean rank of 206. So, the answer is {\"answer\": \"STransE\"}." } ], "images": [ "scigen/train/982.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Link prediction results on two datasets. Higher Hits@10 or lower Mean Rank indicates better performance. Following Nguyen et al. (2016b) and Shen et al. (2016), we divide the models into two groups. The first group contains intrinsic models without using extra information. The second group make use of additional information. Results in the brackets are another set of results STransE reported." ], "reasoning": "To find the model with the best mean rank on WN18 without using additional information, we look at the 'WN18 Mean Rank' column for models with 'No' in the 'Additional Information' column. The model with the lowest mean rank is STransE with a mean rank of 206.", "question": "Which model achieves the best mean rank on WN18 without using additional information?", "answer": "So, the answer is {\"answer\": \"STransE\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim IRN achieves the best Hits@10 on FB15k., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nIRN achieves the best Hits@10 on FB15k.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the 'FB15k Hits@10' column. IRN has a Hits@10 of 92.7, which is the highest value in that column. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/982.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Link prediction results on two datasets. Higher Hits@10 or lower Mean Rank indicates better performance. Following Nguyen et al. (2016b) and Shen et al. (2016), we divide the models into two groups. The first group contains intrinsic models without using extra information. The second group make use of additional information. Results in the brackets are another set of results STransE reported." ], "reasoning": "To verify this statement, we look at the 'FB15k Hits@10' column. IRN has a Hits@10 of 92.7, which is the highest value in that column.", "statement": "IRN achieves the best Hits@10 on FB15k.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Performance of model with dense attention vectors or sparse attention vectors. MR, H10 and Time denotes mean rank, Hits@10 and training time per epoch respectively', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Sparse attention in ITransF generally performs better or comparably to dense attention, and ℓ1 regularization does not produce sparse attention, especially on FB15k.Generally, ITransF with sparse attention has slightly better or comparable performance comparing to dense attention. We see that ℓ1 regularization does not produce a sparse attention, especially on FB15k." } ], "images": [ "scigen/train/983.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of model with dense attention vectors or sparse attention vectors. MR, H10 and Time denotes mean rank, Hits@10 and training time per epoch respectively" ], "reasoning": "Sparse attention in ITransF generally performs better or comparably to dense attention, and ℓ1 regularization does not produce sparse attention, especially on FB15k.", "description": "Generally, ITransF with sparse attention has slightly better or comparable performance comparing to dense attention. We see that ℓ1 regularization does not produce a sparse attention, especially on FB15k.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: Performance of model with dense attention vectors or sparse attention vectors. MR, H10 and Time denotes mean rank, Hits@10 and training time per epoch respectively' to sequentially solve the question presented below. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhich method has the lowest MR score on FB15k?\n" }, { "role": "assistant", "content": "To find the method with the lowest MR score on FB15k, compare the FB15k MR scores for Dense, Dense + ℓ1, and Sparse. The Sparse method has the lowest MR score of 67. So, the answer is {\"answer\": \"Sparse\"}." } ], "images": [ "scigen/train/983.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of model with dense attention vectors or sparse attention vectors. MR, H10 and Time denotes mean rank, Hits@10 and training time per epoch respectively" ], "reasoning": "To find the method with the lowest MR score on FB15k, compare the FB15k MR scores for Dense, Dense + ℓ1, and Sparse. The Sparse method has the lowest MR score of 67.", "question": "Which method has the lowest MR score on FB15k?", "answer": "So, the answer is {\"answer\": \"Sparse\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Dense + ℓ1 method has the highest H10 score on WN18.\n" }, { "role": "assistant", "content": "To verify this statement, compare the WN18 H10 scores for Dense, Dense + ℓ1, and Sparse. The Dense + ℓ1 method has a WN18 H10 score of 94.2, which is the highest among the three methods. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/983.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of model with dense attention vectors or sparse attention vectors. MR, H10 and Time denotes mean rank, Hits@10 and training time per epoch respectively" ], "reasoning": "To verify this statement, compare the WN18 H10 scores for Dense, Dense + ℓ1, and Sparse. The Dense + ℓ1 method has a WN18 H10 score of 94.2, which is the highest among the three methods.", "statement": "The Dense + ℓ1 method has the highest H10 score on WN18.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Different methods to obtain sparse representations'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "ITransF outperforms Sparse Encoding on both WN18 and FB15k benchmarks, likely due to its objective function being optimized for link prediction rather than reconstruction loss.On both benchmarks, ITransF achieves significant improvement against sparse encoding on pretrained model. This performance gap should be expected since the objective function of sparse encoding methods is to minimize the reconstruction loss rather than optimize the criterion for link prediction." } ], "images": [ "scigen/train/984.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Different methods to obtain sparse representations" ], "reasoning": "ITransF outperforms Sparse Encoding on both WN18 and FB15k benchmarks, likely due to its objective function being optimized for link prediction rather than reconstruction loss.", "description": "On both benchmarks, ITransF achieves significant improvement against sparse encoding on pretrained model. This performance gap should be expected since the objective function of sparse encoding methods is to minimize the reconstruction loss rather than optimize the criterion for link prediction.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 4: Different methods to obtain sparse representations', and sequentially solve the question, writing out each step of your reasoning process. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method achieves a higher H10 score on the WN18 benchmark?\n" }, { "role": "assistant", "content": "Compare the H10 scores for WN18 between Sparse Encoding and ITransF. Sparse Encoding has an H10 score of 86.6, while ITransF has an H10 score of 94.2. So, the answer is {\"answer\": \"ITransF\"}" } ], "images": [ "scigen/train/984.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Different methods to obtain sparse representations" ], "reasoning": "Compare the H10 scores for WN18 between Sparse Encoding and ITransF. Sparse Encoding has an H10 score of 86.6, while ITransF has an H10 score of 94.2.", "question": "Which method achieves a higher H10 score on the WN18 benchmark?", "answer": "So, the answer is {\"answer\": \"ITransF\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nSparse Encoding has a lower MR score on the FB15k benchmark compared to ITransF.\n" }, { "role": "assistant", "content": "Check the MR scores for FB15k. Sparse Encoding has an MR score of 66, while ITransF has an MR score of 65. Therefore, Sparse Encoding does not have a lower MR score. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/984.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Different methods to obtain sparse representations" ], "reasoning": "Check the MR scores for FB15k. Sparse Encoding has an MR score of 66, while ITransF has an MR score of 65. Therefore, Sparse Encoding does not have a lower MR score.", "statement": "Sparse Encoding has a lower MR score on the FB15k benchmark compared to ITransF.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Human based evaluation is conducted for 5 different components in the network as well as KV memory networks. AMUSED achieves the highest percent gain over specified baseline model. The scale is 1-10.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Experts rated each neural component in isolation or conjunction to understand their impact on human conversation.To monitor the effect of each neural component, we get it rated by experts either in isolation or in conjunction with other components. Such a study helps us understand the impact of different modules on a human based conversation. Dialogue system proposed by Zhang et al." } ], "images": [ "scigen/train/985.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Human based evaluation is conducted for 5 different components in the network as well as KV memory networks. AMUSED achieves the highest percent gain over specified baseline model. The scale is 1-10." ], "reasoning": "Experts rated each neural component in isolation or conjunction to understand their impact on human conversation.", "description": "To monitor the effect of each neural component, we get it rated by experts either in isolation or in conjunction with other components. Such a study helps us understand the impact of different modules on a human based conversation. Dialogue system proposed by Zhang et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: Human based evaluation is conducted for 5 different components in the network as well as KV memory networks. AMUSED achieves the highest percent gain over specified baseline model. The scale is 1-10.' to sequentially solve the question presented below. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhich model has the highest average rating and what is its %gain?\n" }, { "role": "assistant", "content": "By examining the 'Average Rating' column, the highest value is 8.12 for the model 'GCN and memory networks'. The corresponding %gain is 16.33. So, the answer is {\"answer\": \"GCN and memory networks with 16.33\"}." } ], "images": [ "scigen/train/985.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Human based evaluation is conducted for 5 different components in the network as well as KV memory networks. AMUSED achieves the highest percent gain over specified baseline model. The scale is 1-10." ], "reasoning": "By examining the 'Average Rating' column, the highest value is 8.12 for the model 'GCN and memory networks'. The corresponding %gain is 16.33.", "question": "Which model has the highest average rating and what is its %gain?", "answer": "So, the answer is {\"answer\": \"GCN and memory networks with 16.33\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe BERT only model has a higher average rating than the Bi-GRU & GCN only model.\n" }, { "role": "assistant", "content": "The table shows that the 'BERT only' model has an average rating of 7.06, while the 'Bi-GRU & GCN only' model has an average rating of 6.98. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/985.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Human based evaluation is conducted for 5 different components in the network as well as KV memory networks. AMUSED achieves the highest percent gain over specified baseline model. The scale is 1-10." ], "reasoning": "The table shows that the 'BERT only' model has an average rating of 7.06, while the 'Bi-GRU & GCN only' model has an average rating of 6.98. Therefore, the statement is supported.", "statement": "The BERT only model has a higher average rating than the Bi-GRU & GCN only model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Precision @1 comparison between different methods. Precision@1 % tell us the number of times the correct response from the dataset comes up. Details in Section 5.4.2'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The best accuracy is achieved using the AMUSED(DSTC) method with a precision of 0.78, which involves external knowledge, memory, and GCN modules.Accuracy on this binary classification problem has been used to select the best network. Furthermore, we perform ablation studies using different modules to understand the effect of each component in the network. A 4 layer neural network with ReLU activation in its hidden layers and softmax in the final layer is used as the classifier. External knowledge in conjunction with memory and GCN module has the best accuracy when embeddings of query and response are concatenated together. This is another metric used to judge the effectiveness of our network. It is different from the next sentence prediction task accuracy. It measures that for n trials, the number of times a relevant response is reported with the highest confidence value." } ], "images": [ "scigen/train/986.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Precision @1 comparison between different methods. Precision@1 % tell us the number of times the correct response from the dataset comes up. Details in Section 5.4.2" ], "reasoning": "The best accuracy is achieved using the AMUSED(DSTC) method with a precision of 0.78, which involves external knowledge, memory, and GCN modules.", "description": "Accuracy on this binary classification problem has been used to select the best network. Furthermore, we perform ablation studies using different modules to understand the effect of each component in the network. A 4 layer neural network with ReLU activation in its hidden layers and softmax in the final layer is used as the classifier. External knowledge in conjunction with memory and GCN module has the best accuracy when embeddings of query and response are concatenated together. This is another metric used to judge the effectiveness of our network. It is different from the next sentence prediction task accuracy. It measures that for n trials, the number of times a relevant response is reported with the highest confidence value.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Precision @1 comparison between different methods. Precision@1 % tell us the number of times the correct response from the dataset comes up. Details in Section 5.4.2', answer the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich method achieved the highest Precision@1 and what was its value?\n" }, { "role": "assistant", "content": "The table shows that the AMUSED(DSTC) method achieved the highest Precision@1 with a value of 0.78. So, the answer is {\"answer\": \"AMUSED(DSTC), 0.78\"}." } ], "images": [ "scigen/train/986.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Precision @1 comparison between different methods. Precision@1 % tell us the number of times the correct response from the dataset comes up. Details in Section 5.4.2" ], "reasoning": "The table shows that the AMUSED(DSTC) method achieved the highest Precision@1 with a value of 0.78.", "question": "Which method achieved the highest Precision@1 and what was its value?", "answer": "So, the answer is {\"answer\": \"AMUSED(DSTC), 0.78\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe IR Baseline method achieved a Precision@1 of 0.214.\n" }, { "role": "assistant", "content": "The table lists the Precision@1 for the IR Baseline method as 0.214. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/986.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Precision @1 comparison between different methods. Precision@1 % tell us the number of times the correct response from the dataset comes up. Details in Section 5.4.2" ], "reasoning": "The table lists the Precision@1 for the IR Baseline method as 0.214.", "statement": "The IR Baseline method achieved a Precision@1 of 0.214.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Train and test set character error rate (CER) results for a deep neural network (DNN) without recurrence, recurrent deep neural network with forward temporal connections (RDNN), and a bi-directional recurrent deep neural network (BRDNN). All models have 5 hidden layers. The DNN and RDNN both have 2,048 hidden units in each hidden layer while the BRDNN has 1,824 hidden units per hidden layer to keep its total number of free parameters similar to the other models. For all models we choose the most likely character at each timestep and apply CTC collapsing to obtain a character-level transcript hypothesis.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The table compares the train and test CERs of DNN, RDNN, and BRDNN models while controlling for the number of parameters to evaluate the impact of recurrent connections.Previous experiments with DNN-HMM systems found minimal benefits from recurrent connections in DNN acoustic models. It is natural to wonder whether recurrence, and especially bi-directional recurrence, is an essential aspect of our architecture. To evaluate the impact of recurrent connections we compare the train and test CERs of DNN, RDNN, and BRDNN models while roughly controlling for the total number of free parameters in the model." } ], "images": [ "scigen/train/987.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Train and test set character error rate (CER) results for a deep neural network (DNN) without recurrence, recurrent deep neural network with forward temporal connections (RDNN), and a bi-directional recurrent deep neural network (BRDNN). All models have 5 hidden layers. The DNN and RDNN both have 2,048 hidden units in each hidden layer while the BRDNN has 1,824 hidden units per hidden layer to keep its total number of free parameters similar to the other models. For all models we choose the most likely character at each timestep and apply CTC collapsing to obtain a character-level transcript hypothesis." ], "reasoning": "The table compares the train and test CERs of DNN, RDNN, and BRDNN models while controlling for the number of parameters to evaluate the impact of recurrent connections.", "description": "Previous experiments with DNN-HMM systems found minimal benefits from recurrent connections in DNN acoustic models. It is natural to wonder whether recurrence, and especially bi-directional recurrence, is an essential aspect of our architecture. To evaluate the impact of recurrent connections we compare the train and test CERs of DNN, RDNN, and BRDNN models while roughly controlling for the total number of free parameters in the model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Train and test set character error rate (CER) results for a deep neural network (DNN) without recurrence, recurrent deep neural network with forward temporal connections (RDNN), and a bi-directional recurrent deep neural network (BRDNN). All models have 5 hidden layers. The DNN and RDNN both have 2,048 hidden units in each hidden layer while the BRDNN has 1,824 hidden units per hidden layer to keep its total number of free parameters similar to the other models. For all models we choose the most likely character at each timestep and apply CTC collapsing to obtain a character-level transcript hypothesis.\nQuestion: Which model has the lowest test CER and how many parameters does it have?\nGive a detailed response to the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the lowest test CER, compare the Test CER values of DNN, RDNN, and BRDNN. The BRDNN model has the lowest test CER of 10.7. The number of parameters for BRDNN is 20.9 million. So, the answer is {\"answer\": \"BRDNN with 20.9M parameters\"}." } ], "images": [ "scigen/train/987.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Train and test set character error rate (CER) results for a deep neural network (DNN) without recurrence, recurrent deep neural network with forward temporal connections (RDNN), and a bi-directional recurrent deep neural network (BRDNN). All models have 5 hidden layers. The DNN and RDNN both have 2,048 hidden units in each hidden layer while the BRDNN has 1,824 hidden units per hidden layer to keep its total number of free parameters similar to the other models. For all models we choose the most likely character at each timestep and apply CTC collapsing to obtain a character-level transcript hypothesis." ], "reasoning": "To find the model with the lowest test CER, compare the Test CER values of DNN, RDNN, and BRDNN. The BRDNN model has the lowest test CER of 10.7. The number of parameters for BRDNN is 20.9 million.", "question": "Which model has the lowest test CER and how many parameters does it have?", "answer": "So, the answer is {\"answer\": \"BRDNN with 20.9M parameters\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The DNN model has the highest test CER., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe DNN model has the highest test CER.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Test CER values of DNN, RDNN, and BRDNN. The DNN model has a Test CER of 22.3, which is higher than both RDNN (13.5) and BRDNN (10.7). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/987.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Train and test set character error rate (CER) results for a deep neural network (DNN) without recurrence, recurrent deep neural network with forward temporal connections (RDNN), and a bi-directional recurrent deep neural network (BRDNN). All models have 5 hidden layers. The DNN and RDNN both have 2,048 hidden units in each hidden layer while the BRDNN has 1,824 hidden units per hidden layer to keep its total number of free parameters similar to the other models. For all models we choose the most likely character at each timestep and apply CTC collapsing to obtain a character-level transcript hypothesis." ], "reasoning": "To verify this statement, compare the Test CER values of DNN, RDNN, and BRDNN. The DNN model has a Test CER of 22.3, which is higher than both RDNN (13.5) and BRDNN (10.7).", "statement": "The DNN model has the highest test CER.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1: Word error rate (WER) and character error rate (CER) results from a BDRNN trained with the CTC loss function. As a baseline (No LM) we decode by choosing the most likely label at each timestep and performing standard collapsing as done in CTC training. We compare this baseline against our modified prefix-search decoder using a dictionary constraint and bigram language model.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "We trained a BRDNN with 5 hidden layers, each with 1824 units, totaling 20.9M parameters, using Nesterov accelerated gradient optimization, and trained for 20 passes over 96 hours.We trained a BRDNN with 5 hidden layers, all with 1824 hidden units, for a total of 20.9M free parameters. The third hidden layer of the network has recurrent connections. We use the Nesterov accelerated gradient optimization algorithm as described in Sutskever et al. After each full pass through the training set we divide the learning rate by 1.2 to ensure the overall learning rate decreases over time. We train the network for a total of 20 passes over the training set, which takes about 96 hours using our Python GPU implementation. For decoding with prefix search we use a beam size of 200 and cross-validate with a held-out set to find a good setting of the parameters α and β." } ], "images": [ "scigen/train/988.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Word error rate (WER) and character error rate (CER) results from a BDRNN trained with the CTC loss function. As a baseline (No LM) we decode by choosing the most likely label at each timestep and performing standard collapsing as done in CTC training. We compare this baseline against our modified prefix-search decoder using a dictionary constraint and bigram language model." ], "reasoning": "We trained a BRDNN with 5 hidden layers, each with 1824 units, totaling 20.9M parameters, using Nesterov accelerated gradient optimization, and trained for 20 passes over 96 hours.", "description": "We trained a BRDNN with 5 hidden layers, all with 1824 hidden units, for a total of 20.9M free parameters. The third hidden layer of the network has recurrent connections. We use the Nesterov accelerated gradient optimization algorithm as described in Sutskever et al. After each full pass through the training set we divide the learning rate by 1.2 to ensure the overall learning rate decreases over time. We train the network for a total of 20 passes over the training set, which takes about 96 hours using our Python GPU implementation. For decoding with prefix search we use a beam size of 200 and cross-validate with a held-out set to find a good setting of the parameters α and β.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 1: Word error rate (WER) and character error rate (CER) results from a BDRNN trained with the CTC loss function. As a baseline (No LM) we decode by choosing the most likely label at each timestep and performing standard collapsing as done in CTC training. We compare this baseline against our modified prefix-search decoder using a dictionary constraint and bigram language model.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. \n\nQuestion: What is the Character Error Rate (CER) for the model with the lowest Word Error Rate (WER)?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows that the Bigram LM model has the lowest WER of 14.1. The corresponding CER for this model is 5.7. So, the answer is {\"answer\": \"5.7\"}" } ], "images": [ "scigen/train/988.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Word error rate (WER) and character error rate (CER) results from a BDRNN trained with the CTC loss function. As a baseline (No LM) we decode by choosing the most likely label at each timestep and performing standard collapsing as done in CTC training. We compare this baseline against our modified prefix-search decoder using a dictionary constraint and bigram language model." ], "reasoning": "The table shows that the Bigram LM model has the lowest WER of 14.1. The corresponding CER for this model is 5.7.", "question": "What is the Character Error Rate (CER) for the model with the lowest Word Error Rate (WER)?", "answer": "So, the answer is {\"answer\": \"5.7\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Dictionary LM model has a lower Character Error Rate (CER) than the No LM model.\n" }, { "role": "assistant", "content": "The table shows that the Dictionary LM model has a CER of 8.5, while the No LM model has a CER of 10.0. Therefore, the Dictionary LM model has a lower CER. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/988.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Word error rate (WER) and character error rate (CER) results from a BDRNN trained with the CTC loss function. As a baseline (No LM) we decode by choosing the most likely label at each timestep and performing standard collapsing as done in CTC training. We compare this baseline against our modified prefix-search decoder using a dictionary constraint and bigram language model." ], "reasoning": "The table shows that the Dictionary LM model has a CER of 8.5, while the No LM model has a CER of 10.0. Therefore, the Dictionary LM model has a lower CER.", "statement": "The Dictionary LM model has a lower Character Error Rate (CER) than the No LM model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 6: Performances over different memory types.'.\n" }, { "role": "assistant", "content": "The addressee memory method achieves the best or near-best performance across all metrics, while the complexity of all utterance memory is significantly higher.Comparative Results. It can see that our method, the addressee memory, achieves the best or near-best performances on all metrics. Although memorizing all utterances is competitive, the complexity of all utterance memory is n times compared with the one in the addressee memory, where n is the number of utterances in a context. The speaker memory performs closely to without memory, which indicates that not all memories can improve the performance." } ], "images": [ "scigen/train/989.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Performances over different memory types." ], "reasoning": "The addressee memory method achieves the best or near-best performance across all metrics, while the complexity of all utterance memory is significantly higher.", "description": "Comparative Results. It can see that our method, the addressee memory, achieves the best or near-best performances on all metrics. Although memorizing all utterances is competitive, the complexity of all utterance memory is n times compared with the one in the addressee memory, where n is the number of utterances in a context. The speaker memory performs closely to without memory, which indicates that not all memories can improve the performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 6: Performances over different memory types.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. \n\nQuestion: Which memory type achieves the highest BLEU score and what is its value?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows that the addressee memory type achieves the highest BLEU score with a value of 10.63. So, the answer is {\"answer\": \"addressee memory with 10.63\"}" } ], "images": [ "scigen/train/989.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Performances over different memory types." ], "reasoning": "The table shows that the addressee memory type achieves the highest BLEU score with a value of 10.63.", "question": "Which memory type achieves the highest BLEU score and what is its value?", "answer": "So, the answer is {\"answer\": \"addressee memory with 10.63\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe speaker memory type achieves the highest ROUGE score.\n" }, { "role": "assistant", "content": "The table shows that the all utterance memory type achieves the highest ROUGE score with a value of 8.78, not the speaker memory type. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/989.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Performances over different memory types." ], "reasoning": "The table shows that the all utterance memory type achieves the highest ROUGE score with a value of 8.78, not the speaker memory type.", "statement": "The speaker memory type achieves the highest ROUGE score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: Performances on sparse and plentiful learning data with different numbers of interlocutor’s dialogue turns, where the test data is divided into different intervals according to the number of dialogue turns in training dataset said by target addressee (named as interlocutor’s dialogue turns).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The persona model performs poorly on sparse data but well on plentiful data, while ICRED performs consistently well regardless of data sparsity.Comparative Results. We can clearly see that the persona model has a sparsity issue: it performs very poorly on sparse learning data (e.g., BLEU score = 8.47 on “[0, 100]”) while it achieves good performances on plentiful learning data (e.g., BLEU score = 9.51 on “(5000, +∞)”), which demonstrates that the fixed person vectors in the persona model need to be learned from large-scale training data for each interlocutor. In contrast, ICRED exploits interactive interlocutor representation learned from current dialog context rather than the fixed person vectors obtained from all training dialog utterances. Therefore, ICRED has no sparsity issues and it performs closely on sparse and plentiful learning data." } ], "images": [ "scigen/train/990.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performances on sparse and plentiful learning data with different numbers of interlocutor’s dialogue turns, where the test data is divided into different intervals according to the number of dialogue turns in training dataset said by target addressee (named as interlocutor’s dialogue turns)." ], "reasoning": "The persona model performs poorly on sparse data but well on plentiful data, while ICRED performs consistently well regardless of data sparsity.", "description": "Comparative Results. We can clearly see that the persona model has a sparsity issue: it performs very poorly on sparse learning data (e.g., BLEU score = 8.47 on “[0, 100]”) while it achieves good performances on plentiful learning data (e.g., BLEU score = 9.51 on “(5000, +∞)”), which demonstrates that the fixed person vectors in the persona model need to be learned from large-scale training data for each interlocutor. In contrast, ICRED exploits interactive interlocutor representation learned from current dialog context rather than the fixed person vectors obtained from all training dialog utterances. Therefore, ICRED has no sparsity issues and it performs closely on sparse and plentiful learning data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: Table 4: Performances on sparse and plentiful learning data with different numbers of interlocutor’s dialogue turns, where the test data is divided into different intervals according to the number of dialogue turns in training dataset said by target addressee (named as interlocutor’s dialogue turns).\nQuestion: What is the BLEU score of the ICRED model for dialogue turns between 1000 and 5000?\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for both the Persona Model and ICRED across different ranges of dialogue turns. For the range (1000, 5000], the BLEU score for ICRED is 10.77. So, the answer is {\"answer\": \"10.77\"}" } ], "images": [ "scigen/train/990.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performances on sparse and plentiful learning data with different numbers of interlocutor’s dialogue turns, where the test data is divided into different intervals according to the number of dialogue turns in training dataset said by target addressee (named as interlocutor’s dialogue turns)." ], "reasoning": "The table shows the BLEU scores for both the Persona Model and ICRED across different ranges of dialogue turns. For the range (1000, 5000], the BLEU score for ICRED is 10.77.", "question": "What is the BLEU score of the ICRED model for dialogue turns between 1000 and 5000?", "answer": "So, the answer is {\"answer\": \"10.77\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Persona Model performs better than ICRED on sparse learning data.\n" }, { "role": "assistant", "content": "The table shows that for sparse learning data ([0, 100] dialogue turns), the Persona Model has a BLEU score of 8.47, while ICRED has a BLEU score of 10.63. Therefore, ICRED performs better than the Persona Model on sparse learning data. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/990.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performances on sparse and plentiful learning data with different numbers of interlocutor’s dialogue turns, where the test data is divided into different intervals according to the number of dialogue turns in training dataset said by target addressee (named as interlocutor’s dialogue turns)." ], "reasoning": "The table shows that for sparse learning data ([0, 100] dialogue turns), the Persona Model has a BLEU score of 8.47, while ICRED has a BLEU score of 10.63. Therefore, ICRED performs better than the Persona Model on sparse learning data.", "statement": "The Persona Model performs better than ICRED on sparse learning data.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 5: Ablation Experiments by removing the main components.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Removing any component from the model results in a significant drop in performance, with the absence of 'Ctx_Adr_Vec' leading to the worst results across most metrics, highlighting the critical role of contextual information.Comparative Results. We can see that removing any component causes obvious performance degradation. In particular, “w/o Ctx_Adr_Vec” performs the worst on almost all of the metrics, which demonstrates the importance of contextual information for the target addressee." } ], "images": [ "scigen/train/991.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ablation Experiments by removing the main components." ], "reasoning": "Removing any component from the model results in a significant drop in performance, with the absence of 'Ctx_Adr_Vec' leading to the worst results across most metrics, highlighting the critical role of contextual information.", "description": "Comparative Results. We can see that removing any component causes obvious performance degradation. In particular, “w/o Ctx_Adr_Vec” performs the worst on almost all of the metrics, which demonstrates the importance of contextual information for the target addressee.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 5: Ablation Experiments by removing the main components.'. Solve the following question based on the table.\n\nQuestion: Which model component's removal leads to the worst performance in BLEU and ROUGE metrics?\nLet's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the BLEU and ROUGE scores across different models, it is evident that the model 'w/o Ctx_Adr_Vec' has the lowest scores in both BLEU (9.95) and ROUGE (8.18), indicating the worst performance. So, the answer is {\"answer\": \"Ctx_Adr_Vec\"}" } ], "images": [ "scigen/train/991.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ablation Experiments by removing the main components." ], "reasoning": "By comparing the BLEU and ROUGE scores across different models, it is evident that the model 'w/o Ctx_Adr_Vec' has the lowest scores in both BLEU (9.95) and ROUGE (8.18), indicating the worst performance.", "question": "Which model component's removal leads to the worst performance in BLEU and ROUGE metrics?", "answer": "So, the answer is {\"answer\": \"Ctx_Adr_Vec\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The model without Ctx_Spk_Vec has the highest BLEU score. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model without Ctx_Spk_Vec has the highest BLEU score.\n" }, { "role": "assistant", "content": "The table shows that the model without Ctx_Spk_Vec has a BLEU score of 10.13, which is lower than the BLEU score of the ICRED model (10.63). Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/991.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ablation Experiments by removing the main components." ], "reasoning": "The table shows that the model without Ctx_Spk_Vec has a BLEU score of 10.13, which is lower than the BLEU score of the ICRED model (10.63). Therefore, the statement is refuted.", "statement": "The model without Ctx_Spk_Vec has the highest BLEU score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Zero-shot cross-lingual machine reading comprehension results on Japanese and French SQuAD data. † are extracted in Asai et al. (2018).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "SimpleMatch is proposed as an effective alternative to neural machine translation attention for aligning translated answers to original passage spans, demonstrating its effectiveness on Japanese and French SQuAD datasets.In this paper, we propose a simple but effective approach called SimpleMatch to align translated answer to original passage span. While one may argue that using neural machine translation attention to project source answer to original target passage span is ideal as used in Asai et al. However, to extract attention value in neural machine translation system and apply it to extract the original passage span is bothersome and computationally ineffective. To demonstrate the effectiveness of using SimpleMatch instead of using NMT attention to extract original passage span in zero-shot condition, we applied SimpleMatch to Japanese and French SQuAD (304 samples for each) which is what exactly used in Asai et al." } ], "images": [ "scigen/train/992.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Zero-shot cross-lingual machine reading comprehension results on Japanese and French SQuAD data. † are extracted in Asai et al. (2018)." ], "reasoning": "SimpleMatch is proposed as an effective alternative to neural machine translation attention for aligning translated answers to original passage spans, demonstrating its effectiveness on Japanese and French SQuAD datasets.", "description": "In this paper, we propose a simple but effective approach called SimpleMatch to align translated answer to original passage span. While one may argue that using neural machine translation attention to project source answer to original target passage span is ideal as used in Asai et al. However, to extract attention value in neural machine translation system and apply it to extract the original passage span is bothersome and computationally ineffective. To demonstrate the effectiveness of using SimpleMatch instead of using NMT attention to extract original passage span in zero-shot condition, we applied SimpleMatch to Japanese and French SQuAD (304 samples for each) which is what exactly used in Asai et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: Zero-shot cross-lingual machine reading comprehension results on Japanese and French SQuAD data. † are extracted in Asai et al. (2018).' in mind, break down and resolve the question below step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhat is the F1 score for SimpleMatch on the French SQuAD dataset?\n" }, { "role": "assistant", "content": "To find the F1 score for SimpleMatch on the French SQuAD dataset, locate the row labeled '+SimpleMatch' and then find the value under the 'French F1' column. So, the answer is {\"answer\": \"71.5\"}" } ], "images": [ "scigen/train/992.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Zero-shot cross-lingual machine reading comprehension results on Japanese and French SQuAD data. † are extracted in Asai et al. (2018)." ], "reasoning": "To find the F1 score for SimpleMatch on the French SQuAD dataset, locate the row labeled '+SimpleMatch' and then find the value under the 'French F1' column.", "question": "What is the F1 score for SimpleMatch on the French SQuAD dataset?", "answer": "So, the answer is {\"answer\": \"71.5\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim SimpleMatch achieved a higher EM score than GNMT+BERT Len on the Japanese SQuAD dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nSimpleMatch achieved a higher EM score than GNMT+BERT Len on the Japanese SQuAD dataset.\n" }, { "role": "assistant", "content": "Compare the EM scores for SimpleMatch and GNMT+BERT Len on the Japanese SQuAD dataset. SimpleMatch has an EM score of 37.3, while GNMT+BERT Len has an EM score of 26.9. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/992.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Zero-shot cross-lingual machine reading comprehension results on Japanese and French SQuAD data. † are extracted in Asai et al. (2018)." ], "reasoning": "Compare the EM scores for SimpleMatch and GNMT+BERT Len on the Japanese SQuAD dataset. SimpleMatch has an EM score of 37.3, while GNMT+BERT Len has an EM score of 26.9.", "statement": "SimpleMatch achieved a higher EM score than GNMT+BERT Len on the Japanese SQuAD dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Experimental results on CMRC 2018 and DRCD. † indicates unpublished works (some of the systems are using development set for training, which makes the results not directly comparable.). ♠ indicates zero-shot approach. We mark our system with an ID in the first column for reference simplicity.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "SimpleMatch and Answer Aligner significantly improve performance over zero-shot results, and Answer Verifier surpasses the multi-lingual BERT baseline.As we can see that, without using any alignment approach, the zero-shot results are quite lower regardless of using English BERT-base (#1) or BERT-large (#2). When we apply SimpleMatch (#3), we observe significant improvements demonstrating its effectiveness. The Answer Aligner (#4) could further improve the performance beyond SimpleMatch approach, demonstrating that the machine learning approach could dynamically adjust the span output by learning the semantic relationship between translated answer and target passage. Also, the Answer Verifier (#5) could further boost performance and surpass the multi-lingual BERT baseline (#7) that only use target training data, demonstrating that it is beneficial to adopt rich-resourced language to improve machine reading comprehension in other languages." } ], "images": [ "scigen/train/993.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Experimental results on CMRC 2018 and DRCD. † indicates unpublished works (some of the systems are using development set for training, which makes the results not directly comparable.). ♠ indicates zero-shot approach. We mark our system with an ID in the first column for reference simplicity." ], "reasoning": "SimpleMatch and Answer Aligner significantly improve performance over zero-shot results, and Answer Verifier surpasses the multi-lingual BERT baseline.", "description": "As we can see that, without using any alignment approach, the zero-shot results are quite lower regardless of using English BERT-base (#1) or BERT-large (#2). When we apply SimpleMatch (#3), we observe significant improvements demonstrating its effectiveness. The Answer Aligner (#4) could further improve the performance beyond SimpleMatch approach, demonstrating that the machine learning approach could dynamically adjust the span output by learning the semantic relationship between translated answer and target passage. Also, the Answer Verifier (#5) could further boost performance and surpass the multi-lingual BERT baseline (#7) that only use target training data, demonstrating that it is beneficial to adopt rich-resourced language to improve machine reading comprehension in other languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: Experimental results on CMRC 2018 and DRCD. † indicates unpublished works (some of the systems are using development set for training, which makes the results not directly comparable.). ♠ indicates zero-shot approach. We mark our system with an ID in the first column for reference simplicity.', and tackle the question below in a sequential manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich system achieved the highest F1 score on the CMRC 2018 Dev set?\n" }, { "role": "assistant", "content": "By examining the F1 scores in the CMRC 2018 Dev set column, we see that Human Performance has the highest F1 score of 97.3. So, the answer is {\"answer\": \"Human Performance\"}." } ], "images": [ "scigen/train/993.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Experimental results on CMRC 2018 and DRCD. † indicates unpublished works (some of the systems are using development set for training, which makes the results not directly comparable.). ♠ indicates zero-shot approach. We mark our system with an ID in the first column for reference simplicity." ], "reasoning": "By examining the F1 scores in the CMRC 2018 Dev set column, we see that Human Performance has the highest F1 score of 97.3.", "question": "Which system achieved the highest F1 score on the CMRC 2018 Dev set?", "answer": "So, the answer is {\"answer\": \"Human Performance\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe GNMT+BERT SQ-Len+Verifier system achieved an EM score of 84.7 on the CMRC 2018 Dev set.\n" }, { "role": "assistant", "content": "The table shows that the GNMT+BERT SQ-Len+Verifier system achieved an EM score of 64.7 on the CMRC 2018 Dev set, not 84.7. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/993.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Experimental results on CMRC 2018 and DRCD. † indicates unpublished works (some of the systems are using development set for training, which makes the results not directly comparable.). ♠ indicates zero-shot approach. We mark our system with an ID in the first column for reference simplicity." ], "reasoning": "The table shows that the GNMT+BERT SQ-Len+Verifier system achieved an EM score of 64.7 on the CMRC 2018 Dev set, not 84.7.", "statement": "The GNMT+BERT SQ-Len+Verifier system achieved an EM score of 84.7 on the CMRC 2018 Dev set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: Length normalization fixes translation lengths, but prevents exact search from matching the BLEU score of Beam-10. Experiment conducted on 48.3% of the test set.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Exact search under length normalization improves BLEU score but does not match Beam-10 search, indicating length normalization helps but does not solve the modeling problem.We can find the global best translations under length normalization by generalizing our exact inference scheme to length dependent lower bounds γk. (e.g. zero to 1.2 times the source sentence length). (4) Exact search under length normalization does not suffer from the length deficiency anymore (last row in Tab. but it is not able to match our best BLEU score under Beam-10 search. This suggests that while length normalization biases search towards translations of roughly the correct length, it does not fix the fundamental modelling problem." } ], "images": [ "scigen/train/994.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Length normalization fixes translation lengths, but prevents exact search from matching the BLEU score of Beam-10. Experiment conducted on 48.3% of the test set." ], "reasoning": "Exact search under length normalization improves BLEU score but does not match Beam-10 search, indicating length normalization helps but does not solve the modeling problem.", "description": "We can find the global best translations under length normalization by generalizing our exact inference scheme to length dependent lower bounds γk. (e.g. zero to 1.2 times the source sentence length). (4) Exact search under length normalization does not suffer from the length deficiency anymore (last row in Tab. but it is not able to match our best BLEU score under Beam-10 search. This suggests that while length normalization biases search towards translations of roughly the correct length, it does not fix the fundamental modelling problem.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 4: Length normalization fixes translation lengths, but prevents exact search from matching the BLEU score of Beam-10. Experiment conducted on 48.3% of the test set.'. Solve the following question based on the table.\n\nQuestion: What is the BLEU score for Exact search with length normalization?\nLet's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for different search methods with and without length normalization. For Exact search with length normalization, the BLEU score is listed. So, the answer is {\"answer\": \"36.4\"}" } ], "images": [ "scigen/train/994.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Length normalization fixes translation lengths, but prevents exact search from matching the BLEU score of Beam-10. Experiment conducted on 48.3% of the test set." ], "reasoning": "The table shows the BLEU scores for different search methods with and without length normalization. For Exact search with length normalization, the BLEU score is listed.", "question": "What is the BLEU score for Exact search with length normalization?", "answer": "So, the answer is {\"answer\": \"36.4\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nBeam-30 search with length normalization has a higher BLEU score than Beam-10 search without length normalization.\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for Beam-30 search with length normalization and Beam-10 search without length normalization. Beam-30 with length normalization has a BLEU score of 36.3, which is lower than Beam-10 without length normalization, which has a BLEU score of 37.0. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/994.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Length normalization fixes translation lengths, but prevents exact search from matching the BLEU score of Beam-10. Experiment conducted on 48.3% of the test set." ], "reasoning": "The table shows the BLEU scores for Beam-30 search with length normalization and Beam-10 search without length normalization. Beam-30 with length normalization has a BLEU score of 36.3, which is lower than Beam-10 without length normalization, which has a BLEU score of 37.0.", "statement": "Beam-30 search with length normalization has a higher BLEU score than Beam-10 search without length normalization.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: NMT with exact inference. In the absence of search errors, NMT often prefers the empty translation, causing a dramatic drop in length ratio and BLEU.'.\n" }, { "role": "assistant", "content": "Large beam sizes reduce search errors but result in shorter translations, leading to a drop in BLEU scores.Our main result is shown in Tab. > token. Fig. Large beam sizes reduce the number of search errors, but the BLEU score drops because translations are too short. Even a large beam size of 100 produces 53.62% search errors. Fig. For example, Beam-10 yields 15.9% fewer search errors (absolute) than greedy decoding (57.68% vs. 73.58%), but Beam-100 improves search only slightly (53.62% search errors) despite being 10 times slower than beam-10." } ], "images": [ "scigen/train/995.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: NMT with exact inference. In the absence of search errors, NMT often prefers the empty translation, causing a dramatic drop in length ratio and BLEU." ], "reasoning": "Large beam sizes reduce search errors but result in shorter translations, leading to a drop in BLEU scores.", "description": "Our main result is shown in Tab. > token. Fig. Large beam sizes reduce the number of search errors, but the BLEU score drops because translations are too short. Even a large beam size of 100 produces 53.62% search errors. Fig. For example, Beam-10 yields 15.9% fewer search errors (absolute) than greedy decoding (57.68% vs. 73.58%), but Beam-100 improves search only slightly (53.62% search errors) despite being 10 times slower than beam-10.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 1: NMT with exact inference. In the absence of search errors, NMT often prefers the empty translation, causing a dramatic drop in length ratio and BLEU.' in mind, break down and resolve the question below step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the BLEU score for Beam-10 and how does it compare to Greedy search in terms of search errors?\n" }, { "role": "assistant", "content": "The BLEU score for Beam-10 is 30.3, which is higher than the BLEU score for Greedy search at 29.3. Beam-10 also has fewer search errors (57.7%) compared to Greedy search (73.6%). So, the answer is {\"answer\": \"30.3\"}." } ], "images": [ "scigen/train/995.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: NMT with exact inference. In the absence of search errors, NMT often prefers the empty translation, causing a dramatic drop in length ratio and BLEU." ], "reasoning": "The BLEU score for Beam-10 is 30.3, which is higher than the BLEU score for Greedy search at 29.3. Beam-10 also has fewer search errors (57.7%) compared to Greedy search (73.6%).", "question": "What is the BLEU score for Beam-10 and how does it compare to Greedy search in terms of search errors?", "answer": "So, the answer is {\"answer\": \"30.3\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nBeam-10 has a higher BLEU score than Greedy search.\n" }, { "role": "assistant", "content": "The table shows that Beam-10 has a BLEU score of 30.3, which is higher than the BLEU score of 29.3 for Greedy search. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/995.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: NMT with exact inference. In the absence of search errors, NMT often prefers the empty translation, causing a dramatic drop in length ratio and BLEU." ], "reasoning": "The table shows that Beam-10 has a BLEU score of 30.3, which is higher than the BLEU score of 29.3 for Greedy search.", "statement": "Beam-10 has a higher BLEU score than Greedy search.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: Error rates of our models on 16 datasets against vanilla multi-task learning. ϕ (Books) means that we transfer the knowledge of the other 15 tasks to the target task Books.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The Bi-Channel model with ASP-MTL achieves the best performance due to its ability to store private features in the task-specific layer., we can see the shared layer from ASP-MTL achieves a better performance compared with SP-MTL. Besides, for the two kinds of transfer strategies, the Bi-Channel model performs better. The reason is that the task-specific layer introduced in the Bi-Channel model can store some private features. Overall, the results indicate that we can save the existing knowledge into a shared recurrent layer using adversarial multi-task learning, which is quite useful for a new task." } ], "images": [ "scigen/train/996.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Error rates of our models on 16 datasets against vanilla multi-task learning. ϕ (Books) means that we transfer the knowledge of the other 15 tasks to the target task Books." ], "reasoning": "The Bi-Channel model with ASP-MTL achieves the best performance due to its ability to store private features in the task-specific layer.", "description": ", we can see the shared layer from ASP-MTL achieves a better performance compared with SP-MTL. Besides, for the two kinds of transfer strategies, the Bi-Channel model performs better. The reason is that the task-specific layer introduced in the Bi-Channel model can store some private features. Overall, the results indicate that we can save the existing knowledge into a shared recurrent layer using adversarial multi-task learning, which is quite useful for a new task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: Error rates of our models on 16 datasets against vanilla multi-task learning. ϕ (Books) means that we transfer the knowledge of the other 15 tasks to the target task Books.', answer the following question. Give detailed solution consisting of each step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieves the highest average performance across all tasks?\n" }, { "role": "assistant", "content": "By comparing the average performance across all tasks, the ASP-MTL-BC model achieves the highest average score of 14.7. So, the answer is {\"answer\": \"ASP-MTL-BC\"}." } ], "images": [ "scigen/train/996.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Error rates of our models on 16 datasets against vanilla multi-task learning. ϕ (Books) means that we transfer the knowledge of the other 15 tasks to the target task Books." ], "reasoning": "By comparing the average performance across all tasks, the ASP-MTL-BC model achieves the highest average score of 14.7.", "question": "Which model achieves the highest average performance across all tasks?", "answer": "So, the answer is {\"answer\": \"ASP-MTL-BC\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The SP-MTL-SC model performs better than the ASP-MTL-SC model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe SP-MTL-SC model performs better than the ASP-MTL-SC model.\n" }, { "role": "assistant", "content": "By comparing the average performance across all tasks, the ASP-MTL-SC model achieves a higher average score of 14.7 compared to the SP-MTL-SC model's average score of 15.6. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/996.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Error rates of our models on 16 datasets against vanilla multi-task learning. ϕ (Books) means that we transfer the knowledge of the other 15 tasks to the target task Books." ], "reasoning": "By comparing the average performance across all tasks, the ASP-MTL-SC model achieves a higher average score of 14.7 compared to the SP-MTL-SC model's average score of 15.6.", "statement": "The SP-MTL-SC model performs better than the ASP-MTL-SC model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Error rates of our models on 16 datasets against typical baselines. The numbers in brackets represent the improvements relative to the average performance (Avg.) of three single task baselines.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Multi-task learning significantly improves performance, with ASP-MTL achieving the lowest error rates and a 4.1% average improvement over SP-MTL.The column of “Single Task” shows the results of vanilla LSTM, bidirectional LSTM (BiLSTM), stacked LSTM (sLSTM) and the average error rates of previous three models. The column of “Multiple Tasks” shows the results achieved by corresponding multi-task models. From this table, we can see that the performance of most tasks can be improved with a large margin with the help of multi-task learning, in which our model achieves the lowest error rates. More concretely, compared with SP-MTL, ASP-MTL achieves 4.1% average improvement surpassing SP-MTL with 1.0%, which indicates the importance of adversarial learning. It is noteworthy that for FS-MTL, the performances of some tasks are degraded, since this model puts all private and shared information into a unified space." } ], "images": [ "scigen/train/997.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Error rates of our models on 16 datasets against typical baselines. The numbers in brackets represent the improvements relative to the average performance (Avg.) of three single task baselines." ], "reasoning": "Multi-task learning significantly improves performance, with ASP-MTL achieving the lowest error rates and a 4.1% average improvement over SP-MTL.", "description": "The column of “Single Task” shows the results of vanilla LSTM, bidirectional LSTM (BiLSTM), stacked LSTM (sLSTM) and the average error rates of previous three models. The column of “Multiple Tasks” shows the results achieved by corresponding multi-task models. From this table, we can see that the performance of most tasks can be improved with a large margin with the help of multi-task learning, in which our model achieves the lowest error rates. More concretely, compared with SP-MTL, ASP-MTL achieves 4.1% average improvement surpassing SP-MTL with 1.0%, which indicates the importance of adversarial learning. It is noteworthy that for FS-MTL, the performances of some tasks are degraded, since this model puts all private and shared information into a unified space.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 2: Error rates of our models on 16 datasets against typical baselines. The numbers in brackets represent the improvements relative to the average performance (Avg.) of three single task baselines.' and proceed to solve the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the average error rate improvement of ASP-MTL over SP-MTL?\n" }, { "role": "assistant", "content": "The description states that ASP-MTL achieves a 4.1% average improvement over SP-MTL. So, the answer is {\"answer\": \"4.1%\"}" } ], "images": [ "scigen/train/997.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Error rates of our models on 16 datasets against typical baselines. The numbers in brackets represent the improvements relative to the average performance (Avg.) of three single task baselines." ], "reasoning": "The description states that ASP-MTL achieves a 4.1% average improvement over SP-MTL.", "question": "What is the average error rate improvement of ASP-MTL over SP-MTL?", "answer": "So, the answer is {\"answer\": \"4.1%\"}" }, { "messages": [ { "role": "user", "content": "ASP-MTL achieves the lowest error rates among all models. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nASP-MTL achieves the lowest error rates among all models.\n" }, { "role": "assistant", "content": "The description and table indicate that ASP-MTL achieves the lowest error rates, with an average error rate of 13.9%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/997.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Error rates of our models on 16 datasets against typical baselines. The numbers in brackets represent the improvements relative to the average performance (Avg.) of three single task baselines." ], "reasoning": "The description and table indicate that ASP-MTL achieves the lowest error rates, with an average error rate of 13.9%.", "statement": "ASP-MTL achieves the lowest error rates among all models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Ablation Study on VQAv2 val-set.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Combining image-grid and object-level visual features with language features improves VQA performance, with the best results obtained when both are combined.We perform an extensive ablation study of the proposed model on VQAv2 Goyal et al. This ablation study helps to better understand the contribution of different components of our model towards the overall performance on the VQA task. The objective of this ablation study is to show that when the language features are combined with image- grid and object level visual features, the accuracy of the high level visual reasoning task (i.e. VQA) increases in contrast to only combining language with image or object level features. We observe RAF-I achieve comparable performance in this category. In Category II, RAF-O model extracts only 36 object level features but outperforms the models in Category I. Anderson et al. When we combine image and object level features together in Category III, we observe that the best results are obtained. This proves our hypothesis that the questions relate to both objects, object parts and local attributes, which should be attended for jointly an improved VQA performance." } ], "images": [ "scigen/train/998.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation Study on VQAv2 val-set." ], "reasoning": "Combining image-grid and object-level visual features with language features improves VQA performance, with the best results obtained when both are combined.", "description": "We perform an extensive ablation study of the proposed model on VQAv2 Goyal et al. This ablation study helps to better understand the contribution of different components of our model towards the overall performance on the VQA task. The objective of this ablation study is to show that when the language features are combined with image- grid and object level visual features, the accuracy of the high level visual reasoning task (i.e. VQA) increases in contrast to only combining language with image or object level features. We observe RAF-I achieve comparable performance in this category. In Category II, RAF-O model extracts only 36 object level features but outperforms the models in Category I. Anderson et al. When we combine image and object level features together in Category III, we observe that the best results are obtained. This proves our hypothesis that the questions relate to both objects, object parts and local attributes, which should be attended for jointly an improved VQA performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 3: Ablation Study on VQAv2 val-set.\nQuestion: Which method achieved the highest accuracy in the validation set?\n" }, { "role": "assistant", "content": "By examining the 'Val-set' column, the highest accuracy is observed in Category III with the RAF-IO(ResNeXt-ResNet) method, which achieved 64.2. So, the answer is {\"answer\": \"RAF-IO(ResNeXt-ResNet)\"}" } ], "images": [ "scigen/train/998.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation Study on VQAv2 val-set." ], "reasoning": "By examining the 'Val-set' column, the highest accuracy is observed in Category III with the RAF-IO(ResNeXt-ResNet) method, which achieved 64.2.", "question": "Which method achieved the highest accuracy in the validation set?", "answer": "So, the answer is {\"answer\": \"RAF-IO(ResNeXt-ResNet)\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe RAF-I(ResNet) method achieved the lowest accuracy in the validation set.\n" }, { "role": "assistant", "content": "By examining the 'Val-set' column, the lowest accuracy is observed in Category I with the RAF-I(ResNet) method, which achieved 53.9. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/998.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation Study on VQAv2 val-set." ], "reasoning": "By examining the 'Val-set' column, the lowest accuracy is observed in Category I with the RAF-I(ResNet) method, which achieved 53.9.", "statement": "The RAF-I(ResNet) method achieved the lowest accuracy in the validation set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: Comparison of the state-of-the-art methods with our single model performance on VQAv1.0 test-dev and test-standard server.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Our model outperforms all others in overall accuracy, with a significant boost of 1.2% on the test-dev set and 0.3% on the test-standard set, without using ensembles or data augmentation.Remarkably, our model outperforms all other models in the overall accuracy. We report a significant performance boost of 1.2% on the test-dev set and 0.3% on the test-standard set. It is to be noted that using multiple ensembles and data augmentation with complementary training in Visual Genome QA pairs can increase the accuracy performance of the VQA models. For instance, MCB Fukui et al. , MUTAN Ben-Younes et al. et al. It is interesting to note that except for MFB (7) all other ensemble models are ∼1% less than our reported single model performance. We do not ensemble our model or use data augmentation with complementary dataset as it makes the best results irreproducible and most of the models in the literature do not adopt this strategy." } ], "images": [ "scigen/train/999.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of the state-of-the-art methods with our single model performance on VQAv1.0 test-dev and test-standard server." ], "reasoning": "Our model outperforms all others in overall accuracy, with a significant boost of 1.2% on the test-dev set and 0.3% on the test-standard set, without using ensembles or data augmentation.", "description": "Remarkably, our model outperforms all other models in the overall accuracy. We report a significant performance boost of 1.2% on the test-dev set and 0.3% on the test-standard set. It is to be noted that using multiple ensembles and data augmentation with complementary training in Visual Genome QA pairs can increase the accuracy performance of the VQA models. For instance, MCB Fukui et al. , MUTAN Ben-Younes et al. et al. It is interesting to note that except for MFB (7) all other ensemble models are ∼1% less than our reported single model performance. We do not ensemble our model or use data augmentation with complementary dataset as it makes the best results irreproducible and most of the models in the literature do not adopt this strategy.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 1: Comparison of the state-of-the-art methods with our single model performance on VQAv1.0 test-dev and test-standard server.', solve this question and give detail solutions. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the overall accuracy of the RAF model on the test-standard set?\n" }, { "role": "assistant", "content": "The table shows the overall accuracy of the RAF model on the test-standard set as 68.2. So, the answer is {\"answer\": \"68.2\"}" } ], "images": [ "scigen/train/999.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of the state-of-the-art methods with our single model performance on VQAv1.0 test-dev and test-standard server." ], "reasoning": "The table shows the overall accuracy of the RAF model on the test-standard set as 68.2.", "question": "What is the overall accuracy of the RAF model on the test-standard set?", "answer": "So, the answer is {\"answer\": \"68.2\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe MCB model has a higher overall accuracy on the test-standard set compared to the RAF model.\n" }, { "role": "assistant", "content": "The table shows that the overall accuracy of the MCB model on the test-standard set is 66.9, which is lower than the RAF model's 68.2. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/999.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of the state-of-the-art methods with our single model performance on VQAv1.0 test-dev and test-standard server." ], "reasoning": "The table shows that the overall accuracy of the MCB model on the test-standard set is 66.9, which is lower than the RAF model's 68.2.", "statement": "The MCB model has a higher overall accuracy on the test-standard set compared to the RAF model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Comparison of the state-of-the-art methods with our single model performance on VQAv2.0 test-dev and test-standard server.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The RAF model outperforms all other models in all question categories and overall by a significant margin of 1.7% on the VQAv2 test-standard dataset.in all question categories and overall by a significant margin of 1.7%. The bottom up, adaptive-kTeney et al. et al. reports currently the best performance among on VQAv2 test-standard dataset. This indicates our models superior capability to interpret and incorporate multi-modal relationships for visual reasoning." } ], "images": [ "scigen/train/1000.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of the state-of-the-art methods with our single model performance on VQAv2.0 test-dev and test-standard server." ], "reasoning": "The RAF model outperforms all other models in all question categories and overall by a significant margin of 1.7% on the VQAv2 test-standard dataset.", "description": "in all question categories and overall by a significant margin of 1.7%. The bottom up, adaptive-kTeney et al. et al. reports currently the best performance among on VQAv2 test-standard dataset. This indicates our models superior capability to interpret and incorporate multi-modal relationships for visual reasoning.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 2: Comparison of the state-of-the-art methods with our single model performance on VQAv2.0 test-dev and test-standard server.\nQuestion: Which model has the highest score in the Test-standard All category?\n" }, { "role": "assistant", "content": "By comparing the scores in the Test-standard All category, the RAF model has the highest score of 67.4. So, the answer is {\"answer\": \"RAF\"}" } ], "images": [ "scigen/train/1000.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of the state-of-the-art methods with our single model performance on VQAv2.0 test-dev and test-standard server." ], "reasoning": "By comparing the scores in the Test-standard All category, the RAF model has the highest score of 67.4.", "question": "Which model has the highest score in the Test-standard All category?", "answer": "So, the answer is {\"answer\": \"RAF\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe MUTANBen-Younes et al. (2017) model has the highest score in the Test-standard Y/N category.\n" }, { "role": "assistant", "content": "The table shows that the RAF model has the highest score in the Test-standard Y/N category with a score of 84.2, not the MUTANBen-Younes et al. (2017) model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1000.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of the state-of-the-art methods with our single model performance on VQAv2.0 test-dev and test-standard server." ], "reasoning": "The table shows that the RAF model has the highest score in the Test-standard Y/N category with a score of 84.2, not the MUTANBen-Younes et al. (2017) model.", "statement": "The MUTANBen-Younes et al. (2017) model has the highest score in the Test-standard Y/N category.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: Ablation analysis on the development set for the DSTC7 Ubuntu dataset.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Removing context’s local matching and matching composition from ESIM decreased R@10 and MRR, and further discarding the last words instead of the preceding words degraded them even more. Ensembling these models improved R@10 and MRR significantly.For Ubuntu subtask 1, ESIM achieved 0.854 R@10 and 0.6401 MRR. If we removed context’s local matching and matching composition to accelerate the training process (“-CtxDec”), R@10 and MRR dropped to 0.845 and 0.6210. Further discarding the last words instead of the preceding words for the context (“-CtxDec & -Rev”) degraded R@10 and MRR to 0.840 and 0.6174. Ensembling the above three models (“Ensemble”) achieved 0.887 R@10 and 0.6790 MRR. Ensembling was performed by averaging output from models trained with different parameter initializations and different structures." } ], "images": [ "scigen/train/1001.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Ablation analysis on the development set for the DSTC7 Ubuntu dataset." ], "reasoning": "Removing context’s local matching and matching composition from ESIM decreased R@10 and MRR, and further discarding the last words instead of the preceding words degraded them even more. Ensembling these models improved R@10 and MRR significantly.", "description": "For Ubuntu subtask 1, ESIM achieved 0.854 R@10 and 0.6401 MRR. If we removed context’s local matching and matching composition to accelerate the training process (“-CtxDec”), R@10 and MRR dropped to 0.845 and 0.6210. Further discarding the last words instead of the preceding words for the context (“-CtxDec & -Rev”) degraded R@10 and MRR to 0.840 and 0.6174. Ensembling the above three models (“Ensemble”) achieved 0.887 R@10 and 0.6790 MRR. Ensembling was performed by averaging output from models trained with different parameter initializations and different structures.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 4: Ablation analysis on the development set for the DSTC7 Ubuntu dataset.'. Solve the following question based on the table.\n\nQuestion: What is the R@10 value for the ESIM model in subtask 1 after removing context’s local matching and matching composition?\nLet's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that for subtask 1, the ESIM model achieved an R@10 value of 0.854. After removing context’s local matching and matching composition ('-CtxDec'), the R@10 value dropped to 0.845. So, the answer is {\"answer\": \"0.845\"}" } ], "images": [ "scigen/train/1001.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Ablation analysis on the development set for the DSTC7 Ubuntu dataset." ], "reasoning": "The table shows that for subtask 1, the ESIM model achieved an R@10 value of 0.854. After removing context’s local matching and matching composition ('-CtxDec'), the R@10 value dropped to 0.845.", "question": "What is the R@10 value for the ESIM model in subtask 1 after removing context’s local matching and matching composition?", "answer": "So, the answer is {\"answer\": \"0.845\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The ESIM model in subtask 1 achieved an MRR of 0.6210 after removing context’s local matching and matching composition., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe ESIM model in subtask 1 achieved an MRR of 0.6210 after removing context’s local matching and matching composition.\n" }, { "role": "assistant", "content": "The table indicates that for subtask 1, the ESIM model's MRR dropped to 0.6210 after removing context’s local matching and matching composition ('-CtxDec'). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1001.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Ablation analysis on the development set for the DSTC7 Ubuntu dataset." ], "reasoning": "The table indicates that for subtask 1, the ESIM model's MRR dropped to 0.6210 after removing context’s local matching and matching composition ('-CtxDec').", "statement": "The ESIM model in subtask 1 achieved an MRR of 0.6210 after removing context’s local matching and matching composition.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: Ablation analysis on the development set for the DSTC7 Advising dataset.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Removing context’s local matching and matching composition (-CtxDec) and further discarding the last words instead of the preceding words (-CtxDec & -Rev) both degrade R@10 and MRR, but ensembling these models significantly improves performance.For Ubuntu subtask 1, ESIM achieved 0.854 R@10 and 0.6401 MRR. If we removed context’s local matching and matching composition to accelerate the training process (“-CtxDec”), R@10 and MRR dropped to 0.845 and 0.6210. Further discarding the last words instead of the preceding words for the context (“-CtxDec & -Rev”) degraded R@10 and MRR to 0.840 and 0.6174. Ensembling the above three models (“Ensemble”) achieved 0.887 R@10 and 0.6790 MRR. Ensembling was performed by averaging output from models trained with different parameter initializations and different structures. We used ESIM without context’s local matching and matching composition for computational efficiency. We observed similar trends as on the Ubuntu data set. “-CtxDec & -Rev” degraded R@10 and MRR over “-CtxDec”, yet the ensemble of the two models always produced significant gains over individual models." } ], "images": [ "scigen/train/1002.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ablation analysis on the development set for the DSTC7 Advising dataset." ], "reasoning": "Removing context’s local matching and matching composition (-CtxDec) and further discarding the last words instead of the preceding words (-CtxDec & -Rev) both degrade R@10 and MRR, but ensembling these models significantly improves performance.", "description": "For Ubuntu subtask 1, ESIM achieved 0.854 R@10 and 0.6401 MRR. If we removed context’s local matching and matching composition to accelerate the training process (“-CtxDec”), R@10 and MRR dropped to 0.845 and 0.6210. Further discarding the last words instead of the preceding words for the context (“-CtxDec & -Rev”) degraded R@10 and MRR to 0.840 and 0.6174. Ensembling the above three models (“Ensemble”) achieved 0.887 R@10 and 0.6790 MRR. Ensembling was performed by averaging output from models trained with different parameter initializations and different structures. We used ESIM without context’s local matching and matching composition for computational efficiency. We observed similar trends as on the Ubuntu data set. “-CtxDec & -Rev” degraded R@10 and MRR over “-CtxDec”, yet the ensemble of the two models always produced significant gains over individual models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 5: Ablation analysis on the development set for the DSTC7 Advising dataset.', answer the following question. Give detailed solution consisting of each step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the R@10 value for the Ensemble model in Subtask 3?\n" }, { "role": "assistant", "content": "To find the R@10 value for the Ensemble model in Subtask 3, locate the row corresponding to Subtask 3 and the Ensemble model, then read the R@10 value from that row. So, the answer is {\"answer\": \"0.818\"}" } ], "images": [ "scigen/train/1002.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ablation analysis on the development set for the DSTC7 Advising dataset." ], "reasoning": "To find the R@10 value for the Ensemble model in Subtask 3, locate the row corresponding to Subtask 3 and the Ensemble model, then read the R@10 value from that row.", "question": "What is the R@10 value for the Ensemble model in Subtask 3?", "answer": "So, the answer is {\"answer\": \"0.818\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The -CtxDec model in Subtask 4 has a higher R@1 value than the -CtxDec & -Rev model in Subtask 4., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe -CtxDec model in Subtask 4 has a higher R@1 value than the -CtxDec & -Rev model in Subtask 4.\n" }, { "role": "assistant", "content": "Compare the R@1 values for the -CtxDec and -CtxDec & -Rev models in Subtask 4. The -CtxDec model has an R@1 value of 0.248, which is higher than the -CtxDec & -Rev model's R@1 value of 0.226. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1002.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ablation analysis on the development set for the DSTC7 Advising dataset." ], "reasoning": "Compare the R@1 values for the -CtxDec and -CtxDec & -Rev models in Subtask 4. The -CtxDec model has an R@1 value of 0.248, which is higher than the -CtxDec & -Rev model's R@1 value of 0.226.", "statement": "The -CtxDec model in Subtask 4 has a higher R@1 value than the -CtxDec & -Rev model in Subtask 4.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Ablation study on R52 and Reuters21578. It is clear that LSTM plays an important role by alleviating over-smoothing problem, especially in multi-label classification, which is more prone to over-smoothing.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Removing any part of the proposed model decreases accuracy, with the absence of LSTM causing the most significant drop due to over-smoothing issues.From the results we can see that removing any of the three parts of our proposed model would lead to a decline in accuracy. Among the three parts, the accuracy of the model without LSTM decreases most significantly. We assume that this is because that the over-smoothing problem becomes very severe with relatively big layer number. Furthermore, compared with multiple label classification, smoothing is more acceptable for single label classification. Because if the representation of all the nodes grow to be related to the correct label, it will not hurt the performance. However, over-smoothing is more harmful for multi-label problem, because multi-label requires the representation of different parts related to different labels to be distinguishable." } ], "images": [ "scigen/train/1003.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ablation study on R52 and Reuters21578. It is clear that LSTM plays an important role by alleviating over-smoothing problem, especially in multi-label classification, which is more prone to over-smoothing." ], "reasoning": "Removing any part of the proposed model decreases accuracy, with the absence of LSTM causing the most significant drop due to over-smoothing issues.", "description": "From the results we can see that removing any of the three parts of our proposed model would lead to a decline in accuracy. Among the three parts, the accuracy of the model without LSTM decreases most significantly. We assume that this is because that the over-smoothing problem becomes very severe with relatively big layer number. Furthermore, compared with multiple label classification, smoothing is more acceptable for single label classification. Because if the representation of all the nodes grow to be related to the correct label, it will not hurt the performance. However, over-smoothing is more harmful for multi-label problem, because multi-label requires the representation of different parts related to different labels to be distinguishable.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 5: Ablation study on R52 and Reuters21578. It is clear that LSTM plays an important role by alleviating over-smoothing problem, especially in multi-label classification, which is more prone to over-smoothing.', unfold the steps to solve the question given next. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nQuestion: Which model component's removal leads to the most significant decrease in accuracy for the R52 dataset?\n" }, { "role": "assistant", "content": "The table shows the accuracy for different models on the R52 dataset. The model without LSTM has the lowest accuracy (84.74), indicating the most significant decrease. So, the answer is {\"answer\": \"LSTM\"}" } ], "images": [ "scigen/train/1003.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ablation study on R52 and Reuters21578. It is clear that LSTM plays an important role by alleviating over-smoothing problem, especially in multi-label classification, which is more prone to over-smoothing." ], "reasoning": "The table shows the accuracy for different models on the R52 dataset. The model without LSTM has the lowest accuracy (84.74), indicating the most significant decrease.", "question": "Which model component's removal leads to the most significant decrease in accuracy for the R52 dataset?", "answer": "So, the answer is {\"answer\": \"LSTM\"}" }, { "messages": [ { "role": "user", "content": "The proposed model achieves the highest accuracy on the Reuters21578 dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe proposed model achieves the highest accuracy on the Reuters21578 dataset.\n" }, { "role": "assistant", "content": "The table shows the accuracy of different models on the Reuters21578 dataset. The proposed model has the highest accuracy (82.01). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1003.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ablation study on R52 and Reuters21578. It is clear that LSTM plays an important role by alleviating over-smoothing problem, especially in multi-label classification, which is more prone to over-smoothing." ], "reasoning": "The table shows the accuracy of different models on the Reuters21578 dataset. The proposed model has the highest accuracy (82.01).", "statement": "The proposed model achieves the highest accuracy on the Reuters21578 dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title '(b) SelQA'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The DRCN model achieves the highest MAP and MRR scores, significantly outperforming other models.However, the proposed DRCN using collective attentions over multiple layers, achieves the new state-of-the-art performance, exceeding the current state-of-the-art performance significantly on both datasets." } ], "images": [ "scigen/train/1004.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) SelQA" ], "reasoning": "The DRCN model achieves the highest MAP and MRR scores, significantly outperforming other models.", "description": "However, the proposed DRCN using collective attentions over multiple layers, achieves the new state-of-the-art performance, exceeding the current state-of-the-art performance significantly on both datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model has the highest MAP and MRR scores?\nSolve the above question based on the table titled '(b) SelQA'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the MAP and MRR scores of all models listed in the table, it is evident that the DRCN model has the highest scores in both metrics. So, the answer is {\"answer\": \"DRCN\"}." } ], "images": [ "scigen/train/1004.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) SelQA" ], "reasoning": "By comparing the MAP and MRR scores of all models listed in the table, it is evident that the DRCN model has the highest scores in both metrics.", "question": "Which model has the highest MAP and MRR scores?", "answer": "So, the answer is {\"answer\": \"DRCN\"}." }, { "messages": [ { "role": "user", "content": "The ACNN model has a higher MAP score than the CNN-hinge model. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe ACNN model has a higher MAP score than the CNN-hinge model.\n" }, { "role": "assistant", "content": "The table shows that the ACNN model has a MAP score of 0.874, while the CNN-hinge model has a MAP score of 0.876. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1004.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) SelQA" ], "reasoning": "The table shows that the ACNN model has a MAP score of 0.874, while the CNN-hinge model has a MAP score of 0.876. Therefore, the statement is incorrect.", "statement": "The ACNN model has a higher MAP score than the CNN-hinge model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Classification accuracy for natural language inference on MultiNLI test set. * denotes ensemble methods.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Combining DRCN with ELMo improves performance, surpassing the LM-Transformer with fewer parameters.Our plain DRCN has a competitive performance without any contextualized knowledge. And, by combining DRCN with the ELMo, one of the contextualized embeddings from language models, our model outperforms the LM-Transformer which has 85m parameters with fewer parameters of 61m. From this point of view, the combination of our model with a contextualized knowledge is a good option to enhance the performance." } ], "images": [ "scigen/train/1005.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Classification accuracy for natural language inference on MultiNLI test set. * denotes ensemble methods." ], "reasoning": "Combining DRCN with ELMo improves performance, surpassing the LM-Transformer with fewer parameters.", "description": "Our plain DRCN has a competitive performance without any contextualized knowledge. And, by combining DRCN with the ELMo, one of the contextualized embeddings from language models, our model outperforms the LM-Transformer which has 85m parameters with fewer parameters of 61m. From this point of view, the combination of our model with a contextualized knowledge is a good option to enhance the performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Classification accuracy for natural language inference on MultiNLI test set. * denotes ensemble methods.', answer the following question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest accuracy for matched data?\n" }, { "role": "assistant", "content": "The table shows the accuracy percentages for matched data. The highest value is 82.3%, which corresponds to the DRCN+ELMo* model. So, the answer is {\"answer\": \"DRCN+ELMo*\"}" } ], "images": [ "scigen/train/1005.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Classification accuracy for natural language inference on MultiNLI test set. * denotes ensemble methods." ], "reasoning": "The table shows the accuracy percentages for matched data. The highest value is 82.3%, which corresponds to the DRCN+ELMo* model.", "question": "Which model has the highest accuracy for matched data?", "answer": "So, the answer is {\"answer\": \"DRCN+ELMo*\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe DRCN model has a higher accuracy for mismatched data than the DIIN model.\n" }, { "role": "assistant", "content": "The table shows that the DRCN model has an accuracy of 78.4% for mismatched data, while the DIIN model has an accuracy of 77.8%. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1005.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Classification accuracy for natural language inference on MultiNLI test set. * denotes ensemble methods." ], "reasoning": "The table shows that the DRCN model has an accuracy of 78.4% for mismatched data, while the DIIN model has an accuracy of 77.8%. Therefore, the statement is supported.", "statement": "The DRCN model has a higher accuracy for mismatched data than the DIIN model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled '(a) TrecQA: raw and clean', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The DRCN model achieves the highest MAP and MRR scores in both raw and clean versions, surpassing other models significantly.However, the proposed DRCN using collective attentions over multiple layers, achieves the new state-of-the-art performance, exceeding the current state-of-the-art performance significantly on both datasets." } ], "images": [ "scigen/train/1006.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) TrecQA: raw and clean" ], "reasoning": "The DRCN model achieves the highest MAP and MRR scores in both raw and clean versions, surpassing other models significantly.", "description": "However, the proposed DRCN using collective attentions over multiple layers, achieves the new state-of-the-art performance, exceeding the current state-of-the-art performance significantly on both datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about '(a) TrecQA: raw and clean', solve the following question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model achieves the highest MAP score in the clean version?\n" }, { "role": "assistant", "content": "By comparing the MAP scores of all models in the clean version, DRCN has the highest score of 0.830. So, the answer is {\"answer\": \"DRCN\"}." } ], "images": [ "scigen/train/1006.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) TrecQA: raw and clean" ], "reasoning": "By comparing the MAP scores of all models in the clean version, DRCN has the highest score of 0.830.", "question": "Which model achieves the highest MAP score in the clean version?", "answer": "So, the answer is {\"answer\": \"DRCN\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe DRCN model achieves the highest MRR score in the raw version.\n" }, { "role": "assistant", "content": "By comparing the MRR scores of all models in the raw version, DRCN has the highest score of 0.862. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1006.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) TrecQA: raw and clean" ], "reasoning": "By comparing the MRR scores of all models in the raw version, DRCN has the highest score of 0.862.", "statement": "The DRCN model achieves the highest MRR score in the raw version.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6: Accuracy (%) of Linguistic correctness on MultiNLI dev sets.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Our DRCN model outperforms others in the Quantity/Time category and shows the highest mean and lowest standard deviation for both matched and mismatched problems, indicating competitive and consistent performance.We used annotated subset provided by the MultiNLI dataset, and each sample belongs to one of the 13 linguistic categories. Especially, we can see that ours outperforms much better on the Quantity/Time category which is one of the most difficult problems. Furthermore, our DRCN shows the highest mean and the lowest stddev for both matched and mismatched problems, which indicates that it not only results in a competitive performance but also has a consistent performance." } ], "images": [ "scigen/train/1007.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Accuracy (%) of Linguistic correctness on MultiNLI dev sets." ], "reasoning": "Our DRCN model outperforms others in the Quantity/Time category and shows the highest mean and lowest standard deviation for both matched and mismatched problems, indicating competitive and consistent performance.", "description": "We used annotated subset provided by the MultiNLI dataset, and each sample belongs to one of the 13 linguistic categories. Especially, we can see that ours outperforms much better on the Quantity/Time category which is one of the most difficult problems. Furthermore, our DRCN shows the highest mean and the lowest stddev for both matched and mismatched problems, which indicates that it not only results in a competitive performance but also has a consistent performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model has the highest mean score for mismatched problems?\nSolve the above question based on the table titled 'Table 6: Accuracy (%) of Linguistic correctness on MultiNLI dev sets.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest mean score for mismatched problems, compare the mean scores in the mismatched row for each model. DRCN has the highest mean score of 85.7. So, the answer is {\"answer\": \"DRCN\"}" } ], "images": [ "scigen/train/1007.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Accuracy (%) of Linguistic correctness on MultiNLI dev sets." ], "reasoning": "To find the model with the highest mean score for mismatched problems, compare the mean scores in the mismatched row for each model. DRCN has the highest mean score of 85.7.", "question": "Which model has the highest mean score for mismatched problems?", "answer": "So, the answer is {\"answer\": \"DRCN\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe DRCN model has the lowest standard deviation for matched problems.\n" }, { "role": "assistant", "content": "To verify this statement, compare the standard deviation values for matched problems across all models. DRCN has the lowest standard deviation of 6.7. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1007.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Accuracy (%) of Linguistic correctness on MultiNLI dev sets." ], "reasoning": "To verify this statement, compare the standard deviation values for matched problems across all models. DRCN has the lowest standard deviation of 6.7.", "statement": "The DRCN model has the lowest standard deviation for matched problems.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: InDEE-2019 dataset for five languages, namely, Marathi, Hindi, English, Tamil and Bengali. Number of tags or labels for each dataset and their respective train, validation and test split used in the experiments.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The InDEE-2019 dataset for disaster domain event extraction covers five languages and shows that rule-based approaches outperform baseline models with smaller training sets, while deep learning models excel with larger datasets.We focus on the EE task and model as a sequence labeling problem. We use the following terminologies throughout the paper: Event Trigger : The main word that identifies occurrence of the event mentioned in the document. Event Arguments : Several words that define an events such as place , time , reason , after-effects , participant , casualties. We are interested in extracting event triggers, event arguments from the document. We release the new dataset named InDEE-2019 which consists of tagged event extraction data in disaster domain covering five languages: Marathi, English, Hindi, Bengali and Tamil. We train our marathi model on 15.9K sentences and predict on 43 labels. We train our model on varying training set size, namely, 20%, 40%, 60%, 80% and 100% to ascertain the impact of rules with decreasing amount of dataset. It is observed that at lesser training instances, our rule based approach is able to outperform baseline on 3K(20%) and 6K(40%) training instances. Both macro and micro F1 scores show improvement over baseline model. As training instances increases, deep learning models are able to learn large number of parameters that were difficult to learn on lesser training instances." } ], "images": [ "scigen/train/1008.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: InDEE-2019 dataset for five languages, namely, Marathi, Hindi, English, Tamil and Bengali. Number of tags or labels for each dataset and their respective train, validation and test split used in the experiments." ], "reasoning": "The InDEE-2019 dataset for disaster domain event extraction covers five languages and shows that rule-based approaches outperform baseline models with smaller training sets, while deep learning models excel with larger datasets.", "description": "We focus on the EE task and model as a sequence labeling problem. We use the following terminologies throughout the paper: Event Trigger : The main word that identifies occurrence of the event mentioned in the document. Event Arguments : Several words that define an events such as place , time , reason , after-effects , participant , casualties. We are interested in extracting event triggers, event arguments from the document. We release the new dataset named InDEE-2019 which consists of tagged event extraction data in disaster domain covering five languages: Marathi, English, Hindi, Bengali and Tamil. We train our marathi model on 15.9K sentences and predict on 43 labels. We train our model on varying training set size, namely, 20%, 40%, 60%, 80% and 100% to ascertain the impact of rules with decreasing amount of dataset. It is observed that at lesser training instances, our rule based approach is able to outperform baseline on 3K(20%) and 6K(40%) training instances. Both macro and micro F1 scores show improvement over baseline model. As training instances increases, deep learning models are able to learn large number of parameters that were difficult to learn on lesser training instances.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: InDEE-2019 dataset for five languages, namely, Marathi, Hindi, English, Tamil and Bengali. Number of tags or labels for each dataset and their respective train, validation and test split used in the experiments.', answer the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nHow many sentences are used to train the Marathi model?\n" }, { "role": "assistant", "content": "The table shows that the Marathi model is trained on 15,920 sentences. So, the answer is {\"answer\": \"15920\"}" } ], "images": [ "scigen/train/1008.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: InDEE-2019 dataset for five languages, namely, Marathi, Hindi, English, Tamil and Bengali. Number of tags or labels for each dataset and their respective train, validation and test split used in the experiments." ], "reasoning": "The table shows that the Marathi model is trained on 15,920 sentences.", "question": "How many sentences are used to train the Marathi model?", "answer": "So, the answer is {\"answer\": \"15920\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The dataset includes 47 labels for the Tamil language. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe dataset includes 47 labels for the Tamil language.\n" }, { "role": "assistant", "content": "The table indicates that the Tamil language has 47 labels. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1008.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: InDEE-2019 dataset for five languages, namely, Marathi, Hindi, English, Tamil and Bengali. Number of tags or labels for each dataset and their respective train, validation and test split used in the experiments." ], "reasoning": "The table indicates that the Tamil language has 47 labels.", "statement": "The dataset includes 47 labels for the Tamil language.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 6: BLEU and METEOR scores for human description generation experiments.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The experiment showed that human participants performed better with both image and English descriptions, while the computer algorithm outperformed humans on BLEU metrics but not on METEOR metrics.To evaluate the importance of the different modalities for the image description generation task, we have performed an experiment where we replace the computer algorithm with human participants. The two modalities are the five English description sentences, and the image. The output is a single description sentence in German. The experiment asks the participants for the following tasks: Given both the image and the English descriptions: ’Describe the image in one sentence in German. You can get help from the English sentences provided.’ Given only the image: ’Describe the image in one sentence in German.’ Given only one English sentence: ’Translate the English sentence into German.’ The experiment was performed by 16 native German speakers proficient in English with age ranging from 23 to 54 (coming from Austria, Germany and Switzerland, of which 10 are female and 6 male). The experiment is performed on the first 80 sentences of the validation set. Participants performed 10 repetitions for each task, and not repeating the same image across tasks. For humans, the English description sentences help to obtain better performance. Removing the image altogether and providing only a single English description sentence results in a significant drop. We were surprised to observe such a drop, whereas we expected good translations to obtain competitive results. In addition, we have provided the results of our submission on the same subset of images; humans clearly obtain better performance using METEOR metrics, but our approach is clearly outperforming on the BLEU metrics. The participants were not trained on the train set before performing the tasks, which could be one of the reasons for the difference. Furthermore, given the lower performance of only translating one of the English description sentences on both metrics, it could possibly be caused by existing biases in the data set." } ], "images": [ "scigen/train/1009.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: BLEU and METEOR scores for human description generation experiments." ], "reasoning": "The experiment showed that human participants performed better with both image and English descriptions, while the computer algorithm outperformed humans on BLEU metrics but not on METEOR metrics.", "description": "To evaluate the importance of the different modalities for the image description generation task, we have performed an experiment where we replace the computer algorithm with human participants. The two modalities are the five English description sentences, and the image. The output is a single description sentence in German. The experiment asks the participants for the following tasks: Given both the image and the English descriptions: ’Describe the image in one sentence in German. You can get help from the English sentences provided.’ Given only the image: ’Describe the image in one sentence in German.’ Given only one English sentence: ’Translate the English sentence into German.’ The experiment was performed by 16 native German speakers proficient in English with age ranging from 23 to 54 (coming from Austria, Germany and Switzerland, of which 10 are female and 6 male). The experiment is performed on the first 80 sentences of the validation set. Participants performed 10 repetitions for each task, and not repeating the same image across tasks. For humans, the English description sentences help to obtain better performance. Removing the image altogether and providing only a single English description sentence results in a significant drop. We were surprised to observe such a drop, whereas we expected good translations to obtain competitive results. In addition, we have provided the results of our submission on the same subset of images; humans clearly obtain better performance using METEOR metrics, but our approach is clearly outperforming on the BLEU metrics. The participants were not trained on the train set before performing the tasks, which could be one of the reasons for the difference. Furthermore, given the lower performance of only translating one of the English description sentences on both metrics, it could possibly be caused by existing biases in the data set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 6: BLEU and METEOR scores for human description generation experiments.', answer the question below by showing each progressive step toward the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method achieved the highest METEOR score?\n" }, { "role": "assistant", "content": "To find the highest METEOR score, compare the METEOR scores of all methods listed in the table. The highest METEOR score is 39.16, achieved by the 'Image + sentences' method. So, the answer is {\"answer\": \"Image + sentences\"}." } ], "images": [ "scigen/train/1009.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: BLEU and METEOR scores for human description generation experiments." ], "reasoning": "To find the highest METEOR score, compare the METEOR scores of all methods listed in the table. The highest METEOR score is 39.16, achieved by the 'Image + sentences' method.", "question": "Which method achieved the highest METEOR score?", "answer": "So, the answer is {\"answer\": \"Image + sentences\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe 'Image only' method achieved a higher BLEU-1 score than the 'Image + sentences' method.\n" }, { "role": "assistant", "content": "The BLEU-1 score for the 'Image only' method is 51.26, while the BLEU-1 score for the 'Image + sentences' method is 54.30. Since 51.26 is less than 54.30, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1009.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: BLEU and METEOR scores for human description generation experiments." ], "reasoning": "The BLEU-1 score for the 'Image only' method is 51.26, while the BLEU-1 score for the 'Image + sentences' method is 54.30. Since 51.26 is less than 54.30, the statement is incorrect.", "statement": "The 'Image only' method achieved a higher BLEU-1 score than the 'Image + sentences' method.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Training Data for Task 1.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The dataset includes 29K parallel sentences for training, 1014 for validation, and 1000 for testing, with preprocessing reducing the German vocabulary from 18670 to 15820 tokens.This dataset consists of 29K parallel sentences (direct translations of image descriptions from English to German) for training, 1014 for validation and finally 1000 for the test set. We preprocessed the dataset using the punctuation normalization, tokenization and lowercasing scripts from Moses. This reduces the target vocabulary from 18670 to 15820 unique tokens. During translation generation, the splitted compounds are stitched back together." } ], "images": [ "scigen/train/1010.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Training Data for Task 1." ], "reasoning": "The dataset includes 29K parallel sentences for training, 1014 for validation, and 1000 for testing, with preprocessing reducing the German vocabulary from 18670 to 15820 tokens.", "description": "This dataset consists of 29K parallel sentences (direct translations of image descriptions from English to German) for training, 1014 for validation and finally 1000 for the test set. We preprocessed the dataset using the punctuation normalization, tokenization and lowercasing scripts from Moses. This reduces the target vocabulary from 18670 to 15820 unique tokens. During translation generation, the splitted compounds are stitched back together.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: Training Data for Task 1.'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the total number of words in the English and German datasets combined?\n" }, { "role": "assistant", "content": "The table shows that the English dataset has 377K words and the German dataset has 369K words. Adding these together gives the total number of words. So, the answer is {\"answer\": \"746K\"}" } ], "images": [ "scigen/train/1010.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Training Data for Task 1." ], "reasoning": "The table shows that the English dataset has 377K words and the German dataset has 369K words. Adding these together gives the total number of words.", "question": "What is the total number of words in the English and German datasets combined?", "answer": "So, the answer is {\"answer\": \"746K\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe German vocabulary was reduced to 15820 unique tokens after preprocessing.\n" }, { "role": "assistant", "content": "The description states that preprocessing reduced the German vocabulary from 18670 to 15820 unique tokens, which matches the table. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1010.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Training Data for Task 1." ], "reasoning": "The description states that preprocessing reduced the German vocabulary from 18670 to 15820 unique tokens, which matches the table.", "statement": "The German vocabulary was reduced to 15820 unique tokens after preprocessing.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: BLEU and METEOR scores on detokenized outputs of baseline and submitted Task 1 systems. The METEOR scores in parenthesis are computed with -norm parameter.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Using auxiliary features, we improved test set scores by around 0.4 on METEOR and 0.8 on BLEU over a strong phrase-based baseline.Overall, we were able to improve test set scores by around 0.4 and 0.8 on METEOR and BLEU respectively over a strong phrase-based baseline using auxiliary features." } ], "images": [ "scigen/train/1011.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: BLEU and METEOR scores on detokenized outputs of baseline and submitted Task 1 systems. The METEOR scores in parenthesis are computed with -norm parameter." ], "reasoning": "Using auxiliary features, we improved test set scores by around 0.4 on METEOR and 0.8 on BLEU over a strong phrase-based baseline.", "description": "Overall, we were able to improve test set scores by around 0.4 and 0.8 on METEOR and BLEU respectively over a strong phrase-based baseline using auxiliary features.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: BLEU and METEOR scores on detokenized outputs of baseline and submitted Task 1 systems. The METEOR scores in parenthesis are computed with -norm parameter.', solve the subsequent question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the difference in Test Set BLEU scores between the Phrase-based Baseline and BL+4Features?\n" }, { "role": "assistant", "content": "The Test Set BLEU score for the Phrase-based Baseline is 33.45, and for BL+4Features, it is 34.28. The difference is 34.28 - 33.45 = 0.83. So, the answer is {\"answer\": \"0.83\"}." } ], "images": [ "scigen/train/1011.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: BLEU and METEOR scores on detokenized outputs of baseline and submitted Task 1 systems. The METEOR scores in parenthesis are computed with -norm parameter." ], "reasoning": "The Test Set BLEU score for the Phrase-based Baseline is 33.45, and for BL+4Features, it is 34.28. The difference is 34.28 - 33.45 = 0.83.", "question": "What is the difference in Test Set BLEU scores between the Phrase-based Baseline and BL+4Features?", "answer": "So, the answer is {\"answer\": \"0.83\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe BL+3Features system has a higher Validation Set METEOR score than the Monomodal NMT system.\n" }, { "role": "assistant", "content": "The Validation Set METEOR score for BL+3Features is 54.29, while for Monomodal NMT, it is 51.07. Therefore, BL+3Features has a higher score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1011.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: BLEU and METEOR scores on detokenized outputs of baseline and submitted Task 1 systems. The METEOR scores in parenthesis are computed with -norm parameter." ], "reasoning": "The Validation Set METEOR score for BL+3Features is 54.29, while for Monomodal NMT, it is 51.07. Therefore, BL+3Features has a higher score.", "statement": "The BL+3Features system has a higher Validation Set METEOR score than the Monomodal NMT system.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: BLEU scores for various deep features on the image description generation task using the system of Xu et al. [Xu et al.2015].', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The results increase during the first layers but stabilize from Block-4 on, leading to the selection of 'res4f_relu' for experiments and the use of ResNet-50 features for submission due to computational cost.The results increase during the first layers but stabilize from Block-4 on. Based on these results and considering that a higher spatial resolution is better, we have selected layer ’res4f_relu’ (end of Block-4, after ReLU) for the experiments on multimodal MT. We also compared the features from different networks on the task of image description generation with the system of Xu et al. The results for generating English descriptions Therefore, given the increase in computational cost, we have decided to use ResNet-50 features for our submission." } ], "images": [ "scigen/train/1012.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: BLEU scores for various deep features on the image description generation task using the system of Xu et al. [Xu et al.2015]." ], "reasoning": "The results increase during the first layers but stabilize from Block-4 on, leading to the selection of 'res4f_relu' for experiments and the use of ResNet-50 features for submission due to computational cost.", "description": "The results increase during the first layers but stabilize from Block-4 on. Based on these results and considering that a higher spatial resolution is better, we have selected layer ’res4f_relu’ (end of Block-4, after ReLU) for the experiments on multimodal MT. We also compared the features from different networks on the task of image description generation with the system of Xu et al. The results for generating English descriptions Therefore, given the increase in computational cost, we have decided to use ResNet-50 features for our submission.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: BLEU scores for various deep features on the image description generation task using the system of Xu et al. [Xu et al.2015].'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. \n\nQuestion: Which network has the highest BLEU-4 score and why was it chosen for the submission?\n\nYour Solution:\n" }, { "role": "assistant", "content": "ResNet-50 and ResNet-152 both have the highest BLEU-4 score of 21.1. ResNet-50 was chosen for the submission due to the increase in computational cost. So, the answer is {\"answer\": \"ResNet-50\"}." } ], "images": [ "scigen/train/1012.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: BLEU scores for various deep features on the image description generation task using the system of Xu et al. [Xu et al.2015]." ], "reasoning": "ResNet-50 and ResNet-152 both have the highest BLEU-4 score of 21.1. ResNet-50 was chosen for the submission due to the increase in computational cost.", "question": "Which network has the highest BLEU-4 score and why was it chosen for the submission?", "answer": "So, the answer is {\"answer\": \"ResNet-50\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nResNet-50 has a higher BLEU-2 score than VGG-19.\n" }, { "role": "assistant", "content": "The table shows that ResNet-50 has a BLEU-2 score of 45.2, which is higher than VGG-19's BLEU-2 score of 31.4. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1012.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: BLEU scores for various deep features on the image description generation task using the system of Xu et al. [Xu et al.2015]." ], "reasoning": "The table shows that ResNet-50 has a BLEU-2 score of 45.2, which is higher than VGG-19's BLEU-2 score of 31.4.", "statement": "ResNet-50 has a higher BLEU-2 score than VGG-19.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: BLEU and METEOR scores of our NMT based submissions for Task 2.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The architecture may not effectively integrate image and text representations, and the image context might contain too much irrelevant information.Several explanations can clarify this behavior. First, the architecture is not well suited for integrating image and text representations. This is possible as we did not explore all the possibilities to benefit from both modalities. Another explanation is that the image context contain too much irrelevant information which cannot be discriminated by the lone attention mechanism. This would need a deeper analysis of the attention weights in order to be answered." } ], "images": [ "scigen/train/1013.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: BLEU and METEOR scores of our NMT based submissions for Task 2." ], "reasoning": "The architecture may not effectively integrate image and text representations, and the image context might contain too much irrelevant information.", "description": "Several explanations can clarify this behavior. First, the architecture is not well suited for integrating image and text representations. This is possible as we did not explore all the possibilities to benefit from both modalities. Another explanation is that the image context contain too much irrelevant information which cannot be discriminated by the lone attention mechanism. This would need a deeper analysis of the attention weights in order to be answered.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: BLEU and METEOR scores of our NMT based submissions for Task 2.'.\n\nQuestion: Which system has a higher Validation METEOR score, and what might be a reason for this?\n" }, { "role": "assistant", "content": "The Monomodal system has a higher Validation METEOR score of 36.3 compared to the Multimodal system's 34.4. This could be due to the architecture not being well-suited for integrating image and text representations. So, the answer is {\"answer\": \"Monomodal\"}." } ], "images": [ "scigen/train/1013.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: BLEU and METEOR scores of our NMT based submissions for Task 2." ], "reasoning": "The Monomodal system has a higher Validation METEOR score of 36.3 compared to the Multimodal system's 34.4. This could be due to the architecture not being well-suited for integrating image and text representations.", "question": "Which system has a higher Validation METEOR score, and what might be a reason for this?", "answer": "So, the answer is {\"answer\": \"Monomodal\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Multimodal system has a higher Test BLEU score than the Monomodal system. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Multimodal system has a higher Test BLEU score than the Monomodal system.\n" }, { "role": "assistant", "content": "The table shows that the Multimodal system has a Test BLEU score of 19.2, which is lower than the Monomodal system's Test BLEU score of 23.8. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1013.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: BLEU and METEOR scores of our NMT based submissions for Task 2." ], "reasoning": "The table shows that the Multimodal system has a Test BLEU score of 19.2, which is lower than the Monomodal system's Test BLEU score of 23.8.", "statement": "The Multimodal system has a higher Test BLEU score than the Monomodal system.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3. Class distribution for discuss/stance classification.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The pipeline consists of three stages: relevance classification, neutral/stance classification, and agree/disagree classification, with each stage focusing on different aspects of document classification and showing varying levels of precision and recall.For the first stage of our pipeline (relevance classification), we merge the classes discuss, agree and disagree, to one related class, facilitating a binary unrelated-related classification task. For the second stage (neutral/stance classification), we consider only related documents and merge the classes agree and disagree to one stance class and consider the rest as neutral. We notice that the neutral documents are about twice the stance documents. For the final stage (agree/disagree classification), we only consider the instances of the agree and disagree classes. With respect to stage 1 of our pipeline (relevance classification), precision and recall of the related class (the important class is this stage) is 0.91 and 0.93, respectively. Regarding stage 2 (neutral/stance classification), precision and recall of the important stance class is 0.67 and 0.71, respectively, while that of the neutral class is 0.82 and 0.80, respectively. In general, this task is harder than relevance classification (stage 1). We note that there is room for improvement for the important stance class. The stage 3 of our pipeline deals with the harder problem of agree/disagree classification. We notice that our classifier performs well on the agree class (P = 0.79, R = 0.75), but poorly on the disagree class (P = 0.40, R = 0.44). We observe that, even a dedicated classifier which only considers stance documents struggles detecting many instances of the disagree class. Nevertheless, our method outperforms the existing methods by more than 28% of F1 score. There are two main reasons affecting the performance of the disagree class: i) the classifiers of stage 2 and 3 may filter out many instances belonging to this class, ii)" } ], "images": [ "scigen/train/1014.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. Class distribution for discuss/stance classification." ], "reasoning": "The pipeline consists of three stages: relevance classification, neutral/stance classification, and agree/disagree classification, with each stage focusing on different aspects of document classification and showing varying levels of precision and recall.", "description": "For the first stage of our pipeline (relevance classification), we merge the classes discuss, agree and disagree, to one related class, facilitating a binary unrelated-related classification task. For the second stage (neutral/stance classification), we consider only related documents and merge the classes agree and disagree to one stance class and consider the rest as neutral. We notice that the neutral documents are about twice the stance documents. For the final stage (agree/disagree classification), we only consider the instances of the agree and disagree classes. With respect to stage 1 of our pipeline (relevance classification), precision and recall of the related class (the important class is this stage) is 0.91 and 0.93, respectively. Regarding stage 2 (neutral/stance classification), precision and recall of the important stance class is 0.67 and 0.71, respectively, while that of the neutral class is 0.82 and 0.80, respectively. In general, this task is harder than relevance classification (stage 1). We note that there is room for improvement for the important stance class. The stage 3 of our pipeline deals with the harder problem of agree/disagree classification. We notice that our classifier performs well on the agree class (P = 0.79, R = 0.75), but poorly on the disagree class (P = 0.40, R = 0.44). We observe that, even a dedicated classifier which only considers stance documents struggles detecting many instances of the disagree class. Nevertheless, our method outperforms the existing methods by more than 28% of F1 score. There are two main reasons affecting the performance of the disagree class: i) the classifiers of stage 2 and 3 may filter out many instances belonging to this class, ii)", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 3. Class distribution for discuss/stance classification.'\nQuestion: What is the total number of instances used for training and testing in the pipeline?\n" }, { "role": "assistant", "content": "To find the total number of instances, sum the instances from both the training and testing sets. The training set has 13,427 instances and the testing set has 7,064 instances. Adding these together gives 20,491 instances. So, the answer is {\"answer\": \"20,491\"}." } ], "images": [ "scigen/train/1014.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. Class distribution for discuss/stance classification." ], "reasoning": "To find the total number of instances, sum the instances from both the training and testing sets. The training set has 13,427 instances and the testing set has 7,064 instances. Adding these together gives 20,491 instances.", "question": "What is the total number of instances used for training and testing in the pipeline?", "answer": "So, the answer is {\"answer\": \"20,491\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The classifier performs better on the neutral class than the stance class in stage 2. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe classifier performs better on the neutral class than the stance class in stage 2.\n" }, { "role": "assistant", "content": "The description states that in stage 2, the precision and recall for the neutral class are 0.82 and 0.80, respectively, while for the stance class, they are 0.67 and 0.71, respectively. This indicates that the classifier performs better on the neutral class. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1014.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. Class distribution for discuss/stance classification." ], "reasoning": "The description states that in stage 2, the precision and recall for the neutral class are 0.82 and 0.80, respectively, while for the stance class, they are 0.67 and 0.71, respectively. This indicates that the classifier performs better on the neutral class.", "statement": "The classifier performs better on the neutral class than the stance class in stage 2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3. Class distribution for discuss/stance classification.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The pipeline involves three stages: relevance classification with high precision and recall, neutral/stance classification with moderate performance, and agree/disagree classification where the classifier struggles with the disagree class.For the first stage of our pipeline (relevance classification), we merge the classes discuss, agree and disagree, to one related class, facilitating a binary unrelated-related classification task. For the second stage (neutral/stance classification), we consider only related documents and merge the classes agree and disagree to one stance class and consider the rest as neutral. We notice that the neutral documents are about twice the stance documents. For the final stage (agree/disagree classification), we only consider the instances of the agree and disagree classes. With respect to stage 1 of our pipeline (relevance classification), precision and recall of the related class (the important class is this stage) is 0.91 and 0.93, respectively. Regarding stage 2 (neutral/stance classification), precision and recall of the important stance class is 0.67 and 0.71, respectively, while that of the neutral class is 0.82 and 0.80, respectively. In general, this task is harder than relevance classification (stage 1). We note that there is room for improvement for the important stance class. The stage 3 of our pipeline deals with the harder problem of agree/disagree classification. We notice that our classifier performs well on the agree class (P = 0.79, R = 0.75), but poorly on the disagree class (P = 0.40, R = 0.44). We observe that, even a dedicated classifier which only considers stance documents struggles detecting many instances of the disagree class. Nevertheless, our method outperforms the existing methods by more than 28% of F1 score. There are two main reasons affecting the performance of the disagree class: i) the classifiers of stage 2 and 3 may filter out many instances belonging to this class, ii)" } ], "images": [ "scigen/train/1015.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. Class distribution for discuss/stance classification." ], "reasoning": "The pipeline involves three stages: relevance classification with high precision and recall, neutral/stance classification with moderate performance, and agree/disagree classification where the classifier struggles with the disagree class.", "description": "For the first stage of our pipeline (relevance classification), we merge the classes discuss, agree and disagree, to one related class, facilitating a binary unrelated-related classification task. For the second stage (neutral/stance classification), we consider only related documents and merge the classes agree and disagree to one stance class and consider the rest as neutral. We notice that the neutral documents are about twice the stance documents. For the final stage (agree/disagree classification), we only consider the instances of the agree and disagree classes. With respect to stage 1 of our pipeline (relevance classification), precision and recall of the related class (the important class is this stage) is 0.91 and 0.93, respectively. Regarding stage 2 (neutral/stance classification), precision and recall of the important stance class is 0.67 and 0.71, respectively, while that of the neutral class is 0.82 and 0.80, respectively. In general, this task is harder than relevance classification (stage 1). We note that there is room for improvement for the important stance class. The stage 3 of our pipeline deals with the harder problem of agree/disagree classification. We notice that our classifier performs well on the agree class (P = 0.79, R = 0.75), but poorly on the disagree class (P = 0.40, R = 0.44). We observe that, even a dedicated classifier which only considers stance documents struggles detecting many instances of the disagree class. Nevertheless, our method outperforms the existing methods by more than 28% of F1 score. There are two main reasons affecting the performance of the disagree class: i) the classifiers of stage 2 and 3 may filter out many instances belonging to this class, ii)", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3. Class distribution for discuss/stance classification.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the total number of related instances in both the train and test datasets?\n" }, { "role": "assistant", "content": "To find the total number of related instances, sum the related instances from the train dataset (13,427) and the test dataset (7,064). So, the answer is {\"answer\": \"20491\"}." } ], "images": [ "scigen/train/1015.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. Class distribution for discuss/stance classification." ], "reasoning": "To find the total number of related instances, sum the related instances from the train dataset (13,427) and the test dataset (7,064).", "question": "What is the total number of related instances in both the train and test datasets?", "answer": "So, the answer is {\"answer\": \"20491\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe precision of the related class in stage 1 is higher than the precision of the stance class in stage 2.\n" }, { "role": "assistant", "content": "The precision of the related class in stage 1 is 0.91, while the precision of the stance class in stage 2 is 0.67. Since 0.91 is higher than 0.67, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1015.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. Class distribution for discuss/stance classification." ], "reasoning": "The precision of the related class in stage 1 is 0.91, while the precision of the stance class in stage 2 is 0.67. Since 0.91 is higher than 0.67, the statement is supported.", "statement": "The precision of the related class in stage 1 is higher than the precision of the stance class in stage 2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3. Class distribution for discuss/stance classification.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The pipeline consists of three stages: relevance classification, neutral/stance classification, and agree/disagree classification, with each stage having varying precision and recall metrics.For the first stage of our pipeline (relevance classification), we merge the classes discuss, agree and disagree, to one related class, facilitating a binary unrelated-related classification task. For the second stage (neutral/stance classification), we consider only related documents and merge the classes agree and disagree to one stance class and consider the rest as neutral. We notice that the neutral documents are about twice the stance documents. For the final stage (agree/disagree classification), we only consider the instances of the agree and disagree classes. With respect to stage 1 of our pipeline (relevance classification), precision and recall of the related class (the important class is this stage) is 0.91 and 0.93, respectively. Regarding stage 2 (neutral/stance classification), precision and recall of the important stance class is 0.67 and 0.71, respectively, while that of the neutral class is 0.82 and 0.80, respectively. In general, this task is harder than relevance classification (stage 1). We note that there is room for improvement for the important stance class. The stage 3 of our pipeline deals with the harder problem of agree/disagree classification. We notice that our classifier performs well on the agree class (P = 0.79, R = 0.75), but poorly on the disagree class (P = 0.40, R = 0.44). We observe that, even a dedicated classifier which only considers stance documents struggles detecting many instances of the disagree class. Nevertheless, our method outperforms the existing methods by more than 28% of F1 score. There are two main reasons affecting the performance of the disagree class: i) the classifiers of stage 2 and 3 may filter out many instances belonging to this class, ii)" } ], "images": [ "scigen/train/1016.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. Class distribution for discuss/stance classification." ], "reasoning": "The pipeline consists of three stages: relevance classification, neutral/stance classification, and agree/disagree classification, with each stage having varying precision and recall metrics.", "description": "For the first stage of our pipeline (relevance classification), we merge the classes discuss, agree and disagree, to one related class, facilitating a binary unrelated-related classification task. For the second stage (neutral/stance classification), we consider only related documents and merge the classes agree and disagree to one stance class and consider the rest as neutral. We notice that the neutral documents are about twice the stance documents. For the final stage (agree/disagree classification), we only consider the instances of the agree and disagree classes. With respect to stage 1 of our pipeline (relevance classification), precision and recall of the related class (the important class is this stage) is 0.91 and 0.93, respectively. Regarding stage 2 (neutral/stance classification), precision and recall of the important stance class is 0.67 and 0.71, respectively, while that of the neutral class is 0.82 and 0.80, respectively. In general, this task is harder than relevance classification (stage 1). We note that there is room for improvement for the important stance class. The stage 3 of our pipeline deals with the harder problem of agree/disagree classification. We notice that our classifier performs well on the agree class (P = 0.79, R = 0.75), but poorly on the disagree class (P = 0.40, R = 0.44). We observe that, even a dedicated classifier which only considers stance documents struggles detecting many instances of the disagree class. Nevertheless, our method outperforms the existing methods by more than 28% of F1 score. There are two main reasons affecting the performance of the disagree class: i) the classifiers of stage 2 and 3 may filter out many instances belonging to this class, ii)", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 3. Class distribution for discuss/stance classification.\nQuestion: What is the total number of instances used for training and testing combined?\n" }, { "role": "assistant", "content": "To find the total number of instances, sum the instances from both the training and testing sets. The training set has 4,518 instances and the testing set has 2,600 instances. Adding these together gives 4,518 + 2,600 = 7,118. So, the answer is {\"answer\": \"7118\"}." } ], "images": [ "scigen/train/1016.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. Class distribution for discuss/stance classification." ], "reasoning": "To find the total number of instances, sum the instances from both the training and testing sets. The training set has 4,518 instances and the testing set has 2,600 instances. Adding these together gives 4,518 + 2,600 = 7,118.", "question": "What is the total number of instances used for training and testing combined?", "answer": "So, the answer is {\"answer\": \"7118\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The classifier performs better on the agree class than the disagree class in stage 3., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe classifier performs better on the agree class than the disagree class in stage 3.\n" }, { "role": "assistant", "content": "The description states that the classifier performs well on the agree class with precision and recall of 0.79 and 0.75, respectively, but poorly on the disagree class with precision and recall of 0.40 and 0.44, respectively. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1016.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. Class distribution for discuss/stance classification." ], "reasoning": "The description states that the classifier performs well on the agree class with precision and recall of 0.79 and 0.75, respectively, but poorly on the disagree class with precision and recall of 0.40 and 0.44, respectively.", "statement": "The classifier performs better on the agree class than the disagree class in stage 3.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 5. Document stance classification performance.'.\n" }, { "role": "assistant", "content": "CombNSE achieves the highest FNC score (0.83), but L3S achieves the highest macro-averaged F1 score (0.62).First, we notice that, considering the problematic FNC-I evaluation measure, CombNSE achieves the highest performance (0.83), followed by SOLAT, Athene, UCLMR, StackLSTM, 3-Stage Trad. (0.82) and our pipeline approach (0.81). However, we see that the ranking of the top performing systems is very different if we consider the more robust macro-averaged F1 measure (F1m). Specifically, L3S now achieves the highest score (0.62), outperforming the best baseline system (stackLSTM) by one percentage point, while CombNSE is now in the fourth position (F1m = 0.59)." } ], "images": [ "scigen/train/1017.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5. Document stance classification performance." ], "reasoning": "CombNSE achieves the highest FNC score (0.83), but L3S achieves the highest macro-averaged F1 score (0.62).", "description": "First, we notice that, considering the problematic FNC-I evaluation measure, CombNSE achieves the highest performance (0.83), followed by SOLAT, Athene, UCLMR, StackLSTM, 3-Stage Trad. (0.82) and our pipeline approach (0.81). However, we see that the ranking of the top performing systems is very different if we consider the more robust macro-averaged F1 measure (F1m). Specifically, L3S now achieves the highest score (0.62), outperforming the best baseline system (stackLSTM) by one percentage point, while CombNSE is now in the fourth position (F1m = 0.59).", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 5. Document stance classification performance.'. Solve the following question based on the table.\n\nQuestion: Which system achieves the highest FNC score and which system achieves the highest macro-averaged F1 score?\nLet's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "CombNSE achieves the highest FNC score (0.83) and L3S achieves the highest macro-averaged F1 score (0.62). So, the answer is {\"answer\": \"CombNSE and L3S\"}" } ], "images": [ "scigen/train/1017.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5. Document stance classification performance." ], "reasoning": "CombNSE achieves the highest FNC score (0.83) and L3S achieves the highest macro-averaged F1 score (0.62).", "question": "Which system achieves the highest FNC score and which system achieves the highest macro-averaged F1 score?", "answer": "So, the answer is {\"answer\": \"CombNSE and L3S\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis CombNSE achieves the highest macro-averaged F1 score. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nCombNSE achieves the highest macro-averaged F1 score.\n" }, { "role": "assistant", "content": "The table shows that L3S achieves the highest macro-averaged F1 score (0.62), not CombNSE. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1017.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5. Document stance classification performance." ], "reasoning": "The table shows that L3S achieves the highest macro-averaged F1 score (0.62), not CombNSE.", "statement": "CombNSE achieves the highest macro-averaged F1 score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 7. Confusion matrix of our pipeline system.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The neutral and agree classes are often confused due to their similar nature, while the disagree class is frequently misclassified as unrelated or neutral, highlighting the difficulty of the task.Overall, we note that the neutral and agree classes seem to be frequently confused, what seems intuitive given the very similar nature of these classes, i.e. a document which discusses a claim without explicitly taking a stance is likely to agree with it. The results for the disagree class illustrate that stage 1 misclassifies (as unrelated) 18.5% of all disagree instances (129 instances, in total), while this percentage is less than 7% for the agree and neutral classes. In stage 2, we see that 171 disagree instances (25.5%) are misclassified as neutral, while this percentage is similar for the agree class (26.2%). Finally, in the last stage, 34% of the disagree instances are misclassified as agree, which demonstrates the difficulty of this task. As we explained above, the highly unbalanced data distribution and the limited amount of training data are likely to contribute significantly to this picture." } ], "images": [ "scigen/train/1018.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7. Confusion matrix of our pipeline system." ], "reasoning": "The neutral and agree classes are often confused due to their similar nature, while the disagree class is frequently misclassified as unrelated or neutral, highlighting the difficulty of the task.", "description": "Overall, we note that the neutral and agree classes seem to be frequently confused, what seems intuitive given the very similar nature of these classes, i.e. a document which discusses a claim without explicitly taking a stance is likely to agree with it. The results for the disagree class illustrate that stage 1 misclassifies (as unrelated) 18.5% of all disagree instances (129 instances, in total), while this percentage is less than 7% for the agree and neutral classes. In stage 2, we see that 171 disagree instances (25.5%) are misclassified as neutral, while this percentage is similar for the agree class (26.2%). Finally, in the last stage, 34% of the disagree instances are misclassified as agree, which demonstrates the difficulty of this task. As we explained above, the highly unbalanced data distribution and the limited amount of training data are likely to contribute significantly to this picture.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 7. Confusion matrix of our pipeline system.'\nQuestion: What percentage of disagree instances are misclassified as neutral in stage 2?\n" }, { "role": "assistant", "content": "In stage 2, 171 disagree instances are misclassified as neutral, which is 25.5% of all disagree instances. So, the answer is {\"answer\": \"25.5%\"}" } ], "images": [ "scigen/train/1018.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7. Confusion matrix of our pipeline system." ], "reasoning": "In stage 2, 171 disagree instances are misclassified as neutral, which is 25.5% of all disagree instances.", "question": "What percentage of disagree instances are misclassified as neutral in stage 2?", "answer": "So, the answer is {\"answer\": \"25.5%\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nIn stage 1, 18.5% of all disagree instances are misclassified as unrelated.\n" }, { "role": "assistant", "content": "The description states that stage 1 misclassifies 18.5% of all disagree instances (129 instances) as unrelated. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1018.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7. Confusion matrix of our pipeline system." ], "reasoning": "The description states that stage 1 misclassifies 18.5% of all disagree instances (129 instances) as unrelated.", "statement": "In stage 1, 18.5% of all disagree instances are misclassified as unrelated.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 8. Confusion matrix of different stages.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Stage 1 has a low misclassification rate, Stage 2 misclassifies a significant portion of stances as neutral, and Stage 3 has high misclassification rates for agree and disagree instances.We note the increasing difficulty of each stage. Stage 1 misclassifies a small number of related instances as unrelated (less than 8%). Stage 2 misclassifies 29% of the stance instances as neutral. Finally, stage 3 misclassifies the majority of disagree instances (55%) as agree, and around 25% of the agree instances as disagree. It is evident from these results that there is much room for improvement for the last stage of our pipeline." } ], "images": [ "scigen/train/1019.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8. Confusion matrix of different stages." ], "reasoning": "Stage 1 has a low misclassification rate, Stage 2 misclassifies a significant portion of stances as neutral, and Stage 3 has high misclassification rates for agree and disagree instances.", "description": "We note the increasing difficulty of each stage. Stage 1 misclassifies a small number of related instances as unrelated (less than 8%). Stage 2 misclassifies 29% of the stance instances as neutral. Finally, stage 3 misclassifies the majority of disagree instances (55%) as agree, and around 25% of the agree instances as disagree. It is evident from these results that there is much room for improvement for the last stage of our pipeline.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 8. Confusion matrix of different stages.'. Solve the following question based on the table.\n\nQuestion: What percentage of agree instances are misclassified as disagree in Stage 3?\nLet's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "Stage 3 has 467 agree instances misclassified as disagree out of a total of 1,436 agree instances. The percentage is calculated as (467/1436) * 100. So, the answer is {\"answer\": \"32.52%\"}." } ], "images": [ "scigen/train/1019.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8. Confusion matrix of different stages." ], "reasoning": "Stage 3 has 467 agree instances misclassified as disagree out of a total of 1,436 agree instances. The percentage is calculated as (467/1436) * 100.", "question": "What percentage of agree instances are misclassified as disagree in Stage 3?", "answer": "So, the answer is {\"answer\": \"32.52%\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nStage 2 misclassifies more than 50% of stance instances as neutral.\n" }, { "role": "assistant", "content": "Stage 2 misclassifies 760 stance instances as neutral out of a total of 2,600 stance instances (760 + 1,840). The percentage is (760/2600) * 100, which is 29%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1019.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8. Confusion matrix of different stages." ], "reasoning": "Stage 2 misclassifies 760 stance instances as neutral out of a total of 2,600 stance instances (760 + 1,840). The percentage is (760/2600) * 100, which is 29%.", "statement": "Stage 2 misclassifies more than 50% of stance instances as neutral.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: SLU performances on the DSTC3 evaluation set when removing different modules of our method.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Removing SLU model pretraining on DSTC2 and finetuning on the seed data significantly decreases SLU performance. Further removing the sentence generator decreases performance by 10.3%, and replacing atomic exemplars with act-slot-value triples causes a sharp drop.Ablation Study By removing SLU model pretraining on DSTC2 (“- dstc2”) and finetuning on the seed data (“- dstc3_seed”), we can see a significant decrease in SLU performance. When we subsequently cast aside the sentence generator (“- sentence generator”, i.e. using the atomic exemplars as inputs of SLU model directly), the SLU performance decreases by 10.3%. This shows that the sentence generator can produce more natural utterances. If we replace the atomic exemplars as the corresponding act-slot-value triples (“- atomic templates”), the SLU performance drops sharply. The reason may be that the atomic templates provide a better description of corresponding semantic meanings than the surfaces of the triples." } ], "images": [ "scigen/train/1020.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: SLU performances on the DSTC3 evaluation set when removing different modules of our method." ], "reasoning": "Removing SLU model pretraining on DSTC2 and finetuning on the seed data significantly decreases SLU performance. Further removing the sentence generator decreases performance by 10.3%, and replacing atomic exemplars with act-slot-value triples causes a sharp drop.", "description": "Ablation Study By removing SLU model pretraining on DSTC2 (“- dstc2”) and finetuning on the seed data (“- dstc3_seed”), we can see a significant decrease in SLU performance. When we subsequently cast aside the sentence generator (“- sentence generator”, i.e. using the atomic exemplars as inputs of SLU model directly), the SLU performance decreases by 10.3%. This shows that the sentence generator can produce more natural utterances. If we replace the atomic exemplars as the corresponding act-slot-value triples (“- atomic templates”), the SLU performance drops sharply. The reason may be that the atomic templates provide a better description of corresponding semantic meanings than the surfaces of the triples.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: SLU performances on the DSTC3 evaluation set when removing different modules of our method.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the F1-score when both SLU model pretraining on DSTC2 and finetuning on the seed data are removed?\n" }, { "role": "assistant", "content": "The table shows that removing both SLU model pretraining on DSTC2 and finetuning on the seed data results in an F1-score of 84.3. So, the answer is {\"answer\": \"84.3\"}" } ], "images": [ "scigen/train/1020.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: SLU performances on the DSTC3 evaluation set when removing different modules of our method." ], "reasoning": "The table shows that removing both SLU model pretraining on DSTC2 and finetuning on the seed data results in an F1-score of 84.3.", "question": "What is the F1-score when both SLU model pretraining on DSTC2 and finetuning on the seed data are removed?", "answer": "So, the answer is {\"answer\": \"84.3\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nRemoving the sentence generator results in an F1-score of 74.0.\n" }, { "role": "assistant", "content": "The table indicates that removing the sentence generator results in an F1-score of 74.0. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1020.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: SLU performances on the DSTC3 evaluation set when removing different modules of our method." ], "reasoning": "The table indicates that removing the sentence generator results in an F1-score of 74.0.", "statement": "Removing the sentence generator results in an F1-score of 74.0.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: SLU performances of different systems on the DSTC3 evaluation set.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The hierarchical decoding (HD) model outperforms the zero-shot learning (ZS) method, and data augmentation with atomic templates (AT) significantly enhances SLU performance, especially when combined with seed abridgement.We can see that: The hierarchical decoding (HD) model gets better performance than the zero-shot learning (ZS) method of SLU. The seed data dstc3_seed limits the power of the SLU model, and even the naive augmentation can enhance it. Our data augmentation method with atomic templates (AT) improves the SLU performance dramatically. One reason may be the generated data has higher variety of semantic meaning than the naive augmentation. Combination can make up more dialogue acts and shows better result than Seed abridgement, while Seed abridgement provides more realistic dialogue acts. Thus, their union gives the best result. The best performance of our method is close to the human-designed sentence-level templates Zhu et al." } ], "images": [ "scigen/train/1021.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: SLU performances of different systems on the DSTC3 evaluation set." ], "reasoning": "The hierarchical decoding (HD) model outperforms the zero-shot learning (ZS) method, and data augmentation with atomic templates (AT) significantly enhances SLU performance, especially when combined with seed abridgement.", "description": "We can see that: The hierarchical decoding (HD) model gets better performance than the zero-shot learning (ZS) method of SLU. The seed data dstc3_seed limits the power of the SLU model, and even the naive augmentation can enhance it. Our data augmentation method with atomic templates (AT) improves the SLU performance dramatically. One reason may be the generated data has higher variety of semantic meaning than the naive augmentation. Combination can make up more dialogue acts and shows better result than Seed abridgement, while Seed abridgement provides more realistic dialogue acts. Thus, their union gives the best result. The best performance of our method is close to the human-designed sentence-level templates Zhu et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 2: SLU performances of different systems on the DSTC3 evaluation set.\nQuestion: What is the F1-score of the HD model with AT (seed abridgement + combination) augmentation?\n" }, { "role": "assistant", "content": "The table shows the F1-score for various SLU models and augmentations. The HD model with AT (seed abridgement + combination) augmentation has an F1-score of 88.6. So, the answer is {\"answer\": \"88.6\"}." } ], "images": [ "scigen/train/1021.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: SLU performances of different systems on the DSTC3 evaluation set." ], "reasoning": "The table shows the F1-score for various SLU models and augmentations. The HD model with AT (seed abridgement + combination) augmentation has an F1-score of 88.6.", "question": "What is the F1-score of the HD model with AT (seed abridgement + combination) augmentation?", "answer": "So, the answer is {\"answer\": \"88.6\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The HD model with naive augmentation has a higher F1-score than the HD model without augmentation. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe HD model with naive augmentation has a higher F1-score than the HD model without augmentation.\n" }, { "role": "assistant", "content": "The table shows that the HD model with naive augmentation has an F1-score of 82.9, while the HD model without augmentation has an F1-score of 78.5. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1021.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: SLU performances of different systems on the DSTC3 evaluation set." ], "reasoning": "The table shows that the HD model with naive augmentation has an F1-score of 82.9, while the HD model without augmentation has an F1-score of 78.5. Therefore, the statement is supported.", "statement": "The HD model with naive augmentation has a higher F1-score than the HD model without augmentation.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: Results of child following a parent with swapped direction. “Baseline” is child-only training. “Aligned” is the more natural setup with English appearing on the “correct” side of the parent, the numbers in this column thus correspond to those in Table 2.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The study shows that multilingual settings can improve performance, especially when a common language is aligned, with gains observed in both directions.This interesting result should be studied in more detail. \\newcitefirat-cho-bengio:etal:2016 hinted possible gains even when both languages are distinct from the low-resource languages but in a multilingual setting. Not surprisingly, the improvements are better when the common language is aligned. We see gains in both directions, although not always statistically significant. Future work should investigate if this performance boost is possible even for high-resource languages. Similar behavior has been shown in \\newciteniu-denkowski-carpuat:2018:WNMT2018, where in contrast to our work they mixed the data together and added an artificial token indicating the target language." } ], "images": [ "scigen/train/1022.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of child following a parent with swapped direction. “Baseline” is child-only training. “Aligned” is the more natural setup with English appearing on the “correct” side of the parent, the numbers in this column thus correspond to those in Table 2." ], "reasoning": "The study shows that multilingual settings can improve performance, especially when a common language is aligned, with gains observed in both directions.", "description": "This interesting result should be studied in more detail. \\newcitefirat-cho-bengio:etal:2016 hinted possible gains even when both languages are distinct from the low-resource languages but in a multilingual setting. Not surprisingly, the improvements are better when the common language is aligned. We see gains in both directions, although not always statistically significant. Future work should investigate if this performance boost is possible even for high-resource languages. Similar behavior has been shown in \\newciteniu-denkowski-carpuat:2018:WNMT2018, where in contrast to our work they mixed the data together and added an artificial token indicating the target language.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Results of child following a parent with swapped direction. “Baseline” is child-only training. “Aligned” is the more natural setup with English appearing on the “correct” side of the parent, the numbers in this column thus correspond to those in Table 2.\nQuestion: Which language pair showed the highest improvement in the Aligned setting compared to the Baseline?\nGive a detailed response to the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest improvement, compare the Aligned and Baseline scores for each language pair. The enRU - ETen pair has the highest improvement with a Baseline score of 21.74 and an Aligned score of 23.54. So, the answer is {\"answer\": \"enRU - ETen\"}" } ], "images": [ "scigen/train/1022.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of child following a parent with swapped direction. “Baseline” is child-only training. “Aligned” is the more natural setup with English appearing on the “correct” side of the parent, the numbers in this column thus correspond to those in Table 2." ], "reasoning": "To find the highest improvement, compare the Aligned and Baseline scores for each language pair. The enRU - ETen pair has the highest improvement with a Baseline score of 21.74 and an Aligned score of 23.54.", "question": "Which language pair showed the highest improvement in the Aligned setting compared to the Baseline?", "answer": "So, the answer is {\"answer\": \"enRU - ETen\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe enFI - ETen pair showed an improvement in the Aligned setting compared to the Baseline.\n" }, { "role": "assistant", "content": "The table shows that the enFI - ETen pair has a Baseline score of 21.74 and an Aligned score of 24.18, indicating an improvement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1022.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of child following a parent with swapped direction. “Baseline” is child-only training. “Aligned” is the more natural setup with English appearing on the “correct” side of the parent, the numbers in this column thus correspond to those in Table 2." ], "reasoning": "The table shows that the enFI - ETen pair has a Baseline score of 21.74 and an Aligned score of 24.18, indicating an improvement.", "statement": "The enFI - ETen pair showed an improvement in the Aligned setting compared to the Baseline.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Transfer learning with English reused either in source (encoder) or target (decoder). The column “Transfer” is our method, baselines correspond to training on one of the corpora only. Scores (BLEU) are always for the child language pair and they are comparable only within lines or when the child language pair is the same. “Unrelated” language pairs in bold. Upper part: parent larger, lower part: child larger. (“EN” lowercased just to stand out.)', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The improvement in BLEU score for ENET when the parent model was ENCS (3.38) compared to ENFI (2.71) suggests that the size of the parent training set is more important than the relatedness of languages.Furthermore, the improvement is not restricted only to related languages as Estonian and Finnish as shown in previous works. We reach an improvement of 3.38 BLEU for ENET when parent model was ENCS, compared to improvement of 2.71 from ENFI parent. This statistically significant improvement contradicts \\newcitedabre2017empirical who concluded that the more related the languages are, the better transfer learning works. We see it as an indication that the size of the parent training set is more important than relatedness of languages. On the other hand, this transfer learning works well only when the parent has more training data than the child. This is another indication, that the most important is the size of the parent corpus compared to the child one." } ], "images": [ "scigen/train/1023.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Transfer learning with English reused either in source (encoder) or target (decoder). The column “Transfer” is our method, baselines correspond to training on one of the corpora only. Scores (BLEU) are always for the child language pair and they are comparable only within lines or when the child language pair is the same. “Unrelated” language pairs in bold. Upper part: parent larger, lower part: child larger. (“EN” lowercased just to stand out.)" ], "reasoning": "The improvement in BLEU score for ENET when the parent model was ENCS (3.38) compared to ENFI (2.71) suggests that the size of the parent training set is more important than the relatedness of languages.", "description": "Furthermore, the improvement is not restricted only to related languages as Estonian and Finnish as shown in previous works. We reach an improvement of 3.38 BLEU for ENET when parent model was ENCS, compared to improvement of 2.71 from ENFI parent. This statistically significant improvement contradicts \\newcitedabre2017empirical who concluded that the more related the languages are, the better transfer learning works. We see it as an indication that the size of the parent training set is more important than relatedness of languages. On the other hand, this transfer learning works well only when the parent has more training data than the child. This is another indication, that the most important is the size of the parent corpus compared to the child one.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Transfer learning with English reused either in source (encoder) or target (decoder). The column “Transfer” is our method, baselines correspond to training on one of the corpora only. Scores (BLEU) are always for the child language pair and they are comparable only within lines or when the child language pair is the same. “Unrelated” language pairs in bold. Upper part: parent larger, lower part: child larger. (“EN” lowercased just to stand out.)', answer the following question. Give detailed solution consisting of each step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the BLEU score improvement for ENET when the parent model was ENCS compared to ENFI?\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for ENET with ENCS as the parent model is 20.41, and with ENFI as the parent model is 19.74. The improvement is calculated as 20.41 - 17.03 = 3.38 for ENCS and 19.74 - 17.03 = 2.71 for ENFI. So, the answer is {\"answer\": \"3.38\"}" } ], "images": [ "scigen/train/1023.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Transfer learning with English reused either in source (encoder) or target (decoder). The column “Transfer” is our method, baselines correspond to training on one of the corpora only. Scores (BLEU) are always for the child language pair and they are comparable only within lines or when the child language pair is the same. “Unrelated” language pairs in bold. Upper part: parent larger, lower part: child larger. (“EN” lowercased just to stand out.)" ], "reasoning": "The table shows that the BLEU score for ENET with ENCS as the parent model is 20.41, and with ENFI as the parent model is 19.74. The improvement is calculated as 20.41 - 17.03 = 3.38 for ENCS and 19.74 - 17.03 = 2.71 for ENFI.", "question": "What is the BLEU score improvement for ENET when the parent model was ENCS compared to ENFI?", "answer": "So, the answer is {\"answer\": \"3.38\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe BLEU score improvement for ENET is higher when the parent model is ENCS compared to ENFI.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for ENET with ENCS as the parent model is 20.41, and with ENFI as the parent model is 19.74. The improvement is calculated as 20.41 - 17.03 = 3.38 for ENCS and 19.74 - 17.03 = 2.71 for ENFI, supporting the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1023.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Transfer learning with English reused either in source (encoder) or target (decoder). The column “Transfer” is our method, baselines correspond to training on one of the corpora only. Scores (BLEU) are always for the child language pair and they are comparable only within lines or when the child language pair is the same. “Unrelated” language pairs in bold. Upper part: parent larger, lower part: child larger. (“EN” lowercased just to stand out.)" ], "reasoning": "The table shows that the BLEU score for ENET with ENCS as the parent model is 20.41, and with ENFI as the parent model is 19.74. The improvement is calculated as 20.41 - 17.03 = 3.38 for ENCS and 19.74 - 17.03 = 2.71 for ENFI, supporting the statement.", "statement": "The BLEU score improvement for ENET is higher when the parent model is ENCS compared to ENFI.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Maximal score reached by ENET child for decreasing sizes of child training data, trained off an ENFI parent (all ENFI data are used and models are trained for 800k steps). The baselines use only the reduced ENET data.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Transfer learning shows more significant gains over baseline for smaller datasets, making it beneficial for low-resource languages.It is a common knowledge, that gains from transfer learning are more pronounced for smaller childs. Our transfer learning (“start with a model for whatever parent pair”) may thus resolve the issue of applicability of NMT for low resource languages as pointed out by \\newcitekoehn-knowles:2017:NMT." } ], "images": [ "scigen/train/1024.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Maximal score reached by ENET child for decreasing sizes of child training data, trained off an ENFI parent (all ENFI data are used and models are trained for 800k steps). The baselines use only the reduced ENET data." ], "reasoning": "Transfer learning shows more significant gains over baseline for smaller datasets, making it beneficial for low-resource languages.", "description": "It is a common knowledge, that gains from transfer learning are more pronounced for smaller childs. Our transfer learning (“start with a model for whatever parent pair”) may thus resolve the issue of applicability of NMT for low resource languages as pointed out by \\newcitekoehn-knowles:2017:NMT.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: Maximal score reached by ENET child for decreasing sizes of child training data, trained off an ENFI parent (all ENFI data are used and models are trained for 800k steps). The baselines use only the reduced ENET data.'. Solve the following question based on the table.\n\nQuestion: What is the BLEU score difference between Transfer and Baseline for the smallest dataset?\nLet's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The smallest dataset has 10k sentences. The Transfer BLEU score for 10k sentences is 12.46, and the Baseline BLEU score is 1.95. The difference is 12.46 - 1.95. So, the answer is {\"answer\": \"10.51\"}." } ], "images": [ "scigen/train/1024.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Maximal score reached by ENET child for decreasing sizes of child training data, trained off an ENFI parent (all ENFI data are used and models are trained for 800k steps). The baselines use only the reduced ENET data." ], "reasoning": "The smallest dataset has 10k sentences. The Transfer BLEU score for 10k sentences is 12.46, and the Baseline BLEU score is 1.95. The difference is 12.46 - 1.95.", "question": "What is the BLEU score difference between Transfer and Baseline for the smallest dataset?", "answer": "So, the answer is {\"answer\": \"10.51\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nTransfer learning achieves a BLEU score of 19.74 for 800k sentences.\n" }, { "role": "assistant", "content": "The table shows that for 800k sentences, the Transfer BLEU score is indeed 19.74. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1024.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Maximal score reached by ENET child for decreasing sizes of child training data, trained off an ENFI parent (all ENFI data are used and models are trained for 800k steps). The baselines use only the reduced ENET data." ], "reasoning": "The table shows that for 800k sentences, the Transfer BLEU score is indeed 19.74.", "statement": "Transfer learning achieves a BLEU score of 19.74 for 800k sentences.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 7: Summary of vocabulary overlaps for the various language sets. All figures in % of the shared vocabulary.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The AR-RU-ET-EN language combination has the lowest percentage of subwords (6.2%) available from the parent, indicating minimal word knowledge transfer from the parent., what portion is shared by all the languages and what portion of subwords benefits from the parent training. We see a similar picture across the board, only AR-RU-ET-EN stands out with the very low number of subwords (6.2%) available already in the parent. The parent AR-RU thus offered very little word knowledge to the child and yet lead to a gain in BLEU." } ], "images": [ "scigen/train/1025.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Summary of vocabulary overlaps for the various language sets. All figures in % of the shared vocabulary." ], "reasoning": "The AR-RU-ET-EN language combination has the lowest percentage of subwords (6.2%) available from the parent, indicating minimal word knowledge transfer from the parent.", "description": ", what portion is shared by all the languages and what portion of subwords benefits from the parent training. We see a similar picture across the board, only AR-RU-ET-EN stands out with the very low number of subwords (6.2%) available already in the parent. The parent AR-RU thus offered very little word knowledge to the child and yet lead to a gain in BLEU.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 7: Summary of vocabulary overlaps for the various language sets. All figures in % of the shared vocabulary.' and methodically tackle the question that follows. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich language combination has the lowest percentage of subwords available from the parent?\n" }, { "role": "assistant", "content": "By examining the 'From Parent' column, we see that AR-RU-ET-EN has the lowest percentage of subwords available from the parent at 6.2%. So, the answer is {\"answer\": \"AR-RU-ET-EN\"}." } ], "images": [ "scigen/train/1025.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Summary of vocabulary overlaps for the various language sets. All figures in % of the shared vocabulary." ], "reasoning": "By examining the 'From Parent' column, we see that AR-RU-ET-EN has the lowest percentage of subwords available from the parent at 6.2%.", "question": "Which language combination has the lowest percentage of subwords available from the parent?", "answer": "So, the answer is {\"answer\": \"AR-RU-ET-EN\"}." }, { "messages": [ { "role": "user", "content": "The AR-RU-ET-EN language combination has the highest percentage of subwords available from the parent. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe AR-RU-ET-EN language combination has the highest percentage of subwords available from the parent.\n" }, { "role": "assistant", "content": "The table shows that AR-RU-ET-EN has only 6.2% of subwords available from the parent, which is the lowest percentage among all listed combinations. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1025.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Summary of vocabulary overlaps for the various language sets. All figures in % of the shared vocabulary." ], "reasoning": "The table shows that AR-RU-ET-EN has only 6.2% of subwords available from the parent, which is the lowest percentage among all listed combinations.", "statement": "The AR-RU-ET-EN language combination has the highest percentage of subwords available from the parent.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 9: Candidate total length, BLEU n-gram precisions and brevity penalty (BP). The reference length in the matching tokenization was 36062.'.\n" }, { "role": "assistant", "content": "The improvements in BLEU scores are due to both longer outputs and higher n-gram precisions, not just the reduction in brevity penalty.In the table, we show also individual n-gram precisions and brevity penalty (BP) of BLEU. The longer output clearly helps to reduce the incurred BP but the improvements are also apparent in n-gram precisions. In other words, the observed gain cannot be attributed solely to producing longer outputs." } ], "images": [ "scigen/train/1026.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Candidate total length, BLEU n-gram precisions and brevity penalty (BP). The reference length in the matching tokenization was 36062." ], "reasoning": "The improvements in BLEU scores are due to both longer outputs and higher n-gram precisions, not just the reduction in brevity penalty.", "description": "In the table, we show also individual n-gram precisions and brevity penalty (BP) of BLEU. The longer output clearly helps to reduce the incurred BP but the improvements are also apparent in n-gram precisions. In other words, the observed gain cannot be attributed solely to producing longer outputs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 9: Candidate total length, BLEU n-gram precisions and brevity penalty (BP). The reference length in the matching tokenization was 36062.', and tackle the question below in a sequential manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the BLEU score for ENRU+ENET and how does it compare to Base ENET?\n" }, { "role": "assistant", "content": "The BLEU score for ENRU+ENET is 51.0, which is higher than the BLEU score for Base ENET, which is 48.1. This indicates an improvement. So, the answer is {\"answer\": \"51.0\"}." } ], "images": [ "scigen/train/1026.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Candidate total length, BLEU n-gram precisions and brevity penalty (BP). The reference length in the matching tokenization was 36062." ], "reasoning": "The BLEU score for ENRU+ENET is 51.0, which is higher than the BLEU score for Base ENET, which is 48.1. This indicates an improvement.", "question": "What is the BLEU score for ENRU+ENET and how does it compare to Base ENET?", "answer": "So, the answer is {\"answer\": \"51.0\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim ENCS+ENET has a higher brevity penalty than ENRU+ENET., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nENCS+ENET has a higher brevity penalty than ENRU+ENET.\n" }, { "role": "assistant", "content": "The brevity penalty for ENCS+ENET is 0.996, while for ENRU+ENET it is 0.998. Therefore, ENCS+ENET has a lower brevity penalty. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1026.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Candidate total length, BLEU n-gram precisions and brevity penalty (BP). The reference length in the matching tokenization was 36062." ], "reasoning": "The brevity penalty for ENCS+ENET is 0.996, while for ENRU+ENET it is 0.998. Therefore, ENCS+ENET has a lower brevity penalty.", "statement": "ENCS+ENET has a higher brevity penalty than ENRU+ENET.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Comparison with state-of-the-art approaches on ground-truth MS COCO regions.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Our model outperforms previous methods using vgg16 features and further improves with res101-frcn, achieving the highest accuracy with phrase-guided attentional pooling.First, we compare our model with previous methods using COCO’s ground-truth object bounding boxes as proposals. Results are shown in Table. As all of the previous methods (Line 1-8) used a 16-layer VGGNet (vgg16) as the feature extractor, we run our experiments using the same feature for fair comparison. Note the flat fc7 is a single 4096-dimensional feature which prevents us from using the phrase-guided attentional pooling in Fig. Despite this, our results (Line 9) still outperform all previous state-of-the-art methods. After switching to the res101-based Faster R-CNN (res101-frcn) representation, the comprehension accuracy further improves another ∼3% (Line 10). Note our Faster R-CNN is pre-trained on COCO’s training images, excluding those in RefCOCO, RefCOCO+, and RefCOCOg’s validation+testing. Our full model (Line 11) with phrase-guided attentional pooling achieves the highest accuracy over all others by a large margin." } ], "images": [ "scigen/train/1027.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison with state-of-the-art approaches on ground-truth MS COCO regions." ], "reasoning": "Our model outperforms previous methods using vgg16 features and further improves with res101-frcn, achieving the highest accuracy with phrase-guided attentional pooling.", "description": "First, we compare our model with previous methods using COCO’s ground-truth object bounding boxes as proposals. Results are shown in Table. As all of the previous methods (Line 1-8) used a 16-layer VGGNet (vgg16) as the feature extractor, we run our experiments using the same feature for fair comparison. Note the flat fc7 is a single 4096-dimensional feature which prevents us from using the phrase-guided attentional pooling in Fig. Despite this, our results (Line 9) still outperform all previous state-of-the-art methods. After switching to the res101-based Faster R-CNN (res101-frcn) representation, the comprehension accuracy further improves another ∼3% (Line 10). Note our Faster R-CNN is pre-trained on COCO’s training images, excluding those in RefCOCO, RefCOCO+, and RefCOCOg’s validation+testing. Our full model (Line 11) with phrase-guided attentional pooling achieves the highest accuracy over all others by a large margin.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 1: Comparison with state-of-the-art approaches on ground-truth MS COCO regions.', unfold the question presented below, detailing every step of your calculation. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the comprehension accuracy of the model using res101-frcn on RefCOCO testA?\n" }, { "role": "assistant", "content": "The table shows that the comprehension accuracy of the model using res101-frcn on RefCOCO testA is 82.66%. So, the answer is {\"answer\": \"82.66\"}" } ], "images": [ "scigen/train/1027.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison with state-of-the-art approaches on ground-truth MS COCO regions." ], "reasoning": "The table shows that the comprehension accuracy of the model using res101-frcn on RefCOCO testA is 82.66%.", "question": "What is the comprehension accuracy of the model using res101-frcn on RefCOCO testA?", "answer": "So, the answer is {\"answer\": \"82.66\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model using vgg16 features achieved the highest accuracy on RefCOCOg test.\n" }, { "role": "assistant", "content": "The table shows that the highest accuracy on RefCOCOg test is 78.12%, achieved by the model using res101-frcn features, not vgg16. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1027.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison with state-of-the-art approaches on ground-truth MS COCO regions." ], "reasoning": "The table shows that the highest accuracy on RefCOCOg test is 78.12%, achieved by the model using res101-frcn features, not vgg16.", "statement": "The model using vgg16 features achieved the highest accuracy on RefCOCOg test.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Comparison of segmentation performance on RefCOCO, RefCOCO+, and our results on RefCOCOg.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The model MAttNet with res101-mrcn backbone achieves higher Precision@X and IoU on the test split compared to the validation split, despite being trained with fewer images.(res101-mrcn). We apply the same procedure described in Sec. Then we feed the predicted bounding box to the mask branch to obtain a pixel-wise segmentation. We use Precision@X (X∈{0.5,0.6,0.7,0.8,0.9}) Results are shown in Table. Some referential segmentation examples are shown in Fig. Note this is not a strictly fair comparison as our model was trained with fewer images. Overall, the AP of our implementation is ∼2 points lower. The main reason may due to the shorter 600-pixel edge setting and smaller training batch size. Even though, our pixel-wise comprehension results already outperform the state-of-the-art ones with a huge margin (see Table. and we believe there exists space for further improvements." } ], "images": [ "scigen/train/1028.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of segmentation performance on RefCOCO, RefCOCO+, and our results on RefCOCOg." ], "reasoning": "The model MAttNet with res101-mrcn backbone achieves higher Precision@X and IoU on the test split compared to the validation split, despite being trained with fewer images.", "description": "(res101-mrcn). We apply the same procedure described in Sec. Then we feed the predicted bounding box to the mask branch to obtain a pixel-wise segmentation. We use Precision@X (X∈{0.5,0.6,0.7,0.8,0.9}) Results are shown in Table. Some referential segmentation examples are shown in Fig. Note this is not a strictly fair comparison as our model was trained with fewer images. Overall, the AP of our implementation is ∼2 points lower. The main reason may due to the shorter 600-pixel edge setting and smaller training batch size. Even though, our pixel-wise comprehension results already outperform the state-of-the-art ones with a huge margin (see Table. and we believe there exists space for further improvements.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: Comparison of segmentation performance on RefCOCO, RefCOCO+, and our results on RefCOCOg.', solve this question and give detail solutions. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhat is the IoU score for the MAttNet model with the res101-mrcn backbone on the test split?\n" }, { "role": "assistant", "content": "The table shows the IoU scores for the MAttNet model with the res101-mrcn backbone on both the validation and test splits. The IoU score for the test split is listed in the last column. So, the answer is {\"answer\": \"48.61\"}." } ], "images": [ "scigen/train/1028.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of segmentation performance on RefCOCO, RefCOCO+, and our results on RefCOCOg." ], "reasoning": "The table shows the IoU scores for the MAttNet model with the res101-mrcn backbone on both the validation and test splits. The IoU score for the test split is listed in the last column.", "question": "What is the IoU score for the MAttNet model with the res101-mrcn backbone on the test split?", "answer": "So, the answer is {\"answer\": \"48.61\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The MAttNet model with the res101-mrcn backbone achieves a higher Precision@0.5 on the test split compared to the validation split., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe MAttNet model with the res101-mrcn backbone achieves a higher Precision@0.5 on the test split compared to the validation split.\n" }, { "role": "assistant", "content": "The table shows that the Precision@0.5 for the test split is 65.60, which is higher than the Precision@0.5 for the validation split, which is 64.48. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1028.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of segmentation performance on RefCOCO, RefCOCO+, and our results on RefCOCOg." ], "reasoning": "The table shows that the Precision@0.5 for the test split is 65.60, which is higher than the Precision@0.5 for the validation split, which is 64.48.", "statement": "The MAttNet model with the res101-mrcn backbone achieves a higher Precision@0.5 on the test split compared to the validation split.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 7: Object detection results.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Mask R-CNN (res101-mrcn) outperforms Faster R-CNN (res101-frcn) in object detection due to multi-task training with additional mask supervision.We firstly show the comparison between Faster R-CNN and Mask R-CNN on object detection in Table. Both models are based on ResNet101 and were trained using same setting. In the main paper, we denote them as res101-frcn and res101-mrcn respectively. It shows that Mask R-CNN has higher AP than Faster R-CNN due to the multi-task training (with additional mask supervision)." } ], "images": [ "scigen/train/1029.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Object detection results." ], "reasoning": "Mask R-CNN (res101-mrcn) outperforms Faster R-CNN (res101-frcn) in object detection due to multi-task training with additional mask supervision.", "description": "We firstly show the comparison between Faster R-CNN and Mask R-CNN on object detection in Table. Both models are based on ResNet101 and were trained using same setting. In the main paper, we denote them as res101-frcn and res101-mrcn respectively. It shows that Mask R-CNN has higher AP than Faster R-CNN due to the multi-task training (with additional mask supervision).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 7: Object detection results.\nQuestion: Which model has a higher APbb50 and what is the value?\nSolve the above question based on the table information. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "Comparing the APbb50 values from the table, res101-mrcn has a higher APbb50 of 55.3 compared to res101-frcn's 53.7. So, the answer is {\"answer\": \"res101-mrcn with 55.3\"}." } ], "images": [ "scigen/train/1029.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Object detection results." ], "reasoning": "Comparing the APbb50 values from the table, res101-mrcn has a higher APbb50 of 55.3 compared to res101-frcn's 53.7.", "question": "Which model has a higher APbb50 and what is the value?", "answer": "So, the answer is {\"answer\": \"res101-mrcn with 55.3\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nFaster R-CNN has a higher APbb75 than Mask R-CNN.\n" }, { "role": "assistant", "content": "The table shows that res101-frcn (Faster R-CNN) has an APbb75 of 36.8, while res101-mrcn (Mask R-CNN) has an APbb75 of 38.6, indicating that Mask R-CNN has a higher APbb75. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1029.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Object detection results." ], "reasoning": "The table shows that res101-frcn (Faster R-CNN) has an APbb75 of 36.8, while res101-mrcn (Mask R-CNN) has an APbb75 of 38.6, indicating that Mask R-CNN has a higher APbb75.", "statement": "Faster R-CNN has a higher APbb75 than Mask R-CNN.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 8: Instance segmentation results.'.\n" }, { "role": "assistant", "content": "Our model's AP is approximately 2 points lower than the state-of-the-art due to shorter edge settings and smaller training batch size, but it still outperforms in pixel-wise comprehension.Note this is not a strictly fair comparison as our model was trained with fewer images. Overall, the AP of our implementation is ∼2 points lower. The main reason may due to the shorter 600-pixel edge setting and smaller training batch size. Even though, our pixel-wise comprehension results already outperform the state-of-the-art ones with a huge margin (see Table. and we believe there exists space for further improvements." } ], "images": [ "scigen/train/1030.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Instance segmentation results." ], "reasoning": "Our model's AP is approximately 2 points lower than the state-of-the-art due to shorter edge settings and smaller training batch size, but it still outperforms in pixel-wise comprehension.", "description": "Note this is not a strictly fair comparison as our model was trained with fewer images. Overall, the AP of our implementation is ∼2 points lower. The main reason may due to the shorter 600-pixel edge setting and smaller training batch size. Even though, our pixel-wise comprehension results already outperform the state-of-the-art ones with a huge margin (see Table. and we believe there exists space for further improvements.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 8: Instance segmentation results.', and sequentially solve the question, writing out each step of your reasoning process. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the AP difference between our implementation and the state-of-the-art model?\n" }, { "role": "assistant", "content": "The AP of our implementation is 30.7, while the AP of the state-of-the-art model is 32.7. The difference is 32.7 - 30.7 = 2 points. So, the answer is {\"answer\": \"2\"}" } ], "images": [ "scigen/train/1030.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Instance segmentation results." ], "reasoning": "The AP of our implementation is 30.7, while the AP of the state-of-the-art model is 32.7. The difference is 32.7 - 30.7 = 2 points.", "question": "What is the AP difference between our implementation and the state-of-the-art model?", "answer": "So, the answer is {\"answer\": \"2\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nOur model's AP75 is higher than the state-of-the-art model.\n" }, { "role": "assistant", "content": "The AP75 of our model is 32.4, while the AP75 of the state-of-the-art model is 34.0. Therefore, our model's AP75 is not higher. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1030.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Instance segmentation results." ], "reasoning": "The AP75 of our model is 32.4, while the AP75 of the state-of-the-art model is 34.0. Therefore, our model's AP75 is not higher.", "statement": "Our model's AP75 is higher than the state-of-the-art model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Evaluation results.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The CMT2 model, which includes token reordering, outperforms both GNMT and CMT1 in BLEU, TER, Adequacy, and Fluency scores.Two variants of our system was tested, one without token reordering (CMT1) and one with (CMT2). Manual scoring (in the range 1-5, low to high quality) of Adequacy and Fluency banchs:2015adequacy was done by a bi-lingual linguist, fluent in both En and Bn, with Bn as mother tongue. We can clearly see that our pipeline outperforms GNMT by a fair margin (about 13.34 BLEU, 18.34 TER) and token reordering further improves our system, especially in the case of fluency. Also, for a deeper analysis, we performed two experiments using CMT2." } ], "images": [ "scigen/train/1031.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Evaluation results." ], "reasoning": "The CMT2 model, which includes token reordering, outperforms both GNMT and CMT1 in BLEU, TER, Adequacy, and Fluency scores.", "description": "Two variants of our system was tested, one without token reordering (CMT1) and one with (CMT2). Manual scoring (in the range 1-5, low to high quality) of Adequacy and Fluency banchs:2015adequacy was done by a bi-lingual linguist, fluent in both En and Bn, with Bn as mother tongue. We can clearly see that our pipeline outperforms GNMT by a fair margin (about 13.34 BLEU, 18.34 TER) and token reordering further improves our system, especially in the case of fluency. Also, for a deeper analysis, we performed two experiments using CMT2.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 1: Evaluation results.'. The final result should be presented in the JSON format of {\"answer\": \"\"}. \n\nQuestion: Which model has the highest Fluency score and what is the score?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By comparing the Fluency scores of all models in the table, CMT2 has the highest Fluency score of 3.97. So, the answer is {\"answer\": \"CMT2 with 3.97\"}." } ], "images": [ "scigen/train/1031.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Evaluation results." ], "reasoning": "By comparing the Fluency scores of all models in the table, CMT2 has the highest Fluency score of 3.97.", "question": "Which model has the highest Fluency score and what is the score?", "answer": "So, the answer is {\"answer\": \"CMT2 with 3.97\"}." }, { "messages": [ { "role": "user", "content": "CMT1 has a higher BLEU score than GNMT. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nCMT1 has a higher BLEU score than GNMT.\n" }, { "role": "assistant", "content": "The table shows that CMT1 has a BLEU score of 15.09, which is higher than GNMT's BLEU score of 2.44. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1031.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Evaluation results." ], "reasoning": "The table shows that CMT1 has a BLEU score of 15.09, which is higher than GNMT's BLEU score of 2.44.", "statement": "CMT1 has a higher BLEU score than GNMT.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Error contribution.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "In Exp 1, the Language Tagger module requires the most improvement, followed by Machine Translation and Back Transliteration. In Exp 2, the BLEU and TER scores for MBn and MEn sets indicate that identifying matrix and embedded languages first could improve results.Exp 1. We randomly took 100 instances where BLEU score achieved was less than 15. Then we fed this back to our pipeline and collected outputs from each of the modules. We manually associated each of the errors with the respective module causing it, considering the input to it was correct. Language tagger being the starting module in our pipeline requires the most improvement for better results followed by the machine translation system and the back-transliteration module. All of these are supervised models and can be improved with more training data. Exp 2. A linguist proficient in both English and Bengali manually divided our test data into two sets, one where the matrix language was Bengali (MBn) and the other where matrix language was English (MEn). The size of (MBn) was 1205 and for (MEn) it was 395. When feeding the sets separately to CMT2, the BLEU and TER score achieved on MBn was 16.98 and 55.02 while on MEn it was 9.3 and 65.11 respectively. This problem can be easily solved if matrix and embedded languages are identified first, and then passed on to different systems accordingly, i.e. one for (MBn) type, and one for (MEn) type." } ], "images": [ "scigen/train/1032.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Error contribution." ], "reasoning": "In Exp 1, the Language Tagger module requires the most improvement, followed by Machine Translation and Back Transliteration. In Exp 2, the BLEU and TER scores for MBn and MEn sets indicate that identifying matrix and embedded languages first could improve results.", "description": "Exp 1. We randomly took 100 instances where BLEU score achieved was less than 15. Then we fed this back to our pipeline and collected outputs from each of the modules. We manually associated each of the errors with the respective module causing it, considering the input to it was correct. Language tagger being the starting module in our pipeline requires the most improvement for better results followed by the machine translation system and the back-transliteration module. All of these are supervised models and can be improved with more training data. Exp 2. A linguist proficient in both English and Bengali manually divided our test data into two sets, one where the matrix language was Bengali (MBn) and the other where matrix language was English (MEn). The size of (MBn) was 1205 and for (MEn) it was 395. When feeding the sets separately to CMT2, the BLEU and TER score achieved on MBn was 16.98 and 55.02 while on MEn it was 9.3 and 65.11 respectively. This problem can be easily solved if matrix and embedded languages are identified first, and then passed on to different systems accordingly, i.e. one for (MBn) type, and one for (MEn) type.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich module requires the most improvement according to Exp 1?\nSolve the above question based on the table titled 'Table 2: Error contribution.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "In Exp 1, it is mentioned that the Language Tagger module requires the most improvement for better results. So, the answer is {\"answer\": \"Language Tagger\"}." } ], "images": [ "scigen/train/1032.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Error contribution." ], "reasoning": "In Exp 1, it is mentioned that the Language Tagger module requires the most improvement for better results.", "question": "Which module requires the most improvement according to Exp 1?", "answer": "So, the answer is {\"answer\": \"Language Tagger\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Back Transliteration module has the highest contribution to errors. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Back Transliteration module has the highest contribution to errors.\n" }, { "role": "assistant", "content": "The table shows that the Language Tagger has the highest contribution to errors with a value of 36, while Back Transliteration has a contribution of 12. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1032.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Error contribution." ], "reasoning": "The table shows that the Language Tagger has the highest contribution to errors with a value of 36, while Back Transliteration has a contribution of 12.", "statement": "The Back Transliteration module has the highest contribution to errors.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 8: Word error rate and summarization quality by the models with length count-down. Adaptation results in large gains. The model with length encoding outperforms the baseline and the one with length embedding.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The baseline ASR model achieves some compression but fails to meet length constraints, prompting experiments with explicit length count-downs.While the baseline ASR model achieves some degree of compression after adaptation, it cannot fully comply with length constraints. Therefore, the following experiments examine the effects of training with explicit length count-downs." } ], "images": [ "scigen/train/1033.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Word error rate and summarization quality by the models with length count-down. Adaptation results in large gains. The model with length encoding outperforms the baseline and the one with length embedding." ], "reasoning": "The baseline ASR model achieves some compression but fails to meet length constraints, prompting experiments with explicit length count-downs.", "description": "While the baseline ASR model achieves some degree of compression after adaptation, it cannot fully comply with length constraints. Therefore, the following experiments examine the effects of training with explicit length count-downs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 8: Word error rate and summarization quality by the models with length count-down. Adaptation results in large gains. The model with length encoding outperforms the baseline and the one with length embedding.', answer the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model achieves the lowest WER after adaptation?\n" }, { "role": "assistant", "content": "By examining the WER column, the model with the lowest WER after adaptation is model (6) with a WER of 38.6. So, the answer is {\"answer\": \"(6): + adapt\"}." } ], "images": [ "scigen/train/1033.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Word error rate and summarization quality by the models with length count-down. Adaptation results in large gains. The model with length encoding outperforms the baseline and the one with length embedding." ], "reasoning": "By examining the WER column, the model with the lowest WER after adaptation is model (6) with a WER of 38.6.", "question": "Which model achieves the lowest WER after adaptation?", "answer": "So, the answer is {\"answer\": \"(6): + adapt\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Model (2) achieves the highest R-2 score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nModel (2) achieves the highest R-2 score.\n" }, { "role": "assistant", "content": "By examining the R-2 column, model (2) has an R-2 score of 57.0, which is not the highest. Model (6) has the highest R-2 score of 56.4. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1033.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Word error rate and summarization quality by the models with length count-down. Adaptation results in large gains. The model with length encoding outperforms the baseline and the one with length embedding." ], "reasoning": "By examining the R-2 column, model (2) has an R-2 score of 57.0, which is not the highest. Model (6) has the highest R-2 score of 56.4.", "statement": "Model (2) achieves the highest R-2 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 6: Model performance on the SuperGLUE validation and diagnostic sets. The Avg. column shows the overall SuperGLUE score—an average across the eight tasks, weighting each task equally—as a mean and standard deviation across three restarts.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The Base data yields models that transfer better than the plain RoBERTa or XLNet baseline, and slightly better than 8.5k-example samples of MNLI, MNLI Government, or ANLI.Our first observation is that our overall data collection pipeline worked well for our purposes: Our Base data yields models that transfer substantially better than the plain RoBERTa or XLNet baseline, and at least slightly better than 8.5k-example samples of MNLI, MNLI Government or ANLI." } ], "images": [ "scigen/train/1034.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Model performance on the SuperGLUE validation and diagnostic sets. The Avg. column shows the overall SuperGLUE score—an average across the eight tasks, weighting each task equally—as a mean and standard deviation across three restarts." ], "reasoning": "The Base data yields models that transfer better than the plain RoBERTa or XLNet baseline, and slightly better than 8.5k-example samples of MNLI, MNLI Government, or ANLI.", "description": "Our first observation is that our overall data collection pipeline worked well for our purposes: Our Base data yields models that transfer substantially better than the plain RoBERTa or XLNet baseline, and at least slightly better than 8.5k-example samples of MNLI, MNLI Government or ANLI.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 6: Model performance on the SuperGLUE validation and diagnostic sets. The Avg. column shows the overall SuperGLUE score—an average across the eight tasks, weighting each task equally—as a mean and standard deviation across three restarts.'. Solve the following question based on the table.\n\nQuestion: Which intermediate training data yields the highest ReCoRD F1/EM score for RoBERTa (large)?\nLet's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the ReCoRD F1/EM column for RoBERTa (large), the highest score is 86.7/86.0, which corresponds to the Paragraph intermediate training data. So, the answer is {\"answer\": \"Paragraph\"}." } ], "images": [ "scigen/train/1034.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Model performance on the SuperGLUE validation and diagnostic sets. The Avg. column shows the overall SuperGLUE score—an average across the eight tasks, weighting each task equally—as a mean and standard deviation across three restarts." ], "reasoning": "By examining the ReCoRD F1/EM column for RoBERTa (large), the highest score is 86.7/86.0, which corresponds to the Paragraph intermediate training data.", "question": "Which intermediate training data yields the highest ReCoRD F1/EM score for RoBERTa (large)?", "answer": "So, the answer is {\"answer\": \"Paragraph\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Base data yields the highest average score (Avg. μ (σ)) for RoBERTa (large). and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Base data yields the highest average score (Avg. μ (σ)) for RoBERTa (large).\n" }, { "role": "assistant", "content": "By examining the Avg. μ (σ) column for RoBERTa (large), the highest average score is 72.2 (0.1), which corresponds to the Base intermediate training data. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1034.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Model performance on the SuperGLUE validation and diagnostic sets. The Avg. column shows the overall SuperGLUE score—an average across the eight tasks, weighting each task equally—as a mean and standard deviation across three restarts." ], "reasoning": "By examining the Avg. μ (σ) column for RoBERTa (large), the highest average score is 72.2 (0.1), which corresponds to the Base intermediate training data.", "statement": "The Base data yields the highest average score (Avg. μ (σ)) for RoBERTa (large).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: NLI modeling experiments with RoBERTa, reporting results on the validation sets for MNLI and for the task used for training each model (Self), and the GLUE diagnostic set. We compare the two-class Contrast with a two-class version of MNLI.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The interventions, especially EditPremise, show lower hypothesis-only performance than Base, indicating reduced artifacts and higher evaluation quality.As NLI classifiers trained on Contrast cannot produce the neutral labels used in MNLI, we evaluate them separately, and compare them with two-class variants of the MNLI models. Our first three interventions, especially EditPremise, show much lower hypothesis-only performance than Base. This indicates that these these results cannot be explained away as a consequence of the lower quality of the evaluation sets for these three new datasets. This adds further evidence, alongside our PMI results, that these interventions reduce the presence of such artifacts. While we do not have a direct baseline for the two-class Contrast in this experiment, comparisons with MNLI 8.5k are consistent with the encouraging results seen above." } ], "images": [ "scigen/train/1035.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: NLI modeling experiments with RoBERTa, reporting results on the validation sets for MNLI and for the task used for training each model (Self), and the GLUE diagnostic set. We compare the two-class Contrast with a two-class version of MNLI." ], "reasoning": "The interventions, especially EditPremise, show lower hypothesis-only performance than Base, indicating reduced artifacts and higher evaluation quality.", "description": "As NLI classifiers trained on Contrast cannot produce the neutral labels used in MNLI, we evaluate them separately, and compare them with two-class variants of the MNLI models. Our first three interventions, especially EditPremise, show much lower hypothesis-only performance than Base. This indicates that these these results cannot be explained away as a consequence of the lower quality of the evaluation sets for these three new datasets. This adds further evidence, alongside our PMI results, that these interventions reduce the presence of such artifacts. While we do not have a direct baseline for the two-class Contrast in this experiment, comparisons with MNLI 8.5k are consistent with the encouraging results seen above.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich training data has the highest performance on the MNLI evaluation set?\nSolve the above question based on the table titled 'Table 4: NLI modeling experiments with RoBERTa, reporting results on the validation sets for MNLI and for the task used for training each model (Self), and the GLUE diagnostic set. We compare the two-class Contrast with a two-class version of MNLI.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the MNLI column, the highest performance is achieved by the MNLI (two-class) training data with a score of 94.0. So, the answer is {\"answer\": \"MNLI (two-class)\"}" } ], "images": [ "scigen/train/1035.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: NLI modeling experiments with RoBERTa, reporting results on the validation sets for MNLI and for the task used for training each model (Self), and the GLUE diagnostic set. We compare the two-class Contrast with a two-class version of MNLI." ], "reasoning": "By examining the MNLI column, the highest performance is achieved by the MNLI (two-class) training data with a score of 94.0.", "question": "Which training data has the highest performance on the MNLI evaluation set?", "answer": "So, the answer is {\"answer\": \"MNLI (two-class)\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nEditPremise has a higher performance on the GLUE Diagnostic set than Base.\n" }, { "role": "assistant", "content": "The table shows that EditPremise has a GLUE Diagnostic score of 35.5, while Base has a score of 40.5, indicating that EditPremise has a lower performance. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1035.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: NLI modeling experiments with RoBERTa, reporting results on the validation sets for MNLI and for the task used for training each model (Self), and the GLUE diagnostic set. We compare the two-class Contrast with a two-class version of MNLI." ], "reasoning": "The table shows that EditPremise has a GLUE Diagnostic score of 35.5, while Base has a score of 40.5, indicating that EditPremise has a lower performance.", "statement": "EditPremise has a higher performance on the GLUE Diagnostic set than Base.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Annotated relations between entities. Relations appear within a sentence (intra-sentential) or across sentences (inter-sentential)'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The table shows the distribution of different types of relations, with 'case has finding' being the most frequent, especially in inter-sentential contexts.Entities can appear in a discontinuous way. We model this as a relation between two spans which we call “discontinuous” (cf. Especially findings often appear as discontinuous entities, we found 543 discontinuous finding relations. The numbers for conditions and factors are lower with seven and two, respectively. Entities can also be nested within one another. This happens either when the span of one annotation is completely embedded in the span of another annotation (fully-nested; cf. when there is a partial overlapping between the spans of two different entities (partially-nested; cf. There is a high number of inter-sentential relations in the corpus (cf. This can be explained by the fact that the case entity occurs early in each document; furthermore, it is related to finding and factor annotations that are distributed across different sentences. The most frequently annotated relation in our corpus is the has-relation between a case entity and the findings related to that case. This correlates with the high number of finding entities." } ], "images": [ "scigen/train/1036.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Annotated relations between entities. Relations appear within a sentence (intra-sentential) or across sentences (inter-sentential)" ], "reasoning": "The table shows the distribution of different types of relations, with 'case has finding' being the most frequent, especially in inter-sentential contexts.", "description": "Entities can appear in a discontinuous way. We model this as a relation between two spans which we call “discontinuous” (cf. Especially findings often appear as discontinuous entities, we found 543 discontinuous finding relations. The numbers for conditions and factors are lower with seven and two, respectively. Entities can also be nested within one another. This happens either when the span of one annotation is completely embedded in the span of another annotation (fully-nested; cf. when there is a partial overlapping between the spans of two different entities (partially-nested; cf. There is a high number of inter-sentential relations in the corpus (cf. This can be explained by the fact that the case entity occurs early in each document; furthermore, it is related to finding and factor annotations that are distributed across different sentences. The most frequently annotated relation in our corpus is the has-relation between a case entity and the findings related to that case. This correlates with the high number of finding entities.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 4: Annotated relations between entities. Relations appear within a sentence (intra-sentential) or across sentences (inter-sentential)'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the percentage of 'case has factor' relations that are inter-sentential?\n" }, { "role": "assistant", "content": "To find the percentage of 'case has factor' relations that are inter-sentential, look at the row for 'case has factor' and the column for 'Inter-sentential'. The percentage is 47.1%. So, the answer is {\"answer\": \"47.1%\"}" } ], "images": [ "scigen/train/1036.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Annotated relations between entities. Relations appear within a sentence (intra-sentential) or across sentences (inter-sentential)" ], "reasoning": "To find the percentage of 'case has factor' relations that are inter-sentential, look at the row for 'case has factor' and the column for 'Inter-sentential'. The percentage is 47.1%.", "question": "What is the percentage of 'case has factor' relations that are inter-sentential?", "answer": "So, the answer is {\"answer\": \"47.1%\"}" }, { "messages": [ { "role": "user", "content": "The majority of 'modifier modifies finding' relations are intra-sentential. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe majority of 'modifier modifies finding' relations are intra-sentential.\n" }, { "role": "assistant", "content": "The table shows that 994 out of 1009 'modifier modifies finding' relations are intra-sentential, which is 98.5%. This confirms that the majority are intra-sentential. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1036.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Annotated relations between entities. Relations appear within a sentence (intra-sentential) or across sentences (inter-sentential)" ], "reasoning": "The table shows that 994 out of 1009 'modifier modifies finding' relations are intra-sentential, which is 98.5%. This confirms that the majority are intra-sentential.", "statement": "The majority of 'modifier modifies finding' relations are intra-sentential.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: Span-level precision (P), recall (R) and F1-scores (F1) on four distinct baseline NER systems. All scores are computed as average over five-fold cross validation.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The table evaluates the performance of four systems (CRF, BiLSTM CRF, MTL, BioBERT) by calculating span-level precision, recall, and F1 scores, including micro and macro averages.To evaluate the performance of the four systems, we calculate the span-level precision (P), recall (R) and F1 scores, along with corresponding micro and macro scores." } ], "images": [ "scigen/train/1037.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Span-level precision (P), recall (R) and F1-scores (F1) on four distinct baseline NER systems. All scores are computed as average over five-fold cross validation." ], "reasoning": "The table evaluates the performance of four systems (CRF, BiLSTM CRF, MTL, BioBERT) by calculating span-level precision, recall, and F1 scores, including micro and macro averages.", "description": "To evaluate the performance of the four systems, we calculate the span-level precision (P), recall (R) and F1 scores, along with corresponding micro and macro scores.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 5: Span-level precision (P), recall (R) and F1-scores (F1) on four distinct baseline NER systems. All scores are computed as average over five-fold cross validation.\nQuestion: Which system has the highest macro average F1 score?\n" }, { "role": "assistant", "content": "To find the system with the highest macro average F1 score, compare the F1 scores in the 'macro avg.' row for each system. The highest value is 0.59, which corresponds to the MTL system. So, the answer is {\"answer\": \"MTL\"}." } ], "images": [ "scigen/train/1037.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Span-level precision (P), recall (R) and F1-scores (F1) on four distinct baseline NER systems. All scores are computed as average over five-fold cross validation." ], "reasoning": "To find the system with the highest macro average F1 score, compare the F1 scores in the 'macro avg.' row for each system. The highest value is 0.59, which corresponds to the MTL system.", "question": "Which system has the highest macro average F1 score?", "answer": "So, the answer is {\"answer\": \"MTL\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe BiLSTM CRF system has the lowest precision score for the 'condition' category.\n" }, { "role": "assistant", "content": "To verify this statement, look at the precision scores for the 'condition' category across all systems. The BiLSTM CRF system has a precision score of 0.00, which is the lowest among all systems. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1037.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Span-level precision (P), recall (R) and F1-scores (F1) on four distinct baseline NER systems. All scores are computed as average over five-fold cross validation." ], "reasoning": "To verify this statement, look at the precision scores for the 'condition' category across all systems. The BiLSTM CRF system has a precision score of 0.00, which is the lowest among all systems.", "statement": "The BiLSTM CRF system has the lowest precision score for the 'condition' category.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Results for Validation Seen and Validation Unseen, when trained with a small fraction of Fried-Augmented ordered by scores given by model trained on cma. SPL and SR are reported as percentages and NE and PL in meters.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The model's performance is better when trained on high-quality examples compared to low-quality examples, even though overall performance is low due to the absence of original human-created instructions.Scoring generated instructions. We use this trained model to rank all the paths in Fried-Augmented and train the RCM agent on different portions of the data. Using high-quality examples–as judged by the model–outperforms the ones trained using low-quality examples. Note that the performance is low in both cases because none of the original human-created instructions were used—what is important is the relative performance between examples judged higher or lower. This clearly indicates that the model scores instruction-path pairs effectively." } ], "images": [ "scigen/train/1038.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results for Validation Seen and Validation Unseen, when trained with a small fraction of Fried-Augmented ordered by scores given by model trained on cma. SPL and SR are reported as percentages and NE and PL in meters." ], "reasoning": "The model's performance is better when trained on high-quality examples compared to low-quality examples, even though overall performance is low due to the absence of original human-created instructions.", "description": "Scoring generated instructions. We use this trained model to rank all the paths in Fried-Augmented and train the RCM agent on different portions of the data. Using high-quality examples–as judged by the model–outperforms the ones trained using low-quality examples. Note that the performance is low in both cases because none of the original human-created instructions were used—what is important is the relative performance between examples judged higher or lower. This clearly indicates that the model scores instruction-path pairs effectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2: Results for Validation Seen and Validation Unseen, when trained with a small fraction of Fried-Augmented ordered by scores given by model trained on cma. SPL and SR are reported as percentages and NE and PL in meters.', unfold the steps to solve the question given next. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nQuestion: What is the Validation Seen SR score for the 2% dataset size using the Top strategy?\n" }, { "role": "assistant", "content": "To find the Validation Seen SR score for the 2% dataset size using the Top strategy, locate the row with 2% dataset size and Top strategy, then find the value under the Validation Seen SR column. So, the answer is {\"answer\": \"25.5\"}." } ], "images": [ "scigen/train/1038.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results for Validation Seen and Validation Unseen, when trained with a small fraction of Fried-Augmented ordered by scores given by model trained on cma. SPL and SR are reported as percentages and NE and PL in meters." ], "reasoning": "To find the Validation Seen SR score for the 2% dataset size using the Top strategy, locate the row with 2% dataset size and Top strategy, then find the value under the Validation Seen SR column.", "question": "What is the Validation Seen SR score for the 2% dataset size using the Top strategy?", "answer": "So, the answer is {\"answer\": \"25.5\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Validation Unseen PL score for the 1% dataset size using the Bottom strategy is higher than the Validation Seen PL score for the same dataset size and strategy.\n" }, { "role": "assistant", "content": "Compare the Validation Unseen PL score and the Validation Seen PL score for the 1% dataset size using the Bottom strategy. The Validation Unseen PL score is 10.8, which is higher than the Validation Seen PL score of 10.7. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1038.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results for Validation Seen and Validation Unseen, when trained with a small fraction of Fried-Augmented ordered by scores given by model trained on cma. SPL and SR are reported as percentages and NE and PL in meters." ], "reasoning": "Compare the Validation Unseen PL score and the Validation Seen PL score for the 1% dataset size using the Bottom strategy. The Validation Unseen PL score is 10.8, which is higher than the Validation Seen PL score of 10.7.", "statement": "The Validation Unseen PL score for the 1% dataset size using the Bottom strategy is higher than the Validation Seen PL score for the same dataset size and strategy.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: AUC performance when the model is trained on different combinations of the two tasks and evaluated on the dataset containing only PR and RW negatives.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Training a model on both cma and nvs simultaneously improves its performance on cma alone, as shown by higher AUC scores in both seen and unseen validation sets.Improvements from Adding Coherence Loss. Finally we show that training a model on cma and nvs simultaneously improves the model’s performance when evaluated on cma alone. The model is trained using combined loss αLalignment+(1−α)Lcoherence with α=0.5 and is evaluated on its ability to differentiate incorrect instruction-path pairs from correct ones. As noted earlier, PS negatives are easier to discriminate, therefore, to keep the task challenging, the validation sets were limited to contain validation splits from PR and RW negative sampling strategies only. The area-under ROC curve (AUC) is used as the evaluation metric." } ], "images": [ "scigen/train/1039.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: AUC performance when the model is trained on different combinations of the two tasks and evaluated on the dataset containing only PR and RW negatives." ], "reasoning": "Training a model on both cma and nvs simultaneously improves its performance on cma alone, as shown by higher AUC scores in both seen and unseen validation sets.", "description": "Improvements from Adding Coherence Loss. Finally we show that training a model on cma and nvs simultaneously improves the model’s performance when evaluated on cma alone. The model is trained using combined loss αLalignment+(1−α)Lcoherence with α=0.5 and is evaluated on its ability to differentiate incorrect instruction-path pairs from correct ones. As noted earlier, PS negatives are easier to discriminate, therefore, to keep the task challenging, the validation sets were limited to contain validation splits from PR and RW negative sampling strategies only. The area-under ROC curve (AUC) is used as the evaluation metric.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: AUC performance when the model is trained on different combinations of the two tasks and evaluated on the dataset containing only PR and RW negatives.'. Solve the following question based on the table.\n\nQuestion: What is the AUC score for the model trained on cma + nvs when evaluated on the unseen validation set?\nLet's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the AUC scores for different training sets. For the model trained on cma + nvs, the AUC score for the unseen validation set is 79.2. So, the answer is {\"answer\": \"79.2\"}." } ], "images": [ "scigen/train/1039.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: AUC performance when the model is trained on different combinations of the two tasks and evaluated on the dataset containing only PR and RW negatives." ], "reasoning": "The table shows the AUC scores for different training sets. For the model trained on cma + nvs, the AUC score for the unseen validation set is 79.2.", "question": "What is the AUC score for the model trained on cma + nvs when evaluated on the unseen validation set?", "answer": "So, the answer is {\"answer\": \"79.2\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The model trained on cma + nvs has a higher AUC score on the seen validation set compared to the model trained on cma alone., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model trained on cma + nvs has a higher AUC score on the seen validation set compared to the model trained on cma alone.\n" }, { "role": "assistant", "content": "The table shows that the AUC score for the model trained on cma + nvs on the seen validation set is 84.0, which is higher than the 82.6 score for the model trained on cma alone. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1039.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: AUC performance when the model is trained on different combinations of the two tasks and evaluated on the dataset containing only PR and RW negatives." ], "reasoning": "The table shows that the AUC score for the model trained on cma + nvs on the seen validation set is 84.0, which is higher than the 82.6 score for the model trained on cma alone.", "statement": "The model trained on cma + nvs has a higher AUC score on the seen validation set compared to the model trained on cma alone.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Comparison on R2R Leaderboard Test Set. Our navigation model benefits from transfer learned representations and outperforms the known SOTA on SPL. SPL and SR are reported as percentages and NE and PL in meters.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Our ALTR agent outperforms the SOTA on SPL by 5% and has the lowest NE, while tying with the best models for SR.Our ALTR agent significantly outperforms the SOTA at the time on SPL–the primary metric for R2R–improving it by 5% absolute measure, and it has the lowest navigation error (NE). It furthermore ties the other two best models for SR. Compared to RCM, our ALTR agent is able to learn a more efficient policy resulting in shorter trajectories to reach the goal state, as indicated by its lower path length." } ], "images": [ "scigen/train/1040.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison on R2R Leaderboard Test Set. Our navigation model benefits from transfer learned representations and outperforms the known SOTA on SPL. SPL and SR are reported as percentages and NE and PL in meters." ], "reasoning": "Our ALTR agent outperforms the SOTA on SPL by 5% and has the lowest NE, while tying with the best models for SR.", "description": "Our ALTR agent significantly outperforms the SOTA at the time on SPL–the primary metric for R2R–improving it by 5% absolute measure, and it has the lowest navigation error (NE). It furthermore ties the other two best models for SR. Compared to RCM, our ALTR agent is able to learn a more efficient policy resulting in shorter trajectories to reach the goal state, as indicated by its lower path length.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 4: Comparison on R2R Leaderboard Test Set. Our navigation model benefits from transfer learned representations and outperforms the known SOTA on SPL. SPL and SR are reported as percentages and NE and PL in meters.', answer the question below by showing each progressive step toward the answer. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model has the lowest navigation error (NE) and what is its Success Rate (SR)?\n" }, { "role": "assistant", "content": "The ALTR (Ours) model has the lowest NE of 5.49 and an SR of 48. So, the answer is {\"answer\": \"ALTR (Ours), 48\"}." } ], "images": [ "scigen/train/1040.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison on R2R Leaderboard Test Set. Our navigation model benefits from transfer learned representations and outperforms the known SOTA on SPL. SPL and SR are reported as percentages and NE and PL in meters." ], "reasoning": "The ALTR (Ours) model has the lowest NE of 5.49 and an SR of 48.", "question": "Which model has the lowest navigation error (NE) and what is its Success Rate (SR)?", "answer": "So, the answer is {\"answer\": \"ALTR (Ours), 48\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe ALTR agent has the highest Success Rate (SR) among all models.\n" }, { "role": "assistant", "content": "The table shows that the ALTR agent has an SR of 48, which is tied with Self-Monitoring and The Regretful Agent, but not higher than them. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1040.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison on R2R Leaderboard Test Set. Our navigation model benefits from transfer learned representations and outperforms the known SOTA on SPL. SPL and SR are reported as percentages and NE and PL in meters." ], "reasoning": "The table shows that the ALTR agent has an SR of 48, which is tied with Self-Monitoring and The Regretful Agent, but not higher than them.", "statement": "The ALTR agent has the highest Success Rate (SR) among all models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: Ablations on R2R Validation Seen and Validation Unseen sets, showing results in VLN for different combinations of pre-training tasks. SPL and SR are reported as percentages and NE and PL in meters.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Joint pre-training of CMA and NVS leads to significant improvements in SR and SPL for both SF and RCM agents, while pre-training NVS alone results in a performance drop.The first ablation study analyzes the effectiveness of each task individually in learning representations that can benefit the navigation agent. When pre-trainning CMA and NVS jointly, we see a consistent 11-12% improvement in SR for both the SF and RCM agents as well as improvement in agent’s path length, thereby also improving SPL. When pre-training CMA only, we see a consistent 8-9% improvement in SR for both the SF and RCM agents. When pre-training NVS only, we see a drop in performance. Since there are no cross-modal components to train the language encoder in NVS, training on NVS alone fails to provide a good initialization point for the downstream navigation task that requires cross-modal associations. However, pre-training with NVS and CMA jointly affords the model additional opportunities to improve visual-only pre-training (due to NVS), without compromising cross-modal alignment (due to CMA)." } ], "images": [ "scigen/train/1041.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ablations on R2R Validation Seen and Validation Unseen sets, showing results in VLN for different combinations of pre-training tasks. SPL and SR are reported as percentages and NE and PL in meters." ], "reasoning": "Joint pre-training of CMA and NVS leads to significant improvements in SR and SPL for both SF and RCM agents, while pre-training NVS alone results in a performance drop.", "description": "The first ablation study analyzes the effectiveness of each task individually in learning representations that can benefit the navigation agent. When pre-trainning CMA and NVS jointly, we see a consistent 11-12% improvement in SR for both the SF and RCM agents as well as improvement in agent’s path length, thereby also improving SPL. When pre-training CMA only, we see a consistent 8-9% improvement in SR for both the SF and RCM agents. When pre-training NVS only, we see a drop in performance. Since there are no cross-modal components to train the language encoder in NVS, training on NVS alone fails to provide a good initialization point for the downstream navigation task that requires cross-modal associations. However, pre-training with NVS and CMA jointly affords the model additional opportunities to improve visual-only pre-training (due to NVS), without compromising cross-modal alignment (due to CMA).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: Ablations on R2R Validation Seen and Validation Unseen sets, showing results in VLN for different combinations of pre-training tasks. SPL and SR are reported as percentages and NE and PL in meters.', answer the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the Validation Unseen SR for the RCM (Ours) agent when pre-training with both CMA and NVS?\n" }, { "role": "assistant", "content": "To find the Validation Unseen SR for the RCM (Ours) agent when pre-training with both CMA and NVS, locate the row for RCM (Ours) with both CMA and NVS checked. The Validation Unseen SR is 46.1. So, the answer is {\"answer\": \"46.1\"}." } ], "images": [ "scigen/train/1041.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ablations on R2R Validation Seen and Validation Unseen sets, showing results in VLN for different combinations of pre-training tasks. SPL and SR are reported as percentages and NE and PL in meters." ], "reasoning": "To find the Validation Unseen SR for the RCM (Ours) agent when pre-training with both CMA and NVS, locate the row for RCM (Ours) with both CMA and NVS checked. The Validation Unseen SR is 46.1.", "question": "What is the Validation Unseen SR for the RCM (Ours) agent when pre-training with both CMA and NVS?", "answer": "So, the answer is {\"answer\": \"46.1\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nPre-training with NVS only results in the highest Validation Seen NE for the Speaker-Follower (Ours) agent.\n" }, { "role": "assistant", "content": "To verify this statement, check the Validation Seen NE values for the Speaker-Follower (Ours) agent when pre-training with NVS only. The highest Validation Seen NE is 5.12, which is indeed the highest among the Speaker-Follower (Ours) configurations. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1041.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ablations on R2R Validation Seen and Validation Unseen sets, showing results in VLN for different combinations of pre-training tasks. SPL and SR are reported as percentages and NE and PL in meters." ], "reasoning": "To verify this statement, check the Validation Seen NE values for the Speaker-Follower (Ours) agent when pre-training with NVS only. The highest Validation Seen NE is 5.12, which is indeed the highest among the Speaker-Follower (Ours) configurations.", "statement": "Pre-training with NVS only results in the highest Validation Seen NE for the Speaker-Follower (Ours) agent.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6: Ablations showing the effect of adapting (or not) the learned representations in each branch of our RCM agent on Validation Seen and Validation Unseen. SPL and SR are reported as percentages and NE and PL in meters.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Warm-starting either encoder improves performance on unseen environments, with the best results achieved when both encoders are warm-started.The second ablation analyzes the effect of transferring representations to either of the language and visual encoders. The learned representations help the agent to generalize on previously unseen environments. When either of the encoders is warm-started, the agent outperforms the baseline success rates and SPL on validation unseen dataset. In the absence of learned representations, the agent overfits on seen environments and as a result the performance improves on the validation seen dataset. Among the agents that have at least one of the encoders warm-started, the agent with both encoders warm-started has significantly higher SPL (7%+) on the validation unseen dataset." } ], "images": [ "scigen/train/1042.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Ablations showing the effect of adapting (or not) the learned representations in each branch of our RCM agent on Validation Seen and Validation Unseen. SPL and SR are reported as percentages and NE and PL in meters." ], "reasoning": "Warm-starting either encoder improves performance on unseen environments, with the best results achieved when both encoders are warm-started.", "description": "The second ablation analyzes the effect of transferring representations to either of the language and visual encoders. The learned representations help the agent to generalize on previously unseen environments. When either of the encoders is warm-started, the agent outperforms the baseline success rates and SPL on validation unseen dataset. In the absence of learned representations, the agent overfits on seen environments and as a result the performance improves on the validation seen dataset. Among the agents that have at least one of the encoders warm-started, the agent with both encoders warm-started has significantly higher SPL (7%+) on the validation unseen dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 6: Ablations showing the effect of adapting (or not) the learned representations in each branch of our RCM agent on Validation Seen and Validation Unseen. SPL and SR are reported as percentages and NE and PL in meters.', and tackle the question below in a sequential manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the SPL on the validation unseen dataset when both the image and language encoders are warm-started?\n" }, { "role": "assistant", "content": "The table shows that when both the image and language encoders are warm-started, the SPL on the validation unseen dataset is 43.0. So, the answer is {\"answer\": \"43.0\"}" } ], "images": [ "scigen/train/1042.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Ablations showing the effect of adapting (or not) the learned representations in each branch of our RCM agent on Validation Seen and Validation Unseen. SPL and SR are reported as percentages and NE and PL in meters." ], "reasoning": "The table shows that when both the image and language encoders are warm-started, the SPL on the validation unseen dataset is 43.0.", "question": "What is the SPL on the validation unseen dataset when both the image and language encoders are warm-started?", "answer": "So, the answer is {\"answer\": \"43.0\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe agent with both encoders warm-started has the highest SPL on the validation unseen dataset.\n" }, { "role": "assistant", "content": "The table indicates that the agent with both encoders warm-started has an SPL of 43.0 on the validation unseen dataset, which is higher than any other configuration. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1042.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Ablations showing the effect of adapting (or not) the learned representations in each branch of our RCM agent on Validation Seen and Validation Unseen. SPL and SR are reported as percentages and NE and PL in meters." ], "reasoning": "The table indicates that the agent with both encoders warm-started has an SPL of 43.0 on the validation unseen dataset, which is higher than any other configuration.", "statement": "The agent with both encoders warm-started has the highest SPL on the validation unseen dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Mean Average Precision on Wiki and APR. “∇” means the number is directly from the original paper.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "CGExpan outperforms all baselines significantly, with notable improvements in MAP@50 on both Wiki and APR datasets, demonstrating the effectiveness of class-guided model refinement and error reduction.Overall Performance. We can see that CGExpan along with its ablations in general outperform all the baselines by a large margin. Comparing with SetExpan, the full model CGExpan achieves 24% improvement in MAP@50 on the Wiki dataset and 49% improvement in MAP@50 on the APR dataset, which verifies that our class-guided model can refine the expansion process and reduce the effect of erroneous entities on later iterations. In addition, CGExpan-NoCN outperforms most baseline models, meaning that the pre-trained LM itself is powerful to capture entity similarities. However, it still cannot beat CGExpan-NoFilter model, which shows that we can properly guide the set expansion process by incorporating generated class names. Moreover, by comparing our full model with CGExpan-NoFilter, we can see that negative class names indeed help the expansion process by estimating a clear boundary for the target class and filtering out erroneous entities. Such an improvement is particularly obvious on the APR dataset. The two versions of our full model overall have comparable performance, but CGExpan-MRR consistently outperforms CGExpan-Comb. To explain such a difference, empirically we observe that high-quality entities tend to rank high in most of the ranked lists. Therefore, we use the MRR version for the rest of our experiment, denoted as CGExpan." } ], "images": [ "scigen/train/1043.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Mean Average Precision on Wiki and APR. “∇” means the number is directly from the original paper." ], "reasoning": "CGExpan outperforms all baselines significantly, with notable improvements in MAP@50 on both Wiki and APR datasets, demonstrating the effectiveness of class-guided model refinement and error reduction.", "description": "Overall Performance. We can see that CGExpan along with its ablations in general outperform all the baselines by a large margin. Comparing with SetExpan, the full model CGExpan achieves 24% improvement in MAP@50 on the Wiki dataset and 49% improvement in MAP@50 on the APR dataset, which verifies that our class-guided model can refine the expansion process and reduce the effect of erroneous entities on later iterations. In addition, CGExpan-NoCN outperforms most baseline models, meaning that the pre-trained LM itself is powerful to capture entity similarities. However, it still cannot beat CGExpan-NoFilter model, which shows that we can properly guide the set expansion process by incorporating generated class names. Moreover, by comparing our full model with CGExpan-NoFilter, we can see that negative class names indeed help the expansion process by estimating a clear boundary for the target class and filtering out erroneous entities. Such an improvement is particularly obvious on the APR dataset. The two versions of our full model overall have comparable performance, but CGExpan-MRR consistently outperforms CGExpan-Comb. To explain such a difference, empirically we observe that high-quality entities tend to rank high in most of the ranked lists. Therefore, we use the MRR version for the rest of our experiment, denoted as CGExpan.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Mean Average Precision on Wiki and APR. “∇” means the number is directly from the original paper.', answer the following question. Give detailed solution consisting of each step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the improvement percentage of CGExpan over SetExpan in MAP@50 on the APR dataset?\n" }, { "role": "assistant", "content": "The description states that CGExpan achieves a 49% improvement in MAP@50 on the APR dataset compared to SetExpan. So, the answer is {\"answer\": \"49%\"}" } ], "images": [ "scigen/train/1043.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Mean Average Precision on Wiki and APR. “∇” means the number is directly from the original paper." ], "reasoning": "The description states that CGExpan achieves a 49% improvement in MAP@50 on the APR dataset compared to SetExpan.", "question": "What is the improvement percentage of CGExpan over SetExpan in MAP@50 on the APR dataset?", "answer": "So, the answer is {\"answer\": \"49%\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nCGExpan-MRR outperforms CGExpan-Comb in MAP@50 on both Wiki and APR datasets.\n" }, { "role": "assistant", "content": "The table shows that CGExpan-MRR has higher MAP@50 values (0.902 for Wiki and 0.955 for APR) compared to CGExpan-Comb (0.895 for Wiki and 0.937 for APR). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1043.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Mean Average Precision on Wiki and APR. “∇” means the number is directly from the original paper." ], "reasoning": "The table shows that CGExpan-MRR has higher MAP@50 values (0.902 for Wiki and 0.955 for APR) compared to CGExpan-Comb (0.895 for Wiki and 0.937 for APR).", "statement": "CGExpan-MRR outperforms CGExpan-Comb in MAP@50 on both Wiki and APR datasets.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 6. Experimental results in semantic matching between e-commerce concepts and items.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The model with external knowledge achieves the highest AUC, F1, and Precision at 10, demonstrating the benefits of incorporating external knowledge.Our knowledge-aware deep semantic matching model outperforms all the baselines in terms of AUC, F1 and Precision at 10, showing the benefits brought by external knowledge. To further investigate how knowledge helps, we dig into cases. Using our base model without knowledge injected, the matching score of concept “中秋节礼物 (Gifts for Mid-Autumn Festival)” and item “老式大月饼共800g云南特产荞三香大荞饼荞酥散装多口味 (Old big moon cakes 800g Yunnan…)” is not confident enough to associate those two, since the texts of two sides are not similar. After we introduce external knowledge for “中秋节 (Mid-Autumn Festival)” such as “中秋节自古便有赏月、吃月饼、赏桂花、饮桂花酒等习俗。(It is a tradition for people to eat moon cakes in Mid-Autumn…)”, the attention score for “中秋节 (Mid-Autumn Festival)” and “月饼 (moon cakes) ” increase to bridge the gap of this concept-item pair." } ], "images": [ "scigen/train/1044.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6. Experimental results in semantic matching between e-commerce concepts and items." ], "reasoning": "The model with external knowledge achieves the highest AUC, F1, and Precision at 10, demonstrating the benefits of incorporating external knowledge.", "description": "Our knowledge-aware deep semantic matching model outperforms all the baselines in terms of AUC, F1 and Precision at 10, showing the benefits brought by external knowledge. To further investigate how knowledge helps, we dig into cases. Using our base model without knowledge injected, the matching score of concept “中秋节礼物 (Gifts for Mid-Autumn Festival)” and item “老式大月饼共800g云南特产荞三香大荞饼荞酥散装多口味 (Old big moon cakes 800g Yunnan…)” is not confident enough to associate those two, since the texts of two sides are not similar. After we introduce external knowledge for “中秋节 (Mid-Autumn Festival)” such as “中秋节自古便有赏月、吃月饼、赏桂花、饮桂花酒等习俗。(It is a tradition for people to eat moon cakes in Mid-Autumn…)”, the attention score for “中秋节 (Mid-Autumn Festival)” and “月饼 (moon cakes) ” increase to bridge the gap of this concept-item pair.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 6. Experimental results in semantic matching between e-commerce concepts and items.' to sequentially solve the question presented below. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich model achieves the highest F1 score?\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all models in the table, the model 'Ours + Knowledge' has the highest F1 score of 0.7769. So, the answer is {\"answer\": \"Ours + Knowledge\"}." } ], "images": [ "scigen/train/1044.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6. Experimental results in semantic matching between e-commerce concepts and items." ], "reasoning": "By comparing the F1 scores of all models in the table, the model 'Ours + Knowledge' has the highest F1 score of 0.7769.", "question": "Which model achieves the highest F1 score?", "answer": "So, the answer is {\"answer\": \"Ours + Knowledge\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe RE2 model achieves the highest AUC score.\n" }, { "role": "assistant", "content": "By comparing the AUC scores of all models in the table, the model 'Ours + Knowledge' has the highest AUC score of 0.8713, not the RE2 model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1044.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6. Experimental results in semantic matching between e-commerce concepts and items." ], "reasoning": "By comparing the AUC scores of all models in the table, the model 'Ours + Knowledge' has the highest AUC score of 0.8713, not the RE2 model.", "statement": "The RE2 model achieves the highest AUC score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3. Experimental results of different sampling strategy in hypernym discovery.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "UCS is the most economical sampling strategy, requiring only 325k samples and reducing 35% of samples compared to the random strategy.When it achieves similar MAP score in four active learning strategies, we can find that all the active learning sampling strategies can reduce labeled data to save considerable manual efforts. UCS is the most economical sampling strategy, which only needs 325k samples, reducing 35\\% samples comparing to random strategy. It indicates that high confident negative samples are also important in the task of hypernym discovery." } ], "images": [ "scigen/train/1045.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. Experimental results of different sampling strategy in hypernym discovery." ], "reasoning": "UCS is the most economical sampling strategy, requiring only 325k samples and reducing 35% of samples compared to the random strategy.", "description": "When it achieves similar MAP score in four active learning strategies, we can find that all the active learning sampling strategies can reduce labeled data to save considerable manual efforts. UCS is the most economical sampling strategy, which only needs 325k samples, reducing 35\\% samples comparing to random strategy. It indicates that high confident negative samples are also important in the task of hypernym discovery.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3. Experimental results of different sampling strategy in hypernym discovery.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich sampling strategy achieves the highest MAP score while using the least labeled size?\n" }, { "role": "assistant", "content": "The UCS strategy achieves the highest MAP score of 46.32 while using the least labeled size of 325k samples. So, the answer is {\"answer\": \"UCS\"}." } ], "images": [ "scigen/train/1045.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. Experimental results of different sampling strategy in hypernym discovery." ], "reasoning": "The UCS strategy achieves the highest MAP score of 46.32 while using the least labeled size of 325k samples.", "question": "Which sampling strategy achieves the highest MAP score while using the least labeled size?", "answer": "So, the answer is {\"answer\": \"UCS\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The CS strategy reduces the labeled size by 150k samples compared to the random strategy., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe CS strategy reduces the labeled size by 150k samples compared to the random strategy.\n" }, { "role": "assistant", "content": "The table shows that the CS strategy reduces the labeled size by 100k samples compared to the random strategy, not 150k. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1045.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. Experimental results of different sampling strategy in hypernym discovery." ], "reasoning": "The table shows that the CS strategy reduces the labeled size by 100k samples compared to the random strategy, not 150k.", "statement": "The CS strategy reduces the labeled size by 150k samples compared to the random strategy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4. Experimental results in shopping concept generation.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Adding wide features improves precision by 3%, replacing input embedding with BERT adds another 1.5%, and introducing external knowledge results in a final precision of 0.935, a 7.5% relative gain over the baseline.Comparing to the baseline, which is a base BiLSTM with self attention architecture, adding wide features such as different syntactic features of concept improves the precision by 3\\% in absolute value. When we replace the input embedding with BERT output, the performance improves another 1.5\\%, which shows the advantage of rich semantic information encoded by BERT. After introducing external knowledge into our model, the final performance reaches to 0.935, improving by a relative gain of 7.5\\% against the baseline model, indicating that leveraging external knowledge benefits commonsense reasoning on short concepts." } ], "images": [ "scigen/train/1046.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4. Experimental results in shopping concept generation." ], "reasoning": "Adding wide features improves precision by 3%, replacing input embedding with BERT adds another 1.5%, and introducing external knowledge results in a final precision of 0.935, a 7.5% relative gain over the baseline.", "description": "Comparing to the baseline, which is a base BiLSTM with self attention architecture, adding wide features such as different syntactic features of concept improves the precision by 3\\% in absolute value. When we replace the input embedding with BERT output, the performance improves another 1.5\\%, which shows the advantage of rich semantic information encoded by BERT. After introducing external knowledge into our model, the final performance reaches to 0.935, improving by a relative gain of 7.5\\% against the baseline model, indicating that leveraging external knowledge benefits commonsense reasoning on short concepts.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 4. Experimental results in shopping concept generation.'.\n\nQuestion: What is the precision improvement when wide features and BERT are added to the baseline model?\n" }, { "role": "assistant", "content": "The baseline model has a precision of 0.870. Adding wide features increases it to 0.900, and adding BERT further increases it to 0.915. The total improvement is 0.915 - 0.870. So, the answer is {\"answer\": \"0.045\"}" } ], "images": [ "scigen/train/1046.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4. Experimental results in shopping concept generation." ], "reasoning": "The baseline model has a precision of 0.870. Adding wide features increases it to 0.900, and adding BERT further increases it to 0.915. The total improvement is 0.915 - 0.870.", "question": "What is the precision improvement when wide features and BERT are added to the baseline model?", "answer": "So, the answer is {\"answer\": \"0.045\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nIntroducing external knowledge improves the precision by 7.5% relative to the baseline model.\n" }, { "role": "assistant", "content": "The baseline model has a precision of 0.870. Introducing external knowledge results in a precision of 0.935. The relative gain is calculated as (0.935 - 0.870) / 0.870 * 100%, which is 7.5%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1046.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4. Experimental results in shopping concept generation." ], "reasoning": "The baseline model has a precision of 0.870. Introducing external knowledge results in a precision of 0.935. The relative gain is calculated as (0.935 - 0.870) / 0.870 * 100%, which is 7.5%.", "statement": "Introducing external knowledge improves the precision by 7.5% relative to the baseline model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5. Experimental results in shopping concept tagging.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Adding fuzzy CRF improves F1 by 1.8% over the baseline, and incorporating external knowledge embeddings further enhances the F1 score to 0.8772.Comparing to baseline which is a basic sequence labeling model with Bi-LSTM and CRF, adding fuzzy CRF improves 1.8% on F1, which indicates such multi-path optimization in CRF layer actually contributes to disambiguation. Equipped with external knowledge embeddings to further enhance the textual information, our model continuously outperform to 0.8772 on F1. It demonstrates that introducing external knowledge can benefit tasks dealing with short texts with limited contextual information." } ], "images": [ "scigen/train/1047.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5. Experimental results in shopping concept tagging." ], "reasoning": "Adding fuzzy CRF improves F1 by 1.8% over the baseline, and incorporating external knowledge embeddings further enhances the F1 score to 0.8772.", "description": "Comparing to baseline which is a basic sequence labeling model with Bi-LSTM and CRF, adding fuzzy CRF improves 1.8% on F1, which indicates such multi-path optimization in CRF layer actually contributes to disambiguation. Equipped with external knowledge embeddings to further enhance the textual information, our model continuously outperform to 0.8772 on F1. It demonstrates that introducing external knowledge can benefit tasks dealing with short texts with limited contextual information.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 5. Experimental results in shopping concept tagging.', and tackle the question below in a sequential manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the F1 score improvement when adding fuzzy CRF and external knowledge embeddings compared to the baseline?\n" }, { "role": "assistant", "content": "The baseline F1 score is 0.8523. Adding fuzzy CRF increases the F1 score to 0.8703, and further adding external knowledge embeddings increases it to 0.8772. The total improvement is 0.8772 - 0.8523. So, the answer is {\"answer\": \"0.0249\"}" } ], "images": [ "scigen/train/1047.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5. Experimental results in shopping concept tagging." ], "reasoning": "The baseline F1 score is 0.8523. Adding fuzzy CRF increases the F1 score to 0.8703, and further adding external knowledge embeddings increases it to 0.8772. The total improvement is 0.8772 - 0.8523.", "question": "What is the F1 score improvement when adding fuzzy CRF and external knowledge embeddings compared to the baseline?", "answer": "So, the answer is {\"answer\": \"0.0249\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nAdding fuzzy CRF improves the F1 score by 1.8% over the baseline.\n" }, { "role": "assistant", "content": "The baseline F1 score is 0.8523, and adding fuzzy CRF increases it to 0.8703. The improvement is 0.8703 - 0.8523 = 0.018, which is 1.8%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1047.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5. Experimental results in shopping concept tagging." ], "reasoning": "The baseline F1 score is 0.8523, and adding fuzzy CRF increases it to 0.8703. The improvement is 0.8703 - 0.8523 = 0.018, which is 1.8%.", "statement": "Adding fuzzy CRF improves the F1 score by 1.8% over the baseline.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: E2E NER from speech: Micro-Average scores, with and without LM.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The table shows that using a language model significantly improves the F1 score, precision, and recall for NER tasks.We also studied the effect of language model on the F1 scores. Therefore, based on the quantitative analysis, we conclude that the NER results are closely dependent on the language model, and if an LM is trained on a bigger corpus, then the recall could be increased further." } ], "images": [ "scigen/train/1048.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: E2E NER from speech: Micro-Average scores, with and without LM." ], "reasoning": "The table shows that using a language model significantly improves the F1 score, precision, and recall for NER tasks.", "description": "We also studied the effect of language model on the F1 scores. Therefore, based on the quantitative analysis, we conclude that the NER results are closely dependent on the language model, and if an LM is trained on a bigger corpus, then the recall could be increased further.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 4: E2E NER from speech: Micro-Average scores, with and without LM.'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. \n\nQuestion: What is the difference in F1 score between using a language model and not using one?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The F1 score without a language model is 0.27, and with a language model, it is 0.90. The difference is 0.90 - 0.27. So, the answer is {\"answer\": \"0.63\"}." } ], "images": [ "scigen/train/1048.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: E2E NER from speech: Micro-Average scores, with and without LM." ], "reasoning": "The F1 score without a language model is 0.27, and with a language model, it is 0.90. The difference is 0.90 - 0.27.", "question": "What is the difference in F1 score between using a language model and not using one?", "answer": "So, the answer is {\"answer\": \"0.63\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Using a language model improves the recall for NER tasks. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nUsing a language model improves the recall for NER tasks.\n" }, { "role": "assistant", "content": "The table shows that the recall without a language model is 0.21, and with a language model, it is 0.85, indicating an improvement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1048.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: E2E NER from speech: Micro-Average scores, with and without LM." ], "reasoning": "The table shows that the recall without a language model is 0.21, and with a language model, it is 0.85, indicating an improvement.", "statement": "Using a language model improves the recall for NER tasks.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: Performance comparison for different word embedding and different detection model, where “So-W2v”, “So-Glove” and “So-FastText” represent suicide-oriented word embeddings based on Word2vec, GloVe and FastText respectively. Acc and F1 represent accuracy and F1-score.'.\n" }, { "role": "assistant", "content": "Bert outperforms other word embeddings without a suicide-related dictionary, but FastText achieves the best performance with the dictionary.5.4.1 Effectiveness of Suicide-oriented Word Embeddings We find that without suicide-related dictionary, Bert outperforms other three word embeddings with 2% higher accuracy and 1.5% higher F1-score on both models. After leveraging suicide-related dictionary, suicide-oriented word embeddings based on FastText achieves the best performance with accuracy 88.00% 91.33%, F1-score 88.14%, 90.92% on two models. Obviously, there is a gap between suicide-oriented word embeddings and normal word embeddings which can verify the effectiveness of the former." } ], "images": [ "scigen/train/1049.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Performance comparison for different word embedding and different detection model, where “So-W2v”, “So-Glove” and “So-FastText” represent suicide-oriented word embeddings based on Word2vec, GloVe and FastText respectively. Acc and F1 represent accuracy and F1-score." ], "reasoning": "Bert outperforms other word embeddings without a suicide-related dictionary, but FastText achieves the best performance with the dictionary.", "description": "5.4.1 Effectiveness of Suicide-oriented Word Embeddings We find that without suicide-related dictionary, Bert outperforms other three word embeddings with 2% higher accuracy and 1.5% higher F1-score on both models. After leveraging suicide-related dictionary, suicide-oriented word embeddings based on FastText achieves the best performance with accuracy 88.00% 91.33%, F1-score 88.14%, 90.92% on two models. Obviously, there is a gap between suicide-oriented word embeddings and normal word embeddings which can verify the effectiveness of the former.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 5: Performance comparison for different word embedding and different detection model, where “So-W2v”, “So-Glove” and “So-FastText” represent suicide-oriented word embeddings based on Word2vec, GloVe and FastText respectively. Acc and F1 represent accuracy and F1-score.', solve the following question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich word embedding achieves the highest F1-score on the SDM model after leveraging the suicide-related dictionary?\n" }, { "role": "assistant", "content": "The table shows that after leveraging the suicide-related dictionary, the highest F1-score on the SDM model is achieved by So-FastText with an F1-score of 90.92%. So, the answer is {\"answer\": \"So-FastText\"}" } ], "images": [ "scigen/train/1049.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Performance comparison for different word embedding and different detection model, where “So-W2v”, “So-Glove” and “So-FastText” represent suicide-oriented word embeddings based on Word2vec, GloVe and FastText respectively. Acc and F1 represent accuracy and F1-score." ], "reasoning": "The table shows that after leveraging the suicide-related dictionary, the highest F1-score on the SDM model is achieved by So-FastText with an F1-score of 90.92%.", "question": "Which word embedding achieves the highest F1-score on the SDM model after leveraging the suicide-related dictionary?", "answer": "So, the answer is {\"answer\": \"So-FastText\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Bert achieves the highest accuracy on the LSTM model without a suicide-related dictionary., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nBert achieves the highest accuracy on the LSTM model without a suicide-related dictionary.\n" }, { "role": "assistant", "content": "The table shows that without a suicide-related dictionary, Bert has an accuracy of 85.15% on the LSTM model, which is higher than Word2vec, GloVe, and FastText. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1049.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Performance comparison for different word embedding and different detection model, where “So-W2v”, “So-Glove” and “So-FastText” represent suicide-oriented word embeddings based on Word2vec, GloVe and FastText respectively. Acc and F1 represent accuracy and F1-score." ], "reasoning": "The table shows that without a suicide-related dictionary, Bert has an accuracy of 85.15% on the LSTM model, which is higher than Word2vec, GloVe, and FastText.", "statement": "Bert achieves the highest accuracy on the LSTM model without a suicide-related dictionary.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 6: Performance comparison between different suicide risk detection model, where Acc and F1 represent accuracy and F1-score respectively.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "SDM, using So-FastText word embeddings, improves accuracy by over 3.33% and achieves 2.78% higher F1-score compared to LSTM on the full dataset.In this case, LSTM and SDM employs So-FastText word embeddings as their input. SDM improves the accuracy by over 3.33% and obtains 2.78% higher F1-score on full data set." } ], "images": [ "scigen/train/1050.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Performance comparison between different suicide risk detection model, where Acc and F1 represent accuracy and F1-score respectively." ], "reasoning": "SDM, using So-FastText word embeddings, improves accuracy by over 3.33% and achieves 2.78% higher F1-score compared to LSTM on the full dataset.", "description": "In this case, LSTM and SDM employs So-FastText word embeddings as their input. SDM improves the accuracy by over 3.33% and obtains 2.78% higher F1-score on full data set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nTable title: Table 6: Performance comparison between different suicide risk detection model, where Acc and F1 represent accuracy and F1-score respectively.\n\nQuestion: What is the difference in accuracy between SDM and LSTM on the full test set?\n" }, { "role": "assistant", "content": "The accuracy of SDM on the full test set is 91.33%, while the accuracy of LSTM is 88.00%. The difference is 91.33% - 88.00% = 3.33%. So, the answer is {\"answer\": \"3.33%\"}" } ], "images": [ "scigen/train/1050.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Performance comparison between different suicide risk detection model, where Acc and F1 represent accuracy and F1-score respectively." ], "reasoning": "The accuracy of SDM on the full test set is 91.33%, while the accuracy of LSTM is 88.00%. The difference is 91.33% - 88.00% = 3.33%.", "question": "What is the difference in accuracy between SDM and LSTM on the full test set?", "answer": "So, the answer is {\"answer\": \"3.33%\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim SDM achieves the highest F1-score on the harder sub-test set., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nSDM achieves the highest F1-score on the harder sub-test set.\n" }, { "role": "assistant", "content": "The F1-score of SDM on the harder sub-test set is 84.77, which is higher than the F1-scores of SVM (64.11), NB (62.20), and LSTM (75.32). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1050.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Performance comparison between different suicide risk detection model, where Acc and F1 represent accuracy and F1-score respectively." ], "reasoning": "The F1-score of SDM on the harder sub-test set is 84.77, which is higher than the F1-scores of SVM (64.11), NB (62.20), and LSTM (75.32).", "statement": "SDM achieves the highest F1-score on the harder sub-test set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 7: Ablation test for SDM with different inputs.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The ablation test shows that using more modalities, such as text, image, and user's feature, improves the classification performance, with the highest accuracy and F1-score achieved when all three inputs are used.To show the contribution of different input to the final classification performance, we design an ablation test of SDM after removing different input. All SDMs are based on embedding So-Fasttext. Since not every post contains image and user’s features contain missing value, we do not only use images nor user’s feature as input of SDMs. Besides, user’s features play a more important role than visual information. The more modalities we use, the better performance we get." } ], "images": [ "scigen/train/1051.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Ablation test for SDM with different inputs." ], "reasoning": "The ablation test shows that using more modalities, such as text, image, and user's feature, improves the classification performance, with the highest accuracy and F1-score achieved when all three inputs are used.", "description": "To show the contribution of different input to the final classification performance, we design an ablation test of SDM after removing different input. All SDMs are based on embedding So-Fasttext. Since not every post contains image and user’s features contain missing value, we do not only use images nor user’s feature as input of SDMs. Besides, user’s features play a more important role than visual information. The more modalities we use, the better performance we get.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 7: Ablation test for SDM with different inputs.' and proceed to solve the following question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the F1-score when using both text and user's feature as inputs?\n" }, { "role": "assistant", "content": "To find the F1-score when using both text and user's feature as inputs, locate the row labeled 'Text+User’s feature' and check the corresponding F1-score value. So, the answer is {\"answer\": \"90.17\"}." } ], "images": [ "scigen/train/1051.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Ablation test for SDM with different inputs." ], "reasoning": "To find the F1-score when using both text and user's feature as inputs, locate the row labeled 'Text+User’s feature' and check the corresponding F1-score value.", "question": "What is the F1-score when using both text and user's feature as inputs?", "answer": "So, the answer is {\"answer\": \"90.17\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nUsing only text as input results in the lowest accuracy.\n" }, { "role": "assistant", "content": "The table shows that the accuracy for 'Text' is 88.56, which is lower than the accuracies for 'Text+Image', 'Text+User’s feature', and 'Text+Image+User’s feature'. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1051.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Ablation test for SDM with different inputs." ], "reasoning": "The table shows that the accuracy for 'Text' is 88.56, which is lower than the accuracies for 'Text+Image', 'Text+User’s feature', and 'Text+Image+User’s feature'.", "statement": "Using only text as input results in the lowest accuracy.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 5: BLEU and length ratio of models on Zh→En validation set. †indicates our own implementation.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Our proposed model achieves better length ratio on almost all source sentence lengths in the dev-set compared to seq2seq, BSO, and seq2seq with length reward.We compare our model with seq2seq, BSO and seq2seq with length reward Huang et al. (our proposed method does not require tuning of hyper-parameter). Fig. Our proposed model comparatively achieves better length ratio on almost all source sentence length in dev-set." } ], "images": [ "scigen/train/1052.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: BLEU and length ratio of models on Zh→En validation set. †indicates our own implementation." ], "reasoning": "Our proposed model achieves better length ratio on almost all source sentence lengths in the dev-set compared to seq2seq, BSO, and seq2seq with length reward.", "description": "We compare our model with seq2seq, BSO and seq2seq with length reward Huang et al. (our proposed method does not require tuning of hyper-parameter). Fig. Our proposed model comparatively achieves better length ratio on almost all source sentence length in dev-set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: BLEU and length ratio of models on Zh→En validation set. †indicates our own implementation.\nQuestion: Which model achieved the highest BLEU score and what was its length ratio?\nSolve the above question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows that 'This work' with a train beam of 8 and decode beam of 7 achieved the highest BLEU score of 39.51 with a length ratio of 1.00. So, the answer is {\"answer\": \"This work with BLEU score 39.51 and length ratio 1.00\"}." } ], "images": [ "scigen/train/1052.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: BLEU and length ratio of models on Zh→En validation set. †indicates our own implementation." ], "reasoning": "The table shows that 'This work' with a train beam of 8 and decode beam of 7 achieved the highest BLEU score of 39.51 with a length ratio of 1.00.", "question": "Which model achieved the highest BLEU score and what was its length ratio?", "answer": "So, the answer is {\"answer\": \"This work with BLEU score 39.51 and length ratio 1.00\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The BSO model with a train beam of 4 achieved a BLEU score higher than 37., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe BSO model with a train beam of 4 achieved a BLEU score higher than 37.\n" }, { "role": "assistant", "content": "The table shows that the BSO model with a train beam of 4 achieved a BLEU score of 36.91, which is not higher than 37. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1052.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: BLEU and length ratio of models on Zh→En validation set. †indicates our own implementation." ], "reasoning": "The table shows that the BSO model with a train beam of 4 achieved a BLEU score of 36.91, which is not higher than 37.", "statement": "The BSO model with a train beam of 4 achieved a BLEU score higher than 37.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Ablation study. Compare CAS with not adding LSTM layers (CAS-Subset) and not updating Transformer block parameters (CAS-LSTM).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "CAS-Subset and CAS-LSTM improve performance over naive BERT and GPT, with LSTMs being more effective on smaller datasets due to overfitting in Transformers, while on larger datasets, adapting Transformer parameters is more important.but it fixes all Transformer blocks during fine-tuning. As can be seen, both CAS-Subset and CAS-LSTM improve significantly upon a naive use of BERT and GPT. This is to be expected since fine-tuning improves performance. On the smaller dataset, i.e. PTB, adding LSTMs is more effective. This might be due to the overfitting incurred in updating Transformers. On the other hand, on the larger datasets, i.e. WT-103, adding an LSTM is less effective, which means that adapting the Transformer parameters for better sentence-level representation is more important. Combing both together leads to further improvement. CAS outperforms AWD-LSTM-MoS on all three datasets." } ], "images": [ "scigen/train/1053.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ablation study. Compare CAS with not adding LSTM layers (CAS-Subset) and not updating Transformer block parameters (CAS-LSTM)." ], "reasoning": "CAS-Subset and CAS-LSTM improve performance over naive BERT and GPT, with LSTMs being more effective on smaller datasets due to overfitting in Transformers, while on larger datasets, adapting Transformer parameters is more important.", "description": "but it fixes all Transformer blocks during fine-tuning. As can be seen, both CAS-Subset and CAS-LSTM improve significantly upon a naive use of BERT and GPT. This is to be expected since fine-tuning improves performance. On the smaller dataset, i.e. PTB, adding LSTMs is more effective. This might be due to the overfitting incurred in updating Transformers. On the other hand, on the larger datasets, i.e. WT-103, adding an LSTM is less effective, which means that adapting the Transformer parameters for better sentence-level representation is more important. Combing both together leads to further improvement. CAS outperforms AWD-LSTM-MoS on all three datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 2: Ablation study. Compare CAS with not adding LSTM layers (CAS-Subset) and not updating Transformer block parameters (CAS-LSTM).' and proceed to solve the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model has the highest validation score on the WT-103 dataset?\n" }, { "role": "assistant", "content": "To find the model with the highest validation score on the WT-103 dataset, compare the validation scores in the 'Datasets WT-103 Val' column. The highest score is 53.66, achieved by BERT-CAS-LSTM. So, the answer is {\"answer\": \"BERT-CAS-LSTM\"}." } ], "images": [ "scigen/train/1053.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ablation study. Compare CAS with not adding LSTM layers (CAS-Subset) and not updating Transformer block parameters (CAS-LSTM)." ], "reasoning": "To find the model with the highest validation score on the WT-103 dataset, compare the validation scores in the 'Datasets WT-103 Val' column. The highest score is 53.66, achieved by BERT-CAS-LSTM.", "question": "Which model has the highest validation score on the WT-103 dataset?", "answer": "So, the answer is {\"answer\": \"BERT-CAS-LSTM\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nGPT-CAS-Subset has a higher validation score than BERT-CAS-Subset on the PTB dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the validation scores of GPT-CAS-Subset and BERT-CAS-Subset on the PTB dataset. GPT-CAS-Subset has a validation score of 47.58, which is higher than BERT-CAS-Subset's score of 42.53. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1053.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ablation study. Compare CAS with not adding LSTM layers (CAS-Subset) and not updating Transformer block parameters (CAS-LSTM)." ], "reasoning": "To verify this statement, compare the validation scores of GPT-CAS-Subset and BERT-CAS-Subset on the PTB dataset. GPT-CAS-Subset has a validation score of 47.58, which is higher than BERT-CAS-Subset's score of 42.53.", "statement": "GPT-CAS-Subset has a higher validation score than BERT-CAS-Subset on the PTB dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: Performance of Coordinate Architecture Search (CAS). ‘Val’ and ‘Test’ denote validation and test perplexity respectively.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "AWD-LSTM-MoS outperforms both GPT and BERT on all three datasets, indicating that language modeling requires strong word order dependency capabilities.First note that GPT and BERT are significantly worse than AWD-LSTM-MoS. It confirms our hypothesis that neither BERT nor GPT are effective tools for language modeling. Applying them naively leads to significantly worse results compared to AWS-LSTM-MoS on three datasets. It demonstrates that language modeling requires strong capabilities in modeling the word order dependency within sentences. However, due to the combination of self-attention and positional encoding, both GPT and BERT primarily capture coarse-grained language representation but with limited word-level context." } ], "images": [ "scigen/train/1054.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance of Coordinate Architecture Search (CAS). ‘Val’ and ‘Test’ denote validation and test perplexity respectively." ], "reasoning": "AWD-LSTM-MoS outperforms both GPT and BERT on all three datasets, indicating that language modeling requires strong word order dependency capabilities.", "description": "First note that GPT and BERT are significantly worse than AWD-LSTM-MoS. It confirms our hypothesis that neither BERT nor GPT are effective tools for language modeling. Applying them naively leads to significantly worse results compared to AWS-LSTM-MoS on three datasets. It demonstrates that language modeling requires strong capabilities in modeling the word order dependency within sentences. However, due to the combination of self-attention and positional encoding, both GPT and BERT primarily capture coarse-grained language representation but with limited word-level context.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 1: Performance of Coordinate Architecture Search (CAS). ‘Val’ and ‘Test’ denote validation and test perplexity respectively.', and tackle the question below in a sequential manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model has the lowest validation score on the WT-103 dataset?\n" }, { "role": "assistant", "content": "By examining the validation scores for the WT-103 dataset, BERT-Large-CAS (Our) has the lowest score of 19.67. So, the answer is {\"answer\": \"BERT-Large-CAS (Our)\"}." } ], "images": [ "scigen/train/1054.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance of Coordinate Architecture Search (CAS). ‘Val’ and ‘Test’ denote validation and test perplexity respectively." ], "reasoning": "By examining the validation scores for the WT-103 dataset, BERT-Large-CAS (Our) has the lowest score of 19.67.", "question": "Which model has the lowest validation score on the WT-103 dataset?", "answer": "So, the answer is {\"answer\": \"BERT-Large-CAS (Our)\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nGPT outperforms AWD-LSTM-MoS on the PTB dataset.\n" }, { "role": "assistant", "content": "By comparing the validation and test scores for the PTB dataset, GPT has higher scores (79.44 and 68.79) compared to AWD-LSTM-MoS (43.47 and 38.04), indicating GPT performs worse. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1054.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance of Coordinate Architecture Search (CAS). ‘Val’ and ‘Test’ denote validation and test perplexity respectively." ], "reasoning": "By comparing the validation and test scores for the PTB dataset, GPT has higher scores (79.44 and 68.79) compared to AWD-LSTM-MoS (43.47 and 38.04), indicating GPT performs worse.", "statement": "GPT outperforms AWD-LSTM-MoS on the PTB dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: Efficiency of different search methods on PTB and WT-2.'.\n" }, { "role": "assistant", "content": "BERT-CAS is the most cost-effective method, leveraging prior knowledge to optimize neural network architectures efficiently.As can be seen, BERT-CAS is cheaper than all others. The results indicate that by leveraging the prior knowledge of the design of the neural networks for specific tasks, we could only optimize the architectures in a small confined sub-space, that leads to speed up the search process. For example, BERT-CAS is directly based on BERT, applying search upon such effective neural networks could facilitate the adaptation to similar tasks." } ], "images": [ "scigen/train/1055.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Efficiency of different search methods on PTB and WT-2." ], "reasoning": "BERT-CAS is the most cost-effective method, leveraging prior knowledge to optimize neural network architectures efficiently.", "description": "As can be seen, BERT-CAS is cheaper than all others. The results indicate that by leveraging the prior knowledge of the design of the neural networks for specific tasks, we could only optimize the architectures in a small confined sub-space, that leads to speed up the search process. For example, BERT-CAS is directly based on BERT, applying search upon such effective neural networks could facilitate the adaptation to similar tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: 'Table 5: Efficiency of different search methods on PTB and WT-2.'\nQuestion: Which method has the lowest search cost for PTB and what is the cost?\n" }, { "role": "assistant", "content": "By examining the 'Search Cost (GPU days) PTB' column, BERT-CAS has the lowest cost of 0.15 GPU days. So, the answer is {\"answer\": \"BERT-CAS, 0.15\"}." } ], "images": [ "scigen/train/1055.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Efficiency of different search methods on PTB and WT-2." ], "reasoning": "By examining the 'Search Cost (GPU days) PTB' column, BERT-CAS has the lowest cost of 0.15 GPU days.", "question": "Which method has the lowest search cost for PTB and what is the cost?", "answer": "So, the answer is {\"answer\": \"BERT-CAS, 0.15\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis BERT-CAS has the lowest search cost for both PTB and WT-2. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nBERT-CAS has the lowest search cost for both PTB and WT-2.\n" }, { "role": "assistant", "content": "The table shows that BERT-CAS has the lowest search cost for PTB (0.15 GPU days) and WT-2 (0.38 GPU days) compared to other methods. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1055.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Efficiency of different search methods on PTB and WT-2." ], "reasoning": "The table shows that BERT-CAS has the lowest search cost for PTB (0.15 GPU days) and WT-2 (0.38 GPU days) compared to other methods.", "statement": "BERT-CAS has the lowest search cost for both PTB and WT-2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 6: Compare model parameter size and results with GPT-2. The GPT-2 model size and results are from Radford et al. (2019).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The proposed model BERT-Large-CAS outperforms GPT-2 (762M) on WT-103 despite having fewer parameters, but performs worse on WT-2.We specifically compare the proposed model with the recent state-of-the-art language model GPT-2 Radford et al. WT-103. More surprisingly, the proposed method performs better than GPT-2 (1542M) which has around 4 times more parameters. On WT-103, BERT-Large-CAS is better than GPT-2 (762M) which has around 2 times more parameters. Note that on WT-2, our method performs worse than GPT-2, we suspect the reason is that the WebText still contains the texts that are similar to the Wikipedia. WT-2 is quite small in terms of scale. In contrast, we regard the results on WT-103 (50 times larger than WT-2) as a more reasonable comparison with GPT-2." } ], "images": [ "scigen/train/1056.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Compare model parameter size and results with GPT-2. The GPT-2 model size and results are from Radford et al. (2019)." ], "reasoning": "The proposed model BERT-Large-CAS outperforms GPT-2 (762M) on WT-103 despite having fewer parameters, but performs worse on WT-2.", "description": "We specifically compare the proposed model with the recent state-of-the-art language model GPT-2 Radford et al. WT-103. More surprisingly, the proposed method performs better than GPT-2 (1542M) which has around 4 times more parameters. On WT-103, BERT-Large-CAS is better than GPT-2 (762M) which has around 2 times more parameters. Note that on WT-2, our method performs worse than GPT-2, we suspect the reason is that the WebText still contains the texts that are similar to the Wikipedia. WT-2 is quite small in terms of scale. In contrast, we regard the results on WT-103 (50 times larger than WT-2) as a more reasonable comparison with GPT-2.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 6: Compare model parameter size and results with GPT-2. The GPT-2 model size and results are from Radford et al. (2019).' in mind, break down and resolve the question below step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhich model performs better on WT-103, BERT-Large-CAS or GPT-2 (762M)?\n" }, { "role": "assistant", "content": "By comparing the scores on WT-103, BERT-Large-CAS has a score of 20.42, which is better than GPT-2 (762M)'s score of 22.05. So, the answer is {\"answer\": \"BERT-Large-CAS\"}" } ], "images": [ "scigen/train/1056.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Compare model parameter size and results with GPT-2. The GPT-2 model size and results are from Radford et al. (2019)." ], "reasoning": "By comparing the scores on WT-103, BERT-Large-CAS has a score of 20.42, which is better than GPT-2 (762M)'s score of 22.05.", "question": "Which model performs better on WT-103, BERT-Large-CAS or GPT-2 (762M)?", "answer": "So, the answer is {\"answer\": \"BERT-Large-CAS\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nBERT-Large-CAS performs better than GPT-2 (1542M) on WT-103.\n" }, { "role": "assistant", "content": "The table shows that BERT-Large-CAS has a score of 20.42 on WT-103, while GPT-2 (1542M) has a score of 17.48, indicating that GPT-2 (1542M) performs better. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1056.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Compare model parameter size and results with GPT-2. The GPT-2 model size and results are from Radford et al. (2019)." ], "reasoning": "The table shows that BERT-Large-CAS has a score of 20.42 on WT-103, while GPT-2 (1542M) has a score of 17.48, indicating that GPT-2 (1542M) performs better.", "statement": "BERT-Large-CAS performs better than GPT-2 (1542M) on WT-103.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Results for around 200 custom developer domains. For F1, higher values are better, while for SER lower values are better. * denotes statistically significant SER difference compared to both baselines.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The proposed pre-trained DNN model, which is pre-trained on 4 million utterances and fine-tuned with 50K grammar utterances per domain, significantly outperforms both the baseline CRF/MaxEnt and baseline DNN models across all metrics.For the custom domain experiments, we focus on a low resource experimental setup, where we assume that our only target training data is the data provided by the external developer. We report results for around 200 custom domains, which is a subset of all domains we support. For training the baselines, we use the available data provided by the developer for each domain, e.g., example phrases and gazetteers. This training data size was selected empirically based on baseline model accuracy. The generated utterances may contain repetitions for domains where the external developer provided a small amount of example phrases and few slot values per gazetteer. For the proposed method, we pre-train a DNN model on 4 million utterances and fine tune it per domain using the 50K grammar utterances of that domain and any available gazetteer information (for extracting gazetteer features). This suggests that the baseline DNN models (without pretraining) cannot be trained robustly without large available training data. The proposed pre-trained DNN significantly outperforms both baselines across all metrics (paired t-test, p" } ], "images": [ "scigen/train/1057.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results for around 200 custom developer domains. For F1, higher values are better, while for SER lower values are better. * denotes statistically significant SER difference compared to both baselines." ], "reasoning": "The proposed pre-trained DNN model, which is pre-trained on 4 million utterances and fine-tuned with 50K grammar utterances per domain, significantly outperforms both the baseline CRF/MaxEnt and baseline DNN models across all metrics.", "description": "For the custom domain experiments, we focus on a low resource experimental setup, where we assume that our only target training data is the data provided by the external developer. We report results for around 200 custom domains, which is a subset of all domains we support. For training the baselines, we use the available data provided by the developer for each domain, e.g., example phrases and gazetteers. This training data size was selected empirically based on baseline model accuracy. The generated utterances may contain repetitions for domains where the external developer provided a small amount of example phrases and few slot values per gazetteer. For the proposed method, we pre-train a DNN model on 4 million utterances and fine tune it per domain using the 50K grammar utterances of that domain and any available gazetteer information (for extracting gazetteer features). This suggests that the baseline DNN models (without pretraining) cannot be trained robustly without large available training data. The proposed pre-trained DNN significantly outperforms both baselines across all metrics (paired t-test, p", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 2: Results for around 200 custom developer domains. For F1, higher values are better, while for SER lower values are better. * denotes statistically significant SER difference compared to both baselines.\nQuestion: Which approach has the highest F1 Slot Mean and what is its value?\n" }, { "role": "assistant", "content": "The table shows the F1 Slot Mean for each approach. The highest F1 Slot Mean is 88.6, achieved by the Proposed Pretrained DNN. So, the answer is {\"answer\": \"Proposed Pretrained DNN, 88.6\"}." } ], "images": [ "scigen/train/1057.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results for around 200 custom developer domains. For F1, higher values are better, while for SER lower values are better. * denotes statistically significant SER difference compared to both baselines." ], "reasoning": "The table shows the F1 Slot Mean for each approach. The highest F1 Slot Mean is 88.6, achieved by the Proposed Pretrained DNN.", "question": "Which approach has the highest F1 Slot Mean and what is its value?", "answer": "So, the answer is {\"answer\": \"Proposed Pretrained DNN, 88.6\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Baseline CRF/MaxEnt approach has a higher F1 Intent Mean than the Baseline DNN approach. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Baseline CRF/MaxEnt approach has a higher F1 Intent Mean than the Baseline DNN approach.\n" }, { "role": "assistant", "content": "The table shows that the F1 Intent Mean for Baseline CRF/MaxEnt is 94.6, while for Baseline DNN it is 91.9. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1057.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results for around 200 custom developer domains. For F1, higher values are better, while for SER lower values are better. * denotes statistically significant SER difference compared to both baselines." ], "reasoning": "The table shows that the F1 Intent Mean for Baseline CRF/MaxEnt is 94.6, while for Baseline DNN it is 91.9. Therefore, the statement is supported.", "statement": "The Baseline CRF/MaxEnt approach has a higher F1 Intent Mean than the Baseline DNN approach.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Results on domains A, B and C for the proposed pretrained DNN method and the baseline CRF/MaxEnt method during experimental early stages of domain development. * denotes statistically significant SER difference between proposed and baseline'.\n" }, { "role": "assistant", "content": "The proposed DNN models, pre-trained on 4 million data from mature domains, show significant accuracy gains in early, low-resource stages across domains A, B, and C compared to baseline models.We evaluate our methods on three new built-in domains referred here as domain A (5 intents, 36 slot types), domain B (2 intents, 17 slot types) and domain C (22 intents, 43 slot types). Core data refers to core example utterances, bootstrap data refers to domain data collection and generation of synthetic (grammar) utterances, and user data refers to user interactions with our agent. Here we evaluate whether we can accelerate the development process by achieving accuracy gains in early, low resource stages, and bootstrap a model faster. Results for the non pre-trained DNN baseline are similar, and omitted for lack of space. Our proposed DNN models are pre-trained on 4 million data from mature domains and then fine tuned on the available target data. The baseline CRF/MaxEnt models are trained on the available target data. The types of target data slightly differ across domains according to domain development characteristics. For example, for domain B there was very small amount of core data available and it was combined with the bootstrap data for experiments." } ], "images": [ "scigen/train/1058.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on domains A, B and C for the proposed pretrained DNN method and the baseline CRF/MaxEnt method during experimental early stages of domain development. * denotes statistically significant SER difference between proposed and baseline" ], "reasoning": "The proposed DNN models, pre-trained on 4 million data from mature domains, show significant accuracy gains in early, low-resource stages across domains A, B, and C compared to baseline models.", "description": "We evaluate our methods on three new built-in domains referred here as domain A (5 intents, 36 slot types), domain B (2 intents, 17 slot types) and domain C (22 intents, 43 slot types). Core data refers to core example utterances, bootstrap data refers to domain data collection and generation of synthetic (grammar) utterances, and user data refers to user interactions with our agent. Here we evaluate whether we can accelerate the development process by achieving accuracy gains in early, low resource stages, and bootstrap a model faster. Results for the non pre-trained DNN baseline are similar, and omitted for lack of space. Our proposed DNN models are pre-trained on 4 million data from mature domains and then fine tuned on the available target data. The baseline CRF/MaxEnt models are trained on the available target data. The types of target data slightly differ across domains according to domain development characteristics. For example, for domain B there was very small amount of core data available and it was combined with the bootstrap data for experiments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nTable title: Table 3: Results on domains A, B and C for the proposed pretrained DNN method and the baseline CRF/MaxEnt method during experimental early stages of domain development. * denotes statistically significant SER difference between proposed and baseline\n\nQuestion: What is the F1 slot score for Domain C when using the proposed method with core and bootstrap data?\n" }, { "role": "assistant", "content": "To find the F1 slot score for Domain C using the proposed method with core and bootstrap data, locate the row for Domain C with core and bootstrap data and check the F1 slot score under the proposed method. So, the answer is {\"answer\": \"72.7\"}." } ], "images": [ "scigen/train/1058.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on domains A, B and C for the proposed pretrained DNN method and the baseline CRF/MaxEnt method during experimental early stages of domain development. * denotes statistically significant SER difference between proposed and baseline" ], "reasoning": "To find the F1 slot score for Domain C using the proposed method with core and bootstrap data, locate the row for Domain C with core and bootstrap data and check the F1 slot score under the proposed method.", "question": "What is the F1 slot score for Domain C when using the proposed method with core and bootstrap data?", "answer": "So, the answer is {\"answer\": \"72.7\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The proposed method achieves a higher F1 intent score than the baseline method for Domain A with core data., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe proposed method achieves a higher F1 intent score than the baseline method for Domain A with core data.\n" }, { "role": "assistant", "content": "For Domain A with core data, the F1 intent score for the proposed method is 86.6, while for the baseline method it is 85.0, indicating that the proposed method achieves a higher F1 intent score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1058.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on domains A, B and C for the proposed pretrained DNN method and the baseline CRF/MaxEnt method during experimental early stages of domain development. * denotes statistically significant SER difference between proposed and baseline" ], "reasoning": "For Domain A with core data, the F1 intent score for the proposed method is 86.6, while for the baseline method it is 85.0, indicating that the proposed method achieves a higher F1 intent score.", "statement": "The proposed method achieves a higher F1 intent score than the baseline method for Domain A with core data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: The performance of non-autoregressive machine translation methods on the WMT14 EN-DE and WMT16 EN-RO test data. The Step columns indicate the average number of sequential transformer passes. Shaded results use a small transformer (d_{model}=d_{hidden}=512). Our EN-DE results show the scores after conventional compound splitting (luong-etal-2015-effective; Vaswani2017AttentionIA).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Our re-implementations of CMLM + Mask-Predict outperform Ghazvininejad2019MaskPredictPD in de→en with 10 steps due to tuning on the dropout rate and weight averaging of the 5 best epochs.First, our re-implementations of CMLM + Mask-Predict outperform Ghazvininejad2019MaskPredictPD (e.g., 31.24 vs. 30.53 in de\\rightarrowen with 10 steps). This is probably due to our tuning on the dropout rate and weight averaging of the 5 best epochs based on the validation BLEU performance (Sec." } ], "images": [ "scigen/train/1059.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The performance of non-autoregressive machine translation methods on the WMT14 EN-DE and WMT16 EN-RO test data. The Step columns indicate the average number of sequential transformer passes. Shaded results use a small transformer (d_{model}=d_{hidden}=512). Our EN-DE results show the scores after conventional compound splitting (luong-etal-2015-effective; Vaswani2017AttentionIA)." ], "reasoning": "Our re-implementations of CMLM + Mask-Predict outperform Ghazvininejad2019MaskPredictPD in de→en with 10 steps due to tuning on the dropout rate and weight averaging of the 5 best epochs.", "description": "First, our re-implementations of CMLM + Mask-Predict outperform Ghazvininejad2019MaskPredictPD (e.g., 31.24 vs. 30.53 in de\\rightarrowen with 10 steps). This is probably due to our tuning on the dropout rate and weight averaging of the 5 best epochs based on the validation BLEU performance (Sec.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 1: The performance of non-autoregressive machine translation methods on the WMT14 EN-DE and WMT16 EN-RO test data. The Step columns indicate the average number of sequential transformer passes. Shaded results use a small transformer (d_{model}=d_{hidden}=512). Our EN-DE results show the scores after conventional compound splitting (luong-etal-2015-effective; Vaswani2017AttentionIA).'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}. \n\nQuestion: What is the BLEU score for CMLM + Mask-Predict in en→ro with 10 steps?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the BLEU score for CMLM + Mask-Predict in en→ro with 10 steps, look under 'Our Implementations' and find the row for 'CMLM + Mask-Predict' and the column for 'en→ro BLEU' with 10 steps. So, the answer is {\"answer\": \"33.33\"}" } ], "images": [ "scigen/train/1059.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The performance of non-autoregressive machine translation methods on the WMT14 EN-DE and WMT16 EN-RO test data. The Step columns indicate the average number of sequential transformer passes. Shaded results use a small transformer (d_{model}=d_{hidden}=512). Our EN-DE results show the scores after conventional compound splitting (luong-etal-2015-effective; Vaswani2017AttentionIA)." ], "reasoning": "To find the BLEU score for CMLM + Mask-Predict in en→ro with 10 steps, look under 'Our Implementations' and find the row for 'CMLM + Mask-Predict' and the column for 'en→ro BLEU' with 10 steps.", "question": "What is the BLEU score for CMLM + Mask-Predict in en→ro with 10 steps?", "answer": "So, the answer is {\"answer\": \"33.33\"}" }, { "messages": [ { "role": "user", "content": "The BLEU score for DisCo + Mask-Predict in de→en with 10 steps is higher than 31. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe BLEU score for DisCo + Mask-Predict in de→en with 10 steps is higher than 31.\n" }, { "role": "assistant", "content": "To verify this statement, look under 'Our Implementations' and find the row for 'DisCo + Mask-Predict' and the column for 'de→en BLEU' with 10 steps. The score is 30.89, which is not higher than 31. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1059.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The performance of non-autoregressive machine translation methods on the WMT14 EN-DE and WMT16 EN-RO test data. The Step columns indicate the average number of sequential transformer passes. Shaded results use a small transformer (d_{model}=d_{hidden}=512). Our EN-DE results show the scores after conventional compound splitting (luong-etal-2015-effective; Vaswani2017AttentionIA)." ], "reasoning": "To verify this statement, look under 'Our Implementations' and find the row for 'DisCo + Mask-Predict' and the column for 'de→en BLEU' with 10 steps. The score is 30.89, which is not higher than 31.", "statement": "The BLEU score for DisCo + Mask-Predict in de→en with 10 steps is higher than 31.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: Effects of distillation across different models and inference. All results are BLEU scores from the dev data. T and b denote the max number of iterations and beam size respectively.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Distillation improves the performance of non-autoregressive models, with the DisCo transformer benefiting more than the CMLM under the same conditions.Consistent with previous models (Gu2017NonAutoregressiveNM; UnderstandingKD), we find that distillation facilitates all of the non-autoregressive models. Moreover, the DisCo transformer benefits more from distillation compared to the CMLM under the same mask-predict inference. This is in line with UnderstandingKD who showed that there is correlation between the model capacity and distillation data complexity. The DisCo transformer uses contextless keys and values, resulting in reduced capacity. Autoregressive translation also improves with distillation from a large transformer, but the difference is relatively small. Finally, we can observe that the gain from distillation decreases as we incorporate more global information in inference (more iterations in NAT cases and larger beam size in AT cases)." } ], "images": [ "scigen/train/1060.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Effects of distillation across different models and inference. All results are BLEU scores from the dev data. T and b denote the max number of iterations and beam size respectively." ], "reasoning": "Distillation improves the performance of non-autoregressive models, with the DisCo transformer benefiting more than the CMLM under the same conditions.", "description": "Consistent with previous models (Gu2017NonAutoregressiveNM; UnderstandingKD), we find that distillation facilitates all of the non-autoregressive models. Moreover, the DisCo transformer benefits more from distillation compared to the CMLM under the same mask-predict inference. This is in line with UnderstandingKD who showed that there is correlation between the model capacity and distillation data complexity. The DisCo transformer uses contextless keys and values, resulting in reduced capacity. Autoregressive translation also improves with distillation from a large transformer, but the difference is relatively small. Finally, we can observe that the gain from distillation decreases as we incorporate more global information in inference (more iterations in NAT cases and larger beam size in AT cases).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: Effects of distillation across different models and inference. All results are BLEU scores from the dev data. T and b denote the max number of iterations and beam size respectively.', answer the following question. Give detailed solution consisting of each step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model and setting showed the highest improvement in en→ro performance with distillation?\n" }, { "role": "assistant", "content": "By comparing the en→ro Δ values, the highest improvement is observed in the DisCo + MaskP model with T=4, which has a Δ of 3.2. So, the answer is {\"answer\": \"DisCo + MaskP with T=4\"}" } ], "images": [ "scigen/train/1060.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Effects of distillation across different models and inference. All results are BLEU scores from the dev data. T and b denote the max number of iterations and beam size respectively." ], "reasoning": "By comparing the en→ro Δ values, the highest improvement is observed in the DisCo + MaskP model with T=4, which has a Δ of 3.2.", "question": "Which model and setting showed the highest improvement in en→ro performance with distillation?", "answer": "So, the answer is {\"answer\": \"DisCo + MaskP with T=4\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe CMLM + MaskP model with T=10 showed a higher improvement in en→ro performance with distillation compared to the DisCo + MaskP model with T=10.\n" }, { "role": "assistant", "content": "The table shows that the en→ro Δ for CMLM + MaskP with T=10 is 1.4, while for DisCo + MaskP with T=10 it is 1.7. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1060.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Effects of distillation across different models and inference. All results are BLEU scores from the dev data. T and b denote the max number of iterations and beam size respectively." ], "reasoning": "The table shows that the en→ro Δ for CMLM + MaskP with T=10 is 1.4, while for DisCo + MaskP with T=10 it is 1.7. Therefore, the statement is refuted.", "statement": "The CMLM + MaskP model with T=10 showed a higher improvement in en→ro performance with distillation compared to the DisCo + MaskP model with T=10.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Dev results from bringing training closer to inference.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Easy-first training reduces the average number of iterations during inference but does not improve performance compared to random sampling.Easy-First Training So far we have trained our models to predict every word given a random subset of the other words. But this training scheme yields a gap between training and inference, which might harm the model. We attempt to make training closer to inference by training the DisCo transformer in the easy-first order. Similarly to the inference, we first predict the easy-first order by estimating P(Y_{n}|X) for all n. Then, use that order to determine Y_{{\\tt obs}}^{n}. The overall loss will be the sum of the negative loglikelihood of these two steps. In both directions, this easy-first training does not ameliorate performance, suggesting that randomness helps the model. Notice also that the average number of iterations in inference decreases (4.03 vs. 4.29, 2.94 vs. 3.17). The model gets trapped in a sub-optimal solution with reduced iterations due to lack of exploration." } ], "images": [ "scigen/train/1061.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Dev results from bringing training closer to inference." ], "reasoning": "Easy-first training reduces the average number of iterations during inference but does not improve performance compared to random sampling.", "description": "Easy-First Training So far we have trained our models to predict every word given a random subset of the other words. But this training scheme yields a gap between training and inference, which might harm the model. We attempt to make training closer to inference by training the DisCo transformer in the easy-first order. Similarly to the inference, we first predict the easy-first order by estimating P(Y_{n}|X) for all n. Then, use that order to determine Y_{{\\tt obs}}^{n}. The overall loss will be the sum of the negative loglikelihood of these two steps. In both directions, this easy-first training does not ameliorate performance, suggesting that randomness helps the model. Notice also that the average number of iterations in inference decreases (4.03 vs. 4.29, 2.94 vs. 3.17). The model gets trapped in a sub-optimal solution with reduced iterations due to lack of exploration.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 6: Dev results from bringing training closer to inference.' to sequentially solve the question presented below. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhat is the difference in the average number of iterations for en→de between Random Sampling and Easy-First Training?\n" }, { "role": "assistant", "content": "The table shows that the average number of iterations (Step) for en→de is 4.29 for Random Sampling and 4.03 for Easy-First Training. The difference is 4.29 - 4.03 = 0.26. So, the answer is {\"answer\": \"0.26\"}" } ], "images": [ "scigen/train/1061.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Dev results from bringing training closer to inference." ], "reasoning": "The table shows that the average number of iterations (Step) for en→de is 4.29 for Random Sampling and 4.03 for Easy-First Training. The difference is 4.29 - 4.03 = 0.26.", "question": "What is the difference in the average number of iterations for en→de between Random Sampling and Easy-First Training?", "answer": "So, the answer is {\"answer\": \"0.26\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nEasy-First Training has a higher BLEU score for en→de compared to Random Sampling.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for en→de is 25.60 for Random Sampling and 24.76 for Easy-First Training. Therefore, Easy-First Training does not have a higher BLEU score. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1061.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Dev results from bringing training closer to inference." ], "reasoning": "The table shows that the BLEU score for en→de is 25.60 for Random Sampling and 24.76 for Easy-First Training. Therefore, Easy-First Training does not have a higher BLEU score.", "statement": "Easy-First Training has a higher BLEU score for en→de compared to Random Sampling.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 7: Dev results with different decoding strategies.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Alternative inference algorithms like left-to-right and right-to-left orderings show degraded performance compared to the easy-first ordering, which is simple yet effective.Alternative Inference Algorithms ’s probability conditioned on the easier positions from the previous iteration. We evaluate two alternative orderings: left-to-right and right-to-left. We see that both of them yield much degraded performance. We also attempt to use even broader context than parallel easy-first by computing the probability at each position based on all other positions (all-but-itself, Y_{obs}^{n,t}=Y_{\\neq n}^{t-1}). We again see degraded performance, suggesting that cyclic dependency (e.g., Y_{m}^{t-1}\\in Y_{{\\tt obs}}^{n,t} and Y_{n}^{t-1}\\in Y_{{\\tt obs}}^{m,t}) breaks consistency. For example, a model can have two output candidates: “Hong Kong” and “New York” (Zhang2020POINTERCT). In this case, we might end up producing “Hong York” due to this cyclic dependency. These results suggest that the easy-first ordering we introduced is a simple yet effective approach." } ], "images": [ "scigen/train/1062.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Dev results with different decoding strategies." ], "reasoning": "Alternative inference algorithms like left-to-right and right-to-left orderings show degraded performance compared to the easy-first ordering, which is simple yet effective.", "description": "Alternative Inference Algorithms ’s probability conditioned on the easier positions from the previous iteration. We evaluate two alternative orderings: left-to-right and right-to-left. We see that both of them yield much degraded performance. We also attempt to use even broader context than parallel easy-first by computing the probability at each position based on all other positions (all-but-itself, Y_{obs}^{n,t}=Y_{\\neq n}^{t-1}). We again see degraded performance, suggesting that cyclic dependency (e.g., Y_{m}^{t-1}\\in Y_{{\\tt obs}}^{n,t} and Y_{n}^{t-1}\\in Y_{{\\tt obs}}^{m,t}) breaks consistency. For example, a model can have two output candidates: “Hong Kong” and “New York” (Zhang2020POINTERCT). In this case, we might end up producing “Hong York” due to this cyclic dependency. These results suggest that the easy-first ordering we introduced is a simple yet effective approach.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 7: Dev results with different decoding strategies.\nQuestion: Which inference strategy has the highest BLEU score for the ro→en direction?\n" }, { "role": "assistant", "content": "By examining the table, the highest BLEU score for the ro→en direction is found in the Parallel Easy-First row, with a score of 34.97. So, the answer is {\"answer\": \"Parallel Easy-First\"}." } ], "images": [ "scigen/train/1062.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Dev results with different decoding strategies." ], "reasoning": "By examining the table, the highest BLEU score for the ro→en direction is found in the Parallel Easy-First row, with a score of 34.97.", "question": "Which inference strategy has the highest BLEU score for the ro→en direction?", "answer": "So, the answer is {\"answer\": \"Parallel Easy-First\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe All-But-Itself strategy has the highest BLEU score for the en→ro direction.\n" }, { "role": "assistant", "content": "The table shows that the highest BLEU score for the en→ro direction is 25.60, which is achieved by the Parallel Easy-First strategy, not the All-But-Itself strategy. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1062.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Dev results with different decoding strategies." ], "reasoning": "The table shows that the highest BLEU score for the en→ro direction is 25.60, which is achieved by the Parallel Easy-First strategy, not the All-But-Itself strategy.", "statement": "The All-But-Itself strategy has the highest BLEU score for the en→ro direction.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 8. Extremely large Q&A dataset results.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "TMKD outperforms its teacher model on a large Q&A dataset with an accuracy of 79.22 compared to 77.00.To further evaluate the potential of TMKD, we conduct extensive experiments on CommQA-Unlabeled extremely large-scale corpus data (0.1 billion unlabeled Q&A pairs) and CommQA-Labeled (12M labeled Q&A labeled pairs). Four separate teacher models (T1-T4) are trained with batch size of 128 and learning rate with {2,3,4,5}∗e−5. Max sequence length is set as 200, and number of epochs as 4. Interestingly, on this extremely large Q&A dataset, TMKD even exceeds the performance of its teacher model (ACC: 79.22 vs 77.00), which further verifies the effectiveness of our approach." } ], "images": [ "scigen/train/1063.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8. Extremely large Q&A dataset results." ], "reasoning": "TMKD outperforms its teacher model on a large Q&A dataset with an accuracy of 79.22 compared to 77.00.", "description": "To further evaluate the potential of TMKD, we conduct extensive experiments on CommQA-Unlabeled extremely large-scale corpus data (0.1 billion unlabeled Q&A pairs) and CommQA-Labeled (12M labeled Q&A labeled pairs). Four separate teacher models (T1-T4) are trained with batch size of 128 and learning rate with {2,3,4,5}∗e−5. Max sequence length is set as 200, and number of epochs as 4. Interestingly, on this extremely large Q&A dataset, TMKD even exceeds the performance of its teacher model (ACC: 79.22 vs 77.00), which further verifies the effectiveness of our approach.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 8. Extremely large Q&A dataset results.\nQuestion: What is the accuracy difference between TMKD and BERTlarge?\nSolve the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows the performance of TMKD as 79.22 and BERTlarge as 77.00. Subtracting these values gives the difference. So, the answer is {\"answer\": \"2.22\"}" } ], "images": [ "scigen/train/1063.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8. Extremely large Q&A dataset results." ], "reasoning": "The table shows the performance of TMKD as 79.22 and BERTlarge as 77.00. Subtracting these values gives the difference.", "question": "What is the accuracy difference between TMKD and BERTlarge?", "answer": "So, the answer is {\"answer\": \"2.22\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nTMKD has the highest performance accuracy among the models listed.\n" }, { "role": "assistant", "content": "The table shows the performance accuracy of TMKD as 79.22, which is higher than BERTlarge (77.00), KD (73.22), and MKD (77.32). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1063.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8. Extremely large Q&A dataset results." ], "reasoning": "The table shows the performance accuracy of TMKD as 79.22, which is higher than BERTlarge (77.00), KD (73.22), and MKD (77.32).", "statement": "TMKD has the highest performance accuracy among the models listed.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4. Model comparison between our methods and baseline methods. ACC denotes accuracy (all ACC metrics in the table are percentage numbers with % omitted). Specially for MNLI, we average the results of matched and mismatched validation set.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "TMKD achieves optimal results in inference speed, parameter size, and performance compared to other models, with minimal additional memory consumption.In this section, we conduct experiments to compare TMKD with baselines in terms of three dimensions, i.e. inference speed, parameter size and performance on task specific test set. 1-o-1 and 1avg-o-1 (BERT-3 and Bi-LSTM) obtain pretty good results regarding inference speed and memory capacity. However there are still some gaps compared to the original BERT model in terms of ACC metric. m-o-m performs better than 1-o-1. However, the inference speed and memory consumption increase in proportion to the number of student models used for ensemble. Compared with 1-o-1, 1avg-o-1 and m-o-m, TMKD achieves optimum in all three dimensions. In terms of memory, TMKD only needs small amount of additional memory consumption since the majority of parameters are shared across different distillation tasks compared with the 1-o-1. In addition, TMKD performs significant better than BERT-3, which further proves the effective of our model." } ], "images": [ "scigen/train/1064.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4. Model comparison between our methods and baseline methods. ACC denotes accuracy (all ACC metrics in the table are percentage numbers with % omitted). Specially for MNLI, we average the results of matched and mismatched validation set." ], "reasoning": "TMKD achieves optimal results in inference speed, parameter size, and performance compared to other models, with minimal additional memory consumption.", "description": "In this section, we conduct experiments to compare TMKD with baselines in terms of three dimensions, i.e. inference speed, parameter size and performance on task specific test set. 1-o-1 and 1avg-o-1 (BERT-3 and Bi-LSTM) obtain pretty good results regarding inference speed and memory capacity. However there are still some gaps compared to the original BERT model in terms of ACC metric. m-o-m performs better than 1-o-1. However, the inference speed and memory consumption increase in proportion to the number of student models used for ensemble. Compared with 1-o-1, 1avg-o-1 and m-o-m, TMKD achieves optimum in all three dimensions. In terms of memory, TMKD only needs small amount of additional memory consumption since the majority of parameters are shared across different distillation tasks compared with the 1-o-1. In addition, TMKD performs significant better than BERT-3, which further proves the effective of our model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 4. Model comparison between our methods and baseline methods. ACC denotes accuracy (all ACC metrics in the table are percentage numbers with % omitted). Specially for MNLI, we average the results of matched and mismatched validation set.' to sequentially solve the question presented below. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model achieves the highest performance in the QNLI task?\n" }, { "role": "assistant", "content": "By comparing the ACC values for the QNLI task across all models, TMKDlarge achieves the highest performance with an ACC of 86.44. So, the answer is {\"answer\": \"TMKDlarge\"}." } ], "images": [ "scigen/train/1064.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4. Model comparison between our methods and baseline methods. ACC denotes accuracy (all ACC metrics in the table are percentage numbers with % omitted). Specially for MNLI, we average the results of matched and mismatched validation set." ], "reasoning": "By comparing the ACC values for the QNLI task across all models, TMKDlarge achieves the highest performance with an ACC of 86.44.", "question": "Which model achieves the highest performance in the QNLI task?", "answer": "So, the answer is {\"answer\": \"TMKDlarge\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis TMKDbase has the highest inference speed among all models. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nTMKDbase has the highest inference speed among all models.\n" }, { "role": "assistant", "content": "The table shows that TMKDbase has an inference speed of 217 QPS, which is not the highest. BERT-3 (1-o-1) and BERT-3 (1avg-o-1) also have an inference speed of 217 QPS. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1064.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4. Model comparison between our methods and baseline methods. ACC denotes accuracy (all ACC metrics in the table are percentage numbers with % omitted). Specially for MNLI, we average the results of matched and mismatched validation set." ], "reasoning": "The table shows that TMKDbase has an inference speed of 217 QPS, which is not the highest. BERT-3 (1-o-1) and BERT-3 (1avg-o-1) also have an inference speed of 217 QPS.", "statement": "TMKDbase has the highest inference speed among all models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5. Comparison between KD and TKD'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "TKD shows significant gains in DeepQA and GLUE tasks by leveraging large-scale unsupervised Q&A pairs for distillation pre-training.On DeepQA dataset, TKD shows significant gains by leveraging large-scale unsupervised Q&A pairs for distillation pre-training. (2) Although Q&A task is different with GLUE tasks, the student model of GLUE tasks still benefit a lot from the distillation pre-training stage leveraging Q&A task. This proves the effect of the distillation pre-training stage leveraging Q&A large corpus." } ], "images": [ "scigen/train/1065.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5. Comparison between KD and TKD" ], "reasoning": "TKD shows significant gains in DeepQA and GLUE tasks by leveraging large-scale unsupervised Q&A pairs for distillation pre-training.", "description": "On DeepQA dataset, TKD shows significant gains by leveraging large-scale unsupervised Q&A pairs for distillation pre-training. (2) Although Q&A task is different with GLUE tasks, the student model of GLUE tasks still benefit a lot from the distillation pre-training stage leveraging Q&A task. This proves the effect of the distillation pre-training stage leveraging Q&A large corpus.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5. Comparison between KD and TKD\nQuestion: Which model has the highest performance in QNLI and what is the accuracy?\nGive a detailed response to the above question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the QNLI performance of both models, TKD has the highest accuracy of 85.89. So, the answer is {\"answer\": \"TKD with 85.89\"}." } ], "images": [ "scigen/train/1065.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5. Comparison between KD and TKD" ], "reasoning": "By comparing the QNLI performance of both models, TKD has the highest accuracy of 85.89.", "question": "Which model has the highest performance in QNLI and what is the accuracy?", "answer": "So, the answer is {\"answer\": \"TKD with 85.89\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim TKD outperforms KD in all tasks listed in the table., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nTKD outperforms KD in all tasks listed in the table.\n" }, { "role": "assistant", "content": "By comparing the performance of TKD and KD across all tasks (DeepQA, MNLI, SNLI, QNLI, RTE), TKD has higher accuracy in each task. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1065.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5. Comparison between KD and TKD" ], "reasoning": "By comparing the performance of TKD and KD across all tasks (DeepQA, MNLI, SNLI, QNLI, RTE), TKD has higher accuracy in each task.", "statement": "TKD outperforms KD in all tasks listed in the table.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 9. Compare different number of transformer layer.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "As the number of layers increases, the performance gain in accuracy between consecutive trials decreases significantly.(2) With n increasing, the performance gain between two consecutive trials decreases. That say, when n increases from 1 to 3, the ACC gains of the 5 datasets are (3.87, 9.90, 7.46, 11.44, 11.19) which are very big jump; while n increases from 3 to 5, gains decrease to (1.08, 1.63, 0.53, 2.89, 0.37), without decent add-on value compared with the significantly decreased QPS." } ], "images": [ "scigen/train/1066.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9. Compare different number of transformer layer." ], "reasoning": "As the number of layers increases, the performance gain in accuracy between consecutive trials decreases significantly.", "description": "(2) With n increasing, the performance gain between two consecutive trials decreases. That say, when n increases from 1 to 3, the ACC gains of the 5 datasets are (3.87, 9.90, 7.46, 11.44, 11.19) which are very big jump; while n increases from 3 to 5, gains decrease to (1.08, 1.63, 0.53, 2.89, 0.37), without decent add-on value compared with the significantly decreased QPS.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 9. Compare different number of transformer layer.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the performance gain in accuracy for the QNLI dataset when the number of layers increases from 1 to 3 and from 3 to 5?\n" }, { "role": "assistant", "content": "The accuracy for QNLI increases from 70.60 to 82.04 when the number of layers increases from 1 to 3, resulting in a gain of 11.44. When the number of layers increases from 3 to 5, the accuracy increases from 82.04 to 84.94, resulting in a gain of 2.89. So, the answer is {\"answer\": \"11.44 and 2.89\"}" } ], "images": [ "scigen/train/1066.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9. Compare different number of transformer layer." ], "reasoning": "The accuracy for QNLI increases from 70.60 to 82.04 when the number of layers increases from 1 to 3, resulting in a gain of 11.44. When the number of layers increases from 3 to 5, the accuracy increases from 82.04 to 84.94, resulting in a gain of 2.89.", "question": "What is the performance gain in accuracy for the QNLI dataset when the number of layers increases from 1 to 3 and from 3 to 5?", "answer": "So, the answer is {\"answer\": \"11.44 and 2.89\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe performance gain in accuracy for the MNLI dataset is higher when the number of layers increases from 1 to 3 compared to when it increases from 3 to 5.\n" }, { "role": "assistant", "content": "The accuracy for MNLI increases from 61.23 to 71.13 when the number of layers increases from 1 to 3, resulting in a gain of 9.90. When the number of layers increases from 3 to 5, the accuracy increases from 71.13 to 72.76, resulting in a gain of 1.63. Therefore, the performance gain is higher when the number of layers increases from 1 to 3. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1066.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9. Compare different number of transformer layer." ], "reasoning": "The accuracy for MNLI increases from 61.23 to 71.13 when the number of layers increases from 1 to 3, resulting in a gain of 9.90. When the number of layers increases from 3 to 5, the accuracy increases from 71.13 to 72.76, resulting in a gain of 1.63. Therefore, the performance gain is higher when the number of layers increases from 1 to 3.", "statement": "The performance gain in accuracy for the MNLI dataset is higher when the number of layers increases from 1 to 3 compared to when it increases from 3 to 5.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1. A frequency submatrix ϕ of president entities and their co-occured skip-grams.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The description discusses the concept of flexgrams, which are flexible patterns that merge similar skip-grams to address data sparsity issues in context feature selection.The infrequent “Gorbachev” does not have enough co-occurrence with type-indicative skip-grams and might be even filtered out from the candidate pool What’s more, the skip-gram of “President __ said” might be neglected in the context feature selection process due to its lack of interaction with president entities. These four different skip-grams are actually similar since they can all be filled in with a president entity. Therefore, if we can extract the meaningful part from the original window size, such as “President __” in this example, we will be able to merge these different skip-grams into a common and more flexible pattern: “President __ ∗”, which we named as a flexgram. The last term is an asteroid which serves as a wildcard term to be matched with any words. Then we can update the co-occurrence matrix by Φe,flex(c)=∑flex(c′)=flex(c)Φe,flex(c′) where flex(c) means the flexible transformation of local context c. Then we can merge those infrequent but type-indicative skip-grams together. However, one might say a trivial solution to this data sparsity issue is to exhaust a variety of window sizes, (e.g., [-2,+2],[-1,+1],[-1,0],[0,+1]), as has been done by previous studies(Shen2017SetExpanCS; Yu2019CaSE; Rong2016EgoSetEW). However, this approach very likely ends up generating many skip-grams that are too general. For example, the other half of “President __ and” is “__ and”, which only contains stop words, not informative enough in implying the semantic class of the “skipped” term." } ], "images": [ "scigen/train/1067.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1. A frequency submatrix ϕ of president entities and their co-occured skip-grams." ], "reasoning": "The description discusses the concept of flexgrams, which are flexible patterns that merge similar skip-grams to address data sparsity issues in context feature selection.", "description": "The infrequent “Gorbachev” does not have enough co-occurrence with type-indicative skip-grams and might be even filtered out from the candidate pool What’s more, the skip-gram of “President __ said” might be neglected in the context feature selection process due to its lack of interaction with president entities. These four different skip-grams are actually similar since they can all be filled in with a president entity. Therefore, if we can extract the meaningful part from the original window size, such as “President __” in this example, we will be able to merge these different skip-grams into a common and more flexible pattern: “President __ ∗”, which we named as a flexgram. The last term is an asteroid which serves as a wildcard term to be matched with any words. Then we can update the co-occurrence matrix by Φe,flex(c)=∑flex(c′)=flex(c)Φe,flex(c′) where flex(c) means the flexible transformation of local context c. Then we can merge those infrequent but type-indicative skip-grams together. However, one might say a trivial solution to this data sparsity issue is to exhaust a variety of window sizes, (e.g., [-2,+2],[-1,+1],[-1,0],[0,+1]), as has been done by previous studies(Shen2017SetExpanCS; Yu2019CaSE; Rong2016EgoSetEW). However, this approach very likely ends up generating many skip-grams that are too general. For example, the other half of “President __ and” is “__ and”, which only contains stop words, not informative enough in implying the semantic class of the “skipped” term.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1. A frequency submatrix ϕ of president entities and their co-occured skip-grams.\nQuestion: Which entity has the highest co-occurrence with the flexgram 'President __ *'?\nGive a detailed response to the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the entity with the highest co-occurrence with the flexgram 'President __ *', we look at the column 'President __ *' and identify the entity with the highest value. So, the answer is {\"answer\": \"Bill Clinton\"}." } ], "images": [ "scigen/train/1067.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1. A frequency submatrix ϕ of president entities and their co-occured skip-grams." ], "reasoning": "To find the entity with the highest co-occurrence with the flexgram 'President __ *', we look at the column 'President __ *' and identify the entity with the highest value.", "question": "Which entity has the highest co-occurrence with the flexgram 'President __ *'?", "answer": "So, the answer is {\"answer\": \"Bill Clinton\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nGorbachev has the lowest co-occurrence with 'President __ and'.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the values in the 'President __ and' column for all entities. Gorbachev has a value of 2, which is the lowest among the entities listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1067.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1. A frequency submatrix ϕ of president entities and their co-occured skip-grams." ], "reasoning": "To verify this statement, we compare the values in the 'President __ and' column for all entities. Gorbachev has a value of 2, which is the lowest among the entities listed.", "statement": "Gorbachev has the lowest co-occurrence with 'President __ and'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3. Mean Average Precision across all queries on Wiki and APR.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Set-CoExpan outperforms all other methods, including BERT, especially when the ranking list is longer, indicating its robustness in handling noise and data sparsity.The result clearly shows that our model has the best performance over all the methods in both datasets. Among all the baseline methods, BERT is the strongest one, since it stores a very large pre-trained language model to represent word semantics in a piece of context, which also explains why we have a very large margin over previous iterative-based methods like SetExpan when we incorporate BERT as embedding-based features. However, BERT itself could not outperform our model, which implies that modeling distributional similarity alone is not enough for generating accurate expansion. We can also observe that Set-CoExpan also outperforms the ablations without auxiliary sets or flexgram, which justifies that auxiliary sets indeed improve the set expansion process by providing clearer discriminative context features for multiple sets to prevent them from expanding into each other’s semantic territory. The advantage of Set-CoExpan over the ablation without flexgram also proves that it is necessary to cope with the data sparsity problem by extracting independent units from a fixed local context window. In this way, we relieve the hard-matching constraint brought by skip-gram features by making it more flexible. What’s more, Set-CoExpan has a larger advantage over all the baselines when the ranking list is longer, which indicates that when the seed set gradually grows out of control and more noises appear, our method is able to steer the direction of expansion and to set barriers for out-of-category words to come in." } ], "images": [ "scigen/train/1068.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. Mean Average Precision across all queries on Wiki and APR." ], "reasoning": "Set-CoExpan outperforms all other methods, including BERT, especially when the ranking list is longer, indicating its robustness in handling noise and data sparsity.", "description": "The result clearly shows that our model has the best performance over all the methods in both datasets. Among all the baseline methods, BERT is the strongest one, since it stores a very large pre-trained language model to represent word semantics in a piece of context, which also explains why we have a very large margin over previous iterative-based methods like SetExpan when we incorporate BERT as embedding-based features. However, BERT itself could not outperform our model, which implies that modeling distributional similarity alone is not enough for generating accurate expansion. We can also observe that Set-CoExpan also outperforms the ablations without auxiliary sets or flexgram, which justifies that auxiliary sets indeed improve the set expansion process by providing clearer discriminative context features for multiple sets to prevent them from expanding into each other’s semantic territory. The advantage of Set-CoExpan over the ablation without flexgram also proves that it is necessary to cope with the data sparsity problem by extracting independent units from a fixed local context window. In this way, we relieve the hard-matching constraint brought by skip-gram features by making it more flexible. What’s more, Set-CoExpan has a larger advantage over all the baselines when the ranking list is longer, which indicates that when the seed set gradually grows out of control and more noises appear, our method is able to steer the direction of expansion and to set barriers for out-of-category words to come in.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich method has the highest performance in APR MAP@50?\nSolve the above question based on the table titled 'Table 3. Mean Average Precision across all queries on Wiki and APR.'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the APR MAP@50 values in the table, Set-CoExpan has the highest value of 0.830. So, the answer is {\"answer\": \"Set-CoExpan\"}" } ], "images": [ "scigen/train/1068.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. Mean Average Precision across all queries on Wiki and APR." ], "reasoning": "By comparing the APR MAP@50 values in the table, Set-CoExpan has the highest value of 0.830.", "question": "Which method has the highest performance in APR MAP@50?", "answer": "So, the answer is {\"answer\": \"Set-CoExpan\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nSet-CoExpan without auxiliary sets has the highest performance in Wiki MAP@10.\n" }, { "role": "assistant", "content": "The table shows that Set-CoExpan without auxiliary sets has a Wiki MAP@10 value of 0.964, but Set-CoExpan has a higher value of 0.976. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1068.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. Mean Average Precision across all queries on Wiki and APR." ], "reasoning": "The table shows that Set-CoExpan without auxiliary sets has a Wiki MAP@10 value of 0.964, but Set-CoExpan has a higher value of 0.976.", "statement": "Set-CoExpan without auxiliary sets has the highest performance in Wiki MAP@10.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'TABLE II: Experiment using different fusion strategies.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Element-wise multiplication as a fusion strategy significantly improves training accuracy and validation performance compared to summation and concatenation.The best result is obtained using the element-wise multiplication. Such an approach functions as a filtering strategy that is able to scale down the importance of irrelevant dimensions from the visual-question feature vectors. In other words, vector dimensions with high cross-modal affinity will have their magnitudes increased, differently from the uncorrelated ones that will have their values reduced. Summation does provide the worst results overall, closely followed by the concatenation operator. Moreover, among all the fusion strategies used in this study, multiplication seems to ease the training process as it presents a much higher training set accuracy (≈11% improvement) as well." } ], "images": [ "scigen/train/1069.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Experiment using different fusion strategies." ], "reasoning": "Element-wise multiplication as a fusion strategy significantly improves training accuracy and validation performance compared to summation and concatenation.", "description": "The best result is obtained using the element-wise multiplication. Such an approach functions as a filtering strategy that is able to scale down the importance of irrelevant dimensions from the visual-question feature vectors. In other words, vector dimensions with high cross-modal affinity will have their magnitudes increased, differently from the uncorrelated ones that will have their values reduced. Summation does provide the worst results overall, closely followed by the concatenation operator. Moreover, among all the fusion strategies used in this study, multiplication seems to ease the training process as it presents a much higher training set accuracy (≈11% improvement) as well.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich fusion strategy achieved the highest validation accuracy?\nSolve the above question based on the table titled 'TABLE II: Experiment using different fusion strategies.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the validation accuracies for different fusion strategies. Multiplication has the highest validation accuracy of 58.75. So, the answer is {\"answer\": \"Mult\"}." } ], "images": [ "scigen/train/1069.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Experiment using different fusion strategies." ], "reasoning": "The table shows the validation accuracies for different fusion strategies. Multiplication has the highest validation accuracy of 58.75.", "question": "Which fusion strategy achieved the highest validation accuracy?", "answer": "So, the answer is {\"answer\": \"Mult\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nSummation provides the best validation accuracy.\n" }, { "role": "assistant", "content": "The table shows that summation has a validation accuracy of 54.93, which is lower than both multiplication (58.75) and concatenation (55.07). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1069.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Experiment using different fusion strategies." ], "reasoning": "The table shows that summation has a validation accuracy of 54.93, which is lower than both multiplication (58.75) and concatenation (55.07).", "statement": "Summation provides the best validation accuracy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'TABLE III: Experiment using different attention mechanisms.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Element-wise multiplication was used as the fusion strategy due to its best performance, and attention mechanisms improved VQA accuracy by approximately 6%.For these experiments we used only element-wise multiplication as fusion strategy, given that it presented the best performance in our previous experiments. We observe that attention is a crucial mechanism for VQA, leading to an ≈6% accuracy improvement." } ], "images": [ "scigen/train/1070.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Experiment using different attention mechanisms." ], "reasoning": "Element-wise multiplication was used as the fusion strategy due to its best performance, and attention mechanisms improved VQA accuracy by approximately 6%.", "description": "For these experiments we used only element-wise multiplication as fusion strategy, given that it presented the best performance in our previous experiments. We observe that attention is a crucial mechanism for VQA, leading to an ≈6% accuracy improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'TABLE III: Experiment using different attention mechanisms.' to sequentially solve the question presented below. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich attention mechanism resulted in the highest validation accuracy?\n" }, { "role": "assistant", "content": "By comparing the validation accuracies for different attention mechanisms, we see that 'Top Down (σ=ReLU)' has the highest validation accuracy of 64.12. So, the answer is {\"answer\": \"Top Down (σ=ReLU)\"}." } ], "images": [ "scigen/train/1070.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Experiment using different attention mechanisms." ], "reasoning": "By comparing the validation accuracies for different attention mechanisms, we see that 'Top Down (σ=ReLU)' has the highest validation accuracy of 64.12.", "question": "Which attention mechanism resulted in the highest validation accuracy?", "answer": "So, the answer is {\"answer\": \"Top Down (σ=ReLU)\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Co-Attention (L2 norm) mechanism resulted in the highest training accuracy.\n" }, { "role": "assistant", "content": "By comparing the training accuracies for different attention mechanisms, we see that 'Top Down (σ=ReLU)' has the highest training accuracy of 87.02, not 'Co-Attention (L2 norm)'. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1070.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Experiment using different attention mechanisms." ], "reasoning": "By comparing the training accuracies for different attention mechanisms, we see that 'Top Down (σ=ReLU)' has the highest training accuracy of 87.02, not 'Co-Attention (L2 norm)'.", "statement": "The Co-Attention (L2 norm) mechanism resulted in the highest training accuracy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: P values and win/draw/lose of pairwise comparison of QBC-based methods.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Embedding-based methods are significantly different from TF, TF-IDF, and LDA with p<0.05, but not from each other. BERT achieves the most wins.This shows the win/loss/tie count for each pair of representations and the p-value from the related significance test. The table demonstrate that all embedding-based methods are significantly different from methods based on TF, TF-IDF and LDA with p<0.05. However, embedding-based methods do not have significant difference between each other. Remarkably, BERT achieves the most wins as compared to any other representations." } ], "images": [ "scigen/train/1071.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: P values and win/draw/lose of pairwise comparison of QBC-based methods." ], "reasoning": "Embedding-based methods are significantly different from TF, TF-IDF, and LDA with p<0.05, but not from each other. BERT achieves the most wins.", "description": "This shows the win/loss/tie count for each pair of representations and the p-value from the related significance test. The table demonstrate that all embedding-based methods are significantly different from methods based on TF, TF-IDF and LDA with p<0.05. However, embedding-based methods do not have significant difference between each other. Remarkably, BERT achieves the most wins as compared to any other representations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 3: P values and win/draw/lose of pairwise comparison of QBC-based methods.', answer the question below by showing each progressive step toward the answer. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich representation method achieves the most wins compared to any other representations?\n" }, { "role": "assistant", "content": "The description states that BERT achieves the most wins compared to any other representations, and the table shows BERT has the highest win counts against other methods. So, the answer is {\"answer\": \"BERT\"}." } ], "images": [ "scigen/train/1071.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: P values and win/draw/lose of pairwise comparison of QBC-based methods." ], "reasoning": "The description states that BERT achieves the most wins compared to any other representations, and the table shows BERT has the highest win counts against other methods.", "question": "Which representation method achieves the most wins compared to any other representations?", "answer": "So, the answer is {\"answer\": \"BERT\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Embedding-based methods are significantly different from TF, TF-IDF, and LDA. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nEmbedding-based methods are significantly different from TF, TF-IDF, and LDA.\n" }, { "role": "assistant", "content": "The description and the table both indicate that embedding-based methods have p-values less than 0.05 when compared to TF, TF-IDF, and LDA, showing significant differences. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1071.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: P values and win/draw/lose of pairwise comparison of QBC-based methods." ], "reasoning": "The description and the table both indicate that embedding-based methods have p-values less than 0.05 when compared to TF, TF-IDF, and LDA, showing significant differences.", "statement": "Embedding-based methods are significantly different from TF, TF-IDF, and LDA.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table .5: CauseRelPro-beta’s micro-averaged scores using different degrees of polynomial kernel.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The best F1-score is achieved with the 3rd degree polynomial kernel, while the best precision of 0.6337 is achieved with the 4th degree polynomial kernel.g We also conduct some experiments by using different degrees of polynomial kernel Note that even though the best F1-score is achieved by using the 3rd degree of polynomial kernel, the best precision of 0.6337 is achieved with degree 4." } ], "images": [ "scigen/train/1072.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table .5: CauseRelPro-beta’s micro-averaged scores using different degrees of polynomial kernel." ], "reasoning": "The best F1-score is achieved with the 3rd degree polynomial kernel, while the best precision of 0.6337 is achieved with the 4th degree polynomial kernel.", "description": "g We also conduct some experiments by using different degrees of polynomial kernel Note that even though the best F1-score is achieved by using the 3rd degree of polynomial kernel, the best precision of 0.6337 is achieved with degree 4.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table .5: CauseRelPro-beta’s micro-averaged scores using different degrees of polynomial kernel.', unfold the steps to solve the question given next. Output the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which degree of polynomial kernel achieves the highest recall?\n" }, { "role": "assistant", "content": "To find the degree of polynomial kernel with the highest recall, compare the recall values (R) for each degree. The highest recall value is 0.2516, which corresponds to the 2nd degree polynomial kernel. So, the answer is {\"answer\": \"2nd degree\"}." } ], "images": [ "scigen/train/1072.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table .5: CauseRelPro-beta’s micro-averaged scores using different degrees of polynomial kernel." ], "reasoning": "To find the degree of polynomial kernel with the highest recall, compare the recall values (R) for each degree. The highest recall value is 0.2516, which corresponds to the 2nd degree polynomial kernel.", "question": "Which degree of polynomial kernel achieves the highest recall?", "answer": "So, the answer is {\"answer\": \"2nd degree\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe 3rd degree polynomial kernel achieves the highest precision.\n" }, { "role": "assistant", "content": "The table shows that the precision (P) for the 3rd degree polynomial kernel is 0.5985, while the highest precision of 0.6337 is achieved with the 4th degree polynomial kernel. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1072.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table .5: CauseRelPro-beta’s micro-averaged scores using different degrees of polynomial kernel." ], "reasoning": "The table shows that the precision (P) for the 3rd degree polynomial kernel is 0.5985, while the highest precision of 0.6337 is achieved with the 4th degree polynomial kernel. Therefore, the statement is refuted.", "statement": "The 3rd degree polynomial kernel achieves the highest precision.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table .6: TempRelPro performances evaluated on the TimeBank-Dense test set and compared with CAEVO.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "TempRelPro outperforms CAEVO in overall F1-score (51.1% vs 50.7%) and is significantly better in labeling T-T and E-T pairs.we report the performances of TempRelPro compared with CAEVO. We achieve a small improvement in the overall F1-score, i.e., 51.1% vs 50.7%. For each temporal entity pair type, since we label all possible links, precision and recall are the same. TempRelPro is significantly better than CAEVO in labelling T-T and E-T pairs." } ], "images": [ "scigen/train/1073.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table .6: TempRelPro performances evaluated on the TimeBank-Dense test set and compared with CAEVO." ], "reasoning": "TempRelPro outperforms CAEVO in overall F1-score (51.1% vs 50.7%) and is significantly better in labeling T-T and E-T pairs.", "description": "we report the performances of TempRelPro compared with CAEVO. We achieve a small improvement in the overall F1-score, i.e., 51.1% vs 50.7%. For each temporal entity pair type, since we label all possible links, precision and recall are the same. TempRelPro is significantly better than CAEVO in labelling T-T and E-T pairs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table .6: TempRelPro performances evaluated on the TimeBank-Dense test set and compared with CAEVO.\nQuestion: Which system has a higher precision for T-T pairs?\nGive a detailed response to the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To determine which system has a higher precision for T-T pairs, compare the precision values for T-T pairs in the table. TempRelPro has a precision of 0.780, while CAEVO has a precision of 0.712. So, the answer is {\"answer\": \"TempRelPro\"}." } ], "images": [ "scigen/train/1073.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table .6: TempRelPro performances evaluated on the TimeBank-Dense test set and compared with CAEVO." ], "reasoning": "To determine which system has a higher precision for T-T pairs, compare the precision values for T-T pairs in the table. TempRelPro has a precision of 0.780, while CAEVO has a precision of 0.712.", "question": "Which system has a higher precision for T-T pairs?", "answer": "So, the answer is {\"answer\": \"TempRelPro\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis TempRelPro has a higher recall for E-D pairs compared to CAEVO. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nTempRelPro has a higher recall for E-D pairs compared to CAEVO.\n" }, { "role": "assistant", "content": "To verify this statement, compare the recall values for E-D pairs in the table. TempRelPro has a recall of 0.518, while CAEVO has a recall of 0.553. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1073.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table .6: TempRelPro performances evaluated on the TimeBank-Dense test set and compared with CAEVO." ], "reasoning": "To verify this statement, compare the recall values for E-D pairs in the table. TempRelPro has a recall of 0.518, while CAEVO has a recall of 0.553.", "statement": "TempRelPro has a higher recall for E-D pairs compared to CAEVO.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table .9: TempRelPro performances in terms of coverage (Cov), precision (P), recall (R) and F1-score (F1) for all domains, compared with systems in QA TempEval augmented with TREFL.'.\n" }, { "role": "assistant", "content": "TempRelPro + coref achieves the best performance with 35% recall and 46% F1-score.g The QA TempEval organizers also provide an extra evaluation, augmenting the participating systems with a time expression reasoner (TREFL) as a post-processing step \\parencitellorens-EtAl:2015:SemEval. The TREFL component adds TLINKs between timexes based on their resolved values. \\parencitellorens-saquete-navarro:2010: SemEval, was also provided. TempRelPro + coref achieves the best performance with 35% recall and 46% F1-score." } ], "images": [ "scigen/train/1074.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table .9: TempRelPro performances in terms of coverage (Cov), precision (P), recall (R) and F1-score (F1) for all domains, compared with systems in QA TempEval augmented with TREFL." ], "reasoning": "TempRelPro + coref achieves the best performance with 35% recall and 46% F1-score.", "description": "g The QA TempEval organizers also provide an extra evaluation, augmenting the participating systems with a time expression reasoner (TREFL) as a post-processing step \\parencitellorens-EtAl:2015:SemEval. The TREFL component adds TLINKs between timexes based on their resolved values. \\parencitellorens-saquete-navarro:2010: SemEval, was also provided. TempRelPro + coref achieves the best performance with 35% recall and 46% F1-score.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table .9: TempRelPro performances in terms of coverage (Cov), precision (P), recall (R) and F1-score (F1) for all domains, compared with systems in QA TempEval augmented with TREFL.\nQuestion: Which system has the highest coverage and what is its F1-score?\nGive a detailed response to the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The system with the highest coverage is HLT-FBK + coref + trefl with a coverage of 0.67. Its F1-score is 0.40. So, the answer is {\"answer\": \"0.40\"}" } ], "images": [ "scigen/train/1074.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table .9: TempRelPro performances in terms of coverage (Cov), precision (P), recall (R) and F1-score (F1) for all domains, compared with systems in QA TempEval augmented with TREFL." ], "reasoning": "The system with the highest coverage is HLT-FBK + coref + trefl with a coverage of 0.67. Its F1-score is 0.40.", "question": "Which system has the highest coverage and what is its F1-score?", "answer": "So, the answer is {\"answer\": \"0.40\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nTempRelPro + coref has the highest precision among all systems.\n" }, { "role": "assistant", "content": "TempRelPro + coref has a precision of 0.66, but TIPSem + trefl has a higher precision of 0.68. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1074.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table .9: TempRelPro performances in terms of coverage (Cov), precision (P), recall (R) and F1-score (F1) for all domains, compared with systems in QA TempEval augmented with TREFL." ], "reasoning": "TempRelPro + coref has a precision of 0.66, but TIPSem + trefl has a higher precision of 0.68.", "statement": "TempRelPro + coref has the highest precision among all systems.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table .1: Classifier performances (F1-scores) in different experimental settings S1 and S2, compared with using only traditional features. TP: true positives and FP: false positives.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The classifier performs better in S2 settings compared to S1, with the highest F1 score achieved using Word2Vec in S2.we report the performances of the classifier in different experimental settings S1 and S2, compared with the classifier performance using only traditional features. Since we classify all possible event pairs in the dataset, precision and recall are the same." } ], "images": [ "scigen/train/1075.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table .1: Classifier performances (F1-scores) in different experimental settings S1 and S2, compared with using only traditional features. TP: true positives and FP: false positives." ], "reasoning": "The classifier performs better in S2 settings compared to S1, with the highest F1 score achieved using Word2Vec in S2.", "description": "we report the performances of the classifier in different experimental settings S1 and S2, compared with the classifier performance using only traditional features. Since we classify all possible event pairs in the dataset, precision and recall are the same.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table .1: Classifier performances (F1-scores) in different experimental settings S1 and S2, compared with using only traditional features. TP: true positives and FP: false positives.', solve the subsequent question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhat is the highest F1 score achieved in the S1 setting and which feature vector was used?\n" }, { "role": "assistant", "content": "To find the highest F1 score in the S1 setting, look at the F1 scores under S1 and identify the highest value. The highest F1 score in S1 is 0.4950, achieved using Word2Vec with the feature vector (→w1⊕→w2). So, the answer is {\"answer\": \"0.4950 with Word2Vec (→w1⊕→w2)\"}." } ], "images": [ "scigen/train/1075.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table .1: Classifier performances (F1-scores) in different experimental settings S1 and S2, compared with using only traditional features. TP: true positives and FP: false positives." ], "reasoning": "To find the highest F1 score in the S1 setting, look at the F1 scores under S1 and identify the highest value. The highest F1 score in S1 is 0.4950, achieved using Word2Vec with the feature vector (→w1⊕→w2).", "question": "What is the highest F1 score achieved in the S1 setting and which feature vector was used?", "answer": "So, the answer is {\"answer\": \"0.4950 with Word2Vec (→w1⊕→w2)\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The classifier achieved an F1 score of 0.5760 using GloVe in S2., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe classifier achieved an F1 score of 0.5760 using GloVe in S2.\n" }, { "role": "assistant", "content": "To verify this statement, check the F1 scores under S2 for the GloVe feature vector. The table shows that the highest F1 score of 0.5760 in S2 was achieved using Word2Vec, not GloVe. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1075.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table .1: Classifier performances (F1-scores) in different experimental settings S1 and S2, compared with using only traditional features. TP: true positives and FP: false positives." ], "reasoning": "To verify this statement, check the F1 scores under S2 for the GloVe feature vector. The table shows that the highest F1 score of 0.5760 in S2 was achieved using Word2Vec, not GloVe.", "statement": "The classifier achieved an F1 score of 0.5760 using GloVe in S2.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table .2: F1-scores per TLINK type with different feature vectors. Pairs of word vectors (→w1, →w2) are retrieved from Word2Vec pre-trained vectors.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Combining (→w1⊕→w2) and →f improves the identification of all TLINK types, especially BEGINS/BEGUN_BY and ENDS/ENDED_BY, which are poorly identified when using (→w1⊕→w2) or →f alone.g Pairs of word vectors (→w1,→w2) are retrieved from Word2Vec pre-trained vectors. (→w1−→w2) is shown to be the best in identifying IDENTITY, BEGINS, BEGUN_BY and ENDS relation types, while the rest are best identified by (→w1⊕→w2). Combining (→w1⊕→w2) and →f improves the identification of all TLINK types in general, particularly BEGINS/BEGUN_BY and ENDS/ENDED_BY types, which were barely identified when (→w1⊕→w2) or →f is used individually as features." } ], "images": [ "scigen/train/1076.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table .2: F1-scores per TLINK type with different feature vectors. Pairs of word vectors (→w1, →w2) are retrieved from Word2Vec pre-trained vectors." ], "reasoning": "Combining (→w1⊕→w2) and →f improves the identification of all TLINK types, especially BEGINS/BEGUN_BY and ENDS/ENDED_BY, which are poorly identified when using (→w1⊕→w2) or →f alone.", "description": "g Pairs of word vectors (→w1,→w2) are retrieved from Word2Vec pre-trained vectors. (→w1−→w2) is shown to be the best in identifying IDENTITY, BEGINS, BEGUN_BY and ENDS relation types, while the rest are best identified by (→w1⊕→w2). Combining (→w1⊕→w2) and →f improves the identification of all TLINK types in general, particularly BEGINS/BEGUN_BY and ENDS/ENDED_BY types, which were barely identified when (→w1⊕→w2) or →f is used individually as features.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table .2: F1-scores per TLINK type with different feature vectors. Pairs of word vectors (→w1, →w2) are retrieved from Word2Vec pre-trained vectors.'. Solve the following question based on the table.\n\nQuestion: Which TLINK type has the highest identification score when using (→w1⊕→w2) combined with →f?\nLet's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the column ((→w1⊕→w2) ⊕ →f), the highest identification score is for the IDENTITY TLINK type with a score of 0.6650. So, the answer is {\"answer\": \"IDENTITY\"}." } ], "images": [ "scigen/train/1076.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table .2: F1-scores per TLINK type with different feature vectors. Pairs of word vectors (→w1, →w2) are retrieved from Word2Vec pre-trained vectors." ], "reasoning": "By examining the column ((→w1⊕→w2) ⊕ →f), the highest identification score is for the IDENTITY TLINK type with a score of 0.6650.", "question": "Which TLINK type has the highest identification score when using (→w1⊕→w2) combined with →f?", "answer": "So, the answer is {\"answer\": \"IDENTITY\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The BEGINS TLINK type has the lowest identification score when using (→w1⊕→w2) alone., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe BEGINS TLINK type has the lowest identification score when using (→w1⊕→w2) alone.\n" }, { "role": "assistant", "content": "By examining the column (→w1⊕→w2), the BEGINS TLINK type has a score of 0.0494, which is the lowest among all TLINK types in that column. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1076.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table .2: F1-scores per TLINK type with different feature vectors. Pairs of word vectors (→w1, →w2) are retrieved from Word2Vec pre-trained vectors." ], "reasoning": "By examining the column (→w1⊕→w2), the BEGINS TLINK type has a score of 0.0494, which is the lowest among all TLINK types in that column.", "statement": "The BEGINS TLINK type has the lowest identification score when using (→w1⊕→w2) alone.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table .3: Classifier performance per TLINK type with different feature vectors, evaluated on TempEval-3-platinum. Pairs of word vectors (→w1, →w2) are retrieved from Word2Vec pre-trained vectors.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Using only word embeddings as features results in worse performance compared to traditional features, but combining both improves performance for certain TLINK types, especially SIMULTANEOUS.In general, using only (→w1⊕→w2) as features does not give any benefit since the performance is significantly worse compared to using only traditional features →f, i.e. 0.4271 vs 0.5043 F1-scores. Combining the word embedding and traditional features ((→w1⊕→w2)⊕→f) also does not improve the classifier performance in general. However, if we look into each TLINK type, the classifier performance in identifying IDENTITY, SIMULTANEOUS and IS_INCLUDED is improved, and quite significantly for SIMULTANEOUS." } ], "images": [ "scigen/train/1077.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table .3: Classifier performance per TLINK type with different feature vectors, evaluated on TempEval-3-platinum. Pairs of word vectors (→w1, →w2) are retrieved from Word2Vec pre-trained vectors." ], "reasoning": "Using only word embeddings as features results in worse performance compared to traditional features, but combining both improves performance for certain TLINK types, especially SIMULTANEOUS.", "description": "In general, using only (→w1⊕→w2) as features does not give any benefit since the performance is significantly worse compared to using only traditional features →f, i.e. 0.4271 vs 0.5043 F1-scores. Combining the word embedding and traditional features ((→w1⊕→w2)⊕→f) also does not improve the classifier performance in general. However, if we look into each TLINK type, the classifier performance in identifying IDENTITY, SIMULTANEOUS and IS_INCLUDED is improved, and quite significantly for SIMULTANEOUS.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table .3: Classifier performance per TLINK type with different feature vectors, evaluated on TempEval-3-platinum. Pairs of word vectors (→w1, →w2) are retrieved from Word2Vec pre-trained vectors.', and sequentially solve the question, writing out each step of your reasoning process. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich TLINK type shows the most significant improvement in F1-score when combining word embeddings and traditional features?\n" }, { "role": "assistant", "content": "By comparing the F1-scores for each TLINK type, SIMULTANEOUS shows the most significant improvement from 0.0606 (traditional features) to 0.3667 (combined features). So, the answer is {\"answer\": \"SIMULTANEOUS\"}." } ], "images": [ "scigen/train/1077.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table .3: Classifier performance per TLINK type with different feature vectors, evaluated on TempEval-3-platinum. Pairs of word vectors (→w1, →w2) are retrieved from Word2Vec pre-trained vectors." ], "reasoning": "By comparing the F1-scores for each TLINK type, SIMULTANEOUS shows the most significant improvement from 0.0606 (traditional features) to 0.3667 (combined features).", "question": "Which TLINK type shows the most significant improvement in F1-score when combining word embeddings and traditional features?", "answer": "So, the answer is {\"answer\": \"SIMULTANEOUS\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nCombining word embeddings and traditional features improves the classifier performance for the TLINK type AFTER.\n" }, { "role": "assistant", "content": "The F1-score for AFTER improves from 0.6196 (traditional features) to 0.6049 (combined features), which is not an improvement. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1077.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table .3: Classifier performance per TLINK type with different feature vectors, evaluated on TempEval-3-platinum. Pairs of word vectors (→w1, →w2) are retrieved from Word2Vec pre-trained vectors." ], "reasoning": "The F1-score for AFTER improves from 0.6196 (traditional features) to 0.6049 (combined features), which is not an improvement.", "statement": "Combining word embeddings and traditional features improves the classifier performance for the TLINK type AFTER.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table .4: CauseRelPro-beta’s micro-averaged scores.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "CauseRelPro-beta improves recall by eliminating the middle step and using supersenses, but mirza-tonelli:2014:Coling achieves better precision with token-based features and a specific step for labeling CSIGNALs.The main difference between CauseRelPro-beta and that reported in \\textcitemirza-tonelli:2014: Coling is the elimination of the middle step in which causal signals are identified. This, together with the use of supersenses, contributes to increasing recall. However, using token-based features and having a specific step to label CSIGNALs yield better precision." } ], "images": [ "scigen/train/1078.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table .4: CauseRelPro-beta’s micro-averaged scores." ], "reasoning": "CauseRelPro-beta improves recall by eliminating the middle step and using supersenses, but mirza-tonelli:2014:Coling achieves better precision with token-based features and a specific step for labeling CSIGNALs.", "description": "The main difference between CauseRelPro-beta and that reported in \\textcitemirza-tonelli:2014: Coling is the elimination of the middle step in which causal signals are identified. This, together with the use of supersenses, contributes to increasing recall. However, using token-based features and having a specific step to label CSIGNALs yield better precision.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table .4: CauseRelPro-beta’s micro-averaged scores.' in mind, break down and resolve the question below step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich system has a higher F1 score and why?\n" }, { "role": "assistant", "content": "CauseRelPro-beta has a higher F1 score of 0.3511 compared to mirza-tonelli:2014:Coling's F1 score of 0.3388. This is due to the elimination of the middle step and the use of supersenses, which contribute to increasing recall. So, the answer is {\"answer\": \"CauseRelPro-beta\"}." } ], "images": [ "scigen/train/1078.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table .4: CauseRelPro-beta’s micro-averaged scores." ], "reasoning": "CauseRelPro-beta has a higher F1 score of 0.3511 compared to mirza-tonelli:2014:Coling's F1 score of 0.3388. This is due to the elimination of the middle step and the use of supersenses, which contribute to increasing recall.", "question": "Which system has a higher F1 score and why?", "answer": "So, the answer is {\"answer\": \"CauseRelPro-beta\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence mirza-tonelli:2014:Coling has a higher recall than CauseRelPro-beta. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nmirza-tonelli:2014:Coling has a higher recall than CauseRelPro-beta.\n" }, { "role": "assistant", "content": "The table shows that mirza-tonelli:2014:Coling has a recall (R) of 0.2264, while CauseRelPro-beta has a recall (R) of 0.2484. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1078.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table .4: CauseRelPro-beta’s micro-averaged scores." ], "reasoning": "The table shows that mirza-tonelli:2014:Coling has a recall (R) of 0.2264, while CauseRelPro-beta has a recall (R) of 0.2484. Therefore, the statement is incorrect.", "statement": "mirza-tonelli:2014:Coling has a higher recall than CauseRelPro-beta.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table .6: Impact of increased training data, using EMM-clusters and propagated (prop.) CLINKs, on system performances (micro-averaged scores). TP: true positives, FP: false positives and FN: false negatives.'.\n" }, { "role": "assistant", "content": "(Causal-TimeBank + EMM-clusters + prop. CLINKs) improves the system's F1-score significantly due to increased recall, despite a drop in precision.(Causal-TimeBank + EMM-clusters) improves micro-averaged F1-scores of the system by 5.57% significantly (p < 0.01). We evaluate the performance of the system trained with the enriched training data from CLINK propagation in the same five-fold cross-validation setting as the previous experiment. (Causal-TimeBank + EMM-clusters + prop. CLINKs). The improvement is statistically significant with p-value < 0.001. As expected, the overall improvement is caused by the increased recall, even though the system precision drops." } ], "images": [ "scigen/train/1079.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table .6: Impact of increased training data, using EMM-clusters and propagated (prop.) CLINKs, on system performances (micro-averaged scores). TP: true positives, FP: false positives and FN: false negatives." ], "reasoning": "(Causal-TimeBank + EMM-clusters + prop. CLINKs) improves the system's F1-score significantly due to increased recall, despite a drop in precision.", "description": "(Causal-TimeBank + EMM-clusters) improves micro-averaged F1-scores of the system by 5.57% significantly (p < 0.01). We evaluate the performance of the system trained with the enriched training data from CLINK propagation in the same five-fold cross-validation setting as the previous experiment. (Causal-TimeBank + EMM-clusters + prop. CLINKs). The improvement is statistically significant with p-value < 0.001. As expected, the overall improvement is caused by the increased recall, even though the system precision drops.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table .6: Impact of increased training data, using EMM-clusters and propagated (prop.) CLINKs, on system performances (micro-averaged scores). TP: true positives, FP: false positives and FN: false negatives.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. \n\nQuestion: What is the recall value for the system trained with Causal-TimeBank + EMM-clusters + prop. CLINKs?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows the recall (R) values for different training data configurations. For Causal-TimeBank + EMM-clusters + prop. CLINKs, the recall value is listed. So, the answer is {\"answer\": \"0.3396\"}" } ], "images": [ "scigen/train/1079.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table .6: Impact of increased training data, using EMM-clusters and propagated (prop.) CLINKs, on system performances (micro-averaged scores). TP: true positives, FP: false positives and FN: false negatives." ], "reasoning": "The table shows the recall (R) values for different training data configurations. For Causal-TimeBank + EMM-clusters + prop. CLINKs, the recall value is listed.", "question": "What is the recall value for the system trained with Causal-TimeBank + EMM-clusters + prop. CLINKs?", "answer": "So, the answer is {\"answer\": \"0.3396\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe system trained with Causal-TimeBank + EMM-clusters has a higher precision than the system trained with Causal-TimeBank + EMM-clusters + prop. CLINKs.\n" }, { "role": "assistant", "content": "The table shows the precision (P) values for different training data configurations. The precision for Causal-TimeBank + EMM-clusters is 0.6376, while for Causal-TimeBank + EMM-clusters + prop. CLINKs it is 0.5934. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1079.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table .6: Impact of increased training data, using EMM-clusters and propagated (prop.) CLINKs, on system performances (micro-averaged scores). TP: true positives, FP: false positives and FN: false negatives." ], "reasoning": "The table shows the precision (P) values for different training data configurations. The precision for Causal-TimeBank + EMM-clusters is 0.6376, while for Causal-TimeBank + EMM-clusters + prop. CLINKs it is 0.5934.", "statement": "The system trained with Causal-TimeBank + EMM-clusters has a higher precision than the system trained with Causal-TimeBank + EMM-clusters + prop. CLINKs.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Macro-averaged F1 score for multi-class author classification on the large datasets, using either no discourse (None), grammatical relations (GR), or RST relations (RST).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Discourse-enhanced models generally improve performance on novel-50, with RST features showing the best results, while on IMDB62, discourse features add minimal improvement.Generalization-dataset experiments. On novel-50, most discourse-enhanced models improve the performance of the baseline non-discourse CNN2 to varying degrees. The clear pattern again emerges that RST features work better, with the best F1 score evidenced in the CNN2-DE (global) model (3.5 improvement in F1). On IMDB62, as expected with short text inputs (mean=349 words/review), the discourse features in general do not add further contribution. Even the best model CNN2-DE brings only marginal improvement, confirming our findings from varying the chunk size on novel-9, where discourse features did not help at this input size. Equipped with discourse features, SVM2-PV performs slightly better than SVM2 on novel-50 (by 0.4 with GR, 0.9 with RST features). On IMDB62, the same pattern persists for the SVMs: discourse features do not make noticeable improvements (by 0.0 and 0.5 with GR and RST respectively). GR vs. RST. The RST parser produces a tree of discourse relations for the input text, thus introducing a “global view.” The GR features, on the other hand, are more restricted to a “local view” on entities between consecutive sentences. While a deeper empirical investigation is needed, one can intuitively imagine that identifying authorship by focusing on the local transitions between grammatical relations (as in GR) is more difficult than observing how the entire text is organized (as in RST)." } ], "images": [ "scigen/train/1080.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Macro-averaged F1 score for multi-class author classification on the large datasets, using either no discourse (None), grammatical relations (GR), or RST relations (RST)." ], "reasoning": "Discourse-enhanced models generally improve performance on novel-50, with RST features showing the best results, while on IMDB62, discourse features add minimal improvement.", "description": "Generalization-dataset experiments. On novel-50, most discourse-enhanced models improve the performance of the baseline non-discourse CNN2 to varying degrees. The clear pattern again emerges that RST features work better, with the best F1 score evidenced in the CNN2-DE (global) model (3.5 improvement in F1). On IMDB62, as expected with short text inputs (mean=349 words/review), the discourse features in general do not add further contribution. Even the best model CNN2-DE brings only marginal improvement, confirming our findings from varying the chunk size on novel-9, where discourse features did not help at this input size. Equipped with discourse features, SVM2-PV performs slightly better than SVM2 on novel-50 (by 0.4 with GR, 0.9 with RST features). On IMDB62, the same pattern persists for the SVMs: discourse features do not make noticeable improvements (by 0.0 and 0.5 with GR and RST respectively). GR vs. RST. The RST parser produces a tree of discourse relations for the input text, thus introducing a “global view.” The GR features, on the other hand, are more restricted to a “local view” on entities between consecutive sentences. While a deeper empirical investigation is needed, one can intuitively imagine that identifying authorship by focusing on the local transitions between grammatical relations (as in GR) is more difficult than observing how the entire text is organized (as in RST).", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 7: Macro-averaged F1 score for multi-class author classification on the large datasets, using either no discourse (None), grammatical relations (GR), or RST relations (RST).' and proceed to solve the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model showed the highest improvement in F1 score on novel-50 with RST features?\n" }, { "role": "assistant", "content": "The table shows that the CNN2-DE (global) model with RST features has the highest F1 score of 98.8 on novel-50. So, the answer is {\"answer\": \"CNN2-DE (global)\"}." } ], "images": [ "scigen/train/1080.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Macro-averaged F1 score for multi-class author classification on the large datasets, using either no discourse (None), grammatical relations (GR), or RST relations (RST)." ], "reasoning": "The table shows that the CNN2-DE (global) model with RST features has the highest F1 score of 98.8 on novel-50.", "question": "Which model showed the highest improvement in F1 score on novel-50 with RST features?", "answer": "So, the answer is {\"answer\": \"CNN2-DE (global)\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe CNN2-DE (global) model with RST features achieved the highest F1 score on novel-50.\n" }, { "role": "assistant", "content": "The table shows that the CNN2-DE (global) model with RST features has the highest F1 score of 98.8 on novel-50. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1080.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Macro-averaged F1 score for multi-class author classification on the large datasets, using either no discourse (None), grammatical relations (GR), or RST relations (RST)." ], "reasoning": "The table shows that the CNN2-DE (global) model with RST features has the highest F1 score of 98.8 on novel-50.", "statement": "The CNN2-DE (global) model with RST features achieved the highest F1 score on novel-50.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Decoding accuracy of neural networks of different sizes (Word Error Rate, %)', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Larger neural network models show better performance on the dataset, balancing precision and computational cost.In the next experiment, the neural networks are trained with the same architecture but different layer sizes on the 460x6+500 hours dataset. This shows that larger models are capable of fitting the data and generalizing better, as expected. This allows us to choose the best tradeoff between precision and computational cost depending on each target hardware and assistant needs." } ], "images": [ "scigen/train/1081.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Decoding accuracy of neural networks of different sizes (Word Error Rate, %)" ], "reasoning": "Larger neural network models show better performance on the dataset, balancing precision and computational cost.", "description": "In the next experiment, the neural networks are trained with the same architecture but different layer sizes on the 460x6+500 hours dataset. This shows that larger models are capable of fitting the data and generalizing better, as expected. This allows us to choose the best tradeoff between precision and computational cost depending on each target hardware and assistant needs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Decoding accuracy of neural networks of different sizes (Word Error Rate, %)\nQuestion: Which model has the lowest error rate on the test-clean dataset?\nGive a detailed response to the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the lowest error rate on the test-clean dataset, compare the values in the test-clean column. The lowest value is 4.8, which corresponds to the KALDI model. So, the answer is {\"answer\": \"KALDI\"}." } ], "images": [ "scigen/train/1081.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Decoding accuracy of neural networks of different sizes (Word Error Rate, %)" ], "reasoning": "To find the model with the lowest error rate on the test-clean dataset, compare the values in the test-clean column. The lowest value is 4.8, which corresponds to the KALDI model.", "question": "Which model has the lowest error rate on the test-clean dataset?", "answer": "So, the answer is {\"answer\": \"KALDI\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The nnet-512 model has a lower error rate on the dev-clean dataset than the nnet-256 model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe nnet-512 model has a lower error rate on the dev-clean dataset than the nnet-256 model.\n" }, { "role": "assistant", "content": "The table shows that the error rate for the nnet-512 model on the dev-clean dataset is 6.4, while for the nnet-256 model it is 7.3. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1081.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Decoding accuracy of neural networks of different sizes (Word Error Rate, %)" ], "reasoning": "The table shows that the error rate for the nnet-512 model on the dev-clean dataset is 6.4, while for the nnet-256 model it is 7.3. Therefore, the statement is supported.", "statement": "The nnet-512 model has a lower error rate on the dev-clean dataset than the nnet-256 model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: Comparison of speed and memory performance of nnet-256 and nnet-768. RTF refers to real time ratio.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The nnet-256 model was selected due to its reasonable speed and memory footprint, despite a slight loss in accuracy, which is compensated by the adapted LM and robust NLU.The gain is similar in RAM. In terms of speed, the nnet-256 is 6 to 10 times faster than the nnet-768. These tradeoffs and comparison with other trained models led us to select the nnet-256. It has a reasonable speed and memory footprint, and the loss in accuracy is compensated by the adapted LM and robust NLU." } ], "images": [ "scigen/train/1082.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of speed and memory performance of nnet-256 and nnet-768. RTF refers to real time ratio." ], "reasoning": "The nnet-256 model was selected due to its reasonable speed and memory footprint, despite a slight loss in accuracy, which is compensated by the adapted LM and robust NLU.", "description": "The gain is similar in RAM. In terms of speed, the nnet-256 is 6 to 10 times faster than the nnet-768. These tradeoffs and comparison with other trained models led us to select the nnet-256. It has a reasonable speed and memory footprint, and the loss in accuracy is compensated by the adapted LM and robust NLU.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: 'Table 4: Comparison of speed and memory performance of nnet-256 and nnet-768. RTF refers to real time ratio.'\nQuestion: Why was the nnet-256 model selected over the nnet-768 model?\n" }, { "role": "assistant", "content": "The nnet-256 model was chosen because it is 6 to 10 times faster than the nnet-768, has a smaller memory footprint, and the loss in accuracy is compensated by the adapted LM and robust NLU. So, the answer is {\"answer\": \"reasonable speed and memory footprint\"}." } ], "images": [ "scigen/train/1082.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of speed and memory performance of nnet-256 and nnet-768. RTF refers to real time ratio." ], "reasoning": "The nnet-256 model was chosen because it is 6 to 10 times faster than the nnet-768, has a smaller memory footprint, and the loss in accuracy is compensated by the adapted LM and robust NLU.", "question": "Why was the nnet-256 model selected over the nnet-768 model?", "answer": "So, the answer is {\"answer\": \"reasonable speed and memory footprint\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe nnet-256 model has a smaller size and faster speed compared to the nnet-768 model.\n" }, { "role": "assistant", "content": "The table shows that the nnet-256 model has a size of 10 MB and an RTF of <1, while the nnet-768 model has a size of 59 MB and an RTF of >1, indicating that the nnet-256 is smaller and faster. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1082.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of speed and memory performance of nnet-256 and nnet-768. RTF refers to real time ratio." ], "reasoning": "The table shows that the nnet-256 model has a size of 10 MB and an RTF of <1, while the nnet-768 model has a size of 59 MB and an RTF of >1, indicating that the nnet-256 is smaller and faster.", "statement": "The nnet-256 model has a smaller size and faster speed compared to the nnet-768 model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 6: Precision, recall and F1-score on Braun et al. corpora. *Benchmark run in August 2017 by the authors of bench17 . **Benchmark run in January 2018 by the authors of this paper.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The evaluation used three corpora from StackExchange and a Telegram chatbot, with Snips NLU ranking second highest overall in F1-score.For the raw results and methodology, see https://github.com/snipsco/nlu-benchmark. The main metric used in this benchmark is the average F1-score of intent classification and slot filling. The data consists in three corpora. Two of the corpora were extracted from StackExchange, one from a Telegram chatbot. The exact same splits as in the original paper were used for the Ubuntu and Web Applications corpora. At the date we ran the evaluation, the train and test splits were not explicit for the Chatbot dataset (although they were added later on). In that case, we ran a 5-fold cross-validation. For Rasa, we considered all three possible backends (Spacy, SKLearn + MITIE, MITIE), see the abovementioned GitHub repository for more details. However, only Spacy was run on all 3 datasets, for train time reasons. For fairness, the latest version of Rasa NLU is also displayed. Results show that Snips NLU ranks second highest overall." } ], "images": [ "scigen/train/1083.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Precision, recall and F1-score on Braun et al. corpora. *Benchmark run in August 2017 by the authors of bench17 . **Benchmark run in January 2018 by the authors of this paper." ], "reasoning": "The evaluation used three corpora from StackExchange and a Telegram chatbot, with Snips NLU ranking second highest overall in F1-score.", "description": "For the raw results and methodology, see https://github.com/snipsco/nlu-benchmark. The main metric used in this benchmark is the average F1-score of intent classification and slot filling. The data consists in three corpora. Two of the corpora were extracted from StackExchange, one from a Telegram chatbot. The exact same splits as in the original paper were used for the Ubuntu and Web Applications corpora. At the date we ran the evaluation, the train and test splits were not explicit for the Chatbot dataset (although they were added later on). In that case, we ran a 5-fold cross-validation. For Rasa, we considered all three possible backends (Spacy, SKLearn + MITIE, MITIE), see the abovementioned GitHub repository for more details. However, only Spacy was run on all 3 datasets, for train time reasons. For fairness, the latest version of Rasa NLU is also displayed. Results show that Snips NLU ranks second highest overall.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 6: Precision, recall and F1-score on Braun et al. corpora. *Benchmark run in August 2017 by the authors of bench17 . **Benchmark run in January 2018 by the authors of this paper.\nQuestion: Which NLU provider had the highest F1-score for the chatbot corpus?\n" }, { "role": "assistant", "content": "To find the NLU provider with the highest F1-score for the chatbot corpus, look at the 'F1-score' column under the 'chatbot' rows. The highest F1-score is 0.943, achieved by both Luis* and Rasa*. So, the answer is {\"answer\": \"Luis* and Rasa*\"}." } ], "images": [ "scigen/train/1083.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Precision, recall and F1-score on Braun et al. corpora. *Benchmark run in August 2017 by the authors of bench17 . **Benchmark run in January 2018 by the authors of this paper." ], "reasoning": "To find the NLU provider with the highest F1-score for the chatbot corpus, look at the 'F1-score' column under the 'chatbot' rows. The highest F1-score is 0.943, achieved by both Luis* and Rasa*.", "question": "Which NLU provider had the highest F1-score for the chatbot corpus?", "answer": "So, the answer is {\"answer\": \"Luis* and Rasa*\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Snips NLU had the highest F1-score for the web apps corpus. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSnips NLU had the highest F1-score for the web apps corpus.\n" }, { "role": "assistant", "content": "To verify this statement, check the 'F1-score' column under the 'web apps' rows. The highest F1-score for the web apps corpus is 0.73, achieved by Luis*, not Snips NLU. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1083.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Precision, recall and F1-score on Braun et al. corpora. *Benchmark run in August 2017 by the authors of bench17 . **Benchmark run in January 2018 by the authors of this paper." ], "reasoning": "To verify this statement, check the 'F1-score' column under the 'web apps' rows. The highest F1-score for the web apps corpus is 0.73, achieved by Luis*, not Snips NLU.", "statement": "Snips NLU had the highest F1-score for the web apps corpus.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Precision, recall and F1-score averaged on all slots and on all intents of an in-house dataset, run in June 2017.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Snips NLU outperforms or matches other cloud-based solutions in slot filling accuracy, regardless of the training set size.In this experiment, the comparison is done separately on each intent to focus on slot filling (rather than intent classification). The main metric used in this benchmark is the average F1-score of slot filling on all slots. Three training sets of 70 and 2000 queries have been drawn from the total pool of queries to gain in statistical relevance. Validation sets consist in 100 queries per intent. Five different cloud-based providers are compared to Snips NLU (Microsoft’s Luis, API.AI now Google’s Dialogflow, Facebook’s Wit.ai, and Amazon Alexa). For more details about the specific methodology for each provider and access to the full dataset, see https://github.com/snipsco/nlu-benchmark. Each solution is trained and evaluated on the exact same datasets. Snips NLU is as accurate or better than competing cloud-based solutions in slot filling, regardless of the training set size." } ], "images": [ "scigen/train/1084.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Precision, recall and F1-score averaged on all slots and on all intents of an in-house dataset, run in June 2017." ], "reasoning": "Snips NLU outperforms or matches other cloud-based solutions in slot filling accuracy, regardless of the training set size.", "description": "In this experiment, the comparison is done separately on each intent to focus on slot filling (rather than intent classification). The main metric used in this benchmark is the average F1-score of slot filling on all slots. Three training sets of 70 and 2000 queries have been drawn from the total pool of queries to gain in statistical relevance. Validation sets consist in 100 queries per intent. Five different cloud-based providers are compared to Snips NLU (Microsoft’s Luis, API.AI now Google’s Dialogflow, Facebook’s Wit.ai, and Amazon Alexa). For more details about the specific methodology for each provider and access to the full dataset, see https://github.com/snipsco/nlu-benchmark. Each solution is trained and evaluated on the exact same datasets. Snips NLU is as accurate or better than competing cloud-based solutions in slot filling, regardless of the training set size.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 7: Precision, recall and F1-score averaged on all slots and on all intents of an in-house dataset, run in June 2017.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: Which NLU provider achieved the highest F1-score with a training set size of 2000 queries?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the NLU provider with the highest F1-score for a training set size of 2000 queries, compare the F1-scores in the '2000' train size rows. Luis has the highest F1-score of 0.932. So, the answer is {\"answer\": \"Luis\"}." } ], "images": [ "scigen/train/1084.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Precision, recall and F1-score averaged on all slots and on all intents of an in-house dataset, run in June 2017." ], "reasoning": "To find the NLU provider with the highest F1-score for a training set size of 2000 queries, compare the F1-scores in the '2000' train size rows. Luis has the highest F1-score of 0.932.", "question": "Which NLU provider achieved the highest F1-score with a training set size of 2000 queries?", "answer": "So, the answer is {\"answer\": \"Luis\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Snips NLU has the highest F1-score with a training set size of 70 queries. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nSnips NLU has the highest F1-score with a training set size of 70 queries.\n" }, { "role": "assistant", "content": "To verify this statement, compare the F1-scores for all providers with a training set size of 70 queries. Snips NLU has an F1-score of 0.790, which is higher than the F1-scores of other providers with the same training set size. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1084.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Precision, recall and F1-score averaged on all slots and on all intents of an in-house dataset, run in June 2017." ], "reasoning": "To verify this statement, compare the F1-scores for all providers with a training set size of 70 queries. Snips NLU has an F1-score of 0.790, which is higher than the F1-scores of other providers with the same training set size.", "statement": "Snips NLU has the highest F1-score with a training set size of 70 queries.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 16: Precision, recall and F1-score averaged on all slots in an in-house dataset, run in June 2017.'.\n" }, { "role": "assistant", "content": "The table compares the average F1-score of slot filling for different NLU providers across various intents and training set sizes, showing that Snips NLU performs as well or better than other cloud-based solutions.In this experiment, the comparison is done separately on each intent to focus on slot filling (rather than intent classification). The main metric used in this benchmark is the average F1-score of slot filling on all slots. Three training sets of 70 and 2000 queries have been drawn from the total pool of queries to gain in statistical relevance. Validation sets consist in 100 queries per intent. Five different cloud-based providers are compared to Snips NLU (Microsoft’s Luis, API.AI now Google’s Dialogflow, Facebook’s Wit.ai, and Amazon Alexa). For more details about the specific methodology for each provider and access to the full dataset, see https://github.com/snipsco/nlu-benchmark. Each solution is trained and evaluated on the exact same datasets. Snips NLU is as accurate or better than competing cloud-based solutions in slot filling, regardless of the training set size." } ], "images": [ "scigen/train/1085.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 16: Precision, recall and F1-score averaged on all slots in an in-house dataset, run in June 2017." ], "reasoning": "The table compares the average F1-score of slot filling for different NLU providers across various intents and training set sizes, showing that Snips NLU performs as well or better than other cloud-based solutions.", "description": "In this experiment, the comparison is done separately on each intent to focus on slot filling (rather than intent classification). The main metric used in this benchmark is the average F1-score of slot filling on all slots. Three training sets of 70 and 2000 queries have been drawn from the total pool of queries to gain in statistical relevance. Validation sets consist in 100 queries per intent. Five different cloud-based providers are compared to Snips NLU (Microsoft’s Luis, API.AI now Google’s Dialogflow, Facebook’s Wit.ai, and Amazon Alexa). For more details about the specific methodology for each provider and access to the full dataset, see https://github.com/snipsco/nlu-benchmark. Each solution is trained and evaluated on the exact same datasets. Snips NLU is as accurate or better than competing cloud-based solutions in slot filling, regardless of the training set size.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 16: Precision, recall and F1-score averaged on all slots in an in-house dataset, run in June 2017.\nQuestion: Which NLU provider has the highest F1-score for the 'GetWeather' intent with a training size of 2000?\n" }, { "role": "assistant", "content": "To find the highest F1-score for the 'GetWeather' intent with a training size of 2000, look at the 'GetWeather' rows with a train size of 2000 and compare their F1-scores. Snips has the highest F1-score of 0.943. So, the answer is {\"answer\": \"Snips\"}." } ], "images": [ "scigen/train/1085.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 16: Precision, recall and F1-score averaged on all slots in an in-house dataset, run in June 2017." ], "reasoning": "To find the highest F1-score for the 'GetWeather' intent with a training size of 2000, look at the 'GetWeather' rows with a train size of 2000 and compare their F1-scores. Snips has the highest F1-score of 0.943.", "question": "Which NLU provider has the highest F1-score for the 'GetWeather' intent with a training size of 2000?", "answer": "So, the answer is {\"answer\": \"Snips\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim API.ai has the lowest F1-score for the 'PlayMusic' intent with a training size of 70., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nAPI.ai has the lowest F1-score for the 'PlayMusic' intent with a training size of 70.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'PlayMusic' rows with a train size of 70 and compare their F1-scores. API.ai has an F1-score of 0.595, which is the lowest among the providers listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1085.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 16: Precision, recall and F1-score averaged on all slots in an in-house dataset, run in June 2017." ], "reasoning": "To verify this statement, look at the 'PlayMusic' rows with a train size of 70 and compare their F1-scores. API.ai has an F1-score of 0.595, which is the lowest among the providers listed.", "statement": "API.ai has the lowest F1-score for the 'PlayMusic' intent with a training size of 70.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 17: Precision, recall and F1-score averaged on all slots in an in-house dataset, run in June 2017.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The table compares the F1-scores of different NLU providers for various intents with different training set sizes, showing that Snips NLU performs as well or better than other providers.In this experiment, the comparison is done separately on each intent to focus on slot filling (rather than intent classification). The main metric used in this benchmark is the average F1-score of slot filling on all slots. Three training sets of 70 and 2000 queries have been drawn from the total pool of queries to gain in statistical relevance. Validation sets consist in 100 queries per intent. Five different cloud-based providers are compared to Snips NLU (Microsoft’s Luis, API.AI now Google’s Dialogflow, Facebook’s Wit.ai, and Amazon Alexa). For more details about the specific methodology for each provider and access to the full dataset, see https://github.com/snipsco/nlu-benchmark. Each solution is trained and evaluated on the exact same datasets. Snips NLU is as accurate or better than competing cloud-based solutions in slot filling, regardless of the training set size." } ], "images": [ "scigen/train/1086.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 17: Precision, recall and F1-score averaged on all slots in an in-house dataset, run in June 2017." ], "reasoning": "The table compares the F1-scores of different NLU providers for various intents with different training set sizes, showing that Snips NLU performs as well or better than other providers.", "description": "In this experiment, the comparison is done separately on each intent to focus on slot filling (rather than intent classification). The main metric used in this benchmark is the average F1-score of slot filling on all slots. Three training sets of 70 and 2000 queries have been drawn from the total pool of queries to gain in statistical relevance. Validation sets consist in 100 queries per intent. Five different cloud-based providers are compared to Snips NLU (Microsoft’s Luis, API.AI now Google’s Dialogflow, Facebook’s Wit.ai, and Amazon Alexa). For more details about the specific methodology for each provider and access to the full dataset, see https://github.com/snipsco/nlu-benchmark. Each solution is trained and evaluated on the exact same datasets. Snips NLU is as accurate or better than competing cloud-based solutions in slot filling, regardless of the training set size.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 17: Precision, recall and F1-score averaged on all slots in an in-house dataset, run in June 2017.'. Solve the following question based on the table.\n\nQuestion: Which NLU provider has the highest F1-score for the 'RateBook' intent with a training set size of 2000?\nLet's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest F1-score for the 'RateBook' intent with a training set size of 2000, we look at the rows where the intent is 'RateBook' and the train size is 2000. The highest F1-score among these rows is 0.997, which is achieved by Snips. So, the answer is {\"answer\": \"Snips\"}." } ], "images": [ "scigen/train/1086.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 17: Precision, recall and F1-score averaged on all slots in an in-house dataset, run in June 2017." ], "reasoning": "To find the highest F1-score for the 'RateBook' intent with a training set size of 2000, we look at the rows where the intent is 'RateBook' and the train size is 2000. The highest F1-score among these rows is 0.997, which is achieved by Snips.", "question": "Which NLU provider has the highest F1-score for the 'RateBook' intent with a training set size of 2000?", "answer": "So, the answer is {\"answer\": \"Snips\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nAPI.ai has the highest F1-score for the 'SearchScreeningEvent' intent with a training set size of 2000.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the rows where the intent is 'SearchScreeningEvent' and the train size is 2000. The F1-scores for these rows are 0.979 for Luis, 0.940 for Wit, 0.966 for API.ai, 0.560 for Alexa, and 0.967 for Snips. The highest F1-score is 0.979, achieved by Luis, not API.ai. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1086.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 17: Precision, recall and F1-score averaged on all slots in an in-house dataset, run in June 2017." ], "reasoning": "To verify this statement, we look at the rows where the intent is 'SearchScreeningEvent' and the train size is 2000. The F1-scores for these rows are 0.979 for Luis, 0.940 for Wit, 0.966 for API.ai, 0.560 for Alexa, and 0.967 for Snips. The highest F1-score is 0.979, achieved by Luis, not API.ai.", "statement": "API.ai has the highest F1-score for the 'SearchScreeningEvent' intent with a training set size of 2000.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: Penn Treebank Test Set Results. ‘BPC’ is bits-per-character. ‘Error’ is next-step classification error rate, for either characters or words.'.\n" }, { "role": "assistant", "content": "Combining multiple RNNs, a 5-gram, and a cache model in an ensemble achieved a perplexity of 89.4, showing a significant improvement over individual models.For example, he records a perplexity of 141 for a 5-gram with Keyser-Ney smoothing, 141.8 for a word level feedforward neural network, 131.1 for the state-of-the-art compression algorithm PAQ8 and 123.2 for a dynamically evaluated word-level RNN. However by combining multiple RNNs, a 5-gram and a cache model in an ensemble, he was able to achieve a perplexity of 89.4. Interestingly, the benefit of dynamic evaluation was far more pronounced here than in Mikolov’s thesis (he records a perplexity improvement from 124.7 to 123.2 with word-level RNNs). This suggests that LSTM is better at rapidly adapting to new data than ordinary RNNs." } ], "images": [ "scigen/train/1087.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Penn Treebank Test Set Results. ‘BPC’ is bits-per-character. ‘Error’ is next-step classification error rate, for either characters or words." ], "reasoning": "Combining multiple RNNs, a 5-gram, and a cache model in an ensemble achieved a perplexity of 89.4, showing a significant improvement over individual models.", "description": "For example, he records a perplexity of 141 for a 5-gram with Keyser-Ney smoothing, 141.8 for a word level feedforward neural network, 131.1 for the state-of-the-art compression algorithm PAQ8 and 123.2 for a dynamically evaluated word-level RNN. However by combining multiple RNNs, a 5-gram and a cache model in an ensemble, he was able to achieve a perplexity of 89.4. Interestingly, the benefit of dynamic evaluation was far more pronounced here than in Mikolov’s thesis (he records a perplexity improvement from 124.7 to 123.2 with word-level RNNs). This suggests that LSTM is better at rapidly adapting to new data than ordinary RNNs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 1: Penn Treebank Test Set Results. ‘BPC’ is bits-per-character. ‘Error’ is next-step classification error rate, for either characters or words.' and proceed to solve the following question in a stepwise manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the perplexity for a word-level model with weight noise and dynamic evaluation?\n" }, { "role": "assistant", "content": "From the table, the perplexity for a word-level model with weight noise and dynamic evaluation is 117. So, the answer is {\"answer\": \"117\"}" } ], "images": [ "scigen/train/1087.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Penn Treebank Test Set Results. ‘BPC’ is bits-per-character. ‘Error’ is next-step classification error rate, for either characters or words." ], "reasoning": "From the table, the perplexity for a word-level model with weight noise and dynamic evaluation is 117.", "question": "What is the perplexity for a word-level model with weight noise and dynamic evaluation?", "answer": "So, the answer is {\"answer\": \"117\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nA character-level model with adaptive weight noise and dynamic evaluation has a perplexity of 122.\n" }, { "role": "assistant", "content": "The table shows that a character-level model with adaptive weight noise and dynamic evaluation has a perplexity of 122. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1087.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Penn Treebank Test Set Results. ‘BPC’ is bits-per-character. ‘Error’ is next-step classification error rate, for either characters or words." ], "reasoning": "The table shows that a character-level model with adaptive weight noise and dynamic evaluation has a perplexity of 122.", "statement": "A character-level model with adaptive weight noise and dynamic evaluation has a perplexity of 122.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Wikipedia Results (bits-per-character)'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The network performed better on the validation set with dynamic evaluation than on the training set, likely due to the network underfitting the training data and the varying difficulty of data portions.As with the Penn data, we tested the network on the validation data with and without dynamic evaluation (where the weights are updated as the data is predicted). This is probably because of the long range coherence of Wikipedia data; for example, certain words are much more frequent in some articles than others, and being able to adapt to this during evaluation is advantageous. It may seem surprising that the dynamic results on the validation set were substantially better than on the training set. However this is easily explained by two factors: firstly, the network underfit the training data, and secondly some portions of the data are much more difficult than others (for example, plain text is harder to predict than XML tags)." } ], "images": [ "scigen/train/1088.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Wikipedia Results (bits-per-character)" ], "reasoning": "The network performed better on the validation set with dynamic evaluation than on the training set, likely due to the network underfitting the training data and the varying difficulty of data portions.", "description": "As with the Penn data, we tested the network on the validation data with and without dynamic evaluation (where the weights are updated as the data is predicted). This is probably because of the long range coherence of Wikipedia data; for example, certain words are much more frequent in some articles than others, and being able to adapt to this during evaluation is advantageous. It may seem surprising that the dynamic results on the validation set were substantially better than on the training set. However this is easily explained by two factors: firstly, the network underfit the training data, and secondly some portions of the data are much more difficult than others (for example, plain text is harder to predict than XML tags).", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: Wikipedia Results (bits-per-character)'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. \n\nQuestion: What is the difference in performance between the validation set with dynamic evaluation and the training set?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The performance on the validation set with dynamic evaluation is 1.33, while the performance on the training set is 1.42. The difference is 1.42 - 1.33. So, the answer is {\"answer\": \"0.09\"}." } ], "images": [ "scigen/train/1088.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Wikipedia Results (bits-per-character)" ], "reasoning": "The performance on the validation set with dynamic evaluation is 1.33, while the performance on the training set is 1.42. The difference is 1.42 - 1.33.", "question": "What is the difference in performance between the validation set with dynamic evaluation and the training set?", "answer": "So, the answer is {\"answer\": \"0.09\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe network performed worse on the validation set with dynamic evaluation compared to the training set.\n" }, { "role": "assistant", "content": "The table shows that the performance on the validation set with dynamic evaluation is 1.33, which is better than the performance on the training set, which is 1.42. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1088.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Wikipedia Results (bits-per-character)" ], "reasoning": "The table shows that the performance on the validation set with dynamic evaluation is 1.33, which is better than the performance on the training set, which is 1.42.", "statement": "The network performed worse on the validation set with dynamic evaluation compared to the training set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: Handwriting Synthesis Results. All results recorded on the validation set. ‘Log-Loss’ is the mean value of L(x) in nats. ‘SSE’ is the mean sum-squared-error per data point.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Both networks with and without regularisation performed equally well in terms of SSE, but the regularised network had a higher log-loss.The regularised network appears to generate slightly more realistic sequences, although the difference is hard to discern by eye. Both networks performed considerably better than the best prediction network. In particular the sum-squared-error was reduced by 44%. This is likely due in large part to the improved predictions at the ends of strokes, where the error is largest." } ], "images": [ "scigen/train/1089.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Handwriting Synthesis Results. All results recorded on the validation set. ‘Log-Loss’ is the mean value of L(x) in nats. ‘SSE’ is the mean sum-squared-error per data point." ], "reasoning": "Both networks with and without regularisation performed equally well in terms of SSE, but the regularised network had a higher log-loss.", "description": "The regularised network appears to generate slightly more realistic sequences, although the difference is hard to discern by eye. Both networks performed considerably better than the best prediction network. In particular the sum-squared-error was reduced by 44%. This is likely due in large part to the improved predictions at the ends of strokes, where the error is largest.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 4: Handwriting Synthesis Results. All results recorded on the validation set. ‘Log-Loss’ is the mean value of L(x) in nats. ‘SSE’ is the mean sum-squared-error per data point.'. Solve the following question based on the table.\n\nQuestion: What is the difference in Log-Loss between the network with no regularisation and the network with adaptive weight noise?\nLet's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The Log-Loss for the network with no regularisation is 1096.9, and for the network with adaptive weight noise, it is 1128.2. The difference is 1128.2 - 1096.9. So, the answer is {\"answer\": \"31.3\"}" } ], "images": [ "scigen/train/1089.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Handwriting Synthesis Results. All results recorded on the validation set. ‘Log-Loss’ is the mean value of L(x) in nats. ‘SSE’ is the mean sum-squared-error per data point." ], "reasoning": "The Log-Loss for the network with no regularisation is 1096.9, and for the network with adaptive weight noise, it is 1128.2. The difference is 1128.2 - 1096.9.", "question": "What is the difference in Log-Loss between the network with no regularisation and the network with adaptive weight noise?", "answer": "So, the answer is {\"answer\": \"31.3\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe network with adaptive weight noise regularisation has a lower SSE than the network with no regularisation.\n" }, { "role": "assistant", "content": "The table shows that both the network with no regularisation and the network with adaptive weight noise have the same SSE of 0.23. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1089.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Handwriting Synthesis Results. All results recorded on the validation set. ‘Log-Loss’ is the mean value of L(x) in nats. ‘SSE’ is the mean sum-squared-error per data point." ], "reasoning": "The table shows that both the network with no regularisation and the network with adaptive weight noise have the same SSE of 0.23.", "statement": "The network with adaptive weight noise regularisation has a lower SSE than the network with no regularisation.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: WER of various models on the WSJ corpus. In [13] the CTC baseline is better than ours (5.48%/9.12% with the same architecture and ext. LM), and the eval92 set is used as a validation set.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The EEMMI model consistently outperforms the CTC model in terms of WER, and attention models are inferior to phoneme-based CTC and EEMMI methods.We consider two decoding LMs. The WSJ standard pruned trigram model (std.), and the extended-vocabulary pruned trigram model (ext.). We compare our end-to-end MMI (EEMMI) model to CTC under the same conditions. We see a consistent improvement in WER of EEMMI using bi-grams train LM compared to CTC. It can be observed, that attention models are inferior to phonemes-based CTC and to our method. These well established alignments enable the use of our simple voting scheme. We use models with the same NN architecture we described, trained separately end-to-end. We note that simple posteriors averaging does not work well with CTC, even using models with identical architectures. We perform decoding only once, on the averaged outputs of the models." } ], "images": [ "scigen/train/1090.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: WER of various models on the WSJ corpus. In [13] the CTC baseline is better than ours (5.48%/9.12% with the same architecture and ext. LM), and the eval92 set is used as a validation set." ], "reasoning": "The EEMMI model consistently outperforms the CTC model in terms of WER, and attention models are inferior to phoneme-based CTC and EEMMI methods.", "description": "We consider two decoding LMs. The WSJ standard pruned trigram model (std.), and the extended-vocabulary pruned trigram model (ext.). We compare our end-to-end MMI (EEMMI) model to CTC under the same conditions. We see a consistent improvement in WER of EEMMI using bi-grams train LM compared to CTC. It can be observed, that attention models are inferior to phonemes-based CTC and to our method. These well established alignments enable the use of our simple voting scheme. We use models with the same NN architecture we described, trained separately end-to-end. We note that simple posteriors averaging does not work well with CTC, even using models with identical architectures. We perform decoding only once, on the averaged outputs of the models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: WER of various models on the WSJ corpus. In [13] the CTC baseline is better than ours (5.48%/9.12% with the same architecture and ext. LM), and the eval92 set is used as a validation set.\nQuestion: Which model and LM combination achieved the lowest WER% on dev93?\nSolve the above question based on the table information. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, the lowest WER% on dev93 is 7.34, achieved by the EEMMI model with 3 bi-grams LM. So, the answer is {\"answer\": \"EEMMI, 3 bi-grams\"}." } ], "images": [ "scigen/train/1090.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: WER of various models on the WSJ corpus. In [13] the CTC baseline is better than ours (5.48%/9.12% with the same architecture and ext. LM), and the eval92 set is used as a validation set." ], "reasoning": "By examining the table, the lowest WER% on dev93 is 7.34, achieved by the EEMMI model with 3 bi-grams LM.", "question": "Which model and LM combination achieved the lowest WER% on dev93?", "answer": "So, the answer is {\"answer\": \"EEMMI, 3 bi-grams\"}." }, { "messages": [ { "role": "user", "content": "The EEMMI model with a bi-gram LM achieved a lower WER% on eval92 compared to the CTC model with a standard LM. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe EEMMI model with a bi-gram LM achieved a lower WER% on eval92 compared to the CTC model with a standard LM.\n" }, { "role": "assistant", "content": "The table shows that the EEMMI model with a bi-gram LM (WER% 5.83) has a lower WER% on eval92 compared to the CTC model with a standard LM (WER% 7.66). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1090.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: WER of various models on the WSJ corpus. In [13] the CTC baseline is better than ours (5.48%/9.12% with the same architecture and ext. LM), and the eval92 set is used as a validation set." ], "reasoning": "The table shows that the EEMMI model with a bi-gram LM (WER% 5.83) has a lower WER% on eval92 compared to the CTC model with a standard LM (WER% 7.66).", "statement": "The EEMMI model with a bi-gram LM achieved a lower WER% on eval92 compared to the CTC model with a standard LM.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Results on WMT’14 English-German translation.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our approach, built on top of the Transformer model, shows a notable gain in BLEU scores for English-German translation compared to both the Transformer and RNMT+ models.We compare our approach with Transformer for different numbers of hidden units (i.e. 1024 and 512) and a related RNN-based NMT model RNMT+ Chen et al. Recall that our approach is built on top of the Transformer model. The notable gain in terms of BLEU verifies our English-German translation model." } ], "images": [ "scigen/train/1091.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on WMT’14 English-German translation." ], "reasoning": "Our approach, built on top of the Transformer model, shows a notable gain in BLEU scores for English-German translation compared to both the Transformer and RNMT+ models.", "description": "We compare our approach with Transformer for different numbers of hidden units (i.e. 1024 and 512) and a related RNN-based NMT model RNMT+ Chen et al. Recall that our approach is built on top of the Transformer model. The notable gain in terms of BLEU verifies our English-German translation model.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 4: Results on WMT’14 English-German translation.\nQuestion: Which model achieved the highest BLEU score in the comparison?\n" }, { "role": "assistant", "content": "By examining the BLEU scores in the table, the highest score is 30.01, which is achieved by our Trans.-Big model. So, the answer is {\"answer\": \"30.01\"}." } ], "images": [ "scigen/train/1091.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on WMT’14 English-German translation." ], "reasoning": "By examining the BLEU scores in the table, the highest score is 30.01, which is achieved by our Trans.-Big model.", "question": "Which model achieved the highest BLEU score in the comparison?", "answer": "So, the answer is {\"answer\": \"30.01\"}." }, { "messages": [ { "role": "user", "content": "The RNMT+ model achieved a higher BLEU score than the Transformer-Base model by Vaswani et al. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe RNMT+ model achieved a higher BLEU score than the Transformer-Base model by Vaswani et al.\n" }, { "role": "assistant", "content": "The BLEU score for the RNMT+ model is 28.49, while the BLEU score for the Transformer-Base model by Vaswani et al. is 27.30, indicating that the RNMT+ model has a higher score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1091.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on WMT’14 English-German translation." ], "reasoning": "The BLEU score for the RNMT+ model is 28.49, while the BLEU score for the Transformer-Base model by Vaswani et al. is 27.30, indicating that the RNMT+ model has a higher score.", "statement": "The RNMT+ model achieved a higher BLEU score than the Transformer-Base model by Vaswani et al.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Comparison with baseline methods trained on different backbone models (second column). * indicates the method trained using an extra corpus.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Our method achieves the best results without extra corpora and gains an additional 1-3 points over Sennrich et al. with back-translation.Among all methods trained without extra corpora, our approach achieves the best result across datasets. After incorporating the back-translated corpus, our method yields an additional gain of 1-3 points over Sennrich et al. Since all methods are built on top of the same backbone, the result substantiates the efficacy of our method on the standard benchmarks that contain natural noise. Compared to Miyato et al." } ], "images": [ "scigen/train/1092.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison with baseline methods trained on different backbone models (second column). * indicates the method trained using an extra corpus." ], "reasoning": "Our method achieves the best results without extra corpora and gains an additional 1-3 points over Sennrich et al. with back-translation.", "description": "Among all methods trained without extra corpora, our approach achieves the best result across datasets. After incorporating the back-translated corpus, our method yields an additional gain of 1-3 points over Sennrich et al. Since all methods are built on top of the same backbone, the result substantiates the efficacy of our method on the standard benchmarks that contain natural noise. Compared to Miyato et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: Comparison with baseline methods trained on different backbone models (second column). * indicates the method trained using an extra corpus.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. \n\nQuestion: What is the highest score achieved by 'Ours + BackTranslation' across all datasets?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the highest score achieved by 'Ours + BackTranslation', look at the scores in the row corresponding to 'Ours + BackTranslation' and identify the maximum value. So, the answer is {\"answer\": \"49.13\"}" } ], "images": [ "scigen/train/1092.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison with baseline methods trained on different backbone models (second column). * indicates the method trained using an extra corpus." ], "reasoning": "To find the highest score achieved by 'Ours + BackTranslation', look at the scores in the row corresponding to 'Ours + BackTranslation' and identify the maximum value.", "question": "What is the highest score achieved by 'Ours + BackTranslation' across all datasets?", "answer": "So, the answer is {\"answer\": \"49.13\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nOur method achieves the best result on MT03 without extra corpora.\n" }, { "role": "assistant", "content": "The table shows that 'Ours' achieves a score of 47.83 on MT03, which is higher than any other method trained without extra corpora. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1092.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison with baseline methods trained on different backbone models (second column). * indicates the method trained using an extra corpus." ], "reasoning": "The table shows that 'Ours' achieves a score of 47.83 on MT03, which is higher than any other method trained without extra corpora.", "statement": "Our method achieves the best result on MT03 without extra corpora.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Results on NIST Chinese-English translation.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Our method significantly outperforms the Transformer model by Vaswani et al., achieving an average gain of 2.25 BLEU points across validation and test sets, with up to 2.8 BLEU points on NIST03.We first compare our approach with the Transformer model Vaswani et al. As we see, the introduction of our method to the standard backbone model (Trans.-Base) leads to substantial improvements across the validation and test sets. Specifically, our approach achieves an average gain of 2.25 BLEU points and up to 2.8 BLEU points on NIST03." } ], "images": [ "scigen/train/1093.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on NIST Chinese-English translation." ], "reasoning": "Our method significantly outperforms the Transformer model by Vaswani et al., achieving an average gain of 2.25 BLEU points across validation and test sets, with up to 2.8 BLEU points on NIST03.", "description": "We first compare our approach with the Transformer model Vaswani et al. As we see, the introduction of our method to the standard backbone model (Trans.-Base) leads to substantial improvements across the validation and test sets. Specifically, our approach achieves an average gain of 2.25 BLEU points and up to 2.8 BLEU points on NIST03.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 3: Results on NIST Chinese-English translation.', answer the question below by showing each progressive step toward the answer. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the BLEU score improvement of our method over Vaswani et al.'s method on the MT03 dataset?\n" }, { "role": "assistant", "content": "The BLEU score for Vaswani et al.'s method on MT03 is 43.68, while our method achieves 46.48. The improvement is calculated as 46.48 - 43.68. So, the answer is {\"answer\": \"2.8\"}" } ], "images": [ "scigen/train/1093.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on NIST Chinese-English translation." ], "reasoning": "The BLEU score for Vaswani et al.'s method on MT03 is 43.68, while our method achieves 46.48. The improvement is calculated as 46.48 - 43.68.", "question": "What is the BLEU score improvement of our method over Vaswani et al.'s method on the MT03 dataset?", "answer": "So, the answer is {\"answer\": \"2.8\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nOur method achieves a BLEU score of 47.06 on the MT02 dataset.\n" }, { "role": "assistant", "content": "The table shows that our method achieves a BLEU score of 47.06 on the MT02 dataset, which supports the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1093.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on NIST Chinese-English translation." ], "reasoning": "The table shows that our method achieves a BLEU score of 47.06 on the MT02 dataset, which supports the statement.", "statement": "Our method achieves a BLEU score of 47.06 on the MT02 dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 9: Effect of the ratio value γsrc and γtrg on Chinese-English Translation.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The best BLEU score on the Chinese-English validation set is achieved with γsrc=0.25 and γtrg=0.50, and a non-zero γtrg always improves performance.The hyper-parameters γsrc and γtrg control the ratio of word replacement in the source and target inputs. As we see, the performance is relatively insensitive to the values of these hyper-parameters, and the best configuration on the Chinese-English validation set is obtained at γsrc=0.25 and γtrg=0.50. We found that a non-zero γtrg always yields improvements when compared to the result of γtrg=0. While γsrc=0.25 increases BLEU scores for all the values of γtrg, a larger γsrc seems to be damaging." } ], "images": [ "scigen/train/1094.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Effect of the ratio value γsrc and γtrg on Chinese-English Translation." ], "reasoning": "The best BLEU score on the Chinese-English validation set is achieved with γsrc=0.25 and γtrg=0.50, and a non-zero γtrg always improves performance.", "description": "The hyper-parameters γsrc and γtrg control the ratio of word replacement in the source and target inputs. As we see, the performance is relatively insensitive to the values of these hyper-parameters, and the best configuration on the Chinese-English validation set is obtained at γsrc=0.25 and γtrg=0.50. We found that a non-zero γtrg always yields improvements when compared to the result of γtrg=0. While γsrc=0.25 increases BLEU scores for all the values of γtrg, a larger γsrc seems to be damaging.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 9: Effect of the ratio value γsrc and γtrg on Chinese-English Translation.', answer the question below by showing each progressive step toward the answer. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the BLEU score when γsrc=0.25 and γtrg=0.50?\n" }, { "role": "assistant", "content": "To find the BLEU score for γsrc=0.25 and γtrg=0.50, locate the row for γsrc=0.25 and the column for γtrg=0.50 in the table. The intersection gives the BLEU score. So, the answer is {\"answer\": \"46.95\"}" } ], "images": [ "scigen/train/1094.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Effect of the ratio value γsrc and γtrg on Chinese-English Translation." ], "reasoning": "To find the BLEU score for γsrc=0.25 and γtrg=0.50, locate the row for γsrc=0.25 and the column for γtrg=0.50 in the table. The intersection gives the BLEU score.", "question": "What is the BLEU score when γsrc=0.25 and γtrg=0.50?", "answer": "So, the answer is {\"answer\": \"46.95\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nA non-zero γtrg always yields improvements compared to γtrg=0.\n" }, { "role": "assistant", "content": "The table shows that for each value of γsrc, the BLEU scores for non-zero γtrg (0.25, 0.50, 0.75) are higher than the BLEU score for γtrg=0. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1094.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Effect of the ratio value γsrc and γtrg on Chinese-English Translation." ], "reasoning": "The table shows that for each value of γsrc, the BLEU scores for non-zero γtrg (0.25, 0.50, 0.75) are higher than the BLEU score for γtrg=0.", "statement": "A non-zero γtrg always yields improvements compared to γtrg=0.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'TABLE IV: Comparison with the state-of-the-art systems proposed by [16] on emotion dataset. The metrics P, R and F stand for Precision, Recall and F1-Score.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The proposed system improves the F-scores for all emotions except surprise, achieving an overall improvement of 5 F-Score points over the existing state-of-the-art system.Overall, our proposed system achieves an improvement of 5 F-Score points over the existing state-of-the-art system for emotion analysis. Individually, the proposed system improves the existing F-scores for all the emotions except surprise. This could be attributed to the data scarcity and a very low agreement between the annotators for the emotion surprise." } ], "images": [ "scigen/train/1095.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Comparison with the state-of-the-art systems proposed by [16] on emotion dataset. The metrics P, R and F stand for Precision, Recall and F1-Score." ], "reasoning": "The proposed system improves the F-scores for all emotions except surprise, achieving an overall improvement of 5 F-Score points over the existing state-of-the-art system.", "description": "Overall, our proposed system achieves an improvement of 5 F-Score points over the existing state-of-the-art system for emotion analysis. Individually, the proposed system improves the existing F-scores for all the emotions except surprise. This could be attributed to the data scarcity and a very low agreement between the annotators for the emotion surprise.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: TABLE IV: Comparison with the state-of-the-art systems proposed by [16] on emotion dataset. The metrics P, R and F stand for Precision, Recall and F1-Score.\nQuestion: What is the F-score improvement for emotion sadness in the proposed system compared to the existing state-of-the-art system?\nGive a detailed response to the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The F-score for emotion sadness in the proposed system is 73, while the highest F-score for emotion sadness in the existing systems is 72 (achieved by BiLSTM). The improvement is 73 - 72 = 1 point. So, the answer is {\"answer\": \"1\"}" } ], "images": [ "scigen/train/1095.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Comparison with the state-of-the-art systems proposed by [16] on emotion dataset. The metrics P, R and F stand for Precision, Recall and F1-Score." ], "reasoning": "The F-score for emotion sadness in the proposed system is 73, while the highest F-score for emotion sadness in the existing systems is 72 (achieved by BiLSTM). The improvement is 73 - 72 = 1 point.", "question": "What is the F-score improvement for emotion sadness in the proposed system compared to the existing state-of-the-art system?", "answer": "So, the answer is {\"answer\": \"1\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe proposed system has the highest F-score for emotion anticipation.\n" }, { "role": "assistant", "content": "The F-score for emotion anticipation in the proposed system is 68, which is lower than the highest F-score of 70 achieved by BiLSTM. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1095.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Comparison with the state-of-the-art systems proposed by [16] on emotion dataset. The metrics P, R and F stand for Precision, Recall and F1-Score." ], "reasoning": "The F-score for emotion anticipation in the proposed system is 68, which is lower than the highest F-score of 70 achieved by BiLSTM.", "statement": "The proposed system has the highest F-score for emotion anticipation.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'TABLE III: Comparison with the state-of-the-art systems of SemEval 2016 task 6 on sentiment dataset.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Our system, M2, achieves an F-score of 82.10, which is 3.2 points higher than the previous best system for SemEval 2016 task 6.Our system comfortably surpasses the existing best system at SemEval. Our system manages to improve the existing best system of SemEval 2016 task 6 by 3.2 F-score points for sentiment analysis." } ], "images": [ "scigen/train/1096.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Comparison with the state-of-the-art systems of SemEval 2016 task 6 on sentiment dataset." ], "reasoning": "Our system, M2, achieves an F-score of 82.10, which is 3.2 points higher than the previous best system for SemEval 2016 task 6.", "description": "Our system comfortably surpasses the existing best system at SemEval. Our system manages to improve the existing best system of SemEval 2016 task 6 by 3.2 F-score points for sentiment analysis.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'TABLE III: Comparison with the state-of-the-art systems of SemEval 2016 task 6 on sentiment dataset.' in mind, break down and resolve the question below step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhat is the F-score difference between the proposed M2 system and the SVM + n-grams + sentiment system?\n" }, { "role": "assistant", "content": "The F-score of the proposed M2 system is 82.10, and the F-score of the SVM + n-grams + sentiment system is 78.90. The difference is calculated as 82.10 - 78.90. So, the answer is {\"answer\": \"3.2\"}" } ], "images": [ "scigen/train/1096.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Comparison with the state-of-the-art systems of SemEval 2016 task 6 on sentiment dataset." ], "reasoning": "The F-score of the proposed M2 system is 82.10, and the F-score of the SVM + n-grams + sentiment system is 78.90. The difference is calculated as 82.10 - 78.90.", "question": "What is the F-score difference between the proposed M2 system and the SVM + n-grams + sentiment system?", "answer": "So, the answer is {\"answer\": \"3.2\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe proposed M2 system has the highest F-score among all listed models.\n" }, { "role": "assistant", "content": "The table shows that the proposed M2 system has an F-score of 82.10, which is higher than all other listed models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1096.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Comparison with the state-of-the-art systems of SemEval 2016 task 6 on sentiment dataset." ], "reasoning": "The table shows that the proposed M2 system has an F-score of 82.10, which is higher than all other listed models.", "statement": "The proposed M2 system has the highest F-score among all listed models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'TABLE V: Confusion matrix for sentiment analysis', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The M2 model struggles with certain emotions due to their underrepresentation in the training set, leading to lower confidence in predicting these emotions.We perform quantitative error analysis for both sentiment and emotion for the M2 model. This may be due to the reason that this particular class is the most underrepresented in the training set. These three emotions have the least share of training instances, making the system less confident towards these emotions." } ], "images": [ "scigen/train/1097.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "The M2 model struggles with certain emotions due to their underrepresentation in the training set, leading to lower confidence in predicting these emotions.", "description": "We perform quantitative error analysis for both sentiment and emotion for the M2 model. This may be due to the reason that this particular class is the most underrepresented in the training set. These three emotions have the least share of training instances, making the system less confident towards these emotions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'TABLE V: Confusion matrix for sentiment analysis', answer the question below by showing each progressive step toward the answer. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the total number of instances where the M2 model predicted positive sentiment?\n" }, { "role": "assistant", "content": "To find the total number of instances where the M2 model predicted positive sentiment, sum the values in the 'Predicted positive' column: 88 (negative) + 325 (positive) = 413. So, the answer is {\"answer\": \"413\"}." } ], "images": [ "scigen/train/1097.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "To find the total number of instances where the M2 model predicted positive sentiment, sum the values in the 'Predicted positive' column: 88 (negative) + 325 (positive) = 413.", "question": "What is the total number of instances where the M2 model predicted positive sentiment?", "answer": "So, the answer is {\"answer\": \"413\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe M2 model correctly predicted more negative instances than positive instances.\n" }, { "role": "assistant", "content": "The table shows that the M2 model correctly predicted 1184 negative instances and 325 positive instances. Since 1184 is greater than 325, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1097.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "The table shows that the M2 model correctly predicted 1184 negative instances and 325 positive instances. Since 1184 is greater than 325, the statement is supported.", "statement": "The M2 model correctly predicted more negative instances than positive instances.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'TABLE V: Confusion matrix for sentiment analysis'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The M2 model shows the highest error rates for the least represented emotions in the training set, affecting its confidence in these predictions.We perform quantitative error analysis for both sentiment and emotion for the M2 model. This may be due to the reason that this particular class is the most underrepresented in the training set. These three emotions have the least share of training instances, making the system less confident towards these emotions." } ], "images": [ "scigen/train/1098.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "The M2 model shows the highest error rates for the least represented emotions in the training set, affecting its confidence in these predictions.", "description": "We perform quantitative error analysis for both sentiment and emotion for the M2 model. This may be due to the reason that this particular class is the most underrepresented in the training set. These three emotions have the least share of training instances, making the system less confident towards these emotions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nTable title: TABLE V: Confusion matrix for sentiment analysis\n\nQuestion: What is the total number of instances where the M2 model predicted 'YES'?\n" }, { "role": "assistant", "content": "To find the total number of 'YES' predictions, sum the values in the 'Predicted YES' column: 242 (NO) + 1002 (YES). So, the answer is {\"answer\": \"1244\"}" } ], "images": [ "scigen/train/1098.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "To find the total number of 'YES' predictions, sum the values in the 'Predicted YES' column: 242 (NO) + 1002 (YES).", "question": "What is the total number of instances where the M2 model predicted 'YES'?", "answer": "So, the answer is {\"answer\": \"1244\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe M2 model correctly predicted 'NO' more times than it incorrectly predicted 'YES'.\n" }, { "role": "assistant", "content": "The table shows that the M2 model correctly predicted 'NO' 388 times and incorrectly predicted 'YES' 242 times. Since 388 is greater than 242, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1098.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "The table shows that the M2 model correctly predicted 'NO' 388 times and incorrectly predicted 'YES' 242 times. Since 388 is greater than 242, the statement is supported.", "statement": "The M2 model correctly predicted 'NO' more times than it incorrectly predicted 'YES'.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'TABLE V: Confusion matrix for sentiment analysis'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The M2 model shows the highest error rates for the least represented emotions in the training set, leading to lower confidence in these predictions.We perform quantitative error analysis for both sentiment and emotion for the M2 model. This may be due to the reason that this particular class is the most underrepresented in the training set. These three emotions have the least share of training instances, making the system less confident towards these emotions." } ], "images": [ "scigen/train/1099.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "The M2 model shows the highest error rates for the least represented emotions in the training set, leading to lower confidence in these predictions.", "description": "We perform quantitative error analysis for both sentiment and emotion for the M2 model. This may be due to the reason that this particular class is the most underrepresented in the training set. These three emotions have the least share of training instances, making the system less confident towards these emotions.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'TABLE V: Confusion matrix for sentiment analysis', solve this question and give detail solutions. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the total number of instances where the actual sentiment was YES?\n" }, { "role": "assistant", "content": "To find the total number of instances where the actual sentiment was YES, sum the values in the 'YES' row: 433 (Predicted NO) + 706 (Predicted YES). So, the answer is {\"answer\": \"1139\"}." } ], "images": [ "scigen/train/1099.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "To find the total number of instances where the actual sentiment was YES, sum the values in the 'YES' row: 433 (Predicted NO) + 706 (Predicted YES).", "question": "What is the total number of instances where the actual sentiment was YES?", "answer": "So, the answer is {\"answer\": \"1139\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe M2 model correctly predicted YES more often than it incorrectly predicted NO.\n" }, { "role": "assistant", "content": "The table shows that the model correctly predicted YES 706 times and incorrectly predicted NO 433 times. Since 706 is greater than 433, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1099.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "The table shows that the model correctly predicted YES 706 times and incorrectly predicted NO 433 times. Since 706 is greater than 433, the statement is supported.", "statement": "The M2 model correctly predicted YES more often than it incorrectly predicted NO.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'TABLE V: Confusion matrix for sentiment analysis'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The M2 model shows quantitative error analysis for sentiment and emotion, with the least represented emotions in the training set leading to lower confidence in predictions.We perform quantitative error analysis for both sentiment and emotion for the M2 model. This may be due to the reason that this particular class is the most underrepresented in the training set. These three emotions have the least share of training instances, making the system less confident towards these emotions." } ], "images": [ "scigen/train/1100.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "The M2 model shows quantitative error analysis for sentiment and emotion, with the least represented emotions in the training set leading to lower confidence in predictions.", "description": "We perform quantitative error analysis for both sentiment and emotion for the M2 model. This may be due to the reason that this particular class is the most underrepresented in the training set. These three emotions have the least share of training instances, making the system less confident towards these emotions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'TABLE V: Confusion matrix for sentiment analysis'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: What is the total number of instances where the model predicted 'YES'?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the total number of instances where the model predicted 'YES', sum the values in the 'Predicted YES' column: 277 (NO) + 656 (YES) = 933. So, the answer is {\"answer\": \"933\"}" } ], "images": [ "scigen/train/1100.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "To find the total number of instances where the model predicted 'YES', sum the values in the 'Predicted YES' column: 277 (NO) + 656 (YES) = 933.", "question": "What is the total number of instances where the model predicted 'YES'?", "answer": "So, the answer is {\"answer\": \"933\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model predicted 'NO' correctly more times than it predicted 'YES' correctly.\n" }, { "role": "assistant", "content": "The table shows that the model predicted 'NO' correctly 665 times and 'YES' correctly 656 times. Since 665 is greater than 656, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1100.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "The table shows that the model predicted 'NO' correctly 665 times and 'YES' correctly 656 times. Since 665 is greater than 656, the statement is supported.", "statement": "The model predicted 'NO' correctly more times than it predicted 'YES' correctly.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'TABLE V: Confusion matrix for sentiment analysis'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The M2 model shows higher errors in sentiment and emotion prediction, particularly for underrepresented classes in the training set.We perform quantitative error analysis for both sentiment and emotion for the M2 model. This may be due to the reason that this particular class is the most underrepresented in the training set. These three emotions have the least share of training instances, making the system less confident towards these emotions." } ], "images": [ "scigen/train/1101.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "The M2 model shows higher errors in sentiment and emotion prediction, particularly for underrepresented classes in the training set.", "description": "We perform quantitative error analysis for both sentiment and emotion for the M2 model. This may be due to the reason that this particular class is the most underrepresented in the training set. These three emotions have the least share of training instances, making the system less confident towards these emotions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'TABLE V: Confusion matrix for sentiment analysis'. Output the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: What is the total number of instances where the actual sentiment was YES?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the total number of instances where the actual sentiment was YES, sum the values in the 'YES' row: 445 (Predicted NO) + 317 (Predicted YES). So, the answer is {\"answer\": \"762\"}" } ], "images": [ "scigen/train/1101.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "To find the total number of instances where the actual sentiment was YES, sum the values in the 'YES' row: 445 (Predicted NO) + 317 (Predicted YES).", "question": "What is the total number of instances where the actual sentiment was YES?", "answer": "So, the answer is {\"answer\": \"762\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe M2 model predicted NO correctly more times than YES.\n" }, { "role": "assistant", "content": "The table shows that the M2 model predicted NO correctly 911 times and YES correctly 317 times, indicating more correct predictions for NO. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1101.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "The table shows that the M2 model predicted NO correctly 911 times and YES correctly 317 times, indicating more correct predictions for NO.", "statement": "The M2 model predicted NO correctly more times than YES.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'TABLE V: Confusion matrix for sentiment analysis'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The M2 model shows quantitative error analysis for sentiment and emotion, with the least represented emotions in the training set leading to lower confidence in predictions.We perform quantitative error analysis for both sentiment and emotion for the M2 model. This may be due to the reason that this particular class is the most underrepresented in the training set. These three emotions have the least share of training instances, making the system less confident towards these emotions." } ], "images": [ "scigen/train/1102.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "The M2 model shows quantitative error analysis for sentiment and emotion, with the least represented emotions in the training set leading to lower confidence in predictions.", "description": "We perform quantitative error analysis for both sentiment and emotion for the M2 model. This may be due to the reason that this particular class is the most underrepresented in the training set. These three emotions have the least share of training instances, making the system less confident towards these emotions.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'TABLE V: Confusion matrix for sentiment analysis' in mind, break down and resolve the question below step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the total number of instances where the actual sentiment was YES?\n" }, { "role": "assistant", "content": "To find the total number of instances where the actual sentiment was YES, sum the values in the 'YES' row: 291 (Predicted NO) + 420 (Predicted YES). So, the answer is {\"answer\": \"711\"}" } ], "images": [ "scigen/train/1102.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "To find the total number of instances where the actual sentiment was YES, sum the values in the 'YES' row: 291 (Predicted NO) + 420 (Predicted YES).", "question": "What is the total number of instances where the actual sentiment was YES?", "answer": "So, the answer is {\"answer\": \"711\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The M2 model correctly predicted 'NO' sentiment 886 times. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe M2 model correctly predicted 'NO' sentiment 886 times.\n" }, { "role": "assistant", "content": "The table shows that the M2 model predicted 'NO' sentiment correctly 886 times, as indicated in the 'Predicted NO' column for the 'NO' row. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1102.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "The table shows that the M2 model predicted 'NO' sentiment correctly 886 times, as indicated in the 'Predicted NO' column for the 'NO' row.", "statement": "The M2 model correctly predicted 'NO' sentiment 886 times.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'TABLE V: Confusion matrix for sentiment analysis', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The M2 model shows quantitative error analysis for sentiment and emotion, with the least represented emotions in the training set leading to lower confidence in predictions.We perform quantitative error analysis for both sentiment and emotion for the M2 model. This may be due to the reason that this particular class is the most underrepresented in the training set. These three emotions have the least share of training instances, making the system less confident towards these emotions." } ], "images": [ "scigen/train/1103.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "The M2 model shows quantitative error analysis for sentiment and emotion, with the least represented emotions in the training set leading to lower confidence in predictions.", "description": "We perform quantitative error analysis for both sentiment and emotion for the M2 model. This may be due to the reason that this particular class is the most underrepresented in the training set. These three emotions have the least share of training instances, making the system less confident towards these emotions.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'TABLE V: Confusion matrix for sentiment analysis' in mind, break down and resolve the question below step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhat is the total number of instances where the actual sentiment was YES?\n" }, { "role": "assistant", "content": "To find the total number of instances where the actual sentiment was YES, sum the values in the 'YES' row: 191 (Predicted NO) + 824 (Predicted YES). So, the answer is {\"answer\": \"1015\"}." } ], "images": [ "scigen/train/1103.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "To find the total number of instances where the actual sentiment was YES, sum the values in the 'YES' row: 191 (Predicted NO) + 824 (Predicted YES).", "question": "What is the total number of instances where the actual sentiment was YES?", "answer": "So, the answer is {\"answer\": \"1015\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The M2 model predicted YES correctly more often than it predicted NO correctly. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe M2 model predicted YES correctly more often than it predicted NO correctly.\n" }, { "role": "assistant", "content": "The table shows that the M2 model predicted YES correctly 824 times and NO correctly 413 times. Since 824 is greater than 413, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1103.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "The table shows that the M2 model predicted YES correctly 824 times and NO correctly 413 times. Since 824 is greater than 413, the statement is supported.", "statement": "The M2 model predicted YES correctly more often than it predicted NO correctly.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'TABLE V: Confusion matrix for sentiment analysis', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The M2 model shows the least confidence in predicting the three least represented emotions in the training set, leading to higher error rates in these classes.We perform quantitative error analysis for both sentiment and emotion for the M2 model. This may be due to the reason that this particular class is the most underrepresented in the training set. These three emotions have the least share of training instances, making the system less confident towards these emotions." } ], "images": [ "scigen/train/1104.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "The M2 model shows the least confidence in predicting the three least represented emotions in the training set, leading to higher error rates in these classes.", "description": "We perform quantitative error analysis for both sentiment and emotion for the M2 model. This may be due to the reason that this particular class is the most underrepresented in the training set. These three emotions have the least share of training instances, making the system less confident towards these emotions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. The question is related to the given table titled 'TABLE V: Confusion matrix for sentiment analysis'.\n\nQuestion: What is the total number of instances where the M2 model predicted 'NO'?\n" }, { "role": "assistant", "content": "To find the total number of 'NO' predictions, sum the values in the 'Predicted NO' column: 1312 (Actual NO) + 426 (Actual YES). So, the answer is {\"answer\": \"1738\"}" } ], "images": [ "scigen/train/1104.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "To find the total number of 'NO' predictions, sum the values in the 'Predicted NO' column: 1312 (Actual NO) + 426 (Actual YES).", "question": "What is the total number of instances where the M2 model predicted 'NO'?", "answer": "So, the answer is {\"answer\": \"1738\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The M2 model correctly predicted 'YES' more often than it incorrectly predicted 'YES'., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe M2 model correctly predicted 'YES' more often than it incorrectly predicted 'YES'.\n" }, { "role": "assistant", "content": "The table shows that the M2 model correctly predicted 'YES' 65 times and incorrectly predicted 'YES' 30 times, which means it correctly predicted 'YES' more often. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1104.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "The table shows that the M2 model correctly predicted 'YES' 65 times and incorrectly predicted 'YES' 30 times, which means it correctly predicted 'YES' more often.", "statement": "The M2 model correctly predicted 'YES' more often than it incorrectly predicted 'YES'.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'TABLE V: Confusion matrix for sentiment analysis'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The M2 model shows the least confidence in emotions that are underrepresented in the training set, leading to higher error rates in these classes.We perform quantitative error analysis for both sentiment and emotion for the M2 model. This may be due to the reason that this particular class is the most underrepresented in the training set. These three emotions have the least share of training instances, making the system less confident towards these emotions." } ], "images": [ "scigen/train/1105.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "The M2 model shows the least confidence in emotions that are underrepresented in the training set, leading to higher error rates in these classes.", "description": "We perform quantitative error analysis for both sentiment and emotion for the M2 model. This may be due to the reason that this particular class is the most underrepresented in the training set. These three emotions have the least share of training instances, making the system less confident towards these emotions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: TABLE V: Confusion matrix for sentiment analysis\nQuestion: What is the total number of instances where the model predicted 'YES'?\nSolve the above question based on the table information. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the total number of instances where the model predicted 'YES', sum the values in the 'Predicted YES' column: 150 (NO) + 316 (YES) = 466. So, the answer is {\"answer\": \"466\"}" } ], "images": [ "scigen/train/1105.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "To find the total number of instances where the model predicted 'YES', sum the values in the 'Predicted YES' column: 150 (NO) + 316 (YES) = 466.", "question": "What is the total number of instances where the model predicted 'YES'?", "answer": "So, the answer is {\"answer\": \"466\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe model predicted 'NO' correctly more times than it predicted 'YES' correctly.\n" }, { "role": "assistant", "content": "The table shows that the model predicted 'NO' correctly 1032 times and 'YES' correctly 316 times. Since 1032 is greater than 316, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1105.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Confusion matrix for sentiment analysis" ], "reasoning": "The table shows that the model predicted 'NO' correctly 1032 times and 'YES' correctly 316 times. Since 1032 is greater than 316, the statement is supported.", "statement": "The model predicted 'NO' correctly more times than it predicted 'YES' correctly.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Readmission prediction performance'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Lin et al. used chart events, demographic information, and diagnosis as input to an LSTM+CNN model, achieving a performance boost over the baseline and surpassing state-of-the-art results.Lin et al. uses chart events, demographic information and diagnosis as input to a LSTM+CNN model. We only use written note text and none of the numerical and time-series information. The primary metric area under ROC clearly shows that the CC model produces a performance boost over the baseline and surpassed state-of-the-art results." } ], "images": [ "scigen/train/1106.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Readmission prediction performance" ], "reasoning": "Lin et al. used chart events, demographic information, and diagnosis as input to an LSTM+CNN model, achieving a performance boost over the baseline and surpassing state-of-the-art results.", "description": "Lin et al. uses chart events, demographic information and diagnosis as input to a LSTM+CNN model. We only use written note text and none of the numerical and time-series information. The primary metric area under ROC clearly shows that the CC model produces a performance boost over the baseline and surpassed state-of-the-art results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 4: Readmission prediction performance\n\nQuestion: Which model has the highest A.R. value?\n" }, { "role": "assistant", "content": "By comparing the A.R. values in the table, the CC-LSTM model has the highest A.R. value of 0.804. So, the answer is {\"answer\": \"CC-LSTM\"}." } ], "images": [ "scigen/train/1106.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Readmission prediction performance" ], "reasoning": "By comparing the A.R. values in the table, the CC-LSTM model has the highest A.R. value of 0.804.", "question": "Which model has the highest A.R. value?", "answer": "So, the answer is {\"answer\": \"CC-LSTM\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe LSTM model has a higher A.P. value than the CC-LSTM model.\n" }, { "role": "assistant", "content": "The table shows that the A.P. value for the LSTM model is 0.600, while the A.P. value for the CC-LSTM model is 0.613. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1106.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Readmission prediction performance" ], "reasoning": "The table shows that the A.P. value for the LSTM model is 0.600, while the A.P. value for the CC-LSTM model is 0.613. Therefore, the statement is refuted.", "statement": "The LSTM model has a higher A.P. value than the CC-LSTM model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Performance on language-inferable and non-language-inferable knowledge', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The CC model performs significantly better on Language-inferable (LI) examples with a 77.6% hit rate at top 10, compared to 20.8% for Non-language-inferable (Non-LI) examples.Break-down analysis To measure the effect of semantic generalizability on model performance, and to understand the performance gap between the CC model and TransE , we first sampled 100 examples from the testing set, and labeled them to two categories: Language-inferable (LI) and Non-language-inferable (Non-LI), following our previous definition. First we observe that 3/4 of the triplets contain knowledge that can be inferred from text. This shows that in medical knowledge graphs, a majority of structured knowledge can potentially be carried by text representations. Making use of concept names can be difference-making in medical knowledge embeddings. In the case of the CC model, on LI type examples it gets to 77 percent hit at top10, while for Non-LI type the performance is much lower and is on par with the TransE model." } ], "images": [ "scigen/train/1107.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance on language-inferable and non-language-inferable knowledge" ], "reasoning": "The CC model performs significantly better on Language-inferable (LI) examples with a 77.6% hit rate at top 10, compared to 20.8% for Non-language-inferable (Non-LI) examples.", "description": "Break-down analysis To measure the effect of semantic generalizability on model performance, and to understand the performance gap between the CC model and TransE , we first sampled 100 examples from the testing set, and labeled them to two categories: Language-inferable (LI) and Non-language-inferable (Non-LI), following our previous definition. First we observe that 3/4 of the triplets contain knowledge that can be inferred from text. This shows that in medical knowledge graphs, a majority of structured knowledge can potentially be carried by text representations. Making use of concept names can be difference-making in medical knowledge embeddings. In the case of the CC model, on LI type examples it gets to 77 percent hit at top10, while for Non-LI type the performance is much lower and is on par with the TransE model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the hit rate at top 10 for Non-language-inferable (Non-LI) examples?\nSolve the above question based on the table titled 'Table 3: Performance on language-inferable and non-language-inferable knowledge'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the hit rate at top 10 for Non-LI examples is 20.8%. So, the answer is {\"answer\": \"20.8\"}." } ], "images": [ "scigen/train/1107.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance on language-inferable and non-language-inferable knowledge" ], "reasoning": "The table shows the hit rate at top 10 for Non-LI examples is 20.8%.", "question": "What is the hit rate at top 10 for Non-language-inferable (Non-LI) examples?", "answer": "So, the answer is {\"answer\": \"20.8\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe CC model has a higher hit rate at top 10 for Non-language-inferable (Non-LI) examples compared to Language-inferable (LI) examples.\n" }, { "role": "assistant", "content": "The table shows that the hit rate at top 10 for LI examples is 77.6%, which is significantly higher than the 20.8% for Non-LI examples. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1107.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance on language-inferable and non-language-inferable knowledge" ], "reasoning": "The table shows that the hit rate at top 10 for LI examples is 77.6%, which is significantly higher than the 20.8% for Non-LI examples.", "statement": "The CC model has a higher hit rate at top 10 for Non-language-inferable (Non-LI) examples compared to Language-inferable (LI) examples.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 6: Post-discharge mortality prediction performance'.\n" }, { "role": "assistant", "content": "The CC-LSTM model shows a consistent performance gain over the LSTM model in predicting post ICU discharge mortality for both 30-day and 1-year periods.In this task we predict post ICU discharge mortality. Note that like in the previous task, results from other works are listed mainly for reference rather than direct comparison, for these models use different information from EHR as input. Although not matching with state-of-the-art, the performance gain of the CC model over an LSTM model is consistent." } ], "images": [ "scigen/train/1108.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Post-discharge mortality prediction performance" ], "reasoning": "The CC-LSTM model shows a consistent performance gain over the LSTM model in predicting post ICU discharge mortality for both 30-day and 1-year periods.", "description": "In this task we predict post ICU discharge mortality. Note that like in the previous task, results from other works are listed mainly for reference rather than direct comparison, for these models use different information from EHR as input. Although not matching with state-of-the-art, the performance gain of the CC model over an LSTM model is consistent.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model has the highest 30-day A.R. for predicting post ICU discharge mortality?\nSolve the above question based on the table titled 'Table 6: Post-discharge mortality prediction performance'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the 30-day A.R. values in the table, the model with the highest 30-day A.R. is identified. So, the answer is {\"answer\": \"(retrospective)\"}." } ], "images": [ "scigen/train/1108.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Post-discharge mortality prediction performance" ], "reasoning": "By comparing the 30-day A.R. values in the table, the model with the highest 30-day A.R. is identified.", "question": "Which model has the highest 30-day A.R. for predicting post ICU discharge mortality?", "answer": "So, the answer is {\"answer\": \"(retrospective)\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe LSTM model has a higher 1-year A.R. than the CC-LSTM model.\n" }, { "role": "assistant", "content": "By comparing the 1-year A.R. values for the LSTM and CC-LSTM models, it is clear that the CC-LSTM model has a higher 1-year A.R. than the LSTM model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1108.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Post-discharge mortality prediction performance" ], "reasoning": "By comparing the 1-year A.R. values for the LSTM and CC-LSTM models, it is clear that the CC-LSTM model has a higher 1-year A.R. than the LSTM model.", "statement": "The LSTM model has a higher 1-year A.R. than the CC-LSTM model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Examples from the validation splits of VQA 1.0 (top) and VizWiz (bottom). For each example, we report the pattern of answers provided by annotators (unique answer: frequency), the prediction of the model, and the scores (note that acm, SeS, MaSSeS are computed using threshold 0.9). Answers that are grouped together by SeS are included in square brackets.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "MaSSeS assigns scores based on semantic consistency, while VQA3+ and acm rely on frequency and other metrics, leading to different scores for similar answers.Starting from VQA 1.0, we notice that examples 1 and 2 are considered as 100% correct by both VQA3 + and MaSSeS. The former metric assigns this score because ‘yellow’ and ‘refrigerator’ have frequency equal to or greater than 4. As for MaSSeS, this score is produced because (a) the two answers have max frequency, and (b) the SeS score assigned to the response pattern is the highest (i.e. 1.0) due to their semantic consistency. That is, all the answers are grouped together since their cosine similarity with the centroid is equal or greater than 0.9. Notably, acm produces a similar score in example 2, but very different (i.e., much lower) in example 1, though the words involved are semantically very similar (very similar colors). Moving to example 3, we observe that MaSSeS assigns a lower score (0.67) compared to VQA3+ (1.0) since SeS makes a fine-grained distinction between generic ‘rackets’ and specific ones (i.e., for ‘tennis’). This proves the validity and precision our semantic similarity component, especially in comparison with acm, whose high score does not account for such distinction (0.98). As for example 4, the score output by MaSSeS (1.0) turns out to be higher than both VQA3+ (0.6) and acm (0.7) due to the extremely high semantic consistency of the answers." } ], "images": [ "scigen/train/1109.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Examples from the validation splits of VQA 1.0 (top) and VizWiz (bottom). For each example, we report the pattern of answers provided by annotators (unique answer: frequency), the prediction of the model, and the scores (note that acm, SeS, MaSSeS are computed using threshold 0.9). Answers that are grouped together by SeS are included in square brackets." ], "reasoning": "MaSSeS assigns scores based on semantic consistency, while VQA3+ and acm rely on frequency and other metrics, leading to different scores for similar answers.", "description": "Starting from VQA 1.0, we notice that examples 1 and 2 are considered as 100% correct by both VQA3 + and MaSSeS. The former metric assigns this score because ‘yellow’ and ‘refrigerator’ have frequency equal to or greater than 4. As for MaSSeS, this score is produced because (a) the two answers have max frequency, and (b) the SeS score assigned to the response pattern is the highest (i.e. 1.0) due to their semantic consistency. That is, all the answers are grouped together since their cosine similarity with the centroid is equal or greater than 0.9. Notably, acm produces a similar score in example 2, but very different (i.e., much lower) in example 1, though the words involved are semantically very similar (very similar colors). Moving to example 3, we observe that MaSSeS assigns a lower score (0.67) compared to VQA3+ (1.0) since SeS makes a fine-grained distinction between generic ‘rackets’ and specific ones (i.e., for ‘tennis’). This proves the validity and precision our semantic similarity component, especially in comparison with acm, whose high score does not account for such distinction (0.98). As for example 4, the score output by MaSSeS (1.0) turns out to be higher than both VQA3+ (0.6) and acm (0.7) due to the extremely high semantic consistency of the answers.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Examples from the validation splits of VQA 1.0 (top) and VizWiz (bottom). For each example, we report the pattern of answers provided by annotators (unique answer: frequency), the prediction of the model, and the scores (note that acm, SeS, MaSSeS are computed using threshold 0.9). Answers that are grouped together by SeS are included in square brackets.', answer the question below by showing each progressive step toward the answer. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhy does MaSSeS assign a lower score for example 3 compared to VQA3+?\n" }, { "role": "assistant", "content": "MaSSeS assigns a lower score (0.67) for example 3 compared to VQA3+ (1.0) because it makes a fine-grained distinction between generic 'rackets' and specific ones (i.e., for 'tennis'), proving the precision of its semantic similarity component. So, the answer is {\"answer\": \"MaSSeS makes a fine-grained distinction between generic and specific rackets.\"}." } ], "images": [ "scigen/train/1109.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Examples from the validation splits of VQA 1.0 (top) and VizWiz (bottom). For each example, we report the pattern of answers provided by annotators (unique answer: frequency), the prediction of the model, and the scores (note that acm, SeS, MaSSeS are computed using threshold 0.9). Answers that are grouped together by SeS are included in square brackets." ], "reasoning": "MaSSeS assigns a lower score (0.67) for example 3 compared to VQA3+ (1.0) because it makes a fine-grained distinction between generic 'rackets' and specific ones (i.e., for 'tennis'), proving the precision of its semantic similarity component.", "question": "Why does MaSSeS assign a lower score for example 3 compared to VQA3+?", "answer": "So, the answer is {\"answer\": \"MaSSeS makes a fine-grained distinction between generic and specific rackets.\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nMaSSeS assigns the highest score for example 4 due to the high semantic consistency of the answers.\n" }, { "role": "assistant", "content": "The table shows that MaSSeS assigns a score of 1.0 for example 4, which is higher than both VQA3+ (0.6) and acm (0.7), due to the extremely high semantic consistency of the answers. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1109.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Examples from the validation splits of VQA 1.0 (top) and VizWiz (bottom). For each example, we report the pattern of answers provided by annotators (unique answer: frequency), the prediction of the model, and the scores (note that acm, SeS, MaSSeS are computed using threshold 0.9). Answers that are grouped together by SeS are included in square brackets." ], "reasoning": "The table shows that MaSSeS assigns a score of 1.0 for example 4, which is higher than both VQA3+ (0.6) and acm (0.7), due to the extremely high semantic consistency of the answers.", "statement": "MaSSeS assigns the highest score for example 4 due to the high semantic consistency of the answers.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: Results of VQA3+, WUPS-acm, WUPS–mcm, MaSSeS and its components on four VQA datasets.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Accuracies obtained with both versions of MaSSeS are generally lower compared to those of VQA3+, with the drop being particularly accentuated for VizWiz.Note that columns VQA3 + , WUPS-acm, WUPS-mcm, Ma, MaS, and MaSSeS are accuracies, while S and SeS are reliability scores. As can be noted, accuracies obtained with both versions of MaSSeS are generally lower compared to those of VQA3+, with the drop being particularly accentuated for VizWiz. As can be seen, the scores produced by our metric (blue) are ‘distributed’ across the x-axis (from 0 to 1), while those produced by VQA3+ (red) are grouped into 5 ‘classes’. Moreover, we observe that our metric is much more reluctant to output score 1. (recall that if an element is not max it is not considered as 100% correct by Ma). This drop is further accentuated by multiplying Ma by either S (to obtain MaS) or SeS (to obtain MaSSeS). Since the values of these components cannot exceed 1, the resulting score will be lowered according to the degree of subjectivity of the dataset. Bearing this in mind, it is worth focusing on the scores of S and SeS in each dataset. As can be noticed, S in VQA is relatively high, with most of the answers being grouped in the rightmost bars (0.8 or more). In contrast, we observe an almost normal distribution of S in VizWiz, with very few answers being scored with high values. When injecting semantic information into subjectivity (SeS0.9), however, the distribution changes. Indeed, we observe much less cases scored with extremely low values and much many cases with high values. In numbers, this is reflected in an overall increase of 8 points from S (0.46) to SeS (0.54). A similar pattern is also observed in VQA 1.0 (+5 points). It is worth mentioning that using a lowest similarity threshold (0.7) makes the increase between S and SeS even bigger. This, in turn, makes the MaSSeS score significantly higher and comparable to VQA3+ in the three VQA-based datasets (not for VizWiz)." } ], "images": [ "scigen/train/1110.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results of VQA3+, WUPS-acm, WUPS–mcm, MaSSeS and its components on four VQA datasets." ], "reasoning": "Accuracies obtained with both versions of MaSSeS are generally lower compared to those of VQA3+, with the drop being particularly accentuated for VizWiz.", "description": "Note that columns VQA3 + , WUPS-acm, WUPS-mcm, Ma, MaS, and MaSSeS are accuracies, while S and SeS are reliability scores. As can be noted, accuracies obtained with both versions of MaSSeS are generally lower compared to those of VQA3+, with the drop being particularly accentuated for VizWiz. As can be seen, the scores produced by our metric (blue) are ‘distributed’ across the x-axis (from 0 to 1), while those produced by VQA3+ (red) are grouped into 5 ‘classes’. Moreover, we observe that our metric is much more reluctant to output score 1. (recall that if an element is not max it is not considered as 100% correct by Ma). This drop is further accentuated by multiplying Ma by either S (to obtain MaS) or SeS (to obtain MaSSeS). Since the values of these components cannot exceed 1, the resulting score will be lowered according to the degree of subjectivity of the dataset. Bearing this in mind, it is worth focusing on the scores of S and SeS in each dataset. As can be noticed, S in VQA is relatively high, with most of the answers being grouped in the rightmost bars (0.8 or more). In contrast, we observe an almost normal distribution of S in VizWiz, with very few answers being scored with high values. When injecting semantic information into subjectivity (SeS0.9), however, the distribution changes. Indeed, we observe much less cases scored with extremely low values and much many cases with high values. In numbers, this is reflected in an overall increase of 8 points from S (0.46) to SeS (0.54). A similar pattern is also observed in VQA 1.0 (+5 points). It is worth mentioning that using a lowest similarity threshold (0.7) makes the increase between S and SeS even bigger. This, in turn, makes the MaSSeS score significantly higher and comparable to VQA3+ in the three VQA-based datasets (not for VizWiz).", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 1: Results of VQA3+, WUPS-acm, WUPS–mcm, MaSSeS and its components on four VQA datasets.', and sequentially solve the question, writing out each step of your reasoning process. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich dataset shows the largest drop in accuracy when comparing VQA3+ to MaSSeS?\n" }, { "role": "assistant", "content": "By comparing the accuracies of VQA3+ and MaSSeS across all datasets, VizWiz shows the largest drop from 0.448 (VQA3+) to 0.227 (MaSSeS0.9). So, the answer is {\"answer\": \"VizWiz\"}." } ], "images": [ "scigen/train/1110.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results of VQA3+, WUPS-acm, WUPS–mcm, MaSSeS and its components on four VQA datasets." ], "reasoning": "By comparing the accuracies of VQA3+ and MaSSeS across all datasets, VizWiz shows the largest drop from 0.448 (VQA3+) to 0.227 (MaSSeS0.9).", "question": "Which dataset shows the largest drop in accuracy when comparing VQA3+ to MaSSeS?", "answer": "So, the answer is {\"answer\": \"VizWiz\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe accuracy of MaSSeS0.9 for VQA 1.0 is higher than that of VQA3+.\n" }, { "role": "assistant", "content": "The table shows that the accuracy of MaSSeS0.9 for VQA 1.0 is 0.567, which is higher than the accuracy of VQA3+ for VQA 1.0, which is 0.542. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1110.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results of VQA3+, WUPS-acm, WUPS–mcm, MaSSeS and its components on four VQA datasets." ], "reasoning": "The table shows that the accuracy of MaSSeS0.9 for VQA 1.0 is 0.567, which is higher than the accuracy of VQA3+ for VQA 1.0, which is 0.542.", "statement": "The accuracy of MaSSeS0.9 for VQA 1.0 is higher than that of VQA3+.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Accuracy and macro-F1 (%) for aspect based sentiment analysis on three popular datasets.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Since BERT-based models outperform non-BERT-based models on the ABSA task, comparisons are only made with BERT-based models.Since BERT outperforms previous non-BERT-based studies on ABSA task by a large margin, we are not going to compare our models with non-BERT-based models." } ], "images": [ "scigen/train/1111.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracy and macro-F1 (%) for aspect based sentiment analysis on three popular datasets." ], "reasoning": "Since BERT-based models outperform non-BERT-based models on the ABSA task, comparisons are only made with BERT-based models.", "description": "Since BERT outperforms previous non-BERT-based studies on ABSA task by a large margin, we are not going to compare our models with non-BERT-based models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 2: Accuracy and macro-F1 (%) for aspect based sentiment analysis on three popular datasets.', unfold the question presented below, detailing every step of your calculation. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich BERT-based model has the highest Restaurant F1 score?\n" }, { "role": "assistant", "content": "To find the highest Restaurant F1 score, compare the Restaurant F1 scores of all BERT-based models. The highest score is 75.89, achieved by BERT-PT-Attention. So, the answer is {\"answer\": \"BERT-PT-Attention\"}" } ], "images": [ "scigen/train/1111.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracy and macro-F1 (%) for aspect based sentiment analysis on three popular datasets." ], "reasoning": "To find the highest Restaurant F1 score, compare the Restaurant F1 scores of all BERT-based models. The highest score is 75.89, achieved by BERT-PT-Attention.", "question": "Which BERT-based model has the highest Restaurant F1 score?", "answer": "So, the answer is {\"answer\": \"BERT-PT-Attention\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nBERT-PT-LSTM has the highest Laptop F1 score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Laptop F1 scores of all models. BERT-PT-Attention has the highest Laptop F1 score of 72.57, not BERT-PT-LSTM. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1111.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracy and macro-F1 (%) for aspect based sentiment analysis on three popular datasets." ], "reasoning": "To verify this statement, compare the Laptop F1 scores of all models. BERT-PT-Attention has the highest Laptop F1 score of 72.57, not BERT-PT-LSTM.", "statement": "BERT-PT-LSTM has the highest Laptop F1 score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: Classification accuracy (%) for natural language inference on SNLI dataset. Results with “*” are obtained from the official SNLI leaderboard (https://nlp.stanford.edu/projects/snli/).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "BERT-Attention and BERT-LSTM outperform vanilla BERT\\textscBASE, while MT-DNN-Attention and MT-DNN-LSTM outperform vanilla MT-DNN on Dev set but are slightly inferior on Test set.From the results, BERT-Attention and BERT-LSTM perform better than vanilla BERT\\textscBASE. Furthermore, MT-DNN-Attention and MT-DNN-LSTM outperform vanilla MT-DNN on Dev set, and are slightly inferior to vanilla MT-DNN on Test set. As a whole, our pooling strategies generally improve the vanilla BERT-based model, which draws the same conclusion as on ABSA." } ], "images": [ "scigen/train/1112.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Classification accuracy (%) for natural language inference on SNLI dataset. Results with “*” are obtained from the official SNLI leaderboard (https://nlp.stanford.edu/projects/snli/)." ], "reasoning": "BERT-Attention and BERT-LSTM outperform vanilla BERT\\textscBASE, while MT-DNN-Attention and MT-DNN-LSTM outperform vanilla MT-DNN on Dev set but are slightly inferior on Test set.", "description": "From the results, BERT-Attention and BERT-LSTM perform better than vanilla BERT\\textscBASE. Furthermore, MT-DNN-Attention and MT-DNN-LSTM outperform vanilla MT-DNN on Dev set, and are slightly inferior to vanilla MT-DNN on Test set. As a whole, our pooling strategies generally improve the vanilla BERT-based model, which draws the same conclusion as on ABSA.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: Classification accuracy (%) for natural language inference on SNLI dataset. Results with “*” are obtained from the official SNLI leaderboard (https://nlp.stanford.edu/projects/snli/).'. Solve the following question based on the table.\n\nQuestion: Which model has the highest Dev set score and how does it compare to the vanilla BERT\\textscBASE on the Test set?\nLet's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "MT-DNN-LSTM has the highest Dev set score of 91.50. Comparing it to the vanilla BERT\\textscBASE on the Test set, MT-DNN-LSTM scores 90.91 while BERT\\textscBASE scores 90.66. So, the answer is {\"answer\": \"MT-DNN-LSTM\"}." } ], "images": [ "scigen/train/1112.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Classification accuracy (%) for natural language inference on SNLI dataset. Results with “*” are obtained from the official SNLI leaderboard (https://nlp.stanford.edu/projects/snli/)." ], "reasoning": "MT-DNN-LSTM has the highest Dev set score of 91.50. Comparing it to the vanilla BERT\\textscBASE on the Test set, MT-DNN-LSTM scores 90.91 while BERT\\textscBASE scores 90.66.", "question": "Which model has the highest Dev set score and how does it compare to the vanilla BERT\\textscBASE on the Test set?", "answer": "So, the answer is {\"answer\": \"MT-DNN-LSTM\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nMT-DNN-LSTM has the highest Test set score.\n" }, { "role": "assistant", "content": "The table shows that MT-DNN-LSTM has a Test set score of 90.91, which is not the highest. The highest Test set score is 91.00 by MT-DNN. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1112.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Classification accuracy (%) for natural language inference on SNLI dataset. Results with “*” are obtained from the official SNLI leaderboard (https://nlp.stanford.edu/projects/snli/)." ], "reasoning": "The table shows that MT-DNN-LSTM has a Test set score of 90.91, which is not the highest. The highest Test set score is 91.00 by MT-DNN.", "statement": "MT-DNN-LSTM has the highest Test set score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Test performance (classification error) as polarity classifiers. LL stands for LibLinear (SVM), μi, γu.γi, fA, fT are the recommender systems as in table 2. LL + fA and LL + fT are two hybrid opinion classification models combining the SVM classifier and fA and fT recommender systems.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The hybrid models fA and fT have lower classification errors than other recommender systems, and the LL + fA and LL + fT approaches combine text-based SVM decisions with user profiles.Because they give very poor performance, the bias recommendation models (μ and μu) are not presented here. The item bias μi, second column, gives a baseline, which is improved by the matrix factorization γu.γi, third column. Our hybrid models fA, fourth column, and fT, fifth column, have lower classification errors than all the other recommender systems. The first column, LL is the linear support vector machine (SVM) baseline. It has been learnt on the training set texts, and the regularization hyperparameter has been selected using the validation set. Our implementation relies on liblinear (LL) The resulting hybrid approaches, denoted LL + fA and LL + fT, exploit both text based decision (SVM) and user profile (fA and fT)." } ], "images": [ "scigen/train/1113.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Test performance (classification error) as polarity classifiers. LL stands for LibLinear (SVM), μi, γu.γi, fA, fT are the recommender systems as in table 2. LL + fA and LL + fT are two hybrid opinion classification models combining the SVM classifier and fA and fT recommender systems." ], "reasoning": "The hybrid models fA and fT have lower classification errors than other recommender systems, and the LL + fA and LL + fT approaches combine text-based SVM decisions with user profiles.", "description": "Because they give very poor performance, the bias recommendation models (μ and μu) are not presented here. The item bias μi, second column, gives a baseline, which is improved by the matrix factorization γu.γi, third column. Our hybrid models fA, fourth column, and fT, fifth column, have lower classification errors than all the other recommender systems. The first column, LL is the linear support vector machine (SVM) baseline. It has been learnt on the training set texts, and the regularization hyperparameter has been selected using the validation set. Our implementation relies on liblinear (LL) The resulting hybrid approaches, denoted LL + fA and LL + fT, exploit both text based decision (SVM) and user profile (fA and fT).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Test performance (classification error) as polarity classifiers. LL stands for LibLinear (SVM), μi, γu.γi, fA, fT are the recommender systems as in table 2. LL + fA and LL + fT are two hybrid opinion classification models combining the SVM classifier and fA and fT recommender systems.\nQuestion: Which model has the lowest classification error for the RB_U50_I200 subset?\nSolve the above question based on the table information. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows classification errors for different models across various subsets. For the RB_U50_I200 subset, the LL + fA and LL + fT models have the lowest classification error of 3.79. So, the answer is {\"answer\": \"LL + fA and LL + fT\"}" } ], "images": [ "scigen/train/1113.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Test performance (classification error) as polarity classifiers. LL stands for LibLinear (SVM), μi, γu.γi, fA, fT are the recommender systems as in table 2. LL + fA and LL + fT are two hybrid opinion classification models combining the SVM classifier and fA and fT recommender systems." ], "reasoning": "The table shows classification errors for different models across various subsets. For the RB_U50_I200 subset, the LL + fA and LL + fT models have the lowest classification error of 3.79.", "question": "Which model has the lowest classification error for the RB_U50_I200 subset?", "answer": "So, the answer is {\"answer\": \"LL + fA and LL + fT\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe LL model has the highest classification error for the A_U200_I120 subset.\n" }, { "role": "assistant", "content": "The table shows that for the A_U200_I120 subset, the LL model has a classification error of 10.00, which is lower than the errors for μi (15.83) and γu.γi (22.50), but higher than fA (20.00) and fT (20.83). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1113.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Test performance (classification error) as polarity classifiers. LL stands for LibLinear (SVM), μi, γu.γi, fA, fT are the recommender systems as in table 2. LL + fA and LL + fT are two hybrid opinion classification models combining the SVM classifier and fA and fT recommender systems." ], "reasoning": "The table shows that for the A_U200_I120 subset, the LL model has a classification error of 10.00, which is lower than the errors for μi (15.83) and γu.γi (22.50), but higher than fA (20.00) and fT (20.83).", "statement": "The LL model has the highest classification error for the A_U200_I120 subset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Precision of retrieval for the set-follow operation with various count-min sketches.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The addition of sketches significantly improves precision, especially for larger sets, with substantial gains even for k=10.The results show that sketches are essential for obtaining high precision. This is true especially for larger sets, but even for k=10, substantial gains in precision are obtained by the addition of sketches." } ], "images": [ "scigen/train/1114.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Precision of retrieval for the set-follow operation with various count-min sketches." ], "reasoning": "The addition of sketches significantly improves precision, especially for larger sets, with substantial gains even for k=10.", "description": "The results show that sketches are essential for obtaining high precision. This is true especially for larger sets, but even for k=10, substantial gains in precision are obtained by the addition of sketches.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Precision of retrieval for the set-follow operation with various count-min sketches.', answer the following question. Give detailed solution consisting of each step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the precision for k=1000 with width=2000?\n" }, { "role": "assistant", "content": "To find the precision for k=1000 with width=2000, look at the row labeled 'width=2000' and the column labeled 'k=1000'. The value is 99.98. So, the answer is {\"answer\": \"99.98\"}." } ], "images": [ "scigen/train/1114.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Precision of retrieval for the set-follow operation with various count-min sketches." ], "reasoning": "To find the precision for k=1000 with width=2000, look at the row labeled 'width=2000' and the column labeled 'k=1000'. The value is 99.98.", "question": "What is the precision for k=1000 with width=2000?", "answer": "So, the answer is {\"answer\": \"99.98\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe precision for k=10 with no sketch is lower than with width=200.\n" }, { "role": "assistant", "content": "To verify this statement, compare the precision values for k=10 in the 'no sketch' row and the 'width=200' row. The precision for 'no sketch' is 89.8, and for 'width=200' it is 97.5, which is higher. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1114.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Precision of retrieval for the set-follow operation with various count-min sketches." ], "reasoning": "To verify this statement, compare the precision values for k=10 in the 'no sketch' row and the 'width=200' row. The precision for 'no sketch' is 89.8, and for 'width=200' it is 97.5, which is higher.", "statement": "The precision for k=10 with no sketch is lower than with width=200.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Precision, recall and F1 for model jointly trained on all four reasoning tasks.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table shows that precision is consistently high (99% or better) across all sets and k values, while recall and F1 scores vary depending on the set and k value.We measured the precision, recall and F1 of the top k values in each computed set (averaged across the test cases). The sketches ensure that sets are high precision (for this sketch size, precision is always 99% or better), but the experiments verify that training can make the sets coherent, so relatively small k can be used while still obtaining good recall." } ], "images": [ "scigen/train/1115.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Precision, recall and F1 for model jointly trained on all four reasoning tasks." ], "reasoning": "The table shows that precision is consistently high (99% or better) across all sets and k values, while recall and F1 scores vary depending on the set and k value.", "description": "We measured the precision, recall and F1 of the top k values in each computed set (averaged across the test cases). The sketches ensure that sets are high precision (for this sketch size, precision is always 99% or better), but the experiments verify that training can make the sets coherent, so relatively small k can be used while still obtaining good recall.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 1: Precision, recall and F1 for model jointly trained on all four reasoning tasks.' and methodically tackle the question that follows. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the recall for the Union set when k=100?\n" }, { "role": "assistant", "content": "To find the recall for the Union set when k=100, locate the Union row and the column corresponding to k=100. The recall value is 59.7. So, the answer is {\"answer\": \"59.7\"}." } ], "images": [ "scigen/train/1115.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Precision, recall and F1 for model jointly trained on all four reasoning tasks." ], "reasoning": "To find the recall for the Union set when k=100, locate the Union row and the column corresponding to k=100. The recall value is 59.7.", "question": "What is the recall for the Union set when k=100?", "answer": "So, the answer is {\"answer\": \"59.7\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe precision for the Follow set is 100.0 for all k values.\n" }, { "role": "assistant", "content": "The table shows that the precision (P) for the Follow set is 100.0 for k=1, k=10, k=100, and k=1000. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1115.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Precision, recall and F1 for model jointly trained on all four reasoning tasks." ], "reasoning": "The table shows that the precision (P) for the Follow set is 100.0 for k=1, k=10, k=100, and k=1000.", "statement": "The precision for the Follow set is 100.0 for all k values.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Recall of set-follow operation with joint training vs. single task.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Joint training improves recall performance at all values of k, approaching the theoretical maximum recall.We also report here the maximum recall obtainable by retrieving k results. Joint training improves performance at all values of k, and brings performance close to the theoretical optimum. The benefits of joint training are most likely due to the fact that the requirement of coherency for embeddings is shared by all operations." } ], "images": [ "scigen/train/1116.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Recall of set-follow operation with joint training vs. single task." ], "reasoning": "Joint training improves recall performance at all values of k, approaching the theoretical maximum recall.", "description": "We also report here the maximum recall obtainable by retrieving k results. Joint training improves performance at all values of k, and brings performance close to the theoretical optimum. The benefits of joint training are most likely due to the fact that the requirement of coherency for embeddings is shared by all operations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Recall of set-follow operation with joint training vs. single task.'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: What is the recall value for k=100 when using multi-task training?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows recall values for different k values under single-task and multi-task training. For k=100, the recall value under multi-task training is 81.7. So, the answer is {\"answer\": \"81.7\"}." } ], "images": [ "scigen/train/1116.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Recall of set-follow operation with joint training vs. single task." ], "reasoning": "The table shows recall values for different k values under single-task and multi-task training. For k=100, the recall value under multi-task training is 81.7.", "question": "What is the recall value for k=100 when using multi-task training?", "answer": "So, the answer is {\"answer\": \"81.7\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The recall value for k=10 is higher in multi-task training than in single-task training., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe recall value for k=10 is higher in multi-task training than in single-task training.\n" }, { "role": "assistant", "content": "The table shows that for k=10, the recall value is 52.1 for multi-task training and 49.2 for single-task training, indicating that the recall value is indeed higher in multi-task training. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1116.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Recall of set-follow operation with joint training vs. single task." ], "reasoning": "The table shows that for k=10, the recall value is 52.1 for multi-task training and 49.2 for single-task training, indicating that the recall value is indeed higher in multi-task training.", "statement": "The recall value for k=10 is higher in multi-task training than in single-task training.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: Hits@1 of WebQuestionsSP and MetaQA (2-hop and 3-hop) datasets. (We re-run GRAFT-Net and PullNet on WebQuestionsSP with oracle entities.)'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "EmQL achieved state-of-the-art results on the MetaQA 3-hop and WebQuestionSP datasets, with significant improvements over previous methods.5.2.3 Results We achieved a new state-of-the-art on the MetaQA 3-hop and WebQuestionSP datasets, beating the previous state-of-the-art by a large margin (7.7% and 5.8% hits@1 absolute improvement). The results on the MetaQA 2-hop dataset are comparable to the previous state-of-the-art. We note that unlike PullNet, EmQL does not require any intermediate supervision—it learns only from the final answers." } ], "images": [ "scigen/train/1117.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Hits@1 of WebQuestionsSP and MetaQA (2-hop and 3-hop) datasets. (We re-run GRAFT-Net and PullNet on WebQuestionsSP with oracle entities.)" ], "reasoning": "EmQL achieved state-of-the-art results on the MetaQA 3-hop and WebQuestionSP datasets, with significant improvements over previous methods.", "description": "5.2.3 Results We achieved a new state-of-the-art on the MetaQA 3-hop and WebQuestionSP datasets, beating the previous state-of-the-art by a large margin (7.7% and 5.8% hits@1 absolute improvement). The results on the MetaQA 2-hop dataset are comparable to the previous state-of-the-art. We note that unlike PullNet, EmQL does not require any intermediate supervision—it learns only from the final answers.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 5: Hits@1 of WebQuestionsSP and MetaQA (2-hop and 3-hop) datasets. (We re-run GRAFT-Net and PullNet on WebQuestionsSP with oracle entities.)', and sequentially solve the question, writing out each step of your reasoning process. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model achieved the highest score on the WebQSP dataset?\n" }, { "role": "assistant", "content": "By examining the WebQSP column, EmQL achieved the highest score with 75.5. So, the answer is {\"answer\": \"EmQL\"}" } ], "images": [ "scigen/train/1117.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Hits@1 of WebQuestionsSP and MetaQA (2-hop and 3-hop) datasets. (We re-run GRAFT-Net and PullNet on WebQuestionsSP with oracle entities.)" ], "reasoning": "By examining the WebQSP column, EmQL achieved the highest score with 75.5.", "question": "Which model achieved the highest score on the WebQSP dataset?", "answer": "So, the answer is {\"answer\": \"EmQL\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nPullNet achieved the highest score on the MetaQA3 dataset.\n" }, { "role": "assistant", "content": "By examining the MetaQA3 column, EmQL achieved the highest score with 99.1, not PullNet. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1117.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Hits@1 of WebQuestionsSP and MetaQA (2-hop and 3-hop) datasets. (We re-run GRAFT-Net and PullNet on WebQuestionsSP with oracle entities.)" ], "reasoning": "By examining the MetaQA3 column, EmQL achieved the highest score with 99.1, not PullNet.", "statement": "PullNet achieved the highest score on the MetaQA3 dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 10: Ablated study on WebQuestionsSP'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The performance of EmQL (no-bert) on the WebQuestionsSP dataset is 1.3% lower than the full EmQL model.We did two more experiments on the WebQuestionsSP dataset. Instead, we randomly initialize KB entity and relation embeddings, and train the set operations. The performance of EmQL (no-bert) on the downstream QA task is 1.3% lower than our full model." } ], "images": [ "scigen/train/1118.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Ablated study on WebQuestionsSP" ], "reasoning": "The performance of EmQL (no-bert) on the WebQuestionsSP dataset is 1.3% lower than the full EmQL model.", "description": "We did two more experiments on the WebQuestionsSP dataset. Instead, we randomly initialize KB entity and relation embeddings, and train the set operations. The performance of EmQL (no-bert) on the downstream QA task is 1.3% lower than our full model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the performance difference between EmQL (no-sketch) and EmQL (approx. MIPS) on the WebQuestionsSP dataset?\nSolve the above question based on the table titled 'Table 10: Ablated study on WebQuestionsSP'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The performance of EmQL (no-sketch) is 53.2, and the performance of EmQL (approx. MIPS) is 73.4. The difference is 73.4 - 53.2. So, the answer is {\"answer\": \"20.2\"}" } ], "images": [ "scigen/train/1118.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Ablated study on WebQuestionsSP" ], "reasoning": "The performance of EmQL (no-sketch) is 53.2, and the performance of EmQL (approx. MIPS) is 73.4. The difference is 73.4 - 53.2.", "question": "What is the performance difference between EmQL (no-sketch) and EmQL (approx. MIPS) on the WebQuestionsSP dataset?", "answer": "So, the answer is {\"answer\": \"20.2\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nEmQL (no-constr) performs better than EmQL (no-bert) on the WebQuestionsSP dataset.\n" }, { "role": "assistant", "content": "The performance of EmQL (no-constr) is 65.2, while the performance of EmQL (no-bert) is 74.2. Therefore, EmQL (no-constr) does not perform better than EmQL (no-bert). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1118.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Ablated study on WebQuestionsSP" ], "reasoning": "The performance of EmQL (no-constr) is 65.2, while the performance of EmQL (no-bert) is 74.2. Therefore, EmQL (no-constr) does not perform better than EmQL (no-bert).", "statement": "EmQL (no-constr) performs better than EmQL (no-bert) on the WebQuestionsSP dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: Generalization ability evaluation. We report the results of single MT systems and system combination methods (i.e. Hier and Ours) on the Chinese-English task. N denotes the number of single MT systems. Note that both system combination methods were trained on the outputs of three single MT systems (i.e., N=3) and tested on various number of single systems (i.e., N=2,3,4,5). “†” and “††”: significantly better than the best system among inputs (p<0.05 and p<0.01). “‡” and “‡‡”: significantly better than “Hier” (p<0.05 and p<0.01).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The system combination model can generalize to different numbers of single MT systems during testing by sharing parameters of each hypothesis encoder.We are interested in evaluating whether a system combination method performs well when the number of single MT systems during testing is different from that during training. We shared the parameters of each hypothesis encoder \\mathrm{Encoder}^{\\mathrm{hyp}}(\\cdot) so that the system combination model has the ability to generalize to different number of single systems." } ], "images": [ "scigen/train/1119.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Generalization ability evaluation. We report the results of single MT systems and system combination methods (i.e. Hier and Ours) on the Chinese-English task. N denotes the number of single MT systems. Note that both system combination methods were trained on the outputs of three single MT systems (i.e., N=3) and tested on various number of single systems (i.e., N=2,3,4,5). “†” and “††”: significantly better than the best system among inputs (p<0.05 and p<0.01). “‡” and “‡‡”: significantly better than “Hier” (p<0.05 and p<0.01)." ], "reasoning": "The system combination model can generalize to different numbers of single MT systems during testing by sharing parameters of each hypothesis encoder.", "description": "We are interested in evaluating whether a system combination method performs well when the number of single MT systems during testing is different from that during training. We shared the parameters of each hypothesis encoder \\mathrm{Encoder}^{\\mathrm{hyp}}(\\cdot) so that the system combination model has the ability to generalize to different number of single systems.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 5: Generalization ability evaluation. We report the results of single MT systems and system combination methods (i.e. Hier and Ours) on the Chinese-English task. N denotes the number of single MT systems. Note that both system combination methods were trained on the outputs of three single MT systems (i.e., N=3) and tested on various number of single systems (i.e., N=2,3,4,5). “†” and “††”: significantly better than the best system among inputs (p<0.05 and p<0.01). “‡” and “‡‡”: significantly better than “Hier” (p<0.05 and p<0.01).', answer the question below by showing each progressive step toward the answer. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the highest score achieved by the system combination model when the number of single MT systems during testing is different from that during training?\n" }, { "role": "assistant", "content": "The highest score achieved by the system combination model when the number of single MT systems during testing is different from that during training is found in the row where the training number of single MT systems is 'No' and the test number of single MT systems is 'Yes'. The highest score in this scenario is 49.80. So, the answer is {\"answer\": \"49.80\"}." } ], "images": [ "scigen/train/1119.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Generalization ability evaluation. We report the results of single MT systems and system combination methods (i.e. Hier and Ours) on the Chinese-English task. N denotes the number of single MT systems. Note that both system combination methods were trained on the outputs of three single MT systems (i.e., N=3) and tested on various number of single systems (i.e., N=2,3,4,5). “†” and “††”: significantly better than the best system among inputs (p<0.05 and p<0.01). “‡” and “‡‡”: significantly better than “Hier” (p<0.05 and p<0.01)." ], "reasoning": "The highest score achieved by the system combination model when the number of single MT systems during testing is different from that during training is found in the row where the training number of single MT systems is 'No' and the test number of single MT systems is 'Yes'. The highest score in this scenario is 49.80.", "question": "What is the highest score achieved by the system combination model when the number of single MT systems during testing is different from that during training?", "answer": "So, the answer is {\"answer\": \"49.80\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe system combination model achieved a score of 49.00 when the number of single MT systems during testing is different from that during training.\n" }, { "role": "assistant", "content": "The table shows that the system combination model achieved a score of 49.00 when the training number of single MT systems is 'Yes' and the test number of single MT systems is 'Yes'. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1119.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Generalization ability evaluation. We report the results of single MT systems and system combination methods (i.e. Hier and Ours) on the Chinese-English task. N denotes the number of single MT systems. Note that both system combination methods were trained on the outputs of three single MT systems (i.e., N=3) and tested on various number of single systems (i.e., N=2,3,4,5). “†” and “††”: significantly better than the best system among inputs (p<0.05 and p<0.01). “‡” and “‡‡”: significantly better than “Hier” (p<0.05 and p<0.01)." ], "reasoning": "The table shows that the system combination model achieved a score of 49.00 when the training number of single MT systems is 'Yes' and the test number of single MT systems is 'Yes'.", "statement": "The system combination model achieved a score of 49.00 when the number of single MT systems during testing is different from that during training.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Results on the Chinese-English task. The evaluation metric is case-insensitive tokenized BLEU. “All” is the concatenation of all test sets. The translations of the top three single MT systems are the inputs of the bottom three system combination methods. “††”: significantly better than “Transformer-L2R” (p<0.01). “‡” and “‡‡”: significantly better than “Jane” (p<0.05 and p<0.01). “**”: significantly better than “Hier” (p<0.01).'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Our method significantly outperforms Trans-L2R, Jane, and Hier in all NIST scores, indicating the effectiveness of combining dependency analysis and end-to-end neural network training.We find that our method outperforms the best single system Trans-L2R, the statistical combination method Jane, and the neural combination method Hier. All the differences are statistically significant (p<0.01). The superiority over Jane and Hier suggests that combining the merits of analyzing the dependencies between hypotheses and end-to-end training of neural networks helps to generate better translations." } ], "images": [ "scigen/train/1120.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on the Chinese-English task. The evaluation metric is case-insensitive tokenized BLEU. “All” is the concatenation of all test sets. The translations of the top three single MT systems are the inputs of the bottom three system combination methods. “††”: significantly better than “Transformer-L2R” (p<0.01). “‡” and “‡‡”: significantly better than “Jane” (p<0.05 and p<0.01). “**”: significantly better than “Hier” (p<0.01)." ], "reasoning": "Our method significantly outperforms Trans-L2R, Jane, and Hier in all NIST scores, indicating the effectiveness of combining dependency analysis and end-to-end neural network training.", "description": "We find that our method outperforms the best single system Trans-L2R, the statistical combination method Jane, and the neural combination method Hier. All the differences are statistically significant (p<0.01). The superiority over Jane and Hier suggests that combining the merits of analyzing the dependencies between hypotheses and end-to-end training of neural networks helps to generate better translations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Results on the Chinese-English task. The evaluation metric is case-insensitive tokenized BLEU. “All” is the concatenation of all test sets. The translations of the top three single MT systems are the inputs of the bottom three system combination methods. “††”: significantly better than “Transformer-L2R” (p<0.01). “‡” and “‡‡”: significantly better than “Jane” (p<0.05 and p<0.01). “**”: significantly better than “Hier” (p<0.01).', solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich method has the highest NIST03 score?\n" }, { "role": "assistant", "content": "By comparing the NIST03 scores of all methods, 'Ours' has the highest score of 49.24. So, the answer is {\"answer\": \"Ours\"}." } ], "images": [ "scigen/train/1120.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on the Chinese-English task. The evaluation metric is case-insensitive tokenized BLEU. “All” is the concatenation of all test sets. The translations of the top three single MT systems are the inputs of the bottom three system combination methods. “††”: significantly better than “Transformer-L2R” (p<0.01). “‡” and “‡‡”: significantly better than “Jane” (p<0.05 and p<0.01). “**”: significantly better than “Hier” (p<0.01)." ], "reasoning": "By comparing the NIST03 scores of all methods, 'Ours' has the highest score of 49.24.", "question": "Which method has the highest NIST03 score?", "answer": "So, the answer is {\"answer\": \"Ours\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe method 'Ours' has the highest NIST08 score.\n" }, { "role": "assistant", "content": "The table shows that 'Ours' has a NIST08 score of 49.41, which is higher than all other methods listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1120.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on the Chinese-English task. The evaluation metric is case-insensitive tokenized BLEU. “All” is the concatenation of all test sets. The translations of the top three single MT systems are the inputs of the bottom three system combination methods. “††”: significantly better than “Transformer-L2R” (p<0.01). “‡” and “‡‡”: significantly better than “Jane” (p<0.05 and p<0.01). “**”: significantly better than “Hier” (p<0.01)." ], "reasoning": "The table shows that 'Ours' has a NIST08 score of 49.41, which is higher than all other methods listed.", "statement": "The method 'Ours' has the highest NIST08 score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Results on the English-German task. The evaluation metric is case-sensitive tokenized BLEU. The translations of the top three single MT systems are the inputs of the bottom three system combination methods. “††”: significantly better than “Transformer{}_{\\mathrm{big}}-fb” (p<0.01). “‡‡”: significantly better than “Jane” (p<0.01). “**”: significantly better than “Hier” (p<0.01).'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Our approach significantly outperforms the state-of-the-art methods, including Transformer{}_{\\mathrm{big}}-fb, indicating the importance of the voting mechanism and end-to-end training.Our approach also achieves significant improvements over the state-of-the-art results (p<0.01). The gaps are relatively smaller than those on Chinese-English because the English-German task uses single reference while Chinese-English uses four references. Considering that Transformer{}_{\\mathrm{big}}-fb is nowadays acknowledged strongest single system result, Jane cannot improve the translation quality and Hier improves a little while our approach achieves significant improvements, indicating that voting mechanism and end-to-end training of neural networks is important especially when the translations of single systems already have high quality." } ], "images": [ "scigen/train/1121.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on the English-German task. The evaluation metric is case-sensitive tokenized BLEU. The translations of the top three single MT systems are the inputs of the bottom three system combination methods. “††”: significantly better than “Transformer{}_{\\mathrm{big}}-fb” (p<0.01). “‡‡”: significantly better than “Jane” (p<0.01). “**”: significantly better than “Hier” (p<0.01)." ], "reasoning": "Our approach significantly outperforms the state-of-the-art methods, including Transformer{}_{\\mathrm{big}}-fb, indicating the importance of the voting mechanism and end-to-end training.", "description": "Our approach also achieves significant improvements over the state-of-the-art results (p<0.01). The gaps are relatively smaller than those on Chinese-English because the English-German task uses single reference while Chinese-English uses four references. Considering that Transformer{}_{\\mathrm{big}}-fb is nowadays acknowledged strongest single system result, Jane cannot improve the translation quality and Hier improves a little while our approach achieves significant improvements, indicating that voting mechanism and end-to-end training of neural networks is important especially when the translations of single systems already have high quality.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: Results on the English-German task. The evaluation metric is case-sensitive tokenized BLEU. The translations of the top three single MT systems are the inputs of the bottom three system combination methods. “††”: significantly better than “Transformer{}_{\\mathrm{big}}-fb” (p<0.01). “‡‡”: significantly better than “Jane” (p<0.01). “**”: significantly better than “Hier” (p<0.01).'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich method achieved the highest score on newstest2014?\n" }, { "role": "assistant", "content": "By comparing the scores in the 'newstest2014' column, the method labeled 'Ours' achieved the highest score of 30.52. So, the answer is {\"answer\": \"Ours\"}." } ], "images": [ "scigen/train/1121.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on the English-German task. The evaluation metric is case-sensitive tokenized BLEU. The translations of the top three single MT systems are the inputs of the bottom three system combination methods. “††”: significantly better than “Transformer{}_{\\mathrm{big}}-fb” (p<0.01). “‡‡”: significantly better than “Jane” (p<0.01). “**”: significantly better than “Hier” (p<0.01)." ], "reasoning": "By comparing the scores in the 'newstest2014' column, the method labeled 'Ours' achieved the highest score of 30.52.", "question": "Which method achieved the highest score on newstest2014?", "answer": "So, the answer is {\"answer\": \"Ours\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nJane achieved a higher score than DynamicConv on newstest2014.\n" }, { "role": "assistant", "content": "The table shows that Jane scored 29.62, while DynamicConv scored 29.74 on newstest2014, indicating that Jane did not achieve a higher score. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1121.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on the English-German task. The evaluation metric is case-sensitive tokenized BLEU. The translations of the top three single MT systems are the inputs of the bottom three system combination methods. “††”: significantly better than “Transformer{}_{\\mathrm{big}}-fb” (p<0.01). “‡‡”: significantly better than “Jane” (p<0.01). “**”: significantly better than “Hier” (p<0.01)." ], "reasoning": "The table shows that Jane scored 29.62, while DynamicConv scored 29.74 on newstest2014, indicating that Jane did not achieve a higher score.", "statement": "Jane achieved a higher score than DynamicConv on newstest2014.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Results of human evaluation.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The SMAE model outperforms the CAE and MAE models in content preservation and fluency, while performing comparably in sentiment transformation.We also involve human evaluation to measure the quality of generated text. Each item contains an input and three outputs generated by different systems. Then 200 items are distributed to 2 annotators with linguistic background. The annotators have no idea about which system the output is from. They are asked to score the output on three criteria on a scale from 1 to 10: the transformed sentiment degree, the content preservation degree, and the fluency. Our model has obvious advantage over the baseline systems in content preservation, and also performs well in other aspects." } ], "images": [ "scigen/train/1122.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of human evaluation." ], "reasoning": "The SMAE model outperforms the CAE and MAE models in content preservation and fluency, while performing comparably in sentiment transformation.", "description": "We also involve human evaluation to measure the quality of generated text. Each item contains an input and three outputs generated by different systems. Then 200 items are distributed to 2 annotators with linguistic background. The annotators have no idea about which system the output is from. They are asked to score the output on three criteria on a scale from 1 to 10: the transformed sentiment degree, the content preservation degree, and the fluency. Our model has obvious advantage over the baseline systems in content preservation, and also performs well in other aspects.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: Results of human evaluation.'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest score in content preservation?\n" }, { "role": "assistant", "content": "By comparing the content preservation scores of all models, the SMAE model has the highest score of 5.98. So, the answer is {\"answer\": \"SMAE\"}" } ], "images": [ "scigen/train/1122.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of human evaluation." ], "reasoning": "By comparing the content preservation scores of all models, the SMAE model has the highest score of 5.98.", "question": "Which model has the highest score in content preservation?", "answer": "So, the answer is {\"answer\": \"SMAE\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe CAE model has the highest fluency score.\n" }, { "role": "assistant", "content": "The table shows that the SMAE model has the highest fluency score of 6.69, not the CAE model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1122.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of human evaluation." ], "reasoning": "The table shows that the SMAE model has the highest fluency score of 6.69, not the CAE model.", "statement": "The CAE model has the highest fluency score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Performance of the proposed method and state-of-the-art systems.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Baseline models have low BLEU but high accuracy due to adversarial learning separating emotional and context information, while the SMAE model improves both metrics by explicitly removing emotional words using self-attention.Both baseline models have low BLEU score but high accuracy, which indicates that they may be trapped in a situation that they simply output a sentence with the target sentiment regardless of the content. The main reason is that these methods using adversarial learning attempt to implicitly separate the emotional information from the context information in a sentence vector. However, without parallel data, it is difficult to achieve such a goal. Our proposed SMAE model takes advantage of self-attention mechanism and explicitly removes the emotional words, leading to a significant improvement of content preservation and the state-of-the-art performance in terms of both metrics." } ], "images": [ "scigen/train/1123.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance of the proposed method and state-of-the-art systems." ], "reasoning": "Baseline models have low BLEU but high accuracy due to adversarial learning separating emotional and context information, while the SMAE model improves both metrics by explicitly removing emotional words using self-attention.", "description": "Both baseline models have low BLEU score but high accuracy, which indicates that they may be trapped in a situation that they simply output a sentence with the target sentiment regardless of the content. The main reason is that these methods using adversarial learning attempt to implicitly separate the emotional information from the context information in a sentence vector. However, without parallel data, it is difficult to achieve such a goal. Our proposed SMAE model takes advantage of self-attention mechanism and explicitly removes the emotional words, leading to a significant improvement of content preservation and the state-of-the-art performance in terms of both metrics.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 1: Performance of the proposed method and state-of-the-art systems.', unfold the steps to solve the question given next. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nQuestion: What is the improvement in BLEU score of the SMAE model compared to the MAE model?\n" }, { "role": "assistant", "content": "The BLEU score of the MAE model is 5.45, and the BLEU score of the SMAE model is 24.00. The improvement is calculated as 24.00 - 5.45. So, the answer is {\"answer\": \"18.55\"}" } ], "images": [ "scigen/train/1123.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance of the proposed method and state-of-the-art systems." ], "reasoning": "The BLEU score of the MAE model is 5.45, and the BLEU score of the SMAE model is 24.00. The improvement is calculated as 24.00 - 5.45.", "question": "What is the improvement in BLEU score of the SMAE model compared to the MAE model?", "answer": "So, the answer is {\"answer\": \"18.55\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe SMAE model has the highest accuracy among the models listed.\n" }, { "role": "assistant", "content": "The table shows the accuracy (ACC) for CEA, MAE, and SMAE models. The SMAE model has an accuracy of 76.64, which is higher than both CEA (71.96) and MAE (74.59). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1123.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance of the proposed method and state-of-the-art systems." ], "reasoning": "The table shows the accuracy (ACC) for CEA, MAE, and SMAE models. The SMAE model has an accuracy of 76.64, which is higher than both CEA (71.96) and MAE (74.59).", "statement": "The SMAE model has the highest accuracy among the models listed.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Word similarity Results'.\n" }, { "role": "assistant", "content": "The WED40 and WED70 methods slightly outperform the unmitigated embedding on both Gigaword and Wikipedia, while nWED70 performs worse.the SimLex-999 Spearman rank-order correlation coefficients rs (all are significant, p<0.01). Surprisingly, the WED40 and 70 methods outperform the unmitigated embedding, although the difference in result is small (0.386 and 0.395 vs. 0.385 on Gigaword, 0.371 and 0.367 vs. 0.368 on Wikipedia). nWED70, on the other hand, performs worse than the unmitigated embedding (0.384 vs. 0.385 on Gigaword, 0.367 vs. 0.368 on Wikipedia). CDA and CDS methods do not match the quality of the unmitigated space, but once again the difference is small. author=simone,color=blue!40,size=,fancyline, caption=,todo: author=simone,color=blue!40,size=,fancyline,caption=,Second Part of Reaction to Reviewer 4.It should be noted that since SimLex-999 was produced by human raters, it will reflect the human biases these methods were designed to remove, so worse performance might result from successful bias mitigation." } ], "images": [ "scigen/train/1124.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Word similarity Results" ], "reasoning": "The WED40 and WED70 methods slightly outperform the unmitigated embedding on both Gigaword and Wikipedia, while nWED70 performs worse.", "description": "the SimLex-999 Spearman rank-order correlation coefficients rs (all are significant, p<0.01). Surprisingly, the WED40 and 70 methods outperform the unmitigated embedding, although the difference in result is small (0.386 and 0.395 vs. 0.385 on Gigaword, 0.371 and 0.367 vs. 0.368 on Wikipedia). nWED70, on the other hand, performs worse than the unmitigated embedding (0.384 vs. 0.385 on Gigaword, 0.367 vs. 0.368 on Wikipedia). CDA and CDS methods do not match the quality of the unmitigated space, but once again the difference is small. author=simone,color=blue!40,size=,fancyline, caption=,todo: author=simone,color=blue!40,size=,fancyline,caption=,Second Part of Reaction to Reviewer 4.It should be noted that since SimLex-999 was produced by human raters, it will reflect the human biases these methods were designed to remove, so worse performance might result from successful bias mitigation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: Word similarity Results'.\n\nQuestion: Which method has the highest Spearman rank-order correlation coefficient on Wikipedia?\n" }, { "role": "assistant", "content": "By examining the table, the highest rs value on Wikipedia is 0.375, which corresponds to the WED70 method. So, the answer is {\"answer\": \"WED70\"}." } ], "images": [ "scigen/train/1124.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Word similarity Results" ], "reasoning": "By examining the table, the highest rs value on Wikipedia is 0.375, which corresponds to the WED70 method.", "question": "Which method has the highest Spearman rank-order correlation coefficient on Wikipedia?", "answer": "So, the answer is {\"answer\": \"WED70\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe nWED70 method performs better than the unmitigated embedding on Gigaword.\n" }, { "role": "assistant", "content": "The table shows that the rs value for nWED70 on Gigaword is 0.384, which is lower than the unmitigated embedding's rs value of 0.385. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1124.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Word similarity Results" ], "reasoning": "The table shows that the rs value for nWED70 on Gigaword is 0.384, which is lower than the unmitigated embedding's rs value of 0.385.", "statement": "The nWED70 method performs better than the unmitigated embedding on Gigaword.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Direct bias results', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The table shows the differences and p-values for various methods across different datasets, indicating the significance of these differences.We also compute a two-tailed p-value to determine whether the difference between the various sets is significant." } ], "images": [ "scigen/train/1125.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Direct bias results" ], "reasoning": "The table shows the differences and p-values for various methods across different datasets, indicating the significance of these differences.", "description": "We also compute a two-tailed p-value to determine whether the difference between the various sets is significant.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 1: Direct bias results', answer the following question. Give detailed solution consisting of each step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method has the smallest difference in the Arts-Sciences category for the Gigaword dataset?\n" }, { "role": "assistant", "content": "To find the smallest difference in the Arts-Sciences category for the Gigaword dataset, look at the 'Arts-Sciences d' column under the Gigaword dataset and identify the smallest value. So, the answer is {\"answer\": \"nCDA\"}." } ], "images": [ "scigen/train/1125.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Direct bias results" ], "reasoning": "To find the smallest difference in the Arts-Sciences category for the Gigaword dataset, look at the 'Arts-Sciences d' column under the Gigaword dataset and identify the smallest value.", "question": "Which method has the smallest difference in the Arts-Sciences category for the Gigaword dataset?", "answer": "So, the answer is {\"answer\": \"nCDA\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe gCDA method has a significant difference in the Career-Family category for the Wikipedia dataset.\n" }, { "role": "assistant", "content": "To verify this statement, check the 'Career-Family p' column under the Wikipedia dataset for the gCDA method. A significant difference is indicated by a p-value less than 0.05. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1125.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Direct bias results" ], "reasoning": "To verify this statement, check the 'Career-Family p' column under the Wikipedia dataset for the gCDA method. A significant difference is indicated by a p-value less than 0.05.", "statement": "The gCDA method has a significant difference in the Career-Family category for the Wikipedia dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Evaluation results for models trained on Daily Mail and their generated texts'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The baseline model amplifies bias in the training data, but our method with λ=1 significantly reduces various biases without a significant increase in perplexity.Initially, we measure the co-occurrence bias in the training data. After training the baseline model, we implement our loss function and tune for the λ hyperparameter. Additionally, we implement a combination of our loss function and CDA and tune for λ. Finally, bias evaluation is performed for all the trained models. Causal occupation bias is measured directly from the models using template datasets discussed above and co-occurrence bias is measured from the model-generated texts, which consist of 10,000 documents of 500 words each. It is interesting to observe that the baseline model amplifies the bias in the training data set as measured by BNand BNc. From measurements using the described bias metrics, our method effectively mitigates bias in language modelling without a significant increase in perplexity. At λ value of 1, it reduces BN by 58.95%, BNc by 45.74%, CB|o by 100%, CB|g by 98.52% and EBd by 98.98%. Compared to the results of CDA and REG, it achieves the best results in both occupation biases, CB|g and CB|o, and EBd. We notice that all methods result in GR around 1, indicating that there are near equal amounts of female and male words in the generated texts. In our experiments we note that with increasing λ, the bias steadily decreases and perplexity tends to slightly increase. This indicates that there is a trade-off between bias and perplexity." } ], "images": [ "scigen/train/1126.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Evaluation results for models trained on Daily Mail and their generated texts" ], "reasoning": "The baseline model amplifies bias in the training data, but our method with λ=1 significantly reduces various biases without a significant increase in perplexity.", "description": "Initially, we measure the co-occurrence bias in the training data. After training the baseline model, we implement our loss function and tune for the λ hyperparameter. Additionally, we implement a combination of our loss function and CDA and tune for λ. Finally, bias evaluation is performed for all the trained models. Causal occupation bias is measured directly from the models using template datasets discussed above and co-occurrence bias is measured from the model-generated texts, which consist of 10,000 documents of 500 words each. It is interesting to observe that the baseline model amplifies the bias in the training data set as measured by BNand BNc. From measurements using the described bias metrics, our method effectively mitigates bias in language modelling without a significant increase in perplexity. At λ value of 1, it reduces BN by 58.95%, BNc by 45.74%, CB|o by 100%, CB|g by 98.52% and EBd by 98.98%. Compared to the results of CDA and REG, it achieves the best results in both occupation biases, CB|g and CB|o, and EBd. We notice that all methods result in GR around 1, indicating that there are near equal amounts of female and male words in the generated texts. In our experiments we note that with increasing λ, the bias steadily decreases and perplexity tends to slightly increase. This indicates that there is a trade-off between bias and perplexity.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 3: Evaluation results for models trained on Daily Mail and their generated texts', solve the following question step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the perplexity value for the REG model, and how does it compare to the baseline model?\n" }, { "role": "assistant", "content": "The perplexity value for the REG model is 114.438, which is lower than the baseline model's perplexity of 117.845, indicating better performance in terms of perplexity. So, the answer is {\"answer\": \"114.438\"}." } ], "images": [ "scigen/train/1126.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Evaluation results for models trained on Daily Mail and their generated texts" ], "reasoning": "The perplexity value for the REG model is 114.438, which is lower than the baseline model's perplexity of 117.845, indicating better performance in terms of perplexity.", "question": "What is the perplexity value for the REG model, and how does it compare to the baseline model?", "answer": "So, the answer is {\"answer\": \"114.438\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The model with λ=1 achieves the lowest BN value., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model with λ=1 achieves the lowest BN value.\n" }, { "role": "assistant", "content": "The table shows that the model with λ=1 has a BN value of 0.226, which is not the lowest. The model with λ=0.5 + CDA has the lowest BN value of 0.205. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1126.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Evaluation results for models trained on Daily Mail and their generated texts" ], "reasoning": "The table shows that the model with λ=1 has a BN value of 0.226, which is not the lowest. The model with λ=0.5 + CDA has the lowest BN value of 0.205.", "statement": "The model with λ=1 achieves the lowest BN value.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Results of our model and the baselines (directly reported in the referred articles) on the Chinese-English translation. “-” means that the studies did not test the models on the corresponding datasets.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Our model achieves a BLEU score of 36.51, outperforming the Seq2Seq+Attention baseline by 4.55 BLEU points.We compare our model with our implementation of Seq2Seq+Attention model. For fair comparison, the experimental setting of Seq2Seq+Attention is the same as BAT, so that we can regard it as our proposed model removing the bag-of-words target. The results show that our model achieves the BLEU score of 36.51 on the total test sets, which outperforms the Seq2Seq baseline by the BLEU of 4.55. In order to further evaluate the performance of our model, we compare our model with the recent NMT systems which have been evaluated on the same training set and the test sets as ours. Their results are directly reported in the referred articles." } ], "images": [ "scigen/train/1127.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of our model and the baselines (directly reported in the referred articles) on the Chinese-English translation. “-” means that the studies did not test the models on the corresponding datasets." ], "reasoning": "Our model achieves a BLEU score of 36.51, outperforming the Seq2Seq+Attention baseline by 4.55 BLEU points.", "description": "We compare our model with our implementation of Seq2Seq+Attention model. For fair comparison, the experimental setting of Seq2Seq+Attention is the same as BAT, so that we can regard it as our proposed model removing the bag-of-words target. The results show that our model achieves the BLEU score of 36.51 on the total test sets, which outperforms the Seq2Seq baseline by the BLEU of 4.55. In order to further evaluate the performance of our model, we compare our model with the recent NMT systems which have been evaluated on the same training set and the test sets as ours. Their results are directly reported in the referred articles.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Results of our model and the baselines (directly reported in the referred articles) on the Chinese-English translation. “-” means that the studies did not test the models on the corresponding datasets.\nQuestion: What is the BLEU score difference between the +Bag-of-Words model and the Seq2Seq+Attention model on the total test sets?\nSolve the above question based on the table information. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The +Bag-of-Words model achieves a BLEU score of 36.51, while the Seq2Seq+Attention model achieves 31.96. The difference is 36.51 - 31.96. So, the answer is {\"answer\": \"4.55\"}" } ], "images": [ "scigen/train/1127.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of our model and the baselines (directly reported in the referred articles) on the Chinese-English translation. “-” means that the studies did not test the models on the corresponding datasets." ], "reasoning": "The +Bag-of-Words model achieves a BLEU score of 36.51, while the Seq2Seq+Attention model achieves 31.96. The difference is 36.51 - 31.96.", "question": "What is the BLEU score difference between the +Bag-of-Words model and the Seq2Seq+Attention model on the total test sets?", "answer": "So, the answer is {\"answer\": \"4.55\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe +Bag-of-Words model achieves the highest BLEU score on the MT-04 test set.\n" }, { "role": "assistant", "content": "The table shows that the +Bag-of-Words model has a BLEU score of 40.02 on the MT-04 test set, which is higher than any other model listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1127.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of our model and the baselines (directly reported in the referred articles) on the Chinese-English translation. “-” means that the studies did not test the models on the corresponding datasets." ], "reasoning": "The table shows that the +Bag-of-Words model has a BLEU score of 40.02 on the MT-04 test set, which is higher than any other model listed.", "statement": "The +Bag-of-Words model achieves the highest BLEU score on the MT-04 test set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: Results for experiments between using Word2Vec and fastText embeddings. Also includes results of using attention on top of the model with Word2Vec. Since attention results were not promising, we did not repeat them with fastText.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "fastText embeddings outperform word2vec embeddings due to their ability to use n-grams, which is beneficial for handling rare tokens in command outputs or error messages.Both word2vec and fastText embeddings are trained on all posts in the Ask Ubuntu dataset. As we can see, fastText gives a marked improvement over using embeddings from word2vec. This is probably due to the nature of the vocabulary in our task. Since large portions of questions are spans of command output or error messages a lot of tokens appear very rarely. In fact, out of the 62,501 unique tokens in the dataset, 57% appear just once, and 78% appear 3 or fewer times. However, the characters in these tokens are probably very informative (for example “http” in a token would signal that the token is a URL). Therefore, fastText, which uses n-grams from a token to compute embeddings, would emit more meaningful representations. Given the long tickets in our dataset, and un-reasonably long lengths of spans for labels like command output or error messages, we explored the usefulness of attention in our model. We used the Scaled Dot-Product Attention as in Vaswani et al. We find that weighted attention actually hurts performance. This could be because of the large number of extra parameters introduced in the calculation of Key, Value, and Query matrices. While the un-weighted version gets around this by using the bi-directional GRU hidden states as all 3 matrices, it doesn’t improve results significantly either." } ], "images": [ "scigen/train/1128.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results for experiments between using Word2Vec and fastText embeddings. Also includes results of using attention on top of the model with Word2Vec. Since attention results were not promising, we did not repeat them with fastText." ], "reasoning": "fastText embeddings outperform word2vec embeddings due to their ability to use n-grams, which is beneficial for handling rare tokens in command outputs or error messages.", "description": "Both word2vec and fastText embeddings are trained on all posts in the Ask Ubuntu dataset. As we can see, fastText gives a marked improvement over using embeddings from word2vec. This is probably due to the nature of the vocabulary in our task. Since large portions of questions are spans of command output or error messages a lot of tokens appear very rarely. In fact, out of the 62,501 unique tokens in the dataset, 57% appear just once, and 78% appear 3 or fewer times. However, the characters in these tokens are probably very informative (for example “http” in a token would signal that the token is a URL). Therefore, fastText, which uses n-grams from a token to compute embeddings, would emit more meaningful representations. Given the long tickets in our dataset, and un-reasonably long lengths of spans for labels like command output or error messages, we explored the usefulness of attention in our model. We used the Scaled Dot-Product Attention as in Vaswani et al. We find that weighted attention actually hurts performance. This could be because of the large number of extra parameters introduced in the calculation of Key, Value, and Query matrices. While the un-weighted version gets around this by using the bi-directional GRU hidden states as all 3 matrices, it doesn’t improve results significantly either.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 3: Results for experiments between using Word2Vec and fastText embeddings. Also includes results of using attention on top of the model with Word2Vec. Since attention results were not promising, we did not repeat them with fastText.' and proceed to solve the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model has the highest F1 score and why?\n" }, { "role": "assistant", "content": "The table shows that fastText has the highest F1 score of 75.04. The description explains that fastText's use of n-grams helps it handle rare tokens better, leading to more meaningful representations. So, the answer is {\"answer\": \"fastText\"}." } ], "images": [ "scigen/train/1128.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results for experiments between using Word2Vec and fastText embeddings. Also includes results of using attention on top of the model with Word2Vec. Since attention results were not promising, we did not repeat them with fastText." ], "reasoning": "The table shows that fastText has the highest F1 score of 75.04. The description explains that fastText's use of n-grams helps it handle rare tokens better, leading to more meaningful representations.", "question": "Which model has the highest F1 score and why?", "answer": "So, the answer is {\"answer\": \"fastText\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe weighted attention mechanism improves the performance of word2vec embeddings.\n" }, { "role": "assistant", "content": "The table shows that word2vec with weighted attention has a lower F1 score (59.55) compared to word2vec without attention (61.72), indicating that weighted attention actually hurts performance. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1128.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results for experiments between using Word2Vec and fastText embeddings. Also includes results of using attention on top of the model with Word2Vec. Since attention results were not promising, we did not repeat them with fastText." ], "reasoning": "The table shows that word2vec with weighted attention has a lower F1 score (59.55) compared to word2vec without attention (61.72), indicating that weighted attention actually hurts performance.", "statement": "The weighted attention mechanism improves the performance of word2vec embeddings.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Results comparing the models using various pre-trained embeddings. The en data source is the downloaded pre-trained ELMo model. For simple concatenation, we present the results for the best model at each n combinations of data sources. For example, when concatenating any 2 datasources, the en + config combination gives the best performance.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Combining embeddings from multiple language models outperforms using a single pre-trained model, and contextual weighting is more effective than simple concatenation.For the simple concatenation method, we present results for the best n-way combination of embeddings from different data sources, for each n (1, 2, 3, and 4). We find that combining embeddings from multiple language models trained on different data sources considerably outperforms using embeddings from a single pre-trained model (using both the naive concatenation and CDME). This is an artifact of the support problems containing large sections of non-natural language text. We also find that contextual weighting does better than a simple concatenation." } ], "images": [ "scigen/train/1129.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results comparing the models using various pre-trained embeddings. The en data source is the downloaded pre-trained ELMo model. For simple concatenation, we present the results for the best model at each n combinations of data sources. For example, when concatenating any 2 datasources, the en + config combination gives the best performance." ], "reasoning": "Combining embeddings from multiple language models outperforms using a single pre-trained model, and contextual weighting is more effective than simple concatenation.", "description": "For the simple concatenation method, we present results for the best n-way combination of embeddings from different data sources, for each n (1, 2, 3, and 4). We find that combining embeddings from multiple language models trained on different data sources considerably outperforms using embeddings from a single pre-trained model (using both the naive concatenation and CDME). This is an artifact of the support problems containing large sections of non-natural language text. We also find that contextual weighting does better than a simple concatenation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 4: Results comparing the models using various pre-trained embeddings. The en data source is the downloaded pre-trained ELMo model. For simple concatenation, we present the results for the best model at each n combinations of data sources. For example, when concatenating any 2 datasources, the en + config combination gives the best performance.\nQuestion: Which model achieved the highest F1 score?\n" }, { "role": "assistant", "content": "By examining the F1 scores in the table, the CDME model has the highest F1 score of 78.80. So, the answer is {\"answer\": \"CDME\"}" } ], "images": [ "scigen/train/1129.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results comparing the models using various pre-trained embeddings. The en data source is the downloaded pre-trained ELMo model. For simple concatenation, we present the results for the best model at each n combinations of data sources. For example, when concatenating any 2 datasources, the en + config combination gives the best performance." ], "reasoning": "By examining the F1 scores in the table, the CDME model has the highest F1 score of 78.80.", "question": "Which model achieved the highest F1 score?", "answer": "So, the answer is {\"answer\": \"CDME\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Simple Concat - 3 (en + code + config) model has a higher F1 score than the DME model.\n" }, { "role": "assistant", "content": "The table shows that the Simple Concat - 3 (en + code + config) model has an F1 score of 78.67, while the DME model has an F1 score of 76.61. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1129.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results comparing the models using various pre-trained embeddings. The en data source is the downloaded pre-trained ELMo model. For simple concatenation, we present the results for the best model at each n combinations of data sources. For example, when concatenating any 2 datasources, the en + config combination gives the best performance." ], "reasoning": "The table shows that the Simple Concat - 3 (en + code + config) model has an F1 score of 78.67, while the DME model has an F1 score of 76.61.", "statement": "The Simple Concat - 3 (en + code + config) model has a higher F1 score than the DME model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5: Retrieval results, comparing the performance of querying with the full question against segmented question (gold segments and predicted segments)', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Using separate weights for identified segments of a question improves retrieval accuracy compared to using all tokens from the question.We show that weighing identified segments of the question with separate weights improves retrieval of the correct answer over a query with all tokens from the question. We also present results from the gold annotations of segments for these questions, as an upper-bound of the performance improvement we can hope to achieve." } ], "images": [ "scigen/train/1130.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Retrieval results, comparing the performance of querying with the full question against segmented question (gold segments and predicted segments)" ], "reasoning": "Using separate weights for identified segments of a question improves retrieval accuracy compared to using all tokens from the question.", "description": "We show that weighing identified segments of the question with separate weights improves retrieval of the correct answer over a query with all tokens from the question. We also present results from the gold annotations of segments for these questions, as an upper-bound of the performance improvement we can hope to achieve.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 5: Retrieval results, comparing the performance of querying with the full question against segmented question (gold segments and predicted segments)', and tackle the question below in a sequential manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich method achieved the highest Mean Reciprocal Rank (MRR)?\n" }, { "role": "assistant", "content": "By comparing the MRR values for each method, the 'Segmented Question - Gold' method has the highest MRR of 0.300. So, the answer is {\"answer\": \"Segmented Question - Gold\"}" } ], "images": [ "scigen/train/1130.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Retrieval results, comparing the performance of querying with the full question against segmented question (gold segments and predicted segments)" ], "reasoning": "By comparing the MRR values for each method, the 'Segmented Question - Gold' method has the highest MRR of 0.300.", "question": "Which method achieved the highest Mean Reciprocal Rank (MRR)?", "answer": "So, the answer is {\"answer\": \"Segmented Question - Gold\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The Full Question method achieved the highest MRR., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Full Question method achieved the highest MRR.\n" }, { "role": "assistant", "content": "The table shows that the Full Question method has an MRR of 0.292, which is lower than both the Segmented Question - Gold (0.300) and Segmented Question - Predicted (0.298) methods. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1130.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Retrieval results, comparing the performance of querying with the full question against segmented question (gold segments and predicted segments)" ], "reasoning": "The table shows that the Full Question method has an MRR of 0.292, which is lower than both the Segmented Question - Gold (0.300) and Segmented Question - Predicted (0.298) methods.", "statement": "The Full Question method achieved the highest MRR.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Translation performance on WMT14 English⇒German translation task. “# Para.” denotes the number of parameters, and “Train” and “Decode” respectively denote the training (steps/second) and decoding (sentences/second) speeds.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The linear combination model improves translation performance by 0.42 BLEU points over the Transformer-Base model, highlighting the importance of layer aggregation in deep NMT models.As one would expect, the linear combination (Row 2) improves translation performance by +0.42 BLEU points, indicating the necessity of aggregating layers for deep NMT models." } ], "images": [ "scigen/train/1131.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Translation performance on WMT14 English⇒German translation task. “# Para.” denotes the number of parameters, and “Train” and “Decode” respectively denote the training (steps/second) and decoding (sentences/second) speeds." ], "reasoning": "The linear combination model improves translation performance by 0.42 BLEU points over the Transformer-Base model, highlighting the importance of layer aggregation in deep NMT models.", "description": "As one would expect, the linear combination (Row 2) improves translation performance by +0.42 BLEU points, indicating the necessity of aggregating layers for deep NMT models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 1: Translation performance on WMT14 English⇒German translation task. “# Para.” denotes the number of parameters, and “Train” and “Decode” respectively denote the training (steps/second) and decoding (sentences/second) speeds.' and methodically tackle the question that follows. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model shows the highest improvement in BLEU points over the Transformer-Base model?\n" }, { "role": "assistant", "content": "By comparing the BLEU point improvements (Δ) of all models over the Transformer-Base model, the EM Routing model shows the highest improvement with +1.50 BLEU points. So, the answer is {\"answer\": \"EM Routing\"}" } ], "images": [ "scigen/train/1131.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Translation performance on WMT14 English⇒German translation task. “# Para.” denotes the number of parameters, and “Train” and “Decode” respectively denote the training (steps/second) and decoding (sentences/second) speeds." ], "reasoning": "By comparing the BLEU point improvements (Δ) of all models over the Transformer-Base model, the EM Routing model shows the highest improvement with +1.50 BLEU points.", "question": "Which model shows the highest improvement in BLEU points over the Transformer-Base model?", "answer": "So, the answer is {\"answer\": \"EM Routing\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Dynamic Combination model has a BLEU score of 28.33.\n" }, { "role": "assistant", "content": "According to the table, the Dynamic Combination model indeed has a BLEU score of 28.33. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1131.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Translation performance on WMT14 English⇒German translation task. “# Para.” denotes the number of parameters, and “Train” and “Decode” respectively denote the training (steps/second) and decoding (sentences/second) speeds." ], "reasoning": "According to the table, the Dynamic Combination model indeed has a BLEU score of 28.33.", "statement": "The Dynamic Combination model has a BLEU score of 28.33.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Ablation study on model components. † means we use the model structure as Figure 1(a) and Baseline means the model in Sec 3.2.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The model's accuracy decreases when certain components like hint sentences or data augmentation are removed, indicating their importance.We think the reason is a common sense statement with a similar grammar and syntax does help the model to determine why the input sentence is against common sense." } ], "images": [ "scigen/train/1132.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ablation study on model components. † means we use the model structure as Figure 1(a) and Baseline means the model in Sec 3.2." ], "reasoning": "The model's accuracy decreases when certain components like hint sentences or data augmentation are removed, indicating their importance.", "description": "We think the reason is a common sense statement with a similar grammar and syntax does help the model to determine why the input sentence is against common sense.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 2: Ablation study on model components. † means we use the model structure as Figure 1(a) and Baseline means the model in Sec 3.2.'\nQuestion: Which model component, when removed, results in the largest decrease in accuracy?\n" }, { "role": "assistant", "content": "By comparing the accuracy values, the largest decrease is observed when the hint sentence is removed, dropping the accuracy to 93.47. So, the answer is {\"answer\": \"hint sentence\"}." } ], "images": [ "scigen/train/1132.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ablation study on model components. † means we use the model structure as Figure 1(a) and Baseline means the model in Sec 3.2." ], "reasoning": "By comparing the accuracy values, the largest decrease is observed when the hint sentence is removed, dropping the accuracy to 93.47.", "question": "Which model component, when removed, results in the largest decrease in accuracy?", "answer": "So, the answer is {\"answer\": \"hint sentence\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The baseline model has the lowest accuracy., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe baseline model has the lowest accuracy.\n" }, { "role": "assistant", "content": "The table shows that the baseline model has an accuracy of 93.12, which is lower than all other models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1132.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ablation study on model components. † means we use the model structure as Figure 1(a) and Baseline means the model in Sec 3.2." ], "reasoning": "The table shows that the baseline model has an accuracy of 93.12, which is lower than all other models listed.", "statement": "The baseline model has the lowest accuracy.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Performance with different encoder.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The ALBERT-based model achieves state-of-the-art performance in both Sen-Making and Explanation tasks.The result of our model for subtask a and We have tried different pretraining language model as our encoder, and found that ALBERT based model achieves the state-of-the-art performance." } ], "images": [ "scigen/train/1133.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance with different encoder." ], "reasoning": "The ALBERT-based model achieves state-of-the-art performance in both Sen-Making and Explanation tasks.", "description": "The result of our model for subtask a and We have tried different pretraining language model as our encoder, and found that ALBERT based model achieves the state-of-the-art performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Performance with different encoder.\nQuestion: Which model has the highest Sen-Making score and what is its Explanation score?\nSolve the above question based on the table information. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the 'Our-ensemble' model has the highest Sen-Making score of 95.91, and its Explanation score is 96.39. So, the answer is {\"answer\": \"Our-ensemble\"}" } ], "images": [ "scigen/train/1133.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance with different encoder." ], "reasoning": "The table shows that the 'Our-ensemble' model has the highest Sen-Making score of 95.91, and its Explanation score is 96.39.", "question": "Which model has the highest Sen-Making score and what is its Explanation score?", "answer": "So, the answer is {\"answer\": \"Our-ensemble\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The ALBERT xlarge model has the highest Explanation score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe ALBERT xlarge model has the highest Explanation score.\n" }, { "role": "assistant", "content": "The table shows that the ALBERT xlarge model has an Explanation score of 92.45, which is not the highest. The highest Explanation score is 96.39 by the 'Our-ensemble' model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1133.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance with different encoder." ], "reasoning": "The table shows that the ALBERT xlarge model has an Explanation score of 92.45, which is not the highest. The highest Explanation score is 96.39 by the 'Our-ensemble' model.", "statement": "The ALBERT xlarge model has the highest Explanation score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Ablation study on model components. † means we use the model structure as Figure 1(a) and Baseline means the model in Sec 3.2.'.\n" }, { "role": "assistant", "content": "The table shows the accuracy and delta accuracy of different models, indicating the impact of various methods on performance.We think the reason is a common sense statement with a similar grammar and syntax does help the model to determine why the input sentence is against common sense." } ], "images": [ "scigen/train/1134.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ablation study on model components. † means we use the model structure as Figure 1(a) and Baseline means the model in Sec 3.2." ], "reasoning": "The table shows the accuracy and delta accuracy of different models, indicating the impact of various methods on performance.", "description": "We think the reason is a common sense statement with a similar grammar and syntax does help the model to determine why the input sentence is against common sense.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 2: Ablation study on model components. † means we use the model structure as Figure 1(a) and Baseline means the model in Sec 3.2.' and proceed to solve the following question in a stepwise manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest accuracy and what is its delta accuracy?\n" }, { "role": "assistant", "content": "The 'Our-single' model has the highest accuracy of 95.68, and its delta accuracy is not applicable as it is the reference model. So, the answer is {\"answer\": \"95.68, -\"}." } ], "images": [ "scigen/train/1134.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ablation study on model components. † means we use the model structure as Figure 1(a) and Baseline means the model in Sec 3.2." ], "reasoning": "The 'Our-single' model has the highest accuracy of 95.68, and its delta accuracy is not applicable as it is the reference model.", "question": "Which model has the highest accuracy and what is its delta accuracy?", "answer": "So, the answer is {\"answer\": \"95.68, -\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model without data augmentation has a higher accuracy than the baseline model.\n" }, { "role": "assistant", "content": "The table shows that the model without data augmentation has an accuracy of 95.43, which is higher than the baseline model's accuracy of 93.24. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1134.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ablation study on model components. † means we use the model structure as Figure 1(a) and Baseline means the model in Sec 3.2." ], "reasoning": "The table shows that the model without data augmentation has an accuracy of 95.43, which is higher than the baseline model's accuracy of 93.24.", "statement": "The model without data augmentation has a higher accuracy than the baseline model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 6: Manual evaluation of the generated responses in terms of Content (Cont.) and Emotion (Emot.) .'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "ECM outperforms other methods in both content and emotion metrics, showing significant improvements in emotion expression and content appropriateness.ECM with all options outperforms the other methods in both metrics significantly (2-tailed t-test, p<0.05 for Content, and p<0.005 for Emotion). After incorporating the internal memory and the external memory modules, the performance of ECM in Emotion is improved comparing to Emb, indicating our model can generate more explicit expressions of emotion. Besides, the performance in Content is improved from 1.256 of Emb to 1.299 of ECM, which shows the ability of ECM to control the weight of emotion and generate responses appropriate in content." } ], "images": [ "scigen/train/1135.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Manual evaluation of the generated responses in terms of Content (Cont.) and Emotion (Emot.) ." ], "reasoning": "ECM outperforms other methods in both content and emotion metrics, showing significant improvements in emotion expression and content appropriateness.", "description": "ECM with all options outperforms the other methods in both metrics significantly (2-tailed t-test, p<0.05 for Content, and p<0.005 for Emotion). After incorporating the internal memory and the external memory modules, the performance of ECM in Emotion is improved comparing to Emb, indicating our model can generate more explicit expressions of emotion. Besides, the performance in Content is improved from 1.256 of Emb to 1.299 of ECM, which shows the ability of ECM to control the weight of emotion and generate responses appropriate in content.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 6: Manual evaluation of the generated responses in terms of Content (Cont.) and Emotion (Emot.) .' and proceed to solve the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the improvement in the Overall Emotion score from Emb to ECM?\n" }, { "role": "assistant", "content": "The Overall Emotion score for Emb is 0.363, and for ECM it is 0.424. The improvement is calculated as 0.424 - 0.363. So, the answer is {\"answer\": \"0.061\"}." } ], "images": [ "scigen/train/1135.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Manual evaluation of the generated responses in terms of Content (Cont.) and Emotion (Emot.) ." ], "reasoning": "The Overall Emotion score for Emb is 0.363, and for ECM it is 0.424. The improvement is calculated as 0.424 - 0.363.", "question": "What is the improvement in the Overall Emotion score from Emb to ECM?", "answer": "So, the answer is {\"answer\": \"0.061\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nECM has the highest score in Like Content among all methods.\n" }, { "role": "assistant", "content": "The Like Content scores are 1.308 for Seq2Seq, 1.348 for Emb, and 1.460 for ECM. ECM has the highest score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1135.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Manual evaluation of the generated responses in terms of Content (Cont.) and Emotion (Emot.) ." ], "reasoning": "The Like Content scores are 1.308 for Seq2Seq, 1.348 for Emb, and 1.460 for ECM. ECM has the highest score.", "statement": "ECM has the highest score in Like Content among all methods.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Objective evaluation with perplexity and accuracy.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "ECM achieves the highest emotion accuracy and has better perplexity than Seq2Seq but worse than Emb.As can be seen, ECM obtains the best performance in emotion accuracy, and the performance in perplexity is better than Seq2Seq but worse than Emb. In practice, emotion accuracy is more important than perplexity considering that the generated sentences are already fluent and grammatical with the perplexity of 68.0." } ], "images": [ "scigen/train/1136.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Objective evaluation with perplexity and accuracy." ], "reasoning": "ECM achieves the highest emotion accuracy and has better perplexity than Seq2Seq but worse than Emb.", "description": "As can be seen, ECM obtains the best performance in emotion accuracy, and the performance in perplexity is better than Seq2Seq but worse than Emb. In practice, emotion accuracy is more important than perplexity considering that the generated sentences are already fluent and grammatical with the perplexity of 68.0.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 4: Objective evaluation with perplexity and accuracy.' to sequentially solve the question presented below. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich method has the best performance in emotion accuracy and how does its perplexity compare to Seq2Seq and Emb?\n" }, { "role": "assistant", "content": "ECM has the highest emotion accuracy at 0.773. Its perplexity is 65.9, which is better than Seq2Seq's 68.0 but worse than Emb's 62.5. So, the answer is {\"answer\": \"ECM\"}" } ], "images": [ "scigen/train/1136.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Objective evaluation with perplexity and accuracy." ], "reasoning": "ECM has the highest emotion accuracy at 0.773. Its perplexity is 65.9, which is better than Seq2Seq's 68.0 but worse than Emb's 62.5.", "question": "Which method has the best performance in emotion accuracy and how does its perplexity compare to Seq2Seq and Emb?", "answer": "So, the answer is {\"answer\": \"ECM\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence ECM has the lowest perplexity among all methods. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nECM has the lowest perplexity among all methods.\n" }, { "role": "assistant", "content": "The table shows that ECM has a perplexity of 65.9, which is not the lowest. The lowest perplexity is 61.8 by w/o EMem. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1136.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Objective evaluation with perplexity and accuracy." ], "reasoning": "The table shows that ECM has a perplexity of 65.9, which is not the lowest. The lowest perplexity is 61.8 by w/o EMem.", "statement": "ECM has the lowest perplexity among all methods.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: The percentage of responses in manual evaluation with the score of Content-Emotion. For instance, 2-1 means content score is 2 and emotion score is 1.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "ECM outperforms Emb and Seq2Seq in generating responses with a Content score of 2 and an Emotion score of 1, indicating higher quality in both content and emotion.As we can see, 27.2% of the responses generated by ECM have a Content score of 2 and an Emotion score of 1, while only 22.8% for Emb and 9.0% for Seq2Seq. These indicate that ECM is better in generating high-quality responses in both content and emotion." } ], "images": [ "scigen/train/1137.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The percentage of responses in manual evaluation with the score of Content-Emotion. For instance, 2-1 means content score is 2 and emotion score is 1." ], "reasoning": "ECM outperforms Emb and Seq2Seq in generating responses with a Content score of 2 and an Emotion score of 1, indicating higher quality in both content and emotion.", "description": "As we can see, 27.2% of the responses generated by ECM have a Content score of 2 and an Emotion score of 1, while only 22.8% for Emb and 9.0% for Seq2Seq. These indicate that ECM is better in generating high-quality responses in both content and emotion.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nTable title: Table 5: The percentage of responses in manual evaluation with the score of Content-Emotion. For instance, 2-1 means content score is 2 and emotion score is 1.\n\nQuestion: Which method has the highest percentage of responses with a Content score of 2 and an Emotion score of 1?\n" }, { "role": "assistant", "content": "By comparing the percentages in the 2-1 column, ECM has the highest percentage at 27.2%, followed by Emb at 22.8%, and Seq2Seq at 9.0%. So, the answer is {\"answer\": \"ECM\"}." } ], "images": [ "scigen/train/1137.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The percentage of responses in manual evaluation with the score of Content-Emotion. For instance, 2-1 means content score is 2 and emotion score is 1." ], "reasoning": "By comparing the percentages in the 2-1 column, ECM has the highest percentage at 27.2%, followed by Emb at 22.8%, and Seq2Seq at 9.0%.", "question": "Which method has the highest percentage of responses with a Content score of 2 and an Emotion score of 1?", "answer": "So, the answer is {\"answer\": \"ECM\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Seq2Seq has the highest percentage of responses with a Content score of 2 and an Emotion score of 1., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nSeq2Seq has the highest percentage of responses with a Content score of 2 and an Emotion score of 1.\n" }, { "role": "assistant", "content": "The table shows that Seq2Seq has 9.0% of responses with a Content score of 2 and an Emotion score of 1, which is lower than both ECM (27.2%) and Emb (22.8%). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1137.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The percentage of responses in manual evaluation with the score of Content-Emotion. For instance, 2-1 means content score is 2 and emotion score is 1." ], "reasoning": "The table shows that Seq2Seq has 9.0% of responses with a Content score of 2 and an Emotion score of 1, which is lower than both ECM (27.2%) and Emb (22.8%).", "statement": "Seq2Seq has the highest percentage of responses with a Content score of 2 and an Emotion score of 1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Confusion Matrices for (a) using heuristic h1 with τ=25%n, (b) heuristic h1 with τ=50%n and (c) heuristic h2 with flexible τ.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Heuristic h2, with a flexible depth strategy, achieves the best precision and recall for identifying real mistakes made by the local model.For heuristic h1, we use a fixed depth strategy. In particular, given a document with n mentions, we consider two different depth limits: τ=25%n and τ=50%n, which lead to an average depth of 5 and 10 per document respectively. For heuristic h2, the depth is flexible and determined by the number of mentions that are predicted to be incorrect by h2. This strategy leads to an average depth of 4. In these tables correct/incorrect mean whether the local prediction is correct or not (comparing to the ground truth). Therefore the first cell with value 1134 indicates that there are 1134 mentions in test-b that are correctly predicted by the local model, but deemed as among the top 25% least confident mentions (aka the hard queries) by h1. The confusion matrix for a good heuristic will have small diagonal values and large anti-diagonal values. These results show that heuristic h2 gives the best precision as well as recall of the real mistakes made by the local model." } ], "images": [ "scigen/train/1138.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Confusion Matrices for (a) using heuristic h1 with τ=25%n, (b) heuristic h1 with τ=50%n and (c) heuristic h2 with flexible τ." ], "reasoning": "Heuristic h2, with a flexible depth strategy, achieves the best precision and recall for identifying real mistakes made by the local model.", "description": "For heuristic h1, we use a fixed depth strategy. In particular, given a document with n mentions, we consider two different depth limits: τ=25%n and τ=50%n, which lead to an average depth of 5 and 10 per document respectively. For heuristic h2, the depth is flexible and determined by the number of mentions that are predicted to be incorrect by h2. This strategy leads to an average depth of 4. In these tables correct/incorrect mean whether the local prediction is correct or not (comparing to the ground truth). Therefore the first cell with value 1134 indicates that there are 1134 mentions in test-b that are correctly predicted by the local model, but deemed as among the top 25% least confident mentions (aka the hard queries) by h1. The confusion matrix for a good heuristic will have small diagonal values and large anti-diagonal values. These results show that heuristic h2 gives the best precision as well as recall of the real mistakes made by the local model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Confusion Matrices for (a) using heuristic h1 with τ=25%n, (b) heuristic h1 with τ=50%n and (c) heuristic h2 with flexible τ.', answer the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nHow many mentions are correctly predicted by the local model but deemed as among the top 25% least confident mentions by h1?\n" }, { "role": "assistant", "content": "The table shows that there are 1134 mentions in test-b that are correctly predicted by the local model but are among the top 25% least confident mentions by h1. So, the answer is {\"answer\": \"1134\"}" } ], "images": [ "scigen/train/1138.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Confusion Matrices for (a) using heuristic h1 with τ=25%n, (b) heuristic h1 with τ=50%n and (c) heuristic h2 with flexible τ." ], "reasoning": "The table shows that there are 1134 mentions in test-b that are correctly predicted by the local model but are among the top 25% least confident mentions by h1.", "question": "How many mentions are correctly predicted by the local model but deemed as among the top 25% least confident mentions by h1?", "answer": "So, the answer is {\"answer\": \"1134\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nHeuristic h1 identifies more incorrect mentions than correct mentions in the top 25% least confident mentions.\n" }, { "role": "assistant", "content": "The table shows that heuristic h1 identifies 276 incorrect mentions and 1134 correct mentions in the top 25% least confident mentions, indicating more correct mentions than incorrect ones. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1138.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Confusion Matrices for (a) using heuristic h1 with τ=25%n, (b) heuristic h1 with τ=50%n and (c) heuristic h2 with flexible τ." ], "reasoning": "The table shows that heuristic h1 identifies 276 incorrect mentions and 1134 correct mentions in the top 25% least confident mentions, indicating more correct mentions than incorrect ones.", "statement": "Heuristic h1 identifies more incorrect mentions than correct mentions in the top 25% least confident mentions.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Evaluation on CoNLL 2003 Test-b and TAC-2010', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Our global model outperforms all competitors for both CoNLL 2003 and TAC 2010 datasets, while our local model is solid but inferior to the state-of-the-art local model.To evaluate the model performance we use the standard micro-average accuracies of the top-ranked candidate entities. We use different alias mappings for TAC and CoNLL. Specifically, for TAC we only use anchor-title alias mappings constructed from hyper-links in the Wikipedia. (b) show our performance on the CoNLL and TAC datasets for our local and global models along with other competitive systems respectively. The results show that our global model outperforms all the competitors for both CoNLL 2003 and TAC 2010. It is interesting to note that our local model is solid, but is noticeably inferior to the stat-of-the-art local model." } ], "images": [ "scigen/train/1139.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Evaluation on CoNLL 2003 Test-b and TAC-2010" ], "reasoning": "Our global model outperforms all competitors for both CoNLL 2003 and TAC 2010 datasets, while our local model is solid but inferior to the state-of-the-art local model.", "description": "To evaluate the model performance we use the standard micro-average accuracies of the top-ranked candidate entities. We use different alias mappings for TAC and CoNLL. Specifically, for TAC we only use anchor-title alias mappings constructed from hyper-links in the Wikipedia. (b) show our performance on the CoNLL and TAC datasets for our local and global models along with other competitive systems respectively. The results show that our global model outperforms all the competitors for both CoNLL 2003 and TAC 2010. It is interesting to note that our local model is solid, but is noticeably inferior to the stat-of-the-art local model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Evaluation on CoNLL 2003 Test-b and TAC-2010', answer the following question. Give detailed solution consisting of each step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest In-KB accuracy percentage?\n" }, { "role": "assistant", "content": "By comparing the In-KB accuracy percentages of all models listed in the table, the highest value is 94.44% for our global model. So, the answer is {\"answer\": \"our global model\"}" } ], "images": [ "scigen/train/1139.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Evaluation on CoNLL 2003 Test-b and TAC-2010" ], "reasoning": "By comparing the In-KB accuracy percentages of all models listed in the table, the highest value is 94.44% for our global model.", "question": "Which model has the highest In-KB accuracy percentage?", "answer": "So, the answer is {\"answer\": \"our global model\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe local model by Yamada et al., 2016 has a higher In-KB accuracy percentage than our local model.\n" }, { "role": "assistant", "content": "The table shows that the local model by Yamada et al., 2016 has an In-KB accuracy of 90.9%, while our local model has an In-KB accuracy of 90.89%. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1139.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Evaluation on CoNLL 2003 Test-b and TAC-2010" ], "reasoning": "The table shows that the local model by Yamada et al., 2016 has an In-KB accuracy of 90.9%, while our local model has an In-KB accuracy of 90.89%. Therefore, the statement is refuted.", "statement": "The local model by Yamada et al., 2016 has a higher In-KB accuracy percentage than our local model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Evaluation on CoNLL 2003 Test-b and TAC-2010'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The global model outperforms all competitors for both CoNLL 2003 and TAC 2010 datasets, while the local model is solid but inferior to the state-of-the-art local model.To evaluate the model performance we use the standard micro-average accuracies of the top-ranked candidate entities. We use different alias mappings for TAC and CoNLL. Specifically, for TAC we only use anchor-title alias mappings constructed from hyper-links in the Wikipedia. (b) show our performance on the CoNLL and TAC datasets for our local and global models along with other competitive systems respectively. The results show that our global model outperforms all the competitors for both CoNLL 2003 and TAC 2010. It is interesting to note that our local model is solid, but is noticeably inferior to the stat-of-the-art local model." } ], "images": [ "scigen/train/1140.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Evaluation on CoNLL 2003 Test-b and TAC-2010" ], "reasoning": "The global model outperforms all competitors for both CoNLL 2003 and TAC 2010 datasets, while the local model is solid but inferior to the state-of-the-art local model.", "description": "To evaluate the model performance we use the standard micro-average accuracies of the top-ranked candidate entities. We use different alias mappings for TAC and CoNLL. Specifically, for TAC we only use anchor-title alias mappings constructed from hyper-links in the Wikipedia. (b) show our performance on the CoNLL and TAC datasets for our local and global models along with other competitive systems respectively. The results show that our global model outperforms all the competitors for both CoNLL 2003 and TAC 2010. It is interesting to note that our local model is solid, but is noticeably inferior to the stat-of-the-art local model.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 2: Evaluation on CoNLL 2003 Test-b and TAC-2010'. Solve the following question based on the table.\n\nQuestion: What is the In-KB accuracy percentage of the best performing global model?\nLet's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the In-KB accuracy percentages for various models. The best performing global model is 'our model' with an accuracy of 87.9%. So, the answer is {\"answer\": \"87.9\"}." } ], "images": [ "scigen/train/1140.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Evaluation on CoNLL 2003 Test-b and TAC-2010" ], "reasoning": "The table shows the In-KB accuracy percentages for various models. The best performing global model is 'our model' with an accuracy of 87.9%.", "question": "What is the In-KB accuracy percentage of the best performing global model?", "answer": "So, the answer is {\"answer\": \"87.9\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The local model by Sil et al., 2018 has the highest In-KB accuracy percentage among all local models. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe local model by Sil et al., 2018 has the highest In-KB accuracy percentage among all local models.\n" }, { "role": "assistant", "content": "The table lists the In-KB accuracy percentages for various local models. The local model by Sil et al., 2018 has an accuracy of 87.4%, which is lower than 'our model' local accuracy of 85.73%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1140.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Evaluation on CoNLL 2003 Test-b and TAC-2010" ], "reasoning": "The table lists the In-KB accuracy percentages for various local models. The local model by Sil et al., 2018 has an accuracy of 87.4%, which is lower than 'our model' local accuracy of 85.73%.", "statement": "The local model by Sil et al., 2018 has the highest In-KB accuracy percentage among all local models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Confusion Matrices for (a) using heuristic h1 with τ=25%n, (b) heuristic h1 with τ=50%n and (c) heuristic h2 with flexible τ.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Heuristic h2, which uses a flexible depth strategy, achieves better precision and recall in identifying real mistakes compared to heuristic h1, which uses fixed depth limits.For heuristic h1, we use a fixed depth strategy. In particular, given a document with n mentions, we consider two different depth limits: τ=25%n and τ=50%n, which lead to an average depth of 5 and 10 per document respectively. For heuristic h2, the depth is flexible and determined by the number of mentions that are predicted to be incorrect by h2. This strategy leads to an average depth of 4. In these tables correct/incorrect mean whether the local prediction is correct or not (comparing to the ground truth). Therefore the first cell with value 1134 indicates that there are 1134 mentions in test-b that are correctly predicted by the local model, but deemed as among the top 25% least confident mentions (aka the hard queries) by h1. The confusion matrix for a good heuristic will have small diagonal values and large anti-diagonal values. These results show that heuristic h2 gives the best precision as well as recall of the real mistakes made by the local model." } ], "images": [ "scigen/train/1141.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Confusion Matrices for (a) using heuristic h1 with τ=25%n, (b) heuristic h1 with τ=50%n and (c) heuristic h2 with flexible τ." ], "reasoning": "Heuristic h2, which uses a flexible depth strategy, achieves better precision and recall in identifying real mistakes compared to heuristic h1, which uses fixed depth limits.", "description": "For heuristic h1, we use a fixed depth strategy. In particular, given a document with n mentions, we consider two different depth limits: τ=25%n and τ=50%n, which lead to an average depth of 5 and 10 per document respectively. For heuristic h2, the depth is flexible and determined by the number of mentions that are predicted to be incorrect by h2. This strategy leads to an average depth of 4. In these tables correct/incorrect mean whether the local prediction is correct or not (comparing to the ground truth). Therefore the first cell with value 1134 indicates that there are 1134 mentions in test-b that are correctly predicted by the local model, but deemed as among the top 25% least confident mentions (aka the hard queries) by h1. The confusion matrix for a good heuristic will have small diagonal values and large anti-diagonal values. These results show that heuristic h2 gives the best precision as well as recall of the real mistakes made by the local model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 4: Confusion Matrices for (a) using heuristic h1 with τ=25%n, (b) heuristic h1 with τ=50%n and (c) heuristic h2 with flexible τ.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. \n\nQuestion: What is the total number of mentions that are correctly predicted by the local model when τ=50%?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the total number of correct mentions, sum the values in the 'correct' row for both r ≤ 50% and r > 50%. This is 2071 + 2000. So, the answer is {\"answer\": \"4071\"}" } ], "images": [ "scigen/train/1141.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Confusion Matrices for (a) using heuristic h1 with τ=25%n, (b) heuristic h1 with τ=50%n and (c) heuristic h2 with flexible τ." ], "reasoning": "To find the total number of correct mentions, sum the values in the 'correct' row for both r ≤ 50% and r > 50%. This is 2071 + 2000.", "question": "What is the total number of mentions that are correctly predicted by the local model when τ=50%?", "answer": "So, the answer is {\"answer\": \"4071\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nHeuristic h1 with τ=50% has more incorrect mentions in the r > 50% category than in the r ≤ 50% category.\n" }, { "role": "assistant", "content": "The table shows that for τ=50%, there are 81 incorrect mentions in the r > 50% category and 331 incorrect mentions in the r ≤ 50% category. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1141.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Confusion Matrices for (a) using heuristic h1 with τ=25%n, (b) heuristic h1 with τ=50%n and (c) heuristic h2 with flexible τ." ], "reasoning": "The table shows that for τ=50%, there are 81 incorrect mentions in the r > 50% category and 331 incorrect mentions in the r ≤ 50% category. Therefore, the statement is refuted.", "statement": "Heuristic h1 with τ=50% has more incorrect mentions in the r > 50% category than in the r ≤ 50% category.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Confusion Matrices for (a) using heuristic h1 with τ=25%n, (b) heuristic h1 with τ=50%n and (c) heuristic h2 with flexible τ.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Heuristic h2, with flexible depth, achieves the best precision and recall by correctly identifying the real mistakes made by the local model.For heuristic h1, we use a fixed depth strategy. In particular, given a document with n mentions, we consider two different depth limits: τ=25%n and τ=50%n, which lead to an average depth of 5 and 10 per document respectively. For heuristic h2, the depth is flexible and determined by the number of mentions that are predicted to be incorrect by h2. This strategy leads to an average depth of 4. In these tables correct/incorrect mean whether the local prediction is correct or not (comparing to the ground truth). Therefore the first cell with value 1134 indicates that there are 1134 mentions in test-b that are correctly predicted by the local model, but deemed as among the top 25% least confident mentions (aka the hard queries) by h1. The confusion matrix for a good heuristic will have small diagonal values and large anti-diagonal values. These results show that heuristic h2 gives the best precision as well as recall of the real mistakes made by the local model." } ], "images": [ "scigen/train/1142.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Confusion Matrices for (a) using heuristic h1 with τ=25%n, (b) heuristic h1 with τ=50%n and (c) heuristic h2 with flexible τ." ], "reasoning": "Heuristic h2, with flexible depth, achieves the best precision and recall by correctly identifying the real mistakes made by the local model.", "description": "For heuristic h1, we use a fixed depth strategy. In particular, given a document with n mentions, we consider two different depth limits: τ=25%n and τ=50%n, which lead to an average depth of 5 and 10 per document respectively. For heuristic h2, the depth is flexible and determined by the number of mentions that are predicted to be incorrect by h2. This strategy leads to an average depth of 4. In these tables correct/incorrect mean whether the local prediction is correct or not (comparing to the ground truth). Therefore the first cell with value 1134 indicates that there are 1134 mentions in test-b that are correctly predicted by the local model, but deemed as among the top 25% least confident mentions (aka the hard queries) by h1. The confusion matrix for a good heuristic will have small diagonal values and large anti-diagonal values. These results show that heuristic h2 gives the best precision as well as recall of the real mistakes made by the local model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 4: Confusion Matrices for (a) using heuristic h1 with τ=25%n, (b) heuristic h1 with τ=50%n and (c) heuristic h2 with flexible τ.', and sequentially solve the question, writing out each step of your reasoning process. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nHow many mentions are correctly predicted by the local model but deemed as incorrect by heuristic h2?\n" }, { "role": "assistant", "content": "From the table, the number of mentions correctly predicted by the local model but deemed as incorrect by heuristic h2 is found in the 'correct' row under 'label=0' and 'label=1'. Summing these values gives 805 + 3266. So, the answer is {\"answer\": \"4071\"}" } ], "images": [ "scigen/train/1142.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Confusion Matrices for (a) using heuristic h1 with τ=25%n, (b) heuristic h1 with τ=50%n and (c) heuristic h2 with flexible τ." ], "reasoning": "From the table, the number of mentions correctly predicted by the local model but deemed as incorrect by heuristic h2 is found in the 'correct' row under 'label=0' and 'label=1'. Summing these values gives 805 + 3266.", "question": "How many mentions are correctly predicted by the local model but deemed as incorrect by heuristic h2?", "answer": "So, the answer is {\"answer\": \"4071\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Heuristic h2 has a higher number of incorrect predictions for label=1 than for label=0. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nHeuristic h2 has a higher number of incorrect predictions for label=1 than for label=0.\n" }, { "role": "assistant", "content": "The table shows that for heuristic h2, the number of incorrect predictions for label=1 is 79, while for label=0 it is 333. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1142.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Confusion Matrices for (a) using heuristic h1 with τ=25%n, (b) heuristic h1 with τ=50%n and (c) heuristic h2 with flexible τ." ], "reasoning": "The table shows that for heuristic h2, the number of incorrect predictions for label=1 is 79, while for label=0 it is 333. Therefore, the statement is refuted.", "statement": "Heuristic h2 has a higher number of incorrect predictions for label=1 than for label=0.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5: CoNLL 2003 Test-b'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Using a beam size of 5 improves accuracy over a beam size of 1, and heuristic h2 with flexible depth provides the best performance in both accuracy and efficiency.The results show that using a beam of size 5 improves upon single greedy search, and using heuristic h2 with flexible depth gives the best performance in terms of both prediction accuracy and efficiency (due to smaller search trees). When h1 is used, doubling the depth of the search tree brings about only a marginal improvement in accuracy at the cost of doubling the search depth and thus the prediction time." } ], "images": [ "scigen/train/1143.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: CoNLL 2003 Test-b" ], "reasoning": "Using a beam size of 5 improves accuracy over a beam size of 1, and heuristic h2 with flexible depth provides the best performance in both accuracy and efficiency.", "description": "The results show that using a beam of size 5 improves upon single greedy search, and using heuristic h2 with flexible depth gives the best performance in terms of both prediction accuracy and efficiency (due to smaller search trees). When h1 is used, doubling the depth of the search tree brings about only a marginal improvement in accuracy at the cost of doubling the search depth and thus the prediction time.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 5: CoNLL 2003 Test-b'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the In-KB accuracy percentage when using heuristic h2 with flexible depth and a beam size of 5?\n" }, { "role": "assistant", "content": "The table shows that using heuristic h2 with flexible depth and a beam size of 5 results in an In-KB accuracy of 94.44%. So, the answer is {\"answer\": \"94.44\"}" } ], "images": [ "scigen/train/1143.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: CoNLL 2003 Test-b" ], "reasoning": "The table shows that using heuristic h2 with flexible depth and a beam size of 5 results in an In-KB accuracy of 94.44%.", "question": "What is the In-KB accuracy percentage when using heuristic h2 with flexible depth and a beam size of 5?", "answer": "So, the answer is {\"answer\": \"94.44\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nDoubling the depth of the search tree with heuristic h1 significantly improves accuracy.\n" }, { "role": "assistant", "content": "The table shows that increasing the depth from τ=25% n to τ=50% n with heuristic h1 only marginally improves accuracy from 94.12% to 94.23%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1143.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: CoNLL 2003 Test-b" ], "reasoning": "The table shows that increasing the depth from τ=25% n to τ=50% n with heuristic h1 only marginally improves accuracy from 94.12% to 94.23%.", "statement": "Doubling the depth of the search tree with heuristic h1 significantly improves accuracy.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3. Performance comparison with state-of-the-art entity phrase extraction algorithms for the weakly-supervised entity phrase extraction task.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "ReMine outperforms other baselines in the Wiki-KBP dataset and has a high recall in the NYT dataset, being on par with neural network models in F1-score.1. Performance on Entity Phrase Extraction. The training data is generated through distant supervision described above without type information. Regarding open domain extractions, we train baseline models using the same distant supervision as ReMine, to push them towards a fair comparison. In the Wiki-KBP dataset, ReMine evidently outperforms all the other baselines. In the NYT dataset, ReMine has a rather high recall and is on par with the two neural network models on F1-score." } ], "images": [ "scigen/train/1144.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. Performance comparison with state-of-the-art entity phrase extraction algorithms for the weakly-supervised entity phrase extraction task." ], "reasoning": "ReMine outperforms other baselines in the Wiki-KBP dataset and has a high recall in the NYT dataset, being on par with neural network models in F1-score.", "description": "1. Performance on Entity Phrase Extraction. The training data is generated through distant supervision described above without type information. Regarding open domain extractions, we train baseline models using the same distant supervision as ReMine, to push them towards a fair comparison. In the Wiki-KBP dataset, ReMine evidently outperforms all the other baselines. In the NYT dataset, ReMine has a rather high recall and is on par with the two neural network models on F1-score.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3. Performance comparison with state-of-the-art entity phrase extraction algorithms for the weakly-supervised entity phrase extraction task.'. Solve the following question based on the table.\n\nQuestion: Which method has the highest recall in the NYT dataset?\nLet's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the method with the highest recall in the NYT dataset, look at the 'NYT (riedel2013relation,) Rec' column. ReMine has the highest recall with a value of 0.849. So, the answer is {\"answer\": \"ReMine\"}" } ], "images": [ "scigen/train/1144.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. Performance comparison with state-of-the-art entity phrase extraction algorithms for the weakly-supervised entity phrase extraction task." ], "reasoning": "To find the method with the highest recall in the NYT dataset, look at the 'NYT (riedel2013relation,) Rec' column. ReMine has the highest recall with a value of 0.849.", "question": "Which method has the highest recall in the NYT dataset?", "answer": "So, the answer is {\"answer\": \"ReMine\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nReMine has the highest precision in the Wiki-KBP dataset.\n" }, { "role": "assistant", "content": "To verify this statement, check the 'Wiki-KBP (ling2012fine,) Prec' column. ReMine has a precision of 0.636, which is the highest among the listed methods. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1144.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. Performance comparison with state-of-the-art entity phrase extraction algorithms for the weakly-supervised entity phrase extraction task." ], "reasoning": "To verify this statement, check the 'Wiki-KBP (ling2012fine,) Prec' column. ReMine has a precision of 0.636, which is the highest among the listed methods.", "statement": "ReMine has the highest precision in the Wiki-KBP dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4. Performance comparison with state-of-the-art Open IE systems on two datasets from different domains, using Precision@K, Mean Average Precision (MAP), Normalized Discounted Cumulative Gain (NDCG) and Mean Reciprocal Rank (MRR).'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "ReMine outperforms other methods in both NYT and Twitter datasets, showing less performance drop in Twitter and better ranking performance due to global cohesiveness.2. Performance on Relation Tuple Extraction. On NYT and twitter test set, we compare ReMine with its variants ReMine-L and ReMine-G as well as four baseline open IE systems mentioned above. All methods experience performance drop in Twitter, while ReMine declines less than any other methods on the rank-based measures. In the NYT dataset, all the systems except OLLIE have similar overall precision (i.e. P@300). But ReMine has a “higher” curve since most tuples obtained by Stanford OpenIE and ClausIE will be assigned score 1. Therefore we may not rank them in a very rational way. In contrast, the scores of different tuples obtained by ReMine-G and ReMine are usually distinct from each other. In the Twitter dataset, ReMine shows its power in dealing with short and noisy text. Both ClausIE and MinIE have a rather low score since there are lots of non-standard language usages and grammatical errors in tweets. In twitter, dependency parsing attaches more wrong arguments and labels than usual. All methods investigated depend on dependency parsing to varying degrees, while clause-based methods rely heavily on it and may not achieve a satisfying performance. Model-wise, we believe global cohesiveness helps open IE from two aspects: (1) ranking tuples (2) updating entity phrase pairs. In particular, ReMine-G differs from ReMine-L only on extraction scores, since global cohesiveness σ provides better ranking performance (P@300) over random (ReMine-L). The gain between ReMine and ReMine-G clearly shows the updated extractions have better quality in general." } ], "images": [ "scigen/train/1145.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4. Performance comparison with state-of-the-art Open IE systems on two datasets from different domains, using Precision@K, Mean Average Precision (MAP), Normalized Discounted Cumulative Gain (NDCG) and Mean Reciprocal Rank (MRR)." ], "reasoning": "ReMine outperforms other methods in both NYT and Twitter datasets, showing less performance drop in Twitter and better ranking performance due to global cohesiveness.", "description": "2. Performance on Relation Tuple Extraction. On NYT and twitter test set, we compare ReMine with its variants ReMine-L and ReMine-G as well as four baseline open IE systems mentioned above. All methods experience performance drop in Twitter, while ReMine declines less than any other methods on the rank-based measures. In the NYT dataset, all the systems except OLLIE have similar overall precision (i.e. P@300). But ReMine has a “higher” curve since most tuples obtained by Stanford OpenIE and ClausIE will be assigned score 1. Therefore we may not rank them in a very rational way. In contrast, the scores of different tuples obtained by ReMine-G and ReMine are usually distinct from each other. In the Twitter dataset, ReMine shows its power in dealing with short and noisy text. Both ClausIE and MinIE have a rather low score since there are lots of non-standard language usages and grammatical errors in tweets. In twitter, dependency parsing attaches more wrong arguments and labels than usual. All methods investigated depend on dependency parsing to varying degrees, while clause-based methods rely heavily on it and may not achieve a satisfying performance. Model-wise, we believe global cohesiveness helps open IE from two aspects: (1) ranking tuples (2) updating entity phrase pairs. In particular, ReMine-G differs from ReMine-L only on extraction scores, since global cohesiveness σ provides better ranking performance (P@300) over random (ReMine-L). The gain between ReMine and ReMine-G clearly shows the updated extractions have better quality in general.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nTable title: Table 4. Performance comparison with state-of-the-art Open IE systems on two datasets from different domains, using Precision@K, Mean Average Precision (MAP), Normalized Discounted Cumulative Gain (NDCG) and Mean Reciprocal Rank (MRR).\n\nQuestion: Which method has the highest P@300 score in the NYT dataset?\n" }, { "role": "assistant", "content": "From the table, we can see that ReMine has the highest P@300 score in the NYT dataset with a value of 0.760. So, the answer is {\"answer\": \"ReMine\"}" } ], "images": [ "scigen/train/1145.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4. Performance comparison with state-of-the-art Open IE systems on two datasets from different domains, using Precision@K, Mean Average Precision (MAP), Normalized Discounted Cumulative Gain (NDCG) and Mean Reciprocal Rank (MRR)." ], "reasoning": "From the table, we can see that ReMine has the highest P@300 score in the NYT dataset with a value of 0.760.", "question": "Which method has the highest P@300 score in the NYT dataset?", "answer": "So, the answer is {\"answer\": \"ReMine\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nReMine-G has a higher P@100 score than ReMine in the Twitter dataset.\n" }, { "role": "assistant", "content": "According to the table, ReMine-G has a P@100 score of 0.510, while ReMine has a P@100 score of 0.610 in the Twitter dataset. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1145.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4. Performance comparison with state-of-the-art Open IE systems on two datasets from different domains, using Precision@K, Mean Average Precision (MAP), Normalized Discounted Cumulative Gain (NDCG) and Mean Reciprocal Rank (MRR)." ], "reasoning": "According to the table, ReMine-G has a P@100 score of 0.510, while ReMine has a P@100 score of 0.610 in the Twitter dataset. Therefore, the statement is refuted.", "statement": "ReMine-G has a higher P@100 score than ReMine in the Twitter dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Accuracies of normalization (Id-En, Tr-De) and POS tagging (Tr-De) on test data, comparing the baselines to the best two normalization models.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The multilingual model slightly outperforms the language-aware model on the Tr-De normalization task, while their performances are very close on the Id-En normalization task.Interestingly, the multilingual model slightly outperforms the language-aware model on the Tr-De data. For the Id-En data, the final performances of the two normalization models are very close to each other. Similar as for the normalization, the POS tagger also performs better when using the multilingual model, in general the LAI scores are lower and performance increases are higher compared to the development data." } ], "images": [ "scigen/train/1146.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Accuracies of normalization (Id-En, Tr-De) and POS tagging (Tr-De) on test data, comparing the baselines to the best two normalization models." ], "reasoning": "The multilingual model slightly outperforms the language-aware model on the Tr-De normalization task, while their performances are very close on the Id-En normalization task.", "description": "Interestingly, the multilingual model slightly outperforms the language-aware model on the Tr-De data. For the Id-En data, the final performances of the two normalization models are very close to each other. Similar as for the normalization, the POS tagger also performs better when using the multilingual model, in general the LAI scores are lower and performance increases are higher compared to the development data.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 6: Accuracies of normalization (Id-En, Tr-De) and POS tagging (Tr-De) on test data, comparing the baselines to the best two normalization models.', solve this question and give detail solutions. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich model performs better on the Tr-De normalization task?\n" }, { "role": "assistant", "content": "The table shows that for the Tr-De normalization task, the multilingual model has a score of 78.28, while the language-aware model has a score of 77.83. Therefore, the multilingual model performs better. So, the answer is {\"answer\": \"multilingual\"}" } ], "images": [ "scigen/train/1146.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Accuracies of normalization (Id-En, Tr-De) and POS tagging (Tr-De) on test data, comparing the baselines to the best two normalization models." ], "reasoning": "The table shows that for the Tr-De normalization task, the multilingual model has a score of 78.28, while the language-aware model has a score of 77.83. Therefore, the multilingual model performs better.", "question": "Which model performs better on the Tr-De normalization task?", "answer": "So, the answer is {\"answer\": \"multilingual\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe multilingual model performs better than the language-aware model on the Id-En normalization task.\n" }, { "role": "assistant", "content": "The table shows that for the Id-En normalization task, the multilingual model has a score of 94.27, while the language-aware model has a score of 94.32. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1146.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Accuracies of normalization (Id-En, Tr-De) and POS tagging (Tr-De) on test data, comparing the baselines to the best two normalization models." ], "reasoning": "The table shows that for the Id-En normalization task, the multilingual model has a score of 94.27, while the language-aware model has a score of 94.32. Therefore, the statement is refuted.", "statement": "The multilingual model performs better than the language-aware model on the Id-En normalization task.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Word level accuracies for language identification (10-fold).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "MaChAmp consistently outperforms MarMoT and Bilty across all classes and datasets, with the hardest class being 'Unspecified'.Unsurprisingly, the performances are in line with the chronological order of the introduction of the systems, and their computational complexity. It should be noted that for MaChAmp we used pre-trained embeddings which were trained on the largest amount of external data. The hardest class is ‘Unspecified’; even though this class contains quite some easy cases (punctuation), there are also many harder cases, where a word belongs to any language other than Lang1 and Lang2, or when the annotator is uncertain. From the single label results, we can see that Bilty is more balanced compared to MarMoT, and that MaChAmp consistently outperforms the other two models. \\newcitebarik -etal-2019-normalization use a conditional random fields classifier with a variety of features for this task, and report 90.11 accuracy for the Id-En dataset in a 5-fold cross-validation setting." } ], "images": [ "scigen/train/1147.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Word level accuracies for language identification (10-fold)." ], "reasoning": "MaChAmp consistently outperforms MarMoT and Bilty across all classes and datasets, with the hardest class being 'Unspecified'.", "description": "Unsurprisingly, the performances are in line with the chronological order of the introduction of the systems, and their computational complexity. It should be noted that for MaChAmp we used pre-trained embeddings which were trained on the largest amount of external data. The hardest class is ‘Unspecified’; even though this class contains quite some easy cases (punctuation), there are also many harder cases, where a word belongs to any language other than Lang1 and Lang2, or when the annotator is uncertain. From the single label results, we can see that Bilty is more balanced compared to MarMoT, and that MaChAmp consistently outperforms the other two models. \\newcitebarik -etal-2019-normalization use a conditional random fields classifier with a variety of features for this task, and report 90.11 accuracy for the Id-En dataset in a 5-fold cross-validation setting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 3: Word level accuracies for language identification (10-fold).', and tackle the question below in a sequential manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model has the highest total accuracy for the Turkish-German dataset?\n" }, { "role": "assistant", "content": "By comparing the total accuracy for the Turkish-German dataset across all models, we see that MaChAmp has the highest total accuracy with 95.57. So, the answer is {\"answer\": \"95.57\"}" } ], "images": [ "scigen/train/1147.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Word level accuracies for language identification (10-fold)." ], "reasoning": "By comparing the total accuracy for the Turkish-German dataset across all models, we see that MaChAmp has the highest total accuracy with 95.57.", "question": "Which model has the highest total accuracy for the Turkish-German dataset?", "answer": "So, the answer is {\"answer\": \"95.57\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nBilty has the highest accuracy for the 'Unspecified' class in the Indonesian-English dataset.\n" }, { "role": "assistant", "content": "The table shows that for the 'Unspecified' class in the Indonesian-English dataset, MaChAmp has the highest accuracy with 91.11, not Bilty. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1147.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Word level accuracies for language identification (10-fold)." ], "reasoning": "The table shows that for the 'Unspecified' class in the Indonesian-English dataset, MaChAmp has the highest accuracy with 91.11, not Bilty.", "statement": "Bilty has the highest accuracy for the 'Unspecified' class in the Indonesian-English dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 5: Accuracies for POS tagging, using a variety of normalization strategies.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "MarMoT underperforms in all settings due to misclassification of emoticons as PUNCT, which are absent in the training data.Comparing the different models, we see that MarMoT underperforms on all settings. A manual inspection revealed that this is mainly due to misclassification of emoticons (as PUNCT), which do not occur in the training data. In this setup, performance was generally higher, and the gains when using normalization were smaller." } ], "images": [ "scigen/train/1148.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Accuracies for POS tagging, using a variety of normalization strategies." ], "reasoning": "MarMoT underperforms in all settings due to misclassification of emoticons as PUNCT, which are absent in the training data.", "description": "Comparing the different models, we see that MarMoT underperforms on all settings. A manual inspection revealed that this is mainly due to misclassification of emoticons (as PUNCT), which do not occur in the training data. In this setup, performance was generally higher, and the gains when using normalization were smaller.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: 'Table 5: Accuracies for POS tagging, using a variety of normalization strategies.'\nQuestion: Which model has the highest score in the Gold setting?\n" }, { "role": "assistant", "content": "By comparing the scores in the Gold column, Bilty–POS has the highest score of 70.37. So, the answer is {\"answer\": \"Bilty–POS\"}." } ], "images": [ "scigen/train/1148.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Accuracies for POS tagging, using a variety of normalization strategies." ], "reasoning": "By comparing the scores in the Gold column, Bilty–POS has the highest score of 70.37.", "question": "Which model has the highest score in the Gold setting?", "answer": "So, the answer is {\"answer\": \"Bilty–POS\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nMarMoT–POS has the lowest score in the LAI setting.\n" }, { "role": "assistant", "content": "By comparing the scores in the LAI column, MarMoT–POS has the lowest score of 60.47. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1148.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Accuracies for POS tagging, using a variety of normalization strategies." ], "reasoning": "By comparing the scores in the LAI column, MarMoT–POS has the lowest score of 60.47.", "statement": "MarMoT–POS has the lowest score in the LAI setting.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Model performance on the HotpotQA dataset (top scores marked in bold). Models [1-5] are from (Shen et al.,2017a; Tran et al., 2018; Wang and Jiang, 2016; Bian et al.,2017; Yoon et al., 2019), respectively.'.\n" }, { "role": "assistant", "content": "The dataset provides training and validation subsets, and model performances are reported on these datasets. Early termination is implemented based on devset performance to measure the best performance.Because the dataset only provides training (trainset) and validation (devset) subsets, we report the model performances on these datasets. While training the model, we implement early termination based on the devset performance and measure the best performance. sCARNN In addition to the main proposed model, PS-rnn-elmo, we also report the model performance with a small version of ELMo, PS-rnn-elmo-s." } ], "images": [ "scigen/train/1149.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Model performance on the HotpotQA dataset (top scores marked in bold). Models [1-5] are from (Shen et al.,2017a; Tran et al., 2018; Wang and Jiang, 2016; Bian et al.,2017; Yoon et al., 2019), respectively." ], "reasoning": "The dataset provides training and validation subsets, and model performances are reported on these datasets. Early termination is implemented based on devset performance to measure the best performance.", "description": "Because the dataset only provides training (trainset) and validation (devset) subsets, we report the model performances on these datasets. While training the model, we implement early termination based on the devset performance and measure the best performance. sCARNN In addition to the main proposed model, PS-rnn-elmo, we also report the model performance with a small version of ELMo, PS-rnn-elmo-s.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Model performance on the HotpotQA dataset (top scores marked in bold). Models [1-5] are from (Shen et al.,2017a; Tran et al., 2018; Wang and Jiang, 2016; Bian et al.,2017; Yoon et al., 2019), respectively.\nQuestion: Which model has the highest dev MRR and what is its value?\nGive a detailed response to the above question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest dev MRR, we look at the 'dev MRR' column and identify the highest value, which is 0.853 for the PS-rnn-elmo model. So, the answer is {\"answer\": \"PS-rnn-elmo with 0.853\"}." } ], "images": [ "scigen/train/1149.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Model performance on the HotpotQA dataset (top scores marked in bold). Models [1-5] are from (Shen et al.,2017a; Tran et al., 2018; Wang and Jiang, 2016; Bian et al.,2017; Yoon et al., 2019), respectively." ], "reasoning": "To find the model with the highest dev MRR, we look at the 'dev MRR' column and identify the highest value, which is 0.853 for the PS-rnn-elmo model.", "question": "Which model has the highest dev MRR and what is its value?", "answer": "So, the answer is {\"answer\": \"PS-rnn-elmo with 0.853\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe PS-rnn-elmo-s model has a higher train MAP than the CompAggr model.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the train MAP values of the PS-rnn-elmo-s model (0.813) and the CompAggr model (0.796). Since 0.813 is greater than 0.796, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1149.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Model performance on the HotpotQA dataset (top scores marked in bold). Models [1-5] are from (Shen et al.,2017a; Tran et al., 2018; Wang and Jiang, 2016; Bian et al.,2017; Yoon et al., 2019), respectively." ], "reasoning": "To verify this statement, we compare the train MAP values of the PS-rnn-elmo-s model (0.813) and the CompAggr model (0.796). Since 0.813 is greater than 0.796, the statement is supported.", "statement": "The PS-rnn-elmo-s model has a higher train MAP than the CompAggr model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 5: Model performance with different typologies. The connection strategies between nodes for each type are illustrated in figure 4.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Experiments with different hops showed that hop-4 is the best-performing hyper-parameter, but all variations performed worse than the original PS-rnn-elmo-s model.To reach the best performance, we conduct experiments multiple times by changing the number of hops in the model from 1 to 6 for each case (Type-1 to Type-3). From the experiment, hop-4 is selected as the best-performing hyper-parameter. However, all the model variations undergo performance degradation compared to the original topology (PS-rnn-elmo-s)." } ], "images": [ "scigen/train/1150.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Model performance with different typologies. The connection strategies between nodes for each type are illustrated in figure 4." ], "reasoning": "Experiments with different hops showed that hop-4 is the best-performing hyper-parameter, but all variations performed worse than the original PS-rnn-elmo-s model.", "description": "To reach the best performance, we conduct experiments multiple times by changing the number of hops in the model from 1 to 6 for each case (Type-1 to Type-3). From the experiment, hop-4 is selected as the best-performing hyper-parameter. However, all the model variations undergo performance degradation compared to the original topology (PS-rnn-elmo-s).", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 5: Model performance with different typologies. The connection strategies between nodes for each type are illustrated in figure 4.', and tackle the question below in a sequential manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model variation has the highest dev MAP and how does it compare to the original PS-rnn-elmo-s model?\n" }, { "role": "assistant", "content": "By comparing the dev MAP values, Type-2 (rnn-elmo-s) has the highest dev MAP among the variations with a value of 0.705. However, this is still lower than the original PS-rnn-elmo-s model's dev MAP of 0.716. So, the answer is {\"answer\": \"Type-2 (rnn-elmo-s)\"}." } ], "images": [ "scigen/train/1150.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Model performance with different typologies. The connection strategies between nodes for each type are illustrated in figure 4." ], "reasoning": "By comparing the dev MAP values, Type-2 (rnn-elmo-s) has the highest dev MAP among the variations with a value of 0.705. However, this is still lower than the original PS-rnn-elmo-s model's dev MAP of 0.716.", "question": "Which model variation has the highest dev MAP and how does it compare to the original PS-rnn-elmo-s model?", "answer": "So, the answer is {\"answer\": \"Type-2 (rnn-elmo-s)\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Type-1 (rnn-elmo-s) has a higher train MRR than the original PS-rnn-elmo-s model., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nType-1 (rnn-elmo-s) has a higher train MRR than the original PS-rnn-elmo-s model.\n" }, { "role": "assistant", "content": "The train MRR for Type-1 (rnn-elmo-s) is 0.915, which is lower than the original PS-rnn-elmo-s model's train MRR of 0.916. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1150.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Model performance with different typologies. The connection strategies between nodes for each type are illustrated in figure 4." ], "reasoning": "The train MRR for Type-1 (rnn-elmo-s) is 0.915, which is lower than the original PS-rnn-elmo-s model's train MRR of 0.916.", "statement": "Type-1 (rnn-elmo-s) has a higher train MRR than the original PS-rnn-elmo-s model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 6: Model performance with the different method for computing node representation.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "RNN encoding outperforms average pooling, and averaging ELMo produces worse node representations than GloVe.In all cases, the RNN encoding skims (-rnn) performs better than that of the average pooling (-avg). Interestingly, average pooling with ELMo representation ( PS-avg-elmo-s) performs worse than in the GloVe representation (PS-avg-glove) case. From this result, we find that averaging ELMo does not produce proper node representations. For the PS-rnn-bert case, we do not fine-tune the BERT model and only use its computing word representation. We expect there exists a possibility to enhance model performance by fine-tuning the BERT with the end-to-end training process." } ], "images": [ "scigen/train/1151.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Model performance with the different method for computing node representation." ], "reasoning": "RNN encoding outperforms average pooling, and averaging ELMo produces worse node representations than GloVe.", "description": "In all cases, the RNN encoding skims (-rnn) performs better than that of the average pooling (-avg). Interestingly, average pooling with ELMo representation ( PS-avg-elmo-s) performs worse than in the GloVe representation (PS-avg-glove) case. From this result, we find that averaging ELMo does not produce proper node representations. For the PS-rnn-bert case, we do not fine-tune the BERT model and only use its computing word representation. We expect there exists a possibility to enhance model performance by fine-tuning the BERT with the end-to-end training process.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. The question is related to the given table titled 'Table 6: Model performance with the different method for computing node representation.'.\n\nQuestion: Which model has the highest dev MRR and what is its value?\n" }, { "role": "assistant", "content": "By examining the table, the model PS-rnn-elmo has the highest dev MRR with a value of 0.853. So, the answer is {\"answer\": \"PS-rnn-elmo with 0.853\"}." } ], "images": [ "scigen/train/1151.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Model performance with the different method for computing node representation." ], "reasoning": "By examining the table, the model PS-rnn-elmo has the highest dev MRR with a value of 0.853.", "question": "Which model has the highest dev MRR and what is its value?", "answer": "So, the answer is {\"answer\": \"PS-rnn-elmo with 0.853\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nPS-avg-elmo-s has a higher train MAP than PS-avg-glove.\n" }, { "role": "assistant", "content": "By comparing the train MAP values in the table, PS-avg-elmo-s has a train MAP of 0.483, which is lower than PS-avg-glove's train MAP of 0.876. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1151.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Model performance with the different method for computing node representation." ], "reasoning": "By comparing the train MAP values in the table, PS-avg-elmo-s has a train MAP of 0.483, which is lower than PS-avg-glove's train MAP of 0.876.", "statement": "PS-avg-elmo-s has a higher train MAP than PS-avg-glove.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Comparison of the BiLSTM baseline with SentModel which encode the document sentence by sentence. We report the F1 score and AUC on the Dev set here.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The SentModel, which encodes documents sentence by sentence, achieves similar performance to the BiLSTM model, indicating that the current model fails to capture complex interactions among entities.To test whether current model can capture the complex interaction between entities, we use a SentModel which encodes the document sentence by sentence. Then we locate each entity within a specific sentence and compute its embedding by averaging the word embedding of the entity name. In this way, there will be no interaction between sentences since we encode the whole document sentence by sentence. Surprisingly, the SentModel can achieve very similar performance compared to the BiLSTM model which encodes the whole document as a sequence. Therefore, the current model fails to capture complex interactions among entities, and only local information around each entity is used to predict a relation." } ], "images": [ "scigen/train/1152.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of the BiLSTM baseline with SentModel which encode the document sentence by sentence. We report the F1 score and AUC on the Dev set here." ], "reasoning": "The SentModel, which encodes documents sentence by sentence, achieves similar performance to the BiLSTM model, indicating that the current model fails to capture complex interactions among entities.", "description": "To test whether current model can capture the complex interaction between entities, we use a SentModel which encodes the document sentence by sentence. Then we locate each entity within a specific sentence and compute its embedding by averaging the word embedding of the entity name. In this way, there will be no interaction between sentences since we encode the whole document sentence by sentence. Surprisingly, the SentModel can achieve very similar performance compared to the BiLSTM model which encodes the whole document as a sequence. Therefore, the current model fails to capture complex interactions among entities, and only local information around each entity is used to predict a relation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Comparison of the BiLSTM baseline with SentModel which encode the document sentence by sentence. We report the F1 score and AUC on the Dev set here.', unfold the question presented below, detailing every step of your calculation. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has a higher F1 score and what does this imply about the model's ability to capture complex interactions?\n" }, { "role": "assistant", "content": "The SentModel has a higher F1 score of 50.97 compared to the BiLSTM's 50.94. This implies that despite encoding documents sentence by sentence, the SentModel performs similarly to the BiLSTM, suggesting that the current model fails to capture complex interactions among entities. So, the answer is {\"answer\": \"SentModel\"}" } ], "images": [ "scigen/train/1152.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of the BiLSTM baseline with SentModel which encode the document sentence by sentence. We report the F1 score and AUC on the Dev set here." ], "reasoning": "The SentModel has a higher F1 score of 50.97 compared to the BiLSTM's 50.94. This implies that despite encoding documents sentence by sentence, the SentModel performs similarly to the BiLSTM, suggesting that the current model fails to capture complex interactions among entities.", "question": "Which model has a higher F1 score and what does this imply about the model's ability to capture complex interactions?", "answer": "So, the answer is {\"answer\": \"SentModel\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The BiLSTM model has a higher AUC score than the SentModel., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe BiLSTM model has a higher AUC score than the SentModel.\n" }, { "role": "assistant", "content": "The table shows that the BiLSTM model has an AUC score of 50.26, which is higher than the SentModel's AUC score of 49.31. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1152.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of the BiLSTM baseline with SentModel which encode the document sentence by sentence. We report the F1 score and AUC on the Dev set here." ], "reasoning": "The table shows that the BiLSTM model has an AUC score of 50.26, which is higher than the SentModel's AUC score of 49.31.", "statement": "The BiLSTM model has a higher AUC score than the SentModel.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Comparison of the BERT model with other baselines. We report F1 score on the Dev and Test set.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Using the BERT encoder improves F1 by 2%, and the two-step training process further enhances performance, achieving over 90% accuracy in the second step.We can see that we obtain a 2% F1 improvement by using the BERT encoder, which indicates that it may contain useful information such as common-sense knowledge in order to solve this task. By using the two-step training process, performance is improved further improve. In our experiments, we find that the accuracy for the second step is above 90%, which means the bottleneck lies in the first step, e.g., predicting whether a relation exists for a given entity pair." } ], "images": [ "scigen/train/1153.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of the BERT model with other baselines. We report F1 score on the Dev and Test set." ], "reasoning": "Using the BERT encoder improves F1 by 2%, and the two-step training process further enhances performance, achieving over 90% accuracy in the second step.", "description": "We can see that we obtain a 2% F1 improvement by using the BERT encoder, which indicates that it may contain useful information such as common-sense knowledge in order to solve this task. By using the two-step training process, performance is improved further improve. In our experiments, we find that the accuracy for the second step is above 90%, which means the bottleneck lies in the first step, e.g., predicting whether a relation exists for a given entity pair.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Comparison of the BERT model with other baselines. We report F1 score on the Dev and Test set.\nQuestion: Which model achieved the highest accuracy on the Test set?\nSolve the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the Test set accuracies of all models, BERT-Two-Step has the highest accuracy with 53.92. So, the answer is {\"answer\": \"BERT-Two-Step\"}" } ], "images": [ "scigen/train/1153.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of the BERT model with other baselines. We report F1 score on the Dev and Test set." ], "reasoning": "By comparing the Test set accuracies of all models, BERT-Two-Step has the highest accuracy with 53.92.", "question": "Which model achieved the highest accuracy on the Test set?", "answer": "So, the answer is {\"answer\": \"BERT-Two-Step\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The BERT model achieved a higher accuracy on the Dev set compared to the Context-Aware model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe BERT model achieved a higher accuracy on the Dev set compared to the Context-Aware model.\n" }, { "role": "assistant", "content": "The table shows that the BERT model has a Dev set accuracy of 54.16, while the Context-Aware model has a Dev set accuracy of 51.09. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1153.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of the BERT model with other baselines. We report F1 score on the Dev and Test set." ], "reasoning": "The table shows that the BERT model has a Dev set accuracy of 54.16, while the Context-Aware model has a Dev set accuracy of 51.09.", "statement": "The BERT model achieved a higher accuracy on the Dev set compared to the Context-Aware model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: The effect of GCN.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Models with GCN achieve higher performance in both Restaurant and Laptop datasets compared to models without GCN.It is clear to see that, comparing with GCN-reduced models, the two models with GCN achieve higher performance, respectively. The results verify that the modeling of the sentiment dependencies between different aspects with GCN plays a great role in predicting the sentiment polarities of aspects." } ], "images": [ "scigen/train/1154.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The effect of GCN." ], "reasoning": "Models with GCN achieve higher performance in both Restaurant and Laptop datasets compared to models without GCN.", "description": "It is clear to see that, comparing with GCN-reduced models, the two models with GCN achieve higher performance, respectively. The results verify that the modeling of the sentiment dependencies between different aspects with GCN plays a great role in predicting the sentiment polarities of aspects.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 3: The effect of GCN.', solve the subsequent question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich model achieves the highest Restaurant Macro-F1 score and what is the score?\n" }, { "role": "assistant", "content": "By comparing the Restaurant Macro-F1 scores of all models, the BiAtt+GCN (SDGCN) model achieves the highest score of 75.79. So, the answer is {\"answer\": \"75.79\"}." } ], "images": [ "scigen/train/1154.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The effect of GCN." ], "reasoning": "By comparing the Restaurant Macro-F1 scores of all models, the BiAtt+GCN (SDGCN) model achieves the highest score of 75.79.", "question": "Which model achieves the highest Restaurant Macro-F1 score and what is the score?", "answer": "So, the answer is {\"answer\": \"75.79\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Att+GCN model has a higher Laptop Acc than the BiAtt model., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Att+GCN model has a higher Laptop Acc than the BiAtt model.\n" }, { "role": "assistant", "content": "The table shows that the Att+GCN model has a Laptop Acc of 74.61, while the BiAtt model has a Laptop Acc of 73.51. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1154.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The effect of GCN." ], "reasoning": "The table shows that the Att+GCN model has a Laptop Acc of 74.61, while the BiAtt model has a Laptop Acc of 73.51. Therefore, the statement is supported.", "statement": "The Att+GCN model has a higher Laptop Acc than the BiAtt model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Comparisons with baseline models on the Restaurant dataset and Laptop dataset. The results of baseline models are retrieved from published papers. The best results in GloVe-based models and BERT-based models are all in bold separately. -A means that the model is based on adjacent-relation graph, and -G means the model is based on global-relation graph.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The proposed model achieves the best performance on both GloVe-based and BERT-based models, with SDGCN-BERT obtaining new state-of-the-art results.In order to remove the influence with different word representations and directly compare the performance of different models, we compare GloVe-based models and BERT-based models separately. Our proposed model achieves the best performance on both GloVe-based models and BERT-based models, which demonstrates the effectiveness of our proposed model. In particularly, SDGCN-BERT obtains new state-of-the-art results." } ], "images": [ "scigen/train/1155.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparisons with baseline models on the Restaurant dataset and Laptop dataset. The results of baseline models are retrieved from published papers. The best results in GloVe-based models and BERT-based models are all in bold separately. -A means that the model is based on adjacent-relation graph, and -G means the model is based on global-relation graph." ], "reasoning": "The proposed model achieves the best performance on both GloVe-based and BERT-based models, with SDGCN-BERT obtaining new state-of-the-art results.", "description": "In order to remove the influence with different word representations and directly compare the performance of different models, we compare GloVe-based models and BERT-based models separately. Our proposed model achieves the best performance on both GloVe-based models and BERT-based models, which demonstrates the effectiveness of our proposed model. In particularly, SDGCN-BERT obtains new state-of-the-art results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Comparisons with baseline models on the Restaurant dataset and Laptop dataset. The results of baseline models are retrieved from published papers. The best results in GloVe-based models and BERT-based models are all in bold separately. -A means that the model is based on adjacent-relation graph, and -G means the model is based on global-relation graph.', solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model achieves the highest Restaurant Acc using GloVe embeddings?\n" }, { "role": "assistant", "content": "To find the model with the highest Restaurant Acc using GloVe embeddings, look at the 'Restaurant Acc' column under the 'GloVe' word embedding. The highest value is 82.95, achieved by the SDGCN-G model. So, the answer is {\"answer\": \"SDGCN-G\"}" } ], "images": [ "scigen/train/1155.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparisons with baseline models on the Restaurant dataset and Laptop dataset. The results of baseline models are retrieved from published papers. The best results in GloVe-based models and BERT-based models are all in bold separately. -A means that the model is based on adjacent-relation graph, and -G means the model is based on global-relation graph." ], "reasoning": "To find the model with the highest Restaurant Acc using GloVe embeddings, look at the 'Restaurant Acc' column under the 'GloVe' word embedding. The highest value is 82.95, achieved by the SDGCN-G model.", "question": "Which model achieves the highest Restaurant Acc using GloVe embeddings?", "answer": "So, the answer is {\"answer\": \"SDGCN-G\"}" }, { "messages": [ { "role": "user", "content": "SDGCN-BERT achieves the highest Laptop Macro-F1 score. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nSDGCN-BERT achieves the highest Laptop Macro-F1 score.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'Laptop Macro-F1' column under the 'BERT' word embedding. The highest value is 78.34, achieved by SDGCN-BERT. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1155.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparisons with baseline models on the Restaurant dataset and Laptop dataset. The results of baseline models are retrieved from published papers. The best results in GloVe-based models and BERT-based models are all in bold separately. -A means that the model is based on adjacent-relation graph, and -G means the model is based on global-relation graph." ], "reasoning": "To verify this statement, look at the 'Laptop Macro-F1' column under the 'BERT' word embedding. The highest value is 78.34, achieved by SDGCN-BERT.", "statement": "SDGCN-BERT achieves the highest Laptop Macro-F1 score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: MAP performance on the three BMASS relations with ≥100 unigram analogies. Uni is using unigram embeddings on unigram data, UniM is using MWE embeddings on unigram data, and MWE is performance with MWE embeddings over the full MWE data.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Unigram analogies perform better than MWE analogies for has-lab-number (L2) and has-tradename (L3), but not for tradename-of (L4).Since prior work on analogies has primarily been concerned with unigram data, we also identified a subset of our data for which we could find single-word string realizations for all concepts in an analogy, using the full vocabulary of our trained embeddings. The unigram analogies are slightly better captured than the full MWE data for has-lab-number (L2) and has-tradename (L3); however, lower performance on the unigram subset in tradename-of (L4) shows that unigram analogies are not always easier. We see a small effect from the much larger set of candidate answers in the unigram case (>1m unigrams), as shown by the slightly higher MAP numbers in the UniM case. In general, it is clear that the difficulty of some of the relations in our dataset is not due solely to using MWEs in the analogies." } ], "images": [ "scigen/train/1156.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: MAP performance on the three BMASS relations with ≥100 unigram analogies. Uni is using unigram embeddings on unigram data, UniM is using MWE embeddings on unigram data, and MWE is performance with MWE embeddings over the full MWE data." ], "reasoning": "Unigram analogies perform better than MWE analogies for has-lab-number (L2) and has-tradename (L3), but not for tradename-of (L4).", "description": "Since prior work on analogies has primarily been concerned with unigram data, we also identified a subset of our data for which we could find single-word string realizations for all concepts in an analogy, using the full vocabulary of our trained embeddings. The unigram analogies are slightly better captured than the full MWE data for has-lab-number (L2) and has-tradename (L3); however, lower performance on the unigram subset in tradename-of (L4) shows that unigram analogies are not always easier. We see a small effect from the much larger set of candidate answers in the unigram case (>1m unigrams), as shown by the slightly higher MAP numbers in the UniM case. In general, it is clear that the difficulty of some of the relations in our dataset is not due solely to using MWEs in the analogies.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: MAP performance on the three BMASS relations with ≥100 unigram analogies. Uni is using unigram embeddings on unigram data, UniM is using MWE embeddings on unigram data, and MWE is performance with MWE embeddings over the full MWE data.\nQuestion: Which relation shows the highest performance in the PM-2 Uni M column?\nSolve the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the values in the PM-2 Uni M column, we see that L3 has the highest value of 0.19. So, the answer is {\"answer\": \"L3\"}." } ], "images": [ "scigen/train/1156.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: MAP performance on the three BMASS relations with ≥100 unigram analogies. Uni is using unigram embeddings on unigram data, UniM is using MWE embeddings on unigram data, and MWE is performance with MWE embeddings over the full MWE data." ], "reasoning": "By comparing the values in the PM-2 Uni M column, we see that L3 has the highest value of 0.19.", "question": "Which relation shows the highest performance in the PM-2 Uni M column?", "answer": "So, the answer is {\"answer\": \"L3\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe CBOW Uni M model performs better on L2 than on L3.\n" }, { "role": "assistant", "content": "By comparing the values in the CBOW Uni M column, we see that L2 has a value of 0.14, while L3 has a value of 0.16, indicating that the statement is false. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1156.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: MAP performance on the three BMASS relations with ≥100 unigram analogies. Uni is using unigram embeddings on unigram data, UniM is using MWE embeddings on unigram data, and MWE is performance with MWE embeddings over the full MWE data." ], "reasoning": "By comparing the values in the CBOW Uni M column, we see that L2 has a value of 0.14, while L3 has a value of 0.16, indicating that the statement is false.", "statement": "The CBOW Uni M model performs better on L2 than on L3.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Experiment of our proposed recall mechanism on the MSCOCO Karpathy test split with both cross-entropy loss and CIDEr optimization. We implement our proposed methods: semantic guide (SG), recalled-word slot (RWS) and recalled-word reward (WR) on the baseline model Up-Down. Test results show that our proposed methods have obvious improvement over our baseline. B-1 / B-4 / M / R / C / S refers to BLEU1/ BLEU4 / METEOR / ROUGE-L / CIDEr / SPICE scores.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The SG+RWS+WR model achieved the highest scores in CIDEr optimization training with BLEU4, CIDEr, and SPICE scores of 38.5, 129.1, and 22.4 respectively, outperforming other models., we have proposed semantic guide (SG), recalled-word slot (RWS) in captioning model and recalled-word reward (WR) in CIDEr optimization. Then we test their performances on MSCOCO Karpathy test split. Beam search with beam size 2 is employed to generate captions. Semantic guide with recalled-word slot (SG+RWS) has improved 3.0% on cross-entropy loss, and 6.8% on CIDEr optimization. Our best model (SG+RWS+WR) has obtained 7.5% improvement on CIDEr optimization, and it has obtained BLEU4 / CIDEr / SPICE scores of 36.6 / 116.9 / 21.3 with cross-entropy loss and 38.5 / 129.1/ 22.4 with CIDEr optimization. In addition, compared with other state-of-the-art models, like SCST [Rennie_2017_CVPR], StacKCap [gu2018stack], and CAVP [liu2018context] , our results still outperform theirs. The results of comparison demonstrate the effectiveness of our proposed methods, especially on those more convincing evaluation metrics such as CIDEr, SPICE." } ], "images": [ "scigen/train/1157.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Experiment of our proposed recall mechanism on the MSCOCO Karpathy test split with both cross-entropy loss and CIDEr optimization. We implement our proposed methods: semantic guide (SG), recalled-word slot (RWS) and recalled-word reward (WR) on the baseline model Up-Down. Test results show that our proposed methods have obvious improvement over our baseline. B-1 / B-4 / M / R / C / S refers to BLEU1/ BLEU4 / METEOR / ROUGE-L / CIDEr / SPICE scores." ], "reasoning": "The SG+RWS+WR model achieved the highest scores in CIDEr optimization training with BLEU4, CIDEr, and SPICE scores of 38.5, 129.1, and 22.4 respectively, outperforming other models.", "description": ", we have proposed semantic guide (SG), recalled-word slot (RWS) in captioning model and recalled-word reward (WR) in CIDEr optimization. Then we test their performances on MSCOCO Karpathy test split. Beam search with beam size 2 is employed to generate captions. Semantic guide with recalled-word slot (SG+RWS) has improved 3.0% on cross-entropy loss, and 6.8% on CIDEr optimization. Our best model (SG+RWS+WR) has obtained 7.5% improvement on CIDEr optimization, and it has obtained BLEU4 / CIDEr / SPICE scores of 36.6 / 116.9 / 21.3 with cross-entropy loss and 38.5 / 129.1/ 22.4 with CIDEr optimization. In addition, compared with other state-of-the-art models, like SCST [Rennie_2017_CVPR], StacKCap [gu2018stack], and CAVP [liu2018context] , our results still outperform theirs. The results of comparison demonstrate the effectiveness of our proposed methods, especially on those more convincing evaluation metrics such as CIDEr, SPICE.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: Experiment of our proposed recall mechanism on the MSCOCO Karpathy test split with both cross-entropy loss and CIDEr optimization. We implement our proposed methods: semantic guide (SG), recalled-word slot (RWS) and recalled-word reward (WR) on the baseline model Up-Down. Test results show that our proposed methods have obvious improvement over our baseline. B-1 / B-4 / M / R / C / S refers to BLEU1/ BLEU4 / METEOR / ROUGE-L / CIDEr / SPICE scores.'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model achieved the highest CIDEr score in CIDEr optimization training?\n" }, { "role": "assistant", "content": "By comparing the CIDEr scores in the CIDEr optimization training column, the SG+RWS+WR model has the highest score of 129.1. So, the answer is {\"answer\": \"SG+RWS+WR\"}" } ], "images": [ "scigen/train/1157.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Experiment of our proposed recall mechanism on the MSCOCO Karpathy test split with both cross-entropy loss and CIDEr optimization. We implement our proposed methods: semantic guide (SG), recalled-word slot (RWS) and recalled-word reward (WR) on the baseline model Up-Down. Test results show that our proposed methods have obvious improvement over our baseline. B-1 / B-4 / M / R / C / S refers to BLEU1/ BLEU4 / METEOR / ROUGE-L / CIDEr / SPICE scores." ], "reasoning": "By comparing the CIDEr scores in the CIDEr optimization training column, the SG+RWS+WR model has the highest score of 129.1.", "question": "Which model achieved the highest CIDEr score in CIDEr optimization training?", "answer": "So, the answer is {\"answer\": \"SG+RWS+WR\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe SCST model achieved a CIDEr score of 129.1 in CIDEr optimization training.\n" }, { "role": "assistant", "content": "The table shows that the SCST model achieved a CIDEr score of 114.0 in CIDEr optimization training, not 129.1. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1157.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Experiment of our proposed recall mechanism on the MSCOCO Karpathy test split with both cross-entropy loss and CIDEr optimization. We implement our proposed methods: semantic guide (SG), recalled-word slot (RWS) and recalled-word reward (WR) on the baseline model Up-Down. Test results show that our proposed methods have obvious improvement over our baseline. B-1 / B-4 / M / R / C / S refers to BLEU1/ BLEU4 / METEOR / ROUGE-L / CIDEr / SPICE scores." ], "reasoning": "The table shows that the SCST model achieved a CIDEr score of 114.0 in CIDEr optimization training, not 129.1.", "statement": "The SCST model achieved a CIDEr score of 129.1 in CIDEr optimization training.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Performance of our proposed methods over other state-of-the-art models after cider optimization training.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The proposed methods show an average improvement of 2.3% on att2in, 2.1% on att2all, and 3.3% on stackcap, surpassing the baseline model Up-Down in the MSCOCO online evaluation.To prove the effectiveness and generality of our proposed methods, we have also implemented our proposed methods over other state-of-the-art models: att2in[Rennie_2017_CVPR], att2all[Rennie_2017_CVPR] and stackcap [gu2018look]. We do the comparative experiments over these three models. To be detailed, we have average 2.3% improvement on att2in, 2.1% improvement on att2all, and 3.3% improvement on stackcap. We have conducted the MSCOCO online evaluation and achieved promising results (called “caption-recall”, reported at 19 Oct 2019), which also surpass the online results of our baseline model Up-Down." } ], "images": [ "scigen/train/1158.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of our proposed methods over other state-of-the-art models after cider optimization training." ], "reasoning": "The proposed methods show an average improvement of 2.3% on att2in, 2.1% on att2all, and 3.3% on stackcap, surpassing the baseline model Up-Down in the MSCOCO online evaluation.", "description": "To prove the effectiveness and generality of our proposed methods, we have also implemented our proposed methods over other state-of-the-art models: att2in[Rennie_2017_CVPR], att2all[Rennie_2017_CVPR] and stackcap [gu2018look]. We do the comparative experiments over these three models. To be detailed, we have average 2.3% improvement on att2in, 2.1% improvement on att2all, and 3.3% improvement on stackcap. We have conducted the MSCOCO online evaluation and achieved promising results (called “caption-recall”, reported at 19 Oct 2019), which also surpass the online results of our baseline model Up-Down.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Performance of our proposed methods over other state-of-the-art models after cider optimization training.' to sequentially solve the question presented below. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the highest score achieved in the 'C' metric and by which model?\n" }, { "role": "assistant", "content": "The highest score in the 'C' metric is 126.4, achieved by the model stackcap+SG+RWS+WR. So, the answer is {\"answer\": \"126.4\"}." } ], "images": [ "scigen/train/1158.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of our proposed methods over other state-of-the-art models after cider optimization training." ], "reasoning": "The highest score in the 'C' metric is 126.4, achieved by the model stackcap+SG+RWS+WR.", "question": "What is the highest score achieved in the 'C' metric and by which model?", "answer": "So, the answer is {\"answer\": \"126.4\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model att2all+SG+RWS+WR achieved a score of 57.8 in the 'R' metric.\n" }, { "role": "assistant", "content": "The table shows that the model att2all+SG+RWS+WR achieved a score of 57.8 in the 'R' metric. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1158.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of our proposed methods over other state-of-the-art models after cider optimization training." ], "reasoning": "The table shows that the model att2all+SG+RWS+WR achieved a score of 57.8 in the 'R' metric.", "statement": "The model att2all+SG+RWS+WR achieved a score of 57.8 in the 'R' metric.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Performance of text-retrieval model on MSCOCO Karpathy validation set.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The text-retrieval model retrieves top 5 captions for each image, achieving a balance between relevance and performance as indicated by the cross-entropy loss.The performance of text-retrieval model can directly affect the relevance between the recalled words and image. We assume that our text-retrieval model does not achieve state-of-the-art as in [lee2018stacked], but it is well qualified to retrieve sufficient and relevant words for images. Our corpus is collected from all the captions of MSCOCO Karpathy train splits. For determining an appropriate number of captions to be retrieved, we respectively retrieve top 1, 5 and 15 related captions for each image, and test the performance on cross-entropy loss. is a better choice than 1 or 15. It is necessary to emphasize that we avoid retrieving the ground truth caption for images, and all the retrieved captions only come from the train splits of MSCOCO Karpathy. In the following experiments, top 5 captions retrieved are used to construct recalled words for each image." } ], "images": [ "scigen/train/1159.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of text-retrieval model on MSCOCO Karpathy validation set." ], "reasoning": "The text-retrieval model retrieves top 5 captions for each image, achieving a balance between relevance and performance as indicated by the cross-entropy loss.", "description": "The performance of text-retrieval model can directly affect the relevance between the recalled words and image. We assume that our text-retrieval model does not achieve state-of-the-art as in [lee2018stacked], but it is well qualified to retrieve sufficient and relevant words for images. Our corpus is collected from all the captions of MSCOCO Karpathy train splits. For determining an appropriate number of captions to be retrieved, we respectively retrieve top 1, 5 and 15 related captions for each image, and test the performance on cross-entropy loss. is a better choice than 1 or 15. It is necessary to emphasize that we avoid retrieving the ground truth caption for images, and all the retrieved captions only come from the train splits of MSCOCO Karpathy. In the following experiments, top 5 captions retrieved are used to construct recalled words for each image.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: Performance of text-retrieval model on MSCOCO Karpathy validation set.' in mind, break down and resolve the question below step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the R@5 score for the text-retrieval model?\n" }, { "role": "assistant", "content": "The table shows the R@5 score for the text-retrieval model as 69.0. So, the answer is {\"answer\": \"69.0\"}." } ], "images": [ "scigen/train/1159.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of text-retrieval model on MSCOCO Karpathy validation set." ], "reasoning": "The table shows the R@5 score for the text-retrieval model as 69.0.", "question": "What is the R@5 score for the text-retrieval model?", "answer": "So, the answer is {\"answer\": \"69.0\"}." }, { "messages": [ { "role": "user", "content": "The image-retrieval model has a higher Mean r score than the text-retrieval model. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe image-retrieval model has a higher Mean r score than the text-retrieval model.\n" }, { "role": "assistant", "content": "The table shows that the Mean r score for the image-retrieval model is 11.0, which is higher than the text-retrieval model's Mean r score of 7.7. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1159.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of text-retrieval model on MSCOCO Karpathy validation set." ], "reasoning": "The table shows that the Mean r score for the image-retrieval model is 11.0, which is higher than the text-retrieval model's Mean r score of 7.7.", "statement": "The image-retrieval model has a higher Mean r score than the text-retrieval model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: Experiments on choice of K, the number of captions retrieved for each image. B-1 / B-4 / M / R / C / S refers to BLEU1/ BLEU4 / METEOR / ROUGE-L / CIDEr / SPICE scores. Experiments are conducted on MSCOCO Karpathy validation set.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The text-retrieval model retrieves top 5 captions for each image to construct recalled words, and different λ values are tested for model selection.The performance of text-retrieval model can directly affect the relevance between the recalled words and image. We assume that our text-retrieval model does not achieve state-of-the-art as in [lee2018stacked], but it is well qualified to retrieve sufficient and relevant words for images. Our corpus is collected from all the captions of MSCOCO Karpathy train splits. For determining an appropriate number of captions to be retrieved, we respectively retrieve top 1, 5 and 15 related captions for each image, and test the performance on cross-entropy loss. is a better choice than 1 or 15. It is necessary to emphasize that we avoid retrieving the ground truth caption for images, and all the retrieved captions only come from the train splits of MSCOCO Karpathy. In the following experiments, top 5 captions retrieved are used to construct recalled words for each image. Thus we set different λ values from 0 to 1 to conduct a model selection. As a result, in the following experiments, we set λ=0.5." } ], "images": [ "scigen/train/1160.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Experiments on choice of K, the number of captions retrieved for each image. B-1 / B-4 / M / R / C / S refers to BLEU1/ BLEU4 / METEOR / ROUGE-L / CIDEr / SPICE scores. Experiments are conducted on MSCOCO Karpathy validation set." ], "reasoning": "The text-retrieval model retrieves top 5 captions for each image to construct recalled words, and different λ values are tested for model selection.", "description": "The performance of text-retrieval model can directly affect the relevance between the recalled words and image. We assume that our text-retrieval model does not achieve state-of-the-art as in [lee2018stacked], but it is well qualified to retrieve sufficient and relevant words for images. Our corpus is collected from all the captions of MSCOCO Karpathy train splits. For determining an appropriate number of captions to be retrieved, we respectively retrieve top 1, 5 and 15 related captions for each image, and test the performance on cross-entropy loss. is a better choice than 1 or 15. It is necessary to emphasize that we avoid retrieving the ground truth caption for images, and all the retrieved captions only come from the train splits of MSCOCO Karpathy. In the following experiments, top 5 captions retrieved are used to construct recalled words for each image. Thus we set different λ values from 0 to 1 to conduct a model selection. As a result, in the following experiments, we set λ=0.5.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 4: Experiments on choice of K, the number of captions retrieved for each image. B-1 / B-4 / M / R / C / S refers to BLEU1/ BLEU4 / METEOR / ROUGE-L / CIDEr / SPICE scores. Experiments are conducted on MSCOCO Karpathy validation set.' to sequentially solve the question presented below. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the Cross-Entropy Loss C for K=5?\n" }, { "role": "assistant", "content": "From the table, locate the row where K=5 and find the value under the column 'Cross-Entropy Loss C'. So, the answer is {\"answer\": \"116.7\"}." } ], "images": [ "scigen/train/1160.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Experiments on choice of K, the number of captions retrieved for each image. B-1 / B-4 / M / R / C / S refers to BLEU1/ BLEU4 / METEOR / ROUGE-L / CIDEr / SPICE scores. Experiments are conducted on MSCOCO Karpathy validation set." ], "reasoning": "From the table, locate the row where K=5 and find the value under the column 'Cross-Entropy Loss C'.", "question": "What is the Cross-Entropy Loss C for K=5?", "answer": "So, the answer is {\"answer\": \"116.7\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Cross-Entropy Loss M is highest when K=1.\n" }, { "role": "assistant", "content": "From the table, compare the values under the column 'Cross-Entropy Loss M' for K=1, K=5, and K=15. The value for K=1 is 27.8, which is not the highest. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1160.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Experiments on choice of K, the number of captions retrieved for each image. B-1 / B-4 / M / R / C / S refers to BLEU1/ BLEU4 / METEOR / ROUGE-L / CIDEr / SPICE scores. Experiments are conducted on MSCOCO Karpathy validation set." ], "reasoning": "From the table, compare the values under the column 'Cross-Entropy Loss M' for K=1, K=5, and K=15. The value for K=1 is 27.8, which is not the highest.", "statement": "The Cross-Entropy Loss M is highest when K=1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Macro averaged F1-Scores of our submissions and the other experiments on test data', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The BERT-CNN model achieved the highest average F1-Score among all models, outperforming the baseline SVM with TF-IDF.Macro-averaged F1-Score metric was used for evaluation in this shared task. The baseline model used Term Frequency-Inverse Document Frequency (TF-IDF) with Support Vector Machine (SVM) Using CNNs with the same structure as the main model, but without pre-trained BERT as an embedder. CNN-Text model uses randomly initialized embeddings of size 300, which were trained along with the model. While CNNs could be used to capture local features of the text, LSTM which have shown remarkable performance in text classification tasks, capture the temporal information." } ], "images": [ "scigen/train/1161.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Macro averaged F1-Scores of our submissions and the other experiments on test data" ], "reasoning": "The BERT-CNN model achieved the highest average F1-Score among all models, outperforming the baseline SVM with TF-IDF.", "description": "Macro-averaged F1-Score metric was used for evaluation in this shared task. The baseline model used Term Frequency-Inverse Document Frequency (TF-IDF) with Support Vector Machine (SVM) Using CNNs with the same structure as the main model, but without pre-trained BERT as an embedder. CNN-Text model uses randomly initialized embeddings of size 300, which were trained along with the model. While CNNs could be used to capture local features of the text, LSTM which have shown remarkable performance in text classification tasks, capture the temporal information.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: Macro averaged F1-Scores of our submissions and the other experiments on test data', solve this question and give detail solutions. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich model achieved the highest average F1-Score?\n" }, { "role": "assistant", "content": "By examining the 'Average' column in the table, the BERT-CNN model has the highest average F1-Score of 0.851. So, the answer is {\"answer\": \"BERT-CNN\"}" } ], "images": [ "scigen/train/1161.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Macro averaged F1-Scores of our submissions and the other experiments on test data" ], "reasoning": "By examining the 'Average' column in the table, the BERT-CNN model has the highest average F1-Score of 0.851.", "question": "Which model achieved the highest average F1-Score?", "answer": "So, the answer is {\"answer\": \"BERT-CNN\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe CNN-Text model achieved a higher average F1-Score than the Bi-LSTM model.\n" }, { "role": "assistant", "content": "The table shows that the CNN-Text model has an average F1-Score of 0.805, while the Bi-LSTM model has an average F1-Score of 0.801. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1161.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Macro averaged F1-Scores of our submissions and the other experiments on test data" ], "reasoning": "The table shows that the CNN-Text model has an average F1-Score of 0.805, while the Bi-LSTM model has an average F1-Score of 0.801.", "statement": "The CNN-Text model achieved a higher average F1-Score than the Bi-LSTM model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 8: Comparisons on the 20 Newsgroups dataset for classifying documents into all topics. The disriminative RBM result is from drbm; compressive feature learning and LR-5-grams results are from compressive, and the distributed structured output result is from srikumar.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The new method using unigrams and bigrams with binary weighting and ℓ2 penalty achieved an accuracy of 87.84%, outperforming the strong logistic regression baseline using all 5-grams.The strong logistic regression baseline from \\newcitecompressive uses all 5-grams, heuristic normalization, and elastic net regularization; our method found that unigrams and bigrams, with binary weighting and ℓ2 penalty, achieved far better results." } ], "images": [ "scigen/train/1162.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Comparisons on the 20 Newsgroups dataset for classifying documents into all topics. The disriminative RBM result is from drbm; compressive feature learning and LR-5-grams results are from compressive, and the distributed structured output result is from srikumar." ], "reasoning": "The new method using unigrams and bigrams with binary weighting and ℓ2 penalty achieved an accuracy of 87.84%, outperforming the strong logistic regression baseline using all 5-grams.", "description": "The strong logistic regression baseline from \\newcitecompressive uses all 5-grams, heuristic normalization, and elastic net regularization; our method found that unigrams and bigrams, with binary weighting and ℓ2 penalty, achieved far better results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: Table 8: Comparisons on the 20 Newsgroups dataset for classifying documents into all topics. The disriminative RBM result is from drbm; compressive feature learning and LR-5-grams results are from compressive, and the distributed structured output result is from srikumar.\nQuestion: What is the accuracy of the method that uses unigrams and bigrams with binary weighting and ℓ2 penalty?\n" }, { "role": "assistant", "content": "The description mentions that the new method uses unigrams and bigrams with binary weighting and ℓ2 penalty, and the table shows that 'LR (this work)' achieved an accuracy of 87.84%. So, the answer is {\"answer\": \"87.84\"}." } ], "images": [ "scigen/train/1162.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Comparisons on the 20 Newsgroups dataset for classifying documents into all topics. The disriminative RBM result is from drbm; compressive feature learning and LR-5-grams results are from compressive, and the distributed structured output result is from srikumar." ], "reasoning": "The description mentions that the new method uses unigrams and bigrams with binary weighting and ℓ2 penalty, and the table shows that 'LR (this work)' achieved an accuracy of 87.84%.", "question": "What is the accuracy of the method that uses unigrams and bigrams with binary weighting and ℓ2 penalty?", "answer": "So, the answer is {\"answer\": \"87.84\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe method using all 5-grams achieved the highest accuracy.\n" }, { "role": "assistant", "content": "The table shows that the method 'LR-{1,2,3,4,5}-grams' achieved an accuracy of 82.80%, which is not the highest. The highest accuracy is 87.84% achieved by 'LR (this work)'. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1162.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Comparisons on the 20 Newsgroups dataset for classifying documents into all topics. The disriminative RBM result is from drbm; compressive feature learning and LR-5-grams results are from compressive, and the distributed structured output result is from srikumar." ], "reasoning": "The table shows that the method 'LR-{1,2,3,4,5}-grams' achieved an accuracy of 82.80%, which is not the highest. The highest accuracy is 87.84% achieved by 'LR (this work)'.", "statement": "The method using all 5-grams achieved the highest accuracy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: Comparisons on the Stanford sentiment treebank dataset. Scores are as reported by socher and paragraphvector.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The logistic regression model in this work achieved an accuracy of 82.4%, outperforming the baseline SVM which had an accuracy of 79.4%.Stanford sentiment treebank Our logistic regression model outperforms the baseline SVM reported by \\newcitesocher, who used only unigrams but did not specify the weighting scheme for their SVM baseline." } ], "images": [ "scigen/train/1163.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparisons on the Stanford sentiment treebank dataset. Scores are as reported by socher and paragraphvector." ], "reasoning": "The logistic regression model in this work achieved an accuracy of 82.4%, outperforming the baseline SVM which had an accuracy of 79.4%.", "description": "Stanford sentiment treebank Our logistic regression model outperforms the baseline SVM reported by \\newcitesocher, who used only unigrams but did not specify the weighting scheme for their SVM baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: Table 4: Comparisons on the Stanford sentiment treebank dataset. Scores are as reported by socher and paragraphvector.\nQuestion: What is the accuracy difference between the logistic regression model in this work and the baseline SVM?\n" }, { "role": "assistant", "content": "The logistic regression model in this work has an accuracy of 82.4%, while the baseline SVM has an accuracy of 79.4%. The difference in accuracy is calculated as 82.4% - 79.4%. So, the answer is {\"answer\": \"3.0%\"}" } ], "images": [ "scigen/train/1163.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparisons on the Stanford sentiment treebank dataset. Scores are as reported by socher and paragraphvector." ], "reasoning": "The logistic regression model in this work has an accuracy of 82.4%, while the baseline SVM has an accuracy of 79.4%. The difference in accuracy is calculated as 82.4% - 79.4%.", "question": "What is the accuracy difference between the logistic regression model in this work and the baseline SVM?", "answer": "So, the answer is {\"answer\": \"3.0%\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The logistic regression model in this work achieved a higher accuracy than the recursive neural networks., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe logistic regression model in this work achieved a higher accuracy than the recursive neural networks.\n" }, { "role": "assistant", "content": "The logistic regression model in this work has an accuracy of 82.4%, while the recursive neural networks have an accuracy of 82.4%. Since both have the same accuracy, the statement is not true. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1163.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparisons on the Stanford sentiment treebank dataset. Scores are as reported by socher and paragraphvector." ], "reasoning": "The logistic regression model in this work has an accuracy of 82.4%, while the recursive neural networks have an accuracy of 82.4%. Since both have the same accuracy, the statement is not true.", "statement": "The logistic regression model in this work achieved a higher accuracy than the recursive neural networks.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Comparisons on the Amazon electronics dataset. Scores are as reported by riejohnson.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our method outperformed all reported feed-forward neural networks and SVM variants, achieving the best performance with binary weighting.Our method is on par with the second-best of these, outperforming all of the reported feed-forward neural networks and SVM variants Johnson and Zhang used as baselines. They varied the representations, and used log term frequency and normalization to unit vectors as the weighting scheme, after finding that this outperformed term frequency. Our method achieved the best performance with binary weighting, which they did not consider." } ], "images": [ "scigen/train/1164.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparisons on the Amazon electronics dataset. Scores are as reported by riejohnson." ], "reasoning": "Our method outperformed all reported feed-forward neural networks and SVM variants, achieving the best performance with binary weighting.", "description": "Our method is on par with the second-best of these, outperforming all of the reported feed-forward neural networks and SVM variants Johnson and Zhang used as baselines. They varied the representations, and used log term frequency and normalization to unit vectors as the weighting scheme, after finding that this outperformed term frequency. Our method achieved the best performance with binary weighting, which they did not consider.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 5: Comparisons on the Amazon electronics dataset. Scores are as reported by riejohnson.' in mind, break down and resolve the question below step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich method achieved the highest accuracy according to the table?\n" }, { "role": "assistant", "content": "The table lists various methods and their corresponding accuracies. The method with the highest accuracy is Sequential CNN with an accuracy of 92.22. So, the answer is {\"answer\": \"Sequential CNN\"}" } ], "images": [ "scigen/train/1164.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparisons on the Amazon electronics dataset. Scores are as reported by riejohnson." ], "reasoning": "The table lists various methods and their corresponding accuracies. The method with the highest accuracy is Sequential CNN with an accuracy of 92.22.", "question": "Which method achieved the highest accuracy according to the table?", "answer": "So, the answer is {\"answer\": \"Sequential CNN\"}" }, { "messages": [ { "role": "user", "content": "Our method achieved the highest accuracy among all methods listed. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nOur method achieved the highest accuracy among all methods listed.\n" }, { "role": "assistant", "content": "The table shows that the Sequential CNN method achieved the highest accuracy of 92.22, while our method (LR) achieved an accuracy of 91.56. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1164.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparisons on the Amazon electronics dataset. Scores are as reported by riejohnson." ], "reasoning": "The table shows that the Sequential CNN method achieved the highest accuracy of 92.22, while our method (LR) achieved an accuracy of 91.56.", "statement": "Our method achieved the highest accuracy among all methods listed.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 7: Comparisons on the U.S. congressional vote dataset. SVM-link exploits link structures [Thomas et al.2006]; the min-cut result is from bansal; and SVM-SLE result is reported by ainur.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our method, LR, achieves the highest accuracy of 78.59%, outperforming the best reported result of SVM-SLE with 77.67%.Our method outperforms the best reported results of \\newciteainur, which use a multi-level structured model based on a latent-variable SVM. We show comparisons to two well-known but weaker baselines, as well." } ], "images": [ "scigen/train/1165.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Comparisons on the U.S. congressional vote dataset. SVM-link exploits link structures [Thomas et al.2006]; the min-cut result is from bansal; and SVM-SLE result is reported by ainur." ], "reasoning": "Our method, LR, achieves the highest accuracy of 78.59%, outperforming the best reported result of SVM-SLE with 77.67%.", "description": "Our method outperforms the best reported results of \\newciteainur, which use a multi-level structured model based on a latent-variable SVM. We show comparisons to two well-known but weaker baselines, as well.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 7: Comparisons on the U.S. congressional vote dataset. SVM-link exploits link structures [Thomas et al.2006]; the min-cut result is from bansal; and SVM-SLE result is reported by ainur.\nQuestion: Which method achieves the highest accuracy according to the table?\nSolve the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table lists the accuracies of different methods, with LR (this work) having the highest accuracy of 78.59%. So, the answer is {\"answer\": \"LR (this work)\"}." } ], "images": [ "scigen/train/1165.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Comparisons on the U.S. congressional vote dataset. SVM-link exploits link structures [Thomas et al.2006]; the min-cut result is from bansal; and SVM-SLE result is reported by ainur." ], "reasoning": "The table lists the accuracies of different methods, with LR (this work) having the highest accuracy of 78.59%.", "question": "Which method achieves the highest accuracy according to the table?", "answer": "So, the answer is {\"answer\": \"LR (this work)\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Min-cut method achieves an accuracy higher than 75%. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Min-cut method achieves an accuracy higher than 75%.\n" }, { "role": "assistant", "content": "The table shows that the Min-cut method has an accuracy of exactly 75.00%, which is not higher than 75%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1165.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Comparisons on the U.S. congressional vote dataset. SVM-link exploits link structures [Thomas et al.2006]; the min-cut result is from bansal; and SVM-SLE result is reported by ainur." ], "reasoning": "The table shows that the Min-cut method has an accuracy of exactly 75.00%, which is not higher than 75%.", "statement": "The Min-cut method achieves an accuracy higher than 75%.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: Results for identifying the Arg1 and Arg2 subtree nodes for the SS case under the GS + no EP setting for the three categories.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The performance of the argument extractor shows that subordinating connectives have the highest Arg2 F1 and Rel F1 scores, while discourse adverbials have the lowest scores across all metrics.We next evaluate the performance of the argument extractor. The last column shows the relation level F1 which requires both Arg1 and Arg2 nodes to be matched. We only show the results for the GS + no EP setting to save space. As expected, Arg1 and Arg2 nodes for subordinating connectives are the easiest ones to identify and give a high Arg2 F1 of 97.93% and a Rel F1 of 86.98%. We note that the Arg1 F1 and Arg2 F1 for coordinating connectives are the same, which is strange, as we expect Arg2 nodes to be handled more easily. The error analysis shows that Arg2 spans for coordinating connectives tend to include extra texts that cause the Arg2 nodes to move lower down in the parse tree. For example, “… and Mr. Simpson said he resigned in 1988” contains the extra span “Mr. Simpson said” which causes the Arg2 node moving two levels down the tree. As we discussed, discourse adverbials are difficult to identify as their Arg1 and Arg2 nodes are not strongly bound in the parse trees. However, as they do not occupy a large percentage in the test data, they do not lead to a large degradation as shown in the last row." } ], "images": [ "scigen/train/1166.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results for identifying the Arg1 and Arg2 subtree nodes for the SS case under the GS + no EP setting for the three categories." ], "reasoning": "The performance of the argument extractor shows that subordinating connectives have the highest Arg2 F1 and Rel F1 scores, while discourse adverbials have the lowest scores across all metrics.", "description": "We next evaluate the performance of the argument extractor. The last column shows the relation level F1 which requires both Arg1 and Arg2 nodes to be matched. We only show the results for the GS + no EP setting to save space. As expected, Arg1 and Arg2 nodes for subordinating connectives are the easiest ones to identify and give a high Arg2 F1 of 97.93% and a Rel F1 of 86.98%. We note that the Arg1 F1 and Arg2 F1 for coordinating connectives are the same, which is strange, as we expect Arg2 nodes to be handled more easily. The error analysis shows that Arg2 spans for coordinating connectives tend to include extra texts that cause the Arg2 nodes to move lower down in the parse tree. For example, “… and Mr. Simpson said he resigned in 1988” contains the extra span “Mr. Simpson said” which causes the Arg2 node moving two levels down the tree. As we discussed, discourse adverbials are difficult to identify as their Arg1 and Arg2 nodes are not strongly bound in the parse trees. However, as they do not occupy a large percentage in the test data, they do not lead to a large degradation as shown in the last row.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: 'Table 3: Results for identifying the Arg1 and Arg2 subtree nodes for the SS case under the GS + no EP setting for the three categories.'\nQuestion: What is the Arg1 F1 score for coordinating connectives, and how does it compare to the Arg2 F1 score for the same connectives?\n" }, { "role": "assistant", "content": "The table shows that the Arg1 F1 score for coordinating connectives is 90.34, which is the same as the Arg2 F1 score for coordinating connectives. So, the answer is {\"answer\": \"90.34\"}." } ], "images": [ "scigen/train/1166.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results for identifying the Arg1 and Arg2 subtree nodes for the SS case under the GS + no EP setting for the three categories." ], "reasoning": "The table shows that the Arg1 F1 score for coordinating connectives is 90.34, which is the same as the Arg2 F1 score for coordinating connectives.", "question": "What is the Arg1 F1 score for coordinating connectives, and how does it compare to the Arg2 F1 score for the same connectives?", "answer": "So, the answer is {\"answer\": \"90.34\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The Arg2 F1 score for discourse adverbials is higher than the Arg1 F1 score for subordinating connectives., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Arg2 F1 score for discourse adverbials is higher than the Arg1 F1 score for subordinating connectives.\n" }, { "role": "assistant", "content": "The table shows that the Arg2 F1 score for discourse adverbials is 62.50, which is lower than the Arg1 F1 score for subordinating connectives, which is 88.46. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1166.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results for identifying the Arg1 and Arg2 subtree nodes for the SS case under the GS + no EP setting for the three categories." ], "reasoning": "The table shows that the Arg2 F1 score for discourse adverbials is 62.50, which is lower than the Arg1 F1 score for subordinating connectives, which is 88.46.", "statement": "The Arg2 F1 score for discourse adverbials is higher than the Arg1 F1 score for subordinating connectives.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: Results for the connective classifier.'.\n" }, { "role": "assistant", "content": "Adding lexico-syntactic and path features significantly improves accuracy and F1 scores in both GS and Auto settings.02–21 and tested on Sec. 23. The second and third columns show the accuracy and F1 using the features of P&N, whereas the last two columns show the results when we add in the lexico-syntactic and path features (+new). Introducing the new features significantly (all with p<0.001) increases the accuracy and F1 by 2.04% and 3.01% under the GS setting, and 1.81% and 2.62% under the Auto setting. This confirms the usefulness of integrating the contextual and syntactic information. As the connective classifier is the first component in the pipeline, its high performance is crucial to mitigate the effect of cascaded errors downstream." } ], "images": [ "scigen/train/1167.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results for the connective classifier." ], "reasoning": "Adding lexico-syntactic and path features significantly improves accuracy and F1 scores in both GS and Auto settings.", "description": "02–21 and tested on Sec. 23. The second and third columns show the accuracy and F1 using the features of P&N, whereas the last two columns show the results when we add in the lexico-syntactic and path features (+new). Introducing the new features significantly (all with p<0.001) increases the accuracy and F1 by 2.04% and 3.01% under the GS setting, and 1.81% and 2.62% under the Auto setting. This confirms the usefulness of integrating the contextual and syntactic information. As the connective classifier is the first component in the pipeline, its high performance is crucial to mitigate the effect of cascaded errors downstream.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 1: Results for the connective classifier.', solve this question and give detail solutions. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhat is the increase in F1 score under the GS setting when new features are added?\n" }, { "role": "assistant", "content": "The F1 score under the GS setting with P&N features is 92.75, and with new features, it is 95.76. The increase is 95.76 - 92.75 = 3.01. So, the answer is {\"answer\": \"3.01\"}." } ], "images": [ "scigen/train/1167.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results for the connective classifier." ], "reasoning": "The F1 score under the GS setting with P&N features is 92.75, and with new features, it is 95.76. The increase is 95.76 - 92.75 = 3.01.", "question": "What is the increase in F1 score under the GS setting when new features are added?", "answer": "So, the answer is {\"answer\": \"3.01\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe accuracy under the Auto setting with new features is higher than 95%.\n" }, { "role": "assistant", "content": "The table shows that the accuracy under the Auto setting with new features is 96.02%, which is higher than 95%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1167.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results for the connective classifier." ], "reasoning": "The table shows that the accuracy under the Auto setting with new features is 96.02%, which is higher than 95%.", "statement": "The accuracy under the Auto setting with new features is higher than 95%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: Overall results for the argument extractor.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The GS + no EP setting achieves a high F1 score of 86.24% for partial matching on the relation level, while exact matching results are significantly lower due to annotators' minimality principle.Miltsakaki et al. They found that most of the disagreements for exact match come from partial overlaps which do not show significant semantic difference. We follow such work and report both exact and partial matches. When checking exact match, we require two spans to match identically, excluding any leading and ending punctuation symbols. A partial match is credited if there is any overlap between the verbs and nouns of the two spans. The GS + no EP setting gives a satisfactory F1 of 86.24% for partial matching on relation level. On the other hand, the results for exact matching are much lower than the human agreement. We observe that most misses are due to small portions of text being deleted from or added to the spans by the annotators to follow the minimality principle to include in the argument the minimal span of text that is sufficient for the interpretation of the relation, which poses difficulties for machines to follow." } ], "images": [ "scigen/train/1168.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Overall results for the argument extractor." ], "reasoning": "The GS + no EP setting achieves a high F1 score of 86.24% for partial matching on the relation level, while exact matching results are significantly lower due to annotators' minimality principle.", "description": "Miltsakaki et al. They found that most of the disagreements for exact match come from partial overlaps which do not show significant semantic difference. We follow such work and report both exact and partial matches. When checking exact match, we require two spans to match identically, excluding any leading and ending punctuation symbols. A partial match is credited if there is any overlap between the verbs and nouns of the two spans. The GS + no EP setting gives a satisfactory F1 of 86.24% for partial matching on relation level. On the other hand, the results for exact matching are much lower than the human agreement. We observe that most misses are due to small portions of text being deleted from or added to the spans by the annotators to follow the minimality principle to include in the argument the minimal span of text that is sufficient for the interpretation of the relation, which poses difficulties for machines to follow.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Overall results for the argument extractor.', answer the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the F1 score for Arg2 in the GS + EP setting for partial matching?\n" }, { "role": "assistant", "content": "To find the F1 score for Arg2 in the GS + EP setting for partial matching, locate the row labeled 'Partial' and 'GS + EP' and then find the corresponding Arg2 F1 score. So, the answer is {\"answer\": \"94.98\"}." } ], "images": [ "scigen/train/1168.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Overall results for the argument extractor." ], "reasoning": "To find the F1 score for Arg2 in the GS + EP setting for partial matching, locate the row labeled 'Partial' and 'GS + EP' and then find the corresponding Arg2 F1 score.", "question": "What is the F1 score for Arg2 in the GS + EP setting for partial matching?", "answer": "So, the answer is {\"answer\": \"94.98\"}." }, { "messages": [ { "role": "user", "content": "The Auto + EP setting for exact matching has the lowest F1 score for Arg1. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Auto + EP setting for exact matching has the lowest F1 score for Arg1.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Arg1 F1 scores for exact matching across all settings. The Auto + EP setting has an Arg1 F1 score of 47.68, which is indeed the lowest. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1168.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Overall results for the argument extractor." ], "reasoning": "To verify this statement, compare the Arg1 F1 scores for exact matching across all settings. The Auto + EP setting has an Arg1 F1 score of 47.68, which is indeed the lowest.", "statement": "The Auto + EP setting for exact matching has the lowest F1 score for Arg1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 5: Results for the explicit classifier.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Adding new features improves F1 to 86.77% under the GS + no EP setting, but full automation with error propagation results in a lower F1 of 80.61%.Recall that human agreement on Level 2 types is 84.00% and a baseline classifier that uses only the connectives as features yields an F1 of 86.00% under the GS + no EP setting on Sec. 23. Adding our new features improves F1 to 86.77%. With full automation and error propagation, we obtain an F1 of 80.61%. Pitler and Nenkova 02-22. This actually performs worse than the baseline when trained on Sec. 02-21 and tested on Sec. 23." } ], "images": [ "scigen/train/1169.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results for the explicit classifier." ], "reasoning": "Adding new features improves F1 to 86.77% under the GS + no EP setting, but full automation with error propagation results in a lower F1 of 80.61%.", "description": "Recall that human agreement on Level 2 types is 84.00% and a baseline classifier that uses only the connectives as features yields an F1 of 86.00% under the GS + no EP setting on Sec. 23. Adding our new features improves F1 to 86.77%. With full automation and error propagation, we obtain an F1 of 80.61%. Pitler and Nenkova 02-22. This actually performs worse than the baseline when trained on Sec. 02-21 and tested on Sec. 23.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Results for the explicit classifier.\nQuestion: What is the F1 score for the GS + EP setting?\nGive a detailed response to the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the F1 score for the GS + EP setting as 82.92%. So, the answer is {\"answer\": \"82.92\"}." } ], "images": [ "scigen/train/1169.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results for the explicit classifier." ], "reasoning": "The table shows the F1 score for the GS + EP setting as 82.92%.", "question": "What is the F1 score for the GS + EP setting?", "answer": "So, the answer is {\"answer\": \"82.92\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The F1 score for Auto + EP is higher than GS + EP., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe F1 score for Auto + EP is higher than GS + EP.\n" }, { "role": "assistant", "content": "The table shows that the F1 score for Auto + EP is 80.61%, which is lower than the F1 score for GS + EP at 82.92%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1169.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results for the explicit classifier." ], "reasoning": "The table shows that the F1 score for Auto + EP is 80.61%, which is lower than the F1 score for GS + EP at 82.92%.", "statement": "The F1 score for Auto + EP is higher than GS + EP.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Results for the non-explicit classifier.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The GS + no EP setting achieves a micro F1 of 39.63%, while GS + EP and Auto + EP settings outperform the baseline by about 6%.A single component evaluation (GS + no EP) shows a micro F1 of 39.63%. Although the F1 scores for the GS + EP and Auto + EP settings are unsatisfactory, they still significantly outperform the majority class baseline by about 6%. This performance is in line with the difficulties of classifying Implicit relations discussed in detail in our previous work" } ], "images": [ "scigen/train/1170.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results for the non-explicit classifier." ], "reasoning": "The GS + no EP setting achieves a micro F1 of 39.63%, while GS + EP and Auto + EP settings outperform the baseline by about 6%.", "description": "A single component evaluation (GS + no EP) shows a micro F1 of 39.63%. Although the F1 scores for the GS + EP and Auto + EP settings are unsatisfactory, they still significantly outperform the majority class baseline by about 6%. This performance is in line with the difficulties of classifying Implicit relations discussed in detail in our previous work", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: Results for the non-explicit classifier.\nQuestion: What is the difference in F1 score between the GS + no EP and the Auto + EP settings?\nSolve the above question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The F1 score for GS + no EP is 39.63%, and for Auto + EP it is 25.46%. The difference is 39.63 - 25.46. So, the answer is {\"answer\": \"14.17\"}." } ], "images": [ "scigen/train/1170.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results for the non-explicit classifier." ], "reasoning": "The F1 score for GS + no EP is 39.63%, and for Auto + EP it is 25.46%. The difference is 39.63 - 25.46.", "question": "What is the difference in F1 score between the GS + no EP and the Auto + EP settings?", "answer": "So, the answer is {\"answer\": \"14.17\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe GS + EP setting has a higher F1 score than the Auto + EP setting.\n" }, { "role": "assistant", "content": "The table shows that the F1 score for GS + EP is 26.90%, while for Auto + EP it is 25.46%. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1170.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results for the non-explicit classifier." ], "reasoning": "The table shows that the F1 score for GS + EP is 26.90%, while for Auto + EP it is 25.46%. Therefore, the statement is supported.", "statement": "The GS + EP setting has a higher F1 score than the Auto + EP setting.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 7: Results for the attribution span labeler.'.\n" }, { "role": "assistant", "content": "The degradation in F1 score is mainly due to a drop in precision when error propagation is introduced, and a drop in recall when full automation is added.The final component, the attribution span labeler, is evaluated under both partial and exact match, in accordance with the argument extractor. When error propagation is introduced, the degradation of F1 is largely due to the drop in precision. This is not surprising as at this point, the test data contains a lot of false positives propagated from the previous components. This has effect on the precision calculation but not recall (the recall scores do not change). When full automation is further added, the degradation is largely due to the drop in recall. This is because the automatic parser introduces noise that causes errors in the clause splitting step." } ], "images": [ "scigen/train/1171.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Results for the attribution span labeler." ], "reasoning": "The degradation in F1 score is mainly due to a drop in precision when error propagation is introduced, and a drop in recall when full automation is added.", "description": "The final component, the attribution span labeler, is evaluated under both partial and exact match, in accordance with the argument extractor. When error propagation is introduced, the degradation of F1 is largely due to the drop in precision. This is not surprising as at this point, the test data contains a lot of false positives propagated from the previous components. This has effect on the precision calculation but not recall (the recall scores do not change). When full automation is further added, the degradation is largely due to the drop in recall. This is because the automatic parser introduces noise that causes errors in the clause splitting step.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 7: Results for the attribution span labeler.'. Solve the following question based on the table.\n\nQuestion: What is the F1 score for the exact match with error propagation and automatic parser?\nLet's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the F1 score for the exact match with error propagation and automatic parser, look at the row labeled 'Exact' and 'Auto + EP' under the F1 column. Thus, the answer is {\"answer\": \"42.59\"}." } ], "images": [ "scigen/train/1171.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Results for the attribution span labeler." ], "reasoning": "To find the F1 score for the exact match with error propagation and automatic parser, look at the row labeled 'Exact' and 'Auto + EP' under the F1 column.", "question": "What is the F1 score for the exact match with error propagation and automatic parser?", "answer": "Thus, the answer is {\"answer\": \"42.59\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe recall score remains unchanged when error propagation is introduced.\n" }, { "role": "assistant", "content": "The description states that the recall scores do not change when error propagation is introduced. The table shows that the recall scores for 'GS + EP' and 'Auto + EP' are lower than 'GS + no EP', which refutes the statement. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1171.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Results for the attribution span labeler." ], "reasoning": "The description states that the recall scores do not change when error propagation is introduced. The table shows that the recall scores for 'GS + EP' and 'Auto + EP' are lower than 'GS + no EP', which refutes the statement.", "statement": "The recall score remains unchanged when error propagation is introduced.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Span representation methods.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "BinarySpan achieves a 92.17 LF score for English, while MultiSpan performs better due to label awareness, and LinearRule performs worse than BiaffineRule.We study the two span representation methods, namely the simple concatenating representation v[i,j] We investigate appropriate representations for different models on the English dev dataset. When using sr[i,j] for BinarySpan, the performance drops greatly (92.17→91.80). Similar observations can be found when replacing sr[i,j] with v[i,j] for BiaffineRule. Therefore, we use v[i,j] for the span models and sr[i,j] for the rule models in latter experiments. For English, BinarySpan acheives 92.17 LF score. The multi-class span classifier (MultiSpan) is much better than BinarySpan due to the awareness of label information. Similar phenomenon can be observed on the Chinese dataset. We also test the linear rule (LinearRule) methods. For English, LinearRule obtains 92.03 LF score, which is much worse than BiaffineRule. In general, the performances of BiaffineRule and MultiSpan are quite close both for English and Chinese." } ], "images": [ "scigen/train/1172.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Span representation methods." ], "reasoning": "BinarySpan achieves a 92.17 LF score for English, while MultiSpan performs better due to label awareness, and LinearRule performs worse than BiaffineRule.", "description": "We study the two span representation methods, namely the simple concatenating representation v[i,j] We investigate appropriate representations for different models on the English dev dataset. When using sr[i,j] for BinarySpan, the performance drops greatly (92.17→91.80). Similar observations can be found when replacing sr[i,j] with v[i,j] for BiaffineRule. Therefore, we use v[i,j] for the span models and sr[i,j] for the rule models in latter experiments. For English, BinarySpan acheives 92.17 LF score. The multi-class span classifier (MultiSpan) is much better than BinarySpan due to the awareness of label information. Similar phenomenon can be observed on the Chinese dataset. We also test the linear rule (LinearRule) methods. For English, LinearRule obtains 92.03 LF score, which is much worse than BiaffineRule. In general, the performances of BiaffineRule and MultiSpan are quite close both for English and Chinese.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: Span representation methods.', and tackle the question below in a sequential manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model achieves the highest LF score for the Chinese dataset?\n" }, { "role": "assistant", "content": "By examining the Chinese LF column in the table, BiaffineRule achieves the highest LF score of 91.29. So, the answer is {\"answer\": \"BiaffineRule\"}" } ], "images": [ "scigen/train/1172.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Span representation methods." ], "reasoning": "By examining the Chinese LF column in the table, BiaffineRule achieves the highest LF score of 91.29.", "question": "Which model achieves the highest LF score for the Chinese dataset?", "answer": "So, the answer is {\"answer\": \"BiaffineRule\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nMultiSpan achieves the highest LF score for the English dataset.\n" }, { "role": "assistant", "content": "By examining the English LF column in the table, MultiSpan achieves the highest LF score of 92.44. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1172.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Span representation methods." ], "reasoning": "By examining the English LF column in the table, MultiSpan achieves the highest LF score of 92.44.", "statement": "MultiSpan achieves the highest LF score for the English dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: Hyper-parameters for training.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The model uses pretrained word embeddings for both English and Chinese, with an initial learning rate of 0.1 and a dropout probability of 0.3 for Chinese.Hyper-parameters. These values are tuned using the corresponding development sets. We optimize our models with stochastic gradient descent (SGD). The initial learning rate is 0.1. Our model are initialized with pretrained word embeddings both for English and Chinese. The pretrained word embeddings are the same as those used in \\newcitedyer2016rnng. For Chinese, we find that 0.3 is a good choice for the dropout probability. The number of training epochs is decided by the evaluation performances on development set. In particular, we perform evaluations on development set for every 10,000 examples. The training procedure stops when the results of next 20 evaluations do not become better than the previous best record." } ], "images": [ "scigen/train/1173.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Hyper-parameters for training." ], "reasoning": "The model uses pretrained word embeddings for both English and Chinese, with an initial learning rate of 0.1 and a dropout probability of 0.3 for Chinese.", "description": "Hyper-parameters. These values are tuned using the corresponding development sets. We optimize our models with stochastic gradient descent (SGD). The initial learning rate is 0.1. Our model are initialized with pretrained word embeddings both for English and Chinese. The pretrained word embeddings are the same as those used in \\newcitedyer2016rnng. For Chinese, we find that 0.3 is a good choice for the dropout probability. The number of training epochs is decided by the evaluation performances on development set. In particular, we perform evaluations on development set for every 10,000 examples. The training procedure stops when the results of next 20 evaluations do not become better than the previous best record.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: 'Table 1: Hyper-parameters for training.'\nQuestion: What is the dropout probability for Chinese and the initial learning rate used in the model?\n" }, { "role": "assistant", "content": "The description states that the dropout probability for Chinese is 0.3 and the initial learning rate is 0.1. So, the answer is {\"answer\": \"0.3 and 0.1\"}" } ], "images": [ "scigen/train/1173.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Hyper-parameters for training." ], "reasoning": "The description states that the dropout probability for Chinese is 0.3 and the initial learning rate is 0.1.", "question": "What is the dropout probability for Chinese and the initial learning rate used in the model?", "answer": "So, the answer is {\"answer\": \"0.3 and 0.1\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model uses a dropout probability of 0.5 for English.\n" }, { "role": "assistant", "content": "The table shows that the dropout probability for English is 0.5. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1173.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Hyper-parameters for training." ], "reasoning": "The table shows that the dropout probability for English is 0.5.", "statement": "The model uses a dropout probability of 0.5 for English.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Results on the Chinese Treebank 5.1 test set.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The best model, MultiSpan, improves the labeled F1 score by 1.2 points from 86.1 to 87.3, outperforming the state-of-the-art neural model.Chinese. Under the same settings, all the three models outperform the state-of-the-art neural model Compared with the in-order transition-based parser, our best model improves the labeled F1 score by 1.2 (86.1→87.3)." } ], "images": [ "scigen/train/1174.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on the Chinese Treebank 5.1 test set." ], "reasoning": "The best model, MultiSpan, improves the labeled F1 score by 1.2 points from 86.1 to 87.3, outperforming the state-of-the-art neural model.", "description": "Chinese. Under the same settings, all the three models outperform the state-of-the-art neural model Compared with the in-order transition-based parser, our best model improves the labeled F1 score by 1.2 (86.1→87.3).", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 5: Results on the Chinese Treebank 5.1 test set.', unfold the question presented below, detailing every step of your calculation. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model achieved the highest labeled F1 score under the Parser petrov2007unlex?\n" }, { "role": "assistant", "content": "The table shows that under the Parser petrov2007unlex, the MultiSpan and BiaffineRule models both achieved the highest labeled F1 score of 87.3. So, the answer is {\"answer\": \"MultiSpan\"}" } ], "images": [ "scigen/train/1174.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on the Chinese Treebank 5.1 test set." ], "reasoning": "The table shows that under the Parser petrov2007unlex, the MultiSpan and BiaffineRule models both achieved the highest labeled F1 score of 87.3.", "question": "Which model achieved the highest labeled F1 score under the Parser petrov2007unlex?", "answer": "So, the answer is {\"answer\": \"MultiSpan\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe BinarySpan model achieved a labeled F1 score of 86.5 under the Parser petrov2007unlex.\n" }, { "role": "assistant", "content": "The table indicates that the BinarySpan model achieved a labeled F1 score of 86.5 under the Parser petrov2007unlex. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1174.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on the Chinese Treebank 5.1 test set." ], "reasoning": "The table indicates that the BinarySpan model achieved a labeled F1 score of 86.5 under the Parser petrov2007unlex.", "statement": "The BinarySpan model achieved a labeled F1 score of 86.5 under the Parser petrov2007unlex.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Comparison of QbE system performance on the evaluation set.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "S-RAILS+NAWE shows significant improvements over both RAILS and S-RAILS, with the largest gains in Median Example results and notable improvements in FOM and OTWV for Best Example cases.We find that our approach improves significantly over both RAILS and S-RAILS in terms of all performance metrics at this operating point. The biggest gains from S-RAILS+NAWE are seen in the Median Example results, where there is a relative improvement over S-RAILS of more than 55% across all measures. In terms of FOM and OTWV, we see relative improvements of over 40% in the Best Example case. Although the baselines obtain good P@10, we still find large improvements in this measure as well, from 87.1% to 95.1%." } ], "images": [ "scigen/train/1175.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of QbE system performance on the evaluation set." ], "reasoning": "S-RAILS+NAWE shows significant improvements over both RAILS and S-RAILS, with the largest gains in Median Example results and notable improvements in FOM and OTWV for Best Example cases.", "description": "We find that our approach improves significantly over both RAILS and S-RAILS in terms of all performance metrics at this operating point. The biggest gains from S-RAILS+NAWE are seen in the Median Example results, where there is a relative improvement over S-RAILS of more than 55% across all measures. In terms of FOM and OTWV, we see relative improvements of over 40% in the Best Example case. Although the baselines obtain good P@10, we still find large improvements in this measure as well, from 87.1% to 95.1%.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: Comparison of QbE system performance on the evaluation set.', solve this question and give detail solutions. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the relative improvement in Median Example FOM from S-RAILS to S-RAILS+NAWE?\n" }, { "role": "assistant", "content": "The Median Example FOM for S-RAILS is 24.5, and for S-RAILS+NAWE it is 43.3. The relative improvement is calculated as ((43.3 - 24.5) / 24.5) * 100%. So, the answer is {\"answer\": \"76.7%\"}." } ], "images": [ "scigen/train/1175.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of QbE system performance on the evaluation set." ], "reasoning": "The Median Example FOM for S-RAILS is 24.5, and for S-RAILS+NAWE it is 43.3. The relative improvement is calculated as ((43.3 - 24.5) / 24.5) * 100%.", "question": "What is the relative improvement in Median Example FOM from S-RAILS to S-RAILS+NAWE?", "answer": "So, the answer is {\"answer\": \"76.7%\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nS-RAILS+NAWE has a Best Example P@10 of 95.1%.\n" }, { "role": "assistant", "content": "The table shows that the Best Example P@10 for S-RAILS+NAWE is indeed 95.1%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1175.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of QbE system performance on the evaluation set." ], "reasoning": "The table shows that the Best Example P@10 for S-RAILS+NAWE is indeed 95.1%.", "statement": "S-RAILS+NAWE has a Best Example P@10 of 95.1%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Effect of number of permutations P on S-RAILS+NAWE performance on the development set, for signature length b=1024 and beamwidth B=2,000.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Increasing the number of permutations improves the metrics, especially the FOM score, but incurs a large memory cost.Again in contrast to the S-RAILS system, our method responds strongly to increases in the number of permutations used. This is to be expected if the neural embeddings provide a better measure of speech segment distances, since the increased number of permutations helps provide a more exact estimate of the embedding distance. Further increasing the number of permutations may further improve these metrics, but this incurs a large cost in memory. To obtain higher precision systems, it is more important to use computational resources for increasing the number of permutations or using longer signatures. However, as would be expected, the higher beamwidths help to significantly improve the FOM score, a metric concerned primarily with recall." } ], "images": [ "scigen/train/1176.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Effect of number of permutations P on S-RAILS+NAWE performance on the development set, for signature length b=1024 and beamwidth B=2,000." ], "reasoning": "Increasing the number of permutations improves the metrics, especially the FOM score, but incurs a large memory cost.", "description": "Again in contrast to the S-RAILS system, our method responds strongly to increases in the number of permutations used. This is to be expected if the neural embeddings provide a better measure of speech segment distances, since the increased number of permutations helps provide a more exact estimate of the embedding distance. Further increasing the number of permutations may further improve these metrics, but this incurs a large cost in memory. To obtain higher precision systems, it is more important to use computational resources for increasing the number of permutations or using longer signatures. However, as would be expected, the higher beamwidths help to significantly improve the FOM score, a metric concerned primarily with recall.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: Effect of number of permutations P on S-RAILS+NAWE performance on the development set, for signature length b=1024 and beamwidth B=2,000.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}. \n\nQuestion: What is the Best Example FOM score when b is 2048?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the Best Example FOM score for b=2048, look at the row where b is 2048 and find the value under the Best Example FOM column. So, the answer is {\"answer\": \"85.0\"}" } ], "images": [ "scigen/train/1176.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Effect of number of permutations P on S-RAILS+NAWE performance on the development set, for signature length b=1024 and beamwidth B=2,000." ], "reasoning": "To find the Best Example FOM score for b=2048, look at the row where b is 2048 and find the value under the Best Example FOM column.", "question": "What is the Best Example FOM score when b is 2048?", "answer": "So, the answer is {\"answer\": \"85.0\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Median Example P@10 score increases as the value of b increases.\n" }, { "role": "assistant", "content": "By examining the Median Example P@10 column, it is evident that the scores increase from 42.1 to 54.5 as b increases from 128 to 2048. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1176.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Effect of number of permutations P on S-RAILS+NAWE performance on the development set, for signature length b=1024 and beamwidth B=2,000." ], "reasoning": "By examining the Median Example P@10 column, it is evident that the scores increase from 42.1 to 54.5 as b increases from 128 to 2048.", "statement": "The Median Example P@10 score increases as the value of b increases.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Effect of number of permutations P on S-RAILS+NAWE performance on the development set, for signature length b=1024 and beamwidth B=2,000.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Increasing the number of permutations improves the metrics significantly, especially in terms of recall, but it also increases memory usage.Again in contrast to the S-RAILS system, our method responds strongly to increases in the number of permutations used. This is to be expected if the neural embeddings provide a better measure of speech segment distances, since the increased number of permutations helps provide a more exact estimate of the embedding distance. Further increasing the number of permutations may further improve these metrics, but this incurs a large cost in memory. To obtain higher precision systems, it is more important to use computational resources for increasing the number of permutations or using longer signatures. However, as would be expected, the higher beamwidths help to significantly improve the FOM score, a metric concerned primarily with recall." } ], "images": [ "scigen/train/1177.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Effect of number of permutations P on S-RAILS+NAWE performance on the development set, for signature length b=1024 and beamwidth B=2,000." ], "reasoning": "Increasing the number of permutations improves the metrics significantly, especially in terms of recall, but it also increases memory usage.", "description": "Again in contrast to the S-RAILS system, our method responds strongly to increases in the number of permutations used. This is to be expected if the neural embeddings provide a better measure of speech segment distances, since the increased number of permutations helps provide a more exact estimate of the embedding distance. Further increasing the number of permutations may further improve these metrics, but this incurs a large cost in memory. To obtain higher precision systems, it is more important to use computational resources for increasing the number of permutations or using longer signatures. However, as would be expected, the higher beamwidths help to significantly improve the FOM score, a metric concerned primarily with recall.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 2: Effect of number of permutations P on S-RAILS+NAWE performance on the development set, for signature length b=1024 and beamwidth B=2,000.'. Solve the following question based on the table.\n\nQuestion: What is the Best Example FOM score when P is 16?\nLet's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the Best Example FOM score when P is 16, look at the row where P is 16 and find the value under the Best Example FOM column. So, the answer is {\"answer\": \"84.1\"}." } ], "images": [ "scigen/train/1177.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Effect of number of permutations P on S-RAILS+NAWE performance on the development set, for signature length b=1024 and beamwidth B=2,000." ], "reasoning": "To find the Best Example FOM score when P is 16, look at the row where P is 16 and find the value under the Best Example FOM column.", "question": "What is the Best Example FOM score when P is 16?", "answer": "So, the answer is {\"answer\": \"84.1\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Median Example P@10 score increases as the number of permutations increases.\n" }, { "role": "assistant", "content": "The table shows that the Median Example P@10 score increases from 45.2 to 50.3 to 54.5 as P increases from 4 to 8 to 16. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1177.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Effect of number of permutations P on S-RAILS+NAWE performance on the development set, for signature length b=1024 and beamwidth B=2,000." ], "reasoning": "The table shows that the Median Example P@10 score increases from 45.2 to 50.3 to 54.5 as P increases from 4 to 8 to 16.", "statement": "The Median Example P@10 score increases as the number of permutations increases.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Effect of number of permutations P on S-RAILS+NAWE performance on the development set, for signature length b=1024 and beamwidth B=2,000.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Increasing the number of permutations improves the metrics, but it incurs a large memory cost. Higher beamwidths significantly improve the FOM score, which is primarily concerned with recall.Again in contrast to the S-RAILS system, our method responds strongly to increases in the number of permutations used. This is to be expected if the neural embeddings provide a better measure of speech segment distances, since the increased number of permutations helps provide a more exact estimate of the embedding distance. Further increasing the number of permutations may further improve these metrics, but this incurs a large cost in memory. To obtain higher precision systems, it is more important to use computational resources for increasing the number of permutations or using longer signatures. However, as would be expected, the higher beamwidths help to significantly improve the FOM score, a metric concerned primarily with recall." } ], "images": [ "scigen/train/1178.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Effect of number of permutations P on S-RAILS+NAWE performance on the development set, for signature length b=1024 and beamwidth B=2,000." ], "reasoning": "Increasing the number of permutations improves the metrics, but it incurs a large memory cost. Higher beamwidths significantly improve the FOM score, which is primarily concerned with recall.", "description": "Again in contrast to the S-RAILS system, our method responds strongly to increases in the number of permutations used. This is to be expected if the neural embeddings provide a better measure of speech segment distances, since the increased number of permutations helps provide a more exact estimate of the embedding distance. Further increasing the number of permutations may further improve these metrics, but this incurs a large cost in memory. To obtain higher precision systems, it is more important to use computational resources for increasing the number of permutations or using longer signatures. However, as would be expected, the higher beamwidths help to significantly improve the FOM score, a metric concerned primarily with recall.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Effect of number of permutations P on S-RAILS+NAWE performance on the development set, for signature length b=1024 and beamwidth B=2,000.', and sequentially solve the question, writing out each step of your reasoning process. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the Best Example FOM score when B is 10000?\n" }, { "role": "assistant", "content": "To find the Best Example FOM score for B=10000, look at the row where B is 10000 and find the value under the Best Example FOM column. So, the answer is {\"answer\": \"86.3\"}." } ], "images": [ "scigen/train/1178.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Effect of number of permutations P on S-RAILS+NAWE performance on the development set, for signature length b=1024 and beamwidth B=2,000." ], "reasoning": "To find the Best Example FOM score for B=10000, look at the row where B is 10000 and find the value under the Best Example FOM column.", "question": "What is the Best Example FOM score when B is 10000?", "answer": "So, the answer is {\"answer\": \"86.3\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Median Example FOM score increases as the number of permutations (B) increases.\n" }, { "role": "assistant", "content": "The table shows that the Median Example FOM score is 65.8 for B=1000, 69.1 for B=2000, and 74.6 for B=10000, indicating an increase as B increases. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1178.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Effect of number of permutations P on S-RAILS+NAWE performance on the development set, for signature length b=1024 and beamwidth B=2,000." ], "reasoning": "The table shows that the Median Example FOM score is 65.8 for B=1000, 69.1 for B=2000, and 74.6 for B=10000, indicating an increase as B increases.", "statement": "The Median Example FOM score increases as the number of permutations (B) increases.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Tweet distribution in all corpora.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The French dataset maintains the original distribution of ironic and non-ironic classes without modification, using the same number of records for training and testing to compare with state-of-the-art results.Also, for French, we use the original dataset without any modification, keeping the same number of records for train and test to better compare with state-of-the-art results. For the classes distribution (ironic vs. non ironic), we do not choose a specific ratio but we use the resulted distribution from the random shuffling process." } ], "images": [ "scigen/train/1179.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Tweet distribution in all corpora." ], "reasoning": "The French dataset maintains the original distribution of ironic and non-ironic classes without modification, using the same number of records for training and testing to compare with state-of-the-art results.", "description": "Also, for French, we use the original dataset without any modification, keeping the same number of records for train and test to better compare with state-of-the-art results. For the classes distribution (ironic vs. non ironic), we do not choose a specific ratio but we use the resulted distribution from the random shuffling process.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: Tweet distribution in all corpora.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the total number of records used for training and testing in the French dataset?\n" }, { "role": "assistant", "content": "The table shows that the French dataset has 5,843 records for training and 1,464 records for testing. Adding these together gives the total number of records. So, the answer is {\"answer\": \"7,307\"}." } ], "images": [ "scigen/train/1179.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Tweet distribution in all corpora." ], "reasoning": "The table shows that the French dataset has 5,843 records for training and 1,464 records for testing. Adding these together gives the total number of records.", "question": "What is the total number of records used for training and testing in the French dataset?", "answer": "So, the answer is {\"answer\": \"7,307\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The French dataset has more ironic records than non-ironic records. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe French dataset has more ironic records than non-ironic records.\n" }, { "role": "assistant", "content": "The table shows that the French dataset has 2,425 ironic records and 4,882 non-ironic records. Since 2,425 is less than 4,882, the statement is false. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1179.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Tweet distribution in all corpora." ], "reasoning": "The table shows that the French dataset has 2,425 ironic records and 4,882 non-ironic records. Since 2,425 is less than 4,882, the statement is false.", "statement": "The French dataset has more ironic records than non-ironic records.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Results of the monolingual experiments (in percentage) in terms of accuracy (A), precision (P), recall (R), and macro F-score (F).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The CNN model achieved higher macro F-scores for Arabic and French compared to the RF model, with English scores being comparable to state-of-the-art results.Results. For English, our results, in terms of macro F-score (F), were not comparable to those of [Techc:2014, tay-etal-2018-reasoning], as we used 11% of the original dataset. For French, our scores are in line with those reported in state of the art (cf. best system in the irony shared task achieved F=78.3 [Deft2017]). They outperform those obtained for Arabic (A=71.7) [KarouiACLING:2017] and are comparable to those recently reported in the irony detection shared task in Arabic tweets [idat2019, ghanem2019idat] (F=84.4). Overall, the results show that semantic-based information captured by the embedding space are more productive comparing to standard surface and lexicon-based features." } ], "images": [ "scigen/train/1180.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of the monolingual experiments (in percentage) in terms of accuracy (A), precision (P), recall (R), and macro F-score (F)." ], "reasoning": "The CNN model achieved higher macro F-scores for Arabic and French compared to the RF model, with English scores being comparable to state-of-the-art results.", "description": "Results. For English, our results, in terms of macro F-score (F), were not comparable to those of [Techc:2014, tay-etal-2018-reasoning], as we used 11% of the original dataset. For French, our scores are in line with those reported in state of the art (cf. best system in the irony shared task achieved F=78.3 [Deft2017]). They outperform those obtained for Arabic (A=71.7) [KarouiACLING:2017] and are comparable to those recently reported in the irony detection shared task in Arabic tweets [idat2019, ghanem2019idat] (F=84.4). Overall, the results show that semantic-based information captured by the embedding space are more productive comparing to standard surface and lexicon-based features.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: Results of the monolingual experiments (in percentage) in terms of accuracy (A), precision (P), recall (R), and macro F-score (F).', solve the subsequent question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich model achieved the highest F-score for Arabic?\n" }, { "role": "assistant", "content": "By comparing the F-scores for Arabic in the table, the CNN model achieved the highest F-score of 80.4. So, the answer is {\"answer\": \"CNN\"}" } ], "images": [ "scigen/train/1180.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of the monolingual experiments (in percentage) in terms of accuracy (A), precision (P), recall (R), and macro F-score (F)." ], "reasoning": "By comparing the F-scores for Arabic in the table, the CNN model achieved the highest F-score of 80.4.", "question": "Which model achieved the highest F-score for Arabic?", "answer": "So, the answer is {\"answer\": \"CNN\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The RF model achieved a higher F-score for French than the CNN model., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe RF model achieved a higher F-score for French than the CNN model.\n" }, { "role": "assistant", "content": "The table shows that the RF model achieved an F-score of 61.0 for French, while the CNN model achieved a higher F-score of 73.5. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1180.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of the monolingual experiments (in percentage) in terms of accuracy (A), precision (P), recall (R), and macro F-score (F)." ], "reasoning": "The table shows that the RF model achieved an F-score of 61.0 for French, while the CNN model achieved a higher F-score of 73.5.", "statement": "The RF model achieved a higher F-score for French than the CNN model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: Results of the cross-lingual experiments.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The experiments involve training on one language and testing on another, using a CNN architecture with bilingual embedding and an RF model with surface features, to evaluate irony devices across languages.We use the previous CNN architecture with bilingual embedding and the RF model with surface features (e.g., use of personal pronoun, presence of interjections, emoticon or specific punctuation) : (a) has similar ironic pragmatic devices, and (b) uses similar text-based pattern in the narrative of the ironic tweets. As continuous word embedding spaces exhibit similar structures across (even distant) languages [Mikolov:2013], we use a multilingual word representation which aims to learn a linear mapping from a source to a target embedding space. Many methods have been proposed to learn this mapping such as parallel data supervision and bilingual dictionaries [Mikolov:2013] or unsupervised methods relying on monolingual corpora [Conneau:2017, Artetxe:2018, Wada:2018]. For our experiments, we use Conneau et al ’s approach as it showed superior results with respect to the literature [Conneau:2017]. We perform several experiments by training on one language (lang1) and testing on another one (lang2) ( henceforth lang1→lang2). We get 6 configurations, plus two others to evaluate how irony devices are expressed cross-culturally, i.e. in European vs. non European languages. In each experiment, we took 20% from the training to validate the model before the testing process." } ], "images": [ "scigen/train/1181.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of the cross-lingual experiments." ], "reasoning": "The experiments involve training on one language and testing on another, using a CNN architecture with bilingual embedding and an RF model with surface features, to evaluate irony devices across languages.", "description": "We use the previous CNN architecture with bilingual embedding and the RF model with surface features (e.g., use of personal pronoun, presence of interjections, emoticon or specific punctuation) : (a) has similar ironic pragmatic devices, and (b) uses similar text-based pattern in the narrative of the ironic tweets. As continuous word embedding spaces exhibit similar structures across (even distant) languages [Mikolov:2013], we use a multilingual word representation which aims to learn a linear mapping from a source to a target embedding space. Many methods have been proposed to learn this mapping such as parallel data supervision and bilingual dictionaries [Mikolov:2013] or unsupervised methods relying on monolingual corpora [Conneau:2017, Artetxe:2018, Wada:2018]. For our experiments, we use Conneau et al ’s approach as it showed superior results with respect to the literature [Conneau:2017]. We perform several experiments by training on one language (lang1) and testing on another one (lang2) ( henceforth lang1→lang2). We get 6 configurations, plus two others to evaluate how irony devices are expressed cross-culturally, i.e. in European vs. non European languages. In each experiment, we took 20% from the training to validate the model before the testing process.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. The question is related to the given table titled 'Table 3: Results of the cross-lingual experiments.'.\n\nQuestion: Which training and testing configuration achieved the highest F1 score using the RF model?\n" }, { "role": "assistant", "content": "By examining the RF F column, the highest F1 score is 74.6, achieved by the Ar→(En/Fr) configuration. So, the answer is {\"answer\": \"Ar→(En/Fr)\"}." } ], "images": [ "scigen/train/1181.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of the cross-lingual experiments." ], "reasoning": "By examining the RF F column, the highest F1 score is 74.6, achieved by the Ar→(En/Fr) configuration.", "question": "Which training and testing configuration achieved the highest F1 score using the RF model?", "answer": "So, the answer is {\"answer\": \"Ar→(En/Fr)\"}." }, { "messages": [ { "role": "user", "content": "The CNN model achieved a higher F1 score than the RF model when trained on En and tested on Ar. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe CNN model achieved a higher F1 score than the RF model when trained on En and tested on Ar.\n" }, { "role": "assistant", "content": "The table shows that for the En→Ar configuration, the CNN F1 score is 56.4, while the RF F1 score is 53.0. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1181.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of the cross-lingual experiments." ], "reasoning": "The table shows that for the En→Ar configuration, the CNN F1 score is 56.4, while the RF F1 score is 53.0. Therefore, the statement is supported.", "statement": "The CNN model achieved a higher F1 score than the RF model when trained on En and tested on Ar.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Evaluation results of baselines and our models. CLMN− refers to CLMN without using pre-trained BERT.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The CLMN model achieves the best performance across five evaluation metrics, outperforming other models including the fine-tuned multi-lingual BERT.Overall, our proposed CLMN model achieves the best performance on five evaluation metrics. For simplicity, we mainly discuss the performance of R10@1 in the following part. In details, the advanced unsupervised CLIR models only achieve a fair performance without the collected CLIR-EC dataset. Through utilizing paired data, translation based CLIR models significantly outperform the SOTA unsupervised methods and achieves a decent performance on each metric. Moreover, directly modeling the cross-lingual information retrieval task can further introduce a significant performance improvement over the translation based models. Both the recent proposed POSIT-DRMM and ML-BERT models yield a large increase over the strong translation based models. We also observe that our proposed CLMN− matching model are better than the other matching models and even achieves comparable results with the fine-tuned multi-lingual BERT. With the enhancement of monolingual BERT, our proposed CLMN model outperforms the fine-tuned multi-lingual BERT model." } ], "images": [ "scigen/train/1182.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Evaluation results of baselines and our models. CLMN− refers to CLMN without using pre-trained BERT." ], "reasoning": "The CLMN model achieves the best performance across five evaluation metrics, outperforming other models including the fine-tuned multi-lingual BERT.", "description": "Overall, our proposed CLMN model achieves the best performance on five evaluation metrics. For simplicity, we mainly discuss the performance of R10@1 in the following part. In details, the advanced unsupervised CLIR models only achieve a fair performance without the collected CLIR-EC dataset. Through utilizing paired data, translation based CLIR models significantly outperform the SOTA unsupervised methods and achieves a decent performance on each metric. Moreover, directly modeling the cross-lingual information retrieval task can further introduce a significant performance improvement over the translation based models. Both the recent proposed POSIT-DRMM and ML-BERT models yield a large increase over the strong translation based models. We also observe that our proposed CLMN− matching model are better than the other matching models and even achieves comparable results with the fine-tuned multi-lingual BERT. With the enhancement of monolingual BERT, our proposed CLMN model outperforms the fine-tuned multi-lingual BERT model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Evaluation results of baselines and our models. CLMN− refers to CLMN without using pre-trained BERT.\nQuestion: Which model achieves the highest R10@1 score?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By examining the R10@1 column, the CLMN model achieves the highest score of 86.8. So, the answer is {\"answer\": \"CLMN\"}" } ], "images": [ "scigen/train/1182.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Evaluation results of baselines and our models. CLMN− refers to CLMN without using pre-trained BERT." ], "reasoning": "By examining the R10@1 column, the CLMN model achieves the highest score of 86.8.", "question": "Which model achieves the highest R10@1 score?", "answer": "So, the answer is {\"answer\": \"CLMN\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe CLMN model achieves the highest score in the R2@1 metric.\n" }, { "role": "assistant", "content": "By examining the R2@1 column, the CLMN model achieves the highest score of 97.8. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1182.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Evaluation results of baselines and our models. CLMN− refers to CLMN without using pre-trained BERT." ], "reasoning": "By examining the R2@1 column, the CLMN model achieves the highest score of 97.8.", "statement": "The CLMN model achieves the highest score in the R2@1 metric.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Ablation study of cross-lingual alignment, where mono refers to without cross-lingual alignment learning, en2ch refers to calculating the matching score in the Chinese bilingual space, and ch2en is to compute the relevance score in the English Bilingual Space.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Monolingual BERT performs better due to the small size of domain-specific data, and cross-lingual alignment improves performance by capturing correlations between languages.Through analyzing, we observe that monolingual BERT is superior to train domain-specific word embeddings in our task. We attribute such an observation to the relatively small size of the domain-specific monolingual data, which is far from ideal to train a decent model. As for the influence of cross-lingual alignment, it can effectively capture the correlations between two different languages and thus can introduce performance improvement. Besides, we also notice that the context-dependent mapping strategy is more effective in English bilingual space. This observation is perhaps owing to that the relatively small size of the unordered Chinese product attribute can be easily mapped to another language space compared with a relatively long product description with lexical and syntactic structures." } ], "images": [ "scigen/train/1183.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Ablation study of cross-lingual alignment, where mono refers to without cross-lingual alignment learning, en2ch refers to calculating the matching score in the Chinese bilingual space, and ch2en is to compute the relevance score in the English Bilingual Space." ], "reasoning": "Monolingual BERT performs better due to the small size of domain-specific data, and cross-lingual alignment improves performance by capturing correlations between languages.", "description": "Through analyzing, we observe that monolingual BERT is superior to train domain-specific word embeddings in our task. We attribute such an observation to the relatively small size of the domain-specific monolingual data, which is far from ideal to train a decent model. As for the influence of cross-lingual alignment, it can effectively capture the correlations between two different languages and thus can introduce performance improvement. Besides, we also notice that the context-dependent mapping strategy is more effective in English bilingual space. This observation is perhaps owing to that the relatively small size of the unordered Chinese product attribute can be easily mapped to another language space compared with a relatively long product description with lexical and syntactic structures.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 4: Ablation study of cross-lingual alignment, where mono refers to without cross-lingual alignment learning, en2ch refers to calculating the matching score in the Chinese bilingual space, and ch2en is to compute the relevance score in the English Bilingual Space.', solve the subsequent question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhich model has the highest MRR when initialized with BERT?\n" }, { "role": "assistant", "content": "By comparing the MRR values for models initialized with BERT, the highest MRR is 92.3 for the CLMN model. So, the answer is {\"answer\": \"CLMN\"}" } ], "images": [ "scigen/train/1183.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Ablation study of cross-lingual alignment, where mono refers to without cross-lingual alignment learning, en2ch refers to calculating the matching score in the Chinese bilingual space, and ch2en is to compute the relevance score in the English Bilingual Space." ], "reasoning": "By comparing the MRR values for models initialized with BERT, the highest MRR is 92.3 for the CLMN model.", "question": "Which model has the highest MRR when initialized with BERT?", "answer": "So, the answer is {\"answer\": \"CLMN\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The CLMN model initialized with word embedding has the highest R2@1 score. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe CLMN model initialized with word embedding has the highest R2@1 score.\n" }, { "role": "assistant", "content": "The table shows that the highest R2@1 score for models initialized with word embedding is 96.7, achieved by the CLMN--ch2en model, not the CLMN model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1183.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Ablation study of cross-lingual alignment, where mono refers to without cross-lingual alignment learning, en2ch refers to calculating the matching score in the Chinese bilingual space, and ch2en is to compute the relevance score in the English Bilingual Space." ], "reasoning": "The table shows that the highest R2@1 score for models initialized with word embedding is 96.7, achieved by the CLMN--ch2en model, not the CLMN model.", "statement": "The CLMN model initialized with word embedding has the highest R2@1 score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Performance of Ensemble system on Hinglish and Spanglish test datasets', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The ensemble model achieved higher F1-scores and recall on both Hinglish and Spanglish test datasets compared to previous models.Combining the results of CNN with those of the Self-Attention model was the primary motivation for using an ensemble of the two. The ensemble outperforms all our previous models, achieving a recall of 0.705 with an F1-score of 0.707 on the Hinglish test dataset and a recall of 0.696 with an F1-score of 0.725 on the Spanglish test dataset The confusion matrices for the ensemble on both datasets are shown in figure 6 and 7 (o : neutral, + : positive, - : negative). Our team was ranked 5th among 62 teams in Hinglish and 13th among 29 teams in Spanglish." } ], "images": [ "scigen/train/1184.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of Ensemble system on Hinglish and Spanglish test datasets" ], "reasoning": "The ensemble model achieved higher F1-scores and recall on both Hinglish and Spanglish test datasets compared to previous models.", "description": "Combining the results of CNN with those of the Self-Attention model was the primary motivation for using an ensemble of the two. The ensemble outperforms all our previous models, achieving a recall of 0.705 with an F1-score of 0.707 on the Hinglish test dataset and a recall of 0.696 with an F1-score of 0.725 on the Spanglish test dataset The confusion matrices for the ensemble on both datasets are shown in figure 6 and 7 (o : neutral, + : positive, - : negative). Our team was ranked 5th among 62 teams in Hinglish and 13th among 29 teams in Spanglish.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Performance of Ensemble system on Hinglish and Spanglish test datasets\nQuestion: What is the F1-score for the positive class (+) in the Spanglish test dataset?\nSolve the above question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows the F1-scores for different classes in both Hinglish and Spanglish datasets. For the Spanglish dataset, the F1-score for the positive class (+) is 0.825. So, the answer is {\"answer\": \"0.825\"}" } ], "images": [ "scigen/train/1184.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of Ensemble system on Hinglish and Spanglish test datasets" ], "reasoning": "The table shows the F1-scores for different classes in both Hinglish and Spanglish datasets. For the Spanglish dataset, the F1-score for the positive class (+) is 0.825.", "question": "What is the F1-score for the positive class (+) in the Spanglish test dataset?", "answer": "So, the answer is {\"answer\": \"0.825\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe ensemble model achieved a higher F1 Macro score on the Hinglish dataset compared to the Spanglish dataset.\n" }, { "role": "assistant", "content": "The table shows that the F1 Macro score for the Hinglish dataset is 0.707, while for the Spanglish dataset it is 0.725. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1184.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of Ensemble system on Hinglish and Spanglish test datasets" ], "reasoning": "The table shows that the F1 Macro score for the Hinglish dataset is 0.707, while for the Spanglish dataset it is 0.725. Therefore, the statement is refuted.", "statement": "The ensemble model achieved a higher F1 Macro score on the Hinglish dataset compared to the Spanglish dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Accuracy of models and humans. Values in bold are the highest in the column. *Note that due to an imperfect balancing of data, chance level for humans (computed as majority class) is 0.124.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Broader context improves human performance but decreases model performance, with LSTMs being the best-performing models overall.We have three main results. (1) Broader context helps humans to perform the task, but hurts model performance. This can be seen by comparing the 4-point increase of human accuracy from 1-Sent (0.22) to 3-Sent (0.26) with the generally worse performance of all models (e.g. AttCon-LSTM, from 0.34 to 0.27 in val). (2) All models are significantly better than humans in performing the task at the sentence level (1-Sent), whereas their performance is only slightly better than humans’ in 3-Sent. AttCon-LSTM, which is the best model in the former setting, achieves a significantly higher accuracy than humans’ (0.34 vs 0.22). By contrast, in 3-Sent, the performance of the best model is closer to that of humans (0.29 of Att-LSTM vs 0.26). It can be seen that LSTMs are overall the best-performing architectures, with CNN showing some potential in the handling of longer sequences (3-Sent). Compare ‘few’, ‘a few’, ‘more than half’, ‘some’, and ‘most’: while the first three are generally hard for humans but predictable by the models, the last two show the opposite pattern. Moreover, quantifiers that are guessed by humans to a larger extent in 3-Sent compared to 1-Sent, thus profiting from the broader linguistic context, do not experience the same boost with models. Human accuracy improves notably for ‘few’, ‘a few’, ‘many’, and ‘most’, while model performance on the same quantifiers does not." } ], "images": [ "scigen/train/1185.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracy of models and humans. Values in bold are the highest in the column. *Note that due to an imperfect balancing of data, chance level for humans (computed as majority class) is 0.124." ], "reasoning": "Broader context improves human performance but decreases model performance, with LSTMs being the best-performing models overall.", "description": "We have three main results. (1) Broader context helps humans to perform the task, but hurts model performance. This can be seen by comparing the 4-point increase of human accuracy from 1-Sent (0.22) to 3-Sent (0.26) with the generally worse performance of all models (e.g. AttCon-LSTM, from 0.34 to 0.27 in val). (2) All models are significantly better than humans in performing the task at the sentence level (1-Sent), whereas their performance is only slightly better than humans’ in 3-Sent. AttCon-LSTM, which is the best model in the former setting, achieves a significantly higher accuracy than humans’ (0.34 vs 0.22). By contrast, in 3-Sent, the performance of the best model is closer to that of humans (0.29 of Att-LSTM vs 0.26). It can be seen that LSTMs are overall the best-performing architectures, with CNN showing some potential in the handling of longer sequences (3-Sent). Compare ‘few’, ‘a few’, ‘more than half’, ‘some’, and ‘most’: while the first three are generally hard for humans but predictable by the models, the last two show the opposite pattern. Moreover, quantifiers that are guessed by humans to a larger extent in 3-Sent compared to 1-Sent, thus profiting from the broader linguistic context, do not experience the same boost with models. Human accuracy improves notably for ‘few’, ‘a few’, ‘many’, and ‘most’, while model performance on the same quantifiers does not.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nTable title: Table 2: Accuracy of models and humans. Values in bold are the highest in the column. *Note that due to an imperfect balancing of data, chance level for humans (computed as majority class) is 0.124.\n\nQuestion: Which model shows the highest accuracy in the 1-Sent val setting?\n" }, { "role": "assistant", "content": "To find the model with the highest accuracy in the 1-Sent val setting, compare the values in the 1-Sent val column. AttCon-LSTM has the highest accuracy with 0.343. So, the answer is {\"answer\": \"AttCon-LSTM\"}" } ], "images": [ "scigen/train/1185.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracy of models and humans. Values in bold are the highest in the column. *Note that due to an imperfect balancing of data, chance level for humans (computed as majority class) is 0.124." ], "reasoning": "To find the model with the highest accuracy in the 1-Sent val setting, compare the values in the 1-Sent val column. AttCon-LSTM has the highest accuracy with 0.343.", "question": "Which model shows the highest accuracy in the 1-Sent val setting?", "answer": "So, the answer is {\"answer\": \"AttCon-LSTM\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nHumans perform better than models in the 3-Sent test setting.\n" }, { "role": "assistant", "content": "In the 3-Sent test setting, the highest human accuracy is 0.258, while the highest model accuracy is 0.291 by Att-LSTM. Therefore, models perform better than humans in this setting. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1185.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracy of models and humans. Values in bold are the highest in the column. *Note that due to an imperfect balancing of data, chance level for humans (computed as majority class) is 0.124." ], "reasoning": "In the 3-Sent test setting, the highest human accuracy is 0.258, while the highest model accuracy is 0.291 by Att-LSTM. Therefore, models perform better than humans in this setting.", "statement": "Humans perform better than models in the 3-Sent test setting.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Responses by humans (top) and AttCon-LSTM (bottom) in 3-Sent (val). Values in bold are the highest in the row.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Human and model errors in 3-Sent (val) often involve similar quantifiers, with humans choosing 'some' instead of 'a few' and 'most' instead of 'almost all' or 'more than half', while models show a bias toward 'more than half'.To check whether humans and the models make similar errors, we look into the distribution of responses in 3-Sent (val), which is the most comparable setting with respect to accuracy. Human errors generally involve quantifiers that display a similar magnitude as the correct one. To illustrate, ‘some’ is chosen in place of ‘a few’, and ‘most’ in place of either ‘almost all’ or ‘more than half’. A similar pattern is observed in the model’s predictions, though we note a bias toward ‘more than half’." } ], "images": [ "scigen/train/1186.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Responses by humans (top) and AttCon-LSTM (bottom) in 3-Sent (val). Values in bold are the highest in the row." ], "reasoning": "Human and model errors in 3-Sent (val) often involve similar quantifiers, with humans choosing 'some' instead of 'a few' and 'most' instead of 'almost all' or 'more than half', while models show a bias toward 'more than half'.", "description": "To check whether humans and the models make similar errors, we look into the distribution of responses in 3-Sent (val), which is the most comparable setting with respect to accuracy. Human errors generally involve quantifiers that display a similar magnitude as the correct one. To illustrate, ‘some’ is chosen in place of ‘a few’, and ‘most’ in place of either ‘almost all’ or ‘more than half’. A similar pattern is observed in the model’s predictions, though we note a bias toward ‘more than half’.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: 'Table 3: Responses by humans (top) and AttCon-LSTM (bottom) in 3-Sent (val). Values in bold are the highest in the row.'\nQuestion: What quantifier do models most frequently predict incorrectly in place of 'most'?\n" }, { "role": "assistant", "content": "To find the most frequent incorrect prediction by models for 'most', look at the row labeled 'most' and identify the highest value in that row, which is 'more than half' with a count of 26. So, the answer is {\"answer\": \"more than half\"}." } ], "images": [ "scigen/train/1186.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Responses by humans (top) and AttCon-LSTM (bottom) in 3-Sent (val). Values in bold are the highest in the row." ], "reasoning": "To find the most frequent incorrect prediction by models for 'most', look at the row labeled 'most' and identify the highest value in that row, which is 'more than half' with a count of 26.", "question": "What quantifier do models most frequently predict incorrectly in place of 'most'?", "answer": "So, the answer is {\"answer\": \"more than half\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nModels are more likely to predict 'more than half' instead of 'most'.\n" }, { "role": "assistant", "content": "The table shows that the highest count in the 'most' row is for 'more than half' with a count of 26, supporting the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1186.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Responses by humans (top) and AttCon-LSTM (bottom) in 3-Sent (val). Values in bold are the highest in the row." ], "reasoning": "The table shows that the highest count in the 'most' row is for 'more than half' with a count of 26, supporting the statement.", "statement": "Models are more likely to predict 'more than half' instead of 'most'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Coefficients of a beta regression generalized additive model with ratio of maximum intensity ([s] vs. vowel) as the dependent variable.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The beta regression model used thin-plate smooths for each variable and random smooths for trajectory and other variables in the latent space, with all smooths except z74 being significantly different from 0.The data were fit to a beta regression generalized additive mixed model (in the mgcv package; mgcv) with the ratio as the dependent variable, the seven chosen variables as the parametric term, thin-plate smooths for each variable and random smooths (with first order of penalty; baayen16; soskuthy17) for (i) trajectory and for (ii) value of other variables in the latent space of the Generator network. All smooths (except for z74) are significantly different from 0 In other words, maximum intensity of [s] is increasingly attenuated compared to the intensity of the vowel as z approaches the opposite value from the one identified as predicting the presence of [s] until it completely disappears from the output." } ], "images": [ "scigen/train/1187.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Coefficients of a beta regression generalized additive model with ratio of maximum intensity ([s] vs. vowel) as the dependent variable." ], "reasoning": "The beta regression model used thin-plate smooths for each variable and random smooths for trajectory and other variables in the latent space, with all smooths except z74 being significantly different from 0.", "description": "The data were fit to a beta regression generalized additive mixed model (in the mgcv package; mgcv) with the ratio as the dependent variable, the seven chosen variables as the parametric term, thin-plate smooths for each variable and random smooths (with first order of penalty; baayen16; soskuthy17) for (i) trajectory and for (ii) value of other variables in the latent space of the Generator network. All smooths (except for z74) are significantly different from 0 In other words, maximum intensity of [s] is increasingly attenuated compared to the intensity of the vowel as z approaches the opposite value from the one identified as predicting the presence of [s] until it completely disappears from the output.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 5: Coefficients of a beta regression generalized additive model with ratio of maximum intensity ([s] vs. vowel) as the dependent variable.' and methodically tackle the question that follows. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich variable has the highest F-value among the smooth terms?\n" }, { "role": "assistant", "content": "To find the variable with the highest F-value among the smooth terms, look at the F-value column in section B of the table. The highest F-value is 56.9670, which corresponds to fs(zValuePerc, trajectoryZ, m=1, k=5). So, the answer is {\"answer\": \"fs(zValuePerc, trajectoryZ, m=1, k=5)\"}." } ], "images": [ "scigen/train/1187.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Coefficients of a beta regression generalized additive model with ratio of maximum intensity ([s] vs. vowel) as the dependent variable." ], "reasoning": "To find the variable with the highest F-value among the smooth terms, look at the F-value column in section B of the table. The highest F-value is 56.9670, which corresponds to fs(zValuePerc, trajectoryZ, m=1, k=5).", "question": "Which variable has the highest F-value among the smooth terms?", "answer": "So, the answer is {\"answer\": \"fs(zValuePerc, trajectoryZ, m=1, k=5)\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe variable z74 is significantly different from 0.\n" }, { "role": "assistant", "content": "To determine if z74 is significantly different from 0, check the p-value for z74 in both sections A and B of the table. In section A, the p-value for z74 is 0.0001, which is less than 0.05, indicating it is significantly different from 0. However, in section B, the p-value for z74 is 0.2668, which is greater than 0.05, indicating it is not significantly different from 0 in the smooth terms. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1187.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Coefficients of a beta regression generalized additive model with ratio of maximum intensity ([s] vs. vowel) as the dependent variable." ], "reasoning": "To determine if z74 is significantly different from 0, check the p-value for z74 in both sections A and B of the table. In section A, the p-value for z74 is 0.0001, which is less than 0.05, indicating it is significantly different from 0. However, in section B, the p-value for z74 is 0.2668, which is greater than 0.05, indicating it is not significantly different from 0 in the smooth terms.", "statement": "The variable z74 is significantly different from 0.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: AIC values of five fitted models with corresponding degrees of freedom (df), fitted with Maximum Likelihood. AIC of Select is not listed because it was not fitted with ML; AIC of Select fitted with REML is, however, similar to Excluded (=1,008.46 vs. 1008.54).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Six models were evaluated to predict the presence of [s] in generated outputs, with the LinearExcluded model having the lowest AIC score.Six models are thus fit in an exploratory method to identify variables in the latent space that predict the presence of [s] in generated outputs. The LinearExcluded model has the lowest AIC score. All six models, however, yield similar results." } ], "images": [ "scigen/train/1188.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: AIC values of five fitted models with corresponding degrees of freedom (df), fitted with Maximum Likelihood. AIC of Select is not listed because it was not fitted with ML; AIC of Select fitted with REML is, however, similar to Excluded (=1,008.46 vs. 1008.54)." ], "reasoning": "Six models were evaluated to predict the presence of [s] in generated outputs, with the LinearExcluded model having the lowest AIC score.", "description": "Six models are thus fit in an exploratory method to identify variables in the latent space that predict the presence of [s] in generated outputs. The LinearExcluded model has the lowest AIC score. All six models, however, yield similar results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: AIC values of five fitted models with corresponding degrees of freedom (df), fitted with Maximum Likelihood. AIC of Select is not listed because it was not fitted with ML; AIC of Select fitted with REML is, however, similar to Excluded (=1,008.46 vs. 1008.54).', answer the following question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the lowest AIC score and what is that score?\n" }, { "role": "assistant", "content": "The table lists the AIC scores for six models. The LinearExcluded model has the lowest AIC score of 1007.06. So, the answer is {\"answer\": \"LinearExcluded, 1007.06\"}" } ], "images": [ "scigen/train/1188.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: AIC values of five fitted models with corresponding degrees of freedom (df), fitted with Maximum Likelihood. AIC of Select is not listed because it was not fitted with ML; AIC of Select fitted with REML is, however, similar to Excluded (=1,008.46 vs. 1008.54)." ], "reasoning": "The table lists the AIC scores for six models. The LinearExcluded model has the lowest AIC score of 1007.06.", "question": "Which model has the lowest AIC score and what is that score?", "answer": "So, the answer is {\"answer\": \"LinearExcluded, 1007.06\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Linear model has the highest AIC score among the six models.\n" }, { "role": "assistant", "content": "The table shows the AIC scores for six models. The Linear model has an AIC score of 1036.04, which is the highest among the listed models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1188.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: AIC values of five fitted models with corresponding degrees of freedom (df), fitted with Maximum Likelihood. AIC of Select is not listed because it was not fitted with ML; AIC of Select fitted with REML is, however, similar to Excluded (=1,008.46 vs. 1008.54)." ], "reasoning": "The table shows the AIC scores for six models. The Linear model has an AIC score of 1036.04, which is the highest among the listed models.", "statement": "The Linear model has the highest AIC score among the six models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Coefficients of a Gamma regression model with duration of VOT in the training data as the dependent variable and condition (#TV vs. #sTV) and Place of articulation (with interaction) as independent variables.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The presence of [s] significantly affects VOT duration, with a fitted value difference of 32.35 ms between #TV and #sTV conditions, and the interaction term is significant.Violin plots with box-plots of durations in ms of VOT in the training data based on two conditions: when word-initial #TV sequence is not preceded by [s] (#sTV) and when it is preceded by [s] (#sTV) accross the three places of articulation: [p], [t], [k]. (b) Fitted values of VOT durations with 95% confidence intervals from the Gamma (with log-link) To test the significance of the presence of [s] as a predictor of VOT duration, the data were fit to a Gamma regression model (with log-link) with two predictors: Structure (the presence vs. absence of [s]) and Place of articulation of the target stop (with three levels — [p], [t], [k]) and their interaction. Structure was treatment-coded (with absence of [s] as the reference level), while place of articulation of the stop was sum-coded (with [k] as reference). The interaction term is significant (AIC = 46873.25 vs. 46885.73), which is why it is kept in the final model. (β=−0.84,t=−49.69,p<0.0001). Fitted values for #TV are 56.97 ms [56.41, 57.53] ms and for #sTV 24.62 ms [23.86, 25.41]. The difference between the means is 32.35 ms. The ratio of VOT durations (estimated with the emmeans package; emmeans) between the two conditions (#TV#sTV) equals 2.34, (SE=0.039). The significant interaction #sTV:[t] is not informative for our purposes." } ], "images": [ "scigen/train/1189.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Coefficients of a Gamma regression model with duration of VOT in the training data as the dependent variable and condition (#TV vs. #sTV) and Place of articulation (with interaction) as independent variables." ], "reasoning": "The presence of [s] significantly affects VOT duration, with a fitted value difference of 32.35 ms between #TV and #sTV conditions, and the interaction term is significant.", "description": "Violin plots with box-plots of durations in ms of VOT in the training data based on two conditions: when word-initial #TV sequence is not preceded by [s] (#sTV) and when it is preceded by [s] (#sTV) accross the three places of articulation: [p], [t], [k]. (b) Fitted values of VOT durations with 95% confidence intervals from the Gamma (with log-link) To test the significance of the presence of [s] as a predictor of VOT duration, the data were fit to a Gamma regression model (with log-link) with two predictors: Structure (the presence vs. absence of [s]) and Place of articulation of the target stop (with three levels — [p], [t], [k]) and their interaction. Structure was treatment-coded (with absence of [s] as the reference level), while place of articulation of the stop was sum-coded (with [k] as reference). The interaction term is significant (AIC = 46873.25 vs. 46885.73), which is why it is kept in the final model. (β=−0.84,t=−49.69,p<0.0001). Fitted values for #TV are 56.97 ms [56.41, 57.53] ms and for #sTV 24.62 ms [23.86, 25.41]. The difference between the means is 32.35 ms. The ratio of VOT durations (estimated with the emmeans package; emmeans) between the two conditions (#TV#sTV) equals 2.34, (SE=0.039). The significant interaction #sTV:[t] is not informative for our purposes.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: 'Table 3: Coefficients of a Gamma regression model with duration of VOT in the training data as the dependent variable and condition (#TV vs. #sTV) and Place of articulation (with interaction) as independent variables.'\nQuestion: What is the estimated effect of the presence of [s] on VOT duration?\n" }, { "role": "assistant", "content": "The table shows the estimate for #TV vs. #sTV as -0.8389, indicating the effect of the presence of [s] on VOT duration. So, the answer is {\"answer\": \"-0.8389\"}." } ], "images": [ "scigen/train/1189.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Coefficients of a Gamma regression model with duration of VOT in the training data as the dependent variable and condition (#TV vs. #sTV) and Place of articulation (with interaction) as independent variables." ], "reasoning": "The table shows the estimate for #TV vs. #sTV as -0.8389, indicating the effect of the presence of [s] on VOT duration.", "question": "What is the estimated effect of the presence of [s] on VOT duration?", "answer": "So, the answer is {\"answer\": \"-0.8389\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe interaction term #sTV:[t] is not significant.\n" }, { "role": "assistant", "content": "The table shows that the interaction term #sTV:[t] has a t value of 3.2432 and a p-value of 0.0012, indicating it is significant. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1189.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Coefficients of a Gamma regression model with duration of VOT in the training data as the dependent variable and condition (#TV vs. #sTV) and Place of articulation (with interaction) as independent variables." ], "reasoning": "The table shows that the interaction term #sTV:[t] has a t value of 3.2432 and a p-value of 0.0012, indicating it is significant.", "statement": "The interaction term #sTV:[t] is not significant.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 6: Coefficients of a generalized additive model with center of gravity as the dependent variable with the marginal value of z-variables (strong). The model was fit with correction for autocorrelation with ρ=0.7.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The trajectory for the center of gravity significantly differs between z11 and most other variables, and z74 significantly affects the skew trajectory of [s] but not its amplitude.The trajectory for center of gravity, for example, significantly differs between z11 and most of the other six variables. Overall kurtosis is significantly different when z11 is manipulated, compared to, for example, z26 and z29. Similarly, while z74 does not significantly attenuate amplitude of [s], it significantly differs in skew trajectory of [s]. The main function of z74 is thus likely in its control of spectral properties of frication of [s] (e.g. skew)." } ], "images": [ "scigen/train/1190.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Coefficients of a generalized additive model with center of gravity as the dependent variable with the marginal value of z-variables (strong). The model was fit with correction for autocorrelation with ρ=0.7." ], "reasoning": "The trajectory for the center of gravity significantly differs between z11 and most other variables, and z74 significantly affects the skew trajectory of [s] but not its amplitude.", "description": "The trajectory for center of gravity, for example, significantly differs between z11 and most of the other six variables. Overall kurtosis is significantly different when z11 is manipulated, compared to, for example, z26 and z29. Similarly, while z74 does not significantly attenuate amplitude of [s], it significantly differs in skew trajectory of [s]. The main function of z74 is thus likely in its control of spectral properties of frication of [s] (e.g. skew).", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 6: Coefficients of a generalized additive model with center of gravity as the dependent variable with the marginal value of z-variables (strong). The model was fit with correction for autocorrelation with ρ=0.7.', unfold the question presented below, detailing every step of your calculation. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich variable significantly affects the skew trajectory of [s] but not its amplitude?\n" }, { "role": "assistant", "content": "The description states that z74 significantly differs in the skew trajectory of [s] but does not significantly attenuate its amplitude. This is supported by the table showing z74's p-value for smooth terms. So, the answer is {\"answer\": \"z74\"}" } ], "images": [ "scigen/train/1190.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Coefficients of a generalized additive model with center of gravity as the dependent variable with the marginal value of z-variables (strong). The model was fit with correction for autocorrelation with ρ=0.7." ], "reasoning": "The description states that z74 significantly differs in the skew trajectory of [s] but does not significantly attenuate its amplitude. This is supported by the table showing z74's p-value for smooth terms.", "question": "Which variable significantly affects the skew trajectory of [s] but not its amplitude?", "answer": "So, the answer is {\"answer\": \"z74\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: z11 significantly affects the overall kurtosis compared to z26 and z29., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nz11 significantly affects the overall kurtosis compared to z26 and z29.\n" }, { "role": "assistant", "content": "The description mentions that overall kurtosis is significantly different when z11 is manipulated compared to z26 and z29. The table supports this with the p-value for z11 being < 0.0001, indicating a significant effect. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1190.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Coefficients of a generalized additive model with center of gravity as the dependent variable with the marginal value of z-variables (strong). The model was fit with correction for autocorrelation with ρ=0.7." ], "reasoning": "The description mentions that overall kurtosis is significantly different when z11 is manipulated compared to z26 and z29. The table supports this with the p-value for z11 being < 0.0001, indicating a significant effect.", "statement": "z11 significantly affects the overall kurtosis compared to z26 and z29.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 7: Coefficients of a generalized additive model with kurtosis as the dependent variable with the marginal value of z-variables (strong). The model was fit with correction for autocorrelation with ρ=0.2.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The variables z11, z26, z29, and z74 show significant differences in their effects on various parameters such as center of gravity, kurtosis, and skew trajectory.The trajectory for center of gravity, for example, significantly differs between z11 and most of the other six variables. Overall kurtosis is significantly different when z11 is manipulated, compared to, for example, z26 and z29. Similarly, while z74 does not significantly attenuate amplitude of [s], it significantly differs in skew trajectory of [s]. The main function of z74 is thus likely in its control of spectral properties of frication of [s] (e.g. skew)." } ], "images": [ "scigen/train/1191.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Coefficients of a generalized additive model with kurtosis as the dependent variable with the marginal value of z-variables (strong). The model was fit with correction for autocorrelation with ρ=0.2." ], "reasoning": "The variables z11, z26, z29, and z74 show significant differences in their effects on various parameters such as center of gravity, kurtosis, and skew trajectory.", "description": "The trajectory for center of gravity, for example, significantly differs between z11 and most of the other six variables. Overall kurtosis is significantly different when z11 is manipulated, compared to, for example, z26 and z29. Similarly, while z74 does not significantly attenuate amplitude of [s], it significantly differs in skew trajectory of [s]. The main function of z74 is thus likely in its control of spectral properties of frication of [s] (e.g. skew).", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 7: Coefficients of a generalized additive model with kurtosis as the dependent variable with the marginal value of z-variables (strong). The model was fit with correction for autocorrelation with ρ=0.2.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: Which variable significantly differs in skew trajectory of [s] but does not significantly attenuate its amplitude?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The description states that z74 does not significantly attenuate the amplitude of [s] but significantly differs in the skew trajectory of [s]. So, the answer is {\"answer\": \"z74\"}." } ], "images": [ "scigen/train/1191.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Coefficients of a generalized additive model with kurtosis as the dependent variable with the marginal value of z-variables (strong). The model was fit with correction for autocorrelation with ρ=0.2." ], "reasoning": "The description states that z74 does not significantly attenuate the amplitude of [s] but significantly differs in the skew trajectory of [s].", "question": "Which variable significantly differs in skew trajectory of [s] but does not significantly attenuate its amplitude?", "answer": "So, the answer is {\"answer\": \"z74\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The variable z11 significantly affects the overall kurtosis compared to z26 and z29., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe variable z11 significantly affects the overall kurtosis compared to z26 and z29.\n" }, { "role": "assistant", "content": "The description mentions that overall kurtosis is significantly different when z11 is manipulated compared to z26 and z29. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1191.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Coefficients of a generalized additive model with kurtosis as the dependent variable with the marginal value of z-variables (strong). The model was fit with correction for autocorrelation with ρ=0.2." ], "reasoning": "The description mentions that overall kurtosis is significantly different when z11 is manipulated compared to z26 and z29.", "statement": "The variable z11 significantly affects the overall kurtosis compared to z26 and z29.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 8: Coefficients of a generalized additive model with skew as the dependent variable with the marginal value of z-variables (strong). The model was fit with correction for autocorrelation with ρ=0.7.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The trajectory for the center of gravity and overall kurtosis significantly differ when z11 is manipulated compared to other variables like z26 and z29. Similarly, z74 significantly affects the skew trajectory of [s] but not its amplitude.The trajectory for center of gravity, for example, significantly differs between z11 and most of the other six variables. Overall kurtosis is significantly different when z11 is manipulated, compared to, for example, z26 and z29. Similarly, while z74 does not significantly attenuate amplitude of [s], it significantly differs in skew trajectory of [s]. The main function of z74 is thus likely in its control of spectral properties of frication of [s] (e.g. skew)." } ], "images": [ "scigen/train/1192.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Coefficients of a generalized additive model with skew as the dependent variable with the marginal value of z-variables (strong). The model was fit with correction for autocorrelation with ρ=0.7." ], "reasoning": "The trajectory for the center of gravity and overall kurtosis significantly differ when z11 is manipulated compared to other variables like z26 and z29. Similarly, z74 significantly affects the skew trajectory of [s] but not its amplitude.", "description": "The trajectory for center of gravity, for example, significantly differs between z11 and most of the other six variables. Overall kurtosis is significantly different when z11 is manipulated, compared to, for example, z26 and z29. Similarly, while z74 does not significantly attenuate amplitude of [s], it significantly differs in skew trajectory of [s]. The main function of z74 is thus likely in its control of spectral properties of frication of [s] (e.g. skew).", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 8: Coefficients of a generalized additive model with skew as the dependent variable with the marginal value of z-variables (strong). The model was fit with correction for autocorrelation with ρ=0.7.', solve the following question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich variable significantly affects the skew trajectory of [s] but not its amplitude?\n" }, { "role": "assistant", "content": "The description states that z74 does not significantly attenuate the amplitude of [s] but significantly differs in the skew trajectory of [s]. So, the answer is {\"answer\": \"z74\"}." } ], "images": [ "scigen/train/1192.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Coefficients of a generalized additive model with skew as the dependent variable with the marginal value of z-variables (strong). The model was fit with correction for autocorrelation with ρ=0.7." ], "reasoning": "The description states that z74 does not significantly attenuate the amplitude of [s] but significantly differs in the skew trajectory of [s].", "question": "Which variable significantly affects the skew trajectory of [s] but not its amplitude?", "answer": "So, the answer is {\"answer\": \"z74\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence z11 significantly affects the overall kurtosis compared to z26 and z29. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nz11 significantly affects the overall kurtosis compared to z26 and z29.\n" }, { "role": "assistant", "content": "The description mentions that overall kurtosis is significantly different when z11 is manipulated compared to z26 and z29. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1192.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Coefficients of a generalized additive model with skew as the dependent variable with the marginal value of z-variables (strong). The model was fit with correction for autocorrelation with ρ=0.7." ], "reasoning": "The description mentions that overall kurtosis is significantly different when z11 is manipulated compared to z26 and z29.", "statement": "z11 significantly affects the overall kurtosis compared to z26 and z29.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 9: Coefficients of a generalized additive model with center of gravity as the dependent variable with the value of z-variables at the point before [s] ceases from the output (weak).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The trajectory for center of gravity and overall kurtosis significantly differ when z11 is manipulated, while z74 significantly affects the skew trajectory of [s].The trajectory for center of gravity, for example, significantly differs between z11 and most of the other six variables. Overall kurtosis is significantly different when z11 is manipulated, compared to, for example, z26 and z29. Similarly, while z74 does not significantly attenuate amplitude of [s], it significantly differs in skew trajectory of [s]. The main function of z74 is thus likely in its control of spectral properties of frication of [s] (e.g. skew)." } ], "images": [ "scigen/train/1193.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Coefficients of a generalized additive model with center of gravity as the dependent variable with the value of z-variables at the point before [s] ceases from the output (weak)." ], "reasoning": "The trajectory for center of gravity and overall kurtosis significantly differ when z11 is manipulated, while z74 significantly affects the skew trajectory of [s].", "description": "The trajectory for center of gravity, for example, significantly differs between z11 and most of the other six variables. Overall kurtosis is significantly different when z11 is manipulated, compared to, for example, z26 and z29. Similarly, while z74 does not significantly attenuate amplitude of [s], it significantly differs in skew trajectory of [s]. The main function of z74 is thus likely in its control of spectral properties of frication of [s] (e.g. skew).", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 9: Coefficients of a generalized additive model with center of gravity as the dependent variable with the value of z-variables at the point before [s] ceases from the output (weak).', solve this question and give detail solutions. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhich variable significantly affects the skew trajectory of [s]?\n" }, { "role": "assistant", "content": "The description states that z74 significantly differs in the skew trajectory of [s], and the table shows that z74 has a p-value of 0.8199, indicating it does not significantly affect other parameters. So, the answer is {\"answer\": \"z74\"}." } ], "images": [ "scigen/train/1193.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Coefficients of a generalized additive model with center of gravity as the dependent variable with the value of z-variables at the point before [s] ceases from the output (weak)." ], "reasoning": "The description states that z74 significantly differs in the skew trajectory of [s], and the table shows that z74 has a p-value of 0.8199, indicating it does not significantly affect other parameters.", "question": "Which variable significantly affects the skew trajectory of [s]?", "answer": "So, the answer is {\"answer\": \"z74\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nz11 significantly affects the overall kurtosis.\n" }, { "role": "assistant", "content": "The description mentions that overall kurtosis is significantly different when z11 is manipulated, and the table shows that z11 has a p-value of <0.0001, indicating a significant effect. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1193.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Coefficients of a generalized additive model with center of gravity as the dependent variable with the value of z-variables at the point before [s] ceases from the output (weak)." ], "reasoning": "The description mentions that overall kurtosis is significantly different when z11 is manipulated, and the table shows that z11 has a p-value of <0.0001, indicating a significant effect.", "statement": "z11 significantly affects the overall kurtosis.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 10: Coefficients of a generalized additive model with kurtosis as the dependent variable with the value of z-variables at the point before [s] ceases from the output (weak). The model was fit with correction for autocorrelation with ρ=0.2.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The trajectory for the center of gravity and overall kurtosis significantly differ when z11 is manipulated compared to other variables, while z74 significantly affects the skew trajectory of [s] but not its amplitude.The trajectory for center of gravity, for example, significantly differs between z11 and most of the other six variables. Overall kurtosis is significantly different when z11 is manipulated, compared to, for example, z26 and z29. Similarly, while z74 does not significantly attenuate amplitude of [s], it significantly differs in skew trajectory of [s]. The main function of z74 is thus likely in its control of spectral properties of frication of [s] (e.g. skew)." } ], "images": [ "scigen/train/1194.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Coefficients of a generalized additive model with kurtosis as the dependent variable with the value of z-variables at the point before [s] ceases from the output (weak). The model was fit with correction for autocorrelation with ρ=0.2." ], "reasoning": "The trajectory for the center of gravity and overall kurtosis significantly differ when z11 is manipulated compared to other variables, while z74 significantly affects the skew trajectory of [s] but not its amplitude.", "description": "The trajectory for center of gravity, for example, significantly differs between z11 and most of the other six variables. Overall kurtosis is significantly different when z11 is manipulated, compared to, for example, z26 and z29. Similarly, while z74 does not significantly attenuate amplitude of [s], it significantly differs in skew trajectory of [s]. The main function of z74 is thus likely in its control of spectral properties of frication of [s] (e.g. skew).", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich variable significantly affects the skew trajectory of [s] but not its amplitude?\nSolve the above question based on the table titled 'Table 10: Coefficients of a generalized additive model with kurtosis as the dependent variable with the value of z-variables at the point before [s] ceases from the output (weak). The model was fit with correction for autocorrelation with ρ=0.2.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The description states that z74 does not significantly attenuate the amplitude of [s] but significantly differs in the skew trajectory of [s]. So, the answer is {\"answer\": \"z74\"}." } ], "images": [ "scigen/train/1194.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Coefficients of a generalized additive model with kurtosis as the dependent variable with the value of z-variables at the point before [s] ceases from the output (weak). The model was fit with correction for autocorrelation with ρ=0.2." ], "reasoning": "The description states that z74 does not significantly attenuate the amplitude of [s] but significantly differs in the skew trajectory of [s].", "question": "Which variable significantly affects the skew trajectory of [s] but not its amplitude?", "answer": "So, the answer is {\"answer\": \"z74\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nz11 significantly affects the overall kurtosis compared to z26 and z29.\n" }, { "role": "assistant", "content": "The description mentions that overall kurtosis is significantly different when z11 is manipulated compared to z26 and z29. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1194.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Coefficients of a generalized additive model with kurtosis as the dependent variable with the value of z-variables at the point before [s] ceases from the output (weak). The model was fit with correction for autocorrelation with ρ=0.2." ], "reasoning": "The description mentions that overall kurtosis is significantly different when z11 is manipulated compared to z26 and z29.", "statement": "z11 significantly affects the overall kurtosis compared to z26 and z29.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 11: Coefficients of a generalized additive model with skew as the dependent variable with the value of z-variables at the point before [s] ceases from the output (weak). The model was fit with correction for autocorrelation with ρ=0.3.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The manipulation of z11 significantly affects overall kurtosis and the trajectory for the center of gravity, while z74 significantly affects the skew trajectory of [s] but not its amplitude.The trajectory for center of gravity, for example, significantly differs between z11 and most of the other six variables. Overall kurtosis is significantly different when z11 is manipulated, compared to, for example, z26 and z29. Similarly, while z74 does not significantly attenuate amplitude of [s], it significantly differs in skew trajectory of [s]. The main function of z74 is thus likely in its control of spectral properties of frication of [s] (e.g. skew)." } ], "images": [ "scigen/train/1195.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Coefficients of a generalized additive model with skew as the dependent variable with the value of z-variables at the point before [s] ceases from the output (weak). The model was fit with correction for autocorrelation with ρ=0.3." ], "reasoning": "The manipulation of z11 significantly affects overall kurtosis and the trajectory for the center of gravity, while z74 significantly affects the skew trajectory of [s] but not its amplitude.", "description": "The trajectory for center of gravity, for example, significantly differs between z11 and most of the other six variables. Overall kurtosis is significantly different when z11 is manipulated, compared to, for example, z26 and z29. Similarly, while z74 does not significantly attenuate amplitude of [s], it significantly differs in skew trajectory of [s]. The main function of z74 is thus likely in its control of spectral properties of frication of [s] (e.g. skew).", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}. The question is related to the given table titled 'Table 11: Coefficients of a generalized additive model with skew as the dependent variable with the value of z-variables at the point before [s] ceases from the output (weak). The model was fit with correction for autocorrelation with ρ=0.3.'.\n\nQuestion: Which variable significantly affects the overall kurtosis when manipulated?\n" }, { "role": "assistant", "content": "The description states that overall kurtosis is significantly different when z11 is manipulated. The table shows that z11 has a significant p-value of 0.0009 in the parametric coefficients section. So, the answer is {\"answer\": \"z11\"}." } ], "images": [ "scigen/train/1195.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Coefficients of a generalized additive model with skew as the dependent variable with the value of z-variables at the point before [s] ceases from the output (weak). The model was fit with correction for autocorrelation with ρ=0.3." ], "reasoning": "The description states that overall kurtosis is significantly different when z11 is manipulated. The table shows that z11 has a significant p-value of 0.0009 in the parametric coefficients section.", "question": "Which variable significantly affects the overall kurtosis when manipulated?", "answer": "So, the answer is {\"answer\": \"z11\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nz74 significantly attenuates the amplitude of [s].\n" }, { "role": "assistant", "content": "The description states that z74 does not significantly attenuate the amplitude of [s]. The table shows that z74 has a non-significant p-value of 0.3898 in the smooth terms section. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1195.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Coefficients of a generalized additive model with skew as the dependent variable with the value of z-variables at the point before [s] ceases from the output (weak). The model was fit with correction for autocorrelation with ρ=0.3." ], "reasoning": "The description states that z74 does not significantly attenuate the amplitude of [s]. The table shows that z74 has a non-significant p-value of 0.3898 in the smooth terms section.", "statement": "z74 significantly attenuates the amplitude of [s].", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: Test set performance of the ML models for 20-class document classification.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The linear SVM model performs comparably to the CNN models in terms of classification accuracy, indicating that the non-linear structure of CNNs does not provide a significant advantage for topic categorization tasks.Herein CNN1, CNN2, CNN3 respectively denote neural networks with convolutional filter size H equal to 1, 2 and 3 (i.e. covering 1, 2 or 3 consecutive words in the document). One can see that the linear SVM performs on par with the neural networks, i.e. the non-linear structure of the CNN models does not yield a considerable advantage toward classification accuracy. This can be explained by the fact that for most topic categorization tasks, the different categories can be separated linearly in the very high-dimensional bag-of-words or bag-of-N-grams space thanks to sufficiently disjoint sets of features." } ], "images": [ "scigen/train/1196.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Test set performance of the ML models for 20-class document classification." ], "reasoning": "The linear SVM model performs comparably to the CNN models in terms of classification accuracy, indicating that the non-linear structure of CNNs does not provide a significant advantage for topic categorization tasks.", "description": "Herein CNN1, CNN2, CNN3 respectively denote neural networks with convolutional filter size H equal to 1, 2 and 3 (i.e. covering 1, 2 or 3 consecutive words in the document). One can see that the linear SVM performs on par with the neural networks, i.e. the non-linear structure of the CNN models does not yield a considerable advantage toward classification accuracy. This can be explained by the fact that for most topic categorization tasks, the different categories can be separated linearly in the very high-dimensional bag-of-words or bag-of-N-grams space thanks to sufficiently disjoint sets of features.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Test set performance of the ML models for 20-class document classification.', answer the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model achieved the highest test accuracy?\n" }, { "role": "assistant", "content": "By comparing the test accuracy percentages of all models listed in the table, CNN2 with H=2 and F=800 achieved the highest test accuracy of 80.19%. So, the answer is {\"answer\": \"CNN2\"}" } ], "images": [ "scigen/train/1196.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Test set performance of the ML models for 20-class document classification." ], "reasoning": "By comparing the test accuracy percentages of all models listed in the table, CNN2 with H=2 and F=800 achieved the highest test accuracy of 80.19%.", "question": "Which model achieved the highest test accuracy?", "answer": "So, the answer is {\"answer\": \"CNN2\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe BoW/SVM model achieved the highest test accuracy.\n" }, { "role": "assistant", "content": "The table shows that the BoW/SVM model has a test accuracy of 80.10%, while CNN2 has a higher test accuracy of 80.19%. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1196.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Test set performance of the ML models for 20-class document classification." ], "reasoning": "The table shows that the BoW/SVM model has a test accuracy of 80.10%, while CNN2 has a higher test accuracy of 80.19%. Therefore, the statement is refuted.", "statement": "The BoW/SVM model achieved the highest test accuracy.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Personalization task 2 (Updating API calls). The model correctly learns to focus on the original API call and the changes demanded by the user. It does not require multiple hops to solve this task.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The model accurately completed PT1 and PT2, indicating it can track dialog state and personalize responses based on user profiles.They completed PT1 and PT2 (issuing and updating API calls) with a very high degree of accuracy. This indicates that the model is able to implicitly track dialog state and personalize the bot’s utterance based on the user’s profile." } ], "images": [ "scigen/train/1197.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Personalization task 2 (Updating API calls). The model correctly learns to focus on the original API call and the changes demanded by the user. It does not require multiple hops to solve this task." ], "reasoning": "The model accurately completed PT1 and PT2, indicating it can track dialog state and personalize responses based on user profiles.", "description": "They completed PT1 and PT2 (issuing and updating API calls) with a very high degree of accuracy. This indicates that the model is able to implicitly track dialog state and personalize the bot’s utterance based on the user’s profile.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 6: Personalization task 2 (Updating API calls). The model correctly learns to focus on the original API call and the changes demanded by the user. It does not require multiple hops to solve this task.', answer the question below by showing each progressive step toward the answer. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat was the user's preference change in the dialog and how did the bot respond?\n" }, { "role": "assistant", "content": "The user initially requested a table in Bombay with Spanish food for four in an expensive price range. Later, the user changed the preference to French food, and the bot acknowledged the change and asked if there were any other modifications. So, the answer is {\"answer\": \"French food\"}." } ], "images": [ "scigen/train/1197.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Personalization task 2 (Updating API calls). The model correctly learns to focus on the original API call and the changes demanded by the user. It does not require multiple hops to solve this task." ], "reasoning": "The user initially requested a table in Bombay with Spanish food for four in an expensive price range. Later, the user changed the preference to French food, and the bot acknowledged the change and asked if there were any other modifications.", "question": "What was the user's preference change in the dialog and how did the bot respond?", "answer": "So, the answer is {\"answer\": \"French food\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The bot successfully processed the user's request for a table in Bombay with Spanish food for four in an expensive price range., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe bot successfully processed the user's request for a table in Bombay with Spanish food for four in an expensive price range.\n" }, { "role": "assistant", "content": "The table shows that the bot processed the user's initial request for a table in Bombay with Spanish food for four in an expensive price range, as indicated by the dialog history and the API call. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1197.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Personalization task 2 (Updating API calls). The model correctly learns to focus on the original API call and the changes demanded by the user. It does not require multiple hops to solve this task." ], "reasoning": "The table shows that the bot processed the user's initial request for a table in Bombay with Spanish food for four in an expensive price range, as indicated by the dialog history and the API call.", "statement": "The bot successfully processed the user's request for a table in Bombay with Spanish food for four in an expensive price range.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 5: Personalization task 1 (Issuing API calls). The model solves the task successfully by directing its attention towards the memories containing the values of the fields of the API call. The signal grows stronger on the second hop and switches almost exclusively to the price range in the third. This indicates that the model does not need multiple hops for successfully completing PT1. (Zero attention weight signifies a very small value.)'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The model accurately completed PT1 and PT2, showing its ability to track dialog state and personalize responses based on user profiles.They completed PT1 and PT2 (issuing and updating API calls) with a very high degree of accuracy. This indicates that the model is able to implicitly track dialog state and personalize the bot’s utterance based on the user’s profile. All models were trained on the full dataset." } ], "images": [ "scigen/train/1198.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Personalization task 1 (Issuing API calls). The model solves the task successfully by directing its attention towards the memories containing the values of the fields of the API call. The signal grows stronger on the second hop and switches almost exclusively to the price range in the third. This indicates that the model does not need multiple hops for successfully completing PT1. (Zero attention weight signifies a very small value.)" ], "reasoning": "The model accurately completed PT1 and PT2, showing its ability to track dialog state and personalize responses based on user profiles.", "description": "They completed PT1 and PT2 (issuing and updating API calls) with a very high degree of accuracy. This indicates that the model is able to implicitly track dialog state and personalize the bot’s utterance based on the user’s profile. All models were trained on the full dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 5: Personalization task 1 (Issuing API calls). The model solves the task successfully by directing its attention towards the memories containing the values of the fields of the API call. The signal grows stronger on the second hop and switches almost exclusively to the price range in the third. This indicates that the model does not need multiple hops for successfully completing PT1. (Zero attention weight signifies a very small value.)'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat was the user's preference for the type of cuisine and the location of the restaurant?\n" }, { "role": "assistant", "content": "The user mentioned their preference for French food in the dialog history at time 8 and specified Madrid as the location at time 10. So, the answer is {\"answer\": \"French food, Madrid\"}" } ], "images": [ "scigen/train/1198.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Personalization task 1 (Issuing API calls). The model solves the task successfully by directing its attention towards the memories containing the values of the fields of the API call. The signal grows stronger on the second hop and switches almost exclusively to the price range in the third. This indicates that the model does not need multiple hops for successfully completing PT1. (Zero attention weight signifies a very small value.)" ], "reasoning": "The user mentioned their preference for French food in the dialog history at time 8 and specified Madrid as the location at time 10.", "question": "What was the user's preference for the type of cuisine and the location of the restaurant?", "answer": "So, the answer is {\"answer\": \"French food, Madrid\"}" }, { "messages": [ { "role": "user", "content": "The model was able to accurately predict the user's preference for a cheap price range. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model was able to accurately predict the user's preference for a cheap price range.\n" }, { "role": "assistant", "content": "The user specified a cheap price range at time 14, and the predicted answer matches the correct answer, which includes 'cheap'. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1198.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Personalization task 1 (Issuing API calls). The model solves the task successfully by directing its attention towards the memories containing the values of the fields of the API call. The signal grows stronger on the second hop and switches almost exclusively to the price range in the third. This indicates that the model does not need multiple hops for successfully completing PT1. (Zero attention weight signifies a very small value.)" ], "reasoning": "The user specified a cheap price range at time 14, and the predicted answer matches the correct answer, which includes 'cheap'.", "statement": "The model was able to accurately predict the user's preference for a cheap price range.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 8: Personalization task 3 (Displaying options). The model should ideally be focusing on factors that are used for implicit ranking, such as the user’s profile and the ratings, types and specialities of the various restaurants in the KB facts. It should also pay attention to the restaurants that have already been suggested to the user. However, it attends primarily to the locations, indicating that it is insufficient at reasoning over the KB. We have only shown important utterances in the table.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Memory Networks with split memory significantly improved performance on PT3, leading to better results on PT5.Memory Networks with the split memory feature significantly outperformed the standard architecture for PT3, which lead to better performance on the full dialog task PT5." } ], "images": [ "scigen/train/1199.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Personalization task 3 (Displaying options). The model should ideally be focusing on factors that are used for implicit ranking, such as the user’s profile and the ratings, types and specialities of the various restaurants in the KB facts. It should also pay attention to the restaurants that have already been suggested to the user. However, it attends primarily to the locations, indicating that it is insufficient at reasoning over the KB. We have only shown important utterances in the table." ], "reasoning": "Memory Networks with split memory significantly improved performance on PT3, leading to better results on PT5.", "description": "Memory Networks with the split memory feature significantly outperformed the standard architecture for PT3, which lead to better performance on the full dialog task PT5.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 8: Personalization task 3 (Displaying options). The model should ideally be focusing on factors that are used for implicit ranking, such as the user’s profile and the ratings, types and specialities of the various restaurants in the KB facts. It should also pay attention to the restaurants that have already been suggested to the user. However, it attends primarily to the locations, indicating that it is insufficient at reasoning over the KB. We have only shown important utterances in the table.' and proceed to solve the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat was the predicted answer for the full dialog task PT5?\n" }, { "role": "assistant", "content": "The table shows that the predicted answer for the full dialog task PT5 is 'what food are you looking for' across all hops. So, the answer is {\"answer\": \"what food are you looking for\"}." } ], "images": [ "scigen/train/1199.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Personalization task 3 (Displaying options). The model should ideally be focusing on factors that are used for implicit ranking, such as the user’s profile and the ratings, types and specialities of the various restaurants in the KB facts. It should also pay attention to the restaurants that have already been suggested to the user. However, it attends primarily to the locations, indicating that it is insufficient at reasoning over the KB. We have only shown important utterances in the table." ], "reasoning": "The table shows that the predicted answer for the full dialog task PT5 is 'what food are you looking for' across all hops.", "question": "What was the predicted answer for the full dialog task PT5?", "answer": "So, the answer is {\"answer\": \"what food are you looking for\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The predicted answer for PT3 was 'how about this one: resto_rome_moderate_italian_8stars_1'., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe predicted answer for PT3 was 'how about this one: resto_rome_moderate_italian_8stars_1'.\n" }, { "role": "assistant", "content": "The table shows that the predicted answer for PT3 is 'what food are you looking for', not 'how about this one: resto_rome_moderate_italian_8stars_1'. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1199.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Personalization task 3 (Displaying options). The model should ideally be focusing on factors that are used for implicit ranking, such as the user’s profile and the ratings, types and specialities of the various restaurants in the KB facts. It should also pay attention to the restaurants that have already been suggested to the user. However, it attends primarily to the locations, indicating that it is insufficient at reasoning over the KB. We have only shown important utterances in the table." ], "reasoning": "The table shows that the predicted answer for PT3 is 'what food are you looking for', not 'how about this one: resto_rome_moderate_italian_8stars_1'.", "statement": "The predicted answer for PT3 was 'how about this one: resto_rome_moderate_italian_8stars_1'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'TABLE III: Overall comparison of model precisions (in %). Confidence intervals (±95%) are shown next to the average performance.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "HREDL slightly outperforms HREDG and HREDLG in generative approaches, likely due to the use of specific domain knowledge.As with the Ubuntu corpus, the generative and retrieval based approaches (CR, AR, CAR) are compared and additionally in this setting, they are also tested upon different embedding initialization approaches (HREDL, HREDG and HREDLG) The performance of the generative approaches, HREDL, HREDG, and HREDLG, are relatively similar. However, HREDL slightly outperforms the others. A reason for this could be that utilizing global domain knowledge to generate an answer is more difficult than using specific domain knowledge. Especially for a very homogeneous (and domain specific) corpus, giving standard answers can work better. Nonetheless, similarity comparisons, used by the NNS approaches, could still benefit from richer embeddings." } ], "images": [ "scigen/train/1200.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Overall comparison of model precisions (in %). Confidence intervals (±95%) are shown next to the average performance." ], "reasoning": "HREDL slightly outperforms HREDG and HREDLG in generative approaches, likely due to the use of specific domain knowledge.", "description": "As with the Ubuntu corpus, the generative and retrieval based approaches (CR, AR, CAR) are compared and additionally in this setting, they are also tested upon different embedding initialization approaches (HREDL, HREDG and HREDLG) The performance of the generative approaches, HREDL, HREDG, and HREDLG, are relatively similar. However, HREDL slightly outperforms the others. A reason for this could be that utilizing global domain knowledge to generate an answer is more difficult than using specific domain knowledge. Especially for a very homogeneous (and domain specific) corpus, giving standard answers can work better. Nonetheless, similarity comparisons, used by the NNS approaches, could still benefit from richer embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'TABLE III: Overall comparison of model precisions (in %). Confidence intervals (±95%) are shown next to the average performance.', and tackle the question below in a sequential manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest R@5 score for predicting customer responses?\n" }, { "role": "assistant", "content": "To find the model with the highest R@5 score for predicting customer responses, look at the 'Predicting customer responses R@5' column and identify the highest value. The highest value is 81.5±0.6, which corresponds to the HREDG-AR model. So, the answer is {\"answer\": \"HREDG-AR\"}." } ], "images": [ "scigen/train/1200.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Overall comparison of model precisions (in %). Confidence intervals (±95%) are shown next to the average performance." ], "reasoning": "To find the model with the highest R@5 score for predicting customer responses, look at the 'Predicting customer responses R@5' column and identify the highest value. The highest value is 81.5±0.6, which corresponds to the HREDG-AR model.", "question": "Which model has the highest R@5 score for predicting customer responses?", "answer": "So, the answer is {\"answer\": \"HREDG-AR\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim HREDL-AR has the highest R@1 score for predicting assistant responses., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nHREDL-AR has the highest R@1 score for predicting assistant responses.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'Predicting assistant responses R@1' column. The highest value is 44.0±0.7, which corresponds to the HREDG-CAR model, not HREDL-AR. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1200.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Overall comparison of model precisions (in %). Confidence intervals (±95%) are shown next to the average performance." ], "reasoning": "To verify this statement, look at the 'Predicting assistant responses R@1' column. The highest value is 44.0±0.7, which corresponds to the HREDG-CAR model, not HREDL-AR.", "statement": "HREDL-AR has the highest R@1 score for predicting assistant responses.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Accuracies of the proposed method and the state-of-the-art methods for the factoid QA task.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The results of BOW, BOW-DT, and QANTA were obtained from Xu2016, while QANTA-full used a larger dataset as reported by Iyyer et al. 2014.The results of BOW, BOW-DT, and QANTA were obtained from \\newciteXu2016. We also include the result reported in \\newciteiyyer-EtAl:2014:EMNLP2014 (denoted by QANTA-full), which used a significantly larger dataset than ours for training and testing." } ], "images": [ "scigen/train/1201.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Accuracies of the proposed method and the state-of-the-art methods for the factoid QA task." ], "reasoning": "The results of BOW, BOW-DT, and QANTA were obtained from Xu2016, while QANTA-full used a larger dataset as reported by Iyyer et al. 2014.", "description": "The results of BOW, BOW-DT, and QANTA were obtained from \\newciteXu2016. We also include the result reported in \\newciteiyyer-EtAl:2014:EMNLP2014 (denoted by QANTA-full), which used a significantly larger dataset than ours for training and testing.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 5: Accuracies of the proposed method and the state-of-the-art methods for the factoid QA task.' in mind, break down and resolve the question below step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich model has the highest score in Literature and what is the score?\n" }, { "role": "assistant", "content": "To find the model with the highest score in Literature, we need to compare the Literature scores of all models. The highest score is 95.1, achieved by the NTEE model. So, the answer is {\"answer\": \"NTEE with 95.1\"}." } ], "images": [ "scigen/train/1201.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Accuracies of the proposed method and the state-of-the-art methods for the factoid QA task." ], "reasoning": "To find the model with the highest score in Literature, we need to compare the Literature scores of all models. The highest score is 95.1, achieved by the NTEE model.", "question": "Which model has the highest score in Literature and what is the score?", "answer": "So, the answer is {\"answer\": \"NTEE with 95.1\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The BOW model has the lowest score in both History and Literature., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe BOW model has the lowest score in both History and Literature.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the History and Literature scores of all models. The BOW model has the lowest scores in both History (50.8) and Literature (46.2). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1201.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Accuracies of the proposed method and the state-of-the-art methods for the factoid QA task." ], "reasoning": "To verify this statement, we need to compare the History and Literature scores of all models. The BOW model has the lowest scores in both History (50.8) and Literature (46.2).", "statement": "The BOW model has the lowest score in both History and Literature.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Hyper-parameters used for EL and QA tasks. hidden units is the number of units in the hidden layers, and dropout is the dropout probability.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The number of units in the hidden layer and the dropout probability were tuned using the micro-accuracy on the development set of each dataset.We tuned the following two hyper-parameters using the micro-accuracy on the development set of each dataset: the number of units in the hidden layer and the dropout probability. We tuned these parameters using the development set of each dataset." } ], "images": [ "scigen/train/1202.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Hyper-parameters used for EL and QA tasks. hidden units is the number of units in the hidden layers, and dropout is the dropout probability." ], "reasoning": "The number of units in the hidden layer and the dropout probability were tuned using the micro-accuracy on the development set of each dataset.", "description": "We tuned the following two hyper-parameters using the micro-accuracy on the development set of each dataset: the number of units in the hidden layer and the dropout probability. We tuned these parameters using the development set of each dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Hyper-parameters used for EL and QA tasks. hidden units is the number of units in the hidden layers, and dropout is the dropout probability.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. \n\nQuestion: What is the highest dropout probability used for the CoNLL (SG-proj) dataset?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the highest dropout probability for the CoNLL (SG-proj) dataset, look at the rows corresponding to CoNLL (SG-proj) and compare the dropout values. The highest dropout probability is 0.9. So, the answer is {\"answer\": \"0.9\"}." } ], "images": [ "scigen/train/1202.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Hyper-parameters used for EL and QA tasks. hidden units is the number of units in the hidden layers, and dropout is the dropout probability." ], "reasoning": "To find the highest dropout probability for the CoNLL (SG-proj) dataset, look at the rows corresponding to CoNLL (SG-proj) and compare the dropout values. The highest dropout probability is 0.9.", "question": "What is the highest dropout probability used for the CoNLL (SG-proj) dataset?", "answer": "So, the answer is {\"answer\": \"0.9\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Literature (SG-proj) dataset used a dropout probability of 0.1.\n" }, { "role": "assistant", "content": "Check the rows corresponding to the Literature (SG-proj) dataset and verify the dropout probability. The table shows that the Literature (SG-proj) dataset used a dropout probability of 0.1. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1202.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Hyper-parameters used for EL and QA tasks. hidden units is the number of units in the hidden layers, and dropout is the dropout probability." ], "reasoning": "Check the rows corresponding to the Literature (SG-proj) dataset and verify the dropout probability. The table shows that the Literature (SG-proj) dataset used a dropout probability of 0.1.", "statement": "The Literature (SG-proj) dataset used a dropout probability of 0.1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: Accuracies of the proposed method and the state-of-the-art methods.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The NTEE model achieved the highest scores on both the CoNLL and TAC 2010 datasets, and the string similarity features contributed to its performance.Our method achieved strong results on both the CoNLL and the TAC 2010 datasets. In particular, the NTEE model clearly outperformed the other proposed models. We also tested the performance of the NTEE model without using the string similarity features (strsim) and found that these features also contributed to the performance." } ], "images": [ "scigen/train/1203.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Accuracies of the proposed method and the state-of-the-art methods." ], "reasoning": "The NTEE model achieved the highest scores on both the CoNLL and TAC 2010 datasets, and the string similarity features contributed to its performance.", "description": "Our method achieved strong results on both the CoNLL and the TAC 2010 datasets. In particular, the NTEE model clearly outperformed the other proposed models. We also tested the performance of the NTEE model without using the string similarity features (strsim) and found that these features also contributed to the performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 4: Accuracies of the proposed method and the state-of-the-art methods.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}. \n\nQuestion: What was the performance of the NTEE model on the CoNLL (Micro) dataset without using the string similarity features?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows that the NTEE model without string similarity features (strsim) scored 92.9 on the CoNLL (Micro) dataset. So, the answer is {\"answer\": \"92.9\"}" } ], "images": [ "scigen/train/1203.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Accuracies of the proposed method and the state-of-the-art methods." ], "reasoning": "The table shows that the NTEE model without string similarity features (strsim) scored 92.9 on the CoNLL (Micro) dataset.", "question": "What was the performance of the NTEE model on the CoNLL (Micro) dataset without using the string similarity features?", "answer": "So, the answer is {\"answer\": \"92.9\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe NTEE model without string similarity features performed better than the Fixed NTEE model on the TAC10 (Micro) dataset.\n" }, { "role": "assistant", "content": "The table shows that the NTEE model without string similarity features scored 85.8 on the TAC10 (Micro) dataset, while the Fixed NTEE model scored 85.9. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1203.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Accuracies of the proposed method and the state-of-the-art methods." ], "reasoning": "The table shows that the NTEE model without string similarity features scored 85.8 on the TAC10 (Micro) dataset, while the Fixed NTEE model scored 85.9.", "statement": "The NTEE model without string similarity features performed better than the Fixed NTEE model on the TAC10 (Micro) dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 8: Performance of P2T-Tran', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "P2T-Tran-Syllable and P2T-Tran-Subword have almost negligible performance differences on the test set, unlike P2P-Tran where the differences are small but noticeable.In this subsection, we report results on P2T-Tran, and also compare it against T2T-Tran. Since the outputs of both P2T-Tran and T2T-Tran are in text space, we only report their performances in text space. For P2T-Tran, we also experiment two variants, namely, P2T-Tran-Syllable and P2T-Tran-Subword. On the test set, the performance difference between P2T-Tran-Syllable and P2T-Tran-Subword is very small and almost neglect-able, which is different from the results on P2P-Tran, where differences between P2P-Tran-Syllable and P2P-Tran-Subword is small but noticeable." } ], "images": [ "scigen/train/1204.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Performance of P2T-Tran" ], "reasoning": "P2T-Tran-Syllable and P2T-Tran-Subword have almost negligible performance differences on the test set, unlike P2P-Tran where the differences are small but noticeable.", "description": "In this subsection, we report results on P2T-Tran, and also compare it against T2T-Tran. Since the outputs of both P2T-Tran and T2T-Tran are in text space, we only report their performances in text space. For P2T-Tran, we also experiment two variants, namely, P2T-Tran-Syllable and P2T-Tran-Subword. On the test set, the performance difference between P2T-Tran-Syllable and P2T-Tran-Subword is very small and almost neglect-able, which is different from the results on P2P-Tran, where differences between P2P-Tran-Syllable and P2P-Tran-Subword is small but noticeable.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 8: Performance of P2T-Tran'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. \n\nQuestion: What is the Text Space Test performance of P2T-Tran for Zh-En with subword units?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows the Text Space Test performance for P2T-Tran with Zh subword and En subword units as 42.75. So, the answer is {\"answer\": \"42.75\"}." } ], "images": [ "scigen/train/1204.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Performance of P2T-Tran" ], "reasoning": "The table shows the Text Space Test performance for P2T-Tran with Zh subword and En subword units as 42.75.", "question": "What is the Text Space Test performance of P2T-Tran for Zh-En with subword units?", "answer": "So, the answer is {\"answer\": \"42.75\"}." }, { "messages": [ { "role": "user", "content": "P2T-Tran-Syllable has a higher Text Space Test performance than P2T-Tran-Subword for En-Zh. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nP2T-Tran-Syllable has a higher Text Space Test performance than P2T-Tran-Subword for En-Zh.\n" }, { "role": "assistant", "content": "The table shows that for En-Zh, P2T-Tran-Syllable has a Text Space Test performance of 36.12, while P2T-Tran-Subword has a performance of 36.04. Therefore, P2T-Tran-Syllable has a higher performance. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1204.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Performance of P2T-Tran" ], "reasoning": "The table shows that for En-Zh, P2T-Tran-Syllable has a Text Space Test performance of 36.12, while P2T-Tran-Subword has a performance of 36.04. Therefore, P2T-Tran-Syllable has a higher performance.", "statement": "P2T-Tran-Syllable has a higher Text Space Test performance than P2T-Tran-Subword for En-Zh.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: P2P-Tran with different basic units. The numbers in bracket in first column are the number of basic units.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The subword approach achieves the best performance in both Zh-En and En-Zh directions, while the syllable approach is about 1 BLEU point worse, and the phoneme approach performs the worst.We first compare different basic units in pronunciation space, on the task of P2P-Tran. Specially, we compare 3 types of P2P-Tran, namely, 1) Pinyins for Chinese and phonemes for English, 2) Pinyins for Chinese and syllables for English, and 3) subwords for Chinese and subwords for English. We trained the models for both Zh-En and En-Zh, and for fair comparison, the translations are first transformed into sequences of Pinyins for Chinese and sequences of phonemes for English, then the 4-gram BLEU score papineni2002bleu is calculated by multi-bleu.perl. Note that in this paper, all BLEU scores in the pronunciation space are calculated in this way, and all BLEU scores are single-reference scores. Consistent with the text translation, the subword approach (type 3 of P2P-Tran) achieves the best performance in both directions. The syllable approach (type 2 of P2P-Tran) is about 1 BLEU point worse. However, when the basic unit is switched to phonemes in English side, the performance is much worse, especially in the direction of Zh-En." } ], "images": [ "scigen/train/1205.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: P2P-Tran with different basic units. The numbers in bracket in first column are the number of basic units." ], "reasoning": "The subword approach achieves the best performance in both Zh-En and En-Zh directions, while the syllable approach is about 1 BLEU point worse, and the phoneme approach performs the worst.", "description": "We first compare different basic units in pronunciation space, on the task of P2P-Tran. Specially, we compare 3 types of P2P-Tran, namely, 1) Pinyins for Chinese and phonemes for English, 2) Pinyins for Chinese and syllables for English, and 3) subwords for Chinese and subwords for English. We trained the models for both Zh-En and En-Zh, and for fair comparison, the translations are first transformed into sequences of Pinyins for Chinese and sequences of phonemes for English, then the 4-gram BLEU score papineni2002bleu is calculated by multi-bleu.perl. Note that in this paper, all BLEU scores in the pronunciation space are calculated in this way, and all BLEU scores are single-reference scores. Consistent with the text translation, the subword approach (type 3 of P2P-Tran) achieves the best performance in both directions. The syllable approach (type 2 of P2P-Tran) is about 1 BLEU point worse. However, when the basic unit is switched to phonemes in English side, the performance is much worse, especially in the direction of Zh-En.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: P2P-Tran with different basic units. The numbers in bracket in first column are the number of basic units.'.\n\nQuestion: Which basic unit approach achieves the highest BLEU score in the Zh-En direction?\n" }, { "role": "assistant", "content": "By comparing the BLEU scores for the Zh-En direction, the subword approach achieves the highest score of 71.23. So, the answer is {\"answer\": \"subword\"}" } ], "images": [ "scigen/train/1205.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: P2P-Tran with different basic units. The numbers in bracket in first column are the number of basic units." ], "reasoning": "By comparing the BLEU scores for the Zh-En direction, the subword approach achieves the highest score of 71.23.", "question": "Which basic unit approach achieves the highest BLEU score in the Zh-En direction?", "answer": "So, the answer is {\"answer\": \"subword\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe phoneme approach achieves the best performance in the En-Zh direction.\n" }, { "role": "assistant", "content": "The table shows that the subword approach achieves the highest BLEU score of 50.90 in the En-Zh direction, while the phoneme approach has a lower score of 46.75. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1205.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: P2P-Tran with different basic units. The numbers in bracket in first column are the number of basic units." ], "reasoning": "The table shows that the subword approach achieves the highest BLEU score of 50.90 in the En-Zh direction, while the phoneme approach has a lower score of 46.75.", "statement": "The phoneme approach achieves the best performance in the En-Zh direction.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 7: Performances of P2P-Tran, compared with T2T-Tran'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "P2P-Tran-Subword generally performs better than P2P-Tran-Syllable, and its performance is comparable to T2T-Tran, with slight variations in different translation directions and spaces.For P2P-Tran, we experiments two variants, namely, P2P-Tran-Syllable and P2P-Tran-Subword. In P2P-Tran-Syllable, the basic unit is Pinyin for Chinese and syllable for English. In P2P-Tran-Subword, the basic unit is subword for both Chinese and English. There are some important observations. First, the performances of all models are pretty good. This is probably because our dataset is large and the distributions of dev and test sets are similar to the distribution of train set. Second, the performance of P2P-Tran-Subword is consistently a little better than P2P-Tran-Syllable, which suggests that subwords are also very good basic units for translation in pronunciation space. Second, the performance of P2P-Tran-Subword is in general comparable to the performance of T2T-Tran. In Pronunciation space, P2P-Tran-Subword is slightly better than T2T-Tran (71.23 vs 71.06) in the direction of Zh-En, and T2T-Tran is slightly better than P2P-Tran-Subword (51.02 vs 50.90) in the direction of En-Zh. In Text space, in both directions, T2T-Tran seems to be a little better than P2P-Tran-Subword (43.46 vs 42.86 and 37.13 vs 35.64). However, there are errors in P2T-Conv, and when taking this into account, the differences will be subtle. Another important factor which also affects the performance of P2P-Tran is that a pronunciation sentence usually contains less information than its textual counterpart." } ], "images": [ "scigen/train/1206.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Performances of P2P-Tran, compared with T2T-Tran" ], "reasoning": "P2P-Tran-Subword generally performs better than P2P-Tran-Syllable, and its performance is comparable to T2T-Tran, with slight variations in different translation directions and spaces.", "description": "For P2P-Tran, we experiments two variants, namely, P2P-Tran-Syllable and P2P-Tran-Subword. In P2P-Tran-Syllable, the basic unit is Pinyin for Chinese and syllable for English. In P2P-Tran-Subword, the basic unit is subword for both Chinese and English. There are some important observations. First, the performances of all models are pretty good. This is probably because our dataset is large and the distributions of dev and test sets are similar to the distribution of train set. Second, the performance of P2P-Tran-Subword is consistently a little better than P2P-Tran-Syllable, which suggests that subwords are also very good basic units for translation in pronunciation space. Second, the performance of P2P-Tran-Subword is in general comparable to the performance of T2T-Tran. In Pronunciation space, P2P-Tran-Subword is slightly better than T2T-Tran (71.23 vs 71.06) in the direction of Zh-En, and T2T-Tran is slightly better than P2P-Tran-Subword (51.02 vs 50.90) in the direction of En-Zh. In Text space, in both directions, T2T-Tran seems to be a little better than P2P-Tran-Subword (43.46 vs 42.86 and 37.13 vs 35.64). However, there are errors in P2T-Conv, and when taking this into account, the differences will be subtle. Another important factor which also affects the performance of P2P-Tran is that a pronunciation sentence usually contains less information than its textual counterpart.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 7: Performances of P2P-Tran, compared with T2T-Tran', solve the subsequent question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich model has the highest score in the Pronunciation Space Test for the Zh-En direction?\n" }, { "role": "assistant", "content": "From the table, the highest score in the Pronunciation Space Test for the Zh-En direction is 71.23, achieved by P2P-Tran-Subword. So, the answer is {\"answer\": \"P2P-Tran-Subword\"}" } ], "images": [ "scigen/train/1206.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Performances of P2P-Tran, compared with T2T-Tran" ], "reasoning": "From the table, the highest score in the Pronunciation Space Test for the Zh-En direction is 71.23, achieved by P2P-Tran-Subword.", "question": "Which model has the highest score in the Pronunciation Space Test for the Zh-En direction?", "answer": "So, the answer is {\"answer\": \"P2P-Tran-Subword\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: P2P-Tran-Syllable outperforms P2P-Tran-Subword in the Text Space Test for the En-Zh direction., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nP2P-Tran-Syllable outperforms P2P-Tran-Subword in the Text Space Test for the En-Zh direction.\n" }, { "role": "assistant", "content": "The table shows that in the Text Space Test for the En-Zh direction, P2P-Tran-Syllable scores 34.21, while P2P-Tran-Subword scores 35.64, indicating that P2P-Tran-Subword outperforms P2P-Tran-Syllable. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1206.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Performances of P2P-Tran, compared with T2T-Tran" ], "reasoning": "The table shows that in the Text Space Test for the En-Zh direction, P2P-Tran-Syllable scores 34.21, while P2P-Tran-Subword scores 35.64, indicating that P2P-Tran-Subword outperforms P2P-Tran-Syllable.", "statement": "P2P-Tran-Syllable outperforms P2P-Tran-Subword in the Text Space Test for the En-Zh direction.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Score variance standard deviation', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The translations showed limited differences in meaning and use of standard Bambara between written and oral forms, but significant differences in literalness.The translations of the news broadcast showed a relatively limited difference in meaning and use of standard Bambara between written and oral translations, but significant difference in the literalness of the translations." } ], "images": [ "scigen/train/1207.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Score variance standard deviation" ], "reasoning": "The translations showed limited differences in meaning and use of standard Bambara between written and oral forms, but significant differences in literalness.", "description": "The translations of the news broadcast showed a relatively limited difference in meaning and use of standard Bambara between written and oral translations, but significant difference in the literalness of the translations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: Score variance standard deviation', and tackle the question below in a sequential manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the difference in literalness between Malian news written and oral translations?\n" }, { "role": "assistant", "content": "To find the difference in literalness between Malian news written and oral translations, subtract the literalness score of written (0.130) from oral (0.298). So, the answer is {\"answer\": \"0.168\"}." } ], "images": [ "scigen/train/1207.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Score variance standard deviation" ], "reasoning": "To find the difference in literalness between Malian news written and oral translations, subtract the literalness score of written (0.130) from oral (0.298).", "question": "What is the difference in literalness between Malian news written and oral translations?", "answer": "So, the answer is {\"answer\": \"0.168\"}." }, { "messages": [ { "role": "user", "content": "The overall literalness of Wikipedia translations is higher than that of Malian news translations. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe overall literalness of Wikipedia translations is higher than that of Malian news translations.\n" }, { "role": "assistant", "content": "To verify this statement, compare the overall literalness scores of Wikipedia (0.244) and Malian news (0.243). The overall literalness of Wikipedia translations is indeed higher. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1207.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Score variance standard deviation" ], "reasoning": "To verify this statement, compare the overall literalness scores of Wikipedia (0.244) and Malian news (0.243). The overall literalness of Wikipedia translations is indeed higher.", "statement": "The overall literalness of Wikipedia translations is higher than that of Malian news translations.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'TABLE XVII: Analysis of different Tags for VQG-COCO-dataset. We analyse noun tag (Tag-n), verb tag (Tag-v), and question tag (Tag-wh) for different fusion methods, namely joint, attention, Hadamard, and addition based fusion.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The Hadamard Model with 3 Wh-words as context achieves the highest CIDEr score of 55.0, outperforming other models and contexts.Taking Nouns generated from the captions and questions of the corresponding training example as context, we achieve an increase of 1.6% in Bleu Score and 2% in METEOR and 34.4% in CIDEr Score from the basic Image model. Similarly, taking Verbs as context gives us an increase of 1.3% in Bleu Score and 2.1% in METEOR and 33.5% in CIDEr Score from the basic Image model. And the best result comes when we take 3 Wh-Words as context and apply the Hadamard Model with concatenating the 3 WH-words. Here we show that for 3 words i.e., 3 nouns, 3 verbs, and 3 Wh-words, the Concatenation model performs the best. In this table, the convolution model is using 1D convolution to combine the tags, and the joint model combines all the tags." } ], "images": [ "scigen/train/1208.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE XVII: Analysis of different Tags for VQG-COCO-dataset. We analyse noun tag (Tag-n), verb tag (Tag-v), and question tag (Tag-wh) for different fusion methods, namely joint, attention, Hadamard, and addition based fusion." ], "reasoning": "The Hadamard Model with 3 Wh-words as context achieves the highest CIDEr score of 55.0, outperforming other models and contexts.", "description": "Taking Nouns generated from the captions and questions of the corresponding training example as context, we achieve an increase of 1.6% in Bleu Score and 2% in METEOR and 34.4% in CIDEr Score from the basic Image model. Similarly, taking Verbs as context gives us an increase of 1.3% in Bleu Score and 2.1% in METEOR and 33.5% in CIDEr Score from the basic Image model. And the best result comes when we take 3 Wh-Words as context and apply the Hadamard Model with concatenating the 3 WH-words. Here we show that for 3 words i.e., 3 nouns, 3 verbs, and 3 Wh-words, the Concatenation model performs the best. In this table, the convolution model is using 1D convolution to combine the tags, and the joint model combines all the tags.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'TABLE XVII: Analysis of different Tags for VQG-COCO-dataset. We analyse noun tag (Tag-n), verb tag (Tag-v), and question tag (Tag-wh) for different fusion methods, namely joint, attention, Hadamard, and addition based fusion.', solve this question and give detail solutions. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhich context and method combination achieves the highest CIDEr score?\n" }, { "role": "assistant", "content": "By examining the table, the highest CIDEr score is 55.0, achieved by the Tag-wh context with the HM method. So, the answer is {\"answer\": \"Tag-wh HM\"}." } ], "images": [ "scigen/train/1208.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE XVII: Analysis of different Tags for VQG-COCO-dataset. We analyse noun tag (Tag-n), verb tag (Tag-v), and question tag (Tag-wh) for different fusion methods, namely joint, attention, Hadamard, and addition based fusion." ], "reasoning": "By examining the table, the highest CIDEr score is 55.0, achieved by the Tag-wh context with the HM method.", "question": "Which context and method combination achieves the highest CIDEr score?", "answer": "So, the answer is {\"answer\": \"Tag-wh HM\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Tag-n context with the HM method achieves a higher CIDEr score than the Tag-v context with the HM method., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Tag-n context with the HM method achieves a higher CIDEr score than the Tag-v context with the HM method.\n" }, { "role": "assistant", "content": "The table shows that the Tag-n context with the HM method has a CIDEr score of 53.2, while the Tag-v context with the HM method has a CIDEr score of 52.3. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1208.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE XVII: Analysis of different Tags for VQG-COCO-dataset. We analyse noun tag (Tag-n), verb tag (Tag-v), and question tag (Tag-wh) for different fusion methods, namely joint, attention, Hadamard, and addition based fusion." ], "reasoning": "The table shows that the Tag-n context with the HM method has a CIDEr score of 53.2, while the Tag-v context with the HM method has a CIDEr score of 52.3.", "statement": "The Tag-n context with the HM method achieves a higher CIDEr score than the Tag-v context with the HM method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'TABLE III: Analysis network parameter for DAN'.\n" }, { "role": "assistant", "content": "Using 4 nearest neighbors in the DAN network yields the highest accuracy, but increasing beyond 4 reduces accuracy.In the proposed DAN network, we have a dependency on the number of k-nearest neighbors that should be considered. We, therefore, use 4 nearest neighbors in our experiments. We observe that increasing nearest neighbors beyond 4 nearest neighbors results in a reduction in accuracy. Further, even using a single nearest neighbor results in substantial improvement that is marginally improved as we move to 4 nearest neighbors." } ], "images": [ "scigen/train/1209.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Analysis network parameter for DAN" ], "reasoning": "Using 4 nearest neighbors in the DAN network yields the highest accuracy, but increasing beyond 4 reduces accuracy.", "description": "In the proposed DAN network, we have a dependency on the number of k-nearest neighbors that should be considered. We, therefore, use 4 nearest neighbors in our experiments. We observe that increasing nearest neighbors beyond 4 nearest neighbors results in a reduction in accuracy. Further, even using a single nearest neighbor results in substantial improvement that is marginally improved as we move to 4 nearest neighbors.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'TABLE III: Analysis network parameter for DAN'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the HAT val dataset Rank-correlation for DAN(K=4)+LQIA?\n" }, { "role": "assistant", "content": "The table shows the HAT val dataset Rank-correlation for various models. For DAN(K=4)+LQIA, the Rank-correlation is 0.3206. So, the answer is {\"answer\": \"0.3206\"}" } ], "images": [ "scigen/train/1209.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Analysis network parameter for DAN" ], "reasoning": "The table shows the HAT val dataset Rank-correlation for various models. For DAN(K=4)+LQIA, the Rank-correlation is 0.3206.", "question": "What is the HAT val dataset Rank-correlation for DAN(K=4)+LQIA?", "answer": "So, the answer is {\"answer\": \"0.3206\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nUsing 4 nearest neighbors in the DAN network results in the highest VQA1.0 Open-Ended (test-dev) All accuracy.\n" }, { "role": "assistant", "content": "The table shows that DAN(K=4)+LQIA has an accuracy of 60.2 for VQA1.0 Open-Ended (test-dev) All, which is the highest among the listed models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1209.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Analysis network parameter for DAN" ], "reasoning": "The table shows that DAN(K=4)+LQIA has an accuracy of 60.2 for VQA1.0 Open-Ended (test-dev) All, which is the highest among the listed models.", "statement": "Using 4 nearest neighbors in the DAN network results in the highest VQA1.0 Open-Ended (test-dev) All accuracy.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'TABLE IV: Analysis network parameter for DCN'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The DCN network using multiplication with learned weights and 4 nearest neighbors shows significant improvements in correlation with human attention and accuracy on the VQA-1 dataset, especially when combined with MCB.In the proposed DCN network, we have two different configurations, one where we use the add module (DCN-add) for adding the differential context feature and one where we use the (DCN-mul) multiplication module for adding the differential context feature. We further have a dependency on the number of k-nearest neighbors for the DCN network as well. This is also considered. We next evaluate the effect of using a fixed scaling weight (DCN_v1) for adding the differential context feature against learning a linear scaling weight (DCN_v2) for adding the differential context feature. dataset [VQA] and in correlation with human attention is the version that uses multiplication with learned weights and with 4 nearest neighbors being considered. This results in an improvement of 11% in terms of correlation with human attention and 4.8% improvement in accuracy on the VQA-1 dataset [VQA]. We also observe that incorporating DCN with MCB [Fukui_arXiv2016] further improves the results by 4.5% further on the VQA dataset and results in an improvement of 1.47% improvement in correlation with attention. These configurations are used in comparison with the baselines." } ], "images": [ "scigen/train/1210.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Analysis network parameter for DCN" ], "reasoning": "The DCN network using multiplication with learned weights and 4 nearest neighbors shows significant improvements in correlation with human attention and accuracy on the VQA-1 dataset, especially when combined with MCB.", "description": "In the proposed DCN network, we have two different configurations, one where we use the add module (DCN-add) for adding the differential context feature and one where we use the (DCN-mul) multiplication module for adding the differential context feature. We further have a dependency on the number of k-nearest neighbors for the DCN network as well. This is also considered. We next evaluate the effect of using a fixed scaling weight (DCN_v1) for adding the differential context feature against learning a linear scaling weight (DCN_v2) for adding the differential context feature. dataset [VQA] and in correlation with human attention is the version that uses multiplication with learned weights and with 4 nearest neighbors being considered. This results in an improvement of 11% in terms of correlation with human attention and 4.8% improvement in accuracy on the VQA-1 dataset [VQA]. We also observe that incorporating DCN with MCB [Fukui_arXiv2016] further improves the results by 4.5% further on the VQA dataset and results in an improvement of 1.47% improvement in correlation with attention. These configurations are used in comparison with the baselines.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'TABLE IV: Analysis network parameter for DCN'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich configuration of the DCN network shows the highest improvement in the VQA1.0 Open-Ended (test-dev) All category?\n" }, { "role": "assistant", "content": "By examining the table, the configuration 'DCN Mul_v2(K=4)+MCB' shows the highest score of 65.4 in the VQA1.0 Open-Ended (test-dev) All category. So, the answer is {\"answer\": \"DCN Mul_v2(K=4)+MCB\"}" } ], "images": [ "scigen/train/1210.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Analysis network parameter for DCN" ], "reasoning": "By examining the table, the configuration 'DCN Mul_v2(K=4)+MCB' shows the highest score of 65.4 in the VQA1.0 Open-Ended (test-dev) All category.", "question": "Which configuration of the DCN network shows the highest improvement in the VQA1.0 Open-Ended (test-dev) All category?", "answer": "So, the answer is {\"answer\": \"DCN Mul_v2(K=4)+MCB\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe DCN network using multiplication with learned weights and 4 nearest neighbors achieves the highest rank-correlation on the HAT val dataset.\n" }, { "role": "assistant", "content": "The table shows that the configuration 'DCN Mul_v2(K=4)+MCB' achieves the highest rank-correlation of 0.3387 on the HAT val dataset. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1210.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Analysis network parameter for DCN" ], "reasoning": "The table shows that the configuration 'DCN Mul_v2(K=4)+MCB' achieves the highest rank-correlation of 0.3387 on the HAT val dataset.", "statement": "The DCN network using multiplication with learned weights and 4 nearest neighbors achieves the highest rank-correlation on the HAT val dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'TABLE VI: VQA2.0 accuracy on Validation set for DCN and DAN', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The proposed DCN method with 4 nearest neighbors combined with MCB achieves the highest accuracy of 65.90% on the VQA-2 dataset, outperforming the SAN method.We next evaluate the proposed method on a recently proposed VQA-2 dataset [Goyal_CVPR2017]. The aim of this new dataset is to remove the bias in different questions. It is a more challenging dataset as compared to the previous VQA-1 dataset [VQA]. We provide a comparison of the proposed DAN and DCN methods against the stacked attention network (SAN) [Yang_CVPR2016] method. We observe that our proposed methods are also able to improve the result over the SAN method. DCN with 4 nearest neighbors, when combined with MCB, obtains an accuracy of 65.90%." } ], "images": [ "scigen/train/1211.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VI: VQA2.0 accuracy on Validation set for DCN and DAN" ], "reasoning": "The proposed DCN method with 4 nearest neighbors combined with MCB achieves the highest accuracy of 65.90% on the VQA-2 dataset, outperforming the SAN method.", "description": "We next evaluate the proposed method on a recently proposed VQA-2 dataset [Goyal_CVPR2017]. The aim of this new dataset is to remove the bias in different questions. It is a more challenging dataset as compared to the previous VQA-1 dataset [VQA]. We provide a comparison of the proposed DAN and DCN methods against the stacked attention network (SAN) [Yang_CVPR2016] method. We observe that our proposed methods are also able to improve the result over the SAN method. DCN with 4 nearest neighbors, when combined with MCB, obtains an accuracy of 65.90%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nTable title: TABLE VI: VQA2.0 accuracy on Validation set for DCN and DAN\n\nQuestion: Which method achieves the highest accuracy in the 'All' category on the VQA-2 dataset?\n" }, { "role": "assistant", "content": "By comparing the 'All' category accuracies in the table, the highest accuracy is achieved by the DCN Mul_v2(K=4)+MCB method with 65.90%. So, the answer is {\"answer\": \"DCN Mul_v2(K=4)+MCB\"}" } ], "images": [ "scigen/train/1211.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VI: VQA2.0 accuracy on Validation set for DCN and DAN" ], "reasoning": "By comparing the 'All' category accuracies in the table, the highest accuracy is achieved by the DCN Mul_v2(K=4)+MCB method with 65.90%.", "question": "Which method achieves the highest accuracy in the 'All' category on the VQA-2 dataset?", "answer": "So, the answer is {\"answer\": \"DCN Mul_v2(K=4)+MCB\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe SAN-2 method achieves the highest accuracy in the 'Yes/No' category.\n" }, { "role": "assistant", "content": "The table shows that the SAN-2 method has an accuracy of 52.82% in the 'All' category, but it does not have an entry for the 'Yes/No' category. The highest accuracy in the 'Yes/No' category is achieved by the DCN Mul_v2(K=4)+MCB method with 82.40%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1211.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VI: VQA2.0 accuracy on Validation set for DCN and DAN" ], "reasoning": "The table shows that the SAN-2 method has an accuracy of 52.82% in the 'All' category, but it does not have an entry for the 'Yes/No' category. The highest accuracy in the 'Yes/No' category is achieved by the DCN Mul_v2(K=4)+MCB method with 82.40%.", "statement": "The SAN-2 method achieves the highest accuracy in the 'Yes/No' category.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'TABLE XIV: Full State-of-the-Art comparison on VQG-COCO Dataset. The first block consists of state-of-the-art results, the second block refers to the baselines mentioned in the State-of-the-art section of the main paper, and the third block provides the results for the best method for different ablations of our method.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The proposed MDN-Joint model provides improved embeddings to the decoder, capturing instance-specific differential information that aids in question generation.Although these metrics have not been shown to correlate with the ‘naturalness’ of the question, these still provide a reasonable quantitative measure for comparison. We observe that the proposed MDN provides improved embeddings to the decoder. We believe that these embeddings capture instance-specific differential information that helps in guiding the question generation." } ], "images": [ "scigen/train/1212.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE XIV: Full State-of-the-Art comparison on VQG-COCO Dataset. The first block consists of state-of-the-art results, the second block refers to the baselines mentioned in the State-of-the-art section of the main paper, and the third block provides the results for the best method for different ablations of our method." ], "reasoning": "The proposed MDN-Joint model provides improved embeddings to the decoder, capturing instance-specific differential information that aids in question generation.", "description": "Although these metrics have not been shown to correlate with the ‘naturalness’ of the question, these still provide a reasonable quantitative measure for comparison. We observe that the proposed MDN provides improved embeddings to the decoder. We believe that these embeddings capture instance-specific differential information that helps in guiding the question generation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nTable title: TABLE XIV: Full State-of-the-Art comparison on VQG-COCO Dataset. The first block consists of state-of-the-art results, the second block refers to the baselines mentioned in the State-of-the-art section of the main paper, and the third block provides the results for the best method for different ablations of our method.\n\nQuestion: Which model has the highest BLEU1 score among the automated methods?\n" }, { "role": "assistant", "content": "By comparing the BLEU1 scores of all automated methods, we see that the MDN-Joint model has the highest BLEU1 score of 36.0. So, the answer is {\"answer\": \"MDN-Joint\"}" } ], "images": [ "scigen/train/1212.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE XIV: Full State-of-the-Art comparison on VQG-COCO Dataset. The first block consists of state-of-the-art results, the second block refers to the baselines mentioned in the State-of-the-art section of the main paper, and the third block provides the results for the best method for different ablations of our method." ], "reasoning": "By comparing the BLEU1 scores of all automated methods, we see that the MDN-Joint model has the highest BLEU1 score of 36.0.", "question": "Which model has the highest BLEU1 score among the automated methods?", "answer": "So, the answer is {\"answer\": \"MDN-Joint\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe MDN-Joint model has the highest METEOR score among all models.\n" }, { "role": "assistant", "content": "By comparing the METEOR scores of all models, we see that the Humans model has the highest METEOR score of 60.8, not the MDN-Joint model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1212.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE XIV: Full State-of-the-Art comparison on VQG-COCO Dataset. The first block consists of state-of-the-art results, the second block refers to the baselines mentioned in the State-of-the-art section of the main paper, and the third block provides the results for the best method for different ablations of our method." ], "reasoning": "By comparing the METEOR scores of all models, we see that the Humans model has the highest METEOR score of 60.8, not the MDN-Joint model.", "statement": "The MDN-Joint model has the highest METEOR score among all models.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'TABLE XVIII: Combination of 3 tags of each category for hadamard mixture model namely addition, concatenation, multiplication and 1d-convolution'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The concatenation model with 3 Wh-words as context achieves the highest CIDEr score of 55.0, outperforming other models and contexts.Taking Nouns generated from the captions and questions of the corresponding training example as context, we achieve an increase of 1.6% in Bleu Score and 2% in METEOR and 34.4% in CIDEr Score from the basic Image model. Similarly, taking Verbs as context gives us an increase of 1.3% in Bleu Score and 2.1% in METEOR and 33.5% in CIDEr Score from the basic Image model. And the best result comes when we take 3 Wh-Words as context and apply the Hadamard Model with concatenating the 3 WH-words. Here we show that for 3 words i.e., 3 nouns, 3 verbs, and 3 Wh-words, the Concatenation model performs the best. In this table, the convolution model is using 1D convolution to combine the tags, and the joint model combines all the tags." } ], "images": [ "scigen/train/1213.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE XVIII: Combination of 3 tags of each category for hadamard mixture model namely addition, concatenation, multiplication and 1d-convolution" ], "reasoning": "The concatenation model with 3 Wh-words as context achieves the highest CIDEr score of 55.0, outperforming other models and contexts.", "description": "Taking Nouns generated from the captions and questions of the corresponding training example as context, we achieve an increase of 1.6% in Bleu Score and 2% in METEOR and 34.4% in CIDEr Score from the basic Image model. Similarly, taking Verbs as context gives us an increase of 1.3% in Bleu Score and 2.1% in METEOR and 33.5% in CIDEr Score from the basic Image model. And the best result comes when we take 3 Wh-Words as context and apply the Hadamard Model with concatenating the 3 WH-words. Here we show that for 3 words i.e., 3 nouns, 3 verbs, and 3 Wh-words, the Concatenation model performs the best. In this table, the convolution model is using 1D convolution to combine the tags, and the joint model combines all the tags.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'TABLE XVIII: Combination of 3 tags of each category for hadamard mixture model namely addition, concatenation, multiplication and 1d-convolution', and tackle the question below in a sequential manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich context and model combination achieves the highest BLEU-1 score?\n" }, { "role": "assistant", "content": "By examining the BLEU-1 column, the highest score is 24.8, achieved by the Tag-n3-con context and model combination. So, the answer is {\"answer\": \"Tag-n3-con\"}" } ], "images": [ "scigen/train/1213.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE XVIII: Combination of 3 tags of each category for hadamard mixture model namely addition, concatenation, multiplication and 1d-convolution" ], "reasoning": "By examining the BLEU-1 column, the highest score is 24.8, achieved by the Tag-n3-con context and model combination.", "question": "Which context and model combination achieves the highest BLEU-1 score?", "answer": "So, the answer is {\"answer\": \"Tag-n3-con\"}" }, { "messages": [ { "role": "user", "content": "The Tag-q3-con context and model combination achieves the highest Meteor score. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Tag-q3-con context and model combination achieves the highest Meteor score.\n" }, { "role": "assistant", "content": "By examining the Meteor column, the highest score is 10.8, achieved by the Tag-q3-con context and model combination. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1213.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE XVIII: Combination of 3 tags of each category for hadamard mixture model namely addition, concatenation, multiplication and 1d-convolution" ], "reasoning": "By examining the Meteor column, the highest score is 10.8, achieved by the Tag-q3-con context and model combination.", "statement": "The Tag-q3-con context and model combination achieves the highest Meteor score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'TABLE XIX: VQG-COCO-dataset, Analysis of different number of Exemplars for addition model, hadamard model and joint model, R is random exemplar. All these experiment are for the differential image network. k=5 performs the best and hence we use this value for the results in main paper.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Using random exemplars for the nearest neighbor for MDN shows similar results to the baseline, indicating the model learns to ignore the cue.We had similar concerns and validated this point by using random exemplars for the nearest neighbor for MDN. In this case, the method is similar to the baseline. This suggests that with random exemplar, the model learns to ignore the cue." } ], "images": [ "scigen/train/1214.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE XIX: VQG-COCO-dataset, Analysis of different number of Exemplars for addition model, hadamard model and joint model, R is random exemplar. All these experiment are for the differential image network. k=5 performs the best and hence we use this value for the results in main paper." ], "reasoning": "Using random exemplars for the nearest neighbor for MDN shows similar results to the baseline, indicating the model learns to ignore the cue.", "description": "We had similar concerns and validated this point by using random exemplars for the nearest neighbor for MDN. In this case, the method is similar to the baseline. This suggests that with random exemplar, the model learns to ignore the cue.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich method and exemplar combination achieved the highest CIDEr score?\nSolve the above question based on the table titled 'TABLE XIX: VQG-COCO-dataset, Analysis of different number of Exemplars for addition model, hadamard model and joint model, R is random exemplar. All these experiment are for the differential image network. k=5 performs the best and hence we use this value for the results in main paper.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest CIDEr score, look at the CIDEr column and identify the highest value, which is 38.8. Then, find the corresponding method and exemplar combination, which is JM with IE(K=5). So, the answer is {\"answer\": \"JM with IE(K=5)\"}" } ], "images": [ "scigen/train/1214.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE XIX: VQG-COCO-dataset, Analysis of different number of Exemplars for addition model, hadamard model and joint model, R is random exemplar. All these experiment are for the differential image network. k=5 performs the best and hence we use this value for the results in main paper." ], "reasoning": "To find the highest CIDEr score, look at the CIDEr column and identify the highest value, which is 38.8. Then, find the corresponding method and exemplar combination, which is JM with IE(K=5).", "question": "Which method and exemplar combination achieved the highest CIDEr score?", "answer": "So, the answer is {\"answer\": \"JM with IE(K=5)\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe highest BLEU-1 score was achieved by the HM method with IE(K=5) exemplar.\n" }, { "role": "assistant", "content": "To verify this statement, look at the BLEU-1 column and identify the highest value, which is 30.4. The corresponding method and exemplar combination is JM with IE(K=5), not HM with IE(K=5). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1214.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE XIX: VQG-COCO-dataset, Analysis of different number of Exemplars for addition model, hadamard model and joint model, R is random exemplar. All these experiment are for the differential image network. k=5 performs the best and hence we use this value for the results in main paper." ], "reasoning": "To verify this statement, look at the BLEU-1 column and identify the highest value, which is 30.4. The corresponding method and exemplar combination is JM with IE(K=5), not HM with IE(K=5).", "statement": "The highest BLEU-1 score was achieved by the HM method with IE(K=5) exemplar.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Results on MathQA dataset testing set'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "TP-N2F outperforms both SEQ2PROG-orig and SEQ2PROG-best in terms of operation and execution accuracy, demonstrating the importance of its encoder and decoder components.Given a natural-language math problem, we need to generate a sequence of operations (operators and corresponding arguments) from a set of operators and arguments to solve the given problem. Each operation is regarded as a relational tuple by viewing the operator as relation, e.g., (add,n1,n2). The MathQA dataset consists of about 37k math word problems, each with a corresponding list of multi-choice options and the corresponding operation sequence. In this task, TP-N2F is deployed to generate the operation sequence given the question. The generated operations are executed with the execution script from Amini et al. As there are about 30% noisy data (where the execution script returns the wrong answer when given the ground-truth program; see Sec. TP-N2F is compared to a baseline provided by the seq2prog model in Amini et al. Our model outperforms both the original seq2prog, designated SEQ2PROG-orig, and the best reimplemented seq2prog after an extensive hyperparameter search, designated SEQ2PROG-best. To verify the importance of the TP-N2F encoder and decoder, we conducted experiments to replace either the encoder with a standard LSTM (denoted LSTM2TP) or the decoder with a standard attentional LSTM (denoted TP2LSTM). We observe that both the TPR components of TP-N2F are important for achieving the observed performance gain relative to the baseline." } ], "images": [ "scigen/train/1215.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results on MathQA dataset testing set" ], "reasoning": "TP-N2F outperforms both SEQ2PROG-orig and SEQ2PROG-best in terms of operation and execution accuracy, demonstrating the importance of its encoder and decoder components.", "description": "Given a natural-language math problem, we need to generate a sequence of operations (operators and corresponding arguments) from a set of operators and arguments to solve the given problem. Each operation is regarded as a relational tuple by viewing the operator as relation, e.g., (add,n1,n2). The MathQA dataset consists of about 37k math word problems, each with a corresponding list of multi-choice options and the corresponding operation sequence. In this task, TP-N2F is deployed to generate the operation sequence given the question. The generated operations are executed with the execution script from Amini et al. As there are about 30% noisy data (where the execution script returns the wrong answer when given the ground-truth program; see Sec. TP-N2F is compared to a baseline provided by the seq2prog model in Amini et al. Our model outperforms both the original seq2prog, designated SEQ2PROG-orig, and the best reimplemented seq2prog after an extensive hyperparameter search, designated SEQ2PROG-best. To verify the importance of the TP-N2F encoder and decoder, we conducted experiments to replace either the encoder with a standard LSTM (denoted LSTM2TP) or the decoder with a standard attentional LSTM (denoted TP2LSTM). We observe that both the TPR components of TP-N2F are important for achieving the observed performance gain relative to the baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 1: Results on MathQA dataset testing set\nQuestion: Which model has the highest execution accuracy and what is its value?\n" }, { "role": "assistant", "content": "By comparing the execution accuracy values in the table, TP-N2F has the highest execution accuracy of 55.95%. So, the answer is {\"answer\": \"TP-N2F with 55.95%\"}." } ], "images": [ "scigen/train/1215.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results on MathQA dataset testing set" ], "reasoning": "By comparing the execution accuracy values in the table, TP-N2F has the highest execution accuracy of 55.95%.", "question": "Which model has the highest execution accuracy and what is its value?", "answer": "So, the answer is {\"answer\": \"TP-N2F with 55.95%\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTP2LSTM has a higher operation accuracy than SEQ2PROG-best.\n" }, { "role": "assistant", "content": "By comparing the operation accuracy values in the table, TP2LSTM has an operation accuracy of 68.84%, which is higher than SEQ2PROG-best's 66.97%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1215.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results on MathQA dataset testing set" ], "reasoning": "By comparing the operation accuracy values in the table, TP2LSTM has an operation accuracy of 68.84%, which is higher than SEQ2PROG-best's 66.97%.", "statement": "TP2LSTM has a higher operation accuracy than SEQ2PROG-best.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Results of AlgoLisp dataset', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The TP-N2F model achieves the highest accuracy across all evaluation metrics on both the full and cleaned test sets, outperforming other models.Generating Lisp programs requires sensitivity to structural information because Lisp code can be regarded as tree-structured. Given a natural-language query, we need to generate code containing function calls with parameters. Each function call is a relational tuple, which has a function as the relation and parameters as arguments. We evaluate our model on the AlgoLisp dataset for this task and achieve state-of-the-art performance. Each sample contains a problem description, a corresponding Lisp program tree, and 10 input-output testing pairs. We parse the program tree into a straight-line sequence of tuples (same style as in MathQA). AlgoLisp provides an execution script to run the generated program and has three evaluation metrics: the accuracy of passing all test cases (Acc), the accuracy of passing 50% of test cases (50p-Acc), and the accuracy of generating an exactly matching program (M-Acc). AlgoLisp has about 10% noisy data (details in the Appendix), so we report results both on the full test set and the cleaned test set (in which all noisy testing samples are removed). Ablation experiments with TP2LSTM and LSTM2TP show that, for this task, the TP-N2F Decoder is more helpful than TP-N2F Encoder. This may be because lisp codes rely more heavily on structure representations." } ], "images": [ "scigen/train/1216.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of AlgoLisp dataset" ], "reasoning": "The TP-N2F model achieves the highest accuracy across all evaluation metrics on both the full and cleaned test sets, outperforming other models.", "description": "Generating Lisp programs requires sensitivity to structural information because Lisp code can be regarded as tree-structured. Given a natural-language query, we need to generate code containing function calls with parameters. Each function call is a relational tuple, which has a function as the relation and parameters as arguments. We evaluate our model on the AlgoLisp dataset for this task and achieve state-of-the-art performance. Each sample contains a problem description, a corresponding Lisp program tree, and 10 input-output testing pairs. We parse the program tree into a straight-line sequence of tuples (same style as in MathQA). AlgoLisp provides an execution script to run the generated program and has three evaluation metrics: the accuracy of passing all test cases (Acc), the accuracy of passing 50% of test cases (50p-Acc), and the accuracy of generating an exactly matching program (M-Acc). AlgoLisp has about 10% noisy data (details in the Appendix), so we report results both on the full test set and the cleaned test set (in which all noisy testing samples are removed). Ablation experiments with TP2LSTM and LSTM2TP show that, for this task, the TP-N2F Decoder is more helpful than TP-N2F Encoder. This may be because lisp codes rely more heavily on structure representations.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 2: Results of AlgoLisp dataset'. Solve the following question based on the table.\n\nQuestion: Which model has the highest accuracy for passing all test cases on the full testing set?\nLet's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the 'Full Testing Set Acc' column, the TP-N2F model has the highest accuracy at 84.02%. So, the answer is {\"answer\": \"TP-N2F\"}" } ], "images": [ "scigen/train/1216.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of AlgoLisp dataset" ], "reasoning": "By comparing the 'Full Testing Set Acc' column, the TP-N2F model has the highest accuracy at 84.02%.", "question": "Which model has the highest accuracy for passing all test cases on the full testing set?", "answer": "So, the answer is {\"answer\": \"TP-N2F\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe LSTM2LSTM+atten model has a higher accuracy for passing 50% of test cases on the cleaned test set than the TP2LSTM model.\n" }, { "role": "assistant", "content": "The 'Cleaned Testing Set 50p-Acc' for LSTM2LSTM+atten is 78.86%, while for TP2LSTM it is 80.51%, so the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1216.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of AlgoLisp dataset" ], "reasoning": "The 'Cleaned Testing Set 50p-Acc' for LSTM2LSTM+atten is 78.86%, while for TP2LSTM it is 80.51%, so the statement is refuted.", "statement": "The LSTM2LSTM+atten model has a higher accuracy for passing 50% of test cases on the cleaned test set than the TP2LSTM model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Results of the smoothed model fRS with different K on IMDB using text CNN. “Clean” represents the accuracy on the clean data without adversarial attacking and “Certified” the certified accuracy.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "As K increases, clean accuracy decreases while the gap between clean and certified accuracy decreases, achieving the best certified accuracy at K=100.We can see that the clean accuracy decreases when K increases, while the gap between the clean accuracy and certified accuracy, which measures the smoothness, decreases when K increases. The best certified accuracy is achieved when K=100." } ], "images": [ "scigen/train/1217.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of the smoothed model fRS with different K on IMDB using text CNN. “Clean” represents the accuracy on the clean data without adversarial attacking and “Certified” the certified accuracy." ], "reasoning": "As K increases, clean accuracy decreases while the gap between clean and certified accuracy decreases, achieving the best certified accuracy at K=100.", "description": "We can see that the clean accuracy decreases when K increases, while the gap between the clean accuracy and certified accuracy, which measures the smoothness, decreases when K increases. The best certified accuracy is achieved when K=100.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 3: Results of the smoothed model fRS with different K on IMDB using text CNN. “Clean” represents the accuracy on the clean data without adversarial attacking and “Certified” the certified accuracy.\nQuestion: What is the clean accuracy when the best certified accuracy is achieved?\n" }, { "role": "assistant", "content": "The best certified accuracy is achieved at K=100, where the clean accuracy is 88.09%. So, the answer is {\"answer\": \"88.09%\"}." } ], "images": [ "scigen/train/1217.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of the smoothed model fRS with different K on IMDB using text CNN. “Clean” represents the accuracy on the clean data without adversarial attacking and “Certified” the certified accuracy." ], "reasoning": "The best certified accuracy is achieved at K=100, where the clean accuracy is 88.09%.", "question": "What is the clean accuracy when the best certified accuracy is achieved?", "answer": "So, the answer is {\"answer\": \"88.09%\"}." }, { "messages": [ { "role": "user", "content": "The clean accuracy is highest when K=50. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe clean accuracy is highest when K=50.\n" }, { "role": "assistant", "content": "The clean accuracy for K=50 is 88.48%, which is the highest value in the clean accuracy row. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1217.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of the smoothed model fRS with different K on IMDB using text CNN. “Clean” represents the accuracy on the clean data without adversarial attacking and “Certified” the certified accuracy." ], "reasoning": "The clean accuracy for K=50 is 88.48%, which is the highest value in the clean accuracy row.", "statement": "The clean accuracy is highest when K=50.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: The different syntactic contribution to span-based and dependency-based SRL with end-to-end setup using predicted syntax and BERT pretrained LM features.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The ablation study on the WSJ test set using predicted syntax and BERT pretrained language model features shows that the two syntax-aided methods improve the performance of both span-based and dependency-based SRL.In order to assess the effect of our proposed two syntax aided methods to the span-based SRL and dependency-based SRL respectively, we conducted an ablation study on WSJ test set with end-to-end setup using predicted syntax and BERT pretrained language model (LM) features to explore how the different syntax (constituency and dependency) impact our model. This shows that our two syntactic enhancements are effective." } ], "images": [ "scigen/train/1218.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The different syntactic contribution to span-based and dependency-based SRL with end-to-end setup using predicted syntax and BERT pretrained LM features." ], "reasoning": "The ablation study on the WSJ test set using predicted syntax and BERT pretrained language model features shows that the two syntax-aided methods improve the performance of both span-based and dependency-based SRL.", "description": "In order to assess the effect of our proposed two syntax aided methods to the span-based SRL and dependency-based SRL respectively, we conducted an ablation study on WSJ test set with end-to-end setup using predicted syntax and BERT pretrained language model (LM) features to explore how the different syntax (constituency and dependency) impact our model. This shows that our two syntactic enhancements are effective.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: The different syntactic contribution to span-based and dependency-based SRL with end-to-end setup using predicted syntax and BERT pretrained LM features.\nQuestion: Which method resulted in the highest Dependency SRL P score?\nGive a detailed response to the above question. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the Dependency SRL P scores across all methods, the highest score is observed in the DSA method with a score of 88.1. So, the answer is {\"answer\": \"DSA\"}" } ], "images": [ "scigen/train/1218.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The different syntactic contribution to span-based and dependency-based SRL with end-to-end setup using predicted syntax and BERT pretrained LM features." ], "reasoning": "By comparing the Dependency SRL P scores across all methods, the highest score is observed in the DSA method with a score of 88.1.", "question": "Which method resulted in the highest Dependency SRL P score?", "answer": "So, the answer is {\"answer\": \"DSA\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe CSA method resulted in the highest Span SRL F1 score.\n" }, { "role": "assistant", "content": "By examining the Span SRL F1 scores, the highest score is 87.8, which is achieved by the Baseline method, not the CSA method. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1218.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The different syntactic contribution to span-based and dependency-based SRL with end-to-end setup using predicted syntax and BERT pretrained LM features." ], "reasoning": "By examining the Span SRL F1 scores, the highest score is 87.8, which is achieved by the Baseline method, not the CSA method.", "statement": "The CSA method resulted in the highest Span SRL F1 score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Joint SRL results on CoNLL-2005 and CoNLL-2009 test sets with gold predicates setup, as compared to previous systems. It is noteworthy that predict syntax is used in FitzGerald et al. (2015).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Our single model approach improves dependency by 1.7 and span by 0.7, and further enhancements are possible with pretrained language models like ELMo and BERT.In contrast to previous methods, we used a single model (instead of two independent models) to optimize the joint objectives. It shows that our model yields an absolute improvement on dependency (+1.7) and span (+0.7). With the help of the pretrained language model features (ELMo, BERT), the performance can be further improve and the improvement is orthogonal to our contributions." } ], "images": [ "scigen/train/1219.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Joint SRL results on CoNLL-2005 and CoNLL-2009 test sets with gold predicates setup, as compared to previous systems. It is noteworthy that predict syntax is used in FitzGerald et al. (2015)." ], "reasoning": "Our single model approach improves dependency by 1.7 and span by 0.7, and further enhancements are possible with pretrained language models like ELMo and BERT.", "description": "In contrast to previous methods, we used a single model (instead of two independent models) to optimize the joint objectives. It shows that our model yields an absolute improvement on dependency (+1.7) and span (+0.7). With the help of the pretrained language model features (ELMo, BERT), the performance can be further improve and the improvement is orthogonal to our contributions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: Joint SRL results on CoNLL-2005 and CoNLL-2009 test sets with gold predicates setup, as compared to previous systems. It is noteworthy that predict syntax is used in FitzGerald et al. (2015).'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the CoNLL09 WSJ F1 score for 'Ours + Gold Syntax' with BERT?\n" }, { "role": "assistant", "content": "To find the CoNLL09 WSJ F1 score for 'Ours + Gold Syntax' with BERT, locate the row labeled 'Ours + Gold Syntax' under the 'w/BERT' section and then find the value in the 'CoNLL09 WSJ F1' column. So, the answer is {\"answer\": \"93.0\"}." } ], "images": [ "scigen/train/1219.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Joint SRL results on CoNLL-2005 and CoNLL-2009 test sets with gold predicates setup, as compared to previous systems. It is noteworthy that predict syntax is used in FitzGerald et al. (2015)." ], "reasoning": "To find the CoNLL09 WSJ F1 score for 'Ours + Gold Syntax' with BERT, locate the row labeled 'Ours + Gold Syntax' under the 'w/BERT' section and then find the value in the 'CoNLL09 WSJ F1' column.", "question": "What is the CoNLL09 WSJ F1 score for 'Ours + Gold Syntax' with BERT?", "answer": "So, the answer is {\"answer\": \"93.0\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The 'Ours + Predicted Syntax' model with ELMo has a CoNLL05 WSJ P score of 88.5., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe 'Ours + Predicted Syntax' model with ELMo has a CoNLL05 WSJ P score of 88.5.\n" }, { "role": "assistant", "content": "To verify this statement, locate the row labeled 'Ours + Predicted Syntax' under the 'w/ELMo' section and check the value in the 'CoNLL05 WSJ P' column. The value is indeed 88.5. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1219.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Joint SRL results on CoNLL-2005 and CoNLL-2009 test sets with gold predicates setup, as compared to previous systems. It is noteworthy that predict syntax is used in FitzGerald et al. (2015)." ], "reasoning": "To verify this statement, locate the row labeled 'Ours + Predicted Syntax' under the 'w/ELMo' section and check the value in the 'CoNLL05 WSJ P' column. The value is indeed 88.5.", "statement": "The 'Ours + Predicted Syntax' model with ELMo has a CoNLL05 WSJ P score of 88.5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 10: Percent agreement between adversarial classifiers and human scores on the naturalness of texts.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Sentence-level perplexity does not correlate with human scores, suggesting adversarial classifiers can automate naturalness measurement.We also confirm that sentence-level perplexity is not an appropriate metric. It exhibits no significant correlation with human scores (α=0.05). These results suggest that adversarial classifiers can be useful for automating measurement of naturalness." } ], "images": [ "scigen/train/1220.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Percent agreement between adversarial classifiers and human scores on the naturalness of texts." ], "reasoning": "Sentence-level perplexity does not correlate with human scores, suggesting adversarial classifiers can automate naturalness measurement.", "description": "We also confirm that sentence-level perplexity is not an appropriate metric. It exhibits no significant correlation with human scores (α=0.05). These results suggest that adversarial classifiers can be useful for automating measurement of naturalness.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 10: Percent agreement between adversarial classifiers and human scores on the naturalness of texts.\nQuestion: Which model has the highest Neural Adv. Clf. Relative score?\n" }, { "role": "assistant", "content": "To find the model with the highest Neural Adv. Clf. Relative score, compare the values in the 'Neural Adv. Clf. Relative' column. The highest value is 67.90, which corresponds to the ARAE model. So, the answer is {\"answer\": \"ARAE\"}" } ], "images": [ "scigen/train/1220.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Percent agreement between adversarial classifiers and human scores on the naturalness of texts." ], "reasoning": "To find the model with the highest Neural Adv. Clf. Relative score, compare the values in the 'Neural Adv. Clf. Relative' column. The highest value is 67.90, which corresponds to the ARAE model.", "question": "Which model has the highest Neural Adv. Clf. Relative score?", "answer": "So, the answer is {\"answer\": \"ARAE\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe DAR model has the highest Unigram Adv. Clf. Absolute score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the values in the 'Unigram Adv. Clf. Absolute' column. The highest value is 65.16, which corresponds to the DAR model. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1220.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Percent agreement between adversarial classifiers and human scores on the naturalness of texts." ], "reasoning": "To verify this statement, compare the values in the 'Unigram Adv. Clf. Absolute' column. The highest value is 65.16, which corresponds to the DAR model.", "statement": "The DAR model has the highest Unigram Adv. Clf. Absolute score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Fleiss’ kappas for human judgments of content preservation of unmasked and style-masked texts.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The kappa score for ratings of content preservation based on style-masked texts is 0.297, which is the average of the scores for the CAAE, ARAE, and DAR models.The kappa metric is often levied in a relative fashion, as there are no universally accepted thresholds for agreements that are slight, fair, moderate, etc. For comprehensive experimentation, we compare kappas over the outputs of each style transfer model. The kappa score for ratings of content preservation based on style-masked texts is 0.297." } ], "images": [ "scigen/train/1221.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Fleiss’ kappas for human judgments of content preservation of unmasked and style-masked texts." ], "reasoning": "The kappa score for ratings of content preservation based on style-masked texts is 0.297, which is the average of the scores for the CAAE, ARAE, and DAR models.", "description": "The kappa metric is often levied in a relative fashion, as there are no universally accepted thresholds for agreements that are slight, fair, moderate, etc. For comprehensive experimentation, we compare kappas over the outputs of each style transfer model. The kappa score for ratings of content preservation based on style-masked texts is 0.297.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 5: Fleiss’ kappas for human judgments of content preservation of unmasked and style-masked texts.', unfold the question presented below, detailing every step of your calculation. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the kappa score for the ARAE model in the style-masked setting?\n" }, { "role": "assistant", "content": "The table shows the kappa scores for different models under two settings. For the ARAE model in the style-masked setting, the kappa score is 0.321. So, the answer is {\"answer\": \"0.321\"}." } ], "images": [ "scigen/train/1221.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Fleiss’ kappas for human judgments of content preservation of unmasked and style-masked texts." ], "reasoning": "The table shows the kappa scores for different models under two settings. For the ARAE model in the style-masked setting, the kappa score is 0.321.", "question": "What is the kappa score for the ARAE model in the style-masked setting?", "answer": "So, the answer is {\"answer\": \"0.321\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The kappa score for the DAR model in the unmasked setting is higher than that in the style-masked setting. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe kappa score for the DAR model in the unmasked setting is higher than that in the style-masked setting.\n" }, { "role": "assistant", "content": "The table shows that the kappa score for the DAR model in the unmasked setting is 0.161, while in the style-masked setting, it is 0.281. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1221.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Fleiss’ kappas for human judgments of content preservation of unmasked and style-masked texts." ], "reasoning": "The table shows that the kappa score for the DAR model in the unmasked setting is 0.161, while in the style-masked setting, it is 0.281. Therefore, the statement is refuted.", "statement": "The kappa score for the DAR model in the unmasked setting is higher than that in the style-masked setting.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 6: Fleiss’ kappas for human judgments of absolute naturalness and relative naturalness of texts.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Relative scoring shows higher inter-rater reliability for naturalness compared to absolute scoring across multiple thresholds.For style transfer intensity, kappas for relative scoring do not show improvement over the previously used approach of absolute scoring of x′. However, we observe the opposite for the aspect of naturalness. Despite the two types of tasks having different numbers of categories (2 vs 5), we can compare them by using a threshold τ to bin the absolute score for each text into a “natural” group (x′ is considered to be more natural than x) or “unnatural” one (vice versa), like in relative scoring. For example, τ=2 places texts with absolute scores greater than or equal to 2 into the natural group. Judgments for relative tasks yield greater inter-rater reliability than those of absolute tasks across multiple thresholds (τ∈{2,3}). This suggests that the relative scoring paradigm is preferable in human evaluations of naturalness." } ], "images": [ "scigen/train/1222.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Fleiss’ kappas for human judgments of absolute naturalness and relative naturalness of texts." ], "reasoning": "Relative scoring shows higher inter-rater reliability for naturalness compared to absolute scoring across multiple thresholds.", "description": "For style transfer intensity, kappas for relative scoring do not show improvement over the previously used approach of absolute scoring of x′. However, we observe the opposite for the aspect of naturalness. Despite the two types of tasks having different numbers of categories (2 vs 5), we can compare them by using a threshold τ to bin the absolute score for each text into a “natural” group (x′ is considered to be more natural than x) or “unnatural” one (vice versa), like in relative scoring. For example, τ=2 places texts with absolute scores greater than or equal to 2 into the natural group. Judgments for relative tasks yield greater inter-rater reliability than those of absolute tasks across multiple thresholds (τ∈{2,3}). This suggests that the relative scoring paradigm is preferable in human evaluations of naturalness.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 6: Fleiss’ kappas for human judgments of absolute naturalness and relative naturalness of texts.' and methodically tackle the question that follows. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model has the highest inter-rater reliability for naturalness using relative scoring?\n" }, { "role": "assistant", "content": "By comparing the values in the 'Relative' column, ARAE has the highest inter-rater reliability score of 0.741. So, the answer is {\"answer\": \"ARAE\"}" } ], "images": [ "scigen/train/1222.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Fleiss’ kappas for human judgments of absolute naturalness and relative naturalness of texts." ], "reasoning": "By comparing the values in the 'Relative' column, ARAE has the highest inter-rater reliability score of 0.741.", "question": "Which model has the highest inter-rater reliability for naturalness using relative scoring?", "answer": "So, the answer is {\"answer\": \"ARAE\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The DAR model has the lowest inter-rater reliability for naturalness using absolute scoring with τ=3., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe DAR model has the lowest inter-rater reliability for naturalness using absolute scoring with τ=3.\n" }, { "role": "assistant", "content": "By comparing the values in the 'Absolute τ=3' column, DAR has the lowest score of 0.103. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1222.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Fleiss’ kappas for human judgments of absolute naturalness and relative naturalness of texts." ], "reasoning": "By comparing the values in the 'Absolute τ=3' column, DAR has the lowest score of 0.103.", "statement": "The DAR model has the lowest inter-rater reliability for naturalness using absolute scoring with τ=3.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 10: Results of retraining Infersent on both SNLI and the Comparisons dataset.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The augmented-InferSent model achieved high performance on both the Comparisons and SNLI datasets without losing accuracy, demonstrating its capacity to handle specially designed edge-cases.We then checked whether InferSent can represent this relational structure without losing the information necessary for SNLI. We started with an untrained network, and then trained on an augmented version of the original training data. Here, examples from the SNLI training set were randomly interleaved with examples from our Comparisons training dataset, otherwise using the same training protocols reported in (Conneau \\BOthers. We found that the accuracy obtained this way on the SNLI test set (84.96 %) is comparable to the model trained only on SNLI (84.84 %). Moreover, test accuracy on the Comparisons dataset is close to perfect (99.55 %) and is much higher than the model trained only on SNLI (47.81 %). This establishes that in this case the model has enough capacity to achieve high performance on specially designed edge-cases like the Comparisons dataset, without loss of performance on the more general SNLI dataset. In this section we consider the performance of the augmented-InferSent model. In this section, we analyze its ability to generalize to 3 different kinds of Xs and Zs that have never been encountered during training." } ], "images": [ "scigen/train/1223.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Results of retraining Infersent on both SNLI and the Comparisons dataset." ], "reasoning": "The augmented-InferSent model achieved high performance on both the Comparisons and SNLI datasets without losing accuracy, demonstrating its capacity to handle specially designed edge-cases.", "description": "We then checked whether InferSent can represent this relational structure without losing the information necessary for SNLI. We started with an untrained network, and then trained on an augmented version of the original training data. Here, examples from the SNLI training set were randomly interleaved with examples from our Comparisons training dataset, otherwise using the same training protocols reported in (Conneau \\BOthers. We found that the accuracy obtained this way on the SNLI test set (84.96 %) is comparable to the model trained only on SNLI (84.84 %). Moreover, test accuracy on the Comparisons dataset is close to perfect (99.55 %) and is much higher than the model trained only on SNLI (47.81 %). This establishes that in this case the model has enough capacity to achieve high performance on specially designed edge-cases like the Comparisons dataset, without loss of performance on the more general SNLI dataset. In this section we consider the performance of the augmented-InferSent model. In this section, we analyze its ability to generalize to 3 different kinds of Xs and Zs that have never been encountered during training.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat was the performance percentage on the Comparisons test set after 12 epochs?\nSolve the above question based on the table titled 'Table 10: Results of retraining Infersent on both SNLI and the Comparisons dataset.'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the performance percentages for different epochs. At epoch 12, the performance on the Comparisons test set is 100.00%. So, the answer is {\"answer\": \"100.00%\"}" } ], "images": [ "scigen/train/1223.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Results of retraining Infersent on both SNLI and the Comparisons dataset." ], "reasoning": "The table shows the performance percentages for different epochs. At epoch 12, the performance on the Comparisons test set is 100.00%.", "question": "What was the performance percentage on the Comparisons test set after 12 epochs?", "answer": "So, the answer is {\"answer\": \"100.00%\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The augmented-InferSent model achieved a performance of 84.96% on the SNLI test set after 12 epochs. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe augmented-InferSent model achieved a performance of 84.96% on the SNLI test set after 12 epochs.\n" }, { "role": "assistant", "content": "The table shows that at epoch 12, the performance on the SNLI test set is 84.96%, which supports the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1223.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Results of retraining Infersent on both SNLI and the Comparisons dataset." ], "reasoning": "The table shows that at epoch 12, the performance on the SNLI test set is 84.96%, which supports the statement.", "statement": "The augmented-InferSent model achieved a performance of 84.96% on the SNLI test set after 12 epochs.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: Percentage of entailments split by overlap rate of words in SNLI.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "As the word overlap in sentence pairs increases, the percentage of contradictions decreases, with only 8.5% contradictions in the top 1000 pairs.We carried out an analysis of the SNLI dataset to determine if the overlap heuristic is ecologically valid in it. First, we observed anecdotally that indeed several contradictory sentence pairs have no overlap in words. For example, a contradictory sentence pair in SNLI is: Premise: Several people are trying to climb a ladder in a tree. Hypothesis: People are watching a ball game. CONTRADICTION To quantitatively verify this observation , we ranked all the sentence pairs in SNLI by overlap rate: \\# of overlap wordstotal \\# of words(in non-increasing order). We then considered the top X sentences with highest overlap for different Xs. However, we found that as the word overlap in the sentences increases, the percentage of contradictions drops. When considering only the top 1000 sentence pairs for overlap, we found that 91.5% of them have entailment or neutral labels, with only the remaining 8.5% having a contradiction label." } ], "images": [ "scigen/train/1224.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Percentage of entailments split by overlap rate of words in SNLI." ], "reasoning": "As the word overlap in sentence pairs increases, the percentage of contradictions decreases, with only 8.5% contradictions in the top 1000 pairs.", "description": "We carried out an analysis of the SNLI dataset to determine if the overlap heuristic is ecologically valid in it. First, we observed anecdotally that indeed several contradictory sentence pairs have no overlap in words. For example, a contradictory sentence pair in SNLI is: Premise: Several people are trying to climb a ladder in a tree. Hypothesis: People are watching a ball game. CONTRADICTION To quantitatively verify this observation , we ranked all the sentence pairs in SNLI by overlap rate: \\# of overlap wordstotal \\# of words(in non-increasing order). We then considered the top X sentences with highest overlap for different Xs. However, we found that as the word overlap in the sentences increases, the percentage of contradictions drops. When considering only the top 1000 sentence pairs for overlap, we found that 91.5% of them have entailment or neutral labels, with only the remaining 8.5% having a contradiction label.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 4: Percentage of entailments split by overlap rate of words in SNLI.' and methodically tackle the question that follows. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the percentage of entailment in the top 1000 sentence pairs with the highest overlap?\n" }, { "role": "assistant", "content": "The table shows the percentage of entailment, neutral, and contradiction labels for different top X sentence pairs. For the top 1000 sentence pairs, the percentage of entailment is listed. So, the answer is {\"answer\": \"50.8\"}." } ], "images": [ "scigen/train/1224.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Percentage of entailments split by overlap rate of words in SNLI." ], "reasoning": "The table shows the percentage of entailment, neutral, and contradiction labels for different top X sentence pairs. For the top 1000 sentence pairs, the percentage of entailment is listed.", "question": "What is the percentage of entailment in the top 1000 sentence pairs with the highest overlap?", "answer": "So, the answer is {\"answer\": \"50.8\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe percentage of contradictions in the top 10000 sentence pairs is higher than in the top 1000 sentence pairs.\n" }, { "role": "assistant", "content": "The table shows that the percentage of contradictions in the top 10000 sentence pairs is 24.8%, which is higher than the 8.5% in the top 1000 sentence pairs. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1224.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Percentage of entailments split by overlap rate of words in SNLI." ], "reasoning": "The table shows that the percentage of contradictions in the top 10000 sentence pairs is 24.8%, which is higher than the 8.5% in the top 1000 sentence pairs.", "statement": "The percentage of contradictions in the top 10000 sentence pairs is higher than in the top 1000 sentence pairs.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 11: Zero-shot reasoning: Performance on previously unobserved Xs and Zs.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The augmented-InferSent model significantly outperforms the InferSent model across all test sets, indicating better generalization to unseen data.For each sub-type in the Comparisons dataset (same, more-less and not types), we generated a test set of 1,000 sequences by substituting Xs and Zs of the above kinds. The Ys were sampled in the same way as in the Comparison dataset (random adjectives that appear in SNLI). We then tested on these sentences, and reported the average accuracy. Note that not only had these specific sentences (combinations of X, Y and Z) never been seen during training, even the individual Xs and Zs had not been seen. The held-out nouns are the most similar to the Xs and Zs seen during training since they are also exactly one word, and are nouns sampled from GloVe. It is notable that generalization performance with these is comparable to that with the very different kinds of Xs and Zs such as the made-up words, or longer noun phrases, indicating a fairly abstract representation of relational rules that are not tied to the specific value of X and Z." } ], "images": [ "scigen/train/1225.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Zero-shot reasoning: Performance on previously unobserved Xs and Zs." ], "reasoning": "The augmented-InferSent model significantly outperforms the InferSent model across all test sets, indicating better generalization to unseen data.", "description": "For each sub-type in the Comparisons dataset (same, more-less and not types), we generated a test set of 1,000 sequences by substituting Xs and Zs of the above kinds. The Ys were sampled in the same way as in the Comparison dataset (random adjectives that appear in SNLI). We then tested on these sentences, and reported the average accuracy. Note that not only had these specific sentences (combinations of X, Y and Z) never been seen during training, even the individual Xs and Zs had not been seen. The held-out nouns are the most similar to the Xs and Zs seen during training since they are also exactly one word, and are nouns sampled from GloVe. It is notable that generalization performance with these is comparable to that with the very different kinds of Xs and Zs such as the made-up words, or longer noun phrases, indicating a fairly abstract representation of relational rules that are not tied to the specific value of X and Z.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 11: Zero-shot reasoning: Performance on previously unobserved Xs and Zs.\nQuestion: Which test set had the highest accuracy for the augmented-InferSent model?\nSolve the above question based on the table information. Let's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the accuracy percentages for the augmented-InferSent model across all test sets, the Long noun phrases test set has the highest accuracy at 84.9%. So, the answer is {\"answer\": \"Long noun phrases\"}." } ], "images": [ "scigen/train/1225.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Zero-shot reasoning: Performance on previously unobserved Xs and Zs." ], "reasoning": "By comparing the accuracy percentages for the augmented-InferSent model across all test sets, the Long noun phrases test set has the highest accuracy at 84.9%.", "question": "Which test set had the highest accuracy for the augmented-InferSent model?", "answer": "So, the answer is {\"answer\": \"Long noun phrases\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The InferSent model performed better on the Held-out nouns test set than on the Made up words test set., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe InferSent model performed better on the Held-out nouns test set than on the Made up words test set.\n" }, { "role": "assistant", "content": "The table shows that the InferSent model had an accuracy of 47.9% on the Held-out nouns test set and 48.0% on the Made up words test set, indicating that the statement is false. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1225.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Zero-shot reasoning: Performance on previously unobserved Xs and Zs." ], "reasoning": "The table shows that the InferSent model had an accuracy of 47.9% on the Held-out nouns test set and 48.0% on the Made up words test set, indicating that the statement is false.", "statement": "The InferSent model performed better on the Held-out nouns test set than on the Made up words test set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 12: Type violations: Performance with tokens from the wrong syntactic category, versus with held-out tokens from the right syntactic category'.\n" }, { "role": "assistant", "content": "The augmented-InferSent model shows better generalization to held-out nouns compared to non-noun words, indicating that learned rules apply within fixed categories based on context.We investigated generalization of rules in augmented-Infersent to test items which, unlike in the previous section, had been previously seen, but had only occurred in a different syntactic role (i.e., a different context). We generated a test set of ungrammatical sentences using Xs and Zs that are random non-nouns, in our case random adjectives from SNLI. Crucially, these words had been seen before, but never in the position/context that X and Z occupy in the Comparisons dataset, since appearing in those positions violates syntax. We then evaluated the performance of the augmented-InferSent model in the same way as in the previous section on zero-shot generalization. This indicates that the rules learned, though at least partially abstract as indicated by generalization to held-out nouns, come with restrictions on the type of (known) items they will apply to. This follows closely how humans generalize – that learned rules don’t generalize indiscriminately to all tokens, but rather only within some fixed categories. These categories in turn, like syntactic categories, can be gleaned from the contexts in which these tokens usually appear. In the next section, we examine the role of semantic content in the context of tokens, and how that influences generalization." } ], "images": [ "scigen/train/1226.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 12: Type violations: Performance with tokens from the wrong syntactic category, versus with held-out tokens from the right syntactic category" ], "reasoning": "The augmented-InferSent model shows better generalization to held-out nouns compared to non-noun words, indicating that learned rules apply within fixed categories based on context.", "description": "We investigated generalization of rules in augmented-Infersent to test items which, unlike in the previous section, had been previously seen, but had only occurred in a different syntactic role (i.e., a different context). We generated a test set of ungrammatical sentences using Xs and Zs that are random non-nouns, in our case random adjectives from SNLI. Crucially, these words had been seen before, but never in the position/context that X and Z occupy in the Comparisons dataset, since appearing in those positions violates syntax. We then evaluated the performance of the augmented-InferSent model in the same way as in the previous section on zero-shot generalization. This indicates that the rules learned, though at least partially abstract as indicated by generalization to held-out nouns, come with restrictions on the type of (known) items they will apply to. This follows closely how humans generalize – that learned rules don’t generalize indiscriminately to all tokens, but rather only within some fixed categories. These categories in turn, like syntactic categories, can be gleaned from the contexts in which these tokens usually appear. In the next section, we examine the role of semantic content in the context of tokens, and how that influences generalization.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 12: Type violations: Performance with tokens from the wrong syntactic category, versus with held-out tokens from the right syntactic category\nQuestion: What is the performance difference between InferSent and augmented-InferSent on held-out nouns?\nGive a detailed response to the above question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the performance difference, subtract the InferSent performance on held-out nouns (47.9%) from the augmented-InferSent performance on held-out nouns (82.0%). So, the answer is {\"answer\": \"34.1%\"}" } ], "images": [ "scigen/train/1226.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 12: Type violations: Performance with tokens from the wrong syntactic category, versus with held-out tokens from the right syntactic category" ], "reasoning": "To find the performance difference, subtract the InferSent performance on held-out nouns (47.9%) from the augmented-InferSent performance on held-out nouns (82.0%).", "question": "What is the performance difference between InferSent and augmented-InferSent on held-out nouns?", "answer": "So, the answer is {\"answer\": \"34.1%\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe augmented-InferSent model performs equally well on held-out nouns and non-noun words.\n" }, { "role": "assistant", "content": "The table shows that the augmented-InferSent model performs at 82.0% on held-out nouns and 49.3% on non-noun words, indicating a significant difference in performance. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1226.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 12: Type violations: Performance with tokens from the wrong syntactic category, versus with held-out tokens from the right syntactic category" ], "reasoning": "The table shows that the augmented-InferSent model performs at 82.0% on held-out nouns and 49.3% on non-noun words, indicating a significant difference in performance.", "statement": "The augmented-InferSent model performs equally well on held-out nouns and non-noun words.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 13: Biased exposure: Results from InferSent embeddings augmented with different training sets that manipulate the co-occurrence of context and token.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The control model generalizes well to all test examples with T∗ noun phrases, while Exposed–C1T∗ performs well on C1T∗ but poorly on C2T∗, indicating context-specific generalization issues.All three models received the same number of training examples, with equal numbers of sentence pairs from both contexts C1 and C2. They all also saw T∗ noun phrases appear in both contexts. The three models only differed in which contexts T∗ noun phrases appeared during training. The control model never saw T∗ noun phrases, Exposed–C1T∗ only saw them in the C1 context and Exposed–C2T∗ only saw them in the C2 context. All of these were then tested on the same held-out test set. This is consistent with our initial results on augmentation (see section ‘Augmenting the learning environment’). Further, the control (zero-shot reasoning) condition that never saw T∗ noun phrases in training generalizes well to all the test examples with T∗ noun phrases (first column). This is consistent with our results on zero-shot generalization (see section ‘Zero-shot reasoning’). We discuss the results for the model Exposed–C1T∗ (that saw T∗ noun phrases in C1 type comparisons), a symmetric discussion applies also to Exposed–C2T∗. W found that Exposed–C1T∗ performs well on held-out test examples from the C1T∗ category (99.7 %) – as consistent with our original experiments with augmentation. However, we found that it fails to generalize very well to T∗ type noun phrases in the C2 context, with a significant drop in performance (67.71 %). The crucial comparison is that this low performance is also significantly worse that that of the zero-shot control on the same test set (95.78 %). Neither of these have seen T∗ phrases in the C2 context – yet the control generalizes very well, while the Exposed–C1T∗ fails to. This indicates that while the representations learned can generalize well to previously unseen tokens, this generalization is poorer to tokens that have in fact been seen before, but only in a different context." } ], "images": [ "scigen/train/1227.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 13: Biased exposure: Results from InferSent embeddings augmented with different training sets that manipulate the co-occurrence of context and token." ], "reasoning": "The control model generalizes well to all test examples with T∗ noun phrases, while Exposed–C1T∗ performs well on C1T∗ but poorly on C2T∗, indicating context-specific generalization issues.", "description": "All three models received the same number of training examples, with equal numbers of sentence pairs from both contexts C1 and C2. They all also saw T∗ noun phrases appear in both contexts. The three models only differed in which contexts T∗ noun phrases appeared during training. The control model never saw T∗ noun phrases, Exposed–C1T∗ only saw them in the C1 context and Exposed–C2T∗ only saw them in the C2 context. All of these were then tested on the same held-out test set. This is consistent with our initial results on augmentation (see section ‘Augmenting the learning environment’). Further, the control (zero-shot reasoning) condition that never saw T∗ noun phrases in training generalizes well to all the test examples with T∗ noun phrases (first column). This is consistent with our results on zero-shot generalization (see section ‘Zero-shot reasoning’). We discuss the results for the model Exposed–C1T∗ (that saw T∗ noun phrases in C1 type comparisons), a symmetric discussion applies also to Exposed–C2T∗. W found that Exposed–C1T∗ performs well on held-out test examples from the C1T∗ category (99.7 %) – as consistent with our original experiments with augmentation. However, we found that it fails to generalize very well to T∗ type noun phrases in the C2 context, with a significant drop in performance (67.71 %). The crucial comparison is that this low performance is also significantly worse that that of the zero-shot control on the same test set (95.78 %). Neither of these have seen T∗ phrases in the C2 context – yet the control generalizes very well, while the Exposed–C1T∗ fails to. This indicates that while the representations learned can generalize well to previously unseen tokens, this generalization is poorer to tokens that have in fact been seen before, but only in a different context.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 13: Biased exposure: Results from InferSent embeddings augmented with different training sets that manipulate the co-occurrence of context and token.' in mind, break down and resolve the question below step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich model had the lowest performance on the C1 T∗ test set?\n" }, { "role": "assistant", "content": "By comparing the performance percentages on the C1 T∗ test set, we see that the Exposed–C2T∗ model had the lowest performance at 61.16%. So, the answer is {\"answer\": \"Exposed–C2T∗\"}." } ], "images": [ "scigen/train/1227.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 13: Biased exposure: Results from InferSent embeddings augmented with different training sets that manipulate the co-occurrence of context and token." ], "reasoning": "By comparing the performance percentages on the C1 T∗ test set, we see that the Exposed–C2T∗ model had the lowest performance at 61.16%.", "question": "Which model had the lowest performance on the C1 T∗ test set?", "answer": "So, the answer is {\"answer\": \"Exposed–C2T∗\"}." }, { "messages": [ { "role": "user", "content": "The Exposed–C1T∗ model performed better than the Zero-shot model on the C2 T∗ test set. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Exposed–C1T∗ model performed better than the Zero-shot model on the C2 T∗ test set.\n" }, { "role": "assistant", "content": "The table shows that the Exposed–C1T∗ model had a performance of 67.71% on the C2 T∗ test set, while the Zero-shot model had a performance of 95.78%, indicating that the statement is false. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1227.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 13: Biased exposure: Results from InferSent embeddings augmented with different training sets that manipulate the co-occurrence of context and token." ], "reasoning": "The table shows that the Exposed–C1T∗ model had a performance of 67.71% on the C2 T∗ test set, while the Zero-shot model had a performance of 95.78%, indicating that the statement is false.", "statement": "The Exposed–C1T∗ model performed better than the Zero-shot model on the C2 T∗ test set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 7: Sentiment analysis results on RNN model pre-trained with aligned Uzbek word embeddings. Best results are highlighted in bold. *FastText scores shown in the table are directly taken from the original paper.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The RNN model with aligned word embeddings performs better than the model with fastText embeddings on both Manual and Translated datasets, indicating benefits for low-resource languages.The RNN model trained with aligned word embeddings slightly outperforms the same model trained with fastText word embeddings in both the Manual and Translated datasets. This shows that a low-resource language can benefit from the Meemi model when aligned with resource-rich and/or similar languages." } ], "images": [ "scigen/train/1228.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Sentiment analysis results on RNN model pre-trained with aligned Uzbek word embeddings. Best results are highlighted in bold. *FastText scores shown in the table are directly taken from the original paper." ], "reasoning": "The RNN model with aligned word embeddings performs better than the model with fastText embeddings on both Manual and Translated datasets, indicating benefits for low-resource languages.", "description": "The RNN model trained with aligned word embeddings slightly outperforms the same model trained with fastText word embeddings in both the Manual and Translated datasets. This shows that a low-resource language can benefit from the Meemi model when aligned with resource-rich and/or similar languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 7: Sentiment analysis results on RNN model pre-trained with aligned Uzbek word embeddings. Best results are highlighted in bold. *FastText scores shown in the table are directly taken from the original paper.', unfold the steps to solve the question given next. Output the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which dataset shows a higher improvement in score when using aligned word embeddings compared to fastText embeddings?\n" }, { "role": "assistant", "content": "To determine which dataset shows a higher improvement, subtract the FastText Score from Our Score for both datasets and compare the differences. For the Manual Dataset, the improvement is 0.8825 - 0.8782 = 0.0043. For the Translated Dataset, the improvement is 0.8876 - 0.8832 = 0.0044. The Translated Dataset shows a higher improvement. So, the answer is {\"answer\": \"Translated Dataset\"}" } ], "images": [ "scigen/train/1228.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Sentiment analysis results on RNN model pre-trained with aligned Uzbek word embeddings. Best results are highlighted in bold. *FastText scores shown in the table are directly taken from the original paper." ], "reasoning": "To determine which dataset shows a higher improvement, subtract the FastText Score from Our Score for both datasets and compare the differences. For the Manual Dataset, the improvement is 0.8825 - 0.8782 = 0.0043. For the Translated Dataset, the improvement is 0.8876 - 0.8832 = 0.0044. The Translated Dataset shows a higher improvement.", "question": "Which dataset shows a higher improvement in score when using aligned word embeddings compared to fastText embeddings?", "answer": "So, the answer is {\"answer\": \"Translated Dataset\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe RNN model with aligned word embeddings performs better on the Manual Dataset than on the Translated Dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the scores of the RNN model with aligned word embeddings (Our Score) for both datasets. The score for the Manual Dataset is 0.8825, and the score for the Translated Dataset is 0.8876. Since 0.8876 is greater than 0.8825, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1228.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Sentiment analysis results on RNN model pre-trained with aligned Uzbek word embeddings. Best results are highlighted in bold. *FastText scores shown in the table are directly taken from the original paper." ], "reasoning": "To verify this statement, compare the scores of the RNN model with aligned word embeddings (Our Score) for both datasets. The score for the Manual Dataset is 0.8825, and the score for the Translated Dataset is 0.8876. Since 0.8876 is greater than 0.8825, the statement is refuted.", "statement": "The RNN model with aligned word embeddings performs better on the Manual Dataset than on the Translated Dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Results of systems on HotpotQA.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The biggest improvement is in the EM for the supporting fact, leading to a doubling of the joint EM on previous best results, with consistent improvements in both test and dev set evaluations.More specifically, the biggest improvement comes from the EM for the supporting fact which in turn leads to doubling of the joint EM on previous best results. The scores for answer predictions are also higher than all previous best results with ∼8 absolute points increase on EM and ∼9 absolute points on F1. All the improvements are consistent between test and dev set evaluation." } ], "images": [ "scigen/train/1229.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results of systems on HotpotQA." ], "reasoning": "The biggest improvement is in the EM for the supporting fact, leading to a doubling of the joint EM on previous best results, with consistent improvements in both test and dev set evaluations.", "description": "More specifically, the biggest improvement comes from the EM for the supporting fact which in turn leads to doubling of the joint EM on previous best results. The scores for answer predictions are also higher than all previous best results with ∼8 absolute points increase on EM and ∼9 absolute points on F1. All the improvements are consistent between test and dev set evaluation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 1: Results of systems on HotpotQA.', solve the following question step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the highest Joint F1 score in the Dev set?\n" }, { "role": "assistant", "content": "To find the highest Joint F1 score in the Dev set, compare the Joint F1 scores for all methods in the Dev set. The highest score is 49.2 from the whole pip. method. So, the answer is {\"answer\": \"49.2\"}." } ], "images": [ "scigen/train/1229.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results of systems on HotpotQA." ], "reasoning": "To find the highest Joint F1 score in the Dev set, compare the Joint F1 scores for all methods in the Dev set. The highest score is 49.2 from the whole pip. method.", "question": "What is the highest Joint F1 score in the Dev set?", "answer": "So, the answer is {\"answer\": \"49.2\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe whole pip. method achieved the highest Ans EM score in the Test set.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Ans EM scores for all methods in the Test set. The whole pip. method has the highest Ans EM score of 45.3. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1229.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results of systems on HotpotQA." ], "reasoning": "To verify this statement, compare the Ans EM scores for all methods in the Test set. The whole pip. method has the highest Ans EM score of 45.3.", "statement": "The whole pip. method achieved the highest Ans EM score in the Test set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Performance of systems on FEVER. “F1” indicates the sentence-level evidence F1 score. “LA” indicates Label Acc. without considering the evidence prediction. “FS”=FEVER Score Thorne et al. (2018)'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The full system achieved the highest scores on both the Dev and Test sets, significantly outperforming previous models in Label Accuracy and FEVER Score.Our system obtained substantially higher scores than all previously published results with a ∼4 and ∼3 points absolute improvement on Label Accuracy and FEVER Score. In particular, the system gains 74.62 on the evidence F1, 22 points greater that of the second system, demonstrating its ability on semantic retrieval." } ], "images": [ "scigen/train/1230.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of systems on FEVER. “F1” indicates the sentence-level evidence F1 score. “LA” indicates Label Acc. without considering the evidence prediction. “FS”=FEVER Score Thorne et al. (2018)" ], "reasoning": "The full system achieved the highest scores on both the Dev and Test sets, significantly outperforming previous models in Label Accuracy and FEVER Score.", "description": "Our system obtained substantially higher scores than all previously published results with a ∼4 and ∼3 points absolute improvement on Label Accuracy and FEVER Score. In particular, the system gains 74.62 on the evidence F1, 22 points greater that of the second system, demonstrating its ability on semantic retrieval.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: Performance of systems on FEVER. “F1” indicates the sentence-level evidence F1 score. “LA” indicates Label Acc. without considering the evidence prediction. “FS”=FEVER Score Thorne et al. (2018)'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}. \n\nQuestion: What is the Label Accuracy (LA) score of the Full system (single) on the Test set?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows the Label Accuracy (LA) score for the Full system (single) on the Test set as 72.56. So, the answer is {\"answer\": \"72.56\"}" } ], "images": [ "scigen/train/1230.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of systems on FEVER. “F1” indicates the sentence-level evidence F1 score. “LA” indicates Label Acc. without considering the evidence prediction. “FS”=FEVER Score Thorne et al. (2018)" ], "reasoning": "The table shows the Label Accuracy (LA) score for the Full system (single) on the Test set as 72.56.", "question": "What is the Label Accuracy (LA) score of the Full system (single) on the Test set?", "answer": "So, the answer is {\"answer\": \"72.56\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Full system (single) achieved an F1 score of 74.62 on the Test set.\n" }, { "role": "assistant", "content": "The table indicates that the Full system (single) achieved an F1 score of 74.62 on the Test set. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1230.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of systems on FEVER. “F1” indicates the sentence-level evidence F1 score. “LA” indicates Label Acc. without considering the evidence prediction. “FS”=FEVER Score Thorne et al. (2018)" ], "reasoning": "The table indicates that the Full system (single) achieved an F1 score of 74.62 on the Test set.", "statement": "The Full system (single) achieved an F1 score of 74.62 on the Test set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 5: System performance on different answer types. “PN”= Proper Noun'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The most frequent answer type is 'Person' with 24%, while the least frequent is 'Event' with 2%. The model performs best in 'Yes/No' with an accuracy of 70.6%.We further sample 200 examples from HotpotQA and manually tag them according to several common answer types Yang et al. The most frequent answer type is ’Person’ (24%) and the least frequent answer type is ’Event’ (2%). It is also interesting to note that the model performs the best in Yes/" } ], "images": [ "scigen/train/1231.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: System performance on different answer types. “PN”= Proper Noun" ], "reasoning": "The most frequent answer type is 'Person' with 24%, while the least frequent is 'Event' with 2%. The model performs best in 'Yes/No' with an accuracy of 70.6%.", "description": "We further sample 200 examples from HotpotQA and manually tag them according to several common answer types Yang et al. The most frequent answer type is ’Person’ (24%) and the least frequent answer type is ’Event’ (2%). It is also interesting to note that the model performs the best in Yes/", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 5: System performance on different answer types. “PN”= Proper Noun', solve the following question step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich answer type has the highest accuracy and what is its percentage?\n" }, { "role": "assistant", "content": "By examining the 'Acc. (%)' column, we see that 'Yes/No' has the highest accuracy at 70.6%. So, the answer is {\"answer\": \"Yes/No, 70.6%\"}." } ], "images": [ "scigen/train/1231.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: System performance on different answer types. “PN”= Proper Noun" ], "reasoning": "By examining the 'Acc. (%)' column, we see that 'Yes/No' has the highest accuracy at 70.6%.", "question": "Which answer type has the highest accuracy and what is its percentage?", "answer": "So, the answer is {\"answer\": \"Yes/No, 70.6%\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe model performs best in 'Person' answer type.\n" }, { "role": "assistant", "content": "The table shows that the 'Yes/No' answer type has the highest accuracy at 70.6%, while 'Person' has an accuracy of 56.0%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1231.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: System performance on different answer types. “PN”= Proper Noun" ], "reasoning": "The table shows that the 'Yes/No' answer type has the highest accuracy at 70.6%, while 'Person' has an accuracy of 56.0%.", "statement": "The model performs best in 'Person' answer type.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6: Hyper-parameter selection for the full pipeline system. h and k are the retrieval filtering hyper-parameters mentioned in the main paper. P-level and S-level indicate paragraph-level and sentence-level respectively. “{}” means values enumerated from a set. “[]” means values enumerated from a range with interval=0.1 “BS.”=Batch Size “# E.”=Number of Epochs', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The table outlines the batch size, number of epochs, and hyper-parameters for different modules including P-Level Retrieval, S-Level Retrieval, QA, and Verification.Fact Verification: The input format is the same as that of semantic retrieval and the objective is Jver=−∑iyi⋅log(^yi), where ^yi∈R3 denotes the model’s output for the three verification labels, and yi is a one-hot embedding for the ground-truth label. For verifiable queries, we used ground truth evidential sentences plus some other sentences sampled from upstream retrieved set as new evidential context for NLI. For non-verifiable queries, we only used sentences sampled from upstream retrieved set as context because those queries are not associated with ground truth evidential sentences. Additional training details and hyper-parameter selections are in the Appendix (Sec. The hyper-parameters were chosen based on the performance of the system on the dev set." } ], "images": [ "scigen/train/1232.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Hyper-parameter selection for the full pipeline system. h and k are the retrieval filtering hyper-parameters mentioned in the main paper. P-level and S-level indicate paragraph-level and sentence-level respectively. “{}” means values enumerated from a set. “[]” means values enumerated from a range with interval=0.1 “BS.”=Batch Size “# E.”=Number of Epochs" ], "reasoning": "The table outlines the batch size, number of epochs, and hyper-parameters for different modules including P-Level Retrieval, S-Level Retrieval, QA, and Verification.", "description": "Fact Verification: The input format is the same as that of semantic retrieval and the objective is Jver=−∑iyi⋅log(^yi), where ^yi∈R3 denotes the model’s output for the three verification labels, and yi is a one-hot embedding for the ground-truth label. For verifiable queries, we used ground truth evidential sentences plus some other sentences sampled from upstream retrieved set as new evidential context for NLI. For non-verifiable queries, we only used sentences sampled from upstream retrieved set as context because those queries are not associated with ground truth evidential sentences. Additional training details and hyper-parameter selections are in the Appendix (Sec. The hyper-parameters were chosen based on the performance of the system on the dev set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 6: Hyper-parameter selection for the full pipeline system. h and k are the retrieval filtering hyper-parameters mentioned in the main paper. P-level and S-level indicate paragraph-level and sentence-level respectively. “{}” means values enumerated from a set. “[]” means values enumerated from a range with interval=0.1 “BS.”=Batch Size “# E.”=Number of Epochs\nQuestion: What is the batch size and number of epochs for the S-Level Retrieval module?\n" }, { "role": "assistant", "content": "The table lists the batch size (BS.) and number of epochs (# E.) for each module. For the S-Level Retrieval module, the batch size is 128 and the number of epochs is 3. So, the answer is {\"answer\": \"128 and 3\"}." } ], "images": [ "scigen/train/1232.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Hyper-parameter selection for the full pipeline system. h and k are the retrieval filtering hyper-parameters mentioned in the main paper. P-level and S-level indicate paragraph-level and sentence-level respectively. “{}” means values enumerated from a set. “[]” means values enumerated from a range with interval=0.1 “BS.”=Batch Size “# E.”=Number of Epochs" ], "reasoning": "The table lists the batch size (BS.) and number of epochs (# E.) for each module. For the S-Level Retrieval module, the batch size is 128 and the number of epochs is 3.", "question": "What is the batch size and number of epochs for the S-Level Retrieval module?", "answer": "So, the answer is {\"answer\": \"128 and 3\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe QA module has a batch size of 64.\n" }, { "role": "assistant", "content": "The table lists the batch size (BS.) for each module. For the QA module, the batch size is 32, not 64. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1232.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Hyper-parameter selection for the full pipeline system. h and k are the retrieval filtering hyper-parameters mentioned in the main paper. P-level and S-level indicate paragraph-level and sentence-level respectively. “{}” means values enumerated from a set. “[]” means values enumerated from a range with interval=0.1 “BS.”=Batch Size “# E.”=Number of Epochs" ], "reasoning": "The table lists the batch size (BS.) for each module. For the QA module, the batch size is 32, not 64.", "statement": "The QA module has a batch size of 64.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 14: General-purpose sentence embedding tasks, divided into categories based on resource requirements.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Individual models produce 300-dimensional embeddings, which is fewer than the 2400-4800 dimensions used in prior work. Higher dimensionality does not improve STS task correlation but helps in general sentence embedding tasks.Each of our individual models produces 300-dimensional sentence embeddings, which is far fewer than the several thousands (often 2400-4800) of dimensions used in most prior work. While using higher dimensionality does not improve correlation on the STS tasks, it does help on the general sentence embedding tasks. Using higher dimensionality leads to more trainable parameters in the subsequent classifiers, increasing their ability to linearly separate the data." } ], "images": [ "scigen/train/1233.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 14: General-purpose sentence embedding tasks, divided into categories based on resource requirements." ], "reasoning": "Individual models produce 300-dimensional embeddings, which is fewer than the 2400-4800 dimensions used in prior work. Higher dimensionality does not improve STS task correlation but helps in general sentence embedding tasks.", "description": "Each of our individual models produces 300-dimensional sentence embeddings, which is far fewer than the several thousands (often 2400-4800) of dimensions used in most prior work. While using higher dimensionality does not improve correlation on the STS tasks, it does help on the general sentence embedding tasks. Using higher dimensionality leads to more trainable parameters in the subsequent classifiers, increasing their ability to linearly separate the data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 14: General-purpose sentence embedding tasks, divided into categories based on resource requirements.', answer the question below by showing each progressive step toward the answer. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the dimensionality of the embeddings produced by the FastSent model, and how does it compare to the dimensionality of the embeddings produced by the individual models in our work?\n" }, { "role": "assistant", "content": "The FastSent model produces embeddings with a dimensionality of 100, while the individual models in our work produce embeddings with a dimensionality of 300. This shows that the individual models in our work have higher dimensionality compared to the FastSent model. So, the answer is {\"answer\": \"100 and 300\"}." } ], "images": [ "scigen/train/1233.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 14: General-purpose sentence embedding tasks, divided into categories based on resource requirements." ], "reasoning": "The FastSent model produces embeddings with a dimensionality of 100, while the individual models in our work produce embeddings with a dimensionality of 300. This shows that the individual models in our work have higher dimensionality compared to the FastSent model.", "question": "What is the dimensionality of the embeddings produced by the FastSent model, and how does it compare to the dimensionality of the embeddings produced by the individual models in our work?", "answer": "So, the answer is {\"answer\": \"100 and 300\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe InferSent (SST) model produces embeddings with a dimensionality of 4096.\n" }, { "role": "assistant", "content": "The table shows that the InferSent (SST) model indeed produces embeddings with a dimensionality of 4096, which supports the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1233.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 14: General-purpose sentence embedding tasks, divided into categories based on resource requirements." ], "reasoning": "The table shows that the InferSent (SST) model indeed produces embeddings with a dimensionality of 4096, which supports the statement.", "statement": "The InferSent (SST) model produces embeddings with a dimensionality of 4096.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: Manual evaluation of 100-pair data samples drawn from five ranges of the automatic paraphrase score (first column). Second column shows total count of pairs in that range in ParaNMT-50M. Paraphrase strength and fluency were judged on a 1-3 scale and the table shows counts of each score designation.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The highest paraphrase score range has the highest percentage of strong paraphrase relationships and the highest fluency, indicating high-quality pairs.For each score range, we report the number of pairs, the mean trigram overlap score, and the number of times each paraphrase/fluency label was present in the sample of 100 pairs. There is noise in the dataset but it is largely confined in the bottom two ranges which together comprise only 16% of the entire dataset. In the highest paraphrase score range, 86% of the pairs possess a strong paraphrase relationship. The annotations suggest that ParaNMT-50M contains approximately 30 million strong paraphrase pairs, and that the paraphrase score is a good indicator of quality. With regards to fluency, the vast majority of the back-translations are fluent, even at the low end of the paraphrase score range. At the low ranges, we inspected the data and found there to be many errors in the sentence alignment in the original bitext." } ], "images": [ "scigen/train/1234.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Manual evaluation of 100-pair data samples drawn from five ranges of the automatic paraphrase score (first column). Second column shows total count of pairs in that range in ParaNMT-50M. Paraphrase strength and fluency were judged on a 1-3 scale and the table shows counts of each score designation." ], "reasoning": "The highest paraphrase score range has the highest percentage of strong paraphrase relationships and the highest fluency, indicating high-quality pairs.", "description": "For each score range, we report the number of pairs, the mean trigram overlap score, and the number of times each paraphrase/fluency label was present in the sample of 100 pairs. There is noise in the dataset but it is largely confined in the bottom two ranges which together comprise only 16% of the entire dataset. In the highest paraphrase score range, 86% of the pairs possess a strong paraphrase relationship. The annotations suggest that ParaNMT-50M contains approximately 30 million strong paraphrase pairs, and that the paraphrase score is a good indicator of quality. With regards to fluency, the vast majority of the back-translations are fluent, even at the low end of the paraphrase score range. At the low ranges, we inspected the data and found there to be many errors in the sentence alignment in the original bitext.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: Manual evaluation of 100-pair data samples drawn from five ranges of the automatic paraphrase score (first column). Second column shows total count of pairs in that range in ParaNMT-50M. Paraphrase strength and fluency were judged on a 1-3 scale and the table shows counts of each score designation.' in mind, break down and resolve the question below step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the mean trigram overlap score for the paraphrase score range with the highest number of pairs?\n" }, { "role": "assistant", "content": "The paraphrase score range with the highest number of pairs is (0.8, 1.0] with 18.0 million pairs. The mean trigram overlap score for this range is 0.35. So, the answer is {\"answer\": \"0.35\"}" } ], "images": [ "scigen/train/1234.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Manual evaluation of 100-pair data samples drawn from five ranges of the automatic paraphrase score (first column). Second column shows total count of pairs in that range in ParaNMT-50M. Paraphrase strength and fluency were judged on a 1-3 scale and the table shows counts of each score designation." ], "reasoning": "The paraphrase score range with the highest number of pairs is (0.8, 1.0] with 18.0 million pairs. The mean trigram overlap score for this range is 0.35.", "question": "What is the mean trigram overlap score for the paraphrase score range with the highest number of pairs?", "answer": "So, the answer is {\"answer\": \"0.35\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The majority of pairs in the (0.6, 0.8] paraphrase score range have a strong paraphrase relationship., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe majority of pairs in the (0.6, 0.8] paraphrase score range have a strong paraphrase relationship.\n" }, { "role": "assistant", "content": "In the (0.6, 0.8] paraphrase score range, 56 out of 100 pairs have a strong paraphrase relationship, which is more than half. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1234.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Manual evaluation of 100-pair data samples drawn from five ranges of the automatic paraphrase score (first column). Second column shows total count of pairs in that range in ParaNMT-50M. Paraphrase strength and fluency were judged on a 1-3 scale and the table shows counts of each score designation." ], "reasoning": "In the (0.6, 0.8] paraphrase score range, 56 out of 100 pairs have a strong paraphrase relationship, which is more than half.", "statement": "The majority of pairs in the (0.6, 0.8] paraphrase score range have a strong paraphrase relationship.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 6: Pearson’s r×100 on STS2017 with different mega-batch sizes M.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Aggregating M mini-batches into one mega-batch and selecting negative examples from it improves performance for all models, with larger mega-batches showing greater gains.Therefore, we propose a procedure we call “mega-batching.” We aggregate M mini-batches to create one mega-batch and select negative examples from the mega-batch. Once each pair in the mega-batch has a negative example, the mega-batch is split back up into M mini-batches and training proceeds. For all models, larger mega-batches improve performance. There is a smaller gain when moving from 20 to 40, but all models show clear gains over M=1." } ], "images": [ "scigen/train/1235.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Pearson’s r×100 on STS2017 with different mega-batch sizes M." ], "reasoning": "Aggregating M mini-batches into one mega-batch and selecting negative examples from it improves performance for all models, with larger mega-batches showing greater gains.", "description": "Therefore, we propose a procedure we call “mega-batching.” We aggregate M mini-batches to create one mega-batch and select negative examples from the mega-batch. Once each pair in the mega-batch has a negative example, the mega-batch is split back up into M mini-batches and training proceeds. For all models, larger mega-batches improve performance. There is a smaller gain when moving from 20 to 40, but all models show clear gains over M=1.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 6: Pearson’s r×100 on STS2017 with different mega-batch sizes M.'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the performance gain for the LSTM model when moving from M=1 to M=40?\n" }, { "role": "assistant", "content": "The performance of the LSTM model at M=1 is 81.5, and at M=40 it is 85.0. The gain is calculated as 85.0 - 81.5. So, the answer is {\"answer\": \"3.5\"}." } ], "images": [ "scigen/train/1235.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Pearson’s r×100 on STS2017 with different mega-batch sizes M." ], "reasoning": "The performance of the LSTM model at M=1 is 81.5, and at M=40 it is 85.0. The gain is calculated as 85.0 - 81.5.", "question": "What is the performance gain for the LSTM model when moving from M=1 to M=40?", "answer": "So, the answer is {\"answer\": \"3.5\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Word model shows the highest performance gain when moving from M=1 to M=40. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Word model shows the highest performance gain when moving from M=1 to M=40.\n" }, { "role": "assistant", "content": "The performance of the Word model at M=1 is 82.3, and at M=40 it is 84.1, resulting in a gain of 1.8. The LSTM model shows a gain of 3.5, which is higher. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1235.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Pearson’s r×100 on STS2017 with different mega-batch sizes M." ], "reasoning": "The performance of the Word model at M=1 is 82.3, and at M=40 it is 84.1, resulting in a gain of 1.8. The LSTM model shows a gain of 3.5, which is higher.", "statement": "The Word model shows the highest performance gain when moving from M=1 to M=40.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 10: The means (over all 25 STS competition datasets) of the absolute differences in Pearson’s r between each pair of models.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The Trigram model shows the largest differences from the other two models, indicating it may provide complementary information.Overall, our individual models (Word, Trigram, LSTM) perform similarly. Using 300 dimensions appears to be sufficient; increasing dimensionality does not necessarily improve correlation. When examining particular STS tasks, we found that our individual models showed marked differences on certain tasks. ’s r over all 25 datasets. The Trigram model shows the largest differences from the other two, both of which use word embeddings. This suggests that Trigram may be able to complement the other two by providing information about words that are unknown to models that rely on word embeddings." } ], "images": [ "scigen/train/1236.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: The means (over all 25 STS competition datasets) of the absolute differences in Pearson’s r between each pair of models." ], "reasoning": "The Trigram model shows the largest differences from the other two models, indicating it may provide complementary information.", "description": "Overall, our individual models (Word, Trigram, LSTM) perform similarly. Using 300 dimensions appears to be sufficient; increasing dimensionality does not necessarily improve correlation. When examining particular STS tasks, we found that our individual models showed marked differences on certain tasks. ’s r over all 25 datasets. The Trigram model shows the largest differences from the other two, both of which use word embeddings. This suggests that Trigram may be able to complement the other two by providing information about words that are unknown to models that rely on word embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 10: The means (over all 25 STS competition datasets) of the absolute differences in Pearson’s r between each pair of models.\nQuestion: Which model pair has the largest mean Pearson absolute difference?\nSolve the above question based on the table information. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the mean Pearson absolute differences for each model pair, the Trigram/LSTM pair has the highest value of 2.89. So, the answer is {\"answer\": \"Trigram/LSTM\"}" } ], "images": [ "scigen/train/1236.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: The means (over all 25 STS competition datasets) of the absolute differences in Pearson’s r between each pair of models." ], "reasoning": "By comparing the mean Pearson absolute differences for each model pair, the Trigram/LSTM pair has the highest value of 2.89.", "question": "Which model pair has the largest mean Pearson absolute difference?", "answer": "So, the answer is {\"answer\": \"Trigram/LSTM\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The Word/Trigram model pair has the smallest mean Pearson absolute difference., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Word/Trigram model pair has the smallest mean Pearson absolute difference.\n" }, { "role": "assistant", "content": "The table shows that the Word/LSTM model pair has the smallest mean Pearson absolute difference of 2.17, not the Word/Trigram pair. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1236.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: The means (over all 25 STS competition datasets) of the absolute differences in Pearson’s r between each pair of models." ], "reasoning": "The table shows that the Word/LSTM model pair has the smallest mean Pearson absolute difference of 2.17, not the Word/Trigram pair.", "statement": "The Word/Trigram model pair has the smallest mean Pearson absolute difference.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 15: Difference in correlation (Pearson’s r×100) between “Word , Trigram” and InferSent models trained on two different datasets: AllNLI and SNLI. The first row is the mean difference across all 25 datasets, then the following rows show differences on three individual datasets that are comprised of captions. The InferSent models are much closer to our model on the caption datasets than overall.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The InferSent models trained on SNLI show a larger difference between overall performance and performance on caption datasets compared to the AllNLI models.Effect of Training Domain on InferSent We performed additional experiments to investigate the impact of training domain on downstream tasks. We first compare the performance of our “Word , Trigram (concatenation)” model to the InferSent SNLI and AllNLI models on all STS tasks from 2012-2016. We then compare the overall mean with that of the three caption STS datasets within the collection. The InferSent models are much closer to our Word , Trigram model on the caption datasets than overall, and InferSent trained on SNLI shows the largest difference between its overall performance and its performance on caption data." } ], "images": [ "scigen/train/1237.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 15: Difference in correlation (Pearson’s r×100) between “Word , Trigram” and InferSent models trained on two different datasets: AllNLI and SNLI. The first row is the mean difference across all 25 datasets, then the following rows show differences on three individual datasets that are comprised of captions. The InferSent models are much closer to our model on the caption datasets than overall." ], "reasoning": "The InferSent models trained on SNLI show a larger difference between overall performance and performance on caption datasets compared to the AllNLI models.", "description": "Effect of Training Domain on InferSent We performed additional experiments to investigate the impact of training domain on downstream tasks. We first compare the performance of our “Word , Trigram (concatenation)” model to the InferSent SNLI and AllNLI models on all STS tasks from 2012-2016. We then compare the overall mean with that of the three caption STS datasets within the collection. The InferSent models are much closer to our Word , Trigram model on the caption datasets than overall, and InferSent trained on SNLI shows the largest difference between its overall performance and its performance on caption data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 15: Difference in correlation (Pearson’s r×100) between “Word , Trigram” and InferSent models trained on two different datasets: AllNLI and SNLI. The first row is the mean difference across all 25 datasets, then the following rows show differences on three individual datasets that are comprised of captions. The InferSent models are much closer to our model on the caption datasets than overall.', answer the following question. Give detailed solution consisting of each step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich dataset shows the smallest difference in performance for the SNLI model?\n" }, { "role": "assistant", "content": "By comparing the differences in performance for the SNLI model across all datasets, the smallest difference is found in the 'Images (2015) diff.' dataset with a value of 3.0. So, the answer is {\"answer\": \"Images (2015) diff.\"}." } ], "images": [ "scigen/train/1237.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 15: Difference in correlation (Pearson’s r×100) between “Word , Trigram” and InferSent models trained on two different datasets: AllNLI and SNLI. The first row is the mean difference across all 25 datasets, then the following rows show differences on three individual datasets that are comprised of captions. The InferSent models are much closer to our model on the caption datasets than overall." ], "reasoning": "By comparing the differences in performance for the SNLI model across all datasets, the smallest difference is found in the 'Images (2015) diff.' dataset with a value of 3.0.", "question": "Which dataset shows the smallest difference in performance for the SNLI model?", "answer": "So, the answer is {\"answer\": \"Images (2015) diff.\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe overall mean difference for the AllNLI model is greater than that for the SNLI model.\n" }, { "role": "assistant", "content": "The table shows that the overall mean difference for the AllNLI model is 10.5, which is less than the overall mean difference for the SNLI model, which is 12.5. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1237.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 15: Difference in correlation (Pearson’s r×100) between “Word , Trigram” and InferSent models trained on two different datasets: AllNLI and SNLI. The first row is the mean difference across all 25 datasets, then the following rows show differences on three individual datasets that are comprised of captions. The InferSent models are much closer to our model on the caption datasets than overall." ], "reasoning": "The table shows that the overall mean difference for the AllNLI model is 10.5, which is less than the overall mean difference for the SNLI model, which is 12.5.", "statement": "The overall mean difference for the AllNLI model is greater than that for the SNLI model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: Results for Human Queries (GenX) Our model outperforms LSTM and semantic parsing models on complex human-generated queries, showing it is robust to work on natural language. Better performance than Tree-LSTM (Unsup.) shows the efficacy in representing sub-phrases using explicit denotations. Our model also performs better without an external parser, showing the advantages of latent syntax.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Our model achieves the highest accuracy of 73.7%, outperforming other models including LSTM and semantic parsing baselines.S6SS2SSS0Px3 Performance on Human-generated Language: Our approach outperforms strong LSTM and semantic parsing baselines, despite the semantic parser’s use of hard-coded operators. These results suggest that our method represents an attractive middle ground between minimally structured and highly structured approaches to interpretation. Our model learns to interpret operators such as except that were not considered during development. This shows that our model can learn to parse human language, which contains greater lexical and structural diversity than synthetic questions. Trees induced by the model are linguistically plausible (see Appendix D)." } ], "images": [ "scigen/train/1238.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results for Human Queries (GenX) Our model outperforms LSTM and semantic parsing models on complex human-generated queries, showing it is robust to work on natural language. Better performance than Tree-LSTM (Unsup.) shows the efficacy in representing sub-phrases using explicit denotations. Our model also performs better without an external parser, showing the advantages of latent syntax." ], "reasoning": "Our model achieves the highest accuracy of 73.7%, outperforming other models including LSTM and semantic parsing baselines.", "description": "S6SS2SSS0Px3 Performance on Human-generated Language: Our approach outperforms strong LSTM and semantic parsing baselines, despite the semantic parser’s use of hard-coded operators. These results suggest that our method represents an attractive middle ground between minimally structured and highly structured approaches to interpretation. Our model learns to interpret operators such as except that were not considered during development. This shows that our model can learn to parse human language, which contains greater lexical and structural diversity than synthetic questions. Trees induced by the model are linguistically plausible (see Appendix D).", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: Results for Human Queries (GenX) Our model outperforms LSTM and semantic parsing models on complex human-generated queries, showing it is robust to work on natural language. Better performance than Tree-LSTM (Unsup.) shows the efficacy in representing sub-phrases using explicit denotations. Our model also performs better without an external parser, showing the advantages of latent syntax.'. Solve the following question based on the table.\n\nQuestion: Which model has the highest accuracy and what is its value?\nLet's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the accuracy values in the table, it is evident that 'Our Model' has the highest accuracy of 73.7%. So, the answer is {\"answer\": \"Our Model, 73.7%\"}." } ], "images": [ "scigen/train/1238.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results for Human Queries (GenX) Our model outperforms LSTM and semantic parsing models on complex human-generated queries, showing it is robust to work on natural language. Better performance than Tree-LSTM (Unsup.) shows the efficacy in representing sub-phrases using explicit denotations. Our model also performs better without an external parser, showing the advantages of latent syntax." ], "reasoning": "By comparing the accuracy values in the table, it is evident that 'Our Model' has the highest accuracy of 73.7%.", "question": "Which model has the highest accuracy and what is its value?", "answer": "So, the answer is {\"answer\": \"Our Model, 73.7%\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Tree-LSTM (Unsup.) model has a higher accuracy than the Bi-LSTM model.\n" }, { "role": "assistant", "content": "The table shows that the Tree-LSTM (Unsup.) model has an accuracy of 67.7%, while the Bi-LSTM model has an accuracy of 64.6%, indicating that the statement is true. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1238.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results for Human Queries (GenX) Our model outperforms LSTM and semantic parsing models on complex human-generated queries, showing it is robust to work on natural language. Better performance than Tree-LSTM (Unsup.) shows the efficacy in representing sub-phrases using explicit denotations. Our model also performs better without an external parser, showing the advantages of latent syntax." ], "reasoning": "The table shows that the Tree-LSTM (Unsup.) model has an accuracy of 67.7%, while the Bi-LSTM model has an accuracy of 64.6%, indicating that the statement is true.", "statement": "The Tree-LSTM (Unsup.) model has a higher accuracy than the Bi-LSTM model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: Accuracy on CBT dataset. Results marked with ‡ are of previously published works [Dhingra et al.2017, Cui et al.2016, Yang et al.2017].'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The SAW Reader model outperforms most previous works on the CBT-NE test set and achieves comparable performance with the FG Reader, demonstrating its effectiveness for English reading comprehension.S3SS2SSS0Px3 Cbt To verify if our method can only work for Chinese, we also evaluate the effectiveness of the proposed method on benchmark English dataset. We use CBT dataset as our testbed to evaluate the performance. For a fair comparison, we simply set the same parameters as before. We observe that our model outperforms most of the previously public works, with 2.4 % gains on the CBT-NE test set compared with GA Reader which adopts word and character embedding concatenation. Our SAW Reader also achieves comparable performance with FG Reader who adopts neural gates to combine word-level and character-level representations with assistance of extra features including NE, POS and word frequency while our model is much simpler and faster. This result shows our SAW Reader is not restricted to Chinese reading comprehension, but also for other languages." } ], "images": [ "scigen/train/1239.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Accuracy on CBT dataset. Results marked with ‡ are of previously published works [Dhingra et al.2017, Cui et al.2016, Yang et al.2017]." ], "reasoning": "The SAW Reader model outperforms most previous works on the CBT-NE test set and achieves comparable performance with the FG Reader, demonstrating its effectiveness for English reading comprehension.", "description": "S3SS2SSS0Px3 Cbt To verify if our method can only work for Chinese, we also evaluate the effectiveness of the proposed method on benchmark English dataset. We use CBT dataset as our testbed to evaluate the performance. For a fair comparison, we simply set the same parameters as before. We observe that our model outperforms most of the previously public works, with 2.4 % gains on the CBT-NE test set compared with GA Reader which adopts word and character embedding concatenation. Our SAW Reader also achieves comparable performance with FG Reader who adopts neural gates to combine word-level and character-level representations with assistance of extra features including NE, POS and word frequency while our model is much simpler and faster. This result shows our SAW Reader is not restricted to Chinese reading comprehension, but also for other languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 5: Accuracy on CBT dataset. Results marked with ‡ are of previously published works [Dhingra et al.2017, Cui et al.2016, Yang et al.2017].' to sequentially solve the question presented below. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model achieved the highest score on the CBT-NE Valid set?\n" }, { "role": "assistant", "content": "By examining the CBT-NE Valid column, the FG Reader model has the highest score of 79.1. So, the answer is {\"answer\": \"FG Reader\"}" } ], "images": [ "scigen/train/1239.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Accuracy on CBT dataset. Results marked with ‡ are of previously published works [Dhingra et al.2017, Cui et al.2016, Yang et al.2017]." ], "reasoning": "By examining the CBT-NE Valid column, the FG Reader model has the highest score of 79.1.", "question": "Which model achieved the highest score on the CBT-NE Valid set?", "answer": "So, the answer is {\"answer\": \"FG Reader\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe SAW Reader model outperformed the GA Reader model on the CBT-NE Test set.\n" }, { "role": "assistant", "content": "The table shows that the SAW Reader scored 74.9 on the CBT-NE Test set, while the GA Reader scored 72.5, indicating that the SAW Reader outperformed the GA Reader. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1239.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Accuracy on CBT dataset. Results marked with ‡ are of previously published works [Dhingra et al.2017, Cui et al.2016, Yang et al.2017]." ], "reasoning": "The table shows that the SAW Reader scored 74.9 on the CBT-NE Test set, while the GA Reader scored 72.5, indicating that the SAW Reader outperformed the GA Reader.", "statement": "The SAW Reader model outperformed the GA Reader model on the CBT-NE Test set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Accuracy on CMRC-2017 dataset. Results marked with † are from the latest official CMRC-2017 Leaderboard 777http://www.hfl-tek.com/cmrc2017/leaderboard.html. The best results are in bold face.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "WHU's model performs close to ours on the validation set but significantly worse on the test set, demonstrating our model's better generalization ability.Although WHU’s model achieves the best besides our model on the valid set with only 0.75% below ours, their result on the test set is lower than ours by 2.27%, indicating our model has a satisfactory generalization ability." } ], "images": [ "scigen/train/1240.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracy on CMRC-2017 dataset. Results marked with † are from the latest official CMRC-2017 Leaderboard 777http://www.hfl-tek.com/cmrc2017/leaderboard.html. The best results are in bold face." ], "reasoning": "WHU's model performs close to ours on the validation set but significantly worse on the test set, demonstrating our model's better generalization ability.", "description": "Although WHU’s model achieves the best besides our model on the valid set with only 0.75% below ours, their result on the test set is lower than ours by 2.27%, indicating our model has a satisfactory generalization ability.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Accuracy on CMRC-2017 dataset. Results marked with † are from the latest official CMRC-2017 Leaderboard 777http://www.hfl-tek.com/cmrc2017/leaderboard.html. The best results are in bold face.', solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model has the smallest difference in performance between the CMRC-2017 Valid and Test sets?\n" }, { "role": "assistant", "content": "To find the model with the smallest difference in performance between the CMRC-2017 Valid and Test sets, we need to calculate the difference for each model and compare them. The differences are: Random Guess (0.02), Top Frequency (0.78), AS Reader (1.48), GA Reader (1.20), SJTU BCMI-NLP (1.58), 6ESTATES PTE LTD (1.12), Xinktech (1.78), Ludong University (0.32), ECNU (2.55), WHU (1.67), SAW Reader (0.15). The smallest difference is for Random Guess with 0.02. So, the answer is {\"answer\": \"Random Guess\"}." } ], "images": [ "scigen/train/1240.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracy on CMRC-2017 dataset. Results marked with † are from the latest official CMRC-2017 Leaderboard 777http://www.hfl-tek.com/cmrc2017/leaderboard.html. The best results are in bold face." ], "reasoning": "To find the model with the smallest difference in performance between the CMRC-2017 Valid and Test sets, we need to calculate the difference for each model and compare them. The differences are: Random Guess (0.02), Top Frequency (0.78), AS Reader (1.48), GA Reader (1.20), SJTU BCMI-NLP (1.58), 6ESTATES PTE LTD (1.12), Xinktech (1.78), Ludong University (0.32), ECNU (2.55), WHU (1.67), SAW Reader (0.15). The smallest difference is for Random Guess with 0.02.", "question": "Which model has the smallest difference in performance between the CMRC-2017 Valid and Test sets?", "answer": "So, the answer is {\"answer\": \"Random Guess\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The SAW Reader model has the highest score on the CMRC-2017 Valid set., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe SAW Reader model has the highest score on the CMRC-2017 Valid set.\n" }, { "role": "assistant", "content": "The table shows that the SAW Reader model has a score of 78.95 on the CMRC-2017 Valid set, which is the highest score among all listed models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1240.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracy on CMRC-2017 dataset. Results marked with † are from the latest official CMRC-2017 Leaderboard 777http://www.hfl-tek.com/cmrc2017/leaderboard.html. The best results are in bold face." ], "reasoning": "The table shows that the SAW Reader model has a score of 78.95 on the CMRC-2017 Valid set, which is the highest score among all listed models.", "statement": "The SAW Reader model has the highest score on the CMRC-2017 Valid set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Case study on CMRC-2017.'.\n" }, { "role": "assistant", "content": "Word + BPE outperforms Word + Char, and the mul operation is more effective than concat and sum, indicating that element-wise product is more informative.We also list different integration operations for word and subword embeddings. From the results, we can see that Word + BPE outperforms Word + Char which indicates subword embedding works essentially. We also observe that mul outperforms the other two operations, concat and sum. This reveals that mul might be more informative than concat and sum operations. The superiority might be due to element-wise product being capable of modeling the interactions and eliminating distribution differences between word and subword embedding. Intuitively, this is also similar to endow subword-aware “attention” over the word embedding. In contrast, concatenation operation may cause too high dimension, which leads to serious over-fitting issues, and sum operation is too simple to prevent from detailed information losing." } ], "images": [ "scigen/train/1241.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Case study on CMRC-2017." ], "reasoning": "Word + BPE outperforms Word + Char, and the mul operation is more effective than concat and sum, indicating that element-wise product is more informative.", "description": "We also list different integration operations for word and subword embeddings. From the results, we can see that Word + BPE outperforms Word + Char which indicates subword embedding works essentially. We also observe that mul outperforms the other two operations, concat and sum. This reveals that mul might be more informative than concat and sum operations. The superiority might be due to element-wise product being capable of modeling the interactions and eliminating distribution differences between word and subword embedding. Intuitively, this is also similar to endow subword-aware “attention” over the word embedding. In contrast, concatenation operation may cause too high dimension, which leads to serious over-fitting issues, and sum operation is too simple to prevent from detailed information losing.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}. The question is related to the given table titled 'Table 3: Case study on CMRC-2017.'.\n\nQuestion: Which combination of model and operation achieves the highest score on CMRC-2017 Valid?\n" }, { "role": "assistant", "content": "By comparing the scores in the CMRC-2017 Valid column, the highest score is achieved by the Word + BPE model with the mul operation. So, the answer is {\"answer\": \"Word + BPE with mul\"}" } ], "images": [ "scigen/train/1241.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Case study on CMRC-2017." ], "reasoning": "By comparing the scores in the CMRC-2017 Valid column, the highest score is achieved by the Word + BPE model with the mul operation.", "question": "Which combination of model and operation achieves the highest score on CMRC-2017 Valid?", "answer": "So, the answer is {\"answer\": \"Word + BPE with mul\"}" }, { "messages": [ { "role": "user", "content": "The concat operation with Word + Char model achieves a higher score than the sum operation with Word + BPE model on CMRC-2017 Test. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe concat operation with Word + Char model achieves a higher score than the sum operation with Word + BPE model on CMRC-2017 Test.\n" }, { "role": "assistant", "content": "The concat operation with Word + Char model scores 75.13 on CMRC-2017 Test, while the sum operation with Word + BPE model scores 75.83, which is higher. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1241.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Case study on CMRC-2017." ], "reasoning": "The concat operation with Word + Char model scores 75.13 on CMRC-2017 Test, while the sum operation with Word + BPE model scores 75.83, which is higher.", "statement": "The concat operation with Word + Char model achieves a higher score than the sum operation with Word + BPE model on CMRC-2017 Test.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Accuracy on PD and CFT datasets. Results of AS Reader and CAS Reader are from [Cui et al.2016].'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The SAW Reader outperforms the CAS Reader by 7.0% on the PD test set and by 8.8% on the CFT test set, indicating its effectiveness for out-of-domain learning.Since there is no training set for CFT dataset, our model is trained on PD training set. Note that the CFT dataset is harder for the machine to answer because the test set is further processed by human evaluation, and may not be accordance with the pattern of PD dataset. As we see that, our SAW Reader significantly outperforms the CAS Reader in all types of testing, with improvements of 7.0% on PD and 8.8% on CFT test sets, respectively. Although the domain and topic of PD and CFT datasets are quite different, the results indicate that our model also works effectively for out-of-domain learning." } ], "images": [ "scigen/train/1242.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Accuracy on PD and CFT datasets. Results of AS Reader and CAS Reader are from [Cui et al.2016]." ], "reasoning": "The SAW Reader outperforms the CAS Reader by 7.0% on the PD test set and by 8.8% on the CFT test set, indicating its effectiveness for out-of-domain learning.", "description": "Since there is no training set for CFT dataset, our model is trained on PD training set. Note that the CFT dataset is harder for the machine to answer because the test set is further processed by human evaluation, and may not be accordance with the pattern of PD dataset. As we see that, our SAW Reader significantly outperforms the CAS Reader in all types of testing, with improvements of 7.0% on PD and 8.8% on CFT test sets, respectively. Although the domain and topic of PD and CFT datasets are quite different, the results indicate that our model also works effectively for out-of-domain learning.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 4: Accuracy on PD and CFT datasets. Results of AS Reader and CAS Reader are from [Cui et al.2016].' in mind, break down and resolve the question below step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhat is the performance difference between the SAW Reader and the CAS Reader on the CFT Test-human set?\n" }, { "role": "assistant", "content": "The SAW Reader scores 43.8 on the CFT Test-human set, while the CAS Reader scores 35.0. The difference is 43.8 - 35.0 = 8.8. So, the answer is {\"answer\": \"8.8\"}" } ], "images": [ "scigen/train/1242.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Accuracy on PD and CFT datasets. Results of AS Reader and CAS Reader are from [Cui et al.2016]." ], "reasoning": "The SAW Reader scores 43.8 on the CFT Test-human set, while the CAS Reader scores 35.0. The difference is 43.8 - 35.0 = 8.8.", "question": "What is the performance difference between the SAW Reader and the CAS Reader on the CFT Test-human set?", "answer": "So, the answer is {\"answer\": \"8.8\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe GA Reader performs better than the AS Reader on the PD Test set.\n" }, { "role": "assistant", "content": "The GA Reader scores 69.0 on the PD Test set, while the AS Reader scores 67.2. Therefore, the GA Reader performs better. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1242.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Accuracy on PD and CFT datasets. Results of AS Reader and CAS Reader are from [Cui et al.2016]." ], "reasoning": "The GA Reader scores 69.0 on the PD Test set, while the AS Reader scores 67.2. Therefore, the GA Reader performs better.", "statement": "The GA Reader performs better than the AS Reader on the PD Test set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Performances and training efficiency of GramCTC with different model strides'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Increasing the stride from 2 to 4 reduces the epoch time significantly and improves performance, as indicated by the lower WER.By striding over the input at a faster rate in the early layers, we effectively reduce the time steps of later layers, and reduce the training time in half. From stride 2 to stride 4, the performance also improves a lot probably because larger n-grams align with larger segments of utterance, and thus need lower time resolution." } ], "images": [ "scigen/train/1243.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performances and training efficiency of GramCTC with different model strides" ], "reasoning": "Increasing the stride from 2 to 4 reduces the epoch time significantly and improves performance, as indicated by the lower WER.", "description": "By striding over the input at a faster rate in the early layers, we effectively reduce the time steps of later layers, and reduce the training time in half. From stride 2 to stride 4, the performance also improves a lot probably because larger n-grams align with larger segments of utterance, and thus need lower time resolution.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: Performances and training efficiency of GramCTC with different model strides'.\n\nQuestion: What is the WER when the stride is 4 and how does it compare to the WER when the stride is 2?\n" }, { "role": "assistant", "content": "The WER for stride 4 is 18.27, which is lower than the WER for stride 2, which is 21.46, indicating an improvement in performance. So, the answer is {\"answer\": \"18.27\"}." } ], "images": [ "scigen/train/1243.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performances and training efficiency of GramCTC with different model strides" ], "reasoning": "The WER for stride 4 is 18.27, which is lower than the WER for stride 2, which is 21.46, indicating an improvement in performance.", "question": "What is the WER when the stride is 4 and how does it compare to the WER when the stride is 2?", "answer": "So, the answer is {\"answer\": \"18.27\"}." }, { "messages": [ { "role": "user", "content": "Increasing the stride from 2 to 4 reduces the epoch time by almost half. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nIncreasing the stride from 2 to 4 reduces the epoch time by almost half.\n" }, { "role": "assistant", "content": "The epoch time for stride 2 is 18.3 hours, and for stride 4 it is 9.6 hours, which is approximately half of 18.3 hours. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1243.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performances and training efficiency of GramCTC with different model strides" ], "reasoning": "The epoch time for stride 2 is 18.3 hours, and for stride 4 it is 9.6 hours, which is approximately half of 18.3 hours.", "statement": "Increasing the stride from 2 to 4 reduces the epoch time by almost half.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: Comparison of CTC and GramCTC.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "GramCTC improves WER significantly over CTC despite similar or worse CER, likely due to chunk-based character prediction, but shows potential overfitting on the dev set.GramCTC effectively reduces the learning burden of ASR network in two ways: 1) it decomposes sentences into pronunciation-meaningful n-grams, and 2) it effectively reduces the number of output time steps. Both aspects simplify the rules the network needs to learn, thus reducing the required network capacity of the ASR task. There are some interesting distinctions. First, the CERs of GramCTC are similar or even worse than CTC; however, the WERs of GramCTC are always significantly better than CTC. This is probably because GramCTC predicts in chunks of characters and the characters in the same chunk are dependent, thus more robust. Secondly, we also observe the performance on the dev set is relatively worse than that on the train holdout. Our dev dataset is not drawn from the same distribution of the training data - this exhibits the potential for GramCTC to overfit even a large dataset." } ], "images": [ "scigen/train/1244.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of CTC and GramCTC." ], "reasoning": "GramCTC improves WER significantly over CTC despite similar or worse CER, likely due to chunk-based character prediction, but shows potential overfitting on the dev set.", "description": "GramCTC effectively reduces the learning burden of ASR network in two ways: 1) it decomposes sentences into pronunciation-meaningful n-grams, and 2) it effectively reduces the number of output time steps. Both aspects simplify the rules the network needs to learn, thus reducing the required network capacity of the ASR task. There are some interesting distinctions. First, the CERs of GramCTC are similar or even worse than CTC; however, the WERs of GramCTC are always significantly better than CTC. This is probably because GramCTC predicts in chunks of characters and the characters in the same chunk are dependent, thus more robust. Secondly, we also observe the performance on the dev set is relatively worse than that on the train holdout. Our dev dataset is not drawn from the same distribution of the training data - this exhibits the potential for GramCTC to overfit even a large dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Comparison of CTC and GramCTC.', answer the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the Train Holdout WER for GramCTC and how does it compare to CTC?\n" }, { "role": "assistant", "content": "The Train Holdout WER for GramCTC is 11.37, which is lower than the Train Holdout WER for CTC, which is 12.89. So, the answer is {\"answer\": \"11.37\"}." } ], "images": [ "scigen/train/1244.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of CTC and GramCTC." ], "reasoning": "The Train Holdout WER for GramCTC is 11.37, which is lower than the Train Holdout WER for CTC, which is 12.89.", "question": "What is the Train Holdout WER for GramCTC and how does it compare to CTC?", "answer": "So, the answer is {\"answer\": \"11.37\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence GramCTC has a lower Train Holdout WER than CTC. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nGramCTC has a lower Train Holdout WER than CTC.\n" }, { "role": "assistant", "content": "The table shows that the Train Holdout WER for GramCTC is 11.37, which is lower than the Train Holdout WER for CTC, which is 12.89. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1244.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of CTC and GramCTC." ], "reasoning": "The table shows that the Train Holdout WER for GramCTC is 11.37, which is lower than the Train Holdout WER for CTC, which is 12.89.", "statement": "GramCTC has a lower Train Holdout WER than CTC.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: A deeper look into the dev set, measuring WER with language model decoding of each model on different slices of the dev set. Serving latency (milliseconds) is the 98th percentile latency on the last packet as described in Sec. 4. Training time is in hours per epoch with the data and infrastructure the same. *This model has twice the number of parameters as the Baseline, and suffers from prohibitively large serving latency.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Mix-3 achieves lower error rates on both train and dev sets, especially for farfield and Names categories, but has slightly higher serving latency than the baseline.The model mix of joint training with 3 increasingly difficult losses (CE, CTC, and GramCTC, Mix-1) achieves the best results on the train set far surpassing the other model mixes, and even nearly matching the performance of models with bidirectional layers on the train set. This model has the smallest gain on the dev set amongst all the mix-models, and puts it in the overfitting regime. We know that there exists a model that can generalize better than this one, while achieving the same error rates on the train set: the bidirectional baseline. The model mix of PCEN, LC-BGRU and IR augmentation (Mix-2) performs worse on the train set – additional data augmentation with IR impulses makes the training data harder to fit as we have seen earlier, but PCEN and LC-BGRU is not sufficient to address this difficulty. However, the model does attain better generalization and performs better on the dev set, and actually surpasses our bidirectional target when using a language model. Mix-3 adds CE joint training which helps to address optimization issues and leads to lower error rates on both the train and dev sets. However, the improvement in dev WER disappears when using a language model, again highlighting the language model integration issues when using CE training. Clearly, Mix-3 is significantly better for “farfield” and “Names” speech data, two notably difficult categories for ASR. ASR tasks run into a generalization issue for “Names” categories because they are often required words that is not present in the acoustic training data. Similarly far field audio is hard to obtain and the models are forced to generalize out of the training data, in this case by making use of augmentation. At the same time, the serving latency of Mix-3 is only slightly higher than baseline model, still good for deployment." } ], "images": [ "scigen/train/1245.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: A deeper look into the dev set, measuring WER with language model decoding of each model on different slices of the dev set. Serving latency (milliseconds) is the 98th percentile latency on the last packet as described in Sec. 4. Training time is in hours per epoch with the data and infrastructure the same. *This model has twice the number of parameters as the Baseline, and suffers from prohibitively large serving latency." ], "reasoning": "Mix-3 achieves lower error rates on both train and dev sets, especially for farfield and Names categories, but has slightly higher serving latency than the baseline.", "description": "The model mix of joint training with 3 increasingly difficult losses (CE, CTC, and GramCTC, Mix-1) achieves the best results on the train set far surpassing the other model mixes, and even nearly matching the performance of models with bidirectional layers on the train set. This model has the smallest gain on the dev set amongst all the mix-models, and puts it in the overfitting regime. We know that there exists a model that can generalize better than this one, while achieving the same error rates on the train set: the bidirectional baseline. The model mix of PCEN, LC-BGRU and IR augmentation (Mix-2) performs worse on the train set – additional data augmentation with IR impulses makes the training data harder to fit as we have seen earlier, but PCEN and LC-BGRU is not sufficient to address this difficulty. However, the model does attain better generalization and performs better on the dev set, and actually surpasses our bidirectional target when using a language model. Mix-3 adds CE joint training which helps to address optimization issues and leads to lower error rates on both the train and dev sets. However, the improvement in dev WER disappears when using a language model, again highlighting the language model integration issues when using CE training. Clearly, Mix-3 is significantly better for “farfield” and “Names” speech data, two notably difficult categories for ASR. ASR tasks run into a generalization issue for “Names” categories because they are often required words that is not present in the acoustic training data. Similarly far field audio is hard to obtain and the models are forced to generalize out of the training data, in this case by making use of augmentation. At the same time, the serving latency of Mix-3 is only slightly higher than baseline model, still good for deployment.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 4: A deeper look into the dev set, measuring WER with language model decoding of each model on different slices of the dev set. Serving latency (milliseconds) is the 98th percentile latency on the last packet as described in Sec. 4. Training time is in hours per epoch with the data and infrastructure the same. *This model has twice the number of parameters as the Baseline, and suffers from prohibitively large serving latency.', solve the following question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the lowest error rate for the 'Names' category?\n" }, { "role": "assistant", "content": "By comparing the error rates for the 'Names' category across all models, Mix-3 has the lowest error rate at 17.40. So, the answer is {\"answer\": \"Mix-3\"}." } ], "images": [ "scigen/train/1245.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: A deeper look into the dev set, measuring WER with language model decoding of each model on different slices of the dev set. Serving latency (milliseconds) is the 98th percentile latency on the last packet as described in Sec. 4. Training time is in hours per epoch with the data and infrastructure the same. *This model has twice the number of parameters as the Baseline, and suffers from prohibitively large serving latency." ], "reasoning": "By comparing the error rates for the 'Names' category across all models, Mix-3 has the lowest error rate at 17.40.", "question": "Which model has the lowest error rate for the 'Names' category?", "answer": "So, the answer is {\"answer\": \"Mix-3\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nMix-3 has the highest serving latency among all models.\n" }, { "role": "assistant", "content": "The table shows that the serving latency for Mix-3 is 153, which is higher than the baseline (112) but lower than 2×Baseline* (25933). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1245.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: A deeper look into the dev set, measuring WER with language model decoding of each model on different slices of the dev set. Serving latency (milliseconds) is the 98th percentile latency on the last packet as described in Sec. 4. Training time is in hours per epoch with the data and infrastructure the same. *This model has twice the number of parameters as the Baseline, and suffers from prohibitively large serving latency." ], "reasoning": "The table shows that the serving latency for Mix-3 is 153, which is higher than the baseline (112) but lower than 2×Baseline* (25933).", "statement": "Mix-3 has the highest serving latency among all models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Results on the test set of SemEval Task 11, ROCStories, MCTest and the development set of COIN Task 1. The test set of COIN is not public. DCMN_BERT: BERT + DCMN + PSS + AOI. Previous SOTA: previous state-of-the-art model. All the results are from single models.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our model significantly improves average accuracy over fine-tuned BERT and XLNet on various tasks, demonstrating its strong performance.Passage sentence selection is not used in this dataset because there are only four sentences as the passage. Since the test set of COIN is not publicly available, we report the performance of the model on its development set. To further investigate the contribution of our model, we also report the results of directly fine-tuned BERT/ XLNet on the target datasets. From the comparison, we can see that our model obtains 4.9% and 2.8% absolute improvement in average accuracy over the baseline of directly fine-tuned BERT (88.9% vs. 84.0%) and XLNet (90.6% vs. 87.8%), respectively. These results indicate our proposed model has a heavy impact on the performance no matter how strong the adopted pre-trained language model itself is." } ], "images": [ "scigen/train/1246.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results on the test set of SemEval Task 11, ROCStories, MCTest and the development set of COIN Task 1. The test set of COIN is not public. DCMN_BERT: BERT + DCMN + PSS + AOI. Previous SOTA: previous state-of-the-art model. All the results are from single models." ], "reasoning": "Our model significantly improves average accuracy over fine-tuned BERT and XLNet on various tasks, demonstrating its strong performance.", "description": "Passage sentence selection is not used in this dataset because there are only four sentences as the passage. Since the test set of COIN is not publicly available, we report the performance of the model on its development set. To further investigate the contribution of our model, we also report the results of directly fine-tuned BERT/ XLNet on the target datasets. From the comparison, we can see that our model obtains 4.9% and 2.8% absolute improvement in average accuracy over the baseline of directly fine-tuned BERT (88.9% vs. 84.0%) and XLNet (90.6% vs. 87.8%), respectively. These results indicate our proposed model has a heavy impact on the performance no matter how strong the adopted pre-trained language model itself is.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: Table 6: Results on the test set of SemEval Task 11, ROCStories, MCTest and the development set of COIN Task 1. The test set of COIN is not public. DCMN_BERT: BERT + DCMN + PSS + AOI. Previous SOTA: previous state-of-the-art model. All the results are from single models.\nQuestion: What is the average accuracy improvement of DCMN_XLNet over XLNet?\n" }, { "role": "assistant", "content": "The table shows the average accuracy of XLNet as 87.8% and DCMN_XLNet as 90.6%. The improvement is calculated as 90.6% - 87.8% = 2.8%. So, the answer is {\"answer\": \"2.8%\"}" } ], "images": [ "scigen/train/1246.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results on the test set of SemEval Task 11, ROCStories, MCTest and the development set of COIN Task 1. The test set of COIN is not public. DCMN_BERT: BERT + DCMN + PSS + AOI. Previous SOTA: previous state-of-the-art model. All the results are from single models." ], "reasoning": "The table shows the average accuracy of XLNet as 87.8% and DCMN_XLNet as 90.6%. The improvement is calculated as 90.6% - 87.8% = 2.8%.", "question": "What is the average accuracy improvement of DCMN_XLNet over XLNet?", "answer": "So, the answer is {\"answer\": \"2.8%\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nDCMN_BERT outperforms BERT in all tasks listed in the table.\n" }, { "role": "assistant", "content": "Comparing the accuracy of DCMN_BERT and BERT across all tasks in the table, DCMN_BERT has higher accuracy in each task. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1246.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results on the test set of SemEval Task 11, ROCStories, MCTest and the development set of COIN Task 1. The test set of COIN is not public. DCMN_BERT: BERT + DCMN + PSS + AOI. Previous SOTA: previous state-of-the-art model. All the results are from single models." ], "reasoning": "Comparing the accuracy of DCMN_BERT and BERT across all tasks in the table, DCMN_BERT has higher accuracy in each task.", "statement": "DCMN_BERT outperforms BERT in all tasks listed in the table.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4: Experiment results on RACE test set. All the results are from single models. PSS: Passage Sentence Selection; AOI: Answer Option Interaction. ∗ indicates our implementation.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows the accuracy of various models on the RACE dataset, with XLNetlarge achieving the highest accuracy of 80.1%.In the table, Turkers is the performance of Amazon Turkers on a randomly sampled subset of the RACE test set and Ceiling is the percentage of the unambiguous questions with a correct answer in a subset of the test set. Here we give the results of directly fine-tuned BERTbase, BERTlarge and XLNetlarge on RACE and get the accuracy of 65.0%, 72.0% and 80.1%, respectively." } ], "images": [ "scigen/train/1247.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Experiment results on RACE test set. All the results are from single models. PSS: Passage Sentence Selection; AOI: Answer Option Interaction. ∗ indicates our implementation." ], "reasoning": "The table shows the accuracy of various models on the RACE dataset, with XLNetlarge achieving the highest accuracy of 80.1%.", "description": "In the table, Turkers is the performance of Amazon Turkers on a randomly sampled subset of the RACE test set and Ceiling is the percentage of the unambiguous questions with a correct answer in a subset of the test set. Here we give the results of directly fine-tuned BERTbase, BERTlarge and XLNetlarge on RACE and get the accuracy of 65.0%, 72.0% and 80.1%, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 4: Experiment results on RACE test set. All the results are from single models. PSS: Passage Sentence Selection; AOI: Answer Option Interaction. ∗ indicates our implementation.', unfold the steps to solve the question given next. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nQuestion: Which model achieved the highest accuracy on the RACE dataset?\n" }, { "role": "assistant", "content": "By examining the table, we see that XLNetlarge has the highest accuracy on the RACE dataset with a score of 80.1%. So, the answer is {\"answer\": \"XLNetlarge\"}" } ], "images": [ "scigen/train/1247.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Experiment results on RACE test set. All the results are from single models. PSS: Passage Sentence Selection; AOI: Answer Option Interaction. ∗ indicates our implementation." ], "reasoning": "By examining the table, we see that XLNetlarge has the highest accuracy on the RACE dataset with a score of 80.1%.", "question": "Which model achieved the highest accuracy on the RACE dataset?", "answer": "So, the answer is {\"answer\": \"XLNetlarge\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nBERTbase achieved an accuracy of 65.0% on the RACE dataset.\n" }, { "role": "assistant", "content": "The table shows that BERTbase achieved an accuracy of 65.0% on the RACE dataset. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1247.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Experiment results on RACE test set. All the results are from single models. PSS: Passage Sentence Selection; AOI: Answer Option Interaction. ∗ indicates our implementation." ], "reasoning": "The table shows that BERTbase achieved an accuracy of 65.0% on the RACE dataset.", "statement": "BERTbase achieved an accuracy of 65.0% on the RACE dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 8: Results on RACE and COIN dev set with cosine and bilinear score in PSS. We use BERTbase as encoder here.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Cosine score method performs better on the COIN dataset, while bilinear score method performs better on the RACE dataset.Cosine score method works better on COIN dataset (83.5% vs. 82.8%) and bilinear score works better on RACE dataset (66.8% vs. 66.5%)." } ], "images": [ "scigen/train/1248.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Results on RACE and COIN dev set with cosine and bilinear score in PSS. We use BERTbase as encoder here." ], "reasoning": "Cosine score method performs better on the COIN dataset, while bilinear score method performs better on the RACE dataset.", "description": "Cosine score method works better on COIN dataset (83.5% vs. 82.8%) and bilinear score works better on RACE dataset (66.8% vs. 66.5%).", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: Table 8: Results on RACE and COIN dev set with cosine and bilinear score in PSS. We use BERTbase as encoder here.\nQuestion: Which score method performs better on the COIN dataset and by how much?\n" }, { "role": "assistant", "content": "The table shows that the cosine score method achieves 83.5% on the COIN dataset, while the bilinear score method achieves 82.8%. Therefore, the cosine score method performs better by 0.7%. So, the answer is {\"answer\": \"cosine score method by 0.7%\"}." } ], "images": [ "scigen/train/1248.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Results on RACE and COIN dev set with cosine and bilinear score in PSS. We use BERTbase as encoder here." ], "reasoning": "The table shows that the cosine score method achieves 83.5% on the COIN dataset, while the bilinear score method achieves 82.8%. Therefore, the cosine score method performs better by 0.7%.", "question": "Which score method performs better on the COIN dataset and by how much?", "answer": "So, the answer is {\"answer\": \"cosine score method by 0.7%\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe bilinear score method performs better on the COIN dataset.\n" }, { "role": "assistant", "content": "The table shows that the cosine score method achieves 83.5% on the COIN dataset, while the bilinear score method achieves 82.8%. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1248.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Results on RACE and COIN dev set with cosine and bilinear score in PSS. We use BERTbase as encoder here." ], "reasoning": "The table shows that the cosine score method achieves 83.5% on the COIN dataset, while the bilinear score method achieves 82.8%. Therefore, the statement is refuted.", "statement": "The bilinear score method performs better on the COIN dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Performance of best generic prior when adapted using 50K additional dialogues. Results are given with 95% confidence intervals.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "In-domain adaptation improves performance when sufficient training data is available, making it beneficial to create individual in-domain policies.The results show that additional in-domain adaptation has the potential to improve the performance further. So when enough training data is available, it is beneficial to create individual in-domain policies rather than continuing to train the generic policy." } ], "images": [ "scigen/train/1249.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of best generic prior when adapted using 50K additional dialogues. Results are given with 95% confidence intervals." ], "reasoning": "In-domain adaptation improves performance when sufficient training data is available, making it beneficial to create individual in-domain policies.", "description": "The results show that additional in-domain adaptation has the potential to improve the performance further. So when enough training data is available, it is beneficial to create individual in-domain policies rather than continuing to train the generic policy.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 3: Performance of best generic prior when adapted using 50K additional dialogues. Results are given with 95% confidence intervals.', solve the following question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the SFR Success rate for the adapted policy compared to the best prior policy for the SFH domain?\n" }, { "role": "assistant", "content": "The table shows that the SFR Success rate for the adapted policy in the SFH domain is 92.50±1.64, while the best prior policy has a success rate of 88.80±1.96. This indicates that the adapted policy has a higher success rate. So, the answer is {\"answer\": \"92.50±1.64\"}" } ], "images": [ "scigen/train/1249.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of best generic prior when adapted using 50K additional dialogues. Results are given with 95% confidence intervals." ], "reasoning": "The table shows that the SFR Success rate for the adapted policy in the SFH domain is 92.50±1.64, while the best prior policy has a success rate of 88.80±1.96. This indicates that the adapted policy has a higher success rate.", "question": "What is the SFR Success rate for the adapted policy compared to the best prior policy for the SFH domain?", "answer": "So, the answer is {\"answer\": \"92.50±1.64\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The adapted policy has a higher SFR Reward than the best prior policy in the SFH domain., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe adapted policy has a higher SFR Reward than the best prior policy in the SFH domain.\n" }, { "role": "assistant", "content": "The table shows that the SFR Reward for the adapted policy in the SFH domain is 10.27±0.27, while the best prior policy has a reward of 9.76±0.31. This indicates that the adapted policy has a higher reward. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1249.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of best generic prior when adapted using 50K additional dialogues. Results are given with 95% confidence intervals." ], "reasoning": "The table shows that the SFR Reward for the adapted policy in the SFH domain is 10.27±0.27, while the best prior policy has a reward of 9.76±0.31. This indicates that the adapted policy has a higher reward.", "statement": "The adapted policy has a higher SFR Reward than the best prior policy in the SFH domain.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Selection of committee members for multi-policy Bayesian committee machine for SFR domain. The committee policy is trained on 7500 dialogues equally spread across three domains.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The SFH committee member contributes more to the performance gains in the SFR domain than the L11 committee member.As can be seen from the results, all committee members contribute to performance gains. However not all committee members are equally important. In this case, for good performance on the SFR domain, the SFH committee member is more useful than the L11 committee member." } ], "images": [ "scigen/train/1250.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Selection of committee members for multi-policy Bayesian committee machine for SFR domain. The committee policy is trained on 7500 dialogues equally spread across three domains." ], "reasoning": "The SFH committee member contributes more to the performance gains in the SFR domain than the L11 committee member.", "description": "As can be seen from the results, all committee members contribute to performance gains. However not all committee members are equally important. In this case, for good performance on the SFR domain, the SFH committee member is more useful than the L11 committee member.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 5: Selection of committee members for multi-policy Bayesian committee machine for SFR domain. The committee policy is trained on 7500 dialogues equally spread across three domains.' and proceed to solve the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich committee member contributes more to the SFR domain performance, SFH or L11?\n" }, { "role": "assistant", "content": "Comparing the performance metrics, the SFR+SFH combination has higher SFR Reward and SFR Success than the SFR+L11 combination, indicating that SFH contributes more to performance gains. So, the answer is {\"answer\": \"SFH\"}." } ], "images": [ "scigen/train/1250.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Selection of committee members for multi-policy Bayesian committee machine for SFR domain. The committee policy is trained on 7500 dialogues equally spread across three domains." ], "reasoning": "Comparing the performance metrics, the SFR+SFH combination has higher SFR Reward and SFR Success than the SFR+L11 combination, indicating that SFH contributes more to performance gains.", "question": "Which committee member contributes more to the SFR domain performance, SFH or L11?", "answer": "So, the answer is {\"answer\": \"SFH\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe L11 committee member is more useful than the SFH committee member for good performance on the SFR domain.\n" }, { "role": "assistant", "content": "The table shows that the SFR+SFH combination has higher SFR Reward and SFR Success compared to the SFR+L11 combination, refuting the statement. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1250.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Selection of committee members for multi-policy Bayesian committee machine for SFR domain. The committee policy is trained on 7500 dialogues equally spread across three domains." ], "reasoning": "The table shows that the SFR+SFH combination has higher SFR Reward and SFR Success compared to the SFR+L11 combination, refuting the statement.", "statement": "The L11 committee member is more useful than the SFH committee member for good performance on the SFR domain.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 7: Performance when adding a new agent for the L11 domain to a multi-domain dialogue manager using SCALE training with two partially trained agents for the SFR and SFH domains.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Agents were pre-trained in SFR and SFH, then tested in SFR and L11 after training in L11, and finally tested again after additional training in all three domains.It is also important to understand behaviour when a new domain is added alongside a set of existing agents which themselves are not yet fully trained. We are interested in both the performance in the new domain as well as the existing domains. To investigate this, two agents operating in the SFR and SFH domains were pre-trained with 250 dialogues each using SCALE reward distribution mechanism. Performance was then evaluated in the SFR and the new as yet untrained L11 domain. The L11 agent was then trained with 250 dialogues in that domain. Again, the performance was tested in both L11 and SFR. Finally training continued with another 250 dialogues for each of the three domains - SFR, SFH and L11 and the performance in the SFR and L11 domains tested for a final time." } ], "images": [ "scigen/train/1251.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Performance when adding a new agent for the L11 domain to a multi-domain dialogue manager using SCALE training with two partially trained agents for the SFR and SFH domains." ], "reasoning": "Agents were pre-trained in SFR and SFH, then tested in SFR and L11 after training in L11, and finally tested again after additional training in all three domains.", "description": "It is also important to understand behaviour when a new domain is added alongside a set of existing agents which themselves are not yet fully trained. We are interested in both the performance in the new domain as well as the existing domains. To investigate this, two agents operating in the SFR and SFH domains were pre-trained with 250 dialogues each using SCALE reward distribution mechanism. Performance was then evaluated in the SFR and the new as yet untrained L11 domain. The L11 agent was then trained with 250 dialogues in that domain. Again, the performance was tested in both L11 and SFR. Finally training continued with another 250 dialogues for each of the three domains - SFR, SFH and L11 and the performance in the SFR and L11 domains tested for a final time.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 7: Performance when adding a new agent for the L11 domain to a multi-domain dialogue manager using SCALE training with two partially trained agents for the SFR and SFH domains.' and proceed to solve the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat was the performance in the L11 domain in terms of success after training with 250 dialogues in SFR, SFH, and L11?\n" }, { "role": "assistant", "content": "The table shows the performance in the L11 domain after different training stages. After training with 250 dialogues in SFR, SFH, and L11, the success rate in the L11 domain was 70.47±0.94. So, the answer is {\"answer\": \"70.47±0.94\"}." } ], "images": [ "scigen/train/1251.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Performance when adding a new agent for the L11 domain to a multi-domain dialogue manager using SCALE training with two partially trained agents for the SFR and SFH domains." ], "reasoning": "The table shows the performance in the L11 domain after different training stages. After training with 250 dialogues in SFR, SFH, and L11, the success rate in the L11 domain was 70.47±0.94.", "question": "What was the performance in the L11 domain in terms of success after training with 250 dialogues in SFR, SFH, and L11?", "answer": "So, the answer is {\"answer\": \"70.47±0.94\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe performance in the SFR domain in terms of reward improved after training with 250 dialogues in L11.\n" }, { "role": "assistant", "content": "The table shows that the performance in the SFR domain in terms of reward was 6.12±0.22 after initial training and improved to 6.75±0.21 after training with 250 dialogues in L11. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1251.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Performance when adding a new agent for the L11 domain to a multi-domain dialogue manager using SCALE training with two partially trained agents for the SFR and SFH domains." ], "reasoning": "The table shows that the performance in the SFR domain in terms of reward was 6.12±0.22 after initial training and improved to 6.75±0.21 after training with 250 dialogues in L11.", "statement": "The performance in the SFR domain in terms of reward improved after training with 250 dialogues in L11.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: BLEU scores of different approaches on the validation set. Details of the ensemble models are described in Table 9.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Scheduled sampling and reinforcement learning improve model performance, with ensemble models showing the highest BLEU scores. MNMT outperforms NMT only on the En-Fr task with scheduled sampling.To study the performance of different approaches, we conduct an ablation study. Generally, models with scheduled sampling perform better than baseline models, and reinforcement learning further improves the performance. Ensemble models lead to substantial improvements over the best single model by about +2 to +3 BLEU scores. However, by including image information, MNMT performs better than NMT only on the En-Fr task with scheduled sampling." } ], "images": [ "scigen/train/1252.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: BLEU scores of different approaches on the validation set. Details of the ensemble models are described in Table 9." ], "reasoning": "Scheduled sampling and reinforcement learning improve model performance, with ensemble models showing the highest BLEU scores. MNMT outperforms NMT only on the En-Fr task with scheduled sampling.", "description": "To study the performance of different approaches, we conduct an ablation study. Generally, models with scheduled sampling perform better than baseline models, and reinforcement learning further improves the performance. Ensemble models lead to substantial improvements over the best single model by about +2 to +3 BLEU scores. However, by including image information, MNMT performs better than NMT only on the En-Fr task with scheduled sampling.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2: BLEU scores of different approaches on the validation set. Details of the ensemble models are described in Table 9.', unfold the steps to solve the question given next. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nQuestion: Which model has the highest BLEU score for the En-Fr task?\n" }, { "role": "assistant", "content": "By examining the table, the highest BLEU score for the En-Fr task is found in the MIX Ensemble row, which is 61.45. So, the answer is {\"answer\": \"MIX Ensemble\"}" } ], "images": [ "scigen/train/1252.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: BLEU scores of different approaches on the validation set. Details of the ensemble models are described in Table 9." ], "reasoning": "By examining the table, the highest BLEU score for the En-Fr task is found in the MIX Ensemble row, which is 61.45.", "question": "Which model has the highest BLEU score for the En-Fr task?", "answer": "So, the answer is {\"answer\": \"MIX Ensemble\"}" }, { "messages": [ { "role": "user", "content": "MNMT performs better than NMT on the En-Fr task with scheduled sampling. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nMNMT performs better than NMT on the En-Fr task with scheduled sampling.\n" }, { "role": "assistant", "content": "The table shows that MNMT+SS has a BLEU score of 58.80 for the En-Fr task, while NMT+SS has a BLEU score of 58.67, indicating that MNMT performs better than NMT on the En-Fr task with scheduled sampling. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1252.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: BLEU scores of different approaches on the validation set. Details of the ensemble models are described in Table 9." ], "reasoning": "The table shows that MNMT+SS has a BLEU score of 58.80 for the En-Fr task, while NMT+SS has a BLEU score of 58.67, indicating that MNMT performs better than NMT on the En-Fr task with scheduled sampling.", "statement": "MNMT performs better than NMT on the En-Fr task with scheduled sampling.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 7: BLEU scores on validation set for task 1B'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Models trained using single English to Czech data perform better than those trained using multiple source data.En-Cs, Fr-Cs, De-Cs are models trained from one language to another. (En+Fr+De)-Cs models are trained using multiple source data. Similar to the Shuffle method discussed in multi-reference training Zheng et al. Since we do BPE on the whole training data, we can share the vocabulary of different languages during training. The results show that models trained using single English to Czech data perform much better than the rest." } ], "images": [ "scigen/train/1253.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: BLEU scores on validation set for task 1B" ], "reasoning": "Models trained using single English to Czech data perform better than those trained using multiple source data.", "description": "En-Cs, Fr-Cs, De-Cs are models trained from one language to another. (En+Fr+De)-Cs models are trained using multiple source data. Similar to the Shuffle method discussed in multi-reference training Zheng et al. Since we do BPE on the whole training data, we can share the vocabulary of different languages during training. The results show that models trained using single English to Czech data perform much better than the rest.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 7: BLEU scores on validation set for task 1B' to sequentially solve the question presented below. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich model has the highest performance for En-Cs translation?\n" }, { "role": "assistant", "content": "By comparing the scores for En-Cs translation, the highest score is 31.27, achieved by the NMT model. So, the answer is {\"answer\": \"NMT\"}." } ], "images": [ "scigen/train/1253.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: BLEU scores on validation set for task 1B" ], "reasoning": "By comparing the scores for En-Cs translation, the highest score is 31.27, achieved by the NMT model.", "question": "Which model has the highest performance for En-Cs translation?", "answer": "So, the answer is {\"answer\": \"NMT\"}." }, { "messages": [ { "role": "user", "content": "The MNMT model performs better than the NMT model for Fr-Cs translation. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe MNMT model performs better than the NMT model for Fr-Cs translation.\n" }, { "role": "assistant", "content": "The table shows that the MNMT model has a score of 27.02 for Fr-Cs, while the NMT model has a score of 28.48, indicating that the NMT model performs better. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1253.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: BLEU scores on validation set for task 1B" ], "reasoning": "The table shows that the MNMT model has a score of 27.02 for Fr-Cs, while the NMT model has a score of 28.48, indicating that the NMT model performs better.", "statement": "The MNMT model performs better than the NMT model for Fr-Cs translation.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Results of various coreference resolvers on successfully identifying inter-sentence points of correspondence (PoC) and recall scores of these resolvers split by PoC correspondence type.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "SpaCy has the highest precision, while Stanford CoreNLP achieves the highest F1-score. Event coreference is the most challenging for all resolvers.The three resolvers exhibit similar performance, but the scores on identifying points of correspondence are less than satisfying. The SpaCy resolver has the highest precision (59.2%) and Stanford CoreNLP achieves the highest F1-score (35.3%). We observe that existing coreference resolvers can sometimes struggle to use the high-level reasoning that humans use to determine what connects two sentences together. Next, we go deeper into understanding what PoC types these resolvers struggle with. We present the recall scores of these resolvers split by PoC correspondence type. Event coreference poses the most difficulty by far, which is understandable as coreference resolution only focuses on entities rather than events. More work into detecting event coreference can bring significant improvements in PoC identification. Common-noun coreference also poses a challenge, in part because names and pronouns give strong clues as to the relationships between mentions, while common-noun relationships are more difficult to identify since they lack these clues." } ], "images": [ "scigen/train/1254.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of various coreference resolvers on successfully identifying inter-sentence points of correspondence (PoC) and recall scores of these resolvers split by PoC correspondence type." ], "reasoning": "SpaCy has the highest precision, while Stanford CoreNLP achieves the highest F1-score. Event coreference is the most challenging for all resolvers.", "description": "The three resolvers exhibit similar performance, but the scores on identifying points of correspondence are less than satisfying. The SpaCy resolver has the highest precision (59.2%) and Stanford CoreNLP achieves the highest F1-score (35.3%). We observe that existing coreference resolvers can sometimes struggle to use the high-level reasoning that humans use to determine what connects two sentences together. Next, we go deeper into understanding what PoC types these resolvers struggle with. We present the recall scores of these resolvers split by PoC correspondence type. Event coreference poses the most difficulty by far, which is understandable as coreference resolution only focuses on entities rather than events. More work into detecting event coreference can bring significant improvements in PoC identification. Common-noun coreference also poses a challenge, in part because names and pronouns give strong clues as to the relationships between mentions, while common-noun relationships are more difficult to identify since they lack these clues.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Results of various coreference resolvers on successfully identifying inter-sentence points of correspondence (PoC) and recall scores of these resolvers split by PoC correspondence type.\nQuestion: Which coreference resolver has the highest recall score for common-noun correspondence?\nSolve the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the coreference resolver with the highest recall score for common-noun correspondence, we look at the 'Comm.-Noun' column in the table. The highest value in this column is 17.4, which corresponds to Stanford CoreNLP. So, the answer is {\"answer\": \"Stanford CoreNLP\"}." } ], "images": [ "scigen/train/1254.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of various coreference resolvers on successfully identifying inter-sentence points of correspondence (PoC) and recall scores of these resolvers split by PoC correspondence type." ], "reasoning": "To find the coreference resolver with the highest recall score for common-noun correspondence, we look at the 'Comm.-Noun' column in the table. The highest value in this column is 17.4, which corresponds to Stanford CoreNLP.", "question": "Which coreference resolver has the highest recall score for common-noun correspondence?", "answer": "So, the answer is {\"answer\": \"Stanford CoreNLP\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nAllenNLP has the highest precision score among the coreference resolvers.\n" }, { "role": "assistant", "content": "The table shows the precision scores (P%) for SpaCy, AllenNLP, and Stanford CoreNLP. SpaCy has the highest precision score of 59.2%, not AllenNLP. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1254.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of various coreference resolvers on successfully identifying inter-sentence points of correspondence (PoC) and recall scores of these resolvers split by PoC correspondence type." ], "reasoning": "The table shows the precision scores (P%) for SpaCy, AllenNLP, and Stanford CoreNLP. SpaCy has the highest precision score of 59.2%, not AllenNLP.", "statement": "AllenNLP has the highest precision score among the coreference resolvers.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: Our results on Subtask A: Grouping Verbs to Frame Type Clusters. Purity F1-score is denoted as Pu F1, B-Cubed F1-score is denoted as B3 F1. denotes our final submission (# 536426), denotes our post-competition result, denotes a baseline, and denotes the submission of the winning team.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Agglomerative clustering with Manhattan affinity and 150 clusters using Word2Vec vectors performed best, but ELMo outperformed Word2Vec with 235 clusters.Our final submission (\\faFire) used agglomerative clustering of normalized vectors obtained by concatenating the context and verb vectors from the Word2Vec model. In particular, we found that the best performance is attained for Manhattan affinity and 150 clusters. During our post-competition experiments (\\faHourglassEnd), we found that ELMo performed better than Word2Vec when a higher number of clusters, 235, was specified." } ], "images": [ "scigen/train/1255.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Our results on Subtask A: Grouping Verbs to Frame Type Clusters. Purity F1-score is denoted as Pu F1, B-Cubed F1-score is denoted as B3 F1. denotes our final submission (# 536426), denotes our post-competition result, denotes a baseline, and denotes the submission of the winning team." ], "reasoning": "Agglomerative clustering with Manhattan affinity and 150 clusters using Word2Vec vectors performed best, but ELMo outperformed Word2Vec with 235 clusters.", "description": "Our final submission (\\faFire) used agglomerative clustering of normalized vectors obtained by concatenating the context and verb vectors from the Word2Vec model. In particular, we found that the best performance is attained for Manhattan affinity and 150 clusters. During our post-competition experiments (\\faHourglassEnd), we found that ELMo performed better than Word2Vec when a higher number of clusters, 235, was specified.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. The question is related to the given table titled 'Table 1: Our results on Subtask A: Grouping Verbs to Frame Type Clusters. Purity F1-score is denoted as Pu F1, B-Cubed F1-score is denoted as B3 F1. denotes our final submission (# 536426), denotes our post-competition result, denotes a baseline, and denotes the submission of the winning team.'.\n\nQuestion: Which method achieved the highest Pu F1 score?\n" }, { "role": "assistant", "content": "The table shows the Pu F1 scores for different methods. The 'Winner' method has the highest Pu F1 score of 78.15. So, the answer is {\"answer\": \"Winner\"}." } ], "images": [ "scigen/train/1255.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Our results on Subtask A: Grouping Verbs to Frame Type Clusters. Purity F1-score is denoted as Pu F1, B-Cubed F1-score is denoted as B3 F1. denotes our final submission (# 536426), denotes our post-competition result, denotes a baseline, and denotes the submission of the winning team." ], "reasoning": "The table shows the Pu F1 scores for different methods. The 'Winner' method has the highest Pu F1 score of 78.15.", "question": "Which method achieved the highest Pu F1 score?", "answer": "So, the answer is {\"answer\": \"Winner\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The ELMo method achieved a higher B3 F1 score than the Winner method., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe ELMo method achieved a higher B3 F1 score than the Winner method.\n" }, { "role": "assistant", "content": "The table shows that the ELMo method has a B3 F1 score of 69.50, while the Winner method has a B3 F1 score of 70.70. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1255.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Our results on Subtask A: Grouping Verbs to Frame Type Clusters. Purity F1-score is denoted as Pu F1, B-Cubed F1-score is denoted as B3 F1. denotes our final submission (# 536426), denotes our post-competition result, denotes a baseline, and denotes the submission of the winning team." ], "reasoning": "The table shows that the ELMo method has a B3 F1 score of 69.50, while the Winner method has a B3 F1 score of 70.70. Therefore, the statement is refuted.", "statement": "The ELMo method achieved a higher B3 F1 score than the Winner method.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Our results on Subtask B.1: Clustering Arguments of Verbs to Frame-Specific Slots. Purity F1-score is denoted as Pu F1, B-Cubed F1-score is denoted as B3 F1. denotes our final submission (# 535483), denotes a supervised Logistic Regression submission that does not comply to the task rules, denotes our post-competition result, denotes a baseline, and denotes the submission of the winning team.'.\n" }, { "role": "assistant", "content": "The logistic regression model outperformed clustering techniques in Subtask B.2, and its performance was further improved by combining results from post-competition experiments of Subtask A and Subtask B.2.For our final submission (\\faFire), we merged the frame types obtained by clustering the Word2Vec embeddings of the sentence (context, c) and verb (word, w), and the role labels obtained by clustering the vector of inbound dependencies (ID). However, we observed that the logistic regression model demonstrated better performance in Subtask B.2 than any clustering technique we tried, including our final submission (\\faFire) and the baselines. But this performance was further improved by combining the results from post-competition experiments of Subtask A and Subtask B.2 (\\faHourglassEnd)." } ], "images": [ "scigen/train/1256.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Our results on Subtask B.1: Clustering Arguments of Verbs to Frame-Specific Slots. Purity F1-score is denoted as Pu F1, B-Cubed F1-score is denoted as B3 F1. denotes our final submission (# 535483), denotes a supervised Logistic Regression submission that does not comply to the task rules, denotes our post-competition result, denotes a baseline, and denotes the submission of the winning team." ], "reasoning": "The logistic regression model outperformed clustering techniques in Subtask B.2, and its performance was further improved by combining results from post-competition experiments of Subtask A and Subtask B.2.", "description": "For our final submission (\\faFire), we merged the frame types obtained by clustering the Word2Vec embeddings of the sentence (context, c) and verb (word, w), and the role labels obtained by clustering the vector of inbound dependencies (ID). However, we observed that the logistic regression model demonstrated better performance in Subtask B.2 than any clustering technique we tried, including our final submission (\\faFire) and the baselines. But this performance was further improved by combining the results from post-competition experiments of Subtask A and Subtask B.2 (\\faHourglassEnd).", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Our results on Subtask B.1: Clustering Arguments of Verbs to Frame-Specific Slots. Purity F1-score is denoted as Pu F1, B-Cubed F1-score is denoted as B3 F1. denotes our final submission (# 535483), denotes a supervised Logistic Regression submission that does not comply to the task rules, denotes our post-competition result, denotes a baseline, and denotes the submission of the winning team.', and sequentially solve the question, writing out each step of your reasoning process. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich method achieved the highest Pu F1 score in Subtask B.2?\n" }, { "role": "assistant", "content": "The table shows that the highest Pu F1 score in Subtask B.2 was achieved by the method 'Subtask B.2: w2v[c+w+v]+ID+B+123' with a score of 68.22. So, the answer is {\"answer\": \"68.22\"}." } ], "images": [ "scigen/train/1256.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Our results on Subtask B.1: Clustering Arguments of Verbs to Frame-Specific Slots. Purity F1-score is denoted as Pu F1, B-Cubed F1-score is denoted as B3 F1. denotes our final submission (# 535483), denotes a supervised Logistic Regression submission that does not comply to the task rules, denotes our post-competition result, denotes a baseline, and denotes the submission of the winning team." ], "reasoning": "The table shows that the highest Pu F1 score in Subtask B.2 was achieved by the method 'Subtask B.2: w2v[c+w+v]+ID+B+123' with a score of 68.22.", "question": "Which method achieved the highest Pu F1 score in Subtask B.2?", "answer": "So, the answer is {\"answer\": \"68.22\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe logistic regression model achieved the highest B3 F1 score in Subtask B.2.\n" }, { "role": "assistant", "content": "The table shows that the highest B3 F1 score in Subtask B.2 was achieved by the method 'Subtask B.2: w2v[c+w+v]+ID+B+123' with a score of 58.61, not by the logistic regression model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1256.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Our results on Subtask B.1: Clustering Arguments of Verbs to Frame-Specific Slots. Purity F1-score is denoted as Pu F1, B-Cubed F1-score is denoted as B3 F1. denotes our final submission (# 535483), denotes a supervised Logistic Regression submission that does not comply to the task rules, denotes our post-competition result, denotes a baseline, and denotes the submission of the winning team." ], "reasoning": "The table shows that the highest B3 F1 score in Subtask B.2 was achieved by the method 'Subtask B.2: w2v[c+w+v]+ID+B+123' with a score of 58.61, not by the logistic regression model.", "statement": "The logistic regression model achieved the highest B3 F1 score in Subtask B.2.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: Ablation study on the English development set.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "An ablation study was conducted on the English development set to measure the impact of individual model changes on performance.In order to show the contribution of the modeling choices we made, we performed an ablation study on the English development set In these experiments we made individual changes to the model (one by one) and measured their influence on the model performance." } ], "images": [ "scigen/train/1257.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Ablation study on the English development set." ], "reasoning": "An ablation study was conducted on the English development set to measure the impact of individual model changes on performance.", "description": "In order to show the contribution of the modeling choices we made, we performed an ablation study on the English development set In these experiments we made individual changes to the model (one by one) and measured their influence on the model performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 7: Ablation study on the English development set.'.\n\nQuestion: Which model configuration had the highest precision score?\n" }, { "role": "assistant", "content": "To find the model configuration with the highest precision score, compare the precision (P) values of all configurations listed in the table. So, the answer is {\"answer\": \"Ours (local)\"}." } ], "images": [ "scigen/train/1257.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Ablation study on the English development set." ], "reasoning": "To find the model configuration with the highest precision score, compare the precision (P) values of all configurations listed in the table.", "question": "Which model configuration had the highest precision score?", "answer": "So, the answer is {\"answer\": \"Ours (local)\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nRemoving predicate-specific encoding resulted in the lowest recall score.\n" }, { "role": "assistant", "content": "The table shows that the configuration 'w/o predicate-specific encoding' has a recall (R) score of 79.8, which is the lowest among all configurations. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1257.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Ablation study on the English development set." ], "reasoning": "The table shows that the configuration 'w/o predicate-specific encoding' has a recall (R) score of 79.8, which is the lowest among all configurations.", "statement": "Removing predicate-specific encoding resulted in the lowest recall score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Results on the English in-domain test set.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our model achieved the highest F1 score among local models and performed on par with the state-of-the-art global models.For English, our model significantly outperformed all the local counter-parts (i.e., models which do not perform global inference) on the in-domain tests (see with 87.6% F1 for our model vs. 86.7% for PathLSTM Roth and Lapata When compared with global models, our model performed on-par with the state-of-the-art global version of PathLSTM." } ], "images": [ "scigen/train/1258.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on the English in-domain test set." ], "reasoning": "Our model achieved the highest F1 score among local models and performed on par with the state-of-the-art global models.", "description": "For English, our model significantly outperformed all the local counter-parts (i.e., models which do not perform global inference) on the in-domain tests (see with 87.6% F1 for our model vs. 86.7% for PathLSTM Roth and Lapata When compared with global models, our model performed on-par with the state-of-the-art global version of PathLSTM.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 2: Results on the English in-domain test set.', solve the following question step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the F1 score of the best performing local model?\n" }, { "role": "assistant", "content": "The table lists various models and their performance metrics. The highest F1 score among the local models is 87.7, achieved by 'Ours (local)'. So, the answer is {\"answer\": \"87.7\"}" } ], "images": [ "scigen/train/1258.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on the English in-domain test set." ], "reasoning": "The table lists various models and their performance metrics. The highest F1 score among the local models is 87.7, achieved by 'Ours (local)'.", "question": "What is the F1 score of the best performing local model?", "answer": "So, the answer is {\"answer\": \"87.7\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe global version of PathLSTM achieved an F1 score of 87.7.\n" }, { "role": "assistant", "content": "The table shows that the global version of PathLSTM (roth-lapata:2016:P16-1) achieved an F1 score of 87.7. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1258.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on the English in-domain test set." ], "reasoning": "The table shows that the global version of PathLSTM (roth-lapata:2016:P16-1) achieved an F1 score of 87.7.", "statement": "The global version of PathLSTM achieved an F1 score of 87.7.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: Results of dependency parsing on various treebanks. Entries of the form A→B give results for parsing without mapped embeddings (A) and with mapped embeddings (B). “OOTV %” entries A→B indicate that A% of the test set vocabulary was unseen in the parser training, and B% remain unknown after mapping the embeddings. “OOTV UAS” refers to UAS measured on the subset of the test set sentences that contain at least one OOTV word, and “#Sents” gives the number of sentences in this subset.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The mapper significantly reduces initial OOTV rates, leading to modest UAS gains overall, but larger gains in test sentences containing OOTV words.Due to the small initial OOTV rates (<3%), we only see modest gains of 0.3-0.4% in UAS, with statistical significance at p<0.05 for WSJ and OntoNotes and p<0.07 for Switchboard. The initial OOTV rates are cut in half by our mapper, with the remaining unknown words largely being numerical strings and misspellings. When only considering test sentences containing OOTV words (the row labeled “OOTV subset”), the gains are significantly larger (0.5-0.8% UAS at p<0.05). As expected, the mapper has a much larger impact when parsing these out-of-domain datasets with high OOTV word rates." } ], "images": [ "scigen/train/1259.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results of dependency parsing on various treebanks. Entries of the form A→B give results for parsing without mapped embeddings (A) and with mapped embeddings (B). “OOTV %” entries A→B indicate that A% of the test set vocabulary was unseen in the parser training, and B% remain unknown after mapping the embeddings. “OOTV UAS” refers to UAS measured on the subset of the test set sentences that contain at least one OOTV word, and “#Sents” gives the number of sentences in this subset." ], "reasoning": "The mapper significantly reduces initial OOTV rates, leading to modest UAS gains overall, but larger gains in test sentences containing OOTV words.", "description": "Due to the small initial OOTV rates (<3%), we only see modest gains of 0.3-0.4% in UAS, with statistical significance at p<0.05 for WSJ and OntoNotes and p<0.07 for Switchboard. The initial OOTV rates are cut in half by our mapper, with the remaining unknown words largely being numerical strings and misspellings. When only considering test sentences containing OOTV words (the row labeled “OOTV subset”), the gains are significantly larger (0.5-0.8% UAS at p<0.05). As expected, the mapper has a much larger impact when parsing these out-of-domain datasets with high OOTV word rates.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 1: Results of dependency parsing on various treebanks. Entries of the form A→B give results for parsing without mapped embeddings (A) and with mapped embeddings (B). “OOTV %” entries A→B indicate that A% of the test set vocabulary was unseen in the parser training, and B% remain unknown after mapping the embeddings. “OOTV UAS” refers to UAS measured on the subset of the test set sentences that contain at least one OOTV word, and “#Sents” gives the number of sentences in this subset.\nQuestion: What is the UAS gain for the Higher OOTV word rate Reviews dataset?\n" }, { "role": "assistant", "content": "The UAS for the Higher OOTV word rate Reviews dataset increases from 81.26 to 81.92, resulting in a gain of 0.66. So, the answer is {\"answer\": \"0.66\"}." } ], "images": [ "scigen/train/1259.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results of dependency parsing on various treebanks. Entries of the form A→B give results for parsing without mapped embeddings (A) and with mapped embeddings (B). “OOTV %” entries A→B indicate that A% of the test set vocabulary was unseen in the parser training, and B% remain unknown after mapping the embeddings. “OOTV UAS” refers to UAS measured on the subset of the test set sentences that contain at least one OOTV word, and “#Sents” gives the number of sentences in this subset." ], "reasoning": "The UAS for the Higher OOTV word rate Reviews dataset increases from 81.26 to 81.92, resulting in a gain of 0.66.", "question": "What is the UAS gain for the Higher OOTV word rate Reviews dataset?", "answer": "So, the answer is {\"answer\": \"0.66\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The OOTV % for the Higher OOTV word rate Emails dataset is reduced by more than 7%. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe OOTV % for the Higher OOTV word rate Emails dataset is reduced by more than 7%.\n" }, { "role": "assistant", "content": "The OOTV % for the Higher OOTV word rate Emails dataset is reduced from 10.56 to 3.01, which is a reduction of 7.55%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1259.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results of dependency parsing on various treebanks. Entries of the form A→B give results for parsing without mapped embeddings (A) and with mapped embeddings (B). “OOTV %” entries A→B indicate that A% of the test set vocabulary was unseen in the parser training, and B% remain unknown after mapping the embeddings. “OOTV UAS” refers to UAS measured on the subset of the test set sentences that contain at least one OOTV word, and “#Sents” gives the number of sentences in this subset." ], "reasoning": "The OOTV % for the Higher OOTV word rate Emails dataset is reduced from 10.56 to 3.01, which is a reduction of 7.55%.", "statement": "The OOTV % for the Higher OOTV word rate Emails dataset is reduced by more than 7%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Average Web Treebank development UAS at different threshold settings.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Threshold t3 performs best with a score of 84.97, while t1 and t5 also improve over the baseline of 84.11. Performance drops at t∞.We see that t3 performs best, though settings t1 and t5 also improve over the baseline. At threshold t3 we have approximately 20,000 examples for training the mapper, while at threshold t5 we have only about 10,000 examples. We see a performance drop at t∞, so it appears better to directly use the task-specific embeddings for words that appear frequently in the training data. In other results reported in this paper, we used t3 for the Web Treebank test sets and t1 for the rest." } ], "images": [ "scigen/train/1260.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Average Web Treebank development UAS at different threshold settings." ], "reasoning": "Threshold t3 performs best with a score of 84.97, while t1 and t5 also improve over the baseline of 84.11. Performance drops at t∞.", "description": "We see that t3 performs best, though settings t1 and t5 also improve over the baseline. At threshold t3 we have approximately 20,000 examples for training the mapper, while at threshold t5 we have only about 10,000 examples. We see a performance drop at t∞, so it appears better to directly use the task-specific embeddings for words that appear frequently in the training data. In other results reported in this paper, we used t3 for the Web Treebank test sets and t1 for the rest.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: Average Web Treebank development UAS at different threshold settings.'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich threshold has the highest performance score and how does it compare to the baseline?\n" }, { "role": "assistant", "content": "The table shows that threshold t3 has the highest performance score of 84.97, which is higher than the baseline score of 84.11. So, the answer is {\"answer\": \"t3\"}" } ], "images": [ "scigen/train/1260.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Average Web Treebank development UAS at different threshold settings." ], "reasoning": "The table shows that threshold t3 has the highest performance score of 84.97, which is higher than the baseline score of 84.11.", "question": "Which threshold has the highest performance score and how does it compare to the baseline?", "answer": "So, the answer is {\"answer\": \"t3\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThreshold t5 has a performance score higher than the baseline.\n" }, { "role": "assistant", "content": "The table shows that threshold t5 has a performance score of 84.81, which is higher than the baseline score of 84.11. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1260.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Average Web Treebank development UAS at different threshold settings." ], "reasoning": "The table shows that threshold t5 has a performance score of 84.81, which is higher than the baseline score of 84.11.", "statement": "Threshold t5 has a performance score higher than the baseline.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5: Accuracy on relation approximation'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The sense representation achieves accuracy close to the difference embedding and significantly higher than the global representation, indicating its effectiveness in approximating relations like capital-country, city-state, and nationality-adjective.Results. The accuracy achieved by using the preposition sense representation is significantly close to that of the difference embedding compared to the global representation. This shows that the sense representation is good at approximating the relations between capital-state, capital-country and country-nationality." } ], "images": [ "scigen/train/1261.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Accuracy on relation approximation" ], "reasoning": "The sense representation achieves accuracy close to the difference embedding and significantly higher than the global representation, indicating its effectiveness in approximating relations like capital-country, city-state, and nationality-adjective.", "description": "Results. The accuracy achieved by using the preposition sense representation is significantly close to that of the difference embedding compared to the global representation. This shows that the sense representation is good at approximating the relations between capital-state, capital-country and country-nationality.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 5: Accuracy on relation approximation' in mind, break down and resolve the question below step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich embedding method shows the highest accuracy for the capital-country relation?\n" }, { "role": "assistant", "content": "By comparing the accuracy values for the capital-country relation across the three embedding methods, the difference embedding has the highest accuracy at 0.95. So, the answer is {\"answer\": \"Difference\"}." } ], "images": [ "scigen/train/1261.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Accuracy on relation approximation" ], "reasoning": "By comparing the accuracy values for the capital-country relation across the three embedding methods, the difference embedding has the highest accuracy at 0.95.", "question": "Which embedding method shows the highest accuracy for the capital-country relation?", "answer": "So, the answer is {\"answer\": \"Difference\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe sense representation has a higher accuracy than the global representation for the city-state relation.\n" }, { "role": "assistant", "content": "The table shows that the sense representation has an accuracy of 0.67 for the city-state relation, which is higher than the global representation's accuracy of 0.32. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1261.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Accuracy on relation approximation" ], "reasoning": "The table shows that the sense representation has an accuracy of 0.67 for the city-state relation, which is higher than the global representation's accuracy of 0.32.", "statement": "The sense representation has a higher accuracy than the global representation for the city-state relation.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 14: Precision on Verb Phrase Paraphrasing'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The precision improvement of sense embedding over global embedding is larger on aspectual phrases than on compositional phrases, possibly due to the semantic role of prepositions in aspectual phrases.As we can see from the table, the precision improvement of sense embedding over global embedding is larger on aspectual phrases than on compositional phrases. The reason might be that preposition plays a more important semantic role in aspectual phrases than in compositional phrases." } ], "images": [ "scigen/train/1262.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 14: Precision on Verb Phrase Paraphrasing" ], "reasoning": "The precision improvement of sense embedding over global embedding is larger on aspectual phrases than on compositional phrases, possibly due to the semantic role of prepositions in aspectual phrases.", "description": "As we can see from the table, the precision improvement of sense embedding over global embedding is larger on aspectual phrases than on compositional phrases. The reason might be that preposition plays a more important semantic role in aspectual phrases than in compositional phrases.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: Table 14: Precision on Verb Phrase Paraphrasing\nQuestion: What is the precision improvement of sense embedding over global embedding for aspectual phrases at prec@1?\n" }, { "role": "assistant", "content": "To find the precision improvement, subtract the precision of aspectual global embedding from the precision of aspectual sense embedding at prec@1. Aspectual sense embedding at prec@1 is 0.5 and aspectual global embedding at prec@1 is 0.0625. The improvement is 0.5 - 0.0625 = 0.4375. So, the answer is {\"answer\": \"0.4375\"}." } ], "images": [ "scigen/train/1262.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 14: Precision on Verb Phrase Paraphrasing" ], "reasoning": "To find the precision improvement, subtract the precision of aspectual global embedding from the precision of aspectual sense embedding at prec@1. Aspectual sense embedding at prec@1 is 0.5 and aspectual global embedding at prec@1 is 0.0625. The improvement is 0.5 - 0.0625 = 0.4375.", "question": "What is the precision improvement of sense embedding over global embedding for aspectual phrases at prec@1?", "answer": "So, the answer is {\"answer\": \"0.4375\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe precision improvement of sense embedding over global embedding is larger on aspectual phrases than on idiomatic phrases at prec@1.\n" }, { "role": "assistant", "content": "To verify this, compare the precision improvements for aspectual and idiomatic phrases at prec@1. Aspectual sense embedding at prec@1 is 0.5 and aspectual global embedding at prec@1 is 0.0625, giving an improvement of 0.4375. Idiomatic sense embedding at prec@1 is 0.333 and idiomatic global embedding at prec@1 is 0.190, giving an improvement of 0.143. Since 0.4375 is larger than 0.143, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1262.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 14: Precision on Verb Phrase Paraphrasing" ], "reasoning": "To verify this, compare the precision improvements for aspectual and idiomatic phrases at prec@1. Aspectual sense embedding at prec@1 is 0.5 and aspectual global embedding at prec@1 is 0.0625, giving an improvement of 0.4375. Idiomatic sense embedding at prec@1 is 0.333 and idiomatic global embedding at prec@1 is 0.190, giving an improvement of 0.143. Since 0.4375 is larger than 0.143, the statement is supported.", "statement": "The precision improvement of sense embedding over global embedding is larger on aspectual phrases than on idiomatic phrases at prec@1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Number of labeling functions and the relation types they can annotated w.r.t. two kinds of information'.\n" }, { "role": "assistant", "content": "Heuristic patterns identify more relation types for KBP datasets, while knowledge base provides supervision for more relation types in NYT datasets.We can observe that heuristic patterns can identify more relation types for KBP datasets, while for NYT datasets, knowledge base can provide supervision for more relation types. This observation aligns with our intuition that single kind of information might be insufficient while different kinds of information can complement each other." } ], "images": [ "scigen/train/1263.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Number of labeling functions and the relation types they can annotated w.r.t. two kinds of information" ], "reasoning": "Heuristic patterns identify more relation types for KBP datasets, while knowledge base provides supervision for more relation types in NYT datasets.", "description": "We can observe that heuristic patterns can identify more relation types for KBP datasets, while for NYT datasets, knowledge base can provide supervision for more relation types. This observation aligns with our intuition that single kind of information might be insufficient while different kinds of information can complement each other.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 4: Number of labeling functions and the relation types they can annotated w.r.t. two kinds of information'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich method identifies more relation types for the NYT dataset?\n" }, { "role": "assistant", "content": "The table shows that the KB method identifies 25 relation types for the NYT dataset, while the Pattern method identifies 16 relation types. So, the answer is {\"answer\": \"KB\"}." } ], "images": [ "scigen/train/1263.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Number of labeling functions and the relation types they can annotated w.r.t. two kinds of information" ], "reasoning": "The table shows that the KB method identifies 25 relation types for the NYT dataset, while the Pattern method identifies 16 relation types.", "question": "Which method identifies more relation types for the NYT dataset?", "answer": "So, the answer is {\"answer\": \"KB\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Pattern method identifies more relation types for the Wiki-KBP dataset than the KB method.\n" }, { "role": "assistant", "content": "The table shows that the Pattern method identifies 13 relation types for the Wiki-KBP dataset, while the KB method identifies 7 relation types. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1263.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Number of labeling functions and the relation types they can annotated w.r.t. two kinds of information" ], "reasoning": "The table shows that the Pattern method identifies 13 relation types for the Wiki-KBP dataset, while the KB method identifies 7 relation types.", "statement": "The Pattern method identifies more relation types for the Wiki-KBP dataset than the KB method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 6: Number of relation mentions (RM), relation mentions annotated as None, relation mentions with conflicting annotations and conflicts involving None'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The high number of instances annotated as None and the significant conflicts involving None justify the use of a true label discovery model, especially for the NYT dataset.It can be observed that a large portion of instances is only annotated as None, while lots of conflicts exist among other instances. This phenomenon justifies the motivation to employ true label discovery model to resolve the conflicts among supervision. Also, we can observe most conflicts involve None type, accordingly, our proposed method should have more advantages over traditional true label discovery methods on the relation extraction task comparing to the relation classification task that excludes None type. One could also observe the difference between REHession and the compared methods is more significant on the NYT dataset than on the Wiki-KBP dataset." } ], "images": [ "scigen/train/1264.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Number of relation mentions (RM), relation mentions annotated as None, relation mentions with conflicting annotations and conflicts involving None" ], "reasoning": "The high number of instances annotated as None and the significant conflicts involving None justify the use of a true label discovery model, especially for the NYT dataset.", "description": "It can be observed that a large portion of instances is only annotated as None, while lots of conflicts exist among other instances. This phenomenon justifies the motivation to employ true label discovery model to resolve the conflicts among supervision. Also, we can observe most conflicts involve None type, accordingly, our proposed method should have more advantages over traditional true label discovery methods on the relation extraction task comparing to the relation classification task that excludes None type. One could also observe the difference between REHession and the compared methods is more significant on the NYT dataset than on the Wiki-KBP dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 6: Number of relation mentions (RM), relation mentions annotated as None, relation mentions with conflicting annotations and conflicts involving None', solve the subsequent question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhat is the total number of RM annotated as None in both datasets combined?\n" }, { "role": "assistant", "content": "To find the total number of RM annotated as None in both datasets, sum the values for Wiki-KBP and NYT: 100521 + 356497. So, the answer is {\"answer\": \"457018\"}." } ], "images": [ "scigen/train/1264.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Number of relation mentions (RM), relation mentions annotated as None, relation mentions with conflicting annotations and conflicts involving None" ], "reasoning": "To find the total number of RM annotated as None in both datasets, sum the values for Wiki-KBP and NYT: 100521 + 356497.", "question": "What is the total number of RM annotated as None in both datasets combined?", "answer": "So, the answer is {\"answer\": \"457018\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe NYT dataset has more conflicts involving None than the Wiki-KBP dataset.\n" }, { "role": "assistant", "content": "The table shows that the NYT dataset has 38756 conflicts involving None, while the Wiki-KBP dataset has 30559 conflicts involving None. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1264.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Number of relation mentions (RM), relation mentions annotated as None, relation mentions with conflicting annotations and conflicts involving None" ], "reasoning": "The table shows that the NYT dataset has 38756 conflicts involving None, while the Wiki-KBP dataset has 30559 conflicts involving None.", "statement": "The NYT dataset has more conflicts involving None than the Wiki-KBP dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 8: Comparison among REHession (Ori), REHession-US (US) and REHession-TD (TD) on relation extraction and relation classification'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "REHession outperforms its variants REHession-TD and REHession-US in terms of precision, recall, F1 score, and accuracy, demonstrating the effectiveness of the context-aware true label discovery method.Effectiveness of True Label Discovery. We explore the effectiveness of the proposed context-aware true label discovery component by comparing REHession to its variants REHession-TD and REHession-US, which uses Investment or Universal Schemas to resolve conflicts. We can observe that REHession significantly outperforms its variants. Since the only difference between REHession and its variants is the model employed to resolve conflicts, this gap verifies the effectiveness of the proposed context-aware true label discovery method." } ], "images": [ "scigen/train/1265.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Comparison among REHession (Ori), REHession-US (US) and REHession-TD (TD) on relation extraction and relation classification" ], "reasoning": "REHession outperforms its variants REHession-TD and REHession-US in terms of precision, recall, F1 score, and accuracy, demonstrating the effectiveness of the context-aware true label discovery method.", "description": "Effectiveness of True Label Discovery. We explore the effectiveness of the proposed context-aware true label discovery component by comparing REHession to its variants REHession-TD and REHession-US, which uses Investment or Universal Schemas to resolve conflicts. We can observe that REHession significantly outperforms its variants. Since the only difference between REHession and its variants is the model employed to resolve conflicts, this gap verifies the effectiveness of the proposed context-aware true label discovery method.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 8: Comparison among REHession (Ori), REHession-US (US) and REHession-TD (TD) on relation extraction and relation classification\nQuestion: Which method achieves the highest accuracy on the NYT dataset?\n" }, { "role": "assistant", "content": "By comparing the accuracy values for the NYT dataset, we see that the Ori method has the highest accuracy at 0.8381. So, the answer is {\"answer\": \"Ori\"}." } ], "images": [ "scigen/train/1265.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Comparison among REHession (Ori), REHession-US (US) and REHession-TD (TD) on relation extraction and relation classification" ], "reasoning": "By comparing the accuracy values for the NYT dataset, we see that the Ori method has the highest accuracy at 0.8381.", "question": "Which method achieves the highest accuracy on the NYT dataset?", "answer": "So, the answer is {\"answer\": \"Ori\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe REHession-US method achieves higher precision than the REHession-TD method on the Wiki-KBP dataset.\n" }, { "role": "assistant", "content": "By comparing the precision values for the Wiki-KBP dataset, we see that REHession-US has a precision of 0.3380, which is higher than REHession-TD's precision of 0.3032. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1265.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Comparison among REHession (Ori), REHession-US (US) and REHession-TD (TD) on relation extraction and relation classification" ], "reasoning": "By comparing the precision values for the Wiki-KBP dataset, we see that REHession-US has a precision of 0.3380, which is higher than REHession-TD's precision of 0.3032.", "statement": "The REHession-US method achieves higher precision than the REHession-TD method on the Wiki-KBP dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Illustrative sentence pairs from the STS datasets showing errors made by LSTMavg and avg. The last three columns show the gold similarity score, the similarity score of LSTMavg, and the similarity score of avg. Boldface indicates smaller error compared to gold scores.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The avg model tends to overestimate semantic similarity when sentences have word overlap but key semantic differences, and underestimate when there are one-word-to-multiword paraphrases.We analyze the predictions of avg and the recurrent networks, represented by LSTMavg, on the 20 STS datasets. We choose LSTMavg as it correlates slightly less strongly with avg than the GRAN on the results over all SemEval datasets used for evaluation. We scale the models’ cosine similarities to lie within [0,5], then compare the predicted similarities of LSTMavg and avg to the gold similarities. We analyzed instances in which each model would tend to overestimate or underestimate the gold similarity relative to the other. We find that avg tends to overestimate the semantic similarity of a sentence pair, relative to LSTMavg, when the two sentences have a lot of word or synonym overlap, but have either important differences in key semantic roles or where one sentence has significantly more content than the other. Conversely, avg tends to underestimate similarity when there are one-word-to-multiword paraphrases between the two sentences as shown in examples 3 and 4." } ], "images": [ "scigen/train/1266.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Illustrative sentence pairs from the STS datasets showing errors made by LSTMavg and avg. The last three columns show the gold similarity score, the similarity score of LSTMavg, and the similarity score of avg. Boldface indicates smaller error compared to gold scores." ], "reasoning": "The avg model tends to overestimate semantic similarity when sentences have word overlap but key semantic differences, and underestimate when there are one-word-to-multiword paraphrases.", "description": "We analyze the predictions of avg and the recurrent networks, represented by LSTMavg, on the 20 STS datasets. We choose LSTMavg as it correlates slightly less strongly with avg than the GRAN on the results over all SemEval datasets used for evaluation. We scale the models’ cosine similarities to lie within [0,5], then compare the predicted similarities of LSTMavg and avg to the gold similarities. We analyzed instances in which each model would tend to overestimate or underestimate the gold similarity relative to the other. We find that avg tends to overestimate the semantic similarity of a sentence pair, relative to LSTMavg, when the two sentences have a lot of word or synonym overlap, but have either important differences in key semantic roles or where one sentence has significantly more content than the other. Conversely, avg tends to underestimate similarity when there are one-word-to-multiword paraphrases between the two sentences as shown in examples 3 and 4.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 7: Illustrative sentence pairs from the STS datasets showing errors made by LSTMavg and avg. The last three columns show the gold similarity score, the similarity score of LSTMavg, and the similarity score of avg. Boldface indicates smaller error compared to gold scores.', answer the question below by showing each progressive step toward the answer. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich sentence pair has the largest difference between the avg and Gold similarity scores?\n" }, { "role": "assistant", "content": "To find the sentence pair with the largest difference between the avg and Gold scores, calculate the absolute difference for each pair and identify the maximum. So, the answer is {\"answer\": \"1\"}." } ], "images": [ "scigen/train/1266.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Illustrative sentence pairs from the STS datasets showing errors made by LSTMavg and avg. The last three columns show the gold similarity score, the similarity score of LSTMavg, and the similarity score of avg. Boldface indicates smaller error compared to gold scores." ], "reasoning": "To find the sentence pair with the largest difference between the avg and Gold scores, calculate the absolute difference for each pair and identify the maximum.", "question": "Which sentence pair has the largest difference between the avg and Gold similarity scores?", "answer": "So, the answer is {\"answer\": \"1\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe avg model always overestimates the gold similarity score.\n" }, { "role": "assistant", "content": "By comparing the avg and Gold scores in the table, we see that the avg model does not always overestimate the gold similarity score, as there are instances where avg is lower than Gold. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1266.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Illustrative sentence pairs from the STS datasets showing errors made by LSTMavg and avg. The last three columns show the gold similarity score, the similarity score of LSTMavg, and the similarity score of avg. Boldface indicates smaller error compared to gold scores." ], "reasoning": "By comparing the avg and Gold scores in the table, we see that the avg model does not always overestimate the gold similarity score, as there are instances where avg is lower than Gold.", "statement": "The avg model always overestimates the gold similarity score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: Test results on SemEval semantic textual similarity datasets (Pearson’s r×100) when training on different sources of data: phrase pairs from PPDB or simple-to-standard English Wikipedia sentence pairs from coster2011simple.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Training on sentence pairs reduces the performance gap between avg and LSTM models, and LSTMavg shows significant improvement when moving from PPDB to SimpWiki.We first note that, when training on PPDB, we find the same result as \\newcitewieting-16-full: avg outperforms the LSTM by more than 13 points. However, when training both on sentence pairs, the gap shrinks to about 9 points. It appears that part of the inferior performance for the LSTM in prior work was due to training on phrase pairs rather than on sentence pairs. The avg model also benefits from training on sentences, but not nearly as much as the LSTM. When training on PPDB, moving from LSTM to LSTMavg improves performance by 10 points, closing most of the gap with avg. We also find that LSTMavg improves by moving from PPDB to SimpWiki, though in both cases it still lags behind avg." } ], "images": [ "scigen/train/1267.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Test results on SemEval semantic textual similarity datasets (Pearson’s r×100) when training on different sources of data: phrase pairs from PPDB or simple-to-standard English Wikipedia sentence pairs from coster2011simple." ], "reasoning": "Training on sentence pairs reduces the performance gap between avg and LSTM models, and LSTMavg shows significant improvement when moving from PPDB to SimpWiki.", "description": "We first note that, when training on PPDB, we find the same result as \\newcitewieting-16-full: avg outperforms the LSTM by more than 13 points. However, when training both on sentence pairs, the gap shrinks to about 9 points. It appears that part of the inferior performance for the LSTM in prior work was due to training on phrase pairs rather than on sentence pairs. The avg model also benefits from training on sentences, but not nearly as much as the LSTM. When training on PPDB, moving from LSTM to LSTMavg improves performance by 10 points, closing most of the gap with avg. We also find that LSTMavg improves by moving from PPDB to SimpWiki, though in both cases it still lags behind avg.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 1: Test results on SemEval semantic textual similarity datasets (Pearson’s r×100) when training on different sources of data: phrase pairs from PPDB or simple-to-standard English Wikipedia sentence pairs from coster2011simple.' in mind, break down and resolve the question below step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the performance improvement of LSTMavg when moving from PPDB to SimpWiki?\n" }, { "role": "assistant", "content": "The table shows that LSTMavg scores 64.2 on PPDB and 67.5 on SimpWiki. The improvement is calculated as 67.5 - 64.2. So, the answer is {\"answer\": \"3.3\"}" } ], "images": [ "scigen/train/1267.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Test results on SemEval semantic textual similarity datasets (Pearson’s r×100) when training on different sources of data: phrase pairs from PPDB or simple-to-standard English Wikipedia sentence pairs from coster2011simple." ], "reasoning": "The table shows that LSTMavg scores 64.2 on PPDB and 67.5 on SimpWiki. The improvement is calculated as 67.5 - 64.2.", "question": "What is the performance improvement of LSTMavg when moving from PPDB to SimpWiki?", "answer": "So, the answer is {\"answer\": \"3.3\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe avg model performs better than LSTMavg on SimpWiki.\n" }, { "role": "assistant", "content": "The table shows that the avg model scores 68.4 on SimpWiki, while LSTMavg scores 67.5. Therefore, the avg model performs better than LSTMavg on SimpWiki. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1267.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Test results on SemEval semantic textual similarity datasets (Pearson’s r×100) when training on different sources of data: phrase pairs from PPDB or simple-to-standard English Wikipedia sentence pairs from coster2011simple." ], "reasoning": "The table shows that the avg model scores 68.4 on SimpWiki, while LSTMavg scores 67.5. Therefore, the avg model performs better than LSTMavg on SimpWiki.", "statement": "The avg model performs better than LSTMavg on SimpWiki.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Results on SemEval textual similarity datasets (Pearson’s r×100). The highest score in each row is in boldface.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "LSTMavg and GRAN are more closely correlated in performance than either is to avg, but they significantly outperform avg in datasets like OnWN and FNWN, which contain examples of low similarity with high word overlap.The LSTMavg and gated recurrent averaging network are more closely correlated in performance, in terms of Spearman’s ρ and Pearson’r r, than either is to avg. But they do differ significantly in some datasets, most notably in those comparing machine translation output with its reference. Interestingly, both the LSTMavg and gated recurrent averaging network significantly outperform avg in the datasets focused on comparing glosses like OnWN and FNWN. Upon examination, we found that these datasets, especially 2013 OnWN, contain examples of low similarity with high word overlap. For example, the pair ⟨the act of preserving or protecting something., the act of decreasing or reducing something.⟩ from 2013 OnWN has a gold similarity score of 0.4. It appears that avg was fooled by the high amount of word overlap in such pairs, while the other two models were better able to recognize the semantic differences." } ], "images": [ "scigen/train/1268.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on SemEval textual similarity datasets (Pearson’s r×100). The highest score in each row is in boldface." ], "reasoning": "LSTMavg and GRAN are more closely correlated in performance than either is to avg, but they significantly outperform avg in datasets like OnWN and FNWN, which contain examples of low similarity with high word overlap.", "description": "The LSTMavg and gated recurrent averaging network are more closely correlated in performance, in terms of Spearman’s ρ and Pearson’r r, than either is to avg. But they do differ significantly in some datasets, most notably in those comparing machine translation output with its reference. Interestingly, both the LSTMavg and gated recurrent averaging network significantly outperform avg in the datasets focused on comparing glosses like OnWN and FNWN. Upon examination, we found that these datasets, especially 2013 OnWN, contain examples of low similarity with high word overlap. For example, the pair ⟨the act of preserving or protecting something., the act of decreasing or reducing something.⟩ from 2013 OnWN has a gold similarity score of 0.4. It appears that avg was fooled by the high amount of word overlap in such pairs, while the other two models were better able to recognize the semantic differences.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: Results on SemEval textual similarity datasets (Pearson’s r×100). The highest score in each row is in boldface.', answer the following question. Give detailed solution consisting of each step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model performed best on the 2013 OnWN dataset?\n" }, { "role": "assistant", "content": "The table shows the performance of LSTMavg, avg, and GRAN on various datasets. For the 2013 OnWN dataset, the scores are 84.9 for LSTMavg, 81.4 for avg, and 85.1 for GRAN. GRAN has the highest score. So, the answer is {\"answer\": \"GRAN\"}." } ], "images": [ "scigen/train/1268.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on SemEval textual similarity datasets (Pearson’s r×100). The highest score in each row is in boldface." ], "reasoning": "The table shows the performance of LSTMavg, avg, and GRAN on various datasets. For the 2013 OnWN dataset, the scores are 84.9 for LSTMavg, 81.4 for avg, and 85.1 for GRAN. GRAN has the highest score.", "question": "Which model performed best on the 2013 OnWN dataset?", "answer": "So, the answer is {\"answer\": \"GRAN\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe LSTMavg model outperformed the avg model on the 2013 OnWN dataset.\n" }, { "role": "assistant", "content": "The table shows that for the 2013 OnWN dataset, LSTMavg scored 84.9 while avg scored 81.4. Therefore, LSTMavg outperformed avg. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1268.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on SemEval textual similarity datasets (Pearson’s r×100). The highest score in each row is in boldface." ], "reasoning": "The table shows that for the 2013 OnWN dataset, LSTMavg scored 84.9 while avg scored 81.4. Therefore, LSTMavg outperformed avg.", "statement": "The LSTMavg model outperformed the avg model on the 2013 OnWN dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: Results from supervised training on the STS and SICK datasets (Pearson’s r×100). The last column is the average result on the two datasets.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The gated recurrent averaging network (GRAN) has the best performance on both datasets, and modifications like dropout and scrambling significantly improve LSTM performance.The gated recurrent averaging network has the best performance on both datasets. Dropout helps the word-averaging model in the STS task, unlike in the transfer learning setting. The LSTM benefits slightly from dropout, scrambling, and averaging on their own individually with the exception of word dropout on both datasets and averaging on the SICK dataset. However, when combined, these modifications are able to significantly improve the performance of the LSTM, bringing it much closer in performance to avg. This experiment indicates that these modifications when training LSTMs are beneficial outside the transfer learning setting, and can potentially be used to improve performance for the broad range of problems that use LSTMs to model sentences." } ], "images": [ "scigen/train/1269.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results from supervised training on the STS and SICK datasets (Pearson’s r×100). The last column is the average result on the two datasets." ], "reasoning": "The gated recurrent averaging network (GRAN) has the best performance on both datasets, and modifications like dropout and scrambling significantly improve LSTM performance.", "description": "The gated recurrent averaging network has the best performance on both datasets. Dropout helps the word-averaging model in the STS task, unlike in the transfer learning setting. The LSTM benefits slightly from dropout, scrambling, and averaging on their own individually with the exception of word dropout on both datasets and averaging on the SICK dataset. However, when combined, these modifications are able to significantly improve the performance of the LSTM, bringing it much closer in performance to avg. This experiment indicates that these modifications when training LSTMs are beneficial outside the transfer learning setting, and can potentially be used to improve performance for the broad range of problems that use LSTMs to model sentences.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 5: Results from supervised training on the STS and SICK datasets (Pearson’s r×100). The last column is the average result on the two datasets.', and sequentially solve the question, writing out each step of your reasoning process. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model and regularization combination has the highest average performance?\n" }, { "role": "assistant", "content": "By examining the table, the GRAN model with dropout and scrambling has the highest average performance of 83.4. So, the answer is {\"answer\": \"GRAN with dropout and scrambling\"}." } ], "images": [ "scigen/train/1269.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results from supervised training on the STS and SICK datasets (Pearson’s r×100). The last column is the average result on the two datasets." ], "reasoning": "By examining the table, the GRAN model with dropout and scrambling has the highest average performance of 83.4.", "question": "Which model and regularization combination has the highest average performance?", "answer": "So, the answer is {\"answer\": \"GRAN with dropout and scrambling\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The LSTM model with dropout and scrambling has the highest performance on the SICK dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe LSTM model with dropout and scrambling has the highest performance on the SICK dataset.\n" }, { "role": "assistant", "content": "The table shows that the LSTM model with dropout and scrambling has a performance of 84.2 on the SICK dataset, which is not the highest. The highest performance on the SICK dataset is 85.3 by the GRAN model with scrambling. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1269.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results from supervised training on the STS and SICK datasets (Pearson’s r×100). The last column is the average result on the two datasets." ], "reasoning": "The table shows that the LSTM model with dropout and scrambling has a performance of 84.2 on the SICK dataset, which is not the highest. The highest performance on the SICK dataset is 85.3 by the GRAN model with scrambling.", "statement": "The LSTM model with dropout and scrambling has the highest performance on the SICK dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 8: Impact of initializing and regularizing toward universal models (Pearson’s r×100) in supervised training.'.\n" }, { "role": "assistant", "content": "Initializing and regularizing pretrained models significantly improves performance, demonstrating their dual purpose in semantic similarity and downstream tasks.Initializing and regularizing to the pretrained models significantly improves the performance for all three models, justifying our claim that these models serve a dual purpose: they can be used a black box semantic similarity function, and they possess rich knowledge that can be used to improve the performance of downstream tasks." } ], "images": [ "scigen/train/1270.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Impact of initializing and regularizing toward universal models (Pearson’s r×100) in supervised training." ], "reasoning": "Initializing and regularizing pretrained models significantly improves performance, demonstrating their dual purpose in semantic similarity and downstream tasks.", "description": "Initializing and regularizing to the pretrained models significantly improves the performance for all three models, justifying our claim that these models serve a dual purpose: they can be used a black box semantic similarity function, and they possess rich knowledge that can be used to improve the performance of downstream tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 8: Impact of initializing and regularizing toward universal models (Pearson’s r×100) in supervised training.', solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model and regularization combination achieved the highest SICK score?\n" }, { "role": "assistant", "content": "By examining the SICK scores in the table, the highest score is 86.0, achieved by the GRAN model with dropout, scrambling, and universal regularization. So, the answer is {\"answer\": \"GRAN with dropout, scrambling, universal\"}." } ], "images": [ "scigen/train/1270.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Impact of initializing and regularizing toward universal models (Pearson’s r×100) in supervised training." ], "reasoning": "By examining the SICK scores in the table, the highest score is 86.0, achieved by the GRAN model with dropout, scrambling, and universal regularization.", "question": "Which model and regularization combination achieved the highest SICK score?", "answer": "So, the answer is {\"answer\": \"GRAN with dropout, scrambling, universal\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe avg model with dropout and universal regularization achieved the highest STS score.\n" }, { "role": "assistant", "content": "The table shows that the avg model with dropout and universal regularization achieved an STS score of 82.9, which is not the highest STS score. The highest STS score is 82.7, achieved by the GRAN model with dropout, scrambling, and universal regularization. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1270.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Impact of initializing and regularizing toward universal models (Pearson’s r×100) in supervised training." ], "reasoning": "The table shows that the avg model with dropout and universal regularization achieved an STS score of 82.9, which is not the highest STS score. The highest STS score is 82.7, achieved by the GRAN model with dropout, scrambling, and universal regularization.", "statement": "The avg model with dropout and universal regularization achieved the highest STS score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: The state of the art performance on different datasets. We also compare with existing word segmentation toolkits Jieba and CKIP Hsieh et al. (2012).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our model achieves the best results on all datasets compared to recent neural network-based models and existing word segmentation toolkits Jieba and CKIP.We compare our model with the state-of-the-art results from recent neural network based models, together with existing word segmentation toolkits Jieba and CKIP Hsieh et al. We achieve the best results on all datasets." } ], "images": [ "scigen/train/1271.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The state of the art performance on different datasets. We also compare with existing word segmentation toolkits Jieba and CKIP Hsieh et al. (2012)." ], "reasoning": "Our model achieves the best results on all datasets compared to recent neural network-based models and existing word segmentation toolkits Jieba and CKIP.", "description": "We compare our model with the state-of-the-art results from recent neural network based models, together with existing word segmentation toolkits Jieba and CKIP Hsieh et al. We achieve the best results on all datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: The state of the art performance on different datasets. We also compare with existing word segmentation toolkits Jieba and CKIP Hsieh et al. (2012).' to sequentially solve the question presented below. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich model achieved the highest score on the PKU dataset?\n" }, { "role": "assistant", "content": "By comparing the scores in the PKU column, the highest score is achieved by 'Ours (character level ELMo)' with a score of 97.7. So, the answer is {\"answer\": \"Ours (character level ELMo)\"}." } ], "images": [ "scigen/train/1271.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The state of the art performance on different datasets. We also compare with existing word segmentation toolkits Jieba and CKIP Hsieh et al. (2012)." ], "reasoning": "By comparing the scores in the PKU column, the highest score is achieved by 'Ours (character level ELMo)' with a score of 97.7.", "question": "Which model achieved the highest score on the PKU dataset?", "answer": "So, the answer is {\"answer\": \"Ours (character level ELMo)\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe model 'DBLP:journals/corr/LiuCGQL16' achieved the highest score on the MSR dataset.\n" }, { "role": "assistant", "content": "By examining the MSR column, the highest score is achieved by 'Ours (character level ELMo)' with a score of 98.7, not 'DBLP:journals/corr/LiuCGQL16'. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1271.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The state of the art performance on different datasets. We also compare with existing word segmentation toolkits Jieba and CKIP Hsieh et al. (2012)." ], "reasoning": "By examining the MSR column, the highest score is achieved by 'Ours (character level ELMo)' with a score of 98.7, not 'DBLP:journals/corr/LiuCGQL16'.", "statement": "The model 'DBLP:journals/corr/LiuCGQL16' achieved the highest score on the MSR dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Testing set OOV rate, together with the accuracy achieved on OOV set by our baseline model and our main model, respectively.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The main model outperforms the baseline model by up to 5% in accuracy on OOV words, demonstrating its robustness.We can see that even though the baseline model can have a good accuracy nearly 97% on the whole testing sets of these datasets, the accuracy computed only on OOV words is still stuck in nearly 91%. However, our main model still maintains relatively good performance on OOV words outperforming the baseline model with at most about 5% accuracy, showing the robustness of our model." } ], "images": [ "scigen/train/1272.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Testing set OOV rate, together with the accuracy achieved on OOV set by our baseline model and our main model, respectively." ], "reasoning": "The main model outperforms the baseline model by up to 5% in accuracy on OOV words, demonstrating its robustness.", "description": "We can see that even though the baseline model can have a good accuracy nearly 97% on the whole testing sets of these datasets, the accuracy computed only on OOV words is still stuck in nearly 91%. However, our main model still maintains relatively good performance on OOV words outperforming the baseline model with at most about 5% accuracy, showing the robustness of our model.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: Table 4: Testing set OOV rate, together with the accuracy achieved on OOV set by our baseline model and our main model, respectively.\nQuestion: What is the accuracy percentage of the char-level ELMo model on the UD dataset?\n" }, { "role": "assistant", "content": "The table shows the accuracy percentages for both the baseline model and the char-level ELMo model across different datasets. For the UD dataset, the char-level ELMo model has an accuracy percentage of 95.5%. So, the answer is {\"answer\": \"95.5\"}" } ], "images": [ "scigen/train/1272.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Testing set OOV rate, together with the accuracy achieved on OOV set by our baseline model and our main model, respectively." ], "reasoning": "The table shows the accuracy percentages for both the baseline model and the char-level ELMo model across different datasets. For the UD dataset, the char-level ELMo model has an accuracy percentage of 95.5%.", "question": "What is the accuracy percentage of the char-level ELMo model on the UD dataset?", "answer": "So, the answer is {\"answer\": \"95.5\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe baseline model has an accuracy of 90.3% on the AS dataset.\n" }, { "role": "assistant", "content": "The table lists the accuracy percentages of the baseline model for various datasets. For the AS dataset, the baseline model's accuracy is indeed 90.3%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1272.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Testing set OOV rate, together with the accuracy achieved on OOV set by our baseline model and our main model, respectively." ], "reasoning": "The table lists the accuracy percentages of the baseline model for various datasets. For the AS dataset, the baseline model's accuracy is indeed 90.3%.", "statement": "The baseline model has an accuracy of 90.3% on the AS dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: Evaluation of syntactic parser and SRL system on out-of-domain data. F1 scores are reported on the failure set. SRL model was trained on NW and the syntactic parser was trained on WSJ Section on OntoNote v5.0. Except PT, which is new and old Testament, all failure rate on out-domain data is higher than that of in-domain (11.9% for parsing and 18.1% for SRL) as suspected. The table shows that GBI can be successfully applied to resolve performance degradation on out-of-domain data.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The failure rates for syntactic parsing and SRL vary across genres, but enforcing constraints improves F1 scores significantly on the failure sets.Previously, we saw how GBI performs well even when the underlying network is of lower quality. We now investigate GBI on actual out-of-domain data for which the model quality can suffer. For SRL, we train a SOTA network with ELMo embedding on the NewsWire (NW) section of the OntoNotes v5.0 English PropBank corpus and then test on the other genres provided in the corpus: BC, BN, PT, TC, WB. The failure rate on the within genre data (test set of NW) is 18.10%. Further, by enforcing constraints, we see significant gains on the failure set in terms of F1 score across all genres (ranging from 9.39-16.5 F1), thus, providing additional evidences for answering Q5. As we did for SRL, we train a GMNT seq2seq model on the WSJ NW section in OntoNotes v5.0 The F1 on the within-genre data (test set of WSJ) is 85.03, but the F1 on these genres is much lower, ranging from the mid-forties on BC (46.2–78.5 depending on the subcategory) to the low-eighties on BN (68.3–81.3. depending on the subcategory). Indeed, we find that overall the F1 is lower and in some cases, like WB, the failure rate is much higher (17.6% for WB vs. 11.9% for WSJ). We see that across all genres, the algorithm has high conversion rates (sometimes close to 100%), and that in each case, enforcing the constraints improves the F1. Again, we find support for Q2, Q3 and Q5." } ], "images": [ "scigen/train/1273.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Evaluation of syntactic parser and SRL system on out-of-domain data. F1 scores are reported on the failure set. SRL model was trained on NW and the syntactic parser was trained on WSJ Section on OntoNote v5.0. Except PT, which is new and old Testament, all failure rate on out-domain data is higher than that of in-domain (11.9% for parsing and 18.1% for SRL) as suspected. The table shows that GBI can be successfully applied to resolve performance degradation on out-of-domain data." ], "reasoning": "The failure rates for syntactic parsing and SRL vary across genres, but enforcing constraints improves F1 scores significantly on the failure sets.", "description": "Previously, we saw how GBI performs well even when the underlying network is of lower quality. We now investigate GBI on actual out-of-domain data for which the model quality can suffer. For SRL, we train a SOTA network with ELMo embedding on the NewsWire (NW) section of the OntoNotes v5.0 English PropBank corpus and then test on the other genres provided in the corpus: BC, BN, PT, TC, WB. The failure rate on the within genre data (test set of NW) is 18.10%. Further, by enforcing constraints, we see significant gains on the failure set in terms of F1 score across all genres (ranging from 9.39-16.5 F1), thus, providing additional evidences for answering Q5. As we did for SRL, we train a GMNT seq2seq model on the WSJ NW section in OntoNotes v5.0 The F1 on the within-genre data (test set of WSJ) is 85.03, but the F1 on these genres is much lower, ranging from the mid-forties on BC (46.2–78.5 depending on the subcategory) to the low-eighties on BN (68.3–81.3. depending on the subcategory). Indeed, we find that overall the F1 is lower and in some cases, like WB, the failure rate is much higher (17.6% for WB vs. 11.9% for WSJ). We see that across all genres, the algorithm has high conversion rates (sometimes close to 100%), and that in each case, enforcing the constraints improves the F1. Again, we find support for Q2, Q3 and Q5.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 5: Evaluation of syntactic parser and SRL system on out-of-domain data. F1 scores are reported on the failure set. SRL model was trained on NW and the syntactic parser was trained on WSJ Section on OntoNote v5.0. Except PT, which is new and old Testament, all failure rate on out-domain data is higher than that of in-domain (11.9% for parsing and 18.1% for SRL) as suspected. The table shows that GBI can be successfully applied to resolve performance degradation on out-of-domain data.', answer the following question. Give detailed solution consisting of each step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the SRL failure rate for the genre with the highest syntactic parsing failure rate?\n" }, { "role": "assistant", "content": "The genre with the highest syntactic parsing failure rate is Broadcast Conversation (BC) at 19.3%. The SRL failure rate for BC is 26.86%. So, the answer is {\"answer\": \"26.86%\"}" } ], "images": [ "scigen/train/1273.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Evaluation of syntactic parser and SRL system on out-of-domain data. F1 scores are reported on the failure set. SRL model was trained on NW and the syntactic parser was trained on WSJ Section on OntoNote v5.0. Except PT, which is new and old Testament, all failure rate on out-domain data is higher than that of in-domain (11.9% for parsing and 18.1% for SRL) as suspected. The table shows that GBI can be successfully applied to resolve performance degradation on out-of-domain data." ], "reasoning": "The genre with the highest syntactic parsing failure rate is Broadcast Conversation (BC) at 19.3%. The SRL failure rate for BC is 26.86%.", "question": "What is the SRL failure rate for the genre with the highest syntactic parsing failure rate?", "answer": "So, the answer is {\"answer\": \"26.86%\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The SRL F1 on the failure set for Weblogs (WB) improved by more than 10 points. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe SRL F1 on the failure set for Weblogs (WB) improved by more than 10 points.\n" }, { "role": "assistant", "content": "The SRL F1 on the failure set for Weblogs (WB) improved from 47.6 to 57.39, which is an improvement of 9.39 points, not more than 10 points. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1273.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Evaluation of syntactic parser and SRL system on out-of-domain data. F1 scores are reported on the failure set. SRL model was trained on NW and the syntactic parser was trained on WSJ Section on OntoNote v5.0. Except PT, which is new and old Testament, all failure rate on out-domain data is higher than that of in-domain (11.9% for parsing and 18.1% for SRL) as suspected. The table shows that GBI can be successfully applied to resolve performance degradation on out-of-domain data." ], "reasoning": "The SRL F1 on the failure set for Weblogs (WB) improved from 47.6 to 57.39, which is an improvement of 9.39 points, not more than 10 points.", "statement": "The SRL F1 on the failure set for Weblogs (WB) improved by more than 10 points.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 11: Comparison of runtime for difference inference procedures in the noise-free constraint setting: Viterbi, A*[He et al.2017] and GBI. For SRL-100 refer Table 1 and SRL-NW is a model trained on NW genre.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "GBI is generally faster than A*, except in the BC domain where it is only slightly faster due to higher constraint violation difficulty.In general, we observe that GBI tends to be faster than A*, especially when the dataset is large enough. One exception is the BC domain where GBI is just slightly faster than A *. We hypothesize it might be due to the difficulty of the constraint violations as its failure rate is higher than usual. GBI will spend more time searching for the correct output (more iterations) if it is harder to find the solution." } ], "images": [ "scigen/train/1274.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Comparison of runtime for difference inference procedures in the noise-free constraint setting: Viterbi, A*[He et al.2017] and GBI. For SRL-100 refer Table 1 and SRL-NW is a model trained on NW genre." ], "reasoning": "GBI is generally faster than A*, except in the BC domain where it is only slightly faster due to higher constraint violation difficulty.", "description": "In general, we observe that GBI tends to be faster than A*, especially when the dataset is large enough. One exception is the BC domain where GBI is just slightly faster than A *. We hypothesize it might be due to the difficulty of the constraint violations as its failure rate is higher than usual. GBI will spend more time searching for the correct output (more iterations) if it is harder to find the solution.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 11: Comparison of runtime for difference inference procedures in the noise-free constraint setting: Viterbi, A*[He et al.2017] and GBI. For SRL-100 refer Table 1 and SRL-NW is a model trained on NW genre.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich network has the highest failure rate and what is its inference time using GBI?\n" }, { "role": "assistant", "content": "The network with the highest failure rate is SRL-NW BC with a failure rate of 26.86%. Its inference time using GBI is 110 minutes. So, the answer is {\"answer\": \"110\"}" } ], "images": [ "scigen/train/1274.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Comparison of runtime for difference inference procedures in the noise-free constraint setting: Viterbi, A*[He et al.2017] and GBI. For SRL-100 refer Table 1 and SRL-NW is a model trained on NW genre." ], "reasoning": "The network with the highest failure rate is SRL-NW BC with a failure rate of 26.86%. Its inference time using GBI is 110 minutes.", "question": "Which network has the highest failure rate and what is its inference time using GBI?", "answer": "So, the answer is {\"answer\": \"110\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe SRL-NW PT network has a lower failure rate than the SRL-NW BN network.\n" }, { "role": "assistant", "content": "The failure rate for SRL-NW PT is 10.01%, while the failure rate for SRL-NW BN is 18.51%. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1274.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Comparison of runtime for difference inference procedures in the noise-free constraint setting: Viterbi, A*[He et al.2017] and GBI. For SRL-100 refer Table 1 and SRL-NW is a model trained on NW genre." ], "reasoning": "The failure rate for SRL-NW PT is 10.01%, while the failure rate for SRL-NW BN is 18.51%. Therefore, the statement is supported.", "statement": "The SRL-NW PT network has a lower failure rate than the SRL-NW BN network.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Best delta BLEU by layer when only one head is kept in the WMT model. Underlined numbers indicate that the change is statistically significant with p<0.01.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "To determine if more than one head is needed, we analyze the performance difference when all heads except one are removed within a single layer.This observation begets the question: is more than one head even needed? Therefore, we compute the difference in performance when all heads except one are removed, within a single layer." } ], "images": [ "scigen/train/1275.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Best delta BLEU by layer when only one head is kept in the WMT model. Underlined numbers indicate that the change is statistically significant with p<0.01." ], "reasoning": "To determine if more than one head is needed, we analyze the performance difference when all heads except one are removed within a single layer.", "description": "This observation begets the question: is more than one head even needed? Therefore, we compute the difference in performance when all heads except one are removed, within a single layer.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: 'Table 2: Best delta BLEU by layer when only one head is kept in the WMT model. Underlined numbers indicate that the change is statistically significant with p<0.01.'\nQuestion: What is the performance difference in the Enc-Dec category for Layer 5 when all heads except one are removed?\n" }, { "role": "assistant", "content": "To find the performance difference in the Enc-Dec category for Layer 5, we look at the value in the Enc-Dec column for Layer 5, which is -1.55. So, the answer is {\"answer\": \"-1.55\"}." } ], "images": [ "scigen/train/1275.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Best delta BLEU by layer when only one head is kept in the WMT model. Underlined numbers indicate that the change is statistically significant with p<0.01." ], "reasoning": "To find the performance difference in the Enc-Dec category for Layer 5, we look at the value in the Enc-Dec column for Layer 5, which is -1.55.", "question": "What is the performance difference in the Enc-Dec category for Layer 5 when all heads except one are removed?", "answer": "So, the answer is {\"answer\": \"-1.55\"}." }, { "messages": [ { "role": "user", "content": "The performance difference in the Dec-Dec category for Layer 6 is the highest among all layers. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe performance difference in the Dec-Dec category for Layer 6 is the highest among all layers.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the values in the Dec-Dec column for all layers. The value for Layer 6 is 0.24, which is the highest among all layers. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1275.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Best delta BLEU by layer when only one head is kept in the WMT model. Underlined numbers indicate that the change is statistically significant with p<0.01." ], "reasoning": "To verify this statement, we compare the values in the Dec-Dec column for all layers. The value for Layer 6 is 0.24, which is the highest among all layers.", "statement": "The performance difference in the Dec-Dec category for Layer 6 is the highest among all layers.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Difference in BLEU score for each head of the encoder’s self attention mechanism. Underlined numbers indicate that the change is statistically significant with p<0.01. The base BLEU score is 36.05.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Only 8 out of 96 heads cause a significant change in performance when removed, with half improving the BLEU score, indicating most heads are redundant at test time.Notably, we see that only 8 (out of 96) heads cause a statistically significant change in performance when they are removed from the model, half of which actually result in a higher BLEU score. This leads us to our first observation: at test time, most heads are redundant given the rest of the model." } ], "images": [ "scigen/train/1276.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Difference in BLEU score for each head of the encoder’s self attention mechanism. Underlined numbers indicate that the change is statistically significant with p<0.01. The base BLEU score is 36.05." ], "reasoning": "Only 8 out of 96 heads cause a significant change in performance when removed, with half improving the BLEU score, indicating most heads are redundant at test time.", "description": "Notably, we see that only 8 (out of 96) heads cause a statistically significant change in performance when they are removed from the model, half of which actually result in a higher BLEU score. This leads us to our first observation: at test time, most heads are redundant given the rest of the model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Difference in BLEU score for each head of the encoder’s self attention mechanism. Underlined numbers indicate that the change is statistically significant with p<0.01. The base BLEU score is 36.05.', answer the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nHow many heads result in a higher BLEU score when removed?\n" }, { "role": "assistant", "content": "The table shows 8 heads cause a significant change in performance when removed, and the description states half of these result in a higher BLEU score. So, the answer is {\"answer\": \"4\"}" } ], "images": [ "scigen/train/1276.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Difference in BLEU score for each head of the encoder’s self attention mechanism. Underlined numbers indicate that the change is statistically significant with p<0.01. The base BLEU score is 36.05." ], "reasoning": "The table shows 8 heads cause a significant change in performance when removed, and the description states half of these result in a higher BLEU score.", "question": "How many heads result in a higher BLEU score when removed?", "answer": "So, the answer is {\"answer\": \"4\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nRemoving 8 heads causes a statistically significant change in performance.\n" }, { "role": "assistant", "content": "The description states that only 8 out of 96 heads cause a statistically significant change in performance when removed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1276.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Difference in BLEU score for each head of the encoder’s self attention mechanism. Underlined numbers indicate that the change is statistically significant with p<0.01. The base BLEU score is 36.05." ], "reasoning": "The description states that only 8 out of 96 heads cause a statistically significant change in performance when removed.", "statement": "Removing 8 heads causes a statistically significant change in performance.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Average inference speed of BERT on the MNLI-matched validation set in examples per second (± standard deviation). The speedup relative to the original model is indicated in parentheses.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Pruning half of the model’s heads results in up to a 17.5% increase in inference speed for higher batch sizes, with experiments conducted on two machines equipped with GeForce GTX 1080Ti GPUs.Moreover, we find that actually pruning the heads (and not just masking) results in an appreciable increase in inference speed. Experiments were conducted on two different machines, both equipped with GeForce GTX 1080Ti GPUs. Each experiment is repeated 3 times on each machine (for a total of 6 datapoints for each setting). We find that pruning half of the model’s heads speeds up inference by up to ≈17.5% for higher batch sizes (this difference vanishes for smaller batch sizes)." } ], "images": [ "scigen/train/1277.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Average inference speed of BERT on the MNLI-matched validation set in examples per second (± standard deviation). The speedup relative to the original model is indicated in parentheses." ], "reasoning": "Pruning half of the model’s heads results in up to a 17.5% increase in inference speed for higher batch sizes, with experiments conducted on two machines equipped with GeForce GTX 1080Ti GPUs.", "description": "Moreover, we find that actually pruning the heads (and not just masking) results in an appreciable increase in inference speed. Experiments were conducted on two different machines, both equipped with GeForce GTX 1080Ti GPUs. Each experiment is repeated 3 times on each machine (for a total of 6 datapoints for each setting). We find that pruning half of the model’s heads speeds up inference by up to ≈17.5% for higher batch sizes (this difference vanishes for smaller batch sizes).", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 4: Average inference speed of BERT on the MNLI-matched validation set in examples per second (± standard deviation). The speedup relative to the original model is indicated in parentheses.'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the percentage increase in inference speed when pruning half of the model’s heads for a batch size of 16?\n" }, { "role": "assistant", "content": "The table shows that for a batch size of 16, the inference speed increases by 17.5% when pruning half of the model’s heads. So, the answer is {\"answer\": \"17.5%\"}." } ], "images": [ "scigen/train/1277.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Average inference speed of BERT on the MNLI-matched validation set in examples per second (± standard deviation). The speedup relative to the original model is indicated in parentheses." ], "reasoning": "The table shows that for a batch size of 16, the inference speed increases by 17.5% when pruning half of the model’s heads.", "question": "What is the percentage increase in inference speed when pruning half of the model’s heads for a batch size of 16?", "answer": "So, the answer is {\"answer\": \"17.5%\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nPruning half of the model’s heads results in a 17.5% increase in inference speed for a batch size of 4.\n" }, { "role": "assistant", "content": "The table shows that for a batch size of 4, the inference speed increases by 2.7%, not 17.5%, when pruning half of the model’s heads. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1277.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Average inference speed of BERT on the MNLI-matched validation set in examples per second (± standard deviation). The speedup relative to the original model is indicated in parentheses." ], "reasoning": "The table shows that for a batch size of 4, the inference speed increases by 2.7%, not 17.5%, when pruning half of the model’s heads.", "statement": "Pruning half of the model’s heads results in a 17.5% increase in inference speed for a batch size of 4.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Influence of gold mentions. F1 scores on different test sets are reported. Adding human-annotated gold mentions help both the End2end and our model. Best performed model are indicated with the bold font.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Our model benefits from knowledge graph incorporation and achieves comparable performance to Zhang et al. with gold mentions, despite including a new challenging pronoun type.Providing gold mentions to the End2end model can significantly boost its performance by 6.2 F1 and 2.1 F1 on the CoNLL and i2b2 dataset, respectively. Yet, the performance gain from gold mentions is less for our model. Such results clearly illustrate that our model is able to benefit the mention detection with the help of KG incorporation. Besides that, with the help of gold mentions, our model achieves the comparable (slightly better) performance with the context-and-knowledge model Zhang et al. As we also included one new challenging pronoun type, the demonstrative pronoun, the overall performance of their model is lower than the one reported in the original paper. The reason of our model being better is that more knowledge resources (e.g., OMCS) can be incorporated into our model due to its generalizable design. Moreover, it is more difficult for their method Zhang et al. which is expensive and difficult to acquire." } ], "images": [ "scigen/train/1278.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Influence of gold mentions. F1 scores on different test sets are reported. Adding human-annotated gold mentions help both the End2end and our model. Best performed model are indicated with the bold font." ], "reasoning": "Our model benefits from knowledge graph incorporation and achieves comparable performance to Zhang et al. with gold mentions, despite including a new challenging pronoun type.", "description": "Providing gold mentions to the End2end model can significantly boost its performance by 6.2 F1 and 2.1 F1 on the CoNLL and i2b2 dataset, respectively. Yet, the performance gain from gold mentions is less for our model. Such results clearly illustrate that our model is able to benefit the mention detection with the help of KG incorporation. Besides that, with the help of gold mentions, our model achieves the comparable (slightly better) performance with the context-and-knowledge model Zhang et al. As we also included one new challenging pronoun type, the demonstrative pronoun, the overall performance of their model is lower than the one reported in the original paper. The reason of our model being better is that more knowledge resources (e.g., OMCS) can be incorporated into our model due to its generalizable design. Moreover, it is more difficult for their method Zhang et al. which is expensive and difficult to acquire.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: Table 5: Influence of gold mentions. F1 scores on different test sets are reported. Adding human-annotated gold mentions help both the End2end and our model. Best performed model are indicated with the bold font.\nQuestion: What is the F1 score improvement for the End2end model on the CoNLL dataset when gold mentions are provided?\n" }, { "role": "assistant", "content": "The End2end model's F1 score on the CoNLL dataset improves from 71.6 to 77.8 when gold mentions are provided, resulting in an improvement of 6.2 F1. So, the answer is {\"answer\": \"6.2\"}" } ], "images": [ "scigen/train/1278.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Influence of gold mentions. F1 scores on different test sets are reported. Adding human-annotated gold mentions help both the End2end and our model. Best performed model are indicated with the bold font." ], "reasoning": "The End2end model's F1 score on the CoNLL dataset improves from 71.6 to 77.8 when gold mentions are provided, resulting in an improvement of 6.2 F1.", "question": "What is the F1 score improvement for the End2end model on the CoNLL dataset when gold mentions are provided?", "answer": "So, the answer is {\"answer\": \"6.2\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nOur model achieves a higher F1 score than Zhang et al. (2019b) on the CoNLL dataset with gold mentions.\n" }, { "role": "assistant", "content": "The table shows that our model achieves an F1 score of 80.7 on the CoNLL dataset with gold mentions, while Zhang et al. (2019b) achieves 79.9. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1278.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Influence of gold mentions. F1 scores on different test sets are reported. Adding human-annotated gold mentions help both the End2end and our model. Best performed model are indicated with the bold font." ], "reasoning": "The table shows that our model achieves an F1 score of 80.7 on the CoNLL dataset with gold mentions, while Zhang et al. (2019b) achieves 79.9.", "statement": "Our model achieves a higher F1 score than Zhang et al. (2019b) on the CoNLL dataset with gold mentions.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled '(a) CoNLL'.\n" }, { "role": "assistant", "content": "Our model outperforms all baselines on both datasets, especially on third personal and possessive pronouns, but struggles with demonstrative pronouns.Overall, our model outperforms all baselines on two datasets with respect to all pronoun types. There are several interesting observations. In general, the i2b2 dataset seems simpler than the CoNLL dataset, which might because that i2b2 only involves clinical narratives and its training data is highly similar to the test data. As a result, all neural models perform dramatically good, especially on the third personal and possessive pronouns. In addition, we also notice that it is more challenging for all models to resolve demonstrative pronouns (e.g., this, that) on both datasets, because such pronouns may refer to complex things and occur with low frequency." } ], "images": [ "scigen/train/1279.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) CoNLL" ], "reasoning": "Our model outperforms all baselines on both datasets, especially on third personal and possessive pronouns, but struggles with demonstrative pronouns.", "description": "Overall, our model outperforms all baselines on two datasets with respect to all pronoun types. There are several interesting observations. In general, the i2b2 dataset seems simpler than the CoNLL dataset, which might because that i2b2 only involves clinical narratives and its training data is highly similar to the test data. As a result, all neural models perform dramatically good, especially on the third personal and possessive pronouns. In addition, we also notice that it is more challenging for all models to resolve demonstrative pronouns (e.g., this, that) on both datasets, because such pronouns may refer to complex things and occur with low frequency.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of '(a) CoNLL'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest F1 score for demonstrative pronouns?\n" }, { "role": "assistant", "content": "By examining the Demonstrative F1 column, we see that 'Our Complete Model' has the highest F1 score of 53.9. So, the answer is {\"answer\": \"Our Complete Model\"}." } ], "images": [ "scigen/train/1279.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) CoNLL" ], "reasoning": "By examining the Demonstrative F1 column, we see that 'Our Complete Model' has the highest F1 score of 53.9.", "question": "Which model has the highest F1 score for demonstrative pronouns?", "answer": "So, the answer is {\"answer\": \"Our Complete Model\"}." }, { "messages": [ { "role": "user", "content": "The End2end model has the highest precision for possessive pronouns. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe End2end model has the highest precision for possessive pronouns.\n" }, { "role": "assistant", "content": "By examining the Possessive P column, we see that 'Our Complete Model' has the highest precision of 80.7, not the End2end model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1279.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) CoNLL" ], "reasoning": "By examining the Possessive P column, we see that 'Our Complete Model' has the highest precision of 80.7, not the End2end model.", "statement": "The End2end model has the highest precision for possessive pronouns.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption '(b) i2b2'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our model outperforms all baselines on both datasets, especially on third personal and possessive pronouns, but struggles with demonstrative pronouns.Overall, our model outperforms all baselines on two datasets with respect to all pronoun types. There are several interesting observations. In general, the i2b2 dataset seems simpler than the CoNLL dataset, which might because that i2b2 only involves clinical narratives and its training data is highly similar to the test data. As a result, all neural models perform dramatically good, especially on the third personal and possessive pronouns. In addition, we also notice that it is more challenging for all models to resolve demonstrative pronouns (e.g., this, that) on both datasets, because such pronouns may refer to complex things and occur with low frequency." } ], "images": [ "scigen/train/1280.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) i2b2" ], "reasoning": "Our model outperforms all baselines on both datasets, especially on third personal and possessive pronouns, but struggles with demonstrative pronouns.", "description": "Overall, our model outperforms all baselines on two datasets with respect to all pronoun types. There are several interesting observations. In general, the i2b2 dataset seems simpler than the CoNLL dataset, which might because that i2b2 only involves clinical narratives and its training data is highly similar to the test data. As a result, all neural models perform dramatically good, especially on the third personal and possessive pronouns. In addition, we also notice that it is more challenging for all models to resolve demonstrative pronouns (e.g., this, that) on both datasets, because such pronouns may refer to complex things and occur with low frequency.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: (b) i2b2\nQuestion: Which model has the highest F1 score for possessive pronouns?\nSolve the above question based on the table information. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest F1 score for possessive pronouns, look at the 'Possessive F1' column and identify the highest value. So, the answer is {\"answer\": \"Our Complete Model\"}" } ], "images": [ "scigen/train/1280.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) i2b2" ], "reasoning": "To find the model with the highest F1 score for possessive pronouns, look at the 'Possessive F1' column and identify the highest value.", "question": "Which model has the highest F1 score for possessive pronouns?", "answer": "So, the answer is {\"answer\": \"Our Complete Model\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe End2end model has the highest precision for demonstrative pronouns.\n" }, { "role": "assistant", "content": "Check the 'Demonstrative P' column to see if the End2end model has the highest value. The highest value is 71.9, which belongs to 'Our Complete Model', not End2end. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1280.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) i2b2" ], "reasoning": "Check the 'Demonstrative P' column to see if the End2end model has the highest value. The highest value is 71.9, which belongs to 'Our Complete Model', not End2end.", "statement": "The End2end model has the highest precision for demonstrative pronouns.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 6: Cross-domain performance of different models. F1 on the target domain test sets are reported.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Our model demonstrates better cross-domain performance compared to the End2end model, particularly when trained on CoNLL and tested on i2b2, achieving higher F1 scores.Considering that neural models are intensive data-driven and normally restricted by data nature, they are not easily applied in a cross-domain setting. However, if a model is required to perform in real applications, it has to show promising performance on those cases out of the training data. Overall, all models perform significantly worse if they are used cross domains. Specifically, if we train the End2end model on the CoNLL dataset and test it on the i2b2 dataset, it only achieves 75.2 F1. As a comparison, our model can achieve 80.9 F1 in the same case. This observation confirms the capability of knowledge where our model is able to handle. A similar observation is also drawn for the reversed case. However, even though our model outperforms the End2end model by 22.7 F1 from i2b2 to CoNLL, its overall performance is still poor, which might be explained by that the i2b2 is an in-domain dataset and the knowledge contained in its training data is rarely useful for the general (news) domain dataset. Nevertheless, this experiment clearly shows that the generalization ability of deep models is still crucial for building a successful coreference model, and learns to use knowledge is a promising solution to it." } ], "images": [ "scigen/train/1281.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Cross-domain performance of different models. F1 on the target domain test sets are reported." ], "reasoning": "Our model demonstrates better cross-domain performance compared to the End2end model, particularly when trained on CoNLL and tested on i2b2, achieving higher F1 scores.", "description": "Considering that neural models are intensive data-driven and normally restricted by data nature, they are not easily applied in a cross-domain setting. However, if a model is required to perform in real applications, it has to show promising performance on those cases out of the training data. Overall, all models perform significantly worse if they are used cross domains. Specifically, if we train the End2end model on the CoNLL dataset and test it on the i2b2 dataset, it only achieves 75.2 F1. As a comparison, our model can achieve 80.9 F1 in the same case. This observation confirms the capability of knowledge where our model is able to handle. A similar observation is also drawn for the reversed case. However, even though our model outperforms the End2end model by 22.7 F1 from i2b2 to CoNLL, its overall performance is still poor, which might be explained by that the i2b2 is an in-domain dataset and the knowledge contained in its training data is rarely useful for the general (news) domain dataset. Nevertheless, this experiment clearly shows that the generalization ability of deep models is still crucial for building a successful coreference model, and learns to use knowledge is a promising solution to it.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 6: Cross-domain performance of different models. F1 on the target domain test sets are reported.', solve the following question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the F1 score of the End2end model when trained on i2b2 and tested on CoNLL?\n" }, { "role": "assistant", "content": "The table shows the F1 scores for different models and training/testing data combinations. For the End2end model trained on i2b2 and tested on CoNLL, the F1 score is 20.0. So, the answer is {\"answer\": \"20.0\"}." } ], "images": [ "scigen/train/1281.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Cross-domain performance of different models. F1 on the target domain test sets are reported." ], "reasoning": "The table shows the F1 scores for different models and training/testing data combinations. For the End2end model trained on i2b2 and tested on CoNLL, the F1 score is 20.0.", "question": "What is the F1 score of the End2end model when trained on i2b2 and tested on CoNLL?", "answer": "So, the answer is {\"answer\": \"20.0\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nOur model achieves a higher F1 score than the End2end model when trained on CoNLL and tested on i2b2.\n" }, { "role": "assistant", "content": "The table shows that when trained on CoNLL and tested on i2b2, our model achieves an F1 score of 80.9, while the End2end model achieves 75.2. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1281.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Cross-domain performance of different models. F1 on the target domain test sets are reported." ], "reasoning": "The table shows that when trained on CoNLL and tested on i2b2, our model achieves an F1 score of 80.9, while the End2end model achieves 75.2.", "statement": "Our model achieves a higher F1 score than the End2end model when trained on CoNLL and tested on i2b2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: STREUSLE test set results (%). (Gold): gold POS/lemmas (used in constraints only). (Pred.): predicted POS/lemmas. −Lexcat: excluding lexical category. −SS: excluding supersense. Labeled F: labeled identification F1-score. SNACS: preposition supersenses. MWE LinkAvg P, R, F: evaluates MWE identification with partial credit. Schneider et al. (2018b): previous best tagger for the full SNACS task, reported on a slightly older version of STREUSLE.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Using BERT embeddings with predicted POS tags and lemmas improves performance substantially, even outperforming the use of gold POS tags and lemmas in preposition supersense tagging.For preposition supersenses (SNACS), we compare to the results in Schneider et al. Note that Schneider et al. However, our baseline tagger, even with GloVe embeddings, outperforms Schneider et al. Using BERT embeddings with predicted POS tags and lemmas improves performance substantially; on preposition supersense tagging, it even outperforms using gold POS tags and lemmas. Liu et al." } ], "images": [ "scigen/train/1282.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: STREUSLE test set results (%). (Gold): gold POS/lemmas (used in constraints only). (Pred.): predicted POS/lemmas. −Lexcat: excluding lexical category. −SS: excluding supersense. Labeled F: labeled identification F1-score. SNACS: preposition supersenses. MWE LinkAvg P, R, F: evaluates MWE identification with partial credit. Schneider et al. (2018b): previous best tagger for the full SNACS task, reported on a slightly older version of STREUSLE." ], "reasoning": "Using BERT embeddings with predicted POS tags and lemmas improves performance substantially, even outperforming the use of gold POS tags and lemmas in preposition supersense tagging.", "description": "For preposition supersenses (SNACS), we compare to the results in Schneider et al. Note that Schneider et al. However, our baseline tagger, even with GloVe embeddings, outperforms Schneider et al. Using BERT embeddings with predicted POS tags and lemmas improves performance substantially; on preposition supersense tagging, it even outperforms using gold POS tags and lemmas. Liu et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: STREUSLE test set results (%). (Gold): gold POS/lemmas (used in constraints only). (Pred.): predicted POS/lemmas. −Lexcat: excluding lexical category. −SS: excluding supersense. Labeled F: labeled identification F1-score. SNACS: preposition supersenses. MWE LinkAvg P, R, F: evaluates MWE identification with partial credit. Schneider et al. (2018b): previous best tagger for the full SNACS task, reported on a slightly older version of STREUSLE.', answer the following question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model and configuration achieved the highest F1 score for SNACS Role?\n" }, { "role": "assistant", "content": "By comparing the F1 scores for SNACS Role across different models and configurations, BERT (Pred.) achieved the highest F1 score of 82.4. So, the answer is {\"answer\": \"BERT (Pred.)\"}" } ], "images": [ "scigen/train/1282.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: STREUSLE test set results (%). (Gold): gold POS/lemmas (used in constraints only). (Pred.): predicted POS/lemmas. −Lexcat: excluding lexical category. −SS: excluding supersense. Labeled F: labeled identification F1-score. SNACS: preposition supersenses. MWE LinkAvg P, R, F: evaluates MWE identification with partial credit. Schneider et al. (2018b): previous best tagger for the full SNACS task, reported on a slightly older version of STREUSLE." ], "reasoning": "By comparing the F1 scores for SNACS Role across different models and configurations, BERT (Pred.) achieved the highest F1 score of 82.4.", "question": "Which model and configuration achieved the highest F1 score for SNACS Role?", "answer": "So, the answer is {\"answer\": \"BERT (Pred.)\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nGloVe (Gold) achieved a higher F1 score for SNACS Labeled than BERT (Gold).\n" }, { "role": "assistant", "content": "The table shows that BERT (Gold) has an F1 score of 71.4 for SNACS Labeled, while GloVe (Gold) has an F1 score of 61.0, indicating that the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1282.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: STREUSLE test set results (%). (Gold): gold POS/lemmas (used in constraints only). (Pred.): predicted POS/lemmas. −Lexcat: excluding lexical category. −SS: excluding supersense. Labeled F: labeled identification F1-score. SNACS: preposition supersenses. MWE LinkAvg P, R, F: evaluates MWE identification with partial credit. Schneider et al. (2018b): previous best tagger for the full SNACS task, reported on a slightly older version of STREUSLE." ], "reasoning": "The table shows that BERT (Gold) has an F1 score of 71.4 for SNACS Labeled, while GloVe (Gold) has an F1 score of 61.0, indicating that the statement is refuted.", "statement": "GloVe (Gold) achieved a higher F1 score for SNACS Labeled than BERT (Gold).", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: PARSEME test set results (%). (Gold): gold POS/lemmas (used in constraints only). (Pred.): predicted POS/lemmas. Nerima et al. (2017): a rule-based system which performed best for English in the shared task (Ramisch et al., 2018). Taslimipoor et al. (2019), Rohanian et al. (2019): more recent results on the test set (both used ELMo and dependency parses; only some scores were reported).'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Replacing GloVe embeddings with BERT embeddings improves the MWE-based F-score and exceeds the best reported token-based F-score, demonstrating the effectiveness of pre-training contextualized embeddings on large corpora for out-of-domain generalization.While using GloVe embeddings does not reach the performance of the existing PARSEME systems—some of which were trained on the PARSEME training set—replacing them with BERT embeddings approaches the state of the art MWE-based F-score and exceeds the best reported token-based F-score, despite the challenging zero-shot out-of-domain generalization setting. This demonstrates that pre-training contextualized embeddings on large corpora can help models generalize to out-of-domain settings." } ], "images": [ "scigen/train/1283.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: PARSEME test set results (%). (Gold): gold POS/lemmas (used in constraints only). (Pred.): predicted POS/lemmas. Nerima et al. (2017): a rule-based system which performed best for English in the shared task (Ramisch et al., 2018). Taslimipoor et al. (2019), Rohanian et al. (2019): more recent results on the test set (both used ELMo and dependency parses; only some scores were reported)." ], "reasoning": "Replacing GloVe embeddings with BERT embeddings improves the MWE-based F-score and exceeds the best reported token-based F-score, demonstrating the effectiveness of pre-training contextualized embeddings on large corpora for out-of-domain generalization.", "description": "While using GloVe embeddings does not reach the performance of the existing PARSEME systems—some of which were trained on the PARSEME training set—replacing them with BERT embeddings approaches the state of the art MWE-based F-score and exceeds the best reported token-based F-score, despite the challenging zero-shot out-of-domain generalization setting. This demonstrates that pre-training contextualized embeddings on large corpora can help models generalize to out-of-domain settings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 3: PARSEME test set results (%). (Gold): gold POS/lemmas (used in constraints only). (Pred.): predicted POS/lemmas. Nerima et al. (2017): a rule-based system which performed best for English in the shared task (Ramisch et al., 2018). Taslimipoor et al. (2019), Rohanian et al. (2019): more recent results on the test set (both used ELMo and dependency parses; only some scores were reported).' and proceed to solve the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the MWE-based F-score for BERT (Gold) and how does it compare to the GloVe (Gold) MWE-based F-score?\n" }, { "role": "assistant", "content": "The table shows that the MWE-based F-score for BERT (Gold) is 38.3, while the MWE-based F-score for GloVe (Gold) is 19.3. Comparing these scores, BERT (Gold) has a significantly higher MWE-based F-score than GloVe (Gold). So, the answer is {\"answer\": \"38.3\"}" } ], "images": [ "scigen/train/1283.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: PARSEME test set results (%). (Gold): gold POS/lemmas (used in constraints only). (Pred.): predicted POS/lemmas. Nerima et al. (2017): a rule-based system which performed best for English in the shared task (Ramisch et al., 2018). Taslimipoor et al. (2019), Rohanian et al. (2019): more recent results on the test set (both used ELMo and dependency parses; only some scores were reported)." ], "reasoning": "The table shows that the MWE-based F-score for BERT (Gold) is 38.3, while the MWE-based F-score for GloVe (Gold) is 19.3. Comparing these scores, BERT (Gold) has a significantly higher MWE-based F-score than GloVe (Gold).", "question": "What is the MWE-based F-score for BERT (Gold) and how does it compare to the GloVe (Gold) MWE-based F-score?", "answer": "So, the answer is {\"answer\": \"38.3\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nBERT (Gold) has a higher Token-based F-score than GloVe (Gold).\n" }, { "role": "assistant", "content": "The table shows that the Token-based F-score for BERT (Gold) is 42.7, while the Token-based F-score for GloVe (Gold) is 25.4. Therefore, BERT (Gold) has a higher Token-based F-score than GloVe (Gold). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1283.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: PARSEME test set results (%). (Gold): gold POS/lemmas (used in constraints only). (Pred.): predicted POS/lemmas. Nerima et al. (2017): a rule-based system which performed best for English in the shared task (Ramisch et al., 2018). Taslimipoor et al. (2019), Rohanian et al. (2019): more recent results on the test set (both used ELMo and dependency parses; only some scores were reported)." ], "reasoning": "The table shows that the Token-based F-score for BERT (Gold) is 42.7, while the Token-based F-score for GloVe (Gold) is 25.4. Therefore, BERT (Gold) has a higher Token-based F-score than GloVe (Gold).", "statement": "BERT (Gold) has a higher Token-based F-score than GloVe (Gold).", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: DiMSUM test set results (%). The test set covers three domains: reviews, tweets, and TED talks. (G): gold POS/lemmas (used in constraints only). (P): predicted POS/lemmas. Kirilin et al. (2016): the best performing system from Schneider et al. (2016). Kirilin et al. (2016) and other shared task systems had access to gold POS/lemmas and Twitter training data in addition to all of STREUSLE for training.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The BERT model did not outperform the best shared task system, Kirilin et al., in the full lexical semantic recognition task, as indicated by the lower combined accuracy and F1 scores.Again, this is a zero-shot out-of-domain evaluation setting. In this case, the BERT model did not outperform the best shared task system, likely owing to the comparative difficulty of the full lexical semantic recognition task versus the restricted DiMSUM setting." } ], "images": [ "scigen/train/1284.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: DiMSUM test set results (%). The test set covers three domains: reviews, tweets, and TED talks. (G): gold POS/lemmas (used in constraints only). (P): predicted POS/lemmas. Kirilin et al. (2016): the best performing system from Schneider et al. (2016). Kirilin et al. (2016) and other shared task systems had access to gold POS/lemmas and Twitter training data in addition to all of STREUSLE for training." ], "reasoning": "The BERT model did not outperform the best shared task system, Kirilin et al., in the full lexical semantic recognition task, as indicated by the lower combined accuracy and F1 scores.", "description": "Again, this is a zero-shot out-of-domain evaluation setting. In this case, the BERT model did not outperform the best shared task system, likely owing to the comparative difficulty of the full lexical semantic recognition task versus the restricted DiMSUM setting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: DiMSUM test set results (%). The test set covers three domains: reviews, tweets, and TED talks. (G): gold POS/lemmas (used in constraints only). (P): predicted POS/lemmas. Kirilin et al. (2016): the best performing system from Schneider et al. (2016). Kirilin et al. (2016) and other shared task systems had access to gold POS/lemmas and Twitter training data in addition to all of STREUSLE for training.\nQuestion: Which model achieved the highest combined accuracy?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the model with the highest combined accuracy, compare the 'Combined Accuracy' column for all models. Kirilin et al. achieved the highest combined accuracy with a score of 85.3. So, the answer is {\"answer\": \"Kirilin et al.\"}." } ], "images": [ "scigen/train/1284.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: DiMSUM test set results (%). The test set covers three domains: reviews, tweets, and TED talks. (G): gold POS/lemmas (used in constraints only). (P): predicted POS/lemmas. Kirilin et al. (2016): the best performing system from Schneider et al. (2016). Kirilin et al. (2016) and other shared task systems had access to gold POS/lemmas and Twitter training data in addition to all of STREUSLE for training." ], "reasoning": "To find the model with the highest combined accuracy, compare the 'Combined Accuracy' column for all models. Kirilin et al. achieved the highest combined accuracy with a score of 85.3.", "question": "Which model achieved the highest combined accuracy?", "answer": "So, the answer is {\"answer\": \"Kirilin et al.\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe GloVe (Gold) model achieved higher precision in MWEs than the BERT (Gold) model.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'MWEs P' values for GloVe (Gold) and BERT (Gold). GloVe (Gold) has a precision of 27.9, while BERT (Gold) has a precision of 47.9. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1284.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: DiMSUM test set results (%). The test set covers three domains: reviews, tweets, and TED talks. (G): gold POS/lemmas (used in constraints only). (P): predicted POS/lemmas. Kirilin et al. (2016): the best performing system from Schneider et al. (2016). Kirilin et al. (2016) and other shared task systems had access to gold POS/lemmas and Twitter training data in addition to all of STREUSLE for training." ], "reasoning": "To verify this statement, compare the 'MWEs P' values for GloVe (Gold) and BERT (Gold). GloVe (Gold) has a precision of 27.9, while BERT (Gold) has a precision of 47.9. Therefore, the statement is incorrect.", "statement": "The GloVe (Gold) model achieved higher precision in MWEs than the BERT (Gold) model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: The results of two weighting (WT) strategies based on the soft regularization.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Adaptive weighting assigns larger weights to visual words and smaller weights to non-visual words, improving the effectiveness of visual information usage.Considering that the generation of non-visual words requires little visual information from the image, we propose adaptive weighting, which aims to assign reasonable weights to visual regularization term automatically. The reason is that based on the degree of dependence of the word to be generated on the image, the proposed adaptive weighting can adaptively adjust the weight of the regularization term. The weights corresponding to visual words are larger, which introduces visual agreement regularization to leverage visual information more effectively. For non-visual words, however, a smaller weight will be assigned to the regularization term in the training objective, which prevents the presence of misleading gradient signals." } ], "images": [ "scigen/train/1285.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The results of two weighting (WT) strategies based on the soft regularization." ], "reasoning": "Adaptive weighting assigns larger weights to visual words and smaller weights to non-visual words, improving the effectiveness of visual information usage.", "description": "Considering that the generation of non-visual words requires little visual information from the image, we propose adaptive weighting, which aims to assign reasonable weights to visual regularization term automatically. The reason is that based on the degree of dependence of the word to be generated on the image, the proposed adaptive weighting can adaptively adjust the weight of the regularization term. The weights corresponding to visual words are larger, which introduces visual agreement regularization to leverage visual information more effectively. For non-visual words, however, a smaller weight will be assigned to the regularization term in the training objective, which prevents the presence of misleading gradient signals.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nTable title: Table 4: The results of two weighting (WT) strategies based on the soft regularization.\n\nQuestion: Which method achieved the highest EN→FR BLEU score and what was the score?\n" }, { "role": "assistant", "content": "By comparing the EN→FR BLEU scores for all methods, Adaptive-WT achieved the highest score of 52.4. So, the answer is {\"answer\": \"Adaptive-WT with 52.4\"}." } ], "images": [ "scigen/train/1285.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The results of two weighting (WT) strategies based on the soft regularization." ], "reasoning": "By comparing the EN→FR BLEU scores for all methods, Adaptive-WT achieved the highest score of 52.4.", "question": "Which method achieved the highest EN→FR BLEU score and what was the score?", "answer": "So, the answer is {\"answer\": \"Adaptive-WT with 52.4\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Frozen-WT achieved the highest EN→DE METEOR score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nFrozen-WT achieved the highest EN→DE METEOR score.\n" }, { "role": "assistant", "content": "By comparing the EN→DE METEOR scores for all methods, Adaptive-WT achieved the highest score of 50.9, not Frozen-WT. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1285.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The results of two weighting (WT) strategies based on the soft regularization." ], "reasoning": "By comparing the EN→DE METEOR scores for all methods, Adaptive-WT achieved the highest score of 50.9, not Frozen-WT.", "statement": "Frozen-WT achieved the highest EN→DE METEOR score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 1: The experimental results of Seq2Seq-based systems. The best performance is highlighted in bold.'.\n" }, { "role": "assistant", "content": "The Proposal (Soft) model outperforms the text-only Seq2Seq model and the best-performing baseline by 2.2 and 1.1 BLEU score respectively on the EN→DE language pair, demonstrating the effectiveness of visual features in enhancing machine translation.For instance, our soft regularization model outperforms the text-only Seq2Seq model and the best-performing baseline by 2.2 and 1.1 BLEU score respectively on the EN→DE language pair. This illustrates that visual features are capable of promoting the performance of machine translation, and our approaches can make better use of visual information." } ], "images": [ "scigen/train/1286.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The experimental results of Seq2Seq-based systems. The best performance is highlighted in bold." ], "reasoning": "The Proposal (Soft) model outperforms the text-only Seq2Seq model and the best-performing baseline by 2.2 and 1.1 BLEU score respectively on the EN→DE language pair, demonstrating the effectiveness of visual features in enhancing machine translation.", "description": "For instance, our soft regularization model outperforms the text-only Seq2Seq model and the best-performing baseline by 2.2 and 1.1 BLEU score respectively on the EN→DE language pair. This illustrates that visual features are capable of promoting the performance of machine translation, and our approaches can make better use of visual information.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 1: The experimental results of Seq2Seq-based systems. The best performance is highlighted in bold.'. Solve the following question based on the table.\n\nQuestion: Which method has the highest BLEU score for the EN→DE language pair?\nLet's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the EN→DE BLEU scores in the table, the Proposal (Hard) method has the highest score of 29.3. So, the answer is {\"answer\": \"Proposal (Hard)\"}." } ], "images": [ "scigen/train/1286.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The experimental results of Seq2Seq-based systems. The best performance is highlighted in bold." ], "reasoning": "By examining the EN→DE BLEU scores in the table, the Proposal (Hard) method has the highest score of 29.3.", "question": "Which method has the highest BLEU score for the EN→DE language pair?", "answer": "So, the answer is {\"answer\": \"Proposal (Hard)\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Proposal (Soft) method has the highest METEOR score for the EN→DE language pair. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Proposal (Soft) method has the highest METEOR score for the EN→DE language pair.\n" }, { "role": "assistant", "content": "By examining the EN→DE METEOR scores in the table, the Proposal (Soft) method has a score of 50.9, which is not the highest. The highest METEOR score for EN→DE is 51.2 by Proposal (Hard). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1286.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The experimental results of Seq2Seq-based systems. The best performance is highlighted in bold." ], "reasoning": "By examining the EN→DE METEOR scores in the table, the Proposal (Soft) method has a score of 50.9, which is not the highest. The highest METEOR score for EN→DE is 51.2 by Proposal (Hard).", "statement": "The Proposal (Soft) method has the highest METEOR score for the EN→DE language pair.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: The experimental results of Transformer-based systems. The best performance is highlighted in bold.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Soft regularization generally improves performance across different neural architectures but is less effective than hard regularization due to larger errors in approximating true alignment distribution.This demonstrates that our approaches are universal, which can bring about consistent improvements in performance on different base neural architectures. However, it is worth noting that although soft regularization can achieve better performance than all baselines in most translation directions, its performance is not comparable to hard regularization. This leads to larger error when leveraging attention distribution to approximate the true alignment distribution, and the overall performance would be correspondingly inferior." } ], "images": [ "scigen/train/1287.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The experimental results of Transformer-based systems. The best performance is highlighted in bold." ], "reasoning": "Soft regularization generally improves performance across different neural architectures but is less effective than hard regularization due to larger errors in approximating true alignment distribution.", "description": "This demonstrates that our approaches are universal, which can bring about consistent improvements in performance on different base neural architectures. However, it is worth noting that although soft regularization can achieve better performance than all baselines in most translation directions, its performance is not comparable to hard regularization. This leads to larger error when leveraging attention distribution to approximate the true alignment distribution, and the overall performance would be correspondingly inferior.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: The experimental results of Transformer-based systems. The best performance is highlighted in bold.', answer the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich method achieved the highest EN→DE BLEU score and how does it compare to the highest DE→EN BLEU score?\n" }, { "role": "assistant", "content": "The highest EN→DE BLEU score is 29.5 achieved by Proposal (Soft). The highest DE→EN BLEU score is 33.4 achieved by Proposal (Hard). Comparing these, Proposal (Soft) has a slightly lower DE→EN BLEU score than Proposal (Hard). So, the answer is {\"answer\": \"Proposal (Soft) achieved the highest EN→DE BLEU score of 29.5, while Proposal (Hard) achieved the highest DE→EN BLEU score of 33.4.\"}" } ], "images": [ "scigen/train/1287.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The experimental results of Transformer-based systems. The best performance is highlighted in bold." ], "reasoning": "The highest EN→DE BLEU score is 29.5 achieved by Proposal (Soft). The highest DE→EN BLEU score is 33.4 achieved by Proposal (Hard). Comparing these, Proposal (Soft) has a slightly lower DE→EN BLEU score than Proposal (Hard).", "question": "Which method achieved the highest EN→DE BLEU score and how does it compare to the highest DE→EN BLEU score?", "answer": "So, the answer is {\"answer\": \"Proposal (Soft) achieved the highest EN→DE BLEU score of 29.5, while Proposal (Hard) achieved the highest DE→EN BLEU score of 33.4.\"}" }, { "messages": [ { "role": "user", "content": "Proposal (Soft) outperforms Proposal (Hard) in all translation directions. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nProposal (Soft) outperforms Proposal (Hard) in all translation directions.\n" }, { "role": "assistant", "content": "The table shows that Proposal (Soft) does not outperform Proposal (Hard) in all translation directions. For example, Proposal (Hard) has higher scores in DE→EN BLEU and EN→FR BLEU. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1287.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The experimental results of Transformer-based systems. The best performance is highlighted in bold." ], "reasoning": "The table shows that Proposal (Soft) does not outperform Proposal (Hard) in all translation directions. For example, Proposal (Hard) has higher scores in DE→EN BLEU and EN→FR BLEU.", "statement": "Proposal (Soft) outperforms Proposal (Hard) in all translation directions.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: The results of incremental analysis. The best performance is highlighted in bold and “MHCA” means our proposed multi-head co-attention.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Adding multi-head co-attention and focusing on the same image region improves model performance for machine translation.We treat the text-only Seq2Seq model as the base model and cumulatively add each component until the full model is rebuilt. The results show that the multi-head co-attention model can significantly improve the model performance, illustrating that building bidirectional interactions between textual and visual features can produce better co-dependent representations. By constraining bidirectional models to focus on the same region of the image when generating semantically equivalent visual words, the model can make better use of visual information, resulting in more accurate machine translation hypotheses." } ], "images": [ "scigen/train/1288.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The results of incremental analysis. The best performance is highlighted in bold and “MHCA” means our proposed multi-head co-attention." ], "reasoning": "Adding multi-head co-attention and focusing on the same image region improves model performance for machine translation.", "description": "We treat the text-only Seq2Seq model as the base model and cumulatively add each component until the full model is rebuilt. The results show that the multi-head co-attention model can significantly improve the model performance, illustrating that building bidirectional interactions between textual and visual features can produce better co-dependent representations. By constraining bidirectional models to focus on the same region of the image when generating semantically equivalent visual words, the model can make better use of visual information, resulting in more accurate machine translation hypotheses.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: The results of incremental analysis. The best performance is highlighted in bold and “MHCA” means our proposed multi-head co-attention.' in mind, break down and resolve the question below step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich method achieved the highest EN→DE BLEU score?\n" }, { "role": "assistant", "content": "By comparing the EN→DE BLEU scores across all methods, the Proposal (Hard) method has the highest score of 29.3. So, the answer is {\"answer\": \"Proposal (Hard)\"}." } ], "images": [ "scigen/train/1288.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The results of incremental analysis. The best performance is highlighted in bold and “MHCA” means our proposed multi-head co-attention." ], "reasoning": "By comparing the EN→DE BLEU scores across all methods, the Proposal (Hard) method has the highest score of 29.3.", "question": "Which method achieved the highest EN→DE BLEU score?", "answer": "So, the answer is {\"answer\": \"Proposal (Hard)\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe OnlyText method achieved the highest EN→FR BLEU score.\n" }, { "role": "assistant", "content": "The table shows that the Proposal (Hard) method achieved the highest EN→FR BLEU score of 52.6, not the OnlyText method. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1288.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The results of incremental analysis. The best performance is highlighted in bold and “MHCA” means our proposed multi-head co-attention." ], "reasoning": "The table shows that the Proposal (Hard) method achieved the highest EN→FR BLEU score of 52.6, not the OnlyText method.", "statement": "The OnlyText method achieved the highest EN→FR BLEU score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: The results of visual attention distance (VAD) on the EN→DE and EN→FR translations. The symbol “↓” indicates that lower is better.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The proposed VAD metric measures the difference in visual attention for generating semantically equivalent words, showing greater improvement for visual word pairs compared to non-visual ones.In order to verify that our approach can effectively improve the agreement of visual attention of bidirectional models, here we introduce a new evaluation metric: visual agreement distance (VAD). The proposed VAD aims at characterizing the difference of visual attention when generating semantically equivalent words. Formally, for the given instance (v,x,y), we define VAD as: VAD=1|N(x,y)|∑(x,y)∈N(x,y)ℓ1(Ax→v,Ay→v) (21) where ℓ1(⋅,⋅) denotes ℓ1 distance. N(x,y) denoted mutually aligned word pairs in (x,y), which is defined as: N(x,y)={(x,y)|x∈x,y∈y,x↔y} (22) where x↔y represents that x and y are uniquely aligned with each other. The large VAD means that the model suffers from the poor visual agreement. The results show that our approach can effectively improve the agreement of visual attention to the image of bidirectional models in the generation of semantically equivalent word pairs. In addition, such improvement is more obvious for visual word pairs compared with non-visual word pairs (e.g. 0.44 decline for visual v.s. 0.22 decline for non-visual on EN→DE). As analyzed in the previous section, the generation of visual words exhibits more dependence on the image. Therefore, the adaptive weighting will assign a greater weight to the corresponding regularization, leading to a more significant improvement." } ], "images": [ "scigen/train/1289.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The results of visual attention distance (VAD) on the EN→DE and EN→FR translations. The symbol “↓” indicates that lower is better." ], "reasoning": "The proposed VAD metric measures the difference in visual attention for generating semantically equivalent words, showing greater improvement for visual word pairs compared to non-visual ones.", "description": "In order to verify that our approach can effectively improve the agreement of visual attention of bidirectional models, here we introduce a new evaluation metric: visual agreement distance (VAD). The proposed VAD aims at characterizing the difference of visual attention when generating semantically equivalent words. Formally, for the given instance (v,x,y), we define VAD as: VAD=1|N(x,y)|∑(x,y)∈N(x,y)ℓ1(Ax→v,Ay→v) (21) where ℓ1(⋅,⋅) denotes ℓ1 distance. N(x,y) denoted mutually aligned word pairs in (x,y), which is defined as: N(x,y)={(x,y)|x∈x,y∈y,x↔y} (22) where x↔y represents that x and y are uniquely aligned with each other. The large VAD means that the model suffers from the poor visual agreement. The results show that our approach can effectively improve the agreement of visual attention to the image of bidirectional models in the generation of semantically equivalent word pairs. In addition, such improvement is more obvious for visual word pairs compared with non-visual word pairs (e.g. 0.44 decline for visual v.s. 0.22 decline for non-visual on EN→DE). As analyzed in the previous section, the generation of visual words exhibits more dependence on the image. Therefore, the adaptive weighting will assign a greater weight to the corresponding regularization, leading to a more significant improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 5: The results of visual attention distance (VAD) on the EN→DE and EN→FR translations. The symbol “↓” indicates that lower is better.', answer the question below by showing each progressive step toward the answer. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the decline in VAD for EN→DE visual word pairs when using the proposed approach?\n" }, { "role": "assistant", "content": "The VAD for EN→DE visual word pairs without agreement is 1.18, and with agreement, it is 0.74. The decline is calculated as 1.18 - 0.74. So, the answer is {\"answer\": \"0.44\"}." } ], "images": [ "scigen/train/1289.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The results of visual attention distance (VAD) on the EN→DE and EN→FR translations. The symbol “↓” indicates that lower is better." ], "reasoning": "The VAD for EN→DE visual word pairs without agreement is 1.18, and with agreement, it is 0.74. The decline is calculated as 1.18 - 0.74.", "question": "What is the decline in VAD for EN→DE visual word pairs when using the proposed approach?", "answer": "So, the answer is {\"answer\": \"0.44\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe VAD for EN→FR non-visual word pairs is higher without agreement than with agreement.\n" }, { "role": "assistant", "content": "The table shows that the VAD for EN→FR non-visual word pairs is 1.52 without agreement and 1.45 with agreement, indicating a higher VAD without agreement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1289.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The results of visual attention distance (VAD) on the EN→DE and EN→FR translations. The symbol “↓” indicates that lower is better." ], "reasoning": "The table shows that the VAD for EN→FR non-visual word pairs is 1.52 without agreement and 1.45 with agreement, indicating a higher VAD without agreement.", "statement": "The VAD for EN→FR non-visual word pairs is higher without agreement than with agreement.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Test F1 scores on section 23 of the PTB, by treebank training data conditions: either using only the training sections of the PTB, or using additional silver data (+S).'.\n" }, { "role": "assistant", "content": "Combining scores from different models improves results, with the best performance achieved by using all models for candidates and score combinations, reaching 94.66 F1.Given the success of model combination between the base model and a single generative model, we also investigate the hypothesis that the generative models are complementary. The same trends we observed on the development data, on which the interpolation parameters were tuned, hold here: score combination improves results for all models (row 3 vs. row 2; row 6 vs. row 5), with candidate augmentation from the generative models giving a further increase (rows 4 and 7). For our experiments we train RD and RG using the same silver dataset. As in the PTB training data setting, using all models for candidates and score combinations is best, achieving 94.66 F1 (row 9)." } ], "images": [ "scigen/train/1290.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Test F1 scores on section 23 of the PTB, by treebank training data conditions: either using only the training sections of the PTB, or using additional silver data (+S)." ], "reasoning": "Combining scores from different models improves results, with the best performance achieved by using all models for candidates and score combinations, reaching 94.66 F1.", "description": "Given the success of model combination between the base model and a single generative model, we also investigate the hypothesis that the generative models are complementary. The same trends we observed on the development data, on which the interpolation parameters were tuned, hold here: score combination improves results for all models (row 3 vs. row 2; row 6 vs. row 5), with candidate augmentation from the generative models giving a further increase (rows 4 and 7). For our experiments we train RD and RG using the same silver dataset. As in the PTB training data setting, using all models for candidates and score combinations is best, achieving 94.66 F1 (row 9).", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 3: Test F1 scores on section 23 of the PTB, by treebank training data conditions: either using only the training sections of the PTB, or using additional silver data (+S).', and tackle the question below in a sequential manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the F1 score achieved by using all models for candidates and score combinations?\n" }, { "role": "assistant", "content": "The description states that using all models for candidates and score combinations achieves the best result, and the table shows this score in row 9 as 94.66. So, the answer is {\"answer\": \"94.66\"}" } ], "images": [ "scigen/train/1290.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Test F1 scores on section 23 of the PTB, by treebank training data conditions: either using only the training sections of the PTB, or using additional silver data (+S)." ], "reasoning": "The description states that using all models for candidates and score combinations achieves the best result, and the table shows this score in row 9 as 94.66.", "question": "What is the F1 score achieved by using all models for candidates and score combinations?", "answer": "So, the answer is {\"answer\": \"94.66\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Combining RD and RG models alone achieves the highest F1 score., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nCombining RD and RG models alone achieves the highest F1 score.\n" }, { "role": "assistant", "content": "The table shows that combining RD and RG models alone (row 3) achieves an F1 score of 93.64, which is not the highest. The highest score is achieved by using all models (row 9) with an F1 score of 94.66. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1290.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Test F1 scores on section 23 of the PTB, by treebank training data conditions: either using only the training sections of the PTB, or using additional silver data (+S)." ], "reasoning": "The table shows that combining RD and RG models alone (row 3) achieves an F1 score of 93.64, which is not the highest. The highest score is achieved by using all models (row 9) with an F1 score of 94.66.", "statement": "Combining RD and RG models alone achieves the highest F1 score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1: F1 on the development set for word-synchronous beam search when searching in the RNNG generative (RG) and LSTM generative (LM) models. Ka is set to 10×Kw.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "RG shows larger performance gains with increasing beam sizes compared to LM, but still underperforms LM overall.In practice, we found it to be most effective to use a value for Kw that is a fraction of the value for Ka. In all the experiments we present here, we fix Ka=10×Kw, with Kw ranging from 10 to 100. RG has comparatively larger gains in performance between the larger beam sizes, while still underperforming LM, suggesting that more search is necessary in this model." } ], "images": [ "scigen/train/1291.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: F1 on the development set for word-synchronous beam search when searching in the RNNG generative (RG) and LSTM generative (LM) models. Ka is set to 10×Kw." ], "reasoning": "RG shows larger performance gains with increasing beam sizes compared to LM, but still underperforms LM overall.", "description": "In practice, we found it to be most effective to use a value for Kw that is a fraction of the value for Ka. In all the experiments we present here, we fix Ka=10×Kw, with Kw ranging from 10 to 100. RG has comparatively larger gains in performance between the larger beam sizes, while still underperforming LM, suggesting that more search is necessary in this model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 1: F1 on the development set for word-synchronous beam search when searching in the RNNG generative (RG) and LSTM generative (LM) models. Ka is set to 10×Kw.\nQuestion: What is the performance difference between RG and LM at the largest beam size?\n" }, { "role": "assistant", "content": "To find the performance difference at the largest beam size, subtract the RG score from the LM score at Kw 100. LM has a score of 92.2 and RG has a score of 89.6. So, the answer is {\"answer\": \"2.6\"}." } ], "images": [ "scigen/train/1291.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: F1 on the development set for word-synchronous beam search when searching in the RNNG generative (RG) and LSTM generative (LM) models. Ka is set to 10×Kw." ], "reasoning": "To find the performance difference at the largest beam size, subtract the RG score from the LM score at Kw 100. LM has a score of 92.2 and RG has a score of 89.6.", "question": "What is the performance difference between RG and LM at the largest beam size?", "answer": "So, the answer is {\"answer\": \"2.6\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nRG outperforms LM at the smallest beam size.\n" }, { "role": "assistant", "content": "At the smallest beam size (Kw 10), RG has a score of 74.1 while LM has a score of 83.7. Since 74.1 is less than 83.7, RG does not outperform LM. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1291.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: F1 on the development set for word-synchronous beam search when searching in the RNNG generative (RG) and LSTM generative (LM) models. Ka is set to 10×Kw." ], "reasoning": "At the smallest beam size (Kw 10), RG has a score of 74.1 while LM has a score of 83.7. Since 74.1 is less than 83.7, RG does not outperform LM.", "statement": "RG outperforms LM at the smallest beam size.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Development F1 scores on section 22 of the PTB when using various models to produce candidates and to score them. ∪ denotes taking the union of candidates from each of two models; + denotes using a weighted average of the models’ log-probabilities.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Combining the scores of both models improves performance over using either model alone, and this improvement is statistically significant.Each row is a different candidate set, where the third row in each table presents results for the augmented candidate sets; each column is a different scoring model, where the third column is the score combination setting described below. Going from RD → RG to the augmented candidate setting RD ∪ RG → RG decreases performance from 93.45 F1 to 92.78 F1 on the development set. This difference is statistically significant at the p<0.05 level under a paired bootstrap test. We see a smaller, but still significant, effect in the case of LM: RD → LM achieves 93.66, compared to 93.47 for RD ∪ LM → LM. We find that combining the scores of both models improves on using the score of either model alone, regardless of the source of candidates. These improvements are statistically significant in all cases. Score combination also more than compensates for the decrease in performance we saw previously when adding in candidates from the generative model: RD ∪ RG → RD + RG improves upon both RD → RG and RD ∪ RG → RG, and the same effect holds for LM." } ], "images": [ "scigen/train/1292.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Development F1 scores on section 22 of the PTB when using various models to produce candidates and to score them. ∪ denotes taking the union of candidates from each of two models; + denotes using a weighted average of the models’ log-probabilities." ], "reasoning": "Combining the scores of both models improves performance over using either model alone, and this improvement is statistically significant.", "description": "Each row is a different candidate set, where the third row in each table presents results for the augmented candidate sets; each column is a different scoring model, where the third column is the score combination setting described below. Going from RD → RG to the augmented candidate setting RD ∪ RG → RG decreases performance from 93.45 F1 to 92.78 F1 on the development set. This difference is statistically significant at the p<0.05 level under a paired bootstrap test. We see a smaller, but still significant, effect in the case of LM: RD → LM achieves 93.66, compared to 93.47 for RD ∪ LM → LM. We find that combining the scores of both models improves on using the score of either model alone, regardless of the source of candidates. These improvements are statistically significant in all cases. Score combination also more than compensates for the decrease in performance we saw previously when adding in candidates from the generative model: RD ∪ RG → RD + RG improves upon both RD → RG and RD ∪ RG → RG, and the same effect holds for LM.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the F1 score for the RD ∪ RG candidate set using the RD + RG scoring model?\nSolve the above question based on the table titled 'Table 2: Development F1 scores on section 22 of the PTB when using various models to produce candidates and to score them. ∪ denotes taking the union of candidates from each of two models; + denotes using a weighted average of the models’ log-probabilities.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the F1 score for the RD ∪ RG candidate set using the RD + RG scoring model is 93.92. So, the answer is {\"answer\": \"93.92\"}" } ], "images": [ "scigen/train/1292.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Development F1 scores on section 22 of the PTB when using various models to produce candidates and to score them. ∪ denotes taking the union of candidates from each of two models; + denotes using a weighted average of the models’ log-probabilities." ], "reasoning": "The table shows that the F1 score for the RD ∪ RG candidate set using the RD + RG scoring model is 93.92.", "question": "What is the F1 score for the RD ∪ RG candidate set using the RD + RG scoring model?", "answer": "So, the answer is {\"answer\": \"93.92\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe RD ∪ RG candidate set using the RG scoring model achieves an F1 score higher than 93.\n" }, { "role": "assistant", "content": "The table shows that the F1 score for the RD ∪ RG candidate set using the RG scoring model is 92.78, which is lower than 93. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1292.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Development F1 scores on section 22 of the PTB when using various models to produce candidates and to score them. ∪ denotes taking the union of candidates from each of two models; + denotes using a weighted average of the models’ log-probabilities." ], "reasoning": "The table shows that the F1 score for the RD ∪ RG candidate set using the RG scoring model is 92.78, which is lower than 93.", "statement": "The RD ∪ RG candidate set using the RG scoring model achieves an F1 score higher than 93.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Development F1 scores on section 22 of the PTB when using various models to produce candidates and to score them. ∪ denotes taking the union of candidates from each of two models; + denotes using a weighted average of the models’ log-probabilities.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Combining the scores of both models improves performance over using either model alone, and this improvement is statistically significant.Each row is a different candidate set, where the third row in each table presents results for the augmented candidate sets; each column is a different scoring model, where the third column is the score combination setting described below. Going from RD → RG to the augmented candidate setting RD ∪ RG → RG decreases performance from 93.45 F1 to 92.78 F1 on the development set. This difference is statistically significant at the p<0.05 level under a paired bootstrap test. We see a smaller, but still significant, effect in the case of LM: RD → LM achieves 93.66, compared to 93.47 for RD ∪ LM → LM. We find that combining the scores of both models improves on using the score of either model alone, regardless of the source of candidates. These improvements are statistically significant in all cases. Score combination also more than compensates for the decrease in performance we saw previously when adding in candidates from the generative model: RD ∪ RG → RD + RG improves upon both RD → RG and RD ∪ RG → RG, and the same effect holds for LM." } ], "images": [ "scigen/train/1293.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Development F1 scores on section 22 of the PTB when using various models to produce candidates and to score them. ∪ denotes taking the union of candidates from each of two models; + denotes using a weighted average of the models’ log-probabilities." ], "reasoning": "Combining the scores of both models improves performance over using either model alone, and this improvement is statistically significant.", "description": "Each row is a different candidate set, where the third row in each table presents results for the augmented candidate sets; each column is a different scoring model, where the third column is the score combination setting described below. Going from RD → RG to the augmented candidate setting RD ∪ RG → RG decreases performance from 93.45 F1 to 92.78 F1 on the development set. This difference is statistically significant at the p<0.05 level under a paired bootstrap test. We see a smaller, but still significant, effect in the case of LM: RD → LM achieves 93.66, compared to 93.47 for RD ∪ LM → LM. We find that combining the scores of both models improves on using the score of either model alone, regardless of the source of candidates. These improvements are statistically significant in all cases. Score combination also more than compensates for the decrease in performance we saw previously when adding in candidates from the generative model: RD ∪ RG → RD + RG improves upon both RD → RG and RD ∪ RG → RG, and the same effect holds for LM.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 2: Development F1 scores on section 22 of the PTB when using various models to produce candidates and to score them. ∪ denotes taking the union of candidates from each of two models; + denotes using a weighted average of the models’ log-probabilities.\nQuestion: What is the F1 score for the RD ∪ LM candidate set using the RD + LM scoring model?\n" }, { "role": "assistant", "content": "The table shows that the F1 score for the RD ∪ LM candidate set using the RD + LM scoring model is 94.15. So, the answer is {\"answer\": \"94.15\"}." } ], "images": [ "scigen/train/1293.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Development F1 scores on section 22 of the PTB when using various models to produce candidates and to score them. ∪ denotes taking the union of candidates from each of two models; + denotes using a weighted average of the models’ log-probabilities." ], "reasoning": "The table shows that the F1 score for the RD ∪ LM candidate set using the RD + LM scoring model is 94.15.", "question": "What is the F1 score for the RD ∪ LM candidate set using the RD + LM scoring model?", "answer": "So, the answer is {\"answer\": \"94.15\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The RD ∪ LM candidate set achieves a higher F1 score using the RD + LM scoring model than using the LM scoring model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe RD ∪ LM candidate set achieves a higher F1 score using the RD + LM scoring model than using the LM scoring model.\n" }, { "role": "assistant", "content": "The table shows that the F1 score for the RD ∪ LM candidate set is 94.15 using the RD + LM scoring model, which is higher than the 93.47 F1 score using the LM scoring model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1293.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Development F1 scores on section 22 of the PTB when using various models to produce candidates and to score them. ∪ denotes taking the union of candidates from each of two models; + denotes using a weighted average of the models’ log-probabilities." ], "reasoning": "The table shows that the F1 score for the RD ∪ LM candidate set is 94.15 using the RD + LM scoring model, which is higher than the 93.47 F1 score using the LM scoring model.", "statement": "The RD ∪ LM candidate set achieves a higher F1 score using the RD + LM scoring model than using the LM scoring model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Ablation analysis in preposition selection.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The left context is the most important feature for the CoNLL dataset, while the confusion score is the most important for the SE dataset.We find that the left context is the most important feature in for the CoNLL dataset, whereas the confusion score is the most important for the SE dataset. Pair similarity and triple similarity are less important when compared with the other features. This is because the neural network was able to learn the lexical similarity from the embedding features, thus reducing the importance of the similarity features." } ], "images": [ "scigen/train/1294.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ablation analysis in preposition selection." ], "reasoning": "The left context is the most important feature for the CoNLL dataset, while the confusion score is the most important for the SE dataset.", "description": "We find that the left context is the most important feature in for the CoNLL dataset, whereas the confusion score is the most important for the SE dataset. Pair similarity and triple similarity are less important when compared with the other features. This is because the neural network was able to learn the lexical similarity from the embedding features, thus reducing the importance of the similarity features.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 5: Ablation analysis in preposition selection.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich feature has the highest importance for the SE dataset?\n" }, { "role": "assistant", "content": "The table shows that the confusion score has the highest value (0.3506) for the SE dataset, indicating its highest importance. So, the answer is {\"answer\": \"confusion score\"}" } ], "images": [ "scigen/train/1294.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ablation analysis in preposition selection." ], "reasoning": "The table shows that the confusion score has the highest value (0.3506) for the SE dataset, indicating its highest importance.", "question": "Which feature has the highest importance for the SE dataset?", "answer": "So, the answer is {\"answer\": \"confusion score\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nPair similarity is the most important feature for the CoNLL dataset.\n" }, { "role": "assistant", "content": "The table shows that the left context embedding has the lowest value (0.1558) for the CoNLL dataset, indicating it is the most important feature, not pair similarity. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1294.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ablation analysis in preposition selection." ], "reasoning": "The table shows that the left context embedding has the lowest value (0.1558) for the CoNLL dataset, indicating it is the most important feature, not pair similarity.", "statement": "Pair similarity is the most important feature for the CoNLL dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Cosine similarity between pairs of centered prepositions using some word embeddings'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The proposed tensor embedding for prepositions captures their core linguistic properties better than standard embeddings like Word2vec and GloVe, as evidenced by lower cosine similarity values.Word embeddings have brought a renaissance in NLP research; they have been very successful in capturing word similarities as well as analogies (both syntactic and semantic) and are now mainstream in nearly all downstream NLP tasks (such as question-answering Chen et al. Despite this success, available literature does not highlight any specific properties of word embeddings of prepositions. GloVe (Tensor embedding is our proposed representation for prepositions). While this suggests that using available representations for prepositions diminishes the distinguishing aspect between prepositions, one could hypothesize that this is primarily because standard word embedding algorithms treat prepositions no different from other content words such as verbs and nouns, i.e., embeddings are created based on co-occurrences with other words. However, prepositions are very frequent and co-occur with nearly all words, which means that their co-occurrence ought to be treated differently. Using such a representation, we notice that the resulting tensor is low rank and use it to extract embeddings for both preposition and non-preposition words. In doing so, we use a combination of standard ideas from word representations (such as weighted spectral decomposition as in GloVe Pennington et al. Note that the smaller the cosine similarity is, the more distinct the representations are from each other. We demonstrate that the resulting preposition representation captures the core linguistic properties of prepositions–the attachment and the complement properties. Using both intrinsic evaluations and downstream tasks, we show this by providing new state-of-the-art results on well-known NLP tasks involving prepositions." } ], "images": [ "scigen/train/1295.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Cosine similarity between pairs of centered prepositions using some word embeddings" ], "reasoning": "The proposed tensor embedding for prepositions captures their core linguistic properties better than standard embeddings like Word2vec and GloVe, as evidenced by lower cosine similarity values.", "description": "Word embeddings have brought a renaissance in NLP research; they have been very successful in capturing word similarities as well as analogies (both syntactic and semantic) and are now mainstream in nearly all downstream NLP tasks (such as question-answering Chen et al. Despite this success, available literature does not highlight any specific properties of word embeddings of prepositions. GloVe (Tensor embedding is our proposed representation for prepositions). While this suggests that using available representations for prepositions diminishes the distinguishing aspect between prepositions, one could hypothesize that this is primarily because standard word embedding algorithms treat prepositions no different from other content words such as verbs and nouns, i.e., embeddings are created based on co-occurrences with other words. However, prepositions are very frequent and co-occur with nearly all words, which means that their co-occurrence ought to be treated differently. Using such a representation, we notice that the resulting tensor is low rank and use it to extract embeddings for both preposition and non-preposition words. In doing so, we use a combination of standard ideas from word representations (such as weighted spectral decomposition as in GloVe Pennington et al. Note that the smaller the cosine similarity is, the more distinct the representations are from each other. We demonstrate that the resulting preposition representation captures the core linguistic properties of prepositions–the attachment and the complement properties. Using both intrinsic evaluations and downstream tasks, we show this by providing new state-of-the-art results on well-known NLP tasks involving prepositions.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 1: Cosine similarity between pairs of centered prepositions using some word embeddings' in mind, break down and resolve the question below step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich preposition pair has the lowest cosine similarity in the Tensor embedding?\n" }, { "role": "assistant", "content": "By examining the Tensor column in the table, the preposition pair with the lowest cosine similarity is (amid, despite) with a value of 0.12. So, the answer is {\"answer\": \"(amid, despite)\"}." } ], "images": [ "scigen/train/1295.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Cosine similarity between pairs of centered prepositions using some word embeddings" ], "reasoning": "By examining the Tensor column in the table, the preposition pair with the lowest cosine similarity is (amid, despite) with a value of 0.12.", "question": "Which preposition pair has the lowest cosine similarity in the Tensor embedding?", "answer": "So, the answer is {\"answer\": \"(amid, despite)\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The preposition pair (after, before) has a higher cosine similarity in Word2vec than in GloVe. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe preposition pair (after, before) has a higher cosine similarity in Word2vec than in GloVe.\n" }, { "role": "assistant", "content": "According to the table, the cosine similarity for (after, before) is 0.83 in Word2vec and 0.70 in GloVe, which supports the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1295.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Cosine similarity between pairs of centered prepositions using some word embeddings" ], "reasoning": "According to the table, the cosine similarity for (after, before) is 0.83 in Word2vec and 0.70 in GloVe, which supports the statement.", "statement": "The preposition pair (after, before) has a higher cosine similarity in Word2vec than in GloVe.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Performance on preposition selection.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Tensor embeddings with weighted decomposition achieve the highest F1 score on the CoNLL dataset, while tensor embeddings with ALS decomposition perform best on the SE dataset.Result. We note that the proposed tensor embeddings achieve the best performance among all approaches. In particular, the tensor with weighted decomposition has the highest F1 score on the CoNLL dataset–a 6% improvement over the state-of-the-art. However, the tensor with ALS decomposition performs the best on the SE dataset, achieving a 2% improvement over the state-of-the art. We also note that with the same architecture, tensor embeddings perform much better than word2vec and GloVe embeddings on both the datasets. This validates the representation power of tensor embeddings of prepositions." } ], "images": [ "scigen/train/1296.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance on preposition selection." ], "reasoning": "Tensor embeddings with weighted decomposition achieve the highest F1 score on the CoNLL dataset, while tensor embeddings with ALS decomposition perform best on the SE dataset.", "description": "Result. We note that the proposed tensor embeddings achieve the best performance among all approaches. In particular, the tensor with weighted decomposition has the highest F1 score on the CoNLL dataset–a 6% improvement over the state-of-the-art. However, the tensor with ALS decomposition performs the best on the SE dataset, achieving a 2% improvement over the state-of-the art. We also note that with the same architecture, tensor embeddings perform much better than word2vec and GloVe embeddings on both the datasets. This validates the representation power of tensor embeddings of prepositions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Performance on preposition selection.\nQuestion: Which method achieves the highest F1 score on the SE dataset?\nGive a detailed response to the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that 'Our method (ALS)' achieves the highest F1 score on the SE dataset with a score of 0.3049. So, the answer is {\"answer\": \"Our method (ALS)\"}" } ], "images": [ "scigen/train/1296.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance on preposition selection." ], "reasoning": "The table shows that 'Our method (ALS)' achieves the highest F1 score on the SE dataset with a score of 0.3049.", "question": "Which method achieves the highest F1 score on the SE dataset?", "answer": "So, the answer is {\"answer\": \"Our method (ALS)\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe tensor with weighted decomposition achieves the highest F1 score on the SE dataset.\n" }, { "role": "assistant", "content": "The table shows that 'Our method (ALS)' achieves the highest F1 score on the SE dataset, not the tensor with weighted decomposition. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1296.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance on preposition selection." ], "reasoning": "The table shows that 'Our method (ALS)' achieves the highest F1 score on the SE dataset, not the tensor with weighted decomposition.", "statement": "The tensor with weighted decomposition achieves the highest F1 score on the SE dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 6: Accuracy in prepositional attachment disambiguation.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The LRFR Word2vec method achieves the highest accuracy of 0.903, using POS tag, WordNet, and VerbNet resources.Baselines. For comparison, we include the following state-of-the-art approaches in preposition attachment disambiguation. (1) Head-Prep-Child-Dist (HPCD) Model Belinkov et al. : this compositional neural network is used to train task-specific representations of prepositions. (2) Low-Rank Feature Representation (LRFR) Yu et al. : this method incorporates word parts, contexts and labels into a tensor, and uses decomposed vectors as features for disambiguation. (3) Ontology LSTM (OntoLSTM) Dasigi et al. : the vectors are initialized with GloVe, extended by AutoExtend Rothe and Schütze Similar to the experiments in the preposition selection task (see Section 4), we also include baselines which have the same feed-forward network architecture but generate features with vectors trained by word2vec and GloVe. Since the attachment disambiguation is a selection task, accuracy is a natural evaluation metric. Result. This result is notable considering that prior competitive approaches have used significant linguistic resources such as VerbNet and WordNet, whereas we use none. With the same feed-forward neural network as the classifier, our tensor-based approaches (both ALS and WD) achieve better performance than word2vec and GloVe." } ], "images": [ "scigen/train/1297.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Accuracy in prepositional attachment disambiguation." ], "reasoning": "The LRFR Word2vec method achieves the highest accuracy of 0.903, using POS tag, WordNet, and VerbNet resources.", "description": "Baselines. For comparison, we include the following state-of-the-art approaches in preposition attachment disambiguation. (1) Head-Prep-Child-Dist (HPCD) Model Belinkov et al. : this compositional neural network is used to train task-specific representations of prepositions. (2) Low-Rank Feature Representation (LRFR) Yu et al. : this method incorporates word parts, contexts and labels into a tensor, and uses decomposed vectors as features for disambiguation. (3) Ontology LSTM (OntoLSTM) Dasigi et al. : the vectors are initialized with GloVe, extended by AutoExtend Rothe and Schütze Similar to the experiments in the preposition selection task (see Section 4), we also include baselines which have the same feed-forward network architecture but generate features with vectors trained by word2vec and GloVe. Since the attachment disambiguation is a selection task, accuracy is a natural evaluation metric. Result. This result is notable considering that prior competitive approaches have used significant linguistic resources such as VerbNet and WordNet, whereas we use none. With the same feed-forward neural network as the classifier, our tensor-based approaches (both ALS and WD) achieve better performance than word2vec and GloVe.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: Accuracy in prepositional attachment disambiguation.\nQuestion: Which method achieves the highest accuracy and what resources does it use?\nSolve the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The LRFR Word2vec method achieves the highest accuracy of 0.903 and uses POS tag, WordNet, and VerbNet resources. So, the answer is {\"answer\": \"LRFR Word2vec\"}" } ], "images": [ "scigen/train/1297.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Accuracy in prepositional attachment disambiguation." ], "reasoning": "The LRFR Word2vec method achieves the highest accuracy of 0.903 and uses POS tag, WordNet, and VerbNet resources.", "question": "Which method achieves the highest accuracy and what resources does it use?", "answer": "So, the answer is {\"answer\": \"LRFR Word2vec\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe OntoLSTM Glove-extended method achieves the highest accuracy.\n" }, { "role": "assistant", "content": "The table shows that the LRFR Word2vec method achieves the highest accuracy of 0.903, while the OntoLSTM Glove-extended method has an accuracy of 0.897. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1297.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Accuracy in prepositional attachment disambiguation." ], "reasoning": "The table shows that the LRFR Word2vec method achieves the highest accuracy of 0.903, while the OntoLSTM Glove-extended method has an accuracy of 0.897.", "statement": "The OntoLSTM Glove-extended method achieves the highest accuracy.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: LM Training Parameters'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Both networks used a dropout multiplier of 1.0 and the Adam optimizer with a weight decay of 0.1.For all networks we applied a dropout multiplier which scales the dropout used throughout the network. We used the Adam optimizer with weight decay as indicated in the table." } ], "images": [ "scigen/train/1298.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: LM Training Parameters" ], "reasoning": "Both networks used a dropout multiplier of 1.0 and the Adam optimizer with a weight decay of 0.1.", "description": "For all networks we applied a dropout multiplier which scales the dropout used throughout the network. We used the Adam optimizer with weight decay as indicated in the table.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 1: LM Training Parameters', unfold the question presented below, detailing every step of your calculation. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the learning rate and number of epochs for the Fine-Tune LM during continued training?\n" }, { "role": "assistant", "content": "The table shows that the Fine-Tune LM uses a learning rate of 1*10^-4 and continues training for 10 epochs. So, the answer is {\"answer\": \"1*10^-4, 10 epochs\"}." } ], "images": [ "scigen/train/1298.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: LM Training Parameters" ], "reasoning": "The table shows that the Fine-Tune LM uses a learning rate of 1*10^-4 and continues training for 10 epochs.", "question": "What is the learning rate and number of epochs for the Fine-Tune LM during continued training?", "answer": "So, the answer is {\"answer\": \"1*10^-4, 10 epochs\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The LM and Fine-Tune LM both use a dropout multiplier of 1.0. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe LM and Fine-Tune LM both use a dropout multiplier of 1.0.\n" }, { "role": "assistant", "content": "The table indicates that both the LM and Fine-Tune LM have a dropout multiplier of 1.0. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1298.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: LM Training Parameters" ], "reasoning": "The table indicates that both the LM and Fine-Tune LM have a dropout multiplier of 1.0.", "statement": "The LM and Fine-Tune LM both use a dropout multiplier of 1.0.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Comparative Results'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The best validation F1 score from the 20 seeds tried was 0.8099, which was better than the NBSVM solution and the first random seed selected.The first is the baseline NBSVM solution, with an F1 of 0.7548. Second is our first random seed selected for the classifier which produces a 0.8083 result. While better than the NBSVM solution, we pick the best validation F1 from the 20 seeds we tried. This produced our final submission of 0.8099. Also note that our third place entry was 1.1% worse in F1 score than first place but 1.2% better in F1 than the 4th place entry." } ], "images": [ "scigen/train/1299.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparative Results" ], "reasoning": "The best validation F1 score from the 20 seeds tried was 0.8099, which was better than the NBSVM solution and the first random seed selected.", "description": "The first is the baseline NBSVM solution, with an F1 of 0.7548. Second is our first random seed selected for the classifier which produces a 0.8083 result. While better than the NBSVM solution, we pick the best validation F1 from the 20 seeds we tried. This produced our final submission of 0.8099. Also note that our third place entry was 1.1% worse in F1 score than first place but 1.2% better in F1 than the 4th place entry.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Comparative Results\nQuestion: What is the difference in F1 score between the NBSVM solution and the best seed?\nSolve the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The F1 score for the NBSVM solution is 0.7548, and the F1 score for the best seed is 0.8099. The difference is 0.8099 - 0.7548. So, the answer is {\"answer\": \"0.0551\"}" } ], "images": [ "scigen/train/1299.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparative Results" ], "reasoning": "The F1 score for the NBSVM solution is 0.7548, and the F1 score for the best seed is 0.8099. The difference is 0.8099 - 0.7548.", "question": "What is the difference in F1 score between the NBSVM solution and the best seed?", "answer": "So, the answer is {\"answer\": \"0.0551\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The first seed had a higher recall than the best seed. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe first seed had a higher recall than the best seed.\n" }, { "role": "assistant", "content": "The recall for the first seed is 0.8309, and the recall for the best seed is 0.8416. Since 0.8309 is less than 0.8416, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1299.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparative Results" ], "reasoning": "The recall for the first seed is 0.8309, and the recall for the best seed is 0.8416. Since 0.8309 is less than 0.8416, the statement is refuted.", "statement": "The first seed had a higher recall than the best seed.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2. Performance of question retrieval model. The superscript * denotes statistically significant differences compared to all the baselines (p<0.001).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "BERT-LeaQuR outperforms all baselines in all evaluation metrics, especially as the number of retrieved documents increases.Question retrieval. As we see, BERT-LeaQuR is able to outperform all baselines. It is worth noting that the model’s performance gets better as the number of retrieved documents increases. This indicates that BERT-LeaQuR is able to capture the relevance of query and questions when they lack common terms. In fact, we see that all term-matching retrieval models such as BM25 are significantly outperformed in terms of all evaluation metrics." } ], "images": [ "scigen/train/1300.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2. Performance of question retrieval model. The superscript * denotes statistically significant differences compared to all the baselines (p<0.001)." ], "reasoning": "BERT-LeaQuR outperforms all baselines in all evaluation metrics, especially as the number of retrieved documents increases.", "description": "Question retrieval. As we see, BERT-LeaQuR is able to outperform all baselines. It is worth noting that the model’s performance gets better as the number of retrieved documents increases. This indicates that BERT-LeaQuR is able to capture the relevance of query and questions when they lack common terms. In fact, we see that all term-matching retrieval models such as BM25 are significantly outperformed in terms of all evaluation metrics.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 2. Performance of question retrieval model. The superscript * denotes statistically significant differences compared to all the baselines (p<0.001).'. Solve the following question based on the table.\n\nQuestion: Which method has the highest Recall@30 score?\nLet's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the Recall@30 scores of all methods, BERT-LeaQuR has the highest score of 0.8630. So, the answer is {\"answer\": \"BERT-LeaQuR\"}" } ], "images": [ "scigen/train/1300.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2. Performance of question retrieval model. The superscript * denotes statistically significant differences compared to all the baselines (p<0.001)." ], "reasoning": "By comparing the Recall@30 scores of all methods, BERT-LeaQuR has the highest score of 0.8630.", "question": "Which method has the highest Recall@30 score?", "answer": "So, the answer is {\"answer\": \"BERT-LeaQuR\"}" }, { "messages": [ { "role": "user", "content": "BM25 has the lowest Recall@10 score. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nBM25 has the lowest Recall@10 score.\n" }, { "role": "assistant", "content": "By comparing the Recall@10 scores of all methods, BM25 has a score of 0.5938, which is higher than RM3's score of 0.5970. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1300.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2. Performance of question retrieval model. The superscript * denotes statistically significant differences compared to all the baselines (p<0.001)." ], "reasoning": "By comparing the Recall@10 scores of all methods, BM25 has a score of 0.5938, which is higher than RM3's score of 0.5970.", "statement": "BM25 has the lowest Recall@10 score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3. Performance comparison with baselines. WorstQuestion and BestQuestion respectively determine the lower and upper bounds. The superscript * denotes statistically significant differences compared to all the baselines (p<0.001).', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Clarifying questions significantly improve retrieval system performance, with BestQuestion achieving over 100% relative improvement in key metrics like MRR, P@1, and nDCG@1.Oracle question selection: performance. Here we study the performance of an oracle model, i.e. assuming that an oracle model is aware of the answers to the questions. The goal is to show to what extent clarifying questions can improve the performance of a retrieval system. This shows the high potential gain of asking good clarifying questions on the performance of a conversational system. Particularly, we examine the relative improvement of the system after asking only one question and observe that BestQuestion achieves over 100% relative improvement in terms of different evaluation metrics (MRR: 0.2820→0.5677, P@1: 0.1933→0.4986, nDCG@1: 0.1460→0.3988, nDCG@5: 0.1503→0.2793, nDCG@20: 0.1520→0.2265). It is worth mentioning that we observe the highest relative improvements in terms of nDCG@1 (=173%) and P@1 (=158%), exhibiting a high potential impact on voice-only conversational systems. Question selection. We see that all models outperform OriginalQuery, confirming that asking clarifying questions is crucial in a conversation, leading to high performance gain. For instance, compared to OriginalQuery, a model as simple as σ-QPP achieves a 31% relative improvement in terms of MRR. Also, NeuQS consistently outperforms all the baselines in terms of all evaluation metrics on both data splits. All the improvements are statistically significant. Moreover, NeuQS achieves a remarkable improvement in terms of both P@1 and nDCG@1. These two evaluation metrics are particularly important for voice-only conversational systems where the system must return only one result to the user. The obtained improvements highlight the necessity and effectiveness of asking clarifying questions in a conversational search system, where they are perceived as natural means of interactions with users." } ], "images": [ "scigen/train/1301.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. Performance comparison with baselines. WorstQuestion and BestQuestion respectively determine the lower and upper bounds. The superscript * denotes statistically significant differences compared to all the baselines (p<0.001)." ], "reasoning": "Clarifying questions significantly improve retrieval system performance, with BestQuestion achieving over 100% relative improvement in key metrics like MRR, P@1, and nDCG@1.", "description": "Oracle question selection: performance. Here we study the performance of an oracle model, i.e. assuming that an oracle model is aware of the answers to the questions. The goal is to show to what extent clarifying questions can improve the performance of a retrieval system. This shows the high potential gain of asking good clarifying questions on the performance of a conversational system. Particularly, we examine the relative improvement of the system after asking only one question and observe that BestQuestion achieves over 100% relative improvement in terms of different evaluation metrics (MRR: 0.2820→0.5677, P@1: 0.1933→0.4986, nDCG@1: 0.1460→0.3988, nDCG@5: 0.1503→0.2793, nDCG@20: 0.1520→0.2265). It is worth mentioning that we observe the highest relative improvements in terms of nDCG@1 (=173%) and P@1 (=158%), exhibiting a high potential impact on voice-only conversational systems. Question selection. We see that all models outperform OriginalQuery, confirming that asking clarifying questions is crucial in a conversation, leading to high performance gain. For instance, compared to OriginalQuery, a model as simple as σ-QPP achieves a 31% relative improvement in terms of MRR. Also, NeuQS consistently outperforms all the baselines in terms of all evaluation metrics on both data splits. All the improvements are statistically significant. Moreover, NeuQS achieves a remarkable improvement in terms of both P@1 and nDCG@1. These two evaluation metrics are particularly important for voice-only conversational systems where the system must return only one result to the user. The obtained improvements highlight the necessity and effectiveness of asking clarifying questions in a conversational search system, where they are perceived as natural means of interactions with users.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3. Performance comparison with baselines. WorstQuestion and BestQuestion respectively determine the lower and upper bounds. The superscript * denotes statistically significant differences compared to all the baselines (p<0.001).', solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich method achieves the highest MRR on the Qulac-T dataset?\n" }, { "role": "assistant", "content": "To find the method with the highest MRR on the Qulac-T dataset, compare the MRR values for each method. BestQuestion has the highest MRR of 0.4673. So, the answer is {\"answer\": \"BestQuestion\"}." } ], "images": [ "scigen/train/1301.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. Performance comparison with baselines. WorstQuestion and BestQuestion respectively determine the lower and upper bounds. The superscript * denotes statistically significant differences compared to all the baselines (p<0.001)." ], "reasoning": "To find the method with the highest MRR on the Qulac-T dataset, compare the MRR values for each method. BestQuestion has the highest MRR of 0.4673.", "question": "Which method achieves the highest MRR on the Qulac-T dataset?", "answer": "So, the answer is {\"answer\": \"BestQuestion\"}." }, { "messages": [ { "role": "user", "content": "NeuQS achieves the highest nDCG@1 on the Qulac-F dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nNeuQS achieves the highest nDCG@1 on the Qulac-F dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the nDCG@1 values for each method on the Qulac-F dataset. NeuQS has the highest nDCG@1 value of 0.2110. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1301.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. Performance comparison with baselines. WorstQuestion and BestQuestion respectively determine the lower and upper bounds. The superscript * denotes statistically significant differences compared to all the baselines (p<0.001)." ], "reasoning": "To verify this statement, compare the nDCG@1 values for each method on the Qulac-F dataset. NeuQS has the highest nDCG@1 value of 0.2110.", "statement": "NeuQS achieves the highest nDCG@1 on the Qulac-F dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Human evaluations on the Gigaword dataset. Bold-faced values are the best while red-colored values are the worst among the values in the evaluation metric.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Human evaluation shows that base+E2Tcnn has the best mean rank, followed by gold, base+E2Trnn, and base, with base+E2Tcnn significantly outperforming the RNN variant and base model.Automatic evaluation on the Gigaword dataset shows that the CNN and RNN variants of base+E2T have similar performance. To break the tie between both models, we also conduct human evaluation on the Gigaword dataset. We instruct two annotators to read the input sentence and rank the competing summaries from first to last according to their relevance and fluency: (a) the original summary gold, and from models (b) base, (c) base+E2Tcnn, and (d) base+E2Trnn. We then compute (i) the proportion of every ranking of each model and (ii) the mean rank of each model. The model with the best mean rank is base+E2Tcnn, followed by gold, then by base+E2Trnn and base, respectively. We also perform ANOVA and post-hoc Tukey tests to show that the CNN variant is significantly (p<0.01) better than the RNN variant and the base model. The RNN variant does not perform as well as the CNN variant, contrary to the automatic ROUGE evaluation above. Interestingly, the CNN variant produces better (but with no significant difference) summaries than the gold summaries. We posit that this is due to the fact that the article title does not correspond to the summary of the first sentence." } ], "images": [ "scigen/train/1302.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Human evaluations on the Gigaword dataset. Bold-faced values are the best while red-colored values are the worst among the values in the evaluation metric." ], "reasoning": "Human evaluation shows that base+E2Tcnn has the best mean rank, followed by gold, base+E2Trnn, and base, with base+E2Tcnn significantly outperforming the RNN variant and base model.", "description": "Automatic evaluation on the Gigaword dataset shows that the CNN and RNN variants of base+E2T have similar performance. To break the tie between both models, we also conduct human evaluation on the Gigaword dataset. We instruct two annotators to read the input sentence and rank the competing summaries from first to last according to their relevance and fluency: (a) the original summary gold, and from models (b) base, (c) base+E2Tcnn, and (d) base+E2Trnn. We then compute (i) the proportion of every ranking of each model and (ii) the mean rank of each model. The model with the best mean rank is base+E2Tcnn, followed by gold, then by base+E2Trnn and base, respectively. We also perform ANOVA and post-hoc Tukey tests to show that the CNN variant is significantly (p<0.01) better than the RNN variant and the base model. The RNN variant does not perform as well as the CNN variant, contrary to the automatic ROUGE evaluation above. Interestingly, the CNN variant produces better (but with no significant difference) summaries than the gold summaries. We posit that this is due to the fact that the article title does not correspond to the summary of the first sentence.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 4: Human evaluations on the Gigaword dataset. Bold-faced values are the best while red-colored values are the worst among the values in the evaluation metric.', and sequentially solve the question, writing out each step of your reasoning process. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model has the highest proportion of 1st place rankings and what is its mean rank?\n" }, { "role": "assistant", "content": "The table shows the proportion of 1st place rankings for each model. The model with the highest proportion of 1st place rankings is base+E2Tcnn with 0.47, and its mean rank is 1.93. So, the answer is {\"answer\": \"base+E2Tcnn with a mean rank of 1.93\"}." } ], "images": [ "scigen/train/1302.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Human evaluations on the Gigaword dataset. Bold-faced values are the best while red-colored values are the worst among the values in the evaluation metric." ], "reasoning": "The table shows the proportion of 1st place rankings for each model. The model with the highest proportion of 1st place rankings is base+E2Tcnn with 0.47, and its mean rank is 1.93.", "question": "Which model has the highest proportion of 1st place rankings and what is its mean rank?", "answer": "So, the answer is {\"answer\": \"base+E2Tcnn with a mean rank of 1.93\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe base model has the lowest mean rank among all models.\n" }, { "role": "assistant", "content": "The table shows the mean ranks of all models. The base model has a mean rank of 3.00, which is the highest, not the lowest. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1302.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Human evaluations on the Gigaword dataset. Bold-faced values are the best while red-colored values are the worst among the values in the evaluation metric." ], "reasoning": "The table shows the mean ranks of all models. The base model has a mean rank of 3.00, which is the highest, not the lowest.", "statement": "The base model has the lowest mean rank among all models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 6: Examples with highest/lowest disambiguation gate d values of two example entities (United States and gold). The tagged part of text is marked bold and preceded with at sign (@).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The selective disambiguation gate d assigns high values to incorrectly linked entities and low values to correctly linked entities.We show the effectiveness of the selective disambiguation gate d in selecting which entities to disambiguate or not. In the first example, sentence E1.1 contains the entity “United States” and is linked with the country entity of the same name, however the correct linked entity should be “United States Davis Cup team”, and therefore is given a high d value. On the other hand, sentence E1.2 is linked correctly to the country “United States”, and thus is given a low d value.. The second example provides a similar scenario, where sentence E2.1 is linked to the entity “Gold” but should be linked to the entity “Gold medal”. Sentence E2.2 is linked correctly to the chemical element. Hence, the former case received a high value d while the latter case received a low d value." } ], "images": [ "scigen/train/1303.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Examples with highest/lowest disambiguation gate d values of two example entities (United States and gold). The tagged part of text is marked bold and preceded with at sign (@)." ], "reasoning": "The selective disambiguation gate d assigns high values to incorrectly linked entities and low values to correctly linked entities.", "description": "We show the effectiveness of the selective disambiguation gate d in selecting which entities to disambiguate or not. In the first example, sentence E1.1 contains the entity “United States” and is linked with the country entity of the same name, however the correct linked entity should be “United States Davis Cup team”, and therefore is given a high d value. On the other hand, sentence E1.2 is linked correctly to the country “United States”, and thus is given a low d value.. The second example provides a similar scenario, where sentence E2.1 is linked to the entity “Gold” but should be linked to the entity “Gold medal”. Sentence E2.2 is linked correctly to the chemical element. Hence, the former case received a high value d while the latter case received a low d value.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the d value for the sentence that correctly links to the country 'United States'?\nSolve the above question based on the table titled 'Table 6: Examples with highest/lowest disambiguation gate d values of two example entities (United States and gold). The tagged part of text is marked bold and preceded with at sign (@).'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "Sentence E1.2 is correctly linked to the country 'United States' and has a d value of 0.086. So, the answer is {\"answer\": \"0.086\"}." } ], "images": [ "scigen/train/1303.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Examples with highest/lowest disambiguation gate d values of two example entities (United States and gold). The tagged part of text is marked bold and preceded with at sign (@)." ], "reasoning": "Sentence E1.2 is correctly linked to the country 'United States' and has a d value of 0.086.", "question": "What is the d value for the sentence that correctly links to the country 'United States'?", "answer": "So, the answer is {\"answer\": \"0.086\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The sentence about the medal standings at the Olympic Winter Games is linked to the entity 'Gold medal'. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe sentence about the medal standings at the Olympic Winter Games is linked to the entity 'Gold medal'.\n" }, { "role": "assistant", "content": "The table shows that sentence E2.1, which discusses the medal standings, is linked to the entity 'Gold' but should be linked to 'Gold medal'. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1303.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Examples with highest/lowest disambiguation gate d values of two example entities (United States and gold). The tagged part of text is marked bold and preceded with at sign (@)." ], "reasoning": "The table shows that sentence E2.1, which discusses the medal standings, is linked to the entity 'Gold' but should be linked to 'Gold medal'.", "statement": "The sentence about the medal standings at the Olympic Winter Games is linked to the entity 'Gold medal'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Results on two open-domain QA datasets. See Appendix A for how s/Q is computed.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Our model, DenSPI + Sparc, significantly improves performance over the baseline DenSPI by more than 4% on both datasets and achieves state-of-the-art performance on CuratedTREC with faster inference.On both datasets, our model with contextualized sparse representations ( DenSPI + Sparc) largely improves the performance of the phrase-indexing baseline model (DenSPI) by more than 4%. Also, our method runs significantly faster than other models that need to run heavy QA models during the inference. On CuratedTREC, which is constructed from real user queries, our model achieves state-of-the-art performance at the time of submission. Even though our model is only trained on SQuAD (i.e., zero-shot), it outperforms all other models which are either distant- or semi-supervised with at least 45x faster inference." } ], "images": [ "scigen/train/1304.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results on two open-domain QA datasets. See Appendix A for how s/Q is computed." ], "reasoning": "Our model, DenSPI + Sparc, significantly improves performance over the baseline DenSPI by more than 4% on both datasets and achieves state-of-the-art performance on CuratedTREC with faster inference.", "description": "On both datasets, our model with contextualized sparse representations ( DenSPI + Sparc) largely improves the performance of the phrase-indexing baseline model (DenSPI) by more than 4%. Also, our method runs significantly faster than other models that need to run heavy QA models during the inference. On CuratedTREC, which is constructed from real user queries, our model achieves state-of-the-art performance at the time of submission. Even though our model is only trained on SQuAD (i.e., zero-shot), it outperforms all other models which are either distant- or semi-supervised with at least 45x faster inference.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 1: Results on two open-domain QA datasets. See Appendix A for how s/Q is computed.\nQuestion: What is the improvement in SQuAD-Open EM performance of DenSPI + Sparc over DenSPI?\n" }, { "role": "assistant", "content": "The SQuAD-Open EM performance of DenSPI is 36.4, while DenSPI + Sparc achieves 40.7. The improvement is calculated as 40.7 - 36.4. So, the answer is {\"answer\": \"4.3\"}" } ], "images": [ "scigen/train/1304.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results on two open-domain QA datasets. See Appendix A for how s/Q is computed." ], "reasoning": "The SQuAD-Open EM performance of DenSPI is 36.4, while DenSPI + Sparc achieves 40.7. The improvement is calculated as 40.7 - 36.4.", "question": "What is the improvement in SQuAD-Open EM performance of DenSPI + Sparc over DenSPI?", "answer": "So, the answer is {\"answer\": \"4.3\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nDenSPI + Sparc achieves the highest SQuAD-Open EM performance among all models.\n" }, { "role": "assistant", "content": "The table shows that Multi-passage BERT has the highest SQuAD-Open EM performance with a score of 53.0, while DenSPI + Sparc has a score of 40.7. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1304.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results on two open-domain QA datasets. See Appendix A for how s/Q is computed." ], "reasoning": "The table shows that Multi-passage BERT has the highest SQuAD-Open EM performance with a score of 53.0, while DenSPI + Sparc has a score of 40.7.", "statement": "DenSPI + Sparc achieves the highest SQuAD-Open EM performance among all models.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Results on the SQuAD development set. LSTM+SA+ELMo is a query-agnostic baseline from Seo et al. (2018).'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "While BERT-Large has a higher performance, the gap has been closed to 6.1 F1 score in a query-agnostic setting.While BERT-Large that jointly encodes a passage and a question still has a higher performance than ours, we have closed the gap to 6.1 F1 score in a query-agnostic setting." } ], "images": [ "scigen/train/1305.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on the SQuAD development set. LSTM+SA+ELMo is a query-agnostic baseline from Seo et al. (2018)." ], "reasoning": "While BERT-Large has a higher performance, the gap has been closed to 6.1 F1 score in a query-agnostic setting.", "description": "While BERT-Large that jointly encodes a passage and a question still has a higher performance than ours, we have closed the gap to 6.1 F1 score in a query-agnostic setting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Results on the SQuAD development set. LSTM+SA+ELMo is a query-agnostic baseline from Seo et al. (2018).\nQuestion: What is the F1 score difference between BERT (Devlin et al., 2019) and DenSPI + Sparc in a query-agnostic setting?\nSolve the above question based on the table information. Let's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "BERT (Devlin et al., 2019) has an F1 score of 90.9, and DenSPI + Sparc has an F1 score of 84.8. The difference is 90.9 - 84.8. So, the answer is {\"answer\": \"6.1\"}." } ], "images": [ "scigen/train/1305.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on the SQuAD development set. LSTM+SA+ELMo is a query-agnostic baseline from Seo et al. (2018)." ], "reasoning": "BERT (Devlin et al., 2019) has an F1 score of 90.9, and DenSPI + Sparc has an F1 score of 84.8. The difference is 90.9 - 84.8.", "question": "What is the F1 score difference between BERT (Devlin et al., 2019) and DenSPI + Sparc in a query-agnostic setting?", "answer": "So, the answer is {\"answer\": \"6.1\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nDenSPI + Sparc has a higher F1 score than DrQA (Chen et al., 2017).\n" }, { "role": "assistant", "content": "DenSPI + Sparc has an F1 score of 84.8, while DrQA (Chen et al., 2017) has an F1 score of 78.8. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1305.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on the SQuAD development set. LSTM+SA+ELMo is a query-agnostic baseline from Seo et al. (2018)." ], "reasoning": "DenSPI + Sparc has an F1 score of 84.8, while DrQA (Chen et al., 2017) has an F1 score of 78.8.", "statement": "DenSPI + Sparc has a higher F1 score than DrQA (Chen et al., 2017).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: Exact match scores of Sparc in different search strategies. SFS: Sparse First Search. DFS: Dense First Search. Hybrid: Combination of SFS + DFS. Exact match scores are reported.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "DFS performs better than SFS on CuratedTREC, indicating the effectiveness of dense search when document selection is uncertain.Note that on CuratedTREC where the questions more resemble real user queries, DFS outperforms SFS showing the effectiveness of dense search when not knowing which documents to read." } ], "images": [ "scigen/train/1306.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Exact match scores of Sparc in different search strategies. SFS: Sparse First Search. DFS: Dense First Search. Hybrid: Combination of SFS + DFS. Exact match scores are reported." ], "reasoning": "DFS performs better than SFS on CuratedTREC, indicating the effectiveness of dense search when document selection is uncertain.", "description": "Note that on CuratedTREC where the questions more resemble real user queries, DFS outperforms SFS showing the effectiveness of dense search when not knowing which documents to read.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 5: Exact match scores of Sparc in different search strategies. SFS: Sparse First Search. DFS: Dense First Search. Hybrid: Combination of SFS + DFS. Exact match scores are reported.', answer the following question. Give detailed solution consisting of each step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model shows the highest improvement in SQuAD-Open + Sparc compared to SQuAD-Open DenSPI?\n" }, { "role": "assistant", "content": "To find the model with the highest improvement, compare the differences between SQuAD-Open + Sparc and SQuAD-Open DenSPI for each model. The Hybrid model shows the highest improvement of +4.5. So, the answer is {\"answer\": \"Hybrid\"}." } ], "images": [ "scigen/train/1306.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Exact match scores of Sparc in different search strategies. SFS: Sparse First Search. DFS: Dense First Search. Hybrid: Combination of SFS + DFS. Exact match scores are reported." ], "reasoning": "To find the model with the highest improvement, compare the differences between SQuAD-Open + Sparc and SQuAD-Open DenSPI for each model. The Hybrid model shows the highest improvement of +4.5.", "question": "Which model shows the highest improvement in SQuAD-Open + Sparc compared to SQuAD-Open DenSPI?", "answer": "So, the answer is {\"answer\": \"Hybrid\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Hybrid model outperforms both SFS and DFS in CuratedTREC DenSPI., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Hybrid model outperforms both SFS and DFS in CuratedTREC DenSPI.\n" }, { "role": "assistant", "content": "The table shows that the Hybrid model has a score of 31.6 in CuratedTREC DenSPI, which is higher than both SFS (28.8) and DFS (29.5). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1306.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Exact match scores of Sparc in different search strategies. SFS: Sparse First Search. DFS: Dense First Search. Hybrid: Combination of SFS + DFS. Exact match scores are reported." ], "reasoning": "The table shows that the Hybrid model has a score of 31.6 in CuratedTREC DenSPI, which is higher than both SFS (28.8) and DFS (29.5).", "statement": "The Hybrid model outperforms both SFS and DFS in CuratedTREC DenSPI.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: Effect of the threshold parameters on the hybrid approach on the e-commerce English→Russian task.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Adjusting the coverage threshold or disabling it has minimal impact on translation scores, and varying the focus threshold between 0.0 and 0.3 shows no significant effect.Tuning Again, we retune the system for each choice. Setting the coverage threshold to 1.0 or even disabling the coverage check (by setting τcov=∞) has little effect on the translation scores on this task. This can be explained by the fact that translation from English to Russian is mostly monotonic. We also tried varying the focus threshold τfocus between 0.0 and 0.3 but did not notice any significant effect on this task." } ], "images": [ "scigen/train/1307.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Effect of the threshold parameters on the hybrid approach on the e-commerce English→Russian task." ], "reasoning": "Adjusting the coverage threshold or disabling it has minimal impact on translation scores, and varying the focus threshold between 0.0 and 0.3 shows no significant effect.", "description": "Tuning Again, we retune the system for each choice. Setting the coverage threshold to 1.0 or even disabling the coverage check (by setting τcov=∞) has little effect on the translation scores on this task. This can be explained by the fact that translation from English to Russian is mostly monotonic. We also tried varying the focus threshold τfocus between 0.0 and 0.3 but did not notice any significant effect on this task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: Effect of the threshold parameters on the hybrid approach on the e-commerce English→Russian task.'.\n\nQuestion: What is the Product descr. BLEU score when τcov is set to 1.0?\n" }, { "role": "assistant", "content": "To find the Product descr. BLEU score when τcov is set to 1.0, locate the row where τcov is 1.0 and then find the corresponding Product descr. BLEU score. So, the answer is {\"answer\": \"30.3\"}." } ], "images": [ "scigen/train/1307.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Effect of the threshold parameters on the hybrid approach on the e-commerce English→Russian task." ], "reasoning": "To find the Product descr. BLEU score when τcov is set to 1.0, locate the row where τcov is 1.0 and then find the corresponding Product descr. BLEU score.", "question": "What is the Product descr. BLEU score when τcov is set to 1.0?", "answer": "So, the answer is {\"answer\": \"30.3\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Setting the coverage threshold to 1.0 results in a Product descr. TER score of 50.3. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nSetting the coverage threshold to 1.0 results in a Product descr. TER score of 50.3.\n" }, { "role": "assistant", "content": "The table shows that when τcov is set to 1.0, the Product descr. TER score is indeed 50.3. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1307.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Effect of the threshold parameters on the hybrid approach on the e-commerce English→Russian task." ], "reasoning": "The table shows that when τcov is set to 1.0, the Product descr. TER score is indeed 50.3.", "statement": "Setting the coverage threshold to 1.0 results in a Product descr. TER score of 50.3.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: Translation results of the hybrid approach on the e-commerce English→Russian task with different SMT model combinations. The first row shows results with all models enabled. In the following rows, we either remove or limit exactly one model compared to the full system.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The language model and source word coverage feature are crucial for improving translation quality, while phrase-level scores and source phrase length also impact the results.To analyze the improvements of the hybrid system, we perform experiments in which we either disable or limit some of the SMT models. Without the language model, the hybrid approach has almost no improvements over the NMT baseline. This indicates that the language model is crucial in selecting appropriate phrase candidates. Similarly, when we disable the source word coverage feature, the translation quality is degraded, suggesting that this feature helps choose between phrase hypotheses and word hypotheses during the search. Next, we do not use phrase-level scores. Here, we observe only a small degradation of translation quality. Finally, we limit the source length of phrases used in the search, allowing only one-word source phrases in one experiment and only source phrases with two or more words in another experiment. In both cases, the translation quality decreases. Thus, both one-word phrases and longer phrases are necessary to obtain the best results." } ], "images": [ "scigen/train/1308.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Translation results of the hybrid approach on the e-commerce English→Russian task with different SMT model combinations. The first row shows results with all models enabled. In the following rows, we either remove or limit exactly one model compared to the full system." ], "reasoning": "The language model and source word coverage feature are crucial for improving translation quality, while phrase-level scores and source phrase length also impact the results.", "description": "To analyze the improvements of the hybrid system, we perform experiments in which we either disable or limit some of the SMT models. Without the language model, the hybrid approach has almost no improvements over the NMT baseline. This indicates that the language model is crucial in selecting appropriate phrase candidates. Similarly, when we disable the source word coverage feature, the translation quality is degraded, suggesting that this feature helps choose between phrase hypotheses and word hypotheses during the search. Next, we do not use phrase-level scores. Here, we observe only a small degradation of translation quality. Finally, we limit the source length of phrases used in the search, allowing only one-word source phrases in one experiment and only source phrases with two or more words in another experiment. In both cases, the translation quality decreases. Thus, both one-word phrases and longer phrases are necessary to obtain the best results.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 3: Translation results of the hybrid approach on the e-commerce English→Russian task with different SMT model combinations. The first row shows results with all models enabled. In the following rows, we either remove or limit exactly one model compared to the full system.', solve this question and give detail solutions. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhat is the BLEU score for item descriptions when the source word coverage feature is disabled?\n" }, { "role": "assistant", "content": "To find the BLEU score for item descriptions when the source word coverage feature is disabled, locate the row labeled 'Without source word coverage feature' and find the corresponding BLEU score under 'Item descriptions'. So, the answer is {\"answer\": \"26.7\"}." } ], "images": [ "scigen/train/1308.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Translation results of the hybrid approach on the e-commerce English→Russian task with different SMT model combinations. The first row shows results with all models enabled. In the following rows, we either remove or limit exactly one model compared to the full system." ], "reasoning": "To find the BLEU score for item descriptions when the source word coverage feature is disabled, locate the row labeled 'Without source word coverage feature' and find the corresponding BLEU score under 'Item descriptions'.", "question": "What is the BLEU score for item descriptions when the source word coverage feature is disabled?", "answer": "So, the answer is {\"answer\": \"26.7\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Disabling the language model results in a BLEU score of 26.5 for item descriptions. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nDisabling the language model results in a BLEU score of 26.5 for item descriptions.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for item descriptions without the language model is indeed 26.5. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1308.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Translation results of the hybrid approach on the e-commerce English→Russian task with different SMT model combinations. The first row shows results with all models enabled. In the following rows, we either remove or limit exactly one model compared to the full system." ], "reasoning": "The table shows that the BLEU score for item descriptions without the language model is indeed 26.5.", "statement": "Disabling the language model results in a BLEU score of 26.5 for item descriptions.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Effect of the beam size (word beam size Nw + phrase beam size Np) for the hybrid approach on the e-commerce English→Russian task.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The best translation quality is achieved with a total beam size of 128, using a phrase beam size of 96 and a word beam size of 32, while reducing the beam sizes to Nw=12 and Np=4 results in a slight degradation in BLEU scores.-S4SS1SSSx3 Tuning the beam size Next, we study the effect of different beam sizes on translation quality. Note that we retune the system for each choice. With a total beam size of 128, we get the best results by using a phrase beam size of 96 and a word beam size of 32. When we use a phrase beam size of 116 or 64 instead, the translation quality worsens. In another experiment, we decrease the total beam size to 64. The translation quality degrades only slightly, which means that we can still expect MT quality improvements with hybrid search even if we optimize the system for speed. To further test this, we reduce the beam sizes to Nw=12 and Np=4 after tuning with Nw=32 and Np=96. We get BLEU scores of 27.1% on item descriptions and 30.1% on product descriptions, losing 0.3% and 0.7% BLEU respectively compared to the full beam size." } ], "images": [ "scigen/train/1309.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Effect of the beam size (word beam size Nw + phrase beam size Np) for the hybrid approach on the e-commerce English→Russian task." ], "reasoning": "The best translation quality is achieved with a total beam size of 128, using a phrase beam size of 96 and a word beam size of 32, while reducing the beam sizes to Nw=12 and Np=4 results in a slight degradation in BLEU scores.", "description": "-S4SS1SSSx3 Tuning the beam size Next, we study the effect of different beam sizes on translation quality. Note that we retune the system for each choice. With a total beam size of 128, we get the best results by using a phrase beam size of 96 and a word beam size of 32. When we use a phrase beam size of 116 or 64 instead, the translation quality worsens. In another experiment, we decrease the total beam size to 64. The translation quality degrades only slightly, which means that we can still expect MT quality improvements with hybrid search even if we optimize the system for speed. To further test this, we reduce the beam sizes to Nw=12 and Np=4 after tuning with Nw=32 and Np=96. We get BLEU scores of 27.1% on item descriptions and 30.1% on product descriptions, losing 0.3% and 0.7% BLEU respectively compared to the full beam size.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: Effect of the beam size (word beam size Nw + phrase beam size Np) for the hybrid approach on the e-commerce English→Russian task.', solve this question and give detail solutions. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the BLEU score for item descriptions when the beam size Np is 96 and Nw is 32?\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for different beam sizes. For Np=96 and Nw=32, the BLEU score for item descriptions is 27.4%. So, the answer is {\"answer\": \"27.4\"}." } ], "images": [ "scigen/train/1309.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Effect of the beam size (word beam size Nw + phrase beam size Np) for the hybrid approach on the e-commerce English→Russian task." ], "reasoning": "The table shows the BLEU scores for different beam sizes. For Np=96 and Nw=32, the BLEU score for item descriptions is 27.4%.", "question": "What is the BLEU score for item descriptions when the beam size Np is 96 and Nw is 32?", "answer": "So, the answer is {\"answer\": \"27.4\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The BLEU score for product descriptions is highest when the beam size Np is 96 and Nw is 32. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe BLEU score for product descriptions is highest when the beam size Np is 96 and Nw is 32.\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for product descriptions for different beam sizes. The highest BLEU score for product descriptions is 30.8%, which occurs when Np=96 and Nw=32. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1309.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Effect of the beam size (word beam size Nw + phrase beam size Np) for the hybrid approach on the e-commerce English→Russian task." ], "reasoning": "The table shows the BLEU scores for product descriptions for different beam sizes. The highest BLEU score for product descriptions is 30.8%, which occurs when Np=96 and Nw=32.", "statement": "The BLEU score for product descriptions is highest when the beam size Np is 96 and Nw is 32.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Comparing our methods to baseline based on Bi-LSTM-CRF and BERT-FeedForward on F1 metric. The bold value indicates for the best result. The first version Our Method4 is modified from Bi-LSTM-CRF and the other based on BERT-FeedForward.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Combining CRFs with Bi-LSTM improves tagging accuracy by incorporating dependencies across output labels. Relabeling samples and training separate models for weak and strong classes did not improve results, motivating a novel classification method using RNN-CNN followed by NER prediction.The probability for the sequence y is given by: p(y|X)=exp(S(X,y)∑y′∈exp(S(X,y′)) (4) The objective function is the maximum likelihood of the probability distribution denoted as: lnp(y|X)=S(X,y)−ln∑y′∈yexp(S(X,y′)) (5) The maximum likelihood of the probability of predictions are maximized during training and the final tag y∗ is given by: y∗=argmaxy′∈yS(X,y) (6) It has been shown that combing CRFs with Bi-LSTM for modelling the tagging task improves the tagging accuracy in general by effectively incorporating several dependencies across the output labels. In order to mitigate this issue, we tried relabeling the samples and training two separate models. For the first model, we mask the Weak class to “Other” and train it to predict tags for Strong class and “other””. Similarly for the second model, the Strong classes are masked to “other” and a new model is trained this relabelled data. It is evident that this relabeling method does not improve the results and thus motivates to follow a novel method of classification using RNN-CNN followed by NER prediction. We also compared the derived results to four other baselines include original Bi-LSTM-CRF, BERT- Feedforward and Double version of each architecture. In Double version of each architecture, we train one model for weak classes, one model for strong classes and finally merge two labels to form the final prediction using a simple merging schedule. Sentence type classification is not used in this version. In the result table, the bold value denotes for the best value cross all methods. For each entity category, we compute the F1 score and using global F1, weighted average F1 and macro-average of F1 for all classes by the following formula:" } ], "images": [ "scigen/train/1310.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparing our methods to baseline based on Bi-LSTM-CRF and BERT-FeedForward on F1 metric. The bold value indicates for the best result. The first version Our Method4 is modified from Bi-LSTM-CRF and the other based on BERT-FeedForward." ], "reasoning": "Combining CRFs with Bi-LSTM improves tagging accuracy by incorporating dependencies across output labels. Relabeling samples and training separate models for weak and strong classes did not improve results, motivating a novel classification method using RNN-CNN followed by NER prediction.", "description": "The probability for the sequence y is given by: p(y|X)=exp(S(X,y)∑y′∈exp(S(X,y′)) (4) The objective function is the maximum likelihood of the probability distribution denoted as: lnp(y|X)=S(X,y)−ln∑y′∈yexp(S(X,y′)) (5) The maximum likelihood of the probability of predictions are maximized during training and the final tag y∗ is given by: y∗=argmaxy′∈yS(X,y) (6) It has been shown that combing CRFs with Bi-LSTM for modelling the tagging task improves the tagging accuracy in general by effectively incorporating several dependencies across the output labels. In order to mitigate this issue, we tried relabeling the samples and training two separate models. For the first model, we mask the Weak class to “Other” and train it to predict tags for Strong class and “other””. Similarly for the second model, the Strong classes are masked to “other” and a new model is trained this relabelled data. It is evident that this relabeling method does not improve the results and thus motivates to follow a novel method of classification using RNN-CNN followed by NER prediction. We also compared the derived results to four other baselines include original Bi-LSTM-CRF, BERT- Feedforward and Double version of each architecture. In Double version of each architecture, we train one model for weak classes, one model for strong classes and finally merge two labels to form the final prediction using a simple merging schedule. Sentence type classification is not used in this version. In the result table, the bold value denotes for the best value cross all methods. For each entity category, we compute the F1 score and using global F1, weighted average F1 and macro-average of F1 for all classes by the following formula:", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 4: Comparing our methods to baseline based on Bi-LSTM-CRF and BERT-FeedForward on F1 metric. The bold value indicates for the best result. The first version Our Method4 is modified from Bi-LSTM-CRF and the other based on BERT-FeedForward.', solve the following question step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich method achieved the highest F1 score for the 'Nat' entity category?\n" }, { "role": "assistant", "content": "To find the highest F1 score for the 'Nat' entity category, compare the values in the 'Nat' row across all methods. The highest value is 0.43, achieved by 'Our Method'. So, the answer is {\"answer\": \"Our Method\"}." } ], "images": [ "scigen/train/1310.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparing our methods to baseline based on Bi-LSTM-CRF and BERT-FeedForward on F1 metric. The bold value indicates for the best result. The first version Our Method4 is modified from Bi-LSTM-CRF and the other based on BERT-FeedForward." ], "reasoning": "To find the highest F1 score for the 'Nat' entity category, compare the values in the 'Nat' row across all methods. The highest value is 0.43, achieved by 'Our Method'.", "question": "Which method achieved the highest F1 score for the 'Nat' entity category?", "answer": "So, the answer is {\"answer\": \"Our Method\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Double BERT-FF method achieved the highest weighted average F1 score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the weighted average F1 scores across all methods. The highest weighted average F1 score is 0.84, achieved by both BERT-FF and Double BERT-FF methods. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1310.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparing our methods to baseline based on Bi-LSTM-CRF and BERT-FeedForward on F1 metric. The bold value indicates for the best result. The first version Our Method4 is modified from Bi-LSTM-CRF and the other based on BERT-FeedForward." ], "reasoning": "To verify this statement, compare the weighted average F1 scores across all methods. The highest weighted average F1 score is 0.84, achieved by both BERT-FF and Double BERT-FF methods.", "statement": "The Double BERT-FF method achieved the highest weighted average F1 score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: Label statistic'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The dataset used in the project is highly imbalanced, with the majority of samples belonging to the 'Geo', 'Time', 'Org', 'Per', and 'Gpe' labels, which poses a challenge for model training.Depending on specific applications, the set of entity types might vary. However, the label set most commonly used in academic research contains four labels, including LOC(location), ORG(organization), PER(person) and MISC(miscellaneous). In this project, the given corpus was annotated with 8 types of entities: Art(artifact), Eve(event), Nat(natural phenomena), Geo(geographical entity), Gpe(geopolitical entity), Tim(time indicator), Org(organization), Per(person). In this project, we implemented Bi-LSTM-CRF architecture using Pytorch framework, and we also experimented with one traditional model using CRF (Stanford NLP) and one SOTA transformer based neural network model (BERT). The best result among above models was 0.85 F1 score on test set. However, these models all suffered from the problem of imperfect annotations (wrong annotations) and highly imbalanced dataset. reported from the whole train/test/val corpus. It could be seen that the distribution of labels is highly skewed. Specifically, the total samples of five groups (Strong class): “Geo”, “Time”, “Org”, “Per” and “Gpe” is approximately 50 times more than the total samples of the last three groups (Weak class): “Art”, “Eve” and “Nat”. This characteristic poses a considerable difficulty for almost every models as most of the features learned are biased to the Strong classes. Therefore, in latter part of this report, we will also describe an approach to solve this problem." } ], "images": [ "scigen/train/1311.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Label statistic" ], "reasoning": "The dataset used in the project is highly imbalanced, with the majority of samples belonging to the 'Geo', 'Time', 'Org', 'Per', and 'Gpe' labels, which poses a challenge for model training.", "description": "Depending on specific applications, the set of entity types might vary. However, the label set most commonly used in academic research contains four labels, including LOC(location), ORG(organization), PER(person) and MISC(miscellaneous). In this project, the given corpus was annotated with 8 types of entities: Art(artifact), Eve(event), Nat(natural phenomena), Geo(geographical entity), Gpe(geopolitical entity), Tim(time indicator), Org(organization), Per(person). In this project, we implemented Bi-LSTM-CRF architecture using Pytorch framework, and we also experimented with one traditional model using CRF (Stanford NLP) and one SOTA transformer based neural network model (BERT). The best result among above models was 0.85 F1 score on test set. However, these models all suffered from the problem of imperfect annotations (wrong annotations) and highly imbalanced dataset. reported from the whole train/test/val corpus. It could be seen that the distribution of labels is highly skewed. Specifically, the total samples of five groups (Strong class): “Geo”, “Time”, “Org”, “Per” and “Gpe” is approximately 50 times more than the total samples of the last three groups (Weak class): “Art”, “Eve” and “Nat”. This characteristic poses a considerable difficulty for almost every models as most of the features learned are biased to the Strong classes. Therefore, in latter part of this report, we will also describe an approach to solve this problem.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 1: Label statistic' in mind, break down and resolve the question below step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the total count of samples for the 'Strong class' labels?\n" }, { "role": "assistant", "content": "The 'Strong class' labels are 'Geo', 'Time', 'Org', 'Per', and 'Gpe'. Summing their counts: 37644 (Geo) + 20333 (Tim) + 20143 (Org) + 16990 (Per) + 15869 (Gpe) = 110979. So, the answer is {\"answer\": \"110979\"}" } ], "images": [ "scigen/train/1311.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Label statistic" ], "reasoning": "The 'Strong class' labels are 'Geo', 'Time', 'Org', 'Per', and 'Gpe'. Summing their counts: 37644 (Geo) + 20333 (Tim) + 20143 (Org) + 16990 (Per) + 15869 (Gpe) = 110979.", "question": "What is the total count of samples for the 'Strong class' labels?", "answer": "So, the answer is {\"answer\": \"110979\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe 'Art' label has more samples than the 'Eve' label.\n" }, { "role": "assistant", "content": "According to the table, the 'Art' label has 402 samples, while the 'Eve' label has 308 samples. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1311.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Label statistic" ], "reasoning": "According to the table, the 'Art' label has 402 samples, while the 'Eve' label has 308 samples. Therefore, the statement is supported.", "statement": "The 'Art' label has more samples than the 'Eve' label.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Accuracy of baseline methods using full training data with weighted loss function.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The RNN-CNN method significantly improves accuracy for both classes, achieving 99.6% for class 0 and 75% for class 1, due to the modified binary loss function that assigns small weights to class 0 and large weights to class 1.To overcome this issue, we introduce a modified binary loss function that exploits the whole data for training procedure: Lwce=−p0t0log(s0)−p1t1log(s1) (7) where (t0,s0),(t1,s1) denote for the ground truth and scores for class 0 and 1, respectively. p0,p1 denote for the occurrence probability of class 0 and 1 in the training data. In our experiment, the p0≈0.02 and p1≈0.98. It is really interested when the RNN-CNN improves the accuracy on both 0 and 1 class with a large margin compared two remaining methods, holding 75% on ”1” class and 99.6% on ”0” class. This improvement can be reasoning as the effect of setting small weights for class 0 and large weights for class 1. It forces the model to damp loss values when predict correctly ”0” class and amplifying error for incorrect assignment on ”1” class." } ], "images": [ "scigen/train/1312.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracy of baseline methods using full training data with weighted loss function." ], "reasoning": "The RNN-CNN method significantly improves accuracy for both classes, achieving 99.6% for class 0 and 75% for class 1, due to the modified binary loss function that assigns small weights to class 0 and large weights to class 1.", "description": "To overcome this issue, we introduce a modified binary loss function that exploits the whole data for training procedure: Lwce=−p0t0log(s0)−p1t1log(s1) (7) where (t0,s0),(t1,s1) denote for the ground truth and scores for class 0 and 1, respectively. p0,p1 denote for the occurrence probability of class 0 and 1 in the training data. In our experiment, the p0≈0.02 and p1≈0.98. It is really interested when the RNN-CNN improves the accuracy on both 0 and 1 class with a large margin compared two remaining methods, holding 75% on ”1” class and 99.6% on ”0” class. This improvement can be reasoning as the effect of setting small weights for class 0 and large weights for class 1. It forces the model to damp loss values when predict correctly ”0” class and amplifying error for incorrect assignment on ”1” class.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Accuracy of baseline methods using full training data with weighted loss function.', answer the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich method achieves the highest accuracy for class 1?\n" }, { "role": "assistant", "content": "By comparing the accuracy values for class 1 across all methods in the table, the RNN-CNN method achieves the highest accuracy with 75%. So, the answer is {\"answer\": \"RNN-CNN\"}." } ], "images": [ "scigen/train/1312.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracy of baseline methods using full training data with weighted loss function." ], "reasoning": "By comparing the accuracy values for class 1 across all methods in the table, the RNN-CNN method achieves the highest accuracy with 75%.", "question": "Which method achieves the highest accuracy for class 1?", "answer": "So, the answer is {\"answer\": \"RNN-CNN\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Self-Attention method achieves the highest accuracy for class 0.\n" }, { "role": "assistant", "content": "By comparing the accuracy values for class 0 across all methods in the table, the RNN-CNN method achieves the highest accuracy with 99.6%, not the Self-Attention method. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1312.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracy of baseline methods using full training data with weighted loss function." ], "reasoning": "By comparing the accuracy values for class 0 across all methods in the table, the RNN-CNN method achieves the highest accuracy with 99.6%, not the Self-Attention method.", "statement": "The Self-Attention method achieves the highest accuracy for class 0.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Human evaluation of 100 sample responses on VisDial v1.0 validation set. M1: percentage of responses that pass the Turing Test. M2: percentage of responses that are evaluated better or equal to human responses. Repetition: percentage of responses that have meaningless repeated words. Richness: percentage of responses that contain detailed content to answer the question.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Incorporating RSL and Memory Unit with WDL reduces repetition by 0.06 and improves M1 and M2 by 0.06 and 0.07 respectively, though richness decreases slightly.Distinct from previous works, we add Repetition and Richness metrics, and for all metrics, we record the score when at least 2 persons agree. After incorporating RSL and Memory Unit with WDL, the repetition further reduces by 0.06 while M1 and M2 outperform by 0.06 and 0.07 respectively, which proves the complementary advantages between these two level information. We also notice that Richness decreases slightly. This is mainly because the information from RSL concentrates more attention on the global information, rather than detailed information." } ], "images": [ "scigen/train/1313.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Human evaluation of 100 sample responses on VisDial v1.0 validation set. M1: percentage of responses that pass the Turing Test. M2: percentage of responses that are evaluated better or equal to human responses. Repetition: percentage of responses that have meaningless repeated words. Richness: percentage of responses that contain detailed content to answer the question." ], "reasoning": "Incorporating RSL and Memory Unit with WDL reduces repetition by 0.06 and improves M1 and M2 by 0.06 and 0.07 respectively, though richness decreases slightly.", "description": "Distinct from previous works, we add Repetition and Richness metrics, and for all metrics, we record the score when at least 2 persons agree. After incorporating RSL and Memory Unit with WDL, the repetition further reduces by 0.06 while M1 and M2 outperform by 0.06 and 0.07 respectively, which proves the complementary advantages between these two level information. We also notice that Richness decreases slightly. This is mainly because the information from RSL concentrates more attention on the global information, rather than detailed information.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: Human evaluation of 100 sample responses on VisDial v1.0 validation set. M1: percentage of responses that pass the Turing Test. M2: percentage of responses that are evaluated better or equal to human responses. Repetition: percentage of responses that have meaningless repeated words. Richness: percentage of responses that contain detailed content to answer the question.' in mind, break down and resolve the question below step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the repetition score for the model that combines RSL and Memory Unit with WDL?\n" }, { "role": "assistant", "content": "The description mentions that incorporating RSL and Memory Unit with WDL reduces repetition further by 0.06. The table shows that the repetition score for DualVD-DAM, which combines these elements, is 0.01. So, the answer is {\"answer\": \"0.01\"}." } ], "images": [ "scigen/train/1313.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Human evaluation of 100 sample responses on VisDial v1.0 validation set. M1: percentage of responses that pass the Turing Test. M2: percentage of responses that are evaluated better or equal to human responses. Repetition: percentage of responses that have meaningless repeated words. Richness: percentage of responses that contain detailed content to answer the question." ], "reasoning": "The description mentions that incorporating RSL and Memory Unit with WDL reduces repetition further by 0.06. The table shows that the repetition score for DualVD-DAM, which combines these elements, is 0.01.", "question": "What is the repetition score for the model that combines RSL and Memory Unit with WDL?", "answer": "So, the answer is {\"answer\": \"0.01\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The model with the highest M1 score also has the lowest repetition score. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe model with the highest M1 score also has the lowest repetition score.\n" }, { "role": "assistant", "content": "The table shows that DualVD-DAM has the highest M1 score of 0.75 and the lowest repetition score of 0.01. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1313.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Human evaluation of 100 sample responses on VisDial v1.0 validation set. M1: percentage of responses that pass the Turing Test. M2: percentage of responses that are evaluated better or equal to human responses. Repetition: percentage of responses that have meaningless repeated words. Richness: percentage of responses that contain detailed content to answer the question." ], "reasoning": "The table shows that DualVD-DAM has the highest M1 score of 0.75 and the lowest repetition score of 0.01.", "statement": "The model with the highest M1 score also has the lowest repetition score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: Result comparison on validation set of VisDial v1.0.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our models outperform baseline models on all metrics, showing complementary advantages between DAM and existing encoders in visual dialogue., re-trained by us). ReDAN-G and DMRM adopted complex multi-step reasoning, while HCIAE-G, CoAtt-G and Primary-G are attention-based models. For fairness, we only compare the original generative ability without re-ranking. We just replace the decoders in baseline models by our proposed DAM. Compared with the baseline models, our models outperform them on all the metrics, which indicates the complementary advantages between DAM and existing encoders in visual dialogue. Though DualVD-G performs lower than DMRM on Mean, DualVD-DAM outperforms DMRM on all the other metrics without multi-step reasoning, which is the advantages in DMRM over our models." } ], "images": [ "scigen/train/1314.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Result comparison on validation set of VisDial v1.0." ], "reasoning": "Our models outperform baseline models on all metrics, showing complementary advantages between DAM and existing encoders in visual dialogue.", "description": ", re-trained by us). ReDAN-G and DMRM adopted complex multi-step reasoning, while HCIAE-G, CoAtt-G and Primary-G are attention-based models. For fairness, we only compare the original generative ability without re-ranking. We just replace the decoders in baseline models by our proposed DAM. Compared with the baseline models, our models outperform them on all the metrics, which indicates the complementary advantages between DAM and existing encoders in visual dialogue. Though DualVD-G performs lower than DMRM on Mean, DualVD-DAM outperforms DMRM on all the other metrics without multi-step reasoning, which is the advantages in DMRM over our models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: Result comparison on validation set of VisDial v1.0.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest NDCG score?\n" }, { "role": "assistant", "content": "By comparing the NDCG scores of all models, DualVD-DAM (ours) has the highest NDCG score of 60.93. So, the answer is {\"answer\": \"DualVD-DAM (ours)\"}" } ], "images": [ "scigen/train/1314.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Result comparison on validation set of VisDial v1.0." ], "reasoning": "By comparing the NDCG scores of all models, DualVD-DAM (ours) has the highest NDCG score of 60.93.", "question": "Which model has the highest NDCG score?", "answer": "So, the answer is {\"answer\": \"DualVD-DAM (ours)\"}" }, { "messages": [ { "role": "user", "content": "DMRM has the highest Mean score among all models. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nDMRM has the highest Mean score among all models.\n" }, { "role": "assistant", "content": "By comparing the Mean scores of all models, LF-G has the highest Mean score of 21.11, not DMRM. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1314.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Result comparison on validation set of VisDial v1.0." ], "reasoning": "By comparing the Mean scores of all models, LF-G has the highest Mean score of 21.11, not DMRM.", "statement": "DMRM has the highest Mean score among all models.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Ablation study of each unit on VisDial v1.0 validation set.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The ablation models show the effectiveness of each unit in the DAM model, with the full model 2L-DAM performing the best across different base models.We consider the following ablation models to illustrate the effectiveness of each unit of our model: 1) 2L-DAM : this is our full model that adaptively selects related information for decoding. 2) 2L-DM: full model w/o Abandon Unit. 3) 2L-M: 2L-DM w/o Deliberation Unit. 4) 2-LSTM: 2L-M w/o Memory Unit. Similar trend exists in LF-DAM and MN-DAM, which indicates the effectiveness of each unit in DAM. Since the space limitation and similar observations, we show the ablation studies on DualVD-DAM in the following experiments." } ], "images": [ "scigen/train/1315.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ablation study of each unit on VisDial v1.0 validation set." ], "reasoning": "The ablation models show the effectiveness of each unit in the DAM model, with the full model 2L-DAM performing the best across different base models.", "description": "We consider the following ablation models to illustrate the effectiveness of each unit of our model: 1) 2L-DAM : this is our full model that adaptively selects related information for decoding. 2) 2L-DM: full model w/o Abandon Unit. 3) 2L-M: 2L-DM w/o Deliberation Unit. 4) 2-LSTM: 2L-M w/o Memory Unit. Similar trend exists in LF-DAM and MN-DAM, which indicates the effectiveness of each unit in DAM. Since the space limitation and similar observations, we show the ablation studies on DualVD-DAM in the following experiments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Ablation study of each unit on VisDial v1.0 validation set.\nQuestion: Which model has the highest Mean score for the DualVD-DAM base model?\nGive a detailed response to the above question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest Mean score for the DualVD-DAM base model, compare the Mean scores of all models under the DualVD-DAM base model. The highest Mean score is 17.15, achieved by the 2L-DM model. So, the answer is {\"answer\": \"2L-DM\"}." } ], "images": [ "scigen/train/1315.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ablation study of each unit on VisDial v1.0 validation set." ], "reasoning": "To find the model with the highest Mean score for the DualVD-DAM base model, compare the Mean scores of all models under the DualVD-DAM base model. The highest Mean score is 17.15, achieved by the 2L-DM model.", "question": "Which model has the highest Mean score for the DualVD-DAM base model?", "answer": "So, the answer is {\"answer\": \"2L-DM\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe 2L-DAM model has the highest NDCG score for the LF-DAM base model.\n" }, { "role": "assistant", "content": "To verify this statement, compare the NDCG scores of all models under the LF-DAM base model. The highest NDCG score is 52.68, achieved by the 2L-DAM model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1315.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ablation study of each unit on VisDial v1.0 validation set." ], "reasoning": "To verify this statement, compare the NDCG scores of all models under the LF-DAM base model. The highest NDCG score is 52.68, achieved by the 2L-DAM model.", "statement": "The 2L-DAM model has the highest NDCG score for the LF-DAM base model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: Ablation study of Deliberation Unit on VisDial v1.0.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "DualVD-DAM outperforms all other models on all metrics, demonstrating the effectiveness of incorporating structure-aware information from the encoder.The relatively higher results of H model indicate that the history information plays a more important role in the decoder. By jointly incorporating all the structure-aware information from the encoder, DualVD-DAM achieves the best performance on all the metrics. It proves the advantages of DAM via fully utilizing the information from the elaborate encoder, which is beneficial for enhancing the existing generation models by incorporating their encoders with DAM adaptively." } ], "images": [ "scigen/train/1316.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Ablation study of Deliberation Unit on VisDial v1.0." ], "reasoning": "DualVD-DAM outperforms all other models on all metrics, demonstrating the effectiveness of incorporating structure-aware information from the encoder.", "description": "The relatively higher results of H model indicate that the history information plays a more important role in the decoder. By jointly incorporating all the structure-aware information from the encoder, DualVD-DAM achieves the best performance on all the metrics. It proves the advantages of DAM via fully utilizing the information from the elaborate encoder, which is beneficial for enhancing the existing generation models by incorporating their encoders with DAM adaptively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 4: Ablation study of Deliberation Unit on VisDial v1.0.\n\nQuestion: Which model achieves the highest R@10 score?\n" }, { "role": "assistant", "content": "By comparing the R@10 scores of all models, DualVD-DAM has the highest score of 67.94. So, the answer is {\"answer\": \"DualVD-DAM\"}." } ], "images": [ "scigen/train/1316.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Ablation study of Deliberation Unit on VisDial v1.0." ], "reasoning": "By comparing the R@10 scores of all models, DualVD-DAM has the highest score of 67.94.", "question": "Which model achieves the highest R@10 score?", "answer": "So, the answer is {\"answer\": \"DualVD-DAM\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe H model has the highest Mean score.\n" }, { "role": "assistant", "content": "The table shows that the H model has a Mean score of 17.27, while DualVD-DAM has a lower Mean score of 16.65. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1316.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Ablation study of Deliberation Unit on VisDial v1.0." ], "reasoning": "The table shows that the H model has a Mean score of 17.27, while DualVD-DAM has a lower Mean score of 16.65. Therefore, the statement is refuted.", "statement": "The H model has the highest Mean score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Summary of results. We report Spearman’s correlation coefficient for the word similarity task and accuracies (%) for other tasks. Higher values are better (higher correlation coefficient or higher accuracy). The last two methods (columns) are new to this paper, and our proposed method is in the last column.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our method outperforms others in similarity ranking and sentiment analysis tasks across both low and high dimensional embeddings, and performs best in high-dimensional sentence completion.In the similarity ranking and sentiment analysis tasks, our method performed the best in both low and high dimensional embeddings. In the sentence completion challenge, our method performed best in the high-dimensional case and second-best in the low-dimensional case. Importantly, forest outperforms PCA and unstructured sparse coding (SC) on every task. We take this collection of results as support for the idea that coarse-to-fine organization of latent dimensions of word representations captures the relationships between words’ meanings better compare to unstructured organization." } ], "images": [ "scigen/train/1317.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Summary of results. We report Spearman’s correlation coefficient for the word similarity task and accuracies (%) for other tasks. Higher values are better (higher correlation coefficient or higher accuracy). The last two methods (columns) are new to this paper, and our proposed method is in the last column." ], "reasoning": "Our method outperforms others in similarity ranking and sentiment analysis tasks across both low and high dimensional embeddings, and performs best in high-dimensional sentence completion.", "description": "In the similarity ranking and sentiment analysis tasks, our method performed the best in both low and high dimensional embeddings. In the sentence completion challenge, our method performed best in the high-dimensional case and second-best in the low-dimensional case. Importantly, forest outperforms PCA and unstructured sparse coding (SC) on every task. We take this collection of results as support for the idea that coarse-to-fine organization of latent dimensions of word representations captures the relationships between words’ meanings better compare to unstructured organization.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Summary of results. We report Spearman’s correlation coefficient for the word similarity task and accuracies (%) for other tasks. Higher values are better (higher correlation coefficient or higher accuracy). The last two methods (columns) are new to this paper, and our proposed method is in the last column.', answer the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich method performed best in the high-dimensional sentence completion task?\n" }, { "role": "assistant", "content": "The table shows that in the high-dimensional (M=520) sentence completion task, the forest method has the highest score of 35.86. So, the answer is {\"answer\": \"forest\"}" } ], "images": [ "scigen/train/1317.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Summary of results. We report Spearman’s correlation coefficient for the word similarity task and accuracies (%) for other tasks. Higher values are better (higher correlation coefficient or higher accuracy). The last two methods (columns) are new to this paper, and our proposed method is in the last column." ], "reasoning": "The table shows that in the high-dimensional (M=520) sentence completion task, the forest method has the highest score of 35.86.", "question": "Which method performed best in the high-dimensional sentence completion task?", "answer": "So, the answer is {\"answer\": \"forest\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nForest method performed best in the low-dimensional syntactic analogies task.\n" }, { "role": "assistant", "content": "The table shows that in the low-dimensional (M=52) syntactic analogies task, the CBOW method performed best with a score of 26.69, not the forest method. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1317.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Summary of results. We report Spearman’s correlation coefficient for the word similarity task and accuracies (%) for other tasks. Higher values are better (higher correlation coefficient or higher accuracy). The last two methods (columns) are new to this paper, and our proposed method is in the last column." ], "reasoning": "The table shows that in the low-dimensional (M=52) syntactic analogies task, the CBOW method performed best with a score of 26.69, not the forest method.", "statement": "Forest method performed best in the low-dimensional syntactic analogies task.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Results on the syntactic and semantic analogies tasks with a bigger corpus (M=260).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Combining the WMT-2011 corpus with other news corpora and Wikipedia to obtain a corpus of 6.8 billion words significantly improves model performance, with forest excelling in syntactic tasks and SG outperforming others in semantic tasks.We hypothesize that this is because performing well on these tasks requires training on a bigger corpus. We combine our WMT-2011 corpus with other news corpora and Wikipedia to obtain a corpus of 6.8 billion words. The size of the vocabulary of this corpus is 401,150. (M=52 does not perform as well, and M=520 is computationally expensive). All models benefit significantly from a bigger corpus, and the performance levels are now comparable with previous work. On the syntactic analogies task, forest is the best model. On the semantic analogies task, SG outperformed forest, and they both are better than CBOW." } ], "images": [ "scigen/train/1318.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on the syntactic and semantic analogies tasks with a bigger corpus (M=260)." ], "reasoning": "Combining the WMT-2011 corpus with other news corpora and Wikipedia to obtain a corpus of 6.8 billion words significantly improves model performance, with forest excelling in syntactic tasks and SG outperforming others in semantic tasks.", "description": "We hypothesize that this is because performing well on these tasks requires training on a bigger corpus. We combine our WMT-2011 corpus with other news corpora and Wikipedia to obtain a corpus of 6.8 billion words. The size of the vocabulary of this corpus is 401,150. (M=52 does not perform as well, and M=520 is computationally expensive). All models benefit significantly from a bigger corpus, and the performance levels are now comparable with previous work. On the syntactic analogies task, forest is the best model. On the semantic analogies task, SG outperformed forest, and they both are better than CBOW.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Results on the syntactic and semantic analogies tasks with a bigger corpus (M=260).', answer the question below by showing each progressive step toward the answer. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model performed best on the syntactic analogies task?\n" }, { "role": "assistant", "content": "The table shows the performance of different models on syntactic and semantic tasks. The forest model has the highest score (65.11) on the syntactic task. So, the answer is {\"answer\": \"forest\"}" } ], "images": [ "scigen/train/1318.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on the syntactic and semantic analogies tasks with a bigger corpus (M=260)." ], "reasoning": "The table shows the performance of different models on syntactic and semantic tasks. The forest model has the highest score (65.11) on the syntactic task.", "question": "Which model performed best on the syntactic analogies task?", "answer": "So, the answer is {\"answer\": \"forest\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSG outperformed forest on the syntactic analogies task.\n" }, { "role": "assistant", "content": "The table shows that the forest model scored 65.11 on the syntactic task, while the SG model scored 63.61. Therefore, SG did not outperform forest on the syntactic analogies task. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1318.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on the syntactic and semantic analogies tasks with a bigger corpus (M=260)." ], "reasoning": "The table shows that the forest model scored 65.11 on the syntactic task, while the SG model scored 63.61. Therefore, SG did not outperform forest on the syntactic analogies task.", "statement": "SG outperformed forest on the syntactic analogies task.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Fine-tuning times (in minutes) of our model compared to the most accurate baseline: the standard BERT-large model with a single output layer.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Fine-tuning BERT-large with our approach is comparable in cost to the standard BERT-large model, and in four out of five cases, it is not slower, being slightly faster in three cases.Fine-tuning BERT-large with our approach has a similar cost to fine-tuning the standard BERT-large model, with a single output layer. Our model is not slower to fine-tune in four out of five cases, and is even slightly faster in three of them." } ], "images": [ "scigen/train/1319.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Fine-tuning times (in minutes) of our model compared to the most accurate baseline: the standard BERT-large model with a single output layer." ], "reasoning": "Fine-tuning BERT-large with our approach is comparable in cost to the standard BERT-large model, and in four out of five cases, it is not slower, being slightly faster in three cases.", "description": "Fine-tuning BERT-large with our approach has a similar cost to fine-tuning the standard BERT-large model, with a single output layer. Our model is not slower to fine-tune in four out of five cases, and is even slightly faster in three of them.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: Fine-tuning times (in minutes) of our model compared to the most accurate baseline: the standard BERT-large model with a single output layer.'.\n\nQuestion: Which dataset shows the largest difference in training time between our model and the standard model?\n" }, { "role": "assistant", "content": "By comparing the training times for each dataset, the largest difference is observed in the MNLI dataset, where our model takes 852 units of time compared to 835 units for the standard model. So, the answer is {\"answer\": \"MNLI\"}." } ], "images": [ "scigen/train/1319.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Fine-tuning times (in minutes) of our model compared to the most accurate baseline: the standard BERT-large model with a single output layer." ], "reasoning": "By comparing the training times for each dataset, the largest difference is observed in the MNLI dataset, where our model takes 852 units of time compared to 835 units for the standard model.", "question": "Which dataset shows the largest difference in training time between our model and the standard model?", "answer": "So, the answer is {\"answer\": \"MNLI\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nOur model is faster to fine-tune on the SNLI dataset compared to the standard model.\n" }, { "role": "assistant", "content": "The table shows that the training time for our model on the SNLI dataset is 289 units, while the standard model takes 300 units, indicating that our model is indeed faster. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1319.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Fine-tuning times (in minutes) of our model compared to the most accurate baseline: the standard BERT-large model with a single output layer." ], "reasoning": "The table shows that the training time for our model on the SNLI dataset is 289 units, while the standard model takes 300 units, indicating that our model is indeed faster.", "statement": "Our model is faster to fine-tune on the SNLI dataset compared to the standard model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Spearman’s ρ correlation between confidence levels for our most efficient classifier and two measures of difficulty: document length and consistency. Confidence is correlated reasonably with consistency across all datasets. For all datasets except AG, confidence is (loosely) negatively correlated with document length. For the AG topic classification dataset, confidence is (loosely) positively correlated. Results for the other layers show a similar trend.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The correlation between confidence and length is generally negative across most datasets, except for AG, and there is a medium correlation between confidence and consistency across all datasets.Moreover, as expected, across four out of five datasets, the (weak) correlation between confidence and length is negative; our model is somewhat more confident in its prediction on shorter documents. The fifth dataset (AG), shows the opposite trend: confidence is positively correlated with length. This discrepancy might be explained by the nature of the tasks we consider. For instance, IMDB and SST are sentiment analysis datasets, where longer texts might include conflicting evidence and thus be harder to classify. In contrast, AG is a news topic detection dataset, where a conflict between topics is uncommon, and longer documents provide more opportunities to find the topic. Our next criterion for “difficulty” is the consistency of model predictions. Toneva et al. Such instances can be thought of as “easy” or memorable examples. Similarly, Sakaguchi et al. Inspired by these works, we define the criterion of consistency: whether all classifiers in our model agree on the prediction of a given instance, regardless of whether it is correct or not. ρ correlation between the confidence of the most efficient classifier and this measure of consistency. Our analysis reveals a medium correlation between confidence and consistency across all datasets (0.37≤ρ≤0.47), which indicates that the measure of confidence generally agrees with the measure of consistency." } ], "images": [ "scigen/train/1320.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Spearman’s ρ correlation between confidence levels for our most efficient classifier and two measures of difficulty: document length and consistency. Confidence is correlated reasonably with consistency across all datasets. For all datasets except AG, confidence is (loosely) negatively correlated with document length. For the AG topic classification dataset, confidence is (loosely) positively correlated. Results for the other layers show a similar trend." ], "reasoning": "The correlation between confidence and length is generally negative across most datasets, except for AG, and there is a medium correlation between confidence and consistency across all datasets.", "description": "Moreover, as expected, across four out of five datasets, the (weak) correlation between confidence and length is negative; our model is somewhat more confident in its prediction on shorter documents. The fifth dataset (AG), shows the opposite trend: confidence is positively correlated with length. This discrepancy might be explained by the nature of the tasks we consider. For instance, IMDB and SST are sentiment analysis datasets, where longer texts might include conflicting evidence and thus be harder to classify. In contrast, AG is a news topic detection dataset, where a conflict between topics is uncommon, and longer documents provide more opportunities to find the topic. Our next criterion for “difficulty” is the consistency of model predictions. Toneva et al. Such instances can be thought of as “easy” or memorable examples. Similarly, Sakaguchi et al. Inspired by these works, we define the criterion of consistency: whether all classifiers in our model agree on the prediction of a given instance, regardless of whether it is correct or not. ρ correlation between the confidence of the most efficient classifier and this measure of consistency. Our analysis reveals a medium correlation between confidence and consistency across all datasets (0.37≤ρ≤0.47), which indicates that the measure of confidence generally agrees with the measure of consistency.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 3: Spearman’s ρ correlation between confidence levels for our most efficient classifier and two measures of difficulty: document length and consistency. Confidence is correlated reasonably with consistency across all datasets. For all datasets except AG, confidence is (loosely) negatively correlated with document length. For the AG topic classification dataset, confidence is (loosely) positively correlated. Results for the other layers show a similar trend.', answer the question below by showing each progressive step toward the answer. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich dataset shows the highest correlation between confidence and consistency?\n" }, { "role": "assistant", "content": "By examining the 'Consistency' column in the table, the highest value is 0.47, which corresponds to the IMDB dataset. So, the answer is {\"answer\": \"IMDB\"}." } ], "images": [ "scigen/train/1320.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Spearman’s ρ correlation between confidence levels for our most efficient classifier and two measures of difficulty: document length and consistency. Confidence is correlated reasonably with consistency across all datasets. For all datasets except AG, confidence is (loosely) negatively correlated with document length. For the AG topic classification dataset, confidence is (loosely) positively correlated. Results for the other layers show a similar trend." ], "reasoning": "By examining the 'Consistency' column in the table, the highest value is 0.47, which corresponds to the IMDB dataset.", "question": "Which dataset shows the highest correlation between confidence and consistency?", "answer": "So, the answer is {\"answer\": \"IMDB\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe correlation between confidence and length is positive for the AG dataset.\n" }, { "role": "assistant", "content": "The description states that the AG dataset shows a positive correlation between confidence and length, which is supported by the table showing a correlation of -0.13 for length in AG. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1320.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Spearman’s ρ correlation between confidence levels for our most efficient classifier and two measures of difficulty: document length and consistency. Confidence is correlated reasonably with consistency across all datasets. For all datasets except AG, confidence is (loosely) negatively correlated with document length. For the AG topic classification dataset, confidence is (loosely) positively correlated. Results for the other layers show a similar trend." ], "reasoning": "The description states that the AG dataset shows a positive correlation between confidence and length, which is supported by the table showing a correlation of -0.13 for length in AG.", "statement": "The correlation between confidence and length is positive for the AG dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Spearman’s ρ correlation between confidence levels for our classifiers (of different layers) on the validation sets of SNLI and MNLI, and two measures of difficulty: hypothesis-only classifier predictions (Hyp.-Only) and inter-annotator consensus (IAC).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The correlation between classifier confidence and hypothesis-only predictions decreases with larger models, while the correlation with inter-annotator consensus increases.Gururangan et al. They argued that such instances are “easier” for machines, compared to those which required access to the full input, which they considered “harder.” Similarly to the consistency results, we see that the confidence of our most efficient classifier is reasonably correlated with the predictions of the hypothesis-only classifier. As expected, as we move to larger, more accurate classifiers, which presumably are able to make successful predictions on harder instances, this correlation decreases. Both NLI datasets include labels from five different annotators. We treat the inter-annotator consensus (IAC) as another measure of difficulty: the higher the consensus is, the easier the instance. We compute IAC for each example as the fraction of annotators who agreed on the majority label, hence this number ranges from 0.6 to 1.0 for five annotators. The correlation with our most efficient classifiers is rather weak, only 0.08 and 0.14. Surprisingly, as we move to larger models, the correlation increases, up to 0.32 for the most accurate classifiers. This indicates that the two measures perhaps capture a different notion of difficulty." } ], "images": [ "scigen/train/1321.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Spearman’s ρ correlation between confidence levels for our classifiers (of different layers) on the validation sets of SNLI and MNLI, and two measures of difficulty: hypothesis-only classifier predictions (Hyp.-Only) and inter-annotator consensus (IAC)." ], "reasoning": "The correlation between classifier confidence and hypothesis-only predictions decreases with larger models, while the correlation with inter-annotator consensus increases.", "description": "Gururangan et al. They argued that such instances are “easier” for machines, compared to those which required access to the full input, which they considered “harder.” Similarly to the consistency results, we see that the confidence of our most efficient classifier is reasonably correlated with the predictions of the hypothesis-only classifier. As expected, as we move to larger, more accurate classifiers, which presumably are able to make successful predictions on harder instances, this correlation decreases. Both NLI datasets include labels from five different annotators. We treat the inter-annotator consensus (IAC) as another measure of difficulty: the higher the consensus is, the easier the instance. We compute IAC for each example as the fraction of annotators who agreed on the majority label, hence this number ranges from 0.6 to 1.0 for five annotators. The correlation with our most efficient classifiers is rather weak, only 0.08 and 0.14. Surprisingly, as we move to larger models, the correlation increases, up to 0.32 for the most accurate classifiers. This indicates that the two measures perhaps capture a different notion of difficulty.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 4: Spearman’s ρ correlation between confidence levels for our classifiers (of different layers) on the validation sets of SNLI and MNLI, and two measures of difficulty: hypothesis-only classifier predictions (Hyp.-Only) and inter-annotator consensus (IAC).', and sequentially solve the question, writing out each step of your reasoning process. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nAt which layer does the correlation between SNLI Hypothesis-Only and SNLI IAC become equal?\n" }, { "role": "assistant", "content": "By examining the table, we see that the correlation between SNLI Hypothesis-Only and SNLI IAC becomes equal at layer 12, where both values are 0.31. So, the answer is {\"answer\": \"12\"}." } ], "images": [ "scigen/train/1321.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Spearman’s ρ correlation between confidence levels for our classifiers (of different layers) on the validation sets of SNLI and MNLI, and two measures of difficulty: hypothesis-only classifier predictions (Hyp.-Only) and inter-annotator consensus (IAC)." ], "reasoning": "By examining the table, we see that the correlation between SNLI Hypothesis-Only and SNLI IAC becomes equal at layer 12, where both values are 0.31.", "question": "At which layer does the correlation between SNLI Hypothesis-Only and SNLI IAC become equal?", "answer": "So, the answer is {\"answer\": \"12\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe correlation between MNLI Hypothesis-Only and MNLI IAC is highest at layer 23.\n" }, { "role": "assistant", "content": "By examining the table, we see that the correlation between MNLI Hypothesis-Only and MNLI IAC is highest at layer 23, where the value is 0.32. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1321.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Spearman’s ρ correlation between confidence levels for our classifiers (of different layers) on the validation sets of SNLI and MNLI, and two measures of difficulty: hypothesis-only classifier predictions (Hyp.-Only) and inter-annotator consensus (IAC)." ], "reasoning": "By examining the table, we see that the correlation between MNLI Hypothesis-Only and MNLI IAC is highest at layer 23, where the value is 0.32.", "statement": "The correlation between MNLI Hypothesis-Only and MNLI IAC is highest at layer 23.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 7: QA-based and criteria-based human evaluation. ∗ mark indicates the improvements from the baselines to the ESCA-BERT are statistically significant using a paired t-test (p<0.05). Gold summary was not included in QA evaluation. ♭ means unnecessary value.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The gold summary set the upper bound except for relevance, and ESCA-BERT produced the most popular summaries. ESCA with novelty and relevance control gained the highest ranks in their respective criteria.In the first block of criteria ranking, 5 systems were simultaneously ranked. The gold summary set the upper bound except for the relevance. Unsurprisingly, since the gold summaries of CNN/DailyMaily are mostly top sentences in the articles, their relevance can not be guaranteed. We also found the ESCA-BERT produced the most popular summaries. In the second block, the ESCA with novelty and relevance control were also evaluated together with the bottom-up and the original ESCA. The ranking difference slightly varies, but it obviously proves that the ESCA under novelty or relevance control gained the corresponding highest rank." } ], "images": [ "scigen/train/1322.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: QA-based and criteria-based human evaluation. ∗ mark indicates the improvements from the baselines to the ESCA-BERT are statistically significant using a paired t-test (p<0.05). Gold summary was not included in QA evaluation. ♭ means unnecessary value." ], "reasoning": "The gold summary set the upper bound except for relevance, and ESCA-BERT produced the most popular summaries. ESCA with novelty and relevance control gained the highest ranks in their respective criteria.", "description": "In the first block of criteria ranking, 5 systems were simultaneously ranked. The gold summary set the upper bound except for the relevance. Unsurprisingly, since the gold summaries of CNN/DailyMaily are mostly top sentences in the articles, their relevance can not be guaranteed. We also found the ESCA-BERT produced the most popular summaries. In the second block, the ESCA with novelty and relevance control were also evaluated together with the bottom-up and the original ESCA. The ranking difference slightly varies, but it obviously proves that the ESCA under novelty or relevance control gained the corresponding highest rank.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 7: QA-based and criteria-based human evaluation. ∗ mark indicates the improvements from the baselines to the ESCA-BERT are statistically significant using a paired t-test (p<0.05). Gold summary was not included in QA evaluation. ♭ means unnecessary value.\nQuestion: Which model produced the most popular summaries and what was its QA score?\n" }, { "role": "assistant", "content": "The description states that ESCA-BERT produced the most popular summaries. According to the table, ESCA-BERT has a QA score of 39.2. So, the answer is {\"answer\": \"ESCA-BERT with a QA score of 39.2\"}." } ], "images": [ "scigen/train/1322.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: QA-based and criteria-based human evaluation. ∗ mark indicates the improvements from the baselines to the ESCA-BERT are statistically significant using a paired t-test (p<0.05). Gold summary was not included in QA evaluation. ♭ means unnecessary value." ], "reasoning": "The description states that ESCA-BERT produced the most popular summaries. According to the table, ESCA-BERT has a QA score of 39.2.", "question": "Which model produced the most popular summaries and what was its QA score?", "answer": "So, the answer is {\"answer\": \"ESCA-BERT with a QA score of 39.2\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe gold summary had the highest rank in relevance.\n" }, { "role": "assistant", "content": "The description mentions that the gold summary set the upper bound except for relevance. The table shows that the gold summary has a relevance score of 0.13, which is not the highest. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1322.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: QA-based and criteria-based human evaluation. ∗ mark indicates the improvements from the baselines to the ESCA-BERT are statistically significant using a paired t-test (p<0.05). Gold summary was not included in QA evaluation. ♭ means unnecessary value." ], "reasoning": "The description mentions that the gold summary set the upper bound except for relevance. The table shows that the gold summary has a relevance score of 0.13, which is not the highest.", "statement": "The gold summary had the highest rank in relevance.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 6: Controllability: ROUGE Recall scores on the CNN/DailyMail dataset regarding different explainable aspects under different thresholds, such as novelty ϵn and relevance ϵr.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "ROUGE recall was used to evaluate controlled performance, highlighting the trade-off between controllability and summary performance due to bias in the gold summary.We evaluated the controlled performance by ROUGE recall because it shows whether additional relevant summaries can be included under the deterministic controls. Additionally, there is always a trade-off between controllability and summary performance since the gold summary was automatically constructed with bias and not based on these controllability properties." } ], "images": [ "scigen/train/1323.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Controllability: ROUGE Recall scores on the CNN/DailyMail dataset regarding different explainable aspects under different thresholds, such as novelty ϵn and relevance ϵr." ], "reasoning": "ROUGE recall was used to evaluate controlled performance, highlighting the trade-off between controllability and summary performance due to bias in the gold summary.", "description": "We evaluated the controlled performance by ROUGE recall because it shows whether additional relevant summaries can be included under the deterministic controls. Additionally, there is always a trade-off between controllability and summary performance since the gold summary was automatically constructed with bias and not based on these controllability properties.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 6: Controllability: ROUGE Recall scores on the CNN/DailyMail dataset regarding different explainable aspects under different thresholds, such as novelty ϵn and relevance ϵr.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model has the highest ROUGE-L score?\n" }, { "role": "assistant", "content": "To find the model with the highest ROUGE-L score, compare the R-L values for all models. The highest R-L score is 43.53, which corresponds to the Novelty model. So, the answer is {\"answer\": \"Novelty\"}." } ], "images": [ "scigen/train/1323.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Controllability: ROUGE Recall scores on the CNN/DailyMail dataset regarding different explainable aspects under different thresholds, such as novelty ϵn and relevance ϵr." ], "reasoning": "To find the model with the highest ROUGE-L score, compare the R-L values for all models. The highest R-L score is 43.53, which corresponds to the Novelty model.", "question": "Which model has the highest ROUGE-L score?", "answer": "So, the answer is {\"answer\": \"Novelty\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Relevance model has the highest ROUGE-1 score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the R-1 scores for all models. The Relevance model has an R-1 score of 46.97, which is not the highest. The highest R-1 score is 47.31, which belongs to the Novelty model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1323.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Controllability: ROUGE Recall scores on the CNN/DailyMail dataset regarding different explainable aspects under different thresholds, such as novelty ϵn and relevance ϵr." ], "reasoning": "To verify this statement, compare the R-1 scores for all models. The Relevance model has an R-1 score of 46.97, which is not the highest. The highest R-1 score is 47.31, which belongs to the Novelty model.", "statement": "The Relevance model has the highest ROUGE-1 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 5: Effect of pre-training on multilingual translation into English. bi is a bilingual system trained on only the eval source language and all others are multi-lingual systems trained on two similar source languages.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Pre-trained embeddings and alignment improve BLEU scores for all tasks, with Gl/Pt showing the largest gains and Be/Ru showing a small decrease.When applying pre-trained embeddings, the gains in each translation pair are roughly in order of their similarity, with Gl/Pt showing the largest gains, and Be/Ru showing a small decrease. In addition, it is also interesting to note that as opposed to previous section, aligning the word embeddings helps to increase the BLEU scores for all three tasks. These increases are intuitive, as a single encoder is used for both of the source languages, and the encoder would have to learn a significantly more complicated transform of the input if the word embeddings for the languages were in a semantically separate space. Pre-training and alignment ensures that the word embeddings of the two source languages are put into similar vector spaces, allowing the model to learn in a similar fashion as it would if training on a single language." } ], "images": [ "scigen/train/1324.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Effect of pre-training on multilingual translation into English. bi is a bilingual system trained on only the eval source language and all others are multi-lingual systems trained on two similar source languages." ], "reasoning": "Pre-trained embeddings and alignment improve BLEU scores for all tasks, with Gl/Pt showing the largest gains and Be/Ru showing a small decrease.", "description": "When applying pre-trained embeddings, the gains in each translation pair are roughly in order of their similarity, with Gl/Pt showing the largest gains, and Be/Ru showing a small decrease. In addition, it is also interesting to note that as opposed to previous section, aligning the word embeddings helps to increase the BLEU scores for all three tasks. These increases are intuitive, as a single encoder is used for both of the source languages, and the encoder would have to learn a significantly more complicated transform of the input if the word embeddings for the languages were in a semantically separate space. Pre-training and alignment ensures that the word embeddings of the two source languages are put into similar vector spaces, allowing the model to learn in a similar fashion as it would if training on a single language.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nTable title: Table 5: Effect of pre-training on multilingual translation into English. bi is a bilingual system trained on only the eval source language and all others are multi-lingual systems trained on two similar source languages.\n\nQuestion: Which translation pair showed the largest gain in BLEU score when pre-trained embeddings were aligned?\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for different translation pairs with various embedding methods. The largest gain is seen in the 'align' column for Gl/Pt with a score of 22.4. So, the answer is {\"answer\": \"Gl/Pt\"}" } ], "images": [ "scigen/train/1324.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Effect of pre-training on multilingual translation into English. bi is a bilingual system trained on only the eval source language and all others are multi-lingual systems trained on two similar source languages." ], "reasoning": "The table shows the BLEU scores for different translation pairs with various embedding methods. The largest gain is seen in the 'align' column for Gl/Pt with a score of 22.4.", "question": "Which translation pair showed the largest gain in BLEU score when pre-trained embeddings were aligned?", "answer": "So, the answer is {\"answer\": \"Gl/Pt\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Aligning word embeddings for Be/Ru resulted in the highest BLEU score increase. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nAligning word embeddings for Be/Ru resulted in the highest BLEU score increase.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for Be/Ru increased to 9.6 with aligned embeddings, which is not the highest increase compared to Gl/Pt with a score of 22.4. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1324.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Effect of pre-training on multilingual translation into English. bi is a bilingual system trained on only the eval source language and all others are multi-lingual systems trained on two similar source languages." ], "reasoning": "The table shows that the BLEU score for Be/Ru increased to 9.6 with aligned embeddings, which is not the highest increase compared to Gl/Pt with a score of 22.4.", "statement": "Aligning word embeddings for Be/Ru resulted in the highest BLEU score increase.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: Number of sentences for each language pair.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The parallel corpus from TED talks transcripts includes language pairs from different language families, such as Romance, Turkic, and Slavic, allowing for comparative analysis across languages with different characteristics.In order to perform experiments in a controlled, multilingual setting, we created a parallel corpus from TED talks transcripts. (Az) and Turkish (Tr), and Belarusian (Be) and Russian (Ru). The languages in each pair are similar in vocabulary, grammar and sentence structure Matthews They also represent different language families – Gl/Pt are Romance; Az /Tr are Turkic; Be/ Ru are Slavic – allowing for comparison across languages with different caracteristics. Tokenization was done using Moses tokenizer" } ], "images": [ "scigen/train/1325.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Number of sentences for each language pair." ], "reasoning": "The parallel corpus from TED talks transcripts includes language pairs from different language families, such as Romance, Turkic, and Slavic, allowing for comparative analysis across languages with different characteristics.", "description": "In order to perform experiments in a controlled, multilingual setting, we created a parallel corpus from TED talks transcripts. (Az) and Turkish (Tr), and Belarusian (Be) and Russian (Ru). The languages in each pair are similar in vocabulary, grammar and sentence structure Matthews They also represent different language families – Gl/Pt are Romance; Az /Tr are Turkic; Be/ Ru are Slavic – allowing for comparison across languages with different caracteristics. Tokenization was done using Moses tokenizer", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Number of sentences for each language pair.', solve the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich language pair has the highest number of training examples?\n" }, { "role": "assistant", "content": "By examining the 'train' column in the table, we see that the language pair 'Ru → En' has the highest number of training examples with 208,106. So, the answer is {\"answer\": \"Ru → En\"}" } ], "images": [ "scigen/train/1325.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Number of sentences for each language pair." ], "reasoning": "By examining the 'train' column in the table, we see that the language pair 'Ru → En' has the highest number of training examples with 208,106.", "question": "Which language pair has the highest number of training examples?", "answer": "So, the answer is {\"answer\": \"Ru → En\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Az → En language pair has more training examples than the Be → En language pair.\n" }, { "role": "assistant", "content": "The table shows that the Az → En language pair has 5,946 training examples, while the Be → En language pair has 4,509 training examples. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1325.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Number of sentences for each language pair." ], "reasoning": "The table shows that the Az → En language pair has 5,946 training examples, while the Be → En language pair has 4,509 training examples. Therefore, the statement is supported.", "statement": "The Az → En language pair has more training examples than the Be → En language pair.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Effect of pre-training on BLEU score over six languages. The systems use either random initialization (std) or pre-training (pre) on both the source and target sides.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The increase in scores is more significant with pre-trained source language embeddings, indicating that most of the gain from pre-trained word embeddings comes from better encoding of the source sentence.Comparing the second and third columns, we can see the increase is much more significant with pre-trained source language embeddings. This indicates that the majority of the gain from pre-trained word embeddings results from a better encoding of the source sentence." } ], "images": [ "scigen/train/1326.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Effect of pre-training on BLEU score over six languages. The systems use either random initialization (std) or pre-training (pre) on both the source and target sides." ], "reasoning": "The increase in scores is more significant with pre-trained source language embeddings, indicating that most of the gain from pre-trained word embeddings comes from better encoding of the source sentence.", "description": "Comparing the second and third columns, we can see the increase is much more significant with pre-trained source language embeddings. This indicates that the majority of the gain from pre-trained word embeddings results from a better encoding of the source sentence.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Effect of pre-training on BLEU score over six languages. The systems use either random initialization (std) or pre-training (pre) on both the source and target sides.\nQuestion: Which language pair shows the highest increase in score when using pre-trained source language embeddings compared to standard embeddings?\nSolve the above question based on the table information. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the 'std' and 'pre' columns, the language pair Pt → En shows the highest increase from 26.2 to 30.8. So, the answer is {\"answer\": \"Pt → En\"}" } ], "images": [ "scigen/train/1326.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Effect of pre-training on BLEU score over six languages. The systems use either random initialization (std) or pre-training (pre) on both the source and target sides." ], "reasoning": "By comparing the 'std' and 'pre' columns, the language pair Pt → En shows the highest increase from 26.2 to 30.8.", "question": "Which language pair shows the highest increase in score when using pre-trained source language embeddings compared to standard embeddings?", "answer": "So, the answer is {\"answer\": \"Pt → En\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe score for Tr → En with pre-trained source language embeddings is higher than the score for Ru → En with standard embeddings.\n" }, { "role": "assistant", "content": "The score for Tr → En with pre-trained source language embeddings is 17.9, which is higher than the score for Ru → En with standard embeddings, which is 18.5. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1326.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Effect of pre-training on BLEU score over six languages. The systems use either random initialization (std) or pre-training (pre) on both the source and target sides." ], "reasoning": "The score for Tr → En with pre-trained source language embeddings is 17.9, which is higher than the score for Ru → En with standard embeddings, which is 18.5.", "statement": "The score for Tr → En with pre-trained source language embeddings is higher than the score for Ru → En with standard embeddings.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled '(a) Pairwise comparison between two bilingual models', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Pre-training helps generate specific n-grams, while systems without pre-trained embeddings show inconsistent improvements, mainly on high-frequency words.We additionally performed pairwise comparisons between the top 10 n-grams that each system (selected from the task Gl → En) is better at generating, to further understand what kind of words pre-training is particularly helpful for. On the other hand, the improvements in systems without pre-trained embeddings were not very consistent, and largely focused on high-frequency words." } ], "images": [ "scigen/train/1327.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) Pairwise comparison between two bilingual models" ], "reasoning": "Pre-training helps generate specific n-grams, while systems without pre-trained embeddings show inconsistent improvements, mainly on high-frequency words.", "description": "We additionally performed pairwise comparisons between the top 10 n-grams that each system (selected from the task Gl → En) is better at generating, to further understand what kind of words pre-training is particularly helpful for. On the other hand, the improvements in systems without pre-trained embeddings were not very consistent, and largely focused on high-frequency words.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about '(a) Pairwise comparison between two bilingual models', solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich n-gram has the highest frequency in the bi:pre column?\n" }, { "role": "assistant", "content": "The bi:pre column lists n-grams and their frequencies. The n-gram 'people' has the highest frequency with a count of 0/49. So, the answer is {\"answer\": \"people\"}" } ], "images": [ "scigen/train/1327.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) Pairwise comparison between two bilingual models" ], "reasoning": "The bi:pre column lists n-grams and their frequencies. The n-gram 'people' has the highest frequency with a count of 0/49.", "question": "Which n-gram has the highest frequency in the bi:pre column?", "answer": "So, the answer is {\"answer\": \"people\"}" }, { "messages": [ { "role": "user", "content": "The n-gram 'have to' has a higher frequency in bi:std than in bi:pre. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe n-gram 'have to' has a higher frequency in bi:std than in bi:pre.\n" }, { "role": "assistant", "content": "The table shows 'have to' with a frequency of 5/2 in bi:std and does not list it in bi:pre, indicating it has a higher frequency in bi:std. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1327.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) Pairwise comparison between two bilingual models" ], "reasoning": "The table shows 'have to' with a frequency of 5/2 in bi:std and does not list it in bi:pre, indicating it has a higher frequency in bi:std.", "statement": "The n-gram 'have to' has a higher frequency in bi:std than in bi:pre.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title '(b) Pairwise comparison between two multilingual models', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Pairwise comparisons of top 10 n-grams show pre-training helps with specific words, while non-pre-trained systems focus on high-frequency words.We additionally performed pairwise comparisons between the top 10 n-grams that each system (selected from the task Gl → En) is better at generating, to further understand what kind of words pre-training is particularly helpful for. On the other hand, the improvements in systems without pre-trained embeddings were not very consistent, and largely focused on high-frequency words." } ], "images": [ "scigen/train/1328.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) Pairwise comparison between two multilingual models" ], "reasoning": "Pairwise comparisons of top 10 n-grams show pre-training helps with specific words, while non-pre-trained systems focus on high-frequency words.", "description": "We additionally performed pairwise comparisons between the top 10 n-grams that each system (selected from the task Gl → En) is better at generating, to further understand what kind of words pre-training is particularly helpful for. On the other hand, the improvements in systems without pre-trained embeddings were not very consistent, and largely focused on high-frequency words.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled '(b) Pairwise comparison between two multilingual models' and methodically tackle the question that follows. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich n-gram has the highest frequency in the multi:pre+align 0/14 system?\n" }, { "role": "assistant", "content": "By examining the multi:pre+align 0/14 column, the n-gram 'like' has the highest frequency with a count of 1/20. So, the answer is {\"answer\": \"like\"}." } ], "images": [ "scigen/train/1328.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) Pairwise comparison between two multilingual models" ], "reasoning": "By examining the multi:pre+align 0/14 column, the n-gram 'like' has the highest frequency with a count of 1/20.", "question": "Which n-gram has the highest frequency in the multi:pre+align 0/14 system?", "answer": "So, the answer is {\"answer\": \"like\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The n-gram 'court' has a frequency of 0/9 in the multi:pre+align on the system., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe n-gram 'court' has a frequency of 0/9 in the multi:pre+align on the system.\n" }, { "role": "assistant", "content": "The table shows that the n-gram 'court' indeed has a frequency of 0/9 in the multi:pre+align on the system. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1328.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) Pairwise comparison between two multilingual models" ], "reasoning": "The table shows that the n-gram 'court' indeed has a frequency of 0/9 in the multi:pre+align on the system.", "statement": "The n-gram 'court' has a frequency of 0/9 in the multi:pre+align on the system.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Results on question sentences (§5.3). All of baseline C&C, EasySRL and depccg parsers are retrained on Questions data.'.\n" }, { "role": "assistant", "content": "The plain depccg retrained on Questions data performs the best, with neither ELMo nor the proposed method taking any effect, likely due to the evaluation set containing sentences with similar constructions.Contrary to our expectation, the plain depccg retrained on Questions data performs the best, with neither ELMo nor the proposed method taking any effect. We hypothesize that, since the evaluation set contains sentences with similar constructions, the contributions of the latter two methods are less observable on top of Questions data. Inspection of the output trees reveals that this is actually the case; the majority of differences among parser ’s configurations are irrelevant to question constructions, suggesting that the models capture well the syntax of question in the data." } ], "images": [ "scigen/train/1329.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on question sentences (§5.3). All of baseline C&C, EasySRL and depccg parsers are retrained on Questions data." ], "reasoning": "The plain depccg retrained on Questions data performs the best, with neither ELMo nor the proposed method taking any effect, likely due to the evaluation set containing sentences with similar constructions.", "description": "Contrary to our expectation, the plain depccg retrained on Questions data performs the best, with neither ELMo nor the proposed method taking any effect. We hypothesize that, since the evaluation set contains sentences with similar constructions, the contributions of the latter two methods are less observable on top of Questions data. Inspection of the output trees reveals that this is actually the case; the majority of differences among parser ’s configurations are irrelevant to question constructions, suggesting that the models capture well the syntax of question in the data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 4: Results on question sentences (§5.3). All of baseline C&C, EasySRL and depccg parsers are retrained on Questions data.\nQuestion: Which method has the highest F1 score and what is the value?\n" }, { "role": "assistant", "content": "By examining the table, the method with the highest F1 score is depccg with a value of 90.29. So, the answer is {\"answer\": \"depccg with 90.29\"}" } ], "images": [ "scigen/train/1329.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on question sentences (§5.3). All of baseline C&C, EasySRL and depccg parsers are retrained on Questions data." ], "reasoning": "By examining the table, the method with the highest F1 score is depccg with a value of 90.29.", "question": "Which method has the highest F1 score and what is the value?", "answer": "So, the answer is {\"answer\": \"depccg with 90.29\"}" }, { "messages": [ { "role": "user", "content": "The proposed method has the highest precision score. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe proposed method has the highest precision score.\n" }, { "role": "assistant", "content": "The table shows that the highest precision score is 90.55, achieved by depccg + ELMo, not the proposed method. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1329.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on question sentences (§5.3). All of baseline C&C, EasySRL and depccg parsers are retrained on Questions data." ], "reasoning": "The table shows that the highest precision score is 90.55, achieved by depccg + ELMo, not the proposed method.", "statement": "The proposed method has the highest precision score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: The performance of baseline CCG parsers and the proposed converter on WSJ23, where UF1 and LF1 represents unlabeled and labeled F1, respectively.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The proposed converter outperforms the current best-performing depccg combined with ELMo by 1.5% in unlabeled F1 scores and 2.17% in labeled F1 scores.In short, the method of depccg is equivalent to omitting the dependence on a dependency tree z from P(y|x,z) of our converter model, and running an A* parsing-based decoder on ptag|dep calculated on h1,... ,hN =Ω(ex1,... ,exN), as in our method. In this work, on top of that, we include as a baseline a setting where the affix vectors are replaced by contextualized word representation (ELMo; Peters et al. This can be regarded as evaluating the upper bound of the conversion quality, since the evaluated data comes from the same domain as the converter’s training data. Our converter shows much higher scores compared to the current best-performing depccg combined with ELMo (1.5% and 2.17% up in unlabeled/labeled F1 scores), suggesting that, using the proposed converter, we can obtain CCGbanks of high quality." } ], "images": [ "scigen/train/1330.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The performance of baseline CCG parsers and the proposed converter on WSJ23, where UF1 and LF1 represents unlabeled and labeled F1, respectively." ], "reasoning": "The proposed converter outperforms the current best-performing depccg combined with ELMo by 1.5% in unlabeled F1 scores and 2.17% in labeled F1 scores.", "description": "In short, the method of depccg is equivalent to omitting the dependence on a dependency tree z from P(y|x,z) of our converter model, and running an A* parsing-based decoder on ptag|dep calculated on h1,... ,hN =Ω(ex1,... ,exN), as in our method. In this work, on top of that, we include as a baseline a setting where the affix vectors are replaced by contextualized word representation (ELMo; Peters et al. This can be regarded as evaluating the upper bound of the conversion quality, since the evaluated data comes from the same domain as the converter’s training data. Our converter shows much higher scores compared to the current best-performing depccg combined with ELMo (1.5% and 2.17% up in unlabeled/labeled F1 scores), suggesting that, using the proposed converter, we can obtain CCGbanks of high quality.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 1: The performance of baseline CCG parsers and the proposed converter on WSJ23, where UF1 and LF1 represents unlabeled and labeled F1, respectively.' to sequentially solve the question presented below. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhat is the difference in labeled F1 scores between the depccg method and the Converter method?\n" }, { "role": "assistant", "content": "The labeled F1 score for depccg is 88.8, and for the Converter method, it is 92.68. The difference is 92.68 - 88.8 = 3.88. So, the answer is {\"answer\": \"3.88\"}." } ], "images": [ "scigen/train/1330.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The performance of baseline CCG parsers and the proposed converter on WSJ23, where UF1 and LF1 represents unlabeled and labeled F1, respectively." ], "reasoning": "The labeled F1 score for depccg is 88.8, and for the Converter method, it is 92.68. The difference is 92.68 - 88.8 = 3.88.", "question": "What is the difference in labeled F1 scores between the depccg method and the Converter method?", "answer": "So, the answer is {\"answer\": \"3.88\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Converter method has a higher unlabeled F1 score than the depccg method combined with ELMo.\n" }, { "role": "assistant", "content": "The unlabeled F1 score for the Converter method is 96.48, while for the depccg method combined with ELMo, it is 94.98. Since 96.48 is higher than 94.98, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1330.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The performance of baseline CCG parsers and the proposed converter on WSJ23, where UF1 and LF1 represents unlabeled and labeled F1, respectively." ], "reasoning": "The unlabeled F1 score for the Converter method is 96.48, while for the depccg method combined with ELMo, it is 94.98. Since 96.48 is higher than 94.98, the statement is supported.", "statement": "The Converter method has a higher unlabeled F1 score than the depccg method combined with ELMo.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Results on the biomedical domain dataset (§5.3). P and R represent precision and recall, respectively. The scores of C&C and EasySRL fine-tuned on the GENIA1000 is included for comparison (excerpted from Lewis et al. (2016)).'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The plain depccg method achieves higher scores than other methods and improves further with ELMo, but fine-tuning on GENIA1000 results in mixed outcomes. Fine-tuning on Genia CCGbank yields the highest F1 score of 86.52.The plain depccg already achieves higher scores than these methods, and boosts when combined with ELMo (improvement of 2.73 points in terms of F1). Fine-tuning the parser on GENIA1000 results in a mixed result, with slightly lower scores. This is presumably because the automatically annotated Head First dependencies are not accurate. Finally, by fine-tuning on the Genia CCGbank, we observe another improvement, resulting in the highest 86.52 F1 score." } ], "images": [ "scigen/train/1331.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on the biomedical domain dataset (§5.3). P and R represent precision and recall, respectively. The scores of C&C and EasySRL fine-tuned on the GENIA1000 is included for comparison (excerpted from Lewis et al. (2016))." ], "reasoning": "The plain depccg method achieves higher scores than other methods and improves further with ELMo, but fine-tuning on GENIA1000 results in mixed outcomes. Fine-tuning on Genia CCGbank yields the highest F1 score of 86.52.", "description": "The plain depccg already achieves higher scores than these methods, and boosts when combined with ELMo (improvement of 2.73 points in terms of F1). Fine-tuning the parser on GENIA1000 results in a mixed result, with slightly lower scores. This is presumably because the automatically annotated Head First dependencies are not accurate. Finally, by fine-tuning on the Genia CCGbank, we observe another improvement, resulting in the highest 86.52 F1 score.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 3: Results on the biomedical domain dataset (§5.3). P and R represent precision and recall, respectively. The scores of C&C and EasySRL fine-tuned on the GENIA1000 is included for comparison (excerpted from Lewis et al. (2016)).'.\n\nQuestion: What is the F1 score when the depccg method is combined with ELMo?\n" }, { "role": "assistant", "content": "The table shows the F1 score for the depccg method combined with ELMo as 85.61. So, the answer is {\"answer\": \"85.61\"}." } ], "images": [ "scigen/train/1331.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on the biomedical domain dataset (§5.3). P and R represent precision and recall, respectively. The scores of C&C and EasySRL fine-tuned on the GENIA1000 is included for comparison (excerpted from Lewis et al. (2016))." ], "reasoning": "The table shows the F1 score for the depccg method combined with ELMo as 85.61.", "question": "What is the F1 score when the depccg method is combined with ELMo?", "answer": "So, the answer is {\"answer\": \"85.61\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Fine-tuning the parser on GENIA1000 results in the highest F1 score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nFine-tuning the parser on GENIA1000 results in the highest F1 score.\n" }, { "role": "assistant", "content": "The table shows that fine-tuning on GENIA1000 results in an F1 score of 84.97, which is not the highest. The highest F1 score is 86.52, achieved by fine-tuning on Genia CCGbank. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1331.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on the biomedical domain dataset (§5.3). P and R represent precision and recall, respectively. The scores of C&C and EasySRL fine-tuned on the GENIA1000 is included for comparison (excerpted from Lewis et al. (2016))." ], "reasoning": "The table shows that fine-tuning on GENIA1000 results in an F1 score of 84.97, which is not the highest. The highest F1 score is 86.52, achieved by fine-tuning on Genia CCGbank.", "statement": "Fine-tuning the parser on GENIA1000 results in the highest F1 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 7: Results on speech conversation texts (§5.4), on the whole test set and the manually annotated subset.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The proposed method achieves the highest scores across all metrics, with a notable 95.63% in unlabeled F1 score for predicate argument relations.Though the entire scores are relatively lower, the result suggests that the proposed method is effective to this domain on the whole. By directly evaluating the parser’s performance in terms of predicate argument relations (Subset columns) , we observe that it actually recovers the most of the dependencies, with the fine-tuned depccg achieving as high as 95.63% unlabeled F1 score." } ], "images": [ "scigen/train/1332.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Results on speech conversation texts (§5.4), on the whole test set and the manually annotated subset." ], "reasoning": "The proposed method achieves the highest scores across all metrics, with a notable 95.63% in unlabeled F1 score for predicate argument relations.", "description": "Though the entire scores are relatively lower, the result suggests that the proposed method is effective to this domain on the whole. By directly evaluating the parser’s performance in terms of predicate argument relations (Subset columns) , we observe that it actually recovers the most of the dependencies, with the fine-tuned depccg achieving as high as 95.63% unlabeled F1 score.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: Table 7: Results on speech conversation texts (§5.4), on the whole test set and the manually annotated subset.\nQuestion: What is the Whole F1 score achieved by the proposed method?\n" }, { "role": "assistant", "content": "The table shows the Whole F1 score for each method. The proposed method has a Whole F1 score of 77.54. So, the answer is {\"answer\": \"77.54\"}." } ], "images": [ "scigen/train/1332.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Results on speech conversation texts (§5.4), on the whole test set and the manually annotated subset." ], "reasoning": "The table shows the Whole F1 score for each method. The proposed method has a Whole F1 score of 77.54.", "question": "What is the Whole F1 score achieved by the proposed method?", "answer": "So, the answer is {\"answer\": \"77.54\"}." }, { "messages": [ { "role": "user", "content": "The depccg method achieves a higher Subset UF1 score than the + ELMo method. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe depccg method achieves a higher Subset UF1 score than the + ELMo method.\n" }, { "role": "assistant", "content": "The table shows that the depccg method has a Subset UF1 score of 90.68, while the + ELMo method has a Subset UF1 score of 93.23. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1332.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Results on speech conversation texts (§5.4), on the whole test set and the manually annotated subset." ], "reasoning": "The table shows that the depccg method has a Subset UF1 score of 90.68, while the + ELMo method has a Subset UF1 score of 93.23. Therefore, the statement is refuted.", "statement": "The depccg method achieves a higher Subset UF1 score than the + ELMo method.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 8: Results on math problems (§5.5).', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Adding ELMo to depccg improves labeled F1 by about 4 points, while the new training set improves it by more than 10 points, especially for math expressions.Remarkably, we observe huge additive performance improvement. While, in terms of labeled F1, ELMo contributes about 4 points on top of the plain depccg, adding the new training set (converted from dependency trees) improves more than 10 points. Examining the resulting trees, we observe that the huge gain is primarily involved with expressions unique to math. However, after fine-tuning, it successfully produces the correct “ If S1 and S2, S3” structure, recognizing that the equal sign is a predicate." } ], "images": [ "scigen/train/1333.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Results on math problems (§5.5)." ], "reasoning": "Adding ELMo to depccg improves labeled F1 by about 4 points, while the new training set improves it by more than 10 points, especially for math expressions.", "description": "Remarkably, we observe huge additive performance improvement. While, in terms of labeled F1, ELMo contributes about 4 points on top of the plain depccg, adding the new training set (converted from dependency trees) improves more than 10 points. Examining the resulting trees, we observe that the huge gain is primarily involved with expressions unique to math. However, after fine-tuning, it successfully produces the correct “ If S1 and S2, S3” structure, recognizing that the equal sign is a predicate.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 8: Results on math problems (§5.5).', unfold the question presented below, detailing every step of your calculation. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the labeled F1 score after adding the new training set to depccg?\n" }, { "role": "assistant", "content": "The table shows the labeled F1 scores for different methods. The labeled F1 score for the proposed method, which includes the new training set, is 80.53. So, the answer is {\"answer\": \"80.53\"}." } ], "images": [ "scigen/train/1333.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Results on math problems (§5.5)." ], "reasoning": "The table shows the labeled F1 scores for different methods. The labeled F1 score for the proposed method, which includes the new training set, is 80.53.", "question": "What is the labeled F1 score after adding the new training set to depccg?", "answer": "So, the answer is {\"answer\": \"80.53\"}." }, { "messages": [ { "role": "user", "content": "Adding ELMo to depccg improves the labeled F1 score by about 4 points. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nAdding ELMo to depccg improves the labeled F1 score by about 4 points.\n" }, { "role": "assistant", "content": "The table shows that the labeled F1 score for depccg is 66.15 and for depccg + ELMo is 70.74, which is an improvement of about 4 points. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1333.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Results on math problems (§5.5)." ], "reasoning": "The table shows that the labeled F1 score for depccg is 66.15 and for depccg + ELMo is 70.74, which is an improvement of about 4 points.", "statement": "Adding ELMo to depccg improves the labeled F1 score by about 4 points.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 7: Test performance for various metrics on the dodecaDialogue tasks comparing our multi-task and multi-task + fine-tuned methods to existing approaches (cited). Dashes mean metric was not provided. ∗ was reported on validation only. Score is defined on a per-task basis in the metric column.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Fine-tuning with multi-tasking generally shows improvement across all metrics, but the challenge remains to do better.Here, for the multi-task model we have fine-tuned the decoding hyperparameters per task. We generally find across all metrics a similar story as before when comparing the fine-tuning with multi-tasking: multi-tasking is successful, but the challenge is still to do better." } ], "images": [ "scigen/train/1334.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Test performance for various metrics on the dodecaDialogue tasks comparing our multi-task and multi-task + fine-tuned methods to existing approaches (cited). Dashes mean metric was not provided. ∗ was reported on validation only. Score is defined on a per-task basis in the metric column." ], "reasoning": "Fine-tuning with multi-tasking generally shows improvement across all metrics, but the challenge remains to do better.", "description": "Here, for the multi-task model we have fine-tuned the decoding hyperparameters per task. We generally find across all metrics a similar story as before when comparing the fine-tuning with multi-tasking: multi-tasking is successful, but the challenge is still to do better.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 7: Test performance for various metrics on the dodecaDialogue tasks comparing our multi-task and multi-task + fine-tuned methods to existing approaches (cited). Dashes mean metric was not provided. ∗ was reported on validation only. Score is defined on a per-task basis in the metric column.\nQuestion: What is the MT + FT Score for the Wiz. of Wikipedia approach?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows the MT + FT Score for each approach. For the Wiz. of Wikipedia approach, the MT + FT Score is 38.4. So, the answer is {\"answer\": \"38.4\"}." } ], "images": [ "scigen/train/1334.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Test performance for various metrics on the dodecaDialogue tasks comparing our multi-task and multi-task + fine-tuned methods to existing approaches (cited). Dashes mean metric was not provided. ∗ was reported on validation only. Score is defined on a per-task basis in the metric column." ], "reasoning": "The table shows the MT + FT Score for each approach. For the Wiz. of Wikipedia approach, the MT + FT Score is 38.4.", "question": "What is the MT + FT Score for the Wiz. of Wikipedia approach?", "answer": "So, the answer is {\"answer\": \"38.4\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe MT + FT PPL for the DailyDialog approach is higher than the MT PPL for the same approach.\n" }, { "role": "assistant", "content": "The table shows that the MT + FT PPL for the DailyDialog approach is 10.4, while the MT PPL is 18.2. Since 10.4 is not higher than 18.2, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1334.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Test performance for various metrics on the dodecaDialogue tasks comparing our multi-task and multi-task + fine-tuned methods to existing approaches (cited). Dashes mean metric was not provided. ∗ was reported on validation only. Score is defined on a per-task basis in the metric column." ], "reasoning": "The table shows that the MT + FT PPL for the DailyDialog approach is 10.4, while the MT PPL is 18.2. Since 10.4 is not higher than 18.2, the statement is refuted.", "statement": "The MT + FT PPL for the DailyDialog approach is higher than the MT PPL for the same approach.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Transfer performance of various multi-task models (validation perplexity).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Training on pushshift.io Reddit alone is effective for transfer to other tasks, but fine-tuning on smaller, more similar tasks or multi-tasking on all four tasks yields better performance.We first perform a preliminary study on a subset of the tasks: Reddit, ConvAI2, Wizard of Wikipedia and Empathetic Dialogues, and report the transfer ability of training on some of them, and testing on all of them (using the validation set), reporting perplexity. They show that training on pushshift.io Reddit alone, a huge dataset, is effective at transfer to other tasks, but never as effective as fine-tuning on the task itself. Moreover, fine-tuning on most of the smaller tasks actually provides improvements over pushshift.io Reddit training alone at transfer, likely because the three tasks selected are more similar to each other than to pushshift.io Reddit. Finally, training on all four tasks is the most effective strategy averaged over all tasks compared to any other single model, although this does not beat switching between different fine-tuned models on a per-task basis. The performance is impressive, with some tasks yielding lower perplexity than BERT pre-training + single task fine-tuning. However, it still lags significantly behind fine-tuning applied after pushshift.io Reddit pre-training." } ], "images": [ "scigen/train/1335.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Transfer performance of various multi-task models (validation perplexity)." ], "reasoning": "Training on pushshift.io Reddit alone is effective for transfer to other tasks, but fine-tuning on smaller, more similar tasks or multi-tasking on all four tasks yields better performance.", "description": "We first perform a preliminary study on a subset of the tasks: Reddit, ConvAI2, Wizard of Wikipedia and Empathetic Dialogues, and report the transfer ability of training on some of them, and testing on all of them (using the validation set), reporting perplexity. They show that training on pushshift.io Reddit alone, a huge dataset, is effective at transfer to other tasks, but never as effective as fine-tuning on the task itself. Moreover, fine-tuning on most of the smaller tasks actually provides improvements over pushshift.io Reddit training alone at transfer, likely because the three tasks selected are more similar to each other than to pushshift.io Reddit. Finally, training on all four tasks is the most effective strategy averaged over all tasks compared to any other single model, although this does not beat switching between different fine-tuned models on a per-task basis. The performance is impressive, with some tasks yielding lower perplexity than BERT pre-training + single task fine-tuning. However, it still lags significantly behind fine-tuning applied after pushshift.io Reddit pre-training.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: 'Table 3: Transfer performance of various multi-task models (validation perplexity).'\nQuestion: Which model has the lowest perplexity on the ConvAI2 task?\n" }, { "role": "assistant", "content": "By examining the ConvAI2 column, the model with the lowest perplexity is Multi-Tasking All 4 Tasks with a perplexity of 11.6. So, the answer is {\"answer\": \"Multi-Tasking All 4 Tasks\"}" } ], "images": [ "scigen/train/1335.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Transfer performance of various multi-task models (validation perplexity)." ], "reasoning": "By examining the ConvAI2 column, the model with the lowest perplexity is Multi-Tasking All 4 Tasks with a perplexity of 11.6.", "question": "Which model has the lowest perplexity on the ConvAI2 task?", "answer": "So, the answer is {\"answer\": \"Multi-Tasking All 4 Tasks\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Reddit+Wiz. of Wikipedia model has the lowest perplexity on the Wiz. of Wikipedia task.\n" }, { "role": "assistant", "content": "The table shows that the Reddit+Wiz. of Wikipedia model has a perplexity of 8.7 on the Wiz. of Wikipedia task, which is the lowest in that column. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1335.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Transfer performance of various multi-task models (validation perplexity)." ], "reasoning": "The table shows that the Reddit+Wiz. of Wikipedia model has a perplexity of 8.7 on the Wiz. of Wikipedia task, which is the lowest in that column.", "statement": "The Reddit+Wiz. of Wikipedia model has the lowest perplexity on the Wiz. of Wikipedia task.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 14: Human evaluations on Wizard of Wikipedia (unseen) test set, comparing various decoding schemes for our Image+Seq2Seq model trained on all tasks MT, as well as comparisons with human outputs, using ACUTE-Eval. All scores are statistically significant (binomial test, p'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The experiments compared nucleus sampling with p=0.7 to baseline models and human outputs, showing similar findings to pairwise ACUTE-Eval results.As with our experiments regarding automatic metrics, we additionally explored nucleus sampling, with parameter p=0.7, and compared to both the baseline models as well as human outputs. The findings are similar to the pairwise ACUTE-Eval results in the main paper." } ], "images": [ "scigen/train/1336.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 14: Human evaluations on Wizard of Wikipedia (unseen) test set, comparing various decoding schemes for our Image+Seq2Seq model trained on all tasks MT, as well as comparisons with human outputs, using ACUTE-Eval. All scores are statistically significant (binomial test, p" ], "reasoning": "The experiments compared nucleus sampling with p=0.7 to baseline models and human outputs, showing similar findings to pairwise ACUTE-Eval results.", "description": "As with our experiments regarding automatic metrics, we additionally explored nucleus sampling, with parameter p=0.7, and compared to both the baseline models as well as human outputs. The findings are similar to the pairwise ACUTE-Eval results in the main paper.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 14: Human evaluations on Wizard of Wikipedia (unseen) test set, comparing various decoding schemes for our Image+Seq2Seq model trained on all tasks MT, as well as comparisons with human outputs, using ACUTE-Eval. All scores are statistically significant (binomial test, p', solve this question and give detail solutions. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the win percentage of Image+Seq2Seq Nucleus compared to Human?\n" }, { "role": "assistant", "content": "To find the win percentage of Image+Seq2Seq Nucleus compared to Human, look at the 'Win Percentage Human' column for Image+Seq2Seq Nucleus, which is 72.8%. So, the answer is {\"answer\": \"72.8\"}." } ], "images": [ "scigen/train/1336.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 14: Human evaluations on Wizard of Wikipedia (unseen) test set, comparing various decoding schemes for our Image+Seq2Seq model trained on all tasks MT, as well as comparisons with human outputs, using ACUTE-Eval. All scores are statistically significant (binomial test, p" ], "reasoning": "To find the win percentage of Image+Seq2Seq Nucleus compared to Human, look at the 'Win Percentage Human' column for Image+Seq2Seq Nucleus, which is 72.8%.", "question": "What is the win percentage of Image+Seq2Seq Nucleus compared to Human?", "answer": "So, the answer is {\"answer\": \"72.8\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe win percentage of dinan2018wizard is higher than Image+Seq2Seq Beam.\n" }, { "role": "assistant", "content": "To verify this statement, compare the win percentages of dinan2018wizard and Image+Seq2Seq Beam. dinan2018wizard has a win percentage of 75.8%, while Image+Seq2Seq Beam has 60.5%. Therefore, the statement is correct. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1336.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 14: Human evaluations on Wizard of Wikipedia (unseen) test set, comparing various decoding schemes for our Image+Seq2Seq model trained on all tasks MT, as well as comparisons with human outputs, using ACUTE-Eval. All scores are statistically significant (binomial test, p" ], "reasoning": "To verify this statement, compare the win percentages of dinan2018wizard and Image+Seq2Seq Beam. dinan2018wizard has a win percentage of 75.8%, while Image+Seq2Seq Beam has 60.5%. Therefore, the statement is correct.", "statement": "The win percentage of dinan2018wizard is higher than Image+Seq2Seq Beam.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption '(a)'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table shows the performance of different models on cross-lingual entity alignment tasks, with JAPE SE+AE achieving the highest Hits@k scores.We used a certain proportion of the gold standards as seed alignment while left the remaining as testing data, i.e., the latent aligned entities to discover. The variation of Hits@k with different proportions will be shown shortly. For relationships and attributes, we simply extracted the property pairs with exactly the same labels, which only account for a small portion of the seed alignment. Besides, JE does not give a mandatory constraint on the length of vectors. Instead, it only minimizes ∥v∥22−1 to restrain vector length and brings adverse effect. For MTransE, it models the structures of KBs in different vector spaces, and information loss happens when learning the translation between vector spaces. We found that involving negative triples in structure embedding reduces the random distribution of entities, and involving attribute embedding as constraint further refines the distribution of entities. The two improvements demonstrate that systematic distribution of entities makes for the cross-lingual entity alignment task." } ], "images": [ "scigen/train/1337.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a)" ], "reasoning": "The table shows the performance of different models on cross-lingual entity alignment tasks, with JAPE SE+AE achieving the highest Hits@k scores.", "description": "We used a certain proportion of the gold standards as seed alignment while left the remaining as testing data, i.e., the latent aligned entities to discover. The variation of Hits@k with different proportions will be shown shortly. For relationships and attributes, we simply extracted the property pairs with exactly the same labels, which only account for a small portion of the seed alignment. Besides, JE does not give a mandatory constraint on the length of vectors. Instead, it only minimizes ∥v∥22−1 to restrain vector length and brings adverse effect. For MTransE, it models the structures of KBs in different vector spaces, and information loss happens when learning the translation between vector spaces. We found that involving negative triples in structure embedding reduces the random distribution of entities, and involving attribute embedding as constraint further refines the distribution of entities. The two improvements demonstrate that systematic distribution of entities makes for the cross-lingual entity alignment task.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled '(a)', solve this question and give detail solutions. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich model achieved the highest Hits@1 score for the ZH→EN task?\n" }, { "role": "assistant", "content": "By examining the Hits@1 column for the ZH→EN task, we see that the JAPE SE+AE model achieved the highest score of 41.18. So, the answer is {\"answer\": \"JAPE SE+AE\"}" } ], "images": [ "scigen/train/1337.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a)" ], "reasoning": "By examining the Hits@1 column for the ZH→EN task, we see that the JAPE SE+AE model achieved the highest score of 41.18.", "question": "Which model achieved the highest Hits@1 score for the ZH→EN task?", "answer": "So, the answer is {\"answer\": \"JAPE SE+AE\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe JAPE SE+AE model achieved the highest Hits@50 score for the EN→ZH task.\n" }, { "role": "assistant", "content": "By examining the Hits@50 column for the EN→ZH task, we see that the JAPE SE+AE model achieved the highest score of 86.18. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1337.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a)" ], "reasoning": "By examining the Hits@50 column for the EN→ZH task, we see that the JAPE SE+AE model achieved the highest score of 86.18.", "statement": "The JAPE SE+AE model achieved the highest Hits@50 score for the EN→ZH task.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled '(b)'.\n" }, { "role": "assistant", "content": "The description discusses the impact of different methods on cross-lingual entity alignment, highlighting the benefits of involving negative triples and attribute embedding in refining entity distribution.We used a certain proportion of the gold standards as seed alignment while left the remaining as testing data, i.e., the latent aligned entities to discover. The variation of Hits@k with different proportions will be shown shortly. For relationships and attributes, we simply extracted the property pairs with exactly the same labels, which only account for a small portion of the seed alignment. Besides, JE does not give a mandatory constraint on the length of vectors. Instead, it only minimizes ∥v∥22−1 to restrain vector length and brings adverse effect. For MTransE, it models the structures of KBs in different vector spaces, and information loss happens when learning the translation between vector spaces. We found that involving negative triples in structure embedding reduces the random distribution of entities, and involving attribute embedding as constraint further refines the distribution of entities. The two improvements demonstrate that systematic distribution of entities makes for the cross-lingual entity alignment task." } ], "images": [ "scigen/train/1338.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b)" ], "reasoning": "The description discusses the impact of different methods on cross-lingual entity alignment, highlighting the benefits of involving negative triples and attribute embedding in refining entity distribution.", "description": "We used a certain proportion of the gold standards as seed alignment while left the remaining as testing data, i.e., the latent aligned entities to discover. The variation of Hits@k with different proportions will be shown shortly. For relationships and attributes, we simply extracted the property pairs with exactly the same labels, which only account for a small portion of the seed alignment. Besides, JE does not give a mandatory constraint on the length of vectors. Instead, it only minimizes ∥v∥22−1 to restrain vector length and brings adverse effect. For MTransE, it models the structures of KBs in different vector spaces, and information loss happens when learning the translation between vector spaces. We found that involving negative triples in structure embedding reduces the random distribution of entities, and involving attribute embedding as constraint further refines the distribution of entities. The two improvements demonstrate that systematic distribution of entities makes for the cross-lingual entity alignment task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to '(b)', and sequentially solve the question, writing out each step of your reasoning process. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich method achieved the highest Hits@1 score for EN→JA alignment?\n" }, { "role": "assistant", "content": "To find the highest Hits@1 score for EN→JA alignment, we look at the EN→JA Hits@1 column in the table. The highest value is 38.37, achieved by the JAPE SE+AE method. So, the answer is {\"answer\": \"JAPE SE+AE\"}" } ], "images": [ "scigen/train/1338.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b)" ], "reasoning": "To find the highest Hits@1 score for EN→JA alignment, we look at the EN→JA Hits@1 column in the table. The highest value is 38.37, achieved by the JAPE SE+AE method.", "question": "Which method achieved the highest Hits@1 score for EN→JA alignment?", "answer": "So, the answer is {\"answer\": \"JAPE SE+AE\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe JE method achieved the highest Hits@50 score for JA→EN alignment.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the JA→EN Hits@50 column in the table. The highest value is 85.35, achieved by the JAPE SE+AE method, not the JE method. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1338.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b)" ], "reasoning": "To verify this statement, we look at the JA→EN Hits@50 column in the table. The highest value is 85.35, achieved by the JAPE SE+AE method, not the JE method.", "statement": "The JE method achieved the highest Hits@50 score for JA→EN alignment.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption '(c)'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Involving negative triples and attribute embedding in structure embedding reduces random distribution and refines entity distribution, improving cross-lingual entity alignment.We used a certain proportion of the gold standards as seed alignment while left the remaining as testing data, i.e., the latent aligned entities to discover. The variation of Hits@k with different proportions will be shown shortly. For relationships and attributes, we simply extracted the property pairs with exactly the same labels, which only account for a small portion of the seed alignment. Besides, JE does not give a mandatory constraint on the length of vectors. Instead, it only minimizes ∥v∥22−1 to restrain vector length and brings adverse effect. For MTransE, it models the structures of KBs in different vector spaces, and information loss happens when learning the translation between vector spaces. We found that involving negative triples in structure embedding reduces the random distribution of entities, and involving attribute embedding as constraint further refines the distribution of entities. The two improvements demonstrate that systematic distribution of entities makes for the cross-lingual entity alignment task." } ], "images": [ "scigen/train/1339.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(c)" ], "reasoning": "Involving negative triples and attribute embedding in structure embedding reduces random distribution and refines entity distribution, improving cross-lingual entity alignment.", "description": "We used a certain proportion of the gold standards as seed alignment while left the remaining as testing data, i.e., the latent aligned entities to discover. The variation of Hits@k with different proportions will be shown shortly. For relationships and attributes, we simply extracted the property pairs with exactly the same labels, which only account for a small portion of the seed alignment. Besides, JE does not give a mandatory constraint on the length of vectors. Instead, it only minimizes ∥v∥22−1 to restrain vector length and brings adverse effect. For MTransE, it models the structures of KBs in different vector spaces, and information loss happens when learning the translation between vector spaces. We found that involving negative triples in structure embedding reduces the random distribution of entities, and involving attribute embedding as constraint further refines the distribution of entities. The two improvements demonstrate that systematic distribution of entities makes for the cross-lingual entity alignment task.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of '(c)'. Solve the following question based on the table.\n\nQuestion: Which method achieved the highest Hits@1 score for FR→EN?\nLet's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest Hits@1 score for FR→EN, we look at the 'FR→EN Hits@1' column and identify the highest value, which is 32.39 achieved by JAPE with SE+AE. So, the answer is {\"answer\": \"32.39\"}" } ], "images": [ "scigen/train/1339.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(c)" ], "reasoning": "To find the highest Hits@1 score for FR→EN, we look at the 'FR→EN Hits@1' column and identify the highest value, which is 32.39 achieved by JAPE with SE+AE.", "question": "Which method achieved the highest Hits@1 score for FR→EN?", "answer": "So, the answer is {\"answer\": \"32.39\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nMTransE achieved the highest Hits@10 score for EN→FR.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the 'EN→FR Hits@10' column. The highest value is 65.91, achieved by JAPE with SE+AE, not MTransE. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1339.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(c)" ], "reasoning": "To verify this statement, we look at the 'EN→FR Hits@10' column. The highest value is 65.91, achieved by JAPE with SE+AE, not MTransE.", "statement": "MTransE achieved the highest Hits@10 score for EN→FR.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title '(a)'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The DBP100K dataset was built similarly to DBP15K, with 100 thousand ILLs between English and Chinese, Japanese, and French, and the training for JE takes 2000 epochs., we built three larger datasets by choosing 100 thousand ILLs between English and Chinese, Japanese and French in the same way as DBP15K. The threshold of relationship triples to select ILLs was set to 2. Each dataset contains several hundred thousand entities and several million triples. We set d=100,β=0.1 and keep other parameters the same as DBP15K. For JE, the training takes 2000 epochs as reported in its paper. Due to lack of space, only Hits@10 is reported. We found that similar results and conclusions stand for DBP100K compared with DBP15K, which indicate the scalability and stability of JAPE." } ], "images": [ "scigen/train/1340.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a)" ], "reasoning": "The DBP100K dataset was built similarly to DBP15K, with 100 thousand ILLs between English and Chinese, Japanese, and French, and the training for JE takes 2000 epochs.", "description": ", we built three larger datasets by choosing 100 thousand ILLs between English and Chinese, Japanese and French in the same way as DBP15K. The threshold of relationship triples to select ILLs was set to 2. Each dataset contains several hundred thousand entities and several million triples. We set d=100,β=0.1 and keep other parameters the same as DBP15K. For JE, the training takes 2000 epochs as reported in its paper. Due to lack of space, only Hits@10 is reported. We found that similar results and conclusions stand for DBP100K compared with DBP15K, which indicate the scalability and stability of JAPE.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to '(a)' to sequentially solve the question presented below. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model achieved the highest Hits@10 score for ZH→EN in the DBP100K dataset?\n" }, { "role": "assistant", "content": "To find the model with the highest Hits@10 score for ZH→EN, compare the scores of JE, MTransE, and JAPE in the ZH→EN column. JAPE has the highest score of 41.75. So, the answer is {\"answer\": \"JAPE\"}" } ], "images": [ "scigen/train/1340.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a)" ], "reasoning": "To find the model with the highest Hits@10 score for ZH→EN, compare the scores of JE, MTransE, and JAPE in the ZH→EN column. JAPE has the highest score of 41.75.", "question": "Which model achieved the highest Hits@10 score for ZH→EN in the DBP100K dataset?", "answer": "So, the answer is {\"answer\": \"JAPE\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nMTransE achieved a higher Hits@10 score than JE for EN→ZH in the DBP100K dataset.\n" }, { "role": "assistant", "content": "Compare the Hits@10 scores of MTransE and JE for EN→ZH. MTransE has a score of 29.18, which is higher than JE's score of 16.63. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1340.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a)" ], "reasoning": "Compare the Hits@10 scores of MTransE and JE for EN→ZH. MTransE has a score of 29.18, which is higher than JE's score of 16.63.", "statement": "MTransE achieved a higher Hits@10 score than JE for EN→ZH in the DBP100K dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is '(b)'.\n" }, { "role": "assistant", "content": "The datasets DBP100K and DBP15K show similar results and conclusions, indicating the scalability and stability of JAPE, with Hits@10 reported for 100 thousand ILLs between English and Chinese, Japanese, and French., we built three larger datasets by choosing 100 thousand ILLs between English and Chinese, Japanese and French in the same way as DBP15K. The threshold of relationship triples to select ILLs was set to 2. Each dataset contains several hundred thousand entities and several million triples. We set d=100,β=0.1 and keep other parameters the same as DBP15K. For JE, the training takes 2000 epochs as reported in its paper. Due to lack of space, only Hits@10 is reported. We found that similar results and conclusions stand for DBP100K compared with DBP15K, which indicate the scalability and stability of JAPE." } ], "images": [ "scigen/train/1341.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b)" ], "reasoning": "The datasets DBP100K and DBP15K show similar results and conclusions, indicating the scalability and stability of JAPE, with Hits@10 reported for 100 thousand ILLs between English and Chinese, Japanese, and French.", "description": ", we built three larger datasets by choosing 100 thousand ILLs between English and Chinese, Japanese and French in the same way as DBP15K. The threshold of relationship triples to select ILLs was set to 2. Each dataset contains several hundred thousand entities and several million triples. We set d=100,β=0.1 and keep other parameters the same as DBP15K. For JE, the training takes 2000 epochs as reported in its paper. Due to lack of space, only Hits@10 is reported. We found that similar results and conclusions stand for DBP100K compared with DBP15K, which indicate the scalability and stability of JAPE.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: (b)\nQuestion: What is the highest Hits@10 score for JA→EN?\n" }, { "role": "assistant", "content": "To find the highest Hits@10 score for JA→EN, look at the column labeled 'JA→EN' and identify the maximum value. So, the answer is {\"answer\": \"42.00\"}." } ], "images": [ "scigen/train/1341.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b)" ], "reasoning": "To find the highest Hits@10 score for JA→EN, look at the column labeled 'JA→EN' and identify the maximum value.", "question": "What is the highest Hits@10 score for JA→EN?", "answer": "So, the answer is {\"answer\": \"42.00\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The highest Hits@10 score for EN→JA is 39.30., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe highest Hits@10 score for EN→JA is 39.30.\n" }, { "role": "assistant", "content": "To verify this statement, look at the column labeled 'EN→JA' and identify the highest value, which is 39.30. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1341.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b)" ], "reasoning": "To verify this statement, look at the column labeled 'EN→JA' and identify the highest value, which is 39.30.", "statement": "The highest Hits@10 score for EN→JA is 39.30.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title '(c)', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The datasets DBP100K were built similarly to DBP15K, with 100 thousand ILLs between English and Chinese, Japanese, and French, and the training for JE takes 2000 epochs., we built three larger datasets by choosing 100 thousand ILLs between English and Chinese, Japanese and French in the same way as DBP15K. The threshold of relationship triples to select ILLs was set to 2. Each dataset contains several hundred thousand entities and several million triples. We set d=100,β=0.1 and keep other parameters the same as DBP15K. For JE, the training takes 2000 epochs as reported in its paper. Due to lack of space, only Hits@10 is reported. We found that similar results and conclusions stand for DBP100K compared with DBP15K, which indicate the scalability and stability of JAPE." } ], "images": [ "scigen/train/1342.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(c)" ], "reasoning": "The datasets DBP100K were built similarly to DBP15K, with 100 thousand ILLs between English and Chinese, Japanese, and French, and the training for JE takes 2000 epochs.", "description": ", we built three larger datasets by choosing 100 thousand ILLs between English and Chinese, Japanese and French in the same way as DBP15K. The threshold of relationship triples to select ILLs was set to 2. Each dataset contains several hundred thousand entities and several million triples. We set d=100,β=0.1 and keep other parameters the same as DBP15K. For JE, the training takes 2000 epochs as reported in its paper. Due to lack of space, only Hits@10 is reported. We found that similar results and conclusions stand for DBP100K compared with DBP15K, which indicate the scalability and stability of JAPE.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the '(c)', solve the subsequent question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhat is the highest Hits@10 score for FR→EN?\n" }, { "role": "assistant", "content": "To find the highest Hits@10 score for FR→EN, look at the column labeled 'FR→EN' and identify the maximum value. So, the answer is {\"answer\": \"53.64\"}" } ], "images": [ "scigen/train/1342.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(c)" ], "reasoning": "To find the highest Hits@10 score for FR→EN, look at the column labeled 'FR→EN' and identify the maximum value.", "question": "What is the highest Hits@10 score for FR→EN?", "answer": "So, the answer is {\"answer\": \"53.64\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe highest Hits@10 score for EN→FR is higher than 50.\n" }, { "role": "assistant", "content": "To verify this statement, look at the column labeled 'EN→FR' and identify the highest value. The highest value is 50.51, which is higher than 50. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1342.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(c)" ], "reasoning": "To verify this statement, look at the column labeled 'EN→FR' and identify the highest value. The highest value is 50.51, which is higher than 50.", "statement": "The highest Hits@10 score for EN→FR is higher than 50.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 9: Summary of the results in the 4S corpus. In bold are the highest scores obtained.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Spectral Clustering outperformed Louvain in two metrics, ARI and F1, while Louvain had the highest score in Acc.After having carried out all experiments with the 4S corpus, this section provides a discussion of the obtained results. Contrary to the AVL corpus, results were close between two techniques: Spectral Clustering and Louvain. The former obtained a better overall performance, since it had the highest scores in two of the metrics." } ], "images": [ "scigen/train/1343.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Summary of the results in the 4S corpus. In bold are the highest scores obtained." ], "reasoning": "Spectral Clustering outperformed Louvain in two metrics, ARI and F1, while Louvain had the highest score in Acc.", "description": "After having carried out all experiments with the 4S corpus, this section provides a discussion of the obtained results. Contrary to the AVL corpus, results were close between two techniques: Spectral Clustering and Louvain. The former obtained a better overall performance, since it had the highest scores in two of the metrics.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 9: Summary of the results in the 4S corpus. In bold are the highest scores obtained.' to sequentially solve the question presented below. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhich technique had the highest score in the Acc metric?\n" }, { "role": "assistant", "content": "By examining the Acc column, Louvain had the highest score of 0.48. So, the answer is {\"answer\": \"Louvain\"}" } ], "images": [ "scigen/train/1343.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Summary of the results in the 4S corpus. In bold are the highest scores obtained." ], "reasoning": "By examining the Acc column, Louvain had the highest score of 0.48.", "question": "Which technique had the highest score in the Acc metric?", "answer": "So, the answer is {\"answer\": \"Louvain\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nSpectral Clustering had the highest score in the ARI metric.\n" }, { "role": "assistant", "content": "The table shows that Spectral Clustering had an ARI score of 0.27, which is higher than the scores of the other techniques. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1343.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Summary of the results in the 4S corpus. In bold are the highest scores obtained." ], "reasoning": "The table shows that Spectral Clustering had an ARI score of 0.27, which is higher than the scores of the other techniques.", "statement": "Spectral Clustering had the highest score in the ARI metric.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: AVL corpus used in the experiments.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The AVL corpus was annotated with 15 different relationships across 86 segments, focusing on topics appearing in the majority of documents, resulting in 49 segments being used in experiments.The segmented AVL corpus was then annotated with equivalence relationships. This annotation was done in the same spirit as the 4S corpus, using a user-centered approach. The difficulty in this task is in the presence of multiple topics in a single segment. The annotation process consisted of going through each document individually. In the case of the first document, the segments were tagged with the topic they discussed. On subsequent documents, it was assessed if the segments should be assigned with an existed tag or if a new tag should be created. After the annotation process, a total of 15 different relationships were found in the set of 86 segments. The experiments only used the topics that appeared in the majority of the documents (49 segments)." } ], "images": [ "scigen/train/1344.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: AVL corpus used in the experiments." ], "reasoning": "The AVL corpus was annotated with 15 different relationships across 86 segments, focusing on topics appearing in the majority of documents, resulting in 49 segments being used in experiments.", "description": "The segmented AVL corpus was then annotated with equivalence relationships. This annotation was done in the same spirit as the 4S corpus, using a user-centered approach. The difficulty in this task is in the presence of multiple topics in a single segment. The annotation process consisted of going through each document individually. In the case of the first document, the segments were tagged with the topic they discussed. On subsequent documents, it was assessed if the segments should be assigned with an existed tag or if a new tag should be created. After the annotation process, a total of 15 different relationships were found in the set of 86 segments. The experiments only used the topics that appeared in the majority of the documents (49 segments).", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: AVL corpus used in the experiments.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich topic has the highest number of vocabulary words and how many segments does it have?\n" }, { "role": "assistant", "content": "By examining the table, the topic 'AVL Rebalance' has the highest number of vocabulary words (700). The number of segments for 'AVL Rebalance' is 11. So, the answer is {\"answer\": \"11\"}." } ], "images": [ "scigen/train/1344.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: AVL corpus used in the experiments." ], "reasoning": "By examining the table, the topic 'AVL Rebalance' has the highest number of vocabulary words (700). The number of segments for 'AVL Rebalance' is 11.", "question": "Which topic has the highest number of vocabulary words and how many segments does it have?", "answer": "So, the answer is {\"answer\": \"11\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The topic 'Tree Rotation' has the highest number of segments., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe topic 'Tree Rotation' has the highest number of segments.\n" }, { "role": "assistant", "content": "By examining the table, 'Tree Rotation' and 'Tree Balance' both have 13 segments, which is the highest number of segments among the listed topics. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1344.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: AVL corpus used in the experiments." ], "reasoning": "By examining the table, 'Tree Rotation' and 'Tree Balance' both have 13 segments, which is the highest number of segments among the listed topics.", "statement": "The topic 'Tree Rotation' has the highest number of segments.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: Results of the clustering algorithms in the AVL corpus. Best results are in bold.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Spectral clustering was the best in ARI and F1, while Agglomerative clustering had the highest Acc score.Results show that most of the techniques obtained low scores in all evaluation metrics. The only exceptions were the Agglomerative and Spectral clustering. It should be noted that not all evaluation metrics agree as to the best technique, since Spectral clustering had higher scores in ARI and F1, whereas Agglomerative clustering had the best Acc score. Also, there is not a strict correlation between the evaluation metrics. For example, Mean Shift obtained the lowest ARI, but would be considered as one of the best performing techniques in F1. In this context, we set as a baseline the Spectral clustering technique, since it was the best in two of evaluation metrics and still performed well on the third one." } ], "images": [ "scigen/train/1345.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of the clustering algorithms in the AVL corpus. Best results are in bold." ], "reasoning": "Spectral clustering was the best in ARI and F1, while Agglomerative clustering had the highest Acc score.", "description": "Results show that most of the techniques obtained low scores in all evaluation metrics. The only exceptions were the Agglomerative and Spectral clustering. It should be noted that not all evaluation metrics agree as to the best technique, since Spectral clustering had higher scores in ARI and F1, whereas Agglomerative clustering had the best Acc score. Also, there is not a strict correlation between the evaluation metrics. For example, Mean Shift obtained the lowest ARI, but would be considered as one of the best performing techniques in F1. In this context, we set as a baseline the Spectral clustering technique, since it was the best in two of evaluation metrics and still performed well on the third one.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. The question is related to the given table titled 'Table 4: Results of the clustering algorithms in the AVL corpus. Best results are in bold.'.\n\nQuestion: Which clustering algorithm had the highest ARI and F1 scores?\n" }, { "role": "assistant", "content": "By examining the table, we see that Spectral clustering had the highest ARI score of 0.15 and the highest F1 score of 0.36. So, the answer is {\"answer\": \"Spectral\"}." } ], "images": [ "scigen/train/1345.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of the clustering algorithms in the AVL corpus. Best results are in bold." ], "reasoning": "By examining the table, we see that Spectral clustering had the highest ARI score of 0.15 and the highest F1 score of 0.36.", "question": "Which clustering algorithm had the highest ARI and F1 scores?", "answer": "So, the answer is {\"answer\": \"Spectral\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Mean Shift had the lowest ARI score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nMean Shift had the lowest ARI score.\n" }, { "role": "assistant", "content": "The table shows that Mean Shift had an ARI score of 0.009, which is the lowest among all the clustering algorithms listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1345.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of the clustering algorithms in the AVL corpus. Best results are in bold." ], "reasoning": "The table shows that Mean Shift had an ARI score of 0.009, which is the lowest among all the clustering algorithms listed.", "statement": "Mean Shift had the lowest ARI score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 5: Best results obtained with graph-community detection algorithms. In bold are the best results obtained.'.\n" }, { "role": "assistant", "content": "The Walktraps algorithm performed better in all evaluation metrics, with the highest ARI, F1, and Acc scores.It should be noted that some of the techniques are not sensitive to edge weight. Another aspect to consider is that there are words in the documents that should not be taken into account, since they are either too common or too rare to be representative of a subtopic in a document. In this experiment we also adopted this approach by setting for each segment a cutoff at the top-100 words with the highest tf-idf score. It is possible to observe that the Walktraps algorithm performed better in all evaluation metrics." } ], "images": [ "scigen/train/1346.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Best results obtained with graph-community detection algorithms. In bold are the best results obtained." ], "reasoning": "The Walktraps algorithm performed better in all evaluation metrics, with the highest ARI, F1, and Acc scores.", "description": "It should be noted that some of the techniques are not sensitive to edge weight. Another aspect to consider is that there are words in the documents that should not be taken into account, since they are either too common or too rare to be representative of a subtopic in a document. In this experiment we also adopted this approach by setting for each segment a cutoff at the top-100 words with the highest tf-idf score. It is possible to observe that the Walktraps algorithm performed better in all evaluation metrics.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich algorithm had the highest ARI score and what was the value?\nSolve the above question based on the table titled 'Table 5: Best results obtained with graph-community detection algorithms. In bold are the best results obtained.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the ARI column, the Walktraps algorithm had the highest ARI score of 0.19. So, the answer is {\"answer\": \"Walktraps with 0.19\"}." } ], "images": [ "scigen/train/1346.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Best results obtained with graph-community detection algorithms. In bold are the best results obtained." ], "reasoning": "By examining the ARI column, the Walktraps algorithm had the highest ARI score of 0.19.", "question": "Which algorithm had the highest ARI score and what was the value?", "answer": "So, the answer is {\"answer\": \"Walktraps with 0.19\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Louvain algorithm had the highest accuracy score. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Louvain algorithm had the highest accuracy score.\n" }, { "role": "assistant", "content": "The table shows that the Walktraps algorithm had the highest accuracy score of 0.48, while the Louvain algorithm had an accuracy score of 0.42. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1346.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Best results obtained with graph-community detection algorithms. In bold are the best results obtained." ], "reasoning": "The table shows that the Walktraps algorithm had the highest accuracy score of 0.48, while the Louvain algorithm had an accuracy score of 0.42.", "statement": "The Louvain algorithm had the highest accuracy score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6: Summary of the results in the document relationship identification task.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The Walktraps approach outperformed other methods in document relationship identification, showing significantly higher ARI, F1, and Acc values.The Walktraps approach provided the best performance in the document relationship identification task, obtaining better results in all evaluation metrics used. The most important conclusion from these results is that graph-community detection-based techniques are more suitable for the relationship identification task. The difference in the evaluation metrics is substantial, with a value twice as high in ARI, and a 10% increase in F1 and Acc. The problem with clustering techniques is that they rely on a similarity space, which is not appropriate for the nature of this task. Given the results obtained, we claim that the document relationship identification task should be done by exploring network properties of a co-occurrence graph using a graph-community detection approach." } ], "images": [ "scigen/train/1347.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Summary of the results in the document relationship identification task." ], "reasoning": "The Walktraps approach outperformed other methods in document relationship identification, showing significantly higher ARI, F1, and Acc values.", "description": "The Walktraps approach provided the best performance in the document relationship identification task, obtaining better results in all evaluation metrics used. The most important conclusion from these results is that graph-community detection-based techniques are more suitable for the relationship identification task. The difference in the evaluation metrics is substantial, with a value twice as high in ARI, and a 10% increase in F1 and Acc. The problem with clustering techniques is that they rely on a similarity space, which is not appropriate for the nature of this task. Given the results obtained, we claim that the document relationship identification task should be done by exploring network properties of a co-occurrence graph using a graph-community detection approach.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 6: Summary of the results in the document relationship identification task.', solve this question and give detail solutions. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich algorithm showed the highest performance in ARI, F1, and Acc metrics for document relationship identification?\n" }, { "role": "assistant", "content": "The table shows the ARI, F1, and Acc values for different algorithms. Walktraps has the highest values in all three metrics. So, the answer is {\"answer\": \"Walktraps\"}." } ], "images": [ "scigen/train/1347.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Summary of the results in the document relationship identification task." ], "reasoning": "The table shows the ARI, F1, and Acc values for different algorithms. Walktraps has the highest values in all three metrics.", "question": "Which algorithm showed the highest performance in ARI, F1, and Acc metrics for document relationship identification?", "answer": "So, the answer is {\"answer\": \"Walktraps\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Walktraps approach had a 10% increase in F1 compared to Spectral Clustering.\n" }, { "role": "assistant", "content": "The table shows that Walktraps has an F1 score of 0.46 and Spectral Clustering has an F1 score of 0.36. The difference is 0.10, which is a 10% increase. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1347.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Summary of the results in the document relationship identification task." ], "reasoning": "The table shows that Walktraps has an F1 score of 0.46 and Spectral Clustering has an F1 score of 0.36. The difference is 0.10, which is a 10% increase.", "statement": "The Walktraps approach had a 10% increase in F1 compared to Spectral Clustering.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 7: Results of the clustering algorithms in the 4S corpus. Best results are in bold.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Spectral clustering achieved the best performance, and the heat map analysis of the 4S corpus similarity graph provided similar conclusions to the AVL corpus, indicating many segments are wrongly perceived as similar by the metrics.Spectral clustering obtained again the best performance. The heat map analyzes of the 4S corpus similarity graph provided similar conclusions to the AVL corpus. Therefore, many segments are wrongly perceived as similar by the metrics." } ], "images": [ "scigen/train/1348.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Results of the clustering algorithms in the 4S corpus. Best results are in bold." ], "reasoning": "Spectral clustering achieved the best performance, and the heat map analysis of the 4S corpus similarity graph provided similar conclusions to the AVL corpus, indicating many segments are wrongly perceived as similar by the metrics.", "description": "Spectral clustering obtained again the best performance. The heat map analyzes of the 4S corpus similarity graph provided similar conclusions to the AVL corpus. Therefore, many segments are wrongly perceived as similar by the metrics.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 7: Results of the clustering algorithms in the 4S corpus. Best results are in bold.\nQuestion: Which clustering algorithm achieved the highest ARI score?\n" }, { "role": "assistant", "content": "By examining the ARI column in the table, Spectral clustering has the highest ARI score of 0.27. So, the answer is {\"answer\": \"Spectral\"}." } ], "images": [ "scigen/train/1348.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Results of the clustering algorithms in the 4S corpus. Best results are in bold." ], "reasoning": "By examining the ARI column in the table, Spectral clustering has the highest ARI score of 0.27.", "question": "Which clustering algorithm achieved the highest ARI score?", "answer": "So, the answer is {\"answer\": \"Spectral\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nSpectral clustering achieved the highest F1 score.\n" }, { "role": "assistant", "content": "By examining the F1 column in the table, Spectral clustering has the highest F1 score of 0.41. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1348.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Results of the clustering algorithms in the 4S corpus. Best results are in bold." ], "reasoning": "By examining the F1 column in the table, Spectral clustering has the highest F1 score of 0.41.", "statement": "Spectral clustering achieved the highest F1 score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 8: Best results obtained in the 4S corpus. In bold are the highest scores obtained.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The Louvain technique outperformed others in all metrics, with Count and Best tf-idf being the best weighting schemes and scoring functions.In these experiments the Louvain technique performed better in all metrics. It should be noted that the best weighting scheme and scoring function were Count and Best tf-idf. These two combinations are different from one another in the sense that one just uses raw counts whereas the other takes into account an importance value of the words. The fact that different scoring functions worked better in different scenarios, shows that the corpora have particular characteristics and, thus, different ways to achieve a better model for the data are necessary." } ], "images": [ "scigen/train/1349.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Best results obtained in the 4S corpus. In bold are the highest scores obtained." ], "reasoning": "The Louvain technique outperformed others in all metrics, with Count and Best tf-idf being the best weighting schemes and scoring functions.", "description": "In these experiments the Louvain technique performed better in all metrics. It should be noted that the best weighting scheme and scoring function were Count and Best tf-idf. These two combinations are different from one another in the sense that one just uses raw counts whereas the other takes into account an importance value of the words. The fact that different scoring functions worked better in different scenarios, shows that the corpora have particular characteristics and, thus, different ways to achieve a better model for the data are necessary.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 8: Best results obtained in the 4S corpus. In bold are the highest scores obtained.', unfold the steps to solve the question given next. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which algorithm performed the best in terms of ARI, F1, and Acc metrics?\n" }, { "role": "assistant", "content": "The table shows that the Louvain algorithm has the highest values for ARI (0.21), F1 (0.36), and Acc (0.42), indicating it performed the best in all metrics. So, the answer is {\"answer\": \"Louvain\"}" } ], "images": [ "scigen/train/1349.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Best results obtained in the 4S corpus. In bold are the highest scores obtained." ], "reasoning": "The table shows that the Louvain algorithm has the highest values for ARI (0.21), F1 (0.36), and Acc (0.42), indicating it performed the best in all metrics.", "question": "Which algorithm performed the best in terms of ARI, F1, and Acc metrics?", "answer": "So, the answer is {\"answer\": \"Louvain\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Walktraps algorithm performed better than the CNM algorithm in terms of F1 score.\n" }, { "role": "assistant", "content": "The table shows that the Walktraps algorithm has an F1 score of 0.30, while the CNM algorithm has an F1 score of 0.25, indicating that Walktraps performed better. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1349.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Best results obtained in the 4S corpus. In bold are the highest scores obtained." ], "reasoning": "The table shows that the Walktraps algorithm has an F1 score of 0.30, while the CNM algorithm has an F1 score of 0.25, indicating that Walktraps performed better.", "statement": "The Walktraps algorithm performed better than the CNM algorithm in terms of F1 score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title '5,000', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Training on noisy data improves performance for BPE models, making them as robust as character-level models without sacrificing performance on clean test sets.Adding noise helps. Training on similar type of noisy data improves performance for all vocabularies. By training on similar kinds of noise in the training data, we are able to robustify BPE models to the same level as character level models without sacrificing too much performance on the clean test set. Effect on clean data. However, in the case of BPE 30 000, training on noisy data significantly boosted performance (eg, improvement of 6 BLEU for DE-EN and 1.7 for EN-DE when training with delete and testing on clean data). We hypothesize that the increased diversity of tokens during training (due to the presence of noise) acts as a regularizer boosting performance on the test set." } ], "images": [ "scigen/train/1350.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "5,000" ], "reasoning": "Training on noisy data improves performance for BPE models, making them as robust as character-level models without sacrificing performance on clean test sets.", "description": "Adding noise helps. Training on similar type of noisy data improves performance for all vocabularies. By training on similar kinds of noise in the training data, we are able to robustify BPE models to the same level as character level models without sacrificing too much performance on the clean test set. Effect on clean data. However, in the case of BPE 30 000, training on noisy data significantly boosted performance (eg, improvement of 6 BLEU for DE-EN and 1.7 for EN-DE when training with delete and testing on clean data). We hypothesize that the increased diversity of tokens during training (due to the presence of noise) acts as a regularizer boosting performance on the test set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of '5,000'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the average performance when training on 'replace' and testing on 'switch'?\n" }, { "role": "assistant", "content": "To find the average performance, look at the intersection of the 'replace' row and the 'switch' column, which shows a value of 22.5. So, the answer is {\"answer\": \"22.5\"}" } ], "images": [ "scigen/train/1350.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "5,000" ], "reasoning": "To find the average performance, look at the intersection of the 'replace' row and the 'switch' column, which shows a value of 22.5.", "question": "What is the average performance when training on 'replace' and testing on 'switch'?", "answer": "So, the answer is {\"answer\": \"22.5\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nTraining on 'insert' data and testing on 'clean' data results in a performance of 26.2.\n" }, { "role": "assistant", "content": "The table shows that training on 'insert' data and testing on 'clean' data results in a performance of 26.2. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1350.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "5,000" ], "reasoning": "The table shows that training on 'insert' data and testing on 'clean' data results in a performance of 26.2.", "statement": "Training on 'insert' data and testing on 'clean' data results in a performance of 26.2.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Similarity metrics between test sets and training sets.'.\n" }, { "role": "assistant", "content": "Character level models outperform BPE in low resource settings for out-of-domain datasets except Europarl, while in high resource settings, BPE performs better except for WMT-Biomedical.“% Unseen” is the percentage of words in the test set that are not present in the training corpus. “PPL” is the perplexity measure of the test set using a language model trained on the training data. DE-EN low resource. Character level models are better for all out of domain datasets, except for Europarl. This suggests that character level models outperform BPE when evaluated on data sufficiently different from the training domain in this low resource setting. DE-EN high resource. Character level models are now only better when testing on the WMT-Biomedical test set. For all other test sets, BPE 30 000 leads to the best BLEU scores. EN-DE low resource. We see similar performance for in and out of domain data. Good BPE models on in-domain test set are still better on out-of-domain test sets. A possible explanation is the lower proportion of unseen words as compared to German and seeing the words more frequently in the training corpus." } ], "images": [ "scigen/train/1351.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Similarity metrics between test sets and training sets." ], "reasoning": "Character level models outperform BPE in low resource settings for out-of-domain datasets except Europarl, while in high resource settings, BPE performs better except for WMT-Biomedical.", "description": "“% Unseen” is the percentage of words in the test set that are not present in the training corpus. “PPL” is the perplexity measure of the test set using a language model trained on the training data. DE-EN low resource. Character level models are better for all out of domain datasets, except for Europarl. This suggests that character level models outperform BPE when evaluated on data sufficiently different from the training domain in this low resource setting. DE-EN high resource. Character level models are now only better when testing on the WMT-Biomedical test set. For all other test sets, BPE 30 000 leads to the best BLEU scores. EN-DE low resource. We see similar performance for in and out of domain data. Good BPE models on in-domain test set are still better on out-of-domain test sets. A possible explanation is the lower proportion of unseen words as compared to German and seeing the words more frequently in the training corpus.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 2: Similarity metrics between test sets and training sets.\n\nQuestion: Which dataset has the highest percentage of unseen words for DE-EN low resource?\n" }, { "role": "assistant", "content": "To find the dataset with the highest percentage of unseen words for DE-EN low resource, look at the 'DE-EN % Unseen' column and identify the highest value. So, the answer is {\"answer\": \"WMT-Bio.\"}." } ], "images": [ "scigen/train/1351.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Similarity metrics between test sets and training sets." ], "reasoning": "To find the dataset with the highest percentage of unseen words for DE-EN low resource, look at the 'DE-EN % Unseen' column and identify the highest value.", "question": "Which dataset has the highest percentage of unseen words for DE-EN low resource?", "answer": "So, the answer is {\"answer\": \"WMT-Bio.\"}." }, { "messages": [ { "role": "user", "content": "The perplexity for DE-EN high resource on the WMT-IT dataset is higher than on the IWSLT14 dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe perplexity for DE-EN high resource on the WMT-IT dataset is higher than on the IWSLT14 dataset.\n" }, { "role": "assistant", "content": "Compare the DE-EN (high) PPL values for WMT-IT and IWSLT14 datasets. WMT-IT has a perplexity of 996, while IWSLT14 has a perplexity of 740. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1351.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Similarity metrics between test sets and training sets." ], "reasoning": "Compare the DE-EN (high) PPL values for WMT-IT and IWSLT14 datasets. WMT-IT has a perplexity of 996, while IWSLT14 has a perplexity of 740.", "statement": "The perplexity for DE-EN high resource on the WMT-IT dataset is higher than on the IWSLT14 dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Results for the low resource setting. “PreN + T” refers to an architecture with layer normalization before each sub-layer and transparent attention. Best results for each layer depth are shown in bold.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Training deeper models marginally improves performance for both vocabularies, with character level models improving by 1 BLEU point and BPE 5 000 models by 0.4 BLEU points.While adding transparent attention is beneficial for almost all depths in the character level models, it gives mixed results for the BPE 5 000 model. Hence, we see that by training deeper models we are able to marginally improve performance for both vocabularies. For character level models we improve by 1 BLEU points (from 33.7 to 34.7). For 5 000, the gain is a more modest 0.4 BLEU points (from 35 to 35.4). We are able to narrow but not close the gap between character and BPE." } ], "images": [ "scigen/train/1352.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results for the low resource setting. “PreN + T” refers to an architecture with layer normalization before each sub-layer and transparent attention. Best results for each layer depth are shown in bold." ], "reasoning": "Training deeper models marginally improves performance for both vocabularies, with character level models improving by 1 BLEU point and BPE 5 000 models by 0.4 BLEU points.", "description": "While adding transparent attention is beneficial for almost all depths in the character level models, it gives mixed results for the BPE 5 000 model. Hence, we see that by training deeper models we are able to marginally improve performance for both vocabularies. For character level models we improve by 1 BLEU points (from 33.7 to 34.7). For 5 000, the gain is a more modest 0.4 BLEU points (from 35 to 35.4). We are able to narrow but not close the gap between character and BPE.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Results for the low resource setting. “PreN + T” refers to an architecture with layer normalization before each sub-layer and transparent attention. Best results for each layer depth are shown in bold.\nQuestion: What is the BLEU score improvement for the character level models when using transparent attention?\nGive a detailed response to the above question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The BLEU score for character level models without transparent attention is 33.7, and with transparent attention, it is 34.7, resulting in an improvement of 1 BLEU point. So, the answer is {\"answer\": \"1\"}." } ], "images": [ "scigen/train/1352.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results for the low resource setting. “PreN + T” refers to an architecture with layer normalization before each sub-layer and transparent attention. Best results for each layer depth are shown in bold." ], "reasoning": "The BLEU score for character level models without transparent attention is 33.7, and with transparent attention, it is 34.7, resulting in an improvement of 1 BLEU point.", "question": "What is the BLEU score improvement for the character level models when using transparent attention?", "answer": "So, the answer is {\"answer\": \"1\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe BLEU score for the 5 000 model with transparent attention at depth 24 is 35.4.\n" }, { "role": "assistant", "content": "According to the table, the BLEU score for the 5 000 model with transparent attention at depth 24 is indeed 35.4. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1352.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results for the low resource setting. “PreN + T” refers to an architecture with layer normalization before each sub-layer and transparent attention. Best results for each layer depth are shown in bold." ], "reasoning": "According to the table, the BLEU score for the 5 000 model with transparent attention at depth 24 is indeed 35.4.", "statement": "The BLEU score for the 5 000 model with transparent attention at depth 24 is 35.4.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: Results for the high resource setting.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Increasing the depth of encoder layers improves BLEU scores by 1-2 points beyond 6 layers, with BPE models outperforming character level models., we no longer train models with post layer normalization and restrict ourselves to pre layer normalization and transparent attention. Here, we also experiment with the BPE 30 000 vocabulary. Here again we see an improvement in BLEU scores with increasing depth of 1-2 points when going beyond 6 encoder layers. Transparent attention seems to help consistently for character level models but barely does anything for the two BPE models. Further, with increased depth, BPE 5 000 and 30 000 perform similarly in contrast to shallow models where there is a 1 BLEU difference. However, character level models are still slightly worse than the BPE models with a max score of 37.7 rather than 38 for the BPE models." } ], "images": [ "scigen/train/1353.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results for the high resource setting." ], "reasoning": "Increasing the depth of encoder layers improves BLEU scores by 1-2 points beyond 6 layers, with BPE models outperforming character level models.", "description": ", we no longer train models with post layer normalization and restrict ourselves to pre layer normalization and transparent attention. Here, we also experiment with the BPE 30 000 vocabulary. Here again we see an improvement in BLEU scores with increasing depth of 1-2 points when going beyond 6 encoder layers. Transparent attention seems to help consistently for character level models but barely does anything for the two BPE models. Further, with increased depth, BPE 5 000 and 30 000 perform similarly in contrast to shallow models where there is a 1 BLEU difference. However, character level models are still slightly worse than the BPE models with a max score of 37.7 rather than 38 for the BPE models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Results for the high resource setting.\nQuestion: What is the highest BLEU score achieved by the 30 000 PreN model?\nGive a detailed response to the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The highest BLEU score for the 30 000 PreN model is found in the row with the maximum value, which is 37.9. So, the answer is {\"answer\": \"37.9\"}" } ], "images": [ "scigen/train/1353.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results for the high resource setting." ], "reasoning": "The highest BLEU score for the 30 000 PreN model is found in the row with the maximum value, which is 37.9.", "question": "What is the highest BLEU score achieved by the 30 000 PreN model?", "answer": "So, the answer is {\"answer\": \"37.9\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The 5 000 PreN+T model achieves a BLEU score of 38 with 24 encoder layers. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe 5 000 PreN+T model achieves a BLEU score of 38 with 24 encoder layers.\n" }, { "role": "assistant", "content": "The table shows that the 5 000 PreN+T model achieves a BLEU score of 38 with 32 encoder layers, not 24. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1353.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results for the high resource setting." ], "reasoning": "The table shows that the 5 000 PreN+T model achieves a BLEU score of 38 with 32 encoder layers, not 24.", "statement": "The 5 000 PreN+T model achieves a BLEU score of 38 with 24 encoder layers.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Character'.\n" }, { "role": "assistant", "content": "Training on noisy data improves performance for BPE models, making them as robust as character-level models without sacrificing performance on clean data.Adding noise helps. Training on similar type of noisy data improves performance for all vocabularies. By training on similar kinds of noise in the training data, we are able to robustify BPE models to the same level as character level models without sacrificing too much performance on the clean test set. Effect on clean data. However, in the case of BPE 30 000, training on noisy data significantly boosted performance (eg, improvement of 6 BLEU for DE-EN and 1.7 for EN-DE when training with delete and testing on clean data). We hypothesize that the increased diversity of tokens during training (due to the presence of noise) acts as a regularizer boosting performance on the test set." } ], "images": [ "scigen/train/1354.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Character" ], "reasoning": "Training on noisy data improves performance for BPE models, making them as robust as character-level models without sacrificing performance on clean data.", "description": "Adding noise helps. Training on similar type of noisy data improves performance for all vocabularies. By training on similar kinds of noise in the training data, we are able to robustify BPE models to the same level as character level models without sacrificing too much performance on the clean test set. Effect on clean data. However, in the case of BPE 30 000, training on noisy data significantly boosted performance (eg, improvement of 6 BLEU for DE-EN and 1.7 for EN-DE when training with delete and testing on clean data). We hypothesize that the increased diversity of tokens during training (due to the presence of noise) acts as a regularizer boosting performance on the test set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Character'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the average performance when training on all types of noisy data?\n" }, { "role": "assistant", "content": "To find the average performance when training on all types of noisy data, look at the 'avg' column in the 'all' row. So, the answer is {\"answer\": \"31.5\"}" } ], "images": [ "scigen/train/1354.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Character" ], "reasoning": "To find the average performance when training on all types of noisy data, look at the 'avg' column in the 'all' row.", "question": "What is the average performance when training on all types of noisy data?", "answer": "So, the answer is {\"answer\": \"31.5\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTraining on clean data results in the highest performance on clean test data.\n" }, { "role": "assistant", "content": "The table shows that training on clean data results in a performance of 34.1 on clean test data, which is the highest value in the 'clean' column. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1354.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Character" ], "reasoning": "The table shows that training on clean data results in a performance of 34.1 on clean test data, which is the highest value in the 'clean' column.", "statement": "Training on clean data results in the highest performance on clean test data.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title '5,000'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Training on noisy data improves performance for BPE models, making them as robust as character-level models without sacrificing performance on clean test sets.Adding noise helps. Training on similar type of noisy data improves performance for all vocabularies. By training on similar kinds of noise in the training data, we are able to robustify BPE models to the same level as character level models without sacrificing too much performance on the clean test set. Effect on clean data. However, in the case of BPE 30 000, training on noisy data significantly boosted performance (eg, improvement of 6 BLEU for DE-EN and 1.7 for EN-DE when training with delete and testing on clean data). We hypothesize that the increased diversity of tokens during training (due to the presence of noise) acts as a regularizer boosting performance on the test set." } ], "images": [ "scigen/train/1355.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "5,000" ], "reasoning": "Training on noisy data improves performance for BPE models, making them as robust as character-level models without sacrificing performance on clean test sets.", "description": "Adding noise helps. Training on similar type of noisy data improves performance for all vocabularies. By training on similar kinds of noise in the training data, we are able to robustify BPE models to the same level as character level models without sacrificing too much performance on the clean test set. Effect on clean data. However, in the case of BPE 30 000, training on noisy data significantly boosted performance (eg, improvement of 6 BLEU for DE-EN and 1.7 for EN-DE when training with delete and testing on clean data). We hypothesize that the increased diversity of tokens during training (due to the presence of noise) acts as a regularizer boosting performance on the test set.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled '5,000' in mind, break down and resolve the question below step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhat is the average performance when training on 'all' types of noise and testing on 'clean' data?\n" }, { "role": "assistant", "content": "To find the average performance, look at the intersection of the 'all' row and the 'avg' column. So, the answer is {\"answer\": \"33.0\"}" } ], "images": [ "scigen/train/1355.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "5,000" ], "reasoning": "To find the average performance, look at the intersection of the 'all' row and the 'avg' column.", "question": "What is the average performance when training on 'all' types of noise and testing on 'clean' data?", "answer": "So, the answer is {\"answer\": \"33.0\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nTraining on 'insert' noise and testing on 'insert' data yields the highest performance.\n" }, { "role": "assistant", "content": "The table shows that training on 'insert' noise and testing on 'insert' data yields a performance of 33.8, which is the highest value in the table. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1355.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "5,000" ], "reasoning": "The table shows that training on 'insert' noise and testing on 'insert' data yields a performance of 33.8, which is the highest value in the table.", "statement": "Training on 'insert' noise and testing on 'insert' data yields the highest performance.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption '30,000'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Training on noisy data improves performance for BPE models, making them as robust as character-level models without sacrificing performance on clean test sets.Adding noise helps. Training on similar type of noisy data improves performance for all vocabularies. By training on similar kinds of noise in the training data, we are able to robustify BPE models to the same level as character level models without sacrificing too much performance on the clean test set. Effect on clean data. However, in the case of BPE 30 000, training on noisy data significantly boosted performance (eg, improvement of 6 BLEU for DE-EN and 1.7 for EN-DE when training with delete and testing on clean data). We hypothesize that the increased diversity of tokens during training (due to the presence of noise) acts as a regularizer boosting performance on the test set." } ], "images": [ "scigen/train/1356.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "30,000" ], "reasoning": "Training on noisy data improves performance for BPE models, making them as robust as character-level models without sacrificing performance on clean test sets.", "description": "Adding noise helps. Training on similar type of noisy data improves performance for all vocabularies. By training on similar kinds of noise in the training data, we are able to robustify BPE models to the same level as character level models without sacrificing too much performance on the clean test set. Effect on clean data. However, in the case of BPE 30 000, training on noisy data significantly boosted performance (eg, improvement of 6 BLEU for DE-EN and 1.7 for EN-DE when training with delete and testing on clean data). We hypothesize that the increased diversity of tokens during training (due to the presence of noise) acts as a regularizer boosting performance on the test set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: '30,000'\nQuestion: What is the average performance when training on all types of noise and testing on clean data?\n" }, { "role": "assistant", "content": "To find the average performance when training on all types of noise and testing on clean data, look at the intersection of the 'all' row and the 'avg' column in the table. So, the answer is {\"answer\": \"32.2\"}." } ], "images": [ "scigen/train/1356.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "30,000" ], "reasoning": "To find the average performance when training on all types of noise and testing on clean data, look at the intersection of the 'all' row and the 'avg' column in the table.", "question": "What is the average performance when training on all types of noise and testing on clean data?", "answer": "So, the answer is {\"answer\": \"32.2\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Training on delete noise and testing on clean data results in an average performance of 29.0., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTraining on delete noise and testing on clean data results in an average performance of 29.0.\n" }, { "role": "assistant", "content": "To verify this statement, look at the intersection of the 'delete' row and the 'avg' column in the table. The value is 29.0, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1356.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "30,000" ], "reasoning": "To verify this statement, look at the intersection of the 'delete' row and the 'avg' column in the table. The value is 29.0, which matches the statement.", "statement": "Training on delete noise and testing on clean data results in an average performance of 29.0.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Character', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Training BPE models on noisy data improves their performance on clean test sets, similar to character-level models, by acting as a regularizer.Adding noise helps. Training on similar type of noisy data improves performance for all vocabularies. By training on similar kinds of noise in the training data, we are able to robustify BPE models to the same level as character level models without sacrificing too much performance on the clean test set. Effect on clean data. However, in the case of BPE 30 000, training on noisy data significantly boosted performance (eg, improvement of 6 BLEU for DE-EN and 1.7 for EN-DE when training with delete and testing on clean data). We hypothesize that the increased diversity of tokens during training (due to the presence of noise) acts as a regularizer boosting performance on the test set." } ], "images": [ "scigen/train/1357.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Character" ], "reasoning": "Training BPE models on noisy data improves their performance on clean test sets, similar to character-level models, by acting as a regularizer.", "description": "Adding noise helps. Training on similar type of noisy data improves performance for all vocabularies. By training on similar kinds of noise in the training data, we are able to robustify BPE models to the same level as character level models without sacrificing too much performance on the clean test set. Effect on clean data. However, in the case of BPE 30 000, training on noisy data significantly boosted performance (eg, improvement of 6 BLEU for DE-EN and 1.7 for EN-DE when training with delete and testing on clean data). We hypothesize that the increased diversity of tokens during training (due to the presence of noise) acts as a regularizer boosting performance on the test set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Character', unfold the steps to solve the question given next. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: What is the BLEU score improvement for DE-EN when training with delete noise and testing on clean data?\n" }, { "role": "assistant", "content": "The table shows that training with delete noise and testing on clean data results in a BLEU score of 24.9, compared to 18.9 for clean training, indicating an improvement. So, the answer is {\"answer\": \"6\"}" } ], "images": [ "scigen/train/1357.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Character" ], "reasoning": "The table shows that training with delete noise and testing on clean data results in a BLEU score of 24.9, compared to 18.9 for clean training, indicating an improvement.", "question": "What is the BLEU score improvement for DE-EN when training with delete noise and testing on clean data?", "answer": "So, the answer is {\"answer\": \"6\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence Training on noisy data improves the performance of BPE models on clean test sets. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nTraining on noisy data improves the performance of BPE models on clean test sets.\n" }, { "role": "assistant", "content": "The table shows that training on noisy data (e.g., delete, insert, replace, switch) generally results in higher average BLEU scores compared to training on clean data. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1357.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Character" ], "reasoning": "The table shows that training on noisy data (e.g., delete, insert, replace, switch) generally results in higher average BLEU scores compared to training on clean data.", "statement": "Training on noisy data improves the performance of BPE models on clean test sets.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title '30,000'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Training BPE models on noisy data improves their performance on clean test sets, with significant boosts observed in specific noise types.Adding noise helps. Training on similar type of noisy data improves performance for all vocabularies. By training on similar kinds of noise in the training data, we are able to robustify BPE models to the same level as character level models without sacrificing too much performance on the clean test set. Effect on clean data. However, in the case of BPE 30 000, training on noisy data significantly boosted performance (eg, improvement of 6 BLEU for DE-EN and 1.7 for EN-DE when training with delete and testing on clean data). We hypothesize that the increased diversity of tokens during training (due to the presence of noise) acts as a regularizer boosting performance on the test set." } ], "images": [ "scigen/train/1358.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "30,000" ], "reasoning": "Training BPE models on noisy data improves their performance on clean test sets, with significant boosts observed in specific noise types.", "description": "Adding noise helps. Training on similar type of noisy data improves performance for all vocabularies. By training on similar kinds of noise in the training data, we are able to robustify BPE models to the same level as character level models without sacrificing too much performance on the clean test set. Effect on clean data. However, in the case of BPE 30 000, training on noisy data significantly boosted performance (eg, improvement of 6 BLEU for DE-EN and 1.7 for EN-DE when training with delete and testing on clean data). We hypothesize that the increased diversity of tokens during training (due to the presence of noise) acts as a regularizer boosting performance on the test set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about '30,000', solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the average performance when training with 'all' noise types and testing on 'clean' data?\n" }, { "role": "assistant", "content": "To find the average performance, look at the intersection of the 'all' row and the 'avg' column, which is 25.4. So, the answer is {\"answer\": \"25.4\"}" } ], "images": [ "scigen/train/1358.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "30,000" ], "reasoning": "To find the average performance, look at the intersection of the 'all' row and the 'avg' column, which is 25.4.", "question": "What is the average performance when training with 'all' noise types and testing on 'clean' data?", "answer": "So, the answer is {\"answer\": \"25.4\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nTraining with 'switch' noise type results in the highest performance on 'clean' test data.\n" }, { "role": "assistant", "content": "The highest performance on 'clean' test data is found at the intersection of the 'switch' row and the 'clean' column, which is 26.6, confirming the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1358.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "30,000" ], "reasoning": "The highest performance on 'clean' test data is found at the intersection of the 'switch' row and the 'clean' column, which is 26.6, confirming the statement.", "statement": "Training with 'switch' noise type results in the highest performance on 'clean' test data.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: The result of discourse parsing using different segmentation. The performance is evaluated on intra-sentential, multi-sentential, and text level separately, using the unlabeled and labeled F-score.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Using our segmentation method on the intra-sentential level results in a significant improvement in F-score across Span, Nuclearity, and Relation metrics compared to Joty et al.'s method.As can be seen, on the intra-sentential level, the influence of segmentation is significant. Evaluated on Span, Nuclearity, and Relation, using our own segmentation results in a 10% difference in F-score (p" } ], "images": [ "scigen/train/1359.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The result of discourse parsing using different segmentation. The performance is evaluated on intra-sentential, multi-sentential, and text level separately, using the unlabeled and labeled F-score." ], "reasoning": "Using our segmentation method on the intra-sentential level results in a significant improvement in F-score across Span, Nuclearity, and Relation metrics compared to Joty et al.'s method.", "description": "As can be seen, on the intra-sentential level, the influence of segmentation is significant. Evaluated on Span, Nuclearity, and Relation, using our own segmentation results in a 10% difference in F-score (p", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: The result of discourse parsing using different segmentation. The performance is evaluated on intra-sentential, multi-sentential, and text level separately, using the unlabeled and labeled F-score.\nQuestion: What is the difference in the F-score for the Span metric between our segmentation and Joty et al.'s segmentation on the intra-sentential level?\nSolve the above question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the difference in the F-score for the Span metric between our segmentation and Joty et al.'s segmentation on the intra-sentential level, subtract Joty et al.'s score (78.7) from our score (85.1). So, the answer is {\"answer\": \"6.4\"}." } ], "images": [ "scigen/train/1359.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The result of discourse parsing using different segmentation. The performance is evaluated on intra-sentential, multi-sentential, and text level separately, using the unlabeled and labeled F-score." ], "reasoning": "To find the difference in the F-score for the Span metric between our segmentation and Joty et al.'s segmentation on the intra-sentential level, subtract Joty et al.'s score (78.7) from our score (85.1).", "question": "What is the difference in the F-score for the Span metric between our segmentation and Joty et al.'s segmentation on the intra-sentential level?", "answer": "So, the answer is {\"answer\": \"6.4\"}." }, { "messages": [ { "role": "user", "content": "Our segmentation method achieves higher F-scores in all metrics compared to Joty et al.'s method on the intra-sentential level. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nOur segmentation method achieves higher F-scores in all metrics compared to Joty et al.'s method on the intra-sentential level.\n" }, { "role": "assistant", "content": "Comparing the F-scores for Span, Nuclearity, and Relation metrics on the intra-sentential level, our segmentation method scores higher in all three metrics (85.1, 77.5, 66.8) compared to Joty et al.'s method (78.7, 70.8, 60.8). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1359.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The result of discourse parsing using different segmentation. The performance is evaluated on intra-sentential, multi-sentential, and text level separately, using the unlabeled and labeled F-score." ], "reasoning": "Comparing the F-scores for Span, Nuclearity, and Relation metrics on the intra-sentential level, our segmentation method scores higher in all three metrics (85.1, 77.5, 66.8) compared to Joty et al.'s method (78.7, 70.8, 60.8).", "statement": "Our segmentation method achieves higher F-scores in all metrics compared to Joty et al.'s method on the intra-sentential level.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Characteristics of the training and the test set in RST-DT.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The linear-chain CRF models were trained on 347 documents and tested on 38 documents from the RST Discourse Treebank.We first study how our proposed two-pass discourse segmenter based on pairing features performs against existing segmentation models. In this experiment, we train our linear-chain CRF models on the RST Discourse Treebank (RST-DT) By convention, the corpus is partitioned into a training set of 347 documents and a test set of 38 documents." } ], "images": [ "scigen/train/1360.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Characteristics of the training and the test set in RST-DT." ], "reasoning": "The linear-chain CRF models were trained on 347 documents and tested on 38 documents from the RST Discourse Treebank.", "description": "We first study how our proposed two-pass discourse segmenter based on pairing features performs against existing segmentation models. In this experiment, we train our linear-chain CRF models on the RST Discourse Treebank (RST-DT) By convention, the corpus is partitioned into a training set of 347 documents and a test set of 38 documents.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: Characteristics of the training and the test set in RST-DT.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. \n\nQuestion: How many EDUs are there in the training and test sets combined?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows 18,765 EDUs in the training set and 2,346 EDUs in the test set. Adding these together gives the total number of EDUs. So, the answer is {\"answer\": \"21,111\"}" } ], "images": [ "scigen/train/1360.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Characteristics of the training and the test set in RST-DT." ], "reasoning": "The table shows 18,765 EDUs in the training set and 2,346 EDUs in the test set. Adding these together gives the total number of EDUs.", "question": "How many EDUs are there in the training and test sets combined?", "answer": "So, the answer is {\"answer\": \"21,111\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe training set contains more sentences than the test set.\n" }, { "role": "assistant", "content": "The table shows that the training set has 7,455 sentences, while the test set has 992 sentences, confirming that the training set contains more sentences. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1360.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Characteristics of the training and the test set in RST-DT." ], "reasoning": "The table shows that the training set has 7,455 sentences, while the test set has 992 sentences, confirming that the training set contains more sentences.", "statement": "The training set contains more sentences than the test set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 6: The effect of removing pairing features (−p), removing global features (−g), and removing both (−pg), in comparison with the full model in the first section, across different segmentation frameworks. CRF stands for our standard two-pass segmentation models based on linear-chain CRFs, while LR and SVM stand for two different classifiers in the framework of independent binary classification.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our full models perform similarly across different frameworks with an insignificant absolute difference in F1 scores of less than 0.2%. Models without pairing features show significantly poorer performance.The first section lists the performance of our full model in different segmentation frameworks. As can be seen, our full models perform similarly across different frameworks, where the absolute difference in F1 is less than 0.2% and insignificant. This is consistent with \\newciteHernault:2010 :SMD:2175352.2175383’s finding that, when a large number of contextual features are incorporated, binary classifiers such as SVM can achieve competitive performance with CRFs. The second section lists the performance of our models with no pairing features (−p). For all three resulting models, CRF−p, LR−p, and SVM−p, their performance is significantly poorer (p" } ], "images": [ "scigen/train/1361.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: The effect of removing pairing features (−p), removing global features (−g), and removing both (−pg), in comparison with the full model in the first section, across different segmentation frameworks. CRF stands for our standard two-pass segmentation models based on linear-chain CRFs, while LR and SVM stand for two different classifiers in the framework of independent binary classification." ], "reasoning": "Our full models perform similarly across different frameworks with an insignificant absolute difference in F1 scores of less than 0.2%. Models without pairing features show significantly poorer performance.", "description": "The first section lists the performance of our full model in different segmentation frameworks. As can be seen, our full models perform similarly across different frameworks, where the absolute difference in F1 is less than 0.2% and insignificant. This is consistent with \\newciteHernault:2010 :SMD:2175352.2175383’s finding that, when a large number of contextual features are incorporated, binary classifiers such as SVM can achieve competitive performance with CRFs. The second section lists the performance of our models with no pairing features (−p). For all three resulting models, CRF−p, LR−p, and SVM−p, their performance is significantly poorer (p", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 6: The effect of removing pairing features (−p), removing global features (−g), and removing both (−pg), in comparison with the full model in the first section, across different segmentation frameworks. CRF stands for our standard two-pass segmentation models based on linear-chain CRFs, while LR and SVM stand for two different classifiers in the framework of independent binary classification.'. Solve the following question based on the table.\n\nQuestion: Which model has the highest F1 score among the full models?\nLet's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The full models listed are CRF, LR, and SVM. Among these, the F1 scores are 92.6 for CRF, 92.5 for LR, and 92.7 for SVM. The highest F1 score is 92.7, achieved by the SVM model. So, the answer is {\"answer\": \"SVM\"}." } ], "images": [ "scigen/train/1361.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: The effect of removing pairing features (−p), removing global features (−g), and removing both (−pg), in comparison with the full model in the first section, across different segmentation frameworks. CRF stands for our standard two-pass segmentation models based on linear-chain CRFs, while LR and SVM stand for two different classifiers in the framework of independent binary classification." ], "reasoning": "The full models listed are CRF, LR, and SVM. Among these, the F1 scores are 92.6 for CRF, 92.5 for LR, and 92.7 for SVM. The highest F1 score is 92.7, achieved by the SVM model.", "question": "Which model has the highest F1 score among the full models?", "answer": "So, the answer is {\"answer\": \"SVM\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe CRF model with no pairing features has a higher F1 score than the SVM model with no pairing features.\n" }, { "role": "assistant", "content": "The F1 score for the CRF-p model is 91.2, while the F1 score for the SVM-p model is 91.4. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1361.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: The effect of removing pairing features (−p), removing global features (−g), and removing both (−pg), in comparison with the full model in the first section, across different segmentation frameworks. CRF stands for our standard two-pass segmentation models based on linear-chain CRFs, while LR and SVM stand for two different classifiers in the framework of independent binary classification." ], "reasoning": "The F1 score for the CRF-p model is 91.2, while the F1 score for the SVM-p model is 91.4. Therefore, the statement is refuted.", "statement": "The CRF model with no pairing features has a higher F1 score than the SVM model with no pairing features.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Test results on different datasets. The BiLSTM results reported by other papers are drawn from Zhou et al. (2016),† Wang et al. (2017),‡ and Williams et al. (2017).∗ All of our test results are obtained from the GLUE benchmark website.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The table reports both F1 and accuracy for QQP due to its slight imbalance and provides the average score of each model on the datasets.For QQP, we report both F1 and accuracy, since the dataset is slightly unbalanced. Following GLUE, we report the average score of each model on the datasets." } ], "images": [ "scigen/train/1362.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Test results on different datasets. The BiLSTM results reported by other papers are drawn from Zhou et al. (2016),† Wang et al. (2017),‡ and Williams et al. (2017).∗ All of our test results are obtained from the GLUE benchmark website." ], "reasoning": "The table reports both F1 and accuracy for QQP due to its slight imbalance and provides the average score of each model on the datasets.", "description": "For QQP, we report both F1 and accuracy, since the dataset is slightly unbalanced. Following GLUE, we report the average score of each model on the datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Test results on different datasets. The BiLSTM results reported by other papers are drawn from Zhou et al. (2016),† Wang et al. (2017),‡ and Williams et al. (2017).∗ All of our test results are obtained from the GLUE benchmark website.', solve the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest accuracy on the SST-2 dataset and what is its accuracy?\n" }, { "role": "assistant", "content": "To find the model with the highest accuracy on the SST-2 dataset, look at the 'SST-2 Acc' column and identify the highest value. The highest accuracy is 94.9, achieved by the BERTLARGE model. So, the answer is {\"answer\": \"BERTLARGE, 94.9\"}." } ], "images": [ "scigen/train/1362.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Test results on different datasets. The BiLSTM results reported by other papers are drawn from Zhou et al. (2016),† Wang et al. (2017),‡ and Williams et al. (2017).∗ All of our test results are obtained from the GLUE benchmark website." ], "reasoning": "To find the model with the highest accuracy on the SST-2 dataset, look at the 'SST-2 Acc' column and identify the highest value. The highest accuracy is 94.9, achieved by the BERTLARGE model.", "question": "Which model has the highest accuracy on the SST-2 dataset and what is its accuracy?", "answer": "So, the answer is {\"answer\": \"BERTLARGE, 94.9\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Distilled BiLSTMSOFT has the highest accuracy on the MNLI-m dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nDistilled BiLSTMSOFT has the highest accuracy on the MNLI-m dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the accuracy values in the 'MNLI-m Acc' column. The highest accuracy is 86.7 by BERTLARGE, not Distilled BiLSTMSOFT which has 73.0. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1362.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Test results on different datasets. The BiLSTM results reported by other papers are drawn from Zhou et al. (2016),† Wang et al. (2017),‡ and Williams et al. (2017).∗ All of our test results are obtained from the GLUE benchmark website." ], "reasoning": "To verify this statement, compare the accuracy values in the 'MNLI-m Acc' column. The highest accuracy is 86.7 by BERTLARGE, not Distilled BiLSTMSOFT which has 73.0.", "statement": "Distilled BiLSTMSOFT has the highest accuracy on the MNLI-m dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: First filter performance on Leipzig + SwissCrawl corpora'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The BERT filter achieves an F1-score of 99.8% on the GSW test set, but its performance is expected to decrease on Twitter data due to the nature of short and informal messages.We first evaluate our BERT filter on the test set of the first filter (Leipzig corpora + SwissCrawl). The filter has an F1-score of 99.8% on the GSW test set. However, when this model is applied to Twitter data, we expect a decrease in performance due to having short and also informal messages." } ], "images": [ "scigen/train/1363.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: First filter performance on Leipzig + SwissCrawl corpora" ], "reasoning": "The BERT filter achieves an F1-score of 99.8% on the GSW test set, but its performance is expected to decrease on Twitter data due to the nature of short and informal messages.", "description": "We first evaluate our BERT filter on the test set of the first filter (Leipzig corpora + SwissCrawl). The filter has an F1-score of 99.8% on the GSW test set. However, when this model is applied to Twitter data, we expect a decrease in performance due to having short and also informal messages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: First filter performance on Leipzig + SwissCrawl corpora', unfold the steps to solve the question given next. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nQuestion: What is the F1-score of the BERT filter on the GSW-like test set?\n" }, { "role": "assistant", "content": "The table shows the F1-score for various languages. The F1-score for the GSW-like test set is 0.9959. So, the answer is {\"answer\": \"0.9959\"}" } ], "images": [ "scigen/train/1363.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: First filter performance on Leipzig + SwissCrawl corpora" ], "reasoning": "The table shows the F1-score for various languages. The F1-score for the GSW-like test set is 0.9959.", "question": "What is the F1-score of the BERT filter on the GSW-like test set?", "answer": "So, the answer is {\"answer\": \"0.9959\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The BERT filter has an F1-score of 0.9962 on the German test set., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe BERT filter has an F1-score of 0.9962 on the German test set.\n" }, { "role": "assistant", "content": "The table lists the F1-score for the German test set as 0.9962, which matches the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1363.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: First filter performance on Leipzig + SwissCrawl corpora" ], "reasoning": "The table lists the F1-score for the German test set as 0.9962, which matches the statement.", "statement": "The BERT filter has an F1-score of 0.9962 on the German test set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: Accuracy results on the Cora validation sets w.r.t each data split and each value m>1 of routing iterations for the transductive and inductive settings. Regarding Algorithm 1 when m>1, “Ours” denotes our update rule (bi←^u(i)vi⋅ev), while “Sab.” denotes the update rule (bi←bi+^u(i)vi⋅ev) originally used by sabour2017dynamic .'.\n" }, { "role": "assistant", "content": "The new update rule generally helps obtain higher performance for each setup, as shown by the higher scores in the 'Ours' columns compared to the 'Sab.' columns across different splits and values of m.Therefore, we propose to use the new update rule (bi←^u(i)vi⋅ev) as this new rule generally helps obtain a higher performance for each setup. w.r.t each data split and each value m>1 of routing iterations." } ], "images": [ "scigen/train/1364.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Accuracy results on the Cora validation sets w.r.t each data split and each value m>1 of routing iterations for the transductive and inductive settings. Regarding Algorithm 1 when m>1, “Ours” denotes our update rule (bi←^u(i)vi⋅ev), while “Sab.” denotes the update rule (bi←bi+^u(i)vi⋅ev) originally used by sabour2017dynamic ." ], "reasoning": "The new update rule generally helps obtain higher performance for each setup, as shown by the higher scores in the 'Ours' columns compared to the 'Sab.' columns across different splits and values of m.", "description": "Therefore, we propose to use the new update rule (bi←^u(i)vi⋅ev) as this new rule generally helps obtain a higher performance for each setup. w.r.t each data split and each value m>1 of routing iterations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 5: Accuracy results on the Cora validation sets w.r.t each data split and each value m>1 of routing iterations for the transductive and inductive settings. Regarding Algorithm 1 when m>1, “Ours” denotes our update rule (bi←^u(i)vi⋅ev), while “Sab.” denotes the update rule (bi←bi+^u(i)vi⋅ev) originally used by sabour2017dynamic .'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. \n\nQuestion: What is the overall performance score for the new update rule with Inductive m=7?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the overall performance score for the new update rule with Inductive m=7, look at the 'Overall' row under the 'Inductive m=7' column in the 'Ours' section. So, the answer is {\"answer\": \"69.10\"}." } ], "images": [ "scigen/train/1364.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Accuracy results on the Cora validation sets w.r.t each data split and each value m>1 of routing iterations for the transductive and inductive settings. Regarding Algorithm 1 when m>1, “Ours” denotes our update rule (bi←^u(i)vi⋅ev), while “Sab.” denotes the update rule (bi←bi+^u(i)vi⋅ev) originally used by sabour2017dynamic ." ], "reasoning": "To find the overall performance score for the new update rule with Inductive m=7, look at the 'Overall' row under the 'Inductive m=7' column in the 'Ours' section.", "question": "What is the overall performance score for the new update rule with Inductive m=7?", "answer": "So, the answer is {\"answer\": \"69.10\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The new update rule performs better than the baseline for Transductive m=5., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe new update rule performs better than the baseline for Transductive m=5.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Overall' scores for 'Ours' and 'Sab.' under the 'Transductive m=5' column. The 'Ours' score is 80.21, which is higher than the 'Sab.' score of 79.59. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1364.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Accuracy results on the Cora validation sets w.r.t each data split and each value m>1 of routing iterations for the transductive and inductive settings. Regarding Algorithm 1 when m>1, “Ours” denotes our update rule (bi←^u(i)vi⋅ev), while “Sab.” denotes the update rule (bi←bi+^u(i)vi⋅ev) originally used by sabour2017dynamic ." ], "reasoning": "To verify this statement, compare the 'Overall' scores for 'Ours' and 'Sab.' under the 'Transductive m=5' column. The 'Ours' score is 80.21, which is higher than the 'Sab.' score of 79.59.", "statement": "The new update rule performs better than the baseline for Transductive m=5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Multi-label classification results on PPI, POS and BlogCatalog. Baseline results are from duran2017learning .', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Caps2NE achieves the highest Macro-F1 scores on POS for all γ values, the highest Micro-F1 score on POS when γ=90%, and the highest F1 scores on PPI and BlogCatalog when γ=10% and γ=90%, respectively.PPI, POS and BlogCatalog. the Micro-F1 and Macro-F1 scores on test sets in the transductive setting. Especially, on POS, Caps2NE produces a new state-of-the-art Macro-F1 score for each of the three fraction values γ, the highest Micro-F1 score when γ=90% and the second highest Micro-F1 scores when γ∈{10%,50%}. Caps2NE obtains new highest F1 scores on PPI and BlogCatalog when γ=10% and γ=90%, respectively. On PPI, Caps2NE also achieves the highest Macro-F1 score when γ=50% and the second highest Micro-F1 score when γ=90%. On BlogCatalog, Caps2NE also achieves the second highest Macro-F1 scores when γ∈{10%,50%}." } ], "images": [ "scigen/train/1365.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Multi-label classification results on PPI, POS and BlogCatalog. Baseline results are from duran2017learning ." ], "reasoning": "Caps2NE achieves the highest Macro-F1 scores on POS for all γ values, the highest Micro-F1 score on POS when γ=90%, and the highest F1 scores on PPI and BlogCatalog when γ=10% and γ=90%, respectively.", "description": "PPI, POS and BlogCatalog. the Micro-F1 and Macro-F1 scores on test sets in the transductive setting. Especially, on POS, Caps2NE produces a new state-of-the-art Macro-F1 score for each of the three fraction values γ, the highest Micro-F1 score when γ=90% and the second highest Micro-F1 scores when γ∈{10%,50%}. Caps2NE obtains new highest F1 scores on PPI and BlogCatalog when γ=10% and γ=90%, respectively. On PPI, Caps2NE also achieves the highest Macro-F1 score when γ=50% and the second highest Micro-F1 score when γ=90%. On BlogCatalog, Caps2NE also achieves the second highest Macro-F1 scores when γ∈{10%,50%}.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 2: Multi-label classification results on PPI, POS and BlogCatalog. Baseline results are from duran2017learning .\nQuestion: What is the highest Macro-F1 score achieved by Caps2NE on BlogCatalog when γ=90%?\n" }, { "role": "assistant", "content": "From the table, locate the Macro-F1 scores for Caps2NE under BlogCatalog when γ=90%. The highest score is 26.63. So, the answer is {\"answer\": \"26.63\"}" } ], "images": [ "scigen/train/1365.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Multi-label classification results on PPI, POS and BlogCatalog. Baseline results are from duran2017learning ." ], "reasoning": "From the table, locate the Macro-F1 scores for Caps2NE under BlogCatalog when γ=90%. The highest score is 26.63.", "question": "What is the highest Macro-F1 score achieved by Caps2NE on BlogCatalog when γ=90%?", "answer": "So, the answer is {\"answer\": \"26.63\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nCaps2NE achieves the highest Micro-F1 score on PPI when γ=50%.\n" }, { "role": "assistant", "content": "From the table, the highest Micro-F1 score on PPI when γ=50% is 25.08, achieved by DeepWalk, not Caps2NE. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1365.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Multi-label classification results on PPI, POS and BlogCatalog. Baseline results are from duran2017learning ." ], "reasoning": "From the table, the highest Micro-F1 score on PPI when γ=50% is 25.08, achieved by DeepWalk, not Caps2NE.", "statement": "Caps2NE achieves the highest Micro-F1 score on PPI when γ=50%.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Accuracies on the Cora, Citeseer and Pubmed test sets in the transductive and inductive settings. “Un-sup.” denotes unsupervised graph embedding models, where the best score is in bold while the second best score is in underline. “Sup.” denotes supervised graph embedding models that additionally use node labels when training the models.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Caps2NE outperforms GCN and GAT in both transductive and inductive settings across Cora, Citeseer, and Pubmed datasets.Cora, Citeseer and Pubmed. BoW is evaluated by directly using the pre-computed bag-of-words feature vectors for learning the classifier, thus its performance is the same for both settings. DeepWalk+BoW concatenates the learned embedding of a node from DeepWalk with the pre-computed BoW feature vector of the node. Inductive setting: As previously discussed in the last paragraph in the “The proposed Caps2NE” section, we re-emphasize that our unsupervised Caps2NE model notably outperforms the supervised models GCN and GAT for this inductive setting. In particular, Caps2NE achieves 4+% absolute higher accuracies than both GCN and GAT on the three datasets, clearly showing the effectiveness of Caps2NE to infer embeddings for unseen nodes." } ], "images": [ "scigen/train/1366.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracies on the Cora, Citeseer and Pubmed test sets in the transductive and inductive settings. “Un-sup.” denotes unsupervised graph embedding models, where the best score is in bold while the second best score is in underline. “Sup.” denotes supervised graph embedding models that additionally use node labels when training the models." ], "reasoning": "Caps2NE outperforms GCN and GAT in both transductive and inductive settings across Cora, Citeseer, and Pubmed datasets.", "description": "Cora, Citeseer and Pubmed. BoW is evaluated by directly using the pre-computed bag-of-words feature vectors for learning the classifier, thus its performance is the same for both settings. DeepWalk+BoW concatenates the learned embedding of a node from DeepWalk with the pre-computed BoW feature vector of the node. Inductive setting: As previously discussed in the last paragraph in the “The proposed Caps2NE” section, we re-emphasize that our unsupervised Caps2NE model notably outperforms the supervised models GCN and GAT for this inductive setting. In particular, Caps2NE achieves 4+% absolute higher accuracies than both GCN and GAT on the three datasets, clearly showing the effectiveness of Caps2NE to infer embeddings for unseen nodes.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Accuracies on the Cora, Citeseer and Pubmed test sets in the transductive and inductive settings. “Un-sup.” denotes unsupervised graph embedding models, where the best score is in bold while the second best score is in underline. “Sup.” denotes supervised graph embedding models that additionally use node labels when training the models.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. \n\nQuestion: Which model achieves the highest accuracy on the Pubmed dataset in the inductive setting?\n\nYour Solution:\n" }, { "role": "assistant", "content": "In the inductive setting, the highest accuracy on the Pubmed dataset is achieved by the model with the highest value in the Pubmed column. Our Caps2NE has the highest accuracy of 78.98. So, the answer is {\"answer\": \"Our Caps2NE\"}." } ], "images": [ "scigen/train/1366.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracies on the Cora, Citeseer and Pubmed test sets in the transductive and inductive settings. “Un-sup.” denotes unsupervised graph embedding models, where the best score is in bold while the second best score is in underline. “Sup.” denotes supervised graph embedding models that additionally use node labels when training the models." ], "reasoning": "In the inductive setting, the highest accuracy on the Pubmed dataset is achieved by the model with the highest value in the Pubmed column. Our Caps2NE has the highest accuracy of 78.98.", "question": "Which model achieves the highest accuracy on the Pubmed dataset in the inductive setting?", "answer": "So, the answer is {\"answer\": \"Our Caps2NE\"}." }, { "messages": [ { "role": "user", "content": "GCN achieves higher accuracy than GAT on the Citeseer dataset in the transductive setting. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nGCN achieves higher accuracy than GAT on the Citeseer dataset in the transductive setting.\n" }, { "role": "assistant", "content": "In the transductive setting, GCN has an accuracy of 79.59 on the Citeseer dataset, while GAT has an accuracy of 78.00. Therefore, GCN achieves higher accuracy than GAT. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1366.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracies on the Cora, Citeseer and Pubmed test sets in the transductive and inductive settings. “Un-sup.” denotes unsupervised graph embedding models, where the best score is in bold while the second best score is in underline. “Sup.” denotes supervised graph embedding models that additionally use node labels when training the models." ], "reasoning": "In the transductive setting, GCN has an accuracy of 79.59 on the Citeseer dataset, while GAT has an accuracy of 78.00. Therefore, GCN achieves higher accuracy than GAT.", "statement": "GCN achieves higher accuracy than GAT on the Citeseer dataset in the transductive setting.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table S4: Pearson correlation coefficients for translation-stable words for all language pairs. All p-values are <10−118. These values are included in Fig. 2 and reproduced here for to facilitate comparison.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The study explores the positivity bias in human language by analyzing 24 corpora across 10 languages, focusing on the most frequently used words and their reception.July 16, 2020 \\newmdenv [linecolor=lightgrey,backgroundcolor= white]myframe Human language—our great social technology—reflects that which it describes through the stories it allows to be told, and us, the tellers of those stories. In 1969, Boucher and Osgood framed the Pollyanna Hypothesis: a hypothetical, universal positivity bias in human communication Boucher and Osgood From a selection of small-scale, cross-cultural studies, they marshaled evidence that positive words are likely more prevalent, more meaningful, more diversely used, and more readily learned. However, in being far from an exhaustive, data-driven analysis of language—the approach we take here—their findings could only be regarded as suggestive. To deeply explore the positivity of human language, we constructed 24 corpora spread across 10 languages (see Supplementary Online Material). Our global coverage of linguistically and culturally diverse languages includes English, Spanish, French, German, Brazilian Portuguese, Korean, Chinese (Simplified), Russian, Indonesian, and Arabic. The sources of our corpora are similarly broad, Our work here greatly expands upon our earlier study of English alone, where we found strong evidence for a usage-invariant positivity bias Kloumann et al. We address the social nature of language in two important ways: (1) we focus on the words people most commonly use, and (2) we measure how those same words are received by individuals. We take word usage frequency as the primary organizing measure of a word’s importance. Such a data-driven approach is crucial for both understanding the structure of language and for creating linguistic instruments for principled measurements Dodds et al. By contrast, earlier studies focusing on meaning and emotion have used ‘expert’ generated word lists, and these fail to statistically match frequency distributions of natural language Osgood et al. ; Stone et al. , confounding attempts to make claims about language in general. For each of our corpora we selected between 5,000 to 10,000 of the most frequently used words, choosing the exact numbers so that we obtained approximately 10,000 words for each language. (see also Supplementary Online Material). This happy-sad semantic differential Osgood et al. Participants were restricted to certain regions or countries (for example, Portuguese was rated by residents of Brazil). Overall, we collected 50 ratings per word for a total of around 5,000,000 individual human assessments, and we provide all data sets as part of the Supplementary Online Material." } ], "images": [ "scigen/train/1367.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table S4: Pearson correlation coefficients for translation-stable words for all language pairs. All p-values are <10−118. These values are included in Fig. 2 and reproduced here for to facilitate comparison." ], "reasoning": "The study explores the positivity bias in human language by analyzing 24 corpora across 10 languages, focusing on the most frequently used words and their reception.", "description": "July 16, 2020 \\newmdenv [linecolor=lightgrey,backgroundcolor= white]myframe Human language—our great social technology—reflects that which it describes through the stories it allows to be told, and us, the tellers of those stories. In 1969, Boucher and Osgood framed the Pollyanna Hypothesis: a hypothetical, universal positivity bias in human communication Boucher and Osgood From a selection of small-scale, cross-cultural studies, they marshaled evidence that positive words are likely more prevalent, more meaningful, more diversely used, and more readily learned. However, in being far from an exhaustive, data-driven analysis of language—the approach we take here—their findings could only be regarded as suggestive. To deeply explore the positivity of human language, we constructed 24 corpora spread across 10 languages (see Supplementary Online Material). Our global coverage of linguistically and culturally diverse languages includes English, Spanish, French, German, Brazilian Portuguese, Korean, Chinese (Simplified), Russian, Indonesian, and Arabic. The sources of our corpora are similarly broad, Our work here greatly expands upon our earlier study of English alone, where we found strong evidence for a usage-invariant positivity bias Kloumann et al. We address the social nature of language in two important ways: (1) we focus on the words people most commonly use, and (2) we measure how those same words are received by individuals. We take word usage frequency as the primary organizing measure of a word’s importance. Such a data-driven approach is crucial for both understanding the structure of language and for creating linguistic instruments for principled measurements Dodds et al. By contrast, earlier studies focusing on meaning and emotion have used ‘expert’ generated word lists, and these fail to statistically match frequency distributions of natural language Osgood et al. ; Stone et al. , confounding attempts to make claims about language in general. For each of our corpora we selected between 5,000 to 10,000 of the most frequently used words, choosing the exact numbers so that we obtained approximately 10,000 words for each language. (see also Supplementary Online Material). This happy-sad semantic differential Osgood et al. Participants were restricted to certain regions or countries (for example, Portuguese was rated by residents of Brazil). Overall, we collected 50 ratings per word for a total of around 5,000,000 individual human assessments, and we provide all data sets as part of the Supplementary Online Material.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table S4: Pearson correlation coefficients for translation-stable words for all language pairs. All p-values are <10−118. These values are included in Fig. 2 and reproduced here for to facilitate comparison.', unfold the question presented below, detailing every step of your calculation. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich language has the highest similarity score with English?\n" }, { "role": "assistant", "content": "By examining the row corresponding to English, we find that the highest similarity score is with Indonesian, which is 0.88. So, the answer is {\"answer\": \"Indonesian\"}." } ], "images": [ "scigen/train/1367.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table S4: Pearson correlation coefficients for translation-stable words for all language pairs. All p-values are <10−118. These values are included in Fig. 2 and reproduced here for to facilitate comparison." ], "reasoning": "By examining the row corresponding to English, we find that the highest similarity score is with Indonesian, which is 0.88.", "question": "Which language has the highest similarity score with English?", "answer": "So, the answer is {\"answer\": \"Indonesian\"}." }, { "messages": [ { "role": "user", "content": "Portuguese has a higher similarity score with Spanish than with French. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nPortuguese has a higher similarity score with Spanish than with French.\n" }, { "role": "assistant", "content": "The table shows that the similarity score between Portuguese and Spanish is 0.89, while the similarity score between Portuguese and French is 0.84. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1367.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table S4: Pearson correlation coefficients for translation-stable words for all language pairs. All p-values are <10−118. These values are included in Fig. 2 and reproduced here for to facilitate comparison." ], "reasoning": "The table shows that the similarity score between Portuguese and Spanish is 0.89, while the similarity score between Portuguese and French is 0.84. Therefore, the statement is supported.", "statement": "Portuguese has a higher similarity score with Spanish than with French.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table S5: Spearman correlation coefficients for translation-stable words. All p-values are <10−82.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The study explores the positivity bias in human language by analyzing 24 corpora across 10 languages, focusing on the most frequently used words and their reception by individuals.July 16, 2020 \\newmdenv [linecolor=lightgrey,backgroundcolor= white]myframe Human language—our great social technology—reflects that which it describes through the stories it allows to be told, and us, the tellers of those stories. In 1969, Boucher and Osgood framed the Pollyanna Hypothesis: a hypothetical, universal positivity bias in human communication Boucher and Osgood From a selection of small-scale, cross-cultural studies, they marshaled evidence that positive words are likely more prevalent, more meaningful, more diversely used, and more readily learned. However, in being far from an exhaustive, data-driven analysis of language—the approach we take here—their findings could only be regarded as suggestive. To deeply explore the positivity of human language, we constructed 24 corpora spread across 10 languages (see Supplementary Online Material). Our global coverage of linguistically and culturally diverse languages includes English, Spanish, French, German, Brazilian Portuguese, Korean, Chinese (Simplified), Russian, Indonesian, and Arabic. The sources of our corpora are similarly broad, Our work here greatly expands upon our earlier study of English alone, where we found strong evidence for a usage-invariant positivity bias Kloumann et al. We address the social nature of language in two important ways: (1) we focus on the words people most commonly use, and (2) we measure how those same words are received by individuals. We take word usage frequency as the primary organizing measure of a word’s importance. Such a data-driven approach is crucial for both understanding the structure of language and for creating linguistic instruments for principled measurements Dodds et al. By contrast, earlier studies focusing on meaning and emotion have used ‘expert’ generated word lists, and these fail to statistically match frequency distributions of natural language Osgood et al. ; Stone et al. , confounding attempts to make claims about language in general. For each of our corpora we selected between 5,000 to 10,000 of the most frequently used words, choosing the exact numbers so that we obtained approximately 10,000 words for each language. (see also Supplementary Online Material). This happy-sad semantic differential Osgood et al. Participants were restricted to certain regions or countries (for example, Portuguese was rated by residents of Brazil). Overall, we collected 50 ratings per word for a total of around 5,000,000 individual human assessments, and we provide all data sets as part of the Supplementary Online Material." } ], "images": [ "scigen/train/1368.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table S5: Spearman correlation coefficients for translation-stable words. All p-values are <10−82." ], "reasoning": "The study explores the positivity bias in human language by analyzing 24 corpora across 10 languages, focusing on the most frequently used words and their reception by individuals.", "description": "July 16, 2020 \\newmdenv [linecolor=lightgrey,backgroundcolor= white]myframe Human language—our great social technology—reflects that which it describes through the stories it allows to be told, and us, the tellers of those stories. In 1969, Boucher and Osgood framed the Pollyanna Hypothesis: a hypothetical, universal positivity bias in human communication Boucher and Osgood From a selection of small-scale, cross-cultural studies, they marshaled evidence that positive words are likely more prevalent, more meaningful, more diversely used, and more readily learned. However, in being far from an exhaustive, data-driven analysis of language—the approach we take here—their findings could only be regarded as suggestive. To deeply explore the positivity of human language, we constructed 24 corpora spread across 10 languages (see Supplementary Online Material). Our global coverage of linguistically and culturally diverse languages includes English, Spanish, French, German, Brazilian Portuguese, Korean, Chinese (Simplified), Russian, Indonesian, and Arabic. The sources of our corpora are similarly broad, Our work here greatly expands upon our earlier study of English alone, where we found strong evidence for a usage-invariant positivity bias Kloumann et al. We address the social nature of language in two important ways: (1) we focus on the words people most commonly use, and (2) we measure how those same words are received by individuals. We take word usage frequency as the primary organizing measure of a word’s importance. Such a data-driven approach is crucial for both understanding the structure of language and for creating linguistic instruments for principled measurements Dodds et al. By contrast, earlier studies focusing on meaning and emotion have used ‘expert’ generated word lists, and these fail to statistically match frequency distributions of natural language Osgood et al. ; Stone et al. , confounding attempts to make claims about language in general. For each of our corpora we selected between 5,000 to 10,000 of the most frequently used words, choosing the exact numbers so that we obtained approximately 10,000 words for each language. (see also Supplementary Online Material). This happy-sad semantic differential Osgood et al. Participants were restricted to certain regions or countries (for example, Portuguese was rated by residents of Brazil). Overall, we collected 50 ratings per word for a total of around 5,000,000 individual human assessments, and we provide all data sets as part of the Supplementary Online Material.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table S5: Spearman correlation coefficients for translation-stable words. All p-values are <10−82.'. Solve the following question based on the table.\n\nQuestion: Which language has the highest similarity score with English?\nLet's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the language with the highest similarity score with English, look at the row corresponding to English and identify the highest value excluding the self-similarity score of 1.00. So, the answer is {\"answer\": \"Portuguese\"}." } ], "images": [ "scigen/train/1368.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table S5: Spearman correlation coefficients for translation-stable words. All p-values are <10−82." ], "reasoning": "To find the language with the highest similarity score with English, look at the row corresponding to English and identify the highest value excluding the self-similarity score of 1.00.", "question": "Which language has the highest similarity score with English?", "answer": "So, the answer is {\"answer\": \"Portuguese\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nFrench has a higher similarity score with Spanish than with German.\n" }, { "role": "assistant", "content": "To verify this statement, compare the similarity scores of French with Spanish and German. The score for French with Spanish is 0.81, and with German is 0.80. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1368.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table S5: Spearman correlation coefficients for translation-stable words. All p-values are <10−82." ], "reasoning": "To verify this statement, compare the similarity scores of French with Spanish and German. The score for French with Spanish is 0.81, and with German is 0.80.", "statement": "French has a higher similarity score with Spanish than with German.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table S6: Pearson correlation coefficients and p-values, Spearman correlation coefficients and p-values, and linear fit coefficients, for average word happiness havg as a function of word usage frequency rank r. We use the fit is havg=αr+β for the most common 5000 words in each corpora, determining α and β via ordinary least squares, and order languages by the median of their average word happiness scores (descending). We note that stemming of words may affect these estimates.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The study explores the positivity bias in human language by analyzing 24 corpora across 10 languages, focusing on the most frequently used words and their reception.July 16, 2020 \\newmdenv [linecolor=lightgrey,backgroundcolor= white]myframe Human language—our great social technology—reflects that which it describes through the stories it allows to be told, and us, the tellers of those stories. In 1969, Boucher and Osgood framed the Pollyanna Hypothesis: a hypothetical, universal positivity bias in human communication Boucher and Osgood From a selection of small-scale, cross-cultural studies, they marshaled evidence that positive words are likely more prevalent, more meaningful, more diversely used, and more readily learned. However, in being far from an exhaustive, data-driven analysis of language—the approach we take here—their findings could only be regarded as suggestive. To deeply explore the positivity of human language, we constructed 24 corpora spread across 10 languages (see Supplementary Online Material). Our global coverage of linguistically and culturally diverse languages includes English, Spanish, French, German, Brazilian Portuguese, Korean, Chinese (Simplified), Russian, Indonesian, and Arabic. The sources of our corpora are similarly broad, Our work here greatly expands upon our earlier study of English alone, where we found strong evidence for a usage-invariant positivity bias Kloumann et al. We address the social nature of language in two important ways: (1) we focus on the words people most commonly use, and (2) we measure how those same words are received by individuals. We take word usage frequency as the primary organizing measure of a word’s importance. Such a data-driven approach is crucial for both understanding the structure of language and for creating linguistic instruments for principled measurements Dodds et al. By contrast, earlier studies focusing on meaning and emotion have used ‘expert’ generated word lists, and these fail to statistically match frequency distributions of natural language Osgood et al. ; Stone et al. , confounding attempts to make claims about language in general. For each of our corpora we selected between 5,000 to 10,000 of the most frequently used words, choosing the exact numbers so that we obtained approximately 10,000 words for each language. (see also Supplementary Online Material). This happy-sad semantic differential Osgood et al. Participants were restricted to certain regions or countries (for example, Portuguese was rated by residents of Brazil). Overall, we collected 50 ratings per word for a total of around 5,000,000 individual human assessments, and we provide all data sets as part of the Supplementary Online Material." } ], "images": [ "scigen/train/1369.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table S6: Pearson correlation coefficients and p-values, Spearman correlation coefficients and p-values, and linear fit coefficients, for average word happiness havg as a function of word usage frequency rank r. We use the fit is havg=αr+β for the most common 5000 words in each corpora, determining α and β via ordinary least squares, and order languages by the median of their average word happiness scores (descending). We note that stemming of words may affect these estimates." ], "reasoning": "The study explores the positivity bias in human language by analyzing 24 corpora across 10 languages, focusing on the most frequently used words and their reception.", "description": "July 16, 2020 \\newmdenv [linecolor=lightgrey,backgroundcolor= white]myframe Human language—our great social technology—reflects that which it describes through the stories it allows to be told, and us, the tellers of those stories. In 1969, Boucher and Osgood framed the Pollyanna Hypothesis: a hypothetical, universal positivity bias in human communication Boucher and Osgood From a selection of small-scale, cross-cultural studies, they marshaled evidence that positive words are likely more prevalent, more meaningful, more diversely used, and more readily learned. However, in being far from an exhaustive, data-driven analysis of language—the approach we take here—their findings could only be regarded as suggestive. To deeply explore the positivity of human language, we constructed 24 corpora spread across 10 languages (see Supplementary Online Material). Our global coverage of linguistically and culturally diverse languages includes English, Spanish, French, German, Brazilian Portuguese, Korean, Chinese (Simplified), Russian, Indonesian, and Arabic. The sources of our corpora are similarly broad, Our work here greatly expands upon our earlier study of English alone, where we found strong evidence for a usage-invariant positivity bias Kloumann et al. We address the social nature of language in two important ways: (1) we focus on the words people most commonly use, and (2) we measure how those same words are received by individuals. We take word usage frequency as the primary organizing measure of a word’s importance. Such a data-driven approach is crucial for both understanding the structure of language and for creating linguistic instruments for principled measurements Dodds et al. By contrast, earlier studies focusing on meaning and emotion have used ‘expert’ generated word lists, and these fail to statistically match frequency distributions of natural language Osgood et al. ; Stone et al. , confounding attempts to make claims about language in general. For each of our corpora we selected between 5,000 to 10,000 of the most frequently used words, choosing the exact numbers so that we obtained approximately 10,000 words for each language. (see also Supplementary Online Material). This happy-sad semantic differential Osgood et al. Participants were restricted to certain regions or countries (for example, Portuguese was rated by residents of Brazil). Overall, we collected 50 ratings per word for a total of around 5,000,000 individual human assessments, and we provide all data sets as part of the Supplementary Online Material.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table S6: Pearson correlation coefficients and p-values, Spearman correlation coefficients and p-values, and linear fit coefficients, for average word happiness havg as a function of word usage frequency rank r. We use the fit is havg=αr+β for the most common 5000 words in each corpora, determining α and β via ordinary least squares, and order languages by the median of their average word happiness scores (descending). We note that stemming of words may affect these estimates.', solve the subsequent question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich language and corpus combination has the highest positive pp value?\n" }, { "role": "assistant", "content": "To find the highest positive pp value, we need to look at the pp column and identify the highest positive value. The highest positive pp value is +0.030 for Russian: Google Books. So, the answer is {\"answer\": \"Russian: Google Books\"}." } ], "images": [ "scigen/train/1369.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table S6: Pearson correlation coefficients and p-values, Spearman correlation coefficients and p-values, and linear fit coefficients, for average word happiness havg as a function of word usage frequency rank r. We use the fit is havg=αr+β for the most common 5000 words in each corpora, determining α and β via ordinary least squares, and order languages by the median of their average word happiness scores (descending). We note that stemming of words may affect these estimates." ], "reasoning": "To find the highest positive pp value, we need to look at the pp column and identify the highest positive value. The highest positive pp value is +0.030 for Russian: Google Books.", "question": "Which language and corpus combination has the highest positive pp value?", "answer": "So, the answer is {\"answer\": \"Russian: Google Books\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe pp value for English: Google Books is negative.\n" }, { "role": "assistant", "content": "The table shows that the pp value for English: Google Books is -0.042, which is indeed negative. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1369.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table S6: Pearson correlation coefficients and p-values, Spearman correlation coefficients and p-values, and linear fit coefficients, for average word happiness havg as a function of word usage frequency rank r. We use the fit is havg=αr+β for the most common 5000 words in each corpora, determining α and β via ordinary least squares, and order languages by the median of their average word happiness scores (descending). We note that stemming of words may affect these estimates." ], "reasoning": "The table shows that the pp value for English: Google Books is -0.042, which is indeed negative.", "statement": "The pp value for English: Google Books is negative.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table S7: Pearson correlation coefficients and p-values, Spearman correlation coefficients and p-values, and linear fit coefficients for standard deviation of word happiness hstd as a function of word usage frequency rank r. We consider the fit is hstd=αr+β for the most common 5000 words in each corpora, determining α and β via ordinary least squares, and order corpora according to their emotional variance (descending).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The study expanded on previous research by analyzing 24 corpora across 10 languages to explore the positivity bias in human language, focusing on the most frequently used words and their reception.July 16, 2020 \\newmdenv [linecolor=lightgrey,backgroundcolor= white]myframe Human language—our great social technology—reflects that which it describes through the stories it allows to be told, and us, the tellers of those stories. In 1969, Boucher and Osgood framed the Pollyanna Hypothesis: a hypothetical, universal positivity bias in human communication Boucher and Osgood From a selection of small-scale, cross-cultural studies, they marshaled evidence that positive words are likely more prevalent, more meaningful, more diversely used, and more readily learned. However, in being far from an exhaustive, data-driven analysis of language—the approach we take here—their findings could only be regarded as suggestive. To deeply explore the positivity of human language, we constructed 24 corpora spread across 10 languages (see Supplementary Online Material). Our global coverage of linguistically and culturally diverse languages includes English, Spanish, French, German, Brazilian Portuguese, Korean, Chinese (Simplified), Russian, Indonesian, and Arabic. The sources of our corpora are similarly broad, Our work here greatly expands upon our earlier study of English alone, where we found strong evidence for a usage-invariant positivity bias Kloumann et al. We address the social nature of language in two important ways: (1) we focus on the words people most commonly use, and (2) we measure how those same words are received by individuals. We take word usage frequency as the primary organizing measure of a word’s importance. Such a data-driven approach is crucial for both understanding the structure of language and for creating linguistic instruments for principled measurements Dodds et al. By contrast, earlier studies focusing on meaning and emotion have used ‘expert’ generated word lists, and these fail to statistically match frequency distributions of natural language Osgood et al. ; Stone et al. , confounding attempts to make claims about language in general. For each of our corpora we selected between 5,000 to 10,000 of the most frequently used words, choosing the exact numbers so that we obtained approximately 10,000 words for each language. (see also Supplementary Online Material). This happy-sad semantic differential Osgood et al. Participants were restricted to certain regions or countries (for example, Portuguese was rated by residents of Brazil). Overall, we collected 50 ratings per word for a total of around 5,000,000 individual human assessments, and we provide all data sets as part of the Supplementary Online Material." } ], "images": [ "scigen/train/1370.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table S7: Pearson correlation coefficients and p-values, Spearman correlation coefficients and p-values, and linear fit coefficients for standard deviation of word happiness hstd as a function of word usage frequency rank r. We consider the fit is hstd=αr+β for the most common 5000 words in each corpora, determining α and β via ordinary least squares, and order corpora according to their emotional variance (descending)." ], "reasoning": "The study expanded on previous research by analyzing 24 corpora across 10 languages to explore the positivity bias in human language, focusing on the most frequently used words and their reception.", "description": "July 16, 2020 \\newmdenv [linecolor=lightgrey,backgroundcolor= white]myframe Human language—our great social technology—reflects that which it describes through the stories it allows to be told, and us, the tellers of those stories. In 1969, Boucher and Osgood framed the Pollyanna Hypothesis: a hypothetical, universal positivity bias in human communication Boucher and Osgood From a selection of small-scale, cross-cultural studies, they marshaled evidence that positive words are likely more prevalent, more meaningful, more diversely used, and more readily learned. However, in being far from an exhaustive, data-driven analysis of language—the approach we take here—their findings could only be regarded as suggestive. To deeply explore the positivity of human language, we constructed 24 corpora spread across 10 languages (see Supplementary Online Material). Our global coverage of linguistically and culturally diverse languages includes English, Spanish, French, German, Brazilian Portuguese, Korean, Chinese (Simplified), Russian, Indonesian, and Arabic. The sources of our corpora are similarly broad, Our work here greatly expands upon our earlier study of English alone, where we found strong evidence for a usage-invariant positivity bias Kloumann et al. We address the social nature of language in two important ways: (1) we focus on the words people most commonly use, and (2) we measure how those same words are received by individuals. We take word usage frequency as the primary organizing measure of a word’s importance. Such a data-driven approach is crucial for both understanding the structure of language and for creating linguistic instruments for principled measurements Dodds et al. By contrast, earlier studies focusing on meaning and emotion have used ‘expert’ generated word lists, and these fail to statistically match frequency distributions of natural language Osgood et al. ; Stone et al. , confounding attempts to make claims about language in general. For each of our corpora we selected between 5,000 to 10,000 of the most frequently used words, choosing the exact numbers so that we obtained approximately 10,000 words for each language. (see also Supplementary Online Material). This happy-sad semantic differential Osgood et al. Participants were restricted to certain regions or countries (for example, Portuguese was rated by residents of Brazil). Overall, we collected 50 ratings per word for a total of around 5,000,000 individual human assessments, and we provide all data sets as part of the Supplementary Online Material.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table S7: Pearson correlation coefficients and p-values, Spearman correlation coefficients and p-values, and linear fit coefficients for standard deviation of word happiness hstd as a function of word usage frequency rank r. We consider the fit is hstd=αr+β for the most common 5000 words in each corpora, determining α and β via ordinary least squares, and order corpora according to their emotional variance (descending).', answer the question below by showing each progressive step toward the answer. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich corpus has the highest pp value?\n" }, { "role": "assistant", "content": "To find the corpus with the highest pp value, we need to compare the pp values listed in the table. The highest pp value is +0.187 for the Russian: Google Books corpus. So, the answer is {\"answer\": \"Russian: Google Books\"}." } ], "images": [ "scigen/train/1370.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table S7: Pearson correlation coefficients and p-values, Spearman correlation coefficients and p-values, and linear fit coefficients for standard deviation of word happiness hstd as a function of word usage frequency rank r. We consider the fit is hstd=αr+β for the most common 5000 words in each corpora, determining α and β via ordinary least squares, and order corpora according to their emotional variance (descending)." ], "reasoning": "To find the corpus with the highest pp value, we need to compare the pp values listed in the table. The highest pp value is +0.187 for the Russian: Google Books corpus.", "question": "Which corpus has the highest pp value?", "answer": "So, the answer is {\"answer\": \"Russian: Google Books\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Korean: Movie subtitles corpus has the highest ps value. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Korean: Movie subtitles corpus has the highest ps value.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the ps values listed in the table. The highest ps value is +0.185 for the Korean: Movie subtitles corpus, which supports the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1370.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table S7: Pearson correlation coefficients and p-values, Spearman correlation coefficients and p-values, and linear fit coefficients for standard deviation of word happiness hstd as a function of word usage frequency rank r. We consider the fit is hstd=αr+β for the most common 5000 words in each corpora, determining α and β via ordinary least squares, and order corpora according to their emotional variance (descending)." ], "reasoning": "To verify this statement, we need to compare the ps values listed in the table. The highest ps value is +0.185 for the Korean: Movie subtitles corpus, which supports the statement.", "statement": "The Korean: Movie subtitles corpus has the highest ps value.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Investigating the types of strong precision errors of BERT+Entity trained in Setting I on CoNLL’03/AIDA (testa) on 100 randomly sampled strong precision errors from the validation dataset.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The largest source of errors was no prediction, followed by no obvious reason and gold annotation wrong.The largest source of errors was that BERT+Entity did predict Nil instead of an entity. We hypothesized that most of the no prediction errors are because those entities have only a low frequency in the training data, i.e. this could be solved by increasing the model size and improving the training time. Another source of error we observed was that the context size was too small due to the fragment size. A surprisingly positive result from the error analysis was that in only 3% a wrong span caused the error. Motivated by the observations we devised the follow-up experiment Setting II (see" } ], "images": [ "scigen/train/1371.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Investigating the types of strong precision errors of BERT+Entity trained in Setting I on CoNLL’03/AIDA (testa) on 100 randomly sampled strong precision errors from the validation dataset." ], "reasoning": "The largest source of errors was no prediction, followed by no obvious reason and gold annotation wrong.", "description": "The largest source of errors was that BERT+Entity did predict Nil instead of an entity. We hypothesized that most of the no prediction errors are because those entities have only a low frequency in the training data, i.e. this could be solved by increasing the model size and improving the training time. Another source of error we observed was that the context size was too small due to the fragment size. A surprisingly positive result from the error analysis was that in only 3% a wrong span caused the error. Motivated by the observations we devised the follow-up experiment Setting II (see", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Investigating the types of strong precision errors of BERT+Entity trained in Setting I on CoNLL’03/AIDA (testa) on 100 randomly sampled strong precision errors from the validation dataset.\nQuestion: What are the top three reasons for errors in the BERT+Entity model?\nGive a detailed response to the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the top three reasons for errors are no prediction (57), no obvious reason (13), and gold annotation wrong (12). So, the answer is {\"answer\": \"no prediction, no obvious reason, gold annotation wrong\"}" } ], "images": [ "scigen/train/1371.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Investigating the types of strong precision errors of BERT+Entity trained in Setting I on CoNLL’03/AIDA (testa) on 100 randomly sampled strong precision errors from the validation dataset." ], "reasoning": "The table shows that the top three reasons for errors are no prediction (57), no obvious reason (13), and gold annotation wrong (12).", "question": "What are the top three reasons for errors in the BERT+Entity model?", "answer": "So, the answer is {\"answer\": \"no prediction, no obvious reason, gold annotation wrong\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe most common error in the BERT+Entity model was due to span error.\n" }, { "role": "assistant", "content": "The table shows that the most common error was no prediction (57), while span error only accounted for 3 errors. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1371.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Investigating the types of strong precision errors of BERT+Entity trained in Setting I on CoNLL’03/AIDA (testa) on 100 randomly sampled strong precision errors from the validation dataset." ], "reasoning": "The table shows that the most common error was no prediction (57), while span error only accounted for 3 errors.", "statement": "The most common error in the BERT+Entity model was due to span error.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Comparing entity linking results on CoNLL’03/AIDA. strong F1 and weak F1 denote InKB F1 scores. ED is Precision@1 for InKB. Kolitsas et al. (2018) also study a neural model, however, they only model MD and ED. The independent baseline shows how their model performs when they use mentions detected by Stanford NLP. In Frozen-BERT+Entity BERT is not trained and only the entity classifier on-top is trained.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Kolitsas et al. (2018) achieved higher F1 scores compared to their independent baseline, and BERT+Entity models in Setting II outperformed other configurations.we compare our results to the most recent results by Kolitsas et al. They also provide a baseline in which they show how their classifier performs when MD and ED are independent, i.e. linking mentions detected by Stanford NLP." } ], "images": [ "scigen/train/1372.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparing entity linking results on CoNLL’03/AIDA. strong F1 and weak F1 denote InKB F1 scores. ED is Precision@1 for InKB. Kolitsas et al. (2018) also study a neural model, however, they only model MD and ED. The independent baseline shows how their model performs when they use mentions detected by Stanford NLP. In Frozen-BERT+Entity BERT is not trained and only the entity classifier on-top is trained." ], "reasoning": "Kolitsas et al. (2018) achieved higher F1 scores compared to their independent baseline, and BERT+Entity models in Setting II outperformed other configurations.", "description": "we compare our results to the most recent results by Kolitsas et al. They also provide a baseline in which they show how their classifier performs when MD and ED are independent, i.e. linking mentions detected by Stanford NLP.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 1: Comparing entity linking results on CoNLL’03/AIDA. strong F1 and weak F1 denote InKB F1 scores. ED is Precision@1 for InKB. Kolitsas et al. (2018) also study a neural model, however, they only model MD and ED. The independent baseline shows how their model performs when they use mentions detected by Stanford NLP. In Frozen-BERT+Entity BERT is not trained and only the entity classifier on-top is trained.', solve the following question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model configuration achieved the highest AIDA/testa ED score?\n" }, { "role": "assistant", "content": "By comparing the AIDA/testa ED scores across all model configurations, the highest score is found in the Setting II BERT+Entity configuration with a score of 92.3. So, the answer is {\"answer\": \"92.3\"}." } ], "images": [ "scigen/train/1372.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparing entity linking results on CoNLL’03/AIDA. strong F1 and weak F1 denote InKB F1 scores. ED is Precision@1 for InKB. Kolitsas et al. (2018) also study a neural model, however, they only model MD and ED. The independent baseline shows how their model performs when they use mentions detected by Stanford NLP. In Frozen-BERT+Entity BERT is not trained and only the entity classifier on-top is trained." ], "reasoning": "By comparing the AIDA/testa ED scores across all model configurations, the highest score is found in the Setting II BERT+Entity configuration with a score of 92.3.", "question": "Which model configuration achieved the highest AIDA/testa ED score?", "answer": "So, the answer is {\"answer\": \"92.3\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The BERT model achieved the lowest AIDA/testb strong F1 score. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe BERT model achieved the lowest AIDA/testb strong F1 score.\n" }, { "role": "assistant", "content": "By examining the AIDA/testb strong F1 scores, the BERT model has the lowest score of 49.6 compared to other models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1372.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparing entity linking results on CoNLL’03/AIDA. strong F1 and weak F1 denote InKB F1 scores. ED is Precision@1 for InKB. Kolitsas et al. (2018) also study a neural model, however, they only model MD and ED. The independent baseline shows how their model performs when they use mentions detected by Stanford NLP. In Frozen-BERT+Entity BERT is not trained and only the entity classifier on-top is trained." ], "reasoning": "By examining the AIDA/testb strong F1 scores, the BERT model has the lowest score of 49.6 compared to other models.", "statement": "The BERT model achieved the lowest AIDA/testb strong F1 score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled '(a) SRU', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Pruning a large model consistently outperforms training a small dense model from scratch, and FLOP exceeds the performance of the unstructured AGP method at all tested sparsity levels.The results conform to our expectations and to the results reported in previous work – pruning a large model is consistently better than training a small dense model from scratch. Furthermore, FLOP exceeds the performance of the unstructured AGP method at all sparsity levels tested. For instance, we achieve a loss of 0.01 bits-per-character (BPC) (less than 1% relative performance) using 30% of the parameters, while the AGP baseline has a loss of 0.04 BPC. Again, both pruning methods significantly outperform training small dense models from scratch. Our method achieves results on par with the unstructured pruning baseline, being marginally worse at 90% sparsity but slightly better at 80% sparsity." } ], "images": [ "scigen/train/1373.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) SRU" ], "reasoning": "Pruning a large model consistently outperforms training a small dense model from scratch, and FLOP exceeds the performance of the unstructured AGP method at all tested sparsity levels.", "description": "The results conform to our expectations and to the results reported in previous work – pruning a large model is consistently better than training a small dense model from scratch. Furthermore, FLOP exceeds the performance of the unstructured AGP method at all sparsity levels tested. For instance, we achieve a loss of 0.01 bits-per-character (BPC) (less than 1% relative performance) using 30% of the parameters, while the AGP baseline has a loss of 0.04 BPC. Again, both pruning methods significantly outperform training small dense models from scratch. Our method achieves results on par with the unstructured pruning baseline, being marginally worse at 90% sparsity but slightly better at 80% sparsity.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled '(a) SRU' in mind, break down and resolve the question below step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the BPC loss achieved by the FLOP method using 30% of the parameters?\n" }, { "role": "assistant", "content": "The table shows that using 30% of the parameters (11M), the FLOP method achieves a BPC loss of 1.25. So, the answer is {\"answer\": \"1.25\"}." } ], "images": [ "scigen/train/1373.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) SRU" ], "reasoning": "The table shows that using 30% of the parameters (11M), the FLOP method achieves a BPC loss of 1.25.", "question": "What is the BPC loss achieved by the FLOP method using 30% of the parameters?", "answer": "So, the answer is {\"answer\": \"1.25\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe FLOP method achieves a BPC loss of 1.33 using 10% of the parameters.\n" }, { "role": "assistant", "content": "The table shows that using 10% of the parameters (4.2M), the FLOP method achieves a BPC loss of 1.33. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1373.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) SRU" ], "reasoning": "The table shows that using 10% of the parameters (4.2M), the FLOP method achieves a BPC loss of 1.33.", "statement": "The FLOP method achieves a BPC loss of 1.33 using 10% of the parameters.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title '(a) SRU', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Pruning a large model consistently outperforms training a small dense model from scratch, with FLOP exceeding AGP performance at all sparsity levels tested.The results conform to our expectations and to the results reported in previous work – pruning a large model is consistently better than training a small dense model from scratch. Furthermore, FLOP exceeds the performance of the unstructured AGP method at all sparsity levels tested. For instance, we achieve a loss of 0.01 bits-per-character (BPC) (less than 1% relative performance) using 30% of the parameters, while the AGP baseline has a loss of 0.04 BPC. Again, both pruning methods significantly outperform training small dense models from scratch. Our method achieves results on par with the unstructured pruning baseline, being marginally worse at 90% sparsity but slightly better at 80% sparsity." } ], "images": [ "scigen/train/1374.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) SRU" ], "reasoning": "Pruning a large model consistently outperforms training a small dense model from scratch, with FLOP exceeding AGP performance at all sparsity levels tested.", "description": "The results conform to our expectations and to the results reported in previous work – pruning a large model is consistently better than training a small dense model from scratch. Furthermore, FLOP exceeds the performance of the unstructured AGP method at all sparsity levels tested. For instance, we achieve a loss of 0.01 bits-per-character (BPC) (less than 1% relative performance) using 30% of the parameters, while the AGP baseline has a loss of 0.04 BPC. Again, both pruning methods significantly outperform training small dense models from scratch. Our method achieves results on par with the unstructured pruning baseline, being marginally worse at 90% sparsity but slightly better at 80% sparsity.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: (a) SRU\nQuestion: What is the performance of the Dense Model at 20% parameters?\n" }, { "role": "assistant", "content": "The table shows the performance of different models at various parameter levels. For 20% parameters, the Dense Model has a performance of 1.24. So, the answer is {\"answer\": \"1.24\"}." } ], "images": [ "scigen/train/1374.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) SRU" ], "reasoning": "The table shows the performance of different models at various parameter levels. For 20% parameters, the Dense Model has a performance of 1.24.", "question": "What is the performance of the Dense Model at 20% parameters?", "answer": "So, the answer is {\"answer\": \"1.24\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis FLOP performs better than AGP at 10% parameters. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nFLOP performs better than AGP at 10% parameters.\n" }, { "role": "assistant", "content": "The table shows that at 10% parameters, FLOP has a performance of 1.19, while AGP has a performance of 1.17. Therefore, FLOP does not perform better than AGP at 10% parameters. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1374.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) SRU" ], "reasoning": "The table shows that at 10% parameters, FLOP has a performance of 1.19, while AGP has a performance of 1.17. Therefore, FLOP does not perform better than AGP at 10% parameters.", "statement": "FLOP performs better than AGP at 10% parameters.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: Compression on downstream fine-tuning', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "We achieved nearly 99% performance retention while reducing parameters by 35%, with further sparsity possible by factorizing the embedding layer.We are able to conserve nearly 99% of the performance while reducing the number of parameters by 35%. Our target sparsity level is limited by the fact that the embedding layers consist of a significant portion of the remaining parameters. We believe that higher levels of sparsity could be obtained by also factorizing the embedding layer, similar to Lan et al." } ], "images": [ "scigen/train/1375.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Compression on downstream fine-tuning" ], "reasoning": "We achieved nearly 99% performance retention while reducing parameters by 35%, with further sparsity possible by factorizing the embedding layer.", "description": "We are able to conserve nearly 99% of the performance while reducing the number of parameters by 35%. Our target sparsity level is limited by the fact that the embedding layers consist of a significant portion of the remaining parameters. We believe that higher levels of sparsity could be obtained by also factorizing the embedding layer, similar to Lan et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 4: Compression on downstream fine-tuning'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}. \n\nQuestion: What is the average performance score when the number of parameters is reduced to 80M?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows that with 80M parameters, the average performance score is 89.48. So, the answer is {\"answer\": \"89.48\"}." } ], "images": [ "scigen/train/1375.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Compression on downstream fine-tuning" ], "reasoning": "The table shows that with 80M parameters, the average performance score is 89.48.", "question": "What is the average performance score when the number of parameters is reduced to 80M?", "answer": "So, the answer is {\"answer\": \"89.48\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe performance on the SST2 task is higher when the number of parameters is reduced to 80M.\n" }, { "role": "assistant", "content": "The table shows that the performance on the SST2 task is 92.43 with 125M parameters and 92.09 with 80M parameters, indicating a higher performance with 125M parameters. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1375.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Compression on downstream fine-tuning" ], "reasoning": "The table shows that the performance on the SST2 task is 92.43 with 125M parameters and 92.09 with 80M parameters, indicating a higher performance with 125M parameters.", "statement": "The performance on the SST2 task is higher when the number of parameters is reduced to 80M.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Ablation studies on different pretraining strategies. The retrieval modules (Recall@k) are tested on WebQuestions. ⋆Our reimplementation.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The clustering-based progressive training algorithm in ProQA significantly improves retrieval performance by 7-10% compared to the non-clustering version, and using generated data results in over 15% better performance than ORQA.To validate the sample efficiency of our method, we replicate the inverse-cloze pretraining approach from ORQA using the same amount of resource as we used while training our model, i.e., the same batch size and updates (90k). We also study the effect of the progressive training paradigm by pretraining the model with the same generated data but without the clustering-based sampling. We test the retrieval performance on WebQuestions before any finetuning. We use Recall@k as the evaluation metric, which measures how often the answer paragraphs appear in the top-k retrieval. With the limited results of the non-clutering version of our method, we validate the strong effect of the clustering-based progressive training algorithm, which brings 7-10% improvements on different metrics. We also report the results of early checkpoints of the non-clustering version. We can see that with the limited batch size, the improvements are diminishing as training goes on. This suggests the importance of introducing more challenging negative examples in the batch. Comparing the no-clustering version of our method and ORQA, we see that using the generated data results in much better retrieval performance (more than 15% improvements on all metrics)." } ], "images": [ "scigen/train/1376.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation studies on different pretraining strategies. The retrieval modules (Recall@k) are tested on WebQuestions. ⋆Our reimplementation." ], "reasoning": "The clustering-based progressive training algorithm in ProQA significantly improves retrieval performance by 7-10% compared to the non-clustering version, and using generated data results in over 15% better performance than ORQA.", "description": "To validate the sample efficiency of our method, we replicate the inverse-cloze pretraining approach from ORQA using the same amount of resource as we used while training our model, i.e., the same batch size and updates (90k). We also study the effect of the progressive training paradigm by pretraining the model with the same generated data but without the clustering-based sampling. We test the retrieval performance on WebQuestions before any finetuning. We use Recall@k as the evaluation metric, which measures how often the answer paragraphs appear in the top-k retrieval. With the limited results of the non-clutering version of our method, we validate the strong effect of the clustering-based progressive training algorithm, which brings 7-10% improvements on different metrics. We also report the results of early checkpoints of the non-clustering version. We can see that with the limited batch size, the improvements are diminishing as training goes on. This suggests the importance of introducing more challenging negative examples in the batch. Comparing the no-clustering version of our method and ORQA, we see that using the generated data results in much better retrieval performance (more than 15% improvements on all metrics).", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 3: Ablation studies on different pretraining strategies. The retrieval modules (Recall@k) are tested on WebQuestions. ⋆Our reimplementation.\nQuestion: What is the Recall@10 score for ProQA (90k) and how does it compare to ORQA (90k)?\n" }, { "role": "assistant", "content": "The Recall@10 score for ProQA (90k) is 56.7, which is significantly higher than the Recall@10 score for ORQA (90k), which is 29.6. So, the answer is {\"answer\": \"56.7\"}." } ], "images": [ "scigen/train/1376.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation studies on different pretraining strategies. The retrieval modules (Recall@k) are tested on WebQuestions. ⋆Our reimplementation." ], "reasoning": "The Recall@10 score for ProQA (90k) is 56.7, which is significantly higher than the Recall@10 score for ORQA (90k), which is 29.6.", "question": "What is the Recall@10 score for ProQA (90k) and how does it compare to ORQA (90k)?", "answer": "So, the answer is {\"answer\": \"56.7\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nProQA (90k) has a higher Recall@5 score than ORQA (90k).\n" }, { "role": "assistant", "content": "The table shows that ProQA (90k) has a Recall@5 score of 46.9, while ORQA (90k) has a Recall@5 score of 21.4, indicating that ProQA (90k) indeed has a higher Recall@5 score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1376.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation studies on different pretraining strategies. The retrieval modules (Recall@k) are tested on WebQuestions. ⋆Our reimplementation." ], "reasoning": "The table shows that ProQA (90k) has a Recall@5 score of 46.9, while ORQA (90k) has a Recall@5 score of 21.4, indicating that ProQA (90k) indeed has a higher Recall@5 score.", "statement": "ProQA (90k) has a higher Recall@5 score than ORQA (90k).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Resource comparison with SOTA models. EM scores are measured on NaturalQuestions-Open. batch size and updates all refer to the dense index pretraining. Note that REALM uses ORQA to initialize its parameters and we only report the numbers after ORQA initialization. ”80*8” indicates that we use a batch size of 80 and accumulate the gradients every 8 batches.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "REALM achieves significant improvements with the same parameter size as ProQA but requires more updates and ORQA initialization, making it computationally expensive.It is worth noting that stronger open-domain QA performance have been achieved with much larger pretrained model DBLP:journals/corr/abs-1910-10683, i.e., T5 roberts2020 or better designed pretraining paradigm combined with more updates, i.e., REALM guu2020realm. As T5 simply converts the QA problem into a sequence-to-sequence (decode answers after encoding questions) problem, it does not pretrain the corpus index. The disadvantage of this method is its inefficiency at inference time, as the model is orders of magnitude larger than the others. In contrast, REALM uses the same amount of parameters as our approach and achieves significant improvements. However, it relies on ORQA initialization and further pretraining updates, thus is still computational expensive at pretraining. As our method directly improves the ORQA pretraining, we believe our method is complementary to the REALM approach." } ], "images": [ "scigen/train/1377.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Resource comparison with SOTA models. EM scores are measured on NaturalQuestions-Open. batch size and updates all refer to the dense index pretraining. Note that REALM uses ORQA to initialize its parameters and we only report the numbers after ORQA initialization. ”80*8” indicates that we use a batch size of 80 and accumulate the gradients every 8 batches." ], "reasoning": "REALM achieves significant improvements with the same parameter size as ProQA but requires more updates and ORQA initialization, making it computationally expensive.", "description": "It is worth noting that stronger open-domain QA performance have been achieved with much larger pretrained model DBLP:journals/corr/abs-1910-10683, i.e., T5 roberts2020 or better designed pretraining paradigm combined with more updates, i.e., REALM guu2020realm. As T5 simply converts the QA problem into a sequence-to-sequence (decode answers after encoding questions) problem, it does not pretrain the corpus index. The disadvantage of this method is its inefficiency at inference time, as the model is orders of magnitude larger than the others. In contrast, REALM uses the same amount of parameters as our approach and achieves significant improvements. However, it relies on ORQA initialization and further pretraining updates, thus is still computational expensive at pretraining. As our method directly improves the ORQA pretraining, we believe our method is complementary to the REALM approach.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2: Resource comparison with SOTA models. EM scores are measured on NaturalQuestions-Open. batch size and updates all refer to the dense index pretraining. Note that REALM uses ORQA to initialize its parameters and we only report the numbers after ORQA initialization. ”80*8” indicates that we use a batch size of 80 and accumulate the gradients every 8 batches.', unfold the steps to solve the question given next. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nQuestion: Which method has the highest EM score and what is its model size?\n" }, { "role": "assistant", "content": "The table shows that REALM has the highest EM score of 40.4 and a model size of 330M. So, the answer is {\"answer\": \"REALM, 330M\"}" } ], "images": [ "scigen/train/1377.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Resource comparison with SOTA models. EM scores are measured on NaturalQuestions-Open. batch size and updates all refer to the dense index pretraining. Note that REALM uses ORQA to initialize its parameters and we only report the numbers after ORQA initialization. ”80*8” indicates that we use a batch size of 80 and accumulate the gradients every 8 batches." ], "reasoning": "The table shows that REALM has the highest EM score of 40.4 and a model size of 330M.", "question": "Which method has the highest EM score and what is its model size?", "answer": "So, the answer is {\"answer\": \"REALM, 330M\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nProQA has the smallest batch size among the methods listed.\n" }, { "role": "assistant", "content": "The table shows that ProQA has a batch size of 80*8, which is smaller than ORQA's 4096 and REALM's 512. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1377.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Resource comparison with SOTA models. EM scores are measured on NaturalQuestions-Open. batch size and updates all refer to the dense index pretraining. Note that REALM uses ORQA to initialize its parameters and we only report the numbers after ORQA initialization. ”80*8” indicates that we use a batch size of 80 and accumulate the gradients every 8 batches." ], "reasoning": "The table shows that ProQA has a batch size of 80*8, which is smaller than ORQA's 4096 and REALM's 512.", "statement": "ProQA has the smallest batch size among the methods listed.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 6: Average accuracy of each model using four different random seeds with only answer options as input, and the number of their common correct predictions.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "CEASY is the union of correctly predicted data points by different models and seeds, while CHARD is the difference between the testing set and CEASY.CEASY = (Cseed1GPT∩Cseed2GPT∩Cseed3GPT∩Cseed4GPT) (1) ∪(Cseed1GPT−2∩Cseed2GPT−2∩Cseed3GPT−2∩Cseed4GPT−2) ∪(Cseed1XLNet∩Cseed2XLNet∩Cseed3XLNet∩Cseed4XLNet) ∪(Cseed1RoBERTa∩Cseed2RoBERTa∩Cseed3RoBERTa∩Cseed4RoBERTa), CHARD = CTEST−CEASY, where Cseed1BERT denotes the set of data points which are predicted correctly by BERT\\textscBASE with seed 1, and similarly for the rest. Finally, we get 440 data points from the testing set CTEST and we denote this subset as EASY set CEASY and the other as HARD set CHARD." } ], "images": [ "scigen/train/1378.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Average accuracy of each model using four different random seeds with only answer options as input, and the number of their common correct predictions." ], "reasoning": "CEASY is the union of correctly predicted data points by different models and seeds, while CHARD is the difference between the testing set and CEASY.", "description": "CEASY = (Cseed1GPT∩Cseed2GPT∩Cseed3GPT∩Cseed4GPT) (1) ∪(Cseed1GPT−2∩Cseed2GPT−2∩Cseed3GPT−2∩Cseed4GPT−2) ∪(Cseed1XLNet∩Cseed2XLNet∩Cseed3XLNet∩Cseed4XLNet) ∪(Cseed1RoBERTa∩Cseed2RoBERTa∩Cseed3RoBERTa∩Cseed4RoBERTa), CHARD = CTEST−CEASY, where Cseed1BERT denotes the set of data points which are predicted correctly by BERT\\textscBASE with seed 1, and similarly for the rest. Finally, we get 440 data points from the testing set CTEST and we denote this subset as EASY set CEASY and the other as HARD set CHARD.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. The question is related to the given table titled 'Table 6: Average accuracy of each model using four different random seeds with only answer options as input, and the number of their common correct predictions.'.\n\nQuestion: What is the total number of data points in the EASY set CEASY?\n" }, { "role": "assistant", "content": "The table shows that the Union set, which represents CEASY, has 440 data points. So, the answer is {\"answer\": \"440\"}." } ], "images": [ "scigen/train/1378.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Average accuracy of each model using four different random seeds with only answer options as input, and the number of their common correct predictions." ], "reasoning": "The table shows that the Union set, which represents CEASY, has 440 data points.", "question": "What is the total number of data points in the EASY set CEASY?", "answer": "So, the answer is {\"answer\": \"440\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The number of data points correctly predicted by GPT-2 is higher than those predicted by GPT. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe number of data points correctly predicted by GPT-2 is higher than those predicted by GPT.\n" }, { "role": "assistant", "content": "The table shows that GPT-2 correctly predicted 245 data points, while GPT correctly predicted 238 data points. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1378.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Average accuracy of each model using four different random seeds with only answer options as input, and the number of their common correct predictions." ], "reasoning": "The table shows that GPT-2 correctly predicted 245 data points, while GPT correctly predicted 238 data points.", "statement": "The number of data points correctly predicted by GPT-2 is higher than those predicted by GPT.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 7: Accuracy (%) of models and human performance. The column Input means whether to input context (C), question (Q) and answer options (A). The RACE column represents whether to first use RACE to fine-tune before training on ReClor.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The dataset consists of 100 samples divided into ten tests, with scores averaged to establish a baseline for graduate students.This dataset is built on questions designed for students who apply for admission to graduate schools, thus we randomly choose 100 samples from the testing set and divide them into ten tests, which are distributed to ten different graduate students in a university. We take the average of their scores and present it as the baseline of graduate students. The data of ReClor are carefully chosen and modified from only high-quality questions from standardized graduate entrance exams. We set the ceiling performance to 100% since ambiguous questions are not included in the dataset." } ], "images": [ "scigen/train/1379.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Accuracy (%) of models and human performance. The column Input means whether to input context (C), question (Q) and answer options (A). The RACE column represents whether to first use RACE to fine-tune before training on ReClor." ], "reasoning": "The dataset consists of 100 samples divided into ten tests, with scores averaged to establish a baseline for graduate students.", "description": "This dataset is built on questions designed for students who apply for admission to graduate schools, thus we randomly choose 100 samples from the testing set and divide them into ten tests, which are distributed to ten different graduate students in a university. We take the average of their scores and present it as the baseline of graduate students. The data of ReClor are carefully chosen and modified from only high-quality questions from standardized graduate entrance exams. We set the ceiling performance to 100% since ambiguous questions are not included in the dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 7: Accuracy (%) of models and human performance. The column Input means whether to input context (C), question (Q) and answer options (A). The RACE column represents whether to first use RACE to fine-tune before training on ReClor.', answer the following question. Give detailed solution consisting of each step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model has the highest score on Test-H?\n" }, { "role": "assistant", "content": "To find the model with the highest score on Test-H, compare the Test-H scores of all models. The highest score is 54.3 by RoBERTa LARGE. So, the answer is {\"answer\": \"RoBERTa LARGE\"}" } ], "images": [ "scigen/train/1379.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Accuracy (%) of models and human performance. The column Input means whether to input context (C), question (Q) and answer options (A). The RACE column represents whether to first use RACE to fine-tune before training on ReClor." ], "reasoning": "To find the model with the highest score on Test-H, compare the Test-H scores of all models. The highest score is 54.3 by RoBERTa LARGE.", "question": "Which model has the highest score on Test-H?", "answer": "So, the answer is {\"answer\": \"RoBERTa LARGE\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe BERT BASE model has a higher score on Test-E than the XLNet BASE model.\n" }, { "role": "assistant", "content": "Compare the Test-E scores of BERT BASE (71.6) and XLNet BASE (75.2). XLNet BASE has a higher score. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1379.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Accuracy (%) of models and human performance. The column Input means whether to input context (C), question (Q) and answer options (A). The RACE column represents whether to first use RACE to fine-tune before training on ReClor." ], "reasoning": "Compare the Test-E scores of BERT BASE (71.6) and XLNet BASE (75.2). XLNet BASE has a higher score.", "statement": "The BERT BASE model has a higher score on Test-E than the XLNet BASE model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Figure 4: Effectiveness in disarming the obfuscation attack. These radial histograms show the proportion of obfuscated comments correctly scored with at least the same toxicity of the original comment, out of 1000 variants (red area), as obtained by GP model (green sector) and TP+GP method (amber sector). (a) On the obfuscation-50 dataset. (b) On the obfuscation-99 dataset.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The TP+GP method effectively scores a large proportion of comments in all topics on the obfuscation-50 dataset, and this effectiveness is slightly reduced on the obfuscation-99 dataset due to higher corruption levels.In order to further compare the effectiveness of the proposed method in disarming the obfuscation attack, we illustrate in Fig. It can be seen how on the obfuscation-50 dataset the TP+GP method (amber area) is able to correctly score a large proportion of comments in all topics. This pattern is replicated in the obfuscation-99 dataset, although proportions slightly decline due to the higher corruption level, which yields more acid attacks." } ], "images": [ "scigen/train/1380.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 4: Effectiveness in disarming the obfuscation attack. These radial histograms show the proportion of obfuscated comments correctly scored with at least the same toxicity of the original comment, out of 1000 variants (red area), as obtained by GP model (green sector) and TP+GP method (amber sector). (a) On the obfuscation-50 dataset. (b) On the obfuscation-99 dataset." ], "reasoning": "The TP+GP method effectively scores a large proportion of comments in all topics on the obfuscation-50 dataset, and this effectiveness is slightly reduced on the obfuscation-99 dataset due to higher corruption levels.", "description": "In order to further compare the effectiveness of the proposed method in disarming the obfuscation attack, we illustrate in Fig. It can be seen how on the obfuscation-50 dataset the TP+GP method (amber area) is able to correctly score a large proportion of comments in all topics. This pattern is replicated in the obfuscation-99 dataset, although proportions slightly decline due to the higher corruption level, which yields more acid attacks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Figure 4: Effectiveness in disarming the obfuscation attack. These radial histograms show the proportion of obfuscated comments correctly scored with at least the same toxicity of the original comment, out of 1000 variants (red area), as obtained by GP model (green sector) and TP+GP method (amber sector). (a) On the obfuscation-50 dataset. (b) On the obfuscation-99 dataset.', answer the question below by showing each progressive step toward the answer. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nHow does the TP+GP method perform on the obfuscation-99 dataset compared to the obfuscation-50 dataset?\n" }, { "role": "assistant", "content": "The description states that the TP+GP method is able to correctly score a large proportion of comments in all topics on the obfuscation-50 dataset. This pattern is replicated on the obfuscation-99 dataset, although the proportions slightly decline due to the higher corruption level. So, the answer is {\"answer\": \"slightly reduced effectiveness on obfuscation-99 dataset\"}." } ], "images": [ "scigen/train/1380.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 4: Effectiveness in disarming the obfuscation attack. These radial histograms show the proportion of obfuscated comments correctly scored with at least the same toxicity of the original comment, out of 1000 variants (red area), as obtained by GP model (green sector) and TP+GP method (amber sector). (a) On the obfuscation-50 dataset. (b) On the obfuscation-99 dataset." ], "reasoning": "The description states that the TP+GP method is able to correctly score a large proportion of comments in all topics on the obfuscation-50 dataset. This pattern is replicated on the obfuscation-99 dataset, although the proportions slightly decline due to the higher corruption level.", "question": "How does the TP+GP method perform on the obfuscation-99 dataset compared to the obfuscation-50 dataset?", "answer": "So, the answer is {\"answer\": \"slightly reduced effectiveness on obfuscation-99 dataset\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe TP+GP method is less effective on the obfuscation-99 dataset compared to the obfuscation-50 dataset.\n" }, { "role": "assistant", "content": "The description mentions that the TP+GP method's effectiveness slightly declines on the obfuscation-99 dataset due to the higher corruption level, indicating it is less effective compared to the obfuscation-50 dataset. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1380.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 4: Effectiveness in disarming the obfuscation attack. These radial histograms show the proportion of obfuscated comments correctly scored with at least the same toxicity of the original comment, out of 1000 variants (red area), as obtained by GP model (green sector) and TP+GP method (amber sector). (a) On the obfuscation-50 dataset. (b) On the obfuscation-99 dataset." ], "reasoning": "The description mentions that the TP+GP method's effectiveness slightly declines on the obfuscation-99 dataset due to the higher corruption level, indicating it is less effective compared to the obfuscation-50 dataset.", "statement": "The TP+GP method is less effective on the obfuscation-99 dataset compared to the obfuscation-50 dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Test and Dev accuracy (%) of the models on WikiSQL data, where Accsyn refers to syntactical accuracy and Accex refers to execution accuracy. “+ EG (k)” indicates that model outputs are generated using the execution-guided strategy with beam size k.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Execution accuracy is higher than syntactical accuracy because different programs can produce the same results, and execution-guided decoding is evaluated with beam sizes of 3 and 5.We report both the syntactical accuracy Accsyn corresponding to the ratio of predictions that are exactly the ground truth SQL query, as well as the execution accuracy Accex corresponding to the ratio of predictions that return the same result as the ground truth when executed. Note that the execution accuracy is higher than syntactical accuracy as syntactically different programs can generate the same results (e.g., programs differing only in predicate order). In execution-guided decoding, we report two model variants, one using a beam size of 3 and the other a beam size of 5." } ], "images": [ "scigen/train/1381.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Test and Dev accuracy (%) of the models on WikiSQL data, where Accsyn refers to syntactical accuracy and Accex refers to execution accuracy. “+ EG (k)” indicates that model outputs are generated using the execution-guided strategy with beam size k." ], "reasoning": "Execution accuracy is higher than syntactical accuracy because different programs can produce the same results, and execution-guided decoding is evaluated with beam sizes of 3 and 5.", "description": "We report both the syntactical accuracy Accsyn corresponding to the ratio of predictions that are exactly the ground truth SQL query, as well as the execution accuracy Accex corresponding to the ratio of predictions that return the same result as the ground truth when executed. Note that the execution accuracy is higher than syntactical accuracy as syntactically different programs can generate the same results (e.g., programs differing only in predicate order). In execution-guided decoding, we report two model variants, one using a beam size of 3 and the other a beam size of 5.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 1: Test and Dev accuracy (%) of the models on WikiSQL data, where Accsyn refers to syntactical accuracy and Accex refers to execution accuracy. “+ EG (k)” indicates that model outputs are generated using the execution-guided strategy with beam size k.', solve the following question step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model variant has the highest execution accuracy on the test set?\n" }, { "role": "assistant", "content": "To find the model variant with the highest execution accuracy on the test set, compare the 'Test Accex' values for all models. The highest value is 83.8, which corresponds to the 'Coarse2Fine + EG (5)' model. So, the answer is {\"answer\": \"Coarse2Fine + EG (5)\"}." } ], "images": [ "scigen/train/1381.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Test and Dev accuracy (%) of the models on WikiSQL data, where Accsyn refers to syntactical accuracy and Accex refers to execution accuracy. “+ EG (k)” indicates that model outputs are generated using the execution-guided strategy with beam size k." ], "reasoning": "To find the model variant with the highest execution accuracy on the test set, compare the 'Test Accex' values for all models. The highest value is 83.8, which corresponds to the 'Coarse2Fine + EG (5)' model.", "question": "Which model variant has the highest execution accuracy on the test set?", "answer": "So, the answer is {\"answer\": \"Coarse2Fine + EG (5)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nPointer-SQL + EG (5) has a higher syntactical accuracy on the test set than Coarse2Fine + EG (3).\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Test Accsyn' values for 'Pointer-SQL + EG (5)' and 'Coarse2Fine + EG (3)'. Pointer-SQL + EG (5) has a Test Accsyn of 67.9, while Coarse2Fine + EG (3) has a Test Accsyn of 74.8. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1381.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Test and Dev accuracy (%) of the models on WikiSQL data, where Accsyn refers to syntactical accuracy and Accex refers to execution accuracy. “+ EG (k)” indicates that model outputs are generated using the execution-guided strategy with beam size k." ], "reasoning": "To verify this statement, compare the 'Test Accsyn' values for 'Pointer-SQL + EG (5)' and 'Coarse2Fine + EG (3)'. Pointer-SQL + EG (5) has a Test Accsyn of 67.9, while Coarse2Fine + EG (3) has a Test Accsyn of 74.8. Therefore, the statement is refuted.", "statement": "Pointer-SQL + EG (5) has a higher syntactical accuracy on the test set than Coarse2Fine + EG (3).", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 6: CoNLL development set recall for 5 most frequent error categories with and without training on data with synthesized article/determiner and noun number errors. Wci denotes wrong collocation/idiom errors.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Data augmentation improved the F0.5-score on the development set from 31.55 to 34.81, but decreased recall for error types not included in the additional training data.Effects of Data Augmentation We obtain promising improvements using data augmentation, boosting F0.5-score on the development set from 31.55 to 34.81. The same phenomenon has been observed by \\newciterozovskaya2012ui. Interestingly, the recall of other error types (see \\newciteng2014conll for descriptions) decreases. We surmise this is because the additional training data contains only ArtOrDet and Nn errors, and hence the network is encouraged to simply copy the output when those error types are not present. We hope synthesizing data with a variety of other error types may fix this issue and improve performance." } ], "images": [ "scigen/train/1382.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: CoNLL development set recall for 5 most frequent error categories with and without training on data with synthesized article/determiner and noun number errors. Wci denotes wrong collocation/idiom errors." ], "reasoning": "Data augmentation improved the F0.5-score on the development set from 31.55 to 34.81, but decreased recall for error types not included in the additional training data.", "description": "Effects of Data Augmentation We obtain promising improvements using data augmentation, boosting F0.5-score on the development set from 31.55 to 34.81. The same phenomenon has been observed by \\newciterozovskaya2012ui. Interestingly, the recall of other error types (see \\newciteng2014conll for descriptions) decreases. We surmise this is because the additional training data contains only ArtOrDet and Nn errors, and hence the network is encouraged to simply copy the output when those error types are not present. We hope synthesizing data with a variety of other error types may fix this issue and improve performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 6: CoNLL development set recall for 5 most frequent error categories with and without training on data with synthesized article/determiner and noun number errors. Wci denotes wrong collocation/idiom errors.', solve the following question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat was the recall for Nn errors before and after data augmentation?\n" }, { "role": "assistant", "content": "The table shows the recall for Nn errors as 31.50 without augmentation and 51.00 with augmentation. So, the answer is {\"answer\": \"31.50 and 51.00\"}" } ], "images": [ "scigen/train/1382.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: CoNLL development set recall for 5 most frequent error categories with and without training on data with synthesized article/determiner and noun number errors. Wci denotes wrong collocation/idiom errors." ], "reasoning": "The table shows the recall for Nn errors as 31.50 without augmentation and 51.00 with augmentation.", "question": "What was the recall for Nn errors before and after data augmentation?", "answer": "So, the answer is {\"answer\": \"31.50 and 51.00\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nData augmentation improved the recall for ArtOrDet errors.\n" }, { "role": "assistant", "content": "The table shows that the recall for ArtOrDet errors increased from 20.08 to 29.14 after data augmentation. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1382.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: CoNLL development set recall for 5 most frequent error categories with and without training on data with synthesized article/determiner and noun number errors. Wci denotes wrong collocation/idiom errors." ], "reasoning": "The table shows that the recall for ArtOrDet errors increased from 20.08 to 29.14 after data augmentation.", "statement": "Data augmentation improved the recall for ArtOrDet errors.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1: Performance on Lang-8 test set. Adding the language model results in a negligible increase in performance, illustrating the difficulty of the user-generated forum setting.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The RNN + LM method achieved the highest BLEU score of 61.70, while the Spell check method had the lowest score of 58.91.Note that since there may be multiple ways to correct an error and some errors are left uncorrected, the baseline of using uncorrected sentences is more difficult to improve upon than it may initially appear. We suspect due to proper nouns, acronyms, and inconsistent capitalization conventions in Lang-8, however, this actually decreased BLEU slightly. To the best of our knowledge, no other work has reported results on this challenging task." } ], "images": [ "scigen/train/1383.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance on Lang-8 test set. Adding the language model results in a negligible increase in performance, illustrating the difficulty of the user-generated forum setting." ], "reasoning": "The RNN + LM method achieved the highest BLEU score of 61.70, while the Spell check method had the lowest score of 58.91.", "description": "Note that since there may be multiple ways to correct an error and some errors are left uncorrected, the baseline of using uncorrected sentences is more difficult to improve upon than it may initially appear. We suspect due to proper nouns, acronyms, and inconsistent capitalization conventions in Lang-8, however, this actually decreased BLEU slightly. To the best of our knowledge, no other work has reported results on this challenging task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Performance on Lang-8 test set. Adding the language model results in a negligible increase in performance, illustrating the difficulty of the user-generated forum setting.\nQuestion: Which method achieved the highest BLEU score and what was the score?\nSolve the above question based on the table information. Let's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the BLEU scores of all methods listed in the table, the RNN + LM method has the highest score of 61.70. So, the answer is {\"answer\": \"RNN + LM, 61.70\"}" } ], "images": [ "scigen/train/1383.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance on Lang-8 test set. Adding the language model results in a negligible increase in performance, illustrating the difficulty of the user-generated forum setting." ], "reasoning": "By comparing the BLEU scores of all methods listed in the table, the RNN + LM method has the highest score of 61.70.", "question": "Which method achieved the highest BLEU score and what was the score?", "answer": "So, the answer is {\"answer\": \"RNN + LM, 61.70\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The Spell check method achieved a BLEU score higher than 60., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Spell check method achieved a BLEU score higher than 60.\n" }, { "role": "assistant", "content": "The table shows that the Spell check method achieved a BLEU score of 58.91, which is lower than 60. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1383.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance on Lang-8 test set. Adding the language model results in a negligible increase in performance, illustrating the difficulty of the user-generated forum setting." ], "reasoning": "The table shows that the Spell check method achieved a BLEU score of 58.91, which is lower than 60.", "statement": "The Spell check method achieved a BLEU score higher than 60.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Comparison of results on development set between using conventional distillation and inplace distillation. For DynaBERTW, the average accuracy over four width multipliers are reported. For DynaBERT, the average accuracy over four width multipliers and three depth multipliers are reported. The higher accuracy in each group is highlighted.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Inplace distillation has higher average accuracy on MRPC and CoLA for DynaBERTW, but for DynaBERT, it can lead to worse performance than conventional distillation.As can be seen, inplace distillation has higher average accuracy on MRPC and CoLA when training DynaBERTW. However, in the training of DynaBERT, the model initialized with the inplace distillation can lead to even worse performance than that with the conventional distillation." } ], "images": [ "scigen/train/1384.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison of results on development set between using conventional distillation and inplace distillation. For DynaBERTW, the average accuracy over four width multipliers are reported. For DynaBERT, the average accuracy over four width multipliers and three depth multipliers are reported. The higher accuracy in each group is highlighted." ], "reasoning": "Inplace distillation has higher average accuracy on MRPC and CoLA for DynaBERTW, but for DynaBERT, it can lead to worse performance than conventional distillation.", "description": "As can be seen, inplace distillation has higher average accuracy on MRPC and CoLA when training DynaBERTW. However, in the training of DynaBERT, the model initialized with the inplace distillation can lead to even worse performance than that with the conventional distillation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 5: Comparison of results on development set between using conventional distillation and inplace distillation. For DynaBERTW, the average accuracy over four width multipliers are reported. For DynaBERT, the average accuracy over four width multipliers and three depth multipliers are reported. The higher accuracy in each group is highlighted.', unfold the steps to solve the question given next. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nQuestion: Which distillation type results in the highest accuracy for MRPC when training DynaBERTW?\n" }, { "role": "assistant", "content": "To find the highest accuracy for MRPC when training DynaBERTW, compare the MRPC scores for both conventional and inplace distillation types under DynaBERTW. Inplace distillation has an MRPC score of 87.0, which is higher than the conventional distillation score of 86.1. So, the answer is {\"answer\": \"Inplace\"}." } ], "images": [ "scigen/train/1384.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison of results on development set between using conventional distillation and inplace distillation. For DynaBERTW, the average accuracy over four width multipliers are reported. For DynaBERT, the average accuracy over four width multipliers and three depth multipliers are reported. The higher accuracy in each group is highlighted." ], "reasoning": "To find the highest accuracy for MRPC when training DynaBERTW, compare the MRPC scores for both conventional and inplace distillation types under DynaBERTW. Inplace distillation has an MRPC score of 87.0, which is higher than the conventional distillation score of 86.1.", "question": "Which distillation type results in the highest accuracy for MRPC when training DynaBERTW?", "answer": "So, the answer is {\"answer\": \"Inplace\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nInplace distillation results in higher accuracy for CoLA when training DynaBERT compared to conventional distillation.\n" }, { "role": "assistant", "content": "To verify this statement, compare the CoLA scores for inplace and conventional distillation types under DynaBERT. Inplace distillation has a CoLA score of 54.5, which is lower than the conventional distillation score of 54.8. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1384.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison of results on development set between using conventional distillation and inplace distillation. For DynaBERTW, the average accuracy over four width multipliers are reported. For DynaBERT, the average accuracy over four width multipliers and three depth multipliers are reported. The higher accuracy in each group is highlighted." ], "reasoning": "To verify this statement, compare the CoLA scores for inplace and conventional distillation types under DynaBERT. Inplace distillation has a CoLA score of 54.5, which is lower than the conventional distillation score of 54.8.", "statement": "Inplace distillation results in higher accuracy for CoLA when training DynaBERT compared to conventional distillation.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Results on test set of our proposed DynaBERT and DynaRoBERTa. Note that the evaluation metric for QQP and MRPC here is “F1”.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "DynaBERT achieves similar accuracy to BERTBASE with the same size, while DynaRoBERTa outperforms RoBERTaBASE on seven out of eight tasks due to adaptive width and depth acting as regularization.Again, the proposed DynaBERT achieves comparable accuracy than BERTBASE with the same size. Interestingly, the proposed DynaRoBERTa outperforms RoBERTaBASE on seven out of eight tasks. A possible reason is that allowing adaptive width and depth increases the training difficulty and acts as regularization, and so contributes positively to the performance." } ], "images": [ "scigen/train/1385.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on test set of our proposed DynaBERT and DynaRoBERTa. Note that the evaluation metric for QQP and MRPC here is “F1”." ], "reasoning": "DynaBERT achieves similar accuracy to BERTBASE with the same size, while DynaRoBERTa outperforms RoBERTaBASE on seven out of eight tasks due to adaptive width and depth acting as regularization.", "description": "Again, the proposed DynaBERT achieves comparable accuracy than BERTBASE with the same size. Interestingly, the proposed DynaRoBERTa outperforms RoBERTaBASE on seven out of eight tasks. A possible reason is that allowing adaptive width and depth increases the training difficulty and acts as regularization, and so contributes positively to the performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Results on test set of our proposed DynaBERT and DynaRoBERTa. Note that the evaluation metric for QQP and MRPC here is “F1”.', solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model outperforms RoBERTaBASE on seven out of eight tasks?\n" }, { "role": "assistant", "content": "The table shows the performance of different models on various tasks. According to the description, DynaRoBERTa outperforms RoBERTaBASE on seven out of eight tasks. So, the answer is {\"answer\": \"DynaRoBERTa\"}" } ], "images": [ "scigen/train/1385.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on test set of our proposed DynaBERT and DynaRoBERTa. Note that the evaluation metric for QQP and MRPC here is “F1”." ], "reasoning": "The table shows the performance of different models on various tasks. According to the description, DynaRoBERTa outperforms RoBERTaBASE on seven out of eight tasks.", "question": "Which model outperforms RoBERTaBASE on seven out of eight tasks?", "answer": "So, the answer is {\"answer\": \"DynaRoBERTa\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim DynaBERT achieves higher accuracy than BERTBASE on all tasks., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nDynaBERT achieves higher accuracy than BERTBASE on all tasks.\n" }, { "role": "assistant", "content": "The table shows that DynaBERT has comparable accuracy to BERTBASE, but not higher accuracy on all tasks. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1385.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on test set of our proposed DynaBERT and DynaRoBERTa. Note that the evaluation metric for QQP and MRPC here is “F1”." ], "reasoning": "The table shows that DynaBERT has comparable accuracy to BERTBASE, but not higher accuracy on all tasks.", "statement": "DynaBERT achieves higher accuracy than BERTBASE on all tasks.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: Ablation study in the training of DynaBERT. Results on the development set are reported. The highest average accuracy over four width multipliers for each depth multiplier is highlighted.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Fine-tuning improves average accuracy on SST-2, CoLA, and RTE but harms MRPC performance. The model with higher average accuracy is chosen.We evaluate the effect of knowledge distillation, data augmentation and final fine-tuning in the training of DynaBERT on four of GLUE data sets The DynaBERT trained without knowledge distillation, data augmentation and final fine-tuning is called “vanilla DynaBERT”. Additional fine-tuning further improves the average accuracy on all three depth multipliers on SST-2, CoLA and two on RTE, but harms the performance on MRPC. Empirically, we choose the model with higher average accuracy between with and without fine-tuning." } ], "images": [ "scigen/train/1386.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Ablation study in the training of DynaBERT. Results on the development set are reported. The highest average accuracy over four width multipliers for each depth multiplier is highlighted." ], "reasoning": "Fine-tuning improves average accuracy on SST-2, CoLA, and RTE but harms MRPC performance. The model with higher average accuracy is chosen.", "description": "We evaluate the effect of knowledge distillation, data augmentation and final fine-tuning in the training of DynaBERT on four of GLUE data sets The DynaBERT trained without knowledge distillation, data augmentation and final fine-tuning is called “vanilla DynaBERT”. Additional fine-tuning further improves the average accuracy on all three depth multipliers on SST-2, CoLA and two on RTE, but harms the performance on MRPC. Empirically, we choose the model with higher average accuracy between with and without fine-tuning.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 4: Ablation study in the training of DynaBERT. Results on the development set are reported. The highest average accuracy over four width multipliers for each depth multiplier is highlighted.\nQuestion: What is the average accuracy of DynaBERT on CoLA with fine-tuning?\n" }, { "role": "assistant", "content": "To find the average accuracy of DynaBERT on CoLA with fine-tuning, look at the row labeled '+ Fine-tuning' and the column labeled 'avg.' under CoLA. The value is 57.5. So, the answer is {\"answer\": \"57.5\"}." } ], "images": [ "scigen/train/1386.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Ablation study in the training of DynaBERT. Results on the development set are reported. The highest average accuracy over four width multipliers for each depth multiplier is highlighted." ], "reasoning": "To find the average accuracy of DynaBERT on CoLA with fine-tuning, look at the row labeled '+ Fine-tuning' and the column labeled 'avg.' under CoLA. The value is 57.5.", "question": "What is the average accuracy of DynaBERT on CoLA with fine-tuning?", "answer": "So, the answer is {\"answer\": \"57.5\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nFine-tuning improves the average accuracy on MRPC.\n" }, { "role": "assistant", "content": "The table shows that the average accuracy on MRPC without fine-tuning is 84.8, and with fine-tuning, it is 83.8. Therefore, fine-tuning does not improve the average accuracy on MRPC. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1386.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Ablation study in the training of DynaBERT. Results on the development set are reported. The highest average accuracy over four width multipliers for each depth multiplier is highlighted." ], "reasoning": "The table shows that the average accuracy on MRPC without fine-tuning is 84.8, and with fine-tuning, it is 83.8. Therefore, fine-tuning does not improve the average accuracy on MRPC.", "statement": "Fine-tuning improves the average accuracy on MRPC.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 8: Inter-Language comparison of results. “Increase on SG” denotes the difference between the mentioned systems and the scores of SkipGram embeddings trained on the target languages - fr-SG, de-SG and ur-SG respectively. We are not using ConceptNet-T for this comparison because any equivalent embedding is not available for Hindi.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The increase on SG for Hindi-Urdu is significantly higher than for English-French and English-German, indicating greater similarity.This may be because Hindi-Urdu are very much more similar than either English-French and English-German." } ], "images": [ "scigen/train/1387.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Inter-Language comparison of results. “Increase on SG” denotes the difference between the mentioned systems and the scores of SkipGram embeddings trained on the target languages - fr-SG, de-SG and ur-SG respectively. We are not using ConceptNet-T for this comparison because any equivalent embedding is not available for Hindi." ], "reasoning": "The increase on SG for Hindi-Urdu is significantly higher than for English-French and English-German, indicating greater similarity.", "description": "This may be because Hindi-Urdu are very much more similar than either English-French and English-German.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 8: Inter-Language comparison of results. “Increase on SG” denotes the difference between the mentioned systems and the scores of SkipGram embeddings trained on the target languages - fr-SG, de-SG and ur-SG respectively. We are not using ConceptNet-T for this comparison because any equivalent embedding is not available for Hindi.\nQuestion: Which language pair has the highest increase on SG?\nGive a detailed response to the above question. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the 'Increase on SG' values for each language pair, we see that Hindi-Urdu has the highest value at 15.58. So, the answer is {\"answer\": \"Hindi-Urdu\"}." } ], "images": [ "scigen/train/1387.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Inter-Language comparison of results. “Increase on SG” denotes the difference between the mentioned systems and the scores of SkipGram embeddings trained on the target languages - fr-SG, de-SG and ur-SG respectively. We are not using ConceptNet-T for this comparison because any equivalent embedding is not available for Hindi." ], "reasoning": "By comparing the 'Increase on SG' values for each language pair, we see that Hindi-Urdu has the highest value at 15.58.", "question": "Which language pair has the highest increase on SG?", "answer": "So, the answer is {\"answer\": \"Hindi-Urdu\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe increase on SG for English-German is higher than for English-French.\n" }, { "role": "assistant", "content": "The table shows that the increase on SG for English-German is 2.34, which is lower than the 6.14 for English-French. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1387.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Inter-Language comparison of results. “Increase on SG” denotes the difference between the mentioned systems and the scores of SkipGram embeddings trained on the target languages - fr-SG, de-SG and ur-SG respectively. We are not using ConceptNet-T for this comparison because any equivalent embedding is not available for Hindi." ], "reasoning": "The table shows that the increase on SG for English-German is 2.34, which is lower than the 6.14 for English-French.", "statement": "The increase on SG for English-German is higher than for English-French.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 6: Results of MDE after 100 iterations when removing one of the terms. Best results are in bold.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The removal of S4 has the most negative effect on MDE performance, while S1 has the least effect and improves MRR. S2 negatively influences MRR and Hit@10, despite performing the worst individually.The evaluations on WN18RR and WN18 show that the removal of S4 has the most negative effect on the performance of MDE. The removal of S1 that was one of the good performing terms in the last experiment has the least effect. Nevertheless, S1 improves the MRR in the MDE. Also, when we remove S2, the MRR and Hit@10 are negatively influenced, indicating that it exists cases that S2 performs better than the other terms, although, in the individual tests, it performed the worst between all the terms." } ], "images": [ "scigen/train/1388.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results of MDE after 100 iterations when removing one of the terms. Best results are in bold." ], "reasoning": "The removal of S4 has the most negative effect on MDE performance, while S1 has the least effect and improves MRR. S2 negatively influences MRR and Hit@10, despite performing the worst individually.", "description": "The evaluations on WN18RR and WN18 show that the removal of S4 has the most negative effect on the performance of MDE. The removal of S1 that was one of the good performing terms in the last experiment has the least effect. Nevertheless, S1 improves the MRR in the MDE. Also, when we remove S2, the MRR and Hit@10 are negatively influenced, indicating that it exists cases that S2 performs better than the other terms, although, in the individual tests, it performed the worst between all the terms.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 6: Results of MDE after 100 iterations when removing one of the terms. Best results are in bold.'. Solve the following question based on the table.\n\nQuestion: Which removed term has the most negative effect on the performance of MDE according to the WN18RR evaluation?\nLet's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The description states that the removal of S4 has the most negative effect on the performance of MDE. This is supported by the table where the WN18RR MRR for S4 is 0.366, which is lower than the other terms. So, the answer is {\"answer\": \"S4\"}." } ], "images": [ "scigen/train/1388.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results of MDE after 100 iterations when removing one of the terms. Best results are in bold." ], "reasoning": "The description states that the removal of S4 has the most negative effect on the performance of MDE. This is supported by the table where the WN18RR MRR for S4 is 0.366, which is lower than the other terms.", "question": "Which removed term has the most negative effect on the performance of MDE according to the WN18RR evaluation?", "answer": "So, the answer is {\"answer\": \"S4\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe removal of S1 has the most negative effect on the performance of MDE according to the WN18RR evaluation.\n" }, { "role": "assistant", "content": "The description and table indicate that the removal of S4, not S1, has the most negative effect on the performance of MDE. The WN18RR MRR for S4 is 0.366, which is lower than the WN18RR MRR for S1, which is 0.417. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1388.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results of MDE after 100 iterations when removing one of the terms. Best results are in bold." ], "reasoning": "The description and table indicate that the removal of S4, not S1, has the most negative effect on the performance of MDE. The WN18RR MRR for S4 is 0.366, which is lower than the WN18RR MRR for S1, which is 0.417.", "statement": "The removal of S1 has the most negative effect on the performance of MDE according to the WN18RR evaluation.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: Results of each individual term in MDE on WN18RR and FB15k-237. Best results are in bold.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "S4 outperforms the other terms, while S1 and S3 perform similarly. S2 performs the worst due to inefficiency in modeling antisymmetric patterns.We can see that S4 outperforms the other terms while S1 and S3 perform very similar on these two datasets. Between the four terms, S2 performs the worst since most of the relations in the test datasets follow an antisymmetric pattern and S2 is not efficient in modeling them." } ], "images": [ "scigen/train/1389.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results of each individual term in MDE on WN18RR and FB15k-237. Best results are in bold." ], "reasoning": "S4 outperforms the other terms, while S1 and S3 perform similarly. S2 performs the worst due to inefficiency in modeling antisymmetric patterns.", "description": "We can see that S4 outperforms the other terms while S1 and S3 perform very similar on these two datasets. Between the four terms, S2 performs the worst since most of the relations in the test datasets follow an antisymmetric pattern and S2 is not efficient in modeling them.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 5: Results of each individual term in MDE on WN18RR and FB15k-237. Best results are in bold.\nQuestion: Which term has the highest MRR on the WN18RR dataset?\n" }, { "role": "assistant", "content": "By comparing the MRR values for each term on the WN18RR dataset, S4 has the highest MRR value of 0.323. So, the answer is {\"answer\": \"S4\"}" } ], "images": [ "scigen/train/1389.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results of each individual term in MDE on WN18RR and FB15k-237. Best results are in bold." ], "reasoning": "By comparing the MRR values for each term on the WN18RR dataset, S4 has the highest MRR value of 0.323.", "question": "Which term has the highest MRR on the WN18RR dataset?", "answer": "So, the answer is {\"answer\": \"S4\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nS2 has the lowest Hit@10 score on the FB15k-237 dataset.\n" }, { "role": "assistant", "content": "By comparing the Hit@10 scores for each term on the FB15k-237 dataset, S2 has the lowest score of 0.342. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1389.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results of each individual term in MDE on WN18RR and FB15k-237. Best results are in bold." ], "reasoning": "By comparing the Hit@10 scores for each term on the FB15k-237 dataset, S2 has the lowest score of 0.342.", "statement": "S2 has the lowest Hit@10 score on the FB15k-237 dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: LSTM Language Models for Actionability. (V: Vocabulary Size)'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Languages such as Czech, Finnish, Slovak, and Swedish show the highest accuracies of over 90%, while languages like Arabic, Farsi, and Thai have the lowest accuracies, slightly below 80%, likely due to data sparsity.We examine actionability across languages in our experiments. A pair of training and test datasets is therefore created for each language, as well as one dataset that includes all languages. Some languages have sparse data and smaller number of samples, while others are larger. Our dataset sizes vary from 27k messages for the language with the least data (Farsi), to 8.5 million messages for the language with the most (English). A combined language dataset is also prepared, which contains 1.7 million sampled messages across all languages. Languages such as Czech (cs), Finnish (fi), Slovak (si) and Swedish (sv) show the highest accuracies of over 90%. The lowest accuracies are for languages such as Arabic (ar), Farsi (fa) and Thai (th), where the accuracies are slightly below 80%, though in some cases like fa and th, the low accuracy may be attributed to sparsity of data. As expected, certain languages are more predictable in terms of actionability than others, and we observe a variation of 15% between the least and most accurate models. In our experiments we consider two vocabulary sizes for each language. The first is a smaller vocabulary of size 20,000, and the second is a larger one of size 100,000. The larger vocabulary adds significant overhead in training time, since more params need to be learnt by the model." } ], "images": [ "scigen/train/1390.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: LSTM Language Models for Actionability. (V: Vocabulary Size)" ], "reasoning": "Languages such as Czech, Finnish, Slovak, and Swedish show the highest accuracies of over 90%, while languages like Arabic, Farsi, and Thai have the lowest accuracies, slightly below 80%, likely due to data sparsity.", "description": "We examine actionability across languages in our experiments. A pair of training and test datasets is therefore created for each language, as well as one dataset that includes all languages. Some languages have sparse data and smaller number of samples, while others are larger. Our dataset sizes vary from 27k messages for the language with the least data (Farsi), to 8.5 million messages for the language with the most (English). A combined language dataset is also prepared, which contains 1.7 million sampled messages across all languages. Languages such as Czech (cs), Finnish (fi), Slovak (si) and Swedish (sv) show the highest accuracies of over 90%. The lowest accuracies are for languages such as Arabic (ar), Farsi (fa) and Thai (th), where the accuracies are slightly below 80%, though in some cases like fa and th, the low accuracy may be attributed to sparsity of data. As expected, certain languages are more predictable in terms of actionability than others, and we observe a variation of 15% between the least and most accurate models. In our experiments we consider two vocabulary sizes for each language. The first is a smaller vocabulary of size 20,000, and the second is a larger one of size 100,000. The larger vocabulary adds significant overhead in training time, since more params need to be learnt by the model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1: LSTM Language Models for Actionability. (V: Vocabulary Size)', solve the subsequent question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhich language has the highest test accuracy with a vocabulary size of 20,000?\n" }, { "role": "assistant", "content": "To find the language with the highest test accuracy with a vocabulary size of 20,000, we need to look at the 'Test Accuracy (V=20k)' column and identify the highest value. The highest value is 0.9087, which corresponds to the language 'cs' (Czech). So, the answer is {\"answer\": \"cs\"}" } ], "images": [ "scigen/train/1390.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: LSTM Language Models for Actionability. (V: Vocabulary Size)" ], "reasoning": "To find the language with the highest test accuracy with a vocabulary size of 20,000, we need to look at the 'Test Accuracy (V=20k)' column and identify the highest value. The highest value is 0.9087, which corresponds to the language 'cs' (Czech).", "question": "Which language has the highest test accuracy with a vocabulary size of 20,000?", "answer": "So, the answer is {\"answer\": \"cs\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe combined dataset has a test accuracy of 0.8597 with a vocabulary size of 100,000.\n" }, { "role": "assistant", "content": "The table shows that the combined dataset has a test accuracy of 0.8597 with a vocabulary size of 100,000, which matches the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1390.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: LSTM Language Models for Actionability. (V: Vocabulary Size)" ], "reasoning": "The table shows that the combined dataset has a test accuracy of 0.8597 with a vocabulary size of 100,000, which matches the statement.", "statement": "The combined dataset has a test accuracy of 0.8597 with a vocabulary size of 100,000.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Traditional vs LSTM Model Performance'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The LSTM model shows a significant improvement over the traditional model, with the largest improvement being 15% for Italian, demonstrating the superiority of neural networks in text classification.Further the improvement is significantly large with the LSTM model, the largest improvement being 15% for Italian (it). This clearly demonstrates the superiority of neural networks when it comes to the text classification problems such as actionability." } ], "images": [ "scigen/train/1391.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Traditional vs LSTM Model Performance" ], "reasoning": "The LSTM model shows a significant improvement over the traditional model, with the largest improvement being 15% for Italian, demonstrating the superiority of neural networks in text classification.", "description": "Further the improvement is significantly large with the LSTM model, the largest improvement being 15% for Italian (it). This clearly demonstrates the superiority of neural networks when it comes to the text classification problems such as actionability.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Traditional vs LSTM Model Performance', answer the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich language showed the largest improvement in accuracy with the LSTM model compared to the traditional model?\n" }, { "role": "assistant", "content": "By comparing the accuracy improvements for each language, the largest improvement is seen for Italian, with an increase from 0.72 to 0.87, which is a 15% improvement. So, the answer is {\"answer\": \"Italian\"}" } ], "images": [ "scigen/train/1391.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Traditional vs LSTM Model Performance" ], "reasoning": "By comparing the accuracy improvements for each language, the largest improvement is seen for Italian, with an increase from 0.72 to 0.87, which is a 15% improvement.", "question": "Which language showed the largest improvement in accuracy with the LSTM model compared to the traditional model?", "answer": "So, the answer is {\"answer\": \"Italian\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The LSTM model improved the accuracy for French by 5%. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe LSTM model improved the accuracy for French by 5%.\n" }, { "role": "assistant", "content": "The table shows that the traditional model accuracy for French is 0.76 and the LSTM model accuracy is 0.81, which is an improvement of 5%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1391.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Traditional vs LSTM Model Performance" ], "reasoning": "The table shows that the traditional model accuracy for French is 0.76 and the LSTM model accuracy is 0.81, which is an improvement of 5%.", "statement": "The LSTM model improved the accuracy for French by 5%.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Non-Actionable vs Actionable Classification Examples'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The model's prediction score indicates how actionable a message is, with news being least actionable and explicit issues with calls to action being highly actionable.A useful side effect of the predictions made with the model, is that along with the final classification, the prediction score for a given message is indicative of how strongly actionable a message may be. We find that messages that simply convey some news are classified as the least actionable, and the ones which state an explicit issue with a call to action are classified as highly actionable. Broad complaints or general questions are classified as non-actionable, or weakly actionable, which is a highly desired aspect when sifting through such messages." } ], "images": [ "scigen/train/1392.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Non-Actionable vs Actionable Classification Examples" ], "reasoning": "The model's prediction score indicates how actionable a message is, with news being least actionable and explicit issues with calls to action being highly actionable.", "description": "A useful side effect of the predictions made with the model, is that along with the final classification, the prediction score for a given message is indicative of how strongly actionable a message may be. We find that messages that simply convey some news are classified as the least actionable, and the ones which state an explicit issue with a call to action are classified as highly actionable. Broad complaints or general questions are classified as non-actionable, or weakly actionable, which is a highly desired aspect when sifting through such messages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: Non-Actionable vs Actionable Classification Examples', answer the following question. Give detailed solution consisting of each step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich message has the highest prediction score and what is its classification?\n" }, { "role": "assistant", "content": "By examining the table, the message with the highest prediction score is identified, and its classification is noted. So, the answer is {\"answer\": \"0.96, ACTIONABLE\"}" } ], "images": [ "scigen/train/1392.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Non-Actionable vs Actionable Classification Examples" ], "reasoning": "By examining the table, the message with the highest prediction score is identified, and its classification is noted.", "question": "Which message has the highest prediction score and what is its classification?", "answer": "So, the answer is {\"answer\": \"0.96, ACTIONABLE\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nMessages with a prediction score above 0.5 are classified as actionable.\n" }, { "role": "assistant", "content": "By checking the table, all messages with a prediction score above 0.5 are classified as actionable, supporting the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1392.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Non-Actionable vs Actionable Classification Examples" ], "reasoning": "By checking the table, all messages with a prediction score above 0.5 are classified as actionable, supporting the statement.", "statement": "Messages with a prediction score above 0.5 are classified as actionable.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: SERs (%) for models using all parameters in GLM-Softmax. The Overlapping Speech Model uses the approach of modifying the GLM-Softmax parameters for overlapping speech samples.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Combined v1 shows a 3.0% SER relative reduction over m1=1.10 and 24.6% relative over the baseline, while Combined v2 performs competitively with m1<1 making training harder and m2=0.20 ensuring cos(θt)⩾ψ(θt).The combined approach shows further improvements relative to the single parameter models. Combined v1 has a further 3.0% SER relative reduction over m1=1.10 (and 24.6% relative over the baseline), with Combined v2 performing competitively. The notable property about v2 is that m1<1 makes the training criteria harder as the model becomes more accurate, and m2=0.20 ensures cos(θt)⩾ψ(θt). For the Overlapping Speech Model (using the approach of changing parameters to (m1,m2,m3)=(1,0,0) for overlapping speech samples), since the AMI dataset includes a significant amount of overlapping speech, the best parameters to use are notably different from the conventional approach (of using just one set of parameters). Overall, the reduction in SER relative to the baseline is 17.0% and 40.4% for dev and eval respectively, giving an overall reduction of 29.5%." } ], "images": [ "scigen/train/1393.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: SERs (%) for models using all parameters in GLM-Softmax. The Overlapping Speech Model uses the approach of modifying the GLM-Softmax parameters for overlapping speech samples." ], "reasoning": "Combined v1 shows a 3.0% SER relative reduction over m1=1.10 and 24.6% relative over the baseline, while Combined v2 performs competitively with m1<1 making training harder and m2=0.20 ensuring cos(θt)⩾ψ(θt).", "description": "The combined approach shows further improvements relative to the single parameter models. Combined v1 has a further 3.0% SER relative reduction over m1=1.10 (and 24.6% relative over the baseline), with Combined v2 performing competitively. The notable property about v2 is that m1<1 makes the training criteria harder as the model becomes more accurate, and m2=0.20 ensures cos(θt)⩾ψ(θt). For the Overlapping Speech Model (using the approach of changing parameters to (m1,m2,m3)=(1,0,0) for overlapping speech samples), since the AMI dataset includes a significant amount of overlapping speech, the best parameters to use are notably different from the conventional approach (of using just one set of parameters). Overall, the reduction in SER relative to the baseline is 17.0% and 40.4% for dev and eval respectively, giving an overall reduction of 29.5%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: SERs (%) for models using all parameters in GLM-Softmax. The Overlapping Speech Model uses the approach of modifying the GLM-Softmax parameters for overlapping speech samples.', solve the subsequent question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the evaluation (Eval) SER for the Overlapping Speech Model?\n" }, { "role": "assistant", "content": "The table shows the evaluation (Eval) SER for the Overlapping Speech Model as 10.9. So, the answer is {\"answer\": \"10.9\"}." } ], "images": [ "scigen/train/1393.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: SERs (%) for models using all parameters in GLM-Softmax. The Overlapping Speech Model uses the approach of modifying the GLM-Softmax parameters for overlapping speech samples." ], "reasoning": "The table shows the evaluation (Eval) SER for the Overlapping Speech Model as 10.9.", "question": "What is the evaluation (Eval) SER for the Overlapping Speech Model?", "answer": "So, the answer is {\"answer\": \"10.9\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Combined v1 has a lower Dev SER than Combined v2. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nCombined v1 has a lower Dev SER than Combined v2.\n" }, { "role": "assistant", "content": "The table shows that Combined v1 has a Dev SER of 13.4, which is lower than Combined v2's Dev SER of 13.5. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1393.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: SERs (%) for models using all parameters in GLM-Softmax. The Overlapping Speech Model uses the approach of modifying the GLM-Softmax parameters for overlapping speech samples." ], "reasoning": "The table shows that Combined v1 has a Dev SER of 13.4, which is lower than Combined v2's Dev SER of 13.5.", "statement": "Combined v1 has a lower Dev SER than Combined v2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: Results of various models on specific-domain tasks (%).'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "K-BERT with CN-DBpedia improves performance by 1-2% in domain tasks due to domain knowledge in KG.Similarly, the specific-domain datasets are split into three parts: train, dev, and test, which are used to fine-tune, select and test model, respectively. Compared with BERT, K-BERT has a significant performance improvement in terms of domain tasks. As for F1, K-BERT with CN-DBpedia can improve the performance of all tasks by 1∼2%. The unique gain benefits from the domain knowledge in KG. From these results, we can conclude that KG, especially the domain KG, is very helpful for domain-specific tasks." } ], "images": [ "scigen/train/1394.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of various models on specific-domain tasks (%)." ], "reasoning": "K-BERT with CN-DBpedia improves performance by 1-2% in domain tasks due to domain knowledge in KG.", "description": "Similarly, the specific-domain datasets are split into three parts: train, dev, and test, which are used to fine-tune, select and test model, respectively. Compared with BERT, K-BERT has a significant performance improvement in terms of domain tasks. As for F1, K-BERT with CN-DBpedia can improve the performance of all tasks by 1∼2%. The unique gain benefits from the domain knowledge in KG. From these results, we can conclude that KG, especially the domain KG, is very helpful for domain-specific tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Results of various models on specific-domain tasks (%).\nQuestion: Which model has the highest F1 score for Medicine_NER?\nGive a detailed response to the above question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest F1 score for Medicine_NER, look at the Medicine_NER F1 column and identify the highest value, which is 94.2. So, the answer is {\"answer\": \"94.2\"}" } ], "images": [ "scigen/train/1394.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of various models on specific-domain tasks (%)." ], "reasoning": "To find the highest F1 score for Medicine_NER, look at the Medicine_NER F1 column and identify the highest value, which is 94.2.", "question": "Which model has the highest F1 score for Medicine_NER?", "answer": "So, the answer is {\"answer\": \"94.2\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim K-BERT (HowNet) has the highest F1 score for Finance_Q&A., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nK-BERT (HowNet) has the highest F1 score for Finance_Q&A.\n" }, { "role": "assistant", "content": "The highest F1 score for Finance_Q&A is 84.9, achieved by K-BERT (CN-DBpedia), not K-BERT (HowNet). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1394.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of various models on specific-domain tasks (%)." ], "reasoning": "The highest F1 score for Finance_Q&A is 84.9, achieved by K-BERT (CN-DBpedia), not K-BERT (HowNet).", "statement": "K-BERT (HowNet) has the highest F1 score for Finance_Q&A.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Results of various models on sentence classification tasks on open-domain tasks (Acc. %)'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "For semantic similarity tasks like XNLI and LCQMC, HowNet performs better, while for Q&A and NER tasks like NLPCC-DBQA and MSRA-NER, CN-DBpedia is more suitable.Each of the above datasets is divided into three parts: train, dev, and test. We use the train part to fine-tune the model and then evaluate its performance on the dev and test parts. The language KG (HowNet) performs better than the encyclopedic KG in terms of semantic similarity tasks (i.e., XNLI and LCQMC); (3) For Q&A and NER tasks (i.e., NLPCC-DBQA and MSRA-NER), the encyclopedic KG (CN-DBpedia) is more suitable than the language KG. Therefore, it is important to choose the right KG based on the type of task." } ], "images": [ "scigen/train/1395.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results of various models on sentence classification tasks on open-domain tasks (Acc. %)" ], "reasoning": "For semantic similarity tasks like XNLI and LCQMC, HowNet performs better, while for Q&A and NER tasks like NLPCC-DBQA and MSRA-NER, CN-DBpedia is more suitable.", "description": "Each of the above datasets is divided into three parts: train, dev, and test. We use the train part to fine-tune the model and then evaluate its performance on the dev and test parts. The language KG (HowNet) performs better than the encyclopedic KG in terms of semantic similarity tasks (i.e., XNLI and LCQMC); (3) For Q&A and NER tasks (i.e., NLPCC-DBQA and MSRA-NER), the encyclopedic KG (CN-DBpedia) is more suitable than the language KG. Therefore, it is important to choose the right KG based on the type of task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 1: Results of various models on sentence classification tasks on open-domain tasks (Acc. %)', and tackle the question below in a sequential manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model performs best on the Chnsenticorp Dev dataset?\n" }, { "role": "assistant", "content": "To find the best performing model on the Chnsenticorp Dev dataset, compare the scores of all models in the Chnsenticorp Dev column. The highest score is 95.4 by K-BERT (HowNet) pre-trained on WikiZh and WebtextZh by us. So, the answer is {\"answer\": \"K-BERT (HowNet)\"}." } ], "images": [ "scigen/train/1395.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results of various models on sentence classification tasks on open-domain tasks (Acc. %)" ], "reasoning": "To find the best performing model on the Chnsenticorp Dev dataset, compare the scores of all models in the Chnsenticorp Dev column. The highest score is 95.4 by K-BERT (HowNet) pre-trained on WikiZh and WebtextZh by us.", "question": "Which model performs best on the Chnsenticorp Dev dataset?", "answer": "So, the answer is {\"answer\": \"K-BERT (HowNet)\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: K-BERT (CN-DBpedia) performs better than K-BERT (HowNet) on the LCQMC Test dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nK-BERT (CN-DBpedia) performs better than K-BERT (HowNet) on the LCQMC Test dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the scores of K-BERT (CN-DBpedia) and K-BERT (HowNet) on the LCQMC Test dataset. K-BERT (CN-DBpedia) has a score of 87.1, while K-BERT (HowNet) has a score of 86.9. Therefore, K-BERT (CN-DBpedia) performs better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1395.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results of various models on sentence classification tasks on open-domain tasks (Acc. %)" ], "reasoning": "To verify this statement, compare the scores of K-BERT (CN-DBpedia) and K-BERT (HowNet) on the LCQMC Test dataset. K-BERT (CN-DBpedia) has a score of 87.1, while K-BERT (HowNet) has a score of 86.9. Therefore, K-BERT (CN-DBpedia) performs better.", "statement": "K-BERT (CN-DBpedia) performs better than K-BERT (HowNet) on the LCQMC Test dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Results of various models on NLPCC-DBQA (MRR %) and MSRA-NER (F1 %).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The language KG (HowNet) performs better for semantic similarity tasks, while the encyclopedic KG (CN-DBpedia) is more suitable for Q&A and NER tasks. Using an additional corpus (WebtextZh) can improve performance, but not as significantly as using the right KG.Each of the above datasets is divided into three parts: train, dev, and test. We use the train part to fine-tune the model and then evaluate its performance on the dev and test parts. The language KG (HowNet) performs better than the encyclopedic KG in terms of semantic similarity tasks (i.e., XNLI and LCQMC); (3) For Q&A and NER tasks (i.e., NLPCC-DBQA and MSRA-NER), the encyclopedic KG (CN-DBpedia) is more suitable than the language KG. Therefore, it is important to choose the right KG based on the type of task. In addition, it can be observed that the use of an additional corpus (WebtextZh) can also bring performance boost, but not as significant as KG." } ], "images": [ "scigen/train/1396.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of various models on NLPCC-DBQA (MRR %) and MSRA-NER (F1 %)." ], "reasoning": "The language KG (HowNet) performs better for semantic similarity tasks, while the encyclopedic KG (CN-DBpedia) is more suitable for Q&A and NER tasks. Using an additional corpus (WebtextZh) can improve performance, but not as significantly as using the right KG.", "description": "Each of the above datasets is divided into three parts: train, dev, and test. We use the train part to fine-tune the model and then evaluate its performance on the dev and test parts. The language KG (HowNet) performs better than the encyclopedic KG in terms of semantic similarity tasks (i.e., XNLI and LCQMC); (3) For Q&A and NER tasks (i.e., NLPCC-DBQA and MSRA-NER), the encyclopedic KG (CN-DBpedia) is more suitable than the language KG. Therefore, it is important to choose the right KG based on the type of task. In addition, it can be observed that the use of an additional corpus (WebtextZh) can also bring performance boost, but not as significant as KG.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: Results of various models on NLPCC-DBQA (MRR %) and MSRA-NER (F1 %).'.\n\nQuestion: Which model has the highest performance on the MSRA-NER Test dataset?\n" }, { "role": "assistant", "content": "To find the model with the highest performance on the MSRA-NER Test dataset, we need to compare the scores in the MSRA-NER Test column. The highest score is 95.7, achieved by K-BERT (CN-DBpedia) pre-trained on WikiZh by Google. So, the answer is {\"answer\": \"K-BERT (CN-DBpedia) pre-trained on WikiZh by Google\"}" } ], "images": [ "scigen/train/1396.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of various models on NLPCC-DBQA (MRR %) and MSRA-NER (F1 %)." ], "reasoning": "To find the model with the highest performance on the MSRA-NER Test dataset, we need to compare the scores in the MSRA-NER Test column. The highest score is 95.7, achieved by K-BERT (CN-DBpedia) pre-trained on WikiZh by Google.", "question": "Which model has the highest performance on the MSRA-NER Test dataset?", "answer": "So, the answer is {\"answer\": \"K-BERT (CN-DBpedia) pre-trained on WikiZh by Google\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nK-BERT (HowNet) pre-trained on WikiZh and WebtextZh by us has the highest performance on the NLPCC-DBQA Test dataset.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the scores in the NLPCC-DBQA Test column. K-BERT (HowNet) pre-trained on WikiZh and WebtextZh by us has a score of 93.2, while K-BERT (CN-DBpedia) pre-trained on WikiZh by Google has a higher score of 94.3. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1396.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of various models on NLPCC-DBQA (MRR %) and MSRA-NER (F1 %)." ], "reasoning": "To verify this statement, we need to compare the scores in the NLPCC-DBQA Test column. K-BERT (HowNet) pre-trained on WikiZh and WebtextZh by us has a score of 93.2, while K-BERT (CN-DBpedia) pre-trained on WikiZh by Google has a higher score of 94.3.", "statement": "K-BERT (HowNet) pre-trained on WikiZh and WebtextZh by us has the highest performance on the NLPCC-DBQA Test dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Mean ± standard deviation of various measurements on reviews in the ACL 2017 and ICLR 2017 sections of PeerRead. Note that ACL aspects were written by the reviewers themselves, while ICLR aspects were predicted by our annotators based on the review.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The mean scores for most measurements are similar between ACL 2017 and ICLR 2017, but ACL 2017 reviews are about 50% longer, which may indicate higher quality due to the opt-in nature of ACL reviews.Most of the mean scores are similar in both sections, with a few notable exceptions. The comments in ACL 2017 reviews tend to be about 50% longer than those in the ICLR 2017 reviews. Since review length is often thought of as a measure of its quality, this raises interesting questions about the quality of reviews in ICLR vs. ACL conferences. We note, however, that ACL 2017 reviews were explicitly opted-in while the ICLR 2017 reviews include all official reviews, which is likely to result in a positive bias in review quality of the ACL reviews included in this study." } ], "images": [ "scigen/train/1397.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Mean ± standard deviation of various measurements on reviews in the ACL 2017 and ICLR 2017 sections of PeerRead. Note that ACL aspects were written by the reviewers themselves, while ICLR aspects were predicted by our annotators based on the review." ], "reasoning": "The mean scores for most measurements are similar between ACL 2017 and ICLR 2017, but ACL 2017 reviews are about 50% longer, which may indicate higher quality due to the opt-in nature of ACL reviews.", "description": "Most of the mean scores are similar in both sections, with a few notable exceptions. The comments in ACL 2017 reviews tend to be about 50% longer than those in the ICLR 2017 reviews. Since review length is often thought of as a measure of its quality, this raises interesting questions about the quality of reviews in ICLR vs. ACL conferences. We note, however, that ACL 2017 reviews were explicitly opted-in while the ICLR 2017 reviews include all official reviews, which is likely to result in a positive bias in review quality of the ACL reviews included in this study.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: Table 4: Mean ± standard deviation of various measurements on reviews in the ACL 2017 and ICLR 2017 sections of PeerRead. Note that ACL aspects were written by the reviewers themselves, while ICLR aspects were predicted by our annotators based on the review.\nQuestion: Which conference had a higher mean score for appropriateness, and what does this imply about the quality of reviews?\n" }, { "role": "assistant", "content": "The table shows that ACL 2017 had a higher mean score for appropriateness (4.9) compared to ICLR 2017 (2.6). This implies that ACL 2017 reviews might be of higher quality in terms of appropriateness. So, the answer is {\"answer\": \"ACL 2017\"}." } ], "images": [ "scigen/train/1397.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Mean ± standard deviation of various measurements on reviews in the ACL 2017 and ICLR 2017 sections of PeerRead. Note that ACL aspects were written by the reviewers themselves, while ICLR aspects were predicted by our annotators based on the review." ], "reasoning": "The table shows that ACL 2017 had a higher mean score for appropriateness (4.9) compared to ICLR 2017 (2.6). This implies that ACL 2017 reviews might be of higher quality in terms of appropriateness.", "question": "Which conference had a higher mean score for appropriateness, and what does this imply about the quality of reviews?", "answer": "So, the answer is {\"answer\": \"ACL 2017\"}." }, { "messages": [ { "role": "user", "content": "The overall recommendation scores are the same for both ACL 2017 and ICLR 2017. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe overall recommendation scores are the same for both ACL 2017 and ICLR 2017.\n" }, { "role": "assistant", "content": "The table shows that the overall recommendation score for ACL 2017 is 3.3, and for ICLR 2017, it is also 3.3. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1397.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Mean ± standard deviation of various measurements on reviews in the ACL 2017 and ICLR 2017 sections of PeerRead. Note that ACL aspects were written by the reviewers themselves, while ICLR aspects were predicted by our annotators based on the review." ], "reasoning": "The table shows that the overall recommendation score for ACL 2017 is 3.3, and for ICLR 2017, it is also 3.3. Therefore, the statement is supported.", "statement": "The overall recommendation scores are the same for both ACL 2017 and ICLR 2017.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Mean review scores for each presentation format (oral vs. poster). Raw scores range between 1–5. For reference, the last column shows the sample standard deviation based on all reviews.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The average overall recommendation score for oral presentations is 0.9 higher than for poster presentations, indicating that oral presentations are generally stronger.Notably, the average ‘overall recommendation’ score in reviews recommending an oral presentation is 0.9 higher than in reviews recommending a poster presentation, suggesting that reviewers tend to recommend oral presentation for submissions which are holistically stronger." } ], "images": [ "scigen/train/1398.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Mean review scores for each presentation format (oral vs. poster). Raw scores range between 1–5. For reference, the last column shows the sample standard deviation based on all reviews." ], "reasoning": "The average overall recommendation score for oral presentations is 0.9 higher than for poster presentations, indicating that oral presentations are generally stronger.", "description": "Notably, the average ‘overall recommendation’ score in reviews recommending an oral presentation is 0.9 higher than in reviews recommending a poster presentation, suggesting that reviewers tend to recommend oral presentation for submissions which are holistically stronger.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Mean review scores for each presentation format (oral vs. poster). Raw scores range between 1–5. For reference, the last column shows the sample standard deviation based on all reviews.', answer the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the difference in the average 'substance' score between oral and poster presentations?\n" }, { "role": "assistant", "content": "The table shows that the average 'substance' score for oral presentations is 3.91 and for poster presentations is 3.29. The difference is 3.91 - 3.29 = 0.62. So, the answer is {\"answer\": \"0.62\"}." } ], "images": [ "scigen/train/1398.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Mean review scores for each presentation format (oral vs. poster). Raw scores range between 1–5. For reference, the last column shows the sample standard deviation based on all reviews." ], "reasoning": "The table shows that the average 'substance' score for oral presentations is 3.91 and for poster presentations is 3.29. The difference is 3.91 - 3.29 = 0.62.", "question": "What is the difference in the average 'substance' score between oral and poster presentations?", "answer": "So, the answer is {\"answer\": \"0.62\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The average 'clarity' score for oral presentations is higher than for poster presentations., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe average 'clarity' score for oral presentations is higher than for poster presentations.\n" }, { "role": "assistant", "content": "The table shows that the average 'clarity' score for oral presentations is 4.19 and for poster presentations is 3.72, which means the statement is true. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1398.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Mean review scores for each presentation format (oral vs. poster). Raw scores range between 1–5. For reference, the last column shows the sample standard deviation based on all reviews." ], "reasoning": "The table shows that the average 'clarity' score for oral presentations is 4.19 and for poster presentations is 3.72, which means the statement is true.", "statement": "The average 'clarity' score for oral presentations is higher than for poster presentations.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: Test accuracies (%) for acceptance classification. Our best model outperforms the majority classifiers in all cases.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our model consistently outperforms the majority classifier across all categories, achieving up to a 22% error reduction.Our best model outperforms the majority classifier in all cases, with up to 22% error reduction. Since our models lack the sophistication to assess the quality of the work discussed in the given paper, this might indicate that some of the features we define are correlated with strong papers, or bias reviewers’ judgments." } ], "images": [ "scigen/train/1399.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Test accuracies (%) for acceptance classification. Our best model outperforms the majority classifiers in all cases." ], "reasoning": "Our model consistently outperforms the majority classifier across all categories, achieving up to a 22% error reduction.", "description": "Our best model outperforms the majority classifier in all cases, with up to 22% error reduction. Since our models lack the sophistication to assess the quality of the work discussed in the given paper, this might indicate that some of the features we define are correlated with strong papers, or bias reviewers’ judgments.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 5: Test accuracies (%) for acceptance classification. Our best model outperforms the majority classifiers in all cases.' in mind, break down and resolve the question below step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the error reduction percentage for the cs.cl category?\n" }, { "role": "assistant", "content": "The table shows that the majority classifier has a score of 68.9 for cs.cl, while our model has a score of 75.7. The difference is 75.7 - 68.9 = 6.8, which is the error reduction percentage. So, the answer is {\"answer\": \"6.8\"}." } ], "images": [ "scigen/train/1399.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Test accuracies (%) for acceptance classification. Our best model outperforms the majority classifiers in all cases." ], "reasoning": "The table shows that the majority classifier has a score of 68.9 for cs.cl, while our model has a score of 75.7. The difference is 75.7 - 68.9 = 6.8, which is the error reduction percentage.", "question": "What is the error reduction percentage for the cs.cl category?", "answer": "So, the answer is {\"answer\": \"6.8\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Our model achieves a 7.7% error reduction in the ICLR category. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nOur model achieves a 7.7% error reduction in the ICLR category.\n" }, { "role": "assistant", "content": "The table shows that the majority classifier has a score of 57.6 for ICLR, while our model has a score of 65.3. The difference is 65.3 - 57.6 = 7.7, which supports the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1399.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Test accuracies (%) for acceptance classification. Our best model outperforms the majority classifiers in all cases." ], "reasoning": "The table shows that the majority classifier has a score of 57.6 for ICLR, while our model has a score of 65.3. The difference is 65.3 - 57.6 = 7.7, which supports the statement.", "statement": "Our model achieves a 7.7% error reduction in the ICLR category.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: Controlled generation and Text style transfer accuracy achieved by different methods.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "DE-VAE outperforms other methods in most datasets due to its disentangled feature representation, while DE-VAE-NR performs poorly despite low KL divergence. DAE is the closest competitor, and ctrlGen performs the worst.The superior performance of DE-VAE stems from the fact that it learns a disentangled feature representation where sentiment information is modeled in a single dimension which is independent of other dimensions. So, regulating the sentiment value along this designated dimension gives better control while generating sentences bearing the same sentiment. Further, in case of DE-VAE-NR, we train the parameters of the transformation network by maximizing the likelihood of \\zbs, i.e. \\EEqϕ(\\zbs|\\xb)pθ(\\zbs|\\zbf), such that the minimum KL between qϕ(\\zbf|\\zbs) and pθ(\\zbs|\\zbf) is found to be in the range 0.2−0.4 across all datasets; however, the performance of DE-VAE-NR is significantly inferior compared to DE-VAE. Close inspection reveals that even though the KL was low, as the decoded distribution \\zbs was not exactly the same as the encoded distribution, it was generating sentences very different from the original sentences. This shows the importance of invertible normalizing flow as a design choice. The closest competitor to our method is DAE for controlled text generation with comparable performance to DE-VAE. The reason is that DAE achieves disentanglement between style space and content space using extensive adversarial training and incorporates auxiliary multi-task loss for sentiment or style. Further, in comparison to other methods, ctrlGen performs poorly for controlled text generation. We speculate that ctrlGen has comparatively less control than other methods since it does not model feature representation from sentence representation and tries to control generation by providing only a one hot encoding of sentiment externally. We observe that entangleGen achieves better accuracy on Yelp dataset than our method; however, DE-VAE performs better on all other datasets. Since entangleGen inherently supports binary-valued attributes by design, it can best modify the sentiment value towards its opposite polarity using Fast-Gradient-Iterative-Modification. Hence, its performance suffers in IMDB data where there are more than two sentiment values." } ], "images": [ "scigen/train/1400.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Controlled generation and Text style transfer accuracy achieved by different methods." ], "reasoning": "DE-VAE outperforms other methods in most datasets due to its disentangled feature representation, while DE-VAE-NR performs poorly despite low KL divergence. DAE is the closest competitor, and ctrlGen performs the worst.", "description": "The superior performance of DE-VAE stems from the fact that it learns a disentangled feature representation where sentiment information is modeled in a single dimension which is independent of other dimensions. So, regulating the sentiment value along this designated dimension gives better control while generating sentences bearing the same sentiment. Further, in case of DE-VAE-NR, we train the parameters of the transformation network by maximizing the likelihood of \\zbs, i.e. \\EEqϕ(\\zbs|\\xb)pθ(\\zbs|\\zbf), such that the minimum KL between qϕ(\\zbf|\\zbs) and pθ(\\zbs|\\zbf) is found to be in the range 0.2−0.4 across all datasets; however, the performance of DE-VAE-NR is significantly inferior compared to DE-VAE. Close inspection reveals that even though the KL was low, as the decoded distribution \\zbs was not exactly the same as the encoded distribution, it was generating sentences very different from the original sentences. This shows the importance of invertible normalizing flow as a design choice. The closest competitor to our method is DAE for controlled text generation with comparable performance to DE-VAE. The reason is that DAE achieves disentanglement between style space and content space using extensive adversarial training and incorporates auxiliary multi-task loss for sentiment or style. Further, in comparison to other methods, ctrlGen performs poorly for controlled text generation. We speculate that ctrlGen has comparatively less control than other methods since it does not model feature representation from sentence representation and tries to control generation by providing only a one hot encoding of sentiment externally. We observe that entangleGen achieves better accuracy on Yelp dataset than our method; however, DE-VAE performs better on all other datasets. Since entangleGen inherently supports binary-valued attributes by design, it can best modify the sentiment value towards its opposite polarity using Fast-Gradient-Iterative-Modification. Hence, its performance suffers in IMDB data where there are more than two sentiment values.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: Controlled generation and Text style transfer accuracy achieved by different methods.', answer the question below by showing each progressive step toward the answer. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich method achieves the highest score for Yelp Text style transfer and what is the score?\n" }, { "role": "assistant", "content": "From the table, entangleGen achieves the highest score for Yelp Text style transfer with a score of 0.94. So, the answer is {\"answer\": \"entangleGen, 0.94\"}" } ], "images": [ "scigen/train/1400.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Controlled generation and Text style transfer accuracy achieved by different methods." ], "reasoning": "From the table, entangleGen achieves the highest score for Yelp Text style transfer with a score of 0.94.", "question": "Which method achieves the highest score for Yelp Text style transfer and what is the score?", "answer": "So, the answer is {\"answer\": \"entangleGen, 0.94\"}" }, { "messages": [ { "role": "user", "content": "DE-VAE-NR performs better than ctrlGen for Amazon Controlled generation. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nDE-VAE-NR performs better than ctrlGen for Amazon Controlled generation.\n" }, { "role": "assistant", "content": "The table shows that DE-VAE-NR has a score of 0.59 for Amazon Controlled generation, while ctrlGen has a score of 0.62. Therefore, DE-VAE-NR does not perform better than ctrlGen for Amazon Controlled generation. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1400.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Controlled generation and Text style transfer accuracy achieved by different methods." ], "reasoning": "The table shows that DE-VAE-NR has a score of 0.59 for Amazon Controlled generation, while ctrlGen has a score of 0.62. Therefore, DE-VAE-NR does not perform better than ctrlGen for Amazon Controlled generation.", "statement": "DE-VAE-NR performs better than ctrlGen for Amazon Controlled generation.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1. Overview of the datasets'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The dataset consists of seven product categories with QA pairs aligned to reviews, and helpful votes are used to classify answer helpfulness.Datasets and Evaluation Metrics We experiment with seven datasets from different product categories to validate the model effectiveness. The original question-answer pairs are from a public data collection crawled by Wan and McAuley (amazon-qa2). We also utilize the product ID in the QA dataset to align with the reviews in Amazon review dataset (amazon-review-dataset) so that the corresponding reviews of each product can be obtained. Following previous work (post-helpful; prh-net), we treat the helpful votes given by customers as a proxy of the helpfulness of each answer and model this task as a binary classification problem. Since users’ votes are not always reliable (suryanto2009quality) and people tend to upvote when they decide to vote an answer. We discard answers with less than two votes and treat the answer to be helpful if it receives helpfulness score being one (i.e. X/Y=1, Y≥2) to obtain a high standard notion of helpfulness and a more reliable dataset. However, we observe that answers with only one negative vote often provide reliable examples for unhelpful answers, they are kept in the dataset. We split the dataset in each product category into portions of 80:10:10 for training, validation, and testing respectively." } ], "images": [ "scigen/train/1401.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1. Overview of the datasets" ], "reasoning": "The dataset consists of seven product categories with QA pairs aligned to reviews, and helpful votes are used to classify answer helpfulness.", "description": "Datasets and Evaluation Metrics We experiment with seven datasets from different product categories to validate the model effectiveness. The original question-answer pairs are from a public data collection crawled by Wan and McAuley (amazon-qa2). We also utilize the product ID in the QA dataset to align with the reviews in Amazon review dataset (amazon-review-dataset) so that the corresponding reviews of each product can be obtained. Following previous work (post-helpful; prh-net), we treat the helpful votes given by customers as a proxy of the helpfulness of each answer and model this task as a binary classification problem. Since users’ votes are not always reliable (suryanto2009quality) and people tend to upvote when they decide to vote an answer. We discard answers with less than two votes and treat the answer to be helpful if it receives helpfulness score being one (i.e. X/Y=1, Y≥2) to obtain a high standard notion of helpfulness and a more reliable dataset. However, we observe that answers with only one negative vote often provide reliable examples for unhelpful answers, they are kept in the dataset. We split the dataset in each product category into portions of 80:10:10 for training, validation, and testing respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1. Overview of the datasets', answer the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich product category has the highest average length of answers (La)?\n" }, { "role": "assistant", "content": "To find the product category with the highest average length of answers, compare the La values for each category. The highest La value is 17.7 for Patio Lawn. So, the answer is {\"answer\": \"Patio Lawn\"}" } ], "images": [ "scigen/train/1401.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1. Overview of the datasets" ], "reasoning": "To find the product category with the highest average length of answers, compare the La values for each category. The highest La value is 17.7 for Patio Lawn.", "question": "Which product category has the highest average length of answers (La)?", "answer": "So, the answer is {\"answer\": \"Patio Lawn\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Health category has more QA pairs than the Sports category.\n" }, { "role": "assistant", "content": "The table shows that the Health category has 22,377 QA pairs, while the Sports category has 23,337 QA pairs. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1401.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1. Overview of the datasets" ], "reasoning": "The table shows that the Health category has 22,377 QA pairs, while the Sports category has 23,337 QA pairs. Therefore, the statement is refuted.", "statement": "The Health category has more QA pairs than the Sports category.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2. Comparison of the F1 and AUROC scores between RAHP-Base, RAHP-NLI and comparative models', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "RAHP-Base consistently outperforms other models across all domains, with improvements varying by category, and larger overlapping vocabularies with the SNLI dataset generally lead to larger improvements.Overall, our proposed methods substantially and consistently outperform those baseline methods in all domains. Concretely, we can observe that answer selection models generally provide strong baselines for the concerned helpfulness prediction task. Especially models with advanced architecture (e.g. ESIM model) achieves good performance due to the fact that those complicated models explicitly consider the fine-granularity relations between QA pairs. However, RAHP-Base consistently outperforms them, which demonstrates the effectiveness and necessity of taking relevant reviews into consideration. However, the improvements vary from category to category. To gain some insight of what factor causes such difference, we investigate the ratio of the overlapping vocabularies between datasets of several product categories with the SNLI dataset, since the domain difference is often a key factor in transfer learning (conversation-google). We can see that there exists a trend between the overlapping ratio and the relative improvements: larger overlapping ration generally leads to a larger improvement. For example, the performance on the Phone category has been improved for about 3.5% with the overlapping ratio of 40%, while the performance of the Home category with the overlapping ratio being 18% almost does not change. This result also suggests that one effective approach for further improving the performance, especially for categories with low performance, can be achieved by providing additional in-domain labeled data of those categories." } ], "images": [ "scigen/train/1402.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2. Comparison of the F1 and AUROC scores between RAHP-Base, RAHP-NLI and comparative models" ], "reasoning": "RAHP-Base consistently outperforms other models across all domains, with improvements varying by category, and larger overlapping vocabularies with the SNLI dataset generally lead to larger improvements.", "description": "Overall, our proposed methods substantially and consistently outperform those baseline methods in all domains. Concretely, we can observe that answer selection models generally provide strong baselines for the concerned helpfulness prediction task. Especially models with advanced architecture (e.g. ESIM model) achieves good performance due to the fact that those complicated models explicitly consider the fine-granularity relations between QA pairs. However, RAHP-Base consistently outperforms them, which demonstrates the effectiveness and necessity of taking relevant reviews into consideration. However, the improvements vary from category to category. To gain some insight of what factor causes such difference, we investigate the ratio of the overlapping vocabularies between datasets of several product categories with the SNLI dataset, since the domain difference is often a key factor in transfer learning (conversation-google). We can see that there exists a trend between the overlapping ratio and the relative improvements: larger overlapping ration generally leads to a larger improvement. For example, the performance on the Phone category has been improved for about 3.5% with the overlapping ratio of 40%, while the performance of the Home category with the overlapping ratio being 18% almost does not change. This result also suggests that one effective approach for further improving the performance, especially for categories with low performance, can be achieved by providing additional in-domain labeled data of those categories.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2. Comparison of the F1 and AUROC scores between RAHP-Base, RAHP-NLI and comparative models' and methodically tackle the question that follows. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model achieves the highest F1 score in the Phones category?\n" }, { "role": "assistant", "content": "To find the model with the highest F1 score in the Phones category, we need to compare the F1 scores of all models listed under the Phones F1 column. RAHP-NLI has the highest F1 score of 0.629. So, the answer is {\"answer\": \"RAHP-NLI\"}." } ], "images": [ "scigen/train/1402.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2. Comparison of the F1 and AUROC scores between RAHP-Base, RAHP-NLI and comparative models" ], "reasoning": "To find the model with the highest F1 score in the Phones category, we need to compare the F1 scores of all models listed under the Phones F1 column. RAHP-NLI has the highest F1 score of 0.629.", "question": "Which model achieves the highest F1 score in the Phones category?", "answer": "So, the answer is {\"answer\": \"RAHP-NLI\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe ESIM model achieves the highest AUC score in the Health category.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the AUC scores of all models listed under the Health AUC column. The ESIM model has an AUC score of 0.786, which is the highest in the Health category. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1402.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2. Comparison of the F1 and AUROC scores between RAHP-Base, RAHP-NLI and comparative models" ], "reasoning": "To verify this statement, we need to compare the AUC scores of all models listed under the Health AUC column. The ESIM model has an AUC score of 0.786, which is the highest in the Health category.", "statement": "The ESIM model achieves the highest AUC score in the Health category.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3. Ablation experiments with reported F1 scores'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Removing the RA coherence component from RAHP-Base results in a significant performance decrease, especially in the Health domain, due to the subjective and diverse nature of questions and answers in this domain.To investigate the effectiveness of each component in RAHP, we conduct ablation tests by removing different modules of RAHP-Base. The results show that the model without the RA coherence component (RAHP w/o RA coherence) suffers a large decrease, indicating that considering the opinion coherence between the answer and reviews contribute to the final performance. One interesting phenomenon is that different domains suffer different degrees of such performance decrease. For example, the Health domain has a larger degradation compared with other domains. This may due to the fact that questions and answers in this domain are much more subjective and diverse, making the reviews are less discriminative to help identify helpful answers." } ], "images": [ "scigen/train/1403.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. Ablation experiments with reported F1 scores" ], "reasoning": "Removing the RA coherence component from RAHP-Base results in a significant performance decrease, especially in the Health domain, due to the subjective and diverse nature of questions and answers in this domain.", "description": "To investigate the effectiveness of each component in RAHP, we conduct ablation tests by removing different modules of RAHP-Base. The results show that the model without the RA coherence component (RAHP w/o RA coherence) suffers a large decrease, indicating that considering the opinion coherence between the answer and reviews contribute to the final performance. One interesting phenomenon is that different domains suffer different degrees of such performance decrease. For example, the Health domain has a larger degradation compared with other domains. This may due to the fact that questions and answers in this domain are much more subjective and diverse, making the reviews are less discriminative to help identify helpful answers.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3. Ablation experiments with reported F1 scores', unfold the question presented below, detailing every step of your calculation. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich domain shows the largest performance decrease when the RA coherence component is removed?\n" }, { "role": "assistant", "content": "By comparing the performance of RAHP-Base and RAHP w/o RA coherence across all domains, the Health domain shows the largest decrease from 0.657 to 0.641. So, the answer is {\"answer\": \"Health\"}" } ], "images": [ "scigen/train/1403.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. Ablation experiments with reported F1 scores" ], "reasoning": "By comparing the performance of RAHP-Base and RAHP w/o RA coherence across all domains, the Health domain shows the largest decrease from 0.657 to 0.641.", "question": "Which domain shows the largest performance decrease when the RA coherence component is removed?", "answer": "So, the answer is {\"answer\": \"Health\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe performance of RAHP-Base in the Sports domain is higher than in the Phones domain.\n" }, { "role": "assistant", "content": "The table shows that the performance of RAHP-Base in the Sports domain is 0.655, which is higher than the performance in the Phones domain, which is 0.608. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1403.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. Ablation experiments with reported F1 scores" ], "reasoning": "The table shows that the performance of RAHP-Base in the Sports domain is 0.655, which is higher than the performance in the Phones domain, which is 0.608.", "statement": "The performance of RAHP-Base in the Sports domain is higher than in the Phones domain.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 8: Same as Table 7, except filtering for word types that occur fewer than 5 times in the training set. The cache component is used as expected even on rare words: proper nouns are extremely likely to have been cache-generated, whereas numbers and generic content words are less likely to have been; this indicates both the effectiveness of the prior at determining whether to use the cache and the burstiness of proper nouns.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Proper nouns like 'Nero' and 'Osbert' have high cache-generated probabilities, while numbers and generic words like '246' and 'Lo' have lower probabilities.Tab. The pattern here is similar to the unfiltered list: proper nouns are extremely likely to have been cache-generated, whereas numbers and generic (albeit infrequent ) content words are less likely to have been." } ], "images": [ "scigen/train/1404.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Same as Table 7, except filtering for word types that occur fewer than 5 times in the training set. The cache component is used as expected even on rare words: proper nouns are extremely likely to have been cache-generated, whereas numbers and generic content words are less likely to have been; this indicates both the effectiveness of the prior at determining whether to use the cache and the burstiness of proper nouns." ], "reasoning": "Proper nouns like 'Nero' and 'Osbert' have high cache-generated probabilities, while numbers and generic words like '246' and 'Lo' have lower probabilities.", "description": "Tab. The pattern here is similar to the unfiltered list: proper nouns are extremely likely to have been cache-generated, whereas numbers and generic (albeit infrequent ) content words are less likely to have been.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 8: Same as Table 7, except filtering for word types that occur fewer than 5 times in the training set. The cache component is used as expected even on rare words: proper nouns are extremely likely to have been cache-generated, whereas numbers and generic content words are less likely to have been; this indicates both the effectiveness of the prior at determining whether to use the cache and the burstiness of proper nouns.' and methodically tackle the question that follows. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich word has the highest cache-generated probability in the 'Word Gore' column?\n" }, { "role": "assistant", "content": "To find the word with the highest cache-generated probability in the 'Word Gore' column, look for the word with the highest value in the 'p(z|w)↓' column. The highest value is 0.977, which corresponds to the word 'Nero'. So, the answer is {\"answer\": \"Nero\"}." } ], "images": [ "scigen/train/1404.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Same as Table 7, except filtering for word types that occur fewer than 5 times in the training set. The cache component is used as expected even on rare words: proper nouns are extremely likely to have been cache-generated, whereas numbers and generic content words are less likely to have been; this indicates both the effectiveness of the prior at determining whether to use the cache and the burstiness of proper nouns." ], "reasoning": "To find the word with the highest cache-generated probability in the 'Word Gore' column, look for the word with the highest value in the 'p(z|w)↓' column. The highest value is 0.977, which corresponds to the word 'Nero'.", "question": "Which word has the highest cache-generated probability in the 'Word Gore' column?", "answer": "So, the answer is {\"answer\": \"Nero\"}." }, { "messages": [ { "role": "user", "content": "The word 'Pitcher' has a higher cache-generated probability than 'Poets'. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe word 'Pitcher' has a higher cache-generated probability than 'Poets'.\n" }, { "role": "assistant", "content": "To verify this statement, compare the probabilities of 'Pitcher' and 'Poets' in the 'Word 770' column. 'Pitcher' has a probability of 0.142, while 'Poets' has a probability of 0.143. Since 0.142 is less than 0.143, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1404.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Same as Table 7, except filtering for word types that occur fewer than 5 times in the training set. The cache component is used as expected even on rare words: proper nouns are extremely likely to have been cache-generated, whereas numbers and generic content words are less likely to have been; this indicates both the effectiveness of the prior at determining whether to use the cache and the burstiness of proper nouns." ], "reasoning": "To verify this statement, compare the probabilities of 'Pitcher' and 'Poets' in the 'Word 770' column. 'Pitcher' has a probability of 0.142, while 'Poets' has a probability of 0.143. Since 0.142 is less than 0.143, the statement is refuted.", "statement": "The word 'Pitcher' has a higher cache-generated probability than 'Poets'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: Results on PTB Corpus (bits-per-character). HCLM augmented with a cache obtains the best results among models which have approximately the same numbers of parameter as single layer LSTM with 1,000 hidden units.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The HCLM model achieved 1.276 bpc, outperforming the LSTM with Zoneout regularization, while the HCLM with cache further improved to 1.247 bpc, achieving competitive results with state-of-the-art models.Ptb Tab. Our baseline HCLM model achieved 1.276 bpc which is better performance than the LSTM with Zoneout regularization Krueger et al. And HCLM with cache outperformed the baseline model with 1.247 bpc and achieved competitive results with state-of-the-art models with regularization on recurrence weights, which was not used in our experiments." } ], "images": [ "scigen/train/1405.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on PTB Corpus (bits-per-character). HCLM augmented with a cache obtains the best results among models which have approximately the same numbers of parameter as single layer LSTM with 1,000 hidden units." ], "reasoning": "The HCLM model achieved 1.276 bpc, outperforming the LSTM with Zoneout regularization, while the HCLM with cache further improved to 1.247 bpc, achieving competitive results with state-of-the-art models.", "description": "Ptb Tab. Our baseline HCLM model achieved 1.276 bpc which is better performance than the LSTM with Zoneout regularization Krueger et al. And HCLM with cache outperformed the baseline model with 1.247 bpc and achieved competitive results with state-of-the-art models with regularization on recurrence weights, which was not used in our experiments.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 4: Results on PTB Corpus (bits-per-character). HCLM augmented with a cache obtains the best results among models which have approximately the same numbers of parameter as single layer LSTM with 1,000 hidden units.'. Solve the following question based on the table.\n\nQuestion: Which model achieved the best performance on the test set?\nLet's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the test set bpc values of all models, the 2-LayerNorm HyperLSTM Ha et al. (2017) model achieved the lowest bpc of 1.219. So, the answer is {\"answer\": \"2-LayerNorm HyperLSTM Ha et al. (2017)\"}." } ], "images": [ "scigen/train/1405.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on PTB Corpus (bits-per-character). HCLM augmented with a cache obtains the best results among models which have approximately the same numbers of parameter as single layer LSTM with 1,000 hidden units." ], "reasoning": "By comparing the test set bpc values of all models, the 2-LayerNorm HyperLSTM Ha et al. (2017) model achieved the lowest bpc of 1.219.", "question": "Which model achieved the best performance on the test set?", "answer": "So, the answer is {\"answer\": \"2-LayerNorm HyperLSTM Ha et al. (2017)\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe HCLM model with cache achieved better performance than the HyperNetwork Ha et al. (2017) model.\n" }, { "role": "assistant", "content": "The HCLM with cache achieved a test bpc of 1.247, while the HyperNetwork Ha et al. (2017) model achieved a test bpc of 1.265, indicating that the HCLM with cache performed better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1405.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on PTB Corpus (bits-per-character). HCLM augmented with a cache obtains the best results among models which have approximately the same numbers of parameter as single layer LSTM with 1,000 hidden units." ], "reasoning": "The HCLM with cache achieved a test bpc of 1.247, while the HyperNetwork Ha et al. (2017) model achieved a test bpc of 1.265, indicating that the HCLM with cache performed better.", "statement": "The HCLM model with cache achieved better performance than the HyperNetwork Ha et al. (2017) model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: Results on WikiText-2 Corpus .'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The HCLM with cache model achieved the best performance with 1.500 bpc on the test set, outperforming the LSTM and HCLM models.WikiText-2 Tab. Our baseline, LSTM achieved 1.803 bpc and HCLM model achieved 1.670 bpc. The HCLM with cache outperformed the baseline models and achieved 1.500 bpc. The word level perplexity is 227.30, which is quite high compared to the reported word level baseline result 100.9 with LSTM with ZoneOut and Variational Dropout regularization Merity et al. However, the character-level model is dealing with 76,136 types in training set and 5.87% OOV rate where the word level models only use 33,278 types without OOV in test set. The improvement rate over the HCLM baseline is 10.2% which is much higher than the improvement rate obtained in the PTB experiment." } ], "images": [ "scigen/train/1406.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on WikiText-2 Corpus ." ], "reasoning": "The HCLM with cache model achieved the best performance with 1.500 bpc on the test set, outperforming the LSTM and HCLM models.", "description": "WikiText-2 Tab. Our baseline, LSTM achieved 1.803 bpc and HCLM model achieved 1.670 bpc. The HCLM with cache outperformed the baseline models and achieved 1.500 bpc. The word level perplexity is 227.30, which is quite high compared to the reported word level baseline result 100.9 with LSTM with ZoneOut and Variational Dropout regularization Merity et al. However, the character-level model is dealing with 76,136 types in training set and 5.87% OOV rate where the word level models only use 33,278 types without OOV in test set. The improvement rate over the HCLM baseline is 10.2% which is much higher than the improvement rate obtained in the PTB experiment.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Results on WikiText-2 Corpus .\nQuestion: Which model achieved the lowest bpc on the test set?\nSolve the above question based on the table information. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the bpc values for different models on the test set. The HCLM with cache model achieved the lowest bpc of 1.500. So, the answer is {\"answer\": \"HCLM with Cache\"}" } ], "images": [ "scigen/train/1406.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on WikiText-2 Corpus ." ], "reasoning": "The table shows the bpc values for different models on the test set. The HCLM with cache model achieved the lowest bpc of 1.500.", "question": "Which model achieved the lowest bpc on the test set?", "answer": "So, the answer is {\"answer\": \"HCLM with Cache\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe LSTM model achieved a lower bpc on the test set compared to the HCLM model.\n" }, { "role": "assistant", "content": "The table shows that the LSTM model achieved 1.803 bpc on the test set, while the HCLM model achieved 1.670 bpc, indicating that the statement is false. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1406.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on WikiText-2 Corpus ." ], "reasoning": "The table shows that the LSTM model achieved 1.803 bpc on the test set, while the HCLM model achieved 1.670 bpc, indicating that the statement is false.", "statement": "The LSTM model achieved a lower bpc on the test set compared to the HCLM model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 6: Results on MWC Corpus (bits-per-character).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The cache mechanism improves performance in every language, with the best improvement rate observed in Finnish at 4.7%.Similarly to WikiText-2 experiments, LSTM is strong baseline. We observe that the cache mechanism improve performance in every languages. In English, HCLM with cache achieved 1.538 bpc where the baseline is 1.622 bpc. It is 5.2% improvement. For other languages, the improvement rates were 2.7%, 3.2%, 3.7%, 2.5%, 4.7%, 2.7% in FR, DE, ES, CS, FI, RU respectively. The best improvement rate was obtained in Finnish." } ], "images": [ "scigen/train/1407.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results on MWC Corpus (bits-per-character)." ], "reasoning": "The cache mechanism improves performance in every language, with the best improvement rate observed in Finnish at 4.7%.", "description": "Similarly to WikiText-2 experiments, LSTM is strong baseline. We observe that the cache mechanism improve performance in every languages. In English, HCLM with cache achieved 1.538 bpc where the baseline is 1.622 bpc. It is 5.2% improvement. For other languages, the improvement rates were 2.7%, 3.2%, 3.7%, 2.5%, 4.7%, 2.7% in FR, DE, ES, CS, FI, RU respectively. The best improvement rate was obtained in Finnish.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 6: Results on MWC Corpus (bits-per-character).' to sequentially solve the question presented below. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhat is the bpc improvement rate for HCLM with cache in English compared to the baseline?\n" }, { "role": "assistant", "content": "The baseline bpc for English is 1.622, and HCLM with cache achieved 1.538 bpc. The improvement rate is calculated as (1.622 - 1.538) / 1.622 * 100%, which is 5.2%. So, the answer is {\"answer\": \"5.2%\"}" } ], "images": [ "scigen/train/1407.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results on MWC Corpus (bits-per-character)." ], "reasoning": "The baseline bpc for English is 1.622, and HCLM with cache achieved 1.538 bpc. The improvement rate is calculated as (1.622 - 1.538) / 1.622 * 100%, which is 5.2%.", "question": "What is the bpc improvement rate for HCLM with cache in English compared to the baseline?", "answer": "So, the answer is {\"answer\": \"5.2%\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The improvement rate for HCLM with cache in Russian is higher than in Spanish. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe improvement rate for HCLM with cache in Russian is higher than in Spanish.\n" }, { "role": "assistant", "content": "The improvement rate for Russian is 2.7%, and for Spanish, it is 3.7%. Since 2.7% is less than 3.7%, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1407.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results on MWC Corpus (bits-per-character)." ], "reasoning": "The improvement rate for Russian is 2.7%, and for Spanish, it is 3.7%. Since 2.7% is less than 3.7%, the statement is refuted.", "statement": "The improvement rate for HCLM with cache in Russian is higher than in Spanish.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: BLEU scores on three translation tasks. * and ** mean the improvements over Transformer is statistically significant [collins2005clause] (\\rho<0.01 and \\rho<0.05, respectively).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Our method with \\mathcal{L}_{\\mathrm{c}} significantly outperforms all baselines on CN→EN and EN→RO translation tasks, indicating its effectiveness.We can see that the improvement brought by +RL and +BOW methods are not great. For the +RL method, as we all know, the training is not stable and thus it is difficult to converge to good optima. For the +BOW, the reason may be it does not model the word order in the training loss and hence has a loose supervision to the fluency. On the CN\\rightarrowEN and EN\\rightarrowRO translation tasks, the loss \\mathcal{L}_{\\mathrm{c}} is much more effective than \\mathcal{L}_{\\mathrm{KL}} while on the EN\\rightarrowDE translation the result is reversed. Another finding is that all the methods cannot achieve big improvements on the EN\\rightarrowDE translation. The reason may be the training data is big enough and the two languages EN and DE are closed to each other. As a result, Transformer has already got a good enough optimization and it is difficult for further improvements. Our method with \\mathcal{L}_{\\mathrm{c}} as an additional loss can outperform all the baselines significantly on the CN\\rightarrowEN and EN\\rightarrowRO translation tasks. Therefore, we can conclude that the evaluation module can help improve translation performance and the loss \\mathcal{L}_{\\mathrm{c}} is more reasonable." } ], "images": [ "scigen/train/1408.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: BLEU scores on three translation tasks. * and ** mean the improvements over Transformer is statistically significant [collins2005clause] (\\rho<0.01 and \\rho<0.05, respectively)." ], "reasoning": "Our method with \\mathcal{L}_{\\mathrm{c}} significantly outperforms all baselines on CN→EN and EN→RO translation tasks, indicating its effectiveness.", "description": "We can see that the improvement brought by +RL and +BOW methods are not great. For the +RL method, as we all know, the training is not stable and thus it is difficult to converge to good optima. For the +BOW, the reason may be it does not model the word order in the training loss and hence has a loose supervision to the fluency. On the CN\\rightarrowEN and EN\\rightarrowRO translation tasks, the loss \\mathcal{L}_{\\mathrm{c}} is much more effective than \\mathcal{L}_{\\mathrm{KL}} while on the EN\\rightarrowDE translation the result is reversed. Another finding is that all the methods cannot achieve big improvements on the EN\\rightarrowDE translation. The reason may be the training data is big enough and the two languages EN and DE are closed to each other. As a result, Transformer has already got a good enough optimization and it is difficult for further improvements. Our method with \\mathcal{L}_{\\mathrm{c}} as an additional loss can outperform all the baselines significantly on the CN\\rightarrowEN and EN\\rightarrowRO translation tasks. Therefore, we can conclude that the evaluation module can help improve translation performance and the loss \\mathcal{L}_{\\mathrm{c}} is more reasonable.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 1: BLEU scores on three translation tasks. * and ** mean the improvements over Transformer is statistically significant [collins2005clause] (\\rho<0.01 and \\rho<0.05, respectively).', unfold the steps to solve the question given next. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nQuestion: Which method shows the highest improvement in the CN→EN translation task on MT08?\n" }, { "role": "assistant", "content": "The table shows the scores for different methods on various translation tasks. For the CN→EN translation task on MT08, 'Our Method' has the highest score of 36.78. So, the answer is {\"answer\": \"Our Method\"}" } ], "images": [ "scigen/train/1408.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: BLEU scores on three translation tasks. * and ** mean the improvements over Transformer is statistically significant [collins2005clause] (\\rho<0.01 and \\rho<0.05, respectively)." ], "reasoning": "The table shows the scores for different methods on various translation tasks. For the CN→EN translation task on MT08, 'Our Method' has the highest score of 36.78.", "question": "Which method shows the highest improvement in the CN→EN translation task on MT08?", "answer": "So, the answer is {\"answer\": \"Our Method\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The +RL method shows the highest improvement in the EN→RO translation task. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe +RL method shows the highest improvement in the EN→RO translation task.\n" }, { "role": "assistant", "content": "The table shows that 'Our Method' has the highest improvement in the EN→RO translation task with a score of 34.00 and a delta of +1.15, not the +RL method. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1408.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: BLEU scores on three translation tasks. * and ** mean the improvements over Transformer is statistically significant [collins2005clause] (\\rho<0.01 and \\rho<0.05, respectively)." ], "reasoning": "The table shows that 'Our Method' has the highest improvement in the EN→RO translation task with a score of 34.00 and a delta of +1.15, not the +RL method.", "statement": "The +RL method shows the highest improvement in the EN→RO translation task.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Performance comparison of the translation and evaluation modules on the CN\\rightarrowEN and EN\\rightarrowDE translation tasks. Ground truth is fed to both the modules and only one reference was used for the CN\\rightarrowEN translation.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The CN→EN translation has a larger margin of improvement compared to the EN→DE translation, suggesting that optimizing the evaluation module can significantly enhance the translation module.The margin is bigger on the CN\\rightarrowEN translation than on the EN\\rightarrowDE translation which also gives an explanation to why the improvement on the CN\\rightarrowEN is much bigger. Then we can conclude that it is reasonable to leverage the evaluation module to help optimize the translation module." } ], "images": [ "scigen/train/1409.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance comparison of the translation and evaluation modules on the CN\\rightarrowEN and EN\\rightarrowDE translation tasks. Ground truth is fed to both the modules and only one reference was used for the CN\\rightarrowEN translation." ], "reasoning": "The CN→EN translation has a larger margin of improvement compared to the EN→DE translation, suggesting that optimizing the evaluation module can significantly enhance the translation module.", "description": "The margin is bigger on the CN\\rightarrowEN translation than on the EN\\rightarrowDE translation which also gives an explanation to why the improvement on the CN\\rightarrowEN is much bigger. Then we can conclude that it is reasonable to leverage the evaluation module to help optimize the translation module.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Performance comparison of the translation and evaluation modules on the CN\\rightarrowEN and EN\\rightarrowDE translation tasks. Ground truth is fed to both the modules and only one reference was used for the CN\\rightarrowEN translation.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. \n\nQuestion: What is the average score for the Eval module?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the average score for the Eval module, sum the scores for MT03, MT04, MT05, MT06, and MT08, then divide by the number of scores. The scores are 45.86, 39.34, 44.18, 45.21, and 36.02. The sum is 210.61, and the average is 210.61 / 5 = 42.12. So, the answer is {\"answer\": \"42.12\"}." } ], "images": [ "scigen/train/1409.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance comparison of the translation and evaluation modules on the CN\\rightarrowEN and EN\\rightarrowDE translation tasks. Ground truth is fed to both the modules and only one reference was used for the CN\\rightarrowEN translation." ], "reasoning": "To find the average score for the Eval module, sum the scores for MT03, MT04, MT05, MT06, and MT08, then divide by the number of scores. The scores are 45.86, 39.34, 44.18, 45.21, and 36.02. The sum is 210.61, and the average is 210.61 / 5 = 42.12.", "question": "What is the average score for the Eval module?", "answer": "So, the answer is {\"answer\": \"42.12\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Eval module has a higher average score than the Tran module.\n" }, { "role": "assistant", "content": "The average score for the Eval module is 42.12, while the average score for the Tran module is 27.41. Since 42.12 is greater than 27.41, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1409.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance comparison of the translation and evaluation modules on the CN\\rightarrowEN and EN\\rightarrowDE translation tasks. Ground truth is fed to both the modules and only one reference was used for the CN\\rightarrowEN translation." ], "reasoning": "The average score for the Eval module is 42.12, while the average score for the Tran module is 27.41. Since 42.12 is greater than 27.41, the statement is supported.", "statement": "The Eval module has a higher average score than the Tran module.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: N-gram accuracy and cosine similarity on CN\\rightarrowEN translation. N-gram accuracy is the average on all the test sets. Cosine similarity is calculated with the average embeddings of the translation against that of the reference.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Our method achieves higher accuracy in 1-gram, 2-gram, 3-gram, and 4-gram metrics, as well as a higher cosine similarity, indicating better fluency and faithfulness in translation.Higher 1-gram accuracy indicates that our method can reach more ground truth in optimization and this is another proof that the loss we use can lead to better optima. Besides, the greater accuracy on n-gram, especially on 3-gram and 4-gram, implies better fluency. Furthermore, our method can have a bigger cosine similarity to the reference and this means the generate translation is more faithful in meaning to the source sentence. In conclusion, our method can produce translation with better fluency and faithfulness." } ], "images": [ "scigen/train/1410.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: N-gram accuracy and cosine similarity on CN\\rightarrowEN translation. N-gram accuracy is the average on all the test sets. Cosine similarity is calculated with the average embeddings of the translation against that of the reference." ], "reasoning": "Our method achieves higher accuracy in 1-gram, 2-gram, 3-gram, and 4-gram metrics, as well as a higher cosine similarity, indicating better fluency and faithfulness in translation.", "description": "Higher 1-gram accuracy indicates that our method can reach more ground truth in optimization and this is another proof that the loss we use can lead to better optima. Besides, the greater accuracy on n-gram, especially on 3-gram and 4-gram, implies better fluency. Furthermore, our method can have a bigger cosine similarity to the reference and this means the generate translation is more faithful in meaning to the source sentence. In conclusion, our method can produce translation with better fluency and faithfulness.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: N-gram accuracy and cosine similarity on CN\\rightarrowEN translation. N-gram accuracy is the average on all the test sets. Cosine similarity is calculated with the average embeddings of the translation against that of the reference.', answer the following question. Give detailed solution consisting of each step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich method has the highest 3-gram accuracy and what does it imply?\n" }, { "role": "assistant", "content": "Our method has the highest 3-gram accuracy at 36.90, which implies better fluency in translation. So, the answer is {\"answer\": \"Our method\"}" } ], "images": [ "scigen/train/1410.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: N-gram accuracy and cosine similarity on CN\\rightarrowEN translation. N-gram accuracy is the average on all the test sets. Cosine similarity is calculated with the average embeddings of the translation against that of the reference." ], "reasoning": "Our method has the highest 3-gram accuracy at 36.90, which implies better fluency in translation.", "question": "Which method has the highest 3-gram accuracy and what does it imply?", "answer": "So, the answer is {\"answer\": \"Our method\"}" }, { "messages": [ { "role": "user", "content": "The Transformer method has a higher cosine similarity than Our method. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Transformer method has a higher cosine similarity than Our method.\n" }, { "role": "assistant", "content": "The table shows that the cosine similarity for the Transformer method is 0.873, while for Our method it is 0.877, indicating that the statement is false. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1410.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: N-gram accuracy and cosine similarity on CN\\rightarrowEN translation. N-gram accuracy is the average on all the test sets. Cosine similarity is calculated with the average embeddings of the translation against that of the reference." ], "reasoning": "The table shows that the cosine similarity for the Transformer method is 0.873, while for Our method it is 0.877, indicating that the statement is false.", "statement": "The Transformer method has a higher cosine similarity than Our method.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 10: Nearest neighbours of ‘_monopoly’ in the embedding space.'.\n" }, { "role": "assistant", "content": "The table compares the cosine similarity distances of sub-words between different models, with DPQ variants trained with K=D=128 and no subspace-sharing.Distance between two sub-words is measured by the cosine similarity of their embedding vectors. Baseline is the original full embeddings model. DPQ variants were trained with K=D=128 with no subspace-sharing." } ], "images": [ "scigen/train/1411.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Nearest neighbours of ‘_monopoly’ in the embedding space." ], "reasoning": "The table compares the cosine similarity distances of sub-words between different models, with DPQ variants trained with K=D=128 and no subspace-sharing.", "description": "Distance between two sub-words is measured by the cosine similarity of their embedding vectors. Baseline is the original full embeddings model. DPQ variants were trained with K=D=128 with no subspace-sharing.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 10: Nearest neighbours of ‘_monopoly’ in the embedding space.\nQuestion: Which sub-word has the highest cosine similarity distance in the DPQ-SX_monopoly model?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the sub-word with the highest cosine similarity distance in the DPQ-SX_monopoly model, look for the highest value in the 'Dist' column under 'DPQ-SX_monopoly'. The highest value is 0.762, corresponding to the sub-word 'monopolies'. So, the answer is {\"answer\": \"monopolies\"}" } ], "images": [ "scigen/train/1411.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Nearest neighbours of ‘_monopoly’ in the embedding space." ], "reasoning": "To find the sub-word with the highest cosine similarity distance in the DPQ-SX_monopoly model, look for the highest value in the 'Dist' column under 'DPQ-SX_monopoly'. The highest value is 0.762, corresponding to the sub-word 'monopolies'.", "question": "Which sub-word has the highest cosine similarity distance in the DPQ-SX_monopoly model?", "answer": "So, the answer is {\"answer\": \"monopolies\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe sub-word 'monopolies' has the highest cosine similarity distance in the Baseline_monopoly model.\n" }, { "role": "assistant", "content": "To verify this statement, check the 'Dist' column under 'Baseline_monopoly' for the highest value. The highest value is 1.000, corresponding to the sub-word 'monopolies'. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1411.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Nearest neighbours of ‘_monopoly’ in the embedding space." ], "reasoning": "To verify this statement, check the 'Dist' column under 'Baseline_monopoly' for the highest value. The highest value is 1.000, corresponding to the sub-word 'monopolies'. Therefore, the statement is supported.", "statement": "The sub-word 'monopolies' has the highest cosine similarity distance in the Baseline_monopoly model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Comparison of DPQ against recently proposed embedding compression techniques on the PTB LM task (LSTMs with three model sizes are studied). Metrics are perplexity (PPL) and compression ratio (CR).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "DPQ-SX achieves competitive perplexity scores while offering significantly higher compression ratios compared to other methods.Comparison with more baselines on LM. Here we compare DPQ against the recently proposed embedding compression methods. We find that 1) other than DPQ, only Chen′18+ which requires an extra distillation procedure is able to achieve similar perplexity scores as the full embedding baseline; 2) DPQ variants (particularly DPQ-SX) are able to obtain extremely competitive perplexity scores in all cases, while offering compression ratios that are an order of magnitude larger than all the other alternatives (and is trained end-to-end in a single stage)." } ], "images": [ "scigen/train/1412.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of DPQ against recently proposed embedding compression techniques on the PTB LM task (LSTMs with three model sizes are studied). Metrics are perplexity (PPL) and compression ratio (CR)." ], "reasoning": "DPQ-SX achieves competitive perplexity scores while offering significantly higher compression ratios compared to other methods.", "description": "Comparison with more baselines on LM. Here we compare DPQ against the recently proposed embedding compression methods. We find that 1) other than DPQ, only Chen′18+ which requires an extra distillation procedure is able to achieve similar perplexity scores as the full embedding baseline; 2) DPQ variants (particularly DPQ-SX) are able to obtain extremely competitive perplexity scores in all cases, while offering compression ratios that are an order of magnitude larger than all the other alternatives (and is trained end-to-end in a single stage).", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 4: Comparison of DPQ against recently proposed embedding compression techniques on the PTB LM task (LSTMs with three model sizes are studied). Metrics are perplexity (PPL) and compression ratio (CR).' and methodically tackle the question that follows. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich method achieves the highest compression ratio for large models?\n" }, { "role": "assistant", "content": "By examining the 'Large CR' column, DPQ-SX and DPQ-VQ both achieve the highest compression ratio of 238.3. So, the answer is {\"answer\": \"DPQ-SX\"}." } ], "images": [ "scigen/train/1412.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of DPQ against recently proposed embedding compression techniques on the PTB LM task (LSTMs with three model sizes are studied). Metrics are perplexity (PPL) and compression ratio (CR)." ], "reasoning": "By examining the 'Large CR' column, DPQ-SX and DPQ-VQ both achieve the highest compression ratio of 238.3.", "question": "Which method achieves the highest compression ratio for large models?", "answer": "So, the answer is {\"answer\": \"DPQ-SX\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Chen'18+ achieves the lowest perplexity score for medium models. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nChen'18+ achieves the lowest perplexity score for medium models.\n" }, { "role": "assistant", "content": "By examining the 'Medium PPL' column, Chen'18+ has a perplexity score of 83.1, which is not the lowest. DPQ-SX has the lowest perplexity score of 82.0. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1412.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of DPQ against recently proposed embedding compression techniques on the PTB LM task (LSTMs with three model sizes are studied). Metrics are perplexity (PPL) and compression ratio (CR)." ], "reasoning": "By examining the 'Medium PPL' column, Chen'18+ has a perplexity score of 83.1, which is not the lowest. DPQ-SX has the lowest perplexity score of 82.0.", "statement": "Chen'18+ achieves the lowest perplexity score for medium models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 7: Effect of using DPQ on BERT. DPQ gives a compression ratio of 37× on the embedding table while the model’s performance on downstream tasks remains competitive.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "DPQ-SX was used to replace the embedding layer in BERT for both pre-training and fine-tuning, using the best configuration from WMT19 EnDe experiments.To further test DPQ, we replace the embedding layer in BERT with our DPQ for both pre-training and fine-tuning. We do not perform hyper-parameter search for DPQ, but simply use the best configuration from our experiments on WMT19 EnDe using Transformer, i.e. we use DPQ-SX with no subspace-sharing with K=32,D=128." } ], "images": [ "scigen/train/1413.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Effect of using DPQ on BERT. DPQ gives a compression ratio of 37× on the embedding table while the model’s performance on downstream tasks remains competitive." ], "reasoning": "DPQ-SX was used to replace the embedding layer in BERT for both pre-training and fine-tuning, using the best configuration from WMT19 EnDe experiments.", "description": "To further test DPQ, we replace the embedding layer in BERT with our DPQ for both pre-training and fine-tuning. We do not perform hyper-parameter search for DPQ, but simply use the best configuration from our experiments on WMT19 EnDe using Transformer, i.e. we use DPQ-SX with no subspace-sharing with K=32,D=128.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 7: Effect of using DPQ on BERT. DPQ gives a compression ratio of 37× on the embedding table while the model’s performance on downstream tasks remains competitive.', solve the subsequent question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the CR value for the DPQ-SX embedding?\n" }, { "role": "assistant", "content": "The table lists the CR values for different embeddings. The CR value for DPQ-SX is shown in the second row under the CR column. So, the answer is {\"answer\": \"37.0\"}" } ], "images": [ "scigen/train/1413.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Effect of using DPQ on BERT. DPQ gives a compression ratio of 37× on the embedding table while the model’s performance on downstream tasks remains competitive." ], "reasoning": "The table lists the CR values for different embeddings. The CR value for DPQ-SX is shown in the second row under the CR column.", "question": "What is the CR value for the DPQ-SX embedding?", "answer": "So, the answer is {\"answer\": \"37.0\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Full embedding has a higher MRPC score than the DPQ-SX embedding.\n" }, { "role": "assistant", "content": "The table shows the MRPC scores for both Full and DPQ-SX embeddings. The Full embedding has an MRPC score of 85.9±0.5, while the DPQ-SX embedding has an MRPC score of 85.1±0.6. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1413.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Effect of using DPQ on BERT. DPQ gives a compression ratio of 37× on the embedding table while the model’s performance on downstream tasks remains competitive." ], "reasoning": "The table shows the MRPC scores for both Full and DPQ-SX embeddings. The Full embedding has an MRPC score of 85.9±0.5, while the DPQ-SX embedding has an MRPC score of 85.1±0.6.", "statement": "The Full embedding has a higher MRPC score than the DPQ-SX embedding.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 8: Comparison of end-to-end DPQ against using PQ to reconstruct the embedding table after the model is trained, for the NMT task on WMT19 (En-DE).'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "PQ methods lead to significant performance degradation due to error accumulation, while end-to-end training mitigates this by jointly learning the entire system.We can see that PQ degenerates the performance significantly. This is expected as small approximation errors in the embedding layer accumulate and can be amplified as the errors propagate through the deep neural nets, finally leading to large errors in the output space. End-to-end training does not have this problem as the whole system is jointly learned, so the networks can account for small approximation errors in the early layers." } ], "images": [ "scigen/train/1414.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Comparison of end-to-end DPQ against using PQ to reconstruct the embedding table after the model is trained, for the NMT task on WMT19 (En-DE)." ], "reasoning": "PQ methods lead to significant performance degradation due to error accumulation, while end-to-end training mitigates this by jointly learning the entire system.", "description": "We can see that PQ degenerates the performance significantly. This is expected as small approximation errors in the embedding layer accumulate and can be amplified as the errors propagate through the deep neural nets, finally leading to large errors in the output space. End-to-end training does not have this problem as the whole system is jointly learned, so the networks can account for small approximation errors in the early layers.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 8: Comparison of end-to-end DPQ against using PQ to reconstruct the embedding table after the model is trained, for the NMT task on WMT19 (En-DE).', solve this question and give detail solutions. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich method achieves the highest BLEU score while maintaining a compression ratio of 17?\n" }, { "role": "assistant", "content": "From the table, we see that DPQ-SX (K=32, D=128) achieves a BLEU score of 38.8 with a compression ratio of 17. So, the answer is {\"answer\": \"DPQ-SX (K=32, D=128)\"}" } ], "images": [ "scigen/train/1414.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Comparison of end-to-end DPQ against using PQ to reconstruct the embedding table after the model is trained, for the NMT task on WMT19 (En-DE)." ], "reasoning": "From the table, we see that DPQ-SX (K=32, D=128) achieves a BLEU score of 38.8 with a compression ratio of 17.", "question": "Which method achieves the highest BLEU score while maintaining a compression ratio of 17?", "answer": "So, the answer is {\"answer\": \"DPQ-SX (K=32, D=128)\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe method with the highest BLEU score also has the highest compression ratio.\n" }, { "role": "assistant", "content": "The table shows that the method with the highest BLEU score is DPQ-SX (K=32, D=128) with a BLEU score of 38.8, but it does not have the highest compression ratio. The highest compression ratio is 31.9, achieved by PQ (K=128, D=64). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1414.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Comparison of end-to-end DPQ against using PQ to reconstruct the embedding table after the model is trained, for the NMT task on WMT19 (En-DE)." ], "reasoning": "The table shows that the method with the highest BLEU score is DPQ-SX (K=32, D=128) with a BLEU score of 38.8, but it does not have the highest compression ratio. The highest compression ratio is 31.9, achieved by PQ (K=128, D=64).", "statement": "The method with the highest BLEU score also has the highest compression ratio.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 9: Nearest neighbours of ‘_evolve’ in the embedding space.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The table compares the cosine similarity distances between sub-words for different embedding models, including a baseline and two DPQ variants trained with K=D=128.Distance between two sub-words is measured by the cosine similarity of their embedding vectors. Baseline is the original full embeddings model. DPQ variants were trained with K=D=128 with no subspace-sharing." } ], "images": [ "scigen/train/1415.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Nearest neighbours of ‘_evolve’ in the embedding space." ], "reasoning": "The table compares the cosine similarity distances between sub-words for different embedding models, including a baseline and two DPQ variants trained with K=D=128.", "description": "Distance between two sub-words is measured by the cosine similarity of their embedding vectors. Baseline is the original full embeddings model. DPQ variants were trained with K=D=128 with no subspace-sharing.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 9: Nearest neighbours of ‘_evolve’ in the embedding space.\nQuestion: Which sub-word has the highest cosine similarity distance to '_evolve' in the DPQ-SX model?\nSolve the above question based on the table information. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the sub-word with the highest cosine similarity distance to '_evolve' in the DPQ-SX model, look for the highest value in the 'Dist' column under 'DPQ-SX _evolve'. The highest value is 0.571, corresponding to 'evolved'. So, the answer is {\"answer\": \"evolved\"}." } ], "images": [ "scigen/train/1415.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Nearest neighbours of ‘_evolve’ in the embedding space." ], "reasoning": "To find the sub-word with the highest cosine similarity distance to '_evolve' in the DPQ-SX model, look for the highest value in the 'Dist' column under 'DPQ-SX _evolve'. The highest value is 0.571, corresponding to 'evolved'.", "question": "Which sub-word has the highest cosine similarity distance to '_evolve' in the DPQ-SX model?", "answer": "So, the answer is {\"answer\": \"evolved\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis In the Baseline model, 'develop' has a higher cosine similarity distance to '_evolve' than 'evolving'. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nIn the Baseline model, 'develop' has a higher cosine similarity distance to '_evolve' than 'evolving'.\n" }, { "role": "assistant", "content": "In the Baseline model, the cosine similarity distance for 'develop' is 0.434 and for 'evolving' is 0.493. Since 0.434 is less than 0.493, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1415.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Nearest neighbours of ‘_evolve’ in the embedding space." ], "reasoning": "In the Baseline model, the cosine similarity distance for 'develop' is 0.434 and for 'evolving' is 0.493. Since 0.434 is less than 0.493, the statement is refuted.", "statement": "In the Baseline model, 'develop' has a higher cosine similarity distance to '_evolve' than 'evolving'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 11: Nearest neighbours of ‘_Toronto’ in the embedding space.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The table compares the cosine similarity distances between Toronto and other cities using different embedding models, including Baseline, DPQ-SX, and DPQ-VQ.Distance between two sub-words is measured by the cosine similarity of their embedding vectors. Baseline is the original full embeddings model. DPQ variants were trained with K=D=128 with no subspace-sharing." } ], "images": [ "scigen/train/1416.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Nearest neighbours of ‘_Toronto’ in the embedding space." ], "reasoning": "The table compares the cosine similarity distances between Toronto and other cities using different embedding models, including Baseline, DPQ-SX, and DPQ-VQ.", "description": "Distance between two sub-words is measured by the cosine similarity of their embedding vectors. Baseline is the original full embeddings model. DPQ variants were trained with K=D=128 with no subspace-sharing.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 11: Nearest neighbours of ‘_Toronto’ in the embedding space.'. Solve the following question based on the table.\n\nQuestion: Which city has the highest cosine similarity distance to Toronto in the DPQ-SX model?\nLet's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the city with the highest cosine similarity distance to Toronto in the DPQ-SX model, look for the highest value in the 'Dist' column under 'DPQ-SX_Toronto'. The highest value is 0.475, which corresponds to Chicago. So, the answer is {\"answer\": \"Chicago\"}." } ], "images": [ "scigen/train/1416.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Nearest neighbours of ‘_Toronto’ in the embedding space." ], "reasoning": "To find the city with the highest cosine similarity distance to Toronto in the DPQ-SX model, look for the highest value in the 'Dist' column under 'DPQ-SX_Toronto'. The highest value is 0.475, which corresponds to Chicago.", "question": "Which city has the highest cosine similarity distance to Toronto in the DPQ-SX model?", "answer": "So, the answer is {\"answer\": \"Chicago\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence In the Baseline model, Vancouver has a higher cosine similarity distance to Toronto than Seoul. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nIn the Baseline model, Vancouver has a higher cosine similarity distance to Toronto than Seoul.\n" }, { "role": "assistant", "content": "In the Baseline model, the cosine similarity distance between Vancouver and Toronto is 0.390, while the distance between Seoul and Toronto is 0.329. Since 0.390 is greater than 0.329, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1416.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Nearest neighbours of ‘_Toronto’ in the embedding space." ], "reasoning": "In the Baseline model, the cosine similarity distance between Vancouver and Toronto is 0.390, while the distance between Seoul and Toronto is 0.329. Since 0.390 is greater than 0.329, the statement is supported.", "statement": "In the Baseline model, Vancouver has a higher cosine similarity distance to Toronto than Seoul.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: English-German lower-cased BLEU scores calculated with Moses mteval-v13a.pl.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The combination of Hiero and NMT 8-system ensemble yields the best results, with improvements in BLEU scores across news-test2014, news-test2015, and news-test2016.Tab. test2014, news-test2015, and news-test2016 For SGNMT, we see further improvements of between +0.7 BLEU (news-test2014) and +1.1 BLEU (news-test2015) by using NMT ensembles rather than single NMT. However, these gains are rather small considering the improvements from using ensembles for the (pure) NMT baseline (between +1.9 BLEU and +2.2 BLEU). Our combination scheme makes better use of the ensembles. Back-translation is a technique for making use of monolingual data in NMT training, and we expect our system could benefit from back-translation, although we leave this analysis to future work. The combination procedure we propose is non-trivial. It is not immediately clear how the gains arise as the final scores are mixtures between edit distance costs, NMT scores, and Hiero scores. In the remainder we will try to provide some insight. Unless stated otherwise, we report investigations into the Hiero + NMT 8-system ensemble which yields the best results in Tab." } ], "images": [ "scigen/train/1417.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: English-German lower-cased BLEU scores calculated with Moses mteval-v13a.pl." ], "reasoning": "The combination of Hiero and NMT 8-system ensemble yields the best results, with improvements in BLEU scores across news-test2014, news-test2015, and news-test2016.", "description": "Tab. test2014, news-test2015, and news-test2016 For SGNMT, we see further improvements of between +0.7 BLEU (news-test2014) and +1.1 BLEU (news-test2015) by using NMT ensembles rather than single NMT. However, these gains are rather small considering the improvements from using ensembles for the (pure) NMT baseline (between +1.9 BLEU and +2.2 BLEU). Our combination scheme makes better use of the ensembles. Back-translation is a technique for making use of monolingual data in NMT training, and we expect our system could benefit from back-translation, although we leave this analysis to future work. The combination procedure we propose is non-trivial. It is not immediately clear how the gains arise as the final scores are mixtures between edit distance costs, NMT scores, and Hiero scores. In the remainder we will try to provide some insight. Unless stated otherwise, we report investigations into the Hiero + NMT 8-system ensemble which yields the best results in Tab.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 1: English-German lower-cased BLEU scores calculated with Moses mteval-v13a.pl.', unfold the steps to solve the question given next. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nQuestion: What is the BLEU score for the Edit distance transducer based combination in news-test2016?\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for various setups across different test sets. For the Edit distance transducer based combination in news-test2016, the BLEU score is listed. So, the answer is {\"answer\": \"31.3\"}." } ], "images": [ "scigen/train/1417.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: English-German lower-cased BLEU scores calculated with Moses mteval-v13a.pl." ], "reasoning": "The table shows the BLEU scores for various setups across different test sets. For the Edit distance transducer based combination in news-test2016, the BLEU score is listed.", "question": "What is the BLEU score for the Edit distance transducer based combination in news-test2016?", "answer": "So, the answer is {\"answer\": \"31.3\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe SGNMT (lattice rescoring) setup has a higher BLEU score than Pure NMT in news-test2015.\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for SGNMT (lattice rescoring) and Pure NMT in news-test2015. SGNMT (lattice rescoring) has a BLEU score of 24.6, while Pure NMT has a BLEU score of 21.7. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1417.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: English-German lower-cased BLEU scores calculated with Moses mteval-v13a.pl." ], "reasoning": "The table shows the BLEU scores for SGNMT (lattice rescoring) and Pure NMT in news-test2015. SGNMT (lattice rescoring) has a BLEU score of 24.6, while Pure NMT has a BLEU score of 21.7.", "statement": "The SGNMT (lattice rescoring) setup has a higher BLEU score than Pure NMT in news-test2015.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Projection methods on news-test2016 with NMT 8-ensemble.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The combined translation method achieves the highest BLEU score of 31.3, significantly improving over both the NMT and Hiero baselines.First, we focus on the projection function ΠUNK(⋅) which switches between preserving the input and output label at the UNK symbol to produce the combined translation ˆtcomb (Eq. As explained in Sec. Tab. Remarkably, using the ˆtHiero translations results in 30.4 BLEU, which is a very substantial improvement over the baseline Hiero system (26.0 BLEU). Note that this BLEU score is achieved with hypotheses from the original Hiero lattice H but weighted in combination with the NMT scores and the edit distance. However, these selected paths are often given very low scores by Hiero: in only 8.6% of the sentences is the Hiero hypothesis left unchanged. This indicates the benefit in using lattice-based approaches over n-best lists." } ], "images": [ "scigen/train/1418.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Projection methods on news-test2016 with NMT 8-ensemble." ], "reasoning": "The combined translation method achieves the highest BLEU score of 31.3, significantly improving over both the NMT and Hiero baselines.", "description": "First, we focus on the projection function ΠUNK(⋅) which switches between preserving the input and output label at the UNK symbol to produce the combined translation ˆtcomb (Eq. As explained in Sec. Tab. Remarkably, using the ˆtHiero translations results in 30.4 BLEU, which is a very substantial improvement over the baseline Hiero system (26.0 BLEU). Note that this BLEU score is achieved with hypotheses from the original Hiero lattice H but weighted in combination with the NMT scores and the edit distance. However, these selected paths are often given very low scores by Hiero: in only 8.6% of the sentences is the Hiero hypothesis left unchanged. This indicates the benefit in using lattice-based approaches over n-best lists.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nTable title: Table 2: Projection methods on news-test2016 with NMT 8-ensemble.\n\nQuestion: What is the BLEU score improvement of the combined translation method over the NMT baseline?\n" }, { "role": "assistant", "content": "The BLEU score for the NMT baseline is 25.4, and the BLEU score for the combined translation method is 31.3. The improvement is calculated as 31.3 - 25.4. So, the answer is {\"answer\": \"5.9\"}." } ], "images": [ "scigen/train/1418.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Projection methods on news-test2016 with NMT 8-ensemble." ], "reasoning": "The BLEU score for the NMT baseline is 25.4, and the BLEU score for the combined translation method is 31.3. The improvement is calculated as 31.3 - 25.4.", "question": "What is the BLEU score improvement of the combined translation method over the NMT baseline?", "answer": "So, the answer is {\"answer\": \"5.9\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Hiero hypothesis used for combination achieves a BLEU score higher than 30.\n" }, { "role": "assistant", "content": "According to the table, the Hiero hypothesis used for combination has a BLEU score of 30.4, which is higher than 30. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1418.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Projection methods on news-test2016 with NMT 8-ensemble." ], "reasoning": "According to the table, the Hiero hypothesis used for combination has a BLEU score of 30.4, which is higher than 30.", "statement": "The Hiero hypothesis used for combination achieves a BLEU score higher than 30.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Breakdown of the distances measured between NMT and Hiero along the shortest path in C on news-test2016.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The average edit distance between NMT and Hiero translations is 1.74, with exact matches occurring in only 38.3% of sentences, and UNK often replaced by in-vocabulary words in 55.9% of sentences.Next, we investigate the distance measure between NMT and Hiero translations, which is realized with the UNK insertion transducer U and the modified edit distance transducer E (Sec. Tab. The average edit distance between NMT and Hiero disregarding UNKs on the best path (type III) is 1.74. In 61.7% of the cases the input and output labels differ not only at UNK – i.e. in only 38.3% of the sentences do we have an exact match between NMT and Hiero. We note that UNK is often replaced with an NMT in-vocabulary word (55.9% of the sentences). It seems that NMT often produces an UNK even if a better word is in the NMT vocabulary. This could be due to the over-representation of UNK in the NMT training corpus." } ], "images": [ "scigen/train/1419.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Breakdown of the distances measured between NMT and Hiero along the shortest path in C on news-test2016." ], "reasoning": "The average edit distance between NMT and Hiero translations is 1.74, with exact matches occurring in only 38.3% of sentences, and UNK often replaced by in-vocabulary words in 55.9% of sentences.", "description": "Next, we investigate the distance measure between NMT and Hiero translations, which is realized with the UNK insertion transducer U and the modified edit distance transducer E (Sec. Tab. The average edit distance between NMT and Hiero disregarding UNKs on the best path (type III) is 1.74. In 61.7% of the cases the input and output labels differ not only at UNK – i.e. in only 38.3% of the sentences do we have an exact match between NMT and Hiero. We note that UNK is often replaced with an NMT in-vocabulary word (55.9% of the sentences). It seems that NMT often produces an UNK even if a better word is in the NMT vocabulary. This could be due to the over-representation of UNK in the NMT training corpus.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: Table 3: Breakdown of the distances measured between NMT and Hiero along the shortest path in C on news-test2016.\nQuestion: What is the percentage of sentences where UNK is replaced with an NMT in-vocabulary word?\n" }, { "role": "assistant", "content": "The table shows that UNK→non-OOV substitutions (Type II) occur in 55.9% of sentences. So, the answer is {\"answer\": \"55.9%\"}" } ], "images": [ "scigen/train/1419.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Breakdown of the distances measured between NMT and Hiero along the shortest path in C on news-test2016." ], "reasoning": "The table shows that UNK→non-OOV substitutions (Type II) occur in 55.9% of sentences.", "question": "What is the percentage of sentences where UNK is replaced with an NMT in-vocabulary word?", "answer": "So, the answer is {\"answer\": \"55.9%\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The average number of UNK insertions per sentence is 0.16. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe average number of UNK insertions per sentence is 0.16.\n" }, { "role": "assistant", "content": "The table indicates that the average number of UNK insertions (U) per sentence is indeed 0.16. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1419.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Breakdown of the distances measured between NMT and Hiero along the shortest path in C on news-test2016." ], "reasoning": "The table indicates that the average number of UNK insertions (U) per sentence is indeed 0.16.", "statement": "The average number of UNK insertions per sentence is 0.16.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4: BLEU scores on news-test2016 for different vocabulary sizes (single NMT). Each individual NMT system is combined with Hiero as described in Sec. 2.3.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Increasing the NMT vocabulary size to 50,000 or 60,000 improves the pure NMT BLEU score significantly, but these improvements are already captured by the combination scheme with Hiero.To study the effectiveness of our edit distance transducer based combination scheme in correcting NMT UNKs, we trained individual NMT systems with vocabulary sizes between 10,000 and 60,000. Tab. Increasing the NMT vocabulary to 50k or 60k does improve pure NMT very significantly, but results show that these improvements are already captured by the combination scheme with Hiero." } ], "images": [ "scigen/train/1420.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: BLEU scores on news-test2016 for different vocabulary sizes (single NMT). Each individual NMT system is combined with Hiero as described in Sec. 2.3." ], "reasoning": "Increasing the NMT vocabulary size to 50,000 or 60,000 improves the pure NMT BLEU score significantly, but these improvements are already captured by the combination scheme with Hiero.", "description": "To study the effectiveness of our edit distance transducer based combination scheme in correcting NMT UNKs, we trained individual NMT systems with vocabulary sizes between 10,000 and 60,000. Tab. Increasing the NMT vocabulary to 50k or 60k does improve pure NMT very significantly, but results show that these improvements are already captured by the combination scheme with Hiero.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 4: BLEU scores on news-test2016 for different vocabulary sizes (single NMT). Each individual NMT system is combined with Hiero as described in Sec. 2.3.', and tackle the question below in a sequential manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the BLEU score for pure NMT with a vocabulary size of 50,000?\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for different vocabulary sizes. For a vocabulary size of 50,000, the pure NMT BLEU score is listed. So, the answer is {\"answer\": \"23.2\"}." } ], "images": [ "scigen/train/1420.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: BLEU scores on news-test2016 for different vocabulary sizes (single NMT). Each individual NMT system is combined with Hiero as described in Sec. 2.3." ], "reasoning": "The table shows the BLEU scores for different vocabulary sizes. For a vocabulary size of 50,000, the pure NMT BLEU score is listed.", "question": "What is the BLEU score for pure NMT with a vocabulary size of 50,000?", "answer": "So, the answer is {\"answer\": \"23.2\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe NMT+Hiero BLEU score is higher than the pure NMT BLEU score for all vocabulary sizes.\n" }, { "role": "assistant", "content": "The table shows that for all vocabulary sizes (10,000, 30,000, 50,000, and 60,000), the NMT+Hiero BLEU score is higher than the pure NMT BLEU score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1420.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: BLEU scores on news-test2016 for different vocabulary sizes (single NMT). Each individual NMT system is combined with Hiero as described in Sec. 2.3." ], "reasoning": "The table shows that for all vocabulary sizes (10,000, 30,000, 50,000, and 60,000), the NMT+Hiero BLEU score is higher than the pure NMT BLEU score.", "statement": "The NMT+Hiero BLEU score is higher than the pure NMT BLEU score for all vocabulary sizes.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: Experimental results on iSarcasm. Manual Labelling shows the results using the perceived sarcasm labels provided by third-party human annotators.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "All models perform worse than humans, with MIARN being the best model at 0.364 F-score, and 3CNN the worst at 0.286 F-score.As shown, all the models perform significantly worse than humans, who achieve an F-score of only 0.616. MIARN is the best performing model with a considerably low F-score of 0.364, compared to its performance on the Riloff and Ptacek datasets (0.741 and 0.874 F-scores respectively). 3CNN achieves the lowest performance on iSarcasm with an F-Score of 0.286 compared to 0.675 and 0.788 on SARC balanced and imbalanced, respectively. Similarly, Dense-LSTM achieves 0.318, compared to 0.666 on SemEval-2018." } ], "images": [ "scigen/train/1421.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Experimental results on iSarcasm. Manual Labelling shows the results using the perceived sarcasm labels provided by third-party human annotators." ], "reasoning": "All models perform worse than humans, with MIARN being the best model at 0.364 F-score, and 3CNN the worst at 0.286 F-score.", "description": "As shown, all the models perform significantly worse than humans, who achieve an F-score of only 0.616. MIARN is the best performing model with a considerably low F-score of 0.364, compared to its performance on the Riloff and Ptacek datasets (0.741 and 0.874 F-scores respectively). 3CNN achieves the lowest performance on iSarcasm with an F-Score of 0.286 compared to 0.675 and 0.788 on SARC balanced and imbalanced, respectively. Similarly, Dense-LSTM achieves 0.318, compared to 0.666 on SemEval-2018.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 7: Experimental results on iSarcasm. Manual Labelling shows the results using the perceived sarcasm labels provided by third-party human annotators.'. Solve the following question based on the table.\n\nQuestion: Which model has the highest F-score among the machine learning models?\nLet's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the F-scores of all machine learning models, MIARN has the highest F-score of 0.364. So, the answer is {\"answer\": \"MIARN\"}." } ], "images": [ "scigen/train/1421.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Experimental results on iSarcasm. Manual Labelling shows the results using the perceived sarcasm labels provided by third-party human annotators." ], "reasoning": "By comparing the F-scores of all machine learning models, MIARN has the highest F-score of 0.364.", "question": "Which model has the highest F-score among the machine learning models?", "answer": "So, the answer is {\"answer\": \"MIARN\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Dense-LSTM model has a higher F-score than the Att-LSTM model.\n" }, { "role": "assistant", "content": "The table shows that Dense-LSTM has an F-score of 0.318, while Att-LSTM has an F-score of 0.325, which means Dense-LSTM has a lower F-score. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1421.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Experimental results on iSarcasm. Manual Labelling shows the results using the perceived sarcasm labels provided by third-party human annotators." ], "reasoning": "The table shows that Dense-LSTM has an F-score of 0.318, while Att-LSTM has an F-score of 0.325, which means Dense-LSTM has a lower F-score.", "statement": "The Dense-LSTM model has a higher F-score than the Att-LSTM model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: The agreement between manual annotation and the presence of sarcasm tags in the Riloff dataset, as discussed in Section 3.2.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The Riloff dataset shows a significant disagreement between distant supervision and manual annotation, with 58% of tweets containing predefined hashtags labeled as non-sarcastic.Labels provided by third-party annotators might therefore not reflect the sarcastic intention of the authors of the texts that are being labelled, making this labelling method sub-optimal for capturing intended sarcasm. To investigate this further, we looked at the Riloff dataset, which is published as a list of labelled tweet IDs. We could only retrieve 1,832 tweets, the others being removed from Twitter. We looked at the agreement between the presence of tags and manual annotation. We notice that 58% of the tweets that contained the predefined hashtags were labeled non-sarcastic. This disagreement between distant supervision and manual annotation provides further evidence to doubt the ability of the latter to capture intended sarcasm, at least not the flavor that distant supervision might capture. We could not perform the same analysis on the SemEval-2018 dataset because only the text of the tweets is provided, hashtags are filtered out, and tweet IDs are not available." } ], "images": [ "scigen/train/1422.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The agreement between manual annotation and the presence of sarcasm tags in the Riloff dataset, as discussed in Section 3.2." ], "reasoning": "The Riloff dataset shows a significant disagreement between distant supervision and manual annotation, with 58% of tweets containing predefined hashtags labeled as non-sarcastic.", "description": "Labels provided by third-party annotators might therefore not reflect the sarcastic intention of the authors of the texts that are being labelled, making this labelling method sub-optimal for capturing intended sarcasm. To investigate this further, we looked at the Riloff dataset, which is published as a list of labelled tweet IDs. We could only retrieve 1,832 tweets, the others being removed from Twitter. We looked at the agreement between the presence of tags and manual annotation. We notice that 58% of the tweets that contained the predefined hashtags were labeled non-sarcastic. This disagreement between distant supervision and manual annotation provides further evidence to doubt the ability of the latter to capture intended sarcasm, at least not the flavor that distant supervision might capture. We could not perform the same analysis on the SemEval-2018 dataset because only the text of the tweets is provided, hashtags are filtered out, and tweet IDs are not available.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 2: The agreement between manual annotation and the presence of sarcasm tags in the Riloff dataset, as discussed in Section 3.2.' in mind, break down and resolve the question below step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nHow many tweets were annotated as non-sarcastic and contained a tag?\n" }, { "role": "assistant", "content": "To find the number of tweets annotated as non-sarcastic and containing a tag, look at the intersection of the 'annot. non-sarcastic' row and the 'with tag' column. So, the answer is {\"answer\": \"486\"}." } ], "images": [ "scigen/train/1422.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The agreement between manual annotation and the presence of sarcasm tags in the Riloff dataset, as discussed in Section 3.2." ], "reasoning": "To find the number of tweets annotated as non-sarcastic and containing a tag, look at the intersection of the 'annot. non-sarcastic' row and the 'with tag' column.", "question": "How many tweets were annotated as non-sarcastic and contained a tag?", "answer": "So, the answer is {\"answer\": \"486\"}." }, { "messages": [ { "role": "user", "content": "Most tweets with tags were annotated as sarcastic. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nMost tweets with tags were annotated as sarcastic.\n" }, { "role": "assistant", "content": "To verify this statement, compare the number of tweets with tags annotated as sarcastic (345) to those annotated as non-sarcastic (486). Since 486 is greater than 345, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1422.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The agreement between manual annotation and the presence of sarcasm tags in the Riloff dataset, as discussed in Section 3.2." ], "reasoning": "To verify this statement, compare the number of tweets with tags annotated as sarcastic (345) to those annotated as non-sarcastic (486). Since 486 is greater than 345, the statement is refuted.", "statement": "Most tweets with tags were annotated as sarcastic.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: The agreement between intended labels (int.), provided by the authors, and perceived labels, provided by third-party annotators, (perc.) on iSarcasm test set.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows that 30% of sarcastic tweets were unrecognized by annotators, and 45% of tweets perceived as sarcastic were not intended to be sarcastic, highlighting the subjectivity of sarcasm annotation.As we mentioned earlier, we collected three third-party labels for each tweet in the test set of iSarcasm. as a measure, the pairwise inter-annotator agreement (IAA) scores were κ12=0.37, κ13=0.39 and κ23=0.36, which highlights the high subjectivity of the task. We used majority voting to select the final perceived sarcasm label for each tweet. As shown, 30% of the sarcastic tweets were unrecognised by the annotators, while 45% of the tweets perceived as sarcastic were actually not intended to be sarcastic by their authors. This supports our argument that third-party annotation for sarcasm should not be trusted." } ], "images": [ "scigen/train/1423.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The agreement between intended labels (int.), provided by the authors, and perceived labels, provided by third-party annotators, (perc.) on iSarcasm test set." ], "reasoning": "The table shows that 30% of sarcastic tweets were unrecognized by annotators, and 45% of tweets perceived as sarcastic were not intended to be sarcastic, highlighting the subjectivity of sarcasm annotation.", "description": "As we mentioned earlier, we collected three third-party labels for each tweet in the test set of iSarcasm. as a measure, the pairwise inter-annotator agreement (IAA) scores were κ12=0.37, κ13=0.39 and κ23=0.36, which highlights the high subjectivity of the task. We used majority voting to select the final perceived sarcasm label for each tweet. As shown, 30% of the sarcastic tweets were unrecognised by the annotators, while 45% of the tweets perceived as sarcastic were actually not intended to be sarcastic by their authors. This supports our argument that third-party annotation for sarcasm should not be trusted.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: The agreement between intended labels (int.), provided by the authors, and perceived labels, provided by third-party annotators, (perc.) on iSarcasm test set.', solve the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat percentage of tweets perceived as sarcastic were actually intended to be non-sarcastic?\n" }, { "role": "assistant", "content": "From the table, 50 tweets were perceived as sarcastic but were intended to be non-sarcastic. The total number of tweets perceived as sarcastic is 61 (intended sarcastic) + 50 (intended non-sarcastic) = 111. The percentage is (50/111) * 100. So, the answer is {\"answer\": \"45%\"}." } ], "images": [ "scigen/train/1423.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The agreement between intended labels (int.), provided by the authors, and perceived labels, provided by third-party annotators, (perc.) on iSarcasm test set." ], "reasoning": "From the table, 50 tweets were perceived as sarcastic but were intended to be non-sarcastic. The total number of tweets perceived as sarcastic is 61 (intended sarcastic) + 50 (intended non-sarcastic) = 111. The percentage is (50/111) * 100.", "question": "What percentage of tweets perceived as sarcastic were actually intended to be non-sarcastic?", "answer": "So, the answer is {\"answer\": \"45%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Annotators correctly identified 322 non-sarcastic tweets. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nAnnotators correctly identified 322 non-sarcastic tweets.\n" }, { "role": "assistant", "content": "The table shows that 322 tweets were intended to be non-sarcastic and were perceived as non-sarcastic by the annotators. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1423.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The agreement between intended labels (int.), provided by the authors, and perceived labels, provided by third-party annotators, (perc.) on iSarcasm test set." ], "reasoning": "The table shows that 322 tweets were intended to be non-sarcastic and were perceived as non-sarcastic by the annotators.", "statement": "Annotators correctly identified 322 non-sarcastic tweets.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 6: F-score yielded by our implementations of state-of-the-art models on previous datasets, compared to published results on those datasets.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "We implemented models for each dataset to match the performance of published results.For each previous dataset, we implemented the models reported previously to achieve the best performance on that dataset, and made sure our implementations achieve similar performance to the published one." } ], "images": [ "scigen/train/1424.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: F-score yielded by our implementations of state-of-the-art models on previous datasets, compared to published results on those datasets." ], "reasoning": "We implemented models for each dataset to match the performance of published results.", "description": "For each previous dataset, we implemented the models reported previously to achieve the best performance on that dataset, and made sure our implementations achieve similar performance to the published one.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 6: F-score yielded by our implementations of state-of-the-art models on previous datasets, compared to published results on those datasets.'. Solve the following question based on the table.\n\nQuestion: Which model achieved the highest performance in our implementation for the Ptacek dataset?\nLet's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest performance model for the Ptacek dataset, compare the 'our impl.' scores for each model listed under Ptacek. The highest score is 0.874 for the MIARN model. So, the answer is {\"answer\": \"MIARN\"}." } ], "images": [ "scigen/train/1424.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: F-score yielded by our implementations of state-of-the-art models on previous datasets, compared to published results on those datasets." ], "reasoning": "To find the highest performance model for the Ptacek dataset, compare the 'our impl.' scores for each model listed under Ptacek. The highest score is 0.874 for the MIARN model.", "question": "Which model achieved the highest performance in our implementation for the Ptacek dataset?", "answer": "So, the answer is {\"answer\": \"MIARN\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The CNN model achieved a higher performance in our implementation than the published result for the SARC-unbalanced dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe CNN model achieved a higher performance in our implementation than the published result for the SARC-unbalanced dataset.\n" }, { "role": "assistant", "content": "For the SARC-unbalanced dataset, the published result for the CNN model is 0.780, and our implementation achieved 0.788, which is higher. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1424.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: F-score yielded by our implementations of state-of-the-art models on previous datasets, compared to published results on those datasets." ], "reasoning": "For the SARC-unbalanced dataset, the published result for the CNN model is 0.780, and our implementation achieved 0.788, which is higher.", "statement": "The CNN model achieved a higher performance in our implementation than the published result for the SARC-unbalanced dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: F1 scores, Precision and Recall obtained for the three scenarios achieved by baseline system, best method presented and our method.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Our results were competitive, achieving second place in scenario 1 by a narrow margin of 1.76 F1 score, despite scenario 1 being a composition of scenarios 2 and 3.Our results were very competitive. We were second in the main evaluation (scenario 1) by a narrow margin - only 1.76 of F1 score over 100. This is surprising because scenario 1 is the composition of the other 2 scenarios. The main difference in evaluation among scenarios was that for scenario 1 a subset of 100 sentences out of a 8.800 sentences dataset was randomly selected for evaluation, while in scenarios 2 and 3 a dataset of 100 sentences was given to each one for evaluation. In subtask B, we noticed that not all relations had the same difficulty in being detected. We found that a first set of 6 relations (in-context, subject, target, domain, arg and is-a) were successfully identified with high recall and precision values when tested on development dataset (above 75%). Relations in-place, has-property, same-as and in-time were (in this order) harder to identify and, finally, for part-of, causes and entails relations, results were so bad that models for those relations were discarded." } ], "images": [ "scigen/train/1425.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: F1 scores, Precision and Recall obtained for the three scenarios achieved by baseline system, best method presented and our method." ], "reasoning": "Our results were competitive, achieving second place in scenario 1 by a narrow margin of 1.76 F1 score, despite scenario 1 being a composition of scenarios 2 and 3.", "description": "Our results were very competitive. We were second in the main evaluation (scenario 1) by a narrow margin - only 1.76 of F1 score over 100. This is surprising because scenario 1 is the composition of the other 2 scenarios. The main difference in evaluation among scenarios was that for scenario 1 a subset of 100 sentences out of a 8.800 sentences dataset was randomly selected for evaluation, while in scenarios 2 and 3 a dataset of 100 sentences was given to each one for evaluation. In subtask B, we noticed that not all relations had the same difficulty in being detected. We found that a first set of 6 relations (in-context, subject, target, domain, arg and is-a) were successfully identified with high recall and precision values when tested on development dataset (above 75%). Relations in-place, has-property, same-as and in-time were (in this order) harder to identify and, finally, for part-of, causes and entails relations, results were so bad that models for those relations were discarded.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 1: F1 scores, Precision and Recall obtained for the three scenarios achieved by baseline system, best method presented and our method.\nQuestion: What was the F1 score difference between the baseline and Voting LSTMs (ours) in scenario 1?\n" }, { "role": "assistant", "content": "The baseline F1 score in scenario 1 is 43.09, and the Voting LSTMs (ours) F1 score is 62.18. The difference is 62.18 - 43.09. So, the answer is {\"answer\": \"19.09\"}" } ], "images": [ "scigen/train/1425.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: F1 scores, Precision and Recall obtained for the three scenarios achieved by baseline system, best method presented and our method." ], "reasoning": "The baseline F1 score in scenario 1 is 43.09, and the Voting LSTMs (ours) F1 score is 62.18. The difference is 62.18 - 43.09.", "question": "What was the F1 score difference between the baseline and Voting LSTMs (ours) in scenario 1?", "answer": "So, the answer is {\"answer\": \"19.09\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Voting LSTMs (ours) model had the highest F1 score in scenario 2.\n" }, { "role": "assistant", "content": "In scenario 2, the Voting LSTMs (ours) model had an F1 score of 78.73, while the Joint-BERT-RCNN (best) model had an F1 score of 82.03. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1425.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: F1 scores, Precision and Recall obtained for the three scenarios achieved by baseline system, best method presented and our method." ], "reasoning": "In scenario 2, the Voting LSTMs (ours) model had an F1 score of 78.73, while the Joint-BERT-RCNN (best) model had an F1 score of 82.03.", "statement": "The Voting LSTMs (ours) model had the highest F1 score in scenario 2.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1. Experimental Results for the Subjectivity Detection Task'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Optimized and distilled BERT-based models, along with ensemble techniques, outperform baselines in metrics like Accuracy and F1.Optimized BERT-based models: We use BERT-based models optimized as in (2020roberta) and (2020albert), pretrained on a dataset as large as twelve times as compared to BERTlarge, with bigger batches, and longer sequences. ALBERT, introduced in (2020albert), uses factorized embedding parameterization and cross-layer parameter sharing for parameter reduction. These optimizations have led both the models to outperform BERTlarge in various benchmarking tests, like GLUE for text classification and SQuAD for Question Answering. Distilled BERT-based models: Secondly, we propose to use distilled BERT-based models, as introduced in (DistilBert). They are smaller general-purpose language representation model, pre-trained by leveraging distillation knowledge. This results in significantly smaller and faster models with performance comparable to their undistilled versions. We finetune these pretrained distilled models on the training corpus to efficiently detect subjectivity. BERT-based ensemble models : Lastly, we use the weighted-average ensembling technique to exploit the predictions made by different variations of the above models. Ensembling methodology entails engendering a predictive model by utilizing predictions from multiple models in order to improve Accuracy and F1, decrease variance, and bias. Our proposed methodology, the use of finetuned optimized BERT based models, and BERT-based ensemble models outperform the baselines for all the metrics." } ], "images": [ "scigen/train/1426.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1. Experimental Results for the Subjectivity Detection Task" ], "reasoning": "Optimized and distilled BERT-based models, along with ensemble techniques, outperform baselines in metrics like Accuracy and F1.", "description": "Optimized BERT-based models: We use BERT-based models optimized as in (2020roberta) and (2020albert), pretrained on a dataset as large as twelve times as compared to BERTlarge, with bigger batches, and longer sequences. ALBERT, introduced in (2020albert), uses factorized embedding parameterization and cross-layer parameter sharing for parameter reduction. These optimizations have led both the models to outperform BERTlarge in various benchmarking tests, like GLUE for text classification and SQuAD for Question Answering. Distilled BERT-based models: Secondly, we propose to use distilled BERT-based models, as introduced in (DistilBert). They are smaller general-purpose language representation model, pre-trained by leveraging distillation knowledge. This results in significantly smaller and faster models with performance comparable to their undistilled versions. We finetune these pretrained distilled models on the training corpus to efficiently detect subjectivity. BERT-based ensemble models : Lastly, we use the weighted-average ensembling technique to exploit the predictions made by different variations of the above models. Ensembling methodology entails engendering a predictive model by utilizing predictions from multiple models in order to improve Accuracy and F1, decrease variance, and bias. Our proposed methodology, the use of finetuned optimized BERT based models, and BERT-based ensemble models outperform the baselines for all the metrics.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich single model has the highest F1 score and what is its accuracy?\nSolve the above question based on the table titled 'Table 1. Experimental Results for the Subjectivity Detection Task'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "From the table, the single model with the highest F1 score is RoBERTalarge with an F1 score of 0.702. Its accuracy is 71.09%. So, the answer is {\"answer\": \"RoBERTalarge with 71.09% accuracy\"}" } ], "images": [ "scigen/train/1426.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1. Experimental Results for the Subjectivity Detection Task" ], "reasoning": "From the table, the single model with the highest F1 score is RoBERTalarge with an F1 score of 0.702. Its accuracy is 71.09%.", "question": "Which single model has the highest F1 score and what is its accuracy?", "answer": "So, the answer is {\"answer\": \"RoBERTalarge with 71.09% accuracy\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The ensemble model RoBERTa+ALBERT has the highest precision among all models. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe ensemble model RoBERTa+ALBERT has the highest precision among all models.\n" }, { "role": "assistant", "content": "From the table, the ensemble model RoBERTa+ALBERT has a precision of 0.733, which is the highest among all models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1426.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1. Experimental Results for the Subjectivity Detection Task" ], "reasoning": "From the table, the ensemble model RoBERTa+ALBERT has a precision of 0.733, which is the highest among all models listed.", "statement": "The ensemble model RoBERTa+ALBERT has the highest precision among all models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Comparing Sentence Embeddings for Clustering News Claims.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Google USE Large was chosen for clustering due to its high accuracy and ability to cluster a greater number of claims compared to other methods.This method is limited in how well it can represent the problem, but it can give indications as to a good or bad clustering method or embedding, and can act as a check that the findings we obtained from the Quora dataset will generalise to our domain. We used DBScan as it mirrored the clustering method used to derive the original article clusters. We included TFIDF in the experiment as a baseline to judge other results. That being said, TFIDF performs very well, with only Google USE Large and Infersent coming close in terms of ‘accuracy’. In the case of Infersent, this comes with the penalty of a much smaller number of claims included in the clusters. Google USE Large, however, clusters a greater number and for this reason we chose to use Google’s USE Large." } ], "images": [ "scigen/train/1427.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparing Sentence Embeddings for Clustering News Claims." ], "reasoning": "Google USE Large was chosen for clustering due to its high accuracy and ability to cluster a greater number of claims compared to other methods.", "description": "This method is limited in how well it can represent the problem, but it can give indications as to a good or bad clustering method or embedding, and can act as a check that the findings we obtained from the Quora dataset will generalise to our domain. We used DBScan as it mirrored the clustering method used to derive the original article clusters. We included TFIDF in the experiment as a baseline to judge other results. That being said, TFIDF performs very well, with only Google USE Large and Infersent coming close in terms of ‘accuracy’. In the case of Infersent, this comes with the penalty of a much smaller number of claims included in the clusters. Google USE Large, however, clusters a greater number and for this reason we chose to use Google’s USE Large.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 3: Comparing Sentence Embeddings for Clustering News Claims.\nQuestion: Which embedding method took the least amount of time and how does its percentage of claims in majority clusters compare to Google USE Large?\n" }, { "role": "assistant", "content": "TFIDF took the least amount of time (25.97s) and its percentage of claims in majority clusters (62.85%) is higher than Google USE Large (60.74%). So, the answer is {\"answer\": \"TFIDF\"}" } ], "images": [ "scigen/train/1427.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparing Sentence Embeddings for Clustering News Claims." ], "reasoning": "TFIDF took the least amount of time (25.97s) and its percentage of claims in majority clusters (62.85%) is higher than Google USE Large (60.74%).", "question": "Which embedding method took the least amount of time and how does its percentage of claims in majority clusters compare to Google USE Large?", "answer": "So, the answer is {\"answer\": \"TFIDF\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nGoogle USE Large has the highest percentage of claims in one story.\n" }, { "role": "assistant", "content": "The table shows that Infersent has the highest percentage of claims in one story (10.0%), not Google USE Large (7.02%). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1427.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparing Sentence Embeddings for Clustering News Claims." ], "reasoning": "The table shows that Infersent has the highest percentage of claims in one story (10.0%), not Google USE Large (7.02%).", "statement": "Google USE Large has the highest percentage of claims in one story.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Training and test accuracy on natural language inference task. d is the word embedding size and |θ|M the number of model parameters.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The full tree matching NTI model achieved the highest accuracy of 87.3% on the task, surpassing the previous best model that used an attention mechanism for phrase matching.For comparison, we include the results from the published state-of-the-art systems. While most of the sentence encoder models rely solely on word embeddings, the dependency tree CNN and the SPINN-PI models make use of sentence parser output; which present strong baseline systems. Our best score on this task is 87.3% accuracy obtained with the full tree matching NTI model. The previous best performing model on the task performs phrase matching by using the attention mechanism." } ], "images": [ "scigen/train/1428.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Training and test accuracy on natural language inference task. d is the word embedding size and |θ|M the number of model parameters." ], "reasoning": "The full tree matching NTI model achieved the highest accuracy of 87.3% on the task, surpassing the previous best model that used an attention mechanism for phrase matching.", "description": "For comparison, we include the results from the published state-of-the-art systems. While most of the sentence encoder models rely solely on word embeddings, the dependency tree CNN and the SPINN-PI models make use of sentence parser output; which present strong baseline systems. Our best score on this task is 87.3% accuracy obtained with the full tree matching NTI model. The previous best performing model on the task performs phrase matching by using the attention mechanism.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 1: Training and test accuracy on natural language inference task. d is the word embedding size and |θ|M the number of model parameters.'\nQuestion: Which model achieved the highest test accuracy and what was the accuracy percentage?\n" }, { "role": "assistant", "content": "The table shows that the Full tree matching NTI-SLSTM-LSTM global attention model achieved the highest test accuracy of 87.3%. So, the answer is {\"answer\": \"Full tree matching NTI-SLSTM-LSTM global attention, 87.3%\"}" } ], "images": [ "scigen/train/1428.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Training and test accuracy on natural language inference task. d is the word embedding size and |θ|M the number of model parameters." ], "reasoning": "The table shows that the Full tree matching NTI-SLSTM-LSTM global attention model achieved the highest test accuracy of 87.3%.", "question": "Which model achieved the highest test accuracy and what was the accuracy percentage?", "answer": "So, the answer is {\"answer\": \"Full tree matching NTI-SLSTM-LSTM global attention, 87.3%\"}" }, { "messages": [ { "role": "user", "content": "The Dependency Tree CNN encoders achieved a test accuracy of 82.1%. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Dependency Tree CNN encoders achieved a test accuracy of 82.1%.\n" }, { "role": "assistant", "content": "The table shows that the Dependency Tree CNN encoders achieved a test accuracy of 82.1%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1428.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Training and test accuracy on natural language inference task. d is the word embedding size and |θ|M the number of model parameters." ], "reasoning": "The table shows that the Dependency Tree CNN encoders achieved a test accuracy of 82.1%.", "statement": "The Dependency Tree CNN encoders achieved a test accuracy of 82.1%.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Test set performance on answer sentence selection.'.\n" }, { "role": "assistant", "content": "The NTI model outperforms NASM by approximately 0.4% on MAP, and the Deep LSTM and LSTM attention models outperform the previous best result by nearly 5-6%.The classifier with handcrafted features is a SVM model trained with a set of features. The Bigram-CNN model is a simple convolutional neural net. The Deep LSTM and LSTM attention models outperform the previous best result by a large margin, nearly 5-6%. In NASM, they adopt a deep three-layer LSTM and introduced a latent stochastic attention mechanism over the answer sentence. Our NTI model exceeds NASM by approximately 0.4% on MAP for this task." } ], "images": [ "scigen/train/1429.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Test set performance on answer sentence selection." ], "reasoning": "The NTI model outperforms NASM by approximately 0.4% on MAP, and the Deep LSTM and LSTM attention models outperform the previous best result by nearly 5-6%.", "description": "The classifier with handcrafted features is a SVM model trained with a set of features. The Bigram-CNN model is a simple convolutional neural net. The Deep LSTM and LSTM attention models outperform the previous best result by a large margin, nearly 5-6%. In NASM, they adopt a deep three-layer LSTM and introduced a latent stochastic attention mechanism over the answer sentence. Our NTI model exceeds NASM by approximately 0.4% on MAP for this task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Test set performance on answer sentence selection.', and sequentially solve the question, writing out each step of your reasoning process. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest MRR and by how much does it exceed the second highest MRR?\n" }, { "role": "assistant", "content": "The NTI model has the highest MRR of 0.6884. The second highest MRR is 0.6914 from NASM. The difference is 0.6884 - 0.6914 = -0.0030. So, the answer is {\"answer\": \"NTI exceeds NASM by 0.0030\"}" } ], "images": [ "scigen/train/1429.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Test set performance on answer sentence selection." ], "reasoning": "The NTI model has the highest MRR of 0.6884. The second highest MRR is 0.6914 from NASM. The difference is 0.6884 - 0.6914 = -0.0030.", "question": "Which model has the highest MRR and by how much does it exceed the second highest MRR?", "answer": "So, the answer is {\"answer\": \"NTI exceeds NASM by 0.0030\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Bigram-CNN model has a higher MAP than the 3-layer LSTM model.\n" }, { "role": "assistant", "content": "The Bigram-CNN model has a MAP of 0.6190, while the 3-layer LSTM model has a MAP of 0.6552. Therefore, the statement is incorrect. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1429.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Test set performance on answer sentence selection." ], "reasoning": "The Bigram-CNN model has a MAP of 0.6190, while the 3-layer LSTM model has a MAP of 0.6552. Therefore, the statement is incorrect.", "statement": "The Bigram-CNN model has a higher MAP than the 3-layer LSTM model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Topic clusters in comparison to classic corpora (size in token, mean with standard deviation).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The corpus contains 30 clusters with around 40 documents each, averaging 2413 tokens per document, leading to an average cluster size of 97,880 tokens, and has a high length-adjusted standard deviation.The corpus consists of document clusters for 30 different topics. Each of them contains around 40 documents with on average 2413 tokens, which leads to an average cluster size of 97,880 token. In addition, the documents are also more variable in terms of length, as the (length-adjusted) standard deviation is twice as high as in the other corpora. With these properties, the corpus represents an interesting challenge towards real-world application scenarios, in which users typically have to deal with much more than ten documents." } ], "images": [ "scigen/train/1430.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Topic clusters in comparison to classic corpora (size in token, mean with standard deviation)." ], "reasoning": "The corpus contains 30 clusters with around 40 documents each, averaging 2413 tokens per document, leading to an average cluster size of 97,880 tokens, and has a high length-adjusted standard deviation.", "description": "The corpus consists of document clusters for 30 different topics. Each of them contains around 40 documents with on average 2413 tokens, which leads to an average cluster size of 97,880 token. In addition, the documents are also more variable in terms of length, as the (length-adjusted) standard deviation is twice as high as in the other corpora. With these properties, the corpus represents an interesting challenge towards real-world application scenarios, in which users typically have to deal with much more than ten documents.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: Topic clusters in comparison to classic corpora (size in token, mean with standard deviation).'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: What is the relative standard deviation of the document size in the corpus 'This work'?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows that the relative standard deviation for the document size in the corpus 'This work' is 1.56. So, the answer is {\"answer\": \"1.56\"}" } ], "images": [ "scigen/train/1430.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Topic clusters in comparison to classic corpora (size in token, mean with standard deviation)." ], "reasoning": "The table shows that the relative standard deviation for the document size in the corpus 'This work' is 1.56.", "question": "What is the relative standard deviation of the document size in the corpus 'This work'?", "answer": "So, the answer is {\"answer\": \"1.56\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe average document size in the 'DUC 2006' corpus is 729.2 tokens.\n" }, { "role": "assistant", "content": "The table indicates that the average document size in the 'DUC 2006' corpus is indeed 729.2 tokens. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1430.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Topic clusters in comparison to classic corpora (size in token, mean with standard deviation)." ], "reasoning": "The table indicates that the average document size in the 'DUC 2006' corpus is indeed 729.2 tokens.", "statement": "The average document size in the 'DUC 2006' corpus is 729.2 tokens.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Correlation of peer scores with manual responsiveness scores on TAC2008 topics 01-03.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The study evaluated the importance annotations for summary evaluation using Pearson and Spearman correlations, comparing them to pyramid annotations and ROUGE scores.In addition to the reliability studies, we extrinsically evaluated the annotations in the task of summary evaluation. For each of the 58 peer summaries in TAC2008, we calculated a score as the sum of the importance estimates of the propositions it contains. The results demonstrate that with both task designs, we obtain importance annotations that are similarly useful for summary evaluation as pyramid annotations or gold-standard summaries (used for ROUGE)." } ], "images": [ "scigen/train/1431.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Correlation of peer scores with manual responsiveness scores on TAC2008 topics 01-03." ], "reasoning": "The study evaluated the importance annotations for summary evaluation using Pearson and Spearman correlations, comparing them to pyramid annotations and ROUGE scores.", "description": "In addition to the reliability studies, we extrinsically evaluated the annotations in the task of summary evaluation. For each of the 58 peer summaries in TAC2008, we calculated a score as the sum of the importance estimates of the propositions it contains. The results demonstrate that with both task designs, we obtain importance annotations that are similarly useful for summary evaluation as pyramid annotations or gold-standard summaries (used for ROUGE).", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 1: Correlation of peer scores with manual responsiveness scores on TAC2008 topics 01-03.' to sequentially solve the question presented below. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich peer scoring method has the highest Spearman correlation?\n" }, { "role": "assistant", "content": "To find the peer scoring method with the highest Spearman correlation, compare the Spearman values for all methods. The highest value is 0.4676 for the Modified Pyramid method. So, the answer is {\"answer\": \"Modified Pyramid\"}" } ], "images": [ "scigen/train/1431.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Correlation of peer scores with manual responsiveness scores on TAC2008 topics 01-03." ], "reasoning": "To find the peer scoring method with the highest Spearman correlation, compare the Spearman values for all methods. The highest value is 0.4676 for the Modified Pyramid method.", "question": "Which peer scoring method has the highest Spearman correlation?", "answer": "So, the answer is {\"answer\": \"Modified Pyramid\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe ROUGE-2 method has the lowest Pearson correlation.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Pearson correlations for all methods. The ROUGE-2 method has the lowest Pearson correlation of 0.3062. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1431.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Correlation of peer scores with manual responsiveness scores on TAC2008 topics 01-03." ], "reasoning": "To verify this statement, compare the Pearson correlations for all methods. The ROUGE-2 method has the lowest Pearson correlation of 0.3062.", "statement": "The ROUGE-2 method has the lowest Pearson correlation.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Tokenization Performance.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Although StanfordNLP GUM model performs slightly better on GUM test for tokenization, our tokenizer outperforms it on the AMALGUM sample and overall for the corpus.Although tokenization is marginally better on GUM test for the StanfordNLP GUM model (+0.03%), the results on the AMALGUM sample (+0.73%), as well as qualitative inspection of some of the frequent patterns in the complete AMALGUM data, suggest that our own tokenizer is the better choice overall for this corpus." } ], "images": [ "scigen/train/1432.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Tokenization Performance." ], "reasoning": "Although StanfordNLP GUM model performs slightly better on GUM test for tokenization, our tokenizer outperforms it on the AMALGUM sample and overall for the corpus.", "description": "Although tokenization is marginally better on GUM test for the StanfordNLP GUM model (+0.03%), the results on the AMALGUM sample (+0.73%), as well as qualitative inspection of some of the frequent patterns in the complete AMALGUM data, suggest that our own tokenizer is the better choice overall for this corpus.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model has the highest F1 score on the AMALGUM sample?\nSolve the above question based on the table titled 'Table 2: Tokenization Performance.'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the F1 scores for different models on the AMALGUM sample. The highest F1 score on the AMALGUM sample is 99.88, achieved by 'this paper' model. So, the answer is {\"answer\": \"this paper\"}" } ], "images": [ "scigen/train/1432.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Tokenization Performance." ], "reasoning": "The table shows the F1 scores for different models on the AMALGUM sample. The highest F1 score on the AMALGUM sample is 99.88, achieved by 'this paper' model.", "question": "Which model has the highest F1 score on the AMALGUM sample?", "answer": "So, the answer is {\"answer\": \"this paper\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The StanfordNLP GUM model has the highest F1 score on the GUM test. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe StanfordNLP GUM model has the highest F1 score on the GUM test.\n" }, { "role": "assistant", "content": "The table shows the F1 scores for different models on the GUM test. The highest F1 score on the GUM test is 99.92, achieved by the StanfordNLP GUM model. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1432.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Tokenization Performance." ], "reasoning": "The table shows the F1 scores for different models on the GUM test. The highest F1 score on the GUM test is 99.92, achieved by the StanfordNLP GUM model.", "statement": "The StanfordNLP GUM model has the highest F1 score on the GUM test.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 5: Coreference Resolution Performance. The average F1 of MUC, B3 and CEAFϕ4 is the main evaluation metric.'.\n" }, { "role": "assistant", "content": "The xrenner + S&H model achieves slightly lower scores on GUM test compared to the xrenner model, but significantly outperforms Shibuya's model on AMALGUM.Since the training data for GUM is limited, \\newciteJoshi2019BERTFC’s model does not achieve satisfying scores out-of-the-box on GUM test as in the CoNLL dataset. However in the highest scoring xrenner model, it turns out that entity types for many non-coreferential mentions are more often incorrect than for We therefore considered using that system for mention detection and classification, and report slightly lower scores using this combination of entity recognition and coreference resolution (xrenner + S&H in the table). The modified xrenner significantly outperforms \\newciteshibuya_nested_2019 on AMALGUM and only scores 1.78% lower on GUM test." } ], "images": [ "scigen/train/1433.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Coreference Resolution Performance. The average F1 of MUC, B3 and CEAFϕ4 is the main evaluation metric." ], "reasoning": "The xrenner + S&H model achieves slightly lower scores on GUM test compared to the xrenner model, but significantly outperforms Shibuya's model on AMALGUM.", "description": "Since the training data for GUM is limited, \\newciteJoshi2019BERTFC’s model does not achieve satisfying scores out-of-the-box on GUM test as in the CoNLL dataset. However in the highest scoring xrenner model, it turns out that entity types for many non-coreferential mentions are more often incorrect than for We therefore considered using that system for mention detection and classification, and report slightly lower scores using this combination of entity recognition and coreference resolution (xrenner + S&H in the table). The modified xrenner significantly outperforms \\newciteshibuya_nested_2019 on AMALGUM and only scores 1.78% lower on GUM test.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 5: Coreference Resolution Performance. The average F1 of MUC, B3 and CEAFϕ4 is the main evaluation metric.' to sequentially solve the question presented below. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhich model has the highest average F1 score on the AMALGUM dataset?\n" }, { "role": "assistant", "content": "By comparing the average F1 scores for all models on the AMALGUM dataset, the xrenner model has the highest score of 79.3. So, the answer is {\"answer\": \"xrenner\"}" } ], "images": [ "scigen/train/1433.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Coreference Resolution Performance. The average F1 of MUC, B3 and CEAFϕ4 is the main evaluation metric." ], "reasoning": "By comparing the average F1 scores for all models on the AMALGUM dataset, the xrenner model has the highest score of 79.3.", "question": "Which model has the highest average F1 score on the AMALGUM dataset?", "answer": "So, the answer is {\"answer\": \"xrenner\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe xrenner + S&H model has a higher MUC F1 score on the GUM test than the Joshi2019BERTFC model.\n" }, { "role": "assistant", "content": "The table shows that the xrenner + S&H model has a MUC F1 score of 60.3 on the GUM test, while the Joshi2019BERTFC model has a MUC F1 score of 58.6. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1433.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Coreference Resolution Performance. The average F1 of MUC, B3 and CEAFϕ4 is the main evaluation metric." ], "reasoning": "The table shows that the xrenner + S&H model has a MUC F1 score of 60.3 on the GUM test, while the Joshi2019BERTFC model has a MUC F1 score of 58.6.", "statement": "The xrenner + S&H model has a higher MUC F1 score on the GUM test than the Joshi2019BERTFC model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption '(c) Results on RefCOCOg and Flickr30k Image Retrieval (IR) and Text Retrieval (TR).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Villa-base and Villa-large models outperform UNITER-base and UNITER-large across multiple benchmarks, achieving new state-of-the-art results.Downstream Task Evaluation Compared with existing pre-trained V+L models, our Villa method achieves new state of the art across all the benchmarks. Specifically, Villa-base model outperforms UNITER-base by +0.76 on VQA, +2.4 on VCR for Q→AR, +1.45 on NLVR2, +0.75 on SNLI-VE, +2.22/+0.70 on Flickr30k for Image/Text Retrieval (R@1), and +0.99 on average for the three RE datasets." } ], "images": [ "scigen/train/1434.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(c) Results on RefCOCOg and Flickr30k Image Retrieval (IR) and Text Retrieval (TR)." ], "reasoning": "Villa-base and Villa-large models outperform UNITER-base and UNITER-large across multiple benchmarks, achieving new state-of-the-art results.", "description": "Downstream Task Evaluation Compared with existing pre-trained V+L models, our Villa method achieves new state of the art across all the benchmarks. Specifically, Villa-base model outperforms UNITER-base by +0.76 on VQA, +2.4 on VCR for Q→AR, +1.45 on NLVR2, +0.75 on SNLI-VE, +2.22/+0.70 on Flickr30k for Image/Text Retrieval (R@1), and +0.99 on average for the three RE datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled '(c) Results on RefCOCOg and Flickr30k Image Retrieval (IR) and Text Retrieval (TR).', solve this question and give detail solutions. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nHow much does the Villa-base model outperform the UNITER-base model on the RefCOCOg val d benchmark?\n" }, { "role": "assistant", "content": "To find the difference in performance between the Villa-base and UNITER-base models on the RefCOCOg val d benchmark, subtract the UNITER-base score (74.31) from the Villa-base score (75.90). So, the answer is {\"answer\": \"1.59\"}." } ], "images": [ "scigen/train/1434.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(c) Results on RefCOCOg and Flickr30k Image Retrieval (IR) and Text Retrieval (TR)." ], "reasoning": "To find the difference in performance between the Villa-base and UNITER-base models on the RefCOCOg val d benchmark, subtract the UNITER-base score (74.31) from the Villa-base score (75.90).", "question": "How much does the Villa-base model outperform the UNITER-base model on the RefCOCOg val d benchmark?", "answer": "So, the answer is {\"answer\": \"1.59\"}." }, { "messages": [ { "role": "user", "content": "The Villa-large model achieves higher scores than the UNITER-large model on the RefCOCOg test d benchmark. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Villa-large model achieves higher scores than the UNITER-large model on the RefCOCOg test d benchmark.\n" }, { "role": "assistant", "content": "Compare the scores of the Villa-large model (76.71) and the UNITER-large model (75.77) on the RefCOCOg test d benchmark. Since 76.71 is greater than 75.77, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1434.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(c) Results on RefCOCOg and Flickr30k Image Retrieval (IR) and Text Retrieval (TR)." ], "reasoning": "Compare the scores of the Villa-large model (76.71) and the UNITER-large model (75.77) on the RefCOCOg test d benchmark. Since 76.71 is greater than 75.77, the statement is supported.", "statement": "The Villa-large model achieves higher scores than the UNITER-large model on the RefCOCOg test d benchmark.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled '(b) Results on RefCOCO+ and RefCOCO. The superscript d denotes evaluation using detected proposals.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Villa-base model outperforms UNITER-base across multiple benchmarks, achieving higher scores in VQA, VCR, NLVR2, SNLI-VE, and Flickr30k for Image/Text Retrieval.Downstream Task Evaluation Compared with existing pre-trained V+L models, our Villa method achieves new state of the art across all the benchmarks. Specifically, Villa-base model outperforms UNITER-base by +0.76 on VQA, +2.4 on VCR for Q→AR, +1.45 on NLVR2, +0.75 on SNLI-VE, +2.22/+0.70 on Flickr30k for Image/Text Retrieval (R@1), and +0.99 on average for the three RE datasets." } ], "images": [ "scigen/train/1435.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) Results on RefCOCO+ and RefCOCO. The superscript d denotes evaluation using detected proposals." ], "reasoning": "Villa-base model outperforms UNITER-base across multiple benchmarks, achieving higher scores in VQA, VCR, NLVR2, SNLI-VE, and Flickr30k for Image/Text Retrieval.", "description": "Downstream Task Evaluation Compared with existing pre-trained V+L models, our Villa method achieves new state of the art across all the benchmarks. Specifically, Villa-base model outperforms UNITER-base by +0.76 on VQA, +2.4 on VCR for Q→AR, +1.45 on NLVR2, +0.75 on SNLI-VE, +2.22/+0.70 on Flickr30k for Image/Text Retrieval (R@1), and +0.99 on average for the three RE datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to '(b) Results on RefCOCO+ and RefCOCO. The superscript d denotes evaluation using detected proposals.', and sequentially solve the question, writing out each step of your reasoning process. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich method has the highest score on RefCOCO+ testA?\n" }, { "role": "assistant", "content": "To find the highest score on RefCOCO+ testA, compare the scores of all methods listed in the RefCOCO+ testA column. The highest score is 86.96, achieved by VILLABASE. So, the answer is {\"answer\": \"VILLABASE\"}." } ], "images": [ "scigen/train/1435.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) Results on RefCOCO+ and RefCOCO. The superscript d denotes evaluation using detected proposals." ], "reasoning": "To find the highest score on RefCOCO+ testA, compare the scores of all methods listed in the RefCOCO+ testA column. The highest score is 86.96, achieved by VILLABASE.", "question": "Which method has the highest score on RefCOCO+ testA?", "answer": "So, the answer is {\"answer\": \"VILLABASE\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nVILLALARGE outperforms all other methods on RefCOCO testA.\n" }, { "role": "assistant", "content": "To verify this statement, compare the scores of all methods listed in the RefCOCO testA column. VILLALARGE has a score of 92.96, which is the highest among all methods. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1435.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) Results on RefCOCO+ and RefCOCO. The superscript d denotes evaluation using detected proposals." ], "reasoning": "To verify this statement, compare the scores of all methods listed in the RefCOCO testA column. VILLALARGE has a score of 92.96, which is the highest among all methods.", "statement": "VILLALARGE outperforms all other methods on RefCOCO testA.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Ablation study on Villa-pre (pre-training) and Villa-fine (finetuning) with base model size.'.\n" }, { "role": "assistant", "content": "Adversarial training in both pre-training and finetuning stages improves performance, with the combined approach yielding the highest average gain.Pre-training vs. Finetuning UNITER (reimp.) denotes our re-implementation of the UNITER-base model with standard training. Villa-pre and Villa-fine apply adversarial training to only the pre-training or finetuning stage, respectively. Averaged over the six evaluation tasks, Villa-pre and Villa-fine brings +0.51 and +0.82 points performance gain. By combining the two, +1.15 points gain is achieved. Interestingly, on VQA, though in early epochs UNITER achieves better performance than Villa, Villa catches up quickly after a few hundred of steps, which demonstrates the beneficial regularization effect of adversarial training. More training curves on other tasks can be found in Appendix. On average, adversarial pre-training and finetuning bring +1.48 and +2.21 performance gain, respectively. Combining the two AT stages provides further improvement." } ], "images": [ "scigen/train/1436.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ablation study on Villa-pre (pre-training) and Villa-fine (finetuning) with base model size." ], "reasoning": "Adversarial training in both pre-training and finetuning stages improves performance, with the combined approach yielding the highest average gain.", "description": "Pre-training vs. Finetuning UNITER (reimp.) denotes our re-implementation of the UNITER-base model with standard training. Villa-pre and Villa-fine apply adversarial training to only the pre-training or finetuning stage, respectively. Averaged over the six evaluation tasks, Villa-pre and Villa-fine brings +0.51 and +0.82 points performance gain. By combining the two, +1.15 points gain is achieved. Interestingly, on VQA, though in early epochs UNITER achieves better performance than Villa, Villa catches up quickly after a few hundred of steps, which demonstrates the beneficial regularization effect of adversarial training. More training curves on other tasks can be found in Appendix. On average, adversarial pre-training and finetuning bring +1.48 and +2.21 performance gain, respectively. Combining the two AT stages provides further improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 2: Ablation study on Villa-pre (pre-training) and Villa-fine (finetuning) with base model size.', solve the following question step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the average performance gain when combining adversarial pre-training and finetuning stages?\n" }, { "role": "assistant", "content": "The description states that combining adversarial pre-training and finetuning stages provides a +1.15 points gain, and the table shows that the combined method (VILLA) has the highest average score of 79.21. So, the answer is {\"answer\": \"1.15\"}" } ], "images": [ "scigen/train/1436.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ablation study on Villa-pre (pre-training) and Villa-fine (finetuning) with base model size." ], "reasoning": "The description states that combining adversarial pre-training and finetuning stages provides a +1.15 points gain, and the table shows that the combined method (VILLA) has the highest average score of 79.21.", "question": "What is the average performance gain when combining adversarial pre-training and finetuning stages?", "answer": "So, the answer is {\"answer\": \"1.15\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nVILLA-pre achieves higher performance than UNITER (reimp.) on the VE test.\n" }, { "role": "assistant", "content": "The table shows that VILLA-pre has a score of 78.44 on the VE test, while UNITER (reimp.) has a score of 78.28. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1436.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ablation study on Villa-pre (pre-training) and Villa-fine (finetuning) with base model size." ], "reasoning": "The table shows that VILLA-pre has a score of 78.44 on the VE test, while UNITER (reimp.) has a score of 78.28.", "statement": "VILLA-pre achieves higher performance than UNITER (reimp.) on the VE test.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Probing analysis of the attention heads in pre-trained UNITER and Villa models.'.\n" }, { "role": "assistant", "content": "Villa outperforms UNITERBASE in all ten categories of visual coreference and visual relation, with an average attention weight of 0.223 compared to 0.195.Probing Analysis Pre-trained models are expected to learn intricate knowledge about multimodality correlations, such as visual coreference (i.e., region-phrase alignment) and visual relation (i.e., region-region interaction). To provide a more direct measurement on how well our adversarial pre-trained model captures such multimodal signals, we conduct a probing analysis following Cao et al. We consider five most common visual coreference types in Flickr30k Entities Plummer et al. learned by pre-trained models. Results show that Villa presents higher attention weights across all the ten categories (0.223 vs. 0.195 on average), indicating a higher probability of identifying those relations." } ], "images": [ "scigen/train/1437.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Probing analysis of the attention heads in pre-trained UNITER and Villa models." ], "reasoning": "Villa outperforms UNITERBASE in all ten categories of visual coreference and visual relation, with an average attention weight of 0.223 compared to 0.195.", "description": "Probing Analysis Pre-trained models are expected to learn intricate knowledge about multimodality correlations, such as visual coreference (i.e., region-phrase alignment) and visual relation (i.e., region-region interaction). To provide a more direct measurement on how well our adversarial pre-trained model captures such multimodal signals, we conduct a probing analysis following Cao et al. We consider five most common visual coreference types in Flickr30k Entities Plummer et al. learned by pre-trained models. Results show that Villa presents higher attention weights across all the ten categories (0.223 vs. 0.195 on average), indicating a higher probability of identifying those relations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Probing analysis of the attention heads in pre-trained UNITER and Villa models.\nQuestion: Which model has a higher attention weight for visual coreference in the 'Flickr30k animals' category?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "Compare the attention weights for the 'Flickr30k animals' category between the two models. Villa has an attention weight of 0.299, while UNITERBASE has an attention weight of 0.285. So, the answer is {\"answer\": \"Villa\"}" } ], "images": [ "scigen/train/1437.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Probing analysis of the attention heads in pre-trained UNITER and Villa models." ], "reasoning": "Compare the attention weights for the 'Flickr30k animals' category between the two models. Villa has an attention weight of 0.299, while UNITERBASE has an attention weight of 0.285.", "question": "Which model has a higher attention weight for visual coreference in the 'Flickr30k animals' category?", "answer": "So, the answer is {\"answer\": \"Villa\"}" }, { "messages": [ { "role": "user", "content": "UNITERBASE has a higher average attention weight than Villa. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nUNITERBASE has a higher average attention weight than Villa.\n" }, { "role": "assistant", "content": "The table shows that Villa has an average attention weight of 0.223, while UNITERBASE has an average attention weight of 0.195. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1437.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Probing analysis of the attention heads in pre-trained UNITER and Villa models." ], "reasoning": "The table shows that Villa has an average attention weight of 0.223, while UNITERBASE has an average attention weight of 0.195. Therefore, the statement is refuted.", "statement": "UNITERBASE has a higher average attention weight than Villa.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: Results on LXMERT with Villa-fine (finetuning).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Villa-fine improves the performance of LXMERT on VQA and GQA tasks, demonstrating its regularization effect and preventing overfitting.Results on LXMERT Villa is a generic framework that can be readily applied to any V+L models. To demonstrate its generalization ability, we conduct additional experiments using LXMERT as the backbone. Since adversarial pre-training is highly time-consuming, we only focus on adversarial finetuning for LXMERT. We use VQA and GQA as the evaluation tasks, the same as LXMERT. Compared to LXMERT, Villa-fine achieves higher accuracy on validation set and lower accuracy on training set for both VQA and GQA, clearly demonstrating its regularization effect in preventing overfitting of large-scale models. Results of LXMERT with Villa-fine on NLVR2 In the LXMERT paper, LXMERT has been applied to VQA, GQA, and NLVR2 tasks. Villa-fine instantly provides +0.98 performance boost without changing any model architectures and hyper-parameter settings used in LXMERT. Results on GQA" } ], "images": [ "scigen/train/1438.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on LXMERT with Villa-fine (finetuning)." ], "reasoning": "Villa-fine improves the performance of LXMERT on VQA and GQA tasks, demonstrating its regularization effect and preventing overfitting.", "description": "Results on LXMERT Villa is a generic framework that can be readily applied to any V+L models. To demonstrate its generalization ability, we conduct additional experiments using LXMERT as the backbone. Since adversarial pre-training is highly time-consuming, we only focus on adversarial finetuning for LXMERT. We use VQA and GQA as the evaluation tasks, the same as LXMERT. Compared to LXMERT, Villa-fine achieves higher accuracy on validation set and lower accuracy on training set for both VQA and GQA, clearly demonstrating its regularization effect in preventing overfitting of large-scale models. Results of LXMERT with Villa-fine on NLVR2 In the LXMERT paper, LXMERT has been applied to VQA, GQA, and NLVR2 tasks. Villa-fine instantly provides +0.98 performance boost without changing any model architectures and hyper-parameter settings used in LXMERT. Results on GQA", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 5: Results on LXMERT with Villa-fine (finetuning).'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the Meta-Ave. score for VILLA-fine?\n" }, { "role": "assistant", "content": "The Meta-Ave. score for VILLA-fine is found in the last column of the table under the VILLA-fine row. So, the answer is {\"answer\": \"67.08\"}" } ], "images": [ "scigen/train/1438.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on LXMERT with Villa-fine (finetuning)." ], "reasoning": "The Meta-Ave. score for VILLA-fine is found in the last column of the table under the VILLA-fine row.", "question": "What is the Meta-Ave. score for VILLA-fine?", "answer": "So, the answer is {\"answer\": \"67.08\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nVILLA-fine achieves the highest accuracy on the VQA test-std set.\n" }, { "role": "assistant", "content": "The table shows that VILLA-fine has a VQA test-std score of 73.18, which is higher than the scores of LXMERT and LXMERT (reimp.). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1438.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on LXMERT with Villa-fine (finetuning)." ], "reasoning": "The table shows that VILLA-fine has a VQA test-std score of 73.18, which is higher than the scores of LXMERT and LXMERT (reimp.).", "statement": "VILLA-fine achieves the highest accuracy on the VQA test-std set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 7: Ablation study on Villa-pre (pre-training) and Villa-fine (finetuning) with large model size.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Adversarial pre-training and finetuning individually improve performance by +1.48 and +2.21, respectively, and combining both stages yields further enhancement.On average, adversarial pre-training and finetuning bring +1.48 and +2.21 performance gain, respectively. Combining the two AT stages provides further improvement." } ], "images": [ "scigen/train/1439.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Ablation study on Villa-pre (pre-training) and Villa-fine (finetuning) with large model size." ], "reasoning": "Adversarial pre-training and finetuning individually improve performance by +1.48 and +2.21, respectively, and combining both stages yields further enhancement.", "description": "On average, adversarial pre-training and finetuning bring +1.48 and +2.21 performance gain, respectively. Combining the two AT stages provides further improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 7: Ablation study on Villa-pre (pre-training) and Villa-fine (finetuning) with large model size.', solve the subsequent question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhat is the average performance gain when combining adversarial pre-training and finetuning?\n" }, { "role": "assistant", "content": "The table shows that VILLA-pre has an average score of 73.80 and VILLA-fine has an average score of 74.53. Combining both stages (VILLA) results in an average score of 75.22. The performance gain from combining both stages is 75.22 - 72.32 = 2.90. So, the answer is {\"answer\": \"2.90\"}." } ], "images": [ "scigen/train/1439.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Ablation study on Villa-pre (pre-training) and Villa-fine (finetuning) with large model size." ], "reasoning": "The table shows that VILLA-pre has an average score of 73.80 and VILLA-fine has an average score of 74.53. Combining both stages (VILLA) results in an average score of 75.22. The performance gain from combining both stages is 75.22 - 72.32 = 2.90.", "question": "What is the average performance gain when combining adversarial pre-training and finetuning?", "answer": "So, the answer is {\"answer\": \"2.90\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nCombining adversarial pre-training and finetuning results in the highest average performance.\n" }, { "role": "assistant", "content": "The table shows that the highest average performance score is 75.22, which is achieved by combining both adversarial pre-training and finetuning (VILLA). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1439.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Ablation study on Villa-pre (pre-training) and Villa-fine (finetuning) with large model size." ], "reasoning": "The table shows that the highest average performance score is 75.22, which is achieved by combining both adversarial pre-training and finetuning (VILLA).", "statement": "Combining adversarial pre-training and finetuning results in the highest average performance.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled '(a)', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "We report words occurring at least five times and rank them by overall frequency to indicate their effect on performance.Because many of the most discriminative words are low frequency, we report only words which occur at least five times. We rank the words according to their overall frequency, since this statistic is perhaps more indicative of a word w’s effect on overall performance compared to p(l|w) alone." } ], "images": [ "scigen/train/1440.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a)" ], "reasoning": "We report words occurring at least five times and rank them by overall frequency to indicate their effect on performance.", "description": "Because many of the most discriminative words are low frequency, we report only words which occur at least five times. We rank the words according to their overall frequency, since this statistic is perhaps more indicative of a word w’s effect on overall performance compared to p(l|w) alone.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled '(a)', solve this question and give detail solutions. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhich word has the highest frequency and what is its score?\n" }, { "role": "assistant", "content": "To find the word with the highest frequency, look at the 'Freq' column and identify the highest value, which is 20. The corresponding word is 'instrument' with a score of 0.90. So, the answer is {\"answer\": \"instrument with a score of 0.90\"}." } ], "images": [ "scigen/train/1440.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a)" ], "reasoning": "To find the word with the highest frequency, look at the 'Freq' column and identify the highest value, which is 20. The corresponding word is 'instrument' with a score of 0.90.", "question": "Which word has the highest frequency and what is its score?", "answer": "So, the answer is {\"answer\": \"instrument with a score of 0.90\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The word 'opposing' has the highest score., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe word 'opposing' has the highest score.\n" }, { "role": "assistant", "content": "The table shows that 'opposing' has a score of 1.00, which is the highest score in the 'Score' column. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1440.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a)" ], "reasoning": "The table shows that 'opposing' has a score of 1.00, which is the highest score in the 'Score' column.", "statement": "The word 'opposing' has the highest score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: NLI accuracies on each dataset. Columns ‘Hyp-Only’ and ‘MAJ’ indicates the accuracy of the hypothesis-only model and the majority baseline. |Δ| and Δ% indicate the absolute difference in percentage points and the percentage increase between the Hyp-Only and MAJ. Blue numbers indicate that the hypothesis-model outperforms MAJ. In the right-most section, ‘Baseline’ indicates the original baseline on the test when the dataset was released and ‘SOTA’ indicates current state-of-the-art results. MNLI-1 is the matched version and MNLI-2 is the mismatched for MNLI. The names of datasets are italicized if containing ≤10K labeled examples.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The hypothesis-only model outperforms the majority baseline in six out of ten datasets, indicating exploitable signals that help NLI models perform well without considering contexts.Our goal is to determine whether a hypothesis-only model outperforms the majority baseline and investigate what may cause significant gains. In such cases a hypothesis-only model should be used as a stronger baseline instead of the majority class baseline. For all experiments except for JOCI, we use each NLI dataset’s standard train, dev, and test splits. Across six of the ten datasets, our hypothesis-only model significantly outperforms the majority-baseline, even outperforming the best reported results on one dataset, recast SPR. This indicates that there exists a significant degree of exploitable signal that may help NLI models perform well on their corresponding test set without considering NLI contexts. The largest relative gains are on human-elicited models where the hypothesis-only model more than doubles the majority baseline." } ], "images": [ "scigen/train/1441.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: NLI accuracies on each dataset. Columns ‘Hyp-Only’ and ‘MAJ’ indicates the accuracy of the hypothesis-only model and the majority baseline. |Δ| and Δ% indicate the absolute difference in percentage points and the percentage increase between the Hyp-Only and MAJ. Blue numbers indicate that the hypothesis-model outperforms MAJ. In the right-most section, ‘Baseline’ indicates the original baseline on the test when the dataset was released and ‘SOTA’ indicates current state-of-the-art results. MNLI-1 is the matched version and MNLI-2 is the mismatched for MNLI. The names of datasets are italicized if containing ≤10K labeled examples." ], "reasoning": "The hypothesis-only model outperforms the majority baseline in six out of ten datasets, indicating exploitable signals that help NLI models perform well without considering contexts.", "description": "Our goal is to determine whether a hypothesis-only model outperforms the majority baseline and investigate what may cause significant gains. In such cases a hypothesis-only model should be used as a stronger baseline instead of the majority class baseline. For all experiments except for JOCI, we use each NLI dataset’s standard train, dev, and test splits. Across six of the ten datasets, our hypothesis-only model significantly outperforms the majority-baseline, even outperforming the best reported results on one dataset, recast SPR. This indicates that there exists a significant degree of exploitable signal that may help NLI models perform well on their corresponding test set without considering NLI contexts. The largest relative gains are on human-elicited models where the hypothesis-only model more than doubles the majority baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 2: NLI accuracies on each dataset. Columns ‘Hyp-Only’ and ‘MAJ’ indicates the accuracy of the hypothesis-only model and the majority baseline. |Δ| and Δ% indicate the absolute difference in percentage points and the percentage increase between the Hyp-Only and MAJ. Blue numbers indicate that the hypothesis-model outperforms MAJ. In the right-most section, ‘Baseline’ indicates the original baseline on the test when the dataset was released and ‘SOTA’ indicates current state-of-the-art results. MNLI-1 is the matched version and MNLI-2 is the mismatched for MNLI. The names of datasets are italicized if containing ≤10K labeled examples.', solve the following question step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich dataset shows the largest relative gain in the TEST Δ% column?\n" }, { "role": "assistant", "content": "By examining the TEST Δ% column, the largest relative gain is found in the SNLI dataset with a value of +101.28%. So, the answer is {\"answer\": \"SNLI\"}" } ], "images": [ "scigen/train/1441.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: NLI accuracies on each dataset. Columns ‘Hyp-Only’ and ‘MAJ’ indicates the accuracy of the hypothesis-only model and the majority baseline. |Δ| and Δ% indicate the absolute difference in percentage points and the percentage increase between the Hyp-Only and MAJ. Blue numbers indicate that the hypothesis-model outperforms MAJ. In the right-most section, ‘Baseline’ indicates the original baseline on the test when the dataset was released and ‘SOTA’ indicates current state-of-the-art results. MNLI-1 is the matched version and MNLI-2 is the mismatched for MNLI. The names of datasets are italicized if containing ≤10K labeled examples." ], "reasoning": "By examining the TEST Δ% column, the largest relative gain is found in the SNLI dataset with a value of +101.28%.", "question": "Which dataset shows the largest relative gain in the TEST Δ% column?", "answer": "So, the answer is {\"answer\": \"SNLI\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe hypothesis-only model outperforms the majority baseline in the SICK dataset.\n" }, { "role": "assistant", "content": "In the SICK dataset, the TEST Hyp-Only score is 56.67, which is higher than the TEST MAJ score of 56.67, indicating that the hypothesis-only model does not outperform the majority baseline. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1441.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: NLI accuracies on each dataset. Columns ‘Hyp-Only’ and ‘MAJ’ indicates the accuracy of the hypothesis-only model and the majority baseline. |Δ| and Δ% indicate the absolute difference in percentage points and the percentage increase between the Hyp-Only and MAJ. Blue numbers indicate that the hypothesis-model outperforms MAJ. In the right-most section, ‘Baseline’ indicates the original baseline on the test when the dataset was released and ‘SOTA’ indicates current state-of-the-art results. MNLI-1 is the matched version and MNLI-2 is the mismatched for MNLI. The names of datasets are italicized if containing ≤10K labeled examples." ], "reasoning": "In the SICK dataset, the TEST Hyp-Only score is 56.67, which is higher than the TEST MAJ score of 56.67, indicating that the hypothesis-only model does not outperform the majority baseline.", "statement": "The hypothesis-only model outperforms the majority baseline in the SICK dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption '(b)'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "We report words occurring at least five times and rank them by overall frequency to indicate their effect on performance.Because many of the most discriminative words are low frequency, we report only words which occur at least five times. We rank the words according to their overall frequency, since this statistic is perhaps more indicative of a word w’s effect on overall performance compared to p(l|w) alone." } ], "images": [ "scigen/train/1442.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b)" ], "reasoning": "We report words occurring at least five times and rank them by overall frequency to indicate their effect on performance.", "description": "Because many of the most discriminative words are low frequency, we report only words which occur at least five times. We rank the words according to their overall frequency, since this statistic is perhaps more indicative of a word w’s effect on overall performance compared to p(l|w) alone.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about '(b)', answer the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich word has the highest frequency and what is its SNLI Score?\n" }, { "role": "assistant", "content": "To find the word with the highest frequency, look at the 'SNLI Freq' column and identify the highest value, which is 44. The corresponding word is 'tall' with an SNLI Score of 0.93. So, the answer is {\"answer\": \"tall with an SNLI Score of 0.93\"}." } ], "images": [ "scigen/train/1442.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b)" ], "reasoning": "To find the word with the highest frequency, look at the 'SNLI Freq' column and identify the highest value, which is 44. The corresponding word is 'tall' with an SNLI Score of 0.93.", "question": "Which word has the highest frequency and what is its SNLI Score?", "answer": "So, the answer is {\"answer\": \"tall with an SNLI Score of 0.93\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe word 'birthday' has a frequency higher than 'competition'.\n" }, { "role": "assistant", "content": "The table shows that 'birthday' has a frequency of 20, while 'competition' has a frequency of 24. Therefore, 'birthday' does not have a higher frequency than 'competition'. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1442.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b)" ], "reasoning": "The table shows that 'birthday' has a frequency of 20, while 'competition' has a frequency of 24. Therefore, 'birthday' does not have a higher frequency than 'competition'.", "statement": "The word 'birthday' has a frequency higher than 'competition'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title '(c)'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "We report words occurring at least five times and rank them by overall frequency to indicate their effect on performance.Because many of the most discriminative words are low frequency, we report only words which occur at least five times. We rank the words according to their overall frequency, since this statistic is perhaps more indicative of a word w’s effect on overall performance compared to p(l|w) alone." } ], "images": [ "scigen/train/1443.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(c)" ], "reasoning": "We report words occurring at least five times and rank them by overall frequency to indicate their effect on performance.", "description": "Because many of the most discriminative words are low frequency, we report only words which occur at least five times. We rank the words according to their overall frequency, since this statistic is perhaps more indicative of a word w’s effect on overall performance compared to p(l|w) alone.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about '(c)', and tackle the question below in a sequential manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich word has the highest frequency and what is its score?\n" }, { "role": "assistant", "content": "By examining the table, the word 'sleeping' has the highest frequency of 108. Its corresponding score is 0.88. So, the answer is {\"answer\": \"sleeping with a score of 0.88\"}." } ], "images": [ "scigen/train/1443.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(c)" ], "reasoning": "By examining the table, the word 'sleeping' has the highest frequency of 108. Its corresponding score is 0.88.", "question": "Which word has the highest frequency and what is its score?", "answer": "So, the answer is {\"answer\": \"sleeping with a score of 0.88\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe word 'Nobody' has the highest score.\n" }, { "role": "assistant", "content": "By examining the table, the word 'Nobody' has a score of 1.00, which is the highest score listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1443.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(c)" ], "reasoning": "By examining the table, the word 'Nobody' has a score of 1.00, which is the highest score listed.", "statement": "The word 'Nobody' has the highest score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title '(d)'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "We report words occurring at least five times and rank them by overall frequency to indicate their effect on performance.Because many of the most discriminative words are low frequency, we report only words which occur at least five times. We rank the words according to their overall frequency, since this statistic is perhaps more indicative of a word w’s effect on overall performance compared to p(l|w) alone." } ], "images": [ "scigen/train/1444.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(d)" ], "reasoning": "We report words occurring at least five times and rank them by overall frequency to indicate their effect on performance.", "description": "Because many of the most discriminative words are low frequency, we report only words which occur at least five times. We rank the words according to their overall frequency, since this statistic is perhaps more indicative of a word w’s effect on overall performance compared to p(l|w) alone.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: (d)\n\nQuestion: Which word has the highest frequency and what is its score?\n" }, { "role": "assistant", "content": "To find the word with the highest frequency, look at the 'Freq' column and identify the highest value, then find the corresponding word and its score. So, the answer is {\"answer\": \"an with a score of 0.57\"}." } ], "images": [ "scigen/train/1444.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(d)" ], "reasoning": "To find the word with the highest frequency, look at the 'Freq' column and identify the highest value, then find the corresponding word and its score.", "question": "Which word has the highest frequency and what is its score?", "answer": "So, the answer is {\"answer\": \"an with a score of 0.57\"}." }, { "messages": [ { "role": "user", "content": "The word 'outside' has a frequency of 9 and a score higher than 0.60. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe word 'outside' has a frequency of 9 and a score higher than 0.60.\n" }, { "role": "assistant", "content": "Check the table for the word 'outside' and verify its frequency and score. The table shows 'outside' has a frequency of 9 and a score of 0.67, which is higher than 0.60. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1444.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(d)" ], "reasoning": "Check the table for the word 'outside' and verify its frequency and score. The table shows 'outside' has a frequency of 9 and a score of 0.67, which is higher than 0.60.", "statement": "The word 'outside' has a frequency of 9 and a score higher than 0.60.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title '(e)', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Words are ranked by their overall frequency, and only those occurring at least five times are reported.Because many of the most discriminative words are low frequency, we report only words which occur at least five times. We rank the words according to their overall frequency, since this statistic is perhaps more indicative of a word w’s effect on overall performance compared to p(l|w) alone." } ], "images": [ "scigen/train/1445.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(e)" ], "reasoning": "Words are ranked by their overall frequency, and only those occurring at least five times are reported.", "description": "Because many of the most discriminative words are low frequency, we report only words which occur at least five times. We rank the words according to their overall frequency, since this statistic is perhaps more indicative of a word w’s effect on overall performance compared to p(l|w) alone.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about '(e)'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: Which word has the highest MPE Score and what is its frequency?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the word with the highest MPE Score, look for the highest value in the MPE Score column, which is 0.83 for the word 'smile'. Its frequency is 6. So, the answer is {\"answer\": \"smile, 6\"}." } ], "images": [ "scigen/train/1445.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(e)" ], "reasoning": "To find the word with the highest MPE Score, look for the highest value in the MPE Score column, which is 0.83 for the word 'smile'. Its frequency is 6.", "question": "Which word has the highest MPE Score and what is its frequency?", "answer": "So, the answer is {\"answer\": \"smile, 6\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe word 'smiling' has the highest frequency.\n" }, { "role": "assistant", "content": "The table shows that 'smiling' has a frequency of 16, which is the highest among all the words listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1445.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(e)" ], "reasoning": "The table shows that 'smiling' has a frequency of 16, which is the highest among all the words listed.", "statement": "The word 'smiling' has the highest frequency.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled '(f)', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Words occurring at least five times are ranked by overall frequency to better indicate their impact on performance.Because many of the most discriminative words are low frequency, we report only words which occur at least five times. We rank the words according to their overall frequency, since this statistic is perhaps more indicative of a word w’s effect on overall performance compared to p(l|w) alone." } ], "images": [ "scigen/train/1446.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(f)" ], "reasoning": "Words occurring at least five times are ranked by overall frequency to better indicate their impact on performance.", "description": "Because many of the most discriminative words are low frequency, we report only words which occur at least five times. We rank the words according to their overall frequency, since this statistic is perhaps more indicative of a word w’s effect on overall performance compared to p(l|w) alone.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled '(f)', solve this question and give detail solutions. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich word has the highest frequency and what is its score?\n" }, { "role": "assistant", "content": "To find the word with the highest frequency, look at the 'Freq' column and identify the highest value, which is 88. The corresponding word is 'sitting' with a score of 0.51. So, the answer is {\"answer\": \"sitting with a score of 0.51\"}." } ], "images": [ "scigen/train/1446.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(f)" ], "reasoning": "To find the word with the highest frequency, look at the 'Freq' column and identify the highest value, which is 88. The corresponding word is 'sitting' with a score of 0.51.", "question": "Which word has the highest frequency and what is its score?", "answer": "So, the answer is {\"answer\": \"sitting with a score of 0.51\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The word 'boy' has a frequency higher than 20. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe word 'boy' has a frequency higher than 20.\n" }, { "role": "assistant", "content": "The table shows that the word 'boy' has a frequency of 21, which is indeed higher than 20. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1446.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(f)" ], "reasoning": "The table shows that the word 'boy' has a frequency of 21, which is indeed higher than 20.", "statement": "The word 'boy' has a frequency higher than 20.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Accuracies on FN+ for each class label.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The model shows a significant performance imbalance between the 'entailed' and 'not-entailed' classes, with a much larger Δ % for the 'not-entailed' class.Upon further inspection, we notice an interesting imbalance in how our model performs for each of the two classes." } ], "images": [ "scigen/train/1447.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracies on FN+ for each class label." ], "reasoning": "The model shows a significant performance imbalance between the 'entailed' and 'not-entailed' classes, with a much larger Δ % for the 'not-entailed' class.", "description": "Upon further inspection, we notice an interesting imbalance in how our model performs for each of the two classes.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Accuracies on FN+ for each class label.', unfold the question presented below, detailing every step of your calculation. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the difference in the Δ % between the 'entailed' and 'not-entailed' classes?\n" }, { "role": "assistant", "content": "To find the difference in the Δ % between the 'entailed' and 'not-entailed' classes, subtract the Δ % of the 'entailed' class from the Δ % of the 'not-entailed' class: 34.37 - 2.27. So, the answer is {\"answer\": \"32.10\"}" } ], "images": [ "scigen/train/1447.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracies on FN+ for each class label." ], "reasoning": "To find the difference in the Δ % between the 'entailed' and 'not-entailed' classes, subtract the Δ % of the 'entailed' class from the Δ % of the 'not-entailed' class: 34.37 - 2.27.", "question": "What is the difference in the Δ % between the 'entailed' and 'not-entailed' classes?", "answer": "So, the answer is {\"answer\": \"32.10\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model performs better on the 'not-entailed' class than the 'entailed' class.\n" }, { "role": "assistant", "content": "The table shows that the Δ % for the 'not-entailed' class is 34.37, which is significantly higher than the Δ % for the 'entailed' class, which is 2.27. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1447.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracies on FN+ for each class label." ], "reasoning": "The table shows that the Δ % for the 'not-entailed' class is 34.37, which is significantly higher than the Δ % for the 'entailed' class, which is 2.27.", "statement": "The model performs better on the 'not-entailed' class than the 'entailed' class.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1: Slot filling and intent detection results on two multi-intent datasets. The numbers with * indicate that the improvement of our model over all the compared baselines is statistically significant with p <0.05 under the t-test.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The performance of intent prediction is evaluated by accuracy and macro F1 score, while sentence-level semantic frame parsing is evaluated by overall accuracy.Following Goo et al. The performance of intent prediction are evaluated by accuracy and marco F1 score. The sentence-level semantic frame parsing are evaluated by overall accuracy which represents all metrics are right in an utterance." } ], "images": [ "scigen/train/1448.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Slot filling and intent detection results on two multi-intent datasets. The numbers with * indicate that the improvement of our model over all the compared baselines is statistically significant with p <0.05 under the t-test." ], "reasoning": "The performance of intent prediction is evaluated by accuracy and macro F1 score, while sentence-level semantic frame parsing is evaluated by overall accuracy.", "description": "Following Goo et al. The performance of intent prediction are evaluated by accuracy and marco F1 score. The sentence-level semantic frame parsing are evaluated by overall accuracy which represents all metrics are right in an utterance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1: Slot filling and intent detection results on two multi-intent datasets. The numbers with * indicate that the improvement of our model over all the compared baselines is statistically significant with p <0.05 under the t-test.', solve the subsequent question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich model has the highest overall accuracy on the MixSNIPS dataset?\n" }, { "role": "assistant", "content": "To find the model with the highest overall accuracy on the MixSNIPS dataset, look at the 'MixSNIPS Overall (Acc)' column and identify the highest value. So, the answer is {\"answer\": \"Our model\"}." } ], "images": [ "scigen/train/1448.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Slot filling and intent detection results on two multi-intent datasets. The numbers with * indicate that the improvement of our model over all the compared baselines is statistically significant with p <0.05 under the t-test." ], "reasoning": "To find the model with the highest overall accuracy on the MixSNIPS dataset, look at the 'MixSNIPS Overall (Acc)' column and identify the highest value.", "question": "Which model has the highest overall accuracy on the MixSNIPS dataset?", "answer": "So, the answer is {\"answer\": \"Our model\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Slot-gated Intent model has the highest F1 score for DSTC4 Slot.\n" }, { "role": "assistant", "content": "To verify this statement, compare the F1 scores for DSTC4 Slot in the table. The Slot-gated Intent model has a score of 50.2, while 'Our model' has a higher score of 53.9. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1448.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Slot filling and intent detection results on two multi-intent datasets. The numbers with * indicate that the improvement of our model over all the compared baselines is statistically significant with p <0.05 under the t-test." ], "reasoning": "To verify this statement, compare the F1 scores for DSTC4 Slot in the table. The Slot-gated Intent model has a score of 50.2, while 'Our model' has a higher score of 53.9.", "statement": "The Slot-gated Intent model has the highest F1 score for DSTC4 Slot.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Ablation Study on DSTC4 and MixSNIPS Datasets.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The proposed intent-slot graph interaction layer improves the performance of slot filling and intent prediction, outperforming the simple attention aggregation mechanism.3.5.1 Effectiveness of Intent-Slot Graph Interaction Layer In this section, we conduct experiments to verify the effectiveness of the proposed intent-slot graph interaction layer. Instead of adopting the GAT to model the interaction between the predicted intents and slot, we utilize the attention mechanism to incorporate the intents information for slot filling at token-level. After obtaining the context intent vector, we sum the vector and the hidden state of slot filling decoder to get the final slot prediction. We can see that our framework outperforms the simple attention aggregation mechanism. We attribute it to the fact that multi-layer graph attention network can automatically capture relevant intents information and better aggregate intents information for each token slot prediction." } ], "images": [ "scigen/train/1449.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ablation Study on DSTC4 and MixSNIPS Datasets." ], "reasoning": "The proposed intent-slot graph interaction layer improves the performance of slot filling and intent prediction, outperforming the simple attention aggregation mechanism.", "description": "3.5.1 Effectiveness of Intent-Slot Graph Interaction Layer In this section, we conduct experiments to verify the effectiveness of the proposed intent-slot graph interaction layer. Instead of adopting the GAT to model the interaction between the predicted intents and slot, we utilize the attention mechanism to incorporate the intents information for slot filling at token-level. After obtaining the context intent vector, we sum the vector and the hidden state of slot filling decoder to get the final slot prediction. We can see that our framework outperforms the simple attention aggregation mechanism. We attribute it to the fact that multi-layer graph attention network can automatically capture relevant intents information and better aggregate intents information for each token slot prediction.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 2: Ablation Study on DSTC4 and MixSNIPS Datasets.' and proceed to solve the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the overall accuracy for the Full Model on the MixSNIPS dataset?\n" }, { "role": "assistant", "content": "To find the overall accuracy for the Full Model on the MixSNIPS dataset, look at the 'MixSNIPS Overall (Acc)' column in the 'Full Model' row. So, the answer is {\"answer\": \"76.4\"}." } ], "images": [ "scigen/train/1449.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ablation Study on DSTC4 and MixSNIPS Datasets." ], "reasoning": "To find the overall accuracy for the Full Model on the MixSNIPS dataset, look at the 'MixSNIPS Overall (Acc)' column in the 'Full Model' row.", "question": "What is the overall accuracy for the Full Model on the MixSNIPS dataset?", "answer": "So, the answer is {\"answer\": \"76.4\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Full Model achieves the highest F1 score for DSTC4 Slot.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'DSTC4 Slot (F1)' scores across all models. The Full Model has a score of 53.9, which is higher than the scores of the other models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1449.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ablation Study on DSTC4 and MixSNIPS Datasets." ], "reasoning": "To verify this statement, compare the 'DSTC4 Slot (F1)' scores across all models. The Full Model has a score of 53.9, which is higher than the scores of the other models.", "statement": "The Full Model achieves the highest F1 score for DSTC4 Slot.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Slot filling and intent detection results on two single-intent datasets. The numbers with * indicate that the improvement of our model over all the compared baselines is statistically significant with p <0.05 under the t-test.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Our model outperforms all compared baselines, achieving state-of-the-art performance with a 0.7% improvement on ATIS Overall (Acc) and a 0.4% improvement on SNIPS Overall (Acc).We conduct experiments on two public single-intent benchmark to evaluate the generalizability of the proposed framework. We compare our model with the single-intent state-of-the-art models including SF-ID, Stack-Propagation and multi-intent model including Joint Multiple ID-SF. From the table, we can see that our model significantly outperforms all the compared baselines and achieves the state-of-the-art performance. We obtain 0.7% improvement on Overall (Acc) and 0.4% improvement on Overall (Acc) on ATIS and SNIPS dataset, respectively. This demonstrates the generalizability and effectiveness of our framework whether handling multi-intent or single-intent SLU." } ], "images": [ "scigen/train/1450.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Slot filling and intent detection results on two single-intent datasets. The numbers with * indicate that the improvement of our model over all the compared baselines is statistically significant with p <0.05 under the t-test." ], "reasoning": "Our model outperforms all compared baselines, achieving state-of-the-art performance with a 0.7% improvement on ATIS Overall (Acc) and a 0.4% improvement on SNIPS Overall (Acc).", "description": "We conduct experiments on two public single-intent benchmark to evaluate the generalizability of the proposed framework. We compare our model with the single-intent state-of-the-art models including SF-ID, Stack-Propagation and multi-intent model including Joint Multiple ID-SF. From the table, we can see that our model significantly outperforms all the compared baselines and achieves the state-of-the-art performance. We obtain 0.7% improvement on Overall (Acc) and 0.4% improvement on Overall (Acc) on ATIS and SNIPS dataset, respectively. This demonstrates the generalizability and effectiveness of our framework whether handling multi-intent or single-intent SLU.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 3: Slot filling and intent detection results on two single-intent datasets. The numbers with * indicate that the improvement of our model over all the compared baselines is statistically significant with p <0.05 under the t-test.' and proceed to solve the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the improvement in ATIS Overall (Acc) achieved by our model compared to the best baseline?\n" }, { "role": "assistant", "content": "The best baseline for ATIS Overall (Acc) is Stack-Propagation with 86.5%. Our model achieves 87.2%, which is a 0.7% improvement. So, the answer is {\"answer\": \"0.7%\"}" } ], "images": [ "scigen/train/1450.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Slot filling and intent detection results on two single-intent datasets. The numbers with * indicate that the improvement of our model over all the compared baselines is statistically significant with p <0.05 under the t-test." ], "reasoning": "The best baseline for ATIS Overall (Acc) is Stack-Propagation with 86.5%. Our model achieves 87.2%, which is a 0.7% improvement.", "question": "What is the improvement in ATIS Overall (Acc) achieved by our model compared to the best baseline?", "answer": "So, the answer is {\"answer\": \"0.7%\"}" }, { "messages": [ { "role": "user", "content": "Our model achieves the highest SNIPS Slot (F1) score. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nOur model achieves the highest SNIPS Slot (F1) score.\n" }, { "role": "assistant", "content": "The table shows that our model has a SNIPS Slot (F1) score of 94.8, which is the highest compared to the other models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1450.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Slot filling and intent detection results on two single-intent datasets. The numbers with * indicate that the improvement of our model over all the compared baselines is statistically significant with p <0.05 under the t-test." ], "reasoning": "The table shows that our model has a SNIPS Slot (F1) score of 94.8, which is the highest compared to the other models listed.", "statement": "Our model achieves the highest SNIPS Slot (F1) score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Accuracy taken across all rumours in the LOO setting.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The use of reference rumours significantly improves performance, with accuracy increasing from 70% to around 80% after only 10 annotated tweets, while methods using only the target rumour achieve accuracy similar to the Majority vote classifier.ICM can not use correlations to target rumour in this case and so can not be used. The majority baseline simply assigns the most frequent class from the training set. Most notably, performance increases from 70% to around 80%, after only 10 annotated tweets from However, as the amount of target rumour increases, performance does not increase further, which suggests that even only 10 human-annotated tweets are enough to achieve significant performance benefits. Note also how the use of reference rumours is very important, as methods using only the target rumour obtain accuracy similar to the Majority vote classifier (GP Brown and GP BOW)." } ], "images": [ "scigen/train/1451.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracy taken across all rumours in the LOO setting." ], "reasoning": "The use of reference rumours significantly improves performance, with accuracy increasing from 70% to around 80% after only 10 annotated tweets, while methods using only the target rumour achieve accuracy similar to the Majority vote classifier.", "description": "ICM can not use correlations to target rumour in this case and so can not be used. The majority baseline simply assigns the most frequent class from the training set. Most notably, performance increases from 70% to around 80%, after only 10 annotated tweets from However, as the amount of target rumour increases, performance does not increase further, which suggests that even only 10 human-annotated tweets are enough to achieve significant performance benefits. Note also how the use of reference rumours is very important, as methods using only the target rumour obtain accuracy similar to the Majority vote classifier (GP Brown and GP BOW).", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Accuracy taken across all rumours in the LOO setting.' to sequentially solve the question presented below. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhat is the accuracy of the GP Pooled Brown method?\n" }, { "role": "assistant", "content": "The table shows the accuracy of different methods. The GP Pooled Brown method has an accuracy of 0.72. So, the answer is {\"answer\": \"0.72\"}." } ], "images": [ "scigen/train/1451.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracy taken across all rumours in the LOO setting." ], "reasoning": "The table shows the accuracy of different methods. The GP Pooled Brown method has an accuracy of 0.72.", "question": "What is the accuracy of the GP Pooled Brown method?", "answer": "So, the answer is {\"answer\": \"0.72\"}." }, { "messages": [ { "role": "user", "content": "The Majority method has the highest accuracy. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Majority method has the highest accuracy.\n" }, { "role": "assistant", "content": "The table shows the accuracy of different methods. The Majority method has an accuracy of 0.68, which is lower than the GP Pooled Brown method (0.72) and GP Pooled BOW method (0.69). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1451.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracy taken across all rumours in the LOO setting." ], "reasoning": "The table shows the accuracy of different methods. The Majority method has an accuracy of 0.68, which is lower than the GP Pooled Brown method (0.72) and GP Pooled BOW method (0.69).", "statement": "The Majority method has the highest accuracy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: Sequence training and dev.sub MPE phone accuracy/WER for the 50hr training set. The WERs shown at the last column were computed using the weak pruned biased LM used in MPE training.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The NG approach, which replaces the Hessian component with the empirical FI matrix, leads to faster and improved convergence compared to SGD and HF, making it more effective with large batch sizes and fewer updates.On the 50hr setup, we investigated the efficacy of the proposed optimisation framework and compared to both SGD and HF. The NG approach replaces the ‘Hessian’ component of the HF optimiser with the empirical FI (FI) matrix and this leads to both faster and improved convergence which makes the proposed optimisation framework more effective where only a relatively few updates are performed with large batch sizes. In comparison to SGD baseline, both HF and NG training were observed to be highly stable and did not require any form of additional update clipping. This is attributed to the fact that at each iteration, the direction explored is a modified gradient estimate that has been appropriately rescaled using the local KL curvature information." } ], "images": [ "scigen/train/1452.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Sequence training and dev.sub MPE phone accuracy/WER for the 50hr training set. The WERs shown at the last column were computed using the weak pruned biased LM used in MPE training." ], "reasoning": "The NG approach, which replaces the Hessian component with the empirical FI matrix, leads to faster and improved convergence compared to SGD and HF, making it more effective with large batch sizes and fewer updates.", "description": "On the 50hr setup, we investigated the efficacy of the proposed optimisation framework and compared to both SGD and HF. The NG approach replaces the ‘Hessian’ component of the HF optimiser with the empirical FI (FI) matrix and this leads to both faster and improved convergence which makes the proposed optimisation framework more effective where only a relatively few updates are performed with large batch sizes. In comparison to SGD baseline, both HF and NG training were observed to be highly stable and did not require any form of additional update clipping. This is attributed to the fact that at each iteration, the direction explored is a modified gradient estimate that has been appropriately rescaled using the local KL curvature information.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Sequence training and dev.sub MPE phone accuracy/WER for the 50hr training set. The WERs shown at the last column were computed using the weak pruned biased LM used in MPE training.', answer the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method achieved the highest phone accuracy on the training set?\n" }, { "role": "assistant", "content": "By comparing the 'phone acc. train' column for all methods, NG achieved the highest phone accuracy on the training set with a value of 0.8677. So, the answer is {\"answer\": \"NG\"}." } ], "images": [ "scigen/train/1452.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Sequence training and dev.sub MPE phone accuracy/WER for the 50hr training set. The WERs shown at the last column were computed using the weak pruned biased LM used in MPE training." ], "reasoning": "By comparing the 'phone acc. train' column for all methods, NG achieved the highest phone accuracy on the training set with a value of 0.8677.", "question": "Which method achieved the highest phone accuracy on the training set?", "answer": "So, the answer is {\"answer\": \"NG\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The NG method required the most updates. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe NG method required the most updates.\n" }, { "role": "assistant", "content": "The table shows that the NG method required 60 updates, while the SGD method required 2.62 × 10^5 updates, which is significantly higher. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1452.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Sequence training and dev.sub MPE phone accuracy/WER for the 50hr training set. The WERs shown at the last column were computed using the weak pruned biased LM used in MPE training." ], "reasoning": "The table shows that the NG method required 60 updates, while the SGD method required 2.62 × 10^5 updates, which is significantly higher.", "statement": "The NG method required the most updates.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Performance achieved with different optimisers on the 200 hr setup.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The NG method, requiring fewer updates per epoch, converges to a better solution with a lower WER compared to SGD and HF variants.The experiments on the 200 hr training set were performed to ensure that the sequence training techniques generalise to a somewhat larger training set (and output layer size) and also to present more detailed comparisons of how training proceeds. In this case a total of 8 epochs were performed with SGD and 15 epochs (120 updates) with HF, DSAG-HF and NG. In DSAG-HF, the HF optimiser is equipped with a ‘momentum’ component which essentially contributes to initialising CG with a weighted sum of the current and previous gradient directions. In Fig. In contrast, replacing the GN with the empirical FI matrix leads to progressively better updates at each epoch. In Fig. WER are very similar over the first 8 epochs between NG and SGD, but NG only requires eight updates per epoch. Furthermore, through following a path where the MPE training criterion remains a good approximation to the WER, the NG method allows training to converge to a better solution than either SGD or any of the HF variants." } ], "images": [ "scigen/train/1453.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance achieved with different optimisers on the 200 hr setup." ], "reasoning": "The NG method, requiring fewer updates per epoch, converges to a better solution with a lower WER compared to SGD and HF variants.", "description": "The experiments on the 200 hr training set were performed to ensure that the sequence training techniques generalise to a somewhat larger training set (and output layer size) and also to present more detailed comparisons of how training proceeds. In this case a total of 8 epochs were performed with SGD and 15 epochs (120 updates) with HF, DSAG-HF and NG. In DSAG-HF, the HF optimiser is equipped with a ‘momentum’ component which essentially contributes to initialising CG with a weighted sum of the current and previous gradient directions. In Fig. In contrast, replacing the GN with the empirical FI matrix leads to progressively better updates at each epoch. In Fig. WER are very similar over the first 8 epochs between NG and SGD, but NG only requires eight updates per epoch. Furthermore, through following a path where the MPE training criterion remains a good approximation to the WER, the NG method allows training to converge to a better solution than either SGD or any of the HF variants.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: Performance achieved with different optimisers on the 200 hr setup.' in mind, break down and resolve the question below step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich method achieved the highest phone accuracy on the training set and what was its WER on the dev.sub set?\n" }, { "role": "assistant", "content": "From the table, NG achieved the highest phone accuracy on the training set with 0.8601. The corresponding WER on the dev.sub set for NG is 37.9. So, the answer is {\"answer\": \"NG with WER 37.9\"}." } ], "images": [ "scigen/train/1453.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance achieved with different optimisers on the 200 hr setup." ], "reasoning": "From the table, NG achieved the highest phone accuracy on the training set with 0.8601. The corresponding WER on the dev.sub set for NG is 37.9.", "question": "Which method achieved the highest phone accuracy on the training set and what was its WER on the dev.sub set?", "answer": "So, the answer is {\"answer\": \"NG with WER 37.9\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis SGD required the most updates per epoch among all methods. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nSGD required the most updates per epoch among all methods.\n" }, { "role": "assistant", "content": "The table shows that SGD required 9.27×10^5 updates, which is significantly higher than the 120 updates required by HF, DSAG-HF, and NG. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1453.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance achieved with different optimisers on the 200 hr setup." ], "reasoning": "The table shows that SGD required 9.27×10^5 updates, which is significantly higher than the 120 updates required by HF, DSAG-HF, and NG.", "statement": "SGD required the most updates per epoch among all methods.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: %WER differences between different optimisers on dev.sub with 158k vocabulary bigram/trigram LMs on dev.sub (200 hr).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The NG method consistently results in lower WERs compared to SGD, HF, and DSAG-HF for both bigram and trigram models.It can be seen again that the NG method results in lower WERs than SGD or the HF variants." } ], "images": [ "scigen/train/1454.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: %WER differences between different optimisers on dev.sub with 158k vocabulary bigram/trigram LMs on dev.sub (200 hr)." ], "reasoning": "The NG method consistently results in lower WERs compared to SGD, HF, and DSAG-HF for both bigram and trigram models.", "description": "It can be seen again that the NG method results in lower WERs than SGD or the HF variants.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: %WER differences between different optimisers on dev.sub with 158k vocabulary bigram/trigram LMs on dev.sub (200 hr).', solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method results in the lowest WER for the 158k Trigram model?\n" }, { "role": "assistant", "content": "By comparing the WERs for the 158k Trigram model across all methods, NG has the lowest WER of 29.0. So, the answer is {\"answer\": \"NG\"}." } ], "images": [ "scigen/train/1454.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: %WER differences between different optimisers on dev.sub with 158k vocabulary bigram/trigram LMs on dev.sub (200 hr)." ], "reasoning": "By comparing the WERs for the 158k Trigram model across all methods, NG has the lowest WER of 29.0.", "question": "Which method results in the lowest WER for the 158k Trigram model?", "answer": "So, the answer is {\"answer\": \"NG\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The HF method results in the lowest WER for the 158k Bigram model. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe HF method results in the lowest WER for the 158k Bigram model.\n" }, { "role": "assistant", "content": "The table shows that for the 158k Bigram model, the NG method has the lowest WER of 34.7, not the HF method. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1454.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: %WER differences between different optimisers on dev.sub with 158k vocabulary bigram/trigram LMs on dev.sub (200 hr)." ], "reasoning": "The table shows that for the 158k Bigram model, the NG method has the lowest WER of 34.7, not the HF method.", "statement": "The HF method results in the lowest WER for the 158k Bigram model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: %WER differences between different optimisers on dev.sub2 with 158k trigram (200 hr)', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The NG method consistently provides a statistically significant improvement in WER over both the SGD baseline and the DSAG-HF variant, with approximately a 1% relative reduction.Finally, these DNNs were tested on the dev.sub2 set as an evaluation set that was not used for setting training hyper-parameters to ensure that the trends observed above generalise. Furthermore these improvements have been fairly consistent between the validation dev.sub and evaluation dev.sub2 test sets. On average the NG method can be seen to provide approximately a 1% relative reduction in WER over both the SGD baseline and the DSAG-HF variant. While this improvement it fairly small, it is consistent and a statistical significance test (sign test of the word error rates at the episode level) showed that the improvement due to NG over each of the other methods is highly statistically significant (p<0.001)." } ], "images": [ "scigen/train/1455.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: %WER differences between different optimisers on dev.sub2 with 158k trigram (200 hr)" ], "reasoning": "The NG method consistently provides a statistically significant improvement in WER over both the SGD baseline and the DSAG-HF variant, with approximately a 1% relative reduction.", "description": "Finally, these DNNs were tested on the dev.sub2 set as an evaluation set that was not used for setting training hyper-parameters to ensure that the trends observed above generalise. Furthermore these improvements have been fairly consistent between the validation dev.sub and evaluation dev.sub2 test sets. On average the NG method can be seen to provide approximately a 1% relative reduction in WER over both the SGD baseline and the DSAG-HF variant. While this improvement it fairly small, it is consistent and a statistical significance test (sign test of the word error rates at the episode level) showed that the improvement due to NG over each of the other methods is highly statistically significant (p<0.001).", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: %WER differences between different optimisers on dev.sub2 with 158k trigram (200 hr)'.\n\nQuestion: What is the relative reduction in WER provided by the NG method compared to the SGD baseline and DSAG-HF variant?\n" }, { "role": "assistant", "content": "The description states that the NG method provides approximately a 1% relative reduction in WER over both the SGD baseline and the DSAG-HF variant. So, the answer is {\"answer\": \"1%\"}" } ], "images": [ "scigen/train/1455.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: %WER differences between different optimisers on dev.sub2 with 158k trigram (200 hr)" ], "reasoning": "The description states that the NG method provides approximately a 1% relative reduction in WER over both the SGD baseline and the DSAG-HF variant.", "question": "What is the relative reduction in WER provided by the NG method compared to the SGD baseline and DSAG-HF variant?", "answer": "So, the answer is {\"answer\": \"1%\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The NG method provides a statistically significant improvement in WER over the SGD baseline. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe NG method provides a statistically significant improvement in WER over the SGD baseline.\n" }, { "role": "assistant", "content": "The description mentions that the NG method provides a statistically significant improvement in WER over the SGD baseline, with a p-value of less than 0.001. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1455.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: %WER differences between different optimisers on dev.sub2 with 158k trigram (200 hr)" ], "reasoning": "The description mentions that the NG method provides a statistically significant improvement in WER over the SGD baseline, with a p-value of less than 0.001.", "statement": "The NG method provides a statistically significant improvement in WER over the SGD baseline.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Results of experiments with the multimodal translation task on the validation data. At the time of the submission, the models were not tuned as well as our final models. The first six system are targeted for the translation task. They were trained against one reference – a German translation of one English caption. The last four systems are target to the cross-lingual captioning task. They were trained with 5 independent German captions (5 times bigger data).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The table shows the performance of various systems in multimodal translation and cross-lingual captioning, with the NMT + Moses + image system achieving the highest BLEU score in multimodal translation and the 5 en captions + image system achieving the highest METEOR score in cross-lingual captioning.Sample outputs of our multimodal translation (MMMT) system and cross-lingual captioning (CLC) system in comparison with phrase-based MT and the reference. Our system significantly improved since the competition submission, therefore we report both the performance of the current system and of the submitted systems." } ], "images": [ "scigen/train/1456.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of experiments with the multimodal translation task on the validation data. At the time of the submission, the models were not tuned as well as our final models. The first six system are targeted for the translation task. They were trained against one reference – a German translation of one English caption. The last four systems are target to the cross-lingual captioning task. They were trained with 5 independent German captions (5 times bigger data)." ], "reasoning": "The table shows the performance of various systems in multimodal translation and cross-lingual captioning, with the NMT + Moses + image system achieving the highest BLEU score in multimodal translation and the 5 en captions + image system achieving the highest METEOR score in cross-lingual captioning.", "description": "Sample outputs of our multimodal translation (MMMT) system and cross-lingual captioning (CLC) system in comparison with phrase-based MT and the reference. Our system significantly improved since the competition submission, therefore we report both the performance of the current system and of the submitted systems.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Results of experiments with the multimodal translation task on the validation data. At the time of the submission, the models were not tuned as well as our final models. The first six system are targeted for the translation task. They were trained against one reference – a German translation of one English caption. The last four systems are target to the cross-lingual captioning task. They were trained with 5 independent German captions (5 times bigger data).', answer the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich system achieved the highest BLEU score in multimodal translation?\n" }, { "role": "assistant", "content": "By examining the 'Multimodal translation BLEU' column, the NMT + Moses + image system has the highest score of 37.3. So, the answer is {\"answer\": \"NMT + Moses + image\"}." } ], "images": [ "scigen/train/1456.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of experiments with the multimodal translation task on the validation data. At the time of the submission, the models were not tuned as well as our final models. The first six system are targeted for the translation task. They were trained against one reference – a German translation of one English caption. The last four systems are target to the cross-lingual captioning task. They were trained with 5 independent German captions (5 times bigger data)." ], "reasoning": "By examining the 'Multimodal translation BLEU' column, the NMT + Moses + image system has the highest score of 37.3.", "question": "Which system achieved the highest BLEU score in multimodal translation?", "answer": "So, the answer is {\"answer\": \"NMT + Moses + image\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe NMT system achieved the highest METEOR score in multimodal translation.\n" }, { "role": "assistant", "content": "The table shows that the 'tuned Moses' system achieved the highest METEOR score of 57.4 in multimodal translation, not the NMT system. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1456.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of experiments with the multimodal translation task on the validation data. At the time of the submission, the models were not tuned as well as our final models. The first six system are targeted for the translation task. They were trained against one reference – a German translation of one English caption. The last four systems are target to the cross-lingual captioning task. They were trained with 5 independent German captions (5 times bigger data)." ], "reasoning": "The table shows that the 'tuned Moses' system achieved the highest METEOR score of 57.4 in multimodal translation, not the NMT system.", "statement": "The NMT system achieved the highest METEOR score in multimodal translation.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Link prediction results (of unseen entities) on FB4M-desc.'.\n" }, { "role": "assistant", "content": "The CNN model consistently outperforms the MLP model in both mean rank and hits@10 across different sample sizes, demonstrating the effectiveness of the framework in learning new concepts and performing reasoning.It has been shown in Section 4.2 that our framework well regularizes the neural embedding model for memory storage. Next we use FB4M-desc to evaluate the capability of our framework on learning new concepts and performing reasoning based on learnt embeddings. Note that the test set contains millions of triples, which is very time-consuming in the ranking-based evaluation. So we randomly sample 1k, 10k and 80k triplets from the test set to report the evaluation statistics. We can see that CNN consistently outperforms MLP in terms of both mean rank and hits@10. All the triplets in the test set of FB4M-desc include an entity unseen in the training set on one side, requiring the model to understand natural language descriptions and to do reasoning based on it. As far as we know, no traditional knowledge base embedding model can compete with us on this task, which again claims the novelty of our framework." } ], "images": [ "scigen/train/1457.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Link prediction results (of unseen entities) on FB4M-desc." ], "reasoning": "The CNN model consistently outperforms the MLP model in both mean rank and hits@10 across different sample sizes, demonstrating the effectiveness of the framework in learning new concepts and performing reasoning.", "description": "It has been shown in Section 4.2 that our framework well regularizes the neural embedding model for memory storage. Next we use FB4M-desc to evaluate the capability of our framework on learning new concepts and performing reasoning based on learnt embeddings. Note that the test set contains millions of triples, which is very time-consuming in the ranking-based evaluation. So we randomly sample 1k, 10k and 80k triplets from the test set to report the evaluation statistics. We can see that CNN consistently outperforms MLP in terms of both mean rank and hits@10. All the triplets in the test set of FB4M-desc include an entity unseen in the training set on one side, requiring the model to understand natural language descriptions and to do reasoning based on it. As far as we know, no traditional knowledge base embedding model can compete with us on this task, which again claims the novelty of our framework.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Link prediction results (of unseen entities) on FB4M-desc.\nQuestion: Which model has a better mean rank for 10k samples and what is the value?\nSolve the above question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To determine which model has a better mean rank for 10k samples, compare the mean rank values for both models. The CNN model has a mean rank of 54033, which is better than the MLP model's mean rank of 62914. So, the answer is {\"answer\": \"CNN with 54033\"}." } ], "images": [ "scigen/train/1457.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Link prediction results (of unseen entities) on FB4M-desc." ], "reasoning": "To determine which model has a better mean rank for 10k samples, compare the mean rank values for both models. The CNN model has a mean rank of 54033, which is better than the MLP model's mean rank of 62914.", "question": "Which model has a better mean rank for 10k samples and what is the value?", "answer": "So, the answer is {\"answer\": \"CNN with 54033\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe MLP model has a higher hits@10 percentage than the CNN model for 80k samples.\n" }, { "role": "assistant", "content": "To verify this statement, compare the hits@10 percentages for 80k samples for both models. The MLP model has a hits@10 percentage of 14.06%, while the CNN model has a higher hits@10 percentage of 14.52%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1457.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Link prediction results (of unseen entities) on FB4M-desc." ], "reasoning": "To verify this statement, compare the hits@10 percentages for 80k samples for both models. The MLP model has a hits@10 percentage of 14.06%, while the CNN model has a higher hits@10 percentage of 14.52%.", "statement": "The MLP model has a higher hits@10 percentage than the CNN model for 80k samples.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Link prediction results on FB15k-desc.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our model outperforms TransE in both mean rank and hits@10, demonstrating better utilization of natural language descriptions.The type of concept learning module we use here is CNN. Note that all the triplets in training, validation and test sets of FB15k-desc are the same as FB15k , so we list TransE’s results on FB15k in the same table. Compared to TransE which cannot make use of information in descriptions, our model performs much better, in terms of both mean rank and hits@10. As stated in Section 4.1, all entities in the test set of FB15k are contained in the training set, which, together with the results, shows that our framework well regularizes the embedding model by forcing embeddings to reflect information from natural language descriptions. We demonstrate the concept learning capability in the next subsection." } ], "images": [ "scigen/train/1458.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Link prediction results on FB15k-desc." ], "reasoning": "Our model outperforms TransE in both mean rank and hits@10, demonstrating better utilization of natural language descriptions.", "description": "The type of concept learning module we use here is CNN. Note that all the triplets in training, validation and test sets of FB15k-desc are the same as FB15k , so we list TransE’s results on FB15k in the same table. Compared to TransE which cannot make use of information in descriptions, our model performs much better, in terms of both mean rank and hits@10. As stated in Section 4.1, all entities in the test set of FB15k are contained in the training set, which, together with the results, shows that our framework well regularizes the embedding model by forcing embeddings to reflect information from natural language descriptions. We demonstrate the concept learning capability in the next subsection.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Link prediction results on FB15k-desc.', answer the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the mean rank average for our model compared to TransE?\n" }, { "role": "assistant", "content": "The table shows that the mean rank average for our model is 214, while for TransE it is 243, indicating our model's better performance. So, the answer is {\"answer\": \"214\"}" } ], "images": [ "scigen/train/1458.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Link prediction results on FB15k-desc." ], "reasoning": "The table shows that the mean rank average for our model is 214, while for TransE it is 243, indicating our model's better performance.", "question": "What is the mean rank average for our model compared to TransE?", "answer": "So, the answer is {\"answer\": \"214\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nOur model has a higher Hits@10 (%) Avg than TransE.\n" }, { "role": "assistant", "content": "The table shows that our model has a Hits@10 (%) Avg of 37.7, while TransE has 34.9, indicating that our model performs better. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1458.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Link prediction results on FB15k-desc." ], "reasoning": "The table shows that our model has a Hits@10 (%) Avg of 37.7, while TransE has 34.9, indicating that our model performs better.", "statement": "Our model has a higher Hits@10 (%) Avg than TransE.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 7: Accuracy-flipping on VQA-IR edit test split with zero overlap.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows the performance metrics of three models (C+L, SAAA, SNMN) on accuracy and prediction flips, indicating comparable performance to the overall set.For our fine-tuning experiments, we use a strict subset of IV-VQAwith an overlap score of zero. As we see, the numbers are comparable to the model’s performance on the overall set." } ], "images": [ "scigen/train/1459.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Accuracy-flipping on VQA-IR edit test split with zero overlap." ], "reasoning": "The table shows the performance metrics of three models (C+L, SAAA, SNMN) on accuracy and prediction flips, indicating comparable performance to the overall set.", "description": "For our fine-tuning experiments, we use a strict subset of IV-VQAwith an overlap score of zero. As we see, the numbers are comparable to the model’s performance on the overall set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 7: Accuracy-flipping on VQA-IR edit test split with zero overlap.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model has the highest percentage of predictions flipped from negative to positive?\n" }, { "role": "assistant", "content": "To find the model with the highest percentage of predictions flipped from negative to positive, compare the values in the 'neg→pos' row for each model. The highest value is 6.79% for the C+L model. So, the answer is {\"answer\": \"C+L\"}." } ], "images": [ "scigen/train/1459.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Accuracy-flipping on VQA-IR edit test split with zero overlap." ], "reasoning": "To find the model with the highest percentage of predictions flipped from negative to positive, compare the values in the 'neg→pos' row for each model. The highest value is 6.79% for the C+L model.", "question": "Which model has the highest percentage of predictions flipped from negative to positive?", "answer": "So, the answer is {\"answer\": \"C+L\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe SNMN model has the lowest accuracy among the three models.\n" }, { "role": "assistant", "content": "To verify this statement, compare the accuracy percentages of the three models. The SNMN model has an accuracy of 66.04%, which is higher than the C+L model's 60.21% but lower than the SAAA model's 70.26%. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1459.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Accuracy-flipping on VQA-IR edit test split with zero overlap." ], "reasoning": "To verify this statement, compare the accuracy percentages of the three models. The SNMN model has an accuracy of 66.04%, which is higher than the C+L model's 60.21% but lower than the SAAA model's 70.26%. Therefore, the statement is refuted.", "statement": "The SNMN model has the lowest accuracy among the three models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: IV-VQA and CV-VQA distribution. Real refers to VQA [8] IQAs with uniform answers, realNE refers to IQAs for which no edits are possible (after vocabulary mapping and area-overlap threshold), edit refers to the edited IQA. We split the VQA val into 90:10 ratio, where the former is used for testing purpose and latter for validation.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The IV-VQA dataset is invariant to object removal, while the CV-VQA dataset shows covariant changes in answers when objects are removed.We call our edited set as IV-VQA as removal of objects does not lead to any change in answer, the answer is invariant to the semantic editing. While our algorithm involves both manually curated heuristics to select the objects to remove, and a learned in-painter-based GAN model to perform the removal, the whole pipeline is fully automatic. This allows us to apply it to the large-scale VQA dataset with 658k IQA triplets. We call our edited set as CV-VQA since removal of the object leads to a covariant change in answer. We only target one instance at a time. More such visual examples can be found in supplementary (section B.2) ." } ], "images": [ "scigen/train/1460.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: IV-VQA and CV-VQA distribution. Real refers to VQA [8] IQAs with uniform answers, realNE refers to IQAs for which no edits are possible (after vocabulary mapping and area-overlap threshold), edit refers to the edited IQA. We split the VQA val into 90:10 ratio, where the former is used for testing purpose and latter for validation." ], "reasoning": "The IV-VQA dataset is invariant to object removal, while the CV-VQA dataset shows covariant changes in answers when objects are removed.", "description": "We call our edited set as IV-VQA as removal of objects does not lead to any change in answer, the answer is invariant to the semantic editing. While our algorithm involves both manually curated heuristics to select the objects to remove, and a learned in-painter-based GAN model to perform the removal, the whole pipeline is fully automatic. This allows us to apply it to the large-scale VQA dataset with 658k IQA triplets. We call our edited set as CV-VQA since removal of the object leads to a covariant change in answer. We only target one instance at a time. More such visual examples can be found in supplementary (section B.2) .", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 2: IV-VQA and CV-VQA distribution. Real refers to VQA [8] IQAs with uniform answers, realNE refers to IQAs for which no edits are possible (after vocabulary mapping and area-overlap threshold), edit refers to the edited IQA. We split the VQA val into 90:10 ratio, where the former is used for testing purpose and latter for validation.\n\nQuestion: What is the total number of IQA triplets in the IV-VQA train and CV-VQA train sets combined?\n" }, { "role": "assistant", "content": "To find the total number of IQA triplets in the IV-VQA train and CV-VQA train sets combined, add the values from the IV-VQA train and CV-VQA train columns for each row. The sums are 148013 (real) + 42043 (realNE) + 256604 (edit) for IV-VQA train and 18437 (real) + 13035 (realNE) + 8555 (edit) for CV-VQA train. The total is 148013 + 42043 + 256604 + 18437 + 13035 + 8555 = 488687. So, the answer is {\"answer\": \"488687\"}" } ], "images": [ "scigen/train/1460.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: IV-VQA and CV-VQA distribution. Real refers to VQA [8] IQAs with uniform answers, realNE refers to IQAs for which no edits are possible (after vocabulary mapping and area-overlap threshold), edit refers to the edited IQA. We split the VQA val into 90:10 ratio, where the former is used for testing purpose and latter for validation." ], "reasoning": "To find the total number of IQA triplets in the IV-VQA train and CV-VQA train sets combined, add the values from the IV-VQA train and CV-VQA train columns for each row. The sums are 148013 (real) + 42043 (realNE) + 256604 (edit) for IV-VQA train and 18437 (real) + 13035 (realNE) + 8555 (edit) for CV-VQA train. The total is 148013 + 42043 + 256604 + 18437 + 13035 + 8555 = 488687.", "question": "What is the total number of IQA triplets in the IV-VQA train and CV-VQA train sets combined?", "answer": "So, the answer is {\"answer\": \"488687\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe IV-VQA test set has more IQA triplets than the CV-VQA test set.\n" }, { "role": "assistant", "content": "To verify this statement, compare the total number of IQA triplets in the IV-VQA test set and the CV-VQA test set. The IV-VQA test set has 63219 (real) + 18143 (realNE) + 108239 (edit) = 189601 IQA triplets. The CV-VQA test set has 8042 (real) + 5664 (realNE) + 3743 (edit) = 17449 IQA triplets. Since 189601 is greater than 17449, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1460.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: IV-VQA and CV-VQA distribution. Real refers to VQA [8] IQAs with uniform answers, realNE refers to IQAs for which no edits are possible (after vocabulary mapping and area-overlap threshold), edit refers to the edited IQA. We split the VQA val into 90:10 ratio, where the former is used for testing purpose and latter for validation." ], "reasoning": "To verify this statement, compare the total number of IQA triplets in the IV-VQA test set and the CV-VQA test set. The IV-VQA test set has 63219 (real) + 18143 (realNE) + 108239 (edit) = 189601 IQA triplets. The CV-VQA test set has 8042 (real) + 5664 (realNE) + 3743 (edit) = 17449 IQA triplets. Since 189601 is greater than 17449, the statement is supported.", "statement": "The IV-VQA test set has more IQA triplets than the CV-VQA test set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Accuracy (in %) of different models when trained on VQA v2 train and tested on VQA v2 val.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The models trained by us, such as SAAA and SNMN, outperform other models in their respective categories.VQA models and training. We modified this SAAA code in order to get CL model by removing the attention layers from the network. As we use the VQA v2 val split for consistency evaluation and testing, the models are trained using only the train split. Further details of these models and hyper-parameters used can be found in the supplementary (section B.1). The models chosen by us exceed the performance of other models within the respective categories." } ], "images": [ "scigen/train/1461.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracy (in %) of different models when trained on VQA v2 train and tested on VQA v2 val." ], "reasoning": "The models trained by us, such as SAAA and SNMN, outperform other models in their respective categories.", "description": "VQA models and training. We modified this SAAA code in order to get CL model by removing the attention layers from the network. As we use the VQA v2 val split for consistency evaluation and testing, the models are trained using only the train split. Further details of these models and hyper-parameters used can be found in the supplementary (section B.1). The models chosen by us exceed the performance of other models within the respective categories.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: Table 3: Accuracy (in %) of different models when trained on VQA v2 train and tested on VQA v2 val.\nQuestion: Which model trained by us has the highest performance?\n" }, { "role": "assistant", "content": "By comparing the performance scores of the models trained by us, SAAA has the highest score of 61.14. So, the answer is {\"answer\": \"SAAA\"}." } ], "images": [ "scigen/train/1461.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracy (in %) of different models when trained on VQA v2 train and tested on VQA v2 val." ], "reasoning": "By comparing the performance scores of the models trained by us, SAAA has the highest score of 61.14.", "question": "Which model trained by us has the highest performance?", "answer": "So, the answer is {\"answer\": \"SAAA\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe SNMN model trained by us has a higher performance than the MCB model.\n" }, { "role": "assistant", "content": "The table shows that the SNMN model trained by us has a performance score of 58.34, while the MCB model has a score of 59.71, indicating that the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1461.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracy (in %) of different models when trained on VQA v2 train and tested on VQA v2 val." ], "reasoning": "The table shows that the SNMN model trained by us has a performance score of 58.34, while the MCB model has a score of 59.71, indicating that the statement is incorrect.", "statement": "The SNMN model trained by us has a higher performance than the MCB model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 8: IV-VQAAugmentation: numbers on the left side of the arrow denote the accuracy/flipping for models fine-tuned using just real data whereas numbers on the right side show the performance of models when finetuned with real+synthetic data', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Using synthetic data for finetuning models results in a reduction in prediction flips and sometimes improves accuracy on the original VQA set.In the paper, we show a compact representation of the table by plotting the reduction in flips/improvement in accuracy for models finetuned using real+edit data relative to the models finetuned only using the real data. For instance, for question-type ‘is this a’ for CNN+LSTM: we see there is (12.72-9.77)/12.72 which is about 23% reduction in flips. These numbers show that using synthetic data always leads to a reduction in flips and in some cases- also results in improved accuracy on the original VQA set. n-1 consistent and accurate as well." } ], "images": [ "scigen/train/1462.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: IV-VQAAugmentation: numbers on the left side of the arrow denote the accuracy/flipping for models fine-tuned using just real data whereas numbers on the right side show the performance of models when finetuned with real+synthetic data" ], "reasoning": "Using synthetic data for finetuning models results in a reduction in prediction flips and sometimes improves accuracy on the original VQA set.", "description": "In the paper, we show a compact representation of the table by plotting the reduction in flips/improvement in accuracy for models finetuned using real+edit data relative to the models finetuned only using the real data. For instance, for question-type ‘is this a’ for CNN+LSTM: we see there is (12.72-9.77)/12.72 which is about 23% reduction in flips. These numbers show that using synthetic data always leads to a reduction in flips and in some cases- also results in improved accuracy on the original VQA set. n-1 consistent and accurate as well.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 8: IV-VQAAugmentation: numbers on the left side of the arrow denote the accuracy/flipping for models fine-tuned using just real data whereas numbers on the right side show the performance of models when finetuned with real+synthetic data', answer the question below by showing each progressive step toward the answer. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the percentage reduction in flips for the 'how many' question type for the SAAA model?\n" }, { "role": "assistant", "content": "For the 'how many' question type, the original flips for the SAAA model are 13.35%, and after using synthetic data, it reduces to 11.04%. The percentage reduction is calculated as (13.35 - 11.04) / 13.35 * 100. So, the answer is {\"answer\": \"17.32%\"}" } ], "images": [ "scigen/train/1462.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: IV-VQAAugmentation: numbers on the left side of the arrow denote the accuracy/flipping for models fine-tuned using just real data whereas numbers on the right side show the performance of models when finetuned with real+synthetic data" ], "reasoning": "For the 'how many' question type, the original flips for the SAAA model are 13.35%, and after using synthetic data, it reduces to 11.04%. The percentage reduction is calculated as (13.35 - 11.04) / 13.35 * 100.", "question": "What is the percentage reduction in flips for the 'how many' question type for the SAAA model?", "answer": "So, the answer is {\"answer\": \"17.32%\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nUsing synthetic data for the 'is there a' question type in the SNMN model results in a reduction in prediction flips.\n" }, { "role": "assistant", "content": "For the 'is there a' question type in the SNMN model, the original flips are 8.83%, and after using synthetic data, it reduces to 7.79%, indicating a reduction in prediction flips. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1462.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: IV-VQAAugmentation: numbers on the left side of the arrow denote the accuracy/flipping for models fine-tuned using just real data whereas numbers on the right side show the performance of models when finetuned with real+synthetic data" ], "reasoning": "For the 'is there a' question type in the SNMN model, the original flips are 8.83%, and after using synthetic data, it reduces to 7.79%, indicating a reduction in prediction flips.", "statement": "Using synthetic data for the 'is there a' question type in the SNMN model results in a reduction in prediction flips.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Results on dynamic vs. static mixing ratio (FKGL: lower is better).', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The controller in a multi-armed bandit setup selects actions to maximize expected future payoffs, balancing exploration and exploitation, with action values updated using an exponentially weighted mean.We view the problem of learning the right mixing of tasks as a sequential control problem, where the controller’s goal is to decide the next task/action after every ns training steps in each task-sampling round tb. We model the controller as a M-armed bandits, where it selects a sequence of actions/arms over the current training trajectory to maximize the expected future payoffs (see Fig. At each round tb, the controller selects an arm based on noisy value estimates and observes rewards rtb for the selected arm (we use the negative validation loss of the primary task as the reward in our setup). One problem in bandits learning is the trade-off between exploration and exploitation, where the agent needs to make a decision between taking the action that yields the best payoff on current estimates, or explore new actions whose payoffs are not yet certain. Let πtb be the policy of the bandit controller at round tb, we define this to be: πtb(ai)=exp(Qtb,i/τ)/M∑j=1exp(Qtb,j/τ) (3) where Qtb,i is the estimated action value of each arm i at round tb, and τ is the temperature. If Q0,i is the initial value estimate of arm i, then Qtb,i is the exponentially weighted mean with the decay rate α: Qtb,i=(1−α)tbQ0,i+tb∑k=1α(1−α)tb−krk (4) To further help the exploration process, we follow the principle of optimism under uncertainty and set Q0,i to be above the maximum empirical rewards. Also, we further show ablation study in Sec." } ], "images": [ "scigen/train/1463.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on dynamic vs. static mixing ratio (FKGL: lower is better)." ], "reasoning": "The controller in a multi-armed bandit setup selects actions to maximize expected future payoffs, balancing exploration and exploitation, with action values updated using an exponentially weighted mean.", "description": "We view the problem of learning the right mixing of tasks as a sequential control problem, where the controller’s goal is to decide the next task/action after every ns training steps in each task-sampling round tb. We model the controller as a M-armed bandits, where it selects a sequence of actions/arms over the current training trajectory to maximize the expected future payoffs (see Fig. At each round tb, the controller selects an arm based on noisy value estimates and observes rewards rtb for the selected arm (we use the negative validation loss of the primary task as the reward in our setup). One problem in bandits learning is the trade-off between exploration and exploitation, where the agent needs to make a decision between taking the action that yields the best payoff on current estimates, or explore new actions whose payoffs are not yet certain. Let πtb be the policy of the bandit controller at round tb, we define this to be: πtb(ai)=exp(Qtb,i/τ)/M∑j=1exp(Qtb,j/τ) (3) where Qtb,i is the estimated action value of each arm i at round tb, and τ is the temperature. If Q0,i is the initial value estimate of arm i, then Qtb,i is the exponentially weighted mean with the decay rate α: Qtb,i=(1−α)tbQ0,i+tb∑k=1α(1−α)tb−krk (4) To further help the exploration process, we follow the principle of optimism under uncertainty and set Q0,i to be above the maximum empirical rewards. Also, we further show ablation study in Sec.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: Results on dynamic vs. static mixing ratio (FKGL: lower is better).'. Solve the following question based on the table.\n\nQuestion: What is the difference in BLEU scores between the Static and Dynamic Mixing Ratios for the WikiSmall dataset?\nLet's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the difference in BLEU scores for the WikiSmall dataset, subtract the BLEU score of the Dynamic Mixing Ratio (27.23) from the BLEU score of the Static Mixing Ratio (29.70). So, the answer is {\"answer\": \"2.47\"}." } ], "images": [ "scigen/train/1463.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on dynamic vs. static mixing ratio (FKGL: lower is better)." ], "reasoning": "To find the difference in BLEU scores for the WikiSmall dataset, subtract the BLEU score of the Dynamic Mixing Ratio (27.23) from the BLEU score of the Static Mixing Ratio (29.70).", "question": "What is the difference in BLEU scores between the Static and Dynamic Mixing Ratios for the WikiSmall dataset?", "answer": "So, the answer is {\"answer\": \"2.47\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Dynamic Mixing Ratio has a higher FKGL score than the Static Mixing Ratio for the Newsela dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Dynamic Mixing Ratio has a higher FKGL score than the Static Mixing Ratio for the Newsela dataset.\n" }, { "role": "assistant", "content": "The table shows that for the Newsela dataset, the FKGL score for the Static Mixing Ratio is 1.38, while the FKGL score for the Dynamic Mixing Ratio is 1.32. Since 1.32 is less than 1.38, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1463.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on dynamic vs. static mixing ratio (FKGL: lower is better)." ], "reasoning": "The table shows that for the Newsela dataset, the FKGL score for the Static Mixing Ratio is 1.38, while the FKGL score for the Dynamic Mixing Ratio is 1.32. Since 1.32 is less than 1.38, the statement is refuted.", "statement": "The Dynamic Mixing Ratio has a higher FKGL score than the Static Mixing Ratio for the Newsela dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 5: Multi-task layer ablation results on Newsela.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The final multi-level layer sharing method outperforms other methods in terms of SARI score, while having competitive BLEU and FKGL scores.Sharing Approaches We empirically show that our final multi-level layer sharing method (i.e., higher-level semantic layer sharing with entailment generation, while lower-level lexico-syntactic layer sharing with paraphrase generation) performs better than the following alternative layer sharing methods: (1) both auxiliary tasks with high-level layer sharing, (2) both with low-level layer sharing, and (3) reverse/swapped sharing (i.e., lower-level layer sharing for entailment, and higher-level layer sharing for paraphrasing)." } ], "images": [ "scigen/train/1464.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Multi-task layer ablation results on Newsela." ], "reasoning": "The final multi-level layer sharing method outperforms other methods in terms of SARI score, while having competitive BLEU and FKGL scores.", "description": "Sharing Approaches We empirically show that our final multi-level layer sharing method (i.e., higher-level semantic layer sharing with entailment generation, while lower-level lexico-syntactic layer sharing with paraphrase generation) performs better than the following alternative layer sharing methods: (1) both auxiliary tasks with high-level layer sharing, (2) both with low-level layer sharing, and (3) reverse/swapped sharing (i.e., lower-level layer sharing for entailment, and higher-level layer sharing for paraphrasing).", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 5: Multi-task layer ablation results on Newsela.' and proceed to solve the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model has the highest SARI score and what is its FKGL score?\n" }, { "role": "assistant", "content": "The table shows that the 'Hard-sharing' model has the highest SARI score of 32.36, and its FKGL score is 1.38. So, the answer is {\"answer\": \"1.38\"}" } ], "images": [ "scigen/train/1464.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Multi-task layer ablation results on Newsela." ], "reasoning": "The table shows that the 'Hard-sharing' model has the highest SARI score of 32.36, and its FKGL score is 1.38.", "question": "Which model has the highest SARI score and what is its FKGL score?", "answer": "So, the answer is {\"answer\": \"1.38\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe swapped sharing method has the highest BLEU score.\n" }, { "role": "assistant", "content": "The table shows that the 'Swapped (Low Ent + High PP)' model has a BLEU score of 21.64, which is the highest among all models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1464.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Multi-task layer ablation results on Newsela." ], "reasoning": "The table shows that the 'Swapped (Low Ent + High PP)' model has a BLEU score of 21.64, which is the highest among all models listed.", "statement": "The swapped sharing method has the highest BLEU score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 5: Comparing the previous state-of-the-art results to our best morphologically informed Bi-LSTM-CRF model.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Our model achieved an F1 score of 92.20%, improving from the 89.4% achieved by Georgiev2009FeatureRichNE using regular expressions, gazetteers, and non-local morpho-syntactic characteristics.They achieved an F1 score of 89.4% by sing regular expressions, gazetteers and non-local morpho-syntactic characteristics. Our model improves this to 92.20% without using any external resources." } ], "images": [ "scigen/train/1465.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparing the previous state-of-the-art results to our best morphologically informed Bi-LSTM-CRF model." ], "reasoning": "Our model achieved an F1 score of 92.20%, improving from the 89.4% achieved by Georgiev2009FeatureRichNE using regular expressions, gazetteers, and non-local morpho-syntactic characteristics.", "description": "They achieved an F1 score of 89.4% by sing regular expressions, gazetteers and non-local morpho-syntactic characteristics. Our model improves this to 92.20% without using any external resources.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Comparing the previous state-of-the-art results to our best morphologically informed Bi-LSTM-CRF model.\nQuestion: What is the improvement in F1 score achieved by our model compared to Georgiev2009FeatureRichNE?\nSolve the above question based on the table information. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The F1 score of Georgiev2009FeatureRichNE is 89.40%, and the F1 score of our model is 92.20%. The improvement is calculated as 92.20% - 89.40% = 2.80%. So, the answer is {\"answer\": \"2.80%\"}" } ], "images": [ "scigen/train/1465.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparing the previous state-of-the-art results to our best morphologically informed Bi-LSTM-CRF model." ], "reasoning": "The F1 score of Georgiev2009FeatureRichNE is 89.40%, and the F1 score of our model is 92.20%. The improvement is calculated as 92.20% - 89.40% = 2.80%.", "question": "What is the improvement in F1 score achieved by our model compared to Georgiev2009FeatureRichNE?", "answer": "So, the answer is {\"answer\": \"2.80%\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nOur model achieved an F1 score of 92.20% without using any external resources.\n" }, { "role": "assistant", "content": "The table shows that our model achieved an F1 score of 92.20%. The description states that this was achieved without using any external resources. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1465.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparing the previous state-of-the-art results to our best morphologically informed Bi-LSTM-CRF model." ], "reasoning": "The table shows that our model achieved an F1 score of 92.20%. The description states that this was achieved without using any external resources.", "statement": "Our model achieved an F1 score of 92.20% without using any external resources.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: Evaluation results for Bulgarian POS tagging. Shown are results where the standard input to the Bi-LSTM-CRF model is augmented with different POS tags and morphological features.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The table shows that adding morphological features improves the F1 score of the LSTM-CRF model for Bulgarian NER across various POS configurations.The experimental results suggest that adding grammatical features can have a sizeable impact on the performance of the general LSTM-CRF model for Bulgarian NER." } ], "images": [ "scigen/train/1466.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Evaluation results for Bulgarian POS tagging. Shown are results where the standard input to the Bi-LSTM-CRF model is augmented with different POS tags and morphological features." ], "reasoning": "The table shows that adding morphological features improves the F1 score of the LSTM-CRF model for Bulgarian NER across various POS configurations.", "description": "The experimental results suggest that adding grammatical features can have a sizeable impact on the performance of the general LSTM-CRF model for Bulgarian NER.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: Evaluation results for Bulgarian POS tagging. Shown are results where the standard input to the Bi-LSTM-CRF model is augmented with different POS tags and morphological features.', answer the following question. Give detailed solution consisting of each step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model configuration has the highest F1 score with morphology?\n" }, { "role": "assistant", "content": "To find the highest F1 score with morphology, look at the 'With Morphology F1' column and identify the highest value. The highest value is 92.20, which corresponds to the 'Model + POS-3+11' configuration. So, the answer is {\"answer\": \"Model + POS-3+11\"}." } ], "images": [ "scigen/train/1466.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Evaluation results for Bulgarian POS tagging. Shown are results where the standard input to the Bi-LSTM-CRF model is augmented with different POS tags and morphological features." ], "reasoning": "To find the highest F1 score with morphology, look at the 'With Morphology F1' column and identify the highest value. The highest value is 92.20, which corresponds to the 'Model + POS-3+11' configuration.", "question": "Which model configuration has the highest F1 score with morphology?", "answer": "So, the answer is {\"answer\": \"Model + POS-3+11\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model configuration 'Model + POS-3+11' has the highest precision with morphology.\n" }, { "role": "assistant", "content": "To verify this statement, check the 'With Morphology P' column for the highest value. The highest value is 93.31, which corresponds to the 'Model + POS-3+11' configuration. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1466.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Evaluation results for Bulgarian POS tagging. Shown are results where the standard input to the Bi-LSTM-CRF model is augmented with different POS tags and morphological features." ], "reasoning": "To verify this statement, check the 'With Morphology P' column for the highest value. The highest value is 93.31, which corresponds to the 'Model + POS-3+11' configuration.", "statement": "The model configuration 'Model + POS-3+11' has the highest precision with morphology.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 6: Detailed results for the different kinds of named entities.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The F1 score for Organization is lower than for Location and Person, which is attributed to many organizations being named after persons.We can observe relatively worse F1 score of 84.70 for Organization compared to 95.86 for Location and 94.95 for Person. We explain this drop in F1 score by the fact that many organizations are named after persons." } ], "images": [ "scigen/train/1467.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Detailed results for the different kinds of named entities." ], "reasoning": "The F1 score for Organization is lower than for Location and Person, which is attributed to many organizations being named after persons.", "description": "We can observe relatively worse F1 score of 84.70 for Organization compared to 95.86 for Location and 94.95 for Person. We explain this drop in F1 score by the fact that many organizations are named after persons.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 6: Detailed results for the different kinds of named entities.', answer the following question. Give detailed solution consisting of each step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the F1 score for the entity with the highest precision?\n" }, { "role": "assistant", "content": "The entity with the highest precision is Location with a precision of 97.75. The F1 score for Location is 95.86. So, the answer is {\"answer\": \"95.86\"}" } ], "images": [ "scigen/train/1467.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Detailed results for the different kinds of named entities." ], "reasoning": "The entity with the highest precision is Location with a precision of 97.75. The F1 score for Location is 95.86.", "question": "What is the F1 score for the entity with the highest precision?", "answer": "So, the answer is {\"answer\": \"95.86\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The F1 score for Miscellaneous is higher than for Organization., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe F1 score for Miscellaneous is higher than for Organization.\n" }, { "role": "assistant", "content": "The F1 score for Miscellaneous is 36.76, while the F1 score for Organization is 84.70. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1467.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Detailed results for the different kinds of named entities." ], "reasoning": "The F1 score for Miscellaneous is 36.76, while the F1 score for Organization is 84.70. Therefore, the statement is refuted.", "statement": "The F1 score for Miscellaneous is higher than for Organization.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: The impact of different components and different component combinations on the performance of our best model.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Adding POS and morphological information to the Bi-LSTM-CRF model significantly improves its performance, with POS3 and morphological vectors achieving the highest F1 score of 92.20.The basic model we started with is shown on line (4). Then, on lines (1)-(3) we remove different components from this basic model, and on lines (5)-(7) we add POS and morphological information to it. We can see sizeable improvement for the standard Bi-LSTM-CRF model with only word vector representationa and the model with character-level LSTM. Interestingly, there is almost no difference between the suffix and the prefix vectors. We can further see that adding POS11 (5) vector improves the performance by almost four percent absolute. The morphological vectors and POS3 also improved the F1 score to 92.20 points absolute. These improvements show that using known linguistic knowledge such as grammatical features could improve the representation vectors learned over huge text corpora. From the point of view of feature learning, we speculate that vectors trained over texts in morphologically rich languages do not learn enough grammar such as POS and morphology. The character embeddings also seem not to help much. One explanation for this could be that the suffixes and prefixes in Bulgarian are also highly ambiguous." } ], "images": [ "scigen/train/1468.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: The impact of different components and different component combinations on the performance of our best model." ], "reasoning": "Adding POS and morphological information to the Bi-LSTM-CRF model significantly improves its performance, with POS3 and morphological vectors achieving the highest F1 score of 92.20.", "description": "The basic model we started with is shown on line (4). Then, on lines (1)-(3) we remove different components from this basic model, and on lines (5)-(7) we add POS and morphological information to it. We can see sizeable improvement for the standard Bi-LSTM-CRF model with only word vector representationa and the model with character-level LSTM. Interestingly, there is almost no difference between the suffix and the prefix vectors. We can further see that adding POS11 (5) vector improves the performance by almost four percent absolute. The morphological vectors and POS3 also improved the F1 score to 92.20 points absolute. These improvements show that using known linguistic knowledge such as grammatical features could improve the representation vectors learned over huge text corpora. From the point of view of feature learning, we speculate that vectors trained over texts in morphologically rich languages do not learn enough grammar such as POS and morphology. The character embeddings also seem not to help much. One explanation for this could be that the suffixes and prefixes in Bulgarian are also highly ambiguous.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 7: The impact of different components and different component combinations on the performance of our best model.'\nQuestion: What is the F1 score improvement when adding POS11 to the Bi-LSTM-char model?\n" }, { "role": "assistant", "content": "The F1 score for the Bi-LSTM-char model is 86.44, and adding POS11 to it increases the F1 score to 90.96. The improvement is 90.96 - 86.44 = 4.52. So, the answer is {\"answer\": \"4.52\"}" } ], "images": [ "scigen/train/1468.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: The impact of different components and different component combinations on the performance of our best model." ], "reasoning": "The F1 score for the Bi-LSTM-char model is 86.44, and adding POS11 to it increases the F1 score to 90.96. The improvement is 90.96 - 86.44 = 4.52.", "question": "What is the F1 score improvement when adding POS11 to the Bi-LSTM-char model?", "answer": "So, the answer is {\"answer\": \"4.52\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe model with the highest F1 score includes POS3 and morphological vectors.\n" }, { "role": "assistant", "content": "The table shows that the model with POS3 and Morphological vectors (line 7) has the highest F1 score of 92.20. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1468.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: The impact of different components and different component combinations on the performance of our best model." ], "reasoning": "The table shows that the model with POS3 and Morphological vectors (line 7) has the highest F1 score of 92.20.", "statement": "The model with the highest F1 score includes POS3 and morphological vectors.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 9: Confusion matrix for our best model on the test dataset: the columns represent the true labels and the rows show the predictions.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The model often confuses B-PER and B-LOC tags with the O tag, and also struggles to distinguish between B-LOC and B-ORG tags.In the table, the columns represent the actual expected gold tags, while the rows show the predictions of our model. There are several interesting observations that we can make about this confusion matrix. First, it looks like the biggest problem for the model is with the tag B-PER, which is often confused with the tag O, i.e., Outside. This is probably due to the fact that in Bulgarian the first names sometimes have more than one meaning, which can confuse the model. The same argument holds for the tag B-LOC, which is also often confused with the tag O. Another place for improvements would be to distinguish better between B-LOC and B-ORG, as many places and organization have identical names, or at least the identical first words. The miscellaneous entities such as the names of books or movies can also have names that are identical to those of some organizations. Even more often, Miscellaneous entities could be confused with the Other category as they contain many common Bulgarian words." } ], "images": [ "scigen/train/1469.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Confusion matrix for our best model on the test dataset: the columns represent the true labels and the rows show the predictions." ], "reasoning": "The model often confuses B-PER and B-LOC tags with the O tag, and also struggles to distinguish between B-LOC and B-ORG tags.", "description": "In the table, the columns represent the actual expected gold tags, while the rows show the predictions of our model. There are several interesting observations that we can make about this confusion matrix. First, it looks like the biggest problem for the model is with the tag B-PER, which is often confused with the tag O, i.e., Outside. This is probably due to the fact that in Bulgarian the first names sometimes have more than one meaning, which can confuse the model. The same argument holds for the tag B-LOC, which is also often confused with the tag O. Another place for improvements would be to distinguish better between B-LOC and B-ORG, as many places and organization have identical names, or at least the identical first words. The miscellaneous entities such as the names of books or movies can also have names that are identical to those of some organizations. Even more often, Miscellaneous entities could be confused with the Other category as they contain many common Bulgarian words.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 9: Confusion matrix for our best model on the test dataset: the columns represent the true labels and the rows show the predictions.\nQuestion: Which tag is most frequently confused with the O tag?\n" }, { "role": "assistant", "content": "By examining the table, we see that the B-PER tag has the highest number of instances (58) where it is confused with the O tag. So, the answer is {\"answer\": \"B-PER\"}." } ], "images": [ "scigen/train/1469.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Confusion matrix for our best model on the test dataset: the columns represent the true labels and the rows show the predictions." ], "reasoning": "By examining the table, we see that the B-PER tag has the highest number of instances (58) where it is confused with the O tag.", "question": "Which tag is most frequently confused with the O tag?", "answer": "So, the answer is {\"answer\": \"B-PER\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The model confuses B-LOC with B-ORG more frequently than with O., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe model confuses B-LOC with B-ORG more frequently than with O.\n" }, { "role": "assistant", "content": "The table shows that B-LOC is confused with B-ORG 15 times, while it is confused with O 36 times. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1469.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Confusion matrix for our best model on the test dataset: the columns represent the true labels and the rows show the predictions." ], "reasoning": "The table shows that B-LOC is confused with B-ORG 15 times, while it is confused with O 36 times.", "statement": "The model confuses B-LOC with B-ORG more frequently than with O.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Comparison with re-implemented related work: “RelPos”: relative position encoding Shaw et al. (2018), “DiSAN”: directional SAN Shen et al. (2018), “RNN Encoder”: combining SAN and RNN encoders with multi-column strategy Chen et al. (2018).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "All recurrence enhanced approaches improve over the baseline Transformer-base model, with the 1-Layer BiARN Encoder achieving the best performance.As seen, all the recurrence enhanced approaches achieve improvements over the baseline model Transformer-base, which demonstrates the necessity of modeling recurrence for Transformer. Among these approaches, our approach (i.e., 1-Layer BiARN Encoder) achieves the best performance." } ], "images": [ "scigen/train/1470.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison with re-implemented related work: “RelPos”: relative position encoding Shaw et al. (2018), “DiSAN”: directional SAN Shen et al. (2018), “RNN Encoder”: combining SAN and RNN encoders with multi-column strategy Chen et al. (2018)." ], "reasoning": "All recurrence enhanced approaches improve over the baseline Transformer-base model, with the 1-Layer BiARN Encoder achieving the best performance.", "description": "As seen, all the recurrence enhanced approaches achieve improvements over the baseline model Transformer-base, which demonstrates the necessity of modeling recurrence for Transformer. Among these approaches, our approach (i.e., 1-Layer BiARN Encoder) achieves the best performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 4: Comparison with re-implemented related work: “RelPos”: relative position encoding Shaw et al. (2018), “DiSAN”: directional SAN Shen et al. (2018), “RNN Encoder”: combining SAN and RNN encoders with multi-column strategy Chen et al. (2018).' in mind, break down and resolve the question below step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich model achieves the highest BLEU score among the recurrence enhanced approaches?\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for different models, and the 1-Layer BiARN Encoder achieves the highest score of 28.21. So, the answer is {\"answer\": \"1-Layer BiARN Encoder\"}." } ], "images": [ "scigen/train/1470.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison with re-implemented related work: “RelPos”: relative position encoding Shaw et al. (2018), “DiSAN”: directional SAN Shen et al. (2018), “RNN Encoder”: combining SAN and RNN encoders with multi-column strategy Chen et al. (2018)." ], "reasoning": "The table shows the BLEU scores for different models, and the 1-Layer BiARN Encoder achieves the highest score of 28.21.", "question": "Which model achieves the highest BLEU score among the recurrence enhanced approaches?", "answer": "So, the answer is {\"answer\": \"1-Layer BiARN Encoder\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Transformer-base model achieves the highest BLEU score.\n" }, { "role": "assistant", "content": "The table shows that the Transformer-base model has a BLEU score of 27.31, which is lower than the scores of all recurrence enhanced approaches, especially the 1-Layer BiARN Encoder with a score of 28.21. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1470.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison with re-implemented related work: “RelPos”: relative position encoding Shaw et al. (2018), “DiSAN”: directional SAN Shen et al. (2018), “RNN Encoder”: combining SAN and RNN encoders with multi-column strategy Chen et al. (2018)." ], "reasoning": "The table shows that the Transformer-base model has a BLEU score of 27.31, which is lower than the scores of all recurrence enhanced approaches, especially the 1-Layer BiARN Encoder with a score of 28.21.", "statement": "The Transformer-base model achieves the highest BLEU score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table VI: WER (%) comparison of the 6-layer-BLSTM direct PIT-CE-ASR model on the mixed speech generated from two male speakers (M + M), two female speakers (F + F) and a male and a female speaker (M + F)'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The table shows that same-gender mixed speech, especially between male speakers, has higher WER compared to opposite-gender mixed speech, indicating the difficulty in recognizing same-gender mixed speech.To better understand the results on multi-talker speech recognition, we computed the WER separately for the speech mixed with same and opposite genders. It is observed that the same-gender mixed speech is much more difficult to recognize than the opposite-gender mixed speech, and the gap is even larger when the energy ratio of the two speakers is closer to 1. It is also observed that the mixed speech of two male speakers is hard to recognize than that of two female speakers. These results suggest that effective exploitation of gender information may help to further improve the multi-talker speech recognition system. We will explore this in our future work." } ], "images": [ "scigen/train/1471.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table VI: WER (%) comparison of the 6-layer-BLSTM direct PIT-CE-ASR model on the mixed speech generated from two male speakers (M + M), two female speakers (F + F) and a male and a female speaker (M + F)" ], "reasoning": "The table shows that same-gender mixed speech, especially between male speakers, has higher WER compared to opposite-gender mixed speech, indicating the difficulty in recognizing same-gender mixed speech.", "description": "To better understand the results on multi-talker speech recognition, we computed the WER separately for the speech mixed with same and opposite genders. It is observed that the same-gender mixed speech is much more difficult to recognize than the opposite-gender mixed speech, and the gap is even larger when the energy ratio of the two speakers is closer to 1. It is also observed that the mixed speech of two male speakers is hard to recognize than that of two female speakers. These results suggest that effective exploitation of gender information may help to further improve the multi-talker speech recognition system. We will explore this in our future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table VI: WER (%) comparison of the 6-layer-BLSTM direct PIT-CE-ASR model on the mixed speech generated from two male speakers (M + M), two female speakers (F + F) and a male and a female speaker (M + F)'\nQuestion: What is the WER for male and female mixed speech at 5db SNR condition with high energy ratio?\n" }, { "role": "assistant", "content": "To find the WER for male and female mixed speech at 5db SNR condition with high energy ratio, look at the row where Genders is 'M + F' and SNR Condition is '5db'. The High E WER value in this row is 37.34. So, the answer is {\"answer\": \"37.34\"}." } ], "images": [ "scigen/train/1471.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table VI: WER (%) comparison of the 6-layer-BLSTM direct PIT-CE-ASR model on the mixed speech generated from two male speakers (M + M), two female speakers (F + F) and a male and a female speaker (M + F)" ], "reasoning": "To find the WER for male and female mixed speech at 5db SNR condition with high energy ratio, look at the row where Genders is 'M + F' and SNR Condition is '5db'. The High E WER value in this row is 37.34.", "question": "What is the WER for male and female mixed speech at 5db SNR condition with high energy ratio?", "answer": "So, the answer is {\"answer\": \"37.34\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe WER for female and female mixed speech at 10db SNR condition with low energy ratio is higher than 60.\n" }, { "role": "assistant", "content": "To verify this statement, look at the row where Genders is 'F + F' and SNR Condition is '10db'. The Low E WER value in this row is 65.15, which is higher than 60. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1471.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table VI: WER (%) comparison of the 6-layer-BLSTM direct PIT-CE-ASR model on the mixed speech generated from two male speakers (M + M), two female speakers (F + F) and a male and a female speaker (M + F)" ], "reasoning": "To verify this statement, look at the row where Genders is 'F + F' and SNR Condition is '10db'. The Low E WER value in this row is 65.15, which is higher than 60.", "statement": "The WER for female and female mixed speech at 10db SNR condition with low energy ratio is higher than 60.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table V: WER (%) of the direct PIT-CE-ASR model using different deep learning models on the IHM-2mix dataset', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "BLSTM models outperform DNN models in WER due to stronger modeling power and better tracing ability, with deeper BLSTM models performing better.Although it still gets obvious improvement over the baseline single-speaker model, the gain is much smaller with near 20.0% WER difference in every condition than that from BLSTM-based PIT-CE-ASR model. The difference between DNN and BLSTM models partially attribute to the stronger modeling power of BLSTM models and partially attribute to the better tracing ability of RNNs. It is observed that deeper BLSTM models perform better. This is because the direct PIT-CE-ASR architecture needs to conduct two tasks - separation and recognition, and thus requires additional modeling power." } ], "images": [ "scigen/train/1472.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table V: WER (%) of the direct PIT-CE-ASR model using different deep learning models on the IHM-2mix dataset" ], "reasoning": "BLSTM models outperform DNN models in WER due to stronger modeling power and better tracing ability, with deeper BLSTM models performing better.", "description": "Although it still gets obvious improvement over the baseline single-speaker model, the gain is much smaller with near 20.0% WER difference in every condition than that from BLSTM-based PIT-CE-ASR model. The difference between DNN and BLSTM models partially attribute to the stronger modeling power of BLSTM models and partially attribute to the better tracing ability of RNNs. It is observed that deeper BLSTM models perform better. This is because the direct PIT-CE-ASR architecture needs to conduct two tasks - separation and recognition, and thus requires additional modeling power.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table V: WER (%) of the direct PIT-CE-ASR model using different deep learning models on the IHM-2mix dataset', answer the following question. Give detailed solution consisting of each step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the High E WER for the 6L-DNN model at 10db SNR condition?\n" }, { "role": "assistant", "content": "To find the High E WER for the 6L-DNN model at 10db SNR condition, locate the row with '6L-DNN' under 'Models' and '10db' under 'SNR Condition'. The corresponding 'High E WER' value is 55.27. So, the answer is {\"answer\": \"55.27\"}." } ], "images": [ "scigen/train/1472.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table V: WER (%) of the direct PIT-CE-ASR model using different deep learning models on the IHM-2mix dataset" ], "reasoning": "To find the High E WER for the 6L-DNN model at 10db SNR condition, locate the row with '6L-DNN' under 'Models' and '10db' under 'SNR Condition'. The corresponding 'High E WER' value is 55.27.", "question": "What is the High E WER for the 6L-DNN model at 10db SNR condition?", "answer": "So, the answer is {\"answer\": \"55.27\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe 4L-BLSTM model has a lower High E WER at 0db SNR condition than the 6L-DNN model.\n" }, { "role": "assistant", "content": "Compare the High E WER values for the 4L-BLSTM model at 0db SNR condition (49.74) and the 6L-DNN model at 0db SNR condition (72.95). The 4L-BLSTM model has a lower value. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1472.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table V: WER (%) of the direct PIT-CE-ASR model using different deep learning models on the IHM-2mix dataset" ], "reasoning": "Compare the High E WER values for the 4L-BLSTM model at 0db SNR condition (49.74) and the 6L-DNN model at 0db SNR condition (72.95). The 4L-BLSTM model has a lower value.", "statement": "The 4L-BLSTM model has a lower High E WER at 0db SNR condition than the 6L-DNN model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table VII: WER (%) comparison of the baseline single-speaker BLSTM-RNN system and the proposed direct PIT-CE-ASR model on the IHM-3mix dataset. Diff indicates the mixed speech is from different genders, and Same indicates the mixed speech is from same gender', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The direct PIT-CE-ASR model significantly reduces WER compared to the BLSTM-RNN model, especially in mixed-gender scenarios.The WERs on different gender combinations are also provided. Compared to the results on IHM-2mix, the results on IHM-3mix are significantly worse using the conventional single speaker model. Under this extremely hard setup, the proposed direct PIT-CE-ASR architecture still demonstrated its powerful ability on separating/tracing/recognizing the mixed speech, and achieved 25.0% relative WER reduction across all three speakers. Although the performance gap from two-talker to three-talker is obvious, it is still very promising under this speaker-independent three-talker LVCSR task. Not surprisingly, the mixed speech of different genders is relatively easier to recognize than that of same gender." } ], "images": [ "scigen/train/1473.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table VII: WER (%) comparison of the baseline single-speaker BLSTM-RNN system and the proposed direct PIT-CE-ASR model on the IHM-3mix dataset. Diff indicates the mixed speech is from different genders, and Same indicates the mixed speech is from same gender" ], "reasoning": "The direct PIT-CE-ASR model significantly reduces WER compared to the BLSTM-RNN model, especially in mixed-gender scenarios.", "description": "The WERs on different gender combinations are also provided. Compared to the results on IHM-2mix, the results on IHM-3mix are significantly worse using the conventional single speaker model. Under this extremely hard setup, the proposed direct PIT-CE-ASR architecture still demonstrated its powerful ability on separating/tracing/recognizing the mixed speech, and achieved 25.0% relative WER reduction across all three speakers. Although the performance gap from two-talker to three-talker is obvious, it is still very promising under this speaker-independent three-talker LVCSR task. Not surprisingly, the mixed speech of different genders is relatively easier to recognize than that of same gender.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table VII: WER (%) comparison of the baseline single-speaker BLSTM-RNN system and the proposed direct PIT-CE-ASR model on the IHM-3mix dataset. Diff indicates the mixed speech is from different genders, and Same indicates the mixed speech is from same gender' and methodically tackle the question that follows. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the WER for Speaker2 using the direct PIT-CE-ASR model for different genders?\n" }, { "role": "assistant", "content": "The table shows the WER for Speaker2 using the direct PIT-CE-ASR model for different genders as 65.84. So, the answer is {\"answer\": \"65.84\"}" } ], "images": [ "scigen/train/1473.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table VII: WER (%) comparison of the baseline single-speaker BLSTM-RNN system and the proposed direct PIT-CE-ASR model on the IHM-3mix dataset. Diff indicates the mixed speech is from different genders, and Same indicates the mixed speech is from same gender" ], "reasoning": "The table shows the WER for Speaker2 using the direct PIT-CE-ASR model for different genders as 65.84.", "question": "What is the WER for Speaker2 using the direct PIT-CE-ASR model for different genders?", "answer": "So, the answer is {\"answer\": \"65.84\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe WER for Speaker1 using the BLSTM-RNN model is higher than 90.\n" }, { "role": "assistant", "content": "The table shows that the WER for Speaker1 using the BLSTM-RNN model is 91.0, which is higher than 90. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1473.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table VII: WER (%) comparison of the baseline single-speaker BLSTM-RNN system and the proposed direct PIT-CE-ASR model on the IHM-3mix dataset. Diff indicates the mixed speech is from different genders, and Same indicates the mixed speech is from same gender" ], "reasoning": "The table shows that the WER for Speaker1 using the BLSTM-RNN model is 91.0, which is higher than 90.", "statement": "The WER for Speaker1 using the BLSTM-RNN model is higher than 90.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Inter-annotator agreement (Cohen’s \\kappa values) on the evaluation set for the RNN and PATECH’s Transformer systems and their average. Substantial scores (0.61–0.80) are shown in bold. N/A is given to the error categories that were never used, since no data points could be used to calculate the IAA score.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "IAA scores for Accuracy and its sub-types are higher than those for Fluency, with consistent \\kappa values between Transformer and RNN except for specific categories.For both systems, the IAA scores for Accuracy and its sub-types are considerably higher than those under Fluency. It is an expected result taken into account that accuracy errors are more straightforward and less open to interpretation. The \\kappa values are relatively consistent between Transformer and RNN, except a striking plunge in agreement scores for Transformer in some categories (Function word and its subtypes, Word order and Unintelligible) and the opposite, a considerably lower agreement for RNN, for Entity." } ], "images": [ "scigen/train/1474.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Inter-annotator agreement (Cohen’s \\kappa values) on the evaluation set for the RNN and PATECH’s Transformer systems and their average. Substantial scores (0.61–0.80) are shown in bold. N/A is given to the error categories that were never used, since no data points could be used to calculate the IAA score." ], "reasoning": "IAA scores for Accuracy and its sub-types are higher than those for Fluency, with consistent \\kappa values between Transformer and RNN except for specific categories.", "description": "For both systems, the IAA scores for Accuracy and its sub-types are considerably higher than those under Fluency. It is an expected result taken into account that accuracy errors are more straightforward and less open to interpretation. The \\kappa values are relatively consistent between Transformer and RNN, except a striking plunge in agreement scores for Transformer in some categories (Function word and its subtypes, Word order and Unintelligible) and the opposite, a considerably lower agreement for RNN, for Entity.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: Inter-annotator agreement (Cohen’s \\kappa values) on the evaluation set for the RNN and PATECH’s Transformer systems and their average. Substantial scores (0.61–0.80) are shown in bold. N/A is given to the error categories that were never used, since no data points could be used to calculate the IAA score.', answer the following question. Give detailed solution consisting of each step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich system has the highest IAA score for the 'Untranslated' category?\n" }, { "role": "assistant", "content": "The table shows IAA scores for different categories. For the 'Untranslated' category, the scores are 0.73 for RNN, 0.71 for Transformer, and 0.72 for Both. The highest score is for RNN. So, the answer is {\"answer\": \"RNN\"}." } ], "images": [ "scigen/train/1474.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Inter-annotator agreement (Cohen’s \\kappa values) on the evaluation set for the RNN and PATECH’s Transformer systems and their average. Substantial scores (0.61–0.80) are shown in bold. N/A is given to the error categories that were never used, since no data points could be used to calculate the IAA score." ], "reasoning": "The table shows IAA scores for different categories. For the 'Untranslated' category, the scores are 0.73 for RNN, 0.71 for Transformer, and 0.72 for Both. The highest score is for RNN.", "question": "Which system has the highest IAA score for the 'Untranslated' category?", "answer": "So, the answer is {\"answer\": \"RNN\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Transformer system has a higher IAA score for 'Omission' than the RNN system. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Transformer system has a higher IAA score for 'Omission' than the RNN system.\n" }, { "role": "assistant", "content": "The table shows that the IAA score for 'Omission' is 0.67 for Transformer and 0.52 for RNN, indicating that the Transformer system has a higher score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1474.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Inter-annotator agreement (Cohen’s \\kappa values) on the evaluation set for the RNN and PATECH’s Transformer systems and their average. Substantial scores (0.61–0.80) are shown in bold. N/A is given to the error categories that were never used, since no data points could be used to calculate the IAA score." ], "reasoning": "The table shows that the IAA score for 'Omission' is 0.67 for Transformer and 0.52 for RNN, indicating that the Transformer system has a higher score.", "statement": "The Transformer system has a higher IAA score for 'Omission' than the RNN system.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Automatic evaluation (BLEU scores) of the 3 MT systems on the WMT 2019 news test set.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The BLEU scores indicate that the Transformer models outperform the RNN model, with PATECH achieving the highest score.Before our human error analysis, we would like to compare the recurrent and Transformer MT systems in terms of an automatic evaluation metric. This is not possible from their outputs since they correspond to two different test sets (newstest2017 and newstest2019). In order to be able to compare them, we asked the developer of the recurrent system to provide us with the output from their system for newstest2019. While the gap between the two architectures is large based on BLEU, this is an overall metric and therefore does not provide any insight into which aspects of the translation have improved with Transformer with respect to the recurrent system. To gain further insight we conduct a fine-grained human error analysis in the following section." } ], "images": [ "scigen/train/1475.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Automatic evaluation (BLEU scores) of the 3 MT systems on the WMT 2019 news test set." ], "reasoning": "The BLEU scores indicate that the Transformer models outperform the RNN model, with PATECH achieving the highest score.", "description": "Before our human error analysis, we would like to compare the recurrent and Transformer MT systems in terms of an automatic evaluation metric. This is not possible from their outputs since they correspond to two different test sets (newstest2017 and newstest2019). In order to be able to compare them, we asked the developer of the recurrent system to provide us with the output from their system for newstest2019. While the gap between the two architectures is large based on BLEU, this is an overall metric and therefore does not provide any insight into which aspects of the translation have improved with Transformer with respect to the recurrent system. To gain further insight we conduct a fine-grained human error analysis in the following section.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 1: Automatic evaluation (BLEU scores) of the 3 MT systems on the WMT 2019 news test set.' to sequentially solve the question presented below. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the BLEU score difference between the RNN and the highest performing Transformer model?\n" }, { "role": "assistant", "content": "The BLEU score for the RNN model is 33.1, and the highest score among the Transformer models is 44.6 (PATECH). The difference is 44.6 - 33.1. So, the answer is {\"answer\": \"11.5\"}." } ], "images": [ "scigen/train/1475.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Automatic evaluation (BLEU scores) of the 3 MT systems on the WMT 2019 news test set." ], "reasoning": "The BLEU score for the RNN model is 33.1, and the highest score among the Transformer models is 44.6 (PATECH). The difference is 44.6 - 33.1.", "question": "What is the BLEU score difference between the RNN and the highest performing Transformer model?", "answer": "So, the answer is {\"answer\": \"11.5\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Transformer (KSAI) model has a higher BLEU score than the RNN model.\n" }, { "role": "assistant", "content": "The BLEU score for the Transformer (KSAI) model is 42.4, which is higher than the RNN model's score of 33.1. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1475.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Automatic evaluation (BLEU scores) of the 3 MT systems on the WMT 2019 news test set." ], "reasoning": "The BLEU score for the Transformer (KSAI) model is 42.4, which is higher than the RNN model's score of 33.1.", "statement": "The Transformer (KSAI) model has a higher BLEU score than the RNN model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Total and average inter-annotator agreement (Cohen’s κ values) for the MQM calibration set and evaluation set.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The IAA values improved from the calibration set to the evaluation set, indicating that discussing annotation disagreements can significantly enhance agreement levels.It is worth noting that the IAA values of the evaluation set improve considerably upon those of the calibration set (\\kappa=0.44 versus 0.27). It shows that the discussion of annotation disagreements can contribute to improving the level of agreement notably. The average IAA value (0.44), corresponds to moderate agreement, according to Cohen \\shortcitecohen-kappa-1960. When interpreting these results, it should be taken into account that IAA scores are known to be low in human evaluation of MT. For example, Callison-Burch et al. \\shortcitecallison-burch_meta-_2007 observed fair agreements for fluency and accuracy for eight language pairs, and, though the MQM framework is rigorously defined and supported by clear guidelines, in the experiments by Lommel and Burchardt \\shortcitelommel-iaa-2014 MQM led to relatively low IAA, due to span-level difference, ambiguous categorisation and differences of opinion. Klubička et al. \\shortciteklubicka_quantitative_2018 reported a moderate agreement on English–Croatian, higher than that by Lommel and Burchardt \\shortcitelommel-iaa-2014, probably because the agreement was calculated on errors annotated for each sentence, thus not taking the spans of the annotations into account. Our own IAA results do not differ greatly with aforementioned research." } ], "images": [ "scigen/train/1476.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Total and average inter-annotator agreement (Cohen’s κ values) for the MQM calibration set and evaluation set." ], "reasoning": "The IAA values improved from the calibration set to the evaluation set, indicating that discussing annotation disagreements can significantly enhance agreement levels.", "description": "It is worth noting that the IAA values of the evaluation set improve considerably upon those of the calibration set (\\kappa=0.44 versus 0.27). It shows that the discussion of annotation disagreements can contribute to improving the level of agreement notably. The average IAA value (0.44), corresponds to moderate agreement, according to Cohen \\shortcitecohen-kappa-1960. When interpreting these results, it should be taken into account that IAA scores are known to be low in human evaluation of MT. For example, Callison-Burch et al. \\shortcitecallison-burch_meta-_2007 observed fair agreements for fluency and accuracy for eight language pairs, and, though the MQM framework is rigorously defined and supported by clear guidelines, in the experiments by Lommel and Burchardt \\shortcitelommel-iaa-2014 MQM led to relatively low IAA, due to span-level difference, ambiguous categorisation and differences of opinion. Klubička et al. \\shortciteklubicka_quantitative_2018 reported a moderate agreement on English–Croatian, higher than that by Lommel and Burchardt \\shortcitelommel-iaa-2014, probably because the agreement was calculated on errors annotated for each sentence, thus not taking the spans of the annotations into account. Our own IAA results do not differ greatly with aforementioned research.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 3: Total and average inter-annotator agreement (Cohen’s κ values) for the MQM calibration set and evaluation set.', solve the subsequent question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the IAA value for the Transformer (PATECH) model on the evaluation set?\n" }, { "role": "assistant", "content": "The table shows the IAA values for different models and sets. For the Transformer (PATECH) model on the evaluation set, the IAA value is 0.43. So, the answer is {\"answer\": \"0.43\"}." } ], "images": [ "scigen/train/1476.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Total and average inter-annotator agreement (Cohen’s κ values) for the MQM calibration set and evaluation set." ], "reasoning": "The table shows the IAA values for different models and sets. For the Transformer (PATECH) model on the evaluation set, the IAA value is 0.43.", "question": "What is the IAA value for the Transformer (PATECH) model on the evaluation set?", "answer": "So, the answer is {\"answer\": \"0.43\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe IAA value for the RNN model on the calibration set is higher than that for the Transformer (PATECH) model on the same set.\n" }, { "role": "assistant", "content": "The table shows that the IAA value for the RNN model on the calibration set is 0.31, while for the Transformer (PATECH) model on the calibration set, it is 0.22. Therefore, the statement is correct. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1476.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Total and average inter-annotator agreement (Cohen’s κ values) for the MQM calibration set and evaluation set." ], "reasoning": "The table shows that the IAA value for the RNN model on the calibration set is 0.31, while for the Transformer (PATECH) model on the calibration set, it is 0.22. Therefore, the statement is correct.", "statement": "The IAA value for the RNN model on the calibration set is higher than that for the Transformer (PATECH) model on the same set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Error ratio (%) for each error type and overall. The annotations on RNN and Transformer (PATECH) from both annotators are concatenated. * indicates p-value < 0.05 and ** p-value < 0.001, when a system is compared to the system adjacent to its left side. Numbers shown in bold indicate that the system has significantly more erroneous tokens in the pair comparison.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "PATECH reduces the total error ratio by 34% compared to RNN, with a significant difference (p<0.001), while no significant difference is observed between the two Transformer-based systems.In terms of total error ratio, compared to RNN, the error reduction by PATECH amounts to 34% relative (11.85% versus 17.93%) and is significant (p<0.001). No significant difference is observed between the two Transformer-based systems." } ], "images": [ "scigen/train/1477.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Error ratio (%) for each error type and overall. The annotations on RNN and Transformer (PATECH) from both annotators are concatenated. * indicates p-value < 0.05 and ** p-value < 0.001, when a system is compared to the system adjacent to its left side. Numbers shown in bold indicate that the system has significantly more erroneous tokens in the pair comparison." ], "reasoning": "PATECH reduces the total error ratio by 34% compared to RNN, with a significant difference (p<0.001), while no significant difference is observed between the two Transformer-based systems.", "description": "In terms of total error ratio, compared to RNN, the error reduction by PATECH amounts to 34% relative (11.85% versus 17.93%) and is significant (p<0.001). No significant difference is observed between the two Transformer-based systems.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 6: Error ratio (%) for each error type and overall. The annotations on RNN and Transformer (PATECH) from both annotators are concatenated. * indicates p-value < 0.05 and ** p-value < 0.001, when a system is compared to the system adjacent to its left side. Numbers shown in bold indicate that the system has significantly more erroneous tokens in the pair comparison.', answer the following question. Give detailed solution consisting of each step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the difference in the total error ratio between RNN and Transformer (KSAI)?\n" }, { "role": "assistant", "content": "The total error ratio for RNN is 17.93%, and for Transformer (KSAI) it is 10.40%. The difference is calculated as 17.93% - 10.40%. So, the answer is {\"answer\": \"7.53%\"}" } ], "images": [ "scigen/train/1477.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Error ratio (%) for each error type and overall. The annotations on RNN and Transformer (PATECH) from both annotators are concatenated. * indicates p-value < 0.05 and ** p-value < 0.001, when a system is compared to the system adjacent to its left side. Numbers shown in bold indicate that the system has significantly more erroneous tokens in the pair comparison." ], "reasoning": "The total error ratio for RNN is 17.93%, and for Transformer (KSAI) it is 10.40%. The difference is calculated as 17.93% - 10.40%.", "question": "What is the difference in the total error ratio between RNN and Transformer (KSAI)?", "answer": "So, the answer is {\"answer\": \"7.53%\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe accuracy of Transformer (PATECH) is higher than that of Transformer (KSAI).\n" }, { "role": "assistant", "content": "The table shows that the accuracy of Transformer (PATECH) is 8.29%, while the accuracy of Transformer (KSAI) is 7.41%. Therefore, Transformer (PATECH) has a higher accuracy. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1477.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Error ratio (%) for each error type and overall. The annotations on RNN and Transformer (PATECH) from both annotators are concatenated. * indicates p-value < 0.05 and ** p-value < 0.001, when a system is compared to the system adjacent to its left side. Numbers shown in bold indicate that the system has significantly more erroneous tokens in the pair comparison." ], "reasoning": "The table shows that the accuracy of Transformer (PATECH) is 8.29%, while the accuracy of Transformer (KSAI) is 7.41%. Therefore, Transformer (PATECH) has a higher accuracy.", "statement": "The accuracy of Transformer (PATECH) is higher than that of Transformer (KSAI).", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 5: Test results evaluated through the official submission site and using our updated distilled model. Our official submission results can be seen in Table 9 in the Appendix.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Our official submission obtained an average ELAS of 74.04, ranking fourth out of 9 full submissions, and improved to 76.14 after training distilled models to convergence.Despite focusing on efficiency, our official submission obtained an average ELAS of 74.04 which was the fourth best system (out of 9 full submissions). Our improved score after training distilled models to convergence (or closer to convergence) obtained an average score of 76.14." } ], "images": [ "scigen/train/1478.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Test results evaluated through the official submission site and using our updated distilled model. Our official submission results can be seen in Table 9 in the Appendix." ], "reasoning": "Our official submission obtained an average ELAS of 74.04, ranking fourth out of 9 full submissions, and improved to 76.14 after training distilled models to convergence.", "description": "Despite focusing on efficiency, our official submission obtained an average ELAS of 74.04 which was the fourth best system (out of 9 full submissions). Our improved score after training distilled models to convergence (or closer to convergence) obtained an average score of 76.14.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 5: Test results evaluated through the official submission site and using our updated distilled model. Our official submission results can be seen in Table 9 in the Appendix.' and proceed to solve the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat was the average ELAS score of the official submission and how did it improve after training distilled models to convergence?\n" }, { "role": "assistant", "content": "The official submission had an average ELAS of 74.04, which improved to 76.14 after training distilled models to convergence. So, the answer is {\"answer\": \"74.04 and 76.14\"}." } ], "images": [ "scigen/train/1478.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Test results evaluated through the official submission site and using our updated distilled model. Our official submission results can be seen in Table 9 in the Appendix." ], "reasoning": "The official submission had an average ELAS of 74.04, which improved to 76.14 after training distilled models to convergence.", "question": "What was the average ELAS score of the official submission and how did it improve after training distilled models to convergence?", "answer": "So, the answer is {\"answer\": \"74.04 and 76.14\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The official submission's average ELAS score was 74.04, which was the fourth best system out of 9 full submissions. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe official submission's average ELAS score was 74.04, which was the fourth best system out of 9 full submissions.\n" }, { "role": "assistant", "content": "The description states that the official submission obtained an average ELAS of 74.04, ranking fourth out of 9 full submissions. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1478.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Test results evaluated through the official submission site and using our updated distilled model. Our official submission results can be seen in Table 9 in the Appendix." ], "reasoning": "The description states that the official submission obtained an average ELAS of 74.04, ranking fourth out of 9 full submissions.", "statement": "The official submission's average ELAS score was 74.04, which was the fourth best system out of 9 full submissions.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Attachment scores for both UD trees and EUD graphs for the development treebanks using different dependency parsers: full baseline models (Full), distilled models (dist), and UDPipe v2.5 models (udpipe).'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The distilled models outperform UDPipe models and are close to the full model in UAS and LAS scores, with ELAS results following the dependency scores.We also evaluated the distilled models against the full baseline model and UDPipe v2.5. The distilled models outperform the UDPipe models and are within a point of both UAS and LAS to the full model. The ELAS results for the rule-based system using the predicted dependency trees from each of theses systems are also shown. The performance on ELAS generally follows the dependency scores. Our system is competitive mainly by the grace of our rule-based system which obtains an average 98.20 ELAS when used on the gold development treebanks. Having a rule-based system that can perform so well on gold data means that improving the dependency predictions it is based on for a full pipeline will almost always increase ELAS scores. It also means it could be used to generate new data. Although this would be restricted to generating data for pre-existing UD treebanks. Furthermore, it could be used to highlight annotation inconsistencies in a given treebank and between different treebanks for the same language." } ], "images": [ "scigen/train/1479.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Attachment scores for both UD trees and EUD graphs for the development treebanks using different dependency parsers: full baseline models (Full), distilled models (dist), and UDPipe v2.5 models (udpipe)." ], "reasoning": "The distilled models outperform UDPipe models and are close to the full model in UAS and LAS scores, with ELAS results following the dependency scores.", "description": "We also evaluated the distilled models against the full baseline model and UDPipe v2.5. The distilled models outperform the UDPipe models and are within a point of both UAS and LAS to the full model. The ELAS results for the rule-based system using the predicted dependency trees from each of theses systems are also shown. The performance on ELAS generally follows the dependency scores. Our system is competitive mainly by the grace of our rule-based system which obtains an average 98.20 ELAS when used on the gold development treebanks. Having a rule-based system that can perform so well on gold data means that improving the dependency predictions it is based on for a full pipeline will almost always increase ELAS scores. It also means it could be used to generate new data. Although this would be restricted to generating data for pre-existing UD treebanks. Furthermore, it could be used to highlight annotation inconsistencies in a given treebank and between different treebanks for the same language.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 2: Attachment scores for both UD trees and EUD graphs for the development treebanks using different dependency parsers: full baseline models (Full), distilled models (dist), and UDPipe v2.5 models (udpipe).', solve the following question step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the average ELAS score for the full models across all languages?\n" }, { "role": "assistant", "content": "To find the average ELAS score for the full models, sum the ELAS scores for the full models across all languages and divide by the number of languages. So, the answer is {\"answer\": \"78.0\"}" } ], "images": [ "scigen/train/1479.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Attachment scores for both UD trees and EUD graphs for the development treebanks using different dependency parsers: full baseline models (Full), distilled models (dist), and UDPipe v2.5 models (udpipe)." ], "reasoning": "To find the average ELAS score for the full models, sum the ELAS scores for the full models across all languages and divide by the number of languages.", "question": "What is the average ELAS score for the full models across all languages?", "answer": "So, the answer is {\"answer\": \"78.0\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe distilled models have higher ELAS scores than the UDPipe models for all languages.\n" }, { "role": "assistant", "content": "By comparing the ELAS scores of the distilled models and the UDPipe models for each language, it can be seen that the distilled models consistently have higher ELAS scores. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1479.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Attachment scores for both UD trees and EUD graphs for the development treebanks using different dependency parsers: full baseline models (Full), distilled models (dist), and UDPipe v2.5 models (udpipe)." ], "reasoning": "By comparing the ELAS scores of the distilled models and the UDPipe models for each language, it can be seen that the distilled models consistently have higher ELAS scores.", "statement": "The distilled models have higher ELAS scores than the UDPipe models for all languages.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 6: Hyperparameters for baseline models. The values in parentheses show the values for the distilled and small models used in the main analysis of the shared task.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The network models were tested with 70% and 50% of the nodes in the BILSTM and MLP layers, showing similar trends in performance across different languages, with a notable widening in performance between full and distilled models at larger treebank sizes.these models to two different network sizes, one which has 70% of the number of nodes in both the BILSTM and MLP layers and one that has 50%. Otherwise the structure of the network is the same as the base model. We are limited by what we can extrapolate from the results for Arabic and Finnish other than they appear to follow a similar trend to Dutch and Russian. For the latter languages we observe the performance levelling at larger treebank sizes, which is neither remarkable nor unexpected, but also a widening between the performance of the full and the distilled models." } ], "images": [ "scigen/train/1480.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Hyperparameters for baseline models. The values in parentheses show the values for the distilled and small models used in the main analysis of the shared task." ], "reasoning": "The network models were tested with 70% and 50% of the nodes in the BILSTM and MLP layers, showing similar trends in performance across different languages, with a notable widening in performance between full and distilled models at larger treebank sizes.", "description": "these models to two different network sizes, one which has 70% of the number of nodes in both the BILSTM and MLP layers and one that has 50%. Otherwise the structure of the network is the same as the base model. We are limited by what we can extrapolate from the results for Arabic and Finnish other than they appear to follow a similar trend to Dutch and Russian. For the latter languages we observe the performance levelling at larger treebank sizes, which is neither remarkable nor unexpected, but also a widening between the performance of the full and the distilled models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 6: Hyperparameters for baseline models. The values in parentheses show the values for the distilled and small models used in the main analysis of the shared task.', solve the subsequent question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the embedding dropout rate used in the network models?\n" }, { "role": "assistant", "content": "The table lists various hyperparameters and their values. The embedding dropout rate is specified directly in the table under the 'embedding dropout' row. So, the answer is {\"answer\": \"0.33\"}" } ], "images": [ "scigen/train/1480.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Hyperparameters for baseline models. The values in parentheses show the values for the distilled and small models used in the main analysis of the shared task." ], "reasoning": "The table lists various hyperparameters and their values. The embedding dropout rate is specified directly in the table under the 'embedding dropout' row.", "question": "What is the embedding dropout rate used in the network models?", "answer": "So, the answer is {\"answer\": \"0.33\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe learning rate used in the network models is 0.3.\n" }, { "role": "assistant", "content": "The table lists the learning rate under the 'learning rate' row, which is specified as 0.2, not 0.3. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1480.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Hyperparameters for baseline models. The values in parentheses show the values for the distilled and small models used in the main analysis of the shared task." ], "reasoning": "The table lists the learning rate under the 'learning rate' row, which is specified as 0.2, not 0.3.", "statement": "The learning rate used in the network models is 0.3.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 8: Comparison of attachment scores for the development treebanks for distilled (dist) models and models with the same parameters (small) trained normally.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Normal models generally outperform distilled models, and the use of dropout during distillation might have been too punitive.For the most part the normal models outperform the distilled models. So perhaps without this extra information distillation is less effective. Also, dropout wasn’t used during distillation in the original paper but is here, so perhaps the values used here were too punitive a regularisation. Although we use the same hyperparameters as the original paper, the average LAS for the small normally trained models is 0.4 points less than the large model." } ], "images": [ "scigen/train/1481.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Comparison of attachment scores for the development treebanks for distilled (dist) models and models with the same parameters (small) trained normally." ], "reasoning": "Normal models generally outperform distilled models, and the use of dropout during distillation might have been too punitive.", "description": "For the most part the normal models outperform the distilled models. So perhaps without this extra information distillation is less effective. Also, dropout wasn’t used during distillation in the original paper but is here, so perhaps the values used here were too punitive a regularisation. Although we use the same hyperparameters as the original paper, the average LAS for the small normally trained models is 0.4 points less than the large model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 8: Comparison of attachment scores for the development treebanks for distilled (dist) models and models with the same parameters (small) trained normally.\nQuestion: What is the average LAS for the small normally trained models?\nSolve the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The description mentions that the average LAS for the small normally trained models is 0.4 points less than the large model. The table shows the average LAS for small models is 80.9. So, the answer is {\"answer\": \"80.9\"}" } ], "images": [ "scigen/train/1481.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Comparison of attachment scores for the development treebanks for distilled (dist) models and models with the same parameters (small) trained normally." ], "reasoning": "The description mentions that the average LAS for the small normally trained models is 0.4 points less than the large model. The table shows the average LAS for small models is 80.9.", "question": "What is the average LAS for the small normally trained models?", "answer": "So, the answer is {\"answer\": \"80.9\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe average LAS for the small normally trained models is 0.4 points less than the large model.\n" }, { "role": "assistant", "content": "The description states that the average LAS for the small normally trained models is 0.4 points less than the large model, and the table confirms the average LAS for small models is 80.9. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1481.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Comparison of attachment scores for the development treebanks for distilled (dist) models and models with the same parameters (small) trained normally." ], "reasoning": "The description states that the average LAS for the small normally trained models is 0.4 points less than the large model, and the table confirms the average LAS for small models is 80.9.", "statement": "The average LAS for the small normally trained models is 0.4 points less than the large model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 9: Full test results for our official submission using the shared task’s submission site for evaluation.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The results presented are for fully-trained distilled models using combined development treebanks for each language.This highlighted that our distilled models used for our official score had not converged. We trained new distilled models and the results given here are for these new models. All results, including training costs, in this section are for the full-trained distilled models and unless otherwise stated are using the combined development treebanks for each language." } ], "images": [ "scigen/train/1482.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Full test results for our official submission using the shared task’s submission site for evaluation." ], "reasoning": "The results presented are for fully-trained distilled models using combined development treebanks for each language.", "description": "This highlighted that our distilled models used for our official score had not converged. We trained new distilled models and the results given here are for these new models. All results, including training costs, in this section are for the full-trained distilled models and unless otherwise stated are using the combined development treebanks for each language.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. The question is related to the given table titled 'Table 9: Full test results for our official submission using the shared task’s submission site for evaluation.'.\n\nQuestion: Which language has the highest LAS score and what is that score?\n" }, { "role": "assistant", "content": "To find the language with the highest LAS score, we need to compare the LAS scores for all languages listed in the table. The highest LAS score is 88.2, which corresponds to the Lithuanian language. So, the answer is {\"answer\": \"Lithuanian, 88.2\"}" } ], "images": [ "scigen/train/1482.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Full test results for our official submission using the shared task’s submission site for evaluation." ], "reasoning": "To find the language with the highest LAS score, we need to compare the LAS scores for all languages listed in the table. The highest LAS score is 88.2, which corresponds to the Lithuanian language.", "question": "Which language has the highest LAS score and what is that score?", "answer": "So, the answer is {\"answer\": \"Lithuanian, 88.2\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe average UAS score is higher than the average LAS score.\n" }, { "role": "assistant", "content": "The table shows that the average UAS score is 88.1 and the average LAS score is 82.5. Since 88.1 is higher than 82.5, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1482.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Full test results for our official submission using the shared task’s submission site for evaluation." ], "reasoning": "The table shows that the average UAS score is 88.1 and the average LAS score is 82.5. Since 88.1 is higher than 82.5, the statement is supported.", "statement": "The average UAS score is higher than the average LAS score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title '(b) Delicious', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Training metrics on WikiGloVe embeddings consistently improves correlation with human intuition, while BibSonomy embeddings generally yield poor transfer results except for a slight improvement on WS-353 when trained on MEN.For each line, its header defines the dataset on which the metric was trained, while the column header is the dataset on which the trained metric was then evaluated. In each cell, the first value denotes the Spearman correlation of the cosine measure with the human relatedness scores in the evaluation dataset. The second value is the Spearman correlation of the relatedness scores calculated from the trained metric with the human relatedness scores in the evaluation dataset. Depending on whether the trained metric increased or decreased correlation with human intuition, we depict upwards or downwards arrows. Generally, training a metric on BibSonomy embeddings almost always yields bad transfer results. The only improvement using BibSonomy embeddings is on the WS-353 dataset, when evaluating the metric trained on MEN. The Delicious embeddings are only useful for improving relatedness scores when the trained metric is evaluated on the MEN dataset. The most interesting part of these results is that, using WikiGloVe embeddings, we can always increase correlation with human intuition by a notable margin. However, on the WikiNav embeddings, the results differ only by smaller margins. The only exception here is a notable improvement on the Spearman correlation value of WS-353, when using a metric trained on MEN data. author= chp,color= blue!20todo : author=chp,color =blue!20was sagt uns das jetzt insgesammt? warum ist es nur bei WikiGloVe besser? Overall, it is generally possible to transfer knowledge from one HID to another. However, this is highly dependent on the underlying word representations." } ], "images": [ "scigen/train/1483.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) Delicious" ], "reasoning": "Training metrics on WikiGloVe embeddings consistently improves correlation with human intuition, while BibSonomy embeddings generally yield poor transfer results except for a slight improvement on WS-353 when trained on MEN.", "description": "For each line, its header defines the dataset on which the metric was trained, while the column header is the dataset on which the trained metric was then evaluated. In each cell, the first value denotes the Spearman correlation of the cosine measure with the human relatedness scores in the evaluation dataset. The second value is the Spearman correlation of the relatedness scores calculated from the trained metric with the human relatedness scores in the evaluation dataset. Depending on whether the trained metric increased or decreased correlation with human intuition, we depict upwards or downwards arrows. Generally, training a metric on BibSonomy embeddings almost always yields bad transfer results. The only improvement using BibSonomy embeddings is on the WS-353 dataset, when evaluating the metric trained on MEN. The Delicious embeddings are only useful for improving relatedness scores when the trained metric is evaluated on the MEN dataset. The most interesting part of these results is that, using WikiGloVe embeddings, we can always increase correlation with human intuition by a notable margin. However, on the WikiNav embeddings, the results differ only by smaller margins. The only exception here is a notable improvement on the Spearman correlation value of WS-353, when using a metric trained on MEN data. author= chp,color= blue!20todo : author=chp,color =blue!20was sagt uns das jetzt insgesammt? warum ist es nur bei WikiGloVe besser? Overall, it is generally possible to transfer knowledge from one HID to another. However, this is highly dependent on the underlying word representations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nTable title: (b) Delicious\n\nQuestion: Which dataset shows an improvement in Spearman correlation when the metric trained on MEN is evaluated on WS-353?\n" }, { "role": "assistant", "content": "The table shows that the Spearman correlation for WS-353 when trained on MEN increases from 0.690 to 0.682, indicating an improvement. So, the answer is {\"answer\": \"WS-353\"}." } ], "images": [ "scigen/train/1483.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) Delicious" ], "reasoning": "The table shows that the Spearman correlation for WS-353 when trained on MEN increases from 0.690 to 0.682, indicating an improvement.", "question": "Which dataset shows an improvement in Spearman correlation when the metric trained on MEN is evaluated on WS-353?", "answer": "So, the answer is {\"answer\": \"WS-353\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nTraining a metric on BibSonomy embeddings always yields bad transfer results.\n" }, { "role": "assistant", "content": "The table shows that training on BibSonomy embeddings yields an improvement in Spearman correlation for WS-353 when evaluated on MEN, refuting the statement. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1483.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) Delicious" ], "reasoning": "The table shows that training on BibSonomy embeddings yields an improvement in Spearman correlation for WS-353 when evaluated on MEN, refuting the statement.", "statement": "Training a metric on BibSonomy embeddings always yields bad transfer results.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: Overview of all Human Intution Datasets (HIDs). For each HID, we give the number of word pairs, the number of unique words and the number of judgments per word pair. Also, for each embedding dataset, we give the number of matchable pairs, where both words are present in the dataset’s vocabulary.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The MEN dataset is highly effective for learning semantic relatedness due to its high pair overlap and the large amount of data it contains.As a gold standard for semantic relatedness as it is perceived by humans, we use several datasets with human-generated relatedness scores for word pairs, so called human intuition datasets (HIDs). They will provide training as well as test data. In the following, we will describe all used HIDs briefly. Integrating Different Amounts of User Feedback. It can be seen that in the random sampling experiments, the information in the MEN dataset is generally best suited to learn semantic relatedness. Using crowdsourcing, each human worker was shown two pairs of words and had to determine which of both pairs is more related. The higher a word pair in MEN is rated, the more often it was considered more related than the other one that was given. Our approach exploits very similar constraints for learning. Keeping this explanation in mind, we are able to give a recommendation on how to gather human feedback in order to learn semantic relatedness with our method. On all other embedding datasets, where MEN is very useful to learn the metric, the pair overlap is notably higher. Throughout all settings in this experiment, we observed that using more data results in better relatedness scores, if there was a positive effect." } ], "images": [ "scigen/train/1484.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Overview of all Human Intution Datasets (HIDs). For each HID, we give the number of word pairs, the number of unique words and the number of judgments per word pair. Also, for each embedding dataset, we give the number of matchable pairs, where both words are present in the dataset’s vocabulary." ], "reasoning": "The MEN dataset is highly effective for learning semantic relatedness due to its high pair overlap and the large amount of data it contains.", "description": "As a gold standard for semantic relatedness as it is perceived by humans, we use several datasets with human-generated relatedness scores for word pairs, so called human intuition datasets (HIDs). They will provide training as well as test data. In the following, we will describe all used HIDs briefly. Integrating Different Amounts of User Feedback. It can be seen that in the random sampling experiments, the information in the MEN dataset is generally best suited to learn semantic relatedness. Using crowdsourcing, each human worker was shown two pairs of words and had to determine which of both pairs is more related. The higher a word pair in MEN is rated, the more often it was considered more related than the other one that was given. Our approach exploits very similar constraints for learning. Keeping this explanation in mind, we are able to give a recommendation on how to gather human feedback in order to learn semantic relatedness with our method. On all other embedding datasets, where MEN is very useful to learn the metric, the pair overlap is notably higher. Throughout all settings in this experiment, we observed that using more data results in better relatedness scores, if there was a positive effect.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1: Overview of all Human Intution Datasets (HIDs). For each HID, we give the number of word pairs, the number of unique words and the number of judgments per word pair. Also, for each embedding dataset, we give the number of matchable pairs, where both words are present in the dataset’s vocabulary.', solve the subsequent question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich dataset has the highest number of word pairs and how many pairs does it have?\n" }, { "role": "assistant", "content": "By examining the 'pairs' column in the table, we can see that the MEN dataset has the highest number of word pairs, which is 3000. So, the answer is {\"answer\": \"3000\"}." } ], "images": [ "scigen/train/1484.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Overview of all Human Intution Datasets (HIDs). For each HID, we give the number of word pairs, the number of unique words and the number of judgments per word pair. Also, for each embedding dataset, we give the number of matchable pairs, where both words are present in the dataset’s vocabulary." ], "reasoning": "By examining the 'pairs' column in the table, we can see that the MEN dataset has the highest number of word pairs, which is 3000.", "question": "Which dataset has the highest number of word pairs and how many pairs does it have?", "answer": "So, the answer is {\"answer\": \"3000\"}." }, { "messages": [ { "role": "user", "content": "The MEN dataset has more matches with WikiNav than Bib100. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe MEN dataset has more matches with WikiNav than Bib100.\n" }, { "role": "assistant", "content": "According to the table, the MEN dataset has 1227 matches with WikiNav, while the Bib100 dataset has only 42 matches with WikiNav. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1484.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Overview of all Human Intution Datasets (HIDs). For each HID, we give the number of word pairs, the number of unique words and the number of judgments per word pair. Also, for each embedding dataset, we give the number of matchable pairs, where both words are present in the dataset’s vocabulary." ], "reasoning": "According to the table, the MEN dataset has 1227 matches with WikiNav, while the Bib100 dataset has only 42 matches with WikiNav.", "statement": "The MEN dataset has more matches with WikiNav than Bib100.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Spearman correlation scores for both the high-dimensional representation (ρhigh) and word embeddings (ρemb) of both tagging datasets. It can be seen that the word embeddings encode semantic relations which are more in line with human judgment than the high-dimensional representations.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The use of low-dimensional word embeddings, such as GloVe, is justified for tagging data due to their superior performance in capturing semantic relatedness compared to high-dimensional vectors.We evaluate our approach on several domains. This includes semantic relatedness extracted from tagging data. However, vector representations of words extracted from tagging data are traditionally high-dimensional [cattuto2008semantic, markines2009evaluating], making metric learning in this domain infeasible due to a more than quadratic runtime with regard to the number of vector dimensions [liu2012metric]. Thus, similar to our Wikipedia examples we employ the notion of (low-dimensional) word embeddings which have been shown to outperform their high-dimensional counterparts in terms of correlation with human intuition of semantic relatedness [baroni2014count]. This can also be confirmed when using tagging data as input In this section, we justify our choice of using GloVe to embed words based on tagging data and study the influence of dimensionality on the respective semantic content." } ], "images": [ "scigen/train/1485.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Spearman correlation scores for both the high-dimensional representation (ρhigh) and word embeddings (ρemb) of both tagging datasets. It can be seen that the word embeddings encode semantic relations which are more in line with human judgment than the high-dimensional representations." ], "reasoning": "The use of low-dimensional word embeddings, such as GloVe, is justified for tagging data due to their superior performance in capturing semantic relatedness compared to high-dimensional vectors.", "description": "We evaluate our approach on several domains. This includes semantic relatedness extracted from tagging data. However, vector representations of words extracted from tagging data are traditionally high-dimensional [cattuto2008semantic, markines2009evaluating], making metric learning in this domain infeasible due to a more than quadratic runtime with regard to the number of vector dimensions [liu2012metric]. Thus, similar to our Wikipedia examples we employ the notion of (low-dimensional) word embeddings which have been shown to outperform their high-dimensional counterparts in terms of correlation with human intuition of semantic relatedness [baroni2014count]. This can also be confirmed when using tagging data as input In this section, we justify our choice of using GloVe to embed words based on tagging data and study the influence of dimensionality on the respective semantic content.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: 'Table 2: Spearman correlation scores for both the high-dimensional representation (ρhigh) and word embeddings (ρemb) of both tagging datasets. It can be seen that the word embeddings encode semantic relations which are more in line with human judgment than the high-dimensional representations.'\nQuestion: Which dataset has the highest score for Delicious pemb?\n" }, { "role": "assistant", "content": "To find the dataset with the highest score for Delicious pemb, compare the values in the Delicious pemb column: Bib100 (0.675), MEN (0.752), and WS-353 (0.690). The highest value is 0.752 for the MEN dataset. So, the answer is {\"answer\": \"MEN\"}." } ], "images": [ "scigen/train/1485.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Spearman correlation scores for both the high-dimensional representation (ρhigh) and word embeddings (ρemb) of both tagging datasets. It can be seen that the word embeddings encode semantic relations which are more in line with human judgment than the high-dimensional representations." ], "reasoning": "To find the dataset with the highest score for Delicious pemb, compare the values in the Delicious pemb column: Bib100 (0.675), MEN (0.752), and WS-353 (0.690). The highest value is 0.752 for the MEN dataset.", "question": "Which dataset has the highest score for Delicious pemb?", "answer": "So, the answer is {\"answer\": \"MEN\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Bib100 dataset has a higher score for BibSonomy pemb than for Delicious phigh. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Bib100 dataset has a higher score for BibSonomy pemb than for Delicious phigh.\n" }, { "role": "assistant", "content": "The table shows that the Bib100 dataset has a score of 0.726 for BibSonomy pemb and 0.640 for Delicious phigh. Since 0.726 is greater than 0.640, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1485.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Spearman correlation scores for both the high-dimensional representation (ρhigh) and word embeddings (ρemb) of both tagging datasets. It can be seen that the word embeddings encode semantic relations which are more in line with human judgment than the high-dimensional representations." ], "reasoning": "The table shows that the Bib100 dataset has a score of 0.726 for BibSonomy pemb and 0.640 for Delicious phigh. Since 0.726 is greater than 0.640, the statement is supported.", "statement": "The Bib100 dataset has a higher score for BibSonomy pemb than for Delicious phigh.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption '(a) BibSonomy'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Training metrics on WikiGloVe embeddings consistently improves correlation with human intuition, while BibSonomy embeddings generally yield poor transfer results except for a slight improvement on WS-353 when trained on MEN.For each line, its header defines the dataset on which the metric was trained, while the column header is the dataset on which the trained metric was then evaluated. In each cell, the first value denotes the Spearman correlation of the cosine measure with the human relatedness scores in the evaluation dataset. The second value is the Spearman correlation of the relatedness scores calculated from the trained metric with the human relatedness scores in the evaluation dataset. Depending on whether the trained metric increased or decreased correlation with human intuition, we depict upwards or downwards arrows. Generally, training a metric on BibSonomy embeddings almost always yields bad transfer results. The only improvement using BibSonomy embeddings is on the WS-353 dataset, when evaluating the metric trained on MEN. The Delicious embeddings are only useful for improving relatedness scores when the trained metric is evaluated on the MEN dataset. The most interesting part of these results is that, using WikiGloVe embeddings, we can always increase correlation with human intuition by a notable margin. However, on the WikiNav embeddings, the results differ only by smaller margins. The only exception here is a notable improvement on the Spearman correlation value of WS-353, when using a metric trained on MEN data. author= chp,color= blue!20todo : author=chp,color =blue!20was sagt uns das jetzt insgesammt? warum ist es nur bei WikiGloVe besser? Overall, it is generally possible to transfer knowledge from one HID to another. However, this is highly dependent on the underlying word representations." } ], "images": [ "scigen/train/1486.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) BibSonomy" ], "reasoning": "Training metrics on WikiGloVe embeddings consistently improves correlation with human intuition, while BibSonomy embeddings generally yield poor transfer results except for a slight improvement on WS-353 when trained on MEN.", "description": "For each line, its header defines the dataset on which the metric was trained, while the column header is the dataset on which the trained metric was then evaluated. In each cell, the first value denotes the Spearman correlation of the cosine measure with the human relatedness scores in the evaluation dataset. The second value is the Spearman correlation of the relatedness scores calculated from the trained metric with the human relatedness scores in the evaluation dataset. Depending on whether the trained metric increased or decreased correlation with human intuition, we depict upwards or downwards arrows. Generally, training a metric on BibSonomy embeddings almost always yields bad transfer results. The only improvement using BibSonomy embeddings is on the WS-353 dataset, when evaluating the metric trained on MEN. The Delicious embeddings are only useful for improving relatedness scores when the trained metric is evaluated on the MEN dataset. The most interesting part of these results is that, using WikiGloVe embeddings, we can always increase correlation with human intuition by a notable margin. However, on the WikiNav embeddings, the results differ only by smaller margins. The only exception here is a notable improvement on the Spearman correlation value of WS-353, when using a metric trained on MEN data. author= chp,color= blue!20todo : author=chp,color =blue!20was sagt uns das jetzt insgesammt? warum ist es nur bei WikiGloVe besser? Overall, it is generally possible to transfer knowledge from one HID to another. However, this is highly dependent on the underlying word representations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of '(a) BibSonomy', answer the following question. Give detailed solution consisting of each step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich dataset shows an improvement in Spearman correlation when evaluated on WS-353 using a metric trained on MEN?\n" }, { "role": "assistant", "content": "The table shows that the Spearman correlation for WS-353 when using a metric trained on MEN improves from 0.576 to 0.591, indicated by an upward arrow. So, the answer is {\"answer\": \"WS-353\"}." } ], "images": [ "scigen/train/1486.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) BibSonomy" ], "reasoning": "The table shows that the Spearman correlation for WS-353 when using a metric trained on MEN improves from 0.576 to 0.591, indicated by an upward arrow.", "question": "Which dataset shows an improvement in Spearman correlation when evaluated on WS-353 using a metric trained on MEN?", "answer": "So, the answer is {\"answer\": \"WS-353\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nTraining a metric on BibSonomy embeddings improves the correlation with human intuition for the WS-353 dataset.\n" }, { "role": "assistant", "content": "The table shows that the Spearman correlation for WS-353 improves from 0.576 to 0.591 when using a metric trained on MEN, which is the only improvement using BibSonomy embeddings. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1486.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) BibSonomy" ], "reasoning": "The table shows that the Spearman correlation for WS-353 improves from 0.576 to 0.591 when using a metric trained on MEN, which is the only improvement using BibSonomy embeddings.", "statement": "Training a metric on BibSonomy embeddings improves the correlation with human intuition for the WS-353 dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled '(c) WikiGloVe', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Training metrics on WikiGloVe embeddings consistently improves correlation with human intuition, while BibSonomy embeddings generally yield poor transfer results except for a slight improvement on WS-353 when trained on MEN.For each line, its header defines the dataset on which the metric was trained, while the column header is the dataset on which the trained metric was then evaluated. In each cell, the first value denotes the Spearman correlation of the cosine measure with the human relatedness scores in the evaluation dataset. The second value is the Spearman correlation of the relatedness scores calculated from the trained metric with the human relatedness scores in the evaluation dataset. Depending on whether the trained metric increased or decreased correlation with human intuition, we depict upwards or downwards arrows. Generally, training a metric on BibSonomy embeddings almost always yields bad transfer results. The only improvement using BibSonomy embeddings is on the WS-353 dataset, when evaluating the metric trained on MEN. The Delicious embeddings are only useful for improving relatedness scores when the trained metric is evaluated on the MEN dataset. The most interesting part of these results is that, using WikiGloVe embeddings, we can always increase correlation with human intuition by a notable margin. However, on the WikiNav embeddings, the results differ only by smaller margins. The only exception here is a notable improvement on the Spearman correlation value of WS-353, when using a metric trained on MEN data. author= chp,color= blue!20todo : author=chp,color =blue!20was sagt uns das jetzt insgesammt? warum ist es nur bei WikiGloVe besser? Overall, it is generally possible to transfer knowledge from one HID to another. However, this is highly dependent on the underlying word representations." } ], "images": [ "scigen/train/1487.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(c) WikiGloVe" ], "reasoning": "Training metrics on WikiGloVe embeddings consistently improves correlation with human intuition, while BibSonomy embeddings generally yield poor transfer results except for a slight improvement on WS-353 when trained on MEN.", "description": "For each line, its header defines the dataset on which the metric was trained, while the column header is the dataset on which the trained metric was then evaluated. In each cell, the first value denotes the Spearman correlation of the cosine measure with the human relatedness scores in the evaluation dataset. The second value is the Spearman correlation of the relatedness scores calculated from the trained metric with the human relatedness scores in the evaluation dataset. Depending on whether the trained metric increased or decreased correlation with human intuition, we depict upwards or downwards arrows. Generally, training a metric on BibSonomy embeddings almost always yields bad transfer results. The only improvement using BibSonomy embeddings is on the WS-353 dataset, when evaluating the metric trained on MEN. The Delicious embeddings are only useful for improving relatedness scores when the trained metric is evaluated on the MEN dataset. The most interesting part of these results is that, using WikiGloVe embeddings, we can always increase correlation with human intuition by a notable margin. However, on the WikiNav embeddings, the results differ only by smaller margins. The only exception here is a notable improvement on the Spearman correlation value of WS-353, when using a metric trained on MEN data. author= chp,color= blue!20todo : author=chp,color =blue!20was sagt uns das jetzt insgesammt? warum ist es nur bei WikiGloVe besser? Overall, it is generally possible to transfer knowledge from one HID to another. However, this is highly dependent on the underlying word representations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about '(c) WikiGloVe', solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich dataset shows an improvement in Spearman correlation when the metric trained on MEN is evaluated on WS-353?\n" }, { "role": "assistant", "content": "The table shows that the Spearman correlation for the cosine measure is 0.533 and for the trained metric is 0.604 when the metric trained on MEN is evaluated on WS-353, indicating an improvement. So, the answer is {\"answer\": \"WS-353\"}" } ], "images": [ "scigen/train/1487.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(c) WikiGloVe" ], "reasoning": "The table shows that the Spearman correlation for the cosine measure is 0.533 and for the trained metric is 0.604 when the metric trained on MEN is evaluated on WS-353, indicating an improvement.", "question": "Which dataset shows an improvement in Spearman correlation when the metric trained on MEN is evaluated on WS-353?", "answer": "So, the answer is {\"answer\": \"WS-353\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nTraining a metric on BibSonomy embeddings improves the correlation with human intuition when evaluated on the MEN dataset.\n" }, { "role": "assistant", "content": "The table shows that training a metric on BibSonomy embeddings and evaluating it on the MEN dataset results in a Spearman correlation of 0.693 for the cosine measure and 0.727 for the trained metric, indicating an improvement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1487.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(c) WikiGloVe" ], "reasoning": "The table shows that training a metric on BibSonomy embeddings and evaluating it on the MEN dataset results in a Spearman correlation of 0.693 for the cosine measure and 0.727 for the trained metric, indicating an improvement.", "statement": "Training a metric on BibSonomy embeddings improves the correlation with human intuition when evaluated on the MEN dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption '(d) WikiNav'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Training metrics on WikiGloVe embeddings consistently improves correlation with human intuition, while BibSonomy embeddings generally yield poor transfer results except for a slight improvement on WS-353 when trained on MEN.For each line, its header defines the dataset on which the metric was trained, while the column header is the dataset on which the trained metric was then evaluated. In each cell, the first value denotes the Spearman correlation of the cosine measure with the human relatedness scores in the evaluation dataset. The second value is the Spearman correlation of the relatedness scores calculated from the trained metric with the human relatedness scores in the evaluation dataset. Depending on whether the trained metric increased or decreased correlation with human intuition, we depict upwards or downwards arrows. Generally, training a metric on BibSonomy embeddings almost always yields bad transfer results. The only improvement using BibSonomy embeddings is on the WS-353 dataset, when evaluating the metric trained on MEN. The Delicious embeddings are only useful for improving relatedness scores when the trained metric is evaluated on the MEN dataset. The most interesting part of these results is that, using WikiGloVe embeddings, we can always increase correlation with human intuition by a notable margin. However, on the WikiNav embeddings, the results differ only by smaller margins. The only exception here is a notable improvement on the Spearman correlation value of WS-353, when using a metric trained on MEN data. author= chp,color= blue!20todo : author=chp,color =blue!20was sagt uns das jetzt insgesammt? warum ist es nur bei WikiGloVe besser? Overall, it is generally possible to transfer knowledge from one HID to another. However, this is highly dependent on the underlying word representations." } ], "images": [ "scigen/train/1488.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(d) WikiNav" ], "reasoning": "Training metrics on WikiGloVe embeddings consistently improves correlation with human intuition, while BibSonomy embeddings generally yield poor transfer results except for a slight improvement on WS-353 when trained on MEN.", "description": "For each line, its header defines the dataset on which the metric was trained, while the column header is the dataset on which the trained metric was then evaluated. In each cell, the first value denotes the Spearman correlation of the cosine measure with the human relatedness scores in the evaluation dataset. The second value is the Spearman correlation of the relatedness scores calculated from the trained metric with the human relatedness scores in the evaluation dataset. Depending on whether the trained metric increased or decreased correlation with human intuition, we depict upwards or downwards arrows. Generally, training a metric on BibSonomy embeddings almost always yields bad transfer results. The only improvement using BibSonomy embeddings is on the WS-353 dataset, when evaluating the metric trained on MEN. The Delicious embeddings are only useful for improving relatedness scores when the trained metric is evaluated on the MEN dataset. The most interesting part of these results is that, using WikiGloVe embeddings, we can always increase correlation with human intuition by a notable margin. However, on the WikiNav embeddings, the results differ only by smaller margins. The only exception here is a notable improvement on the Spearman correlation value of WS-353, when using a metric trained on MEN data. author= chp,color= blue!20todo : author=chp,color =blue!20was sagt uns das jetzt insgesammt? warum ist es nur bei WikiGloVe besser? Overall, it is generally possible to transfer knowledge from one HID to another. However, this is highly dependent on the underlying word representations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nTable title: (d) WikiNav\n\nQuestion: Which dataset shows an improvement in Spearman correlation when the metric trained on MEN is evaluated on WS-353?\n" }, { "role": "assistant", "content": "The table shows that the Spearman correlation for WS-353 when trained on MEN is 0.729 for cosine measure and 0.751 for the trained metric, indicating an improvement. So, the answer is {\"answer\": \"WS-353\"}." } ], "images": [ "scigen/train/1488.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(d) WikiNav" ], "reasoning": "The table shows that the Spearman correlation for WS-353 when trained on MEN is 0.729 for cosine measure and 0.751 for the trained metric, indicating an improvement.", "question": "Which dataset shows an improvement in Spearman correlation when the metric trained on MEN is evaluated on WS-353?", "answer": "So, the answer is {\"answer\": \"WS-353\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTraining a metric on BibSonomy embeddings improves the correlation with human intuition for the WS-353 dataset.\n" }, { "role": "assistant", "content": "The table shows that the Spearman correlation for WS-353 when trained on BibSonomy embeddings is 0.729 for cosine measure and 0.718 for the trained metric, indicating a decrease, not an improvement. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1488.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(d) WikiNav" ], "reasoning": "The table shows that the Spearman correlation for WS-353 when trained on BibSonomy embeddings is 0.729 for cosine measure and 0.718 for the trained metric, indicating a decrease, not an improvement.", "statement": "Training a metric on BibSonomy embeddings improves the correlation with human intuition for the WS-353 dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Evaluation results on the test set for MTN w/o QAE models in which maximum history length is range from 1 to 3 or 10 (i.e. all dialogue turns possible). We also experiments when all the source sequences are concatenated into one and the decoder only has one attention block on the concatenated sequence. The auto-encoder components are also removed. Best result in each metric is highlighted in bold.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Concatenating source sequences into one significantly affects the quality of generated responses, while separating them and attending differently improves results, especially with Lmaxhis=10.We experimented our models with text-only input e.g. no video audio or visual features and hence, no auto-encoder layers involved (MTN w/o QAE). We tested cases where the maximum dialogue history length Lmaxhis is limited to 1, 2, or 3 turns only. For each case, we also tried to concatenate all the source sequences, including dialogue history, video caption, and query, into a single sequence and use only one multi-head attention block on this concatenated sequence in each decoding layer (Similar to a Transformer decoder). The results show that concatenating the sequences into one affects the quality of the generated responses significantly. When the input sequences are separated and attended differently by different attention modules, the results improve. This could be explained as different sequences contain different signals to generate responses e.g. dialogue history contains information of references or ellipses in the user queries, user queries include direct signals for feature attention in input videos. Another observation is using all possible dialogue turns in the dialogue history i.e. Lmaxhis=10 achieves the best results. We did not conduct experiments of concatenating source sequences with Lmaxhis=10 due to memory issues with large input sequences." } ], "images": [ "scigen/train/1489.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Evaluation results on the test set for MTN w/o QAE models in which maximum history length is range from 1 to 3 or 10 (i.e. all dialogue turns possible). We also experiments when all the source sequences are concatenated into one and the decoder only has one attention block on the concatenated sequence. The auto-encoder components are also removed. Best result in each metric is highlighted in bold." ], "reasoning": "Concatenating source sequences into one significantly affects the quality of generated responses, while separating them and attending differently improves results, especially with Lmaxhis=10.", "description": "We experimented our models with text-only input e.g. no video audio or visual features and hence, no auto-encoder layers involved (MTN w/o QAE). We tested cases where the maximum dialogue history length Lmaxhis is limited to 1, 2, or 3 turns only. For each case, we also tried to concatenate all the source sequences, including dialogue history, video caption, and query, into a single sequence and use only one multi-head attention block on this concatenated sequence in each decoding layer (Similar to a Transformer decoder). The results show that concatenating the sequences into one affects the quality of the generated responses significantly. When the input sequences are separated and attended differently by different attention modules, the results improve. This could be explained as different sequences contain different signals to generate responses e.g. dialogue history contains information of references or ellipses in the user queries, user queries include direct signals for feature attention in input videos. Another observation is using all possible dialogue turns in the dialogue history i.e. Lmaxhis=10 achieves the best results. We did not conduct experiments of concatenating source sequences with Lmaxhis=10 due to memory issues with large input sequences.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 5: Evaluation results on the test set for MTN w/o QAE models in which maximum history length is range from 1 to 3 or 10 (i.e. all dialogue turns possible). We also experiments when all the source sequences are concatenated into one and the decoder only has one attention block on the concatenated sequence. The auto-encoder components are also removed. Best result in each metric is highlighted in bold.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the BLEU4 score when the maximum dialogue history length is 3 and the source sequences are concatenated?\n" }, { "role": "assistant", "content": "To find the BLEU4 score for Max. HisLen=3 with concatenated source sequences, look at the row where Max. HisLen is 3 and Concat. Source Sequence? is Yes. The BLEU4 score is 0.097. So, the answer is {\"answer\": \"0.097\"}." } ], "images": [ "scigen/train/1489.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Evaluation results on the test set for MTN w/o QAE models in which maximum history length is range from 1 to 3 or 10 (i.e. all dialogue turns possible). We also experiments when all the source sequences are concatenated into one and the decoder only has one attention block on the concatenated sequence. The auto-encoder components are also removed. Best result in each metric is highlighted in bold." ], "reasoning": "To find the BLEU4 score for Max. HisLen=3 with concatenated source sequences, look at the row where Max. HisLen is 3 and Concat. Source Sequence? is Yes. The BLEU4 score is 0.097.", "question": "What is the BLEU4 score when the maximum dialogue history length is 3 and the source sequences are concatenated?", "answer": "So, the answer is {\"answer\": \"0.097\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nUsing all possible dialogue turns in the dialogue history achieves the best results.\n" }, { "role": "assistant", "content": "The description states that using all possible dialogue turns (Lmaxhis=10) achieves the best results. The table shows that for Lmaxhis=10, the scores are BLEU4=0.120, ROUGE-L=0.344, and CIDEr=1.176, which are the highest compared to other configurations. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1489.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Evaluation results on the test set for MTN w/o QAE models in which maximum history length is range from 1 to 3 or 10 (i.e. all dialogue turns possible). We also experiments when all the source sequences are concatenated into one and the decoder only has one attention block on the concatenated sequence. The auto-encoder components are also removed. Best result in each metric is highlighted in bold." ], "reasoning": "The description states that using all possible dialogue turns (Lmaxhis=10) achieves the best results. The table shows that for Lmaxhis=10, the scores are BLEU4=0.120, ROUGE-L=0.344, and CIDEr=1.176, which are the highest compared to other configurations.", "statement": "Using all possible dialogue turns in the dialogue history achieves the best results.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Comparison of MTN (Base) to state-of-the-art visual dialogue models on the test-std v1.0. The best measure is highlighted in bold.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The generative model MTN (Base) achieved the highest NDCG score of 55.33, outperforming other retrieval-based approaches without task-specific fine-tuning.We evaluate our model with Normalized Discounted Cumulative Gain (NDCG) score by submitting the predicted ranks of the response candidates to the evaluation server (as the ground-truth for the test-std v1.0 split is not published). We keep all the training procedures unchanged from the video-grounded dialogue task. It is interesting that our generative model outperforms other retrieval-based approaches in NDCG without any task-specific fine-tuning." } ], "images": [ "scigen/train/1490.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of MTN (Base) to state-of-the-art visual dialogue models on the test-std v1.0. The best measure is highlighted in bold." ], "reasoning": "The generative model MTN (Base) achieved the highest NDCG score of 55.33, outperforming other retrieval-based approaches without task-specific fine-tuning.", "description": "We evaluate our model with Normalized Discounted Cumulative Gain (NDCG) score by submitting the predicted ranks of the response candidates to the evaluation server (as the ground-truth for the test-std v1.0 split is not published). We keep all the training procedures unchanged from the video-grounded dialogue task. It is interesting that our generative model outperforms other retrieval-based approaches in NDCG without any task-specific fine-tuning.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Comparison of MTN (Base) to state-of-the-art visual dialogue models on the test-std v1.0. The best measure is highlighted in bold.', answer the question below by showing each progressive step toward the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieved the highest NDCG score and what was the score?\n" }, { "role": "assistant", "content": "By examining the table, we see that the MTN (Base) model has the highest NDCG score of 55.33. So, the answer is {\"answer\": \"MTN (Base) with 55.33\"}." } ], "images": [ "scigen/train/1490.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of MTN (Base) to state-of-the-art visual dialogue models on the test-std v1.0. The best measure is highlighted in bold." ], "reasoning": "By examining the table, we see that the MTN (Base) model has the highest NDCG score of 55.33.", "question": "Which model achieved the highest NDCG score and what was the score?", "answer": "So, the answer is {\"answer\": \"MTN (Base) with 55.33\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe CorefNMN model achieved an NDCG score higher than 54.70.\n" }, { "role": "assistant", "content": "The table shows that the CorefNMN model achieved an NDCG score of exactly 54.70, not higher. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1490.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of MTN (Base) to state-of-the-art visual dialogue models on the test-std v1.0. The best measure is highlighted in bold." ], "reasoning": "The table shows that the CorefNMN model achieved an NDCG score of exactly 54.70, not higher.", "statement": "The CorefNMN model achieved an NDCG score higher than 54.70.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Evaluated on the test data, the proposed approach achieves better objective measures than the baselines and the submissions to the challenge. The best result in each metric is highlighted in bold.'.\n" }, { "role": "assistant", "content": "The MTN Large model outperforms all previously reported models in the challenge across all metrics, while the MTN Base model outperforms most previous results except for Entry1 in BLEU1-3 and METEOR.We compared MTN models with the baseline Hori et al. The evaluation includes 4 word-overlapping-based objective measures: BLEU (1 to 4) Papineni et al. , CIDEr Vedantam et al. The results were computed based on one reference ground-truth response per test dialogue in the test set. et al. Our Large model outperforms the best previously reported models in the challenge across all the metrics. Even our Base model with smaller parameters outperforms most of the previous results, except for entry1, which we outperform in BLEU1-3 and METEOR measures. While some of the submitted models to the challenge utilized external data or ensemble techniques Alamri et al." } ], "images": [ "scigen/train/1491.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Evaluated on the test data, the proposed approach achieves better objective measures than the baselines and the submissions to the challenge. The best result in each metric is highlighted in bold." ], "reasoning": "The MTN Large model outperforms all previously reported models in the challenge across all metrics, while the MTN Base model outperforms most previous results except for Entry1 in BLEU1-3 and METEOR.", "description": "We compared MTN models with the baseline Hori et al. The evaluation includes 4 word-overlapping-based objective measures: BLEU (1 to 4) Papineni et al. , CIDEr Vedantam et al. The results were computed based on one reference ground-truth response per test dialogue in the test set. et al. Our Large model outperforms the best previously reported models in the challenge across all the metrics. Even our Base model with smaller parameters outperforms most of the previous results, except for entry1, which we outperform in BLEU1-3 and METEOR measures. While some of the submitted models to the challenge utilized external data or ensemble techniques Alamri et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: Evaluated on the test data, the proposed approach achieves better objective measures than the baselines and the submissions to the challenge. The best result in each metric is highlighted in bold.', answer the following question. Give detailed solution consisting of each step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model has the highest CIDEr score?\n" }, { "role": "assistant", "content": "By comparing the CIDEr scores in the table, the MTN (Large) model has the highest score of 1.366. So, the answer is {\"answer\": \"MTN (Large)\"}" } ], "images": [ "scigen/train/1491.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Evaluated on the test data, the proposed approach achieves better objective measures than the baselines and the submissions to the challenge. The best result in each metric is highlighted in bold." ], "reasoning": "By comparing the CIDEr scores in the table, the MTN (Large) model has the highest score of 1.366.", "question": "Which model has the highest CIDEr score?", "answer": "So, the answer is {\"answer\": \"MTN (Large)\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The MTN Base model outperforms all previous models in BLEU1-3 and METEOR. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe MTN Base model outperforms all previous models in BLEU1-3 and METEOR.\n" }, { "role": "assistant", "content": "The MTN Base model outperforms most previous models except for Entry1 in BLEU1-3 and METEOR, so the statement is not entirely true. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1491.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Evaluated on the test data, the proposed approach achieves better objective measures than the baselines and the submissions to the challenge. The best result in each metric is highlighted in bold." ], "reasoning": "The MTN Base model outperforms most previous models except for Entry1 in BLEU1-3 and METEOR, so the statement is not entirely true.", "statement": "The MTN Base model outperforms all previous models in BLEU1-3 and METEOR.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 6: Performance of operator detection using gold sequence labels, and system generated labels', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The system's recall for disjunctions and preferences is significantly lower than the gold standard, particularly for preferences.Operator Labeling: We create rules for commonly occurring operators – disjunctions, negations, and preferences. The default operator between semantic labels is a conjunction. The ‘Gold’ columns denote the performance when using gold semantic label mentions. The ’System’ columns are the performance when using labels generated by our sequence tagger. We find a significant drop in detection of disjunctions and preferences. A detailed study reveals that nearly 70% of all disjunction clauses occur in the context of attribute labels and almost all preferences are expressed for attributes. Since system’s recall for x.attr is low, the pipeline under-performance is not entirely surprising. However, as the next section shows, the low recall for these tags is not very problematic for the end QA task, as they are relatively infrequent. Moreover, even if we miss some attributes, the other attributes can often lead us to correct answers." } ], "images": [ "scigen/train/1492.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Performance of operator detection using gold sequence labels, and system generated labels" ], "reasoning": "The system's recall for disjunctions and preferences is significantly lower than the gold standard, particularly for preferences.", "description": "Operator Labeling: We create rules for commonly occurring operators – disjunctions, negations, and preferences. The default operator between semantic labels is a conjunction. The ‘Gold’ columns denote the performance when using gold semantic label mentions. The ’System’ columns are the performance when using labels generated by our sequence tagger. We find a significant drop in detection of disjunctions and preferences. A detailed study reveals that nearly 70% of all disjunction clauses occur in the context of attribute labels and almost all preferences are expressed for attributes. Since system’s recall for x.attr is low, the pipeline under-performance is not entirely surprising. However, as the next section shows, the low recall for these tags is not very problematic for the end QA task, as they are relatively infrequent. Moreover, even if we miss some attributes, the other attributes can often lead us to correct answers.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: Performance of operator detection using gold sequence labels, and system generated labels\nQuestion: What is the recall difference between the gold and system labels for preferences?\nSolve the above question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The recall for preferences in the gold labels is 60, while in the system labels it is 15. The difference is 60 - 15 = 45. So, the answer is {\"answer\": \"45\"}." } ], "images": [ "scigen/train/1492.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Performance of operator detection using gold sequence labels, and system generated labels" ], "reasoning": "The recall for preferences in the gold labels is 60, while in the system labels it is 15. The difference is 60 - 15 = 45.", "question": "What is the recall difference between the gold and system labels for preferences?", "answer": "So, the answer is {\"answer\": \"45\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The system's precision for negations is higher than its recall., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe system's precision for negations is higher than its recall.\n" }, { "role": "assistant", "content": "The table shows that the system's precision for negations is 85, while its recall is 62, indicating that the precision is indeed higher than the recall. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1492.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Performance of operator detection using gold sequence labels, and system generated labels" ], "reasoning": "The table shows that the system's precision for negations is 85, while its recall is 62, indicating that the precision is indeed higher than the recall.", "statement": "The system's precision for negations is higher than its recall.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: A summary of the average regularization fractions for AE and BE on Twitter and Ngrams. Note that the differences were taken prior to rounding.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Verbs on Twitter are more regular than in Ngrams for American English, but less regular for British English.We found again that verbs on Twitter are more regular than in Ngrams for American English, likely for many of the same reasons that verbs on Twitter are more regular than Ngrams in general. However, we find that in British English the opposite is true: Verbs on Twitter are less regular than in Ngrams. In decreasing order by average regularization fraction, we have AE Twitter, then AE Ngrams, then BE Ngrams, and finally BE Twitter. Then, it may be that British English authors and editors are being influenced by American publications and the language used therein. Finally, it may be that average British users of Twitter are more resistant to the change." } ], "images": [ "scigen/train/1493.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: A summary of the average regularization fractions for AE and BE on Twitter and Ngrams. Note that the differences were taken prior to rounding." ], "reasoning": "Verbs on Twitter are more regular than in Ngrams for American English, but less regular for British English.", "description": "We found again that verbs on Twitter are more regular than in Ngrams for American English, likely for many of the same reasons that verbs on Twitter are more regular than Ngrams in general. However, we find that in British English the opposite is true: Verbs on Twitter are less regular than in Ngrams. In decreasing order by average regularization fraction, we have AE Twitter, then AE Ngrams, then BE Ngrams, and finally BE Twitter. Then, it may be that British English authors and editors are being influenced by American publications and the language used therein. Finally, it may be that average British users of Twitter are more resistant to the change.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich variety of English has the highest average regularization fraction on Twitter?\nSolve the above question based on the table titled 'Table 2: A summary of the average regularization fractions for AE and BE on Twitter and Ngrams. Note that the differences were taken prior to rounding.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the average regularization fractions on Twitter for both American English (0.54) and British English (0.33), we see that American English has the highest average regularization fraction. So, the answer is {\"answer\": \"American English\"}." } ], "images": [ "scigen/train/1493.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: A summary of the average regularization fractions for AE and BE on Twitter and Ngrams. Note that the differences were taken prior to rounding." ], "reasoning": "By comparing the average regularization fractions on Twitter for both American English (0.54) and British English (0.33), we see that American English has the highest average regularization fraction.", "question": "Which variety of English has the highest average regularization fraction on Twitter?", "answer": "So, the answer is {\"answer\": \"American English\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nBritish English verbs are more regular on Twitter than in Ngrams.\n" }, { "role": "assistant", "content": "The table shows that the regularization fraction for British English on Twitter is 0.33, which is less than the 0.42 regularization fraction in Ngrams, indicating that British English verbs are less regular on Twitter. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1493.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: A summary of the average regularization fractions for AE and BE on Twitter and Ngrams. Note that the differences were taken prior to rounding." ], "reasoning": "The table shows that the regularization fraction for British English on Twitter is 0.33, which is less than the 0.42 regularization fraction in Ngrams, indicating that British English verbs are less regular on Twitter.", "statement": "British English verbs are more regular on Twitter than in Ngrams.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Evaluation scores for the Fine-grained classification experiment.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The fine-grained classification experiment shows that different models perform variably across accuracy, F1 score, and recall.The last experiment conducted in the study was to classify the tweets into all five categories: UM, PM, IM, AD, OD. This is essentially the fine grained classification experiment which relies on semantic information as well as lexical choice. We see that models from all the three experiments perform differently given the type of task." } ], "images": [ "scigen/train/1494.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Evaluation scores for the Fine-grained classification experiment." ], "reasoning": "The fine-grained classification experiment shows that different models perform variably across accuracy, F1 score, and recall.", "description": "The last experiment conducted in the study was to classify the tweets into all five categories: UM, PM, IM, AD, OD. This is essentially the fine grained classification experiment which relies on semantic information as well as lexical choice. We see that models from all the three experiments perform differently given the type of task.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 4: Evaluation scores for the Fine-grained classification experiment.'. Solve the following question based on the table.\n\nQuestion: Which model achieved the highest F1 Score in the fine-grained classification experiment?\nLet's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the F1 Scores of all models listed in the table, BERTLarge has the highest F1 Score of 0.714. So, the answer is {\"answer\": \"BERTLarge\"}." } ], "images": [ "scigen/train/1494.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Evaluation scores for the Fine-grained classification experiment." ], "reasoning": "By comparing the F1 Scores of all models listed in the table, BERTLarge has the highest F1 Score of 0.714.", "question": "Which model achieved the highest F1 Score in the fine-grained classification experiment?", "answer": "So, the answer is {\"answer\": \"BERTLarge\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The LSTM model achieved the highest recall in the fine-grained classification experiment. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe LSTM model achieved the highest recall in the fine-grained classification experiment.\n" }, { "role": "assistant", "content": "By comparing the recall values of all models listed in the table, BERTLarge has the highest recall of 0.713, not LSTM. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1494.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Evaluation scores for the Fine-grained classification experiment." ], "reasoning": "By comparing the recall values of all models listed in the table, BERTLarge has the highest recall of 0.713, not LSTM.", "statement": "The LSTM model achieved the highest recall in the fine-grained classification experiment.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Label and ID associated with each class.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The categories for tweets include advertisements, anecdotes, information, and cautionary mentions, with similar patterns observed for e-cigarettes and other drugs.These categories were chosen on the basis of frequency of occurrence, motivated by the general perception of tobacco and non-tobacco drug related tweets. These included advertisements as well anecdotes, information and cautionary tweets. We further noticed that a similar pattern was seen for e-cigarettes and also pertained to some other drugs. While we have explored e-cigarettes in this classification, we have marked the mention of other drugs that were tagged with the same keywords." } ], "images": [ "scigen/train/1495.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Label and ID associated with each class." ], "reasoning": "The categories for tweets include advertisements, anecdotes, information, and cautionary mentions, with similar patterns observed for e-cigarettes and other drugs.", "description": "These categories were chosen on the basis of frequency of occurrence, motivated by the general perception of tobacco and non-tobacco drug related tweets. These included advertisements as well anecdotes, information and cautionary tweets. We further noticed that a similar pattern was seen for e-cigarettes and also pertained to some other drugs. While we have explored e-cigarettes in this classification, we have marked the mention of other drugs that were tagged with the same keywords.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nTable title: Table 1: Label and ID associated with each class.\n\nQuestion: What is the annotation class for Informative or Advisory Mention?\n" }, { "role": "assistant", "content": "To find the annotation class for Informative or Advisory Mention, locate the row with 'Informative or Advisory Mention' in the 'Name' column and read the corresponding value in the 'Annotation Class' column. So, the answer is {\"answer\": \"2\"}" } ], "images": [ "scigen/train/1495.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Label and ID associated with each class." ], "reasoning": "To find the annotation class for Informative or Advisory Mention, locate the row with 'Informative or Advisory Mention' in the 'Name' column and read the corresponding value in the 'Annotation Class' column.", "question": "What is the annotation class for Informative or Advisory Mention?", "answer": "So, the answer is {\"answer\": \"2\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nPersonal or Anecdotal Mention has an annotation class of 1.\n" }, { "role": "assistant", "content": "The table lists 'Personal or Anecdotal Mention' with an annotation class of 1, confirming the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1495.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Label and ID associated with each class." ], "reasoning": "The table lists 'Personal or Anecdotal Mention' with an annotation class of 1, confirming the statement.", "statement": "Personal or Anecdotal Mention has an annotation class of 1.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 5: Average retweets and favorites across classes'.\n" }, { "role": "assistant", "content": "Informative or advisory and personal mentions are the most posted, with advertisements having the highest average favorite count and anecdotal and advisory tweets being the most retweeted.We see that in the span of a week, informative or advisory and personal mentions are the most widely posted. The tweets that provide general information about smokers or the habits of smoking tobacco or e-cigarettes are generated the most, implying that a larger section of the population tweets of smoking in an anecdotal manner. Advertisements have a higher average favorite count than most other classes, while anecdotal and advisory tweets are the most retweeted on average. This difference is an interesting observation, primarily because on further work such as sentiment analysis and doing short text style transfer (luo2019towards) for these categories may provide an effective strategy for advertisers and campaigners alike." } ], "images": [ "scigen/train/1496.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Average retweets and favorites across classes" ], "reasoning": "Informative or advisory and personal mentions are the most posted, with advertisements having the highest average favorite count and anecdotal and advisory tweets being the most retweeted.", "description": "We see that in the span of a week, informative or advisory and personal mentions are the most widely posted. The tweets that provide general information about smokers or the habits of smoking tobacco or e-cigarettes are generated the most, implying that a larger section of the population tweets of smoking in an anecdotal manner. Advertisements have a higher average favorite count than most other classes, while anecdotal and advisory tweets are the most retweeted on average. This difference is an interesting observation, primarily because on further work such as sentiment analysis and doing short text style transfer (luo2019towards) for these categories may provide an effective strategy for advertisers and campaigners alike.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 5: Average retweets and favorites across classes', solve the subsequent question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich category has the highest average favorite count and how does it compare to the category with the highest average retweet count?\n" }, { "role": "assistant", "content": "Advertisements (AD) have the highest average favorite count of 4.586, while personal mentions (PM) have the highest average retweet count of 12171.60. So, the answer is {\"answer\": \"Advertisements have the highest average favorite count of 4.586, while personal mentions have the highest average retweet count of 12171.60.\"}." } ], "images": [ "scigen/train/1496.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Average retweets and favorites across classes" ], "reasoning": "Advertisements (AD) have the highest average favorite count of 4.586, while personal mentions (PM) have the highest average retweet count of 12171.60.", "question": "Which category has the highest average favorite count and how does it compare to the category with the highest average retweet count?", "answer": "So, the answer is {\"answer\": \"Advertisements have the highest average favorite count of 4.586, while personal mentions have the highest average retweet count of 12171.60.\"}." }, { "messages": [ { "role": "user", "content": "Advertisements have the highest average retweet count. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nAdvertisements have the highest average retweet count.\n" }, { "role": "assistant", "content": "The table shows that advertisements (AD) have an average retweet count of 140.81, which is not the highest. Personal mentions (PM) have the highest average retweet count of 12171.60. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1496.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Average retweets and favorites across classes" ], "reasoning": "The table shows that advertisements (AD) have an average retweet count of 140.81, which is not the highest. Personal mentions (PM) have the highest average retweet count of 12171.60.", "statement": "Advertisements have the highest average retweet count.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Statistical information of datasets. #Negative and #Positive are the number of negative instances and positive instances, respectively. lavg is average length of sentences in the dataset, and |V| is the vocabulary size.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "s.parser achieves the best result on MPQA without using external resources, outperforming Tree-CRF and RAE.On the dataset MPQA, the accuracy of s.parser increases by 0.5%, 1.1%, and 14.8% from the best results of SVM, MNB, and LM, respectively. Compared to Voting-w/Rev and HardRule, s.parser achieves 4.5% and 4.4% improvements over them. The RAE-pretrain achieves better results than other methods on this dataset, because the word embeddings pre-trained on Wikipedia can leverage smoothing to relieve the sparsity problem in MPQA. If we do not use any external resources (i.e. Wikipedia), the accuracy of RAE on MPQA is 85.7% which is lower than Tree-CRF and s.parser. The results indicate that s.parser achieves the best result if no external resource is employed." } ], "images": [ "scigen/train/1497.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Statistical information of datasets. #Negative and #Positive are the number of negative instances and positive instances, respectively. lavg is average length of sentences in the dataset, and |V| is the vocabulary size." ], "reasoning": "s.parser achieves the best result on MPQA without using external resources, outperforming Tree-CRF and RAE.", "description": "On the dataset MPQA, the accuracy of s.parser increases by 0.5%, 1.1%, and 14.8% from the best results of SVM, MNB, and LM, respectively. Compared to Voting-w/Rev and HardRule, s.parser achieves 4.5% and 4.4% improvements over them. The RAE-pretrain achieves better results than other methods on this dataset, because the word embeddings pre-trained on Wikipedia can leverage smoothing to relieve the sparsity problem in MPQA. If we do not use any external resources (i.e. Wikipedia), the accuracy of RAE on MPQA is 85.7% which is lower than Tree-CRF and s.parser. The results indicate that s.parser achieves the best result if no external resource is employed.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 3: Statistical information of datasets. #Negative and #Positive are the number of negative instances and positive instances, respectively. lavg is average length of sentences in the dataset, and |V| is the vocabulary size.', solve the following question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the size of the dataset where s.parser achieves the best result without using external resources?\n" }, { "role": "assistant", "content": "The description states that s.parser achieves the best result on the MPQA dataset without using external resources. The table shows that the size of the MPQA dataset is 10,624. So, the answer is {\"answer\": \"10,624\"}" } ], "images": [ "scigen/train/1497.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Statistical information of datasets. #Negative and #Positive are the number of negative instances and positive instances, respectively. lavg is average length of sentences in the dataset, and |V| is the vocabulary size." ], "reasoning": "The description states that s.parser achieves the best result on the MPQA dataset without using external resources. The table shows that the size of the MPQA dataset is 10,624.", "question": "What is the size of the dataset where s.parser achieves the best result without using external resources?", "answer": "So, the answer is {\"answer\": \"10,624\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe MPQA dataset has more positive samples than negative samples.\n" }, { "role": "assistant", "content": "The table shows that the MPQA dataset has 3,316 positive samples and 7,308 negative samples, indicating that there are more negative samples than positive samples. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1497.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Statistical information of datasets. #Negative and #Positive are the number of negative instances and positive instances, respectively. lavg is average length of sentences in the dataset, and |V| is the vocabulary size." ], "reasoning": "The table shows that the MPQA dataset has 3,316 positive samples and 7,308 negative samples, indicating that there are more negative samples than positive samples.", "statement": "The MPQA dataset has more positive samples than negative samples.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: Sentiment classification results on different datasets. The top three methods are in bold and the best is also underlined. SVM-m: Support Vector Machine. MNB-m: Multinomial Naïve Bayes. LM-m: Language Model. Voting-w/Rev: Voting with negation rules. HardRule: Rule based method on dependency tree. Tree-CRF: Dependency tree-based method employing conditional random fields. RAE-pretrain: Recursive autoencoders with pre-trained word vectors. MV-RNN: Matrix-vector recursive neural network. s.parser-LongMatch: The longest matching rules are used. s.parser-w/oComb: Without using the combination rules. s.parser: Our method. Some of results are missing (indicated by “-”) in the table as there is no publicly available implementation or they are hard to scale up.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The s.parser method outperforms other methods on most datasets, achieving the highest scores in RT-C, SST, RT-U, IMDB-U, and MPQ.The top three methods on each dataset are in bold, and the best methods are also underlined. The experimental results show that s.parser achieves better performances than other methods on most datasets." } ], "images": [ "scigen/train/1498.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Sentiment classification results on different datasets. The top three methods are in bold and the best is also underlined. SVM-m: Support Vector Machine. MNB-m: Multinomial Naïve Bayes. LM-m: Language Model. Voting-w/Rev: Voting with negation rules. HardRule: Rule based method on dependency tree. Tree-CRF: Dependency tree-based method employing conditional random fields. RAE-pretrain: Recursive autoencoders with pre-trained word vectors. MV-RNN: Matrix-vector recursive neural network. s.parser-LongMatch: The longest matching rules are used. s.parser-w/oComb: Without using the combination rules. s.parser: Our method. Some of results are missing (indicated by “-”) in the table as there is no publicly available implementation or they are hard to scale up." ], "reasoning": "The s.parser method outperforms other methods on most datasets, achieving the highest scores in RT-C, SST, RT-U, IMDB-U, and MPQ.", "description": "The top three methods on each dataset are in bold, and the best methods are also underlined. The experimental results show that s.parser achieves better performances than other methods on most datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 4: Sentiment classification results on different datasets. The top three methods are in bold and the best is also underlined. SVM-m: Support Vector Machine. MNB-m: Multinomial Naïve Bayes. LM-m: Language Model. Voting-w/Rev: Voting with negation rules. HardRule: Rule based method on dependency tree. Tree-CRF: Dependency tree-based method employing conditional random fields. RAE-pretrain: Recursive autoencoders with pre-trained word vectors. MV-RNN: Matrix-vector recursive neural network. s.parser-LongMatch: The longest matching rules are used. s.parser-w/oComb: Without using the combination rules. s.parser: Our method. Some of results are missing (indicated by “-”) in the table as there is no publicly available implementation or they are hard to scale up.', answer the question below by showing each progressive step toward the answer. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich method achieves the highest score on the IMDB-U dataset?\n" }, { "role": "assistant", "content": "By examining the IMDB-U column, the highest score is 89.3, achieved by the s.parser method. So, the answer is {\"answer\": \"s.parser\"}." } ], "images": [ "scigen/train/1498.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Sentiment classification results on different datasets. The top three methods are in bold and the best is also underlined. SVM-m: Support Vector Machine. MNB-m: Multinomial Naïve Bayes. LM-m: Language Model. Voting-w/Rev: Voting with negation rules. HardRule: Rule based method on dependency tree. Tree-CRF: Dependency tree-based method employing conditional random fields. RAE-pretrain: Recursive autoencoders with pre-trained word vectors. MV-RNN: Matrix-vector recursive neural network. s.parser-LongMatch: The longest matching rules are used. s.parser-w/oComb: Without using the combination rules. s.parser: Our method. Some of results are missing (indicated by “-”) in the table as there is no publicly available implementation or they are hard to scale up." ], "reasoning": "By examining the IMDB-U column, the highest score is 89.3, achieved by the s.parser method.", "question": "Which method achieves the highest score on the IMDB-U dataset?", "answer": "So, the answer is {\"answer\": \"s.parser\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe s.parser method achieves the highest score on the PL05-C dataset.\n" }, { "role": "assistant", "content": "By examining the PL05-C column, the highest score is 79.5, achieved by the s.parser method. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1498.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Sentiment classification results on different datasets. The top three methods are in bold and the best is also underlined. SVM-m: Support Vector Machine. MNB-m: Multinomial Naïve Bayes. LM-m: Language Model. Voting-w/Rev: Voting with negation rules. HardRule: Rule based method on dependency tree. Tree-CRF: Dependency tree-based method employing conditional random fields. RAE-pretrain: Recursive autoencoders with pre-trained word vectors. MV-RNN: Matrix-vector recursive neural network. s.parser-LongMatch: The longest matching rules are used. s.parser-w/oComb: Without using the combination rules. s.parser: Our method. Some of results are missing (indicated by “-”) in the table as there is no publicly available implementation or they are hard to scale up." ], "reasoning": "By examining the PL05-C column, the highest score is 79.5, achieved by the s.parser method.", "statement": "The s.parser method achieves the highest score on the PL05-C dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 6: Comparing our dictionary rule learning method with naive counting. The dictionary rules which are assigned different polarities by these two methods are presented. N represents negative, and P represents positive. The polarity probabilities of fragments are shown in this table, and they demonstrate our method learns more intuitive results than counting directly.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "By considering context and compositionality, the dictionary rule learning method improves the accuracy of polarity probabilities, though some phrases like 'to pay it' may still be misclassified due to data distribution.We further investigate the effect of context for dictionary rule learning. We notice that if we count the fragment occurrence number directly, some polarities of fragments are learned incorrectly. By taking the context into consideration, we obtain more reasonable polarity probabilities of dictionary rules. Our dictionary rule learning method takes compositionality into consideration, i.e. we skip the count if there exist some negation indicators outside the phrase. This constraint tries to ensure that the polarity of fragment is the same as the whole sentence. As shown in the results, the polarity probabilities learned by our method are more reasonable and meet people’s intuitions. However, there are also some negative examples caused by “false subjective”. For instance, the neutral phrase “to pay it” tends to appear in negative sentences, and it is learned as a negative phrase. This makes sense for the data distribution, while it may lead to the mismatch for the combination rules." } ], "images": [ "scigen/train/1499.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Comparing our dictionary rule learning method with naive counting. The dictionary rules which are assigned different polarities by these two methods are presented. N represents negative, and P represents positive. The polarity probabilities of fragments are shown in this table, and they demonstrate our method learns more intuitive results than counting directly." ], "reasoning": "By considering context and compositionality, the dictionary rule learning method improves the accuracy of polarity probabilities, though some phrases like 'to pay it' may still be misclassified due to data distribution.", "description": "We further investigate the effect of context for dictionary rule learning. We notice that if we count the fragment occurrence number directly, some polarities of fragments are learned incorrectly. By taking the context into consideration, we obtain more reasonable polarity probabilities of dictionary rules. Our dictionary rule learning method takes compositionality into consideration, i.e. we skip the count if there exist some negation indicators outside the phrase. This constraint tries to ensure that the polarity of fragment is the same as the whole sentence. As shown in the results, the polarity probabilities learned by our method are more reasonable and meet people’s intuitions. However, there are also some negative examples caused by “false subjective”. For instance, the neutral phrase “to pay it” tends to appear in negative sentences, and it is learned as a negative phrase. This makes sense for the data distribution, while it may lead to the mismatch for the combination rules.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 6: Comparing our dictionary rule learning method with naive counting. The dictionary rules which are assigned different polarities by these two methods are presented. N represents negative, and P represents positive. The polarity probabilities of fragments are shown in this table, and they demonstrate our method learns more intuitive results than counting directly.'. Solve the following question based on the table.\n\nQuestion: What is the polarity of the fragment 'is corny' according to the s.parser method?\nLet's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the fragment 'is corny' with s.parsers N as 0.83 and s.parsers P as 0.17, resulting in an s.parser Polarity of N. So, the answer is {\"answer\": \"N\"}." } ], "images": [ "scigen/train/1499.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Comparing our dictionary rule learning method with naive counting. The dictionary rules which are assigned different polarities by these two methods are presented. N represents negative, and P represents positive. The polarity probabilities of fragments are shown in this table, and they demonstrate our method learns more intuitive results than counting directly." ], "reasoning": "The table shows the fragment 'is corny' with s.parsers N as 0.83 and s.parsers P as 0.17, resulting in an s.parser Polarity of N.", "question": "What is the polarity of the fragment 'is corny' according to the s.parser method?", "answer": "So, the answer is {\"answer\": \"N\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe fragment 'a very good movie' has a positive polarity according to the naive count method.\n" }, { "role": "assistant", "content": "The table shows that the naive count method assigns a polarity of N (negative) to the fragment 'a very good movie'. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1499.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Comparing our dictionary rule learning method with naive counting. The dictionary rules which are assigned different polarities by these two methods are presented. N represents negative, and P represents positive. The polarity probabilities of fragments are shown in this table, and they demonstrate our method learns more intuitive results than counting directly." ], "reasoning": "The table shows that the naive count method assigns a polarity of N (negative) to the fragment 'a very good movie'.", "statement": "The fragment 'a very good movie' has a positive polarity according to the naive count method.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Dataset label distribution (in %)', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Samples are classified into Bad, Loose, and Good categories based on specific thresholds, with additional data augmentation for the Good and Loose categories and synthesized negatives for the Bad category.^y=⎧⎪⎨⎪⎩Badif sBOW≤δ1∧sRFC≤δ1,Looseif sRFC≤δ3∧sRFC% ≥δ2,Goodif sBOW≥δ4∧sRFC≥δ4 (3) where the thresholds are manually set to (δ1,δ2,δ3,δ4)=(0.25,0.4,0.7,0.8). This labels about 69% of all samples into three classes. All others samples with scores that do not fall in the specified ranges are filtered out due to disagreement among the models. We refer to this set as Statistical Classification. We further augment this data with NMT on short sentences with frequent phrases like greetings with the Good label. More samples for the Loose category are generated by adding captions to the source (like whispers, sighs, etc) referred as Added Captions or changing word order in the target to degrade fluency called Scrambled Text samples. This constitutes all the positives (Good and Loose) while we synthesize negatives (Bad) in two ways; we randomly choose a target for each source for easy negatives (called Random Aligned) and to choose a target from a temporally close block for hard negatives (called Drifted alignment)." } ], "images": [ "scigen/train/1500.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Dataset label distribution (in %)" ], "reasoning": "Samples are classified into Bad, Loose, and Good categories based on specific thresholds, with additional data augmentation for the Good and Loose categories and synthesized negatives for the Bad category.", "description": "^y=⎧⎪⎨⎪⎩Badif sBOW≤δ1∧sRFC≤δ1,Looseif sRFC≤δ3∧sRFC% ≥δ2,Goodif sBOW≥δ4∧sRFC≥δ4 (3) where the thresholds are manually set to (δ1,δ2,δ3,δ4)=(0.25,0.4,0.7,0.8). This labels about 69% of all samples into three classes. All others samples with scores that do not fall in the specified ranges are filtered out due to disagreement among the models. We refer to this set as Statistical Classification. We further augment this data with NMT on short sentences with frequent phrases like greetings with the Good label. More samples for the Loose category are generated by adding captions to the source (like whispers, sighs, etc) referred as Added Captions or changing word order in the target to degrade fluency called Scrambled Text samples. This constitutes all the positives (Good and Loose) while we synthesize negatives (Bad) in two ways; we randomly choose a target for each source for easy negatives (called Random Aligned) and to choose a target from a temporally close block for hard negatives (called Drifted alignment).", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 2: Dataset label distribution (in %)', solve the following question step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich language has the highest score in the Good category?\n" }, { "role": "assistant", "content": "To find the language with the highest score in the Good category, compare the Good scores for all languages: French (34.17), German (33.06), Italian (34.33), Portuguese (34.10), and Spanish (29.92). The highest score is 34.33 for Italian. So, the answer is {\"answer\": \"Italian\"}." } ], "images": [ "scigen/train/1500.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Dataset label distribution (in %)" ], "reasoning": "To find the language with the highest score in the Good category, compare the Good scores for all languages: French (34.17), German (33.06), Italian (34.33), Portuguese (34.10), and Spanish (29.92). The highest score is 34.33 for Italian.", "question": "Which language has the highest score in the Good category?", "answer": "So, the answer is {\"answer\": \"Italian\"}." }, { "messages": [ { "role": "user", "content": "Spanish has the highest score in the Bad category. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nSpanish has the highest score in the Bad category.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Bad scores for all languages: French (39.50), German (38.65), Italian (39.40), Portuguese (39.54), and Spanish (42.05). The highest score is 42.05 for Spanish. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1500.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Dataset label distribution (in %)" ], "reasoning": "To verify this statement, compare the Bad scores for all languages: French (39.50), German (38.65), Italian (39.40), Portuguese (39.54), and Spanish (42.05). The highest score is 42.05 for Spanish.", "statement": "Spanish has the highest score in the Bad category.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Model accuracy on train and tests sets.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The model uses two monolingual encoders with BiLSTMs and CNNs to achieve over 91% accuracy across five languages.State-of-the-art monolingual information retrieval systems Huang et al. We extended their idea to build a network with two monolingual encoders for source and target each to extract semantic features followed by a CNN for syntactic features. Input to the model are 300 dimensional embeddings from pretrained FastText Bojanowski et al. We used two BiLSTMs for each encoder with the outputs of both LSTM concatenated. They were then sequentially fed to two convolution modules. CNN output was passed through a fully connected layer before making a three class prediction. We used ReLU activation with dropout and Batch Normalization. We used Adam optimizer Kingma and Ba in all our experiments with a batch size of 8192. We chose a learning rate of 10−3 scheduled to drop by a factor of 10 twice whenever the rate of training loss drop was less than 10−3 before stopping training. We observe that the model performs similarly with accuracy of above 91% for all five languages. This is possibly because shorter ones when paraphrased are harder to detect than longer ones." } ], "images": [ "scigen/train/1501.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Model accuracy on train and tests sets." ], "reasoning": "The model uses two monolingual encoders with BiLSTMs and CNNs to achieve over 91% accuracy across five languages.", "description": "State-of-the-art monolingual information retrieval systems Huang et al. We extended their idea to build a network with two monolingual encoders for source and target each to extract semantic features followed by a CNN for syntactic features. Input to the model are 300 dimensional embeddings from pretrained FastText Bojanowski et al. We used two BiLSTMs for each encoder with the outputs of both LSTM concatenated. They were then sequentially fed to two convolution modules. CNN output was passed through a fully connected layer before making a three class prediction. We used ReLU activation with dropout and Batch Normalization. We used Adam optimizer Kingma and Ba in all our experiments with a batch size of 8192. We chose a learning rate of 10−3 scheduled to drop by a factor of 10 twice whenever the rate of training loss drop was less than 10−3 before stopping training. We observe that the model performs similarly with accuracy of above 91% for all five languages. This is possibly because shorter ones when paraphrased are harder to detect than longer ones.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: Model accuracy on train and tests sets.' and methodically tackle the question that follows. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich language had the highest train accuracy and what was its test F-score?\n" }, { "role": "assistant", "content": "German had the highest train accuracy of 95.18%, and its test F-score was 93.42. So, the answer is {\"answer\": \"93.42\"}" } ], "images": [ "scigen/train/1501.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Model accuracy on train and tests sets." ], "reasoning": "German had the highest train accuracy of 95.18%, and its test F-score was 93.42.", "question": "Which language had the highest train accuracy and what was its test F-score?", "answer": "So, the answer is {\"answer\": \"93.42\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The model achieved the highest test accuracy in Portuguese., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe model achieved the highest test accuracy in Portuguese.\n" }, { "role": "assistant", "content": "The table shows that Portuguese had a test accuracy of 92.73%, which is the highest among the listed languages. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1501.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Model accuracy on train and tests sets." ], "reasoning": "The table shows that Portuguese had a test accuracy of 92.73%, which is the highest among the listed languages.", "statement": "The model achieved the highest test accuracy in Portuguese.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: Miss rate on parallel corpora.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The table compares the number of sentences and false negative rates (FNR) for high-quality subtitles and EuroParl data across different languages.We took a set of high quality subtitles that were translated and validated independently by two sets of distinct human translators. A list of parallel sentences were extracted from them using their timestamp alignment information. These translations that went through two rounds of human audit can safely be assumed to contain no Bad translations. Since data consists only positives constituting of Loose and Good, we use miss rate or false negative rate (FNR) as the performance metric for this experiment. We ran a similar test on public EuroParl data for reference that also has only positives." } ], "images": [ "scigen/train/1502.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Miss rate on parallel corpora." ], "reasoning": "The table compares the number of sentences and false negative rates (FNR) for high-quality subtitles and EuroParl data across different languages.", "description": "We took a set of high quality subtitles that were translated and validated independently by two sets of distinct human translators. A list of parallel sentences were extracted from them using their timestamp alignment information. These translations that went through two rounds of human audit can safely be assumed to contain no Bad translations. Since data consists only positives constituting of Loose and Good, we use miss rate or false negative rate (FNR) as the performance metric for this experiment. We ran a similar test on public EuroParl data for reference that also has only positives.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: Miss rate on parallel corpora.', answer the following question. Give detailed solution consisting of each step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich language has the highest false negative rate (FNR) for high-quality subtitles?\n" }, { "role": "assistant", "content": "To find the language with the highest FNR for high-quality subtitles, compare the FNR values in the 'High-quality subs FNR' column for all languages. So, the answer is {\"answer\": \"German\"}." } ], "images": [ "scigen/train/1502.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Miss rate on parallel corpora." ], "reasoning": "To find the language with the highest FNR for high-quality subtitles, compare the FNR values in the 'High-quality subs FNR' column for all languages.", "question": "Which language has the highest false negative rate (FNR) for high-quality subtitles?", "answer": "So, the answer is {\"answer\": \"German\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The EuroParl data for Portuguese has the highest false negative rate (FNR) among the listed languages. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe EuroParl data for Portuguese has the highest false negative rate (FNR) among the listed languages.\n" }, { "role": "assistant", "content": "To verify this statement, compare the FNR values in the 'EuroParl FNR' column for all languages. The FNR for Portuguese is 4.01, which is the highest among the listed languages. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1502.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Miss rate on parallel corpora." ], "reasoning": "To verify this statement, compare the FNR values in the 'EuroParl FNR' column for all languages. The FNR for Portuguese is 4.01, which is the highest among the listed languages.", "statement": "The EuroParl data for Portuguese has the highest false negative rate (FNR) among the listed languages.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: Accuracy comparison of classification and scoring losses.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The classification model outperformed the scoring model by approximately 4% for each language, with scores bounded between 0 and 1.The model assigns Good samples a score higher than those to Loose which should have been higher than those assigned to Bad. We changed the final fully-connected layer of the model to give only one output followed by a sigmoid to bound the score in the range [0,1]. We can see that the classification loss outperformed the scoring loss by approximately 4% for each language." } ], "images": [ "scigen/train/1503.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Accuracy comparison of classification and scoring losses." ], "reasoning": "The classification model outperformed the scoring model by approximately 4% for each language, with scores bounded between 0 and 1.", "description": "The model assigns Good samples a score higher than those to Loose which should have been higher than those assigned to Bad. We changed the final fully-connected layer of the model to give only one output followed by a sigmoid to bound the score in the range [0,1]. We can see that the classification loss outperformed the scoring loss by approximately 4% for each language.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 5: Accuracy comparison of classification and scoring losses.', solve the following question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich language had the highest classification score and what was the difference between its classification and scoring scores?\n" }, { "role": "assistant", "content": "The highest classification score is for German with 93.90. The difference between its classification and scoring scores is 93.90 - 90.81 = 3.09. So, the answer is {\"answer\": \"German, 3.09\"}." } ], "images": [ "scigen/train/1503.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Accuracy comparison of classification and scoring losses." ], "reasoning": "The highest classification score is for German with 93.90. The difference between its classification and scoring scores is 93.90 - 90.81 = 3.09.", "question": "Which language had the highest classification score and what was the difference between its classification and scoring scores?", "answer": "So, the answer is {\"answer\": \"German, 3.09\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe classification model scored higher than the scoring model for all languages.\n" }, { "role": "assistant", "content": "The table shows that for each language (French, German, Italian, Portuguese, Spanish), the classification score is higher than the scoring score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1503.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Accuracy comparison of classification and scoring losses." ], "reasoning": "The table shows that for each language (French, German, Italian, Portuguese, Spanish), the classification score is higher than the scoring score.", "statement": "The classification model scored higher than the scoring model for all languages.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Test Accuracy for various model architectures.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The proposed hybrid model outperformed all other networks including the CNN by 3%, and LASER models took about 10 epochs on average to meet the stopping criterion while DeepSubQE model took around 34 epochs on average.We found that the existing convolution networks Liu et al. that try to classify bilingual dataset are not able to learn the linguistic nuances of text and fail in many cases. However, recurrent networks that are good with parsing variable length inputs with temporal dependencies complement them. For LSTM network, we concatenated the output of both BiLSTMs and fed to a fully connected layer. For CNN model, we had three convolution modules followed by a fully connected layer. We use the 1024-dimension language agnostic sentence embeddings from LASER. An only-LSTM network works just about as well as the baseline but only-CNN network brings significant gains. The convolutional layer is possibly evaluating semantic retention better Kim et al. LASER FC is a classifier trained with LASER embeddings fed into a fully connected layer that performs worse than baseline. LASER CNN is a CNN on top of LASER embeddings and performs about as well as only-CNN. Proposed hybrid model, however, outperformed all other networks including the CNN by 3%. The LSTM when used in combination with the CNN is consistently improving prediction accuracies across languages. One notable observation from models trained using LASER embeddings was that LASER models took about 10 epochs on average to meet our stopping criterion while DeepSubQE model took around 34 epochs on average." } ], "images": [ "scigen/train/1504.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Test Accuracy for various model architectures." ], "reasoning": "The proposed hybrid model outperformed all other networks including the CNN by 3%, and LASER models took about 10 epochs on average to meet the stopping criterion while DeepSubQE model took around 34 epochs on average.", "description": "We found that the existing convolution networks Liu et al. that try to classify bilingual dataset are not able to learn the linguistic nuances of text and fail in many cases. However, recurrent networks that are good with parsing variable length inputs with temporal dependencies complement them. For LSTM network, we concatenated the output of both BiLSTMs and fed to a fully connected layer. For CNN model, we had three convolution modules followed by a fully connected layer. We use the 1024-dimension language agnostic sentence embeddings from LASER. An only-LSTM network works just about as well as the baseline but only-CNN network brings significant gains. The convolutional layer is possibly evaluating semantic retention better Kim et al. LASER FC is a classifier trained with LASER embeddings fed into a fully connected layer that performs worse than baseline. LASER CNN is a CNN on top of LASER embeddings and performs about as well as only-CNN. Proposed hybrid model, however, outperformed all other networks including the CNN by 3%. The LSTM when used in combination with the CNN is consistently improving prediction accuracies across languages. One notable observation from models trained using LASER embeddings was that LASER models took about 10 epochs on average to meet our stopping criterion while DeepSubQE model took around 34 epochs on average.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: Test Accuracy for various model architectures.\nQuestion: Which model had the highest score for German?\nGive a detailed response to the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest score for German, compare the scores in the German row across all models. The highest score is 93.90, which corresponds to the DeepSubQE model. So, the answer is {\"answer\": \"DeepSubQE\"}." } ], "images": [ "scigen/train/1504.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Test Accuracy for various model architectures." ], "reasoning": "To find the highest score for German, compare the scores in the German row across all models. The highest score is 93.90, which corresponds to the DeepSubQE model.", "question": "Which model had the highest score for German?", "answer": "So, the answer is {\"answer\": \"DeepSubQE\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe LASER FC model performed worse than the baseline for all languages.\n" }, { "role": "assistant", "content": "By comparing the LASER FC scores with the Baseline scores for each language, it is evident that LASER FC scores are lower than the Baseline scores for French, German, Italian, Portuguese, and Spanish. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1504.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Test Accuracy for various model architectures." ], "reasoning": "By comparing the LASER FC scores with the Baseline scores for each language, it is evident that LASER FC scores are lower than the Baseline scores for French, German, Italian, Portuguese, and Spanish.", "statement": "The LASER FC model performed worse than the baseline for all languages.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 8: Dataset size and Performance of RFC', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Datasets for each language are created with a 1:1.2 ratio of correct to incorrect translations, and feature vectors are generated using various similarity and structural features.We create datasets for each language maintaining the ratio of correct and incorrect translations as 1:1.2. We then created a list of features, explained below, to represent a translation in a 273 length feature vector. Average Vector Similarity: Cosine similarity of average of each word’s vector for each sentence. Similarity Features: We create a cosine similarity matrix using aligned bilingual word embeddings Conneau et al. We borrow the ideas from Gupta et al. We repeat process for ngrams for n=[1,6] by taking the vector average for n>2. n -gram Frequencies: Vector of source and target unigram, bigram and trigram probabilities for each ngram in source and target sentence. Structural Features: Number of words in source sentence and target sentence" } ], "images": [ "scigen/train/1505.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Dataset size and Performance of RFC" ], "reasoning": "Datasets for each language are created with a 1:1.2 ratio of correct to incorrect translations, and feature vectors are generated using various similarity and structural features.", "description": "We create datasets for each language maintaining the ratio of correct and incorrect translations as 1:1.2. We then created a list of features, explained below, to represent a translation in a 273 length feature vector. Average Vector Similarity: Cosine similarity of average of each word’s vector for each sentence. Similarity Features: We create a cosine similarity matrix using aligned bilingual word embeddings Conneau et al. We borrow the ideas from Gupta et al. We repeat process for ngrams for n=[1,6] by taking the vector average for n>2. n -gram Frequencies: Vector of source and target unigram, bigram and trigram probabilities for each ngram in source and target sentence. Structural Features: Number of words in source sentence and target sentence", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 8: Dataset size and Performance of RFC' to sequentially solve the question presented below. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich language has the highest training accuracy and what is its test accuracy?\n" }, { "role": "assistant", "content": "By examining the 'Train Accuracy' column, German has the highest training accuracy of 99.97%. The corresponding test accuracy for German is 92.16%. So, the answer is {\"answer\": \"92.16\"}." } ], "images": [ "scigen/train/1505.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Dataset size and Performance of RFC" ], "reasoning": "By examining the 'Train Accuracy' column, German has the highest training accuracy of 99.97%. The corresponding test accuracy for German is 92.16%.", "question": "Which language has the highest training accuracy and what is its test accuracy?", "answer": "So, the answer is {\"answer\": \"92.16\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe dataset for Spanish has more training samples than the dataset for Portuguese.\n" }, { "role": "assistant", "content": "According to the table, Spanish has 703.8k training samples, while Portuguese has 686.7k training samples. Therefore, the statement is correct. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1505.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Dataset size and Performance of RFC" ], "reasoning": "According to the table, Spanish has 703.8k training samples, while Portuguese has 686.7k training samples. Therefore, the statement is correct.", "statement": "The dataset for Spanish has more training samples than the dataset for Portuguese.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Performances of Dir, DirV and JMV2 (MAP, Pr@5, Pr@10). Bold faced numbers indicate runs showing significant improvement over Dir.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "DirV significantly improves MAP of Dir for verbose type of query (SV and LV) except for TREC10, and improves precisions (Pr@5 or Pr@10) for keyword type of query (SK).As for topic measurement τ(D), we selected the information quantity (ε(D)) since JMV2 and DirV using the information quantity is better than those using vocabulary size. We used MLE (Maximum Likelihood Estimation) for P(w|θD) to calculate the information quantity without any smoothing. We selected Dir as the baseline due to its superiority over JM in all test collections. To obtain the best performance of each run, we searched 20 different values between 0.01 and 0.99 for λ, and 22 values between 100 and 30,000 for μ. To check whether or not the proposed method (DirV and JMV2) significantly improves the baseline, we performed the Wilcoxon sign ranked test to examine at 95% and 99% confidence levels. We attached † and ‡ to the performance number of each cell in the table when the test passes at 95% and 99% confidence level, respectively. The results are summarized as follows: DirV significantly improves MAP of Dir for verbose type of query (SV and LV). Exceptionally, TREC10 did not show an improvement for verbose type of query. DirV does not significantly improve MAP of Dir for keyword type of query (SK), but improves precisions (Pr@5 or Pr@10). Especially, on TREC7 and TREC8, Pr@10 is significantly improved over Dir. Although other test collections do not statistically show a significant improvement, there is large portion of the numerical increase. DirV or JMV2 show improvement on a specific test collection even for keyword type of query. For DirV, TREC10 is such a collection by showing a significant improvement of MAP. For JMV2, WT2G is such a test collection. Overall, DirV is slightly better than JMV2 in most of test collections. WT2G is an exceptional collection to show that JMV2 significantly improves DirV." } ], "images": [ "scigen/train/1506.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performances of Dir, DirV and JMV2 (MAP, Pr@5, Pr@10). Bold faced numbers indicate runs showing significant improvement over Dir." ], "reasoning": "DirV significantly improves MAP of Dir for verbose type of query (SV and LV) except for TREC10, and improves precisions (Pr@5 or Pr@10) for keyword type of query (SK).", "description": "As for topic measurement τ(D), we selected the information quantity (ε(D)) since JMV2 and DirV using the information quantity is better than those using vocabulary size. We used MLE (Maximum Likelihood Estimation) for P(w|θD) to calculate the information quantity without any smoothing. We selected Dir as the baseline due to its superiority over JM in all test collections. To obtain the best performance of each run, we searched 20 different values between 0.01 and 0.99 for λ, and 22 values between 100 and 30,000 for μ. To check whether or not the proposed method (DirV and JMV2) significantly improves the baseline, we performed the Wilcoxon sign ranked test to examine at 95% and 99% confidence levels. We attached † and ‡ to the performance number of each cell in the table when the test passes at 95% and 99% confidence level, respectively. The results are summarized as follows: DirV significantly improves MAP of Dir for verbose type of query (SV and LV). Exceptionally, TREC10 did not show an improvement for verbose type of query. DirV does not significantly improve MAP of Dir for keyword type of query (SK), but improves precisions (Pr@5 or Pr@10). Especially, on TREC7 and TREC8, Pr@10 is significantly improved over Dir. Although other test collections do not statistically show a significant improvement, there is large portion of the numerical increase. DirV or JMV2 show improvement on a specific test collection even for keyword type of query. For DirV, TREC10 is such a collection by showing a significant improvement of MAP. For JMV2, WT2G is such a test collection. Overall, DirV is slightly better than JMV2 in most of test collections. WT2G is an exceptional collection to show that JMV2 significantly improves DirV.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Performances of Dir, DirV and JMV2 (MAP, Pr@5, Pr@10). Bold faced numbers indicate runs showing significant improvement over Dir.\nQuestion: Which method shows a significant improvement in MAP for TREC10 with keyword type of query (SK)?\nGive a detailed response to the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that for TREC10 with keyword type of query (SK), DirV has a MAP of 0.2049†, indicating a significant improvement over Dir. So, the answer is {\"answer\": \"DirV\"}." } ], "images": [ "scigen/train/1506.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performances of Dir, DirV and JMV2 (MAP, Pr@5, Pr@10). Bold faced numbers indicate runs showing significant improvement over Dir." ], "reasoning": "The table shows that for TREC10 with keyword type of query (SK), DirV has a MAP of 0.2049†, indicating a significant improvement over Dir.", "question": "Which method shows a significant improvement in MAP for TREC10 with keyword type of query (SK)?", "answer": "So, the answer is {\"answer\": \"DirV\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nJMV2 significantly improves MAP for verbose type of query (LV) in TREC8.\n" }, { "role": "assistant", "content": "The table shows that for TREC8 with verbose type of query (LV), JMV2 has a MAP of 0.2354†, indicating a significant improvement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1506.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performances of Dir, DirV and JMV2 (MAP, Pr@5, Pr@10). Bold faced numbers indicate runs showing significant improvement over Dir." ], "reasoning": "The table shows that for TREC8 with verbose type of query (LV), JMV2 has a MAP of 0.2354†, indicating a significant improvement.", "statement": "JMV2 significantly improves MAP for verbose type of query (LV) in TREC8.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: WER (%) achieved using end-to-end ASR.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "VoiceMask and VTLN-based VC achieve reasonable WER compared to untransformed data, while disentangled representation based VC produces unreasonably high WER.Note that there is no difference between converted data in different attack scenarios, hence the WER does not depend on the attacker. VoiceMask and VTLN-based VC achieve reasonable WER compared to the untransformed data, while the disentangled representation based VC produces unreasonably high WER. Note that these WERs are achieved when ASR is trained solely using converted data. In practice, many techniques can be used optimize the WER, such as using converted data to augment clean data." } ], "images": [ "scigen/train/1507.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: WER (%) achieved using end-to-end ASR." ], "reasoning": "VoiceMask and VTLN-based VC achieve reasonable WER compared to untransformed data, while disentangled representation based VC produces unreasonably high WER.", "description": "Note that there is no difference between converted data in different attack scenarios, hence the WER does not depend on the attacker. VoiceMask and VTLN-based VC achieve reasonable WER compared to the untransformed data, while the disentangled representation based VC produces unreasonably high WER. Note that these WERs are achieved when ASR is trained solely using converted data. In practice, many techniques can be used optimize the WER, such as using converted data to augment clean data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: WER (%) achieved using end-to-end ASR.' and methodically tackle the question that follows. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich transformation strategy produces the highest WER on the test-clean subset?\n" }, { "role": "assistant", "content": "By comparing the WER values for each transformation strategy on the test-clean subset, the disentangled representation based VC random strategy has the highest WER of 115.1. So, the answer is {\"answer\": \"Disentangled representation based VC random\"}." } ], "images": [ "scigen/train/1507.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: WER (%) achieved using end-to-end ASR." ], "reasoning": "By comparing the WER values for each transformation strategy on the test-clean subset, the disentangled representation based VC random strategy has the highest WER of 115.1.", "question": "Which transformation strategy produces the highest WER on the test-clean subset?", "answer": "So, the answer is {\"answer\": \"Disentangled representation based VC random\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nVTLN-based VC perm achieves a WER of 15.9 on the test-clean subset.\n" }, { "role": "assistant", "content": "The table shows that the WER for VTLN-based VC perm on the test-clean subset is indeed 15.9. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1507.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: WER (%) achieved using end-to-end ASR." ], "reasoning": "The table shows that the WER for VTLN-based VC perm on the test-clean subset is indeed 15.9.", "statement": "VTLN-based VC perm achieves a WER of 15.9 on the test-clean subset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: Results of our model and state-of-the-art systems. COPYNET incorporates copying mechanism to solve out-of-vocabulary problem, so its has higher ROUGE scores. Our model does not incorporate this mechanism currently. In the future work, we will implement this technique to further improve the performance. The results are reported on the test sets. (Word: Word level; Char: Character level; R-1: F-score of ROUGE-1; R-2: F-score of ROUGE-2; R-L: F-score of ROUGE-L)'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "COPYNET achieves the highest scores due to its copying mechanism, which addresses out-of-vocabulary word problems.COPYNET has the highest scores, because it incorporates copying mechanism to deals with out-of-vocabulary word problem. In this paper, we do not implement this mechanism in our model. Our model can also be improved with these additional techniques, which, however, are not the focus of this paper." } ], "images": [ "scigen/train/1508.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of our model and state-of-the-art systems. COPYNET incorporates copying mechanism to solve out-of-vocabulary problem, so its has higher ROUGE scores. Our model does not incorporate this mechanism currently. In the future work, we will implement this technique to further improve the performance. The results are reported on the test sets. (Word: Word level; Char: Character level; R-1: F-score of ROUGE-1; R-2: F-score of ROUGE-2; R-L: F-score of ROUGE-L)" ], "reasoning": "COPYNET achieves the highest scores due to its copying mechanism, which addresses out-of-vocabulary word problems.", "description": "COPYNET has the highest scores, because it incorporates copying mechanism to deals with out-of-vocabulary word problem. In this paper, we do not implement this mechanism in our model. Our model can also be improved with these additional techniques, which, however, are not the focus of this paper.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 4: Results of our model and state-of-the-art systems. COPYNET incorporates copying mechanism to solve out-of-vocabulary problem, so its has higher ROUGE scores. Our model does not incorporate this mechanism currently. In the future work, we will implement this technique to further improve the performance. The results are reported on the test sets. (Word: Word level; Char: Character level; R-1: F-score of ROUGE-1; R-2: F-score of ROUGE-2; R-L: F-score of ROUGE-L)'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. \n\nQuestion: Which model has the highest ROUGE-1 score and why?\n\nYour Solution:\n" }, { "role": "assistant", "content": "COPYNET has the highest ROUGE-1 score of 35.0 because it incorporates a copying mechanism to handle out-of-vocabulary words. So, the answer is {\"answer\": \"COPYNET\"}." } ], "images": [ "scigen/train/1508.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of our model and state-of-the-art systems. COPYNET incorporates copying mechanism to solve out-of-vocabulary problem, so its has higher ROUGE scores. Our model does not incorporate this mechanism currently. In the future work, we will implement this technique to further improve the performance. The results are reported on the test sets. (Word: Word level; Char: Character level; R-1: F-score of ROUGE-1; R-2: F-score of ROUGE-2; R-L: F-score of ROUGE-L)" ], "reasoning": "COPYNET has the highest ROUGE-1 score of 35.0 because it incorporates a copying mechanism to handle out-of-vocabulary words.", "question": "Which model has the highest ROUGE-1 score and why?", "answer": "So, the answer is {\"answer\": \"COPYNET\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence COPYNET has the highest ROUGE-2 score. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nCOPYNET has the highest ROUGE-2 score.\n" }, { "role": "assistant", "content": "The table shows that COPYNET has the highest ROUGE-2 score of 22.3. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1508.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of our model and state-of-the-art systems. COPYNET incorporates copying mechanism to solve out-of-vocabulary problem, so its has higher ROUGE scores. Our model does not incorporate this mechanism currently. In the future work, we will implement this technique to further improve the performance. The results are reported on the test sets. (Word: Word level; Char: Character level; R-1: F-score of ROUGE-1; R-2: F-score of ROUGE-2; R-L: F-score of ROUGE-L)" ], "reasoning": "The table shows that COPYNET has the highest ROUGE-2 score of 22.3.", "statement": "COPYNET has the highest ROUGE-2 score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Results of our model and baseline systems. Our models achieve substantial improvement of all ROUGE scores over baseline systems. The results are reported on the test sets.(W: Word level; C: Character level).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The SRB model outperforms both Seq2seq and Seq2seq-Attention models in terms of ROUGE-1, ROUGE-2, and ROUGE-L scores.We compare our model with above baseline systems, including Seq2seq and Seq2seq-Attention. We refer to our proposed Semantic Relevance Based neural model as SRB. Therefore, we implement our model at the character level. Our implementation of Seq2seq-Attention has better score, mainly because we tune the hyper-parameters well on the development set. We can see SRB outperforms both Seq2seq and Seq2seq-Attention with the F-score of 33.3 ROUGE-1, 20.0 ROUGE-2 and 30.1 ROUGE-L." } ], "images": [ "scigen/train/1509.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of our model and baseline systems. Our models achieve substantial improvement of all ROUGE scores over baseline systems. The results are reported on the test sets.(W: Word level; C: Character level)." ], "reasoning": "The SRB model outperforms both Seq2seq and Seq2seq-Attention models in terms of ROUGE-1, ROUGE-2, and ROUGE-L scores.", "description": "We compare our model with above baseline systems, including Seq2seq and Seq2seq-Attention. We refer to our proposed Semantic Relevance Based neural model as SRB. Therefore, we implement our model at the character level. Our implementation of Seq2seq-Attention has better score, mainly because we tune the hyper-parameters well on the development set. We can see SRB outperforms both Seq2seq and Seq2seq-Attention with the F-score of 33.3 ROUGE-1, 20.0 ROUGE-2 and 30.1 ROUGE-L.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Results of our model and baseline systems. Our models achieve substantial improvement of all ROUGE scores over baseline systems. The results are reported on the test sets.(W: Word level; C: Character level).', answer the following question. Give detailed solution consisting of each step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest ROUGE-2 score?\n" }, { "role": "assistant", "content": "By comparing the ROUGE-2 scores of all models in the table, the SRB model has the highest ROUGE-2 score of 20.0. So, the answer is {\"answer\": \"SRB (C) (our proposal)\"}" } ], "images": [ "scigen/train/1509.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of our model and baseline systems. Our models achieve substantial improvement of all ROUGE scores over baseline systems. The results are reported on the test sets.(W: Word level; C: Character level)." ], "reasoning": "By comparing the ROUGE-2 scores of all models in the table, the SRB model has the highest ROUGE-2 score of 20.0.", "question": "Which model has the highest ROUGE-2 score?", "answer": "So, the answer is {\"answer\": \"SRB (C) (our proposal)\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Seq2seq-Attention (C) (our implementation) has a higher ROUGE-1 score than Seq2seq (C) Hu, Chen, and Zhu (2015)., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nSeq2seq-Attention (C) (our implementation) has a higher ROUGE-1 score than Seq2seq (C) Hu, Chen, and Zhu (2015).\n" }, { "role": "assistant", "content": "The table shows that Seq2seq-Attention (C) (our implementation) has a ROUGE-1 score of 30.1, which is higher than the ROUGE-1 score of 21.5 for Seq2seq (C) Hu, Chen, and Zhu (2015). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1509.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of our model and baseline systems. Our models achieve substantial improvement of all ROUGE scores over baseline systems. The results are reported on the test sets.(W: Word level; C: Character level)." ], "reasoning": "The table shows that Seq2seq-Attention (C) (our implementation) has a ROUGE-1 score of 30.1, which is higher than the ROUGE-1 score of 21.5 for Seq2seq (C) Hu, Chen, and Zhu (2015).", "statement": "Seq2seq-Attention (C) (our implementation) has a higher ROUGE-1 score than Seq2seq (C) Hu, Chen, and Zhu (2015).", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Comparison with our model and the recent neural models for text simplification. Our models achieve substantial improvement of BLEU score over baseline systems. The results are reported on the test sets.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our SRB model outperforms Seq2seq-Attention and Seq2seq-Attention-w2v on both PWKP and EW-SEW datasets, achieving BLEU scores of 50.18 and 89.84 respectively.We compare our model with Seq2seq-Attention and Seq2seq-Attention-w2v. Seq2seq -Attention-w2v is a Seq2seq-Attention with pretrain word embeddings. We also implement a Seq2seq-Attention model, and carefully tune it on the development set. Our implementation get 48.26 BLEU score on PWKP, and 88.97 BLEU score on EW-SEW. Our SRB outperforms all of the baseline systems, with the BLEU score of 50.18 on PWKP and the BLEU score of 89.84 on EW-SEW." } ], "images": [ "scigen/train/1510.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison with our model and the recent neural models for text simplification. Our models achieve substantial improvement of BLEU score over baseline systems. The results are reported on the test sets." ], "reasoning": "Our SRB model outperforms Seq2seq-Attention and Seq2seq-Attention-w2v on both PWKP and EW-SEW datasets, achieving BLEU scores of 50.18 and 89.84 respectively.", "description": "We compare our model with Seq2seq-Attention and Seq2seq-Attention-w2v. Seq2seq -Attention-w2v is a Seq2seq-Attention with pretrain word embeddings. We also implement a Seq2seq-Attention model, and carefully tune it on the development set. Our implementation get 48.26 BLEU score on PWKP, and 88.97 BLEU score on EW-SEW. Our SRB outperforms all of the baseline systems, with the BLEU score of 50.18 on PWKP and the BLEU score of 89.84 on EW-SEW.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Comparison with our model and the recent neural models for text simplification. Our models achieve substantial improvement of BLEU score over baseline systems. The results are reported on the test sets.\nQuestion: What is the BLEU score of the Seq2seq-Attention-w2v model on the EW-SEW dataset?\nSolve the above question based on the table information. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table lists the BLEU scores for different models on the PWKP and EW-SEW datasets. The BLEU score for Seq2seq-Attention-w2v on EW-SEW is 87.50. So, the answer is {\"answer\": \"87.50\"}" } ], "images": [ "scigen/train/1510.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison with our model and the recent neural models for text simplification. Our models achieve substantial improvement of BLEU score over baseline systems. The results are reported on the test sets." ], "reasoning": "The table lists the BLEU scores for different models on the PWKP and EW-SEW datasets. The BLEU score for Seq2seq-Attention-w2v on EW-SEW is 87.50.", "question": "What is the BLEU score of the Seq2seq-Attention-w2v model on the EW-SEW dataset?", "answer": "So, the answer is {\"answer\": \"87.50\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The SRB model achieves the highest BLEU score on the PWKP dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe SRB model achieves the highest BLEU score on the PWKP dataset.\n" }, { "role": "assistant", "content": "The table shows that the SRB model has a BLEU score of 50.18 on the PWKP dataset, which is higher than the scores of the other models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1510.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison with our model and the recent neural models for text simplification. Our models achieve substantial improvement of BLEU score over baseline systems. The results are reported on the test sets." ], "reasoning": "The table shows that the SRB model has a BLEU score of 50.18 on the PWKP dataset, which is higher than the scores of the other models listed.", "statement": "The SRB model achieves the highest BLEU score on the PWKP dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: Results of our model and state-of-the-art systems. SRB achieves the best BLEU scores compared with the related systems on PWKP and EW-SEW. The results are reported on the test sets.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "SRB achieves the highest BLEU score on the PWKP dataset with 50.18, outperforming NTS, NTS-w2v, DRESS, and DRESS-LS. On the EW-SEW dataset, WEAN-dot achieves the best BLEU score with 89.84, surpassing PBMT-R, SBMT-SARI, and other neural models.We also compare SRB with other models for text simplification, which are not limit to neural models. On PWKP dataset, we compare SRB with NTS, NTS-w2v, DRESS and DRESS-LS. We run the public release code of NTS and NTS-w2v provided by \\nameciteNisioiEA2017, and get the BLEU score of 47.52 and 48.10 respectively. As for DRESS and DRESS-LS, we use the scores reported by \\nameciteZhangEA2017. The goal of DRESS is not to generate the outputs closer to the references, so BLEU of DRESS and DRESS-LS are relatively lower than NTS and NTS-w2v. SRB achieves a BLEU score of 50.18, outperforming all of the previous systems. On EW-SEW dataset, we compare WEAN-dot with PBMT-R, SBMT-SARI, and the neural models described above. We do not find any public release code of PBMT-R and SBMT-SARI. Fortunately, \\nameciteXuEA2016 provides the predictions of PBMT-R and SBMT-SARI on EW-SEW test set, so that we can compare our model with these systems. It shows that the neural models have better performance in BLEU, and WEAN-dot achieves the best BLEU score with 89.84." } ], "images": [ "scigen/train/1511.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results of our model and state-of-the-art systems. SRB achieves the best BLEU scores compared with the related systems on PWKP and EW-SEW. The results are reported on the test sets." ], "reasoning": "SRB achieves the highest BLEU score on the PWKP dataset with 50.18, outperforming NTS, NTS-w2v, DRESS, and DRESS-LS. On the EW-SEW dataset, WEAN-dot achieves the best BLEU score with 89.84, surpassing PBMT-R, SBMT-SARI, and other neural models.", "description": "We also compare SRB with other models for text simplification, which are not limit to neural models. On PWKP dataset, we compare SRB with NTS, NTS-w2v, DRESS and DRESS-LS. We run the public release code of NTS and NTS-w2v provided by \\nameciteNisioiEA2017, and get the BLEU score of 47.52 and 48.10 respectively. As for DRESS and DRESS-LS, we use the scores reported by \\nameciteZhangEA2017. The goal of DRESS is not to generate the outputs closer to the references, so BLEU of DRESS and DRESS-LS are relatively lower than NTS and NTS-w2v. SRB achieves a BLEU score of 50.18, outperforming all of the previous systems. On EW-SEW dataset, we compare WEAN-dot with PBMT-R, SBMT-SARI, and the neural models described above. We do not find any public release code of PBMT-R and SBMT-SARI. Fortunately, \\nameciteXuEA2016 provides the predictions of PBMT-R and SBMT-SARI on EW-SEW test set, so that we can compare our model with these systems. It shows that the neural models have better performance in BLEU, and WEAN-dot achieves the best BLEU score with 89.84.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: Results of our model and state-of-the-art systems. SRB achieves the best BLEU scores compared with the related systems on PWKP and EW-SEW. The results are reported on the test sets.'.\n\nQuestion: What is the BLEU score of the model that outperforms all others on the PWKP dataset?\n" }, { "role": "assistant", "content": "The table shows that SRB achieves the highest BLEU score on the PWKP dataset with a score of 50.18, which is higher than the scores of NTS, NTS-w2v, DRESS, and DRESS-LS. So, the answer is {\"answer\": \"50.18\"}" } ], "images": [ "scigen/train/1511.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results of our model and state-of-the-art systems. SRB achieves the best BLEU scores compared with the related systems on PWKP and EW-SEW. The results are reported on the test sets." ], "reasoning": "The table shows that SRB achieves the highest BLEU score on the PWKP dataset with a score of 50.18, which is higher than the scores of NTS, NTS-w2v, DRESS, and DRESS-LS.", "question": "What is the BLEU score of the model that outperforms all others on the PWKP dataset?", "answer": "So, the answer is {\"answer\": \"50.18\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSRB achieves the highest BLEU score on the EW-SEW dataset.\n" }, { "role": "assistant", "content": "The table shows that SRB achieves a BLEU score of 89.84 on the EW-SEW dataset, which is higher than the scores of PBMT-R, SBMT-SARI, NTS, NTS-w2v, DRESS, and DRESS-LS. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1511.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results of our model and state-of-the-art systems. SRB achieves the best BLEU scores compared with the related systems on PWKP and EW-SEW. The results are reported on the test sets." ], "reasoning": "The table shows that SRB achieves a BLEU score of 89.84 on the EW-SEW dataset, which is higher than the scores of PBMT-R, SBMT-SARI, NTS, NTS-w2v, DRESS, and DRESS-LS.", "statement": "SRB achieves the highest BLEU score on the EW-SEW dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1. Caption performance comparison between Vanilla (no topic guidance) and TGM (using different latent topic guidance) on the MSR-VTT and Youtube2Text datasets. The best results are marked in bold and the second best results with underline.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The TGM model with predicted multimodal latent topics consistently outperforms other models and topic guidance methods across multiple metrics and datasets, demonstrating the effectiveness of multimodal topic mining.The numbers of textual and multimodal latent topics are optimized on the validation set (50 topics for MSR-VTT and 5 topics for Youtube2Text). We can see that TGMs outperform Vanilla model consistently on all four metrics across MSR-VTT and Youtube2Text datasets, which demonstrates that exploiting topic information is beneficial to generate video descriptions. We can see that the automatically mined topics (both textual and multimodal latent topics), outperform expert-defined topics by a large margin on all four metrics and two datasets, such as over 2% absolute gain on the CIDEr score. The multimodal latent topics further achieve better performance than textual latent topics consistently on multiple metrics across datasets. The improvement is also proved to be significant in the Student’s t-test such as p-value of 0.03 on the BLEU@4 score. These results suggest that the multimodal topic mining approach can discover better underlying topics as guidance for TGM, and such improvement is prominent and robust for different evaluation metrics and datasets. However, the assigned expert-defined topics are very noisy, because the annotations are labelled on the entire videos but the caption generation is only applied on the short segments and the users may also make labelling mistakes. For almost all metrics, the performances of our predicted latent topics are superior to the noisy assigned expert-defined topics on both MSR-VTT and Youtube2Text datasets, which shows that the latent topics learned in an unsupervised approach are competitive or even better than the assigned expert-defined topics with noise." } ], "images": [ "scigen/train/1512.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1. Caption performance comparison between Vanilla (no topic guidance) and TGM (using different latent topic guidance) on the MSR-VTT and Youtube2Text datasets. The best results are marked in bold and the second best results with underline." ], "reasoning": "The TGM model with predicted multimodal latent topics consistently outperforms other models and topic guidance methods across multiple metrics and datasets, demonstrating the effectiveness of multimodal topic mining.", "description": "The numbers of textual and multimodal latent topics are optimized on the validation set (50 topics for MSR-VTT and 5 topics for Youtube2Text). We can see that TGMs outperform Vanilla model consistently on all four metrics across MSR-VTT and Youtube2Text datasets, which demonstrates that exploiting topic information is beneficial to generate video descriptions. We can see that the automatically mined topics (both textual and multimodal latent topics), outperform expert-defined topics by a large margin on all four metrics and two datasets, such as over 2% absolute gain on the CIDEr score. The multimodal latent topics further achieve better performance than textual latent topics consistently on multiple metrics across datasets. The improvement is also proved to be significant in the Student’s t-test such as p-value of 0.03 on the BLEU@4 score. These results suggest that the multimodal topic mining approach can discover better underlying topics as guidance for TGM, and such improvement is prominent and robust for different evaluation metrics and datasets. However, the assigned expert-defined topics are very noisy, because the annotations are labelled on the entire videos but the caption generation is only applied on the short segments and the users may also make labelling mistakes. For almost all metrics, the performances of our predicted latent topics are superior to the noisy assigned expert-defined topics on both MSR-VTT and Youtube2Text datasets, which shows that the latent topics learned in an unsupervised approach are competitive or even better than the assigned expert-defined topics with noise.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1. Caption performance comparison between Vanilla (no topic guidance) and TGM (using different latent topic guidance) on the MSR-VTT and Youtube2Text datasets. The best results are marked in bold and the second best results with underline.', solve the subsequent question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich model and topic guidance combination achieved the highest CIDEr score on the MSR-VTT dataset?\n" }, { "role": "assistant", "content": "To find the highest CIDEr score on the MSR-VTT dataset, we need to look at the 'MSR-VTT CIDEr' column. The highest score is 48.59, achieved by the TGM model with assigned expert-defined topic guidance. So, the answer is {\"answer\": \"TGM with assigned expert-defined topic\"}" } ], "images": [ "scigen/train/1512.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1. Caption performance comparison between Vanilla (no topic guidance) and TGM (using different latent topic guidance) on the MSR-VTT and Youtube2Text datasets. The best results are marked in bold and the second best results with underline." ], "reasoning": "To find the highest CIDEr score on the MSR-VTT dataset, we need to look at the 'MSR-VTT CIDEr' column. The highest score is 48.59, achieved by the TGM model with assigned expert-defined topic guidance.", "question": "Which model and topic guidance combination achieved the highest CIDEr score on the MSR-VTT dataset?", "answer": "So, the answer is {\"answer\": \"TGM with assigned expert-defined topic\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Vanilla model achieved the highest BLEU4 score on the Youtube2Text dataset.\n" }, { "role": "assistant", "content": "To verify this statement, we need to look at the 'Youtube2Text BLEU4' column. The highest score is 47.56, achieved by the TGM model with predicted multimodal latent topic guidance, not the Vanilla model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1512.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1. Caption performance comparison between Vanilla (no topic guidance) and TGM (using different latent topic guidance) on the MSR-VTT and Youtube2Text datasets. The best results are marked in bold and the second best results with underline." ], "reasoning": "To verify this statement, we need to look at the 'Youtube2Text BLEU4' column. The highest score is 47.56, achieved by the TGM model with predicted multimodal latent topic guidance, not the Vanilla model.", "statement": "The Vanilla model achieved the highest BLEU4 score on the Youtube2Text dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3. Caption performance of M&M TGM and state-of-the-art methods on MSR-VTT and Youtube2Text datasets.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Our method, M&M TGM, achieves better performance on the MSR-VTT dataset with a 7.08% relative improvement in BLEU@4 compared to the previous best, and shows significant improvements in METEOR and CIDEr scores on the Youtube2Text dataset, although it performs lower on BLEU@4 than SCN-LSTM.Our method achieves significant better performance than prior works on MSR-VTT dataset, for example, the BLEU@4 achieves 7.08% relative improvement than the previous best performance. For Youtube2Text dataset, the METEOR and CIDERr scores improved significantly but our performance on BLEU@4 is lower than the SCN-LSTM. The BLEU@4 metric focuses on the syntactic agreement, while METEOR and CIDEr concern more about semantic meanings. Therefore, we consider the sentence generated from M&M TGM are more semantically relevant with the video content." } ], "images": [ "scigen/train/1513.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. Caption performance of M&M TGM and state-of-the-art methods on MSR-VTT and Youtube2Text datasets." ], "reasoning": "Our method, M&M TGM, achieves better performance on the MSR-VTT dataset with a 7.08% relative improvement in BLEU@4 compared to the previous best, and shows significant improvements in METEOR and CIDEr scores on the Youtube2Text dataset, although it performs lower on BLEU@4 than SCN-LSTM.", "description": "Our method achieves significant better performance than prior works on MSR-VTT dataset, for example, the BLEU@4 achieves 7.08% relative improvement than the previous best performance. For Youtube2Text dataset, the METEOR and CIDERr scores improved significantly but our performance on BLEU@4 is lower than the SCN-LSTM. The BLEU@4 metric focuses on the syntactic agreement, while METEOR and CIDEr concern more about semantic meanings. Therefore, we consider the sentence generated from M&M TGM are more semantically relevant with the video content.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3. Caption performance of M&M TGM and state-of-the-art methods on MSR-VTT and Youtube2Text datasets.' to sequentially solve the question presented below. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the relative improvement percentage of BLEU@4 score achieved by M&M TGM on the MSR-VTT dataset compared to the previous best performance?\n" }, { "role": "assistant", "content": "The description states that M&M TGM achieves a 7.08% relative improvement in BLEU@4 score on the MSR-VTT dataset compared to the previous best performance. So, the answer is {\"answer\": \"7.08%\"}." } ], "images": [ "scigen/train/1513.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. Caption performance of M&M TGM and state-of-the-art methods on MSR-VTT and Youtube2Text datasets." ], "reasoning": "The description states that M&M TGM achieves a 7.08% relative improvement in BLEU@4 score on the MSR-VTT dataset compared to the previous best performance.", "question": "What is the relative improvement percentage of BLEU@4 score achieved by M&M TGM on the MSR-VTT dataset compared to the previous best performance?", "answer": "So, the answer is {\"answer\": \"7.08%\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nM&M TGM achieves the highest METEOR score on the Youtube2Text dataset.\n" }, { "role": "assistant", "content": "The table shows that M&M TGM has a METEOR score of 34.36 on the Youtube2Text dataset, which is higher than all other models listed for the same dataset. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1513.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. Caption performance of M&M TGM and state-of-the-art methods on MSR-VTT and Youtube2Text datasets." ], "reasoning": "The table shows that M&M TGM has a METEOR score of 34.36 on the Youtube2Text dataset, which is higher than all other models listed for the same dataset.", "statement": "M&M TGM achieves the highest METEOR score on the Youtube2Text dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4. Comparison of generalization ability of the vanilla model and our proposed M&M TGM model. The models are trained on MSR-VTT training set and evaluated on the Youtube2Text testing set.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The M&M TGM model outperforms the Vanilla model on all four metrics (BLEU4, METEOR, ROUGE_L, CIDEr) in cross dataset evaluation, demonstrating better generalization ability.Generalization Ability: To evaluate the generalization ability of our proposed method, we conduct the cross dataset experiment. We train M&M TGM on MSR-VTT dataset and test its performance on the Youtube2Text dataset. We can see that the proposed M&M TGM works significantly better than Vanilla model for cross datasets evaluation on all four metrics, which demonstrates that our method not only can improve the caption performance for in-domain videos but also generalize well on videos in the wild." } ], "images": [ "scigen/train/1514.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4. Comparison of generalization ability of the vanilla model and our proposed M&M TGM model. The models are trained on MSR-VTT training set and evaluated on the Youtube2Text testing set." ], "reasoning": "The M&M TGM model outperforms the Vanilla model on all four metrics (BLEU4, METEOR, ROUGE_L, CIDEr) in cross dataset evaluation, demonstrating better generalization ability.", "description": "Generalization Ability: To evaluate the generalization ability of our proposed method, we conduct the cross dataset experiment. We train M&M TGM on MSR-VTT dataset and test its performance on the Youtube2Text dataset. We can see that the proposed M&M TGM works significantly better than Vanilla model for cross datasets evaluation on all four metrics, which demonstrates that our method not only can improve the caption performance for in-domain videos but also generalize well on videos in the wild.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 4. Comparison of generalization ability of the vanilla model and our proposed M&M TGM model. The models are trained on MSR-VTT training set and evaluated on the Youtube2Text testing set.' in mind, break down and resolve the question below step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the difference in CIDEr scores between the M&M TGM and Vanilla models?\n" }, { "role": "assistant", "content": "To find the difference in CIDEr scores, subtract the Vanilla model's CIDEr score (51.02) from the M&M TGM model's CIDEr score (55.39). So, the answer is {\"answer\": \"4.37\"}." } ], "images": [ "scigen/train/1514.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4. Comparison of generalization ability of the vanilla model and our proposed M&M TGM model. The models are trained on MSR-VTT training set and evaluated on the Youtube2Text testing set." ], "reasoning": "To find the difference in CIDEr scores, subtract the Vanilla model's CIDEr score (51.02) from the M&M TGM model's CIDEr score (55.39).", "question": "What is the difference in CIDEr scores between the M&M TGM and Vanilla models?", "answer": "So, the answer is {\"answer\": \"4.37\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe M&M TGM model has a higher BLEU4 score than the Vanilla model.\n" }, { "role": "assistant", "content": "The table shows that the BLEU4 score for the M&M TGM model is 34.67, which is higher than the Vanilla model's BLEU4 score of 32.92. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1514.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4. Comparison of generalization ability of the vanilla model and our proposed M&M TGM model. The models are trained on MSR-VTT training set and evaluated on the Youtube2Text testing set." ], "reasoning": "The table shows that the BLEU4 score for the M&M TGM model is 34.67, which is higher than the Vanilla model's BLEU4 score of 32.92.", "statement": "The M&M TGM model has a higher BLEU4 score than the Vanilla model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5. Caption performance comparison of different topic prediction losses.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The l2-distance consistently outperforms KL-divergence in caption performance across all datasets and metrics.Topic Loss Selection : We compare the caption performance using l2-distance or KL-divergence as Ltopic in the TGM model. The l2-distance consistently surpasses the KL-divergence across datasets and metrics. So unless otherwise specified, we use l2-distance as the topic prediction loss." } ], "images": [ "scigen/train/1515.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5. Caption performance comparison of different topic prediction losses." ], "reasoning": "The l2-distance consistently outperforms KL-divergence in caption performance across all datasets and metrics.", "description": "Topic Loss Selection : We compare the caption performance using l2-distance or KL-divergence as Ltopic in the TGM model. The l2-distance consistently surpasses the KL-divergence across datasets and metrics. So unless otherwise specified, we use l2-distance as the topic prediction loss.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 5. Caption performance comparison of different topic prediction losses.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. \n\nQuestion: Which loss function achieves the highest BLEU4 score on the Youtube2Text dataset?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By comparing the BLEU4 scores for the Youtube2Text dataset, the l2-distance achieves a score of 47.56, which is higher than the KL-divergence score of 45.98. So, the answer is {\"answer\": \"l2\"}." } ], "images": [ "scigen/train/1515.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5. Caption performance comparison of different topic prediction losses." ], "reasoning": "By comparing the BLEU4 scores for the Youtube2Text dataset, the l2-distance achieves a score of 47.56, which is higher than the KL-divergence score of 45.98.", "question": "Which loss function achieves the highest BLEU4 score on the Youtube2Text dataset?", "answer": "So, the answer is {\"answer\": \"l2\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The KL-divergence loss function achieves a higher METEOR score than the l2-distance on the MSR-VTT dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe KL-divergence loss function achieves a higher METEOR score than the l2-distance on the MSR-VTT dataset.\n" }, { "role": "assistant", "content": "By comparing the METEOR scores for the MSR-VTT dataset, the l2-distance achieves a score of 29.26, while the KL-divergence achieves a score of 29.11. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1515.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5. Caption performance comparison of different topic prediction losses." ], "reasoning": "By comparing the METEOR scores for the MSR-VTT dataset, the l2-distance achieves a score of 29.26, while the KL-divergence achieves a score of 29.11. Therefore, the statement is refuted.", "statement": "The KL-divergence loss function achieves a higher METEOR score than the l2-distance on the MSR-VTT dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 6. The influence of human interactions on Youtube2Text dataset with noisy and clean expert-defined topics assignment.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The topic-guided model's performance improves with human enhancement, as shown by higher scores in BLEU4, METEOR, ROUGE I, and CIDEr for clean topics compared to noisy topics.Besides the improvement on the caption performance, our topic-guided model can provide an interface for users to be involved in the caption generation. For example, users could simply assign a category tag to the uploaded videos with minimum costs to refine the automatic generated video descriptions. To evaluate the performance boosts with the human enhancement to our topic-guided model, we manually re-annotate the expert-defined topics for Youtub2Text dataset as the clean version and compare its caption performance with the noisy assigned topics." } ], "images": [ "scigen/train/1516.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6. The influence of human interactions on Youtube2Text dataset with noisy and clean expert-defined topics assignment." ], "reasoning": "The topic-guided model's performance improves with human enhancement, as shown by higher scores in BLEU4, METEOR, ROUGE I, and CIDEr for clean topics compared to noisy topics.", "description": "Besides the improvement on the caption performance, our topic-guided model can provide an interface for users to be involved in the caption generation. For example, users could simply assign a category tag to the uploaded videos with minimum costs to refine the automatic generated video descriptions. To evaluate the performance boosts with the human enhancement to our topic-guided model, we manually re-annotate the expert-defined topics for Youtub2Text dataset as the clean version and compare its caption performance with the noisy assigned topics.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 6. The influence of human interactions on Youtube2Text dataset with noisy and clean expert-defined topics assignment.', solve this question and give detail solutions. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the difference in CIDEr scores between noisy and clean topics?\n" }, { "role": "assistant", "content": "To find the difference in CIDEr scores, subtract the CIDEr score for noisy topics (74.24) from the CIDEr score for clean topics (82.79). So, the answer is {\"answer\": \"8.55\"}." } ], "images": [ "scigen/train/1516.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6. The influence of human interactions on Youtube2Text dataset with noisy and clean expert-defined topics assignment." ], "reasoning": "To find the difference in CIDEr scores, subtract the CIDEr score for noisy topics (74.24) from the CIDEr score for clean topics (82.79).", "question": "What is the difference in CIDEr scores between noisy and clean topics?", "answer": "So, the answer is {\"answer\": \"8.55\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe clean topic has a higher METEOR score than the noisy topic.\n" }, { "role": "assistant", "content": "The table shows that the METEOR score for clean topics is 35.10, which is higher than the METEOR score for noisy topics, which is 32.99. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1516.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6. The influence of human interactions on Youtube2Text dataset with noisy and clean expert-defined topics assignment." ], "reasoning": "The table shows that the METEOR score for clean topics is 35.10, which is higher than the METEOR score for noisy topics, which is 32.99.", "statement": "The clean topic has a higher METEOR score than the noisy topic.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Evaluation on the word sense induction task to the current state-of-the-art models on SemEval2010 and SemEval2013 tasks.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Combining BERT with embeddings improves its performance by about 3% on both SemEval-2010 and SemEval-2013 datasets, while ELMo with embeddings improves results by about 4% on SemEval-2010.We evaluate lexical substitutes based on neural LMs in the following datasets: SemEval-2013 and SemEval-2010. For example, a combination of BERT with its embeddings (BERT+embs) improves the results of a BERT model by about 3% on both data sets. Likewise, a combination of forward LM, backward LM and proximity of ELMo embeddings between substitute and target word, i.e. ELMo+embs generator, raises results on SemEval-2010 task by about 4%." } ], "images": [ "scigen/train/1517.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Evaluation on the word sense induction task to the current state-of-the-art models on SemEval2010 and SemEval2013 tasks." ], "reasoning": "Combining BERT with embeddings improves its performance by about 3% on both SemEval-2010 and SemEval-2013 datasets, while ELMo with embeddings improves results by about 4% on SemEval-2010.", "description": "We evaluate lexical substitutes based on neural LMs in the following datasets: SemEval-2013 and SemEval-2010. For example, a combination of BERT with its embeddings (BERT+embs) improves the results of a BERT model by about 3% on both data sets. Likewise, a combination of forward LM, backward LM and proximity of ELMo embeddings between substitute and target word, i.e. ELMo+embs generator, raises results on SemEval-2010 task by about 4%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 3: Evaluation on the word sense induction task to the current state-of-the-art models on SemEval2010 and SemEval2013 tasks.', solve the subsequent question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhat is the average score improvement of BERT when combined with embeddings on the SemEval-2013 dataset?\n" }, { "role": "assistant", "content": "The table shows that BERT alone scores 34.5 on SemEval-2013, while BERT+embs scores 36.8. The improvement is 36.8 - 34.5 = 2.3. So, the answer is {\"answer\": \"2.3\"}." } ], "images": [ "scigen/train/1517.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Evaluation on the word sense induction task to the current state-of-the-art models on SemEval2010 and SemEval2013 tasks." ], "reasoning": "The table shows that BERT alone scores 34.5 on SemEval-2013, while BERT+embs scores 36.8. The improvement is 36.8 - 34.5 = 2.3.", "question": "What is the average score improvement of BERT when combined with embeddings on the SemEval-2013 dataset?", "answer": "So, the answer is {\"answer\": \"2.3\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nELMo+embs improves the results on SemEval-2010 by about 4%.\n" }, { "role": "assistant", "content": "The table shows that ELMo scores 41.8 on SemEval-2010, while ELMo+embs scores 45.3. The improvement is 45.3 - 41.8 = 3.5, which is close to 4%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1517.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Evaluation on the word sense induction task to the current state-of-the-art models on SemEval2010 and SemEval2013 tasks." ], "reasoning": "The table shows that ELMo scores 41.8 on SemEval-2010, while ELMo+embs scores 45.3. The improvement is 45.3 - 41.8 = 3.5, which is close to 4%.", "statement": "ELMo+embs improves the results on SemEval-2010 by about 4%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: BLEU scores computed on the development and test sets using different combinations of approaches. WP denotes a word penalty, where we penalizes the number of unknown words to neural networks.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The baseline SMT system built using Moses showed improved performance when features computed by neural networks were added.The baseline phrase-based SMT system was built using Moses with default settings. As expected, adding features computed by neural networks consistently improves the performance over the baseline performance." } ], "images": [ "scigen/train/1518.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: BLEU scores computed on the development and test sets using different combinations of approaches. WP denotes a word penalty, where we penalizes the number of unknown words to neural networks." ], "reasoning": "The baseline SMT system built using Moses showed improved performance when features computed by neural networks were added.", "description": "The baseline phrase-based SMT system was built using Moses with default settings. As expected, adding features computed by neural networks consistently improves the performance over the baseline performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 1: BLEU scores computed on the development and test sets using different combinations of approaches. WP denotes a word penalty, where we penalizes the number of unknown words to neural networks.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: Which model achieved the highest BLEU test score?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the model with the highest BLEU test score, compare the BLEU test scores of all models listed in the table. The model 'CSLM + RNN' has the highest BLEU test score of 34.64. So, the answer is {\"answer\": \"CSLM + RNN\"}." } ], "images": [ "scigen/train/1518.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: BLEU scores computed on the development and test sets using different combinations of approaches. WP denotes a word penalty, where we penalizes the number of unknown words to neural networks." ], "reasoning": "To find the model with the highest BLEU test score, compare the BLEU test scores of all models listed in the table. The model 'CSLM + RNN' has the highest BLEU test score of 34.64.", "question": "Which model achieved the highest BLEU test score?", "answer": "So, the answer is {\"answer\": \"CSLM + RNN\"}." }, { "messages": [ { "role": "user", "content": "The RNN model achieved a higher BLEU dev score than the Baseline model. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe RNN model achieved a higher BLEU dev score than the Baseline model.\n" }, { "role": "assistant", "content": "The table shows that the RNN model has a BLEU dev score of 31.20, while the Baseline model has a BLEU dev score of 30.64. Therefore, the statement is supported by the table. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1518.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: BLEU scores computed on the development and test sets using different combinations of approaches. WP denotes a word penalty, where we penalizes the number of unknown words to neural networks." ], "reasoning": "The table shows that the RNN model has a BLEU dev score of 31.20, while the Baseline model has a BLEU dev score of 30.64. Therefore, the statement is supported by the table.", "statement": "The RNN model achieved a higher BLEU dev score than the Baseline model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: On Switchboard 300h, comparing decoding. All phoneme models here have word-disambiguate symbols. Phoneme single is with end-of-word (EOW) symbol. Grapheme single is with whitespace, which is like a EOW symbol. The optional LSTM LM is on word-level. The advanced decoder is also restricted on the lexicon, and more specifically the unique greedy BPE-split.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The advanced decoder is beneficial for single phone or grapheme labels but detrimental for BPE variants, especially phoneme-BPE. The external LM combination is helpful.For all further experiments, we need to use our advanced decoder, which allows for a word-level LM. It also restricts the search to only label sequences which occur in the lexicon, including only the BPE-splits seen during training, in contrast to the simple decoder, which does not have this restriction. We see that in the case of single phone or grapheme labels, i.e. where we have a weaker model, the restriction on the lexicon by the advanced decoder is helpful, while it is hurtful for the BPE variants, esp. in the case of phoneme-BPE. We also see the effect of the external LM combination, which is helpful (as expected)." } ], "images": [ "scigen/train/1519.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: On Switchboard 300h, comparing decoding. All phoneme models here have word-disambiguate symbols. Phoneme single is with end-of-word (EOW) symbol. Grapheme single is with whitespace, which is like a EOW symbol. The optional LSTM LM is on word-level. The advanced decoder is also restricted on the lexicon, and more specifically the unique greedy BPE-split." ], "reasoning": "The advanced decoder is beneficial for single phone or grapheme labels but detrimental for BPE variants, especially phoneme-BPE. The external LM combination is helpful.", "description": "For all further experiments, we need to use our advanced decoder, which allows for a word-level LM. It also restricts the search to only label sequences which occur in the lexicon, including only the BPE-splits seen during training, in contrast to the simple decoder, which does not have this restriction. We see that in the case of single phone or grapheme labels, i.e. where we have a weaker model, the restriction on the lexicon by the advanced decoder is helpful, while it is hurtful for the BPE variants, esp. in the case of phoneme-BPE. We also see the effect of the external LM combination, which is helpful (as expected).", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 2: On Switchboard 300h, comparing decoding. All phoneme models here have word-disambiguate symbols. Phoneme single is with end-of-word (EOW) symbol. Grapheme single is with whitespace, which is like a EOW symbol. The optional LSTM LM is on word-level. The advanced decoder is also restricted on the lexicon, and more specifically the unique greedy BPE-split.\nQuestion: What is the WER(%) for Phoneme-BPE labels with an advanced decoder and no external LM on the Hub5'00 CH dataset?\n" }, { "role": "assistant", "content": "To find the WER(%) for Phoneme-BPE labels with an advanced decoder and no external LM on the Hub5'00 CH dataset, locate the row for Phoneme-BPE labels, advanced decoder, and no external LM, then find the value under the CH column. So, the answer is {\"answer\": \"22.2\"}" } ], "images": [ "scigen/train/1519.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: On Switchboard 300h, comparing decoding. All phoneme models here have word-disambiguate symbols. Phoneme single is with end-of-word (EOW) symbol. Grapheme single is with whitespace, which is like a EOW symbol. The optional LSTM LM is on word-level. The advanced decoder is also restricted on the lexicon, and more specifically the unique greedy BPE-split." ], "reasoning": "To find the WER(%) for Phoneme-BPE labels with an advanced decoder and no external LM on the Hub5'00 CH dataset, locate the row for Phoneme-BPE labels, advanced decoder, and no external LM, then find the value under the CH column.", "question": "What is the WER(%) for Phoneme-BPE labels with an advanced decoder and no external LM on the Hub5'00 CH dataset?", "answer": "So, the answer is {\"answer\": \"22.2\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe advanced decoder is always better than the simple decoder for all label types and configurations.\n" }, { "role": "assistant", "content": "The table shows that the advanced decoder is not always better than the simple decoder. For example, for Phoneme-BPE labels with no external LM, the WER(%) for the advanced decoder is higher than the simple decoder on the Hub5'00 SWB dataset (11.0 vs. 10.2). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1519.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: On Switchboard 300h, comparing decoding. All phoneme models here have word-disambiguate symbols. Phoneme single is with end-of-word (EOW) symbol. Grapheme single is with whitespace, which is like a EOW symbol. The optional LSTM LM is on word-level. The advanced decoder is also restricted on the lexicon, and more specifically the unique greedy BPE-split." ], "reasoning": "The table shows that the advanced decoder is not always better than the simple decoder. For example, for Phoneme-BPE labels with no external LM, the WER(%) for the advanced decoder is higher than the simple decoder on the Hub5'00 SWB dataset (11.0 vs. 10.2).", "statement": "The advanced decoder is always better than the simple decoder for all label types and configurations.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: On Switchboard 300h, comparing phoneme and grapheme and whole word models. Using simple decoding, using beam size 12. All phoneme models here have word-disambiguate symbols. Phoneme single is with end-of-word (EOW) symbol. Grapheme single is with whitespace, which is like a EOW symbol. All results are without language model.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The experiments using RETURNN and RASR on the Switchboard 300h corpus show that BPE subwords outperform single units and whole words, with BPE-500 performing best.We use RETURNN [zeyer2018:returnn] as the training framework, which builds upon TensorFlow [tensorflow2015]. The advanced decoder is implemented as part of RASR [rybach2011:rasr], while the simple decoder is implemented in pure TensorFlow within RETURNN. All our experiments are performed on the Switchboard 300h English telephone speech corpus [godfrey1992switchboard]. The simple decoder can only produce reasonable results if the label units allow for word disambiguations, such as in the case of graphemes, but also for phonemes with added word-disambiguate symbols. We find that BPE subwords perform much better than single units, both for phonemes and graphemes, and also better than whole words. We also find that a BPE-500 seems to perform best. Note that BPE-500 results in 592 phoneme classes, or 534 grapheme classes." } ], "images": [ "scigen/train/1520.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: On Switchboard 300h, comparing phoneme and grapheme and whole word models. Using simple decoding, using beam size 12. All phoneme models here have word-disambiguate symbols. Phoneme single is with end-of-word (EOW) symbol. Grapheme single is with whitespace, which is like a EOW symbol. All results are without language model." ], "reasoning": "The experiments using RETURNN and RASR on the Switchboard 300h corpus show that BPE subwords outperform single units and whole words, with BPE-500 performing best.", "description": "We use RETURNN [zeyer2018:returnn] as the training framework, which builds upon TensorFlow [tensorflow2015]. The advanced decoder is implemented as part of RASR [rybach2011:rasr], while the simple decoder is implemented in pure TensorFlow within RETURNN. All our experiments are performed on the Switchboard 300h English telephone speech corpus [godfrey1992switchboard]. The simple decoder can only produce reasonable results if the label units allow for word disambiguations, such as in the case of graphemes, but also for phonemes with added word-disambiguate symbols. We find that BPE subwords perform much better than single units, both for phonemes and graphemes, and also better than whole words. We also find that a BPE-500 seems to perform best. Note that BPE-500 results in 592 phoneme classes, or 534 grapheme classes.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 1: On Switchboard 300h, comparing phoneme and grapheme and whole word models. Using simple decoding, using beam size 12. All phoneme models here have word-disambiguate symbols. Phoneme single is with end-of-word (EOW) symbol. Grapheme single is with whitespace, which is like a EOW symbol. All results are without language model.' in mind, break down and resolve the question below step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the WER(%) for BPE-500 phoneme classes on the SWB dataset?\n" }, { "role": "assistant", "content": "The table shows that for BPE-500 phoneme classes, the WER(%) on the SWB dataset is 10.2. So, the answer is {\"answer\": \"10.2\"}." } ], "images": [ "scigen/train/1520.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: On Switchboard 300h, comparing phoneme and grapheme and whole word models. Using simple decoding, using beam size 12. All phoneme models here have word-disambiguate symbols. Phoneme single is with end-of-word (EOW) symbol. Grapheme single is with whitespace, which is like a EOW symbol. All results are without language model." ], "reasoning": "The table shows that for BPE-500 phoneme classes, the WER(%) on the SWB dataset is 10.2.", "question": "What is the WER(%) for BPE-500 phoneme classes on the SWB dataset?", "answer": "So, the answer is {\"answer\": \"10.2\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nBPE-500 grapheme classes have a WER(%) of 10.2 on the SWB dataset.\n" }, { "role": "assistant", "content": "The table shows that BPE-500 grapheme classes have a WER(%) of 9.4 on the SWB dataset, not 10.2. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1520.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: On Switchboard 300h, comparing phoneme and grapheme and whole word models. Using simple decoding, using beam size 12. All phoneme models here have word-disambiguate symbols. Phoneme single is with end-of-word (EOW) symbol. Grapheme single is with whitespace, which is like a EOW symbol. All results are without language model." ], "reasoning": "The table shows that BPE-500 grapheme classes have a WER(%) of 9.4 on the SWB dataset, not 10.2.", "statement": "BPE-500 grapheme classes have a WER(%) of 10.2 on the SWB dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5: On Switchboard 300h, comparing results from the literature. One big difference in varying results is the different amount of training time, which we state as number of epochs. ∗with word-disambiguate symbols.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Longer training times generally lead to better results. Our phoneme-based models perform slightly better than our grapheme-based models.We observe that many other works train for much longer, and that seems to lead to yet better results. Our final phoneme-based models perform slightly better than our final grapheme-based models, although they are very close." } ], "images": [ "scigen/train/1521.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: On Switchboard 300h, comparing results from the literature. One big difference in varying results is the different amount of training time, which we state as number of epochs. ∗with word-disambiguate symbols." ], "reasoning": "Longer training times generally lead to better results. Our phoneme-based models perform slightly better than our grapheme-based models.", "description": "We observe that many other works train for much longer, and that seems to lead to yet better results. Our final phoneme-based models perform slightly better than our final grapheme-based models, although they are very close.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 5: On Switchboard 300h, comparing results from the literature. One big difference in varying results is the different amount of training time, which we state as number of epochs. ∗with word-disambiguate symbols.' to sequentially solve the question presented below. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich model had the lowest WER(%) on Hub500 SWB and how many epochs did it train for?\n" }, { "role": "assistant", "content": "To find the model with the lowest WER(%) on Hub500 SWB, we look at the 'WER(%) Hub500 SWB' column. The lowest value is 6.4, achieved by [tuske2020swbatt], which trained for 250 epochs. So, the answer is {\"answer\": \"[tuske2020swbatt], 250\"}" } ], "images": [ "scigen/train/1521.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: On Switchboard 300h, comparing results from the literature. One big difference in varying results is the different amount of training time, which we state as number of epochs. ∗with word-disambiguate symbols." ], "reasoning": "To find the model with the lowest WER(%) on Hub500 SWB, we look at the 'WER(%) Hub500 SWB' column. The lowest value is 6.4, achieved by [tuske2020swbatt], which trained for 250 epochs.", "question": "Which model had the lowest WER(%) on Hub500 SWB and how many epochs did it train for?", "answer": "So, the answer is {\"answer\": \"[tuske2020swbatt], 250\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model with the highest WER(%) on Hub500 CH trained for 150 epochs.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the 'WER(%) Hub500 CH' column for the highest value, which is 21.6 by [zeyer2019:trafo-vs-lstm-asr]. This model trained for 33 epochs, not 150. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1521.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: On Switchboard 300h, comparing results from the literature. One big difference in varying results is the different amount of training time, which we state as number of epochs. ∗with word-disambiguate symbols." ], "reasoning": "To verify this statement, we look at the 'WER(%) Hub500 CH' column for the highest value, which is 21.6 by [zeyer2019:trafo-vs-lstm-asr]. This model trained for 33 epochs, not 150.", "statement": "The model with the highest WER(%) on Hub500 CH trained for 150 epochs.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6: Performance (Pearson’s r×100) as a function of the number of unknown words in the sentence pairs over all 20 SemEval STS datasets. N is the number of sentence pairs.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Character-based models help address the issue of out-of-vocabulary words, which negatively impact performance, as shown by a Pearson’s r of -0.45 between OOV rate and performance.One of our primary motivations for character-based models is to address the issue of out-of-vocabulary (OOV) words, which were found to be one of the main sources of error for the paragram-phrase model from \\newcitewieting-16-full. They reported a negative correlation (Pearson’s r of -0.45) between OOV rate and performance. We took the 12,108 sentence pairs in all 20 SemEval STS tasks and binned them by the total number of unknown words in the pairs. We computed Pearson ’s r over each bin." } ], "images": [ "scigen/train/1522.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Performance (Pearson’s r×100) as a function of the number of unknown words in the sentence pairs over all 20 SemEval STS datasets. N is the number of sentence pairs." ], "reasoning": "Character-based models help address the issue of out-of-vocabulary words, which negatively impact performance, as shown by a Pearson’s r of -0.45 between OOV rate and performance.", "description": "One of our primary motivations for character-based models is to address the issue of out-of-vocabulary (OOV) words, which were found to be one of the main sources of error for the paragram-phrase model from \\newcitewieting-16-full. They reported a negative correlation (Pearson’s r of -0.45) between OOV rate and performance. We took the 12,108 sentence pairs in all 20 SemEval STS tasks and binned them by the total number of unknown words in the pairs. We computed Pearson ’s r over each bin.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the performance of the charagram-phrase model for sentence pairs with exactly 1 unknown word?\nSolve the above question based on the table titled 'Table 6: Performance (Pearson’s r×100) as a function of the number of unknown words in the sentence pairs over all 20 SemEval STS datasets. N is the number of sentence pairs.'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the performance of the charagram-phrase model for different numbers of unknown words. For sentence pairs with exactly 1 unknown word, the performance is 78.8. So, the answer is {\"answer\": \"78.8\"}." } ], "images": [ "scigen/train/1522.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Performance (Pearson’s r×100) as a function of the number of unknown words in the sentence pairs over all 20 SemEval STS datasets. N is the number of sentence pairs." ], "reasoning": "The table shows the performance of the charagram-phrase model for different numbers of unknown words. For sentence pairs with exactly 1 unknown word, the performance is 78.8.", "question": "What is the performance of the charagram-phrase model for sentence pairs with exactly 1 unknown word?", "answer": "So, the answer is {\"answer\": \"78.8\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The paragram-phrase model performs better than the charagram-phrase model for sentence pairs with 2 unknown words., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe paragram-phrase model performs better than the charagram-phrase model for sentence pairs with 2 unknown words.\n" }, { "role": "assistant", "content": "The table shows that for sentence pairs with 2 unknown words, the paragram-phrase model has a performance of 66.4, while the charagram-phrase model has a performance of 72.8. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1522.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Performance (Pearson’s r×100) as a function of the number of unknown words in the sentence pairs over all 20 SemEval STS datasets. N is the number of sentence pairs." ], "reasoning": "The table shows that for sentence pairs with 2 unknown words, the paragram-phrase model has a performance of 66.4, while the charagram-phrase model has a performance of 72.8. Therefore, the statement is refuted.", "statement": "The paragram-phrase model performs better than the charagram-phrase model for sentence pairs with 2 unknown words.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Word similarity results (Spearman’s ρ × 100) on WS353 and SL999. The inter-annotator agreement is the average Spearman’s ρ between a single annotator with the average over all other annotators.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The charagram model achieves the highest scores on both WS353 and SL999 datasets, outperforming charLSTM and charCNN models.The charagram model outperforms both the charLSTM and charCNN models, and also outperforms recent strong results on SL999." } ], "images": [ "scigen/train/1523.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Word similarity results (Spearman’s ρ × 100) on WS353 and SL999. The inter-annotator agreement is the average Spearman’s ρ between a single annotator with the average over all other annotators." ], "reasoning": "The charagram model achieves the highest scores on both WS353 and SL999 datasets, outperforming charLSTM and charCNN models.", "description": "The charagram model outperforms both the charLSTM and charCNN models, and also outperforms recent strong results on SL999.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 1: Word similarity results (Spearman’s ρ × 100) on WS353 and SL999. The inter-annotator agreement is the average Spearman’s ρ between a single annotator with the average over all other annotators.'. Solve the following question based on the table.\n\nQuestion: Which model achieves the highest score on the SL999 dataset?\nLet's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the SL999 column, the charagram model tuned on SL999 achieves the highest score of 63.33. So, the answer is {\"answer\": \"charagram\"}." } ], "images": [ "scigen/train/1523.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Word similarity results (Spearman’s ρ × 100) on WS353 and SL999. The inter-annotator agreement is the average Spearman’s ρ between a single annotator with the average over all other annotators." ], "reasoning": "By examining the SL999 column, the charagram model tuned on SL999 achieves the highest score of 63.33.", "question": "Which model achieves the highest score on the SL999 dataset?", "answer": "So, the answer is {\"answer\": \"charagram\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe charLSTM model tuned on WS353 outperforms the charCNN model tuned on SL999 on the WS353 dataset.\n" }, { "role": "assistant", "content": "The charLSTM model tuned on WS353 has a score of 51.43 on the WS353 dataset, while the charCNN model tuned on SL999 has a score of 26.31 on the WS353 dataset. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1523.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Word similarity results (Spearman’s ρ × 100) on WS353 and SL999. The inter-annotator agreement is the average Spearman’s ρ between a single annotator with the average over all other annotators." ], "reasoning": "The charLSTM model tuned on WS353 has a score of 51.43 on the WS353 dataset, while the charCNN model tuned on SL999 has a score of 26.31 on the WS353 dataset.", "statement": "The charLSTM model tuned on WS353 outperforms the charCNN model tuned on SL999 on the WS353 dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Spearman’s ρ×100 on SL999. charagram (large) refers to the charagram model described in Section 4.4. This model contains 173,881 character embeddings, more than the 100,283 in the charagram model used to obtain the results in Table 1.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The charagram model's performance on word similarity tasks improved with more character n-grams, achieving a score of 70.6 on SL999 with 173,881 n-gram embeddings.We found that performance of charagram on word similarity tasks can be improved by using more character n-grams. Our best result from these experiments was obtained with the largest model we considered, which contains 173,881 n-gram embeddings. When using WS353 for model selection and training for 25 epochs, this model achieves 70.6 on SL999." } ], "images": [ "scigen/train/1524.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Spearman’s ρ×100 on SL999. charagram (large) refers to the charagram model described in Section 4.4. This model contains 173,881 character embeddings, more than the 100,283 in the charagram model used to obtain the results in Table 1." ], "reasoning": "The charagram model's performance on word similarity tasks improved with more character n-grams, achieving a score of 70.6 on SL999 with 173,881 n-gram embeddings.", "description": "We found that performance of charagram on word similarity tasks can be improved by using more character n-grams. Our best result from these experiments was obtained with the largest model we considered, which contains 173,881 n-gram embeddings. When using WS353 for model selection and training for 25 epochs, this model achieves 70.6 on SL999.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: Spearman’s ρ×100 on SL999. charagram (large) refers to the charagram model described in Section 4.4. This model contains 173,881 character embeddings, more than the 100,283 in the charagram model used to obtain the results in Table 1.' and methodically tackle the question that follows. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model achieved the highest score on SL999 and what was the score?\n" }, { "role": "assistant", "content": "By comparing the SL999 scores of all models listed in the table, the charagram (large) model achieved the highest score of 70.6. So, the answer is {\"answer\": \"charagram (large), 70.6\"}." } ], "images": [ "scigen/train/1524.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Spearman’s ρ×100 on SL999. charagram (large) refers to the charagram model described in Section 4.4. This model contains 173,881 character embeddings, more than the 100,283 in the charagram model used to obtain the results in Table 1." ], "reasoning": "By comparing the SL999 scores of all models listed in the table, the charagram (large) model achieved the highest score of 70.6.", "question": "Which model achieved the highest score on SL999 and what was the score?", "answer": "So, the answer is {\"answer\": \"charagram (large), 70.6\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The charagram (large) model achieved the highest score on SL999., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe charagram (large) model achieved the highest score on SL999.\n" }, { "role": "assistant", "content": "The table shows that the charagram (large) model has the highest score of 70.6 on SL999 compared to other models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1524.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Spearman’s ρ×100 on SL999. charagram (large) refers to the charagram model described in Section 4.4. This model contains 173,881 character embeddings, more than the 100,283 in the charagram model used to obtain the results in Table 1." ], "reasoning": "The table shows that the charagram (large) model has the highest score of 70.6 on SL999 compared to other models.", "statement": "The charagram (large) model achieved the highest score on SL999.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: Results on SemEval textual similarity datasets (Pearson’s r×100). The highest score in each row is in boldface (omitting the official task score columns).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The charagram-phrase model consistently outperforms charCNN and charLSTM models, and often surpasses the paragram-phrase model and top-performing task-tuned systems.For comparison, we report performance for the median (50%), third quartile (75%), and top-performing (Max) systems from the shared tasks. We observe strong performance for the charagram-phrase model. It always does better than the charCNN and charLSTM models, and outperforms the paragram-phrase model on 15 of the 22 tasks. Furthermore, charagram-phrase matches or exceeds the top-performing task-tuned systems on 5 tasks, and is within 0.003 on 2 more. The charLSTM and charCNN models are significantly worse, with the charCNN being the better of the two and beating paragram-phrase on 4 of the tasks." } ], "images": [ "scigen/train/1525.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on SemEval textual similarity datasets (Pearson’s r×100). The highest score in each row is in boldface (omitting the official task score columns)." ], "reasoning": "The charagram-phrase model consistently outperforms charCNN and charLSTM models, and often surpasses the paragram-phrase model and top-performing task-tuned systems.", "description": "For comparison, we report performance for the median (50%), third quartile (75%), and top-performing (Max) systems from the shared tasks. We observe strong performance for the charagram-phrase model. It always does better than the charCNN and charLSTM models, and outperforms the paragram-phrase model on 15 of the 22 tasks. Furthermore, charagram-phrase matches or exceeds the top-performing task-tuned systems on 5 tasks, and is within 0.003 on 2 more. The charLSTM and charCNN models are significantly worse, with the charCNN being the better of the two and beating paragram-phrase on 4 of the tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Results on SemEval textual similarity datasets (Pearson’s r×100). The highest score in each row is in boldface (omitting the official task score columns).', answer the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nHow many tasks does the charagram-phrase model outperform the paragram-phrase model?\n" }, { "role": "assistant", "content": "The description states that the charagram-phrase model outperforms the paragram-phrase model on 15 of the 22 tasks. So, the answer is {\"answer\": \"15\"}" } ], "images": [ "scigen/train/1525.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on SemEval textual similarity datasets (Pearson’s r×100). The highest score in each row is in boldface (omitting the official task score columns)." ], "reasoning": "The description states that the charagram-phrase model outperforms the paragram-phrase model on 15 of the 22 tasks.", "question": "How many tasks does the charagram-phrase model outperform the paragram-phrase model?", "answer": "So, the answer is {\"answer\": \"15\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe charagram-phrase model outperforms the paragram-phrase model on all tasks.\n" }, { "role": "assistant", "content": "The description states that the charagram-phrase model outperforms the paragram-phrase model on 15 of the 22 tasks, not all tasks. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1525.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on SemEval textual similarity datasets (Pearson’s r×100). The highest score in each row is in boldface (omitting the official task score columns)." ], "reasoning": "The description states that the charagram-phrase model outperforms the paragram-phrase model on 15 of the 22 tasks, not all tasks.", "statement": "The charagram-phrase model outperforms the paragram-phrase model on all tasks.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: Results on part-of-speech tagging.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Performance is similar across models, with the charagram model showing slight improvement when a second fully-connected 150 dimensional layer is added.Performance is similar across models. We found that adding a second fully-connected 150 dimensional layer to the charagram model improved results slightly." } ], "images": [ "scigen/train/1526.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on part-of-speech tagging." ], "reasoning": "Performance is similar across models, with the charagram model showing slight improvement when a second fully-connected 150 dimensional layer is added.", "description": "Performance is similar across models. We found that adding a second fully-connected 150 dimensional layer to the charagram model improved results slightly.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 4: Results on part-of-speech tagging.' and methodically tackle the question that follows. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest accuracy and what is its value?\n" }, { "role": "assistant", "content": "By comparing the accuracy values of all models, the charagram (2-layer) model has the highest accuracy at 97.10%. So, the answer is {\"answer\": \"charagram (2-layer)\"}." } ], "images": [ "scigen/train/1526.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on part-of-speech tagging." ], "reasoning": "By comparing the accuracy values of all models, the charagram (2-layer) model has the highest accuracy at 97.10%.", "question": "Which model has the highest accuracy and what is its value?", "answer": "So, the answer is {\"answer\": \"charagram (2-layer)\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The charLSTM model has the lowest accuracy., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe charLSTM model has the lowest accuracy.\n" }, { "role": "assistant", "content": "By comparing the accuracy values of all models, the charLSTM model has the lowest accuracy at 96.90%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1526.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on part-of-speech tagging." ], "reasoning": "By comparing the accuracy values of all models, the charLSTM model has the lowest accuracy at 96.90%.", "statement": "The charLSTM model has the lowest accuracy.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: Results of using different numbers and different combinations of character n-grams.'.\n" }, { "role": "assistant", "content": "The experiments evaluated the performance of charagram and charagram-phrase models on different tasks using various numbers and lengths of character n-grams, reporting metrics such as accuracy and Pearson's r after specific training epochs.We performed a series of experiments to investigate how the charagram and charagram-phrase models perform with different numbers and lengths of character n-grams. For a given k, we took the top k most frequent character n-grams for each value of n in use. We experimented with k values in {100,1000,50000}. If there were fewer than k unique character n-grams for a given n, we used all of them. For these experiments, we did very little tuning, setting the regularization strength to 0 and only tuning over the activation function. We repeated this experiment for all three of our tasks. For word similarity, we report performance on SL999 after training for 5 epochs on the lexical section of PPDB XXL. For sentence similarity, we report the average Pearson’s r over all 22 datasets after training for 5 epochs on the phrasal section of PPDB XL. For tagging, we report accuracy on the validation set after training for 50 epochs." } ], "images": [ "scigen/train/1527.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results of using different numbers and different combinations of character n-grams." ], "reasoning": "The experiments evaluated the performance of charagram and charagram-phrase models on different tasks using various numbers and lengths of character n-grams, reporting metrics such as accuracy and Pearson's r after specific training epochs.", "description": "We performed a series of experiments to investigate how the charagram and charagram-phrase models perform with different numbers and lengths of character n-grams. For a given k, we took the top k most frequent character n-grams for each value of n in use. We experimented with k values in {100,1000,50000}. If there were fewer than k unique character n-grams for a given n, we used all of them. For these experiments, we did very little tuning, setting the regularization strength to 0 and only tuning over the activation function. We repeated this experiment for all three of our tasks. For word similarity, we report performance on SL999 after training for 5 epochs on the lexical section of PPDB XXL. For sentence similarity, we report the average Pearson’s r over all 22 datasets after training for 5 epochs on the phrasal section of PPDB XL. For tagging, we report accuracy on the validation set after training for 50 epochs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 5: Results of using different numbers and different combinations of character n-grams.' to sequentially solve the question presented below. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the highest accuracy achieved for the Tagging task and with which number of n-grams and lengths?\n" }, { "role": "assistant", "content": "To find the highest accuracy for the Tagging task, look at the 'Tagging' rows and compare the accuracy values across different n-grams and lengths. The highest accuracy is 97.09, achieved with 50,000 n-grams and lengths 2,3,4,5,6. So, the answer is {\"answer\": \"97.09\"}." } ], "images": [ "scigen/train/1527.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results of using different numbers and different combinations of character n-grams." ], "reasoning": "To find the highest accuracy for the Tagging task, look at the 'Tagging' rows and compare the accuracy values across different n-grams and lengths. The highest accuracy is 97.09, achieved with 50,000 n-grams and lengths 2,3,4,5,6.", "question": "What is the highest accuracy achieved for the Tagging task and with which number of n-grams and lengths?", "answer": "So, the answer is {\"answer\": \"97.09\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The highest Pearson's r for the Similarity task is achieved with 50,000 n-grams and lengths 2,3,4., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe highest Pearson's r for the Similarity task is achieved with 50,000 n-grams and lengths 2,3,4.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'Similarity' rows and compare the Pearson's r values across different n-grams and lengths. The highest Pearson's r for the Similarity task is 69.5, achieved with 50,000 n-grams and lengths 2,3,4,5. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1527.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results of using different numbers and different combinations of character n-grams." ], "reasoning": "To verify this statement, look at the 'Similarity' rows and compare the Pearson's r values across different n-grams and lengths. The highest Pearson's r for the Similarity task is 69.5, achieved with 50,000 n-grams and lengths 2,3,4,5.", "statement": "The highest Pearson's r for the Similarity task is achieved with 50,000 n-grams and lengths 2,3,4.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: Details of candidate phrases and keyphrases. The coverage show that 9.42% to 22.84% keyphrases are not covered by candidate phrases. This will limit the final performance of our model. The ratio of ci− and ci+ is range from 9.85 to 28.85 meaning that the classification task is imbalanced.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The coverage of keyphrases by candidate phrases varies across datasets, with NUS having the lowest coverage at 77.16%, which affects the model's performance.We use the certain part-of-speech pattern to extract the candidate phrases inspired by \\citeauthorle2016unsupervised \\shortcitele2016unsupervised. (JJ|JJR|JJS|VBG|VBN)∗(NN|NNS|NNP|NNPS|VBG)+ The intersection of candidate phrases and keyphrases are positives denoted as ci+. When determining the match of two phrases, we use Porter Stemmer for preprocessing. The coverage shows that 9.42% to 22.84% keyphrases are not cover by candidate phrases. This will limit the final performance of our model. Our candidates only cover 77.16% keyphrases of NUS, the main reason is NUS contains irregular keyphrases assigned by reader. The ratio of ci− and ci+ is range from 9.85 to 28.85 meaning that data is imbalanced for classification task. Statistical indicators are close among KP dataset which shows this is a reasonable split." } ], "images": [ "scigen/train/1528.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Details of candidate phrases and keyphrases. The coverage show that 9.42% to 22.84% keyphrases are not covered by candidate phrases. This will limit the final performance of our model. The ratio of ci− and ci+ is range from 9.85 to 28.85 meaning that the classification task is imbalanced." ], "reasoning": "The coverage of keyphrases by candidate phrases varies across datasets, with NUS having the lowest coverage at 77.16%, which affects the model's performance.", "description": "We use the certain part-of-speech pattern to extract the candidate phrases inspired by \\citeauthorle2016unsupervised \\shortcitele2016unsupervised. (JJ|JJR|JJS|VBG|VBN)∗(NN|NNS|NNP|NNPS|VBG)+ The intersection of candidate phrases and keyphrases are positives denoted as ci+. When determining the match of two phrases, we use Porter Stemmer for preprocessing. The coverage shows that 9.42% to 22.84% keyphrases are not cover by candidate phrases. This will limit the final performance of our model. Our candidates only cover 77.16% keyphrases of NUS, the main reason is NUS contains irregular keyphrases assigned by reader. The ratio of ci− and ci+ is range from 9.85 to 28.85 meaning that data is imbalanced for classification task. Statistical indicators are close among KP dataset which shows this is a reasonable split.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Details of candidate phrases and keyphrases. The coverage show that 9.42% to 22.84% keyphrases are not covered by candidate phrases. This will limit the final performance of our model. The ratio of ci− and ci+ is range from 9.85 to 28.85 meaning that the classification task is imbalanced.\nQuestion: Which dataset has the highest ratio of # ci− and # ci+?\nSolve the above question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the dataset with the highest ratio of # ci− and # ci+, we compare the values in the 'Ratio of # ci− and # ci+' row. KP Training has the highest ratio at 28.70. So, the answer is {\"answer\": \"KP Training\"}" } ], "images": [ "scigen/train/1528.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Details of candidate phrases and keyphrases. The coverage show that 9.42% to 22.84% keyphrases are not covered by candidate phrases. This will limit the final performance of our model. The ratio of ci− and ci+ is range from 9.85 to 28.85 meaning that the classification task is imbalanced." ], "reasoning": "To find the dataset with the highest ratio of # ci− and # ci+, we compare the values in the 'Ratio of # ci− and # ci+' row. KP Training has the highest ratio at 28.70.", "question": "Which dataset has the highest ratio of # ci− and # ci+?", "answer": "So, the answer is {\"answer\": \"KP Training\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe NUS dataset has the lowest keyphrase coverage by ci+.\n" }, { "role": "assistant", "content": "The table shows the keyphrase coverage by ci+ for each dataset. NUS has the lowest coverage at 77.16%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1528.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Details of candidate phrases and keyphrases. The coverage show that 9.42% to 22.84% keyphrases are not covered by candidate phrases. This will limit the final performance of our model. The ratio of ci− and ci+ is range from 9.85 to 28.85 meaning that the classification task is imbalanced." ], "reasoning": "The table shows the keyphrase coverage by ci+ for each dataset. NUS has the lowest coverage at 77.16%.", "statement": "The NUS dataset has the lowest keyphrase coverage by ci+.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Performance on five benchmark datasets.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The table compares various methods based on their F-measure scores at top 5 and top 10 results across different datasets, with the highest scores highlighted in bold.For each method, the table lists the F-measure at top 5 and top 10 results. The best scores are highlighted in bold." } ], "images": [ "scigen/train/1529.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance on five benchmark datasets." ], "reasoning": "The table compares various methods based on their F-measure scores at top 5 and top 10 results across different datasets, with the highest scores highlighted in bold.", "description": "For each method, the table lists the F-measure at top 5 and top 10 results. The best scores are highlighted in bold.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 2: Performance on five benchmark datasets.' and proceed to solve the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich method has the highest F-measure score at top 5 results for the NUS dataset?\n" }, { "role": "assistant", "content": "To find the method with the highest F-measure score at top 5 results for the NUS dataset, look at the NUS F1 @5 column and identify the highest value, which is highlighted in bold. So, the answer is {\"answer\": \"SKE-Large-Cls\"}." } ], "images": [ "scigen/train/1529.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance on five benchmark datasets." ], "reasoning": "To find the method with the highest F-measure score at top 5 results for the NUS dataset, look at the NUS F1 @5 column and identify the highest value, which is highlighted in bold.", "question": "Which method has the highest F-measure score at top 5 results for the NUS dataset?", "answer": "So, the answer is {\"answer\": \"SKE-Large-Cls\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe SKE-Base-Cls method has the highest F-measure score at top 10 results for the KP20k dataset.\n" }, { "role": "assistant", "content": "To verify this statement, check the KP20k F1 @10 column for the highest value. The highest value is 0.330, which is achieved by the SKE-Large-Cls method, not SKE-Base-Cls. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1529.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance on five benchmark datasets." ], "reasoning": "To verify this statement, check the KP20k F1 @10 column for the highest value. The highest value is 0.330, which is achieved by the SKE-Large-Cls method, not SKE-Base-Cls.", "statement": "The SKE-Base-Cls method has the highest F-measure score at top 10 results for the KP20k dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: The training time of sentence pair classification and our method. As sentence pair classification model takes too much time to train based on BERT-Large, limiting its usage in practical applications, so we don’t report the training time and performance.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our models are significantly faster and more efficient than the BERT-Large sentence pair classification method, making them more practical for real-world applications.Our models achieve significant performance gain and are 66 times faster than sentence pair classification method which is proportional to the size of the training dataset. As the sentence pair classification model takes too much time to train based on BERT-Large, limiting its usage in practical applications, so we don’t report the training time and performance." } ], "images": [ "scigen/train/1530.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The training time of sentence pair classification and our method. As sentence pair classification model takes too much time to train based on BERT-Large, limiting its usage in practical applications, so we don’t report the training time and performance." ], "reasoning": "Our models are significantly faster and more efficient than the BERT-Large sentence pair classification method, making them more practical for real-world applications.", "description": "Our models achieve significant performance gain and are 66 times faster than sentence pair classification method which is proportional to the size of the training dataset. As the sentence pair classification model takes too much time to train based on BERT-Large, limiting its usage in practical applications, so we don’t report the training time and performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: The training time of sentence pair classification and our method. As sentence pair classification model takes too much time to train based on BERT-Large, limiting its usage in practical applications, so we don’t report the training time and performance.' and methodically tackle the question that follows. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method has the highest Nus F1 score at @5 and what is its training time?\n" }, { "role": "assistant", "content": "The table shows that the SKE-Large-Rank and SKE-Large-Cls methods both have the highest Nus F1 score at @5, which is 0.392, and their training time is 7 hours/epoch. So, the answer is {\"answer\": \"SKE-Large-Rank and SKE-Large-Cls with 7 hours/epoch\"}" } ], "images": [ "scigen/train/1530.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The training time of sentence pair classification and our method. As sentence pair classification model takes too much time to train based on BERT-Large, limiting its usage in practical applications, so we don’t report the training time and performance." ], "reasoning": "The table shows that the SKE-Large-Rank and SKE-Large-Cls methods both have the highest Nus F1 score at @5, which is 0.392, and their training time is 7 hours/epoch.", "question": "Which method has the highest Nus F1 score at @5 and what is its training time?", "answer": "So, the answer is {\"answer\": \"SKE-Large-Rank and SKE-Large-Cls with 7 hours/epoch\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe BERT-Base-Pair method has the highest Nus F1 score at @10.\n" }, { "role": "assistant", "content": "The table shows that the highest Nus F1 score at @10 is 0.330, achieved by the SKE-Large-Cls method, not the BERT-Base-Pair method. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1530.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The training time of sentence pair classification and our method. As sentence pair classification model takes too much time to train based on BERT-Large, limiting its usage in practical applications, so we don’t report the training time and performance." ], "reasoning": "The table shows that the highest Nus F1 score at @10 is 0.330, achieved by the SKE-Large-Cls method, not the BERT-Base-Pair method.", "statement": "The BERT-Base-Pair method has the highest Nus F1 score at @10.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 5: We conduct experiment using RNN word representation based Span Keyphrase Extraction classification model named SKE-RNN-Cls. SKE-RNN-Cls achieves 1.8% and 12.9% improvements over CopyRNN on F1@5 and F1@10 respectively.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "SKE-RNN-Cls outperforms CopyRNN on the largest test dataset SKE-RNN-Cls achieves 1.8% and 12.9% improvements over CopyRNN on F1@5 and F1@10 respectively.We compared this model with CopyRNN on the largest test dataset SKE-RNN-Cls achieves 1.8% and 12.9% improvements over CopyRNN on F1@5 and F1@10 respectively." } ], "images": [ "scigen/train/1531.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: We conduct experiment using RNN word representation based Span Keyphrase Extraction classification model named SKE-RNN-Cls. SKE-RNN-Cls achieves 1.8% and 12.9% improvements over CopyRNN on F1@5 and F1@10 respectively." ], "reasoning": "SKE-RNN-Cls outperforms CopyRNN on the largest test dataset SKE-RNN-Cls achieves 1.8% and 12.9% improvements over CopyRNN on F1@5 and F1@10 respectively.", "description": "We compared this model with CopyRNN on the largest test dataset SKE-RNN-Cls achieves 1.8% and 12.9% improvements over CopyRNN on F1@5 and F1@10 respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 5: We conduct experiment using RNN word representation based Span Keyphrase Extraction classification model named SKE-RNN-Cls. SKE-RNN-Cls achieves 1.8% and 12.9% improvements over CopyRNN on F1@5 and F1@10 respectively.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. \n\nQuestion: What is the F1@10 score for the method that achieved the highest F1@5 score?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The method with the highest F1@5 score is SKE-RNN-Cls with a score of 0.339. The F1@10 score for SKE-RNN-Cls is 0.296. So, the answer is {\"answer\": \"0.296\"}." } ], "images": [ "scigen/train/1531.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: We conduct experiment using RNN word representation based Span Keyphrase Extraction classification model named SKE-RNN-Cls. SKE-RNN-Cls achieves 1.8% and 12.9% improvements over CopyRNN on F1@5 and F1@10 respectively." ], "reasoning": "The method with the highest F1@5 score is SKE-RNN-Cls with a score of 0.339. The F1@10 score for SKE-RNN-Cls is 0.296.", "question": "What is the F1@10 score for the method that achieved the highest F1@5 score?", "answer": "So, the answer is {\"answer\": \"0.296\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSKE-RNN-Cls has a higher F1@5 score than Maui.\n" }, { "role": "assistant", "content": "The table shows that SKE-RNN-Cls has an F1@5 score of 0.339, while Maui has an F1@5 score of 0.273. Therefore, SKE-RNN-Cls has a higher F1@5 score than Maui. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1531.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: We conduct experiment using RNN word representation based Span Keyphrase Extraction classification model named SKE-RNN-Cls. SKE-RNN-Cls achieves 1.8% and 12.9% improvements over CopyRNN on F1@5 and F1@10 respectively." ], "reasoning": "The table shows that SKE-RNN-Cls has an F1@5 score of 0.339, while Maui has an F1@5 score of 0.273. Therefore, SKE-RNN-Cls has a higher F1@5 score than Maui.", "statement": "SKE-RNN-Cls has a higher F1@5 score than Maui.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: The F1 scores of our word-level GRU on each of the three datasets when the embedding layer is initialised with one of three probability distributions.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The most appropriate probability distribution to initialize the weights of the embedding layer depends entirely on the dataset, with the US Accidents dataset benefiting significantly from the Cauchy distribution.There is little variance between distributions, although the model trained on the US Accidents dataset benefited relatively significantly from the cauchy distribution. Freezing the weights appears to make no difference to the F1-score. Overall we find that the most appropriate probability distribution to initialise the weights of the embedding layer depends entirely on the dataset." } ], "images": [ "scigen/train/1532.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The F1 scores of our word-level GRU on each of the three datasets when the embedding layer is initialised with one of three probability distributions." ], "reasoning": "The most appropriate probability distribution to initialize the weights of the embedding layer depends entirely on the dataset, with the US Accidents dataset benefiting significantly from the Cauchy distribution.", "description": "There is little variance between distributions, although the model trained on the US Accidents dataset benefited relatively significantly from the cauchy distribution. Freezing the weights appears to make no difference to the F1-score. Overall we find that the most appropriate probability distribution to initialise the weights of the embedding layer depends entirely on the dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 3: The F1 scores of our word-level GRU on each of the three datasets when the embedding layer is initialised with one of three probability distributions.', solve the subsequent question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich dataset showed the highest F1-score when weights were learned using the Cauchy distribution?\n" }, { "role": "assistant", "content": "By examining the 'Weights learned' row for the Cauchy distribution, the highest F1-score is found in the US Acc dataset with a score of 0.8368. So, the answer is {\"answer\": \"US Acc\"}." } ], "images": [ "scigen/train/1532.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The F1 scores of our word-level GRU on each of the three datasets when the embedding layer is initialised with one of three probability distributions." ], "reasoning": "By examining the 'Weights learned' row for the Cauchy distribution, the highest F1-score is found in the US Acc dataset with a score of 0.8368.", "question": "Which dataset showed the highest F1-score when weights were learned using the Cauchy distribution?", "answer": "So, the answer is {\"answer\": \"US Acc\"}." }, { "messages": [ { "role": "user", "content": "Freezing the weights makes no difference to the F1-score for the Twitter dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nFreezing the weights makes no difference to the F1-score for the Twitter dataset.\n" }, { "role": "assistant", "content": "The F1-scores for the Twitter dataset are the same (0.7946) for both 'Weights learned' and 'Weights frozen' under the Uniform distribution, and the same (0.8000) under the Normal distribution. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1532.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The F1 scores of our word-level GRU on each of the three datasets when the embedding layer is initialised with one of three probability distributions." ], "reasoning": "The F1-scores for the Twitter dataset are the same (0.7946) for both 'Weights learned' and 'Weights frozen' under the Uniform distribution, and the same (0.8000) under the Normal distribution.", "statement": "Freezing the weights makes no difference to the F1-score for the Twitter dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: The F1 scores of our word-level GRU on each of the three datasets when the embedding layer is initialised with one of three cooccurrence embedding techniques.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table shows the performance metrics of different embedding techniques before and after applying PCA, with PCA reducing the embedding vector length to 512.In the lower half of the table, we use principle component analysis (PCA) to reduce the length of each embedding vector to 512." } ], "images": [ "scigen/train/1533.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The F1 scores of our word-level GRU on each of the three datasets when the embedding layer is initialised with one of three cooccurrence embedding techniques." ], "reasoning": "The table shows the performance metrics of different embedding techniques before and after applying PCA, with PCA reducing the embedding vector length to 512.", "description": "In the lower half of the table, we use principle component analysis (PCA) to reduce the length of each embedding vector to 512.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: The F1 scores of our word-level GRU on each of the three datasets when the embedding layer is initialised with one of three cooccurrence embedding techniques.\nQuestion: What is the highest cumulative score after applying PCA?\nSolve the above question based on the table information. Let's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest cumulative score after applying PCA, look at the 'Cumul.' column under 'After PCA'. The highest value in this column is 0.7677. So, the answer is {\"answer\": \"0.7677\"}" } ], "images": [ "scigen/train/1533.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The F1 scores of our word-level GRU on each of the three datasets when the embedding layer is initialised with one of three cooccurrence embedding techniques." ], "reasoning": "To find the highest cumulative score after applying PCA, look at the 'Cumul.' column under 'After PCA'. The highest value in this column is 0.7677.", "question": "What is the highest cumulative score after applying PCA?", "answer": "So, the answer is {\"answer\": \"0.7677\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The best score for US Acc after PCA is higher than the best score for Aus Acc after PCA. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe best score for US Acc after PCA is higher than the best score for Aus Acc after PCA.\n" }, { "role": "assistant", "content": "The best score for US Acc after PCA is 0.7677, while the best score for Aus Acc after PCA is 0.7436. Since 0.7677 is higher than 0.7436, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1533.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The F1 scores of our word-level GRU on each of the three datasets when the embedding layer is initialised with one of three cooccurrence embedding techniques." ], "reasoning": "The best score for US Acc after PCA is 0.7677, while the best score for Aus Acc after PCA is 0.7436. Since 0.7677 is higher than 0.7436, the statement is supported.", "statement": "The best score for US Acc after PCA is higher than the best score for Aus Acc after PCA.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: The F1-Scores of our word-level LN model when initialised with each embedding technique. The datasets do not contain the label to denote words that do not require normalisation.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The performance of models dropped significantly from 0.80 to 0.40 for smaller datasets when labels were removed.6.1.4 Effectiveness of the label To determine the effectiveness of augmenting the dataset with labels (whereby a token does not require normalisation) , we retrain and evaluate our models on the original datasets that do not contain these labels. The performance dropped significantly from 0.80 to 0.40 for the smaller datasets (Twitter and Australian Accidents)." } ], "images": [ "scigen/train/1534.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: The F1-Scores of our word-level LN model when initialised with each embedding technique. The datasets do not contain the label to denote words that do not require normalisation." ], "reasoning": "The performance of models dropped significantly from 0.80 to 0.40 for smaller datasets when labels were removed.", "description": "6.1.4 Effectiveness of the label To determine the effectiveness of augmenting the dataset with labels (whereby a token does not require normalisation) , we retrain and evaluate our models on the original datasets that do not contain these labels. The performance dropped significantly from 0.80 to 0.40 for the smaller datasets (Twitter and Australian Accidents).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 6: The F1-Scores of our word-level LN model when initialised with each embedding technique. The datasets do not contain the label to denote words that do not require normalisation.', answer the following question. Give detailed solution consisting of each step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the average performance of the Bi-GRU ELMo model across all datasets?\n" }, { "role": "assistant", "content": "To find the average performance of the Bi-GRU ELMo model, look at the 'Average' row under the 'Bi-GRU ELMo' column. So, the answer is {\"answer\": \"0.5178\"}." } ], "images": [ "scigen/train/1534.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: The F1-Scores of our word-level LN model when initialised with each embedding technique. The datasets do not contain the label to denote words that do not require normalisation." ], "reasoning": "To find the average performance of the Bi-GRU ELMo model, look at the 'Average' row under the 'Bi-GRU ELMo' column.", "question": "What is the average performance of the Bi-GRU ELMo model across all datasets?", "answer": "So, the answer is {\"answer\": \"0.5178\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Bi-GRU Word2Vec model performed better on the US Acc dataset than the Bi-GRU FastText model.\n" }, { "role": "assistant", "content": "Compare the performance values of the Bi-GRU Word2Vec and Bi-GRU FastText models for the US Acc dataset. Bi-GRU Word2Vec has a performance of 0.6804, while Bi-GRU FastText has a performance of 0.7012. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1534.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: The F1-Scores of our word-level LN model when initialised with each embedding technique. The datasets do not contain the label to denote words that do not require normalisation." ], "reasoning": "Compare the performance values of the Bi-GRU Word2Vec and Bi-GRU FastText models for the US Acc dataset. Bi-GRU Word2Vec has a performance of 0.6804, while Bi-GRU FastText has a performance of 0.7012.", "statement": "The Bi-GRU Word2Vec model performed better on the US Acc dataset than the Bi-GRU FastText model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 7: The F1-Scores of each model on each of the three lexical normalisation datasets. The score of the word-level model reflects the best performing word embedding method as determined in Section 6.1.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The best-performing word-level LN model outperforms the character-level LN model across all datasets.Our second result criterion aims to determine how our best-performing word-level LN model compares to a similar character-level LN model." } ], "images": [ "scigen/train/1535.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: The F1-Scores of each model on each of the three lexical normalisation datasets. The score of the word-level model reflects the best performing word embedding method as determined in Section 6.1." ], "reasoning": "The best-performing word-level LN model outperforms the character-level LN model across all datasets.", "description": "Our second result criterion aims to determine how our best-performing word-level LN model compares to a similar character-level LN model.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 7: The F1-Scores of each model on each of the three lexical normalisation datasets. The score of the word-level model reflects the best performing word embedding method as determined in Section 6.1.' in mind, break down and resolve the question below step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich model has the highest average accuracy?\n" }, { "role": "assistant", "content": "To determine the model with the highest average accuracy, compare the average accuracy values for each model. The '+ Dict norm Word' model has the highest average accuracy of 0.8368. So, the answer is {\"answer\": \"0.8368\"}" } ], "images": [ "scigen/train/1535.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: The F1-Scores of each model on each of the three lexical normalisation datasets. The score of the word-level model reflects the best performing word embedding method as determined in Section 6.1." ], "reasoning": "To determine the model with the highest average accuracy, compare the average accuracy values for each model. The '+ Dict norm Word' model has the highest average accuracy of 0.8368.", "question": "Which model has the highest average accuracy?", "answer": "So, the answer is {\"answer\": \"0.8368\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe 'Model only Word' has a higher accuracy than the 'Model only Char' on the US Acc dataset.\n" }, { "role": "assistant", "content": "The table shows that the 'Model only Word' has an accuracy of 0.8378 on the US Acc dataset, while the 'Model only Char' has an accuracy of 0.7804. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1535.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: The F1-Scores of each model on each of the three lexical normalisation datasets. The score of the word-level model reflects the best performing word embedding method as determined in Section 6.1." ], "reasoning": "The table shows that the 'Model only Word' has an accuracy of 0.8378 on the US Acc dataset, while the 'Model only Char' has an accuracy of 0.7804. Therefore, the statement is supported.", "statement": "The 'Model only Word' has a higher accuracy than the 'Model only Char' on the US Acc dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 8: The errors made by the ELMo and Cauchy models on the US Accidents dataset.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The ELMo-initialised model made 29 measurement errors out of 80 normalization errors, while the Cauchy embeddings made no such errors.An analysis of the errors produced by the ELMo-initialised model provides clarity as to why it is outperformed by the Cauchy embeddings for the US Accidents dataset. 29 of the 80 errors made by the ELMo-based model were measurements, such as “10’h” being normalised to the incorrect measurement of “8’ high”. The Cauchy embeddings did not make any errors on such terms." } ], "images": [ "scigen/train/1536.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: The errors made by the ELMo and Cauchy models on the US Accidents dataset." ], "reasoning": "The ELMo-initialised model made 29 measurement errors out of 80 normalization errors, while the Cauchy embeddings made no such errors.", "description": "An analysis of the errors produced by the ELMo-initialised model provides clarity as to why it is outperformed by the Cauchy embeddings for the US Accidents dataset. 29 of the 80 errors made by the ELMo-based model were measurements, such as “10’h” being normalised to the incorrect measurement of “8’ high”. The Cauchy embeddings did not make any errors on such terms.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 8: The errors made by the ELMo and Cauchy models on the US Accidents dataset.', answer the question below by showing each progressive step toward the answer. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nHow many normalization errors did the ELMo-based model make?\n" }, { "role": "assistant", "content": "The table shows that the ELMo-based model made 80 normalization errors. So, the answer is {\"answer\": \"80\"}" } ], "images": [ "scigen/train/1536.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: The errors made by the ELMo and Cauchy models on the US Accidents dataset." ], "reasoning": "The table shows that the ELMo-based model made 80 normalization errors.", "question": "How many normalization errors did the ELMo-based model make?", "answer": "So, the answer is {\"answer\": \"80\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Cauchy embeddings model made 40 normalization errors.\n" }, { "role": "assistant", "content": "The table shows that the Cauchy embeddings model made 40 normalization errors. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1536.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: The errors made by the ELMo and Cauchy models on the US Accidents dataset." ], "reasoning": "The table shows that the Cauchy embeddings model made 40 normalization errors.", "statement": "The Cauchy embeddings model made 40 normalization errors.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Ablation study on DailyMail test dataset with respect to the abstractive ground truth at 75 bytes.'.\n" }, { "role": "assistant", "content": "The ITS model outperforms its variations in all Rouge metrics, demonstrating the importance of each module in ITS.The first variation is the same model without the selective reading module. The second one sets the iteration number to one, that is, a model without iteration process. The last variation is to apply MLP on the output from the last iteration instead of concatenating the hidden states of all decoders. All other settings and parameters are the same. Performances of these models are worse than that of ITS in all metrics, which demonstrates the preeminence of ITS. More importantly, by this controlled experiment, we can verify the contribution of different module in ITS." } ], "images": [ "scigen/train/1537.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Ablation study on DailyMail test dataset with respect to the abstractive ground truth at 75 bytes." ], "reasoning": "The ITS model outperforms its variations in all Rouge metrics, demonstrating the importance of each module in ITS.", "description": "The first variation is the same model without the selective reading module. The second one sets the iteration number to one, that is, a model without iteration process. The last variation is to apply MLP on the output from the last iteration instead of concatenating the hidden states of all decoders. All other settings and parameters are the same. Performances of these models are worse than that of ITS in all metrics, which demonstrates the preeminence of ITS. More importantly, by this controlled experiment, we can verify the contribution of different module in ITS.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: Ablation study on DailyMail test dataset with respect to the abstractive ground truth at 75 bytes.', solve this question and give detail solutions. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich variation of the model has the lowest Rouge-1 score?\n" }, { "role": "assistant", "content": "By comparing the Rouge-1 scores of all variations, the model without iteration has the lowest score of 26.9. So, the answer is {\"answer\": \"w/o iteration\"}" } ], "images": [ "scigen/train/1537.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Ablation study on DailyMail test dataset with respect to the abstractive ground truth at 75 bytes." ], "reasoning": "By comparing the Rouge-1 scores of all variations, the model without iteration has the lowest score of 26.9.", "question": "Which variation of the model has the lowest Rouge-1 score?", "answer": "So, the answer is {\"answer\": \"w/o iteration\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The ITS model has the highest Rouge-2 score. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe ITS model has the highest Rouge-2 score.\n" }, { "role": "assistant", "content": "The table shows that the ITS model has a Rouge-2 score of 11.9, which is the highest among all variations. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1537.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Ablation study on DailyMail test dataset with respect to the abstractive ground truth at 75 bytes." ], "reasoning": "The table shows that the ITS model has a Rouge-2 score of 11.9, which is the highest among all variations.", "statement": "The ITS model has the highest Rouge-2 score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: Comparison with other baselines on DailyMail test dataset using Rouge recall score with respect to the abstractive ground truth at 75 bytes and at 275 bytes.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Our model shows significant improvement over others on 75 bytes, indicating high precision on top rank outputs, but the improvement is smaller on 275 bytes, suggesting lower accuracy for lower rank sentences.Our model performs consistently and significantly better than other models on 75 bytes, while on 275 bytes, the improvement margin is smaller. One possible interpretation is that our model has high precision on top rank outputs, but the accuracy is lower for lower rank sentences." } ], "images": [ "scigen/train/1538.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison with other baselines on DailyMail test dataset using Rouge recall score with respect to the abstractive ground truth at 75 bytes and at 275 bytes." ], "reasoning": "Our model shows significant improvement over others on 75 bytes, indicating high precision on top rank outputs, but the improvement is smaller on 275 bytes, suggesting lower accuracy for lower rank sentences.", "description": "Our model performs consistently and significantly better than other models on 75 bytes, while on 275 bytes, the improvement margin is smaller. One possible interpretation is that our model has high precision on top rank outputs, but the accuracy is lower for lower rank sentences.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Comparison with other baselines on DailyMail test dataset using Rouge recall score with respect to the abstractive ground truth at 75 bytes and at 275 bytes.\nQuestion: Which model has the highest Rouge-1 score on 75 bytes and how does it compare to its Rouge-1 score on 275 bytes?\nGive a detailed response to the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The ITS model has the highest Rouge-1 score on 75 bytes with 27.4. Comparing this to its Rouge-1 score on 275 bytes, which is 17.4, shows a decrease in performance. So, the answer is {\"answer\": \"ITS\"}." } ], "images": [ "scigen/train/1538.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison with other baselines on DailyMail test dataset using Rouge recall score with respect to the abstractive ground truth at 75 bytes and at 275 bytes." ], "reasoning": "The ITS model has the highest Rouge-1 score on 75 bytes with 27.4. Comparing this to its Rouge-1 score on 275 bytes, which is 17.4, shows a decrease in performance.", "question": "Which model has the highest Rouge-1 score on 75 bytes and how does it compare to its Rouge-1 score on 275 bytes?", "answer": "So, the answer is {\"answer\": \"ITS\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The ITS model has the highest Rouge-L score on 275 bytes. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe ITS model has the highest Rouge-L score on 275 bytes.\n" }, { "role": "assistant", "content": "The table shows that the Cheng et.al'16 model has the highest Rouge-L score on 275 bytes with 34.8, not the ITS model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1538.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison with other baselines on DailyMail test dataset using Rouge recall score with respect to the abstractive ground truth at 75 bytes and at 275 bytes." ], "reasoning": "The table shows that the Cheng et.al'16 model has the highest Rouge-L score on 275 bytes with 34.8, not the ITS model.", "statement": "The ITS model has the highest Rouge-L score on 275 bytes.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Comparison with other baselines on CNN test dataset using full-length F1 variants of Rouge.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The ITS model outperforms other models in Rouge-1, Rouge-2, and Rouge-L metrics.To compare with other models, we used full-length Rouge F1 metric as reported by Narayan et al. Results demonstrate that our model has a consistently best performance on different datasets." } ], "images": [ "scigen/train/1539.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison with other baselines on CNN test dataset using full-length F1 variants of Rouge." ], "reasoning": "The ITS model outperforms other models in Rouge-1, Rouge-2, and Rouge-L metrics.", "description": "To compare with other models, we used full-length Rouge F1 metric as reported by Narayan et al. Results demonstrate that our model has a consistently best performance on different datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 2: Comparison with other baselines on CNN test dataset using full-length F1 variants of Rouge.\nQuestion: Which model has the highest Rouge-2 score?\n" }, { "role": "assistant", "content": "By comparing the Rouge-2 scores of all models, the ITS model has the highest score of 12.6. So, the answer is {\"answer\": \"ITS\"}." } ], "images": [ "scigen/train/1539.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison with other baselines on CNN test dataset using full-length F1 variants of Rouge." ], "reasoning": "By comparing the Rouge-2 scores of all models, the ITS model has the highest score of 12.6.", "question": "Which model has the highest Rouge-2 score?", "answer": "So, the answer is {\"answer\": \"ITS\"}." }, { "messages": [ { "role": "user", "content": "The Hybrid MemNet model has the highest Rouge-1 score. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Hybrid MemNet model has the highest Rouge-1 score.\n" }, { "role": "assistant", "content": "The table shows that the ITS model has the highest Rouge-1 score of 30.8, not the Hybrid MemNet model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1539.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison with other baselines on CNN test dataset using full-length F1 variants of Rouge." ], "reasoning": "The table shows that the ITS model has the highest Rouge-1 score of 30.8, not the Hybrid MemNet model.", "statement": "The Hybrid MemNet model has the highest Rouge-1 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Comparison with other baselines on DUC2002 dataset using Rouge recall score with respect to the abstractive ground truth at 75 bytes.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Our model, ITS, outperforms or matches other models including LReg, ILP, and SummaRuNNer in Rouge-1, Rouge-2, and Rouge-L scores, demonstrating its adaptability and high accuracy.Our model outperforms or matches other basic models including LReg and ILR as well as neural network baselines such as SummaRuNNer with respect to the ground truth at 75 bytes, which shows that our model can be adapted to different copora maintaining high accuracy." } ], "images": [ "scigen/train/1540.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison with other baselines on DUC2002 dataset using Rouge recall score with respect to the abstractive ground truth at 75 bytes." ], "reasoning": "Our model, ITS, outperforms or matches other models including LReg, ILP, and SummaRuNNer in Rouge-1, Rouge-2, and Rouge-L scores, demonstrating its adaptability and high accuracy.", "description": "Our model outperforms or matches other basic models including LReg and ILR as well as neural network baselines such as SummaRuNNer with respect to the ground truth at 75 bytes, which shows that our model can be adapted to different copora maintaining high accuracy.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 3: Comparison with other baselines on DUC2002 dataset using Rouge recall score with respect to the abstractive ground truth at 75 bytes.', and tackle the question below in a sequential manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest Rouge-2 score?\n" }, { "role": "assistant", "content": "By comparing the Rouge-2 scores of all models in the table, ITS has the highest Rouge-2 score of 23.4. So, the answer is {\"answer\": \"ITS\"}." } ], "images": [ "scigen/train/1540.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison with other baselines on DUC2002 dataset using Rouge recall score with respect to the abstractive ground truth at 75 bytes." ], "reasoning": "By comparing the Rouge-2 scores of all models in the table, ITS has the highest Rouge-2 score of 23.4.", "question": "Which model has the highest Rouge-2 score?", "answer": "So, the answer is {\"answer\": \"ITS\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nITS has the highest Rouge-1 score among all models.\n" }, { "role": "assistant", "content": "By comparing the Rouge-1 scores of all models in the table, ITS has the highest Rouge-1 score of 47.6. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1540.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison with other baselines on DUC2002 dataset using Rouge recall score with respect to the abstractive ground truth at 75 bytes." ], "reasoning": "By comparing the Rouge-1 scores of all models in the table, ITS has the highest Rouge-1 score of 47.6.", "statement": "ITS has the highest Rouge-1 score among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: System ranking comparison with other baselines on DailyMail corpus. Rank 1 is the best and Rank 4 is the worst. Each score represents the percentage of the summary under this rank.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The gold standard summary is ranked highest in quality, followed by ITS, Hybrid MemNet, and Lead-3.Human Evaluation: We gave human evaluators three system-generated summaries, generated by Lead-3, Hybrid MemNet, ITS, as well as the human-written gold standard summary, and asked them to rank these summaries based on summary informativeness and coherence. It is not surprising that gold standard has the most summaries of the highest quality. Our model has the most summaries under 2nd rank, thus can be considered 2nd best, following are Hybrid MemNet and Lead-3, as they are ranked mostly 3rd and 4th. By case study, we found that a number of summaries generated by Hybrid MemNet have two sentences the same as ITS out of three, however, the third distinct sentence from our model always leads to a better evaluation result considering overall informativeness and coherence. Readers can refer to the appendix to see our case study." } ], "images": [ "scigen/train/1541.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: System ranking comparison with other baselines on DailyMail corpus. Rank 1 is the best and Rank 4 is the worst. Each score represents the percentage of the summary under this rank." ], "reasoning": "The gold standard summary is ranked highest in quality, followed by ITS, Hybrid MemNet, and Lead-3.", "description": "Human Evaluation: We gave human evaluators three system-generated summaries, generated by Lead-3, Hybrid MemNet, ITS, as well as the human-written gold standard summary, and asked them to rank these summaries based on summary informativeness and coherence. It is not surprising that gold standard has the most summaries of the highest quality. Our model has the most summaries under 2nd rank, thus can be considered 2nd best, following are Hybrid MemNet and Lead-3, as they are ranked mostly 3rd and 4th. By case study, we found that a number of summaries generated by Hybrid MemNet have two sentences the same as ITS out of three, however, the third distinct sentence from our model always leads to a better evaluation result considering overall informativeness and coherence. Readers can refer to the appendix to see our case study.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 5: System ranking comparison with other baselines on DailyMail corpus. Rank 1 is the best and Rank 4 is the worst. Each score represents the percentage of the summary under this rank.', solve the following question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model has the highest percentage of summaries ranked 2nd?\n" }, { "role": "assistant", "content": "By examining the table, ITS has the highest percentage of summaries ranked 2nd with 0.34. So, the answer is {\"answer\": \"ITS\"}" } ], "images": [ "scigen/train/1541.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: System ranking comparison with other baselines on DailyMail corpus. Rank 1 is the best and Rank 4 is the worst. Each score represents the percentage of the summary under this rank." ], "reasoning": "By examining the table, ITS has the highest percentage of summaries ranked 2nd with 0.34.", "question": "Which model has the highest percentage of summaries ranked 2nd?", "answer": "So, the answer is {\"answer\": \"ITS\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nLead-3 has the highest percentage of summaries ranked 4th.\n" }, { "role": "assistant", "content": "The table shows that Lead-3 has the highest percentage of summaries ranked 4th with 0.52. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1541.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: System ranking comparison with other baselines on DailyMail corpus. Rank 1 is the best and Rank 4 is the worst. Each score represents the percentage of the summary under this rank." ], "reasoning": "The table shows that Lead-3 has the highest percentage of summaries ranked 4th with 0.52.", "statement": "Lead-3 has the highest percentage of summaries ranked 4th.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: POS tagger comparison on gold-standard tokens in the Tweebank v2 test set.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The POS taggers were tested on the Tweebank v2 test set, with Owoputi et al.'s CRF-based tagger achieving the highest accuracy.We tested the POS taggers on the Tweebank v2 test set. Careful feature engineering and Brown et al. ’s feature-based POS taggers to outperform" } ], "images": [ "scigen/train/1542.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: POS tagger comparison on gold-standard tokens in the Tweebank v2 test set." ], "reasoning": "The POS taggers were tested on the Tweebank v2 test set, with Owoputi et al.'s CRF-based tagger achieving the highest accuracy.", "description": "We tested the POS taggers on the Tweebank v2 test set. Careful feature engineering and Brown et al. ’s feature-based POS taggers to outperform", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 4: POS tagger comparison on gold-standard tokens in the Tweebank v2 test set.' and methodically tackle the question that follows. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich POS tagger achieved the highest accuracy on the Tweebank v2 test set?\n" }, { "role": "assistant", "content": "By comparing the accuracy values of the POS taggers listed in the table, Owoputi et al., 2013 (CRF) has the highest accuracy of 94.6. So, the answer is {\"answer\": \"Owoputi et al., 2013 (CRF)\"}" } ], "images": [ "scigen/train/1542.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: POS tagger comparison on gold-standard tokens in the Tweebank v2 test set." ], "reasoning": "By comparing the accuracy values of the POS taggers listed in the table, Owoputi et al., 2013 (CRF) has the highest accuracy of 94.6.", "question": "Which POS tagger achieved the highest accuracy on the Tweebank v2 test set?", "answer": "So, the answer is {\"answer\": \"Owoputi et al., 2013 (CRF)\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nMa and Hovy, 2016 achieved an accuracy higher than 93.0 on the Tweebank v2 test set.\n" }, { "role": "assistant", "content": "The table shows that Ma and Hovy, 2016 achieved an accuracy of 92.5, which is lower than 93.0. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1542.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: POS tagger comparison on gold-standard tokens in the Tweebank v2 test set." ], "reasoning": "The table shows that Ma and Hovy, 2016 achieved an accuracy of 92.5, which is lower than 93.0.", "statement": "Ma and Hovy, 2016 achieved an accuracy higher than 93.0 on the Tweebank v2 test set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Tokenizer comparison on the Tweebank v2 test set.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our bi-LSTM tokenizer, which uses an LSTM and larger hidden units, achieves the highest F1 score among all tokenizers tested on the Tweebank v2 test set.Experimental results. Our preliminary results showed that our model trained on the combination of UD_English-EWT and Tweebank v2 outperformed the one trained only on the UD_English-EWT or Tweebank v2, consistent with previous work on dialect treebank parsing Wang et al. So we trained our tokenizer on the training portion of Tweebank v2 combined with the UD_English-EWT training set and tested on the Tweebank v2 test set. We report F1 scores, combining precision and recall for token identification. Stanford CoreNLP Manning et al. et al. Compared with UDPipe, we use an LSTM instead of a GRU in our model and we also use a larger size for hidden units (64 vs. 20), which has stronger representational power. Our bi-LSTM tokenizer achieves the best accuracy among all these tokenizers. These results speak to the value of statistical modeling in tokenization for informal texts." } ], "images": [ "scigen/train/1543.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Tokenizer comparison on the Tweebank v2 test set." ], "reasoning": "Our bi-LSTM tokenizer, which uses an LSTM and larger hidden units, achieves the highest F1 score among all tokenizers tested on the Tweebank v2 test set.", "description": "Experimental results. Our preliminary results showed that our model trained on the combination of UD_English-EWT and Tweebank v2 outperformed the one trained only on the UD_English-EWT or Tweebank v2, consistent with previous work on dialect treebank parsing Wang et al. So we trained our tokenizer on the training portion of Tweebank v2 combined with the UD_English-EWT training set and tested on the Tweebank v2 test set. We report F1 scores, combining precision and recall for token identification. Stanford CoreNLP Manning et al. et al. Compared with UDPipe, we use an LSTM instead of a GRU in our model and we also use a larger size for hidden units (64 vs. 20), which has stronger representational power. Our bi-LSTM tokenizer achieves the best accuracy among all these tokenizers. These results speak to the value of statistical modeling in tokenization for informal texts.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 3: Tokenizer comparison on the Tweebank v2 test set.'.\n\nQuestion: Which tokenizer achieved the highest F1 score in the experimental results?\n" }, { "role": "assistant", "content": "The table shows the F1 scores for different tokenizers. The highest F1 score is 98.3, achieved by our bi-LSTM tokenizer. So, the answer is {\"answer\": \"our bi-LSTM tokenizer\"}" } ], "images": [ "scigen/train/1543.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Tokenizer comparison on the Tweebank v2 test set." ], "reasoning": "The table shows the F1 scores for different tokenizers. The highest F1 score is 98.3, achieved by our bi-LSTM tokenizer.", "question": "Which tokenizer achieved the highest F1 score in the experimental results?", "answer": "So, the answer is {\"answer\": \"our bi-LSTM tokenizer\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nStanford CoreNLP achieved an F1 score higher than UDPipe v1.2.\n" }, { "role": "assistant", "content": "The table shows that Stanford CoreNLP has an F1 score of 97.3, while UDPipe v1.2 has an F1 score of 97.4. Therefore, Stanford CoreNLP did not achieve a higher F1 score than UDPipe v1.2. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1543.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Tokenizer comparison on the Tweebank v2 test set." ], "reasoning": "The table shows that Stanford CoreNLP has an F1 score of 97.3, while UDPipe v1.2 has an F1 score of 97.4. Therefore, Stanford CoreNLP did not achieve a higher F1 score than UDPipe v1.2.", "statement": "Stanford CoreNLP achieved an F1 score higher than UDPipe v1.2.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: Owoputi et al. (2013) POS tagging performance with automatic tokenization on the Tweebank v2 test set.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The bi-LSTM tokenizer outperforms the Stanford CoreNLP tokenizer with an F1 score of 93.3% compared to 92.3%.Results of the Owoputi et al. We see that errors in tokenization do propagate, but tagging performance is above 93% with our tokenizer." } ], "images": [ "scigen/train/1544.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Owoputi et al. (2013) POS tagging performance with automatic tokenization on the Tweebank v2 test set." ], "reasoning": "The bi-LSTM tokenizer outperforms the Stanford CoreNLP tokenizer with an F1 score of 93.3% compared to 92.3%.", "description": "Results of the Owoputi et al. We see that errors in tokenization do propagate, but tagging performance is above 93% with our tokenizer.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 5: Owoputi et al. (2013) POS tagging performance with automatic tokenization on the Tweebank v2 test set.' to sequentially solve the question presented below. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhat is the F1 score difference between the Stanford CoreNLP tokenizer and the bi-LSTM tokenizer?\n" }, { "role": "assistant", "content": "The F1 score for the Stanford CoreNLP tokenizer is 92.3%, and for the bi-LSTM tokenizer, it is 93.3%. The difference is calculated by subtracting 92.3 from 93.3. So, the answer is {\"answer\": \"1.0\"}." } ], "images": [ "scigen/train/1544.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Owoputi et al. (2013) POS tagging performance with automatic tokenization on the Tweebank v2 test set." ], "reasoning": "The F1 score for the Stanford CoreNLP tokenizer is 92.3%, and for the bi-LSTM tokenizer, it is 93.3%. The difference is calculated by subtracting 92.3 from 93.3.", "question": "What is the F1 score difference between the Stanford CoreNLP tokenizer and the bi-LSTM tokenizer?", "answer": "So, the answer is {\"answer\": \"1.0\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The bi-LSTM tokenizer has an F1 score below 93%. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe bi-LSTM tokenizer has an F1 score below 93%.\n" }, { "role": "assistant", "content": "The table shows that the bi-LSTM tokenizer has an F1 score of 93.3%, which is above 93%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1544.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Owoputi et al. (2013) POS tagging performance with automatic tokenization on the Tweebank v2 test set." ], "reasoning": "The table shows that the bi-LSTM tokenizer has an F1 score of 93.3%, which is above 93%.", "statement": "The bi-LSTM tokenizer has an F1 score below 93%.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 6: Dependency parser comparison on Tweebank v2 test set, with automatic POS tags. We use Ballesteros et al. (2015) as our baseline and build the ensemble and distilling model over it. The “Kt/s” column shows the parsing speed evaluated by thousands of tokens the model processed per second.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The parser in Dozat et al. (2017) requires superlinear runtime due to graph-based parsing and is re-trained on the same data as our system, but our baseline runs faster and lags behind by nearly two LAS points.The parser in Dozat et al. Both of the compared systems require superlinear runtime due to graph-based parsing. They are re-trained on the same data as our system. Our baseline lags behind by nearly two LAS points but runs faster than both of them." } ], "images": [ "scigen/train/1545.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Dependency parser comparison on Tweebank v2 test set, with automatic POS tags. We use Ballesteros et al. (2015) as our baseline and build the ensemble and distilling model over it. The “Kt/s” column shows the parsing speed evaluated by thousands of tokens the model processed per second." ], "reasoning": "The parser in Dozat et al. (2017) requires superlinear runtime due to graph-based parsing and is re-trained on the same data as our system, but our baseline runs faster and lags behind by nearly two LAS points.", "description": "The parser in Dozat et al. Both of the compared systems require superlinear runtime due to graph-based parsing. They are re-trained on the same data as our system. Our baseline lags behind by nearly two LAS points but runs faster than both of them.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 6: Dependency parser comparison on Tweebank v2 test set, with automatic POS tags. We use Ballesteros et al. (2015) as our baseline and build the ensemble and distilling model over it. The “Kt/s” column shows the parsing speed evaluated by thousands of tokens the model processed per second.', and sequentially solve the question, writing out each step of your reasoning process. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich system has the highest LAS score and what is its Kt/s?\n" }, { "role": "assistant", "content": "To find the system with the highest LAS score, look at the LAS column and identify the highest value, which is 79.4. Then, find the corresponding Kt/s value for that system, which is 0.2. So, the answer is {\"answer\": \"0.2\"}." } ], "images": [ "scigen/train/1545.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Dependency parser comparison on Tweebank v2 test set, with automatic POS tags. We use Ballesteros et al. (2015) as our baseline and build the ensemble and distilling model over it. The “Kt/s” column shows the parsing speed evaluated by thousands of tokens the model processed per second." ], "reasoning": "To find the system with the highest LAS score, look at the LAS column and identify the highest value, which is 79.4. Then, find the corresponding Kt/s value for that system, which is 0.2.", "question": "Which system has the highest LAS score and what is its Kt/s?", "answer": "So, the answer is {\"answer\": \"0.2\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe system with the highest UAS score also has the highest LAS score.\n" }, { "role": "assistant", "content": "The system with the highest UAS score is Ensemble (20) with a UAS of 83.4. The system with the highest LAS score is also Ensemble (20) with a LAS of 79.4. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1545.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Dependency parser comparison on Tweebank v2 test set, with automatic POS tags. We use Ballesteros et al. (2015) as our baseline and build the ensemble and distilling model over it. The “Kt/s” column shows the parsing speed evaluated by thousands of tokens the model processed per second." ], "reasoning": "The system with the highest UAS score is Ensemble (20) with a UAS of 83.4. The system with the highest LAS score is also Ensemble (20) with a LAS of 79.4.", "statement": "The system with the highest UAS score also has the highest LAS score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Evaluating our pipeline against a state-of-the-art pipeline.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Our model outperforms the state-of-the-art pipeline in tokenization, POS tagging, and UD parsing, highlighting the importance of tokenization.We also compare our model with a pipeline of the state-of-the-art systems (labeled “SOTA”): Stanford CoreNLP tokenizer, ’s Our system differs from the state-of-the-art pipeline in the tokenization and parser components. The results also emphasize the importance of tokenization: without gold tokenization UD parsing performance drops by about four points." } ], "images": [ "scigen/train/1546.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Evaluating our pipeline against a state-of-the-art pipeline." ], "reasoning": "Our model outperforms the state-of-the-art pipeline in tokenization, POS tagging, and UD parsing, highlighting the importance of tokenization.", "description": "We also compare our model with a pipeline of the state-of-the-art systems (labeled “SOTA”): Stanford CoreNLP tokenizer, ’s Our system differs from the state-of-the-art pipeline in the tokenization and parser components. The results also emphasize the importance of tokenization: without gold tokenization UD parsing performance drops by about four points.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 7: Evaluating our pipeline against a state-of-the-art pipeline.', solve this question and give detail solutions. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nHow does the performance of our model compare to the state-of-the-art system in UD parsing?\n" }, { "role": "assistant", "content": "The table shows that our model achieves an LAS score of 74.0 in UD parsing, while the state-of-the-art system achieves 71.4, indicating our model performs better. So, the answer is {\"answer\": \"74.0\"}." } ], "images": [ "scigen/train/1546.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Evaluating our pipeline against a state-of-the-art pipeline." ], "reasoning": "The table shows that our model achieves an LAS score of 74.0 in UD parsing, while the state-of-the-art system achieves 71.4, indicating our model performs better.", "question": "How does the performance of our model compare to the state-of-the-art system in UD parsing?", "answer": "So, the answer is {\"answer\": \"74.0\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nOur model performs better than the state-of-the-art system in POS tagging.\n" }, { "role": "assistant", "content": "The table shows that our model has an F1 score of 93.3 in POS tagging, while the state-of-the-art system has an F1 score of 92.2, indicating our model performs better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1546.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Evaluating our pipeline against a state-of-the-art pipeline." ], "reasoning": "The table shows that our model has an F1 score of 93.3 in POS tagging, while the state-of-the-art system has an F1 score of 92.2, indicating our model performs better.", "statement": "Our model performs better than the state-of-the-art system in POS tagging.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Accuracy on Gigaword text summarization task in terms of ROUGE-1 (RG-1), ROUGE-2 (RG-2), and ROUGE-L (RG-L). F stands for F1-score.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "DPN-S2S achieves the highest F1 scores across ROUGE-1, ROUGE-2, and ROUGE-L metrics compared to RNN MRT, WFE, and ConvS2S.DPN-S2S outperforms all the three models by 0.43-1.04 ROUGE-1, 0.43-1.32 ROUGE-2 and 0.64-1.23 ROUGE-L in terms of F1 score." } ], "images": [ "scigen/train/1547.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracy on Gigaword text summarization task in terms of ROUGE-1 (RG-1), ROUGE-2 (RG-2), and ROUGE-L (RG-L). F stands for F1-score." ], "reasoning": "DPN-S2S achieves the highest F1 scores across ROUGE-1, ROUGE-2, and ROUGE-L metrics compared to RNN MRT, WFE, and ConvS2S.", "description": "DPN-S2S outperforms all the three models by 0.43-1.04 ROUGE-1, 0.43-1.32 ROUGE-2 and 0.64-1.23 ROUGE-L in terms of F1 score.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: Accuracy on Gigaword text summarization task in terms of ROUGE-1 (RG-1), ROUGE-2 (RG-2), and ROUGE-L (RG-L). F stands for F1-score.', and sequentially solve the question, writing out each step of your reasoning process. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nBy how much does DPN-S2S outperform the lowest scoring model in terms of ROUGE-2 F1 score?\n" }, { "role": "assistant", "content": "DPN-S2S has a ROUGE-2 F1 score of 17.91, while the lowest scoring model, RNN MRT, has a ROUGE-2 F1 score of 16.59. The difference is 17.91 - 16.59 = 1.32. So, the answer is {\"answer\": \"1.32\"}." } ], "images": [ "scigen/train/1547.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracy on Gigaword text summarization task in terms of ROUGE-1 (RG-1), ROUGE-2 (RG-2), and ROUGE-L (RG-L). F stands for F1-score." ], "reasoning": "DPN-S2S has a ROUGE-2 F1 score of 17.91, while the lowest scoring model, RNN MRT, has a ROUGE-2 F1 score of 16.59. The difference is 17.91 - 16.59 = 1.32.", "question": "By how much does DPN-S2S outperform the lowest scoring model in terms of ROUGE-2 F1 score?", "answer": "So, the answer is {\"answer\": \"1.32\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nDPN-S2S has the highest ROUGE-L F1 score among all models.\n" }, { "role": "assistant", "content": "The table shows that DPN-S2S has a ROUGE-L F1 score of 34.32, which is higher than the ROUGE-L F1 scores of RNN MRT (33.44), WFE (33.88), and ConvS2S (33.29). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1547.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracy on Gigaword text summarization task in terms of ROUGE-1 (RG-1), ROUGE-2 (RG-2), and ROUGE-L (RG-L). F stands for F1-score." ], "reasoning": "The table shows that DPN-S2S has a ROUGE-L F1 score of 34.32, which is higher than the ROUGE-L F1 scores of RNN MRT (33.44), WFE (33.88), and ConvS2S (33.29).", "statement": "DPN-S2S has the highest ROUGE-L F1 score among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Performance of different systems on Nist Chinese-English translation task.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The DPN-S2S model outperforms other models in most Nist test sets, demonstrating significant improvements in BLEU scores.These baselines are RNNSearchs with some well acknowledged techniques including: 1) Using multi-channel information into encoder (MC-NMT) Incorporating word reorder information into NMT (NMT+Distortion) An attention mechanism with a directed-syntax (SD-NMT) We can observe that our method surpasses SAN and CNN baseline by 0.74-0.99 and 0.46-2.96 BLEU score in different test sets. In addition, we also compare our results with some related works, and our model outperforms MC-NMT by 0.47-4.27 BLEU, NMT+Distortion by 0.74- 4.31 BLEU and SD-NMT by 1.44-4.38 BLEU in Nist test sets. These results on Nist Chinese-English dataset prove that our model can also achieve improvement in large dataset." } ], "images": [ "scigen/train/1548.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of different systems on Nist Chinese-English translation task." ], "reasoning": "The DPN-S2S model outperforms other models in most Nist test sets, demonstrating significant improvements in BLEU scores.", "description": "These baselines are RNNSearchs with some well acknowledged techniques including: 1) Using multi-channel information into encoder (MC-NMT) Incorporating word reorder information into NMT (NMT+Distortion) An attention mechanism with a directed-syntax (SD-NMT) We can observe that our method surpasses SAN and CNN baseline by 0.74-0.99 and 0.46-2.96 BLEU score in different test sets. In addition, we also compare our results with some related works, and our model outperforms MC-NMT by 0.47-4.27 BLEU, NMT+Distortion by 0.74- 4.31 BLEU and SD-NMT by 1.44-4.38 BLEU in Nist test sets. These results on Nist Chinese-English dataset prove that our model can also achieve improvement in large dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Performance of different systems on Nist Chinese-English translation task.\nQuestion: How much does DPN-S2S outperform SD-NMT in the Nist06 test set?\nSolve the above question based on the table information. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the difference in BLEU scores between DPN-S2S and SD-NMT in the Nist06 test set, subtract the BLEU score of SD-NMT (34.63) from that of DPN-S2S (38.87). So, the answer is {\"answer\": \"4.24\"}" } ], "images": [ "scigen/train/1548.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of different systems on Nist Chinese-English translation task." ], "reasoning": "To find the difference in BLEU scores between DPN-S2S and SD-NMT in the Nist06 test set, subtract the BLEU score of SD-NMT (34.63) from that of DPN-S2S (38.87).", "question": "How much does DPN-S2S outperform SD-NMT in the Nist06 test set?", "answer": "So, the answer is {\"answer\": \"4.24\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nDPN-S2S has the highest BLEU score in the Nist08 test set.\n" }, { "role": "assistant", "content": "By comparing the BLEU scores of all models in the Nist08 test set, we see that DPN-S2S has a score of 32.66, which is higher than the scores of all other models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1548.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of different systems on Nist Chinese-English translation task." ], "reasoning": "By comparing the BLEU scores of all models in the Nist08 test set, we see that DPN-S2S has a score of 32.66, which is higher than the scores of all other models listed.", "statement": "DPN-S2S has the highest BLEU score in the Nist08 test set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Figure 5: Comparison of different delta functions.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Subtraction helps the model capture better semantics as indicated by its performance on the soft metric VecExt, while addition shows good performance on METEOR.All scores are macro-averaged across datasets. While add shows good performance on METEOR, subtract does on the soft metric (i.e., VecExt), indicating that subtraction can help the model capture the better semantics than the other functions. Both of FlowNet achieve more improvements when generating longer paragraphs. Especially, discourse relations achieve the best performance at length 6 and 7." } ], "images": [ "scigen/train/1549.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 5: Comparison of different delta functions." ], "reasoning": "Subtraction helps the model capture better semantics as indicated by its performance on the soft metric VecExt, while addition shows good performance on METEOR.", "description": "All scores are macro-averaged across datasets. While add shows good performance on METEOR, subtract does on the soft metric (i.e., VecExt), indicating that subtraction can help the model capture the better semantics than the other functions. Both of FlowNet achieve more improvements when generating longer paragraphs. Especially, discourse relations achieve the best performance at length 6 and 7.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: 'Figure 5: Comparison of different delta functions.'\nQuestion: Which function achieves the highest score on the soft metric VecExt?\n" }, { "role": "assistant", "content": "The table shows the scores for subtract, add, and mlp on both METEOR (M) and VecExt (VE). Subtract has the highest score on VecExt with 67.20. So, the answer is {\"answer\": \"subtract\"}." } ], "images": [ "scigen/train/1549.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 5: Comparison of different delta functions." ], "reasoning": "The table shows the scores for subtract, add, and mlp on both METEOR (M) and VecExt (VE). Subtract has the highest score on VecExt with 67.20.", "question": "Which function achieves the highest score on the soft metric VecExt?", "answer": "So, the answer is {\"answer\": \"subtract\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe add function achieves the highest score on METEOR.\n" }, { "role": "assistant", "content": "The table shows the scores for subtract, add, and mlp on METEOR (M). Add has the highest score on METEOR with 3.45. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1549.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 5: Comparison of different delta functions." ], "reasoning": "The table shows the scores for subtract, add, and mlp on METEOR (M). Add has the highest score on METEOR with 3.45.", "statement": "The add function achieves the highest score on METEOR.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: Number of paragraphs in our dataset.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The dataset is split into train, valid, and test sets with a 0.9/0.05/0.05 ratio, and paragraphs of lengths 4 to 7 are parsed into RST trees using a discourse parser.We only use paragraphs whose lengths are from 4 to 7, in order to measure the performance change according to paragraph length. The dataset is randomly split by 0.9/0.05/0.05 for train, valid, and test set, respectively. All paragraphs are parsed into RST trees using the state-of-the-art discourse parser by ji2014representation." } ], "images": [ "scigen/train/1550.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Number of paragraphs in our dataset." ], "reasoning": "The dataset is split into train, valid, and test sets with a 0.9/0.05/0.05 ratio, and paragraphs of lengths 4 to 7 are parsed into RST trees using a discourse parser.", "description": "We only use paragraphs whose lengths are from 4 to 7, in order to measure the performance change according to paragraph length. The dataset is randomly split by 0.9/0.05/0.05 for train, valid, and test set, respectively. All paragraphs are parsed into RST trees using the state-of-the-art discourse parser by ji2014representation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: Number of paragraphs in our dataset.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the total number of paragraphs in the SciFi category across all sets?\n" }, { "role": "assistant", "content": "To find the total number of SciFi paragraphs, sum the values in the Train, Valid, and Test columns for the SciFi row: 157,031 + 8,724 + 8,724. So, the answer is {\"answer\": \"174,479\"}." } ], "images": [ "scigen/train/1550.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Number of paragraphs in our dataset." ], "reasoning": "To find the total number of SciFi paragraphs, sum the values in the Train, Valid, and Test columns for the SciFi row: 157,031 + 8,724 + 8,724.", "question": "What is the total number of paragraphs in the SciFi category across all sets?", "answer": "So, the answer is {\"answer\": \"174,479\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe number of Fantasy paragraphs in the Train set is more than 300,000.\n" }, { "role": "assistant", "content": "The table shows that the number of Fantasy paragraphs in the Train set is 317,654, which is indeed more than 300,000. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1550.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Number of paragraphs in our dataset." ], "reasoning": "The table shows that the number of Fantasy paragraphs in the Train set is 317,654, which is indeed more than 300,000.", "statement": "The number of Fantasy paragraphs in the Train set is more than 300,000.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Results for DN and Chimera tasks. The best result per category (context-based or hybrid) in every column is marked in bold, while setups that were not evaluated have been filled with a dash. Purely form-based methods are not evaluated on the Chimera tasks. The stem-based model is not compatible with the filtered tasks.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The Form-Context model performs better than A La Carte on both tasks, but the Stem-Based model dominates the original DN task. The Full Chimera task allows for better model comparison, with the additive model with a window achieving the best scores.While the Form-Context model performs significantly better than A La Carte on both tasks, the original DN task is completely dominated by the stem-based model. This shows that using known related word forms is an extremely effective approach for estimating a new embedding. The removal of these related words from the training data heavily impacts the scores for all form-based and hybrid methods, but the Form-Context model still manages to perform strongly on the Filtered DN task, showing that the model is robust to varying amounts of information in both the form and context. On the original task, there are no clear performance trends, presumably caused by the small size of the test set. In that respect, the Full Chimera task is much more useful, allowing for a better comparison between models. The additive model with a window achieves the best score on all trials for the Full Chimera task, as well as one for the original (L6, with strong performance L2 and L4 as well). The most advanced additive model (window, subsampling and negative sampling) and the nonlinear A La Carte model also perform very strongly on the Full Chimera task." } ], "images": [ "scigen/train/1551.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results for DN and Chimera tasks. The best result per category (context-based or hybrid) in every column is marked in bold, while setups that were not evaluated have been filled with a dash. Purely form-based methods are not evaluated on the Chimera tasks. The stem-based model is not compatible with the filtered tasks." ], "reasoning": "The Form-Context model performs better than A La Carte on both tasks, but the Stem-Based model dominates the original DN task. The Full Chimera task allows for better model comparison, with the additive model with a window achieving the best scores.", "description": "While the Form-Context model performs significantly better than A La Carte on both tasks, the original DN task is completely dominated by the stem-based model. This shows that using known related word forms is an extremely effective approach for estimating a new embedding. The removal of these related words from the training data heavily impacts the scores for all form-based and hybrid methods, but the Form-Context model still manages to perform strongly on the Filtered DN task, showing that the model is robust to varying amounts of information in both the form and context. On the original task, there are no clear performance trends, presumably caused by the small size of the test set. In that respect, the Full Chimera task is much more useful, allowing for a better comparison between models. The additive model with a window achieves the best score on all trials for the Full Chimera task, as well as one for the original (L6, with strong performance L2 and L4 as well). The most advanced additive model (window, subsampling and negative sampling) and the nonlinear A La Carte model also perform very strongly on the Full Chimera task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: Results for DN and Chimera tasks. The best result per category (context-based or hybrid) in every column is marked in bold, while setups that were not evaluated have been filled with a dash. Purely form-based methods are not evaluated on the Chimera tasks. The stem-based model is not compatible with the filtered tasks.', answer the question below by showing each progressive step toward the answer. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model achieves the highest score on the Full Chimera L6 task?\n" }, { "role": "assistant", "content": "To find the model with the highest score on the Full Chimera L6 task, look at the Full Chimera L6 column and identify the highest value. The highest score is 0.391, achieved by the Additive + Window model. So, the answer is {\"answer\": \"Additive + Window\"}." } ], "images": [ "scigen/train/1551.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results for DN and Chimera tasks. The best result per category (context-based or hybrid) in every column is marked in bold, while setups that were not evaluated have been filled with a dash. Purely form-based methods are not evaluated on the Chimera tasks. The stem-based model is not compatible with the filtered tasks." ], "reasoning": "To find the model with the highest score on the Full Chimera L6 task, look at the Full Chimera L6 column and identify the highest value. The highest score is 0.391, achieved by the Additive + Window model.", "question": "Which model achieves the highest score on the Full Chimera L6 task?", "answer": "So, the answer is {\"answer\": \"Additive + Window\"}." }, { "messages": [ { "role": "user", "content": "The Form-Context model achieves the highest score on the Filtered DN task. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Form-Context model achieves the highest score on the Filtered DN task.\n" }, { "role": "assistant", "content": "To verify this statement, look at the Filtered DN Median column. The highest score in this column is 0.0992, achieved by the Form-Context model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1551.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results for DN and Chimera tasks. The best result per category (context-based or hybrid) in every column is marked in bold, while setups that were not evaluated have been filled with a dash. Purely form-based methods are not evaluated on the Chimera tasks. The stem-based model is not compatible with the filtered tasks." ], "reasoning": "To verify this statement, look at the Filtered DN Median column. The highest score in this column is 0.0992, achieved by the Form-Context model.", "statement": "The Form-Context model achieves the highest score on the Filtered DN task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Results on both CRW tasks. The best result per category in every column is marked in bold. The form-based and hybrid categories are shown together, as a hybrid model using 0 context sentences is effectively form-based. The stem-based model is not compatible with the filtered tasks.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Hybrid methods, particularly FCM, significantly outperform other methods on the CRW task, even without context sentences, highlighting the importance of lexical information.On the original CRW task, hybrid methods dominate, with the FCM outperforming all other results significantly even with no context sentences. This again shows how much lexical information is available." } ], "images": [ "scigen/train/1552.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on both CRW tasks. The best result per category in every column is marked in bold. The form-based and hybrid categories are shown together, as a hybrid model using 0 context sentences is effectively form-based. The stem-based model is not compatible with the filtered tasks." ], "reasoning": "Hybrid methods, particularly FCM, significantly outperform other methods on the CRW task, even without context sentences, highlighting the importance of lexical information.", "description": "On the original CRW task, hybrid methods dominate, with the FCM outperforming all other results significantly even with no context sentences. This again shows how much lexical information is available.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: Results on both CRW tasks. The best result per category in every column is marked in bold. The form-based and hybrid categories are shown together, as a hybrid model using 0 context sentences is effectively form-based. The stem-based model is not compatible with the filtered tasks.', solve this question and give detail solutions. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich method has the highest score for Contextual Rare Words with 0 context sentences?\n" }, { "role": "assistant", "content": "To find the highest score for Contextual Rare Words with 0 context sentences, look at the column under 'Contextual Rare Words Number of Context Sentences' with 0 context sentences. The highest score in this column is 0.49, achieved by both Form-Context and Neural FCM. So, the answer is {\"answer\": \"Form-Context and Neural FCM\"}" } ], "images": [ "scigen/train/1552.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on both CRW tasks. The best result per category in every column is marked in bold. The form-based and hybrid categories are shown together, as a hybrid model using 0 context sentences is effectively form-based. The stem-based model is not compatible with the filtered tasks." ], "reasoning": "To find the highest score for Contextual Rare Words with 0 context sentences, look at the column under 'Contextual Rare Words Number of Context Sentences' with 0 context sentences. The highest score in this column is 0.49, achieved by both Form-Context and Neural FCM.", "question": "Which method has the highest score for Contextual Rare Words with 0 context sentences?", "answer": "So, the answer is {\"answer\": \"Form-Context and Neural FCM\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Additive method has a higher score than the Nonce2Vec method for Contextual Rare Words with 4 context sentences.\n" }, { "role": "assistant", "content": "For Contextual Rare Words with 4 context sentences, the Additive method has a score of 0.17, while the Nonce2Vec method has a score of 0.17. Since both scores are equal, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1552.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on both CRW tasks. The best result per category in every column is marked in bold. The form-based and hybrid categories are shown together, as a hybrid model using 0 context sentences is effectively form-based. The stem-based model is not compatible with the filtered tasks." ], "reasoning": "For Contextual Rare Words with 4 context sentences, the Additive method has a score of 0.17, while the Nonce2Vec method has a score of 0.17. Since both scores are equal, the statement is refuted.", "statement": "The Additive method has a higher score than the Nonce2Vec method for Contextual Rare Words with 4 context sentences.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Translation Performance of Encoder Layers.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The encoder layers achieve high BLEU 1 scores due to word translation in the same order as the source sentence, while the full Transformer model achieves the highest BLEU score due to word re-ordering.To achieve this goal, we feed output representations from an encoder layer to the corresponding linear projection layer, and feed the output of the linear projection layer directly to the decoder classifier, and retrieve tokens with highest probabilities as “translations”. Even though such “translations” from encoder layers have a same length and a same word-order as source sentences, individual source tokens are translated to the target language to some extent. “FULL” is the performance of the whole Transformer model (decoding with a beam size of 4). “Δ” means the improvements obtained by the introduced layer (or the decoder for “FULL”) over the previous layer. It is reasonable that encoder layers achieve a comparably high BLEU 1 score while a low BLEU score, as they perform word translation in the same order as the source sentence without any word re-ordering of the target language. We suggest the BLEU 1 score achieved by only the source embedding layer (i.e. translating with only embeddings) surprising and worth noting." } ], "images": [ "scigen/train/1553.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Translation Performance of Encoder Layers." ], "reasoning": "The encoder layers achieve high BLEU 1 scores due to word translation in the same order as the source sentence, while the full Transformer model achieves the highest BLEU score due to word re-ordering.", "description": "To achieve this goal, we feed output representations from an encoder layer to the corresponding linear projection layer, and feed the output of the linear projection layer directly to the decoder classifier, and retrieve tokens with highest probabilities as “translations”. Even though such “translations” from encoder layers have a same length and a same word-order as source sentences, individual source tokens are translated to the target language to some extent. “FULL” is the performance of the whole Transformer model (decoding with a beam size of 4). “Δ” means the improvements obtained by the introduced layer (or the decoder for “FULL”) over the previous layer. It is reasonable that encoder layers achieve a comparably high BLEU 1 score while a low BLEU score, as they perform word translation in the same order as the source sentence without any word re-ordering of the target language. We suggest the BLEU 1 score achieved by only the source embedding layer (i.e. translating with only embeddings) surprising and worth noting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: Translation Performance of Encoder Layers.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the BLEU score improvement from Layer 2 to Layer 3?\n" }, { "role": "assistant", "content": "The BLEU score for Layer 2 is 11.05, and for Layer 3 it is 11.89. The improvement is calculated as 11.89 - 11.05. So, the answer is {\"answer\": \"0.84\"}" } ], "images": [ "scigen/train/1553.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Translation Performance of Encoder Layers." ], "reasoning": "The BLEU score for Layer 2 is 11.05, and for Layer 3 it is 11.89. The improvement is calculated as 11.89 - 11.05.", "question": "What is the BLEU score improvement from Layer 2 to Layer 3?", "answer": "So, the answer is {\"answer\": \"0.84\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nLayer 4 has a higher BLEU 1 score than Layer 5.\n" }, { "role": "assistant", "content": "The BLEU 1 score for Layer 4 is 46.8, while for Layer 5 it is 48.1. Since 48.1 is greater than 46.8, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1553.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Translation Performance of Encoder Layers." ], "reasoning": "The BLEU 1 score for Layer 4 is 46.8, while for Layer 5 it is 48.1. Since 48.1 is greater than 46.8, the statement is refuted.", "statement": "Layer 4 has a higher BLEU 1 score than Layer 5.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Effects of Encoder / Decoder Depth.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The Transformer with 10 encoder layers and 2 decoder layers decodes 2.32 times faster than the 6-layer Transformer while achieving a slightly higher BLEU score.The decoding speed is measured on the test set which contains 3,003 sentences with a beam size of 4. “Speed up” stands for the decoding acceleration compared to the 6-layer Transformer. Specifically, the Transformer with 10 encoder layers and 2 decoder layers is 2.32 times as fast as the 6-layer Transformer while achieving a slightly higher BLEU." } ], "images": [ "scigen/train/1554.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Effects of Encoder / Decoder Depth." ], "reasoning": "The Transformer with 10 encoder layers and 2 decoder layers decodes 2.32 times faster than the 6-layer Transformer while achieving a slightly higher BLEU score.", "description": "The decoding speed is measured on the test set which contains 3,003 sentences with a beam size of 4. “Speed up” stands for the decoding acceleration compared to the 6-layer Transformer. Specifically, the Transformer with 10 encoder layers and 2 decoder layers is 2.32 times as fast as the 6-layer Transformer while achieving a slightly higher BLEU.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 4: Effects of Encoder / Decoder Depth.', unfold the question presented below, detailing every step of your calculation. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich Transformer configuration achieves the highest BLEU score and what is its decoding speed?\n" }, { "role": "assistant", "content": "The highest BLEU score in the table is 28.61, achieved by the Transformer with 8 encoder layers and 4 decoder layers. Its decoding speed is 31 seconds. So, the answer is {\"answer\": \"31\"}" } ], "images": [ "scigen/train/1554.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Effects of Encoder / Decoder Depth." ], "reasoning": "The highest BLEU score in the table is 28.61, achieved by the Transformer with 8 encoder layers and 4 decoder layers. Its decoding speed is 31 seconds.", "question": "Which Transformer configuration achieves the highest BLEU score and what is its decoding speed?", "answer": "So, the answer is {\"answer\": \"31\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The Transformer with 11 encoder layers and 1 decoder layer has the fastest decoding speed., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Transformer with 11 encoder layers and 1 decoder layer has the fastest decoding speed.\n" }, { "role": "assistant", "content": "The table shows that the Transformer with 11 encoder layers and 1 decoder layer has a decoding speed of 13 seconds, which is the fastest among all configurations listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1554.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Effects of Encoder / Decoder Depth." ], "reasoning": "The table shows that the Transformer with 11 encoder layers and 1 decoder layer has a decoding speed of 13 seconds, which is the fastest among all configurations listed.", "statement": "The Transformer with 11 encoder layers and 1 decoder layer has the fastest decoding speed.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Percentage of Blending Rules', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Blended names are created using specific POS tag rules, with less frequent rules omitted if they account for less than 1% of names.We observed certain rules used in blended names from the description provided by the annotators in Özbal et al. A rule is a combination of unordered POS tags of the blended words (syllables). For example, SplitWise and WiseSplit are created from two syllables whose POS tags are verb and adverb. Hence, they follow the Verb-Adverb rule. Note that the percentage of names created using a certain rule are subject to discretionary choice based on the annotator’s bias. As in, for the same entity having similar properties, there can be two different descriptions from two different annotators leading to different empirical estimates. For example, data platform enables solving problems quickly or data platform is quick in problem solving. Here, we will have the same syllable quick in both sentences. However, in the former case, the POS tag is Adverb whereas, in the latter, it is Adjective. We observed that some of the blending rules were less frequent. Hence, we omitted the rules having less than 1% of the names under their category and called the rest as allowed rules. For example, a name using two syllables coming from words having verb as their POS tag is usually not created. An example can be SplitBreak formed by the syllables Split and Break. With respect to the data, this essentially removed three rules but had a significant reduction in the number of names generated." } ], "images": [ "scigen/train/1555.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Percentage of Blending Rules" ], "reasoning": "Blended names are created using specific POS tag rules, with less frequent rules omitted if they account for less than 1% of names.", "description": "We observed certain rules used in blended names from the description provided by the annotators in Özbal et al. A rule is a combination of unordered POS tags of the blended words (syllables). For example, SplitWise and WiseSplit are created from two syllables whose POS tags are verb and adverb. Hence, they follow the Verb-Adverb rule. Note that the percentage of names created using a certain rule are subject to discretionary choice based on the annotator’s bias. As in, for the same entity having similar properties, there can be two different descriptions from two different annotators leading to different empirical estimates. For example, data platform enables solving problems quickly or data platform is quick in problem solving. Here, we will have the same syllable quick in both sentences. However, in the former case, the POS tag is Adverb whereas, in the latter, it is Adjective. We observed that some of the blending rules were less frequent. Hence, we omitted the rules having less than 1% of the names under their category and called the rest as allowed rules. For example, a name using two syllables coming from words having verb as their POS tag is usually not created. An example can be SplitBreak formed by the syllables Split and Break. With respect to the data, this essentially removed three rules but had a significant reduction in the number of names generated.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 1: Percentage of Blending Rules', solve this question and give detail solutions. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the percentage of names created using the Noun-Noun rule?\n" }, { "role": "assistant", "content": "The table shows the percentage of names created using different rules. The Noun-Noun rule falls under the Adjective-Adverb category with a percentage of 36.36%. So, the answer is {\"answer\": \"36.36\"}" } ], "images": [ "scigen/train/1555.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Percentage of Blending Rules" ], "reasoning": "The table shows the percentage of names created using different rules. The Noun-Noun rule falls under the Adjective-Adverb category with a percentage of 36.36%.", "question": "What is the percentage of names created using the Noun-Noun rule?", "answer": "So, the answer is {\"answer\": \"36.36\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Verb-Verb rule is one of the allowed rules for creating blended names. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Verb-Verb rule is one of the allowed rules for creating blended names.\n" }, { "role": "assistant", "content": "The description states that rules with less than 1% usage are omitted. The table shows that the Verb-Verb rule has 0.00%, indicating it is not an allowed rule. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1555.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Percentage of Blending Rules" ], "reasoning": "The description states that rules with less than 1% usage are omitted. The table shows that the Verb-Verb rule has 0.00%, indicating it is not an allowed rule.", "statement": "The Verb-Verb rule is one of the allowed rules for creating blended names.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 6: The SRCC performance comparison (×100) for SGNS alone and SGNS+PVN/PDE model against word similarity and analogy datasets.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The Verb-143 dataset shows significant improvement with PVN over SGNS, indicating the importance of context order in verb-related tasks.Better results are obtained across all datasets. The improvement over the Verb-143 dataset has a high ranking among all datasets with either joint PVN/PDE or PDE alone. This matches our expectation since the context order has more contribution over verbs." } ], "images": [ "scigen/train/1556.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: The SRCC performance comparison (×100) for SGNS alone and SGNS+PVN/PDE model against word similarity and analogy datasets." ], "reasoning": "The Verb-143 dataset shows significant improvement with PVN over SGNS, indicating the importance of context order in verb-related tasks.", "description": "Better results are obtained across all datasets. The improvement over the Verb-143 dataset has a high ranking among all datasets with either joint PVN/PDE or PDE alone. This matches our expectation since the context order has more contribution over verbs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 6: The SRCC performance comparison (×100) for SGNS alone and SGNS+PVN/PDE model against word similarity and analogy datasets.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich dataset shows the highest improvement in score when using PVN compared to SGNS?\n" }, { "role": "assistant", "content": "To find the dataset with the highest improvement, subtract the SGNS score from the PVN score for each dataset and identify the maximum difference. WS-353-SIM has the highest improvement with a difference of 75.3 - 73.2 = 2.1. So, the answer is {\"answer\": \"WS-353-SIM\"}." } ], "images": [ "scigen/train/1556.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: The SRCC performance comparison (×100) for SGNS alone and SGNS+PVN/PDE model against word similarity and analogy datasets." ], "reasoning": "To find the dataset with the highest improvement, subtract the SGNS score from the PVN score for each dataset and identify the maximum difference. WS-353-SIM has the highest improvement with a difference of 75.3 - 73.2 = 2.1.", "question": "Which dataset shows the highest improvement in score when using PVN compared to SGNS?", "answer": "So, the answer is {\"answer\": \"WS-353-SIM\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe PVN score for the Verb-143 dataset is higher than the SGNS score for the WS-353-REL dataset.\n" }, { "role": "assistant", "content": "The PVN score for Verb-143 is 44.1, and the SGNS score for WS-353-REL is 58.1. Since 44.1 is not higher than 58.1, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1556.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: The SRCC performance comparison (×100) for SGNS alone and SGNS+PVN/PDE model against word similarity and analogy datasets." ], "reasoning": "The PVN score for Verb-143 is 44.1, and the SGNS score for WS-353-REL is 58.1. Since 44.1 is not higher than 58.1, the statement is refuted.", "statement": "The PVN score for the Verb-143 dataset is higher than the SGNS score for the WS-353-REL dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: The SRCC performance comparison (×100) for SGNS alone, SGNS+PPA and SGNS+PVN against word similarity datasets, where the last row is the average performance weighted by the pair number of each dataset.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The SGNS+PVN model outperforms the SGNS+PPA model, with the largest performance gain of 5.2% in average SRCC scores, indicating that PVN is more robust than PPA.We see that the SGNS+PVN performs better than the SGNS+PPA. We observe the largest performance gain of the SGNS+PVN reaches 5.2% in the avarage SRCC scores. It is also interesting to point out that the PVN is more robust than the PPA with different settings in d." } ], "images": [ "scigen/train/1557.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The SRCC performance comparison (×100) for SGNS alone, SGNS+PPA and SGNS+PVN against word similarity datasets, where the last row is the average performance weighted by the pair number of each dataset." ], "reasoning": "The SGNS+PVN model outperforms the SGNS+PPA model, with the largest performance gain of 5.2% in average SRCC scores, indicating that PVN is more robust than PPA.", "description": "We see that the SGNS+PVN performs better than the SGNS+PPA. We observe the largest performance gain of the SGNS+PVN reaches 5.2% in the avarage SRCC scores. It is also interesting to point out that the PVN is more robust than the PPA with different settings in d.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: The SRCC performance comparison (×100) for SGNS alone, SGNS+PPA and SGNS+PVN against word similarity datasets, where the last row is the average performance weighted by the pair number of each dataset.'.\n\nQuestion: What is the average SRCC score for the SGNS+PVN model?\n" }, { "role": "assistant", "content": "The table shows the average SRCC scores for different models. The average SRCC score for the SGNS+PVN model is listed in the 'Average' row under the 'PVN(ours)' column. So, the answer is {\"answer\": \"50.3\"}." } ], "images": [ "scigen/train/1557.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The SRCC performance comparison (×100) for SGNS alone, SGNS+PPA and SGNS+PVN against word similarity datasets, where the last row is the average performance weighted by the pair number of each dataset." ], "reasoning": "The table shows the average SRCC scores for different models. The average SRCC score for the SGNS+PVN model is listed in the 'Average' row under the 'PVN(ours)' column.", "question": "What is the average SRCC score for the SGNS+PVN model?", "answer": "So, the answer is {\"answer\": \"50.3\"}." }, { "messages": [ { "role": "user", "content": "The SGNS+PVN model has the highest score in the WS-353-REL category. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe SGNS+PVN model has the highest score in the WS-353-REL category.\n" }, { "role": "assistant", "content": "The table shows the scores for different models in various categories. In the WS-353-REL category, the SGNS+PVN model has a score of 60.7, which is higher than the scores of SGNS and PPA models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1557.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The SRCC performance comparison (×100) for SGNS alone, SGNS+PPA and SGNS+PVN against word similarity datasets, where the last row is the average performance weighted by the pair number of each dataset." ], "reasoning": "The table shows the scores for different models in various categories. In the WS-353-REL category, the SGNS+PVN model has a score of 60.7, which is higher than the scores of SGNS and PPA models.", "statement": "The SGNS+PVN model has the highest score in the WS-353-REL category.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: The SRCC performance comparison (×100) for SGNS alone, SGNS+PPA and SGNS+PVN against word analogy datasets.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "PVN outperforms PPA in both addition and multiplication evaluations, with PPA performing worse than the baseline in multiplication.We use addition as well as multiplication evaluation methods. PVN performs better than PPA in both. For the multiplication evaluation, the performance of PPA is worse than the baseline. In contrast, the proposed PVN method has no negative effect as it performs consistently well. This can be explained below. When the multiplication evaluation is adopted, the missing dimensions of the PPA influence the relative angles of vectors a lot. This is further verified by the fact that some linguistic properties are captured by these high-variance dimensions and their total elimination is sub-optimal." } ], "images": [ "scigen/train/1558.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The SRCC performance comparison (×100) for SGNS alone, SGNS+PPA and SGNS+PVN against word analogy datasets." ], "reasoning": "PVN outperforms PPA in both addition and multiplication evaluations, with PPA performing worse than the baseline in multiplication.", "description": "We use addition as well as multiplication evaluation methods. PVN performs better than PPA in both. For the multiplication evaluation, the performance of PPA is worse than the baseline. In contrast, the proposed PVN method has no negative effect as it performs consistently well. This can be explained below. When the multiplication evaluation is adopted, the missing dimensions of the PPA influence the relative angles of vectors a lot. This is further verified by the fact that some linguistic properties are captured by these high-variance dimensions and their total elimination is sub-optimal.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: The SRCC performance comparison (×100) for SGNS alone, SGNS+PPA and SGNS+PVN against word analogy datasets.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. \n\nQuestion: Which method performs the worst in the multiplication evaluation for the MSR type?\n\nYour Solution:\n" }, { "role": "assistant", "content": "For the MSR type under multiplication evaluation, the table shows that SGNS has a score of 53.3, PPA has a score of 53.3, and PVN has a score of 54.9. Since SGNS and PPA both have the lowest score of 53.3, they perform the worst. So, the answer is {\"answer\": \"SGNS and PPA\"}." } ], "images": [ "scigen/train/1558.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The SRCC performance comparison (×100) for SGNS alone, SGNS+PPA and SGNS+PVN against word analogy datasets." ], "reasoning": "For the MSR type under multiplication evaluation, the table shows that SGNS has a score of 53.3, PPA has a score of 53.3, and PVN has a score of 54.9. Since SGNS and PPA both have the lowest score of 53.3, they perform the worst.", "question": "Which method performs the worst in the multiplication evaluation for the MSR type?", "answer": "So, the answer is {\"answer\": \"SGNS and PPA\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim PVN performs better than PPA in the addition evaluation for the Semantic type., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nPVN performs better than PPA in the addition evaluation for the Semantic type.\n" }, { "role": "assistant", "content": "The table shows that for the Semantic type under addition evaluation, PPA has a score of 62.4 and PVN has a score of 62.4. Since both scores are equal, PVN does not perform better than PPA. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1558.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The SRCC performance comparison (×100) for SGNS alone, SGNS+PPA and SGNS+PVN against word analogy datasets." ], "reasoning": "The table shows that for the Semantic type under addition evaluation, PPA has a score of 62.4 and PVN has a score of 62.4. Since both scores are equal, PVN does not perform better than PPA.", "statement": "PVN performs better than PPA in the addition evaluation for the Semantic type.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: The SRCC performance comparison (×100) for SGNS alone and SGNS+PDE against word similarity and analogy datasets.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The PDE method improves the quality of word representations by adding ordered information, as evidenced by higher scores in evaluation tasks compared to the SGNS baseline.We adopt the same setting in evaluating the PDE method such as the window size, vocabulary size, number of negative samples, training data, etc. when it is applied to SGNS baseline. The final word representation is composed by two parts: ~v(w)=[vs(w)T,vd(w)T]T, where vs(w) is the static part obtained from dimension reduction using PCA and vd(w)=ATv(w) is the projection of v(w) to dynamic subspace A. Here, we set the dimensions of vs(w) and vd(w) to 240 and 60, respectively. By adding the ordered information via PDE, we see that the quality of word representations is improved in both evaluation tasks." } ], "images": [ "scigen/train/1559.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The SRCC performance comparison (×100) for SGNS alone and SGNS+PDE against word similarity and analogy datasets." ], "reasoning": "The PDE method improves the quality of word representations by adding ordered information, as evidenced by higher scores in evaluation tasks compared to the SGNS baseline.", "description": "We adopt the same setting in evaluating the PDE method such as the window size, vocabulary size, number of negative samples, training data, etc. when it is applied to SGNS baseline. The final word representation is composed by two parts: ~v(w)=[vs(w)T,vd(w)T]T, where vs(w) is the static part obtained from dimension reduction using PCA and vd(w)=ATv(w) is the projection of v(w) to dynamic subspace A. Here, we set the dimensions of vs(w) and vd(w) to 240 and 60, respectively. By adding the ordered information via PDE, we see that the quality of word representations is improved in both evaluation tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: The SRCC performance comparison (×100) for SGNS alone and SGNS+PDE against word similarity and analogy datasets.\nQuestion: What is the difference in score between SGNS and PVN(ours) for the WS-353-REL task?\nSolve the above question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the difference in score between SGNS and PVN(ours) for the WS-353-REL task, subtract the SGNS score from the PVN(ours) score: 59.3 - 58.1. So, the answer is {\"answer\": \"1.2\"}" } ], "images": [ "scigen/train/1559.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The SRCC performance comparison (×100) for SGNS alone and SGNS+PDE against word similarity and analogy datasets." ], "reasoning": "To find the difference in score between SGNS and PVN(ours) for the WS-353-REL task, subtract the SGNS score from the PVN(ours) score: 59.3 - 58.1.", "question": "What is the difference in score between SGNS and PVN(ours) for the WS-353-REL task?", "answer": "So, the answer is {\"answer\": \"1.2\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim PVN(ours) outperforms SGNS in the Google evaluation task., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nPVN(ours) outperforms SGNS in the Google evaluation task.\n" }, { "role": "assistant", "content": "The table shows that the score for PVN(ours) in the Google evaluation task is 60.8, while the score for SGNS is 59.6, indicating that PVN(ours) has a higher score. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1559.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The SRCC performance comparison (×100) for SGNS alone and SGNS+PDE against word similarity and analogy datasets." ], "reasoning": "The table shows that the score for PVN(ours) in the Google evaluation task is 60.8, while the score for SGNS is 59.6, indicating that PVN(ours) has a higher score.", "statement": "PVN(ours) outperforms SGNS in the Google evaluation task.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Pearson’s ρ, Spearman’s r, and Kendall’s τ correlation values between assigned scores and quality categories. BLEU-1 is reported because it is the best of the BLEU scores. All correlations are significant at p<0.05 and all Pearson’s correlations are different at p<0.05 according to a Williams’ test.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The table shows the correlation coefficients (Pearson’s ρ, Spearman’s r, and Kendall’s τ) for different metrics, indicating how well these scores align with quality categories of utterances.We want to see the extent to which these scores correlate with the quality categories of the given utterances. In this situation, we have large numbers of points around certain scores (e.g. 1 for the Literal Listener), and these scores have meaning themselves, so we report Pearson’s ρ. We are also interested in the overall monotonicity of the metric scores across categories—we want to avoid good candidates receiving bad scores and vice versa. As such, we report the Spearman’s r and Kendall ’s τ as well." } ], "images": [ "scigen/train/1560.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Pearson’s ρ, Spearman’s r, and Kendall’s τ correlation values between assigned scores and quality categories. BLEU-1 is reported because it is the best of the BLEU scores. All correlations are significant at p<0.05 and all Pearson’s correlations are different at p<0.05 according to a Williams’ test." ], "reasoning": "The table shows the correlation coefficients (Pearson’s ρ, Spearman’s r, and Kendall’s τ) for different metrics, indicating how well these scores align with quality categories of utterances.", "description": "We want to see the extent to which these scores correlate with the quality categories of the given utterances. In this situation, we have large numbers of points around certain scores (e.g. 1 for the Literal Listener), and these scores have meaning themselves, so we report Pearson’s ρ. We are also interested in the overall monotonicity of the metric scores across categories—we want to avoid good candidates receiving bad scores and vice versa. As such, we report the Spearman’s r and Kendall ’s τ as well.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nTable title: Table 1: Pearson’s ρ, Spearman’s r, and Kendall’s τ correlation values between assigned scores and quality categories. BLEU-1 is reported because it is the best of the BLEU scores. All correlations are significant at p<0.05 and all Pearson’s correlations are different at p<0.05 according to a Williams’ test.\n\nQuestion: Which metric has the highest Pearson’s ρ value?\n" }, { "role": "assistant", "content": "To find the metric with the highest Pearson’s ρ value, we need to compare the ρ values for all metrics listed in the table. The highest ρ value is 0.701, which corresponds to the Human metric. So, the answer is {\"answer\": \"Human\"}." } ], "images": [ "scigen/train/1560.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Pearson’s ρ, Spearman’s r, and Kendall’s τ correlation values between assigned scores and quality categories. BLEU-1 is reported because it is the best of the BLEU scores. All correlations are significant at p<0.05 and all Pearson’s correlations are different at p<0.05 according to a Williams’ test." ], "reasoning": "To find the metric with the highest Pearson’s ρ value, we need to compare the ρ values for all metrics listed in the table. The highest ρ value is 0.701, which corresponds to the Human metric.", "question": "Which metric has the highest Pearson’s ρ value?", "answer": "So, the answer is {\"answer\": \"Human\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe BLEU-1 metric has a higher Spearman’s r value than the METEOR metric.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the Spearman’s r values for BLEU-1 and METEOR. BLEU-1 has an r value of 0.350, while METEOR has an r value of 0.479. Since 0.350 is less than 0.479, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1560.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Pearson’s ρ, Spearman’s r, and Kendall’s τ correlation values between assigned scores and quality categories. BLEU-1 is reported because it is the best of the BLEU scores. All correlations are significant at p<0.05 and all Pearson’s correlations are different at p<0.05 according to a Williams’ test." ], "reasoning": "To verify this statement, we compare the Spearman’s r values for BLEU-1 and METEOR. BLEU-1 has an r value of 0.350, while METEOR has an r value of 0.479. Since 0.350 is less than 0.479, the statement is refuted.", "statement": "The BLEU-1 metric has a higher Spearman’s r value than the METEOR metric.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: P@1 on UN parallel sentence retrieval task.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "LaBSE outperforms other models in en-es, en-fr, and en-ru language pairs, while it is slightly behind the best model in en-zh.For each non-English language, we iterate over English sentences to find the translation sentence from the entire sentence pool from the other language. We compare the proposed model with the current state-of-the-art bilingual models from Yang et al. (m-USE) model with the transformer architecture. Again LaBSE shows the new state-of-the-art performance on 3 of the 4 languages, with P@1 91.1, 88.3, 90.8 for en-es, en-fr, en-ru respectively. The zh-en of LaBSE reaches 87.7, only 0.2 lower than the best bilingual model and nearly 9 points better than the previous best multilingual model." } ], "images": [ "scigen/train/1561.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: P@1 on UN parallel sentence retrieval task." ], "reasoning": "LaBSE outperforms other models in en-es, en-fr, and en-ru language pairs, while it is slightly behind the best model in en-zh.", "description": "For each non-English language, we iterate over English sentences to find the translation sentence from the entire sentence pool from the other language. We compare the proposed model with the current state-of-the-art bilingual models from Yang et al. (m-USE) model with the transformer architecture. Again LaBSE shows the new state-of-the-art performance on 3 of the 4 languages, with P@1 91.1, 88.3, 90.8 for en-es, en-fr, en-ru respectively. The zh-en of LaBSE reaches 87.7, only 0.2 lower than the best bilingual model and nearly 9 points better than the previous best multilingual model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: P@1 on UN parallel sentence retrieval task.', and sequentially solve the question, writing out each step of your reasoning process. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model has the highest P@1 score for the en-fr language pair?\n" }, { "role": "assistant", "content": "By comparing the P@1 scores for the en-fr language pair across all models, LaBSE has the highest score of 88.3. So, the answer is {\"answer\": \"LaBSE\"}." } ], "images": [ "scigen/train/1561.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: P@1 on UN parallel sentence retrieval task." ], "reasoning": "By comparing the P@1 scores for the en-fr language pair across all models, LaBSE has the highest score of 88.3.", "question": "Which model has the highest P@1 score for the en-fr language pair?", "answer": "So, the answer is {\"answer\": \"LaBSE\"}." }, { "messages": [ { "role": "user", "content": "LaBSE has the highest P@1 score for the en-zh language pair. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nLaBSE has the highest P@1 score for the en-zh language pair.\n" }, { "role": "assistant", "content": "The table shows that LaBSE has a P@1 score of 87.7 for the en-zh language pair, which is lower than the 87.9 score of Yang et al. (2019a). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1561.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: P@1 on UN parallel sentence retrieval task." ], "reasoning": "The table shows that LaBSE has a P@1 score of 87.7 for the en-zh language pair, which is lower than the 87.9 score of Yang et al. (2019a).", "statement": "LaBSE has the highest P@1 score for the en-zh language pair.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: [P]recision, [R]ecall and [F]-score of BUCC training set score with cosine similarity scores. The thresholds are chosen for the best F scores on the training set. Following the naming of BUCC task Zweigenbaum et al. (2018), we treat en as the target and the other language as source in forward search. Backward is vice versa.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "LaBSE outperforms previous models in both forward and backward searches across all languages.and Yang et al. Following the original previous work, we perform both of the forward search and backward search. Where forward search treat en as the target and the other language as source in forward search, and backward is vice versa. The LaBSE outperforms the previous models in all languages. It is worth to note that the previous state-of-the-art Yang et al." } ], "images": [ "scigen/train/1562.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: [P]recision, [R]ecall and [F]-score of BUCC training set score with cosine similarity scores. The thresholds are chosen for the best F scores on the training set. Following the naming of BUCC task Zweigenbaum et al. (2018), we treat en as the target and the other language as source in forward search. Backward is vice versa." ], "reasoning": "LaBSE outperforms previous models in both forward and backward searches across all languages.", "description": "and Yang et al. Following the original previous work, we perform both of the forward search and backward search. Where forward search treat en as the target and the other language as source in forward search, and backward is vice versa. The LaBSE outperforms the previous models in all languages. It is worth to note that the previous state-of-the-art Yang et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1: [P]recision, [R]ecall and [F]-score of BUCC training set score with cosine similarity scores. The thresholds are chosen for the best F scores on the training set. Following the naming of BUCC task Zweigenbaum et al. (2018), we treat en as the target and the other language as source in forward search. Backward is vice versa.', solve the subsequent question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model achieved the highest score in the backward search for the fr-en P language pair?\n" }, { "role": "assistant", "content": "By examining the backward search section of the table, we see that LaBSE achieved the highest score for the fr-en P language pair with a score of 87.1. So, the answer is {\"answer\": \"87.1\"}" } ], "images": [ "scigen/train/1562.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: [P]recision, [R]ecall and [F]-score of BUCC training set score with cosine similarity scores. The thresholds are chosen for the best F scores on the training set. Following the naming of BUCC task Zweigenbaum et al. (2018), we treat en as the target and the other language as source in forward search. Backward is vice versa." ], "reasoning": "By examining the backward search section of the table, we see that LaBSE achieved the highest score for the fr-en P language pair with a score of 87.1.", "question": "Which model achieved the highest score in the backward search for the fr-en P language pair?", "answer": "So, the answer is {\"answer\": \"87.1\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Yang et al. (2019a) achieved the highest score in the forward search for the zh-en F language pair., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nYang et al. (2019a) achieved the highest score in the forward search for the zh-en F language pair.\n" }, { "role": "assistant", "content": "By examining the forward search section of the table, we see that LaBSE achieved the highest score for the zh-en F language pair with a score of 88.9, not Yang et al. (2019a). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1562.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: [P]recision, [R]ecall and [F]-score of BUCC training set score with cosine similarity scores. The thresholds are chosen for the best F scores on the training set. Following the naming of BUCC task Zweigenbaum et al. (2018), we treat en as the target and the other language as source in forward search. Backward is vice versa." ], "reasoning": "By examining the forward search section of the table, we see that LaBSE achieved the highest score for the zh-en F language pair with a score of 88.9, not Yang et al. (2019a).", "statement": "Yang et al. (2019a) achieved the highest score in the forward search for the zh-en F language pair.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5: The top and bottom performance of Tatoeba for languages without training data.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The LaBSE model shows good performance across 30+ languages with an average accuracy of around 60%, and languages with low performance tend to have shorter token lengths and longer sequence lengths.There are total of 30+ such languages The performance is surprisingly good for most of the languages with an average accuracy around 60% on those languages. Nearly one third of them have accuracy larger than 75%, and only 7 of them have accuracy lower than 25%. Such positive language transfer across languages is only possible due to the massively multilingual nature of LaBSE. We analyze effect of vocabulary by inspecting the unknown token rate, average token length in characters, and average sentence length in tokens of all languages Arivazhagan et al. The unknown token rates are surprisingly low for all languages, indicating a good coverage of the built vocab. Languages with low performance tend to on average have shorter token lengths and longer sequence lengths indicating which indicates sub-par vocab coverage . A better vocab could potentially benefit those languages." } ], "images": [ "scigen/train/1563.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The top and bottom performance of Tatoeba for languages without training data." ], "reasoning": "The LaBSE model shows good performance across 30+ languages with an average accuracy of around 60%, and languages with low performance tend to have shorter token lengths and longer sequence lengths.", "description": "There are total of 30+ such languages The performance is surprisingly good for most of the languages with an average accuracy around 60% on those languages. Nearly one third of them have accuracy larger than 75%, and only 7 of them have accuracy lower than 25%. Such positive language transfer across languages is only possible due to the massively multilingual nature of LaBSE. We analyze effect of vocabulary by inspecting the unknown token rate, average token length in characters, and average sentence length in tokens of all languages Arivazhagan et al. The unknown token rates are surprisingly low for all languages, indicating a good coverage of the built vocab. Languages with low performance tend to on average have shorter token lengths and longer sequence lengths indicating which indicates sub-par vocab coverage . A better vocab could potentially benefit those languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 5: The top and bottom performance of Tatoeba for languages without training data.', answer the following question. Give detailed solution consisting of each step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich language has the highest Tatoeba accuracy and what is its average token length?\n" }, { "role": "assistant", "content": "The language with the highest Tatoeba accuracy is 'nb' with an accuracy of 98.9%. The average token length for 'nb' is 3.47. So, the answer is {\"answer\": \"nb with an average token length of 3.47\"}." } ], "images": [ "scigen/train/1563.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The top and bottom performance of Tatoeba for languages without training data." ], "reasoning": "The language with the highest Tatoeba accuracy is 'nb' with an accuracy of 98.9%. The average token length for 'nb' is 3.47.", "question": "Which language has the highest Tatoeba accuracy and what is its average token length?", "answer": "So, the answer is {\"answer\": \"nb with an average token length of 3.47\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nLanguages with low performance have shorter token lengths and longer sequence lengths.\n" }, { "role": "assistant", "content": "The table shows that languages with low Tatoeba accuracy (e.g., 'kzj', 'pam', 'dtp', 'ber', 'kab') have shorter average token lengths and longer average sentence lengths, supporting the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1563.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The top and bottom performance of Tatoeba for languages without training data." ], "reasoning": "The table shows that languages with low Tatoeba accuracy (e.g., 'kzj', 'pam', 'dtp', 'ber', 'kab') have shorter average token lengths and longer average sentence lengths, supporting the statement.", "statement": "Languages with low performance have shorter token lengths and longer sequence lengths.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 7: Semantic Textual Similarity (STS) benchmarkCer et al. (2017) performance as measured by Pearson’s r.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The table compares the performance of various sentence embedding models on the STS task, showing both development and test scores, with some models fine-tuned for better performance.versus existing sentence embedding models. Following prior work, the semantic similarity of a sentence pair according to LaBSE is computed as the arccos distance between the pair’s sentence embeddings. In addition to performance using similarity scores commuted directly from sentence embeddings from the various models, we include numbers for SentenceBERT when it is fine-tuned for the STS task as well as ConvEmbed when an additional affine transform is trained to fit the embeddings to STS. Rather than directly measuring sentence embeddings quality, tuning toward STS informs the extent to which the information required to assess semantic similarity is captured in anyway within the model." } ], "images": [ "scigen/train/1564.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Semantic Textual Similarity (STS) benchmarkCer et al. (2017) performance as measured by Pearson’s r." ], "reasoning": "The table compares the performance of various sentence embedding models on the STS task, showing both development and test scores, with some models fine-tuned for better performance.", "description": "versus existing sentence embedding models. Following prior work, the semantic similarity of a sentence pair according to LaBSE is computed as the arccos distance between the pair’s sentence embeddings. In addition to performance using similarity scores commuted directly from sentence embeddings from the various models, we include numbers for SentenceBERT when it is fine-tuned for the STS task as well as ConvEmbed when an additional affine transform is trained to fit the embeddings to STS. Rather than directly measuring sentence embeddings quality, tuning toward STS informs the extent to which the information required to assess semantic similarity is captured in anyway within the model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 7: Semantic Textual Similarity (STS) benchmarkCer et al. (2017) performance as measured by Pearson’s r.', and tackle the question below in a sequential manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model has the highest test score after fine-tuning for the STS task?\n" }, { "role": "assistant", "content": "The table shows that the SentenceBERT-STS Reimers and Gurevych (2019) model has the highest test score of 86.1 after fine-tuning for the STS task. So, the answer is {\"answer\": \"SentenceBERT-STS Reimers and Gurevych (2019)\"}." } ], "images": [ "scigen/train/1564.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Semantic Textual Similarity (STS) benchmarkCer et al. (2017) performance as measured by Pearson’s r." ], "reasoning": "The table shows that the SentenceBERT-STS Reimers and Gurevych (2019) model has the highest test score of 86.1 after fine-tuning for the STS task.", "question": "Which model has the highest test score after fine-tuning for the STS task?", "answer": "So, the answer is {\"answer\": \"SentenceBERT-STS Reimers and Gurevych (2019)\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe m-USE Yang et al. (2019b) model has the highest development score among all models.\n" }, { "role": "assistant", "content": "The table shows that the m-USE Yang et al. (2019b) model has a development score of 83.7, which is the highest among all models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1564.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Semantic Textual Similarity (STS) benchmarkCer et al. (2017) performance as measured by Pearson’s r." ], "reasoning": "The table shows that the m-USE Yang et al. (2019b) model has a development score of 83.7, which is the highest among all models listed.", "statement": "The m-USE Yang et al. (2019b) model has the highest development score among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: CWE embeddings: Spearman ρ correlation between human evaluation and embedding similarities for the three datasets analysed (C1, C2 and C3).', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The performance of embeddings in the similarity task improves with vocabulary size when the training data is clean, but diminishes when the data is noisy.If we compare the relative numbers for the three datasets (C1, C2 and C3) we observe the same trends as before: the performance of the embeddings in the similarity task improves with the vocabulary size when the training data can be considered clean, but the performance diminishes when the data is noisy." } ], "images": [ "scigen/train/1565.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: CWE embeddings: Spearman ρ correlation between human evaluation and embedding similarities for the three datasets analysed (C1, C2 and C3)." ], "reasoning": "The performance of embeddings in the similarity task improves with vocabulary size when the training data is clean, but diminishes when the data is noisy.", "description": "If we compare the relative numbers for the three datasets (C1, C2 and C3) we observe the same trends as before: the performance of the embeddings in the similarity task improves with the vocabulary size when the training data can be considered clean, but the performance diminishes when the data is noisy.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 4: CWE embeddings: Spearman ρ correlation between human evaluation and embedding similarities for the three datasets analysed (C1, C2 and C3).', and tackle the question below in a sequential manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the Spearman correlation for the Yorùbá dataset in C2, and how does it compare to C1 and C3?\n" }, { "role": "assistant", "content": "The Yorùbá Spearman correlation for C2 is 0.345. Comparing this to C1 (0.263) and C3 (0.354), we see that C2 has a higher correlation than C1 but lower than C3. So, the answer is {\"answer\": \"0.345\"}." } ], "images": [ "scigen/train/1565.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: CWE embeddings: Spearman ρ correlation between human evaluation and embedding similarities for the three datasets analysed (C1, C2 and C3)." ], "reasoning": "The Yorùbá Spearman correlation for C2 is 0.345. Comparing this to C1 (0.263) and C3 (0.354), we see that C2 has a higher correlation than C1 but lower than C3.", "question": "What is the Spearman correlation for the Yorùbá dataset in C2, and how does it compare to C1 and C3?", "answer": "So, the answer is {\"answer\": \"0.345\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The Twi Spearman correlation for C3 is higher than for C1., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Twi Spearman correlation for C3 is higher than for C1.\n" }, { "role": "assistant", "content": "The Twi Spearman correlation for C3 is 0.377, which is the same as for C1 (0.377). Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1565.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: CWE embeddings: Spearman ρ correlation between human evaluation and embedding similarities for the three datasets analysed (C1, C2 and C3)." ], "reasoning": "The Twi Spearman correlation for C3 is 0.377, which is the same as for C1 (0.377). Therefore, the statement is refuted.", "statement": "The Twi Spearman correlation for C3 is higher than for C1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: FastText embeddings: Spearman ρ correlation between human judgements and similarity scores on the wordSim-353 for the three datasets analysed (C1, C2 and C3). The comparison with massive fastText embeddings is shown in the top rows.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The pre-trained Twi model has a very limited vocabulary size of 935 tokens, leading to low performance, while the Yorùbá model has a larger vocabulary but still shows low correlation due to mixed dialects and English sentences.For Yorùbá, both versions are available but only embeddings trained on Wikipedia are available for Twi. We consider these embeddings the result of training on what we call massively-extracted corpora. Notice that training settings for both embeddings are not exactly the same, and differences in performance might come both from corpus size/quality but also from the background model. The 294-languages version is trained using skipgram, in dimension 300, with character n-grams of length 5, a window of size 5 and 5 negatives. The 157-languages version is trained using CBOW with position-weights, in dimension 300, with character n-grams of length 5, a window of size 5 and 10 negatives. We train our fastText systems using a skipgram model with an embedding size of 300 dimensions, context window size of 5, 10 negatives and n-grams ranging from 3 to 6 characters similarly to the pre-trained models for both languages. Best results are obtained with minimum word count of 3. Notice that pre-trained embeddings on Wikipedia show a very low correlation with humans on the similarity task for both languages (ρ=0.14) and their performance is even lower when Common Crawl is also considered (ρ=0.07 for Yorùbá). An important reason for the low performance is the limited vocabulary. The pre-trained Twi model has only 935 tokens. For Yorùbá, things are apparently better with more than 150 k tokens when both Wikipedia and Common Crawl are used but correlation is even lower. If we focus only on Wikipedia, we see that many texts are without diacritics in Yorùbá and often make use of mixed dialects and English sentences in Twi. The character-enhanced word embeddings are trained using a skipgram architecture with cluster-based embeddings and an embedding size of 300 dimensions, context window-size of 5, and 5 negative samples." } ], "images": [ "scigen/train/1566.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: FastText embeddings: Spearman ρ correlation between human judgements and similarity scores on the wordSim-353 for the three datasets analysed (C1, C2 and C3). The comparison with massive fastText embeddings is shown in the top rows." ], "reasoning": "The pre-trained Twi model has a very limited vocabulary size of 935 tokens, leading to low performance, while the Yorùbá model has a larger vocabulary but still shows low correlation due to mixed dialects and English sentences.", "description": "For Yorùbá, both versions are available but only embeddings trained on Wikipedia are available for Twi. We consider these embeddings the result of training on what we call massively-extracted corpora. Notice that training settings for both embeddings are not exactly the same, and differences in performance might come both from corpus size/quality but also from the background model. The 294-languages version is trained using skipgram, in dimension 300, with character n-grams of length 5, a window of size 5 and 5 negatives. The 157-languages version is trained using CBOW with position-weights, in dimension 300, with character n-grams of length 5, a window of size 5 and 10 negatives. We train our fastText systems using a skipgram model with an embedding size of 300 dimensions, context window size of 5, 10 negatives and n-grams ranging from 3 to 6 characters similarly to the pre-trained models for both languages. Best results are obtained with minimum word count of 3. Notice that pre-trained embeddings on Wikipedia show a very low correlation with humans on the similarity task for both languages (ρ=0.14) and their performance is even lower when Common Crawl is also considered (ρ=0.07 for Yorùbá). An important reason for the low performance is the limited vocabulary. The pre-trained Twi model has only 935 tokens. For Yorùbá, things are apparently better with more than 150 k tokens when both Wikipedia and Common Crawl are used but correlation is even lower. If we focus only on Wikipedia, we see that many texts are without diacritics in Yorùbá and often make use of mixed dialects and English sentences in Twi. The character-enhanced word embeddings are trained using a skipgram architecture with cluster-based embeddings and an embedding size of 300 dimensions, context window-size of 5, and 5 negative samples.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nTable title: Table 3: FastText embeddings: Spearman ρ correlation between human judgements and similarity scores on the wordSim-353 for the three datasets analysed (C1, C2 and C3). The comparison with massive fastText embeddings is shown in the top rows.\n\nQuestion: Which model has the highest Spearman correlation for Yorùbá?\n" }, { "role": "assistant", "content": "By comparing the Spearman correlation values for Yorùbá across all models, the highest value is found in the C3: Curated Large Dataset (All Clean + Noisy texts) model with a correlation of 0.391. So, the answer is {\"answer\": \"C3: Curated Large Dataset (All Clean + Noisy texts)\"}." } ], "images": [ "scigen/train/1566.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: FastText embeddings: Spearman ρ correlation between human judgements and similarity scores on the wordSim-353 for the three datasets analysed (C1, C2 and C3). The comparison with massive fastText embeddings is shown in the top rows." ], "reasoning": "By comparing the Spearman correlation values for Yorùbá across all models, the highest value is found in the C3: Curated Large Dataset (All Clean + Noisy texts) model with a correlation of 0.391.", "question": "Which model has the highest Spearman correlation for Yorùbá?", "answer": "So, the answer is {\"answer\": \"C3: Curated Large Dataset (All Clean + Noisy texts)\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The pre-trained Twi model has a higher Spearman correlation than the pre-trained Yorùbá model. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe pre-trained Twi model has a higher Spearman correlation than the pre-trained Yorùbá model.\n" }, { "role": "assistant", "content": "The table shows that the pre-trained Twi model (Wiki) has a Spearman correlation of 0.143, while the pre-trained Yorùbá model (Wiki) has a correlation of 0.136. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1566.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: FastText embeddings: Spearman ρ correlation between human judgements and similarity scores on the wordSim-353 for the three datasets analysed (C1, C2 and C3). The comparison with massive fastText embeddings is shown in the top rows." ], "reasoning": "The table shows that the pre-trained Twi model (Wiki) has a Spearman correlation of 0.143, while the pre-trained Yorùbá model (Wiki) has a correlation of 0.136. Therefore, the statement is supported.", "statement": "The pre-trained Twi model has a higher Spearman correlation than the pre-trained Yorùbá model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: NER F1 score on Global Voices Yorùbá corpus after fine-tuning BERT for 10 epochs. Mean F1-score computed after 5 runs'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Fine-tuning the pre-trained BERT on the Yorùbá corpus improves the F1 score, with the model using only Yorùbá vocabulary achieving a 4% higher F1 score than the multilingual vocabulary model.As expected, the fine-tuning of the pre-trained BERT on the Yorùbá corpus in the two configurations generates better representations than the base model. The fine-tuned BERT model with only Yorùbá vocabulary further increases by 4% in F1 score than the BERT model that uses the multilingual vocabulary. Although we do not have enough data to train BERT from scratch, we observe that fine-tuning BERT on a limited amount of monolingual data of a low-resource language helps to improve the quality of the embeddings." } ], "images": [ "scigen/train/1567.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: NER F1 score on Global Voices Yorùbá corpus after fine-tuning BERT for 10 epochs. Mean F1-score computed after 5 runs" ], "reasoning": "Fine-tuning the pre-trained BERT on the Yorùbá corpus improves the F1 score, with the model using only Yorùbá vocabulary achieving a 4% higher F1 score than the multilingual vocabulary model.", "description": "As expected, the fine-tuning of the pre-trained BERT on the Yorùbá corpus in the two configurations generates better representations than the base model. The fine-tuned BERT model with only Yorùbá vocabulary further increases by 4% in F1 score than the BERT model that uses the multilingual vocabulary. Although we do not have enough data to train BERT from scratch, we observe that fine-tuning BERT on a limited amount of monolingual data of a low-resource language helps to improve the quality of the embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 5: NER F1 score on Global Voices Yorùbá corpus after fine-tuning BERT for 10 epochs. Mean F1-score computed after 5 runs'. Solve the following question based on the table.\n\nQuestion: What is the F1 score improvement when fine-tuning the pre-trained BERT model with only Yorùbá vocabulary compared to the multilingual vocabulary?\nLet's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The F1 score for the pre-trained un-cased Multilingual BERT (Multilingual vocab) is 27.1±0.7. The F1 score for the fine-tuned un-cased Multilingual BERT (Yorùbá vocab) is 60.1±0.8. The improvement is calculated as 60.1 - 27.1 = 33.0. So, the answer is {\"answer\": \"33.0\"}" } ], "images": [ "scigen/train/1567.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: NER F1 score on Global Voices Yorùbá corpus after fine-tuning BERT for 10 epochs. Mean F1-score computed after 5 runs" ], "reasoning": "The F1 score for the pre-trained un-cased Multilingual BERT (Multilingual vocab) is 27.1±0.7. The F1 score for the fine-tuned un-cased Multilingual BERT (Yorùbá vocab) is 60.1±0.8. The improvement is calculated as 60.1 - 27.1 = 33.0.", "question": "What is the F1 score improvement when fine-tuning the pre-trained BERT model with only Yorùbá vocabulary compared to the multilingual vocabulary?", "answer": "So, the answer is {\"answer\": \"33.0\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The fine-tuned un-cased Multilingual BERT with Yorùbá vocabulary has a higher F1 score than the fine-tuned un-cased Multilingual BERT with multilingual vocabulary., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe fine-tuned un-cased Multilingual BERT with Yorùbá vocabulary has a higher F1 score than the fine-tuned un-cased Multilingual BERT with multilingual vocabulary.\n" }, { "role": "assistant", "content": "The table shows that the F1 score for the fine-tuned un-cased Multilingual BERT with Yorùbá vocabulary is 60.1±0.8, which is higher than the F1 score of 56.4±2.4 for the fine-tuned un-cased Multilingual BERT with multilingual vocabulary. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1567.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: NER F1 score on Global Voices Yorùbá corpus after fine-tuning BERT for 10 epochs. Mean F1-score computed after 5 runs" ], "reasoning": "The table shows that the F1 score for the fine-tuned un-cased Multilingual BERT with Yorùbá vocabulary is 60.1±0.8, which is higher than the F1 score of 56.4±2.4 for the fine-tuned un-cased Multilingual BERT with multilingual vocabulary.", "statement": "The fine-tuned un-cased Multilingual BERT with Yorùbá vocabulary has a higher F1 score than the fine-tuned un-cased Multilingual BERT with multilingual vocabulary.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Political perspective detection: Baseline classification results.'.\n" }, { "role": "assistant", "content": "The Logistic Regression model using tf-idf features on content achieved the highest AUC score of 0.964.Baseline We experimented with different features representing the articles during the baseline experiments. As in the case of CQA task we tried unigram, bi-gram and tri-gram features. We also identified that tf-idf features used as signal representation achieved a higher result. We used the following baseline algorithms and configurations: SVM with tf-idf features, using sigmoid kernel and penalty variable C=1. Multinomial Naive Bayes classifier on tf-idf features with the alpha variable C=1. Random Forest on tf-idf features with 10 estimators, splitting on the Gini criterion. Decision Tree Classifier on tf-idf features. No limitation on max depth, splitting on the Gini criterion. Logistic Regression on tf-idf features using l2 penalty, with the inverse of regularization at 1 and maximum number of iterations 100." } ], "images": [ "scigen/train/1568.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Political perspective detection: Baseline classification results." ], "reasoning": "The Logistic Regression model using tf-idf features on content achieved the highest AUC score of 0.964.", "description": "Baseline We experimented with different features representing the articles during the baseline experiments. As in the case of CQA task we tried unigram, bi-gram and tri-gram features. We also identified that tf-idf features used as signal representation achieved a higher result. We used the following baseline algorithms and configurations: SVM with tf-idf features, using sigmoid kernel and penalty variable C=1. Multinomial Naive Bayes classifier on tf-idf features with the alpha variable C=1. Random Forest on tf-idf features with 10 estimators, splitting on the Gini criterion. Decision Tree Classifier on tf-idf features. No limitation on max depth, splitting on the Gini criterion. Logistic Regression on tf-idf features using l2 penalty, with the inverse of regularization at 1 and maximum number of iterations 100.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 3: Political perspective detection: Baseline classification results.'.\n\nQuestion: Which model and classification subject combination achieved the highest AUC score?\n" }, { "role": "assistant", "content": "By examining the AUC scores in the table, the highest AUC score is 0.964, achieved by the Logistic Regression model using content as the classification subject. So, the answer is {\"answer\": \"Logistic Regression with Content\"}." } ], "images": [ "scigen/train/1568.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Political perspective detection: Baseline classification results." ], "reasoning": "By examining the AUC scores in the table, the highest AUC score is 0.964, achieved by the Logistic Regression model using content as the classification subject.", "question": "Which model and classification subject combination achieved the highest AUC score?", "answer": "So, the answer is {\"answer\": \"Logistic Regression with Content\"}." }, { "messages": [ { "role": "user", "content": "The SVM model using content as the classification subject achieved an AUC score of 0.962. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe SVM model using content as the classification subject achieved an AUC score of 0.962.\n" }, { "role": "assistant", "content": "The table shows that the SVM model using content as the classification subject indeed achieved an AUC score of 0.962. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1568.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Political perspective detection: Baseline classification results." ], "reasoning": "The table shows that the SVM model using content as the classification subject indeed achieved an AUC score of 0.962.", "statement": "The SVM model using content as the classification subject achieved an AUC score of 0.962.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Computational speed chart. Average running times for the four feature selection methods over all subjects considered. Second column includes time to generate y and adjust X. Final column is percentage increase in total time over Co-occurrence, the baseline method'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Co-occurrence and Correlation Screening are fast and scalable but cannot utilize feature dependence, while Lasso and L1LR can but are more computationally intensive.The primary advantages of Co-occurrence and Correlation Screening are that they are fast, scalable and easily distributed across multiple cores for parallel processing. Unfortunately, as they score each feature independently from the others, they cannot take advantage of any dependence between features to aid summarization. The Lasso and L1LR can, to a certain extent. The down side is that the sparse methods are more computationally intensive than Co-occurence and Correlation Screening. However, this could be mitigated by, for example, moving to a parallel computing environment or doing clever preprocessing such as safe feature elimination [El Ghaoui, Viallon and Rabbani" } ], "images": [ "scigen/train/1569.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Computational speed chart. Average running times for the four feature selection methods over all subjects considered. Second column includes time to generate y and adjust X. Final column is percentage increase in total time over Co-occurrence, the baseline method" ], "reasoning": "Co-occurrence and Correlation Screening are fast and scalable but cannot utilize feature dependence, while Lasso and L1LR can but are more computationally intensive.", "description": "The primary advantages of Co-occurrence and Correlation Screening are that they are fast, scalable and easily distributed across multiple cores for parallel processing. Unfortunately, as they score each feature independently from the others, they cannot take advantage of any dependence between features to aid summarization. The Lasso and L1LR can, to a certain extent. The down side is that the sparse methods are more computationally intensive than Co-occurence and Correlation Screening. However, this could be mitigated by, for example, moving to a parallel computing environment or doing clever preprocessing such as safe feature elimination [El Ghaoui, Viallon and Rabbani", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 3: Computational speed chart. Average running times for the four feature selection methods over all subjects considered. Second column includes time to generate y and adjust X. Final column is percentage increase in total time over Co-occurrence, the baseline method'.\n\nQuestion: Which method has the highest total time for phrase selection and what is its percent increase?\n" }, { "role": "assistant", "content": "By examining the table, L1LR has the highest total time for phrase selection at 124.2 seconds and a percent increase of +511%. So, the answer is {\"answer\": \"L1LR with +511%\"}" } ], "images": [ "scigen/train/1569.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Computational speed chart. Average running times for the four feature selection methods over all subjects considered. Second column includes time to generate y and adjust X. Final column is percentage increase in total time over Co-occurrence, the baseline method" ], "reasoning": "By examining the table, L1LR has the highest total time for phrase selection at 124.2 seconds and a percent increase of +511%.", "question": "Which method has the highest total time for phrase selection and what is its percent increase?", "answer": "So, the answer is {\"answer\": \"L1LR with +511%\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Lasso method is faster than Co-occurrence., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Lasso method is faster than Co-occurrence.\n" }, { "role": "assistant", "content": "The table shows that The Lasso method has a total time of 28.7 seconds, which is higher than Co-occurrence's 20.3 seconds, indicating that The Lasso is slower. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1569.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Computational speed chart. Average running times for the four feature selection methods over all subjects considered. Second column includes time to generate y and adjust X. Final column is percentage increase in total time over Co-occurrence, the baseline method" ], "reasoning": "The table shows that The Lasso method has a total time of 28.7 seconds, which is higher than Co-occurrence's 20.3 seconds, indicating that The Lasso is slower.", "statement": "The Lasso method is faster than Co-occurrence.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Train, validation, and test splits for each Plutchik-8 emotion.'.\n" }, { "role": "assistant", "content": "Tweets are categorized into emotion buckets, with each tweet potentially belonging to multiple buckets. Positive samples are balanced with negative samples, shuffled, and split into train, validation, and test sets.The tweets are assorted into their respective label buckets; because tweets may be labeled with more than one emotion, each belongs to one or more buckets. These buckets represent positive samples (i.e., tweets labeled with that emotion). To create negative samples, we sample an equal amount from other buckets. From here, we shuffle the positive and negative samples and perform an 80/10/10 split to create the train, validation, and test sets." } ], "images": [ "scigen/train/1570.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Train, validation, and test splits for each Plutchik-8 emotion." ], "reasoning": "Tweets are categorized into emotion buckets, with each tweet potentially belonging to multiple buckets. Positive samples are balanced with negative samples, shuffled, and split into train, validation, and test sets.", "description": "The tweets are assorted into their respective label buckets; because tweets may be labeled with more than one emotion, each belongs to one or more buckets. These buckets represent positive samples (i.e., tweets labeled with that emotion). To create negative samples, we sample an equal amount from other buckets. From here, we shuffle the positive and negative samples and perform an 80/10/10 split to create the train, validation, and test sets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 4: Train, validation, and test splits for each Plutchik-8 emotion.\nQuestion: How many tweets labeled with 'Awe' are in the validation set?\n" }, { "role": "assistant", "content": "To find the number of tweets labeled with 'Awe' in the validation set, look at the 'Valid' column in the 'Awe' row. So, the answer is {\"answer\": \"916\"}." } ], "images": [ "scigen/train/1570.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Train, validation, and test splits for each Plutchik-8 emotion." ], "reasoning": "To find the number of tweets labeled with 'Awe' in the validation set, look at the 'Valid' column in the 'Awe' row.", "question": "How many tweets labeled with 'Awe' are in the validation set?", "answer": "So, the answer is {\"answer\": \"916\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThere are more tweets labeled with 'Optimism' in the train set than in the test set.\n" }, { "role": "assistant", "content": "Compare the number of tweets labeled with 'Optimism' in the train set (11,902) with the number in the test set (1,488). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1570.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Train, validation, and test splits for each Plutchik-8 emotion." ], "reasoning": "Compare the number of tweets labeled with 'Optimism' in the train set (11,902) with the number in the test set (1,488).", "statement": "There are more tweets labeled with 'Optimism' in the train set than in the test set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 7: Unsupervised domain adaptation accuracies (abbreviations defined in Table 5). Results are highlighted with blue (↑) and red (↓) with respect to Src-Only. Best viewed in color.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Pre-training adds +1% in average accuracy over Src-Only, but Pretrain-Joint performs worse than both Pretrain-Src and Pretrain-Trg.Overall, we do not find a significant increase in performance over the Src-Only baseline. Pre-training consistently adds +1% in average accuracy, but still leaves a large gap between Pretrain-Src and Trg-Only. Regardless, we have a few observations. First, we do not see a (relatively) large increase in performance for submission, awe, disapproval, and remorse. These emotions may need more explicit strategies to enable domain adaptation. Second, Pretrain-Joint performs worse than both Pretrain-Src and Pretrain-Trg. We posit that, for our emotion tasks, pre-training with a mixture of domains yields a noisier training signal compared to a parameter bias towards the target domain." } ], "images": [ "scigen/train/1571.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Unsupervised domain adaptation accuracies (abbreviations defined in Table 5). Results are highlighted with blue (↑) and red (↓) with respect to Src-Only. Best viewed in color." ], "reasoning": "Pre-training adds +1% in average accuracy over Src-Only, but Pretrain-Joint performs worse than both Pretrain-Src and Pretrain-Trg.", "description": "Overall, we do not find a significant increase in performance over the Src-Only baseline. Pre-training consistently adds +1% in average accuracy, but still leaves a large gap between Pretrain-Src and Trg-Only. Regardless, we have a few observations. First, we do not see a (relatively) large increase in performance for submission, awe, disapproval, and remorse. These emotions may need more explicit strategies to enable domain adaptation. Second, Pretrain-Joint performs worse than both Pretrain-Src and Pretrain-Trg. We posit that, for our emotion tasks, pre-training with a mixture of domains yields a noisier training signal compared to a parameter bias towards the target domain.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 7: Unsupervised domain adaptation accuracies (abbreviations defined in Table 5). Results are highlighted with blue (↑) and red (↓) with respect to Src-Only. Best viewed in color.', and tackle the question below in a sequential manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the average accuracy of the Pretrain-Src model and how does it compare to the Src-Only baseline?\n" }, { "role": "assistant", "content": "The average accuracy of the Pretrain-Src model is 52.0, which is 0.8 higher than the Src-Only baseline of 51.2. So, the answer is {\"answer\": \"52.0\"}." } ], "images": [ "scigen/train/1571.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Unsupervised domain adaptation accuracies (abbreviations defined in Table 5). Results are highlighted with blue (↑) and red (↓) with respect to Src-Only. Best viewed in color." ], "reasoning": "The average accuracy of the Pretrain-Src model is 52.0, which is 0.8 higher than the Src-Only baseline of 51.2.", "question": "What is the average accuracy of the Pretrain-Src model and how does it compare to the Src-Only baseline?", "answer": "So, the answer is {\"answer\": \"52.0\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nPretrain-Joint performs better than Pretrain-Trg in terms of average accuracy.\n" }, { "role": "assistant", "content": "The average accuracy of Pretrain-Joint is 51.6, which is lower than the average accuracy of Pretrain-Trg at 52.9. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1571.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Unsupervised domain adaptation accuracies (abbreviations defined in Table 5). Results are highlighted with blue (↑) and red (↓) with respect to Src-Only. Best viewed in color." ], "reasoning": "The average accuracy of Pretrain-Joint is 51.6, which is lower than the average accuracy of Pretrain-Trg at 52.9.", "statement": "Pretrain-Joint performs better than Pretrain-Trg in terms of average accuracy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 8: Hyperparameters for the baseline modeling experiments (§5).'.\n" }, { "role": "assistant", "content": "Pre-trained models like BERT and RoBERTa use a dropout rate of 0.1 on self-attention layers and employ gradient accumulation for larger mini-batches.For our pre-trained models (e.g., BERT and RoBERTa), we use the default dropout rate (0.1) on the self-attention layers, but do not use additional dropout on the top linear layer. Furthermore, we use gradient accumulation to enable training with larger mini-batches." } ], "images": [ "scigen/train/1572.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Hyperparameters for the baseline modeling experiments (§5)." ], "reasoning": "Pre-trained models like BERT and RoBERTa use a dropout rate of 0.1 on self-attention layers and employ gradient accumulation for larger mini-batches.", "description": "For our pre-trained models (e.g., BERT and RoBERTa), we use the default dropout rate (0.1) on the self-attention layers, but do not use additional dropout on the top linear layer. Furthermore, we use gradient accumulation to enable training with larger mini-batches.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: Table 8: Hyperparameters for the baseline modeling experiments (§5).\nQuestion: What is the learning rate and batch size used for training BERT?\n" }, { "role": "assistant", "content": "The table shows that BERT is trained with a learning rate of 2e-5 and a batch size of 16. So, the answer is {\"answer\": \"2e-5 and 16\"}." } ], "images": [ "scigen/train/1572.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Hyperparameters for the baseline modeling experiments (§5)." ], "reasoning": "The table shows that BERT is trained with a learning rate of 2e-5 and a batch size of 16.", "question": "What is the learning rate and batch size used for training BERT?", "answer": "So, the answer is {\"answer\": \"2e-5 and 16\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nRoBERTa uses a dropout rate of 0.1 on the top linear layer.\n" }, { "role": "assistant", "content": "The description states that no additional dropout is used on the top linear layer for pre-trained models like RoBERTa. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1572.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Hyperparameters for the baseline modeling experiments (§5)." ], "reasoning": "The description states that no additional dropout is used on the top linear layer for pre-trained models like RoBERTa.", "statement": "RoBERTa uses a dropout rate of 0.1 on the top linear layer.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 9: Pre-training using multi-class and binary EmoNet tasks. See Table 6 for styling considerations.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Multi-class pre-training on EmoNet creates better representations for downstream evaluation compared to binary pre-training, leading to higher averaged accuracy.In this section, we explore a fine-grained pre-training scheme. We create Plutchik-8 binary tasks from EmoNet, then fine-tune each emotion model separately on their respective HurricaneEmo tasks. EmoNet-Binary performs markedly worse than EmoNet-Multi and leads to a -2% reduction in averaged accuracy. Therefore, multi-class pre-training creates better representations for downstream evaluation, although they are still not as effective as other pre-training methods (e.g., masked language modeling)." } ], "images": [ "scigen/train/1573.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Pre-training using multi-class and binary EmoNet tasks. See Table 6 for styling considerations." ], "reasoning": "Multi-class pre-training on EmoNet creates better representations for downstream evaluation compared to binary pre-training, leading to higher averaged accuracy.", "description": "In this section, we explore a fine-grained pre-training scheme. We create Plutchik-8 binary tasks from EmoNet, then fine-tune each emotion model separately on their respective HurricaneEmo tasks. EmoNet-Binary performs markedly worse than EmoNet-Multi and leads to a -2% reduction in averaged accuracy. Therefore, multi-class pre-training creates better representations for downstream evaluation, although they are still not as effective as other pre-training methods (e.g., masked language modeling).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 9: Pre-training using multi-class and binary EmoNet tasks. See Table 6 for styling considerations.\nQuestion: What is the average accuracy of the Multi pre-training scheme?\nGive a detailed response to the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the average accuracy (AVG) for different pre-training schemes. The Multi pre-training scheme has an average accuracy of 65.6. So, the answer is {\"answer\": \"65.6\"}" } ], "images": [ "scigen/train/1573.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Pre-training using multi-class and binary EmoNet tasks. See Table 6 for styling considerations." ], "reasoning": "The table shows the average accuracy (AVG) for different pre-training schemes. The Multi pre-training scheme has an average accuracy of 65.6.", "question": "What is the average accuracy of the Multi pre-training scheme?", "answer": "So, the answer is {\"answer\": \"65.6\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The Binary pre-training scheme has a higher average accuracy than the No-Pretrain scheme., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Binary pre-training scheme has a higher average accuracy than the No-Pretrain scheme.\n" }, { "role": "assistant", "content": "The table shows that the Binary pre-training scheme has an average accuracy (AVG) of 62.8, while the No-Pretrain scheme has an average accuracy of 64.1. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1573.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Pre-training using multi-class and binary EmoNet tasks. See Table 6 for styling considerations." ], "reasoning": "The table shows that the Binary pre-training scheme has an average accuracy (AVG) of 62.8, while the No-Pretrain scheme has an average accuracy of 64.1. Therefore, the statement is refuted.", "statement": "The Binary pre-training scheme has a higher average accuracy than the No-Pretrain scheme.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 10: Pre-training using 50-1600K labeled samples from Sentiment. See Table 6 for styling considerations.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Using 200K samples for pre-training saturates the performance for both supervised Sentiment and unsupervised HurricaneExt tasks, with no noticeable benefit from using more data.The Sentiment and HurricaneExt datasets contain significantly more samples than currently used. In this section, we study the effects of using varying amounts of pre-training data on downstream HurricaneEmo performance. For both pre-training datasets, we use 1.6M samples. For both types of pre-training tasks, there is no noticeable benefit to using more pre-training data. The supervised Sentiment and unsupervised HurricaneExt results both saturate around 200K samples, which is what we report in our paper. The results for unsupervised HurricaneExt pre-training are especially compelling because they show that, without any labeled data, we can achieve strong downstream results. Finally, the unsupervised Sentiment task yields almost no gains for most emotions, showing that the type of data used for masked language modeling matters. Through side-by-side comparisons, we notice that the Sentiment samples are shorter in length and the HurricaneExt samples contain more relevant content, such as hurricane-specific hashtags." } ], "images": [ "scigen/train/1574.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Pre-training using 50-1600K labeled samples from Sentiment. See Table 6 for styling considerations." ], "reasoning": "Using 200K samples for pre-training saturates the performance for both supervised Sentiment and unsupervised HurricaneExt tasks, with no noticeable benefit from using more data.", "description": "The Sentiment and HurricaneExt datasets contain significantly more samples than currently used. In this section, we study the effects of using varying amounts of pre-training data on downstream HurricaneEmo performance. For both pre-training datasets, we use 1.6M samples. For both types of pre-training tasks, there is no noticeable benefit to using more pre-training data. The supervised Sentiment and unsupervised HurricaneExt results both saturate around 200K samples, which is what we report in our paper. The results for unsupervised HurricaneExt pre-training are especially compelling because they show that, without any labeled data, we can achieve strong downstream results. Finally, the unsupervised Sentiment task yields almost no gains for most emotions, showing that the type of data used for masked language modeling matters. Through side-by-side comparisons, we notice that the Sentiment samples are shorter in length and the HurricaneExt samples contain more relevant content, such as hurricane-specific hashtags.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 10: Pre-training using 50-1600K labeled samples from Sentiment. See Table 6 for styling considerations.', answer the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the average score (AVG) for the 200K sample pre-training?\n" }, { "role": "assistant", "content": "To find the average score for the 200K sample pre-training, look at the row labeled '200 K' and find the value in the 'AVG' column. So, the answer is {\"answer\": \"66.8\"}." } ], "images": [ "scigen/train/1574.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Pre-training using 50-1600K labeled samples from Sentiment. See Table 6 for styling considerations." ], "reasoning": "To find the average score for the 200K sample pre-training, look at the row labeled '200 K' and find the value in the 'AVG' column.", "question": "What is the average score (AVG) for the 200K sample pre-training?", "answer": "So, the answer is {\"answer\": \"66.8\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nUsing 800K samples for pre-training results in a higher average score than using 200K samples.\n" }, { "role": "assistant", "content": "The table shows that the average score (AVG) for 800K samples is 64.9, which is lower than the average score of 66.8 for 200K samples. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1574.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Pre-training using 50-1600K labeled samples from Sentiment. See Table 6 for styling considerations." ], "reasoning": "The table shows that the average score (AVG) for 800K samples is 64.9, which is lower than the average score of 66.8 for 200K samples.", "statement": "Using 800K samples for pre-training results in a higher average score than using 200K samples.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 11: Pre-training using 50-1600K unlabeled samples from Sentiment. See Table 6 for styling considerations.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Using 200K samples for pre-training saturates the performance for both supervised Sentiment and unsupervised HurricaneExt tasks, with no noticeable benefit from using more data.The Sentiment and HurricaneExt datasets contain significantly more samples than currently used. In this section, we study the effects of using varying amounts of pre-training data on downstream HurricaneEmo performance. For both pre-training datasets, we use 1.6M samples. For both types of pre-training tasks, there is no noticeable benefit to using more pre-training data. The supervised Sentiment and unsupervised HurricaneExt results both saturate around 200K samples, which is what we report in our paper. The results for unsupervised HurricaneExt pre-training are especially compelling because they show that, without any labeled data, we can achieve strong downstream results. Finally, the unsupervised Sentiment task yields almost no gains for most emotions, showing that the type of data used for masked language modeling matters. Through side-by-side comparisons, we notice that the Sentiment samples are shorter in length and the HurricaneExt samples contain more relevant content, such as hurricane-specific hashtags." } ], "images": [ "scigen/train/1575.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Pre-training using 50-1600K unlabeled samples from Sentiment. See Table 6 for styling considerations." ], "reasoning": "Using 200K samples for pre-training saturates the performance for both supervised Sentiment and unsupervised HurricaneExt tasks, with no noticeable benefit from using more data.", "description": "The Sentiment and HurricaneExt datasets contain significantly more samples than currently used. In this section, we study the effects of using varying amounts of pre-training data on downstream HurricaneEmo performance. For both pre-training datasets, we use 1.6M samples. For both types of pre-training tasks, there is no noticeable benefit to using more pre-training data. The supervised Sentiment and unsupervised HurricaneExt results both saturate around 200K samples, which is what we report in our paper. The results for unsupervised HurricaneExt pre-training are especially compelling because they show that, without any labeled data, we can achieve strong downstream results. Finally, the unsupervised Sentiment task yields almost no gains for most emotions, showing that the type of data used for masked language modeling matters. Through side-by-side comparisons, we notice that the Sentiment samples are shorter in length and the HurricaneExt samples contain more relevant content, such as hurricane-specific hashtags.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 11: Pre-training using 50-1600K unlabeled samples from Sentiment. See Table 6 for styling considerations.', unfold the question presented below, detailing every step of your calculation. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the average performance (AVG) when using 200K samples for pre-training?\n" }, { "role": "assistant", "content": "From the table, locate the row corresponding to 200K samples and find the value under the AVG column. So, the answer is {\"answer\": \"63.5\"}." } ], "images": [ "scigen/train/1575.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Pre-training using 50-1600K unlabeled samples from Sentiment. See Table 6 for styling considerations." ], "reasoning": "From the table, locate the row corresponding to 200K samples and find the value under the AVG column.", "question": "What is the average performance (AVG) when using 200K samples for pre-training?", "answer": "So, the answer is {\"answer\": \"63.5\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nUsing 400K samples for pre-training results in a higher average performance (AVG) than using 50K samples.\n" }, { "role": "assistant", "content": "From the table, compare the AVG values for 400K samples (64.5) and 50K samples (64.0). The AVG for 400K samples is indeed higher. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1575.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Pre-training using 50-1600K unlabeled samples from Sentiment. See Table 6 for styling considerations." ], "reasoning": "From the table, compare the AVG values for 400K samples (64.5) and 50K samples (64.0). The AVG for 400K samples is indeed higher.", "statement": "Using 400K samples for pre-training results in a higher average performance (AVG) than using 50K samples.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 12: Pre-training using 50-1600K unlabeled samples from HurricaneExt. See Table 6 for styling considerations.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Using 1.6M samples for pre-training does not provide noticeable benefits over using 200K samples for both supervised Sentiment and unsupervised HurricaneExt tasks, with results saturating around 200K samples.The Sentiment and HurricaneExt datasets contain significantly more samples than currently used. In this section, we study the effects of using varying amounts of pre-training data on downstream HurricaneEmo performance. For both pre-training datasets, we use 1.6M samples. For both types of pre-training tasks, there is no noticeable benefit to using more pre-training data. The supervised Sentiment and unsupervised HurricaneExt results both saturate around 200K samples, which is what we report in our paper. The results for unsupervised HurricaneExt pre-training are especially compelling because they show that, without any labeled data, we can achieve strong downstream results. Finally, the unsupervised Sentiment task yields almost no gains for most emotions, showing that the type of data used for masked language modeling matters. Through side-by-side comparisons, we notice that the Sentiment samples are shorter in length and the HurricaneExt samples contain more relevant content, such as hurricane-specific hashtags." } ], "images": [ "scigen/train/1576.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 12: Pre-training using 50-1600K unlabeled samples from HurricaneExt. See Table 6 for styling considerations." ], "reasoning": "Using 1.6M samples for pre-training does not provide noticeable benefits over using 200K samples for both supervised Sentiment and unsupervised HurricaneExt tasks, with results saturating around 200K samples.", "description": "The Sentiment and HurricaneExt datasets contain significantly more samples than currently used. In this section, we study the effects of using varying amounts of pre-training data on downstream HurricaneEmo performance. For both pre-training datasets, we use 1.6M samples. For both types of pre-training tasks, there is no noticeable benefit to using more pre-training data. The supervised Sentiment and unsupervised HurricaneExt results both saturate around 200K samples, which is what we report in our paper. The results for unsupervised HurricaneExt pre-training are especially compelling because they show that, without any labeled data, we can achieve strong downstream results. Finally, the unsupervised Sentiment task yields almost no gains for most emotions, showing that the type of data used for masked language modeling matters. Through side-by-side comparisons, we notice that the Sentiment samples are shorter in length and the HurricaneExt samples contain more relevant content, such as hurricane-specific hashtags.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 12: Pre-training using 50-1600K unlabeled samples from HurricaneExt. See Table 6 for styling considerations.' and methodically tackle the question that follows. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the average score (AVG) for 200K samples in the table?\n" }, { "role": "assistant", "content": "To find the average score for 200K samples, locate the row labeled '200 K' and find the value in the 'AVG' column. So, the answer is {\"answer\": \"68.2\"}." } ], "images": [ "scigen/train/1576.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 12: Pre-training using 50-1600K unlabeled samples from HurricaneExt. See Table 6 for styling considerations." ], "reasoning": "To find the average score for 200K samples, locate the row labeled '200 K' and find the value in the 'AVG' column.", "question": "What is the average score (AVG) for 200K samples in the table?", "answer": "So, the answer is {\"answer\": \"68.2\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nUsing 800K samples for pre-training yields the highest average score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'AVG' values for all sample sizes. The highest average score is 68.3, which corresponds to the 200K samples, not the 800K samples. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1576.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 12: Pre-training using 50-1600K unlabeled samples from HurricaneExt. See Table 6 for styling considerations." ], "reasoning": "To verify this statement, compare the 'AVG' values for all sample sizes. The highest average score is 68.3, which corresponds to the 200K samples, not the 800K samples.", "statement": "Using 800K samples for pre-training yields the highest average score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: Vocabularies of different methods.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The table compares different methods for generating word embeddings based on their performance on three intrinsic word similarity tasks: WS353, MEN, and SIM999.[leftmargin=0mm] NumAsTok This baseline treats numerals and non-numerical words in the same way, which is very similar to the original skip-gram. The vocabulary includes both high-frequency words and high-frequency numerals. OOV non-numerical words are replaced with symbol \\mathrm{UNK_{word}} and OOV numerals are replaced with symbol \\mathrm{UNK_{num}}. D-LSTM Character-level RNNs are often used to encode OOV words (graves2013generating). Here we apply an LSTM (hochreiter1997long) to the digit sequence of a numeral and use the last hidden state of the LSTM as the embedding of the numeral. We use the embedding to compute the skip-gram objective function and propagate the gradients back to update the LSTM. The vocabulary of digits is: {0-9, ‘.’, ‘+’, ‘-’, ‘e’}. Fixed This baseline fixed embeddings for numerals with no training. Our methods, D-LSTM, and Fixed have finite non-numerical vocabularies but infinite numeral vocabularies. In contrast, the NumAsTok baseline has a finite numeral vocabulary and treats all the OOV numerals as \\mathrm{UNK_{num}}. To ensure that our methods can still generate high quality embeddings for non-numerical words, we evaluate our trained embeddings on classical intrinsic word similarity tasks, including WordSim-353, (Finkelstein:2001: PSC:371920.372094), MEN (Bruni:2014:MDS:2655713.2655714) and Simplex-999 (DBLP: journals/corr/HillRK14). We train 300-dimensional word embeddings on the 1B Wikipedia dump and set the context window size to 5, the number of negative samples to 5, and the vocabulary size to 3\\times 10^{5}. journals/corr/JastrzebskiLC17). Note that while the training data contains numerals, the evaluation tasks do not involve numerals and are only designed to evaluate quality of non-numerical word embeddings." } ], "images": [ "scigen/train/1577.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Vocabularies of different methods." ], "reasoning": "The table compares different methods for generating word embeddings based on their performance on three intrinsic word similarity tasks: WS353, MEN, and SIM999.", "description": "[leftmargin=0mm] NumAsTok This baseline treats numerals and non-numerical words in the same way, which is very similar to the original skip-gram. The vocabulary includes both high-frequency words and high-frequency numerals. OOV non-numerical words are replaced with symbol \\mathrm{UNK_{word}} and OOV numerals are replaced with symbol \\mathrm{UNK_{num}}. D-LSTM Character-level RNNs are often used to encode OOV words (graves2013generating). Here we apply an LSTM (hochreiter1997long) to the digit sequence of a numeral and use the last hidden state of the LSTM as the embedding of the numeral. We use the embedding to compute the skip-gram objective function and propagate the gradients back to update the LSTM. The vocabulary of digits is: {0-9, ‘.’, ‘+’, ‘-’, ‘e’}. Fixed This baseline fixed embeddings for numerals with no training. Our methods, D-LSTM, and Fixed have finite non-numerical vocabularies but infinite numeral vocabularies. In contrast, the NumAsTok baseline has a finite numeral vocabulary and treats all the OOV numerals as \\mathrm{UNK_{num}}. To ensure that our methods can still generate high quality embeddings for non-numerical words, we evaluate our trained embeddings on classical intrinsic word similarity tasks, including WordSim-353, (Finkelstein:2001: PSC:371920.372094), MEN (Bruni:2014:MDS:2655713.2655714) and Simplex-999 (DBLP: journals/corr/HillRK14). We train 300-dimensional word embeddings on the 1B Wikipedia dump and set the context window size to 5, the number of negative samples to 5, and the vocabulary size to 3\\times 10^{5}. journals/corr/JastrzebskiLC17). Note that while the training data contains numerals, the evaluation tasks do not involve numerals and are only designed to evaluate quality of non-numerical word embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 1: Vocabularies of different methods.\nQuestion: Which method achieved the highest score on the MEN task?\n" }, { "role": "assistant", "content": "To find the method with the highest score on the MEN task, look at the MEN column and identify the highest value. The highest score is 74.10, achieved by the SG GoogleNews-100B method. So, the answer is {\"answer\": \"SG GoogleNews-100B\"}" } ], "images": [ "scigen/train/1577.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Vocabularies of different methods." ], "reasoning": "To find the method with the highest score on the MEN task, look at the MEN column and identify the highest value. The highest score is 74.10, achieved by the SG GoogleNews-100B method.", "question": "Which method achieved the highest score on the MEN task?", "answer": "So, the answer is {\"answer\": \"SG GoogleNews-100B\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe NumAsTok method outperformed the D-LSTM method on the WS353 task.\n" }, { "role": "assistant", "content": "To verify this statement, compare the WS353 scores of the NumAsTok and D-LSTM methods. NumAsTok scored 65.30, while D-LSTM scored 63.60. Therefore, NumAsTok did outperform D-LSTM on the WS353 task. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1577.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Vocabularies of different methods." ], "reasoning": "To verify this statement, compare the WS353 scores of the NumAsTok and D-LSTM methods. NumAsTok scored 65.30, while D-LSTM scored 63.60. Therefore, NumAsTok did outperform D-LSTM on the WS353 task.", "statement": "The NumAsTok method outperformed the D-LSTM method on the WS353 task.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: The results of the numeral prediction tasks.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our methods outperform NumAsTok and Fixed baselines on all metrics, while D-LSTM performs well but requires more parameters and computing time.Our methods significantly outperform the NumAsTok and Fixed baselines on all the three metrics. D-LSTM also performs well but needs more parameters and computing time than our methods. We split the dataset to 450k sentences for training, 50k for validation and 100k for testing. We use micro-F1 and macro-F1 in addition to AVGR as the evaluation metrics. The result shows that our methods achieve much better performance compared to the baselines." } ], "images": [ "scigen/train/1578.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The results of the numeral prediction tasks." ], "reasoning": "Our methods outperform NumAsTok and Fixed baselines on all metrics, while D-LSTM performs well but requires more parameters and computing time.", "description": "Our methods significantly outperform the NumAsTok and Fixed baselines on all the three metrics. D-LSTM also performs well but needs more parameters and computing time than our methods. We split the dataset to 450k sentences for training, 50k for validation and 100k for testing. We use micro-F1 and macro-F1 in addition to AVGR as the evaluation metrics. The result shows that our methods achieve much better performance compared to the baselines.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 4: The results of the numeral prediction tasks.'. Solve the following question based on the table.\n\nQuestion: Which method has the highest AVGR score on the Wikipedia-1B, dim 300 \\mathbf{S_{A}} dataset?\nLet's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the method with the highest AVGR score on the Wikipedia-1B, dim 300 \\mathbf{S_{A}} dataset, compare the AVGR scores in the corresponding column. The highest score is 685.58, achieved by the Fixed method. So, the answer is {\"answer\": \"Fixed\"}" } ], "images": [ "scigen/train/1578.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The results of the numeral prediction tasks." ], "reasoning": "To find the method with the highest AVGR score on the Wikipedia-1B, dim 300 \\mathbf{S_{A}} dataset, compare the AVGR scores in the corresponding column. The highest score is 685.58, achieved by the Fixed method.", "question": "Which method has the highest AVGR score on the Wikipedia-1B, dim 300 \\mathbf{S_{A}} dataset?", "answer": "So, the answer is {\"answer\": \"Fixed\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe GMM method has the highest Micro-F1 score on the Numeracy-600k, dim 300 \\mathbf{S_{A}} dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Micro-F1 scores for all methods on the Numeracy-600k, dim 300 \\mathbf{S_{A}} dataset. The highest Micro-F1 score is 44.07, achieved by the GMM method. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1578.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The results of the numeral prediction tasks." ], "reasoning": "To verify this statement, compare the Micro-F1 scores for all methods on the Numeracy-600k, dim 300 \\mathbf{S_{A}} dataset. The highest Micro-F1 score is 44.07, achieved by the GMM method.", "statement": "The GMM method has the highest Micro-F1 score on the Numeracy-600k, dim 300 \\mathbf{S_{A}} dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 5: The results of sequence labeling. We report the accuracy, precision, recall, F1 score for the original, augmented, and harder test sets with different training data sizes. Accuracy is in the token level and the other metrics are in the entity level. We report the standard deviations in Appendix H.'.\n" }, { "role": "assistant", "content": "Our methods outperform baselines in Accuracy, Recall, and F1 metrics, especially in low-resource settings, indicating better generalizability and less annotation required.Our methods consistently outperform all the baselines on the Accuracy, Recall, and F1 metrics in different configurations. NumAsTok trained with 100% training samples has the highest precision on the original and hard test sets probably because it learns high-quality embeddings for high-frequency numerals included in its vocabulary; but its recall is lower than that of our methods, most likely because of its numeral OOV problem. Comparing the results on the original and augmented test sets, we see that NumAsTok shows a more significant drop in performance than the other methods, which suggests that NumAsTok does not generalize well because of the numeral OOV problem. In the low-resource settings, the advantage of our methods over the baselines becomes even larger, indicating better generalizability and less annotation required for our methods to achieve a promising performance." } ], "images": [ "scigen/train/1579.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The results of sequence labeling. We report the accuracy, precision, recall, F1 score for the original, augmented, and harder test sets with different training data sizes. Accuracy is in the token level and the other metrics are in the entity level. We report the standard deviations in Appendix H." ], "reasoning": "Our methods outperform baselines in Accuracy, Recall, and F1 metrics, especially in low-resource settings, indicating better generalizability and less annotation required.", "description": "Our methods consistently outperform all the baselines on the Accuracy, Recall, and F1 metrics in different configurations. NumAsTok trained with 100% training samples has the highest precision on the original and hard test sets probably because it learns high-quality embeddings for high-frequency numerals included in its vocabulary; but its recall is lower than that of our methods, most likely because of its numeral OOV problem. Comparing the results on the original and augmented test sets, we see that NumAsTok shows a more significant drop in performance than the other methods, which suggests that NumAsTok does not generalize well because of the numeral OOV problem. In the low-resource settings, the advantage of our methods over the baselines becomes even larger, indicating better generalizability and less annotation required for our methods to achieve a promising performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: The results of sequence labeling. We report the accuracy, precision, recall, F1 score for the original, augmented, and harder test sets with different training data sizes. Accuracy is in the token level and the other metrics are in the entity level. We report the standard deviations in Appendix H.', answer the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich method has the highest precision on the original test set when trained with 100% training samples?\n" }, { "role": "assistant", "content": "From the table, NumAsTok trained with 100% training samples has the highest precision (91.37) on the original test set. So, the answer is {\"answer\": \"NumAsTok\"}." } ], "images": [ "scigen/train/1579.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The results of sequence labeling. We report the accuracy, precision, recall, F1 score for the original, augmented, and harder test sets with different training data sizes. Accuracy is in the token level and the other metrics are in the entity level. We report the standard deviations in Appendix H." ], "reasoning": "From the table, NumAsTok trained with 100% training samples has the highest precision (91.37) on the original test set.", "question": "Which method has the highest precision on the original test set when trained with 100% training samples?", "answer": "So, the answer is {\"answer\": \"NumAsTok\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nNumAsTok shows a significant drop in performance on the augmented test set compared to the original test set.\n" }, { "role": "assistant", "content": "The table shows that NumAsTok's precision drops from 91.37 on the original test set to 90.90 on the augmented test set, indicating a significant drop in performance. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1579.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The results of sequence labeling. We report the accuracy, precision, recall, F1 score for the original, augmented, and harder test sets with different training data sizes. Accuracy is in the token level and the other metrics are in the entity level. We report the standard deviations in Appendix H." ], "reasoning": "The table shows that NumAsTok's precision drops from 91.37 on the original test set to 90.90 on the augmented test set, indicating a significant drop in performance.", "statement": "NumAsTok shows a significant drop in performance on the augmented test set compared to the original test set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Values of general hyper-parameters for each experiment.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "General hyper-parameters like embedding dimension, context window size, and batch size are set identically for all methods, while specific hyper-parameters vary for SOM and GMM models.We list all of the important hyper-parameters we tune for each model. [leftmargin=0mm] General hyper-parameters: embedding dimension, context window size, SGD learning rate, batch size, vocabulary size, etc. SOM hyper-parameters: number of prototypes, stage of applying the log-squashing function (stage 1: before prototype induction; stage 2: only in the similarity function). GMM hyper-parameters: number of prototypes, whether we apply the log-squashing function to the numerals, EM initialization (from SOM, random initialization, or k-means initialization), type of EM (hard-EM or soft-EM). We find that the general hyper-parameters influence the performance of our methods and the baselines in the same way, so in most cases, these hyper-parameters are set to be identical for all the methods. For large training corpora (Wiki1B, Numeracy-600k), we use 2048 as the batch size for D-LSTM, because D-LSTM consumes much more GPU memory. We set the batch size of the other methods to 4096. For the sequence labeling tasks, because the data is relatively small and confined to a very specific domain (chat log from online apparel shops), we set a small vocabulary size of 500 for all the methods except NumAsTok and set the vocabulary size of NumAsTok to 550 to ensure that different methods have similar numbers of parameters for word embedding training. Consequently, our methods have (500+|\\mathbb{P}|)\\times D parameters for word embedding training and NumAsTok has 550\\times D parameters, where \\mathbb{P} is the prototype set, whose size is typically smaller than 50, and D is the embedding dimension." } ], "images": [ "scigen/train/1580.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Values of general hyper-parameters for each experiment." ], "reasoning": "General hyper-parameters like embedding dimension, context window size, and batch size are set identically for all methods, while specific hyper-parameters vary for SOM and GMM models.", "description": "We list all of the important hyper-parameters we tune for each model. [leftmargin=0mm] General hyper-parameters: embedding dimension, context window size, SGD learning rate, batch size, vocabulary size, etc. SOM hyper-parameters: number of prototypes, stage of applying the log-squashing function (stage 1: before prototype induction; stage 2: only in the similarity function). GMM hyper-parameters: number of prototypes, whether we apply the log-squashing function to the numerals, EM initialization (from SOM, random initialization, or k-means initialization), type of EM (hard-EM or soft-EM). We find that the general hyper-parameters influence the performance of our methods and the baselines in the same way, so in most cases, these hyper-parameters are set to be identical for all the methods. For large training corpora (Wiki1B, Numeracy-600k), we use 2048 as the batch size for D-LSTM, because D-LSTM consumes much more GPU memory. We set the batch size of the other methods to 4096. For the sequence labeling tasks, because the data is relatively small and confined to a very specific domain (chat log from online apparel shops), we set a small vocabulary size of 500 for all the methods except NumAsTok and set the vocabulary size of NumAsTok to 550 to ensure that different methods have similar numbers of parameters for word embedding training. Consequently, our methods have (500+|\\mathbb{P}|)\\times D parameters for word embedding training and NumAsTok has 550\\times D parameters, where \\mathbb{P} is the prototype set, whose size is typically smaller than 50, and D is the embedding dimension.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: 'Table 7: Values of general hyper-parameters for each experiment.'\nQuestion: What is the batch size used for D-LSTM in the Numeral Prediction (Numeracy-600k) task?\n" }, { "role": "assistant", "content": "The description states that for large training corpora like Numeracy-600k, the batch size for D-LSTM is set to 2048 due to higher GPU memory consumption. So, the answer is {\"answer\": \"2048\"}." } ], "images": [ "scigen/train/1580.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Values of general hyper-parameters for each experiment." ], "reasoning": "The description states that for large training corpora like Numeracy-600k, the batch size for D-LSTM is set to 2048 due to higher GPU memory consumption.", "question": "What is the batch size used for D-LSTM in the Numeral Prediction (Numeracy-600k) task?", "answer": "So, the answer is {\"answer\": \"2048\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe vocabulary size for the Sequence Labeling task is set to 500 for all methods.\n" }, { "role": "assistant", "content": "The description specifies that the vocabulary size for the Sequence Labeling task is set to 500 for all methods except NumAsTok, which has a vocabulary size of 550. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1580.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Values of general hyper-parameters for each experiment." ], "reasoning": "The description specifies that the vocabulary size for the Sequence Labeling task is set to 500 for all methods except NumAsTok, which has a vocabulary size of 550.", "statement": "The vocabulary size for the Sequence Labeling task is set to 500 for all methods.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 9: Training speed for each methods.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Our methods, SOM and GMM, are slower than NumAsTok but faster than D-LSTM.The batch size is set to 2048 for all the methods. Our methods are slower than NumAsTok but are faster than D-LSTM." } ], "images": [ "scigen/train/1581.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Training speed for each methods." ], "reasoning": "Our methods, SOM and GMM, are slower than NumAsTok but faster than D-LSTM.", "description": "The batch size is set to 2048 for all the methods. Our methods are slower than NumAsTok but are faster than D-LSTM.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 9: Training speed for each methods.\nQuestion: Which method is the fastest according to the table?\nSolve the above question based on the table information. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the speed of different methods in sentences per second. NumAsTok has the highest speed at 22907.97 sent/s. So, the answer is {\"answer\": \"NumAsTok\"}" } ], "images": [ "scigen/train/1581.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Training speed for each methods." ], "reasoning": "The table shows the speed of different methods in sentences per second. NumAsTok has the highest speed at 22907.97 sent/s.", "question": "Which method is the fastest according to the table?", "answer": "So, the answer is {\"answer\": \"NumAsTok\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim SOM is faster than Fixed., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nSOM is faster than Fixed.\n" }, { "role": "assistant", "content": "The table shows that SOM has a speed of 13590.93 sent/s, which is faster than Fixed with a speed of 13055.08 sent/s. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1581.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Training speed for each methods." ], "reasoning": "The table shows that SOM has a speed of 13590.93 sent/s, which is faster than Fixed with a speed of 13055.08 sent/s.", "statement": "SOM is faster than Fixed.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 10: Probing test results. MLP1 and MLP2 denote MLP with one and two hidden layers respectively.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The dataset is split into 80% for training and 20% for testing, and SGD is used to optimize MSE loss, showing better performance than baselines.For Decoding, the dataset can be directly used. For Subtraction, we randomly sample 10^{5} pairs of numerals (n_{1},n_{2}) from the dataset and assign n_{1}-n_{2} as the prediction target. Following Wallace2019DoNM, we randomly split 80\\% of each dataset for training and 20\\% for testing. We use SGD to optimize the mean square error (MSE) loss. The results show that our methods have better performance than the baselines overall." } ], "images": [ "scigen/train/1582.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Probing test results. MLP1 and MLP2 denote MLP with one and two hidden layers respectively." ], "reasoning": "The dataset is split into 80% for training and 20% for testing, and SGD is used to optimize MSE loss, showing better performance than baselines.", "description": "For Decoding, the dataset can be directly used. For Subtraction, we randomly sample 10^{5} pairs of numerals (n_{1},n_{2}) from the dataset and assign n_{1}-n_{2} as the prediction target. Following Wallace2019DoNM, we randomly split 80\\% of each dataset for training and 20\\% for testing. We use SGD to optimize the mean square error (MSE) loss. The results show that our methods have better performance than the baselines overall.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 10: Probing test results. MLP1 and MLP2 denote MLP with one and two hidden layers respectively.\nQuestion: Which method has the lowest Subtraction MLP2 score?\nSolve the above question based on the table information. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the method with the lowest Subtraction MLP2 score, compare the values in the Subtraction MLP2 column. The lowest value is 4.40, which corresponds to the GMM method. So, the answer is {\"answer\": \"GMM\"}." } ], "images": [ "scigen/train/1582.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Probing test results. MLP1 and MLP2 denote MLP with one and two hidden layers respectively." ], "reasoning": "To find the method with the lowest Subtraction MLP2 score, compare the values in the Subtraction MLP2 column. The lowest value is 4.40, which corresponds to the GMM method.", "question": "Which method has the lowest Subtraction MLP2 score?", "answer": "So, the answer is {\"answer\": \"GMM\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nNumAsTok has a higher Decoding MLP1 score than D-LSTM.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Decoding MLP1 scores of NumAsTok and D-LSTM. NumAsTok has a score of 1027.33, while D-LSTM has a score of 1433.45. Since 1027.33 is less than 1433.45, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1582.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Probing test results. MLP1 and MLP2 denote MLP with one and two hidden layers respectively." ], "reasoning": "To verify this statement, compare the Decoding MLP1 scores of NumAsTok and D-LSTM. NumAsTok has a score of 1027.33, while D-LSTM has a score of 1433.45. Since 1027.33 is less than 1433.45, the statement is refuted.", "statement": "NumAsTok has a higher Decoding MLP1 score than D-LSTM.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Performance comparison of IBM speaker recognition systems with fMLLR features for 2k, 4k, and 10k DNN senones on extended core condition 5 in the NIST SRE 2010.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Increasing the number of senones improves performance, and NDA systems consistently outperform LDA systems.In the next set of experiments, we investigated the impact of the number of senones on speaker recognition performance. “tel-tel” condition which are obtained with i-vectors computed using 2k, 4k, and 10k DNN senones and fMLLR features. Two important observations can be made from this table. First, the larger the number of senones, the better the performance. This is due to the discriminative nature of the DNN where increasing the granularity in the output layer improves the model ability in distinguishing among the various phonetic events. Second, irrespective of the number of senones used to calculate the sufficient statistics, the NDA based systems consistently perform better than the LDA based systems. We note that, in our experiments, increasing the number of senones beyond 10k did not yield much gains in performance." } ], "images": [ "scigen/train/1583.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance comparison of IBM speaker recognition systems with fMLLR features for 2k, 4k, and 10k DNN senones on extended core condition 5 in the NIST SRE 2010." ], "reasoning": "Increasing the number of senones improves performance, and NDA systems consistently outperform LDA systems.", "description": "In the next set of experiments, we investigated the impact of the number of senones on speaker recognition performance. “tel-tel” condition which are obtained with i-vectors computed using 2k, 4k, and 10k DNN senones and fMLLR features. Two important observations can be made from this table. First, the larger the number of senones, the better the performance. This is due to the discriminative nature of the DNN where increasing the granularity in the output layer improves the model ability in distinguishing among the various phonetic events. Second, irrespective of the number of senones used to calculate the sufficient statistics, the NDA based systems consistently perform better than the LDA based systems. We note that, in our experiments, increasing the number of senones beyond 10k did not yield much gains in performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Performance comparison of IBM speaker recognition systems with fMLLR features for 2k, 4k, and 10k DNN senones on extended core condition 5 in the NIST SRE 2010.', unfold the question presented below, detailing every step of your calculation. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the EER for the DNN-NDA system with 10k senones?\n" }, { "role": "assistant", "content": "The table shows the EER values for different systems and senone counts. For the DNN-NDA system with 10k senones, the EER is listed. So, the answer is {\"answer\": \"0.67\"}." } ], "images": [ "scigen/train/1583.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance comparison of IBM speaker recognition systems with fMLLR features for 2k, 4k, and 10k DNN senones on extended core condition 5 in the NIST SRE 2010." ], "reasoning": "The table shows the EER values for different systems and senone counts. For the DNN-NDA system with 10k senones, the EER is listed.", "question": "What is the EER for the DNN-NDA system with 10k senones?", "answer": "So, the answer is {\"answer\": \"0.67\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The DNN-LDA system with 4k senones has a lower minDCF08 than the DNN-NDA system with 2k senones. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe DNN-LDA system with 4k senones has a lower minDCF08 than the DNN-NDA system with 2k senones.\n" }, { "role": "assistant", "content": "The table shows that the minDCF08 for the DNN-LDA system with 4k senones is 0.041, while for the DNN-NDA system with 2k senones, it is 0.043. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1583.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance comparison of IBM speaker recognition systems with fMLLR features for 2k, 4k, and 10k DNN senones on extended core condition 5 in the NIST SRE 2010." ], "reasoning": "The table shows that the minDCF08 for the DNN-LDA system with 4k senones is 0.041, while for the DNN-NDA system with 2k senones, it is 0.043.", "statement": "The DNN-LDA system with 4k senones has a lower minDCF08 than the DNN-NDA system with 2k senones.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: ROUGE scores of the PGNet baseline models and the GPT-2 fine-tuned models on the development set. The GPT-2 res were trained with the “results” section only. Addition of n>1 hint words did not show further gains in ROUGE scores.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The GPT-2 model with hint word n=1 achieves the highest ROUGE scores, indicating its effectiveness in generating good conclusions, while more hint words do not improve performance.Note that the hint words are not considered in score calculation and the output of all models are lower-cased. The GPT-2 fine-tuned model significantly outperforms the pointer-generator (PGNet) baseline on all ROUGE scores, where the best performing model is GPT-2 with hint word n=1, demonstrating the effectiveness of generating good conclusion in our model. However, more hint words do not bring additional gain in ROUGE scores, probably because more constraints hinder the GPT-2 model to explore potentially good conclusions. Moreover, the ablation result shows the significant drop in all ROUGE scores, indicating the importance of including the “background” and “objective” sections in the input for better content understanding." } ], "images": [ "scigen/train/1584.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: ROUGE scores of the PGNet baseline models and the GPT-2 fine-tuned models on the development set. The GPT-2 res were trained with the “results” section only. Addition of n>1 hint words did not show further gains in ROUGE scores." ], "reasoning": "The GPT-2 model with hint word n=1 achieves the highest ROUGE scores, indicating its effectiveness in generating good conclusions, while more hint words do not improve performance.", "description": "Note that the hint words are not considered in score calculation and the output of all models are lower-cased. The GPT-2 fine-tuned model significantly outperforms the pointer-generator (PGNet) baseline on all ROUGE scores, where the best performing model is GPT-2 with hint word n=1, demonstrating the effectiveness of generating good conclusion in our model. However, more hint words do not bring additional gain in ROUGE scores, probably because more constraints hinder the GPT-2 model to explore potentially good conclusions. Moreover, the ablation result shows the significant drop in all ROUGE scores, indicating the importance of including the “background” and “objective” sections in the input for better content understanding.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: ROUGE scores of the PGNet baseline models and the GPT-2 fine-tuned models on the development set. The GPT-2 res were trained with the “results” section only. Addition of n>1 hint words did not show further gains in ROUGE scores.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. \n\nQuestion: Which model and configuration achieved the highest ROUGE-1 score?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By examining the table, the highest ROUGE-1 score is 31.61, achieved by the GPT-2 model with hint word n=1. So, the answer is {\"answer\": \"GPT-2 n=1\"}" } ], "images": [ "scigen/train/1584.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: ROUGE scores of the PGNet baseline models and the GPT-2 fine-tuned models on the development set. The GPT-2 res were trained with the “results” section only. Addition of n>1 hint words did not show further gains in ROUGE scores." ], "reasoning": "By examining the table, the highest ROUGE-1 score is 31.61, achieved by the GPT-2 model with hint word n=1.", "question": "Which model and configuration achieved the highest ROUGE-1 score?", "answer": "So, the answer is {\"answer\": \"GPT-2 n=1\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe PGNet model with n=0 achieved the highest ROUGE-L score.\n" }, { "role": "assistant", "content": "The table shows that the highest ROUGE-L score is 26.71, achieved by the GPT-2 model with hint word n=1, not the PGNet model with n=0. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1584.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: ROUGE scores of the PGNet baseline models and the GPT-2 fine-tuned models on the development set. The GPT-2 res were trained with the “results” section only. Addition of n>1 hint words did not show further gains in ROUGE scores." ], "reasoning": "The table shows that the highest ROUGE-L score is 26.71, achieved by the GPT-2 model with hint word n=1, not the PGNet model with n=0.", "statement": "The PGNet model with n=0 achieved the highest ROUGE-L score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Human evaluation results for text understanding on the annotation questions of 50 randomly selected source documents. Note that some source documents which don’t fit into the binary paradigm of positive or negative results are classified as N/A. TP: True Positive; TN: True Negative; FP: False Positive; FN: False Negative; N/A: Not Applicable.'.\n" }, { "role": "assistant", "content": "The proposed GPT-2 model achieves better accuracy compared to PGnet, but there is still a gap between the generated conclusions and human-written ones.It is clear that the proposed GPT-2 achieves better performance in terms of accuracy (the ratio of true samples). We also include the performance of human-written conclusions in the last row, which serves as the upper bound of this task. However, there is still a gap between human-written conclusions and the generated ones." } ], "images": [ "scigen/train/1585.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Human evaluation results for text understanding on the annotation questions of 50 randomly selected source documents. Note that some source documents which don’t fit into the binary paradigm of positive or negative results are classified as N/A. TP: True Positive; TN: True Negative; FP: False Positive; FN: False Negative; N/A: Not Applicable." ], "reasoning": "The proposed GPT-2 model achieves better accuracy compared to PGnet, but there is still a gap between the generated conclusions and human-written ones.", "description": "It is clear that the proposed GPT-2 achieves better performance in terms of accuracy (the ratio of true samples). We also include the performance of human-written conclusions in the last row, which serves as the upper bound of this task. However, there is still a gap between human-written conclusions and the generated ones.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Human evaluation results for text understanding on the annotation questions of 50 randomly selected source documents. Note that some source documents which don’t fit into the binary paradigm of positive or negative results are classified as N/A. TP: True Positive; TN: True Negative; FP: False Positive; FN: False Negative; N/A: Not Applicable.', unfold the question presented below, detailing every step of your calculation. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the accuracy difference between GPT-2 n=1 and the Target?\n" }, { "role": "assistant", "content": "The accuracy of GPT-2 n=1 is 64%, and the accuracy of the Target is 86%. The difference is 86% - 64% = 22%. So, the answer is {\"answer\": \"22%\"}" } ], "images": [ "scigen/train/1585.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Human evaluation results for text understanding on the annotation questions of 50 randomly selected source documents. Note that some source documents which don’t fit into the binary paradigm of positive or negative results are classified as N/A. TP: True Positive; TN: True Negative; FP: False Positive; FN: False Negative; N/A: Not Applicable." ], "reasoning": "The accuracy of GPT-2 n=1 is 64%, and the accuracy of the Target is 86%. The difference is 86% - 64% = 22%.", "question": "What is the accuracy difference between GPT-2 n=1 and the Target?", "answer": "So, the answer is {\"answer\": \"22%\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The accuracy of PGnet n=1 is higher than GPT-2 n=0. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe accuracy of PGnet n=1 is higher than GPT-2 n=0.\n" }, { "role": "assistant", "content": "The accuracy of PGnet n=1 is 36%, while the accuracy of GPT-2 n=0 is 54%. Therefore, the statement is incorrect. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1585.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Human evaluation results for text understanding on the annotation questions of 50 randomly selected source documents. Note that some source documents which don’t fit into the binary paradigm of positive or negative results are classified as N/A. TP: True Positive; TN: True Negative; FP: False Positive; FN: False Negative; N/A: Not Applicable." ], "reasoning": "The accuracy of PGnet n=1 is 36%, while the accuracy of GPT-2 n=0 is 54%. Therefore, the statement is incorrect.", "statement": "The accuracy of PGnet n=1 is higher than GPT-2 n=0.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Human evaluation results for generation quality on the rating questions.'.\n" }, { "role": "assistant", "content": "GPT-2 models have comparable overall scores around 3.5, with significant improvement in text quality and slight improvement in correctness.The GPT-2 models have comparable scores in overall impression, both scoring around 3.5 out of 5. The most significant improvement of the GPT-2 generated conclusions is the text quality, with the correctness improvement to a lesser extent. The correctness of GPT-2 n=1 is slightly better than that of GPT-2 n=0 in the annotation question, yet in the rating question, GPT-2 n=0 has a higher averaged score. In sum, the human evaluation results demonstrate that our models significantly outperform the baseline pointer generator and tell that the proposed RCT conclusion generation task is not the same as typical summarization task, so deep text understanding is required for better performance." } ], "images": [ "scigen/train/1586.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Human evaluation results for generation quality on the rating questions." ], "reasoning": "GPT-2 models have comparable overall scores around 3.5, with significant improvement in text quality and slight improvement in correctness.", "description": "The GPT-2 models have comparable scores in overall impression, both scoring around 3.5 out of 5. The most significant improvement of the GPT-2 generated conclusions is the text quality, with the correctness improvement to a lesser extent. The correctness of GPT-2 n=1 is slightly better than that of GPT-2 n=0 in the annotation question, yet in the rating question, GPT-2 n=0 has a higher averaged score. In sum, the human evaluation results demonstrate that our models significantly outperform the baseline pointer generator and tell that the proposed RCT conclusion generation task is not the same as typical summarization task, so deep text understanding is required for better performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Human evaluation results for generation quality on the rating questions.', answer the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich system has the highest quality score?\n" }, { "role": "assistant", "content": "By comparing the quality scores of all systems, the Target system has the highest quality score of 4.08. So, the answer is {\"answer\": \"Target\"}" } ], "images": [ "scigen/train/1586.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Human evaluation results for generation quality on the rating questions." ], "reasoning": "By comparing the quality scores of all systems, the Target system has the highest quality score of 4.08.", "question": "Which system has the highest quality score?", "answer": "So, the answer is {\"answer\": \"Target\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nGPT-2 n=1 has a higher overall score than GPT-2 n=0.\n" }, { "role": "assistant", "content": "The table shows that GPT-2 n=0 has an overall score of 3.52, while GPT-2 n=1 has an overall score of 3.50, making the statement false. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1586.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Human evaluation results for generation quality on the rating questions." ], "reasoning": "The table shows that GPT-2 n=0 has an overall score of 3.52, while GPT-2 n=1 has an overall score of 3.50, making the statement false.", "statement": "GPT-2 n=1 has a higher overall score than GPT-2 n=0.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 5: Average accuracy on EPOSG.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The CNN-G model significantly outperforms both the baseline and NN models across all classes and in terms of micro average, demonstrating its superior ability to learn characteristic information for the classes.The CNN outperforms both baselines by large margins, per modal verb and in terms of micro average. Given we employed perfectly balanced training data, the classifier performances reflect their ability to learn characteristic information for the classes. Indeed, the NN has great difficulties distinguishing the senses for können (3 senses) and sollen, and is outperformed by CNN-G by +35.6 and +24.4 pp. gains. The confusion matrices for CNN-G show a clear separation of these classes, in contrast to the NN." } ], "images": [ "scigen/train/1587.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Average accuracy on EPOSG." ], "reasoning": "The CNN-G model significantly outperforms both the baseline and NN models across all classes and in terms of micro average, demonstrating its superior ability to learn characteristic information for the classes.", "description": "The CNN outperforms both baselines by large margins, per modal verb and in terms of micro average. Given we employed perfectly balanced training data, the classifier performances reflect their ability to learn characteristic information for the classes. Indeed, the NN has great difficulties distinguishing the senses for können (3 senses) and sollen, and is outperformed by CNN-G by +35.6 and +24.4 pp. gains. The confusion matrices for CNN-G show a clear separation of these classes, in contrast to the NN.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 5: Average accuracy on EPOSG.'. Solve the following question based on the table.\n\nQuestion: What is the performance gain of CNN-G over NN for the class 'können'?\nLet's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The performance of CNN-G for 'können' is 78.95, while the performance of NN for 'können' is 43.32. The gain is calculated as 78.95 - 43.32. So, the answer is {\"answer\": \"35.63\"}." } ], "images": [ "scigen/train/1587.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Average accuracy on EPOSG." ], "reasoning": "The performance of CNN-G for 'können' is 78.95, while the performance of NN for 'können' is 43.32. The gain is calculated as 78.95 - 43.32.", "question": "What is the performance gain of CNN-G over NN for the class 'können'?", "answer": "So, the answer is {\"answer\": \"35.63\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The NN model performs better than the baseline for the class 'dürfen'. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe NN model performs better than the baseline for the class 'dürfen'.\n" }, { "role": "assistant", "content": "The performance of NN for 'dürfen' is 77.73, while the performance of the baseline for 'dürfen' is 50.00. Since 77.73 is greater than 50.00, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1587.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Average accuracy on EPOSG." ], "reasoning": "The performance of NN for 'dürfen' is 77.73, while the performance of the baseline for 'dürfen' is 50.00. Since 77.73 is greater than 50.00, the statement is supported.", "statement": "The NN model performs better than the baseline for the class 'dürfen'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: TDNN Phone Error Rate'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The best performance was achieved with a context size of 5,5,5,5 and 512 filters, resulting in an average PER of 18.20%. Larger context sizes and fewer filters resulted in worse performance.All the results are worse than the results for our FF networks. Interestingly enough, we obtained the best results for context sizes 5,5,5,5. The larger context sizes resulted in worse network performance. Also, the networks with higher number of filters have better performance. The best average PER we have received was 18.20 % for the network with the context size 5,5,5,5 and 512 filters in each layer." } ], "images": [ "scigen/train/1588.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: TDNN Phone Error Rate" ], "reasoning": "The best performance was achieved with a context size of 5,5,5,5 and 512 filters, resulting in an average PER of 18.20%. Larger context sizes and fewer filters resulted in worse performance.", "description": "All the results are worse than the results for our FF networks. Interestingly enough, we obtained the best results for context sizes 5,5,5,5. The larger context sizes resulted in worse network performance. Also, the networks with higher number of filters have better performance. The best average PER we have received was 18.20 % for the network with the context size 5,5,5,5 and 512 filters in each layer.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 2: TDNN Phone Error Rate\n\nQuestion: What is the Test PER Avg for the network with the context size 5,5,5,5 and 512 filters?\n" }, { "role": "assistant", "content": "The table shows that the network with the context size 5,5,5,5 and 512 filters has a Test PER Avg of 18.20%. So, the answer is {\"answer\": \"18.20%\"}" } ], "images": [ "scigen/train/1588.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: TDNN Phone Error Rate" ], "reasoning": "The table shows that the network with the context size 5,5,5,5 and 512 filters has a Test PER Avg of 18.20%.", "question": "What is the Test PER Avg for the network with the context size 5,5,5,5 and 512 filters?", "answer": "So, the answer is {\"answer\": \"18.20%\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The network with the context size 9,9,9,9 and 1024 filters has the highest Test PER Avg. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe network with the context size 9,9,9,9 and 1024 filters has the highest Test PER Avg.\n" }, { "role": "assistant", "content": "The table shows that the network with the context size 9,9,9,9 and 1024 filters has a Test PER Avg of 19.72%, which is the highest among all listed configurations. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1588.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: TDNN Phone Error Rate" ], "reasoning": "The table shows that the network with the context size 9,9,9,9 and 1024 filters has a Test PER Avg of 19.72%, which is the highest among all listed configurations.", "statement": "The network with the context size 9,9,9,9 and 1024 filters has the highest Test PER Avg.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Feed-Forward DNN Phone Error Rate'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The FF network with sigmoid activation and DBN pre-training has an average PER of 17.04%, but it was outperformed by several simple FF networks with ReLU without pretraining, with the best average PER being 16.91% for the 8x2048 FF network.After we trained all the networks, we have evaluated their performance on the development and test dataset. The FF network with sigmoid activation function and DBN pre-training has average 17.04 % PER, but it was outperformed by several simple FF networks with ReLU without pretraining. The best average PER we have achieved is 16.91 % PER in the case of 8x2048 FF network. However, all the deeper networks with at least 1024 neurons in the hidden layers have similar performance." } ], "images": [ "scigen/train/1589.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Feed-Forward DNN Phone Error Rate" ], "reasoning": "The FF network with sigmoid activation and DBN pre-training has an average PER of 17.04%, but it was outperformed by several simple FF networks with ReLU without pretraining, with the best average PER being 16.91% for the 8x2048 FF network.", "description": "After we trained all the networks, we have evaluated their performance on the development and test dataset. The FF network with sigmoid activation function and DBN pre-training has average 17.04 % PER, but it was outperformed by several simple FF networks with ReLU without pretraining. The best average PER we have achieved is 16.91 % PER in the case of 8x2048 FF network. However, all the deeper networks with at least 1024 neurons in the hidden layers have similar performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 1: Feed-Forward DNN Phone Error Rate', solve the following question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich network achieved the best average PER on the test dataset?\n" }, { "role": "assistant", "content": "The description states that the best average PER achieved is 16.91% by the 8x2048 FF network. By looking at the table, the FF ReLU 8x2048 network has a Test PER Avg of 16.91%. So, the answer is {\"answer\": \"FF ReLU 8x2048\"}" } ], "images": [ "scigen/train/1589.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Feed-Forward DNN Phone Error Rate" ], "reasoning": "The description states that the best average PER achieved is 16.91% by the 8x2048 FF network. By looking at the table, the FF ReLU 8x2048 network has a Test PER Avg of 16.91%.", "question": "Which network achieved the best average PER on the test dataset?", "answer": "So, the answer is {\"answer\": \"FF ReLU 8x2048\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe FF network with sigmoid activation function and DBN pre-training has the highest average PER on the test dataset.\n" }, { "role": "assistant", "content": "The table shows that the FF network with sigmoid activation function and DBN pre-training has an average PER of 17.04% on the test dataset. However, several other networks have lower average PERs, such as the FF ReLU 8x2048 network with 16.91%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1589.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Feed-Forward DNN Phone Error Rate" ], "reasoning": "The table shows that the FF network with sigmoid activation function and DBN pre-training has an average PER of 17.04% on the test dataset. However, several other networks have lower average PERs, such as the FF ReLU 8x2048 network with 16.91%.", "statement": "The FF network with sigmoid activation function and DBN pre-training has the highest average PER on the test dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: LSTM Phone Error Rate', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The network with 4 hidden layers each with 1024 LSTM units achieved the best average PER of 15.58% and the best PER of 15.02%. Networks with 3 and 5 layers with 1024 units had the second and third best PERs of 15.69% and 15.71%, respectively.Our LSTM networks have better performance than other network types used in this work. We have received the best average PER, 15.58 %, for the network with 4 hidden layers each with 1024 LSTM units. The best PER from all experiments was 15.02 %, also belonging to the network with 4 hidden layers with 1024 LSTM units. However, we have also trained other networks with similar PER to the best one. The network with 3 layers with 1024 units and the network with 5 layers with 1024 units have the second and third best PER, 15.69 % and 15.71 % respectively. The networks with 256 units in each layer have comparable or worse PER than FF networks. The network with 6 layers with 1024 units has worse performance than less deep layers due to overfitting." } ], "images": [ "scigen/train/1590.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: LSTM Phone Error Rate" ], "reasoning": "The network with 4 hidden layers each with 1024 LSTM units achieved the best average PER of 15.58% and the best PER of 15.02%. Networks with 3 and 5 layers with 1024 units had the second and third best PERs of 15.69% and 15.71%, respectively.", "description": "Our LSTM networks have better performance than other network types used in this work. We have received the best average PER, 15.58 %, for the network with 4 hidden layers each with 1024 LSTM units. The best PER from all experiments was 15.02 %, also belonging to the network with 4 hidden layers with 1024 LSTM units. However, we have also trained other networks with similar PER to the best one. The network with 3 layers with 1024 units and the network with 5 layers with 1024 units have the second and third best PER, 15.69 % and 15.71 % respectively. The networks with 256 units in each layer have comparable or worse PER than FF networks. The network with 6 layers with 1024 units has worse performance than less deep layers due to overfitting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 3: LSTM Phone Error Rate' and proceed to solve the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich network configuration achieved the best average PER?\n" }, { "role": "assistant", "content": "The description states that the network with 4 hidden layers each with 1024 LSTM units achieved the best average PER of 15.58%. So, the answer is {\"answer\": \"4x1024\"}" } ], "images": [ "scigen/train/1590.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: LSTM Phone Error Rate" ], "reasoning": "The description states that the network with 4 hidden layers each with 1024 LSTM units achieved the best average PER of 15.58%.", "question": "Which network configuration achieved the best average PER?", "answer": "So, the answer is {\"answer\": \"4x1024\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The network with 6 layers and 1024 units has the worst performance due to overfitting. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe network with 6 layers and 1024 units has the worst performance due to overfitting.\n" }, { "role": "assistant", "content": "The description mentions that the network with 6 layers and 1024 units has worse performance than less deep layers due to overfitting. The table shows that the network with 6x1024 units has a Test PER Avg of 15.87%, which is higher than the other networks with 1024 units. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1590.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: LSTM Phone Error Rate" ], "reasoning": "The description mentions that the network with 6 layers and 1024 units has worse performance than less deep layers due to overfitting. The table shows that the network with 6x1024 units has a Test PER Avg of 15.87%, which is higher than the other networks with 1024 units.", "statement": "The network with 6 layers and 1024 units has the worst performance due to overfitting.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Statistic of four low resource languages.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "To investigate mBERT's performance in low-resource languages, monolingual BERT models were trained on Latvian, Afrikaans, Mongolian, and Yoruba, considering various hyperparameters.We have established that mBERT does not perform well in low-resource languages. Is this because we are relying on a multilingual model that favors high-resource over low-resource languages? To answer this question we train mono-lingual BERT models on several low resource languages with different hyperparameters. Since pretraining a BERT model from scratch is computationally intensive, we select four low resource languages: Latvian (lv), Afrikaans (af), Mongolian (mn), and Yoruba (yo)." } ], "images": [ "scigen/train/1591.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Statistic of four low resource languages." ], "reasoning": "To investigate mBERT's performance in low-resource languages, monolingual BERT models were trained on Latvian, Afrikaans, Mongolian, and Yoruba, considering various hyperparameters.", "description": "We have established that mBERT does not perform well in low-resource languages. Is this because we are relying on a multilingual model that favors high-resource over low-resource languages? To answer this question we train mono-lingual BERT models on several low resource languages with different hyperparameters. Since pretraining a BERT model from scratch is computationally intensive, we select four low resource languages: Latvian (lv), Afrikaans (af), Mongolian (mn), and Yoruba (yo).", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nTable title: Table 3: Statistic of four low resource languages.\n\nQuestion: Which language has the smallest number of sentences and what is its WikiSize?\n" }, { "role": "assistant", "content": "From the table, identify the language with the smallest number of sentences, which is Yoruba with 0.1 million sentences. Then, find the corresponding WikiSize for Yoruba, which is 3. So, the answer is {\"answer\": \"3\"}." } ], "images": [ "scigen/train/1591.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Statistic of four low resource languages." ], "reasoning": "From the table, identify the language with the smallest number of sentences, which is Yoruba with 0.1 million sentences. Then, find the corresponding WikiSize for Yoruba, which is 3.", "question": "Which language has the smallest number of sentences and what is its WikiSize?", "answer": "So, the answer is {\"answer\": \"3\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Latvian has a larger mBERT vocabulary percentage than Afrikaans., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nLatvian has a larger mBERT vocabulary percentage than Afrikaans.\n" }, { "role": "assistant", "content": "From the table, compare the mBERT vocabulary percentages for Latvian (49.2%) and Afrikaans (51.3%). Latvian has a smaller percentage than Afrikaans. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1591.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Statistic of four low resource languages." ], "reasoning": "From the table, compare the mBERT vocabulary percentages for Latvian (49.2%) and Afrikaans (51.3%). Latvian has a smaller percentage than Afrikaans.", "statement": "Latvian has a larger mBERT vocabulary percentage than Afrikaans.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Monolingual BERT on four languages with different hyperparameters. Underscore denotes best within monolingual BERT and bold denotes best among all models. Monolingual BERT underperforms mBERT in most cases. “-” denotes same as base case.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The model with smaller sequence length performs best for monolingual BERT and outperforms the base model in 5 out of 8 tasks and languages combination, while the smaller model underperforms the base model in 5 out of 8 cases.The model with smaller sequence length performs best for monolingual BERT and outperforms the base model in 5 out of 8 tasks and languages combination. The model with smaller vocabulary has mixed performance in the low resource languages (mn, yo) but falls short for (relatively) higher resource languages (lv, af). Finally, the smaller model underperforms the base model in 5 out of 8 cases. In conclusion, the best way to pretrain BERT with a limited amount of computation for low resource languages is to use a smaller sequence length to allow a larger batch size. Future work could look into a smaller self-attention span with a restricted transformer Vaswani et al. The Afrikaans-Dutch BERT improves more compared to Latvian-Lithuanian, possibly because Dutch is much larger than Afrikaans, as compared to Latvian and Lithuanian. These experiments suggest that pairing linguistically related languages can benefit representation learning and adding extra languages can further improve the performance as demonstrated by mBERT. It echos the finding of \\newcitelample2019cross where multilingual training improves uni-directional language model perplexity for low resource languages. Concurrent work shows similar findings as the performance of low resource languages (Urdu and Swahili) improves on XNLI when more languages are trained jointly then decrease with an increasing number of languages Conneau et al. However, they do not consider the effect of language similarity." } ], "images": [ "scigen/train/1592.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Monolingual BERT on four languages with different hyperparameters. Underscore denotes best within monolingual BERT and bold denotes best among all models. Monolingual BERT underperforms mBERT in most cases. “-” denotes same as base case." ], "reasoning": "The model with smaller sequence length performs best for monolingual BERT and outperforms the base model in 5 out of 8 tasks and languages combination, while the smaller model underperforms the base model in 5 out of 8 cases.", "description": "The model with smaller sequence length performs best for monolingual BERT and outperforms the base model in 5 out of 8 tasks and languages combination. The model with smaller vocabulary has mixed performance in the low resource languages (mn, yo) but falls short for (relatively) higher resource languages (lv, af). Finally, the smaller model underperforms the base model in 5 out of 8 cases. In conclusion, the best way to pretrain BERT with a limited amount of computation for low resource languages is to use a smaller sequence length to allow a larger batch size. Future work could look into a smaller self-attention span with a restricted transformer Vaswani et al. The Afrikaans-Dutch BERT improves more compared to Latvian-Lithuanian, possibly because Dutch is much larger than Afrikaans, as compared to Latvian and Lithuanian. These experiments suggest that pairing linguistically related languages can benefit representation learning and adding extra languages can further improve the performance as demonstrated by mBERT. It echos the finding of \\newcitelample2019cross where multilingual training improves uni-directional language model perplexity for low resource languages. Concurrent work shows similar findings as the performance of low resource languages (Urdu and Swahili) improves on XNLI when more languages are trained jointly then decrease with an increasing number of languages Conneau et al. However, they do not consider the effect of language similarity.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 4: Monolingual BERT on four languages with different hyperparameters. Underscore denotes best within monolingual BERT and bold denotes best among all models. Monolingual BERT underperforms mBERT in most cases. “-” denotes same as base case.\nQuestion: Which model performs best for Latvian NER?\n" }, { "role": "assistant", "content": "To determine the best performing model for Latvian NER, compare the scores in the 'lv NER' column. The highest score is 93.88, achieved by mBERT. So, the answer is {\"answer\": \"mBERT\"}." } ], "images": [ "scigen/train/1592.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Monolingual BERT on four languages with different hyperparameters. Underscore denotes best within monolingual BERT and bold denotes best among all models. Monolingual BERT underperforms mBERT in most cases. “-” denotes same as base case." ], "reasoning": "To determine the best performing model for Latvian NER, compare the scores in the 'lv NER' column. The highest score is 93.88, achieved by mBERT.", "question": "Which model performs best for Latvian NER?", "answer": "So, the answer is {\"answer\": \"mBERT\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe smaller model outperforms the base model in all tasks for Afrikaans.\n" }, { "role": "assistant", "content": "To verify this statement, compare the scores of the smaller model and the base model in the 'af NER', 'af POS', and 'af Parsing (LAS/UAS)' columns. The smaller model does not outperform the base model in all tasks for Afrikaans. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1592.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Monolingual BERT on four languages with different hyperparameters. Underscore denotes best within monolingual BERT and bold denotes best among all models. Monolingual BERT underperforms mBERT in most cases. “-” denotes same as base case." ], "reasoning": "To verify this statement, compare the scores of the smaller model and the base model in the 'af NER', 'af POS', and 'af Parsing (LAS/UAS)' columns. The smaller model does not outperform the base model in all tasks for Afrikaans.", "statement": "The smaller model outperforms the base model in all tasks for Afrikaans.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Comparing the language model performance on 3-question trust score vs. 10-question trust score. Pearson rdis is dissattenuated Pearson r and MSE is the mean squared error.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Testing on 10-question trust score outperforms the 3-question trust score in both dissattenuated Pearson r and MSE, suggesting less noise in the 10-question trust score.Comparing the second and third settings, we see the result of testing on 10-question trust score outperforms the 3-question trust score by 0.07 margin in dissattenuated Pearson r and MSE by a margin of 0.11. To further understand why 10-question trust seems to be easier to predict, we calculate the variance for both 3-question and 10-question trust, yielding σ2=0.85 and σ2=0.72 respectively. This suggests that 10-question trust has less noise than 3-question trust. Due to these results, in all of the following experiments we only train on 3-question trust labels and test on 10-question trust labels." } ], "images": [ "scigen/train/1593.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparing the language model performance on 3-question trust score vs. 10-question trust score. Pearson rdis is dissattenuated Pearson r and MSE is the mean squared error." ], "reasoning": "Testing on 10-question trust score outperforms the 3-question trust score in both dissattenuated Pearson r and MSE, suggesting less noise in the 10-question trust score.", "description": "Comparing the second and third settings, we see the result of testing on 10-question trust score outperforms the 3-question trust score by 0.07 margin in dissattenuated Pearson r and MSE by a margin of 0.11. To further understand why 10-question trust seems to be easier to predict, we calculate the variance for both 3-question and 10-question trust, yielding σ2=0.85 and σ2=0.72 respectively. This suggests that 10-question trust has less noise than 3-question trust. Due to these results, in all of the following experiments we only train on 3-question trust labels and test on 10-question trust labels.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 2: Comparing the language model performance on 3-question trust score vs. 10-question trust score. Pearson rdis is dissattenuated Pearson r and MSE is the mean squared error.', unfold the question presented below, detailing every step of your calculation. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the Pearson r value when training on 3-question trust labels and testing on 10-question trust labels?\n" }, { "role": "assistant", "content": "From the table, locate the row where the train label is '3-question' and the test label is '10-question'. The Pearson r value in this row is 0.494. So, the answer is {\"answer\": \"0.494\"}" } ], "images": [ "scigen/train/1593.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparing the language model performance on 3-question trust score vs. 10-question trust score. Pearson rdis is dissattenuated Pearson r and MSE is the mean squared error." ], "reasoning": "From the table, locate the row where the train label is '3-question' and the test label is '10-question'. The Pearson r value in this row is 0.494.", "question": "What is the Pearson r value when training on 3-question trust labels and testing on 10-question trust labels?", "answer": "So, the answer is {\"answer\": \"0.494\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nTraining on 3-question trust labels and testing on 10-question trust labels yields a lower MSE than training and testing on 10-question trust labels.\n" }, { "role": "assistant", "content": "From the table, compare the MSE values for '3-question' train and '10-question' test (0.662) with '10-question' train and '10-question' test (0.719). The former is lower. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1593.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparing the language model performance on 3-question trust score vs. 10-question trust score. Pearson rdis is dissattenuated Pearson r and MSE is the mean squared error." ], "reasoning": "From the table, compare the MSE values for '3-question' train and '10-question' test (0.662) with '10-question' train and '10-question' test (0.719). The former is lower.", "statement": "Training on 3-question trust labels and testing on 10-question trust labels yields a lower MSE than training and testing on 10-question trust labels.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Comparing the performance of our language model with sentiment as baseline, using different feature sets: ngr_r: ngrams as relative frequencie, ngr_b: ngrams as boolean variables. Bold indicates the best performance. Pearson rdis is dissattenuated Pearson r and MSE is the mean squared error.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The best model (ngr_r+ngr_b+topics) reduced mean squared error by 8% over sentiment and achieved a Pearson correlation coefficient of 0.494, indicating a strong relationship between language use and a psychological trait.Our best model (ngr_r+ngr_b+topics) had an 8% reduction in mean squared error over sentiment, and achieved a Pearson correlation coefficient of rdis=.494 which is considered a large relationship between a behavior (language use) and a psychological trait Meyer et al. and just below state-of-the-art language-based assessments of other personality traits Park et al." } ], "images": [ "scigen/train/1594.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparing the performance of our language model with sentiment as baseline, using different feature sets: ngr_r: ngrams as relative frequencie, ngr_b: ngrams as boolean variables. Bold indicates the best performance. Pearson rdis is dissattenuated Pearson r and MSE is the mean squared error." ], "reasoning": "The best model (ngr_r+ngr_b+topics) reduced mean squared error by 8% over sentiment and achieved a Pearson correlation coefficient of 0.494, indicating a strong relationship between language use and a psychological trait.", "description": "Our best model (ngr_r+ngr_b+topics) had an 8% reduction in mean squared error over sentiment, and achieved a Pearson correlation coefficient of rdis=.494 which is considered a large relationship between a behavior (language use) and a psychological trait Meyer et al. and just below state-of-the-art language-based assessments of other personality traits Park et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. The question is related to the given table titled 'Table 3: Comparing the performance of our language model with sentiment as baseline, using different feature sets: ngr_r: ngrams as relative frequencie, ngr_b: ngrams as boolean variables. Bold indicates the best performance. Pearson rdis is dissattenuated Pearson r and MSE is the mean squared error.'.\n\nQuestion: What is the mean squared error (MSE) reduction percentage of the ngr_r+ngr_b+topics model compared to the sentiment baseline?\n" }, { "role": "assistant", "content": "The MSE for the sentiment baseline is 0.717, and the MSE for the ngr_r+ngr_b+topics model is 0.662. The reduction in MSE is calculated as ((0.717 - 0.662) / 0.717) * 100%. So, the answer is {\"answer\": \"7.68%\"}." } ], "images": [ "scigen/train/1594.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparing the performance of our language model with sentiment as baseline, using different feature sets: ngr_r: ngrams as relative frequencie, ngr_b: ngrams as boolean variables. Bold indicates the best performance. Pearson rdis is dissattenuated Pearson r and MSE is the mean squared error." ], "reasoning": "The MSE for the sentiment baseline is 0.717, and the MSE for the ngr_r+ngr_b+topics model is 0.662. The reduction in MSE is calculated as ((0.717 - 0.662) / 0.717) * 100%.", "question": "What is the mean squared error (MSE) reduction percentage of the ngr_r+ngr_b+topics model compared to the sentiment baseline?", "answer": "So, the answer is {\"answer\": \"7.68%\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe ngr_r+ngr_b+topics model achieved a Pearson correlation coefficient of 0.494.\n" }, { "role": "assistant", "content": "The table shows that the ngr_r+ngr_b+topics model has a Pearson rdis value of 0.494. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1594.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparing the performance of our language model with sentiment as baseline, using different feature sets: ngr_r: ngrams as relative frequencie, ngr_b: ngrams as boolean variables. Bold indicates the best performance. Pearson rdis is dissattenuated Pearson r and MSE is the mean squared error." ], "reasoning": "The table shows that the ngr_r+ngr_b+topics model has a Pearson rdis value of 0.494.", "statement": "The ngr_r+ngr_b+topics model achieved a Pearson correlation coefficient of 0.494.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: The performance of the unsupervised NMT systems with different learning objectives on en → fr newstest 2014.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The proposed comparative translation loss improves BLEU scores significantly compared to the MLE loss in both LSTM and Transformer models, indicating better handling of noise in training datasets.S4SS4SSS0Px3 The Effect of Comparative Translation Finally, we aim to roughly evaluate the effect of the proposed comparable translation loss in our model. Thus, we compare our model with a two-staged NMT system, where we extract and edit the parallel pairs and retrain the NMT system with the standard maximum likelihood estimation (MLE) loss in a supervised way (by taking the extracted-and-edited sentences as the ground-truth targets). We can observe that with the MLE loss, the translation performance will drop nearly 50%. The results indirectly reflect that the NMT systems are sensitive to noises in the training datasets. Meanwhile, it demonstrates by treating extracted-edit sentences as pivotal points instead of ground truth, our proposed comparative translation loss can avoid the NMT model suffers from the noise." } ], "images": [ "scigen/train/1595.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The performance of the unsupervised NMT systems with different learning objectives on en → fr newstest 2014." ], "reasoning": "The proposed comparative translation loss improves BLEU scores significantly compared to the MLE loss in both LSTM and Transformer models, indicating better handling of noise in training datasets.", "description": "S4SS4SSS0Px3 The Effect of Comparative Translation Finally, we aim to roughly evaluate the effect of the proposed comparable translation loss in our model. Thus, we compare our model with a two-staged NMT system, where we extract and edit the parallel pairs and retrain the NMT system with the standard maximum likelihood estimation (MLE) loss in a supervised way (by taking the extracted-and-edited sentences as the ground-truth targets). We can observe that with the MLE loss, the translation performance will drop nearly 50%. The results indirectly reflect that the NMT systems are sensitive to noises in the training datasets. Meanwhile, it demonstrates by treating extracted-edit sentences as pivotal points instead of ground truth, our proposed comparative translation loss can avoid the NMT model suffers from the noise.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: 'Table 3: The performance of the unsupervised NMT systems with different learning objectives on en → fr newstest 2014.'\nQuestion: What is the BLEU score difference between the LSTM model with MLE Loss and the Transformer model with Comparative Loss?\n" }, { "role": "assistant", "content": "To find the BLEU score difference, subtract the BLEU score of the LSTM model with MLE Loss (12.40) from the BLEU score of the Transformer model with Comparative Loss (25.44). So, the answer is {\"answer\": \"13.04\"}." } ], "images": [ "scigen/train/1595.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The performance of the unsupervised NMT systems with different learning objectives on en → fr newstest 2014." ], "reasoning": "To find the BLEU score difference, subtract the BLEU score of the LSTM model with MLE Loss (12.40) from the BLEU score of the Transformer model with Comparative Loss (25.44).", "question": "What is the BLEU score difference between the LSTM model with MLE Loss and the Transformer model with Comparative Loss?", "answer": "So, the answer is {\"answer\": \"13.04\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Transformer model with Comparative Loss has a higher BLEU score than the LSTM model with Comparative Loss.\n" }, { "role": "assistant", "content": "The table shows that the Transformer model with Comparative Loss has a BLEU score of 25.44, while the LSTM model with Comparative Loss has a BLEU score of 24.54. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1595.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The performance of the unsupervised NMT systems with different learning objectives on en → fr newstest 2014." ], "reasoning": "The table shows that the Transformer model with Comparative Loss has a BLEU score of 25.44, while the LSTM model with Comparative Loss has a BLEU score of 24.54. Therefore, the statement is supported.", "statement": "The Transformer model with Comparative Loss has a higher BLEU score than the LSTM model with Comparative Loss.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: The experimental results of parallel sentence mining on the newstest 2012 en → fr translation dataset with different levels of added sentence noises. Metric: The percentage of Hits@k.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The performance gap between unsupervised and supervised methods narrows as the rank increases, with unsupervised performance growing quickly when k≤10.Although our extraction model structure is different from the supervised extraction model, it can still give us a good insight into the upperbound and gap of performance. We can observe a noticeable gap between unsupervised and supervised methods, but the gap is narrowing as the rank increases. Meanwhile, in our unsupervised method, the performance grows quickly when k≤10." } ], "images": [ "scigen/train/1596.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The experimental results of parallel sentence mining on the newstest 2012 en → fr translation dataset with different levels of added sentence noises. Metric: The percentage of Hits@k." ], "reasoning": "The performance gap between unsupervised and supervised methods narrows as the rank increases, with unsupervised performance growing quickly when k≤10.", "description": "Although our extraction model structure is different from the supervised extraction model, it can still give us a good insight into the upperbound and gap of performance. We can observe a noticeable gap between unsupervised and supervised methods, but the gap is narrowing as the rank increases. Meanwhile, in our unsupervised method, the performance grows quickly when k≤10.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 2: The experimental results of parallel sentence mining on the newstest 2012 en → fr translation dataset with different levels of added sentence noises. Metric: The percentage of Hits@k.\n\nQuestion: What is the Hits@10 performance of the unsupervised model at 50% noise?\n" }, { "role": "assistant", "content": "To find the Hits@10 performance of the unsupervised model at 50% noise, locate the row with 50% noise and the column labeled Hits@10 under the unsupervised model. Thus, the answer is {\"answer\": \"83.2\"}." } ], "images": [ "scigen/train/1596.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The experimental results of parallel sentence mining on the newstest 2012 en → fr translation dataset with different levels of added sentence noises. Metric: The percentage of Hits@k." ], "reasoning": "To find the Hits@10 performance of the unsupervised model at 50% noise, locate the row with 50% noise and the column labeled Hits@10 under the unsupervised model.", "question": "What is the Hits@10 performance of the unsupervised model at 50% noise?", "answer": "Thus, the answer is {\"answer\": \"83.2\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe unsupervised model performs better than the supervised model at Hits@1 with 90% noise.\n" }, { "role": "assistant", "content": "Compare the Hits@1 values for the unsupervised and supervised models at 90% noise. The unsupervised model has a Hits@1 value of 41.5, while the supervised model has a Hits@1 value of 63.7. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1596.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The experimental results of parallel sentence mining on the newstest 2012 en → fr translation dataset with different levels of added sentence noises. Metric: The percentage of Hits@k." ], "reasoning": "Compare the Hits@1 values for the unsupervised and supervised models at 90% noise. The unsupervised model has a Hits@1 value of 41.5, while the supervised model has a Hits@1 value of 63.7.", "statement": "The unsupervised model performs better than the supervised model at Hits@1 with 90% noise.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Human evaluation of our model and other related systems on PWKP and EW-SEW datasets. The results are reported on the test sets.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "WEAN outperforms other systems in fluency, adequacy, and simplicity, achieving the highest scores in human evaluation and significant improvements over DRESS-LS and NTS-w2v.DRESS-LS is based on the reinforcement learning, and it encourages the fluency, simplicity and relevance of the outputs. Therefore, it achieves a high score in our human evaluation. WEAN gains a even better score than DRESS-LS. Besides, WEAN generates more adequate and simpler outputs than the reference on PWKP. The predictions of SBMT-SARI are the most adequate among the compared systems on EW-SEW. In general, WEAN outperforms all of the other systems, considering the balance of fluency, adequate and simplicity. We conduct significance tests based on t-test. The significance tests suggest that WEAN has a very significant improvement over baseline, with p≤0.001 over DRESS-LS in all of the dimension on PWKP, p≤0.05 over DRESS-LS in the dimension of fluency , p≤0.005 over NTS-w2v in the dimension of simplicity and p≤0.005 over DRESS-LS in the dimension of all." } ], "images": [ "scigen/train/1597.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Human evaluation of our model and other related systems on PWKP and EW-SEW datasets. The results are reported on the test sets." ], "reasoning": "WEAN outperforms other systems in fluency, adequacy, and simplicity, achieving the highest scores in human evaluation and significant improvements over DRESS-LS and NTS-w2v.", "description": "DRESS-LS is based on the reinforcement learning, and it encourages the fluency, simplicity and relevance of the outputs. Therefore, it achieves a high score in our human evaluation. WEAN gains a even better score than DRESS-LS. Besides, WEAN generates more adequate and simpler outputs than the reference on PWKP. The predictions of SBMT-SARI are the most adequate among the compared systems on EW-SEW. In general, WEAN outperforms all of the other systems, considering the balance of fluency, adequate and simplicity. We conduct significance tests based on t-test. The significance tests suggest that WEAN has a very significant improvement over baseline, with p≤0.001 over DRESS-LS in all of the dimension on PWKP, p≤0.05 over DRESS-LS in the dimension of fluency , p≤0.005 over NTS-w2v in the dimension of simplicity and p≤0.005 over DRESS-LS in the dimension of all.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: Human evaluation of our model and other related systems on PWKP and EW-SEW datasets. The results are reported on the test sets.', answer the following question. Give detailed solution consisting of each step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich system has the highest overall score on PWKP?\n" }, { "role": "assistant", "content": "By comparing the 'All' column for PWKP, WEAN has the highest score of 3.67. So, the answer is {\"answer\": \"WEAN\"}" } ], "images": [ "scigen/train/1597.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Human evaluation of our model and other related systems on PWKP and EW-SEW datasets. The results are reported on the test sets." ], "reasoning": "By comparing the 'All' column for PWKP, WEAN has the highest score of 3.67.", "question": "Which system has the highest overall score on PWKP?", "answer": "So, the answer is {\"answer\": \"WEAN\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nDRESS-LS has the highest fluency score on PWKP.\n" }, { "role": "assistant", "content": "The table shows that WEAN has the highest fluency score of 3.77 on PWKP, not DRESS-LS. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1597.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Human evaluation of our model and other related systems on PWKP and EW-SEW datasets. The results are reported on the test sets." ], "reasoning": "The table shows that WEAN has the highest fluency score of 3.77 on PWKP, not DRESS-LS.", "statement": "DRESS-LS has the highest fluency score on PWKP.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: ROUGE F1 score on the LCSTS test set. R-1, R-2, and R-L denote ROUGE-1, ROUGE-2, and ROUGE-L, respectively. The models with a suffix of ‘W’ in the table are word-based, while the rest of models are character-based.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our model WEAN achieves the highest ROUGE scores, outperforming the state-of-the-art model DRGD Li et al. with a relative gain of 0.8 ROUGE-1, 1.4 ROUGE-2, and 1.0 ROUGE-L.We report the ROUGE F1 score of our model and the baseline models on the test sets. Our model achieves the score of 37.8 ROUGE-1, 25.6 ROUGE-2, and 35.2 ROUGE-L, outperforming all of the previous models. First, we compare our model with the sequence-to-sequence model. It shows that our model significant outperforms the sequence-to-sequence baseline with a large margin of 5.7 ROUGE-1, 5.7 ROUGE-2, and 6.0 ROUGE-L. Then, we compare our model with other related models. The state-of-the-art model is DRGD Li et al. Our model has a relative gain of 0.8 ROUGE-1, 1.4 ROUGE-2 and 1.0 ROUGE-L over the state-of-the-art models." } ], "images": [ "scigen/train/1598.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: ROUGE F1 score on the LCSTS test set. R-1, R-2, and R-L denote ROUGE-1, ROUGE-2, and ROUGE-L, respectively. The models with a suffix of ‘W’ in the table are word-based, while the rest of models are character-based." ], "reasoning": "Our model WEAN achieves the highest ROUGE scores, outperforming the state-of-the-art model DRGD Li et al. with a relative gain of 0.8 ROUGE-1, 1.4 ROUGE-2, and 1.0 ROUGE-L.", "description": "We report the ROUGE F1 score of our model and the baseline models on the test sets. Our model achieves the score of 37.8 ROUGE-1, 25.6 ROUGE-2, and 35.2 ROUGE-L, outperforming all of the previous models. First, we compare our model with the sequence-to-sequence model. It shows that our model significant outperforms the sequence-to-sequence baseline with a large margin of 5.7 ROUGE-1, 5.7 ROUGE-2, and 6.0 ROUGE-L. Then, we compare our model with other related models. The state-of-the-art model is DRGD Li et al. Our model has a relative gain of 0.8 ROUGE-1, 1.4 ROUGE-2 and 1.0 ROUGE-L over the state-of-the-art models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nTable title: Table 4: ROUGE F1 score on the LCSTS test set. R-1, R-2, and R-L denote ROUGE-1, ROUGE-2, and ROUGE-L, respectively. The models with a suffix of ‘W’ in the table are word-based, while the rest of models are character-based.\n\nQuestion: How much higher is the ROUGE-2 score of WEAN compared to the Seq2seq model?\n" }, { "role": "assistant", "content": "The ROUGE-2 score of WEAN is 25.6, and the ROUGE-2 score of Seq2seq is 19.9. The difference is 25.6 - 19.9 = 5.7. So, the answer is {\"answer\": \"5.7\"}." } ], "images": [ "scigen/train/1598.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: ROUGE F1 score on the LCSTS test set. R-1, R-2, and R-L denote ROUGE-1, ROUGE-2, and ROUGE-L, respectively. The models with a suffix of ‘W’ in the table are word-based, while the rest of models are character-based." ], "reasoning": "The ROUGE-2 score of WEAN is 25.6, and the ROUGE-2 score of Seq2seq is 19.9. The difference is 25.6 - 19.9 = 5.7.", "question": "How much higher is the ROUGE-2 score of WEAN compared to the Seq2seq model?", "answer": "So, the answer is {\"answer\": \"5.7\"}." }, { "messages": [ { "role": "user", "content": "The WEAN model has the highest ROUGE-1 score among all models. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe WEAN model has the highest ROUGE-1 score among all models.\n" }, { "role": "assistant", "content": "The table shows that the WEAN model has a ROUGE-1 score of 37.8, which is higher than all other models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1598.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: ROUGE F1 score on the LCSTS test set. R-1, R-2, and R-L denote ROUGE-1, ROUGE-2, and ROUGE-L, respectively. The models with a suffix of ‘W’ in the table are word-based, while the rest of models are character-based." ], "reasoning": "The table shows that the WEAN model has a ROUGE-1 score of 37.8, which is higher than all other models listed.", "statement": "The WEAN model has the highest ROUGE-1 score among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Performance on modified versions of XQuAD-R where one target answer is removed, either from the same language as the question (−same) or a random other language (−rand).'.\n" }, { "role": "assistant", "content": "The X-Y baseline model shows minimal same-language bias, while the X-X model is most affected due to its training procedure.We rerun the XQuAD-R evaluation, but for each question, we remove one of its 11 target answers from the multilingual candidate pool. If a model is free of same-language bias, the effect of removing a single target should be constant, regardless of whether the removed target was in the same language as the question or not. Looking at the delta between these conditions, the X-Y baseline only displays a minimal bias, falling from 0.62 to 0.61 mAP. The most affected model is X-X, whose training procedure actively encouraged same-language bias. Interestingly even En-En shows a significant delta, indicating that simply fine-tuning mBERT on English QA is not sufficient to produce an embedding space that is strongly language agnostic." } ], "images": [ "scigen/train/1599.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance on modified versions of XQuAD-R where one target answer is removed, either from the same language as the question (−same) or a random other language (−rand)." ], "reasoning": "The X-Y baseline model shows minimal same-language bias, while the X-X model is most affected due to its training procedure.", "description": "We rerun the XQuAD-R evaluation, but for each question, we remove one of its 11 target answers from the multilingual candidate pool. If a model is free of same-language bias, the effect of removing a single target should be constant, regardless of whether the removed target was in the same language as the question or not. Looking at the delta between these conditions, the X-Y baseline only displays a minimal bias, falling from 0.62 to 0.61 mAP. The most affected model is X-X, whose training procedure actively encouraged same-language bias. Interestingly even En-En shows a significant delta, indicating that simply fine-tuning mBERT on English QA is not sufficient to produce an embedding space that is strongly language agnostic.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 3: Performance on modified versions of XQuAD-R where one target answer is removed, either from the same language as the question (−same) or a random other language (−rand).\nQuestion: Which model has the highest mAP when evaluated with random target answers?\n" }, { "role": "assistant", "content": "By comparing the 'mAP - rand' column, the Translate-Test model has the highest mAP of 0.69. So, the answer is {\"answer\": \"Translate-Test\"}" } ], "images": [ "scigen/train/1599.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance on modified versions of XQuAD-R where one target answer is removed, either from the same language as the question (−same) or a random other language (−rand)." ], "reasoning": "By comparing the 'mAP - rand' column, the Translate-Test model has the highest mAP of 0.69.", "question": "Which model has the highest mAP when evaluated with random target answers?", "answer": "So, the answer is {\"answer\": \"Translate-Test\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe X-X model has the smallest delta between random and same-language target answers.\n" }, { "role": "assistant", "content": "The table shows that the X-X model has a % Δ of 0.37, which is the largest delta, not the smallest. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1599.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance on modified versions of XQuAD-R where one target answer is removed, either from the same language as the question (−same) or a random other language (−rand)." ], "reasoning": "The table shows that the X-X model has a % Δ of 0.37, which is the largest delta, not the smallest.", "statement": "The X-X model has the smallest delta between random and same-language target answers.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1: Numbers of questions and candidates per language in XQuAD-R and MLQA-R.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The number of candidates per language varies due to differences in sentence breaking, even though the contexts for XQuAD are parallel across languages.To convert these span-tagging tasks into retrieval tasks, we follow the procedure from ReQA Ahmad et al. While the contexts for XQuAD are parallel across languages, differences in sentence breaking lead to variations in the number of candidates per language." } ], "images": [ "scigen/train/1600.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Numbers of questions and candidates per language in XQuAD-R and MLQA-R." ], "reasoning": "The number of candidates per language varies due to differences in sentence breaking, even though the contexts for XQuAD are parallel across languages.", "description": "To convert these span-tagging tasks into retrieval tasks, we follow the procedure from ReQA Ahmad et al. While the contexts for XQuAD are parallel across languages, differences in sentence breaking lead to variations in the number of candidates per language.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: Numbers of questions and candidates per language in XQuAD-R and MLQA-R.', answer the question below by showing each progressive step toward the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich language has the highest number of MLQA-R candidates?\n" }, { "role": "assistant", "content": "To find the language with the highest number of MLQA-R candidates, compare the values in the 'MLQA-R candidates' column. The highest value is 6264, which corresponds to the 'en' (English) row. So, the answer is {\"answer\": \"en\"}." } ], "images": [ "scigen/train/1600.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Numbers of questions and candidates per language in XQuAD-R and MLQA-R." ], "reasoning": "To find the language with the highest number of MLQA-R candidates, compare the values in the 'MLQA-R candidates' column. The highest value is 6264, which corresponds to the 'en' (English) row.", "question": "Which language has the highest number of MLQA-R candidates?", "answer": "So, the answer is {\"answer\": \"en\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe number of XQuAD-R candidates for the 'hi' language is greater than that for the 'es' language.\n" }, { "role": "assistant", "content": "To verify this statement, compare the values in the 'XQuAD-R candidates' column for 'hi' and 'es'. 'hi' has 1244 candidates, while 'es' has 1215 candidates. Since 1244 is greater than 1215, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1600.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Numbers of questions and candidates per language in XQuAD-R and MLQA-R." ], "reasoning": "To verify this statement, compare the values in the 'XQuAD-R candidates' column for 'hi' and 'es'. 'hi' has 1244 candidates, while 'es' has 1215 candidates. Since 1244 is greater than 1215, the statement is supported.", "statement": "The number of XQuAD-R candidates for the 'hi' language is greater than that for the 'es' language.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Mean average precision (mAP) of baseline models on XQuAD-R and MLQA-R.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The Translate-Test baseline performs the best on both XQuAD-R and MLQA-R, highlighting its effectiveness in language-agnostic retrieval by translating test data to English.On both XQuAD-R and MLQA-R, the strongest model is the Translate-Test baseline. This is unsurprising in that LAReQA demands language-agnostic retrieval, and Translate-Test leverages an external machine translation system to actively “remove” the effects of language, by translating all test data to English. This result supports our claim that LAReQA tests for cross-lingual alignment in a way that existing zero-shot evaluations do not. As an extreme case, X-X scored a mere 0.23 on LAReQA mAP, compared to the best embedding model X-Y and the best overall baseline Translate-Test with 0.63 and 0.70 respectively. More generally, we observe that the more a model separates languages in embedding space, the worse it performs on LAReQA (cf. This ordering is also reflected in the degree to which language ID is predictable from the embeddings. When we use a linear regression to predict language (English vs. Chinese) from the question and candidate embeddings, the accuracies on a one-third holdout are X-X: 99.2%, En-En: 97.7%, X-X-mono: 87.5% and X-Y: 54.0%. This supports the claim that LAReQA is a better test of strong alignment than current zero-shot tasks." } ], "images": [ "scigen/train/1601.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Mean average precision (mAP) of baseline models on XQuAD-R and MLQA-R." ], "reasoning": "The Translate-Test baseline performs the best on both XQuAD-R and MLQA-R, highlighting its effectiveness in language-agnostic retrieval by translating test data to English.", "description": "On both XQuAD-R and MLQA-R, the strongest model is the Translate-Test baseline. This is unsurprising in that LAReQA demands language-agnostic retrieval, and Translate-Test leverages an external machine translation system to actively “remove” the effects of language, by translating all test data to English. This result supports our claim that LAReQA tests for cross-lingual alignment in a way that existing zero-shot evaluations do not. As an extreme case, X-X scored a mere 0.23 on LAReQA mAP, compared to the best embedding model X-Y and the best overall baseline Translate-Test with 0.63 and 0.70 respectively. More generally, we observe that the more a model separates languages in embedding space, the worse it performs on LAReQA (cf. This ordering is also reflected in the degree to which language ID is predictable from the embeddings. When we use a linear regression to predict language (English vs. Chinese) from the question and candidate embeddings, the accuracies on a one-third holdout are X-X: 99.2%, En-En: 97.7%, X-X-mono: 87.5% and X-Y: 54.0%. This supports the claim that LAReQA is a better test of strong alignment than current zero-shot tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: Mean average precision (mAP) of baseline models on XQuAD-R and MLQA-R.', solve the subsequent question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich model has the highest score on XQuAD-R and what is the score?\n" }, { "role": "assistant", "content": "The table shows the scores of different models on XQuAD-R. The Translate-Test model has the highest score of 0.72. So, the answer is {\"answer\": \"Translate-Test with 0.72\"}." } ], "images": [ "scigen/train/1601.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Mean average precision (mAP) of baseline models on XQuAD-R and MLQA-R." ], "reasoning": "The table shows the scores of different models on XQuAD-R. The Translate-Test model has the highest score of 0.72.", "question": "Which model has the highest score on XQuAD-R and what is the score?", "answer": "So, the answer is {\"answer\": \"Translate-Test with 0.72\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe X-Y model has a higher score on MLQA-R than the X-X-mono model.\n" }, { "role": "assistant", "content": "The table shows that the X-Y model has a score of 0.49 on MLQA-R, while the X-X-mono model also has a score of 0.49. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1601.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Mean average precision (mAP) of baseline models on XQuAD-R and MLQA-R." ], "reasoning": "The table shows that the X-Y model has a score of 0.49 on MLQA-R, while the X-X-mono model also has a score of 0.49. Therefore, the statement is refuted.", "statement": "The X-Y model has a higher score on MLQA-R than the X-X-mono model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: mAP on the zero-shot version of XQuAD-R, retrieving a single answer from a monolingual pool that matches the question language. Note, only En-En is strictly “zero-shot”, as the other models see machine translated fine-tuning data.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The X-X(-mono) baselines, trained on within-language examples, outperform other models in the LAReQA task, highlighting weak alignment but good zero-shot performance.Remarkably, the model ranking under this task diverges from that under our proposed LAReQA task of retrieval from a multilingual pool. In particular, the X-X(-mono) baselines which were only trained on “within-language” examples now perform the best, beating the top LAReQA baselines Translate-Test and X-Y. Figure embeddings of all XQuAD-R questions and candidates in English and Chinese (chosen as they are distant languages). The X-X embeddings show a dramatic separation between Chinese and English. This is a clear case of weak alignment, where a model achieves good zero-shot performance (cf. We believe there is significant headroom for models to improve on LAReQA. Our best initial baseline sidesteps the alignment problem by simply translating all test data to English. Among embedding-based models, our strongest baseline (“X-Y”) actively removes language bias by augmenting training data to include machine-translated cross-lingual examples. It is an interesting question for future work whether strong alignment always comes at a cost, or if better training techniques will lead to models that can improve on all these measures simultaneously." } ], "images": [ "scigen/train/1602.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: mAP on the zero-shot version of XQuAD-R, retrieving a single answer from a monolingual pool that matches the question language. Note, only En-En is strictly “zero-shot”, as the other models see machine translated fine-tuning data." ], "reasoning": "The X-X(-mono) baselines, trained on within-language examples, outperform other models in the LAReQA task, highlighting weak alignment but good zero-shot performance.", "description": "Remarkably, the model ranking under this task diverges from that under our proposed LAReQA task of retrieval from a multilingual pool. In particular, the X-X(-mono) baselines which were only trained on “within-language” examples now perform the best, beating the top LAReQA baselines Translate-Test and X-Y. Figure embeddings of all XQuAD-R questions and candidates in English and Chinese (chosen as they are distant languages). The X-X embeddings show a dramatic separation between Chinese and English. This is a clear case of weak alignment, where a model achieves good zero-shot performance (cf. We believe there is significant headroom for models to improve on LAReQA. Our best initial baseline sidesteps the alignment problem by simply translating all test data to English. Among embedding-based models, our strongest baseline (“X-Y”) actively removes language bias by augmenting training data to include machine-translated cross-lingual examples. It is an interesting question for future work whether strong alignment always comes at a cost, or if better training techniques will lead to models that can improve on all these measures simultaneously.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: Table 4: mAP on the zero-shot version of XQuAD-R, retrieving a single answer from a monolingual pool that matches the question language. Note, only En-En is strictly “zero-shot”, as the other models see machine translated fine-tuning data.\nQuestion: Which model has the highest average score in the Zero-shot Rank?\n" }, { "role": "assistant", "content": "By examining the 'Avg' column under the Zero-shot Rank, the X-X-mono model has the highest average score of 0.86. So, the answer is {\"answer\": \"X-X-mono\"}." } ], "images": [ "scigen/train/1602.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: mAP on the zero-shot version of XQuAD-R, retrieving a single answer from a monolingual pool that matches the question language. Note, only En-En is strictly “zero-shot”, as the other models see machine translated fine-tuning data." ], "reasoning": "By examining the 'Avg' column under the Zero-shot Rank, the X-X-mono model has the highest average score of 0.86.", "question": "Which model has the highest average score in the Zero-shot Rank?", "answer": "So, the answer is {\"answer\": \"X-X-mono\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Translate-Test model ranks first in the LAReQA task.\n" }, { "role": "assistant", "content": "The table shows that the Translate-Test model has the highest LAReQA Rank, indicated by the rank of 1. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1602.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: mAP on the zero-shot version of XQuAD-R, retrieving a single answer from a monolingual pool that matches the question language. Note, only En-En is strictly “zero-shot”, as the other models see machine translated fine-tuning data." ], "reasoning": "The table shows that the Translate-Test model has the highest LAReQA Rank, indicated by the rank of 1.", "statement": "The Translate-Test model ranks first in the LAReQA task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Quantitative evaluation of interpretability'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "SPINE vectors have better interpretability in the medical domain compared to SPOWV vectors, despite SPOWV performing better in downstream tasks.5.2.2 Quantitative Evaluation The results agree with those obtained by qualitative evaluation. As it can be seen, although SPOWV vectors were seen to perform better in downstream tasks, vectors trained by SPINE have better interpretability in medical domain." } ], "images": [ "scigen/train/1603.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Quantitative evaluation of interpretability" ], "reasoning": "SPINE vectors have better interpretability in the medical domain compared to SPOWV vectors, despite SPOWV performing better in downstream tasks.", "description": "5.2.2 Quantitative Evaluation The results agree with those obtained by qualitative evaluation. As it can be seen, although SPOWV vectors were seen to perform better in downstream tasks, vectors trained by SPINE have better interpretability in medical domain.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 4: Quantitative evaluation of interpretability', solve the subsequent question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich method has the highest interpretability score for GloVe vectors?\n" }, { "role": "assistant", "content": "To find the method with the highest interpretability score for GloVe vectors, compare the interpretability scores of Original, SPINE, and SPOWV for GloVe. SPINE has the highest score with 18.1. So, the answer is {\"answer\": \"SPINE\"}." } ], "images": [ "scigen/train/1603.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Quantitative evaluation of interpretability" ], "reasoning": "To find the method with the highest interpretability score for GloVe vectors, compare the interpretability scores of Original, SPINE, and SPOWV for GloVe. SPINE has the highest score with 18.1.", "question": "Which method has the highest interpretability score for GloVe vectors?", "answer": "So, the answer is {\"answer\": \"SPINE\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nSPINE vectors have the highest interpretability score for both GloVe and Skip-gram models.\n" }, { "role": "assistant", "content": "The table shows that SPINE vectors have an interpretability score of 18.1 for GloVe and 16.7 for Skip-gram, which are the highest scores in their respective categories. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1603.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Quantitative evaluation of interpretability" ], "reasoning": "The table shows that SPINE vectors have an interpretability score of 18.1 for GloVe and 16.7 for Skip-gram, which are the highest scores in their respective categories.", "statement": "SPINE vectors have the highest interpretability score for both GloVe and Skip-gram models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Word similarity and relatedness results', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Sparse word vectors from SPOWV often outperform their original versions, while SPINE vectors show a slight but tolerable performance loss.One important reason for looking into such comparison is to make sure that the expected interpretability of the sparse methods is achieved at either no or tolerable loss in preserving the similarity and relatedness captured by their original versions. As it can be seen, sparse word vectors obtained by SPOWV have even outperformed the original vectors on several occasions. The sparse vectors obtained by SPINE however, show a slight loss of performance which seems nonetheless tolerable considering the high interpretability it provides, as shown in the next subsection." } ], "images": [ "scigen/train/1604.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Word similarity and relatedness results" ], "reasoning": "Sparse word vectors from SPOWV often outperform their original versions, while SPINE vectors show a slight but tolerable performance loss.", "description": "One important reason for looking into such comparison is to make sure that the expected interpretability of the sparse methods is achieved at either no or tolerable loss in preserving the similarity and relatedness captured by their original versions. As it can be seen, sparse word vectors obtained by SPOWV have even outperformed the original vectors on several occasions. The sparse vectors obtained by SPINE however, show a slight loss of performance which seems nonetheless tolerable considering the high interpretability it provides, as shown in the next subsection.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: Word similarity and relatedness results', answer the question below by showing each progressive step toward the answer. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich sparse word vector method outperforms its original version in the UMNSRS-Sim metric?\n" }, { "role": "assistant", "content": "By comparing the UMNSRS-Sim scores, SPOWV GloVe (0.56) and SPOWV SG (0.60) both outperform their original versions, GloVe (0.56) and Skip-gram (0.56), respectively. So, the answer is {\"answer\": \"SPOWV SG\"}." } ], "images": [ "scigen/train/1604.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Word similarity and relatedness results" ], "reasoning": "By comparing the UMNSRS-Sim scores, SPOWV GloVe (0.56) and SPOWV SG (0.60) both outperform their original versions, GloVe (0.56) and Skip-gram (0.56), respectively.", "question": "Which sparse word vector method outperforms its original version in the UMNSRS-Sim metric?", "answer": "So, the answer is {\"answer\": \"SPOWV SG\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nSPINE GloVe performs better than GloVe in the Bio-SimVerb metric.\n" }, { "role": "assistant", "content": "The table shows that SPINE GloVe has a Bio-SimVerb score of 0.47, which is lower than GloVe's score of 0.53. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1604.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Word similarity and relatedness results" ], "reasoning": "The table shows that SPINE GloVe has a Bio-SimVerb score of 0.47, which is lower than GloVe's score of 0.53.", "statement": "SPINE GloVe performs better than GloVe in the Bio-SimVerb metric.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Extrinsic evaluation results'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The classifiers show a slight improvement in performance with the use of sparse vectors, as indicated by the average scores.As it can be observed, the classifiers benefit slightly from the sparsity of the vectors." } ], "images": [ "scigen/train/1605.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Extrinsic evaluation results" ], "reasoning": "The classifiers show a slight improvement in performance with the use of sparse vectors, as indicated by the average scores.", "description": "As it can be observed, the classifiers benefit slightly from the sparsity of the vectors.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Extrinsic evaluation results\nQuestion: Which vector has the highest average score, and what is that score?\nGive a detailed response to the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the vector with the highest average score, compare the 'Average' column values for all vectors. The highest average score is 71.7, which corresponds to the SPOWV SG vector. So, the answer is {\"answer\": \"71.7\"}." } ], "images": [ "scigen/train/1605.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Extrinsic evaluation results" ], "reasoning": "To find the vector with the highest average score, compare the 'Average' column values for all vectors. The highest average score is 71.7, which corresponds to the SPOWV SG vector.", "question": "Which vector has the highest average score, and what is that score?", "answer": "So, the answer is {\"answer\": \"71.7\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nSPINE SG has a higher average score than GloVe.\n" }, { "role": "assistant", "content": "To verify this statement, compare the average scores of SPINE SG and GloVe. SPINE SG has an average score of 70.4, while GloVe has an average score of 70.5. Therefore, SPINE SG does not have a higher average score than GloVe. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1605.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Extrinsic evaluation results" ], "reasoning": "To verify this statement, compare the average scores of SPINE SG and GloVe. SPINE SG has an average score of 70.4, while GloVe has an average score of 70.5. Therefore, SPINE SG does not have a higher average score than GloVe.", "statement": "SPINE SG has a higher average score than GloVe.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Ablation results of our baseline system on the LDC2015E86 development set.'.\n" }, { "role": "assistant", "content": "The baseline system's performance improved significantly in BLEU score from 18.77 to 24.93 due to the application of BPE and vocabulary sharing techniques.We first show the performance of our baseline system. As mentioned before, BPE and sharing vocabulary are two techniques we applied to relieving data sparsity. From the results we can see that BPE and vocabulary sharing are critical to building our baseline system (an improvement from 18.77 to 24.93 in BLEU), revealing the fact that they are two effective ways to address the issue of data sparseness for AMR-to-text generation." } ], "images": [ "scigen/train/1606.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ablation results of our baseline system on the LDC2015E86 development set." ], "reasoning": "The baseline system's performance improved significantly in BLEU score from 18.77 to 24.93 due to the application of BPE and vocabulary sharing techniques.", "description": "We first show the performance of our baseline system. As mentioned before, BPE and sharing vocabulary are two techniques we applied to relieving data sparsity. From the results we can see that BPE and vocabulary sharing are critical to building our baseline system (an improvement from 18.77 to 24.93 in BLEU), revealing the fact that they are two effective ways to address the issue of data sparseness for AMR-to-text generation.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: Table 2: Ablation results of our baseline system on the LDC2015E86 development set.\nQuestion: What is the BLEU score improvement when both BPE and vocabulary sharing are applied compared to when neither is applied?\n" }, { "role": "assistant", "content": "The BLEU score for the baseline system with both BPE and vocabulary sharing is 24.93, while the BLEU score without both techniques is 18.77. The improvement is calculated as 24.93 - 18.77. So, the answer is {\"answer\": \"6.16\"}" } ], "images": [ "scigen/train/1606.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ablation results of our baseline system on the LDC2015E86 development set." ], "reasoning": "The BLEU score for the baseline system with both BPE and vocabulary sharing is 24.93, while the BLEU score without both techniques is 18.77. The improvement is calculated as 24.93 - 18.77.", "question": "What is the BLEU score improvement when both BPE and vocabulary sharing are applied compared to when neither is applied?", "answer": "So, the answer is {\"answer\": \"6.16\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The baseline system achieved a BLEU score of 24.93 without using BPE and vocabulary sharing., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe baseline system achieved a BLEU score of 24.93 without using BPE and vocabulary sharing.\n" }, { "role": "assistant", "content": "The table shows that the baseline system achieved a BLEU score of 24.93 with the application of BPE and vocabulary sharing, not without them. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1606.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ablation results of our baseline system on the LDC2015E86 development set." ], "reasoning": "The table shows that the baseline system achieved a BLEU score of 24.93 with the application of BPE and vocabulary sharing, not without them.", "statement": "The baseline system achieved a BLEU score of 24.93 without using BPE and vocabulary sharing.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Performance on the test set of our approach with or without modeling structural information of indirectly connected concept pairs.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our approach improves BLEU scores from 29.92 to 31.82 by modeling structural information of indirectly connected concept pairs, and even without this modeling, it outperforms the baseline.It shows that by modeling structural information of indirectly connected concept pairs, our approach improves the performance on the test set from 29.92 to 31.82 in BLEU scores. It also shows that even without modeling structural information of indirectly connected concept pairs, our approach achieves better performance than the baseline." } ], "images": [ "scigen/train/1607.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance on the test set of our approach with or without modeling structural information of indirectly connected concept pairs." ], "reasoning": "Our approach improves BLEU scores from 29.92 to 31.82 by modeling structural information of indirectly connected concept pairs, and even without this modeling, it outperforms the baseline.", "description": "It shows that by modeling structural information of indirectly connected concept pairs, our approach improves the performance on the test set from 29.92 to 31.82 in BLEU scores. It also shows that even without modeling structural information of indirectly connected concept pairs, our approach achieves better performance than the baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: 'Table 4: Performance on the test set of our approach with or without modeling structural information of indirectly connected concept pairs.'\nQuestion: What is the BLEU score improvement when modeling structural information of indirectly connected concept pairs?\n" }, { "role": "assistant", "content": "The BLEU score improves from 29.92 to 31.82 when modeling structural information of indirectly connected concept pairs. So, the answer is {\"answer\": \"1.90\"}" } ], "images": [ "scigen/train/1607.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance on the test set of our approach with or without modeling structural information of indirectly connected concept pairs." ], "reasoning": "The BLEU score improves from 29.92 to 31.82 when modeling structural information of indirectly connected concept pairs.", "question": "What is the BLEU score improvement when modeling structural information of indirectly connected concept pairs?", "answer": "So, the answer is {\"answer\": \"1.90\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Our approach achieves a BLEU score of 31.82 without modeling structural information of indirectly connected concept pairs., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nOur approach achieves a BLEU score of 31.82 without modeling structural information of indirectly connected concept pairs.\n" }, { "role": "assistant", "content": "The table shows that our approach achieves a BLEU score of 31.82 with modeling structural information of indirectly connected concept pairs, but only 29.92 without it. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1607.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance on the test set of our approach with or without modeling structural information of indirectly connected concept pairs." ], "reasoning": "The table shows that our approach achieves a BLEU score of 31.82 with modeling structural information of indirectly connected concept pairs, but only 29.92 without it.", "statement": "Our approach achieves a BLEU score of 31.82 without modeling structural information of indirectly connected concept pairs.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is '(a) RNN Seq2Seq Model'.\n" }, { "role": "assistant", "content": "The approach reduces model sizes and training times significantly for both RNN and CNN models in English-to-Chinese translation tasks.We examine how our approach improves the performance of both the RNN seq2seq model Cho et al. ; Bahdanau et al. During encoding, every logographic word in those sentences is replaced by their corresponding abstract subword (symbol) vectors. New dictionaries are created from those encoded sentences. We then train both the RNN and CNN sequence-to-sequence models with them. Model sizes (NN), We observe that in the English-to-Chinese translation task, the model sizes are reduced by 37% and 77% respectively with RNN and CNN; the training time is 11% and 73% shorter; and the inference time is nearly halved in RNN and 36% shorter in CNN." } ], "images": [ "scigen/train/1608.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) RNN Seq2Seq Model" ], "reasoning": "The approach reduces model sizes and training times significantly for both RNN and CNN models in English-to-Chinese translation tasks.", "description": "We examine how our approach improves the performance of both the RNN seq2seq model Cho et al. ; Bahdanau et al. During encoding, every logographic word in those sentences is replaced by their corresponding abstract subword (symbol) vectors. New dictionaries are created from those encoded sentences. We then train both the RNN and CNN sequence-to-sequence models with them. Model sizes (NN), We observe that in the English-to-Chinese translation task, the model sizes are reduced by 37% and 77% respectively with RNN and CNN; the training time is 11% and 73% shorter; and the inference time is nearly halved in RNN and 36% shorter in CNN.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about '(a) RNN Seq2Seq Model', answer the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the BLEU score for the English-to-Chinese translation task using the L-SW-LO preprocessing method?\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for different translation tasks and preprocessing methods. For the English-to-Chinese translation task with the L-SW-LO preprocessing method, the BLEU score is 49.95. So, the answer is {\"answer\": \"49.95\"}" } ], "images": [ "scigen/train/1608.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) RNN Seq2Seq Model" ], "reasoning": "The table shows the BLEU scores for different translation tasks and preprocessing methods. For the English-to-Chinese translation task with the L-SW-LO preprocessing method, the BLEU score is 49.95.", "question": "What is the BLEU score for the English-to-Chinese translation task using the L-SW-LO preprocessing method?", "answer": "So, the answer is {\"answer\": \"49.95\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe training time for the English-to-Chinese translation task using the L-SW-DA preprocessing method is shorter than using the L-SW-LO preprocessing method.\n" }, { "role": "assistant", "content": "The table shows that the training time for the English-to-Chinese translation task using the L-SW-DA preprocessing method is 13.45 hours, while using the L-SW-LO preprocessing method is 15.39 hours. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1608.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) RNN Seq2Seq Model" ], "reasoning": "The table shows that the training time for the English-to-Chinese translation task using the L-SW-DA preprocessing method is 13.45 hours, while using the L-SW-LO preprocessing method is 15.39 hours. Therefore, the statement is supported.", "statement": "The training time for the English-to-Chinese translation task using the L-SW-DA preprocessing method is shorter than using the L-SW-LO preprocessing method.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title '(b) CNN Seq2Seq Model'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The approach reduces model sizes and training times significantly for both RNN and CNN models in the English-to-Chinese translation task, with notable improvements in inference times.We examine how our approach improves the performance of both the RNN seq2seq model Cho et al. ; Bahdanau et al. During encoding, every logographic word in those sentences is replaced by their corresponding abstract subword (symbol) vectors. New dictionaries are created from those encoded sentences. We then train both the RNN and CNN sequence-to-sequence models with them. Model sizes (NN), We observe that in the English-to-Chinese translation task, the model sizes are reduced by 37% and 77% respectively with RNN and CNN; the training time is 11% and 73% shorter; and the inference time is nearly halved in RNN and 36% shorter in CNN." } ], "images": [ "scigen/train/1609.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) CNN Seq2Seq Model" ], "reasoning": "The approach reduces model sizes and training times significantly for both RNN and CNN models in the English-to-Chinese translation task, with notable improvements in inference times.", "description": "We examine how our approach improves the performance of both the RNN seq2seq model Cho et al. ; Bahdanau et al. During encoding, every logographic word in those sentences is replaced by their corresponding abstract subword (symbol) vectors. New dictionaries are created from those encoded sentences. We then train both the RNN and CNN sequence-to-sequence models with them. Model sizes (NN), We observe that in the English-to-Chinese translation task, the model sizes are reduced by 37% and 77% respectively with RNN and CNN; the training time is 11% and 73% shorter; and the inference time is nearly halved in RNN and 36% shorter in CNN.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about '(b) CNN Seq2Seq Model', answer the following question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the BLEU score for the English-to-Chinese translation task using the L-SW-DA preprocessing method?\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for different preprocessing methods and translation tasks. For the English-to-Chinese task with L-SW-DA preprocessing, the BLEU score is 47.47. So, the answer is {\"answer\": \"47.47\"}" } ], "images": [ "scigen/train/1609.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) CNN Seq2Seq Model" ], "reasoning": "The table shows the BLEU scores for different preprocessing methods and translation tasks. For the English-to-Chinese task with L-SW-DA preprocessing, the BLEU score is 47.47.", "question": "What is the BLEU score for the English-to-Chinese translation task using the L-SW-DA preprocessing method?", "answer": "So, the answer is {\"answer\": \"47.47\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe model size for the English-to-Chinese translation task using the L-SW-LO preprocessing method is 112.2 MB.\n" }, { "role": "assistant", "content": "The table lists the model sizes for different preprocessing methods and translation tasks. For the English-to-Chinese task with L-SW-LO preprocessing, the model size is indeed 112.2 MB. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1609.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) CNN Seq2Seq Model" ], "reasoning": "The table lists the model sizes for different preprocessing methods and translation tasks. For the English-to-Chinese task with L-SW-LO preprocessing, the model size is indeed 112.2 MB.", "statement": "The model size for the English-to-Chinese translation task using the L-SW-LO preprocessing method is 112.2 MB.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: In the task of Chinese-English translation, the BPE approach cannot identify subwords in Chinese logograms and consequently hurts the final BLEU score.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "BPE-based models perform worse in Chinese logograms, affecting BLEU scores, while the proposed models fit better for logographic languages.BPE cannot identify subwords in Chinese logograms. Consequently, it hurts the final BLEU score. Our approaches fit better to logographic languages in this direction. In the direction from English to Chinese, we did not observe the proposed models are better than BPE-based models." } ], "images": [ "scigen/train/1610.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: In the task of Chinese-English translation, the BPE approach cannot identify subwords in Chinese logograms and consequently hurts the final BLEU score." ], "reasoning": "BPE-based models perform worse in Chinese logograms, affecting BLEU scores, while the proposed models fit better for logographic languages.", "description": "BPE cannot identify subwords in Chinese logograms. Consequently, it hurts the final BLEU score. Our approaches fit better to logographic languages in this direction. In the direction from English to Chinese, we did not observe the proposed models are better than BPE-based models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: In the task of Chinese-English translation, the BPE approach cannot identify subwords in Chinese logograms and consequently hurts the final BLEU score.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. \n\nQuestion: Which model has the highest BLEU score and what is its NN(Mb) value?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows BLEU scores for different models. The highest BLEU score is 49.23, achieved by the L-SW-DA model, which has an NN(Mb) value of 234.3. So, the answer is {\"answer\": \"234.3\"}." } ], "images": [ "scigen/train/1610.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: In the task of Chinese-English translation, the BPE approach cannot identify subwords in Chinese logograms and consequently hurts the final BLEU score." ], "reasoning": "The table shows BLEU scores for different models. The highest BLEU score is 49.23, achieved by the L-SW-DA model, which has an NN(Mb) value of 234.3.", "question": "Which model has the highest BLEU score and what is its NN(Mb) value?", "answer": "So, the answer is {\"answer\": \"234.3\"}." }, { "messages": [ { "role": "user", "content": "The L-SW-LO model has a lower BLEU score than the SW-BPE model. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe L-SW-LO model has a lower BLEU score than the SW-BPE model.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for the L-SW-LO model is 48.59, which is higher than the BLEU score of 47.66 for the SW-BPE model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1610.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: In the task of Chinese-English translation, the BPE approach cannot identify subwords in Chinese logograms and consequently hurts the final BLEU score." ], "reasoning": "The table shows that the BLEU score for the L-SW-LO model is 48.59, which is higher than the BLEU score of 47.66 for the SW-BPE model.", "statement": "The L-SW-LO model has a lower BLEU score than the SW-BPE model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Single system results in terms of (Ter-Bleu)/2 (T-b, the lower the better) on 5 million Chinese to English training set. Bp denotes the brevity penalty. NMT results are on a large vocabulary (300k) and with UNK replaced. The second column shows different alignments (Zh → En (one direction), GDFA (“grow-diag-final-and”), and MaxEnt [Ittycheriah and Roukos2005]. A, T, and J mean optimize alignment only, translation only, and jointly. Gau. denotes the smoothed transformation.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The Cov. LVNMT system achieves an average (Ter-Bleu)/2 of 14.24, which is about 0.9 points worse than the Tree-to-string SMT system. Joint optimization J works best among other strategies.The syntax-based statistical machine translation model achieves an average (Ter-Bleu)/2 of 13.36 on three test sets. The Cov. LVNMT system achieves an average (Ter-Bleu)/2 of 14.24, which is about 0.9 points worse than Tree-to-string SMT system. Please note that all systems are single systems. It is highly possible that ensemble of NMT systems with different random seeds can lead to better results over SMT. First, with the same alignment, J joint optimization works best than other optimization strategies (lines 3 to 6). Unfortunately, breaking down the network into two separate parts (A and T) and optimizing them separately do not help (lines 3 to 5). We have to conduct joint optimization J in order to get a comparable or better result (lines 3, 5 and 6) over the baseline system." } ], "images": [ "scigen/train/1611.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Single system results in terms of (Ter-Bleu)/2 (T-b, the lower the better) on 5 million Chinese to English training set. Bp denotes the brevity penalty. NMT results are on a large vocabulary (300k) and with UNK replaced. The second column shows different alignments (Zh → En (one direction), GDFA (“grow-diag-final-and”), and MaxEnt [Ittycheriah and Roukos2005]. A, T, and J mean optimize alignment only, translation only, and jointly. Gau. denotes the smoothed transformation." ], "reasoning": "The Cov. LVNMT system achieves an average (Ter-Bleu)/2 of 14.24, which is about 0.9 points worse than the Tree-to-string SMT system. Joint optimization J works best among other strategies.", "description": "The syntax-based statistical machine translation model achieves an average (Ter-Bleu)/2 of 13.36 on three test sets. The Cov. LVNMT system achieves an average (Ter-Bleu)/2 of 14.24, which is about 0.9 points worse than Tree-to-string SMT system. Please note that all systems are single systems. It is highly possible that ensemble of NMT systems with different random seeds can lead to better results over SMT. First, with the same alignment, J joint optimization works best than other optimization strategies (lines 3 to 6). Unfortunately, breaking down the network into two separate parts (A and T) and optimizing them separately do not help (lines 3 to 5). We have to conduct joint optimization J in order to get a comparable or better result (lines 3, 5 and 6) over the baseline system.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the average (Ter-Bleu)/2 score for the system that performs best with joint optimization J?\nSolve the above question based on the table titled 'Table 1: Single system results in terms of (Ter-Bleu)/2 (T-b, the lower the better) on 5 million Chinese to English training set. Bp denotes the brevity penalty. NMT results are on a large vocabulary (300k) and with UNK replaced. The second column shows different alignments (Zh → En (one direction), GDFA (“grow-diag-final-and”), and MaxEnt [Ittycheriah and Roukos2005]. A, T, and J mean optimize alignment only, translation only, and jointly. Gau. denotes the smoothed transformation.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the system with joint optimization J and MaxEnt alignment achieves the best average (Ter-Bleu)/2 score of 13.24. So, the answer is {\"answer\": \"13.24\"}" } ], "images": [ "scigen/train/1611.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Single system results in terms of (Ter-Bleu)/2 (T-b, the lower the better) on 5 million Chinese to English training set. Bp denotes the brevity penalty. NMT results are on a large vocabulary (300k) and with UNK replaced. The second column shows different alignments (Zh → En (one direction), GDFA (“grow-diag-final-and”), and MaxEnt [Ittycheriah and Roukos2005]. A, T, and J mean optimize alignment only, translation only, and jointly. Gau. denotes the smoothed transformation." ], "reasoning": "The table shows that the system with joint optimization J and MaxEnt alignment achieves the best average (Ter-Bleu)/2 score of 13.24.", "question": "What is the average (Ter-Bleu)/2 score for the system that performs best with joint optimization J?", "answer": "So, the answer is {\"answer\": \"13.24\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Cov. LVNMT system with Zh→En alignment and J optimization achieves an average (Ter-Bleu)/2 score of 14.24.\n" }, { "role": "assistant", "content": "The table shows that the Cov. LVNMT system with Zh→En alignment and J optimization achieves an average (Ter-Bleu)/2 score of 13.97, not 14.24. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1611.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Single system results in terms of (Ter-Bleu)/2 (T-b, the lower the better) on 5 million Chinese to English training set. Bp denotes the brevity penalty. NMT results are on a large vocabulary (300k) and with UNK replaced. The second column shows different alignments (Zh → En (one direction), GDFA (“grow-diag-final-and”), and MaxEnt [Ittycheriah and Roukos2005]. A, T, and J mean optimize alignment only, translation only, and jointly. Gau. denotes the smoothed transformation." ], "reasoning": "The table shows that the Cov. LVNMT system with Zh→En alignment and J optimization achieves an average (Ter-Bleu)/2 score of 13.97, not 14.24.", "statement": "The Cov. LVNMT system with Zh→En alignment and J optimization achieves an average (Ter-Bleu)/2 score of 14.24.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Alignment F1 scores of different models.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The MaxEnt model achieves an F1 score of 75.96 by training on 67k hand-aligned sentences, and tuning alignment and translation jointly boosts the alignment F1 score to 50.97.The MaxEnt model is trained on 67k hand-aligned sentences, and achieves an F1 score of 75.96. For NMT systems, we dump the alignment matrixes and convert them into alignments with following steps. For each target word, we sort the alphas and add the max probability link if it is higher than 0.2. If we only tune the alignment component (A in line 3), we improve the alignment F1 score from 45.76 to 47.87. And we further boost the score to 50.97 by tuning alignment and translation jointly (J in line 7). Interestingly, the system using MaxEnt produces more alignments in the output, and results in a higher recall. This suggests that using MaxEnt can lead to a sharper attention distribution, as we pick the alignment links based on the probabilities of attentions, the sharper the distribution is, more links we can pick. We believe that a sharp attention distribution is a great property of NMT." } ], "images": [ "scigen/train/1612.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Alignment F1 scores of different models." ], "reasoning": "The MaxEnt model achieves an F1 score of 75.96 by training on 67k hand-aligned sentences, and tuning alignment and translation jointly boosts the alignment F1 score to 50.97.", "description": "The MaxEnt model is trained on 67k hand-aligned sentences, and achieves an F1 score of 75.96. For NMT systems, we dump the alignment matrixes and convert them into alignments with following steps. For each target word, we sort the alphas and add the max probability link if it is higher than 0.2. If we only tune the alignment component (A in line 3), we improve the alignment F1 score from 45.76 to 47.87. And we further boost the score to 50.97 by tuning alignment and translation jointly (J in line 7). Interestingly, the system using MaxEnt produces more alignments in the output, and results in a higher recall. This suggests that using MaxEnt can lead to a sharper attention distribution, as we pick the alignment links based on the probabilities of attentions, the sharper the distribution is, more links we can pick. We believe that a sharp attention distribution is a great property of NMT.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2: Alignment F1 scores of different models.', unfold the steps to solve the question given next. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nQuestion: What is the F1 score when tuning alignment and translation jointly for the MaxEnt system?\n" }, { "role": "assistant", "content": "The table shows that tuning alignment and translation jointly (J) for the MaxEnt system results in an F1 score of 50.97. So, the answer is {\"answer\": \"50.97\"}" } ], "images": [ "scigen/train/1612.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Alignment F1 scores of different models." ], "reasoning": "The table shows that tuning alignment and translation jointly (J) for the MaxEnt system results in an F1 score of 50.97.", "question": "What is the F1 score when tuning alignment and translation jointly for the MaxEnt system?", "answer": "So, the answer is {\"answer\": \"50.97\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe MaxEnt system achieves an F1 score of 75.96 when tuning alignment and translation jointly.\n" }, { "role": "assistant", "content": "The table shows that the MaxEnt system achieves an F1 score of 75.96 overall, but when tuning alignment and translation jointly, the F1 score is 50.97. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1612.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Alignment F1 scores of different models." ], "reasoning": "The table shows that the MaxEnt system achieves an F1 score of 75.96 overall, but when tuning alignment and translation jointly, the F1 score is 50.97.", "statement": "The MaxEnt system achieves an F1 score of 75.96 when tuning alignment and translation jointly.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: System Performance of different multi-stage systems'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The two-stage system outperforms both the baseline and three-stage systems in overall performance and in tasks longer than 3 seconds, while also showing robustness in tasks shorter than 3 seconds.First of all, we compare the two-stage system and the three-stage system trained with phonetic sequence annotation and dialect category label with the baseline trained only with dialect category label. The two multi-stage system have the same ResNet14 architecture and use 2-layer BLSTM as the RNN part with 256 nodes. We also observe that both two multi-stage systems perform excellently in long duration ( >3s) task and the two-stage system illustrates its advantageous and robustness in short duration (≤3s) task." } ], "images": [ "scigen/train/1613.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: System Performance of different multi-stage systems" ], "reasoning": "The two-stage system outperforms both the baseline and three-stage systems in overall performance and in tasks longer than 3 seconds, while also showing robustness in tasks shorter than 3 seconds.", "description": "First of all, we compare the two-stage system and the three-stage system trained with phonetic sequence annotation and dialect category label with the baseline trained only with dialect category label. The two multi-stage system have the same ResNet14 architecture and use 2-layer BLSTM as the RNN part with 256 nodes. We also observe that both two multi-stage systems perform excellently in long duration ( >3s) task and the two-stage system illustrates its advantageous and robustness in short duration (≤3s) task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: System Performance of different multi-stage systems', solve the subsequent question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich system performs best in tasks longer than 3 seconds?\n" }, { "role": "assistant", "content": "By comparing the performance of the three systems in the '>3s' column, the two-stage system has the highest score of 90.04. So, the answer is {\"answer\": \"Two-stage\"}." } ], "images": [ "scigen/train/1613.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: System Performance of different multi-stage systems" ], "reasoning": "By comparing the performance of the three systems in the '>3s' column, the two-stage system has the highest score of 90.04.", "question": "Which system performs best in tasks longer than 3 seconds?", "answer": "So, the answer is {\"answer\": \"Two-stage\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The baseline system performs better than the three-stage system in tasks shorter than 3 seconds. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe baseline system performs better than the three-stage system in tasks shorter than 3 seconds.\n" }, { "role": "assistant", "content": "By comparing the performance of the baseline and three-stage systems in the '≤3s' column, the baseline system has a score of 77.60, which is lower than the three-stage system's score of 85.52. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1613.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: System Performance of different multi-stage systems" ], "reasoning": "By comparing the performance of the baseline and three-stage systems in the '≤3s' column, the baseline system has a score of 77.60, which is lower than the three-stage system's score of 85.52.", "statement": "The baseline system performs better than the three-stage system in tasks shorter than 3 seconds.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Comparison of two-stage system with different RNN structures', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "BLSTM outperforms BGRU in both sub-tasks with the same layers and nodes, and a shallow ResNet14 is used for sound-related tasks.We further explore the impact of different RNN structures with bidirectional gated recurrent unit (BGRU) and BLSTM. Moreover, with the same layers and nodes, BLSTM outperforms BGRU in the two sub-tasks. We believe that sound related tasks do not need a very deep network as image related tasks, that is also the reason why we use a shallow ResNet14 as the CNN part." } ], "images": [ "scigen/train/1614.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of two-stage system with different RNN structures" ], "reasoning": "BLSTM outperforms BGRU in both sub-tasks with the same layers and nodes, and a shallow ResNet14 is used for sound-related tasks.", "description": "We further explore the impact of different RNN structures with bidirectional gated recurrent unit (BGRU) and BLSTM. Moreover, with the same layers and nodes, BLSTM outperforms BGRU in the two sub-tasks. We believe that sound related tasks do not need a very deep network as image related tasks, that is also the reason why we use a shallow ResNet14 as the CNN part.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: Comparison of two-stage system with different RNN structures', and sequentially solve the question, writing out each step of your reasoning process. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich RNN structure and configuration achieved the highest performance for the >3s sub-task?\n" }, { "role": "assistant", "content": "By examining the table, the highest performance for the >3s sub-task is achieved by BLSTM with 2 layers and 256 nodes, with a score of 90.04. So, the answer is {\"answer\": \"BLSTM with 2 layers and 256 nodes\"}." } ], "images": [ "scigen/train/1614.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of two-stage system with different RNN structures" ], "reasoning": "By examining the table, the highest performance for the >3s sub-task is achieved by BLSTM with 2 layers and 256 nodes, with a score of 90.04.", "question": "Which RNN structure and configuration achieved the highest performance for the >3s sub-task?", "answer": "So, the answer is {\"answer\": \"BLSTM with 2 layers and 256 nodes\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence BLSTM with 2 layers and 256 nodes achieved the highest performance in the >3s sub-task. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nBLSTM with 2 layers and 256 nodes achieved the highest performance in the >3s sub-task.\n" }, { "role": "assistant", "content": "The table shows that BLSTM with 2 layers and 256 nodes has the highest score of 90.04 in the >3s sub-task. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1614.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of two-stage system with different RNN structures" ], "reasoning": "The table shows that BLSTM with 2 layers and 256 nodes has the highest score of 90.04 in the >3s sub-task.", "statement": "BLSTM with 2 layers and 256 nodes achieved the highest performance in the >3s sub-task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 5: Epochs to converge of different multi-stage systems'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The two-stage system completes its process in 35 units of time, while the three-stage system takes 44 units of time.We can observe that the two-stage system spends less time while having a slightly higher accuracy compared to the three-stage system." } ], "images": [ "scigen/train/1615.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Epochs to converge of different multi-stage systems" ], "reasoning": "The two-stage system completes its process in 35 units of time, while the three-stage system takes 44 units of time.", "description": "We can observe that the two-stage system spends less time while having a slightly higher accuracy compared to the three-stage system.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: Epochs to converge of different multi-stage systems', answer the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nHow much more time does the three-stage system take compared to the two-stage system?\n" }, { "role": "assistant", "content": "The two-stage system takes 29 units of time in the first stage and 6 units in the second stage, totaling 35 units. The three-stage system takes 30 units in the first stage, 7 units in the second stage, and 7 units in the third stage, totaling 44 units. The difference is 44 - 35 = 9 units. So, the answer is {\"answer\": \"9\"}." } ], "images": [ "scigen/train/1615.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Epochs to converge of different multi-stage systems" ], "reasoning": "The two-stage system takes 29 units of time in the first stage and 6 units in the second stage, totaling 35 units. The three-stage system takes 30 units in the first stage, 7 units in the second stage, and 7 units in the third stage, totaling 44 units. The difference is 44 - 35 = 9 units.", "question": "How much more time does the three-stage system take compared to the two-stage system?", "answer": "So, the answer is {\"answer\": \"9\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The two-stage system takes 35 units of time to complete. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe two-stage system takes 35 units of time to complete.\n" }, { "role": "assistant", "content": "The two-stage system takes 29 units of time in the first stage and 6 units in the second stage, totaling 35 units. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1615.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Epochs to converge of different multi-stage systems" ], "reasoning": "The two-stage system takes 29 units of time in the first stage and 6 units in the second stage, totaling 35 units.", "statement": "The two-stage system takes 35 units of time to complete.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6. Confusion matrix for interaction type'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Errors often occur when differentiating between PD and PK interactions, with many mistakes involving unspecified precipitants being classified incorrectly.Lastly, we describe a source of difficulty stemming from incorrectly classifying interaction types. As PD and PK interactions tend to frequently co-occur with effect and trigger mentions respectively, predicted annotations tend to be polarized toward one pair (PD with effect) or the other (PK with trigger). Hence, differentiating between types of interactions for each recognized precipitant is another interesting class of error. Clearly, many errors are due to cases where (1) we mistake unspecified precipitants for PD precipitants and (2) we mistake PK precipitants for unspecified precipitants. We conjecture that making precise implicit connections (not only whether there is evidence in the form of trigger words or phrases, but whether the evidence concerns the particular precipitant) is highly nontrivial. Likely, this aspect may be improved by inclusion of more high quality training data. Confusion between trigger and effect mentions is less concerning; among more than 1000 cases, there are six cases where we mistake effect for trigger and 20 cases where we mistake trigger for effect." } ], "images": [ "scigen/train/1616.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6. Confusion matrix for interaction type" ], "reasoning": "Errors often occur when differentiating between PD and PK interactions, with many mistakes involving unspecified precipitants being classified incorrectly.", "description": "Lastly, we describe a source of difficulty stemming from incorrectly classifying interaction types. As PD and PK interactions tend to frequently co-occur with effect and trigger mentions respectively, predicted annotations tend to be polarized toward one pair (PD with effect) or the other (PK with trigger). Hence, differentiating between types of interactions for each recognized precipitant is another interesting class of error. Clearly, many errors are due to cases where (1) we mistake unspecified precipitants for PD precipitants and (2) we mistake PK precipitants for unspecified precipitants. We conjecture that making precise implicit connections (not only whether there is evidence in the form of trigger words or phrases, but whether the evidence concerns the particular precipitant) is highly nontrivial. Likely, this aspect may be improved by inclusion of more high quality training data. Confusion between trigger and effect mentions is less concerning; among more than 1000 cases, there are six cases where we mistake effect for trigger and 20 cases where we mistake trigger for effect.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nHow many times were actual PK interactions incorrectly classified as unspecified (UN)?\nSolve the above question based on the table titled 'Table 6. Confusion matrix for interaction type'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the number of times actual PK interactions were incorrectly classified as unspecified, look at the row for Actual PK and the column for Predicted UN. So, the answer is {\"answer\": \"147\"}." } ], "images": [ "scigen/train/1616.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6. Confusion matrix for interaction type" ], "reasoning": "To find the number of times actual PK interactions were incorrectly classified as unspecified, look at the row for Actual PK and the column for Predicted UN.", "question": "How many times were actual PK interactions incorrectly classified as unspecified (UN)?", "answer": "So, the answer is {\"answer\": \"147\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThere were more errors in classifying actual PD interactions as unspecified (UN) than in classifying actual PK interactions as unspecified (UN).\n" }, { "role": "assistant", "content": "Compare the number of errors in classifying actual PD interactions as unspecified (68) with the number of errors in classifying actual PK interactions as unspecified (147). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1616.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6. Confusion matrix for interaction type" ], "reasoning": "Compare the number of errors in classifying actual PD interactions as unspecified (68) with the number of errors in classifying actual PK interactions as unspecified (147).", "statement": "There were more errors in classifying actual PD interactions as unspecified (UN) than in classifying actual PK interactions as unspecified (UN).", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 1. Characteristics of various datasets'.\n" }, { "role": "assistant", "content": "The TAC 2018 dataset for drug-drug interaction extraction includes TR22 for training and two test sets, Test Set 1 and Test Set 2, with varying characteristics in terms of sentence annotations and interaction types.We train and evaluate our model on the Text Analysis Conference (TAC) 2018 dataset for drug-drug interaction extraction from drug labels The training data contains 22 drug labels, referred to as TR22, with gold standard annotations. ; we refer to these as NLM180 and DDI2013 respectively. Two official test sets of 57 and 66 drug labels, referred to as Test Set 1 and 2 respectively, with gold standard annotations are used strictly for evaluation. Furthermore, we show that our GCA based model complements our prior BiLSTM model; that is, by combining the two via ensembling, we improve over the prior best by 6 absolute F1 points in overall performance. Among comparable methods, our GCA based method exhibits state-of-the-art performance on all metrics after controlling for available training data. Translating NLM180 and DD2013 to the TAC 2018 format is an imperfect process given structural (breadth and depth of annotations) and semantic (guidelines in addition to annotator experience and vision) differences. For example, differences in how entity boundaries are annotated, such as whether or not modifier terms should be kept as part of a named entity, may have a large impact on model performance. Hence, we expect the translated versions of NLM180 and DDI2013 to be very noisy as training examples for the target task. Next, we discuss the differences between these test sets. However, Test Set 1 is more sparse in the sense that there are more sentences per drug label (144 vs. 27), with a smaller proportion of those sentences having gold annotations (23% vs. 51%). Test Set 2 is unique in that it contains annotations from only two sections, namely DRUG INTERACTIONS and CLINICAL PHARMACOLOGY, the latter of which is not represented in TR22 (nor Test Set 1). Lastly, TR22, Test Set 1, and Test Set 2 all vary with respect to the distribution of interaction types, with TR22, Test Set 1, and Test Set 2 containing a higher proportion of PD, UN, and PK interactions respectively. Overall model performance is assessed using a single metric defined as the average of entity recognition and relation extraction performance across both test sets." } ], "images": [ "scigen/train/1617.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1. Characteristics of various datasets" ], "reasoning": "The TAC 2018 dataset for drug-drug interaction extraction includes TR22 for training and two test sets, Test Set 1 and Test Set 2, with varying characteristics in terms of sentence annotations and interaction types.", "description": "We train and evaluate our model on the Text Analysis Conference (TAC) 2018 dataset for drug-drug interaction extraction from drug labels The training data contains 22 drug labels, referred to as TR22, with gold standard annotations. ; we refer to these as NLM180 and DDI2013 respectively. Two official test sets of 57 and 66 drug labels, referred to as Test Set 1 and 2 respectively, with gold standard annotations are used strictly for evaluation. Furthermore, we show that our GCA based model complements our prior BiLSTM model; that is, by combining the two via ensembling, we improve over the prior best by 6 absolute F1 points in overall performance. Among comparable methods, our GCA based method exhibits state-of-the-art performance on all metrics after controlling for available training data. Translating NLM180 and DD2013 to the TAC 2018 format is an imperfect process given structural (breadth and depth of annotations) and semantic (guidelines in addition to annotator experience and vision) differences. For example, differences in how entity boundaries are annotated, such as whether or not modifier terms should be kept as part of a named entity, may have a large impact on model performance. Hence, we expect the translated versions of NLM180 and DDI2013 to be very noisy as training examples for the target task. Next, we discuss the differences between these test sets. However, Test Set 1 is more sparse in the sense that there are more sentences per drug label (144 vs. 27), with a smaller proportion of those sentences having gold annotations (23% vs. 51%). Test Set 2 is unique in that it contains annotations from only two sections, namely DRUG INTERACTIONS and CLINICAL PHARMACOLOGY, the latter of which is not represented in TR22 (nor Test Set 1). Lastly, TR22, Test Set 1, and Test Set 2 all vary with respect to the distribution of interaction types, with TR22, Test Set 1, and Test Set 2 containing a higher proportion of PD, UN, and PK interactions respectively. Overall model performance is assessed using a single metric defined as the average of entity recognition and relation extraction performance across both test sets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the average number of words per sentence in the training data TR22?\nSolve the above question based on the table titled 'Table 1. Characteristics of various datasets'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the average number of words per sentence for each dataset. For TR22, it is listed as 24. So, the answer is {\"answer\": \"24\"}" } ], "images": [ "scigen/train/1617.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1. Characteristics of various datasets" ], "reasoning": "The table shows the average number of words per sentence for each dataset. For TR22, it is listed as 24.", "question": "What is the average number of words per sentence in the training data TR22?", "answer": "So, the answer is {\"answer\": \"24\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nTest Set 1 has a higher proportion of sentences with annotations compared to Test Set 2.\n" }, { "role": "assistant", "content": "The table shows that Test Set 1 has 23% of sentences with annotations, while Test Set 2 also has 23%. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1617.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1. Characteristics of various datasets" ], "reasoning": "The table shows that Test Set 1 has 23% of sentences with annotations, while Test Set 2 also has 23%. Therefore, the statement is refuted.", "statement": "Test Set 1 has a higher proportion of sentences with annotations compared to Test Set 2.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Ablation study of the BERT-based model on truncated and original CNN/Daily Mail dataset', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "BERT improves model efficiency when used in both encoder and decoder, and is more effective in the encoder when producing embeddings for the Transformer encoder.All hyperparameters were set equal to the ones in experiments in convolutional self-attention. On CNN/Daily Main dataset we test three different models: BERT encoder+Transformer Decoder, BERT-Transformer encoder+Transformer decoder and BERT-Transformer encoder+BERT-Transformer decoder. The version of BERT used in the experiments is BERT-Base. As the baseline, we use the Transformer without Pointer Generator. From the results, we observe that BERT improves the efficiency of the model when it is used in both encoder and decoder. Besides, BERT in the encoder is more effective when it is used to produce embeddings to be used by the standard Transformer encoder than when it is used solely as an encoder. Even without a Pointer Generator, our model outperformed the CopyTransformer baseline by 1.28, 0.5 and 1.24 on ROUGE-1, ROUGE-2 and ROUGE-L." } ], "images": [ "scigen/train/1618.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ablation study of the BERT-based model on truncated and original CNN/Daily Mail dataset" ], "reasoning": "BERT improves model efficiency when used in both encoder and decoder, and is more effective in the encoder when producing embeddings for the Transformer encoder.", "description": "All hyperparameters were set equal to the ones in experiments in convolutional self-attention. On CNN/Daily Main dataset we test three different models: BERT encoder+Transformer Decoder, BERT-Transformer encoder+Transformer decoder and BERT-Transformer encoder+BERT-Transformer decoder. The version of BERT used in the experiments is BERT-Base. As the baseline, we use the Transformer without Pointer Generator. From the results, we observe that BERT improves the efficiency of the model when it is used in both encoder and decoder. Besides, BERT in the encoder is more effective when it is used to produce embeddings to be used by the standard Transformer encoder than when it is used solely as an encoder. Even without a Pointer Generator, our model outperformed the CopyTransformer baseline by 1.28, 0.5 and 1.24 on ROUGE-1, ROUGE-2 and ROUGE-L.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 2: Ablation study of the BERT-based model on truncated and original CNN/Daily Mail dataset'. Solve the following question based on the table.\n\nQuestion: Which model achieved the highest ROUGE-1 score?\nLet's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest ROUGE-1 score, compare the ROUGE-1 scores of all models listed in the table. The model with the highest score is the one with the highest numerical value in the ROUGE-1 column. So, the answer is {\"answer\": \"Bert-transformer Encoder + Bert-transformer Decoder\"}." } ], "images": [ "scigen/train/1618.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ablation study of the BERT-based model on truncated and original CNN/Daily Mail dataset" ], "reasoning": "To find the model with the highest ROUGE-1 score, compare the ROUGE-1 scores of all models listed in the table. The model with the highest score is the one with the highest numerical value in the ROUGE-1 column.", "question": "Which model achieved the highest ROUGE-1 score?", "answer": "So, the answer is {\"answer\": \"Bert-transformer Encoder + Bert-transformer Decoder\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe BERT-Transformer encoder + Transformer decoder model outperformed the CopyTransformer baseline in ROUGE-2.\n" }, { "role": "assistant", "content": "Compare the ROUGE-2 scores of the BERT-Transformer encoder + Transformer decoder model and the CopyTransformer baseline. The BERT-Transformer encoder + Transformer decoder model has a ROUGE-2 score of 14.68, while the CopyTransformer baseline has a ROUGE-2 score of 14.49. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1618.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ablation study of the BERT-based model on truncated and original CNN/Daily Mail dataset" ], "reasoning": "Compare the ROUGE-2 scores of the BERT-Transformer encoder + Transformer decoder model and the CopyTransformer baseline. The BERT-Transformer encoder + Transformer decoder model has a ROUGE-2 score of 14.68, while the CopyTransformer baseline has a ROUGE-2 score of 14.49.", "statement": "The BERT-Transformer encoder + Transformer decoder model outperformed the CopyTransformer baseline in ROUGE-2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Ablation study of model with Convolutional Self-Attention on the CNN/Daily Mail dataset (kernel sizes are 11 and 3)'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Both 1D and 2D convolutions improve ROUGE scores, with standard convolution outperforming circular convolution on ROUGE-1, ROUGE-2, and ROUGE-L by 0.06, 0.13, and 0.09 percent, respectively.We see that both convolutions over tokens and over attention heads improve the ROUGE scores. Standard convolution outperformed circular convolution on ROUGE-1, ROUGE-2 and ROUGE-L by 0.06, 0.13 and 0.09 percent, respectively." } ], "images": [ "scigen/train/1619.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Ablation study of model with Convolutional Self-Attention on the CNN/Daily Mail dataset (kernel sizes are 11 and 3)" ], "reasoning": "Both 1D and 2D convolutions improve ROUGE scores, with standard convolution outperforming circular convolution on ROUGE-1, ROUGE-2, and ROUGE-L by 0.06, 0.13, and 0.09 percent, respectively.", "description": "We see that both convolutions over tokens and over attention heads improve the ROUGE scores. Standard convolution outperformed circular convolution on ROUGE-1, ROUGE-2 and ROUGE-L by 0.06, 0.13 and 0.09 percent, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 1: Ablation study of model with Convolutional Self-Attention on the CNN/Daily Mail dataset (kernel sizes are 11 and 3)\nQuestion: Which method achieved the highest ROUGE-2 score?\n" }, { "role": "assistant", "content": "By comparing the ROUGE-2 scores of all methods, the 2D convolution method achieved the highest score of 15.12. So, the answer is {\"answer\": \"2D convolution\"}" } ], "images": [ "scigen/train/1619.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Ablation study of model with Convolutional Self-Attention on the CNN/Daily Mail dataset (kernel sizes are 11 and 3)" ], "reasoning": "By comparing the ROUGE-2 scores of all methods, the 2D convolution method achieved the highest score of 15.12.", "question": "Which method achieved the highest ROUGE-2 score?", "answer": "So, the answer is {\"answer\": \"2D convolution\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe CopyTransformer method achieved the highest ROUGE-1 score.\n" }, { "role": "assistant", "content": "The table shows that the CopyTransformer method has a ROUGE-1 score of 31.95, which is lower than the scores achieved by the other methods. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1619.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Ablation study of model with Convolutional Self-Attention on the CNN/Daily Mail dataset (kernel sizes are 11 and 3)" ], "reasoning": "The table shows that the CopyTransformer method has a ROUGE-1 score of 31.95, which is lower than the scores achieved by the other methods.", "statement": "The CopyTransformer method achieved the highest ROUGE-1 score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Ablation study of the BERT-based model on the truncated and original SwissText dataset'.\n" }, { "role": "assistant", "content": "The introduction of BERT into the transformer increased ROUGE scores significantly, but its use in the decoder decreased the overall score due to less precise contextualized representations.On SwissText data we use the multilingual version of BERT-Base. The introduction of BERT into the transformer increased the ROUGE-1, ROUGE-2 and ROUGE-L scores by 7.21, 8.91 and 7.51 percent, respectively. At the same time, the usage of BERT in the decoder decreased the overall score. We assume that the reason behind this is that in multilingual BERT, due to its language-independence, the embedding matrix outputs less precise contextualized representations which undermines their benefits for the summarization task." } ], "images": [ "scigen/train/1620.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation study of the BERT-based model on the truncated and original SwissText dataset" ], "reasoning": "The introduction of BERT into the transformer increased ROUGE scores significantly, but its use in the decoder decreased the overall score due to less precise contextualized representations.", "description": "On SwissText data we use the multilingual version of BERT-Base. The introduction of BERT into the transformer increased the ROUGE-1, ROUGE-2 and ROUGE-L scores by 7.21, 8.91 and 7.51 percent, respectively. At the same time, the usage of BERT in the decoder decreased the overall score. We assume that the reason behind this is that in multilingual BERT, due to its language-independence, the embedding matrix outputs less precise contextualized representations which undermines their benefits for the summarization task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Ablation study of the BERT-based model on the truncated and original SwissText dataset\nQuestion: Which model achieved the highest ROUGE-2 score?\nSolve the above question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By examining the table, the model 'Bert-transformer Encoder + Transformer Decoder (full text)' achieved the highest ROUGE-2 score of 30.49. So, the answer is {\"answer\": \"Bert-transformer Encoder + Transformer Decoder (full text)\"}." } ], "images": [ "scigen/train/1620.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation study of the BERT-based model on the truncated and original SwissText dataset" ], "reasoning": "By examining the table, the model 'Bert-transformer Encoder + Transformer Decoder (full text)' achieved the highest ROUGE-2 score of 30.49.", "question": "Which model achieved the highest ROUGE-2 score?", "answer": "So, the answer is {\"answer\": \"Bert-transformer Encoder + Transformer Decoder (full text)\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The CopyTransformer model achieved a higher ROUGE-1 score than the Transformer model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe CopyTransformer model achieved a higher ROUGE-1 score than the Transformer model.\n" }, { "role": "assistant", "content": "The table shows that the CopyTransformer model has a ROUGE-1 score of 39.44, which is higher than the Transformer model's ROUGE-1 score of 36.40. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1620.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation study of the BERT-based model on the truncated and original SwissText dataset" ], "reasoning": "The table shows that the CopyTransformer model has a ROUGE-1 score of 39.44, which is higher than the Transformer model's ROUGE-1 score of 36.40.", "statement": "The CopyTransformer model achieved a higher ROUGE-1 score than the Transformer model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 6: ROUGE scores for our models on the SwissText test set'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The BERT-based model achieved the highest scores in the SwissData dataset, outperforming the baseline by significant margins in ROUGE-1, ROUGE-2, and ROUGE-L.Furthermore, we present the first publicly available benchmark for the SwissData dataset All parameters are equal to the CNN/Daily Mail baseline. BERT-conditioning was used only in the encoder. The networks were trained on the truncated texts in 90,000 training steps. From the results we see that the convolutional CopyTransformer showed much more efficiency than on CNN/Daily Mail dataset, outperforming the baseline by 1.04 percent on ROUGE-1, 1.26 on ROUGE-2 and 1.09 on ROUGE-L. The BERT-based model achieved the highest scores." } ], "images": [ "scigen/train/1621.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: ROUGE scores for our models on the SwissText test set" ], "reasoning": "The BERT-based model achieved the highest scores in the SwissData dataset, outperforming the baseline by significant margins in ROUGE-1, ROUGE-2, and ROUGE-L.", "description": "Furthermore, we present the first publicly available benchmark for the SwissData dataset All parameters are equal to the CNN/Daily Mail baseline. BERT-conditioning was used only in the encoder. The networks were trained on the truncated texts in 90,000 training steps. From the results we see that the convolutional CopyTransformer showed much more efficiency than on CNN/Daily Mail dataset, outperforming the baseline by 1.04 percent on ROUGE-1, 1.26 on ROUGE-2 and 1.09 on ROUGE-L. The BERT-based model achieved the highest scores.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nTable title: Table 6: ROUGE scores for our models on the SwissText test set\n\nQuestion: Which method achieved the highest score in ROUGE-2?\n" }, { "role": "assistant", "content": "The table shows the ROUGE-2 scores for different methods. The BERT+CopyTransformer (enc.) method has the highest ROUGE-2 score of 25.25. So, the answer is {\"answer\": \"BERT+CopyTransformer (enc.)\"}." } ], "images": [ "scigen/train/1621.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: ROUGE scores for our models on the SwissText test set" ], "reasoning": "The table shows the ROUGE-2 scores for different methods. The BERT+CopyTransformer (enc.) method has the highest ROUGE-2 score of 25.25.", "question": "Which method achieved the highest score in ROUGE-2?", "answer": "So, the answer is {\"answer\": \"BERT+CopyTransformer (enc.)\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Convolutional CopyTransformer outperformed the CopyTransformer in all ROUGE metrics.\n" }, { "role": "assistant", "content": "The table shows that the Convolutional CopyTransformer has higher scores than the CopyTransformer in ROUGE-1 (40.54 vs. 39.5), ROUGE-2 (23.62 vs. 22.36), and ROUGE-L (38.06 vs. 36.97). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1621.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: ROUGE scores for our models on the SwissText test set" ], "reasoning": "The table shows that the Convolutional CopyTransformer has higher scores than the CopyTransformer in ROUGE-1 (40.54 vs. 39.5), ROUGE-2 (23.62 vs. 22.36), and ROUGE-L (38.06 vs. 36.97).", "statement": "The Convolutional CopyTransformer outperformed the CopyTransformer in all ROUGE metrics.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: WER (%) of nine recognizers on the evaluation data. The Baseline (B) uses the default 1-best result obtained from the MAP decision rule, while the Proposed (P) uses the word sequence with the maximum mean word confidence.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The WER decrease is marginal but consistent across all 9 recognizers, indicating statistical significance.The WER decrease is marginal in some cases, but there is a decrease in all 9 recognizers, implying that the effect is statistically significant. Combination of n recognizers is done by obtaining n results (via best mean confidence search on the HWCN of every recognizer) and choosing the result with the highest mean word confidence." } ], "images": [ "scigen/train/1622.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: WER (%) of nine recognizers on the evaluation data. The Baseline (B) uses the default 1-best result obtained from the MAP decision rule, while the Proposed (P) uses the word sequence with the maximum mean word confidence." ], "reasoning": "The WER decrease is marginal but consistent across all 9 recognizers, indicating statistical significance.", "description": "The WER decrease is marginal in some cases, but there is a decrease in all 9 recognizers, implying that the effect is statistically significant. Combination of n recognizers is done by obtaining n results (via best mean confidence search on the HWCN of every recognizer) and choosing the result with the highest mean word confidence.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: WER (%) of nine recognizers on the evaluation data. The Baseline (B) uses the default 1-best result obtained from the MAP decision rule, while the Proposed (P) uses the word sequence with the maximum mean word confidence.', solve this question and give detail solutions. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhat is the average WER decrease across all 9 recognizers?\n" }, { "role": "assistant", "content": "To find the average WER decrease, subtract each P value from the corresponding B value, sum the differences, and divide by 9. So, the answer is {\"answer\": \"0.12\"}." } ], "images": [ "scigen/train/1622.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: WER (%) of nine recognizers on the evaluation data. The Baseline (B) uses the default 1-best result obtained from the MAP decision rule, while the Proposed (P) uses the word sequence with the maximum mean word confidence." ], "reasoning": "To find the average WER decrease, subtract each P value from the corresponding B value, sum the differences, and divide by 9.", "question": "What is the average WER decrease across all 9 recognizers?", "answer": "So, the answer is {\"answer\": \"0.12\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe WER decrease for recognizer 5 is greater than for recognizer 1.\n" }, { "role": "assistant", "content": "The WER decrease for recognizer 5 is 4.88 - 4.76 = 0.12, and for recognizer 1 is 12.26 - 12.00 = 0.26. Since 0.12 is less than 0.26, the statement is false. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1622.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: WER (%) of nine recognizers on the evaluation data. The Baseline (B) uses the default 1-best result obtained from the MAP decision rule, while the Proposed (P) uses the word sequence with the maximum mean word confidence." ], "reasoning": "The WER decrease for recognizer 5 is 4.88 - 4.76 = 0.12, and for recognizer 1 is 12.26 - 12.00 = 0.26. Since 0.12 is less than 0.26, the statement is false.", "statement": "The WER decrease for recognizer 5 is greater than for recognizer 1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Case-sensitive BLEU scores (%) on En→De task. The “‡” indicates the results are significantly better (p<0.01) than RNNsearch and Transformer.'.\n" }, { "role": "assistant", "content": "Our method improves RNNSearch and Transformer models by +1.59 and +1.31 BLEU points respectively on the WMT'14 En→De translation task.We also evaluate our approach on the WMT’14 benchmarks on the En→De translation task. Similar with results on the Zh→En task, both scheduled sampling and MIXER could improve the two baseline systems. Our method improves the RNNSearch and Transformer baseline models by +1.59 and +1.31 BLEU points respectively. These results demonstrate that our model works well across different language pairs." } ], "images": [ "scigen/train/1623.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Case-sensitive BLEU scores (%) on En→De task. The “‡” indicates the results are significantly better (p<0.01) than RNNsearch and Transformer." ], "reasoning": "Our method improves RNNSearch and Transformer models by +1.59 and +1.31 BLEU points respectively on the WMT'14 En→De translation task.", "description": "We also evaluate our approach on the WMT’14 benchmarks on the En→De translation task. Similar with results on the Zh→En task, both scheduled sampling and MIXER could improve the two baseline systems. Our method improves the RNNSearch and Transformer baseline models by +1.59 and +1.31 BLEU points respectively. These results demonstrate that our model works well across different language pairs.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 3: Case-sensitive BLEU scores (%) on En→De task. The “‡” indicates the results are significantly better (p<0.01) than RNNsearch and Transformer.', solve this question and give detail solutions. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhat is the BLEU score improvement of the RNNSearch model when using the MIXER method?\n" }, { "role": "assistant", "content": "The RNNSearch model has a baseline BLEU score of 25.82. When using the MIXER method, the BLEU score is 26.76. The improvement is calculated as 26.76 - 25.82. So, the answer is {\"answer\": \"0.94\"}" } ], "images": [ "scigen/train/1623.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Case-sensitive BLEU scores (%) on En→De task. The “‡” indicates the results are significantly better (p<0.01) than RNNsearch and Transformer." ], "reasoning": "The RNNSearch model has a baseline BLEU score of 25.82. When using the MIXER method, the BLEU score is 26.76. The improvement is calculated as 26.76 - 25.82.", "question": "What is the BLEU score improvement of the RNNSearch model when using the MIXER method?", "answer": "So, the answer is {\"answer\": \"0.94\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The OR-NMT method achieves the highest BLEU score for both RNNSearch and Transformer models. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe OR-NMT method achieves the highest BLEU score for both RNNSearch and Transformer models.\n" }, { "role": "assistant", "content": "The table shows that the OR-NMT method achieves a BLEU score of 27.41 for RNNSearch and 28.65 for Transformer, which are the highest scores for each model. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1623.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Case-sensitive BLEU scores (%) on En→De task. The “‡” indicates the results are significantly better (p<0.01) than RNNsearch and Transformer." ], "reasoning": "The table shows that the OR-NMT method achieves a BLEU score of 27.41 for RNNSearch and 28.65 for Transformer, which are the highest scores for each model.", "statement": "The OR-NMT method achieves the highest BLEU score for both RNNSearch and Transformer models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: Case-insensitive BLEU scores (%) on Zh→En translation task. “‡”, “†”, “⋆” and “∗” indicate statistically significant difference (p<0.01) from RNNsearch, SS-NMT, MIXER and Transformer, respectively.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The OR-NMT approach outperforms the baseline RNNsearch and other competitive systems by incorporating sentence-level oracle and noise perturbations, achieving an average improvement of +2.36 BLEU points on four test datasets.Shen et al. Zhang et al. model distortion to enhance the attention model. Compared with them, our baseline system RNNsearch 1) outperforms previous shallow RNN-based NMT system equipped with the coverage model Tu et al. We hope that the strong shallow baseline system used in this work makes the evaluation convincing. We also compare with the other two related methods that aim at solving the exposure bias problem, including the scheduled sampling Bengio et al. (SS-NMT) and the sentence-level training Ranzato et al. (MIXER). While our approach OR-NMT can outperform the baseline system RNNsearch and the competitive comparison systems by directly incorporate the sentence-level oracle and noise perturbations for relieving the overcorrection problem. Particularly, our OR-NMT significantly outperforms the RNNsearch by +2.36 BLEU points averagely on four test datasets. Comparing with the two related models, our approach further gives a significant improvements on most test sets and achieves improvement by about +1.2 BLEU points on average." } ], "images": [ "scigen/train/1624.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Case-insensitive BLEU scores (%) on Zh→En translation task. “‡”, “†”, “⋆” and “∗” indicate statistically significant difference (p<0.01) from RNNsearch, SS-NMT, MIXER and Transformer, respectively." ], "reasoning": "The OR-NMT approach outperforms the baseline RNNsearch and other competitive systems by incorporating sentence-level oracle and noise perturbations, achieving an average improvement of +2.36 BLEU points on four test datasets.", "description": "Shen et al. Zhang et al. model distortion to enhance the attention model. Compared with them, our baseline system RNNsearch 1) outperforms previous shallow RNN-based NMT system equipped with the coverage model Tu et al. We hope that the strong shallow baseline system used in this work makes the evaluation convincing. We also compare with the other two related methods that aim at solving the exposure bias problem, including the scheduled sampling Bengio et al. (SS-NMT) and the sentence-level training Ranzato et al. (MIXER). While our approach OR-NMT can outperform the baseline system RNNsearch and the competitive comparison systems by directly incorporate the sentence-level oracle and noise perturbations for relieving the overcorrection problem. Particularly, our OR-NMT significantly outperforms the RNNsearch by +2.36 BLEU points averagely on four test datasets. Comparing with the two related models, our approach further gives a significant improvements on most test sets and achieves improvement by about +1.2 BLEU points on average.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 1: Case-insensitive BLEU scores (%) on Zh→En translation task. “‡”, “†”, “⋆” and “∗” indicate statistically significant difference (p<0.01) from RNNsearch, SS-NMT, MIXER and Transformer, respectively.', answer the following question. Give detailed solution consisting of each step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the average BLEU score improvement of OR-NMT over RNNsearch?\n" }, { "role": "assistant", "content": "The table shows that the average BLEU score for RNNsearch is 37.73, and for OR-NMT it is 40.09. The improvement is calculated as 40.09 - 37.73. So, the answer is {\"answer\": \"2.36\"}" } ], "images": [ "scigen/train/1624.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Case-insensitive BLEU scores (%) on Zh→En translation task. “‡”, “†”, “⋆” and “∗” indicate statistically significant difference (p<0.01) from RNNsearch, SS-NMT, MIXER and Transformer, respectively." ], "reasoning": "The table shows that the average BLEU score for RNNsearch is 37.73, and for OR-NMT it is 40.09. The improvement is calculated as 40.09 - 37.73.", "question": "What is the average BLEU score improvement of OR-NMT over RNNsearch?", "answer": "So, the answer is {\"answer\": \"2.36\"}" }, { "messages": [ { "role": "user", "content": "The OR-NMT approach achieves an average BLEU score of 40.09. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe OR-NMT approach achieves an average BLEU score of 40.09.\n" }, { "role": "assistant", "content": "The table shows that the average BLEU score for OR-NMT is 40.09, which supports the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1624.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Case-insensitive BLEU scores (%) on Zh→En translation task. “‡”, “†”, “⋆” and “∗” indicate statistically significant difference (p<0.01) from RNNsearch, SS-NMT, MIXER and Transformer, respectively." ], "reasoning": "The table shows that the average BLEU score for OR-NMT is 40.09, which supports the statement.", "statement": "The OR-NMT approach achieves an average BLEU score of 40.09.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Factor analysis on Zh→En translation, the results are average BLEU scores on MT03∼06 datasets.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Incorporating word-level oracle, sentence-level oracle, and Gumbel noise improves the performance of RNNsearch in relieving the overcorrection problem.We propose several strategies to improve the performance of approach on relieving the overcorrection problem, including utilizing the word-level oracle, the sentence-level oracle, and incorporating the Gumbel noise for oracle selection." } ], "images": [ "scigen/train/1625.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Factor analysis on Zh→En translation, the results are average BLEU scores on MT03∼06 datasets." ], "reasoning": "Incorporating word-level oracle, sentence-level oracle, and Gumbel noise improves the performance of RNNsearch in relieving the overcorrection problem.", "description": "We propose several strategies to improve the performance of approach on relieving the overcorrection problem, including utilizing the word-level oracle, the sentence-level oracle, and incorporating the Gumbel noise for oracle selection.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 2: Factor analysis on Zh→En translation, the results are average BLEU scores on MT03∼06 datasets.\nQuestion: What is the average score when both sentence-level oracle and noise are incorporated?\n" }, { "role": "assistant", "content": "The table shows the average scores for different strategies. The average score when both sentence-level oracle and noise are incorporated is 40.09. So, the answer is {\"answer\": \"40.09\"}." } ], "images": [ "scigen/train/1625.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Factor analysis on Zh→En translation, the results are average BLEU scores on MT03∼06 datasets." ], "reasoning": "The table shows the average scores for different strategies. The average score when both sentence-level oracle and noise are incorporated is 40.09.", "question": "What is the average score when both sentence-level oracle and noise are incorporated?", "answer": "So, the answer is {\"answer\": \"40.09\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nIncorporating noise alone results in a higher average score than incorporating word-level oracle alone.\n" }, { "role": "assistant", "content": "The table shows that incorporating noise alone results in an average score of 39.50, while incorporating word-level oracle alone results in an average score of 38.94. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1625.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Factor analysis on Zh→En translation, the results are average BLEU scores on MT03∼06 datasets." ], "reasoning": "The table shows that incorporating noise alone results in an average score of 39.50, while incorporating word-level oracle alone results in an average score of 38.94.", "statement": "Incorporating noise alone results in a higher average score than incorporating word-level oracle alone.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: Training, development and test data for PTB, CTB and PDT. #sentences and #words refer to the number of sentences and the number of words excluding punctuation in each data set, respectively.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Parsing accuracy is measured using UAS, RA, and CM for each set of data regardless of sentence length.All experiments were running using every single sentence in each set of data regardless of length. Parsing accuracy is measured with unlabeled attachment score (UAS): the percentage of words with the correct head, root accuracy (RA): the percentage of correctly identified root words, and the percentage of complete matches (CM)." } ], "images": [ "scigen/train/1626.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Training, development and test data for PTB, CTB and PDT. #sentences and #words refer to the number of sentences and the number of words excluding punctuation in each data set, respectively." ], "reasoning": "Parsing accuracy is measured using UAS, RA, and CM for each set of data regardless of sentence length.", "description": "All experiments were running using every single sentence in each set of data regardless of length. Parsing accuracy is measured with unlabeled attachment score (UAS): the percentage of words with the correct head, root accuracy (RA): the percentage of correctly identified root words, and the percentage of complete matches (CM).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Training, development and test data for PTB, CTB and PDT. #sentences and #words refer to the number of sentences and the number of words excluding punctuation in each data set, respectively.\nQuestion: Which dataset has the highest number of words in the training set?\nSolve the above question based on the table information. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the dataset with the highest number of words in the training set, compare the '# words' column for the 'Training' rows of each dataset. PTB has 370,777 words, CTB has no data, and PDT has 1,255,590 words. So, the answer is {\"answer\": \"PDT\"}" } ], "images": [ "scigen/train/1626.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Training, development and test data for PTB, CTB and PDT. #sentences and #words refer to the number of sentences and the number of words excluding punctuation in each data set, respectively." ], "reasoning": "To find the dataset with the highest number of words in the training set, compare the '# words' column for the 'Training' rows of each dataset. PTB has 370,777 words, CTB has no data, and PDT has 1,255,590 words.", "question": "Which dataset has the highest number of words in the training set?", "answer": "So, the answer is {\"answer\": \"PDT\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The CTB Dev set has fewer sentences than the PTB Dev set. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe CTB Dev set has fewer sentences than the PTB Dev set.\n" }, { "role": "assistant", "content": "The CTB Dev set has 804 sentences, while the PTB Dev set has 1,700 sentences. Since 804 is less than 1,700, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1626.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Training, development and test data for PTB, CTB and PDT. #sentences and #words refer to the number of sentences and the number of words excluding punctuation in each data set, respectively." ], "reasoning": "The CTB Dev set has 804 sentences, while the PTB Dev set has 1,700 sentences. Since 804 is less than 1,700, the statement is supported.", "statement": "The CTB Dev set has fewer sentences than the PTB Dev set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: Accuracy comparisons of different dependency parsers on PTB, CTB and PDT.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Our experimental results show an improvement in performance for English and Chinese over previous works, and for English and Czech, our results are better than two third-order graph-based dependency parsers.Our experimental results show an improvement in performance of English and Chinese over the results in Zhang and Clark \\shortcitezhang:2008, which combining graph-based and transition-based dependency parsing into a single parser using the framework of beam-search, and Zhang and Nivre \\shortcitezhang:ACL11, which are based on a transition-based dependency parser with rich non-local features. For English and Czech, our results are better than the results of the two third-order graph-based dependency parsers in Koo and Collins \\shortciteKoo:2010. The models marked * cannot be compared with our work directly, as they exploit large amount of additional information that is not used in our models, whiling our parses obtain results competitive with these works. For example, Koo et al. \\shortciteKoo-dp:2008 and Suzuki et al. \\shortciteSuzuki:2009 make use of unlabeled data, and the parsing model of Zhang and Clark \\shortcitezhang:2009 utilizes phrase structure annotations." } ], "images": [ "scigen/train/1627.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Accuracy comparisons of different dependency parsers on PTB, CTB and PDT." ], "reasoning": "Our experimental results show an improvement in performance for English and Chinese over previous works, and for English and Czech, our results are better than two third-order graph-based dependency parsers.", "description": "Our experimental results show an improvement in performance of English and Chinese over the results in Zhang and Clark \\shortcitezhang:2008, which combining graph-based and transition-based dependency parsing into a single parser using the framework of beam-search, and Zhang and Nivre \\shortcitezhang:ACL11, which are based on a transition-based dependency parser with rich non-local features. For English and Czech, our results are better than the results of the two third-order graph-based dependency parsers in Koo and Collins \\shortciteKoo:2010. The models marked * cannot be compared with our work directly, as they exploit large amount of additional information that is not used in our models, whiling our parses obtain results competitive with these works. For example, Koo et al. \\shortciteKoo-dp:2008 and Suzuki et al. \\shortciteSuzuki:2009 make use of unlabeled data, and the parsing model of Zhang and Clark \\shortcitezhang:2009 utilizes phrase structure annotations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nTable title: Table 5: Accuracy comparisons of different dependency parsers on PTB, CTB and PDT.\n\nQuestion: What is the English UAS score reported in this paper, and how does it compare to the score in Zhang and Nivre (2011)?\n" }, { "role": "assistant", "content": "The English UAS score reported in this paper is 93.0, which is higher than the score in Zhang and Nivre (2011), which is 92.9. So, the answer is {\"answer\": \"93.0\"}." } ], "images": [ "scigen/train/1627.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Accuracy comparisons of different dependency parsers on PTB, CTB and PDT." ], "reasoning": "The English UAS score reported in this paper is 93.0, which is higher than the score in Zhang and Nivre (2011), which is 92.9.", "question": "What is the English UAS score reported in this paper, and how does it compare to the score in Zhang and Nivre (2011)?", "answer": "So, the answer is {\"answer\": \"93.0\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The English UAS score in this paper is higher than in Koo and Collins (2010, model 2)., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe English UAS score in this paper is higher than in Koo and Collins (2010, model 2).\n" }, { "role": "assistant", "content": "The English UAS score in this paper is 93.0, while in Koo and Collins (2010, model 2) it is 92.9, thus the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1627.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Accuracy comparisons of different dependency parsers on PTB, CTB and PDT." ], "reasoning": "The English UAS score in this paper is 93.0, while in Koo and Collins (2010, model 2) it is 92.9, thus the statement is supported.", "statement": "The English UAS score in this paper is higher than in Koo and Collins (2010, model 2).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: Test results for English-Czech.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The BoW+GCN and CNN+GCN models outperform their respective baselines in BLEU1 and BLEU4 scores, with the BiRNN+GCN model showing the highest improvements.While it is difficult to obtain high absolute BLEU scores on this dataset, we can still see similar relative improvements. Again the BoW baseline scores worst, with the BoW+GCN easily beating that result. The CNN baseline scores BLEU4 of 8.1, but the CNN+GCN improves on that, this time by +1.0 and +0.6 for BLEU1 and BLEU4, respectively. Interestingly, BLEU1 scores for the BoW+GCN and CNN+GCN models are higher than both baselines so far. Finally, the BiRNN baseline scores a BLEU4 of 8.9, but it is again beaten by the BiRNN+GCN model with +1.9 BLEU1 and +0.7 BLEU4." } ], "images": [ "scigen/train/1628.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Test results for English-Czech." ], "reasoning": "The BoW+GCN and CNN+GCN models outperform their respective baselines in BLEU1 and BLEU4 scores, with the BiRNN+GCN model showing the highest improvements.", "description": "While it is difficult to obtain high absolute BLEU scores on this dataset, we can still see similar relative improvements. Again the BoW baseline scores worst, with the BoW+GCN easily beating that result. The CNN baseline scores BLEU4 of 8.1, but the CNN+GCN improves on that, this time by +1.0 and +0.6 for BLEU1 and BLEU4, respectively. Interestingly, BLEU1 scores for the BoW+GCN and CNN+GCN models are higher than both baselines so far. Finally, the BiRNN baseline scores a BLEU4 of 8.9, but it is again beaten by the BiRNN+GCN model with +1.9 BLEU1 and +0.7 BLEU4.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 4: Test results for English-Czech.' and proceed to solve the following question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the BLEU4 score improvement of the BiRNN+GCN model over the BiRNN baseline?\n" }, { "role": "assistant", "content": "The BiRNN baseline has a BLEU4 score of 8.9, and the BiRNN+GCN model has a BLEU4 score of 9.6. The improvement is calculated as 9.6 - 8.9. So, the answer is {\"answer\": \"0.7\"}" } ], "images": [ "scigen/train/1628.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Test results for English-Czech." ], "reasoning": "The BiRNN baseline has a BLEU4 score of 8.9, and the BiRNN+GCN model has a BLEU4 score of 9.6. The improvement is calculated as 9.6 - 8.9.", "question": "What is the BLEU4 score improvement of the BiRNN+GCN model over the BiRNN baseline?", "answer": "So, the answer is {\"answer\": \"0.7\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The CNN+GCN model has a higher BLEU1 score than the BiRNN baseline. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe CNN+GCN model has a higher BLEU1 score than the BiRNN baseline.\n" }, { "role": "assistant", "content": "The CNN+GCN model has a BLEU1 score of 36.1, while the BiRNN baseline has a BLEU1 score of 36.9. Therefore, the statement is incorrect. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1628.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Test results for English-Czech." ], "reasoning": "The CNN+GCN model has a BLEU1 score of 36.1, while the BiRNN baseline has a BLEU1 score of 36.9. Therefore, the statement is incorrect.", "statement": "The CNN+GCN model has a higher BLEU1 score than the BiRNN baseline.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 5: Validation BLEU for English-German and English-Czech for 1- and 2-layer GCNs.'.\n" }, { "role": "assistant", "content": "Adding GCN layers improves BLEU scores for both English-German and English-Czech translations, with each additional layer providing further improvements.S4SS2SSS1Px3 Effect of GCN layers. How many GCN layers do we need? Every layer gives us an extra hop in the graph and expands the syntactic neighborhood of a word. For English-German, using a 1-layer GCN improves BLEU-1, but surprisingly has little effect on BLEU4. Adding an additional layer gives improvements on both BLEU1 and BLEU4 of +1.3 and +0.73, respectively. For English-Czech, performance increases with each added GCN layer." } ], "images": [ "scigen/train/1629.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Validation BLEU for English-German and English-Czech for 1- and 2-layer GCNs." ], "reasoning": "Adding GCN layers improves BLEU scores for both English-German and English-Czech translations, with each additional layer providing further improvements.", "description": "S4SS2SSS1Px3 Effect of GCN layers. How many GCN layers do we need? Every layer gives us an extra hop in the graph and expands the syntactic neighborhood of a word. For English-German, using a 1-layer GCN improves BLEU-1, but surprisingly has little effect on BLEU4. Adding an additional layer gives improvements on both BLEU1 and BLEU4 of +1.3 and +0.73, respectively. For English-Czech, performance increases with each added GCN layer.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 5: Validation BLEU for English-German and English-Czech for 1- and 2-layer GCNs.', unfold the question presented below, detailing every step of your calculation. Output the final answer as JSON in the format {\"answer\": \"\"}.\nHow does adding a second GCN layer affect the BLEU1 and BLEU4 scores for English-German translations?\n" }, { "role": "assistant", "content": "The table shows that adding a second GCN layer increases the BLEU1 score from 45.0 to 46.3 and the BLEU4 score from 14.1 to 14.8 for English-German translations. So, the answer is {\"answer\": \"BLEU1: 46.3, BLEU4: 14.8\"}." } ], "images": [ "scigen/train/1629.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Validation BLEU for English-German and English-Czech for 1- and 2-layer GCNs." ], "reasoning": "The table shows that adding a second GCN layer increases the BLEU1 score from 45.0 to 46.3 and the BLEU4 score from 14.1 to 14.8 for English-German translations.", "question": "How does adding a second GCN layer affect the BLEU1 and BLEU4 scores for English-German translations?", "answer": "So, the answer is {\"answer\": \"BLEU1: 46.3, BLEU4: 14.8\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nAdding a second GCN layer improves the BLEU1 score for English-Czech translations by 1.3 points.\n" }, { "role": "assistant", "content": "The table shows that the BLEU1 score for English-Czech translations increases from 38.3 with one GCN layer to 39.6 with two GCN layers, which is an improvement of 1.3 points. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1629.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Validation BLEU for English-German and English-Czech for 1- and 2-layer GCNs." ], "reasoning": "The table shows that the BLEU1 score for English-Czech translations increases from 38.3 with one GCN layer to 39.6 with two GCN layers, which is an improvement of 1.3 points.", "statement": "Adding a second GCN layer improves the BLEU1 score for English-Czech translations by 1.3 points.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Full-length Rouge F1 (%) evaluation on the NYT50 test set.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "TriBlk reduces the performance of BertSumExt on NYT50 by filtering out sentences with high Rouge gain, indicating limited gains from redundancy removal.NYT50. In contrast to CNN/DailyMail, we observe that TriBlk has harmed the performance of BertSumExt on NYT50. This indicates that TriBlk filters out more sentences that have high Rouge gain on NYT50 than CNN/DailyMail, causing more drop of Rouge. It also shows that sentences in oracle summaries have more trigram overlap on NYT50 than CNN/DailyMail, which implies that redundancy removal on NYT50 may have limited gains and a simple unified rule (TriBlk) applying on all the documents could harm the performance. The results of NeuSum and HiBert are taken from their original papers while we obtained the rest of the results by re-running the models." } ], "images": [ "scigen/train/1630.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Full-length Rouge F1 (%) evaluation on the NYT50 test set." ], "reasoning": "TriBlk reduces the performance of BertSumExt on NYT50 by filtering out sentences with high Rouge gain, indicating limited gains from redundancy removal.", "description": "NYT50. In contrast to CNN/DailyMail, we observe that TriBlk has harmed the performance of BertSumExt on NYT50. This indicates that TriBlk filters out more sentences that have high Rouge gain on NYT50 than CNN/DailyMail, causing more drop of Rouge. It also shows that sentences in oracle summaries have more trigram overlap on NYT50 than CNN/DailyMail, which implies that redundancy removal on NYT50 may have limited gains and a simple unified rule (TriBlk) applying on all the documents could harm the performance. The results of NeuSum and HiBert are taken from their original papers while we obtained the rest of the results by re-running the models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2: Full-length Rouge F1 (%) evaluation on the NYT50 test set.', unfold the steps to solve the question given next. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nQuestion: Which model has the highest RG-2 score?\n" }, { "role": "assistant", "content": "To find the model with the highest RG-2 score, we look at the RG-2 column and identify the highest value, which is 37.92, corresponding to the Oracle model. So, the answer is {\"answer\": \"Oracle\"}" } ], "images": [ "scigen/train/1630.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Full-length Rouge F1 (%) evaluation on the NYT50 test set." ], "reasoning": "To find the model with the highest RG-2 score, we look at the RG-2 column and identify the highest value, which is 37.92, corresponding to the Oracle model.", "question": "Which model has the highest RG-2 score?", "answer": "So, the answer is {\"answer\": \"Oracle\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nBertSumExt+TriBlk has a higher RG-1 score than BertSumExt.\n" }, { "role": "assistant", "content": "The RG-1 score for BertSumExt is 45.46, while the RG-1 score for BertSumExt+TriBlk is 44.90. Since 45.46 is greater than 44.90, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1630.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Full-length Rouge F1 (%) evaluation on the NYT50 test set." ], "reasoning": "The RG-1 score for BertSumExt is 45.46, while the RG-1 score for BertSumExt+TriBlk is 44.90. Since 45.46 is greater than 44.90, the statement is refuted.", "statement": "BertSumExt+TriBlk has a higher RG-1 score than BertSumExt.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Full-length Rouge F1 evaluation (%) on the CNN/DailyMail test set. RG-1, RG-2, RG-L are short for Rouge-1, Rouge-2 and Rouge-L.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "ARedSum-Ctx achieves the best Rouge scores with less harm to precision, balancing salience and redundancy better than other models.CNN/DailyMail. Results are comparable as we use the same non-anonymized version of CNN/DailyMail. We also find that the sequence generation models, i.e., NeuSum and our ARedSum-Seq, do not have clear advantage over other models regardless of their encoder network structure (i.e., Bert or other neural architectures). Our ARedSum-Seq perform similarly to BertSumExt. We attribute this to the fact that the target sequence in NeuSum and ARedSum-Seq is order-sensitive, while the final evaluation of summarization is not. As shown in the figure, BertSumExt has the best P@1,P@2 and P@3 among all, which is reasonable since ^S∗ is the target which it is optimized to learn. It indicates that TriBlk could filter out some sentences that are informative but redundant during selection, which harms precision but improves Rouge. In contrast, P@2 and P@3 of ARedSum-Ctx is between BertSumExt with and without TriBlk. Through learning towards Rouge gain given the previously extracted sentences, ARedSum-Ctx achieves the best Rouge scores with less harm to precision, which means that ARedSum-Ctx can better balance salience and redundancy." } ], "images": [ "scigen/train/1631.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Full-length Rouge F1 evaluation (%) on the CNN/DailyMail test set. RG-1, RG-2, RG-L are short for Rouge-1, Rouge-2 and Rouge-L." ], "reasoning": "ARedSum-Ctx achieves the best Rouge scores with less harm to precision, balancing salience and redundancy better than other models.", "description": "CNN/DailyMail. Results are comparable as we use the same non-anonymized version of CNN/DailyMail. We also find that the sequence generation models, i.e., NeuSum and our ARedSum-Seq, do not have clear advantage over other models regardless of their encoder network structure (i.e., Bert or other neural architectures). Our ARedSum-Seq perform similarly to BertSumExt. We attribute this to the fact that the target sequence in NeuSum and ARedSum-Seq is order-sensitive, while the final evaluation of summarization is not. As shown in the figure, BertSumExt has the best P@1,P@2 and P@3 among all, which is reasonable since ^S∗ is the target which it is optimized to learn. It indicates that TriBlk could filter out some sentences that are informative but redundant during selection, which harms precision but improves Rouge. In contrast, P@2 and P@3 of ARedSum-Ctx is between BertSumExt with and without TriBlk. Through learning towards Rouge gain given the previously extracted sentences, ARedSum-Ctx achieves the best Rouge scores with less harm to precision, which means that ARedSum-Ctx can better balance salience and redundancy.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Full-length Rouge F1 evaluation (%) on the CNN/DailyMail test set. RG-1, RG-2, RG-L are short for Rouge-1, Rouge-2 and Rouge-L.', solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model achieves the highest RG-1 score?\n" }, { "role": "assistant", "content": "By examining the table, the model with the highest RG-1 score is Oracle with a score of 52.59. So, the answer is {\"answer\": \"Oracle\"}" } ], "images": [ "scigen/train/1631.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Full-length Rouge F1 evaluation (%) on the CNN/DailyMail test set. RG-1, RG-2, RG-L are short for Rouge-1, Rouge-2 and Rouge-L." ], "reasoning": "By examining the table, the model with the highest RG-1 score is Oracle with a score of 52.59.", "question": "Which model achieves the highest RG-1 score?", "answer": "So, the answer is {\"answer\": \"Oracle\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim BertSumExt achieves the highest RG-2 score among all models., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nBertSumExt achieves the highest RG-2 score among all models.\n" }, { "role": "assistant", "content": "The table shows that Oracle+TriBlk achieves the highest RG-2 score of 31.24, not BertSumExt. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1631.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Full-length Rouge F1 evaluation (%) on the CNN/DailyMail test set. RG-1, RG-2, RG-L are short for Rouge-1, Rouge-2 and Rouge-L." ], "reasoning": "The table shows that Oracle+TriBlk achieves the highest RG-2 score of 31.24, not BertSumExt.", "statement": "BertSumExt achieves the highest RG-2 score among all models.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Average ranks of our best method and the best baseline on CNN/DailyMail and NYT50 in terms of informativeness (Info), redundancy (Rdnd) and the overall quality by human participants (the lower, the better).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Human evaluations were conducted to compare the best model and the best baseline model on two datasets, sampling summaries with a Rouge-2 score difference greater than 0.05 points.We also conduct human evaluations to analyze how our best model compares against the best baseline model. On both datasets, we randomly sample 20 summaries constructed by the best baseline and our best model from the cases where their Rouge-2 score difference is more than 0.05 points. Following Zhou et al. We allowed ties in the analysis." } ], "images": [ "scigen/train/1632.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Average ranks of our best method and the best baseline on CNN/DailyMail and NYT50 in terms of informativeness (Info), redundancy (Rdnd) and the overall quality by human participants (the lower, the better)." ], "reasoning": "Human evaluations were conducted to compare the best model and the best baseline model on two datasets, sampling summaries with a Rouge-2 score difference greater than 0.05 points.", "description": "We also conduct human evaluations to analyze how our best model compares against the best baseline model. On both datasets, we randomly sample 20 summaries constructed by the best baseline and our best model from the cases where their Rouge-2 score difference is more than 0.05 points. Following Zhou et al. We allowed ties in the analysis.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: Average ranks of our best method and the best baseline on CNN/DailyMail and NYT50 in terms of informativeness (Info), redundancy (Rdnd) and the overall quality by human participants (the lower, the better).', answer the following question. Give detailed solution consisting of each step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the overall score of the best baseline model on the CNN/DailyMail dataset?\n" }, { "role": "assistant", "content": "The best baseline model on the CNN/DailyMail dataset is BertSumExt + TriBlk, which has an overall score of 1.55. So, the answer is {\"answer\": \"1.55\"}." } ], "images": [ "scigen/train/1632.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Average ranks of our best method and the best baseline on CNN/DailyMail and NYT50 in terms of informativeness (Info), redundancy (Rdnd) and the overall quality by human participants (the lower, the better)." ], "reasoning": "The best baseline model on the CNN/DailyMail dataset is BertSumExt + TriBlk, which has an overall score of 1.55.", "question": "What is the overall score of the best baseline model on the CNN/DailyMail dataset?", "answer": "So, the answer is {\"answer\": \"1.55\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe ARedSum-Ctx model has a higher overall score on the NYT50 dataset compared to the CNN/DailyMail dataset.\n" }, { "role": "assistant", "content": "The overall score of ARedSum-Ctx on the NYT50 dataset is 1.35, while on the CNN/DailyMail dataset it is 1.15. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1632.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Average ranks of our best method and the best baseline on CNN/DailyMail and NYT50 in terms of informativeness (Info), redundancy (Rdnd) and the overall quality by human participants (the lower, the better)." ], "reasoning": "The overall score of ARedSum-Ctx on the NYT50 dataset is 1.35, while on the CNN/DailyMail dataset it is 1.15. Therefore, the statement is supported.", "statement": "The ARedSum-Ctx model has a higher overall score on the NYT50 dataset compared to the CNN/DailyMail dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Italian agreement results. Random baseline accuracy is 50% in all three experiments.'.\n" }, { "role": "assistant", "content": "WordNLM shows the strongest performance across all tasks, followed closely by the LSTM CNLM, while the RNN CNLM performs above chance but lags behind the LSTM.WordNLM shows the strongest performance, closely followed by the LSTM CNLM. The RNN CNLM performs strongly above chance (50%), but again lags behind the LSTM. where we used the adverbs più ‘more’, meno ‘less’, tanto ‘so much’. We considered only adjectives that occurred 1K times in the training corpus (as adjectives ending in -a/-o are very common). We excluded all cases in which the adverb-adjective combination occurred in the training corpus, obtaining 88 stimulus pairs. Due to the restriction to common adjectives, there were no WordNLM OOVs. Possibly, the task is made easy by the use of extremely common adverbs and adjectives. Stimulus selection was as in the last experiment, but we used a 500-occurrences threshold for adjectives, as feminine plurals are less common, obtaining 99 pairs. Again, no adverb-adjective combination was attested. There were no OOV items for the WordNLM." } ], "images": [ "scigen/train/1633.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Italian agreement results. Random baseline accuracy is 50% in all three experiments." ], "reasoning": "WordNLM shows the strongest performance across all tasks, followed closely by the LSTM CNLM, while the RNN CNLM performs above chance but lags behind the LSTM.", "description": "WordNLM shows the strongest performance, closely followed by the LSTM CNLM. The RNN CNLM performs strongly above chance (50%), but again lags behind the LSTM. where we used the adverbs più ‘more’, meno ‘less’, tanto ‘so much’. We considered only adjectives that occurred 1K times in the training corpus (as adjectives ending in -a/-o are very common). We excluded all cases in which the adverb-adjective combination occurred in the training corpus, obtaining 88 stimulus pairs. Due to the restriction to common adjectives, there were no WordNLM OOVs. Possibly, the task is made easy by the use of extremely common adverbs and adjectives. Stimulus selection was as in the last experiment, but we used a 500-occurrences threshold for adjectives, as feminine plurals are less common, obtaining 99 pairs. Again, no adverb-adjective combination was attested. There were no OOV items for the WordNLM.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: 'Table 4: Italian agreement results. Random baseline accuracy is 50% in all three experiments.'\nQuestion: Which model has the highest performance in the Adj. Number task?\n" }, { "role": "assistant", "content": "By comparing the performance of the models in the Adj. Number task, WordNLM has the highest score with 100. So, the answer is {\"answer\": \"WordNLM\"}." } ], "images": [ "scigen/train/1633.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Italian agreement results. Random baseline accuracy is 50% in all three experiments." ], "reasoning": "By comparing the performance of the models in the Adj. Number task, WordNLM has the highest score with 100.", "question": "Which model has the highest performance in the Adj. Number task?", "answer": "So, the answer is {\"answer\": \"WordNLM\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe CNLM LSTM model outperforms the WordNLM model in the Noun Gender task.\n" }, { "role": "assistant", "content": "The table shows that the CNLM LSTM model has a score of 93.1 in the Noun Gender task, while the WordNLM model has a higher score of 97.4. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1633.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Italian agreement results. Random baseline accuracy is 50% in all three experiments." ], "reasoning": "The table shows that the CNLM LSTM model has a score of 93.1 in the Noun Gender task, while the WordNLM model has a higher score of 97.4.", "statement": "The CNLM LSTM model outperforms the WordNLM model in the Noun Gender task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1: Performance of language models. For CNLMs, we report bits-per-character (BPC). For WordNLMs, we report perplexity.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows the BPC and perplexity scores for LSTM, RNN, and WordNLM models across English, German, and Italian languages, indicating the performance of different models on various datasets.Recall that we removed whitespace, which is both easy to predict, and aids prediction of other characters. Consequently, the fact that our character-level models are below the state of the art is expected. For example, the best model of \\newcitemerity2018analysis achieved 1.23 English BPC on a Wikipedia-derived dataset. On EuroParl data, \\newcitecotterell2018all report 0.85 for English, 0.90 for German, and 0.82 for Italian. Still, our English BPC is comparable to that reported by \\newciteDBLP:journals/corr/Graves13 for his static character-level LSTM trained on space-delimited Wikipedia data, suggesting that we are achieving reasonable performance. The perplexity of the word-level model might not be comparable to that of highly-optimized state-of-the-art architectures, but it is at the expected level for a well-tuned vanilla LSTM language model. For example, \\newciteGulordava:etal:2018 report 51.9 and 44.9 perplexities respectively in English and Italian for their best LSTMs trained on Wikipedia data with same vocabulary size as ours. This was due to resource availability." } ], "images": [ "scigen/train/1634.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance of language models. For CNLMs, we report bits-per-character (BPC). For WordNLMs, we report perplexity." ], "reasoning": "The table shows the BPC and perplexity scores for LSTM, RNN, and WordNLM models across English, German, and Italian languages, indicating the performance of different models on various datasets.", "description": "Recall that we removed whitespace, which is both easy to predict, and aids prediction of other characters. Consequently, the fact that our character-level models are below the state of the art is expected. For example, the best model of \\newcitemerity2018analysis achieved 1.23 English BPC on a Wikipedia-derived dataset. On EuroParl data, \\newcitecotterell2018all report 0.85 for English, 0.90 for German, and 0.82 for Italian. Still, our English BPC is comparable to that reported by \\newciteDBLP:journals/corr/Graves13 for his static character-level LSTM trained on space-delimited Wikipedia data, suggesting that we are achieving reasonable performance. The perplexity of the word-level model might not be comparable to that of highly-optimized state-of-the-art architectures, but it is at the expected level for a well-tuned vanilla LSTM language model. For example, \\newciteGulordava:etal:2018 report 51.9 and 44.9 perplexities respectively in English and Italian for their best LSTMs trained on Wikipedia data with same vocabulary size as ours. This was due to resource availability.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}. The question is related to the given table titled 'Table 1: Performance of language models. For CNLMs, we report bits-per-character (BPC). For WordNLMs, we report perplexity.'.\n\nQuestion: What is the BPC score for the LSTM model in German, and how does it compare to the RNN model in the same language?\n" }, { "role": "assistant", "content": "The BPC score for the LSTM model in German is 1.51, while the RNN model in German has a BPC score of 1.83. Comparing these, the LSTM model has a lower BPC score than the RNN model in German. So, the answer is {\"answer\": \"1.51\"}" } ], "images": [ "scigen/train/1634.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance of language models. For CNLMs, we report bits-per-character (BPC). For WordNLMs, we report perplexity." ], "reasoning": "The BPC score for the LSTM model in German is 1.51, while the RNN model in German has a BPC score of 1.83. Comparing these, the LSTM model has a lower BPC score than the RNN model in German.", "question": "What is the BPC score for the LSTM model in German, and how does it compare to the RNN model in the same language?", "answer": "So, the answer is {\"answer\": \"1.51\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The WordNLM model has the highest perplexity score in English compared to German and Italian. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe WordNLM model has the highest perplexity score in English compared to German and Italian.\n" }, { "role": "assistant", "content": "The table shows that the WordNLM model has a perplexity score of 48.99 in English, which is higher than the scores in German (37.96) and Italian (42.02). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1634.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance of language models. For CNLMs, we report bits-per-character (BPC). For WordNLMs, we report perplexity." ], "reasoning": "The table shows that the WordNLM model has a perplexity score of 48.99 in English, which is higher than the scores in German (37.96) and Italian (42.02).", "statement": "The WordNLM model has the highest perplexity score in English compared to German and Italian.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: Results on MSR Sentence Completion. For our models (top), we show accuracies for Wikipedia (left) and in-domain (right) training. We compare with language models from prior work (left): Kneser-Ney 5-gram model Mikolov (2012), Word RNN Zweig et al. (2012), Word LSTM and LdTreeLSTM Zhang et al. (2016). We further report models incorporating distributional encodings of semantics (right): Skipgram(+RNNs) from DBLP:journals/corr/abs-1301-3781, the PMI-based model of Woods (2016), and the Context-Embedding-based approach of Melamud et al. (2016).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "In-domain training significantly improves model performance, with LSTM CNLM outperforming many earlier word-level neural models and the RNN performing poorly even with in-domain training.We confirm the importance of in-domain training, as the models trained on Wikipedia perform poorly (but still above chance level, which is at 20%). With in-domain training, the LSTM CNLM outperforms many earlier word-level neural models, and is only slightly below our WordNLM. The RNN is not successful even when trained in-domain, contrasting with the word-based vanilla RNN from the literature, whose performance, while still below LSTMs, is much stronger. Once more, this suggests that capturing word-level generalizations with a word-lexicon-less character model requires the long-span processing abilities of an LSTM." } ], "images": [ "scigen/train/1635.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on MSR Sentence Completion. For our models (top), we show accuracies for Wikipedia (left) and in-domain (right) training. We compare with language models from prior work (left): Kneser-Ney 5-gram model Mikolov (2012), Word RNN Zweig et al. (2012), Word LSTM and LdTreeLSTM Zhang et al. (2016). We further report models incorporating distributional encodings of semantics (right): Skipgram(+RNNs) from DBLP:journals/corr/abs-1301-3781, the PMI-based model of Woods (2016), and the Context-Embedding-based approach of Melamud et al. (2016)." ], "reasoning": "In-domain training significantly improves model performance, with LSTM CNLM outperforming many earlier word-level neural models and the RNN performing poorly even with in-domain training.", "description": "We confirm the importance of in-domain training, as the models trained on Wikipedia perform poorly (but still above chance level, which is at 20%). With in-domain training, the LSTM CNLM outperforms many earlier word-level neural models, and is only slightly below our WordNLM. The RNN is not successful even when trained in-domain, contrasting with the word-based vanilla RNN from the literature, whose performance, while still below LSTMs, is much stronger. Once more, this suggests that capturing word-level generalizations with a word-lexicon-less character model requires the long-span processing abilities of an LSTM.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 5: Results on MSR Sentence Completion. For our models (top), we show accuracies for Wikipedia (left) and in-domain (right) training. We compare with language models from prior work (left): Kneser-Ney 5-gram model Mikolov (2012), Word RNN Zweig et al. (2012), Word LSTM and LdTreeLSTM Zhang et al. (2016). We further report models incorporating distributional encodings of semantics (right): Skipgram(+RNNs) from DBLP:journals/corr/abs-1301-3781, the PMI-based model of Woods (2016), and the Context-Embedding-based approach of Melamud et al. (2016).' and methodically tackle the question that follows. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model has the highest performance with in-domain training?\n" }, { "role": "assistant", "content": "The table shows the performance of different models with both wiki and in-domain training. The WordNLM model has the highest in-domain performance with a score of 63.3. So, the answer is {\"answer\": \"WordNLM\"}." } ], "images": [ "scigen/train/1635.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on MSR Sentence Completion. For our models (top), we show accuracies for Wikipedia (left) and in-domain (right) training. We compare with language models from prior work (left): Kneser-Ney 5-gram model Mikolov (2012), Word RNN Zweig et al. (2012), Word LSTM and LdTreeLSTM Zhang et al. (2016). We further report models incorporating distributional encodings of semantics (right): Skipgram(+RNNs) from DBLP:journals/corr/abs-1301-3781, the PMI-based model of Woods (2016), and the Context-Embedding-based approach of Melamud et al. (2016)." ], "reasoning": "The table shows the performance of different models with both wiki and in-domain training. The WordNLM model has the highest in-domain performance with a score of 63.3.", "question": "Which model has the highest performance with in-domain training?", "answer": "So, the answer is {\"answer\": \"WordNLM\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The RNN model performs better with in-domain training compared to wiki training. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe RNN model performs better with in-domain training compared to wiki training.\n" }, { "role": "assistant", "content": "The table shows that the RNN model has a performance of 24.3 with wiki training and 24.0 with in-domain training, indicating that it performs slightly worse with in-domain training. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1635.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on MSR Sentence Completion. For our models (top), we show accuracies for Wikipedia (left) and in-domain (right) training. We compare with language models from prior work (left): Kneser-Ney 5-gram model Mikolov (2012), Word RNN Zweig et al. (2012), Word LSTM and LdTreeLSTM Zhang et al. (2016). We further report models incorporating distributional encodings of semantics (right): Skipgram(+RNNs) from DBLP:journals/corr/abs-1301-3781, the PMI-based model of Woods (2016), and the Context-Embedding-based approach of Melamud et al. (2016)." ], "reasoning": "The table shows that the RNN model has a performance of 24.3 with wiki training and 24.0 with in-domain training, indicating that it performs slightly worse with in-domain training.", "statement": "The RNN model performs better with in-domain training compared to wiki training.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5: Results on MSR Sentence Completion. For our models (top), we show accuracies for Wikipedia (left) and in-domain (right) training. We compare with language models from prior work (left): Kneser-Ney 5-gram model Mikolov (2012), Word RNN Zweig et al. (2012), Word LSTM and LdTreeLSTM Zhang et al. (2016). We further report models incorporating distributional encodings of semantics (right): Skipgram(+RNNs) from DBLP:journals/corr/abs-1301-3781, the PMI-based model of Woods (2016), and the Context-Embedding-based approach of Melamud et al. (2016).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "In-domain training significantly improves model performance, with LSTM CNLM outperforming many earlier word-level neural models, while RNNs remain less successful.We confirm the importance of in-domain training, as the models trained on Wikipedia perform poorly (but still above chance level, which is at 20%). With in-domain training, the LSTM CNLM outperforms many earlier word-level neural models, and is only slightly below our WordNLM. The RNN is not successful even when trained in-domain, contrasting with the word-based vanilla RNN from the literature, whose performance, while still below LSTMs, is much stronger. Once more, this suggests that capturing word-level generalizations with a word-lexicon-less character model requires the long-span processing abilities of an LSTM." } ], "images": [ "scigen/train/1636.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on MSR Sentence Completion. For our models (top), we show accuracies for Wikipedia (left) and in-domain (right) training. We compare with language models from prior work (left): Kneser-Ney 5-gram model Mikolov (2012), Word RNN Zweig et al. (2012), Word LSTM and LdTreeLSTM Zhang et al. (2016). We further report models incorporating distributional encodings of semantics (right): Skipgram(+RNNs) from DBLP:journals/corr/abs-1301-3781, the PMI-based model of Woods (2016), and the Context-Embedding-based approach of Melamud et al. (2016)." ], "reasoning": "In-domain training significantly improves model performance, with LSTM CNLM outperforming many earlier word-level neural models, while RNNs remain less successful.", "description": "We confirm the importance of in-domain training, as the models trained on Wikipedia perform poorly (but still above chance level, which is at 20%). With in-domain training, the LSTM CNLM outperforms many earlier word-level neural models, and is only slightly below our WordNLM. The RNN is not successful even when trained in-domain, contrasting with the word-based vanilla RNN from the literature, whose performance, while still below LSTMs, is much stronger. Once more, this suggests that capturing word-level generalizations with a word-lexicon-less character model requires the long-span processing abilities of an LSTM.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model has the highest performance in the 'From the literature 48.0' column?\nSolve the above question based on the table titled 'Table 5: Results on MSR Sentence Completion. For our models (top), we show accuracies for Wikipedia (left) and in-domain (right) training. We compare with language models from prior work (left): Kneser-Ney 5-gram model Mikolov (2012), Word RNN Zweig et al. (2012), Word LSTM and LdTreeLSTM Zhang et al. (2016). We further report models incorporating distributional encodings of semantics (right): Skipgram(+RNNs) from DBLP:journals/corr/abs-1301-3781, the PMI-based model of Woods (2016), and the Context-Embedding-based approach of Melamud et al. (2016).'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the values in the 'From the literature 48.0' column, the LdTreeLSTM model has the highest performance with a score of 65.1. So, the answer is {\"answer\": \"LdTreeLSTM\"}" } ], "images": [ "scigen/train/1636.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on MSR Sentence Completion. For our models (top), we show accuracies for Wikipedia (left) and in-domain (right) training. We compare with language models from prior work (left): Kneser-Ney 5-gram model Mikolov (2012), Word RNN Zweig et al. (2012), Word LSTM and LdTreeLSTM Zhang et al. (2016). We further report models incorporating distributional encodings of semantics (right): Skipgram(+RNNs) from DBLP:journals/corr/abs-1301-3781, the PMI-based model of Woods (2016), and the Context-Embedding-based approach of Melamud et al. (2016)." ], "reasoning": "By comparing the values in the 'From the literature 48.0' column, the LdTreeLSTM model has the highest performance with a score of 65.1.", "question": "Which model has the highest performance in the 'From the literature 48.0' column?", "answer": "So, the answer is {\"answer\": \"LdTreeLSTM\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Word RNN model performs better than the Word LSTM model in the 'From the literature 40.0' column.\n" }, { "role": "assistant", "content": "The table shows that the Word RNN model has a score of 45.0, while the Word LSTM model has a score of 56.0 in the 'From the literature 40.0' column, indicating that the statement is incorrect. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1636.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on MSR Sentence Completion. For our models (top), we show accuracies for Wikipedia (left) and in-domain (right) training. We compare with language models from prior work (left): Kneser-Ney 5-gram model Mikolov (2012), Word RNN Zweig et al. (2012), Word LSTM and LdTreeLSTM Zhang et al. (2016). We further report models incorporating distributional encodings of semantics (right): Skipgram(+RNNs) from DBLP:journals/corr/abs-1301-3781, the PMI-based model of Woods (2016), and the Context-Embedding-based approach of Melamud et al. (2016)." ], "reasoning": "The table shows that the Word RNN model has a score of 45.0, while the Word LSTM model has a score of 56.0 in the 'From the literature 40.0' column, indicating that the statement is incorrect.", "statement": "The Word RNN model performs better than the Word LSTM model in the 'From the literature 40.0' column.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'TABLE III: Analysis on the Precision and Recall.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The proposed method improves recall by nearly 9 points while maintaining similar precision, due to increased TP and decreased FN from data augmentation.Obviously, our method can significantly improve the recall by nearly 9 points while keeping the precision basically the same as the baseline. The reason behind the improvement of the recall is that the proposed methods substantially increases TP and decreases FN. This is because the diversity in user utterances created by data augmentation helps the dialogue system recognize more requested slots and further allows the decoder to answer these slots in machine responses. Without data augmentation, some slots are just not detected at all in the baseline (thus a higher FN)." } ], "images": [ "scigen/train/1637.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Analysis on the Precision and Recall." ], "reasoning": "The proposed method improves recall by nearly 9 points while maintaining similar precision, due to increased TP and decreased FN from data augmentation.", "description": "Obviously, our method can significantly improve the recall by nearly 9 points while keeping the precision basically the same as the baseline. The reason behind the improvement of the recall is that the proposed methods substantially increases TP and decreases FN. This is because the diversity in user utterances created by data augmentation helps the dialogue system recognize more requested slots and further allows the decoder to answer these slots in machine responses. Without data augmentation, some slots are just not detected at all in the baseline (thus a higher FN).", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nTable title: TABLE III: Analysis on the Precision and Recall.\n\nQuestion: What is the difference in recall between the baseline and the assembled augmentation method?\n" }, { "role": "assistant", "content": "The recall for the baseline is 0.786, and for the assembled augmentation method, it is 0.875. The difference is 0.875 - 0.786. So, the answer is {\"answer\": \"0.089\"}" } ], "images": [ "scigen/train/1637.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Analysis on the Precision and Recall." ], "reasoning": "The recall for the baseline is 0.786, and for the assembled augmentation method, it is 0.875. The difference is 0.875 - 0.786.", "question": "What is the difference in recall between the baseline and the assembled augmentation method?", "answer": "So, the answer is {\"answer\": \"0.089\"}" }, { "messages": [ { "role": "user", "content": "The assembled augmentation method has a higher precision than the baseline. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe assembled augmentation method has a higher precision than the baseline.\n" }, { "role": "assistant", "content": "The precision for the baseline is 0.885, and for the assembled augmentation method, it is 0.883. The assembled augmentation method has a slightly lower precision. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1637.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Analysis on the Precision and Recall." ], "reasoning": "The precision for the baseline is 0.885, and for the assembled augmentation method, it is 0.883. The assembled augmentation method has a slightly lower precision.", "statement": "The assembled augmentation method has a higher precision than the baseline.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'TABLE II: Experiment results on CamRest676 and KVRET.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Data augmentation methods significantly improve F1 scores over the basic TSCP model, with assembled augmentation achieving the highest performance, while machine utterance augmentation deteriorates performance.First, the results demonstrate that all the proposed data augmentation methods contribute to the significant improvements in F1 over the basic TSCP model. Except for the stop-word deletion method, all other methods perform better than the RL-enhanced TSCP. Second, the sentence-level augmentation methods are better than the word-level methods in most cases as the former provide more variances for user utterances. Third, the assembled augmentation, which combines all data generated by the four data augmentation methods, achieve the new state-of-the-art performance on the two datasets, with more than 2 points higher than the RL-enhanced TSCP model in terms of F1 score. It is clear to observe that machine utterance augmentation seriously deteriorates the performance. The reason for this may be that data augmentation introduces both variance and noise. However, the variance and noise in machine utterances will distract the system." } ], "images": [ "scigen/train/1638.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Experiment results on CamRest676 and KVRET." ], "reasoning": "Data augmentation methods significantly improve F1 scores over the basic TSCP model, with assembled augmentation achieving the highest performance, while machine utterance augmentation deteriorates performance.", "description": "First, the results demonstrate that all the proposed data augmentation methods contribute to the significant improvements in F1 over the basic TSCP model. Except for the stop-word deletion method, all other methods perform better than the RL-enhanced TSCP. Second, the sentence-level augmentation methods are better than the word-level methods in most cases as the former provide more variances for user utterances. Third, the assembled augmentation, which combines all data generated by the four data augmentation methods, achieve the new state-of-the-art performance on the two datasets, with more than 2 points higher than the RL-enhanced TSCP model in terms of F1 score. It is clear to observe that machine utterance augmentation seriously deteriorates the performance. The reason for this may be that data augmentation introduces both variance and noise. However, the variance and noise in machine utterances will distract the system.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'TABLE II: Experiment results on CamRest676 and KVRET.', answer the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich data augmentation method achieved the highest F1 score on the CamRest676 dataset?\n" }, { "role": "assistant", "content": "The table shows that the assembled augmentation method achieved the highest F1 score on the CamRest676 dataset with a score of 0.879. So, the answer is {\"answer\": \"Assembled Augmentation\"}" } ], "images": [ "scigen/train/1638.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Experiment results on CamRest676 and KVRET." ], "reasoning": "The table shows that the assembled augmentation method achieved the highest F1 score on the CamRest676 dataset with a score of 0.879.", "question": "Which data augmentation method achieved the highest F1 score on the CamRest676 dataset?", "answer": "So, the answer is {\"answer\": \"Assembled Augmentation\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe stop-word deletion method performs better than the RL-enhanced TSCP model on the KVRET dataset.\n" }, { "role": "assistant", "content": "The table shows that the stop-word deletion method has an F1 score of 0.831 on the KVRET dataset, which is lower than the RL-enhanced TSCP model's score of 0.831. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1638.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Experiment results on CamRest676 and KVRET." ], "reasoning": "The table shows that the stop-word deletion method has an F1 score of 0.831 on the KVRET dataset, which is lower than the RL-enhanced TSCP model's score of 0.831.", "statement": "The stop-word deletion method performs better than the RL-enhanced TSCP model on the KVRET dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: Comparisons with large pre-trained language model fine-tuning with different amount of training data.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "KagNet shows limited improvement with 10% of the data, suggesting the need for few-shot learning in commonsense reasoning.We conduct the experiments with our in-house splits to investigate whether our KagNet can also work well on other universal language encoders (GPT and Bert-Base), particularly with different fractions of the dataset (say 10%, 50%, 100% of the training data). Furthermore, we find that the improvements in a small data situation (10%) is relatively limited, and we believe an important future research direction is thus few-shot learning for commonsense reasoning." } ], "images": [ "scigen/train/1639.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparisons with large pre-trained language model fine-tuning with different amount of training data." ], "reasoning": "KagNet shows limited improvement with 10% of the data, suggesting the need for few-shot learning in commonsense reasoning.", "description": "We conduct the experiments with our in-house splits to investigate whether our KagNet can also work well on other universal language encoders (GPT and Bert-Base), particularly with different fractions of the dataset (say 10%, 50%, 100% of the training data). Furthermore, we find that the improvements in a small data situation (10%) is relatively limited, and we believe an important future research direction is thus few-shot learning for commonsense reasoning.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 1: Comparisons with large pre-trained language model fine-tuning with different amount of training data.', solve this question and give detail solutions. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model achieved the highest accuracy with 100% of the training data on the IItest set?\n" }, { "role": "assistant", "content": "By examining the table, the highest accuracy with 100% of the training data on the IItest set is achieved by Bert-Large-KagNet with 57.16%. So, the answer is {\"answer\": \"Bert-Large-KagNet\"}" } ], "images": [ "scigen/train/1639.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparisons with large pre-trained language model fine-tuning with different amount of training data." ], "reasoning": "By examining the table, the highest accuracy with 100% of the training data on the IItest set is achieved by Bert-Large-KagNet with 57.16%.", "question": "Which model achieved the highest accuracy with 100% of the training data on the IItest set?", "answer": "So, the answer is {\"answer\": \"Bert-Large-KagNet\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nBert-Base-KagNet achieved higher accuracy than Gpt-KagNet with 50% of the training data on the IItest set.\n" }, { "role": "assistant", "content": "The table shows that Bert-Base-KagNet has an accuracy of 40.32% while Gpt-KagNet has 32.33% with 50% of the training data on the IItest set. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1639.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparisons with large pre-trained language model fine-tuning with different amount of training data." ], "reasoning": "The table shows that Bert-Base-KagNet has an accuracy of 40.32% while Gpt-KagNet has 32.33% with 50% of the training data on the IItest set.", "statement": "Bert-Base-KagNet achieved higher accuracy than Gpt-KagNet with 50% of the training data on the IItest set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Ablation study on the KagNet framework.'.\n" }, { "role": "assistant", "content": "Replacing GCN-LSTM-HPA with traditional R-GCNs results in worse performance due to over-parameterization, while attention mechanisms and pruning effectively improve performance.Ablation study on model components. We find that replacing our GCN-LSTM-HPA architecture with traditional relational GCNs, which uses separate weight matrices for different relation types, results in worse performance, due to its over-parameterization. The attention mechanisms matters almost equally in two levels, and pruning also effectively filters noisy paths." } ], "images": [ "scigen/train/1640.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Ablation study on the KagNet framework." ], "reasoning": "Replacing GCN-LSTM-HPA with traditional R-GCNs results in worse performance due to over-parameterization, while attention mechanisms and pruning effectively improve performance.", "description": "Ablation study on model components. We find that replacing our GCN-LSTM-HPA architecture with traditional relational GCNs, which uses separate weight matrices for different relation types, results in worse performance, due to its over-parameterization. The attention mechanisms matters almost equally in two levels, and pruning also effectively filters noisy paths.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the IHtest performance percentage when using all paths without pruning?\nSolve the above question based on the table titled 'Table 4: Ablation study on the KagNet framework.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the IHtest performance percentage when using all paths without pruning, locate the row labeled 'using all paths (w/o pruning)' and check the IHtest column. So, the answer is {\"answer\": \"55.27\"}." } ], "images": [ "scigen/train/1640.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Ablation study on the KagNet framework." ], "reasoning": "To find the IHtest performance percentage when using all paths without pruning, locate the row labeled 'using all paths (w/o pruning)' and check the IHtest column.", "question": "What is the IHtest performance percentage when using all paths without pruning?", "answer": "So, the answer is {\"answer\": \"55.27\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model without GCN layers has the highest IHdev performance.\n" }, { "role": "assistant", "content": "To verify this statement, compare the IHdev performance percentages of all models. The model without GCN layers has an IHdev performance of 62.05%, which is not the highest as KagNet (standard) has 62.35%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1640.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Ablation study on the KagNet framework." ], "reasoning": "To verify this statement, compare the IHdev performance percentages of all models. The model without GCN layers has an IHdev performance of 62.05%, which is not the highest as KagNet (standard) has 62.35%.", "statement": "The model without GCN layers has the highest IHdev performance.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table A.2: Comparison of text encoders trained on different textual inputs and evaluated on three different prediction tasks. The model uses (a) the initial queries, (b) all attribute tags, or (c) both initial queries and tags as text inputs to predict the target. Each model is evaluated using Accuracy@{1, 3}.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Combining initial queries with tags and paraphrase queries improves classification performance, achieving the highest accuracy when both are used together.We use initial queries as well as paraphrase queries to train the encoder, which has around 16K target-query examples. To analyze the effectiveness of tags in addition to initial queries, we generate pseudo-queries by combining existing queries with sampled subset of tags from the targets. This augmentation strategy is useful to improve the classification performance. We also observe that using the set of tags instead of initial queries as text inputs for a specific target label improves classification performance, indicating that the designed tags can capture the target label well. Finally, when we concatenate user initial queries and tags and use that as text input to the classifier, we achieve Accuracy@1 of 76%. In our full model, we achieve 79% with only querying about five tags." } ], "images": [ "scigen/train/1641.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table A.2: Comparison of text encoders trained on different textual inputs and evaluated on three different prediction tasks. The model uses (a) the initial queries, (b) all attribute tags, or (c) both initial queries and tags as text inputs to predict the target. Each model is evaluated using Accuracy@{1, 3}." ], "reasoning": "Combining initial queries with tags and paraphrase queries improves classification performance, achieving the highest accuracy when both are used together.", "description": "We use initial queries as well as paraphrase queries to train the encoder, which has around 16K target-query examples. To analyze the effectiveness of tags in addition to initial queries, we generate pseudo-queries by combining existing queries with sampled subset of tags from the targets. This augmentation strategy is useful to improve the classification performance. We also observe that using the set of tags instead of initial queries as text inputs for a specific target label improves classification performance, indicating that the designed tags can capture the target label well. Finally, when we concatenate user initial queries and tags and use that as text input to the classifier, we achieve Accuracy@1 of 76%. In our full model, we achieve 79% with only querying about five tags.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table A.2: Comparison of text encoders trained on different textual inputs and evaluated on three different prediction tasks. The model uses (a) the initial queries, (b) all attribute tags, or (c) both initial queries and tags as text inputs to predict the target. Each model is evaluated using Accuracy@{1, 3}.' to sequentially solve the question presented below. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhat is the Accuracy@1 when using both initial queries and tags as text input?\n" }, { "role": "assistant", "content": "To find the Accuracy@1 when using both initial queries and tags, look at the row where both initial queries and tags are used and find the value under the 'Init Query + Tags Acc@1' column. So, the answer is {\"answer\": \"0.36\"}" } ], "images": [ "scigen/train/1641.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table A.2: Comparison of text encoders trained on different textual inputs and evaluated on three different prediction tasks. The model uses (a) the initial queries, (b) all attribute tags, or (c) both initial queries and tags as text inputs to predict the target. Each model is evaluated using Accuracy@{1, 3}." ], "reasoning": "To find the Accuracy@1 when using both initial queries and tags, look at the row where both initial queries and tags are used and find the value under the 'Init Query + Tags Acc@1' column.", "question": "What is the Accuracy@1 when using both initial queries and tags as text input?", "answer": "So, the answer is {\"answer\": \"0.36\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nUsing only initial queries as text input achieves an Accuracy@1 of 0.28.\n" }, { "role": "assistant", "content": "To verify this statement, look at the row where only initial queries are used and find the value under the 'Init Query Acc@1' column. The value is 0.28, which supports the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1641.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table A.2: Comparison of text encoders trained on different textual inputs and evaluated on three different prediction tasks. The model uses (a) the initial queries, (b) all attribute tags, or (c) both initial queries and tags as text inputs to predict the target. Each model is evaluated using Accuracy@{1, 3}." ], "reasoning": "To verify this statement, look at the row where only initial queries are used and find the value under the 'Init Query Acc@1' column. The value is 0.28, which supports the statement.", "statement": "Using only initial queries as text input achieves an Accuracy@1 of 0.28.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: WMT14 en-de-fr Zero-shot results with baseline and aligned models compared against pivoting. Pivoting through English is performed using the baseline multilingual model.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The multilingual model shows a slight regression in BLEU scores compared to single models, which is expected due to its multitasking nature.We see that the multilingual model performs well on the directions for which it received supervision: en↔de,fr. The 1-2 BLEU point regression as compared the one-to-one models is expected given that the multilingual model is trained to perform multiple tasks while using the same capacity." } ], "images": [ "scigen/train/1642.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: WMT14 en-de-fr Zero-shot results with baseline and aligned models compared against pivoting. Pivoting through English is performed using the baseline multilingual model." ], "reasoning": "The multilingual model shows a slight regression in BLEU scores compared to single models, which is expected due to its multitasking nature.", "description": "We see that the multilingual model performs well on the directions for which it received supervision: en↔de,fr. The 1-2 BLEU point regression as compared the one-to-one models is expected given that the multilingual model is trained to perform multiple tasks while using the same capacity.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 1: WMT14 en-de-fr Zero-shot results with baseline and aligned models compared against pivoting. Pivoting through English is performed using the baseline multilingual model.\nQuestion: What is the BLEU score for the multi (drop=0.1) system on the supervised en→fr task?\n" }, { "role": "assistant", "content": "To find the BLEU score for the multi (drop=0.1) system on the supervised en→fr task, locate the row labeled 'multi (drop=0.1)' and the column labeled 'Supervised en→fr'. The intersection of this row and column gives the BLEU score. So, the answer is {\"answer\": \"32.68\"}." } ], "images": [ "scigen/train/1642.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: WMT14 en-de-fr Zero-shot results with baseline and aligned models compared against pivoting. Pivoting through English is performed using the baseline multilingual model." ], "reasoning": "To find the BLEU score for the multi (drop=0.1) system on the supervised en→fr task, locate the row labeled 'multi (drop=0.1)' and the column labeled 'Supervised en→fr'. The intersection of this row and column gives the BLEU score.", "question": "What is the BLEU score for the multi (drop=0.1) system on the supervised en→fr task?", "answer": "So, the answer is {\"answer\": \"32.68\"}." }, { "messages": [ { "role": "user", "content": "The single system has a higher BLEU score for the supervised en→de task compared to the multi (drop=0.3) system. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe single system has a higher BLEU score for the supervised en→de task compared to the multi (drop=0.3) system.\n" }, { "role": "assistant", "content": "To verify this statement, compare the BLEU scores for the single system and the multi (drop=0.3) system in the 'Supervised en→de' column. The single system has a score of 24.31, while the multi (drop=0.3) system has a score of 21.98. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1642.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: WMT14 en-de-fr Zero-shot results with baseline and aligned models compared against pivoting. Pivoting through English is performed using the baseline multilingual model." ], "reasoning": "To verify this statement, compare the BLEU scores for the single system and the multi (drop=0.3) system in the 'Supervised en→de' column. The single system has a score of 24.31, while the multi (drop=0.3) system has a score of 21.98.", "statement": "The single system has a higher BLEU score for the supervised en→de task compared to the multi (drop=0.3) system.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: BLEU on subset of examples predicted in the right language by zero-shot translation through the multilingual model (newstest2012)'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The zero-shot translation system lags behind pivoting by 0.5 BLEU points for French to German and by 2.7 BLEU points for German to French, indicating a generalization problem.Given that a large portion of the errors are due to incorrect language, we try to estimate the improvement to zero-shot translation quality that could potentially be achieved by solving this issue. We discount these errors by re-evaluating the BLEU scores of zero-shot translation and pivoting on only those examples that the multilingual model already zero-shot translates to the right language. We find that although the vanilla zero-shot translation system is much stronger than expected at first glance, it still lags the pivoting by 0.5 BLEU points on French to German and by 2.7 BLEU points on German to French. This gap and the below analysis indicate a generalization problem in the model." } ], "images": [ "scigen/train/1643.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: BLEU on subset of examples predicted in the right language by zero-shot translation through the multilingual model (newstest2012)" ], "reasoning": "The zero-shot translation system lags behind pivoting by 0.5 BLEU points for French to German and by 2.7 BLEU points for German to French, indicating a generalization problem.", "description": "Given that a large portion of the errors are due to incorrect language, we try to estimate the improvement to zero-shot translation quality that could potentially be achieved by solving this issue. We discount these errors by re-evaluating the BLEU scores of zero-shot translation and pivoting on only those examples that the multilingual model already zero-shot translates to the right language. We find that although the vanilla zero-shot translation system is much stronger than expected at first glance, it still lags the pivoting by 0.5 BLEU points on French to German and by 2.7 BLEU points on German to French. This gap and the below analysis indicate a generalization problem in the model.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 3: BLEU on subset of examples predicted in the right language by zero-shot translation through the multilingual model (newstest2012)\nQuestion: What is the BLEU score difference between pivoting and zero-shot translation for German to French?\n" }, { "role": "assistant", "content": "The BLEU score for pivoting from German to French is 19.71, while for zero-shot translation it is 19.22. The difference is 0.49 BLEU points. So, the answer is {\"answer\": \"0.49\"}." } ], "images": [ "scigen/train/1643.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: BLEU on subset of examples predicted in the right language by zero-shot translation through the multilingual model (newstest2012)" ], "reasoning": "The BLEU score for pivoting from German to French is 19.71, while for zero-shot translation it is 19.22. The difference is 0.49 BLEU points.", "question": "What is the BLEU score difference between pivoting and zero-shot translation for German to French?", "answer": "So, the answer is {\"answer\": \"0.49\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The zero-shot translation system performs better than pivoting for French to German. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe zero-shot translation system performs better than pivoting for French to German.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for pivoting from French to German is 24.33, while for zero-shot translation it is 21.63. Therefore, pivoting performs better. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1643.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: BLEU on subset of examples predicted in the right language by zero-shot translation through the multilingual model (newstest2012)" ], "reasoning": "The table shows that the BLEU score for pivoting from French to German is 24.33, while for zero-shot translation it is 21.63. Therefore, pivoting performs better.", "statement": "The zero-shot translation system performs better than pivoting for French to German.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: WMT14 en-de-fr Zero-shot results with baseline and aligned models compared against pivoting. Pivoting through English is performed using the baseline multilingual model.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Pivoting with the pool-cosine model achieved BLEU scores of 26.25 on de→fr and 20.18 on fr→de, outperforming the vanilla model in zero-shot performance.We compare the zero-shot performance of the multilingual models against the pivoting with the same multilingual model. Pivoting was able to achieve BLEU scores of 26.25 on de→fr and 20.18 on fr→de as evaluated on newstest2013. The alignment losses also effectively disentangle the representation of the source sentence from the target language ensuring prediction in the desired language." } ], "images": [ "scigen/train/1644.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: WMT14 en-de-fr Zero-shot results with baseline and aligned models compared against pivoting. Pivoting through English is performed using the baseline multilingual model." ], "reasoning": "Pivoting with the pool-cosine model achieved BLEU scores of 26.25 on de→fr and 20.18 on fr→de, outperforming the vanilla model in zero-shot performance.", "description": "We compare the zero-shot performance of the multilingual models against the pivoting with the same multilingual model. Pivoting was able to achieve BLEU scores of 26.25 on de→fr and 20.18 on fr→de as evaluated on newstest2013. The alignment losses also effectively disentangle the representation of the source sentence from the target language ensuring prediction in the desired language.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nTable title: Table 4: WMT14 en-de-fr Zero-shot results with baseline and aligned models compared against pivoting. Pivoting through English is performed using the baseline multilingual model.\n\nQuestion: Which multilingual system achieved the highest BLEU score for zero-shot de→fr translation?\n" }, { "role": "assistant", "content": "By comparing the zero-shot de→fr scores in the table, the adversarial system achieved the highest score of 26.00. So, the answer is {\"answer\": \"adversarial\"}" } ], "images": [ "scigen/train/1644.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: WMT14 en-de-fr Zero-shot results with baseline and aligned models compared against pivoting. Pivoting through English is performed using the baseline multilingual model." ], "reasoning": "By comparing the zero-shot de→fr scores in the table, the adversarial system achieved the highest score of 26.00.", "question": "Which multilingual system achieved the highest BLEU score for zero-shot de→fr translation?", "answer": "So, the answer is {\"answer\": \"adversarial\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe vanilla system achieved the highest BLEU score for supervised en→fr translation.\n" }, { "role": "assistant", "content": "The table shows that the vanilla system achieved a BLEU score of 32.68 for supervised en→fr translation, which is lower than the scores of the adversarial (32.92) and pool-cosine (32.94) systems. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1644.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: WMT14 en-de-fr Zero-shot results with baseline and aligned models compared against pivoting. Pivoting through English is performed using the baseline multilingual model." ], "reasoning": "The table shows that the vanilla system achieved a BLEU score of 32.68 for supervised en→fr translation, which is lower than the scores of the adversarial (32.92) and pool-cosine (32.94) systems.", "statement": "The vanilla system achieved the highest BLEU score for supervised en→fr translation.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Average BLEU scores for multilingual model on IWSLT-2017; Zero-Shot results are marked (zs).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The baseline model performs well on zero-shot translation due to the multi-way parallel nature of the dataset, and training with auxiliary cosine alignment loss matches the quality of bridging.On this dataset, the baseline model does not seem to have trouble with decoding to the correct language and performs well on zero-shot translation from the start. This may be a symptom of this dataset being multi-way parallel with the English sentences shared across all language pairs. By training with the auxillary cosine alignment loss, we are once again able to match the quality of bridging." } ], "images": [ "scigen/train/1645.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Average BLEU scores for multilingual model on IWSLT-2017; Zero-Shot results are marked (zs)." ], "reasoning": "The baseline model performs well on zero-shot translation due to the multi-way parallel nature of the dataset, and training with auxiliary cosine alignment loss matches the quality of bridging.", "description": "On this dataset, the baseline model does not seem to have trouble with decoding to the correct language and performs well on zero-shot translation from the start. This may be a symptom of this dataset being multi-way parallel with the English sentences shared across all language pairs. By training with the auxillary cosine alignment loss, we are once again able to match the quality of bridging.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: Average BLEU scores for multilingual model on IWSLT-2017; Zero-Shot results are marked (zs).'.\n\nQuestion: What is the performance of the baseline model on zero-shot translation for xx↔yy pairs using vanilla direct?\n" }, { "role": "assistant", "content": "The table shows that the performance of the baseline model on zero-shot translation for xx↔yy pairs using vanilla direct is 16.73. So, the answer is {\"answer\": \"16.73\"}" } ], "images": [ "scigen/train/1645.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Average BLEU scores for multilingual model on IWSLT-2017; Zero-Shot results are marked (zs)." ], "reasoning": "The table shows that the performance of the baseline model on zero-shot translation for xx↔yy pairs using vanilla direct is 16.73.", "question": "What is the performance of the baseline model on zero-shot translation for xx↔yy pairs using vanilla direct?", "answer": "So, the answer is {\"answer\": \"16.73\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe align(cosine) direct method performs better than the vanilla direct method for en↔xx pairs.\n" }, { "role": "assistant", "content": "The table shows that the align(cosine) direct method has a score of 29.95, which is lower than the vanilla direct method score of 30.11 for en↔xx pairs. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1645.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Average BLEU scores for multilingual model on IWSLT-2017; Zero-Shot results are marked (zs)." ], "reasoning": "The table shows that the align(cosine) direct method has a score of 29.95, which is lower than the vanilla direct method score of 30.11 for en↔xx pairs.", "statement": "The align(cosine) direct method performs better than the vanilla direct method for en↔xx pairs.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Kendall’s tau (τ) on WMT12 for alternative cost functions using 4metrics+syntax25+Wiki-GW50.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Using the Kendall cost initially results in slower convergence and poor initialization, but pre-training with logistic cost followed by Kendall cost fine-tuning improves performance.our Kendall cost. We can see that using the Kendall cost enables effective learning, although it is eventually outperformed by the logistic cost. Our investigation revealed that this was due to a combination of slower convergence and poor initialization. Therefore, we further experimented with a setup where we first used the logistic cost to pre-train the neural network, and then we switched to the Kendall cost in order to perform some finer tuning." } ], "images": [ "scigen/train/1646.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Kendall’s tau (τ) on WMT12 for alternative cost functions using 4metrics+syntax25+Wiki-GW50." ], "reasoning": "Using the Kendall cost initially results in slower convergence and poor initialization, but pre-training with logistic cost followed by Kendall cost fine-tuning improves performance.", "description": "our Kendall cost. We can see that using the Kendall cost enables effective learning, although it is eventually outperformed by the logistic cost. Our investigation revealed that this was due to a combination of slower convergence and poor initialization. Therefore, we further experimented with a setup where we first used the logistic cost to pre-train the neural network, and then we switched to the Kendall cost in order to perform some finer tuning.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 5: Kendall’s tau (τ) on WMT12 for alternative cost functions using 4metrics+syntax25+Wiki-GW50.', solve the following question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the average Kendall's τ score when using logistic cost followed by Kendall cost?\n" }, { "role": "assistant", "content": "The table shows the average Kendall's τ score for different methods. The row for 'Log.+Ken.' shows an average score of 29.92. So, the answer is {\"answer\": \"29.92\"}." } ], "images": [ "scigen/train/1646.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Kendall’s tau (τ) on WMT12 for alternative cost functions using 4metrics+syntax25+Wiki-GW50." ], "reasoning": "The table shows the average Kendall's τ score for different methods. The row for 'Log.+Ken.' shows an average score of 29.92.", "question": "What is the average Kendall's τ score when using logistic cost followed by Kendall cost?", "answer": "So, the answer is {\"answer\": \"29.92\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Kendall cost alone has a higher average Kendall's τ score than the logistic cost.\n" }, { "role": "assistant", "content": "The table shows that the average Kendall's τ score for Kendall cost alone is 29.53, while for logistic cost it is 29.70. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1646.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Kendall’s tau (τ) on WMT12 for alternative cost functions using 4metrics+syntax25+Wiki-GW50." ], "reasoning": "The table shows that the average Kendall's τ score for Kendall cost alone is 29.53, while for logistic cost it is 29.70. Therefore, the statement is refuted.", "statement": "The Kendall cost alone has a higher average Kendall's τ score than the logistic cost.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: Kendall’s tau (τ) on the WMT12 dataset for various metrics. ‘AVG’ is the average τ for the four language pairs. The best results are marked in boldface.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The 4metrics achieve Kendall’s τ between 18.5 and 23.5, and adding embedding vectors like syntax25 and Wiki-GW50 improves the τ values significantly, with the combination of both yielding a τ close to 30.We will refer to the set of these four metrics as 4metrics. These metrics are not tuned and achieve Kendall’s τ between 18.5 and 23.5. These are the metrics that are added as pairwise similarity features in our neural network approach (skip arcs). These networks achieve modest τ values around 10, which should not be surprising: they use very general vector representations and have no access to word or n-gram overlap or to length information, which are very important features to compute similarity against the reference. However, as will be discussed below, their contribution is complementary to the four previous evaluation metrics and will lead to significant improvements in combination with them. Indeed, 4metrics is a strong mix that involves not only simple lexical overlap but also approximate matching, paraphrases, edit distance, lengths, etc. Yet, adding to 4metrics the embedding vectors yields sizeable further improvements: +1.5 and +2.0 points absolute when adding syntax25 and Wiki-GW50, respectively. Finally, adding both yields even further improvements close to τ of 30 (+2.64 τ points), showing that lexical semantics and syntactic representations are complementary." } ], "images": [ "scigen/train/1647.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Kendall’s tau (τ) on the WMT12 dataset for various metrics. ‘AVG’ is the average τ for the four language pairs. The best results are marked in boldface." ], "reasoning": "The 4metrics achieve Kendall’s τ between 18.5 and 23.5, and adding embedding vectors like syntax25 and Wiki-GW50 improves the τ values significantly, with the combination of both yielding a τ close to 30.", "description": "We will refer to the set of these four metrics as 4metrics. These metrics are not tuned and achieve Kendall’s τ between 18.5 and 23.5. These are the metrics that are added as pairwise similarity features in our neural network approach (skip arcs). These networks achieve modest τ values around 10, which should not be surprising: they use very general vector representations and have no access to word or n-gram overlap or to length information, which are very important features to compute similarity against the reference. However, as will be discussed below, their contribution is complementary to the four previous evaluation metrics and will lead to significant improvements in combination with them. Indeed, 4metrics is a strong mix that involves not only simple lexical overlap but also approximate matching, paraphrases, edit distance, lengths, etc. Yet, adding to 4metrics the embedding vectors yields sizeable further improvements: +1.5 and +2.0 points absolute when adding syntax25 and Wiki-GW50, respectively. Finally, adding both yields even further improvements close to τ of 30 (+2.64 τ points), showing that lexical semantics and syntactic representations are complementary.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 1: Kendall’s tau (τ) on the WMT12 dataset for various metrics. ‘AVG’ is the average τ for the four language pairs. The best results are marked in boldface.' and methodically tackle the question that follows. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the average Kendall’s τ value for the system using 4metrics and embedding vectors?\n" }, { "role": "assistant", "content": "To find the average Kendall’s τ value for the system using 4metrics and embedding vectors, we look at the row '4metrics+syntax25+Wiki-GW50' under 'III. NN using 4metrics and embedding vectors' and find the AVG column value. So, the answer is {\"answer\": \"29.70\"}." } ], "images": [ "scigen/train/1647.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Kendall’s tau (τ) on the WMT12 dataset for various metrics. ‘AVG’ is the average τ for the four language pairs. The best results are marked in boldface." ], "reasoning": "To find the average Kendall’s τ value for the system using 4metrics and embedding vectors, we look at the row '4metrics+syntax25+Wiki-GW50' under 'III. NN using 4metrics and embedding vectors' and find the AVG column value.", "question": "What is the average Kendall’s τ value for the system using 4metrics and embedding vectors?", "answer": "So, the answer is {\"answer\": \"29.70\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe system using only 4metrics achieves a higher average Kendall’s τ value than the system using 4metrics combined with syntax25.\n" }, { "role": "assistant", "content": "The average Kendall’s τ value for the system using only 4metrics is 27.06, while the system using 4metrics combined with syntax25 has an average Kendall’s τ value of 28.51. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1647.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Kendall’s tau (τ) on the WMT12 dataset for various metrics. ‘AVG’ is the average τ for the four language pairs. The best results are marked in boldface." ], "reasoning": "The average Kendall’s τ value for the system using only 4metrics is 27.06, while the system using 4metrics combined with syntax25 has an average Kendall’s τ value of 28.51. Therefore, the statement is refuted.", "statement": "The system using only 4metrics achieves a higher average Kendall’s τ value than the system using 4metrics combined with syntax25.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Kendall’s τ on WMT12 for neural networks using BLEUcomp, a decomposed version of BLEU. For comparison, the last line shows a combination using BLEU instead of BLEUcomp.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "A single-layer neural network (logistic regression) outperforms BLEU by more than +1.3 τ points on average.We see that using a single-layer neural network, which is equivalent to logistic regression, outperforms BLEU by more than +1.3 τ points absolute." } ], "images": [ "scigen/train/1648.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Kendall’s τ on WMT12 for neural networks using BLEUcomp, a decomposed version of BLEU. For comparison, the last line shows a combination using BLEU instead of BLEUcomp." ], "reasoning": "A single-layer neural network (logistic regression) outperforms BLEU by more than +1.3 τ points on average.", "description": "We see that using a single-layer neural network, which is equivalent to logistic regression, outperforms BLEU by more than +1.3 τ points absolute.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Kendall’s τ on WMT12 for neural networks using BLEUcomp, a decomposed version of BLEU. For comparison, the last line shows a combination using BLEU instead of BLEUcomp.', answer the following question. Give detailed solution consisting of each step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich system has the highest average Kendall's τ score?\n" }, { "role": "assistant", "content": "To find the system with the highest average Kendall's τ score, look at the 'Kendall's τ AVG' column and identify the highest value, which is 26.15. So, the answer is {\"answer\": \"BLEUcomp+syntax25+Wiki-GW50\"}." } ], "images": [ "scigen/train/1648.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Kendall’s τ on WMT12 for neural networks using BLEUcomp, a decomposed version of BLEU. For comparison, the last line shows a combination using BLEU instead of BLEUcomp." ], "reasoning": "To find the system with the highest average Kendall's τ score, look at the 'Kendall's τ AVG' column and identify the highest value, which is 26.15.", "question": "Which system has the highest average Kendall's τ score?", "answer": "So, the answer is {\"answer\": \"BLEUcomp+syntax25+Wiki-GW50\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe BLEUcomp+Wiki-GW50 system has the highest Kendall's τ score for the German language.\n" }, { "role": "assistant", "content": "To verify this statement, check the 'Kendall's τ de' column for the highest value. The highest value is 28.92, which belongs to the BLEUcomp+syntax25+Wiki-GW50 system, not BLEUcomp+Wiki-GW50. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1648.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Kendall’s τ on WMT12 for neural networks using BLEUcomp, a decomposed version of BLEU. For comparison, the last line shows a combination using BLEU instead of BLEUcomp." ], "reasoning": "To verify this statement, check the 'Kendall's τ de' column for the highest value. The highest value is 28.92, which belongs to the BLEUcomp+syntax25+Wiki-GW50 system, not BLEUcomp+Wiki-GW50.", "statement": "The BLEUcomp+Wiki-GW50 system has the highest Kendall's τ score for the German language.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Average Kendall’s τ on WMT12 for semantic vectors trained on different text collections. Shown are results (i) when using the semantic vectors alone, and (ii) when combining them with 4metrics and syntax25. The improvements over Wiki-GW50 are marked in bold.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Larger dimensional vectors (300-400) generally perform better by 1-2 τ points when used alone compared to 50-dimensional vectors, but show minimal improvement or slight decline when combined with 4metrics+syntax25.One interesting aspect to explore is the effect of the dimensionality of the input embeddings. Here, we studied the impact of using semantic vectors of bigger sizes, trained on different and larger text collections. We can see that, compared to the 50-dimensional Wiki-GW50, 300-400 dimensional vectors are generally better by 1-2 τ points absolute when used in isolation; however, when used in combination with 4metrics+syntax25, they do not offer much gain (up to +0.2), and in some cases, we observe a slight drop in performance. We suspect that the variability across the different collections is due to a domain mismatch. Yet, we defer this question for future work." } ], "images": [ "scigen/train/1649.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Average Kendall’s τ on WMT12 for semantic vectors trained on different text collections. Shown are results (i) when using the semantic vectors alone, and (ii) when combining them with 4metrics and syntax25. The improvements over Wiki-GW50 are marked in bold." ], "reasoning": "Larger dimensional vectors (300-400) generally perform better by 1-2 τ points when used alone compared to 50-dimensional vectors, but show minimal improvement or slight decline when combined with 4metrics+syntax25.", "description": "One interesting aspect to explore is the effect of the dimensionality of the input embeddings. Here, we studied the impact of using semantic vectors of bigger sizes, trained on different and larger text collections. We can see that, compared to the 50-dimensional Wiki-GW50, 300-400 dimensional vectors are generally better by 1-2 τ points absolute when used in isolation; however, when used in combination with 4metrics+syntax25, they do not offer much gain (up to +0.2), and in some cases, we observe a slight drop in performance. We suspect that the variability across the different collections is due to a domain mismatch. Yet, we defer this question for future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: Average Kendall’s τ on WMT12 for semantic vectors trained on different text collections. Shown are results (i) when using the semantic vectors alone, and (ii) when combining them with 4metrics and syntax25. The improvements over Wiki-GW50 are marked in bold.', unfold the steps to solve the question given next. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nQuestion: Which source has the highest score when used alone?\n" }, { "role": "assistant", "content": "To find the source with the highest score when used alone, we compare the 'Alone' column values. Composes400 has the highest score of 12.35. So, the answer is {\"answer\": \"Composes400\"}" } ], "images": [ "scigen/train/1649.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Average Kendall’s τ on WMT12 for semantic vectors trained on different text collections. Shown are results (i) when using the semantic vectors alone, and (ii) when combining them with 4metrics and syntax25. The improvements over Wiki-GW50 are marked in bold." ], "reasoning": "To find the source with the highest score when used alone, we compare the 'Alone' column values. Composes400 has the highest score of 12.35.", "question": "Which source has the highest score when used alone?", "answer": "So, the answer is {\"answer\": \"Composes400\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe combination of Wiki-GW300 and 4metrics+syntax25 results in the highest score.\n" }, { "role": "assistant", "content": "The 'Comb.' column shows that the combination of Wiki-GW300 and 4metrics+syntax25 has a score of 29.90, which is the highest among all combinations. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1649.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Average Kendall’s τ on WMT12 for semantic vectors trained on different text collections. Shown are results (i) when using the semantic vectors alone, and (ii) when combining them with 4metrics and syntax25. The improvements over Wiki-GW50 are marked in bold." ], "reasoning": "The 'Comb.' column shows that the combination of Wiki-GW300 and 4metrics+syntax25 has a score of 29.90, which is the highest among all combinations.", "statement": "The combination of Wiki-GW300 and 4metrics+syntax25 results in the highest score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 6: Kendall’s τ on WMT12 for neural networks using different variants of word-embedding fine tuning. All variants are implemented on top of our full system from Section 4, referred to as “Pre-computed sentence embeddings” in this table.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Switching to dynamic composition of sentence-level embeddings results in a 0.16 point drop, moderate fine-tuning improves performance by 0.07, and full fine-tuning yields a 0.25 improvement over the un-tuned setting and 0.09 over the fully normalized baseline., we observe the results on the WMT12 dataset measured by Kendall’s τ in the different settings described above. First, note that the effect of normalization is noticeable. Just by switching to a dynamic composition of sentence-level embeddings, we lose 0.16 points absolute. Allowing the moderate fine-tuning of the embedding matrix only improves performance slightly by 0.07. However, allowing the full fine-tuning of the embedding matrix, yields an improvement of 0.25 over the un-tuned setting, and even slightly over the fully normalized baseline system (+0.09)." } ], "images": [ "scigen/train/1650.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Kendall’s τ on WMT12 for neural networks using different variants of word-embedding fine tuning. All variants are implemented on top of our full system from Section 4, referred to as “Pre-computed sentence embeddings” in this table." ], "reasoning": "Switching to dynamic composition of sentence-level embeddings results in a 0.16 point drop, moderate fine-tuning improves performance by 0.07, and full fine-tuning yields a 0.25 improvement over the un-tuned setting and 0.09 over the fully normalized baseline.", "description": ", we observe the results on the WMT12 dataset measured by Kendall’s τ in the different settings described above. First, note that the effect of normalization is noticeable. Just by switching to a dynamic composition of sentence-level embeddings, we lose 0.16 points absolute. Allowing the moderate fine-tuning of the embedding matrix only improves performance slightly by 0.07. However, allowing the full fine-tuning of the embedding matrix, yields an improvement of 0.25 over the un-tuned setting, and even slightly over the fully normalized baseline system (+0.09).", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 6: Kendall’s τ on WMT12 for neural networks using different variants of word-embedding fine tuning. All variants are implemented on top of our full system from Section 4, referred to as “Pre-computed sentence embeddings” in this table.', and tackle the question below in a sequential manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the improvement in Kendall's τ AVG when switching from pre-computed sentence embeddings to on-the-fly sentence embeddings with full fine-tuning?\n" }, { "role": "assistant", "content": "The Kendall's τ AVG for pre-computed sentence embeddings is 29.72. For on-the-fly sentence embeddings with full fine-tuning, it is 29.81. The improvement is 29.81 - 29.72 = 0.09. So, the answer is {\"answer\": \"0.09\"}" } ], "images": [ "scigen/train/1650.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Kendall’s τ on WMT12 for neural networks using different variants of word-embedding fine tuning. All variants are implemented on top of our full system from Section 4, referred to as “Pre-computed sentence embeddings” in this table." ], "reasoning": "The Kendall's τ AVG for pre-computed sentence embeddings is 29.72. For on-the-fly sentence embeddings with full fine-tuning, it is 29.81. The improvement is 29.81 - 29.72 = 0.09.", "question": "What is the improvement in Kendall's τ AVG when switching from pre-computed sentence embeddings to on-the-fly sentence embeddings with full fine-tuning?", "answer": "So, the answer is {\"answer\": \"0.09\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nOn-the-fly sentence embeddings with moderate fine-tuning have a higher Kendall's τ AVG than pre-computed sentence embeddings.\n" }, { "role": "assistant", "content": "The Kendall's τ AVG for on-the-fly sentence embeddings with moderate fine-tuning is 29.63, while for pre-computed sentence embeddings it is 29.72. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1650.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Kendall’s τ on WMT12 for neural networks using different variants of word-embedding fine tuning. All variants are implemented on top of our full system from Section 4, referred to as “Pre-computed sentence embeddings” in this table." ], "reasoning": "The Kendall's τ AVG for on-the-fly sentence embeddings with moderate fine-tuning is 29.63, while for pre-computed sentence embeddings it is 29.72. Therefore, the statement is refuted.", "statement": "On-the-fly sentence embeddings with moderate fine-tuning have a higher Kendall's τ AVG than pre-computed sentence embeddings.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 7: Kendall’s τ on WMT12 for different variants of LSTM-RNNs.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The CNN model with pre-trained vectors slightly outperforms the averaging baseline, while the Bidirectional LSTM achieves the highest average score.The first row shows the results for the averaging baseline (no semantic composition using CNN or LSTM). The second and the third rows show the results for our CNN model: when it is initialized with pretrained Wiki-GW50 vectors, and when it is randomly initialized, respectively. We can see that CNN with pre-trained vectors is slightly better than our averaging BOW baseline." } ], "images": [ "scigen/train/1651.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Kendall’s τ on WMT12 for different variants of LSTM-RNNs." ], "reasoning": "The CNN model with pre-trained vectors slightly outperforms the averaging baseline, while the Bidirectional LSTM achieves the highest average score.", "description": "The first row shows the results for the averaging baseline (no semantic composition using CNN or LSTM). The second and the third rows show the results for our CNN model: when it is initialized with pretrained Wiki-GW50 vectors, and when it is randomly initialized, respectively. We can see that CNN with pre-trained vectors is slightly better than our averaging BOW baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 7: Kendall’s τ on WMT12 for different variants of LSTM-RNNs.' and proceed to solve the following question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieves the highest average Kendall's τ score?\n" }, { "role": "assistant", "content": "The table shows that the Bidirectional LSTM has the highest average Kendall's τ score of 29.85. So, the answer is {\"answer\": \"Bidirectional LSTM\"}." } ], "images": [ "scigen/train/1651.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Kendall’s τ on WMT12 for different variants of LSTM-RNNs." ], "reasoning": "The table shows that the Bidirectional LSTM has the highest average Kendall's τ score of 29.85.", "question": "Which model achieves the highest average Kendall's τ score?", "answer": "So, the answer is {\"answer\": \"Bidirectional LSTM\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The CNN model with pre-trained vectors has a higher average score than the Bidirectional LSTM., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe CNN model with pre-trained vectors has a higher average score than the Bidirectional LSTM.\n" }, { "role": "assistant", "content": "The table shows that the CNN model with pre-trained vectors has an average score of 29.77, while the Bidirectional LSTM has a higher average score of 29.85. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1651.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Kendall’s τ on WMT12 for different variants of LSTM-RNNs." ], "reasoning": "The table shows that the CNN model with pre-trained vectors has an average score of 29.77, while the Bidirectional LSTM has a higher average score of 29.85.", "statement": "The CNN model with pre-trained vectors has a higher average score than the Bidirectional LSTM.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 8: Results for baseline and LSTM-based model without syntactic embeddings.'.\n" }, { "role": "assistant", "content": "Fine-tuning and composition with LSTMs did not yield significant improvements, with LSTM showing a gain of +0.31.Another general observation is that fine tuning and composition with LSTMs did not yield improvements to the extent that we had expected. In this setting, LSTM yields a larger gain of +0.31." } ], "images": [ "scigen/train/1652.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Results for baseline and LSTM-based model without syntactic embeddings." ], "reasoning": "Fine-tuning and composition with LSTMs did not yield significant improvements, with LSTM showing a gain of +0.31.", "description": "Another general observation is that fine tuning and composition with LSTMs did not yield improvements to the extent that we had expected. In this setting, LSTM yields a larger gain of +0.31.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: Table 8: Results for baseline and LSTM-based model without syntactic embeddings.\nQuestion: What is the average Kendall's τ score for the Bidirectional LSTM without syntactic embeddings?\n" }, { "role": "assistant", "content": "To find the average Kendall's τ score for the Bidirectional LSTM without syntactic embeddings, look at the row labeled 'Bidirectional LSTM, without syntactic embeddings' and find the value under the column 'Kendall’s τ AVG'. So, the answer is {\"answer\": \"29.38\"}." } ], "images": [ "scigen/train/1652.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Results for baseline and LSTM-based model without syntactic embeddings." ], "reasoning": "To find the average Kendall's τ score for the Bidirectional LSTM without syntactic embeddings, look at the row labeled 'Bidirectional LSTM, without syntactic embeddings' and find the value under the column 'Kendall’s τ AVG'.", "question": "What is the average Kendall's τ score for the Bidirectional LSTM without syntactic embeddings?", "answer": "So, the answer is {\"answer\": \"29.38\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Bidirectional LSTM without syntactic embeddings has a higher Kendall's τ score for cz than the Averaging baseline without syntactic embeddings.\n" }, { "role": "assistant", "content": "Compare the Kendall's τ score for cz between the Bidirectional LSTM without syntactic embeddings and the Averaging baseline without syntactic embeddings. The Bidirectional LSTM has a score of 26.31, which is higher than the Averaging baseline's score of 25.67. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1652.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Results for baseline and LSTM-based model without syntactic embeddings." ], "reasoning": "Compare the Kendall's τ score for cz between the Bidirectional LSTM without syntactic embeddings and the Averaging baseline without syntactic embeddings. The Bidirectional LSTM has a score of 26.31, which is higher than the Averaging baseline's score of 25.67.", "statement": "The Bidirectional LSTM without syntactic embeddings has a higher Kendall's τ score for cz than the Averaging baseline without syntactic embeddings.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 9: Comparing to the state of the art at the segment level on the WMT12 dataset, translating into English. Values marked as na were not reported by the authors.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Using absolute scores instead of pairwise comparisons yields better results by 0.9-1.2 Kendall’s τ points, as it removes possible circularities in the pairwise framework.We refer to our metric as NNRK (for Neural Network ReranKing), using subindices to describe the metric variant. The comparison is on the WMT12 dataset, at the segment level. We can see that using absolute scores instead of pairwise comparisons yields better results: by 0.9-1.2 Kendall ’s τ points absolute. We believe that this is because by using an absolute score rather than a pairwise decision, we remove some possible circularities, e.g., in the pairwise framework, we could predict that translation x is better than y, and y is better than z, but z is better than x. This is not possible when working with absolute scores." } ], "images": [ "scigen/train/1653.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Comparing to the state of the art at the segment level on the WMT12 dataset, translating into English. Values marked as na were not reported by the authors." ], "reasoning": "Using absolute scores instead of pairwise comparisons yields better results by 0.9-1.2 Kendall’s τ points, as it removes possible circularities in the pairwise framework.", "description": "We refer to our metric as NNRK (for Neural Network ReranKing), using subindices to describe the metric variant. The comparison is on the WMT12 dataset, at the segment level. We can see that using absolute scores instead of pairwise comparisons yields better results: by 0.9-1.2 Kendall ’s τ points absolute. We believe that this is because by using an absolute score rather than a pairwise decision, we remove some possible circularities, e.g., in the pairwise framework, we could predict that translation x is better than y, and y is better than z, but z is better than x. This is not possible when working with absolute scores.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 9: Comparing to the state of the art at the segment level on the WMT12 dataset, translating into English. Values marked as na were not reported by the authors.', solve the subsequent question in a stepwise manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich system has the highest average Kendall’s τ score?\n" }, { "role": "assistant", "content": "To find the system with the highest average Kendall’s τ score, we need to compare the 'Kendall’s τ (WMT12-style) AVG' column for all systems. The highest value in this column is 30.9, which corresponds to the NNRKMean system. So, the answer is {\"answer\": \"NNRKMean\"}." } ], "images": [ "scigen/train/1653.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Comparing to the state of the art at the segment level on the WMT12 dataset, translating into English. Values marked as na were not reported by the authors." ], "reasoning": "To find the system with the highest average Kendall’s τ score, we need to compare the 'Kendall’s τ (WMT12-style) AVG' column for all systems. The highest value in this column is 30.9, which corresponds to the NNRKMean system.", "question": "Which system has the highest average Kendall’s τ score?", "answer": "So, the answer is {\"answer\": \"NNRKMean\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nNNRKZero has a higher average Kendall’s τ score than NNRKpairwise.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the 'Kendall’s τ (WMT12-style) AVG' scores of NNRKZero and NNRKpairwise. NNRKZero has an average score of 30.6, while NNRKpairwise has an average score of 29.7. Therefore, NNRKZero has a higher average score than NNRKpairwise. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1653.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Comparing to the state of the art at the segment level on the WMT12 dataset, translating into English. Values marked as na were not reported by the authors." ], "reasoning": "To verify this statement, we compare the 'Kendall’s τ (WMT12-style) AVG' scores of NNRKZero and NNRKpairwise. NNRKZero has an average score of 30.6, while NNRKpairwise has an average score of 29.7. Therefore, NNRKZero has a higher average score than NNRKpairwise.", "statement": "NNRKZero has a higher average Kendall’s τ score than NNRKpairwise.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 10: Comparing NNRK to the state of the art at the system level on the WMT12 dataset, translating into English.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The NNRK metric outperformed the WMT12 competition metrics and DiscoTK, and NNRKMean performed better than NNRKZero at the system level.Not surprisingly, they are on par with what we saw at the segment level. Once again, our NNRK metric outperformed the metrics from the WMT12 competition, as well as the best post-competition result of DiscoTK; moreover, our NNRK metric is strong across all language pairs. Also, as expected, the system-level score based on the NNRKMean segment-level score performed better than the one based on NNRKZero." } ], "images": [ "scigen/train/1654.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Comparing NNRK to the state of the art at the system level on the WMT12 dataset, translating into English." ], "reasoning": "The NNRK metric outperformed the WMT12 competition metrics and DiscoTK, and NNRKMean performed better than NNRKZero at the system level.", "description": "Not surprisingly, they are on par with what we saw at the segment level. Once again, our NNRK metric outperformed the metrics from the WMT12 competition, as well as the best post-competition result of DiscoTK; moreover, our NNRK metric is strong across all language pairs. Also, as expected, the system-level score based on the NNRKMean segment-level score performed better than the one based on NNRKZero.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 10: Comparing NNRK to the state of the art at the system level on the WMT12 dataset, translating into English.', unfold the steps to solve the question given next. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nQuestion: Which system has the highest Spearman's rank correlation for the Spanish language pair?\n" }, { "role": "assistant", "content": "By examining the table, the highest Spearman's rank correlation for the Spanish language pair (ρ es) is 97.9, which is achieved by the NNRKMean system. So, the answer is {\"answer\": \"NNRKMean\"}" } ], "images": [ "scigen/train/1654.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Comparing NNRK to the state of the art at the system level on the WMT12 dataset, translating into English." ], "reasoning": "By examining the table, the highest Spearman's rank correlation for the Spanish language pair (ρ es) is 97.9, which is achieved by the NNRKMean system.", "question": "Which system has the highest Spearman's rank correlation for the Spanish language pair?", "answer": "So, the answer is {\"answer\": \"NNRKMean\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe NNRKZero system has a higher average Spearman's rank correlation than the SEMPOS system.\n" }, { "role": "assistant", "content": "The table shows that the average Spearman's rank correlation (ρ AVG) for NNRKZero is 92.1, while for SEMPOS it is 90.2. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1654.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Comparing NNRK to the state of the art at the system level on the WMT12 dataset, translating into English." ], "reasoning": "The table shows that the average Spearman's rank correlation (ρ AVG) for NNRKZero is 92.1, while for SEMPOS it is 90.2. Therefore, the statement is supported.", "statement": "The NNRKZero system has a higher average Spearman's rank correlation than the SEMPOS system.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 11: Comparing to the state of the art at the segment level on the WMT14 dataset, translating into English.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "NNRK outperforms all rivals except for the tuned DiscoTK, which uses more pre-existing metrics. NNRK is best on Hindi-English but weak on Russian-English.Once again, our NNRK metric is very competitive and outperforms all rivals, except for the tuned DiscoTK, which combines about twenty strong pre-existing evaluation metrics (in addition to discourse-based kernels), while we only incorporate four pre-existing metrics. ’s τ points absolute; we are also better on all four language pairs. Overall, we are best on Hindi-English, but we are surprisingly weak on Russian-English." } ], "images": [ "scigen/train/1655.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Comparing to the state of the art at the segment level on the WMT14 dataset, translating into English." ], "reasoning": "NNRK outperforms all rivals except for the tuned DiscoTK, which uses more pre-existing metrics. NNRK is best on Hindi-English but weak on Russian-English.", "description": "Once again, our NNRK metric is very competitive and outperforms all rivals, except for the tuned DiscoTK, which combines about twenty strong pre-existing evaluation metrics (in addition to discourse-based kernels), while we only incorporate four pre-existing metrics. ’s τ points absolute; we are also better on all four language pairs. Overall, we are best on Hindi-English, but we are surprisingly weak on Russian-English.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: 'Table 11: Comparing to the state of the art at the segment level on the WMT14 dataset, translating into English.'\nQuestion: Which system has the highest average Kendall's τ score?\n" }, { "role": "assistant", "content": "To find the system with the highest average Kendall's τ score, look at the 'Kendall's τ (WMT14-style) AVG' column and identify the highest value. DiscoTK has the highest average score of 38.6. So, the answer is {\"answer\": \"DiscoTK\"}." } ], "images": [ "scigen/train/1655.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Comparing to the state of the art at the segment level on the WMT14 dataset, translating into English." ], "reasoning": "To find the system with the highest average Kendall's τ score, look at the 'Kendall's τ (WMT14-style) AVG' column and identify the highest value. DiscoTK has the highest average score of 38.6.", "question": "Which system has the highest average Kendall's τ score?", "answer": "So, the answer is {\"answer\": \"DiscoTK\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nNNRKMean has the highest Kendall's τ score for Hindi-English.\n" }, { "role": "assistant", "content": "To verify this statement, check the 'Kendall's τ (WMT14-style) hi' column. NNRKMean has a score of 44.1, which is the highest in that column. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1655.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Comparing to the state of the art at the segment level on the WMT14 dataset, translating into English." ], "reasoning": "To verify this statement, check the 'Kendall's τ (WMT14-style) hi' column. NNRKMean has a score of 44.1, which is the highest in that column.", "statement": "NNRKMean has the highest Kendall's τ score for Hindi-English.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 12: Comparing to the state of the art at the system level on the WMT14 dataset, translating into English.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "NNRK ranks third overall, excelling in French-English and German-English, while ReVal leads in Czech-English and Russian-English, and LAYERED is best in Hindi-English.Our NNRK metric is the third in the ranking, after DiscoTK and ReVal. While DiscoTK is best overall, it is not the strongest on any of the five languages in the table. In contrast, NNRK has the highest scores on French-English and German-English, while ReVal is strongest on Czech-English and Russian-English, and LAYERED is best on Hindi-English. On this dataset, NNRK is slightly below ReVal; this can be expected, as our metric specializes on segment-level pairwise judgments and it was trained with only the smaller dataset from WMT11. Note that again our overall correlation is strongly penalized by a very low score on Russian-English. The reason for this phenomenon has to be further investigated." } ], "images": [ "scigen/train/1656.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 12: Comparing to the state of the art at the system level on the WMT14 dataset, translating into English." ], "reasoning": "NNRK ranks third overall, excelling in French-English and German-English, while ReVal leads in Czech-English and Russian-English, and LAYERED is best in Hindi-English.", "description": "Our NNRK metric is the third in the ranking, after DiscoTK and ReVal. While DiscoTK is best overall, it is not the strongest on any of the five languages in the table. In contrast, NNRK has the highest scores on French-English and German-English, while ReVal is strongest on Czech-English and Russian-English, and LAYERED is best on Hindi-English. On this dataset, NNRK is slightly below ReVal; this can be expected, as our metric specializes on segment-level pairwise judgments and it was trained with only the smaller dataset from WMT11. Note that again our overall correlation is strongly penalized by a very low score on Russian-English. The reason for this phenomenon has to be further investigated.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 12: Comparing to the state of the art at the system level on the WMT14 dataset, translating into English.', solve this question and give detail solutions. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich metric has the highest Pearson correlation score for Russian-English?\n" }, { "role": "assistant", "content": "The description states that ReVal is strongest on Russian-English. Checking the table, ReVal has the highest Pearson correlation score of 88.1 for Russian-English. So, the answer is {\"answer\": \"ReVal\"}." } ], "images": [ "scigen/train/1656.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 12: Comparing to the state of the art at the system level on the WMT14 dataset, translating into English." ], "reasoning": "The description states that ReVal is strongest on Russian-English. Checking the table, ReVal has the highest Pearson correlation score of 88.1 for Russian-English.", "question": "Which metric has the highest Pearson correlation score for Russian-English?", "answer": "So, the answer is {\"answer\": \"ReVal\"}." }, { "messages": [ { "role": "user", "content": "NNRKMean has the highest Pearson correlation score for French-English. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nNNRKMean has the highest Pearson correlation score for French-English.\n" }, { "role": "assistant", "content": "The description mentions that NNRK has the highest scores on French-English. The table confirms this with NNRKMean having a Pearson correlation score of 98.4 for French-English. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1656.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 12: Comparing to the state of the art at the system level on the WMT14 dataset, translating into English." ], "reasoning": "The description mentions that NNRK has the highest scores on French-English. The table confirms this with NNRKMean having a Pearson correlation score of 98.4 for French-English.", "statement": "NNRKMean has the highest Pearson correlation score for French-English.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 11: The detailed rating scores from AMT.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The AMT platform is used to rate dialogue model responses on fluency, consistency, and engagingness across three datasets, with each response rated from 1 to 9.In addition to perplexity, we use the Amazon Mechanical Turk (AMT) platform for human evaluation of different training processes on the three dialogue data-sets. For the AMT rating, each turker is given a dialogue context, and a randomly permuted set of model sample responses. The turker is then asked to rate each sample response according to its fluency, consistency, and engagingness respectively, using an integer score from 1 to 9. The reference response is also rated for comparison. For each data-model pair, we collect 2,500 ratings. To remove bias among annotators, we use the bayesian inference code from Kulikov et al. Since we use top-k sampling, the BLEU score is not directly suitable for our setting Liu et al." } ], "images": [ "scigen/train/1657.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: The detailed rating scores from AMT." ], "reasoning": "The AMT platform is used to rate dialogue model responses on fluency, consistency, and engagingness across three datasets, with each response rated from 1 to 9.", "description": "In addition to perplexity, we use the Amazon Mechanical Turk (AMT) platform for human evaluation of different training processes on the three dialogue data-sets. For the AMT rating, each turker is given a dialogue context, and a randomly permuted set of model sample responses. The turker is then asked to rate each sample response according to its fluency, consistency, and engagingness respectively, using an integer score from 1 to 9. The reference response is also rated for comparison. For each data-model pair, we collect 2,500 ratings. To remove bias among annotators, we use the bayesian inference code from Kulikov et al. Since we use top-k sampling, the BLEU score is not directly suitable for our setting Liu et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 11: The detailed rating scores from AMT.', answer the following question. Give detailed solution consisting of each step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model received the highest fluency rating for the DailyDialogue dataset?\n" }, { "role": "assistant", "content": "To find the highest fluency rating for the DailyDialogue dataset, compare the fluency scores in the first column. The Reference model has the highest fluency rating of 6.54. So, the answer is {\"answer\": \"Reference\"}" } ], "images": [ "scigen/train/1657.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: The detailed rating scores from AMT." ], "reasoning": "To find the highest fluency rating for the DailyDialogue dataset, compare the fluency scores in the first column. The Reference model has the highest fluency rating of 6.54.", "question": "Which model received the highest fluency rating for the DailyDialogue dataset?", "answer": "So, the answer is {\"answer\": \"Reference\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The NS+Mix-review model received the highest engagingness rating for the Switchboard dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe NS+Mix-review model received the highest engagingness rating for the Switchboard dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the engagingness ratings for the Switchboard dataset. The NS+Mix-review model has an engagingness rating of 5.74, which is the highest among the models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1657.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: The detailed rating scores from AMT." ], "reasoning": "To verify this statement, compare the engagingness ratings for the Switchboard dataset. The NS+Mix-review model has an engagingness rating of 5.74, which is the highest among the models listed.", "statement": "The NS+Mix-review model received the highest engagingness rating for the Switchboard dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 5: Effects of multi-way data on Europarl. ‘‘Yes’’ means with multi-way translation, and ‘‘No’’ means the opposite.'.\n" }, { "role": "assistant", "content": "The performance drop is mainly due to the dataset size, but our methods outperform Chen et al.Prior research Cheng et al. To investigate the affects, we followed the same process in Cheng et al. We conjecture that the performance drop is mainly because of the size of the dataset. Also our methods can easily beat Chen et al." } ], "images": [ "scigen/train/1658.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Effects of multi-way data on Europarl. ‘‘Yes’’ means with multi-way translation, and ‘‘No’’ means the opposite." ], "reasoning": "The performance drop is mainly due to the dataset size, but our methods outperform Chen et al.", "description": "Prior research Cheng et al. To investigate the affects, we followed the same process in Cheng et al. We conjecture that the performance drop is mainly because of the size of the dataset. Also our methods can easily beat Chen et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 5: Effects of multi-way data on Europarl. ‘‘Yes’’ means with multi-way translation, and ‘‘No’’ means the opposite.', and sequentially solve the question, writing out each step of your reasoning process. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model has the highest score for Es→Fr Yes?\n" }, { "role": "assistant", "content": "To find the highest score for Es→Fr Yes, compare the values in the Es→Fr Yes column. The highest value is 40.04, which corresponds to the ZS + LM model. So, the answer is {\"answer\": \"ZS + LM\"}" } ], "images": [ "scigen/train/1658.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Effects of multi-way data on Europarl. ‘‘Yes’’ means with multi-way translation, and ‘‘No’’ means the opposite." ], "reasoning": "To find the highest score for Es→Fr Yes, compare the values in the Es→Fr Yes column. The highest value is 40.04, which corresponds to the ZS + LM model.", "question": "Which model has the highest score for Es→Fr Yes?", "answer": "So, the answer is {\"answer\": \"ZS + LM\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe ZS model has a higher score for De→Fr Yes than the PIV-M model.\n" }, { "role": "assistant", "content": "To verify this statement, compare the scores for De→Fr Yes between the ZS and PIV-M models. The ZS model has a score of 29.59, while the PIV-M model has a score of 31.46. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1658.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Effects of multi-way data on Europarl. ‘‘Yes’’ means with multi-way translation, and ‘‘No’’ means the opposite." ], "reasoning": "To verify this statement, compare the scores for De→Fr Yes between the ZS and PIV-M models. The ZS model has a score of 29.59, while the PIV-M model has a score of 31.46. Therefore, the statement is refuted.", "statement": "The ZS model has a higher score for De→Fr Yes than the PIV-M model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 7: Performance of systems and iterative combination of them. Combination improves both precision and recall even using low performing systems.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The combined model 1+2+3+4+5+6 achieved an F0.5 score of 0.4051, which is a 6 point improvement over the best standalone Nematus model.This generated a 6 point improvement on the dev set of f0.5 when compared the best standalone Nematus model." } ], "images": [ "scigen/train/1659.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Performance of systems and iterative combination of them. Combination improves both precision and recall even using low performing systems." ], "reasoning": "The combined model 1+2+3+4+5+6 achieved an F0.5 score of 0.4051, which is a 6 point improvement over the best standalone Nematus model.", "description": "This generated a 6 point improvement on the dev set of f0.5 when compared the best standalone Nematus model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 7: Performance of systems and iterative combination of them. Combination improves both precision and recall even using low performing systems.' and methodically tackle the question that follows. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the F0.5 score of the best standalone Nematus model?\n" }, { "role": "assistant", "content": "The best standalone Nematus model is Nematus2 with an F0.5 score of 0.3429. The combined model 1+2+3+4+5+6 achieved an F0.5 score of 0.4051, which is a 6 point improvement. So, the answer is {\"answer\": \"0.3429\"}" } ], "images": [ "scigen/train/1659.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Performance of systems and iterative combination of them. Combination improves both precision and recall even using low performing systems." ], "reasoning": "The best standalone Nematus model is Nematus2 with an F0.5 score of 0.3429. The combined model 1+2+3+4+5+6 achieved an F0.5 score of 0.4051, which is a 6 point improvement.", "question": "What is the F0.5 score of the best standalone Nematus model?", "answer": "So, the answer is {\"answer\": \"0.3429\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe combined model 1+2+3+4+5+6 achieved an F0.5 score of 0.4051.\n" }, { "role": "assistant", "content": "The table shows that the combined model 1+2+3+4+5+6 has an F0.5 score of 0.4051. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1659.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Performance of systems and iterative combination of them. Combination improves both precision and recall even using low performing systems." ], "reasoning": "The table shows that the combined model 1+2+3+4+5+6 has an F0.5 score of 0.4051.", "statement": "The combined model 1+2+3+4+5+6 achieved an F0.5 score of 0.4051.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Nematus performance on W&I dev set by training data. The use of more data improves the system, but only when the training from the domain is upsampled.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Training Nematus with additional data sources like Lang8, FCE, and Nucle significantly improved the results, especially when the W&I train set was upsampled.We trained Nematus using several different data sets. First, we trained using only the W&I train set data, we then added Lang8, FCE and Nucle data sources. Since Lang8 is significantly larger than W&I train set, inspired by Junczys-Dowmunt et al. This procedure improved results significantly (" } ], "images": [ "scigen/train/1660.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Nematus performance on W&I dev set by training data. The use of more data improves the system, but only when the training from the domain is upsampled." ], "reasoning": "Training Nematus with additional data sources like Lang8, FCE, and Nucle significantly improved the results, especially when the W&I train set was upsampled.", "description": "We trained Nematus using several different data sets. First, we trained using only the W&I train set data, we then added Lang8, FCE and Nucle data sources. Since Lang8 is significantly larger than W&I train set, inspired by Junczys-Dowmunt et al. This procedure improved results significantly (", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 4: Nematus performance on W&I dev set by training data. The use of more data improves the system, but only when the training from the domain is upsampled.\nQuestion: What was the F0.5 score when the W&I train set was upsampled and combined with Lang8, FCE, and Nucle?\n" }, { "role": "assistant", "content": "The table shows the F0.5 scores for different training data combinations. The highest F0.5 score is achieved when the W&I train set is upsampled and combined with Lang8, FCE, and Nucle. So, the answer is {\"answer\": \"0.333\"}." } ], "images": [ "scigen/train/1660.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Nematus performance on W&I dev set by training data. The use of more data improves the system, but only when the training from the domain is upsampled." ], "reasoning": "The table shows the F0.5 scores for different training data combinations. The highest F0.5 score is achieved when the W&I train set is upsampled and combined with Lang8, FCE, and Nucle.", "question": "What was the F0.5 score when the W&I train set was upsampled and combined with Lang8, FCE, and Nucle?", "answer": "So, the answer is {\"answer\": \"0.333\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe precision score improved when Lang8 and FCE were added to the W&I train set.\n" }, { "role": "assistant", "content": "The table shows that the precision score increased from 0.3187 to 0.4604 when Lang8 and FCE were added to the W&I train set. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1660.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Nematus performance on W&I dev set by training data. The use of more data improves the system, but only when the training from the domain is upsampled." ], "reasoning": "The table shows that the precision score increased from 0.3187 to 0.4604 when Lang8 and FCE were added to the W&I train set.", "statement": "The precision score improved when Lang8 and FCE were added to the W&I train set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 5: Size of synthetic datasets and Nematus scores when trained on them.'.\n" }, { "role": "assistant", "content": "Training Nematus on synthetic errors data from similar domains, such as the W&I train set, yields better performance compared to using data from different domains like Gutenberg Books.We also tried training Nematus over synthetic errors data. We generated errors using data from two different domains. Books from project Gutenberg and gold sentences from W&I train set. These experiments show that relying on the source domain is crucial and it is best to generate data using text from similar domain. When using the synthetic W&I train set we reached a score that is just a little lower than the score when training over W&I train set directly (0.19 vs 0.23). This might suggest that there is potential in using synthetic data when combined with other data sets and promise for synthetic data methods for unsupervised GEC." } ], "images": [ "scigen/train/1661.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Size of synthetic datasets and Nematus scores when trained on them." ], "reasoning": "Training Nematus on synthetic errors data from similar domains, such as the W&I train set, yields better performance compared to using data from different domains like Gutenberg Books.", "description": "We also tried training Nematus over synthetic errors data. We generated errors using data from two different domains. Books from project Gutenberg and gold sentences from W&I train set. These experiments show that relying on the source domain is crucial and it is best to generate data using text from similar domain. When using the synthetic W&I train set we reached a score that is just a little lower than the score when training over W&I train set directly (0.19 vs 0.23). This might suggest that there is potential in using synthetic data when combined with other data sets and promise for synthetic data methods for unsupervised GEC.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 5: Size of synthetic datasets and Nematus scores when trained on them.', unfold the question presented below, detailing every step of your calculation. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the F0.5 score when using 1,300,000 sentences from the W&I train set?\n" }, { "role": "assistant", "content": "The table shows the F0.5 score for different data sources and sizes. For the W&I train set with 1,300,000 sentences, the F0.5 score is 0.1919. So, the answer is {\"answer\": \"0.1919\"}." } ], "images": [ "scigen/train/1661.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Size of synthetic datasets and Nematus scores when trained on them." ], "reasoning": "The table shows the F0.5 score for different data sources and sizes. For the W&I train set with 1,300,000 sentences, the F0.5 score is 0.1919.", "question": "What is the F0.5 score when using 1,300,000 sentences from the W&I train set?", "answer": "So, the answer is {\"answer\": \"0.1919\"}." }, { "messages": [ { "role": "user", "content": "Using 7,000,000 sentences from Gutenberg Books yields a higher F0.5 score than using 650,000 sentences from the same source. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nUsing 7,000,000 sentences from Gutenberg Books yields a higher F0.5 score than using 650,000 sentences from the same source.\n" }, { "role": "assistant", "content": "The table shows that using 7,000,000 sentences from Gutenberg Books results in an F0.5 score of 0.1294, which is lower than the F0.5 score of 0.1483 obtained from using 650,000 sentences from the same source. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1661.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Size of synthetic datasets and Nematus scores when trained on them." ], "reasoning": "The table shows that using 7,000,000 sentences from Gutenberg Books results in an F0.5 score of 0.1294, which is lower than the F0.5 score of 0.1483 obtained from using 650,000 sentences from the same source.", "statement": "Using 7,000,000 sentences from Gutenberg Books yields a higher F0.5 score than using 650,000 sentences from the same source.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 6: Change in performance when avoiding hard errors.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Filtering corrections by error types not only improves system performance but also highlights weak spots for further enhancement without discouraging the system from making those corrections.Even given a single system, we are able to improve the system’s performance by eschewing predictions on low performing error types. Felice et al. In explicitly filtering corrections by error types we gain two additional benefits over the mere score improvement. First, the weak spots of the system are emphasized, and work might be directed to improving components or combining with a relevant strong system. Second, the system itself is not discouraged or changed to stop producing those corrections. So, if future enhancement would improve this type of errors enough, it will show up in results, without discouraging smaller improvements done on the way." } ], "images": [ "scigen/train/1662.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Change in performance when avoiding hard errors." ], "reasoning": "Filtering corrections by error types not only improves system performance but also highlights weak spots for further enhancement without discouraging the system from making those corrections.", "description": "Even given a single system, we are able to improve the system’s performance by eschewing predictions on low performing error types. Felice et al. In explicitly filtering corrections by error types we gain two additional benefits over the mere score improvement. First, the weak spots of the system are emphasized, and work might be directed to improving components or combining with a relevant strong system. Second, the system itself is not discouraged or changed to stop producing those corrections. So, if future enhancement would improve this type of errors enough, it will show up in results, without discouraging smaller improvements done on the way.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 6: Change in performance when avoiding hard errors.', solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich system shows the highest improvement in F0.5 score after filtering corrections by error types?\n" }, { "role": "assistant", "content": "By comparing the F0.5 scores before and after filtering for each system, we find that Grammarly improves from 0.3627 to 0.3754, an increase of 0.0127, which is the highest improvement among all systems. So, the answer is {\"answer\": \"Grammarly\"}." } ], "images": [ "scigen/train/1662.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Change in performance when avoiding hard errors." ], "reasoning": "By comparing the F0.5 scores before and after filtering for each system, we find that Grammarly improves from 0.3627 to 0.3754, an increase of 0.0127, which is the highest improvement among all systems.", "question": "Which system shows the highest improvement in F0.5 score after filtering corrections by error types?", "answer": "So, the answer is {\"answer\": \"Grammarly\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nFiltered Language Tool has a higher precision than Language Tool.\n" }, { "role": "assistant", "content": "The table shows that the precision (P) for Filtered Language Tool is 0.4005, while for Language Tool it is 0.2905, indicating that the statement is correct. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1662.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Change in performance when avoiding hard errors." ], "reasoning": "The table shows that the precision (P) for Filtered Language Tool is 0.4005, while for Language Tool it is 0.2905, indicating that the statement is correct.", "statement": "Filtered Language Tool has a higher precision than Language Tool.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 8: Combining with off the shelf systems helps.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Combining existing approaches can harness their correction power, as shown by the improved F0.5 scores when systems are combined.Off the shelf systems. This implies there is a promise in combining existing approaches which we can’t improve ourselves to harness some of their correction power." } ], "images": [ "scigen/train/1663.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Combining with off the shelf systems helps." ], "reasoning": "Combining existing approaches can harness their correction power, as shown by the improved F0.5 scores when systems are combined.", "description": "Off the shelf systems. This implies there is a promise in combining existing approaches which we can’t improve ourselves to harness some of their correction power.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 8: Combining with off the shelf systems helps.', answer the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich combination of systems achieves the highest F0.5 score?\n" }, { "role": "assistant", "content": "By examining the F0.5 scores for each system and their combinations, the highest F0.5 score is achieved by the combination of systems 1, 2, 3, and 4. So, the answer is {\"answer\": \"1+2+3+4\"}." } ], "images": [ "scigen/train/1663.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Combining with off the shelf systems helps." ], "reasoning": "By examining the F0.5 scores for each system and their combinations, the highest F0.5 score is achieved by the combination of systems 1, 2, 3, and 4.", "question": "Which combination of systems achieves the highest F0.5 score?", "answer": "So, the answer is {\"answer\": \"1+2+3+4\"}." }, { "messages": [ { "role": "user", "content": "The combination of systems 1 and 2 achieves a higher F0.5 score than system 3 alone. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe combination of systems 1 and 2 achieves a higher F0.5 score than system 3 alone.\n" }, { "role": "assistant", "content": "The F0.5 score for the combination of systems 1 and 2 is 0.4105, while the F0.5 score for system 3 alone is 0.3612. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1663.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Combining with off the shelf systems helps." ], "reasoning": "The F0.5 score for the combination of systems 1 and 2 is 0.4105, while the F0.5 score for system 3 alone is 0.3612. Therefore, the statement is supported.", "statement": "The combination of systems 1 and 2 achieves a higher F0.5 score than system 3 alone.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 9: Combining fares better compared to ensemble.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "While average ensemble improves precision but reduces recall, combining models balances precision and recall, maximizing F0.5.Ensemble VS Combining models results. Nematus has average ensembling built-in which enables inference over several RNN models by performing geometric average of the individual models’ probability distributions. It is also important to note that while average ensemble improves precision, it reduces recall. Combination is balancing precision and recall, improving both, in a way that maximizes F0.5. The last observation is far from trivial as most ways to combine systems would emphasize one or the other, e.g., piping would support mainly recall perhaps reducing precision. Lastly, combining is based on the types of errors and is linguistically motivated, and hence could be further improved by smart categorization and perhaps improvements of automatic detection Bryant et al." } ], "images": [ "scigen/train/1664.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Combining fares better compared to ensemble." ], "reasoning": "While average ensemble improves precision but reduces recall, combining models balances precision and recall, maximizing F0.5.", "description": "Ensemble VS Combining models results. Nematus has average ensembling built-in which enables inference over several RNN models by performing geometric average of the individual models’ probability distributions. It is also important to note that while average ensemble improves precision, it reduces recall. Combination is balancing precision and recall, improving both, in a way that maximizes F0.5. The last observation is far from trivial as most ways to combine systems would emphasize one or the other, e.g., piping would support mainly recall perhaps reducing precision. Lastly, combining is based on the types of errors and is linguistically motivated, and hence could be further improved by smart categorization and perhaps improvements of automatic detection Bryant et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich system achieves the highest F0.5 score?\nSolve the above question based on the table titled 'Table 9: Combining fares better compared to ensemble.'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the F0.5 scores of all systems in the table, the 1+2+3+4 combination achieves the highest F0.5 score of 0.3508. So, the answer is {\"answer\": \"1+2+3+4 combination\"}." } ], "images": [ "scigen/train/1664.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Combining fares better compared to ensemble." ], "reasoning": "By comparing the F0.5 scores of all systems in the table, the 1+2+3+4 combination achieves the highest F0.5 score of 0.3508.", "question": "Which system achieves the highest F0.5 score?", "answer": "So, the answer is {\"answer\": \"1+2+3+4 combination\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe 1+2+3+4 ensemble has the highest precision.\n" }, { "role": "assistant", "content": "By comparing the precision (P) values of all systems in the table, the 1+2+3+4 ensemble has the highest precision of 0.5577. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1664.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Combining fares better compared to ensemble." ], "reasoning": "By comparing the precision (P) values of all systems in the table, the 1+2+3+4 ensemble has the highest precision of 0.5577.", "statement": "The 1+2+3+4 ensemble has the highest precision.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Validation Accuracy on BERT-LARGE across sequence length and learning rate.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The BERT-LARGE model performed worse than the BERT-BASE model on a smaller training dataset, achieving a test performance of 75.1% on TIRA.This is a discrepancy from BERT-BASE indicating that the larger model struggled more with training on a small amount of long sequences. For our best trained BERT-LARGE, we submitted the model for evaluation on TIRA. Surprisingly, the test performance (75.1%) of the larger model was worse than the base model. The experiments in Devlin et al. consistently found improvements when using the large model. The main distinction here is a smaller training dataset than in their tasks. The experiments in the remaining sections use the same hyperparameters as the optimal BERT-LARGE." } ], "images": [ "scigen/train/1665.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Validation Accuracy on BERT-LARGE across sequence length and learning rate." ], "reasoning": "The BERT-LARGE model performed worse than the BERT-BASE model on a smaller training dataset, achieving a test performance of 75.1% on TIRA.", "description": "This is a discrepancy from BERT-BASE indicating that the larger model struggled more with training on a small amount of long sequences. For our best trained BERT-LARGE, we submitted the model for evaluation on TIRA. Surprisingly, the test performance (75.1%) of the larger model was worse than the base model. The experiments in Devlin et al. consistently found improvements when using the large model. The main distinction here is a smaller training dataset than in their tasks. The experiments in the remaining sections use the same hyperparameters as the optimal BERT-LARGE.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Validation Accuracy on BERT-LARGE across sequence length and learning rate.', and sequentially solve the question, writing out each step of your reasoning process. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat was the test performance of the BERT-LARGE model on TIRA?\n" }, { "role": "assistant", "content": "The description mentions that the BERT-LARGE model was submitted for evaluation on TIRA and achieved a test performance of 75.1%. So, the answer is {\"answer\": \"75.1%\"}" } ], "images": [ "scigen/train/1665.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Validation Accuracy on BERT-LARGE across sequence length and learning rate." ], "reasoning": "The description mentions that the BERT-LARGE model was submitted for evaluation on TIRA and achieved a test performance of 75.1%.", "question": "What was the test performance of the BERT-LARGE model on TIRA?", "answer": "So, the answer is {\"answer\": \"75.1%\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe BERT-LARGE model achieved a test performance of 75.1% on TIRA.\n" }, { "role": "assistant", "content": "The description explicitly states that the BERT-LARGE model achieved a test performance of 75.1% on TIRA. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1665.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Validation Accuracy on BERT-LARGE across sequence length and learning rate." ], "reasoning": "The description explicitly states that the BERT-LARGE model achieved a test performance of 75.1% on TIRA.", "statement": "The BERT-LARGE model achieved a test performance of 75.1% on TIRA.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 1: Validation accuracy for BERT-base with and without Unsupervised Pre-training (UP).'.\n" }, { "role": "assistant", "content": "Pre-training improves performance for shorter sequence lengths but not for longer sequences, likely due to the small dataset size.Our first experiment was checking the importance of pre-training. We then trained the model on sequence lengths of 100, 250 and 500. The other column shows how well BERT-base trained without pre-training. We found improvements for lower sequence lengths, but not at 500 word pieces. Since the longer chunk should have been more informative, and since our hand-labeled training set only contained 516 articles, this likely indicates that BERT experiences training difficulty when dealing with long sequences on such a small dataset. As the cost to do pre-training was only a one time cost all of our remaining experiments use a pre-trained model." } ], "images": [ "scigen/train/1666.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Validation accuracy for BERT-base with and without Unsupervised Pre-training (UP)." ], "reasoning": "Pre-training improves performance for shorter sequence lengths but not for longer sequences, likely due to the small dataset size.", "description": "Our first experiment was checking the importance of pre-training. We then trained the model on sequence lengths of 100, 250 and 500. The other column shows how well BERT-base trained without pre-training. We found improvements for lower sequence lengths, but not at 500 word pieces. Since the longer chunk should have been more informative, and since our hand-labeled training set only contained 516 articles, this likely indicates that BERT experiences training difficulty when dealing with long sequences on such a small dataset. As the cost to do pre-training was only a one time cost all of our remaining experiments use a pre-trained model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 1: Validation accuracy for BERT-base with and without Unsupervised Pre-training (UP).', and tackle the question below in a sequential manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the performance difference between BERT-base and BERT-base + UP for a sequence length of 250?\n" }, { "role": "assistant", "content": "For a sequence length of 250, BERT-base has a performance of 75.9, while BERT-base + UP has a performance of 82.9. The difference is 82.9 - 75.9. So, the answer is {\"answer\": \"7.0\"}" } ], "images": [ "scigen/train/1666.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Validation accuracy for BERT-base with and without Unsupervised Pre-training (UP)." ], "reasoning": "For a sequence length of 250, BERT-base has a performance of 75.9, while BERT-base + UP has a performance of 82.9. The difference is 82.9 - 75.9.", "question": "What is the performance difference between BERT-base and BERT-base + UP for a sequence length of 250?", "answer": "So, the answer is {\"answer\": \"7.0\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nBERT-base + UP performs better than BERT-base for all sequence lengths.\n" }, { "role": "assistant", "content": "For sequence lengths of 100 and 250, BERT-base + UP performs better than BERT-base. However, for a sequence length of 500, BERT-base performs better than BERT-base + UP. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1666.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Validation accuracy for BERT-base with and without Unsupervised Pre-training (UP)." ], "reasoning": "For sequence lengths of 100 and 250, BERT-base + UP performs better than BERT-base. However, for a sequence length of 500, BERT-base performs better than BERT-base + UP.", "statement": "BERT-base + UP performs better than BERT-base for all sequence lengths.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: Translation results with different sizes of the source vocabulary.'.\n" }, { "role": "assistant", "content": "The best results are achieved with 10k or 20k BPE merges, indicating that a smaller source vocabulary enhances transfer performance.We varied the number of BPE merges on the source side, fixing the target vocabulary to 50k merges. The best results are with 10k or 20k of BPE merges, which shows that the source vocabulary should be reasonably small to maximize the transfer performance. Less BPE merges lead to more language-independent tokens; it is easier for the cross-lingual embedding to find the overlaps in the shared semantic space." } ], "images": [ "scigen/train/1667.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Translation results with different sizes of the source vocabulary." ], "reasoning": "The best results are achieved with 10k or 20k BPE merges, indicating that a smaller source vocabulary enhances transfer performance.", "description": "We varied the number of BPE merges on the source side, fixing the target vocabulary to 50k merges. The best results are with 10k or 20k of BPE merges, which shows that the source vocabulary should be reasonably small to maximize the transfer performance. Less BPE merges lead to more language-independent tokens; it is easier for the cross-lingual embedding to find the overlaps in the shared semantic space.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 5: Translation results with different sizes of the source vocabulary.' and methodically tackle the question that follows. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the BLEU score for sl-en with 50k BPE merges?\n" }, { "role": "assistant", "content": "To find the BLEU score for sl-en with 50k BPE merges, locate the row with 50k BPE merges and then find the corresponding value under the BLEU [%] sl-en column. So, the answer is {\"answer\": \"20.2\"}." } ], "images": [ "scigen/train/1667.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Translation results with different sizes of the source vocabulary." ], "reasoning": "To find the BLEU score for sl-en with 50k BPE merges, locate the row with 50k BPE merges and then find the corresponding value under the BLEU [%] sl-en column.", "question": "What is the BLEU score for sl-en with 50k BPE merges?", "answer": "So, the answer is {\"answer\": \"20.2\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe BLEU score for ben-en with 70k BPE merges is higher than with 10k BPE merges.\n" }, { "role": "assistant", "content": "Compare the BLEU scores for ben-en with 70k and 10k BPE merges. The score for 70k is 10.9, and for 10k it is 11.2, so the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1667.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Translation results with different sizes of the source vocabulary." ], "reasoning": "Compare the BLEU scores for ben-en with 70k and 10k BPE merges. The score for 70k is 10.9, and for 10k it is 11.2, so the statement is incorrect.", "statement": "The BLEU score for ben-en with 70k BPE merges is higher than with 10k BPE merges.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Translation results of different transfer learning setups.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Plain transfer learning improves translation quality, but additional techniques like cross-lingual word embedding, artificial noises, and synthetic data further enhance BLEU scores, especially for challenging language pairs.Plain transfer learning already gives a boost but is still far from a satisfying quality, especially for Basque→-English and Azerbaijani→English. On top of that, each of our three techniques offers clear, incremental improvements in all child language pairs with a maximum of 5.1% Bleu in total. Such tokens play the role of anchors in translation; they are sure to be copied to the target side. The surrounding tokens are spread according to the source language structure, whereas merely copying the target sentence to the source Currey et al. Note that our trick applies also to the languages with completely different alphabets, e.g. Belarusian and German" } ], "images": [ "scigen/train/1668.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Translation results of different transfer learning setups." ], "reasoning": "Plain transfer learning improves translation quality, but additional techniques like cross-lingual word embedding, artificial noises, and synthetic data further enhance BLEU scores, especially for challenging language pairs.", "description": "Plain transfer learning already gives a boost but is still far from a satisfying quality, especially for Basque→-English and Azerbaijani→English. On top of that, each of our three techniques offers clear, incremental improvements in all child language pairs with a maximum of 5.1% Bleu in total. Such tokens play the role of anchors in translation; they are sure to be copied to the target side. The surrounding tokens are spread according to the source language structure, whereas merely copying the target sentence to the source Currey et al. Note that our trick applies also to the languages with completely different alphabets, e.g. Belarusian and German", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Translation results of different transfer learning setups.', answer the following question. Give detailed solution consisting of each step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the BLEU score improvement for Basque→English when using synthetic data compared to the baseline?\n" }, { "role": "assistant", "content": "The BLEU score for Basque→English in the baseline is 1.7%. With synthetic data, it improves to 9.7%. The improvement is 9.7% - 1.7% = 8.0%. So, the answer is {\"answer\": \"8.0%\"}." } ], "images": [ "scigen/train/1668.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Translation results of different transfer learning setups." ], "reasoning": "The BLEU score for Basque→English in the baseline is 1.7%. With synthetic data, it improves to 9.7%. The improvement is 9.7% - 1.7% = 8.0%.", "question": "What is the BLEU score improvement for Basque→English when using synthetic data compared to the baseline?", "answer": "So, the answer is {\"answer\": \"8.0%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nUsing synthetic data results in the highest BLEU score for Slovenian→English.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for Slovenian→English with synthetic data is 22.1%, which is the highest score for this language pair compared to other techniques. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1668.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Translation results of different transfer learning setups." ], "reasoning": "The table shows that the BLEU score for Slovenian→English with synthetic data is 22.1%, which is the highest score for this language pair compared to other techniques.", "statement": "Using synthetic data results in the highest BLEU score for Slovenian→English.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Azerbaijani→English translation results with different types of pre-trained source embeddings.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The cross-lingual embedding mapped to the parent source (de) significantly improves transfer learning compared to monolingual embedding and other cross-lingual mappings.We observe that monolingual embedding without a cross-lingual mapping also improves the transfer learning, but is significantly worse than our proposed embedding, i.e. mapped to the parent source (de) embedding. The target-mapped embedding is not compatible with the pre-trained encoder but directly guides the child model to establish the connection between the new source and the target. It also improves the system, but our method is still the best among the three embedding types." } ], "images": [ "scigen/train/1669.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Azerbaijani→English translation results with different types of pre-trained source embeddings." ], "reasoning": "The cross-lingual embedding mapped to the parent source (de) significantly improves transfer learning compared to monolingual embedding and other cross-lingual mappings.", "description": "We observe that monolingual embedding without a cross-lingual mapping also improves the transfer learning, but is significantly worse than our proposed embedding, i.e. mapped to the parent source (de) embedding. The target-mapped embedding is not compatible with the pre-trained encoder but directly guides the child model to establish the connection between the new source and the target. It also improves the system, but our method is still the best among the three embedding types.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 3: Azerbaijani→English translation results with different types of pre-trained source embeddings.', answer the question below by showing each progressive step toward the answer. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich pre-trained embedding type achieved the highest Bleu score?\n" }, { "role": "assistant", "content": "The table shows different pre-trained embedding types and their corresponding Bleu scores. The highest Bleu score is 7.4%, which corresponds to the cross-lingual (az-de) embedding. So, the answer is {\"answer\": \"Cross-lingual (az-de)\"}" } ], "images": [ "scigen/train/1669.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Azerbaijani→English translation results with different types of pre-trained source embeddings." ], "reasoning": "The table shows different pre-trained embedding types and their corresponding Bleu scores. The highest Bleu score is 7.4%, which corresponds to the cross-lingual (az-de) embedding.", "question": "Which pre-trained embedding type achieved the highest Bleu score?", "answer": "So, the answer is {\"answer\": \"Cross-lingual (az-de)\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe monolingual embedding achieved a higher Bleu score than the cross-lingual (az-en) embedding.\n" }, { "role": "assistant", "content": "The table shows that the monolingual embedding has a Bleu score of 6.3%, while the cross-lingual (az-en) embedding has a higher Bleu score of 7.1%. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1669.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Azerbaijani→English translation results with different types of pre-trained source embeddings." ], "reasoning": "The table shows that the monolingual embedding has a Bleu score of 6.3%, while the cross-lingual (az-en) embedding has a higher Bleu score of 7.1%. Therefore, the statement is refuted.", "statement": "The monolingual embedding achieved a higher Bleu score than the cross-lingual (az-en) embedding.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 6: Slovenian→English translation results with freezing different components of the decoder.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Freezing the decoder components improves translation performance, with the highest BLEU score achieved by freezing the target self-attention.We show only the results when freezing the decoder; in our experiments, freezing any component of the encoder always degrades the translation performance. The experiments were done at the final stage with all of our three proposed methods applied." } ], "images": [ "scigen/train/1670.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Slovenian→English translation results with freezing different components of the decoder." ], "reasoning": "Freezing the decoder components improves translation performance, with the highest BLEU score achieved by freezing the target self-attention.", "description": "We show only the results when freezing the decoder; in our experiments, freezing any component of the encoder always degrades the translation performance. The experiments were done at the final stage with all of our three proposed methods applied.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 6: Slovenian→English translation results with freezing different components of the decoder.', and tackle the question below in a sequential manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich frozen parameter configuration resulted in the highest BLEU score?\n" }, { "role": "assistant", "content": "By examining the BLEU scores for each frozen parameter configuration, the highest score is 22.1%, which corresponds to freezing the target self-attention. So, the answer is {\"answer\": \"Target self-attention\"}." } ], "images": [ "scigen/train/1670.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Slovenian→English translation results with freezing different components of the decoder." ], "reasoning": "By examining the BLEU scores for each frozen parameter configuration, the highest score is 22.1%, which corresponds to freezing the target self-attention.", "question": "Which frozen parameter configuration resulted in the highest BLEU score?", "answer": "So, the answer is {\"answer\": \"Target self-attention\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Freezing the target embedding resulted in a BLEU score higher than 21.8%., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nFreezing the target embedding resulted in a BLEU score higher than 21.8%.\n" }, { "role": "assistant", "content": "The table shows that freezing the target embedding resulted in a BLEU score of 21.4%, which is lower than 21.8%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1670.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Slovenian→English translation results with freezing different components of the decoder." ], "reasoning": "The table shows that freezing the target embedding resulted in a BLEU score of 21.4%, which is lower than 21.8%.", "statement": "Freezing the target embedding resulted in a BLEU score higher than 21.8%.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Accuracies of the (tanh) LSTM-RNN compared with other models.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "LSTM-RNN initially performed worse than other models, but with 300-D GloVe word embeddings, it outperformed all other models in both tasks.The accuracies of LSTM-RNN was taken from the network achieving the highest performance out of 10 runs on the development set. The accuracies of the other models are copied from the corresponding papers. LSTM-RNN clearly performed worse than DCNN, PV, DRNN in both tasks, and worse than CNN in the binary task. In the second experiment, we tried to boost the accuracy of the LSTM-RNN model. Inspired by \\newciteirsoy2014deep, we tried using dropout and better word embeddings. Dropout, however, did not work with LSTM. The reason might be that dropout corrupted its memory, thus making training more difficult. Better word embeddings did pay off, however. We used 300-D GloVe word embeddings trained on a 840B-word corpus. Testing on the development set, we chose the same values for the hyper-parameters as in the first experiment, except setting learning rate 0.01. We also run the model 10 times and selected the networks getting the highest accuracies on the development set. Using the 300-D GloVe word embeddings was very helpful: LSTM-RNN performed on par with DRNN in the fine-grained task, and with CNN in the binary task. Therefore, taking into account both tasks, LSTM-RNN with the 300-D GloVe word embeddings outperformed all other models." } ], "images": [ "scigen/train/1671.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Accuracies of the (tanh) LSTM-RNN compared with other models." ], "reasoning": "LSTM-RNN initially performed worse than other models, but with 300-D GloVe word embeddings, it outperformed all other models in both tasks.", "description": "The accuracies of LSTM-RNN was taken from the network achieving the highest performance out of 10 runs on the development set. The accuracies of the other models are copied from the corresponding papers. LSTM-RNN clearly performed worse than DCNN, PV, DRNN in both tasks, and worse than CNN in the binary task. In the second experiment, we tried to boost the accuracy of the LSTM-RNN model. Inspired by \\newciteirsoy2014deep, we tried using dropout and better word embeddings. Dropout, however, did not work with LSTM. The reason might be that dropout corrupted its memory, thus making training more difficult. Better word embeddings did pay off, however. We used 300-D GloVe word embeddings trained on a 840B-word corpus. Testing on the development set, we chose the same values for the hyper-parameters as in the first experiment, except setting learning rate 0.01. We also run the model 10 times and selected the networks getting the highest accuracies on the development set. Using the 300-D GloVe word embeddings was very helpful: LSTM-RNN performed on par with DRNN in the fine-grained task, and with CNN in the binary task. Therefore, taking into account both tasks, LSTM-RNN with the 300-D GloVe word embeddings outperformed all other models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: Accuracies of the (tanh) LSTM-RNN compared with other models.', answer the question below by showing each progressive step toward the answer. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model achieved the highest accuracy in the fine-grained task?\n" }, { "role": "assistant", "content": "By examining the table, we see that the LSTM-RNN with GloVe-300D achieved the highest accuracy in the fine-grained task with a score of 49.9. So, the answer is {\"answer\": \"LSTM-RNN with GloVe-300D\"}" } ], "images": [ "scigen/train/1671.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Accuracies of the (tanh) LSTM-RNN compared with other models." ], "reasoning": "By examining the table, we see that the LSTM-RNN with GloVe-300D achieved the highest accuracy in the fine-grained task with a score of 49.9.", "question": "Which model achieved the highest accuracy in the fine-grained task?", "answer": "So, the answer is {\"answer\": \"LSTM-RNN with GloVe-300D\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nLSTM-RNN with GloVe-300D outperformed all other models in the binary task.\n" }, { "role": "assistant", "content": "The table shows that LSTM-RNN with GloVe-300D achieved an accuracy of 88.0 in the binary task, which is lower than CNN's 88.1. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1671.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Accuracies of the (tanh) LSTM-RNN compared with other models." ], "reasoning": "The table shows that LSTM-RNN with GloVe-300D achieved an accuracy of 88.0 in the binary task, which is lower than CNN's 88.1.", "statement": "LSTM-RNN with GloVe-300D outperformed all other models in the binary task.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Results of the DAIC-WoZ corpus development set'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Affective conditioning significantly improves performance over the baseline model (HAN), with HAN+L showing higher F1 and UAR scores compared to other models.We observe that affective conditioning significantly improves the performance over the baseline model (HAN). Our HAN+L model also leads to improved F1 and AUR scores over the models proposed in [same]. Overall, we see that conditioning of external psycholinguistic knowledge in this small dataset (189 samples) enhances the performance and the results are comparable to these of the GPC dataset." } ], "images": [ "scigen/train/1672.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results of the DAIC-WoZ corpus development set" ], "reasoning": "Affective conditioning significantly improves performance over the baseline model (HAN), with HAN+L showing higher F1 and UAR scores compared to other models.", "description": "We observe that affective conditioning significantly improves the performance over the baseline model (HAN). Our HAN+L model also leads to improved F1 and AUR scores over the models proposed in [same]. Overall, we see that conditioning of external psycholinguistic knowledge in this small dataset (189 samples) enhances the performance and the results are comparable to these of the GPC dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 5: Results of the DAIC-WoZ corpus development set', solve this question and give detail solutions. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model has the highest F1-macro score and what is the value?\n" }, { "role": "assistant", "content": "By comparing the F1-macro scores of all models in the table, HAN+L has the highest F1-macro score of 0.69. So, the answer is {\"answer\": \"HAN+L, 0.69\"}." } ], "images": [ "scigen/train/1672.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results of the DAIC-WoZ corpus development set" ], "reasoning": "By comparing the F1-macro scores of all models in the table, HAN+L has the highest F1-macro score of 0.69.", "question": "Which model has the highest F1-macro score and what is the value?", "answer": "So, the answer is {\"answer\": \"HAN+L, 0.69\"}." }, { "messages": [ { "role": "user", "content": "The HAN+L model has the highest UAR score. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe HAN+L model has the highest UAR score.\n" }, { "role": "assistant", "content": "By comparing the UAR scores of all models in the table, HAN+L has the highest UAR score of 0.72. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1672.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results of the DAIC-WoZ corpus development set" ], "reasoning": "By comparing the UAR scores of all models in the table, HAN+L has the highest UAR score of 0.72.", "statement": "The HAN+L model has the highest UAR score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: Results of different architectures on the GPC'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The integration of external affective and psycholinguistic features improves model performance, with the best performance achieved by the HAN+L+S model.We compare the performance of the proposed models when given as input the client turns (Client), the therapist turns (Therapist) or the whole dialogue (Client+Therapist). We see that the integration of external affective and psycholinguistic features improves model performance for all model configurations over the HAN and SVM baselines. Furthermore, we notice that when we add the summary information we also gain a performance boost, sometimes greater that the external affective information. Summary and lexica integration leads to a performance increase when we provide only the client data. In addition, we see that the client turns are more important for depression detection, as expected, and incorporation of the therapist turns contributes little to the overall model performance. Based on our results, the best performance can be achieved by the HAN+L+S model, while HAN+L model performs best if such annotation is not available." } ], "images": [ "scigen/train/1673.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of different architectures on the GPC" ], "reasoning": "The integration of external affective and psycholinguistic features improves model performance, with the best performance achieved by the HAN+L+S model.", "description": "We compare the performance of the proposed models when given as input the client turns (Client), the therapist turns (Therapist) or the whole dialogue (Client+Therapist). We see that the integration of external affective and psycholinguistic features improves model performance for all model configurations over the HAN and SVM baselines. Furthermore, we notice that when we add the summary information we also gain a performance boost, sometimes greater that the external affective information. Summary and lexica integration leads to a performance increase when we provide only the client data. In addition, we see that the client turns are more important for depression detection, as expected, and incorporation of the therapist turns contributes little to the overall model performance. Based on our results, the best performance can be achieved by the HAN+L+S model, while HAN+L model performs best if such annotation is not available.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 4: Results of different architectures on the GPC', answer the question below by showing each progressive step toward the answer. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model configuration achieves the highest performance when using only client data?\n" }, { "role": "assistant", "content": "By examining the 'Client' column, we see that the HAN+L+S model achieves the highest performance with a score of 0.715. So, the answer is {\"answer\": \"HAN+L+S\"}." } ], "images": [ "scigen/train/1673.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of different architectures on the GPC" ], "reasoning": "By examining the 'Client' column, we see that the HAN+L+S model achieves the highest performance with a score of 0.715.", "question": "Which model configuration achieves the highest performance when using only client data?", "answer": "So, the answer is {\"answer\": \"HAN+L+S\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe HAN+S model performs better than the HAN+L model when using both client and therapist data.\n" }, { "role": "assistant", "content": "By comparing the 'Client+Therapist' column for HAN+S (0.718) and HAN+L (0.706), we see that HAN+S performs better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1673.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of different architectures on the GPC" ], "reasoning": "By comparing the 'Client+Therapist' column for HAN+S (0.718) and HAN+L (0.706), we see that HAN+S performs better.", "statement": "The HAN+S model performs better than the HAN+L model when using both client and therapist data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 6: Distribution of hate vocabulary over Hatebase categories'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The dataset shows that 9.28% of hate vocabulary overlaps with Hatebase, with most hate words related to ethnicity and gender.Finally, the dataset has been contrasted against the English vocabulary in Hatebase. 9.28% of hate vocabulary overlaps with Hatebase, a higher percentage than for noHate vocabulary, of which 6.57% of the words can be found in Hatebase. Although some percentages are not high, all 8 categories are present in the corpus. Most of the hate words from the dataset belong to ethnicity, followed by gender. This is in agreement with Silva et al." } ], "images": [ "scigen/train/1674.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Distribution of hate vocabulary over Hatebase categories" ], "reasoning": "The dataset shows that 9.28% of hate vocabulary overlaps with Hatebase, with most hate words related to ethnicity and gender.", "description": "Finally, the dataset has been contrasted against the English vocabulary in Hatebase. 9.28% of hate vocabulary overlaps with Hatebase, a higher percentage than for noHate vocabulary, of which 6.57% of the words can be found in Hatebase. Although some percentages are not high, all 8 categories are present in the corpus. Most of the hate words from the dataset belong to ethnicity, followed by gender. This is in agreement with Silva et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 6: Distribution of hate vocabulary over Hatebase categories\nQuestion: What percentage of hate words in the dataset belong to gender and how does it compare to the percentage of hate words related to ethnicity?\n" }, { "role": "assistant", "content": "The table shows that 36.05% of hate words belong to gender and 41.63% belong to ethnicity. Comparing these, ethnicity has a higher percentage of hate words than gender. So, the answer is {\"answer\": \"41.63%\"}." } ], "images": [ "scigen/train/1674.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Distribution of hate vocabulary over Hatebase categories" ], "reasoning": "The table shows that 36.05% of hate words belong to gender and 41.63% belong to ethnicity. Comparing these, ethnicity has a higher percentage of hate words than gender.", "question": "What percentage of hate words in the dataset belong to gender and how does it compare to the percentage of hate words related to ethnicity?", "answer": "So, the answer is {\"answer\": \"41.63%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The majority of hate words in the dataset are related to religion. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe majority of hate words in the dataset are related to religion.\n" }, { "role": "assistant", "content": "The table shows that only 1.34% of hate words are related to religion, which is much lower than the percentages for ethnicity (41.63%) and gender (36.05%). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1674.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Distribution of hate vocabulary over Hatebase categories" ], "reasoning": "The table shows that only 1.34% of hate words are related to religion, which is much lower than the percentages for ethnicity (41.63%) and gender (36.05%).", "statement": "The majority of hate words in the dataset are related to religion.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Inter-annotator agreements on batches 1 and 2'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table shows the number of annotated sentences and categories for each batch, along with the average percentage, average kappa, and Fleiss' kappa values.The number of annotated sentences (# sent) and the number of categories to label (# cat) are also given for each batch. The results are in line with similar works Nobata et al." } ], "images": [ "scigen/train/1675.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Inter-annotator agreements on batches 1 and 2" ], "reasoning": "The table shows the number of annotated sentences and categories for each batch, along with the average percentage, average kappa, and Fleiss' kappa values.", "description": "The number of annotated sentences (# sent) and the number of categories to label (# cat) are also given for each batch. The results are in line with similar works Nobata et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 1: Inter-annotator agreements on batches 1 and 2' and methodically tackle the question that follows. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the average kappa value for the batch with the highest number of categories?\n" }, { "role": "assistant", "content": "The batch with the highest number of categories is batch 2 with 4 categories. The average kappa value for this batch is 0.627. So, the answer is {\"answer\": \"0.627\"}." } ], "images": [ "scigen/train/1675.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Inter-annotator agreements on batches 1 and 2" ], "reasoning": "The batch with the highest number of categories is batch 2 with 4 categories. The average kappa value for this batch is 0.627.", "question": "What is the average kappa value for the batch with the highest number of categories?", "answer": "So, the answer is {\"answer\": \"0.627\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nBatch 1 has a higher average percentage than Batch 2.\n" }, { "role": "assistant", "content": "The average percentage for Batch 1 is 91.03%, while for Batch 2 it is 90.97%. Therefore, Batch 1 has a higher average percentage than Batch 2. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1675.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Inter-annotator agreements on batches 1 and 2" ], "reasoning": "The average percentage for Batch 1 is 91.03%, while for Batch 2 it is 90.97%. Therefore, Batch 1 has a higher average percentage than Batch 2.", "statement": "Batch 1 has a higher average percentage than Batch 2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 1: Classification performance on transfer tasks. We report F1-score for MRPC, Pearson coefficient for SICK-R and accuracy for most others. All sentence representations have been concatenated with ST-LN embeddings. Note that the discrepancy between results reported in this paper and the referenced paper is likely due to differences in minor implementation details and experimental environment. Our models are denoted by †.'.\n" }, { "role": "assistant", "content": "Incorporating self-attention mechanism in the encoder improves performance in 5 out of 8 tasks, but the original models perform better in CR, MPQA, and MRPC.Results show that incorporating self-attention mechanism in the encoder is beneficial for most tasks. However, original models were better in some tasks (CR, MPQA, MRPC), suggesting that self-attention mechanism could sometimes introduce noise in sentence features. Overall, utilizing self-attentive sentence representation further improves performances in 5 out of 8 tasks. Considering that models with self-attention employ smaller LSTM cells (1024) than those without (2048)" } ], "images": [ "scigen/train/1676.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Classification performance on transfer tasks. We report F1-score for MRPC, Pearson coefficient for SICK-R and accuracy for most others. All sentence representations have been concatenated with ST-LN embeddings. Note that the discrepancy between results reported in this paper and the referenced paper is likely due to differences in minor implementation details and experimental environment. Our models are denoted by †." ], "reasoning": "Incorporating self-attention mechanism in the encoder improves performance in 5 out of 8 tasks, but the original models perform better in CR, MPQA, and MRPC.", "description": "Results show that incorporating self-attention mechanism in the encoder is beneficial for most tasks. However, original models were better in some tasks (CR, MPQA, MRPC), suggesting that self-attention mechanism could sometimes introduce noise in sentence features. Overall, utilizing self-attentive sentence representation further improves performances in 5 out of 8 tasks. Considering that models with self-attention employ smaller LSTM cells (1024) than those without (2048)", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Classification performance on transfer tasks. We report F1-score for MRPC, Pearson coefficient for SICK-R and accuracy for most others. All sentence representations have been concatenated with ST-LN embeddings. Note that the discrepancy between results reported in this paper and the referenced paper is likely due to differences in minor implementation details and experimental environment. Our models are denoted by †.\nQuestion: Which method has the highest score in the CR task?\nSolve the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By examining the CR column, the highest score is 92.91, achieved by the 'Att. Cap2Img' method. So, the answer is {\"answer\": \"Att. Cap2Img\"}" } ], "images": [ "scigen/train/1676.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Classification performance on transfer tasks. We report F1-score for MRPC, Pearson coefficient for SICK-R and accuracy for most others. All sentence representations have been concatenated with ST-LN embeddings. Note that the discrepancy between results reported in this paper and the referenced paper is likely due to differences in minor implementation details and experimental environment. Our models are denoted by †." ], "reasoning": "By examining the CR column, the highest score is 92.91, achieved by the 'Att. Cap2Img' method.", "question": "Which method has the highest score in the CR task?", "answer": "So, the answer is {\"answer\": \"Att. Cap2Img\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Cap2All method without self-attention has the highest score in the SICK-R task.\n" }, { "role": "assistant", "content": "By examining the SICK-R column, the highest score is 84.57, achieved by the 'Att. Cap2All' method, not the 'Cap2All' method without self-attention. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1676.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Classification performance on transfer tasks. We report F1-score for MRPC, Pearson coefficient for SICK-R and accuracy for most others. All sentence representations have been concatenated with ST-LN embeddings. Note that the discrepancy between results reported in this paper and the referenced paper is likely due to differences in minor implementation details and experimental environment. Our models are denoted by †." ], "reasoning": "By examining the SICK-R column, the highest score is 84.57, achieved by the 'Att. Cap2All' method, not the 'Cap2All' method without self-attention.", "statement": "The Cap2All method without self-attention has the highest score in the SICK-R task.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: BLEU scores on the WMT test set without (clean) and with synthetic grammar errors. The best performing models for each test set are highlighted. When training and test match (highlighted) we generally observe higher results. However, including all clean and noisy data in the training set (mix-all) yields the best results across all datasets, with the highest average BLEU and the lowest variance.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The system trained on clean data shows a significant drop in performance when tested on noisy data, with BLEU scores dropping by 1.0–3.6 points.The performance of our systems on the synthetic WMT test sets, as measured by detokenized BLEU Papineni et al. When the system is trained only on clean data (first row) and tested on noisy data, it unsurprisingly exhibits degraded performance. We observe significant drops in the range of 1.0–3.6 BLEU." } ], "images": [ "scigen/train/1677.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: BLEU scores on the WMT test set without (clean) and with synthetic grammar errors. The best performing models for each test set are highlighted. When training and test match (highlighted) we generally observe higher results. However, including all clean and noisy data in the training set (mix-all) yields the best results across all datasets, with the highest average BLEU and the lowest variance." ], "reasoning": "The system trained on clean data shows a significant drop in performance when tested on noisy data, with BLEU scores dropping by 1.0–3.6 points.", "description": "The performance of our systems on the synthetic WMT test sets, as measured by detokenized BLEU Papineni et al. When the system is trained only on clean data (first row) and tested on noisy data, it unsurprisingly exhibits degraded performance. We observe significant drops in the range of 1.0–3.6 BLEU.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 4: BLEU scores on the WMT test set without (clean) and with synthetic grammar errors. The best performing models for each test set are highlighted. When training and test match (highlighted) we generally observe higher results. However, including all clean and noisy data in the training set (mix-all) yields the best results across all datasets, with the highest average BLEU and the lowest variance.', solve the following question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the BLEU score for the system trained on clean data and tested on the prep test set?\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for different training and test set combinations. For the system trained on clean data and tested on the prep test set, the BLEU score is 32.0. So, the answer is {\"answer\": \"32.0\"}" } ], "images": [ "scigen/train/1677.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: BLEU scores on the WMT test set without (clean) and with synthetic grammar errors. The best performing models for each test set are highlighted. When training and test match (highlighted) we generally observe higher results. However, including all clean and noisy data in the training set (mix-all) yields the best results across all datasets, with the highest average BLEU and the lowest variance." ], "reasoning": "The table shows the BLEU scores for different training and test set combinations. For the system trained on clean data and tested on the prep test set, the BLEU score is 32.0.", "question": "What is the BLEU score for the system trained on clean data and tested on the prep test set?", "answer": "So, the answer is {\"answer\": \"32.0\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe system trained on clean data and tested on the drop test set has a BLEU score of 29.6.\n" }, { "role": "assistant", "content": "The table shows that the system trained on clean data and tested on the drop test set has a BLEU score of 29.6. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1677.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: BLEU scores on the WMT test set without (clean) and with synthetic grammar errors. The best performing models for each test set are highlighted. When training and test match (highlighted) we generally observe higher results. However, including all clean and noisy data in the training set (mix-all) yields the best results across all datasets, with the highest average BLEU and the lowest variance." ], "reasoning": "The table shows that the system trained on clean data and tested on the drop test set has a BLEU score of 29.6.", "statement": "The system trained on clean data and tested on the drop test set has a BLEU score of 29.6.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: BLEU scores on the JFLEG-es dev and test datasets. Our proposed mix-all model is comparable to the clean model on manually corrected input (cor[0–3]). On noisy input (No corr.) the mix-all outperforms the clean model (25.6>24.2). Preprocessing the noisy input with a GEC model (Auto corr.) further improves results (26.1).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The mix-all model shows smaller BLEU point drops with noisy input compared to the clean model, indicating better robustness.Our mix-all model generally achieves comparable results when presented with each of the four reference corrections of the test set (corX columns). However, when we use the noisy source sentence as input (No corr column) our mix-all model obtains 1.4 BLEU improvements over the clean model. The difference between the performance of the models when presented with clean and noisy input is another indicator for robustness. On the JFLEG-es test set, the noisy source results in a −3.1 BLEU point drop for the clean model, while the drop for our mix-all model is smaller, at −1.7 BLEU points. We used the publicly available JFLEG outputs of the (almost) state-of-the-art model of Junczys-Dowmunt and Grundkiewicz This experiment envisions a pipeline where the noisy source is first automatically corrected and then translated. As expected, this helps the clean model (by +1.1 BLEU), but our mix-all training helps even further (by another +0.8 BLEU). Interestingly, the automatic GEC system only helps in the test set, while there are no improvements in the dev set. Naturally, since automatic GEC systems are imperfect, the performance of this pipeline still lags behind translating on clean data." } ], "images": [ "scigen/train/1678.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: BLEU scores on the JFLEG-es dev and test datasets. Our proposed mix-all model is comparable to the clean model on manually corrected input (cor[0–3]). On noisy input (No corr.) the mix-all outperforms the clean model (25.6>24.2). Preprocessing the noisy input with a GEC model (Auto corr.) further improves results (26.1)." ], "reasoning": "The mix-all model shows smaller BLEU point drops with noisy input compared to the clean model, indicating better robustness.", "description": "Our mix-all model generally achieves comparable results when presented with each of the four reference corrections of the test set (corX columns). However, when we use the noisy source sentence as input (No corr column) our mix-all model obtains 1.4 BLEU improvements over the clean model. The difference between the performance of the models when presented with clean and noisy input is another indicator for robustness. On the JFLEG-es test set, the noisy source results in a −3.1 BLEU point drop for the clean model, while the drop for our mix-all model is smaller, at −1.7 BLEU points. We used the publicly available JFLEG outputs of the (almost) state-of-the-art model of Junczys-Dowmunt and Grundkiewicz This experiment envisions a pipeline where the noisy source is first automatically corrected and then translated. As expected, this helps the clean model (by +1.1 BLEU), but our mix-all training helps even further (by another +0.8 BLEU). Interestingly, the automatic GEC system only helps in the test set, while there are no improvements in the dev set. Naturally, since automatic GEC systems are imperfect, the performance of this pipeline still lags behind translating on clean data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 5: BLEU scores on the JFLEG-es dev and test datasets. Our proposed mix-all model is comparable to the clean model on manually corrected input (cor[0–3]). On noisy input (No corr.) the mix-all outperforms the clean model (25.6>24.2). Preprocessing the noisy input with a GEC model (Auto corr.) further improves results (26.1).', answer the question below by showing each progressive step toward the answer. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the BLEU score improvement of the mix-all model over the clean model when using the noisy source sentence as input?\n" }, { "role": "assistant", "content": "The table shows that the mix-all model has a BLEU score of 32.2 for 'No corr.' while the clean model has a BLEU score of 31.1 for 'No corr.', resulting in an improvement of 1.1 BLEU points. So, the answer is {\"answer\": \"1.1\"}" } ], "images": [ "scigen/train/1678.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: BLEU scores on the JFLEG-es dev and test datasets. Our proposed mix-all model is comparable to the clean model on manually corrected input (cor[0–3]). On noisy input (No corr.) the mix-all outperforms the clean model (25.6>24.2). Preprocessing the noisy input with a GEC model (Auto corr.) further improves results (26.1)." ], "reasoning": "The table shows that the mix-all model has a BLEU score of 32.2 for 'No corr.' while the clean model has a BLEU score of 31.1 for 'No corr.', resulting in an improvement of 1.1 BLEU points.", "question": "What is the BLEU score improvement of the mix-all model over the clean model when using the noisy source sentence as input?", "answer": "So, the answer is {\"answer\": \"1.1\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The mix-all model performs better than the clean model when using automatic correction., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe mix-all model performs better than the clean model when using automatic correction.\n" }, { "role": "assistant", "content": "The table shows that the mix-all model has a BLEU score of 31.6 for 'Auto corr.' while the clean model has a BLEU score of 31.2 for 'Auto corr.', indicating that the mix-all model performs better. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1678.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: BLEU scores on the JFLEG-es dev and test datasets. Our proposed mix-all model is comparable to the clean model on manually corrected input (cor[0–3]). On noisy input (No corr.) the mix-all outperforms the clean model (25.6>24.2). Preprocessing the noisy input with a GEC model (Auto corr.) further improves results (26.1)." ], "reasoning": "The table shows that the mix-all model has a BLEU score of 31.6 for 'Auto corr.' while the clean model has a BLEU score of 31.2 for 'Auto corr.', indicating that the mix-all model performs better.", "statement": "The mix-all model performs better than the clean model when using automatic correction.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: BLEU scores on the JFLEG-es dev and test datasets. Our proposed mix-all model is comparable to the clean model on manually corrected input (cor[0–3]). On noisy input (No corr.) the mix-all outperforms the clean model (25.6>24.2). Preprocessing the noisy input with a GEC model (Auto corr.) further improves results (26.1).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The mix-all model shows robustness with a smaller BLEU drop when using noisy input compared to the clean model, and benefits more from automatic GEC corrections.Our mix-all model generally achieves comparable results when presented with each of the four reference corrections of the test set (corX columns). However, when we use the noisy source sentence as input (No corr column) our mix-all model obtains 1.4 BLEU improvements over the clean model. The difference between the performance of the models when presented with clean and noisy input is another indicator for robustness. On the JFLEG-es test set, the noisy source results in a −3.1 BLEU point drop for the clean model, while the drop for our mix-all model is smaller, at −1.7 BLEU points. We used the publicly available JFLEG outputs of the (almost) state-of-the-art model of Junczys-Dowmunt and Grundkiewicz This experiment envisions a pipeline where the noisy source is first automatically corrected and then translated. As expected, this helps the clean model (by +1.1 BLEU), but our mix-all training helps even further (by another +0.8 BLEU). Interestingly, the automatic GEC system only helps in the test set, while there are no improvements in the dev set. Naturally, since automatic GEC systems are imperfect, the performance of this pipeline still lags behind translating on clean data." } ], "images": [ "scigen/train/1679.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: BLEU scores on the JFLEG-es dev and test datasets. Our proposed mix-all model is comparable to the clean model on manually corrected input (cor[0–3]). On noisy input (No corr.) the mix-all outperforms the clean model (25.6>24.2). Preprocessing the noisy input with a GEC model (Auto corr.) further improves results (26.1)." ], "reasoning": "The mix-all model shows robustness with a smaller BLEU drop when using noisy input compared to the clean model, and benefits more from automatic GEC corrections.", "description": "Our mix-all model generally achieves comparable results when presented with each of the four reference corrections of the test set (corX columns). However, when we use the noisy source sentence as input (No corr column) our mix-all model obtains 1.4 BLEU improvements over the clean model. The difference between the performance of the models when presented with clean and noisy input is another indicator for robustness. On the JFLEG-es test set, the noisy source results in a −3.1 BLEU point drop for the clean model, while the drop for our mix-all model is smaller, at −1.7 BLEU points. We used the publicly available JFLEG outputs of the (almost) state-of-the-art model of Junczys-Dowmunt and Grundkiewicz This experiment envisions a pipeline where the noisy source is first automatically corrected and then translated. As expected, this helps the clean model (by +1.1 BLEU), but our mix-all training helps even further (by another +0.8 BLEU). Interestingly, the automatic GEC system only helps in the test set, while there are no improvements in the dev set. Naturally, since automatic GEC systems are imperfect, the performance of this pipeline still lags behind translating on clean data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 5: BLEU scores on the JFLEG-es dev and test datasets. Our proposed mix-all model is comparable to the clean model on manually corrected input (cor[0–3]). On noisy input (No corr.) the mix-all outperforms the clean model (25.6>24.2). Preprocessing the noisy input with a GEC model (Auto corr.) further improves results (26.1).', unfold the question presented below, detailing every step of your calculation. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the BLEU score improvement of the mix-all model over the clean model when using the noisy source sentence as input?\n" }, { "role": "assistant", "content": "The BLEU score for the clean model with noisy input is 26.2, and for the mix-all model, it is 26.8. The improvement is 26.8 - 26.2 = 0.6 BLEU points. So, the answer is {\"answer\": \"0.6\"}." } ], "images": [ "scigen/train/1679.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: BLEU scores on the JFLEG-es dev and test datasets. Our proposed mix-all model is comparable to the clean model on manually corrected input (cor[0–3]). On noisy input (No corr.) the mix-all outperforms the clean model (25.6>24.2). Preprocessing the noisy input with a GEC model (Auto corr.) further improves results (26.1)." ], "reasoning": "The BLEU score for the clean model with noisy input is 26.2, and for the mix-all model, it is 26.8. The improvement is 26.8 - 26.2 = 0.6 BLEU points.", "question": "What is the BLEU score improvement of the mix-all model over the clean model when using the noisy source sentence as input?", "answer": "So, the answer is {\"answer\": \"0.6\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe mix-all model achieves a higher BLEU score than the clean model when using automatic corrections.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for the clean model with automatic corrections is 27.0, while for the mix-all model, it is 26.7. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1679.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: BLEU scores on the JFLEG-es dev and test datasets. Our proposed mix-all model is comparable to the clean model on manually corrected input (cor[0–3]). On noisy input (No corr.) the mix-all outperforms the clean model (25.6>24.2). Preprocessing the noisy input with a GEC model (Auto corr.) further improves results (26.1)." ], "reasoning": "The table shows that the BLEU score for the clean model with automatic corrections is 27.0, while for the mix-all model, it is 26.7. Therefore, the statement is refuted.", "statement": "The mix-all model achieves a higher BLEU score than the clean model when using automatic corrections.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: Final models comparison', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The table compares the Word Error Rates (WER) of various models on the CHiME-5 and CHiME-6 datasets, highlighting the best single model from the STC CHiME-6 system as the best hybrid system.Note that results from [chime5_e2e] and [yalta2018cnnbased] are obtained for the CHiME-5 data, i.e. without improvements mentioned in Section 3. For the sake of fair comparison between and-to-end and hybrid systems, we also report the best single model from the STC CHiME-6 system [Medennikov_chime6], as it is the best hybrid system known to us at the moment." } ], "images": [ "scigen/train/1680.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Final models comparison" ], "reasoning": "The table compares the Word Error Rates (WER) of various models on the CHiME-5 and CHiME-6 datasets, highlighting the best single model from the STC CHiME-6 system as the best hybrid system.", "description": "Note that results from [chime5_e2e] and [yalta2018cnnbased] are obtained for the CHiME-5 data, i.e. without improvements mentioned in Section 3. For the sake of fair comparison between and-to-end and hybrid systems, we also report the best single model from the STC CHiME-6 system [Medennikov_chime6], as it is the best hybrid system known to us at the moment.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 5: Final models comparison', answer the following question. Give detailed solution consisting of each step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model has the lowest WER according to the table?\n" }, { "role": "assistant", "content": "To find the model with the lowest WER, we need to compare the WER values of all the models listed in the table. The model with the lowest WER is the one with the smallest percentage value. Thus, the answer is {\"answer\": \"Hybrid system (AWD-LSTM-LM) [Medennikov_chime6]\"}." } ], "images": [ "scigen/train/1680.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Final models comparison" ], "reasoning": "To find the model with the lowest WER, we need to compare the WER values of all the models listed in the table. The model with the lowest WER is the one with the smallest percentage value.", "question": "Which model has the lowest WER according to the table?", "answer": "Thus, the answer is {\"answer\": \"Hybrid system (AWD-LSTM-LM) [Medennikov_chime6]\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe RNN-T + train_gss + dev_gss24 model has a lower WER than the CHiME-6 TDNN-F baseline model.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the WER values of the RNN-T + train_gss + dev_gss24 model and the CHiME-6 TDNN-F baseline model. The WER for the RNN-T + train_gss + dev_gss24 model is 49.0%, while the WER for the CHiME-6 TDNN-F baseline model is 51.7%. Since 49.0% is lower than 51.7%, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1680.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Final models comparison" ], "reasoning": "To verify this statement, we need to compare the WER values of the RNN-T + train_gss + dev_gss24 model and the CHiME-6 TDNN-F baseline model. The WER for the RNN-T + train_gss + dev_gss24 model is 49.0%, while the WER for the CHiME-6 TDNN-F baseline model is 51.7%. Since 49.0% is lower than 51.7%, the statement is supported.", "statement": "The RNN-T + train_gss + dev_gss24 model has a lower WER than the CHiME-6 TDNN-F baseline model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: External NNLM for RNN-T beam search'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The use of external RNN-based language models for hypothesis rescoring in RNN-T led to a decrease in recognition accuracy, with the AWD-LSTM-LM showing the least degradation.We also faced a problem in the rescoring process using external RNN-based language models. Character and word-based LSTM LMs (1-layer 1024-units) were trained on the training data transcriptions. However, the use of these LMs for hypothesis rescoring in the beam search algorithm for RNN-T only led to a decrease in recognition accuracy. We also used a pretrained AWD-LSTM-LM, which has shown significant WER improvement during lattice rescoring in the STC CHiME-6 system [Medennikov_chime6]. Unfortunately, none of these models improved accuracy. We have two ideas about this. The first one is that in case of a low-resource task, a regular RNN-T with well-chosen parameters of the Predictor network is able to show sufficiently good results without any rescoring techniques. The second one is that the beam search algorithm we used is not optimal for the hypothesis rescoring. For example, in the case of a hybrid system, there are separate acoustic and language model scores of a word unit. And in the rescoring algorithm, the language score is re-weighted according to the external LSTM LM. But in the case of RNN-T, there is only one single score for word unit. The default ESPnet rescoring just adds the weighted score of external LSTM LM to RNN-T weight. We used the beam size of 10 and NNLM weight of 0.3 for all experiments." } ], "images": [ "scigen/train/1681.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: External NNLM for RNN-T beam search" ], "reasoning": "The use of external RNN-based language models for hypothesis rescoring in RNN-T led to a decrease in recognition accuracy, with the AWD-LSTM-LM showing the least degradation.", "description": "We also faced a problem in the rescoring process using external RNN-based language models. Character and word-based LSTM LMs (1-layer 1024-units) were trained on the training data transcriptions. However, the use of these LMs for hypothesis rescoring in the beam search algorithm for RNN-T only led to a decrease in recognition accuracy. We also used a pretrained AWD-LSTM-LM, which has shown significant WER improvement during lattice rescoring in the STC CHiME-6 system [Medennikov_chime6]. Unfortunately, none of these models improved accuracy. We have two ideas about this. The first one is that in case of a low-resource task, a regular RNN-T with well-chosen parameters of the Predictor network is able to show sufficiently good results without any rescoring techniques. The second one is that the beam search algorithm we used is not optimal for the hypothesis rescoring. For example, in the case of a hybrid system, there are separate acoustic and language model scores of a word unit. And in the rescoring algorithm, the language score is re-weighted according to the external LSTM LM. But in the case of RNN-T, there is only one single score for word unit. The default ESPnet rescoring just adds the weighted score of external LSTM LM to RNN-T weight. We used the beam size of 10 and NNLM weight of 0.3 for all experiments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 4: External NNLM for RNN-T beam search', unfold the question presented below, detailing every step of your calculation. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich NNLM resulted in the highest WER during the experiments?\n" }, { "role": "assistant", "content": "The table shows the WER for different NNLMs, with Char NNLM having the highest WER of 57.5%. So, the answer is {\"answer\": \"Char NNLM\"}." } ], "images": [ "scigen/train/1681.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: External NNLM for RNN-T beam search" ], "reasoning": "The table shows the WER for different NNLMs, with Char NNLM having the highest WER of 57.5%.", "question": "Which NNLM resulted in the highest WER during the experiments?", "answer": "So, the answer is {\"answer\": \"Char NNLM\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe use of AWD-LSTM-LM resulted in the lowest WER among the tested models.\n" }, { "role": "assistant", "content": "The table shows that AWD-LSTM-LM has a WER of 56.3%, which is lower than the WERs of Char NNLM and Word NNLM. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1681.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: External NNLM for RNN-T beam search" ], "reasoning": "The table shows that AWD-LSTM-LM has a WER of 56.3%, which is lower than the WERs of Char NNLM and Word NNLM.", "statement": "The use of AWD-LSTM-LM resulted in the lowest WER among the tested models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Results of the models on the English-Vietnamese translation'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The model with SACT outperforms the Seq2Seq model by 2.17 BLEU points and the NPMT model by 1.43 BLEU points, indicating the importance of the deconvolution-based decoder for low-resource translation.Compared with the attention-based Seq2Seq model, our model with the SACT can outperform it with a clear advantage of 2.17 BLEU score. We also display the most recent model NPMT (Huang et al., Compared with NPMT, our model has an advantage of BLEU score of 1.43. It can be indicated that for low-resource translation, the information from the deconvolution-based decoder is important, which brings significant improvement to the conventional attention-based Seq2Seq model." } ], "images": [ "scigen/train/1682.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of the models on the English-Vietnamese translation" ], "reasoning": "The model with SACT outperforms the Seq2Seq model by 2.17 BLEU points and the NPMT model by 1.43 BLEU points, indicating the importance of the deconvolution-based decoder for low-resource translation.", "description": "Compared with the attention-based Seq2Seq model, our model with the SACT can outperform it with a clear advantage of 2.17 BLEU score. We also display the most recent model NPMT (Huang et al., Compared with NPMT, our model has an advantage of BLEU score of 1.43. It can be indicated that for low-resource translation, the information from the deconvolution-based decoder is important, which brings significant improvement to the conventional attention-based Seq2Seq model.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 2: Results of the models on the English-Vietnamese translation'. Solve the following question based on the table.\n\nQuestion: How much higher is the BLEU score of the model with SACT compared to the Seq2Seq model?\nLet's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The BLEU score of the model with SACT is 29.12, while the BLEU score of the Seq2Seq model is 26.93. The difference is 29.12 - 26.93 = 2.19. So, the answer is {\"answer\": \"2.19\"}" } ], "images": [ "scigen/train/1682.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of the models on the English-Vietnamese translation" ], "reasoning": "The BLEU score of the model with SACT is 29.12, while the BLEU score of the Seq2Seq model is 26.93. The difference is 29.12 - 26.93 = 2.19.", "question": "How much higher is the BLEU score of the model with SACT compared to the Seq2Seq model?", "answer": "So, the answer is {\"answer\": \"2.19\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe model with SACT has the highest BLEU score among the listed models.\n" }, { "role": "assistant", "content": "The table shows the BLEU scores of different models, with the model with SACT having the highest score of 29.12. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1682.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of the models on the English-Vietnamese translation" ], "reasoning": "The table shows the BLEU scores of different models, with the model with SACT having the highest score of 29.12.", "statement": "The model with SACT has the highest BLEU score among the listed models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Results of the models on the Chinese-English translation'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The SACT model achieves the highest average BLEU score of 37.85, outperforming the conventional attention-based Seq2Seq model by 2.94 points.As to the recent models on the same task with the same training data, we extract their results from their original articles. Compared with the baseline models, our model with the SACT for the softness of attention achieves better performance, with the advantages of BLEU score 2.94 over the conventional attention-based Seq2Seq model. The SACT effectively learns the temperature to control the softness of attention so that the model can utilize the information from the source-side contexts more efficiently." } ], "images": [ "scigen/train/1683.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results of the models on the Chinese-English translation" ], "reasoning": "The SACT model achieves the highest average BLEU score of 37.85, outperforming the conventional attention-based Seq2Seq model by 2.94 points.", "description": "As to the recent models on the same task with the same training data, we extract their results from their original articles. Compared with the baseline models, our model with the SACT for the softness of attention achieves better performance, with the advantages of BLEU score 2.94 over the conventional attention-based Seq2Seq model. The SACT effectively learns the temperature to control the softness of attention so that the model can utilize the information from the source-side contexts more efficiently.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 1: Results of the models on the Chinese-English translation', and sequentially solve the question, writing out each step of your reasoning process. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the average BLEU score improvement of the SACT model over the Seq2Seq model?\n" }, { "role": "assistant", "content": "The average BLEU score of the SACT model is 37.85, while the Seq2Seq model has an average BLEU score of 34.91. The improvement is calculated as 37.85 - 34.91 = 2.94. So, the answer is {\"answer\": \"2.94\"}" } ], "images": [ "scigen/train/1683.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results of the models on the Chinese-English translation" ], "reasoning": "The average BLEU score of the SACT model is 37.85, while the Seq2Seq model has an average BLEU score of 34.91. The improvement is calculated as 37.85 - 34.91 = 2.94.", "question": "What is the average BLEU score improvement of the SACT model over the Seq2Seq model?", "answer": "So, the answer is {\"answer\": \"2.94\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The SACT model achieves the highest BLEU score on the MT-04 dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe SACT model achieves the highest BLEU score on the MT-04 dataset.\n" }, { "role": "assistant", "content": "The table shows that the SACT model has a BLEU score of 40.48 on the MT-04 dataset, which is higher than any other model's score on the same dataset. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1683.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results of the models on the Chinese-English translation" ], "reasoning": "The table shows that the SACT model has a BLEU score of 40.48 on the MT-04 dataset, which is higher than any other model's score on the same dataset.", "statement": "The SACT model achieves the highest BLEU score on the MT-04 dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Results of PBSMT at different iterations'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Back-translation significantly improves the quality of the phrase table, but improvements plateau after five iterations, with the sixth iteration selected as the final PBSMT model.From comparing the results, we observe that back-translation can improve the quality of the phrase table significantly, but after five iterations, the phrase table has hardly improved. The PBSMT model at the sixth iteration is selected as the final PBSMT model." } ], "images": [ "scigen/train/1684.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results of PBSMT at different iterations" ], "reasoning": "Back-translation significantly improves the quality of the phrase table, but improvements plateau after five iterations, with the sixth iteration selected as the final PBSMT model.", "description": "From comparing the results, we observe that back-translation can improve the quality of the phrase table significantly, but after five iterations, the phrase table has hardly improved. The PBSMT model at the sixth iteration is selected as the final PBSMT model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: Results of PBSMT at different iterations', answer the question below by showing each progressive step toward the answer. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the BLEU score of the PBSMT model selected as the final model?\n" }, { "role": "assistant", "content": "The description states that the PBSMT model at the sixth iteration is selected as the final model. The table shows that the BLEU score for the sixth iteration is 7.7. So, the answer is {\"answer\": \"7.7\"}." } ], "images": [ "scigen/train/1684.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results of PBSMT at different iterations" ], "reasoning": "The description states that the PBSMT model at the sixth iteration is selected as the final model. The table shows that the BLEU score for the sixth iteration is 7.7.", "question": "What is the BLEU score of the PBSMT model selected as the final model?", "answer": "So, the answer is {\"answer\": \"7.7\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe BLEU score of the PBSMT model improves significantly after the first iteration of back-translation.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score improves from 3.8 to 6.6 after the first iteration of back-translation, which is a significant improvement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1684.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results of PBSMT at different iterations" ], "reasoning": "The table shows that the BLEU score improves from 3.8 to 6.6 after the first iteration of back-translation, which is a significant improvement.", "statement": "The BLEU score of the PBSMT model improves significantly after the first iteration of back-translation.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Unsupervised translation results. We report the scores of several evaluation methods for every step of our approach. Except the result that is listed on the last line, all results are under the condition that the translations are post-processed without patch-up.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Subword-level NMT outperforms word-level NMT by around 1.5 BLEU score, and using pseudo-parallel data from PBSMT improves subword-level NMT but not word-level NMT due to OOV words. Applying UWR to word-level NMT improves its performance by around 2 BLEU score.In the baseline unsupervised NMT models, subword-level NMT outperforms word-level NMT by around a 1.5 BLEU score. Although the unsupervised PBSMT model is worse than the subword-level NMT model, leveraging generated pseudo-parallel data from the PBSMT model to fine-tune the subword-level NMT model can still boost its performance. However, this pseudo-parallel data from the PBSMT model can not improve the word-level NMT model since the large percentage of OOV words limits its performance. After applying unknown words replacement to the word-level NMT model, the performance improves by a BLEU score of around 2. Using the Czech language model to re-score helps the model improve by around a 0.3 BLEU score each time. We also use this language model to create an ensemble of the best word-level and subword-level NMT model and achieve the best performance." } ], "images": [ "scigen/train/1685.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Unsupervised translation results. We report the scores of several evaluation methods for every step of our approach. Except the result that is listed on the last line, all results are under the condition that the translations are post-processed without patch-up." ], "reasoning": "Subword-level NMT outperforms word-level NMT by around 1.5 BLEU score, and using pseudo-parallel data from PBSMT improves subword-level NMT but not word-level NMT due to OOV words. Applying UWR to word-level NMT improves its performance by around 2 BLEU score.", "description": "In the baseline unsupervised NMT models, subword-level NMT outperforms word-level NMT by around a 1.5 BLEU score. Although the unsupervised PBSMT model is worse than the subword-level NMT model, leveraging generated pseudo-parallel data from the PBSMT model to fine-tune the subword-level NMT model can still boost its performance. However, this pseudo-parallel data from the PBSMT model can not improve the word-level NMT model since the large percentage of OOV words limits its performance. After applying unknown words replacement to the word-level NMT model, the performance improves by a BLEU score of around 2. Using the Czech language model to re-score helps the model improve by around a 0.3 BLEU score each time. We also use this language model to create an ensemble of the best word-level and subword-level NMT model and achieve the best performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Unsupervised translation results. We report the scores of several evaluation methods for every step of our approach. Except the result that is listed on the last line, all results are under the condition that the translations are post-processed without patch-up.' to sequentially solve the question presented below. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the BLEU score improvement when fine-tuning the word-level NMT model with UWR and rescoring?\n" }, { "role": "assistant", "content": "The BLEU score for the word-level NMT model is 7.9. After fine-tuning with UWR, it improves to 10.1, and with rescoring, it further improves to 10.4. The total improvement is 10.4 - 7.9 = 2.5. So, the answer is {\"answer\": \"2.5\"}" } ], "images": [ "scigen/train/1685.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Unsupervised translation results. We report the scores of several evaluation methods for every step of our approach. Except the result that is listed on the last line, all results are under the condition that the translations are post-processed without patch-up." ], "reasoning": "The BLEU score for the word-level NMT model is 7.9. After fine-tuning with UWR, it improves to 10.1, and with rescoring, it further improves to 10.4. The total improvement is 10.4 - 7.9 = 2.5.", "question": "What is the BLEU score improvement when fine-tuning the word-level NMT model with UWR and rescoring?", "answer": "So, the answer is {\"answer\": \"2.5\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The subword-level NMT model achieves a BLEU score of 10.3 after fine-tuning and rescoring., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe subword-level NMT model achieves a BLEU score of 10.3 after fine-tuning and rescoring.\n" }, { "role": "assistant", "content": "The table shows that the subword-level NMT model achieves a BLEU score of 10.3 after fine-tuning and rescoring. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1685.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Unsupervised translation results. We report the scores of several evaluation methods for every step of our approach. Except the result that is listed on the last line, all results are under the condition that the translations are post-processed without patch-up." ], "reasoning": "The table shows that the subword-level NMT model achieves a BLEU score of 10.3 after fine-tuning and rescoring.", "statement": "The subword-level NMT model achieves a BLEU score of 10.3 after fine-tuning and rescoring.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Perplexity scores for each of the models.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The conditional ARAE model achieves the lowest perplexity, significantly outperforming both the LSTM and the unconditional ARAE model.In order to assess the performance of the system, we also train a baseline language model that consists of a 1-layer LSTM. From the results we see that while both ARAE models achieve a lower perplexity than the LSTM, the conditional ARAE performs significantly better." } ], "images": [ "scigen/train/1686.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Perplexity scores for each of the models." ], "reasoning": "The conditional ARAE model achieves the lowest perplexity, significantly outperforming both the LSTM and the unconditional ARAE model.", "description": "In order to assess the performance of the system, we also train a baseline language model that consists of a 1-layer LSTM. From the results we see that while both ARAE models achieve a lower perplexity than the LSTM, the conditional ARAE performs significantly better.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Perplexity scores for each of the models.' to sequentially solve the question presented below. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model achieves the lowest perplexity?\n" }, { "role": "assistant", "content": "By comparing the perplexity values in the table, the conditional ARAE model has the lowest perplexity of 125.4. So, the answer is {\"answer\": \"ARAE (condit.)\"}." } ], "images": [ "scigen/train/1686.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Perplexity scores for each of the models." ], "reasoning": "By comparing the perplexity values in the table, the conditional ARAE model has the lowest perplexity of 125.4.", "question": "Which model achieves the lowest perplexity?", "answer": "So, the answer is {\"answer\": \"ARAE (condit.)\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The LSTM model achieves the highest perplexity. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe LSTM model achieves the highest perplexity.\n" }, { "role": "assistant", "content": "The table shows that the LSTM model has a perplexity of 150.0, which is higher than both the unconditional ARAE (148.4) and the conditional ARAE (125.4). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1686.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Perplexity scores for each of the models." ], "reasoning": "The table shows that the LSTM model has a perplexity of 150.0, which is higher than both the unconditional ARAE (148.4) and the conditional ARAE (125.4).", "statement": "The LSTM model achieves the highest perplexity.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: Case-insensitive BLEU scores on the Chinese-English translation tasks. #Param = the number of parameters in NMT model. MP and LS denote the mean-pooling and last-state respectively. ALL = total BLEU score on test sets. We highlight the best results in bold for each test set. “↑/⇑”: significantly better than Moses (p < 0.05/p < 0.01); “+/++”: significantly better than RNNSearch (p < 0.05/p < 0.01).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Cseq2seq-II models outperform RNNSearch and Moses, and parameter sharing in Cseq2seq-II prunes 31% parameters without performance loss.We find that the all NMT models achieve very promising results except the vanilla Seq2seq. This is mainly because that the vanilla Seq2seq model heavily relies on multi-layered deep encoders and decoders which are beyond our research scope. Our implemented RNNSearch model outperforms the Moses up to 3 points in total. Replacing the attention mechanism with GRU-based RNN model, Cseq2seq-I obtains another improvements of 1.94 points with the last-state strategy, and yields the best results across all test sets. Even without the attention mechanism, all Cseq2seq-II models outperform both RNNSearch and Moses system. All these improvements are significant. We then performed parameter sharing to verify whether there are parameters redundant in Cseq2seq-II. Firstly, we shared GRU parameters, which pruned 4.87M parameters. This suggests that different languages can indeed share their recurrent parameters, not only across the encoder or decoder Johnson et al. Secondly, we shared word embeddings, which pruned 18.6M parameters. Under this setting, “Cseq2seq-II + SWord” achieves 34.89 BLEU points, a gain of 0.28 BLEU points over Cseq2seq-II, indicating the two target-side word embeddings indeed share very similar syntactic properties and can be typed together. Finally, we combined the above two approaches, leading to a total of 23.47M parameters being pruned. With only 51.72M parameters, “Cseq2seq-II + SGRU + SWord” achieves 34.89 BLEU points, yielding 0.28 BLEU points improvement. In other words, we pruned 31% parameters in Cseq2seq-II with no performance loss, which will benefit applications in memory-limited devices." } ], "images": [ "scigen/train/1687.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Case-insensitive BLEU scores on the Chinese-English translation tasks. #Param = the number of parameters in NMT model. MP and LS denote the mean-pooling and last-state respectively. ALL = total BLEU score on test sets. We highlight the best results in bold for each test set. “↑/⇑”: significantly better than Moses (p < 0.05/p < 0.01); “+/++”: significantly better than RNNSearch (p < 0.05/p < 0.01)." ], "reasoning": "Cseq2seq-II models outperform RNNSearch and Moses, and parameter sharing in Cseq2seq-II prunes 31% parameters without performance loss.", "description": "We find that the all NMT models achieve very promising results except the vanilla Seq2seq. This is mainly because that the vanilla Seq2seq model heavily relies on multi-layered deep encoders and decoders which are beyond our research scope. Our implemented RNNSearch model outperforms the Moses up to 3 points in total. Replacing the attention mechanism with GRU-based RNN model, Cseq2seq-I obtains another improvements of 1.94 points with the last-state strategy, and yields the best results across all test sets. Even without the attention mechanism, all Cseq2seq-II models outperform both RNNSearch and Moses system. All these improvements are significant. We then performed parameter sharing to verify whether there are parameters redundant in Cseq2seq-II. Firstly, we shared GRU parameters, which pruned 4.87M parameters. This suggests that different languages can indeed share their recurrent parameters, not only across the encoder or decoder Johnson et al. Secondly, we shared word embeddings, which pruned 18.6M parameters. Under this setting, “Cseq2seq-II + SWord” achieves 34.89 BLEU points, a gain of 0.28 BLEU points over Cseq2seq-II, indicating the two target-side word embeddings indeed share very similar syntactic properties and can be typed together. Finally, we combined the above two approaches, leading to a total of 23.47M parameters being pruned. With only 51.72M parameters, “Cseq2seq-II + SGRU + SWord” achieves 34.89 BLEU points, yielding 0.28 BLEU points improvement. In other words, we pruned 31% parameters in Cseq2seq-II with no performance loss, which will benefit applications in memory-limited devices.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nTable title: Table 1: Case-insensitive BLEU scores on the Chinese-English translation tasks. #Param = the number of parameters in NMT model. MP and LS denote the mean-pooling and last-state respectively. ALL = total BLEU score on test sets. We highlight the best results in bold for each test set. “↑/⇑”: significantly better than Moses (p < 0.05/p < 0.01); “+/++”: significantly better than RNNSearch (p < 0.05/p < 0.01).\n\nQuestion: Which model achieves the highest BLEU score in the MT06 test set?\n" }, { "role": "assistant", "content": "By examining the MT06 column in the table, the highest BLEU score is achieved by the Cseq2seq-I (LS) model with a score of 35.61. So, the answer is {\"answer\": \"Cseq2seq-I (LS)\"}" } ], "images": [ "scigen/train/1687.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Case-insensitive BLEU scores on the Chinese-English translation tasks. #Param = the number of parameters in NMT model. MP and LS denote the mean-pooling and last-state respectively. ALL = total BLEU score on test sets. We highlight the best results in bold for each test set. “↑/⇑”: significantly better than Moses (p < 0.05/p < 0.01); “+/++”: significantly better than RNNSearch (p < 0.05/p < 0.01)." ], "reasoning": "By examining the MT06 column in the table, the highest BLEU score is achieved by the Cseq2seq-I (LS) model with a score of 35.61.", "question": "Which model achieves the highest BLEU score in the MT06 test set?", "answer": "So, the answer is {\"answer\": \"Cseq2seq-I (LS)\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Cseq2seq-II + SGRU + SWord model has the fewest parameters among all models.\n" }, { "role": "assistant", "content": "By comparing the #Param column in the table, the Cseq2seq-II + SGRU + SWord model has 51.72M parameters, which is the fewest among all listed models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1687.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Case-insensitive BLEU scores on the Chinese-English translation tasks. #Param = the number of parameters in NMT model. MP and LS denote the mean-pooling and last-state respectively. ALL = total BLEU score on test sets. We highlight the best results in bold for each test set. “↑/⇑”: significantly better than Moses (p < 0.05/p < 0.01); “+/++”: significantly better than RNNSearch (p < 0.05/p < 0.01)." ], "reasoning": "By comparing the #Param column in the table, the Cseq2seq-II + SGRU + SWord model has 51.72M parameters, which is the fewest among all listed models.", "statement": "The Cseq2seq-II + SGRU + SWord model has the fewest parameters among all models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: AER scores of word alignments deduced by different neural systems. The lower the score, the better the alignment quality.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Cseq2seq-I with the last-state strategy achieves the best alignment score, demonstrating that GRU gates inside Cseq2seq learn what should be translated during translation.Secondly, we treated the update gate as word alignments, and evaluated its quality in terms of alignment error rate (AER) following Tu et al. Tu et al. Consistent with the translation results, Cseq2seq-I with last-state strategy achieves the best alignment score. Notice that Cseq2seq does not aim at inducing high-quality word alignments. Instead, these AER results yielded by Cseq2seq demonstrate that the GRU gates inside Cseq2seq indeed learns what should be translated during translation." } ], "images": [ "scigen/train/1688.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: AER scores of word alignments deduced by different neural systems. The lower the score, the better the alignment quality." ], "reasoning": "Cseq2seq-I with the last-state strategy achieves the best alignment score, demonstrating that GRU gates inside Cseq2seq learn what should be translated during translation.", "description": "Secondly, we treated the update gate as word alignments, and evaluated its quality in terms of alignment error rate (AER) following Tu et al. Tu et al. Consistent with the translation results, Cseq2seq-I with last-state strategy achieves the best alignment score. Notice that Cseq2seq does not aim at inducing high-quality word alignments. Instead, these AER results yielded by Cseq2seq demonstrate that the GRU gates inside Cseq2seq indeed learns what should be translated during translation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: AER scores of word alignments deduced by different neural systems. The lower the score, the better the alignment quality.', answer the following question. Give detailed solution consisting of each step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich system achieves the best alignment error rate (AER) according to the table?\n" }, { "role": "assistant", "content": "The table lists various systems and their corresponding AER values. The system with the lowest AER value achieves the best alignment error rate. So, the answer is {\"answer\": \"Tu et al. Tu et al. (2016)\"}" } ], "images": [ "scigen/train/1688.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: AER scores of word alignments deduced by different neural systems. The lower the score, the better the alignment quality." ], "reasoning": "The table lists various systems and their corresponding AER values. The system with the lowest AER value achieves the best alignment error rate.", "question": "Which system achieves the best alignment error rate (AER) according to the table?", "answer": "So, the answer is {\"answer\": \"Tu et al. Tu et al. (2016)\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Cseq2seq-I (MP) achieves the best alignment score., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nCseq2seq-I (MP) achieves the best alignment score.\n" }, { "role": "assistant", "content": "The table shows that Cseq2seq-I (MP) has an AER of 76.36, which is not the lowest. The lowest AER is 50.50, achieved by Tu et al. Tu et al. (2016). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1688.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: AER scores of word alignments deduced by different neural systems. The lower the score, the better the alignment quality." ], "reasoning": "The table shows that Cseq2seq-I (MP) has an AER of 76.36, which is not the lowest. The lowest AER is 50.50, achieved by Tu et al. Tu et al. (2016).", "statement": "Cseq2seq-I (MP) achieves the best alignment score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Summary of adversarial attack potency on three models using the FEVER development data split. For the Before and After columns, the figures are reported on the subset of the data used for the rule based transformation. We also report accuracy on the entire development split reported in Thorne et al. [28] (9999 instances in total, Dev). For the case of the oracle evidence retrieval component, FEVER Score is equal to accuracy.'.\n" }, { "role": "assistant", "content": "65 rule-based transformations generated 29495 adversarial instances from 2708 matches, with only 15% labeled as NotEnoughInfo, leading to a need for balancing by discarding random instances from majority classes.65 rule-based transformations were executed to the data from the development set. There were 2708 instances that matched the regular expression patterns defined in these rules, which were used to generate 29495 novel adversarial instances. In the new set of claims that were generated, only 15% were labeled as NotEnoughInfo (as the negating rules were only applied to claims which were Supported or Refuted) which would preclude comparison against existing results as the FEVER evaluation which assumes balanced class distribution. We balance our data by discarding instances at random from the majority classes." } ], "images": [ "scigen/train/1689.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Summary of adversarial attack potency on three models using the FEVER development data split. For the Before and After columns, the figures are reported on the subset of the data used for the rule based transformation. We also report accuracy on the entire development split reported in Thorne et al. [28] (9999 instances in total, Dev). For the case of the oracle evidence retrieval component, FEVER Score is equal to accuracy." ], "reasoning": "65 rule-based transformations generated 29495 adversarial instances from 2708 matches, with only 15% labeled as NotEnoughInfo, leading to a need for balancing by discarding random instances from majority classes.", "description": "65 rule-based transformations were executed to the data from the development set. There were 2708 instances that matched the regular expression patterns defined in these rules, which were used to generate 29495 novel adversarial instances. In the new set of claims that were generated, only 15% were labeled as NotEnoughInfo (as the negating rules were only applied to claims which were Supported or Refuted) which would preclude comparison against existing results as the FEVER evaluation which assumes balanced class distribution. We balance our data by discarding instances at random from the majority classes.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 2: Summary of adversarial attack potency on three models using the FEVER development data split. For the Before and After columns, the figures are reported on the subset of the data used for the rule based transformation. We also report accuracy on the entire development split reported in Thorne et al. [28] (9999 instances in total, Dev). For the case of the oracle evidence retrieval component, FEVER Score is equal to accuracy.'. Solve the following question based on the table.\n\nQuestion: Which model had the highest Accuracy Before the transformations?\nLet's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest Accuracy Before, compare the Accuracy Before values for all models. The highest value is 87.94, which corresponds to the Oracle + DA model. So, the answer is {\"answer\": \"Oracle + DA\"}" } ], "images": [ "scigen/train/1689.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Summary of adversarial attack potency on three models using the FEVER development data split. For the Before and After columns, the figures are reported on the subset of the data used for the rule based transformation. We also report accuracy on the entire development split reported in Thorne et al. [28] (9999 instances in total, Dev). For the case of the oracle evidence retrieval component, FEVER Score is equal to accuracy." ], "reasoning": "To find the model with the highest Accuracy Before, compare the Accuracy Before values for all models. The highest value is 87.94, which corresponds to the Oracle + DA model.", "question": "Which model had the highest Accuracy Before the transformations?", "answer": "So, the answer is {\"answer\": \"Oracle + DA\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe TFIDF + ESIM model had an Accuracy Delta of -11.41.\n" }, { "role": "assistant", "content": "The table shows that the TFIDF + ESIM model had an Accuracy Delta of -11.41, confirming the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1689.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Summary of adversarial attack potency on three models using the FEVER development data split. For the Before and After columns, the figures are reported on the subset of the data used for the rule based transformation. We also report accuracy on the entire development split reported in Thorne et al. [28] (9999 instances in total, Dev). For the case of the oracle evidence retrieval component, FEVER Score is equal to accuracy." ], "reasoning": "The table shows that the TFIDF + ESIM model had an Accuracy Delta of -11.41, confirming the statement.", "statement": "The TFIDF + ESIM model had an Accuracy Delta of -11.41.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Effect of adversarial attacks on the evidence retrieval component of the pipelines considering sentence-level accuracy of the evidence.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Both ESIM and Decomposable Attention models show a significant drop in accuracy on adversarial examples, with a less pronounced decrease when considering the full pipeline.We observe that in the oracle environment (using manually labeled evidence), both the ESIM and Decomposable Attention models for natural language inference suffer a stark decrease in accuracy when making predictions on the adversarial examples. The decrease is less pronounced when considering the full pipeline (i.e. with a non-perfect evidence retrieval component), which reduces the upper bound for the FEVER Score and accuracy." } ], "images": [ "scigen/train/1690.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Effect of adversarial attacks on the evidence retrieval component of the pipelines considering sentence-level accuracy of the evidence." ], "reasoning": "Both ESIM and Decomposable Attention models show a significant drop in accuracy on adversarial examples, with a less pronounced decrease when considering the full pipeline.", "description": "We observe that in the oracle environment (using manually labeled evidence), both the ESIM and Decomposable Attention models for natural language inference suffer a stark decrease in accuracy when making predictions on the adversarial examples. The decrease is less pronounced when considering the full pipeline (i.e. with a non-perfect evidence retrieval component), which reduces the upper bound for the FEVER Score and accuracy.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 3: Effect of adversarial attacks on the evidence retrieval component of the pipelines considering sentence-level accuracy of the evidence.' and proceed to solve the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model had the highest increase in precision after the adversarial examples?\n" }, { "role": "assistant", "content": "To find the model with the highest increase in precision, compare the 'Precision Delta' values for all models. The highest positive value indicates the greatest increase. So, the answer is {\"answer\": \"UNC\"}." } ], "images": [ "scigen/train/1690.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Effect of adversarial attacks on the evidence retrieval component of the pipelines considering sentence-level accuracy of the evidence." ], "reasoning": "To find the model with the highest increase in precision, compare the 'Precision Delta' values for all models. The highest positive value indicates the greatest increase.", "question": "Which model had the highest increase in precision after the adversarial examples?", "answer": "So, the answer is {\"answer\": \"UNC\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nPapelo had the highest F1 score before the adversarial examples.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'F1 Before' values for all models. The highest value indicates the highest F1 score before the adversarial examples. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1690.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Effect of adversarial attacks on the evidence retrieval component of the pipelines considering sentence-level accuracy of the evidence." ], "reasoning": "To verify this statement, compare the 'F1 Before' values for all models. The highest value indicates the highest F1 score before the adversarial examples.", "statement": "Papelo had the highest F1 score before the adversarial examples.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Breakdown of the potency of the three classes of transformation for each model. For the models with oracle information retrieval, the FEVER Score is equal to the label accuracy.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The Papelo system, despite being ranked 4th in the FEVER shared task, performs the best in this evaluation due to different usage of development data.Considering the performance of the systems we test, we also present a leader-board ranked by the resilience (average FEVER score for all breaker methods). While the Papelo system was ranked 4th on the FEVER shared task, this model performs the best in this evaluation. This may arise from the fact that both teams make use of the development data that we used to generate our claims differently (i.e. the pretrained UCL model we used incorporated development data to increase the size of the training set whereas other models did not). As it is not possible to control how this data is used in the FEVER2.0 shared task, our method for generating claims from transforming existing instances may perform better (i.e. have more potency) when applied to instances that have not previously been released to the public." } ], "images": [ "scigen/train/1691.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Breakdown of the potency of the three classes of transformation for each model. For the models with oracle information retrieval, the FEVER Score is equal to the label accuracy." ], "reasoning": "The Papelo system, despite being ranked 4th in the FEVER shared task, performs the best in this evaluation due to different usage of development data.", "description": "Considering the performance of the systems we test, we also present a leader-board ranked by the resilience (average FEVER score for all breaker methods). While the Papelo system was ranked 4th on the FEVER shared task, this model performs the best in this evaluation. This may arise from the fact that both teams make use of the development data that we used to generate our claims differently (i.e. the pretrained UCL model we used incorporated development data to increase the size of the training set whereas other models did not). As it is not possible to control how this data is used in the FEVER2.0 shared task, our method for generating claims from transforming existing instances may perform better (i.e. have more potency) when applied to instances that have not previously been released to the public.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: Breakdown of the potency of the three classes of transformation for each model. For the models with oracle information retrieval, the FEVER Score is equal to the label accuracy.', answer the following question. Give detailed solution consisting of each step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model had the highest FEVER score after the transformation 'Entailment Preserving'?\n" }, { "role": "assistant", "content": "To find the model with the highest FEVER score after the 'Entailment Preserving' transformation, we need to compare the 'FEVER Score After' values for all models under this transformation. The highest score is 72.32 by the UCL model. So, the answer is {\"answer\": \"UCL\"}." } ], "images": [ "scigen/train/1691.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Breakdown of the potency of the three classes of transformation for each model. For the models with oracle information retrieval, the FEVER Score is equal to the label accuracy." ], "reasoning": "To find the model with the highest FEVER score after the 'Entailment Preserving' transformation, we need to compare the 'FEVER Score After' values for all models under this transformation. The highest score is 72.32 by the UCL model.", "question": "Which model had the highest FEVER score after the transformation 'Entailment Preserving'?", "answer": "So, the answer is {\"answer\": \"UCL\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Papelo model had the highest accuracy before the transformation 'Simple Negations'.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the 'Accuracy Before' values for all models under the 'Simple Negations' transformation. The highest accuracy before transformation is 89.03 by the Oracle+DA model, not Papelo. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1691.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Breakdown of the potency of the three classes of transformation for each model. For the models with oracle information retrieval, the FEVER Score is equal to the label accuracy." ], "reasoning": "To verify this statement, we need to compare the 'Accuracy Before' values for all models under the 'Simple Negations' transformation. The highest accuracy before transformation is 89.03 by the Oracle+DA model, not Papelo.", "statement": "The Papelo model had the highest accuracy before the transformation 'Simple Negations'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: Results on all four Bakeoff-2005 datasets.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Our neural models, without AV features, show comparable performance to Zhao and Kit's traditional models under a strict closed test setting.Note Zhao and Kit The comparison to their results without AV features show that our neural models for the first time present comparable performance against state-of-the-art traditional ones under strict closed test setting." } ], "images": [ "scigen/train/1692.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on all four Bakeoff-2005 datasets." ], "reasoning": "Our neural models, without AV features, show comparable performance to Zhao and Kit's traditional models under a strict closed test setting.", "description": "Note Zhao and Kit The comparison to their results without AV features show that our neural models for the first time present comparable performance against state-of-the-art traditional ones under strict closed test setting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 5: Results on all four Bakeoff-2005 datasets.'\nQuestion: How does the performance of our neural models without AV features compare to Zhao and Kit's models in the MSR dataset?\n" }, { "role": "assistant", "content": "The table shows that Zhao and Kit's model has a score of 97.6 in the MSR dataset, while our neural model without AV features has a score of 97.4, indicating comparable performance. So, the answer is {\"answer\": \"97.4\"}." } ], "images": [ "scigen/train/1692.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on all four Bakeoff-2005 datasets." ], "reasoning": "The table shows that Zhao and Kit's model has a score of 97.6 in the MSR dataset, while our neural model without AV features has a score of 97.4, indicating comparable performance.", "question": "How does the performance of our neural models without AV features compare to Zhao and Kit's models in the MSR dataset?", "answer": "So, the answer is {\"answer\": \"97.4\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nOur neural models without AV features outperform Zhao and Kit's models in the CityU dataset.\n" }, { "role": "assistant", "content": "The table shows that Zhao and Kit's model has a score of 96.1 in the CityU dataset, while our neural model without AV features has a score of 94.8, indicating that Zhao and Kit's model outperforms ours. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1692.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on all four Bakeoff-2005 datasets." ], "reasoning": "The table shows that Zhao and Kit's model has a score of 96.1 in the CityU dataset, while our neural model without AV features has a score of 94.8, indicating that Zhao and Kit's model outperforms ours.", "statement": "Our neural models without AV features outperform Zhao and Kit's models in the CityU dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: The effect of different update methods. #epochs denotes the number of training epochs to convergence.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Early update achieves faster convergence and slightly better performance compared to standard and LaSO update methods.Comparing We find that early update leads to faster convergence and a bit better performance compared to both standard and LaSO update." } ], "images": [ "scigen/train/1693.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The effect of different update methods. #epochs denotes the number of training epochs to convergence." ], "reasoning": "Early update achieves faster convergence and slightly better performance compared to standard and LaSO update methods.", "description": "Comparing We find that early update leads to faster convergence and a bit better performance compared to both standard and LaSO update.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 3: The effect of different update methods. #epochs denotes the number of training epochs to convergence.', and tackle the question below in a sequential manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich method achieves the highest MSR F1 score and how many epochs does it take?\n" }, { "role": "assistant", "content": "The table shows that the Early update method achieves the highest MSR F1 score of 97.1 and it takes 30 epochs. So, the answer is {\"answer\": \"Early update, 30 epochs\"}." } ], "images": [ "scigen/train/1693.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The effect of different update methods. #epochs denotes the number of training epochs to convergence." ], "reasoning": "The table shows that the Early update method achieves the highest MSR F1 score of 97.1 and it takes 30 epochs.", "question": "Which method achieves the highest MSR F1 score and how many epochs does it take?", "answer": "So, the answer is {\"answer\": \"Early update, 30 epochs\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe LaSO update method has the highest PKU F1 score.\n" }, { "role": "assistant", "content": "The table shows that the Early update method has the highest PKU F1 score of 95.8, not the LaSO update method. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1693.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The effect of different update methods. #epochs denotes the number of training epochs to convergence." ], "reasoning": "The table shows that the Early update method has the highest PKU F1 score of 95.8, not the LaSO update method.", "statement": "The LaSO update method has the highest PKU F1 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Comparison with previous models. Results with * are from Cai and Zhao (2016).444To distinguish the performance improvement from model optimization, we especially list the results of stand-alone neural models in Zhang et al. (2016) and Liu et al. (2016). All the running time results are from our runs of released implementations on a single CPU (Intel i7-5960X) with two threads only, except for those of Zhang et al. (2016) which are from personal communication. The results of Xu and Sun (2016) are not listed due to their use of external Chinese idiom dictionary.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Pre-training character embeddings on a large-scale unlabeled corpus improves performance, and the neural word segmenter outperforms state-of-the-art neural systems with less computational cost.Pre-training character embeddings on large-scale unlabeled corpus (not limited to the training corpus) has been shown helpful for extra performance improvement. The results with or without pre-trained character embeddings are listed separately for following the strict closed test setting of SIGHAN Bakeoff in which no linguistic resource other than training corpus is allowed. We also show the state-of-the-art results in (Zhao and Kit, 2008b) of traditional methods. The comparison shows our neural word segmenter outperforms all state-of-the-art neural systems with much less computational cost." } ], "images": [ "scigen/train/1694.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison with previous models. Results with * are from Cai and Zhao (2016).444To distinguish the performance improvement from model optimization, we especially list the results of stand-alone neural models in Zhang et al. (2016) and Liu et al. (2016). All the running time results are from our runs of released implementations on a single CPU (Intel i7-5960X) with two threads only, except for those of Zhang et al. (2016) which are from personal communication. The results of Xu and Sun (2016) are not listed due to their use of external Chinese idiom dictionary." ], "reasoning": "Pre-training character embeddings on a large-scale unlabeled corpus improves performance, and the neural word segmenter outperforms state-of-the-art neural systems with less computational cost.", "description": "Pre-training character embeddings on large-scale unlabeled corpus (not limited to the training corpus) has been shown helpful for extra performance improvement. The results with or without pre-trained character embeddings are listed separately for following the strict closed test setting of SIGHAN Bakeoff in which no linguistic resource other than training corpus is allowed. We also show the state-of-the-art results in (Zhao and Kit, 2008b) of traditional methods. The comparison shows our neural word segmenter outperforms all state-of-the-art neural systems with much less computational cost.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 4: Comparison with previous models. Results with * are from Cai and Zhao (2016).444To distinguish the performance improvement from model optimization, we especially list the results of stand-alone neural models in Zhang et al. (2016) and Liu et al. (2016). All the running time results are from our runs of released implementations on a single CPU (Intel i7-5960X) with two threads only, except for those of Zhang et al. (2016) which are from personal communication. The results of Xu and Sun (2016) are not listed due to their use of external Chinese idiom dictionary.' and methodically tackle the question that follows. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model has the lowest PKU Training time and what is its PKU F1 score?\n" }, { "role": "assistant", "content": "By examining the PKU Training (hours) column, Ma and Hinrichs (2015) has the lowest training time of 1.5 hours. The corresponding PKU F1 score for this model is 95.1. So, the answer is {\"answer\": \"95.1\"}." } ], "images": [ "scigen/train/1694.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison with previous models. Results with * are from Cai and Zhao (2016).444To distinguish the performance improvement from model optimization, we especially list the results of stand-alone neural models in Zhang et al. (2016) and Liu et al. (2016). All the running time results are from our runs of released implementations on a single CPU (Intel i7-5960X) with two threads only, except for those of Zhang et al. (2016) which are from personal communication. The results of Xu and Sun (2016) are not listed due to their use of external Chinese idiom dictionary." ], "reasoning": "By examining the PKU Training (hours) column, Ma and Hinrichs (2015) has the lowest training time of 1.5 hours. The corresponding PKU F1 score for this model is 95.1.", "question": "Which model has the lowest PKU Training time and what is its PKU F1 score?", "answer": "So, the answer is {\"answer\": \"95.1\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The model by Liu et al. (2016) has the highest MSR F1 score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe model by Liu et al. (2016) has the highest MSR F1 score.\n" }, { "role": "assistant", "content": "By examining the MSR F1 column, the highest score is 97.6 by Zhao and Kit (2008c), not Liu et al. (2016) which has a score of 95.21. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1694.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison with previous models. Results with * are from Cai and Zhao (2016).444To distinguish the performance improvement from model optimization, we especially list the results of stand-alone neural models in Zhang et al. (2016) and Liu et al. (2016). All the running time results are from our runs of released implementations on a single CPU (Intel i7-5960X) with two threads only, except for those of Zhang et al. (2016) which are from personal communication. The results of Xu and Sun (2016) are not listed due to their use of external Chinese idiom dictionary." ], "reasoning": "By examining the MSR F1 column, the highest score is 97.6 by Zhao and Kit (2008c), not Liu et al. (2016) which has a score of 95.21.", "statement": "The model by Liu et al. (2016) has the highest MSR F1 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Average number of acceptable realisations out of 3. The difference is significant with p<0.001.'.\n" }, { "role": "assistant", "content": "Crowdsourced judgments showed that the syntax-aware model generated more grammatical realisations with the same meaning as the reference realisation compared to the baseline s2s model.We then crowdsourced acceptability judgments for each example from 100 annotators: we showed the reference realisation of an AMR graph, together with model realisations, and asked each annotator to mark all the grammatical realisations which have the same meaning as the reference realisation. Each annotator was presented 30 examples selected randomly. This shows the utility of separating out syntactic and lexical variation: we model explicitly meaning-preserving invariances, and can therefore generate better paraphrases." } ], "images": [ "scigen/train/1695.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Average number of acceptable realisations out of 3. The difference is significant with p<0.001." ], "reasoning": "Crowdsourced judgments showed that the syntax-aware model generated more grammatical realisations with the same meaning as the reference realisation compared to the baseline s2s model.", "description": "We then crowdsourced acceptability judgments for each example from 100 annotators: we showed the reference realisation of an AMR graph, together with model realisations, and asked each annotator to mark all the grammatical realisations which have the same meaning as the reference realisation. Each annotator was presented 30 examples selected randomly. This shows the utility of separating out syntactic and lexical variation: we model explicitly meaning-preserving invariances, and can therefore generate better paraphrases.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: Average number of acceptable realisations out of 3. The difference is significant with p<0.001.', and tackle the question below in a sequential manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model generated more good realisations according to the crowdsourced judgments?\n" }, { "role": "assistant", "content": "The table shows the number of good realisations for each model. The syntax-aware model has 1.52 good realisations, while the baseline s2s model has 1.19. So, the answer is {\"answer\": \"Syntax-aware model\"}." } ], "images": [ "scigen/train/1695.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Average number of acceptable realisations out of 3. The difference is significant with p<0.001." ], "reasoning": "The table shows the number of good realisations for each model. The syntax-aware model has 1.52 good realisations, while the baseline s2s model has 1.19.", "question": "Which model generated more good realisations according to the crowdsourced judgments?", "answer": "So, the answer is {\"answer\": \"Syntax-aware model\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The baseline s2s model generated more good realisations than the syntax-aware model., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe baseline s2s model generated more good realisations than the syntax-aware model.\n" }, { "role": "assistant", "content": "The table shows that the syntax-aware model generated 1.52 good realisations, while the baseline s2s model generated 1.19 good realisations. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1695.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Average number of acceptable realisations out of 3. The difference is significant with p<0.001." ], "reasoning": "The table shows that the syntax-aware model generated 1.52 good realisations, while the baseline s2s model generated 1.19 good realisations. Therefore, the statement is refuted.", "statement": "The baseline s2s model generated more good realisations than the syntax-aware model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: BLEU results for generation.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Adding syntax into the AMR-to-text model significantly improves performance, achieving state-of-the-art results on both datasets without external training data.As a baseline, we train a straight AMR-to-text model with the same architecture as above to control for the extra regularisation in our model compared to previous work. Our results show that adding syntax into the model dramatically boosts performance, resulting in state-of-the-art single model performance on both datasets without using external training data." } ], "images": [ "scigen/train/1696.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: BLEU results for generation." ], "reasoning": "Adding syntax into the AMR-to-text model significantly improves performance, achieving state-of-the-art results on both datasets without external training data.", "description": "As a baseline, we train a straight AMR-to-text model with the same architecture as above to control for the extra regularisation in our model compared to previous work. Our results show that adding syntax into the model dramatically boosts performance, resulting in state-of-the-art single model performance on both datasets without using external training data.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: BLEU results for generation.' in mind, break down and resolve the question below step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the Test BLEU score of 'Our model' trained on LDC2017T10?\n" }, { "role": "assistant", "content": "To find the Test BLEU score of 'Our model' trained on LDC2017T10, locate the row labeled 'Our model' under the 'Trained on LDC2017T10' section and check the corresponding value in the 'Test BLEU' column. So, the answer is {\"answer\": \"26.8\"}" } ], "images": [ "scigen/train/1696.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: BLEU results for generation." ], "reasoning": "To find the Test BLEU score of 'Our model' trained on LDC2017T10, locate the row labeled 'Our model' under the 'Trained on LDC2017T10' section and check the corresponding value in the 'Test BLEU' column.", "question": "What is the Test BLEU score of 'Our model' trained on LDC2017T10?", "answer": "So, the answer is {\"answer\": \"26.8\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Baseline s2s + copy model has a higher Dev BLEU score than 'Our model' trained on LDC2015E86.\n" }, { "role": "assistant", "content": "Compare the Dev BLEU scores of the Baseline s2s + copy model and 'Our model' trained on LDC2015E86. The Baseline s2s + copy model has a Dev BLEU score of 23.7, while 'Our model' trained on LDC2015E86 has a Dev BLEU score of 23.6. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1696.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: BLEU results for generation." ], "reasoning": "Compare the Dev BLEU scores of the Baseline s2s + copy model and 'Our model' trained on LDC2015E86. The Baseline s2s + copy model has a Dev BLEU score of 23.7, while 'Our model' trained on LDC2015E86 has a Dev BLEU score of 23.6.", "statement": "The Baseline s2s + copy model has a higher Dev BLEU score than 'Our model' trained on LDC2015E86.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled '(b)', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "M-Bert's ability to transfer between languages with different scripts is surprising given its training on separate monolingual corpora without a multilingual objective.M-Bert’s ability to transfer between languages that are written in different scripts, and thus have effectively zero lexical overlap, is surprising given that it was trained on separate monolingual corpora and not with a multilingual objective." } ], "images": [ "scigen/train/1697.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b)" ], "reasoning": "M-Bert's ability to transfer between languages with different scripts is surprising given its training on separate monolingual corpora without a multilingual objective.", "description": "M-Bert’s ability to transfer between languages that are written in different scripts, and thus have effectively zero lexical overlap, is surprising given that it was trained on separate monolingual corpora and not with a multilingual objective.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the '(b)', solve the subsequent question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the accuracy of M-Bert when transferring from English to Japanese?\n" }, { "role": "assistant", "content": "To find the accuracy of M-Bert when transferring from English to Japanese, look at the intersection of the 'en' row and 'ja' column in the table. The value is 49.4. So, the answer is {\"answer\": \"49.4\"}." } ], "images": [ "scigen/train/1697.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b)" ], "reasoning": "To find the accuracy of M-Bert when transferring from English to Japanese, look at the intersection of the 'en' row and 'ja' column in the table. The value is 49.4.", "question": "What is the accuracy of M-Bert when transferring from English to Japanese?", "answer": "So, the answer is {\"answer\": \"49.4\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nM-Bert has the highest accuracy when transferring from Bulgarian to Japanese.\n" }, { "role": "assistant", "content": "To verify this statement, compare the accuracy values in the 'bg' row for the 'ja' column with other values in the same row. The value for 'ja' is 51.6, which is not the highest. The highest value in the 'bg' row is 98.9 for 'bg'. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1697.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b)" ], "reasoning": "To verify this statement, compare the accuracy values in the 'bg' row for the 'ja' column with other values in the same row. The value for 'ja' is 51.6, which is not the highest. The highest value in the 'bg' row is 98.9 for 'bg'.", "statement": "M-Bert has the highest accuracy when transferring from Bulgarian to Japanese.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Ner F1 results on the CoNLL data.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "We conducted NER experiments on CoNLL-2002 and -2003 datasets in four languages: Dutch, Spanish, English, and German.We perform ner experiments on two datasets: the publicly available CoNLL-2002 and -2003 sets, containing Dutch, Spanish, English, and German Tjong Kim Sang" } ], "images": [ "scigen/train/1698.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Ner F1 results on the CoNLL data." ], "reasoning": "We conducted NER experiments on CoNLL-2002 and -2003 datasets in four languages: Dutch, Spanish, English, and German.", "description": "We perform ner experiments on two datasets: the publicly available CoNLL-2002 and -2003 sets, containing Dutch, Spanish, English, and German Tjong Kim Sang", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: 'Table 1: Ner F1 results on the CoNLL data.'\nQuestion: Which language had the highest evaluation score when fine-tuned on English?\n" }, { "role": "assistant", "content": "To find the language with the highest evaluation score when fine-tuned on English, look at the row labeled 'en' and identify the highest score among the columns 'en', 'de', 'nl', and 'es'. The highest score is 90.70 in the 'en' column. So, the answer is {\"answer\": \"English\"}." } ], "images": [ "scigen/train/1698.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Ner F1 results on the CoNLL data." ], "reasoning": "To find the language with the highest evaluation score when fine-tuned on English, look at the row labeled 'en' and identify the highest score among the columns 'en', 'de', 'nl', and 'es'. The highest score is 90.70 in the 'en' column.", "question": "Which language had the highest evaluation score when fine-tuned on English?", "answer": "So, the answer is {\"answer\": \"English\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The evaluation score for Dutch when fine-tuned on German is higher than when fine-tuned on Spanish., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe evaluation score for Dutch when fine-tuned on German is higher than when fine-tuned on Spanish.\n" }, { "role": "assistant", "content": "To verify this statement, compare the evaluation scores for Dutch (nl) when fine-tuned on German (de) and Spanish (es). The score for Dutch when fine-tuned on German is 76.25, and when fine-tuned on Spanish is 64.39. Since 76.25 is higher than 64.39, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1698.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Ner F1 results on the CoNLL data." ], "reasoning": "To verify this statement, compare the evaluation scores for Dutch (nl) when fine-tuned on German (de) and Spanish (es). The score for Dutch when fine-tuned on German is 76.25, and when fine-tuned on Spanish is 64.39. Since 76.25 is higher than 64.39, the statement is supported.", "statement": "The evaluation score for Dutch when fine-tuned on German is higher than when fine-tuned on Spanish.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Pos accuracy on a subset of UD languages.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "M-Bert achieves over 80% accuracy across all language pairs in the Universal Dependencies evaluation.We perform pos experiments using Universal Dependencies (UD) Nivre et al. We use the evaluation sets from Zeman et al. We see that M-Bert generalizes well across languages, achieving over 80% accuracy for all pairs." } ], "images": [ "scigen/train/1699.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Pos accuracy on a subset of UD languages." ], "reasoning": "M-Bert achieves over 80% accuracy across all language pairs in the Universal Dependencies evaluation.", "description": "We perform pos experiments using Universal Dependencies (UD) Nivre et al. We use the evaluation sets from Zeman et al. We see that M-Bert generalizes well across languages, achieving over 80% accuracy for all pairs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Pos accuracy on a subset of UD languages.', answer the following question. Give detailed solution consisting of each step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich language pair achieved the highest accuracy in the evaluation?\n" }, { "role": "assistant", "content": "By examining the table, the highest accuracy is found in the row for fine-tuning on Italian and evaluating on Italian, with a score of 98.11. So, the answer is {\"answer\": \"it-it\"}." } ], "images": [ "scigen/train/1699.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Pos accuracy on a subset of UD languages." ], "reasoning": "By examining the table, the highest accuracy is found in the row for fine-tuning on Italian and evaluating on Italian, with a score of 98.11.", "question": "Which language pair achieved the highest accuracy in the evaluation?", "answer": "So, the answer is {\"answer\": \"it-it\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nM-Bert achieved the lowest accuracy when fine-tuned on Spanish and evaluated on English.\n" }, { "role": "assistant", "content": "The table shows that the accuracy for fine-tuning on Spanish and evaluating on English is 81.64, which is the lowest accuracy in the table. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1699.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Pos accuracy on a subset of UD languages." ], "reasoning": "The table shows that the accuracy for fine-tuning on Spanish and evaluating on English is 81.64, which is the lowest accuracy in the table.", "statement": "M-Bert achieved the lowest accuracy when fine-tuned on Spanish and evaluated on English.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled '(a)'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "M-Bert shows strong transfer capabilities between Hindi and Urdu, despite being trained on separate monolingual corpora.M-Bert’s ability to transfer between languages that are written in different scripts, and thus have effectively zero lexical overlap, is surprising given that it was trained on separate monolingual corpora and not with a multilingual objective." } ], "images": [ "scigen/train/1700.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a)" ], "reasoning": "M-Bert shows strong transfer capabilities between Hindi and Urdu, despite being trained on separate monolingual corpora.", "description": "M-Bert’s ability to transfer between languages that are written in different scripts, and thus have effectively zero lexical overlap, is surprising given that it was trained on separate monolingual corpora and not with a multilingual objective.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of '(a)' and proceed to solve the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the accuracy of M-Bert when transferring from Urdu to Hindi?\n" }, { "role": "assistant", "content": "To find the accuracy of M-Bert when transferring from Urdu to Hindi, look at the intersection of the 'ur' row and 'hi' column in the table. So, the answer is {\"answer\": \"91.1\"}." } ], "images": [ "scigen/train/1700.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a)" ], "reasoning": "To find the accuracy of M-Bert when transferring from Urdu to Hindi, look at the intersection of the 'ur' row and 'hi' column in the table.", "question": "What is the accuracy of M-Bert when transferring from Urdu to Hindi?", "answer": "So, the answer is {\"answer\": \"91.1\"}." }, { "messages": [ { "role": "user", "content": "M-Bert has higher accuracy when transferring from Hindi to Urdu than from Urdu to Hindi. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nM-Bert has higher accuracy when transferring from Hindi to Urdu than from Urdu to Hindi.\n" }, { "role": "assistant", "content": "To verify this statement, compare the accuracy values from Hindi to Urdu (85.9) and from Urdu to Hindi (91.1). The value for Urdu to Hindi is higher. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1700.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a)" ], "reasoning": "To verify this statement, compare the accuracy values from Hindi to Urdu (85.9) and from Urdu to Hindi (91.1). The value for Urdu to Hindi is higher.", "statement": "M-Bert has higher accuracy when transferring from Hindi to Urdu than from Urdu to Hindi.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled '(a) Subj./verb/obj. order.'.\n" }, { "role": "assistant", "content": "Performance is highest when transferring between languages with the same word order, indicating M-Bert's difficulty in adapting to different word orders.The results reported include only zero-shot transfer, i.e. they do not include cases training and testing on the same language. We can see that performance is best when transferring between languages that share word order features, suggesting that while M-Bert’s multilingual representation is able to map learned structures onto new vocabularies, it does not seem to learn systematic transformations of those structures to accommodate a target language with different word order." } ], "images": [ "scigen/train/1701.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) Subj./verb/obj. order." ], "reasoning": "Performance is highest when transferring between languages with the same word order, indicating M-Bert's difficulty in adapting to different word orders.", "description": "The results reported include only zero-shot transfer, i.e. they do not include cases training and testing on the same language. We can see that performance is best when transferring between languages that share word order features, suggesting that while M-Bert’s multilingual representation is able to map learned structures onto new vocabularies, it does not seem to learn systematic transformations of those structures to accommodate a target language with different word order.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: '(a) Subj./verb/obj. order.'\nQuestion: What is the performance score when transferring from SVO to SOV languages?\n" }, { "role": "assistant", "content": "To find the performance score when transferring from SVO to SOV languages, look at the intersection of the SVO row and the SOV column in the table. So, the answer is {\"answer\": \"66.52\"}." } ], "images": [ "scigen/train/1701.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) Subj./verb/obj. order." ], "reasoning": "To find the performance score when transferring from SVO to SOV languages, look at the intersection of the SVO row and the SOV column in the table.", "question": "What is the performance score when transferring from SVO to SOV languages?", "answer": "So, the answer is {\"answer\": \"66.52\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Transferring between languages with different word orders results in lower performance scores. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nTransferring between languages with different word orders results in lower performance scores.\n" }, { "role": "assistant", "content": "The table shows that transferring from SVO to SOV results in a score of 66.52, and from SOV to SVO results in a score of 63.98, both of which are lower than transferring within the same word order. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1701.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) Subj./verb/obj. order." ], "reasoning": "The table shows that transferring from SVO to SOV results in a score of 66.52, and from SOV to SVO results in a score of 63.98, both of which are lower than transferring within the same word order.", "statement": "Transferring between languages with different word orders results in lower performance scores.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title '(b) Adjective/noun order.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows that performance is better when transferring between languages with similar word order features, as indicated by higher scores.The results reported include only zero-shot transfer, i.e. they do not include cases training and testing on the same language. We can see that performance is best when transferring between languages that share word order features, suggesting that while M-Bert’s multilingual representation is able to map learned structures onto new vocabularies, it does not seem to learn systematic transformations of those structures to accommodate a target language with different word order." } ], "images": [ "scigen/train/1702.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) Adjective/noun order." ], "reasoning": "The table shows that performance is better when transferring between languages with similar word order features, as indicated by higher scores.", "description": "The results reported include only zero-shot transfer, i.e. they do not include cases training and testing on the same language. We can see that performance is best when transferring between languages that share word order features, suggesting that while M-Bert’s multilingual representation is able to map learned structures onto new vocabularies, it does not seem to learn systematic transformations of those structures to accommodate a target language with different word order.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: (b) Adjective/noun order.\nQuestion: What is the highest performance score when transferring between languages with different word order features?\nSolve the above question based on the table information. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest performance score when transferring between languages with different word order features, we need to look at the scores in the table and identify the highest value. The highest score is 79.64. So, the answer is {\"answer\": \"79.64\"}." } ], "images": [ "scigen/train/1702.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) Adjective/noun order." ], "reasoning": "To find the highest performance score when transferring between languages with different word order features, we need to look at the scores in the table and identify the highest value. The highest score is 79.64.", "question": "What is the highest performance score when transferring between languages with different word order features?", "answer": "So, the answer is {\"answer\": \"79.64\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe performance score for transferring between languages with different word order features is 70.94.\n" }, { "role": "assistant", "content": "The table shows a performance score of 70.94, which supports the statement that the performance score for transferring between languages with different word order features is 70.94. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1702.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) Adjective/noun order." ], "reasoning": "The table shows a performance score of 70.94, which supports the statement that the performance score for transferring between languages with different word order features is 70.94.", "statement": "The performance score for transferring between languages with different word order features is 70.94.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: Comparison of F1 scores on section 23 of the Penn Treebank. Here we only include models trained without external silver training data. Results in the first two sections are for single-model systems.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The best overall settings achieve an F1 score of 92.56 without pruning and 92.53 with pruning, surpassing previous state-of-the-art scores for single-model parsers.We find that the best overall settings are a beam size of k=2000, a word beam size of kw=200, and ks=20 fast-track candidates per step, as this setup achieves both the highest probabilities under the model and the highest development F1. We achieve F1 scores of 92.56 on the test set without pruning and 92.53 when 1−8/26≈69.2% of Open successors are pruned, obtaining performance well above the previous state-of-the-art scores for single-model parsers. The fact that we match the performance of their reranking parser using the same generative model confirms the efficacy of our approach. We believe that further refinements of our search procedure can continue to push the bar higher, such as the use of a learned heuristic function for forward score estimation, or a more sophisticated approximate decoding scheme making use of specific properties of the model. We look forward to exploring these directions in future work." } ], "images": [ "scigen/train/1703.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison of F1 scores on section 23 of the Penn Treebank. Here we only include models trained without external silver training data. Results in the first two sections are for single-model systems." ], "reasoning": "The best overall settings achieve an F1 score of 92.56 without pruning and 92.53 with pruning, surpassing previous state-of-the-art scores for single-model parsers.", "description": "We find that the best overall settings are a beam size of k=2000, a word beam size of kw=200, and ks=20 fast-track candidates per step, as this setup achieves both the highest probabilities under the model and the highest development F1. We achieve F1 scores of 92.56 on the test set without pruning and 92.53 when 1−8/26≈69.2% of Open successors are pruned, obtaining performance well above the previous state-of-the-art scores for single-model parsers. The fact that we match the performance of their reranking parser using the same generative model confirms the efficacy of our approach. We believe that further refinements of our search procedure can continue to push the bar higher, such as the use of a learned heuristic function for forward score estimation, or a more sophisticated approximate decoding scheme making use of specific properties of the model. We look forward to exploring these directions in future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 5: Comparison of F1 scores on section 23 of the Penn Treebank. Here we only include models trained without external silver training data. Results in the first two sections are for single-model systems.', answer the following question. Give detailed solution consisting of each step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the F1 score of the best result with pruning?\n" }, { "role": "assistant", "content": "The table lists the F1 scores for various parsers, including 'Our Best Result (with pruning)' which has an F1 score of 92.53. So, the answer is {\"answer\": \"92.53\"}." } ], "images": [ "scigen/train/1703.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison of F1 scores on section 23 of the Penn Treebank. Here we only include models trained without external silver training data. Results in the first two sections are for single-model systems." ], "reasoning": "The table lists the F1 scores for various parsers, including 'Our Best Result (with pruning)' which has an F1 score of 92.53.", "question": "What is the F1 score of the best result with pruning?", "answer": "So, the answer is {\"answer\": \"92.53\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The F1 score of the best result without pruning is higher than that of Liu and Zhang (2017)., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe F1 score of the best result without pruning is higher than that of Liu and Zhang (2017).\n" }, { "role": "assistant", "content": "The table shows that 'Our Best Result' without pruning has an F1 score of 92.56, while Liu and Zhang (2017) have an F1 score of 91.7. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1703.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison of F1 scores on section 23 of the Penn Treebank. Here we only include models trained without external silver training data. Results in the first two sections are for single-model systems." ], "reasoning": "The table shows that 'Our Best Result' without pruning has an F1 score of 92.56, while Liu and Zhang (2017) have an F1 score of 91.7.", "statement": "The F1 score of the best result without pruning is higher than that of Liu and Zhang (2017).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 1: Development F1 scores using word-level search with various beam sizes k and two choices of word beam size kw.'.\n" }, { "role": "assistant", "content": "Performance increases with beam size, and word-level search with kw=k/10 consistently outperforms search without a bottleneck.Development set results for word-level search with a variety of beam sizes and with kw We observe that performance in both cases increases steadily with beam size. Word-level search with kw=k/10 consistently outperforms search without a bottleneck at all beam sizes, indicating the utility of this simple diversity-inducing modification. The top result of 92.93 F1 is already quite strong compared to other single-model systems." } ], "images": [ "scigen/train/1704.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Development F1 scores using word-level search with various beam sizes k and two choices of word beam size kw." ], "reasoning": "Performance increases with beam size, and word-level search with kw=k/10 consistently outperforms search without a bottleneck.", "description": "Development set results for word-level search with a variety of beam sizes and with kw We observe that performance in both cases increases steadily with beam size. Word-level search with kw=k/10 consistently outperforms search without a bottleneck at all beam sizes, indicating the utility of this simple diversity-inducing modification. The top result of 92.93 F1 is already quite strong compared to other single-model systems.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 1: Development F1 scores using word-level search with various beam sizes k and two choices of word beam size kw.'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. \n\nQuestion: What is the F1 score for a beam size of 800 with kw=k/10?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the F1 score for a beam size of 800 with kw=k/10, look at the row labeled 'kw=k/10' and the column labeled '800'. So, the answer is {\"answer\": \"92.12\"}." } ], "images": [ "scigen/train/1704.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Development F1 scores using word-level search with various beam sizes k and two choices of word beam size kw." ], "reasoning": "To find the F1 score for a beam size of 800 with kw=k/10, look at the row labeled 'kw=k/10' and the column labeled '800'.", "question": "What is the F1 score for a beam size of 800 with kw=k/10?", "answer": "So, the answer is {\"answer\": \"92.12\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The F1 score for a beam size of 1000 with kw=k is higher than with kw=k/10. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe F1 score for a beam size of 1000 with kw=k is higher than with kw=k/10.\n" }, { "role": "assistant", "content": "Compare the F1 scores for a beam size of 1000 in both rows. The score for kw=k is 91.97, and the score for kw=k/10 is 92.38. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1704.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Development F1 scores using word-level search with various beam sizes k and two choices of word beam size kw." ], "reasoning": "Compare the F1 scores for a beam size of 1000 in both rows. The score for kw=k is 91.97, and the score for kw=k/10 is 92.38.", "statement": "The F1 score for a beam size of 1000 with kw=k is higher than with kw=k/10.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Development F1 scores using the settings from Table 1, together with the fast-track selection strategy from Section 6 with ks=k/100.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The use of fast-tracked candidates improves the top result from 92.93 to 93.18, surpassing prior single-model systems on the development set.Note that the use of fast-tracked candidates offers significant gains under all settings. The top result improves from 92.93 to 93.18 with the use of fast-tracked candidates, surpassing prior single-model systems on the development set." } ], "images": [ "scigen/train/1705.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Development F1 scores using the settings from Table 1, together with the fast-track selection strategy from Section 6 with ks=k/100." ], "reasoning": "The use of fast-tracked candidates improves the top result from 92.93 to 93.18, surpassing prior single-model systems on the development set.", "description": "Note that the use of fast-tracked candidates offers significant gains under all settings. The top result improves from 92.93 to 93.18 with the use of fast-tracked candidates, surpassing prior single-model systems on the development set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Development F1 scores using the settings from Table 1, together with the fast-track selection strategy from Section 6 with ks=k/100.', and sequentially solve the question, writing out each step of your reasoning process. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the improvement in the top result when using fast-tracked candidates?\n" }, { "role": "assistant", "content": "The top result without fast-tracked candidates is 92.93, and with fast-tracked candidates, it is 93.18. The improvement is 93.18 - 92.93. So, the answer is {\"answer\": \"0.25\"}" } ], "images": [ "scigen/train/1705.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Development F1 scores using the settings from Table 1, together with the fast-track selection strategy from Section 6 with ks=k/100." ], "reasoning": "The top result without fast-tracked candidates is 92.93, and with fast-tracked candidates, it is 93.18. The improvement is 93.18 - 92.93.", "question": "What is the improvement in the top result when using fast-tracked candidates?", "answer": "So, the answer is {\"answer\": \"0.25\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe top result with fast-tracked candidates is 93.18.\n" }, { "role": "assistant", "content": "The table shows that with a beam size of 2000 and kw=k/10, the result is 93.18, which is the top result. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1705.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Development F1 scores using the settings from Table 1, together with the fast-track selection strategy from Section 6 with ks=k/100." ], "reasoning": "The table shows that with a beam size of 2000 and kw=k/10, the result is 93.18, which is the top result.", "statement": "The top result with fast-tracked candidates is 93.18.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: Results when the best setting from Section 6 is rerun with Open action pruning with context size c=2 and various pruning fractions p. Lower values of p indicate more aggressive pruning, while p=1 means no pruning is performed.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Performance remains stable with minimal changes in F1 score for p as low as 8/26, but significant degradation occurs when p is set to 6/26.We observe that performance is on par with the unpruned setup (at most 0.1 absolute difference in F1 score) for p as low as 8/26≈0.308. Setting p to 7/26≈0.269 results in a drop of 0.18, and setting p to 6/26≈0.231 results in a drop of 0.40. Hence, degradation begins to occur right around the empirically-motivated threshold of 6/26 given above, but we can prune 1−8/26≈69.2% of Open successors with minimal changes in performance." } ], "images": [ "scigen/train/1706.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results when the best setting from Section 6 is rerun with Open action pruning with context size c=2 and various pruning fractions p. Lower values of p indicate more aggressive pruning, while p=1 means no pruning is performed." ], "reasoning": "Performance remains stable with minimal changes in F1 score for p as low as 8/26, but significant degradation occurs when p is set to 6/26.", "description": "We observe that performance is on par with the unpruned setup (at most 0.1 absolute difference in F1 score) for p as low as 8/26≈0.308. Setting p to 7/26≈0.269 results in a drop of 0.18, and setting p to 6/26≈0.231 results in a drop of 0.40. Hence, degradation begins to occur right around the empirically-motivated threshold of 6/26 given above, but we can prune 1−8/26≈69.2% of Open successors with minimal changes in performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 4: Results when the best setting from Section 6 is rerun with Open action pruning with context size c=2 and various pruning fractions p. Lower values of p indicate more aggressive pruning, while p=1 means no pruning is performed.', unfold the steps to solve the question given next. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nQuestion: What is the F1 score when p is set to 8/26, and how does it compare to the F1 score when p is set to 6/26?\n" }, { "role": "assistant", "content": "The F1 score for p set to 8/26 is 93.08, and for p set to 6/26, it is 92.78. The difference between these scores is 0.30, indicating a drop in performance when p is set to 6/26. So, the answer is {\"answer\": \"0.30\"}" } ], "images": [ "scigen/train/1706.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results when the best setting from Section 6 is rerun with Open action pruning with context size c=2 and various pruning fractions p. Lower values of p indicate more aggressive pruning, while p=1 means no pruning is performed." ], "reasoning": "The F1 score for p set to 8/26 is 93.08, and for p set to 6/26, it is 92.78. The difference between these scores is 0.30, indicating a drop in performance when p is set to 6/26.", "question": "What is the F1 score when p is set to 8/26, and how does it compare to the F1 score when p is set to 6/26?", "answer": "So, the answer is {\"answer\": \"0.30\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nSetting p to 7/26 results in a higher F1 score than setting p to 6/26.\n" }, { "role": "assistant", "content": "The table shows that the F1 score for p set to 7/26 is 93.00, while for p set to 6/26, it is 92.78. Therefore, setting p to 7/26 results in a higher F1 score than setting p to 6/26. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1706.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results when the best setting from Section 6 is rerun with Open action pruning with context size c=2 and various pruning fractions p. Lower values of p indicate more aggressive pruning, while p=1 means no pruning is performed." ], "reasoning": "The table shows that the F1 score for p set to 7/26 is 93.00, while for p set to 6/26, it is 92.78. Therefore, setting p to 7/26 results in a higher F1 score than setting p to 6/26.", "statement": "Setting p to 7/26 results in a higher F1 score than setting p to 6/26.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 9: Independent impact of conditioning & regularization alternatives on WSJ0-2mix (validation set, no dynamic mixing).', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Using FiLM not only improves SDR but also allows for a higher learning rate, enabling faster training.We also ran experiments with the local classifier loss, Eq. Not only reported SDR are better but FiLM allows using a higher learning rate and hence enables faster training." } ], "images": [ "scigen/train/1707.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Independent impact of conditioning & regularization alternatives on WSJ0-2mix (validation set, no dynamic mixing)." ], "reasoning": "Using FiLM not only improves SDR but also allows for a higher learning rate, enabling faster training.", "description": "We also ran experiments with the local classifier loss, Eq. Not only reported SDR are better but FiLM allows using a higher learning rate and hence enables faster training.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 9: Independent impact of conditioning & regularization alternatives on WSJ0-2mix (validation set, no dynamic mixing).\nQuestion: Which model configuration has the highest SDR and what is its value?\nSolve the above question based on the table information. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table lists different model configurations and their corresponding SDR values. The 'Base model' has the highest SDR value of 23.7 dB. So, the answer is {\"answer\": \"Base model with 23.7 dB\"}." } ], "images": [ "scigen/train/1707.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Independent impact of conditioning & regularization alternatives on WSJ0-2mix (validation set, no dynamic mixing)." ], "reasoning": "The table lists different model configurations and their corresponding SDR values. The 'Base model' has the highest SDR value of 23.7 dB.", "question": "Which model configuration has the highest SDR and what is its value?", "answer": "So, the answer is {\"answer\": \"Base model with 23.7 dB\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe model without FiLM has the lowest SDR.\n" }, { "role": "assistant", "content": "The table shows that the model without FiLM has an SDR of 20.7 dB, which is the lowest among the listed configurations. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1707.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Independent impact of conditioning & regularization alternatives on WSJ0-2mix (validation set, no dynamic mixing)." ], "reasoning": "The table shows that the model without FiLM has an SDR of 20.7 dB, which is the lowest among the listed configurations.", "statement": "The model without FiLM has the lowest SDR.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Error Analysis on WSJ0-2mix.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The test set of WSJ0-2mix has a higher percentage of sentences with <5 dB ΔSDR compared to the validation set, and this lower performance is correlated with confusing speakers.We examine further the accuracy on WSJ0-2mix. Our results show that the test set exhibits a larger fraction of sentences with <5 dB ΔSDR than the validation set, i.e. 7.2% versus 1.3%. The WSJ0-2mix validation set contains the same speakers as the training data, we therefore examine further if the lower test performance is due to the representation of the test speakers. We rely on the training speaker classifier from Eq. When the same training identity collects more than 10% of the probability mass in both clusters, we label the sequence as ”confusing speakers” since both clusters contains vectors confused with the same training identity. We observe that low SDR correlates with situations where both speakers in the test recordings are mapped to the same training identity. The training set of WSJ0-2mix has only 101 speakers. We suspect that training on datasets with a wider range of speakers will alleviate this problem and will be studied in future work." } ], "images": [ "scigen/train/1708.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Error Analysis on WSJ0-2mix." ], "reasoning": "The test set of WSJ0-2mix has a higher percentage of sentences with <5 dB ΔSDR compared to the validation set, and this lower performance is correlated with confusing speakers.", "description": "We examine further the accuracy on WSJ0-2mix. Our results show that the test set exhibits a larger fraction of sentences with <5 dB ΔSDR than the validation set, i.e. 7.2% versus 1.3%. The WSJ0-2mix validation set contains the same speakers as the training data, we therefore examine further if the lower test performance is due to the representation of the test speakers. We rely on the training speaker classifier from Eq. When the same training identity collects more than 10% of the probability mass in both clusters, we label the sequence as ”confusing speakers” since both clusters contains vectors confused with the same training identity. We observe that low SDR correlates with situations where both speakers in the test recordings are mapped to the same training identity. The training set of WSJ0-2mix has only 101 speakers. We suspect that training on datasets with a wider range of speakers will alleviate this problem and will be studied in future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Error Analysis on WSJ0-2mix.\nQuestion: What is the percentage of confusing speakers in the test set for sentences with ΔSDR <5.0 dB?\nSolve the above question based on the table information. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the percentage of confusing speakers for both the validation and test sets. For the test set, the percentage of confusing speakers for sentences with ΔSDR <5.0 dB is 56.0%. So, the answer is {\"answer\": \"56.0%\"}." } ], "images": [ "scigen/train/1708.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Error Analysis on WSJ0-2mix." ], "reasoning": "The table shows the percentage of confusing speakers for both the validation and test sets. For the test set, the percentage of confusing speakers for sentences with ΔSDR <5.0 dB is 56.0%.", "question": "What is the percentage of confusing speakers in the test set for sentences with ΔSDR <5.0 dB?", "answer": "So, the answer is {\"answer\": \"56.0%\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe mean ΔSDR for the test set with ΔSDR >=5.0 dB is higher than that of the validation set.\n" }, { "role": "assistant", "content": "The table shows that the mean ΔSDR for the test set with ΔSDR >=5.0 dB is 20.7 dB, while for the validation set it is 22.1 dB. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1708.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Error Analysis on WSJ0-2mix." ], "reasoning": "The table shows that the mean ΔSDR for the test set with ΔSDR >=5.0 dB is 20.7 dB, while for the validation set it is 22.1 dB. Therefore, the statement is refuted.", "statement": "The mean ΔSDR for the test set with ΔSDR >=5.0 dB is higher than that of the validation set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 8: Wavesplit performance when the number of speakers varies (WSJ0-123mix, no dynamic mixing).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The model performs excellently with no speech overlap, but struggles with three active speakers, indicating a potential issue with the loss clipping strategy.The sequences with no speech overlap are excellent, highlighting that the model correctly identifies speaker turns and isolates speakers. Conversely, the sequences with three continuously active speakers report an accuracy lower than our model trained on WSJ0-3mix. We suspect that our loss clipping strategy is sub-optimal on this dataset." } ], "images": [ "scigen/train/1709.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Wavesplit performance when the number of speakers varies (WSJ0-123mix, no dynamic mixing)." ], "reasoning": "The model performs excellently with no speech overlap, but struggles with three active speakers, indicating a potential issue with the loss clipping strategy.", "description": "The sequences with no speech overlap are excellent, highlighting that the model correctly identifies speaker turns and isolates speakers. Conversely, the sequences with three continuously active speakers report an accuracy lower than our model trained on WSJ0-3mix. We suspect that our loss clipping strategy is sub-optimal on this dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 8: Wavesplit performance when the number of speakers varies (WSJ0-123mix, no dynamic mixing).', solve the subsequent question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhat is the ΔSI-SDR (dB) for sequences with three active speakers, and how does it compare to the overall ΔSI-SDR (dB)?\n" }, { "role": "assistant", "content": "The ΔSI-SDR for three active speakers is 13.3 dB, which is lower than the overall ΔSI-SDR of 15.9 dB, indicating reduced accuracy in sequences with three active speakers. So, the answer is {\"answer\": \"13.3\"}." } ], "images": [ "scigen/train/1709.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Wavesplit performance when the number of speakers varies (WSJ0-123mix, no dynamic mixing)." ], "reasoning": "The ΔSI-SDR for three active speakers is 13.3 dB, which is lower than the overall ΔSI-SDR of 15.9 dB, indicating reduced accuracy in sequences with three active speakers.", "question": "What is the ΔSI-SDR (dB) for sequences with three active speakers, and how does it compare to the overall ΔSI-SDR (dB)?", "answer": "So, the answer is {\"answer\": \"13.3\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model's accuracy is highest when there is no speech overlap.\n" }, { "role": "assistant", "content": "The table shows that the ΔSI-SDR and ΔSDR are highest for 1 active speaker (18.6 dB and 19.2 dB respectively), supporting the statement that the model's accuracy is highest with no speech overlap. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1709.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Wavesplit performance when the number of speakers varies (WSJ0-123mix, no dynamic mixing)." ], "reasoning": "The table shows that the ΔSI-SDR and ΔSDR are highest for 1 active speaker (18.6 dB and 19.2 dB respectively), supporting the statement that the model's accuracy is highest with no speech overlap.", "statement": "The model's accuracy is highest when there is no speech overlap.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: Accuracy of models and human on the each dataset, where † denotes the results coming from previous publications. DM denotes Daily Mail and WDW denotes Who-Did-What .'.\n" }, { "role": "assistant", "content": "Turkers perform best on word matching problems but worst on reasoning problems, while Stanford AR does not show stronger performance on word matching than reasoning categories.We compare models’ and human ceiling performance on datasets which have the same evaluation metric with RACE. The compared datasets include RACE, MCTest, CNN/Daily Mail (CNN and DM), CBT and WDW. On CBT, we report performance on two subsets where the missing token is either a common noun (CBT-C) or name entity Turkers do the best on word matching problems while doing the worst on reasoning problems. Sliding window performs better on word matching than problems needing reasoning or paraphrasing. Surprisingly, Stanford AR does not have a stronger performance on the word matching category than reasoning categories. A possible reason is that the proportion of data in reasoning categories is larger than that of data. Also, the candidate answers of simple matching questions may share similar word embeddings. For example, if the question is about color, it is difficult to distinguish candidate answers, “green”, “red”, “blue” and “yellow”, in the embedding vector space." } ], "images": [ "scigen/train/1710.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Accuracy of models and human on the each dataset, where † denotes the results coming from previous publications. DM denotes Daily Mail and WDW denotes Who-Did-What ." ], "reasoning": "Turkers perform best on word matching problems but worst on reasoning problems, while Stanford AR does not show stronger performance on word matching than reasoning categories.", "description": "We compare models’ and human ceiling performance on datasets which have the same evaluation metric with RACE. The compared datasets include RACE, MCTest, CNN/Daily Mail (CNN and DM), CBT and WDW. On CBT, we report performance on two subsets where the missing token is either a common noun (CBT-C) or name entity Turkers do the best on word matching problems while doing the worst on reasoning problems. Sliding window performs better on word matching than problems needing reasoning or paraphrasing. Surprisingly, Stanford AR does not have a stronger performance on the word matching category than reasoning categories. A possible reason is that the proportion of data in reasoning categories is larger than that of data. Also, the candidate answers of simple matching questions may share similar word embeddings. For example, if the question is about color, it is difficult to distinguish candidate answers, “green”, “red”, “blue” and “yellow”, in the embedding vector space.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 5: Accuracy of models and human on the each dataset, where † denotes the results coming from previous publications. DM denotes Daily Mail and WDW denotes Who-Did-What .', unfold the steps to solve the question given next. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nQuestion: Which model has the highest performance on the CNN dataset?\n" }, { "role": "assistant", "content": "By comparing the performance of different models on the CNN dataset, Stanford AR has the highest performance with a score of 73.6. So, the answer is {\"answer\": \"Stanford AR\"}" } ], "images": [ "scigen/train/1710.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Accuracy of models and human on the each dataset, where † denotes the results coming from previous publications. DM denotes Daily Mail and WDW denotes Who-Did-What ." ], "reasoning": "By comparing the performance of different models on the CNN dataset, Stanford AR has the highest performance with a score of 73.6.", "question": "Which model has the highest performance on the CNN dataset?", "answer": "So, the answer is {\"answer\": \"Stanford AR\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nSliding Window performs better on the CBT-N dataset than on the WDW dataset.\n" }, { "role": "assistant", "content": "The table shows that Sliding Window has a performance of 16.8 on the CBT-N dataset and 48.0 on the WDW dataset, indicating that Sliding Window performs better on the WDW dataset. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1710.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Accuracy of models and human on the each dataset, where † denotes the results coming from previous publications. DM denotes Daily Mail and WDW denotes Who-Did-What ." ], "reasoning": "The table shows that Sliding Window has a performance of 16.8 on the CBT-N dataset and 48.0 on the WDW dataset, indicating that Sliding Window performs better on the WDW dataset.", "statement": "Sliding Window performs better on the CBT-N dataset than on the WDW dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 13: Comparison of WER for two speech systems and human level performance on read speech.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The results indicate that further improvements on clean read speech by a generic speech system are limited without domain adaptation.Given this result, we suspect that there is little room for a generic speech system to further improve on clean read speech without further domain adaptation." } ], "images": [ "scigen/train/1711.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 13: Comparison of WER for two speech systems and human level performance on read speech." ], "reasoning": "The results indicate that further improvements on clean read speech by a generic speech system are limited without domain adaptation.", "description": "Given this result, we suspect that there is little room for a generic speech system to further improve on clean read speech without further domain adaptation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 13: Comparison of WER for two speech systems and human level performance on read speech.'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. \n\nQuestion: Which test set has the highest error rate for Read Speech DS1?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the highest error rate for Read Speech DS1, compare the values in the Read Speech DS1 column across all test sets. So, the answer is {\"answer\": \"LibriSpeech test-other\"}." } ], "images": [ "scigen/train/1711.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 13: Comparison of WER for two speech systems and human level performance on read speech." ], "reasoning": "To find the highest error rate for Read Speech DS1, compare the values in the Read Speech DS1 column across all test sets.", "question": "Which test set has the highest error rate for Read Speech DS1?", "answer": "So, the answer is {\"answer\": \"LibriSpeech test-other\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The error rate for Read Speech DS2 on WSJ eval'93 is higher than that for Read Speech Human on LibriSpeech test-clean. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe error rate for Read Speech DS2 on WSJ eval'93 is higher than that for Read Speech Human on LibriSpeech test-clean.\n" }, { "role": "assistant", "content": "Compare the error rate for Read Speech DS2 on WSJ eval'93 (4.98) with the error rate for Read Speech Human on LibriSpeech test-clean (5.83). The former is lower than the latter. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1711.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 13: Comparison of WER for two speech systems and human level performance on read speech." ], "reasoning": "Compare the error rate for Read Speech DS2 on WSJ eval'93 (4.98) with the error rate for Read Speech Human on LibriSpeech test-clean (5.83). The former is lower than the latter.", "statement": "The error rate for Read Speech DS2 on WSJ eval'93 is higher than that for Read Speech Human on LibriSpeech test-clean.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Comparison of WER on a training and development set for various depths of RNN, with and without BatchNorm. The number of parameters is kept constant as the depth increases, thus the number of hidden units per layer decreases. All networks have 38 million parameters. The architecture “M RNN, N total” implies 1 layer of 1D convolution at the input, M consecutive bidirectional RNN layers, and the rest as fully-connected layers with N total layers in the network.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Holding the total number of parameters constant, increasing depth improves performance, but BatchNorm harms generalization error for the shallowest network.For each hidden unit, we compute the mean and variance statistics over all items in the minibatch over the length of the sequence. When comparing depth, in order to control for model size we hold constant the total number of parameters and still see strong performance gains. We would expect to see even larger improvements from depth if we held constant the number of activations per layer and added layers. We also find that BatchNorm harms generalization error for the shallowest network just as it converges slower for shallower networks." } ], "images": [ "scigen/train/1712.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of WER on a training and development set for various depths of RNN, with and without BatchNorm. The number of parameters is kept constant as the depth increases, thus the number of hidden units per layer decreases. All networks have 38 million parameters. The architecture “M RNN, N total” implies 1 layer of 1D convolution at the input, M consecutive bidirectional RNN layers, and the rest as fully-connected layers with N total layers in the network." ], "reasoning": "Holding the total number of parameters constant, increasing depth improves performance, but BatchNorm harms generalization error for the shallowest network.", "description": "For each hidden unit, we compute the mean and variance statistics over all items in the minibatch over the length of the sequence. When comparing depth, in order to control for model size we hold constant the total number of parameters and still see strong performance gains. We would expect to see even larger improvements from depth if we held constant the number of activations per layer and added layers. We also find that BatchNorm harms generalization error for the shallowest network just as it converges slower for shallower networks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 1: Comparison of WER on a training and development set for various depths of RNN, with and without BatchNorm. The number of parameters is kept constant as the depth increases, thus the number of hidden units per layer decreases. All networks have 38 million parameters. The architecture “M RNN, N total” implies 1 layer of 1D convolution at the input, M consecutive bidirectional RNN layers, and the rest as fully-connected layers with N total layers in the network.', and sequentially solve the question, writing out each step of your reasoning process. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the Train BatchNorm error for the architecture with the highest number of hidden units?\n" }, { "role": "assistant", "content": "The architecture with the highest number of hidden units is '1 RNN, 5 total' with 2400 hidden units. The Train BatchNorm error for this architecture is 11.99. So, the answer is {\"answer\": \"11.99\"}." } ], "images": [ "scigen/train/1712.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of WER on a training and development set for various depths of RNN, with and without BatchNorm. The number of parameters is kept constant as the depth increases, thus the number of hidden units per layer decreases. All networks have 38 million parameters. The architecture “M RNN, N total” implies 1 layer of 1D convolution at the input, M consecutive bidirectional RNN layers, and the rest as fully-connected layers with N total layers in the network." ], "reasoning": "The architecture with the highest number of hidden units is '1 RNN, 5 total' with 2400 hidden units. The Train BatchNorm error for this architecture is 11.99.", "question": "What is the Train BatchNorm error for the architecture with the highest number of hidden units?", "answer": "So, the answer is {\"answer\": \"11.99\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe architecture with 7 RNNs and 9 total layers has the lowest Dev BatchNorm error.\n" }, { "role": "assistant", "content": "The table shows that the architecture with 7 RNNs and 9 total layers has a Dev BatchNorm error of 9.52, which is the lowest among all listed architectures. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1712.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of WER on a training and development set for various depths of RNN, with and without BatchNorm. The number of parameters is kept constant as the depth increases, thus the number of hidden units per layer decreases. All networks have 38 million parameters. The architecture “M RNN, N total” implies 1 layer of 1D convolution at the input, M consecutive bidirectional RNN layers, and the rest as fully-connected layers with N total layers in the network." ], "reasoning": "The table shows that the architecture with 7 RNNs and 9 total layers has a Dev BatchNorm error of 9.52, which is the lowest among all listed architectures.", "statement": "The architecture with 7 RNNs and 9 total layers has the lowest Dev BatchNorm error.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Comparison of WER on a training and development set with and without SortaGrad, and with and without batch normalization.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Sorting the training set by utterance length in the first epoch and using BatchNorm improves numerical stability and reduces training and development errors.In the first training epoch, we iterate through the training set in increasing order of the length of the longest utterance in the minibatch. After the first epoch, training reverts back to a random order over minibatches. This effect is particularly pronounced for networks without BatchNorm, since they are numerically less stable. In some sense the two techniques substitute for one another, though we still find gains when applying SortaGrad and BatchNorm together. Even with BatchNorm we find that this curriculum improves numerical stability and sensitivity to small changes in training. Numerical instability can arise from different transcendental function implementations in the CPU and the GPU, especially when computing the CTC cost. This curriculum gives comparable results for both implementations." } ], "images": [ "scigen/train/1713.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of WER on a training and development set with and without SortaGrad, and with and without batch normalization." ], "reasoning": "Sorting the training set by utterance length in the first epoch and using BatchNorm improves numerical stability and reduces training and development errors.", "description": "In the first training epoch, we iterate through the training set in increasing order of the length of the longest utterance in the minibatch. After the first epoch, training reverts back to a random order over minibatches. This effect is particularly pronounced for networks without BatchNorm, since they are numerically less stable. In some sense the two techniques substitute for one another, though we still find gains when applying SortaGrad and BatchNorm together. Even with BatchNorm we find that this curriculum improves numerical stability and sensitivity to small changes in training. Numerical instability can arise from different transcendental function implementations in the CPU and the GPU, especially when computing the CTC cost. This curriculum gives comparable results for both implementations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Comparison of WER on a training and development set with and without SortaGrad, and with and without batch normalization.', answer the following question. Give detailed solution consisting of each step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the Train Baseline error for the Sorted training set without BatchNorm?\n" }, { "role": "assistant", "content": "To find the Train Baseline error for the Sorted training set without BatchNorm, look at the 'Train Baseline' column in the 'Sorted' row. So, the answer is {\"answer\": \"8.76\"}" } ], "images": [ "scigen/train/1713.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of WER on a training and development set with and without SortaGrad, and with and without batch normalization." ], "reasoning": "To find the Train Baseline error for the Sorted training set without BatchNorm, look at the 'Train Baseline' column in the 'Sorted' row.", "question": "What is the Train Baseline error for the Sorted training set without BatchNorm?", "answer": "So, the answer is {\"answer\": \"8.76\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Dev BatchNorm error for the Not Sorted training set is higher than the Dev Baseline error for the Sorted training set. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Dev BatchNorm error for the Not Sorted training set is higher than the Dev Baseline error for the Sorted training set.\n" }, { "role": "assistant", "content": "Compare the Dev BatchNorm error for the Not Sorted training set (9.78) with the Dev Baseline error for the Sorted training set (10.83). Since 9.78 is not higher than 10.83, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1713.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of WER on a training and development set with and without SortaGrad, and with and without batch normalization." ], "reasoning": "Compare the Dev BatchNorm error for the Not Sorted training set (9.78) with the Dev Baseline error for the Sorted training set (10.83). Since 9.78 is not higher than 10.83, the statement is refuted.", "statement": "The Dev BatchNorm error for the Not Sorted training set is higher than the Dev Baseline error for the Sorted training set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Comparison of WER with different amounts of striding for unigram and bigram outputs on a model with 1 layer of 1D-invariant convolution, 7 recurrent layers, and 1 fully connected layer. All models have BatchNorm, SortaGrad, and 35 million parameters. The models are compared on a development set with and without the use of a 5-gram language model.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Bigrams allow for larger strides without increasing the word error rate, reducing the number of time-steps and benefiting computation and memory usage.We observe that bigrams allow for larger strides without any sacrifice in in the word error rate. This allows us to reduce the number of time-steps of the unrolled RNN benefiting both computation and memory usage." } ], "images": [ "scigen/train/1714.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison of WER with different amounts of striding for unigram and bigram outputs on a model with 1 layer of 1D-invariant convolution, 7 recurrent layers, and 1 fully connected layer. All models have BatchNorm, SortaGrad, and 35 million parameters. The models are compared on a development set with and without the use of a 5-gram language model." ], "reasoning": "Bigrams allow for larger strides without increasing the word error rate, reducing the number of time-steps and benefiting computation and memory usage.", "description": "We observe that bigrams allow for larger strides without any sacrifice in in the word error rate. This allows us to reduce the number of time-steps of the unrolled RNN benefiting both computation and memory usage.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 5: Comparison of WER with different amounts of striding for unigram and bigram outputs on a model with 1 layer of 1D-invariant convolution, 7 recurrent layers, and 1 fully connected layer. All models have BatchNorm, SortaGrad, and 35 million parameters. The models are compared on a development set with and without the use of a 5-gram language model.\n\nQuestion: What is the word error rate for Dev LM Bigrams at stride 4, and how does it compare to Dev no LM Unigrams at the same stride?\n" }, { "role": "assistant", "content": "The word error rate for Dev LM Bigrams at stride 4 is 9.93, while for Dev no LM Unigrams at the same stride, it is 18.86. This shows that using bigrams with a language model significantly reduces the word error rate. So, the answer is {\"answer\": \"9.93\"}." } ], "images": [ "scigen/train/1714.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison of WER with different amounts of striding for unigram and bigram outputs on a model with 1 layer of 1D-invariant convolution, 7 recurrent layers, and 1 fully connected layer. All models have BatchNorm, SortaGrad, and 35 million parameters. The models are compared on a development set with and without the use of a 5-gram language model." ], "reasoning": "The word error rate for Dev LM Bigrams at stride 4 is 9.93, while for Dev no LM Unigrams at the same stride, it is 18.86. This shows that using bigrams with a language model significantly reduces the word error rate.", "question": "What is the word error rate for Dev LM Bigrams at stride 4, and how does it compare to Dev no LM Unigrams at the same stride?", "answer": "So, the answer is {\"answer\": \"9.93\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Using bigrams with a language model at stride 3 results in a lower word error rate compared to using unigrams without a language model at stride 2. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nUsing bigrams with a language model at stride 3 results in a lower word error rate compared to using unigrams without a language model at stride 2.\n" }, { "role": "assistant", "content": "The word error rate for Dev LM Bigrams at stride 3 is 10.06, while for Dev no LM Unigrams at stride 2, it is 14.93. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1714.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison of WER with different amounts of striding for unigram and bigram outputs on a model with 1 layer of 1D-invariant convolution, 7 recurrent layers, and 1 fully connected layer. All models have BatchNorm, SortaGrad, and 35 million parameters. The models are compared on a development set with and without the use of a 5-gram language model." ], "reasoning": "The word error rate for Dev LM Bigrams at stride 3 is 10.06, while for Dev no LM Unigrams at stride 2, it is 14.93. Therefore, the statement is supported.", "statement": "Using bigrams with a language model at stride 3 results in a lower word error rate compared to using unigrams without a language model at stride 2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 6: Comparison of WER for English and CER for Mandarin with and without a language model. These are simple RNN models with only one layer of 1D invariant convolution.'.\n" }, { "role": "assistant", "content": "The relative improvement in English and Mandarin decreases as the model architecture changes from 5 layers with 1 recurrent layer to 9 layers with 7 recurrent layers.The relative improvement given by the language model drops from 48% to 36% in English and 27% to 23% in Mandarin, as we go from a model with 5 layers and 1 recurrent layer to a model with 9 layers and 7 recurrent layers. We hypothesize that the network builds a stronger implicit language model with more recurrent layers." } ], "images": [ "scigen/train/1715.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Comparison of WER for English and CER for Mandarin with and without a language model. These are simple RNN models with only one layer of 1D invariant convolution." ], "reasoning": "The relative improvement in English and Mandarin decreases as the model architecture changes from 5 layers with 1 recurrent layer to 9 layers with 7 recurrent layers.", "description": "The relative improvement given by the language model drops from 48% to 36% in English and 27% to 23% in Mandarin, as we go from a model with 5 layers and 1 recurrent layer to a model with 9 layers and 7 recurrent layers. We hypothesize that the network builds a stronger implicit language model with more recurrent layers.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 6: Comparison of WER for English and CER for Mandarin with and without a language model. These are simple RNN models with only one layer of 1D invariant convolution.', answer the following question. Give detailed solution consisting of each step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the relative improvement in Mandarin when the model changes from 5 layers with 1 recurrent layer to 9 layers with 7 recurrent layers?\n" }, { "role": "assistant", "content": "The relative improvement in Mandarin can be calculated by comparing the 'Dev no LM' and 'Dev LM' values for both architectures. For 5 layers with 1 recurrent layer, the improvement is from 9.80 to 7.13. For 9 layers with 7 recurrent layers, the improvement is from 7.55 to 5.81. So, the answer is {\"answer\": \"4.99%\"}." } ], "images": [ "scigen/train/1715.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Comparison of WER for English and CER for Mandarin with and without a language model. These are simple RNN models with only one layer of 1D invariant convolution." ], "reasoning": "The relative improvement in Mandarin can be calculated by comparing the 'Dev no LM' and 'Dev LM' values for both architectures. For 5 layers with 1 recurrent layer, the improvement is from 9.80 to 7.13. For 9 layers with 7 recurrent layers, the improvement is from 7.55 to 5.81.", "question": "What is the relative improvement in Mandarin when the model changes from 5 layers with 1 recurrent layer to 9 layers with 7 recurrent layers?", "answer": "So, the answer is {\"answer\": \"4.99%\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe relative improvement in English is higher with 9 layers and 7 recurrent layers compared to 5 layers and 1 recurrent layer.\n" }, { "role": "assistant", "content": "The table shows that the relative improvement in English decreases from 48% to 36% when the model changes from 5 layers with 1 recurrent layer to 9 layers with 7 recurrent layers. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1715.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Comparison of WER for English and CER for Mandarin with and without a language model. These are simple RNN models with only one layer of 1D invariant convolution." ], "reasoning": "The table shows that the relative improvement in English decreases from 48% to 36% when the model changes from 5 layers with 1 recurrent layer to 9 layers with 7 recurrent layers.", "statement": "The relative improvement in English is higher with 9 layers and 7 recurrent layers compared to 5 layers and 1 recurrent layer.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Comparison of two different all-reduce implementations. All times are in seconds. Performance gain is the ratio of OpenMPI all-reduce time to our all-reduce time.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our implementation of all-reduce is significantly faster than OpenMPI's for up to 8 GPUs, and remains 2-4X faster even as the number of GPUs increases and inter-node communication grows.We report the time spent in all-reduce for a full training run that ran for one epoch on our English dataset using a 5 layer, 3 recurrent layer architecture with 2560 hidden units for all layers. In this table, we use a minibatch of 64 per GPU, expanding the algorithmic minibatch as we scale to more GPUs. We see that our implementation is considerably faster than OpenMPI’s when the communication is within a node (8 GPUs or less). As we increase the number of GPUs and increase the amount of inter-node communication, the gap shrinks, although our implementation is still 2-4X faster." } ], "images": [ "scigen/train/1716.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Comparison of two different all-reduce implementations. All times are in seconds. Performance gain is the ratio of OpenMPI all-reduce time to our all-reduce time." ], "reasoning": "Our implementation of all-reduce is significantly faster than OpenMPI's for up to 8 GPUs, and remains 2-4X faster even as the number of GPUs increases and inter-node communication grows.", "description": "We report the time spent in all-reduce for a full training run that ran for one epoch on our English dataset using a 5 layer, 3 recurrent layer architecture with 2560 hidden units for all layers. In this table, we use a minibatch of 64 per GPU, expanding the algorithmic minibatch as we scale to more GPUs. We see that our implementation is considerably faster than OpenMPI’s when the communication is within a node (8 GPUs or less). As we increase the number of GPUs and increase the amount of inter-node communication, the gap shrinks, although our implementation is still 2-4X faster.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 7: Comparison of two different all-reduce implementations. All times are in seconds. Performance gain is the ratio of OpenMPI all-reduce time to our all-reduce time.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the performance gain of our all-reduce implementation compared to OpenMPI's when using 16 GPUs?\n" }, { "role": "assistant", "content": "To find the performance gain when using 16 GPUs, look at the 'Performance Gain' column corresponding to 16 GPUs. So, the answer is {\"answer\": \"15.5\"}." } ], "images": [ "scigen/train/1716.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Comparison of two different all-reduce implementations. All times are in seconds. Performance gain is the ratio of OpenMPI all-reduce time to our all-reduce time." ], "reasoning": "To find the performance gain when using 16 GPUs, look at the 'Performance Gain' column corresponding to 16 GPUs.", "question": "What is the performance gain of our all-reduce implementation compared to OpenMPI's when using 16 GPUs?", "answer": "So, the answer is {\"answer\": \"15.5\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nOur all-reduce implementation is faster than OpenMPI's for all tested GPU configurations.\n" }, { "role": "assistant", "content": "The table shows that for every tested GPU configuration (4, 8, 16, 32, 64, 128), our all-reduce implementation has a lower time than OpenMPI's. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1716.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Comparison of two different all-reduce implementations. All times are in seconds. Performance gain is the ratio of OpenMPI all-reduce time to our all-reduce time." ], "reasoning": "The table shows that for every tested GPU configuration (4, 8, 16, 32, 64, 128), our all-reduce implementation has a lower time than OpenMPI's.", "statement": "Our all-reduce implementation is faster than OpenMPI's for all tested GPU configurations.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 8: Comparison of time spent in seconds in computing the CTC loss function and gradient in one epoch for two different implementations. Speedup is the ratio of CPU CTC time to GPU CTC time.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The GPU implementation significantly reduces training time per epoch for both English and Mandarin, leading to a 10-20% overall reduction in training time.The GPU implementation saves us 95 minutes per epoch in English, and 25 minutes in Mandarin. This reduces overall training time by 10-20%, which is also an important productivity benefit for our experiments." } ], "images": [ "scigen/train/1717.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Comparison of time spent in seconds in computing the CTC loss function and gradient in one epoch for two different implementations. Speedup is the ratio of CPU CTC time to GPU CTC time." ], "reasoning": "The GPU implementation significantly reduces training time per epoch for both English and Mandarin, leading to a 10-20% overall reduction in training time.", "description": "The GPU implementation saves us 95 minutes per epoch in English, and 25 minutes in Mandarin. This reduces overall training time by 10-20%, which is also an important productivity benefit for our experiments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 8: Comparison of time spent in seconds in computing the CTC loss function and gradient in one epoch for two different implementations. Speedup is the ratio of CPU CTC time to GPU CTC time.', answer the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nHow much time does the GPU implementation save per epoch for English and Mandarin combined?\n" }, { "role": "assistant", "content": "The GPU implementation saves 95 minutes per epoch in English and 25 minutes per epoch in Mandarin. Adding these together gives a total time saving of 120 minutes per epoch. So, the answer is {\"answer\": \"120 minutes\"}" } ], "images": [ "scigen/train/1717.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Comparison of time spent in seconds in computing the CTC loss function and gradient in one epoch for two different implementations. Speedup is the ratio of CPU CTC time to GPU CTC time." ], "reasoning": "The GPU implementation saves 95 minutes per epoch in English and 25 minutes per epoch in Mandarin. Adding these together gives a total time saving of 120 minutes per epoch.", "question": "How much time does the GPU implementation save per epoch for English and Mandarin combined?", "answer": "So, the answer is {\"answer\": \"120 minutes\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe GPU implementation reduces the training time for English by more than 28 times.\n" }, { "role": "assistant", "content": "The table shows that the speedup for English is 28.9 times, which is more than 28 times. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1717.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Comparison of time spent in seconds in computing the CTC loss function and gradient in one epoch for two different implementations. Speedup is the ratio of CPU CTC time to GPU CTC time." ], "reasoning": "The table shows that the speedup for English is 28.9 times, which is more than 28 times.", "statement": "The GPU implementation reduces the training time for English by more than 28 times.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 9: Summary of the datasets used to train DS2 in English. The Wall Street Journal (WSJ), Switchboard and Fisher [13] corpora are all published by the Linguistic Data Consortium. The LibriSpeech dataset [46] is available free on-line. The other datasets are internal Baidu corpora.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The Mandarin speech model uses 9,400 hours of labeled audio from internal Baidu corpora, which includes both read and spontaneous speech.Large-scale deep learning systems require an abundance of labeled training data. We have collected an extensive training dataset for both English and Mandarin speech models, in addition to augmenting our training with publicly available datasets. For the Mandarin system we use 9,400 hours of labeled audio containing 11 million utterances. The Mandarin speech data consists of internal Baidu corpora, representing a mix of read speech and spontaneous speech, in both standard Mandarin and accented Mandarin. The model which performs the best on a held-out development set during training is chosen for evaluation. The learning rate is chosen from [1×10−4,6×10−4] to yield fastest convergence and annealed by a constant factor of 1.2 after each epoch. We use a momentum of 0.99 for all models." } ], "images": [ "scigen/train/1718.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Summary of the datasets used to train DS2 in English. The Wall Street Journal (WSJ), Switchboard and Fisher [13] corpora are all published by the Linguistic Data Consortium. The LibriSpeech dataset [46] is available free on-line. The other datasets are internal Baidu corpora." ], "reasoning": "The Mandarin speech model uses 9,400 hours of labeled audio from internal Baidu corpora, which includes both read and spontaneous speech.", "description": "Large-scale deep learning systems require an abundance of labeled training data. We have collected an extensive training dataset for both English and Mandarin speech models, in addition to augmenting our training with publicly available datasets. For the Mandarin system we use 9,400 hours of labeled audio containing 11 million utterances. The Mandarin speech data consists of internal Baidu corpora, representing a mix of read speech and spontaneous speech, in both standard Mandarin and accented Mandarin. The model which performs the best on a held-out development set during training is chosen for evaluation. The learning rate is chosen from [1×10−4,6×10−4] to yield fastest convergence and annealed by a constant factor of 1.2 after each epoch. We use a momentum of 0.99 for all models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 9: Summary of the datasets used to train DS2 in English. The Wall Street Journal (WSJ), Switchboard and Fisher [13] corpora are all published by the Linguistic Data Consortium. The LibriSpeech dataset [46] is available free on-line. The other datasets are internal Baidu corpora.', answer the following question. Give detailed solution consisting of each step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nHow many hours of read speech data are used in the training dataset?\n" }, { "role": "assistant", "content": "To find the total hours of read speech data, sum the hours from the datasets labeled as 'read': WSJ (80), LibriSpeech (960), and Baidu read (5000). So, the answer is {\"answer\": \"6040\"}" } ], "images": [ "scigen/train/1718.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Summary of the datasets used to train DS2 in English. The Wall Street Journal (WSJ), Switchboard and Fisher [13] corpora are all published by the Linguistic Data Consortium. The LibriSpeech dataset [46] is available free on-line. The other datasets are internal Baidu corpora." ], "reasoning": "To find the total hours of read speech data, sum the hours from the datasets labeled as 'read': WSJ (80), LibriSpeech (960), and Baidu read (5000).", "question": "How many hours of read speech data are used in the training dataset?", "answer": "So, the answer is {\"answer\": \"6040\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The training dataset includes 300 hours of conversational speech from the Switchboard dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe training dataset includes 300 hours of conversational speech from the Switchboard dataset.\n" }, { "role": "assistant", "content": "The table shows that the Switchboard dataset contributes 300 hours of conversational speech to the training dataset. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1718.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Summary of the datasets used to train DS2 in English. The Wall Street Journal (WSJ), Switchboard and Fisher [13] corpora are all published by the Linguistic Data Consortium. The LibriSpeech dataset [46] is available free on-line. The other datasets are internal Baidu corpora." ], "reasoning": "The table shows that the Switchboard dataset contributes 300 hours of conversational speech to the training dataset.", "statement": "The training dataset includes 300 hours of conversational speech from the Switchboard dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 10: Comparison of English WER for Regular and Noisy development sets on increasing training dataset size. The architecture is a 9-layer model with 2 layers of 2D-invariant convolution and 7 recurrent layers with 68M parameters.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The WER decreases by approximately 40% for each tenfold increase in training set size, and there is a consistent 60% gap between regular and noisy datasets.Our English and Mandarin corpora are substantially larger than those commonly reported in speech recognition literature. This is done by randomly sampling the full dataset before training. For each dataset, the model was trained for up to 20 epochs though usually early-stopped based on the error on a held out development set. We note that the WER decreases with a power law for both the regular and noisy development sets. The WER decreases by ∼40% relative for each factor of 10 increase in training set size. We also observe a consistent gap in WER (∼60% relative) between the regular and noisy datasets, implying that more data benefits both cases equally." } ], "images": [ "scigen/train/1719.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Comparison of English WER for Regular and Noisy development sets on increasing training dataset size. The architecture is a 9-layer model with 2 layers of 2D-invariant convolution and 7 recurrent layers with 68M parameters." ], "reasoning": "The WER decreases by approximately 40% for each tenfold increase in training set size, and there is a consistent 60% gap between regular and noisy datasets.", "description": "Our English and Mandarin corpora are substantially larger than those commonly reported in speech recognition literature. This is done by randomly sampling the full dataset before training. For each dataset, the model was trained for up to 20 epochs though usually early-stopped based on the error on a held out development set. We note that the WER decreases with a power law for both the regular and noisy development sets. The WER decreases by ∼40% relative for each factor of 10 increase in training set size. We also observe a consistent gap in WER (∼60% relative) between the regular and noisy datasets, implying that more data benefits both cases equally.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the WER for the regular development set when using 50% of the data?\nSolve the above question based on the table titled 'Table 10: Comparison of English WER for Regular and Noisy development sets on increasing training dataset size. The architecture is a 9-layer model with 2 layers of 2D-invariant convolution and 7 recurrent layers with 68M parameters.'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the WER for the regular development set when using 50% of the data, look at the row corresponding to 50% of the data and find the value under the 'Regular Dev' column. So, the answer is {\"answer\": \"9.51\"}." } ], "images": [ "scigen/train/1719.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Comparison of English WER for Regular and Noisy development sets on increasing training dataset size. The architecture is a 9-layer model with 2 layers of 2D-invariant convolution and 7 recurrent layers with 68M parameters." ], "reasoning": "To find the WER for the regular development set when using 50% of the data, look at the row corresponding to 50% of the data and find the value under the 'Regular Dev' column.", "question": "What is the WER for the regular development set when using 50% of the data?", "answer": "So, the answer is {\"answer\": \"9.51\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The WER for the noisy development set is higher than the regular development set for all fractions of data., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe WER for the noisy development set is higher than the regular development set for all fractions of data.\n" }, { "role": "assistant", "content": "By comparing the 'Regular Dev' and 'Noisy Dev' columns for all fractions of data, it is evident that the WER for the noisy development set is consistently higher than the regular development set. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1719.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Comparison of English WER for Regular and Noisy development sets on increasing training dataset size. The architecture is a 9-layer model with 2 layers of 2D-invariant convolution and 7 recurrent layers with 68M parameters." ], "reasoning": "By comparing the 'Regular Dev' and 'Noisy Dev' columns for all fractions of data, it is evident that the WER for the noisy development set is consistently higher than the regular development set.", "statement": "The WER for the noisy development set is higher than the regular development set for all fractions of data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 14: Comparing WER of the DS1 system to the DS2 system on accented speech.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Human performance is better than DS2 for all accents except the Indian accent.Performance on these test sets is to some extent a measure of the breadth and quality of our training data. However human level performance is still notably better than that of DS2 for all but the Indian accent." } ], "images": [ "scigen/train/1720.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 14: Comparing WER of the DS1 system to the DS2 system on accented speech." ], "reasoning": "Human performance is better than DS2 for all accents except the Indian accent.", "description": "Performance on these test sets is to some extent a measure of the breadth and quality of our training data. However human level performance is still notably better than that of DS2 for all but the Indian accent.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 14: Comparing WER of the DS1 system to the DS2 system on accented speech.' and proceed to solve the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich accent has the smallest performance gap between DS2 and human?\n" }, { "role": "assistant", "content": "Compare the performance differences between DS2 and human for each accent. The smallest gap is for the Indian accent. So, the answer is {\"answer\": \"Indian\"}." } ], "images": [ "scigen/train/1720.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 14: Comparing WER of the DS1 system to the DS2 system on accented speech." ], "reasoning": "Compare the performance differences between DS2 and human for each accent. The smallest gap is for the Indian accent.", "question": "Which accent has the smallest performance gap between DS2 and human?", "answer": "So, the answer is {\"answer\": \"Indian\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim DS2 performs better than human on the Indian accent., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nDS2 performs better than human on the Indian accent.\n" }, { "role": "assistant", "content": "The table shows that DS2 has a score of 22.44 on the Indian accent, while human performance is 22.15, indicating DS2 performs slightly better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1720.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 14: Comparing WER of the DS1 system to the DS2 system on accented speech." ], "reasoning": "The table shows that DS2 has a score of 22.44 on the Indian accent, while human performance is 22.15, indicating DS2 performs slightly better.", "statement": "DS2 performs better than human on the Indian accent.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 15: Comparison of DS1 and DS2 system on noisy speech. “CHiME eval clean” is a noise-free baseline. The “CHiME eval real” dataset is collected in real noisy environments and the “CHiME eval sim” dataset has similar noise synthetically added to clean speech. Note that we use only one of the six channels to test each utterance.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The dataset includes 1320 utterances in various noisy environments and a control set, with results showing the network's ability to handle real and synthetic noise.Noisy Speech This dataset has 1320 utterances from the WSJ test set read in various noisy environments, including a bus, a cafe, a street and a pedestrian area. The CHiME set also includes 1320 utterances with simulated noise from the same environments as well as the control set containing the same utterances delivered by the same speakers in a noise-free environment. Differences between results on the control set and the noisy sets provide a measure of the network’s ability to handle a variety of real and synthetic noise conditions. We use a single channel for all our results, since multi-channel audio is not pervasive on most devices. The relative gap between DS2 and human level performance is larger when the data comes from a real noisy environment instead of synthetically adding noise to clean speech." } ], "images": [ "scigen/train/1721.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 15: Comparison of DS1 and DS2 system on noisy speech. “CHiME eval clean” is a noise-free baseline. The “CHiME eval real” dataset is collected in real noisy environments and the “CHiME eval sim” dataset has similar noise synthetically added to clean speech. Note that we use only one of the six channels to test each utterance." ], "reasoning": "The dataset includes 1320 utterances in various noisy environments and a control set, with results showing the network's ability to handle real and synthetic noise.", "description": "Noisy Speech This dataset has 1320 utterances from the WSJ test set read in various noisy environments, including a bus, a cafe, a street and a pedestrian area. The CHiME set also includes 1320 utterances with simulated noise from the same environments as well as the control set containing the same utterances delivered by the same speakers in a noise-free environment. Differences between results on the control set and the noisy sets provide a measure of the network’s ability to handle a variety of real and synthetic noise conditions. We use a single channel for all our results, since multi-channel audio is not pervasive on most devices. The relative gap between DS2 and human level performance is larger when the data comes from a real noisy environment instead of synthetically adding noise to clean speech.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 15: Comparison of DS1 and DS2 system on noisy speech. “CHiME eval clean” is a noise-free baseline. The “CHiME eval real” dataset is collected in real noisy environments and the “CHiME eval sim” dataset has similar noise synthetically added to clean speech. Note that we use only one of the six channels to test each utterance.', answer the following question. Give detailed solution consisting of each step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the difference in performance between DS2 and human level on the CHiME eval real set?\n" }, { "role": "assistant", "content": "To find the difference in performance between DS2 and human level on the CHiME eval real set, subtract the human level score from the DS2 score: 21.79 - 11.84. So, the answer is {\"answer\": \"9.95\"}" } ], "images": [ "scigen/train/1721.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 15: Comparison of DS1 and DS2 system on noisy speech. “CHiME eval clean” is a noise-free baseline. The “CHiME eval real” dataset is collected in real noisy environments and the “CHiME eval sim” dataset has similar noise synthetically added to clean speech. Note that we use only one of the six channels to test each utterance." ], "reasoning": "To find the difference in performance between DS2 and human level on the CHiME eval real set, subtract the human level score from the DS2 score: 21.79 - 11.84.", "question": "What is the difference in performance between DS2 and human level on the CHiME eval real set?", "answer": "So, the answer is {\"answer\": \"9.95\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nDS1 performs better than DS2 on the CHiME eval clean set.\n" }, { "role": "assistant", "content": "The table shows that DS1 has a score of 6.30 on the CHiME eval clean set, while DS2 has a score of 3.34, indicating that DS1 performs worse than DS2. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1721.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 15: Comparison of DS1 and DS2 system on noisy speech. “CHiME eval clean” is a noise-free baseline. The “CHiME eval real” dataset is collected in real noisy environments and the “CHiME eval sim” dataset has similar noise synthetically added to clean speech. Note that we use only one of the six channels to test each utterance." ], "reasoning": "The table shows that DS1 has a score of 6.30 on the CHiME eval clean set, while DS2 has a score of 3.34, indicating that DS1 performs worse than DS2.", "statement": "DS1 performs better than DS2 on the CHiME eval clean set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 16: Comparison of the improvements in DeepSpeech with architectural improvements. The development and test sets are Baidu internal corpora. All the models in the table have about 80 million parameters each'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The deepest model with 2D-invariant convolution and BatchNorm outperforms the shallow RNN by 48% relative, showing that multiple layers of bidirectional recurrence improve performance substantially.This development set was also used to tune the decoding parameters We see that the deepest model with 2D-invariant convolution and BatchNorm outperforms the shallow RNN by 48% relative, thus continuing the trend that we saw with the English system—multiple layers of bidirectional recurrence improves performance substantially." } ], "images": [ "scigen/train/1722.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 16: Comparison of the improvements in DeepSpeech with architectural improvements. The development and test sets are Baidu internal corpora. All the models in the table have about 80 million parameters each" ], "reasoning": "The deepest model with 2D-invariant convolution and BatchNorm outperforms the shallow RNN by 48% relative, showing that multiple layers of bidirectional recurrence improve performance substantially.", "description": "This development set was also used to tune the decoding parameters We see that the deepest model with 2D-invariant convolution and BatchNorm outperforms the shallow RNN by 48% relative, thus continuing the trend that we saw with the English system—multiple layers of bidirectional recurrence improves performance substantially.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 16: Comparison of the improvements in DeepSpeech with architectural improvements. The development and test sets are Baidu internal corpora. All the models in the table have about 80 million parameters each' and methodically tackle the question that follows. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the test performance improvement percentage of the 9-layer, 7 RNN + BatchNorm + 2D conv model compared to the 5-layer, 1 RNN model?\n" }, { "role": "assistant", "content": "The test performance of the 5-layer, 1 RNN model is 15.41, and the test performance of the 9-layer, 7 RNN + BatchNorm + 2D conv model is 7.93. The improvement percentage is calculated as ((15.41 - 7.93) / 15.41) * 100. So, the answer is {\"answer\": \"48.53%\"}." } ], "images": [ "scigen/train/1722.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 16: Comparison of the improvements in DeepSpeech with architectural improvements. The development and test sets are Baidu internal corpora. All the models in the table have about 80 million parameters each" ], "reasoning": "The test performance of the 5-layer, 1 RNN model is 15.41, and the test performance of the 9-layer, 7 RNN + BatchNorm + 2D conv model is 7.93. The improvement percentage is calculated as ((15.41 - 7.93) / 15.41) * 100.", "question": "What is the test performance improvement percentage of the 9-layer, 7 RNN + BatchNorm + 2D conv model compared to the 5-layer, 1 RNN model?", "answer": "So, the answer is {\"answer\": \"48.53%\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe 5-layer, 3 RNN + BatchNorm model has a better test performance than the 5-layer, 3 RNN model.\n" }, { "role": "assistant", "content": "The test performance of the 5-layer, 3 RNN + BatchNorm model is 9.39, which is better than the test performance of the 5-layer, 3 RNN model, which is 11.85. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1722.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 16: Comparison of the improvements in DeepSpeech with architectural improvements. The development and test sets are Baidu internal corpora. All the models in the table have about 80 million parameters each" ], "reasoning": "The test performance of the 5-layer, 3 RNN + BatchNorm model is 9.39, which is better than the test performance of the 5-layer, 3 RNN model, which is 11.85.", "statement": "The 5-layer, 3 RNN + BatchNorm model has a better test performance than the 5-layer, 3 RNN model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Effect of fine-tuning (FT) segment embedding on development data. For CoNLL03 data, a named entity is “out-of-vocabulary” when it is not included in the training data as a named entity.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Fine-tuning segment embeddings improves performance on MSR due to its low OOV rate, but harms performance on CoNLL03 due to its high OOV rate.We study the effect of fine-tuning the segment embeddings by imposing SEmb-Homo into our model. We find that our models benefit from fixing the segment embeddings on CoNLL03. While on MSR, fine-tuning the embeddings helps. Further study on the out-of-vocabulary rate shows that the OOV rate of MSR is very low, thus fine-tuning on segment embeddings help to learn a better task-specified segment representation. However, on CoNLL03 data whose OOV rate is high, fine-tuning the segment embedding harms the generalization power of pre-trained segment embeddings." } ], "images": [ "scigen/train/1723.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Effect of fine-tuning (FT) segment embedding on development data. For CoNLL03 data, a named entity is “out-of-vocabulary” when it is not included in the training data as a named entity." ], "reasoning": "Fine-tuning segment embeddings improves performance on MSR due to its low OOV rate, but harms performance on CoNLL03 due to its high OOV rate.", "description": "We study the effect of fine-tuning the segment embeddings by imposing SEmb-Homo into our model. We find that our models benefit from fixing the segment embeddings on CoNLL03. While on MSR, fine-tuning the embeddings helps. Further study on the out-of-vocabulary rate shows that the OOV rate of MSR is very low, thus fine-tuning on segment embeddings help to learn a better task-specified segment representation. However, on CoNLL03 data whose OOV rate is high, fine-tuning the segment embedding harms the generalization power of pre-trained segment embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Effect of fine-tuning (FT) segment embedding on development data. For CoNLL03 data, a named entity is “out-of-vocabulary” when it is not included in the training data as a named entity.'. The final result should be presented in the JSON format of {\"answer\": \"\"}. \n\nQuestion: Which model and configuration achieved the highest score on MSR?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By examining the table, the highest score on MSR is 97.32, achieved by the [1pt/3pt] + SEmb-Homo w/FT configuration of the SRNN model. So, the answer is {\"answer\": \"[1pt/3pt] + SEmb-Homo w/FT (SRNN)\"}" } ], "images": [ "scigen/train/1723.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Effect of fine-tuning (FT) segment embedding on development data. For CoNLL03 data, a named entity is “out-of-vocabulary” when it is not included in the training data as a named entity." ], "reasoning": "By examining the table, the highest score on MSR is 97.32, achieved by the [1pt/3pt] + SEmb-Homo w/FT configuration of the SRNN model.", "question": "Which model and configuration achieved the highest score on MSR?", "answer": "So, the answer is {\"answer\": \"[1pt/3pt] + SEmb-Homo w/FT (SRNN)\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nFine-tuning segment embeddings on CoNLL03 data improves the model's performance.\n" }, { "role": "assistant", "content": "The description states that fine-tuning segment embeddings harms the generalization power on CoNLL03 data due to its high OOV rate, and the table shows that the highest score on CoNLL03 is achieved without fine-tuning. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1723.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Effect of fine-tuning (FT) segment embedding on development data. For CoNLL03 data, a named entity is “out-of-vocabulary” when it is not included in the training data as a named entity." ], "reasoning": "The description states that fine-tuning segment embeddings harms the generalization power on CoNLL03 data due to its high OOV rate, and the table shows that the highest score on CoNLL03 is achieved without fine-tuning.", "statement": "Fine-tuning segment embeddings on CoNLL03 data improves the model's performance.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: The NER and CWS results of the baseline models and our neural semi-CRF models with different input composition functions. spd represents the inference speed and is evaluated by the number of tokens processed per millisecond.'.\n" }, { "role": "assistant", "content": "SRNN and SCONCATE perform better than SCNN, and NN-CRF performs better than the best neural semi-CRF model (SCONCATE) on NER, while SRNN and SCONCATE outperform NN-CRF on three CWS datasets.We first consider the problem of representing segments via composing input units and compare different input composition functions. From this table, the SRNN and SCONCATE achieve comparable results and perform better than the SCNN. Although CNN can model input sequence at any length, its invariance to the exact position can be a flaw in representing segments. The experimental results confirm that and show the importance of properly handling the input position. Considering SCNN’s relatively poor performance, we only study SRNN and SCONCATE in the following experiments. Comparing with NN-Labeler, structure prediction models (NN-CRF and neural semi-CRF) generally achieve better performance. But the difference between the neural structure prediction models is not significant. NN-CRF performs better than the best neural semi-CRF model (SCONCATE) on NER while the both SRNN and SCONCATE outperform NN-CRF on three CWS datasets. We address this to the fact either the NN-CRF or the neural semi-CRF merely takes input-level information and not sufficiently adopts segment-level information into the models." } ], "images": [ "scigen/train/1724.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The NER and CWS results of the baseline models and our neural semi-CRF models with different input composition functions. spd represents the inference speed and is evaluated by the number of tokens processed per millisecond." ], "reasoning": "SRNN and SCONCATE perform better than SCNN, and NN-CRF performs better than the best neural semi-CRF model (SCONCATE) on NER, while SRNN and SCONCATE outperform NN-CRF on three CWS datasets.", "description": "We first consider the problem of representing segments via composing input units and compare different input composition functions. From this table, the SRNN and SCONCATE achieve comparable results and perform better than the SCNN. Although CNN can model input sequence at any length, its invariance to the exact position can be a flaw in representing segments. The experimental results confirm that and show the importance of properly handling the input position. Considering SCNN’s relatively poor performance, we only study SRNN and SCONCATE in the following experiments. Comparing with NN-Labeler, structure prediction models (NN-CRF and neural semi-CRF) generally achieve better performance. But the difference between the neural structure prediction models is not significant. NN-CRF performs better than the best neural semi-CRF model (SCONCATE) on NER while the both SRNN and SCONCATE outperform NN-CRF on three CWS datasets. We address this to the fact either the NN-CRF or the neural semi-CRF merely takes input-level information and not sufficiently adopts segment-level information into the models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: The NER and CWS results of the baseline models and our neural semi-CRF models with different input composition functions. spd represents the inference speed and is evaluated by the number of tokens processed per millisecond.', and sequentially solve the question, writing out each step of your reasoning process. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model has the highest test score on the CWS CTB6 dataset?\n" }, { "role": "assistant", "content": "To find the model with the highest test score on the CWS CTB6 dataset, we look at the 'test' column under 'CWS CTB6' and identify the highest value, which is 95.68, achieved by Sparse-CRF. So, the answer is {\"answer\": \"Sparse-CRF\"}." } ], "images": [ "scigen/train/1724.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The NER and CWS results of the baseline models and our neural semi-CRF models with different input composition functions. spd represents the inference speed and is evaluated by the number of tokens processed per millisecond." ], "reasoning": "To find the model with the highest test score on the CWS CTB6 dataset, we look at the 'test' column under 'CWS CTB6' and identify the highest value, which is 95.68, achieved by Sparse-CRF.", "question": "Which model has the highest test score on the CWS CTB6 dataset?", "answer": "So, the answer is {\"answer\": \"Sparse-CRF\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim NN-CRF has the highest test score on the NER CoNLL03 dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nNN-CRF has the highest test score on the NER CoNLL03 dataset.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the 'test' column under 'NER CoNLL03' and compare the scores. NN-CRF has a test score of 89.08, which is higher than the other models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1724.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The NER and CWS results of the baseline models and our neural semi-CRF models with different input composition functions. spd represents the inference speed and is evaluated by the number of tokens processed per millisecond." ], "reasoning": "To verify this statement, we look at the 'test' column under 'NER CoNLL03' and compare the scores. NN-CRF has a test score of 89.08, which is higher than the other models listed.", "statement": "NN-CRF has the highest test score on the NER CoNLL03 dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: Comparison between baselines and our neural semi-CRF model with segment embeddings.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The neural semi-CRF model with segment embeddings significantly improves performance, with a 0.7% increase in NER and over 2.0% in CWS.At last, we compare our neural semi-CRF model leveraging additional segment embeddings with those only represent segment by composing input. Style of segment embeddings (Homo or Hetero) and whether fine-tune it is decided by the development data. From this result, we see that segment-level representation greatly boost up model’s performance. On NER, an improvement of 0.7% is observed and that improvement on CWS is more than 2.0% on average." } ], "images": [ "scigen/train/1725.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison between baselines and our neural semi-CRF model with segment embeddings." ], "reasoning": "The neural semi-CRF model with segment embeddings significantly improves performance, with a 0.7% increase in NER and over 2.0% in CWS.", "description": "At last, we compare our neural semi-CRF model leveraging additional segment embeddings with those only represent segment by composing input. Style of segment embeddings (Homo or Hetero) and whether fine-tune it is decided by the development data. From this result, we see that segment-level representation greatly boost up model’s performance. On NER, an improvement of 0.7% is observed and that improvement on CWS is more than 2.0% on average.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 4: Comparison between baselines and our neural semi-CRF model with segment embeddings.' to sequentially solve the question presented below. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the highest score achieved on the MSR dataset and by which model?\n" }, { "role": "assistant", "content": "By examining the MSR column, the highest score is 97.58, achieved by the [1pt/3pt] SCONCATE +SEmb-Hetero model. So, the answer is {\"answer\": \"97.58\"}" } ], "images": [ "scigen/train/1725.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison between baselines and our neural semi-CRF model with segment embeddings." ], "reasoning": "By examining the MSR column, the highest score is 97.58, achieved by the [1pt/3pt] SCONCATE +SEmb-Hetero model.", "question": "What is the highest score achieved on the MSR dataset and by which model?", "answer": "So, the answer is {\"answer\": \"97.58\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The NN-CRF model achieved the highest score on the CTB6 dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe NN-CRF model achieved the highest score on the CTB6 dataset.\n" }, { "role": "assistant", "content": "The highest score on the CTB6 dataset is 95.48, achieved by the SRNN +SEmb-Hetero model, not the NN-CRF model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1725.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison between baselines and our neural semi-CRF model with segment embeddings." ], "reasoning": "The highest score on the CTB6 dataset is 95.48, achieved by the SRNN +SEmb-Hetero model, not the NN-CRF model.", "statement": "The NN-CRF model achieved the highest score on the CTB6 dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Comparison with the state-of-the-art NER systems.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The neural semi-CRF model with multi-level segment representation achieves a CoNLL03 score of 90.90, outperforming other state-of-the-art NER and CWS systems.We compare our neural semi-CRF model leveraging multi-levels segment representation with other state-of-the-art NER and CWS systems." } ], "images": [ "scigen/train/1726.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison with the state-of-the-art NER systems." ], "reasoning": "The neural semi-CRF model with multi-level segment representation achieves a CoNLL03 score of 90.90, outperforming other state-of-the-art NER and CWS systems.", "description": "We compare our neural semi-CRF model leveraging multi-levels segment representation with other state-of-the-art NER and CWS systems.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 5: Comparison with the state-of-the-art NER systems.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. \n\nQuestion: What is the highest CoNLL03 score achieved by the models listed in the table?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the highest CoNLL03 score, we need to compare all the scores listed in the table. The highest score is 90.90. So, the answer is {\"answer\": \"90.90\"}." } ], "images": [ "scigen/train/1726.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison with the state-of-the-art NER systems." ], "reasoning": "To find the highest CoNLL03 score, we need to compare all the scores listed in the table. The highest score is 90.90.", "question": "What is the highest CoNLL03 score achieved by the models listed in the table?", "answer": "So, the answer is {\"answer\": \"90.90\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The neural semi-CRF model with multi-level segment representation achieved a CoNLL03 score higher than 90.00., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe neural semi-CRF model with multi-level segment representation achieved a CoNLL03 score higher than 90.00.\n" }, { "role": "assistant", "content": "The table shows that the neural semi-CRF model with multi-level segment representation achieved a CoNLL03 score of 90.90, which is higher than 90.00. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1726.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison with the state-of-the-art NER systems." ], "reasoning": "The table shows that the neural semi-CRF model with multi-level segment representation achieved a CoNLL03 score of 90.90, which is higher than 90.00.", "statement": "The neural semi-CRF model with multi-level segment representation achieved a CoNLL03 score higher than 90.00.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 6: Comparison with the state-of-the-art CWS systems.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Our neural semi-CRF model achieves state-of-the-art performance on PKU and MSR data, and performs close to previous models on CTB6 data.Our neural semi-CRF model with multi-level segment representation achieves the state-of-the-art performance on PKU and MSR data. On CTB6 data, our model’s performance is also close to \\citeauthorwang-EtAl:2011: IJCNLP-2011 [\\citeyearwang-EtAl:2011:IJCNLP-2011] which uses semi-supervised features extracted auto-segmented unlabeled data. According to \\citeauthorpei-ge-chang:2014: P14-1 [\\citeyearpei-ge-chang:2014: P14-1], significant improvements can be achieved by replacing character embeddings with character-bigram embeddings. However we didn’t employ this trick considering the unification of our model." } ], "images": [ "scigen/train/1727.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Comparison with the state-of-the-art CWS systems." ], "reasoning": "Our neural semi-CRF model achieves state-of-the-art performance on PKU and MSR data, and performs close to previous models on CTB6 data.", "description": "Our neural semi-CRF model with multi-level segment representation achieves the state-of-the-art performance on PKU and MSR data. On CTB6 data, our model’s performance is also close to \\citeauthorwang-EtAl:2011: IJCNLP-2011 [\\citeyearwang-EtAl:2011:IJCNLP-2011] which uses semi-supervised features extracted auto-segmented unlabeled data. According to \\citeauthorpei-ge-chang:2014: P14-1 [\\citeyearpei-ge-chang:2014: P14-1], significant improvements can be achieved by replacing character embeddings with character-bigram embeddings. However we didn’t employ this trick considering the unification of our model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 6: Comparison with the state-of-the-art CWS systems.', solve the following question step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the performance of the neural semi-CRF model with character-bigram embeddings on the MSR dataset?\n" }, { "role": "assistant", "content": "The table shows that the neural semi-CRF model with character-bigram embeddings achieves a performance of 97.2 on the MSR dataset. So, the answer is {\"answer\": \"97.2\"}." } ], "images": [ "scigen/train/1727.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Comparison with the state-of-the-art CWS systems." ], "reasoning": "The table shows that the neural semi-CRF model with character-bigram embeddings achieves a performance of 97.2 on the MSR dataset.", "question": "What is the performance of the neural semi-CRF model with character-bigram embeddings on the MSR dataset?", "answer": "So, the answer is {\"answer\": \"97.2\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe neural semi-CRF model with character-bigram embeddings achieves the highest performance on the MSR dataset.\n" }, { "role": "assistant", "content": "The table shows that the neural semi-CRF model with character-bigram embeddings achieves a performance of 97.2 on the MSR dataset, but the highest performance on the MSR dataset is 97.58 achieved by 'our best' model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1727.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Comparison with the state-of-the-art CWS systems." ], "reasoning": "The table shows that the neural semi-CRF model with character-bigram embeddings achieves a performance of 97.2 on the MSR dataset, but the highest performance on the MSR dataset is 97.58 achieved by 'our best' model.", "statement": "The neural semi-CRF model with character-bigram embeddings achieves the highest performance on the MSR dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 10: Performance comparison using different idiom representations.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The table shows that idiom embedding consistently outperforms average character embedding and average character embedding + MLP across all models and datasets, indicating the importance of treating idioms as independent semantic units for Chinese reading comprehension.We can observe remarkable drops from idiom embedding to average character embedding + MLP and to average character embedding for all the models, where all the differences are significant (sign test, p-value <0.01). The results indicate that the other two idiom representation methods are worse than treating an idiom as an independent semantic unit. This study also implies that idiom representation is a key factor for the success of Chinese reading comprehension with idiom. In other words, a good cloze test model should have not only a proper model structure, but also a good method to represent idioms." } ], "images": [ "scigen/train/1728.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Performance comparison using different idiom representations." ], "reasoning": "The table shows that idiom embedding consistently outperforms average character embedding and average character embedding + MLP across all models and datasets, indicating the importance of treating idioms as independent semantic units for Chinese reading comprehension.", "description": "We can observe remarkable drops from idiom embedding to average character embedding + MLP and to average character embedding for all the models, where all the differences are significant (sign test, p-value <0.01). The results indicate that the other two idiom representation methods are worse than treating an idiom as an independent semantic unit. This study also implies that idiom representation is a key factor for the success of Chinese reading comprehension with idiom. In other words, a good cloze test model should have not only a proper model structure, but also a good method to represent idioms.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 10: Performance comparison using different idiom representations.', solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model and embedding method combination achieved the highest score on the Ran dataset?\n" }, { "role": "assistant", "content": "To find the highest score on the Ran dataset, we need to compare the scores of all model and embedding method combinations. The highest score is 82.0, achieved by the AR model with idiom embedding. So, the answer is {\"answer\": \"AR with idiom embedding\"}." } ], "images": [ "scigen/train/1728.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Performance comparison using different idiom representations." ], "reasoning": "To find the highest score on the Ran dataset, we need to compare the scores of all model and embedding method combinations. The highest score is 82.0, achieved by the AR model with idiom embedding.", "question": "Which model and embedding method combination achieved the highest score on the Ran dataset?", "answer": "So, the answer is {\"answer\": \"AR with idiom embedding\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe SAR model with average character embedding + MLP achieved a higher score on the Sim dataset than the LM model with idiom embedding.\n" }, { "role": "assistant", "content": "The table shows that the SAR model with average character embedding + MLP scored 60.8 on the Sim dataset, while the LM model with idiom embedding scored 65.6. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1728.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Performance comparison using different idiom representations." ], "reasoning": "The table shows that the SAR model with average character embedding + MLP scored 60.8 on the Sim dataset, while the LM model with idiom embedding scored 65.6. Therefore, the statement is refuted.", "statement": "The SAR model with average character embedding + MLP achieved a higher score on the Sim dataset than the LM model with idiom embedding.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Performance of our generative model on validation dataset with candidate set.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The primary stage outperforms the baseline by 1.1% in MRR, and the synergistic stage further improves MRR by 2.6% when N=10. Increasing N from 10 to 30 in the second stage further increases MRR by 2.1%.It can be seen that the primary stage outperforms baseline by 1.1% with respect to MRR, which generates a strong candidate set for the next stage. Our synergistic stage further improves MRR by 2.6% when N=10, since the primary stage focuses on each word but lacks the understanding of whole answer sequence. In contrast to the discriminative model in which about 90% of correct answers are top ten ranked in the primary stage, only 2 out of 3 correct answers are top sorted by the generative model, so we increase the selected answer number N from 10 to 30 in the second stage, which further increases MRR by 2.1%. Furthermore, our model improves R@5 by 9.2% and R@10 by 8.9%." } ], "images": [ "scigen/train/1729.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of our generative model on validation dataset with candidate set." ], "reasoning": "The primary stage outperforms the baseline by 1.1% in MRR, and the synergistic stage further improves MRR by 2.6% when N=10. Increasing N from 10 to 30 in the second stage further increases MRR by 2.1%.", "description": "It can be seen that the primary stage outperforms baseline by 1.1% with respect to MRR, which generates a strong candidate set for the next stage. Our synergistic stage further improves MRR by 2.6% when N=10, since the primary stage focuses on each word but lacks the understanding of whole answer sequence. In contrast to the discriminative model in which about 90% of correct answers are top ten ranked in the primary stage, only 2 out of 3 correct answers are top sorted by the generative model, so we increase the selected answer number N from 10 to 30 in the second stage, which further increases MRR by 2.1%. Furthermore, our model improves R@5 by 9.2% and R@10 by 8.9%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 3: Performance of our generative model on validation dataset with candidate set.', and tackle the question below in a sequential manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nHow much does the synergistic stage improve MRR when N is increased from 10 to 30?\n" }, { "role": "assistant", "content": "The primary stage improves MRR by 1.1% over the baseline. The synergistic stage improves MRR by 2.6% when N=10. Increasing N from 10 to 30 further increases MRR by 2.1%. So, the answer is {\"answer\": \"2.1%\"}" } ], "images": [ "scigen/train/1729.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of our generative model on validation dataset with candidate set." ], "reasoning": "The primary stage improves MRR by 1.1% over the baseline. The synergistic stage improves MRR by 2.6% when N=10. Increasing N from 10 to 30 further increases MRR by 2.1%.", "question": "How much does the synergistic stage improve MRR when N is increased from 10 to 30?", "answer": "So, the answer is {\"answer\": \"2.1%\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The synergistic stage with N=30 has a higher MRR than the primary stage. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe synergistic stage with N=30 has a higher MRR than the primary stage.\n" }, { "role": "assistant", "content": "The primary stage has an MRR of 49.01, while the synergistic stage with N=30 has an MRR of 53.73, which is higher. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1729.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of our generative model on validation dataset with candidate set." ], "reasoning": "The primary stage has an MRR of 49.01, while the synergistic stage with N=30 has an MRR of 53.73, which is higher.", "statement": "The synergistic stage with N=30 has a higher MRR than the primary stage.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: Performance of discriminative models on test-standard server of Visual Dialog Challenge 2018. We ensemble 10 models with different seeds and varying M from 25 to 35.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "To improve accuracy, the top N answers are selected and ranked by summing scores from 10 models, achieving the highest NDCG on the Visual Dialog Challenge 2018 test-standard server.In the primary stage, some answers are improperly scored due to the limitations of the scoring method. In this synergistic stage, answers are coordinated with the question and image for re-rank. However, easy candidate answers are not needed in further analysis, and we want our second stage model to fully focus on hard answers within its modeling capacity. Therefore, we select the answers with higher probability of being correct based on the predicted scores from the primary stage. which means that the remaining 90 answers are of lower probability and can easily be discriminated. Based on this phenomenon, we first pick the top N answers from At to organize a new candidate set Bt, where Bt=(bt,1,…,bt,N),Bt⊂At. To improve accuracy, we ensemble 10 models with different seeds and M. We rank the answers by summing scores of all models and achieve the highest NDCG on the test-standard server of Visual Dialog Challenge 2018." } ], "images": [ "scigen/train/1730.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance of discriminative models on test-standard server of Visual Dialog Challenge 2018. We ensemble 10 models with different seeds and varying M from 25 to 35." ], "reasoning": "To improve accuracy, the top N answers are selected and ranked by summing scores from 10 models, achieving the highest NDCG on the Visual Dialog Challenge 2018 test-standard server.", "description": "In the primary stage, some answers are improperly scored due to the limitations of the scoring method. In this synergistic stage, answers are coordinated with the question and image for re-rank. However, easy candidate answers are not needed in further analysis, and we want our second stage model to fully focus on hard answers within its modeling capacity. Therefore, we select the answers with higher probability of being correct based on the predicted scores from the primary stage. which means that the remaining 90 answers are of lower probability and can easily be discriminated. Based on this phenomenon, we first pick the top N answers from At to organize a new candidate set Bt, where Bt=(bt,1,…,bt,N),Bt⊂At. To improve accuracy, we ensemble 10 models with different seeds and M. We rank the answers by summing scores of all models and achieve the highest NDCG on the test-standard server of Visual Dialog Challenge 2018.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 1: Performance of discriminative models on test-standard server of Visual Dialog Challenge 2018. We ensemble 10 models with different seeds and varying M from 25 to 35.' and methodically tackle the question that follows. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest R@10 score?\n" }, { "role": "assistant", "content": "To find the model with the highest R@10 score, compare the R@10 values for all models in the table. The highest R@10 score is 90.68, achieved by the Ensemble (ours) model. So, the answer is {\"answer\": \"Ensemble (ours)\"}" } ], "images": [ "scigen/train/1730.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance of discriminative models on test-standard server of Visual Dialog Challenge 2018. We ensemble 10 models with different seeds and varying M from 25 to 35." ], "reasoning": "To find the model with the highest R@10 score, compare the R@10 values for all models in the table. The highest R@10 score is 90.68, achieved by the Ensemble (ours) model.", "question": "Which model has the highest R@10 score?", "answer": "So, the answer is {\"answer\": \"Ensemble (ours)\"}" }, { "messages": [ { "role": "user", "content": "The LF model has the lowest NDCG score. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe LF model has the lowest NDCG score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the NDCG scores of all models in the table. The LF model has an NDCG score of 45.31, which is the lowest among all listed models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1730.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance of discriminative models on test-standard server of Visual Dialog Challenge 2018. We ensemble 10 models with different seeds and varying M from 25 to 35." ], "reasoning": "To verify this statement, compare the NDCG scores of all models in the table. The LF model has an NDCG score of 45.31, which is the lowest among all listed models.", "statement": "The LF model has the lowest NDCG score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Performance of our discriminative model on validation dataset.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The performance of the model improves as τ decreases, with the best results achieved at M=30, but drops at M=40 or N=15 due to less relevant selected answers.We conduct several ablation studies to verify the contribution of each module to our discriminative model. The performance improves as τ decays, because most candidate answers are easily discriminated. This kind of loss is reduced as τ decreases and is nearly zero when τ=0.25 , saturating our primary stage. This makes the model pay more attention to incorrect answers scored near or higher than the correct answer. The last five lines show the results of the synergistic stage with different settings. MRR of this stage drops at N=10 and M=10, as the top answers of the primary stage become stable after several epochs, resulting in the synergistic stage learning bias. Feeding more samples by increasing M improves performance to produce the best model at M=30, showing that synergy can learn a better representation of the image, question, and answer. What of interest is that the performance drops at M=40 or N=15, possibly because the selected answers become less relevant to the inputs, and the second stage model’s ability is to score nearly correct answers but is sensitive to unrelated ones. Conversely, it also addresses the importance of the primary stage to the synergistic stage. And the primary stage is also necessary to balance the memory cost, since each answer learns its own attention map and fusion with the image in the synergistic stage, while in the primary stage, only one attention map is required for the question and image." } ], "images": [ "scigen/train/1731.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of our discriminative model on validation dataset." ], "reasoning": "The performance of the model improves as τ decreases, with the best results achieved at M=30, but drops at M=40 or N=15 due to less relevant selected answers.", "description": "We conduct several ablation studies to verify the contribution of each module to our discriminative model. The performance improves as τ decays, because most candidate answers are easily discriminated. This kind of loss is reduced as τ decreases and is nearly zero when τ=0.25 , saturating our primary stage. This makes the model pay more attention to incorrect answers scored near or higher than the correct answer. The last five lines show the results of the synergistic stage with different settings. MRR of this stage drops at N=10 and M=10, as the top answers of the primary stage become stable after several epochs, resulting in the synergistic stage learning bias. Feeding more samples by increasing M improves performance to produce the best model at M=30, showing that synergy can learn a better representation of the image, question, and answer. What of interest is that the performance drops at M=40 or N=15, possibly because the selected answers become less relevant to the inputs, and the second stage model’s ability is to score nearly correct answers but is sensitive to unrelated ones. Conversely, it also addresses the importance of the primary stage to the synergistic stage. And the primary stage is also necessary to balance the memory cost, since each answer learns its own attention map and fusion with the image in the synergistic stage, while in the primary stage, only one attention map is required for the question and image.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 2: Performance of our discriminative model on validation dataset.\nQuestion: What is the MRR value when M=30 and τ=0.25?\n" }, { "role": "assistant", "content": "To find the MRR value when M=30 and τ=0.25, locate the row in the table where M=30 and τ=0.25, and then read the corresponding MRR value. So, the answer is {\"answer\": \"63.54\"}" } ], "images": [ "scigen/train/1731.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of our discriminative model on validation dataset." ], "reasoning": "To find the MRR value when M=30 and τ=0.25, locate the row in the table where M=30 and τ=0.25, and then read the corresponding MRR value.", "question": "What is the MRR value when M=30 and τ=0.25?", "answer": "So, the answer is {\"answer\": \"63.54\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model achieves the highest MRR when M=30 and τ=0.25.\n" }, { "role": "assistant", "content": "The table shows that the highest MRR value is 63.54, which occurs when M=30 and τ=0.25. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1731.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of our discriminative model on validation dataset." ], "reasoning": "The table shows that the highest MRR value is 63.54, which occurs when M=30 and τ=0.25.", "statement": "The model achieves the highest MRR when M=30 and τ=0.25.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Results of evaluation tasks on the learned word vectors, i.e., word similarity, synonym selection, and word analogy. The sub-sampling rate for the training corpora is 10−4.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The noise distributions SubL1 and SubL2 perform slightly better than Uni3/4, with significant improvements on the small datasets RG and MC. CBOW is more affected by SubL1 and SubL2 than SG, especially on RG and MC with the Wiki10 corpus.5.2.2 Results We implement the task on the mentioned 5 datasets and show the results in the column Word Similarity of Table At the first glance, our noise distributions SubL1 and SubL2 perform slightly better than Uni3/4. Significant improvements can be achieved on two small datasets RG and MC, because they are more sensitive to the vector quality. Another observation is that CBOW is more affected by SubL1 and SubL2 than SG, if comparing results on RG and MC with Wiki10 corpus. These results show that our noise distributions have the potential as high as or even higher than the smoothed unigram distribution in learning good word vectors." } ], "images": [ "scigen/train/1732.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of evaluation tasks on the learned word vectors, i.e., word similarity, synonym selection, and word analogy. The sub-sampling rate for the training corpora is 10−4." ], "reasoning": "The noise distributions SubL1 and SubL2 perform slightly better than Uni3/4, with significant improvements on the small datasets RG and MC. CBOW is more affected by SubL1 and SubL2 than SG, especially on RG and MC with the Wiki10 corpus.", "description": "5.2.2 Results We implement the task on the mentioned 5 datasets and show the results in the column Word Similarity of Table At the first glance, our noise distributions SubL1 and SubL2 perform slightly better than Uni3/4. Significant improvements can be achieved on two small datasets RG and MC, because they are more sensitive to the vector quality. Another observation is that CBOW is more affected by SubL1 and SubL2 than SG, if comparing results on RG and MC with Wiki10 corpus. These results show that our noise distributions have the potential as high as or even higher than the smoothed unigram distribution in learning good word vectors.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: Results of evaluation tasks on the learned word vectors, i.e., word similarity, synonym selection, and word analogy. The sub-sampling rate for the training corpora is 10−4.', solve the subsequent question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich noise distribution achieved the highest Word Similarity score on the RG dataset?\n" }, { "role": "assistant", "content": "To find the highest Word Similarity score on the RG dataset, we need to look at the 'Word Similarity RG' column. The highest score in this column is 77.7, achieved by the SubL2 noise distribution with the SG model and 1.0B size. So, the answer is {\"answer\": \"SubL2\"}." } ], "images": [ "scigen/train/1732.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of evaluation tasks on the learned word vectors, i.e., word similarity, synonym selection, and word analogy. The sub-sampling rate for the training corpora is 10−4." ], "reasoning": "To find the highest Word Similarity score on the RG dataset, we need to look at the 'Word Similarity RG' column. The highest score in this column is 77.7, achieved by the SubL2 noise distribution with the SG model and 1.0B size.", "question": "Which noise distribution achieved the highest Word Similarity score on the RG dataset?", "answer": "So, the answer is {\"answer\": \"SubL2\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe CBOW model with SubL1 noise distribution achieved the highest Word Similarity score on the MC dataset.\n" }, { "role": "assistant", "content": "To verify this statement, we need to look at the 'Word Similarity MC' column. The highest score in this column is 81.9, achieved by the SG model with SubL1 noise distribution and 1.0B size. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1732.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of evaluation tasks on the learned word vectors, i.e., word similarity, synonym selection, and word analogy. The sub-sampling rate for the training corpora is 10−4." ], "reasoning": "To verify this statement, we need to look at the 'Word Similarity MC' column. The highest score in this column is 81.9, achieved by the SG model with SubL1 noise distribution and 1.0B size. Therefore, the statement is refuted.", "statement": "The CBOW model with SubL1 noise distribution achieved the highest Word Similarity score on the MC dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Information of the training corpora.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The corpora are pre-processed by converting all words to lowercase and removing non-alphanumeric characters, with vocabularies built by discarding words below a certain occurrence threshold.We believe a sub-sampled unigram distribution is better for negative sampling since it reduces the amount of frequent words and also maintains the lexical structure of infrequent words. To our best knowledge, we are the first to employ such a noise distribution for negative sampling. The first three large corpora are used for word similarity, synonym selection, and word analogy tasks. The MSR corpus is designated for the MSR sentence completion task. We pre-process the corpora by converting all words into lowercase and removing all the non-alphanumeric. Vocabularies are built by discarding words whose occurrences are less than the threshold shown in the column Mcn. The column Vocab represents the sizes of the resulted vocabularies. The rightmost two columns are the sub-sampling rates for our noise distribution by the wLSE-1 and wLSE-2 estimations, respectively. The values are 106 times of the true ones for readability." } ], "images": [ "scigen/train/1733.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Information of the training corpora." ], "reasoning": "The corpora are pre-processed by converting all words to lowercase and removing non-alphanumeric characters, with vocabularies built by discarding words below a certain occurrence threshold.", "description": "We believe a sub-sampled unigram distribution is better for negative sampling since it reduces the amount of frequent words and also maintains the lexical structure of infrequent words. To our best knowledge, we are the first to employ such a noise distribution for negative sampling. The first three large corpora are used for word similarity, synonym selection, and word analogy tasks. The MSR corpus is designated for the MSR sentence completion task. We pre-process the corpora by converting all words into lowercase and removing all the non-alphanumeric. Vocabularies are built by discarding words whose occurrences are less than the threshold shown in the column Mcn. The column Vocab represents the sizes of the resulted vocabularies. The rightmost two columns are the sub-sampling rates for our noise distribution by the wLSE-1 and wLSE-2 estimations, respectively. The values are 106 times of the true ones for readability.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Information of the training corpora.', solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich corpus has the smallest vocabulary size after pre-processing?\n" }, { "role": "assistant", "content": "By examining the 'Vocab' column, the MSR corpus has the smallest vocabulary size of 77k. So, the answer is {\"answer\": \"MSR\"}" } ], "images": [ "scigen/train/1733.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Information of the training corpora." ], "reasoning": "By examining the 'Vocab' column, the MSR corpus has the smallest vocabulary size of 77k.", "question": "Which corpus has the smallest vocabulary size after pre-processing?", "answer": "So, the answer is {\"answer\": \"MSR\"}" }, { "messages": [ { "role": "user", "content": "The Wiki10 corpus has a vocabulary size of 249k. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Wiki10 corpus has a vocabulary size of 249k.\n" }, { "role": "assistant", "content": "The table shows that the Wiki10 corpus has a vocabulary size of 249k, which matches the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1733.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Information of the training corpora." ], "reasoning": "The table shows that the Wiki10 corpus has a vocabulary size of 249k, which matches the statement.", "statement": "The Wiki10 corpus has a vocabulary size of 249k.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: The results of word analogy task using NCE for the training of word vectors. Each entry is the average result of 2 repeated experiments.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The sub-sampling rate is determined by selecting a critical word with specific properties, which in turn influences the noise distribution.We start our analysis by recalling the probability in Eq. Obviously, we need to choose the sub-sampling rate t to decide the noise distribution. To accomplish this, we firstly introduce a concept critical word denoted by wcrt, which is the word with Pkeep(wcrt)=1. The critical word indicates that words with frequencies lower than it will not be deleted during sub-sampling. It is uniquely decided by the sub-sampling rate. Thus, if we select the critical word with certain properties at first, we are able to obtain a suitable sub-sampling rate in return." } ], "images": [ "scigen/train/1734.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The results of word analogy task using NCE for the training of word vectors. Each entry is the average result of 2 repeated experiments." ], "reasoning": "The sub-sampling rate is determined by selecting a critical word with specific properties, which in turn influences the noise distribution.", "description": "We start our analysis by recalling the probability in Eq. Obviously, we need to choose the sub-sampling rate t to decide the noise distribution. To accomplish this, we firstly introduce a concept critical word denoted by wcrt, which is the word with Pkeep(wcrt)=1. The critical word indicates that words with frequencies lower than it will not be deleted during sub-sampling. It is uniquely decided by the sub-sampling rate. Thus, if we select the critical word with certain properties at first, we are able to obtain a suitable sub-sampling rate in return.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: The results of word analogy task using NCE for the training of word vectors. Each entry is the average result of 2 repeated experiments.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the total score for the 1.0B size with 100 dimensions and Sub L2 noise?\n" }, { "role": "assistant", "content": "To find the total score, locate the row with Size 1.0B, Dim 100, and Noise Sub L2. The corresponding Tot value is 57.6. So, the answer is {\"answer\": \"57.6\"}" } ], "images": [ "scigen/train/1734.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The results of word analogy task using NCE for the training of word vectors. Each entry is the average result of 2 repeated experiments." ], "reasoning": "To find the total score, locate the row with Size 1.0B, Dim 100, and Noise Sub L2. The corresponding Tot value is 57.6.", "question": "What is the total score for the 1.0B size with 100 dimensions and Sub L2 noise?", "answer": "So, the answer is {\"answer\": \"57.6\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The highest total score for the 0.7B size is 57.6. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe highest total score for the 0.7B size is 57.6.\n" }, { "role": "assistant", "content": "Check all rows with Size 0.7B and compare the Tot values. The highest total score is 57.6. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1734.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The results of word analogy task using NCE for the training of word vectors. Each entry is the average result of 2 repeated experiments." ], "reasoning": "Check all rows with Size 0.7B and compare the Tot values. The highest total score is 57.6.", "statement": "The highest total score for the 0.7B size is 57.6.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: The results of MSR sentence completion task by previous word representation models and our approach.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Sub-sampling words in sentences significantly improves conventional methods, and SWM further enhances CMs, outperforming previous word representation models with a vector size of 300.Our focus here is to popularize SWM rather than to compare the noise distributions. The bottom three previous models follow the conventional method. Accordingly, we draw two conclusions: (1) sub-sampling on the words in sentences results in significant improvements to the conventional method; and (2) SWM further improves CMs and beats previous word representation models with a vector size of 300, indicating the success of semantics quantification." } ], "images": [ "scigen/train/1735.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The results of MSR sentence completion task by previous word representation models and our approach." ], "reasoning": "Sub-sampling words in sentences significantly improves conventional methods, and SWM further enhances CMs, outperforming previous word representation models with a vector size of 300.", "description": "Our focus here is to popularize SWM rather than to compare the noise distributions. The bottom three previous models follow the conventional method. Accordingly, we draw two conclusions: (1) sub-sampling on the words in sentences results in significant improvements to the conventional method; and (2) SWM further improves CMs and beats previous word representation models with a vector size of 300, indicating the success of semantics quantification.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 4: The results of MSR sentence completion task by previous word representation models and our approach.' and methodically tackle the question that follows. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model has the highest accuracy and how does it relate to the success of semantics quantification?\n" }, { "role": "assistant", "content": "The table shows that the SWE model has the highest accuracy at 56.2. The description indicates that SWM, which is related to SWE, improves CMs and beats previous models, demonstrating the success of semantics quantification. So, the answer is {\"answer\": \"SWE\"}." } ], "images": [ "scigen/train/1735.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The results of MSR sentence completion task by previous word representation models and our approach." ], "reasoning": "The table shows that the SWE model has the highest accuracy at 56.2. The description indicates that SWM, which is related to SWE, improves CMs and beats previous models, demonstrating the success of semantics quantification.", "question": "Which model has the highest accuracy and how does it relate to the success of semantics quantification?", "answer": "So, the answer is {\"answer\": \"SWE\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe SG model has the lowest accuracy among the listed models.\n" }, { "role": "assistant", "content": "The table shows that the SG model has an accuracy of 48.0, which is the lowest among the listed models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1735.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The results of MSR sentence completion task by previous word representation models and our approach." ], "reasoning": "The table shows that the SG model has an accuracy of 48.0, which is the lowest among the listed models.", "statement": "The SG model has the lowest accuracy among the listed models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: The results of MSR sentence completion task by previous word representation models and our approach.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Sub-sampling words in sentences significantly improves conventional methods, and SWM further enhances CMs, outperforming previous word representation models with a vector size of 300.Our focus here is to popularize SWM rather than to compare the noise distributions. The bottom three previous models follow the conventional method. Accordingly, we draw two conclusions: (1) sub-sampling on the words in sentences results in significant improvements to the conventional method; and (2) SWM further improves CMs and beats previous word representation models with a vector size of 300, indicating the success of semantics quantification." } ], "images": [ "scigen/train/1736.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The results of MSR sentence completion task by previous word representation models and our approach." ], "reasoning": "Sub-sampling words in sentences significantly improves conventional methods, and SWM further enhances CMs, outperforming previous word representation models with a vector size of 300.", "description": "Our focus here is to popularize SWM rather than to compare the noise distributions. The bottom three previous models follow the conventional method. Accordingly, we draw two conclusions: (1) sub-sampling on the words in sentences results in significant improvements to the conventional method; and (2) SWM further improves CMs and beats previous word representation models with a vector size of 300, indicating the success of semantics quantification.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 4: The results of MSR sentence completion task by previous word representation models and our approach.', and sequentially solve the question, writing out each step of your reasoning process. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the highest score achieved by the SWM model with a vector size of 300?\n" }, { "role": "assistant", "content": "To find the highest score achieved by the SWM model with a vector size of 300, we look at the rows where the model is SWM and the dimension is 300. The highest scores in the Uni3/4 and SubL2 columns are 58.0 and 57.9, respectively. So, the answer is {\"answer\": \"58.0\"}" } ], "images": [ "scigen/train/1736.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The results of MSR sentence completion task by previous word representation models and our approach." ], "reasoning": "To find the highest score achieved by the SWM model with a vector size of 300, we look at the rows where the model is SWM and the dimension is 300. The highest scores in the Uni3/4 and SubL2 columns are 58.0 and 57.9, respectively.", "question": "What is the highest score achieved by the SWM model with a vector size of 300?", "answer": "So, the answer is {\"answer\": \"58.0\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe SWM model with a vector size of 100 outperforms the CM model with a vector size of 300.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the scores of the SWM model with a vector size of 100 and the CM model with a vector size of 300. The SWM model with a vector size of 100 has scores of 56.0 and 56.5, while the CM model with a vector size of 300 has scores of 49.9 and 49.5. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1736.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The results of MSR sentence completion task by previous word representation models and our approach." ], "reasoning": "To verify this statement, we compare the scores of the SWM model with a vector size of 100 and the CM model with a vector size of 300. The SWM model with a vector size of 100 has scores of 56.0 and 56.5, while the CM model with a vector size of 300 has scores of 49.9 and 49.5. Therefore, the statement is supported.", "statement": "The SWM model with a vector size of 100 outperforms the CM model with a vector size of 300.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Pearson’s r of Reddit+SNLI model with the transformer encoder on the STS Benchmark.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The un-tuned Reddit model is competitive, outperforming many neural representation models, and the encoder learned from Reddit conversations keeps text with similar semantics close in embedding space.The columns show the accuracy of each model on the dev and test sets. The un-tuned Reddit model is competitive and outperforms many neural representation models. The encoder learned from Reddit conversations keeps text with similar semantics close in embedding space." } ], "images": [ "scigen/train/1737.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Pearson’s r of Reddit+SNLI model with the transformer encoder on the STS Benchmark." ], "reasoning": "The un-tuned Reddit model is competitive, outperforming many neural representation models, and the encoder learned from Reddit conversations keeps text with similar semantics close in embedding space.", "description": "The columns show the accuracy of each model on the dev and test sets. The un-tuned Reddit model is competitive and outperforms many neural representation models. The encoder learned from Reddit conversations keeps text with similar semantics close in embedding space.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: Pearson’s r of Reddit+SNLI model with the transformer encoder on the STS Benchmark.'. Solve the following question based on the table.\n\nQuestion: Which model has the highest accuracy on the test set?\nLet's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest accuracy on the test set, we need to compare the test set accuracy values of all models listed in the table. The highest value is 0.810, which corresponds to the ECNU model. So, the answer is {\"answer\": \"ECNU\"}" } ], "images": [ "scigen/train/1737.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Pearson’s r of Reddit+SNLI model with the transformer encoder on the STS Benchmark." ], "reasoning": "To find the model with the highest accuracy on the test set, we need to compare the test set accuracy values of all models listed in the table. The highest value is 0.810, which corresponds to the ECNU model.", "question": "Which model has the highest accuracy on the test set?", "answer": "So, the answer is {\"answer\": \"ECNU\"}" }, { "messages": [ { "role": "user", "content": "The Reddit+SNLI tuned model has a higher accuracy on the dev set than the ECNU model. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Reddit+SNLI tuned model has a higher accuracy on the dev set than the ECNU model.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the dev set accuracy of the Reddit+SNLI tuned model (0.835) with the ECNU model (0.847). The ECNU model has a higher accuracy on the dev set. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1737.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Pearson’s r of Reddit+SNLI model with the transformer encoder on the STS Benchmark." ], "reasoning": "To verify this statement, we compare the dev set accuracy of the Reddit+SNLI tuned model (0.835) with the ECNU model (0.847). The ECNU model has a higher accuracy on the dev set.", "statement": "The Reddit+SNLI tuned model has a higher accuracy on the dev set than the ECNU model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Precision at N (P@N) results of Reddit model with different encoders on the test set with 1 true response and 99 randomly selected negatives.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The transformer encoder outperforms the DAN encoder in all metrics, achieving a P@1 of 65.7% compared to DAN's 56.1%.The DAN encoder (with n-grams) as used in \\newciteefficientsmartreply can be considered a strong baseline. The transformer encoder outperforms DAN for all values of N. The transformer encoder achieves a P@1 metric of 65.7% while the DAN encoder achieves only 56.1%. Given its greater performance, we use a transformer encoder for the remainder of the experiments reported in this work." } ], "images": [ "scigen/train/1738.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Precision at N (P@N) results of Reddit model with different encoders on the test set with 1 true response and 99 randomly selected negatives." ], "reasoning": "The transformer encoder outperforms the DAN encoder in all metrics, achieving a P@1 of 65.7% compared to DAN's 56.1%.", "description": "The DAN encoder (with n-grams) as used in \\newciteefficientsmartreply can be considered a strong baseline. The transformer encoder outperforms DAN for all values of N. The transformer encoder achieves a P@1 metric of 65.7% while the DAN encoder achieves only 56.1%. Given its greater performance, we use a transformer encoder for the remainder of the experiments reported in this work.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 1: Precision at N (P@N) results of Reddit model with different encoders on the test set with 1 true response and 99 randomly selected negatives.'. Solve the following question based on the table.\n\nQuestion: What is the difference in the P@3 metric between the transformer encoder and the DAN encoder?\nLet's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The P@3 metric for the transformer encoder is 78.7%, while for the DAN encoder it is 70.2%. The difference is calculated by subtracting 70.2 from 78.7. So, the answer is {\"answer\": \"8.5\"}." } ], "images": [ "scigen/train/1738.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Precision at N (P@N) results of Reddit model with different encoders on the test set with 1 true response and 99 randomly selected negatives." ], "reasoning": "The P@3 metric for the transformer encoder is 78.7%, while for the DAN encoder it is 70.2%. The difference is calculated by subtracting 70.2 from 78.7.", "question": "What is the difference in the P@3 metric between the transformer encoder and the DAN encoder?", "answer": "So, the answer is {\"answer\": \"8.5\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe transformer encoder achieves a higher P@10 metric than the DAN encoder.\n" }, { "role": "assistant", "content": "The table shows that the transformer encoder has a P@10 metric of 89.8%, while the DAN encoder has a P@10 metric of 83.6%. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1738.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Precision at N (P@N) results of Reddit model with different encoders on the test set with 1 true response and 99 randomly selected negatives." ], "reasoning": "The table shows that the transformer encoder has a P@10 metric of 89.8%, while the DAN encoder has a P@10 metric of 83.6%. Therefore, the statement is supported.", "statement": "The transformer encoder achieves a higher P@10 metric than the DAN encoder.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Performance on SNLI classification of the Reddit+SNLI model with the transformer encoder.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The multi-task model achieves 84.1% accuracy, learning all parameters from scratch, while InferSent uses pre-trained embeddings and achieves 84.5% accuracy.The multitask model achieves 84.1% accuracy and is close to the performance of InferSent. There are two significant differences between our model and prior work. First, the proposed model learns all model parameters from scratch, including the word embeddings. InferSent uses large pre-trained word embeddings which could result in less out of vocabulary words. In our work, the Reddit dataset is large enough that we do not need to use pre-trained word embeddings. Secondly, our multi-task model learns two tasks simultaneously, balancing performance between them, while InferSent only optimizes performance on SNLI. As will be presented below, our multi-task model performs better on STS. We suspect the Reddit data acts as a regularizer to prevent the resulting sentence embedding from overfitting to the SNLI task, thus improving transfer performance on other tasks." } ], "images": [ "scigen/train/1739.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance on SNLI classification of the Reddit+SNLI model with the transformer encoder." ], "reasoning": "The multi-task model achieves 84.1% accuracy, learning all parameters from scratch, while InferSent uses pre-trained embeddings and achieves 84.5% accuracy.", "description": "The multitask model achieves 84.1% accuracy and is close to the performance of InferSent. There are two significant differences between our model and prior work. First, the proposed model learns all model parameters from scratch, including the word embeddings. InferSent uses large pre-trained word embeddings which could result in less out of vocabulary words. In our work, the Reddit dataset is large enough that we do not need to use pre-trained word embeddings. Secondly, our multi-task model learns two tasks simultaneously, balancing performance between them, while InferSent only optimizes performance on SNLI. As will be presented below, our multi-task model performs better on STS. We suspect the Reddit data acts as a regularizer to prevent the resulting sentence embedding from overfitting to the SNLI task, thus improving transfer performance on other tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: Performance on SNLI classification of the Reddit+SNLI model with the transformer encoder.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. \n\nQuestion: What is the accuracy difference between the multi-task model and the Gumbel TreeLSTM model?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The multi-task model (Reddit+SNLI) achieves 84.1% accuracy, while the Gumbel TreeLSTM model achieves 86.0% accuracy. The difference is 86.0% - 84.1% = 1.9%. So, the answer is {\"answer\": \"1.9%\"}" } ], "images": [ "scigen/train/1739.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance on SNLI classification of the Reddit+SNLI model with the transformer encoder." ], "reasoning": "The multi-task model (Reddit+SNLI) achieves 84.1% accuracy, while the Gumbel TreeLSTM model achieves 86.0% accuracy. The difference is 86.0% - 84.1% = 1.9%.", "question": "What is the accuracy difference between the multi-task model and the Gumbel TreeLSTM model?", "answer": "So, the answer is {\"answer\": \"1.9%\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The multi-task model performs better than InferSent., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe multi-task model performs better than InferSent.\n" }, { "role": "assistant", "content": "The table shows that the multi-task model (Reddit+SNLI) has an accuracy of 84.1%, while InferSent has an accuracy of 84.5%. Therefore, the multi-task model does not perform better than InferSent. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1739.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance on SNLI classification of the Reddit+SNLI model with the transformer encoder." ], "reasoning": "The table shows that the multi-task model (Reddit+SNLI) has an accuracy of 84.1%, while InferSent has an accuracy of 84.5%. Therefore, the multi-task model does not perform better than InferSent.", "statement": "The multi-task model performs better than InferSent.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Pearson’s r of the proposed models on the STS Benchmark with a breakdown by category.'.\n" }, { "role": "assistant", "content": "The Reddit+SNLI tuned model outperforms the other models in almost all categories, especially in dev captions and test captions.To better understand the differences between the proposed models, we compare the Reddit and Reddit+SNLI models in more detail, by breaking down the evaluation of STS Benchmark into different categories. In the test set, there are 625, 500, and 254 sentences pairs for image-captions, news and forums, respectively." } ], "images": [ "scigen/train/1740.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Pearson’s r of the proposed models on the STS Benchmark with a breakdown by category." ], "reasoning": "The Reddit+SNLI tuned model outperforms the other models in almost all categories, especially in dev captions and test captions.", "description": "To better understand the differences between the proposed models, we compare the Reddit and Reddit+SNLI models in more detail, by breaking down the evaluation of STS Benchmark into different categories. In the test set, there are 625, 500, and 254 sentences pairs for image-captions, news and forums, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 4: Pearson’s r of the proposed models on the STS Benchmark with a breakdown by category.', and tackle the question below in a sequential manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest score for test captions and what is the score?\n" }, { "role": "assistant", "content": "By comparing the scores for test captions across all models, the Reddit+SNLI tuned model has the highest score of 0.894. So, the answer is {\"answer\": \"Reddit+SNLI tuned, 0.894\"}" } ], "images": [ "scigen/train/1740.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Pearson’s r of the proposed models on the STS Benchmark with a breakdown by category." ], "reasoning": "By comparing the scores for test captions across all models, the Reddit+SNLI tuned model has the highest score of 0.894.", "question": "Which model has the highest score for test captions and what is the score?", "answer": "So, the answer is {\"answer\": \"Reddit+SNLI tuned, 0.894\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Reddit model has the lowest score for test news.\n" }, { "role": "assistant", "content": "By comparing the scores for test news across all models, the Reddit model has the lowest score of 0.578. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1740.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Pearson’s r of the proposed models on the STS Benchmark with a breakdown by category." ], "reasoning": "By comparing the scores for test news across all models, the Reddit model has the lowest score of 0.578.", "statement": "The Reddit model has the lowest score for test news.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 6: Mean Average Precision (MAP) of the proposed models on CQA subtask B.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The Reddit+SNLI model outperforms SimBow-primary, which is ranked first in the official ranking of the 2017 task, and both models show competitive performance without any tuning on the training data.Similar to the STS experiments, we use cosine similarity between the original question and related question, without considering any interaction between the two questions, or any contextual features. Given a related question Qi and its original question Q, we first encode them into vectors ui and u. Then the related questions are ranked based on the cosine similarity cos(ui,u) with respect to the original question. SimBow Charlet and Damnati Without any tuning on the training data provided by the task, both models show competitive performance. Reddit+SNLI model outperforms SimBow-primary, which is ranked first in the official ranking of the 2017 task." } ], "images": [ "scigen/train/1741.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Mean Average Precision (MAP) of the proposed models on CQA subtask B." ], "reasoning": "The Reddit+SNLI model outperforms SimBow-primary, which is ranked first in the official ranking of the 2017 task, and both models show competitive performance without any tuning on the training data.", "description": "Similar to the STS experiments, we use cosine similarity between the original question and related question, without considering any interaction between the two questions, or any contextual features. Given a related question Qi and its original question Q, we first encode them into vectors ui and u. Then the related questions are ranked based on the cosine similarity cos(ui,u) with respect to the original question. SimBow Charlet and Damnati Without any tuning on the training data provided by the task, both models show competitive performance. Reddit+SNLI model outperforms SimBow-primary, which is ranked first in the official ranking of the 2017 task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 6: Mean Average Precision (MAP) of the proposed models on CQA subtask B.', answer the following question. Give detailed solution consisting of each step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest MAP score and what is the score?\n" }, { "role": "assistant", "content": "To find the model with the highest MAP score, we need to compare the MAP scores of all the models listed in the table. The model with the highest score is KeLP-contrastive1 with a score of 49.00. So, the answer is {\"answer\": \"KeLP-contrastive1 with 49.00\"}." } ], "images": [ "scigen/train/1741.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Mean Average Precision (MAP) of the proposed models on CQA subtask B." ], "reasoning": "To find the model with the highest MAP score, we need to compare the MAP scores of all the models listed in the table. The model with the highest score is KeLP-contrastive1 with a score of 49.00.", "question": "Which model has the highest MAP score and what is the score?", "answer": "So, the answer is {\"answer\": \"KeLP-contrastive1 with 49.00\"}." }, { "messages": [ { "role": "user", "content": "The SimBow-primary model has a higher MAP score than the Reddit model. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe SimBow-primary model has a higher MAP score than the Reddit model.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the MAP scores of the SimBow-primary and Reddit models. According to the table, SimBow-primary has a MAP score of 47.22, while Reddit has a MAP score of 47.07. Therefore, SimBow-primary has a higher MAP score than Reddit. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1741.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Mean Average Precision (MAP) of the proposed models on CQA subtask B." ], "reasoning": "To verify this statement, we need to compare the MAP scores of the SimBow-primary and Reddit models. According to the table, SimBow-primary has a MAP score of 47.22, while Reddit has a MAP score of 47.07. Therefore, SimBow-primary has a higher MAP score than Reddit.", "statement": "The SimBow-primary model has a higher MAP score than the Reddit model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 6: Dependency parsing results by language (UAS/LAS). * denotes delexicalized parsing in the baseline. S and Z denotes supervised learning and zero-shot transfer. Bold and underline denotes best and second best. We order the languages by word order distance to English.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Adding gold POS tags improves mBERT's UAS by 1.6 points and LAS by 4.7 points on average, especially benefiting languages closely related to English.Note in practice, gold POS tags are not always available, especially for low resource languages. Interestingly, the LAS of mBERT tends to weaker than the baseline in languages with less word order distance, in other words, more closely related to English. With the help of gold POS tags, we further observe 1.6 points UAS and 4.7 point LAS absolute improvement on average. It appears that adding gold POS tags, which provide clearer cross-lingual representations, benefit mBERT." } ], "images": [ "scigen/train/1742.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Dependency parsing results by language (UAS/LAS). * denotes delexicalized parsing in the baseline. S and Z denotes supervised learning and zero-shot transfer. Bold and underline denotes best and second best. We order the languages by word order distance to English." ], "reasoning": "Adding gold POS tags improves mBERT's UAS by 1.6 points and LAS by 4.7 points on average, especially benefiting languages closely related to English.", "description": "Note in practice, gold POS tags are not always available, especially for low resource languages. Interestingly, the LAS of mBERT tends to weaker than the baseline in languages with less word order distance, in other words, more closely related to English. With the help of gold POS tags, we further observe 1.6 points UAS and 4.7 point LAS absolute improvement on average. It appears that adding gold POS tags, which provide clearer cross-lingual representations, benefit mBERT.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 6: Dependency parsing results by language (UAS/LAS). * denotes delexicalized parsing in the baseline. S and Z denotes supervised learning and zero-shot transfer. Bold and underline denotes best and second best. We order the languages by word order distance to English.'. Solve the following question based on the table.\n\nQuestion: What is the average LAS improvement when gold POS tags are added to mBERT?\nLet's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The description states that adding gold POS tags results in a 4.7 point LAS improvement on average. So, the answer is {\"answer\": \"4.7\"}" } ], "images": [ "scigen/train/1742.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Dependency parsing results by language (UAS/LAS). * denotes delexicalized parsing in the baseline. S and Z denotes supervised learning and zero-shot transfer. Bold and underline denotes best and second best. We order the languages by word order distance to English." ], "reasoning": "The description states that adding gold POS tags results in a 4.7 point LAS improvement on average.", "question": "What is the average LAS improvement when gold POS tags are added to mBERT?", "answer": "So, the answer is {\"answer\": \"4.7\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nmBERT with gold POS tags has a higher LAS than the baseline for languages with less word order distance.\n" }, { "role": "assistant", "content": "The description mentions that mBERT tends to be weaker than the baseline in languages with less word order distance, but with gold POS tags, there is an improvement. The table shows that mBERT(Z+POS) has higher LAS scores than the baseline for these languages. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1742.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Dependency parsing results by language (UAS/LAS). * denotes delexicalized parsing in the baseline. S and Z denotes supervised learning and zero-shot transfer. Bold and underline denotes best and second best. We order the languages by word order distance to English." ], "reasoning": "The description mentions that mBERT tends to be weaker than the baseline in languages with less word order distance, but with gold POS tags, there is an improvement. The table shows that mBERT(Z+POS) has higher LAS scores than the baseline for these languages.", "statement": "mBERT with gold POS tags has a higher LAS than the baseline for languages with less word order distance.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: NER tagging experiments. ♢ denotes model selection with target language dev set.'.\n" }, { "role": "assistant", "content": "mBERT shows significant improvements over a strong baseline in F1 scores, especially in German, but struggles with transferring to distantly related languages like Chinese.mBERT outperforms a strong baseline by an average of 6.9 points absolute F1 and an 11.8 point absolute improvement in German with a simple one layer 0th-order CRF as a prediction function A large gap remains when transferring to distantly related languages (e.g. Chinese) compared to a supervised baseline. Further effort should focus on transferring between distantly related languages." } ], "images": [ "scigen/train/1743.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: NER tagging experiments. ♢ denotes model selection with target language dev set." ], "reasoning": "mBERT shows significant improvements over a strong baseline in F1 scores, especially in German, but struggles with transferring to distantly related languages like Chinese.", "description": "mBERT outperforms a strong baseline by an average of 6.9 points absolute F1 and an 11.8 point absolute improvement in German with a simple one layer 0th-order CRF as a prediction function A large gap remains when transferring to distantly related languages (e.g. Chinese) compared to a supervised baseline. Further effort should focus on transferring between distantly related languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 4: NER tagging experiments. ♢ denotes model selection with target language dev set.' and methodically tackle the question that follows. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the average F1 score improvement of mBERT over the baseline in zero-shot cross-lingual transfer?\n" }, { "role": "assistant", "content": "To find the average F1 score improvement, compare the average scores of mBERT and the baseline (Xie et al. 2018) in zero-shot cross-lingual transfer. mBERT has an average score of 74.03, while the baseline has 67.13. The improvement is 74.03 - 67.13. So, the answer is {\"answer\": \"6.9\"}" } ], "images": [ "scigen/train/1743.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: NER tagging experiments. ♢ denotes model selection with target language dev set." ], "reasoning": "To find the average F1 score improvement, compare the average scores of mBERT and the baseline (Xie et al. 2018) in zero-shot cross-lingual transfer. mBERT has an average score of 74.03, while the baseline has 67.13. The improvement is 74.03 - 67.13.", "question": "What is the average F1 score improvement of mBERT over the baseline in zero-shot cross-lingual transfer?", "answer": "So, the answer is {\"answer\": \"6.9\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nmBERT achieves the highest F1 score in zero-shot cross-lingual transfer for the Chinese language.\n" }, { "role": "assistant", "content": "The table shows that mBERT has an F1 score of 51.90 for Chinese in zero-shot cross-lingual transfer, which is the highest score listed for Chinese in this category. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1743.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: NER tagging experiments. ♢ denotes model selection with target language dev set." ], "reasoning": "The table shows that mBERT has an F1 score of 51.90 for Chinese in zero-shot cross-lingual transfer, which is the highest score listed for Chinese in this category.", "statement": "mBERT achieves the highest F1 score in zero-shot cross-lingual transfer for the Chinese language.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: POS tagging. Kim et al. (2017) use small amounts of training data in the target language.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "mBERT outperforms Kim et al. (2017) with 320 sentences in German, Polish, Slovak, and Swedish.S5SS1SSS0Px4 Pos We use \\newcitekim-etal-2017-cross as a reference. They utilized a small amount of supervision in the target language as well as English supervision so the results are not directly comparable. Interestingly, mBERT still outperforms \\newcitekim-etal-2017-cross with 320 sentences in German (de), Polish (pl), Slovak (sk) and Swedish (sv)." } ], "images": [ "scigen/train/1744.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: POS tagging. Kim et al. (2017) use small amounts of training data in the target language." ], "reasoning": "mBERT outperforms Kim et al. (2017) with 320 sentences in German, Polish, Slovak, and Swedish.", "description": "S5SS1SSS0Px4 Pos We use \\newcitekim-etal-2017-cross as a reference. They utilized a small amount of supervision in the target language as well as English supervision so the results are not directly comparable. Interestingly, mBERT still outperforms \\newcitekim-etal-2017-cross with 320 sentences in German (de), Polish (pl), Slovak (sk) and Swedish (sv).", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: 'Table 5: POS tagging. Kim et al. (2017) use small amounts of training data in the target language.'\nQuestion: What is the average score of mBERT in the zero-shot cross-lingual transfer setting?\n" }, { "role": "assistant", "content": "To find the average score of mBERT in the zero-shot cross-lingual transfer setting, look at the last row of the table under the 'Average (-en)' column. So, the answer is {\"answer\": \"84.3\"}" } ], "images": [ "scigen/train/1744.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: POS tagging. Kim et al. (2017) use small amounts of training data in the target language." ], "reasoning": "To find the average score of mBERT in the zero-shot cross-lingual transfer setting, look at the last row of the table under the 'Average (-en)' column.", "question": "What is the average score of mBERT in the zero-shot cross-lingual transfer setting?", "answer": "So, the answer is {\"answer\": \"84.3\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nKim et al. (2017) with 1280 sentences has a higher score in German than mBERT in the zero-shot cross-lingual transfer setting.\n" }, { "role": "assistant", "content": "Compare the score of Kim et al. (2017) with 1280 sentences in German (90.7) with the score of mBERT in the zero-shot cross-lingual transfer setting in German (89.8). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1744.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: POS tagging. Kim et al. (2017) use small amounts of training data in the target language." ], "reasoning": "Compare the score of Kim et al. (2017) with 1280 sentences in German (90.7) with the score of mBERT in the zero-shot cross-lingual transfer setting in German (89.8).", "statement": "Kim et al. (2017) with 1280 sentences has a higher score in German than mBERT in the zero-shot cross-lingual transfer setting.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Mentioned troll vs. non-troll comments. Ablation excluding feature groups.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Excluding character n-grams from the features yields the most significant performance gain, improving accuracy by one point.We can see that excluding character n-grams, word suffixes and word prefixes from the features, as well as excluding bag of words with stems or stop words, yields performance gains; the most sizable gain is when excluding char n-grams, which yields one point of improvement. Excluding bad words usage and emoticons also improves the performance but insignificantly, which might be because they are covered by the bag of words features." } ], "images": [ "scigen/train/1745.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Mentioned troll vs. non-troll comments. Ablation excluding feature groups." ], "reasoning": "Excluding character n-grams from the features yields the most significant performance gain, improving accuracy by one point.", "description": "We can see that excluding character n-grams, word suffixes and word prefixes from the features, as well as excluding bag of words with stems or stop words, yields performance gains; the most sizable gain is when excluding char n-grams, which yields one point of improvement. Excluding bad words usage and emoticons also improves the performance but insignificantly, which might be because they are covered by the bag of words features.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich feature exclusion yields the highest accuracy improvement?\nSolve the above question based on the table titled 'Table 3: Mentioned troll vs. non-troll comments. Ablation excluding feature groups.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the accuracy values, excluding character n-grams yields the highest improvement, increasing accuracy to 78.54. So, the answer is {\"answer\": \"char n-grams\"}." } ], "images": [ "scigen/train/1745.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Mentioned troll vs. non-troll comments. Ablation excluding feature groups." ], "reasoning": "By comparing the accuracy values, excluding character n-grams yields the highest improvement, increasing accuracy to 78.54.", "question": "Which feature exclusion yields the highest accuracy improvement?", "answer": "So, the answer is {\"answer\": \"char n-grams\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nExcluding word suffixes results in the highest accuracy improvement.\n" }, { "role": "assistant", "content": "The table shows that excluding character n-grams results in the highest accuracy improvement (78.54), not word suffixes (78.20). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1745.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Mentioned troll vs. non-troll comments. Ablation excluding feature groups." ], "reasoning": "The table shows that excluding character n-grams results in the highest accuracy improvement (78.54), not word suffixes (78.20).", "statement": "Excluding word suffixes results in the highest accuracy improvement.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Paid troll vs. non-troll comments. Ablation excluding feature groups.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The overall accuracy for finding paid trolls is slightly higher than for mentioned trolls, with metadata being the most helpful feature and character n-grams being the least helpful.There are several interesting observations we can make. First, we can see that the overall accuracy for finding paid trolls is slightly higher, namely 81.02, vs. 79.24 for mentioned trolls. The most helpful feature again is metadata, but this time it is less helpful (excluding it yields a drop of 5 points vs. 8 points before). The least helpful feature again are character n-grams. The remaining features fall in between, and most of them yield better performance when excluded, which suggests that there is a lot of redundancy in the features." } ], "images": [ "scigen/train/1746.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Paid troll vs. non-troll comments. Ablation excluding feature groups." ], "reasoning": "The overall accuracy for finding paid trolls is slightly higher than for mentioned trolls, with metadata being the most helpful feature and character n-grams being the least helpful.", "description": "There are several interesting observations we can make. First, we can see that the overall accuracy for finding paid trolls is slightly higher, namely 81.02, vs. 79.24 for mentioned trolls. The most helpful feature again is metadata, but this time it is less helpful (excluding it yields a drop of 5 points vs. 8 points before). The least helpful feature again are character n-grams. The remaining features fall in between, and most of them yield better performance when excluded, which suggests that there is a lot of redundancy in the features.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Paid troll vs. non-troll comments. Ablation excluding feature groups.', answer the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich feature, when excluded, results in the highest drop in accuracy?\n" }, { "role": "assistant", "content": "The description states that excluding metadata yields a drop of 5 points in accuracy, which is the highest drop mentioned. So, the answer is {\"answer\": \"metadata\"}." } ], "images": [ "scigen/train/1746.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Paid troll vs. non-troll comments. Ablation excluding feature groups." ], "reasoning": "The description states that excluding metadata yields a drop of 5 points in accuracy, which is the highest drop mentioned.", "question": "Which feature, when excluded, results in the highest drop in accuracy?", "answer": "So, the answer is {\"answer\": \"metadata\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Excluding character n-grams results in the highest drop in accuracy., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nExcluding character n-grams results in the highest drop in accuracy.\n" }, { "role": "assistant", "content": "The description and table indicate that excluding metadata results in the highest drop in accuracy, not character n-grams. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1746.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Paid troll vs. non-troll comments. Ablation excluding feature groups." ], "reasoning": "The description and table indicate that excluding metadata results in the highest drop in accuracy, not character n-grams.", "statement": "Excluding character n-grams results in the highest drop in accuracy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Mentioned troll comments vs. non-troll comments. Results for individual feature groups.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The metadata features alone outperform the results when using all features by 3.5 points, making them the most important feature group.Next, we look at individual feature groups. We can see that the metadata features are by far the most important: using them alone outperforms the results when using all features by 3.5 points." } ], "images": [ "scigen/train/1747.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Mentioned troll comments vs. non-troll comments. Results for individual feature groups." ], "reasoning": "The metadata features alone outperform the results when using all features by 3.5 points, making them the most important feature group.", "description": "Next, we look at individual feature groups. We can see that the metadata features are by far the most important: using them alone outperforms the results when using all features by 3.5 points.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 5: Mentioned troll comments vs. non-troll comments. Results for individual feature groups.' and proceed to solve the following question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich feature group has the highest F score and how much does it outperform the 'All' features group?\n" }, { "role": "assistant", "content": "The 'Only metadata' feature group has the highest F score of 84.14, which outperforms the 'All' features group with an F score of 78.06 by 6.08 points. So, the answer is {\"answer\": \"6.08\"}" } ], "images": [ "scigen/train/1747.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Mentioned troll comments vs. non-troll comments. Results for individual feature groups." ], "reasoning": "The 'Only metadata' feature group has the highest F score of 84.14, which outperforms the 'All' features group with an F score of 78.06 by 6.08 points.", "question": "Which feature group has the highest F score and how much does it outperform the 'All' features group?", "answer": "So, the answer is {\"answer\": \"6.08\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe 'Only metadata' feature group has the highest accuracy.\n" }, { "role": "assistant", "content": "The table shows that the 'Only metadata' feature group has an accuracy of 81.14, which is higher than any other feature group listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1747.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Mentioned troll comments vs. non-troll comments. Results for individual feature groups." ], "reasoning": "The table shows that the 'Only metadata' feature group has an accuracy of 81.14, which is higher than any other feature group listed.", "statement": "The 'Only metadata' feature group has the highest accuracy.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 6: Paid troll vs. non-troll comments. Results for individual feature groups.'.\n" }, { "role": "assistant", "content": "The combination of sentiment, bad words distribution, POS, metadata, and punctuation is the strongest feature set, while metadata alone is not very useful.The biggest difference is that metadata features are not so useful. Also, the strongest feature set is the combination of sentiment, bad words distribution, POS, metadata, and punctuation. This suggests that paid trolls are smart to post during time intervals and days of the week as non-trolls, but they use comments with slightly different sentiment and bad word use than non-trolls. Features based on words are also very helpful because paid trolls have to defend pre-specified key points, which limits their vocabulary use, while non-trolls are free to express themselves as they wish." } ], "images": [ "scigen/train/1748.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Paid troll vs. non-troll comments. Results for individual feature groups." ], "reasoning": "The combination of sentiment, bad words distribution, POS, metadata, and punctuation is the strongest feature set, while metadata alone is not very useful.", "description": "The biggest difference is that metadata features are not so useful. Also, the strongest feature set is the combination of sentiment, bad words distribution, POS, metadata, and punctuation. This suggests that paid trolls are smart to post during time intervals and days of the week as non-trolls, but they use comments with slightly different sentiment and bad word use than non-trolls. Features based on words are also very helpful because paid trolls have to defend pre-specified key points, which limits their vocabulary use, while non-trolls are free to express themselves as they wish.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 6: Paid troll vs. non-troll comments. Results for individual feature groups.', and sequentially solve the question, writing out each step of your reasoning process. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich feature set has the highest accuracy and what does it suggest about paid trolls' behavior?\n" }, { "role": "assistant", "content": "The feature set 'All' has the highest accuracy of 81.31. This suggests that paid trolls are smart to post during time intervals and days of the week as non-trolls, but they use comments with slightly different sentiment and bad word use than non-trolls. So, the answer is {\"answer\": \"All\"}." } ], "images": [ "scigen/train/1748.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Paid troll vs. non-troll comments. Results for individual feature groups." ], "reasoning": "The feature set 'All' has the highest accuracy of 81.31. This suggests that paid trolls are smart to post during time intervals and days of the week as non-trolls, but they use comments with slightly different sentiment and bad word use than non-trolls.", "question": "Which feature set has the highest accuracy and what does it suggest about paid trolls' behavior?", "answer": "So, the answer is {\"answer\": \"All\"}." }, { "messages": [ { "role": "user", "content": "The feature set 'Only metadata' is the most useful. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe feature set 'Only metadata' is the most useful.\n" }, { "role": "assistant", "content": "The table shows that the feature set 'Only metadata' has an accuracy of 72.54, which is lower than several other feature sets, including 'All' and 'Sent,bad,pos,NE,meta,punct'. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1748.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Paid troll vs. non-troll comments. Results for individual feature groups." ], "reasoning": "The table shows that the feature set 'Only metadata' has an accuracy of 72.54, which is lower than several other feature sets, including 'All' and 'Sent,bad,pos,NE,meta,punct'. Therefore, the statement is refuted.", "statement": "The feature set 'Only metadata' is the most useful.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Zero-shot performance on non-English centric language pairs. We compare with pivot-based translation and two recent approaches from Arivazhagan et al. (2019a) and Kim et al. (2019). The translation quality between these language pairs when parallel data is available is also provided as a baseline. 4 lang. is a multilingual model trained on 4 language pairs (2 languages to and from English), while 30 lang. is our multilingual model trained on all English-centric language pairs.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Training on additional monolingual data significantly improves zero-shot translation quality, resulting in a 3-6 BLEU increase.We next evaluate the effect of training on additional monolingual data on zero-shot translation in multilingual models. To compare with the existing work on zero-shot translation, we also evaluate the performance of multilingual models trained on just the relevant languages (en-fr-de for fr-de translation, en-cs-de for cs-de translation). We observe that the additional monolingual data significantly improves the quality of zero-shot translation, often resulting in 3-6 BLEU increase on all zero-shot directions compared to our multilingual baseline. We hypothesize that the additional monolingual data seen during the self-supervised training process helps better align representations across languages, akin to the smoothing effect in semi-supervised learning Chapelle et al. We leave further exploration of this intriguing phenomenon to future work." } ], "images": [ "scigen/train/1749.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Zero-shot performance on non-English centric language pairs. We compare with pivot-based translation and two recent approaches from Arivazhagan et al. (2019a) and Kim et al. (2019). The translation quality between these language pairs when parallel data is available is also provided as a baseline. 4 lang. is a multilingual model trained on 4 language pairs (2 languages to and from English), while 30 lang. is our multilingual model trained on all English-centric language pairs." ], "reasoning": "Training on additional monolingual data significantly improves zero-shot translation quality, resulting in a 3-6 BLEU increase.", "description": "We next evaluate the effect of training on additional monolingual data on zero-shot translation in multilingual models. To compare with the existing work on zero-shot translation, we also evaluate the performance of multilingual models trained on just the relevant languages (en-fr-de for fr-de translation, en-cs-de for cs-de translation). We observe that the additional monolingual data significantly improves the quality of zero-shot translation, often resulting in 3-6 BLEU increase on all zero-shot directions compared to our multilingual baseline. We hypothesize that the additional monolingual data seen during the self-supervised training process helps better align representations across languages, akin to the smoothing effect in semi-supervised learning Chapelle et al. We leave further exploration of this intriguing phenomenon to future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the BLEU score improvement for fr-de translation when using Multilingual NMT + Mono. compared to Multilingual NMT?\nSolve the above question based on the table titled 'Table 2: Zero-shot performance on non-English centric language pairs. We compare with pivot-based translation and two recent approaches from Arivazhagan et al. (2019a) and Kim et al. (2019). The translation quality between these language pairs when parallel data is available is also provided as a baseline. 4 lang. is a multilingual model trained on 4 language pairs (2 languages to and from English), while 30 lang. is our multilingual model trained on all English-centric language pairs.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The BLEU score for fr-de translation using Multilingual NMT is 11.8, and using Multilingual NMT + Mono. is 18.5. The improvement is 18.5 - 11.8. So, the answer is {\"answer\": \"6.7\"}" } ], "images": [ "scigen/train/1749.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Zero-shot performance on non-English centric language pairs. We compare with pivot-based translation and two recent approaches from Arivazhagan et al. (2019a) and Kim et al. (2019). The translation quality between these language pairs when parallel data is available is also provided as a baseline. 4 lang. is a multilingual model trained on 4 language pairs (2 languages to and from English), while 30 lang. is our multilingual model trained on all English-centric language pairs." ], "reasoning": "The BLEU score for fr-de translation using Multilingual NMT is 11.8, and using Multilingual NMT + Mono. is 18.5. The improvement is 18.5 - 11.8.", "question": "What is the BLEU score improvement for fr-de translation when using Multilingual NMT + Mono. compared to Multilingual NMT?", "answer": "So, the answer is {\"answer\": \"6.7\"}" }, { "messages": [ { "role": "user", "content": "Multilingual NMT + Mono. improves the BLEU score for cs-de translation compared to Multilingual NMT. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nMultilingual NMT + Mono. improves the BLEU score for cs-de translation compared to Multilingual NMT.\n" }, { "role": "assistant", "content": "The BLEU score for cs-de translation using Multilingual NMT is 10.5, and using Multilingual NMT + Mono. is 12.6, showing an improvement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1749.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Zero-shot performance on non-English centric language pairs. We compare with pivot-based translation and two recent approaches from Arivazhagan et al. (2019a) and Kim et al. (2019). The translation quality between these language pairs when parallel data is available is also provided as a baseline. 4 lang. is a multilingual model trained on 4 language pairs (2 languages to and from English), while 30 lang. is our multilingual model trained on all English-centric language pairs." ], "reasoning": "The BLEU score for cs-de translation using Multilingual NMT is 10.5, and using Multilingual NMT + Mono. is 12.6, showing an improvement.", "statement": "Multilingual NMT + Mono. improves the BLEU score for cs-de translation compared to Multilingual NMT.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Translation quality of the new language added to Multilingual NMT using just monolingual data. Multilingual NMT here is a multilingual model with 30 language pairs, Mono. Only is a bilingual model used as a baseline trained with only monolingual data with self-supervised learning, Multilingual NMT-xx is a multilingual model trained on 28 language pairs (xx is the language not present in the model). Multilingual NMT-xx + Mono. is a multilingual model with 28 language pairs but only monolingual data for xx.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Adding monolingual data to a multilingual model significantly improves translation quality, often nearing the performance of fully supervised models without iterative back-translation.We find that simply adding monolingual data for a new language to the training procedure of a multilingual model is sufficient to obtain strong translation quality for several languages, often attaining within a few BLEU points of the fully supervised multilingual baseline, without the need for iterative back-translation. We also notice significant quality improvements over models trained with just self-supervised learning using monolingual data for a variety of languages. et al. This suggests that jump-starting the iterative back-translation process from multilingual models might be a promising avenue to supporting new languages." } ], "images": [ "scigen/train/1750.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Translation quality of the new language added to Multilingual NMT using just monolingual data. Multilingual NMT here is a multilingual model with 30 language pairs, Mono. Only is a bilingual model used as a baseline trained with only monolingual data with self-supervised learning, Multilingual NMT-xx is a multilingual model trained on 28 language pairs (xx is the language not present in the model). Multilingual NMT-xx + Mono. is a multilingual model with 28 language pairs but only monolingual data for xx." ], "reasoning": "Adding monolingual data to a multilingual model significantly improves translation quality, often nearing the performance of fully supervised models without iterative back-translation.", "description": "We find that simply adding monolingual data for a new language to the training procedure of a multilingual model is sufficient to obtain strong translation quality for several languages, often attaining within a few BLEU points of the fully supervised multilingual baseline, without the need for iterative back-translation. We also notice significant quality improvements over models trained with just self-supervised learning using monolingual data for a variety of languages. et al. This suggests that jump-starting the iterative back-translation process from multilingual models might be a promising avenue to supporting new languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 3: Translation quality of the new language added to Multilingual NMT using just monolingual data. Multilingual NMT here is a multilingual model with 30 language pairs, Mono. Only is a bilingual model used as a baseline trained with only monolingual data with self-supervised learning, Multilingual NMT-xx is a multilingual model trained on 28 language pairs (xx is the language not present in the model). Multilingual NMT-xx + Mono. is a multilingual model with 28 language pairs but only monolingual data for xx.', solve the subsequent question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhat is the BLEU score for en_ro using Multilingual NMT - xx + Mono.?\n" }, { "role": "assistant", "content": "To find the BLEU score for en_ro using Multilingual NMT - xx + Mono., locate the en_ro column and the Multilingual NMT - xx + Mono. row in the table. So, the answer is {\"answer\": \"33.0\"}." } ], "images": [ "scigen/train/1750.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Translation quality of the new language added to Multilingual NMT using just monolingual data. Multilingual NMT here is a multilingual model with 30 language pairs, Mono. Only is a bilingual model used as a baseline trained with only monolingual data with self-supervised learning, Multilingual NMT-xx is a multilingual model trained on 28 language pairs (xx is the language not present in the model). Multilingual NMT-xx + Mono. is a multilingual model with 28 language pairs but only monolingual data for xx." ], "reasoning": "To find the BLEU score for en_ro using Multilingual NMT - xx + Mono., locate the en_ro column and the Multilingual NMT - xx + Mono. row in the table.", "question": "What is the BLEU score for en_ro using Multilingual NMT - xx + Mono.?", "answer": "So, the answer is {\"answer\": \"33.0\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The BLEU score for en_fr using Mono. Only is higher than en_de using Multilingual NMT - xx. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe BLEU score for en_fr using Mono. Only is higher than en_de using Multilingual NMT - xx.\n" }, { "role": "assistant", "content": "Compare the BLEU score for en_fr using Mono. Only (7.6) with the BLEU score for en_de using Multilingual NMT - xx (2.6). Since 7.6 is higher than 2.6, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1750.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Translation quality of the new language added to Multilingual NMT using just monolingual data. Multilingual NMT here is a multilingual model with 30 language pairs, Mono. Only is a bilingual model used as a baseline trained with only monolingual data with self-supervised learning, Multilingual NMT-xx is a multilingual model trained on 28 language pairs (xx is the language not present in the model). Multilingual NMT-xx + Mono. is a multilingual model with 28 language pairs but only monolingual data for xx." ], "reasoning": "Compare the BLEU score for en_fr using Mono. Only (7.6) with the BLEU score for en_de using Multilingual NMT - xx (2.6). Since 7.6 is higher than 2.6, the statement is supported.", "statement": "The BLEU score for en_fr using Mono. Only is higher than en_de using Multilingual NMT - xx.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Comparison of the impact of different domain similarity measures on the student’s performance when used for interpolating the predictions of the source domain teacher models. For the results in each column, the domain in the column header is used as target domain and the remaining three domains are used as source domains.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The performance of the student is evaluated based on different measures of domain similarity, which are used to interpolate the predictions of the teachers.Domain similarity. We first evaluate the performance of our student depending on different measures of domain similarity, with which we interpolate the predictions of the teachers. We thus use this measure for the remainder of the experiments." } ], "images": [ "scigen/train/1751.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of the impact of different domain similarity measures on the student’s performance when used for interpolating the predictions of the source domain teacher models. For the results in each column, the domain in the column header is used as target domain and the remaining three domains are used as source domains." ], "reasoning": "The performance of the student is evaluated based on different measures of domain similarity, which are used to interpolate the predictions of the teachers.", "description": "Domain similarity. We first evaluate the performance of our student depending on different measures of domain similarity, with which we interpolate the predictions of the teachers. We thus use this measure for the remainder of the experiments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nTable title: Table 1: Comparison of the impact of different domain similarity measures on the student’s performance when used for interpolating the predictions of the source domain teacher models. For the results in each column, the domain in the column header is used as target domain and the remaining three domains are used as source domains.\n\nQuestion: Which measure of domain similarity resulted in the highest performance for the Kitchen domain?\n" }, { "role": "assistant", "content": "By comparing the values in the Kitchen column, the highest performance is achieved with the Jensen-Shannon divergence measure, which has a value of 0.8523. So, the answer is {\"answer\": \"Jensen-Shannon divergence\"}." } ], "images": [ "scigen/train/1751.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of the impact of different domain similarity measures on the student’s performance when used for interpolating the predictions of the source domain teacher models. For the results in each column, the domain in the column header is used as target domain and the remaining three domains are used as source domains." ], "reasoning": "By comparing the values in the Kitchen column, the highest performance is achieved with the Jensen-Shannon divergence measure, which has a value of 0.8523.", "question": "Which measure of domain similarity resulted in the highest performance for the Kitchen domain?", "answer": "So, the answer is {\"answer\": \"Jensen-Shannon divergence\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Renyi divergence measure resulted in the lowest performance for the DVD domain.\n" }, { "role": "assistant", "content": "By comparing the values in the DVD column, the lowest performance is achieved with the Renyi divergence measure, which has a value of 0.7727. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1751.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of the impact of different domain similarity measures on the student’s performance when used for interpolating the predictions of the source domain teacher models. For the results in each column, the domain in the column header is used as target domain and the remaining three domains are used as source domains." ], "reasoning": "By comparing the values in the DVD column, the lowest performance is achieved with the Renyi divergence measure, which has a value of 0.7727.", "statement": "The Renyi divergence measure resulted in the lowest performance for the DVD domain.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Average results for domain adaptation from multiple sources for the comparison models and ours on the sentiment analysis benchmark. For the results in each column, the domain in the column header is used as target domain and the remaining three domains are used as source domains.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Training the student model on soft targets from both general and source domain teachers significantly improves domain adaptation results over teacher-only and existing approaches.Results. Evaluating the combination of the source teacher models directly on the target domain (Teacher-only) produces the worst results, which underscores the need for methods that allow adaptation to the target domain. Training the student model on the soft targets of the teachers allows us to improve upon the teacher-only baseline significantly, which demonstrates the appropriateness of the teacher-student paradigm to the domain adaptation scenario. This showcases the usefulness of learning from soft targets in the domain adaptation scenario. Training on a general teacher model as well as on a combination of the general teacher and the source domain teachers allows us to improve results even further. Both models improve over existing approaches to domain adaptation from multiple sources and outperform approaches that rely on sentiment analysis-specific information Wu and Huang" } ], "images": [ "scigen/train/1752.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Average results for domain adaptation from multiple sources for the comparison models and ours on the sentiment analysis benchmark. For the results in each column, the domain in the column header is used as target domain and the remaining three domains are used as source domains." ], "reasoning": "Training the student model on soft targets from both general and source domain teachers significantly improves domain adaptation results over teacher-only and existing approaches.", "description": "Results. Evaluating the combination of the source teacher models directly on the target domain (Teacher-only) produces the worst results, which underscores the need for methods that allow adaptation to the target domain. Training the student model on the soft targets of the teachers allows us to improve upon the teacher-only baseline significantly, which demonstrates the appropriateness of the teacher-student paradigm to the domain adaptation scenario. This showcases the usefulness of learning from soft targets in the domain adaptation scenario. Training on a general teacher model as well as on a combination of the general teacher and the source domain teachers allows us to improve results even further. Both models improve over existing approaches to domain adaptation from multiple sources and outperform approaches that rely on sentiment analysis-specific information Wu and Huang", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Average results for domain adaptation from multiple sources for the comparison models and ours on the sentiment analysis benchmark. For the results in each column, the domain in the column header is used as target domain and the remaining three domains are used as source domains.', answer the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieved the highest score in the Kitchen domain?\n" }, { "role": "assistant", "content": "By examining the table, the highest score in the Kitchen domain is found in the 'Student (general teacher)' row with a score of 0.8675. So, the answer is {\"answer\": \"Student (general teacher)\"}." } ], "images": [ "scigen/train/1752.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Average results for domain adaptation from multiple sources for the comparison models and ours on the sentiment analysis benchmark. For the results in each column, the domain in the column header is used as target domain and the remaining three domains are used as source domains." ], "reasoning": "By examining the table, the highest score in the Kitchen domain is found in the 'Student (general teacher)' row with a score of 0.8675.", "question": "Which model achieved the highest score in the Kitchen domain?", "answer": "So, the answer is {\"answer\": \"Student (general teacher)\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Teacher-only model achieved the lowest score in the Electronics domain.\n" }, { "role": "assistant", "content": "By examining the table, the Teacher-only model has a score of 0.7960 in the Electronics domain, which is indeed the lowest score compared to all other models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1752.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Average results for domain adaptation from multiple sources for the comparison models and ours on the sentiment analysis benchmark. For the results in each column, the domain in the column header is used as target domain and the remaining three domains are used as source domains." ], "reasoning": "By examining the table, the Teacher-only model has a score of 0.7960 in the Electronics domain, which is indeed the lowest score compared to all other models listed.", "statement": "The Teacher-only model achieved the lowest score in the Electronics domain.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: Performances of the Seq2Seq models with and without attention on the RCV1-v2 test set, where HL, P, R, and F1 refer to hamming loss, micro-precision, micro-recall and micro-F1. The symbol “+” indicates that the higher the better, while the symbol “-” indicates that the lower the better.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows that the Seq2Seq model with attention has only a slight improvement in precision and F1 score compared to the model without attention, supporting the hypothesis that conventional attention mechanisms may not significantly enhance performance in multi-label text classification.This can be a proof that the conventional attention mechanism does not play a significant role in the improvement of the Seq2Seq model’s performance. We hypothesize that the conventional attention mechanism does not meet the requirements of multi-label text classification. A common sense for such a classification task is that the classification should be based on the salient ideas of the source text. The semantic units, instead of word-level information, mainly determine that the text can be classified into the target categories “youth” and “sports”. For each of a variety of texts, there are always semantic units that construct the semantic meaning of the whole text. Regarding an automatic system for multi-label text classification, the system should be able to extract the semantic units in the source text for better performance in classification. Therefore, we propose our model to tackle this problem." } ], "images": [ "scigen/train/1753.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performances of the Seq2Seq models with and without attention on the RCV1-v2 test set, where HL, P, R, and F1 refer to hamming loss, micro-precision, micro-recall and micro-F1. The symbol “+” indicates that the higher the better, while the symbol “-” indicates that the lower the better." ], "reasoning": "The table shows that the Seq2Seq model with attention has only a slight improvement in precision and F1 score compared to the model without attention, supporting the hypothesis that conventional attention mechanisms may not significantly enhance performance in multi-label text classification.", "description": "This can be a proof that the conventional attention mechanism does not play a significant role in the improvement of the Seq2Seq model’s performance. We hypothesize that the conventional attention mechanism does not meet the requirements of multi-label text classification. A common sense for such a classification task is that the classification should be based on the salient ideas of the source text. The semantic units, instead of word-level information, mainly determine that the text can be classified into the target categories “youth” and “sports”. For each of a variety of texts, there are always semantic units that construct the semantic meaning of the whole text. Regarding an automatic system for multi-label text classification, the system should be able to extract the semantic units in the source text for better performance in classification. Therefore, we propose our model to tackle this problem.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Performances of the Seq2Seq models with and without attention on the RCV1-v2 test set, where HL, P, R, and F1 refer to hamming loss, micro-precision, micro-recall and micro-F1. The symbol “+” indicates that the higher the better, while the symbol “-” indicates that the lower the better.\nQuestion: What is the difference in precision (P) between the Seq2Seq model with attention and without attention?\nSolve the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The precision (P) for the model without attention is 0.883, and for the model with attention, it is 0.889. The difference is 0.889 - 0.883 = 0.006. So, the answer is {\"answer\": \"0.006\"}." } ], "images": [ "scigen/train/1753.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performances of the Seq2Seq models with and without attention on the RCV1-v2 test set, where HL, P, R, and F1 refer to hamming loss, micro-precision, micro-recall and micro-F1. The symbol “+” indicates that the higher the better, while the symbol “-” indicates that the lower the better." ], "reasoning": "The precision (P) for the model without attention is 0.883, and for the model with attention, it is 0.889. The difference is 0.889 - 0.883 = 0.006.", "question": "What is the difference in precision (P) between the Seq2Seq model with attention and without attention?", "answer": "So, the answer is {\"answer\": \"0.006\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Seq2Seq model with attention has a higher recall (R) than the model without attention., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Seq2Seq model with attention has a higher recall (R) than the model without attention.\n" }, { "role": "assistant", "content": "The table shows that the recall (R) for the model without attention is 0.849, and for the model with attention, it is 0.848. Therefore, the model with attention has a slightly lower recall. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1753.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performances of the Seq2Seq models with and without attention on the RCV1-v2 test set, where HL, P, R, and F1 refer to hamming loss, micro-precision, micro-recall and micro-F1. The symbol “+” indicates that the higher the better, while the symbol “-” indicates that the lower the better." ], "reasoning": "The table shows that the recall (R) for the model without attention is 0.849, and for the model with attention, it is 0.848. Therefore, the model with attention has a slightly lower recall.", "statement": "The Seq2Seq model with attention has a higher recall (R) than the model without attention.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Performance on the RCV1-V2 test set. HL, P, R, and F1 denote hamming loss, micro-precision, micro-recall and micro-F1, respectively (p<0.05).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Our proposed model outperforms other models in terms of Hamming loss and micro-F1 score, reducing Hamming loss by 9.8% and improving micro-F1 score by 1.3% compared to the S2S+Attn model.From the results of the conventional baselines, it can be found that the classical methods for multi-label text classification still own competitiveness compared with the machine-learning-based and even deep-learning-based methods, instead of the Seq2Seq-based models. Regarding the Seq2Seq model, both the S2S and the S2S+Attn achieve improvements on the dataset, compared with the baselines above. However, as mentioned previously, the attention mechanism does not play a significant role in the Seq2Seq model for multi-label text classification. By contrast, our proposed mechanism, which is label-classification-oriented, can take both the information of semantic units and that of word units into consideration. Our proposed model achieves the best performance in the evaluation of Hamming loss and micro-F1 score, which reduces 9.8% of Hamming loss and improves 1.3% of micro-F1 score, in comparison with the S2S+Attn." } ], "images": [ "scigen/train/1754.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance on the RCV1-V2 test set. HL, P, R, and F1 denote hamming loss, micro-precision, micro-recall and micro-F1, respectively (p<0.05)." ], "reasoning": "Our proposed model outperforms other models in terms of Hamming loss and micro-F1 score, reducing Hamming loss by 9.8% and improving micro-F1 score by 1.3% compared to the S2S+Attn model.", "description": "From the results of the conventional baselines, it can be found that the classical methods for multi-label text classification still own competitiveness compared with the machine-learning-based and even deep-learning-based methods, instead of the Seq2Seq-based models. Regarding the Seq2Seq model, both the S2S and the S2S+Attn achieve improvements on the dataset, compared with the baselines above. However, as mentioned previously, the attention mechanism does not play a significant role in the Seq2Seq model for multi-label text classification. By contrast, our proposed mechanism, which is label-classification-oriented, can take both the information of semantic units and that of word units into consideration. Our proposed model achieves the best performance in the evaluation of Hamming loss and micro-F1 score, which reduces 9.8% of Hamming loss and improves 1.3% of micro-F1 score, in comparison with the S2S+Attn.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Performance on the RCV1-V2 test set. HL, P, R, and F1 denote hamming loss, micro-precision, micro-recall and micro-F1, respectively (p<0.05).' to sequentially solve the question presented below. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich model achieves the highest recall score?\n" }, { "role": "assistant", "content": "By examining the recall (R(+)) column in the table, the highest recall score is 0.873, which is achieved by Our Model. So, the answer is {\"answer\": \"Our Model\"}." } ], "images": [ "scigen/train/1754.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance on the RCV1-V2 test set. HL, P, R, and F1 denote hamming loss, micro-precision, micro-recall and micro-F1, respectively (p<0.05)." ], "reasoning": "By examining the recall (R(+)) column in the table, the highest recall score is 0.873, which is achieved by Our Model.", "question": "Which model achieves the highest recall score?", "answer": "So, the answer is {\"answer\": \"Our Model\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe CNN model has the highest precision score.\n" }, { "role": "assistant", "content": "By examining the precision (P(+)) column in the table, the highest precision score is 0.922, which is achieved by the CNN model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1754.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance on the RCV1-V2 test set. HL, P, R, and F1 denote hamming loss, micro-precision, micro-recall and micro-F1, respectively (p<0.05)." ], "reasoning": "By examining the precision (P(+)) column in the table, the highest precision score is 0.922, which is achieved by the CNN model.", "statement": "The CNN model has the highest precision score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Performance of the models with different attention mechanisms on the RCV1-V2 test set. HL, P, R, and F1 denote hamming loss, micro-precision, micro-recall and micro-F1, respectively (p<0.05).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The hybrid attention mechanism improves the performance of the Seq2Seq model for multi-label text classification by combining high-level semantic information with word-level details, achieving the highest F1 score.Except for the insignificant effect of the conventional attention mechanism mentioned above, it can be found that the high-level representations generated by the MDC contribute much to the performance of the Seq2Seq model for multi-label text classification, which improves about 0.9 micro-F1 score. Moreover, simple additive attention mechanism, which is equivalent to the element-wise addition of the representations of MDC and those of the conventional mechanism, achieves similar performance to the single MDC, which also demonstrates that conventional attention mechanism in this task makes little contribution. As to our proposed hybrid attention, which is a relatively complex combination of the two mechanisms, can improve the performance of MDC. This shows that although conventional attention mechanism for word-level information does not influence the performance of the SeqSeq model significantly, the hybrid attention which extracts word-level information based on the generated high-level semantic information can provide some information about important details that are relevant to the most contributing semantic units." } ], "images": [ "scigen/train/1755.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance of the models with different attention mechanisms on the RCV1-V2 test set. HL, P, R, and F1 denote hamming loss, micro-precision, micro-recall and micro-F1, respectively (p<0.05)." ], "reasoning": "The hybrid attention mechanism improves the performance of the Seq2Seq model for multi-label text classification by combining high-level semantic information with word-level details, achieving the highest F1 score.", "description": "Except for the insignificant effect of the conventional attention mechanism mentioned above, it can be found that the high-level representations generated by the MDC contribute much to the performance of the Seq2Seq model for multi-label text classification, which improves about 0.9 micro-F1 score. Moreover, simple additive attention mechanism, which is equivalent to the element-wise addition of the representations of MDC and those of the conventional mechanism, achieves similar performance to the single MDC, which also demonstrates that conventional attention mechanism in this task makes little contribution. As to our proposed hybrid attention, which is a relatively complex combination of the two mechanisms, can improve the performance of MDC. This shows that although conventional attention mechanism for word-level information does not influence the performance of the SeqSeq model significantly, the hybrid attention which extracts word-level information based on the generated high-level semantic information can provide some information about important details that are relevant to the most contributing semantic units.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: Performance of the models with different attention mechanisms on the RCV1-V2 test set. HL, P, R, and F1 denote hamming loss, micro-precision, micro-recall and micro-F1, respectively (p<0.05).', answer the following question. Give detailed solution consisting of each step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model achieves the highest F1 score?\n" }, { "role": "assistant", "content": "By examining the F1(+) column in the table, the hybrid model achieves the highest F1 score of 0.882. So, the answer is {\"answer\": \"hybrid\"}" } ], "images": [ "scigen/train/1755.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance of the models with different attention mechanisms on the RCV1-V2 test set. HL, P, R, and F1 denote hamming loss, micro-precision, micro-recall and micro-F1, respectively (p<0.05)." ], "reasoning": "By examining the F1(+) column in the table, the hybrid model achieves the highest F1 score of 0.882.", "question": "Which model achieves the highest F1 score?", "answer": "So, the answer is {\"answer\": \"hybrid\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The MDC model achieves the highest precision score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe MDC model achieves the highest precision score.\n" }, { "role": "assistant", "content": "By examining the P(+) column in the table, the hybrid model achieves the highest precision score of 0.891, not the MDC model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1755.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance of the models with different attention mechanisms on the RCV1-V2 test set. HL, P, R, and F1 denote hamming loss, micro-precision, micro-recall and micro-F1, respectively (p<0.05)." ], "reasoning": "By examining the P(+) column in the table, the hybrid model achieves the highest precision score of 0.891, not the MDC model.", "statement": "The MDC model achieves the highest precision score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: Performance of the hierarchical model and our model on the RCV1-V2 test set. Hier refers to hierarchical model, and the subsequent number refers to the length of sentence (word) for sentence-level representations (p<0.05).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The proposed model outperforms hierarchical models with fixed boundaries in multi-label text classification, indicating that learnable high-level representations are more effective.Moreover, in order to alleviate the influence of the deterministic sentence boundary, we compare the performance of different hierarchical models with different boundaries, which sets the boundaries at the end of every 5, 10, 15 and 20 words respectively. This shows that high-level representations can contribute to the performance of the Seq2Seq model on the multi-label text classification task. Furthermore, as these performances are no better than that of our proposed model, it can reflect that the learnable high-level representations can contribute more than deterministic sentence-level representations, as it can be more flexible to represent information of diverse levels, instead of fixed phrase or sentence level." } ], "images": [ "scigen/train/1756.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Performance of the hierarchical model and our model on the RCV1-V2 test set. Hier refers to hierarchical model, and the subsequent number refers to the length of sentence (word) for sentence-level representations (p<0.05)." ], "reasoning": "The proposed model outperforms hierarchical models with fixed boundaries in multi-label text classification, indicating that learnable high-level representations are more effective.", "description": "Moreover, in order to alleviate the influence of the deterministic sentence boundary, we compare the performance of different hierarchical models with different boundaries, which sets the boundaries at the end of every 5, 10, 15 and 20 words respectively. This shows that high-level representations can contribute to the performance of the Seq2Seq model on the multi-label text classification task. Furthermore, as these performances are no better than that of our proposed model, it can reflect that the learnable high-level representations can contribute more than deterministic sentence-level representations, as it can be more flexible to represent information of diverse levels, instead of fixed phrase or sentence level.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 5: Performance of the hierarchical model and our model on the RCV1-V2 test set. Hier refers to hierarchical model, and the subsequent number refers to the length of sentence (word) for sentence-level representations (p<0.05).'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model has the highest F1 score?\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all models in the table, the proposed model has the highest F1 score of 0.882. So, the answer is {\"answer\": \"Our model\"}" } ], "images": [ "scigen/train/1756.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Performance of the hierarchical model and our model on the RCV1-V2 test set. Hier refers to hierarchical model, and the subsequent number refers to the length of sentence (word) for sentence-level representations (p<0.05)." ], "reasoning": "By comparing the F1 scores of all models in the table, the proposed model has the highest F1 score of 0.882.", "question": "Which model has the highest F1 score?", "answer": "So, the answer is {\"answer\": \"Our model\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nHier-20 has the highest recall score among the hierarchical models.\n" }, { "role": "assistant", "content": "By comparing the recall scores (R) of the hierarchical models, Hier-20 has the highest recall score of 0.881. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1756.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Performance of the hierarchical model and our model on the RCV1-V2 test set. Hier refers to hierarchical model, and the subsequent number refers to the length of sentence (word) for sentence-level representations (p<0.05)." ], "reasoning": "By comparing the recall scores (R) of the hierarchical models, Hier-20 has the highest recall score of 0.881.", "statement": "Hier-20 has the highest recall score among the hierarchical models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 14: Intersection across query strategies using 19 and 9 iterations (mean ± std across runs) and different seeds'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The table compares four uncertainty query strategies: Least Confidence (LC) and Entropy (Ent), with and without deletion of least uncertain samples from the training set, showing the robustness of the approach to variations in the query strategy.Acquisition Functions: We compare four uncertainty query strategies: Least Confidence (LC) and Entropy (Ent), with and without deletion of least uncertain samples from the training set. Deletion of least uncertain samples reduces the dependence on the initial randomly selected set. We present five of the ten possible combinations and again observe the high degree of overlap in the collected samples. It can be concluded that the approach is fairly robust to these variations in the query strategy." } ], "images": [ "scigen/train/1757.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 14: Intersection across query strategies using 19 and 9 iterations (mean ± std across runs) and different seeds" ], "reasoning": "The table compares four uncertainty query strategies: Least Confidence (LC) and Entropy (Ent), with and without deletion of least uncertain samples from the training set, showing the robustness of the approach to variations in the query strategy.", "description": "Acquisition Functions: We compare four uncertainty query strategies: Least Confidence (LC) and Entropy (Ent), with and without deletion of least uncertain samples from the training set. Deletion of least uncertain samples reduces the dependence on the initial randomly selected set. We present five of the ten possible combinations and again observe the high degree of overlap in the collected samples. It can be concluded that the approach is fairly robust to these variations in the query strategy.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 14: Intersection across query strategies using 19 and 9 iterations (mean ± std across runs) and different seeds\nQuestion: What is the highest score for the FTZ Ent-Ent strategy?\nSolve the above question based on the table information. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest score for the FTZ Ent-Ent strategy, look at the column labeled 'FTZ Ent-Ent' and identify the highest value. The highest score is 82.6±0.2 for DBP. So, the answer is {\"answer\": \"82.6±0.2\"}" } ], "images": [ "scigen/train/1757.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 14: Intersection across query strategies using 19 and 9 iterations (mean ± std across runs) and different seeds" ], "reasoning": "To find the highest score for the FTZ Ent-Ent strategy, look at the column labeled 'FTZ Ent-Ent' and identify the highest value. The highest score is 82.6±0.2 for DBP.", "question": "What is the highest score for the FTZ Ent-Ent strategy?", "answer": "So, the answer is {\"answer\": \"82.6±0.2\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe MNB Ent-LC strategy has the lowest score for AGN.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'MNB Ent-LC' column for AGN. The score is 71.5±0.0. Compare this with other scores for AGN in different strategies. The lowest score for AGN is actually 67.3±0.0 in the FTZ Ent-LC strategy. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1757.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 14: Intersection across query strategies using 19 and 9 iterations (mean ± std across runs) and different seeds" ], "reasoning": "To verify this statement, look at the 'MNB Ent-LC' column for AGN. The score is 71.5±0.0. Compare this with other scores for AGN in different strategies. The lowest score for AGN is actually 67.3±0.0 in the FTZ Ent-LC strategy.", "statement": "The MNB Ent-LC strategy has the lowest score for AGN.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Proportion of Support Vectors intersecting with our actively selected set calculated by |SSV∩^Sb||SSV|. Actively selected sets share large overlap with supports of an SVM (critical for classification).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The high percentage overlap between selected data and support vectors indicates that uncertainty sampling effectively targets samples around class boundaries, which are crucial for good classification performance.Indeed, recent work suggests that the learning in deep classification networks may focus on small part of the data closer to class boundaries, thus resembling support vectors Xu et al. To investigate whether uncertainty sampling also exhibits this behavior, we perform below a direct comparison with support vectors from a SVM. For this, we train a FTZ model on the full training data and train a SVM on the resulting features (sentence embeddings) to obtain the support vectors and compute the intersection of support vectors with each selected set. The high percentage overlap is a surprising result which shows that the sampling is indeed biased but in a desirable way. Since the support vectors represent the class boundaries, a large percentage of selected data consists of samples around the class boundaries. This overlap indicates that the actively acquired training sample covers the support vectors well which are important for good classification performance. The overlap with the support vectors of an SVM (a fixed algorithm) also suggests that uncertainty sampling using deep models might generalize beyond FastText, to other learning algorithms." } ], "images": [ "scigen/train/1758.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Proportion of Support Vectors intersecting with our actively selected set calculated by |SSV∩^Sb||SSV|. Actively selected sets share large overlap with supports of an SVM (critical for classification)." ], "reasoning": "The high percentage overlap between selected data and support vectors indicates that uncertainty sampling effectively targets samples around class boundaries, which are crucial for good classification performance.", "description": "Indeed, recent work suggests that the learning in deep classification networks may focus on small part of the data closer to class boundaries, thus resembling support vectors Xu et al. To investigate whether uncertainty sampling also exhibits this behavior, we perform below a direct comparison with support vectors from a SVM. For this, we train a FTZ model on the full training data and train a SVM on the resulting features (sentence embeddings) to obtain the support vectors and compute the intersection of support vectors with each selected set. The high percentage overlap is a surprising result which shows that the sampling is indeed biased but in a desirable way. Since the support vectors represent the class boundaries, a large percentage of selected data consists of samples around the class boundaries. This overlap indicates that the actively acquired training sample covers the support vectors well which are important for good classification performance. The overlap with the support vectors of an SVM (a fixed algorithm) also suggests that uncertainty sampling using deep models might generalize beyond FastText, to other learning algorithms.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: Proportion of Support Vectors intersecting with our actively selected set calculated by |SSV∩^Sb||SSV|. Actively selected sets share large overlap with supports of an SVM (critical for classification).', and sequentially solve the question, writing out each step of your reasoning process. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich dataset has the highest percentage of overlap with support vectors?\n" }, { "role": "assistant", "content": "By examining the 'Common%' column, we see that the dataset DBP has the highest percentage of overlap with support vectors at 86.3%. So, the answer is {\"answer\": \"DBP\"}." } ], "images": [ "scigen/train/1758.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Proportion of Support Vectors intersecting with our actively selected set calculated by |SSV∩^Sb||SSV|. Actively selected sets share large overlap with supports of an SVM (critical for classification)." ], "reasoning": "By examining the 'Common%' column, we see that the dataset DBP has the highest percentage of overlap with support vectors at 86.3%.", "question": "Which dataset has the highest percentage of overlap with support vectors?", "answer": "So, the answer is {\"answer\": \"DBP\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The dataset YRP has the highest number of support vectors. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe dataset YRP has the highest number of support vectors.\n" }, { "role": "assistant", "content": "By looking at the '# SV' column, we see that the dataset YRP has the highest number of support vectors at 31,750. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1758.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Proportion of Support Vectors intersecting with our actively selected set calculated by |SSV∩^Sb||SSV|. Actively selected sets share large overlap with supports of an SVM (critical for classification)." ], "reasoning": "By looking at the '# SV' column, we see that the dataset YRP has the highest number of support vectors at 31,750.", "statement": "The dataset YRP has the highest number of support vectors.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: % Intersection of samples obtained with different seeds (ModelD) compared to same seeds (ModelS) and chance intersection for b=39 queries. We see that FastText is initialization independent (FTZD ≈ FTZS ≫ Chance). NaiveBayes shows significant dependency on the initial set sometimes, while other times performs comparable to FastText.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The results show that FastText is largely initialization independent, while Naive Bayes shows heavy dependence on the initial seed.To investigate the dependence of the actively acquired train set on the initial set, we compare the overlap (intersection) of the incrementally constructed sets from different random initial sets versus the same initial set. We first observe that chance overlaps (column 2) are very low - less than 4%. Columns 3 and 5 present overlaps from different initial sets while 4 and 6 from same initial sets. We note from column 4 and 6 that due to the stochasticity of training in FTZ, we expect non-identical final sets even with same initial samples as well. The results demonstrate that samples obtained using FastText are largely initialization independent (low variation between columns 3 and 4) consistently across datasets while the samples obtained with Naive Bayes can be vastly different showing relatively heavy dependence on the initial seed. This indicates the relative stability of train set obtained with the posterior uncertainty of the actively trained FTZ as an acquisition function." } ], "images": [ "scigen/train/1759.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: % Intersection of samples obtained with different seeds (ModelD) compared to same seeds (ModelS) and chance intersection for b=39 queries. We see that FastText is initialization independent (FTZD ≈ FTZS ≫ Chance). NaiveBayes shows significant dependency on the initial set sometimes, while other times performs comparable to FastText." ], "reasoning": "The results show that FastText is largely initialization independent, while Naive Bayes shows heavy dependence on the initial seed.", "description": "To investigate the dependence of the actively acquired train set on the initial set, we compare the overlap (intersection) of the incrementally constructed sets from different random initial sets versus the same initial set. We first observe that chance overlaps (column 2) are very low - less than 4%. Columns 3 and 5 present overlaps from different initial sets while 4 and 6 from same initial sets. We note from column 4 and 6 that due to the stochasticity of training in FTZ, we expect non-identical final sets even with same initial samples as well. The results demonstrate that samples obtained using FastText are largely initialization independent (low variation between columns 3 and 4) consistently across datasets while the samples obtained with Naive Bayes can be vastly different showing relatively heavy dependence on the initial seed. This indicates the relative stability of train set obtained with the posterior uncertainty of the actively trained FTZ as an acquisition function.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich dataset has the highest overlap in the FTZS column?\nSolve the above question based on the table titled 'Table 4: % Intersection of samples obtained with different seeds (ModelD) compared to same seeds (ModelS) and chance intersection for b=39 queries. We see that FastText is initialization independent (FTZD ≈ FTZS ≫ Chance). NaiveBayes shows significant dependency on the initial set sometimes, while other times performs comparable to FastText.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the FTZS column, the highest overlap value is 81.3, which corresponds to the DBP dataset. So, the answer is {\"answer\": \"DBP\"}." } ], "images": [ "scigen/train/1759.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: % Intersection of samples obtained with different seeds (ModelD) compared to same seeds (ModelS) and chance intersection for b=39 queries. We see that FastText is initialization independent (FTZD ≈ FTZS ≫ Chance). NaiveBayes shows significant dependency on the initial set sometimes, while other times performs comparable to FastText." ], "reasoning": "By examining the FTZS column, the highest overlap value is 81.3, which corresponds to the DBP dataset.", "question": "Which dataset has the highest overlap in the FTZS column?", "answer": "So, the answer is {\"answer\": \"DBP\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe overlap for the YRP dataset in the FTZD column is higher than in the FTZS column.\n" }, { "role": "assistant", "content": "The table shows that the overlap for the YRP dataset in the FTZD column is 42.9, while in the FTZS column it is 43.7. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1759.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: % Intersection of samples obtained with different seeds (ModelD) compared to same seeds (ModelS) and chance intersection for b=39 queries. We see that FastText is initialization independent (FTZD ≈ FTZS ≫ Chance). NaiveBayes shows significant dependency on the initial set sometimes, while other times performs comparable to FastText." ], "reasoning": "The table shows that the overlap for the YRP dataset in the FTZD column is 42.9, while in the FTZS column it is 43.7. Therefore, the statement is incorrect.", "statement": "The overlap for the YRP dataset in the FTZD column is higher than in the FTZS column.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Intersection of samples obtained with different values of b. We see the intersection of samples selected with different number of intersections comparable to highest possible (different seeds) in FastText, far higher compared to chance intersection. This indicates similar samples are selected regardless of sample size. NaiveBayes does not show clear trends but occasionally the queried percentage drops significantly when increasing iterations, occasionally it remains unaffected.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The performance is independent of the query size for FTZ, as indicated by the high overlap of acquired samples across different query sizes, while MNB shows lower overlap and more erratic behavior due to changes in query size.Hoi et al. We experiment with various query sizes - (0.25%, 0.5%, 1%) for DBP, SGN, YRP and AMZP and (0.5%, 1%, 2%) for the rest corresponding to 9, 19 and 39 iterations. We observe a high overlap of the acquired samples across different query sizes indicating that the performance is independent of the query size (compare column 3 to column 4 where the size is held constant) while MNB results in lower overlap with more erratic behavior due to change in the query size (compare column 5 compared to column 6)." } ], "images": [ "scigen/train/1760.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Intersection of samples obtained with different values of b. We see the intersection of samples selected with different number of intersections comparable to highest possible (different seeds) in FastText, far higher compared to chance intersection. This indicates similar samples are selected regardless of sample size. NaiveBayes does not show clear trends but occasionally the queried percentage drops significantly when increasing iterations, occasionally it remains unaffected." ], "reasoning": "The performance is independent of the query size for FTZ, as indicated by the high overlap of acquired samples across different query sizes, while MNB shows lower overlap and more erratic behavior due to changes in query size.", "description": "Hoi et al. We experiment with various query sizes - (0.25%, 0.5%, 1%) for DBP, SGN, YRP and AMZP and (0.5%, 1%, 2%) for the rest corresponding to 9, 19 and 39 iterations. We observe a high overlap of the acquired samples across different query sizes indicating that the performance is independent of the query size (compare column 3 to column 4 where the size is held constant) while MNB results in lower overlap with more erratic behavior due to change in the query size (compare column 5 compared to column 6).", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 5: Intersection of samples obtained with different values of b. We see the intersection of samples selected with different number of intersections comparable to highest possible (different seeds) in FastText, far higher compared to chance intersection. This indicates similar samples are selected regardless of sample size. NaiveBayes does not show clear trends but occasionally the queried percentage drops significantly when increasing iterations, occasionally it remains unaffected.'. Solve the following question based on the table.\n\nQuestion: What is the overlap percentage for FTZ at 39 iterations for the DBP dataset?\nLet's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the overlap percentage for FTZ at 39 iterations for the DBP dataset, look at the column labeled 'FTZ 39∩39∩39' and find the corresponding row for DBP. So, the answer is {\"answer\": \"79.6±0.2\"}." } ], "images": [ "scigen/train/1760.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Intersection of samples obtained with different values of b. We see the intersection of samples selected with different number of intersections comparable to highest possible (different seeds) in FastText, far higher compared to chance intersection. This indicates similar samples are selected regardless of sample size. NaiveBayes does not show clear trends but occasionally the queried percentage drops significantly when increasing iterations, occasionally it remains unaffected." ], "reasoning": "To find the overlap percentage for FTZ at 39 iterations for the DBP dataset, look at the column labeled 'FTZ 39∩39∩39' and find the corresponding row for DBP.", "question": "What is the overlap percentage for FTZ at 39 iterations for the DBP dataset?", "answer": "So, the answer is {\"answer\": \"79.6±0.2\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The overlap percentage for MNB at 9 iterations for the YHA dataset is higher than for FTZ at 9 iterations for the same dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe overlap percentage for MNB at 9 iterations for the YHA dataset is higher than for FTZ at 9 iterations for the same dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the overlap percentage for MNB at 9 iterations (column 'MNB 9∩19∩39') with FTZ at 9 iterations (column 'FTZ 9∩19∩39') for the YHA dataset. MNB shows 92.1±0.0, which is higher than FTZ's 68.3±0.1. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1760.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Intersection of samples obtained with different values of b. We see the intersection of samples selected with different number of intersections comparable to highest possible (different seeds) in FastText, far higher compared to chance intersection. This indicates similar samples are selected regardless of sample size. NaiveBayes does not show clear trends but occasionally the queried percentage drops significantly when increasing iterations, occasionally it remains unaffected." ], "reasoning": "To verify this statement, compare the overlap percentage for MNB at 9 iterations (column 'MNB 9∩19∩39') with FTZ at 9 iterations (column 'FTZ 9∩19∩39') for the YHA dataset. MNB shows 92.1±0.0, which is higher than FTZ's 68.3±0.1.", "statement": "The overlap percentage for MNB at 9 iterations for the YHA dataset is higher than for FTZ at 9 iterations for the same dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 13: Metrics measured after training FastText (FTZ-Ent) model on the resulting sample, with 39 queries, using entropy query strategy. We observe that NLL and Multiclass Brier Score remains low. The model is also well calibrated, i.e. gives calibrated uncertainty estimates.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The FastText model is well calibrated except for YRF and AMZF, as indicated by higher values in NLL, ECE, and other uncertainty measures.NLL denotes the negative log-likelihood, BrierL denotes the Brier Score Loss, ECE denotes the expected calibration error, VarR denotes the variation ratio, ENT denotes the entropy, STD denotes the standard deviation. We measure these properties of the predicted sample and compute their average over the dataset. We observe that the FastText model is well calibrated except for YRF and AMZF. Similar trends are observed in the average uncertainty measures." } ], "images": [ "scigen/train/1761.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 13: Metrics measured after training FastText (FTZ-Ent) model on the resulting sample, with 39 queries, using entropy query strategy. We observe that NLL and Multiclass Brier Score remains low. The model is also well calibrated, i.e. gives calibrated uncertainty estimates." ], "reasoning": "The FastText model is well calibrated except for YRF and AMZF, as indicated by higher values in NLL, ECE, and other uncertainty measures.", "description": "NLL denotes the negative log-likelihood, BrierL denotes the Brier Score Loss, ECE denotes the expected calibration error, VarR denotes the variation ratio, ENT denotes the entropy, STD denotes the standard deviation. We measure these properties of the predicted sample and compute their average over the dataset. We observe that the FastText model is well calibrated except for YRF and AMZF. Similar trends are observed in the average uncertainty measures.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 13: Metrics measured after training FastText (FTZ-Ent) model on the resulting sample, with 39 queries, using entropy query strategy. We observe that NLL and Multiclass Brier Score remains low. The model is also well calibrated, i.e. gives calibrated uncertainty estimates.', answer the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich dataset has the highest entropy (ENT) value?\n" }, { "role": "assistant", "content": "By examining the ENT column, we find that YRF has the highest entropy value of 0.73. So, the answer is {\"answer\": \"YRF\"}." } ], "images": [ "scigen/train/1761.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 13: Metrics measured after training FastText (FTZ-Ent) model on the resulting sample, with 39 queries, using entropy query strategy. We observe that NLL and Multiclass Brier Score remains low. The model is also well calibrated, i.e. gives calibrated uncertainty estimates." ], "reasoning": "By examining the ENT column, we find that YRF has the highest entropy value of 0.73.", "question": "Which dataset has the highest entropy (ENT) value?", "answer": "So, the answer is {\"answer\": \"YRF\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe FastText model shows the highest standard deviation (STD) for the YRP dataset.\n" }, { "role": "assistant", "content": "By examining the STD column, we find that the highest standard deviation is 0.48 for the AMZP dataset, not YRP. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1761.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 13: Metrics measured after training FastText (FTZ-Ent) model on the resulting sample, with 39 queries, using entropy query strategy. We observe that NLL and Multiclass Brier Score remains low. The model is also well calibrated, i.e. gives calibrated uncertainty estimates." ], "reasoning": "By examining the STD column, we find that the highest standard deviation is 0.48 for the AMZP dataset, not YRP.", "statement": "The FastText model shows the highest standard deviation (STD) for the YRP dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 15: Class Bias Experiments: Average Label entropy (mean ± std ) across query iterations, for 39, 19 and 4 query iterations each.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "FTZ with entropy strategy has a balanced representation from all classes with high mean and low standard deviation, while MNB results in more biased queries with lower mean and higher standard deviation.We observe that across queries (∩Q), FTZ with entropy strategy has a balanced representation from all classes (high mean) with a high probability (low std) while Multinomial Naive Bayes (MNB) results in more biased queries (lower mean) with high probability (high std) as studied previously. However, we did not find evidence of class bias in the resulting sample (∩S) in both models:" } ], "images": [ "scigen/train/1762.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 15: Class Bias Experiments: Average Label entropy (mean ± std ) across query iterations, for 39, 19 and 4 query iterations each." ], "reasoning": "FTZ with entropy strategy has a balanced representation from all classes with high mean and low standard deviation, while MNB results in more biased queries with lower mean and higher standard deviation.", "description": "We observe that across queries (∩Q), FTZ with entropy strategy has a balanced representation from all classes (high mean) with a high probability (low std) while Multinomial Naive Bayes (MNB) results in more biased queries (lower mean) with high probability (high std) as studied previously. However, we did not find evidence of class bias in the resulting sample (∩S) in both models:", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 15: Class Bias Experiments: Average Label entropy (mean ± std ) across query iterations, for 39, 19 and 4 query iterations each.', answer the question below by showing each progressive step toward the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model results in more biased queries according to the table and description?\n" }, { "role": "assistant", "content": "The table shows that FTZ has higher mean and lower standard deviation compared to MNB, indicating that MNB results in more biased queries. So, the answer is {\"answer\": \"MNB\"}." } ], "images": [ "scigen/train/1762.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 15: Class Bias Experiments: Average Label entropy (mean ± std ) across query iterations, for 39, 19 and 4 query iterations each." ], "reasoning": "The table shows that FTZ has higher mean and lower standard deviation compared to MNB, indicating that MNB results in more biased queries.", "question": "Which model results in more biased queries according to the table and description?", "answer": "So, the answer is {\"answer\": \"MNB\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence FTZ with entropy strategy has a balanced representation from all classes. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nFTZ with entropy strategy has a balanced representation from all classes.\n" }, { "role": "assistant", "content": "The table shows that FTZ has higher mean and lower standard deviation, supporting the statement that FTZ with entropy strategy has a balanced representation from all classes. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1762.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 15: Class Bias Experiments: Average Label entropy (mean ± std ) across query iterations, for 39, 19 and 4 query iterations each." ], "reasoning": "The table shows that FTZ has higher mean and lower standard deviation, supporting the statement that FTZ with entropy strategy has a balanced representation from all classes.", "statement": "FTZ with entropy strategy has a balanced representation from all classes.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 6: Baseline performance of correctly predicting the label of tweets with a negative stance (for comparison, the best ML system is included; Pr = Precision, Re = Recall, AUC = Area under the Curve).'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The best reported performance is an F1-score of 0.36 and an AUC of 0.66, while rule-based sentiment analyses yield better performances than random baselines.The best reported performance is an F1-score of 0.36 and an AUC of 0.66. Nevertheless, there is room for improvement. The performance of the random baselines, with F1-scores of 0.18 (50%) and 0.13 (15%), indicates that the minimal performance on this task is rather low. The rule-based sentiment analyses yield better performances, at an F1-score of 0.20 for Pattern and 0.25 for Coosto. The Irrelevant category is most often classified with one of the other categories, while the Positive and Negative categories are the biggest confusables. The classifier is possibly identifying features that denote a stance, but struggles to distinguish positive from negative." } ], "images": [ "scigen/train/1763.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Baseline performance of correctly predicting the label of tweets with a negative stance (for comparison, the best ML system is included; Pr = Precision, Re = Recall, AUC = Area under the Curve)." ], "reasoning": "The best reported performance is an F1-score of 0.36 and an AUC of 0.66, while rule-based sentiment analyses yield better performances than random baselines.", "description": "The best reported performance is an F1-score of 0.36 and an AUC of 0.66. Nevertheless, there is room for improvement. The performance of the random baselines, with F1-scores of 0.18 (50%) and 0.13 (15%), indicates that the minimal performance on this task is rather low. The rule-based sentiment analyses yield better performances, at an F1-score of 0.20 for Pattern and 0.25 for Coosto. The Irrelevant category is most often classified with one of the other categories, while the Positive and Negative categories are the biggest confusables. The classifier is possibly identifying features that denote a stance, but struggles to distinguish positive from negative.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 6: Baseline performance of correctly predicting the label of tweets with a negative stance (for comparison, the best ML system is included; Pr = Precision, Re = Recall, AUC = Area under the Curve).', solve the subsequent question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhat is the AUC score of the Pattern rule-based sentiment analysis?\n" }, { "role": "assistant", "content": "The table lists the AUC scores for different methods. The AUC score for the Pattern rule-based sentiment analysis is 0.53. So, the answer is {\"answer\": \"0.53\"}." } ], "images": [ "scigen/train/1763.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Baseline performance of correctly predicting the label of tweets with a negative stance (for comparison, the best ML system is included; Pr = Precision, Re = Recall, AUC = Area under the Curve)." ], "reasoning": "The table lists the AUC scores for different methods. The AUC score for the Pattern rule-based sentiment analysis is 0.53.", "question": "What is the AUC score of the Pattern rule-based sentiment analysis?", "answer": "So, the answer is {\"answer\": \"0.53\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Coosto rule-based sentiment analysis has a higher F1-score than the Pattern rule-based sentiment analysis. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Coosto rule-based sentiment analysis has a higher F1-score than the Pattern rule-based sentiment analysis.\n" }, { "role": "assistant", "content": "The table shows that the F1-score for Coosto is 0.25, which is higher than the F1-score for Pattern, which is 0.20. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1763.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Baseline performance of correctly predicting the label of tweets with a negative stance (for comparison, the best ML system is included; Pr = Precision, Re = Recall, AUC = Area under the Curve)." ], "reasoning": "The table shows that the F1-score for Coosto is 0.25, which is higher than the F1-score for Pattern, which is 0.20.", "statement": "The Coosto rule-based sentiment analysis has a higher F1-score than the Pattern rule-based sentiment analysis.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Agreement scores for all four categorizations; mutual F-score is reported by category.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The percent agreement for Relevance and Subject is higher than for Stance and Sentiment, but the mutual F-scores show that categories annotated more frequently, like Relevant and Vaccine, yield higher scores.To also zoom in on the particular agreement by category, we calculated mutual F-scores for each of the categories. This metric is typically used to evaluate system performance by category on gold standard data, but could also be applied to annotation pairs by alternating the roles of the two annotators between classifier and ground truth. While both the Relevance and Subject categorizations are annotated at a percent agreement of 0.71 and 0.70, their agreement scores are only fair, at α=0.27 and α=0.29. The percent agreement on Stance and Sentiment, which carry more categories than the former two, is 0.54 for both. Their agreement scores are also fair, at α=0.35 and α=0.34. The mutual F-scores show marked differences in agreement by category, where the categories that were annotated most often typically yield a higher score. This holds for the Relevant category (0.81), the Vaccine category (0.79) and the Positive category (0.64). The Negative category yields a mutual F-score of 0.42, which is higher than the more frequently annotated categories Neutral (0.23) and Not clear (0.31). We found that these categories are often confused. After combining the annotations of the two, the stance agreement would be increased to α=0.43." } ], "images": [ "scigen/train/1764.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Agreement scores for all four categorizations; mutual F-score is reported by category." ], "reasoning": "The percent agreement for Relevance and Subject is higher than for Stance and Sentiment, but the mutual F-scores show that categories annotated more frequently, like Relevant and Vaccine, yield higher scores.", "description": "To also zoom in on the particular agreement by category, we calculated mutual F-scores for each of the categories. This metric is typically used to evaluate system performance by category on gold standard data, but could also be applied to annotation pairs by alternating the roles of the two annotators between classifier and ground truth. While both the Relevance and Subject categorizations are annotated at a percent agreement of 0.71 and 0.70, their agreement scores are only fair, at α=0.27 and α=0.29. The percent agreement on Stance and Sentiment, which carry more categories than the former two, is 0.54 for both. Their agreement scores are also fair, at α=0.35 and α=0.34. The mutual F-scores show marked differences in agreement by category, where the categories that were annotated most often typically yield a higher score. This holds for the Relevant category (0.81), the Vaccine category (0.79) and the Positive category (0.64). The Negative category yields a mutual F-score of 0.42, which is higher than the more frequently annotated categories Neutral (0.23) and Not clear (0.31). We found that these categories are often confused. After combining the annotations of the two, the stance agreement would be increased to α=0.43.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 3: Agreement scores for all four categorizations; mutual F-score is reported by category.\n\nQuestion: What is the mutual F-score for the category with the highest percent agreement?\n" }, { "role": "assistant", "content": "The category with the highest percent agreement is Relevance at 0.71. The mutual F-score for the Relevant category is 0.81. So, the answer is {\"answer\": \"0.81\"}." } ], "images": [ "scigen/train/1764.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Agreement scores for all four categorizations; mutual F-score is reported by category." ], "reasoning": "The category with the highest percent agreement is Relevance at 0.71. The mutual F-score for the Relevant category is 0.81.", "question": "What is the mutual F-score for the category with the highest percent agreement?", "answer": "So, the answer is {\"answer\": \"0.81\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Neutral category has a higher mutual F-score than the Positive category. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Neutral category has a higher mutual F-score than the Positive category.\n" }, { "role": "assistant", "content": "The mutual F-score for the Neutral category is 0.23, while the mutual F-score for the Positive category is 0.64. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1764.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Agreement scores for all four categorizations; mutual F-score is reported by category." ], "reasoning": "The mutual F-score for the Neutral category is 0.23, while the mutual F-score for the Positive category is 0.64. Therefore, the statement is incorrect.", "statement": "The Neutral category has a higher mutual F-score than the Positive category.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 7: Confusion table of the classification of tweets in the best ML setting (SVM trained on Polarity labeling with strict data appended by lax data). The vertical axes give gold standard labels, the horizontal axes give the classifier decisions. Numbers given in bold are accurate classifications.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The best performance has an F1-score of 0.36 and an AUC of 0.66, while random baselines have much lower F1-scores, indicating room for improvement.The best reported performance is an F1-score of 0.36 and an AUC of 0.66. Nevertheless, there is room for improvement. The performance of the random baselines, with F1-scores of 0.18 (50%) and 0.13 (15%), indicates that the minimal performance on this task is rather low. The rule-based sentiment analyses yield better performances, at an F1-score of 0.20 for Pattern and 0.25 for Coosto. The Irrelevant category is most often classified with one of the other categories, while the Positive and Negative categories are the biggest confusables. The classifier is possibly identifying features that denote a stance, but struggles to distinguish positive from negative." } ], "images": [ "scigen/train/1765.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Confusion table of the classification of tweets in the best ML setting (SVM trained on Polarity labeling with strict data appended by lax data). The vertical axes give gold standard labels, the horizontal axes give the classifier decisions. Numbers given in bold are accurate classifications." ], "reasoning": "The best performance has an F1-score of 0.36 and an AUC of 0.66, while random baselines have much lower F1-scores, indicating room for improvement.", "description": "The best reported performance is an F1-score of 0.36 and an AUC of 0.66. Nevertheless, there is room for improvement. The performance of the random baselines, with F1-scores of 0.18 (50%) and 0.13 (15%), indicates that the minimal performance on this task is rather low. The rule-based sentiment analyses yield better performances, at an F1-score of 0.20 for Pattern and 0.25 for Coosto. The Irrelevant category is most often classified with one of the other categories, while the Positive and Negative categories are the biggest confusables. The classifier is possibly identifying features that denote a stance, but struggles to distinguish positive from negative.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 7: Confusion table of the classification of tweets in the best ML setting (SVM trained on Polarity labeling with strict data appended by lax data). The vertical axes give gold standard labels, the horizontal axes give the classifier decisions. Numbers given in bold are accurate classifications.', solve this question and give detail solutions. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the F1-score of the best reported performance?\n" }, { "role": "assistant", "content": "The description states that the best reported performance has an F1-score of 0.36. So, the answer is {\"answer\": \"0.36\"}" } ], "images": [ "scigen/train/1765.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Confusion table of the classification of tweets in the best ML setting (SVM trained on Polarity labeling with strict data appended by lax data). The vertical axes give gold standard labels, the horizontal axes give the classifier decisions. Numbers given in bold are accurate classifications." ], "reasoning": "The description states that the best reported performance has an F1-score of 0.36.", "question": "What is the F1-score of the best reported performance?", "answer": "So, the answer is {\"answer\": \"0.36\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe classifier struggles to distinguish between Positive and Negative categories.\n" }, { "role": "assistant", "content": "The description mentions that the Positive and Negative categories are the biggest confusables, indicating that the classifier struggles to distinguish between them. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1765.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Confusion table of the classification of tweets in the best ML setting (SVM trained on Polarity labeling with strict data appended by lax data). The vertical axes give gold standard labels, the horizontal axes give the classifier decisions. Numbers given in bold are accurate classifications." ], "reasoning": "The description mentions that the Positive and Negative categories are the biggest confusables, indicating that the classifier struggles to distinguish between them.", "statement": "The classifier struggles to distinguish between Positive and Negative categories.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 9: Confusion table of the unlabeled Twitter messages predicted as ‘Negative’ or another category between Pattern and the best ML system.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The best ML system has a higher precision in predicting negative messages compared to Pattern, with the highest precision when both systems agree.To gain more insight into the behavior of both approaches, we applied them to 15,577 unlabeled tweets. Again, pattern accounts for the majority of negatively labeled messages, and the overlap is small. Two of the authors validated for a sample of 600 messages whether they actually manifested a negative attitude towards vaccination: 200 messages that were uniquely classified by the best ML system as Negative, 200 messages that were solely labeled as Negative by Pattern and 200 messages that were classified by both systems as Negative. This validation showed the same tendency as for the labeled data, with a higher precision of the best ML system in comparison to Pattern (33.5% versus 21% of the messages correctly predicted) and the highest precision when both systems predicted the negative class (36%)." } ], "images": [ "scigen/train/1766.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Confusion table of the unlabeled Twitter messages predicted as ‘Negative’ or another category between Pattern and the best ML system." ], "reasoning": "The best ML system has a higher precision in predicting negative messages compared to Pattern, with the highest precision when both systems agree.", "description": "To gain more insight into the behavior of both approaches, we applied them to 15,577 unlabeled tweets. Again, pattern accounts for the majority of negatively labeled messages, and the overlap is small. Two of the authors validated for a sample of 600 messages whether they actually manifested a negative attitude towards vaccination: 200 messages that were uniquely classified by the best ML system as Negative, 200 messages that were solely labeled as Negative by Pattern and 200 messages that were classified by both systems as Negative. This validation showed the same tendency as for the labeled data, with a higher precision of the best ML system in comparison to Pattern (33.5% versus 21% of the messages correctly predicted) and the highest precision when both systems predicted the negative class (36%).", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nHow many messages were classified as Negative by both the best ML system and Pattern?\nSolve the above question based on the table titled 'Table 9: Confusion table of the unlabeled Twitter messages predicted as ‘Negative’ or another category between Pattern and the best ML system.'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the number of messages classified by both systems. The intersection of 'Pattern Negative' and 'Best ML system Negative' is 1,015. So, the answer is {\"answer\": \"1,015\"}." } ], "images": [ "scigen/train/1766.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Confusion table of the unlabeled Twitter messages predicted as ‘Negative’ or another category between Pattern and the best ML system." ], "reasoning": "The table shows the number of messages classified by both systems. The intersection of 'Pattern Negative' and 'Best ML system Negative' is 1,015.", "question": "How many messages were classified as Negative by both the best ML system and Pattern?", "answer": "So, the answer is {\"answer\": \"1,015\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe best ML system classified more messages as Negative than Pattern.\n" }, { "role": "assistant", "content": "The table shows that the best ML system classified 2,225 + 1,015 = 3,240 messages as Negative, while Pattern classified 3,383 + 1,015 = 4,398 messages as Negative. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1766.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Confusion table of the unlabeled Twitter messages predicted as ‘Negative’ or another category between Pattern and the best ML system." ], "reasoning": "The table shows that the best ML system classified 2,225 + 1,015 = 3,240 messages as Negative, while Pattern classified 3,383 + 1,015 = 4,398 messages as Negative.", "statement": "The best ML system classified more messages as Negative than Pattern.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 11: Performance of the ensemble system on correctly predicting tweets labeled as ‘Negative’ (AUC = Area under the curve, Total = all tweets in the test set that are labeled as ‘Negative’, Predicted = the number of tweets that were classified as ‘Negative’ by the system, Correct = the number of tweets that were correctly classified as ‘Negative’.'.\n" }, { "role": "assistant", "content": "The ensemble system retrieved 210 out of 343 Negative tweets, resulting in a recall of 0.61, but it predicted 1,168 tweets as Negative, leading to a precision of 0.18.Of the 343 tweets in the test set that are labeled as Negative, 210 are retrieved by the ensemble system. The result is a recall of 0.61. The system does overshoot in its categorization of tweets as Negative: this category is predicted for 1,168 tweets (about 40% of total test set of 2,886 tweets). The result is a precision of 0.18. In comparison to lowering the prediction threshold of the ML system, the ensemble system thus yields a slightly worse trade-off between precision and recall." } ], "images": [ "scigen/train/1767.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Performance of the ensemble system on correctly predicting tweets labeled as ‘Negative’ (AUC = Area under the curve, Total = all tweets in the test set that are labeled as ‘Negative’, Predicted = the number of tweets that were classified as ‘Negative’ by the system, Correct = the number of tweets that were correctly classified as ‘Negative’." ], "reasoning": "The ensemble system retrieved 210 out of 343 Negative tweets, resulting in a recall of 0.61, but it predicted 1,168 tweets as Negative, leading to a precision of 0.18.", "description": "Of the 343 tweets in the test set that are labeled as Negative, 210 are retrieved by the ensemble system. The result is a recall of 0.61. The system does overshoot in its categorization of tweets as Negative: this category is predicted for 1,168 tweets (about 40% of total test set of 2,886 tweets). The result is a precision of 0.18. In comparison to lowering the prediction threshold of the ML system, the ensemble system thus yields a slightly worse trade-off between precision and recall.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: Table 11: Performance of the ensemble system on correctly predicting tweets labeled as ‘Negative’ (AUC = Area under the curve, Total = all tweets in the test set that are labeled as ‘Negative’, Predicted = the number of tweets that were classified as ‘Negative’ by the system, Correct = the number of tweets that were correctly classified as ‘Negative’.\nQuestion: What is the F1-score of the ensemble system for predicting Negative tweets?\n" }, { "role": "assistant", "content": "The table shows the F1-score of the ensemble system as 0.28, which is derived from the precision and recall values. So, the answer is {\"answer\": \"0.28\"}" } ], "images": [ "scigen/train/1767.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Performance of the ensemble system on correctly predicting tweets labeled as ‘Negative’ (AUC = Area under the curve, Total = all tweets in the test set that are labeled as ‘Negative’, Predicted = the number of tweets that were classified as ‘Negative’ by the system, Correct = the number of tweets that were correctly classified as ‘Negative’." ], "reasoning": "The table shows the F1-score of the ensemble system as 0.28, which is derived from the precision and recall values.", "question": "What is the F1-score of the ensemble system for predicting Negative tweets?", "answer": "So, the answer is {\"answer\": \"0.28\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe ensemble system has a recall of 0.61 for Negative tweets.\n" }, { "role": "assistant", "content": "The table indicates that the recall for Negative tweets by the ensemble system is indeed 0.61. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1767.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Performance of the ensemble system on correctly predicting tweets labeled as ‘Negative’ (AUC = Area under the curve, Total = all tweets in the test set that are labeled as ‘Negative’, Predicted = the number of tweets that were classified as ‘Negative’ by the system, Correct = the number of tweets that were correctly classified as ‘Negative’." ], "reasoning": "The table indicates that the recall for Negative tweets by the ensemble system is indeed 0.61.", "statement": "The ensemble system has a recall of 0.61 for Negative tweets.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Performance of our model versus other neural language models on the English Penn Treebank test set. PPL refers to perplexity (lower is better) and size refers to the approximate number of parameters in the model. KN-5 is a Kneser-Ney 5-gram language model which serves as a non-neural baseline. †For these models the authors did not explicitly state the number of parameters, and hence sizes shown here are estimates based on our understanding of their papers or private correspondence with the respective authors.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Despite having approximately 60% fewer parameters, the small model significantly outperforms other neural language models (NLMs) of similar size.Zaremba et al. 2014) , despite having approximately 60% fewer parameters. Our small model significantly outperforms other NLMs of similar size, even though it is penalized by the fact that the dataset already has OOV words replaced with (other models are purely word-level models)." } ], "images": [ "scigen/train/1768.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of our model versus other neural language models on the English Penn Treebank test set. PPL refers to perplexity (lower is better) and size refers to the approximate number of parameters in the model. KN-5 is a Kneser-Ney 5-gram language model which serves as a non-neural baseline. †For these models the authors did not explicitly state the number of parameters, and hence sizes shown here are estimates based on our understanding of their papers or private correspondence with the respective authors." ], "reasoning": "Despite having approximately 60% fewer parameters, the small model significantly outperforms other neural language models (NLMs) of similar size.", "description": "Zaremba et al. 2014) , despite having approximately 60% fewer parameters. Our small model significantly outperforms other NLMs of similar size, even though it is penalized by the fact that the dataset already has OOV words replaced with (other models are purely word-level models).", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: Performance of our model versus other neural language models on the English Penn Treebank test set. PPL refers to perplexity (lower is better) and size refers to the approximate number of parameters in the model. KN-5 is a Kneser-Ney 5-gram language model which serves as a non-neural baseline. †For these models the authors did not explicitly state the number of parameters, and hence sizes shown here are estimates based on our understanding of their papers or private correspondence with the respective authors.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model has the lowest perplexity (PPL) and what is its size?\n" }, { "role": "assistant", "content": "To find the model with the lowest perplexity, look at the PPL column and identify the smallest value. The LSTM-2† (Zaremba et al. 2014) model has the lowest PPL of 78.4. Its size is 52 million parameters. So, the answer is {\"answer\": \"LSTM-2† (Zaremba et al. 2014), 52 m\"}." } ], "images": [ "scigen/train/1768.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of our model versus other neural language models on the English Penn Treebank test set. PPL refers to perplexity (lower is better) and size refers to the approximate number of parameters in the model. KN-5 is a Kneser-Ney 5-gram language model which serves as a non-neural baseline. †For these models the authors did not explicitly state the number of parameters, and hence sizes shown here are estimates based on our understanding of their papers or private correspondence with the respective authors." ], "reasoning": "To find the model with the lowest perplexity, look at the PPL column and identify the smallest value. The LSTM-2† (Zaremba et al. 2014) model has the lowest PPL of 78.4. Its size is 52 million parameters.", "question": "Which model has the lowest perplexity (PPL) and what is its size?", "answer": "So, the answer is {\"answer\": \"LSTM-2† (Zaremba et al. 2014), 52 m\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The LSTM-Word-Small model has a perplexity (PPL) of 97.6 and a size of 5 million parameters. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe LSTM-Word-Small model has a perplexity (PPL) of 97.6 and a size of 5 million parameters.\n" }, { "role": "assistant", "content": "The table shows that the LSTM-Word-Small model has a PPL of 97.6 and a size of 5 million parameters, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1768.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of our model versus other neural language models on the English Penn Treebank test set. PPL refers to perplexity (lower is better) and size refers to the approximate number of parameters in the model. KN-5 is a Kneser-Ney 5-gram language model which serves as a non-neural baseline. †For these models the authors did not explicitly state the number of parameters, and hence sizes shown here are estimates based on our understanding of their papers or private correspondence with the respective authors." ], "reasoning": "The table shows that the LSTM-Word-Small model has a PPL of 97.6 and a size of 5 million parameters, which matches the statement.", "statement": "The LSTM-Word-Small model has a perplexity (PPL) of 97.6 and a size of 5 million parameters.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: Test set perplexities for Data-s. First two rows are from Botha2014b Botha2014b (except on Arabic where we trained our own KN-4 model) while the last six are from this paper. KN-4 is a Kneser-Ney 4-gram language model, and MLBL is the best performing morphological logbilinear model from Botha Botha2014b. Small/Large refer to model size (see Table 2), and Word/Morph/Char are models with words/morphemes/characters as inputs respectively.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The character models outperform their morphological counterparts, even though the morpheme models have more parameters due to the inclusion of word embeddings.The model’s performance on the English PTB is informative to the extent that it facilitates comparison against the large body of existing work. However, English is relatively simple from a morphological standpoint, and thus our next set of results (and arguably the main contribution of this paper) is focused on languages with richer morphology The character models also outperform their morphological counterparts (both MLBL and LSTM architectures), although improvements over the morphological LSTMs are more measured. Note that the morpheme models have strictly more parameters than the word models because word embeddings are used as part of the input." } ], "images": [ "scigen/train/1769.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Test set perplexities for Data-s. First two rows are from Botha2014b Botha2014b (except on Arabic where we trained our own KN-4 model) while the last six are from this paper. KN-4 is a Kneser-Ney 4-gram language model, and MLBL is the best performing morphological logbilinear model from Botha Botha2014b. Small/Large refer to model size (see Table 2), and Word/Morph/Char are models with words/morphemes/characters as inputs respectively." ], "reasoning": "The character models outperform their morphological counterparts, even though the morpheme models have more parameters due to the inclusion of word embeddings.", "description": "The model’s performance on the English PTB is informative to the extent that it facilitates comparison against the large body of existing work. However, English is relatively simple from a morphological standpoint, and thus our next set of results (and arguably the main contribution of this paper) is focused on languages with richer morphology The character models also outperform their morphological counterparts (both MLBL and LSTM architectures), although improvements over the morphological LSTMs are more measured. Note that the morpheme models have strictly more parameters than the word models because word embeddings are used as part of the input.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 4: Test set perplexities for Data-s. First two rows are from Botha2014b Botha2014b (except on Arabic where we trained our own KN-4 model) while the last six are from this paper. KN-4 is a Kneser-Ney 4-gram language model, and MLBL is the best performing morphological logbilinear model from Botha Botha2014b. Small/Large refer to model size (see Table 2), and Word/Morph/Char are models with words/morphemes/characters as inputs respectively.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}. \n\nQuestion: Which model has the lowest score in the Datas Ar column?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By examining the Datas Ar column, the lowest score is found in the Large Morph and Large Char rows, both with a score of 148. So, the answer is {\"answer\": \"Large Morph and Large Char\"}." } ], "images": [ "scigen/train/1769.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Test set perplexities for Data-s. First two rows are from Botha2014b Botha2014b (except on Arabic where we trained our own KN-4 model) while the last six are from this paper. KN-4 is a Kneser-Ney 4-gram language model, and MLBL is the best performing morphological logbilinear model from Botha Botha2014b. Small/Large refer to model size (see Table 2), and Word/Morph/Char are models with words/morphemes/characters as inputs respectively." ], "reasoning": "By examining the Datas Ar column, the lowest score is found in the Large Morph and Large Char rows, both with a score of 148.", "question": "Which model has the lowest score in the Datas Ar column?", "answer": "So, the answer is {\"answer\": \"Large Morph and Large Char\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Small Word model has a higher score in Datas Es than the Large Morph model.\n" }, { "role": "assistant", "content": "The Small Word model has a score of 212 in Datas Es, while the Large Morph model has a score of 177 in the same column, indicating that the statement is true. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1769.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Test set perplexities for Data-s. First two rows are from Botha2014b Botha2014b (except on Arabic where we trained our own KN-4 model) while the last six are from this paper. KN-4 is a Kneser-Ney 4-gram language model, and MLBL is the best performing morphological logbilinear model from Botha Botha2014b. Small/Large refer to model size (see Table 2), and Word/Morph/Char are models with words/morphemes/characters as inputs respectively." ], "reasoning": "The Small Word model has a score of 212 in Datas Es, while the Large Morph model has a score of 177 in the same column, indicating that the statement is true.", "statement": "The Small Word model has a higher score in Datas Es than the Large Morph model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: Test set perplexities on Data-l. First two rows are from Botha2014b Botha2014b, while the last three rows are from the small LSTM models described in the paper. KN-4 is a Kneser-Ney 4-gram language model, and MLBL is the best performing morphological logbilinear model from Botha2014b Botha2014b. Word/Morph/Char are models with words/morphemes/characters as inputs respectively.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The character models outperform word and morpheme models across Spanish, French, and English, with significant perplexity reductions observed even in English when V is large.The model’s performance on the English PTB is informative to the extent that it facilitates comparison against the large body of existing work. However, English is relatively simple from a morphological standpoint, and thus our next set of results (and arguably the main contribution of this paper) is focused on languages with richer morphology Interestingly we do not observe significant differences going from word to morpheme LSTMs on Spanish, French, and English. The character models again outperform the word/morpheme models. We also observe significant perplexity reductions even on English when V is large. We conclude this section by noting that we used the same architecture for all languages and did not perform any language-specific tuning of hyperparameters." } ], "images": [ "scigen/train/1770.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Test set perplexities on Data-l. First two rows are from Botha2014b Botha2014b, while the last three rows are from the small LSTM models described in the paper. KN-4 is a Kneser-Ney 4-gram language model, and MLBL is the best performing morphological logbilinear model from Botha2014b Botha2014b. Word/Morph/Char are models with words/morphemes/characters as inputs respectively." ], "reasoning": "The character models outperform word and morpheme models across Spanish, French, and English, with significant perplexity reductions observed even in English when V is large.", "description": "The model’s performance on the English PTB is informative to the extent that it facilitates comparison against the large body of existing work. However, English is relatively simple from a morphological standpoint, and thus our next set of results (and arguably the main contribution of this paper) is focused on languages with richer morphology Interestingly we do not observe significant differences going from word to morpheme LSTMs on Spanish, French, and English. The character models again outperform the word/morpheme models. We also observe significant perplexity reductions even on English when V is large. We conclude this section by noting that we used the same architecture for all languages and did not perform any language-specific tuning of hyperparameters.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 5: Test set perplexities on Data-l. First two rows are from Botha2014b Botha2014b, while the last three rows are from the small LSTM models described in the paper. KN-4 is a Kneser-Ney 4-gram language model, and MLBL is the best performing morphological logbilinear model from Botha2014b Botha2014b. Word/Morph/Char are models with words/morphemes/characters as inputs respectively.' in mind, break down and resolve the question below step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich model has the lowest perplexity score for English?\n" }, { "role": "assistant", "content": "To find the model with the lowest perplexity score for English, we look at the 'Data-I En' column and identify the lowest value, which is 216 for the Small Char model. So, the answer is {\"answer\": \"Small Char\"}" } ], "images": [ "scigen/train/1770.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Test set perplexities on Data-l. First two rows are from Botha2014b Botha2014b, while the last three rows are from the small LSTM models described in the paper. KN-4 is a Kneser-Ney 4-gram language model, and MLBL is the best performing morphological logbilinear model from Botha2014b Botha2014b. Word/Morph/Char are models with words/morphemes/characters as inputs respectively." ], "reasoning": "To find the model with the lowest perplexity score for English, we look at the 'Data-I En' column and identify the lowest value, which is 216 for the Small Char model.", "question": "Which model has the lowest perplexity score for English?", "answer": "So, the answer is {\"answer\": \"Small Char\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Small Word model has the highest perplexity score for Spanish.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the 'Data-I Es' column. The Small Word model has a perplexity score of 186, which is not the highest. The highest score in this column is 219 for Botha KN-4. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1770.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Test set perplexities on Data-l. First two rows are from Botha2014b Botha2014b, while the last three rows are from the small LSTM models described in the paper. KN-4 is a Kneser-Ney 4-gram language model, and MLBL is the best performing morphological logbilinear model from Botha2014b Botha2014b. Word/Morph/Char are models with words/morphemes/characters as inputs respectively." ], "reasoning": "To verify this statement, we look at the 'Data-I Es' column. The Small Word model has a perplexity score of 186, which is not the highest. The highest score in this column is 219 for Botha KN-4.", "statement": "The Small Word model has the highest perplexity score for Spanish.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 7: Perplexity on the Penn Treebank for small/large models trained with/without highway layers.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Training a model without highway layers decreases performance, and using an MLP layer also results in poor performance.One could also have a multilayer perceptron (MLP) over yk to model interactions between the character n-grams picked up by the filters, but we found that this resulted in worse performance. We train a model without any highway layers, and find that performance decreases significantly. As the difference in performance could be due to the decrease in model size, we also train a model that feeds yk (i.e. word representation from the CharCNN) through a one-layer multilayer perceptron (MLP) to use as input into the LSTM. We find that the MLP does poorly, although this could be due to optimization issues." } ], "images": [ "scigen/train/1771.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Perplexity on the Penn Treebank for small/large models trained with/without highway layers." ], "reasoning": "Training a model without highway layers decreases performance, and using an MLP layer also results in poor performance.", "description": "One could also have a multilayer perceptron (MLP) over yk to model interactions between the character n-grams picked up by the filters, but we found that this resulted in worse performance. We train a model without any highway layers, and find that performance decreases significantly. As the difference in performance could be due to the decrease in model size, we also train a model that feeds yk (i.e. word representation from the CharCNN) through a one-layer multilayer perceptron (MLP) to use as input into the LSTM. We find that the MLP does poorly, although this could be due to optimization issues.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 7: Perplexity on the Penn Treebank for small/large models trained with/without highway layers.\nQuestion: What is the performance of the LSTM-Char Small model with one MLP layer?\nSolve the above question based on the table information. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the performance of different models. For the LSTM-Char Small model with one MLP layer, the performance is 111.2. So, the answer is {\"answer\": \"111.2\"}." } ], "images": [ "scigen/train/1771.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Perplexity on the Penn Treebank for small/large models trained with/without highway layers." ], "reasoning": "The table shows the performance of different models. For the LSTM-Char Small model with one MLP layer, the performance is 111.2.", "question": "What is the performance of the LSTM-Char Small model with one MLP layer?", "answer": "So, the answer is {\"answer\": \"111.2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe LSTM-Char Large model with no highway layers performs better than the LSTM-Char Small model with two highway layers.\n" }, { "role": "assistant", "content": "The table shows that the LSTM-Char Large model with no highway layers has a performance of 84.6, while the LSTM-Char Small model with two highway layers has a performance of 90.1. Since a lower value indicates better performance, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1771.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Perplexity on the Penn Treebank for small/large models trained with/without highway layers." ], "reasoning": "The table shows that the LSTM-Char Large model with no highway layers has a performance of 84.6, while the LSTM-Char Small model with two highway layers has a performance of 90.1. Since a lower value indicates better performance, the statement is supported.", "statement": "The LSTM-Char Large model with no highway layers performs better than the LSTM-Char Small model with two highway layers.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Speech recognition results of various LMs, trained on WSJ portion of PTB dataset. “PPL” is the perplexity on PTB test set. “WER” is the rescoring word error rate on WSJ’92 test data. “#param” is the number of parameter numbers (in millions). “Training time” denotes the total training time for a LM. “Inference time” denotes the average time of rescoring the n-best list for each utterance.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The LSTM-2x1500 model has the lowest perplexity (PPL) and word error rate (WER) among the models, but it requires the longest training time.Speech recognition results on HKUST Chinese dataset. “Valid” is the character error rate (CER) on the valid set and “Test” is the CER on the test set. “+” denotes the log-linear interpolation with equal weights. As the vocabulary size is not large, all LSTM LMs use the standard softmax output layers to ensure the best model performance. The conclusions of this experiments are summarized as follows." } ], "images": [ "scigen/train/1772.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Speech recognition results of various LMs, trained on WSJ portion of PTB dataset. “PPL” is the perplexity on PTB test set. “WER” is the rescoring word error rate on WSJ’92 test data. “#param” is the number of parameter numbers (in millions). “Training time” denotes the total training time for a LM. “Inference time” denotes the average time of rescoring the n-best list for each utterance." ], "reasoning": "The LSTM-2x1500 model has the lowest perplexity (PPL) and word error rate (WER) among the models, but it requires the longest training time.", "description": "Speech recognition results on HKUST Chinese dataset. “Valid” is the character error rate (CER) on the valid set and “Test” is the CER on the test set. “+” denotes the log-linear interpolation with equal weights. As the vocabulary size is not large, all LSTM LMs use the standard softmax output layers to ensure the best model performance. The conclusions of this experiments are summarized as follows.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 1: Speech recognition results of various LMs, trained on WSJ portion of PTB dataset. “PPL” is the perplexity on PTB test set. “WER” is the rescoring word error rate on WSJ’92 test data. “#param” is the number of parameter numbers (in millions). “Training time” denotes the total training time for a LM. “Inference time” denotes the average time of rescoring the n-best list for each utterance.\nQuestion: Which model has the lowest word error rate (WER) and what is its training time?\n" }, { "role": "assistant", "content": "The table shows that the LSTM-2x1500 model has the lowest WER of 7.36%. The training time for this model is 1 day using 1 GPU. So, the answer is {\"answer\": \"1 day (1 GPU)\"}" } ], "images": [ "scigen/train/1772.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Speech recognition results of various LMs, trained on WSJ portion of PTB dataset. “PPL” is the perplexity on PTB test set. “WER” is the rescoring word error rate on WSJ’92 test data. “#param” is the number of parameter numbers (in millions). “Training time” denotes the total training time for a LM. “Inference time” denotes the average time of rescoring the n-best list for each utterance." ], "reasoning": "The table shows that the LSTM-2x1500 model has the lowest WER of 7.36%. The training time for this model is 1 day using 1 GPU.", "question": "Which model has the lowest word error rate (WER) and what is its training time?", "answer": "So, the answer is {\"answer\": \"1 day (1 GPU)\"}" }, { "messages": [ { "role": "user", "content": "The KN5 model has the highest perplexity (PPL) among all models. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe KN5 model has the highest perplexity (PPL) among all models.\n" }, { "role": "assistant", "content": "The table shows that the KN5 model has a perplexity (PPL) of 141.2, which is higher than all other models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1772.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Speech recognition results of various LMs, trained on WSJ portion of PTB dataset. “PPL” is the perplexity on PTB test set. “WER” is the rescoring word error rate on WSJ’92 test data. “#param” is the number of parameter numbers (in millions). “Training time” denotes the total training time for a LM. “Inference time” denotes the average time of rescoring the n-best list for each utterance." ], "reasoning": "The table shows that the KN5 model has a perplexity (PPL) of 141.2, which is higher than all other models listed.", "statement": "The KN5 model has the highest perplexity (PPL) among all models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Speech recognition results on HKUST Chinese dataset. “Valid” is the character error rate (CER) on the valid set and “Test” is the CER on the test set. “+” denotes the log-linear interpolation with equal weights. Other columns has the same meanings as in Table 1.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The neural TRF LM outperforms the classical 5-gram LM with a relative CER reduction of 2.7% and performs close to the LSTM-2×200 with 63% fewer parameters. It is also 5 times faster in rescoring sentences and achieves the lowest WER of 26.87% when combined with the 5-gram LM and the LSTM LM.First, our neural TRF LM outperforms the classical 5-gram LM (denoted by “KN5”) with relative CER reduction 2.7%, and performs close to “LSTM-2×200” (a LSTM LM with 2 hidden layers, 200 hidden units per layer and a standard softmax output layer) with 63% parameters. Second, even though the vocabulary in this experiment is small, containing only about 4000 characters, our neural TRF LM is still 5 times faster than LSMT LM in rescoring sentences. Third, the lowest WER 26.87% is achieved by combining the neural TRF LM with the 5-gram LM and the LSTM LM (denoted by “KN5+LSTM+neural TRF”). Finally, together with the previous experiment on PTB dataset, these results demonstrate the language independence in applying neural TRF LMs." } ], "images": [ "scigen/train/1773.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Speech recognition results on HKUST Chinese dataset. “Valid” is the character error rate (CER) on the valid set and “Test” is the CER on the test set. “+” denotes the log-linear interpolation with equal weights. Other columns has the same meanings as in Table 1." ], "reasoning": "The neural TRF LM outperforms the classical 5-gram LM with a relative CER reduction of 2.7% and performs close to the LSTM-2×200 with 63% fewer parameters. It is also 5 times faster in rescoring sentences and achieves the lowest WER of 26.87% when combined with the 5-gram LM and the LSTM LM.", "description": "First, our neural TRF LM outperforms the classical 5-gram LM (denoted by “KN5”) with relative CER reduction 2.7%, and performs close to “LSTM-2×200” (a LSTM LM with 2 hidden layers, 200 hidden units per layer and a standard softmax output layer) with 63% parameters. Second, even though the vocabulary in this experiment is small, containing only about 4000 characters, our neural TRF LM is still 5 times faster than LSMT LM in rescoring sentences. Third, the lowest WER 26.87% is achieved by combining the neural TRF LM with the 5-gram LM and the LSTM LM (denoted by “KN5+LSTM+neural TRF”). Finally, together with the previous experiment on PTB dataset, these results demonstrate the language independence in applying neural TRF LMs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2: Speech recognition results on HKUST Chinese dataset. “Valid” is the character error rate (CER) on the valid set and “Test” is the CER on the test set. “+” denotes the log-linear interpolation with equal weights. Other columns has the same meanings as in Table 1.', unfold the steps to solve the question given next. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which model combination achieves the lowest WER and what is the value?\n" }, { "role": "assistant", "content": "The table shows that the combination of KN5, LSTM, and neural TRF achieves the lowest WER of 26.87%. So, the answer is {\"answer\": \"KN5+LSTM+neural TRF with 26.87%\"}" } ], "images": [ "scigen/train/1773.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Speech recognition results on HKUST Chinese dataset. “Valid” is the character error rate (CER) on the valid set and “Test” is the CER on the test set. “+” denotes the log-linear interpolation with equal weights. Other columns has the same meanings as in Table 1." ], "reasoning": "The table shows that the combination of KN5, LSTM, and neural TRF achieves the lowest WER of 26.87%.", "question": "Which model combination achieves the lowest WER and what is the value?", "answer": "So, the answer is {\"answer\": \"KN5+LSTM+neural TRF with 26.87%\"}" }, { "messages": [ { "role": "user", "content": "The neural TRF LM has fewer parameters than the LSTM-2×200. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe neural TRF LM has fewer parameters than the LSTM-2×200.\n" }, { "role": "assistant", "content": "The table indicates that the neural TRF LM has 1.4 million parameters, while the LSTM-2×200 has 2.2 million parameters, confirming that the neural TRF LM has fewer parameters. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1773.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Speech recognition results on HKUST Chinese dataset. “Valid” is the character error rate (CER) on the valid set and “Test” is the CER on the test set. “+” denotes the log-linear interpolation with equal weights. Other columns has the same meanings as in Table 1." ], "reasoning": "The table indicates that the neural TRF LM has 1.4 million parameters, while the LSTM-2×200 has 2.2 million parameters, confirming that the neural TRF LM has fewer parameters.", "statement": "The neural TRF LM has fewer parameters than the LSTM-2×200.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Performance comparison between the baseline and the best system.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The AVSD Task received 31 system submissions from 9 teams, and the best system achieved higher BLEU-4, METEOR, CIDEr scores, and human rating compared to the baseline.The AVSD Task received 31 system submission from 9 teams. We built a baseline end-to-end dialog system that can generate answers in response to user questions about events in a video sequence as described in Hori et al. Our architecture is similar to the Hierarchical Recurrent Encoder in Das et al. The question, visual features, and the dialog history are fed into corresponding LSTM-based encoders to build up a context embedding, and then the outputs of the encoders are fed into an LSTM-based decoder to generate an answer. The history consists of encodings of QA pairs. We feed multimodal attention-based video features into the LSTM encoder instead of single static image features. The systems submitted deployed LSTM, BLSTM, and GRU with cross entropy as the objective function. Under this evaluation, the human rating for the original answers was 3.938." } ], "images": [ "scigen/train/1774.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance comparison between the baseline and the best system." ], "reasoning": "The AVSD Task received 31 system submissions from 9 teams, and the best system achieved higher BLEU-4, METEOR, CIDEr scores, and human rating compared to the baseline.", "description": "The AVSD Task received 31 system submission from 9 teams. We built a baseline end-to-end dialog system that can generate answers in response to user questions about events in a video sequence as described in Hori et al. Our architecture is similar to the Hierarchical Recurrent Encoder in Das et al. The question, visual features, and the dialog history are fed into corresponding LSTM-based encoders to build up a context embedding, and then the outputs of the encoders are fed into an LSTM-based decoder to generate an answer. The history consists of encodings of QA pairs. We feed multimodal attention-based video features into the LSTM encoder instead of single static image features. The systems submitted deployed LSTM, BLSTM, and GRU with cross entropy as the objective function. Under this evaluation, the human rating for the original answers was 3.938.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Performance comparison between the baseline and the best system.\nQuestion: What is the difference in human rating between the baseline and the best system?\nGive a detailed response to the above question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The human rating for the baseline system is 2.848, and for the best system, it is 3.491. The difference is calculated as 3.491 - 2.848. So, the answer is {\"answer\": \"0.643\"}." } ], "images": [ "scigen/train/1774.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance comparison between the baseline and the best system." ], "reasoning": "The human rating for the baseline system is 2.848, and for the best system, it is 3.491. The difference is calculated as 3.491 - 2.848.", "question": "What is the difference in human rating between the baseline and the best system?", "answer": "So, the answer is {\"answer\": \"0.643\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe best system achieved a higher METEOR score than the baseline system.\n" }, { "role": "assistant", "content": "The METEOR score for the baseline system is 0.215, and for the best system, it is 0.267. Since 0.267 is greater than 0.215, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1774.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance comparison between the baseline and the best system." ], "reasoning": "The METEOR score for the baseline system is 0.215, and for the best system, it is 0.267. Since 0.267 is greater than 0.215, the statement is supported.", "statement": "The best system achieved a higher METEOR score than the baseline system.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Comparison to the literature. We report the results of the model that performed the best on the validation set among all runs and parametrizations. The ratios used for parametrizations are written as subscripts.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Various Seq2Seq models and hybrid approaches are evaluated on the WikiLarge test set using SARI and FKGL metrics, with ACCESS achieving the highest SARI score.Dissimilar candidates are favored based on their Levenshtein distance to the source. Hybrid Deep semantics sentence representation fed to a monolingual MT system. Seq2Seq trained with reinforcement learning, combined with a lexical simplification model. Seq2Seq model based on the pointer-copy mechanism and trained via multi-task learning on the Entailment and Paraphrase Generation tasks. The second beam search hypothesis is selected during decoding; the hypothesis number is an hyper-parameter fine-tuned with SARI. Seq2Seq with a memory-augmented Neural Semantic Encoder, tuned with SARI. DMASS+DCSS Seq2Seq The database is also used to modify the loss and re-weight word probabilities to favor simpler words." } ], "images": [ "scigen/train/1775.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison to the literature. We report the results of the model that performed the best on the validation set among all runs and parametrizations. The ratios used for parametrizations are written as subscripts." ], "reasoning": "Various Seq2Seq models and hybrid approaches are evaluated on the WikiLarge test set using SARI and FKGL metrics, with ACCESS achieving the highest SARI score.", "description": "Dissimilar candidates are favored based on their Levenshtein distance to the source. Hybrid Deep semantics sentence representation fed to a monolingual MT system. Seq2Seq trained with reinforcement learning, combined with a lexical simplification model. Seq2Seq model based on the pointer-copy mechanism and trained via multi-task learning on the Entailment and Paraphrase Generation tasks. The second beam search hypothesis is selected during decoding; the hypothesis number is an hyper-parameter fine-tuned with SARI. Seq2Seq with a memory-augmented Neural Semantic Encoder, tuned with SARI. DMASS+DCSS Seq2Seq The database is also used to modify the loss and re-weight word probabilities to favor simpler words.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 2: Comparison to the literature. We report the results of the model that performed the best on the validation set among all runs and parametrizations. The ratios used for parametrizations are written as subscripts.', solve the following question step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model achieved the highest SARI score on the WikiLarge test set?\n" }, { "role": "assistant", "content": "By comparing the SARI scores of all models listed in the table, ACCESS achieved the highest SARI score of 41.87. So, the answer is {\"answer\": \"ACCESS\"}." } ], "images": [ "scigen/train/1775.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison to the literature. We report the results of the model that performed the best on the validation set among all runs and parametrizations. The ratios used for parametrizations are written as subscripts." ], "reasoning": "By comparing the SARI scores of all models listed in the table, ACCESS achieved the highest SARI score of 41.87.", "question": "Which model achieved the highest SARI score on the WikiLarge test set?", "answer": "So, the answer is {\"answer\": \"ACCESS\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Hybrid model achieved the lowest FKGL score on the WikiLarge test set., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Hybrid model achieved the lowest FKGL score on the WikiLarge test set.\n" }, { "role": "assistant", "content": "By comparing the FKGL scores of all models listed in the table, the Hybrid model has the lowest FKGL score of 4.56. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1775.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison to the literature. We report the results of the model that performed the best on the validation set among all runs and parametrizations. The ratios used for parametrizations are written as subscripts." ], "reasoning": "By comparing the FKGL scores of all models listed in the table, the Hybrid model has the lowest FKGL score of 4.56.", "statement": "The Hybrid model achieved the lowest FKGL score on the WikiLarge test set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'TABLE VIII: Comparison of pre-training approaches between recent work and the proposed approach on TIMIT [timit]. All the pre-training data are from LibriSpeech [librispeech], if not specified otherwise. All of the TERA models use the combined auxiliary objective of time + channel + mag alteration.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Pre-training TERA models on larger amounts of data improves performance, achieving the best WER of 14.5% with 960 hours of pre-training data, outperforming wav2vec and strong supervised baselines.We then explore how the mismatch of domains between pre-training and downstream tasks affects performance. For the exploration, we pre-train TERA with LibriSpeech [librispeech], and apply the resulting networks to the supervised TIMIT [timit] ASR task. The same Hybrid ASR setting and framework as described above for LibriSpeech ASR is used, except that we adopt a learning rate of 4e^{-4} and a batch size of 8. We also list the results of strong supervised systems [tdfilter, cnnphone, ligru]. All of the TERA models use a combination of time + channel + mag alteration as the auxiliary objectives, and are pre-trained with various amount of data. As expected, pre-training on larger amount of data gives performance benefit, and we achieved the best WER (14.5%) with 960 hours of pre-training data. We find that for TIMIT ASR as the downstream task, using either WS or FT is not helpful, and extracting speech representations from the last layer provides the best performance. Also, there is no significant gain when extracting features from a larger model medium. The reason is likely because there is not enough labeled data in TIMIT. On the other hand, our best model (14.5%) outperforms wav2vec [wav2vec] (14.7%) that uses both LibriSpeech [librispeech] and Wall Street Journal (WSJ) data for pre-training, as well as all the strong supervised baselines. By using only 100 hours of pre-training data, we even surpass the result of wav2vec that uses 960 hours for pre-training (15.2% v.s. 15.6%)." } ], "images": [ "scigen/train/1776.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VIII: Comparison of pre-training approaches between recent work and the proposed approach on TIMIT [timit]. All the pre-training data are from LibriSpeech [librispeech], if not specified otherwise. All of the TERA models use the combined auxiliary objective of time + channel + mag alteration." ], "reasoning": "Pre-training TERA models on larger amounts of data improves performance, achieving the best WER of 14.5% with 960 hours of pre-training data, outperforming wav2vec and strong supervised baselines.", "description": "We then explore how the mismatch of domains between pre-training and downstream tasks affects performance. For the exploration, we pre-train TERA with LibriSpeech [librispeech], and apply the resulting networks to the supervised TIMIT [timit] ASR task. The same Hybrid ASR setting and framework as described above for LibriSpeech ASR is used, except that we adopt a learning rate of 4e^{-4} and a batch size of 8. We also list the results of strong supervised systems [tdfilter, cnnphone, ligru]. All of the TERA models use a combination of time + channel + mag alteration as the auxiliary objectives, and are pre-trained with various amount of data. As expected, pre-training on larger amount of data gives performance benefit, and we achieved the best WER (14.5%) with 960 hours of pre-training data. We find that for TIMIT ASR as the downstream task, using either WS or FT is not helpful, and extracting speech representations from the last layer provides the best performance. Also, there is no significant gain when extracting features from a larger model medium. The reason is likely because there is not enough labeled data in TIMIT. On the other hand, our best model (14.5%) outperforms wav2vec [wav2vec] (14.7%) that uses both LibriSpeech [librispeech] and Wall Street Journal (WSJ) data for pre-training, as well as all the strong supervised baselines. By using only 100 hours of pre-training data, we even surpass the result of wav2vec that uses 960 hours for pre-training (15.2% v.s. 15.6%).", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'TABLE VIII: Comparison of pre-training approaches between recent work and the proposed approach on TIMIT [timit]. All the pre-training data are from LibriSpeech [librispeech], if not specified otherwise. All of the TERA models use the combined auxiliary objective of time + channel + mag alteration.', and tackle the question below in a sequential manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the PER for the liGRU + TERA-base model pre-trained with 960 hours of data?\n" }, { "role": "assistant", "content": "The table shows the PER for various models and pre-training hours. The liGRU + TERA-base model pre-trained with 960 hours of data has a PER of 14.5%. So, the answer is {\"answer\": \"14.5\"}." } ], "images": [ "scigen/train/1776.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VIII: Comparison of pre-training approaches between recent work and the proposed approach on TIMIT [timit]. All the pre-training data are from LibriSpeech [librispeech], if not specified otherwise. All of the TERA models use the combined auxiliary objective of time + channel + mag alteration." ], "reasoning": "The table shows the PER for various models and pre-training hours. The liGRU + TERA-base model pre-trained with 960 hours of data has a PER of 14.5%.", "question": "What is the PER for the liGRU + TERA-base model pre-trained with 960 hours of data?", "answer": "So, the answer is {\"answer\": \"14.5\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The liGRU + TERA-base model pre-trained with 100 hours of data has a lower PER than the wav2vec model pre-trained with 960 hours of data., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe liGRU + TERA-base model pre-trained with 100 hours of data has a lower PER than the wav2vec model pre-trained with 960 hours of data.\n" }, { "role": "assistant", "content": "The table shows that the liGRU + TERA-base model pre-trained with 100 hours of data has a PER of 15.2%, while the wav2vec model pre-trained with 960 hours of data has a PER of 15.6%. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1776.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VIII: Comparison of pre-training approaches between recent work and the proposed approach on TIMIT [timit]. All the pre-training data are from LibriSpeech [librispeech], if not specified otherwise. All of the TERA models use the combined auxiliary objective of time + channel + mag alteration." ], "reasoning": "The table shows that the liGRU + TERA-base model pre-trained with 100 hours of data has a PER of 15.2%, while the wav2vec model pre-trained with 960 hours of data has a PER of 15.6%. Therefore, the statement is supported.", "statement": "The liGRU + TERA-base model pre-trained with 100 hours of data has a lower PER than the wav2vec model pre-trained with 960 hours of data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'TABLE I: Frame-wise phone classification results on LibriSpeech. The training and testing sets are all identical to the ones used in the CPC [cpc] literature. We present testing set accuracy (%) from Linear classifiers and 1 Hidden layer classifiers, measured across various auxiliary objectives and amount of pre-training data.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "TERA and CPC representations outperform surface features like MFCC, FBANK, and fMLLR in both linear and 1 Hidden classifiers, with fMLLR achieving the highest performance among surface features.Comparison of different network depth and size. We show results of TERA pre-trained with different combinations of objectives, and with different amount of pre-training data. TERA here is used for representation extraction (from the last layer), i.e., TERA parameters were frozen when adapting classifiers to phoneme classification. The classification accuracy is compared with four different baselines: MFCC, FBANK, fMLLR features, and CPC representations [cpc]. For the Linear classifier, FBANK outperforms MFCC, and fMLLR outperforms FBANK in terms of linear separability. For the 1 Hidden classifier, fMLLR again achieves the highest performance among surface features. On the other hand, as expected TERA and CPC representations outperform all surface features for both types of classifiers." } ], "images": [ "scigen/train/1777.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Frame-wise phone classification results on LibriSpeech. The training and testing sets are all identical to the ones used in the CPC [cpc] literature. We present testing set accuracy (%) from Linear classifiers and 1 Hidden layer classifiers, measured across various auxiliary objectives and amount of pre-training data." ], "reasoning": "TERA and CPC representations outperform surface features like MFCC, FBANK, and fMLLR in both linear and 1 Hidden classifiers, with fMLLR achieving the highest performance among surface features.", "description": "Comparison of different network depth and size. We show results of TERA pre-trained with different combinations of objectives, and with different amount of pre-training data. TERA here is used for representation extraction (from the last layer), i.e., TERA parameters were frozen when adapting classifiers to phoneme classification. The classification accuracy is compared with four different baselines: MFCC, FBANK, fMLLR features, and CPC representations [cpc]. For the Linear classifier, FBANK outperforms MFCC, and fMLLR outperforms FBANK in terms of linear separability. For the 1 Hidden classifier, fMLLR again achieves the highest performance among surface features. On the other hand, as expected TERA and CPC representations outperform all surface features for both types of classifiers.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'TABLE I: Frame-wise phone classification results on LibriSpeech. The training and testing sets are all identical to the ones used in the CPC [cpc] literature. We present testing set accuracy (%) from Linear classifiers and 1 Hidden layer classifiers, measured across various auxiliary objectives and amount of pre-training data.' and proceed to solve the following question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich representation achieves the highest accuracy with the 960 hr 1 Hidden classifier?\n" }, { "role": "assistant", "content": "By examining the table, the highest accuracy with the 960 hr 1 Hidden classifier is achieved by TERA-base: time (Mockingjay) with an accuracy of 79.1. So, the answer is {\"answer\": \"TERA-base: time (Mockingjay)\"}" } ], "images": [ "scigen/train/1777.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Frame-wise phone classification results on LibriSpeech. The training and testing sets are all identical to the ones used in the CPC [cpc] literature. We present testing set accuracy (%) from Linear classifiers and 1 Hidden layer classifiers, measured across various auxiliary objectives and amount of pre-training data." ], "reasoning": "By examining the table, the highest accuracy with the 960 hr 1 Hidden classifier is achieved by TERA-base: time (Mockingjay) with an accuracy of 79.1.", "question": "Which representation achieves the highest accuracy with the 960 hr 1 Hidden classifier?", "answer": "So, the answer is {\"answer\": \"TERA-base: time (Mockingjay)\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nTERA-base: time + channel achieves the highest accuracy with the 100 hr 1 Hidden classifier.\n" }, { "role": "assistant", "content": "By examining the table, TERA-base: time + channel achieves an accuracy of 77.4 with the 100 hr 1 Hidden classifier, which is the highest among all representations for this classifier. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1777.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Frame-wise phone classification results on LibriSpeech. The training and testing sets are all identical to the ones used in the CPC [cpc] literature. We present testing set accuracy (%) from Linear classifiers and 1 Hidden layer classifiers, measured across various auxiliary objectives and amount of pre-training data." ], "reasoning": "By examining the table, TERA-base: time + channel achieves an accuracy of 77.4 with the 100 hr 1 Hidden classifier, which is the highest among all representations for this classifier.", "statement": "TERA-base: time + channel achieves the highest accuracy with the 100 hr 1 Hidden classifier.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'TABLE II: Comparison of different network depth and size. LibriSpeech [librispeech] frame-wise phone classification results are presented, with the same phone set, train and test set as in Table I.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "As the size of TERA models increases, their performance improves with more pre-training data, but with 960 hours of data, all TERA models perform similarly.Furthermore, we investigate how the network depth (number of layers) affects the performance of TERA in phoneme classification. Classification accuracy of four TERA models: base, medium, large, and xlarge, is presented together with CPC [cpc] and Modified CPC [modified_cpc]. The scores of CPC and Modified CPC are from the original literature. All of the TERA models use the full objective of time + channel + mag. For the case where models are pre-trained with 100 hours of data, all the TERA models outperformed CPC. When 360 hours of data is used for pre-training, we report Concat instead of 1 Hidden to compare TERA with Modified CPC. With 360 hours of data, the base (66.4% / 68.3%) and medium (66.6% / 68.9%) achieved comparable result with Modified CPC (- / 68.9%) that has a network depth of 6 layers, and as the size of TERA increases, TERA representations achieve better separability and yield better performance than Modified CPC. In the case where 100/360 hours of pre-training data is used, a larger model tends to show performance benefit. As for the case where 960 hours of pre-training data is used, model size tends to be irrelevant and all the TERA models achieved similar performance. On the other hand, we also observe a correlation between performance and data size. As we increase the amount of pre-training data, TERA usually yields better performance." } ], "images": [ "scigen/train/1778.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Comparison of different network depth and size. LibriSpeech [librispeech] frame-wise phone classification results are presented, with the same phone set, train and test set as in Table I." ], "reasoning": "As the size of TERA models increases, their performance improves with more pre-training data, but with 960 hours of data, all TERA models perform similarly.", "description": "Furthermore, we investigate how the network depth (number of layers) affects the performance of TERA in phoneme classification. Classification accuracy of four TERA models: base, medium, large, and xlarge, is presented together with CPC [cpc] and Modified CPC [modified_cpc]. The scores of CPC and Modified CPC are from the original literature. All of the TERA models use the full objective of time + channel + mag. For the case where models are pre-trained with 100 hours of data, all the TERA models outperformed CPC. When 360 hours of data is used for pre-training, we report Concat instead of 1 Hidden to compare TERA with Modified CPC. With 360 hours of data, the base (66.4% / 68.3%) and medium (66.6% / 68.9%) achieved comparable result with Modified CPC (- / 68.9%) that has a network depth of 6 layers, and as the size of TERA increases, TERA representations achieve better separability and yield better performance than Modified CPC. In the case where 100/360 hours of pre-training data is used, a larger model tends to show performance benefit. As for the case where 960 hours of pre-training data is used, model size tends to be irrelevant and all the TERA models achieved similar performance. On the other hand, we also observe a correlation between performance and data size. As we increase the amount of pre-training data, TERA usually yields better performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'TABLE II: Comparison of different network depth and size. LibriSpeech [librispeech] frame-wise phone classification results are presented, with the same phone set, train and test set as in Table I.', answer the question below by showing each progressive step toward the answer. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich TERA model achieved the highest accuracy with 100 hours of pre-training data using 1 Hidden layer?\n" }, { "role": "assistant", "content": "By examining the column for 100 hr 1 Hidden, the TERA-large model achieved the highest accuracy of 77.7%. So, the answer is {\"answer\": \"TERA-large\"}." } ], "images": [ "scigen/train/1778.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Comparison of different network depth and size. LibriSpeech [librispeech] frame-wise phone classification results are presented, with the same phone set, train and test set as in Table I." ], "reasoning": "By examining the column for 100 hr 1 Hidden, the TERA-large model achieved the highest accuracy of 77.7%.", "question": "Which TERA model achieved the highest accuracy with 100 hours of pre-training data using 1 Hidden layer?", "answer": "So, the answer is {\"answer\": \"TERA-large\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis TERA-xlarge achieved the highest accuracy with 960 hours of pre-training data using 1 Hidden layer. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nTERA-xlarge achieved the highest accuracy with 960 hours of pre-training data using 1 Hidden layer.\n" }, { "role": "assistant", "content": "By examining the column for 960 hr 1 Hidden, the TERA-base model achieved the highest accuracy of 78.9%, not TERA-xlarge. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1778.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Comparison of different network depth and size. LibriSpeech [librispeech] frame-wise phone classification results are presented, with the same phone set, train and test set as in Table I." ], "reasoning": "By examining the column for 960 hr 1 Hidden, the TERA-base model achieved the highest accuracy of 78.9%, not TERA-xlarge.", "statement": "TERA-xlarge achieved the highest accuracy with 960 hours of pre-training data using 1 Hidden layer.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'TABLE III: Speaker linear classification results on LibriSpeech. We use identical training and testing sets as in the CPC [cpc] and AALBERT [audioalbert] literature. We show both frame-wise linear classification accuracy (Frame, %), and utterance-wise linear classification accuracy (Utterance, %) where we average representations over time.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "TERA pre-trained with time + channel + mag objectives and 100 hours of data achieves the highest accuracy for both Frame and Utterance tasks, outperforming other baselines and configurations.We show results of TERA pre-trained with different combinations of objectives, and with different amount of pre-training data. Here TERA is used as a representation extractor (from the last layer) where parameters are frozen during adapting to speaker classification tasks. We compare the classification accuracy with five baselines: MFCC features, fMLLR features, CPC representations [cpc], AALBERT representations [audioalbert], and Mockingjay representations [audioalbert, mockingjay]. MFCC features perform poorly for the task of Frame since the MFCC extraction process eliminates most of the pitch information from speech, and only reserves partial tone characteristics. FBANK features perform even worse than MFCC for Frame. It shows that the speaker information encoded in FBANK is not linearly accessible. The fMLLR feature extraction process eliminates nearly all of the speaker information and yields an accuracy of 0.4% for Frame. The accuracy is similar to a random guess among the 251 speakers (1/251\\approx 0.003984). On the other hand, when evaluating fMLLR and FBANK with Utterance, a slight improvement is observed, but the performances are still terrible, while MFCC does not improve for Utterance." } ], "images": [ "scigen/train/1779.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Speaker linear classification results on LibriSpeech. We use identical training and testing sets as in the CPC [cpc] and AALBERT [audioalbert] literature. We show both frame-wise linear classification accuracy (Frame, %), and utterance-wise linear classification accuracy (Utterance, %) where we average representations over time." ], "reasoning": "TERA pre-trained with time + channel + mag objectives and 100 hours of data achieves the highest accuracy for both Frame and Utterance tasks, outperforming other baselines and configurations.", "description": "We show results of TERA pre-trained with different combinations of objectives, and with different amount of pre-training data. Here TERA is used as a representation extractor (from the last layer) where parameters are frozen during adapting to speaker classification tasks. We compare the classification accuracy with five baselines: MFCC features, fMLLR features, CPC representations [cpc], AALBERT representations [audioalbert], and Mockingjay representations [audioalbert, mockingjay]. MFCC features perform poorly for the task of Frame since the MFCC extraction process eliminates most of the pitch information from speech, and only reserves partial tone characteristics. FBANK features perform even worse than MFCC for Frame. It shows that the speaker information encoded in FBANK is not linearly accessible. The fMLLR feature extraction process eliminates nearly all of the speaker information and yields an accuracy of 0.4% for Frame. The accuracy is similar to a random guess among the 251 speakers (1/251\\approx 0.003984). On the other hand, when evaluating fMLLR and FBANK with Utterance, a slight improvement is observed, but the performances are still terrible, while MFCC does not improve for Utterance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'TABLE III: Speaker linear classification results on LibriSpeech. We use identical training and testing sets as in the CPC [cpc] and AALBERT [audioalbert] literature. We show both frame-wise linear classification accuracy (Frame, %), and utterance-wise linear classification accuracy (Utterance, %) where we average representations over time.' to sequentially solve the question presented below. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich representation achieves the highest accuracy for the 100 hr Frame task?\n" }, { "role": "assistant", "content": "To find the highest accuracy for the 100 hr Frame task, compare the accuracy values in the '100 hr Frame' column. The highest value is 98.9, achieved by TERA-base: time + channel + mag. So, the answer is {\"answer\": \"TERA-base: time + channel + mag\"}." } ], "images": [ "scigen/train/1779.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Speaker linear classification results on LibriSpeech. We use identical training and testing sets as in the CPC [cpc] and AALBERT [audioalbert] literature. We show both frame-wise linear classification accuracy (Frame, %), and utterance-wise linear classification accuracy (Utterance, %) where we average representations over time." ], "reasoning": "To find the highest accuracy for the 100 hr Frame task, compare the accuracy values in the '100 hr Frame' column. The highest value is 98.9, achieved by TERA-base: time + channel + mag.", "question": "Which representation achieves the highest accuracy for the 100 hr Frame task?", "answer": "So, the answer is {\"answer\": \"TERA-base: time + channel + mag\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis TERA-base: time + channel + mag achieves the highest accuracy for the 960 hr Utterance task. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nTERA-base: time + channel + mag achieves the highest accuracy for the 960 hr Utterance task.\n" }, { "role": "assistant", "content": "To verify this statement, compare the accuracy values in the '960 hr Utterance' column. The highest value is 99.8, achieved by both TERA-base: time + channel and TERA-base: time + channel + mag. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1779.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Speaker linear classification results on LibriSpeech. We use identical training and testing sets as in the CPC [cpc] and AALBERT [audioalbert] literature. We show both frame-wise linear classification accuracy (Frame, %), and utterance-wise linear classification accuracy (Utterance, %) where we average representations over time." ], "reasoning": "To verify this statement, compare the accuracy values in the '960 hr Utterance' column. The highest value is 99.8, achieved by both TERA-base: time + channel and TERA-base: time + channel + mag.", "statement": "TERA-base: time + channel + mag achieves the highest accuracy for the 960 hr Utterance task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'TABLE IV: We investigate how different amount of pre-training data affects performance by reporting ASR results on the LibriSpeech [librispeech] test-clean subset. For all the models, we limit the amount of labeled data to 100 hours to demonstrate the effect of pre-training.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Using all three auxiliary objectives (time + channel + mag) with 100 hours of pre-training data achieves similar performance to using only the time objective with 960 hours of pre-training data, indicating that the combination of objectives can effectively increase data diversity and improve performance.We further apply TERA to speech recognition tasks. Similar to the previous experiments, we show the results of TERA pre-trained with different combinations of objectives, and with different amount of pre-training data. Here we use the Kaldi [kaldi] fglarge lattice for LM rescoring. The LM rescored WER is denoted as Rescore. All ASR models are trained with 100 hours of labels from the train-clean-100 subset. TERA here is used for representation extraction (from the last layer), i.e., TERA parameters were frozen when adopting the DNN/HMM framework to speech recognition, and TERA representations are fed to the liGRU model as input. Applying all of the three objectives yields the best performance, whereas applying the channel objective on top of time is not always better than time objective only. This is because the channel objective mainly helps encode speaker information. However, utilizing all of the three auxiliary objectives can eliminate this disadvantage. Using time + channel + mag with 100 hours of pre-training data achieved similar performance (8.32% / 6.01%) to using time with 960 hours (8.31% / 5.99%). We see that 100 hours with three alteration objectives have a similar effect as 960 hours of data. This concretes the fact that using all three auxiliary objectives is potentially increasing the amount of pre-training data (or diversity of data) and eventually leads to improvement. As for the case of 960 hours of pre-training data, using different combinations of auxiliary objectives does not improve performance." } ], "images": [ "scigen/train/1780.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: We investigate how different amount of pre-training data affects performance by reporting ASR results on the LibriSpeech [librispeech] test-clean subset. For all the models, we limit the amount of labeled data to 100 hours to demonstrate the effect of pre-training." ], "reasoning": "Using all three auxiliary objectives (time + channel + mag) with 100 hours of pre-training data achieves similar performance to using only the time objective with 960 hours of pre-training data, indicating that the combination of objectives can effectively increase data diversity and improve performance.", "description": "We further apply TERA to speech recognition tasks. Similar to the previous experiments, we show the results of TERA pre-trained with different combinations of objectives, and with different amount of pre-training data. Here we use the Kaldi [kaldi] fglarge lattice for LM rescoring. The LM rescored WER is denoted as Rescore. All ASR models are trained with 100 hours of labels from the train-clean-100 subset. TERA here is used for representation extraction (from the last layer), i.e., TERA parameters were frozen when adopting the DNN/HMM framework to speech recognition, and TERA representations are fed to the liGRU model as input. Applying all of the three objectives yields the best performance, whereas applying the channel objective on top of time is not always better than time objective only. This is because the channel objective mainly helps encode speaker information. However, utilizing all of the three auxiliary objectives can eliminate this disadvantage. Using time + channel + mag with 100 hours of pre-training data achieved similar performance (8.32% / 6.01%) to using time with 960 hours (8.31% / 5.99%). We see that 100 hours with three alteration objectives have a similar effect as 960 hours of data. This concretes the fact that using all three auxiliary objectives is potentially increasing the amount of pre-training data (or diversity of data) and eventually leads to improvement. As for the case of 960 hours of pre-training data, using different combinations of auxiliary objectives does not improve performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'TABLE IV: We investigate how different amount of pre-training data affects performance by reporting ASR results on the LibriSpeech [librispeech] test-clean subset. For all the models, we limit the amount of labeled data to 100 hours to demonstrate the effect of pre-training.', solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the WER for liGRU + TERA-base: time + channel + mag with 100 hours of pre-training data after rescoring?\n" }, { "role": "assistant", "content": "The table shows the WER for different models and pre-training data amounts. For liGRU + TERA-base: time + channel + mag with 100 hours of pre-training data, the WER after rescoring is 6.01. So, the answer is {\"answer\": \"6.01\"}." } ], "images": [ "scigen/train/1780.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: We investigate how different amount of pre-training data affects performance by reporting ASR results on the LibriSpeech [librispeech] test-clean subset. For all the models, we limit the amount of labeled data to 100 hours to demonstrate the effect of pre-training." ], "reasoning": "The table shows the WER for different models and pre-training data amounts. For liGRU + TERA-base: time + channel + mag with 100 hours of pre-training data, the WER after rescoring is 6.01.", "question": "What is the WER for liGRU + TERA-base: time + channel + mag with 100 hours of pre-training data after rescoring?", "answer": "So, the answer is {\"answer\": \"6.01\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Using all three auxiliary objectives with 100 hours of pre-training data achieves a WER of 8.32. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nUsing all three auxiliary objectives with 100 hours of pre-training data achieves a WER of 8.32.\n" }, { "role": "assistant", "content": "The table shows that for liGRU + TERA-base: time + channel + mag with 100 hours of pre-training data, the WER is indeed 8.32. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1780.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: We investigate how different amount of pre-training data affects performance by reporting ASR results on the LibriSpeech [librispeech] test-clean subset. For all the models, we limit the amount of labeled data to 100 hours to demonstrate the effect of pre-training." ], "reasoning": "The table shows that for liGRU + TERA-base: time + channel + mag with 100 hours of pre-training data, the WER is indeed 8.32.", "statement": "Using all three auxiliary objectives with 100 hours of pre-training data achieves a WER of 8.32.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'TABLE VII: Ablation Study. We use phone classification, speaker recognition and ASR results to study the effect of different auxiliary objectives, including comparison of pre-training with unidirectional or bidirectional context, channel objective that helps learn speaker identity (highlighted in grey), and pre-training with different acoustic features other than fMLLR (MFCC and FBANK). All of the models are pre-trained on the LibriSpeech train-clean-100 subset, and the numbers are testing results in percentage.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The table evaluates the performance of various TERA pre-training models on different downstream tasks using the last layer's output, with all results reported as percentages on the test set.We perform ablation study to better understand the performance of multi-target auxiliary objectives adopted in the TERA pre-training. For each downstream task, the output of the last layer of TERA is used as the extracted representation if not specified otherwise (no WS or FT). All the reported numbers are percentage and evaluated on test set. The TERA models investigated here are the base architectures pre-trained with 100 hours of LibriSpeech." } ], "images": [ "scigen/train/1781.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VII: Ablation Study. We use phone classification, speaker recognition and ASR results to study the effect of different auxiliary objectives, including comparison of pre-training with unidirectional or bidirectional context, channel objective that helps learn speaker identity (highlighted in grey), and pre-training with different acoustic features other than fMLLR (MFCC and FBANK). All of the models are pre-trained on the LibriSpeech train-clean-100 subset, and the numbers are testing results in percentage." ], "reasoning": "The table evaluates the performance of various TERA pre-training models on different downstream tasks using the last layer's output, with all results reported as percentages on the test set.", "description": "We perform ablation study to better understand the performance of multi-target auxiliary objectives adopted in the TERA pre-training. For each downstream task, the output of the last layer of TERA is used as the extracted representation if not specified otherwise (no WS or FT). All the reported numbers are percentage and evaluated on test set. The TERA models investigated here are the base architectures pre-trained with 100 hours of LibriSpeech.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nTable title: TABLE VII: Ablation Study. We use phone classification, speaker recognition and ASR results to study the effect of different auxiliary objectives, including comparison of pre-training with unidirectional or bidirectional context, channel objective that helps learn speaker identity (highlighted in grey), and pre-training with different acoustic features other than fMLLR (MFCC and FBANK). All of the models are pre-trained on the LibriSpeech train-clean-100 subset, and the numbers are testing results in percentage.\n\nQuestion: Which TERA model configuration achieves the highest score in Phone Classification 1 Hidden?\n" }, { "role": "assistant", "content": "To find the highest score in Phone Classification 1 Hidden, look at the column 'Phone Classification 1 Hidden' and identify the highest value, which is 91.1% achieved by the TERA-base: time + channel + mag (FT) model. So, the answer is {\"answer\": \"TERA-base: time + channel + mag (FT)\"}." } ], "images": [ "scigen/train/1781.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VII: Ablation Study. We use phone classification, speaker recognition and ASR results to study the effect of different auxiliary objectives, including comparison of pre-training with unidirectional or bidirectional context, channel objective that helps learn speaker identity (highlighted in grey), and pre-training with different acoustic features other than fMLLR (MFCC and FBANK). All of the models are pre-trained on the LibriSpeech train-clean-100 subset, and the numbers are testing results in percentage." ], "reasoning": "To find the highest score in Phone Classification 1 Hidden, look at the column 'Phone Classification 1 Hidden' and identify the highest value, which is 91.1% achieved by the TERA-base: time + channel + mag (FT) model.", "question": "Which TERA model configuration achieves the highest score in Phone Classification 1 Hidden?", "answer": "So, the answer is {\"answer\": \"TERA-base: time + channel + mag (FT)\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The TERA-base: time + channel + mag (FT) model achieves the highest score in Speaker Recognition Utterance. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe TERA-base: time + channel + mag (FT) model achieves the highest score in Speaker Recognition Utterance.\n" }, { "role": "assistant", "content": "To verify this statement, look at the column 'Speaker Recognition Utterance' and identify the highest value, which is 99.9% achieved by the TERA-base: time + channel + mag (FBANK) model, not the TERA-base: time + channel + mag (FT) model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1781.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VII: Ablation Study. We use phone classification, speaker recognition and ASR results to study the effect of different auxiliary objectives, including comparison of pre-training with unidirectional or bidirectional context, channel objective that helps learn speaker identity (highlighted in grey), and pre-training with different acoustic features other than fMLLR (MFCC and FBANK). All of the models are pre-trained on the LibriSpeech train-clean-100 subset, and the numbers are testing results in percentage." ], "reasoning": "To verify this statement, look at the column 'Speaker Recognition Utterance' and identify the highest value, which is 99.9% achieved by the TERA-base: time + channel + mag (FBANK) model, not the TERA-base: time + channel + mag (FT) model.", "statement": "The TERA-base: time + channel + mag (FT) model achieves the highest score in Speaker Recognition Utterance.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Mean Square Error (×10−2) of linear regression trained on performance monitoring techniques. All results are reported on test sets.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Entropy gives the lowest MSEs for WSJ and Aurora4, while the RNN predictor achieves the best performance in CHiME4-Sim, CHiME4-Real, and Dirha-Real. MCD measure performs best in unseen simulated domains with reverberant conditions.It is worth noting that decoder features work more effectively than attention probabilities in all cases for predicting CERs. Entropy gives the lowest MSEs for WSJ and Aurora4, domains which have been seen in ASR training. The RNN predictor achieves best performance in CHiME4-Sim (not surprising, since this domain was seen in RNN predictor training) as well as real recordings from CHiME4-Real and Dirha-Real, domains not seen in any training stage at all. MCD measure performs the best at the two unseen simulated domains with reverberant conditions. Overall, entropy, MCD, and RNN prediction all provide reasonably good CER predictions, with average prediction errors (square root of MSE) of 10.1%, 8.8% and 10.1%, respectively, where MCD outperforms the rest of PM measures across all test set." } ], "images": [ "scigen/train/1782.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Mean Square Error (×10−2) of linear regression trained on performance monitoring techniques. All results are reported on test sets." ], "reasoning": "Entropy gives the lowest MSEs for WSJ and Aurora4, while the RNN predictor achieves the best performance in CHiME4-Sim, CHiME4-Real, and Dirha-Real. MCD measure performs best in unseen simulated domains with reverberant conditions.", "description": "It is worth noting that decoder features work more effectively than attention probabilities in all cases for predicting CERs. Entropy gives the lowest MSEs for WSJ and Aurora4, domains which have been seen in ASR training. The RNN predictor achieves best performance in CHiME4-Sim (not surprising, since this domain was seen in RNN predictor training) as well as real recordings from CHiME4-Real and Dirha-Real, domains not seen in any training stage at all. MCD measure performs the best at the two unseen simulated domains with reverberant conditions. Overall, entropy, MCD, and RNN prediction all provide reasonably good CER predictions, with average prediction errors (square root of MSE) of 10.1%, 8.8% and 10.1%, respectively, where MCD outperforms the rest of PM measures across all test set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: Mean Square Error (×10−2) of linear regression trained on performance monitoring techniques. All results are reported on test sets.' and methodically tackle the question that follows. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich measure achieves the lowest MSE for the WSJ dataset?\n" }, { "role": "assistant", "content": "The table shows the MSE values for different measures across various datasets. For the WSJ dataset, the Entropy Dec measure has the lowest MSE value of 0.17. So, the answer is {\"answer\": \"Entropy Dec\"}." } ], "images": [ "scigen/train/1782.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Mean Square Error (×10−2) of linear regression trained on performance monitoring techniques. All results are reported on test sets." ], "reasoning": "The table shows the MSE values for different measures across various datasets. For the WSJ dataset, the Entropy Dec measure has the lowest MSE value of 0.17.", "question": "Which measure achieves the lowest MSE for the WSJ dataset?", "answer": "So, the answer is {\"answer\": \"Entropy Dec\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe RNN predictor achieves the best performance in the Dirha-Sim dataset.\n" }, { "role": "assistant", "content": "The table shows that for the Dirha-Sim dataset, the MCD Dec measure has the lowest MSE value of 1.16, not the RNN predictor, which has an MSE of 1.43. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1782.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Mean Square Error (×10−2) of linear regression trained on performance monitoring techniques. All results are reported on test sets." ], "reasoning": "The table shows that for the Dirha-Sim dataset, the MCD Dec measure has the lowest MSE value of 1.16, not the RNN predictor, which has an MSE of 1.43.", "statement": "The RNN predictor achieves the best performance in the Dirha-Sim dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Results and comparison with DEBIN on DEBIN-Test subset containing 48k functions.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The Transformer model achieves the highest f1-score of 0.199, outperforming DEBIN and Seq2Seq, which have f1-scores of 0.046 and 0.108 respectively.We can see that all solutions are markedly better than the RANDOM strategy. On this dataset the Transformer outperforms the other solutions reaching an f1-score of 0.194, beating DEBIN 0.046 and Seq2Seq 0.108. Variable names reuse common patterns (as an example fd,file,f are standard variable names for files), and this could justify an higher rating than the one obtained by looking only at their performances on function names." } ], "images": [ "scigen/train/1783.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results and comparison with DEBIN on DEBIN-Test subset containing 48k functions." ], "reasoning": "The Transformer model achieves the highest f1-score of 0.199, outperforming DEBIN and Seq2Seq, which have f1-scores of 0.046 and 0.108 respectively.", "description": "We can see that all solutions are markedly better than the RANDOM strategy. On this dataset the Transformer outperforms the other solutions reaching an f1-score of 0.194, beating DEBIN 0.046 and Seq2Seq 0.108. Variable names reuse common patterns (as an example fd,file,f are standard variable names for files), and this could justify an higher rating than the one obtained by looking only at their performances on function names.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model has the highest precision and what is its value?\nSolve the above question based on the table titled 'Table 3: Results and comparison with DEBIN on DEBIN-Test subset containing 48k functions.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, we see that the Transformer model has the highest precision value of 0.229. So, the answer is {\"answer\": \"Transformer with 0.229\"}." } ], "images": [ "scigen/train/1783.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results and comparison with DEBIN on DEBIN-Test subset containing 48k functions." ], "reasoning": "By examining the table, we see that the Transformer model has the highest precision value of 0.229.", "question": "Which model has the highest precision and what is its value?", "answer": "So, the answer is {\"answer\": \"Transformer with 0.229\"}." }, { "messages": [ { "role": "user", "content": "The Seq2Seq model has a higher recall than the DEBIN model. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Seq2Seq model has a higher recall than the DEBIN model.\n" }, { "role": "assistant", "content": "The table shows that the recall for Seq2Seq is 0.086, while the recall for DEBIN is 0.046. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1783.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results and comparison with DEBIN on DEBIN-Test subset containing 48k functions." ], "reasoning": "The table shows that the recall for Seq2Seq is 0.086, while the recall for DEBIN is 0.046. Therefore, the statement is supported.", "statement": "The Seq2Seq model has a higher recall than the DEBIN model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1. NLP Features'.\n" }, { "role": "assistant", "content": "We have introduced two new features, including speech-act features, to enhance rumor stance and veracity detection using a fine-tuned representation on a pre-trained model.We have identified a novel combination of features which are highly effective to provide indicative clues for rumor stance and veracity, when learning the fine-tuned representation on top of pre-trained model. We have introduced two new features in addition to the previously available features. The second one are the speech-act 2019(Gorrell The features extracted has been passed through a linear layer of size d2, hence fi∈Rd2 ∀ i∈ [1,|C|]." } ], "images": [ "scigen/train/1784.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1. NLP Features" ], "reasoning": "We have introduced two new features, including speech-act features, to enhance rumor stance and veracity detection using a fine-tuned representation on a pre-trained model.", "description": "We have identified a novel combination of features which are highly effective to provide indicative clues for rumor stance and veracity, when learning the fine-tuned representation on top of pre-trained model. We have introduced two new features in addition to the previously available features. The second one are the speech-act 2019(Gorrell The features extracted has been passed through a linear layer of size d2, hence fi∈Rd2 ∀ i∈ [1,|C|].", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 1. NLP Features', unfold the question presented below, detailing every step of your calculation. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the total number of features extracted from the combination of Structural Features and Speech-Act Features?\n" }, { "role": "assistant", "content": "The Structural Features have 51 features and the Speech-Act Features have 37 features. Adding these together gives 51 + 37 = 88 features. So, the answer is {\"answer\": \"88\"}." } ], "images": [ "scigen/train/1784.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1. NLP Features" ], "reasoning": "The Structural Features have 51 features and the Speech-Act Features have 37 features. Adding these together gives 51 + 37 = 88 features.", "question": "What is the total number of features extracted from the combination of Structural Features and Speech-Act Features?", "answer": "So, the answer is {\"answer\": \"88\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe combination of Affective Features and Emotion Features results in a total of 23 features.\n" }, { "role": "assistant", "content": "The Affective Features have 7 features and the Emotion Features have 16 features. Adding these together gives 7 + 16 = 23 features. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1784.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1. NLP Features" ], "reasoning": "The Affective Features have 7 features and the Emotion Features have 16 features. Adding these together gives 7 + 16 = 23 features.", "statement": "The combination of Affective Features and Emotion Features results in a total of 23 features.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4. Test results for Task B'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Our proposed method, which averages models D, E, and F with NLP features, shows the best performance in terms of MacroF and RMSE compared to other models and systems.In this section we evaluate our proposed approach against the top-5 systems submitted at RumorEval 2019(Gorrell We have conducted the separate experiments, to properly investigate the performance of a) each of the classifiers used in the ensemble. b) impact of NLP features on each of those classifiers and finally, c) the performance of our proposed system. at the fine-tuned post representation as shown in figure. Similarly, Longformer + Identity Encoder + NLP Features, Longformer + Transformer + NLP Features, Longformer + BiLSTM + NLP Features are corresponding models with NLP features. Specifically, for the model named Longformer + Identity Encoder is only a base model (Longformer) without any encoder to judge the performance boost as compared to when we use BiLSTM or Transformer encoder. Top- Each model is chosen randomly and if it increases the ensemble’s F1 then it has been added to the ensemble. To create the pool of models, we consider only models trained with NLP features since they have better performance, i.e. D, E & F. Finally, Our proposed method represents the model averaging of models based on three architectures(D, E & F) with NLP Features trained with varying parameters (encoder and learning rate). In this paper, we have evaluated our models using the same guidelines as in RumorEval 2019 contest Specifically, they have used macro-averaged F1 to evaluate the performance on both Task A and B. Additionally, they have used RMSE score for Task B to judge the confidence scores. paper(Gorrell it is clear that our proposed method shows the best performance among all the approaches. These results also state the importance of NLP Features and Sentence Encoder. We will discuss the effect of each in the following sections." } ], "images": [ "scigen/train/1785.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4. Test results for Task B" ], "reasoning": "Our proposed method, which averages models D, E, and F with NLP features, shows the best performance in terms of MacroF and RMSE compared to other models and systems.", "description": "In this section we evaluate our proposed approach against the top-5 systems submitted at RumorEval 2019(Gorrell We have conducted the separate experiments, to properly investigate the performance of a) each of the classifiers used in the ensemble. b) impact of NLP features on each of those classifiers and finally, c) the performance of our proposed system. at the fine-tuned post representation as shown in figure. Similarly, Longformer + Identity Encoder + NLP Features, Longformer + Transformer + NLP Features, Longformer + BiLSTM + NLP Features are corresponding models with NLP features. Specifically, for the model named Longformer + Identity Encoder is only a base model (Longformer) without any encoder to judge the performance boost as compared to when we use BiLSTM or Transformer encoder. Top- Each model is chosen randomly and if it increases the ensemble’s F1 then it has been added to the ensemble. To create the pool of models, we consider only models trained with NLP features since they have better performance, i.e. D, E & F. Finally, Our proposed method represents the model averaging of models based on three architectures(D, E & F) with NLP Features trained with varying parameters (encoder and learning rate). In this paper, we have evaluated our models using the same guidelines as in RumorEval 2019 contest Specifically, they have used macro-averaged F1 to evaluate the performance on both Task A and B. Additionally, they have used RMSE score for Task B to judge the confidence scores. paper(Gorrell it is clear that our proposed method shows the best performance among all the approaches. These results also state the importance of NLP Features and Sentence Encoder. We will discuss the effect of each in the following sections.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4. Test results for Task B', solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model has the highest MacroF score among the models using NLP features?\n" }, { "role": "assistant", "content": "Among the models using NLP features (D, E, and F), model E has the highest MacroF score of 0.5327. So, the answer is {\"answer\": \"E\"}" } ], "images": [ "scigen/train/1785.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4. Test results for Task B" ], "reasoning": "Among the models using NLP features (D, E, and F), model E has the highest MacroF score of 0.5327.", "question": "Which model has the highest MacroF score among the models using NLP features?", "answer": "So, the answer is {\"answer\": \"E\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Longformer + Transformer model has a lower RMSE score than the Longformer + Bi-LSTM model. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Longformer + Transformer model has a lower RMSE score than the Longformer + Bi-LSTM model.\n" }, { "role": "assistant", "content": "The table shows that the Longformer + Transformer model (B) has an RMSE score of 0.7212, while the Longformer + Bi-LSTM model (C) has an RMSE score of 0.7394, indicating that the statement is correct. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1785.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4. Test results for Task B" ], "reasoning": "The table shows that the Longformer + Transformer model (B) has an RMSE score of 0.7212, while the Longformer + Bi-LSTM model (C) has an RMSE score of 0.7394, indicating that the statement is correct.", "statement": "The Longformer + Transformer model has a lower RMSE score than the Longformer + Bi-LSTM model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4. Test results for Task B'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The proposed method, which averages models D, E, and F with NLP features, shows the best performance with a MacroF score of 0.6720, outperforming other models and systems.In this section we evaluate our proposed approach against the top-5 systems submitted at RumorEval 2019(Gorrell We have conducted the separate experiments, to properly investigate the performance of a) each of the classifiers used in the ensemble. b) impact of NLP features on each of those classifiers and finally, c) the performance of our proposed system. at the fine-tuned post representation as shown in figure. Similarly, Longformer + Identity Encoder + NLP Features, Longformer + Transformer + NLP Features, Longformer + BiLSTM + NLP Features are corresponding models with NLP features. Specifically, for the model named Longformer + Identity Encoder is only a base model (Longformer) without any encoder to judge the performance boost as compared to when we use BiLSTM or Transformer encoder. Top- Each model is chosen randomly and if it increases the ensemble’s F1 then it has been added to the ensemble. To create the pool of models, we consider only models trained with NLP features since they have better performance, i.e. D, E & F. Finally, Our proposed method represents the model averaging of models based on three architectures(D, E & F) with NLP Features trained with varying parameters (encoder and learning rate). In this paper, we have evaluated our models using the same guidelines as in RumorEval 2019 contest Specifically, they have used macro-averaged F1 to evaluate the performance on both Task A and B. Additionally, they have used RMSE score for Task B to judge the confidence scores. paper(Gorrell it is clear that our proposed method shows the best performance among all the approaches. These results also state the importance of NLP Features and Sentence Encoder. We will discuss the effect of each in the following sections." } ], "images": [ "scigen/train/1786.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4. Test results for Task B" ], "reasoning": "The proposed method, which averages models D, E, and F with NLP features, shows the best performance with a MacroF score of 0.6720, outperforming other models and systems.", "description": "In this section we evaluate our proposed approach against the top-5 systems submitted at RumorEval 2019(Gorrell We have conducted the separate experiments, to properly investigate the performance of a) each of the classifiers used in the ensemble. b) impact of NLP features on each of those classifiers and finally, c) the performance of our proposed system. at the fine-tuned post representation as shown in figure. Similarly, Longformer + Identity Encoder + NLP Features, Longformer + Transformer + NLP Features, Longformer + BiLSTM + NLP Features are corresponding models with NLP features. Specifically, for the model named Longformer + Identity Encoder is only a base model (Longformer) without any encoder to judge the performance boost as compared to when we use BiLSTM or Transformer encoder. Top- Each model is chosen randomly and if it increases the ensemble’s F1 then it has been added to the ensemble. To create the pool of models, we consider only models trained with NLP features since they have better performance, i.e. D, E & F. Finally, Our proposed method represents the model averaging of models based on three architectures(D, E & F) with NLP Features trained with varying parameters (encoder and learning rate). In this paper, we have evaluated our models using the same guidelines as in RumorEval 2019 contest Specifically, they have used macro-averaged F1 to evaluate the performance on both Task A and B. Additionally, they have used RMSE score for Task B to judge the confidence scores. paper(Gorrell it is clear that our proposed method shows the best performance among all the approaches. These results also state the importance of NLP Features and Sentence Encoder. We will discuss the effect of each in the following sections.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4. Test results for Task B', solve this question and give detail solutions. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model has the highest MacroF score among the individual models?\n" }, { "role": "assistant", "content": "By comparing the MacroF scores of the individual models (A, B, C, D, E, F), model F (Longformer + BiLSTM + NLP Features) has the highest score of 0.6487. So, the answer is {\"answer\": \"Longformer + BiLSTM + NLP Features\"}." } ], "images": [ "scigen/train/1786.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4. Test results for Task B" ], "reasoning": "By comparing the MacroF scores of the individual models (A, B, C, D, E, F), model F (Longformer + BiLSTM + NLP Features) has the highest score of 0.6487.", "question": "Which model has the highest MacroF score among the individual models?", "answer": "So, the answer is {\"answer\": \"Longformer + BiLSTM + NLP Features\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe proposed method outperforms all other models and systems in terms of MacroF score.\n" }, { "role": "assistant", "content": "The table shows that the proposed method has a MacroF score of 0.6720, which is higher than all other models and systems listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1786.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4. Test results for Task B" ], "reasoning": "The table shows that the proposed method has a MacroF score of 0.6720, which is higher than all other models and systems listed.", "statement": "The proposed method outperforms all other models and systems in terms of MacroF score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Overview of data sources.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table categorizes health-related variables such as cancer and AHD mortality, and diabetes prevalence across US counties using data from 2014 and 2015.The assigned locations are in the United States. The geotags are categorized into US-counties which represent the set of communities A. The target variables y are health-related variables, for example normalized mortality or prevalence rates. We focus on cancer and AHD mortality as well as on diabetes prevalence. Hence, the quantile-based predictions give a categorization of the Ridge regression predictions on a US-county level. The ranked topics assess what language might relate to higher or lower rates of the corresponding disease. Not all counties are covered in the publicly available datasets, usually being limited to more populous counties. The collected Tweets are from 2014 and 2015. The target variables are the union-averaged values from 2014 and 2015: if the target variable is available for both years the two values are averaged. Conversely, if a county data point is only available for one, but not both years, we use this standalone value." } ], "images": [ "scigen/train/1787.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Overview of data sources." ], "reasoning": "The table categorizes health-related variables such as cancer and AHD mortality, and diabetes prevalence across US counties using data from 2014 and 2015.", "description": "The assigned locations are in the United States. The geotags are categorized into US-counties which represent the set of communities A. The target variables y are health-related variables, for example normalized mortality or prevalence rates. We focus on cancer and AHD mortality as well as on diabetes prevalence. Hence, the quantile-based predictions give a categorization of the Ridge regression predictions on a US-county level. The ranked topics assess what language might relate to higher or lower rates of the corresponding disease. Not all counties are covered in the publicly available datasets, usually being limited to more populous counties. The collected Tweets are from 2014 and 2015. The target variables are the union-averaged values from 2014 and 2015: if the target variable is available for both years the two values are averaged. Conversely, if a county data point is only available for one, but not both years, we use this standalone value.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 1: Overview of data sources.', unfold the steps to solve the question given next. Output the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: How many counties' data were used for diabetes prevalence in 2013?\n" }, { "role": "assistant", "content": "The table lists the number of counties for each health-related variable and the corresponding year. For diabetes prevalence, it shows 3129 counties in 2013. So, the answer is {\"answer\": \"3129\"}." } ], "images": [ "scigen/train/1787.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Overview of data sources." ], "reasoning": "The table lists the number of counties for each health-related variable and the corresponding year. For diabetes prevalence, it shows 3129 counties in 2013.", "question": "How many counties' data were used for diabetes prevalence in 2013?", "answer": "So, the answer is {\"answer\": \"3129\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe data for lung cancer mortality covers more counties than liver cancer mortality.\n" }, { "role": "assistant", "content": "The table shows that lung cancer mortality data covers 1612 counties, while liver cancer mortality data covers 293 counties. Therefore, lung cancer mortality data covers more counties. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1787.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Overview of data sources." ], "reasoning": "The table shows that lung cancer mortality data covers 1612 counties, while liver cancer mortality data covers 293 counties. Therefore, lung cancer mortality data covers more counties.", "statement": "The data for lung cancer mortality covers more counties than liver cancer mortality.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Results of state-of-the-art models with RAUs.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Replacing layers in MCB with RVAU and text attention in MFH with RTAU consistently improves performance on VQA 2.0 validation split.For MCB we remove all the layers after the first MCB operation until the first 2048-d output and replace them with RVAU. Due to GPU memory constraints, we reduced the size of each hidden unit in RVAU’s LSTM from 2048 to 1024. Finally, we replace the convolution text attention in MFH with RTAU (text attention). We train two networks, the standard MFH network and MFH with RTAU, on the VQA 2.0 train split and test on the validation split. Note that the text attention in MFH is “self-attending” which means that the textual attention does not interact with visual content in this setting. This is different from our DRAU model where RTAU uses a joint representation of the question and image to predict the textual attention. This gives insight about the difference of performance between the ablation study and the modified MFH results. While the performance improvement might not look large for some models, it is consistent which shows that RAUs can reliably improve existing state-of-the-art models with different architectures." } ], "images": [ "scigen/train/1788.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of state-of-the-art models with RAUs." ], "reasoning": "Replacing layers in MCB with RVAU and text attention in MFH with RTAU consistently improves performance on VQA 2.0 validation split.", "description": "For MCB we remove all the layers after the first MCB operation until the first 2048-d output and replace them with RVAU. Due to GPU memory constraints, we reduced the size of each hidden unit in RVAU’s LSTM from 2048 to 1024. Finally, we replace the convolution text attention in MFH with RTAU (text attention). We train two networks, the standard MFH network and MFH with RTAU, on the VQA 2.0 train split and test on the validation split. Note that the text attention in MFH is “self-attending” which means that the textual attention does not interact with visual content in this setting. This is different from our DRAU model where RTAU uses a joint representation of the question and image to predict the textual attention. This gives insight about the difference of performance between the ablation study and the modified MFH results. While the performance improvement might not look large for some models, it is consistent which shows that RAUs can reliably improve existing state-of-the-art models with different architectures.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 2: Results of state-of-the-art models with RAUs.', solve the following question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the difference in performance between MFH and MFH with RTAU on the VQA 2.0 validation split for the 'Other' category?\n" }, { "role": "assistant", "content": "To find the difference in performance, subtract the 'Other' category score of MFH from the 'Other' category score of MFH with RTAU on the VQA 2.0 validation split. So, the answer is {\"answer\": \"0.53\"}." } ], "images": [ "scigen/train/1788.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of state-of-the-art models with RAUs." ], "reasoning": "To find the difference in performance, subtract the 'Other' category score of MFH from the 'Other' category score of MFH with RTAU on the VQA 2.0 validation split.", "question": "What is the difference in performance between MFH and MFH with RTAU on the VQA 2.0 validation split for the 'Other' category?", "answer": "So, the answer is {\"answer\": \"0.53\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe performance of MCB with RVAU is higher than MUTAN with RVAU on the VQA 2.0 Test-dev Split All.\n" }, { "role": "assistant", "content": "Compare the scores of MCB with RVAU and MUTAN with RVAU on the VQA 2.0 Test-dev Split All. MCB with RVAU has a score of 62.33, while MUTAN with RVAU has a score of 62.45. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1788.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of state-of-the-art models with RAUs." ], "reasoning": "Compare the scores of MCB with RVAU and MUTAN with RVAU on the VQA 2.0 Test-dev Split All. MCB with RVAU has a score of 62.33, while MUTAN with RVAU has a score of 62.45.", "statement": "The performance of MCB with RVAU is higher than MUTAN with RVAU on the VQA 2.0 Test-dev Split All.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Simple Net model and ablation study of the DRAU model results on the VQA 2.0 validation split. The number of trainable parameters are shown for each model. IF indicates the type of image features the model uses. VG indicates whether the method uses external data augmentation from the Visual Genome dataset.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Using both RTAU and RVAU in the same model improves performance by almost 2%, suggesting that multimodal recurrent text attention is effective in certain architectures.We compare using convolution against our recurrent attention in a simple baseline model. This baseline VQA model uses the same question representation as described in the previous section and the ResNet global image features. The input features are simply concatenated and sent to a visual attention mechanism. The processed attention is fed to the reasoning module. To avoid any type of parameter advantage, both models were designed to have the same number of parameters approximately. We report the mean and standard deviation of the each model. While swapping convolutional text attention with RTAU reduces performance by 0.66%, using both RTAU and RVAU in the same model improves the overall performance by almost 2%. This might indicate that multimodal recurrent text attention thrives with certain architectural designs since it improves performance in our DRAU model." } ], "images": [ "scigen/train/1789.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Simple Net model and ablation study of the DRAU model results on the VQA 2.0 validation split. The number of trainable parameters are shown for each model. IF indicates the type of image features the model uses. VG indicates whether the method uses external data augmentation from the Visual Genome dataset." ], "reasoning": "Using both RTAU and RVAU in the same model improves performance by almost 2%, suggesting that multimodal recurrent text attention is effective in certain architectures.", "description": "We compare using convolution against our recurrent attention in a simple baseline model. This baseline VQA model uses the same question representation as described in the previous section and the ResNet global image features. The input features are simply concatenated and sent to a visual attention mechanism. The processed attention is fed to the reasoning module. To avoid any type of parameter advantage, both models were designed to have the same number of parameters approximately. We report the mean and standard deviation of the each model. While swapping convolutional text attention with RTAU reduces performance by 0.66%, using both RTAU and RVAU in the same model improves the overall performance by almost 2%. This might indicate that multimodal recurrent text attention thrives with certain architectural designs since it improves performance in our DRAU model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Simple Net model and ablation study of the DRAU model results on the VQA 2.0 validation split. The number of trainable parameters are shown for each model. IF indicates the type of image features the model uses. VG indicates whether the method uses external data augmentation from the Visual Genome dataset.', answer the following question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the performance of the DRAU model on the VQA 2.0 Validation Split All?\n" }, { "role": "assistant", "content": "The table shows the performance of various models on different VQA 2.0 validation splits. The DRAU model has a performance of 61.36±0.02 on the VQA 2.0 Validation Split All. So, the answer is {\"answer\": \"61.36±0.02\"}" } ], "images": [ "scigen/train/1789.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Simple Net model and ablation study of the DRAU model results on the VQA 2.0 validation split. The number of trainable parameters are shown for each model. IF indicates the type of image features the model uses. VG indicates whether the method uses external data augmentation from the Visual Genome dataset." ], "reasoning": "The table shows the performance of various models on different VQA 2.0 validation splits. The DRAU model has a performance of 61.36±0.02 on the VQA 2.0 Validation Split All.", "question": "What is the performance of the DRAU model on the VQA 2.0 Validation Split All?", "answer": "So, the answer is {\"answer\": \"61.36±0.02\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe DCA model with RVAU has a higher performance on the VQA 2.0 Validation Split All than the DCA model with RTAU.\n" }, { "role": "assistant", "content": "The table shows that the DCA model with RVAU has a performance of 60.90±0.02 on the VQA 2.0 Validation Split All, while the DCA model with RTAU has a performance of 58.76±0.08. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1789.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Simple Net model and ablation study of the DRAU model results on the VQA 2.0 validation split. The number of trainable parameters are shown for each model. IF indicates the type of image features the model uses. VG indicates whether the method uses external data augmentation from the Visual Genome dataset." ], "reasoning": "The table shows that the DCA model with RVAU has a performance of 60.90±0.02 on the VQA 2.0 Validation Split All, while the DCA model with RTAU has a performance of 58.76±0.08. Therefore, the statement is supported.", "statement": "The DCA model with RVAU has a higher performance on the VQA 2.0 Validation Split All than the DCA model with RTAU.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: DRAU compared to the state-of-the-art on the VQA 1.0 dataset. N corresponds to the number of models used for prediction. WE indicates whether the method uses a pre-trained word embedding. VG indicates whether the method uses external data from the Visual Genome dataset.'.\n" }, { "role": "assistant", "content": "DRAU performs favorably against other models, coming very close to the state-of-the-art single model.S4SS3SSS0Px1 Vqa 1.0 Excluding model ensembles, DRAU performs favorably against other models. To the best of our knowledge, Yu et al. Our single model (DRAU) comes a very close second to the current state-of-the-art single model." } ], "images": [ "scigen/train/1790.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: DRAU compared to the state-of-the-art on the VQA 1.0 dataset. N corresponds to the number of models used for prediction. WE indicates whether the method uses a pre-trained word embedding. VG indicates whether the method uses external data from the Visual Genome dataset." ], "reasoning": "DRAU performs favorably against other models, coming very close to the state-of-the-art single model.", "description": "S4SS3SSS0Px1 Vqa 1.0 Excluding model ensembles, DRAU performs favorably against other models. To the best of our knowledge, Yu et al. Our single model (DRAU) comes a very close second to the current state-of-the-art single model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: DRAU compared to the state-of-the-art on the VQA 1.0 dataset. N corresponds to the number of models used for prediction. WE indicates whether the method uses a pre-trained word embedding. VG indicates whether the method uses external data from the Visual Genome dataset.', answer the following question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest score in the VQA 1.0 Open Ended Task Test-dev for the 'All' category?\n" }, { "role": "assistant", "content": "By examining the 'All' column under the VQA 1.0 Open Ended Task Test-dev, the highest score is 67.42, achieved by the MUTAN model. So, the answer is {\"answer\": \"MUTAN\"}" } ], "images": [ "scigen/train/1790.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: DRAU compared to the state-of-the-art on the VQA 1.0 dataset. N corresponds to the number of models used for prediction. WE indicates whether the method uses a pre-trained word embedding. VG indicates whether the method uses external data from the Visual Genome dataset." ], "reasoning": "By examining the 'All' column under the VQA 1.0 Open Ended Task Test-dev, the highest score is 67.42, achieved by the MUTAN model.", "question": "Which model has the highest score in the VQA 1.0 Open Ended Task Test-dev for the 'All' category?", "answer": "So, the answer is {\"answer\": \"MUTAN\"}" }, { "messages": [ { "role": "user", "content": "The DRAU model has the highest score in the VQA 1.0 Open Ended Task Test-dev for the 'All' category. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe DRAU model has the highest score in the VQA 1.0 Open Ended Task Test-dev for the 'All' category.\n" }, { "role": "assistant", "content": "The table shows that the highest score in the 'All' category for the VQA 1.0 Open Ended Task Test-dev is 67.42, achieved by the MUTAN model, not the DRAU model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1790.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: DRAU compared to the state-of-the-art on the VQA 1.0 dataset. N corresponds to the number of models used for prediction. WE indicates whether the method uses a pre-trained word embedding. VG indicates whether the method uses external data from the Visual Genome dataset." ], "reasoning": "The table shows that the highest score in the 'All' category for the VQA 1.0 Open Ended Task Test-dev is 67.42, achieved by the MUTAN model, not the DRAU model.", "statement": "The DRAU model has the highest score in the VQA 1.0 Open Ended Task Test-dev for the 'All' category.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4. Statistical Information on the Named Entities Annotated in the Three Versions of the Stance-Annotated Tweet Data Sets.'.\n" }, { "role": "assistant", "content": "The NER performance is better on tweets marked with the Favor stance class compared to those marked as Against, and the overall results are better than those reported on randomly-compiled tweet sets.Named Entity Recognition Experiments on the Stance Data Sets In order to carry out NER experiments on the three versions of our stance data sets, we have manually annotated them to use them as the answer keys. The named entity types considered are person, location, and organization names. The most common named entity type is organization names as expected, since each tweet contains the stance target (i.e., the football club name) explicitly and they are named entities of organization name type. Yet, they are favorable because the performance of NER tools is known to decrease considerably when ported to tweets The following two conclusions can be drawn based on these results: Overall, the results are better than those NER results reported on the randomly-compiled tweet sets. This may be attributed to the fact that all of the tweets in the three versions of our stance data sets contain the stance targets (the club names) explicitly, making our procedure ”targeted” NER. The NER performance is far better on tweets marked with the Favor stance class when compared with the performance on tweets marked as Against. The named entities in the tweets from the Favor seem to be commonly well-formed and canonical names of these entities. The considerable difference in the recall rates for these two groups supports this argument." } ], "images": [ "scigen/train/1791.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4. Statistical Information on the Named Entities Annotated in the Three Versions of the Stance-Annotated Tweet Data Sets." ], "reasoning": "The NER performance is better on tweets marked with the Favor stance class compared to those marked as Against, and the overall results are better than those reported on randomly-compiled tweet sets.", "description": "Named Entity Recognition Experiments on the Stance Data Sets In order to carry out NER experiments on the three versions of our stance data sets, we have manually annotated them to use them as the answer keys. The named entity types considered are person, location, and organization names. The most common named entity type is organization names as expected, since each tweet contains the stance target (i.e., the football club name) explicitly and they are named entities of organization name type. Yet, they are favorable because the performance of NER tools is known to decrease considerably when ported to tweets The following two conclusions can be drawn based on these results: Overall, the results are better than those NER results reported on the randomly-compiled tweet sets. This may be attributed to the fact that all of the tweets in the three versions of our stance data sets contain the stance targets (the club names) explicitly, making our procedure ”targeted” NER. The NER performance is far better on tweets marked with the Favor stance class when compared with the performance on tweets marked as Against. The named entities in the tweets from the Favor seem to be commonly well-formed and canonical names of these entities. The considerable difference in the recall rates for these two groups supports this argument.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4. Statistical Information on the Named Entities Annotated in the Three Versions of the Stance-Annotated Tweet Data Sets.', solve this question and give detail solutions. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the total number of named entities in Version-2 for Target-2?\n" }, { "role": "assistant", "content": "To find the total number of named entities in Version-2 for Target-2, sum the number of named entities for person, location, and organization. The values are 6 (person), 24 (location), and 245 (organization). Adding these gives 6 + 24 + 245 = 275. So, the answer is {\"answer\": \"275\"}." } ], "images": [ "scigen/train/1791.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4. Statistical Information on the Named Entities Annotated in the Three Versions of the Stance-Annotated Tweet Data Sets." ], "reasoning": "To find the total number of named entities in Version-2 for Target-2, sum the number of named entities for person, location, and organization. The values are 6 (person), 24 (location), and 245 (organization). Adding these gives 6 + 24 + 245 = 275.", "question": "What is the total number of named entities in Version-2 for Target-2?", "answer": "So, the answer is {\"answer\": \"275\"}." }, { "messages": [ { "role": "user", "content": "The total number of tweets in Version-3 is greater than the total number of tweets in Version-1. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe total number of tweets in Version-3 is greater than the total number of tweets in Version-1.\n" }, { "role": "assistant", "content": "The total number of tweets in Version-3 is 1,065, and the total number of tweets in Version-1 is 700. Since 1,065 is greater than 700, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1791.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4. Statistical Information on the Named Entities Annotated in the Three Versions of the Stance-Annotated Tweet Data Sets." ], "reasoning": "The total number of tweets in Version-3 is 1,065, and the total number of tweets in Version-1 is 700. Since 1,065 is greater than 700, the statement is supported.", "statement": "The total number of tweets in Version-3 is greater than the total number of tweets in Version-1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Distributional discrepancy of generators and classification accuracy on a real corpus. Acc. indicates the classification accuracy. For all of them, the lower the value, the better is the performance.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The performance of language models improves with a larger amount of training data, but real language models generating real sentences were not compared.The gold-standard reference order is estimated, and given the architecture of the LM, a larger number of training data results in a better performance. Unlike the experiment on the syntactic data, we cannot obtain a real LM that generates real sentences. We did not compare these 10 generators together." } ], "images": [ "scigen/train/1792.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Distributional discrepancy of generators and classification accuracy on a real corpus. Acc. indicates the classification accuracy. For all of them, the lower the value, the better is the performance." ], "reasoning": "The performance of language models improves with a larger amount of training data, but real language models generating real sentences were not compared.", "description": "The gold-standard reference order is estimated, and given the architecture of the LM, a larger number of training data results in a better performance. Unlike the experiment on the syntactic data, we cannot obtain a real LM that generates real sentences. We did not compare these 10 generators together.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 3: Distributional discrepancy of generators and classification accuracy on a real corpus. Acc. indicates the classification accuracy. For all of them, the lower the value, the better is the performance.', and tackle the question below in a sequential manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich generator has the highest Valid Acc. and what is its Test Acc.?\n" }, { "role": "assistant", "content": "To find the generator with the highest Valid Acc., we look at the Valid Acc. column and identify LSTM0.2 with 0.758. Then, we find its corresponding Test Acc., which is 0.745. So, the answer is {\"answer\": \"0.745\"}." } ], "images": [ "scigen/train/1792.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Distributional discrepancy of generators and classification accuracy on a real corpus. Acc. indicates the classification accuracy. For all of them, the lower the value, the better is the performance." ], "reasoning": "To find the generator with the highest Valid Acc., we look at the Valid Acc. column and identify LSTM0.2 with 0.758. Then, we find its corresponding Test Acc., which is 0.745.", "question": "Which generator has the highest Valid Acc. and what is its Test Acc.?", "answer": "So, the answer is {\"answer\": \"0.745\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nGPT-20.2 has a higher Valid Acc. than LSTM0.4.\n" }, { "role": "assistant", "content": "By comparing the Valid Acc. values, GPT-20.2 has 0.752, which is higher than LSTM0.4's 0.718. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1792.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Distributional discrepancy of generators and classification accuracy on a real corpus. Acc. indicates the classification accuracy. For all of them, the lower the value, the better is the performance." ], "reasoning": "By comparing the Valid Acc. values, GPT-20.2 has 0.752, which is higher than LSTM0.4's 0.718.", "statement": "GPT-20.2 has a higher Valid Acc. than LSTM0.4.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4: Kendall’s Tau rank correlation on a real corpus. 5LSTM and 5GPT-2 denote the correlations of five LSTM-based and five GPT-2 based generators, respectively.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The DD metric achieves a perfect rank match across both LSTM and GPT-2 architectures, and the novel metric outperforms others despite the LM score's variability.The rank achieved by the DD matches the gold-standard rank perfectly across the two architectures. For comparison of the previous metrics, Kendall’s Tau co-efficiency is computed. Our novel metric still achieves the best performance. Although the LM score works considerably well for GPT-2, it fails to discriminate the generators when an LSTM architecture is adapted." } ], "images": [ "scigen/train/1793.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Kendall’s Tau rank correlation on a real corpus. 5LSTM and 5GPT-2 denote the correlations of five LSTM-based and five GPT-2 based generators, respectively." ], "reasoning": "The DD metric achieves a perfect rank match across both LSTM and GPT-2 architectures, and the novel metric outperforms others despite the LM score's variability.", "description": "The rank achieved by the DD matches the gold-standard rank perfectly across the two architectures. For comparison of the previous metrics, Kendall’s Tau co-efficiency is computed. Our novel metric still achieves the best performance. Although the LM score works considerably well for GPT-2, it fails to discriminate the generators when an LSTM architecture is adapted.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich metric achieves the highest score for both LSTM and GPT-2 architectures?\nSolve the above question based on the table titled 'Table 4: Kendall’s Tau rank correlation on a real corpus. 5LSTM and 5GPT-2 denote the correlations of five LSTM-based and five GPT-2 based generators, respectively.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, the DD metric achieves a score of 1.0 for both LSTM and GPT-2 architectures, indicating it has the highest score for both. So, the answer is {\"answer\": \"DD\"}" } ], "images": [ "scigen/train/1793.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Kendall’s Tau rank correlation on a real corpus. 5LSTM and 5GPT-2 denote the correlations of five LSTM-based and five GPT-2 based generators, respectively." ], "reasoning": "By examining the table, the DD metric achieves a score of 1.0 for both LSTM and GPT-2 architectures, indicating it has the highest score for both.", "question": "Which metric achieves the highest score for both LSTM and GPT-2 architectures?", "answer": "So, the answer is {\"answer\": \"DD\"}" }, { "messages": [ { "role": "user", "content": "The LM score is the best performing metric for both LSTM and GPT-2 architectures. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe LM score is the best performing metric for both LSTM and GPT-2 architectures.\n" }, { "role": "assistant", "content": "The table shows that the LM score is 1.0 for GPT-2 but -0.2 for LSTM, indicating it is not the best performing metric for both architectures. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1793.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Kendall’s Tau rank correlation on a real corpus. 5LSTM and 5GPT-2 denote the correlations of five LSTM-based and five GPT-2 based generators, respectively." ], "reasoning": "The table shows that the LM score is 1.0 for GPT-2 but -0.2 for LSTM, indicating it is not the best performing metric for both architectures.", "statement": "The LM score is the best performing metric for both LSTM and GPT-2 architectures.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5: Kendall’s Tau rank correlation on syntactic data. 10Gs denotes the Tau value when all 10 generators are evaluated together. 5LSTM and 5GPT-2 denote the correlations of five LSTM-based and five GPT-2-based generators, respectively.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The DD metric ranks all 10 generators perfectly in line with the gold-standard order across both models, demonstrating its power and efficiency.We evaluate all 10 generators based on their DD. The rank achieved by the DD matches the gold-standard order perfectly across the two models. It is not necessary to adjust the generators’ softmax temperature. This shows that DD is powerful and efficient." } ], "images": [ "scigen/train/1794.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Kendall’s Tau rank correlation on syntactic data. 10Gs denotes the Tau value when all 10 generators are evaluated together. 5LSTM and 5GPT-2 denote the correlations of five LSTM-based and five GPT-2-based generators, respectively." ], "reasoning": "The DD metric ranks all 10 generators perfectly in line with the gold-standard order across both models, demonstrating its power and efficiency.", "description": "We evaluate all 10 generators based on their DD. The rank achieved by the DD matches the gold-standard order perfectly across the two models. It is not necessary to adjust the generators’ softmax temperature. This shows that DD is powerful and efficient.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Kendall’s Tau rank correlation on syntactic data. 10Gs denotes the Tau value when all 10 generators are evaluated together. 5LSTM and 5GPT-2 denote the correlations of five LSTM-based and five GPT-2-based generators, respectively.\nQuestion: Which metric shows the highest score for the 10Gs generator?\nSolve the above question based on the table information. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, the highest score for the 10Gs generator is found in the DD metric, which has a value of 1.0. So, the answer is {\"answer\": \"DD\"}." } ], "images": [ "scigen/train/1794.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Kendall’s Tau rank correlation on syntactic data. 10Gs denotes the Tau value when all 10 generators are evaluated together. 5LSTM and 5GPT-2 denote the correlations of five LSTM-based and five GPT-2-based generators, respectively." ], "reasoning": "By examining the table, the highest score for the 10Gs generator is found in the DD metric, which has a value of 1.0.", "question": "Which metric shows the highest score for the 10Gs generator?", "answer": "So, the answer is {\"answer\": \"DD\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe 5GPT-2 model has the highest LM score among all metrics.\n" }, { "role": "assistant", "content": "The table shows that the 5GPT-2 model has an LM score of 1.0, which is the highest score among all metrics for this model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1794.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Kendall’s Tau rank correlation on syntactic data. 10Gs denotes the Tau value when all 10 generators are evaluated together. 5LSTM and 5GPT-2 denote the correlations of five LSTM-based and five GPT-2-based generators, respectively." ], "reasoning": "The table shows that the 5GPT-2 model has an LM score of 1.0, which is the highest score among all metrics for this model.", "statement": "The 5GPT-2 model has the highest LM score among all metrics.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Ten generators are ranked according to FED score. The lower the score is, the better is the performance.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The FED scores show minimal discrimination with only a 0.006 difference between the best and worst scores, and multiple generators have the same worst scores but different performances.Considering that the rank of the FED scores is similar to that of our own, we inspected its performance in detail. The discrimination is significantly small, and there is only a 0.006 difference between the best and worst scores. For the worst scores, several generators are assigned the same scores; however, their performances are different from each other." } ], "images": [ "scigen/train/1795.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Ten generators are ranked according to FED score. The lower the score is, the better is the performance." ], "reasoning": "The FED scores show minimal discrimination with only a 0.006 difference between the best and worst scores, and multiple generators have the same worst scores but different performances.", "description": "Considering that the rank of the FED scores is similar to that of our own, we inspected its performance in detail. The discrimination is significantly small, and there is only a 0.006 difference between the best and worst scores. For the worst scores, several generators are assigned the same scores; however, their performances are different from each other.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. The question is related to the given table titled 'Table 6: Ten generators are ranked according to FED score. The lower the score is, the better is the performance.'.\n\nQuestion: What is the difference between the best and worst FED scores?\n" }, { "role": "assistant", "content": "The best score is 0.01 (GPT-21.0) and the worst score is 0.016 (LSTM0.2). The difference is 0.016 - 0.01 = 0.006. So, the answer is {\"answer\": \"0.006\"}." } ], "images": [ "scigen/train/1795.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Ten generators are ranked according to FED score. The lower the score is, the better is the performance." ], "reasoning": "The best score is 0.01 (GPT-21.0) and the worst score is 0.016 (LSTM0.2). The difference is 0.016 - 0.01 = 0.006.", "question": "What is the difference between the best and worst FED scores?", "answer": "So, the answer is {\"answer\": \"0.006\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The FED scores for GPT-20.8 and GPT-20.6 are the same. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe FED scores for GPT-20.8 and GPT-20.6 are the same.\n" }, { "role": "assistant", "content": "The table shows that both GPT-20.8 and GPT-20.6 have a FED score of 0.011. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1795.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Ten generators are ranked according to FED score. The lower the score is, the better is the performance." ], "reasoning": "The table shows that both GPT-20.8 and GPT-20.6 have a FED score of 0.011.", "statement": "The FED scores for GPT-20.8 and GPT-20.6 are the same.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: The Test Classification Accuracy on The Data Sets Generated from 20Newsgroups'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "HLSM achieved the highest average accuracy across all data sets, outperforming other topic models.We did the same data preprocessing as above, and the number of topics in each data set for LDA, PLSA, and asymmetric LDA was set to 4. The last row of the table shows the average accuracy over all data sets. From the table we can observe that HLSM outperformed all other topic models on six data sets." } ], "images": [ "scigen/train/1796.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The Test Classification Accuracy on The Data Sets Generated from 20Newsgroups" ], "reasoning": "HLSM achieved the highest average accuracy across all data sets, outperforming other topic models.", "description": "We did the same data preprocessing as above, and the number of topics in each data set for LDA, PLSA, and asymmetric LDA was set to 4. The last row of the table shows the average accuracy over all data sets. From the table we can observe that HLSM outperformed all other topic models on six data sets.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 2: The Test Classification Accuracy on The Data Sets Generated from 20Newsgroups' in mind, break down and resolve the question below step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich topic model had the highest accuracy on the 'Talk and Rec' data set?\n" }, { "role": "assistant", "content": "By examining the 'Talk and Rec' row in the table, we see that HLSM has the highest accuracy with a value of 0.876. So, the answer is {\"answer\": \"HLSM\"}." } ], "images": [ "scigen/train/1796.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The Test Classification Accuracy on The Data Sets Generated from 20Newsgroups" ], "reasoning": "By examining the 'Talk and Rec' row in the table, we see that HLSM has the highest accuracy with a value of 0.876.", "question": "Which topic model had the highest accuracy on the 'Talk and Rec' data set?", "answer": "So, the answer is {\"answer\": \"HLSM\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim PLSA had the lowest average accuracy among all topic models., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nPLSA had the lowest average accuracy among all topic models.\n" }, { "role": "assistant", "content": "The average accuracy for PLSA is 0.766, which is lower than the average accuracies of LDA (0.779), asymmetric LDA (0.804), TopicMapping (0.834), and HLSM (0.857). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1796.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The Test Classification Accuracy on The Data Sets Generated from 20Newsgroups" ], "reasoning": "The average accuracy for PLSA is 0.766, which is lower than the average accuracies of LDA (0.779), asymmetric LDA (0.804), TopicMapping (0.834), and HLSM (0.857).", "statement": "PLSA had the lowest average accuracy among all topic models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Ablation study results on CoNLL-03 and OntoNotes 5. “- span encoding” removes the BILU match span encoding leaving only the gazetteer types. “- self attention” removes the self-attention. “- uncased matches” removes the uncased matches.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Removing any component from the gazetteer embedding model decreases the F1 score, with the highest degradation occurring when single matches are removed, highlighting the importance of combining gazetteer matching techniques for NER.We remove components of the gazetteer embedding model from the Neural CRF model. In each experiment, we removed only the specified component. Ablations show decreased F1 score on the development and test set if any of the components is removed. The highest degradation is when single matches are removed which underscores the importance of the combining the gazetteer matching techniques for NER. We observe that match span encoding is more important for the CoNLL-02 compared to Ononotes 5 because the former has more entities with multiple tokens. Removing the self-attention shows that self-attention is effective at combining information form multiple gazetteers." } ], "images": [ "scigen/train/1797.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation study results on CoNLL-03 and OntoNotes 5. “- span encoding” removes the BILU match span encoding leaving only the gazetteer types. “- self attention” removes the self-attention. “- uncased matches” removes the uncased matches." ], "reasoning": "Removing any component from the gazetteer embedding model decreases the F1 score, with the highest degradation occurring when single matches are removed, highlighting the importance of combining gazetteer matching techniques for NER.", "description": "We remove components of the gazetteer embedding model from the Neural CRF model. In each experiment, we removed only the specified component. Ablations show decreased F1 score on the development and test set if any of the components is removed. The highest degradation is when single matches are removed which underscores the importance of the combining the gazetteer matching techniques for NER. We observe that match span encoding is more important for the CoNLL-02 compared to Ononotes 5 because the former has more entities with multiple tokens. Removing the self-attention shows that self-attention is effective at combining information form multiple gazetteers.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: 'Table 3: Ablation study results on CoNLL-03 and OntoNotes 5. “- span encoding” removes the BILU match span encoding leaving only the gazetteer types. “- self attention” removes the self-attention. “- uncased matches” removes the uncased matches.'\nQuestion: Which component's removal causes the highest degradation in F1 score on the CoNLL-03 Test set?\n" }, { "role": "assistant", "content": "The table shows that removing single matches results in the lowest F1 score (90.55) on the CoNLL-03 Test set, indicating the highest degradation. So, the answer is {\"answer\": \"single matches\"}." } ], "images": [ "scigen/train/1797.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation study results on CoNLL-03 and OntoNotes 5. “- span encoding” removes the BILU match span encoding leaving only the gazetteer types. “- self attention” removes the self-attention. “- uncased matches” removes the uncased matches." ], "reasoning": "The table shows that removing single matches results in the lowest F1 score (90.55) on the CoNLL-03 Test set, indicating the highest degradation.", "question": "Which component's removal causes the highest degradation in F1 score on the CoNLL-03 Test set?", "answer": "So, the answer is {\"answer\": \"single matches\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Removing span encoding from the model results in the highest degradation in F1 score on the Ontonotes 5 Test set., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nRemoving span encoding from the model results in the highest degradation in F1 score on the Ontonotes 5 Test set.\n" }, { "role": "assistant", "content": "The table shows that removing span encoding results in an F1 score of 86.81 on the Ontonotes 5 Test set, which is not the lowest. Removing single matches results in a lower F1 score of 86.69. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1797.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation study results on CoNLL-03 and OntoNotes 5. “- span encoding” removes the BILU match span encoding leaving only the gazetteer types. “- self attention” removes the self-attention. “- uncased matches” removes the uncased matches." ], "reasoning": "The table shows that removing span encoding results in an F1 score of 86.81 on the Ontonotes 5 Test set, which is not the lowest. Removing single matches results in a lower F1 score of 86.69.", "statement": "Removing span encoding from the model results in the highest degradation in F1 score on the Ontonotes 5 Test set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Results on CoNLL-03 and OntoNotes 5.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The Neural CRF model with ELMo embeddings improves F1 scores for CoNLL-03 and Ontonotes 5 datasets, and GazSelfAttn shows similar improvements with and without ELMo embeddings.The top part of the table shows recently published results. Liu et al. ’s work is using gazetteers with HSCRF and Akbik et al. ’s work is using the Flair language model which is much larger than ELMo. Baevski et al. The bottom part of the table is shows our baseline models and results with included gazetteers. We experiment with the Neural CRF model with and without ELMo embeddings. Including ELMo embeddings the CoNLL-03 and Ontonotes 5, F1 score improves from 92.34 to 92.86 and 89.11 to 89.32 respectively. Without ELMo embeddings the F1 score improves from 90.42 to 91.12 and 86.63 to 87 respectively. We observe that GazSelfAttn relative improvements are similar with and without ELMo embeddings. We obtain slightly better CoNLL-03 F1 score compared to Liu et al. work that uses the HSCRF model, and we match the Ononotes 5 F1 scores of Akbik et al. that uses a much bigger model. Liu et al. Ononotes 5 results use subset of the dataset labels and are not comparable. Note that because of computation constrains, we did not perform extensive hyperparameter tuning except for the gazetteer dropout rate." } ], "images": [ "scigen/train/1798.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on CoNLL-03 and OntoNotes 5." ], "reasoning": "The Neural CRF model with ELMo embeddings improves F1 scores for CoNLL-03 and Ontonotes 5 datasets, and GazSelfAttn shows similar improvements with and without ELMo embeddings.", "description": "The top part of the table shows recently published results. Liu et al. ’s work is using gazetteers with HSCRF and Akbik et al. ’s work is using the Flair language model which is much larger than ELMo. Baevski et al. The bottom part of the table is shows our baseline models and results with included gazetteers. We experiment with the Neural CRF model with and without ELMo embeddings. Including ELMo embeddings the CoNLL-03 and Ontonotes 5, F1 score improves from 92.34 to 92.86 and 89.11 to 89.32 respectively. Without ELMo embeddings the F1 score improves from 90.42 to 91.12 and 86.63 to 87 respectively. We observe that GazSelfAttn relative improvements are similar with and without ELMo embeddings. We obtain slightly better CoNLL-03 F1 score compared to Liu et al. work that uses the HSCRF model, and we match the Ononotes 5 F1 scores of Akbik et al. that uses a much bigger model. Liu et al. Ononotes 5 results use subset of the dataset labels and are not comparable. Note that because of computation constrains, we did not perform extensive hyperparameter tuning except for the gazetteer dropout rate.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: Results on CoNLL-03 and OntoNotes 5.', and tackle the question below in a sequential manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the F1 score improvement for the Neural CRF model on the CoNLL-03 dataset when ELMo embeddings are included?\n" }, { "role": "assistant", "content": "The F1 score for the Neural CRF model on the CoNLL-03 dataset improves from 90.42 to 91.12 when ELMo embeddings are included. So, the answer is {\"answer\": \"0.70\"}." } ], "images": [ "scigen/train/1798.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on CoNLL-03 and OntoNotes 5." ], "reasoning": "The F1 score for the Neural CRF model on the CoNLL-03 dataset improves from 90.42 to 91.12 when ELMo embeddings are included.", "question": "What is the F1 score improvement for the Neural CRF model on the CoNLL-03 dataset when ELMo embeddings are included?", "answer": "So, the answer is {\"answer\": \"0.70\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe F1 score for the Neural CRF model with ELMo embeddings on the Ontonotes 5 dataset is higher than that of Akbik et al. (2019).\n" }, { "role": "assistant", "content": "The F1 score for the Neural CRF model with ELMo embeddings on the Ontonotes 5 dataset is 89.32, while Akbik et al. (2019) has an F1 score of 89.3. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1798.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on CoNLL-03 and OntoNotes 5." ], "reasoning": "The F1 score for the Neural CRF model with ELMo embeddings on the Ontonotes 5 dataset is 89.32, while Akbik et al. (2019) has an F1 score of 89.3.", "statement": "The F1 score for the Neural CRF model with ELMo embeddings on the Ontonotes 5 dataset is higher than that of Akbik et al. (2019).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 7: Binary sentiment classification. Results on the true and fake reviews are very close.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The sentiment classifier performs similarly on real and fake reviews, with some domains like Book and Movie showing better results on fake reviews.To conduct the classification, we randomly sample one 100,000 real reviews from the training data for training the sentiment classifier. As for the evaluation data, a fake review is generated for each test data according to its contexts. We can see that the results on the real and fake reviews are very close to each other. On some domains (e.g., Book and Movie), the results on the fake reviews are even better than the real reviews, showing that the reviews generated by the gC2S model accurately reflect the sentiment polarity." } ], "images": [ "scigen/train/1799.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Binary sentiment classification. Results on the true and fake reviews are very close." ], "reasoning": "The sentiment classifier performs similarly on real and fake reviews, with some domains like Book and Movie showing better results on fake reviews.", "description": "To conduct the classification, we randomly sample one 100,000 real reviews from the training data for training the sentiment classifier. As for the evaluation data, a fake review is generated for each test data according to its contexts. We can see that the results on the real and fake reviews are very close to each other. On some domains (e.g., Book and Movie), the results on the fake reviews are even better than the real reviews, showing that the reviews generated by the gC2S model accurately reflect the sentiment polarity.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: 'Table 7: Binary sentiment classification. Results on the true and fake reviews are very close.'\nQuestion: Which domain has the highest F1 score for fake reviews?\n" }, { "role": "assistant", "content": "By comparing the F1 scores for fake reviews across all domains, the Movie domain has the highest F1 score of 0.984. So, the answer is {\"answer\": \"Movie\"}" } ], "images": [ "scigen/train/1799.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Binary sentiment classification. Results on the true and fake reviews are very close." ], "reasoning": "By comparing the F1 scores for fake reviews across all domains, the Movie domain has the highest F1 score of 0.984.", "question": "Which domain has the highest F1 score for fake reviews?", "answer": "So, the answer is {\"answer\": \"Movie\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe precision for fake reviews in the Hotel domain is higher than for true reviews.\n" }, { "role": "assistant", "content": "The table shows that the precision for fake reviews in the Hotel domain is 0.947, which is lower than the precision for true reviews, which is 0.985. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1799.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Binary sentiment classification. Results on the true and fake reviews are very close." ], "reasoning": "The table shows that the precision for fake reviews in the Hotel domain is 0.947, which is lower than the precision for true reviews, which is 0.985.", "statement": "The precision for fake reviews in the Hotel domain is higher than for true reviews.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Results of language modeling measured by perplexity (P: product, S: sentiment).'.\n" }, { "role": "assistant", "content": "The gC2S model consistently outperforms the C2S model across all contexts and domains, with the product context being more informative than the sentiment context for language modeling.We start with the task of language modeling. This shows that contextual information are indeed helpful for natural language understanding and prediction. Second, the product context seems to be more informative than the sentiment context for language modeling, no matter with the C2S or gC2S model. This may be that there are many words in the reviews that are relevant to the product information. Comparing the C2S and gC2S model, the gC2S model consistently outperforms the C2S model no matter which contexts are used. As explained previously, this is because in the C2S model, the context information may not be able to affect the words that are far away from the beginning of the sequences while the gC2S effectively addresses this through adding the direct dependency between the contexts and the words in the sequences." } ], "images": [ "scigen/train/1800.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of language modeling measured by perplexity (P: product, S: sentiment)." ], "reasoning": "The gC2S model consistently outperforms the C2S model across all contexts and domains, with the product context being more informative than the sentiment context for language modeling.", "description": "We start with the task of language modeling. This shows that contextual information are indeed helpful for natural language understanding and prediction. Second, the product context seems to be more informative than the sentiment context for language modeling, no matter with the C2S or gC2S model. This may be that there are many words in the reviews that are relevant to the product information. Comparing the C2S and gC2S model, the gC2S model consistently outperforms the C2S model no matter which contexts are used. As explained previously, this is because in the C2S model, the context information may not be able to affect the words that are far away from the beginning of the sequences while the gC2S effectively addresses this through adding the direct dependency between the contexts and the words in the sequences.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Results of language modeling measured by perplexity (P: product, S: sentiment).\nQuestion: Which model and context combination achieved the lowest score in the Hotel domain?\nSolve the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the scores in the Hotel domain, the gC2S(P+S) model achieved the lowest score of 21.2. So, the answer is {\"answer\": \"gC2S(P+S)\"}." } ], "images": [ "scigen/train/1800.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of language modeling measured by perplexity (P: product, S: sentiment)." ], "reasoning": "By comparing the scores in the Hotel domain, the gC2S(P+S) model achieved the lowest score of 21.2.", "question": "Which model and context combination achieved the lowest score in the Hotel domain?", "answer": "So, the answer is {\"answer\": \"gC2S(P+S)\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe C2S(P) model outperforms the RNN model in the Electronic domain.\n" }, { "role": "assistant", "content": "In the Electronic domain, the C2S(P) model has a score of 26.2, which is lower than the RNN model's score of 27.4, indicating better performance. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1800.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of language modeling measured by perplexity (P: product, S: sentiment)." ], "reasoning": "In the Electronic domain, the C2S(P) model has a score of 26.2, which is lower than the RNN model's score of 27.4, indicating better performance.", "statement": "The C2S(P) model outperforms the RNN model in the Electronic domain.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Summary of the evaluation data for fake review detection.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The gC2S model's effectiveness for fake review detection is evaluated by generating fake reviews for real reviews from Amazon and TripAdvisor data, ensuring each product has a minimum number of real reviews per rating score.To further evaluate the effectiveness of the gC2S model for natural language generation, we choose the task of fake review detection, which aims to classify whether the reviews are written by real users or generated by the gC2S model. Real reviews are treated as positive, and fake reviews are treated as negative. For the evaluation data, we randomly select some products which have at least two real reviews for each rating score in the Amazon data and one review for each rating score in the TripAdvisor data. For each real review, a fake review is generated with gC2S according to its contexts." } ], "images": [ "scigen/train/1801.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Summary of the evaluation data for fake review detection." ], "reasoning": "The gC2S model's effectiveness for fake review detection is evaluated by generating fake reviews for real reviews from Amazon and TripAdvisor data, ensuring each product has a minimum number of real reviews per rating score.", "description": "To further evaluate the effectiveness of the gC2S model for natural language generation, we choose the task of fake review detection, which aims to classify whether the reviews are written by real users or generated by the gC2S model. Real reviews are treated as positive, and fake reviews are treated as negative. For the evaluation data, we randomly select some products which have at least two real reviews for each rating score in the Amazon data and one review for each rating score in the TripAdvisor data. For each real review, a fake review is generated with gC2S according to its contexts.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 3: Summary of the evaluation data for fake review detection.', solve the following question step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nHow many total reviews are there for the Electronic domain?\n" }, { "role": "assistant", "content": "The table shows that the Electronic domain has 100 products, each with 2 reviews per rating, resulting in a total of 1,000 reviews. So, the answer is {\"answer\": \"1,000\"}" } ], "images": [ "scigen/train/1801.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Summary of the evaluation data for fake review detection." ], "reasoning": "The table shows that the Electronic domain has 100 products, each with 2 reviews per rating, resulting in a total of 1,000 reviews.", "question": "How many total reviews are there for the Electronic domain?", "answer": "So, the answer is {\"answer\": \"1,000\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Book domain has the highest number of products.\n" }, { "role": "assistant", "content": "The table indicates that the Electronic and Movie domains each have 100 products, which is higher than the 74 products in the Book domain. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1801.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Summary of the evaluation data for fake review detection." ], "reasoning": "The table indicates that the Electronic and Movie domains each have 100 products, which is higher than the 74 products in the Book domain.", "statement": "The Book domain has the highest number of products.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Results of fake review classification with human judgments (Positive: real reviews, Negative: fake reviews). In all the domains, more than 50% of the fake reviews are misclassified.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Amazon Mechanical Turk users evaluate the authenticity of product reviews, with quality control ensured by 'gotcha' questions, and only results from users who answer these correctly are kept.Human Evaluation. We use the Amazon Mechanical Turk to evaluate whether the reviews are fake not. We divide all the data into different batches. Each batch contains twenty reviews about the same product. We show the urls of the products, the sentiment rating and the review content to users to ask the turkers to judge whether the reviews are written by real users or not. To control the quality of the results, some “gotcha” questions are inserted in the middle of the list of reviews. Only the results judged by users who answer the “gotcha” questions correctly are kept. The kappa score is …." } ], "images": [ "scigen/train/1802.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of fake review classification with human judgments (Positive: real reviews, Negative: fake reviews). In all the domains, more than 50% of the fake reviews are misclassified." ], "reasoning": "Amazon Mechanical Turk users evaluate the authenticity of product reviews, with quality control ensured by 'gotcha' questions, and only results from users who answer these correctly are kept.", "description": "Human Evaluation. We use the Amazon Mechanical Turk to evaluate whether the reviews are fake not. We divide all the data into different batches. Each batch contains twenty reviews about the same product. We show the urls of the products, the sentiment rating and the review content to users to ask the turkers to judge whether the reviews are written by real users or not. To control the quality of the results, some “gotcha” questions are inserted in the middle of the list of reviews. Only the results judged by users who answer the “gotcha” questions correctly are kept. The kappa score is ….", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 4: Results of fake review classification with human judgments (Positive: real reviews, Negative: fake reviews). In all the domains, more than 50% of the fake reviews are misclassified.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich domain has the highest true positive rate?\n" }, { "role": "assistant", "content": "To find the domain with the highest true positive rate, compare the TP values across all domains. The highest TP value is 85.2 in the Hotel domain. So, the answer is {\"answer\": \"Hotel\"}" } ], "images": [ "scigen/train/1802.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of fake review classification with human judgments (Positive: real reviews, Negative: fake reviews). In all the domains, more than 50% of the fake reviews are misclassified." ], "reasoning": "To find the domain with the highest true positive rate, compare the TP values across all domains. The highest TP value is 85.2 in the Hotel domain.", "question": "Which domain has the highest true positive rate?", "answer": "So, the answer is {\"answer\": \"Hotel\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The false negative rate for the Movie domain is the lowest among all domains. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe false negative rate for the Movie domain is the lowest among all domains.\n" }, { "role": "assistant", "content": "To verify this statement, compare the FN values across all domains. The FN value for the Movie domain is 20.3, which is not the lowest. The lowest FN value is 14.8 in the Hotel domain. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1802.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of fake review classification with human judgments (Positive: real reviews, Negative: fake reviews). In all the domains, more than 50% of the fake reviews are misclassified." ], "reasoning": "To verify this statement, compare the FN values across all domains. The FN value for the Movie domain is 20.3, which is not the lowest. The lowest FN value is 14.8 in the Hotel domain.", "statement": "The false negative rate for the Movie domain is the lowest among all domains.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Fine granularity sentiment classification. Results on the true and fake reviews are very close.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The sentiment classifier's performance on fake reviews is comparable to real reviews, with some domains like Book and Movie showing better results for fake reviews.To conduct the classification, we randomly sample one 100,000 real reviews from the training data for training the sentiment classifier. As for the evaluation data, a fake review is generated for each test data according to its contexts. We can see that the results on the real and fake reviews are very close to each other. On some domains (e.g., Book and Movie), the results on the fake reviews are even better than the real reviews, showing that the reviews generated by the gC2S model accurately reflect the sentiment polarity." } ], "images": [ "scigen/train/1803.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Fine granularity sentiment classification. Results on the true and fake reviews are very close." ], "reasoning": "The sentiment classifier's performance on fake reviews is comparable to real reviews, with some domains like Book and Movie showing better results for fake reviews.", "description": "To conduct the classification, we randomly sample one 100,000 real reviews from the training data for training the sentiment classifier. As for the evaluation data, a fake review is generated for each test data according to its contexts. We can see that the results on the real and fake reviews are very close to each other. On some domains (e.g., Book and Movie), the results on the fake reviews are even better than the real reviews, showing that the reviews generated by the gC2S model accurately reflect the sentiment polarity.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 6: Fine granularity sentiment classification. Results on the true and fake reviews are very close.'. Output the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: Which domain has the highest F1 score for fake reviews?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the domain with the highest F1 score for fake reviews, compare the F1 scores of all fake reviews across different domains. The highest F1 score for fake reviews is 0.529 in the Book domain. So, the answer is {\"answer\": \"Book\"}." } ], "images": [ "scigen/train/1803.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Fine granularity sentiment classification. Results on the true and fake reviews are very close." ], "reasoning": "To find the domain with the highest F1 score for fake reviews, compare the F1 scores of all fake reviews across different domains. The highest F1 score for fake reviews is 0.529 in the Book domain.", "question": "Which domain has the highest F1 score for fake reviews?", "answer": "So, the answer is {\"answer\": \"Book\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The precision for fake reviews in the Movie domain is higher than the precision for true reviews in the same domain., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe precision for fake reviews in the Movie domain is higher than the precision for true reviews in the same domain.\n" }, { "role": "assistant", "content": "The precision for fake reviews in the Movie domain is 0.593, while the precision for true reviews in the Movie domain is 0.536. Since 0.593 is higher than 0.536, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1803.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Fine granularity sentiment classification. Results on the true and fake reviews are very close." ], "reasoning": "The precision for fake reviews in the Movie domain is 0.593, while the precision for true reviews in the Movie domain is 0.536. Since 0.593 is higher than 0.536, the statement is supported.", "statement": "The precision for fake reviews in the Movie domain is higher than the precision for true reviews in the same domain.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Accuracy values (%) for Experiment I: Compositionality detection from contexts', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The PCA subspace representation for both the phrase and context, combined with MSSG embedding, yielded the highest accuracy in both English and Chinese datasets, outperforming the average representation.Detection Results: We used both average and PCA subspace representations for the target phrase and its context. Since the average vector representation is commonly used in recent works, we take “avg phrase + avg context” as our baseline. We note that having a PCA approximation for both the phrase and the context, and the use of MSSG embedding yielded the best accuracy for this task in both the English and the Chinese datasets; this is an instance where the PCA subspace representation is superior to the average representation. We believe that unsupervised improvement beyond the fairly high accuracy rates is likely to require substantially new ideas as compared to those in this paper." } ], "images": [ "scigen/train/1804.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracy values (%) for Experiment I: Compositionality detection from contexts" ], "reasoning": "The PCA subspace representation for both the phrase and context, combined with MSSG embedding, yielded the highest accuracy in both English and Chinese datasets, outperforming the average representation.", "description": "Detection Results: We used both average and PCA subspace representations for the target phrase and its context. Since the average vector representation is commonly used in recent works, we take “avg phrase + avg context” as our baseline. We note that having a PCA approximation for both the phrase and the context, and the use of MSSG embedding yielded the best accuracy for this task in both the English and the Chinese datasets; this is an instance where the PCA subspace representation is superior to the average representation. We believe that unsupervised improvement beyond the fairly high accuracy rates is likely to require substantially new ideas as compared to those in this paper.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: Accuracy values (%) for Experiment I: Compositionality detection from contexts', solve the subsequent question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich representation method yielded the highest accuracy for the Chinese dataset using MSSG embedding?\n" }, { "role": "assistant", "content": "The table shows that the 'pca phrase pca context' method with MSSG embedding yielded the highest accuracy for the Chinese dataset with a value of 88.3. So, the answer is {\"answer\": \"pca phrase pca context\"}" } ], "images": [ "scigen/train/1804.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracy values (%) for Experiment I: Compositionality detection from contexts" ], "reasoning": "The table shows that the 'pca phrase pca context' method with MSSG embedding yielded the highest accuracy for the Chinese dataset with a value of 88.3.", "question": "Which representation method yielded the highest accuracy for the Chinese dataset using MSSG embedding?", "answer": "So, the answer is {\"answer\": \"pca phrase pca context\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe average representation for both phrase and context yielded the highest accuracy for the English dataset using CBOW embedding.\n" }, { "role": "assistant", "content": "The table shows that the 'pca phrase pca context' method with CBOW embedding yielded the highest accuracy for the English dataset with a value of 85.6, not the average representation. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1804.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracy values (%) for Experiment I: Compositionality detection from contexts" ], "reasoning": "The table shows that the 'pca phrase pca context' method with CBOW embedding yielded the highest accuracy for the English dataset with a value of 85.6, not the average representation.", "statement": "The average representation for both phrase and context yielded the highest accuracy for the English dataset using CBOW embedding.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Experiments on ENC, EVPC and GNC Datasets.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table compares the performance of different methods on three datasets using various metrics, highlighting the highest scores in each category.Detection Results: Our subspace representation (SubSpace) uses CBOW and MSSG embeddings, and we use both average and PCA approximations as context embeddings. Their performance is shown in the row of “SubSpace (CBOW)” and “SubSpace (MSSG)” respectively. We have two baseline methods: (1) pointwise mutual information (PMI). PMI=logP(w1w2)P(w1)P(w2), Higher PMI indicates the phrase is more likely to be non-compositional. (2) Average sentence embedding method. and we measure compositionality by the cosine similarity between the target word vector and the sentence vector. The corresponding performance is reported in the rows of “Avg Cxt (CBOW)” and “Avg Cxt (MSSG)”. specifically their methods based on word definitions, synonyms and idiom tags (denoted by ALLDEFS+SYN, ITAG+SYN, ALLDEFS) provided by wikitionary." } ], "images": [ "scigen/train/1805.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Experiments on ENC, EVPC and GNC Datasets." ], "reasoning": "The table compares the performance of different methods on three datasets using various metrics, highlighting the highest scores in each category.", "description": "Detection Results: Our subspace representation (SubSpace) uses CBOW and MSSG embeddings, and we use both average and PCA approximations as context embeddings. Their performance is shown in the row of “SubSpace (CBOW)” and “SubSpace (MSSG)” respectively. We have two baseline methods: (1) pointwise mutual information (PMI). PMI=logP(w1w2)P(w1)P(w2), Higher PMI indicates the phrase is more likely to be non-compositional. (2) Average sentence embedding method. and we measure compositionality by the cosine similarity between the target word vector and the sentence vector. The corresponding performance is reported in the rows of “Avg Cxt (CBOW)” and “Avg Cxt (MSSG)”. specifically their methods based on word definitions, synonyms and idiom tags (denoted by ALLDEFS+SYN, ITAG+SYN, ALLDEFS) provided by wikitionary.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 3: Experiments on ENC, EVPC and GNC Datasets.', answer the question below by showing each progressive step toward the answer. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich method achieved the highest First Component F1 score on the ENC dataset?\n" }, { "role": "assistant", "content": "To find the highest First Component F1 score on the ENC dataset, look at the 'First Component F1 score' column for the ENC dataset rows. The highest score is 84.2% achieved by SubSpace (CBOW). So, the answer is {\"answer\": \"SubSpace (CBOW)\"}." } ], "images": [ "scigen/train/1805.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Experiments on ENC, EVPC and GNC Datasets." ], "reasoning": "To find the highest First Component F1 score on the ENC dataset, look at the 'First Component F1 score' column for the ENC dataset rows. The highest score is 84.2% achieved by SubSpace (CBOW).", "question": "Which method achieved the highest First Component F1 score on the ENC dataset?", "answer": "So, the answer is {\"answer\": \"SubSpace (CBOW)\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The SubSpace (MSSG) method achieved the highest Second Component F1 score on the EVPC dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe SubSpace (MSSG) method achieved the highest Second Component F1 score on the EVPC dataset.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'Second Component F1 score' column for the EVPC dataset rows. The highest score is 70.5% achieved by SubSpace (MSSG). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1805.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Experiments on ENC, EVPC and GNC Datasets." ], "reasoning": "To verify this statement, look at the 'Second Component F1 score' column for the EVPC dataset rows. The highest score is 70.5% achieved by SubSpace (MSSG).", "statement": "The SubSpace (MSSG) method achieved the highest Second Component F1 score on the EVPC dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: Twitter Sarcasm Detection'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The performance metrics for a small dataset using trained word vectors show varying accuracies and F1 scores for different words, providing a baseline for future supervised learning systems.We emphasize that this performance is derived for a very small dataset (for each of the words) and is achieved using entirely only a trained set of word vectors – this would be a baseline to build on for the more sophisticated supervised learning systems." } ], "images": [ "scigen/train/1806.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Twitter Sarcasm Detection" ], "reasoning": "The performance metrics for a small dataset using trained word vectors show varying accuracies and F1 scores for different words, providing a baseline for future supervised learning systems.", "description": "We emphasize that this performance is derived for a very small dataset (for each of the words) and is achieved using entirely only a trained set of word vectors – this would be a baseline to build on for the more sophisticated supervised learning systems.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 4: Twitter Sarcasm Detection', solve the subsequent question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich word has the highest accuracy and what is its F1 score?\n" }, { "role": "assistant", "content": "By examining the accuracy row, the word 'always' has the highest accuracy of 0.792. The corresponding F1 score for 'always' is 0.605. So, the answer is {\"answer\": \"0.605\"}." } ], "images": [ "scigen/train/1806.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Twitter Sarcasm Detection" ], "reasoning": "By examining the accuracy row, the word 'always' has the highest accuracy of 0.792. The corresponding F1 score for 'always' is 0.605.", "question": "Which word has the highest accuracy and what is its F1 score?", "answer": "So, the answer is {\"answer\": \"0.605\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe word 'yeah' has the lowest F1 score.\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all words, 'glad' has the lowest F1 score of 0.582, not 'yeah'. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1806.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Twitter Sarcasm Detection" ], "reasoning": "By comparing the F1 scores of all words, 'glad' has the lowest F1 score of 0.582, not 'yeah'.", "statement": "The word 'yeah' has the lowest F1 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 5: Sarcasm Detection on Reddit Dataset'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The algorithm selects adjectives, adverbs, and verbs based on their POS tags and calculates compositionality scores, choosing the smallest scores as features.Algorithm Description: For a given comment, we first select words that might have sarcastic meaning based on their POS tags: adjectives (like ‘favorite’) , adverbs (like ‘happily’) and verbs (like ‘love’) are likely to be used in irony, and we pick candidate words whose POS tag are JJ (adjective), RB (adverb), or VB (verb). For each of the selected words in a given sentence, we obtain its compositionality score with respect to its local context. Among all these scores, we choose k smallest scores as features (here k=2,3,4 refers to a very small number of features, cf. Results:" } ], "images": [ "scigen/train/1807.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Sarcasm Detection on Reddit Dataset" ], "reasoning": "The algorithm selects adjectives, adverbs, and verbs based on their POS tags and calculates compositionality scores, choosing the smallest scores as features.", "description": "Algorithm Description: For a given comment, we first select words that might have sarcastic meaning based on their POS tags: adjectives (like ‘favorite’) , adverbs (like ‘happily’) and verbs (like ‘love’) are likely to be used in irony, and we pick candidate words whose POS tag are JJ (adjective), RB (adverb), or VB (verb). For each of the selected words in a given sentence, we obtain its compositionality score with respect to its local context. Among all these scores, we choose k smallest scores as features (here k=2,3,4 refers to a very small number of features, cf. Results:", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: Sarcasm Detection on Reddit Dataset'.\n\nQuestion: Which subspace combination achieved the highest F1 score?\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all subspace combinations, the highest F1 score is found in the SubSpace (JJ+RB) column. So, the answer is {\"answer\": \"SubSpace (JJ+RB)\"}." } ], "images": [ "scigen/train/1807.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Sarcasm Detection on Reddit Dataset" ], "reasoning": "By comparing the F1 scores of all subspace combinations, the highest F1 score is found in the SubSpace (JJ+RB) column.", "question": "Which subspace combination achieved the highest F1 score?", "answer": "So, the answer is {\"answer\": \"SubSpace (JJ+RB)\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe SubSpace (VB) combination achieved a higher precision than the Baseline.\n" }, { "role": "assistant", "content": "The precision for SubSpace (VB) is 0.289, which is lower than the Baseline precision of 0.315. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1807.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Sarcasm Detection on Reddit Dataset" ], "reasoning": "The precision for SubSpace (VB) is 0.289, which is lower than the Baseline precision of 0.315.", "statement": "The SubSpace (VB) combination achieved a higher precision than the Baseline.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 6: Metaphor Detection', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The compositionality detection algorithm with only four features achieves an accuracy of 0.729 and an F1 score of 0.744 on the original sentences, which is close to the state-of-the-art performance.Detection Results: On the full set of original sentences, the performance of our compositionality detection algorithm (with only four features in stark contrast to the more than 100 used in the state of the art) is not too far from the baseline." } ], "images": [ "scigen/train/1808.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Metaphor Detection" ], "reasoning": "The compositionality detection algorithm with only four features achieves an accuracy of 0.729 and an F1 score of 0.744 on the original sentences, which is close to the state-of-the-art performance.", "description": "Detection Results: On the full set of original sentences, the performance of our compositionality detection algorithm (with only four features in stark contrast to the more than 100 used in the state of the art) is not too far from the baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 6: Metaphor Detection', unfold the question presented below, detailing every step of your calculation. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the accuracy of the compositionality detection algorithm with four features on longer sentences?\n" }, { "role": "assistant", "content": "The table shows that the compositionality detection algorithm with four features on longer sentences has an accuracy of 0.809. So, the answer is {\"answer\": \"0.809\"}" } ], "images": [ "scigen/train/1808.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Metaphor Detection" ], "reasoning": "The table shows that the compositionality detection algorithm with four features on longer sentences has an accuracy of 0.809.", "question": "What is the accuracy of the compositionality detection algorithm with four features on longer sentences?", "answer": "So, the answer is {\"answer\": \"0.809\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The compositionality detection algorithm with three features on original sentences has an accuracy higher than 0.8. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe compositionality detection algorithm with three features on original sentences has an accuracy higher than 0.8.\n" }, { "role": "assistant", "content": "The table shows that the compositionality detection algorithm with three features on original sentences has an accuracy of 0.735, which is lower than 0.8. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1808.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Metaphor Detection" ], "reasoning": "The table shows that the compositionality detection algorithm with three features on original sentences has an accuracy of 0.735, which is lower than 0.8.", "statement": "The compositionality detection algorithm with three features on original sentences has an accuracy higher than 0.8.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 7: The performance evaluation of ANSP on Twitter16', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The model performs better on 'true' and 'false' types compared to 'half-true' and 'half-false' types due to its ability to differentiate between true and false information, but it struggles with finer distinctions.Specifically, the performance on types “true” and “false” is more remarkable than types “half-true”, “half-false”, etc. The reason is that our model discovers differential features through separating common features between true and false information. But this is coarse-grained due to ignoring the differential features between half-true and half-true types, which is a limitation to our model. In the future, to address this problem, we will focus on capturing the differential features among multiple types to improve the performance of different credibility types of information." } ], "images": [ "scigen/train/1809.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: The performance evaluation of ANSP on Twitter16" ], "reasoning": "The model performs better on 'true' and 'false' types compared to 'half-true' and 'half-false' types due to its ability to differentiate between true and false information, but it struggles with finer distinctions.", "description": "Specifically, the performance on types “true” and “false” is more remarkable than types “half-true”, “half-false”, etc. The reason is that our model discovers differential features through separating common features between true and false information. But this is coarse-grained due to ignoring the differential features between half-true and half-true types, which is a limitation to our model. In the future, to address this problem, we will focus on capturing the differential features among multiple types to improve the performance of different credibility types of information.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 7: The performance evaluation of ANSP on Twitter16\nQuestion: Which method has the highest F1 score for the 'true' type?\nGive a detailed response to the above question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the method with the highest F1 score for the 'true' type, look at the 'TR' column and identify the highest value. So, the answer is {\"answer\": \"ANSP\"}." } ], "images": [ "scigen/train/1809.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: The performance evaluation of ANSP on Twitter16" ], "reasoning": "To find the method with the highest F1 score for the 'true' type, look at the 'TR' column and identify the highest value.", "question": "Which method has the highest F1 score for the 'true' type?", "answer": "So, the answer is {\"answer\": \"ANSP\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The ANSP method has the highest accuracy among all methods., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe ANSP method has the highest accuracy among all methods.\n" }, { "role": "assistant", "content": "To verify this statement, compare the accuracy values of all methods and check if ANSP has the highest value. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1809.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: The performance evaluation of ANSP on Twitter16" ], "reasoning": "To verify this statement, compare the accuracy values of all methods and check if ANSP has the highest value.", "statement": "The ANSP method has the highest accuracy among all methods.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: The parameters of ANSP on different datasets', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The experiments used pre-trained 200-dimensional word2vec embeddings from English and Chinese Wikipedia, with hyperparameters tuned on the validation dataset to achieve optimal performance.Hyperparameters: In our experiments, we use pre-trained 200-dimensional word2vec embeddings from English and Chinese Wikipedia For each task, we strictly turn all the hyperparameters on the validation dataset, and we achieve the best performance via a small grid search." } ], "images": [ "scigen/train/1810.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The parameters of ANSP on different datasets" ], "reasoning": "The experiments used pre-trained 200-dimensional word2vec embeddings from English and Chinese Wikipedia, with hyperparameters tuned on the validation dataset to achieve optimal performance.", "description": "Hyperparameters: In our experiments, we use pre-trained 200-dimensional word2vec embeddings from English and Chinese Wikipedia For each task, we strictly turn all the hyperparameters on the validation dataset, and we achieve the best performance via a small grid search.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 3: The parameters of ANSP on different datasets\nQuestion: What is the dropout rate used for the Weibo task?\n" }, { "role": "assistant", "content": "To find the dropout rate for the Weibo task, look at the 'Dropout' column in the row labeled 'Weibo'. The value is 0.5. So, the answer is {\"answer\": \"0.5\"}." } ], "images": [ "scigen/train/1810.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The parameters of ANSP on different datasets" ], "reasoning": "To find the dropout rate for the Weibo task, look at the 'Dropout' column in the row labeled 'Weibo'. The value is 0.5.", "question": "What is the dropout rate used for the Weibo task?", "answer": "So, the answer is {\"answer\": \"0.5\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe learning rate for the Twitter16 task is 0.01.\n" }, { "role": "assistant", "content": "The table shows that the learning rate (LRate) for the Twitter16 task is indeed 0.01. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1810.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The parameters of ANSP on different datasets" ], "reasoning": "The table shows that the learning rate (LRate) for the Twitter16 task is indeed 0.01.", "statement": "The learning rate for the Twitter16 task is 0.01.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: The performance evaluation of ANSP on LIAR and Weibo'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The model achieves state-of-the-art performance on two datasets with 42.8% and 46.3% overall accuracy, surpassing other methods by significant margins.In details, the model has a 17% average improvement compared with LR and SVMs because our model with neural networks can fully exploit deep semantic representations of text. The model achieves the state-of-the-art performance on two datasets with 42.8%, and 46.3% overall accuracy respectively, surpassing CNN, LSTM, and BiLSTM by a significant margin of 16.8%, and 14.2%. This suggests that private features and common features captured by our model based on multi-task learning are effective for credibility evaluation. The model outperforms attention-based method with 2.1%, 3.1% improvements. This is because our model reduces common, irrelevant-type features from private features and focuses on the differential credibility features." } ], "images": [ "scigen/train/1811.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The performance evaluation of ANSP on LIAR and Weibo" ], "reasoning": "The model achieves state-of-the-art performance on two datasets with 42.8% and 46.3% overall accuracy, surpassing other methods by significant margins.", "description": "In details, the model has a 17% average improvement compared with LR and SVMs because our model with neural networks can fully exploit deep semantic representations of text. The model achieves the state-of-the-art performance on two datasets with 42.8%, and 46.3% overall accuracy respectively, surpassing CNN, LSTM, and BiLSTM by a significant margin of 16.8%, and 14.2%. This suggests that private features and common features captured by our model based on multi-task learning are effective for credibility evaluation. The model outperforms attention-based method with 2.1%, 3.1% improvements. This is because our model reduces common, irrelevant-type features from private features and focuses on the differential credibility features.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: The performance evaluation of ANSP on LIAR and Weibo', solve this question and give detail solutions. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the overall accuracy of the ANSP model on the LIAR dataset, and how does it compare to the Multi-Attention model?\n" }, { "role": "assistant", "content": "The ANSP model has an overall accuracy of 42.8% on the LIAR dataset, while the Multi-Attention model has an overall accuracy of 40.7%. Therefore, the ANSP model outperforms the Multi-Attention model by 2.1%. So, the answer is {\"answer\": \"42.8%\"}" } ], "images": [ "scigen/train/1811.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The performance evaluation of ANSP on LIAR and Weibo" ], "reasoning": "The ANSP model has an overall accuracy of 42.8% on the LIAR dataset, while the Multi-Attention model has an overall accuracy of 40.7%. Therefore, the ANSP model outperforms the Multi-Attention model by 2.1%.", "question": "What is the overall accuracy of the ANSP model on the LIAR dataset, and how does it compare to the Multi-Attention model?", "answer": "So, the answer is {\"answer\": \"42.8%\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe ANSP model achieves the highest precision on the Weibo dataset.\n" }, { "role": "assistant", "content": "The table shows that the ANSP model has a precision of 0.490 on the Weibo dataset, which is the highest compared to other models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1811.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The performance evaluation of ANSP on LIAR and Weibo" ], "reasoning": "The table shows that the ANSP model has a precision of 0.490 on the Weibo dataset, which is the highest compared to other models listed.", "statement": "The ANSP model achieves the highest precision on the Weibo dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: The performance evaluation of different parts of the model'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The additional components in the basic method improve credibility evaluation, with the model including Diff showing significant improvement.We use the above models to test the validity of different components of our model. It indicates that the additional components in the basic method are helpful for credibility evaluation. Especially, the model with Diff outperforms obviously the model without Diff, which demonstrates the effectiveness of negative KL-divergence strategy in our model." } ], "images": [ "scigen/train/1812.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The performance evaluation of different parts of the model" ], "reasoning": "The additional components in the basic method improve credibility evaluation, with the model including Diff showing significant improvement.", "description": "We use the above models to test the validity of different components of our model. It indicates that the additional components in the basic method are helpful for credibility evaluation. Especially, the model with Diff outperforms obviously the model without Diff, which demonstrates the effectiveness of negative KL-divergence strategy in our model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: The performance evaluation of different parts of the model'.\n\nQuestion: Which model shows the highest F1 score for the Weibo dataset?\n" }, { "role": "assistant", "content": "To find the highest F1 score for the Weibo dataset, look at the F1 column for the rows labeled 'Weibo'. The highest value is 0.462 for the 'basic+Adv+Ind+Diff' model. So, the answer is {\"answer\": \"basic+Adv+Ind+Diff\"}." } ], "images": [ "scigen/train/1812.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The performance evaluation of different parts of the model" ], "reasoning": "To find the highest F1 score for the Weibo dataset, look at the F1 column for the rows labeled 'Weibo'. The highest value is 0.462 for the 'basic+Adv+Ind+Diff' model.", "question": "Which model shows the highest F1 score for the Weibo dataset?", "answer": "So, the answer is {\"answer\": \"basic+Adv+Ind+Diff\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The model with Diff outperforms the model without Diff in the LIAR dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe model with Diff outperforms the model without Diff in the LIAR dataset.\n" }, { "role": "assistant", "content": "Comparing the F1 scores for the LIAR dataset, the 'basic+Adv+Ind+Diff' model has an F1 score of 0.410, which is higher than the 'basic+Adv+Ind' model's F1 score of 0.395. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1812.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The performance evaluation of different parts of the model" ], "reasoning": "Comparing the F1 scores for the LIAR dataset, the 'basic+Adv+Ind+Diff' model has an F1 score of 0.410, which is higher than the 'basic+Adv+Ind' model's F1 score of 0.395.", "statement": "The model with Diff outperforms the model without Diff in the LIAR dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 6: The performance of different methods for common features extraction'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The method using adversarial networks with reinforcement learning outperforms the method without reinforcement learning by 3.7% in accuracy, indicating its effectiveness in capturing common features.Furthermore, in order to evaluate the effectiveness of reinforcement learning in common feature extractor, we design two methods, i.e., the method of using adversarial networks with reinforcement learning and the method without reinforcement learning, to demonstrate its performance. We observe that the method of using adversarial networks with reinforcement learning achieves better performance, outperforming the latter with 3.7% on accuracy, which indicates that reinforcement learning contributes to capturing common features from true and false information." } ], "images": [ "scigen/train/1813.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: The performance of different methods for common features extraction" ], "reasoning": "The method using adversarial networks with reinforcement learning outperforms the method without reinforcement learning by 3.7% in accuracy, indicating its effectiveness in capturing common features.", "description": "Furthermore, in order to evaluate the effectiveness of reinforcement learning in common feature extractor, we design two methods, i.e., the method of using adversarial networks with reinforcement learning and the method without reinforcement learning, to demonstrate its performance. We observe that the method of using adversarial networks with reinforcement learning achieves better performance, outperforming the latter with 3.7% on accuracy, which indicates that reinforcement learning contributes to capturing common features from true and false information.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: The performance of different methods for common features extraction\nQuestion: What is the difference in F1 score between the methods with and without reinforcement learning?\nSolve the above question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The F1 score for the method with reinforcement learning is 0.462, while the F1 score for the method without reinforcement learning is 0.413. The difference is 0.462 - 0.413 = 0.049. So, the answer is {\"answer\": \"0.049\"}" } ], "images": [ "scigen/train/1813.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: The performance of different methods for common features extraction" ], "reasoning": "The F1 score for the method with reinforcement learning is 0.462, while the F1 score for the method without reinforcement learning is 0.413. The difference is 0.462 - 0.413 = 0.049.", "question": "What is the difference in F1 score between the methods with and without reinforcement learning?", "answer": "So, the answer is {\"answer\": \"0.049\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe method with reinforcement learning has a higher precision than the method without reinforcement learning.\n" }, { "role": "assistant", "content": "The table shows that the precision (P) for the method with reinforcement learning is 0.490, while the precision for the method without reinforcement learning is 0.447. Since 0.490 is greater than 0.447, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1813.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: The performance of different methods for common features extraction" ], "reasoning": "The table shows that the precision (P) for the method with reinforcement learning is 0.490, while the precision for the method without reinforcement learning is 0.447. Since 0.490 is greater than 0.447, the statement is supported.", "statement": "The method with reinforcement learning has a higher precision than the method without reinforcement learning.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'TABLE I: LDA and RBM performance evaluation', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The RBM model outperforms the LDA model in all datasets, with significant improvements in mAP scores across OMDb, Reuters, and 20NewsGroup datasets.In the first experiment, we investigate the topic modeling performance between LDA and RBM. For the training of the LDA model, the training iteration is 15 and the number of generated topics is 500 which are as the same as the RBM model. The RBM outperforms the LDA in all datasets. For example, using the mAP5 evaluation, the RBM is 30.22% greater than the LDA in OMDb dataset, 18.18% greater in Reuters dataset and 25.25% greater in 20NewsGroup dataset. To have a fair comparison, the RBM model here is based on word only features. In the next we will show the including word pairs can further improve its mAP score." } ], "images": [ "scigen/train/1814.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: LDA and RBM performance evaluation" ], "reasoning": "The RBM model outperforms the LDA model in all datasets, with significant improvements in mAP scores across OMDb, Reuters, and 20NewsGroup datasets.", "description": "In the first experiment, we investigate the topic modeling performance between LDA and RBM. For the training of the LDA model, the training iteration is 15 and the number of generated topics is 500 which are as the same as the RBM model. The RBM outperforms the LDA in all datasets. For example, using the mAP5 evaluation, the RBM is 30.22% greater than the LDA in OMDb dataset, 18.18% greater in Reuters dataset and 25.25% greater in 20NewsGroup dataset. To have a fair comparison, the RBM model here is based on word only features. In the next we will show the including word pairs can further improve its mAP score.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'TABLE I: LDA and RBM performance evaluation', solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nBy how much percentage does the RBM model outperform the LDA model in the mAP5 evaluation for the Reuters dataset?\n" }, { "role": "assistant", "content": "The mAP5 score for the Reuters dataset using LDA is 0.77500, and using RBM is 0.91589. The percentage increase is calculated as ((0.91589 - 0.77500) / 0.77500) * 100. So, the answer is {\"answer\": \"18.18%\"}." } ], "images": [ "scigen/train/1814.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: LDA and RBM performance evaluation" ], "reasoning": "The mAP5 score for the Reuters dataset using LDA is 0.77500, and using RBM is 0.91589. The percentage increase is calculated as ((0.91589 - 0.77500) / 0.77500) * 100.", "question": "By how much percentage does the RBM model outperform the LDA model in the mAP5 evaluation for the Reuters dataset?", "answer": "So, the answer is {\"answer\": \"18.18%\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The RBM model achieves a higher mAP10 score than the LDA model in the OMDb dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe RBM model achieves a higher mAP10 score than the LDA model in the OMDb dataset.\n" }, { "role": "assistant", "content": "The mAP10 score for the OMDb dataset using LDA is 0.03914, and using RBM is 0.05273. Since 0.05273 is greater than 0.03914, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1814.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: LDA and RBM performance evaluation" ], "reasoning": "The mAP10 score for the OMDb dataset using LDA is 0.03914, and using RBM is 0.05273. Since 0.05273 is greater than 0.03914, the statement is supported.", "statement": "The RBM model achieves a higher mAP10 score than the LDA model in the OMDb dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'TABLE II: fixed total feature number word/word pair performance evaluation'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The word/word pair combined model consistently outperforms the word-only model, with the most significant improvements occurring at specific feature sizes for different mAP metrics.the word/word pair combined model almost always performs better than the word-only model. For the mAP1, the mAP5 and the mAP10, the most significant improvement occurs when total feature size is set to = 11000. About 10.48%, 7.97%, and 9.83% improved were found compared to the word-only model. For the mAP3, the most significant improvement occurs when the total feature size is set to = 12000, and about 9.35% improvement is achieved by considering word pair. Again, the word/word pair combined model outperforms the word-only model almost all the time. For the mAP1,3,5 and 10 up to 1.05%, 1.11%, 1.02% and 0.89% improvement are achieved. Similar to previous two datasets, all the results from word/word pair combined model are better than the word-only model. For the mAP1,3,5 and 10, the most significant improvement occurs when when the total feature size is set to = 11500. Up to 10.40%, 11.91%, 12.46% and 12.99% improvements can achieved." } ], "images": [ "scigen/train/1815.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: fixed total feature number word/word pair performance evaluation" ], "reasoning": "The word/word pair combined model consistently outperforms the word-only model, with the most significant improvements occurring at specific feature sizes for different mAP metrics.", "description": "the word/word pair combined model almost always performs better than the word-only model. For the mAP1, the mAP5 and the mAP10, the most significant improvement occurs when total feature size is set to = 11000. About 10.48%, 7.97%, and 9.83% improved were found compared to the word-only model. For the mAP3, the most significant improvement occurs when the total feature size is set to = 12000, and about 9.35% improvement is achieved by considering word pair. Again, the word/word pair combined model outperforms the word-only model almost all the time. For the mAP1,3,5 and 10 up to 1.05%, 1.11%, 1.02% and 0.89% improvement are achieved. Similar to previous two datasets, all the results from word/word pair combined model are better than the word-only model. For the mAP1,3,5 and 10, the most significant improvement occurs when when the total feature size is set to = 11500. Up to 10.40%, 11.91%, 12.46% and 12.99% improvements can achieved.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'TABLE II: fixed total feature number word/word pair performance evaluation', answer the following question. Give detailed solution consisting of each step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the most significant improvement in mAP5 for the 20NewsGroup dataset, and at what feature size does it occur?\n" }, { "role": "assistant", "content": "For the 20NewsGroup dataset, the most significant improvement in mAP5 occurs at a feature size of 11.5K, with an improvement of 12.46% compared to the word-only model. So, the answer is {\"answer\": \"11.5K\"}" } ], "images": [ "scigen/train/1815.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: fixed total feature number word/word pair performance evaluation" ], "reasoning": "For the 20NewsGroup dataset, the most significant improvement in mAP5 occurs at a feature size of 11.5K, with an improvement of 12.46% compared to the word-only model.", "question": "What is the most significant improvement in mAP5 for the 20NewsGroup dataset, and at what feature size does it occur?", "answer": "So, the answer is {\"answer\": \"11.5K\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe word/word pair combined model achieves the highest mAP1 improvement for the OMDB dataset at a feature size of 11K.\n" }, { "role": "assistant", "content": "The table shows that the highest mAP1 improvement for the OMDB dataset occurs at a feature size of 10.5K word pair, not 11K. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1815.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: fixed total feature number word/word pair performance evaluation" ], "reasoning": "The table shows that the highest mAP1 improvement for the OMDB dataset occurs at a feature size of 10.5K word pair, not 11K.", "statement": "The word/word pair combined model achieves the highest mAP1 improvement for the OMDB dataset at a feature size of 11K.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'TABLE III: Different Word Pair Generation Algorithms for OMDb'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "K-means clustering reduces redundancies and compresses the input space by grouping semantically similar word pairs.This is because, although both “Non-K” and “semantic” techniques extract word pairs using natural language processing, without the K-means clustering, semantically similar pairs will be considered separately. Hence there will be lots of redundancies in the input space. This will either increase the size of the input space, or, in order to control the input size, reduce the amount of information captured by the input set. The K-means clustering performs the function of compression and feature extraction." } ], "images": [ "scigen/train/1816.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Different Word Pair Generation Algorithms for OMDb" ], "reasoning": "K-means clustering reduces redundancies and compresses the input space by grouping semantically similar word pairs.", "description": "This is because, although both “Non-K” and “semantic” techniques extract word pairs using natural language processing, without the K-means clustering, semantically similar pairs will be considered separately. Hence there will be lots of redundancies in the input space. This will either increase the size of the input space, or, in order to control the input size, reduce the amount of information captured by the input set. The K-means clustering performs the function of compression and feature extraction.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: TABLE III: Different Word Pair Generation Algorithms for OMDb\nQuestion: Which technique has the highest mAP 1 score?\nSolve the above question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the mAP 1 scores in the table, the Semantic technique has the highest score of 0.14870. So, the answer is {\"answer\": \"Semantic\"}." } ], "images": [ "scigen/train/1816.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Different Word Pair Generation Algorithms for OMDb" ], "reasoning": "By comparing the mAP 1 scores in the table, the Semantic technique has the highest score of 0.14870.", "question": "Which technique has the highest mAP 1 score?", "answer": "So, the answer is {\"answer\": \"Semantic\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Non-K technique has the lowest mAP 10 score.\n" }, { "role": "assistant", "content": "By examining the mAP 10 scores in the table, the N-gram technique has the lowest score of 0.05132, not the Non-K technique. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1816.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Different Word Pair Generation Algorithms for OMDb" ], "reasoning": "By examining the mAP 10 scores in the table, the N-gram technique has the lowest score of 0.05132, not the Non-K technique.", "statement": "The Non-K technique has the lowest mAP 10 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: Performances of models without character embedding on test set.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The deformable stacked structure outperforms the BiLSTM-CRF model in most metrics, indicating its effectiveness in handling OOV words.Deformable stacked structure utilizes the feature of neighbor words. Thus, it should make better predictions when OOV occurs. We conduct experiments on models without character embedding." } ], "images": [ "scigen/train/1817.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Performances of models without character embedding on test set." ], "reasoning": "The deformable stacked structure outperforms the BiLSTM-CRF model in most metrics, indicating its effectiveness in handling OOV words.", "description": "Deformable stacked structure utilizes the feature of neighbor words. Thus, it should make better predictions when OOV occurs. We conduct experiments on models without character embedding.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 5: Performances of models without character embedding on test set.', and sequentially solve the question, writing out each step of your reasoning process. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model has a higher recall score on the OntoNotes 5.0 dataset?\n" }, { "role": "assistant", "content": "Compare the recall scores of both models on the OntoNotes 5.0 dataset. The BiLSTM-CRF model has a recall score of 85.87, while the deformable stacked structure has a recall score of 86.14. So, the answer is {\"answer\": \"Deformable stacked structure\"}." } ], "images": [ "scigen/train/1817.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Performances of models without character embedding on test set." ], "reasoning": "Compare the recall scores of both models on the OntoNotes 5.0 dataset. The BiLSTM-CRF model has a recall score of 85.87, while the deformable stacked structure has a recall score of 86.14.", "question": "Which model has a higher recall score on the OntoNotes 5.0 dataset?", "answer": "So, the answer is {\"answer\": \"Deformable stacked structure\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe BiLSTM-CRF model has a higher precision score on the CoNLL-2003 dataset.\n" }, { "role": "assistant", "content": "Compare the precision scores of both models on the CoNLL-2003 dataset. The BiLSTM-CRF model has a precision score of 87.97, while the deformable stacked structure has a precision score of 87.91. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1817.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Performances of models without character embedding on test set." ], "reasoning": "Compare the precision scores of both models on the CoNLL-2003 dataset. The BiLSTM-CRF model has a precision score of 87.97, while the deformable stacked structure has a precision score of 87.91.", "statement": "The BiLSTM-CRF model has a higher precision score on the CoNLL-2003 dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 6: Effects of different structure setting on OntoNotes 5.0 datset. Structure 1: deformable stacked structure between LSTM layers. Structure 2: deformable stacked structure between LSTM layers and CRF. Structure 3: both structure 1 and structure 2.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Structure 1 uses the deformable stacked structure for feature augmentation in the next LSTM layer, while Structure 2 uses it for feature selection in the CRF layer, leading to improved recall.These two structures differ on the function of their next layers, and the difference in improvement also comes from that. For structure 1, the deformable stacked structure reconstructs the input of the next LSTM layer. Thus, it plays a role of feature augmentation for the next LSTM layer to better extract related information. And better extraction of feature also helps with the decoding procedure. For structure 2, the deformable stacked structure reconstructs the input of the CRF layer and plays a role of feature selection for decoding. Compared with the baseline model, the deformable stacked structure has great improvement in recall. It indicates that the deformable stacked structure can discover more named entity in the sentence." } ], "images": [ "scigen/train/1818.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Effects of different structure setting on OntoNotes 5.0 datset. Structure 1: deformable stacked structure between LSTM layers. Structure 2: deformable stacked structure between LSTM layers and CRF. Structure 3: both structure 1 and structure 2." ], "reasoning": "Structure 1 uses the deformable stacked structure for feature augmentation in the next LSTM layer, while Structure 2 uses it for feature selection in the CRF layer, leading to improved recall.", "description": "These two structures differ on the function of their next layers, and the difference in improvement also comes from that. For structure 1, the deformable stacked structure reconstructs the input of the next LSTM layer. Thus, it plays a role of feature augmentation for the next LSTM layer to better extract related information. And better extraction of feature also helps with the decoding procedure. For structure 2, the deformable stacked structure reconstructs the input of the CRF layer and plays a role of feature selection for decoding. Compared with the baseline model, the deformable stacked structure has great improvement in recall. It indicates that the deformable stacked structure can discover more named entity in the sentence.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: Table 6: Effects of different structure setting on OntoNotes 5.0 datset. Structure 1: deformable stacked structure between LSTM layers. Structure 2: deformable stacked structure between LSTM layers and CRF. Structure 3: both structure 1 and structure 2.\nQuestion: Which structure and number of offsets combination achieved the highest recall?\n" }, { "role": "assistant", "content": "By examining the table, the highest recall (R) value is 88.81, which is achieved by Structure 3 with 3 offsets. So, the answer is {\"answer\": \"Structure 3 with 3 offsets\"}" } ], "images": [ "scigen/train/1818.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Effects of different structure setting on OntoNotes 5.0 datset. Structure 1: deformable stacked structure between LSTM layers. Structure 2: deformable stacked structure between LSTM layers and CRF. Structure 3: both structure 1 and structure 2." ], "reasoning": "By examining the table, the highest recall (R) value is 88.81, which is achieved by Structure 3 with 3 offsets.", "question": "Which structure and number of offsets combination achieved the highest recall?", "answer": "So, the answer is {\"answer\": \"Structure 3 with 3 offsets\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nStructure 2 with 2 offsets has a higher F-score than the baseline model.\n" }, { "role": "assistant", "content": "The table shows that Structure 2 with 2 offsets has an F-score of 87.55, while the baseline model has an F-score of 87.00. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1818.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Effects of different structure setting on OntoNotes 5.0 datset. Structure 1: deformable stacked structure between LSTM layers. Structure 2: deformable stacked structure between LSTM layers and CRF. Structure 3: both structure 1 and structure 2." ], "reasoning": "The table shows that Structure 2 with 2 offsets has an F-score of 87.55, while the baseline model has an F-score of 87.00. Therefore, the statement is supported.", "statement": "Structure 2 with 2 offsets has a higher F-score than the baseline model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: F1 scores, precision (PREC) and recall (REC) for different models for the multi-label classification task. σ is the standard deviation of the F1 score over three runs. All results are the average of three runs.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "When adding more detailed contextual information in the environments, as is the case for CoDiR (capt), the model outperforms the baseline in all cases.When adding more detailed contextual information in the environments, as is the case for CoDiR (capt), our model outperforms the baseline in all cases." } ], "images": [ "scigen/train/1819.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: F1 scores, precision (PREC) and recall (REC) for different models for the multi-label classification task. σ is the standard deviation of the F1 score over three runs. All results are the average of three runs." ], "reasoning": "When adding more detailed contextual information in the environments, as is the case for CoDiR (capt), the model outperforms the baseline in all cases.", "description": "When adding more detailed contextual information in the environments, as is the case for CoDiR (capt), our model outperforms the baseline in all cases.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 1: F1 scores, precision (PREC) and recall (REC) for different models for the multi-label classification task. σ is the standard deviation of the F1 score over three runs. All results are the average of three runs.', unfold the question presented below, detailing every step of your calculation. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model and method combination achieved the highest F1 score?\n" }, { "role": "assistant", "content": "To find the highest F1 score, look at the F1 column and identify the highest value, which is 0.661. Then, find the corresponding model and method combination, which is Inception-v3 with CoDiR (capt). So, the answer is {\"answer\": \"Inception-v3 with CoDiR (capt)\"}" } ], "images": [ "scigen/train/1819.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: F1 scores, precision (PREC) and recall (REC) for different models for the multi-label classification task. σ is the standard deviation of the F1 score over three runs. All results are the average of three runs." ], "reasoning": "To find the highest F1 score, look at the F1 column and identify the highest value, which is 0.661. Then, find the corresponding model and method combination, which is Inception-v3 with CoDiR (capt).", "question": "Which model and method combination achieved the highest F1 score?", "answer": "So, the answer is {\"answer\": \"Inception-v3 with CoDiR (capt)\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nResNet-101 with CoDiR (capt) achieved an F1 score higher than 0.65.\n" }, { "role": "assistant", "content": "Check the F1 score for ResNet-101 with CoDiR (capt). The F1 score is 0.657, which is higher than 0.65. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1819.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: F1 scores, precision (PREC) and recall (REC) for different models for the multi-label classification task. σ is the standard deviation of the F1 score over three runs. All results are the average of three runs." ], "reasoning": "Check the F1 score for ResNet-101 with CoDiR (capt). The F1 score is 0.657, which is higher than 0.65.", "statement": "ResNet-101 with CoDiR (capt) achieved an F1 score higher than 0.65.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Performance of pretraining on different financial corpus.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "FinBERT trained on all corpora achieves the best performance, and Analyst Reports dataset performs well in three tasks despite having fewer word tokens.Corpus Contribution We also train different FinBERT models on three financial corpus separately. It shows that FinBERT trained on all corpora achieves the overall best performance indicating that combining additional financial communication corpus could improve the language model quality. Among three datasets, Analyst Reports dataset appears to perform well in three different tasks, even though it only has 1.1 billion word tokens. Does it suggest that Analyst Reports data contains more information content than corporate reports and earnings call transcripts? We leave it for future research." } ], "images": [ "scigen/train/1820.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of pretraining on different financial corpus." ], "reasoning": "FinBERT trained on all corpora achieves the best performance, and Analyst Reports dataset performs well in three tasks despite having fewer word tokens.", "description": "Corpus Contribution We also train different FinBERT models on three financial corpus separately. It shows that FinBERT trained on all corpora achieves the overall best performance indicating that combining additional financial communication corpus could improve the language model quality. Among three datasets, Analyst Reports dataset appears to perform well in three different tasks, even though it only has 1.1 billion word tokens. Does it suggest that Analyst Reports data contains more information content than corporate reports and earnings call transcripts? We leave it for future research.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 3: Performance of pretraining on different financial corpus.\nQuestion: Which dataset has the highest performance in the AnalystTone task for the FinVocab model?\n" }, { "role": "assistant", "content": "To find the highest performance in the AnalystTone task for the FinVocab model, look at the AnalystTone row under FinVocab and compare the values for each dataset. The highest value is 0.872 for Analyst Reports. So, the answer is {\"answer\": \"Analyst Reports\"}." } ], "images": [ "scigen/train/1820.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of pretraining on different financial corpus." ], "reasoning": "To find the highest performance in the AnalystTone task for the FinVocab model, look at the AnalystTone row under FinVocab and compare the values for each dataset. The highest value is 0.872 for Analyst Reports.", "question": "Which dataset has the highest performance in the AnalystTone task for the FinVocab model?", "answer": "So, the answer is {\"answer\": \"Analyst Reports\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe FinVocab model performs better on the AnalystTone task than the BaseVocab model for the Earnings Call dataset.\n" }, { "role": "assistant", "content": "Compare the performance of the FinVocab and BaseVocab models on the AnalystTone task for the Earnings Call dataset. FinVocab has a score of 0.870, while BaseVocab has a score of 0.862, indicating that FinVocab performs better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1820.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of pretraining on different financial corpus." ], "reasoning": "Compare the performance of the FinVocab and BaseVocab models on the AnalystTone task for the Earnings Call dataset. FinVocab has a score of 0.870, while BaseVocab has a score of 0.862, indicating that FinVocab performs better.", "statement": "The FinVocab model performs better on the AnalystTone task than the BaseVocab model for the Earnings Call dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Language Modeling Perplexity scores on three datasets under two different settings of NLM i.e., S → small-NLM and L → large-NLM. Here, (+) → augment this feature to the model in previous row, NLM → LSTM-LM, (∗) → scores taken from Wang et al. (2018). Here, bold values indicate best performing proposed model in comparison to LSTM-LM baseline, and GAIN(%) indicates the improvement in performance of the same.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "ETA-NLM consistently outperforms LTA-NLM across all datasets, and LETA-NLM further improves performance by combining ETR and LTR vectors, with additional gains when incorporating sentence-level topic representations (+SDT).All topic composition models outperform LSTM-LM baseline which demonstrate the advantage of composing document topical semantics in NLM. Based on the results, here are three key observations: (i) ETA-NLM always performs better than LTA-NLM resulting in 11% (49.34 vs 55.48), 13.2% (59.20 vs 68.21) and 2.7% (95.62 vs 98.31) improvement for APNEWS, IMDB and BNC datasets respectively under small-NLM configuration. This behaviour asserts that ETR vector effectively captures fine-grained document topical semantics compared to LTR vector; (ii) LETA-NLM outperforms both LTA-NLM & ETA-NLM by exploiting complementary semantics of ETR and LTR vectors; (iii) composing sentence-level topic representations i.e., +SDT, further boost the performance by maintaining sentence-level topical discourse and hence LETA-NLM +SDT improve upon LTA-NLM model by 22% (42.98 vs 55.48), 17% (56.65 vs 68.21) and 10% (88.30 vs 98.31) for APNEWS, IMDB and BNC datasets respectively under small-NLM configuration. We evaluate the quality of representations learned by our proposed models via document classification. We use three labeled datasets: 20Newsgroups (20NS), Reuters (R21578) and IMDB movie reviews (IMDB) (See supplementary for data statistics). For each document d, we extract: (1) output state om for each input word xm via NLM component ; and (2) document topic representation vector via NTM component based on the model configuration. For IMDB movie reviews dataset, we use our best models trained on unlabeled IMDB dataset as feature extractor. However, as 20NS and R21578 are news-domain datasets, we employ best models trained on APNEWS because of its bigger corpus size than BNC. Baselines: Document retrieval is used to evaluate applicability of topic models." } ], "images": [ "scigen/train/1821.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Language Modeling Perplexity scores on three datasets under two different settings of NLM i.e., S → small-NLM and L → large-NLM. Here, (+) → augment this feature to the model in previous row, NLM → LSTM-LM, (∗) → scores taken from Wang et al. (2018). Here, bold values indicate best performing proposed model in comparison to LSTM-LM baseline, and GAIN(%) indicates the improvement in performance of the same." ], "reasoning": "ETA-NLM consistently outperforms LTA-NLM across all datasets, and LETA-NLM further improves performance by combining ETR and LTR vectors, with additional gains when incorporating sentence-level topic representations (+SDT).", "description": "All topic composition models outperform LSTM-LM baseline which demonstrate the advantage of composing document topical semantics in NLM. Based on the results, here are three key observations: (i) ETA-NLM always performs better than LTA-NLM resulting in 11% (49.34 vs 55.48), 13.2% (59.20 vs 68.21) and 2.7% (95.62 vs 98.31) improvement for APNEWS, IMDB and BNC datasets respectively under small-NLM configuration. This behaviour asserts that ETR vector effectively captures fine-grained document topical semantics compared to LTR vector; (ii) LETA-NLM outperforms both LTA-NLM & ETA-NLM by exploiting complementary semantics of ETR and LTR vectors; (iii) composing sentence-level topic representations i.e., +SDT, further boost the performance by maintaining sentence-level topical discourse and hence LETA-NLM +SDT improve upon LTA-NLM model by 22% (42.98 vs 55.48), 17% (56.65 vs 68.21) and 10% (88.30 vs 98.31) for APNEWS, IMDB and BNC datasets respectively under small-NLM configuration. We evaluate the quality of representations learned by our proposed models via document classification. We use three labeled datasets: 20Newsgroups (20NS), Reuters (R21578) and IMDB movie reviews (IMDB) (See supplementary for data statistics). For each document d, we extract: (1) output state om for each input word xm via NLM component ; and (2) document topic representation vector via NTM component based on the model configuration. For IMDB movie reviews dataset, we use our best models trained on unlabeled IMDB dataset as feature extractor. However, as 20NS and R21578 are news-domain datasets, we employ best models trained on APNEWS because of its bigger corpus size than BNC. Baselines: Document retrieval is used to evaluate applicability of topic models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: Language Modeling Perplexity scores on three datasets under two different settings of NLM i.e., S → small-NLM and L → large-NLM. Here, (+) → augment this feature to the model in previous row, NLM → LSTM-LM, (∗) → scores taken from Wang et al. (2018). Here, bold values indicate best performing proposed model in comparison to LSTM-LM baseline, and GAIN(%) indicates the improvement in performance of the same.' and methodically tackle the question that follows. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the percentage improvement of LETA-NLM +SDT over LTA-NLM for the IMDB dataset under the small-NLM configuration?\n" }, { "role": "assistant", "content": "LETA-NLM +SDT achieves a score of 66.65 for IMDB under the small-NLM configuration, while LTA-NLM achieves 56.65. The percentage improvement is calculated as ((66.65 - 56.65) / 56.65) * 100. So, the answer is {\"answer\": \"17%\"}" } ], "images": [ "scigen/train/1821.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Language Modeling Perplexity scores on three datasets under two different settings of NLM i.e., S → small-NLM and L → large-NLM. Here, (+) → augment this feature to the model in previous row, NLM → LSTM-LM, (∗) → scores taken from Wang et al. (2018). Here, bold values indicate best performing proposed model in comparison to LSTM-LM baseline, and GAIN(%) indicates the improvement in performance of the same." ], "reasoning": "LETA-NLM +SDT achieves a score of 66.65 for IMDB under the small-NLM configuration, while LTA-NLM achieves 56.65. The percentage improvement is calculated as ((66.65 - 56.65) / 56.65) * 100.", "question": "What is the percentage improvement of LETA-NLM +SDT over LTA-NLM for the IMDB dataset under the small-NLM configuration?", "answer": "So, the answer is {\"answer\": \"17%\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim LETA-NLM +SDT outperforms LTA-NLM by 22% for the APNEWS dataset under the small-NLM configuration., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nLETA-NLM +SDT outperforms LTA-NLM by 22% for the APNEWS dataset under the small-NLM configuration.\n" }, { "role": "assistant", "content": "The table shows that LETA-NLM +SDT achieves a score of 55.48 for APNEWS under the small-NLM configuration, while LTA-NLM achieves 42.98. The percentage improvement is calculated as ((55.48 - 42.98) / 42.98) * 100, which is 22%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1821.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Language Modeling Perplexity scores on three datasets under two different settings of NLM i.e., S → small-NLM and L → large-NLM. Here, (+) → augment this feature to the model in previous row, NLM → LSTM-LM, (∗) → scores taken from Wang et al. (2018). Here, bold values indicate best performing proposed model in comparison to LSTM-LM baseline, and GAIN(%) indicates the improvement in performance of the same." ], "reasoning": "The table shows that LETA-NLM +SDT achieves a score of 55.48 for APNEWS under the small-NLM configuration, while LTA-NLM achieves 42.98. The percentage improvement is calculated as ((55.48 - 42.98) / 42.98) * 100, which is 22%.", "statement": "LETA-NLM +SDT outperforms LTA-NLM by 22% for the APNEWS dataset under the small-NLM configuration.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 6: WSD evaluation results using F1 scores (micro). Among proposed models, Bold values indicates best model compared to BiLSTM-LM.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The proposed models outperform BiLSTM-LM by averaging F1 scores over all test datasets, confirming the advantage of document-level semantic knowledge.Results: Observe that by averaging F1 scores over all test datasets, our proposed models outperform BiLSTM-LM (60.7 vs 59.8) which again confirms the advantage of document-level semantic knowledge in resolving sense ambiguities via composition." } ], "images": [ "scigen/train/1822.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: WSD evaluation results using F1 scores (micro). Among proposed models, Bold values indicates best model compared to BiLSTM-LM." ], "reasoning": "The proposed models outperform BiLSTM-LM by averaging F1 scores over all test datasets, confirming the advantage of document-level semantic knowledge.", "description": "Results: Observe that by averaging F1 scores over all test datasets, our proposed models outperform BiLSTM-LM (60.7 vs 59.8) which again confirms the advantage of document-level semantic knowledge in resolving sense ambiguities via composition.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 6: WSD evaluation results using F1 scores (micro). Among proposed models, Bold values indicates best model compared to BiLSTM-LM.' and proceed to solve the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model has the highest average F1 score across all test datasets?\n" }, { "role": "assistant", "content": "By examining the 'Test ALL' column, the highest average F1 score is 60.7, achieved by the 1-7 LTA-NLM, ETA-NLM, and LETA-NLM models. So, the answer is {\"answer\": \"1-7 LTA-NLM, ETA-NLM, LETA-NLM\"}." } ], "images": [ "scigen/train/1822.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: WSD evaluation results using F1 scores (micro). Among proposed models, Bold values indicates best model compared to BiLSTM-LM." ], "reasoning": "By examining the 'Test ALL' column, the highest average F1 score is 60.7, achieved by the 1-7 LTA-NLM, ETA-NLM, and LETA-NLM models.", "question": "Which model has the highest average F1 score across all test datasets?", "answer": "So, the answer is {\"answer\": \"1-7 LTA-NLM, ETA-NLM, LETA-NLM\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe BiLSTM-LM model has the highest average F1 score across all test datasets.\n" }, { "role": "assistant", "content": "The 'Test ALL' column shows that the highest average F1 score is 60.7, achieved by the 1-7 LTA-NLM, ETA-NLM, and LETA-NLM models, while BiLSTM-LM has an average F1 score of 59.8. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1822.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: WSD evaluation results using F1 scores (micro). Among proposed models, Bold values indicates best model compared to BiLSTM-LM." ], "reasoning": "The 'Test ALL' column shows that the highest average F1 score is 60.7, achieved by the 1-7 LTA-NLM, ETA-NLM, and LETA-NLM models, while BiLSTM-LM has an average F1 score of 59.8.", "statement": "The BiLSTM-LM model has the highest average F1 score across all test datasets.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 7: Evaluation result on the fill-in-the-blank task (in percentage). The word embeddings learned by VSE-C with all classes of contrastive adversarial samples help reach a better performance than those learned by VSE++ [Faghri et al.2017].'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "VSE-based models, particularly VSE-C, outperform GloVe in both noun and preposition filling tasks, with minimal performance gaps between VSE++ and VSE-C in preposition filling.the performance of the proposed VSE-C on filling in both nouns and prepositions. VSE-based models outperform GloVe without visual grounding and concretely correlate word semantics with image embeddings. We found that only small gaps exist between VSE++ and VSE-C on preposition filling, which again shows the limited diversity on visual relations within the dataset." } ], "images": [ "scigen/train/1823.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Evaluation result on the fill-in-the-blank task (in percentage). The word embeddings learned by VSE-C with all classes of contrastive adversarial samples help reach a better performance than those learned by VSE++ [Faghri et al.2017]." ], "reasoning": "VSE-based models, particularly VSE-C, outperform GloVe in both noun and preposition filling tasks, with minimal performance gaps between VSE++ and VSE-C in preposition filling.", "description": "the performance of the proposed VSE-C on filling in both nouns and prepositions. VSE-based models outperform GloVe without visual grounding and concretely correlate word semantics with image embeddings. We found that only small gaps exist between VSE++ and VSE-C on preposition filling, which again shows the limited diversity on visual relations within the dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 7: Evaluation result on the fill-in-the-blank task (in percentage). The word embeddings learned by VSE-C with all classes of contrastive adversarial samples help reach a better performance than those learned by VSE++ [Faghri et al.2017].' and methodically tackle the question that follows. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest R@1 score for noun filling?\n" }, { "role": "assistant", "content": "To find the model with the highest R@1 score for noun filling, compare the R@1 scores for noun filling across all models in the table. VSE-C has the highest score of 27.3. So, the answer is {\"answer\": \"VSE-C\"}" } ], "images": [ "scigen/train/1823.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Evaluation result on the fill-in-the-blank task (in percentage). The word embeddings learned by VSE-C with all classes of contrastive adversarial samples help reach a better performance than those learned by VSE++ [Faghri et al.2017]." ], "reasoning": "To find the model with the highest R@1 score for noun filling, compare the R@1 scores for noun filling across all models in the table. VSE-C has the highest score of 27.3.", "question": "Which model has the highest R@1 score for noun filling?", "answer": "So, the answer is {\"answer\": \"VSE-C\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis VSE++ has a higher R@10 score for preposition filling than VSE-C. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nVSE++ has a higher R@10 score for preposition filling than VSE-C.\n" }, { "role": "assistant", "content": "To verify this statement, compare the R@10 scores for preposition filling between VSE++ and VSE-C. VSE++ has a score of 84.9, while VSE-C has a score of 85.2, indicating that VSE-C has a higher score. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1823.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Evaluation result on the fill-in-the-blank task (in percentage). The word embeddings learned by VSE-C with all classes of contrastive adversarial samples help reach a better performance than those learned by VSE++ [Faghri et al.2017]." ], "reasoning": "To verify this statement, compare the R@10 scores for preposition filling between VSE++ and VSE-C. VSE++ has a score of 84.9, while VSE-C has a score of 85.2, indicating that VSE-C has a higher score.", "statement": "VSE++ has a higher R@10 score for preposition filling than VSE-C.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Evaluation on image-to-caption retrieval. Although VSE++ [Faghri et al.2017] obtains the best performance on original MS-COCO test set, it is more vulnerable to the caption-specific adversarial attack compared with the proposed VSE-C, and so does VSE [Kiros et al.2014].'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "VSE-C models are more robust to adversarial attacks compared to VSE and VSE++, as indicated by their higher performance metrics after adding adversarial samples.VSE-C are more robust to known-typed adversarial attacks than VSE and VSE++. Both VSE and VSE++ have a significant drop in performance after adding adversarial samples, while VSE-C training with contrastive adversarial samples is less vulnerable to the attacks. This phenomenon reflects that the text encoders of VSE and VSE++ do not actually make a good use of the image encodings, as the image encodings are fixed in all experiments." } ], "images": [ "scigen/train/1824.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Evaluation on image-to-caption retrieval. Although VSE++ [Faghri et al.2017] obtains the best performance on original MS-COCO test set, it is more vulnerable to the caption-specific adversarial attack compared with the proposed VSE-C, and so does VSE [Kiros et al.2014]." ], "reasoning": "VSE-C models are more robust to adversarial attacks compared to VSE and VSE++, as indicated by their higher performance metrics after adding adversarial samples.", "description": "VSE-C are more robust to known-typed adversarial attacks than VSE and VSE++. Both VSE and VSE++ have a significant drop in performance after adding adversarial samples, while VSE-C training with contrastive adversarial samples is less vulnerable to the attacks. This phenomenon reflects that the text encoders of VSE and VSE++ do not actually make a good use of the image encodings, as the image encodings are fixed in all experiments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 2: Evaluation on image-to-caption retrieval. Although VSE++ [Faghri et al.2017] obtains the best performance on original MS-COCO test set, it is more vulnerable to the caption-specific adversarial attack compared with the proposed VSE-C, and so does VSE [Kiros et al.2014].', solve the following question step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model has the highest R@1 score in the MS-COCO Test with adversarial samples?\n" }, { "role": "assistant", "content": "By examining the table, the highest R@1 score in the MS-COCO Test with adversarial samples is 47.4, achieved by the VSE-C (+all) model. So, the answer is {\"answer\": \"VSE-C (+all)\"}" } ], "images": [ "scigen/train/1824.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Evaluation on image-to-caption retrieval. Although VSE++ [Faghri et al.2017] obtains the best performance on original MS-COCO test set, it is more vulnerable to the caption-specific adversarial attack compared with the proposed VSE-C, and so does VSE [Kiros et al.2014]." ], "reasoning": "By examining the table, the highest R@1 score in the MS-COCO Test with adversarial samples is 47.4, achieved by the VSE-C (+all) model.", "question": "Which model has the highest R@1 score in the MS-COCO Test with adversarial samples?", "answer": "So, the answer is {\"answer\": \"VSE-C (+all)\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence VSE++ has the highest R@10 score in the MS-COCO Test with adversarial samples. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nVSE++ has the highest R@10 score in the MS-COCO Test with adversarial samples.\n" }, { "role": "assistant", "content": "The table shows that the highest R@10 score in the MS-COCO Test with adversarial samples is 88.8, achieved by the VSE-C (+all) model, not VSE++. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1824.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Evaluation on image-to-caption retrieval. Although VSE++ [Faghri et al.2017] obtains the best performance on original MS-COCO test set, it is more vulnerable to the caption-specific adversarial attack compared with the proposed VSE-C, and so does VSE [Kiros et al.2014]." ], "reasoning": "The table shows that the highest R@10 score in the MS-COCO Test with adversarial samples is 88.8, achieved by the VSE-C (+all) model, not VSE++.", "statement": "VSE++ has the highest R@10 score in the MS-COCO Test with adversarial samples.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Detailed results on each type of adversarial attack. Training VSE-C on one class gains the best performance on robustness against the adversarial attack of the class itself. In addition, training with numeral-typed adversarial samples helps improve the robustness against noun-typed and relation-typed attack. We hypothesis that this is attributed to the singularization or pluralization of the corresponding nouns in the process of numeral-typed adversarial sample generation.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "VSE and VSE++ perform best against noun attacks due to the frequent visual grounding of nouns in the dataset, but struggle with relation and numeral attacks due to less diverse semantics.The three hyper-columns show the ability of the models to defend the adversarial attack of noun, numeral, and relation-typed respectively. Among three types of attacks, VSE and VSE++ suffer least from the noun attack. As the constitution of the dataset ensures the frequency in the entire dataset of the words used for replacement, the visual grounding of these frequent nouns is easy to obtain. However, the semantics of relations (including prepositions and entity relations) or numbers are not diverse enough in the dataset, leading to the poor performance of VSE against these attacks." } ], "images": [ "scigen/train/1825.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Detailed results on each type of adversarial attack. Training VSE-C on one class gains the best performance on robustness against the adversarial attack of the class itself. In addition, training with numeral-typed adversarial samples helps improve the robustness against noun-typed and relation-typed attack. We hypothesis that this is attributed to the singularization or pluralization of the corresponding nouns in the process of numeral-typed adversarial sample generation." ], "reasoning": "VSE and VSE++ perform best against noun attacks due to the frequent visual grounding of nouns in the dataset, but struggle with relation and numeral attacks due to less diverse semantics.", "description": "The three hyper-columns show the ability of the models to defend the adversarial attack of noun, numeral, and relation-typed respectively. Among three types of attacks, VSE and VSE++ suffer least from the noun attack. As the constitution of the dataset ensures the frequency in the entire dataset of the words used for replacement, the visual grounding of these frequent nouns is easy to obtain. However, the semantics of relations (including prepositions and entity relations) or numbers are not diverse enough in the dataset, leading to the poor performance of VSE against these attacks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Detailed results on each type of adversarial attack. Training VSE-C on one class gains the best performance on robustness against the adversarial attack of the class itself. In addition, training with numeral-typed adversarial samples helps improve the robustness against noun-typed and relation-typed attack. We hypothesis that this is attributed to the singularization or pluralization of the corresponding nouns in the process of numeral-typed adversarial sample generation.\nQuestion: Which model has the highest R@1 score for numeral attacks in the MS-COCO Test?\nSolve the above question based on the table information. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest R@1 score for numeral attacks, look at the column 'MS-COCO Test (+num.) R@1' and identify the highest value, which is 53.3 for VSE-C (+num.). So, the answer is {\"answer\": \"VSE-C (+num.)\"}" } ], "images": [ "scigen/train/1825.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Detailed results on each type of adversarial attack. Training VSE-C on one class gains the best performance on robustness against the adversarial attack of the class itself. In addition, training with numeral-typed adversarial samples helps improve the robustness against noun-typed and relation-typed attack. We hypothesis that this is attributed to the singularization or pluralization of the corresponding nouns in the process of numeral-typed adversarial sample generation." ], "reasoning": "To find the model with the highest R@1 score for numeral attacks, look at the column 'MS-COCO Test (+num.) R@1' and identify the highest value, which is 53.3 for VSE-C (+num.).", "question": "Which model has the highest R@1 score for numeral attacks in the MS-COCO Test?", "answer": "So, the answer is {\"answer\": \"VSE-C (+num.)\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim VSE-C (+rel.) has the highest R@10 score for relation attacks in the MS-COCO Test., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nVSE-C (+rel.) has the highest R@10 score for relation attacks in the MS-COCO Test.\n" }, { "role": "assistant", "content": "To verify this statement, check the 'MS-COCO Test (+rel.) R@10' column. The highest value is 90.2, which is indeed for VSE-C (+rel.). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1825.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Detailed results on each type of adversarial attack. Training VSE-C on one class gains the best performance on robustness against the adversarial attack of the class itself. In addition, training with numeral-typed adversarial samples helps improve the robustness against noun-typed and relation-typed attack. We hypothesis that this is attributed to the singularization or pluralization of the corresponding nouns in the process of numeral-typed adversarial sample generation." ], "reasoning": "To verify this statement, check the 'MS-COCO Test (+rel.) R@10' column. The highest value is 90.2, which is indeed for VSE-C (+rel.).", "statement": "VSE-C (+rel.) has the highest R@10 score for relation attacks in the MS-COCO Test.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 7. Fine-grained and overall accuracy on the DM-DSTC dataset. The number in brackets are the accuracy at the session level, and number without brackets are at the turn level.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our proposed model outperforms MEM and RNN in turn-level overall prediction, dialogue act type, and mask prediction, despite challenges with slot-level attention and Res_name slot accuracy.To assess the performance of our proposed model on real dialogue data, we conducted another experiment on DM-DSTC. Different from DMBD, there is only one task in the DM-DSTC dataset. We only reported the results of the methods which predict dialogue act as output. It should be pointed out that in this new dataset, many values in dialogue act annotation didn’t appear exactly in user utterances (such as asian oriental), thus for those values we can not provide precise attention supervision, which will affect the performance of slot-level attention. Moreover, the Res_name slot in this dataset degrades the accuracy because its value does not appear in the dialogue context, and is queried from a knowledge base conditioned on previous search constraints, which is not consistent with our model setting. Compared to MEM and RNN, our proposed method obtains higher accuracy on turn-level overall prediction, as well as the dialogue act type and mask prediction. Although MEM’s accuracy on DA type , slot-value and mask prediction is slightly lower than ours, its overall accuracy on turn-level is far less than our proposed model. This can be attributed to the framework of MEM, where its DA type, mask and slot-value prediction is trained separately, while in our model these three tasks are trained. For the variants of MAD, the experiment results are consistent with what we observed in DMBD. MAD-SM obtains lower accuracy on slot-value prediction compared to MAD, while maintains similar accuracy on DA type and mask. For MAD-Attn, the result is similar to MAD-SM when compared to MAD, but its accuracy on slot-value prediction is obviously higher than that of MAD-SM since it maintains the slot-value memory network. MAD-EM, which removes the external memory, obtains significantly lower accuracy on the prediction of dialogue act type and mask, and its accuracy on slot-value prediction is also reduced." } ], "images": [ "scigen/train/1826.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7. Fine-grained and overall accuracy on the DM-DSTC dataset. The number in brackets are the accuracy at the session level, and number without brackets are at the turn level." ], "reasoning": "Our proposed model outperforms MEM and RNN in turn-level overall prediction, dialogue act type, and mask prediction, despite challenges with slot-level attention and Res_name slot accuracy.", "description": "To assess the performance of our proposed model on real dialogue data, we conducted another experiment on DM-DSTC. Different from DMBD, there is only one task in the DM-DSTC dataset. We only reported the results of the methods which predict dialogue act as output. It should be pointed out that in this new dataset, many values in dialogue act annotation didn’t appear exactly in user utterances (such as asian oriental), thus for those values we can not provide precise attention supervision, which will affect the performance of slot-level attention. Moreover, the Res_name slot in this dataset degrades the accuracy because its value does not appear in the dialogue context, and is queried from a knowledge base conditioned on previous search constraints, which is not consistent with our model setting. Compared to MEM and RNN, our proposed method obtains higher accuracy on turn-level overall prediction, as well as the dialogue act type and mask prediction. Although MEM’s accuracy on DA type , slot-value and mask prediction is slightly lower than ours, its overall accuracy on turn-level is far less than our proposed model. This can be attributed to the framework of MEM, where its DA type, mask and slot-value prediction is trained separately, while in our model these three tasks are trained. For the variants of MAD, the experiment results are consistent with what we observed in DMBD. MAD-SM obtains lower accuracy on slot-value prediction compared to MAD, while maintains similar accuracy on DA type and mask. For MAD-Attn, the result is similar to MAD-SM when compared to MAD, but its accuracy on slot-value prediction is obviously higher than that of MAD-SM since it maintains the slot-value memory network. MAD-EM, which removes the external memory, obtains significantly lower accuracy on the prediction of dialogue act type and mask, and its accuracy on slot-value prediction is also reduced.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 7. Fine-grained and overall accuracy on the DM-DSTC dataset. The number in brackets are the accuracy at the session level, and number without brackets are at the turn level.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. \n\nQuestion: Which model has the highest accuracy for slot-value prediction?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By examining the table, the highest accuracy for slot-value prediction is found in the MAD model with a value of 27.3. So, the answer is {\"answer\": \"MAD\"}" } ], "images": [ "scigen/train/1826.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7. Fine-grained and overall accuracy on the DM-DSTC dataset. The number in brackets are the accuracy at the session level, and number without brackets are at the turn level." ], "reasoning": "By examining the table, the highest accuracy for slot-value prediction is found in the MAD model with a value of 27.3.", "question": "Which model has the highest accuracy for slot-value prediction?", "answer": "So, the answer is {\"answer\": \"MAD\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: MAD-EM has the highest accuracy for mask prediction., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nMAD-EM has the highest accuracy for mask prediction.\n" }, { "role": "assistant", "content": "The table shows that MAD has the highest accuracy for mask prediction at 82.1, while MAD-EM has a lower accuracy of 69.7. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1826.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7. Fine-grained and overall accuracy on the DM-DSTC dataset. The number in brackets are the accuracy at the session level, and number without brackets are at the turn level." ], "reasoning": "The table shows that MAD has the highest accuracy for mask prediction at 82.1, while MAD-EM has a lower accuracy of 69.7.", "statement": "MAD-EM has the highest accuracy for mask prediction.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3. Ontology of the DM-DSTC dataset. The Res_name indicates restaurant name. The average value number of informable slots is 54 which is much higher than that of DMBD dataset. The enhanced complexity of DM-DSTC reflects the characteristics of real-world dialogue data.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The DM-DSTC dataset has a higher average value number of informable slots (54) compared to DMBD, indicating its increased complexity and real-world data characteristics.The resulting DM-DSTC is composed of four informable and nine requestable slots, and the average value number of informable slots is 54, which is much higher than that of DMBD, and the enhanced complexity of DM-DSTC dataset reflects the characteristics of real-world data which is more stochastic and noisy. We also created a special slot for requestable slots in this experiment as we did in the DMBD experiment." } ], "images": [ "scigen/train/1827.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. Ontology of the DM-DSTC dataset. The Res_name indicates restaurant name. The average value number of informable slots is 54 which is much higher than that of DMBD dataset. The enhanced complexity of DM-DSTC reflects the characteristics of real-world dialogue data." ], "reasoning": "The DM-DSTC dataset has a higher average value number of informable slots (54) compared to DMBD, indicating its increased complexity and real-world data characteristics.", "description": "The resulting DM-DSTC is composed of four informable and nine requestable slots, and the average value number of informable slots is 54, which is much higher than that of DMBD, and the enhanced complexity of DM-DSTC dataset reflects the characteristics of real-world data which is more stochastic and noisy. We also created a special slot for requestable slots in this experiment as we did in the DMBD experiment.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3. Ontology of the DM-DSTC dataset. The Res_name indicates restaurant name. The average value number of informable slots is 54 which is much higher than that of DMBD dataset. The enhanced complexity of DM-DSTC reflects the characteristics of real-world dialogue data.' to sequentially solve the question presented below. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the total number of values for the informable slots in the DM-DSTC dataset?\n" }, { "role": "assistant", "content": "To find the total number of values for the informable slots, sum the values in the 'Informable slots #Value' column: 91 (Food) + 3 (Pricerange) + 113 (Res_name) + 5 (Area) = 212. So, the answer is {\"answer\": \"212\"}" } ], "images": [ "scigen/train/1827.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. Ontology of the DM-DSTC dataset. The Res_name indicates restaurant name. The average value number of informable slots is 54 which is much higher than that of DMBD dataset. The enhanced complexity of DM-DSTC reflects the characteristics of real-world dialogue data." ], "reasoning": "To find the total number of values for the informable slots, sum the values in the 'Informable slots #Value' column: 91 (Food) + 3 (Pricerange) + 113 (Res_name) + 5 (Area) = 212.", "question": "What is the total number of values for the informable slots in the DM-DSTC dataset?", "answer": "So, the answer is {\"answer\": \"212\"}" }, { "messages": [ { "role": "user", "content": "The DM-DSTC dataset has more than 200 values for informable slots. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe DM-DSTC dataset has more than 200 values for informable slots.\n" }, { "role": "assistant", "content": "The total number of values for the informable slots in the DM-DSTC dataset is 212, which is more than 200. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1827.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. Ontology of the DM-DSTC dataset. The Res_name indicates restaurant name. The average value number of informable slots is 54 which is much higher than that of DMBD dataset. The enhanced complexity of DM-DSTC reflects the characteristics of real-world dialogue data." ], "reasoning": "The total number of values for the informable slots in the DM-DSTC dataset is 212, which is more than 200.", "statement": "The DM-DSTC dataset has more than 200 values for informable slots.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 5. The accuracy across all tasks and methods. The numbers in brackets are the accuracy at the session level, and numbers without brackets are at the turn level. A session is correct only if all the sentences in the session are predicted correctly.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "MAD achieves the best performance on most tasks with about 100% accuracy at both turn and session-level evaluations, except for Task 1 where it is second to Supervised Ebd and MEMN2N (+match).Overall Performance Analysis Both turn-level and session-level results on all the five tasks are reported. We have the following observations: MAD obtains the best performance on most of the tasks. The model obtains an accuracy of about 100% at both turn and session-level evaluation, which shows the effectiveness of our proposed model. While in Task 1, MAD is at the second place, where Supervised Ebd and MEMN2N (+match) methods obtains 100% accuracy at both turn and session-level evaluation, which is 1% higher than ours. MAD’s defect on Task 1 can be attributed to a potential rule in Task 1, where if the user doesn’t provide enough values to form a query, the agent will request for the value of slots in a fixed order. For example in task 1, the agent requests for slots in an order of (Cuisine → Location → Size → Price). However, this order rule is not essential for a practica application, where the agent can request for values in an arbitrary order as long as it can obtain all necessary values." } ], "images": [ "scigen/train/1828.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5. The accuracy across all tasks and methods. The numbers in brackets are the accuracy at the session level, and numbers without brackets are at the turn level. A session is correct only if all the sentences in the session are predicted correctly." ], "reasoning": "MAD achieves the best performance on most tasks with about 100% accuracy at both turn and session-level evaluations, except for Task 1 where it is second to Supervised Ebd and MEMN2N (+match).", "description": "Overall Performance Analysis Both turn-level and session-level results on all the five tasks are reported. We have the following observations: MAD obtains the best performance on most of the tasks. The model obtains an accuracy of about 100% at both turn and session-level evaluation, which shows the effectiveness of our proposed model. While in Task 1, MAD is at the second place, where Supervised Ebd and MEMN2N (+match) methods obtains 100% accuracy at both turn and session-level evaluation, which is 1% higher than ours. MAD’s defect on Task 1 can be attributed to a potential rule in Task 1, where if the user doesn’t provide enough values to form a query, the agent will request for the value of slots in a fixed order. For example in task 1, the agent requests for slots in an order of (Cuisine → Location → Size → Price). However, this order rule is not essential for a practica application, where the agent can request for values in an arbitrary order as long as it can obtain all necessary values.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 5. The accuracy across all tasks and methods. The numbers in brackets are the accuracy at the session level, and numbers without brackets are at the turn level. A session is correct only if all the sentences in the session are predicted correctly.' and methodically tackle the question that follows. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieves the highest accuracy in Task 3 for displaying options at the session-level?\n" }, { "role": "assistant", "content": "To find the model with the highest accuracy in Task 3 for displaying options at the session-level, look at the '3 Displaying options' column and find the highest value in parentheses. MAD has the highest accuracy with 90.6%. So, the answer is {\"answer\": \"MAD\"}" } ], "images": [ "scigen/train/1828.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5. The accuracy across all tasks and methods. The numbers in brackets are the accuracy at the session level, and numbers without brackets are at the turn level. A session is correct only if all the sentences in the session are predicted correctly." ], "reasoning": "To find the model with the highest accuracy in Task 3 for displaying options at the session-level, look at the '3 Displaying options' column and find the highest value in parentheses. MAD has the highest accuracy with 90.6%.", "question": "Which model achieves the highest accuracy in Task 3 for displaying options at the session-level?", "answer": "So, the answer is {\"answer\": \"MAD\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nMAD achieves 100% accuracy in Task 1 for issuing API calls at the turn-level.\n" }, { "role": "assistant", "content": "To verify this statement, check the '1 Issuing API calls' column for MAD at the turn-level. MAD has an accuracy of 99.0%, not 100%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1828.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5. The accuracy across all tasks and methods. The numbers in brackets are the accuracy at the session level, and numbers without brackets are at the turn level. A session is correct only if all the sentences in the session are predicted correctly." ], "reasoning": "To verify this statement, check the '1 Issuing API calls' column for MAD at the turn-level. MAD has an accuracy of 99.0%, not 100%.", "statement": "MAD achieves 100% accuracy in Task 1 for issuing API calls at the turn-level.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 8. Fine-grained and overall accuracy on the ALDM dataset. The number in bracket is the accuracy at the session level, and the number without bracket is at the turn level.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Despite the higher average slot values in ALDM, high slot-value accuracy is achieved due to its high data quality. Removing slot-value memory significantly decreases accuracy, highlighting its importance in maintaining dialogue states.Although the average number of the slot values in ALDM is much larger than that in the other two dataset, we still obtain high slot-value accuracy. This can be attributed to the high data quality of ALDM which is carefully cleaned before training. By removing the slot-value memory (RNN and MAD-SM) we can see that the slot-value accuracy decreases remarkably, which shows the ability of slot-value memory for maintaining dialogue states. This is because of the nature of the ALDM dataset that the user responses are mainly one-word sentences, which makes no difference between the models with/without attention mechanism." } ], "images": [ "scigen/train/1829.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8. Fine-grained and overall accuracy on the ALDM dataset. The number in bracket is the accuracy at the session level, and the number without bracket is at the turn level." ], "reasoning": "Despite the higher average slot values in ALDM, high slot-value accuracy is achieved due to its high data quality. Removing slot-value memory significantly decreases accuracy, highlighting its importance in maintaining dialogue states.", "description": "Although the average number of the slot values in ALDM is much larger than that in the other two dataset, we still obtain high slot-value accuracy. This can be attributed to the high data quality of ALDM which is carefully cleaned before training. By removing the slot-value memory (RNN and MAD-SM) we can see that the slot-value accuracy decreases remarkably, which shows the ability of slot-value memory for maintaining dialogue states. This is because of the nature of the ALDM dataset that the user responses are mainly one-word sentences, which makes no difference between the models with/without attention mechanism.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 8. Fine-grained and overall accuracy on the ALDM dataset. The number in bracket is the accuracy at the session level, and the number without bracket is at the turn level.', answer the question below by showing each progressive step toward the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the slot-value accuracy for the MAD-EM model?\n" }, { "role": "assistant", "content": "The table shows the slot-value accuracy for different models. For the MAD-EM model, the slot-value accuracy is listed under the 'Slot-value 73.5' column. So, the answer is {\"answer\": \"92.8\"}." } ], "images": [ "scigen/train/1829.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8. Fine-grained and overall accuracy on the ALDM dataset. The number in bracket is the accuracy at the session level, and the number without bracket is at the turn level." ], "reasoning": "The table shows the slot-value accuracy for different models. For the MAD-EM model, the slot-value accuracy is listed under the 'Slot-value 73.5' column.", "question": "What is the slot-value accuracy for the MAD-EM model?", "answer": "So, the answer is {\"answer\": \"92.8\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe MAD model has the highest slot-value accuracy.\n" }, { "role": "assistant", "content": "The table lists the slot-value accuracy for different models. The MAD model has a slot-value accuracy of 100.0, which is the highest among all models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1829.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8. Fine-grained and overall accuracy on the ALDM dataset. The number in bracket is the accuracy at the session level, and the number without bracket is at the turn level." ], "reasoning": "The table lists the slot-value accuracy for different models. The MAD model has a slot-value accuracy of 100.0, which is the highest among all models listed.", "statement": "The MAD model has the highest slot-value accuracy.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 9. Prediction accuracy on the departure city and arrive city slots. The number in bracket is the accuracy at the session level, and that without bracket at the turn level.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The prediction accuracy for Departure_City and Arrive_City slots drops significantly without slot-value memory, with MEM performing better than RNN and MAD-EM but still lower than the proposed model.To verify the model’s ability to combine context information in slot filling, we further analyzed the prediction accuracy on the Departure_City slot and the Arrive_City slot. The results drop dramatically when removing the slot-value memory (RNN and MAD-EM). For MEM, although its accuracy is higher than that of RNN and MAD-EM, it’s still much lower than our proposed model. This is because that 1) the city number is too large for MEM to predict, and 2) MEM fails to identify which slot the value belongs to." } ], "images": [ "scigen/train/1830.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9. Prediction accuracy on the departure city and arrive city slots. The number in bracket is the accuracy at the session level, and that without bracket at the turn level." ], "reasoning": "The prediction accuracy for Departure_City and Arrive_City slots drops significantly without slot-value memory, with MEM performing better than RNN and MAD-EM but still lower than the proposed model.", "description": "To verify the model’s ability to combine context information in slot filling, we further analyzed the prediction accuracy on the Departure_City slot and the Arrive_City slot. The results drop dramatically when removing the slot-value memory (RNN and MAD-EM). For MEM, although its accuracy is higher than that of RNN and MAD-EM, it’s still much lower than our proposed model. This is because that 1) the city number is too large for MEM to predict, and 2) MEM fails to identify which slot the value belongs to.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 9. Prediction accuracy on the departure city and arrive city slots. The number in bracket is the accuracy at the session level, and that without bracket at the turn level.', solve this question and give detail solutions. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model has the highest accuracy for both Departure_City and Arrive_City slots?\n" }, { "role": "assistant", "content": "By comparing the accuracy values in the table, MAD and MAD-Attn both have 100.0 accuracy for both Departure_City and Arrive_City slots. So, the answer is {\"answer\": \"MAD\"}." } ], "images": [ "scigen/train/1830.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9. Prediction accuracy on the departure city and arrive city slots. The number in bracket is the accuracy at the session level, and that without bracket at the turn level." ], "reasoning": "By comparing the accuracy values in the table, MAD and MAD-Attn both have 100.0 accuracy for both Departure_City and Arrive_City slots.", "question": "Which model has the highest accuracy for both Departure_City and Arrive_City slots?", "answer": "So, the answer is {\"answer\": \"MAD\"}." }, { "messages": [ { "role": "user", "content": "MEM has the highest accuracy for the Departure_City slot. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nMEM has the highest accuracy for the Departure_City slot.\n" }, { "role": "assistant", "content": "The table shows that MEM has an accuracy of 2.7 for the Departure_City slot, which is lower than the accuracies of MAD, MAD-Attn, and MAD-EM. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1830.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9. Prediction accuracy on the departure city and arrive city slots. The number in bracket is the accuracy at the session level, and that without bracket at the turn level." ], "reasoning": "The table shows that MEM has an accuracy of 2.7 for the Departure_City slot, which is lower than the accuracies of MAD, MAD-Attn, and MAD-EM.", "statement": "MEM has the highest accuracy for the Departure_City slot.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Performance (average length to failure %) of models on the tree-transduction task.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The structured model with a tree structure bias significantly outperforms both the baseline and simple attention models across all depths.Note that this task is fairly difficult as the encoder is quite simple. The baseline model (unsurprisingly) performs poorly as it has no information about the source ordering. The simple attention model performs better, but is significantly outperformed by the structured model with a tree structure bias. We hypothesize that the model is partially reconstructing the arithmetic tree." } ], "images": [ "scigen/train/1831.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance (average length to failure %) of models on the tree-transduction task." ], "reasoning": "The structured model with a tree structure bias significantly outperforms both the baseline and simple attention models across all depths.", "description": "Note that this task is fairly difficult as the encoder is quite simple. The baseline model (unsurprisingly) performs poorly as it has no information about the source ordering. The simple attention model performs better, but is significantly outperformed by the structured model with a tree structure bias. We hypothesize that the model is partially reconstructing the arithmetic tree.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: Performance (average length to failure %) of models on the tree-transduction task.'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nAt depth 4, how much better does the structured model perform compared to the simple model?\n" }, { "role": "assistant", "content": "At depth 4, the structured model has a performance of 64.5, while the simple model has a performance of 23.3. The difference is 64.5 - 23.3. So, the answer is {\"answer\": \"41.2\"}" } ], "images": [ "scigen/train/1831.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance (average length to failure %) of models on the tree-transduction task." ], "reasoning": "At depth 4, the structured model has a performance of 64.5, while the simple model has a performance of 23.3. The difference is 64.5 - 23.3.", "question": "At depth 4, how much better does the structured model perform compared to the simple model?", "answer": "So, the answer is {\"answer\": \"41.2\"}" }, { "messages": [ { "role": "user", "content": "The simple model performs better than the structured model at depth 5. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe simple model performs better than the structured model at depth 5.\n" }, { "role": "assistant", "content": "At depth 5, the simple model has a performance of 15.0, while the structured model has a performance of 30.8. The simple model does not perform better than the structured model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1831.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance (average length to failure %) of models on the tree-transduction task." ], "reasoning": "At depth 5, the simple model has a performance of 15.0, while the structured model has a performance of 30.8. The simple model does not perform better than the structured model.", "statement": "The simple model performs better than the structured model at depth 5.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Translation performance as measured by BLEU (higher is better) on character-to-word and word-to-word Japanese-English translation for the three different models.'.\n" }, { "role": "assistant", "content": "Structured attention outperforms both simple and sigmoid attention on both character-to-word and word-to-word tasks.Sigmoid attention outperforms simple (softmax) attention on the character-to-word task, potentially because it is able to learn many-to-one alignments. On the word-to-word task, the opposite is true, with simple attention outperforming sigmoid attention. Structured attention outperforms both models on both tasks, although improvements on the word-to-word task are modest and unlikely to be statistically significant." } ], "images": [ "scigen/train/1832.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Translation performance as measured by BLEU (higher is better) on character-to-word and word-to-word Japanese-English translation for the three different models." ], "reasoning": "Structured attention outperforms both simple and sigmoid attention on both character-to-word and word-to-word tasks.", "description": "Sigmoid attention outperforms simple (softmax) attention on the character-to-word task, potentially because it is able to learn many-to-one alignments. On the word-to-word task, the opposite is true, with simple attention outperforming sigmoid attention. Structured attention outperforms both models on both tasks, although improvements on the word-to-word task are modest and unlikely to be statistically significant.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Translation performance as measured by BLEU (higher is better) on character-to-word and word-to-word Japanese-English translation for the three different models.\nQuestion: Which attention model performs best on both the character-to-word and word-to-word tasks?\nGive a detailed response to the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that structured attention has the highest scores for both the character-to-word (14.6) and word-to-word (14.3) tasks. So, the answer is {\"answer\": \"Structured\"}" } ], "images": [ "scigen/train/1832.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Translation performance as measured by BLEU (higher is better) on character-to-word and word-to-word Japanese-English translation for the three different models." ], "reasoning": "The table shows that structured attention has the highest scores for both the character-to-word (14.6) and word-to-word (14.3) tasks.", "question": "Which attention model performs best on both the character-to-word and word-to-word tasks?", "answer": "So, the answer is {\"answer\": \"Structured\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Sigmoid attention performs better than simple attention on the character-to-word task., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSigmoid attention performs better than simple attention on the character-to-word task.\n" }, { "role": "assistant", "content": "The table shows that sigmoid attention has a score of 13.1, which is higher than the simple attention score of 12.6 on the character-to-word task. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1832.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Translation performance as measured by BLEU (higher is better) on character-to-word and word-to-word Japanese-English translation for the three different models." ], "reasoning": "The table shows that sigmoid attention has a score of 13.1, which is higher than the simple attention score of 12.6 on the character-to-word task.", "statement": "Sigmoid attention performs better than simple attention on the character-to-word task.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Answer accuracy (Ans %) and supporting fact selection accuracy (Fact %) of the three QA models on the 1K bAbI dataset. K indicates the number of hops/inference steps used for each task. Task 7 and 8 both contain variable number of facts and hence they are excluded from the fact accuracy measurement. Supporting fact selection accuracy is calculated by taking the average of 10 best runs (out of 20) for each task.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Binary CRF model generally outperforms MemN2N and Unary CRF models in both answer and supporting fact accuracy, particularly in tasks requiring multiple supporting facts.For correct answer seletion (Ans %), we find that MemN2N and the Binary CRF model perform similarly while the Unary CRF model does worse, indicating the importance of including pairwise potentials. Since ground truth supporting facts are provided for each query, we can check the sequence accuracy of supporting facts for each model (i.e. the rate of selecting the exact correct sequence of facts) by taking the highest probability sequence ^z = argmaxp(z1,…,zK|x,q) from the model and checking against the ground truth. Overall the Binary CRF is able to recover supporting facts better than MemN2N. This improvement is significant and can be up to two-fold as seen for task 2, 11, 13 & 17. However we observed that on many tasks it is sufficient to select only the last (or first) fact correctly to predict the answer, and thus higher sequence selection accuracy does not necessarily imply better answer accuracy (and vice versa). For example, all three models get 100% answer accuracy on task 15 but have different supporting fact accuracies." } ], "images": [ "scigen/train/1833.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Answer accuracy (Ans %) and supporting fact selection accuracy (Fact %) of the three QA models on the 1K bAbI dataset. K indicates the number of hops/inference steps used for each task. Task 7 and 8 both contain variable number of facts and hence they are excluded from the fact accuracy measurement. Supporting fact selection accuracy is calculated by taking the average of 10 best runs (out of 20) for each task." ], "reasoning": "Binary CRF model generally outperforms MemN2N and Unary CRF models in both answer and supporting fact accuracy, particularly in tasks requiring multiple supporting facts.", "description": "For correct answer seletion (Ans %), we find that MemN2N and the Binary CRF model perform similarly while the Unary CRF model does worse, indicating the importance of including pairwise potentials. Since ground truth supporting facts are provided for each query, we can check the sequence accuracy of supporting facts for each model (i.e. the rate of selecting the exact correct sequence of facts) by taking the highest probability sequence ^z = argmaxp(z1,…,zK|x,q) from the model and checking against the ground truth. Overall the Binary CRF is able to recover supporting facts better than MemN2N. This improvement is significant and can be up to two-fold as seen for task 2, 11, 13 & 17. However we observed that on many tasks it is sufficient to select only the last (or first) fact correctly to predict the answer, and thus higher sequence selection accuracy does not necessarily imply better answer accuracy (and vice versa). For example, all three models get 100% answer accuracy on task 15 but have different supporting fact accuracies.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 4: Answer accuracy (Ans %) and supporting fact selection accuracy (Fact %) of the three QA models on the 1K bAbI dataset. K indicates the number of hops/inference steps used for each task. Task 7 and 8 both contain variable number of facts and hence they are excluded from the fact accuracy measurement. Supporting fact selection accuracy is calculated by taking the average of 10 best runs (out of 20) for each task.', solve the subsequent question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich model has the highest answer accuracy for Task 14 - Time Reasoning?\n" }, { "role": "assistant", "content": "By examining the table, we see that for Task 14 - Time Reasoning, the Binary CRF model has the highest answer accuracy at 99.7%. So, the answer is {\"answer\": \"Binary CRF\"}" } ], "images": [ "scigen/train/1833.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Answer accuracy (Ans %) and supporting fact selection accuracy (Fact %) of the three QA models on the 1K bAbI dataset. K indicates the number of hops/inference steps used for each task. Task 7 and 8 both contain variable number of facts and hence they are excluded from the fact accuracy measurement. Supporting fact selection accuracy is calculated by taking the average of 10 best runs (out of 20) for each task." ], "reasoning": "By examining the table, we see that for Task 14 - Time Reasoning, the Binary CRF model has the highest answer accuracy at 99.7%.", "question": "Which model has the highest answer accuracy for Task 14 - Time Reasoning?", "answer": "So, the answer is {\"answer\": \"Binary CRF\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Unary CRF model has the lowest supporting fact accuracy for Task 11 - Indefinite Knowledge. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Unary CRF model has the lowest supporting fact accuracy for Task 11 - Indefinite Knowledge.\n" }, { "role": "assistant", "content": "The table shows that for Task 11 - Indefinite Knowledge, the Unary CRF model has a supporting fact accuracy of 0.0%, which is the lowest among the models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1833.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Answer accuracy (Ans %) and supporting fact selection accuracy (Fact %) of the three QA models on the 1K bAbI dataset. K indicates the number of hops/inference steps used for each task. Task 7 and 8 both contain variable number of facts and hence they are excluded from the fact accuracy measurement. Supporting fact selection accuracy is calculated by taking the average of 10 best runs (out of 20) for each task." ], "reasoning": "The table shows that for Task 11 - Indefinite Knowledge, the Unary CRF model has a supporting fact accuracy of 0.0%, which is the lowest among the models.", "statement": "The Unary CRF model has the lowest supporting fact accuracy for Task 11 - Indefinite Knowledge.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Results of our models (bottom) and others (top) on the Stanford NLI test set. Our baseline model has the same architecture as Parikh et al. (2016) but the performance is slightly different due to different settings (e.g. we train for 100 epochs with a batch size of 32 while Parikh et al. (2016) train for 400 epochs with a batch size of 4 using asynchronous SGD.)'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Simple attention and the pipelined model with hard parents improve upon the baseline, while structured attention outperforms both, though pretraining the syntactic attention layer performs worse than training it from scratch.Simple attention improves upon the no attention model, and this is consistent with improvements observed by Parikh et al. The pipelined model with hard parents also slightly improves upon the baseline. Structured attention outperforms both models, though surprisingly, pretraining the syntactic attention layer on the parse trees performs worse than training it from scratch—it is possible that the pretrained attention is too strict for this task." } ], "images": [ "scigen/train/1834.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results of our models (bottom) and others (top) on the Stanford NLI test set. Our baseline model has the same architecture as Parikh et al. (2016) but the performance is slightly different due to different settings (e.g. we train for 100 epochs with a batch size of 32 while Parikh et al. (2016) train for 400 epochs with a batch size of 4 using asynchronous SGD.)" ], "reasoning": "Simple attention and the pipelined model with hard parents improve upon the baseline, while structured attention outperforms both, though pretraining the syntactic attention layer performs worse than training it from scratch.", "description": "Simple attention improves upon the no attention model, and this is consistent with improvements observed by Parikh et al. The pipelined model with hard parents also slightly improves upon the baseline. Structured attention outperforms both models, though surprisingly, pretraining the syntactic attention layer on the parse trees performs worse than training it from scratch—it is possible that the pretrained attention is too strict for this task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 5: Results of our models (bottom) and others (top) on the Stanford NLI test set. Our baseline model has the same architecture as Parikh et al. (2016) but the performance is slightly different due to different settings (e.g. we train for 100 epochs with a batch size of 32 while Parikh et al. (2016) train for 400 epochs with a batch size of 4 using asynchronous SGD.)' and methodically tackle the question that follows. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest accuracy and how does it compare to the No Attention model?\n" }, { "role": "assistant", "content": "The Enhanced BiLSTM Inference Model + ensemble has the highest accuracy at 88.3%, which is 2.5% higher than the No Attention model's accuracy of 85.8%. So, the answer is {\"answer\": \"Enhanced BiLSTM Inference Model + ensemble\"}." } ], "images": [ "scigen/train/1834.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results of our models (bottom) and others (top) on the Stanford NLI test set. Our baseline model has the same architecture as Parikh et al. (2016) but the performance is slightly different due to different settings (e.g. we train for 100 epochs with a batch size of 32 while Parikh et al. (2016) train for 400 epochs with a batch size of 4 using asynchronous SGD.)" ], "reasoning": "The Enhanced BiLSTM Inference Model + ensemble has the highest accuracy at 88.3%, which is 2.5% higher than the No Attention model's accuracy of 85.8%.", "question": "Which model has the highest accuracy and how does it compare to the No Attention model?", "answer": "So, the answer is {\"answer\": \"Enhanced BiLSTM Inference Model + ensemble\"}." }, { "messages": [ { "role": "user", "content": "The Simple Attention model has a higher accuracy than the No Attention model. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Simple Attention model has a higher accuracy than the No Attention model.\n" }, { "role": "assistant", "content": "The table shows that the Simple Attention model has an accuracy of 86.2%, which is higher than the No Attention model's accuracy of 85.8%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1834.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results of our models (bottom) and others (top) on the Stanford NLI test set. Our baseline model has the same architecture as Parikh et al. (2016) but the performance is slightly different due to different settings (e.g. we train for 100 epochs with a batch size of 32 while Parikh et al. (2016) train for 400 epochs with a batch size of 4 using asynchronous SGD.)" ], "reasoning": "The table shows that the Simple Attention model has an accuracy of 86.2%, which is higher than the No Attention model's accuracy of 85.8%.", "statement": "The Simple Attention model has a higher accuracy than the No Attention model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: F1 score on the dev and test sets for using different sources of data for pretraining.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Adding external data sources like Wikipedia and ACM improves performance across all domains, with ACM significantly boosting CS domain performance and Wikipedia benefiting MS and Physics domains.We explore different word embedding pre-training with ScienceIE training set alone (SE), and adding other external resources including Wikipedia (wiki) and Computer Science articles (ACM). All alternatives use word2vec. Compared with using SE alone, introduction of all external data sources improve performance. Moreover, we observe that with the introduction of the ACM dataset, the performance on the CS domain is increased significantly in both the dev and test sets. Adding Wikipedia data benefits all three domains, with more significant improvement on the MS and Physics domains." } ], "images": [ "scigen/train/1835.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: F1 score on the dev and test sets for using different sources of data for pretraining." ], "reasoning": "Adding external data sources like Wikipedia and ACM improves performance across all domains, with ACM significantly boosting CS domain performance and Wikipedia benefiting MS and Physics domains.", "description": "We explore different word embedding pre-training with ScienceIE training set alone (SE), and adding other external resources including Wikipedia (wiki) and Computer Science articles (ACM). All alternatives use word2vec. Compared with using SE alone, introduction of all external data sources improve performance. Moreover, we observe that with the introduction of the ACM dataset, the performance on the CS domain is increased significantly in both the dev and test sets. Adding Wikipedia data benefits all three domains, with more significant improvement on the MS and Physics domains.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: F1 score on the dev and test sets for using different sources of data for pretraining.', unfold the steps to solve the question given next. Output the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which initialization method shows the highest performance on the Dev MS set?\n" }, { "role": "assistant", "content": "By comparing the Dev MS scores across all initialization methods, the highest score is observed with the +wiki method, which is 52.9. So, the answer is {\"answer\": \"52.9\"}" } ], "images": [ "scigen/train/1835.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: F1 score on the dev and test sets for using different sources of data for pretraining." ], "reasoning": "By comparing the Dev MS scores across all initialization methods, the highest score is observed with the +wiki method, which is 52.9.", "question": "Which initialization method shows the highest performance on the Dev MS set?", "answer": "So, the answer is {\"answer\": \"52.9\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The SE initialization method shows the lowest performance on the Test CS set., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe SE initialization method shows the lowest performance on the Test CS set.\n" }, { "role": "assistant", "content": "By comparing the Test CS scores across all initialization methods, the SE method has the lowest score of 30.5. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1835.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: F1 score on the dev and test sets for using different sources of data for pretraining." ], "reasoning": "By comparing the Test CS scores across all initialization methods, the SE method has the lowest score of 30.5.", "statement": "The SE initialization method shows the lowest performance on the Test CS set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: F1 scores of semi-supervised Learning approaches; * shows transductive models.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Incorporating graph-based methods for computing posterior and ULM for CRF training outperforms their counterparts in both Dev and Test scores.In particular we ablate incorporating the graph-based methods of computing the posterior and CRF training (ULM vs. hard decision). The table shows incorporating graph-based methods for computing posterior and ULM for CRF training outperforms their counterparts." } ], "images": [ "scigen/train/1836.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: F1 scores of semi-supervised Learning approaches; * shows transductive models." ], "reasoning": "Incorporating graph-based methods for computing posterior and ULM for CRF training outperforms their counterparts in both Dev and Test scores.", "description": "In particular we ablate incorporating the graph-based methods of computing the posterior and CRF training (ULM vs. hard decision). The table shows incorporating graph-based methods for computing posterior and ULM for CRF training outperforms their counterparts.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 4: F1 scores of semi-supervised Learning approaches; * shows transductive models.' in mind, break down and resolve the question below step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich combination of Posterior and Training methods yields the highest Test score?\n" }, { "role": "assistant", "content": "By examining the table, the combination of GraphFeat* for Posterior and ULM for Training yields the highest Test score of 46.6. So, the answer is {\"answer\": \"GraphFeat* and ULM\"}." } ], "images": [ "scigen/train/1836.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: F1 scores of semi-supervised Learning approaches; * shows transductive models." ], "reasoning": "By examining the table, the combination of GraphFeat* for Posterior and ULM for Training yields the highest Test score of 46.6.", "question": "Which combination of Posterior and Training methods yields the highest Test score?", "answer": "So, the answer is {\"answer\": \"GraphFeat* and ULM\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim GraphInterp with ULM training has the highest Dev score., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nGraphInterp with ULM training has the highest Dev score.\n" }, { "role": "assistant", "content": "The table shows that GraphFeat* with ULM training has the highest Dev score of 52.1, not GraphInterp with ULM training. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1836.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: F1 scores of semi-supervised Learning approaches; * shows transductive models." ], "reasoning": "The table shows that GraphFeat* with ULM training has the highest Dev score of 52.1, not GraphInterp with ULM training.", "statement": "GraphInterp with ULM training has the highest Dev score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: F1 score results on the test set for different categories: T indicates Task, P indicates Process, M is Material and K is Keyword identification (SubTask A). * is transductive model.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Our neural sequence tagging models outperform the state of the art and baselines, with significant improvements from semi-supervised learning, especially using ULM+GraphFeat, which reduces confusion between Process and Material.The results show that our neural sequence tagging models significantly outperforms the state of the art and both baselines. It further shows that our system achieves significant boost from semi-supervised learning using unlabeled data. We observe significant improvement by using ULM+GraphInterp and ULM+GraphFeat over best SemEval and our best supervised system on all three categories at both token and span levels. We further observe that systems’ performance on Task classification is much lower than Process and Material. This is in part because Task is much less frequent than the other types. In addition, Task keyphrases often include verb phrases while the other two domains mainly consists of noun phrases. An analysis of confusion patterns show that the most frequent type confusions are between Process and Material. However, we observe that ULM+GraphFeat* can greatly reduce the confusion, with 3.5% relative improvement of Process and 3.6% relative improvement of Process over ULM+GraphInterp on token level." } ], "images": [ "scigen/train/1837.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: F1 score results on the test set for different categories: T indicates Task, P indicates Process, M is Material and K is Keyword identification (SubTask A). * is transductive model." ], "reasoning": "Our neural sequence tagging models outperform the state of the art and baselines, with significant improvements from semi-supervised learning, especially using ULM+GraphFeat, which reduces confusion between Process and Material.", "description": "The results show that our neural sequence tagging models significantly outperforms the state of the art and both baselines. It further shows that our system achieves significant boost from semi-supervised learning using unlabeled data. We observe significant improvement by using ULM+GraphInterp and ULM+GraphFeat over best SemEval and our best supervised system on all three categories at both token and span levels. We further observe that systems’ performance on Task classification is much lower than Process and Material. This is in part because Task is much less frequent than the other types. In addition, Task keyphrases often include verb phrases while the other two domains mainly consists of noun phrases. An analysis of confusion patterns show that the most frequent type confusions are between Process and Material. However, we observe that ULM+GraphFeat* can greatly reduce the confusion, with 3.5% relative improvement of Process and 3.6% relative improvement of Process over ULM+GraphInterp on token level.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 5: F1 score results on the test set for different categories: T indicates Task, P indicates Process, M is Material and K is Keyword identification (SubTask A). * is transductive model.', unfold the question presented below, detailing every step of your calculation. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich system shows the highest performance on the Material category at the token level?\n" }, { "role": "assistant", "content": "By examining the table, we see that ULM+GraphFeat* has the highest score for the Material category at the token level with a score of 63.4. So, the answer is {\"answer\": \"ULM+GraphFeat*\"}" } ], "images": [ "scigen/train/1837.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: F1 score results on the test set for different categories: T indicates Task, P indicates Process, M is Material and K is Keyword identification (SubTask A). * is transductive model." ], "reasoning": "By examining the table, we see that ULM+GraphFeat* has the highest score for the Material category at the token level with a score of 63.4.", "question": "Which system shows the highest performance on the Material category at the token level?", "answer": "So, the answer is {\"answer\": \"ULM+GraphFeat*\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The supervised system performs better than ULM+GraphInterp on the Task category at the span level. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe supervised system performs better than ULM+GraphInterp on the Task category at the span level.\n" }, { "role": "assistant", "content": "The table shows that at the span level, the supervised system has a score of 13.3 for Task, while ULM+GraphInterp has a score of 17.0, indicating that the supervised system performs worse. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1837.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: F1 score results on the test set for different categories: T indicates Task, P indicates Process, M is Material and K is Keyword identification (SubTask A). * is transductive model." ], "reasoning": "The table shows that at the span level, the supervised system has a score of 13.3 for Task, while ULM+GraphInterp has a score of 17.0, indicating that the supervised system performs worse.", "statement": "The supervised system performs better than ULM+GraphInterp on the Task category at the span level.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 1: Model performance on the official subtask A development set and test set. Lowercase: make all the letters in lowercase except for the first letter. Evidence: add evidence from wiktionary'.\n" }, { "role": "assistant", "content": "Lowercasing statements improved development accuracy slightly, but adding evidence caused a discrepancy between development and test accuracy.We notice that there are some statements whose letters are all capitalized (e.g., A GIRL WON THE RACE WITH HER FRIEND) in Subtask A dataset. We capitalize the first letter and make other letters in lowercase. Then we explore the effect of evidence. By adding evidence to the input statement, we obtain a slightly better result in the development dataset while performance degradation is found in the test dataset. We hypothesize this discrepancy is because the amount of noise data in evidence is unstable." } ], "images": [ "scigen/train/1838.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Model performance on the official subtask A development set and test set. Lowercase: make all the letters in lowercase except for the first letter. Evidence: add evidence from wiktionary" ], "reasoning": "Lowercasing statements improved development accuracy slightly, but adding evidence caused a discrepancy between development and test accuracy.", "description": "We notice that there are some statements whose letters are all capitalized (e.g., A GIRL WON THE RACE WITH HER FRIEND) in Subtask A dataset. We capitalize the first letter and make other letters in lowercase. Then we explore the effect of evidence. By adding evidence to the input statement, we obtain a slightly better result in the development dataset while performance degradation is found in the test dataset. We hypothesize this discrepancy is because the amount of noise data in evidence is unstable.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: 'Table 1: Model performance on the official subtask A development set and test set. Lowercase: make all the letters in lowercase except for the first letter. Evidence: add evidence from wiktionary'\nQuestion: What is the test accuracy when both lowercasing and evidence are added?\n" }, { "role": "assistant", "content": "The table shows the test accuracy for different configurations. When both lowercasing and evidence are added, the test accuracy is 94.1. So, the answer is {\"answer\": \"94.1\"}" } ], "images": [ "scigen/train/1838.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Model performance on the official subtask A development set and test set. Lowercase: make all the letters in lowercase except for the first letter. Evidence: add evidence from wiktionary" ], "reasoning": "The table shows the test accuracy for different configurations. When both lowercasing and evidence are added, the test accuracy is 94.1.", "question": "What is the test accuracy when both lowercasing and evidence are added?", "answer": "So, the answer is {\"answer\": \"94.1\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Adding evidence improved the test accuracy. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nAdding evidence improved the test accuracy.\n" }, { "role": "assistant", "content": "The table shows that adding evidence actually decreased the test accuracy from 95.3 to 94.1. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1838.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Model performance on the official subtask A development set and test set. Lowercase: make all the letters in lowercase except for the first letter. Evidence: add evidence from wiktionary" ], "reasoning": "The table shows that adding evidence actually decreased the test accuracy from 95.3 to 94.1.", "statement": "Adding evidence improved the test accuracy.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Model performance on the official subtask B development set and test set. Extra Words: extra words indicating given statement does not make sense. Reasonable Statement: the corresponding reasonable statement. Wiktionary: the obtained evidence from wiktionary', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Using extra words alone improves accuracy to 93.2% without the additional computational cost of reasonable statements and Wiktionary.We see that the extra words can bring 1.6% absolute improvement since they indicate that the given statement does not make sense and the choice is the reason for that. We also see that using corresponding reasonable statement and wiktionary evidence achieve comparable performance while they involve extra computational cost. Therefore, we only use extra words in the final submission of subtask B and achieve accuracy of 93.2%" } ], "images": [ "scigen/train/1839.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Model performance on the official subtask B development set and test set. Extra Words: extra words indicating given statement does not make sense. Reasonable Statement: the corresponding reasonable statement. Wiktionary: the obtained evidence from wiktionary" ], "reasoning": "Using extra words alone improves accuracy to 93.2% without the additional computational cost of reasonable statements and Wiktionary.", "description": "We see that the extra words can bring 1.6% absolute improvement since they indicate that the given statement does not make sense and the choice is the reason for that. We also see that using corresponding reasonable statement and wiktionary evidence achieve comparable performance while they involve extra computational cost. Therefore, we only use extra words in the final submission of subtask B and achieve accuracy of 93.2%", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 2: Model performance on the official subtask B development set and test set. Extra Words: extra words indicating given statement does not make sense. Reasonable Statement: the corresponding reasonable statement. Wiktionary: the obtained evidence from wiktionary\n\nQuestion: What is the test accuracy when using extra words in the final submission of subtask B?\n" }, { "role": "assistant", "content": "The table shows that using extra words alone achieves a test accuracy of 93.2%. So, the answer is {\"answer\": \"93.2%\"}" } ], "images": [ "scigen/train/1839.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Model performance on the official subtask B development set and test set. Extra Words: extra words indicating given statement does not make sense. Reasonable Statement: the corresponding reasonable statement. Wiktionary: the obtained evidence from wiktionary" ], "reasoning": "The table shows that using extra words alone achieves a test accuracy of 93.2%.", "question": "What is the test accuracy when using extra words in the final submission of subtask B?", "answer": "So, the answer is {\"answer\": \"93.2%\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nUsing extra words and Wiktionary achieves the highest test accuracy.\n" }, { "role": "assistant", "content": "The table shows that using extra words alone achieves a test accuracy of 93.2%, while using extra words and Wiktionary achieves a dev accuracy of 92.4% with no test accuracy reported. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1839.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Model performance on the official subtask B development set and test set. Extra Words: extra words indicating given statement does not make sense. Reasonable Statement: the corresponding reasonable statement. Wiktionary: the obtained evidence from wiktionary" ], "reasoning": "The table shows that using extra words alone achieves a test accuracy of 93.2%, while using extra words and Wiktionary achieves a dev accuracy of 92.4% with no test accuracy reported.", "statement": "Using extra words and Wiktionary achieves the highest test accuracy.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Model performance on the official subtask C development set and test set. Multi-target: the same input has multiple different targets. Extra Words: extra words indicating given statement does not make sense. Reasonable Statement: the corresponding reasonable statement. Wiktionary: the obtained evidence from wiktionary'.\n" }, { "role": "assistant", "content": "The Multi-target method, combined with Extra Words, Reasonable Statement, and Wiktionary, improves the model's performance, achieving a BLEU score of 20.39 on the test set.Compare to the baseline where we simply use the first referential answer of each example in training data as the target of the model output, the Multi-target method provides a larger amount of training data and thus helps the model get better performance. We hypothesize the degradation is because the complexity of the input sentence increases as we add extra material. When Extra Words, Reasonable Statement, and Wiktionary are all added, the benefits outweigh the disadvantages they bring. Therefore, we use all of them in the final submission of subtask C and achieve the BLEU score of 18.5 (3rd place) and human evaluation score of 2.08 (2nd place), which obtains a 0.14 gain over 3rd place and only 0.02 less than 1st place. Note that we might achieve a better human evaluation score accordingly. There are two reasons for that. Firstly, we optimize our evidence-searching approach after the competition, removing a big proportion of noisy data. Secondly, we observe that during the training process, the model performs well at the beginning but turns to mess later. Thus it’s difficult to choose the best model during the training process when we cannot evaluate it on the test set. When the competition has ended, however, we can evaluate our models and choose the best one during the training process." } ], "images": [ "scigen/train/1840.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Model performance on the official subtask C development set and test set. Multi-target: the same input has multiple different targets. Extra Words: extra words indicating given statement does not make sense. Reasonable Statement: the corresponding reasonable statement. Wiktionary: the obtained evidence from wiktionary" ], "reasoning": "The Multi-target method, combined with Extra Words, Reasonable Statement, and Wiktionary, improves the model's performance, achieving a BLEU score of 20.39 on the test set.", "description": "Compare to the baseline where we simply use the first referential answer of each example in training data as the target of the model output, the Multi-target method provides a larger amount of training data and thus helps the model get better performance. We hypothesize the degradation is because the complexity of the input sentence increases as we add extra material. When Extra Words, Reasonable Statement, and Wiktionary are all added, the benefits outweigh the disadvantages they bring. Therefore, we use all of them in the final submission of subtask C and achieve the BLEU score of 18.5 (3rd place) and human evaluation score of 2.08 (2nd place), which obtains a 0.14 gain over 3rd place and only 0.02 less than 1st place. Note that we might achieve a better human evaluation score accordingly. There are two reasons for that. Firstly, we optimize our evidence-searching approach after the competition, removing a big proportion of noisy data. Secondly, we observe that during the training process, the model performs well at the beginning but turns to mess later. Thus it’s difficult to choose the best model during the training process when we cannot evaluate it on the test set. When the competition has ended, however, we can evaluate our models and choose the best one during the training process.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Model performance on the official subtask C development set and test set. Multi-target: the same input has multiple different targets. Extra Words: extra words indicating given statement does not make sense. Reasonable Statement: the corresponding reasonable statement. Wiktionary: the obtained evidence from wiktionary', answer the following question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the BLEU score on the test set when using Multi-target, Extra Words, Reasonable Statement, and Wiktionary?\n" }, { "role": "assistant", "content": "The table shows that the combination of Multi-target, Extra Words, Reasonable Statement, and Wiktionary achieves a BLEU score of 20.39 on the test set. So, the answer is {\"answer\": \"20.39\"}" } ], "images": [ "scigen/train/1840.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Model performance on the official subtask C development set and test set. Multi-target: the same input has multiple different targets. Extra Words: extra words indicating given statement does not make sense. Reasonable Statement: the corresponding reasonable statement. Wiktionary: the obtained evidence from wiktionary" ], "reasoning": "The table shows that the combination of Multi-target, Extra Words, Reasonable Statement, and Wiktionary achieves a BLEU score of 20.39 on the test set.", "question": "What is the BLEU score on the test set when using Multi-target, Extra Words, Reasonable Statement, and Wiktionary?", "answer": "So, the answer is {\"answer\": \"20.39\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Multi-target method alone achieves a higher BLEU score on the test set than when combined with Extra Words.\n" }, { "role": "assistant", "content": "The table shows that the Multi-target method alone achieves a BLEU score of 19.43 on the test set, while the combination with Extra Words does not have a test BLEU score listed, indicating that the statement cannot be verified. So, the answer is {\"answer\": \"not enough info\"}" } ], "images": [ "scigen/train/1840.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Model performance on the official subtask C development set and test set. Multi-target: the same input has multiple different targets. Extra Words: extra words indicating given statement does not make sense. Reasonable Statement: the corresponding reasonable statement. Wiktionary: the obtained evidence from wiktionary" ], "reasoning": "The table shows that the Multi-target method alone achieves a BLEU score of 19.43 on the test set, while the combination with Extra Words does not have a test BLEU score listed, indicating that the statement cannot be verified.", "statement": "The Multi-target method alone achieves a higher BLEU score on the test set than when combined with Extra Words.", "answer": "So, the answer is {\"answer\": \"not enough info\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Average difference (absolute percentage) to the oracle downstream instability when using embedding distance measures as the selection criteria for dimension and precision parameters under fixed memory budgets. Top-performing values are bolded.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The eigenspace instability measure and k-NN measure outperform other baselines in downstream tasks, with eigenspace instability being up to 2.98% closer to the oracle.For the second, more challenging setting, we enumerate all embedding pairs with different dimension-precision combinations which correspond to the same total memory. For each embedding measure, we report the average absolute percentage difference between the downstream instability of the pair selected by the measure to the most stable “oracle” pair, across different memory budgets. We also introduce two naive baselines that do not require an embedding distance measure: high precision, which selects the pair with the highest precision possible at each memory budget, and low precision, which selects the pair with the lowest precision possible at each memory budget. As before, we repeat over three seeds, comparing embedding pairs of the same seed, and report the average. We see that the eigenspace instability measure and k-NN measure again outperform the other baselines on the majority of downstream tasks, with the eigenspace instability measure attaining a distance up to 2.98% (absolute) closer to the oracle than the other baselines, and average distance to the oracle 0.03% (absolute) better to 0.35% (absolute) worse than the k-NN measure across downstream tasks" } ], "images": [ "scigen/train/1841.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Average difference (absolute percentage) to the oracle downstream instability when using embedding distance measures as the selection criteria for dimension and precision parameters under fixed memory budgets. Top-performing values are bolded." ], "reasoning": "The eigenspace instability measure and k-NN measure outperform other baselines in downstream tasks, with eigenspace instability being up to 2.98% closer to the oracle.", "description": "For the second, more challenging setting, we enumerate all embedding pairs with different dimension-precision combinations which correspond to the same total memory. For each embedding measure, we report the average absolute percentage difference between the downstream instability of the pair selected by the measure to the most stable “oracle” pair, across different memory budgets. We also introduce two naive baselines that do not require an embedding distance measure: high precision, which selects the pair with the highest precision possible at each memory budget, and low precision, which selects the pair with the lowest precision possible at each memory budget. As before, we repeat over three seeds, comparing embedding pairs of the same seed, and report the average. We see that the eigenspace instability measure and k-NN measure again outperform the other baselines on the majority of downstream tasks, with the eigenspace instability measure attaining a distance up to 2.98% (absolute) closer to the oracle than the other baselines, and average distance to the oracle 0.03% (absolute) better to 0.35% (absolute) worse than the k-NN measure across downstream tasks", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Average difference (absolute percentage) to the oracle downstream instability when using embedding distance measures as the selection criteria for dimension and precision parameters under fixed memory budgets. Top-performing values are bolded.\nQuestion: Which embedding measure has the lowest average absolute percentage difference in the SST-2 GloVe task?\nSolve the above question based on the table information. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the SST-2 GloVe column, the lowest value is 0.43, which corresponds to the 1-k-NN measure. So, the answer is {\"answer\": \"1-k-NN\"}." } ], "images": [ "scigen/train/1841.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Average difference (absolute percentage) to the oracle downstream instability when using embedding distance measures as the selection criteria for dimension and precision parameters under fixed memory budgets. Top-performing values are bolded." ], "reasoning": "By examining the SST-2 GloVe column, the lowest value is 0.43, which corresponds to the 1-k-NN measure.", "question": "Which embedding measure has the lowest average absolute percentage difference in the SST-2 GloVe task?", "answer": "So, the answer is {\"answer\": \"1-k-NN\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe eigenspace instability measure has the lowest average absolute percentage difference in the Subj CBOW task.\n" }, { "role": "assistant", "content": "By examining the Subj CBOW column, the lowest value is 0.39, which corresponds to the eigenspace instability measure. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1841.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Average difference (absolute percentage) to the oracle downstream instability when using embedding distance measures as the selection criteria for dimension and precision parameters under fixed memory budgets. Top-performing values are bolded." ], "reasoning": "By examining the Subj CBOW column, the lowest value is 0.39, which corresponds to the eigenspace instability measure.", "statement": "The eigenspace instability measure has the lowest average absolute percentage difference in the Subj CBOW task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Spearman correlation scores between embedding distance measures and downstream prediction disagreement across varying dimension-precision pairs for the embedding. Downstream models are trained for sentiment analysis (SST-2, Subj) and NER (CoNLL-2003) tasks. Strongest correlation values are bolded.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The eigenspace instability measure shows strong correlations with downstream instability across various embedding algorithms and tasks, validating its theoretical grounding.We evaluate how predictive the eigenspace instability measure is of downstream instability, showing that the theoretically grounded eigenspace instability measure correlates strongly with downstream instability and is competitive with other embedding distance measures. To do this, we measure the Spearman correlations between the downstream prediction disagreement and the embedding distance measure for each of the five tasks and three embedding algorithms. The Spearman correlation quantifies how similar the ranking of the pairs of embeddings based on the embedding distance measure is to the ranking of the pairs of embeddings based on their downstream prediction disagreement, with a maximum value of 1.0. Moreover, the strong correlation of at least 0.68 for the eigenspace instability measure across embedding algorithms and downstream tasks validates our theoretical claim that this measure relates to downstream disagreement. We now provide additional results validating the strong relationship between the eigenspace instability measure and downstream instability. We see that k-NN measure and the eigenspace instability measure achieve strong correlations since the lines are generally monotonically increasing for both CBOW and MC embedding algorithms." } ], "images": [ "scigen/train/1842.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Spearman correlation scores between embedding distance measures and downstream prediction disagreement across varying dimension-precision pairs for the embedding. Downstream models are trained for sentiment analysis (SST-2, Subj) and NER (CoNLL-2003) tasks. Strongest correlation values are bolded." ], "reasoning": "The eigenspace instability measure shows strong correlations with downstream instability across various embedding algorithms and tasks, validating its theoretical grounding.", "description": "We evaluate how predictive the eigenspace instability measure is of downstream instability, showing that the theoretically grounded eigenspace instability measure correlates strongly with downstream instability and is competitive with other embedding distance measures. To do this, we measure the Spearman correlations between the downstream prediction disagreement and the embedding distance measure for each of the five tasks and three embedding algorithms. The Spearman correlation quantifies how similar the ranking of the pairs of embeddings based on the embedding distance measure is to the ranking of the pairs of embeddings based on their downstream prediction disagreement, with a maximum value of 1.0. Moreover, the strong correlation of at least 0.68 for the eigenspace instability measure across embedding algorithms and downstream tasks validates our theoretical claim that this measure relates to downstream disagreement. We now provide additional results validating the strong relationship between the eigenspace instability measure and downstream instability. We see that k-NN measure and the eigenspace instability measure achieve strong correlations since the lines are generally monotonically increasing for both CBOW and MC embedding algorithms.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 1: Spearman correlation scores between embedding distance measures and downstream prediction disagreement across varying dimension-precision pairs for the embedding. Downstream models are trained for sentiment analysis (SST-2, Subj) and NER (CoNLL-2003) tasks. Strongest correlation values are bolded.' and methodically tackle the question that follows. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the highest Spearman correlation value for the 1-k-NN measure across all embedding algorithms and tasks?\n" }, { "role": "assistant", "content": "To find the highest Spearman correlation value for the 1-k-NN measure, we look at the values in the row labeled '1-k-NN' across all columns. The highest value is 0.92 for the CoNLL-2003 MC task. So, the answer is {\"answer\": \"0.92\"}." } ], "images": [ "scigen/train/1842.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Spearman correlation scores between embedding distance measures and downstream prediction disagreement across varying dimension-precision pairs for the embedding. Downstream models are trained for sentiment analysis (SST-2, Subj) and NER (CoNLL-2003) tasks. Strongest correlation values are bolded." ], "reasoning": "To find the highest Spearman correlation value for the 1-k-NN measure, we look at the values in the row labeled '1-k-NN' across all columns. The highest value is 0.92 for the CoNLL-2003 MC task.", "question": "What is the highest Spearman correlation value for the 1-k-NN measure across all embedding algorithms and tasks?", "answer": "So, the answer is {\"answer\": \"0.92\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The eigenspace instability measure has a correlation of at least 0.68 for all embedding algorithms and downstream tasks. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe eigenspace instability measure has a correlation of at least 0.68 for all embedding algorithms and downstream tasks.\n" }, { "role": "assistant", "content": "To verify this statement, we check the values in the 'Eigenspace Instability' row. All values are 0.68 or higher, confirming the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1842.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Spearman correlation scores between embedding distance measures and downstream prediction disagreement across varying dimension-precision pairs for the embedding. Downstream models are trained for sentiment analysis (SST-2, Subj) and NER (CoNLL-2003) tasks. Strongest correlation values are bolded." ], "reasoning": "To verify this statement, we check the values in the 'Eigenspace Instability' row. All values are 0.68 or higher, confirming the statement.", "statement": "The eigenspace instability measure has a correlation of at least 0.68 for all embedding algorithms and downstream tasks.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Selection error when using embedding distance measures to predict the most stable embedding dimension-precision parameters on sentiment analysis (SST-2, Subj) and NER (CoNLL-2003) downstream tasks. Lowest errors are bolded.'.\n" }, { "role": "assistant", "content": "The eigenspace instability measure achieves between 0.95× to 1.55× the error rate of the k-NN measure across various downstream tasks.For the first, simpler setting, we first form all groupings of two embedding pairs with different dimension-precision combinations. For instance, a grouping may have one embedding pair with dimension 800, precision 32, and another embedding pair with dimension 200, precision 2, where a pair consists of a Wiki’17 and a Wiki’18 embedding from the same algorithm. For each embedding distance measure, we report the fraction of groupings where the embedding distance measure correctly chooses the embedding pair with lower downstream instability on a given task. We repeat over three seeds, comparing embedding pairs of the same seed, and report the average. Moreover, across downstream tasks, the eigenspace instability measure attains 0.95× to 1.55× the error rate of the k-NN measure." } ], "images": [ "scigen/train/1843.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Selection error when using embedding distance measures to predict the most stable embedding dimension-precision parameters on sentiment analysis (SST-2, Subj) and NER (CoNLL-2003) downstream tasks. Lowest errors are bolded." ], "reasoning": "The eigenspace instability measure achieves between 0.95× to 1.55× the error rate of the k-NN measure across various downstream tasks.", "description": "For the first, simpler setting, we first form all groupings of two embedding pairs with different dimension-precision combinations. For instance, a grouping may have one embedding pair with dimension 800, precision 32, and another embedding pair with dimension 200, precision 2, where a pair consists of a Wiki’17 and a Wiki’18 embedding from the same algorithm. For each embedding distance measure, we report the fraction of groupings where the embedding distance measure correctly chooses the embedding pair with lower downstream instability on a given task. We repeat over three seeds, comparing embedding pairs of the same seed, and report the average. Moreover, across downstream tasks, the eigenspace instability measure attains 0.95× to 1.55× the error rate of the k-NN measure.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Selection error when using embedding distance measures to predict the most stable embedding dimension-precision parameters on sentiment analysis (SST-2, Subj) and NER (CoNLL-2003) downstream tasks. Lowest errors are bolded.\nQuestion: Which embedding distance measure has the lowest error rate for the SST-2 MC task?\nSolve the above question based on the table information. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the error rates for the SST-2 MC task, the 1-k-NN measure has the lowest error rate of 0.13. So, the answer is {\"answer\": \"1-k-NN\"}." } ], "images": [ "scigen/train/1843.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Selection error when using embedding distance measures to predict the most stable embedding dimension-precision parameters on sentiment analysis (SST-2, Subj) and NER (CoNLL-2003) downstream tasks. Lowest errors are bolded." ], "reasoning": "By comparing the error rates for the SST-2 MC task, the 1-k-NN measure has the lowest error rate of 0.13.", "question": "Which embedding distance measure has the lowest error rate for the SST-2 MC task?", "answer": "So, the answer is {\"answer\": \"1-k-NN\"}." }, { "messages": [ { "role": "user", "content": "The eigenspace instability measure has the lowest error rate for the CoNLL-2003 MC task. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe eigenspace instability measure has the lowest error rate for the CoNLL-2003 MC task.\n" }, { "role": "assistant", "content": "By comparing the error rates for the CoNLL-2003 MC task, the eigenspace instability measure has an error rate of 0.17, which is higher than the 1-k-NN measure with an error rate of 0.11. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1843.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Selection error when using embedding distance measures to predict the most stable embedding dimension-precision parameters on sentiment analysis (SST-2, Subj) and NER (CoNLL-2003) downstream tasks. Lowest errors are bolded." ], "reasoning": "By comparing the error rates for the CoNLL-2003 MC task, the eigenspace instability measure has an error rate of 0.17, which is higher than the 1-k-NN measure with an error rate of 0.11.", "statement": "The eigenspace instability measure has the lowest error rate for the CoNLL-2003 MC task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4: Hyperparameters for embedding algorithms.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "CBOW uses the default learning rate of 0.05, GloVe uses a learning rate of 0.01 due to NaNs with the default, and MC uses a learning rate of 0.2 found to achieve low loss on Wiki’17.For CBOW, we use the default learning rate. For GloVe, we use a learning rate of 0.01 (as the default of 0.05 resulted in NaNs on 800-dimensional Wiki embeddings). For MC, since we are using our own implementation, we use a learning rate which we found to achieve low loss on Wiki’17." } ], "images": [ "scigen/train/1844.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Hyperparameters for embedding algorithms." ], "reasoning": "CBOW uses the default learning rate of 0.05, GloVe uses a learning rate of 0.01 due to NaNs with the default, and MC uses a learning rate of 0.2 found to achieve low loss on Wiki’17.", "description": "For CBOW, we use the default learning rate. For GloVe, we use a learning rate of 0.01 (as the default of 0.05 resulted in NaNs on 800-dimensional Wiki embeddings). For MC, since we are using our own implementation, we use a learning rate which we found to achieve low loss on Wiki’17.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: Hyperparameters for embedding algorithms.', solve this question and give detail solutions. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the learning rate used for the MC algorithm?\n" }, { "role": "assistant", "content": "The table shows that the learning rate for MC is 0.2, which matches the description stating that MC uses a learning rate found to achieve low loss on Wiki’17. So, the answer is {\"answer\": \"0.2\"}" } ], "images": [ "scigen/train/1844.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Hyperparameters for embedding algorithms." ], "reasoning": "The table shows that the learning rate for MC is 0.2, which matches the description stating that MC uses a learning rate found to achieve low loss on Wiki’17.", "question": "What is the learning rate used for the MC algorithm?", "answer": "So, the answer is {\"answer\": \"0.2\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe learning rate for GloVe is 0.05.\n" }, { "role": "assistant", "content": "The table shows that the learning rate for GloVe is 0.01, not 0.05. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1844.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Hyperparameters for embedding algorithms." ], "reasoning": "The table shows that the learning rate for GloVe is 0.01, not 0.05.", "statement": "The learning rate for GloVe is 0.05.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 7: Hyperparameters for training TransE knowledge graph embeddings. Bolded values indicate we performed a grid search. Other values are from Bordes et al. (2013) and Han et al. (2018).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "We use the OpenKE repository with modified early stopping and normalization procedures, and select the optimal learning rate for the FB15K-95 dataset based on validation set performance.We use the OpenKE repository to generate knowledge graph embeddings Han et al. We follow the training hyperparameters described in Bordes et al. We modify the repository to follow the early stopping procedure and normalization of entity embeddings to follow the protocol of Bordes et al. We additionally sweep the learning rate in {1e-5, 0.0001, 0.001, 0.01, 0.1} using dimension 50 on the FB15K-95 dataset, and choose the learning rate which attains the lowest mean rank (i.e., highest quality) on the validation set for the link prediction task. We also note that unlike with word embeddings, we do not align embeddings with orthogonal Procrustes before compressing the embeddings with uniform quantization. We found alignment to result in a quality drop on knowledge graph embeddings, likely due to the fact that there are two sets of embeddings jointly learned (relation and entity embeddings) which require more advanced alignment techniques." } ], "images": [ "scigen/train/1845.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Hyperparameters for training TransE knowledge graph embeddings. Bolded values indicate we performed a grid search. Other values are from Bordes et al. (2013) and Han et al. (2018)." ], "reasoning": "We use the OpenKE repository with modified early stopping and normalization procedures, and select the optimal learning rate for the FB15K-95 dataset based on validation set performance.", "description": "We use the OpenKE repository to generate knowledge graph embeddings Han et al. We follow the training hyperparameters described in Bordes et al. We modify the repository to follow the early stopping procedure and normalization of entity embeddings to follow the protocol of Bordes et al. We additionally sweep the learning rate in {1e-5, 0.0001, 0.001, 0.01, 0.1} using dimension 50 on the FB15K-95 dataset, and choose the learning rate which attains the lowest mean rank (i.e., highest quality) on the validation set for the link prediction task. We also note that unlike with word embeddings, we do not align embeddings with orthogonal Procrustes before compressing the embeddings with uniform quantization. We found alignment to result in a quality drop on knowledge graph embeddings, likely due to the fact that there are two sets of embeddings jointly learned (relation and entity embeddings) which require more advanced alignment techniques.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 7: Hyperparameters for training TransE knowledge graph embeddings. Bolded values indicate we performed a grid search. Other values are from Bordes et al. (2013) and Han et al. (2018).'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the learning rate used for training the knowledge graph embeddings?\n" }, { "role": "assistant", "content": "The description mentions sweeping the learning rate and choosing the one with the lowest mean rank on the validation set. The table shows the selected learning rate as 0.001. So, the answer is {\"answer\": \"0.001\"}." } ], "images": [ "scigen/train/1845.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Hyperparameters for training TransE knowledge graph embeddings. Bolded values indicate we performed a grid search. Other values are from Bordes et al. (2013) and Han et al. (2018)." ], "reasoning": "The description mentions sweeping the learning rate and choosing the one with the lowest mean rank on the validation set. The table shows the selected learning rate as 0.001.", "question": "What is the learning rate used for training the knowledge graph embeddings?", "answer": "So, the answer is {\"answer\": \"0.001\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The optimizer used for training the knowledge graph embeddings is Adam., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe optimizer used for training the knowledge graph embeddings is Adam.\n" }, { "role": "assistant", "content": "The table lists the optimizer used as SGD, not Adam. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1845.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Hyperparameters for training TransE knowledge graph embeddings. Bolded values indicate we performed a grid search. Other values are from Bordes et al. (2013) and Han et al. (2018)." ], "reasoning": "The table lists the optimizer used as SGD, not Adam.", "statement": "The optimizer used for training the knowledge graph embeddings is Adam.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 10: Worst-case absolute percentage error when using each embedding distance measure to predict the most stable embedding parameters on downstream tasks over of all pairs of parameters. Downstream models are trained for sentiment (SST-2, Subj) and NER (CoNLL-2003) tasks. Lowest errors are bolded.'.\n" }, { "role": "assistant", "content": "The worst-case performance of embedding distance measures is evaluated by comparing the maximum increase in instability and the worst-case absolute percentage error when choosing dimension-precision parameters.We also evaluate the the worst-case performance of the embedding distance measures when used as a selection criterion for dimension-precision parameters. First, on the easier task of choosing the more stable dimension-precision pair out of two choices, we define the worst-case performance as the maximum increase in instability that may occur by using the embedding distance measure to choose the dimension-precision parameters (rather than the ground truth choice). On the more challenging task of choosing the most stable dimension-precision pair under a memory budget, we define the worst-case performance as the worst-case absolute percentage error to the oracle parameters under a given memory budget." } ], "images": [ "scigen/train/1846.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Worst-case absolute percentage error when using each embedding distance measure to predict the most stable embedding parameters on downstream tasks over of all pairs of parameters. Downstream models are trained for sentiment (SST-2, Subj) and NER (CoNLL-2003) tasks. Lowest errors are bolded." ], "reasoning": "The worst-case performance of embedding distance measures is evaluated by comparing the maximum increase in instability and the worst-case absolute percentage error when choosing dimension-precision parameters.", "description": "We also evaluate the the worst-case performance of the embedding distance measures when used as a selection criterion for dimension-precision parameters. First, on the easier task of choosing the more stable dimension-precision pair out of two choices, we define the worst-case performance as the maximum increase in instability that may occur by using the embedding distance measure to choose the dimension-precision parameters (rather than the ground truth choice). On the more challenging task of choosing the most stable dimension-precision pair under a memory budget, we define the worst-case performance as the worst-case absolute percentage error to the oracle parameters under a given memory budget.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nTable title: Table 10: Worst-case absolute percentage error when using each embedding distance measure to predict the most stable embedding parameters on downstream tasks over of all pairs of parameters. Downstream models are trained for sentiment (SST-2, Subj) and NER (CoNLL-2003) tasks. Lowest errors are bolded.\n\nQuestion: What is the worst-case performance for the 1-k-NN measure on the SST-2 CBOW task?\n" }, { "role": "assistant", "content": "The worst-case performance for the 1-k-NN measure on the SST-2 CBOW task is given directly in the table under the '1-k-NN' row and 'SST-2 CBOW' column. So, the answer is {\"answer\": \"10.43\"}." } ], "images": [ "scigen/train/1846.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Worst-case absolute percentage error when using each embedding distance measure to predict the most stable embedding parameters on downstream tasks over of all pairs of parameters. Downstream models are trained for sentiment (SST-2, Subj) and NER (CoNLL-2003) tasks. Lowest errors are bolded." ], "reasoning": "The worst-case performance for the 1-k-NN measure on the SST-2 CBOW task is given directly in the table under the '1-k-NN' row and 'SST-2 CBOW' column.", "question": "What is the worst-case performance for the 1-k-NN measure on the SST-2 CBOW task?", "answer": "So, the answer is {\"answer\": \"10.43\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe worst-case performance for the 1-Eigenspace Overlap measure on the Subj MC task is 7.10.\n" }, { "role": "assistant", "content": "The table shows the worst-case performance for the 1-Eigenspace Overlap measure on the Subj MC task as 7.10, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1846.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Worst-case absolute percentage error when using each embedding distance measure to predict the most stable embedding parameters on downstream tasks over of all pairs of parameters. Downstream models are trained for sentiment (SST-2, Subj) and NER (CoNLL-2003) tasks. Lowest errors are bolded." ], "reasoning": "The table shows the worst-case performance for the 1-Eigenspace Overlap measure on the Subj MC task as 7.10, which matches the statement.", "statement": "The worst-case performance for the 1-Eigenspace Overlap measure on the Subj MC task is 7.10.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 11: Worst-case absolute percentage error to the oracle downstream instability when using embedding distance measures as the selection criteria for dimension and precision parameters. Smallest errors are bolded.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The worst-case performance of embedding distance measures is evaluated by the maximum increase in instability for choosing dimension-precision parameters and the worst-case absolute percentage error under a memory budget.We also evaluate the the worst-case performance of the embedding distance measures when used as a selection criterion for dimension-precision parameters. First, on the easier task of choosing the more stable dimension-precision pair out of two choices, we define the worst-case performance as the maximum increase in instability that may occur by using the embedding distance measure to choose the dimension-precision parameters (rather than the ground truth choice). On the more challenging task of choosing the most stable dimension-precision pair under a memory budget, we define the worst-case performance as the worst-case absolute percentage error to the oracle parameters under a given memory budget." } ], "images": [ "scigen/train/1847.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Worst-case absolute percentage error to the oracle downstream instability when using embedding distance measures as the selection criteria for dimension and precision parameters. Smallest errors are bolded." ], "reasoning": "The worst-case performance of embedding distance measures is evaluated by the maximum increase in instability for choosing dimension-precision parameters and the worst-case absolute percentage error under a memory budget.", "description": "We also evaluate the the worst-case performance of the embedding distance measures when used as a selection criterion for dimension-precision parameters. First, on the easier task of choosing the more stable dimension-precision pair out of two choices, we define the worst-case performance as the maximum increase in instability that may occur by using the embedding distance measure to choose the dimension-precision parameters (rather than the ground truth choice). On the more challenging task of choosing the most stable dimension-precision pair under a memory budget, we define the worst-case performance as the worst-case absolute percentage error to the oracle parameters under a given memory budget.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 11: Worst-case absolute percentage error to the oracle downstream instability when using embedding distance measures as the selection criteria for dimension and precision parameters. Smallest errors are bolded.\nQuestion: What is the highest value for '1−k-NN' across all embedding algorithms and tasks?\nSolve the above question based on the table information. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest value for '1−k-NN', we need to look at the row corresponding to '1−k-NN' and identify the maximum value across all columns. So, the answer is {\"answer\": \"11.37\"}." } ], "images": [ "scigen/train/1847.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Worst-case absolute percentage error to the oracle downstream instability when using embedding distance measures as the selection criteria for dimension and precision parameters. Smallest errors are bolded." ], "reasoning": "To find the highest value for '1−k-NN', we need to look at the row corresponding to '1−k-NN' and identify the maximum value across all columns.", "question": "What is the highest value for '1−k-NN' across all embedding algorithms and tasks?", "answer": "So, the answer is {\"answer\": \"11.37\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe highest value for 'PIP Loss' is greater than 10.\n" }, { "role": "assistant", "content": "To verify this statement, we need to check the values in the 'PIP Loss' row across all columns. The highest value in this row is 7.96, which is less than 10. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1847.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Worst-case absolute percentage error to the oracle downstream instability when using embedding distance measures as the selection criteria for dimension and precision parameters. Smallest errors are bolded." ], "reasoning": "To verify this statement, we need to check the values in the 'PIP Loss' row across all columns. The highest value in this row is 7.96, which is less than 10.", "statement": "The highest value for 'PIP Loss' is greater than 10.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Long-range Arcs Precision & Recall', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The LSTM model shows more than a 3% improvement in both precision and recall over the Cubic baseline for dependency lengths greater than 7, and its performance degrades more slowly as dependency length increases.Despite the models’ similar overall accuracy, our LSTM model outperforms the Cubic baseline by more than 3% in both precision and recall for dependency lengths greater than 7, and that the LSTM’s performance degrades more slowly as dependency length increases." } ], "images": [ "scigen/train/1848.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Long-range Arcs Precision & Recall" ], "reasoning": "The LSTM model shows more than a 3% improvement in both precision and recall over the Cubic baseline for dependency lengths greater than 7, and its performance degrades more slowly as dependency length increases.", "description": "Despite the models’ similar overall accuracy, our LSTM model outperforms the Cubic baseline by more than 3% in both precision and recall for dependency lengths greater than 7, and that the LSTM’s performance degrades more slowly as dependency length increases.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Long-range Arcs Precision & Recall\nQuestion: What is the precision of the LSTM model for dependency lengths between 7 and 49?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the precision of the LSTM model for dependency lengths between 7 and 49, look at the row labeled '7-49' under the 'Precision' column for the LSTM model. So, the answer is {\"answer\": \"73.5\"}." } ], "images": [ "scigen/train/1848.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Long-range Arcs Precision & Recall" ], "reasoning": "To find the precision of the LSTM model for dependency lengths between 7 and 49, look at the row labeled '7-49' under the 'Precision' column for the LSTM model.", "question": "What is the precision of the LSTM model for dependency lengths between 7 and 49?", "answer": "So, the answer is {\"answer\": \"73.5\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe LSTM model has a recall of 69.5 for dependency lengths between 7 and 49.\n" }, { "role": "assistant", "content": "To verify this statement, check the row labeled '7-49' under the 'Recall' column for the LSTM model. The value is 69.5, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1848.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Long-range Arcs Precision & Recall" ], "reasoning": "To verify this statement, check the row labeled '7-49' under the 'Recall' column for the LSTM model. The value is 69.5, which matches the statement.", "statement": "The LSTM model has a recall of 69.5 for dependency lengths between 7 and 49.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Google Web Treebank LAS Result', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The LSTM model achieves higher accuracy than the feedforward baseline with Tanh activation on the WSJ Test set, and performs competitively with the Cubic baseline.Our LSTM model outperforms the feedforward baseline with the same Tanh activation function (87.5 vs 86.4 on WSJ Test), while achieving competitive accuracy with the Cubic baseline." } ], "images": [ "scigen/train/1849.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Google Web Treebank LAS Result" ], "reasoning": "The LSTM model achieves higher accuracy than the feedforward baseline with Tanh activation on the WSJ Test set, and performs competitively with the Cubic baseline.", "description": "Our LSTM model outperforms the feedforward baseline with the same Tanh activation function (87.5 vs 86.4 on WSJ Test), while achieving competitive accuracy with the Cubic baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Google Web Treebank LAS Result\nQuestion: Which model achieves the highest accuracy on the Weblogs dataset?\nGive a detailed response to the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest accuracy on the Weblogs dataset, compare the accuracy values for F-T, F-C, and LSTM. The LSTM model has the highest accuracy with 81.2. So, the answer is {\"answer\": \"LSTM\"}." } ], "images": [ "scigen/train/1849.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Google Web Treebank LAS Result" ], "reasoning": "To find the model with the highest accuracy on the Weblogs dataset, compare the accuracy values for F-T, F-C, and LSTM. The LSTM model has the highest accuracy with 81.2.", "question": "Which model achieves the highest accuracy on the Weblogs dataset?", "answer": "So, the answer is {\"answer\": \"LSTM\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe feedforward model with Tanh activation outperforms the LSTM model on the WSJ Dev set.\n" }, { "role": "assistant", "content": "The table shows that on the WSJ Dev set, the LSTM model has an accuracy of 87.8, which is higher than the feedforward model with Tanh activation, which has an accuracy of 86.0. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1849.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Google Web Treebank LAS Result" ], "reasoning": "The table shows that on the WSJ Dev set, the LSTM model has an accuracy of 87.8, which is higher than the feedforward model with Tanh activation, which has an accuracy of 86.0.", "statement": "The feedforward model with Tanh activation outperforms the LSTM model on the WSJ Dev set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Effect of Dropout on UAS Accuracy'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The LSTM parser shows a significant improvement with dropout, becoming competitive with F-Cubic, which has a smaller improvement.We discover that regularisation is important for the LSTM parser, more so than feedforward architectures. Observe that LSTM becomes competitive only with dropout." } ], "images": [ "scigen/train/1850.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Effect of Dropout on UAS Accuracy" ], "reasoning": "The LSTM parser shows a significant improvement with dropout, becoming competitive with F-Cubic, which has a smaller improvement.", "description": "We discover that regularisation is important for the LSTM parser, more so than feedforward architectures. Observe that LSTM becomes competitive only with dropout.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: Effect of Dropout on UAS Accuracy' to sequentially solve the question presented below. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nHow much does the LSTM parser's performance improve with dropout compared to without dropout?\n" }, { "role": "assistant", "content": "The table shows that the LSTM parser's performance improves from 87.4 to 89.5 with dropout, resulting in an improvement of 2.1. So, the answer is {\"answer\": \"2.1\"}." } ], "images": [ "scigen/train/1850.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Effect of Dropout on UAS Accuracy" ], "reasoning": "The table shows that the LSTM parser's performance improves from 87.4 to 89.5 with dropout, resulting in an improvement of 2.1.", "question": "How much does the LSTM parser's performance improve with dropout compared to without dropout?", "answer": "So, the answer is {\"answer\": \"2.1\"}." }, { "messages": [ { "role": "user", "content": "The F-Cubic model shows a larger improvement with dropout compared to the LSTM model. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe F-Cubic model shows a larger improvement with dropout compared to the LSTM model.\n" }, { "role": "assistant", "content": "The table shows that the F-Cubic model improves by 0.4 with dropout, while the LSTM model improves by 2.1. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1850.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Effect of Dropout on UAS Accuracy" ], "reasoning": "The table shows that the F-Cubic model improves by 0.4 with dropout, while the LSTM model improves by 2.1. Therefore, the statement is refuted.", "statement": "The F-Cubic model shows a larger improvement with dropout compared to the LSTM model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: UAS Accuracy of Various Regularisation'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Experiments were conducted on the first 80,000 tokens of the WSJ training set to determine the benefits of dropout between embedding-hidden and hidden-output connections.To investigate what kind of dropout is beneficial, we conducted further experiments on a subset of the training data (the first 80,000 tokens of the WSJ training set). E-H and H-O indicate dropout between the embedding-hidden and hidden-output connections, respectively." } ], "images": [ "scigen/train/1851.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: UAS Accuracy of Various Regularisation" ], "reasoning": "Experiments were conducted on the first 80,000 tokens of the WSJ training set to determine the benefits of dropout between embedding-hidden and hidden-output connections.", "description": "To investigate what kind of dropout is beneficial, we conducted further experiments on a subset of the training data (the first 80,000 tokens of the WSJ training set). E-H and H-O indicate dropout between the embedding-hidden and hidden-output connections, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 4: UAS Accuracy of Various Regularisation', and tackle the question below in a sequential manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the highest test score achieved with dropout applied to both embedding-hidden and hidden-output connections?\n" }, { "role": "assistant", "content": "To find the highest test score with dropout applied to both connections, look at the rows labeled 'Both' under the Dropout column and identify the highest value in the Test column. So, the answer is {\"answer\": \"85.9\"}." } ], "images": [ "scigen/train/1851.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: UAS Accuracy of Various Regularisation" ], "reasoning": "To find the highest test score with dropout applied to both connections, look at the rows labeled 'Both' under the Dropout column and identify the highest value in the Test column.", "question": "What is the highest test score achieved with dropout applied to both embedding-hidden and hidden-output connections?", "answer": "So, the answer is {\"answer\": \"85.9\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe highest development score was achieved with a dropout rate of 0.6 between embedding-hidden connections.\n" }, { "role": "assistant", "content": "To verify this statement, look at the rows labeled 'E-H' under the Dropout column and identify the highest value in the Dev column. The highest development score is 86.2, which corresponds to a dropout rate of 0.6. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1851.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: UAS Accuracy of Various Regularisation" ], "reasoning": "To verify this statement, look at the rows labeled 'E-H' under the Dropout column and identify the highest value in the Dev column. The highest development score is 86.2, which corresponds to a dropout rate of 0.6.", "statement": "The highest development score was achieved with a dropout rate of 0.6 between embedding-hidden connections.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: German→English translation quality (Bleu) of systems with different ratios of real:syn data.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The model trained on a 1:4 real to synthetic data ratio achieves the best results, but the performance is close to the model trained on a 1:1 ratio.Comparing the systems using ratios of (1:1) and (1:4), we see that the latter achieves lower perplexity during training. The Bleu scores show that the translation quality does not improve linearly with the size of the synthetic data. The model trained on (1:4) real to synthetic ratio of training data achieves the best results, however, the performance is close to the model trained on (1:1) training data." } ], "images": [ "scigen/train/1852.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: German→English translation quality (Bleu) of systems with different ratios of real:syn data." ], "reasoning": "The model trained on a 1:4 real to synthetic data ratio achieves the best results, but the performance is close to the model trained on a 1:1 ratio.", "description": "Comparing the systems using ratios of (1:1) and (1:4), we see that the latter achieves lower perplexity during training. The Bleu scores show that the translation quality does not improve linearly with the size of the synthetic data. The model trained on (1:4) real to synthetic ratio of training data achieves the best results, however, the performance is close to the model trained on (1:1) training data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: German→English translation quality (Bleu) of systems with different ratios of real:syn data.\nQuestion: Which model achieves the best results in 2016, and how does it compare to the model trained on a 1:1 ratio?\nSolve the above question based on the table information. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The model trained on a 1:4 real to synthetic data ratio achieves a perplexity of 36.9 in 2016, which is higher than the 36.3 achieved by the model trained on a 1:1 ratio. So, the answer is {\"answer\": \"1:4\"}." } ], "images": [ "scigen/train/1852.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: German→English translation quality (Bleu) of systems with different ratios of real:syn data." ], "reasoning": "The model trained on a 1:4 real to synthetic data ratio achieves a perplexity of 36.9 in 2016, which is higher than the 36.3 achieved by the model trained on a 1:1 ratio.", "question": "Which model achieves the best results in 2016, and how does it compare to the model trained on a 1:1 ratio?", "answer": "So, the answer is {\"answer\": \"1:4\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe model trained on a 1:10 real to synthetic data ratio achieves the best results in 2015.\n" }, { "role": "assistant", "content": "The table shows that the model trained on a 1:10 ratio achieves a perplexity of 23.6 in 2015, which is lower than the perplexities achieved by other models. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1852.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: German→English translation quality (Bleu) of systems with different ratios of real:syn data." ], "reasoning": "The table shows that the model trained on a 1:10 ratio achieves a perplexity of 23.6 in 2015, which is lower than the perplexities achieved by other models.", "statement": "The model trained on a 1:10 real to synthetic data ratio achieves the best results in 2015.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: English→German translation quality (Bleu) of systems using forward and reverse models for generating synthetic data.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The experiment shows that using synthetic source data improves translation quality more than using synthetic target data, contrary to previous findings for PBMT systems.We perform a small experiment to measure whether back-translating from source to target is also beneficial for improving translation quality. This is in contrast to the findings of \\newciteLambert:2011:ITM:2132960.2132997 for PBMT systems where they show that using synthetic target data does not lead to improvements in translation quality." } ], "images": [ "scigen/train/1853.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: English→German translation quality (Bleu) of systems using forward and reverse models for generating synthetic data." ], "reasoning": "The experiment shows that using synthetic source data improves translation quality more than using synthetic target data, contrary to previous findings for PBMT systems.", "description": "We perform a small experiment to measure whether back-translating from source to target is also beneficial for improving translation quality. This is in contrast to the findings of \\newciteLambert:2011:ITM:2132960.2132997 for PBMT systems where they show that using synthetic target data does not lead to improvements in translation quality.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 2: English→German translation quality (Bleu) of systems using forward and reverse models for generating synthetic data.', unfold the question presented below, detailing every step of your calculation. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the improvement in translation quality from 2014 to 2016 when using synthetic source data?\n" }, { "role": "assistant", "content": "To find the improvement, compare the scores for 2014 and 2016 when using synthetic source data. The score in 2014 is 24.0 and in 2016 is 30.7. The improvement is 30.7 - 24.0. So, the answer is {\"answer\": \"6.7\"}." } ], "images": [ "scigen/train/1853.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: English→German translation quality (Bleu) of systems using forward and reverse models for generating synthetic data." ], "reasoning": "To find the improvement, compare the scores for 2014 and 2016 when using synthetic source data. The score in 2014 is 24.0 and in 2016 is 30.7. The improvement is 30.7 - 24.0.", "question": "What is the improvement in translation quality from 2014 to 2016 when using synthetic source data?", "answer": "So, the answer is {\"answer\": \"6.7\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Using synthetic target data improves translation quality in 2016 compared to the baseline. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nUsing synthetic target data improves translation quality in 2016 compared to the baseline.\n" }, { "role": "assistant", "content": "Compare the 2016 scores for the baseline and synthetic target data. The baseline score is 28.0, and the synthetic target data score is 29.8, showing an improvement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1853.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: English→German translation quality (Bleu) of systems using forward and reverse models for generating synthetic data." ], "reasoning": "Compare the 2016 scores for the baseline and synthetic target data. The baseline score is 28.0, and the synthetic target data score is 29.8, showing an improvement.", "statement": "Using synthetic target data improves translation quality in 2016 compared to the baseline.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: German→English translation quality (Bleu).'.\n" }, { "role": "assistant", "content": "Re-training with additional parallel data yields slightly higher Bleu scores than with synthetic data, indicating that back-translation with a good reverse model achieves results close to using manually translated data.As to be expected, re-training with additional parallel data yields higher performance than re-training with additional synthetic data. However, the differences between the Bleu scores of these two models are surprisingly small. This indicates that performing back-translation with a reasonably good reverse model already achieves results that are close to a system that uses additional manually translated data. This is inline with findings of \\newcitesennrich-haddow-birch:2016: P16-11 who observed that the same monolingual data translated with three translation systems of different quality and used in re-training the translation model yields similar results." } ], "images": [ "scigen/train/1854.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: German→English translation quality (Bleu)." ], "reasoning": "Re-training with additional parallel data yields slightly higher Bleu scores than with synthetic data, indicating that back-translation with a good reverse model achieves results close to using manually translated data.", "description": "As to be expected, re-training with additional parallel data yields higher performance than re-training with additional synthetic data. However, the differences between the Bleu scores of these two models are surprisingly small. This indicates that performing back-translation with a reasonably good reverse model already achieves results that are close to a system that uses additional manually translated data. This is inline with findings of \\newcitesennrich-haddow-birch:2016: P16-11 who observed that the same monolingual data translated with three translation systems of different quality and used in re-training the translation model yields similar results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: German→English translation quality (Bleu).'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. \n\nQuestion: What is the difference in Bleu scores between the models re-trained with synthetic data and ground truth data in 2017?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows that the Bleu score for the model re-trained with synthetic data in 2017 is 27.5, while the score for the model re-trained with ground truth data is 28.1. The difference is 28.1 - 27.5. So, the answer is {\"answer\": \"0.6\"}." } ], "images": [ "scigen/train/1854.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: German→English translation quality (Bleu)." ], "reasoning": "The table shows that the Bleu score for the model re-trained with synthetic data in 2017 is 27.5, while the score for the model re-trained with ground truth data is 28.1. The difference is 28.1 - 27.5.", "question": "What is the difference in Bleu scores between the models re-trained with synthetic data and ground truth data in 2017?", "answer": "So, the answer is {\"answer\": \"0.6\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model re-trained with synthetic data in 2016 outperforms the baseline model in 2016.\n" }, { "role": "assistant", "content": "The table shows that the Bleu score for the model re-trained with synthetic data in 2016 is 32.2, while the score for the baseline model in 2016 is 29.5. Since 32.2 is greater than 29.5, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1854.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: German→English translation quality (Bleu)." ], "reasoning": "The table shows that the Bleu score for the model re-trained with synthetic data in 2016 is 32.2, while the score for the baseline model in 2016 is 29.5. Since 32.2 is greater than 29.5, the statement is supported.", "statement": "The model re-trained with synthetic data in 2016 outperforms the baseline model in 2016.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 8: German↔English translation quality (Bleu). Experiments marked † are averaged over 3 runs. PredLoss is the contextual prediction loss and MeanPredLoss is the average loss. token and Sword are context selection definitions from neighboring tokens and subword units respectively. Note that token includes both subword units and full words. emb is computing context similarities with token embeddings and match is comparing the context tokens.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Experiments show that using word embeddings for similarity measures yields the best results across all test sets, indicating the importance of context diversity over exact ngrams.In these experiments, we set the hyperparameters s and w to 0.75 and 4, respectively. Comparing the experiments with different similarity measures, match and emb, we observe that in all test sets we achieve the best results when using word embeddings. This indicate that for targeted sampling, it is more beneficial to have diversity in the context of difficult words as opposed to having the exact ngrams." } ], "images": [ "scigen/train/1855.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: German↔English translation quality (Bleu). Experiments marked † are averaged over 3 runs. PredLoss is the contextual prediction loss and MeanPredLoss is the average loss. token and Sword are context selection definitions from neighboring tokens and subword units respectively. Note that token includes both subword units and full words. emb is computing context similarities with token embeddings and match is comparing the context tokens." ], "reasoning": "Experiments show that using word embeddings for similarity measures yields the best results across all test sets, indicating the importance of context diversity over exact ngrams.", "description": "In these experiments, we set the hyperparameters s and w to 0.75 and 4, respectively. Comparing the experiments with different similarity measures, match and emb, we observe that in all test sets we achieve the best results when using word embeddings. This indicate that for targeted sampling, it is more beneficial to have diversity in the context of difficult words as opposed to having the exact ngrams.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nTable title: Table 8: German↔English translation quality (Bleu). Experiments marked † are averaged over 3 runs. PredLoss is the contextual prediction loss and MeanPredLoss is the average loss. token and Sword are context selection definitions from neighboring tokens and subword units respectively. Note that token includes both subword units and full words. emb is computing context similarities with token embeddings and match is comparing the context tokens.\n\nQuestion: What is the highest score achieved in the De-En test2016 set and which system achieved it?\n" }, { "role": "assistant", "content": "By examining the De-En test2016 column, the highest score is 37.9, achieved by the MeanPredLoss system with tokens context and emb similarity. So, the answer is {\"answer\": \"37.9 by MeanPredLoss\"}." } ], "images": [ "scigen/train/1855.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: German↔English translation quality (Bleu). Experiments marked † are averaged over 3 runs. PredLoss is the contextual prediction loss and MeanPredLoss is the average loss. token and Sword are context selection definitions from neighboring tokens and subword units respectively. Note that token includes both subword units and full words. emb is computing context similarities with token embeddings and match is comparing the context tokens." ], "reasoning": "By examining the De-En test2016 column, the highest score is 37.9, achieved by the MeanPredLoss system with tokens context and emb similarity.", "question": "What is the highest score achieved in the De-En test2016 set and which system achieved it?", "answer": "So, the answer is {\"answer\": \"37.9 by MeanPredLoss\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The PredLoss system with sentence context and emb similarity achieved the lowest score in the En-De test2014 set., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe PredLoss system with sentence context and emb similarity achieved the lowest score in the En-De test2014 set.\n" }, { "role": "assistant", "content": "By examining the En-De test2014 column, the lowest score is 22.0, achieved by the PredLoss system with sentence context and emb similarity. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1855.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: German↔English translation quality (Bleu). Experiments marked † are averaged over 3 runs. PredLoss is the contextual prediction loss and MeanPredLoss is the average loss. token and Sword are context selection definitions from neighboring tokens and subword units respectively. Note that token includes both subword units and full words. emb is computing context similarities with token embeddings and match is comparing the context tokens." ], "reasoning": "By examining the En-De test2014 column, the lowest score is 22.0, achieved by the PredLoss system with sentence context and emb similarity.", "statement": "The PredLoss system with sentence context and emb similarity achieved the lowest score in the En-De test2014 set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4.1: WER(%) obtained in a distant-talking scenario with real and simulated data across different acoustic models and microphone processing.'.\n" }, { "role": "assistant", "content": "Beamforming improves performance across all models and datasets, with DNN models showing a 12% improvement in real data and 13% in simulated data.The “mono” model is a context-independent GMM model (monophones), “tri4” is a context-dependent GMM model based on Speaker Adaptive Training (SAT), while “DNN” represents the considered FF-DNN model. One can also note that, as hoped, a similar performance trend between the datasets is reached when applying beamforming. For instance, in the case of real data coupled with FF-DNN acoustic models, delay-and-sum beamforming leads to a relative improvement of about 12% over the single microphone case, which is similar to the improvement of 13% obtained with the simulated data." } ], "images": [ "scigen/train/1856.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4.1: WER(%) obtained in a distant-talking scenario with real and simulated data across different acoustic models and microphone processing." ], "reasoning": "Beamforming improves performance across all models and datasets, with DNN models showing a 12% improvement in real data and 13% in simulated data.", "description": "The “mono” model is a context-independent GMM model (monophones), “tri4” is a context-dependent GMM model based on Speaker Adaptive Training (SAT), while “DNN” represents the considered FF-DNN model. One can also note that, as hoped, a similar performance trend between the datasets is reached when applying beamforming. For instance, in the case of real data coupled with FF-DNN acoustic models, delay-and-sum beamforming leads to a relative improvement of about 12% over the single microphone case, which is similar to the improvement of 13% obtained with the simulated data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 4.1: WER(%) obtained in a distant-talking scenario with real and simulated data across different acoustic models and microphone processing.' to sequentially solve the question presented below. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhat is the relative improvement in performance for the DNN model when applying D&S beamforming to real data compared to single distant microphone real data?\n" }, { "role": "assistant", "content": "The performance for the DNN model with single distant microphone real data is 12.0, and with D&S beamforming real data, it is 10.7. The relative improvement is calculated as (12.0 - 10.7) / 12.0 * 100%. So, the answer is {\"answer\": \"10.83%\"}." } ], "images": [ "scigen/train/1856.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4.1: WER(%) obtained in a distant-talking scenario with real and simulated data across different acoustic models and microphone processing." ], "reasoning": "The performance for the DNN model with single distant microphone real data is 12.0, and with D&S beamforming real data, it is 10.7. The relative improvement is calculated as (12.0 - 10.7) / 12.0 * 100%.", "question": "What is the relative improvement in performance for the DNN model when applying D&S beamforming to real data compared to single distant microphone real data?", "answer": "So, the answer is {\"answer\": \"10.83%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The mono model shows a greater improvement with beamforming in simulated data than in real data. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe mono model shows a greater improvement with beamforming in simulated data than in real data.\n" }, { "role": "assistant", "content": "The mono model's performance improves from 64.7 to 58.8 in simulated data (a difference of 5.9) and from 62.2 to 56.8 in real data (a difference of 5.4). The improvement is greater in simulated data. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1856.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4.1: WER(%) obtained in a distant-talking scenario with real and simulated data across different acoustic models and microphone processing." ], "reasoning": "The mono model's performance improves from 64.7 to 58.8 in simulated data (a difference of 5.9) and from 62.2 to 56.8 in real data (a difference of 5.4). The improvement is greater in simulated data.", "statement": "The mono model shows a greater improvement with beamforming in simulated data than in real data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4.3: PER(%) obtained in distant-talking scenarios with the standard and with the proposed technique based on close-talking labels (CT-lab).'.\n" }, { "role": "assistant", "content": "The proposed approach shows a relative improvement of 10% for APASCI and 12% for Euronews, even under challenging acoustic conditions.Results show that the proposed approach provides a substantial performance improvement, over both real and simulated data. Specifically, a relative improvement of 10% and 12% was achieved for APASCI and Euronews, respectively. Results shows that the benefits of this approach holds also under challenging acoustic conditions characterized by noise and reverberation." } ], "images": [ "scigen/train/1857.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4.3: PER(%) obtained in distant-talking scenarios with the standard and with the proposed technique based on close-talking labels (CT-lab)." ], "reasoning": "The proposed approach shows a relative improvement of 10% for APASCI and 12% for Euronews, even under challenging acoustic conditions.", "description": "Results show that the proposed approach provides a substantial performance improvement, over both real and simulated data. Specifically, a relative improvement of 10% and 12% was achieved for APASCI and Euronews, respectively. Results shows that the benefits of this approach holds also under challenging acoustic conditions characterized by noise and reverberation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 4.3: PER(%) obtained in distant-talking scenarios with the standard and with the proposed technique based on close-talking labels (CT-lab).'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. \n\nQuestion: What is the relative improvement in performance for APASCI under Real-Rev conditions when using the CT-lab approach?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the relative improvement, compare the scores for APASCI under Real-Rev conditions between the Standard and CT-lab approaches. The Standard score is 40.2, and the CT-lab score is 35.9. The relative improvement is calculated as (40.2 - 35.9) / 40.2 * 100%. So, the answer is {\"answer\": \"10.7%\"}." } ], "images": [ "scigen/train/1857.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4.3: PER(%) obtained in distant-talking scenarios with the standard and with the proposed technique based on close-talking labels (CT-lab)." ], "reasoning": "To find the relative improvement, compare the scores for APASCI under Real-Rev conditions between the Standard and CT-lab approaches. The Standard score is 40.2, and the CT-lab score is 35.9. The relative improvement is calculated as (40.2 - 35.9) / 40.2 * 100%.", "question": "What is the relative improvement in performance for APASCI under Real-Rev conditions when using the CT-lab approach?", "answer": "So, the answer is {\"answer\": \"10.7%\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe proposed approach provides a 12% improvement for Euronews under Sim-Rev conditions.\n" }, { "role": "assistant", "content": "To verify this statement, compare the scores for Euronews under Sim-Rev conditions between the Standard and CT-lab approaches. The Standard score is 36.1, and the CT-lab score is 32.1. The relative improvement is calculated as (36.1 - 32.1) / 36.1 * 100%, which is approximately 11.1%, not 12%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1857.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4.3: PER(%) obtained in distant-talking scenarios with the standard and with the proposed technique based on close-talking labels (CT-lab)." ], "reasoning": "To verify this statement, compare the scores for Euronews under Sim-Rev conditions between the Standard and CT-lab approaches. The Standard score is 36.1, and the CT-lab score is 32.1. The relative improvement is calculated as (36.1 - 32.1) / 36.1 * 100%, which is approximately 11.1%, not 12%.", "statement": "The proposed approach provides a 12% improvement for Euronews under Sim-Rev conditions.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4.4: PER(%) obtained in distant-talking scenarios with a standard RBM pre-training (Stand-PT) and with the proposed supervised close-talking pre-training (CT-PT).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The improvement is observed in both real and simulated data, including challenging scenarios with noise and reverberation.This improvement is obtained over both real and simulated data and also arises in the challenging scenarios characterized by both noise and reverberation (rev&noise)." } ], "images": [ "scigen/train/1858.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4.4: PER(%) obtained in distant-talking scenarios with a standard RBM pre-training (Stand-PT) and with the proposed supervised close-talking pre-training (CT-PT)." ], "reasoning": "The improvement is observed in both real and simulated data, including challenging scenarios with noise and reverberation.", "description": "This improvement is obtained over both real and simulated data and also arises in the challenging scenarios characterized by both noise and reverberation (rev&noise).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 4.4: PER(%) obtained in distant-talking scenarios with a standard RBM pre-training (Stand-PT) and with the proposed supervised close-talking pre-training (CT-PT).', unfold the steps to solve the question given next. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nQuestion: What is the highest score obtained in the Sim-Rev&Noise scenario?\n" }, { "role": "assistant", "content": "To find the highest score in the Sim-Rev&Noise scenario, look at the row labeled 'Sim-Rev&Noise' and identify the highest value across all columns. So, the answer is {\"answer\": \"47.3\"}." } ], "images": [ "scigen/train/1858.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4.4: PER(%) obtained in distant-talking scenarios with a standard RBM pre-training (Stand-PT) and with the proposed supervised close-talking pre-training (CT-PT)." ], "reasoning": "To find the highest score in the Sim-Rev&Noise scenario, look at the row labeled 'Sim-Rev&Noise' and identify the highest value across all columns.", "question": "What is the highest score obtained in the Sim-Rev&Noise scenario?", "answer": "So, the answer is {\"answer\": \"47.3\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe score for Real-Rev in the APASCI (6 h) Stand-PT scenario is higher than in the Euronews (100 h) CT-PT scenario.\n" }, { "role": "assistant", "content": "Compare the scores for Real-Rev in the APASCI (6 h) Stand-PT and Euronews (100 h) CT-PT columns. The score for Real-Rev in APASCI (6 h) Stand-PT is 35.9, which is higher than 33.0 in Euronews (100 h) CT-PT. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1858.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4.4: PER(%) obtained in distant-talking scenarios with a standard RBM pre-training (Stand-PT) and with the proposed supervised close-talking pre-training (CT-PT)." ], "reasoning": "Compare the scores for Real-Rev in the APASCI (6 h) Stand-PT and Euronews (100 h) CT-PT columns. The score for Real-Rev in APASCI (6 h) Stand-PT is 35.9, which is higher than 33.0 in Euronews (100 h) CT-PT.", "statement": "The score for Real-Rev in the APASCI (6 h) Stand-PT scenario is higher than in the Euronews (100 h) CT-PT scenario.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5.5: PER(%) of the GRU models with and without batch normalization (TIMIT dataset, MFCC features).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table compares the final speech recognition performance of RNN models on the TIMIT dataset, highlighting the impact of batch normalization., we now compare RNN models in terms of their final speech recognition performance on the TIMIT dataset. In particular, this section studies the effects of batch normalization on the proposed model." } ], "images": [ "scigen/train/1859.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5.5: PER(%) of the GRU models with and without batch normalization (TIMIT dataset, MFCC features)." ], "reasoning": "The table compares the final speech recognition performance of RNN models on the TIMIT dataset, highlighting the impact of batch normalization.", "description": ", we now compare RNN models in terms of their final speech recognition performance on the TIMIT dataset. In particular, this section studies the effects of batch normalization on the proposed model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 5.5: PER(%) of the GRU models with and without batch normalization (TIMIT dataset, MFCC features).' and proceed to solve the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich RNN model shows the greatest improvement in performance with batch normalization?\n" }, { "role": "assistant", "content": "To determine the model with the greatest improvement, compare the performance of each model with and without batch normalization. The GRU model improves from 18.4 to 17.1, the M-GRU model improves from 18.6 to 17.2, and the Li-GRU model improves from 20.4 to 16.7. The Li-GRU model shows the greatest improvement. So, the answer is {\"answer\": \"Li-GRU\"}" } ], "images": [ "scigen/train/1859.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5.5: PER(%) of the GRU models with and without batch normalization (TIMIT dataset, MFCC features)." ], "reasoning": "To determine the model with the greatest improvement, compare the performance of each model with and without batch normalization. The GRU model improves from 18.4 to 17.1, the M-GRU model improves from 18.6 to 17.2, and the Li-GRU model improves from 20.4 to 16.7. The Li-GRU model shows the greatest improvement.", "question": "Which RNN model shows the greatest improvement in performance with batch normalization?", "answer": "So, the answer is {\"answer\": \"Li-GRU\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Batch normalization improves the performance of all RNN models on the TIMIT dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nBatch normalization improves the performance of all RNN models on the TIMIT dataset.\n" }, { "role": "assistant", "content": "The table shows that all RNN models (GRU, M-GRU, and Li-GRU) have lower performance values with batch normalization compared to without batch normalization, indicating improved performance. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1859.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5.5: PER(%) of the GRU models with and without batch normalization (TIMIT dataset, MFCC features)." ], "reasoning": "The table shows that all RNN models (GRU, M-GRU, and Li-GRU) have lower performance values with batch normalization compared to without batch normalization, indicating improved performance.", "statement": "Batch normalization improves the performance of all RNN models on the TIMIT dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 5.6: PER(%) obtained for the test set of TIMIT with various CTC RNN architectures.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The Li-GRU model outperforms the standard GRU in both batch normalization settings, showing its effectiveness in an end-to-end ASR framework.In these experiments, the Li-GRU clearly outperforms the standard GRU, showing the effectiveness of the proposed model even in a end-to-end ASR framework. The improvement is obtained both with and without batch normalization and, similarly to what observed for hybrid systems, the latter technique leads to better performance when coupled with Li-GRU. However, a smaller performance gain is observed when batch normalization is applied to the CTC. This result could also be related to the different choice of the regularizer, as weight noise was used instead of recurrent dropout." } ], "images": [ "scigen/train/1860.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5.6: PER(%) obtained for the test set of TIMIT with various CTC RNN architectures." ], "reasoning": "The Li-GRU model outperforms the standard GRU in both batch normalization settings, showing its effectiveness in an end-to-end ASR framework.", "description": "In these experiments, the Li-GRU clearly outperforms the standard GRU, showing the effectiveness of the proposed model even in a end-to-end ASR framework. The improvement is obtained both with and without batch normalization and, similarly to what observed for hybrid systems, the latter technique leads to better performance when coupled with Li-GRU. However, a smaller performance gain is observed when batch normalization is applied to the CTC. This result could also be related to the different choice of the regularizer, as weight noise was used instead of recurrent dropout.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the performance gain of Li-GRU over GRU when batch normalization is applied?\nSolve the above question based on the table titled 'Table 5.6: PER(%) obtained for the test set of TIMIT with various CTC RNN architectures.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "When batch normalization is applied, the performance of GRU is 22.0, while the performance of Li-GRU is 20.9. The performance gain is the difference between these values. So, the answer is {\"answer\": \"1.1\"}." } ], "images": [ "scigen/train/1860.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5.6: PER(%) obtained for the test set of TIMIT with various CTC RNN architectures." ], "reasoning": "When batch normalization is applied, the performance of GRU is 22.0, while the performance of Li-GRU is 20.9. The performance gain is the difference between these values.", "question": "What is the performance gain of Li-GRU over GRU when batch normalization is applied?", "answer": "So, the answer is {\"answer\": \"1.1\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nLi-GRU performs better than GRU without batch normalization.\n" }, { "role": "assistant", "content": "Without batch normalization, the performance of GRU is 22.1, while the performance of Li-GRU is 21.1. Since 21.1 is less than 22.1, Li-GRU performs better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1860.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5.6: PER(%) obtained for the test set of TIMIT with various CTC RNN architectures." ], "reasoning": "Without batch normalization, the performance of GRU is 22.1, while the performance of Li-GRU is 21.1. Since 21.1 is less than 22.1, Li-GRU performs better.", "statement": "Li-GRU performs better than GRU without batch normalization.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6.1: Performance of the proposed joint training approach compared with other competitive DNN-based systems. Training is performed with WSJ-rev, while test is performed with DIRHA-English-WSJ (set2, real part).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The table compares the performance of different neural network training approaches on TIMIT and WSJ datasets, showing that joint training of speech enhancement and speech recognition yields the best results, especially in noisy conditions.In particular, the first line shows the results obtained with a single neural network. The size of the network has been optimized on the development set (4 hidden layers of 1024 neurons for TIMIT, 6 hidden layers of 2048 neurons for WSJ cases). The second line shows the performance obtained when the speech enhancement neural network (4 hidden layers of 2048 neurons for TIMIT, 6 hidden layers of 2048 neurons for WSJ) is trained independently and later coupled with the aforementioned close-talking DNN. These results are particularly critical because, especially in adverse acoustic conditions, the speech enhancement model introduces significant distortions. The close-talking DNN, trained in the usual way, is thus not able to cope with this significant amount of mismatch. To partially recover such a critical mismatch, one approach is to first train the speech enhancement, then pass all the training features through the speech enhancement DNN, and, lastly, train the speech recognition DNN with the dataset processed by the speech enhancement. The third line shows results obtained with such a matched training approach. The last line reports the performance achieved with the proposed joint training approach." } ], "images": [ "scigen/train/1861.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6.1: Performance of the proposed joint training approach compared with other competitive DNN-based systems. Training is performed with WSJ-rev, while test is performed with DIRHA-English-WSJ (set2, real part)." ], "reasoning": "The table compares the performance of different neural network training approaches on TIMIT and WSJ datasets, showing that joint training of speech enhancement and speech recognition yields the best results, especially in noisy conditions.", "description": "In particular, the first line shows the results obtained with a single neural network. The size of the network has been optimized on the development set (4 hidden layers of 1024 neurons for TIMIT, 6 hidden layers of 2048 neurons for WSJ cases). The second line shows the performance obtained when the speech enhancement neural network (4 hidden layers of 2048 neurons for TIMIT, 6 hidden layers of 2048 neurons for WSJ) is trained independently and later coupled with the aforementioned close-talking DNN. These results are particularly critical because, especially in adverse acoustic conditions, the speech enhancement model introduces significant distortions. The close-talking DNN, trained in the usual way, is thus not able to cope with this significant amount of mismatch. To partially recover such a critical mismatch, one approach is to first train the speech enhancement, then pass all the training features through the speech enhancement DNN, and, lastly, train the speech recognition DNN with the dataset processed by the speech enhancement. The third line shows results obtained with such a matched training approach. The last line reports the performance achieved with the proposed joint training approach.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 6.1: Performance of the proposed joint training approach compared with other competitive DNN-based systems. Training is performed with WSJ-rev, while test is performed with DIRHA-English-WSJ (set2, real part).', and sequentially solve the question, writing out each step of your reasoning process. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich training approach yields the lowest error rate on the WSJ Rev+Noise dataset?\n" }, { "role": "assistant", "content": "By examining the WSJ Rev+Noise column, the lowest error rate is found in the SE + SR joint training row, which is 12.7. So, the answer is {\"answer\": \"12.7\"}." } ], "images": [ "scigen/train/1861.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6.1: Performance of the proposed joint training approach compared with other competitive DNN-based systems. Training is performed with WSJ-rev, while test is performed with DIRHA-English-WSJ (set2, real part)." ], "reasoning": "By examining the WSJ Rev+Noise column, the lowest error rate is found in the SE + SR joint training row, which is 12.7.", "question": "Which training approach yields the lowest error rate on the WSJ Rev+Noise dataset?", "answer": "So, the answer is {\"answer\": \"12.7\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe SE + clean SR approach performs better than the Single big DNN approach on the TIMIT Rev dataset.\n" }, { "role": "assistant", "content": "The table shows that the SE + clean SR approach has an error rate of 31.4 on the TIMIT Rev dataset, while the Single big DNN approach has an error rate of 31.9, indicating that the SE + clean SR approach performs better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1861.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6.1: Performance of the proposed joint training approach compared with other competitive DNN-based systems. Training is performed with WSJ-rev, while test is performed with DIRHA-English-WSJ (set2, real part)." ], "reasoning": "The table shows that the SE + clean SR approach has an error rate of 31.4 on the TIMIT Rev dataset, while the Single big DNN approach has an error rate of 31.9, indicating that the SE + clean SR approach performs better.", "statement": "The SE + clean SR approach performs better than the Single big DNN approach on the TIMIT Rev dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 6.2: Analysis of the role played by batch normalization within the proposed joint training framework.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Batch normalization improves performance and convergence speed in the joint training framework, especially in noisy and reverberant scenarios.the impact of batch normalization on the joint training framework is shown. The first two columns report, respectively, the results obtained with and without batch normalization when no pre-training techniques are employed. The impact of pre-training is studied in the last two columns. The pre-training strategy considered here consists of initializing the two DNNs with the matched training system discussed in the previous section, and performing a fine-tuning phase with a reduced learning rate. For instance, a relative improvement of about 23% is achieved when batch normalization is adopted for the WSJ task in a noisy and reverberant scenario. From the figure it is clear that batch normalization, when applied to the considered deep joint architecture, ensures a faster convergence and a significantly better performance." } ], "images": [ "scigen/train/1862.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6.2: Analysis of the role played by batch normalization within the proposed joint training framework." ], "reasoning": "Batch normalization improves performance and convergence speed in the joint training framework, especially in noisy and reverberant scenarios.", "description": "the impact of batch normalization on the joint training framework is shown. The first two columns report, respectively, the results obtained with and without batch normalization when no pre-training techniques are employed. The impact of pre-training is studied in the last two columns. The pre-training strategy considered here consists of initializing the two DNNs with the matched training system discussed in the previous section, and performing a fine-tuning phase with a reduced learning rate. For instance, a relative improvement of about 23% is achieved when batch normalization is adopted for the WSJ task in a noisy and reverberant scenario. From the figure it is clear that batch normalization, when applied to the considered deep joint architecture, ensures a faster convergence and a significantly better performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 6.2: Analysis of the role played by batch normalization within the proposed joint training framework.'. Solve the following question based on the table.\n\nQuestion: What is the relative improvement in performance for the WSJ-Rev+Noise task when batch normalization is applied without pre-training?\nLet's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the relative improvement, compare the scores without pre-training for WSJ-Rev+Noise with and without batch normalization. The scores are 15.7 and 12.7, respectively. The relative improvement is calculated as (15.7 - 12.7) / 15.7 * 100%. So, the answer is {\"answer\": \"19.11%\"}." } ], "images": [ "scigen/train/1862.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6.2: Analysis of the role played by batch normalization within the proposed joint training framework." ], "reasoning": "To find the relative improvement, compare the scores without pre-training for WSJ-Rev+Noise with and without batch normalization. The scores are 15.7 and 12.7, respectively. The relative improvement is calculated as (15.7 - 12.7) / 15.7 * 100%.", "question": "What is the relative improvement in performance for the WSJ-Rev+Noise task when batch normalization is applied without pre-training?", "answer": "So, the answer is {\"answer\": \"19.11%\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nBatch normalization without pre-training improves the performance for the TIMIT-Rev task.\n" }, { "role": "assistant", "content": "The table shows that for the TIMIT-Rev task, the score without pre-training and without batch normalization is 34.2, while with batch normalization it is 29.1. Since a lower score indicates better performance, batch normalization improves performance. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1862.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6.2: Analysis of the role played by batch normalization within the proposed joint training framework." ], "reasoning": "The table shows that for the TIMIT-Rev task, the score without pre-training and without batch normalization is 34.2, while with batch normalization it is 29.1. Since a lower score indicates better performance, batch normalization improves performance.", "statement": "Batch normalization without pre-training improves the performance for the TIMIT-Rev task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 6.3: Performance of the proposed network of DNN approach compared with other competitive DNN-based systems (PER% for TIMIT, WER% for WSJ). For WSJ experiments, test is performed with DIRHA-English-WSJ (set2, real-part).'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The network of deep neural networks achieves the best performance across all datasets, outperforming both the single DNN and the joint SE-SR training methods.The first line reports the results obtained with a single neural network. In this case, only the speech recognition labels are used and the DNN is not forced to perform any speech enhancement task. This multi-task architecture shares the first half of the hidden layers across the tasks, while the second half of the architecture is task-dependent. This approach aims to discover (within the shared layers) more general and robust features which can be exploited to better solve both correlated tasks. The third line reports the performance achieved with the joint training approach described in the previous section. In this case a bigger DNN composed of a cascade of a speech enhancement and a speech recognition DNNs is jointly trained by back-propagating the speech recognition gradient also into the speech enhancement DNN. The last line finally shows the performance achieved with the proposed network of deep neural network approach (unfolded up to level 2). To allow a fair comparison, batch normalization is adopted for all the considered systems. For instance, a relative improvement of about 14% over the single DNN baseline is obtained for the WSJ rev+noise case. The network of deep neural networks also outperforms the considered joint training method. This result suggests that the improved cooperation between the networks achieved with our full communication scheme can overtake the standard DSR pipeline based on a partial and unidirectional information flow (which is still considered in the context of joint training approaches)." } ], "images": [ "scigen/train/1863.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6.3: Performance of the proposed network of DNN approach compared with other competitive DNN-based systems (PER% for TIMIT, WER% for WSJ). For WSJ experiments, test is performed with DIRHA-English-WSJ (set2, real-part)." ], "reasoning": "The network of deep neural networks achieves the best performance across all datasets, outperforming both the single DNN and the joint SE-SR training methods.", "description": "The first line reports the results obtained with a single neural network. In this case, only the speech recognition labels are used and the DNN is not forced to perform any speech enhancement task. This multi-task architecture shares the first half of the hidden layers across the tasks, while the second half of the architecture is task-dependent. This approach aims to discover (within the shared layers) more general and robust features which can be exploited to better solve both correlated tasks. The third line reports the performance achieved with the joint training approach described in the previous section. In this case a bigger DNN composed of a cascade of a speech enhancement and a speech recognition DNNs is jointly trained by back-propagating the speech recognition gradient also into the speech enhancement DNN. The last line finally shows the performance achieved with the proposed network of deep neural network approach (unfolded up to level 2). To allow a fair comparison, batch normalization is adopted for all the considered systems. For instance, a relative improvement of about 14% over the single DNN baseline is obtained for the WSJ rev+noise case. The network of deep neural networks also outperforms the considered joint training method. This result suggests that the improved cooperation between the networks achieved with our full communication scheme can overtake the standard DSR pipeline based on a partial and unidirectional information flow (which is still considered in the context of joint training approaches).", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 6.3: Performance of the proposed network of DNN approach compared with other competitive DNN-based systems (PER% for TIMIT, WER% for WSJ). For WSJ experiments, test is performed with DIRHA-English-WSJ (set2, real-part).'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}. \n\nQuestion: What is the relative improvement in performance for the WSJ rev+noise dataset when using the Network of DNNs compared to the Single DNN?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The performance of the Single DNN for WSJ rev+noise is 14.3, and the performance of the Network of DNNs is 12.3. The relative improvement is calculated as (14.3 - 12.3) / 14.3 * 100%. So, the answer is {\"answer\": \"13.99%\"}" } ], "images": [ "scigen/train/1863.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6.3: Performance of the proposed network of DNN approach compared with other competitive DNN-based systems (PER% for TIMIT, WER% for WSJ). For WSJ experiments, test is performed with DIRHA-English-WSJ (set2, real-part)." ], "reasoning": "The performance of the Single DNN for WSJ rev+noise is 14.3, and the performance of the Network of DNNs is 12.3. The relative improvement is calculated as (14.3 - 12.3) / 14.3 * 100%.", "question": "What is the relative improvement in performance for the WSJ rev+noise dataset when using the Network of DNNs compared to the Single DNN?", "answer": "So, the answer is {\"answer\": \"13.99%\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Network of DNNs achieves the lowest error rate for the TIMIT rev dataset.\n" }, { "role": "assistant", "content": "The table shows that the Network of DNNs has an error rate of 28.7 for the TIMIT rev dataset, which is lower than the error rates of the other methods. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1863.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6.3: Performance of the proposed network of DNN approach compared with other competitive DNN-based systems (PER% for TIMIT, WER% for WSJ). For WSJ experiments, test is performed with DIRHA-English-WSJ (set2, real-part)." ], "reasoning": "The table shows that the Network of DNNs has an error rate of 28.7 for the TIMIT rev dataset, which is lower than the error rates of the other methods.", "statement": "The Network of DNNs achieves the lowest error rate for the TIMIT rev dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 6.4: Performance of the proposed network of DNN achieved at various levels of the architecture.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Performance improves progressively with increasing network levels up to level 2, with the best performance at level 2.One can note that the performance become progressively better as the level of the network of DNNs increases till level 2. As expected, the first level speech recognizer (SR DNN-0) performs similarly to the single DNN baseline. The second level (SR DNN-1) is based on a simple cascade between a speech enhancement and a speech recognition DNNs, and thus provides results similar to that obtained with standard joint training. The third level (SR DNN-2) achieves the best performance, confirming that the progressive interaction of the DNNs involved in the DSR process helps in improving the system performance. No additional benefits have been observed for the considered tasks by adding more than 3 levels. The number of communication levels, however, can be regarded as an hyperparameter of the architecture and its best value might be task-dependent. In general, one should anyway expect a diminishing return when increasing N." } ], "images": [ "scigen/train/1864.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6.4: Performance of the proposed network of DNN achieved at various levels of the architecture." ], "reasoning": "Performance improves progressively with increasing network levels up to level 2, with the best performance at level 2.", "description": "One can note that the performance become progressively better as the level of the network of DNNs increases till level 2. As expected, the first level speech recognizer (SR DNN-0) performs similarly to the single DNN baseline. The second level (SR DNN-1) is based on a simple cascade between a speech enhancement and a speech recognition DNNs, and thus provides results similar to that obtained with standard joint training. The third level (SR DNN-2) achieves the best performance, confirming that the progressive interaction of the DNNs involved in the DSR process helps in improving the system performance. No additional benefits have been observed for the considered tasks by adding more than 3 levels. The number of communication levels, however, can be regarded as an hyperparameter of the architecture and its best value might be task-dependent. In general, one should anyway expect a diminishing return when increasing N.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nTable title: Table 6.4: Performance of the proposed network of DNN achieved at various levels of the architecture.\n\nQuestion: What is the performance of the WSJ rev dataset at level 2?\n" }, { "role": "assistant", "content": "The table shows the performance metrics for different datasets at various levels. For the WSJ rev dataset, the performance at level 2 is given. So, the answer is {\"answer\": \"7.6\"}." } ], "images": [ "scigen/train/1864.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6.4: Performance of the proposed network of DNN achieved at various levels of the architecture." ], "reasoning": "The table shows the performance metrics for different datasets at various levels. For the WSJ rev dataset, the performance at level 2 is given.", "question": "What is the performance of the WSJ rev dataset at level 2?", "answer": "So, the answer is {\"answer\": \"7.6\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe performance of the TIMIT rev dataset improves at each level up to level 2.\n" }, { "role": "assistant", "content": "The table shows the performance of the TIMIT rev dataset at levels 0, 1, and 2. The values decrease from 31.4 at level 0 to 29.1 at level 1 and further to 28.7 at level 2, indicating improvement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1864.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6.4: Performance of the proposed network of DNN achieved at various levels of the architecture." ], "reasoning": "The table shows the performance of the TIMIT rev dataset at levels 0, 1, and 2. The values decrease from 31.4 at level 0 to 29.1 at level 1 and further to 28.7 at level 2, indicating improvement.", "statement": "The performance of the TIMIT rev dataset improves at each level up to level 2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 6: An ablation study of our proposed model. For model without lattice-aware self-attention (-LASA), we take character sequence as input, and one character just computes multi-head self-attention weights with its adjacent characters and the shared pivot node. For model without porous mechanism (-PM), we directly utilize multi-head LASA to aggregate the weighted information of each word with fully-connected attention connections. For PLTE-LASA-PMHA, we apply multi-head self-attention to each pair of elements from the input character sequence.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The porous mechanism (PM) enhances local context capture for NER, and PLTE-LASA outperforms PLTE-LASA-PM on most datasets, indicating PM benefits self-attention with character input., we conduct an ablation study on four datasets. (2) By introducing the porous mechanism (PM), we can enhance the ability of capturing useful local context which is beneficial to NER, while maintaining the strength of capturing long-term dependencies. (3) PLTE-PM performs worse than PLTE-LASA-PM, which confirms that the standard LASA is not suitable for NER because it takes into account all the signals and disperses the distribution of attention, while NER may be benefited more from localness modeling. (4) PLTE-LASA outperforms PLTE-LASA-PM on most datasets, which shows that the porous mechanism can also benefit self-attention when only taking characters as input." } ], "images": [ "scigen/train/1865.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: An ablation study of our proposed model. For model without lattice-aware self-attention (-LASA), we take character sequence as input, and one character just computes multi-head self-attention weights with its adjacent characters and the shared pivot node. For model without porous mechanism (-PM), we directly utilize multi-head LASA to aggregate the weighted information of each word with fully-connected attention connections. For PLTE-LASA-PMHA, we apply multi-head self-attention to each pair of elements from the input character sequence." ], "reasoning": "The porous mechanism (PM) enhances local context capture for NER, and PLTE-LASA outperforms PLTE-LASA-PM on most datasets, indicating PM benefits self-attention with character input.", "description": ", we conduct an ablation study on four datasets. (2) By introducing the porous mechanism (PM), we can enhance the ability of capturing useful local context which is beneficial to NER, while maintaining the strength of capturing long-term dependencies. (3) PLTE-PM performs worse than PLTE-LASA-PM, which confirms that the standard LASA is not suitable for NER because it takes into account all the signals and disperses the distribution of attention, while NER may be benefited more from localness modeling. (4) PLTE-LASA outperforms PLTE-LASA-PM on most datasets, which shows that the porous mechanism can also benefit self-attention when only taking characters as input.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 6: An ablation study of our proposed model. For model without lattice-aware self-attention (-LASA), we take character sequence as input, and one character just computes multi-head self-attention weights with its adjacent characters and the shared pivot node. For model without porous mechanism (-PM), we directly utilize multi-head LASA to aggregate the weighted information of each word with fully-connected attention connections. For PLTE-LASA-PMHA, we apply multi-head self-attention to each pair of elements from the input character sequence.', solve the following question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model performs the best on the Weibo dataset?\n" }, { "role": "assistant", "content": "To determine the best performing model on the Weibo dataset, compare the scores of all models listed under the Weibo column. The highest score indicates the best performance. So, the answer is {\"answer\": \"PLTE\"}" } ], "images": [ "scigen/train/1865.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: An ablation study of our proposed model. For model without lattice-aware self-attention (-LASA), we take character sequence as input, and one character just computes multi-head self-attention weights with its adjacent characters and the shared pivot node. For model without porous mechanism (-PM), we directly utilize multi-head LASA to aggregate the weighted information of each word with fully-connected attention connections. For PLTE-LASA-PMHA, we apply multi-head self-attention to each pair of elements from the input character sequence." ], "reasoning": "To determine the best performing model on the Weibo dataset, compare the scores of all models listed under the Weibo column. The highest score indicates the best performance.", "question": "Which model performs the best on the Weibo dataset?", "answer": "So, the answer is {\"answer\": \"PLTE\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nPLTE-LASA-PM performs better than PLTE on the MSRA dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the scores of PLTE-LASA-PM and PLTE under the MSRA column. PLTE has a score of 93.26, while PLTE-LASA-PM has a score of 92.48, indicating that PLTE performs better. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1865.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: An ablation study of our proposed model. For model without lattice-aware self-attention (-LASA), we take character sequence as input, and one character just computes multi-head self-attention weights with its adjacent characters and the shared pivot node. For model without porous mechanism (-PM), we directly utilize multi-head LASA to aggregate the weighted information of each word with fully-connected attention connections. For PLTE-LASA-PMHA, we apply multi-head self-attention to each pair of elements from the input character sequence." ], "reasoning": "To verify this statement, compare the scores of PLTE-LASA-PM and PLTE under the MSRA column. PLTE has a score of 93.26, while PLTE-LASA-PM has a score of 92.48, indicating that PLTE performs better.", "statement": "PLTE-LASA-PM performs better than PLTE on the MSRA dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 11: Average performance on ERC with pre-trained weights: {θBERT+θcornellcxt} for VHRED, θcornellcxt contain additional parameters modeling the latent prior state.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Transferring parameters from VHRED to HRED shows no significant difference, but in-domain tuning on ERC resources and transferring re-tuned weights for the ERC task results in sub-optimal generation and no improvement in classification.As a result, our set of transferred parameters contain the additional parameters of MLPθ, included in θsourcecxt. Unfortunately, we do not find significant difference between the parameters from VHRED as opposed to HRED. However, the lack of degradation in the results promise possible future improvements in such designs. We try in-domain tuning of the generative HRED model by performing conversation modeling on the ERC resources. Finally, we transfer these re-tuned weights for the discriminative ERC task. TL between generative tasks, especially with small-scale target resources, is a challenging task. As a result, we find sub-optimal generation in ERC datasets whose further transfer for the classification does not provide any improvement." } ], "images": [ "scigen/train/1866.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Average performance on ERC with pre-trained weights: {θBERT+θcornellcxt} for VHRED, θcornellcxt contain additional parameters modeling the latent prior state." ], "reasoning": "Transferring parameters from VHRED to HRED shows no significant difference, but in-domain tuning on ERC resources and transferring re-tuned weights for the ERC task results in sub-optimal generation and no improvement in classification.", "description": "As a result, our set of transferred parameters contain the additional parameters of MLPθ, included in θsourcecxt. Unfortunately, we do not find significant difference between the parameters from VHRED as opposed to HRED. However, the lack of degradation in the results promise possible future improvements in such designs. We try in-domain tuning of the generative HRED model by performing conversation modeling on the ERC resources. Finally, we transfer these re-tuned weights for the discriminative ERC task. TL between generative tasks, especially with small-scale target resources, is a challenging task. As a result, we find sub-optimal generation in ERC datasets whose further transfer for the classification does not provide any improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 11: Average performance on ERC with pre-trained weights: {θBERT+θcornellcxt} for VHRED, θcornellcxt contain additional parameters modeling the latent prior state.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. \n\nQuestion: What is the F-Score for the IEMOCAP dataset when using Source + Target training?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows the F-Scores for IEMOCAP and Dailydialog datasets under different training conditions. For IEMOCAP, the F-Score for Source + Target training is 58.0. So, the answer is {\"answer\": \"58.0\"}." } ], "images": [ "scigen/train/1866.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Average performance on ERC with pre-trained weights: {θBERT+θcornellcxt} for VHRED, θcornellcxt contain additional parameters modeling the latent prior state." ], "reasoning": "The table shows the F-Scores for IEMOCAP and Dailydialog datasets under different training conditions. For IEMOCAP, the F-Score for Source + Target training is 58.0.", "question": "What is the F-Score for the IEMOCAP dataset when using Source + Target training?", "answer": "So, the answer is {\"answer\": \"58.0\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe F-Score for the Dailydialog dataset decreases when using Source + Target training compared to Source training.\n" }, { "role": "assistant", "content": "The table shows that the F-Score for Dailydialog is 48.0 for Source training and 47.2 for Source + Target training, indicating a decrease. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1866.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Average performance on ERC with pre-trained weights: {θBERT+θcornellcxt} for VHRED, θcornellcxt contain additional parameters modeling the latent prior state." ], "reasoning": "The table shows that the F-Score for Dailydialog is 48.0 for Source training and 47.2 for Source + Target training, indicating a decrease.", "statement": "The F-Score for the Dailydialog dataset decreases when using Source + Target training compared to Source training.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Table illustrates the sizes of the datasets used in this work. #D represents the number of dialogues whereas #U represents the total number of constituting utterances.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "IEMOCAP is a balanced dataset, while DailyDialog is skewed towards sentences with no emotion, leading to different metrics for each dataset.For both IEMOCAP and SEMAINE, we generate the validation sets by random-sampling of 20% dialogue videos from the training sets. From the table, IEMOCAP is observed a fairly balanced dataset whereas DailyDialog is highly skewed towards sentences with no emotion. As such, we decide upon different metrics for each dataset as discussed next." } ], "images": [ "scigen/train/1867.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Table illustrates the sizes of the datasets used in this work. #D represents the number of dialogues whereas #U represents the total number of constituting utterances." ], "reasoning": "IEMOCAP is a balanced dataset, while DailyDialog is skewed towards sentences with no emotion, leading to different metrics for each dataset.", "description": "For both IEMOCAP and SEMAINE, we generate the validation sets by random-sampling of 20% dialogue videos from the training sets. From the table, IEMOCAP is observed a fairly balanced dataset whereas DailyDialog is highly skewed towards sentences with no emotion. As such, we decide upon different metrics for each dataset as discussed next.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 1: Table illustrates the sizes of the datasets used in this work. #D represents the number of dialogues whereas #U represents the total number of constituting utterances.', solve the following question step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nHow many dialogue videos are used for validation in the IEMOCAP dataset?\n" }, { "role": "assistant", "content": "The table shows that for IEMOCAP, both the #D and #U splits have 120 and 5810 dialogue videos respectively in the validation set. So, the answer is {\"answer\": \"120 and 5810\"}." } ], "images": [ "scigen/train/1867.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Table illustrates the sizes of the datasets used in this work. #D represents the number of dialogues whereas #U represents the total number of constituting utterances." ], "reasoning": "The table shows that for IEMOCAP, both the #D and #U splits have 120 and 5810 dialogue videos respectively in the validation set.", "question": "How many dialogue videos are used for validation in the IEMOCAP dataset?", "answer": "So, the answer is {\"answer\": \"120 and 5810\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe DailyDialog dataset has more training dialogue videos than the IEMOCAP dataset.\n" }, { "role": "assistant", "content": "The table shows that DailyDialog has 11,118 (#D) and 87,170 (#U) training dialogue videos, while IEMOCAP has 120 (#D) and 5810 (#U) training dialogue videos. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1867.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Table illustrates the sizes of the datasets used in this work. #D represents the number of dialogues whereas #U represents the total number of constituting utterances." ], "reasoning": "The table shows that DailyDialog has 11,118 (#D) and 87,170 (#U) training dialogue videos, while IEMOCAP has 120 (#D) and 5810 (#U) training dialogue videos.", "statement": "The DailyDialog dataset has more training dialogue videos than the IEMOCAP dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Table illustrates the sizes of the datasets used in this work. #D represents the number of dialogues whereas #U represents the total number of constituting utterances.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "IEMOCAP is a balanced dataset, while DailyDialog is skewed towards sentences with no emotion.For both IEMOCAP and SEMAINE, we generate the validation sets by random-sampling of 20% dialogue videos from the training sets. From the table, IEMOCAP is observed a fairly balanced dataset whereas DailyDialog is highly skewed towards sentences with no emotion. As such, we decide upon different metrics for each dataset as discussed next." } ], "images": [ "scigen/train/1868.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Table illustrates the sizes of the datasets used in this work. #D represents the number of dialogues whereas #U represents the total number of constituting utterances." ], "reasoning": "IEMOCAP is a balanced dataset, while DailyDialog is skewed towards sentences with no emotion.", "description": "For both IEMOCAP and SEMAINE, we generate the validation sets by random-sampling of 20% dialogue videos from the training sets. From the table, IEMOCAP is observed a fairly balanced dataset whereas DailyDialog is highly skewed towards sentences with no emotion. As such, we decide upon different metrics for each dataset as discussed next.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 1: Table illustrates the sizes of the datasets used in this work. #D represents the number of dialogues whereas #U represents the total number of constituting utterances.' and methodically tackle the question that follows. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the total number of 'neu' (neutral) sentences in the DailyDialog dataset?\n" }, { "role": "assistant", "content": "To find the total number of 'neu' sentences in the DailyDialog dataset, sum the values from the DailyDialog train, val, and test columns for 'neu'. So, the answer is {\"answer\": \"85892\"}." } ], "images": [ "scigen/train/1868.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Table illustrates the sizes of the datasets used in this work. #D represents the number of dialogues whereas #U represents the total number of constituting utterances." ], "reasoning": "To find the total number of 'neu' sentences in the DailyDialog dataset, sum the values from the DailyDialog train, val, and test columns for 'neu'.", "question": "What is the total number of 'neu' (neutral) sentences in the DailyDialog dataset?", "answer": "So, the answer is {\"answer\": \"85892\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The IEMOCAP dataset has more 'frus' (frustrated) sentences in the test set than the train/val set., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe IEMOCAP dataset has more 'frus' (frustrated) sentences in the test set than the train/val set.\n" }, { "role": "assistant", "content": "The table shows that the IEMOCAP train/val set has 1468 'frus' sentences, while the test set has 381 'frus' sentences, indicating that the train/val set has more 'frus' sentences. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1868.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Table illustrates the sizes of the datasets used in this work. #D represents the number of dialogues whereas #U represents the total number of constituting utterances." ], "reasoning": "The table shows that the IEMOCAP train/val set has 1468 'frus' sentences, while the test set has 381 'frus' sentences, indicating that the train/val set has more 'frus' sentences.", "statement": "The IEMOCAP dataset has more 'frus' (frustrated) sentences in the test set than the train/val set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 9: Average performance on ERC with pre-trained weights: {θBERT+θcornellcxt}. Note: DD here means DailyDialog.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "TL-ERC outperforms other models without using attention mechanisms or multi-hop memory networks, and fine-tuning is more effective than freezing parameters.As seen, our proposed TL-ERC comfortably outperforms both non-contextual and contextual baselines. It achieves this without the aid of attention mechanisms that is used in c-LSTM + Att, multi-hop memory networks used in Memnet, and CMN. It also achieves competitive performance against DialogueRNN, which has three layers of inter-utterance recurrent layers, while TL-ERC has one. These trends indicate TL to be effective in our setup and provided promising directions for future research. We try two primary adaptation techniques used in inductive TL, freezed or fine-tuned. In the former setting, the borrowed weights are used for feature extraction, while in the latter, we train the weights along with the other new parameters of the target task’s model. Fine-tuning can also be performed using other techniques such as gradual unfreezing Peters et al. We notice a degradation in performance with more frozen parameters. The datasets in ERC contain multi-class annotations with varying label distributions. With frozen parameters, our transferred model is unable to account for the label distribution and results in low recall for infrequent classes. We thus find the fine-tuning approach to be more effective in this setup." } ], "images": [ "scigen/train/1869.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Average performance on ERC with pre-trained weights: {θBERT+θcornellcxt}. Note: DD here means DailyDialog." ], "reasoning": "TL-ERC outperforms other models without using attention mechanisms or multi-hop memory networks, and fine-tuning is more effective than freezing parameters.", "description": "As seen, our proposed TL-ERC comfortably outperforms both non-contextual and contextual baselines. It achieves this without the aid of attention mechanisms that is used in c-LSTM + Att, multi-hop memory networks used in Memnet, and CMN. It also achieves competitive performance against DialogueRNN, which has three layers of inter-utterance recurrent layers, while TL-ERC has one. These trends indicate TL to be effective in our setup and provided promising directions for future research. We try two primary adaptation techniques used in inductive TL, freezed or fine-tuned. In the former setting, the borrowed weights are used for feature extraction, while in the latter, we train the weights along with the other new parameters of the target task’s model. Fine-tuning can also be performed using other techniques such as gradual unfreezing Peters et al. We notice a degradation in performance with more frozen parameters. The datasets in ERC contain multi-class annotations with varying label distributions. With frozen parameters, our transferred model is unable to account for the label distribution and results in low recall for infrequent classes. We thus find the fine-tuning approach to be more effective in this setup.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 9: Average performance on ERC with pre-trained weights: {θBERT+θcornellcxt}. Note: DD here means DailyDialog.', unfold the question presented below, detailing every step of your calculation. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the F-Score for the IEMOCAP dataset when using the θBERT+θcornellcxt adaptation strategy?\n" }, { "role": "assistant", "content": "The table shows the F-Scores for different adaptation strategies. For the IEMOCAP dataset using the θBERT+θcornellcxt strategy, the F-Score is 9.3. So, the answer is {\"answer\": \"9.3\"}" } ], "images": [ "scigen/train/1869.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Average performance on ERC with pre-trained weights: {θBERT+θcornellcxt}. Note: DD here means DailyDialog." ], "reasoning": "The table shows the F-Scores for different adaptation strategies. For the IEMOCAP dataset using the θBERT+θcornellcxt strategy, the F-Score is 9.3.", "question": "What is the F-Score for the IEMOCAP dataset when using the θBERT+θcornellcxt adaptation strategy?", "answer": "So, the answer is {\"answer\": \"9.3\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe fixed weights adaptation strategy achieves the highest F-Score for the DD dataset.\n" }, { "role": "assistant", "content": "The table shows that the fixed weights adaptation strategy has an F-Score of 48.0 for the DD dataset, which is higher than the F-Scores for the other strategies. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1869.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Average performance on ERC with pre-trained weights: {θBERT+θcornellcxt}. Note: DD here means DailyDialog." ], "reasoning": "The table shows that the fixed weights adaptation strategy has an F-Score of 48.0 for the DD dataset, which is higher than the F-Scores for the other strategies.", "statement": "The fixed weights adaptation strategy achieves the highest F-Score for the DD dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 9: Average performance on ERC with pre-trained weights: {θBERT+θcornellcxt}. Note: DD here means DailyDialog.'.\n" }, { "role": "assistant", "content": "TL-ERC outperforms other models without using attention mechanisms or multi-hop memory networks, and fine-tuning is more effective than freezing parameters.As seen, our proposed TL-ERC comfortably outperforms both non-contextual and contextual baselines. It achieves this without the aid of attention mechanisms that is used in c-LSTM + Att, multi-hop memory networks used in Memnet, and CMN. It also achieves competitive performance against DialogueRNN, which has three layers of inter-utterance recurrent layers, while TL-ERC has one. These trends indicate TL to be effective in our setup and provided promising directions for future research. We try two primary adaptation techniques used in inductive TL, freezed or fine-tuned. In the former setting, the borrowed weights are used for feature extraction, while in the latter, we train the weights along with the other new parameters of the target task’s model. Fine-tuning can also be performed using other techniques such as gradual unfreezing Peters et al. We notice a degradation in performance with more frozen parameters. The datasets in ERC contain multi-class annotations with varying label distributions. With frozen parameters, our transferred model is unable to account for the label distribution and results in low recall for infrequent classes. We thus find the fine-tuning approach to be more effective in this setup." } ], "images": [ "scigen/train/1870.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Average performance on ERC with pre-trained weights: {θBERT+θcornellcxt}. Note: DD here means DailyDialog." ], "reasoning": "TL-ERC outperforms other models without using attention mechanisms or multi-hop memory networks, and fine-tuning is more effective than freezing parameters.", "description": "As seen, our proposed TL-ERC comfortably outperforms both non-contextual and contextual baselines. It achieves this without the aid of attention mechanisms that is used in c-LSTM + Att, multi-hop memory networks used in Memnet, and CMN. It also achieves competitive performance against DialogueRNN, which has three layers of inter-utterance recurrent layers, while TL-ERC has one. These trends indicate TL to be effective in our setup and provided promising directions for future research. We try two primary adaptation techniques used in inductive TL, freezed or fine-tuned. In the former setting, the borrowed weights are used for feature extraction, while in the latter, we train the weights along with the other new parameters of the target task’s model. Fine-tuning can also be performed using other techniques such as gradual unfreezing Peters et al. We notice a degradation in performance with more frozen parameters. The datasets in ERC contain multi-class annotations with varying label distributions. With frozen parameters, our transferred model is unable to account for the label distribution and results in low recall for infrequent classes. We thus find the fine-tuning approach to be more effective in this setup.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 9: Average performance on ERC with pre-trained weights: {θBERT+θcornellcxt}. Note: DD here means DailyDialog.' and methodically tackle the question that follows. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieves the highest F-Score on the IEMOCAP dataset?\n" }, { "role": "assistant", "content": "By examining the F-Score column for the IEMOCAP dataset, DialogueRNN achieves the highest F-Score of 59.8. So, the answer is {\"answer\": \"DialogueRNN\"}" } ], "images": [ "scigen/train/1870.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Average performance on ERC with pre-trained weights: {θBERT+θcornellcxt}. Note: DD here means DailyDialog." ], "reasoning": "By examining the F-Score column for the IEMOCAP dataset, DialogueRNN achieves the highest F-Score of 59.8.", "question": "Which model achieves the highest F-Score on the IEMOCAP dataset?", "answer": "So, the answer is {\"answer\": \"DialogueRNN\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim TL-ERC achieves the highest correlation score on the SEMAINE DV dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTL-ERC achieves the highest correlation score on the SEMAINE DV dataset.\n" }, { "role": "assistant", "content": "The table shows that TL-ERC has a correlation score (r) of 0.66 on the SEMAINE DV dataset, which is the highest among all models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1870.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Average performance on ERC with pre-trained weights: {θBERT+θcornellcxt}. Note: DD here means DailyDialog." ], "reasoning": "The table shows that TL-ERC has a correlation score (r) of 0.66 on the SEMAINE DV dataset, which is the highest among all models listed.", "statement": "TL-ERC achieves the highest correlation score on the SEMAINE DV dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 11: Average performance on ERC with pre-trained weights: {θBERT+θcornellcxt} for VHRED, θcornellcxt contain additional parameters modeling the latent prior state.'.\n" }, { "role": "assistant", "content": "The generative HRED model was tuned on ERC resources and its re-tuned weights were transferred for the ERC task, but no significant improvement was observed compared to VHRED.As a result, our set of transferred parameters contain the additional parameters of MLPθ, included in θsourcecxt. Unfortunately, we do not find significant difference between the parameters from VHRED as opposed to HRED. However, the lack of degradation in the results promise possible future improvements in such designs. We try in-domain tuning of the generative HRED model by performing conversation modeling on the ERC resources. Finally, we transfer these re-tuned weights for the discriminative ERC task. TL between generative tasks, especially with small-scale target resources, is a challenging task. As a result, we find sub-optimal generation in ERC datasets whose further transfer for the classification does not provide any improvement." } ], "images": [ "scigen/train/1871.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Average performance on ERC with pre-trained weights: {θBERT+θcornellcxt} for VHRED, θcornellcxt contain additional parameters modeling the latent prior state." ], "reasoning": "The generative HRED model was tuned on ERC resources and its re-tuned weights were transferred for the ERC task, but no significant improvement was observed compared to VHRED.", "description": "As a result, our set of transferred parameters contain the additional parameters of MLPθ, included in θsourcecxt. Unfortunately, we do not find significant difference between the parameters from VHRED as opposed to HRED. However, the lack of degradation in the results promise possible future improvements in such designs. We try in-domain tuning of the generative HRED model by performing conversation modeling on the ERC resources. Finally, we transfer these re-tuned weights for the discriminative ERC task. TL between generative tasks, especially with small-scale target resources, is a challenging task. As a result, we find sub-optimal generation in ERC datasets whose further transfer for the classification does not provide any improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the F-Score difference between HRED and VHRED on the DailyDialog dataset?\nSolve the above question based on the table titled 'Table 11: Average performance on ERC with pre-trained weights: {θBERT+θcornellcxt} for VHRED, θcornellcxt contain additional parameters modeling the latent prior state.'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The F-Score for HRED on DailyDialog is 48.0, and for VHRED it is 48.4. The difference is 48.4 - 48.0 = 0.4. So, the answer is {\"answer\": \"0.4\"}." } ], "images": [ "scigen/train/1871.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Average performance on ERC with pre-trained weights: {θBERT+θcornellcxt} for VHRED, θcornellcxt contain additional parameters modeling the latent prior state." ], "reasoning": "The F-Score for HRED on DailyDialog is 48.0, and for VHRED it is 48.4. The difference is 48.4 - 48.0 = 0.4.", "question": "What is the F-Score difference between HRED and VHRED on the DailyDialog dataset?", "answer": "So, the answer is {\"answer\": \"0.4\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nVHRED has a higher F-Score than HRED on both IEMOCAP and DailyDialog datasets.\n" }, { "role": "assistant", "content": "The table shows that VHRED has an F-Score of 58.6 on IEMOCAP and 48.4 on DailyDialog, while HRED has an F-Score of 58.5 on IEMOCAP and 48.0 on DailyDialog. VHRED scores higher on both datasets. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1871.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Average performance on ERC with pre-trained weights: {θBERT+θcornellcxt} for VHRED, θcornellcxt contain additional parameters modeling the latent prior state." ], "reasoning": "The table shows that VHRED has an F-Score of 58.6 on IEMOCAP and 48.4 on DailyDialog, while HRED has an F-Score of 58.5 on IEMOCAP and 48.0 on DailyDialog. VHRED scores higher on both datasets.", "statement": "VHRED has a higher F-Score than HRED on both IEMOCAP and DailyDialog datasets.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: BLEU-2 scores with different generation methods.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The BLEU results align with subjective evaluations, except the SMT-based approach outperforms the RNNLM-based approach due to BLEU's focus on keyword co-occurrence.It can be seen that the BLEU results are highly consistent to the results of the subjective evaluation. A minor exception is that the SMT-based approach outperforms the RNNLM-based approach. A possible reason is that the BLEU metric focus on keyword co-occurrence rather than reasonable word sequence, which is therefore more amiable to the phrase-based SMT model that we used than RNN." } ], "images": [ "scigen/train/1872.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: BLEU-2 scores with different generation methods." ], "reasoning": "The BLEU results align with subjective evaluations, except the SMT-based approach outperforms the RNNLM-based approach due to BLEU's focus on keyword co-occurrence.", "description": "It can be seen that the BLEU results are highly consistent to the results of the subjective evaluation. A minor exception is that the SMT-based approach outperforms the RNNLM-based approach. A possible reason is that the BLEU metric focus on keyword co-occurrence rather than reasonable word sequence, which is therefore more amiable to the phrase-based SMT model that we used than RNN.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 4: BLEU-2 scores with different generation methods.', and sequentially solve the question, writing out each step of your reasoning process. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model has the highest BLEU score and why might it be favored by the BLEU metric?\n" }, { "role": "assistant", "content": "The Attention-All model has the highest BLEU score of 0.1482. The BLEU metric favors models that focus on keyword co-occurrence, which might explain why Attention-All performs best. So, the answer is {\"answer\": \"Attention-All\"}." } ], "images": [ "scigen/train/1872.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: BLEU-2 scores with different generation methods." ], "reasoning": "The Attention-All model has the highest BLEU score of 0.1482. The BLEU metric favors models that focus on keyword co-occurrence, which might explain why Attention-All performs best.", "question": "Which model has the highest BLEU score and why might it be favored by the BLEU metric?", "answer": "So, the answer is {\"answer\": \"Attention-All\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe SMT-based approach has a higher BLEU score than the RNNLM-based approach.\n" }, { "role": "assistant", "content": "The table shows that the SMT-based approach has a BLEU score of 0.0598, which is higher than the RNNLM-based approach's BLEU score of 0.0330. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1872.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: BLEU-2 scores with different generation methods." ], "reasoning": "The table shows that the SMT-based approach has a BLEU score of 0.0598, which is higher than the RNNLM-based approach's BLEU score of 0.0330.", "statement": "The SMT-based approach has a higher BLEU score than the RNNLM-based approach.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Averaged ratings for Song iambics generation with different methods and settings. ‘Partridge Sky’ and ‘Pusaman’ are the two tunes in the evaluation.'.\n" }, { "role": "assistant", "content": "The Attention-11 model outperforms both the SMT and RNNLM-based approaches in the Chinese poem generation task, achieving higher average scores.It can be seen that our model outperforms both the SMT-based and the RNNLM-based approaches that are often used in Chinese poem Generation task. Note that in this experiment, the RNNLM-based approach is also based on LSTM, and so holds the same advantage as the attention model in learning long-distance patterns. However the RNN model tends to swift from the initial theme, hence causing inconsistency during the generation." } ], "images": [ "scigen/train/1873.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Averaged ratings for Song iambics generation with different methods and settings. ‘Partridge Sky’ and ‘Pusaman’ are the two tunes in the evaluation." ], "reasoning": "The Attention-11 model outperforms both the SMT and RNNLM-based approaches in the Chinese poem generation task, achieving higher average scores.", "description": "It can be seen that our model outperforms both the SMT-based and the RNNLM-based approaches that are often used in Chinese poem Generation task. Note that in this experiment, the RNNLM-based approach is also based on LSTM, and so holds the same advantage as the attention model in learning long-distance patterns. However the RNN model tends to swift from the initial theme, hence causing inconsistency during the generation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Averaged ratings for Song iambics generation with different methods and settings. ‘Partridge Sky’ and ‘Pusaman’ are the two tunes in the evaluation.', unfold the question presented below, detailing every step of your calculation. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model has the highest average score in the Chinese poem generation task?\n" }, { "role": "assistant", "content": "By comparing the average scores of all models in the table, the Human model has the highest average score of 4.45. So, the answer is {\"answer\": \"Human\"}." } ], "images": [ "scigen/train/1873.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Averaged ratings for Song iambics generation with different methods and settings. ‘Partridge Sky’ and ‘Pusaman’ are the two tunes in the evaluation." ], "reasoning": "By comparing the average scores of all models in the table, the Human model has the highest average score of 4.45.", "question": "Which model has the highest average score in the Chinese poem generation task?", "answer": "So, the answer is {\"answer\": \"Human\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Attention-1 model has a higher average score than the RNNLM-based approach. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Attention-1 model has a higher average score than the RNNLM-based approach.\n" }, { "role": "assistant", "content": "The table shows that the Attention-1 model has an average score of 3.69, while the RNNLM-based approach has an average score of 3.02. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1873.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Averaged ratings for Song iambics generation with different methods and settings. ‘Partridge Sky’ and ‘Pusaman’ are the two tunes in the evaluation." ], "reasoning": "The table shows that the Attention-1 model has an average score of 3.69, while the RNNLM-based approach has an average score of 3.02.", "statement": "The Attention-1 model has a higher average score than the RNNLM-based approach.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Accuracy on the test sets of SST and MR (%). The size of training sets means the size of the original training sets sampled from the full set. w/ indicates that the training sets are augmented by C-BERT / SentiLR-B.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The model SentiLR-B shows a significant performance boost in sentence-level classification tasks, especially with the RNN classifier, compared to C-BERT.Thus, we tested the augmentation ability of our model on two sentence-level classification datasets, i.e., SST and MR. Specifically, we sampled some subsets at different scales from the original dataset as our training sets. For each sentence in the training sets, we constructed 4 augmented samples, which were then added to the training data. The detailed augmentation settings are described in the Appendix. The results also show that SentiLR-B can acquire augmented data with more inductive biases than C-BERT. In particular, for the RNN classifier, the performance boost with training data augmented by SentiLR-B is more significant. It demonstrates that our model can generate more plausible augmented samples with enhanced representations that capture the dependency within contexts and the relationship between labels and words." } ], "images": [ "scigen/train/1874.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracy on the test sets of SST and MR (%). The size of training sets means the size of the original training sets sampled from the full set. w/ indicates that the training sets are augmented by C-BERT / SentiLR-B." ], "reasoning": "The model SentiLR-B shows a significant performance boost in sentence-level classification tasks, especially with the RNN classifier, compared to C-BERT.", "description": "Thus, we tested the augmentation ability of our model on two sentence-level classification datasets, i.e., SST and MR. Specifically, we sampled some subsets at different scales from the original dataset as our training sets. For each sentence in the training sets, we constructed 4 augmented samples, which were then added to the training data. The detailed augmentation settings are described in the Appendix. The results also show that SentiLR-B can acquire augmented data with more inductive biases than C-BERT. In particular, for the RNN classifier, the performance boost with training data augmented by SentiLR-B is more significant. It demonstrates that our model can generate more plausible augmented samples with enhanced representations that capture the dependency within contexts and the relationship between labels and words.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 3: Accuracy on the test sets of SST and MR (%). The size of training sets means the size of the original training sets sampled from the full set. w/ indicates that the training sets are augmented by C-BERT / SentiLR-B.', and tackle the question below in a sequential manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the highest accuracy achieved by the RNN classifier with SentiLR-B on the MR dataset?\n" }, { "role": "assistant", "content": "To find the highest accuracy achieved by the RNN classifier with SentiLR-B on the MR dataset, look at the 'w/SentiLR-B' row under the 'RNN' section for the MR dataset columns. The highest value is 75.12 for the MR Full dataset. So, the answer is {\"answer\": \"75.12\"}" } ], "images": [ "scigen/train/1874.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracy on the test sets of SST and MR (%). The size of training sets means the size of the original training sets sampled from the full set. w/ indicates that the training sets are augmented by C-BERT / SentiLR-B." ], "reasoning": "To find the highest accuracy achieved by the RNN classifier with SentiLR-B on the MR dataset, look at the 'w/SentiLR-B' row under the 'RNN' section for the MR dataset columns. The highest value is 75.12 for the MR Full dataset.", "question": "What is the highest accuracy achieved by the RNN classifier with SentiLR-B on the MR dataset?", "answer": "So, the answer is {\"answer\": \"75.12\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The CNN classifier with C-BERT achieved an accuracy of 78.91 on the MR Full dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe CNN classifier with C-BERT achieved an accuracy of 78.91 on the MR Full dataset.\n" }, { "role": "assistant", "content": "To verify this statement, check the 'CNN' section under the 'w/C-BERT' row for the MR Full dataset column. The accuracy listed is 78.91, which supports the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1874.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracy on the test sets of SST and MR (%). The size of training sets means the size of the original training sets sampled from the full set. w/ indicates that the training sets are augmented by C-BERT / SentiLR-B." ], "reasoning": "To verify this statement, check the 'CNN' section under the 'w/C-BERT' row for the MR Full dataset column. The accuracy listed is 78.91, which supports the statement.", "statement": "The CNN classifier with C-BERT achieved an accuracy of 78.91 on the MR Full dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Accuracy on sentence-level sentiment classification (SSC) benchmarks (%).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "SentiLR-R outperforms all other models on sentence-level sentiment classification datasets, demonstrating the effectiveness of its knowledge-aware representation and pre-training tasks.We can observe that SentiLR-R performs better than all the other baselines on the sentence-level sentiment classification datasets, which indicates that our knowledge-aware representation promotes the understanding of the sentiment in the whole text. We can also see that SentiLR-B / SentiLR-R enhances the performance of vanilla BERT / RoBERTa. This demonstrates that linguistic knowledge and the well-designed pre-training task of SentiLR can adapt to different kinds of BERT-style pre-trained models and improve their performances on the sentence-level sentiment classification benchmarks." } ], "images": [ "scigen/train/1875.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Accuracy on sentence-level sentiment classification (SSC) benchmarks (%)." ], "reasoning": "SentiLR-R outperforms all other models on sentence-level sentiment classification datasets, demonstrating the effectiveness of its knowledge-aware representation and pre-training tasks.", "description": "We can observe that SentiLR-R performs better than all the other baselines on the sentence-level sentiment classification datasets, which indicates that our knowledge-aware representation promotes the understanding of the sentiment in the whole text. We can also see that SentiLR-B / SentiLR-R enhances the performance of vanilla BERT / RoBERTa. This demonstrates that linguistic knowledge and the well-designed pre-training task of SentiLR can adapt to different kinds of BERT-style pre-trained models and improve their performances on the sentence-level sentiment classification benchmarks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 1: Accuracy on sentence-level sentiment classification (SSC) benchmarks (%).'.\n\nQuestion: Which model has the highest performance on the IMDB dataset?\n" }, { "role": "assistant", "content": "By comparing the IMDB scores of all models in the table, SentiLR-R has the highest score of 95.25. So, the answer is {\"answer\": \"SentiLR-R\"}" } ], "images": [ "scigen/train/1875.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Accuracy on sentence-level sentiment classification (SSC) benchmarks (%)." ], "reasoning": "By comparing the IMDB scores of all models in the table, SentiLR-R has the highest score of 95.25.", "question": "Which model has the highest performance on the IMDB dataset?", "answer": "So, the answer is {\"answer\": \"SentiLR-R\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence SentiLR-B performs better than TransBERT on the Yelp-5 dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nSentiLR-B performs better than TransBERT on the Yelp-5 dataset.\n" }, { "role": "assistant", "content": "The table shows that SentiLR-B has a score of 70.86 on the Yelp-5 dataset, while TransBERT has a score of 69.53, indicating that SentiLR-B performs better. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1875.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Accuracy on sentence-level sentiment classification (SSC) benchmarks (%)." ], "reasoning": "The table shows that SentiLR-B has a score of 70.86 on the Yelp-5 dataset, while TransBERT has a score of 69.53, indicating that SentiLR-B performs better.", "statement": "SentiLR-B performs better than TransBERT on the Yelp-5 dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: F1, accuracy (Acc.) and Macro-F1 (MF1.) on four aspect-level sentiment analysis tasks including aspect term extraction (ATE), aspect term sentiment classification (ATSC), aspect category detection (ACD) and aspect category sentiment classification (ACSC) (%).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "SentiLR-B and SentiLR-R outperform baselines on most datasets, excelling in aspect-level sentiment classification, aspect term extraction, and aspect category detection.We can see that SentiLR-B / SentiLR-R outperforms the baselines on most of the datasets. Interestingly, in addition to aspect-level sentiment classification, our model also performs well in aspect term extraction and aspect category detection. Since the aspect words are mostly nouns, part-of-speech tag may provide additional knowledge for the extraction task. Furthermore, the aspect terms can be signaled by neighboring sentiment words. This may explain why our sentiment-aware representation can help to extract / detect the aspect term / category." } ], "images": [ "scigen/train/1876.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: F1, accuracy (Acc.) and Macro-F1 (MF1.) on four aspect-level sentiment analysis tasks including aspect term extraction (ATE), aspect term sentiment classification (ATSC), aspect category detection (ACD) and aspect category sentiment classification (ACSC) (%)." ], "reasoning": "SentiLR-B and SentiLR-R outperform baselines on most datasets, excelling in aspect-level sentiment classification, aspect term extraction, and aspect category detection.", "description": "We can see that SentiLR-B / SentiLR-R outperforms the baselines on most of the datasets. Interestingly, in addition to aspect-level sentiment classification, our model also performs well in aspect term extraction and aspect category detection. Since the aspect words are mostly nouns, part-of-speech tag may provide additional knowledge for the extraction task. Furthermore, the aspect terms can be signaled by neighboring sentiment words. This may explain why our sentiment-aware representation can help to extract / detect the aspect term / category.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: F1, accuracy (Acc.) and Macro-F1 (MF1.) on four aspect-level sentiment analysis tasks including aspect term extraction (ATE), aspect term sentiment classification (ATSC), aspect category detection (ACD) and aspect category sentiment classification (ACSC) (%).'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. \n\nQuestion: Which model has the highest F1 score for ATE on the Res14 dataset?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the model with the highest F1 score for ATE on the Res14 dataset, look at the 'ATE Res14' column under 'F1'. The highest value is 90.83, which corresponds to the SentiLR-R model. So, the answer is {\"answer\": \"SentiLR-R\"}" } ], "images": [ "scigen/train/1876.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: F1, accuracy (Acc.) and Macro-F1 (MF1.) on four aspect-level sentiment analysis tasks including aspect term extraction (ATE), aspect term sentiment classification (ATSC), aspect category detection (ACD) and aspect category sentiment classification (ACSC) (%)." ], "reasoning": "To find the model with the highest F1 score for ATE on the Res14 dataset, look at the 'ATE Res14' column under 'F1'. The highest value is 90.83, which corresponds to the SentiLR-R model.", "question": "Which model has the highest F1 score for ATE on the Res14 dataset?", "answer": "So, the answer is {\"answer\": \"SentiLR-R\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence SentiLR-B has the highest accuracy for ACSC on the Res14 dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nSentiLR-B has the highest accuracy for ACSC on the Res14 dataset.\n" }, { "role": "assistant", "content": "To verify this statement, check the 'ACSC Res14' column under 'Acc.'. The highest accuracy value is 90.59, which corresponds to the SentiLR-R model, not SentiLR-B. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1876.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: F1, accuracy (Acc.) and Macro-F1 (MF1.) on four aspect-level sentiment analysis tasks including aspect term extraction (ATE), aspect term sentiment classification (ATSC), aspect category detection (ACD) and aspect category sentiment classification (ACSC) (%)." ], "reasoning": "To verify this statement, check the 'ACSC Res14' column under 'Acc.'. The highest accuracy value is 90.59, which corresponds to the SentiLR-R model, not SentiLR-B.", "statement": "SentiLR-B has the highest accuracy for ACSC on the Res14 dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Ablation test on sentence-level / aspect-level sentiment analysis tasks, where F1, accuracy (Acc.) and Macro-F1 (MF1.) are reported (%). The early fusion (EF) / late supervision (LS) sub-task and the linguistic knowledge (Know.) are removed in different settings, respectively.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Late supervision plays a more important role in sentiment analysis tasks, contributing more to classification performance compared to early fusion.Since the two sub-tasks are separate, the setting of -EF Additionally, the -Know. setting means that we removed the part of speech and word-level sentiment polarity in the input embeddings as well as in the supervision signals of the two sub-tasks, and reset the probability of masking sentiment words to 15%. The late supervision sub-task plays a more important role in most of the tasks including sentence-level / aspect-level sentiment analysis. Intuitively, compared with early fusion, the setting of late supervision is closer to sentiment classification, thereby contributing more to the performance on the classification tasks." } ], "images": [ "scigen/train/1877.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Ablation test on sentence-level / aspect-level sentiment analysis tasks, where F1, accuracy (Acc.) and Macro-F1 (MF1.) are reported (%). The early fusion (EF) / late supervision (LS) sub-task and the linguistic knowledge (Know.) are removed in different settings, respectively." ], "reasoning": "Late supervision plays a more important role in sentiment analysis tasks, contributing more to classification performance compared to early fusion.", "description": "Since the two sub-tasks are separate, the setting of -EF Additionally, the -Know. setting means that we removed the part of speech and word-level sentiment polarity in the input embeddings as well as in the supervision signals of the two sub-tasks, and reset the probability of masking sentiment words to 15%. The late supervision sub-task plays a more important role in most of the tasks including sentence-level / aspect-level sentiment analysis. Intuitively, compared with early fusion, the setting of late supervision is closer to sentiment classification, thereby contributing more to the performance on the classification tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 4: Ablation test on sentence-level / aspect-level sentiment analysis tasks, where F1, accuracy (Acc.) and Macro-F1 (MF1.) are reported (%). The early fusion (EF) / late supervision (LS) sub-task and the linguistic knowledge (Know.) are removed in different settings, respectively.\nQuestion: Which model has the highest F1 score for the ATE Res14 dataset?\n" }, { "role": "assistant", "content": "By comparing the F1 scores for the ATE Res14 dataset across all models, we see that SentiLR-B has the highest F1 score of 90.03. So, the answer is {\"answer\": \"SentiLR-B\"}." } ], "images": [ "scigen/train/1877.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Ablation test on sentence-level / aspect-level sentiment analysis tasks, where F1, accuracy (Acc.) and Macro-F1 (MF1.) are reported (%). The early fusion (EF) / late supervision (LS) sub-task and the linguistic knowledge (Know.) are removed in different settings, respectively." ], "reasoning": "By comparing the F1 scores for the ATE Res14 dataset across all models, we see that SentiLR-B has the highest F1 score of 90.03.", "question": "Which model has the highest F1 score for the ATE Res14 dataset?", "answer": "So, the answer is {\"answer\": \"SentiLR-B\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe -Know. setting has the highest accuracy for the SSC MR dataset.\n" }, { "role": "assistant", "content": "By comparing the accuracy for the SSC MR dataset across all settings, we see that the -Know. setting has an accuracy of 87.43, which is higher than the other settings. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1877.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Ablation test on sentence-level / aspect-level sentiment analysis tasks, where F1, accuracy (Acc.) and Macro-F1 (MF1.) are reported (%). The early fusion (EF) / late supervision (LS) sub-task and the linguistic knowledge (Know.) are removed in different settings, respectively." ], "reasoning": "By comparing the accuracy for the SSC MR dataset across all settings, we see that the -Know. setting has an accuracy of 87.43, which is higher than the other settings.", "statement": "The -Know. setting has the highest accuracy for the SSC MR dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: Accuracy on the SCT dataset in the story ending prediction task (%).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Integrating sentiment-related linguistic knowledge into pre-trained models improves sentiment analysis and potentially other NLP tasks.This indicates that sentiment-related linguistic knowledge can be successfully integrated into pre-trained language representation model to not only benefit the sentiment analysis tasks but generalize to other NLP tasks. We leave the generalization experiments on more NLP tasks as the future work." } ], "images": [ "scigen/train/1878.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Accuracy on the SCT dataset in the story ending prediction task (%)." ], "reasoning": "Integrating sentiment-related linguistic knowledge into pre-trained models improves sentiment analysis and potentially other NLP tasks.", "description": "This indicates that sentiment-related linguistic knowledge can be successfully integrated into pre-trained language representation model to not only benefit the sentiment analysis tasks but generalize to other NLP tasks. We leave the generalization experiments on more NLP tasks as the future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: Accuracy on the SCT dataset in the story ending prediction task (%).'.\n\nQuestion: Which model has the highest accuracy according to the table?\n" }, { "role": "assistant", "content": "The table shows the accuracy of different models. By comparing the accuracy values, SentiLR-R has the highest accuracy with 91.29. So, the answer is {\"answer\": \"SentiLR-R\"}." } ], "images": [ "scigen/train/1878.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Accuracy on the SCT dataset in the story ending prediction task (%)." ], "reasoning": "The table shows the accuracy of different models. By comparing the accuracy values, SentiLR-R has the highest accuracy with 91.29.", "question": "Which model has the highest accuracy according to the table?", "answer": "So, the answer is {\"answer\": \"SentiLR-R\"}." }, { "messages": [ { "role": "user", "content": "RoBERTa has the lowest accuracy among the models listed. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRoBERTa has the lowest accuracy among the models listed.\n" }, { "role": "assistant", "content": "The table shows the accuracy of different models. RoBERTa has an accuracy of 90.75, which is higher than BERT (87.58) and SentiLR-B (87.99), but lower than SentiLR-R (91.29). Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1878.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Accuracy on the SCT dataset in the story ending prediction task (%)." ], "reasoning": "The table shows the accuracy of different models. RoBERTa has an accuracy of 90.75, which is higher than BERT (87.58) and SentiLR-B (87.99), but lower than SentiLR-R (91.29). Therefore, the statement is refuted.", "statement": "RoBERTa has the lowest accuracy among the models listed.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 13: Macro F1 of targeted cross-lingual models on Twitter data in 10 target languages. Twitter refers to models that have been trained on the English data mentioned in Table 10, while USAGE and SemEval are trained on the English data from the datasets mentioned in Section 4.1.2.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The table shows that MT generally outperforms Muse, VecMap, and Blse in most languages, as indicated by the higher average scores.The final column is the average over all languages. Given the results from the earlier experiments, we hypothesize that MT should outperform Muse, VecMap and Blse for most of the languages." } ], "images": [ "scigen/train/1879.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 13: Macro F1 of targeted cross-lingual models on Twitter data in 10 target languages. Twitter refers to models that have been trained on the English data mentioned in Table 10, while USAGE and SemEval are trained on the English data from the datasets mentioned in Section 4.1.2." ], "reasoning": "The table shows that MT generally outperforms Muse, VecMap, and Blse in most languages, as indicated by the higher average scores.", "description": "The final column is the average over all languages. Given the results from the earlier experiments, we hypothesize that MT should outperform Muse, VecMap and Blse for most of the languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 13: Macro F1 of targeted cross-lingual models on Twitter data in 10 target languages. Twitter refers to models that have been trained on the English data mentioned in Table 10, while USAGE and SemEval are trained on the English data from the datasets mentioned in Section 4.1.2.', solve the subsequent question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich model has the highest average score for Binary classification?\n" }, { "role": "assistant", "content": "To find the model with the highest average score for Binary classification, we need to compare the average scores in the 'avg.' column for each model under the Binary classification section. MT has the highest average score of 55.8. So, the answer is {\"answer\": \"MT\"}" } ], "images": [ "scigen/train/1879.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 13: Macro F1 of targeted cross-lingual models on Twitter data in 10 target languages. Twitter refers to models that have been trained on the English data mentioned in Table 10, while USAGE and SemEval are trained on the English data from the datasets mentioned in Section 4.1.2." ], "reasoning": "To find the model with the highest average score for Binary classification, we need to compare the average scores in the 'avg.' column for each model under the Binary classification section. MT has the highest average score of 55.8.", "question": "Which model has the highest average score for Binary classification?", "answer": "So, the answer is {\"answer\": \"MT\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: MT has the highest average score for Multi-class classification., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nMT has the highest average score for Multi-class classification.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the average scores in the 'avg.' column for each model under the Multi-class classification section. MT has an average score of 19.4, which is not the highest. The highest average score is 35.1 by the Ensemble model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1879.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 13: Macro F1 of targeted cross-lingual models on Twitter data in 10 target languages. Twitter refers to models that have been trained on the English data mentioned in Table 10, while USAGE and SemEval are trained on the English data from the datasets mentioned in Section 4.1.2." ], "reasoning": "To verify this statement, we need to compare the average scores in the 'avg.' column for each model under the Multi-class classification section. MT has an average score of 19.4, which is not the highest. The highest average score is 35.1 by the Ensemble model.", "statement": "MT has the highest average score for Multi-class classification.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Number of aspect-polarity tuples for the targeted datasets.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The experiments involve training on a source language corpus and testing on a target language corpus, with both binary and multiclass classification setups, excluding the Strong Negative class for OpeNER and MultiBooked datasets.4.1.2 Targeted Datasets We use the following corpora to set up the experiments in which we train on a source language corpus CS and test on a target language corpus CT. We include a binary classification setup, where neutral has been removed and strong positive and strong negative have been mapped to positive and negative, as well as a multiclass setup, where the original labels are used. Similarly, models do not learn the Strong Negative class on the OpeNER and MultiBooked datasets." } ], "images": [ "scigen/train/1880.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Number of aspect-polarity tuples for the targeted datasets." ], "reasoning": "The experiments involve training on a source language corpus and testing on a target language corpus, with both binary and multiclass classification setups, excluding the Strong Negative class for OpeNER and MultiBooked datasets.", "description": "4.1.2 Targeted Datasets We use the following corpora to set up the experiments in which we train on a source language corpus CS and test on a target language corpus CT. We include a binary classification setup, where neutral has been removed and strong positive and strong negative have been mapped to positive and negative, as well as a multiclass setup, where the original labels are used. Similarly, models do not learn the Strong Negative class on the OpeNER and MultiBooked datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: Number of aspect-polarity tuples for the targeted datasets.', and sequentially solve the question, writing out each step of your reasoning process. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nHow many instances are there in the Binary + category for the USAGE dataset in English?\n" }, { "role": "assistant", "content": "To find the number of instances in the Binary + category for the USAGE dataset in English, locate the row labeled 'USAGE' and the column labeled 'Binary +'. The value in this cell is 2985. So, the answer is {\"answer\": \"2985\"}." } ], "images": [ "scigen/train/1880.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Number of aspect-polarity tuples for the targeted datasets." ], "reasoning": "To find the number of instances in the Binary + category for the USAGE dataset in English, locate the row labeled 'USAGE' and the column labeled 'Binary +'. The value in this cell is 2985.", "question": "How many instances are there in the Binary + category for the USAGE dataset in English?", "answer": "So, the answer is {\"answer\": \"2985\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The MultiBooked dataset in Catalan has more instances in the Binary - category than in the Multiclass + category. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe MultiBooked dataset in Catalan has more instances in the Binary - category than in the Multiclass + category.\n" }, { "role": "assistant", "content": "To verify this statement, compare the values in the Binary - and Multiclass + categories for the MultiBooked dataset in Catalan. The Binary - category has 883 instances, while the Multiclass + category has 808 instances. Since 883 is greater than 808, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1880.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Number of aspect-polarity tuples for the targeted datasets." ], "reasoning": "To verify this statement, compare the values in the Binary - and Multiclass + categories for the MultiBooked dataset in Catalan. The Binary - category has 883 instances, while the Multiclass + category has 808 instances. Since 883 is greater than 808, the statement is supported.", "statement": "The MultiBooked dataset in Catalan has more instances in the Binary - category than in the Multiclass + category.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: Macro F1 of four models trained on English and tested on Spanish (ES), Catalan (CA), and Basque (EU). The bold numbers show the best results for each metric per column and the highlighted numbers show where Blse is better than the other projection methods, VecMap, Muse, and Barista (* p < 0.01).', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our method, Blse, significantly outperforms other projection methods in four out of six experiments and performs nearly as well as the more resource-intensive upper bounds in binary classification tasks.Our method outperforms the other projection methods (the baselines VecMap, Muse, and Barista) on four of the six experiments substantially. It performs only slightly worse than the more resource-costly upper bounds (MT and Mono). This is especially noticeable for the binary classification task, where Blse performs nearly as well as machine translation and significantly better than the other methods. Unsup also performs similarly to Blse on the binary tasks, while giving stronger performance on the 4-class setup." } ], "images": [ "scigen/train/1881.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Macro F1 of four models trained on English and tested on Spanish (ES), Catalan (CA), and Basque (EU). The bold numbers show the best results for each metric per column and the highlighted numbers show where Blse is better than the other projection methods, VecMap, Muse, and Barista (* p < 0.01)." ], "reasoning": "Our method, Blse, significantly outperforms other projection methods in four out of six experiments and performs nearly as well as the more resource-intensive upper bounds in binary classification tasks.", "description": "Our method outperforms the other projection methods (the baselines VecMap, Muse, and Barista) on four of the six experiments substantially. It performs only slightly worse than the more resource-costly upper bounds (MT and Mono). This is especially noticeable for the binary classification task, where Blse performs nearly as well as machine translation and significantly better than the other methods. Unsup also performs similarly to Blse on the binary tasks, while giving stronger performance on the 4-class setup.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Macro F1 of four models trained on English and tested on Spanish (ES), Catalan (CA), and Basque (EU). The bold numbers show the best results for each metric per column and the highlighted numbers show where Blse is better than the other projection methods, VecMap, Muse, and Barista (* p < 0.01).\nQuestion: How does Blse perform compared to the baselines in the binary classification tasks?\nSolve the above question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "Blse performs significantly better than the baselines VecMap, Muse, and Barista in the binary classification tasks, with scores of 80.3, 85.0, and 73.5 respectively. So, the answer is {\"answer\": \"significantly better\"}." } ], "images": [ "scigen/train/1881.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Macro F1 of four models trained on English and tested on Spanish (ES), Catalan (CA), and Basque (EU). The bold numbers show the best results for each metric per column and the highlighted numbers show where Blse is better than the other projection methods, VecMap, Muse, and Barista (* p < 0.01)." ], "reasoning": "Blse performs significantly better than the baselines VecMap, Muse, and Barista in the binary classification tasks, with scores of 80.3, 85.0, and 73.5 respectively.", "question": "How does Blse perform compared to the baselines in the binary classification tasks?", "answer": "So, the answer is {\"answer\": \"significantly better\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nBlse performs worse than the baselines in the 4-class EU experiment.\n" }, { "role": "assistant", "content": "In the 4-class EU experiment, Blse scores 50.5, which is higher than all the baselines (Unsup: 39.3, VecMap: 21.3, Muse: 25.8, Barista: 33.8). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1881.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Macro F1 of four models trained on English and tested on Spanish (ES), Catalan (CA), and Basque (EU). The bold numbers show the best results for each metric per column and the highlighted numbers show where Blse is better than the other projection methods, VecMap, Muse, and Barista (* p < 0.01)." ], "reasoning": "In the 4-class EU experiment, Blse scores 50.5, which is higher than all the baselines (Unsup: 39.3, VecMap: 21.3, Muse: 25.8, Barista: 33.8).", "statement": "Blse performs worse than the baselines in the 4-class EU experiment.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 7: Macro F1 results for all corpora and techniques. We denote the best performing projection-based method per column with a blue box and the best overall method per column with a green box.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The final column shows the average score across all corpora, and the final row in each setup shows the macro F1 score for a classifier that always chooses the majority class.The final column is the average over all corpora. The final row in each setup shows the macro F1 for a classifier that always chooses the majority class." } ], "images": [ "scigen/train/1882.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Macro F1 results for all corpora and techniques. We denote the best performing projection-based method per column with a blue box and the best overall method per column with a green box." ], "reasoning": "The final column shows the average score across all corpora, and the final row in each setup shows the macro F1 score for a classifier that always chooses the majority class.", "description": "The final column is the average over all corpora. The final row in each setup shows the macro F1 for a classifier that always chooses the majority class.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 7: Macro F1 results for all corpora and techniques. We denote the best performing projection-based method per column with a blue box and the best overall method per column with a green box.\n\nQuestion: What is the highest average score for the Binary Sent setup?\n" }, { "role": "assistant", "content": "To find the highest average score for the Binary Sent setup, look at the 'Average' column for all rows where the first column is 'Binary' and the second column is 'Sent'. The highest value is 71.6. So, the answer is {\"answer\": \"71.6\"}." } ], "images": [ "scigen/train/1882.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Macro F1 results for all corpora and techniques. We denote the best performing projection-based method per column with a blue box and the best overall method per column with a green box." ], "reasoning": "To find the highest average score for the Binary Sent setup, look at the 'Average' column for all rows where the first column is 'Binary' and the second column is 'Sent'. The highest value is 71.6.", "question": "What is the highest average score for the Binary Sent setup?", "answer": "So, the answer is {\"answer\": \"71.6\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe highest macro F1 score for the Multiclass Split setup is 40.6.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'Multiclass' rows with 'Split' in the second column and find the highest value in the 'Average' column. The highest value is 40.6. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1882.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Macro F1 results for all corpora and techniques. We denote the best performing projection-based method per column with a blue box and the best overall method per column with a green box." ], "reasoning": "To verify this statement, look at the 'Multiclass' rows with 'Split' in the second column and find the highest value in the 'Average' column. The highest value is 40.6.", "statement": "The highest macro F1 score for the Multiclass Split setup is 40.6.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 8: Average length of tokens of correctly and incorrectly classified targets on the OpeNER Spanish binary corpus.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows that as the size of the targets increases, the average correct scores do not consistently improve, indicating a potential detrimental effect of averaging.This indicates that averaging may have a detrimental effect as the size of the targets increases." } ], "images": [ "scigen/train/1883.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Average length of tokens of correctly and incorrectly classified targets on the OpeNER Spanish binary corpus." ], "reasoning": "The table shows that as the size of the targets increases, the average correct scores do not consistently improve, indicating a potential detrimental effect of averaging.", "description": "This indicates that averaging may have a detrimental effect as the size of the targets increases.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 8: Average length of tokens of correctly and incorrectly classified targets on the OpeNER Spanish binary corpus.', answer the following question. Give detailed solution consisting of each step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich method has the highest correct score and what is its incorrect score?\n" }, { "role": "assistant", "content": "By comparing the correct scores in the table, VecMap has the highest correct score of 2.5. The corresponding incorrect score for VecMap is 2.1. So, the answer is {\"answer\": \"2.1\"}." } ], "images": [ "scigen/train/1883.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Average length of tokens of correctly and incorrectly classified targets on the OpeNER Spanish binary corpus." ], "reasoning": "By comparing the correct scores in the table, VecMap has the highest correct score of 2.5. The corresponding incorrect score for VecMap is 2.1.", "question": "Which method has the highest correct score and what is its incorrect score?", "answer": "So, the answer is {\"answer\": \"2.1\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Barista method has the lowest correct score.\n" }, { "role": "assistant", "content": "By comparing the correct scores in the table, Barista has the lowest correct score of 1.7. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1883.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Average length of tokens of correctly and incorrectly classified targets on the OpeNER Spanish binary corpus." ], "reasoning": "By comparing the correct scores in the table, Barista has the lowest correct score of 1.7.", "statement": "The Barista method has the lowest correct score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: RMSE and correlation results (reflecting median fold, except as indicated)'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "To rank synthesizers, we use five-fold cross-validation to predict MOS for each utterance using the AutoMOS instance from which it was held-out, then average at the synthesizer-level, resulting in 36 Esynth(MOSpredicted),Esynth(MOStrue) pairs for evaluation.How well can we rank synthesizers relative to one another? To perform this evaluation, we use the above five-fold cross validation to predict a MOS for each utterance using the AutoMOS instance from which it was held-out. We then average at the synthesizer-level, giving us a total of 36 Esynth(MOSpredicted),Esynth(MOStrue) pairs upon which we evaluate." } ], "images": [ "scigen/train/1884.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: RMSE and correlation results (reflecting median fold, except as indicated)" ], "reasoning": "To rank synthesizers, we use five-fold cross-validation to predict MOS for each utterance using the AutoMOS instance from which it was held-out, then average at the synthesizer-level, resulting in 36 Esynth(MOSpredicted),Esynth(MOStrue) pairs for evaluation.", "description": "How well can we rank synthesizers relative to one another? To perform this evaluation, we use the above five-fold cross validation to predict a MOS for each utterance using the AutoMOS instance from which it was held-out. We then average at the synthesizer-level, giving us a total of 36 Esynth(MOSpredicted),Esynth(MOStrue) pairs upon which we evaluate.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: 'Table 2: RMSE and correlation results (reflecting median fold, except as indicated)'\nQuestion: Which model has the lowest RMSE at the synthesizer-level?\n" }, { "role": "assistant", "content": "To find the model with the lowest RMSE at the synthesizer-level, we compare the RMSE values in the 'Synthesizer-level means (n=36; uses all folds)' row across all models. So, the answer is {\"answer\": \"AutoMOS Quantized\"}." } ], "images": [ "scigen/train/1884.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: RMSE and correlation results (reflecting median fold, except as indicated)" ], "reasoning": "To find the model with the lowest RMSE at the synthesizer-level, we compare the RMSE values in the 'Synthesizer-level means (n=36; uses all folds)' row across all models.", "question": "Which model has the lowest RMSE at the synthesizer-level?", "answer": "So, the answer is {\"answer\": \"AutoMOS Quantized\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe AutoMOS Raw model has a higher Pearson r value at the synthesizer-level than the Baselines NNet(utt. length) model.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the Pearson r values at the synthesizer-level for the AutoMOS Raw model and the Baselines NNet(utt. length) model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1884.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: RMSE and correlation results (reflecting median fold, except as indicated)" ], "reasoning": "To verify this statement, we compare the Pearson r values at the synthesizer-level for the AutoMOS Raw model and the Baselines NNet(utt. length) model.", "statement": "The AutoMOS Raw model has a higher Pearson r value at the synthesizer-level than the Baselines NNet(utt. length) model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Comparison of accuracy scores on [Davidson et al.2017] dataset', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Our model with Glove embeddings achieves an accuracy of 0.89, outperforming Mathur et al. and our model with Word2Vec embeddings.We also compare results on pre-trained embeddings. As shown in the table, our model when given Glove embeddings performs better than all other models." } ], "images": [ "scigen/train/1885.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of accuracy scores on [Davidson et al.2017] dataset" ], "reasoning": "Our model with Glove embeddings achieves an accuracy of 0.89, outperforming Mathur et al. and our model with Word2Vec embeddings.", "description": "We also compare results on pre-trained embeddings. As shown in the table, our model when given Glove embeddings performs better than all other models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 4: Comparison of accuracy scores on [Davidson et al.2017] dataset', answer the question below by showing each progressive step toward the answer. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the second highest accuracy?\n" }, { "role": "assistant", "content": "The table shows that Davidson et al. has the highest accuracy of 0.90. The next highest accuracy is 0.89, achieved by our model with Glove embeddings. So, the answer is {\"answer\": \"Our Model with embeddings trained on Glove\"}" } ], "images": [ "scigen/train/1885.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of accuracy scores on [Davidson et al.2017] dataset" ], "reasoning": "The table shows that Davidson et al. has the highest accuracy of 0.90. The next highest accuracy is 0.89, achieved by our model with Glove embeddings.", "question": "Which model has the second highest accuracy?", "answer": "So, the answer is {\"answer\": \"Our Model with embeddings trained on Glove\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Our model with Word2Vec embeddings performs better than Mathur et al., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nOur model with Word2Vec embeddings performs better than Mathur et al.\n" }, { "role": "assistant", "content": "The table shows that our model with Word2Vec embeddings has an accuracy of 0.86, while Mathur et al. has an accuracy of 0.75. Therefore, our model with Word2Vec embeddings performs better. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1885.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of accuracy scores on [Davidson et al.2017] dataset" ], "reasoning": "The table shows that our model with Word2Vec embeddings has an accuracy of 0.86, while Mathur et al. has an accuracy of 0.75. Therefore, our model with Word2Vec embeddings performs better.", "statement": "Our model with Word2Vec embeddings performs better than Mathur et al.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Comparison of accuracy scores on HEOT dataset'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Our model achieves the highest accuracy of 0.87 when using Glove embeddings, outperforming all other models.We also compare results on pre-trained embeddings. As shown in the table, our model when given Glove embeddings performs better than all other models." } ], "images": [ "scigen/train/1886.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of accuracy scores on HEOT dataset" ], "reasoning": "Our model achieves the highest accuracy of 0.87 when using Glove embeddings, outperforming all other models.", "description": "We also compare results on pre-trained embeddings. As shown in the table, our model when given Glove embeddings performs better than all other models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Comparison of accuracy scores on HEOT dataset\nQuestion: Which model achieves the highest accuracy and what is the value?\nGive a detailed response to the above question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, we see that 'Our Model with embeddings trained on Glove' achieves the highest accuracy of 0.87. So, the answer is {\"answer\": \"Our Model with embeddings trained on Glove, 0.87\"}" } ], "images": [ "scigen/train/1886.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of accuracy scores on HEOT dataset" ], "reasoning": "By examining the table, we see that 'Our Model with embeddings trained on Glove' achieves the highest accuracy of 0.87.", "question": "Which model achieves the highest accuracy and what is the value?", "answer": "So, the answer is {\"answer\": \"Our Model with embeddings trained on Glove, 0.87\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Our model with pre-trained Word2Vec embeddings performs better than Davidson et al., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nOur model with pre-trained Word2Vec embeddings performs better than Davidson et al.\n" }, { "role": "assistant", "content": "The table shows that 'Our Model with pre-trained Word2Vec embeddings' has an accuracy of 0.59, which is higher than Davidson et al.'s accuracy of 0.57. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1886.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of accuracy scores on HEOT dataset" ], "reasoning": "The table shows that 'Our Model with pre-trained Word2Vec embeddings' has an accuracy of 0.59, which is higher than Davidson et al.'s accuracy of 0.57.", "statement": "Our model with pre-trained Word2Vec embeddings performs better than Davidson et al.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Average silhouette scores derived from the term-based clustering approach'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The system generated 39 clusters with an average Silhouette score of 0.0000, indicating poor clustering performance due to the complexity of climate change topics.Silhouette evaluates the clustering performance by determining the appropriateness of documents assigned to a cluster rather than the other clusters. These documents are represented as coordinates. Silhouette calculates the pairwise difference in both inter-cluster and intra-cluster distance. As shown in the table, the system generated 39 clusters based on the climate change terms annotated by the ClimaPinion service. The interpretation based on the score is the data points are assigned nearly to the decision boundaries of the clusters. Especially, when salient sentences contain multiple climate change topics, clear clustering boundaries are difficult to achieve. This circumstance indicates that such a simple clustering approach is less applicable for grouping semantically similar sentences together and that the task asks for more sophisticated ways for achieving a better performance." } ], "images": [ "scigen/train/1887.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Average silhouette scores derived from the term-based clustering approach" ], "reasoning": "The system generated 39 clusters with an average Silhouette score of 0.0000, indicating poor clustering performance due to the complexity of climate change topics.", "description": "Silhouette evaluates the clustering performance by determining the appropriateness of documents assigned to a cluster rather than the other clusters. These documents are represented as coordinates. Silhouette calculates the pairwise difference in both inter-cluster and intra-cluster distance. As shown in the table, the system generated 39 clusters based on the climate change terms annotated by the ClimaPinion service. The interpretation based on the score is the data points are assigned nearly to the decision boundaries of the clusters. Especially, when salient sentences contain multiple climate change topics, clear clustering boundaries are difficult to achieve. This circumstance indicates that such a simple clustering approach is less applicable for grouping semantically similar sentences together and that the task asks for more sophisticated ways for achieving a better performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: Average silhouette scores derived from the term-based clustering approach', and tackle the question below in a sequential manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat does the average Silhouette score of 0.0000 indicate about the clustering performance?\n" }, { "role": "assistant", "content": "The average Silhouette score of 0.0000 indicates that the data points are assigned nearly to the decision boundaries of the clusters, suggesting poor clustering performance. So, the answer is {\"answer\": \"poor clustering performance\"}" } ], "images": [ "scigen/train/1887.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Average silhouette scores derived from the term-based clustering approach" ], "reasoning": "The average Silhouette score of 0.0000 indicates that the data points are assigned nearly to the decision boundaries of the clusters, suggesting poor clustering performance.", "question": "What does the average Silhouette score of 0.0000 indicate about the clustering performance?", "answer": "So, the answer is {\"answer\": \"poor clustering performance\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe system generated 39 clusters with a high average Silhouette score.\n" }, { "role": "assistant", "content": "The table shows that the average Silhouette score is 0.0000, which is not high. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1887.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Average silhouette scores derived from the term-based clustering approach" ], "reasoning": "The table shows that the average Silhouette score is 0.0000, which is not high. Therefore, the statement is refuted.", "statement": "The system generated 39 clusters with a high average Silhouette score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1: ROUGE scores derived from each feature in the Salient Sentence Selection task', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The evaluation used ROUGE metrics to assess system summaries, revealing that sentence position is the most effective feature.4.1.1 Evaluation We used ROUGE-1, ROUGE-2, and ROUGE-SU4 evaluation metrics to evaluate the quality of the system summaries, i.e. the selection of salient sentences. As reference summaries we used the manually generated summaries from the freely available dataset, SSSD. Our results revealed that sentence position outperforms other features indicating that the most salient sentences are always. In addition, other useful key features are debate title words feature, and cosine similarity of debate title words and sentences feature." } ], "images": [ "scigen/train/1888.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: ROUGE scores derived from each feature in the Salient Sentence Selection task" ], "reasoning": "The evaluation used ROUGE metrics to assess system summaries, revealing that sentence position is the most effective feature.", "description": "4.1.1 Evaluation We used ROUGE-1, ROUGE-2, and ROUGE-SU4 evaluation metrics to evaluate the quality of the system summaries, i.e. the selection of salient sentences. As reference summaries we used the manually generated summaries from the freely available dataset, SSSD. Our results revealed that sentence position outperforms other features indicating that the most salient sentences are always. In addition, other useful key features are debate title words feature, and cosine similarity of debate title words and sentences feature.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 1: ROUGE scores derived from each feature in the Salient Sentence Selection task', solve the following question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich feature had the highest ROUGE-1 score?\n" }, { "role": "assistant", "content": "To find the feature with the highest ROUGE-1 score, look at the ROUGE-1 row and identify the highest value. The highest value is 0.6124, which corresponds to the SP feature. So, the answer is {\"answer\": \"SP\"}." } ], "images": [ "scigen/train/1888.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: ROUGE scores derived from each feature in the Salient Sentence Selection task" ], "reasoning": "To find the feature with the highest ROUGE-1 score, look at the ROUGE-1 row and identify the highest value. The highest value is 0.6124, which corresponds to the SP feature.", "question": "Which feature had the highest ROUGE-1 score?", "answer": "So, the answer is {\"answer\": \"SP\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The COS_TTS feature had the highest ROUGE-2 score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe COS_TTS feature had the highest ROUGE-2 score.\n" }, { "role": "assistant", "content": "To verify this statement, look at the ROUGE-2 row and compare the values. The highest value is 0.5375, which corresponds to the SP feature, not COS_TTS. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1888.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: ROUGE scores derived from each feature in the Salient Sentence Selection task" ], "reasoning": "To verify this statement, look at the ROUGE-2 row and compare the values. The highest value is 0.5375, which corresponds to the SP feature, not COS_TTS.", "statement": "The COS_TTS feature had the highest ROUGE-2 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Precision@k for identifying the best parsing transfer language, for the k typological neighbors.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows that TD has the best alignment with the highest precision@k values, indicating the best parsing performance among the methods.Typology vs. Parser Transferability: The implicit assumption of all the typology based methods is that the typological similarity of two languages is a good indicator of their parsing transferability. As a measure of parser transferability, for each language we select the oracle source language which results in the best transfer performance. We then compute precision@k for the nearest k neighbors in the typological space, i.e. whether the best source appears in the k nearest neighbors. TD has the best alignment, which is consistent with its corresponding best parsing performance. Overall, this divergence motivates the development of approaches that better match the two distributions. We use Euclidean distance as our metric, another extreme simplification." } ], "images": [ "scigen/train/1889.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Precision@k for identifying the best parsing transfer language, for the k typological neighbors." ], "reasoning": "The table shows that TD has the best alignment with the highest precision@k values, indicating the best parsing performance among the methods.", "description": "Typology vs. Parser Transferability: The implicit assumption of all the typology based methods is that the typological similarity of two languages is a good indicator of their parsing transferability. As a measure of parser transferability, for each language we select the oracle source language which results in the best transfer performance. We then compute precision@k for the nearest k neighbors in the typological space, i.e. whether the best source appears in the k nearest neighbors. TD has the best alignment, which is consistent with its corresponding best parsing performance. Overall, this divergence motivates the development of approaches that better match the two distributions. We use Euclidean distance as our metric, another extreme simplification.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 4: Precision@k for identifying the best parsing transfer language, for the k typological neighbors.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method has the highest precision@10 value?\n" }, { "role": "assistant", "content": "By comparing the precision@10 values in the table, TD has the highest value of 93. So, the answer is {\"answer\": \"TD\"}." } ], "images": [ "scigen/train/1889.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Precision@k for identifying the best parsing transfer language, for the k typological neighbors." ], "reasoning": "By comparing the precision@10 values in the table, TD has the highest value of 93.", "question": "Which method has the highest precision@10 value?", "answer": "So, the answer is {\"answer\": \"TD\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTD has the highest precision@5 value.\n" }, { "role": "assistant", "content": "The table shows that TD has a precision@5 value of 67, which is the highest among the methods listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1889.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Precision@k for identifying the best parsing transfer language, for the k typological neighbors." ], "reasoning": "The table shows that TD has a precision@5 value of 67, which is the highest among the methods listed.", "statement": "TD has the highest precision@5 value.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Performance of typology prediction using hidden states of the parser’s encoder, compared to a majority baseline which predicts the most frequent category.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The parser struggles due to limited examples and the redundancy of additional typology features, which are nearly linearly separable.This can likely be attributed to the training routine, which poses two inherent difficulties: 1) the parser has few examples (entire languages) to generalize from, making it hard from a learning perspective and 2) a naïve encoder can already implicitly capture important typological features within its hidden state, using only the surface forms of the input. This renders the additional typology features redundant. We find they are nearly linearly separable — logistic regression achieves greater than 90% accuracy on average." } ], "images": [ "scigen/train/1890.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of typology prediction using hidden states of the parser’s encoder, compared to a majority baseline which predicts the most frequent category." ], "reasoning": "The parser struggles due to limited examples and the redundancy of additional typology features, which are nearly linearly separable.", "description": "This can likely be attributed to the training routine, which poses two inherent difficulties: 1) the parser has few examples (entire languages) to generalize from, making it hard from a learning perspective and 2) a naïve encoder can already implicitly capture important typological features within its hidden state, using only the surface forms of the input. This renders the additional typology features redundant. We find they are nearly linearly separable — logistic regression achieves greater than 90% accuracy on average.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: Performance of typology prediction using hidden states of the parser’s encoder, compared to a majority baseline which predicts the most frequent category.', solve the subsequent question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the accuracy of logistic regression for WALS ID 85A?\n" }, { "role": "assistant", "content": "The table shows the accuracy of logistic regression (Logreg) for different WALS IDs. For WALS ID 85A, the accuracy is 97. So, the answer is {\"answer\": \"97\"}" } ], "images": [ "scigen/train/1890.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of typology prediction using hidden states of the parser’s encoder, compared to a majority baseline which predicts the most frequent category." ], "reasoning": "The table shows the accuracy of logistic regression (Logreg) for different WALS IDs. For WALS ID 85A, the accuracy is 97.", "question": "What is the accuracy of logistic regression for WALS ID 85A?", "answer": "So, the answer is {\"answer\": \"97\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe majority classifier achieves higher accuracy than logistic regression for WALS ID 87A.\n" }, { "role": "assistant", "content": "The table shows that for WALS ID 87A, the accuracy of the majority classifier is 51, while the accuracy of logistic regression is 94. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1890.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of typology prediction using hidden states of the parser’s encoder, compared to a majority baseline which predicts the most frequent category." ], "reasoning": "The table shows that for WALS ID 87A, the accuracy of the majority classifier is 51, while the accuracy of logistic regression is 94. Therefore, the statement is refuted.", "statement": "The majority classifier achieves higher accuracy than logistic regression for WALS ID 87A.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4: Amazon results for two and five class sentiment classification tasks.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "CNN-pool-k performs comparably to the best baselines on both classification tasks, indicating that pooling should be the default method for variable length input.From the table, we see that CNN-pool-k performs comparably to the best baselines on both classification tasks. Max pooling again produces better performance than padding leading to conclusion that pooling should be used as the default method to deal with variable length input. Strong performance on the 5-class classification task suggest that our model is capable of successfully learning fine-grained differences between sentiment directly from unlabeled text. Overall, together with IMDB these results further indicate that the convolutional architecture is well suited for unsupervised NLP, and can be used to learn robust embedding models." } ], "images": [ "scigen/train/1891.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Amazon results for two and five class sentiment classification tasks." ], "reasoning": "CNN-pool-k performs comparably to the best baselines on both classification tasks, indicating that pooling should be the default method for variable length input.", "description": "From the table, we see that CNN-pool-k performs comparably to the best baselines on both classification tasks. Max pooling again produces better performance than padding leading to conclusion that pooling should be used as the default method to deal with variable length input. Strong performance on the 5-class classification task suggest that our model is capable of successfully learning fine-grained differences between sentiment directly from unlabeled text. Overall, together with IMDB these results further indicate that the convolutional architecture is well suited for unsupervised NLP, and can be used to learn robust embedding models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Amazon results for two and five class sentiment classification tasks.', solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich method has the highest performance on the 5-class classification task?\n" }, { "role": "assistant", "content": "By comparing the 5-class classification task scores, CNN-pool-k has the highest performance with a score of 51.73. So, the answer is {\"answer\": \"CNN-pool-k\"}." } ], "images": [ "scigen/train/1891.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Amazon results for two and five class sentiment classification tasks." ], "reasoning": "By comparing the 5-class classification task scores, CNN-pool-k has the highest performance with a score of 51.73.", "question": "Which method has the highest performance on the 5-class classification task?", "answer": "So, the answer is {\"answer\": \"CNN-pool-k\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nCNN-pool-k outperforms all other methods on the 2-class classification task.\n" }, { "role": "assistant", "content": "By comparing the 2-class classification task scores, skip-thought-pr has the highest performance with a score of 89.38, not CNN-pool-k. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1891.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Amazon results for two and five class sentiment classification tasks." ], "reasoning": "By comparing the 2-class classification task scores, skip-thought-pr has the highest performance with a score of 89.38, not CNN-pool-k.", "statement": "CNN-pool-k outperforms all other methods on the 2-class classification task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Inference speed.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The CNN architecture, despite having 7 hidden layers, is over 10 times faster than the uni-directional skip-thought model and over 20 times faster than the bi-directional skip-thought model.After GLU layers we apply max pooling (for CNN-pool) and a single fully connected layer that outputs the 300-dimensional embedding. These results are generated by doing inference with batch size 1 to remove the effects of batch CPU/GPU parallelization. From the table it is our CNN architecture is over 10x faster than uni-directional skip-thought and over 20x faster than the bi-directional version. This is despite the fact that CNN has 7 hidden layers vs skip-thought RNN only has one." } ], "images": [ "scigen/train/1892.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Inference speed." ], "reasoning": "The CNN architecture, despite having 7 hidden layers, is over 10 times faster than the uni-directional skip-thought model and over 20 times faster than the bi-directional skip-thought model.", "description": "After GLU layers we apply max pooling (for CNN-pool) and a single fully connected layer that outputs the 300-dimensional embedding. These results are generated by doing inference with batch size 1 to remove the effects of batch CPU/GPU parallelization. From the table it is our CNN architecture is over 10x faster than uni-directional skip-thought and over 20x faster than the bi-directional version. This is despite the fact that CNN has 7 hidden layers vs skip-thought RNN only has one.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Inference speed.\nQuestion: How many times faster is the CNN-pool model compared to the skip-thought-bi model?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The CNN-pool model processes 277,932 tokens per second, while the skip-thought-bi model processes 14,374 tokens per second. Dividing 277,932 by 14,374 gives approximately 19.33, indicating that the CNN-pool model is over 19 times faster. So, the answer is {\"answer\": \"19.33\"}" } ], "images": [ "scigen/train/1892.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Inference speed." ], "reasoning": "The CNN-pool model processes 277,932 tokens per second, while the skip-thought-bi model processes 14,374 tokens per second. Dividing 277,932 by 14,374 gives approximately 19.33, indicating that the CNN-pool model is over 19 times faster.", "question": "How many times faster is the CNN-pool model compared to the skip-thought-bi model?", "answer": "So, the answer is {\"answer\": \"19.33\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe CNN-pad model is over 10 times faster than the skip-thought-uni model.\n" }, { "role": "assistant", "content": "The CNN-pad model processes 312,744 tokens per second, while the skip-thought-uni model processes 27,493 tokens per second. Dividing 312,744 by 27,493 gives approximately 11.38, indicating that the CNN-pad model is indeed over 10 times faster. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1892.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Inference speed." ], "reasoning": "The CNN-pad model processes 312,744 tokens per second, while the skip-thought-uni model processes 27,493 tokens per second. Dividing 312,744 by 27,493 gives approximately 11.38, indicating that the CNN-pad model is indeed over 10 times faster.", "statement": "The CNN-pad model is over 10 times faster than the skip-thought-uni model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: IMDB sentiment classification test set accuracy. Test reviews are excluded during unsupervised model training.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "CNN-pool-k achieves the highest accuracy, surpassing 90%, and CNN-pool models outperform CNN-pad and RNN-based skip-thought approaches.From the table, we see that our best model CNN-pool-k outperforms all baselines and passes the difficult 90% accuracy level. We also see that CNN-pool generally performs better than CNN-pad, suggesting that max pooling is more effective than padding and truncation. Both CNN-pool models significantly outperform the RNN-based skip-thought approach. These results indicate that convolution with max pooling is a good alternative to recurrence for unsupervised learning with variable length textual input." } ], "images": [ "scigen/train/1893.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: IMDB sentiment classification test set accuracy. Test reviews are excluded during unsupervised model training." ], "reasoning": "CNN-pool-k achieves the highest accuracy, surpassing 90%, and CNN-pool models outperform CNN-pad and RNN-based skip-thought approaches.", "description": "From the table, we see that our best model CNN-pool-k outperforms all baselines and passes the difficult 90% accuracy level. We also see that CNN-pool generally performs better than CNN-pad, suggesting that max pooling is more effective than padding and truncation. Both CNN-pool models significantly outperform the RNN-based skip-thought approach. These results indicate that convolution with max pooling is a good alternative to recurrence for unsupervised learning with variable length textual input.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: IMDB sentiment classification test set accuracy. Test reviews are excluded during unsupervised model training.', and sequentially solve the question, writing out each step of your reasoning process. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieves the highest accuracy and how does it compare to the skip-thought-pr model?\n" }, { "role": "assistant", "content": "The CNN-pool-k model achieves the highest accuracy at 90.64%. Comparing this to the skip-thought-pr model, which has an accuracy of 82.57%, CNN-pool-k significantly outperforms it. So, the answer is {\"answer\": \"CNN-pool-k\"}." } ], "images": [ "scigen/train/1893.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: IMDB sentiment classification test set accuracy. Test reviews are excluded during unsupervised model training." ], "reasoning": "The CNN-pool-k model achieves the highest accuracy at 90.64%. Comparing this to the skip-thought-pr model, which has an accuracy of 82.57%, CNN-pool-k significantly outperforms it.", "question": "Which model achieves the highest accuracy and how does it compare to the skip-thought-pr model?", "answer": "So, the answer is {\"answer\": \"CNN-pool-k\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe CNN-pool model achieves an accuracy of 90.02%.\n" }, { "role": "assistant", "content": "The table shows that the CNN-pool model has an accuracy of 90.02%, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1893.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: IMDB sentiment classification test set accuracy. Test reviews are excluded during unsupervised model training." ], "reasoning": "The table shows that the CNN-pool model has an accuracy of 90.02%, which matches the statement.", "statement": "The CNN-pool model achieves an accuracy of 90.02%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: Injecting External Syntax Information. Bold number is the best performance in each column, same below.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "All three representations improve over the baseline system, with Input and Auto-Encoder methods performing equally well, but the extra complexity of the auto-encoder model is not justified.When used as input features, all three representations improve over our baseline system. This shows that syntactic representations provide additional useful information, which is beyond the dynamic context embeddings from ELMo, to SRL task. Both the Input and Auto-Encoder methods work equally well. We conclude that the extra complexity of the auto-encoder model is not justified. In particular, Dep with auto-encoder hurts SRL accuracy (0.6% behind the model with the constituency features)." } ], "images": [ "scigen/train/1894.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Injecting External Syntax Information. Bold number is the best performance in each column, same below." ], "reasoning": "All three representations improve over the baseline system, with Input and Auto-Encoder methods performing equally well, but the extra complexity of the auto-encoder model is not justified.", "description": "When used as input features, all three representations improve over our baseline system. This shows that syntactic representations provide additional useful information, which is beyond the dynamic context embeddings from ELMo, to SRL task. Both the Input and Auto-Encoder methods work equally well. We conclude that the extra complexity of the auto-encoder model is not justified. In particular, Dep with auto-encoder hurts SRL accuracy (0.6% behind the model with the constituency features).", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 1: Injecting External Syntax Information. Bold number is the best performance in each column, same below.' and proceed to solve the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model and injection method combination had the highest accuracy on the CoNLL'05 WSJ dataset?\n" }, { "role": "assistant", "content": "By examining the table, the highest accuracy on the CoNLL'05 WSJ dataset is 88.2, achieved by both the Input SRL-C and Auto Encoder Full-C models. So, the answer is {\"answer\": \"Input SRL-C and Auto Encoder Full-C\"}." } ], "images": [ "scigen/train/1894.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Injecting External Syntax Information. Bold number is the best performance in each column, same below." ], "reasoning": "By examining the table, the highest accuracy on the CoNLL'05 WSJ dataset is 88.2, achieved by both the Input SRL-C and Auto Encoder Full-C models.", "question": "Which model and injection method combination had the highest accuracy on the CoNLL'05 WSJ dataset?", "answer": "So, the answer is {\"answer\": \"Input SRL-C and Auto Encoder Full-C\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Dep model with auto-encoder has the lowest accuracy on the CoNLL'05 Brown dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Dep model with auto-encoder has the lowest accuracy on the CoNLL'05 Brown dataset.\n" }, { "role": "assistant", "content": "By examining the table, the Dep model with auto-encoder has an accuracy of 78.1 on the CoNLL'05 Brown dataset, which is indeed the lowest. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1894.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Injecting External Syntax Information. Bold number is the best performance in each column, same below." ], "reasoning": "By examining the table, the Dep model with auto-encoder has an accuracy of 78.1 on the CoNLL'05 Brown dataset, which is indeed the lowest.", "statement": "The Dep model with auto-encoder has the lowest accuracy on the CoNLL'05 Brown dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Comparison with existing systems. \\lx@sectionsign indicates ensemble models.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our model outperforms state-of-the-art non-ensemble SRL models on CoNLL’05 and CoNLL’12 in-domain tests by 0.6% and 0.2% respectively, and achieves competitive results on CoNLL’05 Brown Test.We improve upon the state-of-the-art results for non-ensemble SRL models on in-domain test by 0.6% and 0.2% on CoNLL’05 and CoNLL’12 respectively. Our model also achieves a competitive result on CoNLL’05 Brown Test. Comparing with the strong ensemble model in Ouchi et al." } ], "images": [ "scigen/train/1895.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison with existing systems. \\lx@sectionsign indicates ensemble models." ], "reasoning": "Our model outperforms state-of-the-art non-ensemble SRL models on CoNLL’05 and CoNLL’12 in-domain tests by 0.6% and 0.2% respectively, and achieves competitive results on CoNLL’05 Brown Test.", "description": "We improve upon the state-of-the-art results for non-ensemble SRL models on in-domain test by 0.6% and 0.2% on CoNLL’05 and CoNLL’12 respectively. Our model also achieves a competitive result on CoNLL’05 Brown Test. Comparing with the strong ensemble model in Ouchi et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: Comparison with existing systems. \\lx@sectionsign indicates ensemble models.'.\n\nQuestion: How much better is 'Our best model' compared to 'He et al. (2018)' on CoNLL’05 WSJ and CoNLL’12 Test?\n" }, { "role": "assistant", "content": "To find the difference, subtract the scores of 'He et al. (2018)' from 'Our best model' for both CoNLL’05 WSJ and CoNLL’12 Test. For CoNLL’05 WSJ, it is 88.2 - 87.4 = 0.8%. For CoNLL’12 Test, it is 86.4 - 85.5 = 0.9%. So, the answer is {\"answer\": \"0.8% on CoNLL’05 WSJ and 0.9% on CoNLL’12 Test\"}" } ], "images": [ "scigen/train/1895.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison with existing systems. \\lx@sectionsign indicates ensemble models." ], "reasoning": "To find the difference, subtract the scores of 'He et al. (2018)' from 'Our best model' for both CoNLL’05 WSJ and CoNLL’12 Test. For CoNLL’05 WSJ, it is 88.2 - 87.4 = 0.8%. For CoNLL’12 Test, it is 86.4 - 85.5 = 0.9%.", "question": "How much better is 'Our best model' compared to 'He et al. (2018)' on CoNLL’05 WSJ and CoNLL’12 Test?", "answer": "So, the answer is {\"answer\": \"0.8% on CoNLL’05 WSJ and 0.9% on CoNLL’12 Test\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nOur best model achieves the highest score on CoNLL’05 WSJ.\n" }, { "role": "assistant", "content": "Comparing the scores in the CoNLL’05 WSJ column, 'Our best model' has the highest score of 88.2. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1895.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison with existing systems. \\lx@sectionsign indicates ensemble models." ], "reasoning": "Comparing the scores in the CoNLL’05 WSJ column, 'Our best model' has the highest score of 88.2.", "statement": "Our best model achieves the highest score on CoNLL’05 WSJ.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: SRL Performance with Gold Trees', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Using gold trees for syntactic features in an oracle experiment bounds the potential improvement in SRL performance.Finally, we conduct an oracle experiment where all syntactic features are derived from gold trees. This bounds the improvement in SRL that one can expect with improved syntactic parses." } ], "images": [ "scigen/train/1896.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: SRL Performance with Gold Trees" ], "reasoning": "Using gold trees for syntactic features in an oracle experiment bounds the potential improvement in SRL performance.", "description": "Finally, we conduct an oracle experiment where all syntactic features are derived from gold trees. This bounds the improvement in SRL that one can expect with improved syntactic parses.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: SRL Performance with Gold Trees'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest score in the CoNLL'12 Test dataset?\n" }, { "role": "assistant", "content": "To find the model with the highest score in the CoNLL'12 Test dataset, compare the scores in the CoNLL'12 Test column. The highest score is 91.4, achieved by the Full-C model. So, the answer is {\"answer\": \"Full-C\"}." } ], "images": [ "scigen/train/1896.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: SRL Performance with Gold Trees" ], "reasoning": "To find the model with the highest score in the CoNLL'12 Test dataset, compare the scores in the CoNLL'12 Test column. The highest score is 91.4, achieved by the Full-C model.", "question": "Which model has the highest score in the CoNLL'12 Test dataset?", "answer": "So, the answer is {\"answer\": \"Full-C\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Full-C model has the highest score in the CoNLL'05 WSJ dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the scores in the CoNLL'05 WSJ column. The Full-C model has a score of 92.2, which is the highest in that column. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1896.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: SRL Performance with Gold Trees" ], "reasoning": "To verify this statement, compare the scores in the CoNLL'05 WSJ column. The Full-C model has a score of 92.2, which is the highest in that column.", "statement": "The Full-C model has the highest score in the CoNLL'05 WSJ dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Results of the proposed technique with the parameter settings established within the previous experiment and of other methods on the considered benchmark data sets.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The proposed method achieves competitive results for CCAT-50 and the source code is available online.The other rows contain results by various techniques that were applied for the same instances. Subsequent work suggests that this particular result may be fragile insofar as it relies on parameter settings that are difficult to discern. The next best result for this data set is achieved by the proposed methodology. In order to make our results reproducible, the source code is made available at the webpage https://github.com/catalinstoean/FCGR-LR. For CCAT-50, the proposed method achieves competitive results as compared to the ones found in the literature." } ], "images": [ "scigen/train/1897.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of the proposed technique with the parameter settings established within the previous experiment and of other methods on the considered benchmark data sets." ], "reasoning": "The proposed method achieves competitive results for CCAT-50 and the source code is available online.", "description": "The other rows contain results by various techniques that were applied for the same instances. Subsequent work suggests that this particular result may be fragile insofar as it relies on parameter settings that are difficult to discern. The next best result for this data set is achieved by the proposed methodology. In order to make our results reproducible, the source code is made available at the webpage https://github.com/catalinstoean/FCGR-LR. For CCAT-50, the proposed method achieves competitive results as compared to the ones found in the literature.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 4: Results of the proposed technique with the parameter settings established within the previous experiment and of other methods on the considered benchmark data sets.'. The final result should be presented in the JSON format of {\"answer\": \"\"}. \n\nQuestion: What is the PAN-12 score achieved by the proposed method FCGR+LR?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows that the proposed method FCGR+LR achieved a PAN-12 score of 14/16. So, the answer is {\"answer\": \"14/16\"}" } ], "images": [ "scigen/train/1897.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of the proposed technique with the parameter settings established within the previous experiment and of other methods on the considered benchmark data sets." ], "reasoning": "The table shows that the proposed method FCGR+LR achieved a PAN-12 score of 14/16.", "question": "What is the PAN-12 score achieved by the proposed method FCGR+LR?", "answer": "So, the answer is {\"answer\": \"14/16\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe proposed method FCGR+LR achieved the highest score for CCAT-10.\n" }, { "role": "assistant", "content": "The table shows that the highest score for CCAT-10 is 86.4% achieved by SVM (Escalante et al., 2011), while FCGR+LR achieved 82.2%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1897.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of the proposed technique with the parameter settings established within the previous experiment and of other methods on the considered benchmark data sets." ], "reasoning": "The table shows that the highest score for CCAT-10 is 86.4% achieved by SVM (Escalante et al., 2011), while FCGR+LR achieved 82.2%.", "statement": "The proposed method FCGR+LR achieved the highest score for CCAT-10.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: The equivalence classes and their corresponding base 4 representation.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Characters are grouped into 16 equivalence classes based on frequency and phonetic similarity, and encoded in base 4 to balance the spread of characters.The most successful representation among the ones tried is obtained by encoding each distinct character as a pair of numbers in base 4. There are only 16 different pairs of numbers in base 4, so some characters are replaced by others in the initial text in order to reach only 16 equivalence classes. The classes of characters are chosen and ordered so that several criteria are met: The letters that are very common are left alone in order to reach a rough balance with respect to the occurrence of each equivalence class of characters. The letters that are less common are grouped in the same equivalence class if they have similar sounds or similar phonetic usage. White space (tabs, spaces, and line breaks) are grouped into one equivalence class. The punctuation and the digits are gathered in the same class. Other characters (that do not belong to the Latin alphabet) are omitted. Beside the grouping of the characters, special attention is paid to the choice of the base 4 representation for each equivalence class. It is intended to have a good balance in the spread of characters that are encoded in base 4 and start, and end respectively, with a certain digit." } ], "images": [ "scigen/train/1898.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The equivalence classes and their corresponding base 4 representation." ], "reasoning": "Characters are grouped into 16 equivalence classes based on frequency and phonetic similarity, and encoded in base 4 to balance the spread of characters.", "description": "The most successful representation among the ones tried is obtained by encoding each distinct character as a pair of numbers in base 4. There are only 16 different pairs of numbers in base 4, so some characters are replaced by others in the initial text in order to reach only 16 equivalence classes. The classes of characters are chosen and ordered so that several criteria are met: The letters that are very common are left alone in order to reach a rough balance with respect to the occurrence of each equivalence class of characters. The letters that are less common are grouped in the same equivalence class if they have similar sounds or similar phonetic usage. White space (tabs, spaces, and line breaks) are grouped into one equivalence class. The punctuation and the digits are gathered in the same class. Other characters (that do not belong to the Latin alphabet) are omitted. Beside the grouping of the characters, special attention is paid to the choice of the base 4 representation for each equivalence class. It is intended to have a good balance in the spread of characters that are encoded in base 4 and start, and end respectively, with a certain digit.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 1: The equivalence classes and their corresponding base 4 representation.', unfold the question presented below, detailing every step of your calculation. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the base 4 encoding for the class containing the characters 'c', 'k', 'q', 'x', and 'z'?\n" }, { "role": "assistant", "content": "The table shows that the class containing 'c', 'k', 'q', 'x', and 'z' is encoded as 12 in base 4. So, the answer is {\"answer\": \"12\"}." } ], "images": [ "scigen/train/1898.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The equivalence classes and their corresponding base 4 representation." ], "reasoning": "The table shows that the class containing 'c', 'k', 'q', 'x', and 'z' is encoded as 12 in base 4.", "question": "What is the base 4 encoding for the class containing the characters 'c', 'k', 'q', 'x', and 'z'?", "answer": "So, the answer is {\"answer\": \"12\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The character 'e' is encoded as 22 in base 4., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe character 'e' is encoded as 22 in base 4.\n" }, { "role": "assistant", "content": "The table shows that the character 'e' is in the class {p} and is encoded as 22 in base 4. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1898.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The equivalence classes and their corresponding base 4 representation." ], "reasoning": "The table shows that the character 'e' is in the class {p} and is encoded as 22 in base 4.", "statement": "The character 'e' is encoded as 22 in base 4.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Average accuracy results for the classification on validation and test sets over 30 trials, each with random samples in the training and validation sets. The test set comprises the disputed essays and the ground truth is considered with Madison as author.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The classification accuracy is higher on the first validation set for all classifiers, and the test set shows that 10 of the 12 essays are attributed to Madison by both classifiers, with essays 62 and 63 sometimes attributed to Hamilton.Accordingly, the parameter settings of chunk size 8500 and k=7 are used for the subsequent test on the Federalist Papers, while randomly varying the training and validation samples in 30 different repeated runs. The aim is to ascertain that the methods are not sensitive to the choice of used training and validation sets. It can be observed that all of them have higher classification accuracy on the first validation set as compared to the second one: the former is obtained by gathering the FCGR images produced from the final chunk of each essay from the training set, therefore it is natural that those text parts resemble more the rest of the essays that remained in the training sets. As concerns the test set, in all trials, 10 of the 12 essays are attributed to Madison by both classifiers. The Federalist Papers 62 and 63 are the ones that, in some trials, get attributed to Hamilton. For LR this happens in 2 of the 30 trials for essay 62, and in 1 trial for essay 63. For SVM the situation is quite different: in 24 times of the 30 trials the essay 62 is attributed to Hamilton, and in 25 trials the same situation occurs for essay 63. The results of FTT+PCA are more similar to those of the SVM: in 27 of 30 trials it assigns essay 62 to Hamilton and in 29 cases essay 63 is labeled also with Hamilton. As concerns the other disputed essays, there is only one minor exception for essay 50, which is assigned by FTT+PCA in 1 trial out of 30 to Hamilton. historians feel weakest about”, so it can be observed that the results of the proposed methodology get complicated for the exact essays that are considered tricky." } ], "images": [ "scigen/train/1899.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Average accuracy results for the classification on validation and test sets over 30 trials, each with random samples in the training and validation sets. The test set comprises the disputed essays and the ground truth is considered with Madison as author." ], "reasoning": "The classification accuracy is higher on the first validation set for all classifiers, and the test set shows that 10 of the 12 essays are attributed to Madison by both classifiers, with essays 62 and 63 sometimes attributed to Hamilton.", "description": "Accordingly, the parameter settings of chunk size 8500 and k=7 are used for the subsequent test on the Federalist Papers, while randomly varying the training and validation samples in 30 different repeated runs. The aim is to ascertain that the methods are not sensitive to the choice of used training and validation sets. It can be observed that all of them have higher classification accuracy on the first validation set as compared to the second one: the former is obtained by gathering the FCGR images produced from the final chunk of each essay from the training set, therefore it is natural that those text parts resemble more the rest of the essays that remained in the training sets. As concerns the test set, in all trials, 10 of the 12 essays are attributed to Madison by both classifiers. The Federalist Papers 62 and 63 are the ones that, in some trials, get attributed to Hamilton. For LR this happens in 2 of the 30 trials for essay 62, and in 1 trial for essay 63. For SVM the situation is quite different: in 24 times of the 30 trials the essay 62 is attributed to Hamilton, and in 25 trials the same situation occurs for essay 63. The results of FTT+PCA are more similar to those of the SVM: in 27 of 30 trials it assigns essay 62 to Hamilton and in 29 cases essay 63 is labeled also with Hamilton. As concerns the other disputed essays, there is only one minor exception for essay 50, which is assigned by FTT+PCA in 1 trial out of 30 to Hamilton. historians feel weakest about”, so it can be observed that the results of the proposed methodology get complicated for the exact essays that are considered tricky.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 3: Average accuracy results for the classification on validation and test sets over 30 trials, each with random samples in the training and validation sets. The test set comprises the disputed essays and the ground truth is considered with Madison as author.', answer the question below by showing each progressive step toward the answer. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich classifier has the highest accuracy on the first validation set?\n" }, { "role": "assistant", "content": "By comparing the accuracy percentages in the first validation set column, SVM has the highest accuracy at 95.9%. So, the answer is {\"answer\": \"SVM\"}" } ], "images": [ "scigen/train/1899.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Average accuracy results for the classification on validation and test sets over 30 trials, each with random samples in the training and validation sets. The test set comprises the disputed essays and the ground truth is considered with Madison as author." ], "reasoning": "By comparing the accuracy percentages in the first validation set column, SVM has the highest accuracy at 95.9%.", "question": "Which classifier has the highest accuracy on the first validation set?", "answer": "So, the answer is {\"answer\": \"SVM\"}" }, { "messages": [ { "role": "user", "content": "FTT+PCA has the highest accuracy on the test set. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nFTT+PCA has the highest accuracy on the test set.\n" }, { "role": "assistant", "content": "The table shows that LR has the highest accuracy on the test set at 99.17%, while FTT+PCA has an accuracy of 84.17%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1899.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Average accuracy results for the classification on validation and test sets over 30 trials, each with random samples in the training and validation sets. The test set comprises the disputed essays and the ground truth is considered with Madison as author." ], "reasoning": "The table shows that LR has the highest accuracy on the test set at 99.17%, while FTT+PCA has an accuracy of 84.17%.", "statement": "FTT+PCA has the highest accuracy on the test set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: Results in percents for AA and genre classification for the Brazilian newspaper data set. The results of the proposed methods using LR and FTT+PCA options are presented beside results presented by other methods in the literature. For the proposed methods, percents for cases when the correct author is second and 3rd or 4th respectively are also shown.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The FCGR+LR classifier outperforms the FCGR+FTT+PCA method in both author and genre identification, with higher percentages in correct author and genre classification.The testing proceeds as follows. The FTT+PCA method had settings to retain the lowest 100x100 submatrix of Fourier frequencies, and the largest 28 singular values and corresponding right singular vectors. For each processed text image, the 12 closest neighbors are found. A score for the classifier is constructed, as before, based on reciprocal distances between neighbors and test vector. The prospective author with top score is the main guess. In 63.3% of the tests this guess is in fact the correct author. Another 12.2% have the correct author as second highest scoring prospective author. Nearly 9% of the correct authors are found amongst the third and fourth highest scoring prospective authors. Loosely, nearly 85% of the test cases have authors in the top four guesses. As it can be observed, for the LR method the results are even better. Again each test can be ascribed to any of 100 different authors, and this classifier ascribes an internal probability to each author for a given test image. In 82% of the test cases the correct author is in fact the one that the classifier has identified as having the highest probability. Moreover, in 93% of the tests, the correct author lies amongst the top four candidates. Not surprisingly the correctness percentages go up. With the FTT+PCA method the correct genre is identified in 82.5% of the tests, with the second guess being the correct genre in another 9.25% of the test cases. LR correctly identifies genre in 86.8% of the tests, with another 9.2% having the correct genre as second best guess." } ], "images": [ "scigen/train/1900.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results in percents for AA and genre classification for the Brazilian newspaper data set. The results of the proposed methods using LR and FTT+PCA options are presented beside results presented by other methods in the literature. For the proposed methods, percents for cases when the correct author is second and 3rd or 4th respectively are also shown." ], "reasoning": "The FCGR+LR classifier outperforms the FCGR+FTT+PCA method in both author and genre identification, with higher percentages in correct author and genre classification.", "description": "The testing proceeds as follows. The FTT+PCA method had settings to retain the lowest 100x100 submatrix of Fourier frequencies, and the largest 28 singular values and corresponding right singular vectors. For each processed text image, the 12 closest neighbors are found. A score for the classifier is constructed, as before, based on reciprocal distances between neighbors and test vector. The prospective author with top score is the main guess. In 63.3% of the tests this guess is in fact the correct author. Another 12.2% have the correct author as second highest scoring prospective author. Nearly 9% of the correct authors are found amongst the third and fourth highest scoring prospective authors. Loosely, nearly 85% of the test cases have authors in the top four guesses. As it can be observed, for the LR method the results are even better. Again each test can be ascribed to any of 100 different authors, and this classifier ascribes an internal probability to each author for a given test image. In 82% of the test cases the correct author is in fact the one that the classifier has identified as having the highest probability. Moreover, in 93% of the tests, the correct author lies amongst the top four candidates. Not surprisingly the correctness percentages go up. With the FTT+PCA method the correct genre is identified in 82.5% of the tests, with the second guess being the correct genre in another 9.25% of the test cases. LR correctly identifies genre in 86.8% of the tests, with another 9.2% having the correct genre as second best guess.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: Results in percents for AA and genre classification for the Brazilian newspaper data set. The results of the proposed methods using LR and FTT+PCA options are presented beside results presented by other methods in the literature. For the proposed methods, percents for cases when the correct author is second and 3rd or 4th respectively are also shown.', solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich classifier has the highest percentage of correct genre classification?\n" }, { "role": "assistant", "content": "By comparing the genre classification percentages in the table, the SVM (Varela et al., 2011) classifier has the highest percentage at 86%. So, the answer is {\"answer\": \"SVM (Varela et al., 2011)\"}." } ], "images": [ "scigen/train/1900.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results in percents for AA and genre classification for the Brazilian newspaper data set. The results of the proposed methods using LR and FTT+PCA options are presented beside results presented by other methods in the literature. For the proposed methods, percents for cases when the correct author is second and 3rd or 4th respectively are also shown." ], "reasoning": "By comparing the genre classification percentages in the table, the SVM (Varela et al., 2011) classifier has the highest percentage at 86%.", "question": "Which classifier has the highest percentage of correct genre classification?", "answer": "So, the answer is {\"answer\": \"SVM (Varela et al., 2011)\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe FCGR+FTT+PCA method correctly identifies the author in 63.3% of the tests.\n" }, { "role": "assistant", "content": "The table shows that the FCGR+FTT+PCA method has a correct author identification percentage of 63.3%, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1900.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results in percents for AA and genre classification for the Brazilian newspaper data set. The results of the proposed methods using LR and FTT+PCA options are presented beside results presented by other methods in the literature. For the proposed methods, percents for cases when the correct author is second and 3rd or 4th respectively are also shown." ], "reasoning": "The table shows that the FCGR+FTT+PCA method has a correct author identification percentage of 63.3%, which matches the statement.", "statement": "The FCGR+FTT+PCA method correctly identifies the author in 63.3% of the tests.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Soundex encoding table.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Soundex converts a string into a code with one character and three numbers by keeping the first letter and encoding consonants, while Phonex and Phonix improve encoding quality with pronunciation-based rules, and Double Metaphone is better for non-English languages.Soundex [HolmesM02], one of the best known phonetic functions, converts a string into a code according to an encoding table. A soundex code is comprised of one character and three numbers. The Soundex code is generated as follows: (i) Keep the first letter of a string and ignore all other occurrences of vowels (a, e, i, o, u) and h, w, y; (iii) Code two consecutive letters as a single number; and (iv) Pad with 0 if there are less than three numbers. For example, using the soundex encoding table, both “daniel” and “damiel” return the same soundex code “d540”. Phonex/Phonix [Randell93] is an alternative function to Soundex, which was designed to improve the encoding quality by preprocessing names based on their pronunciations. Phonix [Gadd1990], an extension of Phonex, uses more than one hundred rules on groups of characters [Christen06]. The rules are applied to only some parts of names, e.g., the beginning, middle or end of names. Double Metaphone [Philips] performs better for string matching in non-English languages, like European and Asian, rather than the soundex function that is suitable for English. Thus it uses more complex rules that consider letter positions as well as previous and following letters in a string, compared with the soundex function." } ], "images": [ "scigen/train/1901.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Soundex encoding table." ], "reasoning": "Soundex converts a string into a code with one character and three numbers by keeping the first letter and encoding consonants, while Phonex and Phonix improve encoding quality with pronunciation-based rules, and Double Metaphone is better for non-English languages.", "description": "Soundex [HolmesM02], one of the best known phonetic functions, converts a string into a code according to an encoding table. A soundex code is comprised of one character and three numbers. The Soundex code is generated as follows: (i) Keep the first letter of a string and ignore all other occurrences of vowels (a, e, i, o, u) and h, w, y; (iii) Code two consecutive letters as a single number; and (iv) Pad with 0 if there are less than three numbers. For example, using the soundex encoding table, both “daniel” and “damiel” return the same soundex code “d540”. Phonex/Phonix [Randell93] is an alternative function to Soundex, which was designed to improve the encoding quality by preprocessing names based on their pronunciations. Phonix [Gadd1990], an extension of Phonex, uses more than one hundred rules on groups of characters [Christen06]. The rules are applied to only some parts of names, e.g., the beginning, middle or end of names. Double Metaphone [Philips] performs better for string matching in non-English languages, like European and Asian, rather than the soundex function that is suitable for English. Thus it uses more complex rules that consider letter positions as well as previous and following letters in a string, compared with the soundex function.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Soundex encoding table.', answer the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the Soundex code for the name 'Robert'?\n" }, { "role": "assistant", "content": "Keep the first letter 'R'. Encode the consonants: 'b' (1), 'r' (6), 't' (3). The code is 'R163'. So, the answer is {\"answer\": \"R163\"}." } ], "images": [ "scigen/train/1901.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Soundex encoding table." ], "reasoning": "Keep the first letter 'R'. Encode the consonants: 'b' (1), 'r' (6), 't' (3). The code is 'R163'.", "question": "What is the Soundex code for the name 'Robert'?", "answer": "So, the answer is {\"answer\": \"R163\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Soundex code for the name 'David' is D130. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Soundex code for the name 'David' is D130.\n" }, { "role": "assistant", "content": "Keep the first letter 'D'. Encode the consonants: 'v' (1), 'd' (3). The code is 'D130'. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1901.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Soundex encoding table." ], "reasoning": "Keep the first letter 'D'. Encode the consonants: 'v' (1), 'd' (3). The code is 'D130'.", "statement": "The Soundex code for the name 'David' is D130.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Parsing speeds on the PTB test set. {}^{\\spadesuit} means the models use Transformer as their encoders. “w. Cython” stands for using Cython to optimize the python code. “w/o tree inference” stands for evaluating without tree inference. The model in DBLP:conf/acl/KleinK18 is ran by ourselves, and other speeds are extracted from their original papers.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our model is faster than most local and global models due to its simplicity and parallelism, except for DBLP:conf/acl/BengioSCJLS18 without tree inference and DBLP:conf/naacl/VilaresAS19.Although our inference complexity is \\mathcal{O}(n\\log n), our speed is faster than other local models, except DBLP:conf/acl/BengioSCJLS18 which evaluates without tree inference and DBLP:conf/naacl/VilaresAS19 which utilizes a pure sequence tagging framework. This is mainly due to the simplicity of our model and the parallelism of matrix operations for structure prediction. Compared with globally normalized parsers like DBLP:conf/acl/ZhouZ19 and DBLP: conf/acl/KleinK18, our model is also faster even if they use optimization for python code Other global model like DBLP: conf/acl/SternAK17 which infers in O(n^{3}) complexity is much slower than ours, and this shows the superiority of our linearization in speed." } ], "images": [ "scigen/train/1902.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Parsing speeds on the PTB test set. {}^{\\spadesuit} means the models use Transformer as their encoders. “w. Cython” stands for using Cython to optimize the python code. “w/o tree inference” stands for evaluating without tree inference. The model in DBLP:conf/acl/KleinK18 is ran by ourselves, and other speeds are extracted from their original papers." ], "reasoning": "Our model is faster than most local and global models due to its simplicity and parallelism, except for DBLP:conf/acl/BengioSCJLS18 without tree inference and DBLP:conf/naacl/VilaresAS19.", "description": "Although our inference complexity is \\mathcal{O}(n\\log n), our speed is faster than other local models, except DBLP:conf/acl/BengioSCJLS18 which evaluates without tree inference and DBLP:conf/naacl/VilaresAS19 which utilizes a pure sequence tagging framework. This is mainly due to the simplicity of our model and the parallelism of matrix operations for structure prediction. Compared with globally normalized parsers like DBLP:conf/acl/ZhouZ19 and DBLP: conf/acl/KleinK18, our model is also faster even if they use optimization for python code Other global model like DBLP: conf/acl/SternAK17 which infers in O(n^{3}) complexity is much slower than ours, and this shows the superiority of our linearization in speed.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 6: Parsing speeds on the PTB test set. {}^{\\spadesuit} means the models use Transformer as their encoders. “w. Cython” stands for using Cython to optimize the python code. “w/o tree inference” stands for evaluating without tree inference. The model in DBLP:conf/acl/KleinK18 is ran by ourselves, and other speeds are extracted from their original papers.', solve the subsequent question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich model is faster than ours but uses a pure sequence tagging framework?\n" }, { "role": "assistant", "content": "From the description, DBLP:conf/naacl/VilaresAS19 is mentioned as using a pure sequence tagging framework and is faster than our model. The table confirms this with a speed of 942 sents/sec compared to our model's 220 sents/sec. So, the answer is {\"answer\": \"DBLP:conf/naacl/VilaresAS19\"}" } ], "images": [ "scigen/train/1902.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Parsing speeds on the PTB test set. {}^{\\spadesuit} means the models use Transformer as their encoders. “w. Cython” stands for using Cython to optimize the python code. “w/o tree inference” stands for evaluating without tree inference. The model in DBLP:conf/acl/KleinK18 is ran by ourselves, and other speeds are extracted from their original papers." ], "reasoning": "From the description, DBLP:conf/naacl/VilaresAS19 is mentioned as using a pure sequence tagging framework and is faster than our model. The table confirms this with a speed of 942 sents/sec compared to our model's 220 sents/sec.", "question": "Which model is faster than ours but uses a pure sequence tagging framework?", "answer": "So, the answer is {\"answer\": \"DBLP:conf/naacl/VilaresAS19\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nOur model is faster than DBLP:conf/acl/SternAK17.\n" }, { "role": "assistant", "content": "The table shows that DBLP:conf/acl/SternAK17 has a speed of 20 sents/sec, while our model has a speed of 220 sents/sec, confirming that our model is indeed faster. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1902.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Parsing speeds on the PTB test set. {}^{\\spadesuit} means the models use Transformer as their encoders. “w. Cython” stands for using Cython to optimize the python code. “w/o tree inference” stands for evaluating without tree inference. The model in DBLP:conf/acl/KleinK18 is ran by ourselves, and other speeds are extracted from their original papers." ], "reasoning": "The table shows that DBLP:conf/acl/SternAK17 has a speed of 20 sents/sec, while our model has a speed of 220 sents/sec, confirming that our model is indeed faster.", "statement": "Our model is faster than DBLP:conf/acl/SternAK17.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Comparison on different phrases types. Here we only list top nine types.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our model outperforms the PSN model in all constituent types, with significant improvements in SBAR, ADJP, and QP.S4SS3SSS0Px2 Influence of Constituent Type Then we compare the accuracy of different constituent types. Our model all performs better than PSN model, especially in types SBAR, ADJP and QP. When optimizing the representation of one split point, our model can consider all of the words before it, which can be helpful to predict some types. For example, when we predict an adjective phrase (ADJP), its representation has fused the words’ information before it (e.g. linking verb like “is”), which can narrow the scope of prediction." } ], "images": [ "scigen/train/1903.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison on different phrases types. Here we only list top nine types." ], "reasoning": "Our model outperforms the PSN model in all constituent types, with significant improvements in SBAR, ADJP, and QP.", "description": "S4SS3SSS0Px2 Influence of Constituent Type Then we compare the accuracy of different constituent types. Our model all performs better than PSN model, especially in types SBAR, ADJP and QP. When optimizing the representation of one split point, our model can consider all of the words before it, which can be helpful to predict some types. For example, when we predict an adjective phrase (ADJP), its representation has fused the words’ information before it (e.g. linking verb like “is”), which can narrow the scope of prediction.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 1: Comparison on different phrases types. Here we only list top nine types.', solve this question and give detail solutions. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhich constituent type shows the greatest improvement in accuracy when using our model compared to the PSN model?\n" }, { "role": "assistant", "content": "By comparing the 'Difference' row, we see that QP has the highest improvement of +2.13. So, the answer is {\"answer\": \"QP\"}" } ], "images": [ "scigen/train/1903.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison on different phrases types. Here we only list top nine types." ], "reasoning": "By comparing the 'Difference' row, we see that QP has the highest improvement of +2.13.", "question": "Which constituent type shows the greatest improvement in accuracy when using our model compared to the PSN model?", "answer": "So, the answer is {\"answer\": \"QP\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nOur model performs better than the PSN model in predicting ADVP constituent types.\n" }, { "role": "assistant", "content": "The table shows that the accuracy for ADVP is 87.39 for our model and 86.89 for the PSN model, indicating better performance by our model. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1903.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison on different phrases types. Here we only list top nine types." ], "reasoning": "The table shows that the accuracy for ADVP is 87.39 for our model and 86.89 for the PSN model, indicating better performance by our model.", "statement": "Our model performs better than the PSN model in predicting ADVP constituent types.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: Evaluation results on the SRE18 (CMN2) dev set', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Model MP achieves the best results with a 9.3% EER reduction and a 12% to 14% DCF score reduction when regularization is applied.Similar to SRE16 results, model MP achieves the best result: 9.3% EER reduction and 12% to 14% DCF score reduction are achieved when regularization is applied." } ], "images": [ "scigen/train/1904.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Evaluation results on the SRE18 (CMN2) dev set" ], "reasoning": "Model MP achieves the best results with a 9.3% EER reduction and a 12% to 14% DCF score reduction when regularization is applied.", "description": "Similar to SRE16 results, model MP achieves the best result: 9.3% EER reduction and 12% to 14% DCF score reduction are achieved when regularization is applied.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: Evaluation results on the SRE18 (CMN2) dev set', answer the following question. Give detailed solution consisting of each step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the EER reduction percentage for model MP when regularization is applied?\n" }, { "role": "assistant", "content": "The EER for model MP without regularization (λ=0.0) is 7.16, and with regularization (λ=0.001) it is 6.61. The reduction is calculated as (7.16 - 6.61) / 7.16 * 100 = 7.69%. So, the answer is {\"answer\": \"7.69%\"}" } ], "images": [ "scigen/train/1904.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Evaluation results on the SRE18 (CMN2) dev set" ], "reasoning": "The EER for model MP without regularization (λ=0.0) is 7.16, and with regularization (λ=0.001) it is 6.61. The reduction is calculated as (7.16 - 6.61) / 7.16 * 100 = 7.69%.", "question": "What is the EER reduction percentage for model MP when regularization is applied?", "answer": "So, the answer is {\"answer\": \"7.69%\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence Model MP achieves a 12% DCF(0.01) score reduction when regularization is applied. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nModel MP achieves a 12% DCF(0.01) score reduction when regularization is applied.\n" }, { "role": "assistant", "content": "The DCF(0.01) for model MP without regularization (λ=0.0) is 0.567, and with regularization (λ=0.001) it is 0.506. The reduction is calculated as (0.567 - 0.506) / 0.567 * 100 = 10.76%, which is not 12%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1904.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Evaluation results on the SRE18 (CMN2) dev set" ], "reasoning": "The DCF(0.01) for model MP without regularization (λ=0.0) is 0.567, and with regularization (λ=0.001) it is 0.506. The reduction is calculated as (0.567 - 0.506) / 0.567 * 100 = 10.76%, which is not 12%.", "statement": "Model MP achieves a 12% DCF(0.01) score reduction when regularization is applied.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: DCF scores for SRE16 (pooled) test set'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The proposed MP model with regularization shows a 19% EER reduction and a 14.6% and 12.3% DCF score reduction for Ptarget = 0.01 and 0.005 respectively, compared to the baseline x-vector model.Overall, comparing the result from our proposed model MP with regularization, against the baseline x-vector model, we observe a relative 19% EER reduction. There is a 14.6% and 12.3% DCF score reduction compared with the baseline for Ptarget = 0.01 and 0.005 respectively." } ], "images": [ "scigen/train/1905.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: DCF scores for SRE16 (pooled) test set" ], "reasoning": "The proposed MP model with regularization shows a 19% EER reduction and a 14.6% and 12.3% DCF score reduction for Ptarget = 0.01 and 0.005 respectively, compared to the baseline x-vector model.", "description": "Overall, comparing the result from our proposed model MP with regularization, against the baseline x-vector model, we observe a relative 19% EER reduction. There is a 14.6% and 12.3% DCF score reduction compared with the baseline for Ptarget = 0.01 and 0.005 respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 3: DCF scores for SRE16 (pooled) test set' and proceed to solve the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the DCF score for the MP model with λ=0.001 and Ptarget=0.01, and how does it compare to the x-vector model?\n" }, { "role": "assistant", "content": "The table shows that the DCF score for the MP model with λ=0.001 and Ptarget=0.01 is 0.506. The DCF score for the x-vector model with the same parameters is 0.594. Comparing these values, the MP model has a lower DCF score. So, the answer is {\"answer\": \"0.506\"}." } ], "images": [ "scigen/train/1905.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: DCF scores for SRE16 (pooled) test set" ], "reasoning": "The table shows that the DCF score for the MP model with λ=0.001 and Ptarget=0.01 is 0.506. The DCF score for the x-vector model with the same parameters is 0.594. Comparing these values, the MP model has a lower DCF score.", "question": "What is the DCF score for the MP model with λ=0.001 and Ptarget=0.01, and how does it compare to the x-vector model?", "answer": "So, the answer is {\"answer\": \"0.506\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe MP model with λ=0.001 and Ptarget=0.005 has a higher DCF score than the B model with the same parameters.\n" }, { "role": "assistant", "content": "The table shows that the DCF score for the MP model with λ=0.001 and Ptarget=0.005 is 0.571, while the DCF score for the B model with the same parameters is 0.586. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1905.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: DCF scores for SRE16 (pooled) test set" ], "reasoning": "The table shows that the DCF score for the MP model with λ=0.001 and Ptarget=0.005 is 0.571, while the DCF score for the B model with the same parameters is 0.586. Therefore, the statement is refuted.", "statement": "The MP model with λ=0.001 and Ptarget=0.005 has a higher DCF score than the B model with the same parameters.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Results using global constraints.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Performing one iteration of LBP inference yields the highest F1 score of 44.9, which is 14.9 points higher than Reschke’s best system and significantly better than the unconstrained model.Performing one iteration of LBP inference produces our highest performance, an F1 of 44.9. This is 14.9 points higher than Reschke’s best system, and a statistically significant improvement over the unconstrained model (p ≤0.0313)." } ], "images": [ "scigen/train/1906.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results using global constraints." ], "reasoning": "Performing one iteration of LBP inference yields the highest F1 score of 44.9, which is 14.9 points higher than Reschke’s best system and significantly better than the unconstrained model.", "description": "Performing one iteration of LBP inference produces our highest performance, an F1 of 44.9. This is 14.9 points higher than Reschke’s best system, and a statistically significant improvement over the unconstrained model (p ≤0.0313).", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nTable title: Table 2: Results using global constraints.\n\nQuestion: What is the F1 score for performing one iteration of LBP inference?\n" }, { "role": "assistant", "content": "The table shows that performing one iteration of LBP inference (BP=1) results in an F1 score of 44.9. So, the answer is {\"answer\": \"44.9\"}" } ], "images": [ "scigen/train/1906.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results using global constraints." ], "reasoning": "The table shows that performing one iteration of LBP inference (BP=1) results in an F1 score of 44.9.", "question": "What is the F1 score for performing one iteration of LBP inference?", "answer": "So, the answer is {\"answer\": \"44.9\"}" }, { "messages": [ { "role": "user", "content": "Performing two iterations of LBP inference results in a higher F1 score than performing one iteration. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nPerforming two iterations of LBP inference results in a higher F1 score than performing one iteration.\n" }, { "role": "assistant", "content": "The table shows that performing one iteration of LBP inference (BP=1) results in an F1 score of 44.9, while performing two iterations (BP=2) results in an F1 score of 42.8. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1906.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results using global constraints." ], "reasoning": "The table shows that performing one iteration of LBP inference (BP=1) results in an F1 score of 44.9, while performing two iterations (BP=2) results in an F1 score of 42.8. Therefore, the statement is refuted.", "statement": "Performing two iterations of LBP inference results in a higher F1 score than performing one iteration.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Event extraction results across different systems on the Stanford Plane Crash Dataset.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The RAC architecture with sum-based aggregation outperforms the best existing systems by at least 9.8 F1, due to end-to-end training with aggregation and a cluster-level loss function.In comparison to previous work, our any configuration of our RAC architecture with sum-based aggregation outperforms the best existing systems by a minimum of 9.8 F1. In comparison to the various Mention-CNN systems, it is clear that this improvement is not a result of different features or the use of pre-trained word embeddings, or even the representational power of the CNN-based embeddings. Rather, it is attributable to training end-to-end with aggregation and a cluster-level loss function." } ], "images": [ "scigen/train/1907.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Event extraction results across different systems on the Stanford Plane Crash Dataset." ], "reasoning": "The RAC architecture with sum-based aggregation outperforms the best existing systems by at least 9.8 F1, due to end-to-end training with aggregation and a cluster-level loss function.", "description": "In comparison to previous work, our any configuration of our RAC architecture with sum-based aggregation outperforms the best existing systems by a minimum of 9.8 F1. In comparison to the various Mention-CNN systems, it is clear that this improvement is not a result of different features or the use of pre-trained word embeddings, or even the representational power of the CNN-based embeddings. Rather, it is attributable to training end-to-end with aggregation and a cluster-level loss function.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: Table 1: Event extraction results across different systems on the Stanford Plane Crash Dataset.\nQuestion: What is the F1 score of the RAC-CNN-BP-1 system with Topic aggregation?\n" }, { "role": "assistant", "content": "To find the F1 score of the RAC-CNN-BP-1 system with Topic aggregation, locate the row labeled 'RAC-CNN-BP-1' and the column labeled 'Score F1'. So, the answer is {\"answer\": \"44.9\"}." } ], "images": [ "scigen/train/1907.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Event extraction results across different systems on the Stanford Plane Crash Dataset." ], "reasoning": "To find the F1 score of the RAC-CNN-BP-1 system with Topic aggregation, locate the row labeled 'RAC-CNN-BP-1' and the column labeled 'Score F1'.", "question": "What is the F1 score of the RAC-CNN-BP-1 system with Topic aggregation?", "answer": "So, the answer is {\"answer\": \"44.9\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Mention-CNN with None aggregation has the highest Nulls F1 score. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Mention-CNN with None aggregation has the highest Nulls F1 score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Nulls F1 scores across all systems. The highest Nulls F1 score is 64.6, which belongs to Mention-CNN with None aggregation. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1907.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Event extraction results across different systems on the Stanford Plane Crash Dataset." ], "reasoning": "To verify this statement, compare the Nulls F1 scores across all systems. The highest Nulls F1 score is 64.6, which belongs to Mention-CNN with None aggregation.", "statement": "The Mention-CNN with None aggregation has the highest Nulls F1 score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 1: Quantitative results for model architectures and EHR contexts used in experiments.'.\n" }, { "role": "assistant", "content": "The table shows various models with different contexts and their performance metrics such as PPL, ACC, R1, R2, B-R1, Sex, and Age.[* * 2101-7-12 **] 5:44 PM CT HEAD W/O CONTRAST Clip # Reason: eval for ICH _____________________________________________________________________________ _ [* *Hospital 2 **] MEDICAL CONDITION: History: 79M with fall REASON FOR THIS EXAMINATION: eval for ICH No contraindications for IV contrast ______________________________________________________________________________ WET READ: [* * First Name9 (NamePattern2) 1333 * *] [* *Doctor First Name 141 **] [* *2101-7-12 **] 6:02 PM no acute intracranial process WET READ VERSION #1 ____________________________________________________________________________ __ FINAL REPORT HISTORY: 79-year-old male with fall. COMPARISON: None available. TECHNIQUE: Non-contrast head CT was obtained. FINDINGS: There is no evidence of acute intracranial hemorrhage, mass effect, shift of normally midline structures, hydrocephalus, or acute major vascular territorial infarction. The [ **Doctor Last Name 107**]-white matter differentiation is preserved. The visualized paranasal sinuses and mastoid air cells are well aerated. IMPRESSION: No acute intracranial process." } ], "images": [ "scigen/train/1908.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Quantitative results for model architectures and EHR contexts used in experiments." ], "reasoning": "The table shows various models with different contexts and their performance metrics such as PPL, ACC, R1, R2, B-R1, Sex, and Age.", "description": "[* * 2101-7-12 **] 5:44 PM CT HEAD W/O CONTRAST Clip # Reason: eval for ICH _____________________________________________________________________________ _ [* *Hospital 2 **] MEDICAL CONDITION: History: 79M with fall REASON FOR THIS EXAMINATION: eval for ICH No contraindications for IV contrast ______________________________________________________________________________ WET READ: [* * First Name9 (NamePattern2) 1333 * *] [* *Doctor First Name 141 **] [* *2101-7-12 **] 6:02 PM no acute intracranial process WET READ VERSION #1 ____________________________________________________________________________ __ FINAL REPORT HISTORY: 79-year-old male with fall. COMPARISON: None available. TECHNIQUE: Non-contrast head CT was obtained. FINDINGS: There is no evidence of acute intracranial hemorrhage, mass effect, shift of normally midline structures, hydrocephalus, or acute major vascular territorial infarction. The [ **Doctor Last Name 107**]-white matter differentiation is preserved. The visualized paranasal sinuses and mastoid air cells are well aerated. IMPRESSION: No acute intracranial process.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Quantitative results for model architectures and EHR contexts used in experiments.', answer the following question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest ACC value?\n" }, { "role": "assistant", "content": "To find the model with the highest ACC value, compare the ACC values of all models in the table. The highest ACC value is 63.4, which corresponds to the T-DMCA model with the THDMLNp context. So, the answer is {\"answer\": \"T-DMCA with THDMLNp context\"}." } ], "images": [ "scigen/train/1908.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Quantitative results for model architectures and EHR contexts used in experiments." ], "reasoning": "To find the model with the highest ACC value, compare the ACC values of all models in the table. The highest ACC value is 63.4, which corresponds to the T-DMCA model with the THDMLNp context.", "question": "Which model has the highest ACC value?", "answer": "So, the answer is {\"answer\": \"T-DMCA with THDMLNp context\"}." }, { "messages": [ { "role": "user", "content": "The T-ED model with the THDMLNp context has the highest R2 value. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe T-ED model with the THDMLNp context has the highest R2 value.\n" }, { "role": "assistant", "content": "To verify this statement, compare the R2 values of all models in the table. The highest R2 value is 28.5, which corresponds to the T-DMCA model with the THDMLNp context, not the T-ED model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1908.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Quantitative results for model architectures and EHR contexts used in experiments." ], "reasoning": "To verify this statement, compare the R2 values of all models in the table. The highest R2 value is 28.5, which corresponds to the T-DMCA model with the THDMLNp context, not the T-ED model.", "statement": "The T-ED model with the THDMLNp context has the highest R2 value.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: Comparison of DARE vs the previous SOTA and two baselines suited for imbalanced datasets. Only statistically significant results to the second best model are marked in bold. Statistical significance is determined with a McNemar p-test at 0.05 significance level.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "DARE consistently outperforms the SOTA and other baselines in terms of F1 score across the CDR, ChemProt, and DDI2013 datasets.Taking into account the previous observations, we proceed to compare DARE against the SOTA and the two previously described baselines. For the multi-class datasets we report the micro-F score in order to make our results comparable with previous works. Also, in the Supplementary Material we report the per class results for DARE against the SOTA and the class weighting baseline, for the two multi-class datasets in order to ease comparison with past or future works." } ], "images": [ "scigen/train/1909.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of DARE vs the previous SOTA and two baselines suited for imbalanced datasets. Only statistically significant results to the second best model are marked in bold. Statistical significance is determined with a McNemar p-test at 0.05 significance level." ], "reasoning": "DARE consistently outperforms the SOTA and other baselines in terms of F1 score across the CDR, ChemProt, and DDI2013 datasets.", "description": "Taking into account the previous observations, we proceed to compare DARE against the SOTA and the two previously described baselines. For the multi-class datasets we report the micro-F score in order to make our results comparable with previous works. Also, in the Supplementary Material we report the per class results for DARE against the SOTA and the class weighting baseline, for the two multi-class datasets in order to ease comparison with past or future works.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 4: Comparison of DARE vs the previous SOTA and two baselines suited for imbalanced datasets. Only statistically significant results to the second best model are marked in bold. Statistical significance is determined with a McNemar p-test at 0.05 significance level.' and methodically tackle the question that follows. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich configuration achieved the highest F1 score for the DDI2013 dataset?\n" }, { "role": "assistant", "content": "To find the highest F1 score for the DDI2013 dataset, we need to compare the F1 scores of all configurations listed under the DDI2013 dataset. The highest F1 score is 0.78, achieved by the BERT+DARE configuration. So, the answer is {\"answer\": \"BERT+DARE\"}." } ], "images": [ "scigen/train/1909.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of DARE vs the previous SOTA and two baselines suited for imbalanced datasets. Only statistically significant results to the second best model are marked in bold. Statistical significance is determined with a McNemar p-test at 0.05 significance level." ], "reasoning": "To find the highest F1 score for the DDI2013 dataset, we need to compare the F1 scores of all configurations listed under the DDI2013 dataset. The highest F1 score is 0.78, achieved by the BERT+DARE configuration.", "question": "Which configuration achieved the highest F1 score for the DDI2013 dataset?", "answer": "So, the answer is {\"answer\": \"BERT+DARE\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nBERT+DARE achieved the highest F1 score for the ChemProt dataset.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the F1 scores of all configurations listed under the ChemProt dataset. The highest F1 score for ChemProt is 0.73, achieved by the BERT+DARE configuration. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1909.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of DARE vs the previous SOTA and two baselines suited for imbalanced datasets. Only statistically significant results to the second best model are marked in bold. Statistical significance is determined with a McNemar p-test at 0.05 significance level." ], "reasoning": "To verify this statement, we need to compare the F1 scores of all configurations listed under the ChemProt dataset. The highest F1 score for ChemProt is 0.73, achieved by the BERT+DARE configuration.", "statement": "BERT+DARE achieved the highest F1 score for the ChemProt dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Figure 1: DARE vs balanced bagging(BB) for different sizes of positive samples on CDR dataset. Both methods employ ensembles of BERT RE classifiers.'.\n" }, { "role": "assistant", "content": "DARE consistently outperforms the balanced bagging baseline, especially when fewer positive samples are available, with the difference diminishing as more samples are added.In all cases, our approach has a steady, significant advantage over the balanced bagging baseline, their difference reaching up to 11 F1 score points when only few positives (≤250) are available. As we add more samples, the differences start to smooth out as expected. These results clearly illustrate that DARE can boost the predictive power of a classifier when dealing with few positive samples, by cheaply generating training data of arbitrary sizes." } ], "images": [ "scigen/train/1910.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 1: DARE vs balanced bagging(BB) for different sizes of positive samples on CDR dataset. Both methods employ ensembles of BERT RE classifiers." ], "reasoning": "DARE consistently outperforms the balanced bagging baseline, especially when fewer positive samples are available, with the difference diminishing as more samples are added.", "description": "In all cases, our approach has a steady, significant advantage over the balanced bagging baseline, their difference reaching up to 11 F1 score points when only few positives (≤250) are available. As we add more samples, the differences start to smooth out as expected. These results clearly illustrate that DARE can boost the predictive power of a classifier when dealing with few positive samples, by cheaply generating training data of arbitrary sizes.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Figure 1: DARE vs balanced bagging(BB) for different sizes of positive samples on CDR dataset. Both methods employ ensembles of BERT RE classifiers.'. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the F1 score difference between DARE and BB when 250 samples are available?\n" }, { "role": "assistant", "content": "The table shows that for 250 samples, the F1 score for BB is 0.42 and for DARE is 0.53. The difference is 0.53 - 0.42 = 0.11. So, the answer is {\"answer\": \"0.11\"}." } ], "images": [ "scigen/train/1910.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 1: DARE vs balanced bagging(BB) for different sizes of positive samples on CDR dataset. Both methods employ ensembles of BERT RE classifiers." ], "reasoning": "The table shows that for 250 samples, the F1 score for BB is 0.42 and for DARE is 0.53. The difference is 0.53 - 0.42 = 0.11.", "question": "What is the F1 score difference between DARE and BB when 250 samples are available?", "answer": "So, the answer is {\"answer\": \"0.11\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nDARE has a higher F1 score than BB when 1000 samples are available.\n" }, { "role": "assistant", "content": "The table shows that for 1000 samples, the F1 score for BB is 0.63 and for DARE is 0.68, indicating that DARE has a higher F1 score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1910.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 1: DARE vs balanced bagging(BB) for different sizes of positive samples on CDR dataset. Both methods employ ensembles of BERT RE classifiers." ], "reasoning": "The table shows that for 1000 samples, the F1 score for BB is 0.63 and for DARE is 0.68, indicating that DARE has a higher F1 score.", "statement": "DARE has a higher F1 score than BB when 1000 samples are available.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Experiment on silver standard annotation of Indonesian NER evaluated on Gold test set [1] in large distantly supervised NER scenario. Bold F1 scores are best result per scenarios (Baseline, Supervised Cross-lingual Transfer, Cross-lingual using ELMo from EN, Mono-lingual ELMo and Unsupervised-Supervised Cross-lingual Transfer). * is the best model on a dataset (DEE, MDEE, or +Gazz) on all model scenarios'.\n" }, { "role": "assistant", "content": "Alfina et al. observed many false negatives in orthographically similar location aliases in Indonesian Wikipedia and noted marginal improvements using monolingual Indonesian language models compared to English language models due to the larger corpus size available for English.Alfina et al We also confirmed that, there are many cases of false negative in orthographically similar LOCATION alias such as ”Pacific” to ”Pasifik” in Indonesian Wikipedia. using pre-trained weights from state-of-the-arts NER model that uses Bidirectional language model. Our motivation to propose this method is because we observed that there are only marginal improvement using monolingual Indonesia LM of 82M Tokens from Wikipedia compared to using English LM trained on 1B Tokens on applying ELMo to Distantly Supervised NER dataset. This might be attributed due to large difference of publicly available unlabeled corpus size, such as 82M in benchmark or 2.9B English Wikipedia available to train." } ], "images": [ "scigen/train/1911.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Experiment on silver standard annotation of Indonesian NER evaluated on Gold test set [1] in large distantly supervised NER scenario. Bold F1 scores are best result per scenarios (Baseline, Supervised Cross-lingual Transfer, Cross-lingual using ELMo from EN, Mono-lingual ELMo and Unsupervised-Supervised Cross-lingual Transfer). * is the best model on a dataset (DEE, MDEE, or +Gazz) on all model scenarios" ], "reasoning": "Alfina et al. observed many false negatives in orthographically similar location aliases in Indonesian Wikipedia and noted marginal improvements using monolingual Indonesian language models compared to English language models due to the larger corpus size available for English.", "description": "Alfina et al We also confirmed that, there are many cases of false negative in orthographically similar LOCATION alias such as ”Pacific” to ”Pasifik” in Indonesian Wikipedia. using pre-trained weights from state-of-the-arts NER model that uses Bidirectional language model. Our motivation to propose this method is because we observed that there are only marginal improvement using monolingual Indonesia LM of 82M Tokens from Wikipedia compared to using English LM trained on 1B Tokens on applying ELMo to Distantly Supervised NER dataset. This might be attributed due to large difference of publicly available unlabeled corpus size, such as 82M in benchmark or 2.9B English Wikipedia available to train.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Experiment on silver standard annotation of Indonesian NER evaluated on Gold test set [1] in large distantly supervised NER scenario. Bold F1 scores are best result per scenarios (Baseline, Supervised Cross-lingual Transfer, Cross-lingual using ELMo from EN, Mono-lingual ELMo and Unsupervised-Supervised Cross-lingual Transfer). * is the best model on a dataset (DEE, MDEE, or +Gazz) on all model scenarios\nQuestion: Which model achieved the highest score in the +Gazz column?\nGive a detailed response to the above question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest score in the +Gazz column, we need to look at all the values in that column and identify the maximum value. So, the answer is {\"answer\": \"64.35\"}." } ], "images": [ "scigen/train/1911.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Experiment on silver standard annotation of Indonesian NER evaluated on Gold test set [1] in large distantly supervised NER scenario. Bold F1 scores are best result per scenarios (Baseline, Supervised Cross-lingual Transfer, Cross-lingual using ELMo from EN, Mono-lingual ELMo and Unsupervised-Supervised Cross-lingual Transfer). * is the best model on a dataset (DEE, MDEE, or +Gazz) on all model scenarios" ], "reasoning": "To find the highest score in the +Gazz column, we need to look at all the values in that column and identify the maximum value.", "question": "Which model achieved the highest score in the +Gazz column?", "answer": "So, the answer is {\"answer\": \"64.35\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe ELMo EN-1B Tokens model achieved the highest score in the DEE column.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the DEE scores of all models and check if the ELMo EN-1B Tokens model has the highest score. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1911.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Experiment on silver standard annotation of Indonesian NER evaluated on Gold test set [1] in large distantly supervised NER scenario. Bold F1 scores are best result per scenarios (Baseline, Supervised Cross-lingual Transfer, Cross-lingual using ELMo from EN, Mono-lingual ELMo and Unsupervised-Supervised Cross-lingual Transfer). * is the best model on a dataset (DEE, MDEE, or +Gazz) on all model scenarios" ], "reasoning": "To verify this statement, we need to compare the DEE scores of all models and check if the ELMo EN-1B Tokens model has the highest score.", "statement": "The ELMo EN-1B Tokens model achieved the highest score in the DEE column.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: Experiment on silver standard annotation of Indonesian NER evaluated on Gold test set [1] in large distantly supervised NER scenario. Bold F1 scores are best result per scenarios (Baseline, Supervised Cross-lingual Transfer, Cross-lingual using ELMo from EN, Mono-lingual ELMo and Unsupervised-Supervised Cross-lingual Transfer). * is the best model on a dataset (DEE, MDEE, or +Gazz) on all model scenarios', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Alfina et al. observed that using a monolingual Indonesian language model with 82M tokens from Wikipedia showed only marginal improvement compared to using an English language model trained on 1B tokens, likely due to the large difference in corpus size.Alfina et al We also confirmed that, there are many cases of false negative in orthographically similar LOCATION alias such as ”Pacific” to ”Pasifik” in Indonesian Wikipedia. using pre-trained weights from state-of-the-arts NER model that uses Bidirectional language model. Our motivation to propose this method is because we observed that there are only marginal improvement using monolingual Indonesia LM of 82M Tokens from Wikipedia compared to using English LM trained on 1B Tokens on applying ELMo to Distantly Supervised NER dataset. This might be attributed due to large difference of publicly available unlabeled corpus size, such as 82M in benchmark or 2.9B English Wikipedia available to train." } ], "images": [ "scigen/train/1912.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Experiment on silver standard annotation of Indonesian NER evaluated on Gold test set [1] in large distantly supervised NER scenario. Bold F1 scores are best result per scenarios (Baseline, Supervised Cross-lingual Transfer, Cross-lingual using ELMo from EN, Mono-lingual ELMo and Unsupervised-Supervised Cross-lingual Transfer). * is the best model on a dataset (DEE, MDEE, or +Gazz) on all model scenarios" ], "reasoning": "Alfina et al. observed that using a monolingual Indonesian language model with 82M tokens from Wikipedia showed only marginal improvement compared to using an English language model trained on 1B tokens, likely due to the large difference in corpus size.", "description": "Alfina et al We also confirmed that, there are many cases of false negative in orthographically similar LOCATION alias such as ”Pacific” to ”Pasifik” in Indonesian Wikipedia. using pre-trained weights from state-of-the-arts NER model that uses Bidirectional language model. Our motivation to propose this method is because we observed that there are only marginal improvement using monolingual Indonesia LM of 82M Tokens from Wikipedia compared to using English LM trained on 1B Tokens on applying ELMo to Distantly Supervised NER dataset. This might be attributed due to large difference of publicly available unlabeled corpus size, such as 82M in benchmark or 2.9B English Wikipedia available to train.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Experiment on silver standard annotation of Indonesian NER evaluated on Gold test set [1] in large distantly supervised NER scenario. Bold F1 scores are best result per scenarios (Baseline, Supervised Cross-lingual Transfer, Cross-lingual using ELMo from EN, Mono-lingual ELMo and Unsupervised-Supervised Cross-lingual Transfer). * is the best model on a dataset (DEE, MDEE, or +Gazz) on all model scenarios\nQuestion: Which model achieved the highest F1 score and what was the score?\nSolve the above question based on the table information. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, the model G+J achieved the highest F1 score of 83.68. So, the answer is {\"answer\": \"G+J\"}." } ], "images": [ "scigen/train/1912.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Experiment on silver standard annotation of Indonesian NER evaluated on Gold test set [1] in large distantly supervised NER scenario. Bold F1 scores are best result per scenarios (Baseline, Supervised Cross-lingual Transfer, Cross-lingual using ELMo from EN, Mono-lingual ELMo and Unsupervised-Supervised Cross-lingual Transfer). * is the best model on a dataset (DEE, MDEE, or +Gazz) on all model scenarios" ], "reasoning": "By examining the table, the model G+J achieved the highest F1 score of 83.68.", "question": "Which model achieved the highest F1 score and what was the score?", "answer": "So, the answer is {\"answer\": \"G+J\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model G+J achieved an F1 score higher than 83.\n" }, { "role": "assistant", "content": "The table shows that the model G+J achieved an F1 score of 83.68, which is higher than 83. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1912.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Experiment on silver standard annotation of Indonesian NER evaluated on Gold test set [1] in large distantly supervised NER scenario. Bold F1 scores are best result per scenarios (Baseline, Supervised Cross-lingual Transfer, Cross-lingual using ELMo from EN, Mono-lingual ELMo and Unsupervised-Supervised Cross-lingual Transfer). * is the best model on a dataset (DEE, MDEE, or +Gazz) on all model scenarios" ], "reasoning": "The table shows that the model G+J achieved an F1 score of 83.68, which is higher than 83.", "statement": "The model G+J achieved an F1 score higher than 83.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: CER obtained for a monolingual model with different pre-training strategies. The model was then fine-tuned on 150 hours of the target language (DE), except * where no fine-tuning was performed.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The gated language-universal model, even without fine-tuning, outperformed other models, including those trained with more data.While the primary goal of this work was to create a language-universal model, we found that this model is also a good initial model for creating a language-specific monolingual model when the training data is limited. Notably, initializing with the gated language-universal model outperformed all of the other approaches, including bootstrapping from an EN model trained with significantly more data. The second best performance was obtained by the gated language-universal model directly, even without further fine-tuning on the DE training data." } ], "images": [ "scigen/train/1913.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: CER obtained for a monolingual model with different pre-training strategies. The model was then fine-tuned on 150 hours of the target language (DE), except * where no fine-tuning was performed." ], "reasoning": "The gated language-universal model, even without fine-tuning, outperformed other models, including those trained with more data.", "description": "While the primary goal of this work was to create a language-universal model, we found that this model is also a good initial model for creating a language-specific monolingual model when the training data is limited. Notably, initializing with the gated language-universal model outperformed all of the other approaches, including bootstrapping from an EN model trained with significantly more data. The second best performance was obtained by the gated language-universal model directly, even without further fine-tuning on the DE training data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: CER obtained for a monolingual model with different pre-training strategies. The model was then fine-tuned on 150 hours of the target language (DE), except * where no fine-tuning was performed.' and methodically tackle the question that follows. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model had the lowest % CER DE?\n" }, { "role": "assistant", "content": "The table shows that the 'EN + ES + DE + gate' model had the lowest % CER DE with values of 19.4 and 20.6. So, the answer is {\"answer\": \"EN + ES + DE + gate\"}." } ], "images": [ "scigen/train/1913.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: CER obtained for a monolingual model with different pre-training strategies. The model was then fine-tuned on 150 hours of the target language (DE), except * where no fine-tuning was performed." ], "reasoning": "The table shows that the 'EN + ES + DE + gate' model had the lowest % CER DE with values of 19.4 and 20.6.", "question": "Which model had the lowest % CER DE?", "answer": "So, the answer is {\"answer\": \"EN + ES + DE + gate\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe EN model with 1000 pre-training hours had a % CER DE of 21.4.\n" }, { "role": "assistant", "content": "The table indicates that the EN model with 1000 pre-training hours had a % CER DE of 21.4. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1913.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: CER obtained for a monolingual model with different pre-training strategies. The model was then fine-tuned on 150 hours of the target language (DE), except * where no fine-tuning was performed." ], "reasoning": "The table indicates that the EN model with 1000 pre-training hours had a % CER DE of 21.4.", "statement": "The EN model with 1000 pre-training hours had a % CER DE of 21.4.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: Initial CER results for conventional single-task (stl), multi-task (mtl), and universal character set (univ) networks using monolingual and multilingual training data.'.\n" }, { "role": "assistant", "content": "Models trained with multi-task learning and universal label sets performed comparably, but increasing multilingual data from two to three languages degraded performance.We first evaluated both multi-task and language-universal architectures on German and Spanish data, along with conventional monolingual training. In addition, we observed that models trained with multi-task learning with language-specific output labels and models trained with a universal label set performed comparably, demonstrating the potential of a universal label set. However, with a conventional BLSTM topology, the benefit gained from the multilingual data was far less than simply doubling the amount of training data from the target language to 300 hours. More concerning, the performance actually degraded when we increased the multilingual data from two to three language. These initial results indicate that simply training conventional models with multilingual data is not very helpful. However, we note that these results may differ in extremely low resource scenarios where the data available per language could be an order of magnitude smaller." } ], "images": [ "scigen/train/1914.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Initial CER results for conventional single-task (stl), multi-task (mtl), and universal character set (univ) networks using monolingual and multilingual training data." ], "reasoning": "Models trained with multi-task learning and universal label sets performed comparably, but increasing multilingual data from two to three languages degraded performance.", "description": "We first evaluated both multi-task and language-universal architectures on German and Spanish data, along with conventional monolingual training. In addition, we observed that models trained with multi-task learning with language-specific output labels and models trained with a universal label set performed comparably, demonstrating the potential of a universal label set. However, with a conventional BLSTM topology, the benefit gained from the multilingual data was far less than simply doubling the amount of training data from the target language to 300 hours. More concerning, the performance actually degraded when we increased the multilingual data from two to three language. These initial results indicate that simply training conventional models with multilingual data is not very helpful. However, we note that these results may differ in extremely low resource scenarios where the data available per language could be an order of magnitude smaller.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 1: Initial CER results for conventional single-task (stl), multi-task (mtl), and universal character set (univ) networks using monolingual and multilingual training data.', unfold the question presented below, detailing every step of your calculation. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the CER for the DE language when trained with 300 hours of data using the stl model?\n" }, { "role": "assistant", "content": "To find the CER for the DE language trained with 300 hours of data using the stl model, locate the row with 'DE' in the Training Languages column, '300' in the Total Hrs column, and 'stl' in the Model Arch column. The CER is 15.8%. So, the answer is {\"answer\": \"15.8%\"}" } ], "images": [ "scigen/train/1914.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Initial CER results for conventional single-task (stl), multi-task (mtl), and universal character set (univ) networks using monolingual and multilingual training data." ], "reasoning": "To find the CER for the DE language trained with 300 hours of data using the stl model, locate the row with 'DE' in the Training Languages column, '300' in the Total Hrs column, and 'stl' in the Model Arch column. The CER is 15.8%.", "question": "What is the CER for the DE language when trained with 300 hours of data using the stl model?", "answer": "So, the answer is {\"answer\": \"15.8%\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTraining with 450 hours of multilingual data resulted in the lowest CER for the ES language.\n" }, { "role": "assistant", "content": "The table shows that the lowest CER for the ES language is 11.7%, achieved with 300 hours of training using the stl model, not with 450 hours of multilingual data. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1914.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Initial CER results for conventional single-task (stl), multi-task (mtl), and universal character set (univ) networks using monolingual and multilingual training data." ], "reasoning": "The table shows that the lowest CER for the ES language is 11.7%, achieved with 300 hours of training using the stl model, not with 450 hours of multilingual data.", "statement": "Training with 450 hours of multilingual data resulted in the lowest CER for the ES language.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Performance of the language-universal modeling approach with and without the language-specific gating. Baseline monolingual performance is also shown. Each language has 150 h of training data.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The proposed language-universal model with language-specific gating showed significant improvements in CER and WER for EN, DE, and ES compared to a monolingual model, with additional gains when the number of training languages increased from two to three.We next evaluated the proposed language-universal model with language-specific gating. Our model showed 10.7%, 11.4%, and 14.1% relative improvement in CER compared to a monolingual for EN, DE, and ES respectively. The relative improvements in WER, which was calculated without using any language model, were 7.0%, 8.6%, and 11.1%, for EN, DE, and ES respectively. Furthermore, when language-specific gating is used, additional improvement is obtained when the number of training languages increased from two to three." } ], "images": [ "scigen/train/1915.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of the language-universal modeling approach with and without the language-specific gating. Baseline monolingual performance is also shown. Each language has 150 h of training data." ], "reasoning": "The proposed language-universal model with language-specific gating showed significant improvements in CER and WER for EN, DE, and ES compared to a monolingual model, with additional gains when the number of training languages increased from two to three.", "description": "We next evaluated the proposed language-universal model with language-specific gating. Our model showed 10.7%, 11.4%, and 14.1% relative improvement in CER compared to a monolingual for EN, DE, and ES respectively. The relative improvements in WER, which was calculated without using any language model, were 7.0%, 8.6%, and 11.1%, for EN, DE, and ES respectively. Furthermore, when language-specific gating is used, additional improvement is obtained when the number of training languages increased from two to three.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: Performance of the language-universal modeling approach with and without the language-specific gating. Baseline monolingual performance is also shown. Each language has 150 h of training data.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. \n\nQuestion: What is the relative improvement in WER for ES when using the language-universal model with language-specific gating compared to the monolingual model?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The WER for ES in the monolingual model is 39.9%. With language-specific gating and training on EN + DE + ES, the WER for ES is 35.5%. The relative improvement is calculated as (39.9 - 35.5) / 39.9 * 100%. So, the answer is {\"answer\": \"11.1%\"}." } ], "images": [ "scigen/train/1915.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of the language-universal modeling approach with and without the language-specific gating. Baseline monolingual performance is also shown. Each language has 150 h of training data." ], "reasoning": "The WER for ES in the monolingual model is 39.9%. With language-specific gating and training on EN + DE + ES, the WER for ES is 35.5%. The relative improvement is calculated as (39.9 - 35.5) / 39.9 * 100%.", "question": "What is the relative improvement in WER for ES when using the language-universal model with language-specific gating compared to the monolingual model?", "answer": "So, the answer is {\"answer\": \"11.1%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The language-universal model with language-specific gating showed a 14.1% relative improvement in CER for ES compared to the monolingual model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe language-universal model with language-specific gating showed a 14.1% relative improvement in CER for ES compared to the monolingual model.\n" }, { "role": "assistant", "content": "The description states that the model showed a 14.1% relative improvement in CER for ES compared to the monolingual model. The table shows that the CER for ES in the monolingual model is 13.7% and with language-specific gating and training on EN + DE + ES, it is 11.7%. The relative improvement is (13.7 - 11.7) / 13.7 * 100%, which is approximately 14.1%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1915.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of the language-universal modeling approach with and without the language-specific gating. Baseline monolingual performance is also shown. Each language has 150 h of training data." ], "reasoning": "The description states that the model showed a 14.1% relative improvement in CER for ES compared to the monolingual model. The table shows that the CER for ES in the monolingual model is 13.7% and with language-specific gating and training on EN + DE + ES, it is 11.7%. The relative improvement is (13.7 - 11.7) / 13.7 * 100%, which is approximately 14.1%.", "statement": "The language-universal model with language-specific gating showed a 14.1% relative improvement in CER for ES compared to the monolingual model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: Performance comparison of different models on the Quora dataset', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The table lists models by decreasing test accuracy, with validation accuracy provided for some. Our baselines, highlighted in bold, show that Word2Vec is the most consistent embedding approach.Each model’s validation accuracy (if published) and test accuracy are listed in the columns. The models are sorted by decreasing test accuracy. Note that all models with ♠ were reported by Wang et al. in their work [WHF17]. Our baselines are denoted in bold in the last four rows of the table. Each baseline is labeled by the embedding approach used during training. Our experimental results surpass some models in terms of test accuracy and are also competitive with the state-of-the-art models. Among our models, we found Word2Vec to be the most consistent embedding approach." } ], "images": [ "scigen/train/1916.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance comparison of different models on the Quora dataset" ], "reasoning": "The table lists models by decreasing test accuracy, with validation accuracy provided for some. Our baselines, highlighted in bold, show that Word2Vec is the most consistent embedding approach.", "description": "Each model’s validation accuracy (if published) and test accuracy are listed in the columns. The models are sorted by decreasing test accuracy. Note that all models with ♠ were reported by Wang et al. in their work [WHF17]. Our baselines are denoted in bold in the last four rows of the table. Each baseline is labeled by the embedding approach used during training. Our experimental results surpass some models in terms of test accuracy and are also competitive with the state-of-the-art models. Among our models, we found Word2Vec to be the most consistent embedding approach.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 1: Performance comparison of different models on the Quora dataset', unfold the question presented below, detailing every step of your calculation. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich baseline model has the highest test accuracy?\n" }, { "role": "assistant", "content": "Among the baseline models listed in bold, W2V has the highest test accuracy of 87.22. So, the answer is {\"answer\": \"W2V\"}" } ], "images": [ "scigen/train/1916.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance comparison of different models on the Quora dataset" ], "reasoning": "Among the baseline models listed in bold, W2V has the highest test accuracy of 87.22.", "question": "Which baseline model has the highest test accuracy?", "answer": "So, the answer is {\"answer\": \"W2V\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The model with the highest test accuracy is DIIN. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe model with the highest test accuracy is DIIN.\n" }, { "role": "assistant", "content": "The table shows that the model with the highest test accuracy is RE2 with 89.2, not DIIN. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1916.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance comparison of different models on the Quora dataset" ], "reasoning": "The table shows that the model with the highest test accuracy is RE2 with 89.2, not DIIN.", "statement": "The model with the highest test accuracy is DIIN.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2. F1-measure for the models across all the topics in PHM2017 dataset.'.\n" }, { "role": "assistant", "content": "WESPAD outperforms all baselines in most topics, demonstrating its ability to generalize better, especially compared to ME+lex and LSTM+GRNN.The experiments show that our model WESPAD outperforms all the baselines in the majority of the topics. The substantial difference in terms of F1-measure between ME+lex and WESPAD models, shows that our model has successfully managed to learn the characteristics of the small set of the positive tweets, and to generalize better. We can also see that CNN, although with small amount of training data, is working surprisingly well. On the other hand, the complex LSTM+GRNN model is outperformed on all the topics by our WESPAD classifier." } ], "images": [ "scigen/train/1917.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2. F1-measure for the models across all the topics in PHM2017 dataset." ], "reasoning": "WESPAD outperforms all baselines in most topics, demonstrating its ability to generalize better, especially compared to ME+lex and LSTM+GRNN.", "description": "The experiments show that our model WESPAD outperforms all the baselines in the majority of the topics. The substantial difference in terms of F1-measure between ME+lex and WESPAD models, shows that our model has successfully managed to learn the characteristics of the small set of the positive tweets, and to generalize better. We can also see that CNN, although with small amount of training data, is working surprisingly well. On the other hand, the complex LSTM+GRNN model is outperformed on all the topics by our WESPAD classifier.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2. F1-measure for the models across all the topics in PHM2017 dataset.' and methodically tackle the question that follows. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest F1-measure for Parkinson's and what is the value?\n" }, { "role": "assistant", "content": "By examining the table, we see that WESPAD has the highest F1-measure for Parkinson's with a value of 0.672. So, the answer is {\"answer\": \"WESPAD with 0.672\"}." } ], "images": [ "scigen/train/1917.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2. F1-measure for the models across all the topics in PHM2017 dataset." ], "reasoning": "By examining the table, we see that WESPAD has the highest F1-measure for Parkinson's with a value of 0.672.", "question": "Which model has the highest F1-measure for Parkinson's and what is the value?", "answer": "So, the answer is {\"answer\": \"WESPAD with 0.672\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: WESPAD has the highest F1-measure for Depression., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nWESPAD has the highest F1-measure for Depression.\n" }, { "role": "assistant", "content": "The table shows that CNN has the highest F1-measure for Depression with a value of 0.768, not WESPAD. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1917.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2. F1-measure for the models across all the topics in PHM2017 dataset." ], "reasoning": "The table shows that CNN has the highest F1-measure for Depression with a value of 0.768, not WESPAD.", "statement": "WESPAD has the highest F1-measure for Depression.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3. Average F1-measure, precision, and recall in PHM2017 dataset.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "WESPAD achieves the highest recall, leading to its main improvement, while LSTM+GRNN has the lowest precision due to its complex structure.The results show that the main improvement of WESPAD comes from the higher recall, i.e., detecting additional true health mentions. On the other hand, LSTM+GRNN has the lowest precision, and this can be attributed to the complex structure of the network which expects to be fine-tuned during the training." } ], "images": [ "scigen/train/1918.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. Average F1-measure, precision, and recall in PHM2017 dataset." ], "reasoning": "WESPAD achieves the highest recall, leading to its main improvement, while LSTM+GRNN has the lowest precision due to its complex structure.", "description": "The results show that the main improvement of WESPAD comes from the higher recall, i.e., detecting additional true health mentions. On the other hand, LSTM+GRNN has the lowest precision, and this can be attributed to the complex structure of the network which expects to be fine-tuned during the training.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3. Average F1-measure, precision, and recall in PHM2017 dataset.', and sequentially solve the question, writing out each step of your reasoning process. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest F1 score and what is its recall?\n" }, { "role": "assistant", "content": "The table shows that WESPAD has the highest F1 score of 0.695. The recall for WESPAD is 0.628. So, the answer is {\"answer\": \"0.628\"}." } ], "images": [ "scigen/train/1918.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. Average F1-measure, precision, and recall in PHM2017 dataset." ], "reasoning": "The table shows that WESPAD has the highest F1 score of 0.695. The recall for WESPAD is 0.628.", "question": "Which model has the highest F1 score and what is its recall?", "answer": "So, the answer is {\"answer\": \"0.628\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nLSTM+GRNN has the highest precision among all models.\n" }, { "role": "assistant", "content": "The table shows that LSTM+GRNN has a precision of 0.638, which is not the highest. The highest precision is 0.834 by ME+lex. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1918.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. Average F1-measure, precision, and recall in PHM2017 dataset." ], "reasoning": "The table shows that LSTM+GRNN has a precision of 0.638, which is not the highest. The highest precision is 0.834 by ME+lex.", "statement": "LSTM+GRNN has the highest precision among all models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4. F1-measure, precision, and recall in FLU2013 dataset.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "WESPAD outperforms all baselines in both datasets, with statistically significant improvements over ME+lex using a paired t-test at p<0.05.The results show that WESPAD outperforms all the baselines, even though there are considerable differences between PHM2017 and FLU2013 datasets (in terms of the proportion of the positive tweets). It is also worth mentioning that, all the improvements of WESPAD model over the lexical baseline ME+lex in both datasets are statistically significant using paired t-test at p<0.05." } ], "images": [ "scigen/train/1919.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4. F1-measure, precision, and recall in FLU2013 dataset." ], "reasoning": "WESPAD outperforms all baselines in both datasets, with statistically significant improvements over ME+lex using a paired t-test at p<0.05.", "description": "The results show that WESPAD outperforms all the baselines, even though there are considerable differences between PHM2017 and FLU2013 datasets (in terms of the proportion of the positive tweets). It is also worth mentioning that, all the improvements of WESPAD model over the lexical baseline ME+lex in both datasets are statistically significant using paired t-test at p<0.05.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4. F1-measure, precision, and recall in FLU2013 dataset.', answer the following question in a stepwise manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest recall and how does it compare to the ME+lex model in terms of F1 score?\n" }, { "role": "assistant", "content": "The model with the highest recall is WESPAD with a recall of 0.858. Comparing it to the ME+lex model, WESPAD has a higher F1 score of 0.851 compared to ME+lex's F1 score of 0.838. So, the answer is {\"answer\": \"WESPAD\"}" } ], "images": [ "scigen/train/1919.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4. F1-measure, precision, and recall in FLU2013 dataset." ], "reasoning": "The model with the highest recall is WESPAD with a recall of 0.858. Comparing it to the ME+lex model, WESPAD has a higher F1 score of 0.851 compared to ME+lex's F1 score of 0.838.", "question": "Which model has the highest recall and how does it compare to the ME+lex model in terms of F1 score?", "answer": "So, the answer is {\"answer\": \"WESPAD\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The CNN model has the highest precision among all models. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe CNN model has the highest precision among all models.\n" }, { "role": "assistant", "content": "The table shows that the CNN model has a precision of 0.864, which is the highest precision value among all the models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1919.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4. F1-measure, precision, and recall in FLU2013 dataset." ], "reasoning": "The table shows that the CNN model has a precision of 0.864, which is the highest precision value among all the models listed.", "statement": "The CNN model has the highest precision among all models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5. Feature ablation of WESPAD on PHM2017 dataset.'.\n" }, { "role": "assistant", "content": "The we_distortion and we_partitioning feature sets have the highest impact on F1-measure, with we_partitioning performing better in terms of precision.Feature ablation: The experiment shows that we_distortion and we_partitioning feature sets have the highest impact, in terms of F1-measure. We also observe that, in terms of precision, we_partitioning performs better than we_distortion. One possible explanation is that due to the small size of the positive sets, IG-weighting may fail to accurately assign the weights to the word vectors, and thus, the tweet centroid is drifted." } ], "images": [ "scigen/train/1920.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5. Feature ablation of WESPAD on PHM2017 dataset." ], "reasoning": "The we_distortion and we_partitioning feature sets have the highest impact on F1-measure, with we_partitioning performing better in terms of precision.", "description": "Feature ablation: The experiment shows that we_distortion and we_partitioning feature sets have the highest impact, in terms of F1-measure. We also observe that, in terms of precision, we_partitioning performs better than we_distortion. One possible explanation is that due to the small size of the positive sets, IG-weighting may fail to accurately assign the weights to the word vectors, and thus, the tweet centroid is drifted.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich feature set has the highest precision and what is its value?\nSolve the above question based on the table titled 'Table 5. Feature ablation of WESPAD on PHM2017 dataset.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the Precision column, we see that the highest precision value is 0.804, which corresponds to the we_distortion feature set. So, the answer is {\"answer\": \"we_distortion\"}" } ], "images": [ "scigen/train/1920.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5. Feature ablation of WESPAD on PHM2017 dataset." ], "reasoning": "By examining the Precision column, we see that the highest precision value is 0.804, which corresponds to the we_distortion feature set.", "question": "Which feature set has the highest precision and what is its value?", "answer": "So, the answer is {\"answer\": \"we_distortion\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The we_distortion feature set has a higher F1-measure than the we_partitioning feature set. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe we_distortion feature set has a higher F1-measure than the we_partitioning feature set.\n" }, { "role": "assistant", "content": "The table shows that the F1-measure for we_distortion is 0.643, while for we_partitioning it is 0.652. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1920.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5. Feature ablation of WESPAD on PHM2017 dataset." ], "reasoning": "The table shows that the F1-measure for we_distortion is 0.643, while for we_partitioning it is 0.652. Therefore, the statement is refuted.", "statement": "The we_distortion feature set has a higher F1-measure than the we_partitioning feature set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: Enhancement performance obtained for each setup and loss function. SNR: signal-to-noise-ratio in decibel (dB), PESQ: perceptual evaluation of speech quality, A: Audio-only enhancer, AV: audiovisual enhancer, FC: fully connected, LSTM: Long short-term memory', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The results indicate that audio-only enhancers achieve significant gains, and LSTM-based audio encoders outperform FC-based encoders due to better temporal modeling.The results show that considerable gains are achieved using an audio-only enhancer (columns labeled A), which was not expected a priori. One reason for this is that although the target mixture for the noisy signal was 0dB SNR, mixtures of 1dB emerged due to short pauses in the target and background speech. This 1dB difference between target and background acoustic speech gives the network a clue for enhancing the target speaker, even without visual cues. That said, the results show that the addition of visual cues still provides improvement in performance for all setups. The results also show that using an LSTM-based audio encoder yields better performance compared to FC-based encoders. This can be attributed to the temporal modeling of LSTMs. Finally, the results show that using the proposed hybrid loss function gives improvements over using MSE for the majority of the setups." } ], "images": [ "scigen/train/1921.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Enhancement performance obtained for each setup and loss function. SNR: signal-to-noise-ratio in decibel (dB), PESQ: perceptual evaluation of speech quality, A: Audio-only enhancer, AV: audiovisual enhancer, FC: fully connected, LSTM: Long short-term memory" ], "reasoning": "The results indicate that audio-only enhancers achieve significant gains, and LSTM-based audio encoders outperform FC-based encoders due to better temporal modeling.", "description": "The results show that considerable gains are achieved using an audio-only enhancer (columns labeled A), which was not expected a priori. One reason for this is that although the target mixture for the noisy signal was 0dB SNR, mixtures of 1dB emerged due to short pauses in the target and background speech. This 1dB difference between target and background acoustic speech gives the network a clue for enhancing the target speaker, even without visual cues. That said, the results show that the addition of visual cues still provides improvement in performance for all setups. The results also show that using an LSTM-based audio encoder yields better performance compared to FC-based encoders. This can be attributed to the temporal modeling of LSTMs. Finally, the results show that using the proposed hybrid loss function gives improvements over using MSE for the majority of the setups.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Enhancement performance obtained for each setup and loss function. SNR: signal-to-noise-ratio in decibel (dB), PESQ: perceptual evaluation of speech quality, A: Audio-only enhancer, AV: audiovisual enhancer, FC: fully connected, LSTM: Long short-term memory\nQuestion: Which audio encoder and loss combination yields the highest SNR A value?\nSolve the above question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the highest SNR A value, compare all the SNR A values in the table. The highest SNR A value is 5.17, which corresponds to the LSTM audio encoder with the MAE+Cosine loss. So, the answer is {\"answer\": \"LSTM with MAE+Cosine\"}" } ], "images": [ "scigen/train/1921.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Enhancement performance obtained for each setup and loss function. SNR: signal-to-noise-ratio in decibel (dB), PESQ: perceptual evaluation of speech quality, A: Audio-only enhancer, AV: audiovisual enhancer, FC: fully connected, LSTM: Long short-term memory" ], "reasoning": "To find the highest SNR A value, compare all the SNR A values in the table. The highest SNR A value is 5.17, which corresponds to the LSTM audio encoder with the MAE+Cosine loss.", "question": "Which audio encoder and loss combination yields the highest SNR A value?", "answer": "So, the answer is {\"answer\": \"LSTM with MAE+Cosine\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nUsing an LSTM-based audio encoder with MAE+Cosine loss yields the highest PESQ AV value.\n" }, { "role": "assistant", "content": "To verify this statement, compare the PESQ AV values for all LSTM-based audio encoders with different loss functions. The highest PESQ AV value is 2.95, which corresponds to the LSTM audio encoder with the MAE+Cosine loss. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1921.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Enhancement performance obtained for each setup and loss function. SNR: signal-to-noise-ratio in decibel (dB), PESQ: perceptual evaluation of speech quality, A: Audio-only enhancer, AV: audiovisual enhancer, FC: fully connected, LSTM: Long short-term memory" ], "reasoning": "To verify this statement, compare the PESQ AV values for all LSTM-based audio encoders with different loss functions. The highest PESQ AV value is 2.95, which corresponds to the LSTM audio encoder with the MAE+Cosine loss.", "statement": "Using an LSTM-based audio encoder with MAE+Cosine loss yields the highest PESQ AV value.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Viseme classification performance obtained using visual embeddings extracted from the video encoder in the enhancement model.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The classifier predicts visemes like /SH/, /P/, and /F/ more accurately than those articulated towards the back of the mouth, achieving an overall unweighted accuracy of 33.5%, compared to a chance performance of 7.7%.We find that the visual embeddings are discriminative toward visemes, giving an overall unweighted accuracy of 33.5%, where 7.7% is the chance performance. We find that our classifier predicts apparent visemes, such as /Z/, /F/, /P/, and /SH/, relatively accurately compared to predicting visemes articulated more towards the back of the mouth, such as /T/ and /G/. The trends that we observe for viseme prediction performance using visual embeddings are similar to those observed in viseme classification tasks. As a benchmark, we were able to obtain an unweighted accuracy of 49.2% using a separate VGG-M neural network trained from scratch specifically to detect visemes, which suggests that our self-supervised visual features were able to close a large proportion of the performance gap. This demonstrates the efficacy of audiovisual speech enhancement as a self-supervised task for learning strong visual features." } ], "images": [ "scigen/train/1922.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Viseme classification performance obtained using visual embeddings extracted from the video encoder in the enhancement model." ], "reasoning": "The classifier predicts visemes like /SH/, /P/, and /F/ more accurately than those articulated towards the back of the mouth, achieving an overall unweighted accuracy of 33.5%, compared to a chance performance of 7.7%.", "description": "We find that the visual embeddings are discriminative toward visemes, giving an overall unweighted accuracy of 33.5%, where 7.7% is the chance performance. We find that our classifier predicts apparent visemes, such as /Z/, /F/, /P/, and /SH/, relatively accurately compared to predicting visemes articulated more towards the back of the mouth, such as /T/ and /G/. The trends that we observe for viseme prediction performance using visual embeddings are similar to those observed in viseme classification tasks. As a benchmark, we were able to obtain an unweighted accuracy of 49.2% using a separate VGG-M neural network trained from scratch specifically to detect visemes, which suggests that our self-supervised visual features were able to close a large proportion of the performance gap. This demonstrates the efficacy of audiovisual speech enhancement as a self-supervised task for learning strong visual features.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: Viseme classification performance obtained using visual embeddings extracted from the video encoder in the enhancement model.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the recall percentage for the viseme /SH/ and how does it compare to the average recall percentage?\n" }, { "role": "assistant", "content": "The recall percentage for the viseme /SH/ is 68.7%. The average recall percentage is 33.5%. Comparing these, the recall percentage for /SH/ is significantly higher than the average recall percentage. So, the answer is {\"answer\": \"68.7%\"}." } ], "images": [ "scigen/train/1922.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Viseme classification performance obtained using visual embeddings extracted from the video encoder in the enhancement model." ], "reasoning": "The recall percentage for the viseme /SH/ is 68.7%. The average recall percentage is 33.5%. Comparing these, the recall percentage for /SH/ is significantly higher than the average recall percentage.", "question": "What is the recall percentage for the viseme /SH/ and how does it compare to the average recall percentage?", "answer": "So, the answer is {\"answer\": \"68.7%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe classifier's recall percentage for the viseme /T/ is lower than the chance performance.\n" }, { "role": "assistant", "content": "The recall percentage for the viseme /T/ is 4.2%, while the chance performance is 7.7%. Since 4.2% is lower than 7.7%, the statement is supported by the table. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1922.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Viseme classification performance obtained using visual embeddings extracted from the video encoder in the enhancement model." ], "reasoning": "The recall percentage for the viseme /T/ is 4.2%, while the chance performance is 7.7%. Since 4.2% is lower than 7.7%, the statement is supported by the table.", "statement": "The classifier's recall percentage for the viseme /T/ is lower than the chance performance.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Discourse connective evaluation: Rankings of the different models for each language pair, ranked first by their Accuracy and then by the percentage where ANY connective is produced. Each set of rankings is followed by the results of the manual analysis on a subset of the translation data for Omissions, Synonyms, Mistranslations.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Sen2Sen models are the most accurate and omit connectives less often, while San leads in De-En accuracy but produces fewer connectives.The ranking shows that Sen2Sen models are on average the most accurate and omit the connectives less often. Anaph continues its high performance in Ru-En, and while San leads the board for De-En in terms of accuracy, it has a low percentage of cases overall in which any connective is produced." } ], "images": [ "scigen/train/1923.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Discourse connective evaluation: Rankings of the different models for each language pair, ranked first by their Accuracy and then by the percentage where ANY connective is produced. Each set of rankings is followed by the results of the manual analysis on a subset of the translation data for Omissions, Synonyms, Mistranslations." ], "reasoning": "Sen2Sen models are the most accurate and omit connectives less often, while San leads in De-En accuracy but produces fewer connectives.", "description": "The ranking shows that Sen2Sen models are on average the most accurate and omit the connectives less often. Anaph continues its high performance in Ru-En, and while San leads the board for De-En in terms of accuracy, it has a low percentage of cases overall in which any connective is produced.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 7: Discourse connective evaluation: Rankings of the different models for each language pair, ranked first by their Accuracy and then by the percentage where ANY connective is produced. Each set of rankings is followed by the results of the manual analysis on a subset of the translation data for Omissions, Synonyms, Mistranslations.' in mind, break down and resolve the question below step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich model has the highest accuracy for De-En and what is its omission rate?\n" }, { "role": "assistant", "content": "San has the highest accuracy for De-En with 52.29%, and its omission rate is 33. So, the answer is {\"answer\": \"San with 33 omissions\"}." } ], "images": [ "scigen/train/1923.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Discourse connective evaluation: Rankings of the different models for each language pair, ranked first by their Accuracy and then by the percentage where ANY connective is produced. Each set of rankings is followed by the results of the manual analysis on a subset of the translation data for Omissions, Synonyms, Mistranslations." ], "reasoning": "San has the highest accuracy for De-En with 52.29%, and its omission rate is 33.", "question": "Which model has the highest accuracy for De-En and what is its omission rate?", "answer": "So, the answer is {\"answer\": \"San with 33 omissions\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Anaph has the highest accuracy for Ru-En., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nAnaph has the highest accuracy for Ru-En.\n" }, { "role": "assistant", "content": "The table shows that Sen2Sen has the highest accuracy for Ru-En with 40.17%, not Anaph. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1923.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Discourse connective evaluation: Rankings of the different models for each language pair, ranked first by their Accuracy and then by the percentage where ANY connective is produced. Each set of rankings is followed by the results of the manual analysis on a subset of the translation data for Omissions, Synonyms, Mistranslations." ], "reasoning": "The table shows that Sen2Sen has the highest accuracy for Ru-En with 40.17%, not Anaph.", "statement": "Anaph has the highest accuracy for Ru-En.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 8: Results of the re-trained pronoun scoring model.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The model was re-trained with WMT13-18 data and tested on WMT-19 data, achieving an accuracy of 86.76%.The pronoun evaluation model results reported in Jwalapuram et al. We re-train the model with more up-to-date data from WMT13-18, and test the model on WMT-19 data. Note that this training data is taken from WMT submissions, which do not overlap with the benchmarked MT models; there is therefore no conflict in using this trained model to evaluate the benchmarked model translations. Their model scores the translations in context; we provide the previous two sentences from the reference translation as context according to their settings." } ], "images": [ "scigen/train/1924.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Results of the re-trained pronoun scoring model." ], "reasoning": "The model was re-trained with WMT13-18 data and tested on WMT-19 data, achieving an accuracy of 86.76%.", "description": "The pronoun evaluation model results reported in Jwalapuram et al. We re-train the model with more up-to-date data from WMT13-18, and test the model on WMT-19 data. Note that this training data is taken from WMT submissions, which do not overlap with the benchmarked MT models; there is therefore no conflict in using this trained model to evaluate the benchmarked model translations. Their model scores the translations in context; we provide the previous two sentences from the reference translation as context according to their settings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 8: Results of the re-trained pronoun scoring model.', answer the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the accuracy of the model when trained on WMT13-18 data and tested on WMT-19 data?\n" }, { "role": "assistant", "content": "The table shows that the model trained on WMT13-18 data and tested on WMT-19 data achieved an accuracy of 86.76%. So, the answer is {\"answer\": \"86.76%\"}." } ], "images": [ "scigen/train/1924.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Results of the re-trained pronoun scoring model." ], "reasoning": "The table shows that the model trained on WMT13-18 data and tested on WMT-19 data achieved an accuracy of 86.76%.", "question": "What is the accuracy of the model when trained on WMT13-18 data and tested on WMT-19 data?", "answer": "So, the answer is {\"answer\": \"86.76%\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The model achieved an accuracy of 86.76% when tested on WMT-19 data., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model achieved an accuracy of 86.76% when tested on WMT-19 data.\n" }, { "role": "assistant", "content": "The table indicates that the model, when trained on WMT13-18 data and tested on WMT-19 data, achieved an accuracy of 86.76%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1924.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Results of the re-trained pronoun scoring model." ], "reasoning": "The table indicates that the model, when trained on WMT13-18 data and tested on WMT-19 data, achieved an accuracy of 86.76%.", "statement": "The model achieved an accuracy of 86.76% when tested on WMT-19 data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 10: Models ranked according to their performance (best to worst) in anaphora according to the evaluation model, with BLEU score for comparison. Model scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The models are ranked from best to worst based on their De-En Model Scores, with lower scores indicating better performance.The models are ranked based on these scores from lowest score (best performing) to highest score (worst performing)." } ], "images": [ "scigen/train/1925.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Models ranked according to their performance (best to worst) in anaphora according to the evaluation model, with BLEU score for comparison. Model scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores)." ], "reasoning": "The models are ranked from best to worst based on their De-En Model Scores, with lower scores indicating better performance.", "description": "The models are ranked based on these scores from lowest score (best performing) to highest score (worst performing).", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nTable title: Table 10: Models ranked according to their performance (best to worst) in anaphora according to the evaluation model, with BLEU score for comparison. Model scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores).\n\nQuestion: Which model has the second-best performance based on the De-En Model Score?\n" }, { "role": "assistant", "content": "The second-best performing model will have the second-lowest De-En Model Score. The lowest score is 109.0793 (Sen2Sen), and the second-lowest score is 206.7197 (Concat). So, the answer is {\"answer\": \"Concat\"}." } ], "images": [ "scigen/train/1925.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Models ranked according to their performance (best to worst) in anaphora according to the evaluation model, with BLEU score for comparison. Model scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores)." ], "reasoning": "The second-best performing model will have the second-lowest De-En Model Score. The lowest score is 109.0793 (Sen2Sen), and the second-lowest score is 206.7197 (Concat).", "question": "Which model has the second-best performance based on the De-En Model Score?", "answer": "So, the answer is {\"answer\": \"Concat\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nHan has a better De-En Model Score than San.\n" }, { "role": "assistant", "content": "Comparing the De-En Model Scores, Han has a score of 211.4651, and San has a score of 214.5966. Since 211.4651 is lower than 214.5966, Han has a better score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1925.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Models ranked according to their performance (best to worst) in anaphora according to the evaluation model, with BLEU score for comparison. Model scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores)." ], "reasoning": "Comparing the De-En Model Scores, Han has a score of 211.4651, and San has a score of 214.5966. Since 211.4651 is lower than 214.5966, Han has a better score.", "statement": "Han has a better De-En Model Score than San.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 10: Models ranked according to their performance (best to worst) in anaphora according to the evaluation model, with BLEU score for comparison. Model scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The models are ranked from best to worst based on their Fr-En Model Score, with the lowest score indicating the best performance.The models are ranked based on these scores from lowest score (best performing) to highest score (worst performing)." } ], "images": [ "scigen/train/1926.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Models ranked according to their performance (best to worst) in anaphora according to the evaluation model, with BLEU score for comparison. Model scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores)." ], "reasoning": "The models are ranked from best to worst based on their Fr-En Model Score, with the lowest score indicating the best performance.", "description": "The models are ranked based on these scores from lowest score (best performing) to highest score (worst performing).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 10: Models ranked according to their performance (best to worst) in anaphora according to the evaluation model, with BLEU score for comparison. Model scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores).\nQuestion: Which model has the highest Fr-En BLEU score and what is its Fr-En Model Score?\nSolve the above question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the model with the highest Fr-En BLEU score, look for the highest value in the Fr-En BLEU column. The highest Fr-En BLEU score is 35.34, which corresponds to the Concat model. The Fr-En Model Score for Concat is 118.7841. So, the answer is {\"answer\": \"Concat with a Fr-En Model Score of 118.7841\"}." } ], "images": [ "scigen/train/1926.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Models ranked according to their performance (best to worst) in anaphora according to the evaluation model, with BLEU score for comparison. Model scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores)." ], "reasoning": "To find the model with the highest Fr-En BLEU score, look for the highest value in the Fr-En BLEU column. The highest Fr-En BLEU score is 35.34, which corresponds to the Concat model. The Fr-En Model Score for Concat is 118.7841.", "question": "Which model has the highest Fr-En BLEU score and what is its Fr-En Model Score?", "answer": "So, the answer is {\"answer\": \"Concat with a Fr-En Model Score of 118.7841\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The Anaph model has a better Fr-En Model Score than the San model., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Anaph model has a better Fr-En Model Score than the San model.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Fr-En Model Scores of Anaph and San. Anaph has a Fr-En Model Score of 63.0893, while San has a Fr-En Model Score of 128.7549. Since a lower score indicates better performance, Anaph has a better score than San. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1926.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Models ranked according to their performance (best to worst) in anaphora according to the evaluation model, with BLEU score for comparison. Model scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores)." ], "reasoning": "To verify this statement, compare the Fr-En Model Scores of Anaph and San. Anaph has a Fr-En Model Score of 63.0893, while San has a Fr-En Model Score of 128.7549. Since a lower score indicates better performance, Anaph has a better score than San.", "statement": "The Anaph model has a better Fr-En Model Score than the San model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 10: Models ranked according to their performance (best to worst) in anaphora according to the evaluation model, with BLEU score for comparison. Model scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The models are ranked from best to worst based on their Ru-En Model Score, with Anaph being the best performing and San being the worst performing.The models are ranked based on these scores from lowest score (best performing) to highest score (worst performing)." } ], "images": [ "scigen/train/1927.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Models ranked according to their performance (best to worst) in anaphora according to the evaluation model, with BLEU score for comparison. Model scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores)." ], "reasoning": "The models are ranked from best to worst based on their Ru-En Model Score, with Anaph being the best performing and San being the worst performing.", "description": "The models are ranked based on these scores from lowest score (best performing) to highest score (worst performing).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 10: Models ranked according to their performance (best to worst) in anaphora according to the evaluation model, with BLEU score for comparison. Model scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores).\nQuestion: Which model has the second-best Ru-En Model Score?\nGive a detailed response to the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the second-best model, look for the second-lowest Ru-En Model Score. The second-lowest score is 160.9542, which corresponds to the Sen2Sen model. So, the answer is {\"answer\": \"Sen2Sen\"}." } ], "images": [ "scigen/train/1927.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Models ranked according to their performance (best to worst) in anaphora according to the evaluation model, with BLEU score for comparison. Model scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores)." ], "reasoning": "To find the second-best model, look for the second-lowest Ru-En Model Score. The second-lowest score is 160.9542, which corresponds to the Sen2Sen model.", "question": "Which model has the second-best Ru-En Model Score?", "answer": "So, the answer is {\"answer\": \"Sen2Sen\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Han model has a better Ru-En Model Score than the Concat model.\n" }, { "role": "assistant", "content": "The Ru-En Model Score for Han is 259.4806, and for Concat, it is 267.5505. Since a lower score is better, Han has a better score than Concat. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1927.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Models ranked according to their performance (best to worst) in anaphora according to the evaluation model, with BLEU score for comparison. Model scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores)." ], "reasoning": "The Ru-En Model Score for Han is 259.4806, and for Concat, it is 267.5505. Since a lower score is better, Han has a better score than Concat.", "statement": "The Han model has a better Ru-En Model Score than the Concat model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 11: Models ranked according to their performance (best to worst) in coherence according our evaluation, with BLEU for comparison. Coherence scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores).', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The models are ranked from best to worst based on their scores, with the lowest score indicating the best performance.The models are ranked based on these scores from lowest score (best performing) to highest score (worst performing)." } ], "images": [ "scigen/train/1928.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Models ranked according to their performance (best to worst) in coherence according our evaluation, with BLEU for comparison. Coherence scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores)." ], "reasoning": "The models are ranked from best to worst based on their scores, with the lowest score indicating the best performance.", "description": "The models are ranked based on these scores from lowest score (best performing) to highest score (worst performing).", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: Table 11: Models ranked according to their performance (best to worst) in coherence according our evaluation, with BLEU for comparison. Coherence scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores).\nQuestion: Which model has the highest De-En Coherence Score?\n" }, { "role": "assistant", "content": "To find the model with the highest De-En Coherence Score, look at the De-En Coherence Score column and identify the highest value. The highest score is 2393.837, which corresponds to the model Han. So, the answer is {\"answer\": \"Han\"}." } ], "images": [ "scigen/train/1928.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Models ranked according to their performance (best to worst) in coherence according our evaluation, with BLEU for comparison. Coherence scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores)." ], "reasoning": "To find the model with the highest De-En Coherence Score, look at the De-En Coherence Score column and identify the highest value. The highest score is 2393.837, which corresponds to the model Han.", "question": "Which model has the highest De-En Coherence Score?", "answer": "So, the answer is {\"answer\": \"Han\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe model Concat has the highest De-En BLEU score.\n" }, { "role": "assistant", "content": "To verify this statement, look at the De-En BLEU score column and identify the highest value. The highest De-En BLEU score is 31.96, which corresponds to the model Concat. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1928.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Models ranked according to their performance (best to worst) in coherence according our evaluation, with BLEU for comparison. Coherence scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores)." ], "reasoning": "To verify this statement, look at the De-En BLEU score column and identify the highest value. The highest De-En BLEU score is 31.96, which corresponds to the model Concat.", "statement": "The model Concat has the highest De-En BLEU score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 11: Models ranked according to their performance (best to worst) in coherence according our evaluation, with BLEU for comparison. Coherence scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The models are ranked from best to worst based on their scores, with the lowest score indicating the best performance.The models are ranked based on these scores from lowest score (best performing) to highest score (worst performing)." } ], "images": [ "scigen/train/1929.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Models ranked according to their performance (best to worst) in coherence according our evaluation, with BLEU for comparison. Coherence scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores)." ], "reasoning": "The models are ranked from best to worst based on their scores, with the lowest score indicating the best performance.", "description": "The models are ranked based on these scores from lowest score (best performing) to highest score (worst performing).", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 11: Models ranked according to their performance (best to worst) in coherence according our evaluation, with BLEU for comparison. Coherence scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores).' and methodically tackle the question that follows. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest Fr-En Coherence Score and what is its Fr-En BLEU score?\n" }, { "role": "assistant", "content": "To find the model with the highest Fr-En Coherence Score, look for the highest value in the Fr-En Coherence Score column. The highest score is 306.905, which corresponds to the Han model. The Fr-En BLEU score for Han is 33.30. So, the answer is {\"answer\": \"Han with a Fr-En BLEU score of 33.30\"}." } ], "images": [ "scigen/train/1929.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Models ranked according to their performance (best to worst) in coherence according our evaluation, with BLEU for comparison. Coherence scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores)." ], "reasoning": "To find the model with the highest Fr-En Coherence Score, look for the highest value in the Fr-En Coherence Score column. The highest score is 306.905, which corresponds to the Han model. The Fr-En BLEU score for Han is 33.30.", "question": "Which model has the highest Fr-En Coherence Score and what is its Fr-En BLEU score?", "answer": "So, the answer is {\"answer\": \"Han with a Fr-En BLEU score of 33.30\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Concat model has the highest Fr-En BLEU score. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Concat model has the highest Fr-En BLEU score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Fr-En BLEU scores of all models. The Concat model has a Fr-En BLEU score of 35.34, which is the highest among the listed models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1929.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Models ranked according to their performance (best to worst) in coherence according our evaluation, with BLEU for comparison. Coherence scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores)." ], "reasoning": "To verify this statement, compare the Fr-En BLEU scores of all models. The Concat model has a Fr-En BLEU score of 35.34, which is the highest among the listed models.", "statement": "The Concat model has the highest Fr-En BLEU score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 11: Models ranked according to their performance (best to worst) in coherence according our evaluation, with BLEU for comparison. Coherence scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The models are ranked from best to worst based on their scores, with the lowest score indicating the best performance.The models are ranked based on these scores from lowest score (best performing) to highest score (worst performing)." } ], "images": [ "scigen/train/1930.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Models ranked according to their performance (best to worst) in coherence according our evaluation, with BLEU for comparison. Coherence scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores)." ], "reasoning": "The models are ranked from best to worst based on their scores, with the lowest score indicating the best performance.", "description": "The models are ranked based on these scores from lowest score (best performing) to highest score (worst performing).", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 11: Models ranked according to their performance (best to worst) in coherence according our evaluation, with BLEU for comparison. Coherence scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores).'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. \n\nQuestion: Which model has the highest Ru-En BLEU score and what is its Ru-En Coherence score?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the model with the highest Ru-En BLEU score, look for the highest value in the Ru-En BLEU column. The highest Ru-En BLEU score is 35.34, which corresponds to the Concat model. The Ru-En Coherence score for Concat is 4016.302. So, the answer is {\"answer\": \"Concat with a Ru-En Coherence score of 4016.302\"}." } ], "images": [ "scigen/train/1930.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Models ranked according to their performance (best to worst) in coherence according our evaluation, with BLEU for comparison. Coherence scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores)." ], "reasoning": "To find the model with the highest Ru-En BLEU score, look for the highest value in the Ru-En BLEU column. The highest Ru-En BLEU score is 35.34, which corresponds to the Concat model. The Ru-En Coherence score for Concat is 4016.302.", "question": "Which model has the highest Ru-En BLEU score and what is its Ru-En Coherence score?", "answer": "So, the answer is {\"answer\": \"Concat with a Ru-En Coherence score of 4016.302\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Anaph model has a higher Ru-En BLEU score than the Sen2Sen model.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Ru-En BLEU scores of the Anaph and Sen2Sen models. The Anaph model has a Ru-En BLEU score of 34.32, while the Sen2Sen model has a Ru-En BLEU score of 35.12. Since 34.32 is less than 35.12, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1930.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Models ranked according to their performance (best to worst) in coherence according our evaluation, with BLEU for comparison. Coherence scores given here are obtained by subtracting the score for the model translation from the score for the reference translation, and summing the absolute score differences across the dataset. Hence, smaller model scores indicate better performance (closer to reference scores)." ], "reasoning": "To verify this statement, compare the Ru-En BLEU scores of the Anaph and Sen2Sen models. The Anaph model has a Ru-En BLEU score of 34.32, while the Sen2Sen model has a Ru-En BLEU score of 35.12. Since 34.32 is less than 35.12, the statement is incorrect.", "statement": "The Anaph model has a higher Ru-En BLEU score than the Sen2Sen model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 10: Summary of cases involving an “emotional condition” and a discussion of causality. Different columns correspond to the number of dropped phrases. First column is only “emotion” and “condition.” Rest are adding more and more phrases. Column B adds “ecab,” Column C “depression,” and Column D “xx” (for illustration purposes). Column D includes many other terms.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table shows how dropping specific terms affects the selection of phrases in legal case summaries, highlighting the dependencies between phrases.We see fairly general terms and some boilerplate language. Here it is necessary to explore the raw text to discover the contexts for these phrases. This is easily done using our package. For example, one positively marked decision has: Not every injury or illness that is related to a claimant’s employment is compensable. In the case of Lillian Cutler, the Board explained some distinctions as to the type of employment situations giving rise to a compensable emotional condition under FECA. C.E., Docket No. 10-461 (issued November 23, 2010) (emphasis added). Another is: To establish that an emotional condition arose in the performance of duty, a claimant must submit the following: (1) medical evidence establishing that she has an emotional or psychiatric disorder; (2) factual evidence identifying employment factors or incidents alleged to have caused or contributed to the condition; and (3) rationalized medical opinion evidence establishing that the emotional condition is causally related to the identified compensable employment factors. T.G., 58 ECAB 189 (2006) (emphasis added). Each column corresponds to dropping more and more terms from consideration. Note that the transition from the third to fourth column drops one of the case-references, “Lillian Cutler,” even though we did not explicitly drop those words and phrases. CCS selected phrases are picked in the context of other phrases. Because we removed “xx” (indicating a case number), “Lillian Cutler” is no longer selected, along with other phrases including parts of this phrase and “xx.” Instead we obtain a cluster of phrases showcasing different aspects of these cases. Dropping phrases can only affect a summary if those phrases are in the summary. The final two columns are the same because none of the additional phrases were in the summary from column 4. Care must be taken to understand the complex dependencies between phrases." } ], "images": [ "scigen/train/1931.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Summary of cases involving an “emotional condition” and a discussion of causality. Different columns correspond to the number of dropped phrases. First column is only “emotion” and “condition.” Rest are adding more and more phrases. Column B adds “ecab,” Column C “depression,” and Column D “xx” (for illustration purposes). Column D includes many other terms." ], "reasoning": "The table shows how dropping specific terms affects the selection of phrases in legal case summaries, highlighting the dependencies between phrases.", "description": "We see fairly general terms and some boilerplate language. Here it is necessary to explore the raw text to discover the contexts for these phrases. This is easily done using our package. For example, one positively marked decision has: Not every injury or illness that is related to a claimant’s employment is compensable. In the case of Lillian Cutler, the Board explained some distinctions as to the type of employment situations giving rise to a compensable emotional condition under FECA. C.E., Docket No. 10-461 (issued November 23, 2010) (emphasis added). Another is: To establish that an emotional condition arose in the performance of duty, a claimant must submit the following: (1) medical evidence establishing that she has an emotional or psychiatric disorder; (2) factual evidence identifying employment factors or incidents alleged to have caused or contributed to the condition; and (3) rationalized medical opinion evidence establishing that the emotional condition is causally related to the identified compensable employment factors. T.G., 58 ECAB 189 (2006) (emphasis added). Each column corresponds to dropping more and more terms from consideration. Note that the transition from the third to fourth column drops one of the case-references, “Lillian Cutler,” even though we did not explicitly drop those words and phrases. CCS selected phrases are picked in the context of other phrases. Because we removed “xx” (indicating a case number), “Lillian Cutler” is no longer selected, along with other phrases including parts of this phrase and “xx.” Instead we obtain a cluster of phrases showcasing different aspects of these cases. Dropping phrases can only affect a summary if those phrases are in the summary. The final two columns are the same because none of the additional phrases were in the summary from column 4. Care must be taken to understand the complex dependencies between phrases.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 10: Summary of cases involving an “emotional condition” and a discussion of causality. Different columns correspond to the number of dropped phrases. First column is only “emotion” and “condition.” Rest are adding more and more phrases. Column B adds “ecab,” Column C “depression,” and Column D “xx” (for illustration purposes). Column D includes many other terms.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. \n\nQuestion: Which phrase is consistently selected across all columns?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By examining the table, we see that the phrase 'illness that is' has a value in every column, indicating it is consistently selected. So, the answer is {\"answer\": \"illness that is\"}." } ], "images": [ "scigen/train/1931.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Summary of cases involving an “emotional condition” and a discussion of causality. Different columns correspond to the number of dropped phrases. First column is only “emotion” and “condition.” Rest are adding more and more phrases. Column B adds “ecab,” Column C “depression,” and Column D “xx” (for illustration purposes). Column D includes many other terms." ], "reasoning": "By examining the table, we see that the phrase 'illness that is' has a value in every column, indicating it is consistently selected.", "question": "Which phrase is consistently selected across all columns?", "answer": "So, the answer is {\"answer\": \"illness that is\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe phrase 'depression' is selected in the main column.\n" }, { "role": "assistant", "content": "The table shows that the phrase 'depression' has a value of 0.00 in the main column, indicating it is not selected. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1931.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Summary of cases involving an “emotional condition” and a discussion of causality. Different columns correspond to the number of dropped phrases. First column is only “emotion” and “condition.” Rest are adding more and more phrases. Column B adds “ecab,” Column C “depression,” and Column D “xx” (for illustration purposes). Column D includes many other terms." ], "reasoning": "The table shows that the phrase 'depression' has a value of 0.00 in the main column, indicating it is not selected.", "statement": "The phrase 'depression' is selected in the main column.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Needed C to Prune Rare Perfect Predictors.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The table shows the impact of different regularization cutoffs (C*) on the pruning of perfect predictors for various values of q, μ, and r.For this C∗, any perfect predictor of r documents will be pruned. This cutoff will generally be overly aggressive; if other predictors also predict these documents, then the gain of including the perfect predictor is potentially less. This provides a human interpretation of the impact of the regularization." } ], "images": [ "scigen/train/1932.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Needed C to Prune Rare Perfect Predictors." ], "reasoning": "The table shows the impact of different regularization cutoffs (C*) on the pruning of perfect predictors for various values of q, μ, and r.", "description": "For this C∗, any perfect predictor of r documents will be pruned. This cutoff will generally be overly aggressive; if other predictors also predict these documents, then the gain of including the perfect predictor is potentially less. This provides a human interpretation of the impact of the regularization.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Needed C to Prune Rare Perfect Predictors.\nQuestion: What is the value of C* when q is 4/3 and μ is approximately -1 with r equal to 2?\nSolve the above question based on the table information. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the value of C* when q is 4/3 and μ is approximately -1 with r equal to 2, locate the row where q is 4/3 and the column where μ is approximately -1 with r equal to 2. The intersection of this row and column gives the value of C*. So, the answer is {\"answer\": \"≈4.75≈5.7\"}" } ], "images": [ "scigen/train/1932.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Needed C to Prune Rare Perfect Predictors." ], "reasoning": "To find the value of C* when q is 4/3 and μ is approximately -1 with r equal to 2, locate the row where q is 4/3 and the column where μ is approximately -1 with r equal to 2. The intersection of this row and column gives the value of C*.", "question": "What is the value of C* when q is 4/3 and μ is approximately -1 with r equal to 2?", "answer": "So, the answer is {\"answer\": \"≈4.75≈5.7\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nWhen q is 2 and μ is approximately 0 with r equal to 4, the value of C* is 4.\n" }, { "role": "assistant", "content": "To verify this statement, locate the row where q is 2 and the column where μ is approximately 0 with r equal to 4. The intersection of this row and column shows the value of C*. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1932.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Needed C to Prune Rare Perfect Predictors." ], "reasoning": "To verify this statement, locate the row where q is 2 and the column where μ is approximately 0 with r equal to 4. The intersection of this row and column shows the value of C*.", "statement": "When q is 2 and μ is approximately 0 with r equal to 4, the value of C* is 4.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results of comparison study on OSHA reports (top) and ECAB legal decisions (bottom). Runtime does not include time to generate the document-term matrix for the Lasso and MNIR.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The average runtimes for the Lasso method increased by about 16% to a mean of 101 seconds when moving from unigrams to pentagrams, and the textreg methods showed a substantial increase in time when moving to trigrams.For each labeling and each method, we calculated different statistics on the word lists. We also timed the total time to generate a list. A few general trends are evident. We were quite surprised by the time statistics being insensitive to number of terms for the glmnet package. Here average runtimes for the Lasso increased modestly by about 16% to a mean of 101 seconds. We now see a substantially increased time from moving to unigrams to trigrams for the Lasso and the timing of the textreg methods exploded. In investigating this time differential further, we found many selected phrases had 5 or more words. Furthermore, most runs reached the maximum number of iterations before convergence, indicating flat surfaces. This is a weakness of greedy coordinate descent, which we discuss further below. Overall we potentially have, due to the use of boilerplate language in legal writing, long yet informative phrases that we wish to see in our summaries. This could make pre-generation of candidate phrases prohibitively expensive. The MNIR method selected all unigrams; each unigram apparently has enough difference in use across judges to be selected under the BIC penalization." } ], "images": [ "scigen/train/1933.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of comparison study on OSHA reports (top) and ECAB legal decisions (bottom). Runtime does not include time to generate the document-term matrix for the Lasso and MNIR." ], "reasoning": "The average runtimes for the Lasso method increased by about 16% to a mean of 101 seconds when moving from unigrams to pentagrams, and the textreg methods showed a substantial increase in time when moving to trigrams.", "description": "For each labeling and each method, we calculated different statistics on the word lists. We also timed the total time to generate a list. A few general trends are evident. We were quite surprised by the time statistics being insensitive to number of terms for the glmnet package. Here average runtimes for the Lasso increased modestly by about 16% to a mean of 101 seconds. We now see a substantially increased time from moving to unigrams to trigrams for the Lasso and the timing of the textreg methods exploded. In investigating this time differential further, we found many selected phrases had 5 or more words. Furthermore, most runs reached the maximum number of iterations before convergence, indicating flat surfaces. This is a weakness of greedy coordinate descent, which we discuss further below. Overall we potentially have, due to the use of boilerplate language in legal writing, long yet informative phrases that we wish to see in our summaries. This could make pre-generation of candidate phrases prohibitively expensive. The MNIR method selected all unigrams; each unigram apparently has enough difference in use across judges to be selected under the BIC penalization.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Results of comparison study on OSHA reports (top) and ECAB legal decisions (bottom). Runtime does not include time to generate the document-term matrix for the Lasso and MNIR.', and sequentially solve the question, writing out each step of your reasoning process. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the average word frequency for the lasso method when using trigrams?\n" }, { "role": "assistant", "content": "To find the average word frequency for the lasso method using trigrams, locate the row labeled 'lasso (trigrams)' and find the value under the 'word freq (avg)' column. So, the answer is {\"answer\": \"898\"}" } ], "images": [ "scigen/train/1933.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of comparison study on OSHA reports (top) and ECAB legal decisions (bottom). Runtime does not include time to generate the document-term matrix for the Lasso and MNIR." ], "reasoning": "To find the average word frequency for the lasso method using trigrams, locate the row labeled 'lasso (trigrams)' and find the value under the 'word freq (avg)' column.", "question": "What is the average word frequency for the lasso method when using trigrams?", "answer": "So, the answer is {\"answer\": \"898\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The MNIR method selected all unigrams. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe MNIR method selected all unigrams.\n" }, { "role": "assistant", "content": "The description states that the MNIR method selected all unigrams, and the table shows that the MNIR method has a list length of 25,990, which supports the claim that it selected a large number of unigrams. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1933.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of comparison study on OSHA reports (top) and ECAB legal decisions (bottom). Runtime does not include time to generate the document-term matrix for the Lasso and MNIR." ], "reasoning": "The description states that the MNIR method selected all unigrams, and the table shows that the MNIR method has a list length of 25,990, which supports the claim that it selected a large number of unigrams.", "statement": "The MNIR method selected all unigrams.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Word list characteristics of different rescaling norms. Length is length of list, mean size is average number of words in the selected phrases, frequency refers to phrase occurrence in the corpus. Mean C is the average regularization value.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Higher q values result in longer lists with more common words, while lower q values produce shorter lists with more targeted phrases.As anticipated, different values of q produce lists of different quality. Higher q corresponds to lists with relatively more common words and phrases. Low q produces lists that tend to have phrases with more words. For the q=1.2 list, more than half the phrases were 3 words or longer. We also found that the length of the list increased with q. For q=4 we had longer lists with more common words. For low q the algorithm selected very targeted phrases, and not too many of them. The regularization parameter C is not comparable across q. Therefore we recalculated it via the permutation approach for each run and value of q. Mean values for C are shown on the table for reference." } ], "images": [ "scigen/train/1934.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Word list characteristics of different rescaling norms. Length is length of list, mean size is average number of words in the selected phrases, frequency refers to phrase occurrence in the corpus. Mean C is the average regularization value." ], "reasoning": "Higher q values result in longer lists with more common words, while lower q values produce shorter lists with more targeted phrases.", "description": "As anticipated, different values of q produce lists of different quality. Higher q corresponds to lists with relatively more common words and phrases. Low q produces lists that tend to have phrases with more words. For the q=1.2 list, more than half the phrases were 3 words or longer. We also found that the length of the list increased with q. For q=4 we had longer lists with more common words. For low q the algorithm selected very targeted phrases, and not too many of them. The regularization parameter C is not comparable across q. Therefore we recalculated it via the permutation approach for each run and value of q. Mean values for C are shown on the table for reference.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Word list characteristics of different rescaling norms. Length is length of list, mean size is average number of words in the selected phrases, frequency refers to phrase occurrence in the corpus. Mean C is the average regularization value.', solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the average phrase length for the method with the longest list length?\n" }, { "role": "assistant", "content": "The method with the longest list length is textreg.4 with a list length of 35. The average phrase length for textreg.4 is 1.8. So, the answer is {\"answer\": \"1.8\"}" } ], "images": [ "scigen/train/1934.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Word list characteristics of different rescaling norms. Length is length of list, mean size is average number of words in the selected phrases, frequency refers to phrase occurrence in the corpus. Mean C is the average regularization value." ], "reasoning": "The method with the longest list length is textreg.4 with a list length of 35. The average phrase length for textreg.4 is 1.8.", "question": "What is the average phrase length for the method with the longest list length?", "answer": "So, the answer is {\"answer\": \"1.8\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe method with the highest average word frequency also has the highest average value for C.\n" }, { "role": "assistant", "content": "The method with the highest average word frequency is textreg.4 with 8035, and it also has the highest average value for C, which is 19.0. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1934.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Word list characteristics of different rescaling norms. Length is length of list, mean size is average number of words in the selected phrases, frequency refers to phrase occurrence in the corpus. Mean C is the average regularization value." ], "reasoning": "The method with the highest average word frequency is textreg.4 with 8035, and it also has the highest average value for C, which is 19.0.", "statement": "The method with the highest average word frequency also has the highest average value for C.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 11: Different Summaries of Carbon Monoxide (when comparing against all other cases)', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The comparison of CO cases to the full set of cases in the database shows broad similarities, with Appendix A providing supplemental tables for alternate summaries.We also compared CO cases to the full set of cases in the database. That is, we summarized the same collection of reports, but used a different baseline point of comparison. They are broadly similar. Appendix A consists of supplemental tables showing alternate summaries of the case studies discussed above." } ], "images": [ "scigen/train/1935.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Different Summaries of Carbon Monoxide (when comparing against all other cases)" ], "reasoning": "The comparison of CO cases to the full set of cases in the database shows broad similarities, with Appendix A providing supplemental tables for alternate summaries.", "description": "We also compared CO cases to the full set of cases in the database. That is, we summarized the same collection of reports, but used a different baseline point of comparison. They are broadly similar. Appendix A consists of supplemental tables showing alternate summaries of the case studies discussed above.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 11: Different Summaries of Carbon Monoxide (when comparing against all other cases)' and methodically tackle the question that follows. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the percentage of reports tagged for the phrase 'gasoline powered'?\n" }, { "role": "assistant", "content": "To find the percentage of reports tagged for 'gasoline powered', locate the row for 'gasoline powered' and check the '% tagged' column. So, the answer is {\"answer\": \"45.00\"}." } ], "images": [ "scigen/train/1935.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Different Summaries of Carbon Monoxide (when comparing against all other cases)" ], "reasoning": "To find the percentage of reports tagged for 'gasoline powered', locate the row for 'gasoline powered' and check the '% tagged' column.", "question": "What is the percentage of reports tagged for the phrase 'gasoline powered'?", "answer": "So, the answer is {\"answer\": \"45.00\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The phrase 'were hospitalized' has the highest number of reports tagged. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe phrase 'were hospitalized' has the highest number of reports tagged.\n" }, { "role": "assistant", "content": "To verify this statement, compare the '# tagged' values for all phrases. The phrase 'were hospitalized' has 42 reports tagged, which is not the highest. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1935.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Different Summaries of Carbon Monoxide (when comparing against all other cases)" ], "reasoning": "To verify this statement, compare the '# tagged' values for all phrases. The phrase 'were hospitalized' has 42 reports tagged, which is not the highest.", "statement": "The phrase 'were hospitalized' has the highest number of reports tagged.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 12: Different Summaries of Carbon Monoxide (using stemming, compared to chemical family)', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Stemming collapses phrases, which can be helpful but hampers human readability, as shown in Appendix A with alternate summaries of case studies.We also analyzed the data using stemming. Results are again broadly similar (but possibly harder to read). Stemming collapses phrases, which can be helpful, but hampers human readability. Appendix A consists of supplemental tables showing alternate summaries of the case studies discussed above." } ], "images": [ "scigen/train/1936.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 12: Different Summaries of Carbon Monoxide (using stemming, compared to chemical family)" ], "reasoning": "Stemming collapses phrases, which can be helpful but hampers human readability, as shown in Appendix A with alternate summaries of case studies.", "description": "We also analyzed the data using stemming. Results are again broadly similar (but possibly harder to read). Stemming collapses phrases, which can be helpful, but hampers human readability. Appendix A consists of supplemental tables showing alternate summaries of the case studies discussed above.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 12: Different Summaries of Carbon Monoxide (using stemming, compared to chemical family)' to sequentially solve the question presented below. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhich phrase has the highest percentage of tagged reports?\n" }, { "role": "assistant", "content": "To find the phrase with the highest percentage of tagged reports, look at the '% tagged' column and identify the highest value, which is 100%. The corresponding phrases are 'poison+ at+', 'were+ use+ a+ gasolin+', 'was oper+ a+ propan+', and 'XXX cubic+'. So, the answer is {\"answer\": \"poison+ at+\"}." } ], "images": [ "scigen/train/1936.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 12: Different Summaries of Carbon Monoxide (using stemming, compared to chemical family)" ], "reasoning": "To find the phrase with the highest percentage of tagged reports, look at the '% tagged' column and identify the highest value, which is 100%. The corresponding phrases are 'poison+ at+', 'were+ use+ a+ gasolin+', 'was oper+ a+ propan+', and 'XXX cubic+'.", "question": "Which phrase has the highest percentage of tagged reports?", "answer": "So, the answer is {\"answer\": \"poison+ at+\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe phrase 'generat+' has more than 100 reports.\n" }, { "role": "assistant", "content": "To verify this statement, check the '# reports' column for the phrase 'generat+'. The value is 110, which is more than 100. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1936.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 12: Different Summaries of Carbon Monoxide (using stemming, compared to chemical family)" ], "reasoning": "To verify this statement, check the '# reports' column for the phrase 'generat+'. The value is 110, which is more than 100.", "statement": "The phrase 'generat+' has more than 100 reports.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Accuracy of the dialogue act identification using the character-level language model utterance representation for 42 classes using a single MLP layer with 64 neurons.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The average feature vector representation, which has the highest accuracy and lowest standard deviation, is preferred for future experiments in emotion detection.The hidden vector st obtained after the last character is called the last feature vector, as it stores the information related to the character language model and the sentiment of the utterance. However, it was shown that the average vector over all characters in the utterance works better for emotion detection Hence, we extract the last feature vector and also the average feature vector representations for each utterance. The standard deviation (SD) is computed over ten runs. The average vector seems to carry more information related to the DA; hence we use it for future experiments. There is an advantage of using domain-independent data: it is rich regarding features being trained on big data, perhaps surpassing the limitation of scalability as mentioned in Ji et al. (2016)." } ], "images": [ "scigen/train/1937.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracy of the dialogue act identification using the character-level language model utterance representation for 42 classes using a single MLP layer with 64 neurons." ], "reasoning": "The average feature vector representation, which has the highest accuracy and lowest standard deviation, is preferred for future experiments in emotion detection.", "description": "The hidden vector st obtained after the last character is called the last feature vector, as it stores the information related to the character language model and the sentiment of the utterance. However, it was shown that the average vector over all characters in the utterance works better for emotion detection Hence, we extract the last feature vector and also the average feature vector representations for each utterance. The standard deviation (SD) is computed over ten runs. The average vector seems to carry more information related to the DA; hence we use it for future experiments. There is an advantage of using domain-independent data: it is rich regarding features being trained on big data, perhaps surpassing the limitation of scalability as mentioned in Ji et al. (2016).", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Accuracy of the dialogue act identification using the character-level language model utterance representation for 42 classes using a single MLP layer with 64 neurons.', and sequentially solve the question, writing out each step of your reasoning process. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model input has the highest accuracy and what is its standard deviation?\n" }, { "role": "assistant", "content": "The table shows the accuracy and standard deviation for different model inputs. The average feature vector has the highest accuracy of 73.96% and a standard deviation of 0.26. So, the answer is {\"answer\": \"73.96% with a standard deviation of 0.26\"}." } ], "images": [ "scigen/train/1937.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracy of the dialogue act identification using the character-level language model utterance representation for 42 classes using a single MLP layer with 64 neurons." ], "reasoning": "The table shows the accuracy and standard deviation for different model inputs. The average feature vector has the highest accuracy of 73.96% and a standard deviation of 0.26.", "question": "Which model input has the highest accuracy and what is its standard deviation?", "answer": "So, the answer is {\"answer\": \"73.96% with a standard deviation of 0.26\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe concatenated vector has the highest accuracy among the model inputs.\n" }, { "role": "assistant", "content": "The table shows that the average feature vector has the highest accuracy of 73.96%, while the concatenated vector has an accuracy of 73.18%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1937.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracy of the dialogue act identification using the character-level language model utterance representation for 42 classes using a single MLP layer with 64 neurons." ], "reasoning": "The table shows that the average feature vector has the highest accuracy of 73.96%, while the concatenated vector has an accuracy of 73.18%.", "statement": "The concatenated vector has the highest accuracy among the model inputs.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Accuracy of the dialogue act identification with the context-learning approach.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Using 64 hidden units for the RNN and minimal context information, the model's performance increased from 74% to 77% with context, showing robustness with minimal variance.We examined different values for the number of the hidden units of the RNN, empirically 64 was identified as best and used throughout the experiments. We also experimented with the various representations for the speaker id that is concatenated with the respective utterances but could find no differences. As a result, our proposed model uses minimal information for the context. The performance increases from 74% to about 77% with context. We run each experiment for ten times and take the average. The model shows robustness providing minimal variance, and using a minimum number of preceding utterances as a context can produce fair results." } ], "images": [ "scigen/train/1938.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracy of the dialogue act identification with the context-learning approach." ], "reasoning": "Using 64 hidden units for the RNN and minimal context information, the model's performance increased from 74% to 77% with context, showing robustness with minimal variance.", "description": "We examined different values for the number of the hidden units of the RNN, empirically 64 was identified as best and used throughout the experiments. We also experimented with the various representations for the speaker id that is concatenated with the respective utterances but could find no differences. As a result, our proposed model uses minimal information for the context. The performance increases from 74% to about 77% with context. We run each experiment for ten times and take the average. The model shows robustness providing minimal variance, and using a minimum number of preceding utterances as a context can produce fair results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: 'Table 3: Accuracy of the dialogue act identification with the context-learning approach.'\nQuestion: What is the accuracy and standard deviation of the RNN model with 3 utterances in context?\n" }, { "role": "assistant", "content": "The table shows the performance metrics for different RNN setups. The RNN model with 3 utterances in context has an accuracy of 77.34% and a standard deviation of 0.21. So, the answer is {\"answer\": \"77.34% accuracy, 0.21 SD\"}" } ], "images": [ "scigen/train/1938.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracy of the dialogue act identification with the context-learning approach." ], "reasoning": "The table shows the performance metrics for different RNN setups. The RNN model with 3 utterances in context has an accuracy of 77.34% and a standard deviation of 0.21.", "question": "What is the accuracy and standard deviation of the RNN model with 3 utterances in context?", "answer": "So, the answer is {\"answer\": \"77.34% accuracy, 0.21 SD\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe RNN model with 1 utterance in context has a higher accuracy than the RNN model with 3 utterances in context.\n" }, { "role": "assistant", "content": "The table shows that the RNN model with 1 utterance in context has an accuracy of 76.57%, while the RNN model with 3 utterances in context has an accuracy of 77.34%. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1938.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracy of the dialogue act identification with the context-learning approach." ], "reasoning": "The table shows that the RNN model with 1 utterance in context has an accuracy of 76.57%, while the RNN model with 3 utterances in context has an accuracy of 77.34%. Therefore, the statement is refuted.", "statement": "The RNN model with 1 utterance in context has a higher accuracy than the RNN model with 3 utterances in context.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Multi-class classification results based on weighted average of the F1 scores.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The B-LSTM model using translation data augmentation and FastText embedding achieved the highest f1-score of 69.33%, outperforming the machine learning baseline of 67.62%.Just to remind you, initially the dataset has been labeled by five emotions ranged from -2 to +2. 68.344% f1-score has been recorded for its multi-classification rate. On one hand, the top f1-score goes to the suggested B-LSTM model using translation DA and embedded by FastText with 69.33%. It is higher than its relevant machine learning baseline which is 67.62%." } ], "images": [ "scigen/train/1939.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Multi-class classification results based on weighted average of the F1 scores." ], "reasoning": "The B-LSTM model using translation data augmentation and FastText embedding achieved the highest f1-score of 69.33%, outperforming the machine learning baseline of 67.62%.", "description": "Just to remind you, initially the dataset has been labeled by five emotions ranged from -2 to +2. 68.344% f1-score has been recorded for its multi-classification rate. On one hand, the top f1-score goes to the suggested B-LSTM model using translation DA and embedded by FastText with 69.33%. It is higher than its relevant machine learning baseline which is 67.62%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: Multi-class classification results based on weighted average of the F1 scores.', solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the f1-score of the B-LSTM model using translation data augmentation and FastText embedding, and how does it compare to the machine learning baseline?\n" }, { "role": "assistant", "content": "The B-LSTM model using translation data augmentation and FastText embedding achieved an f1-score of 69.33%, which is higher than the machine learning baseline f1-score of 67.62%. So, the answer is {\"answer\": \"69.33%\"}" } ], "images": [ "scigen/train/1939.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Multi-class classification results based on weighted average of the F1 scores." ], "reasoning": "The B-LSTM model using translation data augmentation and FastText embedding achieved an f1-score of 69.33%, which is higher than the machine learning baseline f1-score of 67.62%.", "question": "What is the f1-score of the B-LSTM model using translation data augmentation and FastText embedding, and how does it compare to the machine learning baseline?", "answer": "So, the answer is {\"answer\": \"69.33%\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The B-LSTM model using translation data augmentation and FastText embedding achieved an f1-score of 69.33%. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe B-LSTM model using translation data augmentation and FastText embedding achieved an f1-score of 69.33%.\n" }, { "role": "assistant", "content": "The table shows that the B-LSTM model using translation data augmentation and FastText embedding achieved an f1-score of 69.33%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1939.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Multi-class classification results based on weighted average of the F1 scores." ], "reasoning": "The table shows that the B-LSTM model using translation data augmentation and FastText embedding achieved an f1-score of 69.33%.", "statement": "The B-LSTM model using translation data augmentation and FastText embedding achieved an f1-score of 69.33%.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Chosen hyperparameters for our POS tagging and parsing models. both means the feature is common to both the POS tagger and parser.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The POS tagging and parsing models use Bi-LSTM architectures with specific embedding dimensions and are trained for fifty epochs without splitting the synthetic Faroese treebanks.For POS tagging, we adopt a standard architecture with a word and character-level Bi-LSTM Plank et al. These representations are passed to a multilayer perceptron (MLP) classifier followed by a softmax function to choose a tag with the highest probability. For both the POS tagging and parsing models, we use a word embedding dimension of size 100 and a character embedding dimension of size 64. POS tag embeddings of dimension 50 are included in the parser. We train our Faroese models for fifty epochs. We do not split the synthetic Faroese treebanks into training/development portions though we suspect doing so will help the models to not overfit on the training data. For all experiments we report labelled attachment scores produced by the official CoNLL 2018 evaluation script." } ], "images": [ "scigen/train/1940.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Chosen hyperparameters for our POS tagging and parsing models. both means the feature is common to both the POS tagger and parser." ], "reasoning": "The POS tagging and parsing models use Bi-LSTM architectures with specific embedding dimensions and are trained for fifty epochs without splitting the synthetic Faroese treebanks.", "description": "For POS tagging, we adopt a standard architecture with a word and character-level Bi-LSTM Plank et al. These representations are passed to a multilayer perceptron (MLP) classifier followed by a softmax function to choose a tag with the highest probability. For both the POS tagging and parsing models, we use a word embedding dimension of size 100 and a character embedding dimension of size 64. POS tag embeddings of dimension 50 are included in the parser. We train our Faroese models for fifty epochs. We do not split the synthetic Faroese treebanks into training/development portions though we suspect doing so will help the models to not overfit on the training data. For all experiments we report labelled attachment scores produced by the official CoNLL 2018 evaluation script.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: Chosen hyperparameters for our POS tagging and parsing models. both means the feature is common to both the POS tagger and parser.', answer the question below by showing each progressive step toward the answer. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the size of the Arc MLP in the parser architecture?\n" }, { "role": "assistant", "content": "The table lists the parameters for both the POS tagger and parser architectures. The Arc MLP size for the parser is specified under the parser architecture parameters. So, the answer is {\"answer\": \"500\"}" } ], "images": [ "scigen/train/1940.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Chosen hyperparameters for our POS tagging and parsing models. both means the feature is common to both the POS tagger and parser." ], "reasoning": "The table lists the parameters for both the POS tagger and parser architectures. The Arc MLP size for the parser is specified under the parser architecture parameters.", "question": "What is the size of the Arc MLP in the parser architecture?", "answer": "So, the answer is {\"answer\": \"500\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe learning rate for training the models is 0.01.\n" }, { "role": "assistant", "content": "The table provides the training parameters, including the learning rate, which is listed as 0.001, not 0.01. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1940.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Chosen hyperparameters for our POS tagging and parsing models. both means the feature is common to both the POS tagger and parser." ], "reasoning": "The table provides the training parameters, including the learning rate, which is listed as 0.001, not 0.01.", "statement": "The learning rate for training the models is 0.01.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Comparison to previous work. LAS on Faroese test set. Note that the first results uses predicted segmentation and tokenization whereas the rest used gold.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The highest LAS score of 71.51 was achieved by combining strong monolingual source models with multi-treebank POS tagging and parsing models.The highest scoring system in the 2018 CoNLL shared task was that of \\newciterosa-marecek-2018-cuni who achieved a LAS score of 49.4 on the Faroese test set. \\newcitetyers-etal-2018-multi report an LAS of 64.43 with a monolingual multi-source approach. Our implementation which uses a different parser (AllenNLP versus UDPipe) and pre-trained word embeddings achieves an LAS of 68. Our highest score of 71.51 is achieved through the combination of projecting from strong monolingual source models and then training multi-treebank POS tagging and parsing models on the outputs." } ], "images": [ "scigen/train/1941.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Comparison to previous work. LAS on Faroese test set. Note that the first results uses predicted segmentation and tokenization whereas the rest used gold." ], "reasoning": "The highest LAS score of 71.51 was achieved by combining strong monolingual source models with multi-treebank POS tagging and parsing models.", "description": "The highest scoring system in the 2018 CoNLL shared task was that of \\newciterosa-marecek-2018-cuni who achieved a LAS score of 49.4 on the Faroese test set. \\newcitetyers-etal-2018-multi report an LAS of 64.43 with a monolingual multi-source approach. Our implementation which uses a different parser (AllenNLP versus UDPipe) and pre-trained word embeddings achieves an LAS of 68. Our highest score of 71.51 is achieved through the combination of projecting from strong monolingual source models and then training multi-treebank POS tagging and parsing models on the outputs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 6: Comparison to previous work. LAS on Faroese test set. Note that the first results uses predicted segmentation and tokenization whereas the rest used gold.'.\n\nQuestion: What is the difference in LAS score between the highest scoring system and the system by rosa-marecek-2018-cuni?\n" }, { "role": "assistant", "content": "The highest scoring system achieved an LAS score of 71.51, while rosa-marecek-2018-cuni achieved 49.4. The difference is 71.51 - 49.4. So, the answer is {\"answer\": \"22.11\"}" } ], "images": [ "scigen/train/1941.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Comparison to previous work. LAS on Faroese test set. Note that the first results uses predicted segmentation and tokenization whereas the rest used gold." ], "reasoning": "The highest scoring system achieved an LAS score of 71.51, while rosa-marecek-2018-cuni achieved 49.4. The difference is 71.51 - 49.4.", "question": "What is the difference in LAS score between the highest scoring system and the system by rosa-marecek-2018-cuni?", "answer": "So, the answer is {\"answer\": \"22.11\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe system by tyers-etal-2018-multi achieved a higher LAS score than 'Our implementation'.\n" }, { "role": "assistant", "content": "The table shows that tyers-etal-2018-multi achieved an LAS score of 64.4, while 'Our implementation' achieved 68.0. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1941.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Comparison to previous work. LAS on Faroese test set. Note that the first results uses predicted segmentation and tokenization whereas the rest used gold." ], "reasoning": "The table shows that tyers-etal-2018-multi achieved an LAS score of 64.4, while 'Our implementation' achieved 68.0.", "statement": "The system by tyers-etal-2018-multi achieved a higher LAS score than 'Our implementation'.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Formants measurement in Hertz. Our measurements are compared with Hawkings et. al., Hunter et. al., Deterding.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Formants are influenced by age and gender, making phoneme identification challenging but achievable with around 70% accuracy.This is supported by observations in which it was seen that formants do not only depend on the geometry of the vocal tract of each person, but also are statistically dependent on the age, as shown in (Hawkings & Midgley, 2005); as well as on gender, as shown in (Hillenbrand, Getty, Clark, & Wheeler, 1995). Therefore, we see that identifying a phoneme by its formants is not trivial, but as we showed, can be done with an estimated accuracy of 70%. Despite the low accuracy, formants keep being used for vowel pronunciation research, as in (Rasilo & Räsänen, 2017), in which the process of infants learning their native language was simulated through a Leaning Virtual Infant that received sounds from its caregivers. At first the Learning Virtual Infant babbled randomly, but as the learning process advanced, it began to compute formants to identify similar sounding vowels and to improve its pronunciation." } ], "images": [ "scigen/train/1942.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Formants measurement in Hertz. Our measurements are compared with Hawkings et. al., Hunter et. al., Deterding." ], "reasoning": "Formants are influenced by age and gender, making phoneme identification challenging but achievable with around 70% accuracy.", "description": "This is supported by observations in which it was seen that formants do not only depend on the geometry of the vocal tract of each person, but also are statistically dependent on the age, as shown in (Hawkings & Midgley, 2005); as well as on gender, as shown in (Hillenbrand, Getty, Clark, & Wheeler, 1995). Therefore, we see that identifying a phoneme by its formants is not trivial, but as we showed, can be done with an estimated accuracy of 70%. Despite the low accuracy, formants keep being used for vowel pronunciation research, as in (Rasilo & Räsänen, 2017), in which the process of infants learning their native language was simulated through a Leaning Virtual Infant that received sounds from its caregivers. At first the Learning Virtual Infant babbled randomly, but as the learning process advanced, it began to compute formants to identify similar sounding vowels and to improve its pronunciation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 2: Formants measurement in Hertz. Our measurements are compared with Hawkings et. al., Hunter et. al., Deterding.\nQuestion: What is the F1 value for the phoneme /æ/ according to Deterding?\n" }, { "role": "assistant", "content": "To find the F1 value for the phoneme /æ/ according to Deterding, locate the row for /æ/ and then find the corresponding value under the Deterding F1 column. So, the answer is {\"answer\": \"748\"}" } ], "images": [ "scigen/train/1942.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Formants measurement in Hertz. Our measurements are compared with Hawkings et. al., Hunter et. al., Deterding." ], "reasoning": "To find the F1 value for the phoneme /æ/ according to Deterding, locate the row for /æ/ and then find the corresponding value under the Deterding F1 column.", "question": "What is the F1 value for the phoneme /æ/ according to Deterding?", "answer": "So, the answer is {\"answer\": \"748\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The F2 value for the phoneme /ɪ/ according to Hawkings et al. is 1160. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe F2 value for the phoneme /ɪ/ according to Hawkings et al. is 1160.\n" }, { "role": "assistant", "content": "To verify this statement, locate the row for the phoneme /ɪ/ and then check the value under the Hawkings et al. F2 column. The table shows that the F2 value for /ɪ/ according to Hawkings et al. is indeed 1160. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1942.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Formants measurement in Hertz. Our measurements are compared with Hawkings et. al., Hunter et. al., Deterding." ], "reasoning": "To verify this statement, locate the row for the phoneme /ɪ/ and then check the value under the Hawkings et al. F2 column. The table shows that the F2 value for /ɪ/ according to Hawkings et al. is indeed 1160.", "statement": "The F2 value for the phoneme /ɪ/ according to Hawkings et al. is 1160.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: The scores of all the participants in the WNUT-2017 shared task. The metrics of the shared task are entity and surface form F1-scores. Our results are highlighted.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our approach, UH-RiTUAL, achieved the highest F1-scores for both entities (41.86%) and surface forms (40.24%), indicating the difficulty of the task and that the problem is far from being solved.Regarding the shared-task metrics, our approach achieves a 41.86% F1-score for entities and 40.24% for surface forms. In general, the final scores are low which states the difficulty of the task and that the problem is far from being solved." } ], "images": [ "scigen/train/1943.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The scores of all the participants in the WNUT-2017 shared task. The metrics of the shared task are entity and surface form F1-scores. Our results are highlighted." ], "reasoning": "Our approach, UH-RiTUAL, achieved the highest F1-scores for both entities (41.86%) and surface forms (40.24%), indicating the difficulty of the task and that the problem is far from being solved.", "description": "Regarding the shared-task metrics, our approach achieves a 41.86% F1-score for entities and 40.24% for surface forms. In general, the final scores are low which states the difficulty of the task and that the problem is far from being solved.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 3: The scores of all the participants in the WNUT-2017 shared task. The metrics of the shared task are entity and surface form F1-scores. Our results are highlighted.', solve the following question step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich participant achieved the highest F1-score for entities?\n" }, { "role": "assistant", "content": "By examining the F1-E (%) column, UH-RiTUAL has the highest score of 41.86%. So, the answer is {\"answer\": \"UH-RiTUAL\"}." } ], "images": [ "scigen/train/1943.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The scores of all the participants in the WNUT-2017 shared task. The metrics of the shared task are entity and surface form F1-scores. Our results are highlighted." ], "reasoning": "By examining the F1-E (%) column, UH-RiTUAL has the highest score of 41.86%.", "question": "Which participant achieved the highest F1-score for entities?", "answer": "So, the answer is {\"answer\": \"UH-RiTUAL\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nDrexel-CCI achieved the lowest F1-score for surface forms.\n" }, { "role": "assistant", "content": "By examining the F1-SF (%) column, Drexel-CCI has the lowest score of 25.26%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1943.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The scores of all the participants in the WNUT-2017 shared task. The metrics of the shared task are entity and surface form F1-scores. Our results are highlighted." ], "reasoning": "By examining the F1-SF (%) column, Drexel-CCI has the lowest score of 25.26%.", "statement": "Drexel-CCI achieved the lowest F1-score for surface forms.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: This table shows the final results of our submission. The hardest class to predict for is creative-work, while the easiest is person.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our approach is most effective for person and location categories, while it struggles the most with creative-work and product categories due to their less restrictive patterns.Our approach obtains the best results for the person and location categories. It is less effective for corporation, and the most difficult categories for our system are creative-work and product. Our intuition is that the latter two classes are the most difficult to predict for because they grow faster and have less restrictive patterns than the rest. For instance, products can have any type of letters or numbers in their names, or in the case of creative works, as many words as their titles can hold (e.g. name of movies, books, songs, etc.)." } ], "images": [ "scigen/train/1944.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: This table shows the final results of our submission. The hardest class to predict for is creative-work, while the easiest is person." ], "reasoning": "Our approach is most effective for person and location categories, while it struggles the most with creative-work and product categories due to their less restrictive patterns.", "description": "Our approach obtains the best results for the person and location categories. It is less effective for corporation, and the most difficult categories for our system are creative-work and product. Our intuition is that the latter two classes are the most difficult to predict for because they grow faster and have less restrictive patterns than the rest. For instance, products can have any type of letters or numbers in their names, or in the case of creative works, as many words as their titles can hold (e.g. name of movies, books, songs, etc.).", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: This table shows the final results of our submission. The hardest class to predict for is creative-work, while the easiest is person.', solve this question and give detail solutions. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich category has the highest F1 score and why is it considered the best result?\n" }, { "role": "assistant", "content": "The person category has the highest F1 score of 58.66%, indicating it is the best result. This is because the approach is most effective for this category. So, the answer is {\"answer\": \"person\"}" } ], "images": [ "scigen/train/1944.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: This table shows the final results of our submission. The hardest class to predict for is creative-work, while the easiest is person." ], "reasoning": "The person category has the highest F1 score of 58.66%, indicating it is the best result. This is because the approach is most effective for this category.", "question": "Which category has the highest F1 score and why is it considered the best result?", "answer": "So, the answer is {\"answer\": \"person\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The approach is least effective for the corporation category. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe approach is least effective for the corporation category.\n" }, { "role": "assistant", "content": "The table shows that the creative-work category has the lowest F1 score of 12.79%, making it the least effective category, not corporation. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1944.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: This table shows the final results of our submission. The hardest class to predict for is creative-work, while the easiest is person." ], "reasoning": "The table shows that the creative-work category has the lowest F1 score of 12.79%, making it the least effective category, not corporation.", "statement": "The approach is least effective for the corporation category.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: Parse and disfluency detection F1 scores on the test set. The best model has statistically significant gains over the text-only baseline with p-value <0.02.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The best model shows statistically significant improvement with acoustic-prosodic features over the text-only baseline, with a p-value <0.02.Again, adding the acoustic-prosodic features improves over the text-only baseline. The gains are statistically significant for the best model with p-value <0.02, again using a bootstrap test with simulated 105 random test draws on the two models." } ], "images": [ "scigen/train/1945.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Parse and disfluency detection F1 scores on the test set. The best model has statistically significant gains over the text-only baseline with p-value <0.02." ], "reasoning": "The best model shows statistically significant improvement with acoustic-prosodic features over the text-only baseline, with a p-value <0.02.", "description": "Again, adding the acoustic-prosodic features improves over the text-only baseline. The gains are statistically significant for the best model with p-value <0.02, again using a bootstrap test with simulated 105 random test draws on the two models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 4: Parse and disfluency detection F1 scores on the test set. The best model has statistically significant gains over the text-only baseline with p-value <0.02.' and proceed to solve the following question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the parse score of the best model?\n" }, { "role": "assistant", "content": "The table lists the parse scores for different models, and the best model has the highest parse score. So, the answer is {\"answer\": \"88.50\"}" } ], "images": [ "scigen/train/1945.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Parse and disfluency detection F1 scores on the test set. The best model has statistically significant gains over the text-only baseline with p-value <0.02." ], "reasoning": "The table lists the parse scores for different models, and the best model has the highest parse score.", "question": "What is the parse score of the best model?", "answer": "So, the answer is {\"answer\": \"88.50\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe best model has the highest disfluency score.\n" }, { "role": "assistant", "content": "The table shows the disfluency scores for different models, and the best model has the highest disfluency score of 77.47. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1945.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Parse and disfluency detection F1 scores on the test set. The best model has statistically significant gains over the text-only baseline with p-value <0.02." ], "reasoning": "The table shows the disfluency scores for different models, and the best model has the highest disfluency score of 77.47.", "statement": "The best model has the highest disfluency score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Scores of text-only models on the dev set: 2044 fluent and 3725 disfluent sentences. C-attn denotes content-only attention; CL-attn denotes content+location attention.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The CL-attn text-only model outperforms both the Berkeley parser and the C-attn model in terms of F1 and flat-F1 scores, particularly excelling in parsing disfluent sentences.We first show our results on the model using only text (i.e. xi=ei) to establish a strong baseline, on top of which we can add acoustic-prosodic features. We experiment with the content-only attention model used by \\newciteVinyals2015 and the content+location attention of \\newciteChorowski2015. For comparison with previous non-neural models, we use a high-quality latent-variable parser, the Berkeley parser Petrov et al. In terms of F1, the content+location attention beats the Berkeley parser by about 2.5% and content-only attention by about 4.5%. Flat-F1 scores for both encoder-decoder models is lower than their corresponding F1 scores, suggesting that the encoder-decoder models do well on predicting the internal structure of EDIT nodes while the reverse is true for the Berkeley parser. It is clear that most of the gains for content+location attention are from disfluent sentences. A possible explanation is the presence of duplicate words or phrases in disfluent sentences, which can be problematic for a content-only attention model. Since our best model is the content+location attention model, we will henceforth refer to it as the “CL-attn” text-only model. All models using acoustic-prosodic features are extensions of this model, which provides a strong text-only baseline. One major challenge of parsing conversational speech is the presence of disfluencies, which are grammatical and prosodic interruptions. Disfluencies include repetitions (‘I am + I am’), repairs (‘I am + we are’), and restarts (‘ What I + Today is the…’), where the ‘+’ corresponds to an interruption point. Repairs often involve parallel grammatical constructions, but they can be more complex, involving hedging, clarifications, etc. However, more explicit modeling of disfluency pattern match characteristics in a dependency parser Honnibal and Johnson (F = 84.1 vs. 76.7 for our text only model). Pattern match features also benefit a neural model for disfluency detection alone (F = 87.0) Zayats et al. F = 87.5) Wang et al. Experiments with oracle disfluencies as features improve the CL-attn text-only parsing performance from 87.85 to 89.38 on the test set, showing that more accurate disfluency modeling is a potential area of improvement." } ], "images": [ "scigen/train/1946.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Scores of text-only models on the dev set: 2044 fluent and 3725 disfluent sentences. C-attn denotes content-only attention; CL-attn denotes content+location attention." ], "reasoning": "The CL-attn text-only model outperforms both the Berkeley parser and the C-attn model in terms of F1 and flat-F1 scores, particularly excelling in parsing disfluent sentences.", "description": "We first show our results on the model using only text (i.e. xi=ei) to establish a strong baseline, on top of which we can add acoustic-prosodic features. We experiment with the content-only attention model used by \\newciteVinyals2015 and the content+location attention of \\newciteChorowski2015. For comparison with previous non-neural models, we use a high-quality latent-variable parser, the Berkeley parser Petrov et al. In terms of F1, the content+location attention beats the Berkeley parser by about 2.5% and content-only attention by about 4.5%. Flat-F1 scores for both encoder-decoder models is lower than their corresponding F1 scores, suggesting that the encoder-decoder models do well on predicting the internal structure of EDIT nodes while the reverse is true for the Berkeley parser. It is clear that most of the gains for content+location attention are from disfluent sentences. A possible explanation is the presence of duplicate words or phrases in disfluent sentences, which can be problematic for a content-only attention model. Since our best model is the content+location attention model, we will henceforth refer to it as the “CL-attn” text-only model. All models using acoustic-prosodic features are extensions of this model, which provides a strong text-only baseline. One major challenge of parsing conversational speech is the presence of disfluencies, which are grammatical and prosodic interruptions. Disfluencies include repetitions (‘I am + I am’), repairs (‘I am + we are’), and restarts (‘ What I + Today is the…’), where the ‘+’ corresponds to an interruption point. Repairs often involve parallel grammatical constructions, but they can be more complex, involving hedging, clarifications, etc. However, more explicit modeling of disfluency pattern match characteristics in a dependency parser Honnibal and Johnson (F = 84.1 vs. 76.7 for our text only model). Pattern match features also benefit a neural model for disfluency detection alone (F = 87.0) Zayats et al. F = 87.5) Wang et al. Experiments with oracle disfluencies as features improve the CL-attn text-only parsing performance from 87.85 to 89.38 on the test set, showing that more accurate disfluency modeling is a potential area of improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 1: Scores of text-only models on the dev set: 2044 fluent and 3725 disfluent sentences. C-attn denotes content-only attention; CL-attn denotes content+location attention.'.\n\nQuestion: Which model has the highest F1 score for disfluent sentences and what is the score?\n" }, { "role": "assistant", "content": "The table shows the F1 scores for disfluent sentences for three models: Berkeley, C-attn, and CL-attn. The CL-attn model has the highest F1 score for disfluent sentences. So, the answer is {\"answer\": \"85.95\"}." } ], "images": [ "scigen/train/1946.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Scores of text-only models on the dev set: 2044 fluent and 3725 disfluent sentences. C-attn denotes content-only attention; CL-attn denotes content+location attention." ], "reasoning": "The table shows the F1 scores for disfluent sentences for three models: Berkeley, C-attn, and CL-attn. The CL-attn model has the highest F1 score for disfluent sentences.", "question": "Which model has the highest F1 score for disfluent sentences and what is the score?", "answer": "So, the answer is {\"answer\": \"85.95\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The Berkeley parser has a higher F1 score for fluent sentences compared to the CL-attn model., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Berkeley parser has a higher F1 score for fluent sentences compared to the CL-attn model.\n" }, { "role": "assistant", "content": "The table shows that the F1 score for fluent sentences is 90.52 for the Berkeley parser and 92.07 for the CL-attn model, indicating that the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1946.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Scores of text-only models on the dev set: 2044 fluent and 3725 disfluent sentences. C-attn denotes content-only attention; CL-attn denotes content+location attention." ], "reasoning": "The table shows that the F1 score for fluent sentences is 90.52 for the Berkeley parser and 92.07 for the CL-attn model, indicating that the statement is incorrect.", "statement": "The Berkeley parser has a higher F1 score for fluent sentences compared to the CL-attn model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Parse and disfluency detection F1 scores on the dev set. Flat-F1 scores were consistently 0.1%-0.3% lower for our models, but 0.2% higher for the Berkeley parser (85.64).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Adding any combination of acoustic-prosodic features improves performance over the text-only baseline, with the text + p + δ + f0/E-CNN model achieving the best performance.First, we note that adding any combination of acoustic-prosodic features (individually or in sets) improves performance over the text-only baseline. However, certain combinations of acoustic-prosodic features are not always better than their subsets. The text + p + δ + f0/E-CNN model that uses all three types of features has the best performance with a gain of 0.7% over the already-strong text-only baseline. We will henceforth refer to the text + p + δ + f0/E-CNN model as our “best model”." } ], "images": [ "scigen/train/1947.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Parse and disfluency detection F1 scores on the dev set. Flat-F1 scores were consistently 0.1%-0.3% lower for our models, but 0.2% higher for the Berkeley parser (85.64)." ], "reasoning": "Adding any combination of acoustic-prosodic features improves performance over the text-only baseline, with the text + p + δ + f0/E-CNN model achieving the best performance.", "description": "First, we note that adding any combination of acoustic-prosodic features (individually or in sets) improves performance over the text-only baseline. However, certain combinations of acoustic-prosodic features are not always better than their subsets. The text + p + δ + f0/E-CNN model that uses all three types of features has the best performance with a gain of 0.7% over the already-strong text-only baseline. We will henceforth refer to the text + p + δ + f0/E-CNN model as our “best model”.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Parse and disfluency detection F1 scores on the dev set. Flat-F1 scores were consistently 0.1%-0.3% lower for our models, but 0.2% higher for the Berkeley parser (85.64).\nQuestion: What is the performance gain of the text + p + δ + f0/E-CNN model over the Berkeley (text only) model in terms of Parse?\nSolve the above question based on the table information. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The Parse score for the Berkeley (text only) model is 85.41, and the Parse score for the text + p + δ + f0/E-CNN model is 88.59. The performance gain is 88.59 - 85.41. So, the answer is {\"answer\": \"3.18\"}." } ], "images": [ "scigen/train/1947.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Parse and disfluency detection F1 scores on the dev set. Flat-F1 scores were consistently 0.1%-0.3% lower for our models, but 0.2% higher for the Berkeley parser (85.64)." ], "reasoning": "The Parse score for the Berkeley (text only) model is 85.41, and the Parse score for the text + p + δ + f0/E-CNN model is 88.59. The performance gain is 88.59 - 85.41.", "question": "What is the performance gain of the text + p + δ + f0/E-CNN model over the Berkeley (text only) model in terms of Parse?", "answer": "So, the answer is {\"answer\": \"3.18\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe text + p + δ + f0/E-CNN model has the highest Disf score.\n" }, { "role": "assistant", "content": "The Disf score for the text + p + δ + f0/E-CNN model is 80.84, which is not the highest. The highest Disf score is 81.19 for the + p + f0/E-CNN model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1947.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Parse and disfluency detection F1 scores on the dev set. Flat-F1 scores were consistently 0.1%-0.3% lower for our models, but 0.2% higher for the Berkeley parser (85.64)." ], "reasoning": "The Disf score for the text + p + δ + f0/E-CNN model is 80.84, which is not the highest. The highest Disf score is 81.19 for the + p + f0/E-CNN model.", "statement": "The text + p + δ + f0/E-CNN model has the highest Disf score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: Parse and disfluency detection F1 scores on the test set comparing to other reported results.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The table shows that different models and experimental setups yield varying results in parsing and disfluency detection, with our framework leveraging sentence-internal prosodic cues more effectively.It is difficult to compare systems directly, because of the many differences in the experimental set-up. For example, the original \\newciteCharniak2001 result (reporting F=85.9 for parsing and F=78.2 for disfluencies) leverages punctuation in the text stream, which is not realistic for speech transcripts and not used in most other work. Our work benefits from more text training material than others, but others benefit from gold part-of-speech tags. \\newciteKahn2005 use a modified sentence segmentation. There are probably minor differences in handling of word fragments and scoring edit regions. Thus, this table primarily shows that our framework leads to more benefits from sentence-internal prosodic cues than others have obtained." } ], "images": [ "scigen/train/1948.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Parse and disfluency detection F1 scores on the test set comparing to other reported results." ], "reasoning": "The table shows that different models and experimental setups yield varying results in parsing and disfluency detection, with our framework leveraging sentence-internal prosodic cues more effectively.", "description": "It is difficult to compare systems directly, because of the many differences in the experimental set-up. For example, the original \\newciteCharniak2001 result (reporting F=85.9 for parsing and F=78.2 for disfluencies) leverages punctuation in the text stream, which is not realistic for speech transcripts and not used in most other work. Our work benefits from more text training material than others, but others benefit from gold part-of-speech tags. \\newciteKahn2005 use a modified sentence segmentation. There are probably minor differences in handling of word fragments and scoring edit regions. Thus, this table primarily shows that our framework leads to more benefits from sentence-internal prosodic cues than others have obtained.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 5: Parse and disfluency detection F1 scores on the test set comparing to other reported results.\nQuestion: Which model achieved the highest parsing score?\n" }, { "role": "assistant", "content": "By examining the 'Parse' column, the highest score is 88.50, achieved by the 'best model'. So, the answer is {\"answer\": \"best model\"}." } ], "images": [ "scigen/train/1948.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Parse and disfluency detection F1 scores on the test set comparing to other reported results." ], "reasoning": "By examining the 'Parse' column, the highest score is 88.50, achieved by the 'best model'.", "question": "Which model achieved the highest parsing score?", "answer": "So, the answer is {\"answer\": \"best model\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The CL-attn (text only) model achieved a higher parsing score than the Kahn et al. (2005) model. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe CL-attn (text only) model achieved a higher parsing score than the Kahn et al. (2005) model.\n" }, { "role": "assistant", "content": "The table shows that the CL-attn (text only) model has a parsing score of 87.99, while the Kahn et al. (2005) model has a parsing score of 86.4, confirming the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1948.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Parse and disfluency detection F1 scores on the test set comparing to other reported results." ], "reasoning": "The table shows that the CL-attn (text only) model has a parsing score of 87.99, while the Kahn et al. (2005) model has a parsing score of 86.4, confirming the statement.", "statement": "The CL-attn (text only) model achieved a higher parsing score than the Kahn et al. (2005) model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 6: Dev set F1-score of text-only and best model on fluent (2029) vs. disfluent (3689) sentences.10'.\n" }, { "role": "assistant", "content": "Because sentence boundaries are given, and many fluent sentences in spontaneous speech are short, there is less potential for benefit from prosody in the fluent set.Because sentence boundaries are given, and so many fluent sentences in spontaneous speech are short, there is less potential for benefit from prosody in the fluent set." } ], "images": [ "scigen/train/1949.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Dev set F1-score of text-only and best model on fluent (2029) vs. disfluent (3689) sentences.10" ], "reasoning": "Because sentence boundaries are given, and many fluent sentences in spontaneous speech are short, there is less potential for benefit from prosody in the fluent set.", "description": "Because sentence boundaries are given, and so many fluent sentences in spontaneous speech are short, there is less potential for benefit from prosody in the fluent set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 6: Dev set F1-score of text-only and best model on fluent (2029) vs. disfluent (3689) sentences.10' to sequentially solve the question presented below. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhich model performs better on disfluent sentences?\n" }, { "role": "assistant", "content": "Compare the scores of the 'text-only' and 'best model' for disfluent sentences. The 'text-only' model has a score of 85.90, while the 'best model' has a score of 87.02. So, the answer is {\"answer\": \"best model\"}" } ], "images": [ "scigen/train/1949.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Dev set F1-score of text-only and best model on fluent (2029) vs. disfluent (3689) sentences.10" ], "reasoning": "Compare the scores of the 'text-only' and 'best model' for disfluent sentences. The 'text-only' model has a score of 85.90, while the 'best model' has a score of 87.02.", "question": "Which model performs better on disfluent sentences?", "answer": "So, the answer is {\"answer\": \"best model\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe text-only model performs better on fluent sentences.\n" }, { "role": "assistant", "content": "Compare the scores of the 'text-only' and 'best model' for fluent sentences. The 'text-only' model has a score of 92.07, while the 'best model' has a score of 92.03. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1949.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Dev set F1-score of text-only and best model on fluent (2029) vs. disfluent (3689) sentences.10" ], "reasoning": "Compare the scores of the 'text-only' and 'best model' for fluent sentences. The 'text-only' model has a score of 92.07, while the 'best model' has a score of 92.03.", "statement": "The text-only model performs better on fluent sentences.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Human ratings demonstrating a comparison between our models to the ground truth responses on the metrics of readability, coherence and emotional appropriateness'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our fine-tuned model achieves higher scores than the emo-prepend model in readability, coherence, and emotional appropriateness according to human ratings.We find that our fine-tuned model outperforms the emo-prepend on all three metrics from the ratings provided by the human ratings." } ], "images": [ "scigen/train/1950.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Human ratings demonstrating a comparison between our models to the ground truth responses on the metrics of readability, coherence and emotional appropriateness" ], "reasoning": "Our fine-tuned model achieves higher scores than the emo-prepend model in readability, coherence, and emotional appropriateness according to human ratings.", "description": "We find that our fine-tuned model outperforms the emo-prepend on all three metrics from the ratings provided by the human ratings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: Human ratings demonstrating a comparison between our models to the ground truth responses on the metrics of readability, coherence and emotional appropriateness', answer the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model has the highest score in readability and what is the score?\n" }, { "role": "assistant", "content": "To find the model with the highest readability score, compare the readability scores of all models. The fine-tuned model has the highest readability score of 4.14. So, the answer is {\"answer\": \"4.14\"}." } ], "images": [ "scigen/train/1950.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Human ratings demonstrating a comparison between our models to the ground truth responses on the metrics of readability, coherence and emotional appropriateness" ], "reasoning": "To find the model with the highest readability score, compare the readability scores of all models. The fine-tuned model has the highest readability score of 4.14.", "question": "Which model has the highest score in readability and what is the score?", "answer": "So, the answer is {\"answer\": \"4.14\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe emo-prepend model has a higher coherence score than the ground truth.\n" }, { "role": "assistant", "content": "The coherence score for the emo-prepend model is 3.4, while the ground truth has a coherence score of 3.86. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1950.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Human ratings demonstrating a comparison between our models to the ground truth responses on the metrics of readability, coherence and emotional appropriateness" ], "reasoning": "The coherence score for the emo-prepend model is 3.4, while the ground truth has a coherence score of 3.86. Therefore, the statement is refuted.", "statement": "The emo-prepend model has a higher coherence score than the ground truth.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Comparison of the performance of our model to the baseline model proposed by Rashkin et al (2019) across a variety of automated metrics to provide a thorough comparison. x indicates that these metrics were not provided in the Rashkin et al (2019) work.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our approach achieves lower perplexity and higher BLEU scores compared to the baseline, and our emo-prepend model shows higher readability and diversity.We first compare the performance of our approach with the baseline results obtained from Rashkin et al. that uses a full transformer architecture Vaswani et al. We find that fine-tuning the GPT-2 language model using a transfer learning approach helps us achieve a lower perplexity and a higher BLEU scores. The results from our approach are consistent with the empirical study conducted by Edunov et al that demonstrate the effectiveness of the using pre-trained model diminishes when added to the decoder network in an seq2seq approach. We also perform a comparison between our two models on the metrics of length, diversity, readability and coherence. We find that our baseline model produces less diverse responses compared to when the model is conditioned on emotion. We find that the our emo-prepend model also higher a slightly higher readability score that our baseline model." } ], "images": [ "scigen/train/1951.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of the performance of our model to the baseline model proposed by Rashkin et al (2019) across a variety of automated metrics to provide a thorough comparison. x indicates that these metrics were not provided in the Rashkin et al (2019) work." ], "reasoning": "Our approach achieves lower perplexity and higher BLEU scores compared to the baseline, and our emo-prepend model shows higher readability and diversity.", "description": "We first compare the performance of our approach with the baseline results obtained from Rashkin et al. that uses a full transformer architecture Vaswani et al. We find that fine-tuning the GPT-2 language model using a transfer learning approach helps us achieve a lower perplexity and a higher BLEU scores. The results from our approach are consistent with the empirical study conducted by Edunov et al that demonstrate the effectiveness of the using pre-trained model diminishes when added to the decoder network in an seq2seq approach. We also perform a comparison between our two models on the metrics of length, diversity, readability and coherence. We find that our baseline model produces less diverse responses compared to when the model is conditioned on emotion. We find that the our emo-prepend model also higher a slightly higher readability score that our baseline model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Comparison of the performance of our model to the baseline model proposed by Rashkin et al (2019) across a variety of automated metrics to provide a thorough comparison. x indicates that these metrics were not provided in the Rashkin et al (2019) work.', unfold the question presented below, detailing every step of your calculation. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest BLEU score and what is its value?\n" }, { "role": "assistant", "content": "By comparing the BLEU scores in the table, we see that 'Our Model Fine-Tuned' has the highest BLEU score of 7.71. So, the answer is {\"answer\": \"Our Model Fine-Tuned with a BLEU score of 7.71\"}" } ], "images": [ "scigen/train/1951.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of the performance of our model to the baseline model proposed by Rashkin et al (2019) across a variety of automated metrics to provide a thorough comparison. x indicates that these metrics were not provided in the Rashkin et al (2019) work." ], "reasoning": "By comparing the BLEU scores in the table, we see that 'Our Model Fine-Tuned' has the highest BLEU score of 7.71.", "question": "Which model has the highest BLEU score and what is its value?", "answer": "So, the answer is {\"answer\": \"Our Model Fine-Tuned with a BLEU score of 7.71\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Baseline Fine-Tuned model by Rashkin et al. (2019) has a higher readability score than Our Model Fine-Tuned.\n" }, { "role": "assistant", "content": "The table shows that the readability score for the Baseline Fine-Tuned model by Rashkin et al. (2019) is not provided, while Our Model Fine-Tuned has a readability score of 0.78. So, the answer is {\"answer\": \"not enough info\"}" } ], "images": [ "scigen/train/1951.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of the performance of our model to the baseline model proposed by Rashkin et al (2019) across a variety of automated metrics to provide a thorough comparison. x indicates that these metrics were not provided in the Rashkin et al (2019) work." ], "reasoning": "The table shows that the readability score for the Baseline Fine-Tuned model by Rashkin et al. (2019) is not provided, while Our Model Fine-Tuned has a readability score of 0.78.", "statement": "The Baseline Fine-Tuned model by Rashkin et al. (2019) has a higher readability score than Our Model Fine-Tuned.", "answer": "So, the answer is {\"answer\": \"not enough info\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: Leaderboard results under different experimental setups. NL, SR, and SPL are Navigation Length, Success Rate and Success rate weighted by Path Length. The primary metric for each setup is in italics. The best results are in bold font and the second best results are underlined.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our agent model outperforms all other models in all experimental setups on the Vision and Language Navigation leaderboard, evaluated on a private unseen test set with 18 new environments.In this section, we compare our agent model with the models in previous works on the Vision and Language Navigation (VLN) leaderboard. The models on the leaderboard are evaluated on a private unseen test set which contains 18 new environments. For the result, our model outperforms all other models in all experimental setups." } ], "images": [ "scigen/train/1952.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Leaderboard results under different experimental setups. NL, SR, and SPL are Navigation Length, Success Rate and Success rate weighted by Path Length. The primary metric for each setup is in italics. The best results are in bold font and the second best results are underlined." ], "reasoning": "Our agent model outperforms all other models in all experimental setups on the Vision and Language Navigation leaderboard, evaluated on a private unseen test set with 18 new environments.", "description": "In this section, we compare our agent model with the models in previous works on the Vision and Language Navigation (VLN) leaderboard. The models on the leaderboard are evaluated on a private unseen test set which contains 18 new environments. For the result, our model outperforms all other models in all experimental setups.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 1: Leaderboard results under different experimental setups. NL, SR, and SPL are Navigation Length, Success Rate and Success rate weighted by Path Length. The primary metric for each setup is in italics. The best results are in bold font and the second best results are underlined.' and proceed to solve the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model has the highest Success Rate (SR%) in the Test Unseen (Leader-Board) Beam Search setup?\n" }, { "role": "assistant", "content": "By comparing the SR% values in the Test Unseen (Leader-Board) Beam Search setup, our model has the highest SR% of 68.9. So, the answer is {\"answer\": \"Ours\"}" } ], "images": [ "scigen/train/1952.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Leaderboard results under different experimental setups. NL, SR, and SPL are Navigation Length, Success Rate and Success rate weighted by Path Length. The primary metric for each setup is in italics. The best results are in bold font and the second best results are underlined." ], "reasoning": "By comparing the SR% values in the Test Unseen (Leader-Board) Beam Search setup, our model has the highest SR% of 68.9.", "question": "Which model has the highest Success Rate (SR%) in the Test Unseen (Leader-Board) Beam Search setup?", "answer": "So, the answer is {\"answer\": \"Ours\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Self-Monitoring Ma et al. (2019) model has the highest Navigation Length (NL) in the Test Unseen (Leader-Board) Beam Search setup.\n" }, { "role": "assistant", "content": "By comparing the NL values in the Test Unseen (Leader-Board) Beam Search setup, the Self-Monitoring Ma et al. (2019) model has an NL of 373, which is the highest. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1952.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Leaderboard results under different experimental setups. NL, SR, and SPL are Navigation Length, Success Rate and Success rate weighted by Path Length. The primary metric for each setup is in italics. The best results are in bold font and the second best results are underlined." ], "reasoning": "By comparing the NL values in the Test Unseen (Leader-Board) Beam Search setup, the Self-Monitoring Ma et al. (2019) model has an NL of 373, which is the highest.", "statement": "The Self-Monitoring Ma et al. (2019) model has the highest Navigation Length (NL) in the Test Unseen (Leader-Board) Beam Search setup.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Results of the dependency arc prediction task. L0–L4 denote the different layers of the model. DP refers to the RNN trained with dependency parsing supervision.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The dependency parser shows the best performance, peaking at approximately 95% in the last layer, while other models show improvements of 12 to 20 accuracy points above the input layer.We see the best performance from the dependency parser, finding that the performance for the dependency parser’s representations continue to improve in the deepest layers, with a maximum performance of approximately 95% on the last layer. This result is unsurprising, as this closely related to the task on which the model was explicitly trained. In the three other models, we find peaks that occur 12 to 20 accuracy points above the input layer’s performance. These results support the findings from the constituency label prediction task and show that these findings hold up across syntactic formalisms." } ], "images": [ "scigen/train/1953.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of the dependency arc prediction task. L0–L4 denote the different layers of the model. DP refers to the RNN trained with dependency parsing supervision." ], "reasoning": "The dependency parser shows the best performance, peaking at approximately 95% in the last layer, while other models show improvements of 12 to 20 accuracy points above the input layer.", "description": "We see the best performance from the dependency parser, finding that the performance for the dependency parser’s representations continue to improve in the deepest layers, with a maximum performance of approximately 95% on the last layer. This result is unsurprising, as this closely related to the task on which the model was explicitly trained. In the three other models, we find peaks that occur 12 to 20 accuracy points above the input layer’s performance. These results support the findings from the constituency label prediction task and show that these findings hold up across syntactic formalisms.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model shows the highest performance in the last layer?\nSolve the above question based on the table titled 'Table 2: Results of the dependency arc prediction task. L0–L4 denote the different layers of the model. DP refers to the RNN trained with dependency parsing supervision.'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the performance of different models across various layers. The dependency parser (DP) has the highest performance in the last layer (L4) with a value of 0.95. So, the answer is {\"answer\": \"DP\"}." } ], "images": [ "scigen/train/1953.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of the dependency arc prediction task. L0–L4 denote the different layers of the model. DP refers to the RNN trained with dependency parsing supervision." ], "reasoning": "The table shows the performance of different models across various layers. The dependency parser (DP) has the highest performance in the last layer (L4) with a value of 0.95.", "question": "Which model shows the highest performance in the last layer?", "answer": "So, the answer is {\"answer\": \"DP\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe SRL model shows the highest performance in the third layer.\n" }, { "role": "assistant", "content": "The table indicates that the SRL model has a performance of 0.79 in the third layer (L3), which is not the highest among all models. The LM model has a higher performance of 0.80 in the same layer. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1953.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of the dependency arc prediction task. L0–L4 denote the different layers of the model. DP refers to the RNN trained with dependency parsing supervision." ], "reasoning": "The table indicates that the SRL model has a performance of 0.79 in the third layer (L3), which is not the highest among all models. The LM model has a higher performance of 0.80 in the same layer.", "statement": "The SRL model shows the highest performance in the third layer.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 6: Experimental results'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The performance of the models is evaluated using Precision, Recall, and F-measure, with Model 9 achieving the highest scores across all metrics.We are based on the standard metrics of Precision, Recall and F-measure to evaluate the performance of the experimenting models." } ], "images": [ "scigen/train/1954.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Experimental results" ], "reasoning": "The performance of the models is evaluated using Precision, Recall, and F-measure, with Model 9 achieving the highest scores across all metrics.", "description": "We are based on the standard metrics of Precision, Recall and F-measure to evaluate the performance of the experimenting models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 6: Experimental results', solve the following question step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model has the highest F-measure and what is its Precision score?\n" }, { "role": "assistant", "content": "Model 9 has the highest F-measure of 0.862. The Precision score for Model 9 is 0.867. So, the answer is {\"answer\": \"0.867\"}." } ], "images": [ "scigen/train/1954.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Experimental results" ], "reasoning": "Model 9 has the highest F-measure of 0.862. The Precision score for Model 9 is 0.867.", "question": "Which model has the highest F-measure and what is its Precision score?", "answer": "So, the answer is {\"answer\": \"0.867\"}." }, { "messages": [ { "role": "user", "content": "Model 7 has the highest Recall score. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nModel 7 has the highest Recall score.\n" }, { "role": "assistant", "content": "The table shows that Model 9 has the highest Recall score of 0.857, not Model 7. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1954.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Experimental results" ], "reasoning": "The table shows that Model 9 has the highest Recall score of 0.857, not Model 7.", "statement": "Model 7 has the highest Recall score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: Mean reciprocal rank for co-author retrieval.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The NEA-smoothed version of the ATM's parameters achieved the highest mean reciprocal rank (MRR) for finding similar authors in the NIPS corpus, outperforming both the ATM and tf-idf baseline.We also studied the performance of NEA for smoothing the author-topic distributions. The ATM could be used for a variety of applications such as automated reviewer recommendations Rosen-Zvi et al. Since these applications are based on searching for similar authors, we can treat them as a ranking problem. Following Rosen-Zvi et al. Using this distance metric, we searched for similar authors in the NIPS corpus for the 125 out of 2037 authors who wrote at least 5 papers. We reported the mean reciprocal rank (MRR) based on the rank of the most similar co-author. Further improvement was achieved by the NEA-smoothed version of the ATM’s parameters which also outperformed author vectors generated from a tf-idf baseline at this task." } ], "images": [ "scigen/train/1955.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Mean reciprocal rank for co-author retrieval." ], "reasoning": "The NEA-smoothed version of the ATM's parameters achieved the highest mean reciprocal rank (MRR) for finding similar authors in the NIPS corpus, outperforming both the ATM and tf-idf baseline.", "description": "We also studied the performance of NEA for smoothing the author-topic distributions. The ATM could be used for a variety of applications such as automated reviewer recommendations Rosen-Zvi et al. Since these applications are based on searching for similar authors, we can treat them as a ranking problem. Following Rosen-Zvi et al. Using this distance metric, we searched for similar authors in the NIPS corpus for the 125 out of 2037 authors who wrote at least 5 papers. We reported the mean reciprocal rank (MRR) based on the rank of the most similar co-author. Further improvement was achieved by the NEA-smoothed version of the ATM’s parameters which also outperformed author vectors generated from a tf-idf baseline at this task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 4: Mean reciprocal rank for co-author retrieval.', solve the subsequent question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich method achieved the highest mean reciprocal rank (MRR) for finding similar authors in the NIPS corpus?\n" }, { "role": "assistant", "content": "The table shows the MRR values for different methods. NEA smoothing has the highest value of 0.021, indicating it achieved the highest MRR. So, the answer is {\"answer\": \"NEA smoothing\"}" } ], "images": [ "scigen/train/1955.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Mean reciprocal rank for co-author retrieval." ], "reasoning": "The table shows the MRR values for different methods. NEA smoothing has the highest value of 0.021, indicating it achieved the highest MRR.", "question": "Which method achieved the highest mean reciprocal rank (MRR) for finding similar authors in the NIPS corpus?", "answer": "So, the answer is {\"answer\": \"NEA smoothing\"}" }, { "messages": [ { "role": "user", "content": "The ATM method achieved the highest mean reciprocal rank (MRR) for finding similar authors in the NIPS corpus. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe ATM method achieved the highest mean reciprocal rank (MRR) for finding similar authors in the NIPS corpus.\n" }, { "role": "assistant", "content": "The table shows that the ATM method has an MRR of 0.016, which is lower than the MRR values for NEA embeddings (0.018), tf-idf (0.019), and NEA smoothing (0.021). Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1955.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Mean reciprocal rank for co-author retrieval." ], "reasoning": "The table shows that the ATM method has an MRR of 0.016, which is lower than the MRR values for NEA embeddings (0.018), tf-idf (0.019), and NEA smoothing (0.021). Therefore, the statement is refuted.", "statement": "The ATM method achieved the highest mean reciprocal rank (MRR) for finding similar authors in the NIPS corpus.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Figure 1: The worst four topics produced by LDA, in terms of per-topic coherence score, and their corresponding NEA topics, with LDA trained on the NIPS corpus for K=7,000.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "NEA improves the quality of topics with few words by replacing stop words with semantically related ones, especially when K is large.In this case, NEA generated slightly more meaningful topics than LDA. We also identified the most improved topics based on the difference between per-topic coherence scores. We observe that these LDA topics were uninterpretable, and likely had very few words assigned to them. NEA tends to improve the quality of these “bad” topics, e.g. by replacing stop words (or words at the top of the dictionary) with more semantically related ones. In particular, we found that NEA gave the most improvement for topics with few words assigned to them (see and when K becomes large, the majority of topics have few assigned words (see As a result, NEA improves the quality of most of the topics." } ], "images": [ "scigen/train/1956.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 1: The worst four topics produced by LDA, in terms of per-topic coherence score, and their corresponding NEA topics, with LDA trained on the NIPS corpus for K=7,000." ], "reasoning": "NEA improves the quality of topics with few words by replacing stop words with semantically related ones, especially when K is large.", "description": "In this case, NEA generated slightly more meaningful topics than LDA. We also identified the most improved topics based on the difference between per-topic coherence scores. We observe that these LDA topics were uninterpretable, and likely had very few words assigned to them. NEA tends to improve the quality of these “bad” topics, e.g. by replacing stop words (or words at the top of the dictionary) with more semantically related ones. In particular, we found that NEA gave the most improvement for topics with few words assigned to them (see and when K becomes large, the majority of topics have few assigned words (see As a result, NEA improves the quality of most of the topics.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Figure 1: The worst four topics produced by LDA, in terms of per-topic coherence score, and their corresponding NEA topics, with LDA trained on the NIPS corpus for K=7,000.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich method improves the quality of topics with few words assigned to them?\n" }, { "role": "assistant", "content": "The description states that NEA improves the quality of topics with few words assigned to them by replacing stop words with more semantically related ones. So, the answer is {\"answer\": \"NEA\"}" } ], "images": [ "scigen/train/1956.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 1: The worst four topics produced by LDA, in terms of per-topic coherence score, and their corresponding NEA topics, with LDA trained on the NIPS corpus for K=7,000." ], "reasoning": "The description states that NEA improves the quality of topics with few words assigned to them by replacing stop words with more semantically related ones.", "question": "Which method improves the quality of topics with few words assigned to them?", "answer": "So, the answer is {\"answer\": \"NEA\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nNEA improves the quality of topics by replacing stop words with semantically related ones.\n" }, { "role": "assistant", "content": "The description explicitly mentions that NEA tends to improve the quality of 'bad' topics by replacing stop words with more semantically related ones. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1956.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 1: The worst four topics produced by LDA, in terms of per-topic coherence score, and their corresponding NEA topics, with LDA trained on the NIPS corpus for K=7,000." ], "reasoning": "The description explicitly mentions that NEA tends to improve the quality of 'bad' topics by replacing stop words with more semantically related ones.", "statement": "NEA improves the quality of topics by replacing stop words with semantically related ones.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Figure 2: The four topics that were most improved by NEA over the original LDA topic, in terms of the difference between per-topic coherence score, with LDA trained on the Reuters-150 corpus for K=7,000.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "NEA improves the quality of topics with few words by replacing stop words with semantically related ones, especially when K is large.In this case, NEA generated slightly more meaningful topics than LDA. We also identified the most improved topics based on the difference between per-topic coherence scores. We observe that these LDA topics were uninterpretable, and likely had very few words assigned to them. NEA tends to improve the quality of these “bad” topics, e.g. by replacing stop words (or words at the top of the dictionary) with more semantically related ones. In particular, we found that NEA gave the most improvement for topics with few words assigned to them (see and when K becomes large, the majority of topics have few assigned words (see As a result, NEA improves the quality of most of the topics." } ], "images": [ "scigen/train/1957.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 2: The four topics that were most improved by NEA over the original LDA topic, in terms of the difference between per-topic coherence score, with LDA trained on the Reuters-150 corpus for K=7,000." ], "reasoning": "NEA improves the quality of topics with few words by replacing stop words with semantically related ones, especially when K is large.", "description": "In this case, NEA generated slightly more meaningful topics than LDA. We also identified the most improved topics based on the difference between per-topic coherence scores. We observe that these LDA topics were uninterpretable, and likely had very few words assigned to them. NEA tends to improve the quality of these “bad” topics, e.g. by replacing stop words (or words at the top of the dictionary) with more semantically related ones. In particular, we found that NEA gave the most improvement for topics with few words assigned to them (see and when K becomes large, the majority of topics have few assigned words (see As a result, NEA improves the quality of most of the topics.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Figure 2: The four topics that were most improved by NEA over the original LDA topic, in terms of the difference between per-topic coherence score, with LDA trained on the Reuters-150 corpus for K=7,000.\nQuestion: Which method tends to improve the quality of topics with few words assigned to them?\nGive a detailed response to the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The description states that NEA tends to improve the quality of 'bad' topics, particularly those with few words assigned to them. So, the answer is {\"answer\": \"NEA\"}" } ], "images": [ "scigen/train/1957.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 2: The four topics that were most improved by NEA over the original LDA topic, in terms of the difference between per-topic coherence score, with LDA trained on the Reuters-150 corpus for K=7,000." ], "reasoning": "The description states that NEA tends to improve the quality of 'bad' topics, particularly those with few words assigned to them.", "question": "Which method tends to improve the quality of topics with few words assigned to them?", "answer": "So, the answer is {\"answer\": \"NEA\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: NEA improves the quality of topics by replacing stop words with more semantically related ones., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nNEA improves the quality of topics by replacing stop words with more semantically related ones.\n" }, { "role": "assistant", "content": "The description mentions that NEA improves the quality of 'bad' topics by replacing stop words with more semantically related ones. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1957.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 2: The four topics that were most improved by NEA over the original LDA topic, in terms of the difference between per-topic coherence score, with LDA trained on the Reuters-150 corpus for K=7,000." ], "reasoning": "The description mentions that NEA improves the quality of 'bad' topics by replacing stop words with more semantically related ones.", "statement": "NEA improves the quality of topics by replacing stop words with more semantically related ones.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Figure 8: Randomly selected topic pairs from LDA and NEA, with LDA trained on the NIPS corpus for K=2,000.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Both models produced interpretable topics, and NEA was able to approximately recover the original LDA’s topics.In this section, we demonstrate our results on other datasets by repeating the similar experiments on them. First, we found that most of the topics produced by both models are interpretable, and NEA was able to approximately recover the original LDA’s topics." } ], "images": [ "scigen/train/1958.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 8: Randomly selected topic pairs from LDA and NEA, with LDA trained on the NIPS corpus for K=2,000." ], "reasoning": "Both models produced interpretable topics, and NEA was able to approximately recover the original LDA’s topics.", "description": "In this section, we demonstrate our results on other datasets by repeating the similar experiments on them. First, we found that most of the topics produced by both models are interpretable, and NEA was able to approximately recover the original LDA’s topics.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Figure 8: Randomly selected topic pairs from LDA and NEA, with LDA trained on the NIPS corpus for K=2,000.' and proceed to solve the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model includes the topic 'image recognition' and what are the associated words?\n" }, { "role": "assistant", "content": "The table shows that the topic 'image recognition' is included under LDA, and the associated words are vision, pixel, techniques, pixels, visual, computed, applied. So, the answer is {\"answer\": \"LDA: vision, pixel, techniques, pixels, visual, computed, applied\"}." } ], "images": [ "scigen/train/1958.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 8: Randomly selected topic pairs from LDA and NEA, with LDA trained on the NIPS corpus for K=2,000." ], "reasoning": "The table shows that the topic 'image recognition' is included under LDA, and the associated words are vision, pixel, techniques, pixels, visual, computed, applied.", "question": "Which model includes the topic 'image recognition' and what are the associated words?", "answer": "So, the answer is {\"answer\": \"LDA: vision, pixel, techniques, pixels, visual, computed, applied\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nNEA includes the topic 'regression' with associated words 'linear, ridge, quadratic, squared, nonparametric, variables, smoothing, friedman'.\n" }, { "role": "assistant", "content": "The table shows that NEA includes the topic 'regression' but the associated words are 'linear, ridge, quadratic, variables, nonparametric, squared, multivariate, kernel, basis', not 'smoothing, friedman'. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1958.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 8: Randomly selected topic pairs from LDA and NEA, with LDA trained on the NIPS corpus for K=2,000." ], "reasoning": "The table shows that NEA includes the topic 'regression' but the associated words are 'linear, ridge, quadratic, variables, nonparametric, squared, multivariate, kernel, basis', not 'smoothing, friedman'.", "statement": "NEA includes the topic 'regression' with associated words 'linear, ridge, quadratic, squared, nonparametric, variables, smoothing, friedman'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: Comparing NEA in document categorization tasks with MMSG, when both models trained for MMSGTM. Classification accuracy is shown for two different corpus: Reuters−150, and Ohsumed.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Both NEA and MMSG improve accuracy over MMSGTM for Reuters-150 and Ohsumed datasets, with MMSG having slightly higher accuracy than NEA.Both NEA and MMSG improves accuracy of this downstream task comparing to MMSGTM for Reuters−150, and Ohsumed dataset while MMSG maintains slightly higher accuracy than NEA." } ], "images": [ "scigen/train/1959.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparing NEA in document categorization tasks with MMSG, when both models trained for MMSGTM. Classification accuracy is shown for two different corpus: Reuters−150, and Ohsumed." ], "reasoning": "Both NEA and MMSG improve accuracy over MMSGTM for Reuters-150 and Ohsumed datasets, with MMSG having slightly higher accuracy than NEA.", "description": "Both NEA and MMSG improves accuracy of this downstream task comparing to MMSGTM for Reuters−150, and Ohsumed dataset while MMSG maintains slightly higher accuracy than NEA.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: Comparing NEA in document categorization tasks with MMSG, when both models trained for MMSGTM. Classification accuracy is shown for two different corpus: Reuters−150, and Ohsumed.', solve the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method has the highest accuracy for the Ohsumed dataset?\n" }, { "role": "assistant", "content": "To find the method with the highest accuracy for the Ohsumed dataset, compare the accuracy values of MMSGTM, NEA, and MMSG in the Ohsumed row. MMSG has the highest accuracy with 34.78. Thus, the answer is {\"answer\": \"MMSG\"}." } ], "images": [ "scigen/train/1959.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparing NEA in document categorization tasks with MMSG, when both models trained for MMSGTM. Classification accuracy is shown for two different corpus: Reuters−150, and Ohsumed." ], "reasoning": "To find the method with the highest accuracy for the Ohsumed dataset, compare the accuracy values of MMSGTM, NEA, and MMSG in the Ohsumed row. MMSG has the highest accuracy with 34.78.", "question": "Which method has the highest accuracy for the Ohsumed dataset?", "answer": "Thus, the answer is {\"answer\": \"MMSG\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nNEA has the highest accuracy for the Reuters-150 dataset.\n" }, { "role": "assistant", "content": "The table shows that for the Reuters-150 dataset, NEA has an accuracy of 68.14, while MMSG has a higher accuracy of 68.26. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1959.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparing NEA in document categorization tasks with MMSG, when both models trained for MMSGTM. Classification accuracy is shown for two different corpus: Reuters−150, and Ohsumed." ], "reasoning": "The table shows that for the Reuters-150 dataset, NEA has an accuracy of 68.14, while MMSG has a higher accuracy of 68.26. Therefore, the statement is refuted.", "statement": "NEA has the highest accuracy for the Reuters-150 dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 7: BLEU scores and perplexity for eval-AWD-LSTM-based evaluation on texts generated from the COCO image dataset by different GAN models: SeqGAN Yu et al. (2017), MaliGAN Che et al. (2017), RankGAN Lin et al. (2017), LeakGAN Guo et al. (2018), and TextGAN Zhang et al. (2017).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The eval-AWD-LSTM model trained with the COCO image dataset and PTB hyperparameters achieved a perplexity score of 65.41.Nevertheless, other metrics would not provide a better evaluation in this case. The perplexity scores were computed by using the eval-AWD-LSTM model trained with the COCO image dataset and the hyperparameters for the PTB dataset. The perplexity of AWD-LSTM when trained with that dataset was 65.41." } ], "images": [ "scigen/train/1960.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: BLEU scores and perplexity for eval-AWD-LSTM-based evaluation on texts generated from the COCO image dataset by different GAN models: SeqGAN Yu et al. (2017), MaliGAN Che et al. (2017), RankGAN Lin et al. (2017), LeakGAN Guo et al. (2018), and TextGAN Zhang et al. (2017)." ], "reasoning": "The eval-AWD-LSTM model trained with the COCO image dataset and PTB hyperparameters achieved a perplexity score of 65.41.", "description": "Nevertheless, other metrics would not provide a better evaluation in this case. The perplexity scores were computed by using the eval-AWD-LSTM model trained with the COCO image dataset and the hyperparameters for the PTB dataset. The perplexity of AWD-LSTM when trained with that dataset was 65.41.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 7: BLEU scores and perplexity for eval-AWD-LSTM-based evaluation on texts generated from the COCO image dataset by different GAN models: SeqGAN Yu et al. (2017), MaliGAN Che et al. (2017), RankGAN Lin et al. (2017), LeakGAN Guo et al. (2018), and TextGAN Zhang et al. (2017).', solve the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the lowest perplexity score when trained with the COCO image dataset?\n" }, { "role": "assistant", "content": "By examining the 'eval-AWD-LSTM' row in the table, the lowest perplexity score is found under the 'ImageCoco' column, which is 44.17. So, the answer is {\"answer\": \"ImageCoco\"}" } ], "images": [ "scigen/train/1960.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: BLEU scores and perplexity for eval-AWD-LSTM-based evaluation on texts generated from the COCO image dataset by different GAN models: SeqGAN Yu et al. (2017), MaliGAN Che et al. (2017), RankGAN Lin et al. (2017), LeakGAN Guo et al. (2018), and TextGAN Zhang et al. (2017)." ], "reasoning": "By examining the 'eval-AWD-LSTM' row in the table, the lowest perplexity score is found under the 'ImageCoco' column, which is 44.17.", "question": "Which model has the lowest perplexity score when trained with the COCO image dataset?", "answer": "So, the answer is {\"answer\": \"ImageCoco\"}" }, { "messages": [ { "role": "user", "content": "The perplexity score of eval-AWD-LSTM when trained with the COCO image dataset is 65.41. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe perplexity score of eval-AWD-LSTM when trained with the COCO image dataset is 65.41.\n" }, { "role": "assistant", "content": "The table shows that the perplexity score of eval-AWD-LSTM when trained with the COCO image dataset is 44.17, not 65.41. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1960.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: BLEU scores and perplexity for eval-AWD-LSTM-based evaluation on texts generated from the COCO image dataset by different GAN models: SeqGAN Yu et al. (2017), MaliGAN Che et al. (2017), RankGAN Lin et al. (2017), LeakGAN Guo et al. (2018), and TextGAN Zhang et al. (2017)." ], "reasoning": "The table shows that the perplexity score of eval-AWD-LSTM when trained with the COCO image dataset is 44.17, not 65.41.", "statement": "The perplexity score of eval-AWD-LSTM when trained with the COCO image dataset is 65.41.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'TABLE X: Ablation analysis over Ranking-Based BFS Sampling Strategy.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "PF-HIN outperforms PF-HIN +BFS and PF-HIN +random, indicating that choosing nodes with higher importance is better for describing HIN features.PF-HIN +BFS outperforms PF-HIN +random, which is because that aggregating the node’s closest neighborhood’s information is more expressive than randomly chosen neighboring nodes. PF-HIN still outperforms PF-HIN +BFS, which indicates that choosing the nodes with higher importance is better for describing the features of a HIN." } ], "images": [ "scigen/train/1961.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE X: Ablation analysis over Ranking-Based BFS Sampling Strategy." ], "reasoning": "PF-HIN outperforms PF-HIN +BFS and PF-HIN +random, indicating that choosing nodes with higher importance is better for describing HIN features.", "description": "PF-HIN +BFS outperforms PF-HIN +random, which is because that aggregating the node’s closest neighborhood’s information is more expressive than randomly chosen neighboring nodes. PF-HIN still outperforms PF-HIN +BFS, which indicates that choosing the nodes with higher importance is better for describing the features of a HIN.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'TABLE X: Ablation analysis over Ranking-Based BFS Sampling Strategy.'\nQuestion: Which model has the highest LR AUC score and why is it significant?\n" }, { "role": "assistant", "content": "The model PF-HIN has the highest LR AUC score of 0.649. This is significant because it shows that choosing nodes with higher importance is better for describing the features of a HIN. So, the answer is {\"answer\": \"PF-HIN\"}." } ], "images": [ "scigen/train/1961.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE X: Ablation analysis over Ranking-Based BFS Sampling Strategy." ], "reasoning": "The model PF-HIN has the highest LR AUC score of 0.649. This is significant because it shows that choosing nodes with higher importance is better for describing the features of a HIN.", "question": "Which model has the highest LR AUC score and why is it significant?", "answer": "So, the answer is {\"answer\": \"PF-HIN\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nPF-HIN +BFS has a higher NC NMI score than PF-HIN +random.\n" }, { "role": "assistant", "content": "The table shows that PF-HIN +BFS has an NC NMI score of 0.712, which is higher than the NC NMI score of PF-HIN +random, which is 0.641. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1961.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE X: Ablation analysis over Ranking-Based BFS Sampling Strategy." ], "reasoning": "The table shows that PF-HIN +BFS has an NC NMI score of 0.712, which is higher than the NC NMI score of PF-HIN +random, which is 0.641.", "statement": "PF-HIN +BFS has a higher NC NMI score than PF-HIN +random.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE II: Experimental results on the link prediction task.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "PF-HIN outperforms all other models, including HetGNN and traditional homogeneous and heterogeneous models, indicating the effectiveness of its fine-tuning framework based on bi-directional transformers.From it we could observe that the outputs become better with a decreasing scale of the datasets. Traditional homogeneous models like DeepWalk, LINE and node2vec perform worse than traditional heterogeneous meta-path based models metapath2vec, HINE and HIN2Vec, which indicates that meta-path captures the network structure better than random walks. However, homogeneous GNN models like GCN, GraphSAGE and GAT have even better outputs comparing traditional heterogeneous methods. We attribute this to the fact that deep neural network explores the whole network in a better way, generating better representations for link prediction. HetGNN outperforms those homogeneous GNN models, since it takes the node types into consideration. Our model PF-HIN outperforms all the baselines consistently and significantly, which verifies that our fine-tuning framework based on bi-directional transformers is effective on modeling the relationships between nodes, so as to predict the links in between." } ], "images": [ "scigen/train/1962.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Experimental results on the link prediction task." ], "reasoning": "PF-HIN outperforms all other models, including HetGNN and traditional homogeneous and heterogeneous models, indicating the effectiveness of its fine-tuning framework based on bi-directional transformers.", "description": "From it we could observe that the outputs become better with a decreasing scale of the datasets. Traditional homogeneous models like DeepWalk, LINE and node2vec perform worse than traditional heterogeneous meta-path based models metapath2vec, HINE and HIN2Vec, which indicates that meta-path captures the network structure better than random walks. However, homogeneous GNN models like GCN, GraphSAGE and GAT have even better outputs comparing traditional heterogeneous methods. We attribute this to the fact that deep neural network explores the whole network in a better way, generating better representations for link prediction. HetGNN outperforms those homogeneous GNN models, since it takes the node types into consideration. Our model PF-HIN outperforms all the baselines consistently and significantly, which verifies that our fine-tuning framework based on bi-directional transformers is effective on modeling the relationships between nodes, so as to predict the links in between.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'TABLE II: Experimental results on the link prediction task.', answer the following question. Give detailed solution consisting of each step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest F1 score for the Freebase dataset?\n" }, { "role": "assistant", "content": "By examining the Freebase F1 column, PF-HIN has the highest F1 score of 0.686, which is higher than all other models listed. So, the answer is {\"answer\": \"PF-HIN\"}" } ], "images": [ "scigen/train/1962.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Experimental results on the link prediction task." ], "reasoning": "By examining the Freebase F1 column, PF-HIN has the highest F1 score of 0.686, which is higher than all other models listed.", "question": "Which model has the highest F1 score for the Freebase dataset?", "answer": "So, the answer is {\"answer\": \"PF-HIN\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence HetGNN has the highest AUC score for the YAGO dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nHetGNN has the highest AUC score for the YAGO dataset.\n" }, { "role": "assistant", "content": "By examining the YAGO AUC column, PF-HIN has the highest AUC score of 0.804, not HetGNN, which has an AUC score of 0.793. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1962.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Experimental results on the link prediction task." ], "reasoning": "By examining the YAGO AUC column, PF-HIN has the highest AUC score of 0.804, not HetGNN, which has an AUC score of 0.793.", "statement": "HetGNN has the highest AUC score for the YAGO dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'TABLE III: Experimental results on the similarity search task.'.\n" }, { "role": "assistant", "content": "Both traditional heterogeneous models and homogeneous GNN models achieve comparable outputs, but PF-HIN performs the best in all cases, demonstrating the effectiveness of the pre-training and fine-tuning framework.The best results are highlighted in bold. According to this table, we could observe that traditional heterogeneous models and homogeneous GNN models achieve comparable outputs, which means that both meta-path based mechanism and deep neural networks can generate expressive node embeddings for similarity search. HetGNN is still the best baseline which proves the power of the combination of GNN and type features. PF-HIN performs the best in all cases, illustrating the effectiveness of our pre-training and fine-tuning framework on learning the node representations for similarity search." } ], "images": [ "scigen/train/1963.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Experimental results on the similarity search task." ], "reasoning": "Both traditional heterogeneous models and homogeneous GNN models achieve comparable outputs, but PF-HIN performs the best in all cases, demonstrating the effectiveness of the pre-training and fine-tuning framework.", "description": "The best results are highlighted in bold. According to this table, we could observe that traditional heterogeneous models and homogeneous GNN models achieve comparable outputs, which means that both meta-path based mechanism and deep neural networks can generate expressive node embeddings for similarity search. HetGNN is still the best baseline which proves the power of the combination of GNN and type features. PF-HIN performs the best in all cases, illustrating the effectiveness of our pre-training and fine-tuning framework on learning the node representations for similarity search.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'TABLE III: Experimental results on the similarity search task.'. Solve the following question based on the table.\n\nQuestion: Which model achieves the highest AUC score on the YAGO dataset?\nLet's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the AUC scores for the YAGO dataset, PF-HIN has the highest score of 0.707. So, the answer is {\"answer\": \"PF-HIN\"}" } ], "images": [ "scigen/train/1963.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Experimental results on the similarity search task." ], "reasoning": "By examining the AUC scores for the YAGO dataset, PF-HIN has the highest score of 0.707.", "question": "Which model achieves the highest AUC score on the YAGO dataset?", "answer": "So, the answer is {\"answer\": \"PF-HIN\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim HetGNN achieves the highest AUC score on the Freebase dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nHetGNN achieves the highest AUC score on the Freebase dataset.\n" }, { "role": "assistant", "content": "The table shows that PF-HIN has the highest AUC score on the Freebase dataset with 0.783, not HetGNN. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1963.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Experimental results on the similarity search task." ], "reasoning": "The table shows that PF-HIN has the highest AUC score on the Freebase dataset with 0.783, not HetGNN.", "statement": "HetGNN achieves the highest AUC score on the Freebase dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'TABLE IV: Experimental results on the multi-label node classification task.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "GNN-based models are strong baselines for node classification, but PF-HIN achieves the best results due to its fine-tuning framework.From which we could observe that GNN based models are the best baselines, showing the advancement of deep neural network on exploring the features of the network data for classification. Our PF-HIN still obtains the best experimental results thanks to our fine-tuning framework which aggregates the whole sequence information for node classification." } ], "images": [ "scigen/train/1964.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Experimental results on the multi-label node classification task." ], "reasoning": "GNN-based models are strong baselines for node classification, but PF-HIN achieves the best results due to its fine-tuning framework.", "description": "From which we could observe that GNN based models are the best baselines, showing the advancement of deep neural network on exploring the features of the network data for classification. Our PF-HIN still obtains the best experimental results thanks to our fine-tuning framework which aggregates the whole sequence information for node classification.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: 'TABLE IV: Experimental results on the multi-label node classification task.'\nQuestion: Which model achieves the highest MIC-F1 score on the Freebase dataset?\n" }, { "role": "assistant", "content": "By examining the Freebase MIC-F1 column, PF-HIN has the highest score of 0.591. So, the answer is {\"answer\": \"PF-HIN\"}." } ], "images": [ "scigen/train/1964.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Experimental results on the multi-label node classification task." ], "reasoning": "By examining the Freebase MIC-F1 column, PF-HIN has the highest score of 0.591.", "question": "Which model achieves the highest MIC-F1 score on the Freebase dataset?", "answer": "So, the answer is {\"answer\": \"PF-HIN\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim PF-HIN achieves the highest MAC-F1 score on the YAGO dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nPF-HIN achieves the highest MAC-F1 score on the YAGO dataset.\n" }, { "role": "assistant", "content": "By examining the YAGO MAC-F1 column, PF-HIN has the highest score of 0.480. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1964.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Experimental results on the multi-label node classification task." ], "reasoning": "By examining the YAGO MAC-F1 column, PF-HIN has the highest score of 0.480.", "statement": "PF-HIN achieves the highest MAC-F1 score on the YAGO dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'TABLE V: Experimental results on the node clustering task.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Despite the strong structural information capture by homogeneous GNN models, they perform slightly worse than traditional heterogeneous models due to the lack of node type information. PF-HIN outperforms all baselines, proving its effectiveness in generating node embeddings for clustering.According to this table, we find that despite the homogeneous GNN models’ strong ability of capturing the structural information of a network, they still perform slightly worse than those traditional heterogeneous models. And we attribute this to taking node type information into consideration, which could make a real difference when clustering the nodes. No doubt HetGNN is the best baseline combining both type information and deep neural networks. Our PF-HIN still outperforms all the baselines, proving that PF-HIN is able to generate effective node embeddings for node clustering." } ], "images": [ "scigen/train/1965.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Experimental results on the node clustering task." ], "reasoning": "Despite the strong structural information capture by homogeneous GNN models, they perform slightly worse than traditional heterogeneous models due to the lack of node type information. PF-HIN outperforms all baselines, proving its effectiveness in generating node embeddings for clustering.", "description": "According to this table, we find that despite the homogeneous GNN models’ strong ability of capturing the structural information of a network, they still perform slightly worse than those traditional heterogeneous models. And we attribute this to taking node type information into consideration, which could make a real difference when clustering the nodes. No doubt HetGNN is the best baseline combining both type information and deep neural networks. Our PF-HIN still outperforms all the baselines, proving that PF-HIN is able to generate effective node embeddings for node clustering.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model achieves the highest ARI score on the Freebase dataset?\nSolve the above question based on the table titled 'TABLE V: Experimental results on the node clustering task.'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To determine the model with the highest ARI score on the Freebase dataset, we need to compare the ARI scores of all models listed under the Freebase ARI column. PF-HIN has the highest ARI score of 0.962. So, the answer is {\"answer\": \"PF-HIN\"}." } ], "images": [ "scigen/train/1965.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Experimental results on the node clustering task." ], "reasoning": "To determine the model with the highest ARI score on the Freebase dataset, we need to compare the ARI scores of all models listed under the Freebase ARI column. PF-HIN has the highest ARI score of 0.962.", "question": "Which model achieves the highest ARI score on the Freebase dataset?", "answer": "So, the answer is {\"answer\": \"PF-HIN\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nHetGNN achieves the highest NMI score on the YAGO dataset.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the NMI scores of all models listed under the YAGO NMI column. PF-HIN has the highest NMI score of 0.911, not HetGNN. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1965.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Experimental results on the node clustering task." ], "reasoning": "To verify this statement, we need to compare the NMI scores of all models listed under the YAGO NMI column. PF-HIN has the highest NMI score of 0.911, not HetGNN.", "statement": "HetGNN achieves the highest NMI score on the YAGO dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'TABLE VI: Computation cost on different tasks.'.\n" }, { "role": "assistant", "content": "PF-HIN is more efficient than other GNN-based models like GCN, GraphSAGE, GAT, and HetGNN due to the use of pre-trained parameters and embeddings, despite employing bi-directional transformer encoders.To evaluate the efficiency of our fine-tuning framework comparing other models, here we conduct the computation cost analysis. In specific, we first extract a subset of DBLP used for downstream task, i.e., 15% of the original DBLP dataset. Then in this subset, we analyze the running time of each model in each task, using the early stopping mechanism. For simplicity consideration, here we use LP, SS, MC and NC to represent link prediction, similarity search, multi-label node classification and node clustering tasks, respectively. The models are all conducted on GPU GTX-1080. running time is only a bit longer than those three traditional homogeneous models, DeepWalk, LINE and node2vec which are based on random walks. GNN based models like GCN, GraphSAGE, GAT and HetGNN cost much more time than other models, since the complexity of traditional deep neural networks is much higher than other algorithms. Although PF-HIN employs the bi-directional transformer encoders which is a variant of GNN, it is much more efficient than the above GNN based models. This is because that pre-trained parameters and embeddings could help the cost function converge much faster." } ], "images": [ "scigen/train/1966.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VI: Computation cost on different tasks." ], "reasoning": "PF-HIN is more efficient than other GNN-based models like GCN, GraphSAGE, GAT, and HetGNN due to the use of pre-trained parameters and embeddings, despite employing bi-directional transformer encoders.", "description": "To evaluate the efficiency of our fine-tuning framework comparing other models, here we conduct the computation cost analysis. In specific, we first extract a subset of DBLP used for downstream task, i.e., 15% of the original DBLP dataset. Then in this subset, we analyze the running time of each model in each task, using the early stopping mechanism. For simplicity consideration, here we use LP, SS, MC and NC to represent link prediction, similarity search, multi-label node classification and node clustering tasks, respectively. The models are all conducted on GPU GTX-1080. running time is only a bit longer than those three traditional homogeneous models, DeepWalk, LINE and node2vec which are based on random walks. GNN based models like GCN, GraphSAGE, GAT and HetGNN cost much more time than other models, since the complexity of traditional deep neural networks is much higher than other algorithms. Although PF-HIN employs the bi-directional transformer encoders which is a variant of GNN, it is much more efficient than the above GNN based models. This is because that pre-trained parameters and embeddings could help the cost function converge much faster.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'TABLE VI: Computation cost on different tasks.' to sequentially solve the question presented below. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich model has the longest running time for the link prediction (LP) task?\n" }, { "role": "assistant", "content": "To find the model with the longest running time for the LP task, we need to compare the 'Time (s)' values under the 'LR' column for all models. The highest value is 1863 seconds for GCN. So, the answer is {\"answer\": \"GCN\"}." } ], "images": [ "scigen/train/1966.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VI: Computation cost on different tasks." ], "reasoning": "To find the model with the longest running time for the LP task, we need to compare the 'Time (s)' values under the 'LR' column for all models. The highest value is 1863 seconds for GCN.", "question": "Which model has the longest running time for the link prediction (LP) task?", "answer": "So, the answer is {\"answer\": \"GCN\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nPF-HIN has the shortest running time for the node clustering (NC) task.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the 'Time (s)' values under the 'NC' column for all models. The shortest time is 291 seconds for DeepWalk, not PF-HIN. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1966.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VI: Computation cost on different tasks." ], "reasoning": "To verify this statement, we need to compare the 'Time (s)' values under the 'NC' column for all models. The shortest time is 291 seconds for DeepWalk, not PF-HIN.", "statement": "PF-HIN has the shortest running time for the node clustering (NC) task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'TABLE VII: Ablation analysis over pre-training tasks.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Removing MNM significantly reduces performance, while PF-HIN-ANP performs slightly worse than PF-HIN on node classification and clustering but much worse on link prediction and similarity search.We have the following observations. The MNM task plays a more important role for pre-training the model, as the performance drops dramatically after removing MNM. PF-HIN-ANP is slightly worse than PF-HIN on node classification and clustering tasks, while the gap is much larger on link prediction and similarity search tasks. This is because that ANP has a larger influence on fine-tuning framework with sequence pairs, which models the relationships between nodes." } ], "images": [ "scigen/train/1967.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VII: Ablation analysis over pre-training tasks." ], "reasoning": "Removing MNM significantly reduces performance, while PF-HIN-ANP performs slightly worse than PF-HIN on node classification and clustering but much worse on link prediction and similarity search.", "description": "We have the following observations. The MNM task plays a more important role for pre-training the model, as the performance drops dramatically after removing MNM. PF-HIN-ANP is slightly worse than PF-HIN on node classification and clustering tasks, while the gap is much larger on link prediction and similarity search tasks. This is because that ANP has a larger influence on fine-tuning framework with sequence pairs, which models the relationships between nodes.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'TABLE VII: Ablation analysis over pre-training tasks.', unfold the steps to solve the question given next. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nQuestion: Which model shows the largest drop in performance on the LR AUC metric?\n" }, { "role": "assistant", "content": "By comparing the LR AUC values for PF-HIN, PF-HIN-ANP, and PF-HIN-MNM, we see that PF-HIN-MNM has the lowest LR AUC value, indicating the largest drop in performance. So, the answer is {\"answer\": \"PF-HIN-MNM\"}" } ], "images": [ "scigen/train/1967.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VII: Ablation analysis over pre-training tasks." ], "reasoning": "By comparing the LR AUC values for PF-HIN, PF-HIN-ANP, and PF-HIN-MNM, we see that PF-HIN-MNM has the lowest LR AUC value, indicating the largest drop in performance.", "question": "Which model shows the largest drop in performance on the LR AUC metric?", "answer": "So, the answer is {\"answer\": \"PF-HIN-MNM\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nPF-HIN-ANP performs better than PF-HIN-MNM on the NC NMI metric.\n" }, { "role": "assistant", "content": "The NC NMI value for PF-HIN-ANP is 0.693, while for PF-HIN-MNM it is 0.348, indicating that PF-HIN-ANP performs better. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1967.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VII: Ablation analysis over pre-training tasks." ], "reasoning": "The NC NMI value for PF-HIN-ANP is 0.693, while for PF-HIN-MNM it is 0.348, indicating that PF-HIN-ANP performs better.", "statement": "PF-HIN-ANP performs better than PF-HIN-MNM on the NC NMI metric.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'TABLE VIII: Ablation analysis over transformer encoders.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "PF-HIN consistently outperforms traditional deep neural network models like CNN, LSTM, and attention mechanism based models on four tasks.We could observe that CNN, LSTM and attention mechanism based models achieve comparable results on four tasks. However, PF-HIN consistently outperforms all the traditional deep neural network models, which further proves that our bi-directional transformer encoders’ advancement on mining the information behind a HIN." } ], "images": [ "scigen/train/1968.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VIII: Ablation analysis over transformer encoders." ], "reasoning": "PF-HIN consistently outperforms traditional deep neural network models like CNN, LSTM, and attention mechanism based models on four tasks.", "description": "We could observe that CNN, LSTM and attention mechanism based models achieve comparable results on four tasks. However, PF-HIN consistently outperforms all the traditional deep neural network models, which further proves that our bi-directional transformer encoders’ advancement on mining the information behind a HIN.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'TABLE VIII: Ablation analysis over transformer encoders.'\nQuestion: Which model achieves the highest LR AUC score?\n" }, { "role": "assistant", "content": "By comparing the LR AUC scores of all models, PF-HIN achieves the highest score of 0.649. So, the answer is {\"answer\": \"PF-HIN\"}" } ], "images": [ "scigen/train/1968.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VIII: Ablation analysis over transformer encoders." ], "reasoning": "By comparing the LR AUC scores of all models, PF-HIN achieves the highest score of 0.649.", "question": "Which model achieves the highest LR AUC score?", "answer": "So, the answer is {\"answer\": \"PF-HIN\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim PF-HIN + CNN achieves the highest NC NMI score., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nPF-HIN + CNN achieves the highest NC NMI score.\n" }, { "role": "assistant", "content": "The table shows that PF-HIN has the highest NC NMI score of 0.734, while PF-HIN + CNN has a score of 0.706. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1968.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VIII: Ablation analysis over transformer encoders." ], "reasoning": "The table shows that PF-HIN has the highest NC NMI score of 0.734, while PF-HIN + CNN has a score of 0.706.", "statement": "PF-HIN + CNN achieves the highest NC NMI score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'TABLE IX: Ablation analysis over input representation.'.\n" }, { "role": "assistant", "content": "Type and rank embeddings significantly improve model performance, especially in node clustering tasks, as removing them deteriorates results.Type and rank embeddings could help improve the model performance as PF-HIN obtains the best results on all tasks. Offering the model with a sense of type information has a large influence on the performance, since removing it deteriorates the results a lot, especially on node clustering task. This is because that node clustering task is more sensitive to the type information. Removing rank embeddings also hurts the performance, since the ranking information could help assign higher weights on important nodes in a HIN. No doubt that the model PF-HIN-type-rank achieves the worst performance." } ], "images": [ "scigen/train/1969.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IX: Ablation analysis over input representation." ], "reasoning": "Type and rank embeddings significantly improve model performance, especially in node clustering tasks, as removing them deteriorates results.", "description": "Type and rank embeddings could help improve the model performance as PF-HIN obtains the best results on all tasks. Offering the model with a sense of type information has a large influence on the performance, since removing it deteriorates the results a lot, especially on node clustering task. This is because that node clustering task is more sensitive to the type information. Removing rank embeddings also hurts the performance, since the ranking information could help assign higher weights on important nodes in a HIN. No doubt that the model PF-HIN-type-rank achieves the worst performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'TABLE IX: Ablation analysis over input representation.', solve the subsequent question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhich model achieves the worst performance in terms of LR AUC?\n" }, { "role": "assistant", "content": "The table shows the LR AUC values for different models. PF-HIN-type-rank has the lowest LR AUC value of 0.604. So, the answer is {\"answer\": \"PF-HIN-type-rank\"}" } ], "images": [ "scigen/train/1969.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IX: Ablation analysis over input representation." ], "reasoning": "The table shows the LR AUC values for different models. PF-HIN-type-rank has the lowest LR AUC value of 0.604.", "question": "Which model achieves the worst performance in terms of LR AUC?", "answer": "So, the answer is {\"answer\": \"PF-HIN-type-rank\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim PF-HIN-type-rank achieves the best performance on all tasks., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nPF-HIN-type-rank achieves the best performance on all tasks.\n" }, { "role": "assistant", "content": "The table shows that PF-HIN-type-rank has the lowest performance metrics across all tasks compared to other models. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1969.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IX: Ablation analysis over input representation." ], "reasoning": "The table shows that PF-HIN-type-rank has the lowest performance metrics across all tasks compared to other models.", "statement": "PF-HIN-type-rank achieves the best performance on all tasks.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: Ablation studies for our proposed metrics with human judgements.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The KPQA-metric outperforms uniform weights, especially for the MS-MARCO dataset, and setting stopwords' weight to zero is generally effective except for the SemEval dataset.For the results in the second row, we substitute KPW in BERTScore-KPQA with uniform weight whose weight for each token is all set to one. By doing this, we can see the effect of our importance weighting by KPW that is conditioned on the question. We can observe that performance of KPQA-metric is higher than uniform weights. This gap is especially higher for the MS-MARCO dataset, where the average number of tokens is longer than other datasets. Since our KPW already assigns lower weights to unnecessary words, the effect of setting the weight of stopwords to zero to remove them is slight and even negative for SemEval dataset. But it is usually effective to make stopwords’ weight zero so that it cannot be used completely in other three datasets." } ], "images": [ "scigen/train/1970.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ablation studies for our proposed metrics with human judgements." ], "reasoning": "The KPQA-metric outperforms uniform weights, especially for the MS-MARCO dataset, and setting stopwords' weight to zero is generally effective except for the SemEval dataset.", "description": "For the results in the second row, we substitute KPW in BERTScore-KPQA with uniform weight whose weight for each token is all set to one. By doing this, we can see the effect of our importance weighting by KPW that is conditioned on the question. We can observe that performance of KPQA-metric is higher than uniform weights. This gap is especially higher for the MS-MARCO dataset, where the average number of tokens is longer than other datasets. Since our KPW already assigns lower weights to unnecessary words, the effect of setting the weight of stopwords to zero to remove them is slight and even negative for SemEval dataset. But it is usually effective to make stopwords’ weight zero so that it cannot be used completely in other three datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 5: Ablation studies for our proposed metrics with human judgements.' in mind, break down and resolve the question below step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich dataset shows the highest performance gap between BERTScore-KPQA and uniform weights?\n" }, { "role": "assistant", "content": "By comparing the performance metrics (r and ρ) for BERTScore-KPQA and -KPW across all datasets, the MS-MARCO dataset shows the largest difference. So, the answer is {\"answer\": \"MS-MARCO\"}." } ], "images": [ "scigen/train/1970.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ablation studies for our proposed metrics with human judgements." ], "reasoning": "By comparing the performance metrics (r and ρ) for BERTScore-KPQA and -KPW across all datasets, the MS-MARCO dataset shows the largest difference.", "question": "Which dataset shows the highest performance gap between BERTScore-KPQA and uniform weights?", "answer": "So, the answer is {\"answer\": \"MS-MARCO\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nSetting the weight of stopwords to zero is effective for the SemEval dataset.\n" }, { "role": "assistant", "content": "The description states that setting the weight of stopwords to zero is slightly negative for the SemEval dataset, and the table shows a slight decrease in performance for SemEval when stopwords are removed. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1970.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ablation studies for our proposed metrics with human judgements." ], "reasoning": "The description states that setting the weight of stopwords to zero is slightly negative for the SemEval dataset, and the table shows a slight decrease in performance for SemEval when stopwords are removed.", "statement": "Setting the weight of stopwords to zero is effective for the SemEval dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Inter annotator agreement'.\n" }, { "role": "assistant", "content": "Noisy responses are filtered using z-score, ensuring at least five annotations per sample, and the dataset's inter-annotator agreement is validated with Krippendorff’s alpha.Filtering Noisy Workers Some workers did not follow the instructions, producing poor-quality judgements. To solve this problem, we filter noisy responses using z-score as in jung2011improving. We first compute the z-score among ten responses for each sample. Then, we consider the responses whose z-score is higher than 1 as noise and remove them up to five in the order of the z-score. As a result, all of the samples have at least five annotations after removing the noisy responses. We use the average score of the annotators for each sample as a ground-truth evaluation score to assess the quality of the evaluation metric. Inter-Annotator Agreement The final dataset is further validated with Krippendorff’s alpha krippendorff2011computing, a statistical measure of inter-rater agreement for multiple annotators. These coefficient numbers indicate a “near-perfect” agreement according to one of the general guidelines landis1977measurement for kappa-like measures." } ], "images": [ "scigen/train/1971.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Inter annotator agreement" ], "reasoning": "Noisy responses are filtered using z-score, ensuring at least five annotations per sample, and the dataset's inter-annotator agreement is validated with Krippendorff’s alpha.", "description": "Filtering Noisy Workers Some workers did not follow the instructions, producing poor-quality judgements. To solve this problem, we filter noisy responses using z-score as in jung2011improving. We first compute the z-score among ten responses for each sample. Then, we consider the responses whose z-score is higher than 1 as noise and remove them up to five in the order of the z-score. As a result, all of the samples have at least five annotations after removing the noisy responses. We use the average score of the annotators for each sample as a ground-truth evaluation score to assess the quality of the evaluation metric. Inter-Annotator Agreement The final dataset is further validated with Krippendorff’s alpha krippendorff2011computing, a statistical measure of inter-rater agreement for multiple annotators. These coefficient numbers indicate a “near-perfect” agreement according to one of the general guidelines landis1977measurement for kappa-like measures.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 2: Inter annotator agreement' and proceed to solve the following question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich dataset has a higher inter-annotator agreement according to Krippendorff’s alpha?\n" }, { "role": "assistant", "content": "Compare the Krippendorff’s alpha values for both datasets. MS MARCO has a Krippendorff’s alpha of 0.832, while AVSD has 0.808. Therefore, MS MARCO has a higher inter-annotator agreement. So, the answer is {\"answer\": \"MS MARCO\"}." } ], "images": [ "scigen/train/1971.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Inter annotator agreement" ], "reasoning": "Compare the Krippendorff’s alpha values for both datasets. MS MARCO has a Krippendorff’s alpha of 0.832, while AVSD has 0.808. Therefore, MS MARCO has a higher inter-annotator agreement.", "question": "Which dataset has a higher inter-annotator agreement according to Krippendorff’s alpha?", "answer": "So, the answer is {\"answer\": \"MS MARCO\"}." }, { "messages": [ { "role": "user", "content": "The AVSD dataset has more annotators on average than the MS MARCO dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe AVSD dataset has more annotators on average than the MS MARCO dataset.\n" }, { "role": "assistant", "content": "Compare the average number of annotators for both datasets. AVSD has 7.32 annotators on average, while MS MARCO has 7.24. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1971.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Inter annotator agreement" ], "reasoning": "Compare the average number of annotators for both datasets. AVSD has 7.32 annotators on average, while MS MARCO has 7.24. Therefore, the statement is supported.", "statement": "The AVSD dataset has more annotators on average than the MS MARCO dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Correlation between metrics and human judgments of correctness using Pearson Correlation(r), Spearman’s Correlation(ρ). Some of the results of NarrativeQA and SemEval are from chen2019evaluating.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The proposed KPQA-metric, BERTScore-KPQA, and ROUGE-L-KPQA show higher correlation scores with human judgment compared to existing metrics, especially for the MS-MARCO dataset, indicating their effectiveness in evaluating abstractive answers.The correlation between human judgement and most of the existing metrics such as BLEU or ROUGE-L is very low, especially for the MS-MARCO dataset, which has longer and more abstractive answers than the other three datasets. Hence, most of the widely used n-gram similarity metrics are inadequate to evaluate the correctness of the answer for genQA especially for the datasets that have abstractive answer. We also observe a higher correlation score for our proposed KPQA-metric, BERTScore-KPQA and ROUGE-L-KPQA, compared to existing metrics including original BERTScore and ROUGE-L. We observe that this gap of correlation is especially higher for MS-MARCO dataset and we argue that our proposed metric is especially more effective in evaluating abstractive answers than existing metrics. The next best metric after our proposed metric is the original BERTScore, which uses contextual embeddings and adopt IDF (Inverse Document Frequency) based importance weighting. When computing the original BERTScore, we compute the IDF dictionary with reference texts and adopt importance weighting with IDF as in zhang2019bertscore. By comparing BERTScore-KPQA and BERTScore, we show that our importance weighting method using KPQA is more effective than IDF for evaluating correctness. Since IDF is dependent on the frequency, it assigns a lower weight to some important words that frequently occur in the reference sentence. Hence, IDF based importance weighting might not be helpful for some cases. On the other hand, our KPW is computed utilizing questions so that it can assign the weights to words in the context of the questions in the generated answer, and this leads to better correlation with human evaluation." } ], "images": [ "scigen/train/1972.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Correlation between metrics and human judgments of correctness using Pearson Correlation(r), Spearman’s Correlation(ρ). Some of the results of NarrativeQA and SemEval are from chen2019evaluating." ], "reasoning": "The proposed KPQA-metric, BERTScore-KPQA, and ROUGE-L-KPQA show higher correlation scores with human judgment compared to existing metrics, especially for the MS-MARCO dataset, indicating their effectiveness in evaluating abstractive answers.", "description": "The correlation between human judgement and most of the existing metrics such as BLEU or ROUGE-L is very low, especially for the MS-MARCO dataset, which has longer and more abstractive answers than the other three datasets. Hence, most of the widely used n-gram similarity metrics are inadequate to evaluate the correctness of the answer for genQA especially for the datasets that have abstractive answer. We also observe a higher correlation score for our proposed KPQA-metric, BERTScore-KPQA and ROUGE-L-KPQA, compared to existing metrics including original BERTScore and ROUGE-L. We observe that this gap of correlation is especially higher for MS-MARCO dataset and we argue that our proposed metric is especially more effective in evaluating abstractive answers than existing metrics. The next best metric after our proposed metric is the original BERTScore, which uses contextual embeddings and adopt IDF (Inverse Document Frequency) based importance weighting. When computing the original BERTScore, we compute the IDF dictionary with reference texts and adopt importance weighting with IDF as in zhang2019bertscore. By comparing BERTScore-KPQA and BERTScore, we show that our importance weighting method using KPQA is more effective than IDF for evaluating correctness. Since IDF is dependent on the frequency, it assigns a lower weight to some important words that frequently occur in the reference sentence. Hence, IDF based importance weighting might not be helpful for some cases. On the other hand, our KPW is computed utilizing questions so that it can assign the weights to words in the context of the questions in the generated answer, and this leads to better correlation with human evaluation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: Correlation between metrics and human judgments of correctness using Pearson Correlation(r), Spearman’s Correlation(ρ). Some of the results of NarrativeQA and SemEval are from chen2019evaluating.', unfold the steps to solve the question given next. Output the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which metric shows the highest correlation with human judgment for the NarrativeQA dataset?\n" }, { "role": "assistant", "content": "To find the metric with the highest correlation for the NarrativeQA dataset, we need to look at the 'NarrativeQA ρ' column and identify the highest value. The highest value in this column is 0.770, which corresponds to BERTScore-KPQA. So, the answer is {\"answer\": \"BERTScore-KPQA\"}." } ], "images": [ "scigen/train/1972.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Correlation between metrics and human judgments of correctness using Pearson Correlation(r), Spearman’s Correlation(ρ). Some of the results of NarrativeQA and SemEval are from chen2019evaluating." ], "reasoning": "To find the metric with the highest correlation for the NarrativeQA dataset, we need to look at the 'NarrativeQA ρ' column and identify the highest value. The highest value in this column is 0.770, which corresponds to BERTScore-KPQA.", "question": "Which metric shows the highest correlation with human judgment for the NarrativeQA dataset?", "answer": "So, the answer is {\"answer\": \"BERTScore-KPQA\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nROUGE-L-KPQA has a higher correlation score than BERTScore for the AVSD dataset.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the correlation scores of ROUGE-L-KPQA and BERTScore for the AVSD dataset. According to the table, ROUGE-L-KPQA has a correlation score of 0.709, while BERTScore has a correlation score of 0.587. Since 0.709 is higher than 0.587, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1972.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Correlation between metrics and human judgments of correctness using Pearson Correlation(r), Spearman’s Correlation(ρ). Some of the results of NarrativeQA and SemEval are from chen2019evaluating." ], "reasoning": "To verify this statement, we need to compare the correlation scores of ROUGE-L-KPQA and BERTScore for the AVSD dataset. According to the table, ROUGE-L-KPQA has a correlation score of 0.709, while BERTScore has a correlation score of 0.587. Since 0.709 is higher than 0.587, the statement is supported.", "statement": "ROUGE-L-KPQA has a higher correlation score than BERTScore for the AVSD dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Mean average precision of tf-idf (TFIDF), BM25, word vector centroid (CENTROID) and our semantic approach (SEM) on the TREC set.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "CENTROID maps queries and documents to vectors using word embeddings, but it performs worse than BM25 and SEM on TREC 2006 and 2007.CENTROID maps each query and document to a vector by taking a centroid of word embedding vectors, and the cosine similarity between two vectors is used for scoring and ranking documents. As mentioned earlier, this approach is not effective when multiple topics exist in a document. From the table, the embedding approach boosts the average precision of BM25 by 19% and 6% on TREC 2006 and 2007, respectively. However, CENTROID provides scores lower than BM25 and SEM approaches." } ], "images": [ "scigen/train/1973.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Mean average precision of tf-idf (TFIDF), BM25, word vector centroid (CENTROID) and our semantic approach (SEM) on the TREC set." ], "reasoning": "CENTROID maps queries and documents to vectors using word embeddings, but it performs worse than BM25 and SEM on TREC 2006 and 2007.", "description": "CENTROID maps each query and document to a vector by taking a centroid of word embedding vectors, and the cosine similarity between two vectors is used for scoring and ranking documents. As mentioned earlier, this approach is not effective when multiple topics exist in a document. From the table, the embedding approach boosts the average precision of BM25 by 19% and 6% on TREC 2006 and 2007, respectively. However, CENTROID provides scores lower than BM25 and SEM approaches.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: Table 2: Mean average precision of tf-idf (TFIDF), BM25, word vector centroid (CENTROID) and our semantic approach (SEM) on the TREC set.\nQuestion: Which method had the highest average precision on TREC 2006?\n" }, { "role": "assistant", "content": "To find the method with the highest average precision on TREC 2006, compare the values in the TREC 2006 column. SEM has the highest value of 0.3732. So, the answer is {\"answer\": \"SEM\"}." } ], "images": [ "scigen/train/1973.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Mean average precision of tf-idf (TFIDF), BM25, word vector centroid (CENTROID) and our semantic approach (SEM) on the TREC set." ], "reasoning": "To find the method with the highest average precision on TREC 2006, compare the values in the TREC 2006 column. SEM has the highest value of 0.3732.", "question": "Which method had the highest average precision on TREC 2006?", "answer": "So, the answer is {\"answer\": \"SEM\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis CENTROID had the lowest average precision on TREC 2006. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nCENTROID had the lowest average precision on TREC 2006.\n" }, { "role": "assistant", "content": "To verify this statement, compare the values in the TREC 2006 column. CENTROID has the lowest value of 0.2363, which is lower than TFIDF, BM25, and SEM. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1973.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Mean average precision of tf-idf (TFIDF), BM25, word vector centroid (CENTROID) and our semantic approach (SEM) on the TREC set." ], "reasoning": "To verify this statement, compare the values in the TREC 2006 column. CENTROID has the lowest value of 0.2363, which is lower than TFIDF, BM25, and SEM.", "statement": "CENTROID had the lowest average precision on TREC 2006.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: BLEU scores where the target pair’s parallel corpus is constrained to be 5%, 10%, 20% and 40% of the original size. We report the BLEU scores on the development and test sets (separated by /) by the single-pair model (Single), the single-pair model with monolingual corpus (Single+DF) and the proposed multi-way, multilingual model (Multi).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The multi-language model outperforms the single-pair model, even when the latter is enhanced with a target-side monolingual corpus, and this performance gap widens as the size of the target parallel corpus decreases.Low-Resource Translation This is true even when the single-pair model is strengthened with a target-side monolingual corpus (Single+DF). This suggests that the benefit of generalization from having multiple languages goes beyond that of simply having more target-side monolingual corpus. The performance gap grows as the size of target parallel corpus decreases." } ], "images": [ "scigen/train/1974.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: BLEU scores where the target pair’s parallel corpus is constrained to be 5%, 10%, 20% and 40% of the original size. We report the BLEU scores on the development and test sets (separated by /) by the single-pair model (Single), the single-pair model with monolingual corpus (Single+DF) and the proposed multi-way, multilingual model (Multi)." ], "reasoning": "The multi-language model outperforms the single-pair model, even when the latter is enhanced with a target-side monolingual corpus, and this performance gap widens as the size of the target parallel corpus decreases.", "description": "Low-Resource Translation This is true even when the single-pair model is strengthened with a target-side monolingual corpus (Single+DF). This suggests that the benefit of generalization from having multiple languages goes beyond that of simply having more target-side monolingual corpus. The performance gap grows as the size of target parallel corpus decreases.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: BLEU scores where the target pair’s parallel corpus is constrained to be 5%, 10%, 20% and 40% of the original size. We report the BLEU scores on the development and test sets (separated by /) by the single-pair model (Single), the single-pair model with monolingual corpus (Single+DF) and the proposed multi-way, multilingual model (Multi).'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the performance of the multi-language model for En→Fi with a 100k target parallel corpus?\n" }, { "role": "assistant", "content": "To find the performance of the multi-language model for En→Fi with a 100k target parallel corpus, look at the 'Multi' column in the row corresponding to 'En→Fi' and '100k'. The values are 6.2/5.17. So, the answer is {\"answer\": \"6.2/5.17\"}." } ], "images": [ "scigen/train/1974.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: BLEU scores where the target pair’s parallel corpus is constrained to be 5%, 10%, 20% and 40% of the original size. We report the BLEU scores on the development and test sets (separated by /) by the single-pair model (Single), the single-pair model with monolingual corpus (Single+DF) and the proposed multi-way, multilingual model (Multi)." ], "reasoning": "To find the performance of the multi-language model for En→Fi with a 100k target parallel corpus, look at the 'Multi' column in the row corresponding to 'En→Fi' and '100k'. The values are 6.2/5.17.", "question": "What is the performance of the multi-language model for En→Fi with a 100k target parallel corpus?", "answer": "So, the answer is {\"answer\": \"6.2/5.17\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The performance of the multi-language model for De→En with a 420k target parallel corpus is better than the single-pair model., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe performance of the multi-language model for De→En with a 420k target parallel corpus is better than the single-pair model.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Multi' column with the 'Single' column for De→En with a 420k target parallel corpus. The 'Multi' values are 18.57/17.12, and the 'Single' values are 18.32/17.32. Since 18.57 is greater than 18.32 and 17.12 is less than 17.32, the statement is partially true. Thus, the answer is {\"answer\": \"not enough info\"}." } ], "images": [ "scigen/train/1974.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: BLEU scores where the target pair’s parallel corpus is constrained to be 5%, 10%, 20% and 40% of the original size. We report the BLEU scores on the development and test sets (separated by /) by the single-pair model (Single), the single-pair model with monolingual corpus (Single+DF) and the proposed multi-way, multilingual model (Multi)." ], "reasoning": "To verify this statement, compare the 'Multi' column with the 'Single' column for De→En with a 420k target parallel corpus. The 'Multi' values are 18.57/17.12, and the 'Single' values are 18.32/17.32. Since 18.57 is greater than 18.32 and 17.12 is less than 17.32, the statement is partially true.", "statement": "The performance of the multi-language model for De→En with a 420k target parallel corpus is better than the single-pair model.", "answer": "Thus, the answer is {\"answer\": \"not enough info\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Macro-averaged F1-score of automatic essay classification into the big five dimensions of personality. All improvements over MB that were statistically significant are shown in bold.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The table compares the precision, recall, and F1-score of various classifiers against the gold labels for five personality dimensions, with a baseline classifier predicting the majority class.Upon classification, the results were compared with the gold labels of yes or no for each of the five personality dimension to determine precision, recall, and F1-score. We also present the results for a simple baseline classifier that always predicts the majority class." } ], "images": [ "scigen/train/1975.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Macro-averaged F1-score of automatic essay classification into the big five dimensions of personality. All improvements over MB that were statistically significant are shown in bold." ], "reasoning": "The table compares the precision, recall, and F1-score of various classifiers against the gold labels for five personality dimensions, with a baseline classifier predicting the majority class.", "description": "Upon classification, the results were compared with the gold labels of yes or no for each of the five personality dimension to determine precision, recall, and F1-score. We also present the results for a simple baseline classifier that always predicts the majority class.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Macro-averaged F1-score of automatic essay classification into the big five dimensions of personality. All improvements over MB that were statistically significant are shown in bold.\nQuestion: Which classifier achieved the highest score for the OPN dimension?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest score for the OPN dimension, look at the OPN column and identify the highest value. The highest score in the OPN column is 60.61, achieved by the 'f. MB + FineEmo' classifier. So, the answer is {\"answer\": \"f. MB + FineEmo\"}." } ], "images": [ "scigen/train/1975.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Macro-averaged F1-score of automatic essay classification into the big five dimensions of personality. All improvements over MB that were statistically significant are shown in bold." ], "reasoning": "To find the highest score for the OPN dimension, look at the OPN column and identify the highest value. The highest score in the OPN column is 60.61, achieved by the 'f. MB + FineEmo' classifier.", "question": "Which classifier achieved the highest score for the OPN dimension?", "answer": "So, the answer is {\"answer\": \"f. MB + FineEmo\"}." }, { "messages": [ { "role": "user", "content": "The 'c. MB + AIC' classifier achieved the highest score for the NEU dimension. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe 'c. MB + AIC' classifier achieved the highest score for the NEU dimension.\n" }, { "role": "assistant", "content": "To verify this statement, look at the NEU column and compare the scores. The highest score in the NEU column is 58.25, achieved by the 'f. MB + FineEmo' classifier, not 'c. MB + AIC'. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1975.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Macro-averaged F1-score of automatic essay classification into the big five dimensions of personality. All improvements over MB that were statistically significant are shown in bold." ], "reasoning": "To verify this statement, look at the NEU column and compare the scores. The highest score in the NEU column is 58.25, achieved by the 'f. MB + FineEmo' classifier, not 'c. MB + AIC'.", "statement": "The 'c. MB + AIC' classifier achieved the highest score for the NEU dimension.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6: Accuracy (%) of models, on the test split of LDC201T10, for different categories of contrastive errors: antecedent (Antec.), pronoun type (Type), number (Num.), and gender (Gender).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The sequential encoder performs better or on par with the tree encoder, except for pronoun number and gender replacements where the graph encoder outperforms the sequential encoder.From the test split of LDC2017T10, we generated 251 contrastive examples due to antecedent replacements, 912 due to pronoun type replacements, 1840 due to number replacements and 95 due to gender replacements. The sequential encoder performs surprisingly well at this task, with better or on par performance with respect to the tree encoder. The graph encoder outperforms the sequential encoder only for pronoun number and gender replacements. Future work is required to more precisely analyze if the different models cope with pronomial mentions in significantly different ways. Other approaches to inspect phenomena of co-reference and control verbs can also be explored, for instance by devising specific training objectives Linzen et al." } ], "images": [ "scigen/train/1976.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Accuracy (%) of models, on the test split of LDC201T10, for different categories of contrastive errors: antecedent (Antec.), pronoun type (Type), number (Num.), and gender (Gender)." ], "reasoning": "The sequential encoder performs better or on par with the tree encoder, except for pronoun number and gender replacements where the graph encoder outperforms the sequential encoder.", "description": "From the test split of LDC2017T10, we generated 251 contrastive examples due to antecedent replacements, 912 due to pronoun type replacements, 1840 due to number replacements and 95 due to gender replacements. The sequential encoder performs surprisingly well at this task, with better or on par performance with respect to the tree encoder. The graph encoder outperforms the sequential encoder only for pronoun number and gender replacements. Future work is required to more precisely analyze if the different models cope with pronomial mentions in significantly different ways. Other approaches to inspect phenomena of co-reference and control verbs can also be explored, for instance by devising specific training objectives Linzen et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 6: Accuracy (%) of models, on the test split of LDC201T10, for different categories of contrastive errors: antecedent (Antec.), pronoun type (Type), number (Num.), and gender (Gender).' and proceed to solve the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model performs best for pronoun type replacements?\n" }, { "role": "assistant", "content": "The table shows the performance of different models for various replacements. For pronoun type replacements, the sequential encoder has the highest score of 97.70. So, the answer is {\"answer\": \"Seq\"}" } ], "images": [ "scigen/train/1976.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Accuracy (%) of models, on the test split of LDC201T10, for different categories of contrastive errors: antecedent (Antec.), pronoun type (Type), number (Num.), and gender (Gender)." ], "reasoning": "The table shows the performance of different models for various replacements. For pronoun type replacements, the sequential encoder has the highest score of 97.70.", "question": "Which model performs best for pronoun type replacements?", "answer": "So, the answer is {\"answer\": \"Seq\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe tree encoder performs better than the sequential encoder for pronoun number replacements.\n" }, { "role": "assistant", "content": "The table shows that the sequential encoder has a score of 94.89 for pronoun number replacements, while the tree encoder has a lower score of 93.70. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1976.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Accuracy (%) of models, on the test split of LDC201T10, for different categories of contrastive errors: antecedent (Antec.), pronoun type (Type), number (Num.), and gender (Gender)." ], "reasoning": "The table shows that the sequential encoder has a score of 94.89 for pronoun number replacements, while the tree encoder has a lower score of 93.70.", "statement": "The tree encoder performs better than the sequential encoder for pronoun number replacements.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: BLEU and Meteor (%) scores on the development split of LDC2015E86.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The table compares the performance of different models on BLEU and Meteor scores, highlighting the impact of structural and sequential components.We first evaluate the overall performance of the models, after which we focus on two phenomena that we expect to benefit most from structural encoders: reentrancies and long-range dependencies. The sequential encoder (Seq) is a re-implementation of \\newcitekonstas2017neural. We test both approaches of stacking structural and sequential components: structure on top of sequence (SeqTreeLSTM and SeqGCN), and sequence on top of structure (TreeLSTMSeq and GCNSeq). To inspect the effect of the sequential component, we run ablation tests by removing the RNNs altogether (TreeLSTM and GCN). GCN-based models are used both as tree encoders (reentrancies are removed) and graph encoders (reentrancies are maintained)." } ], "images": [ "scigen/train/1977.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: BLEU and Meteor (%) scores on the development split of LDC2015E86." ], "reasoning": "The table compares the performance of different models on BLEU and Meteor scores, highlighting the impact of structural and sequential components.", "description": "We first evaluate the overall performance of the models, after which we focus on two phenomena that we expect to benefit most from structural encoders: reentrancies and long-range dependencies. The sequential encoder (Seq) is a re-implementation of \\newcitekonstas2017neural. We test both approaches of stacking structural and sequential components: structure on top of sequence (SeqTreeLSTM and SeqGCN), and sequence on top of structure (TreeLSTMSeq and GCNSeq). To inspect the effect of the sequential component, we run ablation tests by removing the RNNs altogether (TreeLSTM and GCN). GCN-based models are used both as tree encoders (reentrancies are removed) and graph encoders (reentrancies are maintained).", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 1: BLEU and Meteor (%) scores on the development split of LDC2015E86.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: Which model achieved the highest Meteor score and what was the input type?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the highest Meteor score, we look at the Meteor column and identify the highest value, which is 24.00. The corresponding model is GCNSeq with an input type of Graph. So, the answer is {\"answer\": \"Graph GCNSeq\"}." } ], "images": [ "scigen/train/1977.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: BLEU and Meteor (%) scores on the development split of LDC2015E86." ], "reasoning": "To find the highest Meteor score, we look at the Meteor column and identify the highest value, which is 24.00. The corresponding model is GCNSeq with an input type of Graph.", "question": "Which model achieved the highest Meteor score and what was the input type?", "answer": "So, the answer is {\"answer\": \"Graph GCNSeq\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe SeqTreeLSTM model achieved a higher BLEU score than the TreeLSTM model.\n" }, { "role": "assistant", "content": "The BLEU score for SeqTreeLSTM is 21.84, while the BLEU score for TreeLSTM is 22.07. Since 21.84 is less than 22.07, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1977.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: BLEU and Meteor (%) scores on the development split of LDC2015E86." ], "reasoning": "The BLEU score for SeqTreeLSTM is 21.84, while the BLEU score for TreeLSTM is 22.07. Since 21.84 is less than 22.07, the statement is incorrect.", "statement": "The SeqTreeLSTM model achieved a higher BLEU score than the TreeLSTM model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Scores on the test split of LDC2015E86 and LDC2017T10. Tree is the tree-based GCNSeq and Graph is the graph-based GCNSeq.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The graph encoder achieves the highest BLEU and Meteor scores, demonstrating its superiority over other models and previous work.In order to mitigate the effects of random seeds, we train five models with different random seeds and report the results of the median model, according to their BLEU score on the development set Beck et al. We achieve state-of-the-art results with both tree and graph encoders, demonstrating the efficacy of our GCNSeq approach. The graph encoder outperforms the other systems and previous work on both datasets. These results demonstrate the benefit of structural encoders over purely sequential ones as well as the advantage of explicitly including reentrancies. The differences between our graph encoder and that of \\newcitesong" } ], "images": [ "scigen/train/1978.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Scores on the test split of LDC2015E86 and LDC2017T10. Tree is the tree-based GCNSeq and Graph is the graph-based GCNSeq." ], "reasoning": "The graph encoder achieves the highest BLEU and Meteor scores, demonstrating its superiority over other models and previous work.", "description": "In order to mitigate the effects of random seeds, we train five models with different random seeds and report the results of the median model, according to their BLEU score on the development set Beck et al. We achieve state-of-the-art results with both tree and graph encoders, demonstrating the efficacy of our GCNSeq approach. The graph encoder outperforms the other systems and previous work on both datasets. These results demonstrate the benefit of structural encoders over purely sequential ones as well as the advantage of explicitly including reentrancies. The differences between our graph encoder and that of \\newcitesong", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Scores on the test split of LDC2015E86 and LDC2017T10. Tree is the tree-based GCNSeq and Graph is the graph-based GCNSeq.', answer the following question. Give detailed solution consisting of each step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieved the highest BLEU score on the LDC2017T10 dataset?\n" }, { "role": "assistant", "content": "By comparing the BLEU scores for the LDC2017T10 dataset, the Graph model has the highest score of 24.54. So, the answer is {\"answer\": \"Graph\"}." } ], "images": [ "scigen/train/1978.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Scores on the test split of LDC2015E86 and LDC2017T10. Tree is the tree-based GCNSeq and Graph is the graph-based GCNSeq." ], "reasoning": "By comparing the BLEU scores for the LDC2017T10 dataset, the Graph model has the highest score of 24.54.", "question": "Which model achieved the highest BLEU score on the LDC2017T10 dataset?", "answer": "So, the answer is {\"answer\": \"Graph\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Seq model achieved a BLEU score of 21.43 on the LDC2015E86 dataset.\n" }, { "role": "assistant", "content": "The table shows that the Seq model has a BLEU score of 21.43 on the LDC2015E86 dataset. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1978.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Scores on the test split of LDC2015E86 and LDC2017T10. Tree is the tree-based GCNSeq and Graph is the graph-based GCNSeq." ], "reasoning": "The table shows that the Seq model has a BLEU score of 21.43 on the LDC2015E86 dataset.", "statement": "The Seq model achieved a BLEU score of 21.43 on the LDC2015E86 dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Counts of reentrancies for the development and test split of LDC2017T10'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Graph encoders are expected to perform better with examples containing more reentrancies, as they explicitly model these structures.Overall scores show an advantage of graph encoder over tree and sequential encoders, but they do not shed light into how this is achieved. Because graph encoders are the only ones to model reentrancies explicitly, we expect them to deal better with these structures. It is, however, possible that the other models are capable of handling these structures implicitly. It is expected that the benefit of the graph models will be more evident for those examples containing more reentrancies. To test this hypothesis, we evaluate the various scenarios as a function of the number of reentrancies in each example, using the Meteor score as a metric." } ], "images": [ "scigen/train/1979.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Counts of reentrancies for the development and test split of LDC2017T10" ], "reasoning": "Graph encoders are expected to perform better with examples containing more reentrancies, as they explicitly model these structures.", "description": "Overall scores show an advantage of graph encoder over tree and sequential encoders, but they do not shed light into how this is achieved. Because graph encoders are the only ones to model reentrancies explicitly, we expect them to deal better with these structures. It is, however, possible that the other models are capable of handling these structures implicitly. It is expected that the benefit of the graph models will be more evident for those examples containing more reentrancies. To test this hypothesis, we evaluate the various scenarios as a function of the number of reentrancies in each example, using the Meteor score as a metric.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: Counts of reentrancies for the development and test split of LDC2017T10'. Solve the following question based on the table.\n\nQuestion: How many development sentences contain 1-5 reentrancies?\nLet's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the number of development sentences with 1-5 reentrancies, look at the row labeled '1-5' under the column '# dev sents.'. So, the answer is {\"answer\": \"679\"}." } ], "images": [ "scigen/train/1979.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Counts of reentrancies for the development and test split of LDC2017T10" ], "reasoning": "To find the number of development sentences with 1-5 reentrancies, look at the row labeled '1-5' under the column '# dev sents.'.", "question": "How many development sentences contain 1-5 reentrancies?", "answer": "So, the answer is {\"answer\": \"679\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThere are more test sentences with 0 reentrancies than development sentences with 6-20 reentrancies.\n" }, { "role": "assistant", "content": "Compare the number of test sentences with 0 reentrancies (622) to the number of development sentences with 6-20 reentrancies (70). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1979.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Counts of reentrancies for the development and test split of LDC2017T10" ], "reasoning": "Compare the number of test sentences with 0 reentrancies (622) to the number of development sentences with 6-20 reentrancies (70).", "statement": "There are more test sentences with 0 reentrancies than development sentences with 6-20 reentrancies.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Measuring redundancy of extractions. MNO stands for Mean Number of Occurrences. IOU stands for Intersection over Union.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "IMoJIE has lower redundancy than gold triples due to imperfect recall, as indicated by lower values in Metric MNO and Metric IOU.These measures were calculated after removing stop words from tuples. Higher value of these measures suggest higher redundancy among the extractions. Interestingly, IMoJIE has a lower redundancy than even the gold triples; this is due to imperfect recall." } ], "images": [ "scigen/train/1980.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Measuring redundancy of extractions. MNO stands for Mean Number of Occurrences. IOU stands for Intersection over Union." ], "reasoning": "IMoJIE has lower redundancy than gold triples due to imperfect recall, as indicated by lower values in Metric MNO and Metric IOU.", "description": "These measures were calculated after removing stop words from tuples. Higher value of these measures suggest higher redundancy among the extractions. Interestingly, IMoJIE has a lower redundancy than even the gold triples; this is due to imperfect recall.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 7: Measuring redundancy of extractions. MNO stands for Mean Number of Occurrences. IOU stands for Intersection over Union.', answer the following question. Give detailed solution consisting of each step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich extraction method has the highest redundancy according to Metric MNO?\n" }, { "role": "assistant", "content": "Higher values in Metric MNO suggest higher redundancy. CopyAttention+BERT has the highest value of 2.805 in Metric MNO. So, the answer is {\"answer\": \"CopyAttention+BERT\"}" } ], "images": [ "scigen/train/1980.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Measuring redundancy of extractions. MNO stands for Mean Number of Occurrences. IOU stands for Intersection over Union." ], "reasoning": "Higher values in Metric MNO suggest higher redundancy. CopyAttention+BERT has the highest value of 2.805 in Metric MNO.", "question": "Which extraction method has the highest redundancy according to Metric MNO?", "answer": "So, the answer is {\"answer\": \"CopyAttention+BERT\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: IMoJIE has the highest number of tuples among the extraction methods., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nIMoJIE has the highest number of tuples among the extraction methods.\n" }, { "role": "assistant", "content": "The table shows that IMoJIE has 1620 tuples, which is less than both CopyAttention+BERT (3159) and Gold (2650). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/1980.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Measuring redundancy of extractions. MNO stands for Mean Number of Occurrences. IOU stands for Intersection over Union." ], "reasoning": "The table shows that IMoJIE has 1620 tuples, which is less than both CopyAttention+BERT (3159) and Gold (2650).", "statement": "IMoJIE has the highest number of tuples among the extraction methods.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Comparison of various OpenIE systems - non-neural, neural and proposed models. (*) Cannot compute AUC as Sense-OIE, MinIE do not emit confidence values for extractions and released code for Span-OIE does not provision calculation of confidence values. In these cases, we report the Last F1 as the Opt. F1'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The best non-neural system achieved a Last F1 score of 51.5, while the best neural model achieved 49.2. CopyAttention produces redundant extractions, and RnnOIE struggles with auxiliary verbs and prepositions.Among the systems under consideration, the best non-neural system reached Last F1 of 51.5, whereas the best existing neural model could only reach 49.2. Deeper analysis reveals that CopyAttention produces redundant extractions conveying nearly the same information, which CaRB effectively penalizes. RnnOIE performs much better, however suffers due to its lack of generating auxilliary verbs and implied prepositions. Example, it can only generate (Trump; President; US) instead of (Trump; is President of; US) from the sentence “US President Trump…”. Moreover, it is trained only on limited number of pseudo-gold extractions, generated by Michael et al." } ], "images": [ "scigen/train/1981.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of various OpenIE systems - non-neural, neural and proposed models. (*) Cannot compute AUC as Sense-OIE, MinIE do not emit confidence values for extractions and released code for Span-OIE does not provision calculation of confidence values. In these cases, we report the Last F1 as the Opt. F1" ], "reasoning": "The best non-neural system achieved a Last F1 score of 51.5, while the best neural model achieved 49.2. CopyAttention produces redundant extractions, and RnnOIE struggles with auxiliary verbs and prepositions.", "description": "Among the systems under consideration, the best non-neural system reached Last F1 of 51.5, whereas the best existing neural model could only reach 49.2. Deeper analysis reveals that CopyAttention produces redundant extractions conveying nearly the same information, which CaRB effectively penalizes. RnnOIE performs much better, however suffers due to its lack of generating auxilliary verbs and implied prepositions. Example, it can only generate (Trump; President; US) instead of (Trump; is President of; US) from the sentence “US President Trump…”. Moreover, it is trained only on limited number of pseudo-gold extractions, generated by Michael et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 3: Comparison of various OpenIE systems - non-neural, neural and proposed models. (*) Cannot compute AUC as Sense-OIE, MinIE do not emit confidence values for extractions and released code for Span-OIE does not provision calculation of confidence values. In these cases, we report the Last F1 as the Opt. F1' and proceed to solve the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich system achieved the highest Metric Last F1 score?\n" }, { "role": "assistant", "content": "By examining the 'Metric Last F1' column, we see that IMoJIE achieved the highest score of 53.3. So, the answer is {\"answer\": \"IMoJIE\"}" } ], "images": [ "scigen/train/1981.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of various OpenIE systems - non-neural, neural and proposed models. (*) Cannot compute AUC as Sense-OIE, MinIE do not emit confidence values for extractions and released code for Span-OIE does not provision calculation of confidence values. In these cases, we report the Last F1 as the Opt. F1" ], "reasoning": "By examining the 'Metric Last F1' column, we see that IMoJIE achieved the highest score of 53.3.", "question": "Which system achieved the highest Metric Last F1 score?", "answer": "So, the answer is {\"answer\": \"IMoJIE\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The best existing neural model achieved a Metric Last F1 score of 49.2., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe best existing neural model achieved a Metric Last F1 score of 49.2.\n" }, { "role": "assistant", "content": "The table shows that RNN-OIE, a neural model, achieved a Metric Last F1 score of 49.2, supporting the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1981.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of various OpenIE systems - non-neural, neural and proposed models. (*) Cannot compute AUC as Sense-OIE, MinIE do not emit confidence values for extractions and released code for Span-OIE does not provision calculation of confidence values. In these cases, we report the Last F1 as the Opt. F1" ], "reasoning": "The table shows that RNN-OIE, a neural model, achieved a Metric Last F1 score of 49.2, supporting the statement.", "statement": "The best existing neural model achieved a Metric Last F1 score of 49.2.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 6: Performance of IMoJIE on aggregated dataset OpenIE-4+ClausIE+RnnOIE, with different filtering techniques. For comparison, SenseOIE trained on multiple system extractions gives an F1 of 17.2 on CaRB.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "IMoJIE uses a scoring and filtering approach to aggregate extractions from multiple systems, improving performance in Last F1 by 15.9 points and Optimal F1 by 3.8 points compared to no filtering.IMoJIE aggregates extractions from multiple systems through the scoring and filtering approach. It uses extractions from OpenIE-4 (190K), ClausIE (202K) and RnnOIE (230K) to generate a set of 215K tuples. We also try two supervised approaches to aggregation, by utilizing the gold extractions from CaRB’s dev set. Extraction Filtering: For every sentence-tuple pair, we use a binary classifier that decides whether or not to consider that extraction. The input features of the classifier are the [CLS]-embeddings generated from BERT after processing the concatenated sentence and extraction. The classifier is trained over tuples from CaRB’s dev set. Sentence Filtering: We use an IMoJIE model (bootstrapped over OpenIE-4), to score all the tuples. Then, a Multilayer Perceptron (MLP) predicts a confidence threshold to perform the filtering. Only extractions with scores greater than this threshold will be considered. The input features of the MLP include the length of sentence, IMoJIE (OpenIE-4) scores, and GPT Radford et al. This MLP is trained over sentences from CaRB’s dev set and the gold optimal confidence threshold calculated by CaRB. We observe that the Extraction, Sentence Filtering are better than no filtering by by 7.5, 11.2 pts in Last F1, but worse at Opt. F1 and AUC. We hypothesise that this is because the training data for the MLP (640 sentences in CaRB’s dev set), is not sufficient and the features given to it are not sufficiently discriminative. Thereby, we see the value of our unsupervised Score-and-Filter that improves the performance of IMoJIE by (3.8, 15.9) pts in (Optimal F1, Last F1). The 1.2 pt decrease in AUC is due to the fact that the IMoJIE (no filtering) produces many low-precision extractions, that inflates the AUC." } ], "images": [ "scigen/train/1982.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Performance of IMoJIE on aggregated dataset OpenIE-4+ClausIE+RnnOIE, with different filtering techniques. For comparison, SenseOIE trained on multiple system extractions gives an F1 of 17.2 on CaRB." ], "reasoning": "IMoJIE uses a scoring and filtering approach to aggregate extractions from multiple systems, improving performance in Last F1 by 15.9 points and Optimal F1 by 3.8 points compared to no filtering.", "description": "IMoJIE aggregates extractions from multiple systems through the scoring and filtering approach. It uses extractions from OpenIE-4 (190K), ClausIE (202K) and RnnOIE (230K) to generate a set of 215K tuples. We also try two supervised approaches to aggregation, by utilizing the gold extractions from CaRB’s dev set. Extraction Filtering: For every sentence-tuple pair, we use a binary classifier that decides whether or not to consider that extraction. The input features of the classifier are the [CLS]-embeddings generated from BERT after processing the concatenated sentence and extraction. The classifier is trained over tuples from CaRB’s dev set. Sentence Filtering: We use an IMoJIE model (bootstrapped over OpenIE-4), to score all the tuples. Then, a Multilayer Perceptron (MLP) predicts a confidence threshold to perform the filtering. Only extractions with scores greater than this threshold will be considered. The input features of the MLP include the length of sentence, IMoJIE (OpenIE-4) scores, and GPT Radford et al. This MLP is trained over sentences from CaRB’s dev set and the gold optimal confidence threshold calculated by CaRB. We observe that the Extraction, Sentence Filtering are better than no filtering by by 7.5, 11.2 pts in Last F1, but worse at Opt. F1 and AUC. We hypothesise that this is because the training data for the MLP (640 sentences in CaRB’s dev set), is not sufficient and the features given to it are not sufficiently discriminative. Thereby, we see the value of our unsupervised Score-and-Filter that improves the performance of IMoJIE by (3.8, 15.9) pts in (Optimal F1, Last F1). The 1.2 pt decrease in AUC is due to the fact that the IMoJIE (no filtering) produces many low-precision extractions, that inflates the AUC.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 6: Performance of IMoJIE on aggregated dataset OpenIE-4+ClausIE+RnnOIE, with different filtering techniques. For comparison, SenseOIE trained on multiple system extractions gives an F1 of 17.2 on CaRB.', solve this question and give detail solutions. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich filtering method resulted in the highest Last F1 score?\n" }, { "role": "assistant", "content": "By comparing the Last F1 scores in the table, the Score-And-Filter method has the highest Last F1 score of 53.3. So, the answer is {\"answer\": \"Score-And-Filter\"}." } ], "images": [ "scigen/train/1982.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Performance of IMoJIE on aggregated dataset OpenIE-4+ClausIE+RnnOIE, with different filtering techniques. For comparison, SenseOIE trained on multiple system extractions gives an F1 of 17.2 on CaRB." ], "reasoning": "By comparing the Last F1 scores in the table, the Score-And-Filter method has the highest Last F1 score of 53.3.", "question": "Which filtering method resulted in the highest Last F1 score?", "answer": "So, the answer is {\"answer\": \"Score-And-Filter\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Sentence-based filtering method resulted in a higher Optimal F1 score than the Extraction-based filtering method.\n" }, { "role": "assistant", "content": "The table shows that the Sentence-based filtering method has an Optimal F1 score of 49.5, while the Extraction-based filtering method has an Optimal F1 score of 46. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1982.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Performance of IMoJIE on aggregated dataset OpenIE-4+ClausIE+RnnOIE, with different filtering techniques. For comparison, SenseOIE trained on multiple system extractions gives an F1 of 17.2 on CaRB." ], "reasoning": "The table shows that the Sentence-based filtering method has an Optimal F1 score of 49.5, while the Extraction-based filtering method has an Optimal F1 score of 46.", "statement": "The Sentence-based filtering method resulted in a higher Optimal F1 score than the Extraction-based filtering method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5: Evaluation results of the CRF-dictionary vs. CNN-enhanced tagging on London1 city and San Francisco datasets', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The dataset London1 was used to evaluate word tagging approaches, with basic pre-processing steps included, and divided into training and testing sub-corpora for the fused NER model.To better evaluate our claims we have also tested our proposed word tagging approaches on a more realistic dataset collected from London referenced as London1. Though basic pre-processing steps such as tokenizing and stop word removals have been included in the pipeline. The dataset is divided into two sub-corpora for training and testing the fused NER model." } ], "images": [ "scigen/train/1983.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Evaluation results of the CRF-dictionary vs. CNN-enhanced tagging on London1 city and San Francisco datasets" ], "reasoning": "The dataset London1 was used to evaluate word tagging approaches, with basic pre-processing steps included, and divided into training and testing sub-corpora for the fused NER model.", "description": "To better evaluate our claims we have also tested our proposed word tagging approaches on a more realistic dataset collected from London referenced as London1. Though basic pre-processing steps such as tokenizing and stop word removals have been included in the pipeline. The dataset is divided into two sub-corpora for training and testing the fused NER model.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 5: Evaluation results of the CRF-dictionary vs. CNN-enhanced tagging on London1 city and San Francisco datasets' in mind, break down and resolve the question below step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the CNN-enhanced LOCATION Tagging Precision for the London1 data?\n" }, { "role": "assistant", "content": "To find the CNN-enhanced LOCATION Tagging Precision for the London1 data, locate the row labeled 'London1 data' and the column labeled 'CNN-enhanced LOCATION Tagging Precision'. The value in this cell is the required precision. So, the answer is {\"answer\": \"0.43\"}." } ], "images": [ "scigen/train/1983.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Evaluation results of the CRF-dictionary vs. CNN-enhanced tagging on London1 city and San Francisco datasets" ], "reasoning": "To find the CNN-enhanced LOCATION Tagging Precision for the London1 data, locate the row labeled 'London1 data' and the column labeled 'CNN-enhanced LOCATION Tagging Precision'. The value in this cell is the required precision.", "question": "What is the CNN-enhanced LOCATION Tagging Precision for the London1 data?", "answer": "So, the answer is {\"answer\": \"0.43\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe CRF-dictionary Tagging Recall for the San Francisco data is higher than for the London1 data.\n" }, { "role": "assistant", "content": "To verify this statement, compare the CRF-dictionary Tagging Recall values for San Francisco data and London1 data. The value for San Francisco data is 0.96, and for London1 data, it is 0.49. Since 0.96 is higher than 0.49, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1983.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Evaluation results of the CRF-dictionary vs. CNN-enhanced tagging on London1 city and San Francisco datasets" ], "reasoning": "To verify this statement, compare the CRF-dictionary Tagging Recall values for San Francisco data and London1 data. The value for San Francisco data is 0.96, and for London1 data, it is 0.49. Since 0.96 is higher than 0.49, the statement is supported.", "statement": "The CRF-dictionary Tagging Recall for the San Francisco data is higher than for the London1 data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Similarity analysis on London2 dataset: different Twitter class distributions compared against the ground-truth traffic sensor driven data distribution (first row), different Twitter class distributions compared against the ground-truth sociocultural data parsed form TimeOut London (second row).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The smallest average distance of 0.74 corresponds to Traffic events, indicating good tweet classification performance, while higher similarity measurements for traffic, cultural, and social tweet event classes suggest these are high-traffic zones.The comparison results are reported in terms of classes average distance (represented with μ) from their nearest authority (web) record which was described in sec. To compute the similarities from the average distance values, we have first rescaled the averaged distances by the within-class maximum average distance (sport class distance) and then subtracted the result from one. Doing so, the similarity values are confined to be between 0 and 1 where closer to one values will guarantee higher similarity and values close to zero show a higher level of dissimilarity. First row results in The results showed that the smallest average distance of 0.74, considering the variance intervals, correspond to the Traffic events, which is a proof of an acceptable tweet classification performance. Additionally, a comparison of Twitter traffic report times with their nearest neighbour authority traffic record time-stamp, showed that 49.5% of the Twitter traffic alerts are reported on average 297.5 minutes. This is approximately 5 hours prior to the authority’s official reports. This finding highlights the advantage of utilising the social media, particularly Twitter driven knowledge in facilitating and speeding up the city traffic management and potentially smoothing the task-handling. Second row results in In our experiments, we discovered higher similarity measurements for traffic, cultural and social tweet event classes with 1.73, 2.95 and 2.00 respective average distance values. This, in fact, proves that the popular and well-advertised cultural activities are potential high-traffic zones. It is important to point out that the Time Out London sociocultural event set does not separate the social events, such as special events held in pubs and restaurants, from cultural events (i.e. exhibitions and ceremonies) while our proposed pipeline labels these events differently." } ], "images": [ "scigen/train/1984.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Similarity analysis on London2 dataset: different Twitter class distributions compared against the ground-truth traffic sensor driven data distribution (first row), different Twitter class distributions compared against the ground-truth sociocultural data parsed form TimeOut London (second row)." ], "reasoning": "The smallest average distance of 0.74 corresponds to Traffic events, indicating good tweet classification performance, while higher similarity measurements for traffic, cultural, and social tweet event classes suggest these are high-traffic zones.", "description": "The comparison results are reported in terms of classes average distance (represented with μ) from their nearest authority (web) record which was described in sec. To compute the similarities from the average distance values, we have first rescaled the averaged distances by the within-class maximum average distance (sport class distance) and then subtracted the result from one. Doing so, the similarity values are confined to be between 0 and 1 where closer to one values will guarantee higher similarity and values close to zero show a higher level of dissimilarity. First row results in The results showed that the smallest average distance of 0.74, considering the variance intervals, correspond to the Traffic events, which is a proof of an acceptable tweet classification performance. Additionally, a comparison of Twitter traffic report times with their nearest neighbour authority traffic record time-stamp, showed that 49.5% of the Twitter traffic alerts are reported on average 297.5 minutes. This is approximately 5 hours prior to the authority’s official reports. This finding highlights the advantage of utilising the social media, particularly Twitter driven knowledge in facilitating and speeding up the city traffic management and potentially smoothing the task-handling. Second row results in In our experiments, we discovered higher similarity measurements for traffic, cultural and social tweet event classes with 1.73, 2.95 and 2.00 respective average distance values. This, in fact, proves that the popular and well-advertised cultural activities are potential high-traffic zones. It is important to point out that the Time Out London sociocultural event set does not separate the social events, such as special events held in pubs and restaurants, from cultural events (i.e. exhibitions and ceremonies) while our proposed pipeline labels these events differently.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 7: Similarity analysis on London2 dataset: different Twitter class distributions compared against the ground-truth traffic sensor driven data distribution (first row), different Twitter class distributions compared against the ground-truth sociocultural data parsed form TimeOut London (second row).', answer the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich event class has the highest similarity value in the TimeOut report?\n" }, { "role": "assistant", "content": "To find the event class with the highest similarity value in the TimeOut report, look at the 'Similarity' row under 'TimeOut Rep.' The highest value is 0.67, which corresponds to the 'Trans.' (Transportation) class. So, the answer is {\"answer\": \"Trans.\"}." } ], "images": [ "scigen/train/1984.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Similarity analysis on London2 dataset: different Twitter class distributions compared against the ground-truth traffic sensor driven data distribution (first row), different Twitter class distributions compared against the ground-truth sociocultural data parsed form TimeOut London (second row)." ], "reasoning": "To find the event class with the highest similarity value in the TimeOut report, look at the 'Similarity' row under 'TimeOut Rep.' The highest value is 0.67, which corresponds to the 'Trans.' (Transportation) class.", "question": "Which event class has the highest similarity value in the TimeOut report?", "answer": "So, the answer is {\"answer\": \"Trans.\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe average distance for the Sport class in the Road report is 1.69.\n" }, { "role": "assistant", "content": "The table shows that the average distance (μ) for the Sport class in the Road report is 0.90, not 1.69. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1984.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Similarity analysis on London2 dataset: different Twitter class distributions compared against the ground-truth traffic sensor driven data distribution (first row), different Twitter class distributions compared against the ground-truth sociocultural data parsed form TimeOut London (second row)." ], "reasoning": "The table shows that the average distance (μ) for the Sport class in the Road report is 0.90, not 1.69. Therefore, the statement is incorrect.", "statement": "The average distance for the Sport class in the Road report is 1.69.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title '(a) Pretraining tasks using captions.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Bicaptioning and forward captioning outperform token classification, and all three models trained with language outperform those trained with object labels.Bicaptioning outperforms forward captioning; we hypothesize that bidirectional modeling gives a denser supervisory signal from each caption, improving the visual features. Bicaptioning and forward captioning both outperform Token Classification, demonstrating that learning to model the sequential structure of language also results in better visual features. All three models trained with language outperform the two models trained with object labels, showing the benefit of learning from the semantically denser language." } ], "images": [ "scigen/train/1985.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) Pretraining tasks using captions." ], "reasoning": "Bicaptioning and forward captioning outperform token classification, and all three models trained with language outperform those trained with object labels.", "description": "Bicaptioning outperforms forward captioning; we hypothesize that bidirectional modeling gives a denser supervisory signal from each caption, improving the visual features. Bicaptioning and forward captioning both outperform Token Classification, demonstrating that learning to model the sequential structure of language also results in better visual features. All three models trained with language outperform the two models trained with object labels, showing the benefit of learning from the semantically denser language.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about '(a) Pretraining tasks using captions.', solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich pretraining task has the highest IN-1k score?\n" }, { "role": "assistant", "content": "By comparing the IN-1k scores of all pretraining tasks, Bicaptioning has the highest score of 52.8. So, the answer is {\"answer\": \"Bicaptioning\"}" } ], "images": [ "scigen/train/1985.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) Pretraining tasks using captions." ], "reasoning": "By comparing the IN-1k scores of all pretraining tasks, Bicaptioning has the highest score of 52.8.", "question": "Which pretraining task has the highest IN-1k score?", "answer": "So, the answer is {\"answer\": \"Bicaptioning\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nToken Classification has the highest VOC07 score.\n" }, { "role": "assistant", "content": "The table shows that both Bicaptioning and Token Classification have the same highest VOC07 score of 88.1. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1985.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) Pretraining tasks using captions." ], "reasoning": "The table shows that both Bicaptioning and Token Classification have the same highest VOC07 score of 88.1.", "statement": "Token Classification has the highest VOC07 score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption '(b) Pretraining tasks using boxes/masks.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Bicaptioning outperforms forward captioning and Token Classification, indicating that bidirectional modeling and learning the sequential structure of language improve visual features.Bicaptioning outperforms forward captioning; we hypothesize that bidirectional modeling gives a denser supervisory signal from each caption, improving the visual features. Bicaptioning and forward captioning both outperform Token Classification, demonstrating that learning to model the sequential structure of language also results in better visual features. All three models trained with language outperform the two models trained with object labels, showing the benefit of learning from the semantically denser language." } ], "images": [ "scigen/train/1986.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) Pretraining tasks using boxes/masks." ], "reasoning": "Bicaptioning outperforms forward captioning and Token Classification, indicating that bidirectional modeling and learning the sequential structure of language improve visual features.", "description": "Bicaptioning outperforms forward captioning; we hypothesize that bidirectional modeling gives a denser supervisory signal from each caption, improving the visual features. Bicaptioning and forward captioning both outperform Token Classification, demonstrating that learning to model the sequential structure of language also results in better visual features. All three models trained with language outperform the two models trained with object labels, showing the benefit of learning from the semantically denser language.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled '(b) Pretraining tasks using boxes/masks.' in mind, break down and resolve the question below step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich pretraining task achieves the highest VOC07 score and what is the score?\n" }, { "role": "assistant", "content": "By examining the table, Bicaptioning achieves the highest VOC07 score of 88.1. So, the answer is {\"answer\": \"88.1\"}." } ], "images": [ "scigen/train/1986.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) Pretraining tasks using boxes/masks." ], "reasoning": "By examining the table, Bicaptioning achieves the highest VOC07 score of 88.1.", "question": "Which pretraining task achieves the highest VOC07 score and what is the score?", "answer": "So, the answer is {\"answer\": \"88.1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nInstance Segmentation outperforms Multi-Label Classification in IN-1k.\n" }, { "role": "assistant", "content": "The table shows that Instance Segmentation has an IN-1k score of 50.1, while Multi-Label Classification has a score of 46.4, indicating that Instance Segmentation outperforms Multi-Label Classification in IN-1k. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1986.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) Pretraining tasks using boxes/masks." ], "reasoning": "The table shows that Instance Segmentation has an IN-1k score of 50.1, while Multi-Label Classification has a score of 46.4, indicating that Instance Segmentation outperforms Multi-Label Classification in IN-1k.", "statement": "Instance Segmentation outperforms Multi-Label Classification in IN-1k.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Ablations: Transformer Size. Comparison across varying Transformer width (hidden size H) and depth (layers L). All models use ResNet-50 as the visual backbone. We show that larger transformers improve downstream linear classification performance – both, wider (left) and deeper (right).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Increasing both width and depth consistently improves downstream classification performance, as observed in the VirTex model with ResNet-50 visual backbone.Increasing width consistently improves downstream classification performance. Similar to width, increasing depth consistently improves downstream classification performance. However, based on these empirical observations, we think that even larger widths and depths may continue to improve performance. (c) Object Detection on PASCAL VOC In all these fine-tuning experiments, we use the VirTex model with ResNet-50 visual backbone, and textual head with depth L=1, width H=2048" } ], "images": [ "scigen/train/1987.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ablations: Transformer Size. Comparison across varying Transformer width (hidden size H) and depth (layers L). All models use ResNet-50 as the visual backbone. We show that larger transformers improve downstream linear classification performance – both, wider (left) and deeper (right)." ], "reasoning": "Increasing both width and depth consistently improves downstream classification performance, as observed in the VirTex model with ResNet-50 visual backbone.", "description": "Increasing width consistently improves downstream classification performance. Similar to width, increasing depth consistently improves downstream classification performance. However, based on these empirical observations, we think that even larger widths and depths may continue to improve performance. (c) Object Detection on PASCAL VOC In all these fine-tuning experiments, we use the VirTex model with ResNet-50 visual backbone, and textual head with depth L=1, width H=2048", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Ablations: Transformer Size. Comparison across varying Transformer width (hidden size H) and depth (layers L). All models use ResNet-50 as the visual backbone. We show that larger transformers improve downstream linear classification performance – both, wider (left) and deeper (right).' to sequentially solve the question presented below. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the VOC07 performance score for a model with a width of 2048?\n" }, { "role": "assistant", "content": "The table shows the VOC07 performance scores for different widths. For a width of 2048, the VOC07 score is 88.1. So, the answer is {\"answer\": \"88.1\"}." } ], "images": [ "scigen/train/1987.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ablations: Transformer Size. Comparison across varying Transformer width (hidden size H) and depth (layers L). All models use ResNet-50 as the visual backbone. We show that larger transformers improve downstream linear classification performance – both, wider (left) and deeper (right)." ], "reasoning": "The table shows the VOC07 performance scores for different widths. For a width of 2048, the VOC07 score is 88.1.", "question": "What is the VOC07 performance score for a model with a width of 2048?", "answer": "So, the answer is {\"answer\": \"88.1\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nIncreasing the width to 2048 improves the IN-1k score to above 52.\n" }, { "role": "assistant", "content": "The table shows that increasing the width to 2048 results in an IN-1k score of 52.8, which is above 52. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1987.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ablations: Transformer Size. Comparison across varying Transformer width (hidden size H) and depth (layers L). All models use ResNet-50 as the visual backbone. We show that larger transformers improve downstream linear classification performance – both, wider (left) and deeper (right)." ], "reasoning": "The table shows that increasing the width to 2048 results in an IN-1k score of 52.8, which is above 52.", "statement": "Increasing the width to 2048 improves the IN-1k score to above 52.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Ablations: Transformer Size. Comparison across varying Transformer width (hidden size H) and depth (layers L). All models use ResNet-50 as the visual backbone. We show that larger transformers improve downstream linear classification performance – both, wider (left) and deeper (right).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Increasing both depth and width consistently improves downstream classification performance, as shown in the table for object detection on PASCAL VOC.Increasing width consistently improves downstream classification performance. Similar to width, increasing depth consistently improves downstream classification performance. However, based on these empirical observations, we think that even larger widths and depths may continue to improve performance. (c) Object Detection on PASCAL VOC In all these fine-tuning experiments, we use the VirTex model with ResNet-50 visual backbone, and textual head with depth L=1, width H=2048" } ], "images": [ "scigen/train/1988.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ablations: Transformer Size. Comparison across varying Transformer width (hidden size H) and depth (layers L). All models use ResNet-50 as the visual backbone. We show that larger transformers improve downstream linear classification performance – both, wider (left) and deeper (right)." ], "reasoning": "Increasing both depth and width consistently improves downstream classification performance, as shown in the table for object detection on PASCAL VOC.", "description": "Increasing width consistently improves downstream classification performance. Similar to width, increasing depth consistently improves downstream classification performance. However, based on these empirical observations, we think that even larger widths and depths may continue to improve performance. (c) Object Detection on PASCAL VOC In all these fine-tuning experiments, we use the VirTex model with ResNet-50 visual backbone, and textual head with depth L=1, width H=2048", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Ablations: Transformer Size. Comparison across varying Transformer width (hidden size H) and depth (layers L). All models use ResNet-50 as the visual backbone. We show that larger transformers improve downstream linear classification performance – both, wider (left) and deeper (right).\nQuestion: What is the VOC07 performance for a model with depth L=3 and width H=1024?\nSolve the above question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the VOC07 performance for a model with depth L=3 and width H=1024, look at the row where Depth L is 3 and find the corresponding VOC07 value. So, the answer is {\"answer\": \"87.5\"}." } ], "images": [ "scigen/train/1988.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ablations: Transformer Size. Comparison across varying Transformer width (hidden size H) and depth (layers L). All models use ResNet-50 as the visual backbone. We show that larger transformers improve downstream linear classification performance – both, wider (left) and deeper (right)." ], "reasoning": "To find the VOC07 performance for a model with depth L=3 and width H=1024, look at the row where Depth L is 3 and find the corresponding VOC07 value.", "question": "What is the VOC07 performance for a model with depth L=3 and width H=1024?", "answer": "So, the answer is {\"answer\": \"87.5\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nIncreasing the depth from L=1 to L=4 improves the IN-1k performance.\n" }, { "role": "assistant", "content": "To verify this statement, compare the IN-1k performance values for depths L=1 and L=4. The IN-1k performance increases from 50.6 to 52.1. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1988.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ablations: Transformer Size. Comparison across varying Transformer width (hidden size H) and depth (layers L). All models use ResNet-50 as the visual backbone. We show that larger transformers improve downstream linear classification performance – both, wider (left) and deeper (right)." ], "reasoning": "To verify this statement, compare the IN-1k performance values for depths L=1 and L=4. The IN-1k performance increases from 50.6 to 52.1.", "statement": "Increasing the depth from L=1 to L=4 improves the IN-1k performance.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Ablations: Backbone Size. We show that bigger visual backbones improve downstream linear classification performance – both, wider (left) and deeper (right). All VirTex models use textual heads with depth L=1 and width H=1024. VirTex continues to closely match, or outperform IN-sup models (IN: gray) on VOC07 with bigger backbones, similar to ResNet-50 (Figure 3).'.\n" }, { "role": "assistant", "content": "Scaling the backbone capacity in VirTex models, such as using ResNet-50 w2× or ResNet-50 w2× (IN), improves performance on downstream tasks like PASCAL VOC and ImageNet-1k.Do bigger visual backbones help? We investigate whether scaling backbone capacity in VirTex models can improve performance on downstream tasks. We train two VirTex models with bigger visual backbones and compare them with base model using ResNet-50 – (a) ResNet-50 w2× (2× channel width), and (b) ResNet-101 ( 2× depth). Both variants use textual head with L=1,H=1024. Using higher-capacity backbones improve downstream performance on both PASCAL VOC and ImageNet-1k." } ], "images": [ "scigen/train/1989.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablations: Backbone Size. We show that bigger visual backbones improve downstream linear classification performance – both, wider (left) and deeper (right). All VirTex models use textual heads with depth L=1 and width H=1024. VirTex continues to closely match, or outperform IN-sup models (IN: gray) on VOC07 with bigger backbones, similar to ResNet-50 (Figure 3)." ], "reasoning": "Scaling the backbone capacity in VirTex models, such as using ResNet-50 w2× or ResNet-50 w2× (IN), improves performance on downstream tasks like PASCAL VOC and ImageNet-1k.", "description": "Do bigger visual backbones help? We investigate whether scaling backbone capacity in VirTex models can improve performance on downstream tasks. We train two VirTex models with bigger visual backbones and compare them with base model using ResNet-50 – (a) ResNet-50 w2× (2× channel width), and (b) ResNet-101 ( 2× depth). Both variants use textual head with L=1,H=1024. Using higher-capacity backbones improve downstream performance on both PASCAL VOC and ImageNet-1k.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Ablations: Backbone Size. We show that bigger visual backbones improve downstream linear classification performance – both, wider (left) and deeper (right). All VirTex models use textual heads with depth L=1 and width H=1024. VirTex continues to closely match, or outperform IN-sup models (IN: gray) on VOC07 with bigger backbones, similar to ResNet-50 (Figure 3).'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}. \n\nQuestion: Which visual backbone shows the highest performance on ImageNet-1k?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the highest performance on ImageNet-1k, compare the IN-1k column values for each visual backbone. The highest value is 76.7 for ResNet-50 w2× (IN). So, the answer is {\"answer\": \"ResNet-50 w2× (IN)\"}." } ], "images": [ "scigen/train/1989.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablations: Backbone Size. We show that bigger visual backbones improve downstream linear classification performance – both, wider (left) and deeper (right). All VirTex models use textual heads with depth L=1 and width H=1024. VirTex continues to closely match, or outperform IN-sup models (IN: gray) on VOC07 with bigger backbones, similar to ResNet-50 (Figure 3)." ], "reasoning": "To find the highest performance on ImageNet-1k, compare the IN-1k column values for each visual backbone. The highest value is 76.7 for ResNet-50 w2× (IN).", "question": "Which visual backbone shows the highest performance on ImageNet-1k?", "answer": "So, the answer is {\"answer\": \"ResNet-50 w2× (IN)\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nResNet-50 w2× (IN) has the highest performance on PASCAL VOC.\n" }, { "role": "assistant", "content": "To verify this statement, compare the VOC07 column values for each visual backbone. The highest value is 87.6 for ResNet-50 w2× (IN), which supports the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1989.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablations: Backbone Size. We show that bigger visual backbones improve downstream linear classification performance – both, wider (left) and deeper (right). All VirTex models use textual heads with depth L=1 and width H=1024. VirTex continues to closely match, or outperform IN-sup models (IN: gray) on VOC07 with bigger backbones, similar to ResNet-50 (Figure 3)." ], "reasoning": "To verify this statement, compare the VOC07 column values for each visual backbone. The highest value is 87.6 for ResNet-50 w2× (IN), which supports the statement.", "statement": "ResNet-50 w2× (IN) has the highest performance on PASCAL VOC.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Ablations: Backbone Size. We show that bigger visual backbones improve downstream linear classification performance – both, wider (left) and deeper (right). All VirTex models use textual heads with depth L=1 and width H=1024. VirTex continues to closely match, or outperform IN-sup models (IN: gray) on VOC07 with bigger backbones, similar to ResNet-50 (Figure 3).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Scaling backbone capacity in VirTex models with ResNet-101 improves performance on both PASCAL VOC and ImageNet-1k compared to the base ResNet-50 model.Do bigger visual backbones help? We investigate whether scaling backbone capacity in VirTex models can improve performance on downstream tasks. We train two VirTex models with bigger visual backbones and compare them with base model using ResNet-50 – (a) ResNet-50 w2× (2× channel width), and (b) ResNet-101 ( 2× depth). Both variants use textual head with L=1,H=1024. Using higher-capacity backbones improve downstream performance on both PASCAL VOC and ImageNet-1k." } ], "images": [ "scigen/train/1990.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablations: Backbone Size. We show that bigger visual backbones improve downstream linear classification performance – both, wider (left) and deeper (right). All VirTex models use textual heads with depth L=1 and width H=1024. VirTex continues to closely match, or outperform IN-sup models (IN: gray) on VOC07 with bigger backbones, similar to ResNet-50 (Figure 3)." ], "reasoning": "Scaling backbone capacity in VirTex models with ResNet-101 improves performance on both PASCAL VOC and ImageNet-1k compared to the base ResNet-50 model.", "description": "Do bigger visual backbones help? We investigate whether scaling backbone capacity in VirTex models can improve performance on downstream tasks. We train two VirTex models with bigger visual backbones and compare them with base model using ResNet-50 – (a) ResNet-50 w2× (2× channel width), and (b) ResNet-101 ( 2× depth). Both variants use textual head with L=1,H=1024. Using higher-capacity backbones improve downstream performance on both PASCAL VOC and ImageNet-1k.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: Ablations: Backbone Size. We show that bigger visual backbones improve downstream linear classification performance – both, wider (left) and deeper (right). All VirTex models use textual heads with depth L=1 and width H=1024. VirTex continues to closely match, or outperform IN-sup models (IN: gray) on VOC07 with bigger backbones, similar to ResNet-50 (Figure 3).' in mind, break down and resolve the question below step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich visual backbone has the highest performance on ImageNet-1k?\n" }, { "role": "assistant", "content": "By comparing the IN-1k column values for each visual backbone, ResNet-101 (IN) has the highest performance with a score of 77.1. So, the answer is {\"answer\": \"ResNet-101 (IN)\"}." } ], "images": [ "scigen/train/1990.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablations: Backbone Size. We show that bigger visual backbones improve downstream linear classification performance – both, wider (left) and deeper (right). All VirTex models use textual heads with depth L=1 and width H=1024. VirTex continues to closely match, or outperform IN-sup models (IN: gray) on VOC07 with bigger backbones, similar to ResNet-50 (Figure 3)." ], "reasoning": "By comparing the IN-1k column values for each visual backbone, ResNet-101 (IN) has the highest performance with a score of 77.1.", "question": "Which visual backbone has the highest performance on ImageNet-1k?", "answer": "So, the answer is {\"answer\": \"ResNet-101 (IN)\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nResNet-50 has the highest performance on PASCAL VOC.\n" }, { "role": "assistant", "content": "By comparing the VOC07 column values for each visual backbone, ResNet-101 has the highest performance with a score of 87.7, not ResNet-50. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1990.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablations: Backbone Size. We show that bigger visual backbones improve downstream linear classification performance – both, wider (left) and deeper (right). All VirTex models use textual heads with depth L=1 and width H=1024. VirTex continues to closely match, or outperform IN-sup models (IN: gray) on VOC07 with bigger backbones, similar to ResNet-50 (Figure 3)." ], "reasoning": "By comparing the VOC07 column values for each visual backbone, ResNet-101 has the highest performance with a score of 87.7, not ResNet-50.", "statement": "ResNet-50 has the highest performance on PASCAL VOC.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Figure 4: Image Captioning Results. Left: We evaluate VirTex models on COCO val2017 split. We report CIDEr [89] and SPICE [1] metrics for the checkpoint chosen by best VOC07 mAP (at iteration Iter). Changes that improve captioning also tend to improve downstream performance – except few models which are chosen at earlier iterations (orange), indicating that the textual head is slightly under-trained. Right: Qualitative results of captions predicted by our best model. it describes objects in coherent language. The second column shows failure modes: word repetitions and miscounting objects.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Using image captioning as a pretraining task improves transferable visual features, which correlates with better performance in downstream visual recognition tasks.Our goal is to learn transferable visual features via textual supervision. To do so, we use image captioning as a pretraining task. This shows that improvements in our pretraining task tend to correlate with improvements in downstream visual recognition tasks." } ], "images": [ "scigen/train/1991.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 4: Image Captioning Results. Left: We evaluate VirTex models on COCO val2017 split. We report CIDEr [89] and SPICE [1] metrics for the checkpoint chosen by best VOC07 mAP (at iteration Iter). Changes that improve captioning also tend to improve downstream performance – except few models which are chosen at earlier iterations (orange), indicating that the textual head is slightly under-trained. Right: Qualitative results of captions predicted by our best model. it describes objects in coherent language. The second column shows failure modes: word repetitions and miscounting objects." ], "reasoning": "Using image captioning as a pretraining task improves transferable visual features, which correlates with better performance in downstream visual recognition tasks.", "description": "Our goal is to learn transferable visual features via textual supervision. To do so, we use image captioning as a pretraining task. This shows that improvements in our pretraining task tend to correlate with improvements in downstream visual recognition tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model configuration achieved the highest SPICE score and what was its iteration count?\nSolve the above question based on the table titled 'Figure 4: Image Captioning Results. Left: We evaluate VirTex models on COCO val2017 split. We report CIDEr [89] and SPICE [1] metrics for the checkpoint chosen by best VOC07 mAP (at iteration Iter). Changes that improve captioning also tend to improve downstream performance – except few models which are chosen at earlier iterations (orange), indicating that the textual head is slightly under-trained. Right: Qualitative results of captions predicted by our best model. it describes objects in coherent language. The second column shows failure modes: word repetitions and miscounting objects.'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model configuration with the highest SPICE score, look through the SPICE column and identify the highest value, which is 18.5. The corresponding model configuration is ResNet-101 with an iteration count of 498K. So, the answer is {\"answer\": \"498K\"}." } ], "images": [ "scigen/train/1991.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 4: Image Captioning Results. Left: We evaluate VirTex models on COCO val2017 split. We report CIDEr [89] and SPICE [1] metrics for the checkpoint chosen by best VOC07 mAP (at iteration Iter). Changes that improve captioning also tend to improve downstream performance – except few models which are chosen at earlier iterations (orange), indicating that the textual head is slightly under-trained. Right: Qualitative results of captions predicted by our best model. it describes objects in coherent language. The second column shows failure modes: word repetitions and miscounting objects." ], "reasoning": "To find the model configuration with the highest SPICE score, look through the SPICE column and identify the highest value, which is 18.5. The corresponding model configuration is ResNet-101 with an iteration count of 498K.", "question": "Which model configuration achieved the highest SPICE score and what was its iteration count?", "answer": "So, the answer is {\"answer\": \"498K\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The ResNet-50 model with a depth of 3 and a width of 1024 achieved the highest CIDEr score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe ResNet-50 model with a depth of 3 and a width of 1024 achieved the highest CIDEr score.\n" }, { "role": "assistant", "content": "To verify this statement, look at the rows with ResNet-50, depth of 3, and width of 1024. The CIDEr score for this configuration is 92.0, which is indeed the highest CIDEr score in the table. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1991.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 4: Image Captioning Results. Left: We evaluate VirTex models on COCO val2017 split. We report CIDEr [89] and SPICE [1] metrics for the checkpoint chosen by best VOC07 mAP (at iteration Iter). Changes that improve captioning also tend to improve downstream performance – except few models which are chosen at earlier iterations (orange), indicating that the textual head is slightly under-trained. Right: Qualitative results of captions predicted by our best model. it describes objects in coherent language. The second column shows failure modes: word repetitions and miscounting objects." ], "reasoning": "To verify this statement, look at the rows with ResNet-50, depth of 3, and width of 1024. The CIDEr score for this configuration is 92.0, which is indeed the highest CIDEr score in the table.", "statement": "The ResNet-50 model with a depth of 3 and a width of 1024 achieved the highest CIDEr score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: One Billion Word benchmark. Perplexity on the test set for single models. Our result is obtained after 5 epochs.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Our model achieves a perplexity lower than 50 on a single GPU, which is comparable to Jozefowicz et al.'s model that required 32 GPUs.In comparison, only Jozefowicz et al. 8× bigger than ours and trained over 32 GPUs during 3 weeks. We also note that for models of similar size, we achieve similar perplexity than the method introduced by Jozefowicz et al. As far as we know, ours the first method to achieve a perplexity lower than 50 on a single GPU." } ], "images": [ "scigen/train/1992.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: One Billion Word benchmark. Perplexity on the test set for single models. Our result is obtained after 5 epochs." ], "reasoning": "Our model achieves a perplexity lower than 50 on a single GPU, which is comparable to Jozefowicz et al.'s model that required 32 GPUs.", "description": "In comparison, only Jozefowicz et al. 8× bigger than ours and trained over 32 GPUs during 3 weeks. We also note that for models of similar size, we achieve similar perplexity than the method introduced by Jozefowicz et al. As far as we know, ours the first method to achieve a perplexity lower than 50 on a single GPU.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: One Billion Word benchmark. Perplexity on the test set for single models. Our result is obtained after 5 epochs.', solve the subsequent question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the test perplexity of the model that is 8 times bigger than ours and trained over 32 GPUs during 3 weeks?\n" }, { "role": "assistant", "content": "The description mentions that Jozefowicz et al.'s model is 8 times bigger than ours and trained over 32 GPUs during 3 weeks. From the table, Jozefowicz et al.'s model (LSTM-2048-512) has a test perplexity of 43.7. So, the answer is {\"answer\": \"43.7\"}." } ], "images": [ "scigen/train/1992.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: One Billion Word benchmark. Perplexity on the test set for single models. Our result is obtained after 5 epochs." ], "reasoning": "The description mentions that Jozefowicz et al.'s model is 8 times bigger than ours and trained over 32 GPUs during 3 weeks. From the table, Jozefowicz et al.'s model (LSTM-2048-512) has a test perplexity of 43.7.", "question": "What is the test perplexity of the model that is 8 times bigger than ours and trained over 32 GPUs during 3 weeks?", "answer": "So, the answer is {\"answer\": \"43.7\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nOur model achieves a test perplexity lower than 40.\n" }, { "role": "assistant", "content": "The table shows that our 2-layer LSTM-2048 model achieves a test perplexity of 39.8, which is lower than 40. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1992.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: One Billion Word benchmark. Perplexity on the test set for single models. Our result is obtained after 5 epochs." ], "reasoning": "The table shows that our 2-layer LSTM-2048 model achieves a test perplexity of 39.8, which is lower than 40.", "statement": "Our model achieves a test perplexity lower than 40.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Text8: perplexity and training time after 5 epochs. Our approach is significantly better than other published approximate strategies. We also show that improving the baseline D-softmax [*] as discussed in text improve the results, but is slower than our proposal. Note, approximate strategies are comparatively less interesting for small vocabularies such as in this case.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our approach achieves a perplexity of 147, which is close to the full softmax's 144, while being the fastest with a training time of 30 minutes.S5SS0SSS0Px4 Comparison with the state of the art. On this small vocabulary, approximate methods are comparatively less interesting. Our approach is the only one to approach the result of the full soft-max (below by 3 points of perplexity), while being the fastest. Our improved variant D-softmax [*] of the work by Chen et al." } ], "images": [ "scigen/train/1993.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Text8: perplexity and training time after 5 epochs. Our approach is significantly better than other published approximate strategies. We also show that improving the baseline D-softmax [*] as discussed in text improve the results, but is slower than our proposal. Note, approximate strategies are comparatively less interesting for small vocabularies such as in this case." ], "reasoning": "Our approach achieves a perplexity of 147, which is close to the full softmax's 144, while being the fastest with a training time of 30 minutes.", "description": "S5SS0SSS0Px4 Comparison with the state of the art. On this small vocabulary, approximate methods are comparatively less interesting. Our approach is the only one to approach the result of the full soft-max (below by 3 points of perplexity), while being the fastest. Our improved variant D-softmax [*] of the work by Chen et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Text8: perplexity and training time after 5 epochs. Our approach is significantly better than other published approximate strategies. We also show that improving the baseline D-softmax [*] as discussed in text improve the results, but is slower than our proposal. Note, approximate strategies are comparatively less interesting for small vocabularies such as in this case.\nQuestion: Which method has the lowest perplexity and how long does it take to train?\nSolve the above question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The full softmax method has the lowest perplexity of 144 and takes 83 minutes to train. So, the answer is {\"answer\": \"full softmax, 83 min\"}." } ], "images": [ "scigen/train/1993.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Text8: perplexity and training time after 5 epochs. Our approach is significantly better than other published approximate strategies. We also show that improving the baseline D-softmax [*] as discussed in text improve the results, but is slower than our proposal. Note, approximate strategies are comparatively less interesting for small vocabularies such as in this case." ], "reasoning": "The full softmax method has the lowest perplexity of 144 and takes 83 minutes to train.", "question": "Which method has the lowest perplexity and how long does it take to train?", "answer": "So, the answer is {\"answer\": \"full softmax, 83 min\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nOur approach is faster than the D-softmax [*] variant.\n" }, { "role": "assistant", "content": "The table shows that our approach has a training time of 30 minutes, while the D-softmax [*] variant has a training time of 54 minutes. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/1993.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Text8: perplexity and training time after 5 epochs. Our approach is significantly better than other published approximate strategies. We also show that improving the baseline D-softmax [*] as discussed in text improve the results, but is slower than our proposal. Note, approximate strategies are comparatively less interesting for small vocabularies such as in this case." ], "reasoning": "The table shows that our approach has a training time of 30 minutes, while the D-softmax [*] variant has a training time of 54 minutes.", "statement": "Our approach is faster than the D-softmax [*] variant.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Europarl. Perplexity after 5 epochs for different languages as a function of time t (minutes).', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our method outperforms all competitors by a large margin and is 2× to 3× faster than the improved D-softmax.Our approach significantly outperforms all competitors by a large margin. For reference, we also show the performance (D-softmax [*]) obtained by improving the D-softmax, to make it more comparable to our method. Our method is 2× to 3× faster than this improved competitor, which demonstrates how critical is our optimization strategy." } ], "images": [ "scigen/train/1994.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Europarl. Perplexity after 5 epochs for different languages as a function of time t (minutes)." ], "reasoning": "Our method outperforms all competitors by a large margin and is 2× to 3× faster than the improved D-softmax.", "description": "Our approach significantly outperforms all competitors by a large margin. For reference, we also show the performance (D-softmax [*]) obtained by improving the D-softmax, to make it more comparable to our method. Our method is 2× to 3× faster than this improved competitor, which demonstrates how critical is our optimization strategy.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 3: Europarl. Perplexity after 5 epochs for different languages as a function of time t (minutes).', solve the following question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the performance of the 'Ours' method for the 'de 143k' language in terms of 'ppl'?\n" }, { "role": "assistant", "content": "To find the performance of the 'Ours' method for the 'de 143k' language, locate the 'Ours' row and the 'de 143k' column in the table. The value under 'ppl' is 110. So, the answer is {\"answer\": \"110\"}" } ], "images": [ "scigen/train/1994.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Europarl. Perplexity after 5 epochs for different languages as a function of time t (minutes)." ], "reasoning": "To find the performance of the 'Ours' method for the 'de 143k' language, locate the 'Ours' row and the 'de 143k' column in the table. The value under 'ppl' is 110.", "question": "What is the performance of the 'Ours' method for the 'de 143k' language in terms of 'ppl'?", "answer": "So, the answer is {\"answer\": \"110\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The 'Ours' method has the lowest 'ppl' value for the 'el 100k' language. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe 'Ours' method has the lowest 'ppl' value for the 'el 100k' language.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'ppl' values for the 'el 100k' language across all methods. The 'Ours' method has a 'ppl' value of 36, which is the lowest among all methods. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1994.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Europarl. Perplexity after 5 epochs for different languages as a function of time t (minutes)." ], "reasoning": "To verify this statement, compare the 'ppl' values for the 'el 100k' language across all methods. The 'Ours' method has a 'ppl' value of 36, which is the lowest among all methods.", "statement": "The 'Ours' method has the lowest 'ppl' value for the 'el 100k' language.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Results (LAS%) on test sets. Languages are split by language families with dashed lines. AVG. means the average of results from all target languages. Statistically significant differences between our methods and the mBERT model are marked with *, with p-value '. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Our approach with either SVD or GD outperforms both FT-SVD and mBERT, with GD being slightly better than SVD in most languages. Combining FT-SVD with GD or SVD further improves performance.Results on the test sets are shown in Table Overall, our approach with either SVD or GD outperforms both FT-SVD and mBERT by a substantial margin (+2.91% in averaged LAS), among which GD turns out to be slightly better than SVD in most of the languages. When combined with FT-SVD, the performances can be further improved by 0.33% in LAS for the GD method and 0.51% for SVD (see the Appendix for more details). Interestingly, the mBERT model which is trained without any cross-lingual supervision but using a shared multilingual wordpiece vocabulary works surprisingly well in some languages, especially in those linguistically close to English. Similar observations have also been identified in other works pires2019multilingual; wu2019beto." } ], "images": [ "scigen/train/1995.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results (LAS%) on test sets. Languages are split by language families with dashed lines. AVG. means the average of results from all target languages. Statistically significant differences between our methods and the mBERT model are marked with *, with p-value " ], "reasoning": "Our approach with either SVD or GD outperforms both FT-SVD and mBERT, with GD being slightly better than SVD in most languages. Combining FT-SVD with GD or SVD further improves performance.", "description": "Results on the test sets are shown in Table Overall, our approach with either SVD or GD outperforms both FT-SVD and mBERT by a substantial margin (+2.91% in averaged LAS), among which GD turns out to be slightly better than SVD in most of the languages. When combined with FT-SVD, the performances can be further improved by 0.33% in LAS for the GD method and 0.51% for SVD (see the Appendix for more details). Interestingly, the mBERT model which is trained without any cross-lingual supervision but using a shared multilingual wordpiece vocabulary works surprisingly well in some languages, especially in those linguistically close to English. Similar observations have also been identified in other works pires2019multilingual; wu2019beto.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1: Results (LAS%) on test sets. Languages are split by language families with dashed lines. AVG. means the average of results from all target languages. Statistically significant differences between our methods and the mBERT model are marked with *, with p-value ', solve the subsequent question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich method achieves the highest average LAS score?\n" }, { "role": "assistant", "content": "To find the highest average LAS score, compare the average scores of all methods. The averages are: Static FT-SVD (60.63), Contextualized mBERT (60.53), Contextualized CLBT (SVD) (63.09), and Contextualized CLBT (GD) (63.54). So, the answer is {\"answer\": \"Contextualized CLBT (GD)\"}." } ], "images": [ "scigen/train/1995.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results (LAS%) on test sets. Languages are split by language families with dashed lines. AVG. means the average of results from all target languages. Statistically significant differences between our methods and the mBERT model are marked with *, with p-value " ], "reasoning": "To find the highest average LAS score, compare the average scores of all methods. The averages are: Static FT-SVD (60.63), Contextualized mBERT (60.53), Contextualized CLBT (SVD) (63.09), and Contextualized CLBT (GD) (63.54).", "question": "Which method achieves the highest average LAS score?", "answer": "So, the answer is {\"answer\": \"Contextualized CLBT (GD)\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Contextualized CLBT (GD) method outperforms the Contextualized CLBT (SVD) method in the Italian language.\n" }, { "role": "assistant", "content": "For the Italian language, the scores are: Contextualized CLBT (SVD) (79.46) and Contextualized CLBT (GD) (78.98). Since 79.46 is greater than 78.98, the statement is false. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1995.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results (LAS%) on test sets. Languages are split by language families with dashed lines. AVG. means the average of results from all target languages. Statistically significant differences between our methods and the mBERT model are marked with *, with p-value " ], "reasoning": "For the Italian language, the scores are: Contextualized CLBT (SVD) (79.46) and Contextualized CLBT (GD) (78.98). Since 79.46 is greater than 78.98, the statement is false.", "statement": "The Contextualized CLBT (GD) method outperforms the Contextualized CLBT (SVD) method in the Italian language.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Performance on three tasks of countries dataset with AUC-PR metric. minerva significantly outperforms all other methods on the hardest task (S3). Also variance across runs for minerva is lower compared to other methods.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Minerva outperforms all baseline models in task S3, which is the hardest task, and compares favorably in other tasks except S2.Dataset The countries dataset ontains countries, regions, and subregions as entities and is carefully designed to explicitly test the logical rule learning and reasoning capabilities of link prediction models. The queries are of the form LocatedIn(c, ?) and the answer is a region (e.g. LocatedIn(Egypt, ?) with the answer as Africa). Following the design of the countries dataset, for task S1 and S2, we set the maximum path length T=2 and for S3, we set T=3. The Unified Medical Language System (UMLS) dataset, is from biomedicine. The entities are biomedical concepts (e.g. disease, antibiotic) and relations are like treats and diagnoses. The Kinship dataset contains kinship relationships among members of the Alyawarra tribe from Central Australia. For these two task we use maximum path length T=2. We can see that minerva compares favorably or outperforms all the baseline models except on the task S2 of countries, where the ensemble model ntp-λ and ConvE outperforms it, albeit with a higher variance across runs. Our gains are much more prominent in task S3, which is the hardest among all the tasks." } ], "images": [ "scigen/train/1996.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance on three tasks of countries dataset with AUC-PR metric. minerva significantly outperforms all other methods on the hardest task (S3). Also variance across runs for minerva is lower compared to other methods." ], "reasoning": "Minerva outperforms all baseline models in task S3, which is the hardest task, and compares favorably in other tasks except S2.", "description": "Dataset The countries dataset ontains countries, regions, and subregions as entities and is carefully designed to explicitly test the logical rule learning and reasoning capabilities of link prediction models. The queries are of the form LocatedIn(c, ?) and the answer is a region (e.g. LocatedIn(Egypt, ?) with the answer as Africa). Following the design of the countries dataset, for task S1 and S2, we set the maximum path length T=2 and for S3, we set T=3. The Unified Medical Language System (UMLS) dataset, is from biomedicine. The entities are biomedical concepts (e.g. disease, antibiotic) and relations are like treats and diagnoses. The Kinship dataset contains kinship relationships among members of the Alyawarra tribe from Central Australia. For these two task we use maximum path length T=2. We can see that minerva compares favorably or outperforms all the baseline models except on the task S2 of countries, where the ensemble model ntp-λ and ConvE outperforms it, albeit with a higher variance across runs. Our gains are much more prominent in task S3, which is the hardest among all the tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 2: Performance on three tasks of countries dataset with AUC-PR metric. minerva significantly outperforms all other methods on the hardest task (S3). Also variance across runs for minerva is lower compared to other methods.', unfold the question presented below, detailing every step of your calculation. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest performance in task S3?\n" }, { "role": "assistant", "content": "To determine the highest performance in task S3, compare the values in the S3 row. MINERVA has the highest value of 95.10±1.20. So, the answer is {\"answer\": \"MINERVA\"}" } ], "images": [ "scigen/train/1996.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance on three tasks of countries dataset with AUC-PR metric. minerva significantly outperforms all other methods on the hardest task (S3). Also variance across runs for minerva is lower compared to other methods." ], "reasoning": "To determine the highest performance in task S3, compare the values in the S3 row. MINERVA has the highest value of 95.10±1.20.", "question": "Which model has the highest performance in task S3?", "answer": "So, the answer is {\"answer\": \"MINERVA\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim ConvE outperforms all other models in task S2., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nConvE outperforms all other models in task S2.\n" }, { "role": "assistant", "content": "In task S2, ConvE has a performance of 99.0±1.00, which is higher than all other models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1996.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance on three tasks of countries dataset with AUC-PR metric. minerva significantly outperforms all other methods on the hardest task (S3). Also variance across runs for minerva is lower compared to other methods." ], "reasoning": "In task S2, ConvE has a performance of 99.0±1.00, which is higher than all other models listed.", "statement": "ConvE outperforms all other models in task S2.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Query answering results on Kinship and UMLS datasets.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Minerva outperforms NeuralLP and NTP on Kinship and matches NTP's hits@10 performance on UMLS, but pre-trained embeddings of ComplEx do not significantly increase Minerva's performance.The Kinship and UMLS datasets are small KB datasets with around 100 entities each On Kinship, minerva outperforms both NeuralLP and NTP and matches the hits@10 performance of NTP on UMLS. Unlike countries, these datasets were not designed to test the logical rule learning ability of models and given the small size, embedding based models are able to get really high performance. Combination of both methods gives a slight increase in performance as can be seen from the results of NTP-λ. However, when we initialized minerva with pre-trained embeddings of ComplEx, we did not find a significant increase in performance." } ], "images": [ "scigen/train/1997.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Query answering results on Kinship and UMLS datasets." ], "reasoning": "Minerva outperforms NeuralLP and NTP on Kinship and matches NTP's hits@10 performance on UMLS, but pre-trained embeddings of ComplEx do not significantly increase Minerva's performance.", "description": "The Kinship and UMLS datasets are small KB datasets with around 100 entities each On Kinship, minerva outperforms both NeuralLP and NTP and matches the hits@10 performance of NTP on UMLS. Unlike countries, these datasets were not designed to test the logical rule learning ability of models and given the small size, embedding based models are able to get really high performance. Combination of both methods gives a slight increase in performance as can be seen from the results of NTP-λ. However, when we initialized minerva with pre-trained embeddings of ComplEx, we did not find a significant increase in performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: Query answering results on Kinship and UMLS datasets.' and methodically tackle the question that follows. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model has the highest hits@10 performance on the UMLS dataset?\n" }, { "role": "assistant", "content": "By examining the hits@10 column for the UMLS dataset, we see that NTP-λ has the highest value of 1.000. So, the answer is {\"answer\": \"NTP-λ\"}" } ], "images": [ "scigen/train/1997.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Query answering results on Kinship and UMLS datasets." ], "reasoning": "By examining the hits@10 column for the UMLS dataset, we see that NTP-λ has the highest value of 1.000.", "question": "Which model has the highest hits@10 performance on the UMLS dataset?", "answer": "So, the answer is {\"answer\": \"NTP-λ\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nMinerva outperforms NeuralLP on the hits@1 metric for the Kinship dataset.\n" }, { "role": "assistant", "content": "The hits@1 metric for the Kinship dataset shows Minerva with a score of 0.605 and NeuralLP with a score of 0.475, indicating that Minerva outperforms NeuralLP. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1997.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Query answering results on Kinship and UMLS datasets." ], "reasoning": "The hits@1 metric for the Kinship dataset shows Minerva with a score of 0.605 and NeuralLP with a score of 0.475, indicating that Minerva outperforms NeuralLP.", "statement": "Minerva outperforms NeuralLP on the hits@1 metric for the Kinship dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: Query answering results on wn18rr, fb15k-237 and nell-995 datasets. NeuralLP does not scale to nell-995 and hence the entries are kept blank.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Minerva outperforms other baseline methods significantly by focusing on paths relevant to the query, unlike random path sampling methods which lose important paths or fail to aggregate predictive features.We could not include the results of NeuralLP on nell-995 since it didn’t scale to that size. Similarly NTP did not scale to any of the larger datasets. Apart from these, we are the first to report a comprehensive summary of performance of all baseline methods on these datasets. As we can see minerva outperforms this baseline significantly. This shows that a model which predicts based on a set of randomly sampled paths does not do as well as minerva because it either loses important paths during random walking or it fails to aggregate predictive features from all the k paths, many of which would be irrelevant to answer the given query. The latter is akin to the problem with distant supervision However, by taking each step conditioned on the query relation, minerva can effectively reduce the search space and focus on paths relevant to answer the query." } ], "images": [ "scigen/train/1998.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Query answering results on wn18rr, fb15k-237 and nell-995 datasets. NeuralLP does not scale to nell-995 and hence the entries are kept blank." ], "reasoning": "Minerva outperforms other baseline methods significantly by focusing on paths relevant to the query, unlike random path sampling methods which lose important paths or fail to aggregate predictive features.", "description": "We could not include the results of NeuralLP on nell-995 since it didn’t scale to that size. Similarly NTP did not scale to any of the larger datasets. Apart from these, we are the first to report a comprehensive summary of performance of all baseline methods on these datasets. As we can see minerva outperforms this baseline significantly. This shows that a model which predicts based on a set of randomly sampled paths does not do as well as minerva because it either loses important paths during random walking or it fails to aggregate predictive features from all the k paths, many of which would be irrelevant to answer the given query. The latter is akin to the problem with distant supervision However, by taking each step conditioned on the query relation, minerva can effectively reduce the search space and focus on paths relevant to answer the query.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 4: Query answering results on wn18rr, fb15k-237 and nell-995 datasets. NeuralLP does not scale to nell-995 and hence the entries are kept blank.', answer the question below by showing each progressive step toward the answer. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model has the highest hits@10 score on the nell-995 dataset?\n" }, { "role": "assistant", "content": "By examining the hits@10 scores for the nell-995 dataset, we see that MINERVA has the highest score of 0.831. So, the answer is {\"answer\": \"MINERVA\"}" } ], "images": [ "scigen/train/1998.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Query answering results on wn18rr, fb15k-237 and nell-995 datasets. NeuralLP does not scale to nell-995 and hence the entries are kept blank." ], "reasoning": "By examining the hits@10 scores for the nell-995 dataset, we see that MINERVA has the highest score of 0.831.", "question": "Which model has the highest hits@10 score on the nell-995 dataset?", "answer": "So, the answer is {\"answer\": \"MINERVA\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nNeuralLP did not scale to the nell-995 dataset.\n" }, { "role": "assistant", "content": "The table shows no results for NeuralLP on the nell-995 dataset, supporting the statement that NeuralLP did not scale to this dataset. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/1998.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Query answering results on wn18rr, fb15k-237 and nell-995 datasets. NeuralLP does not scale to nell-995 and hence the entries are kept blank." ], "reasoning": "The table shows no results for NeuralLP on the nell-995 dataset, supporting the statement that NeuralLP did not scale to this dataset.", "statement": "NeuralLP did not scale to the nell-995 dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 9: Few example 1-to-M relations from fb15k-237 with high cardinality ratio of tail to head.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "In the validation set of fb15k-237, 54% of the queries are 1-to-M with an average ratio of 13.39, which is significantly higher than 1.5, explaining the suboptimal performance of minerva.Following Bordes et al. , we classify a relation as 1-to-M if the ratio of cardinality of tail to head entities is greater than 1.5 and as M-to-1 if it is lesser than 0.67. In the validation set of fb15k-237, 54% of the queries are 1-to-M, whereas only 26% are M-to-1. Contrasting it with nell-995, 27% are 1-to-M and 36% are M-to-1 or umls where only 18% are 1-to The average ratio for 1-to-M relations in fb15k-237 is 13.39 (substantially higher than 1.5). As explained before, the current evaluation scheme is not suited when it comes to 1-to-M relations and the high percentage of 1-to-M relations in fb15k-237 also explains the sub optimal performance of minerva." } ], "images": [ "scigen/train/1999.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Few example 1-to-M relations from fb15k-237 with high cardinality ratio of tail to head." ], "reasoning": "In the validation set of fb15k-237, 54% of the queries are 1-to-M with an average ratio of 13.39, which is significantly higher than 1.5, explaining the suboptimal performance of minerva.", "description": "Following Bordes et al. , we classify a relation as 1-to-M if the ratio of cardinality of tail to head entities is greater than 1.5 and as M-to-1 if it is lesser than 0.67. In the validation set of fb15k-237, 54% of the queries are 1-to-M, whereas only 26% are M-to-1. Contrasting it with nell-995, 27% are 1-to-M and 36% are M-to-1 or umls where only 18% are 1-to The average ratio for 1-to-M relations in fb15k-237 is 13.39 (substantially higher than 1.5). As explained before, the current evaluation scheme is not suited when it comes to 1-to-M relations and the high percentage of 1-to-M relations in fb15k-237 also explains the sub optimal performance of minerva.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 9: Few example 1-to-M relations from fb15k-237 with high cardinality ratio of tail to head.\nQuestion: What is the tail/head ratio for the relation '/organization/role/leaders./organization/leadership/organization'?\nGive a detailed response to the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table lists the tail/head ratios for various relations. The ratio for '/organization/role/leaders./organization/leadership/organization' is 65.15. So, the answer is {\"answer\": \"65.15\"}." } ], "images": [ "scigen/train/1999.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Few example 1-to-M relations from fb15k-237 with high cardinality ratio of tail to head." ], "reasoning": "The table lists the tail/head ratios for various relations. The ratio for '/organization/role/leaders./organization/leadership/organization' is 65.15.", "question": "What is the tail/head ratio for the relation '/organization/role/leaders./organization/leadership/organization'?", "answer": "So, the answer is {\"answer\": \"65.15\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe relation '/location/country/second_level_divisions' has a tail/head ratio greater than 50.\n" }, { "role": "assistant", "content": "The table shows that the tail/head ratio for '/location/country/second_level_divisions' is 49.18, which is less than 50. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/1999.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Few example 1-to-M relations from fb15k-237 with high cardinality ratio of tail to head." ], "reasoning": "The table shows that the tail/head ratio for '/location/country/second_level_divisions' is 49.18, which is less than 50.", "statement": "The relation '/location/country/second_level_divisions' has a tail/head ratio greater than 50.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Retrofitting to WordNet. Reported values are Spearman correlations for the word similarity tasks and mean cosine similarity for the syntatic relation task. These are deterministic evaluations, so the only source of stochasticity is the optimization of the FR-Neural model.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The flexible FR framework shows consistent improvements in word similarity and syntactic relation tasks compared to the naïve retrofitting model.Here, we evaluate standard lexical metrics for word embeddings: word similarity and syntatic relations. For word similiarity tasks, the evaluation metric is the Spearman correlation between predicted and annotated similarities; for syntatic relation, the evaluation metric is the mean cosine similarity between the learned representation of the correct answer and the prediction by the vector offset method In contrast to our other experiments, here the only stochastic behavior is due to stochastic gradient descent training, not sampling of evaluation samples. Even though the WordNet knowledge graph largely satisfies the assumptions of the naïve retrofitting model, the flexible FR framework achieves sustained improvements for both word similarity datasets (WordSim-353; \\newcitefinkelstein2001placing," } ], "images": [ "scigen/train/2000.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Retrofitting to WordNet. Reported values are Spearman correlations for the word similarity tasks and mean cosine similarity for the syntatic relation task. These are deterministic evaluations, so the only source of stochasticity is the optimization of the FR-Neural model." ], "reasoning": "The flexible FR framework shows consistent improvements in word similarity and syntactic relation tasks compared to the naïve retrofitting model.", "description": "Here, we evaluate standard lexical metrics for word embeddings: word similarity and syntatic relations. For word similiarity tasks, the evaluation metric is the Spearman correlation between predicted and annotated similarities; for syntatic relation, the evaluation metric is the mean cosine similarity between the learned representation of the correct answer and the prediction by the vector offset method In contrast to our other experiments, here the only stochastic behavior is due to stochastic gradient descent training, not sampling of evaluation samples. Even though the WordNet knowledge graph largely satisfies the assumptions of the naïve retrofitting model, the flexible FR framework achieves sustained improvements for both word similarity datasets (WordSim-353; \\newcitefinkelstein2001placing,", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Retrofitting to WordNet. Reported values are Spearman correlations for the word similarity tasks and mean cosine similarity for the syntatic relation task. These are deterministic evaluations, so the only source of stochasticity is the optimization of the FR-Neural model.', answer the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich retrofitting model achieved the highest mean cosine similarity for syntactic relations?\n" }, { "role": "assistant", "content": "The table shows the mean cosine similarity for syntactic relations under different retrofitting models. The highest value is 0.793, achieved by the FR-Linear model. So, the answer is {\"answer\": \"FR-Linear\"}." } ], "images": [ "scigen/train/2000.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Retrofitting to WordNet. Reported values are Spearman correlations for the word similarity tasks and mean cosine similarity for the syntatic relation task. These are deterministic evaluations, so the only source of stochasticity is the optimization of the FR-Neural model." ], "reasoning": "The table shows the mean cosine similarity for syntactic relations under different retrofitting models. The highest value is 0.793, achieved by the FR-Linear model.", "question": "Which retrofitting model achieved the highest mean cosine similarity for syntactic relations?", "answer": "So, the answer is {\"answer\": \"FR-Linear\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe FR-Neural model achieved the highest score in the Word Similarity WordSim-353 task.\n" }, { "role": "assistant", "content": "The table shows the scores for the Word Similarity WordSim-353 task. The highest score is 0.542, achieved by the FR-Linear model, not the FR-Neural model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2000.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Retrofitting to WordNet. Reported values are Spearman correlations for the word similarity tasks and mean cosine similarity for the syntatic relation task. These are deterministic evaluations, so the only source of stochasticity is the optimization of the FR-Neural model." ], "reasoning": "The table shows the scores for the Word Similarity WordSim-353 task. The highest score is 0.542, achieved by the FR-Linear model, not the FR-Neural model.", "statement": "The FR-Neural model achieved the highest score in the Word Similarity WordSim-353 task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Retrofitting to SNOMED-CT. Reported values are mean and standard deviation of the link prediction accuracies over three experiments. The number of edges used for (training/testing) is shown below each edge type.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Simple retrofitting methods are effective for SNOMED-CT due to its synonymy-like relations, and users should select function classes based on relation semantics.As the SNOMED-CT ontology is dominated by synonymy-like relations, we expect the simple retrofitting methods to perform well. Our implementation supports the use of different function classes to represent different relation types; in practice, we recommend that users select function classes in accordance with relation semantics." } ], "images": [ "scigen/train/2001.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Retrofitting to SNOMED-CT. Reported values are mean and standard deviation of the link prediction accuracies over three experiments. The number of edges used for (training/testing) is shown below each edge type." ], "reasoning": "Simple retrofitting methods are effective for SNOMED-CT due to its synonymy-like relations, and users should select function classes based on relation semantics.", "description": "As the SNOMED-CT ontology is dominated by synonymy-like relations, we expect the simple retrofitting methods to perform well. Our implementation supports the use of different function classes to represent different relation types; in practice, we recommend that users select function classes in accordance with relation semantics.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Retrofitting to SNOMED-CT. Reported values are mean and standard deviation of the link prediction accuracies over three experiments. The number of edges used for (training/testing) is shown below each edge type.\nQuestion: Which retrofitting model performs best for the 'Has Pathological Process' relation?\nSolve the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To determine the best-performing retrofitting model for the 'Has Pathological Process' relation, compare the performance values in the corresponding column. The highest value indicates the best performance. So, the answer is {\"answer\": \"FR-Linear\"}." } ], "images": [ "scigen/train/2001.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Retrofitting to SNOMED-CT. Reported values are mean and standard deviation of the link prediction accuracies over three experiments. The number of edges used for (training/testing) is shown below each edge type." ], "reasoning": "To determine the best-performing retrofitting model for the 'Has Pathological Process' relation, compare the performance values in the corresponding column. The highest value indicates the best performance.", "question": "Which retrofitting model performs best for the 'Has Pathological Process' relation?", "answer": "So, the answer is {\"answer\": \"FR-Linear\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe FR-Identity model performs better than the None model for the 'Due to' relation.\n" }, { "role": "assistant", "content": "Compare the performance values of the FR-Identity model and the None model for the 'Due to' relation. The FR-Identity model has a performance value of 94.69±0.61, which is higher than the None model's 91.47±0.88. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2001.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Retrofitting to SNOMED-CT. Reported values are mean and standard deviation of the link prediction accuracies over three experiments. The number of edges used for (training/testing) is shown below each edge type." ], "reasoning": "Compare the performance values of the FR-Identity model and the None model for the 'Due to' relation. The FR-Identity model has a performance value of 94.69±0.61, which is higher than the None model's 91.47±0.88.", "statement": "The FR-Identity model performs better than the None model for the 'Due to' relation.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 8: Structure of the SNOMED-CT knowledge graph.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "SNOMED-CT concepts are linked to Wikipedia articles using WikiData, and TF-IDF weighted aggregation of pre-trained term embeddings is used to create 300-dimensional vectors for each entity.SNOMED-CT is an ontology of clinical healthcare terms and concepts including diseases, treatments, anatomical terms, and many other types of entities. To create distributional embeddings, we first link each SNOMED-CT concept to a set of Wikipedia articles by indexing the associated search terms in WikiData. We aggregate each article set by the method of \\newcitearora2016simple, which performs TF-IDF weighted aggregation of pre-trained term embeddings to create sophisticated distributional embeddings of SNOMED-CT concepts. This creates a single 300-dimensional vector for each entity." } ], "images": [ "scigen/train/2002.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Structure of the SNOMED-CT knowledge graph." ], "reasoning": "SNOMED-CT concepts are linked to Wikipedia articles using WikiData, and TF-IDF weighted aggregation of pre-trained term embeddings is used to create 300-dimensional vectors for each entity.", "description": "SNOMED-CT is an ontology of clinical healthcare terms and concepts including diseases, treatments, anatomical terms, and many other types of entities. To create distributional embeddings, we first link each SNOMED-CT concept to a set of Wikipedia articles by indexing the associated search terms in WikiData. We aggregate each article set by the method of \\newcitearora2016simple, which performs TF-IDF weighted aggregation of pre-trained term embeddings to create sophisticated distributional embeddings of SNOMED-CT concepts. This creates a single 300-dimensional vector for each entity.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 8: Structure of the SNOMED-CT knowledge graph.\nQuestion: What is the total count of the edge types 'has_finding_site' and 'has_method'?\nSolve the above question based on the table information. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The count for 'has_finding_site' is 205263 and the count for 'has_method' is 200507. Adding these together gives 405770. So, the answer is {\"answer\": \"405770\"}" } ], "images": [ "scigen/train/2002.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Structure of the SNOMED-CT knowledge graph." ], "reasoning": "The count for 'has_finding_site' is 205263 and the count for 'has_method' is 200507. Adding these together gives 405770.", "question": "What is the total count of the edge types 'has_finding_site' and 'has_method'?", "answer": "So, the answer is {\"answer\": \"405770\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe edge type 'has_direct_procedure_site' has a higher count than 'has_procedure_site'.\n" }, { "role": "assistant", "content": "The count for 'has_direct_procedure_site' is 45693, while the count for 'has_procedure_site' is 79171. Since 45693 is less than 79171, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2002.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Structure of the SNOMED-CT knowledge graph." ], "reasoning": "The count for 'has_direct_procedure_site' is 45693, while the count for 'has_procedure_site' is 79171. Since 45693 is less than 79171, the statement is refuted.", "statement": "The edge type 'has_direct_procedure_site' has a higher count than 'has_procedure_site'.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Mean squared error (MSE) of selected models on a 2:1 training-validation-split of the Clickbait Challenge 2017 training dataset (left), and means squared error of selected submitted approaches on the challenge’s test dataset (right).', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The best-performing model achieved a mean squared error of 0.0297 with 12,008 features, which is 32.0% of the original features, showing a 10% performance improvement over the full feature set.In total, we processed one million remove-train-predict iterations for our feature assessment (i.e., a feature was removed from an average of 26 feature subsets). After the outputs of the iterations were accumulated, the leave-many-out scores per feature was computed to rank the features. From this ranking, we then choose feature subsets of increasing size to examine the effect of removing the lowest ranking features and training a model on the remaining ones. We constructed 49 subsets including the top-ranked 100%, …, 2% of the features in steps of 2%, and three other subsets containing only the top-ranked 1.5%, 1.0%, and 0.5% of the features. For every subset, we trained a ridge regression model on 2/3 of the training data and validated it on the remaining 1/3 of the training data. In this case the split was kept constant for all the 52 feature subsets. The best-performing model in this experiment series achieves a mean squared error of 0.0297 with 12,008 features (32.0% of the original features). This corresponds to about 10% performance improvement over the full feature set (see The smallest subset size that does not perform worse than the full feature set uses 375 features (1.0%). This means that Icarfish, based only on its heuristic feature selection, achieves a performance gain of about 20% over the baseline and would have been placed second in the challenge." } ], "images": [ "scigen/train/2003.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Mean squared error (MSE) of selected models on a 2:1 training-validation-split of the Clickbait Challenge 2017 training dataset (left), and means squared error of selected submitted approaches on the challenge’s test dataset (right)." ], "reasoning": "The best-performing model achieved a mean squared error of 0.0297 with 12,008 features, which is 32.0% of the original features, showing a 10% performance improvement over the full feature set.", "description": "In total, we processed one million remove-train-predict iterations for our feature assessment (i.e., a feature was removed from an average of 26 feature subsets). After the outputs of the iterations were accumulated, the leave-many-out scores per feature was computed to rank the features. From this ranking, we then choose feature subsets of increasing size to examine the effect of removing the lowest ranking features and training a model on the remaining ones. We constructed 49 subsets including the top-ranked 100%, …, 2% of the features in steps of 2%, and three other subsets containing only the top-ranked 1.5%, 1.0%, and 0.5% of the features. For every subset, we trained a ridge regression model on 2/3 of the training data and validated it on the remaining 1/3 of the training data. In this case the split was kept constant for all the 52 feature subsets. The best-performing model in this experiment series achieves a mean squared error of 0.0297 with 12,008 features (32.0% of the original features). This corresponds to about 10% performance improvement over the full feature set (see The smallest subset size that does not perform worse than the full feature set uses 375 features (1.0%). This means that Icarfish, based only on its heuristic feature selection, achieves a performance gain of about 20% over the baseline and would have been placed second in the challenge.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Mean squared error (MSE) of selected models on a 2:1 training-validation-split of the Clickbait Challenge 2017 training dataset (left), and means squared error of selected submitted approaches on the challenge’s test dataset (right).\nQuestion: What is the mean squared error (MSE) of the model with the smallest subset size that does not perform worse than the full feature set?\nGive a detailed response to the above question. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The description states that the smallest subset size that does not perform worse than the full feature set uses 375 features (1.0%). The table shows that the MSE for 375 features is 0.0323. So, the answer is {\"answer\": \"0.0323\"}." } ], "images": [ "scigen/train/2003.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Mean squared error (MSE) of selected models on a 2:1 training-validation-split of the Clickbait Challenge 2017 training dataset (left), and means squared error of selected submitted approaches on the challenge’s test dataset (right)." ], "reasoning": "The description states that the smallest subset size that does not perform worse than the full feature set uses 375 features (1.0%). The table shows that the MSE for 375 features is 0.0323.", "question": "What is the mean squared error (MSE) of the model with the smallest subset size that does not perform worse than the full feature set?", "answer": "So, the answer is {\"answer\": \"0.0323\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe model with 187 features has a mean squared error (MSE) lower than the model with 12,008 features.\n" }, { "role": "assistant", "content": "The table shows that the MSE for the model with 187 features is 0.0342, while the MSE for the model with 12,008 features is 0.0297. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2003.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Mean squared error (MSE) of selected models on a 2:1 training-validation-split of the Clickbait Challenge 2017 training dataset (left), and means squared error of selected submitted approaches on the challenge’s test dataset (right)." ], "reasoning": "The table shows that the MSE for the model with 187 features is 0.0342, while the MSE for the model with 12,008 features is 0.0297. Therefore, the statement is refuted.", "statement": "The model with 187 features has a mean squared error (MSE) lower than the model with 12,008 features.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Mean squared error (MSE) of selected models on a 2:1 training-validation-split of the Clickbait Challenge 2017 training dataset (left), and means squared error of selected submitted approaches on the challenge’s test dataset (right).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The best-performing model achieves a mean squared error of 0.0297 with 12,008 features, corresponding to a 10% performance improvement over the full feature set, while the smallest subset size that does not perform worse than the full feature set uses 375 features.In total, we processed one million remove-train-predict iterations for our feature assessment (i.e., a feature was removed from an average of 26 feature subsets). After the outputs of the iterations were accumulated, the leave-many-out scores per feature was computed to rank the features. From this ranking, we then choose feature subsets of increasing size to examine the effect of removing the lowest ranking features and training a model on the remaining ones. We constructed 49 subsets including the top-ranked 100%, …, 2% of the features in steps of 2%, and three other subsets containing only the top-ranked 1.5%, 1.0%, and 0.5% of the features. For every subset, we trained a ridge regression model on 2/3 of the training data and validated it on the remaining 1/3 of the training data. In this case the split was kept constant for all the 52 feature subsets. The best-performing model in this experiment series achieves a mean squared error of 0.0297 with 12,008 features (32.0% of the original features). This corresponds to about 10% performance improvement over the full feature set (see The smallest subset size that does not perform worse than the full feature set uses 375 features (1.0%). This means that Icarfish, based only on its heuristic feature selection, achieves a performance gain of about 20% over the baseline and would have been placed second in the challenge." } ], "images": [ "scigen/train/2004.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Mean squared error (MSE) of selected models on a 2:1 training-validation-split of the Clickbait Challenge 2017 training dataset (left), and means squared error of selected submitted approaches on the challenge’s test dataset (right)." ], "reasoning": "The best-performing model achieves a mean squared error of 0.0297 with 12,008 features, corresponding to a 10% performance improvement over the full feature set, while the smallest subset size that does not perform worse than the full feature set uses 375 features.", "description": "In total, we processed one million remove-train-predict iterations for our feature assessment (i.e., a feature was removed from an average of 26 feature subsets). After the outputs of the iterations were accumulated, the leave-many-out scores per feature was computed to rank the features. From this ranking, we then choose feature subsets of increasing size to examine the effect of removing the lowest ranking features and training a model on the remaining ones. We constructed 49 subsets including the top-ranked 100%, …, 2% of the features in steps of 2%, and three other subsets containing only the top-ranked 1.5%, 1.0%, and 0.5% of the features. For every subset, we trained a ridge regression model on 2/3 of the training data and validated it on the remaining 1/3 of the training data. In this case the split was kept constant for all the 52 feature subsets. The best-performing model in this experiment series achieves a mean squared error of 0.0297 with 12,008 features (32.0% of the original features). This corresponds to about 10% performance improvement over the full feature set (see The smallest subset size that does not perform worse than the full feature set uses 375 features (1.0%). This means that Icarfish, based only on its heuristic feature selection, achieves a performance gain of about 20% over the baseline and would have been placed second in the challenge.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: Table 1: Mean squared error (MSE) of selected models on a 2:1 training-validation-split of the Clickbait Challenge 2017 training dataset (left), and means squared error of selected submitted approaches on the challenge’s test dataset (right).\nQuestion: What is the mean squared error (MSE) of the best-performing model in the experiment series?\n" }, { "role": "assistant", "content": "The description states that the best-performing model in the experiment series achieves a mean squared error of 0.0297 with 12,008 features. So, the answer is {\"answer\": \"0.0297\"}" } ], "images": [ "scigen/train/2004.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Mean squared error (MSE) of selected models on a 2:1 training-validation-split of the Clickbait Challenge 2017 training dataset (left), and means squared error of selected submitted approaches on the challenge’s test dataset (right)." ], "reasoning": "The description states that the best-performing model in the experiment series achieves a mean squared error of 0.0297 with 12,008 features.", "question": "What is the mean squared error (MSE) of the best-performing model in the experiment series?", "answer": "So, the answer is {\"answer\": \"0.0297\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Icarfish model achieves a mean squared error (MSE) of 0.0351.\n" }, { "role": "assistant", "content": "The table shows that the Icarfish model has an MSE of 0.0351. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2004.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Mean squared error (MSE) of selected models on a 2:1 training-validation-split of the Clickbait Challenge 2017 training dataset (left), and means squared error of selected submitted approaches on the challenge’s test dataset (right)." ], "reasoning": "The table shows that the Icarfish model has an MSE of 0.0351.", "statement": "The Icarfish model achieves a mean squared error (MSE) of 0.0351.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Features by category with a leave-many-out score >10−5. All scores are multiplied by 10−4. Feature categories shown are character n-grams (CNG), word n-grams (WNG), word list features, and engineered features.'.\n" }, { "role": "assistant", "content": "Engineered features generally have a high impact on prediction performance, but some can harm it. Combining high-impact individual features into new engineered features could improve model performance.All feature types include good and bad features with differences in strength and frequency. Most of the 12 engineered features have a high impact about one order of magnitude larger than that of any other feature. However, two of the engineered features harm prediction performance (the average word length and whether a tweet starts with a number). One reason for the relatively high impact of the engineered features is that they are not as sparse as the n-grams or word lists. Interestingly, word n-grams are the only feature category without high-impact confusing features. An interesting side note is that the question mark seems to be discriminatory as a word token, but confusing as a character token, while the exclamation mark is discriminatory in both categories. Maybe more compact models could thus be constructed if the information of individual features with high impact could be combined into engineered features with higher impact. An example for a new engineered feature could be something like the “number of words ending with -ly or -ing” (counting adverbs, gerunds, etc.) since these suffixes seem to be rather discriminating character n-gram features. However, the hypothesis of creating good engineered features from individual other features is only partially supported by a qualitative analysis of the high-impact features. While the engineered “number of stopwords” feature is very discriminatory and most of the high-impact word 1-grams are actually stop words, the engineered “number of mentions” feature (counting the number of @ ’s in a tweet) is not in the table of the high-impact engineered features, even though 3 of the 18 most discriminatory character tokens involve the ’@’ character." } ], "images": [ "scigen/train/2005.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Features by category with a leave-many-out score >10−5. All scores are multiplied by 10−4. Feature categories shown are character n-grams (CNG), word n-grams (WNG), word list features, and engineered features." ], "reasoning": "Engineered features generally have a high impact on prediction performance, but some can harm it. Combining high-impact individual features into new engineered features could improve model performance.", "description": "All feature types include good and bad features with differences in strength and frequency. Most of the 12 engineered features have a high impact about one order of magnitude larger than that of any other feature. However, two of the engineered features harm prediction performance (the average word length and whether a tweet starts with a number). One reason for the relatively high impact of the engineered features is that they are not as sparse as the n-grams or word lists. Interestingly, word n-grams are the only feature category without high-impact confusing features. An interesting side note is that the question mark seems to be discriminatory as a word token, but confusing as a character token, while the exclamation mark is discriminatory in both categories. Maybe more compact models could thus be constructed if the information of individual features with high impact could be combined into engineered features with higher impact. An example for a new engineered feature could be something like the “number of words ending with -ly or -ing” (counting adverbs, gerunds, etc.) since these suffixes seem to be rather discriminating character n-gram features. However, the hypothesis of creating good engineered features from individual other features is only partially supported by a qualitative analysis of the high-impact features. While the engineered “number of stopwords” feature is very discriminatory and most of the high-impact word 1-grams are actually stop words, the engineered “number of mentions” feature (counting the number of @ ’s in a tweet) is not in the table of the high-impact engineered features, even though 3 of the 18 most discriminatory character tokens involve the ’@’ character.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: Features by category with a leave-many-out score >10−5. All scores are multiplied by 10−4. Feature categories shown are character n-grams (CNG), word n-grams (WNG), word list features, and engineered features.', solve this question and give detail solutions. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich character n-gram has the highest positive impact on prediction performance?\n" }, { "role": "assistant", "content": "To find the character n-gram with the highest positive impact, look for the highest positive score in the 'Score' column of the table. The '@' character n-gram has the highest positive score of 0.73. So, the answer is {\"answer\": \"@\"}." } ], "images": [ "scigen/train/2005.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Features by category with a leave-many-out score >10−5. All scores are multiplied by 10−4. Feature categories shown are character n-grams (CNG), word n-grams (WNG), word list features, and engineered features." ], "reasoning": "To find the character n-gram with the highest positive impact, look for the highest positive score in the 'Score' column of the table. The '@' character n-gram has the highest positive score of 0.73.", "question": "Which character n-gram has the highest positive impact on prediction performance?", "answer": "So, the answer is {\"answer\": \"@\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe character n-gram 'thi' has the most negative impact on prediction performance.\n" }, { "role": "assistant", "content": "To verify this statement, look for the most negative score in the 'Score' column of the table. The 'thi' character n-gram has the most negative score of -0.80. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2005.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Features by category with a leave-many-out score >10−5. All scores are multiplied by 10−4. Feature categories shown are character n-grams (CNG), word n-grams (WNG), word list features, and engineered features." ], "reasoning": "To verify this statement, look for the most negative score in the 'Score' column of the table. The 'thi' character n-gram has the most negative score of -0.80.", "statement": "The character n-gram 'thi' has the most negative impact on prediction performance.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Features by category with a leave-many-out score >10−5. All scores are multiplied by 10−4. Feature categories shown are character n-grams (CNG), word n-grams (WNG), word list features, and engineered features.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Engineered features generally have a high impact on prediction performance, but some can harm it, while word n-grams are consistently high-impact without confusion.All feature types include good and bad features with differences in strength and frequency. Most of the 12 engineered features have a high impact about one order of magnitude larger than that of any other feature. However, two of the engineered features harm prediction performance (the average word length and whether a tweet starts with a number). One reason for the relatively high impact of the engineered features is that they are not as sparse as the n-grams or word lists. Interestingly, word n-grams are the only feature category without high-impact confusing features. An interesting side note is that the question mark seems to be discriminatory as a word token, but confusing as a character token, while the exclamation mark is discriminatory in both categories. Maybe more compact models could thus be constructed if the information of individual features with high impact could be combined into engineered features with higher impact. An example for a new engineered feature could be something like the “number of words ending with -ly or -ing” (counting adverbs, gerunds, etc.) since these suffixes seem to be rather discriminating character n-gram features. However, the hypothesis of creating good engineered features from individual other features is only partially supported by a qualitative analysis of the high-impact features. While the engineered “number of stopwords” feature is very discriminatory and most of the high-impact word 1-grams are actually stop words, the engineered “number of mentions” feature (counting the number of @ ’s in a tweet) is not in the table of the high-impact engineered features, even though 3 of the 18 most discriminatory character tokens involve the ’@’ character." } ], "images": [ "scigen/train/2006.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Features by category with a leave-many-out score >10−5. All scores are multiplied by 10−4. Feature categories shown are character n-grams (CNG), word n-grams (WNG), word list features, and engineered features." ], "reasoning": "Engineered features generally have a high impact on prediction performance, but some can harm it, while word n-grams are consistently high-impact without confusion.", "description": "All feature types include good and bad features with differences in strength and frequency. Most of the 12 engineered features have a high impact about one order of magnitude larger than that of any other feature. However, two of the engineered features harm prediction performance (the average word length and whether a tweet starts with a number). One reason for the relatively high impact of the engineered features is that they are not as sparse as the n-grams or word lists. Interestingly, word n-grams are the only feature category without high-impact confusing features. An interesting side note is that the question mark seems to be discriminatory as a word token, but confusing as a character token, while the exclamation mark is discriminatory in both categories. Maybe more compact models could thus be constructed if the information of individual features with high impact could be combined into engineered features with higher impact. An example for a new engineered feature could be something like the “number of words ending with -ly or -ing” (counting adverbs, gerunds, etc.) since these suffixes seem to be rather discriminating character n-gram features. However, the hypothesis of creating good engineered features from individual other features is only partially supported by a qualitative analysis of the high-impact features. While the engineered “number of stopwords” feature is very discriminatory and most of the high-impact word 1-grams are actually stop words, the engineered “number of mentions” feature (counting the number of @ ’s in a tweet) is not in the table of the high-impact engineered features, even though 3 of the 18 most discriminatory character tokens involve the ’@’ character.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 2: Features by category with a leave-many-out score >10−5. All scores are multiplied by 10−4. Feature categories shown are character n-grams (CNG), word n-grams (WNG), word list features, and engineered features.\nQuestion: Which word n-gram has the highest impact score?\n" }, { "role": "assistant", "content": "The table lists various word n-grams (WNG) and their corresponding impact scores. The word n-gram with the highest score is 'this' with a score of 3.16. So, the answer is {\"answer\": \"this\"}." } ], "images": [ "scigen/train/2006.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Features by category with a leave-many-out score >10−5. All scores are multiplied by 10−4. Feature categories shown are character n-grams (CNG), word n-grams (WNG), word list features, and engineered features." ], "reasoning": "The table lists various word n-grams (WNG) and their corresponding impact scores. The word n-gram with the highest score is 'this' with a score of 3.16.", "question": "Which word n-gram has the highest impact score?", "answer": "So, the answer is {\"answer\": \"this\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe word n-gram 'how' has a higher impact score than 'a'.\n" }, { "role": "assistant", "content": "According to the table, the impact score for 'how' is 0.83, while the impact score for 'a' is 0.57. Therefore, 'how' has a higher impact score than 'a'. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2006.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Features by category with a leave-many-out score >10−5. All scores are multiplied by 10−4. Feature categories shown are character n-grams (CNG), word n-grams (WNG), word list features, and engineered features." ], "reasoning": "According to the table, the impact score for 'how' is 0.83, while the impact score for 'a' is 0.57. Therefore, 'how' has a higher impact score than 'a'.", "statement": "The word n-gram 'how' has a higher impact score than 'a'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Features by category with a leave-many-out score >10−5. All scores are multiplied by 10−4. Feature categories shown are character n-grams (CNG), word n-grams (WNG), word list features, and engineered features.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Engineered features generally have a high impact on prediction performance, but some can harm it, while word n-grams do not have high-impact confusing features.All feature types include good and bad features with differences in strength and frequency. Most of the 12 engineered features have a high impact about one order of magnitude larger than that of any other feature. However, two of the engineered features harm prediction performance (the average word length and whether a tweet starts with a number). One reason for the relatively high impact of the engineered features is that they are not as sparse as the n-grams or word lists. Interestingly, word n-grams are the only feature category without high-impact confusing features. An interesting side note is that the question mark seems to be discriminatory as a word token, but confusing as a character token, while the exclamation mark is discriminatory in both categories. Maybe more compact models could thus be constructed if the information of individual features with high impact could be combined into engineered features with higher impact. An example for a new engineered feature could be something like the “number of words ending with -ly or -ing” (counting adverbs, gerunds, etc.) since these suffixes seem to be rather discriminating character n-gram features. However, the hypothesis of creating good engineered features from individual other features is only partially supported by a qualitative analysis of the high-impact features. While the engineered “number of stopwords” feature is very discriminatory and most of the high-impact word 1-grams are actually stop words, the engineered “number of mentions” feature (counting the number of @ ’s in a tweet) is not in the table of the high-impact engineered features, even though 3 of the 18 most discriminatory character tokens involve the ’@’ character." } ], "images": [ "scigen/train/2007.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Features by category with a leave-many-out score >10−5. All scores are multiplied by 10−4. Feature categories shown are character n-grams (CNG), word n-grams (WNG), word list features, and engineered features." ], "reasoning": "Engineered features generally have a high impact on prediction performance, but some can harm it, while word n-grams do not have high-impact confusing features.", "description": "All feature types include good and bad features with differences in strength and frequency. Most of the 12 engineered features have a high impact about one order of magnitude larger than that of any other feature. However, two of the engineered features harm prediction performance (the average word length and whether a tweet starts with a number). One reason for the relatively high impact of the engineered features is that they are not as sparse as the n-grams or word lists. Interestingly, word n-grams are the only feature category without high-impact confusing features. An interesting side note is that the question mark seems to be discriminatory as a word token, but confusing as a character token, while the exclamation mark is discriminatory in both categories. Maybe more compact models could thus be constructed if the information of individual features with high impact could be combined into engineered features with higher impact. An example for a new engineered feature could be something like the “number of words ending with -ly or -ing” (counting adverbs, gerunds, etc.) since these suffixes seem to be rather discriminating character n-gram features. However, the hypothesis of creating good engineered features from individual other features is only partially supported by a qualitative analysis of the high-impact features. While the engineered “number of stopwords” feature is very discriminatory and most of the high-impact word 1-grams are actually stop words, the engineered “number of mentions” feature (counting the number of @ ’s in a tweet) is not in the table of the high-impact engineered features, even though 3 of the 18 most discriminatory character tokens involve the ’@’ character.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Features by category with a leave-many-out score >10−5. All scores are multiplied by 10−4. Feature categories shown are character n-grams (CNG), word n-grams (WNG), word list features, and engineered features.\nQuestion: Which word list has the highest score and what does this imply about its impact?\nSolve the above question based on the table information. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the scores of various word lists, with 'Easy Words' having the highest score of 2.60, implying it has the highest positive impact. So, the answer is {\"answer\": \"Easy Words\"}." } ], "images": [ "scigen/train/2007.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Features by category with a leave-many-out score >10−5. All scores are multiplied by 10−4. Feature categories shown are character n-grams (CNG), word n-grams (WNG), word list features, and engineered features." ], "reasoning": "The table shows the scores of various word lists, with 'Easy Words' having the highest score of 2.60, implying it has the highest positive impact.", "question": "Which word list has the highest score and what does this imply about its impact?", "answer": "So, the answer is {\"answer\": \"Easy Words\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe 'number of mentions' feature is one of the high-impact engineered features.\n" }, { "role": "assistant", "content": "The description states that the 'number of mentions' feature is not in the table of high-impact engineered features, and it is not listed in the provided table. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2007.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Features by category with a leave-many-out score >10−5. All scores are multiplied by 10−4. Feature categories shown are character n-grams (CNG), word n-grams (WNG), word list features, and engineered features." ], "reasoning": "The description states that the 'number of mentions' feature is not in the table of high-impact engineered features, and it is not listed in the provided table.", "statement": "The 'number of mentions' feature is one of the high-impact engineered features.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Features by category with a leave-many-out score >10−5. All scores are multiplied by 10−4. Feature categories shown are character n-grams (CNG), word n-grams (WNG), word list features, and engineered features.'.\n" }, { "role": "assistant", "content": "Most engineered features have a high impact, but two harm prediction performance. Combining high-impact features into new engineered features could improve models.All feature types include good and bad features with differences in strength and frequency. Most of the 12 engineered features have a high impact about one order of magnitude larger than that of any other feature. However, two of the engineered features harm prediction performance (the average word length and whether a tweet starts with a number). One reason for the relatively high impact of the engineered features is that they are not as sparse as the n-grams or word lists. Interestingly, word n-grams are the only feature category without high-impact confusing features. An interesting side note is that the question mark seems to be discriminatory as a word token, but confusing as a character token, while the exclamation mark is discriminatory in both categories. Maybe more compact models could thus be constructed if the information of individual features with high impact could be combined into engineered features with higher impact. An example for a new engineered feature could be something like the “number of words ending with -ly or -ing” (counting adverbs, gerunds, etc.) since these suffixes seem to be rather discriminating character n-gram features. However, the hypothesis of creating good engineered features from individual other features is only partially supported by a qualitative analysis of the high-impact features. While the engineered “number of stopwords” feature is very discriminatory and most of the high-impact word 1-grams are actually stop words, the engineered “number of mentions” feature (counting the number of @ ’s in a tweet) is not in the table of the high-impact engineered features, even though 3 of the 18 most discriminatory character tokens involve the ’@’ character." } ], "images": [ "scigen/train/2008.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Features by category with a leave-many-out score >10−5. All scores are multiplied by 10−4. Feature categories shown are character n-grams (CNG), word n-grams (WNG), word list features, and engineered features." ], "reasoning": "Most engineered features have a high impact, but two harm prediction performance. Combining high-impact features into new engineered features could improve models.", "description": "All feature types include good and bad features with differences in strength and frequency. Most of the 12 engineered features have a high impact about one order of magnitude larger than that of any other feature. However, two of the engineered features harm prediction performance (the average word length and whether a tweet starts with a number). One reason for the relatively high impact of the engineered features is that they are not as sparse as the n-grams or word lists. Interestingly, word n-grams are the only feature category without high-impact confusing features. An interesting side note is that the question mark seems to be discriminatory as a word token, but confusing as a character token, while the exclamation mark is discriminatory in both categories. Maybe more compact models could thus be constructed if the information of individual features with high impact could be combined into engineered features with higher impact. An example for a new engineered feature could be something like the “number of words ending with -ly or -ing” (counting adverbs, gerunds, etc.) since these suffixes seem to be rather discriminating character n-gram features. However, the hypothesis of creating good engineered features from individual other features is only partially supported by a qualitative analysis of the high-impact features. While the engineered “number of stopwords” feature is very discriminatory and most of the high-impact word 1-grams are actually stop words, the engineered “number of mentions” feature (counting the number of @ ’s in a tweet) is not in the table of the high-impact engineered features, even though 3 of the 18 most discriminatory character tokens involve the ’@’ character.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Features by category with a leave-many-out score >10−5. All scores are multiplied by 10−4. Feature categories shown are character n-grams (CNG), word n-grams (WNG), word list features, and engineered features.\nQuestion: Which engineered feature has the highest impact on prediction performance?\nSolve the above question based on the table information. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table lists the scores of various engineered features. The feature with the highest score has the highest impact. So, the answer is {\"answer\": \"Number of dots\"}." } ], "images": [ "scigen/train/2008.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Features by category with a leave-many-out score >10−5. All scores are multiplied by 10−4. Feature categories shown are character n-grams (CNG), word n-grams (WNG), word list features, and engineered features." ], "reasoning": "The table lists the scores of various engineered features. The feature with the highest score has the highest impact.", "question": "Which engineered feature has the highest impact on prediction performance?", "answer": "So, the answer is {\"answer\": \"Number of dots\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The 'Character sum' feature has a higher impact than the 'Part of day' feature. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe 'Character sum' feature has a higher impact than the 'Part of day' feature.\n" }, { "role": "assistant", "content": "The table shows the scores for 'Character sum' and 'Part of day'. Comparing their scores, 'Character sum' has a higher score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2008.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Features by category with a leave-many-out score >10−5. All scores are multiplied by 10−4. Feature categories shown are character n-grams (CNG), word n-grams (WNG), word list features, and engineered features." ], "reasoning": "The table shows the scores for 'Character sum' and 'Part of day'. Comparing their scores, 'Character sum' has a higher score.", "statement": "The 'Character sum' feature has a higher impact than the 'Part of day' feature.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Comparison of different models on the EXEQ-300k and OFEQ-10k test sets according to math evaluation metrics. Edit Distance(m) and Edit Distance(s) evaluate those that are dissimilar (the smaller the better). Exact Match is the number of math tokens accurately generated in math headlines (the larger the better).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "MathSum achieves the best performance on all metrics for EXEQ-300k and the best performance for Exact Match on OFEX-10k, while Transformer performs slightly better on Edit Distance(m) and Edit Distance(s) for OFEX-10k.In addition, we also evaluate the gap between the generated headlines and human-written headlines. The Edit Distance(m), Edit Distance(s) and The results show that extractive models perform worse, if we use the metric Edit Distance(s) instead of Edit Distance(m) for evaluation. Since extractive models directly select sentences from source questions, some selected sentences may not contain math equations. For abstractive baselines, the Transformer obtains the best performance. This observation reinforces the claim that a mutli-head attention mechanism can construct a better representation for math equations. On EXEQ-300k, our model, MathSum, achieves the best performance on all metrics. On OFEX-10k, MathSum gets the best performance for Exact Match and second best performance (slightly weaker than Transformer) for Edit Distance(m) and Edit Distance(s). A possible reason is that in OFEX-10k, the lengths of math equations in source questions are usually long, while the ones in headlines are often short. Compared to the Transformer, the copying mechanism could cause MathSum to copy long equations from the source questions, which may result in a slight decreased performance for Edit Distance(m) and Edit Distance(s) metrics." } ], "images": [ "scigen/train/2009.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of different models on the EXEQ-300k and OFEQ-10k test sets according to math evaluation metrics. Edit Distance(m) and Edit Distance(s) evaluate those that are dissimilar (the smaller the better). Exact Match is the number of math tokens accurately generated in math headlines (the larger the better)." ], "reasoning": "MathSum achieves the best performance on all metrics for EXEQ-300k and the best performance for Exact Match on OFEX-10k, while Transformer performs slightly better on Edit Distance(m) and Edit Distance(s) for OFEX-10k.", "description": "In addition, we also evaluate the gap between the generated headlines and human-written headlines. The Edit Distance(m), Edit Distance(s) and The results show that extractive models perform worse, if we use the metric Edit Distance(s) instead of Edit Distance(m) for evaluation. Since extractive models directly select sentences from source questions, some selected sentences may not contain math equations. For abstractive baselines, the Transformer obtains the best performance. This observation reinforces the claim that a mutli-head attention mechanism can construct a better representation for math equations. On EXEQ-300k, our model, MathSum, achieves the best performance on all metrics. On OFEX-10k, MathSum gets the best performance for Exact Match and second best performance (slightly weaker than Transformer) for Edit Distance(m) and Edit Distance(s). A possible reason is that in OFEX-10k, the lengths of math equations in source questions are usually long, while the ones in headlines are often short. Compared to the Transformer, the copying mechanism could cause MathSum to copy long equations from the source questions, which may result in a slight decreased performance for Edit Distance(m) and Edit Distance(s) metrics.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 4: Comparison of different models on the EXEQ-300k and OFEQ-10k test sets according to math evaluation metrics. Edit Distance(m) and Edit Distance(s) evaluate those that are dissimilar (the smaller the better). Exact Match is the number of math tokens accurately generated in math headlines (the larger the better).', unfold the question presented below, detailing every step of your calculation. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model has the best performance for EXEQ-300k Exact Match and what is its score?\n" }, { "role": "assistant", "content": "The table shows the scores for different models on various metrics. For EXEQ-300k Exact Match, MathSum has the highest score of 15.21. So, the answer is {\"answer\": \"MathSum with a score of 15.21\"}" } ], "images": [ "scigen/train/2009.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of different models on the EXEQ-300k and OFEQ-10k test sets according to math evaluation metrics. Edit Distance(m) and Edit Distance(s) evaluate those that are dissimilar (the smaller the better). Exact Match is the number of math tokens accurately generated in math headlines (the larger the better)." ], "reasoning": "The table shows the scores for different models on various metrics. For EXEQ-300k Exact Match, MathSum has the highest score of 15.21.", "question": "Which model has the best performance for EXEQ-300k Exact Match and what is its score?", "answer": "So, the answer is {\"answer\": \"MathSum with a score of 15.21\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Transformer model performs better than MathSum on OFEX-10k Edit Distance(m)., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Transformer model performs better than MathSum on OFEX-10k Edit Distance(m).\n" }, { "role": "assistant", "content": "The table shows that for OFEX-10k Edit Distance(m), the Transformer model has a score of 5.56, which is better than MathSum's score of 5.71. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2009.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of different models on the EXEQ-300k and OFEQ-10k test sets according to math evaluation metrics. Edit Distance(m) and Edit Distance(s) evaluate those that are dissimilar (the smaller the better). Exact Match is the number of math tokens accurately generated in math headlines (the larger the better)." ], "reasoning": "The table shows that for OFEX-10k Edit Distance(m), the Transformer model has a score of 5.56, which is better than MathSum's score of 5.71.", "statement": "The Transformer model performs better than MathSum on OFEX-10k Edit Distance(m).", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Comparison of different models on the EXEQ-300k and OFEQ-10 test sets for F1 scores of R1 (ROUGE-1), R2 (ROUGE-2), RL (ROUGE-L), BLEU-4, and METEOR.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "MathSum outperforms all other models on both EXEQ-300k and OFEQ-10k datasets, addressing challenges in the NAME task.All models perform better on EXEQ-300k than OFEQ-10k. For extractive models, we find that Lead obtains a good performance on EXEQ-300k, while TextRank performs well on OFEX-10k. Since OFEX-10k contains more sentences for each question, TextRank is more likely to pick out the accurate sentence. Unsurprisingly, abstractive models perform better than extractive models on both datasets. Compared to ordinary Seq2Seq, PtGen gets better performance, since it uses a copying strategy to directly copy tokens from the source question. The transformer can outperform PtGen, which implies that by utilizing multi-head attention mechanism, we obtain a better learning of representation. MathSum significantly outperforms other models for all evaluation metrics on both datasets. Thus, MathSum initially addresses some of the challenges of NAME task and generates satisfactory headlines for questions." } ], "images": [ "scigen/train/2010.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of different models on the EXEQ-300k and OFEQ-10 test sets for F1 scores of R1 (ROUGE-1), R2 (ROUGE-2), RL (ROUGE-L), BLEU-4, and METEOR." ], "reasoning": "MathSum outperforms all other models on both EXEQ-300k and OFEQ-10k datasets, addressing challenges in the NAME task.", "description": "All models perform better on EXEQ-300k than OFEQ-10k. For extractive models, we find that Lead obtains a good performance on EXEQ-300k, while TextRank performs well on OFEX-10k. Since OFEX-10k contains more sentences for each question, TextRank is more likely to pick out the accurate sentence. Unsurprisingly, abstractive models perform better than extractive models on both datasets. Compared to ordinary Seq2Seq, PtGen gets better performance, since it uses a copying strategy to directly copy tokens from the source question. The transformer can outperform PtGen, which implies that by utilizing multi-head attention mechanism, we obtain a better learning of representation. MathSum significantly outperforms other models for all evaluation metrics on both datasets. Thus, MathSum initially addresses some of the challenges of NAME task and generates satisfactory headlines for questions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: 'Table 3: Comparison of different models on the EXEQ-300k and OFEQ-10 test sets for F1 scores of R1 (ROUGE-1), R2 (ROUGE-2), RL (ROUGE-L), BLEU-4, and METEOR.'\nQuestion: Which model has the highest score for EXEQ-300k BLEU-4?\n" }, { "role": "assistant", "content": "By comparing the BLEU-4 scores for EXEQ-300k across all models, MathSum has the highest score of 52.00. So, the answer is {\"answer\": \"MathSum\"}" } ], "images": [ "scigen/train/2010.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of different models on the EXEQ-300k and OFEQ-10 test sets for F1 scores of R1 (ROUGE-1), R2 (ROUGE-2), RL (ROUGE-L), BLEU-4, and METEOR." ], "reasoning": "By comparing the BLEU-4 scores for EXEQ-300k across all models, MathSum has the highest score of 52.00.", "question": "Which model has the highest score for EXEQ-300k BLEU-4?", "answer": "So, the answer is {\"answer\": \"MathSum\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nLead performs better than TextRank on EXEQ-300k R1.\n" }, { "role": "assistant", "content": "By comparing the EXEQ-300k R1 scores, Lead has a score of 42.23, which is higher than TextRank's score of 42.19. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2010.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of different models on the EXEQ-300k and OFEQ-10 test sets for F1 scores of R1 (ROUGE-1), R2 (ROUGE-2), RL (ROUGE-L), BLEU-4, and METEOR." ], "reasoning": "By comparing the EXEQ-300k R1 scores, Lead has a score of 42.23, which is higher than TextRank's score of 42.19.", "statement": "Lead performs better than TextRank on EXEQ-300k R1.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: End-to-end model performance evaluated with new fluent references. Comparing average single reference scores (1Ref) vs multi-reference scores using both generated references (2Ref).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The fluent model achieves higher BLEU scores compared to the disfluent model, while METEOR scores are similar for both models.Comparing the disfluent and fluent models, we see that METEOR scores are almost the same while BLEU scores are lower with the disfluent model. This is as we would hope: with our fluent model, we want to generate translations that are semantically the same but with disfluencies removed. Therefore similar METEOR scores with similar recall (52) on the fluent references are encouraging. For BLEU, however, the disfluencies generated by the disfluent model break up n-grams in the fluent references, thereby lowering scores. A filtering or tagging system may not capture all false starts or corrections, leading to lower fluency, and requires labeled spans. Treating the post-processing step as a monolingual translation task (MonoMT) rather than a filtering task allows for reordering and insertions, which we saw boost fluency. We trained a 4-layer BiLSTM encoder-decoder model to translate between the disfluent and fluent English references and applied this to the output of the end-to-end disfluent model." } ], "images": [ "scigen/train/2011.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: End-to-end model performance evaluated with new fluent references. Comparing average single reference scores (1Ref) vs multi-reference scores using both generated references (2Ref)." ], "reasoning": "The fluent model achieves higher BLEU scores compared to the disfluent model, while METEOR scores are similar for both models.", "description": "Comparing the disfluent and fluent models, we see that METEOR scores are almost the same while BLEU scores are lower with the disfluent model. This is as we would hope: with our fluent model, we want to generate translations that are semantically the same but with disfluencies removed. Therefore similar METEOR scores with similar recall (52) on the fluent references are encouraging. For BLEU, however, the disfluencies generated by the disfluent model break up n-grams in the fluent references, thereby lowering scores. A filtering or tagging system may not capture all false starts or corrections, leading to lower fluency, and requires labeled spans. Treating the post-processing step as a monolingual translation task (MonoMT) rather than a filtering task allows for reordering and insertions, which we saw boost fluency. We trained a 4-layer BiLSTM encoder-decoder model to translate between the disfluent and fluent English references and applied this to the output of the end-to-end disfluent model.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: End-to-end model performance evaluated with new fluent references. Comparing average single reference scores (1Ref) vs multi-reference scores using both generated references (2Ref).' in mind, break down and resolve the question below step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the difference in BLEU scores between the disfluent and fluent models for the test 2Ref metric?\n" }, { "role": "assistant", "content": "The BLEU score for the disfluent model on test 2Ref is 17.0, while for the fluent model it is 18.1. The difference is 18.1 - 17.0. So, the answer is {\"answer\": \"1.1\"}." } ], "images": [ "scigen/train/2011.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: End-to-end model performance evaluated with new fluent references. Comparing average single reference scores (1Ref) vs multi-reference scores using both generated references (2Ref)." ], "reasoning": "The BLEU score for the disfluent model on test 2Ref is 17.0, while for the fluent model it is 18.1. The difference is 18.1 - 17.0.", "question": "What is the difference in BLEU scores between the disfluent and fluent models for the test 2Ref metric?", "answer": "So, the answer is {\"answer\": \"1.1\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe disfluent model has a higher METEOR score than the fluent model for the dev 1Ref metric.\n" }, { "role": "assistant", "content": "The METEOR score for the disfluent model on dev 1Ref is 22.2, while for the fluent model it is 22.3. The fluent model has a slightly higher score. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2011.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: End-to-end model performance evaluated with new fluent references. Comparing average single reference scores (1Ref) vs multi-reference scores using both generated references (2Ref)." ], "reasoning": "The METEOR score for the disfluent model on dev 1Ref is 22.2, while for the fluent model it is 22.3. The fluent model has a slightly higher score.", "statement": "The disfluent model has a higher METEOR score than the fluent model for the dev 1Ref metric.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Classification results on the three data sets of the English monolingual track of the 2018 CWI Shared Task. The methods are evaluated in terms of the classification accuracy and the F1-score. The results marked with an asterisk are obtained during the competition. The other results are obtained after the competition.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The system had a bug causing NaN values, which was fixed only for the RBF kernel on the English Wikipedia data set, leading to improved post-competition results.Results. We would like to note that, before the competition ended, we observed a bug in the code that was used in most of our submissions. In the feature extraction stage, the code produced NaN (not a number) values for some features. In order to make the submissions in time, we had to eliminate the samples containing NaN values in the feature vector. Consequently, most of our results during the competition were lower than expected. However, we managed to fix this bug and recompute the features in time to resubmit new results, but only for the RBF kernel on the English Wikipedia data set. Nevertheless, for a fair comparison with the other systems, we include our F1-scores and rankings during the competition as well as the post-competition F1-scores and rankings. Our best F1-score on the English News data set is 0.8594, which is nearly 1.4% lower than the top scoring system, which attained 0.8736 during the competition. On the English WikiNews data set, our best F1-score (0.8201) is once again about 2% lower than the top scoring system, which obtained 0.8400 during the competition. On the English Wikipedia data set, our best F1-score is 0.7919. With this score, we ranked as the third team on the English Wikipedia data set. Two systems performed better on English Wikipedia, one that reached the top F1-score of 0.8115 and one that reached the second-best scored of 0.7965. Overall, our system performed quite well, but it can surely benefit from the addition of more features." } ], "images": [ "scigen/train/2012.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Classification results on the three data sets of the English monolingual track of the 2018 CWI Shared Task. The methods are evaluated in terms of the classification accuracy and the F1-score. The results marked with an asterisk are obtained during the competition. The other results are obtained after the competition." ], "reasoning": "The system had a bug causing NaN values, which was fixed only for the RBF kernel on the English Wikipedia data set, leading to improved post-competition results.", "description": "Results. We would like to note that, before the competition ended, we observed a bug in the code that was used in most of our submissions. In the feature extraction stage, the code produced NaN (not a number) values for some features. In order to make the submissions in time, we had to eliminate the samples containing NaN values in the feature vector. Consequently, most of our results during the competition were lower than expected. However, we managed to fix this bug and recompute the features in time to resubmit new results, but only for the RBF kernel on the English Wikipedia data set. Nevertheless, for a fair comparison with the other systems, we include our F1-scores and rankings during the competition as well as the post-competition F1-scores and rankings. Our best F1-score on the English News data set is 0.8594, which is nearly 1.4% lower than the top scoring system, which attained 0.8736 during the competition. On the English WikiNews data set, our best F1-score (0.8201) is once again about 2% lower than the top scoring system, which obtained 0.8400 during the competition. On the English Wikipedia data set, our best F1-score is 0.7919. With this score, we ranked as the third team on the English Wikipedia data set. Two systems performed better on English Wikipedia, one that reached the top F1-score of 0.8115 and one that reached the second-best scored of 0.7965. Overall, our system performed quite well, but it can surely benefit from the addition of more features.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: Classification results on the three data sets of the English monolingual track of the 2018 CWI Shared Task. The methods are evaluated in terms of the classification accuracy and the F1-score. The results marked with an asterisk are obtained during the competition. The other results are obtained after the competition.', and tackle the question below in a sequential manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat was the post-competition rank for the English WikiNews data set using the RBF kernel?\n" }, { "role": "assistant", "content": "The table shows the post-competition rank for the English WikiNews data set using the RBF kernel as 4. So, the answer is {\"answer\": \"4\"}." } ], "images": [ "scigen/train/2012.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Classification results on the three data sets of the English monolingual track of the 2018 CWI Shared Task. The methods are evaluated in terms of the classification accuracy and the F1-score. The results marked with an asterisk are obtained during the competition. The other results are obtained after the competition." ], "reasoning": "The table shows the post-competition rank for the English WikiNews data set using the RBF kernel as 4.", "question": "What was the post-competition rank for the English WikiNews data set using the RBF kernel?", "answer": "So, the answer is {\"answer\": \"4\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe system ranked third in the competition for the English Wikipedia data set using the RBF kernel.\n" }, { "role": "assistant", "content": "The table shows that the system ranked third in the competition for the English Wikipedia data set using the RBF kernel. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2012.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Classification results on the three data sets of the English monolingual track of the 2018 CWI Shared Task. The methods are evaluated in terms of the classification accuracy and the F1-score. The results marked with an asterisk are obtained during the competition. The other results are obtained after the competition." ], "reasoning": "The table shows that the system ranked third in the competition for the English Wikipedia data set using the RBF kernel.", "statement": "The system ranked third in the competition for the English Wikipedia data set using the RBF kernel.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Regression results on the three data sets of the English monolingual track of the 2018 CWI Shared Task. The methods are evaluated in terms of the mean absolute error (MAE). The reported results are obtained after the competition.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The RBF kernel generally provides better results than the linear kernel, achieving the best MAE values on English News and English WikiNews, and the second-best on English Wikipedia.Results. The systems are evaluated in terms of the mean absolute error (MAE). As in the classification task, we can observe that the RBF kernel provides generally better results than the linear kernel. On two data sets, English News and English WikiNews, we obtain better MAE values than all the systems that participated in the competition. Indeed, the best MAE on English News reported during the competition is 0.0510, and we obtain a smaller MAE (0.0492) using the RBF kernel. Similarly, with a MAE of 0.0667 for the RBF kernel, we surpass the top system on English WikiNews, which attained a MAE of 0.0674 during the competition. On the third data set, English Wikipedia, we attain the second-best score (0.0805), after the top system, that obtained a MAE of 0.0739 during the competition. Compared to the classification task, we report better post-competition rankings in the regression task. This could be explained by two factors. First of all, the number of participants in the regression task was considerably lower. Second of all, we believe that ν-SVR is a very good regressor which is not commonly used, surpassing alternative regression methods in other tasks as well, e.g. image difficulty prediction Ionescu et al." } ], "images": [ "scigen/train/2013.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Regression results on the three data sets of the English monolingual track of the 2018 CWI Shared Task. The methods are evaluated in terms of the mean absolute error (MAE). The reported results are obtained after the competition." ], "reasoning": "The RBF kernel generally provides better results than the linear kernel, achieving the best MAE values on English News and English WikiNews, and the second-best on English Wikipedia.", "description": "Results. The systems are evaluated in terms of the mean absolute error (MAE). As in the classification task, we can observe that the RBF kernel provides generally better results than the linear kernel. On two data sets, English News and English WikiNews, we obtain better MAE values than all the systems that participated in the competition. Indeed, the best MAE on English News reported during the competition is 0.0510, and we obtain a smaller MAE (0.0492) using the RBF kernel. Similarly, with a MAE of 0.0667 for the RBF kernel, we surpass the top system on English WikiNews, which attained a MAE of 0.0674 during the competition. On the third data set, English Wikipedia, we attain the second-best score (0.0805), after the top system, that obtained a MAE of 0.0739 during the competition. Compared to the classification task, we report better post-competition rankings in the regression task. This could be explained by two factors. First of all, the number of participants in the regression task was considerably lower. Second of all, we believe that ν-SVR is a very good regressor which is not commonly used, surpassing alternative regression methods in other tasks as well, e.g. image difficulty prediction Ionescu et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Regression results on the three data sets of the English monolingual track of the 2018 CWI Shared Task. The methods are evaluated in terms of the mean absolute error (MAE). The reported results are obtained after the competition.', solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich kernel achieved the best post-competition rank on the English News data set?\n" }, { "role": "assistant", "content": "The table shows that the RBF kernel achieved a post-competition rank of 1 on the English News data set, which is the best rank. So, the answer is {\"answer\": \"RBF\"}." } ], "images": [ "scigen/train/2013.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Regression results on the three data sets of the English monolingual track of the 2018 CWI Shared Task. The methods are evaluated in terms of the mean absolute error (MAE). The reported results are obtained after the competition." ], "reasoning": "The table shows that the RBF kernel achieved a post-competition rank of 1 on the English News data set, which is the best rank.", "question": "Which kernel achieved the best post-competition rank on the English News data set?", "answer": "So, the answer is {\"answer\": \"RBF\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The linear kernel achieved the best post-competition rank on the English WikiNews data set., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe linear kernel achieved the best post-competition rank on the English WikiNews data set.\n" }, { "role": "assistant", "content": "The table shows that the RBF kernel, not the linear kernel, achieved the best post-competition rank (1) on the English WikiNews data set. The linear kernel had a rank of 4. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2013.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Regression results on the three data sets of the English monolingual track of the 2018 CWI Shared Task. The methods are evaluated in terms of the mean absolute error (MAE). The reported results are obtained after the competition." ], "reasoning": "The table shows that the RBF kernel, not the linear kernel, achieved the best post-competition rank (1) on the English WikiNews data set. The linear kernel had a rank of 4.", "statement": "The linear kernel achieved the best post-competition rank on the English WikiNews data set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Top: Semantic similarity between paraphrases and reference sentences, as scored by human annotators on a 0-100 scale (least to most similar). Results are grouped by length of reference sentences {5, 10, 20, 40}. System names and numbers correspond to Tab. 3. Improvements over ParaNMT (Czech-English only) in bold. Asterisk (*) indicates best in column. Bottom: Lexical diversity between generated paraphrases and reference sentences, as computed by a modified BLEU score with no length penalty. Results are grouped by length of reference sentences, and BLEU is computed over concatenated references and concatenated paraphrases. Lower BLEU scores indicate greater lexical divergence; the lowest per column (bottom half) is indicated by (*).'.\n" }, { "role": "assistant", "content": "For every length, at least one ParaBank system shows higher lexical diversity than ParaNMT, with systems applying more lexical constraints, like system 21, yielding the greatest diversity.The top half of Tab. The bottom half of Tab. For every length, there is at least one ParaBank system that exhibits higher lexical diversity than ParaNMT; unsurprisingly, the ParaBank systems that apply the greatest number of lexical constraints tend to yield the greatest lexical diversity (e.g., system 21)." } ], "images": [ "scigen/train/2014.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Top: Semantic similarity between paraphrases and reference sentences, as scored by human annotators on a 0-100 scale (least to most similar). Results are grouped by length of reference sentences {5, 10, 20, 40}. System names and numbers correspond to Tab. 3. Improvements over ParaNMT (Czech-English only) in bold. Asterisk (*) indicates best in column. Bottom: Lexical diversity between generated paraphrases and reference sentences, as computed by a modified BLEU score with no length penalty. Results are grouped by length of reference sentences, and BLEU is computed over concatenated references and concatenated paraphrases. Lower BLEU scores indicate greater lexical divergence; the lowest per column (bottom half) is indicated by (*)." ], "reasoning": "For every length, at least one ParaBank system shows higher lexical diversity than ParaNMT, with systems applying more lexical constraints, like system 21, yielding the greatest diversity.", "description": "The top half of Tab. The bottom half of Tab. For every length, there is at least one ParaBank system that exhibits higher lexical diversity than ParaNMT; unsurprisingly, the ParaBank systems that apply the greatest number of lexical constraints tend to yield the greatest lexical diversity (e.g., system 21).", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 4: Top: Semantic similarity between paraphrases and reference sentences, as scored by human annotators on a 0-100 scale (least to most similar). Results are grouped by length of reference sentences {5, 10, 20, 40}. System names and numbers correspond to Tab. 3. Improvements over ParaNMT (Czech-English only) in bold. Asterisk (*) indicates best in column. Bottom: Lexical diversity between generated paraphrases and reference sentences, as computed by a modified BLEU score with no length penalty. Results are grouped by length of reference sentences, and BLEU is computed over concatenated references and concatenated paraphrases. Lower BLEU scores indicate greater lexical divergence; the lowest per column (bottom half) is indicated by (*).\n\nQuestion: Which system exhibits the highest lexical diversity for Czech-English len=40?\n" }, { "role": "assistant", "content": "To find the system with the highest lexical diversity for Czech-English len=40, compare the values in the Czech-English len=40 column under Lexical Diversity. System 21 has the highest value of 28.90. So, the answer is {\"answer\": \"21\"}." } ], "images": [ "scigen/train/2014.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Top: Semantic similarity between paraphrases and reference sentences, as scored by human annotators on a 0-100 scale (least to most similar). Results are grouped by length of reference sentences {5, 10, 20, 40}. System names and numbers correspond to Tab. 3. Improvements over ParaNMT (Czech-English only) in bold. Asterisk (*) indicates best in column. Bottom: Lexical diversity between generated paraphrases and reference sentences, as computed by a modified BLEU score with no length penalty. Results are grouped by length of reference sentences, and BLEU is computed over concatenated references and concatenated paraphrases. Lower BLEU scores indicate greater lexical divergence; the lowest per column (bottom half) is indicated by (*)." ], "reasoning": "To find the system with the highest lexical diversity for Czech-English len=40, compare the values in the Czech-English len=40 column under Lexical Diversity. System 21 has the highest value of 28.90.", "question": "Which system exhibits the highest lexical diversity for Czech-English len=40?", "answer": "So, the answer is {\"answer\": \"21\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nSystem 21 has the highest lexical diversity for French-English len=5.\n" }, { "role": "assistant", "content": "To verify this statement, compare the values in the French-English len=5 column under Lexical Diversity. System 21 has a value of 1.21, which is not the highest. System 28 has the highest value of 14.07. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2014.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Top: Semantic similarity between paraphrases and reference sentences, as scored by human annotators on a 0-100 scale (least to most similar). Results are grouped by length of reference sentences {5, 10, 20, 40}. System names and numbers correspond to Tab. 3. Improvements over ParaNMT (Czech-English only) in bold. Asterisk (*) indicates best in column. Bottom: Lexical diversity between generated paraphrases and reference sentences, as computed by a modified BLEU score with no length penalty. Results are grouped by length of reference sentences, and BLEU is computed over concatenated references and concatenated paraphrases. Lower BLEU scores indicate greater lexical divergence; the lowest per column (bottom half) is indicated by (*)." ], "reasoning": "To verify this statement, compare the values in the French-English len=5 column under Lexical Diversity. System 21 has a value of 1.21, which is not the highest. System 28 has the highest value of 14.07.", "statement": "System 21 has the highest lexical diversity for French-English len=5.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Tokens assessed, along with their IDFs, of the sentence “I told her I was proud to work for them.”'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Tokens with only lower-cased alphabetical letters were assessed, and after applying IDF thresholds and exceptions for prepositions, the tokens proud, told, work, for, and to were selected based on descending IDF values.First, tokens with only lower-cased alphabetical letters are assessed; they are listed in Tab. After applying the IDF thresholds and exception for prepositions, the following tokens are in the candidate pool, from which we choose tokens to constrain on (listed in descending order of IDF values): proud, told, work, for, and to." } ], "images": [ "scigen/train/2015.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Tokens assessed, along with their IDFs, of the sentence “I told her I was proud to work for them.”" ], "reasoning": "Tokens with only lower-cased alphabetical letters were assessed, and after applying IDF thresholds and exceptions for prepositions, the tokens proud, told, work, for, and to were selected based on descending IDF values.", "description": "First, tokens with only lower-cased alphabetical letters are assessed; they are listed in Tab. After applying the IDF thresholds and exception for prepositions, the following tokens are in the candidate pool, from which we choose tokens to constrain on (listed in descending order of IDF values): proud, told, work, for, and to.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Tokens assessed, along with their IDFs, of the sentence “I told her I was proud to work for them.”\nQuestion: Which token has the highest IDF value after applying the thresholds and exceptions?\nSolve the above question based on the table information. Let's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "First, identify the tokens with only lower-cased alphabetical letters. Then, apply the IDF thresholds and exceptions for prepositions to find the token with the highest IDF value. So, the answer is {\"answer\": \"proud\"}." } ], "images": [ "scigen/train/2015.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Tokens assessed, along with their IDFs, of the sentence “I told her I was proud to work for them.”" ], "reasoning": "First, identify the tokens with only lower-cased alphabetical letters. Then, apply the IDF thresholds and exceptions for prepositions to find the token with the highest IDF value.", "question": "Which token has the highest IDF value after applying the thresholds and exceptions?", "answer": "So, the answer is {\"answer\": \"proud\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe token 'work' has a higher IDF value than the token 'told'.\n" }, { "role": "assistant", "content": "Compare the IDF values of the tokens 'work' and 'told' from the table. 'work' has an IDF value of 7.4, while 'told' has an IDF value of 7.9. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2015.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Tokens assessed, along with their IDFs, of the sentence “I told her I was proud to work for them.”" ], "reasoning": "Compare the IDF values of the tokens 'work' and 'told' from the table. 'work' has an IDF value of 7.4, while 'told' has an IDF value of 7.9.", "statement": "The token 'work' has a higher IDF value than the token 'told'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 6: Multimodal sentiment analysis experiments with different number of MTL layers. The number in the square bracket indicates the number of MTL layers.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The best FMT performance is achieved with 8 MTL layers, showing the lowest MAE of 0.837 and the highest correlation of 0.744.We study the effect of number of MTL on FMT performance. The best performance is achieved using 8 MTL layers (which was also the maximum layers we tried in our hyperparameter search)." } ], "images": [ "scigen/train/2016.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Multimodal sentiment analysis experiments with different number of MTL layers. The number in the square bracket indicates the number of MTL layers." ], "reasoning": "The best FMT performance is achieved with 8 MTL layers, showing the lowest MAE of 0.837 and the highest correlation of 0.744.", "description": "We study the effect of number of MTL on FMT performance. The best performance is achieved using 8 MTL layers (which was also the maximum layers we tried in our hyperparameter search).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 6: Multimodal sentiment analysis experiments with different number of MTL layers. The number in the square bracket indicates the number of MTL layers.', solve the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the correlation value for the model with the lowest MAE?\n" }, { "role": "assistant", "content": "The model with the lowest MAE is FMT[8] with an MAE of 0.837. The corresponding correlation value for this model is 0.744. So, the answer is {\"answer\": \"0.744\"}." } ], "images": [ "scigen/train/2016.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Multimodal sentiment analysis experiments with different number of MTL layers. The number in the square bracket indicates the number of MTL layers." ], "reasoning": "The model with the lowest MAE is FMT[8] with an MAE of 0.837. The corresponding correlation value for this model is 0.744.", "question": "What is the correlation value for the model with the lowest MAE?", "answer": "So, the answer is {\"answer\": \"0.744\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nFMT[6] has a lower MAE than FMT[7].\n" }, { "role": "assistant", "content": "According to the table, FMT[6] has an MAE of 0.852, which is lower than the MAE of FMT[7], which is 0.859. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2016.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Multimodal sentiment analysis experiments with different number of MTL layers. The number in the square bracket indicates the number of MTL layers." ], "reasoning": "According to the table, FMT[6] has an MAE of 0.852, which is lower than the MAE of FMT[7], which is 0.859.", "statement": "FMT[6] has a lower MAE than FMT[7].", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: FMT ablation studies on CMU-MOSI dataset. UNI, BI, TRI denote removing all unimodal, bimodal and trimodal factors respectively. L, A, V denote using only language, audio, and visual factors respectively. S denotes the model with summarization networks replaced by simple addition. All factors, modalities, and components are needed for achieving best performance.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Removing unimodal, bimodal, and trimodal attentions, using only one modality, and replacing summarization networks with vector addition all result in decreased performance compared to the full FMT model.We study the importance of the factorization in FMT. We first remove the unimodal, bimodal and trimodal attentions from the FMT model, resulting in 3 alternative implementations of FMT. Furthermore, we use only one modality as input for FMT, to understand the importance of each modality (all other factors removed). We also replace the summarization networks with simple vector addition operation. All factors, modalities, and summarization components are needed for achieving best performance." } ], "images": [ "scigen/train/2017.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: FMT ablation studies on CMU-MOSI dataset. UNI, BI, TRI denote removing all unimodal, bimodal and trimodal factors respectively. L, A, V denote using only language, audio, and visual factors respectively. S denotes the model with summarization networks replaced by simple addition. All factors, modalities, and components are needed for achieving best performance." ], "reasoning": "Removing unimodal, bimodal, and trimodal attentions, using only one modality, and replacing summarization networks with vector addition all result in decreased performance compared to the full FMT model.", "description": "We study the importance of the factorization in FMT. We first remove the unimodal, bimodal and trimodal attentions from the FMT model, resulting in 3 alternative implementations of FMT. Furthermore, we use only one modality as input for FMT, to understand the importance of each modality (all other factors removed). We also replace the summarization networks with simple vector addition operation. All factors, modalities, and summarization components are needed for achieving best performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: FMT ablation studies on CMU-MOSI dataset. UNI, BI, TRI denote removing all unimodal, bimodal and trimodal factors respectively. L, A, V denote using only language, audio, and visual factors respectively. S denotes the model with summarization networks replaced by simple addition. All factors, modalities, and components are needed for achieving best performance.', solve this question and give detail solutions. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich FMT model variant has the highest MAE?\n" }, { "role": "assistant", "content": "By comparing the MAE values in the table, the FMT model variant with the highest MAE is FMT [V] with an MAE of 1.357. So, the answer is {\"answer\": \"FMT [V]\"}." } ], "images": [ "scigen/train/2017.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: FMT ablation studies on CMU-MOSI dataset. UNI, BI, TRI denote removing all unimodal, bimodal and trimodal factors respectively. L, A, V denote using only language, audio, and visual factors respectively. S denotes the model with summarization networks replaced by simple addition. All factors, modalities, and components are needed for achieving best performance." ], "reasoning": "By comparing the MAE values in the table, the FMT model variant with the highest MAE is FMT [V] with an MAE of 1.357.", "question": "Which FMT model variant has the highest MAE?", "answer": "So, the answer is {\"answer\": \"FMT [V]\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe FMT model with only the auditory modality as input has the lowest correlation.\n" }, { "role": "assistant", "content": "By comparing the correlation values in the table, the FMT model with only the auditory modality (FMT [A]) has a correlation of 0.306, which is the lowest among all models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2017.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: FMT ablation studies on CMU-MOSI dataset. UNI, BI, TRI denote removing all unimodal, bimodal and trimodal factors respectively. L, A, V denote using only language, audio, and visual factors respectively. S denotes the model with summarization networks replaced by simple addition. All factors, modalities, and components are needed for achieving best performance." ], "reasoning": "By comparing the correlation values in the table, the FMT model with only the auditory modality (FMT [A]) has a correlation of 0.306, which is the lowest among all models.", "statement": "The FMT model with only the auditory modality as input has the lowest correlation.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: Multimodal sentiment analysis experiments with different number of FMS units inside MTL. The number in the square bracket indicates the number of FMS. The total number of attention is 7 times the number in bracket.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The model with 6 FMT units achieves the highest performance with 81.5/83.5 BA and 81.4/83.5 F1, and fewer attentions than MulT.We also perform experiments to understand the effect of number of FMT units within each MTL. The model with 6 number of FMS (42 attentions in total) achieves the highest performance (6 is also the highest number we experimented with). Tsai et al. FMT uses fewer number of attentions than MulT, yet achieves better performance. We also experiment with number of heads for original transformer model" } ], "images": [ "scigen/train/2018.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Multimodal sentiment analysis experiments with different number of FMS units inside MTL. The number in the square bracket indicates the number of FMS. The total number of attention is 7 times the number in bracket." ], "reasoning": "The model with 6 FMT units achieves the highest performance with 81.5/83.5 BA and 81.4/83.5 F1, and fewer attentions than MulT.", "description": "We also perform experiments to understand the effect of number of FMT units within each MTL. The model with 6 number of FMS (42 attentions in total) achieves the highest performance (6 is also the highest number we experimented with). Tsai et al. FMT uses fewer number of attentions than MulT, yet achieves better performance. We also experiment with number of heads for original transformer model", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 5: Multimodal sentiment analysis experiments with different number of FMS units inside MTL. The number in the square bracket indicates the number of FMS. The total number of attention is 7 times the number in bracket.' in mind, break down and resolve the question below step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model achieves the highest correlation (Corr) and what is its value?\n" }, { "role": "assistant", "content": "By examining the table, the model FMT[6] achieves the highest correlation value of 0.744. So, the answer is {\"answer\": \"FMT[6] with 0.744\"}." } ], "images": [ "scigen/train/2018.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Multimodal sentiment analysis experiments with different number of FMS units inside MTL. The number in the square bracket indicates the number of FMS. The total number of attention is 7 times the number in bracket." ], "reasoning": "By examining the table, the model FMT[6] achieves the highest correlation value of 0.744.", "question": "Which model achieves the highest correlation (Corr) and what is its value?", "answer": "So, the answer is {\"answer\": \"FMT[6] with 0.744\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nFMT[3] has the highest MAE value among all models.\n" }, { "role": "assistant", "content": "By comparing the MAE values in the table, FMT[3] has the highest MAE value of 0.905. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2018.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Multimodal sentiment analysis experiments with different number of FMS units inside MTL. The number in the square bracket indicates the number of FMS. The total number of attention is 7 times the number in bracket." ], "reasoning": "By comparing the MAE values in the table, FMT[3] has the highest MAE value of 0.905.", "statement": "FMT[3] has the highest MAE value among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Results of experiments with different number of heads for OTF. The number in the square bracket indicates the number of heads.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Increasing the number of attention heads from 1 to 35 in the original transformer model does not necessarily lead to superior performance, as even with fewer heads, the FMT achieves higher performance.In this section, we discuss the effect of increasing the number of heads on the original transformer model (OTF, Vaswani et al. Please note that we implement the OTF to allow for all attention heads to have full input receptive field (from 1…T), similar to FMT. We increase the attention heads from 1 to 35 (after 35 does not fit on a Tesla-V100 GPU with batchsize of 20). We observe that achieving superior performance is not a matter of increasing the attention heads. Even using 1 FMS unit, which leads to 7 total attention, FMT achieves higher performance than counterpart OTF." } ], "images": [ "scigen/train/2019.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Results of experiments with different number of heads for OTF. The number in the square bracket indicates the number of heads." ], "reasoning": "Increasing the number of attention heads from 1 to 35 in the original transformer model does not necessarily lead to superior performance, as even with fewer heads, the FMT achieves higher performance.", "description": "In this section, we discuss the effect of increasing the number of heads on the original transformer model (OTF, Vaswani et al. Please note that we implement the OTF to allow for all attention heads to have full input receptive field (from 1…T), similar to FMT. We increase the attention heads from 1 to 35 (after 35 does not fit on a Tesla-V100 GPU with batchsize of 20). We observe that achieving superior performance is not a matter of increasing the attention heads. Even using 1 FMS unit, which leads to 7 total attention, FMT achieves higher performance than counterpart OTF.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 7: Results of experiments with different number of heads for OTF. The number in the square bracket indicates the number of heads.', solve this question and give detail solutions. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the correlation (Corr) value for the original transformer model (OTF) with 21 attention heads?\n" }, { "role": "assistant", "content": "To find the correlation value for OTF with 21 attention heads, locate the row labeled 'OTF [21]' and find the value under the 'Corr' column. So, the answer is {\"answer\": \"0.682\"}." } ], "images": [ "scigen/train/2019.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Results of experiments with different number of heads for OTF. The number in the square bracket indicates the number of heads." ], "reasoning": "To find the correlation value for OTF with 21 attention heads, locate the row labeled 'OTF [21]' and find the value under the 'Corr' column.", "question": "What is the correlation (Corr) value for the original transformer model (OTF) with 21 attention heads?", "answer": "So, the answer is {\"answer\": \"0.682\"}." }, { "messages": [ { "role": "user", "content": "The original transformer model (OTF) with 35 attention heads has the highest Mean Absolute Error (MAE). Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe original transformer model (OTF) with 35 attention heads has the highest Mean Absolute Error (MAE).\n" }, { "role": "assistant", "content": "To verify this statement, compare the MAE values for all rows. The MAE value for OTF with 35 attention heads is 1.174, which is the highest among all listed models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2019.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Results of experiments with different number of heads for OTF. The number in the square bracket indicates the number of heads." ], "reasoning": "To verify this statement, compare the MAE values for all rows. The MAE value for OTF with 35 attention heads is 1.174, which is the highest among all listed models.", "statement": "The original transformer model (OTF) with 35 attention heads has the highest Mean Absolute Error (MAE).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: Performance of different model variants.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The RE-ID model, especially when initialized with word2vec, significantly outperforms the W2V and LDA baselines in both AUC and 1/MRR metrics.The RE-ID model outperforms the W2V and LDA baselines for both criteria, with substantial gains in 1/MRR (lower is better). Further, the version of RE-ID initialized with word2vec did better than the one that was initialized randomly even though the randomly initialized version was trained for twice as long." } ], "images": [ "scigen/train/2020.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance of different model variants." ], "reasoning": "The RE-ID model, especially when initialized with word2vec, significantly outperforms the W2V and LDA baselines in both AUC and 1/MRR metrics.", "description": "The RE-ID model outperforms the W2V and LDA baselines for both criteria, with substantial gains in 1/MRR (lower is better). Further, the version of RE-ID initialized with word2vec did better than the one that was initialized randomly even though the randomly initialized version was trained for twice as long.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: Performance of different model variants.', answer the question below by showing each progressive step toward the answer. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich strategy achieved the lowest 1/MRR score?\n" }, { "role": "assistant", "content": "By comparing the 1/MRR scores of all strategies, the RE-ID (W2V init) strategy has the lowest score of 12. So, the answer is {\"answer\": \"RE-ID (W2V init)\"}." } ], "images": [ "scigen/train/2020.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance of different model variants." ], "reasoning": "By comparing the 1/MRR scores of all strategies, the RE-ID (W2V init) strategy has the lowest score of 12.", "question": "Which strategy achieved the lowest 1/MRR score?", "answer": "So, the answer is {\"answer\": \"RE-ID (W2V init)\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe RE-ID model initialized with word2vec achieved an AUC of 98.5.\n" }, { "role": "assistant", "content": "The table shows that the RE-ID (W2V init) strategy has an AUC of 98.5, which supports the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2020.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance of different model variants." ], "reasoning": "The table shows that the RE-ID (W2V init) strategy has an AUC of 98.5, which supports the statement.", "statement": "The RE-ID model initialized with word2vec achieved an AUC of 98.5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: W2V+RE-ID results by community'.\n" }, { "role": "assistant", "content": "The communities were selected based on themes familiar to the authors, avoiding bias towards celebrities and ensuring diversity in topics and characteristics.To define a “community,” volunteers manually selected a set of users that fit with a theme that they had familiarity with. Thus, the specific 16 communities were determined based on themes of interest to the authors and their friends and colleagues, where we could be reasonably confident about membership decisions. In addition, we tried to avoid themes that might be biased towards well-known celebrities, and we made an effort to have diversity in the characteristics of the communities. The communities were selected to span a range of topics, sizes (6-130 accounts), individuals vs. organizations, and other characteristics. A few of the communities are comprised of organizations rather than individuals such as the high school drama departments and the Pittsburgh food truck communities. (The community names are invented by the authors for purposes of describing the data in this paper; they are not part of the retrieval task.) Sample size does not seem to be a good indicator of performance: the two smallest communities (Cartoonists, Fresno City Council) had the worst and one of the best results, respectively. Anecdotally, we observed that the sample of cartoonists were more likely to Tweet about topics outside their main interest (e.g., politics or sports). We hypothesize that the diversity of interests of the members of a community affects the difficulty of the retrieval task, but our test set is too small to confirm this hypothesis." } ], "images": [ "scigen/train/2021.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: W2V+RE-ID results by community" ], "reasoning": "The communities were selected based on themes familiar to the authors, avoiding bias towards celebrities and ensuring diversity in topics and characteristics.", "description": "To define a “community,” volunteers manually selected a set of users that fit with a theme that they had familiarity with. Thus, the specific 16 communities were determined based on themes of interest to the authors and their friends and colleagues, where we could be reasonably confident about membership decisions. In addition, we tried to avoid themes that might be biased towards well-known celebrities, and we made an effort to have diversity in the characteristics of the communities. The communities were selected to span a range of topics, sizes (6-130 accounts), individuals vs. organizations, and other characteristics. A few of the communities are comprised of organizations rather than individuals such as the high school drama departments and the Pittsburgh food truck communities. (The community names are invented by the authors for purposes of describing the data in this paper; they are not part of the retrieval task.) Sample size does not seem to be a good indicator of performance: the two smallest communities (Cartoonists, Fresno City Council) had the worst and one of the best results, respectively. Anecdotally, we observed that the sample of cartoonists were more likely to Tweet about topics outside their main interest (e.g., politics or sports). We hypothesize that the diversity of interests of the members of a community affects the difficulty of the retrieval task, but our test set is too small to confirm this hypothesis.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: W2V+RE-ID results by community', solve the subsequent question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich community has the highest 1/MRR score and what is its size?\n" }, { "role": "assistant", "content": "By examining the table, the Cartoonists community has the highest 1/MRR score of 58.1, and its size is 8. So, the answer is {\"answer\": \"8\"}." } ], "images": [ "scigen/train/2021.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: W2V+RE-ID results by community" ], "reasoning": "By examining the table, the Cartoonists community has the highest 1/MRR score of 58.1, and its size is 8.", "question": "Which community has the highest 1/MRR score and what is its size?", "answer": "So, the answer is {\"answer\": \"8\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe community with the smallest size has a 1/MRR score of 3.0.\n" }, { "role": "assistant", "content": "The table shows that the Fresno City Council community has the smallest size of 6 and a 1/MRR score of 3.0. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2021.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: W2V+RE-ID results by community" ], "reasoning": "The table shows that the Fresno City Council community has the smallest size of 6 and a 1/MRR score of 3.0.", "statement": "The community with the smallest size has a 1/MRR score of 3.0.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Figure 5: Performance of different J-K-fold tuning procedures for a logistic regression part-of-speech tagger over 1,000 different partitions (3 s.f).', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Increasing J reduces variability, but vanilla 10-fold CV produces consistently near-optimal models, though variability in C value selection remains a concern.Our most unstable tuning is by 1-10-fold CV. However when checked on the held-out population, the tuned model with the worst true performance only loses 0.05% accuracy from the parameter choice with best global performance. So although increasing J does reduce variability, it is not always necessary for effective tuning, as vanilla 10-fold CV produced consistently near-optimal models. The size of variability, however, is still a concern for reproducibility, as different random partitions can lead to choosing C as 10, 50 or 100. Once again, at each computational cost, the most effective tuning (in terms of gamma and variability of the performance estimate of the tuned model) is from the lower choice of K and higher J. Note that for C variability, there is an slight increase when moving from 1-10-fold to 2-5-fold CV, however, this corresponds to a large drop in the other variabilities and so 2-5-fold CV still provides more effective tuning overall. Also note that we see a larger reduction in gamma variability than C variability for larger J choices. Note that effective parameter tuning corresponds to a single predominately large bin, i.e. selecting a single parameter choice irrespective of the partitioning. Also note that the accuracy estimate for the model tuned by 1-10-fold CV is not stable enough to allow comparison with other models of close performance. To be able to reliably distinguish between our tuned model and alternatives with performance differing by only a couple of percentage points, we require higher choices of J." } ], "images": [ "scigen/train/2022.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 5: Performance of different J-K-fold tuning procedures for a logistic regression part-of-speech tagger over 1,000 different partitions (3 s.f)." ], "reasoning": "Increasing J reduces variability, but vanilla 10-fold CV produces consistently near-optimal models, though variability in C value selection remains a concern.", "description": "Our most unstable tuning is by 1-10-fold CV. However when checked on the held-out population, the tuned model with the worst true performance only loses 0.05% accuracy from the parameter choice with best global performance. So although increasing J does reduce variability, it is not always necessary for effective tuning, as vanilla 10-fold CV produced consistently near-optimal models. The size of variability, however, is still a concern for reproducibility, as different random partitions can lead to choosing C as 10, 50 or 100. Once again, at each computational cost, the most effective tuning (in terms of gamma and variability of the performance estimate of the tuned model) is from the lower choice of K and higher J. Note that for C variability, there is an slight increase when moving from 1-10-fold to 2-5-fold CV, however, this corresponds to a large drop in the other variabilities and so 2-5-fold CV still provides more effective tuning overall. Also note that we see a larger reduction in gamma variability than C variability for larger J choices. Note that effective parameter tuning corresponds to a single predominately large bin, i.e. selecting a single parameter choice irrespective of the partitioning. Also note that the accuracy estimate for the model tuned by 1-10-fold CV is not stable enough to allow comparison with other models of close performance. To be able to reliably distinguish between our tuned model and alternatives with performance differing by only a couple of percentage points, we require higher choices of J.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Figure 5: Performance of different J-K-fold tuning procedures for a logistic regression part-of-speech tagger over 1,000 different partitions (3 s.f).', unfold the question presented below, detailing every step of your calculation. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the SD of the accuracy estimate for the model with the lowest gamma value?\n" }, { "role": "assistant", "content": "The table shows the SD of the accuracy estimate for different C values and their corresponding gamma values. The lowest gamma value is 0.0563, which corresponds to an SD of the accuracy estimate of 0.330%. So, the answer is {\"answer\": \"0.330 %\"}." } ], "images": [ "scigen/train/2022.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 5: Performance of different J-K-fold tuning procedures for a logistic regression part-of-speech tagger over 1,000 different partitions (3 s.f)." ], "reasoning": "The table shows the SD of the accuracy estimate for different C values and their corresponding gamma values. The lowest gamma value is 0.0563, which corresponds to an SD of the accuracy estimate of 0.330%.", "question": "What is the SD of the accuracy estimate for the model with the lowest gamma value?", "answer": "So, the answer is {\"answer\": \"0.330 %\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model with a C value of 20.4 has a higher SD of the accuracy estimate than the model with a C value of 21.3.\n" }, { "role": "assistant", "content": "The table shows that the SD of the accuracy estimate for the model with a C value of 20.4 is 0.445%, while for the model with a C value of 21.3, it is 0.580%. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2022.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 5: Performance of different J-K-fold tuning procedures for a logistic regression part-of-speech tagger over 1,000 different partitions (3 s.f)." ], "reasoning": "The table shows that the SD of the accuracy estimate for the model with a C value of 20.4 is 0.445%, while for the model with a C value of 21.3, it is 0.580%. Therefore, the statement is refuted.", "statement": "The model with a C value of 20.4 has a higher SD of the accuracy estimate than the model with a C value of 21.3.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: Standard deviation (SD) performance of J-K-fold tuning on the IMDB dataset over 1,000 different partitions (3 s.f).'.\n" }, { "role": "assistant", "content": "Reducing K and using the saved computation for increasing J results in less variability, making 2-5-fold CV superior to 1-10-fold CV and 1-20-fold CV.We see that between 1-10-fold CV and 2-5-fold CV, and between 2-10-fold, 4-5-fold and 1-20-fold (choices of equivalent computational cost), we have the least variability when reducing K and using the saved computation for increasing J. Note that the naive approach of using all available computation to increase K (1-20-fold CV) produces a wider range of parameter choices than 2-10-fold and 4-5-fold CV. This is despite any bias reductions from choosing a higher K and so we do not recommend 1-J∗K-fold CV. This analysis questions the rule of thumb selection of K=10 common in NLP, as 2-5 CV seems to produce superior tuning at the same cost, a hypothesis we now test across a range of NLP tasks." } ], "images": [ "scigen/train/2023.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Standard deviation (SD) performance of J-K-fold tuning on the IMDB dataset over 1,000 different partitions (3 s.f)." ], "reasoning": "Reducing K and using the saved computation for increasing J results in less variability, making 2-5-fold CV superior to 1-10-fold CV and 1-20-fold CV.", "description": "We see that between 1-10-fold CV and 2-5-fold CV, and between 2-10-fold, 4-5-fold and 1-20-fold (choices of equivalent computational cost), we have the least variability when reducing K and using the saved computation for increasing J. Note that the naive approach of using all available computation to increase K (1-20-fold CV) produces a wider range of parameter choices than 2-10-fold and 4-5-fold CV. This is despite any bias reductions from choosing a higher K and so we do not recommend 1-J∗K-fold CV. This analysis questions the rule of thumb selection of K=10 common in NLP, as 2-5 CV seems to produce superior tuning at the same cost, a hypothesis we now test across a range of NLP tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 1: Standard deviation (SD) performance of J-K-fold tuning on the IMDB dataset over 1,000 different partitions (3 s.f).'\nQuestion: Which CV method has the least variability in the SD of the chosen max features value?\n" }, { "role": "assistant", "content": "By comparing the SD of the chosen max features value across all CV methods, 4-5-fold CV has the lowest SD of 0.0278. So, the answer is {\"answer\": \"4-5-fold CV\"}." } ], "images": [ "scigen/train/2023.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Standard deviation (SD) performance of J-K-fold tuning on the IMDB dataset over 1,000 different partitions (3 s.f)." ], "reasoning": "By comparing the SD of the chosen max features value across all CV methods, 4-5-fold CV has the lowest SD of 0.0278.", "question": "Which CV method has the least variability in the SD of the chosen max features value?", "answer": "So, the answer is {\"answer\": \"4-5-fold CV\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\n2-5-fold CV has a higher SD of the accuracy estimate than 1-20-fold CV.\n" }, { "role": "assistant", "content": "The table shows that the SD of the accuracy estimate for 2-5-fold CV is 0.528%, while for 1-20-fold CV it is 0.483%. Therefore, the statement is correct. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2023.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Standard deviation (SD) performance of J-K-fold tuning on the IMDB dataset over 1,000 different partitions (3 s.f)." ], "reasoning": "The table shows that the SD of the accuracy estimate for 2-5-fold CV is 0.528%, while for 1-20-fold CV it is 0.483%. Therefore, the statement is correct.", "statement": "2-5-fold CV has a higher SD of the accuracy estimate than 1-20-fold CV.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Figure 8: Performance of J-K-fold tuning when choosing the width of the LSTM (3 s.f).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Tuning the LSTM model with 8-5-fold CV consistently chooses a width of 70 and l2 regularisation of 0.001, with performance differences being small and requiring larger J for specificity.We perform two experiments: tuning the number of nodes in the LSTM layer (known as width) across the grid {10,20,30,..,90}, and separately tuning the amount of l2 regularisation on the inputs and biases (for a fixed width of 50) each across {0.00001,0.001,0.1}]. In contrast, tuning with 8-5-fold produces much more consistent choices, the majority of the time choosing 70 for the optimal width and 0.001 Although variability in our chosen LSTM parameters does reduce as we increase J, it remains significantly higher than the gaps in our parameter grid. When coupled with the relative stability of the accuracy estimates of this tuned model, this suggests that the performance differences between the most frequent choices by 8-5-fold CV are small. However, to consistently tune the model to this specificity we require larger choices of J than 8." } ], "images": [ "scigen/train/2024.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 8: Performance of J-K-fold tuning when choosing the width of the LSTM (3 s.f)." ], "reasoning": "Tuning the LSTM model with 8-5-fold CV consistently chooses a width of 70 and l2 regularisation of 0.001, with performance differences being small and requiring larger J for specificity.", "description": "We perform two experiments: tuning the number of nodes in the LSTM layer (known as width) across the grid {10,20,30,..,90}, and separately tuning the amount of l2 regularisation on the inputs and biases (for a fixed width of 50) each across {0.00001,0.001,0.1}]. In contrast, tuning with 8-5-fold produces much more consistent choices, the majority of the time choosing 70 for the optimal width and 0.001 Although variability in our chosen LSTM parameters does reduce as we increase J, it remains significantly higher than the gaps in our parameter grid. When coupled with the relative stability of the accuracy estimates of this tuned model, this suggests that the performance differences between the most frequent choices by 8-5-fold CV are small. However, to consistently tune the model to this specificity we require larger choices of J than 8.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Figure 8: Performance of J-K-fold tuning when choosing the width of the LSTM (3 s.f).' in mind, break down and resolve the question below step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhat is the standard deviation of the accuracy estimate for the lowest chosen bias regularisation?\n" }, { "role": "assistant", "content": "Identify the row with the lowest chosen bias regularisation (0.00815) and find the corresponding standard deviation of the accuracy estimate (0.148%). So, the answer is {\"answer\": \"0.148 %\"}." } ], "images": [ "scigen/train/2024.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 8: Performance of J-K-fold tuning when choosing the width of the LSTM (3 s.f)." ], "reasoning": "Identify the row with the lowest chosen bias regularisation (0.00815) and find the corresponding standard deviation of the accuracy estimate (0.148%).", "question": "What is the standard deviation of the accuracy estimate for the lowest chosen bias regularisation?", "answer": "So, the answer is {\"answer\": \"0.148 %\"}." }, { "messages": [ { "role": "user", "content": "The highest standard deviation of the accuracy estimate corresponds to a chosen bias regularisation of 0.0404. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe highest standard deviation of the accuracy estimate corresponds to a chosen bias regularisation of 0.0404.\n" }, { "role": "assistant", "content": "The table shows that the highest standard deviation of the accuracy estimate is 0.365%, which corresponds to a chosen bias regularisation of 0.0404. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2024.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 8: Performance of J-K-fold tuning when choosing the width of the LSTM (3 s.f)." ], "reasoning": "The table shows that the highest standard deviation of the accuracy estimate is 0.365%, which corresponds to a chosen bias regularisation of 0.0404.", "statement": "The highest standard deviation of the accuracy estimate corresponds to a chosen bias regularisation of 0.0404.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: Accuracy of BERT, with and without defenses, on MRPC when attacked under the ‘all’ attack setting.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Simple 1-2 character attacks significantly reduce BERT's accuracy, but word recognition models like pass-through and neutral variants can recover most of the accuracy.We re-trained the ScRNN model variants on the MRPC training set for these experiments. Again, we find that simple 1-2 character attacks can bring down the accuracy of BERT significantly (89% to 31%). Word recognition models can provide an effective defense, with both our pass-through and neutral variants recovering most of the accuracy. While the neutral backoff model is effective on 2-char attacks, it hurts performance in the no attack setting, since it incorrectly modifies certain correctly spelled entity names. Since the two variants are already effective, we did not train a background model for this task." } ], "images": [ "scigen/train/2025.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Accuracy of BERT, with and without defenses, on MRPC when attacked under the ‘all’ attack setting." ], "reasoning": "Simple 1-2 character attacks significantly reduce BERT's accuracy, but word recognition models like pass-through and neutral variants can recover most of the accuracy.", "description": "We re-trained the ScRNN model variants on the MRPC training set for these experiments. Again, we find that simple 1-2 character attacks can bring down the accuracy of BERT significantly (89% to 31%). Word recognition models can provide an effective defense, with both our pass-through and neutral variants recovering most of the accuracy. While the neutral backoff model is effective on 2-char attacks, it hurts performance in the no attack setting, since it incorrectly modifies certain correctly spelled entity names. Since the two variants are already effective, we did not train a background model for this task.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 4: Accuracy of BERT, with and without defenses, on MRPC when attacked under the ‘all’ attack setting.'. Solve the following question based on the table.\n\nQuestion: Which model has the highest accuracy under 2-character attacks?\nLet's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest accuracy under 2-character attacks, compare the accuracy values in the 'All attacks 2-char' column. The highest value is 82.5, which corresponds to the 'BERT + Neutral' model. So, the answer is {\"answer\": \"BERT + Neutral\"}" } ], "images": [ "scigen/train/2025.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Accuracy of BERT, with and without defenses, on MRPC when attacked under the ‘all’ attack setting." ], "reasoning": "To find the model with the highest accuracy under 2-character attacks, compare the accuracy values in the 'All attacks 2-char' column. The highest value is 82.5, which corresponds to the 'BERT + Neutral' model.", "question": "Which model has the highest accuracy under 2-character attacks?", "answer": "So, the answer is {\"answer\": \"BERT + Neutral\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis BERT + ATD has the highest accuracy in the no attack setting. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nBERT + ATD has the highest accuracy in the no attack setting.\n" }, { "role": "assistant", "content": "To verify this statement, compare the accuracy values in the 'No Attack' column. The highest value is 89.9, which corresponds to the 'BERT + ATD' model. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2025.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Accuracy of BERT, with and without defenses, on MRPC when attacked under the ‘all’ attack setting." ], "reasoning": "To verify this statement, compare the accuracy values in the 'No Attack' column. The highest value is 89.9, which corresponds to the 'BERT + ATD' model.", "statement": "BERT + ATD has the highest accuracy in the no attack setting.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Word Error Rates (WER) of ScRNN with each backoff strategy, plus ATD and an ScRNN trained only on the background corpus (78K vocabulary) The error rates include 5.25% OOV words.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The most accurate word recognition model is the ScRNN with a background backoff variant, achieving a 6.9% error rate, which is a 32% relative error reduction compared to the vanilla ScRNN model.Note that ATD incorrectly predicts 11.2 words for every 100 words (in the ‘all’ setting), whereas, all of the backoff variations of the ScRNN reconstruct better. The most accurate variant involves backing off to the background model, resulting in a low error rate of 6.9%, leading to the best performance on word recognition. This is a 32% relative error reduction compared to the vanilla ScRNN model with a pass-through backoff strategy. We can attribute the improved performance to the fact that there are 5.25% words in the test corpus that are unseen in the training corpus, and are thus only recoverable by backing off to a larger corpus. Notably, only training on the larger background corpus does worse, at 8.7%, since the distribution of word frequencies is different in the background corpus compared to the foreground corpus." } ], "images": [ "scigen/train/2026.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Word Error Rates (WER) of ScRNN with each backoff strategy, plus ATD and an ScRNN trained only on the background corpus (78K vocabulary) The error rates include 5.25% OOV words." ], "reasoning": "The most accurate word recognition model is the ScRNN with a background backoff variant, achieving a 6.9% error rate, which is a 32% relative error reduction compared to the vanilla ScRNN model.", "description": "Note that ATD incorrectly predicts 11.2 words for every 100 words (in the ‘all’ setting), whereas, all of the backoff variations of the ScRNN reconstruct better. The most accurate variant involves backing off to the background model, resulting in a low error rate of 6.9%, leading to the best performance on word recognition. This is a 32% relative error reduction compared to the vanilla ScRNN model with a pass-through backoff strategy. We can attribute the improved performance to the fact that there are 5.25% words in the test corpus that are unseen in the training corpus, and are thus only recoverable by backing off to a larger corpus. Notably, only training on the larger background corpus does worse, at 8.7%, since the distribution of word frequencies is different in the background corpus compared to the foreground corpus.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Word Error Rates (WER) of ScRNN with each backoff strategy, plus ATD and an ScRNN trained only on the background corpus (78K vocabulary) The error rates include 5.25% OOV words.' to sequentially solve the question presented below. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the error rate of the most accurate word recognition model?\n" }, { "role": "assistant", "content": "The table shows that the ScRNN (10K) with Background backoff variant has the lowest error rate of 6.9% in the 'Word Recognition All' column. So, the answer is {\"answer\": \"6.9%\"}" } ], "images": [ "scigen/train/2026.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Word Error Rates (WER) of ScRNN with each backoff strategy, plus ATD and an ScRNN trained only on the background corpus (78K vocabulary) The error rates include 5.25% OOV words." ], "reasoning": "The table shows that the ScRNN (10K) with Background backoff variant has the lowest error rate of 6.9% in the 'Word Recognition All' column.", "question": "What is the error rate of the most accurate word recognition model?", "answer": "So, the answer is {\"answer\": \"6.9%\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe ScRNN (10K) with Pass-Through backoff variant has a lower error rate than the ATD model in the 'Word Recognition All' setting.\n" }, { "role": "assistant", "content": "The table shows that the ScRNN (10K) with Pass-Through backoff variant has an error rate of 10.2%, which is lower than the ATD model's error rate of 11.2% in the 'Word Recognition All' setting. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2026.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Word Error Rates (WER) of ScRNN with each backoff strategy, plus ATD and an ScRNN trained only on the background corpus (78K vocabulary) The error rates include 5.25% OOV words." ], "reasoning": "The table shows that the ScRNN (10K) with Pass-Through backoff variant has an error rate of 10.2%, which is lower than the ATD model's error rate of 11.2% in the 'Word Recognition All' setting.", "statement": "The ScRNN (10K) with Pass-Through backoff variant has a lower error rate than the ATD model in the 'Word Recognition All' setting.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Experiment results of the ablation study. CoAttn denotes the traditional co-attention mechanism. Ortho. represents parameter orthogonalization. ✔ means including this module/method in the model.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "An ablation study was conducted to compare different settings of the proposed model with the traditional co-attention model.In order to better understand the efficacy of the proposed methods, we further conduct an ablation study on different settings of our model. We also use the traditional co-attention model for comparison." } ], "images": [ "scigen/train/2027.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Experiment results of the ablation study. CoAttn denotes the traditional co-attention mechanism. Ortho. represents parameter orthogonalization. ✔ means including this module/method in the model." ], "reasoning": "An ablation study was conducted to compare different settings of the proposed model with the traditional co-attention model.", "description": "In order to better understand the efficacy of the proposed methods, we further conduct an ablation study on different settings of our model. We also use the traditional co-attention model for comparison.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 4: Experiment results of the ablation study. CoAttn denotes the traditional co-attention mechanism. Ortho. represents parameter orthogonalization. ✔ means including this module/method in the model.\nQuestion: Which model has the highest Seq2Seq Architecture RC@10 score?\n" }, { "role": "assistant", "content": "To find the model with the highest Seq2Seq Architecture RC@10 score, compare the values in the Seq2Seq Architecture RC@10 column for all models. The highest value is 58.4, which corresponds to the Ours-MPA+Ortho.+GAM model. So, the answer is {\"answer\": \"Ours-MPA+Ortho.+GAM\"}." } ], "images": [ "scigen/train/2027.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Experiment results of the ablation study. CoAttn denotes the traditional co-attention mechanism. Ortho. represents parameter orthogonalization. ✔ means including this module/method in the model." ], "reasoning": "To find the model with the highest Seq2Seq Architecture RC@10 score, compare the values in the Seq2Seq Architecture RC@10 column for all models. The highest value is 58.4, which corresponds to the Ours-MPA+Ortho.+GAM model.", "question": "Which model has the highest Seq2Seq Architecture RC@10 score?", "answer": "So, the answer is {\"answer\": \"Ours-MPA+Ortho.+GAM\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The traditional co-attention model has the highest Transformer Architecture RC@10 score., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe traditional co-attention model has the highest Transformer Architecture RC@10 score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Transformer Architecture RC@10 scores for all models. The highest score is 62.0, which corresponds to the Ours-MPA+Ortho.+GAM model, not the traditional co-attention model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2027.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Experiment results of the ablation study. CoAttn denotes the traditional co-attention mechanism. Ortho. represents parameter orthogonalization. ✔ means including this module/method in the model." ], "reasoning": "To verify this statement, compare the Transformer Architecture RC@10 scores for all models. The highest score is 62.0, which corresponds to the Ours-MPA+Ortho.+GAM model, not the traditional co-attention model.", "statement": "The traditional co-attention model has the highest Transformer Architecture RC@10 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 8: Answer quality for misspelled questions.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "DrQA's F1 score drops by almost 8% with misspelled questions, while DocQA's F1 score drops by about 2%, indicating DocQA's stability.DrQA seems to be most sensible to misspellings: The difference in F1 is almost 8% (scores are lower for misspelled questions). DocQA has most stable behavior: The difference in F1 scores is about 2%." } ], "images": [ "scigen/train/2028.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Answer quality for misspelled questions." ], "reasoning": "DrQA's F1 score drops by almost 8% with misspelled questions, while DocQA's F1 score drops by about 2%, indicating DocQA's stability.", "description": "DrQA seems to be most sensible to misspellings: The difference in F1 is almost 8% (scores are lower for misspelled questions). DocQA has most stable behavior: The difference in F1 scores is about 2%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 8: Answer quality for misspelled questions.', unfold the steps to solve the question given next. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nQuestion: Which model shows the smallest difference in F1 scores between correct and misspelled questions?\n" }, { "role": "assistant", "content": "By comparing the F1 scores for correct and misspelled questions, DocQA shows the smallest difference of about 2% (77.5% to 75.4%). So, the answer is {\"answer\": \"DocQA\"}." } ], "images": [ "scigen/train/2028.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Answer quality for misspelled questions." ], "reasoning": "By comparing the F1 scores for correct and misspelled questions, DocQA shows the smallest difference of about 2% (77.5% to 75.4%).", "question": "Which model shows the smallest difference in F1 scores between correct and misspelled questions?", "answer": "So, the answer is {\"answer\": \"DocQA\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nDocQA is the most stable model in terms of F1 score when dealing with misspelled questions.\n" }, { "role": "assistant", "content": "The table shows that DocQA has the smallest difference in F1 scores between correct and misspelled questions, which is about 2%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2028.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Answer quality for misspelled questions." ], "reasoning": "The table shows that DocQA has the smallest difference in F1 scores between correct and misspelled questions, which is about 2%.", "statement": "DocQA is the most stable model in terms of F1 score when dealing with misspelled questions.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: Distribution of answers by constituent types (NP – noun phrase, PP – prepositional phrase, VP – verb phrase, ADJP – adjective phrase, ADVP – adverb phrase, non-R – words in non-Russian characters; None – not recognized).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Noun phrases are the most frequent answer types, followed by prepositional phrases. Verb phrases represent a significant share of answers, and non-factoid verb phrases are the most difficult to answer.Following [squad], we complemented our analysis of answers with syntactic parsing. When AOT produced multiple parses, we picked the parse with the longest span. Not surprisingly, noun phrases are most frequent answer types, followed by prepositional phrases. Verb phrases represent a non-negligible share of answers (7.1%), which is quite different from a traditional QA setting where answers are predominantly noun phrases [DBLP: journals/ftir/Prager06]. Among them, non-factoid verb phrases stand out as most difficult ones (all models perform worse on such questions). In contrast, answers expressed as prepositional phrases are easier to answer compared to both noun and verb phrases. Noun phrases—most common syntactic units among answers—are second-easiest structure among others to answer. However, with exception for BERT, F1 scores for noun phrases are lower than average." } ], "images": [ "scigen/train/2029.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Distribution of answers by constituent types (NP – noun phrase, PP – prepositional phrase, VP – verb phrase, ADJP – adjective phrase, ADVP – adverb phrase, non-R – words in non-Russian characters; None – not recognized)." ], "reasoning": "Noun phrases are the most frequent answer types, followed by prepositional phrases. Verb phrases represent a significant share of answers, and non-factoid verb phrases are the most difficult to answer.", "description": "Following [squad], we complemented our analysis of answers with syntactic parsing. When AOT produced multiple parses, we picked the parse with the longest span. Not surprisingly, noun phrases are most frequent answer types, followed by prepositional phrases. Verb phrases represent a non-negligible share of answers (7.1%), which is quite different from a traditional QA setting where answers are predominantly noun phrases [DBLP: journals/ftir/Prager06]. Among them, non-factoid verb phrases stand out as most difficult ones (all models perform worse on such questions). In contrast, answers expressed as prepositional phrases are easier to answer compared to both noun and verb phrases. Noun phrases—most common syntactic units among answers—are second-easiest structure among others to answer. However, with exception for BERT, F1 scores for noun phrases are lower than average.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Distribution of answers by constituent types (NP – noun phrase, PP – prepositional phrase, VP – verb phrase, ADJP – adjective phrase, ADVP – adverb phrase, non-R – words in non-Russian characters; None – not recognized).'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}. \n\nQuestion: Which model has the highest F1 score for prepositional phrases, and what is that score?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the model with the highest F1 score for prepositional phrases, look at the 'PP' row and compare the scores across all models. The highest score is 89.1 by BERT. So, the answer is {\"answer\": \"BERT with 89.1\"}." } ], "images": [ "scigen/train/2029.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Distribution of answers by constituent types (NP – noun phrase, PP – prepositional phrase, VP – verb phrase, ADJP – adjective phrase, ADVP – adverb phrase, non-R – words in non-Russian characters; None – not recognized)." ], "reasoning": "To find the model with the highest F1 score for prepositional phrases, look at the 'PP' row and compare the scores across all models. The highest score is 89.1 by BERT.", "question": "Which model has the highest F1 score for prepositional phrases, and what is that score?", "answer": "So, the answer is {\"answer\": \"BERT with 89.1\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim BERT has the highest F1 score for noun phrases., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nBERT has the highest F1 score for noun phrases.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'NP' row and compare the scores across all models. BERT has the highest score of 84.5, which supports the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2029.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Distribution of answers by constituent types (NP – noun phrase, PP – prepositional phrase, VP – verb phrase, ADJP – adjective phrase, ADVP – adverb phrase, non-R – words in non-Russian characters; None – not recognized)." ], "reasoning": "To verify this statement, look at the 'NP' row and compare the scores across all models. BERT has the highest score of 84.5, which supports the statement.", "statement": "BERT has the highest F1 score for noun phrases.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 5: Training parameters. LR stands for learning rate.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "BiDAF and DocQA models required position corrections for about 10% of answer strings, mostly achieved automatically by ignoring punctuation, with 12 manual interventions.This initialization is used for both questions and paragraphs. For BiDAF and DocQA about 10% of answer strings in both training and testing sets require a correction of positions, which can be nearly always achieved automatically by ignoring punctuation (12 answers required a manual intervention). Models were trained on GPU nVidia Tesla V100 16Gb." } ], "images": [ "scigen/train/2030.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Training parameters. LR stands for learning rate." ], "reasoning": "BiDAF and DocQA models required position corrections for about 10% of answer strings, mostly achieved automatically by ignoring punctuation, with 12 manual interventions.", "description": "This initialization is used for both questions and paragraphs. For BiDAF and DocQA about 10% of answer strings in both training and testing sets require a correction of positions, which can be nearly always achieved automatically by ignoring punctuation (12 answers required a manual intervention). Models were trained on GPU nVidia Tesla V100 16Gb.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 5: Training parameters. LR stands for learning rate.'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. \n\nQuestion: Which model had the highest initial learning rate and how many epochs was it trained for?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows that DocQA had the highest initial learning rate of 1 and it was trained for 26 epochs. So, the answer is {\"answer\": \"DocQA, 26\"}." } ], "images": [ "scigen/train/2030.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Training parameters. LR stands for learning rate." ], "reasoning": "The table shows that DocQA had the highest initial learning rate of 1 and it was trained for 26 epochs.", "question": "Which model had the highest initial learning rate and how many epochs was it trained for?", "answer": "So, the answer is {\"answer\": \"DocQA, 26\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence R-Net was trained with a batch size of 32 and an initial learning rate of 0.5. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nR-Net was trained with a batch size of 32 and an initial learning rate of 0.5.\n" }, { "role": "assistant", "content": "The table indicates that R-Net was indeed trained with a batch size of 32 and an initial learning rate of 0.5. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2030.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Training parameters. LR stands for learning rate." ], "reasoning": "The table indicates that R-Net was indeed trained with a batch size of 32 and an initial learning rate of 0.5.", "statement": "R-Net was trained with a batch size of 32 and an initial learning rate of 0.5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 7: Model performance on SQuAD and SberQuAD; SQuAD part shows single-model scores on test set taken from respective papers.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Models perform worse on the Russian dataset SberQuAD than on SQuAD, with a bigger drop in exact match scores compared to F1 scores.It can be seen that all the models perform worse on the Russian dataset SberQuAD than on SQuAD. In that, there is a bigger difference in exact matching scores compared to F1. For example, for BERT the F1 score drops from 91.8 to 84.8 whereas the exact match score drops from 85.1 to 66.6. The relative performance of models is consistent for both datasets, although there is a greater variability among four neural “pre-BERT” models. One explanation for lower scores is that SberQuAD has always only one correct answer, whereas SQuAD can have multiple answer variants (1.7 on the development set). Furthermore, SberQuAD contains many fewer answers that are named entities than SQuAD (13.8% vs. 52.4%), which—as we discuss below—maybe another reason for lower scores. Another plausible reason is a poorer quality of annotations: We have found a number of deficiencies including but not limited to misspellings in questions and answers." } ], "images": [ "scigen/train/2031.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Model performance on SQuAD and SberQuAD; SQuAD part shows single-model scores on test set taken from respective papers." ], "reasoning": "Models perform worse on the Russian dataset SberQuAD than on SQuAD, with a bigger drop in exact match scores compared to F1 scores.", "description": "It can be seen that all the models perform worse on the Russian dataset SberQuAD than on SQuAD. In that, there is a bigger difference in exact matching scores compared to F1. For example, for BERT the F1 score drops from 91.8 to 84.8 whereas the exact match score drops from 85.1 to 66.6. The relative performance of models is consistent for both datasets, although there is a greater variability among four neural “pre-BERT” models. One explanation for lower scores is that SberQuAD has always only one correct answer, whereas SQuAD can have multiple answer variants (1.7 on the development set). Furthermore, SberQuAD contains many fewer answers that are named entities than SQuAD (13.8% vs. 52.4%), which—as we discuss below—maybe another reason for lower scores. Another plausible reason is a poorer quality of annotations: We have found a number of deficiencies including but not limited to misspellings in questions and answers.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 7: Model performance on SQuAD and SberQuAD; SQuAD part shows single-model scores on test set taken from respective papers.\nQuestion: Which model has the highest exact match score on the SberQuAD dataset?\n" }, { "role": "assistant", "content": "By comparing the exact match (EM) scores for all models on the SberQuAD dataset, BERT has the highest EM score of 66.6. So, the answer is {\"answer\": \"BERT\"}." } ], "images": [ "scigen/train/2031.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Model performance on SQuAD and SberQuAD; SQuAD part shows single-model scores on test set taken from respective papers." ], "reasoning": "By comparing the exact match (EM) scores for all models on the SberQuAD dataset, BERT has the highest EM score of 66.6.", "question": "Which model has the highest exact match score on the SberQuAD dataset?", "answer": "So, the answer is {\"answer\": \"BERT\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe exact match score for BiDAF on the SQuAD dataset is higher than on the SberQuAD dataset.\n" }, { "role": "assistant", "content": "The table shows that BiDAF has an exact match score of 68.0 on the SQuAD dataset and 51.7 on the SberQuAD dataset, confirming that the score is higher on SQuAD. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2031.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Model performance on SQuAD and SberQuAD; SQuAD part shows single-model scores on test set taken from respective papers." ], "reasoning": "The table shows that BiDAF has an exact match score of 68.0 on the SQuAD dataset and 51.7 on the SberQuAD dataset, confirming that the score is higher on SQuAD.", "statement": "The exact match score for BiDAF on the SQuAD dataset is higher than on the SberQuAD dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 10: Qualitative analysis of 100 difficult questions (questions can be assigned to more than one category).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Most errors in model performance are due to poor data quality, with incomplete answers and vague questions being the most common issues.Finally, we sampled 100 questions where all models achieved zero F1 score (i.e., they returned a span with no overlap with a ground truth answer). We manually grouped the sampled questions into the following categories: An entire paragraph or its significant part can be seen as an answer to a broad/general question. An answer is incomplete, because it contains only a part of an acceptable longer answer. For example for Q31929 ‘Who did notice an enemy airplane?’ only the word pilots is marked as ground truth in the context: On July 15, during a reconnaissance east to Zolotaya Lipa, pilots of the 2nd Siberian Corps Air Squadron Lieutenant Pokrovsky and Cornet Plonsky noticed an enemy airplane. Vague questions are related to the corresponding paragraph but seem to be a result of a misinterpretation of the context by a crowdsource worker. For example, in Q70465 ‘What are the disadvantages of TNT comparing to dynamite and other explosives?’ the ground truth answer ‘ a detonator needs to be used’ is not mentioned as a disadvantage in the paragraph. A couple of these questions use paronyms of concepts mentioned in the paragraph. For example, Q46229 asks about ‘discrete policy’, while the paragraph mentions ‘discretionary policy’. No answer in the paragraph and incorrect answer constitute more straightforward error cases. Some questions require reasoning and co-reference resolution. A small fraction of questions uses synonyms and paraphrases that are not directly borrowed from the paragraph. A relatively large fraction of ‘difficult’ questions contains misspellings and imply yes/no answers. One can see from the table that most potential causes of degraded performance can be attributed to poor data quality: Only 25% of cases can be explained by a need to deal with linguistic phenomena such as co-reference resolution, reasoning, and paraphrase detection." } ], "images": [ "scigen/train/2032.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Qualitative analysis of 100 difficult questions (questions can be assigned to more than one category)." ], "reasoning": "Most errors in model performance are due to poor data quality, with incomplete answers and vague questions being the most common issues.", "description": "Finally, we sampled 100 questions where all models achieved zero F1 score (i.e., they returned a span with no overlap with a ground truth answer). We manually grouped the sampled questions into the following categories: An entire paragraph or its significant part can be seen as an answer to a broad/general question. An answer is incomplete, because it contains only a part of an acceptable longer answer. For example for Q31929 ‘Who did notice an enemy airplane?’ only the word pilots is marked as ground truth in the context: On July 15, during a reconnaissance east to Zolotaya Lipa, pilots of the 2nd Siberian Corps Air Squadron Lieutenant Pokrovsky and Cornet Plonsky noticed an enemy airplane. Vague questions are related to the corresponding paragraph but seem to be a result of a misinterpretation of the context by a crowdsource worker. For example, in Q70465 ‘What are the disadvantages of TNT comparing to dynamite and other explosives?’ the ground truth answer ‘ a detonator needs to be used’ is not mentioned as a disadvantage in the paragraph. A couple of these questions use paronyms of concepts mentioned in the paragraph. For example, Q46229 asks about ‘discrete policy’, while the paragraph mentions ‘discretionary policy’. No answer in the paragraph and incorrect answer constitute more straightforward error cases. Some questions require reasoning and co-reference resolution. A small fraction of questions uses synonyms and paraphrases that are not directly borrowed from the paragraph. A relatively large fraction of ‘difficult’ questions contains misspellings and imply yes/no answers. One can see from the table that most potential causes of degraded performance can be attributed to poor data quality: Only 25% of cases can be explained by a need to deal with linguistic phenomena such as co-reference resolution, reasoning, and paraphrase detection.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 10: Qualitative analysis of 100 difficult questions (questions can be assigned to more than one category).', answer the question below by showing each progressive step toward the answer. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat percentage of questions were categorized as requiring reasoning or co-reference resolution?\n" }, { "role": "assistant", "content": "To find the percentage of questions requiring reasoning or co-reference resolution, add the percentages for 'Reasoning' (10%) and 'Co-reference resolution' (12%). So, the answer is {\"answer\": \"22\"}." } ], "images": [ "scigen/train/2032.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Qualitative analysis of 100 difficult questions (questions can be assigned to more than one category)." ], "reasoning": "To find the percentage of questions requiring reasoning or co-reference resolution, add the percentages for 'Reasoning' (10%) and 'Co-reference resolution' (12%).", "question": "What percentage of questions were categorized as requiring reasoning or co-reference resolution?", "answer": "So, the answer is {\"answer\": \"22\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The majority of the sampled questions were categorized as having incomplete answers., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe majority of the sampled questions were categorized as having incomplete answers.\n" }, { "role": "assistant", "content": "The table shows that 29% of the questions were categorized as having incomplete answers, which is the highest percentage among all categories. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2032.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Qualitative analysis of 100 difficult questions (questions can be assigned to more than one category)." ], "reasoning": "The table shows that 29% of the questions were categorized as having incomplete answers, which is the highest percentage among all categories.", "statement": "The majority of the sampled questions were categorized as having incomplete answers.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Results on the TriviaQA-Wikipedia test set: Joshi et al. (2017)1, Hu et al. (2018)2, Wang et al. (2018b)3, Chen et al. (2017)4, Clark and Gardner (2018)5, Back et al. (2018)6, Weissenborn et al. (2017)7, and Yan et al. (2019)8. † indicates unpublished works.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our best model, RE3QA LARGE, achieves 71.0 EM and 75.2 F1, outperforming previous methods, but the 80.3 EM on the verified set shows room for improvement.As we can see, our best model achieves 71.0 EM and 75.2 F1, firmly outperforming previous methods. Besides, Joshi et al. However, the score of 80.3 EM on the verified set implies that there is still room for improvement." } ], "images": [ "scigen/train/2033.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on the TriviaQA-Wikipedia test set: Joshi et al. (2017)1, Hu et al. (2018)2, Wang et al. (2018b)3, Chen et al. (2017)4, Clark and Gardner (2018)5, Back et al. (2018)6, Weissenborn et al. (2017)7, and Yan et al. (2019)8. † indicates unpublished works." ], "reasoning": "Our best model, RE3QA LARGE, achieves 71.0 EM and 75.2 F1, outperforming previous methods, but the 80.3 EM on the verified set shows room for improvement.", "description": "As we can see, our best model achieves 71.0 EM and 75.2 F1, firmly outperforming previous methods. Besides, Joshi et al. However, the score of 80.3 EM on the verified set implies that there is still room for improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: Results on the TriviaQA-Wikipedia test set: Joshi et al. (2017)1, Hu et al. (2018)2, Wang et al. (2018b)3, Chen et al. (2017)4, Clark and Gardner (2018)5, Back et al. (2018)6, Weissenborn et al. (2017)7, and Yan et al. (2019)8. † indicates unpublished works.' in mind, break down and resolve the question below step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model achieves the highest Full EM score?\n" }, { "role": "assistant", "content": "By comparing the Full EM scores of all models, RE3QA LARGE achieves the highest Full EM score of 71.0. So, the answer is {\"answer\": \"RE3QA LARGE\"}" } ], "images": [ "scigen/train/2033.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on the TriviaQA-Wikipedia test set: Joshi et al. (2017)1, Hu et al. (2018)2, Wang et al. (2018b)3, Chen et al. (2017)4, Clark and Gardner (2018)5, Back et al. (2018)6, Weissenborn et al. (2017)7, and Yan et al. (2019)8. † indicates unpublished works." ], "reasoning": "By comparing the Full EM scores of all models, RE3QA LARGE achieves the highest Full EM score of 71.0.", "question": "Which model achieves the highest Full EM score?", "answer": "So, the answer is {\"answer\": \"RE3QA LARGE\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe MemoReader6 model achieves a higher Full EM score than the S-Norm5 model.\n" }, { "role": "assistant", "content": "The table shows that MemoReader6 has a Full EM score of 64.4, while S-Norm5 has a Full EM score of 64.0, indicating that MemoReader6 has a higher score. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2033.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on the TriviaQA-Wikipedia test set: Joshi et al. (2017)1, Hu et al. (2018)2, Wang et al. (2018b)3, Chen et al. (2017)4, Clark and Gardner (2018)5, Back et al. (2018)6, Weissenborn et al. (2017)7, and Yan et al. (2019)8. † indicates unpublished works." ], "reasoning": "The table shows that MemoReader6 has a Full EM score of 64.4, while S-Norm5 has a Full EM score of 64.0, indicating that MemoReader6 has a higher score.", "statement": "The MemoReader6 model achieves a higher Full EM score than the S-Norm5 model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Results on the SQuAD-document dev set.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The RE3QA\\textscLARGE model achieves an F1 score of 87.2, which is only 3.7% lower than the BERT\\textscLARGE model's 90.9 F1 score on the original SQuAD dataset.We find our approach adapts well: the best model achieves 87.2 F1. Note that the BERT\\textscLARGE model has obtained 90.9 F1 on the original SQuAD dataset (single-paragraph setting), which is only 3.7% ahead of us." } ], "images": [ "scigen/train/2034.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on the SQuAD-document dev set." ], "reasoning": "The RE3QA\\textscLARGE model achieves an F1 score of 87.2, which is only 3.7% lower than the BERT\\textscLARGE model's 90.9 F1 score on the original SQuAD dataset.", "description": "We find our approach adapts well: the best model achieves 87.2 F1. Note that the BERT\\textscLARGE model has obtained 90.9 F1 on the original SQuAD dataset (single-paragraph setting), which is only 3.7% ahead of us.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 4: Results on the SQuAD-document dev set.', unfold the question presented below, detailing every step of your calculation. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the difference in EM scores between the RE3QA\\textscBASE and RE3QA\\textscLARGE models?\n" }, { "role": "assistant", "content": "The EM score for RE3QA\\textscBASE is 77.90 and for RE3QA\\textscLARGE is 80.71. The difference is calculated as 80.71 - 77.90. So, the answer is {\"answer\": \"2.81\"}" } ], "images": [ "scigen/train/2034.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on the SQuAD-document dev set." ], "reasoning": "The EM score for RE3QA\\textscBASE is 77.90 and for RE3QA\\textscLARGE is 80.71. The difference is calculated as 80.71 - 77.90.", "question": "What is the difference in EM scores between the RE3QA\\textscBASE and RE3QA\\textscLARGE models?", "answer": "So, the answer is {\"answer\": \"2.81\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The RE3QA\\textscLARGE model has a higher F1 score than the S-Norm Clark and Gardner (2018) model., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe RE3QA\\textscLARGE model has a higher F1 score than the S-Norm Clark and Gardner (2018) model.\n" }, { "role": "assistant", "content": "The F1 score for RE3QA\\textscLARGE is 87.20, while the F1 score for the S-Norm Clark and Gardner (2018) model is 72.37. Since 87.20 is greater than 72.37, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2034.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on the SQuAD-document dev set." ], "reasoning": "The F1 score for RE3QA\\textscLARGE is 87.20, while the F1 score for the S-Norm Clark and Gardner (2018) model is 72.37. Since 87.20 is greater than 72.37, the statement is supported.", "statement": "The RE3QA\\textscLARGE model has a higher F1 score than the S-Norm Clark and Gardner (2018) model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: Results on TriviaQA-unfiltered test set and SQuAD-open dev set: Chen et al. (2017)1, Wang et al. (2018a)2, Lin et al. (2018)3, Wang et al. (2018b)4, Min et al. (2018)5, Das et al. (2019)6, Clark and Gardner (2018)7, Pang et al. (2019)8 and Yang et al. (2019)9.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "RE3QA achieves the highest F1 scores on both TriviaQA-unfiltered and SQuAD-open, outperforming other models significantly.Again, RE3QA surpasses prior works by an evident margin: our best model achieves 71.2 F1 on TriviaQA-unfiltered, and outperforms a BERT baseline by 4 F1 on SQuAD-open, indicating that our approach is effective for the challenging multi-document RC task." } ], "images": [ "scigen/train/2035.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on TriviaQA-unfiltered test set and SQuAD-open dev set: Chen et al. (2017)1, Wang et al. (2018a)2, Lin et al. (2018)3, Wang et al. (2018b)4, Min et al. (2018)5, Das et al. (2019)6, Clark and Gardner (2018)7, Pang et al. (2019)8 and Yang et al. (2019)9." ], "reasoning": "RE3QA achieves the highest F1 scores on both TriviaQA-unfiltered and SQuAD-open, outperforming other models significantly.", "description": "Again, RE3QA surpasses prior works by an evident margin: our best model achieves 71.2 F1 on TriviaQA-unfiltered, and outperforms a BERT baseline by 4 F1 on SQuAD-open, indicating that our approach is effective for the challenging multi-document RC task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Results on TriviaQA-unfiltered test set and SQuAD-open dev set: Chen et al. (2017)1, Wang et al. (2018a)2, Lin et al. (2018)3, Wang et al. (2018b)4, Min et al. (2018)5, Das et al. (2019)6, Clark and Gardner (2018)7, Pang et al. (2019)8 and Yang et al. (2019)9.\nQuestion: Which model achieves the highest F1 score on TriviaQA-unfiltered?\nSolve the above question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the F1 scores on TriviaQA-unfiltered, RE3QA \textsc{LARGE} has the highest score of 71.2. So, the answer is {\"answer\": \"RE3QA \textsc{LARGE}\"}" } ], "images": [ "scigen/train/2035.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on TriviaQA-unfiltered test set and SQuAD-open dev set: Chen et al. (2017)1, Wang et al. (2018a)2, Lin et al. (2018)3, Wang et al. (2018b)4, Min et al. (2018)5, Das et al. (2019)6, Clark and Gardner (2018)7, Pang et al. (2019)8 and Yang et al. (2019)9." ], "reasoning": "By comparing the F1 scores on TriviaQA-unfiltered, RE3QA \textsc{LARGE} has the highest score of 71.2.", "question": "Which model achieves the highest F1 score on TriviaQA-unfiltered?", "answer": "So, the answer is {\"answer\": \"RE3QA \textsc{LARGE}\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis RE3QA \textsc{BASE} outperforms BERTserini9 on SQuAD-open F1. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nRE3QA \textsc{BASE} outperforms BERTserini9 on SQuAD-open F1.\n" }, { "role": "assistant", "content": "RE3QA \textsc{BASE} has an F1 score of 48.4 on SQuAD-open, while BERTserini9 has an F1 score of 46.1, indicating that RE3QA \textsc{BASE} outperforms BERTserini9. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2035.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on TriviaQA-unfiltered test set and SQuAD-open dev set: Chen et al. (2017)1, Wang et al. (2018a)2, Lin et al. (2018)3, Wang et al. (2018b)4, Min et al. (2018)5, Das et al. (2019)6, Clark and Gardner (2018)7, Pang et al. (2019)8 and Yang et al. (2019)9." ], "reasoning": "RE3QA \textsc{BASE} has an F1 score of 48.4 on SQuAD-open, while BERTserini9 has an F1 score of 46.1, indicating that RE3QA \textsc{BASE} outperforms BERTserini9.", "statement": "RE3QA \textsc{BASE} outperforms BERTserini9 on SQuAD-open F1.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 6: Comparison between our approach and the pipelined method. “Speed” denotes the number of instances processed per second during inference.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Our approach, RE3QA, outperforms BERT\\textscpipe by 1.6/1.2 F1 on two datasets respectively, and is also 2.3/2.1 times faster during inference.Our approach outperforms BERT\\textscpipe by 1.6/1.2 F1 on two datasets respectively, and is also 2.3/2.1 times faster during inference. Moreover, RE3QA also beats the BERT\\textscpipe* baseline by 1.1/0.8 F1, even as the parameters of retriever and reader are trained sequentially in BERT\\textscpipe *. The above results confirm that the end-to-end training can indeed mitigate the context inconsistency problem, perhaps due to multi-task learning and parameter sharing. Our approach can also obtain inference speedups because of the fact that it avoids re-encoding inputs by sharing contextualized representations." } ], "images": [ "scigen/train/2036.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Comparison between our approach and the pipelined method. “Speed” denotes the number of instances processed per second during inference." ], "reasoning": "Our approach, RE3QA, outperforms BERT\\textscpipe by 1.6/1.2 F1 on two datasets respectively, and is also 2.3/2.1 times faster during inference.", "description": "Our approach outperforms BERT\\textscpipe by 1.6/1.2 F1 on two datasets respectively, and is also 2.3/2.1 times faster during inference. Moreover, RE3QA also beats the BERT\\textscpipe* baseline by 1.1/0.8 F1, even as the parameters of retriever and reader are trained sequentially in BERT\\textscpipe *. The above results confirm that the end-to-end training can indeed mitigate the context inconsistency problem, perhaps due to multi-task learning and parameter sharing. Our approach can also obtain inference speedups because of the fact that it avoids re-encoding inputs by sharing contextualized representations.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 6: Comparison between our approach and the pipelined method. “Speed” denotes the number of instances processed per second during inference.', solve this question and give detail solutions. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nHow much faster is RE3QA compared to BERT\\textscpipe on the SQuAD-document dataset?\n" }, { "role": "assistant", "content": "RE3QA has a speed of 3.76 on the SQuAD-document dataset, while BERT\\textscpipe has a speed of 1.78. The difference in speed is 3.76 - 1.78. So, the answer is {\"answer\": \"1.98\"}" } ], "images": [ "scigen/train/2036.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Comparison between our approach and the pipelined method. “Speed” denotes the number of instances processed per second during inference." ], "reasoning": "RE3QA has a speed of 3.76 on the SQuAD-document dataset, while BERT\\textscpipe has a speed of 1.78. The difference in speed is 3.76 - 1.78.", "question": "How much faster is RE3QA compared to BERT\\textscpipe on the SQuAD-document dataset?", "answer": "So, the answer is {\"answer\": \"1.98\"}" }, { "messages": [ { "role": "user", "content": "RE3QA is faster than BERT\\textscpipe* on the TriviaQA-Wikipedia dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nRE3QA is faster than BERT\\textscpipe* on the TriviaQA-Wikipedia dataset.\n" }, { "role": "assistant", "content": "The table shows that RE3QA has a speed of 4.62 on the TriviaQA-Wikipedia dataset, while BERT\\textscpipe* has a speed of 2.08, confirming that RE3QA is faster. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2036.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Comparison between our approach and the pipelined method. “Speed” denotes the number of instances processed per second during inference." ], "reasoning": "The table shows that RE3QA has a speed of 4.62 on the TriviaQA-Wikipedia dataset, while BERT\\textscpipe* has a speed of 2.08, confirming that RE3QA is faster.", "statement": "RE3QA is faster than BERT\\textscpipe* on the TriviaQA-Wikipedia dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 7: Retrieving performance with different number of blocks J used for the early-stopped retriever.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "As J increases, the retrieving metrics such as MAP, Top-3, and Top-5 improve, but speed decreases. The best F1 score is achieved at J=3.S5SS2SSS0Px3 Effect of early-stopped retriever We assess whether the early-stopped retriever is sufficient for the segment retrieving task. As we can see, the model performs worst but maintains a high speed when J is 1. As J becomes larger, the retrieving metrices such as MAP, Top-3 and Top-5 significantly increase on both datasets. On the other hand, the speed continues to decline since more computations have been done during retrieving. A J of 6 eventually leads to an out-of-memory issue on both datasets. As for the F1 score, the model achieves the best result when J reaches 3, and starts to degrade as J continues rising. We experiment with the RE3QA\\textscLARGE model and observe similar results, where the best J is 6. A likely reason for this observation may be that sharing high-level features with the retriever could disturb the reading prediction. Therefore, the above results demonstrate that an early-stopped retriever with a relatively small J is able to reach a good trade-off between efficiency and effectiveness." } ], "images": [ "scigen/train/2037.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Retrieving performance with different number of blocks J used for the early-stopped retriever." ], "reasoning": "As J increases, the retrieving metrics such as MAP, Top-3, and Top-5 improve, but speed decreases. The best F1 score is achieved at J=3.", "description": "S5SS2SSS0Px3 Effect of early-stopped retriever We assess whether the early-stopped retriever is sufficient for the segment retrieving task. As we can see, the model performs worst but maintains a high speed when J is 1. As J becomes larger, the retrieving metrices such as MAP, Top-3 and Top-5 significantly increase on both datasets. On the other hand, the speed continues to decline since more computations have been done during retrieving. A J of 6 eventually leads to an out-of-memory issue on both datasets. As for the F1 score, the model achieves the best result when J reaches 3, and starts to degrade as J continues rising. We experiment with the RE3QA\\textscLARGE model and observe similar results, where the best J is 6. A likely reason for this observation may be that sharing high-level features with the retriever could disturb the reading prediction. Therefore, the above results demonstrate that an early-stopped retriever with a relatively small J is able to reach a good trade-off between efficiency and effectiveness.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 7: Retrieving performance with different number of blocks J used for the early-stopped retriever.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. \n\nQuestion: What is the F1 score for TriviaQA-Wikipedia when J is 3, and how does it compare to the F1 score for SQuAD-document at the same J?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The F1 score for TriviaQA-Wikipedia when J is 3 is 72.7. The F1 score for SQuAD-document at J=3 is 84.8. Comparing these, the F1 score for SQuAD-document is higher. So, the answer is {\"answer\": \"84.8\"}." } ], "images": [ "scigen/train/2037.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Retrieving performance with different number of blocks J used for the early-stopped retriever." ], "reasoning": "The F1 score for TriviaQA-Wikipedia when J is 3 is 72.7. The F1 score for SQuAD-document at J=3 is 84.8. Comparing these, the F1 score for SQuAD-document is higher.", "question": "What is the F1 score for TriviaQA-Wikipedia when J is 3, and how does it compare to the F1 score for SQuAD-document at the same J?", "answer": "So, the answer is {\"answer\": \"84.8\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model achieves the highest MAP score for TriviaQA-Wikipedia when J is 5.\n" }, { "role": "assistant", "content": "The table shows that the highest MAP score for TriviaQA-Wikipedia is 80.6, which occurs when J is 5. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2037.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Retrieving performance with different number of blocks J used for the early-stopped retriever." ], "reasoning": "The table shows that the highest MAP score for TriviaQA-Wikipedia is 80.6, which occurs when J is 5.", "statement": "The model achieves the highest MAP score for TriviaQA-Wikipedia when J is 5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 8: Reranking performance with different ablations. yhard and ysoft refer to the two labels used to train the reranker.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Removing NMS negatively impacts performance, and hard labels have a larger impact on TriviaQA while soft labels have a larger impact on SQuAD.As we can see, removing the non-maximum suppression (NMS) algorithm has a negative impact on the performance, suggesting it is necessary to prune highly-overlapped candidate answers before reranking. Ablating the hard label leads to a drop of 0.81 and 0.64 F1 scores on two datasets respectively, while the F1 drops by 0.39 and 0.76 points after removing the soft label. This implies that the hard label has a larger impact than the soft label on the TriviaQA dataset, but vice versa on SQuAD." } ], "images": [ "scigen/train/2038.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Reranking performance with different ablations. yhard and ysoft refer to the two labels used to train the reranker." ], "reasoning": "Removing NMS negatively impacts performance, and hard labels have a larger impact on TriviaQA while soft labels have a larger impact on SQuAD.", "description": "As we can see, removing the non-maximum suppression (NMS) algorithm has a negative impact on the performance, suggesting it is necessary to prune highly-overlapped candidate answers before reranking. Ablating the hard label leads to a drop of 0.81 and 0.64 F1 scores on two datasets respectively, while the F1 drops by 0.39 and 0.76 points after removing the soft label. This implies that the hard label has a larger impact than the soft label on the TriviaQA dataset, but vice versa on SQuAD.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 8: Reranking performance with different ablations. yhard and ysoft refer to the two labels used to train the reranker.', answer the question below by showing each progressive step toward the answer. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the impact on the F1 score of the TriviaQA-Wikipedia dataset when the hard label is ablated?\n" }, { "role": "assistant", "content": "The F1 score drops from 72.68 to 71.87 when the hard label is ablated, indicating a drop of 0.81 points. So, the answer is {\"answer\": \"0.81\"}." } ], "images": [ "scigen/train/2038.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Reranking performance with different ablations. yhard and ysoft refer to the two labels used to train the reranker." ], "reasoning": "The F1 score drops from 72.68 to 71.87 when the hard label is ablated, indicating a drop of 0.81 points.", "question": "What is the impact on the F1 score of the TriviaQA-Wikipedia dataset when the hard label is ablated?", "answer": "So, the answer is {\"answer\": \"0.81\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Ablating the soft label has a larger impact on the SQuAD-document dataset than on the TriviaQA-Wikipedia dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nAblating the soft label has a larger impact on the SQuAD-document dataset than on the TriviaQA-Wikipedia dataset.\n" }, { "role": "assistant", "content": "The F1 score drops by 0.76 points on the SQuAD-document dataset and by 0.39 points on the TriviaQA-Wikipedia dataset when the soft label is ablated, indicating a larger impact on the SQuAD-document dataset. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2038.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Reranking performance with different ablations. yhard and ysoft refer to the two labels used to train the reranker." ], "reasoning": "The F1 score drops by 0.76 points on the SQuAD-document dataset and by 0.39 points on the TriviaQA-Wikipedia dataset when the soft label is ablated, indicating a larger impact on the SQuAD-document dataset.", "statement": "Ablating the soft label has a larger impact on the SQuAD-document dataset than on the TriviaQA-Wikipedia dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 9: A sampled case (ID: sfq_21220) from the TriviaQA-Wikipedia dev set shows that although candidate [2] and candidate [3] get higher reranking and reading scores, the candidate [1] is preferred by the retrieving component and is therefore chosen as the final answer. The ground truth answer is “Women’s Institute”.'.\n" }, { "role": "assistant", "content": "The model uses the weighted sum of retrieving, reading, and reranking scores to determine the correct answer, even when there are confusing candidates.Under such circumstance, it is crucial to perform additional answer verifications to identify the final answer. In this example, we can see that the retriever becomes the key factor when the reader and reranker are distracted by confusing candidates (e.g., the second and third predictions). By taking the weighted sum of the three scores, our model eventually predicts the correct answer since the first prediction has the largest retrieving score." } ], "images": [ "scigen/train/2039.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: A sampled case (ID: sfq_21220) from the TriviaQA-Wikipedia dev set shows that although candidate [2] and candidate [3] get higher reranking and reading scores, the candidate [1] is preferred by the retrieving component and is therefore chosen as the final answer. The ground truth answer is “Women’s Institute”." ], "reasoning": "The model uses the weighted sum of retrieving, reading, and reranking scores to determine the correct answer, even when there are confusing candidates.", "description": "Under such circumstance, it is crucial to perform additional answer verifications to identify the final answer. In this example, we can see that the retriever becomes the key factor when the reader and reranker are distracted by confusing candidates (e.g., the second and third predictions). By taking the weighted sum of the three scores, our model eventually predicts the correct answer since the first prediction has the largest retrieving score.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 9: A sampled case (ID: sfq_21220) from the TriviaQA-Wikipedia dev set shows that although candidate [2] and candidate [3] get higher reranking and reading scores, the candidate [1] is preferred by the retrieving component and is therefore chosen as the final answer. The ground truth answer is “Women’s Institute”.', answer the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich candidate answer has the highest combined score?\n" }, { "role": "assistant", "content": "To find the candidate with the highest combined score, sum the scores for retrieving, reading, and reranking for each candidate. The candidate with the highest total is the answer. So, the answer is {\"answer\": \"Young Women's Christian Association\"}." } ], "images": [ "scigen/train/2039.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: A sampled case (ID: sfq_21220) from the TriviaQA-Wikipedia dev set shows that although candidate [2] and candidate [3] get higher reranking and reading scores, the candidate [1] is preferred by the retrieving component and is therefore chosen as the final answer. The ground truth answer is “Women’s Institute”." ], "reasoning": "To find the candidate with the highest combined score, sum the scores for retrieving, reading, and reranking for each candidate. The candidate with the highest total is the answer.", "question": "Which candidate answer has the highest combined score?", "answer": "So, the answer is {\"answer\": \"Young Women's Christian Association\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Women's Institute has the highest retrieving score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Women's Institute has the highest retrieving score.\n" }, { "role": "assistant", "content": "The table shows that the Women's Institute has a retrieving score of 0.517, which is higher than the retrieving scores of the other candidates. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2039.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: A sampled case (ID: sfq_21220) from the TriviaQA-Wikipedia dev set shows that although candidate [2] and candidate [3] get higher reranking and reading scores, the candidate [1] is preferred by the retrieving component and is therefore chosen as the final answer. The ground truth answer is “Women’s Institute”." ], "reasoning": "The table shows that the Women's Institute has a retrieving score of 0.517, which is higher than the retrieving scores of the other candidates.", "statement": "The Women's Institute has the highest retrieving score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 10: A sampled case (ID: sfq_10640) from the TriviaQA-Wikipedia dev set shows that although the candidate [2] gets higher retrieving and reading scores, the candidate [1] is chosen as the final answer since it has the highest reranking score. The ground truth answer is “Macau”.'.\n" }, { "role": "assistant", "content": "Despite Kowloon having the highest retrieving and reading scores, Macau is chosen as the final answer due to a higher reranking score.On the one hand, despite the confusing candidate “Kowloon” has the highest retrieving and reading scores, the reranker assigns a larger confidence on the candidate “Macau”. As a result, “Macau” is chosen as the final answer. On the other hand, we find that the reranking scores of some candidates (e.g., the third and fourth predictions) are zero. This is due to the span-level non-maximum suppression algorithm, where redundant spans such as “Macau, China” will be pruned before the reranking step. Therefore, the final weighted-sum scores of these candidates will be significantly lower than the top predictions, which is beneficial for filtering distractors out." } ], "images": [ "scigen/train/2040.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: A sampled case (ID: sfq_10640) from the TriviaQA-Wikipedia dev set shows that although the candidate [2] gets higher retrieving and reading scores, the candidate [1] is chosen as the final answer since it has the highest reranking score. The ground truth answer is “Macau”." ], "reasoning": "Despite Kowloon having the highest retrieving and reading scores, Macau is chosen as the final answer due to a higher reranking score.", "description": "On the one hand, despite the confusing candidate “Kowloon” has the highest retrieving and reading scores, the reranker assigns a larger confidence on the candidate “Macau”. As a result, “Macau” is chosen as the final answer. On the other hand, we find that the reranking scores of some candidates (e.g., the third and fourth predictions) are zero. This is due to the span-level non-maximum suppression algorithm, where redundant spans such as “Macau, China” will be pruned before the reranking step. Therefore, the final weighted-sum scores of these candidates will be significantly lower than the top predictions, which is beneficial for filtering distractors out.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nTable title: Table 10: A sampled case (ID: sfq_10640) from the TriviaQA-Wikipedia dev set shows that although the candidate [2] gets higher retrieving and reading scores, the candidate [1] is chosen as the final answer since it has the highest reranking score. The ground truth answer is “Macau”.\n\nQuestion: Why is Macau chosen as the final answer despite Kowloon having higher retrieving and reading scores?\n" }, { "role": "assistant", "content": "Macau is chosen as the final answer because the reranker assigns a larger confidence to Macau, giving it a higher reranking score compared to Kowloon. So, the answer is {\"answer\": \"Macau\"}" } ], "images": [ "scigen/train/2040.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: A sampled case (ID: sfq_10640) from the TriviaQA-Wikipedia dev set shows that although the candidate [2] gets higher retrieving and reading scores, the candidate [1] is chosen as the final answer since it has the highest reranking score. The ground truth answer is “Macau”." ], "reasoning": "Macau is chosen as the final answer because the reranker assigns a larger confidence to Macau, giving it a higher reranking score compared to Kowloon.", "question": "Why is Macau chosen as the final answer despite Kowloon having higher retrieving and reading scores?", "answer": "So, the answer is {\"answer\": \"Macau\"}" }, { "messages": [ { "role": "user", "content": "Kowloon has the highest reranking score. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nKowloon has the highest reranking score.\n" }, { "role": "assistant", "content": "The table shows that Kowloon has a reranking score of 1.795, which is lower than Macau's reranking score of 2.502. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2040.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: A sampled case (ID: sfq_10640) from the TriviaQA-Wikipedia dev set shows that although the candidate [2] gets higher retrieving and reading scores, the candidate [1] is chosen as the final answer since it has the highest reranking score. The ground truth answer is “Macau”." ], "reasoning": "The table shows that Kowloon has a reranking score of 1.795, which is lower than Macau's reranking score of 2.502.", "statement": "Kowloon has the highest reranking score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Density of polarity words in novels and fairy tales: number of polar words in every 10,000 words.'.\n" }, { "role": "assistant", "content": "Fairy tales have fewer negative words and more positive words compared to the CEN, with statistical significance (p<0.001).The table states, for example, that for every 10,000 words in the CEN, one can expect to see about 1670 negative words. We find that fairy tales, on average, have a significantly lower number of negative terms, and a significantly higher number of positive words (p<0.001)." } ], "images": [ "scigen/train/2041.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Density of polarity words in novels and fairy tales: number of polar words in every 10,000 words." ], "reasoning": "Fairy tales have fewer negative words and more positive words compared to the CEN, with statistical significance (p<0.001).", "description": "The table states, for example, that for every 10,000 words in the CEN, one can expect to see about 1670 negative words. We find that fairy tales, on average, have a significantly lower number of negative terms, and a significantly higher number of positive words (p<0.001).", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 2: Density of polarity words in novels and fairy tales: number of polar words in every 10,000 words.' and proceed to solve the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the difference in the mean number of positive words between CEN and FTC?\n" }, { "role": "assistant", "content": "The mean number of positive words in CEN is 2602, and in FTC it is 2808. The difference is 2808 - 2602. So, the answer is {\"answer\": \"206\"}." } ], "images": [ "scigen/train/2041.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Density of polarity words in novels and fairy tales: number of polar words in every 10,000 words." ], "reasoning": "The mean number of positive words in CEN is 2602, and in FTC it is 2808. The difference is 2808 - 2602.", "question": "What is the difference in the mean number of positive words between CEN and FTC?", "answer": "So, the answer is {\"answer\": \"206\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence FTC has a higher standard deviation for negative words compared to CEN. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nFTC has a higher standard deviation for negative words compared to CEN.\n" }, { "role": "assistant", "content": "The table shows that the standard deviation for negative words in FTC is 613, while in CEN it is 243, indicating a higher standard deviation in FTC. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2041.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Density of polarity words in novels and fairy tales: number of polar words in every 10,000 words." ], "reasoning": "The table shows that the standard deviation for negative words in FTC is 613, while in CEN it is 243, indicating a higher standard deviation in FTC.", "statement": "FTC has a higher standard deviation for negative words compared to CEN.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'TABLE VII: Performance of the language model {LM}(D{HR}) tested on cover letters', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The model's performance was poor as indicated by the R2 values, which were not above zero for any of the Big Five factors.We applied {LM}(D{HR}) to the cover letters to produce Big Five trait analyses, and we compared the results to the scores from the personality questionnaire. As it can be seen in the table, model performance is poor and R2 was not above zero for any of the factors." } ], "images": [ "scigen/train/2042.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VII: Performance of the language model {LM}(D{HR}) tested on cover letters" ], "reasoning": "The model's performance was poor as indicated by the R2 values, which were not above zero for any of the Big Five factors.", "description": "We applied {LM}(D{HR}) to the cover letters to produce Big Five trait analyses, and we compared the results to the scores from the personality questionnaire. As it can be seen in the table, model performance is poor and R2 was not above zero for any of the factors.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'TABLE VII: Performance of the language model {LM}(D{HR}) tested on cover letters' in mind, break down and resolve the question below step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the R2 value for the factor with the highest Mean Absolute Error (MAE)?\n" }, { "role": "assistant", "content": "The factor with the highest MAE is Agreeableness with an MAE of 1.02. The R2 value for Agreeableness is -4.56. So, the answer is {\"answer\": \"-4.56\"}." } ], "images": [ "scigen/train/2042.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VII: Performance of the language model {LM}(D{HR}) tested on cover letters" ], "reasoning": "The factor with the highest MAE is Agreeableness with an MAE of 1.02. The R2 value for Agreeableness is -4.56.", "question": "What is the R2 value for the factor with the highest Mean Absolute Error (MAE)?", "answer": "So, the answer is {\"answer\": \"-4.56\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe model performed best on the Openness factor.\n" }, { "role": "assistant", "content": "The model's performance can be evaluated by looking at the R2 values. The R2 value for Openness is -0.69, which is not the highest among the factors. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2042.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VII: Performance of the language model {LM}(D{HR}) tested on cover letters" ], "reasoning": "The model's performance can be evaluated by looking at the R2 values. The R2 value for Openness is -0.69, which is not the highest among the factors. Therefore, the statement is refuted.", "statement": "The model performed best on the Openness factor.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'TABLE VI: Cross validated accuracy for all the binary models on D{HR} and D{LR}'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The interval-based representation of [−3,3] is preferred for its balance between granularity and simplicity, and binary classifiers were trained to compare performance with state-of-the-art methods, distinguishing between low and high factor values.We consider our [−3,3] interval-based representation to be preferable, as it is sufficiently granular to express realistic nuances while remaining simple enough not to overtax annotators with too many choices. Nevertheless, to gain some understanding of how our approach would compare to the state of the art, we modified our methods to train binary classifiers on the large and small datasets. For the purposes of this training a factor value below zero was regarded as low and values above as high, and the classifiers learnt to distinguish only these two classes. Note that a direct comparison with earlier systems is problematic due to the differences in datasets. This test merely serves to ensure that our approach is not out of line with the general performance in the field." } ], "images": [ "scigen/train/2043.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VI: Cross validated accuracy for all the binary models on D{HR} and D{LR}" ], "reasoning": "The interval-based representation of [−3,3] is preferred for its balance between granularity and simplicity, and binary classifiers were trained to compare performance with state-of-the-art methods, distinguishing between low and high factor values.", "description": "We consider our [−3,3] interval-based representation to be preferable, as it is sufficiently granular to express realistic nuances while remaining simple enough not to overtax annotators with too many choices. Nevertheless, to gain some understanding of how our approach would compare to the state of the art, we modified our methods to train binary classifiers on the large and small datasets. For the purposes of this training a factor value below zero was regarded as low and values above as high, and the classifiers learnt to distinguish only these two classes. Note that a direct comparison with earlier systems is problematic due to the differences in datasets. This test merely serves to ensure that our approach is not out of line with the general performance in the field.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'TABLE VI: Cross validated accuracy for all the binary models on D{HR} and D{LR}', unfold the steps to solve the question given next. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nQuestion: What is the highest accuracy achieved for the Agreeableness factor across all classifiers?\n" }, { "role": "assistant", "content": "To find the highest accuracy for the Agreeableness factor, compare the values in the row labeled 'Agreeableness' across all columns. The highest value is 0.90 under D{HR} ACC{LM}. So, the answer is {\"answer\": \"0.90\"}." } ], "images": [ "scigen/train/2043.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VI: Cross validated accuracy for all the binary models on D{HR} and D{LR}" ], "reasoning": "To find the highest accuracy for the Agreeableness factor, compare the values in the row labeled 'Agreeableness' across all columns. The highest value is 0.90 under D{HR} ACC{LM}.", "question": "What is the highest accuracy achieved for the Agreeableness factor across all classifiers?", "answer": "So, the answer is {\"answer\": \"0.90\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The lowest accuracy for the Stability factor is 0.48. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe lowest accuracy for the Stability factor is 0.48.\n" }, { "role": "assistant", "content": "To verify this statement, look at the row labeled 'Stability' and find the lowest value across all columns. The lowest value is indeed 0.48 under D{HR} ACC{Dum}. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2043.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VI: Cross validated accuracy for all the binary models on D{HR} and D{LR}" ], "reasoning": "To verify this statement, look at the row labeled 'Stability' and find the lowest value across all columns. The lowest value is indeed 0.48 under D{HR} ACC{Dum}.", "statement": "The lowest accuracy for the Stability factor is 0.48.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 6: Summarization performance with different training data.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Adding TGSum as an extra training set improves ROUGE scores, and training solely on TGSum achieves comparable performance, especially on DUC 02.To verify the effect of TGSum, we examine whether it can be used to improve the performance of summarization systems on DUC benchmarks. To be concrete, each sentence in the training set is scored by ROUGE-2. Then we extract the features such as TF (the averaged TF scores of the sentence), LENGTH (sentence length), and STOP-RATIO (the ratio of stopwords), and train SVR to measure the saliency of sentences. It is a standard learning-based summarization model, which is enough to emphasize the effect of training data. We train this summarizer on different datasets and test it on DUC. Here “DUC” stands for DUC datasets except the testing year, and “TWT_REF” means the same documents as TGSum but directly use tweets as reference summaries. Seeing from this table, ROUGE scores always enjoy a considerable increase when adding TGSum as an extra training set. In addition, even only given the TGSum dataset for training, the summarizer can still achieve comparable performance, especially on DUC 02. Thus, the effect of TGSum references is similar to the manual annotations of DUC, although the former is generated according to tweets automatically. In comparison to TGSum, directly using tweets as references does not always improve the summarization performance. The noise in tweets may misguide the model learning. For example, when treating tweets as references, the feature STOP-RATIO holds extremely high positive weight. This improper feature weight can be ascribed to the informal writing style on Twitter, and it obviously fails to match the informativeness requirement." } ], "images": [ "scigen/train/2044.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Summarization performance with different training data." ], "reasoning": "Adding TGSum as an extra training set improves ROUGE scores, and training solely on TGSum achieves comparable performance, especially on DUC 02.", "description": "To verify the effect of TGSum, we examine whether it can be used to improve the performance of summarization systems on DUC benchmarks. To be concrete, each sentence in the training set is scored by ROUGE-2. Then we extract the features such as TF (the averaged TF scores of the sentence), LENGTH (sentence length), and STOP-RATIO (the ratio of stopwords), and train SVR to measure the saliency of sentences. It is a standard learning-based summarization model, which is enough to emphasize the effect of training data. We train this summarizer on different datasets and test it on DUC. Here “DUC” stands for DUC datasets except the testing year, and “TWT_REF” means the same documents as TGSum but directly use tweets as reference summaries. Seeing from this table, ROUGE scores always enjoy a considerable increase when adding TGSum as an extra training set. In addition, even only given the TGSum dataset for training, the summarizer can still achieve comparable performance, especially on DUC 02. Thus, the effect of TGSum references is similar to the manual annotations of DUC, although the former is generated according to tweets automatically. In comparison to TGSum, directly using tweets as references does not always improve the summarization performance. The noise in tweets may misguide the model learning. For example, when treating tweets as references, the feature STOP-RATIO holds extremely high positive weight. This improper feature weight can be ascribed to the informal writing style on Twitter, and it obviously fails to match the informativeness requirement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 6: Summarization performance with different training data.' to sequentially solve the question presented below. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhat is the highest ROUGE-2 score achieved when TGSum is used as part of the training set?\n" }, { "role": "assistant", "content": "To find the highest ROUGE-2 score with TGSum, we look at the rows where TGSum is part of the training set and identify the maximum ROUGE-2 score. So, the answer is {\"answer\": \"9.51\"}" } ], "images": [ "scigen/train/2044.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Summarization performance with different training data." ], "reasoning": "To find the highest ROUGE-2 score with TGSum, we look at the rows where TGSum is part of the training set and identify the maximum ROUGE-2 score.", "question": "What is the highest ROUGE-2 score achieved when TGSum is used as part of the training set?", "answer": "So, the answer is {\"answer\": \"9.51\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nTraining on DUC+TGSum achieves the highest ROUGE-1 score on DUC 04.\n" }, { "role": "assistant", "content": "We check the ROUGE-1 scores for DUC 04 and see that DUC+TGSum has the highest score of 36.64. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2044.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Summarization performance with different training data." ], "reasoning": "We check the ROUGE-1 scores for DUC 04 and see that DUC+TGSum has the highest score of 36.64.", "statement": "Training on DUC+TGSum achieves the highest ROUGE-1 score on DUC 04.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: ROUGE(%) comparison.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The ILP-based ROUGE upper bound generation algorithm achieves higher ROUGE scores compared to the greedy algorithm, with UB-2 and UB-Combination reaching the upper bound of ROUGE-2, and UB-Combination having a higher ROUGE-1 score.Now we verify the accuracy of our ILP-based ROUGE upper bound generation algorithm. We choose λ∈0,1,0.0001, which means selecting sentences according to ROUGE-2, ROUGE-1 and their combination. We refer to the corresponding model summaries as UB-1, UB-2 and UB-Combination respectively. As a contrast, we design a baseline applying the greedy algorithm. It iteratively adds the sentence bringing the maximal ROUGE gain into the summary. Likewise, the summaries generated according to ROUGE-1 and ROUGE-2 are called GA-1 and GA-2. Obviously, the ILP solution achieves much larger ROUGE scores than the greedy algorithm. Then we compare three types of summaries derived from different λ values. It is noted that both UB-2 and UB-Combination reach the upper bound of ROUGE-2, but the ROUGE-1 score of UB-Combination is higher. We thereby include the summaries generated by UB-Combination in TGSum as reference summaries." } ], "images": [ "scigen/train/2045.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: ROUGE(%) comparison." ], "reasoning": "The ILP-based ROUGE upper bound generation algorithm achieves higher ROUGE scores compared to the greedy algorithm, with UB-2 and UB-Combination reaching the upper bound of ROUGE-2, and UB-Combination having a higher ROUGE-1 score.", "description": "Now we verify the accuracy of our ILP-based ROUGE upper bound generation algorithm. We choose λ∈0,1,0.0001, which means selecting sentences according to ROUGE-2, ROUGE-1 and their combination. We refer to the corresponding model summaries as UB-1, UB-2 and UB-Combination respectively. As a contrast, we design a baseline applying the greedy algorithm. It iteratively adds the sentence bringing the maximal ROUGE gain into the summary. Likewise, the summaries generated according to ROUGE-1 and ROUGE-2 are called GA-1 and GA-2. Obviously, the ILP solution achieves much larger ROUGE scores than the greedy algorithm. Then we compare three types of summaries derived from different λ values. It is noted that both UB-2 and UB-Combination reach the upper bound of ROUGE-2, but the ROUGE-1 score of UB-Combination is higher. We thereby include the summaries generated by UB-Combination in TGSum as reference summaries.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 4: ROUGE(%) comparison.'.\n\nQuestion: Which method achieves the highest ROUGE-1 score?\n" }, { "role": "assistant", "content": "By comparing the ROUGE-1 scores of all methods in the table, UB-1 has the highest ROUGE-1 score of 55.24. So, the answer is {\"answer\": \"UB-1\"}." } ], "images": [ "scigen/train/2045.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: ROUGE(%) comparison." ], "reasoning": "By comparing the ROUGE-1 scores of all methods in the table, UB-1 has the highest ROUGE-1 score of 55.24.", "question": "Which method achieves the highest ROUGE-1 score?", "answer": "So, the answer is {\"answer\": \"UB-1\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nUB-Combination achieves the highest ROUGE-2 score.\n" }, { "role": "assistant", "content": "The table shows that both UB-2 and UB-Combination have the same highest ROUGE-2 score of 34.74. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2045.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: ROUGE(%) comparison." ], "reasoning": "The table shows that both UB-2 and UB-Combination have the same highest ROUGE-2 score of 34.74.", "statement": "UB-Combination achieves the highest ROUGE-2 score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: Manual evaluation of TGSum’s quality.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table shows that TGSum has higher scores in both informativeness and readability compared to linked tweets, indicating better quality in generated summaries.We manually evaluate the sentence quality in the generated reference summaries. Two metrics are used, i.e., informativeness and readability. Since TGSum serves for learning-based summarization models, we do not consider the coherence of an entire summary. For each metrics, a summary sentence is classified into three levels, namely good, OK and bad. Linked tweets are also evaluated for comparison. As seen, most sentences in the reference summaries are both informative and readable. The former owes to the saliency of most linked tweets, while the candidate sentence selection strategy brings the nice readability. Since a generated reference is the ROUGE upper bound measured by the set of linked tweets, a small amount of noised tweets will be excluded. Thus the number of unimportant sentences in reference summaries is much smaller than that in linked tweets. A bad case we find is the cluster about “#BBCBizQuiz”. All the tweets are questions. As a result, the reference summary fails to locate the salient sentences. With regard to readability, a large part of linked tweets are merely regarded as OK because they are not the complete sentences at all. By contrast, sentences in reference summaries are usually formal and readable. Quite a few summary sentences marked as OK are due to the splitting errors. For instance, sometimes a section title is attached to a sentence. Meanwhile, a pronoun at the beginning of a sentence brings the co-reference ambiguity problem." } ], "images": [ "scigen/train/2046.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Manual evaluation of TGSum’s quality." ], "reasoning": "The table shows that TGSum has higher scores in both informativeness and readability compared to linked tweets, indicating better quality in generated summaries.", "description": "We manually evaluate the sentence quality in the generated reference summaries. Two metrics are used, i.e., informativeness and readability. Since TGSum serves for learning-based summarization models, we do not consider the coherence of an entire summary. For each metrics, a summary sentence is classified into three levels, namely good, OK and bad. Linked tweets are also evaluated for comparison. As seen, most sentences in the reference summaries are both informative and readable. The former owes to the saliency of most linked tweets, while the candidate sentence selection strategy brings the nice readability. Since a generated reference is the ROUGE upper bound measured by the set of linked tweets, a small amount of noised tweets will be excluded. Thus the number of unimportant sentences in reference summaries is much smaller than that in linked tweets. A bad case we find is the cluster about “#BBCBizQuiz”. All the tweets are questions. As a result, the reference summary fails to locate the salient sentences. With regard to readability, a large part of linked tweets are merely regarded as OK because they are not the complete sentences at all. By contrast, sentences in reference summaries are usually formal and readable. Quite a few summary sentences marked as OK are due to the splitting errors. For instance, sometimes a section title is attached to a sentence. Meanwhile, a pronoun at the beginning of a sentence brings the co-reference ambiguity problem.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 5: Manual evaluation of TGSum’s quality.', and tackle the question below in a sequential manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich metric and level combination has the highest score for TGSum?\n" }, { "role": "assistant", "content": "By examining the table, we see that the 'Readability Good' level has the highest score for TGSum with a value of 0.64. So, the answer is {\"answer\": \"Readability Good\"}." } ], "images": [ "scigen/train/2046.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Manual evaluation of TGSum’s quality." ], "reasoning": "By examining the table, we see that the 'Readability Good' level has the highest score for TGSum with a value of 0.64.", "question": "Which metric and level combination has the highest score for TGSum?", "answer": "So, the answer is {\"answer\": \"Readability Good\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nTGSum has a higher score for 'Readability OK' than for 'Informativeness OK'.\n" }, { "role": "assistant", "content": "The table shows that TGSum has a score of 0.23 for 'Readability OK' and a score of 0.29 for 'Informativeness OK', indicating that the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2046.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Manual evaluation of TGSum’s quality." ], "reasoning": "The table shows that TGSum has a score of 0.23 for 'Readability OK' and a score of 0.29 for 'Informativeness OK', indicating that the statement is incorrect.", "statement": "TGSum has a higher score for 'Readability OK' than for 'Informativeness OK'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Visual recognition of SR, accuracy in %.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Adding more training data and unaries to the dish node significantly improves the recognition accuracy for all nodes, especially the dish node.We first evaluate the output of our visual recognition, the SR. We notice that the recognition of the handled object (in many cases the ingredient), is the most difficult, achieving only 33.2% compared to 60.8% or more for the other nodes. This is in contrast to the importance for verbalization where the activity (second lowest) and handled object are naturally most important (see also Sec. However, the dish recognition of 8.1% is too low and enforcing consistency by conditioning on the node prediction confuses the other nodes, leading to a drop in performance for most nodes. Next we improve the dish recognition accuracy by adding more training data during the CRF training. that correspond to dishes of the TACoS subset. This data was previously only used for learning attribute/unary classifiers. This leads to an improvement not only for the dish node, but for all nodes (see line 4). As a next step we add unaries to the dish node. During training and test time we use the ground-truth segmentation for computing the features. Comparing this to the same approach without dish features (line 5 versus 4) improves the dish node significantly from 29% to 46%. As a last step we add our hand centric color Sift features as second unary for all states from the nodes tool, object, source, and target. This leads to a significant improvement for objects of 6.7% and for dish of 9.9% (line 6 versus 5)." } ], "images": [ "scigen/train/2047.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Visual recognition of SR, accuracy in %." ], "reasoning": "Adding more training data and unaries to the dish node significantly improves the recognition accuracy for all nodes, especially the dish node.", "description": "We first evaluate the output of our visual recognition, the SR. We notice that the recognition of the handled object (in many cases the ingredient), is the most difficult, achieving only 33.2% compared to 60.8% or more for the other nodes. This is in contrast to the importance for verbalization where the activity (second lowest) and handled object are naturally most important (see also Sec. However, the dish recognition of 8.1% is too low and enforcing consistency by conditioning on the node prediction confuses the other nodes, leading to a drop in performance for most nodes. Next we improve the dish recognition accuracy by adding more training data during the CRF training. that correspond to dishes of the TACoS subset. This data was previously only used for learning attribute/unary classifiers. This leads to an improvement not only for the dish node, but for all nodes (see line 4). As a next step we add unaries to the dish node. During training and test time we use the ground-truth segmentation for computing the features. Comparing this to the same approach without dish features (line 5 versus 4) improves the dish node significantly from 29% to 46%. As a last step we add our hand centric color Sift features as second unary for all states from the nodes tool, object, source, and target. This leads to a significant improvement for objects of 6.7% and for dish of 9.9% (line 6 versus 5).", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 3: Visual recognition of SR, accuracy in %.\nQuestion: What is the improvement in dish recognition accuracy when hand centric features are added?\n" }, { "role": "assistant", "content": "Initially, the dish recognition accuracy is 46.3% with dish unaries. Adding hand centric features improves it to 56.2%, an increase of 9.9%. So, the answer is {\"answer\": \"9.9%\"}" } ], "images": [ "scigen/train/2047.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Visual recognition of SR, accuracy in %." ], "reasoning": "Initially, the dish recognition accuracy is 46.3% with dish unaries. Adding hand centric features improves it to 56.2%, an increase of 9.9%.", "question": "What is the improvement in dish recognition accuracy when hand centric features are added?", "answer": "So, the answer is {\"answer\": \"9.9%\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The object recognition accuracy improved by more than 10% with hand centric features., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe object recognition accuracy improved by more than 10% with hand centric features.\n" }, { "role": "assistant", "content": "The object recognition accuracy improved from 42.4% to 49.1% with hand centric features, an increase of 6.7%, which is less than 10%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2047.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Visual recognition of SR, accuracy in %." ], "reasoning": "The object recognition accuracy improved from 42.4% to 49.1% with hand centric features, an increase of 6.7%, which is less than 10%.", "statement": "The object recognition accuracy improved by more than 10% with hand centric features.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Evaluating detailed descriptions. BLEU@4 in %. Human judgments from 1-5, where 5 is best.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The SMT approach with probabilistic input improves BLEU scores and human judgments significantly, indicating the effectiveness of the suggested features and segmentation quality.Judges rate especially the readability much higher (+0.8) which is due to our increased consistency introduced by the dish node. Also correctness (+0.3) and relevance (+0.2) is rated higher, and the BLEU score improves by 1.9% and 8.1%. To estimate the effect of our hand centric features we evaluate our SMT without them, which reaches a BLEU score of 24.1% (-1.0%) per sentence and 61.1% (-2.7%) for full descriptions. This indicates that the suggested features have a strong effect not only on the visual recognition performance but also on the quality of our descriptions. Again all scores increase. Most notably the BLEU@4 score by 2.3% and readability by 0.3. We can further improve readability to 3.8 by applying linguistic post-processing to the description (see Sec. This indicates the good quality of our segmentation. Third, surprisingly the relevance slightly improves for our approaches by 0.1/0.2. This might be due to our background classifier which removes unimportant segments." } ], "images": [ "scigen/train/2048.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Evaluating detailed descriptions. BLEU@4 in %. Human judgments from 1-5, where 5 is best." ], "reasoning": "The SMT approach with probabilistic input improves BLEU scores and human judgments significantly, indicating the effectiveness of the suggested features and segmentation quality.", "description": "Judges rate especially the readability much higher (+0.8) which is due to our increased consistency introduced by the dish node. Also correctness (+0.3) and relevance (+0.2) is rated higher, and the BLEU score improves by 1.9% and 8.1%. To estimate the effect of our hand centric features we evaluate our SMT without them, which reaches a BLEU score of 24.1% (-1.0%) per sentence and 61.1% (-2.7%) for full descriptions. This indicates that the suggested features have a strong effect not only on the visual recognition performance but also on the quality of our descriptions. Again all scores increase. Most notably the BLEU@4 score by 2.3% and readability by 0.3. We can further improve readability to 3.8 by applying linguistic post-processing to the description (see Sec. This indicates the good quality of our segmentation. Third, surprisingly the relevance slightly improves for our approaches by 0.1/0.2. This might be due to our background classifier which removes unimportant segments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Evaluating detailed descriptions. BLEU@4 in %. Human judgments from 1-5, where 5 is best.', answer the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the BLEU score per description for the SMT approach on TACoS gt intervals?\n" }, { "role": "assistant", "content": "The BLEU score per description for the SMT approach on TACoS gt intervals is found in the table under the 'PER DESCRIPTION' column for the 'SMT' row. So, the answer is {\"answer\": \"55.7\"}" } ], "images": [ "scigen/train/2048.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Evaluating detailed descriptions. BLEU@4 in %. Human judgments from 1-5, where 5 is best." ], "reasoning": "The BLEU score per description for the SMT approach on TACoS gt intervals is found in the table under the 'PER DESCRIPTION' column for the 'SMT' row.", "question": "What is the BLEU score per description for the SMT approach on TACoS gt intervals?", "answer": "So, the answer is {\"answer\": \"55.7\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The SMT approach with probabilistic input has a readability score of 3.2. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe SMT approach with probabilistic input has a readability score of 3.2.\n" }, { "role": "assistant", "content": "The table shows that the SMT approach with probabilistic input has a readability score of 3.2 under the 'PER DESCRIPTION Human judgments' column. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2048.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Evaluating detailed descriptions. BLEU@4 in %. Human judgments from 1-5, where 5 is best." ], "reasoning": "The table shows that the SMT approach with probabilistic input has a readability score of 3.2 under the 'PER DESCRIPTION Human judgments' column.", "statement": "The SMT approach with probabilistic input has a readability score of 3.2.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 9: The Generation Quality of Different Outer Hop Concept Selectors. Both Bleu and Nist are used to calculate the relevance between generated responses and golden responses.'.\n" }, { "role": "assistant", "content": "Full outperforms Distract, Rand, and Base on all evaluation metrics, demonstrating the effectiveness of ConceptFlow in generating better responses.Rand outperforms Base on most evaluation metrics, which illustrates the quality of generated response can be improved with more concepts included. Distract outperforms Rand on all evaluation metrics, which indicates that concepts appearing in golden responses are meaningful and important for the conversation system to generate a more on-topic and informative response. On the other hand, Full outperforms Distract significantly, even though not all golden concepts are included. The better performance thrives from the underlying related concepts selected by our ConceptFlow (select). This experiment further demonstrates the effectiveness of our ConceptFlow to generate a better response." } ], "images": [ "scigen/train/2049.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: The Generation Quality of Different Outer Hop Concept Selectors. Both Bleu and Nist are used to calculate the relevance between generated responses and golden responses." ], "reasoning": "Full outperforms Distract, Rand, and Base on all evaluation metrics, demonstrating the effectiveness of ConceptFlow in generating better responses.", "description": "Rand outperforms Base on most evaluation metrics, which illustrates the quality of generated response can be improved with more concepts included. Distract outperforms Rand on all evaluation metrics, which indicates that concepts appearing in golden responses are meaningful and important for the conversation system to generate a more on-topic and informative response. On the other hand, Full outperforms Distract significantly, even though not all golden concepts are included. The better performance thrives from the underlying related concepts selected by our ConceptFlow (select). This experiment further demonstrates the effectiveness of our ConceptFlow to generate a better response.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 9: The Generation Quality of Different Outer Hop Concept Selectors. Both Bleu and Nist are used to calculate the relevance between generated responses and golden responses.', solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich version has the highest score for the Bleu-4 metric?\n" }, { "role": "assistant", "content": "By comparing the Bleu-4 scores across all versions, Full has the highest score of 0.0195. So, the answer is {\"answer\": \"Full\"}." } ], "images": [ "scigen/train/2049.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: The Generation Quality of Different Outer Hop Concept Selectors. Both Bleu and Nist are used to calculate the relevance between generated responses and golden responses." ], "reasoning": "By comparing the Bleu-4 scores across all versions, Full has the highest score of 0.0195.", "question": "Which version has the highest score for the Bleu-4 metric?", "answer": "So, the answer is {\"answer\": \"Full\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Rand outperforms Base on the Nist-1 metric. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nRand outperforms Base on the Nist-1 metric.\n" }, { "role": "assistant", "content": "The table shows that Rand has a Nist-1 score of 1.0046, which is higher than Base's Nist-1 score of 0.9962. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2049.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: The Generation Quality of Different Outer Hop Concept Selectors. Both Bleu and Nist are used to calculate the relevance between generated responses and golden responses." ], "reasoning": "The table shows that Rand has a Nist-1 score of 1.0046, which is higher than Base's Nist-1 score of 0.9962.", "statement": "Rand outperforms Base on the Nist-1 metric.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Human Evaluation on Appropriate (App.) and Informativeness (Inf.). The Average Score takes the average from human judgments. Best@1 Ratio indicates the fraction of judges consider the case as the best. The number of parameters are also presented.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "ConceptFlow outperforms other models in all scenarios while using only 30% of the parameters compared to GPT-2, demonstrating the advantage of modeling conversation flow with structured semantics.The first is the mean of five judges; the latter calculates the fraction of judges that consider the corresponding response the best among four systems. ConceptFlow outperforms all other models in all scenarios, while only using 30% of parameters compared to GPT-2. This demonstrates the advantage of explicitly modeling conversation flow with structured semantics." } ], "images": [ "scigen/train/2050.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Human Evaluation on Appropriate (App.) and Informativeness (Inf.). The Average Score takes the average from human judgments. Best@1 Ratio indicates the fraction of judges consider the case as the best. The number of parameters are also presented." ], "reasoning": "ConceptFlow outperforms other models in all scenarios while using only 30% of the parameters compared to GPT-2, demonstrating the advantage of modeling conversation flow with structured semantics.", "description": "The first is the mean of five judges; the latter calculates the fraction of judges that consider the corresponding response the best among four systems. ConceptFlow outperforms all other models in all scenarios, while only using 30% of parameters compared to GPT-2. This demonstrates the advantage of explicitly modeling conversation flow with structured semantics.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Human Evaluation on Appropriate (App.) and Informativeness (Inf.). The Average Score takes the average from human judgments. Best@1 Ratio indicates the fraction of judges consider the case as the best. The number of parameters are also presented.', answer the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model has the highest Best@1 Ratio in the Inf. scenario and how does it compare to ConceptFlow?\n" }, { "role": "assistant", "content": "The table shows that the Golden model has the highest Best@1 Ratio in the Inf. scenario at 81.8%, while ConceptFlow has a Best@1 Ratio of 25.6% in the same scenario. So, the answer is {\"answer\": \"Golden\"}" } ], "images": [ "scigen/train/2050.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Human Evaluation on Appropriate (App.) and Informativeness (Inf.). The Average Score takes the average from human judgments. Best@1 Ratio indicates the fraction of judges consider the case as the best. The number of parameters are also presented." ], "reasoning": "The table shows that the Golden model has the highest Best@1 Ratio in the Inf. scenario at 81.8%, while ConceptFlow has a Best@1 Ratio of 25.6% in the same scenario.", "question": "Which model has the highest Best@1 Ratio in the Inf. scenario and how does it compare to ConceptFlow?", "answer": "So, the answer is {\"answer\": \"Golden\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nConceptFlow has the highest Average Score in the Inf. scenario.\n" }, { "role": "assistant", "content": "The table shows that the Golden model has the highest Average Score in the Inf. scenario at 3.110, while ConceptFlow has an Average Score of 2.192. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2050.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Human Evaluation on Appropriate (App.) and Informativeness (Inf.). The Average Score takes the average from human judgments. Best@1 Ratio indicates the fraction of judges consider the case as the best. The number of parameters are also presented." ], "reasoning": "The table shows that the Golden model has the highest Average Score in the Inf. scenario at 3.110, while ConceptFlow has an Average Score of 2.192.", "statement": "ConceptFlow has the highest Average Score in the Inf. scenario.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: Fleiss’ Kappa of Human Agreement. Two testing scenarios Appropriate (App.) and Informativeness (Inf.) are used to evaluate the the quality of generated response. The Fleiss’ Kappa evaluates agreement from various annotators and focuses on the comparison of two models with three categories: win, tie and loss.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The table compares ConceptFlow's performance against CCM and GPT-2 (conv) in terms of appropriateness (App.) and informativeness (Inf.), with human evaluation agreement calculated using Fleiss’ Kappa.For each case, the response from ConceptFlow is compared to the responses from two baseline models, CCM and GPT-2 (conv). The comparison result is divided into three categories: win, tie and loss. Then the human evaluation agreement is calculated with Fleiss’ Kappa (κ). The κ value ranges from 0.21 to 0.40 indicating fair agreement, which confirms the quality of human evaluation." } ], "images": [ "scigen/train/2051.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Fleiss’ Kappa of Human Agreement. Two testing scenarios Appropriate (App.) and Informativeness (Inf.) are used to evaluate the the quality of generated response. The Fleiss’ Kappa evaluates agreement from various annotators and focuses on the comparison of two models with three categories: win, tie and loss." ], "reasoning": "The table compares ConceptFlow's performance against CCM and GPT-2 (conv) in terms of appropriateness (App.) and informativeness (Inf.), with human evaluation agreement calculated using Fleiss’ Kappa.", "description": "For each case, the response from ConceptFlow is compared to the responses from two baseline models, CCM and GPT-2 (conv). The comparison result is divided into three categories: win, tie and loss. Then the human evaluation agreement is calculated with Fleiss’ Kappa (κ). The κ value ranges from 0.21 to 0.40 indicating fair agreement, which confirms the quality of human evaluation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 4: Fleiss’ Kappa of Human Agreement. Two testing scenarios Appropriate (App.) and Informativeness (Inf.) are used to evaluate the the quality of generated response. The Fleiss’ Kappa evaluates agreement from various annotators and focuses on the comparison of two models with three categories: win, tie and loss.', solve the subsequent question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich model has the highest appropriateness score?\n" }, { "role": "assistant", "content": "To determine the model with the highest appropriateness score, compare the 'App.' values for ConceptFlow-CCM and ConceptFlow-GPT2. ConceptFlow-CCM has an appropriateness score of 0.3724, which is higher than ConceptFlow-GPT2's score of 0.2468. So, the answer is {\"answer\": \"ConceptFlow-CCM\"}" } ], "images": [ "scigen/train/2051.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Fleiss’ Kappa of Human Agreement. Two testing scenarios Appropriate (App.) and Informativeness (Inf.) are used to evaluate the the quality of generated response. The Fleiss’ Kappa evaluates agreement from various annotators and focuses on the comparison of two models with three categories: win, tie and loss." ], "reasoning": "To determine the model with the highest appropriateness score, compare the 'App.' values for ConceptFlow-CCM and ConceptFlow-GPT2. ConceptFlow-CCM has an appropriateness score of 0.3724, which is higher than ConceptFlow-GPT2's score of 0.2468.", "question": "Which model has the highest appropriateness score?", "answer": "So, the answer is {\"answer\": \"ConceptFlow-CCM\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nConceptFlow-GPT2 has a higher informativeness score than ConceptFlow-CCM.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Inf.' values for ConceptFlow-GPT2 and ConceptFlow-CCM. ConceptFlow-GPT2 has an informativeness score of 0.2824, which is higher than ConceptFlow-CCM's score of 0.2641. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2051.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Fleiss’ Kappa of Human Agreement. Two testing scenarios Appropriate (App.) and Informativeness (Inf.) are used to evaluate the the quality of generated response. The Fleiss’ Kappa evaluates agreement from various annotators and focuses on the comparison of two models with three categories: win, tie and loss." ], "reasoning": "To verify this statement, compare the 'Inf.' values for ConceptFlow-GPT2 and ConceptFlow-CCM. ConceptFlow-GPT2 has an informativeness score of 0.2824, which is higher than ConceptFlow-CCM's score of 0.2641.", "statement": "ConceptFlow-GPT2 has a higher informativeness score than ConceptFlow-CCM.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 7: More Metrics on Relevance of Generated Responses. The relevance is calculated between the generated response and the golden response. Concept-PPL is the method used for calculating Perplexity in CCM (Zhou et al., 2018a), which combines the distribution of both words and concepts together. The Concept-PPL is meaningless when utilizing different numbers of concepts (more concepts included, better Perplexity shows).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "ConceptFlow outperforms other baselines in all evaluation metrics, including Bleu and Nist scores, and has the lowest Concept-PPL.Commonsense knowledge aware conversation generation with graph attention.. In Proceedings of IJCAI, pp. 4623–4629. External Links: ConceptFlow outperforms other baselines with large margins among all evaluation metrics. Concept-PPL is the Perplexity that calculated by the code from previous work Zhou et al. Zhou et al. It is evident that more entities will lead to a better result in terms of Concept-PPL because the vocabulary size of entities is always smaller than word vocabulary size. These supplementary results compare the generated response with the user post to measure the repeatability of the post and generated responses. A lower score indicates better performance because the repetitive and dull response will degenerate the model performance. ConceptFlow presents competitive performance with other baselines, which illustrate our model provides an informative response for users." } ], "images": [ "scigen/train/2052.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: More Metrics on Relevance of Generated Responses. The relevance is calculated between the generated response and the golden response. Concept-PPL is the method used for calculating Perplexity in CCM (Zhou et al., 2018a), which combines the distribution of both words and concepts together. The Concept-PPL is meaningless when utilizing different numbers of concepts (more concepts included, better Perplexity shows)." ], "reasoning": "ConceptFlow outperforms other baselines in all evaluation metrics, including Bleu and Nist scores, and has the lowest Concept-PPL.", "description": "Commonsense knowledge aware conversation generation with graph attention.. In Proceedings of IJCAI, pp. 4623–4629. External Links: ConceptFlow outperforms other baselines with large margins among all evaluation metrics. Concept-PPL is the Perplexity that calculated by the code from previous work Zhou et al. Zhou et al. It is evident that more entities will lead to a better result in terms of Concept-PPL because the vocabulary size of entities is always smaller than word vocabulary size. These supplementary results compare the generated response with the user post to measure the repeatability of the post and generated responses. A lower score indicates better performance because the repetitive and dull response will degenerate the model performance. ConceptFlow presents competitive performance with other baselines, which illustrate our model provides an informative response for users.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 7: More Metrics on Relevance of Generated Responses. The relevance is calculated between the generated response and the golden response. Concept-PPL is the method used for calculating Perplexity in CCM (Zhou et al., 2018a), which combines the distribution of both words and concepts together. The Concept-PPL is meaningless when utilizing different numbers of concepts (more concepts included, better Perplexity shows).\nQuestion: Which model has the lowest Concept-PPL and how does it perform in terms of Bleu-1 and Nist-3 scores?\nSolve the above question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "ConceptFlow has the lowest Concept-PPL of 26.76 and it also has the highest Bleu-1 score of 0.2451 and the highest Nist-3 score of 1.8265. So, the answer is {\"answer\": \"ConceptFlow\"}" } ], "images": [ "scigen/train/2052.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: More Metrics on Relevance of Generated Responses. The relevance is calculated between the generated response and the golden response. Concept-PPL is the method used for calculating Perplexity in CCM (Zhou et al., 2018a), which combines the distribution of both words and concepts together. The Concept-PPL is meaningless when utilizing different numbers of concepts (more concepts included, better Perplexity shows)." ], "reasoning": "ConceptFlow has the lowest Concept-PPL of 26.76 and it also has the highest Bleu-1 score of 0.2451 and the highest Nist-3 score of 1.8265.", "question": "Which model has the lowest Concept-PPL and how does it perform in terms of Bleu-1 and Nist-3 scores?", "answer": "So, the answer is {\"answer\": \"ConceptFlow\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nConceptFlow has the highest Bleu-2 score among all models.\n" }, { "role": "assistant", "content": "The table shows that ConceptFlow has a Bleu-2 score of 0.1047, which is higher than all other models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2052.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: More Metrics on Relevance of Generated Responses. The relevance is calculated between the generated response and the golden response. Concept-PPL is the method used for calculating Perplexity in CCM (Zhou et al., 2018a), which combines the distribution of both words and concepts together. The Concept-PPL is meaningless when utilizing different numbers of concepts (more concepts included, better Perplexity shows)." ], "reasoning": "The table shows that ConceptFlow has a Bleu-2 score of 0.1047, which is higher than all other models listed.", "statement": "ConceptFlow has the highest Bleu-2 score among all models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 7: More Metrics on Relevance of Generated Responses. The relevance is calculated between the generated response and the golden response. Concept-PPL is the method used for calculating Perplexity in CCM (Zhou et al., 2018a), which combines the distribution of both words and concepts together. The Concept-PPL is meaningless when utilizing different numbers of concepts (more concepts included, better Perplexity shows).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "ConceptFlow outperforms other baselines in terms of Concept-PPL due to a smaller vocabulary size of entities compared to word vocabulary size.Commonsense knowledge aware conversation generation with graph attention.. In Proceedings of IJCAI, pp. 4623–4629. External Links: ConceptFlow outperforms other baselines with large margins among all evaluation metrics. Concept-PPL is the Perplexity that calculated by the code from previous work Zhou et al. Zhou et al. It is evident that more entities will lead to a better result in terms of Concept-PPL because the vocabulary size of entities is always smaller than word vocabulary size. These supplementary results compare the generated response with the user post to measure the repeatability of the post and generated responses. A lower score indicates better performance because the repetitive and dull response will degenerate the model performance. ConceptFlow presents competitive performance with other baselines, which illustrate our model provides an informative response for users." } ], "images": [ "scigen/train/2053.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: More Metrics on Relevance of Generated Responses. The relevance is calculated between the generated response and the golden response. Concept-PPL is the method used for calculating Perplexity in CCM (Zhou et al., 2018a), which combines the distribution of both words and concepts together. The Concept-PPL is meaningless when utilizing different numbers of concepts (more concepts included, better Perplexity shows)." ], "reasoning": "ConceptFlow outperforms other baselines in terms of Concept-PPL due to a smaller vocabulary size of entities compared to word vocabulary size.", "description": "Commonsense knowledge aware conversation generation with graph attention.. In Proceedings of IJCAI, pp. 4623–4629. External Links: ConceptFlow outperforms other baselines with large margins among all evaluation metrics. Concept-PPL is the Perplexity that calculated by the code from previous work Zhou et al. Zhou et al. It is evident that more entities will lead to a better result in terms of Concept-PPL because the vocabulary size of entities is always smaller than word vocabulary size. These supplementary results compare the generated response with the user post to measure the repeatability of the post and generated responses. A lower score indicates better performance because the repetitive and dull response will degenerate the model performance. ConceptFlow presents competitive performance with other baselines, which illustrate our model provides an informative response for users.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 7: More Metrics on Relevance of Generated Responses. The relevance is calculated between the generated response and the golden response. Concept-PPL is the method used for calculating Perplexity in CCM (Zhou et al., 2018a), which combines the distribution of both words and concepts together. The Concept-PPL is meaningless when utilizing different numbers of concepts (more concepts included, better Perplexity shows).' and methodically tackle the question that follows. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model has the highest score in Novelty w.r.t. Input(↓) Nist-3?\n" }, { "role": "assistant", "content": "To find the model with the highest score in Novelty w.r.t. Input(↓) Nist-3, compare the values in the Novelty w.r.t. Input(↓) Nist-3 column. GPT-2 (conv) has the highest score of 2.5277. So, the answer is {\"answer\": \"GPT-2 (conv)\"}" } ], "images": [ "scigen/train/2053.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: More Metrics on Relevance of Generated Responses. The relevance is calculated between the generated response and the golden response. Concept-PPL is the method used for calculating Perplexity in CCM (Zhou et al., 2018a), which combines the distribution of both words and concepts together. The Concept-PPL is meaningless when utilizing different numbers of concepts (more concepts included, better Perplexity shows)." ], "reasoning": "To find the model with the highest score in Novelty w.r.t. Input(↓) Nist-3, compare the values in the Novelty w.r.t. Input(↓) Nist-3 column. GPT-2 (conv) has the highest score of 2.5277.", "question": "Which model has the highest score in Novelty w.r.t. Input(↓) Nist-3?", "answer": "So, the answer is {\"answer\": \"GPT-2 (conv)\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: ConceptFlow has the lowest score in Novelty w.r.t. Input(↓) Bleu-2., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nConceptFlow has the lowest score in Novelty w.r.t. Input(↓) Bleu-2.\n" }, { "role": "assistant", "content": "To verify if ConceptFlow has the lowest score in Novelty w.r.t. Input(↓) Bleu-2, compare its score with other models in the same column. ConceptFlow has a score of 0.0685, which is indeed the lowest. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2053.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: More Metrics on Relevance of Generated Responses. The relevance is calculated between the generated response and the golden response. Concept-PPL is the method used for calculating Perplexity in CCM (Zhou et al., 2018a), which combines the distribution of both words and concepts together. The Concept-PPL is meaningless when utilizing different numbers of concepts (more concepts included, better Perplexity shows)." ], "reasoning": "To verify if ConceptFlow has the lowest score in Novelty w.r.t. Input(↓) Bleu-2, compare its score with other models in the same column. ConceptFlow has a score of 0.0685, which is indeed the lowest.", "statement": "ConceptFlow has the lowest score in Novelty w.r.t. Input(↓) Bleu-2.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: A comparison between our mention-level (ML) model (with three different attention mechanisms), and E2EET on the modified Wiki, Ontonotes and BBN datasets. Top scores (prior to rounding) are in bold.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "E2EET uses BERT embeddings and evaluates only across entities using F1 calculations, ignoring non-entities incorrectly labeled as entities.when trained and evaluated upon the modified datasets. Here, E2EET is using BERT embeddings and is evaluated using the same F1 calculations as the mention-level model, i.e. the scores are only calculated across entities as opposed to across all tokens. This means that any non-entities that are incorrectly labelled as entities by E2EET are ignored." } ], "images": [ "scigen/train/2054.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: A comparison between our mention-level (ML) model (with three different attention mechanisms), and E2EET on the modified Wiki, Ontonotes and BBN datasets. Top scores (prior to rounding) are in bold." ], "reasoning": "E2EET uses BERT embeddings and evaluates only across entities using F1 calculations, ignoring non-entities incorrectly labeled as entities.", "description": "when trained and evaluated upon the modified datasets. Here, E2EET is using BERT embeddings and is evaluated using the same F1 calculations as the mention-level model, i.e. the scores are only calculated across entities as opposed to across all tokens. This means that any non-entities that are incorrectly labelled as entities by E2EET are ignored.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 4: A comparison between our mention-level (ML) model (with three different attention mechanisms), and E2EET on the modified Wiki, Ontonotes and BBN datasets. Top scores (prior to rounding) are in bold.', unfold the steps to solve the question given next. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nQuestion: Which model and attention combination achieved the highest M-BBN Mi-F1 score?\n" }, { "role": "assistant", "content": "By comparing the M-BBN Mi-F1 scores across all model and attention combinations, the highest score is identified. So, the answer is {\"answer\": \"E2E\"}." } ], "images": [ "scigen/train/2054.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: A comparison between our mention-level (ML) model (with three different attention mechanisms), and E2EET on the modified Wiki, Ontonotes and BBN datasets. Top scores (prior to rounding) are in bold." ], "reasoning": "By comparing the M-BBN Mi-F1 scores across all model and attention combinations, the highest score is identified.", "question": "Which model and attention combination achieved the highest M-BBN Mi-F1 score?", "answer": "So, the answer is {\"answer\": \"E2E\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe ML model with Scalar attention achieved the highest M-Ontonotes Ma-F1 score.\n" }, { "role": "assistant", "content": "By examining the M-Ontonotes Ma-F1 scores, the highest score is found to be 0.861, achieved by the ML model with Dynamic attention, not Scalar attention. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2054.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: A comparison between our mention-level (ML) model (with three different attention mechanisms), and E2EET on the modified Wiki, Ontonotes and BBN datasets. Top scores (prior to rounding) are in bold." ], "reasoning": "By examining the M-Ontonotes Ma-F1 scores, the highest score is found to be 0.861, achieved by the ML model with Dynamic attention, not Scalar attention.", "statement": "The ML model with Scalar attention achieved the highest M-Ontonotes Ma-F1 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: A comparison of our mention-level (ML) model, with three different attention mechanisms, against state-of-the-art mention-level typing systems. The system using handcrafted features, AFET, is highlighted in grey. Grey cells with bold text indicate the best performing model not using handcrafted features. Non-highlighted cells with bold text are the best performing model even when compared to systems using handcrafted features.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "AFET, which relies on handcrafted features, is highlighted in grey, and the dynamic attention model excels on cleaner, modified datasets.AFET, which relies on handcrafted features, is highlighted in grey. Another noticeable result is that, for the mention-level typing models, there is a clear relationship between the complexity of the attention model and the overall performance. The dynamic attention model in particular excels on the modified datasets, which are significantly cleaner than the original datasets as a result of being taken from the hand-labelled test set of their respective original dataset. The dynamic attention mechanism is clearly most effective when the dataset is clean and when there is consistency between the training and testing sets." } ], "images": [ "scigen/train/2055.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: A comparison of our mention-level (ML) model, with three different attention mechanisms, against state-of-the-art mention-level typing systems. The system using handcrafted features, AFET, is highlighted in grey. Grey cells with bold text indicate the best performing model not using handcrafted features. Non-highlighted cells with bold text are the best performing model even when compared to systems using handcrafted features." ], "reasoning": "AFET, which relies on handcrafted features, is highlighted in grey, and the dynamic attention model excels on cleaner, modified datasets.", "description": "AFET, which relies on handcrafted features, is highlighted in grey. Another noticeable result is that, for the mention-level typing models, there is a clear relationship between the complexity of the attention model and the overall performance. The dynamic attention model in particular excels on the modified datasets, which are significantly cleaner than the original datasets as a result of being taken from the hand-labelled test set of their respective original dataset. The dynamic attention mechanism is clearly most effective when the dataset is clean and when there is consistency between the training and testing sets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 2: A comparison of our mention-level (ML) model, with three different attention mechanisms, against state-of-the-art mention-level typing systems. The system using handcrafted features, AFET, is highlighted in grey. Grey cells with bold text indicate the best performing model not using handcrafted features. Non-highlighted cells with bold text are the best performing model even when compared to systems using handcrafted features.', unfold the question presented below, detailing every step of your calculation. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model has the highest Wiki Mi-F1 score and what is the score?\n" }, { "role": "assistant", "content": "By examining the Wiki Mi-F1 column, the highest score is 0.718, achieved by the METIC model. So, the answer is {\"answer\": \"METIC with 0.718\"}." } ], "images": [ "scigen/train/2055.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: A comparison of our mention-level (ML) model, with three different attention mechanisms, against state-of-the-art mention-level typing systems. The system using handcrafted features, AFET, is highlighted in grey. Grey cells with bold text indicate the best performing model not using handcrafted features. Non-highlighted cells with bold text are the best performing model even when compared to systems using handcrafted features." ], "reasoning": "By examining the Wiki Mi-F1 column, the highest score is 0.718, achieved by the METIC model.", "question": "Which model has the highest Wiki Mi-F1 score and what is the score?", "answer": "So, the answer is {\"answer\": \"METIC with 0.718\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe dynamic attention model has the highest Ontonotes Ma-F1 score.\n" }, { "role": "assistant", "content": "The Ontonotes Ma-F1 column shows that the dynamic attention model (ML (Dynamic)) has a score of 0.577, which is the highest in that column. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2055.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: A comparison of our mention-level (ML) model, with three different attention mechanisms, against state-of-the-art mention-level typing systems. The system using handcrafted features, AFET, is highlighted in grey. Grey cells with bold text indicate the best performing model not using handcrafted features. Non-highlighted cells with bold text are the best performing model even when compared to systems using handcrafted features." ], "reasoning": "The Ontonotes Ma-F1 column shows that the dynamic attention model (ML (Dynamic)) has a score of 0.577, which is the highest in that column.", "statement": "The dynamic attention model has the highest Ontonotes Ma-F1 score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: The results of E2EET model on the modified Wiki, Ontonotes and BBN datasets using various embedding techniques. Misleading metrics are in grey.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The model's performance is evaluated using strict accuracy, macro, and micro F1 scores with four different embedding techniques.5.2.1 Baseline performance To evaluate E2EET we test the model after its embedding layer has been initialised with four different embedding techniques. Here, the strict accuracy, macro, and micro F1 scores are calculated with respect to the model’s predictions of every single token in the corpus." } ], "images": [ "scigen/train/2056.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The results of E2EET model on the modified Wiki, Ontonotes and BBN datasets using various embedding techniques. Misleading metrics are in grey." ], "reasoning": "The model's performance is evaluated using strict accuracy, macro, and micro F1 scores with four different embedding techniques.", "description": "5.2.1 Baseline performance To evaluate E2EET we test the model after its embedding layer has been initialised with four different embedding techniques. Here, the strict accuracy, macro, and micro F1 scores are calculated with respect to the model’s predictions of every single token in the corpus.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: The results of E2EET model on the modified Wiki, Ontonotes and BBN datasets using various embedding techniques. Misleading metrics are in grey.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich embedding technique achieved the highest macro F1 score on the Ontonotes dataset?\n" }, { "role": "assistant", "content": "To find the highest macro F1 score on the Ontonotes dataset, compare the values in the 'M-Ontonotes Ma-F1' column for each embedding technique. So, the answer is {\"answer\": \"BERT\"}" } ], "images": [ "scigen/train/2056.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The results of E2EET model on the modified Wiki, Ontonotes and BBN datasets using various embedding techniques. Misleading metrics are in grey." ], "reasoning": "To find the highest macro F1 score on the Ontonotes dataset, compare the values in the 'M-Ontonotes Ma-F1' column for each embedding technique.", "question": "Which embedding technique achieved the highest macro F1 score on the Ontonotes dataset?", "answer": "So, the answer is {\"answer\": \"BERT\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Word2Vec embedding technique achieved the highest accuracy on the BBN dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the accuracy values in the 'M-BBN Acc' column for each embedding technique. The highest value is 0.975, achieved by BERT, not Word2Vec. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2056.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The results of E2EET model on the modified Wiki, Ontonotes and BBN datasets using various embedding techniques. Misleading metrics are in grey." ], "reasoning": "To verify this statement, compare the accuracy values in the 'M-BBN Acc' column for each embedding technique. The highest value is 0.975, achieved by BERT, not Word2Vec.", "statement": "The Word2Vec embedding technique achieved the highest accuracy on the BBN dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Results of the CNN / Daily Mail test dataset. ROUGE scores have a 95% confidence interval of at most ±0.24 as reported by the official ROUGE script.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Random hierarchical encoders achieve ROUGE scores close to trained models, with a gap of about 1.1 points, and even outperform a competitive baseline in ROUGE-2.Of note, our hierarchical random encoder models (Random H-LSTM and Random LSTM + ESN) obtain ROUGE scores close to other trained models. The gap between our trained and random hierarchical encoders is about 1.1 point for all ROUGE scores. Hierarchical random encoders even outperform a competitive baseline nallapati2016abstractive in terms of ROUGE-2, even though the cited model uses pre-trained embeddings. With respect to the trained H-LSTM, the Random H-LSTM achieves ROUGE scores that are very similar: the gap is 3.5% in ROUGE-1, 8.5% in ROUGE-2 and 3.9% in ROUGE-L. We also tested the Identity H-LSTM to get an idea of the role that trained word embeddings play in the performance. To measure the representational power of random projection in the encoder, we compare the ROUGE scores of random hierarchical encoder models with those of the Identity H-LSTM model. This has brought us closer to gauging the effectiveness of randomly projected recurrent encoder hidden states over an accumulation of word embeddings." } ], "images": [ "scigen/train/2057.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results of the CNN / Daily Mail test dataset. ROUGE scores have a 95% confidence interval of at most ±0.24 as reported by the official ROUGE script." ], "reasoning": "Random hierarchical encoders achieve ROUGE scores close to trained models, with a gap of about 1.1 points, and even outperform a competitive baseline in ROUGE-2.", "description": "Of note, our hierarchical random encoder models (Random H-LSTM and Random LSTM + ESN) obtain ROUGE scores close to other trained models. The gap between our trained and random hierarchical encoders is about 1.1 point for all ROUGE scores. Hierarchical random encoders even outperform a competitive baseline nallapati2016abstractive in terms of ROUGE-2, even though the cited model uses pre-trained embeddings. With respect to the trained H-LSTM, the Random H-LSTM achieves ROUGE scores that are very similar: the gap is 3.5% in ROUGE-1, 8.5% in ROUGE-2 and 3.9% in ROUGE-L. We also tested the Identity H-LSTM to get an idea of the role that trained word embeddings play in the performance. To measure the representational power of random projection in the encoder, we compare the ROUGE scores of random hierarchical encoder models with those of the Identity H-LSTM model. This has brought us closer to gauging the effectiveness of randomly projected recurrent encoder hidden states over an accumulation of word embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 1: Results of the CNN / Daily Mail test dataset. ROUGE scores have a 95% confidence interval of at most ±0.24 as reported by the official ROUGE script.', unfold the question presented below, detailing every step of your calculation. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the ROUGE-2 score difference between the Random H-LSTM and the Identity H-LSTM models?\n" }, { "role": "assistant", "content": "The ROUGE-2 score for the Random H-LSTM model is 13.89, and for the Identity H-LSTM model, it is 8.14. The difference is calculated as 13.89 - 8.14. So, the answer is {\"answer\": \"5.75\"}." } ], "images": [ "scigen/train/2057.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results of the CNN / Daily Mail test dataset. ROUGE scores have a 95% confidence interval of at most ±0.24 as reported by the official ROUGE script." ], "reasoning": "The ROUGE-2 score for the Random H-LSTM model is 13.89, and for the Identity H-LSTM model, it is 8.14. The difference is calculated as 13.89 - 8.14.", "question": "What is the ROUGE-2 score difference between the Random H-LSTM and the Identity H-LSTM models?", "answer": "So, the answer is {\"answer\": \"5.75\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Random LSTM + ESN model has a higher ROUGE-1 score than the seq2seq + attn (50K vocab) model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Random LSTM + ESN model has a higher ROUGE-1 score than the seq2seq + attn (50K vocab) model.\n" }, { "role": "assistant", "content": "The ROUGE-1 score for the Random LSTM + ESN model is 34.60, while for the seq2seq + attn (50K vocab) model, it is 31.33. Since 34.60 is higher than 31.33, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2057.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results of the CNN / Daily Mail test dataset. ROUGE scores have a 95% confidence interval of at most ±0.24 as reported by the official ROUGE script." ], "reasoning": "The ROUGE-1 score for the Random LSTM + ESN model is 34.60, while for the seq2seq + attn (50K vocab) model, it is 31.33. Since 34.60 is higher than 31.33, the statement is supported.", "statement": "The Random LSTM + ESN model has a higher ROUGE-1 score than the seq2seq + attn (50K vocab) model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Test perplexity of trained and random hierarchical encoder models on the CNN / Daily Mail dataset (lower is better). Note that Enc = encoder hidden size and Dec = decoder hidden size. Percentages in parentheses are the relative perplexity degradation in random encoder models with respect to the associated Trained H-LSTM encoder model.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Increasing the capacity of a random encoder reduces the performance gap with a fully trained encoder, and similar improvements are observed when increasing the parameters of a trainable decoder.We chose to base our analysis on perplexity instead of ROUGE to isolate model performance from the effect of word sampling methods such as beam search and to show the quality of overall predicted word probabilities. It is shown that increased model capacity leads to lower test perplexity, which implies better target word prediction. The improvement in performance, however, is not equal across models. We notice that random encoders close the performance gap with the fully trained counterpart as the encoder hidden size increases. For instance, as we vary the encoder hidden size of the Random H-LSTM from 64 to 1024, the relative perplexity gap with the Trained H-LSTM diminishes from 45% to 18%. This pattern aligns with the previous work from notraining, where authors discovered that the performance of random sentence encoder converged to that of trained one as the dimensionality of sentence embeddings increased. We perform similar experiments with the decoder hidden size which varies from 64 to 1024, while fixing the encoder hidden size to 256. We first expected that the hidden size of a trained decoder would play a larger role in enhancing the model performance than that of a random encoder. Moreover, the performance gaps of the previously mentioned configuration with respect to its fully trained counterpart are just as small (19% vs 18% and 20% vs 19%). There are two conclusions that we can draw from this result. First, increasing the capacity of random encoder closes the performance gap between fully trained and random encoder models. Second, increasing the number of parameters in a random encoder yields similar improvements to increasing the number of parameters of a trainable decoder. This illustrates important advantages of using random encoder in terms of the number of parameters to train." } ], "images": [ "scigen/train/2058.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Test perplexity of trained and random hierarchical encoder models on the CNN / Daily Mail dataset (lower is better). Note that Enc = encoder hidden size and Dec = decoder hidden size. Percentages in parentheses are the relative perplexity degradation in random encoder models with respect to the associated Trained H-LSTM encoder model." ], "reasoning": "Increasing the capacity of a random encoder reduces the performance gap with a fully trained encoder, and similar improvements are observed when increasing the parameters of a trainable decoder.", "description": "We chose to base our analysis on perplexity instead of ROUGE to isolate model performance from the effect of word sampling methods such as beam search and to show the quality of overall predicted word probabilities. It is shown that increased model capacity leads to lower test perplexity, which implies better target word prediction. The improvement in performance, however, is not equal across models. We notice that random encoders close the performance gap with the fully trained counterpart as the encoder hidden size increases. For instance, as we vary the encoder hidden size of the Random H-LSTM from 64 to 1024, the relative perplexity gap with the Trained H-LSTM diminishes from 45% to 18%. This pattern aligns with the previous work from notraining, where authors discovered that the performance of random sentence encoder converged to that of trained one as the dimensionality of sentence embeddings increased. We perform similar experiments with the decoder hidden size which varies from 64 to 1024, while fixing the encoder hidden size to 256. We first expected that the hidden size of a trained decoder would play a larger role in enhancing the model performance than that of a random encoder. Moreover, the performance gaps of the previously mentioned configuration with respect to its fully trained counterpart are just as small (19% vs 18% and 20% vs 19%). There are two conclusions that we can draw from this result. First, increasing the capacity of random encoder closes the performance gap between fully trained and random encoder models. Second, increasing the number of parameters in a random encoder yields similar improvements to increasing the number of parameters of a trainable decoder. This illustrates important advantages of using random encoder in terms of the number of parameters to train.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: Test perplexity of trained and random hierarchical encoder models on the CNN / Daily Mail dataset (lower is better). Note that Enc = encoder hidden size and Dec = decoder hidden size. Percentages in parentheses are the relative perplexity degradation in random encoder models with respect to the associated Trained H-LSTM encoder model.'.\n\nQuestion: What is the perplexity of the Random H-LSTM model with an encoder hidden size of 256 and a decoder hidden size of 1024?\n" }, { "role": "assistant", "content": "To find the perplexity of the Random H-LSTM model with an encoder hidden size of 256 and a decoder hidden size of 1024, look at the row where Enc is 256 and Dec is 1024 under the Random H-LSTM column. So, the answer is {\"answer\": \"16.75\"}" } ], "images": [ "scigen/train/2058.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Test perplexity of trained and random hierarchical encoder models on the CNN / Daily Mail dataset (lower is better). Note that Enc = encoder hidden size and Dec = decoder hidden size. Percentages in parentheses are the relative perplexity degradation in random encoder models with respect to the associated Trained H-LSTM encoder model." ], "reasoning": "To find the perplexity of the Random H-LSTM model with an encoder hidden size of 256 and a decoder hidden size of 1024, look at the row where Enc is 256 and Dec is 1024 under the Random H-LSTM column.", "question": "What is the perplexity of the Random H-LSTM model with an encoder hidden size of 256 and a decoder hidden size of 1024?", "answer": "So, the answer is {\"answer\": \"16.75\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe perplexity of the Trained H-LSTM model with an encoder hidden size of 256 and a decoder hidden size of 64 is 17.07.\n" }, { "role": "assistant", "content": "To verify this statement, look at the row where Enc is 256 and Dec is 64 under the Trained H-LSTM column. The value is 17.07, which matches the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2058.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Test perplexity of trained and random hierarchical encoder models on the CNN / Daily Mail dataset (lower is better). Note that Enc = encoder hidden size and Dec = decoder hidden size. Percentages in parentheses are the relative perplexity degradation in random encoder models with respect to the associated Trained H-LSTM encoder model." ], "reasoning": "To verify this statement, look at the row where Enc is 256 and Dec is 64 under the Trained H-LSTM column. The value is 17.07, which matches the statement.", "statement": "The perplexity of the Trained H-LSTM model with an encoder hidden size of 256 and a decoder hidden size of 64 is 17.07.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'TABLE VII: Mortality prediction performance on MIMIC III dataset.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "TeSAN outperforms all baseline models in PR-AUC and ROC-AUC due to its ability to attend to details in patient records, while GloVe performs poorly because it assigns near-zero vectors to low-frequency medical concepts.We find that TeSAN again consistently outperforms all baseline models. Achieving high specificity in mortality prediction is relatively easy as there are many more negative samples than positive ones. However, correctly identifying positive cases while ignoring negative ones requires a model differentiate between positive cases. This means attending to the details of patient records, such as the relationship between the diagnosis codes and temporal intervals. This is why TeSAN demonstrates a significant improvement in PR-AUC and ROC-AUC. Also, we note that GloVe shows very poor PR-AUC and ROC-AUC, and we observe that medical concepts with low frequencies are assigned near-zero vectors in this model, which might explain its poor performance." } ], "images": [ "scigen/train/2059.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VII: Mortality prediction performance on MIMIC III dataset." ], "reasoning": "TeSAN outperforms all baseline models in PR-AUC and ROC-AUC due to its ability to attend to details in patient records, while GloVe performs poorly because it assigns near-zero vectors to low-frequency medical concepts.", "description": "We find that TeSAN again consistently outperforms all baseline models. Achieving high specificity in mortality prediction is relatively easy as there are many more negative samples than positive ones. However, correctly identifying positive cases while ignoring negative ones requires a model differentiate between positive cases. This means attending to the details of patient records, such as the relationship between the diagnosis codes and temporal intervals. This is why TeSAN demonstrates a significant improvement in PR-AUC and ROC-AUC. Also, we note that GloVe shows very poor PR-AUC and ROC-AUC, and we observe that medical concepts with low frequencies are assigned near-zero vectors in this model, which might explain its poor performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'TABLE VII: Mortality prediction performance on MIMIC III dataset.', answer the question below by showing each progressive step toward the answer. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model has the highest test ROC-AUC and why?\n" }, { "role": "assistant", "content": "TeSAN has the highest test ROC-AUC of 0.8064. The description explains that TeSAN's ability to attend to details in patient records, such as the relationship between diagnosis codes and temporal intervals, contributes to its superior performance. So, the answer is {\"answer\": \"TeSAN\"}." } ], "images": [ "scigen/train/2059.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VII: Mortality prediction performance on MIMIC III dataset." ], "reasoning": "TeSAN has the highest test ROC-AUC of 0.8064. The description explains that TeSAN's ability to attend to details in patient records, such as the relationship between diagnosis codes and temporal intervals, contributes to its superior performance.", "question": "Which model has the highest test ROC-AUC and why?", "answer": "So, the answer is {\"answer\": \"TeSAN\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nGloVe has the lowest test PR-AUC among all models.\n" }, { "role": "assistant", "content": "The table shows that GloVe has a test PR-AUC of 0.4172, which is the lowest among all the models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2059.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VII: Mortality prediction performance on MIMIC III dataset." ], "reasoning": "The table shows that GloVe has a test PR-AUC of 0.4172, which is the lowest among all the models listed.", "statement": "GloVe has the lowest test PR-AUC among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'TABLE VIII: Mortality prediction performance on MIMIC III dataset.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "TeSAN+ achieves the best performance on medical concept embedding, outperforming the best ablated model by 2.75% on PR-AUC and 2.54% on ROC-AUC over MIMIC III.As can be seen from the table, the proposed model achieves the best performance compared to the ablated models on medical concept embedding. We observe the same trend as the ablated performance of unsupervised tasks, in which Multi_Sa+ outperforms Normal_Sa+. TeSAN outperforms the best ablated model by 2.75% on PR-AUC and 2.54% on ROC-AUC over MIMIC III." } ], "images": [ "scigen/train/2060.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VIII: Mortality prediction performance on MIMIC III dataset." ], "reasoning": "TeSAN+ achieves the best performance on medical concept embedding, outperforming the best ablated model by 2.75% on PR-AUC and 2.54% on ROC-AUC over MIMIC III.", "description": "As can be seen from the table, the proposed model achieves the best performance compared to the ablated models on medical concept embedding. We observe the same trend as the ablated performance of unsupervised tasks, in which Multi_Sa+ outperforms Normal_Sa+. TeSAN outperforms the best ablated model by 2.75% on PR-AUC and 2.54% on ROC-AUC over MIMIC III.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'TABLE VIII: Mortality prediction performance on MIMIC III dataset.' in mind, break down and resolve the question below step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich model achieves the highest test PR-AUC and by how much does it outperform the best ablated model?\n" }, { "role": "assistant", "content": "TeSAN+ achieves the highest test PR-AUC of 0.5544. The best ablated model is Multi_SA+ with a test PR-AUC of 0.5269. The difference is 0.5544 - 0.5269 = 0.0275 or 2.75%. So, the answer is {\"answer\": \"TeSAN+, 2.75%\"}." } ], "images": [ "scigen/train/2060.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VIII: Mortality prediction performance on MIMIC III dataset." ], "reasoning": "TeSAN+ achieves the highest test PR-AUC of 0.5544. The best ablated model is Multi_SA+ with a test PR-AUC of 0.5269. The difference is 0.5544 - 0.5269 = 0.0275 or 2.75%.", "question": "Which model achieves the highest test PR-AUC and by how much does it outperform the best ablated model?", "answer": "So, the answer is {\"answer\": \"TeSAN+, 2.75%\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nTeSAN+ has the lowest test loss among all models.\n" }, { "role": "assistant", "content": "The table shows that TeSAN+ has a test loss of 0.6736, which is lower than Normal_SA+ (0.6762), Interval+ (0.6764), and Multi_SA+ (0.6759). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2060.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VIII: Mortality prediction performance on MIMIC III dataset." ], "reasoning": "The table shows that TeSAN+ has a test loss of 0.6736, which is lower than Normal_SA+ (0.6762), Interval+ (0.6764), and Multi_SA+ (0.6759).", "statement": "TeSAN+ has the lowest test loss among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: The human evaluation results. We calculate each score by averaging the rank of each model in corresponding metrics. Lower is better.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Three annotators ranked responses from different models on coherence and informativeness for two datasets, showing substantial improvements in these aspects.We randomly select 200 samples from the test sets in the two dialogue datasets separately. Each sample consists of query, future turn and responses generated by different models. We invite three annotators to rank the generated responses with respect to two aspects: coherence and informativeness. Ties are allowed. Coherence indicates how likely the generated response is relevant to both query and future turn. Informativeness specifies how much the information related to the context is contained in the generated response. The annotators are all well educated and are ignorant of the models by which the responses are generated. To ensure a stable comparison, the evaluated models consist of 2 Seq2Sea-att models with different initialization, Adver-REGS, DP-GAN, and 3 Posterior-GAN outputs. We also report the inter-annotator agreement to demonstrate the consistency of three annotators. The spearman’s rank correlation coefficient for coherence and informativeness is 0.3948 and 0.3406, with p<0.0001. Whereas, our model obtains substantial and consistent improvements in terms of coherence and informativeness on two public datasets, DailyDialog and Opensubtitles (OSDb)." } ], "images": [ "scigen/train/2061.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The human evaluation results. We calculate each score by averaging the rank of each model in corresponding metrics. Lower is better." ], "reasoning": "Three annotators ranked responses from different models on coherence and informativeness for two datasets, showing substantial improvements in these aspects.", "description": "We randomly select 200 samples from the test sets in the two dialogue datasets separately. Each sample consists of query, future turn and responses generated by different models. We invite three annotators to rank the generated responses with respect to two aspects: coherence and informativeness. Ties are allowed. Coherence indicates how likely the generated response is relevant to both query and future turn. Informativeness specifies how much the information related to the context is contained in the generated response. The annotators are all well educated and are ignorant of the models by which the responses are generated. To ensure a stable comparison, the evaluated models consist of 2 Seq2Sea-att models with different initialization, Adver-REGS, DP-GAN, and 3 Posterior-GAN outputs. We also report the inter-annotator agreement to demonstrate the consistency of three annotators. The spearman’s rank correlation coefficient for coherence and informativeness is 0.3948 and 0.3406, with p<0.0001. Whereas, our model obtains substantial and consistent improvements in terms of coherence and informativeness on two public datasets, DailyDialog and Opensubtitles (OSDb).", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model has the highest coherence score on the OpenSubtitles (OSDb) dataset?\nSolve the above question based on the table titled 'Table 2: The human evaluation results. We calculate each score by averaging the rank of each model in corresponding metrics. Lower is better.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest coherence score on the OpenSubtitles (OSDb) dataset, look at the 'Coherence' column under the OpenSubtitles (OSDb) section. The highest score is 4.0365, which corresponds to the Adver-REGS model. So, the answer is {\"answer\": \"Adver-REGS\"}." } ], "images": [ "scigen/train/2061.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The human evaluation results. We calculate each score by averaging the rank of each model in corresponding metrics. Lower is better." ], "reasoning": "To find the model with the highest coherence score on the OpenSubtitles (OSDb) dataset, look at the 'Coherence' column under the OpenSubtitles (OSDb) section. The highest score is 4.0365, which corresponds to the Adver-REGS model.", "question": "Which model has the highest coherence score on the OpenSubtitles (OSDb) dataset?", "answer": "So, the answer is {\"answer\": \"Adver-REGS\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Posterior-GAN model has the lowest informativeness score on the DailyDialog dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Posterior-GAN model has the lowest informativeness score on the DailyDialog dataset.\n" }, { "role": "assistant", "content": "To verify this statement, check the 'DailyDialog Informativeness' column. The Posterior-GAN model has a score of 3.2250, which is indeed the lowest among the listed models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2061.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The human evaluation results. We calculate each score by averaging the rank of each model in corresponding metrics. Lower is better." ], "reasoning": "To verify this statement, check the 'DailyDialog Informativeness' column. The Posterior-GAN model has a score of 3.2250, which is indeed the lowest among the listed models.", "statement": "The Posterior-GAN model has the lowest informativeness score on the DailyDialog dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: The automatic metrics evaluation results. Higher is better. “(F)”, “(B)” and “(A)” represent Posterior-GAN with a forward generative discriminator, a backward generative discriminator and both two discriminators, respectively.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Posterior-GAN achieves the best performance on all automatic evaluation metrics on both corpora, demonstrating the effectiveness of query-response-future turn triple training.DP-GAN effectively improves the response diversity by utilizing the language model cross-entropy rewards while the performance on BLEU and embedding-based similarities do not witness similar improvements in our settings. Posterior-GAN achieves the best performance on all the automatic evaluation metrics on both corpora, indicating the superiority of the query-response-future turn triple training, enabled by the forward and backward generative discriminators, in comparison with the state-of-the-art generative approaches. And the improvements of our model are significant with p≤0.001 (T-test)." } ], "images": [ "scigen/train/2062.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The automatic metrics evaluation results. Higher is better. “(F)”, “(B)” and “(A)” represent Posterior-GAN with a forward generative discriminator, a backward generative discriminator and both two discriminators, respectively." ], "reasoning": "Posterior-GAN achieves the best performance on all automatic evaluation metrics on both corpora, demonstrating the effectiveness of query-response-future turn triple training.", "description": "DP-GAN effectively improves the response diversity by utilizing the language model cross-entropy rewards while the performance on BLEU and embedding-based similarities do not witness similar improvements in our settings. Posterior-GAN achieves the best performance on all the automatic evaluation metrics on both corpora, indicating the superiority of the query-response-future turn triple training, enabled by the forward and backward generative discriminators, in comparison with the state-of-the-art generative approaches. And the improvements of our model are significant with p≤0.001 (T-test).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 1: The automatic metrics evaluation results. Higher is better. “(F)”, “(B)” and “(A)” represent Posterior-GAN with a forward generative discriminator, a backward generative discriminator and both two discriminators, respectively.', unfold the steps to solve the question given next. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nQuestion: Which model achieves the highest DailyDialog BLEU score?\n" }, { "role": "assistant", "content": "To find the model with the highest DailyDialog BLEU score, look at the DailyDialog BLEU column. Posterior-GAN(A) has the highest score of 0.2183. So, the answer is {\"answer\": \"Posterior-GAN(A)\"}" } ], "images": [ "scigen/train/2062.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The automatic metrics evaluation results. Higher is better. “(F)”, “(B)” and “(A)” represent Posterior-GAN with a forward generative discriminator, a backward generative discriminator and both two discriminators, respectively." ], "reasoning": "To find the model with the highest DailyDialog BLEU score, look at the DailyDialog BLEU column. Posterior-GAN(A) has the highest score of 0.2183.", "question": "Which model achieves the highest DailyDialog BLEU score?", "answer": "So, the answer is {\"answer\": \"Posterior-GAN(A)\"}" }, { "messages": [ { "role": "user", "content": "DP-GAN has the highest DailyDialog Dist-1 score. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nDP-GAN has the highest DailyDialog Dist-1 score.\n" }, { "role": "assistant", "content": "To verify this statement, check the DailyDialog Dist-1 column. Posterior-GAN(A) has the highest score of 0.0678, not DP-GAN. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2062.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The automatic metrics evaluation results. Higher is better. “(F)”, “(B)” and “(A)” represent Posterior-GAN with a forward generative discriminator, a backward generative discriminator and both two discriminators, respectively." ], "reasoning": "To verify this statement, check the DailyDialog Dist-1 column. Posterior-GAN(A) has the highest score of 0.0678, not DP-GAN.", "statement": "DP-GAN has the highest DailyDialog Dist-1 score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: The results of Embedding-based Averaged Greedy Matching for response y with the given query x and future conversations z, which reflects the coherence of the response, and Frequency-based Similarity, which illustrates the informativeness of the response.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Posterior-GAN outperforms other models in both Averaged Greedy Matching and Frequency-based Similarity, indicating it generates more informative and coherent responses.The informativeness of the responses is reflected by comparing the word frequency similarity between the generated response and the ground-truth response, where the responses are represented as a vector, and each element in the vector is denoted as the frequency of a word. Here, we use 2350 most frequent words from the training set of DailyDialog corpus without stop words and meaningless words. Regarding both the frequency similarity and matching degree, our model consistently outperforms the comparison models, which indicates that our model generates more informative and coherent responses." } ], "images": [ "scigen/train/2063.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The results of Embedding-based Averaged Greedy Matching for response y with the given query x and future conversations z, which reflects the coherence of the response, and Frequency-based Similarity, which illustrates the informativeness of the response." ], "reasoning": "Posterior-GAN outperforms other models in both Averaged Greedy Matching and Frequency-based Similarity, indicating it generates more informative and coherent responses.", "description": "The informativeness of the responses is reflected by comparing the word frequency similarity between the generated response and the ground-truth response, where the responses are represented as a vector, and each element in the vector is denoted as the frequency of a word. Here, we use 2350 most frequent words from the training set of DailyDialog corpus without stop words and meaningless words. Regarding both the frequency similarity and matching degree, our model consistently outperforms the comparison models, which indicates that our model generates more informative and coherent responses.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: The results of Embedding-based Averaged Greedy Matching for response y with the given query x and future conversations z, which reflects the coherence of the response, and Frequency-based Similarity, which illustrates the informativeness of the response.', answer the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest Averaged Greedy Matching score?\n" }, { "role": "assistant", "content": "By comparing the Averaged Greedy Matching scores of all models, Posterior-GAN has the highest score of 0.7276. So, the answer is {\"answer\": \"Posterior-GAN\"}." } ], "images": [ "scigen/train/2063.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The results of Embedding-based Averaged Greedy Matching for response y with the given query x and future conversations z, which reflects the coherence of the response, and Frequency-based Similarity, which illustrates the informativeness of the response." ], "reasoning": "By comparing the Averaged Greedy Matching scores of all models, Posterior-GAN has the highest score of 0.7276.", "question": "Which model has the highest Averaged Greedy Matching score?", "answer": "So, the answer is {\"answer\": \"Posterior-GAN\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nSeq2Seq-att has the lowest Frequency-based Similarity score.\n" }, { "role": "assistant", "content": "By comparing the Frequency-based Similarity scores of all models, Seq2Seq-att has the lowest score of 0.5478. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2063.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The results of Embedding-based Averaged Greedy Matching for response y with the given query x and future conversations z, which reflects the coherence of the response, and Frequency-based Similarity, which illustrates the informativeness of the response." ], "reasoning": "By comparing the Frequency-based Similarity scores of all models, Seq2Seq-att has the lowest score of 0.5478.", "statement": "Seq2Seq-att has the lowest Frequency-based Similarity score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Frequencies of various phenomena in WiRe57.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The resource contains 343 tuples, with 74% being binary relations and 21% having three arguments.Our resource is comprised of 343 relational facts (or tuples), three quarters of them binary relations. One in five have three arguments, sometimes “two objects” as in (This performance ; has made ; some economists ; optimistic) or more frequently a complement as in (His parents ; had to flee ; from Hungary ; during World War II). Five percent of them have four arguments or more : for instance (Tokyo ; ranked ; third ; in the International Financial Centres Development IndexEdit ; twice) and (The International Monetary Fund ; saw ; advanced economies ; advancing 1.8 percent ; [in] 2017)." } ], "images": [ "scigen/train/2064.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Frequencies of various phenomena in WiRe57." ], "reasoning": "The resource contains 343 tuples, with 74% being binary relations and 21% having three arguments.", "description": "Our resource is comprised of 343 relational facts (or tuples), three quarters of them binary relations. One in five have three arguments, sometimes “two objects” as in (This performance ; has made ; some economists ; optimistic) or more frequently a complement as in (His parents ; had to flee ; from Hungary ; during World War II). Five percent of them have four arguments or more : for instance (Tokyo ; ranked ; third ; in the International Financial Centres Development IndexEdit ; twice) and (The International Monetary Fund ; saw ; advanced economies ; advancing 1.8 percent ; [in] 2017).", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 1: Frequencies of various phenomena in WiRe57.\nQuestion: What percentage of tuples have three arguments and how many tuples are binary relations?\n" }, { "role": "assistant", "content": "The table shows that 21% of tuples have three arguments (n-ary, n = 3) and 254 tuples are binary relations. So, the answer is {\"answer\": \"21% and 254\"}." } ], "images": [ "scigen/train/2064.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Frequencies of various phenomena in WiRe57." ], "reasoning": "The table shows that 21% of tuples have three arguments (n-ary, n = 3) and 254 tuples are binary relations.", "question": "What percentage of tuples have three arguments and how many tuples are binary relations?", "answer": "So, the answer is {\"answer\": \"21% and 254\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The resource contains 186 tuples with inferred words. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe resource contains 186 tuples with inferred words.\n" }, { "role": "assistant", "content": "The table indicates that 186 tuples contain inferred words. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2064.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Frequencies of various phenomena in WiRe57." ], "reasoning": "The table indicates that 186 tuples contain inferred words.", "statement": "The resource contains 186 tuples with inferred words.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1: Comparison of initialization with phone CTC vs. AWE/AGWE, in terms of SWB dev WER (%). “AWE init” refers to initialization of the parameters of fAWE. “AGWE init” refers to initialization of the rows of A(2).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Segmental models do not improve significantly with larger vocabulary sizes, and setting a larger dropout value is important as vocabulary size increases.We find that, unlike our A2W CTC models and those of prior work [settle2019_a2w, yu2018_amt], the segmental models do not improve significantly (or at all) with larger vocabulary. One possible reason is that word representations in segmental models, especially for rare words, are harder to learn as they must be robust to variations in segment duration and content. Segmental models may require more data and overfit when many rare words are included. We find that it is important to set a larger dropout value as the vocabulary size increases. Initialization with the pre-trained AWE model reduces WER by 1–2% over phone CTC initialization. Initialization of A(2) This is consistent with our expectation: Since the AGWEs are composed from character sequences, they are less impacted by vocabulary size, helping with recognition of rare words." } ], "images": [ "scigen/train/2065.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of initialization with phone CTC vs. AWE/AGWE, in terms of SWB dev WER (%). “AWE init” refers to initialization of the parameters of fAWE. “AGWE init” refers to initialization of the rows of A(2)." ], "reasoning": "Segmental models do not improve significantly with larger vocabulary sizes, and setting a larger dropout value is important as vocabulary size increases.", "description": "We find that, unlike our A2W CTC models and those of prior work [settle2019_a2w, yu2018_amt], the segmental models do not improve significantly (or at all) with larger vocabulary. One possible reason is that word representations in segmental models, especially for rare words, are harder to learn as they must be robust to variations in segment duration and content. Segmental models may require more data and overfit when many rare words are included. We find that it is important to set a larger dropout value as the vocabulary size increases. Initialization with the pre-trained AWE model reduces WER by 1–2% over phone CTC initialization. Initialization of A(2) This is consistent with our expectation: Since the AGWEs are composed from character sequences, they are less impacted by vocabulary size, helping with recognition of rare words.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 1: Comparison of initialization with phone CTC vs. AWE/AGWE, in terms of SWB dev WER (%). “AWE init” refers to initialization of the parameters of fAWE. “AGWE init” refers to initialization of the rows of A(2).'\nQuestion: What is the WER for the A2W Segmental with AWE init system with a vocabulary size of 10K?\n" }, { "role": "assistant", "content": "The table shows the WER for different systems and vocabulary sizes. For the A2W Segmental with AWE init system, the WER for a vocabulary size of 10K is 16.0. So, the answer is {\"answer\": \"16.0\"}." } ], "images": [ "scigen/train/2065.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of initialization with phone CTC vs. AWE/AGWE, in terms of SWB dev WER (%). “AWE init” refers to initialization of the parameters of fAWE. “AGWE init” refers to initialization of the rows of A(2)." ], "reasoning": "The table shows the WER for different systems and vocabulary sizes. For the A2W Segmental with AWE init system, the WER for a vocabulary size of 10K is 16.0.", "question": "What is the WER for the A2W Segmental with AWE init system with a vocabulary size of 10K?", "answer": "So, the answer is {\"answer\": \"16.0\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The A2W Segmental with phone CTC init system has a lower WER than the A2W CTC with phone CTC init system for all vocabulary sizes., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe A2W Segmental with phone CTC init system has a lower WER than the A2W CTC with phone CTC init system for all vocabulary sizes.\n" }, { "role": "assistant", "content": "The table shows that the A2W Segmental with phone CTC init system has WERs of 18.2, 17.9, and 18.0 for vocab sizes 5K, 10K, and 20K respectively, while the A2W CTC with phone CTC init system has WERs of 19.3, 18.0, and 17.7 for the same vocab sizes. The statement is not true for the 20K vocab size. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2065.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of initialization with phone CTC vs. AWE/AGWE, in terms of SWB dev WER (%). “AWE init” refers to initialization of the parameters of fAWE. “AGWE init” refers to initialization of the rows of A(2)." ], "reasoning": "The table shows that the A2W Segmental with phone CTC init system has WERs of 18.2, 17.9, and 18.0 for vocab sizes 5K, 10K, and 20K respectively, while the A2W CTC with phone CTC init system has WERs of 19.3, 18.0, and 17.7 for the same vocab sizes. The statement is not true for the 20K vocab size.", "statement": "The A2W Segmental with phone CTC init system has a lower WER than the A2W CTC with phone CTC init system for all vocabulary sizes.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: WER (%) results on SWB/CH evaluation sets.'.\n" }, { "role": "assistant", "content": "Training with SpecAugment improves performance by approximately 1% across all vocabulary sizes, and the model outperforms previous models on the SWB test set except for the one trained with speed perturbation.Training with SpecAugment [park2019specaugment] produces an additional gain of ∼1% across all vocabulary sizes. On the SWB test set, our model also improves over all previous models except for the one of [yu2018_amt], which is larger and trained with speed perturbation. On the CH test set performance also improves over CTC except at the largest vocabulary size. We hypothesize that CH has even larger variability within segments, but this remains to be investigated. We leave as future work the incorporation of larger models and speed perturbation." } ], "images": [ "scigen/train/2066.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: WER (%) results on SWB/CH evaluation sets." ], "reasoning": "Training with SpecAugment improves performance by approximately 1% across all vocabulary sizes, and the model outperforms previous models on the SWB test set except for the one trained with speed perturbation.", "description": "Training with SpecAugment [park2019specaugment] produces an additional gain of ∼1% across all vocabulary sizes. On the SWB test set, our model also improves over all previous models except for the one of [yu2018_amt], which is larger and trained with speed perturbation. On the CH test set performance also improves over CTC except at the largest vocabulary size. We hypothesize that CH has even larger variability within segments, but this remains to be investigated. We leave as future work the incorporation of larger models and speed perturbation.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 2: WER (%) results on SWB/CH evaluation sets.' in mind, break down and resolve the question below step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich system has the lowest WER for the Vocab 20K size?\n" }, { "role": "assistant", "content": "By examining the table, the system with the lowest WER for the Vocab 20K size is the one with Speed Perturbation, which has a WER of 20.8. Thus, the answer is {\"answer\": \"Speed Perturbation\"}." } ], "images": [ "scigen/train/2066.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: WER (%) results on SWB/CH evaluation sets." ], "reasoning": "By examining the table, the system with the lowest WER for the Vocab 20K size is the one with Speed Perturbation, which has a WER of 20.8.", "question": "Which system has the lowest WER for the Vocab 20K size?", "answer": "Thus, the answer is {\"answer\": \"Speed Perturbation\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The SpecAugment system has the lowest WER for the Vocab 20K size. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe SpecAugment system has the lowest WER for the Vocab 20K size.\n" }, { "role": "assistant", "content": "The table shows that the SpecAugment system has a WER of 22.5 for the Vocab 20K size, while the Speed Perturbation system has a lower WER of 20.8. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2066.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: WER (%) results on SWB/CH evaluation sets." ], "reasoning": "The table shows that the SpecAugment system has a WER of 22.5 for the Vocab 20K size, while the Speed Perturbation system has a lower WER of 20.8.", "statement": "The SpecAugment system has the lowest WER for the Vocab 20K size.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: accuracy, precisionmicro and recallmicro for the models.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The KNN model outperformed the MLP model in terms of accuracy and recall, but had slightly lower precision.The KNN model had a higher accuracy of 0.554 compared to MPL’s accuracy of 0.37 and the KNN model had a higher recall but slightly lower precision than the MLP model." } ], "images": [ "scigen/train/2067.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: accuracy, precisionmicro and recallmicro for the models." ], "reasoning": "The KNN model outperformed the MLP model in terms of accuracy and recall, but had slightly lower precision.", "description": "The KNN model had a higher accuracy of 0.554 compared to MPL’s accuracy of 0.37 and the KNN model had a higher recall but slightly lower precision than the MLP model.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 4: accuracy, precisionmicro and recallmicro for the models.'. Solve the following question based on the table.\n\nQuestion: Which model had a higher recall and what was its value?\nLet's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the KNN model had a recall of 0.677, which is higher than the MLP model's recall of 0.597. So, the answer is {\"answer\": \"KNN with 0.677\"}." } ], "images": [ "scigen/train/2067.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: accuracy, precisionmicro and recallmicro for the models." ], "reasoning": "The table shows that the KNN model had a recall of 0.677, which is higher than the MLP model's recall of 0.597.", "question": "Which model had a higher recall and what was its value?", "answer": "So, the answer is {\"answer\": \"KNN with 0.677\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The MLP model had a higher precision than the KNN model., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe MLP model had a higher precision than the KNN model.\n" }, { "role": "assistant", "content": "The table shows that the MLP model had a precision of 0.816, which is higher than the KNN model's precision of 0.807. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2067.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: accuracy, precisionmicro and recallmicro for the models." ], "reasoning": "The table shows that the MLP model had a precision of 0.816, which is higher than the KNN model's precision of 0.807.", "statement": "The MLP model had a higher precision than the KNN model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Comparison of model architecture perplexity (PPL) trained from scratch for 200K iterations. The top half of the table are iso-parameter count experiments while the bottom half are iso-architecture experiments. Note that h, l, and A define the sizes of the encoder and decoder transformers individually.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Models with similar parameter counts show GCC-Dec has an advantage, and processing reference and conversation together in a single decoder results in superior performance.We see that for models with similar parameter counts the GCC-Dec has the advantage, and that under similar decoder sizes having direct access to the reference material (i.e. processing the reference and conversation together in a single decoder) results in superior performance. This indicates that the added complexity from additional encoding is not needed and that concatenating all relevant context is both the simplest, and most effective means of incorporating previous information. Since the parameters are shared and no latent variable bottleneck is used, the model has full access to the information from the references. With this, the self attention operation is able to automatically modify the model’s output distribution in complex, non-linear ways without the need for manual architecture design." } ], "images": [ "scigen/train/2068.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of model architecture perplexity (PPL) trained from scratch for 200K iterations. The top half of the table are iso-parameter count experiments while the bottom half are iso-architecture experiments. Note that h, l, and A define the sizes of the encoder and decoder transformers individually." ], "reasoning": "Models with similar parameter counts show GCC-Dec has an advantage, and processing reference and conversation together in a single decoder results in superior performance.", "description": "We see that for models with similar parameter counts the GCC-Dec has the advantage, and that under similar decoder sizes having direct access to the reference material (i.e. processing the reference and conversation together in a single decoder) results in superior performance. This indicates that the added complexity from additional encoding is not needed and that concatenating all relevant context is both the simplest, and most effective means of incorporating previous information. Since the parameters are shared and no latent variable bottleneck is used, the model has full access to the information from the references. With this, the self attention operation is able to automatically modify the model’s output distribution in complex, non-linear ways without the need for manual architecture design.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Comparison of model architecture perplexity (PPL) trained from scratch for 200K iterations. The top half of the table are iso-parameter count experiments while the bottom half are iso-architecture experiments. Note that h, l, and A define the sizes of the encoder and decoder transformers individually.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. \n\nQuestion: Which model has the lowest perplexity (PPL) and what are its parameters?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By examining the table, the model with the lowest PPL is GCC-Dec with a PPL of 19.10. The parameters for this model are 355M. So, the answer is {\"answer\": \"GCC-Dec with 355M parameters\"}." } ], "images": [ "scigen/train/2068.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of model architecture perplexity (PPL) trained from scratch for 200K iterations. The top half of the table are iso-parameter count experiments while the bottom half are iso-architecture experiments. Note that h, l, and A define the sizes of the encoder and decoder transformers individually." ], "reasoning": "By examining the table, the model with the lowest PPL is GCC-Dec with a PPL of 19.10. The parameters for this model are 355M.", "question": "Which model has the lowest perplexity (PPL) and what are its parameters?", "answer": "So, the answer is {\"answer\": \"GCC-Dec with 355M parameters\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis GCC-Dec has the lowest perplexity among models with similar parameter counts. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nGCC-Dec has the lowest perplexity among models with similar parameter counts.\n" }, { "role": "assistant", "content": "The table shows that GCC-Dec has a perplexity of 19.10, which is the lowest among models with similar parameter counts (355M). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2068.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of model architecture perplexity (PPL) trained from scratch for 200K iterations. The top half of the table are iso-parameter count experiments while the bottom half are iso-architecture experiments. Note that h, l, and A define the sizes of the encoder and decoder transformers individually." ], "reasoning": "The table shows that GCC-Dec has a perplexity of 19.10, which is the lowest among models with similar parameter counts (355M).", "statement": "GCC-Dec has the lowest perplexity among models with similar parameter counts.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Comparison of models with and without pre-trained initializations. All models in this study have 355M parameters with h=1024, l=24, and A=16.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Pre-trained models achieve better perplexity with less data, and including reference history further improves performance.We observe that with less data, the pre-trained model quickly eclipses the model trained from scratch and achieves better perplexity, highlighting the need for models with robust linguistic features learned from non-Reddit corpora. Additionally, including reference history improves performance as well. This difference of 0.47, while smaller than differences between results from different model sizes, is notable due to the large amount of out of sample data that the models were tested on." } ], "images": [ "scigen/train/2069.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of models with and without pre-trained initializations. All models in this study have 355M parameters with h=1024, l=24, and A=16." ], "reasoning": "Pre-trained models achieve better perplexity with less data, and including reference history further improves performance.", "description": "We observe that with less data, the pre-trained model quickly eclipses the model trained from scratch and achieves better perplexity, highlighting the need for models with robust linguistic features learned from non-Reddit corpora. Additionally, including reference history improves performance as well. This difference of 0.47, while smaller than differences between results from different model sizes, is notable due to the large amount of out of sample data that the models were tested on.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Comparison of models with and without pre-trained initializations. All models in this study have 355M parameters with h=1024, l=24, and A=16.\nQuestion: Which model achieved the best perplexity and what was its batch size?\nSolve the above question based on the table information. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the GCC-Dec model with pre-training and 70K iterations achieved the best perplexity of 18.92, and its batch size was 128. So, the answer is {\"answer\": \"GCC-Dec with pre-training and batch size 128\"}." } ], "images": [ "scigen/train/2069.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of models with and without pre-trained initializations. All models in this study have 355M parameters with h=1024, l=24, and A=16." ], "reasoning": "The table shows that the GCC-Dec model with pre-training and 70K iterations achieved the best perplexity of 18.92, and its batch size was 128.", "question": "Which model achieved the best perplexity and what was its batch size?", "answer": "So, the answer is {\"answer\": \"GCC-Dec with pre-training and batch size 128\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model with the highest number of iterations achieved the best perplexity.\n" }, { "role": "assistant", "content": "The table shows that the model with the highest number of iterations (200K) had a perplexity of 19.10, which is not the best. The best perplexity (18.92) was achieved by a model with 70K iterations. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2069.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of models with and without pre-trained initializations. All models in this study have 355M parameters with h=1024, l=24, and A=16." ], "reasoning": "The table shows that the model with the highest number of iterations (200K) had a perplexity of 19.10, which is not the best. The best perplexity (18.92) was achieved by a model with 70K iterations.", "statement": "The model with the highest number of iterations achieved the best perplexity.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Comparison of model performance as size varies. All models are trained from a pre-trained checkpoint for 70K iterations with a batch size of 128.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The ablation study on GCC-Dec models shows that increasing the model size significantly improves performance, as indicated by lower perplexity (PPL) values.Finally, we performed an ablation study on the size of GCC-Dec used. All models fine-tuned from a pre-trained checkpoint for 70,000 iterations at a global batch size of 128. As shown in Shoeybi et al. This increase in performance is significant as it has been shown that for conversational models there is a correlation between held-out perplexity measures and human-likeness of sampled turns, especially for models within the same family Adiwardana et al." } ], "images": [ "scigen/train/2070.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison of model performance as size varies. All models are trained from a pre-trained checkpoint for 70K iterations with a batch size of 128." ], "reasoning": "The ablation study on GCC-Dec models shows that increasing the model size significantly improves performance, as indicated by lower perplexity (PPL) values.", "description": "Finally, we performed an ablation study on the size of GCC-Dec used. All models fine-tuned from a pre-trained checkpoint for 70,000 iterations at a global batch size of 128. As shown in Shoeybi et al. This increase in performance is significant as it has been shown that for conversational models there is a correlation between held-out perplexity measures and human-likeness of sampled turns, especially for models within the same family Adiwardana et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 5: Comparison of model performance as size varies. All models are trained from a pre-trained checkpoint for 70K iterations with a batch size of 128.', unfold the steps to solve the question given next. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nQuestion: What is the perplexity (PPL) of the GCC-Dec model with the highest number of parameters?\n" }, { "role": "assistant", "content": "To find the perplexity of the GCC-Dec model with the highest number of parameters, identify the row with the largest 'Params' value, which is 8.3B, and then look at the corresponding PPL value, which is 13.24. So, the answer is {\"answer\": \"13.24\"}." } ], "images": [ "scigen/train/2070.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison of model performance as size varies. All models are trained from a pre-trained checkpoint for 70K iterations with a batch size of 128." ], "reasoning": "To find the perplexity of the GCC-Dec model with the highest number of parameters, identify the row with the largest 'Params' value, which is 8.3B, and then look at the corresponding PPL value, which is 13.24.", "question": "What is the perplexity (PPL) of the GCC-Dec model with the highest number of parameters?", "answer": "So, the answer is {\"answer\": \"13.24\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe GCC-Dec model with 1.2B parameters has a perplexity (PPL) of 16.08.\n" }, { "role": "assistant", "content": "The table shows that the GCC-Dec model with 1.2B parameters indeed has a perplexity (PPL) of 16.08. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2070.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison of model performance as size varies. All models are trained from a pre-trained checkpoint for 70K iterations with a batch size of 128." ], "reasoning": "The table shows that the GCC-Dec model with 1.2B parameters indeed has a perplexity (PPL) of 16.08.", "statement": "The GCC-Dec model with 1.2B parameters has a perplexity (PPL) of 16.08.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 10: Mean statistic of the hypothesis sentences word overlapped with premises tables across all discussed train-test splits (standard deviation is in range 0.17 to 0.22)'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The overlap values for α1, α2, and α3 are similar across the labels Entail, Neutral, and Contradict.Stop words like a, the, it, of, etc. are removed. We observe that the overlap is almost similar across labels." } ], "images": [ "scigen/train/2071.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Mean statistic of the hypothesis sentences word overlapped with premises tables across all discussed train-test splits (standard deviation is in range 0.17 to 0.22)" ], "reasoning": "The overlap values for α1, α2, and α3 are similar across the labels Entail, Neutral, and Contradict.", "description": "Stop words like a, the, it, of, etc. are removed. We observe that the overlap is almost similar across labels.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 10: Mean statistic of the hypothesis sentences word overlapped with premises tables across all discussed train-test splits (standard deviation is in range 0.17 to 0.22)', solve this question and give detail solutions. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the highest training score among the labels?\n" }, { "role": "assistant", "content": "To find the highest training score, compare the 'Train' column values for Entail (0.52), Neutral (0.46), and Contradict (0.44). The highest value is 0.52. So, the answer is {\"answer\": \"0.52\"}" } ], "images": [ "scigen/train/2071.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Mean statistic of the hypothesis sentences word overlapped with premises tables across all discussed train-test splits (standard deviation is in range 0.17 to 0.22)" ], "reasoning": "To find the highest training score, compare the 'Train' column values for Entail (0.52), Neutral (0.46), and Contradict (0.44). The highest value is 0.52.", "question": "What is the highest training score among the labels?", "answer": "So, the answer is {\"answer\": \"0.52\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The development score for the Neutral label is higher than for the Contradict label., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe development score for the Neutral label is higher than for the Contradict label.\n" }, { "role": "assistant", "content": "The development score for Neutral is 0.44, while for Contradict it is 0.43. Since 0.44 is higher than 0.43, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2071.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Mean statistic of the hypothesis sentences word overlapped with premises tables across all discussed train-test splits (standard deviation is in range 0.17 to 0.22)" ], "reasoning": "The development score for Neutral is 0.44, while for Contradict it is 0.43. Since 0.44 is higher than 0.43, the statement is supported.", "statement": "The development score for the Neutral label is higher than for the Contradict label.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Number of tables and premise-hypothesis pairs for each data split'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The dataset consists of 23,738 sentences, divided almost equally among entailments, contradictions, and neutrals, with similar average hypothesis lengths across all splits.In total, we collected 23,738 sentences split almost equally among entailments, contradictions, and neutrals. In all the splits, the average length of the hypotheses is similar." } ], "images": [ "scigen/train/2072.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Number of tables and premise-hypothesis pairs for each data split" ], "reasoning": "The dataset consists of 23,738 sentences, divided almost equally among entailments, contradictions, and neutrals, with similar average hypothesis lengths across all splits.", "description": "In total, we collected 23,738 sentences split almost equally among entailments, contradictions, and neutrals. In all the splits, the average length of the hypotheses is similar.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 1: Number of tables and premise-hypothesis pairs for each data split', unfold the question presented below, detailing every step of your calculation. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nHow many pairs are there in the training and development splits combined?\n" }, { "role": "assistant", "content": "The training split has 16,538 pairs and the development split has 1,800 pairs. Adding these together gives 16,538 + 1,800 = 18,338 pairs. So, the answer is {\"answer\": \"18338\"}" } ], "images": [ "scigen/train/2072.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Number of tables and premise-hypothesis pairs for each data split" ], "reasoning": "The training split has 16,538 pairs and the development split has 1,800 pairs. Adding these together gives 16,538 + 1,800 = 18,338 pairs.", "question": "How many pairs are there in the training and development splits combined?", "answer": "So, the answer is {\"answer\": \"18338\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The dataset has 1,800 pairs in each of the α test splits. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe dataset has 1,800 pairs in each of the α test splits.\n" }, { "role": "assistant", "content": "The table shows that each of the α1, α2, and α3 test splits has 1,800 pairs. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2072.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Number of tables and premise-hypothesis pairs for each data split" ], "reasoning": "The table shows that each of the α1, α2, and α3 test splits has 1,800 pairs.", "statement": "The dataset has 1,800 pairs in each of the α test splits.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: Accuracy of test splits with structured representation of premises with RoBERTaL trained on SNLI and MultiNLI training data', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Full premises improve performance over WMD-1 due to the ineffectiveness of WMD in identifying the correct premise sentence and the need for multi-row reasoning.Results and Analysis: We observe that full premises slightly improve performance over the WMD-1 ones. This might be due to a) ineffectiveness of WMD to identify the correct premise sentence, and b) multi-row reasoning." } ], "images": [ "scigen/train/2073.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Accuracy of test splits with structured representation of premises with RoBERTaL trained on SNLI and MultiNLI training data" ], "reasoning": "Full premises improve performance over WMD-1 due to the ineffectiveness of WMD in identifying the correct premise sentence and the need for multi-row reasoning.", "description": "Results and Analysis: We observe that full premises slightly improve performance over the WMD-1 ones. This might be due to a) ineffectiveness of WMD to identify the correct premise sentence, and b) multi-row reasoning.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 5: Accuracy of test splits with structured representation of premises with RoBERTaL trained on SNLI and MultiNLI training data'. Solve the following question based on the table.\n\nQuestion: What is the performance difference between WMD-1 and Para when trained on MultiNLI for the α1 metric?\nLet's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the performance difference, subtract the α1 score of WMD-1 (44.67) from the α1 score of Para (53.83). So, the answer is {\"answer\": \"9.16\"}" } ], "images": [ "scigen/train/2073.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Accuracy of test splits with structured representation of premises with RoBERTaL trained on SNLI and MultiNLI training data" ], "reasoning": "To find the performance difference, subtract the α1 score of WMD-1 (44.67) from the α1 score of Para (53.83).", "question": "What is the performance difference between WMD-1 and Para when trained on MultiNLI for the α1 metric?", "answer": "So, the answer is {\"answer\": \"9.16\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nPara performs better than WMD-1 on the Dev metric when trained on SNLI.\n" }, { "role": "assistant", "content": "The table shows that for the Dev metric, Para has a score of 54.44, which is higher than the WMD-1 score of 49.44 when trained on SNLI. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2073.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Accuracy of test splits with structured representation of premises with RoBERTaL trained on SNLI and MultiNLI training data" ], "reasoning": "The table shows that for the Dev metric, Para has a score of 54.44, which is higher than the WMD-1 score of 49.44 when trained on SNLI.", "statement": "Para performs better than WMD-1 on the Dev metric when trained on SNLI.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 1: Size of test sets: total number of test instances and with regard to the distance between sentences requiring consistency (in the number of sentences). For ellipsis, the two sets correspond to whether a model has to predict correct NP inflection, or correct verb sense (VP ellipsis).'.\n" }, { "role": "assistant", "content": "The test suite includes four sets with contrastive examples to evaluate system adaptation to context, with specific development and test sets for deixis and lexical cohesion, and two test sets for ellipsis.There are four test sets in the suite. Each test set contains contrastive examples. It is specifically designed to test the ability of a system to adapt to contextual information and handle the phenomenon under consideration. Each test instance consists of a true example (a sequence of sentences and their reference translation from the data) and several contrastive translations which differ from the true one only in one specific aspect. All contrastive translations are correct and plausible translations at the sentence level, and only context reveals the inconsistencies between them. The system is asked to score each candidate translation, and we compute the system accuracy as the proportion of times the true translation is preferred to the contrastive ones. The suites for deixis and lexical cohesion are split into development and test sets, with 500 examples from each used for validation purposes and the rest for testing. Convergence of both consistency scores on these development sets and BLEU score on a general development set are used as early stopping criteria in models training. For ellipsis, there is no dedicated development set, so we evaluate on all the ellipsis data and do not use it for development. There are two different test sets for ellipsis." } ], "images": [ "scigen/train/2074.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Size of test sets: total number of test instances and with regard to the distance between sentences requiring consistency (in the number of sentences). For ellipsis, the two sets correspond to whether a model has to predict correct NP inflection, or correct verb sense (VP ellipsis)." ], "reasoning": "The test suite includes four sets with contrastive examples to evaluate system adaptation to context, with specific development and test sets for deixis and lexical cohesion, and two test sets for ellipsis.", "description": "There are four test sets in the suite. Each test set contains contrastive examples. It is specifically designed to test the ability of a system to adapt to contextual information and handle the phenomenon under consideration. Each test instance consists of a true example (a sequence of sentences and their reference translation from the data) and several contrastive translations which differ from the true one only in one specific aspect. All contrastive translations are correct and plausible translations at the sentence level, and only context reveals the inconsistencies between them. The system is asked to score each candidate translation, and we compute the system accuracy as the proportion of times the true translation is preferred to the contrastive ones. The suites for deixis and lexical cohesion are split into development and test sets, with 500 examples from each used for validation purposes and the rest for testing. Convergence of both consistency scores on these development sets and BLEU score on a general development set are used as early stopping criteria in models training. For ellipsis, there is no dedicated development set, so we evaluate on all the ellipsis data and do not use it for development. There are two different test sets for ellipsis.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 1: Size of test sets: total number of test instances and with regard to the distance between sentences requiring consistency (in the number of sentences). For ellipsis, the two sets correspond to whether a model has to predict correct NP inflection, or correct verb sense (VP ellipsis).' to sequentially solve the question presented below. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nHow many examples are used for validation purposes in the deixis and lexical cohesion test suites?\n" }, { "role": "assistant", "content": "The description states that the suites for deixis and lexical cohesion are split into development and test sets, with 500 examples from each used for validation purposes. So, the answer is {\"answer\": \"1000\"}." } ], "images": [ "scigen/train/2074.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Size of test sets: total number of test instances and with regard to the distance between sentences requiring consistency (in the number of sentences). For ellipsis, the two sets correspond to whether a model has to predict correct NP inflection, or correct verb sense (VP ellipsis)." ], "reasoning": "The description states that the suites for deixis and lexical cohesion are split into development and test sets, with 500 examples from each used for validation purposes.", "question": "How many examples are used for validation purposes in the deixis and lexical cohesion test suites?", "answer": "So, the answer is {\"answer\": \"1000\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe ellipsis test suite has a dedicated development set.\n" }, { "role": "assistant", "content": "The description mentions that for ellipsis, there is no dedicated development set, so we evaluate on all the ellipsis data and do not use it for development. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2074.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Size of test sets: total number of test instances and with regard to the distance between sentences requiring consistency (in the number of sentences). For ellipsis, the two sets correspond to whether a model has to predict correct NP inflection, or correct verb sense (VP ellipsis)." ], "reasoning": "The description mentions that for ellipsis, there is no dedicated development set, so we evaluate on all the ellipsis data and do not use it for development.", "statement": "The ellipsis test suite has a dedicated development set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: BLEU scores. For CADec, the original implementation was used.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "DocRepair achieves the highest BLEU score by accounting for extra-sentential dependencies, outperforming both the baseline and CADec models.(we evaluate translations of 4-sentence fragments). To see which part of the improvement is due to fixing agreement between sentences rather than simply sentence-level post-editing, we train the same repair model at the sentence level. Each sentence in a group is now corrected separately, then they are put back together in a group. One can see that most of the improvement comes from accounting for extra-sentential dependencies. DocRepair outperforms the baseline and CADec by 0.7 BLEU, and its sentence-level repair version by 0.5 BLEU." } ], "images": [ "scigen/train/2075.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: BLEU scores. For CADec, the original implementation was used." ], "reasoning": "DocRepair achieves the highest BLEU score by accounting for extra-sentential dependencies, outperforming both the baseline and CADec models.", "description": "(we evaluate translations of 4-sentence fragments). To see which part of the improvement is due to fixing agreement between sentences rather than simply sentence-level post-editing, we train the same repair model at the sentence level. Each sentence in a group is now corrected separately, then they are put back together in a group. One can see that most of the improvement comes from accounting for extra-sentential dependencies. DocRepair outperforms the baseline and CADec by 0.7 BLEU, and its sentence-level repair version by 0.5 BLEU.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2: BLEU scores. For CADec, the original implementation was used.', unfold the steps to solve the question given next. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nQuestion: Which model achieves the highest BLEU score and by how much does it outperform the baseline?\n" }, { "role": "assistant", "content": "DocRepair achieves the highest BLEU score of 34.60, outperforming the baseline model by 0.69 BLEU points. So, the answer is {\"answer\": \"DocRepair, 0.69\"}." } ], "images": [ "scigen/train/2075.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: BLEU scores. For CADec, the original implementation was used." ], "reasoning": "DocRepair achieves the highest BLEU score of 34.60, outperforming the baseline model by 0.69 BLEU points.", "question": "Which model achieves the highest BLEU score and by how much does it outperform the baseline?", "answer": "So, the answer is {\"answer\": \"DocRepair, 0.69\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe sentence-level repair model outperforms the CADec model.\n" }, { "role": "assistant", "content": "The sentence-level repair model has a BLEU score of 34.12, which is higher than the CADec model's BLEU score of 33.86. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2075.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: BLEU scores. For CADec, the original implementation was used." ], "reasoning": "The sentence-level repair model has a BLEU score of 34.12, which is higher than the CADec model's BLEU score of 33.86.", "statement": "The sentence-level repair model outperforms the CADec model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: Results on contrastive test sets for specific contextual phenomena (deixis, lexical consistency, ellipsis (inflection), and VP ellipsis).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "DocRepair shows substantial improvements over both the baseline and CADec in deixis, lexical cohesion, and ellipsis (infl.), but there is a drop in VP ellipsis.For deixis, lexical cohesion and ellipsis (infl.) we see substantial improvements over both the baseline and CADec. The largest improvement over CADec (22.5 percentage points) is for lexical cohesion. However, there is a drop of almost 5 percentage points for VP ellipsis. We hypothesize that this is because it is hard to learn to correct inconsistencies in translations caused by VP ellipsis relying on monolingual data alone. There is no VP ellipsis in Russian, and when translating auxiliary “did” the model has to guess the main verb. When translating from Russian, main verbs are unlikely to be translated as the auxiliary “do” in English, and hence the VP ellipsis is rarely present on the English side. This implies the model trained using the round-trip translations will not be exposed to many VP ellipsis examples in training." } ], "images": [ "scigen/train/2076.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on contrastive test sets for specific contextual phenomena (deixis, lexical consistency, ellipsis (inflection), and VP ellipsis)." ], "reasoning": "DocRepair shows substantial improvements over both the baseline and CADec in deixis, lexical cohesion, and ellipsis (infl.), but there is a drop in VP ellipsis.", "description": "For deixis, lexical cohesion and ellipsis (infl.) we see substantial improvements over both the baseline and CADec. The largest improvement over CADec (22.5 percentage points) is for lexical cohesion. However, there is a drop of almost 5 percentage points for VP ellipsis. We hypothesize that this is because it is hard to learn to correct inconsistencies in translations caused by VP ellipsis relying on monolingual data alone. There is no VP ellipsis in Russian, and when translating auxiliary “did” the model has to guess the main verb. When translating from Russian, main verbs are unlikely to be translated as the auxiliary “do” in English, and hence the VP ellipsis is rarely present on the English side. This implies the model trained using the round-trip translations will not be exposed to many VP ellipsis examples in training.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Results on contrastive test sets for specific contextual phenomena (deixis, lexical consistency, ellipsis (inflection), and VP ellipsis).', answer the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the percentage point improvement of DocRepair over CADec for lexical cohesion?\n" }, { "role": "assistant", "content": "The table shows that DocRepair has a lexical cohesion score of 80.6, while CADec has a score of 58.1. The improvement is calculated as 80.6 - 58.1. So, the answer is {\"answer\": \"22.5\"}." } ], "images": [ "scigen/train/2076.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on contrastive test sets for specific contextual phenomena (deixis, lexical consistency, ellipsis (inflection), and VP ellipsis)." ], "reasoning": "The table shows that DocRepair has a lexical cohesion score of 80.6, while CADec has a score of 58.1. The improvement is calculated as 80.6 - 58.1.", "question": "What is the percentage point improvement of DocRepair over CADec for lexical cohesion?", "answer": "So, the answer is {\"answer\": \"22.5\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: DocRepair has the highest score for VP ellipsis., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nDocRepair has the highest score for VP ellipsis.\n" }, { "role": "assistant", "content": "The table shows that CADec has a VP ellipsis score of 80.0, which is higher than DocRepair's score of 75.2. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2076.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on contrastive test sets for specific contextual phenomena (deixis, lexical consistency, ellipsis (inflection), and VP ellipsis)." ], "reasoning": "The table shows that CADec has a VP ellipsis score of 80.0, which is higher than DocRepair's score of 75.2.", "statement": "DocRepair has the highest score for VP ellipsis.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5: Human evaluation results, comparing DocRepair with baseline.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Annotators marked 52% of translations as equal in quality, and among the remaining cases, 73% preferred DocRepair translations over baseline ones.In about 52% of the cases annotators marked translations as having equal quality. Among the cases where one of the translations was marked better than the other, the DocRepair translation was marked better in 73% of the cases. This shows a strong preference of the annotators for corrected translations over the baseline ones." } ], "images": [ "scigen/train/2077.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Human evaluation results, comparing DocRepair with baseline." ], "reasoning": "Annotators marked 52% of translations as equal in quality, and among the remaining cases, 73% preferred DocRepair translations over baseline ones.", "description": "In about 52% of the cases annotators marked translations as having equal quality. Among the cases where one of the translations was marked better than the other, the DocRepair translation was marked better in 73% of the cases. This shows a strong preference of the annotators for corrected translations over the baseline ones.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 5: Human evaluation results, comparing DocRepair with baseline.' to sequentially solve the question presented below. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat percentage of cases were marked as better translations?\n" }, { "role": "assistant", "content": "From the table, 35% of the cases were marked as better translations. So, the answer is {\"answer\": \"35%\"}." } ], "images": [ "scigen/train/2077.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Human evaluation results, comparing DocRepair with baseline." ], "reasoning": "From the table, 35% of the cases were marked as better translations.", "question": "What percentage of cases were marked as better translations?", "answer": "So, the answer is {\"answer\": \"35%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Annotators marked 13% of the translations as worse. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nAnnotators marked 13% of the translations as worse.\n" }, { "role": "assistant", "content": "The table shows that 13% of the translations were marked as worse. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2077.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Human evaluation results, comparing DocRepair with baseline." ], "reasoning": "The table shows that 13% of the translations were marked as worse.", "statement": "Annotators marked 13% of the translations as worse.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'TABLE IV: Language modeling task'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "MorphMine outperforms other morpheme-enriched embeddings in language modeling due to its utilization of morphemes of multiple granularity, capturing semantic meaning of rare and OOV words.We observe that across all segmentation-based morpheme-enriched embeddings perform better in language modeling over traditional skip-gram. In contrast, FastText’s indiscriminate enumeration of all possible morphemes appears to perform much poorer in this task. Finally, MorphMine outperforms the other morpheme enriched baselines. This may be due to MorphMine utilizing morphemes of mutliple granularity which closely capture semantic meaning of rare and OOV words at the largest granularity." } ], "images": [ "scigen/train/2078.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Language modeling task" ], "reasoning": "MorphMine outperforms other morpheme-enriched embeddings in language modeling due to its utilization of morphemes of multiple granularity, capturing semantic meaning of rare and OOV words.", "description": "We observe that across all segmentation-based morpheme-enriched embeddings perform better in language modeling over traditional skip-gram. In contrast, FastText’s indiscriminate enumeration of all possible morphemes appears to perform much poorer in this task. Finally, MorphMine outperforms the other morpheme enriched baselines. This may be due to MorphMine utilizing morphemes of mutliple granularity which closely capture semantic meaning of rare and OOV words at the largest granularity.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'TABLE IV: Language modeling task' and methodically tackle the question that follows. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich method has the lowest perplexity across all languages?\n" }, { "role": "assistant", "content": "To find the method with the lowest perplexity across all languages, we need to compare the perplexity values for each method in English, German, and Turkish. MorphMine has the lowest perplexity values in all three languages. So, the answer is {\"answer\": \"MorphMine\"}." } ], "images": [ "scigen/train/2078.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Language modeling task" ], "reasoning": "To find the method with the lowest perplexity across all languages, we need to compare the perplexity values for each method in English, German, and Turkish. MorphMine has the lowest perplexity values in all three languages.", "question": "Which method has the lowest perplexity across all languages?", "answer": "So, the answer is {\"answer\": \"MorphMine\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nFastText performs better than SkipGram in German.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the perplexity values of FastText and SkipGram in German. FastText has a perplexity of 376, while SkipGram has a perplexity of 381, indicating that FastText performs better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2078.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Language modeling task" ], "reasoning": "To verify this statement, we compare the perplexity values of FastText and SkipGram in German. FastText has a perplexity of 376, while SkipGram has a perplexity of 381, indicating that FastText performs better.", "statement": "FastText performs better than SkipGram in German.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'TABLE I: Morpheme Segmentation Performance.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "MorphMine variants outperform other methods in F1 score across all three languages, primarily due to higher recall. Implementing global refinement generally improves performance in English and German, while in Turkish, performance is comparable between MorphMine variants.As both BytePair Encoding and Unigram-LM require a morpheme vocabulary size parameter, for these methods, we perform a parameter sweep and report results from the highest performing run. Across all three languages, variants of MorphMine outperform with respect to F1 score. Further analysis shows this is primarily due to a higher recall. In comparison to MorphMine without global refinement, we see that implementing global refinement generally improves performance as seen in English and German and in the case of Turkish, performance between the MorphMine variants were overall comparable." } ], "images": [ "scigen/train/2079.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Morpheme Segmentation Performance." ], "reasoning": "MorphMine variants outperform other methods in F1 score across all three languages, primarily due to higher recall. Implementing global refinement generally improves performance in English and German, while in Turkish, performance is comparable between MorphMine variants.", "description": "As both BytePair Encoding and Unigram-LM require a morpheme vocabulary size parameter, for these methods, we perform a parameter sweep and report results from the highest performing run. Across all three languages, variants of MorphMine outperform with respect to F1 score. Further analysis shows this is primarily due to a higher recall. In comparison to MorphMine without global refinement, we see that implementing global refinement generally improves performance as seen in English and German and in the case of Turkish, performance between the MorphMine variants were overall comparable.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'TABLE I: Morpheme Segmentation Performance.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich method has the highest recall for German and what is its value?\n" }, { "role": "assistant", "content": "To find the method with the highest recall for German, we look at the 'German R' column and identify the highest value, which is 0.7520 for MorphMine-NoRefine. So, the answer is {\"answer\": \"MorphMine-NoRefine\"}." } ], "images": [ "scigen/train/2079.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Morpheme Segmentation Performance." ], "reasoning": "To find the method with the highest recall for German, we look at the 'German R' column and identify the highest value, which is 0.7520 for MorphMine-NoRefine.", "question": "Which method has the highest recall for German and what is its value?", "answer": "So, the answer is {\"answer\": \"MorphMine-NoRefine\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence MorphMine has the highest F1 score for English. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nMorphMine has the highest F1 score for English.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the 'English F1' column and see that MorphMine has the highest value of 0.7600. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2079.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Morpheme Segmentation Performance." ], "reasoning": "To verify this statement, we look at the 'English F1' column and see that MorphMine has the highest value of 0.7600.", "statement": "MorphMine has the highest F1 score for English.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'TABLE II: Multilingual word similarity.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "MorphMine outperforms all baselines in word similarity tasks, especially for rare and out-of-vocabulary words, due to its ability to generate morphemes at multiple granularities.Additionally, methods that discriminately generate these morphemes outperform FastText that indiscriminately generate all subwords. Finally, while most subword enriched embeddings perform well on word similarity, MorphMine shines particularly in the similarity task on rare words where it outperforms all baselines. This is likely because MorphMine generates morphemes at multiple granularity which is more likely semantically link a rare word to a frequent word via a semantically-meaningful morpheme. This performance gap is even higher for out-of-vocabulary words were MorphMine significantly outperforms all other baselines at the word similarity task." } ], "images": [ "scigen/train/2080.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Multilingual word similarity." ], "reasoning": "MorphMine outperforms all baselines in word similarity tasks, especially for rare and out-of-vocabulary words, due to its ability to generate morphemes at multiple granularities.", "description": "Additionally, methods that discriminately generate these morphemes outperform FastText that indiscriminately generate all subwords. Finally, while most subword enriched embeddings perform well on word similarity, MorphMine shines particularly in the similarity task on rare words where it outperforms all baselines. This is likely because MorphMine generates morphemes at multiple granularity which is more likely semantically link a rare word to a frequent word via a semantically-meaningful morpheme. This performance gap is even higher for out-of-vocabulary words were MorphMine significantly outperforms all other baselines at the word similarity task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'TABLE II: Multilingual word similarity.', unfold the steps to solve the question given next. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nQuestion: Which method performs best on the English RW-OOV dataset and why?\n" }, { "role": "assistant", "content": "MorphMine performs best on the English RW-OOV dataset with a score of 0.42. This is likely because it generates morphemes at multiple granularities, linking rare words to frequent words via semantically meaningful morphemes. So, the answer is {\"answer\": \"MorphMine\"}." } ], "images": [ "scigen/train/2080.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Multilingual word similarity." ], "reasoning": "MorphMine performs best on the English RW-OOV dataset with a score of 0.42. This is likely because it generates morphemes at multiple granularities, linking rare words to frequent words via semantically meaningful morphemes.", "question": "Which method performs best on the English RW-OOV dataset and why?", "answer": "So, the answer is {\"answer\": \"MorphMine\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence FastText performs better than MorphMine on the English RW-Frequent dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nFastText performs better than MorphMine on the English RW-Frequent dataset.\n" }, { "role": "assistant", "content": "The table shows that FastText has a score of 0.34 on the English RW-Frequent dataset, while MorphMine has a higher score of 0.46. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2080.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Multilingual word similarity." ], "reasoning": "The table shows that FastText has a score of 0.34 on the English RW-Frequent dataset, while MorphMine has a higher score of 0.46.", "statement": "FastText performs better than MorphMine on the English RW-Frequent dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'TABLE III: Word analogies.'.\n" }, { "role": "assistant", "content": "Utilizing high-quality subwords improves embedding performance, as seen with MorphMine's superior syntactic performance and comparable semantic performance to SkipGram.This does not extend to semantic analogies whereby utilizing subword-information seems to cause a deterioration in performance. This is intuitive as words without valid morphemes learn noisy embeddings when false morphemes are identified and used to enrich their representation. This is seen in the performance gap between FastText and SkipGram on semantic analogies whereby FastText’s large number indiscriminate subwords degrades the quality of the final embedding. This degradation is mitigated by utilizing more-refined morpheme methods such as BytePair Encoding, Unigram-LM, Morfessor, and MorphMine. Overall, embeddings enriched with MorphMine morphemes demonstrate superior syntactic performance to all baselines while demonstrating comparable semantic performance to SkipGram. This supports our intuition that utilizing more subwords is useful, but only when they are of high quality; indiscriminately generating all enumerations of subword degrades quality." } ], "images": [ "scigen/train/2081.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Word analogies." ], "reasoning": "Utilizing high-quality subwords improves embedding performance, as seen with MorphMine's superior syntactic performance and comparable semantic performance to SkipGram.", "description": "This does not extend to semantic analogies whereby utilizing subword-information seems to cause a deterioration in performance. This is intuitive as words without valid morphemes learn noisy embeddings when false morphemes are identified and used to enrich their representation. This is seen in the performance gap between FastText and SkipGram on semantic analogies whereby FastText’s large number indiscriminate subwords degrades the quality of the final embedding. This degradation is mitigated by utilizing more-refined morpheme methods such as BytePair Encoding, Unigram-LM, Morfessor, and MorphMine. Overall, embeddings enriched with MorphMine morphemes demonstrate superior syntactic performance to all baselines while demonstrating comparable semantic performance to SkipGram. This supports our intuition that utilizing more subwords is useful, but only when they are of high quality; indiscriminately generating all enumerations of subword degrades quality.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: TABLE III: Word analogies.\nQuestion: Which dataset method demonstrates the highest English syntactic performance?\nGive a detailed response to the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the 'English Syn' column, MorphMine has the highest score of 78, indicating the best English syntactic performance. So, the answer is {\"answer\": \"MorphMine\"}." } ], "images": [ "scigen/train/2081.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Word analogies." ], "reasoning": "By examining the 'English Syn' column, MorphMine has the highest score of 78, indicating the best English syntactic performance.", "question": "Which dataset method demonstrates the highest English syntactic performance?", "answer": "So, the answer is {\"answer\": \"MorphMine\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nFastText has the highest German syntactic performance.\n" }, { "role": "assistant", "content": "The 'German Syn' column shows that both FastText and MorphMine have a score of 53, indicating that FastText does not have the highest German syntactic performance alone. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2081.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Word analogies." ], "reasoning": "The 'German Syn' column shows that both FastText and MorphMine have a score of 53, indicating that FastText does not have the highest German syntactic performance alone.", "statement": "FastText has the highest German syntactic performance.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Multi-label topic ID average precision on LORELEI languages, with the number of speech segments in parentheses.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The table evaluates topic ID performance on speech corpora of Turkish, Uzbek, and Mandarin using different models and features, reporting overall and in-domain topic scores.5.2.1 Experimental setup We further evaluate our topic ID performance on the speech corpora of three languages released by the DARPA LORELEI (Low Resource Languages for Emergent Incidents) Program. For each language there are a number of audio speech files, and each speech file is cut into segments of various lengths (up to 120 seconds). Each speech segment is seen as either in-domain or out-of-domain. In-domain data is defined as any speech segment relating to an incident or incidents, and in-domain data will fall into a set of domain-specific categories; these categories are known as situation types, or in-domain topics. There are 11 situation types: “Civil Unrest or Wide-spread Crime”, “Elections and Politics”, “Evacuation”, “Food Supply”, “Urgent Rescue”, “Utilities, Energy, or Sanitation”, “Infrastructure”, “Medical Assistance”, “Shelter”, “Terrorism or other Extreme Violence”, and “Water Supply”. We consider “Out-of-domain” as the 12th topic label, so each speech segment either corresponds to one or multiple in-domain topics, or is “Out-of-domain”. For each configuration, only a single 10-fold CV result is reported, since we observe less variance in results here than in Switchboard. We have 16.5 hours in-domain data and 8.5 hours out-of-domain data for Turkish, 2.9 and 13.2 hours for Uzbek, and 7.7 and 7.2 hours for Mandarin. Also we use mini-batch size 30 and fix the training epochs as 100. All CNNs use word2vec pre-training." } ], "images": [ "scigen/train/2082.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Multi-label topic ID average precision on LORELEI languages, with the number of speech segments in parentheses." ], "reasoning": "The table evaluates topic ID performance on speech corpora of Turkish, Uzbek, and Mandarin using different models and features, reporting overall and in-domain topic scores.", "description": "5.2.1 Experimental setup We further evaluate our topic ID performance on the speech corpora of three languages released by the DARPA LORELEI (Low Resource Languages for Emergent Incidents) Program. For each language there are a number of audio speech files, and each speech file is cut into segments of various lengths (up to 120 seconds). Each speech segment is seen as either in-domain or out-of-domain. In-domain data is defined as any speech segment relating to an incident or incidents, and in-domain data will fall into a set of domain-specific categories; these categories are known as situation types, or in-domain topics. There are 11 situation types: “Civil Unrest or Wide-spread Crime”, “Elections and Politics”, “Evacuation”, “Food Supply”, “Urgent Rescue”, “Utilities, Energy, or Sanitation”, “Infrastructure”, “Medical Assistance”, “Shelter”, “Terrorism or other Extreme Violence”, and “Water Supply”. We consider “Out-of-domain” as the 12th topic label, so each speech segment either corresponds to one or multiple in-domain topics, or is “Out-of-domain”. For each configuration, only a single 10-fold CV result is reported, since we observe less variance in results here than in Switchboard. We have 16.5 hours in-domain data and 8.5 hours out-of-domain data for Turkish, 2.9 and 13.2 hours for Uzbek, and 7.7 and 7.2 hours for Mandarin. Also we use mini-batch size 30 and fix the training epochs as 100. All CNNs use word2vec pre-training.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nTable title: Table 2: Multi-label topic ID average precision on LORELEI languages, with the number of speech segments in parentheses.\n\nQuestion: Which model and feature combination achieved the highest overall score for the Uzbek dataset?\n" }, { "role": "assistant", "content": "To find the highest overall score for the Uzbek dataset, look at the 'Overall' column for rows where 'Dataset' is Uzbek. The highest score is 0.807, achieved by the AUD feature with the CNN model. So, the answer is {\"answer\": \"AUD CNN\"}." } ], "images": [ "scigen/train/2082.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Multi-label topic ID average precision on LORELEI languages, with the number of speech segments in parentheses." ], "reasoning": "To find the highest overall score for the Uzbek dataset, look at the 'Overall' column for rows where 'Dataset' is Uzbek. The highest score is 0.807, achieved by the AUD feature with the CNN model.", "question": "Which model and feature combination achieved the highest overall score for the Uzbek dataset?", "answer": "So, the answer is {\"answer\": \"AUD CNN\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe SVM model with the AUD feature achieved the highest in-domain topic score for the Turkish dataset.\n" }, { "role": "assistant", "content": "To verify this, look at the 'In-domain topics' column for rows where 'Dataset' is Turkish. The highest in-domain topic score is 0.580, achieved by the ASR feature with the SVM model, not the AUD feature. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2082.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Multi-label topic ID average precision on LORELEI languages, with the number of speech segments in parentheses." ], "reasoning": "To verify this, look at the 'In-domain topics' column for rows where 'Dataset' is Turkish. The highest in-domain topic score is 0.580, achieved by the ASR feature with the SVM model, not the AUD feature.", "statement": "The SVM model with the AUD feature achieved the highest in-domain topic score for the Turkish dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Label distribution across our datasets; and distribution into train, validation and test sets.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The Ptacek dataset, consisting of 27,177 tweets, was divided into ten stratified buckets by users, ensuring class balance across training, validation, and testing sets.We divided the dataset into ten buckets, using eight for training, one for validation and one for testing. The division into buckets is stratified by users, i.e. all tweets from a user end up in the same bucket. Stratification makes sure any specific embedding is only used during training, during validation, or during testing. We further ensured the overall class balance is represented in all of the three sets. In a similar setting as with Riloff we could only collect 27,177 tweets and corresponding timelines. We divided them into ten buckets and stratified by users. During preprocessing we removed all sarcasm-marking tags from both the training tweets and the historical tweets." } ], "images": [ "scigen/train/2083.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Label distribution across our datasets; and distribution into train, validation and test sets." ], "reasoning": "The Ptacek dataset, consisting of 27,177 tweets, was divided into ten stratified buckets by users, ensuring class balance across training, validation, and testing sets.", "description": "We divided the dataset into ten buckets, using eight for training, one for validation and one for testing. The division into buckets is stratified by users, i.e. all tweets from a user end up in the same bucket. Stratification makes sure any specific embedding is only used during training, during validation, or during testing. We further ensured the overall class balance is represented in all of the three sets. In a similar setting as with Riloff we could only collect 27,177 tweets and corresponding timelines. We divided them into ten buckets and stratified by users. During preprocessing we removed all sarcasm-marking tags from both the training tweets and the historical tweets.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 1: Label distribution across our datasets; and distribution into train, validation and test sets.\nQuestion: How many tweets were used for validation in the Ptacek dataset?\n" }, { "role": "assistant", "content": "The table shows the Ptacek dataset has 2,711 tweets used for validation. So, the answer is {\"answer\": \"2,711\"}." } ], "images": [ "scigen/train/2083.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Label distribution across our datasets; and distribution into train, validation and test sets." ], "reasoning": "The table shows the Ptacek dataset has 2,711 tweets used for validation.", "question": "How many tweets were used for validation in the Ptacek dataset?", "answer": "So, the answer is {\"answer\": \"2,711\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Riloff dataset has more sarcastic tweets than non-sarcastic tweets.\n" }, { "role": "assistant", "content": "The table shows the Riloff dataset has 192 sarcastic tweets and 509 non-sarcastic tweets, indicating there are more non-sarcastic tweets. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2083.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Label distribution across our datasets; and distribution into train, validation and test sets." ], "reasoning": "The table shows the Riloff dataset has 192 sarcastic tweets and 509 non-sarcastic tweets, indicating there are more non-sarcastic tweets.", "statement": "The Riloff dataset has more sarcastic tweets than non-sarcastic tweets.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: F1 score achieved on the Riloff and Ptacek datasets for both exclusive and inclusive models. Best results for each model class are highlighted in bold.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "User embeddings significantly improve the f1-score on the Ptacek dataset using the EX-W-CASCADE model compared to the baseline, but are less effective on the Riloff dataset.User embeddings show remarkable predictive power on the Ptacek dataset. In particular, using the EX-W-CASCADE model, we get better results (f1-score 0.922) than the baseline (f1-score 0.863) without even looking at the tweet being predicted. On the Riloff dataset, however, user embeddings seem to be far less informative, with EX-W-CASCADE yielding an f1-score of only 0.478. Out of the exclusive models, we get the highest f1-score of 0.546 using EX-ED on Riloff. By contrast we get 0.873 on Ptacek using EX-ED." } ], "images": [ "scigen/train/2084.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: F1 score achieved on the Riloff and Ptacek datasets for both exclusive and inclusive models. Best results for each model class are highlighted in bold." ], "reasoning": "User embeddings significantly improve the f1-score on the Ptacek dataset using the EX-W-CASCADE model compared to the baseline, but are less effective on the Riloff dataset.", "description": "User embeddings show remarkable predictive power on the Ptacek dataset. In particular, using the EX-W-CASCADE model, we get better results (f1-score 0.922) than the baseline (f1-score 0.863) without even looking at the tweet being predicted. On the Riloff dataset, however, user embeddings seem to be far less informative, with EX-W-CASCADE yielding an f1-score of only 0.478. Out of the exclusive models, we get the highest f1-score of 0.546 using EX-ED on Riloff. By contrast we get 0.873 on Ptacek using EX-ED.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: F1 score achieved on the Riloff and Ptacek datasets for both exclusive and inclusive models. Best results for each model class are highlighted in bold.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. \n\nQuestion: Which model achieved the highest f1-score on the Ptacek dataset?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By comparing the f1-scores of all models on the Ptacek dataset, the highest score is 0.934 achieved by the IN-W-CASCADE model. So, the answer is {\"answer\": \"IN-W-CASCADE\"}" } ], "images": [ "scigen/train/2084.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: F1 score achieved on the Riloff and Ptacek datasets for both exclusive and inclusive models. Best results for each model class are highlighted in bold." ], "reasoning": "By comparing the f1-scores of all models on the Ptacek dataset, the highest score is 0.934 achieved by the IN-W-CASCADE model.", "question": "Which model achieved the highest f1-score on the Ptacek dataset?", "answer": "So, the answer is {\"answer\": \"IN-W-CASCADE\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe EX-ED model achieved the highest f1-score on the Riloff dataset.\n" }, { "role": "assistant", "content": "The table shows that the EX-ED model has an f1-score of 0.546 on the Riloff dataset, which is the highest among the exclusive models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2084.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: F1 score achieved on the Riloff and Ptacek datasets for both exclusive and inclusive models. Best results for each model class are highlighted in bold." ], "reasoning": "The table shows that the EX-ED model has an f1-score of 0.546 on the Riloff dataset, which is the highest among the exclusive models.", "statement": "The EX-ED model achieved the highest f1-score on the Riloff dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: F1 score achieved by the exclusive models on the #Riloff dataset, compared to Riloff dataset. Best results are highlighted in bold.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The re-labelled #Riloff dataset showed higher predictive power for sarcasm detection across all models compared to the original Riloff dataset, suggesting the importance of differentiating between intended and perceived sarcasm.To investigate further, we re-labelled the Riloff dataset via distant supervision considering these tags as markers of sarcasm, to create the #Riloff dataset. We applied the exclusive models on #Riloff and noticed a considerably higher predictive power than on Riloff. Author history seems therefore predictive of authorial sarcastic intention, but not of external perception. This could indicate that future work should differentiate between the two types of sarcasm: intended and perceived. Both are important to detect, for applications such as opinion mining for the former and hate speech detection for the latter." } ], "images": [ "scigen/train/2085.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: F1 score achieved by the exclusive models on the #Riloff dataset, compared to Riloff dataset. Best results are highlighted in bold." ], "reasoning": "The re-labelled #Riloff dataset showed higher predictive power for sarcasm detection across all models compared to the original Riloff dataset, suggesting the importance of differentiating between intended and perceived sarcasm.", "description": "To investigate further, we re-labelled the Riloff dataset via distant supervision considering these tags as markers of sarcasm, to create the #Riloff dataset. We applied the exclusive models on #Riloff and noticed a considerably higher predictive power than on Riloff. Author history seems therefore predictive of authorial sarcastic intention, but not of external perception. This could indicate that future work should differentiate between the two types of sarcasm: intended and perceived. Both are important to detect, for applications such as opinion mining for the former and hate speech detection for the latter.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 3: F1 score achieved by the exclusive models on the #Riloff dataset, compared to Riloff dataset. Best results are highlighted in bold.', solve this question and give detail solutions. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich model showed the highest predictive power on the #Riloff dataset?\n" }, { "role": "assistant", "content": "By comparing the predictive power values for all models on the #Riloff dataset, the EX-ED model has the highest value of 0.827. So, the answer is {\"answer\": \"EX-ED\"}." } ], "images": [ "scigen/train/2085.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: F1 score achieved by the exclusive models on the #Riloff dataset, compared to Riloff dataset. Best results are highlighted in bold." ], "reasoning": "By comparing the predictive power values for all models on the #Riloff dataset, the EX-ED model has the highest value of 0.827.", "question": "Which model showed the highest predictive power on the #Riloff dataset?", "answer": "So, the answer is {\"answer\": \"EX-ED\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe EX-W-CASCADE model showed higher predictive power on the Riloff dataset than the EX-SUMMARY model.\n" }, { "role": "assistant", "content": "By comparing the predictive power values on the Riloff dataset, the EX-W-CASCADE model has a value of 0.478, which is lower than the EX-SUMMARY model's value of 0.492. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2085.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: F1 score achieved by the exclusive models on the #Riloff dataset, compared to Riloff dataset. Best results are highlighted in bold." ], "reasoning": "By comparing the predictive power values on the Riloff dataset, the EX-W-CASCADE model has a value of 0.478, which is lower than the EX-SUMMARY model's value of 0.492.", "statement": "The EX-W-CASCADE model showed higher predictive power on the Riloff dataset than the EX-SUMMARY model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3.4: ABX error rates (lower is better) of models by others (Renshaw et al., 2015; Thiollière et al., 2015) on English and Xitsonga data. Both within-speaker and across-speaker discriminability are shown.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Renshaw et al. found that cAE representations are more discriminative than MFCCs and stacked AEs, and outperform denoising AEs by using realistic noise.More importantly, Renshaw et al. compared the cAE to more models using ABX evaluation on data from two languages: English and Xitsonga. found that, as in this chapter, cAE representations are more discriminative than MFCCs as well as representations from stacked AEs on both languages. Importantly, they also showed that the cAE outperforms a denoising AE (dAE). the cAE uses ‘realistic’ noise obtained from data, instead of using artificial noise as in the standard dAE. Instead of using a reconstruction criterion as in the cAE, they used Siamese networks: tied networks that take pairs of speech frames and minimize or maximize a distance depending on whether they come from same or different word classes (as predicted by UTD)." } ], "images": [ "scigen/train/2086.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3.4: ABX error rates (lower is better) of models by others (Renshaw et al., 2015; Thiollière et al., 2015) on English and Xitsonga data. Both within-speaker and across-speaker discriminability are shown." ], "reasoning": "Renshaw et al. found that cAE representations are more discriminative than MFCCs and stacked AEs, and outperform denoising AEs by using realistic noise.", "description": "More importantly, Renshaw et al. compared the cAE to more models using ABX evaluation on data from two languages: English and Xitsonga. found that, as in this chapter, cAE representations are more discriminative than MFCCs as well as representations from stacked AEs on both languages. Importantly, they also showed that the cAE outperforms a denoising AE (dAE). the cAE uses ‘realistic’ noise obtained from data, instead of using artificial noise as in the standard dAE. Instead of using a reconstruction criterion as in the cAE, they used Siamese networks: tied networks that take pairs of speech frames and minimize or maximize a distance depending on whether they come from same or different word classes (as predicted by UTD).", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 3.4: ABX error rates (lower is better) of models by others (Renshaw et al., 2015; Thiollière et al., 2015) on English and Xitsonga data. Both within-speaker and across-speaker discriminability are shown.', solve the subsequent question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model has the lowest English ABX (%) Within score?\n" }, { "role": "assistant", "content": "To find the model with the lowest English ABX (%) Within score, compare the scores in the 'English ABX (%) Within' column. The Siamese DNN model has the lowest score of 12.0. So, the answer is {\"answer\": \"Siamese DNN\"}" } ], "images": [ "scigen/train/2086.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3.4: ABX error rates (lower is better) of models by others (Renshaw et al., 2015; Thiollière et al., 2015) on English and Xitsonga data. Both within-speaker and across-speaker discriminability are shown." ], "reasoning": "To find the model with the lowest English ABX (%) Within score, compare the scores in the 'English ABX (%) Within' column. The Siamese DNN model has the lowest score of 12.0.", "question": "Which model has the lowest English ABX (%) Within score?", "answer": "So, the answer is {\"answer\": \"Siamese DNN\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The Correspondence AE has a lower English ABX (%) Within score than the Denoising AE., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Correspondence AE has a lower English ABX (%) Within score than the Denoising AE.\n" }, { "role": "assistant", "content": "The Correspondence AE has an English ABX (%) Within score of 13.5, while the Denoising AE has a score of 15.8. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2086.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3.4: ABX error rates (lower is better) of models by others (Renshaw et al., 2015; Thiollière et al., 2015) on English and Xitsonga data. Both within-speaker and across-speaker discriminability are shown." ], "reasoning": "The Correspondence AE has an English ABX (%) Within score of 13.5, while the Denoising AE has a score of 15.8. Therefore, the statement is supported.", "statement": "The Correspondence AE has a lower English ABX (%) Within score than the Denoising AE.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3.1: Average precision (AP) on the test set using MFCCs, the UBM and partitioned UBM, the stacked and correspondence AEs trained on the 100k gold standard word pairs, and supervised NNs. Best performance when using weak top-down constraints is highlighted.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The cAE trained on the gold standard 100k word-pair set significantly outperforms MFCCs and stacked AE baselines, achieving an AP of 0.469, which is within 23% of the 10-hour supervised NN baseline.The partitioned UBM and the cAE were both trained on the gold standard 100k word-pair set. The optimal cAE network on the development set The second-to-last encoding layer was also used for obtaining representations from the stacked AE. In this case, the cAE is truly used as an unsupervised frame-level representation learning method. The first UTD run produced 25k word pairs at an accuracy of 46%, while the second (applied to additional unlabelled speech data) produced 80k pairs at 36%. Correspondence AEs were trained separately on the two sets of weak top-down constraints, with each optimized on the development data. In both cases, representations from the second-to-last encoding layer were again used. Both cAEs significantly outperform the MFCCs and stacked AE baselines by more than 57% relative in AP, coming to within 23% of the 10-hour supervised NN baseline." } ], "images": [ "scigen/train/2087.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3.1: Average precision (AP) on the test set using MFCCs, the UBM and partitioned UBM, the stacked and correspondence AEs trained on the 100k gold standard word pairs, and supervised NNs. Best performance when using weak top-down constraints is highlighted." ], "reasoning": "The cAE trained on the gold standard 100k word-pair set significantly outperforms MFCCs and stacked AE baselines, achieving an AP of 0.469, which is within 23% of the 10-hour supervised NN baseline.", "description": "The partitioned UBM and the cAE were both trained on the gold standard 100k word-pair set. The optimal cAE network on the development set The second-to-last encoding layer was also used for obtaining representations from the stacked AE. In this case, the cAE is truly used as an unsupervised frame-level representation learning method. The first UTD run produced 25k word pairs at an accuracy of 46%, while the second (applied to additional unlabelled speech data) produced 80k pairs at 36%. Correspondence AEs were trained separately on the two sets of weak top-down constraints, with each optimized on the development data. In both cases, representations from the second-to-last encoding layer were again used. Both cAEs significantly outperform the MFCCs and stacked AE baselines by more than 57% relative in AP, coming to within 23% of the 10-hour supervised NN baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nTable title: Table 3.1: Average precision (AP) on the test set using MFCCs, the UBM and partitioned UBM, the stacked and correspondence AEs trained on the 100k gold standard word pairs, and supervised NNs. Best performance when using weak top-down constraints is highlighted.\n\nQuestion: What is the AP of the 100-unit, 13-layer correspondence AE with pretraining?\n" }, { "role": "assistant", "content": "The table lists the AP values for various features. The AP for the 100-unit, 13-layer correspondence AE with pretraining is highlighted as 0.469. So, the answer is {\"answer\": \"0.469\"}." } ], "images": [ "scigen/train/2087.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3.1: Average precision (AP) on the test set using MFCCs, the UBM and partitioned UBM, the stacked and correspondence AEs trained on the 100k gold standard word pairs, and supervised NNs. Best performance when using weak top-down constraints is highlighted." ], "reasoning": "The table lists the AP values for various features. The AP for the 100-unit, 13-layer correspondence AE with pretraining is highlighted as 0.469.", "question": "What is the AP of the 100-unit, 13-layer correspondence AE with pretraining?", "answer": "So, the answer is {\"answer\": \"0.469\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The AP of the 100-unit, 13-layer correspondence AE with no pretraining is higher than the AP of the 1024-UBM partitioned into 100 components. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe AP of the 100-unit, 13-layer correspondence AE with no pretraining is higher than the AP of the 1024-UBM partitioned into 100 components.\n" }, { "role": "assistant", "content": "The table shows that the AP of the 100-unit, 13-layer correspondence AE with no pretraining is 0.024, while the AP of the 1024-UBM partitioned into 100 components is 0.286. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2087.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3.1: Average precision (AP) on the test set using MFCCs, the UBM and partitioned UBM, the stacked and correspondence AEs trained on the 100k gold standard word pairs, and supervised NNs. Best performance when using weak top-down constraints is highlighted." ], "reasoning": "The table shows that the AP of the 100-unit, 13-layer correspondence AE with no pretraining is 0.024, while the AP of the 1024-UBM partitioned into 100 components is 0.286. Therefore, the statement is incorrect.", "statement": "The AP of the 100-unit, 13-layer correspondence AE with no pretraining is higher than the AP of the 1024-UBM partitioned into 100 components.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3.2: Average precision (AP) on the test set using the partitioned UBM (Jansen et al., 2013b) and correspondence AEs when varying the number of gold standard word pairs N, with F the resulting number of frame pairs.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The cAE consistently outperforms the partitioned UBM and baseline MFCCs, even with fewer word pairs, indicating a trade-off between UTD accuracy and the number of pairs produced.For every set, the cAE was optimized on the development data. In all cases the cAE outperforms the partitioned UBM and the baseline MFCCs. With as few as 1k word pairs, the cAE gives the same performance as the partitioned UBM trained with all pairs. This is attributed to the lower word-pair accuracy of the former, and shows that there is a trade-off between UTD accuracy and the number of pairs produced. A finer-grained investigation of the trade-off between the number of word pairs and accuracy, which can be varied by searching more data or by adjusting the search threshold, could be the focus of future work." } ], "images": [ "scigen/train/2088.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3.2: Average precision (AP) on the test set using the partitioned UBM (Jansen et al., 2013b) and correspondence AEs when varying the number of gold standard word pairs N, with F the resulting number of frame pairs." ], "reasoning": "The cAE consistently outperforms the partitioned UBM and baseline MFCCs, even with fewer word pairs, indicating a trade-off between UTD accuracy and the number of pairs produced.", "description": "For every set, the cAE was optimized on the development data. In all cases the cAE outperforms the partitioned UBM and the baseline MFCCs. With as few as 1k word pairs, the cAE gives the same performance as the partitioned UBM trained with all pairs. This is attributed to the lower word-pair accuracy of the former, and shows that there is a trade-off between UTD accuracy and the number of pairs produced. A finer-grained investigation of the trade-off between the number of word pairs and accuracy, which can be varied by searching more data or by adjusting the search threshold, could be the focus of future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3.2: Average precision (AP) on the test set using the partitioned UBM (Jansen et al., 2013b) and correspondence AEs when varying the number of gold standard word pairs N, with F the resulting number of frame pairs.' in mind, break down and resolve the question below step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the Correspondence AE AP value when N is 105?\n" }, { "role": "assistant", "content": "To find the Correspondence AE AP value when N is 105, look at the row where N is 105 and find the value under the Correspondence AE AP column. So, the answer is {\"answer\": \"0.469\"}." } ], "images": [ "scigen/train/2088.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3.2: Average precision (AP) on the test set using the partitioned UBM (Jansen et al., 2013b) and correspondence AEs when varying the number of gold standard word pairs N, with F the resulting number of frame pairs." ], "reasoning": "To find the Correspondence AE AP value when N is 105, look at the row where N is 105 and find the value under the Correspondence AE AP column.", "question": "What is the Correspondence AE AP value when N is 105?", "answer": "So, the answer is {\"answer\": \"0.469\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Correspondence AE AP value is higher than the Partitioned UBM AP value for all N values.\n" }, { "role": "assistant", "content": "Compare the Correspondence AE AP values with the Partitioned UBM AP values for all N values in the table. In each case, the Correspondence AE AP value is higher. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2088.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3.2: Average precision (AP) on the test set using the partitioned UBM (Jansen et al., 2013b) and correspondence AEs when varying the number of gold standard word pairs N, with F the resulting number of frame pairs." ], "reasoning": "Compare the Correspondence AE AP values with the Partitioned UBM AP values for all N values in the table. In each case, the Correspondence AE AP value is higher.", "statement": "The Correspondence AE AP value is higher than the Partitioned UBM AP value for all N values.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4.1: Performance of the unconstrained segmental Bayesian model on TIDigits1 over iterations in which the reference set is refined.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The model's WER improves from 35.4% in the first iteration to around 21% in iterations 3 and 4, with cluster purity consistently above 86.5% and word boundary F-scores around 70%.Random word boundary initialization is used throughout. Unconstrained modelling represents the most realistic setting where vocabulary size is not known upfront. Standard deviations are less than 0.3% absolute for all metrics. Despite being allowed to discover many more clusters (up to 100) than the true number of word types (11), the model achieves a WER of 35.4% in the first iteration, which improves to around 21% in iterations 3 and 4. This shows that to achieve good performance, it is essential to use top-down refinement for the acoustic word embeddings used here. Error rate increases slightly in iteration 5. Cluster purity over all iterations is above 86.5%, which is higher than the scores of around 85% reported by Sun and Van hamme Word boundary F-scores are around 70% over all iterations. As mentioned, the Bayesian GMM is biased not to use all of its 100 components. Despite this, none of the models empty out any of their components." } ], "images": [ "scigen/train/2089.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4.1: Performance of the unconstrained segmental Bayesian model on TIDigits1 over iterations in which the reference set is refined." ], "reasoning": "The model's WER improves from 35.4% in the first iteration to around 21% in iterations 3 and 4, with cluster purity consistently above 86.5% and word boundary F-scores around 70%.", "description": "Random word boundary initialization is used throughout. Unconstrained modelling represents the most realistic setting where vocabulary size is not known upfront. Standard deviations are less than 0.3% absolute for all metrics. Despite being allowed to discover many more clusters (up to 100) than the true number of word types (11), the model achieves a WER of 35.4% in the first iteration, which improves to around 21% in iterations 3 and 4. This shows that to achieve good performance, it is essential to use top-down refinement for the acoustic word embeddings used here. Error rate increases slightly in iteration 5. Cluster purity over all iterations is above 86.5%, which is higher than the scores of around 85% reported by Sun and Van hamme Word boundary F-scores are around 70% over all iterations. As mentioned, the Bayesian GMM is biased not to use all of its 100 components. Despite this, none of the models empty out any of their components.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 4.1: Performance of the unconstrained segmental Bayesian model on TIDigits1 over iterations in which the reference set is refined.', answer the question below by showing each progressive step toward the answer. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the Word Error Rate (WER) in the fifth iteration and how does it compare to the fourth iteration?\n" }, { "role": "assistant", "content": "The WER in the fifth iteration is 22.9%, which is higher than the WER of 21.2% in the fourth iteration, indicating a slight increase in error rate. So, the answer is {\"answer\": \"22.9%\"}" } ], "images": [ "scigen/train/2089.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4.1: Performance of the unconstrained segmental Bayesian model on TIDigits1 over iterations in which the reference set is refined." ], "reasoning": "The WER in the fifth iteration is 22.9%, which is higher than the WER of 21.2% in the fourth iteration, indicating a slight increase in error rate.", "question": "What is the Word Error Rate (WER) in the fifth iteration and how does it compare to the fourth iteration?", "answer": "So, the answer is {\"answer\": \"22.9%\"}" }, { "messages": [ { "role": "user", "content": "The model's cluster purity is above 90% in all iterations. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model's cluster purity is above 90% in all iterations.\n" }, { "role": "assistant", "content": "The table shows that the highest cluster purity achieved is 89.7% in the second iteration, which means the statement is incorrect. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2089.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4.1: Performance of the unconstrained segmental Bayesian model on TIDigits1 over iterations in which the reference set is refined." ], "reasoning": "The table shows that the highest cluster purity achieved is 89.7% in the second iteration, which means the statement is incorrect.", "statement": "The model's cluster purity is above 90% in all iterations.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4.2: WER (%) on TIDigits1 of the unsupervised discrete HMM system of Walter et al. (2013) and the segmental Bayesian model.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The constrained segmental Bayesian model performs better than the discrete HMM with random initialization, but the discrete HMM performs better with UTD initialization.Constrained model evaluation and comparison To compare with the discrete HMM-based system of Walter et al. Under random initialization, the constrained segmental Bayesian model performs 12.7% absolute better than the discrete HMM. When using UTD for initialization, the discrete HMM does better by 1.3% absolute. Despite only mapping eleven out of 100 clusters to true labels, this unconstrained model still yields 10.6% absolute lower WER than the randomly-initialized discrete HMM with the correct number of clusters. By comparing rows two and three, we observe that there is only a 2.1% absolute gain in WER by constraining the Bayesian model to a stricter number of types." } ], "images": [ "scigen/train/2090.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4.2: WER (%) on TIDigits1 of the unsupervised discrete HMM system of Walter et al. (2013) and the segmental Bayesian model." ], "reasoning": "The constrained segmental Bayesian model performs better than the discrete HMM with random initialization, but the discrete HMM performs better with UTD initialization.", "description": "Constrained model evaluation and comparison To compare with the discrete HMM-based system of Walter et al. Under random initialization, the constrained segmental Bayesian model performs 12.7% absolute better than the discrete HMM. When using UTD for initialization, the discrete HMM does better by 1.3% absolute. Despite only mapping eleven out of 100 clusters to true labels, this unconstrained model still yields 10.6% absolute lower WER than the randomly-initialized discrete HMM with the correct number of clusters. By comparing rows two and three, we observe that there is only a 2.1% absolute gain in WER by constraining the Bayesian model to a stricter number of types.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nTable title: Table 4.2: WER (%) on TIDigits1 of the unsupervised discrete HMM system of Walter et al. (2013) and the segmental Bayesian model.\n\nQuestion: What is the absolute difference in WER between the constrained segmental Bayesian model and the discrete HMM with random initialization?\n" }, { "role": "assistant", "content": "The WER for the constrained segmental Bayesian model with random initialization is 19.4%, and for the discrete HMM, it is 32.1%. The absolute difference is 32.1% - 19.4% = 12.7%. So, the answer is {\"answer\": \"12.7%\"}." } ], "images": [ "scigen/train/2090.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4.2: WER (%) on TIDigits1 of the unsupervised discrete HMM system of Walter et al. (2013) and the segmental Bayesian model." ], "reasoning": "The WER for the constrained segmental Bayesian model with random initialization is 19.4%, and for the discrete HMM, it is 32.1%. The absolute difference is 32.1% - 19.4% = 12.7%.", "question": "What is the absolute difference in WER between the constrained segmental Bayesian model and the discrete HMM with random initialization?", "answer": "So, the answer is {\"answer\": \"12.7%\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe unconstrained segmental Bayesian model performs better than the constrained segmental Bayesian model with random initialization.\n" }, { "role": "assistant", "content": "The WER for the unconstrained segmental Bayesian model with random initialization is 21.5%, while for the constrained segmental Bayesian model, it is 19.4%. Since 21.5% is higher than 19.4%, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2090.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4.2: WER (%) on TIDigits1 of the unsupervised discrete HMM system of Walter et al. (2013) and the segmental Bayesian model." ], "reasoning": "The WER for the unconstrained segmental Bayesian model with random initialization is 21.5%, while for the constrained segmental Bayesian model, it is 19.4%. Since 21.5% is higher than 19.4%, the statement is refuted.", "statement": "The unconstrained segmental Bayesian model performs better than the constrained segmental Bayesian model with random initialization.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4.3: Performance of the Bayesian segmental model on TIDigits1 and TIDigits2, using random initialization. Results from both the constrained (K=15) and unconstrained (K=100) segmental Bayesian models are shown.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Performance on TIDigits2 is better than on TIDigits1 across all metrics, with significant improvements in WER, cluster purity, and boundary F-score.TIDigits2 was kept as unseen data up to this point. Using the setup developed on TIDigits1, we repeated exemplar extraction and segmentation separately on TIDigits2. Three iterations of exemplar refinement were used. Across all metrics, performance is better on TIDigits2 than on TIDigits1: WERs drop by 6.2% and 3.9% absolute for the constrained and unconstrained models, respectively; cluster purity improves by around 3% absolute; and boundary F-score is higher by 6% absolute. The figure shows that every cluster is dominated by data from a single ground truth digit. Furthermore, all digits apart from ‘eight’ are found in a single cluster. Although these digits are consistently decoded as the same sequence of clusters, WER counts the extra clusters as insertion errors. These small differences in the discovered word types results in a non-negligible difference in WER between TIDigits1 and TIDigits2." } ], "images": [ "scigen/train/2091.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4.3: Performance of the Bayesian segmental model on TIDigits1 and TIDigits2, using random initialization. Results from both the constrained (K=15) and unconstrained (K=100) segmental Bayesian models are shown." ], "reasoning": "Performance on TIDigits2 is better than on TIDigits1 across all metrics, with significant improvements in WER, cluster purity, and boundary F-score.", "description": "TIDigits2 was kept as unseen data up to this point. Using the setup developed on TIDigits1, we repeated exemplar extraction and segmentation separately on TIDigits2. Three iterations of exemplar refinement were used. Across all metrics, performance is better on TIDigits2 than on TIDigits1: WERs drop by 6.2% and 3.9% absolute for the constrained and unconstrained models, respectively; cluster purity improves by around 3% absolute; and boundary F-score is higher by 6% absolute. The figure shows that every cluster is dominated by data from a single ground truth digit. Furthermore, all digits apart from ‘eight’ are found in a single cluster. Although these digits are consistently decoded as the same sequence of clusters, WER counts the extra clusters as insertion errors. These small differences in the discovered word types results in a non-negligible difference in WER between TIDigits1 and TIDigits2.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4.3: Performance of the Bayesian segmental model on TIDigits1 and TIDigits2, using random initialization. Results from both the constrained (K=15) and unconstrained (K=100) segmental Bayesian models are shown.\nQuestion: What is the difference in WER between TIDigits1 and TIDigits2 for the constrained model?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The WER for TIDigits1 with the constrained model is 19.4%, and for TIDigits2 it is 13.2%. The difference is 19.4% - 13.2% = 6.2%. So, the answer is {\"answer\": \"6.2%\"}." } ], "images": [ "scigen/train/2091.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4.3: Performance of the Bayesian segmental model on TIDigits1 and TIDigits2, using random initialization. Results from both the constrained (K=15) and unconstrained (K=100) segmental Bayesian models are shown." ], "reasoning": "The WER for TIDigits1 with the constrained model is 19.4%, and for TIDigits2 it is 13.2%. The difference is 19.4% - 13.2% = 6.2%.", "question": "What is the difference in WER between TIDigits1 and TIDigits2 for the constrained model?", "answer": "So, the answer is {\"answer\": \"6.2%\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe cluster purity for the unconstrained model is higher on TIDigits2 than on TIDigits1.\n" }, { "role": "assistant", "content": "The table shows that the cluster purity for the unconstrained model is 89.2% on TIDigits1 and 92.5% on TIDigits2, indicating an improvement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2091.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4.3: Performance of the Bayesian segmental model on TIDigits1 and TIDigits2, using random initialization. Results from both the constrained (K=15) and unconstrained (K=100) segmental Bayesian models are shown." ], "reasoning": "The table shows that the cluster purity for the unconstrained model is 89.2% on TIDigits1 and 92.5% on TIDigits2, indicating an improvement.", "statement": "The cluster purity for the unconstrained model is higher on TIDigits2 than on TIDigits1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5.2: Same-different performance of different representations. In the first two rows, DTW alignment over all frames is used for segment comparison; dimensionality (dim.) is at the frame level. In the last two rows, cosine distance between fixed-dimensional acoustic word embeddings (obtained by downsampling to ten frames) is used and the size of the embedding vectors is given. Higher AP is better.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The cAE features outperform MFCC features in average precision across all datasets, with the most significant improvement observed in the Xitsonga dataset.Here each test set contains all words of at least 5 characters and 0.5 seconds in duration from all speakers in the corresponding dataset (i.e. speaker-independent evaluation). For the cAE, we use activations from the 13-unit bottleneck layer as features, as described in the previous section. This is especially pronounced on Xitsonga, which has the lowest AP (0.281) for the MFCC features, but the highest AP (0.552) for the cAE features. The performance when downsampling each test segment to ten frames (the output of fe) is given in the third and fourth rows using the MFCC and cAE features, respectively. Again, cAE features perform best. The improvements on English2, however, are not as pronounced as they are on the other two datasets. The biggest improvement is again on Xitsonga." } ], "images": [ "scigen/train/2092.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5.2: Same-different performance of different representations. In the first two rows, DTW alignment over all frames is used for segment comparison; dimensionality (dim.) is at the frame level. In the last two rows, cosine distance between fixed-dimensional acoustic word embeddings (obtained by downsampling to ten frames) is used and the size of the embedding vectors is given. Higher AP is better." ], "reasoning": "The cAE features outperform MFCC features in average precision across all datasets, with the most significant improvement observed in the Xitsonga dataset.", "description": "Here each test set contains all words of at least 5 characters and 0.5 seconds in duration from all speakers in the corresponding dataset (i.e. speaker-independent evaluation). For the cAE, we use activations from the 13-unit bottleneck layer as features, as described in the previous section. This is especially pronounced on Xitsonga, which has the lowest AP (0.281) for the MFCC features, but the highest AP (0.552) for the cAE features. The performance when downsampling each test segment to ten frames (the output of fe) is given in the third and fourth rows using the MFCC and cAE features, respectively. Again, cAE features perform best. The improvements on English2, however, are not as pronounced as they are on the other two datasets. The biggest improvement is again on Xitsonga.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 5.2: Same-different performance of different representations. In the first two rows, DTW alignment over all frames is used for segment comparison; dimensionality (dim.) is at the frame level. In the last two rows, cosine distance between fixed-dimensional acoustic word embeddings (obtained by downsampling to ten frames) is used and the size of the embedding vectors is given. Higher AP is better.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich feature set has the highest average precision on the Xitsonga dataset?\n" }, { "role": "assistant", "content": "By comparing the average precision values for the Xitsonga dataset, the 13-dimensional cAE features have the highest value of 0.552. So, the answer is {\"answer\": \"13-dimensional cAE features\"}" } ], "images": [ "scigen/train/2092.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5.2: Same-different performance of different representations. In the first two rows, DTW alignment over all frames is used for segment comparison; dimensionality (dim.) is at the frame level. In the last two rows, cosine distance between fixed-dimensional acoustic word embeddings (obtained by downsampling to ten frames) is used and the size of the embedding vectors is given. Higher AP is better." ], "reasoning": "By comparing the average precision values for the Xitsonga dataset, the 13-dimensional cAE features have the highest value of 0.552.", "question": "Which feature set has the highest average precision on the Xitsonga dataset?", "answer": "So, the answer is {\"answer\": \"13-dimensional cAE features\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe downsampled MFCC embeddings have the lowest average precision on the Xitsonga dataset.\n" }, { "role": "assistant", "content": "The table shows that the downsampled MFCC embeddings have an average precision of 0.147 on the Xitsonga dataset, which is the lowest among all feature sets listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2092.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5.2: Same-different performance of different representations. In the first two rows, DTW alignment over all frames is used for segment comparison; dimensionality (dim.) is at the frame level. In the last two rows, cosine distance between fixed-dimensional acoustic word embeddings (obtained by downsampling to ten frames) is used and the size of the embedding vectors is given. Higher AP is better." ], "reasoning": "The table shows that the downsampled MFCC embeddings have an average precision of 0.147 on the Xitsonga dataset, which is the lowest among all feature sets listed.", "statement": "The downsampled MFCC embeddings have the lowest average precision on the Xitsonga dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5.4: A breakdown of the errors on English2 for the speaker-dependent models in Table 5.3. The word boundary detection tolerance is 20 ms. The greedy one-to-one cluster mapping is used for error rate computations.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Using inferred boundaries rather than fixed syllable boundaries results in higher purity and lower WER, with BayesSegMinDur-MFCC achieving the highest purity at 56%.SyllableBayesClust gives an upper-bound for word boundary recall since every syllable boundary is set as a word boundary. The low recall (28.9% and 24.8%) could potentially be improved by using a better syllabification method, but we decided to use the method as provided, and leave further investigation for future work. However, this is accompanied by large reductions in substitution and insertion error rates, resulting in overall WER improvements and more accurate clusters when boundaries are inferred (45.1% purity, BayesSeg-MFCC) rather than using fixed syllable boundaries (42%, SyllableBayesClust), with further improvements when not allowing short word candidates (56%, BayesSegMinDur-MFCC)." } ], "images": [ "scigen/train/2093.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5.4: A breakdown of the errors on English2 for the speaker-dependent models in Table 5.3. The word boundary detection tolerance is 20 ms. The greedy one-to-one cluster mapping is used for error rate computations." ], "reasoning": "Using inferred boundaries rather than fixed syllable boundaries results in higher purity and lower WER, with BayesSegMinDur-MFCC achieving the highest purity at 56%.", "description": "SyllableBayesClust gives an upper-bound for word boundary recall since every syllable boundary is set as a word boundary. The low recall (28.9% and 24.8%) could potentially be improved by using a better syllabification method, but we decided to use the method as provided, and leave further investigation for future work. However, this is accompanied by large reductions in substitution and insertion error rates, resulting in overall WER improvements and more accurate clusters when boundaries are inferred (45.1% purity, BayesSeg-MFCC) rather than using fixed syllable boundaries (42%, SyllableBayesClust), with further improvements when not allowing short word candidates (56%, BayesSegMinDur-MFCC).", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 5.4: A breakdown of the errors on English2 for the speaker-dependent models in Table 5.3. The word boundary detection tolerance is 20 ms. The greedy one-to-one cluster mapping is used for error rate computations.' and methodically tackle the question that follows. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model and embedding combination achieved the highest purity?\n" }, { "role": "assistant", "content": "By examining the 'Purity Avg. (%)' column, BayesSegMinDur with MFCC embeddings achieved the highest purity at 56%. So, the answer is {\"answer\": \"BayesSegMinDur-MFCC\"}." } ], "images": [ "scigen/train/2093.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5.4: A breakdown of the errors on English2 for the speaker-dependent models in Table 5.3. The word boundary detection tolerance is 20 ms. The greedy one-to-one cluster mapping is used for error rate computations." ], "reasoning": "By examining the 'Purity Avg. (%)' column, BayesSegMinDur with MFCC embeddings achieved the highest purity at 56%.", "question": "Which model and embedding combination achieved the highest purity?", "answer": "So, the answer is {\"answer\": \"BayesSegMinDur-MFCC\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nBayesSegMinDur with MFCC embeddings has the lowest word boundary recall.\n" }, { "role": "assistant", "content": "The 'Word boundary (%) Rec.' column shows that BayesSegMinDur with MFCC embeddings has a recall of 12.4%, which is the lowest among all models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2093.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5.4: A breakdown of the errors on English2 for the speaker-dependent models in Table 5.3. The word boundary detection tolerance is 20 ms. The greedy one-to-one cluster mapping is used for error rate computations." ], "reasoning": "The 'Word boundary (%) Rec.' column shows that BayesSegMinDur with MFCC embeddings has a recall of 12.4%, which is the lowest among all models.", "statement": "BayesSegMinDur with MFCC embeddings has the lowest word boundary recall.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5.5: A breakdown of the errors on Xitsonga for the speaker-dependent models in Table 5.3. The word boundary detection tolerance is 20 ms. The greedy one-to-one cluster mapping is used for error rate computations.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "SyllableBayesClust has low word boundary recall due to treating every syllable as a word, while cAE embeddings significantly improve WER and cluster purity for BayesSeg models on Xitsonga.SyllableBayesClust gives an upper-bound for word boundary recall since every syllable boundary is set as a word boundary. The low recall (28.9% and 24.8%) could potentially be improved by using a better syllabification method, but we decided to use the method as provided, and leave further investigation for future work. This is not surprising since every syllable is treated as a word, while most of the true Xitsonga words are multisyllabic. At the cost of more deletions and poorer word boundary detection, BayesSeg-MFCC and BayesSegMinDur-MFCC systematically reduces substitution and insertion errors, again resulting in better overall WER and average cluster purity. Where the cAE-based models on English2 performed more-or-less on par with their MFCC counterparts, on Xitsonga the cAE embeddings yield large improvements on some metrics: by switching to cAE embeddings, the WER of BayesSeg improves by 8.3% absolute, while average cluster purity is 13.6% better for BayesSegMinDur." } ], "images": [ "scigen/train/2094.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5.5: A breakdown of the errors on Xitsonga for the speaker-dependent models in Table 5.3. The word boundary detection tolerance is 20 ms. The greedy one-to-one cluster mapping is used for error rate computations." ], "reasoning": "SyllableBayesClust has low word boundary recall due to treating every syllable as a word, while cAE embeddings significantly improve WER and cluster purity for BayesSeg models on Xitsonga.", "description": "SyllableBayesClust gives an upper-bound for word boundary recall since every syllable boundary is set as a word boundary. The low recall (28.9% and 24.8%) could potentially be improved by using a better syllabification method, but we decided to use the method as provided, and leave further investigation for future work. This is not surprising since every syllable is treated as a word, while most of the true Xitsonga words are multisyllabic. At the cost of more deletions and poorer word boundary detection, BayesSeg-MFCC and BayesSegMinDur-MFCC systematically reduces substitution and insertion errors, again resulting in better overall WER and average cluster purity. Where the cAE-based models on English2 performed more-or-less on par with their MFCC counterparts, on Xitsonga the cAE embeddings yield large improvements on some metrics: by switching to cAE embeddings, the WER of BayesSeg improves by 8.3% absolute, while average cluster purity is 13.6% better for BayesSegMinDur.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. The question is related to the given table titled 'Table 5.5: A breakdown of the errors on Xitsonga for the speaker-dependent models in Table 5.3. The word boundary detection tolerance is 20 ms. The greedy one-to-one cluster mapping is used for error rate computations.'.\n\nQuestion: Which model has the highest average cluster purity and what is its word boundary recall?\n" }, { "role": "assistant", "content": "From the table, BayesSegMinDur with cAE embeddings has the highest average cluster purity at 63.7%. Its word boundary recall is 8.9%. So, the answer is {\"answer\": \"BayesSegMinDur with cAE embeddings has the highest average cluster purity of 63.7% and a word boundary recall of 8.9%.\"}" } ], "images": [ "scigen/train/2094.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5.5: A breakdown of the errors on Xitsonga for the speaker-dependent models in Table 5.3. The word boundary detection tolerance is 20 ms. The greedy one-to-one cluster mapping is used for error rate computations." ], "reasoning": "From the table, BayesSegMinDur with cAE embeddings has the highest average cluster purity at 63.7%. Its word boundary recall is 8.9%.", "question": "Which model has the highest average cluster purity and what is its word boundary recall?", "answer": "So, the answer is {\"answer\": \"BayesSegMinDur with cAE embeddings has the highest average cluster purity of 63.7% and a word boundary recall of 8.9%.\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence BayesSegMinDur with MFCC embeddings has a higher word boundary recall than BayesSeg with cAE embeddings. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nBayesSegMinDur with MFCC embeddings has a higher word boundary recall than BayesSeg with cAE embeddings.\n" }, { "role": "assistant", "content": "From the table, BayesSegMinDur with MFCC embeddings has a word boundary recall of 10.8%, while BayesSeg with cAE embeddings has a word boundary recall of 18.3%. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2094.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5.5: A breakdown of the errors on Xitsonga for the speaker-dependent models in Table 5.3. The word boundary detection tolerance is 20 ms. The greedy one-to-one cluster mapping is used for error rate computations." ], "reasoning": "From the table, BayesSegMinDur with MFCC embeddings has a word boundary recall of 10.8%, while BayesSeg with cAE embeddings has a word boundary recall of 18.3%. Therefore, the statement is refuted.", "statement": "BayesSegMinDur with MFCC embeddings has a higher word boundary recall than BayesSeg with cAE embeddings.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5.6: Performance on the three datasets for speaker-independent models.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The cAE-based BayesSegMinDur model is the best performing speaker-independent model for Xitsonga with a WER less than 100%, showing significant improvements over MFCC-based models.As in the speaker-dependent case, BayesSegMinDur is the best performing MFCC system, followed by BayesSeg, and SyllableBayesClust performs worst. In the speaker-dependent experiments, some MFCC-based models slightly outperformed their cAE counterparts. Here, however, the WERs of cAE models are identical or improved in all cases; for Xitsonga in particular, improvements are obtained by using cAE features in both BayesSeg (improvement of 26.3% absolute in WER) and BayesSegMinDur (7.4%). The cAE-based BayesSegMinDur model is the only speaker-independent Xitsonga model with a WER less than 100%. Again, by allowing more than one cluster to be mapped to the same true word type, WERm scores are lower than WER. Although the cAE-based models do not result in better scores on the English data, the cAE features yield large improvements on Xitsonga. Word boundary scores and substitution, deletion and insertion errors (not shown) follow a similar pattern to that of the speaker-dependent models." } ], "images": [ "scigen/train/2095.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5.6: Performance on the three datasets for speaker-independent models." ], "reasoning": "The cAE-based BayesSegMinDur model is the best performing speaker-independent model for Xitsonga with a WER less than 100%, showing significant improvements over MFCC-based models.", "description": "As in the speaker-dependent case, BayesSegMinDur is the best performing MFCC system, followed by BayesSeg, and SyllableBayesClust performs worst. In the speaker-dependent experiments, some MFCC-based models slightly outperformed their cAE counterparts. Here, however, the WERs of cAE models are identical or improved in all cases; for Xitsonga in particular, improvements are obtained by using cAE features in both BayesSeg (improvement of 26.3% absolute in WER) and BayesSegMinDur (7.4%). The cAE-based BayesSegMinDur model is the only speaker-independent Xitsonga model with a WER less than 100%. Again, by allowing more than one cluster to be mapped to the same true word type, WERm scores are lower than WER. Although the cAE-based models do not result in better scores on the English data, the cAE features yield large improvements on Xitsonga. Word boundary scores and substitution, deletion and insertion errors (not shown) follow a similar pattern to that of the speaker-dependent models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 5.6: Performance on the three datasets for speaker-independent models.' and proceed to solve the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model has the lowest one-to-one WER for Xitsonga?\n" }, { "role": "assistant", "content": "By examining the 'One-to-one WER (%) Xitsonga' column, the BayesSegMinDur model with cAE embeddings has the lowest WER of 95.0%. So, the answer is {\"answer\": \"BayesSegMinDur (cAE)\"}" } ], "images": [ "scigen/train/2095.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5.6: Performance on the three datasets for speaker-independent models." ], "reasoning": "By examining the 'One-to-one WER (%) Xitsonga' column, the BayesSegMinDur model with cAE embeddings has the lowest WER of 95.0%.", "question": "Which model has the lowest one-to-one WER for Xitsonga?", "answer": "So, the answer is {\"answer\": \"BayesSegMinDur (cAE)\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe SyllableBayesClust model has the highest one-to-one WER for Xitsonga.\n" }, { "role": "assistant", "content": "By looking at the 'One-to-one WER (%) Xitsonga' column, the SyllableBayesClust model has the highest WER of 167.2%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2095.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5.6: Performance on the three datasets for speaker-independent models." ], "reasoning": "By looking at the 'One-to-one WER (%) Xitsonga' column, the SyllableBayesClust model has the highest WER of 167.2%.", "statement": "The SyllableBayesClust model has the highest one-to-one WER for Xitsonga.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5.7: Average speaker-independent cluster (clust.), speaker (spk.), and gender (gndr) purity for BayesSegMinDur on the three datasets.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "A speaker-independent system should have high cluster purity and low speaker and gender purities, with speaker purity being 100% if every cluster contains tokens from a single speaker and gender purity being 100% if every cluster contains tokens from a single gender.Average cluster purity is as defined before. Average speaker purity is similarly defined, but instead of considering the mapped ground truth label of a segmented token, it considers the speaker who produced it: speaker purity is 100% if every cluster contains tokens from a single speaker, while it is 1/12=8.3% if all clusters are completely speaker balanced for the English sets and 1/24=4.2% for Xitsonga. Average gender purity is similarly defined: it is 100% if every cluster contains tokens from a single gender, while 1/2=50% indicates a perfectly gender-balanced cluster. Ideally, a speaker-independent system should have high cluster purity and low speaker and gender purities." } ], "images": [ "scigen/train/2096.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5.7: Average speaker-independent cluster (clust.), speaker (spk.), and gender (gndr) purity for BayesSegMinDur on the three datasets." ], "reasoning": "A speaker-independent system should have high cluster purity and low speaker and gender purities, with speaker purity being 100% if every cluster contains tokens from a single speaker and gender purity being 100% if every cluster contains tokens from a single gender.", "description": "Average cluster purity is as defined before. Average speaker purity is similarly defined, but instead of considering the mapped ground truth label of a segmented token, it considers the speaker who produced it: speaker purity is 100% if every cluster contains tokens from a single speaker, while it is 1/12=8.3% if all clusters are completely speaker balanced for the English sets and 1/24=4.2% for Xitsonga. Average gender purity is similarly defined: it is 100% if every cluster contains tokens from a single gender, while 1/2=50% indicates a perfectly gender-balanced cluster. Ideally, a speaker-independent system should have high cluster purity and low speaker and gender purities.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: 'Table 5.7: Average speaker-independent cluster (clust.), speaker (spk.), and gender (gndr) purity for BayesSegMinDur on the three datasets.'\nQuestion: Which embedding method has the highest cluster purity for the Xitsonga dataset?\n" }, { "role": "assistant", "content": "To find the highest cluster purity for the Xitsonga dataset, compare the values in the 'Xitsonga (%) Clust.' column for both embedding methods. MFCC has a cluster purity of 24.5%, while cAE has a cluster purity of 33.1%. So, the answer is {\"answer\": \"cAE\"}." } ], "images": [ "scigen/train/2096.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5.7: Average speaker-independent cluster (clust.), speaker (spk.), and gender (gndr) purity for BayesSegMinDur on the three datasets." ], "reasoning": "To find the highest cluster purity for the Xitsonga dataset, compare the values in the 'Xitsonga (%) Clust.' column for both embedding methods. MFCC has a cluster purity of 24.5%, while cAE has a cluster purity of 33.1%.", "question": "Which embedding method has the highest cluster purity for the Xitsonga dataset?", "answer": "So, the answer is {\"answer\": \"cAE\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim MFCC has a higher speaker purity than cAE for the English1 dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nMFCC has a higher speaker purity than cAE for the English1 dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the values in the 'English1 (%) Spk.' column for both embedding methods. MFCC has a speaker purity of 56.7%, while cAE has a speaker purity of 37.9%. Therefore, MFCC has a higher speaker purity than cAE for the English1 dataset. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2096.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5.7: Average speaker-independent cluster (clust.), speaker (spk.), and gender (gndr) purity for BayesSegMinDur on the three datasets." ], "reasoning": "To verify this statement, compare the values in the 'English1 (%) Spk.' column for both embedding methods. MFCC has a speaker purity of 56.7%, while cAE has a speaker purity of 37.9%. Therefore, MFCC has a higher speaker purity than cAE for the English1 dataset.", "statement": "MFCC has a higher speaker purity than cAE for the English1 dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 7: The performance of text-independent speaker recognition task using both Train250k and Train50k as training sets and Eva200 as the test set. All deep speaker models are trained with softmax pre-training.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Using the full dataset significantly improves performance, reducing identification error and EER for both ResCNN and GRU models.We do not experiment with the i-vector baseline here, as it’s too time consuming and computationally expensive. It is clear that using tens of millions of samples results in a performance boost. Compared to using only around 1/5th the data, the using the full dataset reduces the identification error and EER by 17.94% and 21.65% for ResCNN and 15.16% and 13.88% for GRU." } ], "images": [ "scigen/train/2097.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: The performance of text-independent speaker recognition task using both Train250k and Train50k as training sets and Eva200 as the test set. All deep speaker models are trained with softmax pre-training." ], "reasoning": "Using the full dataset significantly improves performance, reducing identification error and EER for both ResCNN and GRU models.", "description": "We do not experiment with the i-vector baseline here, as it’s too time consuming and computationally expensive. It is clear that using tens of millions of samples results in a performance boost. Compared to using only around 1/5th the data, the using the full dataset reduces the identification error and EER by 17.94% and 21.65% for ResCNN and 15.16% and 13.88% for GRU.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 7: The performance of text-independent speaker recognition task using both Train250k and Train50k as training sets and Eva200 as the test set. All deep speaker models are trained with softmax pre-training.', solve the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the EER reduction percentage for ResCNN when using the full dataset compared to using only 50k samples?\n" }, { "role": "assistant", "content": "The EER for ResCNN on Train50k is 2.23%, and on Train250k is 1.83%. The reduction percentage is calculated as ((2.23 - 1.83) / 2.23) * 100. So, the answer is {\"answer\": \"17.94%\"}" } ], "images": [ "scigen/train/2097.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: The performance of text-independent speaker recognition task using both Train250k and Train50k as training sets and Eva200 as the test set. All deep speaker models are trained with softmax pre-training." ], "reasoning": "The EER for ResCNN on Train50k is 2.23%, and on Train250k is 1.83%. The reduction percentage is calculated as ((2.23 - 1.83) / 2.23) * 100.", "question": "What is the EER reduction percentage for ResCNN when using the full dataset compared to using only 50k samples?", "answer": "So, the answer is {\"answer\": \"17.94%\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nUsing the full dataset reduces the EER for GRU by more than 15%.\n" }, { "role": "assistant", "content": "The EER for GRU on Train50k is 2.77%, and on Train250k is 2.35%. The reduction percentage is calculated as ((2.77 - 2.35) / 2.77) * 100, which is approximately 15.16%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2097.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: The performance of text-independent speaker recognition task using both Train250k and Train50k as training sets and Eva200 as the test set. All deep speaker models are trained with softmax pre-training." ], "reasoning": "The EER for GRU on Train50k is 2.77%, and on Train250k is 2.35%. The reduction percentage is calculated as ((2.77 - 2.35) / 2.77) * 100, which is approximately 15.16%.", "statement": "Using the full dataset reduces the EER for GRU by more than 15%.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: Architecture of ResCNN. “Average” denotes the temporal pooling layer and “ln” denotes the length normalization layer. A bracket describes the structure of a ResBlock as shown in Fig. 2'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Three ResBlocks are stacked, and when the number of channels increases, a single convolutional layer with a 5×5 filter and 2×2 stride is used to keep the frequency dimension constant.Each block has an identical structure, and the skip connection is the identity mapping of x. Three ResBlocks are stacked in our architecture. When the number of channels increases, we use a single convolutional layer with filter size of 5×5 and a stride of 2×2. Thus, the the frequency dimension is kept constant in all convolution layers. ’s findings." } ], "images": [ "scigen/train/2098.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Architecture of ResCNN. “Average” denotes the temporal pooling layer and “ln” denotes the length normalization layer. A bracket describes the structure of a ResBlock as shown in Fig. 2" ], "reasoning": "Three ResBlocks are stacked, and when the number of channels increases, a single convolutional layer with a 5×5 filter and 2×2 stride is used to keep the frequency dimension constant.", "description": "Each block has an identical structure, and the skip connection is the identity mapping of x. Three ResBlocks are stacked in our architecture. When the number of channels increases, we use a single convolutional layer with filter size of 5×5 and a stride of 2×2. Thus, the the frequency dimension is kept constant in all convolution layers. ’s findings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 1: Architecture of ResCNN. “Average” denotes the temporal pooling layer and “ln” denotes the length normalization layer. A bracket describes the structure of a ResBlock as shown in Fig. 2'\nQuestion: How many parameters does the conv256-s layer have?\n" }, { "role": "assistant", "content": "The table shows that the conv256-s layer has a structure of 5×5,256 and a stride of 2×2, with 823K parameters. So, the answer is {\"answer\": \"823K\"}." } ], "images": [ "scigen/train/2098.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Architecture of ResCNN. “Average” denotes the temporal pooling layer and “ln” denotes the length normalization layer. A bracket describes the structure of a ResBlock as shown in Fig. 2" ], "reasoning": "The table shows that the conv256-s layer has a structure of 5×5,256 and a stride of 2×2, with 823K parameters.", "question": "How many parameters does the conv256-s layer have?", "answer": "So, the answer is {\"answer\": \"823K\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe res512 layer has more parameters than the conv512-s layer.\n" }, { "role": "assistant", "content": "The table shows that the res512 layer has 2.4M parameters, while the conv512-s layer has 3.3M parameters, indicating that the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2098.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Architecture of ResCNN. “Average” denotes the temporal pooling layer and “ln” denotes the length normalization layer. A bracket describes the structure of a ResBlock as shown in Fig. 2" ], "reasoning": "The table shows that the res512 layer has 2.4M parameters, while the conv512-s layer has 3.3M parameters, indicating that the statement is incorrect.", "statement": "The res512 layer has more parameters than the conv512-s layer.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: The performance of Mandarin text-independent speaker recognition task using Train50k as train set and Eva200 as test set. Results from different neural network architectures and training methodologies are reported.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Deep Speaker models, using different neural network architectures and strategies, significantly outperform the DNN-based i-vector system on the UIDs dataset, achieving notable improvements in both EER and ACC.First, we compare the DNN-based i-vector system and Deep Speaker on the UIDs dataset, with the Train50k partition as the training set and Eva200 partition as the test set. Deep Speaker models are trained with different neural network architectures (GRU or ResCNN), and different strategies (softmax, triplet loss, or softmax + triplet loss, which is softmax pretraining followed by triplet loss fine-tuning). All Deep Speaker models achieve notable improvements over the baseline, roughly 50 - 80% relative reduction on EER, and 60 - 70% relative improvement on ACC." } ], "images": [ "scigen/train/2099.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The performance of Mandarin text-independent speaker recognition task using Train50k as train set and Eva200 as test set. Results from different neural network architectures and training methodologies are reported." ], "reasoning": "Deep Speaker models, using different neural network architectures and strategies, significantly outperform the DNN-based i-vector system on the UIDs dataset, achieving notable improvements in both EER and ACC.", "description": "First, we compare the DNN-based i-vector system and Deep Speaker on the UIDs dataset, with the Train50k partition as the training set and Eva200 partition as the test set. Deep Speaker models are trained with different neural network architectures (GRU or ResCNN), and different strategies (softmax, triplet loss, or softmax + triplet loss, which is softmax pretraining followed by triplet loss fine-tuning). All Deep Speaker models achieve notable improvements over the baseline, roughly 50 - 80% relative reduction on EER, and 60 - 70% relative improvement on ACC.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 5: The performance of Mandarin text-independent speaker recognition task using Train50k as train set and Eva200 as test set. Results from different neural network architectures and training methodologies are reported.', answer the following question. Give detailed solution consisting of each step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich Deep Speaker model achieved the highest accuracy (ACC) on the UIDs dataset?\n" }, { "role": "assistant", "content": "To find the Deep Speaker model with the highest accuracy, we compare the ACC values of all the Deep Speaker models listed in the table. The model with the highest ACC is the ResCNN with softmax pre-training followed by triplet loss fine-tuning, which achieved an ACC of 90.53%. So, the answer is {\"answer\": \"ResCNN, softmax (pre-train) + triplet\"}." } ], "images": [ "scigen/train/2099.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The performance of Mandarin text-independent speaker recognition task using Train50k as train set and Eva200 as test set. Results from different neural network architectures and training methodologies are reported." ], "reasoning": "To find the Deep Speaker model with the highest accuracy, we compare the ACC values of all the Deep Speaker models listed in the table. The model with the highest ACC is the ResCNN with softmax pre-training followed by triplet loss fine-tuning, which achieved an ACC of 90.53%.", "question": "Which Deep Speaker model achieved the highest accuracy (ACC) on the UIDs dataset?", "answer": "So, the answer is {\"answer\": \"ResCNN, softmax (pre-train) + triplet\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe GRU, triplet model achieved a lower EER than the ResCNN, softmax model.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the EER values of the GRU, triplet model and the ResCNN, softmax model. The GRU, triplet model has an EER of 3.23%, while the ResCNN, softmax model has an EER of 6.13%. Since 3.23% is lower than 6.13%, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2099.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The performance of Mandarin text-independent speaker recognition task using Train50k as train set and Eva200 as test set. Results from different neural network architectures and training methodologies are reported." ], "reasoning": "To verify this statement, we compare the EER values of the GRU, triplet model and the ResCNN, softmax model. The GRU, triplet model has an EER of 3.23%, while the ResCNN, softmax model has an EER of 6.13%. Since 3.23% is lower than 6.13%, the statement is supported.", "statement": "The GRU, triplet model achieved a lower EER than the ResCNN, softmax model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 9: The text-dependent speaker recognition performance of different systems on XiaoDu dataset.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The flag 'on Train50k' means models are trained only on Train50k, while 'finetuned' indicates models are first trained on Train50k and then fine-tuned with the XiaoDu dataset.The flag “on Train50k” means the Deep Speaker models are only trained on Train50k, while the flag “finetuned” indicates that we first trained the model on Train50k, then used the XiaoDu dataset to fine-tune with triplet loss for Deep Speaker systems, and i-vector extraction for the DNN i-vector system." } ], "images": [ "scigen/train/2100.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: The text-dependent speaker recognition performance of different systems on XiaoDu dataset." ], "reasoning": "The flag 'on Train50k' means models are trained only on Train50k, while 'finetuned' indicates models are first trained on Train50k and then fine-tuned with the XiaoDu dataset.", "description": "The flag “on Train50k” means the Deep Speaker models are only trained on Train50k, while the flag “finetuned” indicates that we first trained the model on Train50k, then used the XiaoDu dataset to fine-tune with triplet loss for Deep Speaker systems, and i-vector extraction for the DNN i-vector system.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 9: The text-dependent speaker recognition performance of different systems on XiaoDu dataset.', solve the subsequent question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich system has the lowest EER and what is its ACC?\n" }, { "role": "assistant", "content": "To find the system with the lowest EER, we look at the EER column and identify the lowest value, which is 2.78% for the finetuned GRU system. The corresponding ACC for this system is 95.75%. So, the answer is {\"answer\": \"finetuned GRU with ACC 95.75%\"}." } ], "images": [ "scigen/train/2100.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: The text-dependent speaker recognition performance of different systems on XiaoDu dataset." ], "reasoning": "To find the system with the lowest EER, we look at the EER column and identify the lowest value, which is 2.78% for the finetuned GRU system. The corresponding ACC for this system is 95.75%.", "question": "Which system has the lowest EER and what is its ACC?", "answer": "So, the answer is {\"answer\": \"finetuned GRU with ACC 95.75%\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The finetuned ResCNN system has a higher ACC than the GRU on Train50k system., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe finetuned ResCNN system has a higher ACC than the GRU on Train50k system.\n" }, { "role": "assistant", "content": "The ACC for the finetuned ResCNN system is 94.85%, while the ACC for the GRU on Train50k system is 94.45%. Since 94.85% is higher than 94.45%, the statement is supported by the table. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2100.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: The text-dependent speaker recognition performance of different systems on XiaoDu dataset." ], "reasoning": "The ACC for the finetuned ResCNN system is 94.85%, while the ACC for the GRU on Train50k system is 94.45%. Since 94.85% is higher than 94.45%, the statement is supported by the table.", "statement": "The finetuned ResCNN system has a higher ACC than the GRU on Train50k system.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 10: The text-independent speaker recognition performance of different systems on MTurk dataset.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The Deep Speaker models trained on Train50k and fine-tuned on the MTurk dataset using triplet loss show improved performance, while the DNN i-vector baseline is not fine-tuned due to the difficulty in adapting to different phone sets of Mandarin and English.The flag “on Train50k” means the Deep Speaker models are only trained on Train50k, while the flag “finetuned” means models are first trained on Train50k, and then fine-tuned on the MTurk dataset using triplet loss. Note that this is a non-trivial task, since Mandarin and English speech sounds disparate. We don’t report a fine-tuned result for DNN i-vector baseline here. Because Mandarin and English have different phone sets, the ASR-DNN model is difficult to adapt." } ], "images": [ "scigen/train/2101.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: The text-independent speaker recognition performance of different systems on MTurk dataset." ], "reasoning": "The Deep Speaker models trained on Train50k and fine-tuned on the MTurk dataset using triplet loss show improved performance, while the DNN i-vector baseline is not fine-tuned due to the difficulty in adapting to different phone sets of Mandarin and English.", "description": "The flag “on Train50k” means the Deep Speaker models are only trained on Train50k, while the flag “finetuned” means models are first trained on Train50k, and then fine-tuned on the MTurk dataset using triplet loss. Note that this is a non-trivial task, since Mandarin and English speech sounds disparate. We don’t report a fine-tuned result for DNN i-vector baseline here. Because Mandarin and English have different phone sets, the ASR-DNN model is difficult to adapt.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 10: The text-independent speaker recognition performance of different systems on MTurk dataset.', answer the following question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the lowest EER and what is its ACC?\n" }, { "role": "assistant", "content": "To find the model with the lowest EER, we look at the EER column and identify the lowest value, which is 2.40 for the finetuned GRU model. The corresponding ACC for this model is 94.88%. So, the answer is {\"answer\": \"finetuned GRU with ACC 94.88%\"}." } ], "images": [ "scigen/train/2101.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: The text-independent speaker recognition performance of different systems on MTurk dataset." ], "reasoning": "To find the model with the lowest EER, we look at the EER column and identify the lowest value, which is 2.40 for the finetuned GRU model. The corresponding ACC for this model is 94.88%.", "question": "Which model has the lowest EER and what is its ACC?", "answer": "So, the answer is {\"answer\": \"finetuned GRU with ACC 94.88%\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe GRU model trained on Train50k has an EER higher than 5%.\n" }, { "role": "assistant", "content": "The table shows that the GRU model trained on Train50k has an EER of 5.57%, which is higher than 5%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2101.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: The text-independent speaker recognition performance of different systems on MTurk dataset." ], "reasoning": "The table shows that the GRU model trained on Train50k has an EER of 5.57%, which is higher than 5%.", "statement": "The GRU model trained on Train50k has an EER higher than 5%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Evaluation of coreference results on the test dataset. We force the model to decode the gold target sequence, and only evaluate algorithms for solving coreference. The Avg. F1 is computed by averaging F1 scores of MUC, B3 and CEAFe.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The copying mechanism achieves the highest scores across all metrics, with an average F1 of 97.82, while the heuristic baseline achieves an average F1 of 81.94. The random baseline performs significantly worse.Since coreference in our setup occurs at the sentence level, the proposed copying mechanism achieves high scores in all three metrics, and the average F1 is 97.82. The heuristic baseline, which solves coreference only based on syntactic heads, also achieves a relatively high average F1 of 81.94. Under the MUC metric, the copying mechanism performs significantly better than the heuristic baseline. The random baseline limits the choice of coreference to preceding syntactic heads, ignoring all other tokens, achieving scores much lower than the other two approaches in all three metrics." } ], "images": [ "scigen/train/2102.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Evaluation of coreference results on the test dataset. We force the model to decode the gold target sequence, and only evaluate algorithms for solving coreference. The Avg. F1 is computed by averaging F1 scores of MUC, B3 and CEAFe." ], "reasoning": "The copying mechanism achieves the highest scores across all metrics, with an average F1 of 97.82, while the heuristic baseline achieves an average F1 of 81.94. The random baseline performs significantly worse.", "description": "Since coreference in our setup occurs at the sentence level, the proposed copying mechanism achieves high scores in all three metrics, and the average F1 is 97.82. The heuristic baseline, which solves coreference only based on syntactic heads, also achieves a relatively high average F1 of 81.94. Under the MUC metric, the copying mechanism performs significantly better than the heuristic baseline. The random baseline limits the choice of coreference to preceding syntactic heads, ignoring all other tokens, achieving scores much lower than the other two approaches in all three metrics.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Evaluation of coreference results on the test dataset. We force the model to decode the gold target sequence, and only evaluate algorithms for solving coreference. The Avg. F1 is computed by averaging F1 scores of MUC, B3 and CEAFe.' to sequentially solve the question presented below. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhich approach achieves the highest MUC F1 score?\n" }, { "role": "assistant", "content": "The table shows that the Seq2Seq+Copy approach achieves the highest MUC F1 score of 96.05. So, the answer is {\"answer\": \"Seq2Seq+Copy\"}" } ], "images": [ "scigen/train/2102.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Evaluation of coreference results on the test dataset. We force the model to decode the gold target sequence, and only evaluate algorithms for solving coreference. The Avg. F1 is computed by averaging F1 scores of MUC, B3 and CEAFe." ], "reasoning": "The table shows that the Seq2Seq+Copy approach achieves the highest MUC F1 score of 96.05.", "question": "Which approach achieves the highest MUC F1 score?", "answer": "So, the answer is {\"answer\": \"Seq2Seq+Copy\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Seq2Seq+Heuristics approach achieves a higher average F1 score than the Seq2Seq+Copy approach.\n" }, { "role": "assistant", "content": "The table shows that the Seq2Seq+Heuristics approach has an average F1 score of 81.94, which is lower than the Seq2Seq+Copy approach's average F1 score of 97.82. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2102.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Evaluation of coreference results on the test dataset. We force the model to decode the gold target sequence, and only evaluate algorithms for solving coreference. The Avg. F1 is computed by averaging F1 scores of MUC, B3 and CEAFe." ], "reasoning": "The table shows that the Seq2Seq+Heuristics approach has an average F1 score of 81.94, which is lower than the Seq2Seq+Copy approach's average F1 score of 97.82.", "statement": "The Seq2Seq+Heuristics approach achieves a higher average F1 score than the Seq2Seq+Copy approach.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Precision, recall, and F1 scores for the evaluation metric S(ϕ,ψ) on the test dataset.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Seq2Seq+Copy achieves the highest precision, recall, and F1 scores compared to Seq2Seq+Heuristic, Seq2Seq+Random, and Pipeline approaches.Results: Overall, our proposed approach, Seq2Seq+Copy, achieves the best precision, recall, and F1. The two baselines, Seq2Seq+Heuristic and Seq2Seq+Random also achieve reasonable results. These two baselines both employ sequence-to-sequence models to predict meaning representations, which can be considered a replica of state-of-the-art approaches for structured prediction Vinyals et al. Our proposed approach outperforms these two strong baselines: the copying mechanism, aiming for solving coreference in cross-lingual semantic parsing, results in both precision and recall gains. The detailed gains achieved by the copying mechanism are discussed in the following section. In the Pipeline approach, each component is trained independently. During testing, residual errors from each component are propagated through the pipeline. As expected, Pipeline shows a significant performance drop compared to the other end-to-end learning approaches." } ], "images": [ "scigen/train/2103.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Precision, recall, and F1 scores for the evaluation metric S(ϕ,ψ) on the test dataset." ], "reasoning": "Seq2Seq+Copy achieves the highest precision, recall, and F1 scores compared to Seq2Seq+Heuristic, Seq2Seq+Random, and Pipeline approaches.", "description": "Results: Overall, our proposed approach, Seq2Seq+Copy, achieves the best precision, recall, and F1. The two baselines, Seq2Seq+Heuristic and Seq2Seq+Random also achieve reasonable results. These two baselines both employ sequence-to-sequence models to predict meaning representations, which can be considered a replica of state-of-the-art approaches for structured prediction Vinyals et al. Our proposed approach outperforms these two strong baselines: the copying mechanism, aiming for solving coreference in cross-lingual semantic parsing, results in both precision and recall gains. The detailed gains achieved by the copying mechanism are discussed in the following section. In the Pipeline approach, each component is trained independently. During testing, residual errors from each component are propagated through the pipeline. As expected, Pipeline shows a significant performance drop compared to the other end-to-end learning approaches.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Precision, recall, and F1 scores for the evaluation metric S(ϕ,ψ) on the test dataset.', unfold the question presented below, detailing every step of your calculation. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich approach has the highest F1 score and what is the value?\n" }, { "role": "assistant", "content": "The table shows the F1 scores for different approaches. Seq2Seq+Copy has the highest F1 score of 38.38. So, the answer is {\"answer\": \"38.38\"}." } ], "images": [ "scigen/train/2103.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Precision, recall, and F1 scores for the evaluation metric S(ϕ,ψ) on the test dataset." ], "reasoning": "The table shows the F1 scores for different approaches. Seq2Seq+Copy has the highest F1 score of 38.38.", "question": "Which approach has the highest F1 score and what is the value?", "answer": "So, the answer is {\"answer\": \"38.38\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Pipeline approach has the lowest precision score.\n" }, { "role": "assistant", "content": "The table shows the precision scores for different approaches. The Pipeline approach has the lowest precision score of 28.50. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2103.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Precision, recall, and F1 scores for the evaluation metric S(ϕ,ψ) on the test dataset." ], "reasoning": "The table shows the precision scores for different approaches. The Pipeline approach has the lowest precision score of 28.50.", "statement": "The Pipeline approach has the lowest precision score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Precision@k of semantic alignment on test set.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The experiment used Procrustes alignment and CSLS method to evaluate the semantic alignment of embeddings, reporting Precision@k for top 50 frequent words and remaining 450 words.Semantic Alignment: To further verify if predicted embedding is semantically aligned to pre-trained embedding, we applied Procrustes alignment Conneau et al. Top 50 frequent words were selected to be the training dictionary, and we evaluated on the remaining 450 words with cross-domain similarity local scaling (CSLS) method. Precision@k (P@k, k=1,5) were reported as measurements. This experiment reinforced that our proposals can constrain hidden states to the similar structure of word embedding space." } ], "images": [ "scigen/train/2104.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Precision@k of semantic alignment on test set." ], "reasoning": "The experiment used Procrustes alignment and CSLS method to evaluate the semantic alignment of embeddings, reporting Precision@k for top 50 frequent words and remaining 450 words.", "description": "Semantic Alignment: To further verify if predicted embedding is semantically aligned to pre-trained embedding, we applied Procrustes alignment Conneau et al. Top 50 frequent words were selected to be the training dictionary, and we evaluated on the remaining 450 words with cross-domain similarity local scaling (CSLS) method. Precision@k (P@k, k=1,5) were reported as measurements. This experiment reinforced that our proposals can constrain hidden states to the similar structure of word embedding space.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 3: Precision@k of semantic alignment on test set.', solve the subsequent question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhat is the Precision@1 for CD after 160 hours?\n" }, { "role": "assistant", "content": "To find the Precision@1 for CD after 160 hours, look at the row labeled 'CD' and the column labeled '160 hours P@1'. The value is 61.48. So, the answer is {\"answer\": \"61.48\"}." } ], "images": [ "scigen/train/2104.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Precision@k of semantic alignment on test set." ], "reasoning": "To find the Precision@1 for CD after 160 hours, look at the row labeled 'CD' and the column labeled '160 hours P@1'. The value is 61.48.", "question": "What is the Precision@1 for CD after 160 hours?", "answer": "So, the answer is {\"answer\": \"61.48\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The Precision@5 for CS after 40 hours is higher than the Precision@1 for ME after 160 hours., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Precision@5 for CS after 40 hours is higher than the Precision@1 for ME after 160 hours.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Precision@5 for CS after 40 hours (25.19) with the Precision@1 for ME after 160 hours (1.85). Since 25.19 is higher than 1.85, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2104.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Precision@k of semantic alignment on test set." ], "reasoning": "To verify this statement, compare the Precision@5 for CS after 40 hours (25.19) with the Precision@1 for ME after 160 hours (1.85). Since 25.19 is higher than 1.85, the statement is supported.", "statement": "The Precision@5 for CS after 40 hours is higher than the Precision@1 for ME after 160 hours.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Eigenvector similarity.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The eigenvector similarity metric shows that CD is the most similar case and ME is the most different case, indicating that the proposed methods effectively constrain hidden states in the pre-trained embedding space.Eigenvector Similarity: To verify our proposed methods can constrain hidden states in the word embedding space, we computed eigenvector similarity between predicted embedding and pre-trained embedding space. The metric derives from Laplacian eigenvalues and represents how similar between two spaces, the lower value on the metric, the more approximately isospectral between the two spaces. Previous works showed that the metric is correlated to the performance of translation task Søgaard et al. CD is the most similar case among the three cases, and ME is the most different case. Results indicated that our proposals constrain hidden states in pre-trained embedding space." } ], "images": [ "scigen/train/2105.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Eigenvector similarity." ], "reasoning": "The eigenvector similarity metric shows that CD is the most similar case and ME is the most different case, indicating that the proposed methods effectively constrain hidden states in the pre-trained embedding space.", "description": "Eigenvector Similarity: To verify our proposed methods can constrain hidden states in the word embedding space, we computed eigenvector similarity between predicted embedding and pre-trained embedding space. The metric derives from Laplacian eigenvalues and represents how similar between two spaces, the lower value on the metric, the more approximately isospectral between the two spaces. Previous works showed that the metric is correlated to the performance of translation task Søgaard et al. CD is the most similar case among the three cases, and ME is the most different case. Results indicated that our proposals constrain hidden states in pre-trained embedding space.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Eigenvector similarity.', answer the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich case has the lowest eigenvector similarity in the 160 hours dev set?\n" }, { "role": "assistant", "content": "To find the case with the lowest eigenvector similarity in the 160 hours dev set, look at the values in the '160 hours dev' column. The lowest value is 2.60, which corresponds to the CD case. So, the answer is {\"answer\": \"CD\"}" } ], "images": [ "scigen/train/2105.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Eigenvector similarity." ], "reasoning": "To find the case with the lowest eigenvector similarity in the 160 hours dev set, look at the values in the '160 hours dev' column. The lowest value is 2.60, which corresponds to the CD case.", "question": "Which case has the lowest eigenvector similarity in the 160 hours dev set?", "answer": "So, the answer is {\"answer\": \"CD\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis ME has the highest eigenvector similarity in the 40 hours test set. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nME has the highest eigenvector similarity in the 40 hours test set.\n" }, { "role": "assistant", "content": "To verify this statement, look at the values in the '40 hours test' column. The highest value is 15.09, which corresponds to the ME case. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2105.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Eigenvector similarity." ], "reasoning": "To verify this statement, look at the values in the '40 hours test' column. The highest value is 15.09, which corresponds to the ME case.", "statement": "ME has the highest eigenvector similarity in the 40 hours test set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Multiscale vs. single scale features. Holding the number of filters constant, single scale convolution with High frequency (1ms window, 1/4ms stride), Mid frequency (4ms, 1ms) or Low frequency (40ms, 10ms), are outperformed by a combination of all three (as diagrammed in Figure 1).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The High, Mid, and Low filter banks capture different frequency bands, and jointly learning several scales results in the lowest WER with fewer parameters.It is clear that the High (2800Hz), Mid (1700Hz), and Low (940Hz) filter banks live up to their names, each learning filters that capture different frequency bands. When we jointly learn several scales, the filters exhibit a an heightened preference for representing different bands. Relaxing the requirement of each bank to cover the entire spectrum causes the High (3500Hz) banks to increase in mean frequency, and the Mid (1300Hz) and Low (480Hz) banks to decrease. Further, the WER is lowest for the multiscale features, despite the fact that it has fewer parameters and three times less large filters." } ], "images": [ "scigen/train/2106.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Multiscale vs. single scale features. Holding the number of filters constant, single scale convolution with High frequency (1ms window, 1/4ms stride), Mid frequency (4ms, 1ms) or Low frequency (40ms, 10ms), are outperformed by a combination of all three (as diagrammed in Figure 1)." ], "reasoning": "The High, Mid, and Low filter banks capture different frequency bands, and jointly learning several scales results in the lowest WER with fewer parameters.", "description": "It is clear that the High (2800Hz), Mid (1700Hz), and Low (940Hz) filter banks live up to their names, each learning filters that capture different frequency bands. When we jointly learn several scales, the filters exhibit a an heightened preference for representing different bands. Relaxing the requirement of each bank to cover the entire spectrum causes the High (3500Hz) banks to increase in mean frequency, and the Mid (1300Hz) and Low (480Hz) banks to decrease. Further, the WER is lowest for the multiscale features, despite the fact that it has fewer parameters and three times less large filters.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 3: Multiscale vs. single scale features. Holding the number of filters constant, single scale convolution with High frequency (1ms window, 1/4ms stride), Mid frequency (4ms, 1ms) or Low frequency (40ms, 10ms), are outperformed by a combination of all three (as diagrammed in Figure 1).', solve the following question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the WER when using a combination of 61 High, 50 Mid, and 50 Low features?\n" }, { "role": "assistant", "content": "The table shows that the WER is 25.67% when using a combination of 61 High, 50 Mid, and 50 Low features. So, the answer is {\"answer\": \"25.67\"}" } ], "images": [ "scigen/train/2106.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Multiscale vs. single scale features. Holding the number of filters constant, single scale convolution with High frequency (1ms window, 1/4ms stride), Mid frequency (4ms, 1ms) or Low frequency (40ms, 10ms), are outperformed by a combination of all three (as diagrammed in Figure 1)." ], "reasoning": "The table shows that the WER is 25.67% when using a combination of 61 High, 50 Mid, and 50 Low features.", "question": "What is the WER when using a combination of 61 High, 50 Mid, and 50 Low features?", "answer": "So, the answer is {\"answer\": \"25.67\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe WER is lowest when using only High features.\n" }, { "role": "assistant", "content": "The table shows that the WER is lowest (25.67%) when using a combination of High, Mid, and Low features, not when using only High features (32.84%). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2106.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Multiscale vs. single scale features. Holding the number of filters constant, single scale convolution with High frequency (1ms window, 1/4ms stride), Mid frequency (4ms, 1ms) or Low frequency (40ms, 10ms), are outperformed by a combination of all three (as diagrammed in Figure 1)." ], "reasoning": "The table shows that the WER is lowest (25.67%) when using a combination of High, Mid, and Low features, not when using only High features (32.84%).", "statement": "The WER is lowest when using only High features.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Single scale waveform convolution outperforms the spectrogram baseline at low strides. The trend is visualized in Figure 2.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "As the stride of the convolution decreases below 2ms, the performance improves, surpassing the spectrogram, but at the cost of increased computational resources.We decided to test this hypothesis that a convolution with low stride and pooling can find at least as good a basis as the spectrogram. For each decrease in stride of the convolution, the stride of the pooling is increased to give a consistent total stride of 20ms, which is the same as the stride of the spectrogram with pooling. At comparable strides to the spectrogram (10ms) the convolution is unable to perform as well, likely due to needing to represent phase shifts as well as frequency variation. However, as the stride dips below 2ms, the convolution asymptotically reaches a superior level of performance to the spectrogram. To be fair, this improved performance comes at increased computational cost and memory usage." } ], "images": [ "scigen/train/2107.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Single scale waveform convolution outperforms the spectrogram baseline at low strides. The trend is visualized in Figure 2." ], "reasoning": "As the stride of the convolution decreases below 2ms, the performance improves, surpassing the spectrogram, but at the cost of increased computational resources.", "description": "We decided to test this hypothesis that a convolution with low stride and pooling can find at least as good a basis as the spectrogram. For each decrease in stride of the convolution, the stride of the pooling is increased to give a consistent total stride of 20ms, which is the same as the stride of the spectrogram with pooling. At comparable strides to the spectrogram (10ms) the convolution is unable to perform as well, likely due to needing to represent phase shifts as well as frequency variation. However, as the stride dips below 2ms, the convolution asymptotically reaches a superior level of performance to the spectrogram. To be fair, this improved performance comes at increased computational cost and memory usage.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 2: Single scale waveform convolution outperforms the spectrogram baseline at low strides. The trend is visualized in Figure 2.', solve the following question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the WER(%) when the convolution stride is 0.5ms?\n" }, { "role": "assistant", "content": "To find the WER(%) for a convolution stride of 0.5ms, locate the row where the Spectrogram / Convolution Stride is 0.5ms and read the corresponding WER(%). So, the answer is {\"answer\": \"26.13\"}." } ], "images": [ "scigen/train/2107.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Single scale waveform convolution outperforms the spectrogram baseline at low strides. The trend is visualized in Figure 2." ], "reasoning": "To find the WER(%) for a convolution stride of 0.5ms, locate the row where the Spectrogram / Convolution Stride is 0.5ms and read the corresponding WER(%).", "question": "What is the WER(%) when the convolution stride is 0.5ms?", "answer": "So, the answer is {\"answer\": \"26.13\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The WER(%) for a convolution stride of 1ms is higher than that for a stride of 2ms., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe WER(%) for a convolution stride of 1ms is higher than that for a stride of 2ms.\n" }, { "role": "assistant", "content": "Compare the WER(%) values for convolution strides of 1ms and 2ms. The WER(%) for 1ms is 26.35, and for 2ms it is 26.90, which means the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2107.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Single scale waveform convolution outperforms the spectrogram baseline at low strides. The trend is visualized in Figure 2." ], "reasoning": "Compare the WER(%) values for convolution strides of 1ms and 2ms. The WER(%) for 1ms is 26.35, and for 2ms it is 26.90, which means the statement is incorrect.", "statement": "The WER(%) for a convolution stride of 1ms is higher than that for a stride of 2ms.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: Decoupling time and frequency resolution. Window size shown in parentheses. Adding additional filters at the same stride (1/4ms, 1ms, 10ms) significantly improves performance. All models have an additional bottleneck layer (Fully Connected, 161) inserted after pooling to maintain the same number of features as previous experiments. ∗Increasing the bottleneck layer from 161 to 800 leads to a small improvement.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Increasing the number of filters at long strides improves WER, with the highest improvement of 23.28% achieved by using 160, 320, and 640 filters for high, mid, and low features respectively.We explore the value of increased resolution by performing the same multiscale experiments as the previous section, but increasing the number of filters. A fully-connected layer with output dimension of 161 is added above the concatenated feature maps in order to produce the same number of features as the previous experiments. Since using more filters at short strides is more costly in terms of both computation and memory, we specifically increase the number of filters at long strides. Further increasing the number of filters, and expanding the size of the bottle neck leads to smaller gains. showing an impressive 20.7% cumulative improvement in WER relative to the spectrogram baseline." } ], "images": [ "scigen/train/2108.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Decoupling time and frequency resolution. Window size shown in parentheses. Adding additional filters at the same stride (1/4ms, 1ms, 10ms) significantly improves performance. All models have an additional bottleneck layer (Fully Connected, 161) inserted after pooling to maintain the same number of features as previous experiments. ∗Increasing the bottleneck layer from 161 to 800 leads to a small improvement." ], "reasoning": "Increasing the number of filters at long strides improves WER, with the highest improvement of 23.28% achieved by using 160, 320, and 640 filters for high, mid, and low features respectively.", "description": "We explore the value of increased resolution by performing the same multiscale experiments as the previous section, but increasing the number of filters. A fully-connected layer with output dimension of 161 is added above the concatenated feature maps in order to produce the same number of features as the previous experiments. Since using more filters at short strides is more costly in terms of both computation and memory, we specifically increase the number of filters at long strides. Further increasing the number of filters, and expanding the size of the bottle neck leads to smaller gains. showing an impressive 20.7% cumulative improvement in WER relative to the spectrogram baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nTable title: Table 4: Decoupling time and frequency resolution. Window size shown in parentheses. Adding additional filters at the same stride (1/4ms, 1ms, 10ms) significantly improves performance. All models have an additional bottleneck layer (Fully Connected, 161) inserted after pooling to maintain the same number of features as previous experiments. ∗Increasing the bottleneck layer from 161 to 800 leads to a small improvement.\n\nQuestion: What is the WER when the number of features for high, mid, and low are 160, 320, and 640 respectively?\n" }, { "role": "assistant", "content": "The table shows that when the number of features for high, mid, and low are 160, 320, and 640 respectively, the WER is 23.28%. So, the answer is {\"answer\": \"23.28\"}." } ], "images": [ "scigen/train/2108.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Decoupling time and frequency resolution. Window size shown in parentheses. Adding additional filters at the same stride (1/4ms, 1ms, 10ms) significantly improves performance. All models have an additional bottleneck layer (Fully Connected, 161) inserted after pooling to maintain the same number of features as previous experiments. ∗Increasing the bottleneck layer from 161 to 800 leads to a small improvement." ], "reasoning": "The table shows that when the number of features for high, mid, and low are 160, 320, and 640 respectively, the WER is 23.28%.", "question": "What is the WER when the number of features for high, mid, and low are 160, 320, and 640 respectively?", "answer": "So, the answer is {\"answer\": \"23.28\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nUsing 161 features for high, mid, and low results in a WER of 23.78%.\n" }, { "role": "assistant", "content": "The table shows that using 161 features for high, mid, and low results in a WER of 23.78%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2108.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Decoupling time and frequency resolution. Window size shown in parentheses. Adding additional filters at the same stride (1/4ms, 1ms, 10ms) significantly improves performance. All models have an additional bottleneck layer (Fully Connected, 161) inserted after pooling to maintain the same number of features as previous experiments. ∗Increasing the bottleneck layer from 161 to 800 leads to a small improvement." ], "reasoning": "The table shows that using 161 features for high, mid, and low results in a WER of 23.78%.", "statement": "Using 161 features for high, mid, and low results in a WER of 23.78%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: The baseline and proposed approach results', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The hybridFrameWrk approach outperforms both baselines and other clustering methods in both Pk and WD measures.The first row and the fourth row present the baseline performances where we used the BayesTopic and BestCluster algorithm. The second and third rows present our proposed clustering based approaches, OptimAggloCluster refers to the approach of segmentation using agglomerative clustering, then optimized with fixed boundary clumping and OptimKMedoidCluster refers to the performances of iteratively optimized of the K-medoids clustered segmentation. We present the results with two metrics in two columns. For all the five cases the Pk measure performances marginally work better than the corresponding WD measure performances. Between the two baselines, the BayesTopic was harder to overcome. OptimKMedoidCluster performs better than the OptimAggloCluster approach. The BestCluster, which is our second baseline, shows better performance than the standalone cluster based approaches but it shows lower performance than the first baseline the BayesTopic. The best performance is performed by our proposed hybrid approach hybridFrameWrk that beats both of the baselines for both of the Pk and WD measures. It is possible to use this method without any change for a range of conversational applications like hybrid, online, multilingual and low resource language systems." } ], "images": [ "scigen/train/2109.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The baseline and proposed approach results" ], "reasoning": "The hybridFrameWrk approach outperforms both baselines and other clustering methods in both Pk and WD measures.", "description": "The first row and the fourth row present the baseline performances where we used the BayesTopic and BestCluster algorithm. The second and third rows present our proposed clustering based approaches, OptimAggloCluster refers to the approach of segmentation using agglomerative clustering, then optimized with fixed boundary clumping and OptimKMedoidCluster refers to the performances of iteratively optimized of the K-medoids clustered segmentation. We present the results with two metrics in two columns. For all the five cases the Pk measure performances marginally work better than the corresponding WD measure performances. Between the two baselines, the BayesTopic was harder to overcome. OptimKMedoidCluster performs better than the OptimAggloCluster approach. The BestCluster, which is our second baseline, shows better performance than the standalone cluster based approaches but it shows lower performance than the first baseline the BayesTopic. The best performance is performed by our proposed hybrid approach hybridFrameWrk that beats both of the baselines for both of the Pk and WD measures. It is possible to use this method without any change for a range of conversational applications like hybrid, online, multilingual and low resource language systems.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: 'Table 1: The baseline and proposed approach results'\nQuestion: Which algorithm shows the best performance in both Pk and WD measures?\n" }, { "role": "assistant", "content": "The table shows that the hybridFrameWrk (proposed) algorithm has the lowest Pk (0.190) and WD (0.248) values, indicating the best performance. So, the answer is {\"answer\": \"hybridFrameWrk\"}." } ], "images": [ "scigen/train/2109.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The baseline and proposed approach results" ], "reasoning": "The table shows that the hybridFrameWrk (proposed) algorithm has the lowest Pk (0.190) and WD (0.248) values, indicating the best performance.", "question": "Which algorithm shows the best performance in both Pk and WD measures?", "answer": "So, the answer is {\"answer\": \"hybridFrameWrk\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The BestCluster algorithm performs better than the BayesTopic algorithm. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe BestCluster algorithm performs better than the BayesTopic algorithm.\n" }, { "role": "assistant", "content": "The table shows that the BayesTopic algorithm has a Pk value of 0.239 and a WD value of 0.312, while the BestCluster algorithm has higher values of 0.317 and 0.379 respectively, indicating that BayesTopic performs better. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2109.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The baseline and proposed approach results" ], "reasoning": "The table shows that the BayesTopic algorithm has a Pk value of 0.239 and a WD value of 0.312, while the BestCluster algorithm has higher values of 0.317 and 0.379 respectively, indicating that BayesTopic performs better.", "statement": "The BestCluster algorithm performs better than the BayesTopic algorithm.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: BayesTopic, BestCluster and Hybrid Approach Individual Conversation Analysis Results'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The standard deviation of the window difference (WD) for the BayesTopic approach is higher than that of other approaches, while the Pk value is marginally higher.Still the performances are comparable to the best performances in each of these two cases. In the same table we also compute the means and the standard deviation of all the measures in total. We observe that comparatively the standard deviation of the window difference (WD) of the BayesTopic approach is higher than that of other approaches, whereas the Pk of the same approach is marginally higher than the other values of the two rest approaches." } ], "images": [ "scigen/train/2110.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: BayesTopic, BestCluster and Hybrid Approach Individual Conversation Analysis Results" ], "reasoning": "The standard deviation of the window difference (WD) for the BayesTopic approach is higher than that of other approaches, while the Pk value is marginally higher.", "description": "Still the performances are comparable to the best performances in each of these two cases. In the same table we also compute the means and the standard deviation of all the measures in total. We observe that comparatively the standard deviation of the window difference (WD) of the BayesTopic approach is higher than that of other approaches, whereas the Pk of the same approach is marginally higher than the other values of the two rest approaches.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: BayesTopic, BestCluster and Hybrid Approach Individual Conversation Analysis Results', and tackle the question below in a sequential manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich approach has the highest standard deviation for the window difference (WD)?\n" }, { "role": "assistant", "content": "By comparing the standard deviation values for the window difference (WD) across all approaches, we see that BayesTopic has the highest standard deviation of 0.106. So, the answer is {\"answer\": \"BayesTopic\"}" } ], "images": [ "scigen/train/2110.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: BayesTopic, BestCluster and Hybrid Approach Individual Conversation Analysis Results" ], "reasoning": "By comparing the standard deviation values for the window difference (WD) across all approaches, we see that BayesTopic has the highest standard deviation of 0.106.", "question": "Which approach has the highest standard deviation for the window difference (WD)?", "answer": "So, the answer is {\"answer\": \"BayesTopic\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe BayesTopic approach has the highest mean Pk value.\n" }, { "role": "assistant", "content": "By comparing the mean Pk values across all approaches, we see that the BestCluster approach has the highest mean Pk value of 0.317, not BayesTopic. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2110.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: BayesTopic, BestCluster and Hybrid Approach Individual Conversation Analysis Results" ], "reasoning": "By comparing the mean Pk values across all approaches, we see that the BestCluster approach has the highest mean Pk value of 0.317, not BayesTopic.", "statement": "The BayesTopic approach has the highest mean Pk value.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Number of segments used for BayesTopic, BestCluster and Hybrid Approach for the Best Results (including the time for each of the meetings)', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The correlation between BayesTopic and BestCluster is the least, while BestCluster and Hybrid have the highest correlation. The Bmr009 transcript shows a short meeting with two discussion topics.We perform Spearman’s ρ correlation test on each pairwise columns. We find that there is least (ρ=0.10) correlation between the BayesTopic and BestCluster number of segment distribution, on the other hand there is more (ρ=0.46) correlation between the BestCluster and Hybrid approach the number of segment distribution, whereas the segments between the BayesTopic and Hybrid approach are also not well-correlated (ρ=0.26). So in case a meeting runs much longer than the others , it does not necessarily mean that there are many local discourse or many topics to discuss. We observe from the Bmr009 transcript that the participants tried to make the meeting short with two discussion-topics namely, signal processing and transcription issues, then the individual digit recording started." } ], "images": [ "scigen/train/2111.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Number of segments used for BayesTopic, BestCluster and Hybrid Approach for the Best Results (including the time for each of the meetings)" ], "reasoning": "The correlation between BayesTopic and BestCluster is the least, while BestCluster and Hybrid have the highest correlation. The Bmr009 transcript shows a short meeting with two discussion topics.", "description": "We perform Spearman’s ρ correlation test on each pairwise columns. We find that there is least (ρ=0.10) correlation between the BayesTopic and BestCluster number of segment distribution, on the other hand there is more (ρ=0.46) correlation between the BestCluster and Hybrid approach the number of segment distribution, whereas the segments between the BayesTopic and Hybrid approach are also not well-correlated (ρ=0.26). So in case a meeting runs much longer than the others , it does not necessarily mean that there are many local discourse or many topics to discuss. We observe from the Bmr009 transcript that the participants tried to make the meeting short with two discussion-topics namely, signal processing and transcription issues, then the individual digit recording started.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: Number of segments used for BayesTopic, BestCluster and Hybrid Approach for the Best Results (including the time for each of the meetings)', and sequentially solve the question, writing out each step of your reasoning process. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich meeting had the least correlation between BayesTopic and BestCluster, and what were the discussion topics?\n" }, { "role": "assistant", "content": "The least correlation (ρ=0.10) is between BayesTopic and BestCluster. The Bmr009 transcript had a short meeting with two discussion topics: signal processing and transcription issues. So, the answer is {\"answer\": \"Bmr009\"}" } ], "images": [ "scigen/train/2111.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Number of segments used for BayesTopic, BestCluster and Hybrid Approach for the Best Results (including the time for each of the meetings)" ], "reasoning": "The least correlation (ρ=0.10) is between BayesTopic and BestCluster. The Bmr009 transcript had a short meeting with two discussion topics: signal processing and transcription issues.", "question": "Which meeting had the least correlation between BayesTopic and BestCluster, and what were the discussion topics?", "answer": "So, the answer is {\"answer\": \"Bmr009\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Bmr009 meeting had the highest correlation between BestCluster and Hybrid.\n" }, { "role": "assistant", "content": "The description states that the highest correlation (ρ=0.46) is between BestCluster and Hybrid, but it does not specify which meeting had this correlation. Therefore, it is not enough information to confirm this statement. So, the answer is {\"answer\": \"not enough info\"}" } ], "images": [ "scigen/train/2111.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Number of segments used for BayesTopic, BestCluster and Hybrid Approach for the Best Results (including the time for each of the meetings)" ], "reasoning": "The description states that the highest correlation (ρ=0.46) is between BestCluster and Hybrid, but it does not specify which meeting had this correlation. Therefore, it is not enough information to confirm this statement.", "statement": "The Bmr009 meeting had the highest correlation between BestCluster and Hybrid.", "answer": "So, the answer is {\"answer\": \"not enough info\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Mean Opinion Score (MOS) evaluations with 95% confidence intervals of Deep Voice 1, Deep Voice 2, and Tacotron. Using the crowdMOS toolkit, batches of samples from these models were presented to raters on Mechanical Turk. Since batches contained samples from all models, the experiment naturally induces a comparison between the models.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Deep Voice 2 shows significant quality improvements over Deep Voice 1, and Tacotron with WaveNet conversion outperforms the Griffin-Lim algorithm.We train Deep Voice 1, Deep Voice 2, and Tacotron on an internal English speech database containing approximately 20 hours of single-speaker data. The results show conclusively that the architecture improvements in Deep Voice 2 yield significant gains in quality over Deep Voice 1. They also demonstrate that converting Tacotron-generated spectrograms to audio using WaveNet is preferable to using the iterative Griffin-Lim algorithm." } ], "images": [ "scigen/train/2112.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Mean Opinion Score (MOS) evaluations with 95% confidence intervals of Deep Voice 1, Deep Voice 2, and Tacotron. Using the crowdMOS toolkit, batches of samples from these models were presented to raters on Mechanical Turk. Since batches contained samples from all models, the experiment naturally induces a comparison between the models." ], "reasoning": "Deep Voice 2 shows significant quality improvements over Deep Voice 1, and Tacotron with WaveNet conversion outperforms the Griffin-Lim algorithm.", "description": "We train Deep Voice 1, Deep Voice 2, and Tacotron on an internal English speech database containing approximately 20 hours of single-speaker data. The results show conclusively that the architecture improvements in Deep Voice 2 yield significant gains in quality over Deep Voice 1. They also demonstrate that converting Tacotron-generated spectrograms to audio using WaveNet is preferable to using the iterative Griffin-Lim algorithm.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 1: Mean Opinion Score (MOS) evaluations with 95% confidence intervals of Deep Voice 1, Deep Voice 2, and Tacotron. Using the crowdMOS toolkit, batches of samples from these models were presented to raters on Mechanical Turk. Since batches contained samples from all models, the experiment naturally induces a comparison between the models.', answer the following question. Give detailed solution consisting of each step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest MOS score and what is its sampling frequency?\n" }, { "role": "assistant", "content": "From the table, Tacotron (WaveNet) has the highest MOS score of 4.17±0.18, and its sampling frequency is 24 KHz. So, the answer is {\"answer\": \"Tacotron (WaveNet) with 24 KHz\"}" } ], "images": [ "scigen/train/2112.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Mean Opinion Score (MOS) evaluations with 95% confidence intervals of Deep Voice 1, Deep Voice 2, and Tacotron. Using the crowdMOS toolkit, batches of samples from these models were presented to raters on Mechanical Turk. Since batches contained samples from all models, the experiment naturally induces a comparison between the models." ], "reasoning": "From the table, Tacotron (WaveNet) has the highest MOS score of 4.17±0.18, and its sampling frequency is 24 KHz.", "question": "Which model has the highest MOS score and what is its sampling frequency?", "answer": "So, the answer is {\"answer\": \"Tacotron (WaveNet) with 24 KHz\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nDeep Voice 2 has a higher MOS score than Tacotron (Griffin-Lim).\n" }, { "role": "assistant", "content": "The table shows that Deep Voice 2 has a MOS score of 2.96±0.38, while Tacotron (Griffin-Lim) has a MOS score of 2.57±0.28, indicating that Deep Voice 2 has a higher MOS score. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2112.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Mean Opinion Score (MOS) evaluations with 95% confidence intervals of Deep Voice 1, Deep Voice 2, and Tacotron. Using the crowdMOS toolkit, batches of samples from these models were presented to raters on Mechanical Turk. Since batches contained samples from all models, the experiment naturally induces a comparison between the models." ], "reasoning": "The table shows that Deep Voice 2 has a MOS score of 2.96±0.38, while Tacotron (Griffin-Lim) has a MOS score of 2.57±0.28, indicating that Deep Voice 2 has a higher MOS score.", "statement": "Deep Voice 2 has a higher MOS score than Tacotron (Griffin-Lim).", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Performance of agents with simulated user.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "HRL agents achieved higher success rates and required fewer conversation turns compared to rule-based and flat RL agents.Recall that our subgoal discovery approach takes as input a set of state trajectories which lead to successful outcomes. In practice, one can collect a large set of successful state trajectories, either by asking human experts to demonstrate (e.g., in a call center), or by rolling out a reasonably good policy (e.g., a policy designed by human experts). We only collect the successful dialogue sessions from the roll-outs of the rule-based agent, and try to learn the subgoals from these dialogue state trajectories. The remaining 20% were used as a validation set. Each learning curve was averaged over 5 runs. We find that the HRL agents generated higher success rates and needed fewer conversation turns to achieve the users’ goals than the rule-based agent and the flat RL agent. The performance of the m-HRL agent is tied with that of the h-HRL agent, even though the latter requires high-quality subgoals designed by human experts." } ], "images": [ "scigen/train/2113.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance of agents with simulated user." ], "reasoning": "HRL agents achieved higher success rates and required fewer conversation turns compared to rule-based and flat RL agents.", "description": "Recall that our subgoal discovery approach takes as input a set of state trajectories which lead to successful outcomes. In practice, one can collect a large set of successful state trajectories, either by asking human experts to demonstrate (e.g., in a call center), or by rolling out a reasonably good policy (e.g., a policy designed by human experts). We only collect the successful dialogue sessions from the roll-outs of the rule-based agent, and try to learn the subgoals from these dialogue state trajectories. The remaining 20% were used as a validation set. Each learning curve was averaged over 5 runs. We find that the HRL agents generated higher success rates and needed fewer conversation turns to achieve the users’ goals than the rule-based agent and the flat RL agent. The performance of the m-HRL agent is tied with that of the h-HRL agent, even though the latter requires high-quality subgoals designed by human experts.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 1: Performance of agents with simulated user.' to sequentially solve the question presented below. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich agent had the highest reward and how many turns did it take?\n" }, { "role": "assistant", "content": "The h-HRL agent had the highest reward of 35.32 and it took 44.23 turns. So, the answer is {\"answer\": \"h-HRL\"}" } ], "images": [ "scigen/train/2113.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance of agents with simulated user." ], "reasoning": "The h-HRL agent had the highest reward of 35.32 and it took 44.23 turns.", "question": "Which agent had the highest reward and how many turns did it take?", "answer": "So, the answer is {\"answer\": \"h-HRL\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe rule-based agent had the lowest success rate.\n" }, { "role": "assistant", "content": "The table shows that the rule-based agent had a success rate of .3220, which is lower than the success rates of the RL, h-HRL, and m-HRL agents. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2113.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance of agents with simulated user." ], "reasoning": "The table shows that the rule-based agent had a success rate of .3220, which is lower than the success rates of the RL, h-HRL, and m-HRL agents.", "statement": "The rule-based agent had the lowest success rate.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'TABLE V: Comparison Results'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table compares the accuracy of a Hybrid Hierarchical CNN-LSTM model with an SMS Organizer application across various categories.We compared our classification results with one of the most popular application, which employs similar kind of architecture to retrieve information from SMS and displays it in a subtle way." } ], "images": [ "scigen/train/2114.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Comparison Results" ], "reasoning": "The table compares the accuracy of a Hybrid Hierarchical CNN-LSTM model with an SMS Organizer application across various categories.", "description": "We compared our classification results with one of the most popular application, which employs similar kind of architecture to retrieve information from SMS and displays it in a subtle way.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'TABLE V: Comparison Results' and methodically tackle the question that follows. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich category has the highest accuracy difference between the Hybrid Hierarchical CNN-LSTM model and the SMS Organizer application?\n" }, { "role": "assistant", "content": "To find the category with the highest accuracy difference, calculate the difference between the accuracies of the two models for each category and identify the maximum difference. So, the answer is {\"answer\": \"Reminder_Train\"}." } ], "images": [ "scigen/train/2114.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Comparison Results" ], "reasoning": "To find the category with the highest accuracy difference, calculate the difference between the accuracies of the two models for each category and identify the maximum difference.", "question": "Which category has the highest accuracy difference between the Hybrid Hierarchical CNN-LSTM model and the SMS Organizer application?", "answer": "So, the answer is {\"answer\": \"Reminder_Train\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Hybrid Hierarchical CNN-LSTM model has a higher accuracy than the SMS Organizer application in all categories., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Hybrid Hierarchical CNN-LSTM model has a higher accuracy than the SMS Organizer application in all categories.\n" }, { "role": "assistant", "content": "By comparing the accuracies of both models across all categories, it is evident that the Hybrid Hierarchical CNN-LSTM model has a higher accuracy in each category. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2114.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Comparison Results" ], "reasoning": "By comparing the accuracies of both models across all categories, it is evident that the Hybrid Hierarchical CNN-LSTM model has a higher accuracy in each category.", "statement": "The Hybrid Hierarchical CNN-LSTM model has a higher accuracy than the SMS Organizer application in all categories.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'TABLE IV: Accuracy Comparison of Various Models'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The hybrid model performs almost as well as the All-CNN-LSTM model, which has the highest accuracy among the tested models.We compared different machine learning architectures in terms of their accuracy across a test data. We used test data of 50 SMS for each class, making the entire test data of 900 SMS. basically implies that the same model has been used at both the levels of hierarchy. Our hybrid model performs either at par or almost equivalent to other computationally expensive models." } ], "images": [ "scigen/train/2115.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Accuracy Comparison of Various Models" ], "reasoning": "The hybrid model performs almost as well as the All-CNN-LSTM model, which has the highest accuracy among the tested models.", "description": "We compared different machine learning architectures in terms of their accuracy across a test data. We used test data of 50 SMS for each class, making the entire test data of 900 SMS. basically implies that the same model has been used at both the levels of hierarchy. Our hybrid model performs either at par or almost equivalent to other computationally expensive models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'TABLE IV: Accuracy Comparison of Various Models', unfold the steps to solve the question given next. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nQuestion: Which model has the highest accuracy and how does the hybrid model compare to it?\n" }, { "role": "assistant", "content": "The All-CNN-LSTM model has the highest accuracy at 93.23%, and the hybrid model has an accuracy of 93.18%, which is almost equivalent. So, the answer is {\"answer\": \"All-CNN-LSTM with 93.23%, Hybrid with 93.18%\"}." } ], "images": [ "scigen/train/2115.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Accuracy Comparison of Various Models" ], "reasoning": "The All-CNN-LSTM model has the highest accuracy at 93.23%, and the hybrid model has an accuracy of 93.18%, which is almost equivalent.", "question": "Which model has the highest accuracy and how does the hybrid model compare to it?", "answer": "So, the answer is {\"answer\": \"All-CNN-LSTM with 93.23%, Hybrid with 93.18%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The All-SVM model has the lowest accuracy among the tested models. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe All-SVM model has the lowest accuracy among the tested models.\n" }, { "role": "assistant", "content": "The table shows that the All-SVM model has an accuracy of 88.87%, which is lower than all other models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2115.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Accuracy Comparison of Various Models" ], "reasoning": "The table shows that the All-SVM model has an accuracy of 88.87%, which is lower than all other models listed.", "statement": "The All-SVM model has the lowest accuracy among the tested models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Optimal value of t for each corpus.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The model's performance was evaluated for different t values, with the average per-corpus evaluated parameter settings being 1006. The model achieved a maximum recall of 0.697 for the Nokia Phone corpus without a threshold, but with a precision of 0.151.As we mentioned earlier, to evaluate the impact of the t parameter whose value was not reported by \\newciteliu_opinion_2012, we let the model return the unfiltered aspect candidates and evaluate the performance for t∈[10,20,...,tmax]. Note that tmax might be different each time. Because of this, the number of parameter configurations we tried for each corpora is slightly different. Instead of reporting each value, we rather report the average of per-corpus evaluated parameter settings, which was 1006. In this sense, when we experimented without setting a threshold we obtained a maximum recall of 0.697 —for the Nokia Phone corpus— but at the cost of precision 0.151. When we set t=10, we obtained a maximum precision of 0.9 but at the cost of recall being lower than five percent. These results mean the model does not seem to generalize well." } ], "images": [ "scigen/train/2116.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Optimal value of t for each corpus." ], "reasoning": "The model's performance was evaluated for different t values, with the average per-corpus evaluated parameter settings being 1006. The model achieved a maximum recall of 0.697 for the Nokia Phone corpus without a threshold, but with a precision of 0.151.", "description": "As we mentioned earlier, to evaluate the impact of the t parameter whose value was not reported by \\newciteliu_opinion_2012, we let the model return the unfiltered aspect candidates and evaluate the performance for t∈[10,20,...,tmax]. Note that tmax might be different each time. Because of this, the number of parameter configurations we tried for each corpora is slightly different. Instead of reporting each value, we rather report the average of per-corpus evaluated parameter settings, which was 1006. In this sense, when we experimented without setting a threshold we obtained a maximum recall of 0.697 —for the Nokia Phone corpus— but at the cost of precision 0.151. When we set t=10, we obtained a maximum precision of 0.9 but at the cost of recall being lower than five percent. These results mean the model does not seem to generalize well.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: Optimal value of t for each corpus.' in mind, break down and resolve the question below step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhat was the maximum t value used for the Creative MP3 Player corpus?\n" }, { "role": "assistant", "content": "The table lists the t values for different corpora. The t value for the Creative MP3 Player corpus is 200, which is the highest among the listed values. So, the answer is {\"answer\": \"200\"}" } ], "images": [ "scigen/train/2116.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Optimal value of t for each corpus." ], "reasoning": "The table lists the t values for different corpora. The t value for the Creative MP3 Player corpus is 200, which is the highest among the listed values.", "question": "What was the maximum t value used for the Creative MP3 Player corpus?", "answer": "So, the answer is {\"answer\": \"200\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe model achieved a maximum recall of 0.697 for the Creative MP3 Player corpus.\n" }, { "role": "assistant", "content": "The description states that the maximum recall of 0.697 was achieved for the Nokia Phone corpus, not the Creative MP3 Player corpus. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2116.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Optimal value of t for each corpus." ], "reasoning": "The description states that the maximum recall of 0.697 was achieved for the Nokia Phone corpus, not the Creative MP3 Player corpus.", "statement": "The model achieved a maximum recall of 0.697 for the Creative MP3 Player corpus.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: Performance comparison for FBA.'.\n" }, { "role": "assistant", "content": "The experiments showed a significant drop in precision and recall across all datasets, with minsup being the most sensitive parameter.We remain unable to replicate the performance reported by the authors and see a big drop for both precision and recall in all the datasets. In our experiments, we noted that the most sensitive parameter was minsup for itemset mining. We also experimented omitting the pruning steps and observed that precision and recall were not too different from the results we obtained with pruning." } ], "images": [ "scigen/train/2117.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance comparison for FBA." ], "reasoning": "The experiments showed a significant drop in precision and recall across all datasets, with minsup being the most sensitive parameter.", "description": "We remain unable to replicate the performance reported by the authors and see a big drop for both precision and recall in all the datasets. In our experiments, we noted that the most sensitive parameter was minsup for itemset mining. We also experimented omitting the pruning steps and observed that precision and recall were not too different from the results we obtained with pruning.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 1: Performance comparison for FBA.' to sequentially solve the question presented below. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhat is the difference in average precision between the original and our experiments?\n" }, { "role": "assistant", "content": "The average precision in the original experiments is 0.8, while in our experiments it is 0.325. The difference is 0.8 - 0.325. So, the answer is {\"answer\": \"0.475\"}." } ], "images": [ "scigen/train/2117.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance comparison for FBA." ], "reasoning": "The average precision in the original experiments is 0.8, while in our experiments it is 0.325. The difference is 0.8 - 0.325.", "question": "What is the difference in average precision between the original and our experiments?", "answer": "So, the answer is {\"answer\": \"0.475\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The precision for the Nikon Camera in our experiments is higher than the recall. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe precision for the Nikon Camera in our experiments is higher than the recall.\n" }, { "role": "assistant", "content": "The table shows that the precision for the Nikon Camera in our experiments is 0.265, while the recall is 0.457. Therefore, the precision is not higher than the recall. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2117.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance comparison for FBA." ], "reasoning": "The table shows that the precision for the Nikon Camera in our experiments is 0.265, while the recall is 0.457. Therefore, the precision is not higher than the recall.", "statement": "The precision for the Nikon Camera in our experiments is higher than the recall.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Performance comparison for DBA.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The results show significant differences between our precision values and those reported in the original paper, with our precision values being particularly low.Again, we see huge differences between our results and the ones reported by the original paper. Moreover, in this case we observe particularly low values for precision. A detailed review of the extracted aspects showed us that in fact many of the extracted terms do not correspond to aspects but rather to common nouns that are not related to the product." } ], "images": [ "scigen/train/2118.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance comparison for DBA." ], "reasoning": "The results show significant differences between our precision values and those reported in the original paper, with our precision values being particularly low.", "description": "Again, we see huge differences between our results and the ones reported by the original paper. Moreover, in this case we observe particularly low values for precision. A detailed review of the extracted aspects showed us that in fact many of the extracted terms do not correspond to aspects but rather to common nouns that are not related to the product.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Performance comparison for DBA.', answer the question below by showing each progressive step toward the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the average precision value reported in the original paper?\n" }, { "role": "assistant", "content": "To find the average precision value reported in the original paper, look at the 'Original P' column and find the average value listed at the bottom. So, the answer is {\"answer\": \"0.88\"}." } ], "images": [ "scigen/train/2118.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance comparison for DBA." ], "reasoning": "To find the average precision value reported in the original paper, look at the 'Original P' column and find the average value listed at the bottom.", "question": "What is the average precision value reported in the original paper?", "answer": "So, the answer is {\"answer\": \"0.88\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe precision value for the Creative MP3 Player in our results is higher than the recall value.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Ours P' and 'Ours R' values for the Creative MP3 Player. The precision value (0.180) is lower than the recall value (0.319). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2118.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance comparison for DBA." ], "reasoning": "To verify this statement, compare the 'Ours P' and 'Ours R' values for the Creative MP3 Player. The precision value (0.180) is lower than the recall value (0.319).", "statement": "The precision value for the Creative MP3 Player in our results is higher than the recall value.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'TABLE I: Comparison of the speaker recognition models on the TIMIT and MIT dataset using the metrics Frame Error Rate (FER), Classification Error Rate (CER), Inference Time, Number of Parameters, and Model Size.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The AM-Softmax loss function improved the performance of the models, with AM-MobileNet1D achieving the best results in terms of FER and CER on the TIMIT dataset, and the proposed models being significantly faster and more efficient in terms of parameters and disk space.The best result in each dataset is highlighted in bold. Based on these results, it is possible to see that in every scenario, the AM-Softmax loss function improved the performance of the models. Indeed, the benefit of using the AM-Softmax loss function on the Speaker Recognition models is quite clear, mainly on the SincNet model for the TIMIT dataset. The FER and CER calculated on the TIMIT dataset for the proposed method MobileNet1D are about 40% and 50% smaller (better) than the ones measured from SincNet, respectively. Also, when comparing the MobileNet1D with the AM-SincNet in the same dataset, they are practically equivalent, the MobileNet1D got a slightly better FER, while the AM-SincNet got a slightly better CER. For this dataset, the model with the best results was the proposed AM-MobileNet1D, it got the lowest (best) FER and CER over the models. In the same way, in terms of CER it was 24% better than the MobileNet1D, 14% better than the AM-SincNet, and 62% better than the SincNet. On the other hand, the SincNet and the AM-SincNet overcome the proposed models in terms of FER and CER for the MIT dataset. Still, the proposed models are quite good on this dataset showing an accuracy of about 98%. The inference time results are in the format time±standard deviation. The results show that the proposed models are about 7 times faster, have 8 times fewer parameters, and take up to 8 times less space on disk than the SincNet and the AM-SincNet. The proposed MobileNet1D and AM-MobileNet1D take only 11.6 megabytes on disk, against 91.2mb from the SincNet and AM-SincNet. In fact, these results show that the proposed models are suitable for being embedded on Mobile devices, showing high performance and low inference latency for the speaker recognition problem." } ], "images": [ "scigen/train/2119.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Comparison of the speaker recognition models on the TIMIT and MIT dataset using the metrics Frame Error Rate (FER), Classification Error Rate (CER), Inference Time, Number of Parameters, and Model Size." ], "reasoning": "The AM-Softmax loss function improved the performance of the models, with AM-MobileNet1D achieving the best results in terms of FER and CER on the TIMIT dataset, and the proposed models being significantly faster and more efficient in terms of parameters and disk space.", "description": "The best result in each dataset is highlighted in bold. Based on these results, it is possible to see that in every scenario, the AM-Softmax loss function improved the performance of the models. Indeed, the benefit of using the AM-Softmax loss function on the Speaker Recognition models is quite clear, mainly on the SincNet model for the TIMIT dataset. The FER and CER calculated on the TIMIT dataset for the proposed method MobileNet1D are about 40% and 50% smaller (better) than the ones measured from SincNet, respectively. Also, when comparing the MobileNet1D with the AM-SincNet in the same dataset, they are practically equivalent, the MobileNet1D got a slightly better FER, while the AM-SincNet got a slightly better CER. For this dataset, the model with the best results was the proposed AM-MobileNet1D, it got the lowest (best) FER and CER over the models. In the same way, in terms of CER it was 24% better than the MobileNet1D, 14% better than the AM-SincNet, and 62% better than the SincNet. On the other hand, the SincNet and the AM-SincNet overcome the proposed models in terms of FER and CER for the MIT dataset. Still, the proposed models are quite good on this dataset showing an accuracy of about 98%. The inference time results are in the format time±standard deviation. The results show that the proposed models are about 7 times faster, have 8 times fewer parameters, and take up to 8 times less space on disk than the SincNet and the AM-SincNet. The proposed MobileNet1D and AM-MobileNet1D take only 11.6 megabytes on disk, against 91.2mb from the SincNet and AM-SincNet. In fact, these results show that the proposed models are suitable for being embedded on Mobile devices, showing high performance and low inference latency for the speaker recognition problem.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'TABLE I: Comparison of the speaker recognition models on the TIMIT and MIT dataset using the metrics Frame Error Rate (FER), Classification Error Rate (CER), Inference Time, Number of Parameters, and Model Size.', answer the following question. Give detailed solution consisting of each step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model has the lowest FER and CER on the TIMIT dataset?\n" }, { "role": "assistant", "content": "The table shows the FER and CER for different models on the TIMIT dataset. The AM-MobileNet1D model has the lowest FER (21.30%) and CER (0.43%) on the TIMIT dataset. So, the answer is {\"answer\": \"AM-MobileNet1D\"}." } ], "images": [ "scigen/train/2119.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Comparison of the speaker recognition models on the TIMIT and MIT dataset using the metrics Frame Error Rate (FER), Classification Error Rate (CER), Inference Time, Number of Parameters, and Model Size." ], "reasoning": "The table shows the FER and CER for different models on the TIMIT dataset. The AM-MobileNet1D model has the lowest FER (21.30%) and CER (0.43%) on the TIMIT dataset.", "question": "Which model has the lowest FER and CER on the TIMIT dataset?", "answer": "So, the answer is {\"answer\": \"AM-MobileNet1D\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The AM-SincNet model has the lowest FER on the TIMIT dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe AM-SincNet model has the lowest FER on the TIMIT dataset.\n" }, { "role": "assistant", "content": "The table shows that the AM-MobileNet1D model has the lowest FER on the TIMIT dataset with 21.30%, while the AM-SincNet model has a FER of 27.86%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2119.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Comparison of the speaker recognition models on the TIMIT and MIT dataset using the metrics Frame Error Rate (FER), Classification Error Rate (CER), Inference Time, Number of Parameters, and Model Size." ], "reasoning": "The table shows that the AM-MobileNet1D model has the lowest FER on the TIMIT dataset with 21.30%, while the AM-SincNet model has a FER of 27.86%.", "statement": "The AM-SincNet model has the lowest FER on the TIMIT dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: Training batches on the WMT’14 English–German translation task. “Len.” denotes the length of the sentence. The proposed method provides a much easier curriculum at the beginning of the training of the model.'.\n" }, { "role": "assistant", "content": "CL suggests that organizing mini-batches from easy to difficult can enhance NMT performance and training speed.CL supposes that if mini-batches are bucketed in a particular way (e.g. with examples from easy to difficult), this would boost the performance of NMT and speed up the training process as well. That is, upgrading the sample(⋅) to B∗1,⋯,B∗t,⋯,B∗T =sample∗(D) (4) where the order from easy to difficult (i.e. B∗1→B∗T ) can be: 1) sentences with lengths from short to long; 2) sentences with words whose frequency goes from high to low (i.e. word rarity); and 3) uncertainty of sentences (from low to high uncertainties) measured by models trained in previous epochs or pre-trained language models." } ], "images": [ "scigen/train/2120.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Training batches on the WMT’14 English–German translation task. “Len.” denotes the length of the sentence. The proposed method provides a much easier curriculum at the beginning of the training of the model." ], "reasoning": "CL suggests that organizing mini-batches from easy to difficult can enhance NMT performance and training speed.", "description": "CL supposes that if mini-batches are bucketed in a particular way (e.g. with examples from easy to difficult), this would boost the performance of NMT and speed up the training process as well. That is, upgrading the sample(⋅) to B∗1,⋯,B∗t,⋯,B∗T =sample∗(D) (4) where the order from easy to difficult (i.e. B∗1→B∗T ) can be: 1) sentences with lengths from short to long; 2) sentences with words whose frequency goes from high to low (i.e. word rarity); and 3) uncertainty of sentences (from low to high uncertainties) measured by models trained in previous epochs or pre-trained language models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1: Training batches on the WMT’14 English–German translation task. “Len.” denotes the length of the sentence. The proposed method provides a much easier curriculum at the beginning of the training of the model.', solve the subsequent question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the length of the shortest sentence in the proposed method batches?\n" }, { "role": "assistant", "content": "To find the shortest sentence length in the proposed method batches, look at the 'Len.' column for B∗1 and B∗2 batches. The shortest length is 3. So, the answer is {\"answer\": \"3\"}" } ], "images": [ "scigen/train/2120.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Training batches on the WMT’14 English–German translation task. “Len.” denotes the length of the sentence. The proposed method provides a much easier curriculum at the beginning of the training of the model." ], "reasoning": "To find the shortest sentence length in the proposed method batches, look at the 'Len.' column for B∗1 and B∗2 batches. The shortest length is 3.", "question": "What is the length of the shortest sentence in the proposed method batches?", "answer": "So, the answer is {\"answer\": \"3\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The longest sentence in the Vanilla batches is 38 characters long. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe longest sentence in the Vanilla batches is 38 characters long.\n" }, { "role": "assistant", "content": "The table shows that the longest sentence in the Vanilla batches (B1 and B2) has a length of 38. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2120.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Training batches on the WMT’14 English–German translation task. “Len.” denotes the length of the sentence. The proposed method provides a much easier curriculum at the beginning of the training of the model." ], "reasoning": "The table shows that the longest sentence in the Vanilla batches (B1 and B2) has a length of 38.", "statement": "The longest sentence in the Vanilla batches is 38 characters long.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Effects of different λm of the norm-based model competence function and λw of the norm-based sentence weight function.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The best performance is achieved with λm set to 2.5, resulting in a curriculum length of nearly 29K steps, and satisfactory results are obtained with λw set to 0.5, balancing simple and hard examples.For each experiment, we kept the other parameters unchanged and only adjusted the hyperparameter. For λm, controlling curriculum length, the higher the value, the longer the curriculum length. When setting λm to 2.5 with the curriculum length of nearly 29K steps, it achieves the best performance. For λw, the scaling sentence weight of the objective function, one achieves satisfactory results with a value of 0.5, which maintains the right balance between the learning of simple and hard examples." } ], "images": [ "scigen/train/2121.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Effects of different λm of the norm-based model competence function and λw of the norm-based sentence weight function." ], "reasoning": "The best performance is achieved with λm set to 2.5, resulting in a curriculum length of nearly 29K steps, and satisfactory results are obtained with λw set to 0.5, balancing simple and hard examples.", "description": "For each experiment, we kept the other parameters unchanged and only adjusted the hyperparameter. For λm, controlling curriculum length, the higher the value, the longer the curriculum length. When setting λm to 2.5 with the curriculum length of nearly 29K steps, it achieves the best performance. For λw, the scaling sentence weight of the objective function, one achieves satisfactory results with a value of 0.5, which maintains the right balance between the learning of simple and hard examples.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 3: Effects of different λm of the norm-based model competence function and λw of the norm-based sentence weight function.', solve this question and give detail solutions. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhat is the development score when λm is set to 2.5 and λw is set to 0.5?\n" }, { "role": "assistant", "content": "The table shows that the development score for λm set to 2.5 is 26.89, and for λw set to 0.5, the development score is also 26.89. So, the answer is {\"answer\": \"26.89\"}." } ], "images": [ "scigen/train/2121.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Effects of different λm of the norm-based model competence function and λw of the norm-based sentence weight function." ], "reasoning": "The table shows that the development score for λm set to 2.5 is 26.89, and for λw set to 0.5, the development score is also 26.89.", "question": "What is the development score when λm is set to 2.5 and λw is set to 0.5?", "answer": "So, the answer is {\"answer\": \"26.89\"}." }, { "messages": [ { "role": "user", "content": "The highest development score is achieved with λm set to 2.5. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe highest development score is achieved with λm set to 2.5.\n" }, { "role": "assistant", "content": "The table shows that the highest development score for λm is 26.89, which is achieved when λm is set to 2.5. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2121.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Effects of different λm of the norm-based model competence function and λw of the norm-based sentence weight function." ], "reasoning": "The table shows that the highest development score for λm is 26.89, which is achieved when λm is set to 2.5.", "statement": "The highest development score is achieved with λm set to 2.5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 5: Analysis of test set results (% hits@1) for KB vs. Synthetic Docs on WikiMovies.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The performance drop from KB to synthetic documents is due to sentence representation, with further drops from conjunctions and coreference, making the hardest synthetic dataset more challenging than real Wikipedia documents.For a given movie, we use a simple grammar to construct a synthetic “Wikipedia” document based on the KB triples: for each relation type we have a set of template phrases (100 in total) used to generate the fact, e.g. “Blade Runner came out in 1982” for the entry Blade Runner release_year 1982. We can then parameterize the complexity of our synthetic documents: (i) using one template, or all of them; (ii) using conjunctions to combine facts into single sentences or not; and (iii) using coreference between sentences where we replace the movie name with “it”. The purpose of this experiment is to find which aspects are responsible for the gap in performance to a KB. They indicate that some of the loss (93.9% for KB to 82.9% for One Template Sentence) in performance is due directly to representing in sentence form, making the subject, relation and object harder to extract. Moving to a larger number of templates does not deteriorate performance much (80%). The remaining performance drop seems to be split roughly equally between conjunctions (74%) and coreference (76%). The hardest synthetic dataset combines these (All Templates + Conj. + Coref.) and is actually harder than using the real Wikipedia documents (72.5% vs. 76.2%). This is possibly because the amount of conjunctions and coreferences we make are artificially too high (50% and 80% of the time, respectively)." } ], "images": [ "scigen/train/2122.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Analysis of test set results (% hits@1) for KB vs. Synthetic Docs on WikiMovies." ], "reasoning": "The performance drop from KB to synthetic documents is due to sentence representation, with further drops from conjunctions and coreference, making the hardest synthetic dataset more challenging than real Wikipedia documents.", "description": "For a given movie, we use a simple grammar to construct a synthetic “Wikipedia” document based on the KB triples: for each relation type we have a set of template phrases (100 in total) used to generate the fact, e.g. “Blade Runner came out in 1982” for the entry Blade Runner release_year 1982. We can then parameterize the complexity of our synthetic documents: (i) using one template, or all of them; (ii) using conjunctions to combine facts into single sentences or not; and (iii) using coreference between sentences where we replace the movie name with “it”. The purpose of this experiment is to find which aspects are responsible for the gap in performance to a KB. They indicate that some of the loss (93.9% for KB to 82.9% for One Template Sentence) in performance is due directly to representing in sentence form, making the subject, relation and object harder to extract. Moving to a larger number of templates does not deteriorate performance much (80%). The remaining performance drop seems to be split roughly equally between conjunctions (74%) and coreference (76%). The hardest synthetic dataset combines these (All Templates + Conj. + Coref.) and is actually harder than using the real Wikipedia documents (72.5% vs. 76.2%). This is possibly because the amount of conjunctions and coreferences we make are artificially too high (50% and 80% of the time, respectively).", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 5: Analysis of test set results (% hits@1) for KB vs. Synthetic Docs on WikiMovies.' and methodically tackle the question that follows. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the KV-MemNN performance for the hardest synthetic dataset?\n" }, { "role": "assistant", "content": "The hardest synthetic dataset combines all templates, conjunctions, and coreference, which has a KV-MemNN performance of 72.5%. So, the answer is {\"answer\": \"72.5\"}" } ], "images": [ "scigen/train/2122.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Analysis of test set results (% hits@1) for KB vs. Synthetic Docs on WikiMovies." ], "reasoning": "The hardest synthetic dataset combines all templates, conjunctions, and coreference, which has a KV-MemNN performance of 72.5%.", "question": "What is the KV-MemNN performance for the hardest synthetic dataset?", "answer": "So, the answer is {\"answer\": \"72.5\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Using one template sentence has a higher KV-MemNN performance than using all templates sentences., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nUsing one template sentence has a higher KV-MemNN performance than using all templates sentences.\n" }, { "role": "assistant", "content": "The table shows that using one template sentence has a KV-MemNN performance of 82.9%, while using all templates sentences has a performance of 80.0%, indicating that the statement is correct. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2122.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Analysis of test set results (% hits@1) for KB vs. Synthetic Docs on WikiMovies." ], "reasoning": "The table shows that using one template sentence has a KV-MemNN performance of 82.9%, while using all templates sentences has a performance of 80.0%, indicating that the statement is correct.", "statement": "Using one template sentence has a higher KV-MemNN performance than using all templates sentences.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 6: Test results on WikiQA.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Memory Networks perform poorly compared to Key-Value Memory Networks, which highlights the importance of key-value memories.The results are given in Memory Networks, which cannot easily pair windows to sentences, perform much worse, highlighting the importance of key-value memories." } ], "images": [ "scigen/train/2123.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Test results on WikiQA." ], "reasoning": "Memory Networks perform poorly compared to Key-Value Memory Networks, which highlights the importance of key-value memories.", "description": "The results are given in Memory Networks, which cannot easily pair windows to sentences, perform much worse, highlighting the importance of key-value memories.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 6: Test results on WikiQA.'.\n\nQuestion: Which method has the highest MRR score, and what does this indicate about the importance of key-value memories?\n" }, { "role": "assistant", "content": "The Key-Value Memory Network has the highest MRR score of 0.7265, indicating that key-value memories are crucial for better performance. So, the answer is {\"answer\": \"Key-Value Memory Network\"}" } ], "images": [ "scigen/train/2123.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Test results on WikiQA." ], "reasoning": "The Key-Value Memory Network has the highest MRR score of 0.7265, indicating that key-value memories are crucial for better performance.", "question": "Which method has the highest MRR score, and what does this indicate about the importance of key-value memories?", "answer": "So, the answer is {\"answer\": \"Key-Value Memory Network\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Memory Network method has the lowest MAP score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Memory Network method has the lowest MAP score.\n" }, { "role": "assistant", "content": "The table shows that the Memory Network method has a MAP score of 0.5170, which is not the lowest. The Word Cnt method has the lowest MAP score of 0.4891. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2123.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Test results on WikiQA." ], "reasoning": "The table shows that the Memory Network method has a MAP score of 0.5170, which is not the lowest. The Word Cnt method has the lowest MAP score of 0.4891.", "statement": "The Memory Network method has the lowest MAP score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: BLEU scores over the test sets. Average of 10 models independently trained with different seeds.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Adding ReWE has significantly improved the baseline in all cases, with the highest improvement of 2.54 BLEU points in the eu-en dataset.The values of our experiments are for blind runs over the test sets, averaged over 10 independent runs with different seeds. The results show that adding ReWE has significantly improved the baseline in all cases, with an average of 1.46 BLEU points. In the case of the eu-en dataset, the improvement has reached 2.54 BLEU points. We have also run unpaired t-tests between our baseline and ReWE, and the differences have proved statistically significant (p-values <0.05) in all cases. Using BPE has proved beneficial for the cs-en and eu-en pairs, but not for the en-fr pair. We speculate that English and French may be closer to each other at word level and, therefore, less likely to benefit from the use of sub-word units. Conversely, Czech and Basque are morphologically very rich, justifying the improvements with BPE. This shows that the proposed model is competitive with contemporary NMT techniques." } ], "images": [ "scigen/train/2124.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: BLEU scores over the test sets. Average of 10 models independently trained with different seeds." ], "reasoning": "Adding ReWE has significantly improved the baseline in all cases, with the highest improvement of 2.54 BLEU points in the eu-en dataset.", "description": "The values of our experiments are for blind runs over the test sets, averaged over 10 independent runs with different seeds. The results show that adding ReWE has significantly improved the baseline in all cases, with an average of 1.46 BLEU points. In the case of the eu-en dataset, the improvement has reached 2.54 BLEU points. We have also run unpaired t-tests between our baseline and ReWE, and the differences have proved statistically significant (p-values <0.05) in all cases. Using BPE has proved beneficial for the cs-en and eu-en pairs, but not for the en-fr pair. We speculate that English and French may be closer to each other at word level and, therefore, less likely to benefit from the use of sub-word units. Conversely, Czech and Basque are morphologically very rich, justifying the improvements with BPE. This shows that the proposed model is competitive with contemporary NMT techniques.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: BLEU scores over the test sets. Average of 10 models independently trained with different seeds.'.\n\nQuestion: What is the BLEU score improvement for the eu-en dataset when using ReWE compared to the baseline?\n" }, { "role": "assistant", "content": "The baseline BLEU score for eu-en Word is 12.16, and with ReWE, it is 13.73. The improvement is 13.73 - 12.16 = 1.57 BLEU points. So, the answer is {\"answer\": \"1.57\"}." } ], "images": [ "scigen/train/2124.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: BLEU scores over the test sets. Average of 10 models independently trained with different seeds." ], "reasoning": "The baseline BLEU score for eu-en Word is 12.16, and with ReWE, it is 13.73. The improvement is 13.73 - 12.16 = 1.57 BLEU points.", "question": "What is the BLEU score improvement for the eu-en dataset when using ReWE compared to the baseline?", "answer": "So, the answer is {\"answer\": \"1.57\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Using BPE has proved beneficial for the cs-en and eu-en pairs., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nUsing BPE has proved beneficial for the cs-en and eu-en pairs.\n" }, { "role": "assistant", "content": "The description states that using BPE has proved beneficial for the cs-en and eu-en pairs, and the table shows higher BLEU scores for cs-en BPE (23.60) and eu-en BPE (19.71) compared to their Word counterparts (21.83 and 13.73, respectively). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2124.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: BLEU scores over the test sets. Average of 10 models independently trained with different seeds." ], "reasoning": "The description states that using BPE has proved beneficial for the cs-en and eu-en pairs, and the table shows higher BLEU scores for cs-en BPE (23.60) and eu-en BPE (19.71) compared to their Word counterparts (21.83 and 13.73, respectively).", "statement": "Using BPE has proved beneficial for the cs-en and eu-en pairs.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 7: Results of the Contrastive A experiment (λ=0.2; average of 10 models trained independently from different random seeds).'.\n" }, { "role": "assistant", "content": "The value of λ was tuned to 0.2 for the er-fr validation set, achieving the highest score, but it rarely improved over the baseline and performed worse with the en-fr pair, indicating that joint learning is more effective.For this experiment, the value of λ has been specifically tuned over the er-fr validation set (highest score for λ=0.2). However, this configuration has rarely improved over our baseline (e.g., on the eu-en dataset), and it has performed considerably worse with the en-fr pair. This shows that, in comparison, the proposed joint learning is a much more effective setting." } ], "images": [ "scigen/train/2125.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Results of the Contrastive A experiment (λ=0.2; average of 10 models trained independently from different random seeds)." ], "reasoning": "The value of λ was tuned to 0.2 for the er-fr validation set, achieving the highest score, but it rarely improved over the baseline and performed worse with the en-fr pair, indicating that joint learning is more effective.", "description": "For this experiment, the value of λ has been specifically tuned over the er-fr validation set (highest score for λ=0.2). However, this configuration has rarely improved over our baseline (e.g., on the eu-en dataset), and it has performed considerably worse with the en-fr pair. This shows that, in comparison, the proposed joint learning is a much more effective setting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 7: Results of the Contrastive A experiment (λ=0.2; average of 10 models trained independently from different random seeds).', solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the BLEU score for the en-fr dataset using BPE, and how does it compare to the BLEU score using Word?\n" }, { "role": "assistant", "content": "The BLEU score for the en-fr dataset using BPE is 33.37, which is slightly lower than the BLEU score using Word, which is 33.82. So, the answer is {\"answer\": \"33.37\"}." } ], "images": [ "scigen/train/2125.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Results of the Contrastive A experiment (λ=0.2; average of 10 models trained independently from different random seeds)." ], "reasoning": "The BLEU score for the en-fr dataset using BPE is 33.37, which is slightly lower than the BLEU score using Word, which is 33.82.", "question": "What is the BLEU score for the en-fr dataset using BPE, and how does it compare to the BLEU score using Word?", "answer": "So, the answer is {\"answer\": \"33.37\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe BLEU score for the eu-en dataset using BPE is higher than using Word.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for the eu-en dataset using BPE is 17.53, which is higher than the BLEU score using Word, which is 12.15. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2125.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Results of the Contrastive A experiment (λ=0.2; average of 10 models trained independently from different random seeds)." ], "reasoning": "The table shows that the BLEU score for the eu-en dataset using BPE is 17.53, which is higher than the BLEU score using Word, which is 12.15.", "statement": "The BLEU score for the eu-en dataset using BPE is higher than using Word.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Precision and recall of the parsers evaluated on the out-of-knowledge base dataset. The uniform prior places equal probability mass on all semantic statements. The “type-correct” prior places higher mass (+4 in log probability) on semantic statements with subject and object types that agree with the domain and range of the relation predicate. The “knowledge base prior” is similar to the type-correct prior, except that it additionally places higher probability mass on semantic statements that correspond to true beliefs in the NELL knowledge base (additional +4 in log probability).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The parser with no prior achieves the highest F1 score, while the parser with type-correct prior and KB prior have higher precision but lower recall.Our parser is trained with the simple grammar in the simple relation extraction task, and with the grammar that models verb morphology in the temporally-scoped relation extraction task. As expected, the more informative priors do not uniformly improve parsing performance in this evaluation. Interestingly, the parser behaves more conservatively when incorporating the stronger priors, outputting a smaller set of the most confident responses, which results in higher precision and reduced recall. Our parser is indeed capable of extracting the correct semantic statements from sentences that refer to beliefs outside the knowledge base, and the use of the informative priors does not obviously hurt performance." } ], "images": [ "scigen/train/2126.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Precision and recall of the parsers evaluated on the out-of-knowledge base dataset. The uniform prior places equal probability mass on all semantic statements. The “type-correct” prior places higher mass (+4 in log probability) on semantic statements with subject and object types that agree with the domain and range of the relation predicate. The “knowledge base prior” is similar to the type-correct prior, except that it additionally places higher probability mass on semantic statements that correspond to true beliefs in the NELL knowledge base (additional +4 in log probability)." ], "reasoning": "The parser with no prior achieves the highest F1 score, while the parser with type-correct prior and KB prior have higher precision but lower recall.", "description": "Our parser is trained with the simple grammar in the simple relation extraction task, and with the grammar that models verb morphology in the temporally-scoped relation extraction task. As expected, the more informative priors do not uniformly improve parsing performance in this evaluation. Interestingly, the parser behaves more conservatively when incorporating the stronger priors, outputting a smaller set of the most confident responses, which results in higher precision and reduced recall. Our parser is indeed capable of extracting the correct semantic statements from sentences that refer to beliefs outside the knowledge base, and the use of the informative priors does not obviously hurt performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: Table 2: Precision and recall of the parsers evaluated on the out-of-knowledge base dataset. The uniform prior places equal probability mass on all semantic statements. The “type-correct” prior places higher mass (+4 in log probability) on semantic statements with subject and object types that agree with the domain and range of the relation predicate. The “knowledge base prior” is similar to the type-correct prior, except that it additionally places higher probability mass on semantic statements that correspond to true beliefs in the NELL knowledge base (additional +4 in log probability).\nQuestion: Which parser configuration achieves the highest F1 score in temporally-scoped relation extraction?\n" }, { "role": "assistant", "content": "By comparing the F1 scores in the table, the parser with no prior achieves the highest F1 score of 0.73. So, the answer is {\"answer\": \"our parser (no prior)\"}." } ], "images": [ "scigen/train/2126.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Precision and recall of the parsers evaluated on the out-of-knowledge base dataset. The uniform prior places equal probability mass on all semantic statements. The “type-correct” prior places higher mass (+4 in log probability) on semantic statements with subject and object types that agree with the domain and range of the relation predicate. The “knowledge base prior” is similar to the type-correct prior, except that it additionally places higher probability mass on semantic statements that correspond to true beliefs in the NELL knowledge base (additional +4 in log probability)." ], "reasoning": "By comparing the F1 scores in the table, the parser with no prior achieves the highest F1 score of 0.73.", "question": "Which parser configuration achieves the highest F1 score in temporally-scoped relation extraction?", "answer": "So, the answer is {\"answer\": \"our parser (no prior)\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe parser with type-correct prior has the highest recall.\n" }, { "role": "assistant", "content": "The table shows that the parser with type-correct prior has a recall of 0.59, which is lower than the recall of the parser with no prior (0.68). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2126.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Precision and recall of the parsers evaluated on the out-of-knowledge base dataset. The uniform prior places equal probability mass on all semantic statements. The “type-correct” prior places higher mass (+4 in log probability) on semantic statements with subject and object types that agree with the domain and range of the relation predicate. The “knowledge base prior” is similar to the type-correct prior, except that it additionally places higher probability mass on semantic statements that correspond to true beliefs in the NELL knowledge base (additional +4 in log probability)." ], "reasoning": "The table shows that the parser with type-correct prior has a recall of 0.59, which is lower than the recall of the parser with no prior (0.68).", "statement": "The parser with type-correct prior has the highest recall.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Precision and recall of the parsers evaluated on the out-of-knowledge base dataset. The uniform prior places equal probability mass on all semantic statements. The “type-correct” prior places higher mass (+4 in log probability) on semantic statements with subject and object types that agree with the domain and range of the relation predicate. The “knowledge base prior” is similar to the type-correct prior, except that it additionally places higher probability mass on semantic statements that correspond to true beliefs in the NELL knowledge base (additional +4 in log probability).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The parser with stronger priors outputs fewer but more confident responses, resulting in higher precision and lower recall.Our parser is trained with the simple grammar in the simple relation extraction task, and with the grammar that models verb morphology in the temporally-scoped relation extraction task. As expected, the more informative priors do not uniformly improve parsing performance in this evaluation. Interestingly, the parser behaves more conservatively when incorporating the stronger priors, outputting a smaller set of the most confident responses, which results in higher precision and reduced recall. Our parser is indeed capable of extracting the correct semantic statements from sentences that refer to beliefs outside the knowledge base, and the use of the informative priors does not obviously hurt performance." } ], "images": [ "scigen/train/2127.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Precision and recall of the parsers evaluated on the out-of-knowledge base dataset. The uniform prior places equal probability mass on all semantic statements. The “type-correct” prior places higher mass (+4 in log probability) on semantic statements with subject and object types that agree with the domain and range of the relation predicate. The “knowledge base prior” is similar to the type-correct prior, except that it additionally places higher probability mass on semantic statements that correspond to true beliefs in the NELL knowledge base (additional +4 in log probability)." ], "reasoning": "The parser with stronger priors outputs fewer but more confident responses, resulting in higher precision and lower recall.", "description": "Our parser is trained with the simple grammar in the simple relation extraction task, and with the grammar that models verb morphology in the temporally-scoped relation extraction task. As expected, the more informative priors do not uniformly improve parsing performance in this evaluation. Interestingly, the parser behaves more conservatively when incorporating the stronger priors, outputting a smaller set of the most confident responses, which results in higher precision and reduced recall. Our parser is indeed capable of extracting the correct semantic statements from sentences that refer to beliefs outside the knowledge base, and the use of the informative priors does not obviously hurt performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: 'Table 2: Precision and recall of the parsers evaluated on the out-of-knowledge base dataset. The uniform prior places equal probability mass on all semantic statements. The “type-correct” prior places higher mass (+4 in log probability) on semantic statements with subject and object types that agree with the domain and range of the relation predicate. The “knowledge base prior” is similar to the type-correct prior, except that it additionally places higher probability mass on semantic statements that correspond to true beliefs in the NELL knowledge base (additional +4 in log probability).'\nQuestion: Which parser configuration achieved the highest precision in the simple relation extraction task?\n" }, { "role": "assistant", "content": "The table shows that 'our parser (KB prior)' achieved the highest precision with a value of 0.90. So, the answer is {\"answer\": \"our parser (KB prior)\"}." } ], "images": [ "scigen/train/2127.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Precision and recall of the parsers evaluated on the out-of-knowledge base dataset. The uniform prior places equal probability mass on all semantic statements. The “type-correct” prior places higher mass (+4 in log probability) on semantic statements with subject and object types that agree with the domain and range of the relation predicate. The “knowledge base prior” is similar to the type-correct prior, except that it additionally places higher probability mass on semantic statements that correspond to true beliefs in the NELL knowledge base (additional +4 in log probability)." ], "reasoning": "The table shows that 'our parser (KB prior)' achieved the highest precision with a value of 0.90.", "question": "Which parser configuration achieved the highest precision in the simple relation extraction task?", "answer": "So, the answer is {\"answer\": \"our parser (KB prior)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe parser with no prior achieved the highest recall in the simple relation extraction task.\n" }, { "role": "assistant", "content": "The table indicates that 'our parser (no prior)' achieved the highest recall with a value of 0.58. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2127.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Precision and recall of the parsers evaluated on the out-of-knowledge base dataset. The uniform prior places equal probability mass on all semantic statements. The “type-correct” prior places higher mass (+4 in log probability) on semantic statements with subject and object types that agree with the domain and range of the relation predicate. The “knowledge base prior” is similar to the type-correct prior, except that it additionally places higher probability mass on semantic statements that correspond to true beliefs in the NELL knowledge base (additional +4 in log probability)." ], "reasoning": "The table indicates that 'our parser (no prior)' achieved the highest recall with a value of 0.58.", "statement": "The parser with no prior achieved the highest recall in the simple relation extraction task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: The results of NER evaluation task. The scores are macro average F1 scores of the three named entity classes, excluding score for class ”Other”. The columns show fastText, ELMoForManyLangs (EFML), and EMBEDDIA ELMo embeddings.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Training models for 10 epochs using categorical cross-entropy as a loss function showed significant improvements, especially for Slovenian with EFML embeddings, achieving a score of 0.82F1 in 5 epochs.We used categorical cross-entropy as a loss function and trained each model for 10 epochs (except Slovenian with EFML embeddings, where we trained for 5 epochs, since it gives a much better score (0.82F1 vs. 0.68F1)). In English, there is still improvement, but a smaller one, in part due to already high performance using fastText embeddings." } ], "images": [ "scigen/train/2128.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The results of NER evaluation task. The scores are macro average F1 scores of the three named entity classes, excluding score for class ”Other”. The columns show fastText, ELMoForManyLangs (EFML), and EMBEDDIA ELMo embeddings." ], "reasoning": "Training models for 10 epochs using categorical cross-entropy as a loss function showed significant improvements, especially for Slovenian with EFML embeddings, achieving a score of 0.82F1 in 5 epochs.", "description": "We used categorical cross-entropy as a loss function and trained each model for 10 epochs (except Slovenian with EFML embeddings, where we trained for 5 epochs, since it gives a much better score (0.82F1 vs. 0.68F1)). In English, there is still improvement, but a smaller one, in part due to already high performance using fastText embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 5: The results of NER evaluation task. The scores are macro average F1 scores of the three named entity classes, excluding score for class ”Other”. The columns show fastText, ELMoForManyLangs (EFML), and EMBEDDIA ELMo embeddings.'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich language showed the highest improvement in F1 score when using EFML embeddings compared to fastText embeddings?\n" }, { "role": "assistant", "content": "To find the language with the highest improvement, compare the F1 scores of fastText and EFML embeddings for each language. The highest improvement is seen in Lithuanian, where the score increased from 0.44 to 0.74. So, the answer is {\"answer\": \"Lithuanian\"}" } ], "images": [ "scigen/train/2128.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The results of NER evaluation task. The scores are macro average F1 scores of the three named entity classes, excluding score for class ”Other”. The columns show fastText, ELMoForManyLangs (EFML), and EMBEDDIA ELMo embeddings." ], "reasoning": "To find the language with the highest improvement, compare the F1 scores of fastText and EFML embeddings for each language. The highest improvement is seen in Lithuanian, where the score increased from 0.44 to 0.74.", "question": "Which language showed the highest improvement in F1 score when using EFML embeddings compared to fastText embeddings?", "answer": "So, the answer is {\"answer\": \"Lithuanian\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe English language model achieved the highest F1 score using EMBEDDIA embeddings.\n" }, { "role": "assistant", "content": "The table shows that the English language model achieved an F1 score of 0.92 using EMBEDDIA embeddings, which is the highest score among all languages listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2128.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The results of NER evaluation task. The scores are macro average F1 scores of the three named entity classes, excluding score for class ”Other”. The columns show fastText, ELMoForManyLangs (EFML), and EMBEDDIA ELMo embeddings." ], "reasoning": "The table shows that the English language model achieved an F1 score of 0.92 using EMBEDDIA embeddings, which is the highest score among all languages listed.", "statement": "The English language model achieved the highest F1 score using EMBEDDIA embeddings.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Recall-oriented ROUGE scores in DUC-2004.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The proposed method, including LDPE and LRPE, significantly improved performance compared to the bare Transformer and achieved competitive scores to state-of-the-art methods, except for R-2 of LenInit.Following the evaluation protocol Over et al. The table indicates that LDPE and LRPE significantly improved the performance compared to the bare Transformer, and achieved better performance than the baselines except for R-2 of LenInit. This table also shows the scores reported in the previous studies. The proposed method outperformed the previous methods that control the output length and achieved the competitive score to the state-of-the-art scores. Since the proposed method consists of a character-based decoder, it sometimes generated words unrelated to a source sentence. Thus, we applied a simple re-ranking to each n-best headlines generated by the proposed method (n=20 in this experiment) based on the contained words. Our re-ranking strategy selects a headline that contains source-side words the most." } ], "images": [ "scigen/train/2129.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Recall-oriented ROUGE scores in DUC-2004." ], "reasoning": "The proposed method, including LDPE and LRPE, significantly improved performance compared to the bare Transformer and achieved competitive scores to state-of-the-art methods, except for R-2 of LenInit.", "description": "Following the evaluation protocol Over et al. The table indicates that LDPE and LRPE significantly improved the performance compared to the bare Transformer, and achieved better performance than the baselines except for R-2 of LenInit. This table also shows the scores reported in the previous studies. The proposed method outperformed the previous methods that control the output length and achieved the competitive score to the state-of-the-art scores. Since the proposed method consists of a character-based decoder, it sometimes generated words unrelated to a source sentence. Thus, we applied a simple re-ranking to each n-best headlines generated by the proposed method (n=20 in this experiment) based on the contained words. Our re-ranking strategy selects a headline that contains source-side words the most.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 3: Recall-oriented ROUGE scores in DUC-2004.\nQuestion: Which model achieved the highest R-1 score among the proposed methods?\n" }, { "role": "assistant", "content": "To find the highest R-1 score among the proposed methods, look at the R-1 column under the 'Proposed method' section. The highest score is 32.85 achieved by the +Ensemble (5 models). So, the answer is {\"answer\": \"32.85\"}." } ], "images": [ "scigen/train/2129.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Recall-oriented ROUGE scores in DUC-2004." ], "reasoning": "To find the highest R-1 score among the proposed methods, look at the R-1 column under the 'Proposed method' section. The highest score is 32.85 achieved by the +Ensemble (5 models).", "question": "Which model achieved the highest R-1 score among the proposed methods?", "answer": "So, the answer is {\"answer\": \"32.85\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe proposed method with LDPE achieved a higher R-2 score than the Transformer.\n" }, { "role": "assistant", "content": "The R-2 score for the Transformer is 9.14, while the R-2 score for the Transformer+ LDPE is 10.53, which is higher. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2129.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Recall-oriented ROUGE scores in DUC-2004." ], "reasoning": "The R-2 score for the Transformer is 9.14, while the R-2 score for the Transformer+ LDPE is 10.53, which is higher.", "statement": "The proposed method with LDPE achieved a higher R-2 score than the Transformer.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: Evaluation results for suggested methods', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The paper proposed five methods for APP, each evaluated separately for the Big Five personality traits, achieving the highest average accuracy in five traits.Base Methods Outputs This paper proposed five distinct methods for APP, each of which performs the prediction independently, during the first step. Specifically, the term frequency vector-based, ontology-based, enriched ontology-based, LSA-based, and deep learning-based (BiLSTM) methods were called into action. The performance of each method, is evaluated separately in each of the personality traits in the Big Five model. This method has also achieved the highest average accuracy value in five traits." } ], "images": [ "scigen/train/2130.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Evaluation results for suggested methods" ], "reasoning": "The paper proposed five methods for APP, each evaluated separately for the Big Five personality traits, achieving the highest average accuracy in five traits.", "description": "Base Methods Outputs This paper proposed five distinct methods for APP, each of which performs the prediction independently, during the first step. Specifically, the term frequency vector-based, ontology-based, enriched ontology-based, LSA-based, and deep learning-based (BiLSTM) methods were called into action. The performance of each method, is evaluated separately in each of the personality traits in the Big Five model. This method has also achieved the highest average accuracy value in five traits.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 5: Evaluation results for suggested methods\nQuestion: Which method achieved the highest average accuracy in the Big Five personality traits?\n" }, { "role": "assistant", "content": "By examining the 'Accuracy' row in the table, the method with the highest average accuracy value is Ensemble Modeling with an average accuracy of 60.24. So, the answer is {\"answer\": \"Ensemble Modeling\"}." } ], "images": [ "scigen/train/2130.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Evaluation results for suggested methods" ], "reasoning": "By examining the 'Accuracy' row in the table, the method with the highest average accuracy value is Ensemble Modeling with an average accuracy of 60.24.", "question": "Which method achieved the highest average accuracy in the Big Five personality traits?", "answer": "So, the answer is {\"answer\": \"Ensemble Modeling\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Ontology-Based method achieved the highest recall in the Big Five personality traits.\n" }, { "role": "assistant", "content": "By examining the 'Recall' row in the table, the Ontology-Based method achieved a recall of 100 in all five traits, which is the highest possible value. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2130.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Evaluation results for suggested methods" ], "reasoning": "By examining the 'Recall' row in the table, the Ontology-Based method achieved a recall of 100 in all five traits, which is the highest possible value.", "statement": "The Ontology-Based method achieved the highest recall in the Big Five personality traits.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Evaluation results based on the context-response test set 2. RN – random negatives model; BL – RN model with responses filter; HNr – hard negatives model based on responses only; HNr+c – proposed hard negatives model based on both responses and contexts. The metrics are described in Section 3.4', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The HNr+c model achieves the highest values in almost all metrics, but it does not tend to rank input contexts highly according to rankcontext, and the diffresponse metric shows that the context can be ranked higher than the ground-truth response.As we can see, the proposed HNr+c model achieves the highest values in almost all metrics compared to other approaches. According to rankcontext, it turns out that this model does not tend to highly rank input contexts and have them in the top response candidates. Still, according to the diffresponse metric, the average score of a ground-truth response is lower than the score of a context, which means that the context can be ranked higher than the ground-truth response." } ], "images": [ "scigen/train/2131.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Evaluation results based on the context-response test set 2. RN – random negatives model; BL – RN model with responses filter; HNr – hard negatives model based on responses only; HNr+c – proposed hard negatives model based on both responses and contexts. The metrics are described in Section 3.4" ], "reasoning": "The HNr+c model achieves the highest values in almost all metrics, but it does not tend to rank input contexts highly according to rankcontext, and the diffresponse metric shows that the context can be ranked higher than the ground-truth response.", "description": "As we can see, the proposed HNr+c model achieves the highest values in almost all metrics compared to other approaches. According to rankcontext, it turns out that this model does not tend to highly rank input contexts and have them in the top response candidates. Still, according to the diffresponse metric, the average score of a ground-truth response is lower than the score of a context, which means that the context can be ranked higher than the ground-truth response.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: Evaluation results based on the context-response test set 2. RN – random negatives model; BL – RN model with responses filter; HNr – hard negatives model based on responses only; HNr+c – proposed hard negatives model based on both responses and contexts. The metrics are described in Section 3.4'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest Recall@10 value?\n" }, { "role": "assistant", "content": "By comparing the Recall@10 values in the table, the HNr model has the highest value of 0.54. So, the answer is {\"answer\": \"HNr\"}." } ], "images": [ "scigen/train/2131.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Evaluation results based on the context-response test set 2. RN – random negatives model; BL – RN model with responses filter; HNr – hard negatives model based on responses only; HNr+c – proposed hard negatives model based on both responses and contexts. The metrics are described in Section 3.4" ], "reasoning": "By comparing the Recall@10 values in the table, the HNr model has the highest value of 0.54.", "question": "Which model has the highest Recall@10 value?", "answer": "So, the answer is {\"answer\": \"HNr\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe HNr+c model has the highest diffresponse score.\n" }, { "role": "assistant", "content": "The table shows that the diffresponse score for the HNr+c model is -0.09, which is not the highest compared to other models. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2131.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Evaluation results based on the context-response test set 2. RN – random negatives model; BL – RN model with responses filter; HNr – hard negatives model based on responses only; HNr+c – proposed hard negatives model based on both responses and contexts. The metrics are described in Section 3.4" ], "reasoning": "The table shows that the diffresponse score for the HNr+c model is -0.09, which is not the highest compared to other models.", "statement": "The HNr+c model has the highest diffresponse score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Spearman’s ρ correlation coefficient for Word Similarity and Lexical Entailment tasks using SGNS and Poincaré embeddings.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "SGNS embeddings outperform Poincaré embeddings in preserving word similarities, while Poincaré embeddings are better at preserving lexical entailment, despite their best score being only 0.259.SGNS embeddings are superior for preserving word similarities, while Poincaré embeddings are superior for preserving lexical entailment. However, the best score for Poincaré embeddings is only 0.259, which is quite low. In comparison to the unsupervised baselines studied in Vulić et al." } ], "images": [ "scigen/train/2132.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Spearman’s ρ correlation coefficient for Word Similarity and Lexical Entailment tasks using SGNS and Poincaré embeddings." ], "reasoning": "SGNS embeddings outperform Poincaré embeddings in preserving word similarities, while Poincaré embeddings are better at preserving lexical entailment, despite their best score being only 0.259.", "description": "SGNS embeddings are superior for preserving word similarities, while Poincaré embeddings are superior for preserving lexical entailment. However, the best score for Poincaré embeddings is only 0.259, which is quite low. In comparison to the unsupervised baselines studied in Vulić et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 4: Spearman’s ρ correlation coefficient for Word Similarity and Lexical Entailment tasks using SGNS and Poincaré embeddings.' and proceed to solve the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich embedding method and dimension combination achieved the highest score for the WordSim-353 task?\n" }, { "role": "assistant", "content": "To find the highest score for the WordSim-353 task, compare the scores of SGNS and Poincaré embeddings across all dimensions. The highest score is 0.689 for SGNS embeddings at Dimension 100. So, the answer is {\"answer\": \"SGNS at Dimension 100\"}." } ], "images": [ "scigen/train/2132.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Spearman’s ρ correlation coefficient for Word Similarity and Lexical Entailment tasks using SGNS and Poincaré embeddings." ], "reasoning": "To find the highest score for the WordSim-353 task, compare the scores of SGNS and Poincaré embeddings across all dimensions. The highest score is 0.689 for SGNS embeddings at Dimension 100.", "question": "Which embedding method and dimension combination achieved the highest score for the WordSim-353 task?", "answer": "So, the answer is {\"answer\": \"SGNS at Dimension 100\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Poincaré embeddings achieved a higher score than SGNS embeddings for the HyperLex task at Dimension 5., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nPoincaré embeddings achieved a higher score than SGNS embeddings for the HyperLex task at Dimension 5.\n" }, { "role": "assistant", "content": "For the HyperLex task at Dimension 5, Poincaré embeddings scored 0.259, while SGNS embeddings scored -0.002. Therefore, Poincaré embeddings did achieve a higher score than SGNS embeddings. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2132.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Spearman’s ρ correlation coefficient for Word Similarity and Lexical Entailment tasks using SGNS and Poincaré embeddings." ], "reasoning": "For the HyperLex task at Dimension 5, Poincaré embeddings scored 0.259, while SGNS embeddings scored -0.002. Therefore, Poincaré embeddings did achieve a higher score than SGNS embeddings.", "statement": "Poincaré embeddings achieved a higher score than SGNS embeddings for the HyperLex task at Dimension 5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Words in order of increasing hyperbolic norm which contain the substring indicated in the top row. Their counts in the text8 corpus are also shown. Dimension size d=20.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Words with fewer contexts have higher hyperbolic norms, indicating increased specificity, as seen with 'bulldogs' having a higher norm than 'dog' and 'greatest' having a higher norm than 'great'.We also show the counts in the corpus of these words, which are correlated to the number of contexts they occur in. As expected, words occurring in fewer contexts have higher hyperbolic norm, and this corresponds to increased specificity as we move down the list; for example “bulldogs” has a higher norm than “dog”, and “greatest” has a higher norm than “great”. The Spearman correlation between 1/f, where f is the frequency of a word in the corpus, and the norm of its embedding is 0.77." } ], "images": [ "scigen/train/2133.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Words in order of increasing hyperbolic norm which contain the substring indicated in the top row. Their counts in the text8 corpus are also shown. Dimension size d=20." ], "reasoning": "Words with fewer contexts have higher hyperbolic norms, indicating increased specificity, as seen with 'bulldogs' having a higher norm than 'dog' and 'greatest' having a higher norm than 'great'.", "description": "We also show the counts in the corpus of these words, which are correlated to the number of contexts they occur in. As expected, words occurring in fewer contexts have higher hyperbolic norm, and this corresponds to increased specificity as we move down the list; for example “bulldogs” has a higher norm than “dog”, and “greatest” has a higher norm than “great”. The Spearman correlation between 1/f, where f is the frequency of a word in the corpus, and the norm of its embedding is 0.77.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Words in order of increasing hyperbolic norm which contain the substring indicated in the top row. Their counts in the text8 corpus are also shown. Dimension size d=20.'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. \n\nQuestion: Which word has the highest hyperbolic norm in the 'dog' category?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the word with the highest hyperbolic norm in the 'dog' category, we compare the norms of all words listed under the 'dog' category. 'sheepdog' has the highest norm of 7.73. So, the answer is {\"answer\": \"sheepdog\"}." } ], "images": [ "scigen/train/2133.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Words in order of increasing hyperbolic norm which contain the substring indicated in the top row. Their counts in the text8 corpus are also shown. Dimension size d=20." ], "reasoning": "To find the word with the highest hyperbolic norm in the 'dog' category, we compare the norms of all words listed under the 'dog' category. 'sheepdog' has the highest norm of 7.73.", "question": "Which word has the highest hyperbolic norm in the 'dog' category?", "answer": "So, the answer is {\"answer\": \"sheepdog\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The word 'banknote' has the highest hyperbolic norm in the 'bank' category., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe word 'banknote' has the highest hyperbolic norm in the 'bank' category.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the hyperbolic norms of all words listed under the 'bank' category. 'banknote' has a norm of 6.62, which is indeed the highest in the 'bank' category. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2133.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Words in order of increasing hyperbolic norm which contain the substring indicated in the top row. Their counts in the text8 corpus are also shown. Dimension size d=20." ], "reasoning": "To verify this statement, we compare the hyperbolic norms of all words listed under the 'bank' category. 'banknote' has a norm of 6.62, which is indeed the highest in the 'bank' category.", "statement": "The word 'banknote' has the highest hyperbolic norm in the 'bank' category.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: Sentences from Movie Reviews dataset with their norms. Each row represents a nearest neighbor to and with a greater norm than the sentence in the row above.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Sentence embeddings exhibit hierarchical structure, with norms increasing as we move upwards in the tree, indicated by a Spearman correlation of 0.671 between norm and node height.Similar to word embeddings, we expect that sentence (phrase) embeddings will be organized in a hierarchical manner such that sentences (phrases) that appear in a variety of contexts are closer to the origin. However, unlike word embeddings where we could compare hyperbolic norm to frequency in the corpus, this effect is hard to measure directly for sentences (phrases) because most only appear a small number of times in the corpus. Instead, we check whether the embeddings exhibit a known hierarchical structure: constituent parses of sentences. The Spearman correlation between the norm of the resulting embedding and the height of the node in its tree, computed over all nodes in the set, is 0.671. The norms generally increase as we move upwards, indicating that the learned embeddings encode some of this particular form of hierarchical structure." } ], "images": [ "scigen/train/2134.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Sentences from Movie Reviews dataset with their norms. Each row represents a nearest neighbor to and with a greater norm than the sentence in the row above." ], "reasoning": "Sentence embeddings exhibit hierarchical structure, with norms increasing as we move upwards in the tree, indicated by a Spearman correlation of 0.671 between norm and node height.", "description": "Similar to word embeddings, we expect that sentence (phrase) embeddings will be organized in a hierarchical manner such that sentences (phrases) that appear in a variety of contexts are closer to the origin. However, unlike word embeddings where we could compare hyperbolic norm to frequency in the corpus, this effect is hard to measure directly for sentences (phrases) because most only appear a small number of times in the corpus. Instead, we check whether the embeddings exhibit a known hierarchical structure: constituent parses of sentences. The Spearman correlation between the norm of the resulting embedding and the height of the node in its tree, computed over all nodes in the set, is 0.671. The norms generally increase as we move upwards, indicating that the learned embeddings encode some of this particular form of hierarchical structure.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 5: Sentences from Movie Reviews dataset with their norms. Each row represents a nearest neighbor to and with a greater norm than the sentence in the row above.' in mind, break down and resolve the question below step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the norm of the sentence 'an imponderably stilted and self-consciously arty movie' and how does it relate to the hierarchical structure described?\n" }, { "role": "assistant", "content": "The norm of the sentence 'an imponderably stilted and self-consciously arty movie' is 6.65. According to the description, higher norms indicate higher positions in the hierarchical structure, suggesting this sentence is higher in the hierarchy. So, the answer is {\"answer\": \"6.65\"}." } ], "images": [ "scigen/train/2134.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Sentences from Movie Reviews dataset with their norms. Each row represents a nearest neighbor to and with a greater norm than the sentence in the row above." ], "reasoning": "The norm of the sentence 'an imponderably stilted and self-consciously arty movie' is 6.65. According to the description, higher norms indicate higher positions in the hierarchical structure, suggesting this sentence is higher in the hierarchy.", "question": "What is the norm of the sentence 'an imponderably stilted and self-consciously arty movie' and how does it relate to the hierarchical structure described?", "answer": "So, the answer is {\"answer\": \"6.65\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The sentence 'a trifle of a movie, with a few laughs ... unremarkable soft center' has the highest norm in the table., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe sentence 'a trifle of a movie, with a few laughs ... unremarkable soft center' has the highest norm in the table.\n" }, { "role": "assistant", "content": "The table shows that the norm of the sentence 'a trifle of a movie, with a few laughs ... unremarkable soft center' is 6.86, which is the highest norm listed in the table. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2134.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Sentences from Movie Reviews dataset with their norms. Each row represents a nearest neighbor to and with a greater norm than the sentence in the row above." ], "reasoning": "The table shows that the norm of the sentence 'a trifle of a movie, with a few laughs ... unremarkable soft center' is 6.86, which is the highest norm listed in the table.", "statement": "The sentence 'a trifle of a movie, with a few laughs ... unremarkable soft center' has the highest norm in the table.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 6: Held out set perplexity and downstream task performance for sentence embeddings of various sizes. ∗Perplexity of the Skip-Thoughts model is not comparable to our methods since it only uses uni-directional local context.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Poincaré embeddings achieve lower perplexity compared to Euclidean embeddings, and perform similarly on downstream tasks except for the MPQA task where they perform significantly better.Next, following common practice for evaluating sentence representations, we evaluate the trained Poincaré encoder as a black-box feature extractor for downstream tasks. We choose four binary classification benchmarks from the original Skip-Thoughts evaluation – CR, MR, MPQA and SUBJ – and two entailment tasks – MultiNLI For the binary classification tasks we train SVM models with a kernel based on hyperbolic distance between sentences, and for the entailment tasks we train multi-layer perceptrons on top of the premise and hypothesis embeddings and their element-wise products and differences. As a baseline, we compare to embeddings trained using the Euclidean distance metric. Poincaré embeddings achieve a lower perplexity in each case, suggesting a more efficient use of the embedding space. However, both sets of embeddings perform similarly on downstream tasks, except for the MPQA opinion polarity task where Poincaré embeddings do significantly better. Training with embedding sizes greater than 1000 did not show any further improvements in our experiments." } ], "images": [ "scigen/train/2135.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Held out set perplexity and downstream task performance for sentence embeddings of various sizes. ∗Perplexity of the Skip-Thoughts model is not comparable to our methods since it only uses uni-directional local context." ], "reasoning": "Poincaré embeddings achieve lower perplexity compared to Euclidean embeddings, and perform similarly on downstream tasks except for the MPQA task where they perform significantly better.", "description": "Next, following common practice for evaluating sentence representations, we evaluate the trained Poincaré encoder as a black-box feature extractor for downstream tasks. We choose four binary classification benchmarks from the original Skip-Thoughts evaluation – CR, MR, MPQA and SUBJ – and two entailment tasks – MultiNLI For the binary classification tasks we train SVM models with a kernel based on hyperbolic distance between sentences, and for the entailment tasks we train multi-layer perceptrons on top of the premise and hypothesis embeddings and their element-wise products and differences. As a baseline, we compare to embeddings trained using the Euclidean distance metric. Poincaré embeddings achieve a lower perplexity in each case, suggesting a more efficient use of the embedding space. However, both sets of embeddings perform similarly on downstream tasks, except for the MPQA opinion polarity task where Poincaré embeddings do significantly better. Training with embedding sizes greater than 1000 did not show any further improvements in our experiments.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 6: Held out set perplexity and downstream task performance for sentence embeddings of various sizes. ∗Perplexity of the Skip-Thoughts model is not comparable to our methods since it only uses uni-directional local context.'. Solve the following question based on the table.\n\nQuestion: Which method and encoder dimension combination achieved the lowest perplexity?\nLet's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, the lowest perplexity value is 46, which is achieved by the Poincaré method with an encoder dimension of 1000. So, the answer is {\"answer\": \"Poincaré with 1000 encoder dimension\"}" } ], "images": [ "scigen/train/2135.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Held out set perplexity and downstream task performance for sentence embeddings of various sizes. ∗Perplexity of the Skip-Thoughts model is not comparable to our methods since it only uses uni-directional local context." ], "reasoning": "By examining the table, the lowest perplexity value is 46, which is achieved by the Poincaré method with an encoder dimension of 1000.", "question": "Which method and encoder dimension combination achieved the lowest perplexity?", "answer": "So, the answer is {\"answer\": \"Poincaré with 1000 encoder dimension\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Skip-Thoughts embeddings achieved the highest accuracy on the SUBJ task., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSkip-Thoughts embeddings achieved the highest accuracy on the SUBJ task.\n" }, { "role": "assistant", "content": "The table shows that Skip-Thoughts embeddings have an accuracy of 0.938 on the SUBJ task, which is the highest among all methods listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2135.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Held out set perplexity and downstream task performance for sentence embeddings of various sizes. ∗Perplexity of the Skip-Thoughts model is not comparable to our methods since it only uses uni-directional local context." ], "reasoning": "The table shows that Skip-Thoughts embeddings have an accuracy of 0.938 on the SUBJ task, which is the highest among all methods listed.", "statement": "Skip-Thoughts embeddings achieved the highest accuracy on the SUBJ task.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: Comparison to other document-level NMT methods on Chinese-English translation.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Our method outperforms both the concatenation method and the method by Zhang et al., achieving higher BLEU scores on both datasets.It is clear that our method outperforms these two methods. Although the concatenation method [Tiedemann2017Neural] can improve document-level NMT over the RNNSearch model [o2017The], it fails to improve the Transformer model. Comparied with the method by Zhang et al. [Zhang2018Improving] that is also based on Transformer, our work outperforms their model by 0.72 and 0.65 BLEU points on the two datasets." } ], "images": [ "scigen/train/2136.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison to other document-level NMT methods on Chinese-English translation." ], "reasoning": "Our method outperforms both the concatenation method and the method by Zhang et al., achieving higher BLEU scores on both datasets.", "description": "It is clear that our method outperforms these two methods. Although the concatenation method [Tiedemann2017Neural] can improve document-level NMT over the RNNSearch model [o2017The], it fails to improve the Transformer model. Comparied with the method by Zhang et al. [Zhang2018Improving] that is also based on Transformer, our work outperforms their model by 0.72 and 0.65 BLEU points on the two datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 5: Comparison to other document-level NMT methods on Chinese-English translation.\n\nQuestion: How much does our work outperform the method by Zhang et al. on the 2.8M dataset?\n" }, { "role": "assistant", "content": "The table shows that our work scores 44.17 on the 2.8M dataset, while the method by Zhang et al. scores 43.52. The difference is 44.17 - 43.52 = 0.65 BLEU points. So, the answer is {\"answer\": \"0.65\"}" } ], "images": [ "scigen/train/2136.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison to other document-level NMT methods on Chinese-English translation." ], "reasoning": "The table shows that our work scores 44.17 on the 2.8M dataset, while the method by Zhang et al. scores 43.52. The difference is 44.17 - 43.52 = 0.65 BLEU points.", "question": "How much does our work outperform the method by Zhang et al. on the 2.8M dataset?", "answer": "So, the answer is {\"answer\": \"0.65\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe concatenation method by Tiedemann and Scherrer improves the Transformer model.\n" }, { "role": "assistant", "content": "The description states that the concatenation method fails to improve the Transformer model, and the table shows that the score for the concatenation method is lower than the baseline for the 900k dataset and is not available for the 2.8M dataset. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2136.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison to other document-level NMT methods on Chinese-English translation." ], "reasoning": "The description states that the concatenation method fails to improve the Transformer model, and the table shows that the score for the concatenation method is lower than the baseline for the 900k dataset and is not available for the 2.8M dataset.", "statement": "The concatenation method by Tiedemann and Scherrer improves the Transformer model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Comparison between the joint training method and the explicit contextual integration method on Chinese-English translation.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The joint training method improves translation quality by 0.84 BLEU points over the baseline, while explicit contextual integration improves it by 0.26 BLEU points more than joint training.In our joint training method, during the testing phase, we do not explicitly use any contextual information for the current source sentence as the jointly trained NMT model implicitly learns the potential context information by learning to predict the preceding / succeeding source sentence during the training phase. In order to study how much potential context information can be captured by this implicit method, we conducted a comparison experiment. We obtain the representations of the preceding / succeeding source sentences via the pre-encoder and next-encoder and integrate them into the encoder of the current sentence by using self-attention that treats the encoder outputs of the current sentence as q and the encoder outputs of the previous / next sentence as k and v. We refer to this method as “Explicit Contextual Integration”. The explicit contextual integration method is better than our joint training method by only 0.26 BLEU points. Comparing this with the improvement of 0.84 BLEU points achieved by our joint training method over the baseline, we find that the joint training method is able to learn sufficient contextual information for translation in an implicit way." } ], "images": [ "scigen/train/2137.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison between the joint training method and the explicit contextual integration method on Chinese-English translation." ], "reasoning": "The joint training method improves translation quality by 0.84 BLEU points over the baseline, while explicit contextual integration improves it by 0.26 BLEU points more than joint training.", "description": "In our joint training method, during the testing phase, we do not explicitly use any contextual information for the current source sentence as the jointly trained NMT model implicitly learns the potential context information by learning to predict the preceding / succeeding source sentence during the training phase. In order to study how much potential context information can be captured by this implicit method, we conducted a comparison experiment. We obtain the representations of the preceding / succeeding source sentences via the pre-encoder and next-encoder and integrate them into the encoder of the current sentence by using self-attention that treats the encoder outputs of the current sentence as q and the encoder outputs of the previous / next sentence as k and v. We refer to this method as “Explicit Contextual Integration”. The explicit contextual integration method is better than our joint training method by only 0.26 BLEU points. Comparing this with the improvement of 0.84 BLEU points achieved by our joint training method over the baseline, we find that the joint training method is able to learn sufficient contextual information for translation in an implicit way.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Comparison between the joint training method and the explicit contextual integration method on Chinese-English translation.', solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nHow much does the Explicit Contextual Integration method improve the BLEU score over the baseline?\n" }, { "role": "assistant", "content": "The baseline BLEU score is 39.49, and the Explicit Contextual Integration method achieves 40.59. The improvement is 40.59 - 39.49 = 1.10 BLEU points. So, the answer is {\"answer\": \"1.10\"}" } ], "images": [ "scigen/train/2137.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison between the joint training method and the explicit contextual integration method on Chinese-English translation." ], "reasoning": "The baseline BLEU score is 39.49, and the Explicit Contextual Integration method achieves 40.59. The improvement is 40.59 - 39.49 = 1.10 BLEU points.", "question": "How much does the Explicit Contextual Integration method improve the BLEU score over the baseline?", "answer": "So, the answer is {\"answer\": \"1.10\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe joint training method achieves a BLEU score of 40.33.\n" }, { "role": "assistant", "content": "According to the table, the joint training method has an average BLEU score of 40.33. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2137.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison between the joint training method and the explicit contextual integration method on Chinese-English translation." ], "reasoning": "According to the table, the joint training method has an average BLEU score of 40.33.", "statement": "The joint training method achieves a BLEU score of 40.33.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: BLEU scores (average results on the 5 test sets) of the two encoders vs. the single shared encoder for the pre-training model on the 900K-sentence corpus.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The two-encoder network outperforms the single shared encoder, indicating better capture of discourse dependencies.We conducted experiments to compare the two encoders vs. the single shared encoder for the pre-training model. Obviously, the two-encoder network is better than the single shared encoder. This indicates that the two separate encoders are better at capturing the discourse dependencies between the previous and the current sentence and between the next and the current sentence than the single encoder. We conjecture that this is because the dependencies on the previous sentence are different from those on the next sentence." } ], "images": [ "scigen/train/2138.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: BLEU scores (average results on the 5 test sets) of the two encoders vs. the single shared encoder for the pre-training model on the 900K-sentence corpus." ], "reasoning": "The two-encoder network outperforms the single shared encoder, indicating better capture of discourse dependencies.", "description": "We conducted experiments to compare the two encoders vs. the single shared encoder for the pre-training model. Obviously, the two-encoder network is better than the single shared encoder. This indicates that the two separate encoders are better at capturing the discourse dependencies between the previous and the current sentence and between the next and the current sentence than the single encoder. We conjecture that this is because the dependencies on the previous sentence are different from those on the next sentence.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 7: BLEU scores (average results on the 5 test sets) of the two encoders vs. the single shared encoder for the pre-training model on the 900K-sentence corpus.', solve this question and give detail solutions. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich method achieved the highest BLEU score in the experiments?\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for different methods. The 'Two Encoders' method has the highest BLEU score of 40.42. So, the answer is {\"answer\": \"Two Encoders\"}." } ], "images": [ "scigen/train/2138.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: BLEU scores (average results on the 5 test sets) of the two encoders vs. the single shared encoder for the pre-training model on the 900K-sentence corpus." ], "reasoning": "The table shows the BLEU scores for different methods. The 'Two Encoders' method has the highest BLEU score of 40.42.", "question": "Which method achieved the highest BLEU score in the experiments?", "answer": "So, the answer is {\"answer\": \"Two Encoders\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe single shared encoder achieved a BLEU score higher than 40.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for the single shared encoder is 39.95, which is less than 40. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2138.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: BLEU scores (average results on the 5 test sets) of the two encoders vs. the single shared encoder for the pre-training model on the 900K-sentence corpus." ], "reasoning": "The table shows that the BLEU score for the single shared encoder is 39.95, which is less than 40.", "statement": "The single shared encoder achieved a BLEU score higher than 40.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 8: BLEU scores for the ablation study of the fine-tuning model on the two corpora. “Pre+Next+Input Embedding” is the method that inputs the sum of the outputs from the pre- and next-encoder and input embeddings into the fine-tuning model. “Input Embedding” only uses input embeddings of the pre-training model for the input to the fine-tuning model.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The addition of pre-encoder and next-encoder outputs to input embeddings results in significant BLEU score improvements over the baseline, indicating that the pretrained encoders capture additional contextual information.Here we empirically investigate the impact of these three parts on the fine-tuning model via ablation study. and +0.66 BLEU points over baseline on the two corpora. When we add the outputs from both the pre-encoder and next-encoder, we obtain further improvements of +0.5 and +0.62 BLEU points over the input word embeddings. This clearly suggests that the pretrained two encoders learn additional contextual information." } ], "images": [ "scigen/train/2139.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: BLEU scores for the ablation study of the fine-tuning model on the two corpora. “Pre+Next+Input Embedding” is the method that inputs the sum of the outputs from the pre- and next-encoder and input embeddings into the fine-tuning model. “Input Embedding” only uses input embeddings of the pre-training model for the input to the fine-tuning model." ], "reasoning": "The addition of pre-encoder and next-encoder outputs to input embeddings results in significant BLEU score improvements over the baseline, indicating that the pretrained encoders capture additional contextual information.", "description": "Here we empirically investigate the impact of these three parts on the fine-tuning model via ablation study. and +0.66 BLEU points over baseline on the two corpora. When we add the outputs from both the pre-encoder and next-encoder, we obtain further improvements of +0.5 and +0.62 BLEU points over the input word embeddings. This clearly suggests that the pretrained two encoders learn additional contextual information.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 8: BLEU scores for the ablation study of the fine-tuning model on the two corpora. “Pre+Next+Input Embedding” is the method that inputs the sum of the outputs from the pre- and next-encoder and input embeddings into the fine-tuning model. “Input Embedding” only uses input embeddings of the pre-training model for the input to the fine-tuning model.\nQuestion: What is the BLEU score improvement when using Pre+Next+Input Embedding over the Baseline for the 2.8M corpus?\n" }, { "role": "assistant", "content": "The BLEU score for the Baseline method on the 2.8M corpus is 42.89, and for the Pre+Next+Input Embedding method, it is 44.17. The improvement is calculated as 44.17 - 42.89. So, the answer is {\"answer\": \"1.28\"}." } ], "images": [ "scigen/train/2139.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: BLEU scores for the ablation study of the fine-tuning model on the two corpora. “Pre+Next+Input Embedding” is the method that inputs the sum of the outputs from the pre- and next-encoder and input embeddings into the fine-tuning model. “Input Embedding” only uses input embeddings of the pre-training model for the input to the fine-tuning model." ], "reasoning": "The BLEU score for the Baseline method on the 2.8M corpus is 42.89, and for the Pre+Next+Input Embedding method, it is 44.17. The improvement is calculated as 44.17 - 42.89.", "question": "What is the BLEU score improvement when using Pre+Next+Input Embedding over the Baseline for the 2.8M corpus?", "answer": "So, the answer is {\"answer\": \"1.28\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Input Embedding method achieves a higher BLEU score than the Baseline method on the 900k corpus. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Input Embedding method achieves a higher BLEU score than the Baseline method on the 900k corpus.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for the Input Embedding method on the 900k corpus is 39.83, while the Baseline method achieves 39.49. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2139.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: BLEU scores for the ablation study of the fine-tuning model on the two corpora. “Pre+Next+Input Embedding” is the method that inputs the sum of the outputs from the pre- and next-encoder and input embeddings into the fine-tuning model. “Input Embedding” only uses input embeddings of the pre-training model for the input to the fine-tuning model." ], "reasoning": "The table shows that the BLEU score for the Input Embedding method on the 900k corpus is 39.83, while the Baseline method achieves 39.49. Therefore, the statement is supported.", "statement": "The Input Embedding method achieves a higher BLEU score than the Baseline method on the 900k corpus.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 9: The number of parameters used in the baseline and our method and performance comparison.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The joint training method achieves a higher BLEU score than the baseline with the same number of parameters, while the pre-training & fine-tuning method, despite using more parameters, shows improved performance due to learned contextual knowledge.The joint training method uses the same number of parameters as the baseline, achieving a higher BLEU score. The number of parameters in the pre-training & fine-tuning method is about 30% larger than that of the baseline due to the additional two encoders for the preceding and succeeding source sentence. If we do not pre-train the model, the performance significantly drops, even worse than the baseline. This suggests that the improvement of the pre-training & fine-tuning method over the baseline is achieved not because of using additional parameters but the learned contextual knowledge in the pre-training on the monolingual document data." } ], "images": [ "scigen/train/2140.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: The number of parameters used in the baseline and our method and performance comparison." ], "reasoning": "The joint training method achieves a higher BLEU score than the baseline with the same number of parameters, while the pre-training & fine-tuning method, despite using more parameters, shows improved performance due to learned contextual knowledge.", "description": "The joint training method uses the same number of parameters as the baseline, achieving a higher BLEU score. The number of parameters in the pre-training & fine-tuning method is about 30% larger than that of the baseline due to the additional two encoders for the preceding and succeeding source sentence. If we do not pre-train the model, the performance significantly drops, even worse than the baseline. This suggests that the improvement of the pre-training & fine-tuning method over the baseline is achieved not because of using additional parameters but the learned contextual knowledge in the pre-training on the monolingual document data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 9: The number of parameters used in the baseline and our method and performance comparison.\nQuestion: Which method achieves the highest BLEU score and how many parameters does it use?\nSolve the above question based on the table information. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the pre-training & fine-tuning method achieves the highest BLEU score of 40.42 and uses 128M parameters. So, the answer is {\"answer\": \"Pre-training & Fine-tuning, 128M\"}." } ], "images": [ "scigen/train/2140.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: The number of parameters used in the baseline and our method and performance comparison." ], "reasoning": "The table shows that the pre-training & fine-tuning method achieves the highest BLEU score of 40.42 and uses 128M parameters.", "question": "Which method achieves the highest BLEU score and how many parameters does it use?", "answer": "So, the answer is {\"answer\": \"Pre-training & Fine-tuning, 128M\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe joint training method uses more parameters than the baseline.\n" }, { "role": "assistant", "content": "The table shows that both the joint training method and the baseline use the same number of parameters, 90.2M. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2140.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: The number of parameters used in the baseline and our method and performance comparison." ], "reasoning": "The table shows that both the joint training method and the baseline use the same number of parameters, 90.2M.", "statement": "The joint training method uses more parameters than the baseline.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: The results of the automated evaluation. The bold indicates the system with a lower similarity at the target rhyme density.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The models initially achieved a higher rhyme density than the artist's average, causing the regression line to hit the average rhyme density at a negative iteration.For each artist, we calculate their average rhyme density across all verses. We then use this value to determine at which iteration this rhyme density is achieved during generation (using the regression line for rhyme density). Next, we use the maximum similarity regression line to determine the maximum similarity score at that iteration. Low maximum similarity score indicates that we have maintained stylistic similarity while producing new, previously unseen lyrics. The reason is that in the beginning of training (in the LSTM’s case) and at a low n-gram length (for the baseline model), the models actually achieved a rhyme density that exceeded the artist’s average rhyme density. As a result, the rhyme density regression line hits the average rhyme density on a negative iteration." } ], "images": [ "scigen/train/2141.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The results of the automated evaluation. The bold indicates the system with a lower similarity at the target rhyme density." ], "reasoning": "The models initially achieved a higher rhyme density than the artist's average, causing the regression line to hit the average rhyme density at a negative iteration.", "description": "For each artist, we calculate their average rhyme density across all verses. We then use this value to determine at which iteration this rhyme density is achieved during generation (using the regression line for rhyme density). Next, we use the maximum similarity regression line to determine the maximum similarity score at that iteration. Low maximum similarity score indicates that we have maintained stylistic similarity while producing new, previously unseen lyrics. The reason is that in the beginning of training (in the LSTM’s case) and at a low n-gram length (for the baseline model), the models actually achieved a rhyme density that exceeded the artist’s average rhyme density. As a result, the rhyme density regression line hits the average rhyme density on a negative iteration.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 3: The results of the automated evaluation. The bold indicates the system with a lower similarity at the target rhyme density.', solve the subsequent question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhich artist has the highest LSTM similarity score?\n" }, { "role": "assistant", "content": "To find the artist with the highest LSTM similarity score, compare the LSTM similarity scores of all artists in the table. The highest score is 0.569096, which belongs to Fabolous. So, the answer is {\"answer\": \"Fabolous\"}" } ], "images": [ "scigen/train/2141.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The results of the automated evaluation. The bold indicates the system with a lower similarity at the target rhyme density." ], "reasoning": "To find the artist with the highest LSTM similarity score, compare the LSTM similarity scores of all artists in the table. The highest score is 0.569096, which belongs to Fabolous.", "question": "Which artist has the highest LSTM similarity score?", "answer": "So, the answer is {\"answer\": \"Fabolous\"}" }, { "messages": [ { "role": "user", "content": "Jay Z has a higher baseline similarity score than Eminem. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nJay Z has a higher baseline similarity score than Eminem.\n" }, { "role": "assistant", "content": "To verify this statement, compare the baseline similarity scores of Jay Z and Eminem. Jay Z has a baseline similarity score of 0.498746, while Eminem has a score of 0.337271. Therefore, Jay Z has a higher baseline similarity score than Eminem. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2141.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The results of the automated evaluation. The bold indicates the system with a lower similarity at the target rhyme density." ], "reasoning": "To verify this statement, compare the baseline similarity scores of Jay Z and Eminem. Jay Z has a baseline similarity score of 0.498746, while Eminem has a score of 0.337271. Therefore, Jay Z has a higher baseline similarity score than Eminem.", "statement": "Jay Z has a higher baseline similarity score than Eminem.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: The maximum lengths of generated verses and % of training completed on which the verse is generated'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Although most models can generate long verses, the correlation between the average verse length for an artist and the verse length achieved by the model is weak, indicating a need for improvement in modeling verse structure.One aspect of the generated verse we have not discussed so far is the structure of the generated verse. For example, the length of the generated verses should be evaluated, since the models we examined do generate line breaks and also decide when to end the verse. We note that although 10 of the 11 models are able to generate long verses (up to a full standard deviation above the average verse length for that author), it takes a substantial amount of time, and the correlation between the average verse length for a given an artist and the verse length achieved by the model is weak (0.258). This suggests that modeling the specific verse structure, including length, is one aspect that requires improvement." } ], "images": [ "scigen/train/2142.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The maximum lengths of generated verses and % of training completed on which the verse is generated" ], "reasoning": "Although most models can generate long verses, the correlation between the average verse length for an artist and the verse length achieved by the model is weak, indicating a need for improvement in modeling verse structure.", "description": "One aspect of the generated verse we have not discussed so far is the structure of the generated verse. For example, the length of the generated verses should be evaluated, since the models we examined do generate line breaks and also decide when to end the verse. We note that although 10 of the 11 models are able to generate long verses (up to a full standard deviation above the average verse length for that author), it takes a substantial amount of time, and the correlation between the average verse length for a given an artist and the verse length achieved by the model is weak (0.258). This suggests that modeling the specific verse structure, including length, is one aspect that requires improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 5: The maximum lengths of generated verses and % of training completed on which the verse is generated' to sequentially solve the question presented below. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich artist has the highest percentage of training completed and what is their maximum verse length?\n" }, { "role": "assistant", "content": "To find the artist with the highest percentage of training completed, look at the '% of training completed' column and identify the highest value. Then, find the corresponding artist and their maximum verse length. So, the answer is {\"answer\": \"Jay Z with a maximum verse length of 449\"}." } ], "images": [ "scigen/train/2142.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The maximum lengths of generated verses and % of training completed on which the verse is generated" ], "reasoning": "To find the artist with the highest percentage of training completed, look at the '% of training completed' column and identify the highest value. Then, find the corresponding artist and their maximum verse length.", "question": "Which artist has the highest percentage of training completed and what is their maximum verse length?", "answer": "So, the answer is {\"answer\": \"Jay Z with a maximum verse length of 449\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe artist with the lowest maximum verse length has completed less than 50% of their training.\n" }, { "role": "assistant", "content": "Identify the artist with the lowest maximum verse length from the 'Max Len' column. Then, check the '% of training completed' for that artist to see if it is less than 50%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2142.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The maximum lengths of generated verses and % of training completed on which the verse is generated" ], "reasoning": "Identify the artist with the lowest maximum verse length from the 'Max Len' column. Then, check the '% of training completed' for that artist to see if it is less than 50%.", "statement": "The artist with the lowest maximum verse length has completed less than 50% of their training.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: EDS parsing test set results.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "EDS parsing is simpler than DMRS due to fewer argument labels and graph edges, resulting in similar accuracies for ACE but higher accuracy for the model on EDM metrics.The EDS parsing task is slightly simpler than DMRS, due to the absence of rich argument labels and additional graph edges that allow the recovery of full MRS. We see that for ACE the accuracies are very similar, while for our model EDS parsing is more accurate on the EDM metrics. We hypothesize that most of the extra information in DMRS can be obtained through the ERG, to which ACE has access but our model doesn’t." } ], "images": [ "scigen/train/2143.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: EDS parsing test set results." ], "reasoning": "EDS parsing is simpler than DMRS due to fewer argument labels and graph edges, resulting in similar accuracies for ACE but higher accuracy for the model on EDM metrics.", "description": "The EDS parsing task is slightly simpler than DMRS, due to the absence of rich argument labels and additional graph edges that allow the recovery of full MRS. We see that for ACE the accuracies are very similar, while for our model EDS parsing is more accurate on the EDM metrics. We hypothesize that most of the extra information in DMRS can be obtained through the ERG, to which ACE has access but our model doesn’t.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: EDS parsing test set results.\nQuestion: Which model has the highest accuracy for the ACE parser on the Smatch metric?\nGive a detailed response to the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest accuracy for the ACE parser on the Smatch metric, look at the Smatch row under the ACE column. So, the answer is {\"answer\": \"93.52\"}" } ], "images": [ "scigen/train/2143.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: EDS parsing test set results." ], "reasoning": "To find the highest accuracy for the ACE parser on the Smatch metric, look at the Smatch row under the ACE column.", "question": "Which model has the highest accuracy for the ACE parser on the Smatch metric?", "answer": "So, the answer is {\"answer\": \"93.52\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe AE RNN model has higher accuracy than the ACE model on the EDM P metric.\n" }, { "role": "assistant", "content": "The table shows that the AE RNN model has an accuracy of 88.14 on the EDM P metric, while the ACE model has an accuracy of 91.82 on the same metric, indicating that the statement is incorrect. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2143.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: EDS parsing test set results." ], "reasoning": "The table shows that the AE RNN model has an accuracy of 88.14 on the EDM P metric, while the ACE model has an accuracy of 91.82 on the same metric, indicating that the statement is incorrect.", "statement": "The AE RNN model has higher accuracy than the ACE model on the EDM P metric.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1: DMRS development set results for attention-based encoder-decoder models with alignments encoded in the linearization, for top-down (TD) and arc-eager (AE) linearizations, and lexicalized and unlexicalized predicate prediction.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table compares the performance of top-down and arc-eager linearizations, with and without delexicalizing predicates, showing that delexicalizing generally improves performance across all metrics.First we report results using standard attention-based encoder-decoders, with the alignments encoded as token strings in the linearization. We compare the top-down (TD) and arc-eager (AE) linearizations, as well as the effect of delexicalizing the predicates (factorizing lemmas out of the linearization and predicting them separately.) In both cases constants are predicted with a dictionary lookup based on the predicted spans. A special label is predicted for predicates not in the ERG lexicon – the words and POS tags that make up those predicates are recovered through the alignments during post-processing." } ], "images": [ "scigen/train/2144.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: DMRS development set results for attention-based encoder-decoder models with alignments encoded in the linearization, for top-down (TD) and arc-eager (AE) linearizations, and lexicalized and unlexicalized predicate prediction." ], "reasoning": "The table compares the performance of top-down and arc-eager linearizations, with and without delexicalizing predicates, showing that delexicalizing generally improves performance across all metrics.", "description": "First we report results using standard attention-based encoder-decoders, with the alignments encoded as token strings in the linearization. We compare the top-down (TD) and arc-eager (AE) linearizations, as well as the effect of delexicalizing the predicates (factorizing lemmas out of the linearization and predicting them separately.) In both cases constants are predicted with a dictionary lookup based on the predicted spans. A special label is predicted for predicates not in the ERG lexicon – the words and POS tags that make up those predicates are recovered through the alignments during post-processing.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: DMRS development set results for attention-based encoder-decoder models with alignments encoded in the linearization, for top-down (TD) and arc-eager (AE) linearizations, and lexicalized and unlexicalized predicate prediction.', answer the following question in a stepwise manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model and configuration achieved the highest EDM score?\n" }, { "role": "assistant", "content": "To find the highest EDM score, we compare the EDM values across all models and configurations. The highest EDM score is 82.56, achieved by the AE unlex model. So, the answer is {\"answer\": \"AE unlex\"}." } ], "images": [ "scigen/train/2144.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: DMRS development set results for attention-based encoder-decoder models with alignments encoded in the linearization, for top-down (TD) and arc-eager (AE) linearizations, and lexicalized and unlexicalized predicate prediction." ], "reasoning": "To find the highest EDM score, we compare the EDM values across all models and configurations. The highest EDM score is 82.56, achieved by the AE unlex model.", "question": "Which model and configuration achieved the highest EDM score?", "answer": "So, the answer is {\"answer\": \"AE unlex\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nDelexicalizing predicates improves the EDM P score for both top-down and arc-eager linearizations.\n" }, { "role": "assistant", "content": "By comparing the EDM P scores for lex and unlex configurations in both TD and AE models, we see that delexicalizing (unlex) improves the EDM P score in both cases: TD lex (85.20) to TD unlex (85.59) and AE lex (85.79) to AE unlex (86.76). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2144.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: DMRS development set results for attention-based encoder-decoder models with alignments encoded in the linearization, for top-down (TD) and arc-eager (AE) linearizations, and lexicalized and unlexicalized predicate prediction." ], "reasoning": "By comparing the EDM P scores for lex and unlex configurations in both TD and AE models, we see that delexicalizing (unlex) improves the EDM P score in both cases: TD lex (85.20) to TD unlex (85.59) and AE lex (85.79) to AE unlex (86.76).", "statement": "Delexicalizing predicates improves the EDM P score for both top-down and arc-eager linearizations.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: DMRS development set results of encoder-decoder models with pointer-based alignment prediction, delexicalized predicates and hard or soft attention.'.\n" }, { "role": "assistant", "content": "The arc-eager models, especially the stack-based architecture, outperform the top-down models, with hard attention used for natural alignment prediction.The results show that the arc-eager models performs better than those based on top-down representation. For the arc-eager model we use hard attention, due to the natural interpretation of the alignment prediction corresponding to the transition system. The stack-based architecture gives further improvements." } ], "images": [ "scigen/train/2145.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: DMRS development set results of encoder-decoder models with pointer-based alignment prediction, delexicalized predicates and hard or soft attention." ], "reasoning": "The arc-eager models, especially the stack-based architecture, outperform the top-down models, with hard attention used for natural alignment prediction.", "description": "The results show that the arc-eager models performs better than those based on top-down representation. For the arc-eager model we use hard attention, due to the natural interpretation of the alignment prediction corresponding to the transition system. The stack-based architecture gives further improvements.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: DMRS development set results of encoder-decoder models with pointer-based alignment prediction, delexicalized predicates and hard or soft attention.', and tackle the question below in a sequential manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model has the highest EDM score?\n" }, { "role": "assistant", "content": "To find the model with the highest EDM score, compare the EDM scores of all models listed in the table. The AE stack model has the highest EDM score of 85.28. So, the answer is {\"answer\": \"AE stack\"}." } ], "images": [ "scigen/train/2145.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: DMRS development set results of encoder-decoder models with pointer-based alignment prediction, delexicalized predicates and hard or soft attention." ], "reasoning": "To find the model with the highest EDM score, compare the EDM scores of all models listed in the table. The AE stack model has the highest EDM score of 85.28.", "question": "Which model has the highest EDM score?", "answer": "So, the answer is {\"answer\": \"AE stack\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The AE hard model has a higher EDM P score than the TD hard model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe AE hard model has a higher EDM P score than the TD hard model.\n" }, { "role": "assistant", "content": "The table shows that the EDM P score for the AE hard model is 87.77, while the EDM P score for the TD hard model is 86.37. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2145.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: DMRS development set results of encoder-decoder models with pointer-based alignment prediction, delexicalized predicates and hard or soft attention." ], "reasoning": "The table shows that the EDM P score for the AE hard model is 87.77, while the EDM P score for the TD hard model is 86.37. Therefore, the statement is supported.", "statement": "The AE hard model has a higher EDM P score than the TD hard model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: DMRS parsing test set results, comparing the standard top-down attention-based and arc-eager stack-based RNN models to the grammar-based ACE parser.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Start EDM is calculated by matching only the start of the alignment spans, and its performance is compared against other models like ACE and the ERG-based parser.Start EDM is calculated by requiring only the start of the alignment spans to match, not the ends. We compare the performance of our baseline and stack-based models against ACE, the ERG-based parser." } ], "images": [ "scigen/train/2146.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: DMRS parsing test set results, comparing the standard top-down attention-based and arc-eager stack-based RNN models to the grammar-based ACE parser." ], "reasoning": "Start EDM is calculated by matching only the start of the alignment spans, and its performance is compared against other models like ACE and the ERG-based parser.", "description": "Start EDM is calculated by requiring only the start of the alignment spans to match, not the ends. We compare the performance of our baseline and stack-based models against ACE, the ERG-based parser.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: DMRS parsing test set results, comparing the standard top-down attention-based and arc-eager stack-based RNN models to the grammar-based ACE parser.', answer the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model has the highest performance in the ACE category?\n" }, { "role": "assistant", "content": "To find the model with the highest performance in the ACE category, we need to compare the ACE scores of all models listed in the table. The model with the highest ACE score is Smatch with a score of 93.50. So, the answer is {\"answer\": \"Smatch\"}." } ], "images": [ "scigen/train/2146.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: DMRS parsing test set results, comparing the standard top-down attention-based and arc-eager stack-based RNN models to the grammar-based ACE parser." ], "reasoning": "To find the model with the highest performance in the ACE category, we need to compare the ACE scores of all models listed in the table. The model with the highest ACE score is Smatch with a score of 93.50.", "question": "Which model has the highest performance in the ACE category?", "answer": "So, the answer is {\"answer\": \"Smatch\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nStart EDM has a higher AE RNN score than EDM P.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the AE RNN scores of Start EDM and EDM P. Start EDM has an AE RNN score of 87.81, while EDM P has a score of 87.54. Since 87.81 is greater than 87.54, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2146.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: DMRS parsing test set results, comparing the standard top-down attention-based and arc-eager stack-based RNN models to the grammar-based ACE parser." ], "reasoning": "To verify this statement, we compare the AE RNN scores of Start EDM and EDM P. Start EDM has an AE RNN score of 87.81, while EDM P has a score of 87.54. Since 87.81 is greater than 87.54, the statement is supported.", "statement": "Start EDM has a higher AE RNN score than EDM P.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Development set results for AMR parsing. All the models except the first predict alignments with pointer networks.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Arc-eager-based models outperform others in AMR parsing due to better concept prediction accuracy, but concept prediction remains a significant weakness.We apply the same approach to AMR parsing. The arc-eager-based models again give better performance, mainly due to improved concept prediction accuracy. However, concept prediction remains the most important weakness of the model; \\newciteDamonteCS16 reports that state-of-the-art AMR parsers score 83% on concept prediction." } ], "images": [ "scigen/train/2147.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Development set results for AMR parsing. All the models except the first predict alignments with pointer networks." ], "reasoning": "Arc-eager-based models outperform others in AMR parsing due to better concept prediction accuracy, but concept prediction remains a significant weakness.", "description": "We apply the same approach to AMR parsing. The arc-eager-based models again give better performance, mainly due to improved concept prediction accuracy. However, concept prediction remains the most important weakness of the model; \\newciteDamonteCS16 reports that state-of-the-art AMR parsers score 83% on concept prediction.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model has the highest Concept F1 score and what is its Smatch score?\nSolve the above question based on the table titled 'Table 5: Development set results for AMR parsing. All the models except the first predict alignments with pointer networks.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The model with the highest Concept F1 score is AE stack unlex with a score of 77.93. Its corresponding Smatch score is 61.21. So, the answer is {\"answer\": \"61.21\"}." } ], "images": [ "scigen/train/2147.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Development set results for AMR parsing. All the models except the first predict alignments with pointer networks." ], "reasoning": "The model with the highest Concept F1 score is AE stack unlex with a score of 77.93. Its corresponding Smatch score is 61.21.", "question": "Which model has the highest Concept F1 score and what is its Smatch score?", "answer": "So, the answer is {\"answer\": \"61.21\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe AE stack unlex model has the highest Smatch score among all models.\n" }, { "role": "assistant", "content": "The table shows that the AE stack unlex model has a Smatch score of 61.21, which is the highest among all listed models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2147.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Development set results for AMR parsing. All the models except the first predict alignments with pointer networks." ], "reasoning": "The table shows that the AE stack unlex model has a Smatch score of 61.21, which is the highest among all listed models.", "statement": "The AE stack unlex model has the highest Smatch score among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 6: AMR parsing test set results (Smatch F1 scores). Published results follow the number of decimals which were reported.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our best neural model surpasses the JAMR parser, AMR Eager, and Synchronous Hyperedge Replacement Grammar models, which use external resources.Our best neural model outperforms the baseline JAMR parser Flanigan et al. AMR Eager Damonte et al. These models make extensive use of external resources, including syntactic parsers and semantic role labellers. Our model also outperforms a Synchronous Hyperedge Replacement Grammar model Peng and Gildea" } ], "images": [ "scigen/train/2148.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: AMR parsing test set results (Smatch F1 scores). Published results follow the number of decimals which were reported." ], "reasoning": "Our best neural model surpasses the JAMR parser, AMR Eager, and Synchronous Hyperedge Replacement Grammar models, which use external resources.", "description": "Our best neural model outperforms the baseline JAMR parser Flanigan et al. AMR Eager Damonte et al. These models make extensive use of external resources, including syntactic parsers and semantic role labellers. Our model also outperforms a Synchronous Hyperedge Replacement Grammar model Peng and Gildea", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 6: AMR parsing test set results (Smatch F1 scores). Published results follow the number of decimals which were reported.', solve the subsequent question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich model has the highest Smatch score?\n" }, { "role": "assistant", "content": "To find the model with the highest Smatch score, compare the Smatch scores of all models listed in the table. The model with the highest score is WangEa16 with a score of 66.54. So, the answer is {\"answer\": \"WangEa16\"}." } ], "images": [ "scigen/train/2148.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: AMR parsing test set results (Smatch F1 scores). Published results follow the number of decimals which were reported." ], "reasoning": "To find the model with the highest Smatch score, compare the Smatch scores of all models listed in the table. The model with the highest score is WangEa16 with a score of 66.54.", "question": "Which model has the highest Smatch score?", "answer": "So, the answer is {\"answer\": \"WangEa16\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe PengG16 model has a higher Smatch score than the FlaniganTCDS14 model.\n" }, { "role": "assistant", "content": "The table shows that the PengG16 model has a Smatch score of 55, while the FlaniganTCDS14 model has a Smatch score of 56. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2148.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: AMR parsing test set results (Smatch F1 scores). Published results follow the number of decimals which were reported." ], "reasoning": "The table shows that the PengG16 model has a Smatch score of 55, while the FlaniganTCDS14 model has a Smatch score of 56. Therefore, the statement is incorrect.", "statement": "The PengG16 model has a higher Smatch score than the FlaniganTCDS14 model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: BLEU scores of DeepAPI and related techniques (%)', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "DeepAPI achieves the highest BLEU scores across all top 1, top 5, and top 10 results, demonstrating its superior accuracy compared to SWIM and Lucene+UP-Miner.Results: Each column shows the average BLEU score for a method. As the results indicate, DeepAPI produces API sequences with higher accuracy. When only the top 1 result is examined, the BLEU score achieved by DeepAPI is 54.42, which is greater than that of SWIM (BLEU=19.90) and Code Search (BLEU=11.97). The improvement over SWIM is 173% and the improvement over Code Search is 355%. Similar results are obtained when the top 5 and 10 results are examined. The evaluation results confirm the effectiveness of the deep learning method used by DeepAPI." } ], "images": [ "scigen/train/2149.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: BLEU scores of DeepAPI and related techniques (%)" ], "reasoning": "DeepAPI achieves the highest BLEU scores across all top 1, top 5, and top 10 results, demonstrating its superior accuracy compared to SWIM and Lucene+UP-Miner.", "description": "Results: Each column shows the average BLEU score for a method. As the results indicate, DeepAPI produces API sequences with higher accuracy. When only the top 1 result is examined, the BLEU score achieved by DeepAPI is 54.42, which is greater than that of SWIM (BLEU=19.90) and Code Search (BLEU=11.97). The improvement over SWIM is 173% and the improvement over Code Search is 355%. Similar results are obtained when the top 5 and 10 results are examined. The evaluation results confirm the effectiveness of the deep learning method used by DeepAPI.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: BLEU scores of DeepAPI and related techniques (%)'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the BLEU score improvement percentage of DeepAPI over SWIM for the top 1 result?\n" }, { "role": "assistant", "content": "The BLEU score for DeepAPI in the top 1 result is 54.42, and for SWIM, it is 19.90. The improvement percentage is calculated as ((54.42 - 19.90) / 19.90) * 100. So, the answer is {\"answer\": \"173%\"}." } ], "images": [ "scigen/train/2149.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: BLEU scores of DeepAPI and related techniques (%)" ], "reasoning": "The BLEU score for DeepAPI in the top 1 result is 54.42, and for SWIM, it is 19.90. The improvement percentage is calculated as ((54.42 - 19.90) / 19.90) * 100.", "question": "What is the BLEU score improvement percentage of DeepAPI over SWIM for the top 1 result?", "answer": "So, the answer is {\"answer\": \"173%\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nDeepAPI achieves a BLEU score of 67.83 for the top 10 results.\n" }, { "role": "assistant", "content": "The table shows that DeepAPI has a BLEU score of 67.83 for the top 10 results, confirming the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2149.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: BLEU scores of DeepAPI and related techniques (%)" ], "reasoning": "The table shows that DeepAPI has a BLEU score of 67.83 for the top 10 results, confirming the statement.", "statement": "DeepAPI achieves a BLEU score of 67.83 for the top 10 results.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: BLEU scores of different RNN Encoder-Decoder Models (%)'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The attention-based RNN Encoder-Decoder and the proposed cost function enhance the model's performance, showing improvements in BLEU scores for top 1, 5, and 10 results.The attention-based RNN Encoder-Decoder outperforms the basic RNN Encoder-Decoder model on API learning. The relative improvement in the top 1, 5, and 10 results (in terms of BLEU score) is 8%, 5% and 4%, respectively. This result confirms the effectiveness of the attention-based RNN Encoder-Decoder used in our approach. The improvement in the top 1, 5, and 10 results (in terms of BLEU score) is 4%, 2% and 1%, respectively. The results confirm the usefulness of the proposed cost function for enhancing the RNN Encoder-Decoder model." } ], "images": [ "scigen/train/2150.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: BLEU scores of different RNN Encoder-Decoder Models (%)" ], "reasoning": "The attention-based RNN Encoder-Decoder and the proposed cost function enhance the model's performance, showing improvements in BLEU scores for top 1, 5, and 10 results.", "description": "The attention-based RNN Encoder-Decoder outperforms the basic RNN Encoder-Decoder model on API learning. The relative improvement in the top 1, 5, and 10 results (in terms of BLEU score) is 8%, 5% and 4%, respectively. This result confirms the effectiveness of the attention-based RNN Encoder-Decoder used in our approach. The improvement in the top 1, 5, and 10 results (in terms of BLEU score) is 4%, 2% and 1%, respectively. The results confirm the usefulness of the proposed cost function for enhancing the RNN Encoder-Decoder model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: BLEU scores of different RNN Encoder-Decoder Models (%)', unfold the question presented below, detailing every step of your calculation. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the BLEU score improvement for the top 1 result when using the RNN+Attention+New Cost Function model compared to the basic RNN model?\n" }, { "role": "assistant", "content": "The BLEU score for the top 1 result using the basic RNN model is 48.83, and using the RNN+Attention+New Cost Function model is 54.42. The improvement is calculated as 54.42 - 48.83. So, the answer is {\"answer\": \"5.59\"}" } ], "images": [ "scigen/train/2150.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: BLEU scores of different RNN Encoder-Decoder Models (%)" ], "reasoning": "The BLEU score for the top 1 result using the basic RNN model is 48.83, and using the RNN+Attention+New Cost Function model is 54.42. The improvement is calculated as 54.42 - 48.83.", "question": "What is the BLEU score improvement for the top 1 result when using the RNN+Attention+New Cost Function model compared to the basic RNN model?", "answer": "So, the answer is {\"answer\": \"5.59\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe RNN+Attention model has a higher BLEU score for the top 5 results compared to the RNN+Attention+New Cost Function model.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for the top 5 results is 63.81 for the RNN+Attention model and 64.89 for the RNN+Attention+New Cost Function model, indicating that the latter has a higher score. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2150.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: BLEU scores of different RNN Encoder-Decoder Models (%)" ], "reasoning": "The table shows that the BLEU score for the top 5 results is 63.81 for the RNN+Attention model and 64.89 for the RNN+Attention+New Cost Function model, indicating that the latter has a higher score.", "statement": "The RNN+Attention model has a higher BLEU score for the top 5 results compared to the RNN+Attention+New Cost Function model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Ablation study for feature selection on IWSLT’14 En→De dataset.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table compares BLEU, AP, and AL scores for wait-3 and wait-5 strategies using full action space, future information only, and past information only on IWSLT'14 En→De.(II) Action space selection: In previous experiments, both future information and past information are leveraged. That is, given a strategy wait-k, the controller φ can sample a strategy wait-k′ where k′≥k or k′≤k. We want to study the effect of using past information or future information only. For any wait-k, we build another two action spaces for φ: Kp(k)={1,2,⋯,k}; Kf(k)={k,k+1,⋯,K}. We evaluate wait-3,5 on IWSLT’14 En→De with the above two action spaces." } ], "images": [ "scigen/train/2151.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ablation study for feature selection on IWSLT’14 En→De dataset." ], "reasoning": "The table compares BLEU, AP, and AL scores for wait-3 and wait-5 strategies using full action space, future information only, and past information only on IWSLT'14 En→De.", "description": "(II) Action space selection: In previous experiments, both future information and past information are leveraged. That is, given a strategy wait-k, the controller φ can sample a strategy wait-k′ where k′≥k or k′≤k. We want to study the effect of using past information or future information only. For any wait-k, we build another two action spaces for φ: Kp(k)={1,2,⋯,k}; Kf(k)={k,k+1,⋯,K}. We evaluate wait-3,5 on IWSLT’14 En→De with the above two action spaces.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Ablation study for feature selection on IWSLT’14 En→De dataset.\nQuestion: What is the BLEU score for wait-3 using past information only?\nGive a detailed response to the above question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the BLEU score for wait-3 using past information only, look at the row labeled 'Kp(k)' and the column labeled 'k=3 BLEU'. The value is 22.80. So, the answer is {\"answer\": \"22.80\"}." } ], "images": [ "scigen/train/2151.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ablation study for feature selection on IWSLT’14 En→De dataset." ], "reasoning": "To find the BLEU score for wait-3 using past information only, look at the row labeled 'Kp(k)' and the column labeled 'k=3 BLEU'. The value is 22.80.", "question": "What is the BLEU score for wait-3 using past information only?", "answer": "So, the answer is {\"answer\": \"22.80\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe AP score for wait-5 using future information only is higher than using the full action space.\n" }, { "role": "assistant", "content": "To verify this statement, compare the AP scores for wait-5 using future information only and the full action space. The AP score for wait-5 using future information only (Kf(k)) is 0.730, while the AP score for the full action space is 0.723. Since 0.730 is higher than 0.723, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2151.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ablation study for feature selection on IWSLT’14 En→De dataset." ], "reasoning": "To verify this statement, compare the AP scores for wait-5 using future information only and the full action space. The AP score for wait-5 using future information only (Kf(k)) is 0.730, while the AP score for the full action space is 0.723. Since 0.730 is higher than 0.723, the statement is supported.", "statement": "The AP score for wait-5 using future information only is higher than using the full action space.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: The performance of the proposed MDVC framework with different input modalities (V-visual, A-audio, S-speech) and feature fusion approaches: probability averaging and concatenation of two fully-connected layers (Concat. + 2 FC). Also, we report the comparison between audio-visual MDVC with visual-only MDVC with similar model capacities (2 FC).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The final model with all three modalities and concatenation with two fully connected layers performs best, highlighting the importance of a multi-modal setting in dense video captioning.Secondly, we evaluate the contribution of different modalities in our framework. Tab. Specifically, averaging of the output probabilities and concatenation of the outputs of all modalities and applying two fully connected (FC) layers on top. We observe that audio-only model has the worst performance, followed by the visual only model, and the combination of these two. Moreover, the concatenation and FC layers result in better performance than averaging. To further assess if the performance gain is due to the additional modalities or to the extra capacity in the FC layers, we trained a visual-only model with two additional FC layers. The results indicate that such configuration performs worse than any bi-modal setup. Overall, we conclude that the final model with all three modalities performs best among all tested set-ups, which highlights the importance of multi-modal setting in dense video captioning task." } ], "images": [ "scigen/train/2152.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The performance of the proposed MDVC framework with different input modalities (V-visual, A-audio, S-speech) and feature fusion approaches: probability averaging and concatenation of two fully-connected layers (Concat. + 2 FC). Also, we report the comparison between audio-visual MDVC with visual-only MDVC with similar model capacities (2 FC)." ], "reasoning": "The final model with all three modalities and concatenation with two fully connected layers performs best, highlighting the importance of a multi-modal setting in dense video captioning.", "description": "Secondly, we evaluate the contribution of different modalities in our framework. Tab. Specifically, averaging of the output probabilities and concatenation of the outputs of all modalities and applying two fully connected (FC) layers on top. We observe that audio-only model has the worst performance, followed by the visual only model, and the combination of these two. Moreover, the concatenation and FC layers result in better performance than averaging. To further assess if the performance gain is due to the additional modalities or to the extra capacity in the FC layers, we trained a visual-only model with two additional FC layers. The results indicate that such configuration performs worse than any bi-modal setup. Overall, we conclude that the final model with all three modalities performs best among all tested set-ups, which highlights the importance of multi-modal setting in dense video captioning task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nTable title: Table 3: The performance of the proposed MDVC framework with different input modalities (V-visual, A-audio, S-speech) and feature fusion approaches: probability averaging and concatenation of two fully-connected layers (Concat. + 2 FC). Also, we report the comparison between audio-visual MDVC with visual-only MDVC with similar model capacities (2 FC).\n\nQuestion: Which model configuration achieved the highest Metric B@4 score?\n" }, { "role": "assistant", "content": "By examining the table, the model with all three modalities (V, A, S) and concatenation with two fully connected layers achieved the highest Metric B@4 score of 1.81. So, the answer is {\"answer\": \"1.81\"}." } ], "images": [ "scigen/train/2152.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The performance of the proposed MDVC framework with different input modalities (V-visual, A-audio, S-speech) and feature fusion approaches: probability averaging and concatenation of two fully-connected layers (Concat. + 2 FC). Also, we report the comparison between audio-visual MDVC with visual-only MDVC with similar model capacities (2 FC)." ], "reasoning": "By examining the table, the model with all three modalities (V, A, S) and concatenation with two fully connected layers achieved the highest Metric B@4 score of 1.81.", "question": "Which model configuration achieved the highest Metric B@4 score?", "answer": "So, the answer is {\"answer\": \"1.81\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe audio-only model has the worst performance in terms of Metric M.\n" }, { "role": "assistant", "content": "The table shows that the audio-only model has a Metric M score of 8.01, which is the lowest among all configurations. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2152.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The performance of the proposed MDVC framework with different input modalities (V-visual, A-audio, S-speech) and feature fusion approaches: probability averaging and concatenation of two fully-connected layers (Concat. + 2 FC). Also, we report the comparison between audio-visual MDVC with visual-only MDVC with similar model capacities (2 FC)." ], "reasoning": "The table shows that the audio-only model has a Metric M score of 8.01, which is the lowest among all configurations.", "statement": "The audio-only model has the worst performance in terms of Metric M.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: The results of the dense video captioning task on the ActivityNet Captions validation sets in terms of BLEU–3,4 (B@3, B@4) and METEOR (M). The related methods are compared with the proposed approach (MDVC) in two settings: on the full validation dataset and a part of it with the videos with all modalities present for a fair comparison (“no missings”). Methods are additionally split into the ones which “saw” all training videos and another ones which trained on partially available data. The results are presented for both ground truth (GT) and learned proposals.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "MDVC achieves comparable or better performance despite a smaller training set and missing validation videos, and outperforms all baselines when all modalities are present.The obtained results are presented in Tab. Our method (MDVC) achieves comparable or better performance, even though we have access to smaller training set and 9 % of the validation videos are missing (replaced with random input features). Furthermore, if all three modalities are present, our method outperforms all baseline approaches in the case of both GT and learned proposals. This shows the superior performance of our captioning module which, yet, trained on the smaller amount of data." } ], "images": [ "scigen/train/2153.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The results of the dense video captioning task on the ActivityNet Captions validation sets in terms of BLEU–3,4 (B@3, B@4) and METEOR (M). The related methods are compared with the proposed approach (MDVC) in two settings: on the full validation dataset and a part of it with the videos with all modalities present for a fair comparison (“no missings”). Methods are additionally split into the ones which “saw” all training videos and another ones which trained on partially available data. The results are presented for both ground truth (GT) and learned proposals." ], "reasoning": "MDVC achieves comparable or better performance despite a smaller training set and missing validation videos, and outperforms all baselines when all modalities are present.", "description": "The obtained results are presented in Tab. Our method (MDVC) achieves comparable or better performance, even though we have access to smaller training set and 9 % of the validation videos are missing (replaced with random input features). Furthermore, if all three modalities are present, our method outperforms all baseline approaches in the case of both GT and learned proposals. This shows the superior performance of our captioning module which, yet, trained on the smaller amount of data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: 'Table 1: The results of the dense video captioning task on the ActivityNet Captions validation sets in terms of BLEU–3,4 (B@3, B@4) and METEOR (M). The related methods are compared with the proposed approach (MDVC) in two settings: on the full validation dataset and a part of it with the videos with all modalities present for a fair comparison (“no missings”). Methods are additionally split into the ones which “saw” all training videos and another ones which trained on partially available data. The results are presented for both ground truth (GT) and learned proposals.'\nQuestion: What is the GT Proposals M score for MDVC with no missings?\n" }, { "role": "assistant", "content": "The table shows the GT Proposals M score for MDVC with no missings as 11.72, which is higher than the other methods listed. So, the answer is {\"answer\": \"11.72\"}." } ], "images": [ "scigen/train/2153.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The results of the dense video captioning task on the ActivityNet Captions validation sets in terms of BLEU–3,4 (B@3, B@4) and METEOR (M). The related methods are compared with the proposed approach (MDVC) in two settings: on the full validation dataset and a part of it with the videos with all modalities present for a fair comparison (“no missings”). Methods are additionally split into the ones which “saw” all training videos and another ones which trained on partially available data. The results are presented for both ground truth (GT) and learned proposals." ], "reasoning": "The table shows the GT Proposals M score for MDVC with no missings as 11.72, which is higher than the other methods listed.", "question": "What is the GT Proposals M score for MDVC with no missings?", "answer": "So, the answer is {\"answer\": \"11.72\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nMDVC with no missings has the highest GT Proposals B@3 score.\n" }, { "role": "assistant", "content": "The table shows that MDVC with no missings has a GT Proposals B@3 score of 5.83, which is higher than the scores of the other methods listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2153.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The results of the dense video captioning task on the ActivityNet Captions validation sets in terms of BLEU–3,4 (B@3, B@4) and METEOR (M). The related methods are compared with the proposed approach (MDVC) in two settings: on the full validation dataset and a part of it with the videos with all modalities present for a fair comparison (“no missings”). Methods are additionally split into the ones which “saw” all training videos and another ones which trained on partially available data. The results are presented for both ground truth (GT) and learned proposals." ], "reasoning": "The table shows that MDVC with no missings has a GT Proposals B@3 score of 5.83, which is higher than the scores of the other methods listed.", "statement": "MDVC with no missings has the highest GT Proposals B@3 score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Comparison of the Feature Transformer and the Bi-directional GRU (Bi-GRU) architectures in terms of BLEU-4 (B@4), METEOR (M), and a number of model parameters. The input to all models is visual modality (I3D). The results indicate the superior performance of the Feature Transformer on all metrics. Additionally, we report the random input baseline which acts as a lower performance bound. The best results are highlighted.'.\n" }, { "role": "assistant", "content": "The feature transformer-based model uses fewer parameters and achieves better performance in dense video captioning compared to Bi-GRU and random baseline models.Firstly, we assess the selection of the model architecture. To this end, we implemented a version of our method where the transformer was replaced by Bidirectional Recurrent Neural Network with Gated Recurrent Units with attention To distil the effect of the change in architecture, the results are shown for visual-only models. Both Bi-GRU and the transformer input I3D features extracted from 64 RGB and optical flow frames (the final model inputs 24 frames). Finally, we set a lower bound for the feature performance by training a transformer model with random video features. Tab. To conclude, we observe that the feature transformer-based model is not only uses less parameters but also achieves better performance in dense video captioning task. Moreover, both method clearly surpasses the random baseline." } ], "images": [ "scigen/train/2154.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of the Feature Transformer and the Bi-directional GRU (Bi-GRU) architectures in terms of BLEU-4 (B@4), METEOR (M), and a number of model parameters. The input to all models is visual modality (I3D). The results indicate the superior performance of the Feature Transformer on all metrics. Additionally, we report the random input baseline which acts as a lower performance bound. The best results are highlighted." ], "reasoning": "The feature transformer-based model uses fewer parameters and achieves better performance in dense video captioning compared to Bi-GRU and random baseline models.", "description": "Firstly, we assess the selection of the model architecture. To this end, we implemented a version of our method where the transformer was replaced by Bidirectional Recurrent Neural Network with Gated Recurrent Units with attention To distil the effect of the change in architecture, the results are shown for visual-only models. Both Bi-GRU and the transformer input I3D features extracted from 64 RGB and optical flow frames (the final model inputs 24 frames). Finally, we set a lower bound for the feature performance by training a transformer model with random video features. Tab. To conclude, we observe that the feature transformer-based model is not only uses less parameters but also achieves better performance in dense video captioning task. Moreover, both method clearly surpasses the random baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Comparison of the Feature Transformer and the Bi-directional GRU (Bi-GRU) architectures in terms of BLEU-4 (B@4), METEOR (M), and a number of model parameters. The input to all models is visual modality (I3D). The results indicate the superior performance of the Feature Transformer on all metrics. Additionally, we report the random input baseline which acts as a lower performance bound. The best results are highlighted.', answer the question below by showing each progressive step toward the answer. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model achieves the highest Metric B@4 score and how many parameters does it use?\n" }, { "role": "assistant", "content": "The table shows that the Feature Transformer model achieves the highest Metric B@4 score of 1.84 and uses 42 million parameters. So, the answer is {\"answer\": \"Feature Transformer\"}" } ], "images": [ "scigen/train/2154.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of the Feature Transformer and the Bi-directional GRU (Bi-GRU) architectures in terms of BLEU-4 (B@4), METEOR (M), and a number of model parameters. The input to all models is visual modality (I3D). The results indicate the superior performance of the Feature Transformer on all metrics. Additionally, we report the random input baseline which acts as a lower performance bound. The best results are highlighted." ], "reasoning": "The table shows that the Feature Transformer model achieves the highest Metric B@4 score of 1.84 and uses 42 million parameters.", "question": "Which model achieves the highest Metric B@4 score and how many parameters does it use?", "answer": "So, the answer is {\"answer\": \"Feature Transformer\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Bi-GRU model uses fewer parameters than the Feature Transformer model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Bi-GRU model uses fewer parameters than the Feature Transformer model.\n" }, { "role": "assistant", "content": "The table shows that the Bi-GRU model uses 55 million parameters, while the Feature Transformer model uses 42 million parameters, indicating that the statement is false. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2154.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of the Feature Transformer and the Bi-directional GRU (Bi-GRU) architectures in terms of BLEU-4 (B@4), METEOR (M), and a number of model parameters. The input to all models is visual modality (I3D). The results indicate the superior performance of the Feature Transformer on all metrics. Additionally, we report the random input baseline which acts as a lower performance bound. The best results are highlighted." ], "reasoning": "The table shows that the Bi-GRU model uses 55 million parameters, while the Feature Transformer model uses 42 million parameters, indicating that the statement is false.", "statement": "The Bi-GRU model uses fewer parameters than the Feature Transformer model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Final untranslated term count and number of I-tags after filtering based off of the relevancy constraint (§2). That is, only the raw untranslated source terms that appear in the translator’s transcript are truly considered untranslated.'.\n" }, { "role": "assistant", "content": "The number of untranslated terms is highest in B-rank and lowest in S-rank, with capitalized terms constituting about 14% of B-rank, 13% of A-rank, and 15% of S-rank terms.Since the number of per-word I-tags is only slightly higher than the number of untranslated terms, most such terms consist of only a single word of about 6.5 average characters for all ranks. Capitalized terms (i.e., named entities/locations) constitute about 14% of B-rank, 13% of A-rank, and 15% of S-rank terms. Numbers represent about 5% of untranslated terms for each rank." } ], "images": [ "scigen/train/2155.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Final untranslated term count and number of I-tags after filtering based off of the relevancy constraint (§2). That is, only the raw untranslated source terms that appear in the translator’s transcript are truly considered untranslated." ], "reasoning": "The number of untranslated terms is highest in B-rank and lowest in S-rank, with capitalized terms constituting about 14% of B-rank, 13% of A-rank, and 15% of S-rank terms.", "description": "Since the number of per-word I-tags is only slightly higher than the number of untranslated terms, most such terms consist of only a single word of about 6.5 average characters for all ranks. Capitalized terms (i.e., named entities/locations) constitute about 14% of B-rank, 13% of A-rank, and 15% of S-rank terms. Numbers represent about 5% of untranslated terms for each rank.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: Final untranslated term count and number of I-tags after filtering based off of the relevancy constraint (§2). That is, only the raw untranslated source terms that appear in the translator’s transcript are truly considered untranslated.', solve this question and give detail solutions. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the percentage of I-tags of nouns/numbers in A-rank?\n" }, { "role": "assistant", "content": "The table shows the percentage of I-tags of nouns/numbers for each rank. For A-rank, this percentage is 43.6%. So, the answer is {\"answer\": \"43.6%\"}" } ], "images": [ "scigen/train/2155.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Final untranslated term count and number of I-tags after filtering based off of the relevancy constraint (§2). That is, only the raw untranslated source terms that appear in the translator’s transcript are truly considered untranslated." ], "reasoning": "The table shows the percentage of I-tags of nouns/numbers for each rank. For A-rank, this percentage is 43.6%.", "question": "What is the percentage of I-tags of nouns/numbers in A-rank?", "answer": "So, the answer is {\"answer\": \"43.6%\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The percentage of I-tags of all terms is highest in B-rank., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe percentage of I-tags of all terms is highest in B-rank.\n" }, { "role": "assistant", "content": "The table shows the percentage of I-tags of all terms for each rank. B-rank has 10.8%, A-rank has 10.4%, and S-rank has 7.0%. Therefore, B-rank has the highest percentage. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2155.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Final untranslated term count and number of I-tags after filtering based off of the relevancy constraint (§2). That is, only the raw untranslated source terms that appear in the translator’s transcript are truly considered untranslated." ], "reasoning": "The table shows the percentage of I-tags of all terms for each rank. B-rank has 10.8%, A-rank has 10.4%, and S-rank has 7.0%. Therefore, B-rank has the highest percentage.", "statement": "The percentage of I-tags of all terms is highest in B-rank.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Translated, non-literally translated, and raw untranslated term annotations obtained in the annotation process using the NAIST STC for (T)ranslator, and {B,A,S}-rank SI. Note that these raw untranslated term figures are directly from the annotation process, prior to filtering based off of the term relevancy constraint from §2.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Translators achieved the highest term coverage with 80% literal translations, while S-rank interpreters had the highest number of non-literal translations and left 17% fewer terms uncovered than B-rank interpreters.Since translators performed in an offline setting without time constraints, they were able to translate the largest number of source terms into the target language, with 80% being literally translated, and 6% being non-literally translated. On the other hand, interpreters tend to leave many source terms uncovered in their translations. The A-rank and B-rank interpreters achieve roughly the same level of term coverage, with the A-rank being only slightly more effective than B-rank at translating terms literally and non-literally. This is in contrast with Shimizu et al. ’s automatic analysis of translation quality on a three-talk subset, in which A-rank has slightly higher translation error rate and lower BLEU score The most experienced S-rank interpreter leaves 17% fewer terms than B-rank uncovered in the translations. More interestingly, the number of non-literally translated terms also correlates with experience-level. In fact, the S-rank interpreter actually exceeds the translator in the number of non-literal translations produced. Non-literal translations can occur when the interpreter fully comprehended the source expression, but chose to generate it in a way that better fit the translation in terms of fluency." } ], "images": [ "scigen/train/2156.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Translated, non-literally translated, and raw untranslated term annotations obtained in the annotation process using the NAIST STC for (T)ranslator, and {B,A,S}-rank SI. Note that these raw untranslated term figures are directly from the annotation process, prior to filtering based off of the term relevancy constraint from §2." ], "reasoning": "Translators achieved the highest term coverage with 80% literal translations, while S-rank interpreters had the highest number of non-literal translations and left 17% fewer terms uncovered than B-rank interpreters.", "description": "Since translators performed in an offline setting without time constraints, they were able to translate the largest number of source terms into the target language, with 80% being literally translated, and 6% being non-literally translated. On the other hand, interpreters tend to leave many source terms uncovered in their translations. The A-rank and B-rank interpreters achieve roughly the same level of term coverage, with the A-rank being only slightly more effective than B-rank at translating terms literally and non-literally. This is in contrast with Shimizu et al. ’s automatic analysis of translation quality on a three-talk subset, in which A-rank has slightly higher translation error rate and lower BLEU score The most experienced S-rank interpreter leaves 17% fewer terms than B-rank uncovered in the translations. More interestingly, the number of non-literally translated terms also correlates with experience-level. In fact, the S-rank interpreter actually exceeds the translator in the number of non-literal translations produced. Non-literal translations can occur when the interpreter fully comprehended the source expression, but chose to generate it in a way that better fit the translation in terms of fluency.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 1: Translated, non-literally translated, and raw untranslated term annotations obtained in the annotation process using the NAIST STC for (T)ranslator, and {B,A,S}-rank SI. Note that these raw untranslated term figures are directly from the annotation process, prior to filtering based off of the term relevancy constraint from §2.', solve the following question step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich interpreter rank leaves the fewest terms uncovered in their translations?\n" }, { "role": "assistant", "content": "The table shows that the S-rank interpreter has the lowest number of raw untranslated terms (1,071) and the lowest percentage of raw untranslated terms (39%). So, the answer is {\"answer\": \"S-rank\"}" } ], "images": [ "scigen/train/2156.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Translated, non-literally translated, and raw untranslated term annotations obtained in the annotation process using the NAIST STC for (T)ranslator, and {B,A,S}-rank SI. Note that these raw untranslated term figures are directly from the annotation process, prior to filtering based off of the term relevancy constraint from §2." ], "reasoning": "The table shows that the S-rank interpreter has the lowest number of raw untranslated terms (1,071) and the lowest percentage of raw untranslated terms (39%).", "question": "Which interpreter rank leaves the fewest terms uncovered in their translations?", "answer": "So, the answer is {\"answer\": \"S-rank\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence A-rank interpreters have a higher percentage of non-literal translations than B-rank interpreters. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nA-rank interpreters have a higher percentage of non-literal translations than B-rank interpreters.\n" }, { "role": "assistant", "content": "The table shows that A-rank interpreters have a non-literal translation percentage of 4%, while B-rank interpreters have a non-literal translation percentage of 3%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2156.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Translated, non-literally translated, and raw untranslated term annotations obtained in the annotation process using the NAIST STC for (T)ranslator, and {B,A,S}-rank SI. Note that these raw untranslated term figures are directly from the annotation process, prior to filtering based off of the term relevancy constraint from §2." ], "reasoning": "The table shows that A-rank interpreters have a non-literal translation percentage of 4%, while B-rank interpreters have a non-literal translation percentage of 3%.", "statement": "A-rank interpreters have a higher percentage of non-literal translations than B-rank interpreters.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: Average precision score cross-validation results with feature ablation for the untranslated term class on test data. Optimal word frequency threshold is determined on dev set of each fold. Evaluation performed on a word-level. Highest numbers per column are bolded. Each setting is statistically significant at p<0.05 by paired bootstrap (Koehn, 2004).'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The SVM method improves average precision scores by ablating uninformative features, with the highest improvement seen in B-rank interpreters when word frequency is removed.Since we are ultimately interested in the precision and recall trade-off among the methods, we evaluate our results using precision-recall curves in Fig. If the method is embedded in a CAI system, then the user could theoretically adjust the precision-recall threshold to balance helpful term suggestions with cognitive load. Overall, we tend to see that all methods perform best when tested on data from the B-rank interpreter, and observe a decline in performance across all methods with an increase in interpreter experience. We believe that this is due to a decrease in the number of untranslated terminology as experience increases (i.e., class imbalance) coupled with the difficulty of predicting such exclusive word occurrences from only source speech and textual cues. While the elapsed time and word timing features tend to cause a degradation in performance when removed, ablating the word frequency and characteristic/syntax features can actually improve average precision score. Word frequency, which is a recall-based feature, seems to be more helpful for B- and S-rank interpreters because it is challenging to recall the smaller number of untranslated terms from the data. Although the characteristic/syntax features are also recall-based, we see a decline in performance for them across all interpreter ranks because they are simply too noisy. When ablating the uninformative features for each rank, the SVM is able to increase AP vs. the optimal word frequency baseline by about 20%, 15%, and 30% for the B, A, and S-rank interpreters, respectively." } ], "images": [ "scigen/train/2157.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Average precision score cross-validation results with feature ablation for the untranslated term class on test data. Optimal word frequency threshold is determined on dev set of each fold. Evaluation performed on a word-level. Highest numbers per column are bolded. Each setting is statistically significant at p<0.05 by paired bootstrap (Koehn, 2004)." ], "reasoning": "The SVM method improves average precision scores by ablating uninformative features, with the highest improvement seen in B-rank interpreters when word frequency is removed.", "description": "Since we are ultimately interested in the precision and recall trade-off among the methods, we evaluate our results using precision-recall curves in Fig. If the method is embedded in a CAI system, then the user could theoretically adjust the precision-recall threshold to balance helpful term suggestions with cognitive load. Overall, we tend to see that all methods perform best when tested on data from the B-rank interpreter, and observe a decline in performance across all methods with an increase in interpreter experience. We believe that this is due to a decrease in the number of untranslated terminology as experience increases (i.e., class imbalance) coupled with the difficulty of predicting such exclusive word occurrences from only source speech and textual cues. While the elapsed time and word timing features tend to cause a degradation in performance when removed, ablating the word frequency and characteristic/syntax features can actually improve average precision score. Word frequency, which is a recall-based feature, seems to be more helpful for B- and S-rank interpreters because it is challenging to recall the smaller number of untranslated terms from the data. Although the characteristic/syntax features are also recall-based, we see a decline in performance for them across all interpreter ranks because they are simply too noisy. When ablating the uninformative features for each rank, the SVM is able to increase AP vs. the optimal word frequency baseline by about 20%, 15%, and 30% for the B, A, and S-rank interpreters, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 3: Average precision score cross-validation results with feature ablation for the untranslated term class on test data. Optimal word frequency threshold is determined on dev set of each fold. Evaluation performed on a word-level. Highest numbers per column are bolded. Each setting is statistically significant at p<0.05 by paired bootstrap (Koehn, 2004).'.\n\nQuestion: Which method achieves the highest average precision score for B-rank interpreters?\n" }, { "role": "assistant", "content": "To find the method with the highest average precision score for B-rank interpreters, we look at the AP B column and identify the highest value, which is 59.4 for the method 'SVM - word freq'. So, the answer is {\"answer\": \"SVM - word freq\"}." } ], "images": [ "scigen/train/2157.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Average precision score cross-validation results with feature ablation for the untranslated term class on test data. Optimal word frequency threshold is determined on dev set of each fold. Evaluation performed on a word-level. Highest numbers per column are bolded. Each setting is statistically significant at p<0.05 by paired bootstrap (Koehn, 2004)." ], "reasoning": "To find the method with the highest average precision score for B-rank interpreters, we look at the AP B column and identify the highest value, which is 59.4 for the method 'SVM - word freq'.", "question": "Which method achieves the highest average precision score for B-rank interpreters?", "answer": "So, the answer is {\"answer\": \"SVM - word freq\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nRemoving word frequency improves the average precision score for A-rank interpreters.\n" }, { "role": "assistant", "content": "The table shows that removing word frequency results in an AP of 52.5 for A-rank interpreters, which is lower than the AP of 53.0 when all features are included. Therefore, removing word frequency does not improve the average precision score for A-rank interpreters. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2157.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Average precision score cross-validation results with feature ablation for the untranslated term class on test data. Optimal word frequency threshold is determined on dev set of each fold. Evaluation performed on a word-level. Highest numbers per column are bolded. Each setting is statistically significant at p<0.05 by paired bootstrap (Koehn, 2004)." ], "reasoning": "The table shows that removing word frequency results in an AP of 52.5 for A-rank interpreters, which is lower than the AP of 53.0 when all features are included. Therefore, removing word frequency does not improve the average precision score for A-rank interpreters.", "statement": "Removing word frequency improves the average precision score for A-rank interpreters.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'TABLE IV: Overall accuracy of all pitch detectors.'.\n" }, { "role": "assistant", "content": "The proposed pitch detector significantly reduces voicing errors while maintaining state-of-the-art GPE and FPE capabilities, resulting in a 3.8% FFE, which is a 40% relative reduction over the best baseline (RAPT).We summarize now the overall pitch tracking performance along the 4 main objective metrics. For the proposed technique, we used either K-means,T or MLP for voicing detection, followed by the median filter for F0 estimation. Our proposed algorithm provides a significant reduction of the voicing errors, while maintaining state-of-art GPE and FPE capabilities. It is worth noticing that most pitch trackers avoid estimating F0 when unsure, which leads to better GPE and FPE but worse VDE. Overall, the proposed pitch detector yields a FFE of 3.8%, which is a 40% relative reduction over the best baseline (RAPT)." } ], "images": [ "scigen/train/2158.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Overall accuracy of all pitch detectors." ], "reasoning": "The proposed pitch detector significantly reduces voicing errors while maintaining state-of-the-art GPE and FPE capabilities, resulting in a 3.8% FFE, which is a 40% relative reduction over the best baseline (RAPT).", "description": "We summarize now the overall pitch tracking performance along the 4 main objective metrics. For the proposed technique, we used either K-means,T or MLP for voicing detection, followed by the median filter for F0 estimation. Our proposed algorithm provides a significant reduction of the voicing errors, while maintaining state-of-art GPE and FPE capabilities. It is worth noticing that most pitch trackers avoid estimating F0 when unsure, which leads to better GPE and FPE but worse VDE. Overall, the proposed pitch detector yields a FFE of 3.8%, which is a 40% relative reduction over the best baseline (RAPT).", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nTable title: TABLE IV: Overall accuracy of all pitch detectors.\n\nQuestion: What is the FFE of the proposed pitch detector using MLP, and how does it compare to the FFE of RAPT?\n" }, { "role": "assistant", "content": "The FFE of the proposed pitch detector using MLP is 3.8%, which is a 40% relative reduction compared to the FFE of RAPT, which is 6.26%. So, the answer is {\"answer\": \"3.8%\"}" } ], "images": [ "scigen/train/2158.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Overall accuracy of all pitch detectors." ], "reasoning": "The FFE of the proposed pitch detector using MLP is 3.8%, which is a 40% relative reduction compared to the FFE of RAPT, which is 6.26%.", "question": "What is the FFE of the proposed pitch detector using MLP, and how does it compare to the FFE of RAPT?", "answer": "So, the answer is {\"answer\": \"3.8%\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The proposed pitch detector using K-means,T has a lower VDE than SWIPE'., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe proposed pitch detector using K-means,T has a lower VDE than SWIPE'.\n" }, { "role": "assistant", "content": "The table shows that the VDE for the proposed pitch detector using K-means,T is 4.31, which is lower than the VDE for SWIPE', which is 5.42. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2158.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Overall accuracy of all pitch detectors." ], "reasoning": "The table shows that the VDE for the proposed pitch detector using K-means,T is 4.31, which is lower than the VDE for SWIPE', which is 5.42.", "statement": "The proposed pitch detector using K-means,T has a lower VDE than SWIPE'.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'TABLE II: Voicing decision error rate (in %) for the baselines and proposed approaches.'.\n" }, { "role": "assistant", "content": "Harvest performed poorly with a VDE of 23.59%, while RAPT and SWIPE’ achieved the best results with a VDE around 5.5%. Adding MS features provided a slight improvement, but embedding contextual frames resulted in a significant 15% improvement.Harvest was found to be particularly poor (VDE=23.59%), although we used the original implementation released by its authors with default settings. Across the state-of-the-art methods, RAPT and SWIPE’ provide the best results, with a VDE around 5.5%. Through our experiments, we found out that adding the MS features only slightly helped (3% relative improvement), while it turned out to be important to embed contextual frames. Indeed, stacking the previous and next frames to the current one brought a 15% relative improvement for both unsupervised and supervised approaches. Extending the context to more frames did not help." } ], "images": [ "scigen/train/2159.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Voicing decision error rate (in %) for the baselines and proposed approaches." ], "reasoning": "Harvest performed poorly with a VDE of 23.59%, while RAPT and SWIPE’ achieved the best results with a VDE around 5.5%. Adding MS features provided a slight improvement, but embedding contextual frames resulted in a significant 15% improvement.", "description": "Harvest was found to be particularly poor (VDE=23.59%), although we used the original implementation released by its authors with default settings. Across the state-of-the-art methods, RAPT and SWIPE’ provide the best results, with a VDE around 5.5%. Through our experiments, we found out that adding the MS features only slightly helped (3% relative improvement), while it turned out to be important to embed contextual frames. Indeed, stacking the previous and next frames to the current one brought a 15% relative improvement for both unsupervised and supervised approaches. Extending the context to more frames did not help.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'TABLE II: Voicing decision error rate (in %) for the baselines and proposed approaches.', solve the following question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method achieved the lowest VDE according to the table?\n" }, { "role": "assistant", "content": "The table lists various methods and their corresponding VDE values. The method with the lowest VDE value is MLP with a VDE of 3.11. So, the answer is {\"answer\": \"MLP\"}." } ], "images": [ "scigen/train/2159.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Voicing decision error rate (in %) for the baselines and proposed approaches." ], "reasoning": "The table lists various methods and their corresponding VDE values. The method with the lowest VDE value is MLP with a VDE of 3.11.", "question": "Which method achieved the lowest VDE according to the table?", "answer": "So, the answer is {\"answer\": \"MLP\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence RAPT and SWIPE’ achieved the best results with a VDE around 5.5%. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nRAPT and SWIPE’ achieved the best results with a VDE around 5.5%.\n" }, { "role": "assistant", "content": "The table shows that RAPT has a VDE of 5.58 and SWIPE’ has a VDE of 5.42, which are close to 5.5%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2159.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Voicing decision error rate (in %) for the baselines and proposed approaches." ], "reasoning": "The table shows that RAPT has a VDE of 5.58 and SWIPE’ has a VDE of 5.42, which are close to 5.5%.", "statement": "RAPT and SWIPE’ achieved the best results with a VDE around 5.5%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'TABLE III: Gross pitch error rate (in %) for the various F0 estimation techniques.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Using only the 3 best estimators (F0AC, F0SSH, and F0AC,MS) with a context of ±2 frames and applying median filtering outperformed all other approaches, including CREPE.Out of the 7 pitch estimators generated by our feature extraction scheme, F0CPP and F0CPP,MS were discarded as they led to a GPE rate beyond 30%. Across our experiments, we have tried to include the voicing measurements (i.e. the 16 features used for voicing detection) as additional input to the regressor. We have also experimented with reducing the set of F0 estimators, as F0SRH and F0SSH,MS gave higher GPE and could hence impair the final estimate. Finally, we have tried to include contextual values of the estimators to get more reliable predictions. The outcome of those experiments was that: i) considering voicing measurements does not help, ii) keeping only the 3 best estimators (F0AC, F0SSH and F0AC,MS) gave better results, iii) contextual frames gave more robust results. As a result, we used only the 3 estimators with a context of ±2 frames. Those 3 methods are the KNN regressor, the MLP predicting the index of the most accurate F0 estimator and the median filter. It is interestingly observed that a simple combination of the 3 estimators by median filtering outperformed all other approaches (including CREPE). One area that we have not explored though is dynamic programming. Both RAPT and CREPE use Viterbi decoding to smooth the F0 contour. Disabling it for CREPE degraded the GPE from 3.51% to 4.48%." } ], "images": [ "scigen/train/2160.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Gross pitch error rate (in %) for the various F0 estimation techniques." ], "reasoning": "Using only the 3 best estimators (F0AC, F0SSH, and F0AC,MS) with a context of ±2 frames and applying median filtering outperformed all other approaches, including CREPE.", "description": "Out of the 7 pitch estimators generated by our feature extraction scheme, F0CPP and F0CPP,MS were discarded as they led to a GPE rate beyond 30%. Across our experiments, we have tried to include the voicing measurements (i.e. the 16 features used for voicing detection) as additional input to the regressor. We have also experimented with reducing the set of F0 estimators, as F0SRH and F0SSH,MS gave higher GPE and could hence impair the final estimate. Finally, we have tried to include contextual values of the estimators to get more reliable predictions. The outcome of those experiments was that: i) considering voicing measurements does not help, ii) keeping only the 3 best estimators (F0AC, F0SSH and F0AC,MS) gave better results, iii) contextual frames gave more robust results. As a result, we used only the 3 estimators with a context of ±2 frames. Those 3 methods are the KNN regressor, the MLP predicting the index of the most accurate F0 estimator and the median filter. It is interestingly observed that a simple combination of the 3 estimators by median filtering outperformed all other approaches (including CREPE). One area that we have not explored though is dynamic programming. Both RAPT and CREPE use Viterbi decoding to smooth the F0 contour. Disabling it for CREPE degraded the GPE from 3.51% to 4.48%.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'TABLE III: Gross pitch error rate (in %) for the various F0 estimation techniques.', solve this question and give detail solutions. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhich method had the lowest GPE rate according to the table?\n" }, { "role": "assistant", "content": "The table shows the GPE rates for different methods. The method with the lowest GPE rate is highlighted in yellow, which is the Med. Filt. with a GPE rate of 1.95%. So, the answer is {\"answer\": \"Med. Filt.\"}." } ], "images": [ "scigen/train/2160.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Gross pitch error rate (in %) for the various F0 estimation techniques." ], "reasoning": "The table shows the GPE rates for different methods. The method with the lowest GPE rate is highlighted in yellow, which is the Med. Filt. with a GPE rate of 1.95%.", "question": "Which method had the lowest GPE rate according to the table?", "answer": "So, the answer is {\"answer\": \"Med. Filt.\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The KNN regressor had a lower GPE rate than the Linear Regressor., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe KNN regressor had a lower GPE rate than the Linear Regressor.\n" }, { "role": "assistant", "content": "According to the table, the GPE rate for the KNN regressor is 2.53%, while the GPE rate for the Linear Regressor is 3.89%. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2160.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Gross pitch error rate (in %) for the various F0 estimation techniques." ], "reasoning": "According to the table, the GPE rate for the KNN regressor is 2.53%, while the GPE rate for the Linear Regressor is 3.89%. Therefore, the statement is supported.", "statement": "The KNN regressor had a lower GPE rate than the Linear Regressor.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Ablation tests of stance features and temporal modeling for veracity prediction on PHEME dataset.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Removing stance features or the GRU layer reduces both Macro-F1 and accuracy, indicating their importance for veracity prediction.5.4.2 Ablation Tests Effect of Stance Features To understand the importance of stance features for veracity prediction, we conduct an ablation study: we only input the content features of all tweets in a conversation to the top component RNN. It means that the RNN only models the temporal variation of tweet contents during spreading, but does not consider their stances and is not “stance-aware”. Hence, combining the low-level content features and the high-level stance features is crucial to improve rumor veracity prediction. Effect of Temporal Evolution Modeling We modify the Stance-Aware RNN by two ways: (i) we replace the GRU layer by a CNN that only captures local temporal information; (ii) we remove the GRU layer." } ], "images": [ "scigen/train/2161.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Ablation tests of stance features and temporal modeling for veracity prediction on PHEME dataset." ], "reasoning": "Removing stance features or the GRU layer reduces both Macro-F1 and accuracy, indicating their importance for veracity prediction.", "description": "5.4.2 Ablation Tests Effect of Stance Features To understand the importance of stance features for veracity prediction, we conduct an ablation study: we only input the content features of all tweets in a conversation to the top component RNN. It means that the RNN only models the temporal variation of tweet contents during spreading, but does not consider their stances and is not “stance-aware”. Hence, combining the low-level content features and the high-level stance features is crucial to improve rumor veracity prediction. Effect of Temporal Evolution Modeling We modify the Stance-Aware RNN by two ways: (i) we replace the GRU layer by a CNN that only captures local temporal information; (ii) we remove the GRU layer.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: Ablation tests of stance features and temporal modeling for veracity prediction on PHEME dataset.', answer the following question. Give detailed solution consisting of each step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the Macro-F1 score when the GRU layer is replaced by a CNN?\n" }, { "role": "assistant", "content": "The table shows the Macro-F1 score for different methods. When the GRU layer is replaced by a CNN, the Macro-F1 score is 0.312. So, the answer is {\"answer\": \"0.312\"}" } ], "images": [ "scigen/train/2161.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Ablation tests of stance features and temporal modeling for veracity prediction on PHEME dataset." ], "reasoning": "The table shows the Macro-F1 score for different methods. When the GRU layer is replaced by a CNN, the Macro-F1 score is 0.312.", "question": "What is the Macro-F1 score when the GRU layer is replaced by a CNN?", "answer": "So, the answer is {\"answer\": \"0.312\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe accuracy of the model decreases when stance features are removed.\n" }, { "role": "assistant", "content": "The table shows that the accuracy decreases from 0.361 to 0.338 when stance features are removed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2161.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Ablation tests of stance features and temporal modeling for veracity prediction on PHEME dataset." ], "reasoning": "The table shows that the accuracy decreases from 0.361 to 0.338 when stance features are removed.", "statement": "The accuracy of the model decreases when stance features are removed.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Results of veracity prediction. Single-task setting means that stance labels cannot be used to train models.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Hierarchical GCN-RNN outperforms TD-RvNN in both accuracy and speed, demonstrating the effectiveness of the hierarchical framework in modeling conversation structures and learning tweet representations.By comparing single-task methods, Hierarchical GCN-RNN performs better than TD-RvNN, which indicates that our hierarchical framework can effectively model conversation structures to learn high-quality tweet representations. The recursive operation in TD-RvNN is performed in a fixed direction and runs over all tweets, thus may not obtain enough useful information. Moreover, the training speed of Hierarchical GCN-RNN is significantly faster than TD-RvNN: in the condition of batch-wise optimization for training one step over a batch containing 32 conversations, our method takes only 0.18 seconds, while TD-RvNN takes 5.02 seconds." } ], "images": [ "scigen/train/2162.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of veracity prediction. Single-task setting means that stance labels cannot be used to train models." ], "reasoning": "Hierarchical GCN-RNN outperforms TD-RvNN in both accuracy and speed, demonstrating the effectiveness of the hierarchical framework in modeling conversation structures and learning tweet representations.", "description": "By comparing single-task methods, Hierarchical GCN-RNN performs better than TD-RvNN, which indicates that our hierarchical framework can effectively model conversation structures to learn high-quality tweet representations. The recursive operation in TD-RvNN is performed in a fixed direction and runs over all tweets, thus may not obtain enough useful information. Moreover, the training speed of Hierarchical GCN-RNN is significantly faster than TD-RvNN: in the condition of batch-wise optimization for training one step over a batch containing 32 conversations, our method takes only 0.18 seconds, while TD-RvNN takes 5.02 seconds.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 3: Results of veracity prediction. Single-task setting means that stance labels cannot be used to train models.\nQuestion: Which method has the highest SemEval dataset Macro-F1 score and how does it compare to the PHEME dataset Macro-F1 score?\n" }, { "role": "assistant", "content": "The table shows that Hierarchical-PSV (Ours, λ=1) has the highest SemEval dataset Macro-F1 score of 0.588. Comparing this to the PHEME dataset Macro-F1 score, Hierarchical-PSV also has the highest score of 0.333. So, the answer is {\"answer\": \"Hierarchical-PSV (Ours, λ=1)\"}." } ], "images": [ "scigen/train/2162.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of veracity prediction. Single-task setting means that stance labels cannot be used to train models." ], "reasoning": "The table shows that Hierarchical-PSV (Ours, λ=1) has the highest SemEval dataset Macro-F1 score of 0.588. Comparing this to the PHEME dataset Macro-F1 score, Hierarchical-PSV also has the highest score of 0.333.", "question": "Which method has the highest SemEval dataset Macro-F1 score and how does it compare to the PHEME dataset Macro-F1 score?", "answer": "So, the answer is {\"answer\": \"Hierarchical-PSV (Ours, λ=1)\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nHierarchical GCN-RNN has a higher SemEval dataset Macro-F1 score than TD-RvNN.\n" }, { "role": "assistant", "content": "The table shows that Hierarchical GCN-RNN has a SemEval dataset Macro-F1 score of 0.540, while TD-RvNN has a score of 0.509, indicating that Hierarchical GCN-RNN performs better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2162.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of veracity prediction. Single-task setting means that stance labels cannot be used to train models." ], "reasoning": "The table shows that Hierarchical GCN-RNN has a SemEval dataset Macro-F1 score of 0.540, while TD-RvNN has a score of 0.509, indicating that Hierarchical GCN-RNN performs better.", "statement": "Hierarchical GCN-RNN has a higher SemEval dataset Macro-F1 score than TD-RvNN.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Percentage of summary edges that can be covered by an automatically constructed source graph.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Sentence-level graph expansion increases the average number of edges by a factor of 15, improving edge prediction performance to 64% for gold-standard and 46.7% for JAMR parses.We investigated how well these automatically constructed source graphs cover the gold-standard summary graphs produced by AMR annotators. ‘Unlabeled’ counts edges as matching if both the source and destination concepts have identical labels, but ignores the edge label. In order to improve edge coverage, we explore expanding the source graph by adding every possible edge between every pair of concepts within the same sentence. We also explored adding every possible edge between every pair of concepts in the entire source graph. Edges that are newly introduced during expansion receive a default label ‘null’. Subgraph prediction became infeasable with the document-level expansion, so we conducted our experiments using only sentence-level expansion. Sentence-level graph expansion increases the average number of edges by a factor of 15, to 3,292. Fig. Document-level expansion covers the gold-standard summary edge “chase-01” → “garden,” yet the expansion is computationally prohibitive; sentence-level expansion adds an edge “dog” → “garden,” which enables the prediction of a structure with similar semantic meaning: “ Joe’s dog was in the garden chasing a cat.” Oracle results for the subgraph prediction stage are obtained using the ILP decoder to minimize the cost of the output graph, given the gold-standard. We assign wrong nodes and edges a score of −1, correct nodes and edges a score of 0, then decode with the same structural constraints as in subgraph prediction. The resulting graph is the best summary graph in the hypothesis space of our model, and provides an upper bound on performance achievable within our framework. Oracle performance on node prediction is in the range of 80% when using gold-standard AMR annotations, and 70% when using JAMR output. Edge prediction has lower performance, yielding 52.2% for gold-standard and 31.1% for JAMR parses. When graph expansion was applied, the numbers increased to 64% and 46.7%, respectively." } ], "images": [ "scigen/train/2163.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Percentage of summary edges that can be covered by an automatically constructed source graph." ], "reasoning": "Sentence-level graph expansion increases the average number of edges by a factor of 15, improving edge prediction performance to 64% for gold-standard and 46.7% for JAMR parses.", "description": "We investigated how well these automatically constructed source graphs cover the gold-standard summary graphs produced by AMR annotators. ‘Unlabeled’ counts edges as matching if both the source and destination concepts have identical labels, but ignores the edge label. In order to improve edge coverage, we explore expanding the source graph by adding every possible edge between every pair of concepts within the same sentence. We also explored adding every possible edge between every pair of concepts in the entire source graph. Edges that are newly introduced during expansion receive a default label ‘null’. Subgraph prediction became infeasable with the document-level expansion, so we conducted our experiments using only sentence-level expansion. Sentence-level graph expansion increases the average number of edges by a factor of 15, to 3,292. Fig. Document-level expansion covers the gold-standard summary edge “chase-01” → “garden,” yet the expansion is computationally prohibitive; sentence-level expansion adds an edge “dog” → “garden,” which enables the prediction of a structure with similar semantic meaning: “ Joe’s dog was in the garden chasing a cat.” Oracle results for the subgraph prediction stage are obtained using the ILP decoder to minimize the cost of the output graph, given the gold-standard. We assign wrong nodes and edges a score of −1, correct nodes and edges a score of 0, then decode with the same structural constraints as in subgraph prediction. The resulting graph is the best summary graph in the hypothesis space of our model, and provides an upper bound on performance achievable within our framework. Oracle performance on node prediction is in the range of 80% when using gold-standard AMR annotations, and 70% when using JAMR output. Edge prediction has lower performance, yielding 52.2% for gold-standard and 31.1% for JAMR parses. When graph expansion was applied, the numbers increased to 64% and 46.7%, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: Percentage of summary edges that can be covered by an automatically constructed source graph.' and methodically tackle the question that follows. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the summary edge coverage percentage for the development set when using document-level expansion?\n" }, { "role": "assistant", "content": "The table shows the summary edge coverage percentages for different sets and methods. For the development set with document-level expansion, the percentage is 91.8%. So, the answer is {\"answer\": \"91.8\"}." } ], "images": [ "scigen/train/2163.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Percentage of summary edges that can be covered by an automatically constructed source graph." ], "reasoning": "The table shows the summary edge coverage percentages for different sets and methods. For the development set with document-level expansion, the percentage is 91.8%.", "question": "What is the summary edge coverage percentage for the development set when using document-level expansion?", "answer": "So, the answer is {\"answer\": \"91.8\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The summary edge coverage for the test set using labeled edges is higher than using sentence-level expansion., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe summary edge coverage for the test set using labeled edges is higher than using sentence-level expansion.\n" }, { "role": "assistant", "content": "The table shows that the summary edge coverage for the test set using labeled edges is 63.0%, while using sentence-level expansion it is 75.0%. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2163.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Percentage of summary edges that can be covered by an automatically constructed source graph." ], "reasoning": "The table shows that the summary edge coverage for the test set using labeled edges is 63.0%, while using sentence-level expansion it is 75.0%. Therefore, the statement is refuted.", "statement": "The summary edge coverage for the test set using labeled edges is higher than using sentence-level expansion.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Subgraph prediction and summarization (to bag of words) results on test set. Gold-standard AMR annotations are used for model training in all conditions. “+ Expand” means the result is obtained using source graph with expansion; edge performance is measured ignoring labels.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Graph expansion marginally affects system performance, slightly hurting edge prediction but significantly increasing oracle performance.Gold-standard AMR annotations are used for model training in all conditions. During testing, we apply the trained model to source graphs constructed using either gold-standard or JAMR parses. In all of these experiments, we use the number of edges in the gold-standard summary graph to fix the number of edges in the predicted subgraph, allowing direct comparison across conditions. “+ Expand” means the results are obtained using the expanded source graph. We find that graph expansion only marginally affects system performance. Graph expansion slightly hurts the system performance on edge prediction. For example, using ramp loss with JAMR parser as input, we obtained 50.7% and 19.0% for node and edge prediction with graph expansion; 51.5% and 20.0% without edge expansion. On the other hand, it increases the oracle performance by a large margin. This suggests that with more training data, or a more sophisticated model that is able to better discriminate among the enlarged output space, graph expansion still has promise to be helpful." } ], "images": [ "scigen/train/2164.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Subgraph prediction and summarization (to bag of words) results on test set. Gold-standard AMR annotations are used for model training in all conditions. “+ Expand” means the result is obtained using source graph with expansion; edge performance is measured ignoring labels." ], "reasoning": "Graph expansion marginally affects system performance, slightly hurting edge prediction but significantly increasing oracle performance.", "description": "Gold-standard AMR annotations are used for model training in all conditions. During testing, we apply the trained model to source graphs constructed using either gold-standard or JAMR parses. In all of these experiments, we use the number of edges in the gold-standard summary graph to fix the number of edges in the predicted subgraph, allowing direct comparison across conditions. “+ Expand” means the results are obtained using the expanded source graph. We find that graph expansion only marginally affects system performance. Graph expansion slightly hurts the system performance on edge prediction. For example, using ramp loss with JAMR parser as input, we obtained 50.7% and 19.0% for node and edge prediction with graph expansion; 51.5% and 20.0% without edge expansion. On the other hand, it increases the oracle performance by a large margin. This suggests that with more training data, or a more sophisticated model that is able to better discriminate among the enlarged output space, graph expansion still has promise to be helpful.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nTable title: Table 5: Subgraph prediction and summarization (to bag of words) results on test set. Gold-standard AMR annotations are used for model training in all conditions. “+ Expand” means the result is obtained using source graph with expansion; edge performance is measured ignoring labels.\n\nQuestion: What is the F1 score for subgraph prediction nodes using the ramp loss with JAMR parser and graph expansion?\n" }, { "role": "assistant", "content": "To find the F1 score for subgraph prediction nodes using the ramp loss with JAMR parser and graph expansion, locate the row 'Ramp + Expand' under 'JAMR parses' and find the F1 score in the 'Subgraph Prediction Nodes' column. So, the answer is {\"answer\": \"51.5\"}" } ], "images": [ "scigen/train/2164.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Subgraph prediction and summarization (to bag of words) results on test set. Gold-standard AMR annotations are used for model training in all conditions. “+ Expand” means the result is obtained using source graph with expansion; edge performance is measured ignoring labels." ], "reasoning": "To find the F1 score for subgraph prediction nodes using the ramp loss with JAMR parser and graph expansion, locate the row 'Ramp + Expand' under 'JAMR parses' and find the F1 score in the 'Subgraph Prediction Nodes' column.", "question": "What is the F1 score for subgraph prediction nodes using the ramp loss with JAMR parser and graph expansion?", "answer": "So, the answer is {\"answer\": \"51.5\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nGraph expansion improves the F1 score for subgraph prediction edges using the ramp loss with JAMR parser.\n" }, { "role": "assistant", "content": "The table shows that the F1 score for subgraph prediction edges using the ramp loss with JAMR parser is 20.0% without graph expansion and 19.0% with graph expansion, indicating that graph expansion slightly hurts the performance. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2164.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Subgraph prediction and summarization (to bag of words) results on test set. Gold-standard AMR annotations are used for model training in all conditions. “+ Expand” means the result is obtained using source graph with expansion; edge performance is measured ignoring labels." ], "reasoning": "The table shows that the F1 score for subgraph prediction edges using the ramp loss with JAMR parser is 20.0% without graph expansion and 19.0% with graph expansion, indicating that graph expansion slightly hurts the performance.", "statement": "Graph expansion improves the F1 score for subgraph prediction edges using the ramp loss with JAMR parser.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 6: Performance of all models on forecasting market movement as varying the daily latest time up to which news headlines are collected.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The prediction accuracy increases as news articles are collected closer to market closing time, with MSIN offering the best interpretation due to joint-training of time series and news articles.In order to further observe the impact of textual news on the models’ classification accuracy, we vary the latest time, up to which the daily news headlines are collected, to 9:00 (before market open), 15:40 (before market close), 19:00, and 23:59 on the same day the market performance is predicted. A clear trend is seen that, the prediction accuracy is higher as the news articles are collected closer to the market closing time, which confirms the indicative information embedded in the textual news toward predicting the S&P500 performance. While most of neural models tend to perform feature engineering better than that of SVM, only our MSIN can further offer better interpretation due to its join-training of both evolving time series and the news articles. It is noted that evaluations at the time stamp of 19:00 (or 23:59) will shift a model from a predictive system to a purely explanatory one." } ], "images": [ "scigen/train/2165.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Performance of all models on forecasting market movement as varying the daily latest time up to which news headlines are collected." ], "reasoning": "The prediction accuracy increases as news articles are collected closer to market closing time, with MSIN offering the best interpretation due to joint-training of time series and news articles.", "description": "In order to further observe the impact of textual news on the models’ classification accuracy, we vary the latest time, up to which the daily news headlines are collected, to 9:00 (before market open), 15:40 (before market close), 19:00, and 23:59 on the same day the market performance is predicted. A clear trend is seen that, the prediction accuracy is higher as the news articles are collected closer to the market closing time, which confirms the indicative information embedded in the textual news toward predicting the S&P500 performance. While most of neural models tend to perform feature engineering better than that of SVM, only our MSIN can further offer better interpretation due to its join-training of both evolving time series and the news articles. It is noted that evaluations at the time stamp of 19:00 (or 23:59) will shift a model from a predictive system to a purely explanatory one.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 6: Performance of all models on forecasting market movement as varying the daily latest time up to which news headlines are collected.', and sequentially solve the question, writing out each step of your reasoning process. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest prediction accuracy at 23:59?\n" }, { "role": "assistant", "content": "By examining the table, the highest prediction accuracy at 23:59 is found in the MSIN model with an accuracy of 79.3. So, the answer is {\"answer\": \"MSIN\"}." } ], "images": [ "scigen/train/2165.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Performance of all models on forecasting market movement as varying the daily latest time up to which news headlines are collected." ], "reasoning": "By examining the table, the highest prediction accuracy at 23:59 is found in the MSIN model with an accuracy of 79.3.", "question": "Which model has the highest prediction accuracy at 23:59?", "answer": "So, the answer is {\"answer\": \"MSIN\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The SVM model has the highest prediction accuracy at 15:40. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe SVM model has the highest prediction accuracy at 15:40.\n" }, { "role": "assistant", "content": "The table shows that the CNNtxt model has the highest prediction accuracy at 15:40 with an accuracy of 60.1, not the SVM model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2165.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Performance of all models on forecasting market movement as varying the daily latest time up to which news headlines are collected." ], "reasoning": "The table shows that the CNNtxt model has the highest prediction accuracy at 15:40 with an accuracy of 60.1, not the SVM model.", "statement": "The SVM model has the highest prediction accuracy at 15:40.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: News headlines from three days selected from the test set, along with the attention mass (annotated in the numeric columns) by: GRUtxt, LSTMw/o and MSIN. Colored headlines are relevant news outputted by our model based on setting the accumulated probability mass ≥50%.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "MSIN focuses more on headlines directly reporting market performance, while LSTMw/o and GRUtxt spread attention across multiple headlines.As observed, our model MSIN places more probability mass on the headlines whose contents directly report the market performance, while zeros out mass on headlines that report company-events or local markets. Its attention mass is also more condense, with clear focus on a small set of top relevant news, as compared to those of LSTMw/o and GRUtxt that both spread out on multiple headlines." } ], "images": [ "scigen/train/2166.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: News headlines from three days selected from the test set, along with the attention mass (annotated in the numeric columns) by: GRUtxt, LSTMw/o and MSIN. Colored headlines are relevant news outputted by our model based on setting the accumulated probability mass ≥50%." ], "reasoning": "MSIN focuses more on headlines directly reporting market performance, while LSTMw/o and GRUtxt spread attention across multiple headlines.", "description": "As observed, our model MSIN places more probability mass on the headlines whose contents directly report the market performance, while zeros out mass on headlines that report company-events or local markets. Its attention mass is also more condense, with clear focus on a small set of top relevant news, as compared to those of LSTMw/o and GRUtxt that both spread out on multiple headlines.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 4: News headlines from three days selected from the test set, along with the attention mass (annotated in the numeric columns) by: GRUtxt, LSTMw/o and MSIN. Colored headlines are relevant news outputted by our model based on setting the accumulated probability mass ≥50%.', unfold the question presented below, detailing every step of your calculation. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model places the highest probability mass on the headline 'wallstreet dip gdp but finish week high'?\n" }, { "role": "assistant", "content": "The headline 'wallstreet dip gdp but finish week high' has the highest probability mass of 0.73 under the MSIN model, compared to 0.28 for GRUtxt and 0.80 for LSTMw/o. So, the answer is {\"answer\": \"MSIN\"}." } ], "images": [ "scigen/train/2166.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: News headlines from three days selected from the test set, along with the attention mass (annotated in the numeric columns) by: GRUtxt, LSTMw/o and MSIN. Colored headlines are relevant news outputted by our model based on setting the accumulated probability mass ≥50%." ], "reasoning": "The headline 'wallstreet dip gdp but finish week high' has the highest probability mass of 0.73 under the MSIN model, compared to 0.28 for GRUtxt and 0.80 for LSTMw/o.", "question": "Which model places the highest probability mass on the headline 'wallstreet dip gdp but finish week high'?", "answer": "So, the answer is {\"answer\": \"MSIN\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe GRUtxt model places the highest probability mass on the headline 'global share euro fall sharply renew euro zone fear'.\n" }, { "role": "assistant", "content": "The headline 'global share euro fall sharply renew euro zone fear' has a probability mass of 0.39 under the MSIN model, which is higher than the 0.15 under GRUtxt and 0.07 under LSTMw/o. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2166.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: News headlines from three days selected from the test set, along with the attention mass (annotated in the numeric columns) by: GRUtxt, LSTMw/o and MSIN. Colored headlines are relevant news outputted by our model based on setting the accumulated probability mass ≥50%." ], "reasoning": "The headline 'global share euro fall sharply renew euro zone fear' has a probability mass of 0.39 under the MSIN model, which is higher than the 0.15 under GRUtxt and 0.07 under LSTMw/o.", "statement": "The GRUtxt model places the highest probability mass on the headline 'global share euro fall sharply renew euro zone fear'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Performance of all models on forecasting market movement. Precision and recall are reported w.r.t. two prediction of up and down market respectively.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "MSIN achieves the highest prediction accuracy and a balanced classification boundary, while SVM's performance is less comparable to neural networks.Two values in each entry of precision and recall columns are respectively reported for the forecasting of up and down market on the next day respectively. As observed, there is not much difference in the prediction accuracy of GRUtxt and CNNtxt though they analyze the textual news with different network topologies. Without exploiting the temporal order in the time series and the semantic dependencies of textual news, SVM’s performance seems less comparable to its counterpart neural networks. Out of all models, our MSIN achieves higher prediction accuracy. Its solution also converges to a more balanced classification boundary as reflected in the precision and recall measures." } ], "images": [ "scigen/train/2167.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Performance of all models on forecasting market movement. Precision and recall are reported w.r.t. two prediction of up and down market respectively." ], "reasoning": "MSIN achieves the highest prediction accuracy and a balanced classification boundary, while SVM's performance is less comparable to neural networks.", "description": "Two values in each entry of precision and recall columns are respectively reported for the forecasting of up and down market on the next day respectively. As observed, there is not much difference in the prediction accuracy of GRUtxt and CNNtxt though they analyze the textual news with different network topologies. Without exploiting the temporal order in the time series and the semantic dependencies of textual news, SVM’s performance seems less comparable to its counterpart neural networks. Out of all models, our MSIN achieves higher prediction accuracy. Its solution also converges to a more balanced classification boundary as reflected in the precision and recall measures.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 5: Performance of all models on forecasting market movement. Precision and recall are reported w.r.t. two prediction of up and down market respectively.', unfold the question presented below, detailing every step of your calculation. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest recall for predicting the up market?\n" }, { "role": "assistant", "content": "To find the model with the highest recall for predicting the up market, look at the first value in the 'Rec.' column for each model. The highest value is 71.7, which corresponds to the SVM model. So, the answer is {\"answer\": \"SVM\"}." } ], "images": [ "scigen/train/2167.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Performance of all models on forecasting market movement. Precision and recall are reported w.r.t. two prediction of up and down market respectively." ], "reasoning": "To find the model with the highest recall for predicting the up market, look at the first value in the 'Rec.' column for each model. The highest value is 71.7, which corresponds to the SVM model.", "question": "Which model has the highest recall for predicting the up market?", "answer": "So, the answer is {\"answer\": \"SVM\"}." }, { "messages": [ { "role": "user", "content": "MSIN has the highest precision for predicting the up market. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nMSIN has the highest precision for predicting the up market.\n" }, { "role": "assistant", "content": "To verify this statement, look at the first value in the 'Pre.' column for each model. The highest value is 63.9, which corresponds to the LSTMw/o model, not MSIN. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2167.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Performance of all models on forecasting market movement. Precision and recall are reported w.r.t. two prediction of up and down market respectively." ], "reasoning": "To verify this statement, look at the first value in the 'Pre.' column for each model. The highest value is 63.9, which corresponds to the LSTMw/o model, not MSIN.", "statement": "MSIN has the highest precision for predicting the up market.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Accuracy results for the SNLI dev-set experiment.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "All pretrained embeddings outperform random vectors, with orthogonal initialization generally better than Gaussian, especially for GloVe and retrofitted GloVe.After a single epoch, all kinds of pretraining outperform random vectors. Surprisingly, though, GloVe and word2vec also outperform their retrofitted counterparts. In addition, for all but random and word2vec, orthogonal initialization is notably better than Gaussian, and results in a mean gain of 0.8% across all the pretrained embeddings, with gains of 1.0% for GloVe vectors and 2.9% for retrofitted Glove." } ], "images": [ "scigen/train/2168.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Accuracy results for the SNLI dev-set experiment." ], "reasoning": "All pretrained embeddings outperform random vectors, with orthogonal initialization generally better than Gaussian, especially for GloVe and retrofitted GloVe.", "description": "After a single epoch, all kinds of pretraining outperform random vectors. Surprisingly, though, GloVe and word2vec also outperform their retrofitted counterparts. In addition, for all but random and word2vec, orthogonal initialization is notably better than Gaussian, and results in a mean gain of 0.8% across all the pretrained embeddings, with gains of 1.0% for GloVe vectors and 2.9% for retrofitted Glove.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Accuracy results for the SNLI dev-set experiment.\nQuestion: Which pretrained embedding shows the highest performance with orthonormal initialization?\nSolve the above question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the orthonormal initialization column, word2vec has the highest performance with a score of 81.11. So, the answer is {\"answer\": \"word2vec\"}" } ], "images": [ "scigen/train/2168.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Accuracy results for the SNLI dev-set experiment." ], "reasoning": "By comparing the orthonormal initialization column, word2vec has the highest performance with a score of 81.11.", "question": "Which pretrained embedding shows the highest performance with orthonormal initialization?", "answer": "So, the answer is {\"answer\": \"word2vec\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Orthogonal initialization results in a mean gain of 0.8% across all pretrained embeddings., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nOrthogonal initialization results in a mean gain of 0.8% across all pretrained embeddings.\n" }, { "role": "assistant", "content": "The description states that orthogonal initialization results in a mean gain of 0.8% across all pretrained embeddings, which is supported by the table showing higher scores for orthonormal initialization compared to Gaussian for most embeddings. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2168.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Accuracy results for the SNLI dev-set experiment." ], "reasoning": "The description states that orthogonal initialization results in a mean gain of 0.8% across all pretrained embeddings, which is supported by the table showing higher scores for orthonormal initialization compared to Gaussian for most embeddings.", "statement": "Orthogonal initialization results in a mean gain of 0.8% across all pretrained embeddings.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Confusion matrix for error detection', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The proposed model has a detection recall of 91.07% and a considerable number of true-positive errors, where correct words are detected as errors.The proposed model achieves 91.07% detection recall. There are considerable number of ture-positive errors, which are correct words but detected as errors. When using this type of errors for training or testing, we use the word itself as the intended word for each error." } ], "images": [ "scigen/train/2169.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Confusion matrix for error detection" ], "reasoning": "The proposed model has a detection recall of 91.07% and a considerable number of true-positive errors, where correct words are detected as errors.", "description": "The proposed model achieves 91.07% detection recall. There are considerable number of ture-positive errors, which are correct words but detected as errors. When using this type of errors for training or testing, we use the word itself as the intended word for each error.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: Confusion matrix for error detection'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the total number of instances where the model detection was correct?\n" }, { "role": "assistant", "content": "To find the total number of instances where the model detection was correct, sum the values in the 'Model Detection Correct' column. So, the answer is {\"answer\": \"80764\"}." } ], "images": [ "scigen/train/2169.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Confusion matrix for error detection" ], "reasoning": "To find the total number of instances where the model detection was correct, sum the values in the 'Model Detection Correct' column.", "question": "What is the total number of instances where the model detection was correct?", "answer": "So, the answer is {\"answer\": \"80764\"}." }, { "messages": [ { "role": "user", "content": "The model made 2457 detection errors. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe model made 2457 detection errors.\n" }, { "role": "assistant", "content": "The table shows that the number of model detection errors is 2457 under the 'Model Detection Error' column. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2169.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Confusion matrix for error detection" ], "reasoning": "The table shows that the number of model detection errors is 2457 under the 'Model Detection Error' column.", "statement": "The model made 2457 detection errors.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: The number of detected errors and recall of bounded and unbounded detections'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Unbounded detection can be corrected but has inaccurate feature scores, influencing correction accuracy, and multiple unbounded errors may be detected for one ground truth error.An error may be “partially” detected if an overlapped but non-identical character sequence is treated as an error. We call this “partially” detected case as a success unbounded detection, where the correct recognition of the character sequence as success bounded detection. Unbounded detection can potentially be corrected, but it has inaccurate features scores that will influence the correction accuracy. For example, if an error < spend→sp end> is unbounded and detected as end, sp will exists in the context and candidate edit distance will be computed with end instead of sp end. In addition, there may exist multiple unbounded errors detected for one ground truth error, because of the splitting mistakes. For every ground truth error, we count at most one successful unbounded detection." } ], "images": [ "scigen/train/2170.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The number of detected errors and recall of bounded and unbounded detections" ], "reasoning": "Unbounded detection can be corrected but has inaccurate feature scores, influencing correction accuracy, and multiple unbounded errors may be detected for one ground truth error.", "description": "An error may be “partially” detected if an overlapped but non-identical character sequence is treated as an error. We call this “partially” detected case as a success unbounded detection, where the correct recognition of the character sequence as success bounded detection. Unbounded detection can potentially be corrected, but it has inaccurate features scores that will influence the correction accuracy. For example, if an error < spend→sp end> is unbounded and detected as end, sp will exists in the context and candidate edit distance will be computed with end instead of sp end. In addition, there may exist multiple unbounded errors detected for one ground truth error, because of the splitting mistakes. For every ground truth error, we count at most one successful unbounded detection.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: The number of detected errors and recall of bounded and unbounded detections', unfold the question presented below, detailing every step of your calculation. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the recall percentage for bounded detection?\n" }, { "role": "assistant", "content": "The table shows the recall percentages for different detection categories. The recall percentage for bounded detection is listed directly in the table. So, the answer is {\"answer\": \"73.94%\"}." } ], "images": [ "scigen/train/2170.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The number of detected errors and recall of bounded and unbounded detections" ], "reasoning": "The table shows the recall percentages for different detection categories. The recall percentage for bounded detection is listed directly in the table.", "question": "What is the recall percentage for bounded detection?", "answer": "So, the answer is {\"answer\": \"73.94%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The number of unbounded detections is greater than the number of bounded detections. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe number of unbounded detections is greater than the number of bounded detections.\n" }, { "role": "assistant", "content": "The table shows that the number of bounded detections is 1995, while the number of unbounded detections is 462. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2170.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The number of detected errors and recall of bounded and unbounded detections" ], "reasoning": "The table shows that the number of bounded detections is 1995, while the number of unbounded detections is 462. Therefore, the statement is incorrect.", "statement": "The number of unbounded detections is greater than the number of bounded detections.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: Class-wise comparison of model accuracy for ProBERT and GREP. Off-diagonal terms show cases where GREP fixes errors made by ProBERT and vice-versa.'.\n" }, { "role": "assistant", "content": "GREP shows higher confidence and accuracy, especially in resolving NEITHER instances, compared to ProBERT.It can be seen that GREP is more confident in its predictions (all distributions appear translated closer to 1.0), and the improvement is overwhelmingly evident for the NEITHER class. The diagonal terms show the number of instances that the two models agree on, and the off-diagonal terms show where they disagree. The numbers reveal that the evidence pooling module not only boosts the model confidence but also helps in correctly resolving Neither instances (44 vs 11), indicating that the model is successfully able to build evidence for or against the given candidates." } ], "images": [ "scigen/train/2171.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Class-wise comparison of model accuracy for ProBERT and GREP. Off-diagonal terms show cases where GREP fixes errors made by ProBERT and vice-versa." ], "reasoning": "GREP shows higher confidence and accuracy, especially in resolving NEITHER instances, compared to ProBERT.", "description": "It can be seen that GREP is more confident in its predictions (all distributions appear translated closer to 1.0), and the improvement is overwhelmingly evident for the NEITHER class. The diagonal terms show the number of instances that the two models agree on, and the off-diagonal terms show where they disagree. The numbers reveal that the evidence pooling module not only boosts the model confidence but also helps in correctly resolving Neither instances (44 vs 11), indicating that the model is successfully able to build evidence for or against the given candidates.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 5: Class-wise comparison of model accuracy for ProBERT and GREP. Off-diagonal terms show cases where GREP fixes errors made by ProBERT and vice-versa.' and methodically tackle the question that follows. Output the final answer as JSON in the format {\"answer\": \"\"}.\nHow many NEITHER instances did GREP correctly resolve compared to ProBERT?\n" }, { "role": "assistant", "content": "The table shows that GREP correctly resolved 146 NEITHER instances, while ProBERT only resolved 11. So, the answer is {\"answer\": \"146\"}." } ], "images": [ "scigen/train/2171.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Class-wise comparison of model accuracy for ProBERT and GREP. Off-diagonal terms show cases where GREP fixes errors made by ProBERT and vice-versa." ], "reasoning": "The table shows that GREP correctly resolved 146 NEITHER instances, while ProBERT only resolved 11.", "question": "How many NEITHER instances did GREP correctly resolve compared to ProBERT?", "answer": "So, the answer is {\"answer\": \"146\"}." }, { "messages": [ { "role": "user", "content": "GREP incorrectly resolved more instances than ProBERT. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nGREP incorrectly resolved more instances than ProBERT.\n" }, { "role": "assistant", "content": "The table shows that GREP incorrectly resolved 121 instances, while ProBERT incorrectly resolved 126 instances. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2171.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Class-wise comparison of model accuracy for ProBERT and GREP. Off-diagonal terms show cases where GREP fixes errors made by ProBERT and vice-versa." ], "reasoning": "The table shows that GREP incorrectly resolved 121 instances, while ProBERT incorrectly resolved 126 instances.", "statement": "GREP incorrectly resolved more instances than ProBERT.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Single model performance on gap-test set by gender. M: masculine, F: feminine, B: (bias) ratio of feminine to masculine performance, O: overall. Log loss is not available for systems that only produce labels. †As reported by Webster et al. Webster et al. (2018). ‡As reported by Liu et al. Liu et al. (2019), their model does not use gold-two-mention labeled span information for prediction.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The GREP model with bert-large-uncased achieves state-of-the-art performance, significantly benefiting from evidence pooling with improvements in log loss and F1 accuracy.The GREP model (with bert-large-uncased as the language model) achieves a powerful state-of-the-art performance on this task. The model significantly benefits from evidence pooling, gaining 6 points in terms of log loss and 2.8 points in F1 accuracy." } ], "images": [ "scigen/train/2172.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Single model performance on gap-test set by gender. M: masculine, F: feminine, B: (bias) ratio of feminine to masculine performance, O: overall. Log loss is not available for systems that only produce labels. †As reported by Webster et al. Webster et al. (2018). ‡As reported by Liu et al. Liu et al. (2019), their model does not use gold-two-mention labeled span information for prediction." ], "reasoning": "The GREP model with bert-large-uncased achieves state-of-the-art performance, significantly benefiting from evidence pooling with improvements in log loss and F1 accuracy.", "description": "The GREP model (with bert-large-uncased as the language model) achieves a powerful state-of-the-art performance on this task. The model significantly benefits from evidence pooling, gaining 6 points in terms of log loss and 2.8 points in F1 accuracy.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: Single model performance on gap-test set by gender. M: masculine, F: feminine, B: (bias) ratio of feminine to masculine performance, O: overall. Log loss is not available for systems that only produce labels. †As reported by Webster et al. Webster et al. (2018). ‡As reported by Liu et al. Liu et al. (2019), their model does not use gold-two-mention labeled span information for prediction.', and sequentially solve the question, writing out each step of your reasoning process. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the log loss improvement of the GREP model with bert-large-uncased compared to ProBERT (bert-large-uncased)?\n" }, { "role": "assistant", "content": "The log loss for GREP (bert-large-uncased) is 0.317, while for ProBERT (bert-large-uncased) it is 0.376. The improvement is calculated as 0.376 - 0.317. So, the answer is {\"answer\": \"0.059\"}" } ], "images": [ "scigen/train/2172.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Single model performance on gap-test set by gender. M: masculine, F: feminine, B: (bias) ratio of feminine to masculine performance, O: overall. Log loss is not available for systems that only produce labels. †As reported by Webster et al. Webster et al. (2018). ‡As reported by Liu et al. Liu et al. (2019), their model does not use gold-two-mention labeled span information for prediction." ], "reasoning": "The log loss for GREP (bert-large-uncased) is 0.317, while for ProBERT (bert-large-uncased) it is 0.376. The improvement is calculated as 0.376 - 0.317.", "question": "What is the log loss improvement of the GREP model with bert-large-uncased compared to ProBERT (bert-large-uncased)?", "answer": "So, the answer is {\"answer\": \"0.059\"}" }, { "messages": [ { "role": "user", "content": "The GREP model with bert-large-uncased has the highest F1 accuracy in the table. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe GREP model with bert-large-uncased has the highest F1 accuracy in the table.\n" }, { "role": "assistant", "content": "The table shows that the GREP model with bert-large-uncased has an F1 accuracy of 94.0, which is the highest among the listed models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2172.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Single model performance on gap-test set by gender. M: masculine, F: feminine, B: (bias) ratio of feminine to masculine performance, O: overall. Log loss is not available for systems that only produce labels. †As reported by Webster et al. Webster et al. (2018). ‡As reported by Liu et al. Liu et al. (2019), their model does not use gold-two-mention labeled span information for prediction." ], "reasoning": "The table shows that the GREP model with bert-large-uncased has an F1 accuracy of 94.0, which is the highest among the listed models.", "statement": "The GREP model with bert-large-uncased has the highest F1 accuracy in the table.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: GREP model performance results in the Kaggle competition. Out-of-fold (OOF) error is reported on all data, i.e. gap-development, gap-validation, gap-test, and gpr-neither, as well as on gap-test explicitly for comparison against single model performance results. Since early stopping is based on OOF samples, OOF errors reported here cannot be considered as an estimate of test error. Nevertheless, stage 2 test performance benchmarks the model. †Due to a bug, the model did not fully leverage coref evidence, further gains are expected with the fixed version.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Ensemble models achieve larger gains in log loss compared to F1 accuracy, as they make more confident decisions on easier examples. Model accuracy benefits from more training data, but the gains are marginal.This performance comes very close to human performance and has almost no gender bias. As the table shows, the ensemble models achieve much larger gains in log loss as compared to F1 accuracy. This is expected since the committee of models makes more confident decisions on “easier” examples. (1) model accuracy benefits from more training data, although the gains are marginal at best (92.5 vs 93.9) given that the model was trained on approximately twice the amount of data; (2) ensembling has a similar effect as evidence pooling , i.e., models become more confident in their predictions." } ], "images": [ "scigen/train/2173.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: GREP model performance results in the Kaggle competition. Out-of-fold (OOF) error is reported on all data, i.e. gap-development, gap-validation, gap-test, and gpr-neither, as well as on gap-test explicitly for comparison against single model performance results. Since early stopping is based on OOF samples, OOF errors reported here cannot be considered as an estimate of test error. Nevertheless, stage 2 test performance benchmarks the model. †Due to a bug, the model did not fully leverage coref evidence, further gains are expected with the fixed version." ], "reasoning": "Ensemble models achieve larger gains in log loss compared to F1 accuracy, as they make more confident decisions on easier examples. Model accuracy benefits from more training data, but the gains are marginal.", "description": "This performance comes very close to human performance and has almost no gender bias. As the table shows, the ensemble models achieve much larger gains in log loss as compared to F1 accuracy. This is expected since the committee of models makes more confident decisions on “easier” examples. (1) model accuracy benefits from more training data, although the gains are marginal at best (92.5 vs 93.9) given that the model was trained on approximately twice the amount of data; (2) ensembling has a similar effect as evidence pooling , i.e., models become more confident in their predictions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 4: GREP model performance results in the Kaggle competition. Out-of-fold (OOF) error is reported on all data, i.e. gap-development, gap-validation, gap-test, and gpr-neither, as well as on gap-test explicitly for comparison against single model performance results. Since early stopping is based on OOF samples, OOF errors reported here cannot be considered as an estimate of test error. Nevertheless, stage 2 test performance benchmarks the model. †Due to a bug, the model did not fully leverage coref evidence, further gains are expected with the fixed version.' to sequentially solve the question presented below. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhat is the log loss for the ensemble model trained on the OOF all dataset with bert-large-cased and multiple seeds?\n" }, { "role": "assistant", "content": "The table shows the log loss for different models and datasets. For the ensemble model trained on the OOF all dataset with bert-large-cased and multiple seeds, the log loss is 0.187. So, the answer is {\"answer\": \"0.187\"}." } ], "images": [ "scigen/train/2173.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: GREP model performance results in the Kaggle competition. Out-of-fold (OOF) error is reported on all data, i.e. gap-development, gap-validation, gap-test, and gpr-neither, as well as on gap-test explicitly for comparison against single model performance results. Since early stopping is based on OOF samples, OOF errors reported here cannot be considered as an estimate of test error. Nevertheless, stage 2 test performance benchmarks the model. †Due to a bug, the model did not fully leverage coref evidence, further gains are expected with the fixed version." ], "reasoning": "The table shows the log loss for different models and datasets. For the ensemble model trained on the OOF all dataset with bert-large-cased and multiple seeds, the log loss is 0.187.", "question": "What is the log loss for the ensemble model trained on the OOF all dataset with bert-large-cased and multiple seeds?", "answer": "So, the answer is {\"answer\": \"0.187\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The ensemble model with bert-large-uncased and multiple seeds has a lower log loss on the OOF gap-test dataset compared to the OOF all dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe ensemble model with bert-large-uncased and multiple seeds has a lower log loss on the OOF gap-test dataset compared to the OOF all dataset.\n" }, { "role": "assistant", "content": "The table shows that the log loss for the ensemble model with bert-large-uncased and multiple seeds is 0.193 on the OOF gap-test dataset and 0.195 on the OOF all dataset, indicating a lower log loss on the OOF gap-test dataset. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2173.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: GREP model performance results in the Kaggle competition. Out-of-fold (OOF) error is reported on all data, i.e. gap-development, gap-validation, gap-test, and gpr-neither, as well as on gap-test explicitly for comparison against single model performance results. Since early stopping is based on OOF samples, OOF errors reported here cannot be considered as an estimate of test error. Nevertheless, stage 2 test performance benchmarks the model. †Due to a bug, the model did not fully leverage coref evidence, further gains are expected with the fixed version." ], "reasoning": "The table shows that the log loss for the ensemble model with bert-large-uncased and multiple seeds is 0.193 on the OOF gap-test dataset and 0.195 on the OOF all dataset, indicating a lower log loss on the OOF gap-test dataset.", "statement": "The ensemble model with bert-large-uncased and multiple seeds has a lower log loss on the OOF gap-test dataset compared to the OOF all dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 8: Example 1 - A comparison of probabilities assigned by ProBERT and GREP', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "ProBERT is uncertain between entities A and B, while GREP, using coreference solvers, confidently identifies A as the correct resolution.While the example is trivial for a human to resolve, a machine would require knowledge of the world to understand “death” and its implications. ProBERT is unsure about the resolution and ends up assigning comparable probabilities to both entities A and B. GREP, on the other hand, is able to shift nearly all the probability mass from B to the correct resolution A, in light of strong evidence presented by the coreference solvers. The above observations indicate that by pooling evidence from various sources, the model is able to reason over a larger space and build a rudimentary form of world knowledge." } ], "images": [ "scigen/train/2174.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Example 1 - A comparison of probabilities assigned by ProBERT and GREP" ], "reasoning": "ProBERT is uncertain between entities A and B, while GREP, using coreference solvers, confidently identifies A as the correct resolution.", "description": "While the example is trivial for a human to resolve, a machine would require knowledge of the world to understand “death” and its implications. ProBERT is unsure about the resolution and ends up assigning comparable probabilities to both entities A and B. GREP, on the other hand, is able to shift nearly all the probability mass from B to the correct resolution A, in light of strong evidence presented by the coreference solvers. The above observations indicate that by pooling evidence from various sources, the model is able to reason over a larger space and build a rudimentary form of world knowledge.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 8: Example 1 - A comparison of probabilities assigned by ProBERT and GREP', solve this question and give detail solutions. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhich model has a higher probability of correctly identifying entity A?\n" }, { "role": "assistant", "content": "By comparing the probabilities assigned to entity A by both models, GREP assigns a higher probability (0.718) compared to ProBERT (0.405). So, the answer is {\"answer\": \"GREP\"}." } ], "images": [ "scigen/train/2174.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Example 1 - A comparison of probabilities assigned by ProBERT and GREP" ], "reasoning": "By comparing the probabilities assigned to entity A by both models, GREP assigns a higher probability (0.718) compared to ProBERT (0.405).", "question": "Which model has a higher probability of correctly identifying entity A?", "answer": "So, the answer is {\"answer\": \"GREP\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nProBERT is more confident in identifying entity B than entity A.\n" }, { "role": "assistant", "content": "The table shows that ProBERT assigns a higher probability to entity B (0.452) than to entity A (0.405), indicating greater confidence in entity B. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2174.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Example 1 - A comparison of probabilities assigned by ProBERT and GREP" ], "reasoning": "The table shows that ProBERT assigns a higher probability to entity B (0.452) than to entity A (0.405), indicating greater confidence in entity B.", "statement": "ProBERT is more confident in identifying entity B than entity A.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 13: Example 3 - A comparison of probabilities assigned by ProBERT and GREP', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The model evaluates the probabilities of different outcomes by considering the interactions between various evidence sources.This example shows that the model doesn’t simply make a majority decision, rather considers interactions between the global structure exposed by the various evidence sources." } ], "images": [ "scigen/train/2175.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 13: Example 3 - A comparison of probabilities assigned by ProBERT and GREP" ], "reasoning": "The model evaluates the probabilities of different outcomes by considering the interactions between various evidence sources.", "description": "This example shows that the model doesn’t simply make a majority decision, rather considers interactions between the global structure exposed by the various evidence sources.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 13: Example 3 - A comparison of probabilities assigned by ProBERT and GREP\nQuestion: Which model has a higher probability of predicting P(A) and what is that probability?\nSolve the above question based on the table information. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To determine which model has a higher probability of predicting P(A), compare the P(A) values for ProBERT and GREP. GREP has a higher P(A) value of 0.724 compared to ProBERT's 0.028. So, the answer is {\"answer\": \"GREP with 0.724\"}." } ], "images": [ "scigen/train/2175.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 13: Example 3 - A comparison of probabilities assigned by ProBERT and GREP" ], "reasoning": "To determine which model has a higher probability of predicting P(A), compare the P(A) values for ProBERT and GREP. GREP has a higher P(A) value of 0.724 compared to ProBERT's 0.028.", "question": "Which model has a higher probability of predicting P(A) and what is that probability?", "answer": "So, the answer is {\"answer\": \"GREP with 0.724\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis ProBERT has a higher probability of predicting P(B) than GREP. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nProBERT has a higher probability of predicting P(B) than GREP.\n" }, { "role": "assistant", "content": "The table shows that ProBERT has a P(B) value of 0.968, which is higher than GREP's P(B) value of 0.263. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2175.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 13: Example 3 - A comparison of probabilities assigned by ProBERT and GREP" ], "reasoning": "The table shows that ProBERT has a P(B) value of 0.968, which is higher than GREP's P(B) value of 0.263.", "statement": "ProBERT has a higher probability of predicting P(B) than GREP.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Gold standard evaluation on domain-specific subtask. “Embed” is short for “Embedding”.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Neural models outperform term embedding averaging in all metrics, with CNN-based networks generally performing better than RNN-based ones using word embedding, while sense embedding shows poorer results due to noise from averaging.To investigate the performance of neural models on specific domains, we conduct experiments on medical and medicine subtask. All the neural models outperform term embedding averaging in terms of all the metrics and CNN-based network also performs better than RNN-based ones in most of the metrics using word embedding, which verifies our hypothesis in the general-purpose task. Compared with word embedding, the sense embedding shows a much poorer result though they work closely in general-purpose subtask. The reason might be the simple averaging of sense embedding of various domains for a word, which may introduce too much noise and bias the overall sense representation. This also discloses that modeling the sense embedding of specific domains could be quite important for further improvement." } ], "images": [ "scigen/train/2176.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Gold standard evaluation on domain-specific subtask. “Embed” is short for “Embedding”." ], "reasoning": "Neural models outperform term embedding averaging in all metrics, with CNN-based networks generally performing better than RNN-based ones using word embedding, while sense embedding shows poorer results due to noise from averaging.", "description": "To investigate the performance of neural models on specific domains, we conduct experiments on medical and medicine subtask. All the neural models outperform term embedding averaging in terms of all the metrics and CNN-based network also performs better than RNN-based ones in most of the metrics using word embedding, which verifies our hypothesis in the general-purpose task. Compared with word embedding, the sense embedding shows a much poorer result though they work closely in general-purpose subtask. The reason might be the simple averaging of sense embedding of various domains for a word, which may introduce too much noise and bias the overall sense representation. This also discloses that modeling the sense embedding of specific domains could be quite important for further improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: Gold standard evaluation on domain-specific subtask. “Embed” is short for “Embedding”.'. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model using word embedding has the highest medical MAP score?\n" }, { "role": "assistant", "content": "To find the model with the highest medical MAP score using word embedding, look at the 'medical MAP' column under the 'Word' embedding rows. The highest score is 18.31, achieved by the CNN model. So, the answer is {\"answer\": \"CNN\"}." } ], "images": [ "scigen/train/2176.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Gold standard evaluation on domain-specific subtask. “Embed” is short for “Embedding”." ], "reasoning": "To find the model with the highest medical MAP score using word embedding, look at the 'medical MAP' column under the 'Word' embedding rows. The highest score is 18.31, achieved by the CNN model.", "question": "Which model using word embedding has the highest medical MAP score?", "answer": "So, the answer is {\"answer\": \"CNN\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe GRU model using word embedding has a higher medical MRR score than the LSTM model using word embedding.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'medical MRR' scores of the GRU and LSTM models under the 'Word' embedding rows. The GRU model has a score of 21.89, while the LSTM model has a score of 21.11, indicating that the statement is true. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2176.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Gold standard evaluation on domain-specific subtask. “Embed” is short for “Embedding”." ], "reasoning": "To verify this statement, compare the 'medical MRR' scores of the GRU and LSTM models under the 'Word' embedding rows. The GRU model has a score of 21.89, while the LSTM model has a score of 21.11, indicating that the statement is true.", "statement": "The GRU model using word embedding has a higher medical MRR score than the LSTM model using word embedding.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Gold standard evaluation on general-purpose subtask.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Neural models outperform term embedding averaging in all metrics, with CNN-based networks performing better than RNN-based networks, indicating the importance of local features over long-term dependencies.All the neural models outperform term embedding averaging in terms of all the metrics. This result indicates simply averaging the embedding of words in a phrase is not an appropriate solution to represent a phrase. Convolution or recurrent gated mechanisms in either CNN-based (CNN, RCNN) or RNN (GRU, LSTM) based neural networks could essentially be helpful of modeling the semantic connections between words in a phrase, and guide the networks to discover the hypernym relationships. We also observe CNN-based network performance is better than RNN-based, which indicates local features between words could be more important than long-term dependency in this task where the term length is up to trigrams." } ], "images": [ "scigen/train/2177.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Gold standard evaluation on general-purpose subtask." ], "reasoning": "Neural models outperform term embedding averaging in all metrics, with CNN-based networks performing better than RNN-based networks, indicating the importance of local features over long-term dependencies.", "description": "All the neural models outperform term embedding averaging in terms of all the metrics. This result indicates simply averaging the embedding of words in a phrase is not an appropriate solution to represent a phrase. Convolution or recurrent gated mechanisms in either CNN-based (CNN, RCNN) or RNN (GRU, LSTM) based neural networks could essentially be helpful of modeling the semantic connections between words in a phrase, and guide the networks to discover the hypernym relationships. We also observe CNN-based network performance is better than RNN-based, which indicates local features between words could be more important than long-term dependency in this task where the term length is up to trigrams.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Gold standard evaluation on general-purpose subtask.', answer the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model has the highest P@3 score and what does this indicate about the importance of local features?\n" }, { "role": "assistant", "content": "The RCNN model with word embedding has the highest P@3 score of 9.67, indicating that local features between words are crucial for this task. So, the answer is {\"answer\": \"RCNN\"}." } ], "images": [ "scigen/train/2177.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Gold standard evaluation on general-purpose subtask." ], "reasoning": "The RCNN model with word embedding has the highest P@3 score of 9.67, indicating that local features between words are crucial for this task.", "question": "Which model has the highest P@3 score and what does this indicate about the importance of local features?", "answer": "So, the answer is {\"answer\": \"RCNN\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe GRU model with word embedding has the highest MAP score.\n" }, { "role": "assistant", "content": "The table shows that the RCNN model with word embedding has the highest MAP score of 8.74, not the GRU model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2177.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Gold standard evaluation on general-purpose subtask." ], "reasoning": "The table shows that the RCNN model with word embedding has the highest MAP score of 8.74, not the GRU model.", "statement": "The GRU model with word embedding has the highest MAP score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 16: The top shows the most used words in happy moments for instruction sets I1 and I2. The bottom shows the frequencies in I1 and I2 w.r.t. V, the vocabulary in our positive examples.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The χ2-test showed that the word usage in happy moments is influenced by the presence of positive examples in the instructions, leading to a significant difference in word frequencies between I1 and I2 batches.Upon collecting our first batch of 300 happy moments, we tabulated the top 20 most frequent words (nouns/verbs/adjectives) that occur in the happy moments. It was surprising to observe that the words used in the positive examples often appear in the top 20 most frequent words. For example, the bold words “morning”, “enjoy”, “woke”, “son”, and “great” ( excluding “went”), which are words we use in our positive examples, appear highly among the top 20 words. Consequently, we experimented with an instruction set without the positive examples (I2) and collected another 300 happy moments. When we compare the moments collected under I1 and I2, it is evident that the bold words no longer appear highly ranked. Here, V denotes the set of all words (no duplicates and no stopwords) obtained from our 5 positive examples in the I1 instruction set. We counted the number of times words in V occur in the happy moments obtained from I1 instruction and, respectively, I2 instruction. In addition, we also counted the number of times words outside V occur in the respective batches of happy moments. Specifically, our null hypothesis is that the word usage of the happy moments is independent of whether the instruction set contained positive examples or not. The χ2-test rejected the null hypothesis with p-value <0.001. Hence, we conclude that MTurk workers were influenced by the positive examples in our instructions when reporting their happy moments and decided against positive example sentences in the instructions for collecting happy moments." } ], "images": [ "scigen/train/2178.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 16: The top shows the most used words in happy moments for instruction sets I1 and I2. The bottom shows the frequencies in I1 and I2 w.r.t. V, the vocabulary in our positive examples." ], "reasoning": "The χ2-test showed that the word usage in happy moments is influenced by the presence of positive examples in the instructions, leading to a significant difference in word frequencies between I1 and I2 batches.", "description": "Upon collecting our first batch of 300 happy moments, we tabulated the top 20 most frequent words (nouns/verbs/adjectives) that occur in the happy moments. It was surprising to observe that the words used in the positive examples often appear in the top 20 most frequent words. For example, the bold words “morning”, “enjoy”, “woke”, “son”, and “great” ( excluding “went”), which are words we use in our positive examples, appear highly among the top 20 words. Consequently, we experimented with an instruction set without the positive examples (I2) and collected another 300 happy moments. When we compare the moments collected under I1 and I2, it is evident that the bold words no longer appear highly ranked. Here, V denotes the set of all words (no duplicates and no stopwords) obtained from our 5 positive examples in the I1 instruction set. We counted the number of times words in V occur in the happy moments obtained from I1 instruction and, respectively, I2 instruction. In addition, we also counted the number of times words outside V occur in the respective batches of happy moments. Specifically, our null hypothesis is that the word usage of the happy moments is independent of whether the instruction set contained positive examples or not. The χ2-test rejected the null hypothesis with p-value <0.001. Hence, we conclude that MTurk workers were influenced by the positive examples in our instructions when reporting their happy moments and decided against positive example sentences in the instructions for collecting happy moments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 16: The top shows the most used words in happy moments for instruction sets I1 and I2. The bottom shows the frequencies in I1 and I2 w.r.t. V, the vocabulary in our positive examples.', unfold the question presented below, detailing every step of your calculation. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the total number of words in the I1 batch?\n" }, { "role": "assistant", "content": "To find the total number of words in the I1 batch, sum the number of words from V and the number of words not from V in the I1 row: 213 + 3,115. So, the answer is {\"answer\": \"3328\"}." } ], "images": [ "scigen/train/2178.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 16: The top shows the most used words in happy moments for instruction sets I1 and I2. The bottom shows the frequencies in I1 and I2 w.r.t. V, the vocabulary in our positive examples." ], "reasoning": "To find the total number of words in the I1 batch, sum the number of words from V and the number of words not from V in the I1 row: 213 + 3,115.", "question": "What is the total number of words in the I1 batch?", "answer": "So, the answer is {\"answer\": \"3328\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The number of words from V in the I2 batch is less than the number of words from V in the I1 batch. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe number of words from V in the I2 batch is less than the number of words from V in the I1 batch.\n" }, { "role": "assistant", "content": "The table shows that the number of words from V in the I2 batch is 126, which is less than the 213 words from V in the I1 batch. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2178.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 16: The top shows the most used words in happy moments for instruction sets I1 and I2. The bottom shows the frequencies in I1 and I2 w.r.t. V, the vocabulary in our positive examples." ], "reasoning": "The table shows that the number of words from V in the I2 batch is 126, which is less than the 213 words from V in the I1 batch.", "statement": "The number of words from V in the I2 batch is less than the number of words from V in the I1 batch.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: HappyDB Topics Distributions', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "80% of HappyDB sentences pertain to 9 main topics, with 'people' being a superset of 'family' and other topics, while the remaining sentences cover a variety of less common topics.Note that a happy moment may be related to multiple topics. For instance, “running with my son” is related to both “family” and “exercise”. All of the keywords lists are disjoint except for “people”: this is a superset of “family”, and also contains some words from other topics, for example “co-worker” which is also in “work”. We can observe that 80% of HappyDB pertains to these 9 topics. The remaining sentences that did not fit into any of these topics contain all sorts of topics, such as rare surprises (“finding a $100 dollar bill inside my pants pockets”) or situations that turned out to be better than expected (“There was almost no traffic today”). None of these other categories covered a large enough portion of our corpus to justify adding them to our dictionaries. However, this suggests that there is a long tail of topics in the corpus." } ], "images": [ "scigen/train/2179.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: HappyDB Topics Distributions" ], "reasoning": "80% of HappyDB sentences pertain to 9 main topics, with 'people' being a superset of 'family' and other topics, while the remaining sentences cover a variety of less common topics.", "description": "Note that a happy moment may be related to multiple topics. For instance, “running with my son” is related to both “family” and “exercise”. All of the keywords lists are disjoint except for “people”: this is a superset of “family”, and also contains some words from other topics, for example “co-worker” which is also in “work”. We can observe that 80% of HappyDB pertains to these 9 topics. The remaining sentences that did not fit into any of these topics contain all sorts of topics, such as rare surprises (“finding a $100 dollar bill inside my pants pockets”) or situations that turned out to be better than expected (“There was almost no traffic today”). None of these other categories covered a large enough portion of our corpus to justify adding them to our dictionaries. However, this suggests that there is a long tail of topics in the corpus.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: HappyDB Topics Distributions', solve the subsequent question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich topic has the largest keywords list and what percentage of sentences does it cover?\n" }, { "role": "assistant", "content": "By examining the table, we see that 'food' has the largest keywords list with 1073 keywords and covers 16.2% of sentences. So, the answer is {\"answer\": \"food, 16.2\"}." } ], "images": [ "scigen/train/2179.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: HappyDB Topics Distributions" ], "reasoning": "By examining the table, we see that 'food' has the largest keywords list with 1073 keywords and covers 16.2% of sentences.", "question": "Which topic has the largest keywords list and what percentage of sentences does it cover?", "answer": "So, the answer is {\"answer\": \"food, 16.2\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe 'exercise' topic covers more sentences than the 'entertainment' topic.\n" }, { "role": "assistant", "content": "The table shows that 'exercise' covers 8.4% of sentences, while 'entertainment' covers 8.8%, so 'exercise' covers fewer sentences. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2179.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: HappyDB Topics Distributions" ], "reasoning": "The table shows that 'exercise' covers 8.4% of sentences, while 'entertainment' covers 8.8%, so 'exercise' covers fewer sentences.", "statement": "The 'exercise' topic covers more sentences than the 'entertainment' topic.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: Average LIWC Scores for (cleaned) HappyDB compared to those of other text corpora of expressive writing, novels, and blogs [Pennebaker et al.2015a]. All three categories provide scores in the range from 1 to 99.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "HappyDB has a high tone score, close analytic score to Novels, and a high authentic score similar to Expressive Writing.These categories are defined as follows: analytic refers to a measurement of the author’s logical thinking, as opposed to narrative and informal thinking; authentic approximates how honest and disclosing the writing is; and tone measures how positive or negative the text is. As expected, HappyDB has a higher score for tone than any of the other corpora analyzed. More interestingly, the analytic score for HappyDB is quite high and very close to that of Novels, yet the authentic score (also quite high) is closer to that for Expressive Writing. Our analysis of LIWC scores suggest that our corpus is very disclosing and honest which makes it an ideal corpus for studying emotions expressed in text." } ], "images": [ "scigen/train/2180.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Average LIWC Scores for (cleaned) HappyDB compared to those of other text corpora of expressive writing, novels, and blogs [Pennebaker et al.2015a]. All three categories provide scores in the range from 1 to 99." ], "reasoning": "HappyDB has a high tone score, close analytic score to Novels, and a high authentic score similar to Expressive Writing.", "description": "These categories are defined as follows: analytic refers to a measurement of the author’s logical thinking, as opposed to narrative and informal thinking; authentic approximates how honest and disclosing the writing is; and tone measures how positive or negative the text is. As expected, HappyDB has a higher score for tone than any of the other corpora analyzed. More interestingly, the analytic score for HappyDB is quite high and very close to that of Novels, yet the authentic score (also quite high) is closer to that for Expressive Writing. Our analysis of LIWC scores suggest that our corpus is very disclosing and honest which makes it an ideal corpus for studying emotions expressed in text.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 5: Average LIWC Scores for (cleaned) HappyDB compared to those of other text corpora of expressive writing, novels, and blogs [Pennebaker et al.2015a]. All three categories provide scores in the range from 1 to 99.', solve this question and give detail solutions. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich corpus has the highest score for the authentic category?\n" }, { "role": "assistant", "content": "By examining the 'authentic' row in the table, we see that HappyDB has a score of 77.14, Expressive has 76.01, Novels has 21.56, and Blogs has 60.93. Therefore, HappyDB has the highest score for the authentic category. So, the answer is {\"answer\": \"HappyDB\"}." } ], "images": [ "scigen/train/2180.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Average LIWC Scores for (cleaned) HappyDB compared to those of other text corpora of expressive writing, novels, and blogs [Pennebaker et al.2015a]. All three categories provide scores in the range from 1 to 99." ], "reasoning": "By examining the 'authentic' row in the table, we see that HappyDB has a score of 77.14, Expressive has 76.01, Novels has 21.56, and Blogs has 60.93. Therefore, HappyDB has the highest score for the authentic category.", "question": "Which corpus has the highest score for the authentic category?", "answer": "So, the answer is {\"answer\": \"HappyDB\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe analytic score for Blogs is higher than that for Expressive Writing.\n" }, { "role": "assistant", "content": "By comparing the 'analytic' scores in the table, Blogs has a score of 49.89, while Expressive Writing has a score of 44.88. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2180.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Average LIWC Scores for (cleaned) HappyDB compared to those of other text corpora of expressive writing, novels, and blogs [Pennebaker et al.2015a]. All three categories provide scores in the range from 1 to 99." ], "reasoning": "By comparing the 'analytic' scores in the table, Blogs has a score of 49.89, while Expressive Writing has a score of 44.88. Therefore, the statement is supported.", "statement": "The analytic score for Blogs is higher than that for Expressive Writing.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 8: Percentage of happy moments in each topic, separated by reflection period. All differences between the columns are statistically significant at p<10−3 except for pets, work, and none.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Topics like food and entertainment are more frequently mentioned in the 24-hour reflection period compared to the 3-month reflection period, as daily events are more likely to be remembered if they occurred recently.Topic Mentions by Reflection Period : We analyzed the incidence of different topics separately for each reflection period. For instance, we observe that the categories “food” and “entertainment” have higher percentage of coverage in 24 hour (19.2%, and 9.6%) compared to the 3 months reflection period (13.1%, and 7.8%). Naturally, people are more likely to talk about a meal or a movie because these are more frequent daily events that are more likely to be remembered if they occurred recently. When people are asked to reflect on the past 3 months, they tend to remember events that are more prominent such as school, big achievements, and time spent with friends and family." } ], "images": [ "scigen/train/2181.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Percentage of happy moments in each topic, separated by reflection period. All differences between the columns are statistically significant at p<10−3 except for pets, work, and none." ], "reasoning": "Topics like food and entertainment are more frequently mentioned in the 24-hour reflection period compared to the 3-month reflection period, as daily events are more likely to be remembered if they occurred recently.", "description": "Topic Mentions by Reflection Period : We analyzed the incidence of different topics separately for each reflection period. For instance, we observe that the categories “food” and “entertainment” have higher percentage of coverage in 24 hour (19.2%, and 9.6%) compared to the 3 months reflection period (13.1%, and 7.8%). Naturally, people are more likely to talk about a meal or a movie because these are more frequent daily events that are more likely to be remembered if they occurred recently. When people are asked to reflect on the past 3 months, they tend to remember events that are more prominent such as school, big achievements, and time spent with friends and family.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. The question is related to the given table titled 'Table 8: Percentage of happy moments in each topic, separated by reflection period. All differences between the columns are statistically significant at p<10−3 except for pets, work, and none.'.\n\nQuestion: Which topic has the highest percentage of sentences in the 3 Months Reflection period?\n" }, { "role": "assistant", "content": "To find the topic with the highest percentage of sentences in the 3 Months Reflection period, we need to compare the percentages in the second column of the table. The topic 'people' has the highest percentage at 47.9%. So, the answer is {\"answer\": \"people\"}." } ], "images": [ "scigen/train/2181.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Percentage of happy moments in each topic, separated by reflection period. All differences between the columns are statistically significant at p<10−3 except for pets, work, and none." ], "reasoning": "To find the topic with the highest percentage of sentences in the 3 Months Reflection period, we need to compare the percentages in the second column of the table. The topic 'people' has the highest percentage at 47.9%.", "question": "Which topic has the highest percentage of sentences in the 3 Months Reflection period?", "answer": "So, the answer is {\"answer\": \"people\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The percentage of sentences about pets is higher in the 24 Hours Reflection period than in the 3 Months Reflection period., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe percentage of sentences about pets is higher in the 24 Hours Reflection period than in the 3 Months Reflection period.\n" }, { "role": "assistant", "content": "The table shows that the percentage of sentences about pets is 4.6% in both the 24 Hours Reflection and the 3 Months Reflection periods. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2181.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Percentage of happy moments in each topic, separated by reflection period. All differences between the columns are statistically significant at p<10−3 except for pets, work, and none." ], "reasoning": "The table shows that the percentage of sentences about pets is 4.6% in both the 24 Hours Reflection and the 3 Months Reflection periods. Therefore, the statement is refuted.", "statement": "The percentage of sentences about pets is higher in the 24 Hours Reflection period than in the 3 Months Reflection period.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 10: Precision/recall/F1 scores for each category as well as the % of moments per category for each reflection period. All differences between the reflection periods are statistically significant at p<10−5 except for bonding.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The classifier shows room for improvement in categories like 'Leisure' and 'Enjoy the moment', indicating that word distributions alone are insufficient for this task.We developed a multi-class classifier using Logistic Regression with a bag of words representation of happy moments as features. To obtain training data, we crowdsourced a batch of 15,000 happy moments to obtain category labels. Every happy moment was shown to 5 workers, and we only considered labels that at least 3 workers agreed on. Clearly the classifier has room for further improvement on categories such as “Leisure” and “Enjoy the moment” which shows that word distributions are not sufficient for this task. Building a classifier with sufficiently high precision/recall scores on all categories can be a challenging task in general, as it usually involves inferring some information or context that is not explicitly mentioned in text. Notice that HappyDB has roughly the same number of moments for each reflection period. The higher frequency of moments in “Exercise”, “Nature”, and “Leisure” under the 24 hours reflection period confirms our theory that daily tasks are sources of short-term happiness. Longer-term happiness is more likely to come from loved ones or achievements." } ], "images": [ "scigen/train/2182.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Precision/recall/F1 scores for each category as well as the % of moments per category for each reflection period. All differences between the reflection periods are statistically significant at p<10−5 except for bonding." ], "reasoning": "The classifier shows room for improvement in categories like 'Leisure' and 'Enjoy the moment', indicating that word distributions alone are insufficient for this task.", "description": "We developed a multi-class classifier using Logistic Regression with a bag of words representation of happy moments as features. To obtain training data, we crowdsourced a batch of 15,000 happy moments to obtain category labels. Every happy moment was shown to 5 workers, and we only considered labels that at least 3 workers agreed on. Clearly the classifier has room for further improvement on categories such as “Leisure” and “Enjoy the moment” which shows that word distributions are not sufficient for this task. Building a classifier with sufficiently high precision/recall scores on all categories can be a challenging task in general, as it usually involves inferring some information or context that is not explicitly mentioned in text. Notice that HappyDB has roughly the same number of moments for each reflection period. The higher frequency of moments in “Exercise”, “Nature”, and “Leisure” under the 24 hours reflection period confirms our theory that daily tasks are sources of short-term happiness. Longer-term happiness is more likely to come from loved ones or achievements.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 10: Precision/recall/F1 scores for each category as well as the % of moments per category for each reflection period. All differences between the reflection periods are statistically significant at p<10−5 except for bonding.', unfold the steps to solve the question given next. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nQuestion: Which category has the highest F1 score and what is its percentage of moments in the 24 hours reflection period?\n" }, { "role": "assistant", "content": "The category with the highest F1 score is 'Affection' with an F1 score of 92.0. The percentage of moments in the 24 hours reflection period for 'Affection' is 32.7%. So, the answer is {\"answer\": \"Affection, 32.7%\"}." } ], "images": [ "scigen/train/2182.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Precision/recall/F1 scores for each category as well as the % of moments per category for each reflection period. All differences between the reflection periods are statistically significant at p<10−5 except for bonding." ], "reasoning": "The category with the highest F1 score is 'Affection' with an F1 score of 92.0. The percentage of moments in the 24 hours reflection period for 'Affection' is 32.7%.", "question": "Which category has the highest F1 score and what is its percentage of moments in the 24 hours reflection period?", "answer": "So, the answer is {\"answer\": \"Affection, 32.7%\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe 'Exercise' category has the lowest recall score.\n" }, { "role": "assistant", "content": "The table shows that the 'Exercise' category has a recall score of 59.3, which is higher than the recall score of 'Enjoy the moment' at 49.9 and 'Nature' at 52.4. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2182.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Precision/recall/F1 scores for each category as well as the % of moments per category for each reflection period. All differences between the reflection periods are statistically significant at p<10−5 except for bonding." ], "reasoning": "The table shows that the 'Exercise' category has a recall score of 59.3, which is higher than the recall score of 'Enjoy the moment' at 49.9 and 'Nature' at 52.4.", "statement": "The 'Exercise' category has the lowest recall score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 16: The top shows the most used words in happy moments for instruction sets I1 and I2. The bottom shows the frequencies in I1 and I2 w.r.t. V, the vocabulary in our positive examples.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The χ2-test showed that the word usage in happy moments is influenced by the presence of positive examples in the instructions, leading to the decision to exclude positive example sentences.Upon collecting our first batch of 300 happy moments, we tabulated the top 20 most frequent words (nouns/verbs/adjectives) that occur in the happy moments. It was surprising to observe that the words used in the positive examples often appear in the top 20 most frequent words. For example, the bold words “morning”, “enjoy”, “woke”, “son”, and “great” ( excluding “went”), which are words we use in our positive examples, appear highly among the top 20 words. Consequently, we experimented with an instruction set without the positive examples (I2) and collected another 300 happy moments. When we compare the moments collected under I1 and I2, it is evident that the bold words no longer appear highly ranked. Here, V denotes the set of all words (no duplicates and no stopwords) obtained from our 5 positive examples in the I1 instruction set. We counted the number of times words in V occur in the happy moments obtained from I1 instruction and, respectively, I2 instruction. In addition, we also counted the number of times words outside V occur in the respective batches of happy moments. Specifically, our null hypothesis is that the word usage of the happy moments is independent of whether the instruction set contained positive examples or not. The χ2-test rejected the null hypothesis with p-value <0.001. Hence, we conclude that MTurk workers were influenced by the positive examples in our instructions when reporting their happy moments and decided against positive example sentences in the instructions for collecting happy moments." } ], "images": [ "scigen/train/2183.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 16: The top shows the most used words in happy moments for instruction sets I1 and I2. The bottom shows the frequencies in I1 and I2 w.r.t. V, the vocabulary in our positive examples." ], "reasoning": "The χ2-test showed that the word usage in happy moments is influenced by the presence of positive examples in the instructions, leading to the decision to exclude positive example sentences.", "description": "Upon collecting our first batch of 300 happy moments, we tabulated the top 20 most frequent words (nouns/verbs/adjectives) that occur in the happy moments. It was surprising to observe that the words used in the positive examples often appear in the top 20 most frequent words. For example, the bold words “morning”, “enjoy”, “woke”, “son”, and “great” ( excluding “went”), which are words we use in our positive examples, appear highly among the top 20 words. Consequently, we experimented with an instruction set without the positive examples (I2) and collected another 300 happy moments. When we compare the moments collected under I1 and I2, it is evident that the bold words no longer appear highly ranked. Here, V denotes the set of all words (no duplicates and no stopwords) obtained from our 5 positive examples in the I1 instruction set. We counted the number of times words in V occur in the happy moments obtained from I1 instruction and, respectively, I2 instruction. In addition, we also counted the number of times words outside V occur in the respective batches of happy moments. Specifically, our null hypothesis is that the word usage of the happy moments is independent of whether the instruction set contained positive examples or not. The χ2-test rejected the null hypothesis with p-value <0.001. Hence, we conclude that MTurk workers were influenced by the positive examples in our instructions when reporting their happy moments and decided against positive example sentences in the instructions for collecting happy moments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: Table 16: The top shows the most used words in happy moments for instruction sets I1 and I2. The bottom shows the frequencies in I1 and I2 w.r.t. V, the vocabulary in our positive examples.\nQuestion: What is the difference in the count of the word 'morning' between I1 and I2 instruction sets?\n" }, { "role": "assistant", "content": "The word 'morning' appears 21 times in the I1 instruction set and 14 times in the I2 instruction set. The difference is 21 - 14. So, the answer is {\"answer\": \"7\"}." } ], "images": [ "scigen/train/2183.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 16: The top shows the most used words in happy moments for instruction sets I1 and I2. The bottom shows the frequencies in I1 and I2 w.r.t. V, the vocabulary in our positive examples." ], "reasoning": "The word 'morning' appears 21 times in the I1 instruction set and 14 times in the I2 instruction set. The difference is 21 - 14.", "question": "What is the difference in the count of the word 'morning' between I1 and I2 instruction sets?", "answer": "So, the answer is {\"answer\": \"7\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe word 'great' appears more frequently in the I1 instruction set than in the I2 instruction set.\n" }, { "role": "assistant", "content": "The table shows that the word 'great' appears 25 times in the I1 instruction set and does not appear in the I2 instruction set. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2183.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 16: The top shows the most used words in happy moments for instruction sets I1 and I2. The bottom shows the frequencies in I1 and I2 w.r.t. V, the vocabulary in our positive examples." ], "reasoning": "The table shows that the word 'great' appears 25 times in the I1 instruction set and does not appear in the I2 instruction set.", "statement": "The word 'great' appears more frequently in the I1 instruction set than in the I2 instruction set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Accuracy'.\n" }, { "role": "assistant", "content": "The table compares the performance of different loss functions and an ensemble approach on various datasets using a neural network model implemented in TensorFlow.Since this work is a proof of concept, the neural networks of each application are simply a softmax of the linear combination of input features plus bias: ^y=softmax(x⋅W+b) where the input features x are the word frequencies in the input text. Thus, θ in our notation is composed of W and b. We use Python and its TensorFlow package for implementing the proposed approach. The table compares the results of using individual loss functions and the ensemble loss." } ], "images": [ "scigen/train/2184.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracy" ], "reasoning": "The table compares the performance of different loss functions and an ensemble approach on various datasets using a neural network model implemented in TensorFlow.", "description": "Since this work is a proof of concept, the neural networks of each application are simply a softmax of the linear combination of input features plus bias: ^y=softmax(x⋅W+b) where the input features x are the word frequencies in the input text. Thus, θ in our notation is composed of W and b. We use Python and its TensorFlow package for implementing the proposed approach. The table compares the results of using individual loss functions and the ensemble loss.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Accuracy\nQuestion: Which dataset achieved the highest performance using the ensemble loss function?\nGive a detailed response to the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the dataset with the highest performance using the ensemble loss function, look at the 'Ensemble' column and identify the highest value. The highest value is 0.97, which corresponds to the Email-Classification (TREC) dataset. So, the answer is {\"answer\": \"Email-Classification (TREC)\"}." } ], "images": [ "scigen/train/2184.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracy" ], "reasoning": "To find the dataset with the highest performance using the ensemble loss function, look at the 'Ensemble' column and identify the highest value. The highest value is 0.97, which corresponds to the Email-Classification (TREC) dataset.", "question": "Which dataset achieved the highest performance using the ensemble loss function?", "answer": "So, the answer is {\"answer\": \"Email-Classification (TREC)\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The hinge loss function performed better than the cross-entropy loss function on the Reuters dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe hinge loss function performed better than the cross-entropy loss function on the Reuters dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the values for the hinge and cross-entropy loss functions in the Reuters row. The hinge loss function has a value of 0.79, while the cross-entropy loss function has a value of 0.79. Since both values are equal, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2184.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracy" ], "reasoning": "To verify this statement, compare the values for the hinge and cross-entropy loss functions in the Reuters row. The hinge loss function has a value of 0.79, while the cross-entropy loss function has a value of 0.79. Since both values are equal, the statement is refuted.", "statement": "The hinge loss function performed better than the cross-entropy loss function on the Reuters dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Accuracy on Yelp.'.\n" }, { "role": "assistant", "content": "The VAE approach achieves the best accuracy in text classification tasks, especially with fewer training samples, demonstrating its ability to learn good latent representations without a pretrained inference network.Performance on a downstream task - Text classification: The goal of VAE is to learn a good representation of the data for downstream tasks. Here, we evaluate the quality of latent representations by training a one-layer linear classifier based on the mean of the posterior distribution. We use a downsampled version of the Yelp sentiment dataset shen2017style. li-etal-2019-surprisingly further sampled various labeled data to train the classifier. To compare with them fairly, we use the same samples in li-etal-2019-surprisingly. Our approach achieves the best accuracy in all the settings. For 10k training samples, all the methods get a good result. However, when only using 100 training samples, different methods vary a lot in accuracy. The text classification task shows that our approach can learn a good latent representation even without a pretrained inference network." } ], "images": [ "scigen/train/2185.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracy on Yelp." ], "reasoning": "The VAE approach achieves the best accuracy in text classification tasks, especially with fewer training samples, demonstrating its ability to learn good latent representations without a pretrained inference network.", "description": "Performance on a downstream task - Text classification: The goal of VAE is to learn a good representation of the data for downstream tasks. Here, we evaluate the quality of latent representations by training a one-layer linear classifier based on the mean of the posterior distribution. We use a downsampled version of the Yelp sentiment dataset shen2017style. li-etal-2019-surprisingly further sampled various labeled data to train the classifier. To compare with them fairly, we use the same samples in li-etal-2019-surprisingly. Our approach achieves the best accuracy in all the settings. For 10k training samples, all the methods get a good result. However, when only using 100 training samples, different methods vary a lot in accuracy. The text classification task shows that our approach can learn a good latent representation even without a pretrained inference network.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Accuracy on Yelp.', solve the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method achieves the highest accuracy with 100 training samples?\n" }, { "role": "assistant", "content": "By examining the table, the highest accuracy with 100 training samples is achieved by BN-VAE (0.7) with an accuracy of 88.8. So, the answer is {\"answer\": \"BN-VAE (0.7)\"}" } ], "images": [ "scigen/train/2185.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracy on Yelp." ], "reasoning": "By examining the table, the highest accuracy with 100 training samples is achieved by BN-VAE (0.7) with an accuracy of 88.8.", "question": "Which method achieves the highest accuracy with 100 training samples?", "answer": "So, the answer is {\"answer\": \"BN-VAE (0.7)\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nAE+FB (6) achieves the highest accuracy with 10k training samples.\n" }, { "role": "assistant", "content": "The table shows that BN-VAE (0.7) achieves the highest accuracy with 10k training samples at 95.4, while AE+FB (6) achieves 94.9. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2185.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracy on Yelp." ], "reasoning": "The table shows that BN-VAE (0.7) achieves the highest accuracy with 10k training samples at 95.4, while AE+FB (6) achieves 94.9.", "statement": "AE+FB (6) achieves the highest accuracy with 10k training samples.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Comparison on dialogue generation.'.\n" }, { "role": "assistant", "content": "CVAE suffers from posterior collapse as indicated by low KL values, while CVAE (BOW) and BN-VAE avoid it. CVAE has slightly better BLEU-4 scores, but CVAE (BOW) and BN-VAE outperform CVAE in recall.Automatic evaluation: From the KL values, we find that CVAE suffers from posterior collapse while CVAE (BOW) and our approach avoid it effectively. For BLEU-4, we observe the same phenomenon in the previous work liu2019cyclical; zhao2017learning that CVAE is slightly better than the others. This is because CVAE tends to generate the most likely and safe responses repeatedly with the collapsed posterior. As for precision, these three models do not differ much. However, CVAE (BOW) and our BN-VAE outperform CVAE in recall with a large margin. This indicates that BN-VAE can also produce diverse responses with good quality like CVAE (BOW)." } ], "images": [ "scigen/train/2186.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison on dialogue generation." ], "reasoning": "CVAE suffers from posterior collapse as indicated by low KL values, while CVAE (BOW) and BN-VAE avoid it. CVAE has slightly better BLEU-4 scores, but CVAE (BOW) and BN-VAE outperform CVAE in recall.", "description": "Automatic evaluation: From the KL values, we find that CVAE suffers from posterior collapse while CVAE (BOW) and our approach avoid it effectively. For BLEU-4, we observe the same phenomenon in the previous work liu2019cyclical; zhao2017learning that CVAE is slightly better than the others. This is because CVAE tends to generate the most likely and safe responses repeatedly with the collapsed posterior. As for precision, these three models do not differ much. However, CVAE (BOW) and our BN-VAE outperform CVAE in recall with a large margin. This indicates that BN-VAE can also produce diverse responses with good quality like CVAE (BOW).", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 4: Comparison on dialogue generation.', answer the question below by showing each progressive step toward the answer. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest A-bow Precision and how does it compare to the others in terms of KL values?\n" }, { "role": "assistant", "content": "The table shows that BN-VAE has the highest A-bow Precision at 96.64. Comparing KL values, BN-VAE has a KL value of 5.18, which is higher than CVAE's 0.15 but lower than CVAE (BOW)'s 9.30. So, the answer is {\"answer\": \"BN-VAE\"}" } ], "images": [ "scigen/train/2186.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison on dialogue generation." ], "reasoning": "The table shows that BN-VAE has the highest A-bow Precision at 96.64. Comparing KL values, BN-VAE has a KL value of 5.18, which is higher than CVAE's 0.15 but lower than CVAE (BOW)'s 9.30.", "question": "Which model has the highest A-bow Precision and how does it compare to the others in terms of KL values?", "answer": "So, the answer is {\"answer\": \"BN-VAE\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nBN-VAE has the lowest PPL among the three models.\n" }, { "role": "assistant", "content": "The table shows that CVAE (BOW) has the lowest PPL at 24.49, while BN-VAE has a PPL of 30.67. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2186.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison on dialogue generation." ], "reasoning": "The table shows that CVAE (BOW) has the lowest PPL at 24.49, while BN-VAE has a PPL of 30.67.", "statement": "BN-VAE has the lowest PPL among the three models.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 8: Results on Yahoo and Yelp datasets. We report mean values across 5 different random runs. ∗ indicates the results are from our experiments, while others are from previous report.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The training procedure for different models shows that while DKL increases in the basic VAE with annealing, its MI remains small, leading to posterior collapse. In contrast, the proposed approach achieves high MI with a small DKL value similar to aggressive VAE.We investigate the training procedure for different models. We plot the MI Iq, DKL in the ELBO and the distance between the approximated posterior and the prior, DKL(qϕ(z)||p(z)). As in Eq. 4 in the main paper, DKL in the ELBO is the sum of the other two. Fig. Although DKL is the upper bound of the mutual information, we notice that the gap is usually large. In the initial training stage, DKL increases in the basic VAE with annealing, while its MI remains small. With the weight decreases, the method finally suffers from posterior collapse. In contrast, our approach can obtain a high MI with a small DKL value like aggressive VAE." } ], "images": [ "scigen/train/2187.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Results on Yahoo and Yelp datasets. We report mean values across 5 different random runs. ∗ indicates the results are from our experiments, while others are from previous report." ], "reasoning": "The training procedure for different models shows that while DKL increases in the basic VAE with annealing, its MI remains small, leading to posterior collapse. In contrast, the proposed approach achieves high MI with a small DKL value similar to aggressive VAE.", "description": "We investigate the training procedure for different models. We plot the MI Iq, DKL in the ELBO and the distance between the approximated posterior and the prior, DKL(qϕ(z)||p(z)). As in Eq. 4 in the main paper, DKL in the ELBO is the sum of the other two. Fig. Although DKL is the upper bound of the mutual information, we notice that the gap is usually large. In the initial training stage, DKL increases in the basic VAE with annealing, while its MI remains small. With the weight decreases, the method finally suffers from posterior collapse. In contrast, our approach can obtain a high MI with a small DKL value like aggressive VAE.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 8: Results on Yahoo and Yelp datasets. We report mean values across 5 different random runs. ∗ indicates the results are from our experiments, while others are from previous report.', solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model has the highest Yahoo MI and what is its value?\n" }, { "role": "assistant", "content": "To find the model with the highest Yahoo MI, we need to look at the Yahoo MI column and identify the highest value. The highest Yahoo MI value is 8.0, which corresponds to the δ-VAE (0.25) model. So, the answer is {\"answer\": \"δ-VAE (0.25)\"}." } ], "images": [ "scigen/train/2187.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Results on Yahoo and Yelp datasets. We report mean values across 5 different random runs. ∗ indicates the results are from our experiments, while others are from previous report." ], "reasoning": "To find the model with the highest Yahoo MI, we need to look at the Yahoo MI column and identify the highest value. The highest Yahoo MI value is 8.0, which corresponds to the δ-VAE (0.25) model.", "question": "Which model has the highest Yahoo MI and what is its value?", "answer": "So, the answer is {\"answer\": \"δ-VAE (0.25)\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe BN-VAE (0.6) model has the highest Yelp AU value.\n" }, { "role": "assistant", "content": "To verify this statement, we need to check the Yelp AU column and compare the values. The highest Yelp AU value is 361.5, which corresponds to the vMF-VAE (30) model, not the BN-VAE (0.6) model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2187.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Results on Yahoo and Yelp datasets. We report mean values across 5 different random runs. ∗ indicates the results are from our experiments, while others are from previous report." ], "reasoning": "To verify this statement, we need to check the Yelp AU column and compare the values. The highest Yelp AU value is 361.5, which corresponds to the vMF-VAE (30) model, not the BN-VAE (0.6) model.", "statement": "The BN-VAE (0.6) model has the highest Yelp AU value.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled '((a)) Varing T for RNNLM softmax (λ=0.9).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The model achieves the best performance on the development set when λ=0.9 and T=5.0, referred to as 'Seq2Seq+LST'.Then we fix T at 5.0 and evaluate the influence of λ. The parameter λ controls the proportion of the ground truth hard labels to the soft labels of the RNNLM. We can see that when λ=0.9, the model achieves the best performance on the development set. We refer to the model trained with λ=0.9 and T=5.0 as “Seq2Seq+LST” in the rest." } ], "images": [ "scigen/train/2188.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "((a)) Varing T for RNNLM softmax (λ=0.9)." ], "reasoning": "The model achieves the best performance on the development set when λ=0.9 and T=5.0, referred to as 'Seq2Seq+LST'.", "description": "Then we fix T at 5.0 and evaluate the influence of λ. The parameter λ controls the proportion of the ground truth hard labels to the soft labels of the RNNLM. We can see that when λ=0.9, the model achieves the best performance on the development set. We refer to the model trained with λ=0.9 and T=5.0 as “Seq2Seq+LST” in the rest.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of '((a)) Varing T for RNNLM softmax (λ=0.9).', answer the question below by showing each progressive step toward the answer. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the Character Error Rate (CER%) when the temperature T is set to 5.0?\n" }, { "role": "assistant", "content": "The table shows the CER% for different temperatures. When T=5.0, the CER% is 8.0. So, the answer is {\"answer\": \"8.0\"}." } ], "images": [ "scigen/train/2188.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "((a)) Varing T for RNNLM softmax (λ=0.9)." ], "reasoning": "The table shows the CER% for different temperatures. When T=5.0, the CER% is 8.0.", "question": "What is the Character Error Rate (CER%) when the temperature T is set to 5.0?", "answer": "So, the answer is {\"answer\": \"8.0\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model achieves the lowest CER% when the temperature T is set to 5.0.\n" }, { "role": "assistant", "content": "The table shows the CER% for different temperatures. The lowest CER% is 8.0, which occurs when T=5.0. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2188.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "((a)) Varing T for RNNLM softmax (λ=0.9)." ], "reasoning": "The table shows the CER% for different temperatures. The lowest CER% is 8.0, which occurs when T=5.0.", "statement": "The model achieves the lowest CER% when the temperature T is set to 5.0.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: The perplexities on transcriptions of AISHELL-1 development set.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The RNNLM trained on external text achieves the lowest perplexity, showing a 28% reduction over the 3-gram model trained on external text, which itself significantly reduces perplexity compared to the 3-gram model trained on training transcriptions.5.3.1 The effectiveness of external text Firstly, we demonstrate the effectiveness of the external text data and the RNNLM. Note that the data is in character level, so the perplexities are relatively smaller. We can see that compared with the 3-gram with Kneser-Ney smoothing trained on training transcriptions, the 3-gram trained on external text achieves a significant reduction of perplexity. Moreover, the RNNLM achieves about a 28% relative reduction over the 3-gram trained on the external text." } ], "images": [ "scigen/train/2189.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The perplexities on transcriptions of AISHELL-1 development set." ], "reasoning": "The RNNLM trained on external text achieves the lowest perplexity, showing a 28% reduction over the 3-gram model trained on external text, which itself significantly reduces perplexity compared to the 3-gram model trained on training transcriptions.", "description": "5.3.1 The effectiveness of external text Firstly, we demonstrate the effectiveness of the external text data and the RNNLM. Note that the data is in character level, so the perplexities are relatively smaller. We can see that compared with the 3-gram with Kneser-Ney smoothing trained on training transcriptions, the 3-gram trained on external text achieves a significant reduction of perplexity. Moreover, the RNNLM achieves about a 28% relative reduction over the 3-gram trained on the external text.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 2: The perplexities on transcriptions of AISHELL-1 development set.' and proceed to solve the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the perplexity reduction percentage of the RNNLM over the 3-gram model trained on external text?\n" }, { "role": "assistant", "content": "The RNNLM achieves a perplexity of 34, while the 3-gram model trained on external text achieves a perplexity of 47. The description states that the RNNLM achieves about a 28% relative reduction over the 3-gram model trained on external text. So, the answer is {\"answer\": \"28%\"}" } ], "images": [ "scigen/train/2189.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The perplexities on transcriptions of AISHELL-1 development set." ], "reasoning": "The RNNLM achieves a perplexity of 34, while the 3-gram model trained on external text achieves a perplexity of 47. The description states that the RNNLM achieves about a 28% relative reduction over the 3-gram model trained on external text.", "question": "What is the perplexity reduction percentage of the RNNLM over the 3-gram model trained on external text?", "answer": "So, the answer is {\"answer\": \"28%\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The 3-gram model trained on external text achieves a perplexity of 70., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe 3-gram model trained on external text achieves a perplexity of 70.\n" }, { "role": "assistant", "content": "The table shows that the 3-gram model trained on external text achieves a perplexity of 47, not 70. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2189.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The perplexities on transcriptions of AISHELL-1 development set." ], "reasoning": "The table shows that the 3-gram model trained on external text achieves a perplexity of 47, not 70.", "statement": "The 3-gram model trained on external text achieves a perplexity of 70.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled '((b)) Varing λ for interpolation (T=5.0).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "When λ is set to 0.9 and T is fixed at 5.0, the model achieves the best performance on the development set with a CER of 8.0%.Then we fix T at 5.0 and evaluate the influence of λ. The parameter λ controls the proportion of the ground truth hard labels to the soft labels of the RNNLM. We can see that when λ=0.9, the model achieves the best performance on the development set. We refer to the model trained with λ=0.9 and T=5.0 as “Seq2Seq+LST” in the rest." } ], "images": [ "scigen/train/2190.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "((b)) Varing λ for interpolation (T=5.0)." ], "reasoning": "When λ is set to 0.9 and T is fixed at 5.0, the model achieves the best performance on the development set with a CER of 8.0%.", "description": "Then we fix T at 5.0 and evaluate the influence of λ. The parameter λ controls the proportion of the ground truth hard labels to the soft labels of the RNNLM. We can see that when λ=0.9, the model achieves the best performance on the development set. We refer to the model trained with λ=0.9 and T=5.0 as “Seq2Seq+LST” in the rest.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of '((b)) Varing λ for interpolation (T=5.0).', answer the following question. Give detailed solution consisting of each step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the Character Error Rate (CER) when λ is set to 0.85?\n" }, { "role": "assistant", "content": "From the table, locate the row where λ=0.85 and find the corresponding CER value. So, the answer is {\"answer\": \"8.9\"}." } ], "images": [ "scigen/train/2190.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "((b)) Varing λ for interpolation (T=5.0)." ], "reasoning": "From the table, locate the row where λ=0.85 and find the corresponding CER value.", "question": "What is the Character Error Rate (CER) when λ is set to 0.85?", "answer": "So, the answer is {\"answer\": \"8.9\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model achieves the worst performance when λ is set to 0.3.\n" }, { "role": "assistant", "content": "From the table, the highest CER value is 9.4, which corresponds to λ=0.3, indicating the worst performance. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2190.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "((b)) Varing λ for interpolation (T=5.0)." ], "reasoning": "From the table, the highest CER value is 9.4, which corresponds to λ=0.3, indicating the worst performance.", "statement": "The model achieves the worst performance when λ is set to 0.3.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Comparisons on the test set. LST represents our proposed “Learn Spelling from Teachers” approach. SF means using shallow fusion during decoding.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Seq2Seq+LST achieves an 18.42% relative reduction in character error rate compared to Seq2Seq, indicating the effectiveness of the data-driven approach without assuming prior distributions.“Seq2Seq” is the plain Seq2Seq without regularization. Compared to “Seq2Seq”, “Seq2Seq+LST” achieves an 18.42% relative reduction in character error rate. We analyze that the assumptions of label smoothing (uniform distribution) and unigram smoothing (unigram frequency) do not match real situations. However, LST, which is a data-driven approach, does not assume prior distributions." } ], "images": [ "scigen/train/2191.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparisons on the test set. LST represents our proposed “Learn Spelling from Teachers” approach. SF means using shallow fusion during decoding." ], "reasoning": "Seq2Seq+LST achieves an 18.42% relative reduction in character error rate compared to Seq2Seq, indicating the effectiveness of the data-driven approach without assuming prior distributions.", "description": "“Seq2Seq” is the plain Seq2Seq without regularization. Compared to “Seq2Seq”, “Seq2Seq+LST” achieves an 18.42% relative reduction in character error rate. We analyze that the assumptions of label smoothing (uniform distribution) and unigram smoothing (unigram frequency) do not match real situations. However, LST, which is a data-driven approach, does not assume prior distributions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 4: Comparisons on the test set. LST represents our proposed “Learn Spelling from Teachers” approach. SF means using shallow fusion during decoding.', unfold the steps to solve the question given next. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: What is the character error rate (CER%) of the Seq2Seq model with Proposed LST?\n" }, { "role": "assistant", "content": "To find the CER% of the Seq2Seq model with Proposed LST, locate the row labeled 'Seq2Seq + Proposed LST' in the table and read the corresponding CER% value. So, the answer is {\"answer\": \"9.3\"}" } ], "images": [ "scigen/train/2191.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparisons on the test set. LST represents our proposed “Learn Spelling from Teachers” approach. SF means using shallow fusion during decoding." ], "reasoning": "To find the CER% of the Seq2Seq model with Proposed LST, locate the row labeled 'Seq2Seq + Proposed LST' in the table and read the corresponding CER% value.", "question": "What is the character error rate (CER%) of the Seq2Seq model with Proposed LST?", "answer": "So, the answer is {\"answer\": \"9.3\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Seq2Seq with Original Unigram Smoothing has the highest character error rate (CER%)., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSeq2Seq with Original Unigram Smoothing has the highest character error rate (CER%).\n" }, { "role": "assistant", "content": "To verify this statement, compare the CER% of 'Seq2Seq + Original Unigram Smoothing' with all other models in the table. The CER% for 'Seq2Seq + Original Unigram Smoothing' is 15.7, which is higher than any other model listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2191.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparisons on the test set. LST represents our proposed “Learn Spelling from Teachers” approach. SF means using shallow fusion during decoding." ], "reasoning": "To verify this statement, compare the CER% of 'Seq2Seq + Original Unigram Smoothing' with all other models in the table. The CER% for 'Seq2Seq + Original Unigram Smoothing' is 15.7, which is higher than any other model listed.", "statement": "Seq2Seq with Original Unigram Smoothing has the highest character error rate (CER%).", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Evaluation on CoNLL-YAGO.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The model achieves comparable results to the state-of-the-art but does not outperform it due to a small training set and reliance on domain adaptation.On this dataset our model achieves comparable results, however it does not outperform the state-of-the-art, probably because of the relatively small training set and our reliance on domain adaptation." } ], "images": [ "scigen/train/2192.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Evaluation on CoNLL-YAGO." ], "reasoning": "The model achieves comparable results to the state-of-the-art but does not outperform it due to a small training set and reliance on domain adaptation.", "description": "On this dataset our model achieves comparable results, however it does not outperform the state-of-the-art, probably because of the relatively small training set and our reliance on domain adaptation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Evaluation on CoNLL-YAGO.\nQuestion: Which model achieved the highest Micro P@1 score on the CoNLL-YAGO test-b dataset?\nSolve the above question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the highest Micro P@1 score, look at the 'Micro P@1' column and identify the highest value, which is 93.1 achieved by Yamada (2016) global. So, the answer is {\"answer\": \"Yamada (2016) global\"}." } ], "images": [ "scigen/train/2192.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Evaluation on CoNLL-YAGO." ], "reasoning": "To find the highest Micro P@1 score, look at the 'Micro P@1' column and identify the highest value, which is 93.1 achieved by Yamada (2016) global.", "question": "Which model achieved the highest Micro P@1 score on the CoNLL-YAGO test-b dataset?", "answer": "So, the answer is {\"answer\": \"Yamada (2016) global\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nOur ARNN + GBRT model achieved a higher Macro P@1 score than Yamada (2016) local on the CoNLL-YAGO test-b dataset.\n" }, { "role": "assistant", "content": "Compare the Macro P@1 scores of 'Our ARNN + GBRT' (88.6) and 'Yamada (2016) local' (92.4). Since 88.6 is less than 92.4, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2192.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Evaluation on CoNLL-YAGO." ], "reasoning": "Compare the Macro P@1 scores of 'Our ARNN + GBRT' (88.6) and 'Yamada (2016) local' (92.4). Since 88.6 is less than 92.4, the statement is refuted.", "statement": "Our ARNN + GBRT model achieved a higher Macro P@1 score than Yamada (2016) local on the CoNLL-YAGO test-b dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Evaluation on noisy web data (WikilinksNED)'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Our RNN model with attention outperforms Yamada et al. by over 5 points, showing the benefits of more expressive RNNs and the attention mechanism.We used standard micro P@1 accuracy for evaluation. Our RNN model significantly outperforms Yamada at el. on this data by over 5 points, indicating that the more expressive RNNs are indeed beneficial for this task. We find that the attention mechanism further improves our results by a small, yet statistically significant, margin." } ], "images": [ "scigen/train/2193.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Evaluation on noisy web data (WikilinksNED)" ], "reasoning": "Our RNN model with attention outperforms Yamada et al. by over 5 points, showing the benefits of more expressive RNNs and the attention mechanism.", "description": "We used standard micro P@1 accuracy for evaluation. Our RNN model significantly outperforms Yamada at el. on this data by over 5 points, indicating that the more expressive RNNs are indeed beneficial for this task. We find that the attention mechanism further improves our results by a small, yet statistically significant, margin.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Evaluation on noisy web data (WikilinksNED)', answer the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the accuracy difference between Yamada (2016) and Our Attention-RNN on the Wikilinks Test-Set Evaluation Full Test Set (300K)?\n" }, { "role": "assistant", "content": "Yamada (2016) has an accuracy of 66.9, while Our Attention-RNN has an accuracy of 73 on the Full Test Set. The difference is 73 - 66.9 = 6.1 points. So, the answer is {\"answer\": \"6.1\"}" } ], "images": [ "scigen/train/2193.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Evaluation on noisy web data (WikilinksNED)" ], "reasoning": "Yamada (2016) has an accuracy of 66.9, while Our Attention-RNN has an accuracy of 73 on the Full Test Set. The difference is 73 - 66.9 = 6.1 points.", "question": "What is the accuracy difference between Yamada (2016) and Our Attention-RNN on the Wikilinks Test-Set Evaluation Full Test Set (300K)?", "answer": "So, the answer is {\"answer\": \"6.1\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nOur RNN model without attention performs better than Yamada (2016) on the Wikilinks Test-Set Evaluation Sampled Test Set (10K).\n" }, { "role": "assistant", "content": "Our RNN without attention has an accuracy of 72.1, while Yamada (2016) has an accuracy of 67.6 on the Sampled Test Set. Therefore, our RNN model without attention performs better. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2193.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Evaluation on noisy web data (WikilinksNED)" ], "reasoning": "Our RNN without attention has an accuracy of 72.1, while Yamada (2016) has an accuracy of 67.6 on the Sampled Test Set. Therefore, our RNN model without attention performs better.", "statement": "Our RNN model without attention performs better than Yamada (2016) on the Wikilinks Test-Set Evaluation Sampled Test Set (10K).", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Evaluation of training steps on CoNLL-YAGO.'.\n" }, { "role": "assistant", "content": "Domain adaptation significantly boosts performance, and incorporating extra statistical and string features yields a small extra improvement.the micro and macro P@1 scores on CoNLL-YAGO test-b for the different training steps. We find that when using only the training set of CoNLL-YAGO our model is under-trained and that the domain adaptation significant boosts performance. We find that incorporating extra statistical and string features yields a small extra improvement in performance." } ], "images": [ "scigen/train/2194.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Evaluation of training steps on CoNLL-YAGO." ], "reasoning": "Domain adaptation significantly boosts performance, and incorporating extra statistical and string features yields a small extra improvement.", "description": "the micro and macro P@1 scores on CoNLL-YAGO test-b for the different training steps. We find that when using only the training set of CoNLL-YAGO our model is under-trained and that the domain adaptation significant boosts performance. We find that incorporating extra statistical and string features yields a small extra improvement in performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: Evaluation of training steps on CoNLL-YAGO.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the macro P@1 score when using the CoNLL training set with domain adaptation and GBRT?\n" }, { "role": "assistant", "content": "To find the macro P@1 score when using the CoNLL training set with domain adaptation and GBRT, look at the row labeled '+ GBRT' under the CoNLL training set section and the column labeled 'Macro P@1'. The value is 88.6. So, the answer is {\"answer\": \"88.6\"}." } ], "images": [ "scigen/train/2194.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Evaluation of training steps on CoNLL-YAGO." ], "reasoning": "To find the macro P@1 score when using the CoNLL training set with domain adaptation and GBRT, look at the row labeled '+ GBRT' under the CoNLL training set section and the column labeled 'Macro P@1'. The value is 88.6.", "question": "What is the macro P@1 score when using the CoNLL training set with domain adaptation and GBRT?", "answer": "So, the answer is {\"answer\": \"88.6\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe macro P@1 score for the Yago training set with domain adaptation is higher than the micro P@1 score for the CoNLL training set with domain adaptation.\n" }, { "role": "assistant", "content": "The macro P@1 score for the Yago training set with domain adaptation is 85.1, while the micro P@1 score for the CoNLL training set with domain adaptation is 86.6. Since 85.1 is not higher than 86.6, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2194.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Evaluation of training steps on CoNLL-YAGO." ], "reasoning": "The macro P@1 score for the Yago training set with domain adaptation is 85.1, while the micro P@1 score for the CoNLL training set with domain adaptation is 86.6. Since 85.1 is not higher than 86.6, the statement is refuted.", "statement": "The macro P@1 score for the Yago training set with domain adaptation is higher than the micro P@1 score for the CoNLL training set with domain adaptation.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Corrupt-sampling and Initialization'.\n" }, { "role": "assistant", "content": "Using pre-initialized embeddings and Near-Misses corrupt-sampling resulted in the highest micro accuracy of 72.5 in the Wikilinks Evaluation-Set Model.We performed a study of the effects of using pre-initialized embeddings for our model, and of using either All-Entity or Near-Misses corrupt-sampling." } ], "images": [ "scigen/train/2195.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Corrupt-sampling and Initialization" ], "reasoning": "Using pre-initialized embeddings and Near-Misses corrupt-sampling resulted in the highest micro accuracy of 72.5 in the Wikilinks Evaluation-Set Model.", "description": "We performed a study of the effects of using pre-initialized embeddings for our model, and of using either All-Entity or Near-Misses corrupt-sampling.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: Corrupt-sampling and Initialization', solve this question and give detail solutions. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich combination of initialization and corrupt-sampling method resulted in the highest micro accuracy in the Wikilinks Evaluation-Set Model?\n" }, { "role": "assistant", "content": "By comparing the micro accuracy values for each combination of initialization and corrupt-sampling methods, we find that 'Near-misses, with init.' has the highest micro accuracy of 72.5. So, the answer is {\"answer\": \"Near-misses, with init.\"}." } ], "images": [ "scigen/train/2195.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Corrupt-sampling and Initialization" ], "reasoning": "By comparing the micro accuracy values for each combination of initialization and corrupt-sampling methods, we find that 'Near-misses, with init.' has the highest micro accuracy of 72.5.", "question": "Which combination of initialization and corrupt-sampling method resulted in the highest micro accuracy in the Wikilinks Evaluation-Set Model?", "answer": "So, the answer is {\"answer\": \"Near-misses, with init.\"}." }, { "messages": [ { "role": "user", "content": "The All-Entity model with random initialization achieved a micro accuracy of 67.1. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe All-Entity model with random initialization achieved a micro accuracy of 67.1.\n" }, { "role": "assistant", "content": "The table shows that the All-Entity model with random initialization indeed achieved a micro accuracy of 67.1. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2195.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Corrupt-sampling and Initialization" ], "reasoning": "The table shows that the All-Entity model with random initialization indeed achieved a micro accuracy of 67.1.", "statement": "The All-Entity model with random initialization achieved a micro accuracy of 67.1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Overall accuracy top1 on VQA-CP v1'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The RUBi approach significantly improves overall accuracy across different models, with the highest gain observed in the SAN model.Our RUBi approach consistently leads to significant gains over the classical learning strategy with a gain of +9.8 overall accuracy point with our baseline architecture, +19.2 with SAN and +7.66 with UpDn." } ], "images": [ "scigen/train/2196.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Overall accuracy top1 on VQA-CP v1" ], "reasoning": "The RUBi approach significantly improves overall accuracy across different models, with the highest gain observed in the SAN model.", "description": "Our RUBi approach consistently leads to significant gains over the classical learning strategy with a gain of +9.8 overall accuracy point with our baseline architecture, +19.2 with SAN and +7.66 with UpDn.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Overall accuracy top1 on VQA-CP v1\nQuestion: What is the overall accuracy gain when using the RUBi approach with the SAN model?\nGive a detailed response to the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The overall accuracy for SAN is 26.88, and with RUBi, it is 46.08. The gain is calculated as 46.08 - 26.88. So, the answer is {\"answer\": \"19.2\"}" } ], "images": [ "scigen/train/2196.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Overall accuracy top1 on VQA-CP v1" ], "reasoning": "The overall accuracy for SAN is 26.88, and with RUBi, it is 46.08. The gain is calculated as 46.08 - 26.88.", "question": "What is the overall accuracy gain when using the RUBi approach with the SAN model?", "answer": "So, the answer is {\"answer\": \"19.2\"}" }, { "messages": [ { "role": "user", "content": "The RUBi approach leads to a gain of +7.66 overall accuracy points with the UpDn model. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe RUBi approach leads to a gain of +7.66 overall accuracy points with the UpDn model.\n" }, { "role": "assistant", "content": "The overall accuracy for UpDn is 37.15, and with RUBi, it is 44.81. The gain is calculated as 44.81 - 37.15, which is 7.66. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2196.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Overall accuracy top1 on VQA-CP v1" ], "reasoning": "The overall accuracy for UpDn is 37.15, and with RUBi, it is 44.81. The gain is calculated as 44.81 - 37.15, which is 7.66.", "statement": "The RUBi approach leads to a gain of +7.66 overall accuracy points with the UpDn model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: State-of-the-art results on VQA-CP v2 test. All reported models use the same features from Anderson_2018_CVPR . Models with * have been trained by ramakrishnan2018overcoming . Models with ** have been trained by shrestha2019answer .', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Our RUBi approach achieves an average overall accuracy of 47.11% with a low standard deviation of ±0.51, significantly outperforming the second best approach UpDn + Q-Adv + DoE which only achieves a +1.43 gain in overall accuracy over their baseline UpDn., we compare our approach consisting of our baseline architecture trained with RUBi on VQA-CP v2 against the state of the art. We compute the average accuracy over 5 experiments with different random seeds. Our RUBi approach reaches an average overall accuracy of 47.11% with a low standard deviation of ±0.51. RUBi reaches a +8.65 improvement over our baseline model trained with the classical cross-entropy. In comparison, the second best approach UpDn + Q-Adv + DoE only achieves a +1.43 gain in overall accuracy over their baseline UpDn. In addition, our approach does not significantly reduce the accuracy over our baseline for the answer type Other, while the second best approach reduces it by 10.57 point." } ], "images": [ "scigen/train/2197.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: State-of-the-art results on VQA-CP v2 test. All reported models use the same features from Anderson_2018_CVPR . Models with * have been trained by ramakrishnan2018overcoming . Models with ** have been trained by shrestha2019answer ." ], "reasoning": "Our RUBi approach achieves an average overall accuracy of 47.11% with a low standard deviation of ±0.51, significantly outperforming the second best approach UpDn + Q-Adv + DoE which only achieves a +1.43 gain in overall accuracy over their baseline UpDn.", "description": ", we compare our approach consisting of our baseline architecture trained with RUBi on VQA-CP v2 against the state of the art. We compute the average accuracy over 5 experiments with different random seeds. Our RUBi approach reaches an average overall accuracy of 47.11% with a low standard deviation of ±0.51. RUBi reaches a +8.65 improvement over our baseline model trained with the classical cross-entropy. In comparison, the second best approach UpDn + Q-Adv + DoE only achieves a +1.43 gain in overall accuracy over their baseline UpDn. In addition, our approach does not significantly reduce the accuracy over our baseline for the answer type Other, while the second best approach reduces it by 10.57 point.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: State-of-the-art results on VQA-CP v2 test. All reported models use the same features from Anderson_2018_CVPR . Models with * have been trained by ramakrishnan2018overcoming . Models with ** have been trained by shrestha2019answer .\nQuestion: What is the overall accuracy improvement of RUBi over the baseline model trained with classical cross-entropy?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The description states that RUBi reaches a +8.65 improvement over the baseline model trained with classical cross-entropy. This is calculated by comparing the overall accuracy of RUBi (47.11%) with the baseline model. So, the answer is {\"answer\": \"+8.65\"}." } ], "images": [ "scigen/train/2197.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: State-of-the-art results on VQA-CP v2 test. All reported models use the same features from Anderson_2018_CVPR . Models with * have been trained by ramakrishnan2018overcoming . Models with ** have been trained by shrestha2019answer ." ], "reasoning": "The description states that RUBi reaches a +8.65 improvement over the baseline model trained with classical cross-entropy. This is calculated by comparing the overall accuracy of RUBi (47.11%) with the baseline model.", "question": "What is the overall accuracy improvement of RUBi over the baseline model trained with classical cross-entropy?", "answer": "So, the answer is {\"answer\": \"+8.65\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe second best approach reduces the accuracy for the answer type Other by 10.57 points.\n" }, { "role": "assistant", "content": "The description states that the second best approach UpDn + Q-Adv + DoE reduces the accuracy for the answer type Other by 10.57 points. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2197.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: State-of-the-art results on VQA-CP v2 test. All reported models use the same features from Anderson_2018_CVPR . Models with * have been trained by ramakrishnan2018overcoming . Models with ** have been trained by shrestha2019answer ." ], "reasoning": "The description states that the second best approach UpDn + Q-Adv + DoE reduces the accuracy for the answer type Other by 10.57 points.", "statement": "The second best approach reduces the accuracy for the answer type Other by 10.57 points.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Overall accuracy of the RUBi learning strategy on VQA v2 val and test-dev splits.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "RUBi can be applied to existing VQA models without changing their architecture, leading to significant accuracy gains over the baselines.S4SS1SSS0Px3 Architecture agnostic RUBi can be used on existing VQA models without changing the underlying architecture. First, we show that applying RUBi on these architectures leads to important gains over the baselines trained with their original learning strategy. We report a gain of +11.73 accuracy point for SAN and +4.5 for UpDn. This lower gap in accuracy may show that UpDn is less driven by biases than SAN. We report a gain of +3.4 with SAN + RUBi over SAN + Q-Adv + DoE, and +3.06 with UpDn + RUBi over UpDn + Q-Adv + DoE. Full results splitted by question type are available in the supplementary materials." } ], "images": [ "scigen/train/2198.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Overall accuracy of the RUBi learning strategy on VQA v2 val and test-dev splits." ], "reasoning": "RUBi can be applied to existing VQA models without changing their architecture, leading to significant accuracy gains over the baselines.", "description": "S4SS1SSS0Px3 Architecture agnostic RUBi can be used on existing VQA models without changing the underlying architecture. First, we show that applying RUBi on these architectures leads to important gains over the baselines trained with their original learning strategy. We report a gain of +11.73 accuracy point for SAN and +4.5 for UpDn. This lower gap in accuracy may show that UpDn is less driven by biases than SAN. We report a gain of +3.4 with SAN + RUBi over SAN + Q-Adv + DoE, and +3.06 with UpDn + RUBi over UpDn + Q-Adv + DoE. Full results splitted by question type are available in the supplementary materials.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Overall accuracy of the RUBi learning strategy on VQA v2 val and test-dev splits.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. \n\nQuestion: What is the difference in test-dev accuracy between the Baseline and RUBi models?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The test-dev accuracy for the Baseline model is 64.75, and for the RUBi model, it is 63.18. The difference is 64.75 - 63.18. So, the answer is {\"answer\": \"1.57\"}" } ], "images": [ "scigen/train/2198.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Overall accuracy of the RUBi learning strategy on VQA v2 val and test-dev splits." ], "reasoning": "The test-dev accuracy for the Baseline model is 64.75, and for the RUBi model, it is 63.18. The difference is 64.75 - 63.18.", "question": "What is the difference in test-dev accuracy between the Baseline and RUBi models?", "answer": "So, the answer is {\"answer\": \"1.57\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe RUBi model has a higher validation accuracy than the Baseline model.\n" }, { "role": "assistant", "content": "The table shows that the validation accuracy for the Baseline model is 63.10, while for the RUBi model, it is 61.16. Therefore, the statement is incorrect. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2198.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Overall accuracy of the RUBi learning strategy on VQA v2 val and test-dev splits." ], "reasoning": "The table shows that the validation accuracy for the Baseline model is 63.10, while for the RUBi model, it is 61.16. Therefore, the statement is incorrect.", "statement": "The RUBi model has a higher validation accuracy than the Baseline model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Ablation study of the question-only loss LQO on VQA-CP v2.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Using LQO results in consistent performance gains across all architectures, with the highest gain observed in the UpDn architecture.The absence of LQO implies that the question-only classifier cq is never used, and nnq only receives gradients from the main loss LQM. Using LQO leads to consistent gains on all three architectures. We report a gain of +0.89 for our Baseline architecture, +0.22 for SAN, +4.76 for UpDn." } ], "images": [ "scigen/train/2199.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Ablation study of the question-only loss LQO on VQA-CP v2." ], "reasoning": "Using LQO results in consistent performance gains across all architectures, with the highest gain observed in the UpDn architecture.", "description": "The absence of LQO implies that the question-only classifier cq is never used, and nnq only receives gradients from the main loss LQM. Using LQO leads to consistent gains on all three architectures. We report a gain of +0.89 for our Baseline architecture, +0.22 for SAN, +4.76 for UpDn.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Ablation study of the question-only loss LQO on VQA-CP v2.', answer the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the overall performance gain for the Baseline architecture when LQO is used?\n" }, { "role": "assistant", "content": "The overall performance for the Baseline architecture with LQO is 47.11, and without LQO is 46.11. The gain is calculated as 47.11 - 46.11. So, the answer is {\"answer\": \"1.00\"}" } ], "images": [ "scigen/train/2199.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Ablation study of the question-only loss LQO on VQA-CP v2." ], "reasoning": "The overall performance for the Baseline architecture with LQO is 47.11, and without LQO is 46.11. The gain is calculated as 47.11 - 46.11.", "question": "What is the overall performance gain for the Baseline architecture when LQO is used?", "answer": "So, the answer is {\"answer\": \"1.00\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nUsing LQO results in a higher performance gain for the SAN architecture compared to the Baseline architecture.\n" }, { "role": "assistant", "content": "The performance gain for the SAN architecture with LQO is 37.63 - 36.96 = 0.67, while for the Baseline architecture it is 47.11 - 46.11 = 1.00. The gain for the Baseline architecture is higher. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2199.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Ablation study of the question-only loss LQO on VQA-CP v2." ], "reasoning": "The performance gain for the SAN architecture with LQO is 37.63 - 36.96 = 0.67, while for the Baseline architecture it is 47.11 - 46.11 = 1.00. The gain for the Baseline architecture is higher.", "statement": "Using LQO results in a higher performance gain for the SAN architecture compared to the Baseline architecture.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: Parsing results with fixed composition function on PTB §23 (D=discriminative, G=generative, S=semisupervised). ⋆ indicates the (Vinyals et al., 2015) model trained only on the WSJ corpus without ensembling. † indicates RNNG models with the buggy composition function implementation.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Fixing the implementation error in both generative and discriminative RNNGs led to a significant improvement in parsing accuracy, highlighting the importance of proper composition and information propagation.The implementation error affected both the generative and discriminative RNNGs. The considerable improvement in parsing accuracy indicates that properly composing the constituent and propagating information upwards is crucial. Despite slightly higher language modeling perplexity on PTB §23, the fixed RNNG still outperforms a highly optimized sequential LSTM baseline." } ], "images": [ "scigen/train/2200.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Parsing results with fixed composition function on PTB §23 (D=discriminative, G=generative, S=semisupervised). ⋆ indicates the (Vinyals et al., 2015) model trained only on the WSJ corpus without ensembling. † indicates RNNG models with the buggy composition function implementation." ], "reasoning": "Fixing the implementation error in both generative and discriminative RNNGs led to a significant improvement in parsing accuracy, highlighting the importance of proper composition and information propagation.", "description": "The implementation error affected both the generative and discriminative RNNGs. The considerable improvement in parsing accuracy indicates that properly composing the constituent and propagating information upwards is crucial. Despite slightly higher language modeling perplexity on PTB §23, the fixed RNNG still outperforms a highly optimized sequential LSTM baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 5: Parsing results with fixed composition function on PTB §23 (D=discriminative, G=generative, S=semisupervised). ⋆ indicates the (Vinyals et al., 2015) model trained only on the WSJ corpus without ensembling. † indicates RNNG models with the buggy composition function implementation.', unfold the steps to solve the question given next. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nQuestion: What is the F1 score of the fixed generative RNNG model?\n" }, { "role": "assistant", "content": "The table lists the F1 scores of various models. The fixed generative RNNG model has an F1 score of 93.3. So, the answer is {\"answer\": \"93.3\"}." } ], "images": [ "scigen/train/2200.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Parsing results with fixed composition function on PTB §23 (D=discriminative, G=generative, S=semisupervised). ⋆ indicates the (Vinyals et al., 2015) model trained only on the WSJ corpus without ensembling. † indicates RNNG models with the buggy composition function implementation." ], "reasoning": "The table lists the F1 scores of various models. The fixed generative RNNG model has an F1 score of 93.3.", "question": "What is the F1 score of the fixed generative RNNG model?", "answer": "So, the answer is {\"answer\": \"93.3\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The fixed discriminative RNNG model has a higher F1 score than the fixed generative RNNG model., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe fixed discriminative RNNG model has a higher F1 score than the fixed generative RNNG model.\n" }, { "role": "assistant", "content": "The table shows that the fixed discriminative RNNG model has an F1 score of 91.7, while the fixed generative RNNG model has an F1 score of 93.3. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2200.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Parsing results with fixed composition function on PTB §23 (D=discriminative, G=generative, S=semisupervised). ⋆ indicates the (Vinyals et al., 2015) model trained only on the WSJ corpus without ensembling. † indicates RNNG models with the buggy composition function implementation." ], "reasoning": "The table shows that the fixed discriminative RNNG model has an F1 score of 91.7, while the fixed generative RNNG model has an F1 score of 93.3. Therefore, the statement is refuted.", "statement": "The fixed discriminative RNNG model has a higher F1 score than the fixed generative RNNG model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6: Parsing results on CTB 5.1 including results with the buggy composition function implementation (indicated by †) and with the correct implementation.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Fixing the implementation error in both generative and discriminative RNNGs led to a significant improvement in parsing accuracy, highlighting the importance of proper composition and information propagation.The implementation error affected both the generative and discriminative RNNGs. The considerable improvement in parsing accuracy indicates that properly composing the constituent and propagating information upwards is crucial. Despite slightly higher language modeling perplexity on PTB §23, the fixed RNNG still outperforms a highly optimized sequential LSTM baseline." } ], "images": [ "scigen/train/2201.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Parsing results on CTB 5.1 including results with the buggy composition function implementation (indicated by †) and with the correct implementation." ], "reasoning": "Fixing the implementation error in both generative and discriminative RNNGs led to a significant improvement in parsing accuracy, highlighting the importance of proper composition and information propagation.", "description": "The implementation error affected both the generative and discriminative RNNGs. The considerable improvement in parsing accuracy indicates that properly composing the constituent and propagating information upwards is crucial. Despite slightly higher language modeling perplexity on PTB §23, the fixed RNNG still outperforms a highly optimized sequential LSTM baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 6: Parsing results on CTB 5.1 including results with the buggy composition function implementation (indicated by †) and with the correct implementation.', and sequentially solve the question, writing out each step of your reasoning process. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the F1 score of the correct generative RNNG model?\n" }, { "role": "assistant", "content": "The table lists the F1 scores for various models. The correct generative RNNG model has an F1 score of 86.9. So, the answer is {\"answer\": \"86.9\"}" } ], "images": [ "scigen/train/2201.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Parsing results on CTB 5.1 including results with the buggy composition function implementation (indicated by †) and with the correct implementation." ], "reasoning": "The table lists the F1 scores for various models. The correct generative RNNG model has an F1 score of 86.9.", "question": "What is the F1 score of the correct generative RNNG model?", "answer": "So, the answer is {\"answer\": \"86.9\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The discriminative RNNG model with the implementation error has a higher F1 score than the generative RNNG model with the implementation error. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe discriminative RNNG model with the implementation error has a higher F1 score than the generative RNNG model with the implementation error.\n" }, { "role": "assistant", "content": "The table shows that the discriminative RNNG model with the implementation error has an F1 score of 80.7, while the generative RNNG model with the implementation error has an F1 score of 82.7. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2201.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Parsing results on CTB 5.1 including results with the buggy composition function implementation (indicated by †) and with the correct implementation." ], "reasoning": "The table shows that the discriminative RNNG model with the implementation error has an F1 score of 80.7, while the generative RNNG model with the implementation error has an F1 score of 82.7. Therefore, the statement is refuted.", "statement": "The discriminative RNNG model with the implementation error has a higher F1 score than the generative RNNG model with the implementation error.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 7: PTB and CTB language modeling results including results with the buggy composition function implementation (indicated by †) and with the correct implementation.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Fixing the implementation error in RNNGs led to a significant improvement in parsing accuracy and better performance compared to a sequential LSTM baseline, despite a slight increase in language modeling perplexity on PTB §23.The implementation error affected both the generative and discriminative RNNGs. The considerable improvement in parsing accuracy indicates that properly composing the constituent and propagating information upwards is crucial. Despite slightly higher language modeling perplexity on PTB §23, the fixed RNNG still outperforms a highly optimized sequential LSTM baseline." } ], "images": [ "scigen/train/2202.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: PTB and CTB language modeling results including results with the buggy composition function implementation (indicated by †) and with the correct implementation." ], "reasoning": "Fixing the implementation error in RNNGs led to a significant improvement in parsing accuracy and better performance compared to a sequential LSTM baseline, despite a slight increase in language modeling perplexity on PTB §23.", "description": "The implementation error affected both the generative and discriminative RNNGs. The considerable improvement in parsing accuracy indicates that properly composing the constituent and propagating information upwards is crucial. Despite slightly higher language modeling perplexity on PTB §23, the fixed RNNG still outperforms a highly optimized sequential LSTM baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}. The question is related to the given table titled 'Table 7: PTB and CTB language modeling results including results with the buggy composition function implementation (indicated by †) and with the correct implementation.'.\n\nQuestion: What is the test perplexity (ppl) on CTB for the correct RNNG model?\n" }, { "role": "assistant", "content": "The table shows the test perplexity (ppl) values for different models on PTB and CTB datasets. The correct RNNG model has a test perplexity of 148.5 on CTB. So, the answer is {\"answer\": \"148.5\"}." } ], "images": [ "scigen/train/2202.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: PTB and CTB language modeling results including results with the buggy composition function implementation (indicated by †) and with the correct implementation." ], "reasoning": "The table shows the test perplexity (ppl) values for different models on PTB and CTB datasets. The correct RNNG model has a test perplexity of 148.5 on CTB.", "question": "What is the test perplexity (ppl) on CTB for the correct RNNG model?", "answer": "So, the answer is {\"answer\": \"148.5\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe correct RNNG model has a higher test perplexity on PTB compared to the buggy RNNG model.\n" }, { "role": "assistant", "content": "The table shows that the correct RNNG model has a test perplexity of 105.2 on PTB, while the buggy RNNG model has a test perplexity of 102.4 on PTB. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2202.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: PTB and CTB language modeling results including results with the buggy composition function implementation (indicated by †) and with the correct implementation." ], "reasoning": "The table shows that the correct RNNG model has a test perplexity of 105.2 on PTB, while the buggy RNNG model has a test perplexity of 102.4 on PTB. Therefore, the statement is supported.", "statement": "The correct RNNG model has a higher test perplexity on PTB compared to the buggy RNNG model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: A comparison of NER systems. The results are F1 scores, where a correct span annotation exactly matches a gold span annotation (start, length, and entity type must all be correct). Results of the systems described in the text are shown for English, German, Spanish, and Dutch. BTS* shows the results of the BTS model trained separately on each language while BTS is a single model trained on all 4 languages together. The top set of results leverage resources beyond the training data; the middle set do not, and thus are most comparable to our results (bottom set).'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Nothman et al. used Wikipedia and Freebase to create noisy training data for multilingual NER, achieving good results across many languages.There is relatively little work on multilingual NER, and most research is focused on building systems that are unsupervised in the sense that they use resources like Wikipedia and Freebase rather than manually annotated data. Nothman et al. \\shortcitenothman2013learning use Wikipedia anchor links and disambiguation pages joined with Freebase types to create a huge amount of somewhat noisy training data and are able to achieve good results on many languages (with some extra heuristics)." } ], "images": [ "scigen/train/2203.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: A comparison of NER systems. The results are F1 scores, where a correct span annotation exactly matches a gold span annotation (start, length, and entity type must all be correct). Results of the systems described in the text are shown for English, German, Spanish, and Dutch. BTS* shows the results of the BTS model trained separately on each language while BTS is a single model trained on all 4 languages together. The top set of results leverage resources beyond the training data; the middle set do not, and thus are most comparable to our results (bottom set)." ], "reasoning": "Nothman et al. used Wikipedia and Freebase to create noisy training data for multilingual NER, achieving good results across many languages.", "description": "There is relatively little work on multilingual NER, and most research is focused on building systems that are unsupervised in the sense that they use resources like Wikipedia and Freebase rather than manually annotated data. Nothman et al. \\shortcitenothman2013learning use Wikipedia anchor links and disambiguation pages joined with Freebase types to create a huge amount of somewhat noisy training data and are able to achieve good results on many languages (with some extra heuristics).", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: A comparison of NER systems. The results are F1 scores, where a correct span annotation exactly matches a gold span annotation (start, length, and entity type must all be correct). Results of the systems described in the text are shown for English, German, Spanish, and Dutch. BTS* shows the results of the BTS model trained separately on each language while BTS is a single model trained on all 4 languages together. The top set of results leverage resources beyond the training data; the middle set do not, and thus are most comparable to our results (bottom set).', solve this question and give detail solutions. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model achieved the highest score in the English (en) column?\n" }, { "role": "assistant", "content": "To find the highest score in the English (en) column, compare all the values listed under the 'en' column. The highest value is 90.90, achieved by the Passos model. So, the answer is {\"answer\": \"Passos\"}." } ], "images": [ "scigen/train/2203.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: A comparison of NER systems. The results are F1 scores, where a correct span annotation exactly matches a gold span annotation (start, length, and entity type must all be correct). Results of the systems described in the text are shown for English, German, Spanish, and Dutch. BTS* shows the results of the BTS model trained separately on each language while BTS is a single model trained on all 4 languages together. The top set of results leverage resources beyond the training data; the middle set do not, and thus are most comparable to our results (bottom set)." ], "reasoning": "To find the highest score in the English (en) column, compare all the values listed under the 'en' column. The highest value is 90.90, achieved by the Passos model.", "question": "Which model achieved the highest score in the English (en) column?", "answer": "So, the answer is {\"answer\": \"Passos\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The BTS model achieved the highest score in the Spanish (es) column., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe BTS model achieved the highest score in the Spanish (es) column.\n" }, { "role": "assistant", "content": "To verify this statement, compare the scores in the Spanish (es) column. The highest score is 82.95, achieved by the BTS model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2203.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: A comparison of NER systems. The results are F1 scores, where a correct span annotation exactly matches a gold span annotation (start, length, and entity type must all be correct). Results of the systems described in the text are shown for English, German, Spanish, and Dutch. BTS* shows the results of the BTS model trained separately on each language while BTS is a single model trained on all 4 languages together. The top set of results leverage resources beyond the training data; the middle set do not, and thus are most comparable to our results (bottom set)." ], "reasoning": "To verify this statement, compare the scores in the Spanish (es) column. The highest score is 82.95, achieved by the BTS model.", "statement": "The BTS model achieved the highest score in the Spanish (es) column.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: Part-of-speech tagging accuracy for two CRF baselines and 2 versions of BTS. CRF+ uses resources external to the training data (word clusters) and CRF uses only the training data. BTS (unlike CRF+ and CRF) is a single model trained on all the languages together, while BTS* is a separate Byte-to-Span model for each language.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The BTS model generally outperforms CRF models, especially in Finnish, and is more storage-efficient.The single BTS model improves on average over the CRF models trained using the same data, though clearly there is some benefit in using external resources. Note that BTS is particularly strong in Finnish, surpassing even CRF+ by nearly 1.5% (absolute), probably because the byte representation generalizes better to agglutinative languages than word-based models, a finding validated by Ling et al. \\shortciteling2015finding. In addition, the baseline CRF models, including the (compressed) cluster tables, require about 50 MB per language, while BTS is under 10 MB. BTS improves on average over BTS*, suggesting that it is learning some language-independent representation." } ], "images": [ "scigen/train/2204.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Part-of-speech tagging accuracy for two CRF baselines and 2 versions of BTS. CRF+ uses resources external to the training data (word clusters) and CRF uses only the training data. BTS (unlike CRF+ and CRF) is a single model trained on all the languages together, while BTS* is a separate Byte-to-Span model for each language." ], "reasoning": "The BTS model generally outperforms CRF models, especially in Finnish, and is more storage-efficient.", "description": "The single BTS model improves on average over the CRF models trained using the same data, though clearly there is some benefit in using external resources. Note that BTS is particularly strong in Finnish, surpassing even CRF+ by nearly 1.5% (absolute), probably because the byte representation generalizes better to agglutinative languages than word-based models, a finding validated by Ling et al. \\shortciteling2015finding. In addition, the baseline CRF models, including the (compressed) cluster tables, require about 50 MB per language, while BTS is under 10 MB. BTS improves on average over BTS*, suggesting that it is learning some language-independent representation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Part-of-speech tagging accuracy for two CRF baselines and 2 versions of BTS. CRF+ uses resources external to the training data (word clusters) and CRF uses only the training data. BTS (unlike CRF+ and CRF) is a single model trained on all the languages together, while BTS* is a separate Byte-to-Span model for each language.\nQuestion: Which language shows the greatest improvement in performance when using BTS compared to CRF+?\nSolve the above question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the language with the greatest improvement, subtract the BTS score from the CRF+ score for each language and identify the maximum difference. So, the answer is {\"answer\": \"Finnish\"}" } ], "images": [ "scigen/train/2204.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Part-of-speech tagging accuracy for two CRF baselines and 2 versions of BTS. CRF+ uses resources external to the training data (word clusters) and CRF uses only the training data. BTS (unlike CRF+ and CRF) is a single model trained on all the languages together, while BTS* is a separate Byte-to-Span model for each language." ], "reasoning": "To find the language with the greatest improvement, subtract the BTS score from the CRF+ score for each language and identify the maximum difference.", "question": "Which language shows the greatest improvement in performance when using BTS compared to CRF+?", "answer": "So, the answer is {\"answer\": \"Finnish\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nBTS requires less storage space compared to CRF models.\n" }, { "role": "assistant", "content": "The description states that CRF models require about 50 MB per language, while BTS is under 10 MB. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2204.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Part-of-speech tagging accuracy for two CRF baselines and 2 versions of BTS. CRF+ uses resources external to the training data (word clusters) and CRF uses only the training data. BTS (unlike CRF+ and CRF) is a single model trained on all the languages together, while BTS* is a separate Byte-to-Span model for each language." ], "reasoning": "The description states that CRF models require about 50 MB per language, while BTS is under 10 MB.", "statement": "BTS requires less storage space compared to CRF models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: BTS Part-of-speech tagging average accuracy across all 13 evaluated languages and Named Entity Recognition average F1 across all 4 evaluated languages with various modifications to the vanilla training setup. Dropout is standard in Neural Network model training because it often improves generalization; Byte-dropout randomly replaces input bytes with a special DROP marker.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Applying dropout and byte-dropout during training significantly improves POS accuracy and NER F1 scores, reducing overfitting.Without any kind of dropout, the training process starts to overfit (development data perplexity starts increasing) relatively quickly. For POS tagging, we set dropout and byte-dropout to 0.2, while for NER, we set both to 0.3. This significantly reduces the overfitting problem." } ], "images": [ "scigen/train/2205.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: BTS Part-of-speech tagging average accuracy across all 13 evaluated languages and Named Entity Recognition average F1 across all 4 evaluated languages with various modifications to the vanilla training setup. Dropout is standard in Neural Network model training because it often improves generalization; Byte-dropout randomly replaces input bytes with a special DROP marker." ], "reasoning": "Applying dropout and byte-dropout during training significantly improves POS accuracy and NER F1 scores, reducing overfitting.", "description": "Without any kind of dropout, the training process starts to overfit (development data perplexity starts increasing) relatively quickly. For POS tagging, we set dropout and byte-dropout to 0.2, while for NER, we set both to 0.3. This significantly reduces the overfitting problem.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: BTS Part-of-speech tagging average accuracy across all 13 evaluated languages and Named Entity Recognition average F1 across all 4 evaluated languages with various modifications to the vanilla training setup. Dropout is standard in Neural Network model training because it often improves generalization; Byte-dropout randomly replaces input bytes with a special DROP marker.\nQuestion: What is the POS accuracy when both dropout and byte-dropout are applied during training?\nSolve the above question based on the table information. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the POS accuracy for different training methods. When both dropout and byte-dropout are applied, the POS accuracy is 95.85. So, the answer is {\"answer\": \"95.85\"}." } ], "images": [ "scigen/train/2205.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: BTS Part-of-speech tagging average accuracy across all 13 evaluated languages and Named Entity Recognition average F1 across all 4 evaluated languages with various modifications to the vanilla training setup. Dropout is standard in Neural Network model training because it often improves generalization; Byte-dropout randomly replaces input bytes with a special DROP marker." ], "reasoning": "The table shows the POS accuracy for different training methods. When both dropout and byte-dropout are applied, the POS accuracy is 95.85.", "question": "What is the POS accuracy when both dropout and byte-dropout are applied during training?", "answer": "So, the answer is {\"answer\": \"95.85\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nApplying dropout improves the NER F1 score compared to vanilla training.\n" }, { "role": "assistant", "content": "The table shows that the NER F1 score for vanilla training is 74.75, while with dropout, it improves to 78.76. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2205.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: BTS Part-of-speech tagging average accuracy across all 13 evaluated languages and Named Entity Recognition average F1 across all 4 evaluated languages with various modifications to the vanilla training setup. Dropout is standard in Neural Network model training because it often improves generalization; Byte-dropout randomly replaces input bytes with a special DROP marker." ], "reasoning": "The table shows that the NER F1 score for vanilla training is 74.75, while with dropout, it improves to 78.76.", "statement": "Applying dropout improves the NER F1 score compared to vanilla training.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Macro-averaged (across 4 languages) F1 for the NER task using different model architectures.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Increasing the depth of the model improves performance more significantly than increasing the width, and the benefit of a wider model diminishes after using 4 stacked LSTMs.This observation holds for both models trained on a single language and models trained on many languages. Because the vocabulary is so small, the total number of parameters is dominated by the size of the recurrent matrices. All the results reported below use the same architecture (unless otherwise noted) and thus have roughly 900k parameters. Increasing the width of the model improves performance less than increasing the depth, and once we use 4 stacked LSTMs, the added benefit of a much wider model has disappeared. This result suggests that rather than learning to partition the space of inputs according to the source language, the model is learning some lanugage-independent representation at the deeper levels." } ], "images": [ "scigen/train/2206.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Macro-averaged (across 4 languages) F1 for the NER task using different model architectures." ], "reasoning": "Increasing the depth of the model improves performance more significantly than increasing the width, and the benefit of a wider model diminishes after using 4 stacked LSTMs.", "description": "This observation holds for both models trained on a single language and models trained on many languages. Because the vocabulary is so small, the total number of parameters is dominated by the size of the recurrent matrices. All the results reported below use the same architecture (unless otherwise noted) and thus have roughly 900k parameters. Increasing the width of the model improves performance less than increasing the depth, and once we use 4 stacked LSTMs, the added benefit of a much wider model has disappeared. This result suggests that rather than learning to partition the space of inputs according to the source language, the model is learning some lanugage-independent representation at the deeper levels.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nTable title: Table 4: Macro-averaged (across 4 languages) F1 for the NER task using different model architectures.\n\nQuestion: What is the performance improvement when increasing the depth from 1 to 4 for a model with Width=320?\n" }, { "role": "assistant", "content": "The performance at Depth=1 for Width=320 is 76.15, and at Depth=4 for Width=320 is 82.13. The improvement is 82.13 - 76.15. So, the answer is {\"answer\": \"5.98\"}" } ], "images": [ "scigen/train/2206.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Macro-averaged (across 4 languages) F1 for the NER task using different model architectures." ], "reasoning": "The performance at Depth=1 for Width=320 is 76.15, and at Depth=4 for Width=320 is 82.13. The improvement is 82.13 - 76.15.", "question": "What is the performance improvement when increasing the depth from 1 to 4 for a model with Width=320?", "answer": "So, the answer is {\"answer\": \"5.98\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Increasing the width of the model from 320 to 640 at Depth=4 significantly improves performance., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nIncreasing the width of the model from 320 to 640 at Depth=4 significantly improves performance.\n" }, { "role": "assistant", "content": "The performance at Depth=4 for Width=320 is 82.13, and for Width=640 is 82.18. The improvement is only 0.05, which is not significant. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2206.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Macro-averaged (across 4 languages) F1 for the NER task using different model architectures." ], "reasoning": "The performance at Depth=4 for Width=320 is 82.13, and for Width=640 is 82.18. The improvement is only 0.05, which is not significant.", "statement": "Increasing the width of the model from 320 to 640 at Depth=4 significantly improves performance.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Hyperparameters for sentiment model.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The model uses a high dropout rate of 0.75 in the hidden layers to prevent overfitting and employs two hidden layers with 300 units each to extract abstract features.Due to the relatively small amount of labeled training data, our proposed model architecture is designed to prevent overfitting by using a restricted view of the training data via a high rate of dropout in the hidden layers. Additionally, we use two hidden layers to extract a more abstracted form of the input. Additionally, because neutral sentiment is much broader in scope and has fewer training examples, resulting in covariate shift, we compute a threshold for classifying positive and negative sentiment using the formula min=avg(sim)+α*σ(sim), where σ is standard deviation and α is a constant, which we set to 0.2. If a given test sentence does not have positive or negative outputs that exceed this threshold, the sentence is classified as neutral even if neutral is not the maximal output." } ], "images": [ "scigen/train/2207.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Hyperparameters for sentiment model." ], "reasoning": "The model uses a high dropout rate of 0.75 in the hidden layers to prevent overfitting and employs two hidden layers with 300 units each to extract abstract features.", "description": "Due to the relatively small amount of labeled training data, our proposed model architecture is designed to prevent overfitting by using a restricted view of the training data via a high rate of dropout in the hidden layers. Additionally, we use two hidden layers to extract a more abstracted form of the input. Additionally, because neutral sentiment is much broader in scope and has fewer training examples, resulting in covariate shift, we compute a threshold for classifying positive and negative sentiment using the formula min=avg(sim)+α*σ(sim), where σ is standard deviation and α is a constant, which we set to 0.2. If a given test sentence does not have positive or negative outputs that exceed this threshold, the sentence is classified as neutral even if neutral is not the maximal output.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 4: Hyperparameters for sentiment model.' in mind, break down and resolve the question below step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the dropout rate used in the hidden layers to prevent overfitting?\n" }, { "role": "assistant", "content": "The description mentions using a high rate of dropout in the hidden layers to prevent overfitting, and the table specifies that the dropout rate is 0.75. So, the answer is {\"answer\": \"0.75\"}" } ], "images": [ "scigen/train/2207.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Hyperparameters for sentiment model." ], "reasoning": "The description mentions using a high rate of dropout in the hidden layers to prevent overfitting, and the table specifies that the dropout rate is 0.75.", "question": "What is the dropout rate used in the hidden layers to prevent overfitting?", "answer": "So, the answer is {\"answer\": \"0.75\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model uses the Adam optimizer.\n" }, { "role": "assistant", "content": "The table lists the optimizer used as Adam, which matches the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2207.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Hyperparameters for sentiment model." ], "reasoning": "The table lists the optimizer used as Adam, which matches the statement.", "statement": "The model uses the Adam optimizer.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Results of the clinical sentiment extraction task.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The Pattern sentiment lexicon underclassifies positive and negative examples, leading to poor recall but moderate precision, while neutral examples are correctly classified more often.The ‘All’ row for each model configuration was computed by averaging the scores of the sentiment models for each risk factor domain. Applying the Pattern sentiment lexicon to our test corpus showed a strong trend towards underclassification of positive and negative examples, which led to poor recall scores while maintaining moderate precision. Neutral examples, however, were correctly classified significantly more often. This confirms that many of the most informative words in terms of clinical sentiment (e.g. ‘hallucination’, ‘depressed’, ‘employed’, etc.) do not hold significance in general-domain sentiment and are therefore not part of the Pattern lexicon." } ], "images": [ "scigen/train/2208.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of the clinical sentiment extraction task." ], "reasoning": "The Pattern sentiment lexicon underclassifies positive and negative examples, leading to poor recall but moderate precision, while neutral examples are correctly classified more often.", "description": "The ‘All’ row for each model configuration was computed by averaging the scores of the sentiment models for each risk factor domain. Applying the Pattern sentiment lexicon to our test corpus showed a strong trend towards underclassification of positive and negative examples, which led to poor recall scores while maintaining moderate precision. Neutral examples, however, were correctly classified significantly more often. This confirms that many of the most informative words in terms of clinical sentiment (e.g. ‘hallucination’, ‘depressed’, ‘employed’, etc.) do not hold significance in general-domain sentiment and are therefore not part of the Pattern lexicon.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: Results of the clinical sentiment extraction task.' and methodically tackle the question that follows. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model configuration has the highest recall score for negative examples in the 'All' domain?\n" }, { "role": "assistant", "content": "To find the model configuration with the highest recall score for negative examples in the 'All' domain, compare the 'Neg R' values for the 'All' domain across all model configurations. The highest recall score for negative examples is 0.733, which is achieved by the Semi-Supervised MLP (Self-Training) model. So, the answer is {\"answer\": \"Semi-Supervised MLP (Self-Training)\"}." } ], "images": [ "scigen/train/2208.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of the clinical sentiment extraction task." ], "reasoning": "To find the model configuration with the highest recall score for negative examples in the 'All' domain, compare the 'Neg R' values for the 'All' domain across all model configurations. The highest recall score for negative examples is 0.733, which is achieved by the Semi-Supervised MLP (Self-Training) model.", "question": "Which model configuration has the highest recall score for negative examples in the 'All' domain?", "answer": "So, the answer is {\"answer\": \"Semi-Supervised MLP (Self-Training)\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Fully Supervised MLP model has the highest precision score for positive examples in the 'All' domain., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Fully Supervised MLP model has the highest precision score for positive examples in the 'All' domain.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Pos P' values for the 'All' domain across all model configurations. The Fully Supervised MLP model has a 'Pos P' value of 0.62, which is not the highest. The highest 'Pos P' value is 0.612, achieved by the Baseline (Pattern) model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2208.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of the clinical sentiment extraction task." ], "reasoning": "To verify this statement, compare the 'Pos P' values for the 'All' domain across all model configurations. The Fully Supervised MLP model has a 'Pos P' value of 0.62, which is not the highest. The highest 'Pos P' value is 0.612, achieved by the Baseline (Pattern) model.", "statement": "The Fully Supervised MLP model has the highest precision score for positive examples in the 'All' domain.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 9: Ablation study of the DGLSTM-CRF model on the OntoNotes English dataset.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The model's F1 score drops by 0.3 points with self connection, 0.4 points with concatenation, and 0.4 points without dependency relation embedding.With self connection as interaction function, the F1 drops 0.3 points. The model achieves comparable performance with concatenation as interaction function but F1 drops about 0.4 points with the addition interaction function. We believe that the addition potentially leads to certain information loss. Without the dependency relation embedding vr in the input representation, the F1 drops about 0.4 points." } ], "images": [ "scigen/train/2209.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Ablation study of the DGLSTM-CRF model on the OntoNotes English dataset." ], "reasoning": "The model's F1 score drops by 0.3 points with self connection, 0.4 points with concatenation, and 0.4 points without dependency relation embedding.", "description": "With self connection as interaction function, the F1 drops 0.3 points. The model achieves comparable performance with concatenation as interaction function but F1 drops about 0.4 points with the addition interaction function. We believe that the addition potentially leads to certain information loss. Without the dependency relation embedding vr in the input representation, the F1 drops about 0.4 points.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 9: Ablation study of the DGLSTM-CRF model on the OntoNotes English dataset.' and methodically tackle the question that follows. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the F1 score of the model with concatenation as the interaction function?\n" }, { "role": "assistant", "content": "The table shows that with concatenation as the interaction function, the F1 score is 89.76. So, the answer is {\"answer\": \"89.76\"}" } ], "images": [ "scigen/train/2209.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Ablation study of the DGLSTM-CRF model on the OntoNotes English dataset." ], "reasoning": "The table shows that with concatenation as the interaction function, the F1 score is 89.76.", "question": "What is the F1 score of the model with concatenation as the interaction function?", "answer": "So, the answer is {\"answer\": \"89.76\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The F1 score drops by 0.4 points without the dependency relation embedding., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe F1 score drops by 0.4 points without the dependency relation embedding.\n" }, { "role": "assistant", "content": "The description states that without the dependency relation embedding, the F1 drops about 0.4 points, which is supported by the table showing an F1 score of 89.46 without the dependency relation embedding. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2209.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Ablation study of the DGLSTM-CRF model on the OntoNotes English dataset." ], "reasoning": "The description states that without the dependency relation embedding, the F1 drops about 0.4 points, which is supported by the table showing an F1 score of 89.46 without the dependency relation embedding.", "statement": "The F1 score drops by 0.4 points without the dependency relation embedding.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Performance comparison on the OntoNotes 5.0 English dataset.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The DGLSTM-CRF model with L=2 significantly outperforms the BiLSTM-CRF and BiLSTM-GCN-CRF models, but increasing the number of layers to L=3 does not provide further improvements.Without the LSTM layers (i.e., L=0), the proposed model with dependency information significantly improves the NER performance with more than 2 points in F1 compared to the baseline BiLSTM-CRF (L=0), which demonstrate the effectiveness of dependencies for the NER task. Li et al. This baseline also outperforms the CNN-based models Strubell et al. Li et al. The BiLSTM-GCN-CRF model outperforms the BiLSTM-CRF model but achieves inferior performance compared to the proposed DGLSTM-CRF model. We believe it is challenging to preserve the surrounding context information with stacking GCN layers while contextual information is important for NER Peters et al. Overall, the 2-layer DGLSTM-CRF model significantly (with p<0.01) outperforms the best BiLSTM-CRF baseline and the BiLSTM-GCN-CRF model. As we can see from the table, increasing the number of layers (e.g., L = 3) does not give us further improvements for both BiLSTM-CRF and DGLSTM-CRF because such third-order information (e.g., the relationship among a word’s parent, its grandparent, and great-grandparent) does not play an important role in indicating the presence of named entities." } ], "images": [ "scigen/train/2210.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance comparison on the OntoNotes 5.0 English dataset." ], "reasoning": "The DGLSTM-CRF model with L=2 significantly outperforms the BiLSTM-CRF and BiLSTM-GCN-CRF models, but increasing the number of layers to L=3 does not provide further improvements.", "description": "Without the LSTM layers (i.e., L=0), the proposed model with dependency information significantly improves the NER performance with more than 2 points in F1 compared to the baseline BiLSTM-CRF (L=0), which demonstrate the effectiveness of dependencies for the NER task. Li et al. This baseline also outperforms the CNN-based models Strubell et al. Li et al. The BiLSTM-GCN-CRF model outperforms the BiLSTM-CRF model but achieves inferior performance compared to the proposed DGLSTM-CRF model. We believe it is challenging to preserve the surrounding context information with stacking GCN layers while contextual information is important for NER Peters et al. Overall, the 2-layer DGLSTM-CRF model significantly (with p<0.01) outperforms the best BiLSTM-CRF baseline and the BiLSTM-GCN-CRF model. As we can see from the table, increasing the number of layers (e.g., L = 3) does not give us further improvements for both BiLSTM-CRF and DGLSTM-CRF because such third-order information (e.g., the relationship among a word’s parent, its grandparent, and great-grandparent) does not play an important role in indicating the presence of named entities.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 3: Performance comparison on the OntoNotes 5.0 English dataset.', answer the question below by showing each progressive step toward the answer. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model achieves the highest F1 score without using ELMo?\n" }, { "role": "assistant", "content": "To find the model with the highest F1 score without using ELMo, we need to compare the F1 scores of all models that do not include '+ ELMo' in their names. The DGLSTM-CRF (L=2) model has the highest F1 score of 88.52. So, the answer is {\"answer\": \"DGLSTM-CRF (L=2)\"}" } ], "images": [ "scigen/train/2210.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance comparison on the OntoNotes 5.0 English dataset." ], "reasoning": "To find the model with the highest F1 score without using ELMo, we need to compare the F1 scores of all models that do not include '+ ELMo' in their names. The DGLSTM-CRF (L=2) model has the highest F1 score of 88.52.", "question": "Which model achieves the highest F1 score without using ELMo?", "answer": "So, the answer is {\"answer\": \"DGLSTM-CRF (L=2)\"}" }, { "messages": [ { "role": "user", "content": "The BiLSTM-GCN-CRF model achieves a higher F1 score than the BiLSTM-CRF model with L=3. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe BiLSTM-GCN-CRF model achieves a higher F1 score than the BiLSTM-CRF model with L=3.\n" }, { "role": "assistant", "content": "The F1 score of the BiLSTM-GCN-CRF model is 88.18, while the F1 score of the BiLSTM-CRF model with L=3 is 87.65. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2210.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance comparison on the OntoNotes 5.0 English dataset." ], "reasoning": "The F1 score of the BiLSTM-GCN-CRF model is 88.18, while the F1 score of the BiLSTM-CRF model with L=3 is 87.65. Therefore, the statement is supported.", "statement": "The BiLSTM-GCN-CRF model achieves a higher F1 score than the BiLSTM-CRF model with L=3.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Results on the SemEval-2010 Task 1 datasets.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The DGLSTM-CRF model, especially with ELMo and L larger than 1, shows substantial improvements in F1 scores on the Catalan and Spanish datasets compared to the best performing BiLSTM-CRF baseline.Overall, we observe substantial improvements of the DGLSTM-CRF on the Catalan and Spanish datasets (with p<0.001 marked in bold against the best performing BiLSTM-CRF baseline), especially for DGLSTM-CRF with ELMo and L larger than 1. With word embeddings, the best DGLSTM-CRF model outperforms the best performing BiLSTM-CRF baseline with more than 10 and 9 points in F1 on the Catalan and Spanish datasets, respectively. The BiLSTM-GCN-CRF model also performs much better than the BiLSTM-CRF baselines but is worse than the DGLSTM-CRF model with L≥2. Both precision and recall significantly improve with a large margin compared to the best performing BiLSTM-CRF, especially for the recall (with more than 10 points improvement) on these two datasets. With ELMo, the best performing DGLSTM-CRF model outperforms the BiLSTM-CRF baseline with about 6 and 7 points in F1 on these two datasets, respectively. The substantial improvements show that the structural dependency information is extremely helpful for these two datasets." } ], "images": [ "scigen/train/2211.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on the SemEval-2010 Task 1 datasets." ], "reasoning": "The DGLSTM-CRF model, especially with ELMo and L larger than 1, shows substantial improvements in F1 scores on the Catalan and Spanish datasets compared to the best performing BiLSTM-CRF baseline.", "description": "Overall, we observe substantial improvements of the DGLSTM-CRF on the Catalan and Spanish datasets (with p<0.001 marked in bold against the best performing BiLSTM-CRF baseline), especially for DGLSTM-CRF with ELMo and L larger than 1. With word embeddings, the best DGLSTM-CRF model outperforms the best performing BiLSTM-CRF baseline with more than 10 and 9 points in F1 on the Catalan and Spanish datasets, respectively. The BiLSTM-GCN-CRF model also performs much better than the BiLSTM-CRF baselines but is worse than the DGLSTM-CRF model with L≥2. Both precision and recall significantly improve with a large margin compared to the best performing BiLSTM-CRF, especially for the recall (with more than 10 points improvement) on these two datasets. With ELMo, the best performing DGLSTM-CRF model outperforms the BiLSTM-CRF baseline with about 6 and 7 points in F1 on these two datasets, respectively. The substantial improvements show that the structural dependency information is extremely helpful for these two datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 5: Results on the SemEval-2010 Task 1 datasets.', solve the following question step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the F1 score of the best performing DGLSTM-CRF model with ELMo on the Spanish dataset?\n" }, { "role": "assistant", "content": "The best performing DGLSTM-CRF model with ELMo on the Spanish dataset is DGLSTM-CRF (L=2) + ELMo, which has an F1 score of 87.56. So, the answer is {\"answer\": \"87.56\"}" } ], "images": [ "scigen/train/2211.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on the SemEval-2010 Task 1 datasets." ], "reasoning": "The best performing DGLSTM-CRF model with ELMo on the Spanish dataset is DGLSTM-CRF (L=2) + ELMo, which has an F1 score of 87.56.", "question": "What is the F1 score of the best performing DGLSTM-CRF model with ELMo on the Spanish dataset?", "answer": "So, the answer is {\"answer\": \"87.56\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe DGLSTM-CRF model with L=1 and ELMo has a higher F1 score on the Catalan dataset than the BiLSTM-GCN-CRF model with ELMo.\n" }, { "role": "assistant", "content": "The F1 score of the DGLSTM-CRF model with L=1 and ELMo on the Catalan dataset is 82.33, while the F1 score of the BiLSTM-GCN-CRF model with ELMo is 83.42. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2211.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on the SemEval-2010 Task 1 datasets." ], "reasoning": "The F1 score of the DGLSTM-CRF model with L=1 and ELMo on the Catalan dataset is 82.33, while the F1 score of the BiLSTM-GCN-CRF model with ELMo is 83.42. Therefore, the statement is refuted.", "statement": "The DGLSTM-CRF model with L=1 and ELMo has a higher F1 score on the Catalan dataset than the BiLSTM-GCN-CRF model with ELMo.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Performance on the CoNLL-2003 English dataset.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "DGLSTM-CRF outperforms BiLSTM-CRF by 0.2 points in F1 score, but the improvement is not significant. Modifying dependencies to ensure each entity forms a subtree improves the F1 score to 92.7, which is significantly better than BiLSTM-CRF.The dependencies are predicted from Spacy Honnibal and Montani With the contextualized word representations, DGLSTM-CRF outperforms BiLSTM-CRF with 0.2 points in F1 (p<0.09). The improvement is not significant due to the relatively lower equality of the dependency trees. To further study the effect of the dependencies, we modified the predicted dependencies to ensure each entity form a subtree in the complete dataset. Such modification improves the F1 to 92.7, which is significantly better (p<0.05) than the BiLSTM-CRF." } ], "images": [ "scigen/train/2212.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Performance on the CoNLL-2003 English dataset." ], "reasoning": "DGLSTM-CRF outperforms BiLSTM-CRF by 0.2 points in F1 score, but the improvement is not significant. Modifying dependencies to ensure each entity forms a subtree improves the F1 score to 92.7, which is significantly better than BiLSTM-CRF.", "description": "The dependencies are predicted from Spacy Honnibal and Montani With the contextualized word representations, DGLSTM-CRF outperforms BiLSTM-CRF with 0.2 points in F1 (p<0.09). The improvement is not significant due to the relatively lower equality of the dependency trees. To further study the effect of the dependencies, we modified the predicted dependencies to ensure each entity form a subtree in the complete dataset. Such modification improves the F1 to 92.7, which is significantly better (p<0.05) than the BiLSTM-CRF.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 6: Performance on the CoNLL-2003 English dataset.', and sequentially solve the question, writing out each step of your reasoning process. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the F1 score of the DGLSTM-CRF + ELMo (L=2) model after modifying the dependencies to ensure each entity forms a subtree?\n" }, { "role": "assistant", "content": "The description states that modifying the dependencies to ensure each entity forms a subtree improves the F1 score to 92.7, which is significantly better than BiLSTM-CRF. So, the answer is {\"answer\": \"92.7\"}" } ], "images": [ "scigen/train/2212.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Performance on the CoNLL-2003 English dataset." ], "reasoning": "The description states that modifying the dependencies to ensure each entity forms a subtree improves the F1 score to 92.7, which is significantly better than BiLSTM-CRF.", "question": "What is the F1 score of the DGLSTM-CRF + ELMo (L=2) model after modifying the dependencies to ensure each entity forms a subtree?", "answer": "So, the answer is {\"answer\": \"92.7\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The DGLSTM-CRF + ELMo (L=2) model has a higher F1 score than the BiLSTM-CRF + ELMo (L=2) model., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe DGLSTM-CRF + ELMo (L=2) model has a higher F1 score than the BiLSTM-CRF + ELMo (L=2) model.\n" }, { "role": "assistant", "content": "The table shows that the DGLSTM-CRF + ELMo (L=2) model has an F1 score of 92.4, while the BiLSTM-CRF + ELMo (L=2) model has an F1 score of 92.2. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2212.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Performance on the CoNLL-2003 English dataset." ], "reasoning": "The table shows that the DGLSTM-CRF + ELMo (L=2) model has an F1 score of 92.4, while the BiLSTM-CRF + ELMo (L=2) model has an F1 score of 92.2.", "statement": "The DGLSTM-CRF + ELMo (L=2) model has a higher F1 score than the BiLSTM-CRF + ELMo (L=2) model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 7: Low-resource NER performance on the SemEval-2010 Task 1 datasets.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "DGLSTM-CRF significantly improves recall over BiLSTM-CRF, and using gold dependencies further enhances both precision and recall.We assume that the contextualized word representations are not available and dependencies are predicted. With limited amount of training data, BiLSTM-CRF suffers from low recall and the DGLSTM-CRF largely improves it on these two datasets. Using gold dependencies further significantly improves the precision and recall." } ], "images": [ "scigen/train/2213.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Low-resource NER performance on the SemEval-2010 Task 1 datasets." ], "reasoning": "DGLSTM-CRF significantly improves recall over BiLSTM-CRF, and using gold dependencies further enhances both precision and recall.", "description": "We assume that the contextualized word representations are not available and dependencies are predicted. With limited amount of training data, BiLSTM-CRF suffers from low recall and the DGLSTM-CRF largely improves it on these two datasets. Using gold dependencies further significantly improves the precision and recall.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 7: Low-resource NER performance on the SemEval-2010 Task 1 datasets.', answer the following question. Give detailed solution consisting of each step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model and condition achieved the highest recall for the Spanish dataset?\n" }, { "role": "assistant", "content": "By comparing the recall values for the Spanish dataset across all models and conditions, the highest recall is achieved by the model with gold dependency. So, the answer is {\"answer\": \"with gold dependency\"}." } ], "images": [ "scigen/train/2213.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Low-resource NER performance on the SemEval-2010 Task 1 datasets." ], "reasoning": "By comparing the recall values for the Spanish dataset across all models and conditions, the highest recall is achieved by the model with gold dependency.", "question": "Which model and condition achieved the highest recall for the Spanish dataset?", "answer": "So, the answer is {\"answer\": \"with gold dependency\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe BiLSTM-CRF model achieved the highest precision for the Catalan dataset.\n" }, { "role": "assistant", "content": "By comparing the precision values for the Catalan dataset, the highest precision is achieved by the model with gold dependency, not BiLSTM-CRF. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2213.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Low-resource NER performance on the SemEval-2010 Task 1 datasets." ], "reasoning": "By comparing the precision values for the Catalan dataset, the highest precision is achieved by the model with gold dependency, not BiLSTM-CRF.", "statement": "The BiLSTM-CRF model achieved the highest precision for the Catalan dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 8: F1 performance of DGLSTM-CRF with predicted dependencies against the best performing BiLSTM-CRF. †: LAS is label attachment score which is the metric for dependency evaluation.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The DGLSTM-CRF model with predicted dependencies outperforms the BiLSTM-CRF model in all four languages, but still performs worse than the DGLSTM-CRF model with gold dependencies, especially in Catalan and Spanish.We implemented the dependency parser using the AllenNLP package Gardner et al. DGLSTM-CRF even with predicted dependencies is able to consistently outperform the BiLSTM-CRF on four languages. However, the performance is still worse than the DGLSTM-CRF with gold dependencies, especially on the Catalan and Spanish. Such results suggest that it is essential to have high-quality dependency annotations available for the proposed model." } ], "images": [ "scigen/train/2214.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: F1 performance of DGLSTM-CRF with predicted dependencies against the best performing BiLSTM-CRF. †: LAS is label attachment score which is the metric for dependency evaluation." ], "reasoning": "The DGLSTM-CRF model with predicted dependencies outperforms the BiLSTM-CRF model in all four languages, but still performs worse than the DGLSTM-CRF model with gold dependencies, especially in Catalan and Spanish.", "description": "We implemented the dependency parser using the AllenNLP package Gardner et al. DGLSTM-CRF even with predicted dependencies is able to consistently outperform the BiLSTM-CRF on four languages. However, the performance is still worse than the DGLSTM-CRF with gold dependencies, especially on the Catalan and Spanish. Such results suggest that it is essential to have high-quality dependency annotations available for the proposed model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 8: F1 performance of DGLSTM-CRF with predicted dependencies against the best performing BiLSTM-CRF. †: LAS is label attachment score which is the metric for dependency evaluation.', answer the question below by showing each progressive step toward the answer. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich language shows the highest improvement in performance when using the DGLSTM-CRF model with predicted dependencies compared to the BiLSTM-CRF model?\n" }, { "role": "assistant", "content": "By comparing the improvement values, Catalan shows the highest improvement of +3.91 when using the DGLSTM-CRF model with predicted dependencies compared to the BiLSTM-CRF model. So, the answer is {\"answer\": \"Catalan\"}." } ], "images": [ "scigen/train/2214.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: F1 performance of DGLSTM-CRF with predicted dependencies against the best performing BiLSTM-CRF. †: LAS is label attachment score which is the metric for dependency evaluation." ], "reasoning": "By comparing the improvement values, Catalan shows the highest improvement of +3.91 when using the DGLSTM-CRF model with predicted dependencies compared to the BiLSTM-CRF model.", "question": "Which language shows the highest improvement in performance when using the DGLSTM-CRF model with predicted dependencies compared to the BiLSTM-CRF model?", "answer": "So, the answer is {\"answer\": \"Catalan\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe DGLSTM-CRF model with predicted dependencies performs better than the BiLSTM-CRF model in Chinese.\n" }, { "role": "assistant", "content": "The table shows that the DGLSTM-CRF model with predicted dependencies has a score of 79.59 in Chinese, which is higher than the BiLSTM-CRF model's score of 79.20. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2214.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: F1 performance of DGLSTM-CRF with predicted dependencies against the best performing BiLSTM-CRF. †: LAS is label attachment score which is the metric for dependency evaluation." ], "reasoning": "The table shows that the DGLSTM-CRF model with predicted dependencies has a score of 79.59 in Chinese, which is higher than the BiLSTM-CRF model's score of 79.20.", "statement": "The DGLSTM-CRF model with predicted dependencies performs better than the BiLSTM-CRF model in Chinese.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: The evaluation results for 10k test sessions on 10×10 maps. The average navigation success rates are reported as percentages. The second number in each cell represents the standard deviation over twelve test runs, each run corresponding to one pass (out of three final passes) of one trained model (out of four random initializations).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The GFT agents perform reasonably well on larger maps with more goals and obstacles, outperforming comparison methods.Y=10, with 6 goals and 24 obstacles, and X=Y=11, with 8 goals and 28 obstacles. Although the performance is not as good as on 8×8 maps (except for nav_avoid whose chance performance tends to peak as there are more goals on the map before the map size becoming too large), the GFT agents achieve reasonable generalizations and still greatly outperform the comparison methods. This further demonstrates that our GFT agents are not trained to memorize environments in specific settings." } ], "images": [ "scigen/train/2215.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The evaluation results for 10k test sessions on 10×10 maps. The average navigation success rates are reported as percentages. The second number in each cell represents the standard deviation over twelve test runs, each run corresponding to one pass (out of three final passes) of one trained model (out of four random initializations)." ], "reasoning": "The GFT agents perform reasonably well on larger maps with more goals and obstacles, outperforming comparison methods.", "description": "Y=10, with 6 goals and 24 obstacles, and X=Y=11, with 8 goals and 28 obstacles. Although the performance is not as good as on 8×8 maps (except for nav_avoid whose chance performance tends to peak as there are more goals on the map before the map size becoming too large), the GFT agents achieve reasonable generalizations and still greatly outperform the comparison methods. This further demonstrates that our GFT agents are not trained to memorize environments in specific settings.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 5: The evaluation results for 10k test sessions on 10×10 maps. The average navigation success rates are reported as percentages. The second number in each cell represents the standard deviation over twelve test runs, each run corresponding to one pass (out of three final passes) of one trained model (out of four random initializations).\nQuestion: Which method achieves the highest performance in the nav_avoid 3D task?\n" }, { "role": "assistant", "content": "By examining the nav_avoid 3D column, the highest performance is achieved by GFT-2 with a score of 98.6±1.3. So, the answer is {\"answer\": \"GFT-2\"}" } ], "images": [ "scigen/train/2215.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The evaluation results for 10k test sessions on 10×10 maps. The average navigation success rates are reported as percentages. The second number in each cell represents the standard deviation over twelve test runs, each run corresponding to one pass (out of three final passes) of one trained model (out of four random initializations)." ], "reasoning": "By examining the nav_avoid 3D column, the highest performance is achieved by GFT-2 with a score of 98.6±1.3.", "question": "Which method achieves the highest performance in the nav_avoid 3D task?", "answer": "So, the answer is {\"answer\": \"GFT-2\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nGFT-1 outperforms all other methods in the nav_bw 2D task.\n" }, { "role": "assistant", "content": "By examining the nav_bw 2D column, GFT-1 has the highest score of 78.7±2.9, outperforming all other methods. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2215.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The evaluation results for 10k test sessions on 10×10 maps. The average navigation success rates are reported as percentages. The second number in each cell represents the standard deviation over twelve test runs, each run corresponding to one pass (out of three final passes) of one trained model (out of four random initializations)." ], "reasoning": "By examining the nav_bw 2D column, GFT-1 has the highest score of 78.7±2.9, outperforming all other methods.", "statement": "GFT-1 outperforms all other methods in the nav_bw 2D task.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: The evaluation results for 10k test sessions. The average navigation success rates are reported as percentages. Numbers in bold represent the best ones. The second number in each cell represents the standard deviation over twelve test runs, each run corresponding to one pass (out of three final passes) of one trained model (out of four random initializations).'.\n" }, { "role": "assistant", "content": "GFT-2 outperforms all other methods in all tasks, with nav_avoid having the highest success rate and nav_dir the lowest.Results. We observe that GFT-1 has some marginal improvement on the success rates of the best-performing comparison methods such as FiLM and Gated. As we perform a second feature transformation, GFT-2 produces a performance jump. Our explanation is that the visual recognition challenge, with random object yaws and scales in each session, requires an expressive language grounding function that can be better modeled by multiple steps of Eq. GFT-2 produces the best numbers in all the five columns. Unsurprisingly, nav_avoid has the highest rate because the agent only has to go to an arbitrary target which is not the specified one. nav has the lowest rate because the agent cannot exploit any object arrangement pattern like in nav_bw. See Results. We observe that GFT-1 already has a huge advantage over the best-performing comparison method FiLM. On top of this, GFT-2 shows a faster performance increase during the training time. This suggests that the original feature space cannot easily comply with various language commands. A rotation of the feature space depending on the input command, an operation which FiLM lacks but GFT-1 owns, is important for producing better grounding results. Unlike in the 2D case, now for GFT-2, nav has the second-best success rate while nav_dir has the lowest rate. Visualization of the nav_dir test cases reveals that due to severe perspective distortion in 3D, the agent has some difficulty of grounding the spatial-relation words, especially when multiple objects are located nearby. But still, GFT agents obtain much better nav_dir results than the comparison methods (over a 15% increase on average)." } ], "images": [ "scigen/train/2216.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The evaluation results for 10k test sessions. The average navigation success rates are reported as percentages. Numbers in bold represent the best ones. The second number in each cell represents the standard deviation over twelve test runs, each run corresponding to one pass (out of three final passes) of one trained model (out of four random initializations)." ], "reasoning": "GFT-2 outperforms all other methods in all tasks, with nav_avoid having the highest success rate and nav_dir the lowest.", "description": "Results. We observe that GFT-1 has some marginal improvement on the success rates of the best-performing comparison methods such as FiLM and Gated. As we perform a second feature transformation, GFT-2 produces a performance jump. Our explanation is that the visual recognition challenge, with random object yaws and scales in each session, requires an expressive language grounding function that can be better modeled by multiple steps of Eq. GFT-2 produces the best numbers in all the five columns. Unsurprisingly, nav_avoid has the highest rate because the agent only has to go to an arbitrary target which is not the specified one. nav has the lowest rate because the agent cannot exploit any object arrangement pattern like in nav_bw. See Results. We observe that GFT-1 already has a huge advantage over the best-performing comparison method FiLM. On top of this, GFT-2 shows a faster performance increase during the training time. This suggests that the original feature space cannot easily comply with various language commands. A rotation of the feature space depending on the input command, an operation which FiLM lacks but GFT-1 owns, is important for producing better grounding results. Unlike in the 2D case, now for GFT-2, nav has the second-best success rate while nav_dir has the lowest rate. Visualization of the nav_dir test cases reveals that due to severe perspective distortion in 3D, the agent has some difficulty of grounding the spatial-relation words, especially when multiple objects are located nearby. But still, GFT agents obtain much better nav_dir results than the comparison methods (over a 15% increase on average).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: The evaluation results for 10k test sessions. The average navigation success rates are reported as percentages. Numbers in bold represent the best ones. The second number in each cell represents the standard deviation over twelve test runs, each run corresponding to one pass (out of three final passes) of one trained model (out of four random initializations).', answer the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method has the highest success rate for the nav_avoid 3D task?\n" }, { "role": "assistant", "content": "By comparing the success rates for the nav_avoid 3D task across all methods, GFT-2 has the highest success rate at 96.3±1.9. So, the answer is {\"answer\": \"GFT-2\"}." } ], "images": [ "scigen/train/2216.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The evaluation results for 10k test sessions. The average navigation success rates are reported as percentages. Numbers in bold represent the best ones. The second number in each cell represents the standard deviation over twelve test runs, each run corresponding to one pass (out of three final passes) of one trained model (out of four random initializations)." ], "reasoning": "By comparing the success rates for the nav_avoid 3D task across all methods, GFT-2 has the highest success rate at 96.3±1.9.", "question": "Which method has the highest success rate for the nav_avoid 3D task?", "answer": "So, the answer is {\"answer\": \"GFT-2\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nGFT-1 has a higher success rate than FiLM in the nav_bw 2D task.\n" }, { "role": "assistant", "content": "The table shows that GFT-1 has a success rate of 85.6±2.3 in the nav_bw 2D task, while FiLM has a success rate of 78.6±4.2, indicating that GFT-1 has a higher success rate. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2216.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The evaluation results for 10k test sessions. The average navigation success rates are reported as percentages. Numbers in bold represent the best ones. The second number in each cell represents the standard deviation over twelve test runs, each run corresponding to one pass (out of three final passes) of one trained model (out of four random initializations)." ], "reasoning": "The table shows that GFT-1 has a success rate of 85.6±2.3 in the nav_bw 2D task, while FiLM has a success rate of 78.6±4.2, indicating that GFT-1 has a higher success rate.", "statement": "GFT-1 has a higher success rate than FiLM in the nav_bw 2D task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4: The evaluation results for 10k test sessions on 9×9 maps. The average navigation success rates are reported as percentages. The second number in each cell represents the standard deviation over twelve test runs, each run corresponding to one pass (out of three final passes) of one trained model (out of four random initializations).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "GFT agents achieve reasonable generalizations and outperform comparison methods on larger maps, demonstrating they are not trained to memorize specific environments.Y=10, with 6 goals and 24 obstacles, and X=Y=11, with 8 goals and 28 obstacles. Although the performance is not as good as on 8×8 maps (except for nav_avoid whose chance performance tends to peak as there are more goals on the map before the map size becoming too large), the GFT agents achieve reasonable generalizations and still greatly outperform the comparison methods. This further demonstrates that our GFT agents are not trained to memorize environments in specific settings." } ], "images": [ "scigen/train/2217.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The evaluation results for 10k test sessions on 9×9 maps. The average navigation success rates are reported as percentages. The second number in each cell represents the standard deviation over twelve test runs, each run corresponding to one pass (out of three final passes) of one trained model (out of four random initializations)." ], "reasoning": "GFT agents achieve reasonable generalizations and outperform comparison methods on larger maps, demonstrating they are not trained to memorize specific environments.", "description": "Y=10, with 6 goals and 24 obstacles, and X=Y=11, with 8 goals and 28 obstacles. Although the performance is not as good as on 8×8 maps (except for nav_avoid whose chance performance tends to peak as there are more goals on the map before the map size becoming too large), the GFT agents achieve reasonable generalizations and still greatly outperform the comparison methods. This further demonstrates that our GFT agents are not trained to memorize environments in specific settings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 4: The evaluation results for 10k test sessions on 9×9 maps. The average navigation success rates are reported as percentages. The second number in each cell represents the standard deviation over twelve test runs, each run corresponding to one pass (out of three final passes) of one trained model (out of four random initializations).'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich method achieved the highest performance in nav_avoid 3D?\n" }, { "role": "assistant", "content": "By comparing the values in the nav_avoid 3D column, GFT-2 achieved the highest performance with a score of 98.1±0.9. So, the answer is {\"answer\": \"GFT-2\"}" } ], "images": [ "scigen/train/2217.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The evaluation results for 10k test sessions on 9×9 maps. The average navigation success rates are reported as percentages. The second number in each cell represents the standard deviation over twelve test runs, each run corresponding to one pass (out of three final passes) of one trained model (out of four random initializations)." ], "reasoning": "By comparing the values in the nav_avoid 3D column, GFT-2 achieved the highest performance with a score of 98.1±0.9.", "question": "Which method achieved the highest performance in nav_avoid 3D?", "answer": "So, the answer is {\"answer\": \"GFT-2\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nGFT-1 outperformed all other methods in nav_bw 2D.\n" }, { "role": "assistant", "content": "By comparing the values in the nav_bw 2D column, GFT-1 achieved the highest performance with a score of 78.3±4.4, which is higher than all other methods. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2217.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The evaluation results for 10k test sessions on 9×9 maps. The average navigation success rates are reported as percentages. The second number in each cell represents the standard deviation over twelve test runs, each run corresponding to one pass (out of three final passes) of one trained model (out of four random initializations)." ], "reasoning": "By comparing the values in the nav_bw 2D column, GFT-1 achieved the highest performance with a score of 78.3±4.4, which is higher than all other methods.", "statement": "GFT-1 outperformed all other methods in nav_bw 2D.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 6: The evaluation results for 10k test sessions on 11×11 maps. The average navigation success rates are reported as percentages. The second number in each cell represents the standard deviation over twelve test runs, each run corresponding to one pass (out of three final passes) of one trained model (out of four random initializations).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "GFT agents perform well on larger maps with more goals and obstacles, demonstrating strong generalization and outperforming comparison methods.Y=10, with 6 goals and 24 obstacles, and X=Y=11, with 8 goals and 28 obstacles. Although the performance is not as good as on 8×8 maps (except for nav_avoid whose chance performance tends to peak as there are more goals on the map before the map size becoming too large), the GFT agents achieve reasonable generalizations and still greatly outperform the comparison methods. This further demonstrates that our GFT agents are not trained to memorize environments in specific settings." } ], "images": [ "scigen/train/2218.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: The evaluation results for 10k test sessions on 11×11 maps. The average navigation success rates are reported as percentages. The second number in each cell represents the standard deviation over twelve test runs, each run corresponding to one pass (out of three final passes) of one trained model (out of four random initializations)." ], "reasoning": "GFT agents perform well on larger maps with more goals and obstacles, demonstrating strong generalization and outperforming comparison methods.", "description": "Y=10, with 6 goals and 24 obstacles, and X=Y=11, with 8 goals and 28 obstacles. Although the performance is not as good as on 8×8 maps (except for nav_avoid whose chance performance tends to peak as there are more goals on the map before the map size becoming too large), the GFT agents achieve reasonable generalizations and still greatly outperform the comparison methods. This further demonstrates that our GFT agents are not trained to memorize environments in specific settings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 6: The evaluation results for 10k test sessions on 11×11 maps. The average navigation success rates are reported as percentages. The second number in each cell represents the standard deviation over twelve test runs, each run corresponding to one pass (out of three final passes) of one trained model (out of four random initializations).\nQuestion: Which method achieved the highest performance in nav_avoid 3D?\n" }, { "role": "assistant", "content": "By examining the nav_avoid 3D column, the highest performance is achieved by GFT-2 with a score of 98.0±1.3. So, the answer is {\"answer\": \"GFT-2\"}" } ], "images": [ "scigen/train/2218.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: The evaluation results for 10k test sessions on 11×11 maps. The average navigation success rates are reported as percentages. The second number in each cell represents the standard deviation over twelve test runs, each run corresponding to one pass (out of three final passes) of one trained model (out of four random initializations)." ], "reasoning": "By examining the nav_avoid 3D column, the highest performance is achieved by GFT-2 with a score of 98.0±1.3.", "question": "Which method achieved the highest performance in nav_avoid 3D?", "answer": "So, the answer is {\"answer\": \"GFT-2\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nGFT-1 outperformed all other methods in nav_bw 2D.\n" }, { "role": "assistant", "content": "By examining the nav_bw 2D column, GFT-1 achieved a score of 74.3±3.3, which is higher than all other methods. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2218.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: The evaluation results for 10k test sessions on 11×11 maps. The average navigation success rates are reported as percentages. The second number in each cell represents the standard deviation over twelve test runs, each run corresponding to one pass (out of three final passes) of one trained model (out of four random initializations)." ], "reasoning": "By examining the nav_bw 2D column, GFT-1 achieved a score of 74.3±3.3, which is higher than all other methods.", "statement": "GFT-1 outperformed all other methods in nav_bw 2D.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Common hyper-parameters for all the approaches'.\n" }, { "role": "assistant", "content": "The experiment settings for embedding-based entity alignment approaches in OpenEA include a fixed batch size for relation triples and specific termination conditions, with hyper-parameters tuned for optimal results.Comparative approaches and settings. We evaluate all the embedding-based entity alignment approaches implemented in OpenEA. To make a fair comparison, we try our best efforts to unify the experiment settings. So, we use a fixed batch size for relation triples to avoid its interference. For other settings specific to each approach, we follow the reported details in literature as carefully as we can. For the key hyper-parameters and the unreported ones, we try our best to tune them. For example, we constrain the L2-norm of entity embeddings to 1 for many approaches, e.g., IMUSE, because we find that such normalization yields better results. All hyper-parameter settings of each approach on our datasets are available online." } ], "images": [ "scigen/train/2219.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Common hyper-parameters for all the approaches" ], "reasoning": "The experiment settings for embedding-based entity alignment approaches in OpenEA include a fixed batch size for relation triples and specific termination conditions, with hyper-parameters tuned for optimal results.", "description": "Comparative approaches and settings. We evaluate all the embedding-based entity alignment approaches implemented in OpenEA. To make a fair comparison, we try our best efforts to unify the experiment settings. So, we use a fixed batch size for relation triples to avoid its interference. For other settings specific to each approach, we follow the reported details in literature as carefully as we can. For the key hyper-parameters and the unreported ones, we try our best to tune them. For example, we constrain the L2-norm of entity embeddings to 1 for many approaches, e.g., IMUSE, because we find that such normalization yields better results. All hyper-parameter settings of each approach on our datasets are available online.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the batch size for relation triples in the 100K setting?\nSolve the above question based on the table titled 'Table 4: Common hyper-parameters for all the approaches'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the batch size for relation triples in the 100K setting is 20,000. So, the answer is {\"answer\": \"20,000\"}." } ], "images": [ "scigen/train/2219.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Common hyper-parameters for all the approaches" ], "reasoning": "The table shows that the batch size for relation triples in the 100K setting is 20,000.", "question": "What is the batch size for relation triples in the 100K setting?", "answer": "So, the answer is {\"answer\": \"20,000\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe maximum number of epochs for the 15K setting is 2000.\n" }, { "role": "assistant", "content": "The table indicates that the maximum number of epochs for both the 15K and 100K settings is 2000. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2219.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Common hyper-parameters for all the approaches" ], "reasoning": "The table indicates that the maximum number of epochs for both the 15K and 100K settings is 2000.", "statement": "The maximum number of epochs for the 15K setting is 2000.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 13. Number of total lists (log scale) and percent of lists that are frozen. There is no correlation between size and frozenness, but note that news is far more frozen than any other data source.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Wine reviews have specialized vocabulary with some binomials frozen, unlike on Reddit, but this does not have a general effect.Wine reviews are a highly stylized form of text. In this case reviews are often just a few sentences, and they use a specialized vocabulary meant for wine tasting. There is some evidence of an additional freezing effect in binomials such as (’aromas’, ’flavors’) and (’scents’, ’flavors’) which both are frozen in the wine reviews, but are not frozen on Reddit. However, this does not seem to have a more general effect. Additionally, there are a number of binomials which appear frozen on Reddit, but have low asymmetry in the wine reviews, such as [’lemon’, ’lime’]." } ], "images": [ "scigen/train/2220.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 13. Number of total lists (log scale) and percent of lists that are frozen. There is no correlation between size and frozenness, but note that news is far more frozen than any other data source." ], "reasoning": "Wine reviews have specialized vocabulary with some binomials frozen, unlike on Reddit, but this does not have a general effect.", "description": "Wine reviews are a highly stylized form of text. In this case reviews are often just a few sentences, and they use a specialized vocabulary meant for wine tasting. There is some evidence of an additional freezing effect in binomials such as (’aromas’, ’flavors’) and (’scents’, ’flavors’) which both are frozen in the wine reviews, but are not frozen on Reddit. However, this does not seem to have a more general effect. Additionally, there are a number of binomials which appear frozen on Reddit, but have low asymmetry in the wine reviews, such as [’lemon’, ’lime’].", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 13. Number of total lists (log scale) and percent of lists that are frozen. There is no correlation between size and frozenness, but note that news is far more frozen than any other data source.', answer the question below by showing each progressive step toward the answer. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich category has the highest size and what is its frozenness?\n" }, { "role": "assistant", "content": "To find the category with the highest size, compare the 'Size' values in the table. The highest size is 16.4, which corresponds to 'r/politics'. Then, check the 'Frozenness' value for 'r/politics', which is 0.28. So, the answer is {\"answer\": \"r/politics with frozenness 0.28\"}." } ], "images": [ "scigen/train/2220.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 13. Number of total lists (log scale) and percent of lists that are frozen. There is no correlation between size and frozenness, but note that news is far more frozen than any other data source." ], "reasoning": "To find the category with the highest size, compare the 'Size' values in the table. The highest size is 16.4, which corresponds to 'r/politics'. Then, check the 'Frozenness' value for 'r/politics', which is 0.28.", "question": "Which category has the highest size and what is its frozenness?", "answer": "So, the answer is {\"answer\": \"r/politics with frozenness 0.28\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Wine reviews have the lowest frozenness among the categories listed., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nWine reviews have the lowest frozenness among the categories listed.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Frozenness' values in the table. The lowest frozenness is 0.16, which corresponds to 'f/nba', not 'wine'. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2220.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 13. Number of total lists (log scale) and percent of lists that are frozen. There is no correlation between size and frozenness, but note that news is far more frozen than any other data source." ], "reasoning": "To verify this statement, compare the 'Frozenness' values in the table. The lowest frozenness is 0.16, which corresponds to 'f/nba', not 'wine'.", "statement": "Wine reviews have the lowest frozenness among the categories listed.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2. Covariance table for movement in r/nba, r/nfl and r/politics.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The table shows the covariance for movement across subreddits, indicating how pairs of terms change in relation to each other within and across different subreddits.Movement. The vast majority of binomials have low movement. However, the exceptions to this can be very informative. Within r/nba a few of these pairs show clear change in linguistics and/or culture. The binomial [‘rpm’, ‘vorp’] (a pair of basketball statistics) started at 0.74 ordinality and within three years dropped to 0.32 ordinality, showing a potential change in users’ representation of how these statistics relate to each other. In r/politics, [‘daughter’, ‘son’] moved from 0.07 ordinality to 0.36 ordinality over ten years. This may represent a cultural shift in how users refer to children, or a shift in topics discussed relating to children. And in r/politics, [’dems’, ’obama’] went from 0.75 ordinality to 0.43 ordinality from 2009–2018, potentially reflecting changes in Obama’s role as a defining feature of the Democratic Party. Meanwhile the ratio of unigram frequency of ‘dems’ to ‘obama’ actually increased from 10% to 20% from 2010 to 2017. Similarly, [‘fdr’, ‘lincoln’] moved from 0.49 ordinality to 0.17 ordinality from 2015–2018. This is particularly interesting, since in 2016 ‘fdr’ had a unigram frequency 20% higher than ‘lincoln’, but in 2017 they are almost the same. This suggests that movement could be unrelated to unigram frequency changes. Note also that the covariance for movement across subreddits is" } ], "images": [ "scigen/train/2221.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2. Covariance table for movement in r/nba, r/nfl and r/politics." ], "reasoning": "The table shows the covariance for movement across subreddits, indicating how pairs of terms change in relation to each other within and across different subreddits.", "description": "Movement. The vast majority of binomials have low movement. However, the exceptions to this can be very informative. Within r/nba a few of these pairs show clear change in linguistics and/or culture. The binomial [‘rpm’, ‘vorp’] (a pair of basketball statistics) started at 0.74 ordinality and within three years dropped to 0.32 ordinality, showing a potential change in users’ representation of how these statistics relate to each other. In r/politics, [‘daughter’, ‘son’] moved from 0.07 ordinality to 0.36 ordinality over ten years. This may represent a cultural shift in how users refer to children, or a shift in topics discussed relating to children. And in r/politics, [’dems’, ’obama’] went from 0.75 ordinality to 0.43 ordinality from 2009–2018, potentially reflecting changes in Obama’s role as a defining feature of the Democratic Party. Meanwhile the ratio of unigram frequency of ‘dems’ to ‘obama’ actually increased from 10% to 20% from 2010 to 2017. Similarly, [‘fdr’, ‘lincoln’] moved from 0.49 ordinality to 0.17 ordinality from 2015–2018. This is particularly interesting, since in 2016 ‘fdr’ had a unigram frequency 20% higher than ‘lincoln’, but in 2017 they are almost the same. This suggests that movement could be unrelated to unigram frequency changes. Note also that the covariance for movement across subreddits is", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2. Covariance table for movement in r/nba, r/nfl and r/politics.', unfold the steps to solve the question given next. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which subreddit pair has the highest covariance for movement?\n" }, { "role": "assistant", "content": "By comparing the covariance values in the table, the highest value is 0.00579 for the pair r/politics and r/politics. So, the answer is {\"answer\": \"r/politics and r/politics\"}." } ], "images": [ "scigen/train/2221.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2. Covariance table for movement in r/nba, r/nfl and r/politics." ], "reasoning": "By comparing the covariance values in the table, the highest value is 0.00579 for the pair r/politics and r/politics.", "question": "Which subreddit pair has the highest covariance for movement?", "answer": "So, the answer is {\"answer\": \"r/politics and r/politics\"}." }, { "messages": [ { "role": "user", "content": "The covariance for movement between r/nba and r/politics is higher than that between r/nfl and r/politics. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe covariance for movement between r/nba and r/politics is higher than that between r/nfl and r/politics.\n" }, { "role": "assistant", "content": "The table shows that the covariance for movement between r/nba and r/politics is 0.00560, while the covariance between r/nfl and r/politics is 0.00320. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2221.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2. Covariance table for movement in r/nba, r/nfl and r/politics." ], "reasoning": "The table shows that the covariance for movement between r/nba and r/politics is 0.00560, while the covariance between r/nfl and r/politics is 0.00320.", "statement": "The covariance for movement between r/nba and r/politics is higher than that between r/nfl and r/politics.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 7. Paired prediction results.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Predictive power is measured by comparing the asymmetry of two binomials using different predictors, such as length and phonemes.Paired Predictions. Another measure of predictive power is predicting which of two binomials has higher asymmetry. In this case, we take two binomials with very different asymmetry and try to predict which has higher asymmetry by our measures (we use the top-1000 and bottom-1000 binomials in terms of asymmetry for these tasks). For instance, we may predict that [‘red’, ‘turquoise’] is more asymmetric than [‘red’, ‘blue’] because the differences in lengths is more extreme." } ], "images": [ "scigen/train/2222.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7. Paired prediction results." ], "reasoning": "Predictive power is measured by comparing the asymmetry of two binomials using different predictors, such as length and phonemes.", "description": "Paired Predictions. Another measure of predictive power is predicting which of two binomials has higher asymmetry. In this case, we take two binomials with very different asymmetry and try to predict which has higher asymmetry by our measures (we use the top-1000 and bottom-1000 binomials in terms of asymmetry for these tasks). For instance, we may predict that [‘red’, ‘turquoise’] is more asymmetric than [‘red’, ‘blue’] because the differences in lengths is more extreme.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 7. Paired prediction results.', solve this question and give detail solutions. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhich predictor has the highest asymmetry score for r/The_Donald?\n" }, { "role": "assistant", "content": "To find the predictor with the highest asymmetry score for r/The_Donald, compare the values in the r/The_Donald column. The highest value is 0.631, which corresponds to the Alphabetical predictor. So, the answer is {\"answer\": \"Alphabetical\"}." } ], "images": [ "scigen/train/2222.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7. Paired prediction results." ], "reasoning": "To find the predictor with the highest asymmetry score for r/The_Donald, compare the values in the r/The_Donald column. The highest value is 0.631, which corresponds to the Alphabetical predictor.", "question": "Which predictor has the highest asymmetry score for r/The_Donald?", "answer": "So, the answer is {\"answer\": \"Alphabetical\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Length predictor has the lowest asymmetry score for r/nfl.\n" }, { "role": "assistant", "content": "To verify this statement, compare the values in the r/nfl column. The Length predictor has a score of 0.324, which is not the lowest. The lowest score is 0.257, which corresponds to the Syllables predictor. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2222.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7. Paired prediction results." ], "reasoning": "To verify this statement, compare the values in the r/nfl column. The Length predictor has a score of 0.324, which is not the lowest. The lowest score is 0.257, which corresponds to the Syllables predictor.", "statement": "The Length predictor has the lowest asymmetry score for r/nfl.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 9. Results of logistic regression based on word embeddings. This is by far our most successful model. Note that not all words in our binomials were found in the word embeddings, leaving about 70–97% of the binomials usable.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Word embeddings are used to predict the ordering of binomials in a corpus by calculating the dot product of a vector with the difference between word vectors, achieving the best prediction accuracy.Word Embeddings. Word embeddings assign a vector xi to each word i in the corpus, such that the relative position of these vectors in space encode information lingustically relevant relationships among the words. Using the Google News word embeddings, via a simple logistic model, we produce a vector v∗ and predict the ordering of a binomial on words i and j from v∗⋅(xi−xj). In this sense, v∗ can be thought of as a “sweep-line” direction through the space containing the word vectors, such that the ordering along this sweep-line is the predicted ordering of all binomials in the corpus. This is by far the best prediction method we tested. It is important to note that not all words in our binomials could be associated with an embedding, so it was necessary to remove binomials containing words such as names or slang. However, retesting our basic features on this data set did not show any improvement, implying that the drastic change in predictive power is not due to the changed data set." } ], "images": [ "scigen/train/2223.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9. Results of logistic regression based on word embeddings. This is by far our most successful model. Note that not all words in our binomials were found in the word embeddings, leaving about 70–97% of the binomials usable." ], "reasoning": "Word embeddings are used to predict the ordering of binomials in a corpus by calculating the dot product of a vector with the difference between word vectors, achieving the best prediction accuracy.", "description": "Word Embeddings. Word embeddings assign a vector xi to each word i in the corpus, such that the relative position of these vectors in space encode information lingustically relevant relationships among the words. Using the Google News word embeddings, via a simple logistic model, we produce a vector v∗ and predict the ordering of a binomial on words i and j from v∗⋅(xi−xj). In this sense, v∗ can be thought of as a “sweep-line” direction through the space containing the word vectors, such that the ordering along this sweep-line is the predicted ordering of all binomials in the corpus. This is by far the best prediction method we tested. It is important to note that not all words in our binomials could be associated with an embedding, so it was necessary to remove binomials containing words such as names or slang. However, retesting our basic features on this data set did not show any improvement, implying that the drastic change in predictive power is not due to the changed data set.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 9. Results of logistic regression based on word embeddings. This is by far our most successful model. Note that not all words in our binomials were found in the word embeddings, leaving about 70–97% of the binomials usable.'. Solve the following question based on the table.\n\nQuestion: Which subreddit has the highest accuracy in the table?\nLet's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the subreddit with the highest accuracy, we need to compare the accuracy values for each subreddit listed in the table. The highest accuracy value is 0.87, which corresponds to r/Libertarian. So, the answer is {\"answer\": \"r/Libertarian\"}." } ], "images": [ "scigen/train/2223.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9. Results of logistic regression based on word embeddings. This is by far our most successful model. Note that not all words in our binomials were found in the word embeddings, leaving about 70–97% of the binomials usable." ], "reasoning": "To find the subreddit with the highest accuracy, we need to compare the accuracy values for each subreddit listed in the table. The highest accuracy value is 0.87, which corresponds to r/Libertarian.", "question": "Which subreddit has the highest accuracy in the table?", "answer": "So, the answer is {\"answer\": \"r/Libertarian\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The accuracy for r/food is higher than that for r/Conservative., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe accuracy for r/food is higher than that for r/Conservative.\n" }, { "role": "assistant", "content": "The table shows that the accuracy for r/food is 0.85, while the accuracy for r/Conservative is 0.83. Since 0.85 is greater than 0.83, the statement is supported by the table. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2223.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9. Results of logistic regression based on word embeddings. This is by far our most successful model. Note that not all words in our binomials were found in the word embeddings, leaving about 70–97% of the binomials usable." ], "reasoning": "The table shows that the accuracy for r/food is 0.85, while the accuracy for r/Conservative is 0.83. Since 0.85 is greater than 0.83, the statement is supported by the table.", "statement": "The accuracy for r/food is higher than that for r/Conservative.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: F1 scores of rule-based system and ablation test results. Macro-averaged F1 scores were calculated using Condition and Next (ALL).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The use of sub-label rules improved the F1 scores by 7.8% on Condition and 11.1% on Next, demonstrating the effectiveness of fine-grained types.The high performance with a macro-averaged F1 score of 0.887 shows the effectiveness of the rules. To confirm the effectiveness of the fine-grained types or sub-labels, we compared the F1 score with three settings. In the first setting, we extracted the relations without using material sub-labels (– Material-∗), by applying the rule of M-O to all of the Material types and ignoring the rule of O-M. In the second setting, we extracted relations without using Property sub-labels (– Property-∗), by applying the rule of Po-OM to all of the Property types and ignoring the rule of P-O. The final setting was without either Material or Property sub-labels (– both sub-levels). According to the ablation tests, the F1 scores were improved by 7.8% on Condition and 11.1% on Next when applying the sub-label rules." } ], "images": [ "scigen/train/2224.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: F1 scores of rule-based system and ablation test results. Macro-averaged F1 scores were calculated using Condition and Next (ALL)." ], "reasoning": "The use of sub-label rules improved the F1 scores by 7.8% on Condition and 11.1% on Next, demonstrating the effectiveness of fine-grained types.", "description": "The high performance with a macro-averaged F1 score of 0.887 shows the effectiveness of the rules. To confirm the effectiveness of the fine-grained types or sub-labels, we compared the F1 score with three settings. In the first setting, we extracted the relations without using material sub-labels (– Material-∗), by applying the rule of M-O to all of the Material types and ignoring the rule of O-M. In the second setting, we extracted relations without using Property sub-labels (– Property-∗), by applying the rule of Po-OM to all of the Property types and ignoring the rule of P-O. The final setting was without either Material or Property sub-labels (– both sub-levels). According to the ablation tests, the F1 scores were improved by 7.8% on Condition and 11.1% on Next when applying the sub-label rules.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 6: F1 scores of rule-based system and ablation test results. Macro-averaged F1 scores were calculated using Condition and Next (ALL).', solve this question and give detail solutions. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the F1 score for the Condition setting when both Material and Property sub-labels are not used?\n" }, { "role": "assistant", "content": "To find the F1 score for the Condition setting without both Material and Property sub-labels, look at the row labeled '– both sub-levels' under the Condition column. So, the answer is {\"answer\": \"0.836\"}" } ], "images": [ "scigen/train/2224.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: F1 scores of rule-based system and ablation test results. Macro-averaged F1 scores were calculated using Condition and Next (ALL)." ], "reasoning": "To find the F1 score for the Condition setting without both Material and Property sub-labels, look at the row labeled '– both sub-levels' under the Condition column.", "question": "What is the F1 score for the Condition setting when both Material and Property sub-labels are not used?", "answer": "So, the answer is {\"answer\": \"0.836\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The F1 score for the Next setting without using Material sub-labels is higher than the F1 score for the Next setting without using Property sub-labels. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe F1 score for the Next setting without using Material sub-labels is higher than the F1 score for the Next setting without using Property sub-labels.\n" }, { "role": "assistant", "content": "The F1 score for the Next setting without using Material sub-labels is 0.749, while the F1 score for the Next setting without using Property sub-labels is 0.860. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2224.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: F1 scores of rule-based system and ablation test results. Macro-averaged F1 scores were calculated using Condition and Next (ALL)." ], "reasoning": "The F1 score for the Next setting without using Material sub-labels is 0.749, while the F1 score for the Next setting without using Property sub-labels is 0.860. Therefore, the statement is refuted.", "statement": "The F1 score for the Next setting without using Material sub-labels is higher than the F1 score for the Next setting without using Property sub-labels.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: F1 scores of sequence-labeling models with different base representations on development dataset. Macro-averaged F1 scores were calculated using all three coarse-grained types (ALL). The highest and second-highest for each metric are indicated in bold and underline, respectively.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Mat-ELMo achieved the highest F1 scores on Material and ALL, while SciBERT achieved the highest F1 score on Operation.The results show reasonably high performance, in which Mat-ELMo achieved the highest performances, with an F1 score of 0.917 on Material, and 0.826 on ALL, while SciBERT achieved the best score on Operation." } ], "images": [ "scigen/train/2225.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: F1 scores of sequence-labeling models with different base representations on development dataset. Macro-averaged F1 scores were calculated using all three coarse-grained types (ALL). The highest and second-highest for each metric are indicated in bold and underline, respectively." ], "reasoning": "Mat-ELMo achieved the highest F1 scores on Material and ALL, while SciBERT achieved the highest F1 score on Operation.", "description": "The results show reasonably high performance, in which Mat-ELMo achieved the highest performances, with an F1 score of 0.917 on Material, and 0.826 on ALL, while SciBERT achieved the best score on Operation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 4: F1 scores of sequence-labeling models with different base representations on development dataset. Macro-averaged F1 scores were calculated using all three coarse-grained types (ALL). The highest and second-highest for each metric are indicated in bold and underline, respectively.\nQuestion: Which model achieved the highest F1 score on Operation and what was the score?\n" }, { "role": "assistant", "content": "By examining the table, SciBERT achieved the highest F1 score on Operation with a score of 0.839. So, the answer is {\"answer\": \"SciBERT with 0.839\"}." } ], "images": [ "scigen/train/2225.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: F1 scores of sequence-labeling models with different base representations on development dataset. Macro-averaged F1 scores were calculated using all three coarse-grained types (ALL). The highest and second-highest for each metric are indicated in bold and underline, respectively." ], "reasoning": "By examining the table, SciBERT achieved the highest F1 score on Operation with a score of 0.839.", "question": "Which model achieved the highest F1 score on Operation and what was the score?", "answer": "So, the answer is {\"answer\": \"SciBERT with 0.839\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nMat-ELMo achieved the highest F1 score on Property.\n" }, { "role": "assistant", "content": "By examining the table, Mat-ELMo did not achieve the highest F1 score on Property; SciBERT did with a score of 0.884. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2225.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: F1 scores of sequence-labeling models with different base representations on development dataset. Macro-averaged F1 scores were calculated using all three coarse-grained types (ALL). The highest and second-highest for each metric are indicated in bold and underline, respectively." ], "reasoning": "By examining the table, Mat-ELMo did not achieve the highest F1 score on Property; SciBERT did with a score of 0.884.", "statement": "Mat-ELMo achieved the highest F1 score on Property.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: F1 scores of the sequence labeling models with Mat-ELMo on the development dataset by type. Material and Property indicate macro-averaged F1 scores of each fine-grained types, respectively. ALL is the macro-averaged scores of three coarse-grained types (i.e., Material, Operation, and Property in this table).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Material-Start achieved the highest F1 score among Material types, while Property-Time achieved the highest F1 score among Property types.Among the Material types, Material-Start achieved the highest F1 score of 0.887. The F1 score of Operation was 0.821, which was higher than the average. Among the Property types, Property-Time achieved the highest F1 score of 0.928. However, the F1 score of Material-Intermedium was 0.105 in the sequence tagging, which were 11 phrases incorrectly detected as Material-Start, and 21 phrases as Material-Final out of the 36 phrases. This may be because it is difficult to extract Material-Intermedium without understanding the whole structure of the synthesis process." } ], "images": [ "scigen/train/2226.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: F1 scores of the sequence labeling models with Mat-ELMo on the development dataset by type. Material and Property indicate macro-averaged F1 scores of each fine-grained types, respectively. ALL is the macro-averaged scores of three coarse-grained types (i.e., Material, Operation, and Property in this table)." ], "reasoning": "Material-Start achieved the highest F1 score among Material types, while Property-Time achieved the highest F1 score among Property types.", "description": "Among the Material types, Material-Start achieved the highest F1 score of 0.887. The F1 score of Operation was 0.821, which was higher than the average. Among the Property types, Property-Time achieved the highest F1 score of 0.928. However, the F1 score of Material-Intermedium was 0.105 in the sequence tagging, which were 11 phrases incorrectly detected as Material-Start, and 21 phrases as Material-Final out of the 36 phrases. This may be because it is difficult to extract Material-Intermedium without understanding the whole structure of the synthesis process.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 5: F1 scores of the sequence labeling models with Mat-ELMo on the development dataset by type. Material and Property indicate macro-averaged F1 scores of each fine-grained types, respectively. ALL is the macro-averaged scores of three coarse-grained types (i.e., Material, Operation, and Property in this table).'. Solve the following question based on the table.\n\nQuestion: Which type achieved the highest F1 score overall?\nLet's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all types, Property-Time has the highest F1 score of 0.928. So, the answer is {\"answer\": \"Property-Time\"}." } ], "images": [ "scigen/train/2226.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: F1 scores of the sequence labeling models with Mat-ELMo on the development dataset by type. Material and Property indicate macro-averaged F1 scores of each fine-grained types, respectively. ALL is the macro-averaged scores of three coarse-grained types (i.e., Material, Operation, and Property in this table)." ], "reasoning": "By comparing the F1 scores of all types, Property-Time has the highest F1 score of 0.928.", "question": "Which type achieved the highest F1 score overall?", "answer": "So, the answer is {\"answer\": \"Property-Time\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nMaterial-Intermedium had the lowest F1 score among all types.\n" }, { "role": "assistant", "content": "The F1 score of Material-Intermedium is 0.105, which is the lowest among all the types listed in the table. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2226.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: F1 scores of the sequence labeling models with Mat-ELMo on the development dataset by type. Material and Property indicate macro-averaged F1 scores of each fine-grained types, respectively. ALL is the macro-averaged scores of three coarse-grained types (i.e., Material, Operation, and Property in this table)." ], "reasoning": "The F1 score of Material-Intermedium is 0.105, which is the lowest among all the types listed in the table.", "statement": "Material-Intermedium had the lowest F1 score among all types.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 7: Coverage and accuracy of our rules applied to training data.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Rules Po-OM and P-O had the highest coverage and accuracy, significantly contributing to extraction performance, while rule O-M had low coverage and accuracy but was essential for constructing the synthesis graph.By comparing the rule coverage and accuracy, it could be observed that the rules of Po-OM and P-O, which exhibited wide coverage and high accuracy (over 25% and 85%, respectively), contributed significantly to the extraction performance. This indicates that the rules for extracting the relation between the Property and Material or Operation successfully mimicked the manner of reading a paper. Although the coverage of the rule O-M was extremely low and the accuracy was relatively low (4.6% and 48.9%, respectively), this rule was essential for constructing the synthesis graph and could not be omitted." } ], "images": [ "scigen/train/2227.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Coverage and accuracy of our rules applied to training data." ], "reasoning": "Rules Po-OM and P-O had the highest coverage and accuracy, significantly contributing to extraction performance, while rule O-M had low coverage and accuracy but was essential for constructing the synthesis graph.", "description": "By comparing the rule coverage and accuracy, it could be observed that the rules of Po-OM and P-O, which exhibited wide coverage and high accuracy (over 25% and 85%, respectively), contributed significantly to the extraction performance. This indicates that the rules for extracting the relation between the Property and Material or Operation successfully mimicked the manner of reading a paper. Although the coverage of the rule O-M was extremely low and the accuracy was relatively low (4.6% and 48.9%, respectively), this rule was essential for constructing the synthesis graph and could not be omitted.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}. The question is related to the given table titled 'Table 7: Coverage and accuracy of our rules applied to training data.'.\n\nQuestion: Which rule had the highest accuracy and what was its coverage?\n" }, { "role": "assistant", "content": "By examining the table, the rule P-O had the highest accuracy of 0.951 and its coverage was 0.254. So, the answer is {\"answer\": \"P-O with coverage 0.254\"}" } ], "images": [ "scigen/train/2227.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Coverage and accuracy of our rules applied to training data." ], "reasoning": "By examining the table, the rule P-O had the highest accuracy of 0.951 and its coverage was 0.254.", "question": "Which rule had the highest accuracy and what was its coverage?", "answer": "So, the answer is {\"answer\": \"P-O with coverage 0.254\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe rule O-M had the lowest accuracy among all the rules.\n" }, { "role": "assistant", "content": "By comparing the accuracy values in the table, the rule O-M had the lowest accuracy of 0.489. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2227.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Coverage and accuracy of our rules applied to training data." ], "reasoning": "By comparing the accuracy values in the table, the rule O-M had the lowest accuracy of 0.489.", "statement": "The rule O-M had the lowest accuracy among all the rules.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Results of Chinese word segmentation.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "CRF slightly outperforms MLP, and the {B,M,E,S} tag set is marginally better than the {app,seg} tag set. Joint training of Chinese word segmentation and dependency parsing provides comparable or slightly better results than training segmentation alone.The first two rows present the difference between whether utilizing CRF on the top of MLP. CRF’s performance is slightly better than its counterpart. The first row and the third row display the comparison between different tag scenario, the {B,M,E,S} tag set is slightly better than the {app,seg} tag set. Jointly training Chinese word segmentation and dependency parsing achieves comparable or slightly better Chinese word segmentation than training Chinese word segmentation alone. Although head prediction is not directly related to Chinese word segmentation, the head character can only be the end of a word, therefore combination between Chinese word segmentation and character dependency parsing actually introduces more supervision for the former task. On CTB-5, the Joint-binary and Joint-multi models are slightly worse than the joint-SegOnly model. The reason may be that the CTB-5 dataset is relatively small and the complicated models suffer from the overfitting. We assume this is because the extra supervision from dependency parsing labels is informative for word segmentation." } ], "images": [ "scigen/train/2228.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of Chinese word segmentation." ], "reasoning": "CRF slightly outperforms MLP, and the {B,M,E,S} tag set is marginally better than the {app,seg} tag set. Joint training of Chinese word segmentation and dependency parsing provides comparable or slightly better results than training segmentation alone.", "description": "The first two rows present the difference between whether utilizing CRF on the top of MLP. CRF’s performance is slightly better than its counterpart. The first row and the third row display the comparison between different tag scenario, the {B,M,E,S} tag set is slightly better than the {app,seg} tag set. Jointly training Chinese word segmentation and dependency parsing achieves comparable or slightly better Chinese word segmentation than training Chinese word segmentation alone. Although head prediction is not directly related to Chinese word segmentation, the head character can only be the end of a word, therefore combination between Chinese word segmentation and character dependency parsing actually introduces more supervision for the former task. On CTB-5, the Joint-binary and Joint-multi models are slightly worse than the joint-SegOnly model. The reason may be that the CTB-5 dataset is relatively small and the complicated models suffer from the overfitting. We assume this is because the extra supervision from dependency parsing labels is informative for word segmentation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Results of Chinese word segmentation.\nQuestion: Which model and tag set combination achieves the highest CTB-5 Pseg score?\nSolve the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the highest CTB-5 Pseg score, look at the CTB-5 Pseg column and identify the highest value. The highest value is 98.81, achieved by the Joint-Multi-BERT model with the {app, dep1,..., depK} tag set. So, the answer is {\"answer\": \"Joint-Multi-BERT with {app, dep1,..., depK}\"}" } ], "images": [ "scigen/train/2228.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of Chinese word segmentation." ], "reasoning": "To find the highest CTB-5 Pseg score, look at the CTB-5 Pseg column and identify the highest value. The highest value is 98.81, achieved by the Joint-Multi-BERT model with the {app, dep1,..., depK} tag set.", "question": "Which model and tag set combination achieves the highest CTB-5 Pseg score?", "answer": "So, the answer is {\"answer\": \"Joint-Multi-BERT with {app, dep1,..., depK}\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Joint-SegOnly model achieves the highest CTB-5 F1 seg score.\n" }, { "role": "assistant", "content": "To verify this statement, look at the CTB-5 F1 seg column. The highest score in this column is 98.50, achieved by the Joint-SegOnly model. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2228.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of Chinese word segmentation." ], "reasoning": "To verify this statement, look at the CTB-5 F1 seg column. The highest score in this column is 98.50, achieved by the Joint-SegOnly model.", "statement": "The Joint-SegOnly model achieves the highest CTB-5 F1 seg score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Main results in the test set of different datasets. Our Joint-Multi model achieves superior performance than previous joint models. The Joint-Multi-BERT further enhances the performance of dependency parsing significantly.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The joint model with labeled dependency parsing (Joint-Multi) outperforms its counterpart (Joint-Binary) in both Chinese word segmentation and dependency parsing, and the use of BERT enhances the performance further.Zhang et al. Kurita et al. Another difference between our joint models and previous works is the combination of POS tags, the previous models all used the POS task as one componential task. Despite the lack of POS tag information, our models still achieve much better results. Other than the performance distinction between previous work, our joint model with or without dependency labels also differ from each other. our joint model with labeled dependency parsing (Joint-Multi) outperforms its counterpart (Joint-Binary) in both Chinese word segmentation and dependency parsing. With respect to the enhancement of dependency parsing caused by the arc labels, we believe it can be credited to two aspects. The first one is the more accurate Chinese word segmentation. The second one is that label information between two characters will give extra supervision for the search of head characters. Owing to the joint decoding of Chinese word segmentation and dependency parsing, we can utilize the character-level pre-trained language model BERT. We presume this indicates BERT can better extract the contextualized information to help the dependency parsing." } ], "images": [ "scigen/train/2229.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Main results in the test set of different datasets. Our Joint-Multi model achieves superior performance than previous joint models. The Joint-Multi-BERT further enhances the performance of dependency parsing significantly." ], "reasoning": "The joint model with labeled dependency parsing (Joint-Multi) outperforms its counterpart (Joint-Binary) in both Chinese word segmentation and dependency parsing, and the use of BERT enhances the performance further.", "description": "Zhang et al. Kurita et al. Another difference between our joint models and previous works is the combination of POS tags, the previous models all used the POS task as one componential task. Despite the lack of POS tag information, our models still achieve much better results. Other than the performance distinction between previous work, our joint model with or without dependency labels also differ from each other. our joint model with labeled dependency parsing (Joint-Multi) outperforms its counterpart (Joint-Binary) in both Chinese word segmentation and dependency parsing. With respect to the enhancement of dependency parsing caused by the arc labels, we believe it can be credited to two aspects. The first one is the more accurate Chinese word segmentation. The second one is that label information between two characters will give extra supervision for the search of head characters. Owing to the joint decoding of Chinese word segmentation and dependency parsing, we can utilize the character-level pre-trained language model BERT. We presume this indicates BERT can better extract the contextualized information to help the dependency parsing.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nTable title: Table 2: Main results in the test set of different datasets. Our Joint-Multi model achieves superior performance than previous joint models. The Joint-Multi-BERT further enhances the performance of dependency parsing significantly.\n\nQuestion: Which model achieves the highest CTB-9 F1 score in segmentation?\n" }, { "role": "assistant", "content": "By examining the CTB-9 F1 seg column, the highest score is achieved by the Joint-Multi model with a score of 97.20. So, the answer is {\"answer\": \"Joint-Multi\"}" } ], "images": [ "scigen/train/2229.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Main results in the test set of different datasets. Our Joint-Multi model achieves superior performance than previous joint models. The Joint-Multi-BERT further enhances the performance of dependency parsing significantly." ], "reasoning": "By examining the CTB-9 F1 seg column, the highest score is achieved by the Joint-Multi model with a score of 97.20.", "question": "Which model achieves the highest CTB-9 F1 score in segmentation?", "answer": "So, the answer is {\"answer\": \"Joint-Multi\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Joint-Multi model achieves a higher CTB-5 F1 segmentation score than the Joint-Multi-BERT model.\n" }, { "role": "assistant", "content": "The table shows that the Joint-Multi model has a CTB-5 F1 seg score of 98.48, while the Joint-Multi-BERT model has a score of 98.46, which is lower. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2229.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Main results in the test set of different datasets. Our Joint-Multi model achieves superior performance than previous joint models. The Joint-Multi-BERT further enhances the performance of dependency parsing significantly." ], "reasoning": "The table shows that the Joint-Multi model has a CTB-5 F1 seg score of 98.48, while the Joint-Multi-BERT model has a score of 98.46, which is lower.", "statement": "The Joint-Multi model achieves a higher CTB-5 F1 segmentation score than the Joint-Multi-BERT model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Ablation experiments for Joint-Multi models.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The model without pre-trained embeddings performs better in Chinese word segmentation, while the model with pre-trained character embeddings performs better in dependency parsing.As our model uses various n-gram pre-trained embeddings, we explore the influence of these pre-trained embeddings. We also tried to remove the bigram and trigram. Compared with the Joint-Multi model, without bigram and trigram, it performs worse in all metrics. However, the comparison between the second row and the third row shows divergence in Chinese word segmentation and dependency parsing for dataset CTB-5 and CTB-7. For Chinese word segmentation, the model without pre-trained embeddings gets superior performance than without bigram and trigram feature. While for all dependency parsing related metrics, the model with pre-trained character embedding gets better performance. We assume the n-gram features are important to Chinese word segmentation. But for the dependency parsing task, the relation between two characters are more beneficial, when pre-trained embeddings are combined, the model can exploit the relationship encoded in the pre-trained embeddings. Additionally, for CTB-5 and CTB-7, even though the third row has inferior F1seg (in average 0.16% lower than the second row), it still achieves much better F1udep (in average 0.95% higher than the second row). We believe this is a proof that joint Chinese word segmentation and dependency parsing is resistant to error propagation. The higher segmentation and dependency parsing performance for the model without pre-trained embedding in CTB-9 might owing to its large training set, which can achieve better results even from randomly initialized embeddings." } ], "images": [ "scigen/train/2230.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ablation experiments for Joint-Multi models." ], "reasoning": "The model without pre-trained embeddings performs better in Chinese word segmentation, while the model with pre-trained character embeddings performs better in dependency parsing.", "description": "As our model uses various n-gram pre-trained embeddings, we explore the influence of these pre-trained embeddings. We also tried to remove the bigram and trigram. Compared with the Joint-Multi model, without bigram and trigram, it performs worse in all metrics. However, the comparison between the second row and the third row shows divergence in Chinese word segmentation and dependency parsing for dataset CTB-5 and CTB-7. For Chinese word segmentation, the model without pre-trained embeddings gets superior performance than without bigram and trigram feature. While for all dependency parsing related metrics, the model with pre-trained character embedding gets better performance. We assume the n-gram features are important to Chinese word segmentation. But for the dependency parsing task, the relation between two characters are more beneficial, when pre-trained embeddings are combined, the model can exploit the relationship encoded in the pre-trained embeddings. Additionally, for CTB-5 and CTB-7, even though the third row has inferior F1seg (in average 0.16% lower than the second row), it still achieves much better F1udep (in average 0.95% higher than the second row). We believe this is a proof that joint Chinese word segmentation and dependency parsing is resistant to error propagation. The higher segmentation and dependency parsing performance for the model without pre-trained embedding in CTB-9 might owing to its large training set, which can achieve better results even from randomly initialized embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 5: Ablation experiments for Joint-Multi models.', unfold the steps to solve the question given next. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nQuestion: Which model achieves the highest F1seg score for CTB-5?\n" }, { "role": "assistant", "content": "By comparing the F1seg scores for CTB-5 across all models, the Joint-Multi model has the highest score of 98.48. So, the answer is {\"answer\": \"Joint-Multi\"}" } ], "images": [ "scigen/train/2230.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ablation experiments for Joint-Multi models." ], "reasoning": "By comparing the F1seg scores for CTB-5 across all models, the Joint-Multi model has the highest score of 98.48.", "question": "Which model achieves the highest F1seg score for CTB-5?", "answer": "So, the answer is {\"answer\": \"Joint-Multi\"}" }, { "messages": [ { "role": "user", "content": "The model without pre-trained embeddings achieves the highest F1udep score for CTB-7. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe model without pre-trained embeddings achieves the highest F1udep score for CTB-7.\n" }, { "role": "assistant", "content": "The table shows that the highest F1udep score for CTB-7 is 81.80, achieved by the Joint-Multi model, not the model without pre-trained embeddings. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2230.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ablation experiments for Joint-Multi models." ], "reasoning": "The table shows that the highest F1udep score for CTB-7 is 81.80, achieved by the Joint-Multi model, not the model without pre-trained embeddings.", "statement": "The model without pre-trained embeddings achieves the highest F1udep score for CTB-7.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Experimental results on Visual Genome (F1). “#” stands for the number of query sentences categorized into that phenomenon.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Hybrid outperforms Simple in all phenomena, with significant improvements in Q, Rel, and Neg, indicating the effectiveness of complex translation for inference.Hybrid shows better performance for all phenomena than Simple one, improving by 19.59% on Q, 9.97% on Rel and 7.01% on Neg, over Simple, suggesting that the proposed complex translation is useful for inference using semantically complex sentences including quantifier and negation. Every green tree is tall and (b) No umbrella is colorful, each containing universal quantifier and negation, respectively. Our system successfully performs inference on these queries, returning the correct images, while excluding wrong ones (note that the third picture in (a) contains short trees)." } ], "images": [ "scigen/train/2231.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Experimental results on Visual Genome (F1). “#” stands for the number of query sentences categorized into that phenomenon." ], "reasoning": "Hybrid outperforms Simple in all phenomena, with significant improvements in Q, Rel, and Neg, indicating the effectiveness of complex translation for inference.", "description": "Hybrid shows better performance for all phenomena than Simple one, improving by 19.59% on Q, 9.97% on Rel and 7.01% on Neg, over Simple, suggesting that the proposed complex translation is useful for inference using semantically complex sentences including quantifier and negation. Every green tree is tall and (b) No umbrella is colorful, each containing universal quantifier and negation, respectively. Our system successfully performs inference on these queries, returning the correct images, while excluding wrong ones (note that the third picture in (a) contains short trees).", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 5: Experimental results on Visual Genome (F1). “#” stands for the number of query sentences categorized into that phenomenon.'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the percentage improvement of Hybrid over Simple for the Q phenomenon?\n" }, { "role": "assistant", "content": "The table shows that the Simple score for Q is 8.59 and the Hybrid score is 28.18. The improvement is calculated as (28.18 - 8.59) / 8.59 * 100%. So, the answer is {\"answer\": \"228.06%\"}" } ], "images": [ "scigen/train/2231.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Experimental results on Visual Genome (F1). “#” stands for the number of query sentences categorized into that phenomenon." ], "reasoning": "The table shows that the Simple score for Q is 8.59 and the Hybrid score is 28.18. The improvement is calculated as (28.18 - 8.59) / 8.59 * 100%.", "question": "What is the percentage improvement of Hybrid over Simple for the Q phenomenon?", "answer": "So, the answer is {\"answer\": \"228.06%\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nHybrid shows a 7.01% improvement over Simple for the Neg phenomenon.\n" }, { "role": "assistant", "content": "The table shows that the Simple score for Neg is 66.38 and the Hybrid score is 73.39. The improvement is calculated as (73.39 - 66.38) / 66.38 * 100%, which is approximately 10.56%, not 7.01%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2231.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Experimental results on Visual Genome (F1). “#” stands for the number of query sentences categorized into that phenomenon." ], "reasoning": "The table shows that the Simple score for Neg is 66.38 and the Hybrid score is 73.39. The improvement is calculated as (73.39 - 66.38) / 66.38 * 100%, which is approximately 10.56%, not 7.01%.", "statement": "Hybrid shows a 7.01% improvement over Simple for the Neg phenomenon.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: Performance of different algorithms and down-sampling strategies with models trained on corpora with and without subsampling. Bold values are best or not significantly different by independent t-tests (with p<0.05).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Probabilistic down-sampling and weighting for SVD\\textscPPMI lead to small improvements in MEN and MTurk similarity tasks, but other tasks showed no improvements.Measured with j@10 metric (higher is better). Accuracy differences between test sets are in line with prior observations and general performance on WIKI is roughly in-line with the data reported in Levy et al. The strongest counterexample is a combination of probabilistic down-sampling for df and weighting for ff which lead to small, yet significant improvements in the MEN (0.703±0.001) and MTurk (0.568±0.015) similarity tasks (cf. However, other accuracy tasks showed no improvements and the stability of this approach (0.475±0.001) was far closer to SVD\\textscPPMI with fully probabilistic down-sampling than to the perfect stability of SVD\\textscwPPMI. Analyses on individual corpora would have resulted in insufficient samples given the pre-conditions of our tests." } ], "images": [ "scigen/train/2232.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance of different algorithms and down-sampling strategies with models trained on corpora with and without subsampling. Bold values are best or not significantly different by independent t-tests (with p<0.05)." ], "reasoning": "Probabilistic down-sampling and weighting for SVD\\textscPPMI lead to small improvements in MEN and MTurk similarity tasks, but other tasks showed no improvements.", "description": "Measured with j@10 metric (higher is better). Accuracy differences between test sets are in line with prior observations and general performance on WIKI is roughly in-line with the data reported in Levy et al. The strongest counterexample is a combination of probabilistic down-sampling for df and weighting for ff which lead to small, yet significant improvements in the MEN (0.703±0.001) and MTurk (0.568±0.015) similarity tasks (cf. However, other accuracy tasks showed no improvements and the stability of this approach (0.475±0.001) was far closer to SVD\\textscPPMI with fully probabilistic down-sampling than to the perfect stability of SVD\\textscwPPMI. Analyses on individual corpora would have resulted in insufficient samples given the pre-conditions of our tests.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 1: Performance of different algorithms and down-sampling strategies with models trained on corpora with and without subsampling. Bold values are best or not significantly different by independent t-tests (with p<0.05).', and sequentially solve the question, writing out each step of your reasoning process. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich combination of down-sampling and weighting for SVD\\textscPPMI showed the highest stability?\n" }, { "role": "assistant", "content": "The table shows that the combination of no down-sampling and weighting for SVD\\textscPPMI has a stability of 1.000, which is the highest stability value. So, the answer is {\"answer\": \"none\"}" } ], "images": [ "scigen/train/2232.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance of different algorithms and down-sampling strategies with models trained on corpora with and without subsampling. Bold values are best or not significantly different by independent t-tests (with p<0.05)." ], "reasoning": "The table shows that the combination of no down-sampling and weighting for SVD\\textscPPMI has a stability of 1.000, which is the highest stability value.", "question": "Which combination of down-sampling and weighting for SVD\\textscPPMI showed the highest stability?", "answer": "So, the answer is {\"answer\": \"none\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe combination of probabilistic down-sampling and weighting for SVD\\textscPPMI showed significant improvements in all tasks.\n" }, { "role": "assistant", "content": "The table shows that the combination of probabilistic down-sampling and weighting for SVD\\textscPPMI showed improvements in MEN and MTurk similarity tasks but not in all tasks. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2232.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance of different algorithms and down-sampling strategies with models trained on corpora with and without subsampling. Bold values are best or not significantly different by independent t-tests (with p<0.05)." ], "reasoning": "The table shows that the combination of probabilistic down-sampling and weighting for SVD\\textscPPMI showed improvements in MEN and MTurk similarity tasks but not in all tasks.", "statement": "The combination of probabilistic down-sampling and weighting for SVD\\textscPPMI showed significant improvements in all tasks.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Accuracy and macro-averaged F-Measure, precision and recall on BLESS and DiffVec. Models marked with † use external resources. The results with * indicate that WordNet was used for both the development of the model and the construction of the dataset. All models concatenate their encoded representations with the baseline vector difference of standard FastText word embeddings.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our model outperforms FastText embeddings and knowledge-enhanced embeddings in the BLESS dataset, except for DiffVec precision.Our model consistently outperforms the FastText embeddings baseline and comparison systems, with the only exception being the precision score for DiffVec. Despite being completely unsupervised, it is also surprising that our model manages to outperform the knowledge-enhanced embeddings of Retrofitting and Attract-Repel in the BLESS dataset. For DiffVec, let us recall that both these approaches have the unfair advantage of having had WordNet as source knowledge base, used both to construct the test set and to enhance the word embeddings." } ], "images": [ "scigen/train/2233.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Accuracy and macro-averaged F-Measure, precision and recall on BLESS and DiffVec. Models marked with † use external resources. The results with * indicate that WordNet was used for both the development of the model and the construction of the dataset. All models concatenate their encoded representations with the baseline vector difference of standard FastText word embeddings." ], "reasoning": "Our model outperforms FastText embeddings and knowledge-enhanced embeddings in the BLESS dataset, except for DiffVec precision.", "description": "Our model consistently outperforms the FastText embeddings baseline and comparison systems, with the only exception being the precision score for DiffVec. Despite being completely unsupervised, it is also surprising that our model manages to outperform the knowledge-enhanced embeddings of Retrofitting and Attract-Repel in the BLESS dataset. For DiffVec, let us recall that both these approaches have the unfair advantage of having had WordNet as source knowledge base, used both to construct the test set and to enhance the word embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 1: Accuracy and macro-averaged F-Measure, precision and recall on BLESS and DiffVec. Models marked with † use external resources. The results with * indicate that WordNet was used for both the development of the model and the construction of the dataset. All models concatenate their encoded representations with the baseline vector difference of standard FastText word embeddings.', solve this question and give detail solutions. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich model has the highest BLESS F1 score and what is its DiffVec precision?\n" }, { "role": "assistant", "content": "The table shows that the model with the highest BLESS F1 score is 'rwe' with a score of 92.8. Its DiffVec precision is 65.1. So, the answer is {\"answer\": \"rwe with DiffVec precision of 65.1\"}." } ], "images": [ "scigen/train/2233.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Accuracy and macro-averaged F-Measure, precision and recall on BLESS and DiffVec. Models marked with † use external resources. The results with * indicate that WordNet was used for both the development of the model and the construction of the dataset. All models concatenate their encoded representations with the baseline vector difference of standard FastText word embeddings." ], "reasoning": "The table shows that the model with the highest BLESS F1 score is 'rwe' with a score of 92.8. Its DiffVec precision is 65.1.", "question": "Which model has the highest BLESS F1 score and what is its DiffVec precision?", "answer": "So, the answer is {\"answer\": \"rwe with DiffVec precision of 65.1\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe rwe model has the highest DiffVec accuracy.\n" }, { "role": "assistant", "content": "The table shows that the rwe model has a DiffVec accuracy of 85.3, which is not the highest. The highest DiffVec accuracy is 86.1 by the Retrofitting model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2233.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Accuracy and macro-averaged F-Measure, precision and recall on BLESS and DiffVec. Models marked with † use external resources. The results with * indicate that WordNet was used for both the development of the model and the construction of the dataset. All models concatenate their encoded representations with the baseline vector difference of standard FastText word embeddings." ], "reasoning": "The table shows that the rwe model has a DiffVec accuracy of 85.3, which is not the highest. The highest DiffVec accuracy is 86.1 by the Retrofitting model.", "statement": "The rwe model has the highest DiffVec accuracy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Results on the McRae feature norms dataset (Macro F-Score) and QVEC (correlation score). Models marked with † use external resources. The results with * indicate that WordNet was used for both the development of the model and the construction of the dataset.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our relational word embeddings achieve the best overall results in the McRae Feature Norms dataset, outperforming other models including Pair2Vec and FastText.In the case of the McRae Feature Norms dataset, our relational word embeddings achieve the best overall results, although there is some variation for the individual features. These results suggest that attributional information is encoded well in our relational word embeddings. Interestingly, our results also suggest that Retrofitting and Attract-Repel, which use pairs of related words during training, may be too naïve to capture the complex relationships proposed in these benchmarks. In fact, they perform considerably lower than the baseline FastText model. On the other hand, Pair2Vec, which we recall is the most similar to our model, yields slightly better results than the FastText baseline, but still worse than our relational word embedding model. This is especially remarkable considering its much lower computational cost." } ], "images": [ "scigen/train/2234.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on the McRae feature norms dataset (Macro F-Score) and QVEC (correlation score). Models marked with † use external resources. The results with * indicate that WordNet was used for both the development of the model and the construction of the dataset." ], "reasoning": "Our relational word embeddings achieve the best overall results in the McRae Feature Norms dataset, outperforming other models including Pair2Vec and FastText.", "description": "In the case of the McRae Feature Norms dataset, our relational word embeddings achieve the best overall results, although there is some variation for the individual features. These results suggest that attributional information is encoded well in our relational word embeddings. Interestingly, our results also suggest that Retrofitting and Attract-Repel, which use pairs of related words during training, may be too naïve to capture the complex relationships proposed in these benchmarks. In fact, they perform considerably lower than the baseline FastText model. On the other hand, Pair2Vec, which we recall is the most similar to our model, yields slightly better results than the FastText baseline, but still worse than our relational word embedding model. This is especially remarkable considering its much lower computational cost.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Results on the McRae feature norms dataset (Macro F-Score) and QVEC (correlation score). Models marked with † use external resources. The results with * indicate that WordNet was used for both the development of the model and the construction of the dataset.\nQuestion: Which model achieves the highest score in the McRae Feature Norms is_large category?\nSolve the above question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By examining the McRae Feature Norms is_large column, Retrofitting† achieves the highest score with 90.6. So, the answer is {\"answer\": \"Retrofitting†\"}" } ], "images": [ "scigen/train/2234.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on the McRae feature norms dataset (Macro F-Score) and QVEC (correlation score). Models marked with † use external resources. The results with * indicate that WordNet was used for both the development of the model and the construction of the dataset." ], "reasoning": "By examining the McRae Feature Norms is_large column, Retrofitting† achieves the highest score with 90.6.", "question": "Which model achieves the highest score in the McRae Feature Norms is_large category?", "answer": "So, the answer is {\"answer\": \"Retrofitting†\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe FastText model performs better than the Attract-Repel† model in the McRae Feature Norms is_round category.\n" }, { "role": "assistant", "content": "In the McRae Feature Norms is_round category, FastText scores 39.0, which is higher than Attract-Repel†'s score of 24.2. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2234.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on the McRae feature norms dataset (Macro F-Score) and QVEC (correlation score). Models marked with † use external resources. The results with * indicate that WordNet was used for both the development of the model and the construction of the dataset." ], "reasoning": "In the McRae Feature Norms is_round category, FastText scores 39.0, which is higher than Attract-Repel†'s score of 24.2.", "statement": "The FastText model performs better than the Attract-Repel† model in the McRae Feature Norms is_round category.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Pearson (r) and Spearman (ρ) correlation on a subset of the HyperLex lexical split. Models marked with † use external resources. All models concatenate their encoded representations with the baseline vector difference of standard FastText word embeddings.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our model generalizes better than others, as methods like Retrofitting and Attract-Repel, which are tuned towards similarity, perform worse than FastText vectors.Even though the results are low overall (noting e.g. that results for the random split are in some cases above 50% as reported in the literature), our model can clearly generalize better than other models. Interestingly, methods such as Retrofitting and Attract-Repel perform worse than the FastText vectors. This can be attributed to the fact that these models have been mainly tuned towards similarity, which is a feature that loses relevance in this setting. Likewise, the relation-based embeddings of Pair2Vec do not help, probably due to the high-capacity of their model, which makes the word embeddings less informative." } ], "images": [ "scigen/train/2235.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Pearson (r) and Spearman (ρ) correlation on a subset of the HyperLex lexical split. Models marked with † use external resources. All models concatenate their encoded representations with the baseline vector difference of standard FastText word embeddings." ], "reasoning": "Our model generalizes better than others, as methods like Retrofitting and Attract-Repel, which are tuned towards similarity, perform worse than FastText vectors.", "description": "Even though the results are low overall (noting e.g. that results for the random split are in some cases above 50% as reported in the literature), our model can clearly generalize better than other models. Interestingly, methods such as Retrofitting and Attract-Repel perform worse than the FastText vectors. This can be attributed to the fact that these models have been mainly tuned towards similarity, which is a feature that loses relevance in this setting. Likewise, the relation-based embeddings of Pair2Vec do not help, probably due to the high-capacity of their model, which makes the word embeddings less informative.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Pearson (r) and Spearman (ρ) correlation on a subset of the HyperLex lexical split. Models marked with † use external resources. All models concatenate their encoded representations with the baseline vector difference of standard FastText word embeddings.\nQuestion: Which model has the highest r and ρ values, and how does it compare to FastText?\nSolve the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The model with the highest r and ρ values is rwe with 38.8 and 38.4, respectively. FastText has lower values of 37.2 and 35.8 for Mult+Avg encoding. So, the answer is {\"answer\": \"rwe\"}" } ], "images": [ "scigen/train/2235.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Pearson (r) and Spearman (ρ) correlation on a subset of the HyperLex lexical split. Models marked with † use external resources. All models concatenate their encoded representations with the baseline vector difference of standard FastText word embeddings." ], "reasoning": "The model with the highest r and ρ values is rwe with 38.8 and 38.4, respectively. FastText has lower values of 37.2 and 35.8 for Mult+Avg encoding.", "question": "Which model has the highest r and ρ values, and how does it compare to FastText?", "answer": "So, the answer is {\"answer\": \"rwe\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nRetrofitting has higher r and ρ values than FastText.\n" }, { "role": "assistant", "content": "The table shows that Retrofitting has r and ρ values of 29.5 and 28.9, respectively, which are lower than FastText's values of 37.2 and 35.8 for Mult+Avg encoding. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2235.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Pearson (r) and Spearman (ρ) correlation on a subset of the HyperLex lexical split. Models marked with † use external resources. All models concatenate their encoded representations with the baseline vector difference of standard FastText word embeddings." ], "reasoning": "The table shows that Retrofitting has r and ρ values of 29.5 and 28.9, respectively, which are lower than FastText's values of 37.2 and 35.8 for Mult+Avg encoding.", "statement": "Retrofitting has higher r and ρ values than FastText.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Comparison of various 12 layer Transformers normalization variants against ReZero and the training iterations required to reach 1.2 BPB on enwiki8 validation set.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Post-Norm requires learning rate warm-up for convergence, while ReZero with α=0 converges faster than the vanilla Transformer but increases memory requirements.To verify this, we re-ran the Post-Norm setup with 100 steps of learning rate warm-up and find that the model is able to converge to 1.2 BPB in 13,690 iterations. Under this setting, we compared other LayerNorm placement schemes against Post-Norm. We find that the other placements led to initially faster convergence, but ultimately Post-Norm catches up in performance, resulting in relatively slower convergence for Pre-Norm and GPT2-Norm. However, other LayerNorm placements have an advantage over Post-Norm in that they do not require learning rate warm-up, thus have fewer hyperparameters to tune. ReZero with α=1 does not show an improvement over the vanilla Transformer, indicating the importance of initializing α=0. With our proposed initialization of α=0, ReZero converges 56% faster than the vanilla Transformer. This cost comes from an increase in memory requirements and poor signal propagation. We find that trying to train deep vanilla Transformers leads to convergence difficulties. When scaled to 64 layers, the vanilla Transformer fails to converge even with a warm-up schedule. A ReZero Transformer with initialization of α=1 diverges, supporting our theoretically motivated initialization at α=0. The deeper ReZero Transformers are able to attain better performance than the shallower Transformers." } ], "images": [ "scigen/train/2236.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of various 12 layer Transformers normalization variants against ReZero and the training iterations required to reach 1.2 BPB on enwiki8 validation set." ], "reasoning": "Post-Norm requires learning rate warm-up for convergence, while ReZero with α=0 converges faster than the vanilla Transformer but increases memory requirements.", "description": "To verify this, we re-ran the Post-Norm setup with 100 steps of learning rate warm-up and find that the model is able to converge to 1.2 BPB in 13,690 iterations. Under this setting, we compared other LayerNorm placement schemes against Post-Norm. We find that the other placements led to initially faster convergence, but ultimately Post-Norm catches up in performance, resulting in relatively slower convergence for Pre-Norm and GPT2-Norm. However, other LayerNorm placements have an advantage over Post-Norm in that they do not require learning rate warm-up, thus have fewer hyperparameters to tune. ReZero with α=1 does not show an improvement over the vanilla Transformer, indicating the importance of initializing α=0. With our proposed initialization of α=0, ReZero converges 56% faster than the vanilla Transformer. This cost comes from an increase in memory requirements and poor signal propagation. We find that trying to train deep vanilla Transformers leads to convergence difficulties. When scaled to 64 layers, the vanilla Transformer fails to converge even with a warm-up schedule. A ReZero Transformer with initialization of α=1 diverges, supporting our theoretically motivated initialization at α=0. The deeper ReZero Transformers are able to attain better performance than the shallower Transformers.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: Comparison of various 12 layer Transformers normalization variants against ReZero and the training iterations required to reach 1.2 BPB on enwiki8 validation set.' in mind, break down and resolve the question below step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich model with 64 layers has the best BPB?\n" }, { "role": "assistant", "content": "From the table, we see that the Char-TX model with 64 layers has a BPB of 1.06, which is the best among the models with 64 layers. So, the answer is {\"answer\": \"Char-TX\"}" } ], "images": [ "scigen/train/2236.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of various 12 layer Transformers normalization variants against ReZero and the training iterations required to reach 1.2 BPB on enwiki8 validation set." ], "reasoning": "From the table, we see that the Char-TX model with 64 layers has a BPB of 1.06, which is the best among the models with 64 layers.", "question": "Which model with 64 layers has the best BPB?", "answer": "So, the answer is {\"answer\": \"Char-TX\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe TX + ReZero α=0 model with 64 layers has a BPB of 1.11.\n" }, { "role": "assistant", "content": "The table shows that the TX + ReZero α=0 model with 64 layers indeed has a BPB of 1.11. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2236.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of various 12 layer Transformers normalization variants against ReZero and the training iterations required to reach 1.2 BPB on enwiki8 validation set." ], "reasoning": "The table shows that the TX + ReZero α=0 model with 64 layers indeed has a BPB of 1.11.", "statement": "The TX + ReZero α=0 model with 64 layers has a BPB of 1.11.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5. Performance on 1000 human-recorded questions.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "A supplementary test was conducted on 1000 non-synthetic, human-recorded questions using the best performing models from the ZS section.Finally, a small, supplementary test is run on non-synthetic, human-recorded questions to see if the models would perform differently on real-world audio inputs. 1000 samples were randomly selected from val, and the best performing models from the ZS section were used for evaluation." } ], "images": [ "scigen/train/2237.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5. Performance on 1000 human-recorded questions." ], "reasoning": "A supplementary test was conducted on 1000 non-synthetic, human-recorded questions using the best performing models from the ZS section.", "description": "Finally, a small, supplementary test is run on non-synthetic, human-recorded questions to see if the models would perform differently on real-world audio inputs. 1000 samples were randomly selected from val, and the best performing models from the ZS section were used for evaluation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5. Performance on 1000 human-recorded questions.\nQuestion: Which model had the highest performance in the 'Number' category?\nGive a detailed response to the above question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the 'Number' column across all models, TextMod (Original Text) had the highest performance with a score of 41.54. So, the answer is {\"answer\": \"TextMod (Original Text)\"}." } ], "images": [ "scigen/train/2237.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5. Performance on 1000 human-recorded questions." ], "reasoning": "By comparing the 'Number' column across all models, TextMod (Original Text) had the highest performance with a score of 41.54.", "question": "Which model had the highest performance in the 'Number' category?", "answer": "So, the answer is {\"answer\": \"TextMod (Original Text)\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim SpeechMod (Recorded) had the lowest performance in the 'All' category., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nSpeechMod (Recorded) had the lowest performance in the 'All' category.\n" }, { "role": "assistant", "content": "By comparing the 'All' column across all models, SpeechMod (Recorded) had the lowest performance with a score of 21.46. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2237.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5. Performance on 1000 human-recorded questions." ], "reasoning": "By comparing the 'All' column across all models, SpeechMod (Recorded) had the lowest performance with a score of 21.46.", "statement": "SpeechMod (Recorded) had the lowest performance in the 'All' category.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1. Dimensions for the conv layers. Example shown with a 2 second long audio waveform, sampled at 16 kHz. The final output dimensions are (3, 512)'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Convolution layers reduce the dimensionality of waveforms, with each layer halving the filter length and doubling the number of filters, resulting in a final output dimension of (x, 512) for LSTM input.Convolution layers are used to encode waveforms because they reduce dimensionality of data while finding salient patterns. The maximum length of a spoken question in our dataset is 6.7 seconds and corresponds to a waveform length of 107,360 elements, while the minimum is 0.63 seconds and corresponds to 10,080 elements. One could directly feed the input waveform to a LSTM, but a LSTM will be unable to learn from sequences that are excessively long, so dimensionality reduction is a necessity. Each consecutive convolution layer halves in filter length but doubles the number of filters. This is done for simplicity rather than for performance optimization. The main consideration taken in choosing the parameters is that the last convolution should output dimensions of (x, 512), where x must be a positive integer. x represents the length of a sequence of 512-dim vectors. The sequence is then fed into an LSTM, which then outputs a single vector of (512). Thus, x should not be too big, and the CNN parameters are chosen to ensure a sensible sequence length. The longest and shortest waveforms correspond to final convolution outputs of size (13, 512) and (1, 512) respectively. 512 is used as the dimension of the LSTM to be consistent with TextMod and the original VQA baseline." } ], "images": [ "scigen/train/2238.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1. Dimensions for the conv layers. Example shown with a 2 second long audio waveform, sampled at 16 kHz. The final output dimensions are (3, 512)" ], "reasoning": "Convolution layers reduce the dimensionality of waveforms, with each layer halving the filter length and doubling the number of filters, resulting in a final output dimension of (x, 512) for LSTM input.", "description": "Convolution layers are used to encode waveforms because they reduce dimensionality of data while finding salient patterns. The maximum length of a spoken question in our dataset is 6.7 seconds and corresponds to a waveform length of 107,360 elements, while the minimum is 0.63 seconds and corresponds to 10,080 elements. One could directly feed the input waveform to a LSTM, but a LSTM will be unable to learn from sequences that are excessively long, so dimensionality reduction is a necessity. Each consecutive convolution layer halves in filter length but doubles the number of filters. This is done for simplicity rather than for performance optimization. The main consideration taken in choosing the parameters is that the last convolution should output dimensions of (x, 512), where x must be a positive integer. x represents the length of a sequence of 512-dim vectors. The sequence is then fed into an LSTM, which then outputs a single vector of (512). Thus, x should not be too big, and the CNN parameters are chosen to ensure a sensible sequence length. The longest and shortest waveforms correspond to final convolution outputs of size (13, 512) and (1, 512) respectively. 512 is used as the dimension of the LSTM to be consistent with TextMod and the original VQA baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 1. Dimensions for the conv layers. Example shown with a 2 second long audio waveform, sampled at 16 kHz. The final output dimensions are (3, 512)'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: What is the output dimension after the second pooling layer?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows that the output dimension after the second pooling layer (pool2) is 2,000. So, the answer is {\"answer\": \"2,000\"}." } ], "images": [ "scigen/train/2238.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1. Dimensions for the conv layers. Example shown with a 2 second long audio waveform, sampled at 16 kHz. The final output dimensions are (3, 512)" ], "reasoning": "The table shows that the output dimension after the second pooling layer (pool2) is 2,000.", "question": "What is the output dimension after the second pooling layer?", "answer": "So, the answer is {\"answer\": \"2,000\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe filter length of conv3 is 16.\n" }, { "role": "assistant", "content": "The table indicates that the filter length of conv3 is indeed 16. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2238.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1. Dimensions for the conv layers. Example shown with a 2 second long audio waveform, sampled at 16 kHz. The final output dimensions are (3, 512)" ], "reasoning": "The table indicates that the filter length of conv3 is indeed 16.", "statement": "The filter length of conv3 is 16.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2. Word Error Rate from Kaldi speech recognition'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "WER increases as the noise percentage increases, indicating a higher error rate in transcription with more noise.Word error rate (WER) is used to measure the accuracy of speech to text transcriptions. WER is defined as follows: WER=(S+D+I)/N Where S is the number of substitutions, D is the number of deletions, and I is the number of insertions. N is the total number of words in the sentence being translated. WER is not expected to be a perfect measure of transcription accuracy, since some words are more essential to the meaning of a sentence than other words. For example, missing the word dog in the sentence what is the dog eating is more detrimental than missing the word the, but we nevertheless employ it to convey a general notion of how many words are understood by the ASR. Naturally the more noise there is, the higher the word error rate becomes. Due to transcription errors, there are resulting questions that contain words not seen in the original datasets. These words, as mentioned above, are indexed as 0 and are masked when fed into TextMod." } ], "images": [ "scigen/train/2239.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2. Word Error Rate from Kaldi speech recognition" ], "reasoning": "WER increases as the noise percentage increases, indicating a higher error rate in transcription with more noise.", "description": "Word error rate (WER) is used to measure the accuracy of speech to text transcriptions. WER is defined as follows: WER=(S+D+I)/N Where S is the number of substitutions, D is the number of deletions, and I is the number of insertions. N is the total number of words in the sentence being translated. WER is not expected to be a perfect measure of transcription accuracy, since some words are more essential to the meaning of a sentence than other words. For example, missing the word dog in the sentence what is the dog eating is more detrimental than missing the word the, but we nevertheless employ it to convey a general notion of how many words are understood by the ASR. Naturally the more noise there is, the higher the word error rate becomes. Due to transcription errors, there are resulting questions that contain words not seen in the original datasets. These words, as mentioned above, are indexed as 0 and are masked when fed into TextMod.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2. Word Error Rate from Kaldi speech recognition\nQuestion: What is the WER when the noise level is at 30%?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the WER at 30% noise, locate the row with 30% noise in the table and read the corresponding WER value. So, the answer is {\"answer\": \"25.41\"}." } ], "images": [ "scigen/train/2239.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2. Word Error Rate from Kaldi speech recognition" ], "reasoning": "To find the WER at 30% noise, locate the row with 30% noise in the table and read the corresponding WER value.", "question": "What is the WER when the noise level is at 30%?", "answer": "So, the answer is {\"answer\": \"25.41\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The WER is below 10% when there is no noise., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe WER is below 10% when there is no noise.\n" }, { "role": "assistant", "content": "Check the WER value in the row where the noise percentage is 0%. The WER is 8.46%, which is below 10%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2239.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2. Word Error Rate from Kaldi speech recognition" ], "reasoning": "Check the WER value in the row where the noise percentage is 0%. The WER is 8.46%, which is below 10%.", "statement": "The WER is below 10% when there is no noise.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3. Accuracy on test-dev with different levels of noise added. (Higher is better)'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The experiments aimed to evaluate model performance with varying noise levels, reporting results in overall accuracy, yes/no questions, counting questions, and other questions.The goal of the main experiments were to observe how each model performs with and without different levels of noise added. The standard format of reporting results from VQA1.0 is followed: All is the overall accuracy, Y/N is for questions with yes or no as answers, Number is for questions that are answered by counting, and Other covers the rest." } ], "images": [ "scigen/train/2240.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. Accuracy on test-dev with different levels of noise added. (Higher is better)" ], "reasoning": "The experiments aimed to evaluate model performance with varying noise levels, reporting results in overall accuracy, yes/no questions, counting questions, and other questions.", "description": "The goal of the main experiments were to observe how each model performs with and without different levels of noise added. The standard format of reporting results from VQA1.0 is followed: All is the overall accuracy, Y/N is for questions with yes or no as answers, Number is for questions that are answered by counting, and Other covers the rest.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3. Accuracy on test-dev with different levels of noise added. (Higher is better)' and methodically tackle the question that follows. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model and noise level combination achieved the highest overall accuracy?\n" }, { "role": "assistant", "content": "To find the highest overall accuracy, compare the 'All' column values for both TextMod and SpeechMod across all noise levels. The highest value is 56.66 for TextMod with OQ noise level. So, the answer is {\"answer\": \"TextMod with OQ\"}." } ], "images": [ "scigen/train/2240.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. Accuracy on test-dev with different levels of noise added. (Higher is better)" ], "reasoning": "To find the highest overall accuracy, compare the 'All' column values for both TextMod and SpeechMod across all noise levels. The highest value is 56.66 for TextMod with OQ noise level.", "question": "Which model and noise level combination achieved the highest overall accuracy?", "answer": "So, the answer is {\"answer\": \"TextMod with OQ\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nSpeechMod with 30% noise level has a higher accuracy for Number questions than TextMod with 30% noise level.\n" }, { "role": "assistant", "content": "Compare the 'Number' column values for SpeechMod with 30% noise level (27.82) and TextMod with 30% noise level (31.45). SpeechMod has a lower accuracy. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2240.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. Accuracy on test-dev with different levels of noise added. (Higher is better)" ], "reasoning": "Compare the 'Number' column values for SpeechMod with 30% noise level (27.82) and TextMod with 30% noise level (31.45). SpeechMod has a lower accuracy.", "statement": "SpeechMod with 30% noise level has a higher accuracy for Number questions than TextMod with 30% noise level.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 6: Test set results for knowledge base embeddings on MCScript 2.0.'.\n" }, { "role": "assistant", "content": "ConceptNet provides the largest performance boost, while ATOMIC gives the smallest due to less overlap with the task text.Each knowledge base improves accuracy, with ConceptNet giving the largest performance boost. ATOMIC gives the smallest boost, likely because the ATOMIC edges involve longer phrases, which means less matches, and the overlap between ATOMIC text and the text present in the task is not as large as either ConceptNet or WebChild. The knowledge embeddings provide a similar +1% absolute improvement (85.5 vs. 84.5), suggesting that the knowledge embeddings cover different aspects and relationships in the text than learned during fine-tuning on RACE." } ], "images": [ "scigen/train/2241.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Test set results for knowledge base embeddings on MCScript 2.0." ], "reasoning": "ConceptNet provides the largest performance boost, while ATOMIC gives the smallest due to less overlap with the task text.", "description": "Each knowledge base improves accuracy, with ConceptNet giving the largest performance boost. ATOMIC gives the smallest boost, likely because the ATOMIC edges involve longer phrases, which means less matches, and the overlap between ATOMIC text and the text present in the task is not as large as either ConceptNet or WebChild. The knowledge embeddings provide a similar +1% absolute improvement (85.5 vs. 84.5), suggesting that the knowledge embeddings cover different aspects and relationships in the text than learned during fine-tuning on RACE.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 6: Test set results for knowledge base embeddings on MCScript 2.0.', answer the following question. Give detailed solution consisting of each step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich knowledge base provides the smallest performance boost and why?\n" }, { "role": "assistant", "content": "The table shows that the system with ATOMIC has an accuracy of 82.5, which is the smallest boost compared to other knowledge bases. The description explains that this is because ATOMIC edges involve longer phrases and have less overlap with the task text. So, the answer is {\"answer\": \"ATOMIC\"}" } ], "images": [ "scigen/train/2241.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Test set results for knowledge base embeddings on MCScript 2.0." ], "reasoning": "The table shows that the system with ATOMIC has an accuracy of 82.5, which is the smallest boost compared to other knowledge bases. The description explains that this is because ATOMIC edges involve longer phrases and have less overlap with the task text.", "question": "Which knowledge base provides the smallest performance boost and why?", "answer": "So, the answer is {\"answer\": \"ATOMIC\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe system with all KB + RACE selected has the highest accuracy.\n" }, { "role": "assistant", "content": "The table shows that the system with all KB + RACE selected has an accuracy of 85.5, which is not the highest. The highest accuracy is 97.4 by Human Ostermann et al. (2019). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2241.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Test set results for knowledge base embeddings on MCScript 2.0." ], "reasoning": "The table shows that the system with all KB + RACE selected has an accuracy of 85.5, which is not the highest. The highest accuracy is 97.4 by Human Ostermann et al. (2019).", "statement": "The system with all KB + RACE selected has the highest accuracy.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: Results on the Telephony test set when adapting the multidomain model and the Voicesearch-Dictation model using in-domain data. Also shown are the corresponding baselines from Tab. 6, and when using a model trained only on Telephony training data.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Using varying amounts of in-domain data for adaptation significantly improves performance on the Telephony domain, as shown by the reduction in word error rates.One of the most important advantages of multidomain modeling is that it allows for easy and quicker adaptation to new conditions. To demonstrate this, we use varying amounts of in-domain data to improve performance on the Telephony domain, which was not used to train the models in Sec. We experiment with 10 hours, 30 hours, 100 hours and 700 hours (approx.) of training data. For adaptation, we use the relatively simpler fine-tuning approach. All layers of the model are tuned. A lower learning rate and early stopping is used to prevent over-fitting. We also train a separate model using the 700 hours training set, with model parameters initialized from scratch. Results are shown in Tab." } ], "images": [ "scigen/train/2242.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Results on the Telephony test set when adapting the multidomain model and the Voicesearch-Dictation model using in-domain data. Also shown are the corresponding baselines from Tab. 6, and when using a model trained only on Telephony training data." ], "reasoning": "Using varying amounts of in-domain data for adaptation significantly improves performance on the Telephony domain, as shown by the reduction in word error rates.", "description": "One of the most important advantages of multidomain modeling is that it allows for easy and quicker adaptation to new conditions. To demonstrate this, we use varying amounts of in-domain data to improve performance on the Telephony domain, which was not used to train the models in Sec. We experiment with 10 hours, 30 hours, 100 hours and 700 hours (approx.) of training data. For adaptation, we use the relatively simpler fine-tuning approach. All layers of the model are tuned. A lower learning rate and early stopping is used to prevent over-fitting. We also train a separate model using the 700 hours training set, with model parameters initialized from scratch. Results are shown in Tab.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 7: Results on the Telephony test set when adapting the multidomain model and the Voicesearch-Dictation model using in-domain data. Also shown are the corresponding baselines from Tab. 6, and when using a model trained only on Telephony training data.\nQuestion: What is the word error rate after fine-tuning with 700 hours of training data?\nSolve the above question based on the table information. Let's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the word error rates for different amounts of training data. The word error rate after fine-tuning with 700 hours of training data is listed as 10.1. So, the answer is {\"answer\": \"10.1\"}." } ], "images": [ "scigen/train/2242.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Results on the Telephony test set when adapting the multidomain model and the Voicesearch-Dictation model using in-domain data. Also shown are the corresponding baselines from Tab. 6, and when using a model trained only on Telephony training data." ], "reasoning": "The table shows the word error rates for different amounts of training data. The word error rate after fine-tuning with 700 hours of training data is listed as 10.1.", "question": "What is the word error rate after fine-tuning with 700 hours of training data?", "answer": "So, the answer is {\"answer\": \"10.1\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nFine-tuning with 30 hours of training data results in a word error rate of 12.2.\n" }, { "role": "assistant", "content": "The table shows the word error rates for different amounts of training data. For 30 hours of training data, the word error rate is listed as 12.2. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2242.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Results on the Telephony test set when adapting the multidomain model and the Voicesearch-Dictation model using in-domain data. Also shown are the corresponding baselines from Tab. 6, and when using a model trained only on Telephony training data." ], "reasoning": "The table shows the word error rates for different amounts of training data. For 30 hours of training data, the word error rate is listed as 12.2.", "statement": "Fine-tuning with 30 hours of training data results in a word error rate of 12.2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Pairwise clustering metrics between each application domain and the Telephony domain. Lower silhouette scores and higher cluster similarity indicate more overlap.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The silhouette score measures how well-defined a cluster is, and the cluster similarity quantifies how similar each application domain is to the Telephony domain.Xi, a(i) is the mean Euclidean distance of Xi to other points in the same cluster, and b(i) is the mean Euclidean distance of Xi to the nearest neighboring cluster. The higher the silhouette score , the better the point sits within its designated cluster. The silhouette averaged over a cluster thus suggests how well-defined the cluster is; over the entire data set, it quantifies how distinct the clusters are on average. To understand how similar each application domain is to the held-out Telephony domain, we perform pairwise silhouette analysis. Rij=Si+SjMij, (2) where Si is a dispersion measure of cluster i and Mij is a distance measure between clusters i and j. Here we define Si as the average Euclidean distance of points in cluster i to the cluster centroid, and Mij as the Euclidean distance between the centroids of clusters i and j. Results from 50 examples sampled from each application domain (Tab." } ], "images": [ "scigen/train/2243.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Pairwise clustering metrics between each application domain and the Telephony domain. Lower silhouette scores and higher cluster similarity indicate more overlap." ], "reasoning": "The silhouette score measures how well-defined a cluster is, and the cluster similarity quantifies how similar each application domain is to the Telephony domain.", "description": "Xi, a(i) is the mean Euclidean distance of Xi to other points in the same cluster, and b(i) is the mean Euclidean distance of Xi to the nearest neighboring cluster. The higher the silhouette score , the better the point sits within its designated cluster. The silhouette averaged over a cluster thus suggests how well-defined the cluster is; over the entire data set, it quantifies how distinct the clusters are on average. To understand how similar each application domain is to the held-out Telephony domain, we perform pairwise silhouette analysis. Rij=Si+SjMij, (2) where Si is a dispersion measure of cluster i and Mij is a distance measure between clusters i and j. Here we define Si as the average Euclidean distance of points in cluster i to the cluster centroid, and Mij as the Euclidean distance between the centroids of clusters i and j. Results from 50 examples sampled from each application domain (Tab.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: Pairwise clustering metrics between each application domain and the Telephony domain. Lower silhouette scores and higher cluster similarity indicate more overlap.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich domain has the highest cluster similarity and what is its average silhouette score?\n" }, { "role": "assistant", "content": "To find the domain with the highest cluster similarity, look at the 'Cluster similarity' column and identify the highest value. Then, find the corresponding 'Average silhouette' score for that domain. The domain with the highest cluster similarity is Call-center with an average silhouette score of 0.0248. So, the answer is {\"answer\": \"0.0248\"}." } ], "images": [ "scigen/train/2243.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Pairwise clustering metrics between each application domain and the Telephony domain. Lower silhouette scores and higher cluster similarity indicate more overlap." ], "reasoning": "To find the domain with the highest cluster similarity, look at the 'Cluster similarity' column and identify the highest value. Then, find the corresponding 'Average silhouette' score for that domain.", "question": "Which domain has the highest cluster similarity and what is its average silhouette score?", "answer": "The domain with the highest cluster similarity is Call-center with an average silhouette score of 0.0248. So, the answer is {\"answer\": \"0.0248\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Farfield domain has a higher average silhouette score than the Dictation domain.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Average silhouette' scores of the Farfield and Dictation domains. Farfield has an average silhouette score of 0.0443, while Dictation has an average silhouette score of 0.0440. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2243.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Pairwise clustering metrics between each application domain and the Telephony domain. Lower silhouette scores and higher cluster similarity indicate more overlap." ], "reasoning": "To verify this statement, compare the 'Average silhouette' scores of the Farfield and Dictation domains. Farfield has an average silhouette score of 0.0443, while Dictation has an average silhouette score of 0.0440.", "statement": "The Farfield domain has a higher average silhouette score than the Dictation domain.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: Results using models trained w/ and w/o noise.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Training with simulated noise improves performance in noisy conditions but slightly degrades performance in clean conditions.4.3.1 Noise To evaluate noise simulation , we train using Voicesearch, Dictation and Other search sets, with and without simulated background noise. Results are shown in Tab. As shown, training with simulated noisy data using the MTR settings described in Sec. The word error rates (WERs) in clean conditions are almost identical when using models trained with and without noise. Unsurprisingly, training with noise significantly improves performance in noisy conditions. For the Dictation set, MTR training improves WER by around 60% (relative). It is also interesting to note that MTR training only marginally affects performance of the 8 kHz test set. When doing noise simulation, we selectively add noise to only those domains for which the input data is relatively clean. Specifically, we add noise only to Voicesearch, Dictation, and Call-center. When the multidomain model is trained with noise simulation, it performs better on the noisy sets but degrades on the clean sets. Moreover, the performance in noisy conditions doesn’t match those of the domain-specific model trained with noise (Tab. This is likely because the model doesn’t see as much clean and noisy data as the domain-specific models because of the imbalance in training data distribution. Adding noise also makes the underlying task harder since the model has to learn to normalize out variations in the training data along several dimensions like application domain, noise and sample rate. It is interesting to see that even though large scale training works in some conditions, combinations of conditions makes it harder for the model to learn without any additional signal during training. As with noise, the multidomain model with codec simulation shows degradation on clean and noisy tests without codecs, as well as for the AAC 23k condition. It works better only on MP3 23k condition. In contrast, the domain-specific codec model performed better than its non-codec counterpart all around." } ], "images": [ "scigen/train/2244.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results using models trained w/ and w/o noise." ], "reasoning": "Training with simulated noise improves performance in noisy conditions but slightly degrades performance in clean conditions.", "description": "4.3.1 Noise To evaluate noise simulation , we train using Voicesearch, Dictation and Other search sets, with and without simulated background noise. Results are shown in Tab. As shown, training with simulated noisy data using the MTR settings described in Sec. The word error rates (WERs) in clean conditions are almost identical when using models trained with and without noise. Unsurprisingly, training with noise significantly improves performance in noisy conditions. For the Dictation set, MTR training improves WER by around 60% (relative). It is also interesting to note that MTR training only marginally affects performance of the 8 kHz test set. When doing noise simulation, we selectively add noise to only those domains for which the input data is relatively clean. Specifically, we add noise only to Voicesearch, Dictation, and Call-center. When the multidomain model is trained with noise simulation, it performs better on the noisy sets but degrades on the clean sets. Moreover, the performance in noisy conditions doesn’t match those of the domain-specific model trained with noise (Tab. This is likely because the model doesn’t see as much clean and noisy data as the domain-specific models because of the imbalance in training data distribution. Adding noise also makes the underlying task harder since the model has to learn to normalize out variations in the training data along several dimensions like application domain, noise and sample rate. It is interesting to see that even though large scale training works in some conditions, combinations of conditions makes it harder for the model to learn without any additional signal during training. As with noise, the multidomain model with codec simulation shows degradation on clean and noisy tests without codecs, as well as for the AAC 23k condition. It works better only on MP3 23k condition. In contrast, the domain-specific codec model performed better than its non-codec counterpart all around.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 3: Results using models trained w/ and w/o noise.', solve this question and give detail solutions. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the word error rate (WER) improvement for the Dictation set at 16 kHz when noise is added?\n" }, { "role": "assistant", "content": "The WER for the Dictation set at 16 kHz without noise is 30.6, and with noise, it is 12.3. The improvement is calculated as 30.6 - 12.3. So, the answer is {\"answer\": \"18.3\"}." } ], "images": [ "scigen/train/2244.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results using models trained w/ and w/o noise." ], "reasoning": "The WER for the Dictation set at 16 kHz without noise is 30.6, and with noise, it is 12.3. The improvement is calculated as 30.6 - 12.3.", "question": "What is the word error rate (WER) improvement for the Dictation set at 16 kHz when noise is added?", "answer": "So, the answer is {\"answer\": \"18.3\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nAdding noise to the Dictation set at 16 kHz improves the word error rate (WER) by more than 50%.\n" }, { "role": "assistant", "content": "The WER for the Dictation set at 16 kHz without noise is 30.6, and with noise, it is 12.3. The improvement is 30.6 - 12.3 = 18.3, which is more than 50% of 30.6. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2244.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results using models trained w/ and w/o noise." ], "reasoning": "The WER for the Dictation set at 16 kHz without noise is 30.6, and with noise, it is 12.3. The improvement is 30.6 - 12.3 = 18.3, which is more than 50% of 30.6.", "statement": "Adding noise to the Dictation set at 16 kHz improves the word error rate (WER) by more than 50%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Results using models trained with and without codec simulation. All of the results are on the Voicesearch test set.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Training with codecs improves performance under both seen and unseen codecs, making it close to not using any codec, even at low bitrates.4.3.2 Codecs Tab. We evaluate on the Voicesearch set under various codec conditions. For the baseline model, performance worsens as the bitrate decreases since lower bitrates imply more lossy encoding. Training with codecs makes the performance under seen and unseen codecs close to not using any codec, even at low bitrates: For MP3 23k, training with codecs improves WER by almost 20%." } ], "images": [ "scigen/train/2245.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results using models trained with and without codec simulation. All of the results are on the Voicesearch test set." ], "reasoning": "Training with codecs improves performance under both seen and unseen codecs, making it close to not using any codec, even at low bitrates.", "description": "4.3.2 Codecs Tab. We evaluate on the Voicesearch set under various codec conditions. For the baseline model, performance worsens as the bitrate decreases since lower bitrates imply more lossy encoding. Training with codecs makes the performance under seen and unseen codecs close to not using any codec, even at low bitrates: For MP3 23k, training with codecs improves WER by almost 20%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Results using models trained with and without codec simulation. All of the results are on the Voicesearch test set.', solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the Word Error Rate (WER) improvement for MP3 23k when training with codecs?\n" }, { "role": "assistant", "content": "The WER for MP3 23k without codec training is 13.6, and with codec training, it is 10.6. The improvement is 13.6 - 10.6 = 3.0. So, the answer is {\"answer\": \"3.0\"}." } ], "images": [ "scigen/train/2245.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results using models trained with and without codec simulation. All of the results are on the Voicesearch test set." ], "reasoning": "The WER for MP3 23k without codec training is 13.6, and with codec training, it is 10.6. The improvement is 13.6 - 10.6 = 3.0.", "question": "What is the Word Error Rate (WER) improvement for MP3 23k when training with codecs?", "answer": "So, the answer is {\"answer\": \"3.0\"}." }, { "messages": [ { "role": "user", "content": "Training with codecs improves the Word Error Rate (WER) for AAC 64k. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nTraining with codecs improves the Word Error Rate (WER) for AAC 64k.\n" }, { "role": "assistant", "content": "The table shows that the WER for AAC 64k without codec training is 10.6, and with codec training, it is 10.0, indicating an improvement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2245.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results using models trained with and without codec simulation. All of the results are on the Voicesearch test set." ], "reasoning": "The table shows that the WER for AAC 64k without codec training is 10.6, and with codec training, it is 10.0, indicating an improvement.", "statement": "Training with codecs improves the Word Error Rate (WER) for AAC 64k.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 5: Results using domain-specific models.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The YouTube model generalizes better across different test sets due to its varied training data, outperforming domain-specific models especially on the Telephony and Call-center sets.Next, we look at performance of application domain specific models. Results are shown in Tab. We present results using 3 domain-specific models trained on Voicesearch and Dictation, YouTube, and Call-Center data, respectively. Unsurprisingly, the models work well when the test domains match the model domains, and poorly when the domain changes. The YouTube model, which is trained with the largest dataset among the 3 models, generalizes better. This is most likely because YouTube training set includes data from a varied set of sources. Comparing these results with those in Tab. It also generalizes better: It has better performance on the Telephony set compared to the domain-specific models, and also on the noisy test sets compared to the baseline model trained without noise simulation. Finally, on the Call-center set, it outperforms the domain-specific model, which shows that multidomain training partly addresses data sparsity issues." } ], "images": [ "scigen/train/2246.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results using domain-specific models." ], "reasoning": "The YouTube model generalizes better across different test sets due to its varied training data, outperforming domain-specific models especially on the Telephony and Call-center sets.", "description": "Next, we look at performance of application domain specific models. Results are shown in Tab. We present results using 3 domain-specific models trained on Voicesearch and Dictation, YouTube, and Call-Center data, respectively. Unsurprisingly, the models work well when the test domains match the model domains, and poorly when the domain changes. The YouTube model, which is trained with the largest dataset among the 3 models, generalizes better. This is most likely because YouTube training set includes data from a varied set of sources. Comparing these results with those in Tab. It also generalizes better: It has better performance on the Telephony set compared to the domain-specific models, and also on the noisy test sets compared to the baseline model trained without noise simulation. Finally, on the Call-center set, it outperforms the domain-specific model, which shows that multidomain training partly addresses data sparsity issues.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model has the lowest word error rate on the Dictation test set at 16 kHz sample rate?\nSolve the above question based on the table titled 'Table 5: Results using domain-specific models.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows word error rates for different models on various test sets. For the Dictation test set at 16 kHz, the Voicesearch-Dictation model has a word error rate of 7.7, which is the lowest compared to the Call-center and YouTube models. So, the answer is {\"answer\": \"Voicesearch-Dictation\"}" } ], "images": [ "scigen/train/2246.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results using domain-specific models." ], "reasoning": "The table shows word error rates for different models on various test sets. For the Dictation test set at 16 kHz, the Voicesearch-Dictation model has a word error rate of 7.7, which is the lowest compared to the Call-center and YouTube models.", "question": "Which model has the lowest word error rate on the Dictation test set at 16 kHz sample rate?", "answer": "So, the answer is {\"answer\": \"Voicesearch-Dictation\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The YouTube model has the highest word error rate on the Voicesearch test set. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe YouTube model has the highest word error rate on the Voicesearch test set.\n" }, { "role": "assistant", "content": "The table shows that the YouTube model has a word error rate of 16.3 on the Voicesearch test set, which is lower than the Call-center model's 98.6, making the statement incorrect. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2246.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results using domain-specific models." ], "reasoning": "The table shows that the YouTube model has a word error rate of 16.3 on the Voicesearch test set, which is lower than the Call-center model's 98.6, making the statement incorrect.", "statement": "The YouTube model has the highest word error rate on the Voicesearch test set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5: Phoneme Error Rate using multiple self-supervised learning methods.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "CSTNet models outperform all systems except Wav2Vec, with the best CSTNet model (en-fr) trailing Wav2Vec by 8pp and the worst CSTNet model (en-de) outperforming ResDAVENet by 6pp.Representations learned by CSTNet significantly outperform all other systems except Wav2Vec, where the best CSTNet system trained on English-French lags by 8 percentage points (pp). Our worst model, English-German, outperforms ResDAVENet by 6pp. For ResDAVENet, we compute the PER using features from every layer and report the best results. This is an encouraging result showing that CSTNet can learn better linguistic information than the ResDAVENet trained on the audio-visual retrieval task. We do not train any of the comparison features extractors on our own, but use the publicly available checkpoints released by the authors of the respective methods." } ], "images": [ "scigen/train/2247.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Phoneme Error Rate using multiple self-supervised learning methods." ], "reasoning": "CSTNet models outperform all systems except Wav2Vec, with the best CSTNet model (en-fr) trailing Wav2Vec by 8pp and the worst CSTNet model (en-de) outperforming ResDAVENet by 6pp.", "description": "Representations learned by CSTNet significantly outperform all other systems except Wav2Vec, where the best CSTNet system trained on English-French lags by 8 percentage points (pp). Our worst model, English-German, outperforms ResDAVENet by 6pp. For ResDAVENet, we compute the PER using features from every layer and report the best results. This is an encouraging result showing that CSTNet can learn better linguistic information than the ResDAVENet trained on the audio-visual retrieval task. We do not train any of the comparison features extractors on our own, but use the publicly available checkpoints released by the authors of the respective methods.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 5: Phoneme Error Rate using multiple self-supervised learning methods.', solve the subsequent question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nHow much better is the best CSTNet model compared to ResDAVENet in terms of PER?\n" }, { "role": "assistant", "content": "The best CSTNet model (en-fr) has a PER of 29.2, while ResDAVENet has a PER of 38.5. The difference is 38.5 - 29.2 = 9.3pp. So, the answer is {\"answer\": \"9.3\"}" } ], "images": [ "scigen/train/2247.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Phoneme Error Rate using multiple self-supervised learning methods." ], "reasoning": "The best CSTNet model (en-fr) has a PER of 29.2, while ResDAVENet has a PER of 38.5. The difference is 38.5 - 29.2 = 9.3pp.", "question": "How much better is the best CSTNet model compared to ResDAVENet in terms of PER?", "answer": "So, the answer is {\"answer\": \"9.3\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe en-es CSTNet model has a lower PER than MockingJay.\n" }, { "role": "assistant", "content": "The en-es CSTNet model has a PER of 30.3, while MockingJay has a PER of 41.2. Therefore, the en-es CSTNet model has a lower PER than MockingJay. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2247.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Phoneme Error Rate using multiple self-supervised learning methods." ], "reasoning": "The en-es CSTNet model has a PER of 30.3, while MockingJay has a PER of 41.2. Therefore, the en-es CSTNet model has a lower PER than MockingJay.", "statement": "The en-es CSTNet model has a lower PER than MockingJay.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Experimental results for speech to text and text to speech retrieval task.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The table shows the retrieval performance of CSTNet for different language pairs in the MuST-C corpus, with metrics for both Speech to Text and Text to Speech tasks.This gives us an indication of how well CSTNet is doing at the actual task that it is trained on. Rows correspond to retrieval performance for the model trained on different language pairs of the MuST-C corpus." } ], "images": [ "scigen/train/2248.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Experimental results for speech to text and text to speech retrieval task." ], "reasoning": "The table shows the retrieval performance of CSTNet for different language pairs in the MuST-C corpus, with metrics for both Speech to Text and Text to Speech tasks.", "description": "This gives us an indication of how well CSTNet is doing at the actual task that it is trained on. Rows correspond to retrieval performance for the model trained on different language pairs of the MuST-C corpus.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Experimental results for speech to text and text to speech retrieval task.', answer the following question. Give detailed solution consisting of each step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich language pair has the highest retrieval performance for the Speech to Text task at R@1?\n" }, { "role": "assistant", "content": "To find the highest retrieval performance for the Speech to Text task at R@1, compare the values in the Speech → Text R@1 column for all language pairs. The highest value is 49.6 for the en-es language pair. So, the answer is {\"answer\": \"en-es\"}." } ], "images": [ "scigen/train/2248.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Experimental results for speech to text and text to speech retrieval task." ], "reasoning": "To find the highest retrieval performance for the Speech to Text task at R@1, compare the values in the Speech → Text R@1 column for all language pairs. The highest value is 49.6 for the en-es language pair.", "question": "Which language pair has the highest retrieval performance for the Speech to Text task at R@1?", "answer": "So, the answer is {\"answer\": \"en-es\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The en-fr language pair has the highest retrieval performance for the Text to Speech task at R@10. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe en-fr language pair has the highest retrieval performance for the Text to Speech task at R@10.\n" }, { "role": "assistant", "content": "To verify this statement, compare the values in the Text → Speech R@10 column for all language pairs. The highest value is 77.2 for the en-es language pair, not en-fr. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2248.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Experimental results for speech to text and text to speech retrieval task." ], "reasoning": "To verify this statement, compare the values in the Text → Speech R@10 column for all language pairs. The highest value is 77.2 for the en-es language pair, not en-fr.", "statement": "The en-fr language pair has the highest retrieval performance for the Text to Speech task at R@10.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: ABX Scores on ZRC19 Challenge English Test Set'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "CSTNet's audio encoder outperforms Wavenet-VQ in ABX scores across different language pairs, particularly in the middle layers of the network.We compute the ABX score using all the layers of the pre-trained CSTNet’s audio encoder trained on different language pairs. Here, we present the best results that is usually obtained using the representations in the middle of the network (for layer numbers 5, 6). The network hits a sweet spot in the middle layers, where the receptive field is approximately 100-140 ms. A similar trend is observed for all the languages. We significantly outperform Wavenet-VQ (ZS), the best performing submission to the ZRC19 challenge, based on Vector Quantization VAE (VQ-VAE) To have a fair comparison, we also compare our model against Wavenet-VQ (MuST-C) that is trained on the English speech from the MuST-C corpus on which CSTNet is also trained. CSTNet still outperforms Wavenet-VQ. Hence, we show that our framework could be an alternative to reconstruction based representation learning methods." } ], "images": [ "scigen/train/2249.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: ABX Scores on ZRC19 Challenge English Test Set" ], "reasoning": "CSTNet's audio encoder outperforms Wavenet-VQ in ABX scores across different language pairs, particularly in the middle layers of the network.", "description": "We compute the ABX score using all the layers of the pre-trained CSTNet’s audio encoder trained on different language pairs. Here, we present the best results that is usually obtained using the representations in the middle of the network (for layer numbers 5, 6). The network hits a sweet spot in the middle layers, where the receptive field is approximately 100-140 ms. A similar trend is observed for all the languages. We significantly outperform Wavenet-VQ (ZS), the best performing submission to the ZRC19 challenge, based on Vector Quantization VAE (VQ-VAE) To have a fair comparison, we also compare our model against Wavenet-VQ (MuST-C) that is trained on the English speech from the MuST-C corpus on which CSTNet is also trained. CSTNet still outperforms Wavenet-VQ. Hence, we show that our framework could be an alternative to reconstruction based representation learning methods.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: ABX Scores on ZRC19 Challenge English Test Set'. Solve the following question based on the table.\n\nQuestion: Which method has the lowest ABX score and what is the score?\nLet's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, we see that ResDAVENet has the lowest ABX score of 10.8. So, the answer is {\"answer\": \"ResDAVENet with 10.8\"}" } ], "images": [ "scigen/train/2249.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: ABX Scores on ZRC19 Challenge English Test Set" ], "reasoning": "By examining the table, we see that ResDAVENet has the lowest ABX score of 10.8.", "question": "Which method has the lowest ABX score and what is the score?", "answer": "So, the answer is {\"answer\": \"ResDAVENet with 10.8\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: CSTNet outperforms Wavenet-VQ (MuST-C) in ABX scores., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nCSTNet outperforms Wavenet-VQ (MuST-C) in ABX scores.\n" }, { "role": "assistant", "content": "The table shows that CSTNet's ABX scores for all language pairs (en-es, en-fr, en-it, en-de, en-pt) are lower than Wavenet-VQ (MuST-C)'s score of 20.1. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2249.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: ABX Scores on ZRC19 Challenge English Test Set" ], "reasoning": "The table shows that CSTNet's ABX scores for all language pairs (en-es, en-fr, en-it, en-de, en-pt) are lower than Wavenet-VQ (MuST-C)'s score of 20.1.", "statement": "CSTNet outperforms Wavenet-VQ (MuST-C) in ABX scores.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: Phone Recognition Results with features extracted from different layers of CSTNet on WSJ eval92. LX stands for Layer #X.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The phone recognition performance improves with higher layers in the audio network, with the best performance from the last layer, and the best language pair is English-French.Rows in the table correspond to the CSTNet trained on different language pairs and the different columns correspond to the layer number of the pre-trained audio network. As can be seen from the table, as we go higher up in the audio network, the phone recognition performance improves with the best performance achieved using the representations extracted from the last layer. The improvement in PER is not consistent with the increase in layer size but the best performance is always from last layer. This is not surprising as the last layer has the highest receptive field and hence has access to more global information essential for the task of phone recognition. The best performance is obtained using the CSTNet trained on the language pair English-French, but the performance gap between different language pairs is not very significant, with a gap of 2.8 percentage points between the best (en-fr) and the worst (en-de) language pairs." } ], "images": [ "scigen/train/2250.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Phone Recognition Results with features extracted from different layers of CSTNet on WSJ eval92. LX stands for Layer #X." ], "reasoning": "The phone recognition performance improves with higher layers in the audio network, with the best performance from the last layer, and the best language pair is English-French.", "description": "Rows in the table correspond to the CSTNet trained on different language pairs and the different columns correspond to the layer number of the pre-trained audio network. As can be seen from the table, as we go higher up in the audio network, the phone recognition performance improves with the best performance achieved using the representations extracted from the last layer. The improvement in PER is not consistent with the increase in layer size but the best performance is always from last layer. This is not surprising as the last layer has the highest receptive field and hence has access to more global information essential for the task of phone recognition. The best performance is obtained using the CSTNet trained on the language pair English-French, but the performance gap between different language pairs is not very significant, with a gap of 2.8 percentage points between the best (en-fr) and the worst (en-de) language pairs.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 4: Phone Recognition Results with features extracted from different layers of CSTNet on WSJ eval92. LX stands for Layer #X.\nQuestion: Which language pair achieves the best phone recognition performance and at which layer?\n" }, { "role": "assistant", "content": "The best phone recognition performance is achieved by the language pair English-French at layer 13 with a PER of 29.2. So, the answer is {\"answer\": \"English-French at layer 13\"}." } ], "images": [ "scigen/train/2250.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Phone Recognition Results with features extracted from different layers of CSTNet on WSJ eval92. LX stands for Layer #X." ], "reasoning": "The best phone recognition performance is achieved by the language pair English-French at layer 13 with a PER of 29.2.", "question": "Which language pair achieves the best phone recognition performance and at which layer?", "answer": "So, the answer is {\"answer\": \"English-French at layer 13\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe phone recognition performance for the en-de language pair is the worst at layer 13.\n" }, { "role": "assistant", "content": "The table shows that the en-de language pair has a PER of 32.5 at layer 13, which is the highest (worst) PER among all language pairs at that layer. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2250.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Phone Recognition Results with features extracted from different layers of CSTNet on WSJ eval92. LX stands for Layer #X." ], "reasoning": "The table shows that the en-de language pair has a PER of 32.5 at layer 13, which is the highest (worst) PER among all language pairs at that layer.", "statement": "The phone recognition performance for the en-de language pair is the worst at layer 13.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'TABLE VII: Comparison with the state-of-the-art methods on RefCOCO, RefCOCO+ and RefCOCOg when using detected proposals. The best performing method is marked in bold.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The proposed CMRIN model outperforms existing state-of-the-art models in grounding referring expressions, showing significant improvements in average precision and Precision@1 across multiple datasets.We have also evaluated the performance of the proposed CMRIN for grounding referring expressions using automatically detected objects in the three datasets. The proposed CMRIN outperforms existing state-of-the-art models, which demonstrates the robustness of CMRIN with respect to object detection results. Specifically, CMRIN improves the average precision in the person category achieved with the existing best-performing method by 3.80%, and it improves the Precision@1 on RefCOCO+’s test A and test B by 5.50% and 1.27%, respectively." } ], "images": [ "scigen/train/2251.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VII: Comparison with the state-of-the-art methods on RefCOCO, RefCOCO+ and RefCOCOg when using detected proposals. The best performing method is marked in bold." ], "reasoning": "The proposed CMRIN model outperforms existing state-of-the-art models in grounding referring expressions, showing significant improvements in average precision and Precision@1 across multiple datasets.", "description": "We have also evaluated the performance of the proposed CMRIN for grounding referring expressions using automatically detected objects in the three datasets. The proposed CMRIN outperforms existing state-of-the-art models, which demonstrates the robustness of CMRIN with respect to object detection results. Specifically, CMRIN improves the average precision in the person category achieved with the existing best-performing method by 3.80%, and it improves the Precision@1 on RefCOCO+’s test A and test B by 5.50% and 1.27%, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'TABLE VII: Comparison with the state-of-the-art methods on RefCOCO, RefCOCO+ and RefCOCOg when using detected proposals. The best performing method is marked in bold.'. Solve the following question based on the table.\n\nQuestion: How much does CMRIN improve the Precision@1 on RefCOCO+ test A compared to the existing best-performing method?\nLet's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the Precision@1 on RefCOCO+ test A for the existing best-performing method (MAttNet) is 70.26, while CMRIN achieves 75.76. The improvement is calculated as 75.76 - 70.26 = 5.50%. So, the answer is {\"answer\": \"5.50%\"}." } ], "images": [ "scigen/train/2251.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VII: Comparison with the state-of-the-art methods on RefCOCO, RefCOCO+ and RefCOCOg when using detected proposals. The best performing method is marked in bold." ], "reasoning": "The table shows that the Precision@1 on RefCOCO+ test A for the existing best-performing method (MAttNet) is 70.26, while CMRIN achieves 75.76. The improvement is calculated as 75.76 - 70.26 = 5.50%.", "question": "How much does CMRIN improve the Precision@1 on RefCOCO+ test A compared to the existing best-performing method?", "answer": "So, the answer is {\"answer\": \"5.50%\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nCMRIN achieves the highest Precision@1 on RefCOCO test A.\n" }, { "role": "assistant", "content": "The table shows that CMRIN achieves a Precision@1 of 82.53 on RefCOCO test A, which is higher than any other model listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2251.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VII: Comparison with the state-of-the-art methods on RefCOCO, RefCOCO+ and RefCOCOg when using detected proposals. The best performing method is marked in bold." ], "reasoning": "The table shows that CMRIN achieves a Precision@1 of 82.53 on RefCOCO test A, which is higher than any other model listed.", "statement": "CMRIN achieves the highest Precision@1 on RefCOCO test A.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'TABLE I: Comparison with the state-of-the-art methods on RefCOCO, RefCOCO+ and RefCOCOg. The two best performing methods using VGG-16 are marked in red and blue. The best performing method using ResNet is marked in bold.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "CMRIN outperforms existing methods across all datasets, improving Precision@1 by 1.80%, 5.17%, and 3.14% on RefCOCO, RefCOCO+, and RefCOCOg respectively with VGG-16, and further improves by ~4.40% with ResNet-101.Our proposed CMRIN consistently outperforms existing methods across all the datasets by a large margin, which indicates that our CMRIN performs well on datasets with different characteristics. Specifically, CMRIN improves the average Precision@1 over validation and testing sets achieved by the existing best-performing algorithm by 1.80%, 5.17% and 3.14% respectively on the RefCOCO, RefCOCO+ and RefCOCOg datasets when VGG-16 is used as the backbone network. Our CMRIN significantly improves the precision in the person category (test A of RefCOCO and RefCOCO+), which indicates that casting appearance attributes (e.g. shirt, glasses and shoes) of a person as external relationships between person and appearance attributes can effectively distinguish the target person from other persons. After we switch to the visual features extracted by ResNet-101 based Faster R-CNN, the Precision@1 of our CMRIN is further improved by another ∼4.40%. Note that our CMRIN only uses the 2048-dimensional features from pool5 while MattNet uses the feature maps generated from the last convolutional layers of both the third and fourth stages." } ], "images": [ "scigen/train/2252.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Comparison with the state-of-the-art methods on RefCOCO, RefCOCO+ and RefCOCOg. The two best performing methods using VGG-16 are marked in red and blue. The best performing method using ResNet is marked in bold." ], "reasoning": "CMRIN outperforms existing methods across all datasets, improving Precision@1 by 1.80%, 5.17%, and 3.14% on RefCOCO, RefCOCO+, and RefCOCOg respectively with VGG-16, and further improves by ~4.40% with ResNet-101.", "description": "Our proposed CMRIN consistently outperforms existing methods across all the datasets by a large margin, which indicates that our CMRIN performs well on datasets with different characteristics. Specifically, CMRIN improves the average Precision@1 over validation and testing sets achieved by the existing best-performing algorithm by 1.80%, 5.17% and 3.14% respectively on the RefCOCO, RefCOCO+ and RefCOCOg datasets when VGG-16 is used as the backbone network. Our CMRIN significantly improves the precision in the person category (test A of RefCOCO and RefCOCO+), which indicates that casting appearance attributes (e.g. shirt, glasses and shoes) of a person as external relationships between person and appearance attributes can effectively distinguish the target person from other persons. After we switch to the visual features extracted by ResNet-101 based Faster R-CNN, the Precision@1 of our CMRIN is further improved by another ∼4.40%. Note that our CMRIN only uses the 2048-dimensional features from pool5 while MattNet uses the feature maps generated from the last convolutional layers of both the third and fourth stages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: TABLE I: Comparison with the state-of-the-art methods on RefCOCO, RefCOCO+ and RefCOCOg. The two best performing methods using VGG-16 are marked in red and blue. The best performing method using ResNet is marked in bold.\nQuestion: What is the Precision@1 improvement of CMRIN on RefCOCO+ testA when switching from VGG-16 to ResNet-101?\n" }, { "role": "assistant", "content": "The Precision@1 for CMRIN on RefCOCO+ testA with VGG-16 is 75.63%. With ResNet-101, it is 81.44%. The improvement is 81.44% - 75.63% = 5.81%. So, the answer is {\"answer\": \"5.81%\"}" } ], "images": [ "scigen/train/2252.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Comparison with the state-of-the-art methods on RefCOCO, RefCOCO+ and RefCOCOg. The two best performing methods using VGG-16 are marked in red and blue. The best performing method using ResNet is marked in bold." ], "reasoning": "The Precision@1 for CMRIN on RefCOCO+ testA with VGG-16 is 75.63%. With ResNet-101, it is 81.44%. The improvement is 81.44% - 75.63% = 5.81%.", "question": "What is the Precision@1 improvement of CMRIN on RefCOCO+ testA when switching from VGG-16 to ResNet-101?", "answer": "So, the answer is {\"answer\": \"5.81%\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: CMRIN achieves the highest Precision@1 on RefCOCOg test when using ResNet-101., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nCMRIN achieves the highest Precision@1 on RefCOCOg test when using ResNet-101.\n" }, { "role": "assistant", "content": "The Precision@1 for CMRIN on RefCOCOg test with ResNet-101 is 81.71%, which is the highest value in the RefCOCOg test column. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2252.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Comparison with the state-of-the-art methods on RefCOCO, RefCOCO+ and RefCOCOg. The two best performing methods using VGG-16 are marked in red and blue. The best performing method using ResNet is marked in bold." ], "reasoning": "The Precision@1 for CMRIN on RefCOCOg test with ResNet-101 is 81.71%, which is the highest value in the RefCOCOg test column.", "statement": "CMRIN achieves the highest Precision@1 on RefCOCOg test when using ResNet-101.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'TABLE IV: Comparison of different phrase designs on RefCOCO, RefCOCO+ and RefCOCOg.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The final version using both words and phrases (row 3) performs better than the variant using only words (row 1), demonstrating the effectiveness of phrases in improving accuracy.The performance of the variant using words to highlight the vertices of the spatial relation graph (row 1) is worse than that of the final version using both words and phrases (row 3), which demonstrates the effectiveness of phrases in improving the accuracy of vertex highlighting. It is worth noting that we implicitly considered the word context (phrase-level information) in our conference version (row 2) by using the contextual features of words to attend the vertices instead of using the word embeddings. However, the contextual features of words introduce the global noise of the expressions, which increases the difficulty of learning the correspondence between words and vertices. The performance of CMRIN with implicit phrases is worse than that of it with explicit phrases in the object category (i.e., test B), because the visual contents of object category is sensitive to contextual noise. In addition, explicit use of phrases can help align between the linguistic words and visual objects." } ], "images": [ "scigen/train/2253.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Comparison of different phrase designs on RefCOCO, RefCOCO+ and RefCOCOg." ], "reasoning": "The final version using both words and phrases (row 3) performs better than the variant using only words (row 1), demonstrating the effectiveness of phrases in improving accuracy.", "description": "The performance of the variant using words to highlight the vertices of the spatial relation graph (row 1) is worse than that of the final version using both words and phrases (row 3), which demonstrates the effectiveness of phrases in improving the accuracy of vertex highlighting. It is worth noting that we implicitly considered the word context (phrase-level information) in our conference version (row 2) by using the contextual features of words to attend the vertices instead of using the word embeddings. However, the contextual features of words introduce the global noise of the expressions, which increases the difficulty of learning the correspondence between words and vertices. The performance of CMRIN with implicit phrases is worse than that of it with explicit phrases in the object category (i.e., test B), because the visual contents of object category is sensitive to contextual noise. In addition, explicit use of phrases can help align between the linguistic words and visual objects.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'TABLE IV: Comparison of different phrase designs on RefCOCO, RefCOCO+ and RefCOCOg.', answer the following question. Give detailed solution consisting of each step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich version has the highest performance on RefCOCO+ testB?\n" }, { "role": "assistant", "content": "By comparing the values in the RefCOCO+ testB column, the CMRIN version (row 3) has the highest performance with a score of 69.58. So, the answer is {\"answer\": \"CMRIN\"}" } ], "images": [ "scigen/train/2253.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Comparison of different phrase designs on RefCOCO, RefCOCO+ and RefCOCOg." ], "reasoning": "By comparing the values in the RefCOCO+ testB column, the CMRIN version (row 3) has the highest performance with a score of 69.58.", "question": "Which version has the highest performance on RefCOCO+ testB?", "answer": "So, the answer is {\"answer\": \"CMRIN\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe performance of the implicit phrase version is better than the w/o phrase version in RefCOCO testA.\n" }, { "role": "assistant", "content": "The implicit phrase version (row 2) has a score of 87.63 in RefCOCO testA, which is higher than the w/o phrase version (row 1) score of 86.95. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2253.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Comparison of different phrase designs on RefCOCO, RefCOCO+ and RefCOCOg." ], "reasoning": "The implicit phrase version (row 2) has a score of 87.63 in RefCOCO testA, which is higher than the w/o phrase version (row 1) score of 86.95.", "statement": "The performance of the implicit phrase version is better than the w/o phrase version in RefCOCO testA.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'TABLE V: Ablation studies on variants of spatial relation graph of our proposed CMRIN on RefCOCO, RefCOCO+ and RefCOCOg. The variant is denoted as design of edge type with condition of existence for edges. The numbers in parentheses following the “type”, “dis” and “num” represent the number of types of edges, the threshold of normalized distance and the maximum number of edges at each vertices respectively. The two best performing models are marked in red and blue. CMRIN consistently outperforms existing state-of-the-art models on all the three benchmark datasets.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The 'type(11)' design is more efficient and performs better on the RefCOCO+ dataset compared to the 'soft' design, while different conditions for connecting objects show similar performance except for 'center dis(0.7)' which introduces noise.On one hand, we adopt three types of designs for edges, i.e, “type(11)”, “type(7)” and “soft”. the “soft” is a fine-grained version of “type(11)” (row 1) is slightly worse than that of it with “type(11)” (row 7), because the design of “type(7)” is coarse than the design of “type(11)”. The CMRIN with “soft” (row 2) and “type(11)” (row 3) have similar performance on RefCOCO and RefCOCOg datasets, but the performance of latter is better than that of the former on RefCOCO+ dataset, which indicates that “type(11)” is fine enough to capture spatial relationships. In addition, “type(11)” is more memory- and computation-efficient than “soft”. On the other hand, we evaluate different conditions for connecting between objects, i.e, “edge num”, “axis dis” and “center dis”. In particular, the “edge num(5)” constraints the maximum out-degrees of each vertices to 5 and a vertex is connected to its 5 nearest nodes based on the distances between their normalized center coordinates (i.e., center distances) whose center distance is smaller than the threshold. (row 6) has relative lower precision than that with other conditions (row 3, 4, 5 and 7), because the “center dis(0.7)” covers several redundant edges which introduces noisy information. The CMRIN with remaining conditions have similar performance, and their differences in average precision over the validation and testing sets on RefCOCO, RefCOCO+ and RefCOCOg datasets are within ±0.07%, ±0.27% and ±0.55%, respectively." } ], "images": [ "scigen/train/2254.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Ablation studies on variants of spatial relation graph of our proposed CMRIN on RefCOCO, RefCOCO+ and RefCOCOg. The variant is denoted as design of edge type with condition of existence for edges. The numbers in parentheses following the “type”, “dis” and “num” represent the number of types of edges, the threshold of normalized distance and the maximum number of edges at each vertices respectively. The two best performing models are marked in red and blue. CMRIN consistently outperforms existing state-of-the-art models on all the three benchmark datasets." ], "reasoning": "The 'type(11)' design is more efficient and performs better on the RefCOCO+ dataset compared to the 'soft' design, while different conditions for connecting objects show similar performance except for 'center dis(0.7)' which introduces noise.", "description": "On one hand, we adopt three types of designs for edges, i.e, “type(11)”, “type(7)” and “soft”. the “soft” is a fine-grained version of “type(11)” (row 1) is slightly worse than that of it with “type(11)” (row 7), because the design of “type(7)” is coarse than the design of “type(11)”. The CMRIN with “soft” (row 2) and “type(11)” (row 3) have similar performance on RefCOCO and RefCOCOg datasets, but the performance of latter is better than that of the former on RefCOCO+ dataset, which indicates that “type(11)” is fine enough to capture spatial relationships. In addition, “type(11)” is more memory- and computation-efficient than “soft”. On the other hand, we evaluate different conditions for connecting between objects, i.e, “edge num”, “axis dis” and “center dis”. In particular, the “edge num(5)” constraints the maximum out-degrees of each vertices to 5 and a vertex is connected to its 5 nearest nodes based on the distances between their normalized center coordinates (i.e., center distances) whose center distance is smaller than the threshold. (row 6) has relative lower precision than that with other conditions (row 3, 4, 5 and 7), because the “center dis(0.7)” covers several redundant edges which introduces noisy information. The CMRIN with remaining conditions have similar performance, and their differences in average precision over the validation and testing sets on RefCOCO, RefCOCO+ and RefCOCOg datasets are within ±0.07%, ±0.27% and ±0.55%, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: TABLE V: Ablation studies on variants of spatial relation graph of our proposed CMRIN on RefCOCO, RefCOCO+ and RefCOCOg. The variant is denoted as design of edge type with condition of existence for edges. The numbers in parentheses following the “type”, “dis” and “num” represent the number of types of edges, the threshold of normalized distance and the maximum number of edges at each vertices respectively. The two best performing models are marked in red and blue. CMRIN consistently outperforms existing state-of-the-art models on all the three benchmark datasets.\nQuestion: Which design and condition combination has the highest performance on the RefCOCOg test dataset?\n" }, { "role": "assistant", "content": "To find the highest performance on the RefCOCOg test dataset, we need to compare the values in the RefCOCOg test column. The highest value is 81.07, which corresponds to the 'type(11) + edge num(5)' combination in row 3. So, the answer is {\"answer\": \"type(11) + edge num(5)\"}." } ], "images": [ "scigen/train/2254.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Ablation studies on variants of spatial relation graph of our proposed CMRIN on RefCOCO, RefCOCO+ and RefCOCOg. The variant is denoted as design of edge type with condition of existence for edges. The numbers in parentheses following the “type”, “dis” and “num” represent the number of types of edges, the threshold of normalized distance and the maximum number of edges at each vertices respectively. The two best performing models are marked in red and blue. CMRIN consistently outperforms existing state-of-the-art models on all the three benchmark datasets." ], "reasoning": "To find the highest performance on the RefCOCOg test dataset, we need to compare the values in the RefCOCOg test column. The highest value is 81.07, which corresponds to the 'type(11) + edge num(5)' combination in row 3.", "question": "Which design and condition combination has the highest performance on the RefCOCOg test dataset?", "answer": "So, the answer is {\"answer\": \"type(11) + edge num(5)\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The 'type(11) + center dis(0.5)' combination has the highest performance on the RefCOCO+ testA dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe 'type(11) + center dis(0.5)' combination has the highest performance on the RefCOCO+ testA dataset.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the values in the RefCOCO+ testA column. The highest value is 81.63, which corresponds to the 'type(11) + edge num(5)' combination in row 3, not the 'type(11) + center dis(0.5)' combination in row 7. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2254.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Ablation studies on variants of spatial relation graph of our proposed CMRIN on RefCOCO, RefCOCO+ and RefCOCOg. The variant is denoted as design of edge type with condition of existence for edges. The numbers in parentheses following the “type”, “dis” and “num” represent the number of types of edges, the threshold of normalized distance and the maximum number of edges at each vertices respectively. The two best performing models are marked in red and blue. CMRIN consistently outperforms existing state-of-the-art models on all the three benchmark datasets." ], "reasoning": "To verify this statement, we need to compare the values in the RefCOCO+ testA column. The highest value is 81.63, which corresponds to the 'type(11) + edge num(5)' combination in row 3, not the 'type(11) + center dis(0.5)' combination in row 7.", "statement": "The 'type(11) + center dis(0.5)' combination has the highest performance on the RefCOCO+ testA dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'TABLE VI: Experimental results of spatial/semantic relation graph branch on RefCOCO, RefCOCO+ and RefCOCOg.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The joint model with both spatial and semantic branches achieves the highest precision, indicating effective cooperation between the two types of relationship representations.Effectiveness. We compare the CMRINs with single spatial relation graph branch, with single semantic relation graph branch and joint model including both branches. The performance of single semantic branch is worse than that of single spatial branch, because the object relationship detector cannot recognize the semantic relations completely accurately in the highly unrestricted scenes. In addition, the model including both branches achieves the best precision, which indicate the possibility of cooperation between the spatial and semantic relationship representations." } ], "images": [ "scigen/train/2255.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VI: Experimental results of spatial/semantic relation graph branch on RefCOCO, RefCOCO+ and RefCOCOg." ], "reasoning": "The joint model with both spatial and semantic branches achieves the highest precision, indicating effective cooperation between the two types of relationship representations.", "description": "Effectiveness. We compare the CMRINs with single spatial relation graph branch, with single semantic relation graph branch and joint model including both branches. The performance of single semantic branch is worse than that of single spatial branch, because the object relationship detector cannot recognize the semantic relations completely accurately in the highly unrestricted scenes. In addition, the model including both branches achieves the best precision, which indicate the possibility of cooperation between the spatial and semantic relationship representations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: TABLE VI: Experimental results of spatial/semantic relation graph branch on RefCOCO, RefCOCO+ and RefCOCOg.\nQuestion: Which model achieves the highest precision on the RefCOCOg test?\nSolve the above question based on the table information. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the precision values for the RefCOCOg test, the spatial+semantic model achieves the highest precision with a value of 81.71. So, the answer is {\"answer\": \"spatial+semantic\"}." } ], "images": [ "scigen/train/2255.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VI: Experimental results of spatial/semantic relation graph branch on RefCOCO, RefCOCO+ and RefCOCOg." ], "reasoning": "By comparing the precision values for the RefCOCOg test, the spatial+semantic model achieves the highest precision with a value of 81.71.", "question": "Which model achieves the highest precision on the RefCOCOg test?", "answer": "So, the answer is {\"answer\": \"spatial+semantic\"}." }, { "messages": [ { "role": "user", "content": "The semantic model performs better than the spatial model on the RefCOCO testA. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe semantic model performs better than the spatial model on the RefCOCO testA.\n" }, { "role": "assistant", "content": "By comparing the precision values for the RefCOCO testA, the semantic model has a precision of 87.63, which is higher than the spatial model's precision of 87.36. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2255.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VI: Experimental results of spatial/semantic relation graph branch on RefCOCO, RefCOCO+ and RefCOCOg." ], "reasoning": "By comparing the precision values for the RefCOCO testA, the semantic model has a precision of 87.63, which is higher than the spatial model's precision of 87.36.", "statement": "The semantic model performs better than the spatial model on the RefCOCO testA.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: CER (%) results of multilingual ASR using DARTS-ASR under different fine-tuning approaches.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The fine-tuning approach 'Adapt arch.+param.' achieved the best performance on average CER, while 'Adapt pruned arch.+param.' reduced computational cost with minimal performance drop.Especially for “Adapt pruned arch.+params”, the architecture was pruned by removing all transformations but the top three ones with the highest {αfi,j} values in each edge. Then the pruned {αi,j} kept being fine-tuned jointly with remaining parameter weights. fine-tuning approach obtained the best performance on average CER. However, “Adapt only param.” and “Adapt pruned arch.+param.” were only a little worse than “Adapt arch.+param.”. It indicates after pre-training, DARTS-ASR can find a generally good architecture and parameter weights for different languages. And the pruned architecture can reduce computational cost while suffering little performance drop. We used “Adapt arch.+param.” fine-tuning approach for DARTS-ASR in the following experiments." } ], "images": [ "scigen/train/2256.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: CER (%) results of multilingual ASR using DARTS-ASR under different fine-tuning approaches." ], "reasoning": "The fine-tuning approach 'Adapt arch.+param.' achieved the best performance on average CER, while 'Adapt pruned arch.+param.' reduced computational cost with minimal performance drop.", "description": "Especially for “Adapt pruned arch.+params”, the architecture was pruned by removing all transformations but the top three ones with the highest {αfi,j} values in each edge. Then the pruned {αi,j} kept being fine-tuned jointly with remaining parameter weights. fine-tuning approach obtained the best performance on average CER. However, “Adapt only param.” and “Adapt pruned arch.+param.” were only a little worse than “Adapt arch.+param.”. It indicates after pre-training, DARTS-ASR can find a generally good architecture and parameter weights for different languages. And the pruned architecture can reduce computational cost while suffering little performance drop. We used “Adapt arch.+param.” fine-tuning approach for DARTS-ASR in the following experiments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: CER (%) results of multilingual ASR using DARTS-ASR under different fine-tuning approaches.', and tackle the question below in a sequential manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich language had the highest CER using the 'Fine-tuning of DARTS-ASR Adapt pruned arch.+param.' approach?\n" }, { "role": "assistant", "content": "By examining the 'Fine-tuning of DARTS-ASR Adapt pruned arch.+param.' column, Kurmanji had the highest CER with a value of 53.2. So, the answer is {\"answer\": \"Kurmanji\"}." } ], "images": [ "scigen/train/2256.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: CER (%) results of multilingual ASR using DARTS-ASR under different fine-tuning approaches." ], "reasoning": "By examining the 'Fine-tuning of DARTS-ASR Adapt pruned arch.+param.' column, Kurmanji had the highest CER with a value of 53.2.", "question": "Which language had the highest CER using the 'Fine-tuning of DARTS-ASR Adapt pruned arch.+param.' approach?", "answer": "So, the answer is {\"answer\": \"Kurmanji\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe 'Adapt only param.' approach had the lowest CER for Swahili.\n" }, { "role": "assistant", "content": "By examining the 'Adapt only param.' column, Swahili had a CER of 33.2, which is the lowest among the languages listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2256.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: CER (%) results of multilingual ASR using DARTS-ASR under different fine-tuning approaches." ], "reasoning": "By examining the 'Adapt only param.' column, Swahili had a CER of 33.2, which is the lowest among the languages listed.", "statement": "The 'Adapt only param.' approach had the lowest CER for Swahili.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: CER (%) results of monolingual ASR using different CNN modules.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "DARTS-ASR significantly outperformed both VGG-Small and VGG-Large in average CER, indicating the importance of architecture in training DNNs.The results of DARTS-ASR using all the seven kinds of transformations mentioned in the last subsection are listed in the third column. We can observe DARTS-ASR significantly outperformed both VGG-Small and VGG-Large, showing that the connection pattern of nodes in DARTS-ASR contributed a lot to the huge performance boosting. It is worth noting that even though the parameter size of VGG-Large was four times as many as VGG-Small, the CERs of Vietnamese and Tamil became worse due to overfitting and the CERs of Swahili and Kurmanji improved only a little. In comparison, the parameter size of DARTS-ASR was also much larger than VGG-Small. However, DARTS-ASR outperformed VGG-Small by 10.2% relative reduction on average CER. It indicates the role of architecture for training DNN is very important." } ], "images": [ "scigen/train/2257.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: CER (%) results of monolingual ASR using different CNN modules." ], "reasoning": "DARTS-ASR significantly outperformed both VGG-Small and VGG-Large in average CER, indicating the importance of architecture in training DNNs.", "description": "The results of DARTS-ASR using all the seven kinds of transformations mentioned in the last subsection are listed in the third column. We can observe DARTS-ASR significantly outperformed both VGG-Small and VGG-Large, showing that the connection pattern of nodes in DARTS-ASR contributed a lot to the huge performance boosting. It is worth noting that even though the parameter size of VGG-Large was four times as many as VGG-Small, the CERs of Vietnamese and Tamil became worse due to overfitting and the CERs of Swahili and Kurmanji improved only a little. In comparison, the parameter size of DARTS-ASR was also much larger than VGG-Small. However, DARTS-ASR outperformed VGG-Small by 10.2% relative reduction on average CER. It indicates the role of architecture for training DNN is very important.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nTable title: Table 1: CER (%) results of monolingual ASR using different CNN modules.\n\nQuestion: What is the average CER reduction percentage of DARTS-ASR compared to VGG-Small?\n" }, { "role": "assistant", "content": "To find the average CER reduction percentage, calculate the average CER for VGG-Small and DARTS-ASR across all languages, then find the relative reduction percentage. So, the answer is {\"answer\": \"10.2%\"}" } ], "images": [ "scigen/train/2257.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: CER (%) results of monolingual ASR using different CNN modules." ], "reasoning": "To find the average CER reduction percentage, calculate the average CER for VGG-Small and DARTS-ASR across all languages, then find the relative reduction percentage.", "question": "What is the average CER reduction percentage of DARTS-ASR compared to VGG-Small?", "answer": "So, the answer is {\"answer\": \"10.2%\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The CER for Tamil using DARTS-ASR is lower than using VGG-Large., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe CER for Tamil using DARTS-ASR is lower than using VGG-Large.\n" }, { "role": "assistant", "content": "The table shows that the CER for Tamil using DARTS-ASR is 48.0, which is lower than the CER using VGG-Large, which is 60.1. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2257.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: CER (%) results of monolingual ASR using different CNN modules." ], "reasoning": "The table shows that the CER for Tamil using DARTS-ASR is 48.0, which is lower than the CER using VGG-Large, which is 60.1.", "statement": "The CER for Tamil using DARTS-ASR is lower than using VGG-Large.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: CER (%) results of multilingual ASR using different CNN modules.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "DARTS-ASR outperformed both VGG-Small and VGG-Large in multilingual ASR, achieving the lowest average CER across all languages.Then we compared DARTS-ASR with VGG-Small and VGG-Large. All three kinds of CNN modules got much better performance on multilingual ASR than monolingual ASR. On multilingual ASR, VGG-Large achieved better results than VGG-Small on average CER. Among those, DARTS-ASR still outperformed both VGG-Small and VGG-Large by a significant margin. It indicates DARTS-ASR can also benefit from multilingual learning to build a shared acoustic pre-trained model with a better architecture and parameter weights." } ], "images": [ "scigen/train/2258.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: CER (%) results of multilingual ASR using different CNN modules." ], "reasoning": "DARTS-ASR outperformed both VGG-Small and VGG-Large in multilingual ASR, achieving the lowest average CER across all languages.", "description": "Then we compared DARTS-ASR with VGG-Small and VGG-Large. All three kinds of CNN modules got much better performance on multilingual ASR than monolingual ASR. On multilingual ASR, VGG-Large achieved better results than VGG-Small on average CER. Among those, DARTS-ASR still outperformed both VGG-Small and VGG-Large by a significant margin. It indicates DARTS-ASR can also benefit from multilingual learning to build a shared acoustic pre-trained model with a better architecture and parameter weights.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 3: CER (%) results of multilingual ASR using different CNN modules.', solve the following question step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich CNN module achieved the lowest CER for Swahili?\n" }, { "role": "assistant", "content": "By comparing the CER values for Swahili across all CNN modules, DARTS-ASR has the lowest CER of 32.3. So, the answer is {\"answer\": \"DARTS-ASR\"}" } ], "images": [ "scigen/train/2258.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: CER (%) results of multilingual ASR using different CNN modules." ], "reasoning": "By comparing the CER values for Swahili across all CNN modules, DARTS-ASR has the lowest CER of 32.3.", "question": "Which CNN module achieved the lowest CER for Swahili?", "answer": "So, the answer is {\"answer\": \"DARTS-ASR\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis VGG-Small achieved the lowest CER for Vietnamese. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nVGG-Small achieved the lowest CER for Vietnamese.\n" }, { "role": "assistant", "content": "The table shows that for Vietnamese, VGG-Small has a CER of 45.3, while VGG-Large has 43.2 and DARTS-ASR has 40.9. Therefore, VGG-Small did not achieve the lowest CER. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2258.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: CER (%) results of multilingual ASR using different CNN modules." ], "reasoning": "The table shows that for Vietnamese, VGG-Small has a CER of 45.3, while VGG-Large has 43.2 and DARTS-ASR has 40.9. Therefore, VGG-Small did not achieve the lowest CER.", "statement": "VGG-Small achieved the lowest CER for Vietnamese.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Dominant Word Classes in some datasets', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Classes were clustered into macro-categories based on dominance scores between 1.2 and 0.8, focusing on significant results and dataset-specific categories.In the following we clustered these classes according to macro-categories that emerged from the analysis. To keep only significant results, we made a cutoff for dominance scores included between 1.2 and 0.8, as proposed by Mihalcea and Strapparava (\\citeyearrada:carlo:ACL-09). We also analyze those categories that are dominant in only some datasets or that are representative of a specific dataset." } ], "images": [ "scigen/train/2259.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Dominant Word Classes in some datasets" ], "reasoning": "Classes were clustered into macro-categories based on dominance scores between 1.2 and 0.8, focusing on significant results and dataset-specific categories.", "description": "In the following we clustered these classes according to macro-categories that emerged from the analysis. To keep only significant results, we made a cutoff for dominance scores included between 1.2 and 0.8, as proposed by Mihalcea and Strapparava (\\citeyearrada:carlo:ACL-09). We also analyze those categories that are dominant in only some datasets or that are representative of a specific dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 3: Dominant Word Classes in some datasets\nQuestion: Which class has the highest dominance score in the Cite dataset?\n" }, { "role": "assistant", "content": "To find the class with the highest dominance score in the Cite dataset, look at the Cite column and identify the highest value. The highest value is 2.04, which corresponds to the HEAR class. So, the answer is {\"answer\": \"HEAR\"}." } ], "images": [ "scigen/train/2259.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Dominant Word Classes in some datasets" ], "reasoning": "To find the class with the highest dominance score in the Cite dataset, look at the Cite column and identify the highest value. The highest value is 2.04, which corresponds to the HEAR class.", "question": "Which class has the highest dominance score in the Cite dataset?", "answer": "So, the answer is {\"answer\": \"HEAR\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe class SEE has a dominance score of 1.91 in the Downl dataset.\n" }, { "role": "assistant", "content": "To verify this statement, check the Downl column for the SEE class. The table shows that the dominance score for SEE in the Downl dataset is indeed 1.91. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2259.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Dominant Word Classes in some datasets" ], "reasoning": "To verify this statement, check the Downl column for the SEE class. The table shows that the dominance score for SEE in the Downl dataset is indeed 1.91.", "statement": "The class SEE has a dominance score of 1.91 in the Downl dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Dominant Word Classes in all three datasets'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Classes were clustered into macro-categories based on dominance scores between 1.2 and 0.8, representing a basic form of virality common to all datasets.In the following we clustered these classes according to macro-categories that emerged from the analysis. To keep only significant results, we made a cutoff for dominance scores included between 1.2 and 0.8, as proposed by Mihalcea and Strapparava (\\citeyearrada:carlo:ACL-09). These categories represent a basic form of virality, common to all datasets." } ], "images": [ "scigen/train/2260.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Dominant Word Classes in all three datasets" ], "reasoning": "Classes were clustered into macro-categories based on dominance scores between 1.2 and 0.8, representing a basic form of virality common to all datasets.", "description": "In the following we clustered these classes according to macro-categories that emerged from the analysis. To keep only significant results, we made a cutoff for dominance scores included between 1.2 and 0.8, as proposed by Mihalcea and Strapparava (\\citeyearrada:carlo:ACL-09). These categories represent a basic form of virality, common to all datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: Dominant Word Classes in all three datasets', and tackle the question below in a sequential manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich class has the highest dominance score in the Downl dataset?\n" }, { "role": "assistant", "content": "To find the class with the highest dominance score in the Downl dataset, look for the highest value in the Downl column. The highest value is 3.70, which corresponds to the class 'WE'. So, the answer is {\"answer\": \"WE\"}." } ], "images": [ "scigen/train/2260.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Dominant Word Classes in all three datasets" ], "reasoning": "To find the class with the highest dominance score in the Downl dataset, look for the highest value in the Downl column. The highest value is 3.70, which corresponds to the class 'WE'.", "question": "Which class has the highest dominance score in the Downl dataset?", "answer": "So, the answer is {\"answer\": \"WE\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The class 'PAST' has a dominance score of 0.40 in the Downl dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe class 'PAST' has a dominance score of 0.40 in the Downl dataset.\n" }, { "role": "assistant", "content": "To verify this statement, check the dominance score for the class 'PAST' in the Downl column. The table shows that the dominance score for 'PAST' in the Downl dataset is indeed 0.40. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2260.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Dominant Word Classes in all three datasets" ], "reasoning": "To verify this statement, check the dominance score for the class 'PAST' in the Downl column. The table shows that the dominance score for 'PAST' in the Downl dataset is indeed 0.40.", "statement": "The class 'PAST' has a dominance score of 0.40 in the Downl dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Impact of Reward-RefNet on fluency and answerability. %preference denotes the percentage of times annotators prefer the generated output from the model for fluency in case of BLEU Reward signal and answerability in case of Answerability Reward signal.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Reward-RefNet is preferred by annotators for fluency in 67% of cases and for answerability in 70% of cases.In this section, we analyze the impact of employing different reward signals in Reward-RefNet. We validated these results through human evaluations across 200 samples. Annotators prefer the Reward-RefNet model in 67% of the cases for fluency. Similarly when we use Answerability score as a reward signal, answerability improves for the model and annotators prefer the Reward-RefNet in 70% of the cases for answerability." } ], "images": [ "scigen/train/2261.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Impact of Reward-RefNet on fluency and answerability. %preference denotes the percentage of times annotators prefer the generated output from the model for fluency in case of BLEU Reward signal and answerability in case of Answerability Reward signal." ], "reasoning": "Reward-RefNet is preferred by annotators for fluency in 67% of cases and for answerability in 70% of cases.", "description": "In this section, we analyze the impact of employing different reward signals in Reward-RefNet. We validated these results through human evaluations across 200 samples. Annotators prefer the Reward-RefNet model in 67% of the cases for fluency. Similarly when we use Answerability score as a reward signal, answerability improves for the model and annotators prefer the Reward-RefNet in 70% of the cases for answerability.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 6: Impact of Reward-RefNet on fluency and answerability. %preference denotes the percentage of times annotators prefer the generated output from the model for fluency in case of BLEU Reward signal and answerability in case of Answerability Reward signal.'. Solve the following question based on the table.\n\nQuestion: What is the BLEU Reward Signal BLEU4 score for the model preferred by annotators in 67.1% of cases?\nLet's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the Reward-RefNet model is preferred by annotators in 67.1% of cases for BLEU Reward Signal. The BLEU Reward Signal BLEU4 score for Reward-RefNet is 18.52. So, the answer is {\"answer\": \"18.52\"}" } ], "images": [ "scigen/train/2261.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Impact of Reward-RefNet on fluency and answerability. %preference denotes the percentage of times annotators prefer the generated output from the model for fluency in case of BLEU Reward signal and answerability in case of Answerability Reward signal." ], "reasoning": "The table shows that the Reward-RefNet model is preferred by annotators in 67.1% of cases for BLEU Reward Signal. The BLEU Reward Signal BLEU4 score for Reward-RefNet is 18.52.", "question": "What is the BLEU Reward Signal BLEU4 score for the model preferred by annotators in 67.1% of cases?", "answer": "So, the answer is {\"answer\": \"18.52\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe RefNet model has a higher Answerability Reward Signal Ans. score than Reward-RefNet.\n" }, { "role": "assistant", "content": "The table shows that the Answerability Reward Signal Ans. score for RefNet is 36.9, while for Reward-RefNet it is 37.5. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2261.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Impact of Reward-RefNet on fluency and answerability. %preference denotes the percentage of times annotators prefer the generated output from the model for fluency in case of BLEU Reward signal and answerability in case of Answerability Reward signal." ], "reasoning": "The table shows that the Answerability Reward Signal Ans. score for RefNet is 36.9, while for Reward-RefNet it is 37.5. Therefore, the statement is refuted.", "statement": "The RefNet model has a higher Answerability Reward Signal Ans. score than Reward-RefNet.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Comparsion of RefNet model with existing approaches and EAD model. Here * denotes our implementation of the corresponding work.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "RefNet consistently outperforms existing models across all n-gram scores (BLEU, ROUGE-L, and METEOR) and shows improvements on Q-BLEU4, indicating better answerability and fluency.On BLEU-4 metric, RefNet beats the existing state-of-the-art model by 12.30%, 9.74%, 17.48%, and 3.71% respectively on SQuAD (sentence), HOTPOT-QA, DROP and SQuAD (passage) dataset. Also it outperforms EAD by 7.83%, 7.57%, 15.25% and 3.85% respectively on SQuAD (sentence), HOTPOT-QA, DROP and SQuAD (passage). In general, RefNet is consistently better than existing models across all n-gram scores (BLEU, ROUGE-L and METEOR). Along with n-gram scores, we also observe improvements on Q-BLEU4 as well, which as described earlier, gives a measure of both answerability and fluency." } ], "images": [ "scigen/train/2262.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparsion of RefNet model with existing approaches and EAD model. Here * denotes our implementation of the corresponding work." ], "reasoning": "RefNet consistently outperforms existing models across all n-gram scores (BLEU, ROUGE-L, and METEOR) and shows improvements on Q-BLEU4, indicating better answerability and fluency.", "description": "On BLEU-4 metric, RefNet beats the existing state-of-the-art model by 12.30%, 9.74%, 17.48%, and 3.71% respectively on SQuAD (sentence), HOTPOT-QA, DROP and SQuAD (passage) dataset. Also it outperforms EAD by 7.83%, 7.57%, 15.25% and 3.85% respectively on SQuAD (sentence), HOTPOT-QA, DROP and SQuAD (passage). In general, RefNet is consistently better than existing models across all n-gram scores (BLEU, ROUGE-L and METEOR). Along with n-gram scores, we also observe improvements on Q-BLEU4 as well, which as described earlier, gives a measure of both answerability and fluency.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Comparsion of RefNet model with existing approaches and EAD model. Here * denotes our implementation of the corresponding work.' to sequentially solve the question presented below. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the BLEU-4 score for RefNet on the HOTPOT-QA dataset, and how much does it outperform EAD on this metric?\n" }, { "role": "assistant", "content": "The BLEU-4 score for RefNet on the HOTPOT-QA dataset is 21.17. RefNet outperforms EAD by 21.17 - 18.36 = 2.81 on this metric. So, the answer is {\"answer\": \"21.17\"}." } ], "images": [ "scigen/train/2262.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparsion of RefNet model with existing approaches and EAD model. Here * denotes our implementation of the corresponding work." ], "reasoning": "The BLEU-4 score for RefNet on the HOTPOT-QA dataset is 21.17. RefNet outperforms EAD by 21.17 - 18.36 = 2.81 on this metric.", "question": "What is the BLEU-4 score for RefNet on the HOTPOT-QA dataset, and how much does it outperform EAD on this metric?", "answer": "So, the answer is {\"answer\": \"21.17\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nRefNet has the highest QBLEU4 score on the DROP dataset.\n" }, { "role": "assistant", "content": "The QBLEU4 score for RefNet on the DROP dataset is 33.6, which is higher than the scores for Zhao et al. (2018) and EAD, which are 31.4 and 31.8 respectively. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2262.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparsion of RefNet model with existing approaches and EAD model. Here * denotes our implementation of the corresponding work." ], "reasoning": "The QBLEU4 score for RefNet on the DROP dataset is 33.6, which is higher than the scores for Zhao et al. (2018) and EAD, which are 31.4 and 31.8 respectively.", "statement": "RefNet has the highest QBLEU4 score on the DROP dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: Comparison between Preliminary Decoder and Refinement Decoder in RefNet Model for SQuAD Sentence Level QG.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The RefNet model improves the QBLEU-4 score more significantly than the Initial Draft model, especially when A3 is included.The two decoders impact each other through two paths: (i) indirect path, where they share the encoder and the output projection to the vocabulary V, (ii) direct path, via the dual attention network, where the initial draft of the question is attended by the Refinement Decoder. This suggests that generating two variants of the question improves the performance of the first decoder pass as well. This is perhaps due to the additional feedback that the shared encoder and output layer get from the Refinement Decoder. As discussed earlier, Q-Metric measures Answerability using four components, viz., Named Entities, Important Words, Function Words, and Question Type. We observe that the increase in Q-Metric for refined questions is because the RefNet model can correct/add the relevant Named Entities in the question. In particular, we observe that the Named Entity component score in Q-Metric increases from 32.42 for the first draft to 37.81 for the refined draft." } ], "images": [ "scigen/train/2263.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison between Preliminary Decoder and Refinement Decoder in RefNet Model for SQuAD Sentence Level QG." ], "reasoning": "The RefNet model improves the QBLEU-4 score more significantly than the Initial Draft model, especially when A3 is included.", "description": "The two decoders impact each other through two paths: (i) indirect path, where they share the encoder and the output projection to the vocabulary V, (ii) direct path, via the dual attention network, where the initial draft of the question is attended by the Refinement Decoder. This suggests that generating two variants of the question improves the performance of the first decoder pass as well. This is perhaps due to the additional feedback that the shared encoder and output layer get from the Refinement Decoder. As discussed earlier, Q-Metric measures Answerability using four components, viz., Named Entities, Important Words, Function Words, and Question Type. We observe that the increase in Q-Metric for refined questions is because the RefNet model can correct/add the relevant Named Entities in the question. In particular, we observe that the Named Entity component score in Q-Metric increases from 32.42 for the first draft to 37.81 for the refined draft.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: Comparison between Preliminary Decoder and Refinement Decoder in RefNet Model for SQuAD Sentence Level QG.', answer the following question. Give detailed solution consisting of each step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the QBLEU-4 score for the RefNet decoder with A3?\n" }, { "role": "assistant", "content": "To find the QBLEU-4 score for the RefNet decoder with A3, look at the row where the Model is 'with A3' and the Decoder is 'RefNet'. The QBLEU-4 score in this row is 27.40. So, the answer is {\"answer\": \"27.40\"}." } ], "images": [ "scigen/train/2263.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison between Preliminary Decoder and Refinement Decoder in RefNet Model for SQuAD Sentence Level QG." ], "reasoning": "To find the QBLEU-4 score for the RefNet decoder with A3, look at the row where the Model is 'with A3' and the Decoder is 'RefNet'. The QBLEU-4 score in this row is 27.40.", "question": "What is the QBLEU-4 score for the RefNet decoder with A3?", "answer": "So, the answer is {\"answer\": \"27.40\"}." }, { "messages": [ { "role": "user", "content": "The Initial Draft decoder has a higher BLEU-4 score than the RefNet decoder without A3. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Initial Draft decoder has a higher BLEU-4 score than the RefNet decoder without A3.\n" }, { "role": "assistant", "content": "To verify this statement, compare the BLEU-4 scores of the Initial Draft and RefNet decoders without A3. The Initial Draft decoder has a BLEU-4 score of 17.59, while the RefNet decoder has a BLEU-4 score of 17.16. Since 17.59 is higher than 17.16, the statement is correct. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2263.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison between Preliminary Decoder and Refinement Decoder in RefNet Model for SQuAD Sentence Level QG." ], "reasoning": "To verify this statement, compare the BLEU-4 scores of the Initial Draft and RefNet decoders without A3. The Initial Draft decoder has a BLEU-4 score of 17.59, while the RefNet decoder has a BLEU-4 score of 17.16. Since 17.59 is higher than 17.16, the statement is correct.", "statement": "The Initial Draft decoder has a higher BLEU-4 score than the RefNet decoder without A3.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 6: Testing results', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Ensemble models achieve a higher MAR@6 score of 0.5662 compared to single models at 0.5404, indicating better performance on testing data.Ensemble models achieve about 0.5662 MAR@6 score, while only using single type of model only gets 0.5404. This indicates single type of model may be slightly worse in testing data. Applying ensemble methods does solve this problem. Overall, our proposed system successfully outperforms with either using original pre-trained language model to fine-tune or EmotionGIF official baseline. Our approach achieves high MAR@6 score both on validation data and testing data in this competition." } ], "images": [ "scigen/train/2264.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Testing results" ], "reasoning": "Ensemble models achieve a higher MAR@6 score of 0.5662 compared to single models at 0.5404, indicating better performance on testing data.", "description": "Ensemble models achieve about 0.5662 MAR@6 score, while only using single type of model only gets 0.5404. This indicates single type of model may be slightly worse in testing data. Applying ensemble methods does solve this problem. Overall, our proposed system successfully outperforms with either using original pre-trained language model to fine-tune or EmotionGIF official baseline. Our approach achieves high MAR@6 score both on validation data and testing data in this competition.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 6: Testing results', unfold the steps to solve the question given next. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nQuestion: What is the MAR@6 score difference between the Ensemble models and the Official majority baseline?\n" }, { "role": "assistant", "content": "To find the difference, subtract the MAR@6 score of the Official majority baseline (0.4065) from the MAR@6 score of the Ensemble models (0.5662). So, the answer is {\"answer\": \"0.1597\"}." } ], "images": [ "scigen/train/2264.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Testing results" ], "reasoning": "To find the difference, subtract the MAR@6 score of the Official majority baseline (0.4065) from the MAR@6 score of the Ensemble models (0.5662).", "question": "What is the MAR@6 score difference between the Ensemble models and the Official majority baseline?", "answer": "So, the answer is {\"answer\": \"0.1597\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The LMRoBERTa-base + MLCRoBERTa-base model achieves a higher MAR@6 score than the Ensemble models., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe LMRoBERTa-base + MLCRoBERTa-base model achieves a higher MAR@6 score than the Ensemble models.\n" }, { "role": "assistant", "content": "The table shows that the MAR@6 score for LMRoBERTa-base + MLCRoBERTa-base is 0.5404, which is lower than the Ensemble models' score of 0.5662. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2264.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Testing results" ], "reasoning": "The table shows that the MAR@6 score for LMRoBERTa-base + MLCRoBERTa-base is 0.5404, which is lower than the Ensemble models' score of 0.5662.", "statement": "The LMRoBERTa-base + MLCRoBERTa-base model achieves a higher MAR@6 score than the Ensemble models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: First 6 out-of-vocab tokens'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows the frequency of out-of-vocabulary tokens, with 'pensive' being the most frequent and 'medium-dark' being the least frequent.Although there are still some unknown tokens, we can observe that some words like medium-dark might be able to be tokenized into expected tokens like medium, -, and dark." } ], "images": [ "scigen/train/2265.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: First 6 out-of-vocab tokens" ], "reasoning": "The table shows the frequency of out-of-vocabulary tokens, with 'pensive' being the most frequent and 'medium-dark' being the least frequent.", "description": "Although there are still some unknown tokens, we can observe that some words like medium-dark might be able to be tokenized into expected tokens like medium, -, and dark.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 4: First 6 out-of-vocab tokens\n\nQuestion: What is the total count of out-of-vocab tokens that have a count greater than 200?\n" }, { "role": "assistant", "content": "To find the total count of out-of-vocab tokens with a count greater than 200, sum the counts of 'pensive', 'dependable', and 'Scotty'. So, the answer is {\"answer\": \"701\"}." } ], "images": [ "scigen/train/2265.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: First 6 out-of-vocab tokens" ], "reasoning": "To find the total count of out-of-vocab tokens with a count greater than 200, sum the counts of 'pensive', 'dependable', and 'Scotty'.", "question": "What is the total count of out-of-vocab tokens that have a count greater than 200?", "answer": "So, the answer is {\"answer\": \"701\"}." }, { "messages": [ { "role": "user", "content": "The token 'backhand' has a higher count than '6pm'. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe token 'backhand' has a higher count than '6pm'.\n" }, { "role": "assistant", "content": "The table shows that 'backhand' has a count of 194, while '6pm' has a count of 163, indicating that 'backhand' has a higher count. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2265.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: First 6 out-of-vocab tokens" ], "reasoning": "The table shows that 'backhand' has a count of 194, while '6pm' has a count of 163, indicating that 'backhand' has a higher count.", "statement": "The token 'backhand' has a higher count than '6pm'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Performance of Human-Alice and Qbot-Alice teams measured by MR (lower is better). We observe that AI-AI teams outperform human-AI teams.'.\n" }, { "role": "assistant", "content": "AI-Alice teams outperform human-Alice teams, with Qbot (RL)-AliceRL teams performing the best, taking fewer guesses to arrive at the correct secret image.AI-Alice teams versus human-Alice teams. Interestingly, we observe that AI-Alice teams outperform human-Alice teams. On average, a Qbot (SL)-AliceSL team takes about 5.6 guesses to arrive at the correct secret image (as opposed to 6.86 guesses for a human-AliceSL team). Similarly, a Qbot (RL)-AliceRL team takes 4.7 guesses as opposed to a human-AliceRL team which takes 7.19 guesses. When we compare AI-AI teams (see Row 2 and 3) under different settings, we observe that teams having Qbot (RL) as the questioner outperform those with Qbot (SL). Qualitatively, we found that Qbot (SL) tends to ask repeating questions in a dialog and that questions from Qbot (RL) tend to be more visually grounded compared to Qbot (SL). Also, note that among the four teams Alice does not seem to affect performance across SL and RL." } ], "images": [ "scigen/train/2266.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of Human-Alice and Qbot-Alice teams measured by MR (lower is better). We observe that AI-AI teams outperform human-AI teams." ], "reasoning": "AI-Alice teams outperform human-Alice teams, with Qbot (RL)-AliceRL teams performing the best, taking fewer guesses to arrive at the correct secret image.", "description": "AI-Alice teams versus human-Alice teams. Interestingly, we observe that AI-Alice teams outperform human-Alice teams. On average, a Qbot (SL)-AliceSL team takes about 5.6 guesses to arrive at the correct secret image (as opposed to 6.86 guesses for a human-AliceSL team). Similarly, a Qbot (RL)-AliceRL team takes 4.7 guesses as opposed to a human-AliceRL team which takes 7.19 guesses. When we compare AI-AI teams (see Row 2 and 3) under different settings, we observe that teams having Qbot (RL) as the questioner outperform those with Qbot (SL). Qualitatively, we found that Qbot (SL) tends to ask repeating questions in a dialog and that questions from Qbot (RL) tend to be more visually grounded compared to Qbot (SL). Also, note that among the four teams Alice does not seem to affect performance across SL and RL.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Performance of Human-Alice and Qbot-Alice teams measured by MR (lower is better). We observe that AI-AI teams outperform human-AI teams.', answer the following question. Give detailed solution consisting of each step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich team takes the fewest guesses to arrive at the correct secret image?\n" }, { "role": "assistant", "content": "By comparing the number of guesses taken by each team in the table, we see that the Qbot (RL)-AliceRL team takes the fewest guesses, with 4.7 guesses. So, the answer is {\"answer\": \"Qbot (RL)-AliceRL\"}." } ], "images": [ "scigen/train/2266.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of Human-Alice and Qbot-Alice teams measured by MR (lower is better). We observe that AI-AI teams outperform human-AI teams." ], "reasoning": "By comparing the number of guesses taken by each team in the table, we see that the Qbot (RL)-AliceRL team takes the fewest guesses, with 4.7 guesses.", "question": "Which team takes the fewest guesses to arrive at the correct secret image?", "answer": "So, the answer is {\"answer\": \"Qbot (RL)-AliceRL\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nHuman-AliceSL teams take fewer guesses than Qbot (SL)-AliceSL teams.\n" }, { "role": "assistant", "content": "The table shows that Human-AliceSL teams take 6.9 guesses, while Qbot (SL)-AliceSL teams take 5.6 guesses, indicating that the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2266.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of Human-Alice and Qbot-Alice teams measured by MR (lower is better). We observe that AI-AI teams outperform human-AI teams." ], "reasoning": "The table shows that Human-AliceSL teams take 6.9 guesses, while Qbot (SL)-AliceSL teams take 5.6 guesses, indicating that the statement is incorrect.", "statement": "Human-AliceSL teams take fewer guesses than Qbot (SL)-AliceSL teams.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 9: Words suitable for pronunciation analysis. Data come from the 2017 manually transcribed data. Numbers indicate the number of occurrences, divided into test and training, with good and bad pronounciations.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Manual checking and annotation can enhance the modeling of pronunciation errors, as indicated by the table data.Finally, as mentioned above, further manual checking and annotation could be introduced to improve modelling of pronunciation errors." } ], "images": [ "scigen/train/2267.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Words suitable for pronunciation analysis. Data come from the 2017 manually transcribed data. Numbers indicate the number of occurrences, divided into test and training, with good and bad pronounciations." ], "reasoning": "Manual checking and annotation can enhance the modeling of pronunciation errors, as indicated by the table data.", "description": "Finally, as mentioned above, further manual checking and annotation could be introduced to improve modelling of pronunciation errors.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich German word has the highest total occurrence and what is its count?\nSolve the above question based on the table titled 'Table 9: Words suitable for pronunciation analysis. Data come from the 2017 manually transcribed data. Numbers indicate the number of occurrences, divided into test and training, with good and bad pronounciations.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the 'tot occ' column for German words, 'ich' has the highest total occurrence with a count of 1132. So, the answer is {\"answer\": \"ich\"}." } ], "images": [ "scigen/train/2267.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Words suitable for pronunciation analysis. Data come from the 2017 manually transcribed data. Numbers indicate the number of occurrences, divided into test and training, with good and bad pronounciations." ], "reasoning": "By examining the 'tot occ' column for German words, 'ich' has the highest total occurrence with a count of 1132.", "question": "Which German word has the highest total occurrence and what is its count?", "answer": "So, the answer is {\"answer\": \"ich\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The word 'pet' has more occurrences in the English test set than in the English training set., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe word 'pet' has more occurrences in the English test set than in the English training set.\n" }, { "role": "assistant", "content": "The table shows that 'pet' has 49 occurrences in the English test set and 96 occurrences in the English training set, which refutes the statement. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2267.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Words suitable for pronunciation analysis. Data come from the 2017 manually transcribed data. Numbers indicate the number of occurrences, divided into test and training, with good and bad pronounciations." ], "reasoning": "The table shows that 'pet' has 49 occurrences in the English test set and 96 occurrences in the English training set, which refutes the statement.", "statement": "The word 'pet' has more occurrences in the English test set than in the English training set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Evaluation of L2 linguistic competences in Trentino: level, grade, age and number of pupils participating in the evaluation campaigns. Most of the pupils did both the English and the German tests.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The TLT-school corpus refers to the 2018 campaign, which was split into a 2017 try-out data set and the actual 2018 data, involving about 3000 students in total.In Trentino, an autonomous region in northern Italy, there is a series of evaluation campaigns underway for testing L2 linguistic competence of Italian students taking proficiency tests in both English and German. A set of three evaluation campaigns is underway, two having been completed in 2016 and 2018, and a final one scheduled in 2020. Note that the “TLT-school” corpus refers to only the 2018 campaign, that was split in two parts: 2017 try-out data set (involving about 500 pupils) and the actual 2018 data (about 2500 pupils). Each of the three campaigns (i.e. 2016, 2018 and 2020) involves about 3000 students ranging from 9 to 16 years, belonging to four different school grade levels and three proficiency levels (A1, A2, B1). Several tests, aimed at assessing the language learning skills of the students, were carried out by means of multiple-choice questions, which can be evaluated automatically. However, a detailed linguistic evaluation cannot be performed without allowing the students to express themselves in both written sentences and spoken utterances, which typically require the intervention of human experts to be scored." } ], "images": [ "scigen/train/2268.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Evaluation of L2 linguistic competences in Trentino: level, grade, age and number of pupils participating in the evaluation campaigns. Most of the pupils did both the English and the German tests." ], "reasoning": "The TLT-school corpus refers to the 2018 campaign, which was split into a 2017 try-out data set and the actual 2018 data, involving about 3000 students in total.", "description": "In Trentino, an autonomous region in northern Italy, there is a series of evaluation campaigns underway for testing L2 linguistic competence of Italian students taking proficiency tests in both English and German. A set of three evaluation campaigns is underway, two having been completed in 2016 and 2018, and a final one scheduled in 2020. Note that the “TLT-school” corpus refers to only the 2018 campaign, that was split in two parts: 2017 try-out data set (involving about 500 pupils) and the actual 2018 data (about 2500 pupils). Each of the three campaigns (i.e. 2016, 2018 and 2020) involves about 3000 students ranging from 9 to 16 years, belonging to four different school grade levels and three proficiency levels (A1, A2, B1). Several tests, aimed at assessing the language learning skills of the students, were carried out by means of multiple-choice questions, which can be evaluated automatically. However, a detailed linguistic evaluation cannot be performed without allowing the students to express themselves in both written sentences and spoken utterances, which typically require the intervention of human experts to be scored.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: 'Table 1: Evaluation of L2 linguistic competences in Trentino: level, grade, age and number of pupils participating in the evaluation campaigns. Most of the pupils did both the English and the German tests.'\nQuestion: How many pupils were involved in the 2018 campaign for the A2 proficiency level?\n" }, { "role": "assistant", "content": "According to the table, the number of pupils involved in the 2018 campaign for the A2 proficiency level is 614. So, the answer is {\"answer\": \"614\"}." } ], "images": [ "scigen/train/2268.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Evaluation of L2 linguistic competences in Trentino: level, grade, age and number of pupils participating in the evaluation campaigns. Most of the pupils did both the English and the German tests." ], "reasoning": "According to the table, the number of pupils involved in the 2018 campaign for the A2 proficiency level is 614.", "question": "How many pupils were involved in the 2018 campaign for the A2 proficiency level?", "answer": "So, the answer is {\"answer\": \"614\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe 2016 campaign involved more pupils at the A1 proficiency level than the 2018 campaign.\n" }, { "role": "assistant", "content": "The table shows that the number of pupils at the A1 proficiency level in 2016 was 1074, while in 2018 it was 517. Therefore, the 2016 campaign involved more pupils at the A1 proficiency level than the 2018 campaign. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2268.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Evaluation of L2 linguistic competences in Trentino: level, grade, age and number of pupils participating in the evaluation campaigns. Most of the pupils did both the English and the German tests." ], "reasoning": "The table shows that the number of pupils at the A1 proficiency level in 2016 was 1074, while in 2018 it was 517. Therefore, the 2016 campaign involved more pupils at the A1 proficiency level than the 2018 campaign.", "statement": "The 2016 campaign involved more pupils at the A1 proficiency level than the 2018 campaign.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Optimal hyperparameters used for final training on the ADE and CoNLL04 datasets.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The optimal number of task-specific layers differed between the ADE and CoNLL04 datasets, highlighting the importance of treating the number of shared and task-specific layers as a hyperparameter.In evaluating NER performance on these datasets, a predicted entity is only considered a true positive if both the entity’s span and span type are correctly predicted. In evaluating RE performance, we follow previous work in adopting a strict evaluation method wherein a predicted relation is only considered correct if the spans corresponding to the two arguments of this relation and the entity types of these spans are also predicted correctly. We experimented with LSTMs and GRUs for all BiRNN layers in the model and experimented with using 1−3 shared BiRNN layers and 0−3 task-specific BiRNN layers for each task. The fact that the optimal number of task-specific layers differed between the two datasets demonstrates the value of taking the number of shared and task-specific layers to be a hyperparameter of our model architecture. We suspect that this is due to the limited number of relations and entities in the ADE dataset. For most examples in this dataset, it is sufficient to correctly identify a single Drug entity, a single Adverse-Effect entity, and an Adverse-Effect relation between the two entities. Thus, the NER and RE tasks for this dataset are more closely related than they are in the case of the CoNLL04 dataset. Intuitively, cases in which the NER and RE problems can be solved by relying on more shared information should require fewer task-specific layers." } ], "images": [ "scigen/train/2269.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Optimal hyperparameters used for final training on the ADE and CoNLL04 datasets." ], "reasoning": "The optimal number of task-specific layers differed between the ADE and CoNLL04 datasets, highlighting the importance of treating the number of shared and task-specific layers as a hyperparameter.", "description": "In evaluating NER performance on these datasets, a predicted entity is only considered a true positive if both the entity’s span and span type are correctly predicted. In evaluating RE performance, we follow previous work in adopting a strict evaluation method wherein a predicted relation is only considered correct if the spans corresponding to the two arguments of this relation and the entity types of these spans are also predicted correctly. We experimented with LSTMs and GRUs for all BiRNN layers in the model and experimented with using 1−3 shared BiRNN layers and 0−3 task-specific BiRNN layers for each task. The fact that the optimal number of task-specific layers differed between the two datasets demonstrates the value of taking the number of shared and task-specific layers to be a hyperparameter of our model architecture. We suspect that this is due to the limited number of relations and entities in the ADE dataset. For most examples in this dataset, it is sufficient to correctly identify a single Drug entity, a single Adverse-Effect entity, and an Adverse-Effect relation between the two entities. Thus, the NER and RE tasks for this dataset are more closely related than they are in the case of the CoNLL04 dataset. Intuitively, cases in which the NER and RE problems can be solved by relying on more shared information should require fewer task-specific layers.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 1: Optimal hyperparameters used for final training on the ADE and CoNLL04 datasets.' and methodically tackle the question that follows. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the Non-Character BiRNN Size for the ADE dataset and what is the FFNN (e1) Activation function for the CoNLL04 dataset?\n" }, { "role": "assistant", "content": "The Non-Character BiRNN Size for the ADE dataset is 128, and the FFNN (e1) Activation function for the CoNLL04 dataset is tanh. So, the answer is {\"answer\": \"128 and tanh\"}" } ], "images": [ "scigen/train/2269.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Optimal hyperparameters used for final training on the ADE and CoNLL04 datasets." ], "reasoning": "The Non-Character BiRNN Size for the ADE dataset is 128, and the FFNN (e1) Activation function for the CoNLL04 dataset is tanh.", "question": "What is the Non-Character BiRNN Size for the ADE dataset and what is the FFNN (e1) Activation function for the CoNLL04 dataset?", "answer": "So, the answer is {\"answer\": \"128 and tanh\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe ADE dataset uses a GRU BiRNN type and a ReLU activation function for FFNN (e1).\n" }, { "role": "assistant", "content": "The table shows that the ADE dataset uses a GRU BiRNN type and a ReLU activation function for FFNN (e1), which supports the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2269.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Optimal hyperparameters used for final training on the ADE and CoNLL04 datasets." ], "reasoning": "The table shows that the ADE dataset uses a GRU BiRNN type and a ReLU activation function for FFNN (e1), which supports the statement.", "statement": "The ADE dataset uses a GRU BiRNN type and a ReLU activation function for FFNN (e1).", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Precision, Recall, and F1 scores for our model and other recent models on the ADE and CoNLL04 datasets. Because our scores are averaged across multiple trials, F1 scores shown here cannot be directly calculated from the precision and recall scores shown here. Note that Nguyen and Verspoor do not report precision and recall scores.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our model achieves state-of-the-art results on both the ADE and CoNLL04 datasets, with the highest average F1 scores across NER and RE tasks.In addition to precision, recall, and F1 scores for both tasks, we show the average of the F1 scores across both tasks. On the ADE dataset, we achieve SOTA results for both the NER and RE tasks. On the CoNLL04 dataset, we achieve SOTA results on the NER task, while our performance on the RE task is competitive with other recent models. On both datasets, we achieve SOTA results when considering the average F1 score across both tasks. The largest gain relative to the previous SOTA performance is on the RE task of the ADE dataset, where we see an absolute improvement of 4.5 on the macro-average F1 score." } ], "images": [ "scigen/train/2270.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Precision, Recall, and F1 scores for our model and other recent models on the ADE and CoNLL04 datasets. Because our scores are averaged across multiple trials, F1 scores shown here cannot be directly calculated from the precision and recall scores shown here. Note that Nguyen and Verspoor do not report precision and recall scores." ], "reasoning": "Our model achieves state-of-the-art results on both the ADE and CoNLL04 datasets, with the highest average F1 scores across NER and RE tasks.", "description": "In addition to precision, recall, and F1 scores for both tasks, we show the average of the F1 scores across both tasks. On the ADE dataset, we achieve SOTA results for both the NER and RE tasks. On the CoNLL04 dataset, we achieve SOTA results on the NER task, while our performance on the RE task is competitive with other recent models. On both datasets, we achieve SOTA results when considering the average F1 score across both tasks. The largest gain relative to the previous SOTA performance is on the RE task of the ADE dataset, where we see an absolute improvement of 4.5 on the macro-average F1 score.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Precision, Recall, and F1 scores for our model and other recent models on the ADE and CoNLL04 datasets. Because our scores are averaged across multiple trials, F1 scores shown here cannot be directly calculated from the precision and recall scores shown here. Note that Nguyen and Verspoor do not report precision and recall scores.', solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the average F1 score of our model on the ADE dataset?\n" }, { "role": "assistant", "content": "The table shows the average F1 score for our model on the ADE dataset as 86.61. So, the answer is {\"answer\": \"86.61\"}" } ], "images": [ "scigen/train/2270.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Precision, Recall, and F1 scores for our model and other recent models on the ADE and CoNLL04 datasets. Because our scores are averaged across multiple trials, F1 scores shown here cannot be directly calculated from the precision and recall scores shown here. Note that Nguyen and Verspoor do not report precision and recall scores." ], "reasoning": "The table shows the average F1 score for our model on the ADE dataset as 86.61.", "question": "What is the average F1 score of our model on the ADE dataset?", "answer": "So, the answer is {\"answer\": \"86.61\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Our model achieves the highest RE F1 score on the ADE dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nOur model achieves the highest RE F1 score on the ADE dataset.\n" }, { "role": "assistant", "content": "The table shows that our model has an RE F1 score of 83.74 on the ADE dataset, which is the highest among the listed models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2270.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Precision, Recall, and F1 scores for our model and other recent models on the ADE and CoNLL04 datasets. Because our scores are averaged across multiple trials, F1 scores shown here cannot be directly calculated from the precision and recall scores shown here. Note that Nguyen and Verspoor do not report precision and recall scores." ], "reasoning": "The table shows that our model has an RE F1 score of 83.74 on the ADE dataset, which is the highest among the listed models.", "statement": "Our model achieves the highest RE F1 score on the ADE dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Results from an ablation study using the CoNLL04 dataset. All models have the same number of total parameters.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The RE task benefits more from task-specific layers than the NER task, indicating the RE task's higher difficulty on the CoNLL04 dataset.These results show that the proposed architecture benefits from the inclusion of both NER- and RE-specific layers. However, the RE task benefits much more from the inclusion of these task-specific layers than does the NER task. We take this to reflect the fact that the RE task is more difficult than the NER task for the CoNLL04 dataset, and therefore benefits the most from its own task-specific layers. This is consistent with the fact that the hyperparameter setting that performs best on the RE task is that with no NER-specific BiRNN layers, i.e. the setting that retained RE-specific BiRNN layers. In contrast, the inclusion of task-specific BiRNN layers of any kind had relatively little impact on the performance on the NER task." } ], "images": [ "scigen/train/2271.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results from an ablation study using the CoNLL04 dataset. All models have the same number of total parameters." ], "reasoning": "The RE task benefits more from task-specific layers than the NER task, indicating the RE task's higher difficulty on the CoNLL04 dataset.", "description": "These results show that the proposed architecture benefits from the inclusion of both NER- and RE-specific layers. However, the RE task benefits much more from the inclusion of these task-specific layers than does the NER task. We take this to reflect the fact that the RE task is more difficult than the NER task for the CoNLL04 dataset, and therefore benefits the most from its own task-specific layers. This is consistent with the fact that the hyperparameter setting that performs best on the RE task is that with no NER-specific BiRNN layers, i.e. the setting that retained RE-specific BiRNN layers. In contrast, the inclusion of task-specific BiRNN layers of any kind had relatively little impact on the performance on the NER task.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 3: Results from an ablation study using the CoNLL04 dataset. All models have the same number of total parameters.\nQuestion: Which model type shows the highest RE F1 score?\n" }, { "role": "assistant", "content": "The table shows the RE F1 scores for different model types. The highest RE F1 score is 72.63, which is achieved by the Baseline model. So, the answer is {\"answer\": \"Baseline\"}" } ], "images": [ "scigen/train/2271.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results from an ablation study using the CoNLL04 dataset. All models have the same number of total parameters." ], "reasoning": "The table shows the RE F1 scores for different model types. The highest RE F1 score is 72.63, which is achieved by the Baseline model.", "question": "Which model type shows the highest RE F1 score?", "answer": "So, the answer is {\"answer\": \"Baseline\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe No NER-Specific BiRNNs model type has the highest NER F1 score.\n" }, { "role": "assistant", "content": "The table shows the NER F1 scores for different model types. The highest NER F1 score is 89.78, which is achieved by the Baseline model, not the No NER-Specific BiRNNs model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2271.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results from an ablation study using the CoNLL04 dataset. All models have the same number of total parameters." ], "reasoning": "The table shows the NER F1 scores for different model types. The highest NER F1 score is 89.78, which is achieved by the Baseline model, not the No NER-Specific BiRNNs model.", "statement": "The No NER-Specific BiRNNs model type has the highest NER F1 score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Summary of averaged metric scores for different methods under permuted task orders using models at last epoch of last task. The Average and Std columns respectively are the average and standard deviation of the averaged scores for each row of the methods. Multitasked learning as an upper bound is shown at the bottom.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "LAMOL0.2GEN is the best performing method, only 1.8% worse than Multitasked, and the order of tasks significantly affects performance, especially for WOZ.For an initial understanding of the performance on all of the methods and the effect of task order, we first conducted a small-scale experiment on three small datasets: SST, QA-SRL, and WOZ. We trained all but the the multitasked method on all six permutations of the task order. The final score for each order was obtained by evaluating the model at the conclusion of the training process. Note that LAMOL with γ=0 is not the same as Fine-tuned, as the LM loss is still optimized. [leftmargin=*] Fine-tuned, EWC, MAS, and LAMOL with γ=0 show similar performance and are much worse than LAMOL with γ>0. LAMOL0.2GEN, our best performing method, is only 1.8 percent away from Multitasked, which implies almost no forgetting during LLL. The order of the tasks is crucial to the performance. For instance, the WOZ score drops significantly after training other tasks. Thus, if WOZ is not the last task, the performance is usually noticeably worse. When using LAMOL, the performance of old tasks maintains almost the same level throughout the training process. When the sampling ratio γ is increased, the performance also increases, especially when increased from 0 to 0.05. When γ=0, adding task-specific tokens harms performance, because the model must fit additional special tokens that are useless. Adding task-specific tokens is also not helpful if γ=0.2. We believe that 0.2 is enough for three tasks; thus task-specific tokens are redundant. However, when γ=0.05, task-specific tokens are beneficial because the tokens are needed to help retain a substantial presence of the first task when training the third task. We see that a better LLL method usually has a smaller standard deviation, which implies that it is effected less by task order. Adding task-specific tokens also has a stabilizing effect." } ], "images": [ "scigen/train/2272.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Summary of averaged metric scores for different methods under permuted task orders using models at last epoch of last task. The Average and Std columns respectively are the average and standard deviation of the averaged scores for each row of the methods. Multitasked learning as an upper bound is shown at the bottom." ], "reasoning": "LAMOL0.2GEN is the best performing method, only 1.8% worse than Multitasked, and the order of tasks significantly affects performance, especially for WOZ.", "description": "For an initial understanding of the performance on all of the methods and the effect of task order, we first conducted a small-scale experiment on three small datasets: SST, QA-SRL, and WOZ. We trained all but the the multitasked method on all six permutations of the task order. The final score for each order was obtained by evaluating the model at the conclusion of the training process. Note that LAMOL with γ=0 is not the same as Fine-tuned, as the LM loss is still optimized. [leftmargin=*] Fine-tuned, EWC, MAS, and LAMOL with γ=0 show similar performance and are much worse than LAMOL with γ>0. LAMOL0.2GEN, our best performing method, is only 1.8 percent away from Multitasked, which implies almost no forgetting during LLL. The order of the tasks is crucial to the performance. For instance, the WOZ score drops significantly after training other tasks. Thus, if WOZ is not the last task, the performance is usually noticeably worse. When using LAMOL, the performance of old tasks maintains almost the same level throughout the training process. When the sampling ratio γ is increased, the performance also increases, especially when increased from 0 to 0.05. When γ=0, adding task-specific tokens harms performance, because the model must fit additional special tokens that are useless. Adding task-specific tokens is also not helpful if γ=0.2. We believe that 0.2 is enough for three tasks; thus task-specific tokens are redundant. However, when γ=0.05, task-specific tokens are beneficial because the tokens are needed to help retain a substantial presence of the first task when training the third task. We see that a better LLL method usually has a smaller standard deviation, which implies that it is effected less by task order. Adding task-specific tokens also has a stabilizing effect.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 3: Summary of averaged metric scores for different methods under permuted task orders using models at last epoch of last task. The Average and Std columns respectively are the average and standard deviation of the averaged scores for each row of the methods. Multitasked learning as an upper bound is shown at the bottom.', answer the question below by showing each progressive step toward the answer. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich method has the smallest standard deviation and what is its average score?\n" }, { "role": "assistant", "content": "From the table, LAMOL0.2TASK has the smallest standard deviation of 0.5. Its average score is 79.4. So, the answer is {\"answer\": \"79.4\"}" } ], "images": [ "scigen/train/2272.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Summary of averaged metric scores for different methods under permuted task orders using models at last epoch of last task. The Average and Std columns respectively are the average and standard deviation of the averaged scores for each row of the methods. Multitasked learning as an upper bound is shown at the bottom." ], "reasoning": "From the table, LAMOL0.2TASK has the smallest standard deviation of 0.5. Its average score is 79.4.", "question": "Which method has the smallest standard deviation and what is its average score?", "answer": "So, the answer is {\"answer\": \"79.4\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence LAMOL0.2GEN performs better than LAMOL0.05GEN. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nLAMOL0.2GEN performs better than LAMOL0.05GEN.\n" }, { "role": "assistant", "content": "The table shows that LAMOL0.2GEN has an average score of 79.7, while LAMOL0.05GEN has an average score of 79.6, indicating that LAMOL0.2GEN performs slightly better. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2272.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Summary of averaged metric scores for different methods under permuted task orders using models at last epoch of last task. The Average and Std columns respectively are the average and standard deviation of the averaged scores for each row of the methods. Multitasked learning as an upper bound is shown at the bottom." ], "reasoning": "The table shows that LAMOL0.2GEN has an average score of 79.7, while LAMOL0.05GEN has an average score of 79.6, indicating that LAMOL0.2GEN performs slightly better.", "statement": "LAMOL0.2GEN performs better than LAMOL0.05GEN.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Summary of tasks, datasets, dataset sizes, and their corresponding metrics. As this work uses no development set, only the training and test datasets are shown. nF1 is the normalized version of the F1 score; EM represents an exact match between texts: for text classification, this amounts to accuracy; for WOZ, it is equivalent to dfEM (turn-based dialogue state exact match); for WikiSQL, it is equivalent to lfEM (exact match of logical forms).', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "We do not train on all datasets due to computational constraints, and each task has a specific evaluation metric with scores ranging from 0 to 100%.We do not train on all datasets from both papers due to a lack of computational resources. For each task, there is a corresponding evaluation metric. Note that the score of any metric lies between 0 and 100%." } ], "images": [ "scigen/train/2273.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Summary of tasks, datasets, dataset sizes, and their corresponding metrics. As this work uses no development set, only the training and test datasets are shown. nF1 is the normalized version of the F1 score; EM represents an exact match between texts: for text classification, this amounts to accuracy; for WOZ, it is equivalent to dfEM (turn-based dialogue state exact match); for WikiSQL, it is equivalent to lfEM (exact match of logical forms)." ], "reasoning": "We do not train on all datasets due to computational constraints, and each task has a specific evaluation metric with scores ranging from 0 to 100%.", "description": "We do not train on all datasets from both papers due to a lack of computational resources. For each task, there is a corresponding evaluation metric. Note that the score of any metric lies between 0 and 100%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 1: Summary of tasks, datasets, dataset sizes, and their corresponding metrics. As this work uses no development set, only the training and test datasets are shown. nF1 is the normalized version of the F1 score; EM represents an exact match between texts: for text classification, this amounts to accuracy; for WOZ, it is equivalent to dfEM (turn-based dialogue state exact match); for WikiSQL, it is equivalent to lfEM (exact match of logical forms).' to sequentially solve the question presented below. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich dataset has the highest number of training examples?\n" }, { "role": "assistant", "content": "By comparing the '# Train' column for all datasets, the dataset with the highest number of training examples is identified. So, the answer is {\"answer\": \"AGNews\"}" } ], "images": [ "scigen/train/2273.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Summary of tasks, datasets, dataset sizes, and their corresponding metrics. As this work uses no development set, only the training and test datasets are shown. nF1 is the normalized version of the F1 score; EM represents an exact match between texts: for text classification, this amounts to accuracy; for WOZ, it is equivalent to dfEM (turn-based dialogue state exact match); for WikiSQL, it is equivalent to lfEM (exact match of logical forms)." ], "reasoning": "By comparing the '# Train' column for all datasets, the dataset with the highest number of training examples is identified.", "question": "Which dataset has the highest number of training examples?", "answer": "So, the answer is {\"answer\": \"AGNews\"}" }, { "messages": [ { "role": "user", "content": "The dataset with the highest number of test examples is WikiSQL. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe dataset with the highest number of test examples is WikiSQL.\n" }, { "role": "assistant", "content": "By comparing the '# Test' column for all datasets, WikiSQL has the highest number of test examples with 15878. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2273.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Summary of tasks, datasets, dataset sizes, and their corresponding metrics. As this work uses no development set, only the training and test datasets are shown. nF1 is the normalized version of the F1 score; EM represents an exact match between texts: for text classification, this amounts to accuracy; for WOZ, it is equivalent to dfEM (turn-based dialogue state exact match); for WikiSQL, it is equivalent to lfEM (exact match of logical forms)." ], "reasoning": "By comparing the '# Test' column for all datasets, WikiSQL has the highest number of test examples with 15878.", "statement": "The dataset with the highest number of test examples is WikiSQL.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Comparison of GPT-2 and other methods on single task scores. Other scores are retrieved from McCann2018decaNLP or d2019episodic. Better performance in boldface.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The GPT-2 model outperforms the BERT-based model on text classification datasets, indicating its potential for superior LLL performance if catastrophic forgetting is prevented.To establish a reference on the capability of the GPT-2 model on every dataset, we trained the model on each dataset independently. We observe that the performance of the GPT-2 model is actually quite good, even beating the BERT-based model (d2019episodic) on text classification datasets by a large margin. Thus, the GPT-2 model has the potential for superior LLL performance, as long as we can prevent catastrophic forgetting." } ], "images": [ "scigen/train/2274.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of GPT-2 and other methods on single task scores. Other scores are retrieved from McCann2018decaNLP or d2019episodic. Better performance in boldface." ], "reasoning": "The GPT-2 model outperforms the BERT-based model on text classification datasets, indicating its potential for superior LLL performance if catastrophic forgetting is prevented.", "description": "To establish a reference on the capability of the GPT-2 model on every dataset, we trained the model on each dataset independently. We observe that the performance of the GPT-2 model is actually quite good, even beating the BERT-based model (d2019episodic) on text classification datasets by a large margin. Thus, the GPT-2 model has the potential for superior LLL performance, as long as we can prevent catastrophic forgetting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich dataset shows the largest margin of improvement for GPT-2 over other scores?\nSolve the above question based on the table titled 'Table 2: Comparison of GPT-2 and other methods on single task scores. Other scores are retrieved from McCann2018decaNLP or d2019episodic. Better performance in boldface.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the largest margin of improvement, subtract the 'Other scores' from the 'GPT-2 score' for each dataset and identify the maximum difference. So, the answer is {\"answer\": \"Amazon\"}." } ], "images": [ "scigen/train/2274.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of GPT-2 and other methods on single task scores. Other scores are retrieved from McCann2018decaNLP or d2019episodic. Better performance in boldface." ], "reasoning": "To find the largest margin of improvement, subtract the 'Other scores' from the 'GPT-2 score' for each dataset and identify the maximum difference.", "question": "Which dataset shows the largest margin of improvement for GPT-2 over other scores?", "answer": "So, the answer is {\"answer\": \"Amazon\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nGPT-2 performs better than other models on the WikiSQL dataset.\n" }, { "role": "assistant", "content": "Comparing the scores for the WikiSQL dataset, GPT-2 has a score of 70.7 while the other scores are 72.6, indicating that GPT-2 does not perform better. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2274.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of GPT-2 and other methods on single task scores. Other scores are retrieved from McCann2018decaNLP or d2019episodic. Better performance in boldface." ], "reasoning": "Comparing the scores for the WikiSQL dataset, GPT-2 has a score of 70.7 while the other scores are 72.6, indicating that GPT-2 does not perform better.", "statement": "GPT-2 performs better than other models on the WikiSQL dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: Summary of averaged score on five tasks. The scores are reported as the averaged score over all tasks of the models after training on every task. The rightmost three columns – LAMOL with γ=0.05 and γ=0.2 of real samples from previous tasks and Multitasked – are upper bounds for comparison. Best performance in boldface.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The performance of LAMOL improves as the sampling ratio γ increases, with the highest performance observed when task-specific tokens are used.Also, as expected, the performance of LAMOL improves as the sampling ratio γ increases and task-specific tokens are used." } ], "images": [ "scigen/train/2275.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Summary of averaged score on five tasks. The scores are reported as the averaged score over all tasks of the models after training on every task. The rightmost three columns – LAMOL with γ=0.05 and γ=0.2 of real samples from previous tasks and Multitasked – are upper bounds for comparison. Best performance in boldface." ], "reasoning": "The performance of LAMOL improves as the sampling ratio γ increases, with the highest performance observed when task-specific tokens are used.", "description": "Also, as expected, the performance of LAMOL improves as the sampling ratio γ increases and task-specific tokens are used.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nTable title: Table 4: Summary of averaged score on five tasks. The scores are reported as the averaged score over all tasks of the models after training on every task. The rightmost three columns – LAMOL with γ=0.05 and γ=0.2 of real samples from previous tasks and Multitasked – are upper bounds for comparison. Best performance in boldface.\n\nQuestion: What is the performance of LAMOL0.2REAL compared to LAMOL0.05REAL?\n" }, { "role": "assistant", "content": "The table shows that LAMOL0.2REAL has a performance of 76.0, while LAMOL0.05REAL has a performance of 74.5. Comparing these values, LAMOL0.2REAL performs better. So, the answer is {\"answer\": \"76.0\"}." } ], "images": [ "scigen/train/2275.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Summary of averaged score on five tasks. The scores are reported as the averaged score over all tasks of the models after training on every task. The rightmost three columns – LAMOL with γ=0.05 and γ=0.2 of real samples from previous tasks and Multitasked – are upper bounds for comparison. Best performance in boldface." ], "reasoning": "The table shows that LAMOL0.2REAL has a performance of 76.0, while LAMOL0.05REAL has a performance of 74.5. Comparing these values, LAMOL0.2REAL performs better.", "question": "What is the performance of LAMOL0.2REAL compared to LAMOL0.05REAL?", "answer": "So, the answer is {\"answer\": \"76.0\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nLAMOL0.2TASK has the highest performance among all methods.\n" }, { "role": "assistant", "content": "The table shows that LAMOL0.2TASK has a performance of 74.3, which is not the highest. The highest performance is 76.6 by Multitasked. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2275.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Summary of averaged score on five tasks. The scores are reported as the averaged score over all tasks of the models after training on every task. The rightmost three columns – LAMOL with γ=0.05 and γ=0.2 of real samples from previous tasks and Multitasked – are upper bounds for comparison. Best performance in boldface." ], "reasoning": "The table shows that LAMOL0.2TASK has a performance of 74.3, which is not the highest. The highest performance is 76.6 by Multitasked.", "statement": "LAMOL0.2TASK has the highest performance among all methods.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: The results of our approach compared to existing work. The numbers of other systems are derived from the evaluation script'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The KSA-BiGRU model achieves the highest F1 score of 84.9%, outperforming other models including the state-of-the-art BiLSTM model.Experiments are performed to evaluate the proposed KSA-BiGRU model in our ambiguous question answering tasks, relative to other existing models and variations of KSA-BiGRU. Our KSA-BiGRU outperforms the state-of-the-art model[petrochuk2018simplequestions] and achieves 84.9 % F1." } ], "images": [ "scigen/train/2276.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The results of our approach compared to existing work. The numbers of other systems are derived from the evaluation script" ], "reasoning": "The KSA-BiGRU model achieves the highest F1 score of 84.9%, outperforming other models including the state-of-the-art BiLSTM model.", "description": "Experiments are performed to evaluate the proposed KSA-BiGRU model in our ambiguous question answering tasks, relative to other existing models and variations of KSA-BiGRU. Our KSA-BiGRU outperforms the state-of-the-art model[petrochuk2018simplequestions] and achieves 84.9 % F1.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: The results of our approach compared to existing work. The numbers of other systems are derived from the evaluation script' and methodically tackle the question that follows. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest recall and what is its F1 score?\n" }, { "role": "assistant", "content": "The table shows that the KSA-BiGRU model has the highest recall of 86.7%. Its corresponding F1 score is 84.9%. So, the answer is {\"answer\": \"84.9\"}." } ], "images": [ "scigen/train/2276.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The results of our approach compared to existing work. The numbers of other systems are derived from the evaluation script" ], "reasoning": "The table shows that the KSA-BiGRU model has the highest recall of 86.7%. Its corresponding F1 score is 84.9%.", "question": "Which model has the highest recall and what is its F1 score?", "answer": "So, the answer is {\"answer\": \"84.9\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The BiLSTM model has a higher precision than the KS-BiGRU model. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe BiLSTM model has a higher precision than the KS-BiGRU model.\n" }, { "role": "assistant", "content": "The table shows that the BiLSTM model has a precision of 82.6%, while the KS-BiGRU model has a precision of 82.4%. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2276.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The results of our approach compared to existing work. The numbers of other systems are derived from the evaluation script" ], "reasoning": "The table shows that the BiLSTM model has a precision of 82.6%, while the KS-BiGRU model has a precision of 82.4%. Therefore, the statement is supported.", "statement": "The BiLSTM model has a higher precision than the KS-BiGRU model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Results of type classification task on three typing systems: FIGER, BBN, OntoNotesfine'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The BERT-based entity embeddings outperform the baseline by 8.31, 10.43, and 9.33 F1mi points on three typing systems, demonstrating better latent entity type information capture.Specifically, our method improves over the baseline with an absolute 8.31, 10.43 and 9.33 F1mi point than the baseline on three typing systems respectively. This demonstrates that our proposed entity embeddings from BERT indeed capture better latent entity type information than \\citeauthorganea2017deep \\shortciteganea2017deep." } ], "images": [ "scigen/train/2277.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of type classification task on three typing systems: FIGER, BBN, OntoNotesfine" ], "reasoning": "The BERT-based entity embeddings outperform the baseline by 8.31, 10.43, and 9.33 F1mi points on three typing systems, demonstrating better latent entity type information capture.", "description": "Specifically, our method improves over the baseline with an absolute 8.31, 10.43 and 9.33 F1mi point than the baseline on three typing systems respectively. This demonstrates that our proposed entity embeddings from BERT indeed capture better latent entity type information than \\citeauthorganea2017deep \\shortciteganea2017deep.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: Results of type classification task on three typing systems: FIGER, BBN, OntoNotesfine' to sequentially solve the question presented below. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the improvement in F1mi points for the BERT-based entity embeddings over the baseline on the BBN typing system?\n" }, { "role": "assistant", "content": "The BERT-based entity embeddings have an F1mi score of 91.30 on the BBN typing system, while the baseline has an F1mi score of 84.10. The improvement is calculated as 91.30 - 84.10. So, the answer is {\"answer\": \"7.2\"}" } ], "images": [ "scigen/train/2277.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of type classification task on three typing systems: FIGER, BBN, OntoNotesfine" ], "reasoning": "The BERT-based entity embeddings have an F1mi score of 91.30 on the BBN typing system, while the baseline has an F1mi score of 84.10. The improvement is calculated as 91.30 - 84.10.", "question": "What is the improvement in F1mi points for the BERT-based entity embeddings over the baseline on the BBN typing system?", "answer": "So, the answer is {\"answer\": \"7.2\"}" }, { "messages": [ { "role": "user", "content": "The BERT-based entity embeddings have a higher F1ma score on the OntoNotesfine typing system compared to the baseline. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe BERT-based entity embeddings have a higher F1ma score on the OntoNotesfine typing system compared to the baseline.\n" }, { "role": "assistant", "content": "The table shows that the BERT-based entity embeddings have an F1ma score of 92.52 on the OntoNotesfine typing system, while the baseline has an F1ma score of 83.54. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2277.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of type classification task on three typing systems: FIGER, BBN, OntoNotesfine" ], "reasoning": "The table shows that the BERT-based entity embeddings have an F1ma score of 92.52 on the OntoNotesfine typing system, while the baseline has an F1ma score of 83.54.", "statement": "The BERT-based entity embeddings have a higher F1ma score on the OntoNotesfine typing system compared to the baseline.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: F1 scores on AIDA-B (test set).'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "BERT-Entity-Sim outperforms all previous methods, achieving 93.54 F1 with global modeling, which is an improvement of 1.32 over ganea2017deep and 0.47 over le-titov-2018-improving.The models are divided into two groups: local models and local & global models. As we can see, our proposed model, BERT-Entity-Sim, outperforms all previous methods. Equipped with the global modeling method of \\citeauthorganea2017deep \\shortciteganea2017deep, the performance of our model further increase to 93.54 with an average 1.32 improvement in terms of F1 over \\citeauthorganea2017deep \\shortciteganea2017deep. In addition, our method outperforms \\citeauthorle-titov-2018-improving \\shortcitele-titov-2018-improving model by 0.47 point. The model of \\citeauthorle-titov-2018-improving \\shortcitele-titov-2018-improving is a multi-relational extension of \\citeauthorganea2017deep \\shortciteganea2017deep’s global modeling method while keeps exactly the same local context model. Our better local context model should be orthogonal with them and has potential more applications on short texts (e.g. tweets) where global modeling has little benefits. The reason is that BERT-based context representation space and \\citeauthorganea2017deep’s entity embeddings space are heterogeneous. \\citeauthorganea2017deep ’s entity embeddings are bootstrapped from word embeddings which mainly capture topic level entity relatedness, while BERT-based context representation is derived from BERT which naturally captures type information. The non-parallel information in both context and entity sides makes it difficult to learn the alignment parameter W and results in the poor generalization performance. all the state-of-the-art entity linking models by a large margin, even surpass \\citeauthorle-titov-2018-improving \\shortcitele-titov-2018-improving by 3.28 F1 points on AIDA-CoNLL test set. This result shows that a better type prediction system can further improve upon the state-of-the-state entity linking systems. However, in the predict setting, the type injection models have worse performance than the baseline. The degradation might be attributed to the poor performance of the two state-of-the-art fine grained entity typing systems. To verify this, we measure the performance of the two typing systems on AIDA-CoNLL development set." } ], "images": [ "scigen/train/2278.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: F1 scores on AIDA-B (test set)." ], "reasoning": "BERT-Entity-Sim outperforms all previous methods, achieving 93.54 F1 with global modeling, which is an improvement of 1.32 over ganea2017deep and 0.47 over le-titov-2018-improving.", "description": "The models are divided into two groups: local models and local & global models. As we can see, our proposed model, BERT-Entity-Sim, outperforms all previous methods. Equipped with the global modeling method of \\citeauthorganea2017deep \\shortciteganea2017deep, the performance of our model further increase to 93.54 with an average 1.32 improvement in terms of F1 over \\citeauthorganea2017deep \\shortciteganea2017deep. In addition, our method outperforms \\citeauthorle-titov-2018-improving \\shortcitele-titov-2018-improving model by 0.47 point. The model of \\citeauthorle-titov-2018-improving \\shortcitele-titov-2018-improving is a multi-relational extension of \\citeauthorganea2017deep \\shortciteganea2017deep’s global modeling method while keeps exactly the same local context model. Our better local context model should be orthogonal with them and has potential more applications on short texts (e.g. tweets) where global modeling has little benefits. The reason is that BERT-based context representation space and \\citeauthorganea2017deep’s entity embeddings space are heterogeneous. \\citeauthorganea2017deep ’s entity embeddings are bootstrapped from word embeddings which mainly capture topic level entity relatedness, while BERT-based context representation is derived from BERT which naturally captures type information. The non-parallel information in both context and entity sides makes it difficult to learn the alignment parameter W and results in the poor generalization performance. all the state-of-the-art entity linking models by a large margin, even surpass \\citeauthorle-titov-2018-improving \\shortcitele-titov-2018-improving by 3.28 F1 points on AIDA-CoNLL test set. This result shows that a better type prediction system can further improve upon the state-of-the-state entity linking systems. However, in the predict setting, the type injection models have worse performance than the baseline. The degradation might be attributed to the poor performance of the two state-of-the-art fine grained entity typing systems. To verify this, we measure the performance of the two typing systems on AIDA-CoNLL development set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: F1 scores on AIDA-B (test set).\nQuestion: What is the F1 score improvement of BERT-Entity-Sim (local & global) over ganea2017deep (global)?\nSolve the above question based on the table information. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "BERT-Entity-Sim (local & global) has an F1 score of 93.54, while ganea2017deep (global) has an F1 score of 92.22. The improvement is calculated as 93.54 - 92.22. So, the answer is {\"answer\": \"1.32\"}" } ], "images": [ "scigen/train/2278.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: F1 scores on AIDA-B (test set)." ], "reasoning": "BERT-Entity-Sim (local & global) has an F1 score of 93.54, while ganea2017deep (global) has an F1 score of 92.22. The improvement is calculated as 93.54 - 92.22.", "question": "What is the F1 score improvement of BERT-Entity-Sim (local & global) over ganea2017deep (global)?", "answer": "So, the answer is {\"answer\": \"1.32\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nBERT-Entity-Sim (local & global) outperforms le-titov-2018-improving by 0.47 points.\n" }, { "role": "assistant", "content": "The table shows that BERT-Entity-Sim (local & global) has an F1 score of 93.54, while le-titov-2018-improving has an F1 score of 93.07. The difference is 93.54 - 93.07, which is 0.47 points. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2278.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: F1 scores on AIDA-B (test set)." ], "reasoning": "The table shows that BERT-Entity-Sim (local & global) has an F1 score of 93.54, while le-titov-2018-improving has an F1 score of 93.07. The difference is 93.54 - 93.07, which is 0.47 points.", "statement": "BERT-Entity-Sim (local & global) outperforms le-titov-2018-improving by 0.47 points.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: F1 scores on five out-domain test sets. Underlined scores denote the corresponding model outperforms the baseline.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The BERT-Entity-Sim model outperforms other state-of-the-art entity linking models, including surpassing le-titov-2018-improving by 3.28 F1 points on the AIDA-CoNLL test set, but performs worse in the predict setting due to poor performance of fine-grained entity typing systems.On average, our proposed model (BERT-Entity-Sim) outperforms the local & global version of \\citeauthorganea2017deep,le-titov-2018-improving \\shortciteganea2017deep ,le-titov-2018-improving by an average 0.80 and 0.51 on F1. all the state-of-the-art entity linking models by a large margin, even surpass \\citeauthorle-titov-2018-improving \\shortcitele-titov-2018-improving by 3.28 F1 points on AIDA-CoNLL test set. This result shows that a better type prediction system can further improve upon the state-of-the-state entity linking systems. However, in the predict setting, the type injection models have worse performance than the baseline. The degradation might be attributed to the poor performance of the two state-of-the-art fine grained entity typing systems. To verify this, we measure the performance of the two typing systems on AIDA-CoNLL development set." } ], "images": [ "scigen/train/2279.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: F1 scores on five out-domain test sets. Underlined scores denote the corresponding model outperforms the baseline." ], "reasoning": "The BERT-Entity-Sim model outperforms other state-of-the-art entity linking models, including surpassing le-titov-2018-improving by 3.28 F1 points on the AIDA-CoNLL test set, but performs worse in the predict setting due to poor performance of fine-grained entity typing systems.", "description": "On average, our proposed model (BERT-Entity-Sim) outperforms the local & global version of \\citeauthorganea2017deep,le-titov-2018-improving \\shortciteganea2017deep ,le-titov-2018-improving by an average 0.80 and 0.51 on F1. all the state-of-the-art entity linking models by a large margin, even surpass \\citeauthorle-titov-2018-improving \\shortcitele-titov-2018-improving by 3.28 F1 points on AIDA-CoNLL test set. This result shows that a better type prediction system can further improve upon the state-of-the-state entity linking systems. However, in the predict setting, the type injection models have worse performance than the baseline. The degradation might be attributed to the poor performance of the two state-of-the-art fine grained entity typing systems. To verify this, we measure the performance of the two typing systems on AIDA-CoNLL development set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 2: F1 scores on five out-domain test sets. Underlined scores denote the corresponding model outperforms the baseline.\nQuestion: What is the average F1 score of the BERT-Entity-Sim (local & global) model across all datasets?\n" }, { "role": "assistant", "content": "To find the average F1 score of the BERT-Entity-Sim (local & global) model, look at the 'Avg' column in the table for the corresponding row. So, the answer is {\"answer\": \"86.02\"}." } ], "images": [ "scigen/train/2279.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: F1 scores on five out-domain test sets. Underlined scores denote the corresponding model outperforms the baseline." ], "reasoning": "To find the average F1 score of the BERT-Entity-Sim (local & global) model, look at the 'Avg' column in the table for the corresponding row.", "question": "What is the average F1 score of the BERT-Entity-Sim (local & global) model across all datasets?", "answer": "So, the answer is {\"answer\": \"86.02\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The BERT-Entity-Sim (local & global) model has the highest average F1 score among all models., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe BERT-Entity-Sim (local & global) model has the highest average F1 score among all models.\n" }, { "role": "assistant", "content": "To verify this statement, compare the average F1 score of the BERT-Entity-Sim (local & global) model with the average F1 scores of all other models listed in the 'Avg' column. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2279.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: F1 scores on five out-domain test sets. Underlined scores denote the corresponding model outperforms the baseline." ], "reasoning": "To verify this statement, compare the average F1 score of the BERT-Entity-Sim (local & global) model with the average F1 scores of all other models listed in the 'Avg' column.", "statement": "The BERT-Entity-Sim (local & global) model has the highest average F1 score among all models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Figure 7: Input for the sentence I am not that rich, without and with POS-tags. POS-tags are inserted as super characters. The +-symbols represent spaces (word boundaries).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "By creating a hybrid model that combines word and character level input, we can leverage syntactic information to improve performance in neural semantic parsing.We are not necessarily restricted to only using characters as input. For example, we can view the AMR relations (e.g. :ARG0, :mod) as atomic instead of a set of characters. This ensures that the characters for relations (e.g. m, o and d for :mod) do not influence the general character embeddings of the concepts, which might improve performance. This way, we create a hybrid model that is a combination of word and character level input. We might still be able to benefit from syntactic information, even though we use a character-level neural semantic parser. To show this, we parse the sentences with the POS-tagger of the C&C tools Clark et al. Put differently, we create a new super character for each unique tag and add this to the input sentence. On the one hand, this will increase the size of the input. On the other hand, just a single character will add a lot of general, potentially useful, information. For example, proper nouns correlate with the :name relation, while adjectives correlate with the :mod relation." } ], "images": [ "scigen/train/2280.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 7: Input for the sentence I am not that rich, without and with POS-tags. POS-tags are inserted as super characters. The +-symbols represent spaces (word boundaries)." ], "reasoning": "By creating a hybrid model that combines word and character level input, we can leverage syntactic information to improve performance in neural semantic parsing.", "description": "We are not necessarily restricted to only using characters as input. For example, we can view the AMR relations (e.g. :ARG0, :mod) as atomic instead of a set of characters. This ensures that the characters for relations (e.g. m, o and d for :mod) do not influence the general character embeddings of the concepts, which might improve performance. This way, we create a hybrid model that is a combination of word and character level input. We might still be able to benefit from syntactic information, even though we use a character-level neural semantic parser. To show this, we parse the sentences with the POS-tagger of the C&C tools Clark et al. Put differently, we create a new super character for each unique tag and add this to the input sentence. On the one hand, this will increase the size of the input. On the other hand, just a single character will add a lot of general, potentially useful, information. For example, proper nouns correlate with the :name relation, while adjectives correlate with the :mod relation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Figure 7: Input for the sentence I am not that rich, without and with POS-tags. POS-tags are inserted as super characters. The +-symbols represent spaces (word boundaries).', answer the question below by showing each progressive step toward the answer. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the POS tag for the word 'not' in the sentence?\n" }, { "role": "assistant", "content": "The table shows the sentence with POS tags. The word 'not' is tagged as 'RB' in the table. So, the answer is {\"answer\": \"RB\"}" } ], "images": [ "scigen/train/2280.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 7: Input for the sentence I am not that rich, without and with POS-tags. POS-tags are inserted as super characters. The +-symbols represent spaces (word boundaries)." ], "reasoning": "The table shows the sentence with POS tags. The word 'not' is tagged as 'RB' in the table.", "question": "What is the POS tag for the word 'not' in the sentence?", "answer": "So, the answer is {\"answer\": \"RB\"}" }, { "messages": [ { "role": "user", "content": "The word 'am' in the sentence is tagged as a verb. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe word 'am' in the sentence is tagged as a verb.\n" }, { "role": "assistant", "content": "The table shows the sentence with POS tags. The word 'am' is tagged as 'VBP', which is a verb. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2280.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 7: Input for the sentence I am not that rich, without and with POS-tags. POS-tags are inserted as super characters. The +-symbols represent spaces (word boundaries)." ], "reasoning": "The table shows the sentence with POS tags. The word 'am' is tagged as 'VBP', which is a verb.", "statement": "The word 'am' in the sentence is tagged as a verb.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Parameter settings of the seq2seq model.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The model was trained with bidirectional encoding and general attention, using a heuristic parameter search to optimize performance, and training was stopped after no improvement in validation perplexity for 3 epochs.We train a model with bidirectional encoding and general attention Luong et al. Since training a full model takes two to three days on a GPU, we perform a heuristic parameter search instead of an exhaustive one. We started out with a default model and changed only one parameter value in separate experiments. If we improved over the default, the setting was kept and combined with other parameter settings that improved performance. All models were only tested on the development set. All our described models in this paper are trained with these settings. Training is stopped 3 epochs after there is no improvement in validation perplexity on the development set anymore. The best performing model on the development set is then used to decode the test set." } ], "images": [ "scigen/train/2281.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Parameter settings of the seq2seq model." ], "reasoning": "The model was trained with bidirectional encoding and general attention, using a heuristic parameter search to optimize performance, and training was stopped after no improvement in validation perplexity for 3 epochs.", "description": "We train a model with bidirectional encoding and general attention Luong et al. Since training a full model takes two to three days on a GPU, we perform a heuristic parameter search instead of an exhaustive one. We started out with a default model and changed only one parameter value in separate experiments. If we improved over the default, the setting was kept and combined with other parameter settings that improved performance. All models were only tested on the development set. All our described models in this paper are trained with these settings. Training is stopped 3 epochs after there is no improvement in validation perplexity on the development set anymore. The best performing model on the development set is then used to decode the test set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: Parameter settings of the seq2seq model.'. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the learning rate and optimizer used for training the model?\n" }, { "role": "assistant", "content": "The table lists the learning rate as 0.1 and the optimizer as sgd under the 'Parameter Layers' column. So, the answer is {\"answer\": \"learning rate: 0.1, optimizer: sgd\"}." } ], "images": [ "scigen/train/2281.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Parameter settings of the seq2seq model." ], "reasoning": "The table lists the learning rate as 0.1 and the optimizer as sgd under the 'Parameter Layers' column.", "question": "What is the learning rate and optimizer used for training the model?", "answer": "So, the answer is {\"answer\": \"learning rate: 0.1, optimizer: sgd\"}." }, { "messages": [ { "role": "user", "content": "The model was trained with a beam size of 5. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe model was trained with a beam size of 5.\n" }, { "role": "assistant", "content": "The table lists the beam size as 5 under the 'Parameter RNN type' column. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2281.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Parameter settings of the seq2seq model." ], "reasoning": "The table lists the beam size as 5 under the 'Parameter RNN type' column.", "statement": "The model was trained with a beam size of 5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: Baseline Results Semantic Parsing on LDC2016E25.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The table shows that various post-processing techniques such as Pruning, Restoring Co-reference, and Wikification each increase the F-score by about a percentage point, with all post-processing combined resulting in a significant increase.Compared to the F-score of 43.0 by Barzdins and Gosko This is probably due to the higher amount of training data and the fact that they used Tensorflow instead of OpenNMT. We also reproduced their results by using the exact same data, software and parameter settings as they did, obtaining an F1-score of 42.3. all increase the F-score by about a percentage point each. This small gain of performance is what one could expect as each single operation has only a small impact on the overall contents of an AMR." } ], "images": [ "scigen/train/2282.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Baseline Results Semantic Parsing on LDC2016E25." ], "reasoning": "The table shows that various post-processing techniques such as Pruning, Restoring Co-reference, and Wikification each increase the F-score by about a percentage point, with all post-processing combined resulting in a significant increase.", "description": "Compared to the F-score of 43.0 by Barzdins and Gosko This is probably due to the higher amount of training data and the fact that they used Tensorflow instead of OpenNMT. We also reproduced their results by using the exact same data, software and parameter settings as they did, obtaining an F1-score of 42.3. all increase the F-score by about a percentage point each. This small gain of performance is what one could expect as each single operation has only a small impact on the overall contents of an AMR.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Baseline Results Semantic Parsing on LDC2016E25.\nQuestion: What is the combined increase in the F-score for the test set when all post-processing techniques are applied?\nGive a detailed response to the above question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows individual increases for Pruning (+0.6), Restoring Co-reference (+1.1), and Wikification (+1.0). When all post-processing techniques are applied, the increase is +2.4. So, the answer is {\"answer\": \"+2.4\"}" } ], "images": [ "scigen/train/2282.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Baseline Results Semantic Parsing on LDC2016E25." ], "reasoning": "The table shows individual increases for Pruning (+0.6), Restoring Co-reference (+1.1), and Wikification (+1.0). When all post-processing techniques are applied, the increase is +2.4.", "question": "What is the combined increase in the F-score for the test set when all post-processing techniques are applied?", "answer": "So, the answer is {\"answer\": \"+2.4\"}" }, { "messages": [ { "role": "user", "content": "Restoring Co-reference increases the F-score for the test set by more than 1 percentage point. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nRestoring Co-reference increases the F-score for the test set by more than 1 percentage point.\n" }, { "role": "assistant", "content": "The table shows that Restoring Co-reference increases the F-score for the test set by +1.1 percentage points. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2282.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Baseline Results Semantic Parsing on LDC2016E25." ], "reasoning": "The table shows that Restoring Co-reference increases the F-score for the test set by +1.1 percentage points.", "statement": "Restoring Co-reference increases the F-score for the test set by more than 1 percentage point.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Figure 6: Input for the AMR (t / thing :quant 1 :polarity -) representing the sentence Not one thing, with and without super characters. The +-symbols represent spaces.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "By treating AMR relations as atomic units instead of character sets, we can prevent character embeddings from influencing concept embeddings, potentially improving performance.We are not necessarily restricted to only using characters as input. For example, we can view the AMR relations (e.g. :ARG0, :mod) as atomic instead of a set of characters. This ensures that the characters for relations (e.g. m, o and d for :mod) do not influence the general character embeddings of the concepts, which might improve performance. This way, we create a hybrid model that is a combination of word and character level input." } ], "images": [ "scigen/train/2283.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 6: Input for the AMR (t / thing :quant 1 :polarity -) representing the sentence Not one thing, with and without super characters. The +-symbols represent spaces." ], "reasoning": "By treating AMR relations as atomic units instead of character sets, we can prevent character embeddings from influencing concept embeddings, potentially improving performance.", "description": "We are not necessarily restricted to only using characters as input. For example, we can view the AMR relations (e.g. :ARG0, :mod) as atomic instead of a set of characters. This ensures that the characters for relations (e.g. m, o and d for :mod) do not influence the general character embeddings of the concepts, which might improve performance. This way, we create a hybrid model that is a combination of word and character level input.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Figure 6: Input for the AMR (t / thing :quant 1 :polarity -) representing the sentence Not one thing, with and without super characters. The +-symbols represent spaces.', answer the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nHow does treating AMR relations as atomic units instead of character sets potentially improve performance?\n" }, { "role": "assistant", "content": "Treating AMR relations as atomic units ensures that characters for relations do not influence the general character embeddings of the concepts, which might improve performance. So, the answer is {\"answer\": \"It prevents character embeddings from influencing concept embeddings.\"}" } ], "images": [ "scigen/train/2283.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 6: Input for the AMR (t / thing :quant 1 :polarity -) representing the sentence Not one thing, with and without super characters. The +-symbols represent spaces." ], "reasoning": "Treating AMR relations as atomic units ensures that characters for relations do not influence the general character embeddings of the concepts, which might improve performance.", "question": "How does treating AMR relations as atomic units instead of character sets potentially improve performance?", "answer": "So, the answer is {\"answer\": \"It prevents character embeddings from influencing concept embeddings.\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nTreating AMR relations as atomic units ensures that the characters for relations influence the general character embeddings of the concepts.\n" }, { "role": "assistant", "content": "The description states that treating AMR relations as atomic units ensures that the characters for relations do not influence the general character embeddings of the concepts. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2283.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 6: Input for the AMR (t / thing :quant 1 :polarity -) representing the sentence Not one thing, with and without super characters. The +-symbols represent spaces." ], "reasoning": "The description states that treating AMR relations as atomic units ensures that the characters for relations do not influence the general character embeddings of the concepts.", "statement": "Treating AMR relations as atomic units ensures that the characters for relations influence the general character embeddings of the concepts.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5: F-scores on the dev set for adding different ratios of CAMR and JAMR parsed AMRs to our initial data set. All scores are without postprocessing improvement methods.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "CAMR generally produces higher quality AMRs, but introducing JAMR-parsed AMRs can add variety. Experiments show that a mix of CAMR and JAMR AMRs can achieve high F-scores, with the highest being 65.8 when 100% or 75% CAMR AMRs are used.We now have to determine which AMR to add to our silver data set. CAMR produces higher quality AMRs in general (64.0 vs 55.0 on the test set), but it might be beneficial to introduce some variety by also adding JAMR-parsed AMRs. We never add both CAMR and JAMR for the same sentence. We performed five experiments in which we added 100k silver AMRs, either containing 100%, 75%, 67%, 50% or 0% CAMR-parsed AMRs." } ], "images": [ "scigen/train/2284.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: F-scores on the dev set for adding different ratios of CAMR and JAMR parsed AMRs to our initial data set. All scores are without postprocessing improvement methods." ], "reasoning": "CAMR generally produces higher quality AMRs, but introducing JAMR-parsed AMRs can add variety. Experiments show that a mix of CAMR and JAMR AMRs can achieve high F-scores, with the highest being 65.8 when 100% or 75% CAMR AMRs are used.", "description": "We now have to determine which AMR to add to our silver data set. CAMR produces higher quality AMRs in general (64.0 vs 55.0 on the test set), but it might be beneficial to introduce some variety by also adding JAMR-parsed AMRs. We never add both CAMR and JAMR for the same sentence. We performed five experiments in which we added 100k silver AMRs, either containing 100%, 75%, 67%, 50% or 0% CAMR-parsed AMRs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 5: F-scores on the dev set for adding different ratios of CAMR and JAMR parsed AMRs to our initial data set. All scores are without postprocessing improvement methods.' and proceed to solve the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the F-score when the dataset contains an equal number of CAMR and JAMR AMRs?\n" }, { "role": "assistant", "content": "The table shows the F-scores for different proportions of CAMR and JAMR AMRs. When the dataset contains 50,000 CAMR and 50,000 JAMR AMRs, the F-score is 65.3. So, the answer is {\"answer\": \"65.3\"}." } ], "images": [ "scigen/train/2284.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: F-scores on the dev set for adding different ratios of CAMR and JAMR parsed AMRs to our initial data set. All scores are without postprocessing improvement methods." ], "reasoning": "The table shows the F-scores for different proportions of CAMR and JAMR AMRs. When the dataset contains 50,000 CAMR and 50,000 JAMR AMRs, the F-score is 65.3.", "question": "What is the F-score when the dataset contains an equal number of CAMR and JAMR AMRs?", "answer": "So, the answer is {\"answer\": \"65.3\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe highest F-score is achieved when the dataset contains only CAMR AMRs.\n" }, { "role": "assistant", "content": "The table shows that the highest F-score of 65.8 is achieved when the dataset contains either 100,000 CAMR AMRs or 75,000 CAMR and 25,000 JAMR AMRs. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2284.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: F-scores on the dev set for adding different ratios of CAMR and JAMR parsed AMRs to our initial data set. All scores are without postprocessing improvement methods." ], "reasoning": "The table shows that the highest F-score of 65.8 is achieved when the dataset contains either 100,000 CAMR AMRs or 75,000 CAMR and 25,000 JAMR AMRs.", "statement": "The highest F-score is achieved when the dataset contains only CAMR AMRs.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 6: Results of the improvements in isolation (without post-processing).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Re-ordering and adding silver standard data significantly improve performance, with the latter reaching a maximum of 65.8 on the dev set.Re-ordering has a clear positive effect, both for using the best re-ordering (+2.0) and adding that re-ordering to the existing data set (+5.2). Constructing super characters and adding POS-tags both lead to a similar increase in performance. Pre-training and subsequently fine-tuning also results in a substantial improvement, but creating an average model only has a slight positive effect. The biggest improvement comes from adding silver standard data to our training set, reaching a maximum of 65.8 on the dev set. However, there is a limit with regards to adding silver data, since adding 500k silver AMRs performed worse than adding 50k, 75k or 100k silver AMRs. Finding the optimal number of silver AMRs is difficult due to the long training times and is therefore left for future work." } ], "images": [ "scigen/train/2285.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results of the improvements in isolation (without post-processing)." ], "reasoning": "Re-ordering and adding silver standard data significantly improve performance, with the latter reaching a maximum of 65.8 on the dev set.", "description": "Re-ordering has a clear positive effect, both for using the best re-ordering (+2.0) and adding that re-ordering to the existing data set (+5.2). Constructing super characters and adding POS-tags both lead to a similar increase in performance. Pre-training and subsequently fine-tuning also results in a substantial improvement, but creating an average model only has a slight positive effect. The biggest improvement comes from adding silver standard data to our training set, reaching a maximum of 65.8 on the dev set. However, there is a limit with regards to adding silver data, since adding 500k silver AMRs performed worse than adding 50k, 75k or 100k silver AMRs. Finding the optimal number of silver AMRs is difficult due to the long training times and is therefore left for future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: Results of the improvements in isolation (without post-processing).\nQuestion: What is the maximum improvement in the dev set score achieved by adding silver standard data?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows that adding 100k silver standard data results in the highest dev set score of 65.8, which is an improvement of 11.0. So, the answer is {\"answer\": \"65.8\"}." } ], "images": [ "scigen/train/2285.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results of the improvements in isolation (without post-processing)." ], "reasoning": "The table shows that adding 100k silver standard data results in the highest dev set score of 65.8, which is an improvement of 11.0.", "question": "What is the maximum improvement in the dev set score achieved by adding silver standard data?", "answer": "So, the answer is {\"answer\": \"65.8\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nAdding 500k silver AMRs results in the highest improvement in the dev set score.\n" }, { "role": "assistant", "content": "The table shows that adding 100k silver AMRs results in the highest dev set score of 65.8, while adding 500k silver AMRs results in a lower score of 63.8. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2285.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results of the improvements in isolation (without post-processing)." ], "reasoning": "The table shows that adding 100k silver AMRs results in the highest dev set score of 65.8, while adding 500k silver AMRs results in a lower score of 63.8.", "statement": "Adding 500k silver AMRs results in the highest improvement in the dev set score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 9: Comparison with previous parsers using the evaluation script of Damonte et al. (2017). We also included precision and recall scores for our system.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our system outperforms other parsers on five of the eight metrics excluding Smatch, with higher precision than recall for each metric.Damonte et al. As the table shows, our system scores higher than the other parsers on five of the eight metrics other than Smatch. In general, our system is quite conservative, obtaining a higher precision than recall for each metric. Note that the other parsers score also relatively bad at these metrics. Compared to the other systems, our system scores worse on concepts, named entities, and wikification. A possible method to increase performance in the first two of those metrics is to adopt an anonymization or generalization approach for named entities and concepts, similar to Peng et al." } ], "images": [ "scigen/train/2286.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Comparison with previous parsers using the evaluation script of Damonte et al. (2017). We also included precision and recall scores for our system." ], "reasoning": "Our system outperforms other parsers on five of the eight metrics excluding Smatch, with higher precision than recall for each metric.", "description": "Damonte et al. As the table shows, our system scores higher than the other parsers on five of the eight metrics other than Smatch. In general, our system is quite conservative, obtaining a higher precision than recall for each metric. Note that the other parsers score also relatively bad at these metrics. Compared to the other systems, our system scores worse on concepts, named entities, and wikification. A possible method to increase performance in the first two of those metrics is to adopt an anonymization or generalization approach for named entities and concepts, similar to Peng et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 9: Comparison with previous parsers using the evaluation script of Damonte et al. (2017). We also included precision and recall scores for our system.', solve the subsequent question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nOn how many metrics does our system score higher than the other parsers?\n" }, { "role": "assistant", "content": "The description states that our system scores higher than the other parsers on five of the eight metrics other than Smatch. So, the answer is {\"answer\": \"five\"}" } ], "images": [ "scigen/train/2286.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Comparison with previous parsers using the evaluation script of Damonte et al. (2017). We also included precision and recall scores for our system." ], "reasoning": "The description states that our system scores higher than the other parsers on five of the eight metrics other than Smatch.", "question": "On how many metrics does our system score higher than the other parsers?", "answer": "So, the answer is {\"answer\": \"five\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nOur system scores higher than the other parsers on named entities.\n" }, { "role": "assistant", "content": "The table shows that our system scores 79 on named entities, while other parsers score 75, 79, and 83, indicating that our system does not score higher than all other parsers on this metric. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2286.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Comparison with previous parsers using the evaluation script of Damonte et al. (2017). We also included precision and recall scores for our system." ], "reasoning": "The table shows that our system scores 79 on named entities, while other parsers score 75, 79, and 83, indicating that our system does not score higher than all other parsers on this metric.", "statement": "Our system scores higher than the other parsers on named entities.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Automatic evaluation results on test set. Results were obtained using Wiseman’s trained extractive evaluation models with relexicalization Li and Wan (2018). ∗ We include delayed copy (DEL)’s result in the paper Li and Wan (2018) for comparison.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Each component in the model contributes to the overall performance, with position embedding and record fusion gate being critical for modeling time dimension information and performance metrics respectively.Row, column and time dimension information are important to the modeling of tables because subtracting any of them will result in performance drop. Also, position embedding is critical when modeling time dimension information according to the results. In addition, record fusion gate plays an important role because BLEU, CO, RG P% and CS P% drop significantly after subtracting it from full model. Results show that each component in the model contributes to the overall performance. Wiseman et al. Puduppully et al. \\newciteLi ’s model generate a template at first and then fill in the slots with delayed copy mechanism. Since its result in \\newciteLi’s paper was evaluated by IE model trained by \\newciteWiseman and “relexicalization” by \\newciteLi, we adopted the corresponding IE model and re-implement “relexicalization” as suggested by \\newciteLi for fair comparison. Please note that CC’s evaluation results via our re-implemented “relexicalization” is comparable to the reported result in \\newciteLi. ’s paper." } ], "images": [ "scigen/train/2287.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Automatic evaluation results on test set. Results were obtained using Wiseman’s trained extractive evaluation models with relexicalization Li and Wan (2018). ∗ We include delayed copy (DEL)’s result in the paper Li and Wan (2018) for comparison." ], "reasoning": "Each component in the model contributes to the overall performance, with position embedding and record fusion gate being critical for modeling time dimension information and performance metrics respectively.", "description": "Row, column and time dimension information are important to the modeling of tables because subtracting any of them will result in performance drop. Also, position embedding is critical when modeling time dimension information according to the results. In addition, record fusion gate plays an important role because BLEU, CO, RG P% and CS P% drop significantly after subtracting it from full model. Results show that each component in the model contributes to the overall performance. Wiseman et al. Puduppully et al. \\newciteLi ’s model generate a template at first and then fill in the slots with delayed copy mechanism. Since its result in \\newciteLi’s paper was evaluated by IE model trained by \\newciteWiseman and “relexicalization” by \\newciteLi, we adopted the corresponding IE model and re-implement “relexicalization” as suggested by \\newciteLi for fair comparison. Please note that CC’s evaluation results via our re-implemented “relexicalization” is comparable to the reported result in \\newciteLi. ’s paper.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Automatic evaluation results on test set. Results were obtained using Wiseman’s trained extractive evaluation models with relexicalization Li and Wan (2018). ∗ We include delayed copy (DEL)’s result in the paper Li and Wan (2018) for comparison.' to sequentially solve the question presented below. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich model has the highest RG P% and what is its BLEU score?\n" }, { "role": "assistant", "content": "The model with the highest RG P% is TEM with a score of 99.97%. The BLEU score for TEM is 8.33. So, the answer is {\"answer\": \"8.33\"}" } ], "images": [ "scigen/train/2287.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Automatic evaluation results on test set. Results were obtained using Wiseman’s trained extractive evaluation models with relexicalization Li and Wan (2018). ∗ We include delayed copy (DEL)’s result in the paper Li and Wan (2018) for comparison." ], "reasoning": "The model with the highest RG P% is TEM with a score of 99.97%. The BLEU score for TEM is 8.33.", "question": "Which model has the highest RG P% and what is its BLEU score?", "answer": "So, the answer is {\"answer\": \"8.33\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model 'Ours' has the highest BLEU score.\n" }, { "role": "assistant", "content": "The table shows that the model 'Ours' has a BLEU score of 16.69, which is not the highest. The highest BLEU score is 100.00 for the Gold model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2287.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Automatic evaluation results on test set. Results were obtained using Wiseman’s trained extractive evaluation models with relexicalization Li and Wan (2018). ∗ We include delayed copy (DEL)’s result in the paper Li and Wan (2018) for comparison." ], "reasoning": "The table shows that the model 'Ours' has a BLEU score of 16.69, which is not the highest. The highest BLEU score is 100.00 for the Gold model.", "statement": "The model 'Ours' has the highest BLEU score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Human evaluation results.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The template-based system includes the most supporting facts and the least contradicting facts, while our model produces fewer contradicting facts than other neural models and slightly more supporting facts than gold texts.Following human evaluation settings in \\newcitePuduppully, we conducted the following human evaluation experiments at the same scale. The second experiment is to assess whether the improvement on relation generation metric reported in automatic evaluation is supported by human evaluation. We compared our full model with gold texts, template-based system, CC Wiseman et al. Puduppully et al. We randomly sampled 30 examples from test set. Then, we randomly sampled 4 sentences from each model’s output for each example. We provided the raters of those sampled sentences with the corresponding NBA game statistics. They were asked to count the number of supporting and contradicting facts in each sentence. Each sentence is rated independently. Unsurprisingly, template-based system includes most supporting facts and least contradicting facts in its texts because the template consists of a large number of facts and all of those facts are extracted from the table. Also, our model produces less contradicting facts than other two neural models. Although our model produces less supporting facts than NCP and CC, it still includes enough supporting facts (slightly more than gold texts). Also, comparing to NCP+CC (NCP) ’s tendency to include vast information that contain redundant information, our model’s ability to select and accurately convey information is better. All other results (Gold, CC, NCP and ours) are significantly different from template-based system’s results in terms of number of supporting facts according to one-way ANOVA with posthoc Tukey HSD tests. All significance difference reported in this paper are less than 0.05. Our model is also significantly different from the NCP model. As for average number of contradicting facts, our model is significantly different from other two neural models. Surprisingly, gold texts were found containing contradicting facts. We checked the raters’s result and found that gold texts occasionally include wrong field-goal or three-point percent or wrong points difference between the winner and the defeated team. We can treat the average contradicting facts number of gold texts as a lower bound." } ], "images": [ "scigen/train/2288.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Human evaluation results." ], "reasoning": "The template-based system includes the most supporting facts and the least contradicting facts, while our model produces fewer contradicting facts than other neural models and slightly more supporting facts than gold texts.", "description": "Following human evaluation settings in \\newcitePuduppully, we conducted the following human evaluation experiments at the same scale. The second experiment is to assess whether the improvement on relation generation metric reported in automatic evaluation is supported by human evaluation. We compared our full model with gold texts, template-based system, CC Wiseman et al. Puduppully et al. We randomly sampled 30 examples from test set. Then, we randomly sampled 4 sentences from each model’s output for each example. We provided the raters of those sampled sentences with the corresponding NBA game statistics. They were asked to count the number of supporting and contradicting facts in each sentence. Each sentence is rated independently. Unsurprisingly, template-based system includes most supporting facts and least contradicting facts in its texts because the template consists of a large number of facts and all of those facts are extracted from the table. Also, our model produces less contradicting facts than other two neural models. Although our model produces less supporting facts than NCP and CC, it still includes enough supporting facts (slightly more than gold texts). Also, comparing to NCP+CC (NCP) ’s tendency to include vast information that contain redundant information, our model’s ability to select and accurately convey information is better. All other results (Gold, CC, NCP and ours) are significantly different from template-based system’s results in terms of number of supporting facts according to one-way ANOVA with posthoc Tukey HSD tests. All significance difference reported in this paper are less than 0.05. Our model is also significantly different from the NCP model. As for average number of contradicting facts, our model is significantly different from other two neural models. Surprisingly, gold texts were found containing contradicting facts. We checked the raters’s result and found that gold texts occasionally include wrong field-goal or three-point percent or wrong points difference between the winner and the defeated team. We can treat the average contradicting facts number of gold texts as a lower bound.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Human evaluation results.', unfold the question presented below, detailing every step of your calculation. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest number of supporting facts and the lowest number of contradicting facts?\n" }, { "role": "assistant", "content": "The table shows that the template-based system (Temp) has the highest number of supporting facts (#Sup = 7.83) and the lowest number of contradicting facts (#Cont = 0.00). So, the answer is {\"answer\": \"Temp\"}." } ], "images": [ "scigen/train/2288.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Human evaluation results." ], "reasoning": "The table shows that the template-based system (Temp) has the highest number of supporting facts (#Sup = 7.83) and the lowest number of contradicting facts (#Cont = 0.00).", "question": "Which model has the highest number of supporting facts and the lowest number of contradicting facts?", "answer": "So, the answer is {\"answer\": \"Temp\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe NCP model has the highest number of contradicting facts.\n" }, { "role": "assistant", "content": "The table shows that the CC model has the highest number of contradicting facts (#Cont = 1.23), not the NCP model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2288.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Human evaluation results." ], "reasoning": "The table shows that the CC model has the highest number of contradicting facts (#Cont = 1.23), not the NCP model.", "statement": "The NCP model has the highest number of contradicting facts.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test set. We reimplement the baselines for fair comparison.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "CF-VQA improves the performance of language prior based strategies by over 2% in all cases, with RUBi and Sum showing improvements of over 6% with different baseline architectures.How general is CF-VQA? Here, “RUBi/Harmonic/Sum” means using the language prior based strategies cadene2019rubi; clark2019don that train the ensemble model and test with only the vision-language branch. It can be clearly seen that CF-VQA improves the language prior based strategies by over 2% in all cases. In particular, RUBi and Sum are improved by over 6% with different baseline architectures." } ], "images": [ "scigen/train/2289.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test set. We reimplement the baselines for fair comparison." ], "reasoning": "CF-VQA improves the performance of language prior based strategies by over 2% in all cases, with RUBi and Sum showing improvements of over 6% with different baseline architectures.", "description": "How general is CF-VQA? Here, “RUBi/Harmonic/Sum” means using the language prior based strategies cadene2019rubi; clark2019don that train the ensemble model and test with only the vision-language branch. It can be clearly seen that CF-VQA improves the language prior based strategies by over 2% in all cases. In particular, RUBi and Sum are improved by over 6% with different baseline architectures.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 1: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test set. We reimplement the baselines for fair comparison.', answer the following question. Give detailed solution consisting of each step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nBy how much does CF-VQA improve the performance of the Sum strategy in the 'All' category?\n" }, { "role": "assistant", "content": "The performance of the Sum strategy in the 'All' category is 48.27. With CF-VQA, it improves to 54.95. The improvement is 54.95 - 48.27 = 6.68. So, the answer is {\"answer\": \"6.68\"}" } ], "images": [ "scigen/train/2289.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test set. We reimplement the baselines for fair comparison." ], "reasoning": "The performance of the Sum strategy in the 'All' category is 48.27. With CF-VQA, it improves to 54.95. The improvement is 54.95 - 48.27 = 6.68.", "question": "By how much does CF-VQA improve the performance of the Sum strategy in the 'All' category?", "answer": "So, the answer is {\"answer\": \"6.68\"}" }, { "messages": [ { "role": "user", "content": "CF-VQA improves the performance of the Harmonic strategy in the 'Y/N' category by over 5%. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nCF-VQA improves the performance of the Harmonic strategy in the 'Y/N' category by over 5%.\n" }, { "role": "assistant", "content": "The performance of the Harmonic strategy in the 'Y/N' category is 73.20. With CF-VQA, it improves to 78.62. The improvement is 78.62 - 73.20 = 5.42, which is over 5%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2289.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test set. We reimplement the baselines for fair comparison." ], "reasoning": "The performance of the Harmonic strategy in the 'Y/N' category is 73.20. With CF-VQA, it improves to 78.62. The improvement is 78.62 - 73.20 = 5.42, which is over 5%.", "statement": "CF-VQA improves the performance of the Harmonic strategy in the 'Y/N' category by over 5%.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test set. We reimplement the baselines for fair comparison.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "CF-VQA improves the performance of language prior based strategies by over 2% in all cases, with RUBi and Sum showing improvements of over 6% with different baseline architectures.How general is CF-VQA? Here, “RUBi/Harmonic/Sum” means using the language prior based strategies cadene2019rubi; clark2019don that train the ensemble model and test with only the vision-language branch. It can be clearly seen that CF-VQA improves the language prior based strategies by over 2% in all cases. In particular, RUBi and Sum are improved by over 6% with different baseline architectures." } ], "images": [ "scigen/train/2290.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test set. We reimplement the baselines for fair comparison." ], "reasoning": "CF-VQA improves the performance of language prior based strategies by over 2% in all cases, with RUBi and Sum showing improvements of over 6% with different baseline architectures.", "description": "How general is CF-VQA? Here, “RUBi/Harmonic/Sum” means using the language prior based strategies cadene2019rubi; clark2019don that train the ensemble model and test with only the vision-language branch. It can be clearly seen that CF-VQA improves the language prior based strategies by over 2% in all cases. In particular, RUBi and Sum are improved by over 6% with different baseline architectures.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 1: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test set. We reimplement the baselines for fair comparison.', solve this question and give detail solutions. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the percentage improvement in the 'All' category for the Sum strategy when using CF-VQA?\n" }, { "role": "assistant", "content": "The 'All' category for Sum without CF-VQA is 43.98%. With CF-VQA, it is 50.15%. The improvement is 50.15% - 43.98% = 6.17%. So, the answer is {\"answer\": \"6.17%\"}" } ], "images": [ "scigen/train/2290.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test set. We reimplement the baselines for fair comparison." ], "reasoning": "The 'All' category for Sum without CF-VQA is 43.98%. With CF-VQA, it is 50.15%. The improvement is 50.15% - 43.98% = 6.17%.", "question": "What is the percentage improvement in the 'All' category for the Sum strategy when using CF-VQA?", "answer": "So, the answer is {\"answer\": \"6.17%\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim CF-VQA improves the 'Y/N' category for the Harmonic strategy by over 7%., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nCF-VQA improves the 'Y/N' category for the Harmonic strategy by over 7%.\n" }, { "role": "assistant", "content": "The 'Y/N' category for Harmonic without CF-VQA is 70.37%. With CF-VQA, it is 77.68%. The improvement is 77.68% - 70.37% = 7.31%, which is over 7%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2290.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test set. We reimplement the baselines for fair comparison." ], "reasoning": "The 'Y/N' category for Harmonic without CF-VQA is 70.37%. With CF-VQA, it is 77.68%. The improvement is 77.68% - 70.37% = 7.31%, which is over 7%.", "statement": "CF-VQA improves the 'Y/N' category for the Harmonic strategy by over 7%.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test set. We reimplement the baselines for fair comparison.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "CF-VQA improves the performance of language prior based strategies by over 2% in all cases, with RUBi and Sum showing improvements of over 6% with different baseline architectures.How general is CF-VQA? Here, “RUBi/Harmonic/Sum” means using the language prior based strategies cadene2019rubi; clark2019don that train the ensemble model and test with only the vision-language branch. It can be clearly seen that CF-VQA improves the language prior based strategies by over 2% in all cases. In particular, RUBi and Sum are improved by over 6% with different baseline architectures." } ], "images": [ "scigen/train/2291.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test set. We reimplement the baselines for fair comparison." ], "reasoning": "CF-VQA improves the performance of language prior based strategies by over 2% in all cases, with RUBi and Sum showing improvements of over 6% with different baseline architectures.", "description": "How general is CF-VQA? Here, “RUBi/Harmonic/Sum” means using the language prior based strategies cadene2019rubi; clark2019don that train the ensemble model and test with only the vision-language branch. It can be clearly seen that CF-VQA improves the language prior based strategies by over 2% in all cases. In particular, RUBi and Sum are improved by over 6% with different baseline architectures.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: 'Table 1: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test set. We reimplement the baselines for fair comparison.'\nQuestion: What is the percentage improvement in the 'All' category for the Sum strategy when using CF-VQA?\n" }, { "role": "assistant", "content": "The 'All' category for Sum without CF-VQA is 47.29%, and with CF-VQA it is 53.69%. The improvement is 53.69% - 47.29% = 6.4%. So, the answer is {\"answer\": \"6.4%\"}" } ], "images": [ "scigen/train/2291.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test set. We reimplement the baselines for fair comparison." ], "reasoning": "The 'All' category for Sum without CF-VQA is 47.29%, and with CF-VQA it is 53.69%. The improvement is 53.69% - 47.29% = 6.4%.", "question": "What is the percentage improvement in the 'All' category for the Sum strategy when using CF-VQA?", "answer": "So, the answer is {\"answer\": \"6.4%\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nCF-VQA improves the 'Y/N' category performance of the Harmonic strategy by over 5%.\n" }, { "role": "assistant", "content": "The 'Y/N' category for Harmonic without CF-VQA is 69.19%, and with CF-VQA it is 74.82%. The improvement is 74.82% - 69.19% = 5.63%, which is over 5%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2291.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test set. We reimplement the baselines for fair comparison." ], "reasoning": "The 'Y/N' category for Harmonic without CF-VQA is 69.19%, and with CF-VQA it is 74.82%. The improvement is 74.82% - 69.19% = 5.63%, which is over 5%.", "statement": "CF-VQA improves the 'Y/N' category performance of the Harmonic strategy by over 5%.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Experimental results on VQA-CP v2 and VQA v2. Best and second best numbers are highlighted in each column. The results on VQA v2 test-std are obtained via our reimplementation.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "CF-VQA shows significant improvement in reducing language bias, especially in 'Yes/No' questions, but over-correction is a concern as seen with the entropy penalty in LM+H.Can CF-VQA overcome language bias? The out-distribution setting aims to evaluate whether VQA models effectively reduce language bias. With a deep look at the question type, we find that the improvement on “Yes/No” questions is extremely large (from ∼70% to ∼90%), which indicates that the language bias affects variously on different types of questions. Is CF-VQA robust? The in-distribution and independent-distribution settings are conducted to evaluate whether VQA models over-correct language bias. Note that LM clark2019don achieves a competitive performance on VQA-CP v2 test split (∼52%) with an additional language entropy penalty (LM+H). However, the accuracy drops significantly by ∼7% on the in-domain setting (i.e., VQA v2 val split), which indicates that the entropy penalty forces the model to over-correct the language bias, especially on “Yes/No” questions." } ], "images": [ "scigen/train/2292.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Experimental results on VQA-CP v2 and VQA v2. Best and second best numbers are highlighted in each column. The results on VQA v2 test-std are obtained via our reimplementation." ], "reasoning": "CF-VQA shows significant improvement in reducing language bias, especially in 'Yes/No' questions, but over-correction is a concern as seen with the entropy penalty in LM+H.", "description": "Can CF-VQA overcome language bias? The out-distribution setting aims to evaluate whether VQA models effectively reduce language bias. With a deep look at the question type, we find that the improvement on “Yes/No” questions is extremely large (from ∼70% to ∼90%), which indicates that the language bias affects variously on different types of questions. Is CF-VQA robust? The in-distribution and independent-distribution settings are conducted to evaluate whether VQA models over-correct language bias. Note that LM clark2019don achieves a competitive performance on VQA-CP v2 test split (∼52%) with an additional language entropy penalty (LM+H). However, the accuracy drops significantly by ∼7% on the in-domain setting (i.e., VQA v2 val split), which indicates that the entropy penalty forces the model to over-correct the language bias, especially on “Yes/No” questions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: Experimental results on VQA-CP v2 and VQA v2. Best and second best numbers are highlighted in each column. The results on VQA v2 test-std are obtained via our reimplementation.', and sequentially solve the question, writing out each step of your reasoning process. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the accuracy of CF-VQA (Sum) on the VQA-CP v2 test split for 'Yes/No' questions?\n" }, { "role": "assistant", "content": "To find the accuracy of CF-VQA (Sum) on the VQA-CP v2 test split for 'Yes/No' questions, locate the CF-VQA (Sum) row and the VQA-CP v2 test column under the 'Y/N' section. So, the answer is {\"answer\": \"90.56\"}" } ], "images": [ "scigen/train/2292.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Experimental results on VQA-CP v2 and VQA v2. Best and second best numbers are highlighted in each column. The results on VQA v2 test-std are obtained via our reimplementation." ], "reasoning": "To find the accuracy of CF-VQA (Sum) on the VQA-CP v2 test split for 'Yes/No' questions, locate the CF-VQA (Sum) row and the VQA-CP v2 test column under the 'Y/N' section.", "question": "What is the accuracy of CF-VQA (Sum) on the VQA-CP v2 test split for 'Yes/No' questions?", "answer": "So, the answer is {\"answer\": \"90.56\"}" }, { "messages": [ { "role": "user", "content": "CF-VQA (Har.) achieves over 80% accuracy on 'Yes/No' questions in the VQA v2 val split. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nCF-VQA (Har.) achieves over 80% accuracy on 'Yes/No' questions in the VQA v2 val split.\n" }, { "role": "assistant", "content": "Check the CF-VQA (Har.) row and the VQA v2 val column under the 'Y/N' section to verify if the accuracy is over 80%. The accuracy is 82.23%, which supports the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2292.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Experimental results on VQA-CP v2 and VQA v2. Best and second best numbers are highlighted in each column. The results on VQA v2 test-std are obtained via our reimplementation." ], "reasoning": "Check the CF-VQA (Har.) row and the VQA v2 val column under the 'Y/N' section to verify if the accuracy is over 80%. The accuracy is 82.23%, which supports the statement.", "statement": "CF-VQA (Har.) achieves over 80% accuracy on 'Yes/No' questions in the VQA v2 val split.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 5: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v1 test split. We reimplement the baselines for fair comparison.'.\n" }, { "role": "assistant", "content": "CF-VQA generally improves the performance of RUBi, Harmonic, and Sum, except for a slight decrease in accuracy with the Harmonic fusion strategy using the UpDn baseline architecture.Specifically, CF-VQA improves RUBi, Harmonic and Sum in most cases. The exception is using UpDn baseline architecture with Harmonic fusion strategy, where using CF-VQA slightly decreases the accuracy from 55.75% to 55.16%." } ], "images": [ "scigen/train/2293.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v1 test split. We reimplement the baselines for fair comparison." ], "reasoning": "CF-VQA generally improves the performance of RUBi, Harmonic, and Sum, except for a slight decrease in accuracy with the Harmonic fusion strategy using the UpDn baseline architecture.", "description": "Specifically, CF-VQA improves RUBi, Harmonic and Sum in most cases. The exception is using UpDn baseline architecture with Harmonic fusion strategy, where using CF-VQA slightly decreases the accuracy from 55.75% to 55.16%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 5: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v1 test split. We reimplement the baselines for fair comparison.'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the accuracy of the Harmonic fusion strategy with and without CF-VQA for the 'Num.' category?\n" }, { "role": "assistant", "content": "The table shows that the accuracy for the 'Num.' category using the Harmonic fusion strategy is 20.57% without CF-VQA and 16.88% with CF-VQA. So, the answer is {\"answer\": \"20.57% without CF-VQA and 16.88% with CF-VQA\"}." } ], "images": [ "scigen/train/2293.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v1 test split. We reimplement the baselines for fair comparison." ], "reasoning": "The table shows that the accuracy for the 'Num.' category using the Harmonic fusion strategy is 20.57% without CF-VQA and 16.88% with CF-VQA.", "question": "What is the accuracy of the Harmonic fusion strategy with and without CF-VQA for the 'Num.' category?", "answer": "So, the answer is {\"answer\": \"20.57% without CF-VQA and 16.88% with CF-VQA\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim CF-VQA improves the accuracy of the Sum fusion strategy in the 'Y/N' category., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nCF-VQA improves the accuracy of the Sum fusion strategy in the 'Y/N' category.\n" }, { "role": "assistant", "content": "The table shows that the accuracy for the 'Y/N' category using the Sum fusion strategy is 49.88% without CF-VQA and 84.94% with CF-VQA, indicating an improvement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2293.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v1 test split. We reimplement the baselines for fair comparison." ], "reasoning": "The table shows that the accuracy for the 'Y/N' category using the Sum fusion strategy is 49.88% without CF-VQA and 84.94% with CF-VQA, indicating an improvement.", "statement": "CF-VQA improves the accuracy of the Sum fusion strategy in the 'Y/N' category.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v1 test split. We reimplement the baselines for fair comparison.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "CF-VQA generally improves accuracy over RUBi, Harmonic, and Sum, except when using the Harmonic fusion strategy with the UpDn baseline, where it slightly decreases accuracy.Specifically, CF-VQA improves RUBi, Harmonic and Sum in most cases. The exception is using UpDn baseline architecture with Harmonic fusion strategy, where using CF-VQA slightly decreases the accuracy from 55.75% to 55.16%." } ], "images": [ "scigen/train/2294.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v1 test split. We reimplement the baselines for fair comparison." ], "reasoning": "CF-VQA generally improves accuracy over RUBi, Harmonic, and Sum, except when using the Harmonic fusion strategy with the UpDn baseline, where it slightly decreases accuracy.", "description": "Specifically, CF-VQA improves RUBi, Harmonic and Sum in most cases. The exception is using UpDn baseline architecture with Harmonic fusion strategy, where using CF-VQA slightly decreases the accuracy from 55.75% to 55.16%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v1 test split. We reimplement the baselines for fair comparison.', answer the following question in a stepwise manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the accuracy change when using CF-VQA with the Harmonic fusion strategy compared to without it?\n" }, { "role": "assistant", "content": "The table shows that the accuracy for Harmonic without CF-VQA is 55.75%, and with CF-VQA, it is 55.16%. The change is a decrease of 0.59%. So, the answer is {\"answer\": \"0.59% decrease\"}" } ], "images": [ "scigen/train/2294.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v1 test split. We reimplement the baselines for fair comparison." ], "reasoning": "The table shows that the accuracy for Harmonic without CF-VQA is 55.75%, and with CF-VQA, it is 55.16%. The change is a decrease of 0.59%.", "question": "What is the accuracy change when using CF-VQA with the Harmonic fusion strategy compared to without it?", "answer": "So, the answer is {\"answer\": \"0.59% decrease\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Using CF-VQA with the Sum fusion strategy improves accuracy compared to not using it., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nUsing CF-VQA with the Sum fusion strategy improves accuracy compared to not using it.\n" }, { "role": "assistant", "content": "The table shows that the accuracy for Sum without CF-VQA is 52.78%, and with CF-VQA, it is 57.39%, indicating an improvement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2294.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v1 test split. We reimplement the baselines for fair comparison." ], "reasoning": "The table shows that the accuracy for Sum without CF-VQA is 52.78%, and with CF-VQA, it is 57.39%, indicating an improvement.", "statement": "Using CF-VQA with the Sum fusion strategy improves accuracy compared to not using it.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v1 test split. We reimplement the baselines for fair comparison.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "CF-VQA generally improves accuracy over RUBi, Harmonic, and Sum, except for a slight decrease with the Harmonic strategy using the UpDn baseline.Specifically, CF-VQA improves RUBi, Harmonic and Sum in most cases. The exception is using UpDn baseline architecture with Harmonic fusion strategy, where using CF-VQA slightly decreases the accuracy from 55.75% to 55.16%." } ], "images": [ "scigen/train/2295.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v1 test split. We reimplement the baselines for fair comparison." ], "reasoning": "CF-VQA generally improves accuracy over RUBi, Harmonic, and Sum, except for a slight decrease with the Harmonic strategy using the UpDn baseline.", "description": "Specifically, CF-VQA improves RUBi, Harmonic and Sum in most cases. The exception is using UpDn baseline architecture with Harmonic fusion strategy, where using CF-VQA slightly decreases the accuracy from 55.75% to 55.16%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 5: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v1 test split. We reimplement the baselines for fair comparison.' to sequentially solve the question presented below. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the accuracy of the Harmonic fusion strategy using CF-VQA for the 'All' category?\n" }, { "role": "assistant", "content": "The table shows the accuracy for different fusion strategies. For the Harmonic fusion strategy using CF-VQA, the accuracy for the 'All' category is listed. So, the answer is {\"answer\": \"55.26\"}." } ], "images": [ "scigen/train/2295.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v1 test split. We reimplement the baselines for fair comparison." ], "reasoning": "The table shows the accuracy for different fusion strategies. For the Harmonic fusion strategy using CF-VQA, the accuracy for the 'All' category is listed.", "question": "What is the accuracy of the Harmonic fusion strategy using CF-VQA for the 'All' category?", "answer": "So, the answer is {\"answer\": \"55.26\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Using CF-VQA with the Sum fusion strategy results in an accuracy of 57.03% for the 'All' category., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nUsing CF-VQA with the Sum fusion strategy results in an accuracy of 57.03% for the 'All' category.\n" }, { "role": "assistant", "content": "The table shows the accuracy for different fusion strategies. For the Sum fusion strategy using CF-VQA, the accuracy for the 'All' category is 57.03%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2295.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v1 test split. We reimplement the baselines for fair comparison." ], "reasoning": "The table shows the accuracy for different fusion strategies. For the Sum fusion strategy using CF-VQA, the accuracy for the 'All' category is 57.03%.", "statement": "Using CF-VQA with the Sum fusion strategy results in an accuracy of 57.03% for the 'All' category.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 6: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test split using the simplified VQA causal graph. We reimplement the baselines for fair comparison.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "CF-VQA significantly improves performance across different VQA architectures and fusion strategies.Similar to the results using the complete causal graph, CF-VQA achieves significant improvement for different baseline VQA architectures and fusion strategies." } ], "images": [ "scigen/train/2296.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test split using the simplified VQA causal graph. We reimplement the baselines for fair comparison." ], "reasoning": "CF-VQA significantly improves performance across different VQA architectures and fusion strategies.", "description": "Similar to the results using the complete causal graph, CF-VQA achieves significant improvement for different baseline VQA architectures and fusion strategies.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 6: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test split using the simplified VQA causal graph. We reimplement the baselines for fair comparison.', and tackle the question below in a sequential manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the highest score achieved in the 'Y/N' category?\n" }, { "role": "assistant", "content": "To find the highest score in the 'Y/N' category, compare the values in the 'Y/N' column. The highest value is 88.43, achieved by '+ CF-VQA' with the baseline architecture. So, the answer is {\"answer\": \"88.43\"}" } ], "images": [ "scigen/train/2296.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test split using the simplified VQA causal graph. We reimplement the baselines for fair comparison." ], "reasoning": "To find the highest score in the 'Y/N' category, compare the values in the 'Y/N' column. The highest value is 88.43, achieved by '+ CF-VQA' with the baseline architecture.", "question": "What is the highest score achieved in the 'Y/N' category?", "answer": "So, the answer is {\"answer\": \"88.43\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The baseline architecture with CF-VQA achieves the highest score in the 'All' category. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe baseline architecture with CF-VQA achieves the highest score in the 'All' category.\n" }, { "role": "assistant", "content": "The highest score in the 'All' category is 49.85, achieved by '+ CF-VQA' with the Sum architecture, not the baseline. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2296.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test split using the simplified VQA causal graph. We reimplement the baselines for fair comparison." ], "reasoning": "The highest score in the 'All' category is 49.85, achieved by '+ CF-VQA' with the Sum architecture, not the baseline.", "statement": "The baseline architecture with CF-VQA achieves the highest score in the 'All' category.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 6: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test split using the simplified VQA causal graph. We reimplement the baselines for fair comparison.'.\n" }, { "role": "assistant", "content": "CF-VQA significantly improves performance across various VQA architectures and fusion strategies compared to their baselines.Similar to the results using the complete causal graph, CF-VQA achieves significant improvement for different baseline VQA architectures and fusion strategies." } ], "images": [ "scigen/train/2297.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test split using the simplified VQA causal graph. We reimplement the baselines for fair comparison." ], "reasoning": "CF-VQA significantly improves performance across various VQA architectures and fusion strategies compared to their baselines.", "description": "Similar to the results using the complete causal graph, CF-VQA achieves significant improvement for different baseline VQA architectures and fusion strategies.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 6: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test split using the simplified VQA causal graph. We reimplement the baselines for fair comparison.' to sequentially solve the question presented below. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the improvement in the 'All' category for the Sum architecture when CF-VQA is applied?\n" }, { "role": "assistant", "content": "The 'All' category score for the Sum architecture without CF-VQA is 47.10. With CF-VQA, it is 53.55. The improvement is 53.55 - 47.10. So, the answer is {\"answer\": \"6.45\"}." } ], "images": [ "scigen/train/2297.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test split using the simplified VQA causal graph. We reimplement the baselines for fair comparison." ], "reasoning": "The 'All' category score for the Sum architecture without CF-VQA is 47.10. With CF-VQA, it is 53.55. The improvement is 53.55 - 47.10.", "question": "What is the improvement in the 'All' category for the Sum architecture when CF-VQA is applied?", "answer": "So, the answer is {\"answer\": \"6.45\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Harmonic architecture with CF-VQA has a higher 'Y/N' score than the RUBi architecture without CF-VQA.\n" }, { "role": "assistant", "content": "The 'Y/N' score for Harmonic with CF-VQA is 77.02, while for RUBi without CF-VQA, it is 56.48. Since 77.02 is greater than 56.48, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2297.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test split using the simplified VQA causal graph. We reimplement the baselines for fair comparison." ], "reasoning": "The 'Y/N' score for Harmonic with CF-VQA is 77.02, while for RUBi without CF-VQA, it is 56.48. Since 77.02 is greater than 56.48, the statement is supported.", "statement": "The Harmonic architecture with CF-VQA has a higher 'Y/N' score than the RUBi architecture without CF-VQA.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 6: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test split using the simplified VQA causal graph. We reimplement the baselines for fair comparison.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "CF-VQA significantly improves performance across different VQA architectures and fusion strategies.Similar to the results using the complete causal graph, CF-VQA achieves significant improvement for different baseline VQA architectures and fusion strategies." } ], "images": [ "scigen/train/2298.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test split using the simplified VQA causal graph. We reimplement the baselines for fair comparison." ], "reasoning": "CF-VQA significantly improves performance across different VQA architectures and fusion strategies.", "description": "Similar to the results using the complete causal graph, CF-VQA achieves significant improvement for different baseline VQA architectures and fusion strategies.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 6: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test split using the simplified VQA causal graph. We reimplement the baselines for fair comparison.' in mind, break down and resolve the question below step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the highest score achieved in the 'Y/N' category?\n" }, { "role": "assistant", "content": "To find the highest score in the 'Y/N' category, compare the values in the 'Y/N' column. The highest value is 90.69, achieved by Sum + CF-VQA. So, the answer is {\"answer\": \"90.69\"}." } ], "images": [ "scigen/train/2298.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test split using the simplified VQA causal graph. We reimplement the baselines for fair comparison." ], "reasoning": "To find the highest score in the 'Y/N' category, compare the values in the 'Y/N' column. The highest value is 90.69, achieved by Sum + CF-VQA.", "question": "What is the highest score achieved in the 'Y/N' category?", "answer": "So, the answer is {\"answer\": \"90.69\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Sum + CF-VQA strategy achieves the highest score in the 'All' category.\n" }, { "role": "assistant", "content": "To verify this statement, compare the values in the 'All' column. The highest value is 54.52, achieved by Sum + CF-VQA. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2298.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Ablations of (1) baseline models (SAN, UpDn and S-MRL), (2) fusion strategies (RUBi, Harmonic, Sum) on VQA-CP v2 test split using the simplified VQA causal graph. We reimplement the baselines for fair comparison." ], "reasoning": "To verify this statement, compare the values in the 'All' column. The highest value is 54.52, achieved by Sum + CF-VQA.", "statement": "The Sum + CF-VQA strategy achieves the highest score in the 'All' category.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 7: Improving RUBi cadene2019rubi and Learned-Mixin (LM) clark2019don using our CF-VQA on VQA-CP v2 test split. We reimplement these methods for fair comparison.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "CF-VQA improves RUBi and LM without re-training, unlike LM+H which requires re-training with different entropy penalties.Learned-Mixin (LM) clark2019don by replacing NIE with TIE for inference. Therefore, CF-VQA could improve RUBi and LM without re-training the model, and the hyper-parameter can be selected on one trained model. As a comparison, LM+H clark2019don needs to re-train LM model with different entropy penalty, which leads to a high computation cost. Therefore, these two fusion strategies are not preferred in practice." } ], "images": [ "scigen/train/2299.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Improving RUBi cadene2019rubi and Learned-Mixin (LM) clark2019don using our CF-VQA on VQA-CP v2 test split. We reimplement these methods for fair comparison." ], "reasoning": "CF-VQA improves RUBi and LM without re-training, unlike LM+H which requires re-training with different entropy penalties.", "description": "Learned-Mixin (LM) clark2019don by replacing NIE with TIE for inference. Therefore, CF-VQA could improve RUBi and LM without re-training the model, and the hyper-parameter can be selected on one trained model. As a comparison, LM+H clark2019don needs to re-train LM model with different entropy penalty, which leads to a high computation cost. Therefore, these two fusion strategies are not preferred in practice.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 7: Improving RUBi cadene2019rubi and Learned-Mixin (LM) clark2019don using our CF-VQA on VQA-CP v2 test split. We reimplement these methods for fair comparison.', solve this question and give detail solutions. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhat is the 'All' score for LM clark2019don after applying CF-VQA?\n" }, { "role": "assistant", "content": "The table shows the 'All' score for LM clark2019don as 48.36, and after applying CF-VQA, it increases to 57.18. So, the answer is {\"answer\": \"57.18\"}" } ], "images": [ "scigen/train/2299.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Improving RUBi cadene2019rubi and Learned-Mixin (LM) clark2019don using our CF-VQA on VQA-CP v2 test split. We reimplement these methods for fair comparison." ], "reasoning": "The table shows the 'All' score for LM clark2019don as 48.36, and after applying CF-VQA, it increases to 57.18.", "question": "What is the 'All' score for LM clark2019don after applying CF-VQA?", "answer": "So, the answer is {\"answer\": \"57.18\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nCF-VQA improves the 'Num.' score for RUBi cadene2019rubi.\n" }, { "role": "assistant", "content": "The table shows that the 'Num.' score for RUBi cadene2019rubi is 19.78, and after applying CF-VQA, it increases to 23.69. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2299.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Improving RUBi cadene2019rubi and Learned-Mixin (LM) clark2019don using our CF-VQA on VQA-CP v2 test split. We reimplement these methods for fair comparison." ], "reasoning": "The table shows that the 'Num.' score for RUBi cadene2019rubi is 19.78, and after applying CF-VQA, it increases to 23.69.", "statement": "CF-VQA improves the 'Num.' score for RUBi cadene2019rubi.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'TABLE I: Overview of the results. The column Train indicates the training set used: we always use both code-only and code-description function representations from the training set. Test is the test set: cd means code-description, co means code-only. Recall from Section V-A that cd simulates the situation when we have descriptions, and co simulates when we only have code, but both cd and co test sets have the same functions from the holdout set. The top four columns are results simulating when no description data is available (co). The algorithm lr 1.8k is the linear baseline using vocab size of 1.8k, nn+w is the neural architecture with Wikipedia embedding, nn+co is the code-only embedding, and nn+cd is the code-description embedding. P, R, and F correspond to precision, recall, and f-measure. run is the run number associated with the downloadable model and full output available in our online appendix; we include it to assist replication of our experiments and use of the models we created (see Section IX).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The performance of linear regression models drops significantly when text description data is not available, with F1 scores decreasing by over 40% on standard and challenge datasets, but remains stable or slightly improves on the libs dataset.These lines correspond to the linear regression for code-only (line 1) and code-description (line 5) data. In line 5, the F1 score on the standard dataset is 65, and precision/recall is 68/64. These results are broadly similar to the 67% F1 score reported by Wang et. when using text description data (mined from the web) with linear regression to classify Java projects from SourceForge (though the results are not directly comparable due to different categories, datasets, and programming language). However, when text data is not available, we observe a drop in performance to 34% F1 score, 43/33 precision/recall – a nearly 50% performance penalty as measured by F1 score. It achieves 78% F1 score with descriptions, compared to 46% without – over a 40% performance drop. Challenge results are even more extreme, from 40 to 17 F1 score. Interestingly, performance on the libs dataset is relatively stable, even with slightly improved results when description data is removed: from 48 to 52. One likely explanation is that the library functions could be called by functions in the training set, which would lead to the model learning to recognize those libraries as part of a category. Still, on the standard and challenge datasets, there is evidence for a large performance gap between code-only and code-description data." } ], "images": [ "scigen/train/2300.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Overview of the results. The column Train indicates the training set used: we always use both code-only and code-description function representations from the training set. Test is the test set: cd means code-description, co means code-only. Recall from Section V-A that cd simulates the situation when we have descriptions, and co simulates when we only have code, but both cd and co test sets have the same functions from the holdout set. The top four columns are results simulating when no description data is available (co). The algorithm lr 1.8k is the linear baseline using vocab size of 1.8k, nn+w is the neural architecture with Wikipedia embedding, nn+co is the code-only embedding, and nn+cd is the code-description embedding. P, R, and F correspond to precision, recall, and f-measure. run is the run number associated with the downloadable model and full output available in our online appendix; we include it to assist replication of our experiments and use of the models we created (see Section IX)." ], "reasoning": "The performance of linear regression models drops significantly when text description data is not available, with F1 scores decreasing by over 40% on standard and challenge datasets, but remains stable or slightly improves on the libs dataset.", "description": "These lines correspond to the linear regression for code-only (line 1) and code-description (line 5) data. In line 5, the F1 score on the standard dataset is 65, and precision/recall is 68/64. These results are broadly similar to the 67% F1 score reported by Wang et. when using text description data (mined from the web) with linear regression to classify Java projects from SourceForge (though the results are not directly comparable due to different categories, datasets, and programming language). However, when text data is not available, we observe a drop in performance to 34% F1 score, 43/33 precision/recall – a nearly 50% performance penalty as measured by F1 score. It achieves 78% F1 score with descriptions, compared to 46% without – over a 40% performance drop. Challenge results are even more extreme, from 40 to 17 F1 score. Interestingly, performance on the libs dataset is relatively stable, even with slightly improved results when description data is removed: from 48 to 52. One likely explanation is that the library functions could be called by functions in the training set, which would lead to the model learning to recognize those libraries as part of a category. Still, on the standard and challenge datasets, there is evidence for a large performance gap between code-only and code-description data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'TABLE I: Overview of the results. The column Train indicates the training set used: we always use both code-only and code-description function representations from the training set. Test is the test set: cd means code-description, co means code-only. Recall from Section V-A that cd simulates the situation when we have descriptions, and co simulates when we only have code, but both cd and co test sets have the same functions from the holdout set. The top four columns are results simulating when no description data is available (co). The algorithm lr 1.8k is the linear baseline using vocab size of 1.8k, nn+w is the neural architecture with Wikipedia embedding, nn+co is the code-only embedding, and nn+cd is the code-description embedding. P, R, and F correspond to precision, recall, and f-measure. run is the run number associated with the downloadable model and full output available in our online appendix; we include it to assist replication of our experiments and use of the models we created (see Section IX).'. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the F1 score on the challenge dataset when using code-only data with linear regression?\n" }, { "role": "assistant", "content": "To find the F1 score on the challenge dataset when using code-only data with linear regression, look at the row where 'Train' is 'cd+co' and 'Test' is 'co' with 'Algo.' as 'lr 1.8k'. The F1 score under the 'Challenge F' column is 20. So, the answer is {\"answer\": \"20\"}." } ], "images": [ "scigen/train/2300.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Overview of the results. The column Train indicates the training set used: we always use both code-only and code-description function representations from the training set. Test is the test set: cd means code-description, co means code-only. Recall from Section V-A that cd simulates the situation when we have descriptions, and co simulates when we only have code, but both cd and co test sets have the same functions from the holdout set. The top four columns are results simulating when no description data is available (co). The algorithm lr 1.8k is the linear baseline using vocab size of 1.8k, nn+w is the neural architecture with Wikipedia embedding, nn+co is the code-only embedding, and nn+cd is the code-description embedding. P, R, and F correspond to precision, recall, and f-measure. run is the run number associated with the downloadable model and full output available in our online appendix; we include it to assist replication of our experiments and use of the models we created (see Section IX)." ], "reasoning": "To find the F1 score on the challenge dataset when using code-only data with linear regression, look at the row where 'Train' is 'cd+co' and 'Test' is 'co' with 'Algo.' as 'lr 1.8k'. The F1 score under the 'Challenge F' column is 20.", "question": "What is the F1 score on the challenge dataset when using code-only data with linear regression?", "answer": "So, the answer is {\"answer\": \"20\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The F1 score on the libs dataset improves when description data is removed. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe F1 score on the libs dataset improves when description data is removed.\n" }, { "role": "assistant", "content": "The table shows that the F1 score on the libs dataset increases from 48 to 52 when moving from 'cd+co' to 'cd+co' without descriptions, supporting the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2300.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Overview of the results. The column Train indicates the training set used: we always use both code-only and code-description function representations from the training set. Test is the test set: cd means code-description, co means code-only. Recall from Section V-A that cd simulates the situation when we have descriptions, and co simulates when we only have code, but both cd and co test sets have the same functions from the holdout set. The top four columns are results simulating when no description data is available (co). The algorithm lr 1.8k is the linear baseline using vocab size of 1.8k, nn+w is the neural architecture with Wikipedia embedding, nn+co is the code-only embedding, and nn+cd is the code-description embedding. P, R, and F correspond to precision, recall, and f-measure. run is the run number associated with the downloadable model and full output available in our online appendix; we include it to assist replication of our experiments and use of the models we created (see Section IX)." ], "reasoning": "The table shows that the F1 score on the libs dataset increases from 48 to 52 when moving from 'cd+co' to 'cd+co' without descriptions, supporting the statement.", "statement": "The F1 score on the libs dataset improves when description data is removed.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: DERs (%) on different overlapping conditions. For the evaluation on simulated mixtures, the proposed model is trained using 100,000 mixtures generated with β=2. For the evaluation on the CALLHOME dataset, the proposed model is trained with 26,712 telephone recordings. The DER without the domain adaptation is shown in the parenthesis.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The proposed method showed degraded performance on less overlapping speech and produced worse DER than baseline systems on the CALLHOME dataset due to different overlap ratios.To investigate the robustness to variable conditions, we evaluated different overlap ratio controlled by the average interval β. Larger β values generate less overlapping speech. The baseline systems show better performance on less overlapping speech as expected. However, the proposed method unexpectedly showed degraded performance on less overlapping speech. The result suggests that the network had overfit to the specific overlap ratio: 27.3%. Investigation with various overlap ratio settings of training data is among our future work. In addition, we evaluated the proposed method on real telephone conversations using the CALLHOME dataset. We split two-speaker recordings from the CALLHOME dataset into two subsets: an adaptation set of 155 recordings and a test set of 148 recordings. The overlap ratio of the training data was 5.8%. Then, it was retrained with the adaptation set. For this retraining, we used the Adam optimizer with initial learning rate of 10−6 and ran 5 epochs. For the postprocessing, we adjusted the threshold to 0.6 so that the DER of the adaptation set has the minimum value. Unfortunately, the proposed method produces worse DER than the baseline systems. This is likely because our training set has very different overlap ratio (5.8%) from the CALLHOME test set (11.8%). To reduce this condition mismatch, we tried domain adaptation. The result showed a significant DER reduction. The relative improvement introduced by domain adaptation was 25.6%. Although the DER of the proposed method was still behind those of the baseline systems, we expect it will be much improved by developing better simulation techniques of training data or just by feeding more real data, as was suggested by the result with simulated data. We will address these directions in our future work." } ], "images": [ "scigen/train/2301.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: DERs (%) on different overlapping conditions. For the evaluation on simulated mixtures, the proposed model is trained using 100,000 mixtures generated with β=2. For the evaluation on the CALLHOME dataset, the proposed model is trained with 26,712 telephone recordings. The DER without the domain adaptation is shown in the parenthesis." ], "reasoning": "The proposed method showed degraded performance on less overlapping speech and produced worse DER than baseline systems on the CALLHOME dataset due to different overlap ratios.", "description": "To investigate the robustness to variable conditions, we evaluated different overlap ratio controlled by the average interval β. Larger β values generate less overlapping speech. The baseline systems show better performance on less overlapping speech as expected. However, the proposed method unexpectedly showed degraded performance on less overlapping speech. The result suggests that the network had overfit to the specific overlap ratio: 27.3%. Investigation with various overlap ratio settings of training data is among our future work. In addition, we evaluated the proposed method on real telephone conversations using the CALLHOME dataset. We split two-speaker recordings from the CALLHOME dataset into two subsets: an adaptation set of 155 recordings and a test set of 148 recordings. The overlap ratio of the training data was 5.8%. Then, it was retrained with the adaptation set. For this retraining, we used the Adam optimizer with initial learning rate of 10−6 and ran 5 epochs. For the postprocessing, we adjusted the threshold to 0.6 so that the DER of the adaptation set has the minimum value. Unfortunately, the proposed method produces worse DER than the baseline systems. This is likely because our training set has very different overlap ratio (5.8%) from the CALLHOME test set (11.8%). To reduce this condition mismatch, we tried domain adaptation. The result showed a significant DER reduction. The relative improvement introduced by domain adaptation was 25.6%. Although the DER of the proposed method was still behind those of the baseline systems, we expect it will be much improved by developing better simulation techniques of training data or just by feeding more real data, as was suggested by the result with simulated data. We will address these directions in our future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: DERs (%) on different overlapping conditions. For the evaluation on simulated mixtures, the proposed model is trained using 100,000 mixtures generated with β=2. For the evaluation on the CALLHOME dataset, the proposed model is trained with 26,712 telephone recordings. The DER without the domain adaptation is shown in the parenthesis.\nQuestion: What was the DER of the proposed method (EEND) on the CALLHOME test set after domain adaptation?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The description mentions that the proposed method was evaluated on the CALLHOME dataset and produced a DER of 23.07%, which was reduced by domain adaptation. The table shows the DER of the proposed method (EEND) on the CALLHOME test set as 23.07% (31.01%). So, the answer is {\"answer\": \"23.07\"}." } ], "images": [ "scigen/train/2301.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: DERs (%) on different overlapping conditions. For the evaluation on simulated mixtures, the proposed model is trained using 100,000 mixtures generated with β=2. For the evaluation on the CALLHOME dataset, the proposed model is trained with 26,712 telephone recordings. The DER without the domain adaptation is shown in the parenthesis." ], "reasoning": "The description mentions that the proposed method was evaluated on the CALLHOME dataset and produced a DER of 23.07%, which was reduced by domain adaptation. The table shows the DER of the proposed method (EEND) on the CALLHOME test set as 23.07% (31.01%).", "question": "What was the DER of the proposed method (EEND) on the CALLHOME test set after domain adaptation?", "answer": "So, the answer is {\"answer\": \"23.07\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe proposed method (EEND) performed better on the CALLHOME test set than on the simulated mixtures with an overlap ratio of 27.3%.\n" }, { "role": "assistant", "content": "The table shows that the DER of the proposed method (EEND) on the CALLHOME test set is 23.07%, while the DER on the simulated mixtures with an overlap ratio of 27.3% is 12.28%. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2301.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: DERs (%) on different overlapping conditions. For the evaluation on simulated mixtures, the proposed model is trained using 100,000 mixtures generated with β=2. For the evaluation on the CALLHOME dataset, the proposed model is trained with 26,712 telephone recordings. The DER without the domain adaptation is shown in the parenthesis." ], "reasoning": "The table shows that the DER of the proposed method (EEND) on the CALLHOME test set is 23.07%, while the DER on the simulated mixtures with an overlap ratio of 27.3% is 12.28%. Therefore, the statement is refuted.", "statement": "The proposed method (EEND) performed better on the CALLHOME test set than on the simulated mixtures with an overlap ratio of 27.3%.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Effect of loss functions evaluated on simulated speech generated with β=2. The models are trained using 10,000 mixtures genarated with β=2.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The PIT loss is crucial for training the neural network, and the DPCL loss further enhances performance, as indicated by the lower DER percentages.We evaluated the effect of the proposed loss functions. With PIT and DPCL losses, we set the mixing parameter α=0.5. It is observed that PIT loss is essential for training of our neural network. It also demonstrates that DPCL loss helps improve performance." } ], "images": [ "scigen/train/2302.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Effect of loss functions evaluated on simulated speech generated with β=2. The models are trained using 10,000 mixtures genarated with β=2." ], "reasoning": "The PIT loss is crucial for training the neural network, and the DPCL loss further enhances performance, as indicated by the lower DER percentages.", "description": "We evaluated the effect of the proposed loss functions. With PIT and DPCL losses, we set the mixing parameter α=0.5. It is observed that PIT loss is essential for training of our neural network. It also demonstrates that DPCL loss helps improve performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 1: Effect of loss functions evaluated on simulated speech generated with β=2. The models are trained using 10,000 mixtures genarated with β=2.', solve this question and give detail solutions. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhat is the DER percentage when both PIT and DPCL losses are applied?\n" }, { "role": "assistant", "content": "The table shows the DER percentages for different combinations of PIT and DPCL losses. When both PIT and DPCL losses are applied, the DER percentage is 23.79%. So, the answer is {\"answer\": \"23.79\"}." } ], "images": [ "scigen/train/2302.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Effect of loss functions evaluated on simulated speech generated with β=2. The models are trained using 10,000 mixtures genarated with β=2." ], "reasoning": "The table shows the DER percentages for different combinations of PIT and DPCL losses. When both PIT and DPCL losses are applied, the DER percentage is 23.79%.", "question": "What is the DER percentage when both PIT and DPCL losses are applied?", "answer": "So, the answer is {\"answer\": \"23.79\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The DER percentage is lowest when both PIT and DPCL losses are applied., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe DER percentage is lowest when both PIT and DPCL losses are applied.\n" }, { "role": "assistant", "content": "The table shows that the DER percentage is 23.79% when both PIT and DPCL losses are applied, which is the lowest among the given values. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2302.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Effect of loss functions evaluated on simulated speech generated with β=2. The models are trained using 10,000 mixtures genarated with β=2." ], "reasoning": "The table shows that the DER percentage is 23.79% when both PIT and DPCL losses are applied, which is the lowest among the given values.", "statement": "The DER percentage is lowest when both PIT and DPCL losses are applied.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Effect of the number of training mixtures evaluated on simulated speech generated with β=2. The models are trained with β=2.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Increasing the number of training samples reduces the DER percentage, indicating improved performance.It is observed that increasing the number of training samples improves the performance. Because our proposed method can be trained with any speech mixture with corresponding time annotations, it is possible to utilize large scale speech corpora for improving robustness of the system." } ], "images": [ "scigen/train/2303.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Effect of the number of training mixtures evaluated on simulated speech generated with β=2. The models are trained with β=2." ], "reasoning": "Increasing the number of training samples reduces the DER percentage, indicating improved performance.", "description": "It is observed that increasing the number of training samples improves the performance. Because our proposed method can be trained with any speech mixture with corresponding time annotations, it is possible to utilize large scale speech corpora for improving robustness of the system.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Effect of the number of training mixtures evaluated on simulated speech generated with β=2. The models are trained with β=2.', answer the following question in a stepwise manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the DER percentage when the number of training mixtures is doubled from 10,000 to 20,000?\n" }, { "role": "assistant", "content": "The table shows that when the number of training mixtures increases from 10,000 to 20,000, the DER percentage decreases from 23.79% to 14.66%. So, the answer is {\"answer\": \"14.66\"}." } ], "images": [ "scigen/train/2303.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Effect of the number of training mixtures evaluated on simulated speech generated with β=2. The models are trained with β=2." ], "reasoning": "The table shows that when the number of training mixtures increases from 10,000 to 20,000, the DER percentage decreases from 23.79% to 14.66%.", "question": "What is the DER percentage when the number of training mixtures is doubled from 10,000 to 20,000?", "answer": "So, the answer is {\"answer\": \"14.66\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe DER percentage decreases as the number of training mixtures increases.\n" }, { "role": "assistant", "content": "The table shows that as the number of training mixtures increases from 10,000 to 100,000, the DER percentage decreases from 23.79% to 12.28%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2303.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Effect of the number of training mixtures evaluated on simulated speech generated with β=2. The models are trained with β=2." ], "reasoning": "The table shows that as the number of training mixtures increases from 10,000 to 100,000, the DER percentage decreases from 23.79% to 12.28%.", "statement": "The DER percentage decreases as the number of training mixtures increases.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Detailed DERs (%) evaluated on simulated speech generated with the β=2. MI, FA and CF denote miss, false alarm and confusion error rates, respectively. The proposed model is trained using 100,000 mixtures generated with β=2.'.\n" }, { "role": "assistant", "content": "The proposed method significantly reduces the miss rate and DER compared to baseline systems, and it performs better in confusion error rate than the i-vector system but worse than the x-vector system.We compared the proposed method with the baseline systems using the simulated speech mixtures. It is observed that miss rate is dominant in the DER of the baseline systems, due to the lack of capability for overlapping speech. In contrast, the proposed method achieved significantly low miss rate. The results indicate that the proposed method successfully detects overlapping segments as well as single-speaker segments and silence segments. Regarding the confusion error rate, the proposed method is better than the i-vector system, while it is worse than the x-vector system. For reducing the confusion errors, it is possible to use data augmentation for learning noise and speaker variations, which is utilized in the x-vector system." } ], "images": [ "scigen/train/2304.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Detailed DERs (%) evaluated on simulated speech generated with the β=2. MI, FA and CF denote miss, false alarm and confusion error rates, respectively. The proposed model is trained using 100,000 mixtures generated with β=2." ], "reasoning": "The proposed method significantly reduces the miss rate and DER compared to baseline systems, and it performs better in confusion error rate than the i-vector system but worse than the x-vector system.", "description": "We compared the proposed method with the baseline systems using the simulated speech mixtures. It is observed that miss rate is dominant in the DER of the baseline systems, due to the lack of capability for overlapping speech. In contrast, the proposed method achieved significantly low miss rate. The results indicate that the proposed method successfully detects overlapping segments as well as single-speaker segments and silence segments. Regarding the confusion error rate, the proposed method is better than the i-vector system, while it is worse than the x-vector system. For reducing the confusion errors, it is possible to use data augmentation for learning noise and speaker variations, which is utilized in the x-vector system.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: 'Table 3: Detailed DERs (%) evaluated on simulated speech generated with the β=2. MI, FA and CF denote miss, false alarm and confusion error rates, respectively. The proposed model is trained using 100,000 mixtures generated with β=2.'\nQuestion: Which method has the lowest miss rate and how does it compare to the baseline systems in terms of DER?\n" }, { "role": "assistant", "content": "The proposed method (EEND) has the lowest miss rate (4.47) compared to the i-vector (25.82) and x-vector (25.82) systems. It also has a significantly lower DER (12.28) compared to the i-vector (33.74) and x-vector (28.77) systems. So, the answer is {\"answer\": \"EEND\"}." } ], "images": [ "scigen/train/2304.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Detailed DERs (%) evaluated on simulated speech generated with the β=2. MI, FA and CF denote miss, false alarm and confusion error rates, respectively. The proposed model is trained using 100,000 mixtures generated with β=2." ], "reasoning": "The proposed method (EEND) has the lowest miss rate (4.47) compared to the i-vector (25.82) and x-vector (25.82) systems. It also has a significantly lower DER (12.28) compared to the i-vector (33.74) and x-vector (28.77) systems.", "question": "Which method has the lowest miss rate and how does it compare to the baseline systems in terms of DER?", "answer": "So, the answer is {\"answer\": \"EEND\"}." }, { "messages": [ { "role": "user", "content": "The proposed method has a higher confusion error rate than the i-vector system. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe proposed method has a higher confusion error rate than the i-vector system.\n" }, { "role": "assistant", "content": "The table shows that the proposed method (EEND) has a confusion error rate (CF) of 2.61, which is lower than the i-vector system's confusion error rate of 6.88. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2304.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Detailed DERs (%) evaluated on simulated speech generated with the β=2. MI, FA and CF denote miss, false alarm and confusion error rates, respectively. The proposed model is trained using 100,000 mixtures generated with β=2." ], "reasoning": "The table shows that the proposed method (EEND) has a confusion error rate (CF) of 2.61, which is lower than the i-vector system's confusion error rate of 6.88.", "statement": "The proposed method has a higher confusion error rate than the i-vector system.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 6: ROUGE F-measure results on the French, Portuguese and Spanish corpora. The best ROUGE results are in bold.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The ILP methods (ILP:80% and ILP:∞) generally outperform the baseline systems in ROUGE scores for Portuguese and Spanish, with POS-LM post-processing further improving the scores for these languages.Based on these results, a further analysis was done for the 80% and ∞ configurations. The first two columns display the evaluation of the two baseline systems; the ROUGE scores measured with our method using either 80% or ∞ maximum compression lengths are shown in the next two columns and the last two columns respectively. The outputs produced by all of these systems for two sample clusters in Spanish and Portuguese can be found in the Appendix. Globally, all versions of our ILP method outperform both baselines according to ROUGE F-measures for the Portuguese and Spanish corpora, and our ILP systems (ILP:80% and ILP:∞) obtained similar results to BM13 for the French corpus. The POS-LM post-processing further improved the ROUGE scores for Portuguese and Spanish, but unfortunately not for the French corpus." } ], "images": [ "scigen/train/2305.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: ROUGE F-measure results on the French, Portuguese and Spanish corpora. The best ROUGE results are in bold." ], "reasoning": "The ILP methods (ILP:80% and ILP:∞) generally outperform the baseline systems in ROUGE scores for Portuguese and Spanish, with POS-LM post-processing further improving the scores for these languages.", "description": "Based on these results, a further analysis was done for the 80% and ∞ configurations. The first two columns display the evaluation of the two baseline systems; the ROUGE scores measured with our method using either 80% or ∞ maximum compression lengths are shown in the next two columns and the last two columns respectively. The outputs produced by all of these systems for two sample clusters in Spanish and Portuguese can be found in the Appendix. Globally, all versions of our ILP method outperform both baselines according to ROUGE F-measures for the Portuguese and Spanish corpora, and our ILP systems (ILP:80% and ILP:∞) obtained similar results to BM13 for the French corpus. The POS-LM post-processing further improved the ROUGE scores for Portuguese and Spanish, but unfortunately not for the French corpus.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 6: ROUGE F-measure results on the French, Portuguese and Spanish corpora. The best ROUGE results are in bold.', and tackle the question below in a sequential manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich method achieved the highest ROUGE-2 score for the Portuguese corpus?\n" }, { "role": "assistant", "content": "To find the highest ROUGE-2 score for the Portuguese corpus, look at the ROUGE-2 row under the Portuguese section and compare the scores across all methods. The highest score is 0.3249, achieved by Methods ILP:∞+LM. So, the answer is {\"answer\": \"Methods ILP:∞+LM\"}." } ], "images": [ "scigen/train/2305.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: ROUGE F-measure results on the French, Portuguese and Spanish corpora. The best ROUGE results are in bold." ], "reasoning": "To find the highest ROUGE-2 score for the Portuguese corpus, look at the ROUGE-2 row under the Portuguese section and compare the scores across all methods. The highest score is 0.3249, achieved by Methods ILP:∞+LM.", "question": "Which method achieved the highest ROUGE-2 score for the Portuguese corpus?", "answer": "So, the answer is {\"answer\": \"Methods ILP:∞+LM\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe ILP:80% method achieved the highest ROUGE-1 score for the Spanish corpus.\n" }, { "role": "assistant", "content": "To verify this statement, look at the ROUGE-1 row under the Spanish section and compare the scores across all methods. The highest score is 0.5500, achieved by Methods ILP:80%+LM, not ILP:80%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2305.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: ROUGE F-measure results on the French, Portuguese and Spanish corpora. The best ROUGE results are in bold." ], "reasoning": "To verify this statement, look at the ROUGE-1 row under the Spanish section and compare the scores across all methods. The highest score is 0.5500, achieved by Methods ILP:80%+LM, not ILP:80%.", "statement": "The ILP:80% method achieved the highest ROUGE-1 score for the Spanish corpus.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 15: Mutual Information Cuts'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The experiment shows that not cutting rare words or pairs and only altering the MI cut results in lower accuracy when word-pairs are considered.Perhaps it is a mistake, then to cut rare words and rare word-pairs, when using a bag-of-pairs model? In this experiment, no rare words or pairs were cut; only the cut for the MI was altered. Thus, somehow, when word-pairs come into play, failing to cut rare words and phrases makes things worse!" } ], "images": [ "scigen/train/2306.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 15: Mutual Information Cuts" ], "reasoning": "The experiment shows that not cutting rare words or pairs and only altering the MI cut results in lower accuracy when word-pairs are considered.", "description": "Perhaps it is a mistake, then to cut rare words and rare word-pairs, when using a bag-of-pairs model? In this experiment, no rare words or pairs were cut; only the cut for the MI was altered. Thus, somehow, when word-pairs come into play, failing to cut rare words and phrases makes things worse!", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 15: Mutual Information Cuts', unfold the steps to solve the question given next. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nQuestion: What is the mean accuracy when no rare words or pairs are cut and the MI cut is greater than 2?\n" }, { "role": "assistant", "content": "The table shows the mean accuracy for different MI cuts. For MI>2, the mean accuracy is 0.5871. So, the answer is {\"answer\": \"0.5871\"}." } ], "images": [ "scigen/train/2306.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 15: Mutual Information Cuts" ], "reasoning": "The table shows the mean accuracy for different MI cuts. For MI>2, the mean accuracy is 0.5871.", "question": "What is the mean accuracy when no rare words or pairs are cut and the MI cut is greater than 2?", "answer": "So, the answer is {\"answer\": \"0.5871\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe highest mean accuracy is achieved when the MI cut is greater than 6.\n" }, { "role": "assistant", "content": "The table shows that the highest mean accuracy is 0.6036 for 'Best Pairs', not for MI>6, which has a mean accuracy of 0.5340. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2306.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 15: Mutual Information Cuts" ], "reasoning": "The table shows that the highest mean accuracy is 0.6036 for 'Best Pairs', not for MI>6, which has a mean accuracy of 0.5340.", "statement": "The highest mean accuracy is achieved when the MI cut is greater than 6.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5: Training Confusion Matrix, Group 1 vs. Group 2'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The model fits the training data very well but shows lower accuracy on the test set, indicating overfitting.Running the the classifier once, for a given set of parameters, results in a single model being created. The precise model, and its accuracy, depends on the training parameters, such as run-time, the number of features selected, the number of representations comprising the model, and other variables. In all cases (for all parameter settings), the resulting model fits the training data very well. When this model is evaluated on the test set, the accuracy and other measures are, of course, sharply lower. In essence, the model is over-fit on the train set." } ], "images": [ "scigen/train/2307.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Training Confusion Matrix, Group 1 vs. Group 2" ], "reasoning": "The model fits the training data very well but shows lower accuracy on the test set, indicating overfitting.", "description": "Running the the classifier once, for a given set of parameters, results in a single model being created. The precise model, and its accuracy, depends on the training parameters, such as run-time, the number of features selected, the number of representations comprising the model, and other variables. In all cases (for all parameter settings), the resulting model fits the training data very well. When this model is evaluated on the test set, the accuracy and other measures are, of course, sharply lower. In essence, the model is over-fit on the train set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 5: Training Confusion Matrix, Group 1 vs. Group 2', and sequentially solve the question, writing out each step of your reasoning process. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the total number of correct predictions made by the model?\n" }, { "role": "assistant", "content": "To find the total number of correct predictions, sum the values where the predicted group matches the expected group: 277 (Expected Grp 1, Predicted Grp 1) and 265 (Expected Grp 2, Predicted Grp 2). So, the answer is {\"answer\": \"542\"}" } ], "images": [ "scigen/train/2307.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Training Confusion Matrix, Group 1 vs. Group 2" ], "reasoning": "To find the total number of correct predictions, sum the values where the predicted group matches the expected group: 277 (Expected Grp 1, Predicted Grp 1) and 265 (Expected Grp 2, Predicted Grp 2).", "question": "What is the total number of correct predictions made by the model?", "answer": "So, the answer is {\"answer\": \"542\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe model made more incorrect predictions for Expected Grp 1 than for Expected Grp 2.\n" }, { "role": "assistant", "content": "The table shows that the model made 3 incorrect predictions for Expected Grp 1 and 11 incorrect predictions for Expected Grp 2. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2307.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Training Confusion Matrix, Group 1 vs. Group 2" ], "reasoning": "The table shows that the model made 3 incorrect predictions for Expected Grp 1 and 11 incorrect predictions for Expected Grp 2. Therefore, the statement is refuted.", "statement": "The model made more incorrect predictions for Expected Grp 1 than for Expected Grp 2.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Training Confusion Matrix, Group 1 vs. Group 2'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The model fits the training data very well but performs poorly on the test set, indicating overfitting.Running the the classifier once, for a given set of parameters, results in a single model being created. The precise model, and its accuracy, depends on the training parameters, such as run-time, the number of features selected, the number of representations comprising the model, and other variables. In all cases (for all parameter settings), the resulting model fits the training data very well. When this model is evaluated on the test set, the accuracy and other measures are, of course, sharply lower. In essence, the model is over-fit on the train set." } ], "images": [ "scigen/train/2308.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Training Confusion Matrix, Group 1 vs. Group 2" ], "reasoning": "The model fits the training data very well but performs poorly on the test set, indicating overfitting.", "description": "Running the the classifier once, for a given set of parameters, results in a single model being created. The precise model, and its accuracy, depends on the training parameters, such as run-time, the number of features selected, the number of representations comprising the model, and other variables. In all cases (for all parameter settings), the resulting model fits the training data very well. When this model is evaluated on the test set, the accuracy and other measures are, of course, sharply lower. In essence, the model is over-fit on the train set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: Training Confusion Matrix, Group 1 vs. Group 2'.\n\nQuestion: What is the precision of the model and how many correct predictions does it represent?\n" }, { "role": "assistant", "content": "The precision of the model is given in the table as 0.9888, which corresponds to 265 correct predictions out of 268 total predictions. So, the answer is {\"answer\": \"0.9888 (265 correct out of 268 total)\"}." } ], "images": [ "scigen/train/2308.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Training Confusion Matrix, Group 1 vs. Group 2" ], "reasoning": "The precision of the model is given in the table as 0.9888, which corresponds to 265 correct predictions out of 268 total predictions.", "question": "What is the precision of the model and how many correct predictions does it represent?", "answer": "So, the answer is {\"answer\": \"0.9888 (265 correct out of 268 total)\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model has a recall rate of 0.9601.\n" }, { "role": "assistant", "content": "The table shows that the recall rate of the model is indeed 0.9601. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2308.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Training Confusion Matrix, Group 1 vs. Group 2" ], "reasoning": "The table shows that the recall rate of the model is indeed 0.9601.", "statement": "The model has a recall rate of 0.9601.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 6: Test Confusion Matrix, Group 3 vs. Group 2'.\n" }, { "role": "assistant", "content": "The model fits the training data very well but performs poorly on the test set, indicating overfitting.Running the the classifier once, for a given set of parameters, results in a single model being created. The precise model, and its accuracy, depends on the training parameters, such as run-time, the number of features selected, the number of representations comprising the model, and other variables. In all cases (for all parameter settings), the resulting model fits the training data very well. When this model is evaluated on the test set, the accuracy and other measures are, of course, sharply lower. In essence, the model is over-fit on the train set." } ], "images": [ "scigen/train/2309.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Test Confusion Matrix, Group 3 vs. Group 2" ], "reasoning": "The model fits the training data very well but performs poorly on the test set, indicating overfitting.", "description": "Running the the classifier once, for a given set of parameters, results in a single model being created. The precise model, and its accuracy, depends on the training parameters, such as run-time, the number of features selected, the number of representations comprising the model, and other variables. In all cases (for all parameter settings), the resulting model fits the training data very well. When this model is evaluated on the test set, the accuracy and other measures are, of course, sharply lower. In essence, the model is over-fit on the train set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 6: Test Confusion Matrix, Group 3 vs. Group 2', and tackle the question below in a sequential manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the total number of correct predictions made by the model?\n" }, { "role": "assistant", "content": "To find the total number of correct predictions, sum the values where the predicted group matches the expected group: 43 (Expected Grp 1, Predicted Grp 1) + 47 (Expected Grp 2, Predicted Grp 2). So, the answer is {\"answer\": \"90\"}" } ], "images": [ "scigen/train/2309.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Test Confusion Matrix, Group 3 vs. Group 2" ], "reasoning": "To find the total number of correct predictions, sum the values where the predicted group matches the expected group: 43 (Expected Grp 1, Predicted Grp 1) + 47 (Expected Grp 2, Predicted Grp 2).", "question": "What is the total number of correct predictions made by the model?", "answer": "So, the answer is {\"answer\": \"90\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The model correctly predicted 43 instances for Expected Grp 1., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model correctly predicted 43 instances for Expected Grp 1.\n" }, { "role": "assistant", "content": "The table shows that for Expected Grp 1, the model predicted 43 instances correctly under Predicted Grp 1. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2309.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Test Confusion Matrix, Group 3 vs. Group 2" ], "reasoning": "The table shows that for Expected Grp 1, the model predicted 43 instances correctly under Predicted Grp 1.", "statement": "The model correctly predicted 43 instances for Expected Grp 1.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Test Confusion Matrix, Group 3 vs. Group 2'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The model fits the training data well but performs poorly on the test set, indicating overfitting.Running the the classifier once, for a given set of parameters, results in a single model being created. The precise model, and its accuracy, depends on the training parameters, such as run-time, the number of features selected, the number of representations comprising the model, and other variables. In all cases (for all parameter settings), the resulting model fits the training data very well. When this model is evaluated on the test set, the accuracy and other measures are, of course, sharply lower. In essence, the model is over-fit on the train set." } ], "images": [ "scigen/train/2310.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Test Confusion Matrix, Group 3 vs. Group 2" ], "reasoning": "The model fits the training data well but performs poorly on the test set, indicating overfitting.", "description": "Running the the classifier once, for a given set of parameters, results in a single model being created. The precise model, and its accuracy, depends on the training parameters, such as run-time, the number of features selected, the number of representations comprising the model, and other variables. In all cases (for all parameter settings), the resulting model fits the training data very well. When this model is evaluated on the test set, the accuracy and other measures are, of course, sharply lower. In essence, the model is over-fit on the train set.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 6: Test Confusion Matrix, Group 3 vs. Group 2'. Solve the following question based on the table.\n\nQuestion: What is the precision of the model and how many correct predictions does it represent?\nLet's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the precision as 0.6351, which corresponds to 47 correct predictions out of 74 total predictions. So, the answer is {\"answer\": \"0.6351 (47 correct out of 74 total)\"}" } ], "images": [ "scigen/train/2310.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Test Confusion Matrix, Group 3 vs. Group 2" ], "reasoning": "The table shows the precision as 0.6351, which corresponds to 47 correct predictions out of 74 total predictions.", "question": "What is the precision of the model and how many correct predictions does it represent?", "answer": "So, the answer is {\"answer\": \"0.6351 (47 correct out of 74 total)\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model has a recall rate higher than its precision rate.\n" }, { "role": "assistant", "content": "The table shows that the recall rate is 0.6812, which is higher than the precision rate of 0.6351. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2310.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Test Confusion Matrix, Group 3 vs. Group 2" ], "reasoning": "The table shows that the recall rate is 0.6812, which is higher than the precision rate of 0.6351.", "statement": "The model has a recall rate higher than its precision rate.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 11: Data Cuts'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Excluding infrequent words from model building might seem beneficial, but it actually worsens the model's performance when word-pairs are considered.A different measure of the importance of infrequent words can be obtained by excluding them from model building: that is, by creating models from word lists that include only those words that occur 2 or 4 or more times in the text. Superficially, this seems like a wise idea. If a word appears in only one patient record, and it is found during the training phase, then it is impossible that this word will also appear in one of the test patient records. Thus, it cannot contribute to the accuracy of the model on the test set: both the positive and negative cohorts will be missing this word; it has no predictive value. If a word appears in only two patient records, then it is unlikely that one of those locations will be in the test-set (due to the 4/5’ths - 1/5’th split). Thus, one might also believe that such words have little or no predictive value. Perhaps accuracy can be increased by cutting down the dataset, and discarding all words that appear fewer than M times in the dataset. But this is very much not the case. Perhaps it is a mistake, then to cut rare words and rare word-pairs, when using a bag-of-pairs model? In this experiment, no rare words or pairs were cut; only the cut for the MI was altered. Thus, somehow, when word-pairs come into play, failing to cut rare words and phrases makes things worse!" } ], "images": [ "scigen/train/2311.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Data Cuts" ], "reasoning": "Excluding infrequent words from model building might seem beneficial, but it actually worsens the model's performance when word-pairs are considered.", "description": "A different measure of the importance of infrequent words can be obtained by excluding them from model building: that is, by creating models from word lists that include only those words that occur 2 or 4 or more times in the text. Superficially, this seems like a wise idea. If a word appears in only one patient record, and it is found during the training phase, then it is impossible that this word will also appear in one of the test patient records. Thus, it cannot contribute to the accuracy of the model on the test set: both the positive and negative cohorts will be missing this word; it has no predictive value. If a word appears in only two patient records, then it is unlikely that one of those locations will be in the test-set (due to the 4/5’ths - 1/5’th split). Thus, one might also believe that such words have little or no predictive value. Perhaps accuracy can be increased by cutting down the dataset, and discarding all words that appear fewer than M times in the dataset. But this is very much not the case. Perhaps it is a mistake, then to cut rare words and rare word-pairs, when using a bag-of-pairs model? In this experiment, no rare words or pairs were cut; only the cut for the MI was altered. Thus, somehow, when word-pairs come into play, failing to cut rare words and phrases makes things worse!", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the mean accuracy when no words are cut and the threshold is set to 3?\nSolve the above question based on the table titled 'Table 11: Data Cuts'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the mean accuracy when no words are cut and the threshold is set to 3, look at the row labeled 'none' and the column labeled 'thresh=3 Mean'. The value is 0.5645. So, the answer is {\"answer\": \"0.5645\"}." } ], "images": [ "scigen/train/2311.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Data Cuts" ], "reasoning": "To find the mean accuracy when no words are cut and the threshold is set to 3, look at the row labeled 'none' and the column labeled 'thresh=3 Mean'. The value is 0.5645.", "question": "What is the mean accuracy when no words are cut and the threshold is set to 3?", "answer": "So, the answer is {\"answer\": \"0.5645\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Cutting words that appear fewer than 16 times results in a mean accuracy of 0.4996 when the threshold is set to 1. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nCutting words that appear fewer than 16 times results in a mean accuracy of 0.4996 when the threshold is set to 1.\n" }, { "role": "assistant", "content": "To verify this statement, look at the row labeled '16' and the column labeled 'thresh=1 Mean'. The value is 0.4996, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2311.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Data Cuts" ], "reasoning": "To verify this statement, look at the row labeled '16' and the column labeled 'thresh=1 Mean'. The value is 0.4996, which matches the statement.", "statement": "Cutting words that appear fewer than 16 times results in a mean accuracy of 0.4996 when the threshold is set to 1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 12: Bag of Phrases Tuning Results', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "All bag-of-phrases models outperform the best bag-of-words model, indicating that including word-pairs without cutting rare words and pairs worsens performance.Observe that all of the bag-of-phrases models outperform the best bag-of-words model. Figure 6: N-grams Perhaps it is a mistake, then to cut rare words and rare word-pairs, when using a bag-of-pairs model? In this experiment, no rare words or pairs were cut; only the cut for the MI was altered. Thus, somehow, when word-pairs come into play, failing to cut rare words and phrases makes things worse!" } ], "images": [ "scigen/train/2312.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 12: Bag of Phrases Tuning Results" ], "reasoning": "All bag-of-phrases models outperform the best bag-of-words model, indicating that including word-pairs without cutting rare words and pairs worsens performance.", "description": "Observe that all of the bag-of-phrases models outperform the best bag-of-words model. Figure 6: N-grams Perhaps it is a mistake, then to cut rare words and rare word-pairs, when using a bag-of-pairs model? In this experiment, no rare words or pairs were cut; only the cut for the MI was altered. Thus, somehow, when word-pairs come into play, failing to cut rare words and phrases makes things worse!", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 12: Bag of Phrases Tuning Results'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the highest performance score for the word pairs model and how does it compare to the highest score for the single words model?\n" }, { "role": "assistant", "content": "The highest performance score for the word pairs model is 0.6283 at 5 dynamic features, while the highest score for the single words model is 0.5942 at 75 dynamic features. Thus, the word pairs model outperforms the single words model. So, the answer is {\"answer\": \"0.6283\"}." } ], "images": [ "scigen/train/2312.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 12: Bag of Phrases Tuning Results" ], "reasoning": "The highest performance score for the word pairs model is 0.6283 at 5 dynamic features, while the highest score for the single words model is 0.5942 at 75 dynamic features. Thus, the word pairs model outperforms the single words model.", "question": "What is the highest performance score for the word pairs model and how does it compare to the highest score for the single words model?", "answer": "So, the answer is {\"answer\": \"0.6283\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe word pairs model performs better than the single words model at 75 dynamic features.\n" }, { "role": "assistant", "content": "At 75 dynamic features, the word pairs model has a score of 0.5899, while the single words model has a score of 0.5942. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2312.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 12: Bag of Phrases Tuning Results" ], "reasoning": "At 75 dynamic features, the word pairs model has a score of 0.5899, while the single words model has a score of 0.5942. Therefore, the statement is refuted.", "statement": "The word pairs model performs better than the single words model at 75 dynamic features.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 13: Choosing Significant Words'.\n" }, { "role": "assistant", "content": "Feature selection is a faster method for obtaining significant words and achieves slightly higher mean accuracy compared to the ensemble method.Creating the set of corpus-pairs requires having previously computed a list of ’significant words’. Creating that list is time-consuming, since it requires training an ensemble, extracting the words, and then training again, with pairs. Thus, it is natural to ask if there are simpler ways of obtaining a list of ’significant words’ that are just as good. To make the two methods comparable, a simple mutual-information-maximizing feature selection step was performed to select 371 words, the same number of words as obtained from the ensemble. Feature selection runs in seconds, whereas training an ensemble of 40 models can take hours. Perhaps it is a mistake, then to cut rare words and rare word-pairs, when using a bag-of-pairs model? In this experiment, no rare words or pairs were cut; only the cut for the MI was altered. Thus, somehow, when word-pairs come into play, failing to cut rare words and phrases makes things worse!" } ], "images": [ "scigen/train/2313.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 13: Choosing Significant Words" ], "reasoning": "Feature selection is a faster method for obtaining significant words and achieves slightly higher mean accuracy compared to the ensemble method.", "description": "Creating the set of corpus-pairs requires having previously computed a list of ’significant words’. Creating that list is time-consuming, since it requires training an ensemble, extracting the words, and then training again, with pairs. Thus, it is natural to ask if there are simpler ways of obtaining a list of ’significant words’ that are just as good. To make the two methods comparable, a simple mutual-information-maximizing feature selection step was performed to select 371 words, the same number of words as obtained from the ensemble. Feature selection runs in seconds, whereas training an ensemble of 40 models can take hours. Perhaps it is a mistake, then to cut rare words and rare word-pairs, when using a bag-of-pairs model? In this experiment, no rare words or pairs were cut; only the cut for the MI was altered. Thus, somehow, when word-pairs come into play, failing to cut rare words and phrases makes things worse!", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 13: Choosing Significant Words', unfold the steps to solve the question given next. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which method has a higher mean accuracy for obtaining significant words?\n" }, { "role": "assistant", "content": "The table shows the mean accuracy for both methods. Feature selection has a mean accuracy of 0.6036, which is higher than the ensemble method's mean accuracy of 0.5986. So, the answer is {\"answer\": \"Feature Selection\"}." } ], "images": [ "scigen/train/2313.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 13: Choosing Significant Words" ], "reasoning": "The table shows the mean accuracy for both methods. Feature selection has a mean accuracy of 0.6036, which is higher than the ensemble method's mean accuracy of 0.5986.", "question": "Which method has a higher mean accuracy for obtaining significant words?", "answer": "So, the answer is {\"answer\": \"Feature Selection\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe ensemble method has a lower standard deviation than the feature selection method.\n" }, { "role": "assistant", "content": "The table shows that the standard deviation for the ensemble method is 0.0324, which is higher than the standard deviation for the feature selection method, which is 0.0295. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2313.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 13: Choosing Significant Words" ], "reasoning": "The table shows that the standard deviation for the ensemble method is 0.0324, which is higher than the standard deviation for the feature selection method, which is 0.0295.", "statement": "The ensemble method has a lower standard deviation than the feature selection method.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 14: All-Word-Pair Cuts', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Cutting low-MI pairs from the all-pairs dataset improves the score slightly compared to the bag-of-words model, but not as significantly as using corpus-pairs.By selecting corpus pairs, thousands of the highest-MI pairs are discarded, as well as most of the low-MI pairs, as well. Perhaps it is possible to replicate the corpus-pairs results by applying a simple cut to the all-pairs dataset, and merely discarding the low-MI pairs? This table compares a bag-of-words model to several different all-pairs models, with different MI cuts applied. Including all pairs does not improve the score over the bag-of-words. By cutting out low-MI pairs, the score can be improved somewhat, but the effect is not dramatic; certainly not as strong as the decision to use corpus-pairs." } ], "images": [ "scigen/train/2314.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 14: All-Word-Pair Cuts" ], "reasoning": "Cutting low-MI pairs from the all-pairs dataset improves the score slightly compared to the bag-of-words model, but not as significantly as using corpus-pairs.", "description": "By selecting corpus pairs, thousands of the highest-MI pairs are discarded, as well as most of the low-MI pairs, as well. Perhaps it is possible to replicate the corpus-pairs results by applying a simple cut to the all-pairs dataset, and merely discarding the low-MI pairs? This table compares a bag-of-words model to several different all-pairs models, with different MI cuts applied. Including all pairs does not improve the score over the bag-of-words. By cutting out low-MI pairs, the score can be improved somewhat, but the effect is not dramatic; certainly not as strong as the decision to use corpus-pairs.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 14: All-Word-Pair Cuts\nQuestion: Which MI cut applied to all pairs results in the highest mean accuracy?\n" }, { "role": "assistant", "content": "By comparing the mean accuracy values for different MI cuts, the highest mean accuracy is found for All Pairs MI>0 with a value of 0.5764. So, the answer is {\"answer\": \"All Pairs MI>0\"}" } ], "images": [ "scigen/train/2314.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 14: All-Word-Pair Cuts" ], "reasoning": "By comparing the mean accuracy values for different MI cuts, the highest mean accuracy is found for All Pairs MI>0 with a value of 0.5764.", "question": "Which MI cut applied to all pairs results in the highest mean accuracy?", "answer": "So, the answer is {\"answer\": \"All Pairs MI>0\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The bag-of-words model has a higher mean accuracy than the all-pairs model with MI>2. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe bag-of-words model has a higher mean accuracy than the all-pairs model with MI>2.\n" }, { "role": "assistant", "content": "The mean accuracy for the bag-of-words model (Single Words) is 0.5617, while the mean accuracy for the all-pairs model with MI>2 is 0.5646, which is higher. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2314.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 14: All-Word-Pair Cuts" ], "reasoning": "The mean accuracy for the bag-of-words model (Single Words) is 0.5617, while the mean accuracy for the all-pairs model with MI>2 is 0.5646, which is higher.", "statement": "The bag-of-words model has a higher mean accuracy than the all-pairs model with MI>2.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 16: Cutting Phrases'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Cutting infrequent words improves scores more than cutting infrequent phrases.To improve scores, are the relevant cuts to the rare words, to the rare phrases, or both? Of the two, cutting infrequent words seems to provide the greater benefit." } ], "images": [ "scigen/train/2315.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 16: Cutting Phrases" ], "reasoning": "Cutting infrequent words improves scores more than cutting infrequent phrases.", "description": "To improve scores, are the relevant cuts to the rare words, to the rare phrases, or both? Of the two, cutting infrequent words seems to provide the greater benefit.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 16: Cutting Phrases' to sequentially solve the question presented below. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the highest mean accuracy achieved by cutting infrequent words and phrases?\n" }, { "role": "assistant", "content": "To find the highest mean accuracy, we need to look at the 'Mean Accuracy' column and identify the highest value. The highest mean accuracy is 0.6036, achieved with a Word Count Cut of 4 and a Pair Count Cut of 4. So, the answer is {\"answer\": \"0.6036\"}" } ], "images": [ "scigen/train/2315.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 16: Cutting Phrases" ], "reasoning": "To find the highest mean accuracy, we need to look at the 'Mean Accuracy' column and identify the highest value. The highest mean accuracy is 0.6036, achieved with a Word Count Cut of 4 and a Pair Count Cut of 4.", "question": "What is the highest mean accuracy achieved by cutting infrequent words and phrases?", "answer": "So, the answer is {\"answer\": \"0.6036\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nCutting infrequent words to a count of 4 and infrequent phrases to a count of 4 results in the highest mean accuracy.\n" }, { "role": "assistant", "content": "The table shows that the highest mean accuracy of 0.6036 is achieved with a Word Count Cut of 4 and a Pair Count Cut of 4. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2315.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 16: Cutting Phrases" ], "reasoning": "The table shows that the highest mean accuracy of 0.6036 is achieved with a Word Count Cut of 4 and a Pair Count Cut of 4.", "statement": "Cutting infrequent words to a count of 4 and infrequent phrases to a count of 4 results in the highest mean accuracy.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 18: Training Time Dependence'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The highest score of 63.68% occurs at 5000 evaluations and 90 dynamical features, with most high scores occurring at 9000 or fewer evaluations unless the number of features is very small.Training times are measured in terms of the number of evaluations of the scoring function: a single comparison of the model to the training table counts as one evaluation. The highest score for each row is marked in bold (magenta); the second-highest score in italic (cyan). The very highest score, over the entire table, of 63.68%, occurs at a training time of 5000 evaluations and 90 dynamical features (indicated in bold sans-serif). Most of the high scores occur when 9000 or fewer training evaluations are performed. The exceptions occur when the number of dynamical features is extremely small: this suggests that the model builder is starved for features at this point, and must iterate over many trials before finding the appropriate features." } ], "images": [ "scigen/train/2316.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 18: Training Time Dependence" ], "reasoning": "The highest score of 63.68% occurs at 5000 evaluations and 90 dynamical features, with most high scores occurring at 9000 or fewer evaluations unless the number of features is very small.", "description": "Training times are measured in terms of the number of evaluations of the scoring function: a single comparison of the model to the training table counts as one evaluation. The highest score for each row is marked in bold (magenta); the second-highest score in italic (cyan). The very highest score, over the entire table, of 63.68%, occurs at a training time of 5000 evaluations and 90 dynamical features (indicated in bold sans-serif). Most of the high scores occur when 9000 or fewer training evaluations are performed. The exceptions occur when the number of dynamical features is extremely small: this suggests that the model builder is starved for features at this point, and must iterate over many trials before finding the appropriate features.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 18: Training Time Dependence', and sequentially solve the question, writing out each step of your reasoning process. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the highest score achieved with 90 dynamical features?\n" }, { "role": "assistant", "content": "The table shows that the highest score for 90 dynamical features is 63.68%, which occurs at 5000 evaluations. So, the answer is {\"answer\": \"63.68%\"}" } ], "images": [ "scigen/train/2316.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 18: Training Time Dependence" ], "reasoning": "The table shows that the highest score for 90 dynamical features is 63.68%, which occurs at 5000 evaluations.", "question": "What is the highest score achieved with 90 dynamical features?", "answer": "So, the answer is {\"answer\": \"63.68%\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe highest score for 180 dynamical features is achieved at 9000 evaluations.\n" }, { "role": "assistant", "content": "The table shows that the highest score for 180 dynamical features is 60.70%, which occurs at 9000 evaluations. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2316.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 18: Training Time Dependence" ], "reasoning": "The table shows that the highest score for 180 dynamical features is 60.70%, which occurs at 9000 evaluations.", "statement": "The highest score for 180 dynamical features is achieved at 9000 evaluations.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: The experiments on the number of layers for encoders and decoders.'.\n" }, { "role": "assistant", "content": "Translation performance improves significantly from 2 to 4 layers, but shows minimal improvement beyond 4 layers, leading to the choice of 4 layers for both encoders and decoders.To determine the number of layers for encoders and decoders in our system beforehand, we conduct experiments on English-German translation tasks to test how the amount of layers in encoders and decoders affects the translation performance. We can find that the translation performance achieves substantial improvement with the layer number increasing from 2 to 4. However, with layer number set larger than 4, we get little improvement. To make a trade-off between the translation performance and the computation complexity, we set the layer number as 4 for our encoders and decoders." } ], "images": [ "scigen/train/2317.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The experiments on the number of layers for encoders and decoders." ], "reasoning": "Translation performance improves significantly from 2 to 4 layers, but shows minimal improvement beyond 4 layers, leading to the choice of 4 layers for both encoders and decoders.", "description": "To determine the number of layers for encoders and decoders in our system beforehand, we conduct experiments on English-German translation tasks to test how the amount of layers in encoders and decoders affects the translation performance. We can find that the translation performance achieves substantial improvement with the layer number increasing from 2 to 4. However, with layer number set larger than 4, we get little improvement. To make a trade-off between the translation performance and the computation complexity, we set the layer number as 4 for our encoders and decoders.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 4: The experiments on the number of layers for encoders and decoders.\n\nQuestion: What is the translation performance for English-German (en-de) when the layer number is set to 4?\n" }, { "role": "assistant", "content": "The table shows the translation performance for different layer numbers. For layer number 4, the en-de performance is listed. So, the answer is {\"answer\": \"12.86\"}." } ], "images": [ "scigen/train/2317.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The experiments on the number of layers for encoders and decoders." ], "reasoning": "The table shows the translation performance for different layer numbers. For layer number 4, the en-de performance is listed.", "question": "What is the translation performance for English-German (en-de) when the layer number is set to 4?", "answer": "So, the answer is {\"answer\": \"12.86\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The translation performance for German-English (de-en) improves significantly when the layer number is increased from 4 to 5., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe translation performance for German-English (de-en) improves significantly when the layer number is increased from 4 to 5.\n" }, { "role": "assistant", "content": "The table shows that the de-en performance increases from 15.62 to 15.83 when the layer number is increased from 4 to 5, which is a minimal improvement. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2317.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The experiments on the number of layers for encoders and decoders." ], "reasoning": "The table shows that the de-en performance increases from 15.62 to 15.83 when the layer number is increased from 4 to 5, which is a minimal improvement.", "statement": "The translation performance for German-English (de-en) improves significantly when the layer number is increased from 4 to 5.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: The translation performance on English-German, English-French and Chinese-to-English test sets. The results of Lample et al. (2017) are copied directly from their paper. We do not present the results of Artetxe et al. (2017b) since we use different training sets.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The proposed approach significantly improves translation quality over the word-by-word baseline, achieving at least +5.01 BLEU points in English-to-German and up to +13.37 BLEU points in English-to-French.As it can be seen, the proposed approach obtains significant improvements than the word-by-word baseline system, with at least +5.01 BLEU points in English-to-German translation and up to +13.37 BLEU points in English-to-French translation. This shows that the proposed model only trained with monolingual data effectively learns to use the context information and the internal structure of each language. Compared to the work of Lample et al. We believe that the unsupervised NMT is very promising. However, there is still a large room for improvement compared to the supervised upper bound. The gap between the supervised and unsupervised model is as large as 12.3-25.5 BLEU points depending on the language pair and translation direction." } ], "images": [ "scigen/train/2318.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The translation performance on English-German, English-French and Chinese-to-English test sets. The results of Lample et al. (2017) are copied directly from their paper. We do not present the results of Artetxe et al. (2017b) since we use different training sets." ], "reasoning": "The proposed approach significantly improves translation quality over the word-by-word baseline, achieving at least +5.01 BLEU points in English-to-German and up to +13.37 BLEU points in English-to-French.", "description": "As it can be seen, the proposed approach obtains significant improvements than the word-by-word baseline system, with at least +5.01 BLEU points in English-to-German translation and up to +13.37 BLEU points in English-to-French translation. This shows that the proposed model only trained with monolingual data effectively learns to use the context information and the internal structure of each language. Compared to the work of Lample et al. We believe that the unsupervised NMT is very promising. However, there is still a large room for improvement compared to the supervised upper bound. The gap between the supervised and unsupervised model is as large as 12.3-25.5 BLEU points depending on the language pair and translation direction.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: The translation performance on English-German, English-French and Chinese-to-English test sets. The results of Lample et al. (2017) are copied directly from their paper. We do not present the results of Artetxe et al. (2017b) since we use different training sets.'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the BLEU score improvement of the proposed approach over the word-by-word baseline for English-to-French translation?\n" }, { "role": "assistant", "content": "The BLEU score for the word-by-word baseline in English-to-French translation is 3.60, and for the proposed approach, it is 16.97. The improvement is 16.97 - 3.60. So, the answer is {\"answer\": \"13.37\"}." } ], "images": [ "scigen/train/2318.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The translation performance on English-German, English-French and Chinese-to-English test sets. The results of Lample et al. (2017) are copied directly from their paper. We do not present the results of Artetxe et al. (2017b) since we use different training sets." ], "reasoning": "The BLEU score for the word-by-word baseline in English-to-French translation is 3.60, and for the proposed approach, it is 16.97. The improvement is 16.97 - 3.60.", "question": "What is the BLEU score improvement of the proposed approach over the word-by-word baseline for English-to-French translation?", "answer": "So, the answer is {\"answer\": \"13.37\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The proposed approach achieves a BLEU score of 14.62 for German-to-English translation. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe proposed approach achieves a BLEU score of 14.62 for German-to-English translation.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for the proposed approach in German-to-English (de-en) translation is 14.62. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2318.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The translation performance on English-German, English-French and Chinese-to-English test sets. The results of Lample et al. (2017) are copied directly from their paper. We do not present the results of Artetxe et al. (2017b) since we use different training sets." ], "reasoning": "The table shows that the BLEU score for the proposed approach in German-to-English (de-en) translation is 14.62.", "statement": "The proposed approach achieves a BLEU score of 14.62 for German-to-English translation.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Ablation study on English-German, English-French and Chinese-to-English translation tasks. Without weight sharing means no layers are shared in the two AEs.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The weight-sharing constraint is the most critical component, and the GANs significantly improve translation performance, with global GANs achieving up to +0.78 BLEU points and local GANs up to +0.57 BLEU points on English-to-French translation., we perform an ablation study by training multiple versions of our model with some missing components: the local GANs, the global GANs, the directional self-attention, the weight-sharing, the embedding-reinforced encoders, etc. We do not test the the importance of the auto-encoding, back-translation and the pre-trained embeddings because they have been widely tested in Lample et al. The most critical component is the weight-sharing constraint, which is vital to map sentences of different languages to the shared-latent space. The embedding-reinforced encoder also brings some improvement on all of the translation tasks. When we remove the directional self-attention, we get up to -0.3 BLEU points decline. This indicates that it deserves more efforts to investigate the temporal order information in self-attention mechanism. The GANs also significantly improve the translation performance of our system. Specifically, the global GANs achieve improvement up to +0.78 BLEU points on English-to-French translation and the local GANs also obtain improvement up to +0.57 BLEU points on English-to-French translation. This reveals that the proposed model benefits a lot from the cross-domain loss defined by GANs." } ], "images": [ "scigen/train/2319.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation study on English-German, English-French and Chinese-to-English translation tasks. Without weight sharing means no layers are shared in the two AEs." ], "reasoning": "The weight-sharing constraint is the most critical component, and the GANs significantly improve translation performance, with global GANs achieving up to +0.78 BLEU points and local GANs up to +0.57 BLEU points on English-to-French translation.", "description": ", we perform an ablation study by training multiple versions of our model with some missing components: the local GANs, the global GANs, the directional self-attention, the weight-sharing, the embedding-reinforced encoders, etc. We do not test the the importance of the auto-encoding, back-translation and the pre-trained embeddings because they have been widely tested in Lample et al. The most critical component is the weight-sharing constraint, which is vital to map sentences of different languages to the shared-latent space. The embedding-reinforced encoder also brings some improvement on all of the translation tasks. When we remove the directional self-attention, we get up to -0.3 BLEU points decline. This indicates that it deserves more efforts to investigate the temporal order information in self-attention mechanism. The GANs also significantly improve the translation performance of our system. Specifically, the global GANs achieve improvement up to +0.78 BLEU points on English-to-French translation and the local GANs also obtain improvement up to +0.57 BLEU points on English-to-French translation. This reveals that the proposed model benefits a lot from the cross-domain loss defined by GANs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Ablation study on English-German, English-French and Chinese-to-English translation tasks. Without weight sharing means no layers are shared in the two AEs.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. \n\nQuestion: What is the BLEU score for English-to-French translation without the global GANs?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the BLEU score for English-to-French translation without the global GANs, look at the row labeled 'Without Global GANs' and the column labeled 'en-fr'. The score is 16.19. So, the answer is {\"answer\": \"16.19\"}" } ], "images": [ "scigen/train/2319.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation study on English-German, English-French and Chinese-to-English translation tasks. Without weight sharing means no layers are shared in the two AEs." ], "reasoning": "To find the BLEU score for English-to-French translation without the global GANs, look at the row labeled 'Without Global GANs' and the column labeled 'en-fr'. The score is 16.19.", "question": "What is the BLEU score for English-to-French translation without the global GANs?", "answer": "So, the answer is {\"answer\": \"16.19\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The full model achieves the highest BLEU score for English-to-French translation. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe full model achieves the highest BLEU score for English-to-French translation.\n" }, { "role": "assistant", "content": "To verify this statement, compare the BLEU scores for English-to-French translation across all rows. The full model has a score of 16.97, which is higher than any other row. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2319.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation study on English-German, English-French and Chinese-to-English translation tasks. Without weight sharing means no layers are shared in the two AEs." ], "reasoning": "To verify this statement, compare the BLEU scores for English-to-French translation across all rows. The full model has a score of 16.97, which is higher than any other row.", "statement": "The full model achieves the highest BLEU score for English-to-French translation.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Per-relation classification results of SRC and MRC best systems on SciERC.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "SRC and MRC perform identically on SciERC data, with Part-Of and Feature-Of relations having the lowest classification performance.Feature-Of and Part-Of. Since these two relations are not the most underrepresented in the corpus, we theorize that their low classification performance is owed to the natural language text diversity from which they are deduced. In this case, obtaining more annotated instances is one way to boost classifier performance. In terms of the ranked order of performances on the relations, SRC and MRC perform identically on SciERC data." } ], "images": [ "scigen/train/2320.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Per-relation classification results of SRC and MRC best systems on SciERC." ], "reasoning": "SRC and MRC perform identically on SciERC data, with Part-Of and Feature-Of relations having the lowest classification performance.", "description": "Feature-Of and Part-Of. Since these two relations are not the most underrepresented in the corpus, we theorize that their low classification performance is owed to the natural language text diversity from which they are deduced. In this case, obtaining more annotated instances is one way to boost classifier performance. In terms of the ranked order of performances on the relations, SRC and MRC perform identically on SciERC data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 4: Per-relation classification results of SRC and MRC best systems on SciERC.\nQuestion: Which relationship type has the lowest F1 score for both SRC and MRC on SciERC data?\n" }, { "role": "assistant", "content": "By comparing the F1 scores for both SRC and MRC, Feature-Of has the lowest F1 score for both SRC (60.00) and MRC (57.73). So, the answer is {\"answer\": \"Feature-Of\"}" } ], "images": [ "scigen/train/2320.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Per-relation classification results of SRC and MRC best systems on SciERC." ], "reasoning": "By comparing the F1 scores for both SRC and MRC, Feature-Of has the lowest F1 score for both SRC (60.00) and MRC (57.73).", "question": "Which relationship type has the lowest F1 score for both SRC and MRC on SciERC data?", "answer": "So, the answer is {\"answer\": \"Feature-Of\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The Feature-Of relationship type has the lowest precision score for both SRC and MRC on SciERC data., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Feature-Of relationship type has the lowest precision score for both SRC and MRC on SciERC data.\n" }, { "role": "assistant", "content": "By comparing the precision scores for both SRC and MRC, Feature-Of has the lowest precision score for both SRC (59.02) and MRC (47.46). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2320.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Per-relation classification results of SRC and MRC best systems on SciERC." ], "reasoning": "By comparing the precision scores for both SRC and MRC, Feature-Of has the lowest precision score for both SRC (59.02) and MRC (47.46).", "statement": "The Feature-Of relationship type has the lowest precision score for both SRC and MRC on SciERC data.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Scientific relation classification results over three datasets (SemEval18, SciERC, & Combined), four Bert model variants (Bert cased & uncased; SciBert cased & uncased), and two classification strategies (SRC & MRC). Acc. is accuracy and F1 is the macro F1-score; Top scores are in bold; Scores in red from our surveyed systems are those that are on par with the state-of-the-art reported results [6, 26] on SemEval18 and SciERC.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The table shows the accuracy and F1 scores of eight classifiers derived from two classification strategies using four Bert model variants for scientific relation classification.5.2.1 RQ1: What is the impact of the eight classifiers on scientific relation classification? The eight classifiers are obtained from two classification strategies built over four Bert model variants." } ], "images": [ "scigen/train/2321.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Scientific relation classification results over three datasets (SemEval18, SciERC, & Combined), four Bert model variants (Bert cased & uncased; SciBert cased & uncased), and two classification strategies (SRC & MRC). Acc. is accuracy and F1 is the macro F1-score; Top scores are in bold; Scores in red from our surveyed systems are those that are on par with the state-of-the-art reported results [6, 26] on SemEval18 and SciERC." ], "reasoning": "The table shows the accuracy and F1 scores of eight classifiers derived from two classification strategies using four Bert model variants for scientific relation classification.", "description": "5.2.1 RQ1: What is the impact of the eight classifiers on scientific relation classification? The eight classifiers are obtained from two classification strategies built over four Bert model variants.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Scientific relation classification results over three datasets (SemEval18, SciERC, & Combined), four Bert model variants (Bert cased & uncased; SciBert cased & uncased), and two classification strategies (SRC & MRC). Acc. is accuracy and F1 is the macro F1-score; Top scores are in bold; Scores in red from our surveyed systems are those that are on par with the state-of-the-art reported results [6, 26] on SemEval18 and SciERC.', answer the following question. Give detailed solution consisting of each step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich Bert model variant achieved the highest accuracy for the SRC SciERC classifier?\n" }, { "role": "assistant", "content": "To find the highest accuracy for the SRC SciERC classifier, look at the 'SRC SciERC' column under 'Acc.' and identify the highest value. The highest accuracy is 86.86, achieved by the Scibert cased model. So, the answer is {\"answer\": \"Scibert cased\"}." } ], "images": [ "scigen/train/2321.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Scientific relation classification results over three datasets (SemEval18, SciERC, & Combined), four Bert model variants (Bert cased & uncased; SciBert cased & uncased), and two classification strategies (SRC & MRC). Acc. is accuracy and F1 is the macro F1-score; Top scores are in bold; Scores in red from our surveyed systems are those that are on par with the state-of-the-art reported results [6, 26] on SemEval18 and SciERC." ], "reasoning": "To find the highest accuracy for the SRC SciERC classifier, look at the 'SRC SciERC' column under 'Acc.' and identify the highest value. The highest accuracy is 86.86, achieved by the Scibert cased model.", "question": "Which Bert model variant achieved the highest accuracy for the SRC SciERC classifier?", "answer": "So, the answer is {\"answer\": \"Scibert cased\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Bert-base uncased model achieved the highest F1 score for the MRC SemEval18 classifier. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Bert-base uncased model achieved the highest F1 score for the MRC SemEval18 classifier.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'MRC SemEval18' column under 'F1' and compare the scores of all models. The highest F1 score is 83.42, achieved by the Scibert cased model, not the Bert-base uncased model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2321.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Scientific relation classification results over three datasets (SemEval18, SciERC, & Combined), four Bert model variants (Bert cased & uncased; SciBert cased & uncased), and two classification strategies (SRC & MRC). Acc. is accuracy and F1 is the macro F1-score; Top scores are in bold; Scores in red from our surveyed systems are those that are on par with the state-of-the-art reported results [6, 26] on SemEval18 and SciERC." ], "reasoning": "To verify this statement, look at the 'MRC SemEval18' column under 'F1' and compare the scores of all models. The highest F1 score is 83.42, achieved by the Scibert cased model, not the Bert-base uncased model.", "statement": "The Bert-base uncased model achieved the highest F1 score for the MRC SemEval18 classifier.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Human Accuracy increases after fine-tuning the models. Humans also prefer gradient-based reduced examples over randomly reduced ones, indicating that the reduced examples are more meaningful to humans after regularization.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Human preference and performance increased for reduced examples compared to random ones, indicating that model pathologies have been mitigated.We also repeat the vs. Random experiment : we re-generate the random examples to match the lengths of the new reduced examples from input reduction, and find humans now prefer the reduced examples to random ones. The increase in both human performance and preference suggests that the reduced examples are more reasonable; model pathologies have been mitigated." } ], "images": [ "scigen/train/2322.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Human Accuracy increases after fine-tuning the models. Humans also prefer gradient-based reduced examples over randomly reduced ones, indicating that the reduced examples are more meaningful to humans after regularization." ], "reasoning": "Human preference and performance increased for reduced examples compared to random ones, indicating that model pathologies have been mitigated.", "description": "We also repeat the vs. Random experiment : we re-generate the random examples to match the lengths of the new reduced examples from input reduction, and find humans now prefer the reduced examples to random ones. The increase in both human performance and preference suggests that the reduced examples are more reasonable; model pathologies have been mitigated.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 3: Human Accuracy increases after fine-tuning the models. Humans also prefer gradient-based reduced examples over randomly reduced ones, indicating that the reduced examples are more meaningful to humans after regularization.', solve this question and give detail solutions. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich dataset showed the highest increase in accuracy after input reduction?\n" }, { "role": "assistant", "content": "To find the dataset with the highest increase in accuracy, subtract the 'Accuracy Before' from the 'Accuracy After' for each dataset and compare the results. So, the answer is {\"answer\": \"SQuAD\"}." } ], "images": [ "scigen/train/2322.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Human Accuracy increases after fine-tuning the models. Humans also prefer gradient-based reduced examples over randomly reduced ones, indicating that the reduced examples are more meaningful to humans after regularization." ], "reasoning": "To find the dataset with the highest increase in accuracy, subtract the 'Accuracy Before' from the 'Accuracy After' for each dataset and compare the results.", "question": "Which dataset showed the highest increase in accuracy after input reduction?", "answer": "So, the answer is {\"answer\": \"SQuAD\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe VQA dataset had the highest accuracy before input reduction.\n" }, { "role": "assistant", "content": "The table shows the 'Accuracy Before' for each dataset. Comparing these values, SNLI-C had the highest accuracy before input reduction, not VQA. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2322.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Human Accuracy increases after fine-tuning the models. Humans also prefer gradient-based reduced examples over randomly reduced ones, indicating that the reduced examples are more meaningful to humans after regularization." ], "reasoning": "The table shows the 'Accuracy Before' for each dataset. Comparing these values, SNLI-C had the highest accuracy before input reduction, not VQA.", "statement": "The VQA dataset had the highest accuracy before input reduction.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Results on word similarity, and analogical tasks. Best performances bolded per task. Dimensionality of the meta embedding is shown next to the source embedding names.'.\n" }, { "role": "assistant", "content": "Concatenation generally performs better overall, but averaging improves performance for SL and GL tasks with CBOW and GloVe, and maintains a manageable dimensionality.In our experiments concatenation obtains better overall performance. However, averaging offers improvements over the source embedding sets for semantic similarity task SL and word analogy task GL, on the combination of CBOW and GloVe. HLBL has a negative effect on CBOW and GloVe, but the performance of averaging is close to that of concatenation. An advantage of averaging when compared against concatenation, is that the dimensionality of the produced meta-embedding is not increased beyond the maximum dimension present within the source embeddings, resulting in a meta-embedding which is easier to process and store." } ], "images": [ "scigen/train/2323.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on word similarity, and analogical tasks. Best performances bolded per task. Dimensionality of the meta embedding is shown next to the source embedding names." ], "reasoning": "Concatenation generally performs better overall, but averaging improves performance for SL and GL tasks with CBOW and GloVe, and maintains a manageable dimensionality.", "description": "In our experiments concatenation obtains better overall performance. However, averaging offers improvements over the source embedding sets for semantic similarity task SL and word analogy task GL, on the combination of CBOW and GloVe. HLBL has a negative effect on CBOW and GloVe, but the performance of averaging is close to that of concatenation. An advantage of averaging when compared against concatenation, is that the dimensionality of the produced meta-embedding is not increased beyond the maximum dimension present within the source embeddings, resulting in a meta-embedding which is easier to process and store.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: Results on word similarity, and analogical tasks. Best performances bolded per task. Dimensionality of the meta embedding is shown next to the source embedding names.'.\n\nQuestion: Which embedding method shows the highest performance in the MC task?\n" }, { "role": "assistant", "content": "To find the highest performance in the MC task, we need to compare the MC scores across all embedding methods. The highest MC score is 88.8, achieved by the concatenation of GloVe and CBOW 600. So, the answer is {\"answer\": \"GloVe+CBOW 600\"}" } ], "images": [ "scigen/train/2323.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on word similarity, and analogical tasks. Best performances bolded per task. Dimensionality of the meta embedding is shown next to the source embedding names." ], "reasoning": "To find the highest performance in the MC task, we need to compare the MC scores across all embedding methods. The highest MC score is 88.8, achieved by the concatenation of GloVe and CBOW 600.", "question": "Which embedding method shows the highest performance in the MC task?", "answer": "So, the answer is {\"answer\": \"GloVe+CBOW 600\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nAveraging GloVe and CBOW 300 embeddings results in the highest performance for the SL task.\n" }, { "role": "assistant", "content": "The table shows that the highest performance for the SL task is 46.5, achieved by averaging GloVe and CBOW 300 embeddings. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2323.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on word similarity, and analogical tasks. Best performances bolded per task. Dimensionality of the meta embedding is shown next to the source embedding names." ], "reasoning": "The table shows that the highest performance for the SL task is 46.5, achieved by averaging GloVe and CBOW 300 embeddings.", "statement": "Averaging GloVe and CBOW 300 embeddings results in the highest performance for the SL task.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Comparison of ReDAN to state-of-the-art visual dialog models on the blind test-std v1.0 set, as reported by the test server. (†) taken from https://evalai.cloudcv.org/web/challenges/challenge-page/161/leaderboard/483. (‡) taken from https://evalai.cloudcv.org/web/challenges/challenge-page/103/leaderboard/298.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "ReDAN+ achieves the highest NDCG score of 64.47, outperforming the state-of-the-art method DAN Kottur et al. (2019) on the VisDial test-std v1.0 set.S4SS4SSS0Px2 Results on VisDial test-std v1.0 We also evaluate the proposed ReDAN on the blind test-std v1.0 set, by submitting results to the online evaluation server. By using a diverse set of ensembles, ReDAN+ outperforms the state of the art method, DAN Kottur et al." } ], "images": [ "scigen/train/2324.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of ReDAN to state-of-the-art visual dialog models on the blind test-std v1.0 set, as reported by the test server. (†) taken from https://evalai.cloudcv.org/web/challenges/challenge-page/161/leaderboard/483. (‡) taken from https://evalai.cloudcv.org/web/challenges/challenge-page/103/leaderboard/298." ], "reasoning": "ReDAN+ achieves the highest NDCG score of 64.47, outperforming the state-of-the-art method DAN Kottur et al. (2019) on the VisDial test-std v1.0 set.", "description": "S4SS4SSS0Px2 Results on VisDial test-std v1.0 We also evaluate the proposed ReDAN on the blind test-std v1.0 set, by submitting results to the online evaluation server. By using a diverse set of ensembles, ReDAN+ outperforms the state of the art method, DAN Kottur et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 4: Comparison of ReDAN to state-of-the-art visual dialog models on the blind test-std v1.0 set, as reported by the test server. (†) taken from https://evalai.cloudcv.org/web/challenges/challenge-page/161/leaderboard/483. (‡) taken from https://evalai.cloudcv.org/web/challenges/challenge-page/103/leaderboard/298.\nQuestion: Which model has the highest NDCG score and what is that score?\n" }, { "role": "assistant", "content": "By examining the NDCG column, we see that ReDAN+ has the highest score of 64.47. So, the answer is {\"answer\": \"ReDAN+ with 64.47\"}." } ], "images": [ "scigen/train/2324.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of ReDAN to state-of-the-art visual dialog models on the blind test-std v1.0 set, as reported by the test server. (†) taken from https://evalai.cloudcv.org/web/challenges/challenge-page/161/leaderboard/483. (‡) taken from https://evalai.cloudcv.org/web/challenges/challenge-page/103/leaderboard/298." ], "reasoning": "By examining the NDCG column, we see that ReDAN+ has the highest score of 64.47.", "question": "Which model has the highest NDCG score and what is that score?", "answer": "So, the answer is {\"answer\": \"ReDAN+ with 64.47\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nReDAN+ has a higher NDCG score than NMN Kottur et al. (2018).\n" }, { "role": "assistant", "content": "ReDAN+ has an NDCG score of 64.47, while NMN Kottur et al. (2018) has an NDCG score of 58.10. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2324.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of ReDAN to state-of-the-art visual dialog models on the blind test-std v1.0 set, as reported by the test server. (†) taken from https://evalai.cloudcv.org/web/challenges/challenge-page/161/leaderboard/483. (‡) taken from https://evalai.cloudcv.org/web/challenges/challenge-page/103/leaderboard/298." ], "reasoning": "ReDAN+ has an NDCG score of 64.47, while NMN Kottur et al. (2018) has an NDCG score of 58.10.", "statement": "ReDAN+ has a higher NDCG score than NMN Kottur et al. (2018).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 5: Question-type analysis of the NDCG score achieved by different models on the val v1.0 set.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Generative models perform better on Yes/no questions, which make up 75% of all questions, leading to their overall better performance. Combining discriminative and generative models boosts the final NDCG score significantly.We further perform a question-type analysis of the NDCG scores achieved by different models. We classify questions into 4 categories: Yes/no, Number, Color, and Others. no questions, while discriminative models performed better on all the other types of questions. We hypothesize that this is due to that generative models tend to ranking short answers higher, thus is beneficial for Yes/no questions. Since Yes/no questions take a majority of all the questions (75%), the better performance of generative models on the Yes/no questions translated into an overall better performance of generative models. Aggregating the ranking results of both discriminative and generative models results in the mutual enhancement of each other, and therefore boosting the final NDCG score by a large margin. Also, we observe that the Number questions are most difficult to answer, since training a model to count is a challenging research problem." } ], "images": [ "scigen/train/2325.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Question-type analysis of the NDCG score achieved by different models on the val v1.0 set." ], "reasoning": "Generative models perform better on Yes/no questions, which make up 75% of all questions, leading to their overall better performance. Combining discriminative and generative models boosts the final NDCG score significantly.", "description": "We further perform a question-type analysis of the NDCG scores achieved by different models. We classify questions into 4 categories: Yes/no, Number, Color, and Others. no questions, while discriminative models performed better on all the other types of questions. We hypothesize that this is due to that generative models tend to ranking short answers higher, thus is beneficial for Yes/no questions. Since Yes/no questions take a majority of all the questions (75%), the better performance of generative models on the Yes/no questions translated into an overall better performance of generative models. Aggregating the ranking results of both discriminative and generative models results in the mutual enhancement of each other, and therefore boosting the final NDCG score by a large margin. Also, we observe that the Number questions are most difficult to answer, since training a model to count is a challenging research problem.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 5: Question-type analysis of the NDCG score achieved by different models on the val v1.0 set.' and proceed to solve the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the NDCG score for the ReDAN+ model on Number questions?\n" }, { "role": "assistant", "content": "To find the NDCG score for the ReDAN+ model on Number questions, look at the intersection of the 'ReDAN+' row and the 'Number' column in the table. So, the answer is {\"answer\": \"50.10\"}." } ], "images": [ "scigen/train/2325.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Question-type analysis of the NDCG score achieved by different models on the val v1.0 set." ], "reasoning": "To find the NDCG score for the ReDAN+ model on Number questions, look at the intersection of the 'ReDAN+' row and the 'Number' column in the table.", "question": "What is the NDCG score for the ReDAN+ model on Number questions?", "answer": "So, the answer is {\"answer\": \"50.10\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The generative model performs better than the discriminative model on Color questions., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe generative model performs better than the discriminative model on Color questions.\n" }, { "role": "assistant", "content": "The table shows that the NDCG score for the generative model on Color questions is 52.16, while the score for the discriminative model is 58.13. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2325.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Question-type analysis of the NDCG score achieved by different models on the val v1.0 set." ], "reasoning": "The table shows that the NDCG score for the generative model on Color questions is 52.16, while the score for the discriminative model is 58.13. Therefore, the statement is incorrect.", "statement": "The generative model performs better than the discriminative model on Color questions.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'TABLE I: The WER and the CER performance (%) of the SR algorithm with respect to the beam width.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The performance of the proposed system improves with larger RNNs, increased beam width, and the application of word-level LM, while maintaining energy efficiency by avoiding WFST networks.The word error rate (WER) and character error rate ( CER) performances of the proposed system with respect to the size of the RNNs and The small-model represents the system with 3×256 RNN AM and 2×256 RNN LM while the large-model employs 4×512 RNN AM and 2×512 RNN LM. The table shows that the performance improves when the beam width or the network size increases. Also, combining the word-level LM improves the performance especially when the network size is small. Of course, the best advantage we expect is the energy efficiency since we do not employ a WFST network which demands a large amount of computation and memory accesses. The algorithm also uses the WFST decoding network to combine the results of acoustic modeling, lexicon, and the word-level LM. Note that the compared system does not use the character-level RNN because the WFST network embeds the lexicon. However, the WFST-based decoding demands a large memory space to search, and thus the algorithm is hard to be power efficient. On the other hand, our algorithm employs the character-level LM in addition to the word-level LM, and uses simple beam-search in decoding that requires far less memory. The RNNs of the proposed algorithm are implemented using only on-chip memory for energy efficiency. Note that the recognition performance of our system can be further improved by employing larger RNNs or increasing the beam width." } ], "images": [ "scigen/train/2326.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: The WER and the CER performance (%) of the SR algorithm with respect to the beam width." ], "reasoning": "The performance of the proposed system improves with larger RNNs, increased beam width, and the application of word-level LM, while maintaining energy efficiency by avoiding WFST networks.", "description": "The word error rate (WER) and character error rate ( CER) performances of the proposed system with respect to the size of the RNNs and The small-model represents the system with 3×256 RNN AM and 2×256 RNN LM while the large-model employs 4×512 RNN AM and 2×512 RNN LM. The table shows that the performance improves when the beam width or the network size increases. Also, combining the word-level LM improves the performance especially when the network size is small. Of course, the best advantage we expect is the energy efficiency since we do not employ a WFST network which demands a large amount of computation and memory accesses. The algorithm also uses the WFST decoding network to combine the results of acoustic modeling, lexicon, and the word-level LM. Note that the compared system does not use the character-level RNN because the WFST network embeds the lexicon. However, the WFST-based decoding demands a large memory space to search, and thus the algorithm is hard to be power efficient. On the other hand, our algorithm employs the character-level LM in addition to the word-level LM, and uses simple beam-search in decoding that requires far less memory. The RNNs of the proposed algorithm are implemented using only on-chip memory for energy efficiency. Note that the recognition performance of our system can be further improved by employing larger RNNs or increasing the beam width.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'TABLE I: The WER and the CER performance (%) of the SR algorithm with respect to the beam width.', solve this question and give detail solutions. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the WER/CER for the large model with a beam width of 256 and no word LM applied?\n" }, { "role": "assistant", "content": "To find the WER/CER for the large model with a beam width of 256 and no word LM applied, locate the row in the table where the network is 'Large model', the word LM is 'none', and the beam width is 256. The corresponding WER/CER is 8.86 / 3.71. So, the answer is {\"answer\": \"8.86 / 3.71\"}." } ], "images": [ "scigen/train/2326.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: The WER and the CER performance (%) of the SR algorithm with respect to the beam width." ], "reasoning": "To find the WER/CER for the large model with a beam width of 256 and no word LM applied, locate the row in the table where the network is 'Large model', the word LM is 'none', and the beam width is 256. The corresponding WER/CER is 8.86 / 3.71.", "question": "What is the WER/CER for the large model with a beam width of 256 and no word LM applied?", "answer": "So, the answer is {\"answer\": \"8.86 / 3.71\"}." }, { "messages": [ { "role": "user", "content": "The small model with a beam width of 128 and no word LM applied has a WER of 13.45. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe small model with a beam width of 128 and no word LM applied has a WER of 13.45.\n" }, { "role": "assistant", "content": "To verify the statement, locate the row in the table where the network is 'Small model', the word LM is 'none', and the beam width is 128. The corresponding WER is 13.45, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2326.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: The WER and the CER performance (%) of the SR algorithm with respect to the beam width." ], "reasoning": "To verify the statement, locate the row in the table where the network is 'Small model', the word LM is 'none', and the beam width is 128. The corresponding WER is 13.45, which matches the statement.", "statement": "The small model with a beam width of 128 and no word LM applied has a WER of 13.45.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: Experimental results'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "BERT outperforms BiDAF by 14.5 to 19 percentage points, while Human performance exceeds BERT by 14.8 to 15.5 percentage points, indicating potential for future model improvements.From this table, it is obvious that BERT is 14.5∼19 percentage points higher than BiDAF, and Human performance is 14.8∼15.5 percentage points higher that BERT. This implies that models could be improved markedly in future research." } ], "images": [ "scigen/train/2327.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Experimental results" ], "reasoning": "BERT outperforms BiDAF by 14.5 to 19 percentage points, while Human performance exceeds BERT by 14.8 to 15.5 percentage points, indicating potential for future model improvements.", "description": "From this table, it is obvious that BERT is 14.5∼19 percentage points higher than BiDAF, and Human performance is 14.8∼15.5 percentage points higher that BERT. This implies that models could be improved markedly in future research.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: Experimental results' to sequentially solve the question presented below. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nBy how many percentage points does Human performance exceed BiDAF in the Criminal category?\n" }, { "role": "assistant", "content": "Human performance in the Criminal category is 92.7, while BiDAF performance is 62.7. The difference is 92.7 - 62.7 = 30 percentage points. So, the answer is {\"answer\": \"30\"}." } ], "images": [ "scigen/train/2327.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Experimental results" ], "reasoning": "Human performance in the Criminal category is 92.7, while BiDAF performance is 62.7. The difference is 92.7 - 62.7 = 30 percentage points.", "question": "By how many percentage points does Human performance exceed BiDAF in the Criminal category?", "answer": "So, the answer is {\"answer\": \"30\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nBERT's performance in the Civil category is 18.2 percentage points higher than BiDAF.\n" }, { "role": "assistant", "content": "BERT's performance in the Civil category is 80.1, while BiDAF's performance is 61.1. The difference is 80.1 - 61.1 = 19 percentage points, not 18.2. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2327.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Experimental results" ], "reasoning": "BERT's performance in the Civil category is 80.1, while BiDAF's performance is 61.1. The difference is 80.1 - 61.1 = 19 percentage points, not 18.2.", "statement": "BERT's performance in the Civil category is 18.2 percentage points higher than BiDAF.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Comparisons of different types of questions.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Human agreement on criminal data is lower than on civil data due to more experience gained while marking civil data, leading to more consistent granularity on 'Span' questions.We observe that humans maintain high consistency on all types of questions, especially on the “YES” questions. The human agreement on criminal data is lower than that on civil data. This is partly because that we firstly annotate the criminal data, and then have more experience when marking the civil data. It could result in a more consistent granularity of the selected segments on the “Span” questions." } ], "images": [ "scigen/train/2328.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparisons of different types of questions." ], "reasoning": "Human agreement on criminal data is lower than on civil data due to more experience gained while marking civil data, leading to more consistent granularity on 'Span' questions.", "description": "We observe that humans maintain high consistency on all types of questions, especially on the “YES” questions. The human agreement on criminal data is lower than that on civil data. This is partly because that we firstly annotate the criminal data, and then have more experience when marking the civil data. It could result in a more consistent granularity of the selected segments on the “Span” questions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 5: Comparisons of different types of questions.' and methodically tackle the question that follows. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the human agreement percentage on 'YES' questions for civil data during the development phase?\n" }, { "role": "assistant", "content": "To find the human agreement percentage on 'YES' questions for civil data during the development phase, look at the 'Development Human' column under 'Civil' and 'YES'. The value is 96.9. So, the answer is {\"answer\": \"96.9\"}" } ], "images": [ "scigen/train/2328.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparisons of different types of questions." ], "reasoning": "To find the human agreement percentage on 'YES' questions for civil data during the development phase, look at the 'Development Human' column under 'Civil' and 'YES'. The value is 96.9.", "question": "What is the human agreement percentage on 'YES' questions for civil data during the development phase?", "answer": "So, the answer is {\"answer\": \"96.9\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence Human agreement on 'NO' questions for criminal data during the development phase is higher than for civil data. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nHuman agreement on 'NO' questions for criminal data during the development phase is higher than for civil data.\n" }, { "role": "assistant", "content": "To verify this statement, compare the human agreement percentages on 'NO' questions for criminal and civil data during the development phase. For criminal data, it is 87.8, and for civil data, it is 94.2. Since 87.8 is not higher than 94.2, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2328.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparisons of different types of questions." ], "reasoning": "To verify this statement, compare the human agreement percentages on 'NO' questions for criminal and civil data during the development phase. For criminal data, it is 87.8, and for civil data, it is 94.2. Since 87.8 is not higher than 94.2, the statement is refuted.", "statement": "Human agreement on 'NO' questions for criminal data during the development phase is higher than for civil data.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 6: Comparison data of unanswerable questions and “NO” questions, where unanswerable+ denotes adding extra unanswerable questions on the training set of the civil data.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Unanswerable questions have lower performance than 'NO' questions on criminal data despite having more questions, indicating they are harder. Increasing unanswerable questions in civil data also results in lower performance compared to 'NO' questions.It is easy to verify the first cause via observing the corpus. To verify the second point, we compare the unanswerable questions and the “NO” questions. The first two rows show that unanswerable questions presents a lower performance than the other on the criminal data, even though the former owns more questions. This has basically illustrated that the unanswerable questions are more hard. We have further experimented with increasing the number of unanswerable questions of civil data on the training set. However, despite having a larger amount of questions for unanswerable questions, it presents a lower score than “NO” questions." } ], "images": [ "scigen/train/2329.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Comparison data of unanswerable questions and “NO” questions, where unanswerable+ denotes adding extra unanswerable questions on the training set of the civil data." ], "reasoning": "Unanswerable questions have lower performance than 'NO' questions on criminal data despite having more questions, indicating they are harder. Increasing unanswerable questions in civil data also results in lower performance compared to 'NO' questions.", "description": "It is easy to verify the first cause via observing the corpus. To verify the second point, we compare the unanswerable questions and the “NO” questions. The first two rows show that unanswerable questions presents a lower performance than the other on the criminal data, even though the former owns more questions. This has basically illustrated that the unanswerable questions are more hard. We have further experimented with increasing the number of unanswerable questions of civil data on the training set. However, despite having a larger amount of questions for unanswerable questions, it presents a lower score than “NO” questions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 6: Comparison data of unanswerable questions and “NO” questions, where unanswerable+ denotes adding extra unanswerable questions on the training set of the civil data.', unfold the steps to solve the question given next. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nQuestion: What is the performance difference between 'Unanswerable' and 'NO' questions on the criminal test set?\n" }, { "role": "assistant", "content": "To find the performance difference, subtract the performance of 'Unanswerable' questions (65.6) from the performance of 'NO' questions (69.7) on the criminal test set. So, the answer is {\"answer\": \"4.1\"}" } ], "images": [ "scigen/train/2329.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Comparison data of unanswerable questions and “NO” questions, where unanswerable+ denotes adding extra unanswerable questions on the training set of the civil data." ], "reasoning": "To find the performance difference, subtract the performance of 'Unanswerable' questions (65.6) from the performance of 'NO' questions (69.7) on the criminal test set.", "question": "What is the performance difference between 'Unanswerable' and 'NO' questions on the criminal test set?", "answer": "So, the answer is {\"answer\": \"4.1\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence Unanswerable questions have a higher performance than 'NO' questions on the civil test set. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nUnanswerable questions have a higher performance than 'NO' questions on the civil test set.\n" }, { "role": "assistant", "content": "The table shows that 'Unanswerable' questions have a performance of 67.7 on the civil test set, while 'NO' questions have a performance of 72.9, indicating that 'Unanswerable' questions have a lower performance. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2329.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Comparison data of unanswerable questions and “NO” questions, where unanswerable+ denotes adding extra unanswerable questions on the training set of the civil data." ], "reasoning": "The table shows that 'Unanswerable' questions have a performance of 67.7 on the civil test set, while 'NO' questions have a performance of 72.9, indicating that 'Unanswerable' questions have a lower performance.", "statement": "Unanswerable questions have a higher performance than 'NO' questions on the civil test set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: Influence of unanswerable questions. Implement BERT and BiDAF on development set and test set. +Train stands for increasing the number of unanswerable questions on the training set. -Dev-Test means no adjusting the number of unanswerable questions on the development set and the test set.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Increasing the proportion of unanswerable questions in development and test sets is more effective in increasing the difficulty of the dataset compared to reducing the number of unanswerable questions in the training set.CJRC represents that we only increase the number of unanswerable answers on the development and the test set without changes on the training set. CJRC+Train stands for adjusting all the datasets. CJRC-Dev-Test means no adjusting any of the datasets. CJRC+Train-Dev-Test means only increasing the number of unanswerable questions of the training set. In terms of BERT, before adjustment, the gap with human indicator is 9.8%, but after adjustment, the gap increases to 15.2%. By comparing CJRC+Train and CJRC (or comparing CJRC+Train-Dev-Test and CJRC-Dev-Test), we can conclude that BiDAF cannot handle unanswerable questions effectively. Increasing the proportion of unanswerable questions in development and test sets is more effective in increasing the difficulty of the dataset, compared with reducing the number of unanswerable questions of the training set (get the conclusion by observing CJRC, CJRC+Train and CJRC-Dev-Test)." } ], "images": [ "scigen/train/2330.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Influence of unanswerable questions. Implement BERT and BiDAF on development set and test set. +Train stands for increasing the number of unanswerable questions on the training set. -Dev-Test means no adjusting the number of unanswerable questions on the development set and the test set." ], "reasoning": "Increasing the proportion of unanswerable questions in development and test sets is more effective in increasing the difficulty of the dataset compared to reducing the number of unanswerable questions in the training set.", "description": "CJRC represents that we only increase the number of unanswerable answers on the development and the test set without changes on the training set. CJRC+Train stands for adjusting all the datasets. CJRC-Dev-Test means no adjusting any of the datasets. CJRC+Train-Dev-Test means only increasing the number of unanswerable questions of the training set. In terms of BERT, before adjustment, the gap with human indicator is 9.8%, but after adjustment, the gap increases to 15.2%. By comparing CJRC+Train and CJRC (or comparing CJRC+Train-Dev-Test and CJRC-Dev-Test), we can conclude that BiDAF cannot handle unanswerable questions effectively. Increasing the proportion of unanswerable questions in development and test sets is more effective in increasing the difficulty of the dataset, compared with reducing the number of unanswerable questions of the training set (get the conclusion by observing CJRC, CJRC+Train and CJRC-Dev-Test).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 7: Influence of unanswerable questions. Implement BERT and BiDAF on development set and test set. +Train stands for increasing the number of unanswerable questions on the training set. -Dev-Test means no adjusting the number of unanswerable questions on the development set and the test set.', unfold the steps to solve the question given next. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nQuestion: What is the overall BERT score for CJRC+Train-Dev-Test on the test set?\n" }, { "role": "assistant", "content": "To find the overall BERT score for CJRC+Train-Dev-Test on the test set, look at the 'Test' row under 'Overall' in the 'Bert' columns. The score is 83.3. So, the answer is {\"answer\": \"83.3\"}" } ], "images": [ "scigen/train/2330.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Influence of unanswerable questions. Implement BERT and BiDAF on development set and test set. +Train stands for increasing the number of unanswerable questions on the training set. -Dev-Test means no adjusting the number of unanswerable questions on the development set and the test set." ], "reasoning": "To find the overall BERT score for CJRC+Train-Dev-Test on the test set, look at the 'Test' row under 'Overall' in the 'Bert' columns. The score is 83.3.", "question": "What is the overall BERT score for CJRC+Train-Dev-Test on the test set?", "answer": "So, the answer is {\"answer\": \"83.3\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nCJRC+Train-Dev-Test has a higher overall BERT score on the development set compared to CJRC-Dev-Test.\n" }, { "role": "assistant", "content": "By comparing the overall BERT scores on the development set, CJRC+Train-Dev-Test has a score of 83.3, while CJRC-Dev-Test has a score of 82.9. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2330.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Influence of unanswerable questions. Implement BERT and BiDAF on development set and test set. +Train stands for increasing the number of unanswerable questions on the training set. -Dev-Test means no adjusting the number of unanswerable questions on the development set and the test set." ], "reasoning": "By comparing the overall BERT scores on the development set, CJRC+Train-Dev-Test has a score of 83.3, while CJRC-Dev-Test has a score of 82.9. Therefore, the statement is supported.", "statement": "CJRC+Train-Dev-Test has a higher overall BERT score on the development set compared to CJRC-Dev-Test.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 6: UNK number after using the revision memory.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The revision memory reduces the number of UNK tokens in final translation outputs by 16.1%, indicating it helps the model avoid repeating mistakes.We also compare the number of UNK in final translation outputs, between adopting revision memory or not. This indicates that the revision memory enables our model to remember the past revisions, which helps avoid making same mistakes, e.g., UNK." } ], "images": [ "scigen/train/2331.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: UNK number after using the revision memory." ], "reasoning": "The revision memory reduces the number of UNK tokens in final translation outputs by 16.1%, indicating it helps the model avoid repeating mistakes.", "description": "We also compare the number of UNK in final translation outputs, between adopting revision memory or not. This indicates that the revision memory enables our model to remember the past revisions, which helps avoid making same mistakes, e.g., UNK.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: Table 6: UNK number after using the revision memory.\nQuestion: What is the percentage reduction in the number of UNK tokens when using revision memory?\n" }, { "role": "assistant", "content": "The table shows that the number of UNK tokens decreases from 2052 to 1723 when using revision memory, which is a reduction of 16.1%. So, the answer is {\"answer\": \"16.1%\"}." } ], "images": [ "scigen/train/2331.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: UNK number after using the revision memory." ], "reasoning": "The table shows that the number of UNK tokens decreases from 2052 to 1723 when using revision memory, which is a reduction of 16.1%.", "question": "What is the percentage reduction in the number of UNK tokens when using revision memory?", "answer": "So, the answer is {\"answer\": \"16.1%\"}." }, { "messages": [ { "role": "user", "content": "The use of revision memory increases the number of UNK tokens in the final translation outputs. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe use of revision memory increases the number of UNK tokens in the final translation outputs.\n" }, { "role": "assistant", "content": "The table shows that the number of UNK tokens decreases from 2052 to 1723 when using revision memory, indicating a reduction rather than an increase. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2331.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: UNK number after using the revision memory." ], "reasoning": "The table shows that the number of UNK tokens decreases from 2052 to 1723 when using revision memory, indicating a reduction rather than an increase.", "statement": "The use of revision memory increases the number of UNK tokens in the final translation outputs.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Average BLEU score of our CAMIT’s bi-directional decoder after multiple interactions on the NIST ZH-EN task.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "BiDiR achieves a significant BLEU improvement over the Transformer baseline, especially after multiple revisions, with RNNSearch obtaining higher BLEU scores after 4 revisions.We also apply BiDiR decoder on Transformer with the same setting of RNNSearch. By revision once, BiDiR achieves an absolute BLEU improvement of 6.93, upon a strong Transformer baseline (43.65 to 50.58), significantly outperforming UniDiR (+2.04) and \\text{UniDiR}_{\\text{G}} (+4.57). Results show that although the baseline of Transformer is significantly better than RNNSearch, both of them give pretty high BLEU scores (around 60) after 4 revisions. RNNSearch even obtains higher BLEU scores. For convenience, we will only report results based on RNNSearch in followed experiments." } ], "images": [ "scigen/train/2332.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Average BLEU score of our CAMIT’s bi-directional decoder after multiple interactions on the NIST ZH-EN task." ], "reasoning": "BiDiR achieves a significant BLEU improvement over the Transformer baseline, especially after multiple revisions, with RNNSearch obtaining higher BLEU scores after 4 revisions.", "description": "We also apply BiDiR decoder on Transformer with the same setting of RNNSearch. By revision once, BiDiR achieves an absolute BLEU improvement of 6.93, upon a strong Transformer baseline (43.65 to 50.58), significantly outperforming UniDiR (+2.04) and \\text{UniDiR}_{\\text{G}} (+4.57). Results show that although the baseline of Transformer is significantly better than RNNSearch, both of them give pretty high BLEU scores (around 60) after 4 revisions. RNNSearch even obtains higher BLEU scores. For convenience, we will only report results based on RNNSearch in followed experiments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Average BLEU score of our CAMIT’s bi-directional decoder after multiple interactions on the NIST ZH-EN task.', solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the BLEU score of BiDiR after 4 revisions?\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for different models and revisions. For BiDiR, the BLEU score after 4 revisions is 59.91. So, the answer is {\"answer\": \"59.91\"}" } ], "images": [ "scigen/train/2332.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Average BLEU score of our CAMIT’s bi-directional decoder after multiple interactions on the NIST ZH-EN task." ], "reasoning": "The table shows the BLEU scores for different models and revisions. For BiDiR, the BLEU score after 4 revisions is 59.91.", "question": "What is the BLEU score of BiDiR after 4 revisions?", "answer": "So, the answer is {\"answer\": \"59.91\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nBiDiR achieves a BLEU score of 57.21 after 4 revisions.\n" }, { "role": "assistant", "content": "The table shows that BiDiR achieves a BLEU score of 57.21 after 4 revisions. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2332.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Average BLEU score of our CAMIT’s bi-directional decoder after multiple interactions on the NIST ZH-EN task." ], "reasoning": "The table shows that BiDiR achieves a BLEU score of 57.21 after 4 revisions.", "statement": "BiDiR achieves a BLEU score of 57.21 after 4 revisions.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: The performance gain from online learning (OL) and revision memory (RM) on the NIST ZH-EN task. BiDiR is CAMIT without OL and RM .', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Incorporating history learning into BiDiR improves the BLEU score by 0.95, achieving over 50 BLEU score points, indicating that learning from interaction history enhances translation performance.Additionally, we find that incorporating history learning into BiDiR can still give about 1 absolute BLEU score improvement (+0.95), which is achieved on a very strong baseline with over 50 BLEU score points. This experiment shows that learning from the interaction history is helpful to boost the translation performance. In such case, human will take less revision number for obtaining satisfactory translations." } ], "images": [ "scigen/train/2333.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The performance gain from online learning (OL) and revision memory (RM) on the NIST ZH-EN task. BiDiR is CAMIT without OL and RM ." ], "reasoning": "Incorporating history learning into BiDiR improves the BLEU score by 0.95, achieving over 50 BLEU score points, indicating that learning from interaction history enhances translation performance.", "description": "Additionally, we find that incorporating history learning into BiDiR can still give about 1 absolute BLEU score improvement (+0.95), which is achieved on a very strong baseline with over 50 BLEU score points. This experiment shows that learning from the interaction history is helpful to boost the translation performance. In such case, human will take less revision number for obtaining satisfactory translations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: The performance gain from online learning (OL) and revision memory (RM) on the NIST ZH-EN task. BiDiR is CAMIT without OL and RM .', and sequentially solve the question, writing out each step of your reasoning process. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the average score (AVE.) for the CAMIT model under the BiDiR framework?\n" }, { "role": "assistant", "content": "The table shows the average score (AVE.) for the CAMIT model under the BiDiR framework as 52.66. So, the answer is {\"answer\": \"52.66\"}" } ], "images": [ "scigen/train/2333.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The performance gain from online learning (OL) and revision memory (RM) on the NIST ZH-EN task. BiDiR is CAMIT without OL and RM ." ], "reasoning": "The table shows the average score (AVE.) for the CAMIT model under the BiDiR framework as 52.66.", "question": "What is the average score (AVE.) for the CAMIT model under the BiDiR framework?", "answer": "So, the answer is {\"answer\": \"52.66\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe CAMIT model under the BiDiR framework achieved the highest average score.\n" }, { "role": "assistant", "content": "The table shows that the CAMIT model under the BiDiR framework has the highest average score (AVE.) of 52.66, which is higher than any other model listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2333.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The performance gain from online learning (OL) and revision memory (RM) on the NIST ZH-EN task. BiDiR is CAMIT without OL and RM ." ], "reasoning": "The table shows that the CAMIT model under the BiDiR framework has the highest average score (AVE.) of 52.66, which is higher than any other model listed.", "statement": "The CAMIT model under the BiDiR framework achieved the highest average score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: Comparison between annotators 1 and 3.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Annotators 2 and 3 have higher k-alpha values for most variables, especially psychological terms, but show low agreement on 'happiness' and 'valence'. Annotator 1 shows the greatest discrepancies, particularly with annotator 3, where only 'Illusion' has a k-alpha greater than 0.21.There is a slight coincidence (0>K<0.21) for the other variables considering the annotations of the 3 experts. In some cases the K is high (close to 0.21 although somewhat lower), something that occurs especially in psychological terms. The greatest discrepancies are seen in the variables: arousal 0.0912, happiness 0.0279, valence 0.0051. which pair of annotators are compared. Again, the validation of variables is done for those with k-alpha greater than 0.21. For annotators 2 and 3 the following variables are validated, representing 77.42% of the total variables (that includes all psychological variables): ’Anxiety’, ’Aversion’, ’Compulsion’, ’Depression ’, ’Disappointment’, ’Dramatización’, ’Daydream’, ’Grandeur’, ’Idealization’, ’Illusion’, ’Helplessness’, ’Inestability’, ’Insecurity’, ’Anger’, ’Irritability’, ’Obsesión’, ’Pride’, ’Prejudice’, ’Solitude’, ’Temor’, ’Vulnerability’, ’anger’, ’disgust’, ’fear’. For annotators 2 and 3 the k-alpha is higher for most of the variables comparing it with the previous analysis (including for all the psychological terms). However, it is especially low (including one negative value) for the following variables: ‘happiness’, ‘valence’. The biggest discrepancies are with the annotator 1, especially between 1 and 3 where only one variable has k-alpha greater than 0.21: ‘Illusion’." } ], "images": [ "scigen/train/2334.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison between annotators 1 and 3." ], "reasoning": "Annotators 2 and 3 have higher k-alpha values for most variables, especially psychological terms, but show low agreement on 'happiness' and 'valence'. Annotator 1 shows the greatest discrepancies, particularly with annotator 3, where only 'Illusion' has a k-alpha greater than 0.21.", "description": "There is a slight coincidence (0>K<0.21) for the other variables considering the annotations of the 3 experts. In some cases the K is high (close to 0.21 although somewhat lower), something that occurs especially in psychological terms. The greatest discrepancies are seen in the variables: arousal 0.0912, happiness 0.0279, valence 0.0051. which pair of annotators are compared. Again, the validation of variables is done for those with k-alpha greater than 0.21. For annotators 2 and 3 the following variables are validated, representing 77.42% of the total variables (that includes all psychological variables): ’Anxiety’, ’Aversion’, ’Compulsion’, ’Depression ’, ’Disappointment’, ’Dramatización’, ’Daydream’, ’Grandeur’, ’Idealization’, ’Illusion’, ’Helplessness’, ’Inestability’, ’Insecurity’, ’Anger’, ’Irritability’, ’Obsesión’, ’Pride’, ’Prejudice’, ’Solitude’, ’Temor’, ’Vulnerability’, ’anger’, ’disgust’, ’fear’. For annotators 2 and 3 the k-alpha is higher for most of the variables comparing it with the previous analysis (including for all the psychological terms). However, it is especially low (including one negative value) for the following variables: ‘happiness’, ‘valence’. The biggest discrepancies are with the annotator 1, especially between 1 and 3 where only one variable has k-alpha greater than 0.21: ‘Illusion’.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 4: Comparison between annotators 1 and 3.', and tackle the question below in a sequential manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich variable has the highest k-alpha value for annotators 2 and 3?\n" }, { "role": "assistant", "content": "From the table, the highest k-alpha value for annotators 2 and 3 is for the variable 'Compulsion' with a k-alpha of 0.2079. So, the answer is {\"answer\": \"Compulsion\"}." } ], "images": [ "scigen/train/2334.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison between annotators 1 and 3." ], "reasoning": "From the table, the highest k-alpha value for annotators 2 and 3 is for the variable 'Compulsion' with a k-alpha of 0.2079.", "question": "Which variable has the highest k-alpha value for annotators 2 and 3?", "answer": "So, the answer is {\"answer\": \"Compulsion\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Annotators 2 and 3 have a k-alpha value greater than 0.21 for the variable 'Illusion'., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nAnnotators 2 and 3 have a k-alpha value greater than 0.21 for the variable 'Illusion'.\n" }, { "role": "assistant", "content": "The table shows that the k-alpha value for 'Illusion' is 0.2382, which is greater than 0.21, supporting the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2334.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison between annotators 1 and 3." ], "reasoning": "The table shows that the k-alpha value for 'Illusion' is 0.2382, which is greater than 0.21, supporting the statement.", "statement": "Annotators 2 and 3 have a k-alpha value greater than 0.21 for the variable 'Illusion'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: K-alpha values comparing the agreement of all annotators for each variable.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Aversión, Vulnerabilidad, and Dramatización have agreement levels higher than 0.21, while Valence has a high level of discrepancy with a K-value close to 0.This shows the following information. Regarding the results for each psychological tag, Aversión, Vulnerabilidad and Dramatización have an agreement level higher than 0.21. Placing this together with the other k-values, we can consider acceptable results those regarding Fear, Anger, Sadness, but for Valence, the level of discrepancy was too high (K-value for the three annotators is almost 0)." } ], "images": [ "scigen/train/2335.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: K-alpha values comparing the agreement of all annotators for each variable." ], "reasoning": "Aversión, Vulnerabilidad, and Dramatización have agreement levels higher than 0.21, while Valence has a high level of discrepancy with a K-value close to 0.", "description": "This shows the following information. Regarding the results for each psychological tag, Aversión, Vulnerabilidad and Dramatización have an agreement level higher than 0.21. Placing this together with the other k-values, we can consider acceptable results those regarding Fear, Anger, Sadness, but for Valence, the level of discrepancy was too high (K-value for the three annotators is almost 0).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: K-alpha values comparing the agreement of all annotators for each variable.', solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich psychological tags have an agreement level higher than 0.21 for Anxiety?\n" }, { "role": "assistant", "content": "By examining the K-alpha values for Anxiety, we find that Aversión (0.2948), Vulnerabilidad (0.2826), and Dramatización (0.2300) have agreement levels higher than 0.21. So, the answer is {\"answer\": \"Aversión, Vulnerabilidad, Dramatización\"}" } ], "images": [ "scigen/train/2335.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: K-alpha values comparing the agreement of all annotators for each variable." ], "reasoning": "By examining the K-alpha values for Anxiety, we find that Aversión (0.2948), Vulnerabilidad (0.2826), and Dramatización (0.2300) have agreement levels higher than 0.21.", "question": "Which psychological tags have an agreement level higher than 0.21 for Anxiety?", "answer": "So, the answer is {\"answer\": \"Aversión, Vulnerabilidad, Dramatización\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The K-value for Valence is almost 0. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe K-value for Valence is almost 0.\n" }, { "role": "assistant", "content": "The table shows that the K-alpha value for Valence is 0.0051, which is very close to 0. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2335.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: K-alpha values comparing the agreement of all annotators for each variable." ], "reasoning": "The table shows that the K-alpha value for Valence is 0.0051, which is very close to 0.", "statement": "The K-value for Valence is almost 0.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Comparison between annotators 1 and 2.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The annotations of the 3 experts show slight coincidence for most variables, with the greatest discrepancies seen in arousal, happiness, and valence.There is a slight coincidence (0>K<0.21) for the other variables considering the annotations of the 3 experts. In some cases the K is high (close to 0.21 although somewhat lower), something that occurs especially in psychological terms. The greatest discrepancies are seen in the variables: arousal 0.0912, happiness 0.0279, valence 0.0051. which pair of annotators are compared. Again, the validation of variables is done for those with k-alpha greater than 0.21. In particular, the following is checked:" } ], "images": [ "scigen/train/2336.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison between annotators 1 and 2." ], "reasoning": "The annotations of the 3 experts show slight coincidence for most variables, with the greatest discrepancies seen in arousal, happiness, and valence.", "description": "There is a slight coincidence (0>K<0.21) for the other variables considering the annotations of the 3 experts. In some cases the K is high (close to 0.21 although somewhat lower), something that occurs especially in psychological terms. The greatest discrepancies are seen in the variables: arousal 0.0912, happiness 0.0279, valence 0.0051. which pair of annotators are compared. Again, the validation of variables is done for those with k-alpha greater than 0.21. In particular, the following is checked:", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 3: Comparison between annotators 1 and 2.', solve the following question step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich variables have a K-alpha greater than 0.21?\n" }, { "role": "assistant", "content": "By examining the table, we identify variables with K-alpha values greater than 0.21 in both columns. So, the answer is {\"answer\": \"Dramatisation, Insecurity, Obsession, Vulnerability, Imageability\"}." } ], "images": [ "scigen/train/2336.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison between annotators 1 and 2." ], "reasoning": "By examining the table, we identify variables with K-alpha values greater than 0.21 in both columns.", "question": "Which variables have a K-alpha greater than 0.21?", "answer": "So, the answer is {\"answer\": \"Dramatisation, Insecurity, Obsession, Vulnerability, Imageability\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe variable 'Fear (binary)' has a K-alpha greater than 0.21.\n" }, { "role": "assistant", "content": "The table shows that the K-alpha for 'Fear (binary)' is 0.2402, which is greater than 0.21. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2336.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison between annotators 1 and 2." ], "reasoning": "The table shows that the K-alpha for 'Fear (binary)' is 0.2402, which is greater than 0.21.", "statement": "The variable 'Fear (binary)' has a K-alpha greater than 0.21.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: Comparison between annotators 2 and 3.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Annotators 2 and 3 have higher k-alpha values for most variables, especially psychological terms, but show low agreement on 'happiness' and 'valence'. Annotator 1 shows the greatest discrepancies, particularly with annotator 3, where only 'Illusion' has a k-alpha greater than 0.21.There is a slight coincidence (0>K<0.21) for the other variables considering the annotations of the 3 experts. In some cases the K is high (close to 0.21 although somewhat lower), something that occurs especially in psychological terms. The greatest discrepancies are seen in the variables: arousal 0.0912, happiness 0.0279, valence 0.0051. which pair of annotators are compared. Again, the validation of variables is done for those with k-alpha greater than 0.21. For annotators 2 and 3 the following variables are validated, representing 77.42% of the total variables (that includes all psychological variables): ’Anxiety’, ’Aversion’, ’Compulsion’, ’Depression ’, ’Disappointment’, ’Dramatización’, ’Daydream’, ’Grandeur’, ’Idealization’, ’Illusion’, ’Helplessness’, ’Inestability’, ’Insecurity’, ’Anger’, ’Irritability’, ’Obsesión’, ’Pride’, ’Prejudice’, ’Solitude’, ’Temor’, ’Vulnerability’, ’anger’, ’disgust’, ’fear’. For annotators 2 and 3 the k-alpha is higher for most of the variables comparing it with the previous analysis (including for all the psychological terms). However, it is especially low (including one negative value) for the following variables: ‘happiness’, ‘valence’. The biggest discrepancies are with the annotator 1, especially between 1 and 3 where only one variable has k-alpha greater than 0.21: ‘Illusion’." } ], "images": [ "scigen/train/2337.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison between annotators 2 and 3." ], "reasoning": "Annotators 2 and 3 have higher k-alpha values for most variables, especially psychological terms, but show low agreement on 'happiness' and 'valence'. Annotator 1 shows the greatest discrepancies, particularly with annotator 3, where only 'Illusion' has a k-alpha greater than 0.21.", "description": "There is a slight coincidence (0>K<0.21) for the other variables considering the annotations of the 3 experts. In some cases the K is high (close to 0.21 although somewhat lower), something that occurs especially in psychological terms. The greatest discrepancies are seen in the variables: arousal 0.0912, happiness 0.0279, valence 0.0051. which pair of annotators are compared. Again, the validation of variables is done for those with k-alpha greater than 0.21. For annotators 2 and 3 the following variables are validated, representing 77.42% of the total variables (that includes all psychological variables): ’Anxiety’, ’Aversion’, ’Compulsion’, ’Depression ’, ’Disappointment’, ’Dramatización’, ’Daydream’, ’Grandeur’, ’Idealization’, ’Illusion’, ’Helplessness’, ’Inestability’, ’Insecurity’, ’Anger’, ’Irritability’, ’Obsesión’, ’Pride’, ’Prejudice’, ’Solitude’, ’Temor’, ’Vulnerability’, ’anger’, ’disgust’, ’fear’. For annotators 2 and 3 the k-alpha is higher for most of the variables comparing it with the previous analysis (including for all the psychological terms). However, it is especially low (including one negative value) for the following variables: ‘happiness’, ‘valence’. The biggest discrepancies are with the annotator 1, especially between 1 and 3 where only one variable has k-alpha greater than 0.21: ‘Illusion’.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 5: Comparison between annotators 2 and 3.' and proceed to solve the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich variable has the highest k-alpha value for annotators 2 and 3?\n" }, { "role": "assistant", "content": "From the table, the highest k-alpha value for annotators 2 and 3 is found in the 'Grandeur' variable with a k-alpha of 0.5543. So, the answer is {\"answer\": \"Grandeur\"}." } ], "images": [ "scigen/train/2337.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison between annotators 2 and 3." ], "reasoning": "From the table, the highest k-alpha value for annotators 2 and 3 is found in the 'Grandeur' variable with a k-alpha of 0.5543.", "question": "Which variable has the highest k-alpha value for annotators 2 and 3?", "answer": "So, the answer is {\"answer\": \"Grandeur\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nAnnotators 2 and 3 have a k-alpha value greater than 0.21 for the variable 'Grandeur'.\n" }, { "role": "assistant", "content": "The table shows that the k-alpha value for 'Grandeur' is 0.5543, which is greater than 0.21. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2337.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison between annotators 2 and 3." ], "reasoning": "The table shows that the k-alpha value for 'Grandeur' is 0.5543, which is greater than 0.21.", "statement": "Annotators 2 and 3 have a k-alpha value greater than 0.21 for the variable 'Grandeur'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Number of words (original, after lemmatization or stemming) in the original DISCO PAL corpus (total and per psychological category)'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Lemmatization and stemming techniques improve the number of words from the sonnets present in the source corpora, with lemmatization being slightly more effective.Several scenarios are proposed, in which we show the results of the original words for both the DISCO PAL corpus and the source corpora, as well as the words after applying lemmatization and stemming (with SnowBall stemming algorithm [porter2001snowball]) techniques. It can be seen that using lemmatization or stemming techniques improves, as expected, the number of words from the sonnets present in the source corpora. Since there is not a huge difference between lemmatization and stemming percentages, the analyses carried out in this paper will deal with lemmatization words. Lemmatization and stemming scenarios also include the elimination of stopwords." } ], "images": [ "scigen/train/2338.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Number of words (original, after lemmatization or stemming) in the original DISCO PAL corpus (total and per psychological category)" ], "reasoning": "Lemmatization and stemming techniques improve the number of words from the sonnets present in the source corpora, with lemmatization being slightly more effective.", "description": "Several scenarios are proposed, in which we show the results of the original words for both the DISCO PAL corpus and the source corpora, as well as the words after applying lemmatization and stemming (with SnowBall stemming algorithm [porter2001snowball]) techniques. It can be seen that using lemmatization or stemming techniques improves, as expected, the number of words from the sonnets present in the source corpora. Since there is not a huge difference between lemmatization and stemming percentages, the analyses carried out in this paper will deal with lemmatization words. Lemmatization and stemming scenarios also include the elimination of stopwords.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 6: Number of words (original, after lemmatization or stemming) in the original DISCO PAL corpus (total and per psychological category)' and proceed to solve the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the difference in the number of words between the original and lemmatized versions for the category 'Dramatización'?\n" }, { "role": "assistant", "content": "The original number of words for 'Dramatización' is 2757, and the lemmatized number of words is 2158. The difference is 2757 - 2158. So, the answer is {\"answer\": \"599\"}." } ], "images": [ "scigen/train/2338.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Number of words (original, after lemmatization or stemming) in the original DISCO PAL corpus (total and per psychological category)" ], "reasoning": "The original number of words for 'Dramatización' is 2757, and the lemmatized number of words is 2158. The difference is 2757 - 2158.", "question": "What is the difference in the number of words between the original and lemmatized versions for the category 'Dramatización'?", "answer": "So, the answer is {\"answer\": \"599\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The category 'Ansiedad' has more words in the original version than in the stemmed version., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe category 'Ansiedad' has more words in the original version than in the stemmed version.\n" }, { "role": "assistant", "content": "The original number of words for 'Ansiedad' is 1589, and the stemmed number of words is 1248. Since 1589 is greater than 1248, the statement is true. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2338.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Number of words (original, after lemmatization or stemming) in the original DISCO PAL corpus (total and per psychological category)" ], "reasoning": "The original number of words for 'Ansiedad' is 1589, and the stemmed number of words is 1248. Since 1589 is greater than 1248, the statement is true.", "statement": "The category 'Ansiedad' has more words in the original version than in the stemmed version.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 7: Fraction of words (original) from the original DISCO PAL corpus (total and per psychological category) in the different source corpora'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Lemmatization and stemming techniques improve the number of words from the sonnets present in the source corpora, with the analyses focusing on lemmatization words.Several scenarios are proposed, in which we show the results of the original words for both the DISCO PAL corpus and the source corpora, as well as the words after applying lemmatization and stemming (with SnowBall stemming algorithm [porter2001snowball]) techniques. It can be seen that using lemmatization or stemming techniques improves, as expected, the number of words from the sonnets present in the source corpora. Since there is not a huge difference between lemmatization and stemming percentages, the analyses carried out in this paper will deal with lemmatization words. Lemmatization and stemming scenarios also include the elimination of stopwords." } ], "images": [ "scigen/train/2339.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Fraction of words (original) from the original DISCO PAL corpus (total and per psychological category) in the different source corpora" ], "reasoning": "Lemmatization and stemming techniques improve the number of words from the sonnets present in the source corpora, with the analyses focusing on lemmatization words.", "description": "Several scenarios are proposed, in which we show the results of the original words for both the DISCO PAL corpus and the source corpora, as well as the words after applying lemmatization and stemming (with SnowBall stemming algorithm [porter2001snowball]) techniques. It can be seen that using lemmatization or stemming techniques improves, as expected, the number of words from the sonnets present in the source corpora. Since there is not a huge difference between lemmatization and stemming percentages, the analyses carried out in this paper will deal with lemmatization words. Lemmatization and stemming scenarios also include the elimination of stopwords.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 7: Fraction of words (original) from the original DISCO PAL corpus (total and per psychological category) in the different source corpora' and methodically tackle the question that follows. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich category has the highest value in the [stadtthagen2017norms] column?\n" }, { "role": "assistant", "content": "To find the category with the highest value in the [stadtthagen2017norms] column, we need to compare all the values in that column. The highest value is 0.53, which corresponds to the category 'Irritabilidad'. So, the answer is {\"answer\": \"Irritabilidad\"}." } ], "images": [ "scigen/train/2339.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Fraction of words (original) from the original DISCO PAL corpus (total and per psychological category) in the different source corpora" ], "reasoning": "To find the category with the highest value in the [stadtthagen2017norms] column, we need to compare all the values in that column. The highest value is 0.53, which corresponds to the category 'Irritabilidad'.", "question": "Which category has the highest value in the [stadtthagen2017norms] column?", "answer": "So, the answer is {\"answer\": \"Irritabilidad\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The category 'Depresión' has the highest value in the [ferre2017moved] column., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe category 'Depresión' has the highest value in the [ferre2017moved] column.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the values in the [ferre2017moved] column. The highest value in this column is 0.18, which is shared by 'Depresión', 'Desilusión', 'Ira', and 'Soledad'. Therefore, the statement is correct. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2339.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Fraction of words (original) from the original DISCO PAL corpus (total and per psychological category) in the different source corpora" ], "reasoning": "To verify this statement, we need to compare the values in the [ferre2017moved] column. The highest value in this column is 0.18, which is shared by 'Depresión', 'Desilusión', 'Ira', and 'Soledad'. Therefore, the statement is correct.", "statement": "The category 'Depresión' has the highest value in the [ferre2017moved] column.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 11: Fraction of words (after lemmatization or stemming) from the original DISCO PAL corpus (total and per psychological category) in the different source corpora'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Lemmatization and stemming techniques improve the number of words from the sonnets present in the source corpora, with no significant difference between the two methods.Several scenarios are proposed, in which we show the results of the original words for both the DISCO PAL corpus and the source corpora, as well as the words after applying lemmatization and stemming (with SnowBall stemming algorithm [porter2001snowball]) techniques. It can be seen that using lemmatization or stemming techniques improves, as expected, the number of words from the sonnets present in the source corpora. Since there is not a huge difference between lemmatization and stemming percentages, the analyses carried out in this paper will deal with lemmatization words. Lemmatization and stemming scenarios also include the elimination of stopwords." } ], "images": [ "scigen/train/2340.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Fraction of words (after lemmatization or stemming) from the original DISCO PAL corpus (total and per psychological category) in the different source corpora" ], "reasoning": "Lemmatization and stemming techniques improve the number of words from the sonnets present in the source corpora, with no significant difference between the two methods.", "description": "Several scenarios are proposed, in which we show the results of the original words for both the DISCO PAL corpus and the source corpora, as well as the words after applying lemmatization and stemming (with SnowBall stemming algorithm [porter2001snowball]) techniques. It can be seen that using lemmatization or stemming techniques improves, as expected, the number of words from the sonnets present in the source corpora. Since there is not a huge difference between lemmatization and stemming percentages, the analyses carried out in this paper will deal with lemmatization words. Lemmatization and stemming scenarios also include the elimination of stopwords.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 11: Fraction of words (after lemmatization or stemming) from the original DISCO PAL corpus (total and per psychological category) in the different source corpora'. Solve the following question based on the table.\n\nQuestion: Which category has the highest percentage in the [stadtthagen2017norms] stem column?\nLet's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest percentage in the [stadtthagen2017norms] stem column, we need to compare all the values in that column and identify the maximum value. So, the answer is {\"answer\": \"Depresión\"}." } ], "images": [ "scigen/train/2340.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Fraction of words (after lemmatization or stemming) from the original DISCO PAL corpus (total and per psychological category) in the different source corpora" ], "reasoning": "To find the highest percentage in the [stadtthagen2017norms] stem column, we need to compare all the values in that column and identify the maximum value.", "question": "Which category has the highest percentage in the [stadtthagen2017norms] stem column?", "answer": "So, the answer is {\"answer\": \"Depresión\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The category 'Ansiedad' has a higher percentage in the [stadtthagen2017norms] lem column than in the [ferre2017moved] lem column., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe category 'Ansiedad' has a higher percentage in the [stadtthagen2017norms] lem column than in the [ferre2017moved] lem column.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the percentages of 'Ansiedad' in the [stadtthagen2017norms] lem column (0.73) and the [ferre2017moved] lem column (0.23). Since 0.73 is greater than 0.23, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2340.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Fraction of words (after lemmatization or stemming) from the original DISCO PAL corpus (total and per psychological category) in the different source corpora" ], "reasoning": "To verify this statement, we compare the percentages of 'Ansiedad' in the [stadtthagen2017norms] lem column (0.73) and the [ferre2017moved] lem column (0.23). Since 0.73 is greater than 0.23, the statement is supported.", "statement": "The category 'Ansiedad' has a higher percentage in the [stadtthagen2017norms] lem column than in the [ferre2017moved] lem column.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 14: Partial dependence analysis between GAM features and their annotated counterparts (I)'.\n" }, { "role": "assistant", "content": "A linear regression model is trained using GAM features as independent variables and an annotated feature as the dependent variable. The p-value, coefficient, and adjusted r-squared value are checked to determine the significance and fit of the model.As mentioned before, a linear regression model is trained over all sonnets, using all GAM features as independent variables, and using one of the annotated features as dependent variable. Then, we get the p-value of the corresponding GAM feature, and see if that value is relevant, using a threshold of 0.1. (p<0.1 meaning it is significant). We also check the coefficient of that feature to see that it is >0 (a negative coefficient would mean that even if the model is fitted properly, the relationship between both features is not coherent). We also check the adjusted r-squared value in order to see if the model is well-fitted." } ], "images": [ "scigen/train/2341.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 14: Partial dependence analysis between GAM features and their annotated counterparts (I)" ], "reasoning": "A linear regression model is trained using GAM features as independent variables and an annotated feature as the dependent variable. The p-value, coefficient, and adjusted r-squared value are checked to determine the significance and fit of the model.", "description": "As mentioned before, a linear regression model is trained over all sonnets, using all GAM features as independent variables, and using one of the annotated features as dependent variable. Then, we get the p-value of the corresponding GAM feature, and see if that value is relevant, using a threshold of 0.1. (p<0.1 meaning it is significant). We also check the coefficient of that feature to see that it is >0 (a negative coefficient would mean that even if the model is fitted properly, the relationship between both features is not coherent). We also check the adjusted r-squared value in order to see if the model is well-fitted.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 14: Partial dependence analysis between GAM features and their annotated counterparts (I)'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: Which feature under the label 'Compulsión' has a significant p-value and a positive coefficient?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the feature under the label 'Compulsión' with a significant p-value (p < 0.1) and a positive coefficient, we look at the rows with the label 'Compulsión' and check the 'p-val' and 'coeff' columns. The feature 'sadness' has a p-value of 0.08 (significant) and a coefficient of 0.1 (positive). So, the answer is {\"answer\": \"sadness\"}." } ], "images": [ "scigen/train/2341.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 14: Partial dependence analysis between GAM features and their annotated counterparts (I)" ], "reasoning": "To find the feature under the label 'Compulsión' with a significant p-value (p < 0.1) and a positive coefficient, we look at the rows with the label 'Compulsión' and check the 'p-val' and 'coeff' columns. The feature 'sadness' has a p-value of 0.08 (significant) and a coefficient of 0.1 (positive).", "question": "Which feature under the label 'Compulsión' has a significant p-value and a positive coefficient?", "answer": "So, the answer is {\"answer\": \"sadness\"}." }, { "messages": [ { "role": "user", "content": "The feature 'disgust' under the label 'Depresión' has a significant p-value and a positive coefficient. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe feature 'disgust' under the label 'Depresión' has a significant p-value and a positive coefficient.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the row with the label 'Depresión' and the feature 'disgust'. The p-value is 0.58, which is not significant (p > 0.1), and the coefficient is -0.05, which is not positive. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2341.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 14: Partial dependence analysis between GAM features and their annotated counterparts (I)" ], "reasoning": "To verify this statement, we look at the row with the label 'Depresión' and the feature 'disgust'. The p-value is 0.58, which is not significant (p > 0.1), and the coefficient is -0.05, which is not positive.", "statement": "The feature 'disgust' under the label 'Depresión' has a significant p-value and a positive coefficient.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 15: Partial dependence analysis between GAM features and their annotated counterparts (II)'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "A linear regression model is trained using GAM features as independent variables and an annotated feature as the dependent variable, checking the p-value, coefficient, and adjusted r-squared value for significance and coherence.As mentioned before, a linear regression model is trained over all sonnets, using all GAM features as independent variables, and using one of the annotated features as dependent variable. Then, we get the p-value of the corresponding GAM feature, and see if that value is relevant, using a threshold of 0.1. (p<0.1 meaning it is significant). We also check the coefficient of that feature to see that it is >0 (a negative coefficient would mean that even if the model is fitted properly, the relationship between both features is not coherent). We also check the adjusted r-squared value in order to see if the model is well-fitted." } ], "images": [ "scigen/train/2342.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 15: Partial dependence analysis between GAM features and their annotated counterparts (II)" ], "reasoning": "A linear regression model is trained using GAM features as independent variables and an annotated feature as the dependent variable, checking the p-value, coefficient, and adjusted r-squared value for significance and coherence.", "description": "As mentioned before, a linear regression model is trained over all sonnets, using all GAM features as independent variables, and using one of the annotated features as dependent variable. Then, we get the p-value of the corresponding GAM feature, and see if that value is relevant, using a threshold of 0.1. (p<0.1 meaning it is significant). We also check the coefficient of that feature to see that it is >0 (a negative coefficient would mean that even if the model is fitted properly, the relationship between both features is not coherent). We also check the adjusted r-squared value in order to see if the model is well-fitted.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 15: Partial dependence analysis between GAM features and their annotated counterparts (II)', and tackle the question below in a sequential manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich feature under the label 'Dramatización' has a significant p-value and a positive coefficient?\n" }, { "role": "assistant", "content": "Under the label 'Dramatización', the feature 'sadness' has a p-value of 0.01, which is less than 0.1, and a coefficient of 0.09, which is positive. So, the answer is {\"answer\": \"sadness\"}" } ], "images": [ "scigen/train/2342.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 15: Partial dependence analysis between GAM features and their annotated counterparts (II)" ], "reasoning": "Under the label 'Dramatización', the feature 'sadness' has a p-value of 0.01, which is less than 0.1, and a coefficient of 0.09, which is positive.", "question": "Which feature under the label 'Dramatización' has a significant p-value and a positive coefficient?", "answer": "So, the answer is {\"answer\": \"sadness\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The feature 'fear' under the label 'Idealización' has a significant p-value and a positive coefficient., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe feature 'fear' under the label 'Idealización' has a significant p-value and a positive coefficient.\n" }, { "role": "assistant", "content": "Under the label 'Idealización', the feature 'fear' has a p-value of 0.15, which is greater than 0.1, making it not significant. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2342.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 15: Partial dependence analysis between GAM features and their annotated counterparts (II)" ], "reasoning": "Under the label 'Idealización', the feature 'fear' has a p-value of 0.15, which is greater than 0.1, making it not significant.", "statement": "The feature 'fear' under the label 'Idealización' has a significant p-value and a positive coefficient.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 17: One-way ANOVA between GAM values according to their psychological tag'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "A One-way ANOVA was performed to analyze significant differences in GAM between subsets with and without specific psychological labels, yielding significant differences in 96 out of 190 combinations.Finally, we analyse if there are significant differences in the GAM (using the annotated value) between subsets depending on whether they refer to a specific psychological label or not. We perform a One-way ANOVA hypothesis contrast for each combination between GAM feature and psychological label. That table also includes the mean value for the GAM considering the sonnets annotated with that psychological tag, M (=1), and the other ones, M (=0). As we can see, from among the 190 possible combinations, 96 of them yielded significant differences in the GAM depending on the subset considered." } ], "images": [ "scigen/train/2343.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 17: One-way ANOVA between GAM values according to their psychological tag" ], "reasoning": "A One-way ANOVA was performed to analyze significant differences in GAM between subsets with and without specific psychological labels, yielding significant differences in 96 out of 190 combinations.", "description": "Finally, we analyse if there are significant differences in the GAM (using the annotated value) between subsets depending on whether they refer to a specific psychological label or not. We perform a One-way ANOVA hypothesis contrast for each combination between GAM feature and psychological label. That table also includes the mean value for the GAM considering the sonnets annotated with that psychological tag, M (=1), and the other ones, M (=0). As we can see, from among the 190 possible combinations, 96 of them yielded significant differences in the GAM depending on the subset considered.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 17: One-way ANOVA between GAM values according to their psychological tag\nQuestion: How many combinations showed significant differences in the GAM feature 'fear' with the psychological label 'Soledad'?\nSolve the above question based on the table information. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the number of significant differences for the GAM feature 'fear' with the psychological label 'Soledad', we look at the 'GAM fear' column and the corresponding 'Psycho. Tag Inseguridad' column for 'Soledad'. There is one row with 'Soledad' and a p-value of 0.01, indicating a significant difference. So, the answer is {\"answer\": \"1\"}." } ], "images": [ "scigen/train/2343.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 17: One-way ANOVA between GAM values according to their psychological tag" ], "reasoning": "To find the number of significant differences for the GAM feature 'fear' with the psychological label 'Soledad', we look at the 'GAM fear' column and the corresponding 'Psycho. Tag Inseguridad' column for 'Soledad'. There is one row with 'Soledad' and a p-value of 0.01, indicating a significant difference.", "question": "How many combinations showed significant differences in the GAM feature 'fear' with the psychological label 'Soledad'?", "answer": "So, the answer is {\"answer\": \"1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe GAM feature 'disgust' with the psychological label 'Ansiedad' showed a significant difference.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the 'GAM disgust' column and the corresponding 'Psycho. Tag Ansiedad' column. The row shows a p-value of 0.03, indicating a significant difference. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2343.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 17: One-way ANOVA between GAM values according to their psychological tag" ], "reasoning": "To verify this statement, we look at the 'GAM disgust' column and the corresponding 'Psycho. Tag Ansiedad' column. The row shows a p-value of 0.03, indicating a significant difference.", "statement": "The GAM feature 'disgust' with the psychological label 'Ansiedad' showed a significant difference.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: Naturalness and speaker similarity MOS of cross-language voice cloning of the full multilingual model using phoneme inputs.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Incorporating adversarial loss improves naturalness MOS scores above 3.85 across all language pairs, but consistently conditioning on the EN language embedding reduces naturalness but increases similarity MOS scores.Incorporating the adversarial loss forces the text representation to be less language-specific, instead relying on the language embedding to capture language-dependent information. Across all language pairs, the model synthesizes speech in all voices with naturalness MOS above 3.85, demonstrating that increasing training speaker diversity improves generalization. In most cases synthesizing EN and ES speech (except EN-to-ES) approaches the ground truth scores. In contrast, naturalness of CN speech is consistently lower than the ground truth. When consistently conditioning on the EN language embedding regardless of the target language (second row), the model produces more English accented ES and CN speech, which leads to lower naturalness but higher similarity MOS scores." } ], "images": [ "scigen/train/2344.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Naturalness and speaker similarity MOS of cross-language voice cloning of the full multilingual model using phoneme inputs." ], "reasoning": "Incorporating adversarial loss improves naturalness MOS scores above 3.85 across all language pairs, but consistently conditioning on the EN language embedding reduces naturalness but increases similarity MOS scores.", "description": "Incorporating the adversarial loss forces the text representation to be less language-specific, instead relying on the language embedding to capture language-dependent information. Across all language pairs, the model synthesizes speech in all voices with naturalness MOS above 3.85, demonstrating that increasing training speaker diversity improves generalization. In most cases synthesizing EN and ES speech (except EN-to-ES) approaches the ground truth scores. In contrast, naturalness of CN speech is consistently lower than the ground truth. When consistently conditioning on the EN language embedding regardless of the target language (second row), the model produces more English accented ES and CN speech, which leads to lower naturalness but higher similarity MOS scores.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Naturalness and speaker similarity MOS of cross-language voice cloning of the full multilingual model using phoneme inputs.\nQuestion: What is the naturalness MOS score for CN target speech when the source language is CN?\nSolve the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the naturalness MOS score for CN target speech when the source language is CN, look at the row where the source language is CN and find the value under the CN target Naturalness column. So, the answer is {\"answer\": \"4.49±0.08\"}" } ], "images": [ "scigen/train/2344.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Naturalness and speaker similarity MOS of cross-language voice cloning of the full multilingual model using phoneme inputs." ], "reasoning": "To find the naturalness MOS score for CN target speech when the source language is CN, look at the row where the source language is CN and find the value under the CN target Naturalness column.", "question": "What is the naturalness MOS score for CN target speech when the source language is CN?", "answer": "So, the answer is {\"answer\": \"4.49±0.08\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model produces more natural CN speech when the source language is CN compared to when the source language is EN.\n" }, { "role": "assistant", "content": "Compare the CN target Naturalness scores for the source languages CN and EN. The score for CN source language (4.49±0.08) is higher than for EN source language (3.94±0.09), supporting the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2344.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Naturalness and speaker similarity MOS of cross-language voice cloning of the full multilingual model using phoneme inputs." ], "reasoning": "Compare the CN target Naturalness scores for the source languages CN and EN. The score for CN source language (4.49±0.08) is higher than for EN source language (3.94±0.09), supporting the statement.", "statement": "The model produces more natural CN speech when the source language is CN compared to when the source language is EN.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: Speaker similarity Mean Opinion Score (MOS) comparing ground truth audio from speakers of different languages. Raters are native speakers of the target language.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The CN speaker has more natural variability, leading to lower self-similarity, and English raters often consider heavily accented synthetic CN speech more similar to the target EN speaker.For each language, we chose one speaker to use for similarity tests. The CN speaker has more natural variability compared to EN and ES, leading to a lower self similarity. The scores are consistent when EN and CN raters evaluate the same EN and CN test set. However, when rating synthetic speech, we observed that English speaking raters often considered “heavy accented” synthetic CN speech to sound more similar to the target EN speaker, compared to more fluent speech from the same speaker. This indicates that accent and speaker identity are not fully disentangled. We encourage readers to listen to samples on the companion webpage. This is a consequence of the low speaker coverage of CN compared to EN in the training data, as well as the large distance between CN and other languages." } ], "images": [ "scigen/train/2345.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Speaker similarity Mean Opinion Score (MOS) comparing ground truth audio from speakers of different languages. Raters are native speakers of the target language." ], "reasoning": "The CN speaker has more natural variability, leading to lower self-similarity, and English raters often consider heavily accented synthetic CN speech more similar to the target EN speaker.", "description": "For each language, we chose one speaker to use for similarity tests. The CN speaker has more natural variability compared to EN and ES, leading to a lower self similarity. The scores are consistent when EN and CN raters evaluate the same EN and CN test set. However, when rating synthetic speech, we observed that English speaking raters often considered “heavy accented” synthetic CN speech to sound more similar to the target EN speaker, compared to more fluent speech from the same speaker. This indicates that accent and speaker identity are not fully disentangled. We encourage readers to listen to samples on the companion webpage. This is a consequence of the low speaker coverage of CN compared to EN in the training data, as well as the large distance between CN and other languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Speaker similarity Mean Opinion Score (MOS) comparing ground truth audio from speakers of different languages. Raters are native speakers of the target language.\nQuestion: Which source language has the highest similarity score when the target language is CN?\nSolve the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the scores in the 'Target Language CN' column, the highest similarity score is 3.51±0.12, which corresponds to the CN source language. So, the answer is {\"answer\": \"CN\"}" } ], "images": [ "scigen/train/2345.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Speaker similarity Mean Opinion Score (MOS) comparing ground truth audio from speakers of different languages. Raters are native speakers of the target language." ], "reasoning": "By comparing the scores in the 'Target Language CN' column, the highest similarity score is 3.51±0.12, which corresponds to the CN source language.", "question": "Which source language has the highest similarity score when the target language is CN?", "answer": "So, the answer is {\"answer\": \"CN\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe EN source language has the highest similarity score when the target language is ES.\n" }, { "role": "assistant", "content": "By examining the 'Target Language ES' column, the highest similarity score is 4.39±0.06, which corresponds to the ES source language, not EN. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2345.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Speaker similarity Mean Opinion Score (MOS) comparing ground truth audio from speakers of different languages. Raters are native speakers of the target language." ], "reasoning": "By examining the 'Target Language ES' column, the highest similarity score is 4.39±0.06, which corresponds to the ES source language, not EN.", "statement": "The EN source language has the highest similarity score when the target language is ES.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Naturalness MOS of monolingual and multilingual models synthesizing speech of in different languages.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Phoneme-based models outperform char- and byte-based models for Mandarin due to rare and OOV words, and all languages achieve MOS scores above 4.0 with phoneme inputs.For Mandarin, the phoneme-based model performs significantly better than char- or byte-based variants due to rare and OOV words. Compared to the monolingual system, multilingual phoneme-based systems have similar performance on ES and CN but are slightly worse on EN. CN has a larger gap to ground truth (top) due to unseen word segmentation (for simplicity, we didn’t add word boundary during training). The multispeaker model (bottom) performs about the same as the single speaker per-language variant (middle). Overall, when using phoneme inputs all the languages obtain MOS scores above 4.0." } ], "images": [ "scigen/train/2346.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Naturalness MOS of monolingual and multilingual models synthesizing speech of in different languages." ], "reasoning": "Phoneme-based models outperform char- and byte-based models for Mandarin due to rare and OOV words, and all languages achieve MOS scores above 4.0 with phoneme inputs.", "description": "For Mandarin, the phoneme-based model performs significantly better than char- or byte-based variants due to rare and OOV words. Compared to the monolingual system, multilingual phoneme-based systems have similar performance on ES and CN but are slightly worse on EN. CN has a larger gap to ground truth (top) due to unseen word segmentation (for simplicity, we didn’t add word boundary during training). The multispeaker model (bottom) performs about the same as the single speaker per-language variant (middle). Overall, when using phoneme inputs all the languages obtain MOS scores above 4.0.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: Naturalness MOS of monolingual and multilingual models synthesizing speech of in different languages.', solve the subsequent question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich input type achieves the highest MOS score for Language CN in the Multilingual 84EN 3ES 5CN model?\n" }, { "role": "assistant", "content": "By comparing the MOS scores for Language CN in the Multilingual 84EN 3ES 5CN model, the phoneme input achieves the highest score of 4.09±0.10. So, the answer is {\"answer\": \"phoneme\"}" } ], "images": [ "scigen/train/2346.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Naturalness MOS of monolingual and multilingual models synthesizing speech of in different languages." ], "reasoning": "By comparing the MOS scores for Language CN in the Multilingual 84EN 3ES 5CN model, the phoneme input achieves the highest score of 4.09±0.10.", "question": "Which input type achieves the highest MOS score for Language CN in the Multilingual 84EN 3ES 5CN model?", "answer": "So, the answer is {\"answer\": \"phoneme\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe monolingual phoneme-based model performs better than the multilingual phoneme-based model for Language EN.\n" }, { "role": "assistant", "content": "The monolingual phoneme-based model has a MOS score of 4.59±0.06 for Language EN, while the multilingual phoneme-based model has a MOS score of 4.37±0.12 for Language EN, indicating that the statement is correct. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2346.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Naturalness MOS of monolingual and multilingual models synthesizing speech of in different languages." ], "reasoning": "The monolingual phoneme-based model has a MOS score of 4.59±0.06 for Language EN, while the multilingual phoneme-based model has a MOS score of 4.37±0.12 for Language EN, indicating that the statement is correct.", "statement": "The monolingual phoneme-based model performs better than the multilingual phoneme-based model for Language EN.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: Effect of EN speaker cloning with no residual encoder.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The Naturalness MOS for EN-to-CN cloning decreases by 0.4 points without the residual encoder, indicating the VAE prior helps stabilize attention.Naturalness MOS decreases by 0.4 points for EN-to-CN cloning without the residual encoder (bottom row). In informal comparisons of the outputs of the two models we find that the model without the residual encoder tends to skip rare words or inserts unnatural pauses in the output speech. This indicates the VAE prior learns a mode which helps stabilize attention." } ], "images": [ "scigen/train/2347.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Effect of EN speaker cloning with no residual encoder." ], "reasoning": "The Naturalness MOS for EN-to-CN cloning decreases by 0.4 points without the residual encoder, indicating the VAE prior helps stabilize attention.", "description": "Naturalness MOS decreases by 0.4 points for EN-to-CN cloning without the residual encoder (bottom row). In informal comparisons of the outputs of the two models we find that the model without the residual encoder tends to skip rare words or inserts unnatural pauses in the output speech. This indicates the VAE prior learns a mode which helps stabilize attention.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 5: Effect of EN speaker cloning with no residual encoder.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the Naturalness MOS for EN-to-CN cloning with and without the residual encoder?\n" }, { "role": "assistant", "content": "The table shows the Naturalness MOS for EN-to-CN cloning with the residual encoder as 3.94 and without the residual encoder as 3.52. So, the answer is {\"answer\": \"3.94 with residual encoder, 3.52 without residual encoder\"}" } ], "images": [ "scigen/train/2347.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Effect of EN speaker cloning with no residual encoder." ], "reasoning": "The table shows the Naturalness MOS for EN-to-CN cloning with the residual encoder as 3.94 and without the residual encoder as 3.52.", "question": "What is the Naturalness MOS for EN-to-CN cloning with and without the residual encoder?", "answer": "So, the answer is {\"answer\": \"3.94 with residual encoder, 3.52 without residual encoder\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Naturalness MOS for EN-to-CN cloning without the residual encoder is 3.52.\n" }, { "role": "assistant", "content": "The table shows that the Naturalness MOS for EN-to-CN cloning without the residual encoder is indeed 3.52. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2347.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Effect of EN speaker cloning with no residual encoder." ], "reasoning": "The table shows that the Naturalness MOS for EN-to-CN cloning without the residual encoder is indeed 3.52.", "statement": "The Naturalness MOS for EN-to-CN cloning without the residual encoder is 3.52.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: Performance of models, evaluated with accuracy (%).We report results on the full validation and test sets (Overall), as well as results on informative subsets of the data: evaluated on in-domain, versus zero-shot situations, along with performance on the underlying data sources (ActivityNet versus WikiHow). All models substantially underperform humans: the gap is over 45% on in-domain categories, and 50% on zero-shot categories.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Despite being used as the adversarial filter, BERT-Large performs the strongest overall at 47.3%, indicating that making the dataset adversarial for BERT also affected other models.Surprisingly, despite BERT-Large having been used as the adversarial filter, it still performs the strongest at 47.3% overall. By making the dataset adversarial for BERT, it seems to also have become adversarial for every other model. For instance, while ESIM+ELMo obtained 59% accuracy on SWAG, it obtains only 33.3% accuracy on HellaSwag." } ], "images": [ "scigen/train/2348.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance of models, evaluated with accuracy (%).We report results on the full validation and test sets (Overall), as well as results on informative subsets of the data: evaluated on in-domain, versus zero-shot situations, along with performance on the underlying data sources (ActivityNet versus WikiHow). All models substantially underperform humans: the gap is over 45% on in-domain categories, and 50% on zero-shot categories." ], "reasoning": "Despite being used as the adversarial filter, BERT-Large performs the strongest overall at 47.3%, indicating that making the dataset adversarial for BERT also affected other models.", "description": "Surprisingly, despite BERT-Large having been used as the adversarial filter, it still performs the strongest at 47.3% overall. By making the dataset adversarial for BERT, it seems to also have become adversarial for every other model. For instance, while ESIM+ELMo obtained 59% accuracy on SWAG, it obtains only 33.3% accuracy on HellaSwag.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 1: Performance of models, evaluated with accuracy (%).We report results on the full validation and test sets (Overall), as well as results on informative subsets of the data: evaluated on in-domain, versus zero-shot situations, along with performance on the underlying data sources (ActivityNet versus WikiHow). All models substantially underperform humans: the gap is over 45% on in-domain categories, and 50% on zero-shot categories.\nQuestion: What is the accuracy of ESIM+ELMo on the HellaSwag dataset compared to the SWAG dataset?\n" }, { "role": "assistant", "content": "The description mentions that ESIM+ELMo obtained 59% accuracy on SWAG and only 33.3% accuracy on HellaSwag. The table shows that ESIM+ELMo has an overall test accuracy of 33.8%. So, the answer is {\"answer\": \"33.3% on HellaSwag and 59% on SWAG\"}" } ], "images": [ "scigen/train/2348.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance of models, evaluated with accuracy (%).We report results on the full validation and test sets (Overall), as well as results on informative subsets of the data: evaluated on in-domain, versus zero-shot situations, along with performance on the underlying data sources (ActivityNet versus WikiHow). All models substantially underperform humans: the gap is over 45% on in-domain categories, and 50% on zero-shot categories." ], "reasoning": "The description mentions that ESIM+ELMo obtained 59% accuracy on SWAG and only 33.3% accuracy on HellaSwag. The table shows that ESIM+ELMo has an overall test accuracy of 33.8%.", "question": "What is the accuracy of ESIM+ELMo on the HellaSwag dataset compared to the SWAG dataset?", "answer": "So, the answer is {\"answer\": \"33.3% on HellaSwag and 59% on SWAG\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence BERT-Large has the highest accuracy on the ActivityNet Test dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nBERT-Large has the highest accuracy on the ActivityNet Test dataset.\n" }, { "role": "assistant", "content": "The table shows that BERT-Large has an accuracy of 51.7% on the ActivityNet Test dataset, which is the highest among the listed models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2348.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance of models, evaluated with accuracy (%).We report results on the full validation and test sets (Overall), as well as results on informative subsets of the data: evaluated on in-domain, versus zero-shot situations, along with performance on the underlying data sources (ActivityNet versus WikiHow). All models substantially underperform humans: the gap is over 45% on in-domain categories, and 50% on zero-shot categories." ], "reasoning": "The table shows that BERT-Large has an accuracy of 51.7% on the ActivityNet Test dataset, which is the highest among the listed models.", "statement": "BERT-Large has the highest accuracy on the ActivityNet Test dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: The comparison of three baselines: unadapted, copy, and back, between cases with and without pre-training when adapting them from the domain of LAW to MED and IT. Validation set BLEU scores are reported.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Pre-training on unrelated Wikipedia text significantly improves performance across all baselines in domain adaptation from LAW to MED and IT.Through the experiments, we find that initializing the translation model with pre-trained parameters can not only benefit our method but also the baselines. Unadapted, Copy, Back, where we adapt from the LAW domain to MED and IT domains. For all three baselines, pre-training consistently brings in substantial improvements, although the pre-training process is performed via unsupervised language modeling training on Wikipedia text that is irrelevant to the target domain data we used here. This shows that proper initialization of models is crucial to the domain adaptation problem to circumvent the lack of supervised data in the target domain." } ], "images": [ "scigen/train/2349.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The comparison of three baselines: unadapted, copy, and back, between cases with and without pre-training when adapting them from the domain of LAW to MED and IT. Validation set BLEU scores are reported." ], "reasoning": "Pre-training on unrelated Wikipedia text significantly improves performance across all baselines in domain adaptation from LAW to MED and IT.", "description": "Through the experiments, we find that initializing the translation model with pre-trained parameters can not only benefit our method but also the baselines. Unadapted, Copy, Back, where we adapt from the LAW domain to MED and IT domains. For all three baselines, pre-training consistently brings in substantial improvements, although the pre-training process is performed via unsupervised language modeling training on Wikipedia text that is irrelevant to the target domain data we used here. This shows that proper initialization of models is crucial to the domain adaptation problem to circumvent the lack of supervised data in the target domain.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 4: The comparison of three baselines: unadapted, copy, and back, between cases with and without pre-training when adapting them from the domain of LAW to MED and IT. Validation set BLEU scores are reported.', and tackle the question below in a sequential manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the improvement in the 'Back' baseline for the IT domain when pre-trained parameters are used?\n" }, { "role": "assistant", "content": "The 'Back' baseline for the IT domain improves from 9.21 to 28.32 when pre-trained parameters are used. So, the answer is {\"answer\": \"19.11\"}." } ], "images": [ "scigen/train/2349.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The comparison of three baselines: unadapted, copy, and back, between cases with and without pre-training when adapting them from the domain of LAW to MED and IT. Validation set BLEU scores are reported." ], "reasoning": "The 'Back' baseline for the IT domain improves from 9.21 to 28.32 when pre-trained parameters are used.", "question": "What is the improvement in the 'Back' baseline for the IT domain when pre-trained parameters are used?", "answer": "So, the answer is {\"answer\": \"19.11\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nPre-training improves the 'Copy' baseline for the MED domain by more than 5 points.\n" }, { "role": "assistant", "content": "The 'Copy' baseline for the MED domain improves from 17.42 to 24.38, which is an improvement of 6.96 points. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2349.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The comparison of three baselines: unadapted, copy, and back, between cases with and without pre-training when adapting them from the domain of LAW to MED and IT. Validation set BLEU scores are reported." ], "reasoning": "The 'Copy' baseline for the MED domain improves from 17.42 to 24.38, which is an improvement of 6.96 points.", "statement": "Pre-training improves the 'Copy' baseline for the MED domain by more than 5 points.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Translation accuracy (BLEU) under different settings. The second and third columns are source and target domains respectively. “DE”, “EN”, and “RO” denote German, English, and Romanian, respectively. DALI and DAFE results are the best results from the original papers, except that numbers marked by † are from our re-implementation. Settings (7) IBT+Src and (8) IBT+Back uses the out-of-domain data and back-translated data to minimize the supervised machine translation loss, respectively. (9) “MT (Sup.)” results are obtained by training an NMT model on the supervised target domain data.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The Back method significantly improves adaptation performance and outperforms other baselines like DALI and DAFE, while IBT shows substantial performance using only target domain non-parallel data.Adapting between Specific Domains In this table, the second row lists the source domains whereas the third row shows the target domains. From this table, we see that the unadapted baseline model, Unadapted, performs very poorly, verifying the previous statement that current NMT models cannot generalize well to test data from a new domain. In contrast, the copy method, Copy, and back-translation method, Back, can significantly improve the adaptation performance, with Back showing much superior performance. To be noted, Back even outperforms the other two baselines: DALI and DAFE, by a large margin in the majority of cases, although it was proposed earlier and is much simpler. Notably, IBT only needs the target domain non-parallel data but can still substantially outperform those baselines that rely on the parallel source domain data (e.g., Back, DALI, DAFE), indicating that previous methods have not exhausted the potential contained within the target domain data." } ], "images": [ "scigen/train/2350.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Translation accuracy (BLEU) under different settings. The second and third columns are source and target domains respectively. “DE”, “EN”, and “RO” denote German, English, and Romanian, respectively. DALI and DAFE results are the best results from the original papers, except that numbers marked by † are from our re-implementation. Settings (7) IBT+Src and (8) IBT+Back uses the out-of-domain data and back-translated data to minimize the supervised machine translation loss, respectively. (9) “MT (Sup.)” results are obtained by training an NMT model on the supervised target domain data." ], "reasoning": "The Back method significantly improves adaptation performance and outperforms other baselines like DALI and DAFE, while IBT shows substantial performance using only target domain non-parallel data.", "description": "Adapting between Specific Domains In this table, the second row lists the source domains whereas the third row shows the target domains. From this table, we see that the unadapted baseline model, Unadapted, performs very poorly, verifying the previous statement that current NMT models cannot generalize well to test data from a new domain. In contrast, the copy method, Copy, and back-translation method, Back, can significantly improve the adaptation performance, with Back showing much superior performance. To be noted, Back even outperforms the other two baselines: DALI and DAFE, by a large margin in the majority of cases, although it was proposed earlier and is much simpler. Notably, IBT only needs the target domain non-parallel data but can still substantially outperform those baselines that rely on the parallel source domain data (e.g., Back, DALI, DAFE), indicating that previous methods have not exhausted the potential contained within the target domain data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: Translation accuracy (BLEU) under different settings. The second and third columns are source and target domains respectively. “DE”, “EN”, and “RO” denote German, English, and Romanian, respectively. DALI and DAFE results are the best results from the original papers, except that numbers marked by † are from our re-implementation. Settings (7) IBT+Src and (8) IBT+Back uses the out-of-domain data and back-translated data to minimize the supervised machine translation loss, respectively. (9) “MT (Sup.)” results are obtained by training an NMT model on the supervised target domain data.', solve the subsequent question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich method shows the highest performance for DE to EN WMT-16 in the MED domain?\n" }, { "role": "assistant", "content": "To find the highest performance for DE to EN WMT-16 in the MED domain, we need to look at the column labeled 'DE to EN WMT-16 MED' and identify the highest value. The highest value in this column is 42.08, which corresponds to the IBT+Back method. So, the answer is {\"answer\": \"IBT+Back\"}" } ], "images": [ "scigen/train/2350.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Translation accuracy (BLEU) under different settings. The second and third columns are source and target domains respectively. “DE”, “EN”, and “RO” denote German, English, and Romanian, respectively. DALI and DAFE results are the best results from the original papers, except that numbers marked by † are from our re-implementation. Settings (7) IBT+Src and (8) IBT+Back uses the out-of-domain data and back-translated data to minimize the supervised machine translation loss, respectively. (9) “MT (Sup.)” results are obtained by training an NMT model on the supervised target domain data." ], "reasoning": "To find the highest performance for DE to EN WMT-16 in the MED domain, we need to look at the column labeled 'DE to EN WMT-16 MED' and identify the highest value. The highest value in this column is 42.08, which corresponds to the IBT+Back method.", "question": "Which method shows the highest performance for DE to EN WMT-16 in the MED domain?", "answer": "So, the answer is {\"answer\": \"IBT+Back\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Copy method performs better than the Unadapted method in the DE to EN LAW domain. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Copy method performs better than the Unadapted method in the DE to EN LAW domain.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the performance of the Copy and Unadapted methods in the DE to EN LAW domain. The Copy method has a score of 23.57, while the Unadapted method has a score of 18.76, indicating that the Copy method performs better. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2350.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Translation accuracy (BLEU) under different settings. The second and third columns are source and target domains respectively. “DE”, “EN”, and “RO” denote German, English, and Romanian, respectively. DALI and DAFE results are the best results from the original papers, except that numbers marked by † are from our re-implementation. Settings (7) IBT+Src and (8) IBT+Back uses the out-of-domain data and back-translated data to minimize the supervised machine translation loss, respectively. (9) “MT (Sup.)” results are obtained by training an NMT model on the supervised target domain data." ], "reasoning": "To verify this statement, we compare the performance of the Copy and Unadapted methods in the DE to EN LAW domain. The Copy method has a score of 23.57, while the Unadapted method has a score of 18.76, indicating that the Copy method performs better.", "statement": "The Copy method performs better than the Unadapted method in the DE to EN LAW domain.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5: Test set BLEU scores after adding extra non-parallel data (“+Data”) from the source WMT domain (“WMT”) or from the target TED domain (“TED”).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Our method shows continuous improvement with increasing non-parallel data, as demonstrated by adaptation from WMT to TED for DE-EN and RO-EN language pairs.One advantage of our method is that it keeps gaining improvement if the non-parallel data get larger. Specifically, we studied the adaptation from the WMT data to TED for both DE-EN and RO-EN language pairs. We consider two sources of extra non-parallel data: source domain and target domain. The source domain data source can be the WMT data itself, whereas for the target domain, we collected an additional dataset of TED talks. Note that for any language pair, we made sure that the transcript of a TED talk only appeared once in either the source or the target side to avoid any parallel sentences. However, if we could not get more non-parallel data from the target domain, those in the source domain that are more readily to be obtained may also potentially improve the adaptation performance. This set of experiments have shown the great potential of our method: we can always seek to collecting more non-parallel data to keep improving the adaptation performance." } ], "images": [ "scigen/train/2351.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Test set BLEU scores after adding extra non-parallel data (“+Data”) from the source WMT domain (“WMT”) or from the target TED domain (“TED”)." ], "reasoning": "Our method shows continuous improvement with increasing non-parallel data, as demonstrated by adaptation from WMT to TED for DE-EN and RO-EN language pairs.", "description": "One advantage of our method is that it keeps gaining improvement if the non-parallel data get larger. Specifically, we studied the adaptation from the WMT data to TED for both DE-EN and RO-EN language pairs. We consider two sources of extra non-parallel data: source domain and target domain. The source domain data source can be the WMT data itself, whereas for the target domain, we collected an additional dataset of TED talks. Note that for any language pair, we made sure that the transcript of a TED talk only appeared once in either the source or the target side to avoid any parallel sentences. However, if we could not get more non-parallel data from the target domain, those in the source domain that are more readily to be obtained may also potentially improve the adaptation performance. This set of experiments have shown the great potential of our method: we can always seek to collecting more non-parallel data to keep improving the adaptation performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 5: Test set BLEU scores after adding extra non-parallel data (“+Data”) from the source WMT domain (“WMT”) or from the target TED domain (“TED”).'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. \n\nQuestion: What is the highest score achieved for the WMT16→TED (RO-EN) adaptation?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the highest score for the WMT16→TED (RO-EN) adaptation, look at the WMT16→TED (RO-EN) column and identify the highest value. So, the answer is {\"answer\": \"42.22\"}" } ], "images": [ "scigen/train/2351.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Test set BLEU scores after adding extra non-parallel data (“+Data”) from the source WMT domain (“WMT”) or from the target TED domain (“TED”)." ], "reasoning": "To find the highest score for the WMT16→TED (RO-EN) adaptation, look at the WMT16→TED (RO-EN) column and identify the highest value.", "question": "What is the highest score achieved for the WMT16→TED (RO-EN) adaptation?", "answer": "So, the answer is {\"answer\": \"42.22\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The IBT+Back model with TED data achieved the highest score for the WMT14→TED (DE-EN) adaptation. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe IBT+Back model with TED data achieved the highest score for the WMT14→TED (DE-EN) adaptation.\n" }, { "role": "assistant", "content": "Check the WMT14→TED (DE-EN) column for the highest score and verify if it corresponds to the IBT+Back model with TED data. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2351.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Test set BLEU scores after adding extra non-parallel data (“+Data”) from the source WMT domain (“WMT”) or from the target TED domain (“TED”)." ], "reasoning": "Check the WMT14→TED (DE-EN) column for the highest score and verify if it corresponds to the IBT+Back model with TED data.", "statement": "The IBT+Back model with TED data achieved the highest score for the WMT14→TED (DE-EN) adaptation.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 5: Comparison studies of RMFN on CMU-MOSI. Modeling cross-modal interactions using multistage fusion and attention weights are crucial in multimodal language analysis.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "RMFN shows improved performance, indicating that multistage fusion is both effective and efficient for human multimodal language modeling.RMFN shows improved performance, indicating that multistage fusion is both effective and efficient for human multimodal language modeling. Q4: RMFN (no MFP) represents a system of LSTHMs without the integration of zt from the MFP to model cross-modal interactions. Q5: RMFN (no HIGHLIGHT) removes the HIGHLIGHT module from MFP during multistage fusion." } ], "images": [ "scigen/train/2352.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison studies of RMFN on CMU-MOSI. Modeling cross-modal interactions using multistage fusion and attention weights are crucial in multimodal language analysis." ], "reasoning": "RMFN shows improved performance, indicating that multistage fusion is both effective and efficient for human multimodal language modeling.", "description": "RMFN shows improved performance, indicating that multistage fusion is both effective and efficient for human multimodal language modeling. Q4: RMFN (no MFP) represents a system of LSTHMs without the integration of zt from the MFP to model cross-modal interactions. Q5: RMFN (no HIGHLIGHT) removes the HIGHLIGHT module from MFP during multistage fusion.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 5: Comparison studies of RMFN on CMU-MOSI. Modeling cross-modal interactions using multistage fusion and attention weights are crucial in multimodal language analysis.\nQuestion: Which system has the highest A2 score in the CMU-MOSI dataset?\n" }, { "role": "assistant", "content": "The table shows the A2 scores for different systems in the CMU-MOSI dataset. RMFN has the highest A2 score of 78.4. So, the answer is {\"answer\": \"RMFN\"}." } ], "images": [ "scigen/train/2352.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison studies of RMFN on CMU-MOSI. Modeling cross-modal interactions using multistage fusion and attention weights are crucial in multimodal language analysis." ], "reasoning": "The table shows the A2 scores for different systems in the CMU-MOSI dataset. RMFN has the highest A2 score of 78.4.", "question": "Which system has the highest A2 score in the CMU-MOSI dataset?", "answer": "So, the answer is {\"answer\": \"RMFN\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nRMFN (no HIGHLIGHT) has a higher F1 score than RMFN (no MFP) in the CMU-MOSI dataset.\n" }, { "role": "assistant", "content": "The table shows that RMFN (no HIGHLIGHT) has an F1 score of 77.9, which is higher than the F1 score of RMFN (no MFP) which is 76.5. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2352.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison studies of RMFN on CMU-MOSI. Modeling cross-modal interactions using multistage fusion and attention weights are crucial in multimodal language analysis." ], "reasoning": "The table shows that RMFN (no HIGHLIGHT) has an F1 score of 77.9, which is higher than the F1 score of RMFN (no MFP) which is 76.5.", "statement": "RMFN (no HIGHLIGHT) has a higher F1 score than RMFN (no MFP) in the CMU-MOSI dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: Effect of varying the number of stages on CMU-MOSI sentiment analysis performance. Multistage fusion improves performance as compared to single stage fusion.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The RMFN-R3 model, which performs fusion across three stages, achieves the highest performance in terms of A2, F1, A7, and Corr metrics, while maintaining a low MAE.Q1: To study the effectiveness of the multistage fusion process, we test the baseline RMFN-R1 which performs fusion in only one stage instead of across multiple stages. This model makes the strong assumption that all cross-modal interactions can be modeled during only one stage. Q2: We test baselines RMFN-RK which perform K stages of fusion. Further increases led to decreases in performance and we hypothesize this is due to overfitting on the dataset." } ], "images": [ "scigen/train/2353.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Effect of varying the number of stages on CMU-MOSI sentiment analysis performance. Multistage fusion improves performance as compared to single stage fusion." ], "reasoning": "The RMFN-R3 model, which performs fusion across three stages, achieves the highest performance in terms of A2, F1, A7, and Corr metrics, while maintaining a low MAE.", "description": "Q1: To study the effectiveness of the multistage fusion process, we test the baseline RMFN-R1 which performs fusion in only one stage instead of across multiple stages. This model makes the strong assumption that all cross-modal interactions can be modeled during only one stage. Q2: We test baselines RMFN-RK which perform K stages of fusion. Further increases led to decreases in performance and we hypothesize this is due to overfitting on the dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 4: Effect of varying the number of stages on CMU-MOSI sentiment analysis performance. Multistage fusion improves performance as compared to single stage fusion.', unfold the steps to solve the question given next. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which RMFN model achieves the highest A2 score and what is its corresponding MAE?\n" }, { "role": "assistant", "content": "By examining the table, the RMFN-R3 model achieves the highest A2 score of 78.4. The corresponding MAE for RMFN-R3 is 0.922. So, the answer is {\"answer\": \"0.922\"}." } ], "images": [ "scigen/train/2353.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Effect of varying the number of stages on CMU-MOSI sentiment analysis performance. Multistage fusion improves performance as compared to single stage fusion." ], "reasoning": "By examining the table, the RMFN-R3 model achieves the highest A2 score of 78.4. The corresponding MAE for RMFN-R3 is 0.922.", "question": "Which RMFN model achieves the highest A2 score and what is its corresponding MAE?", "answer": "So, the answer is {\"answer\": \"0.922\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe RMFN-R3 model achieves the highest performance across all metrics.\n" }, { "role": "assistant", "content": "The RMFN-R3 model achieves the highest performance in A2, F1, A7, and Corr metrics, but not in MAE where RMFN-R2 has a lower value. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2353.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Effect of varying the number of stages on CMU-MOSI sentiment analysis performance. Multistage fusion improves performance as compared to single stage fusion." ], "reasoning": "The RMFN-R3 model achieves the highest performance in A2, F1, A7, and Corr metrics, but not in MAE where RMFN-R2 has a lower value.", "statement": "The RMFN-R3 model achieves the highest performance across all metrics.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5. Performance with different fine-tuning strategies'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Applying all three strategies (STL, GU, DFT) results in the best performance in terms of test loss and accuracy, showing that gradual unfreezing is crucial.For measuring the performance of the techniques against catastrophic forgetting, we try four different settings: No adjustment (NA), only with slanted triangular learning rate (STL), slanted triangular learning rate and gradual unfreezing (STL+GU) and the techniques in the previous one, together with discriminative fine-tuning. We report the performance of these four settings with loss on test function and trajectory of validation loss over training epochs. Applying all three of the strategies produce the best performance in terms of test loss and accuracy. Gradual unfreezing and discriminative fine-tuning have the same reasoning behind them: higher level features should be fine-tuned more than the lower level ones, since information learned from language modeling are mostly present in the lower levels. that using only discriminative fine-tuning with slanted triangular learning rates performs worse than using the slanted triangular learning rates alone. This shows that gradual unfreezing is the most important technique for our case. One way that catastrophic forgetting can show itself is the sudden increase in validation loss after several epochs. As model is trained, it quickly starts to overfit when no measure is taken accordingly. The model achieves the best performance on validation set after the first epoch and then starts to overfit. While with all three techniques applied, model is much more stable. The other combinations lie between these two cases." } ], "images": [ "scigen/train/2354.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5. Performance with different fine-tuning strategies" ], "reasoning": "Applying all three strategies (STL, GU, DFT) results in the best performance in terms of test loss and accuracy, showing that gradual unfreezing is crucial.", "description": "For measuring the performance of the techniques against catastrophic forgetting, we try four different settings: No adjustment (NA), only with slanted triangular learning rate (STL), slanted triangular learning rate and gradual unfreezing (STL+GU) and the techniques in the previous one, together with discriminative fine-tuning. We report the performance of these four settings with loss on test function and trajectory of validation loss over training epochs. Applying all three of the strategies produce the best performance in terms of test loss and accuracy. Gradual unfreezing and discriminative fine-tuning have the same reasoning behind them: higher level features should be fine-tuned more than the lower level ones, since information learned from language modeling are mostly present in the lower levels. that using only discriminative fine-tuning with slanted triangular learning rates performs worse than using the slanted triangular learning rates alone. This shows that gradual unfreezing is the most important technique for our case. One way that catastrophic forgetting can show itself is the sudden increase in validation loss after several epochs. As model is trained, it quickly starts to overfit when no measure is taken accordingly. The model achieves the best performance on validation set after the first epoch and then starts to overfit. While with all three techniques applied, model is much more stable. The other combinations lie between these two cases.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 5. Performance with different fine-tuning strategies' to sequentially solve the question presented below. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich strategy combination results in the lowest test loss?\n" }, { "role": "assistant", "content": "By examining the 'Loss' column in the table, the strategy combination 'All three' results in the lowest test loss of 0.37. So, the answer is {\"answer\": \"All three\"}" } ], "images": [ "scigen/train/2354.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5. Performance with different fine-tuning strategies" ], "reasoning": "By examining the 'Loss' column in the table, the strategy combination 'All three' results in the lowest test loss of 0.37.", "question": "Which strategy combination results in the lowest test loss?", "answer": "So, the answer is {\"answer\": \"All three\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nUsing only discriminative fine-tuning with slanted triangular learning rates performs worse than using the slanted triangular learning rates alone.\n" }, { "role": "assistant", "content": "The table shows that the 'STL + DFT' strategy has a higher loss (0.42) compared to the 'STL' strategy (0.40), supporting the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2354.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5. Performance with different fine-tuning strategies" ], "reasoning": "The table shows that the 'STL + DFT' strategy has a higher loss (0.42) compared to the 'STL' strategy (0.40), supporting the statement.", "statement": "Using only discriminative fine-tuning with slanted triangular learning rates performs worse than using the slanted triangular learning rates alone.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3. Experimental Results on FiQA Sentiment Dataset'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "FinBERT outperforms ULMFit and other methods in all metrics, showing the effectiveness of language model pre-training.FinBERT outperforms ULMFit, and consequently all of the other methods in all metrics. In order to measure the performance of the models on different sizes of labeled training datasets, we ran LSTM classifiers, ULMFit and FinBERT on 5 different configurations. 100 training examples is too low for all of the models. However, once the training size becomes 250, ULMFit and FinBERT starts to successfully differentiate between labels, with an accuracy as high as 80% for FinBERT. All of the methods consistently get better with more data, but ULMFit and FinBERT does better with 250 examples than LSTM classifiers do with the whole dataset. This shows the effectiveness of language model pre-training. Our model outperforms state-of-the-art models for both MSE and R2. Since we don’t have access to that we report the results on 10-Fold cross validation." } ], "images": [ "scigen/train/2355.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. Experimental Results on FiQA Sentiment Dataset" ], "reasoning": "FinBERT outperforms ULMFit and other methods in all metrics, showing the effectiveness of language model pre-training.", "description": "FinBERT outperforms ULMFit, and consequently all of the other methods in all metrics. In order to measure the performance of the models on different sizes of labeled training datasets, we ran LSTM classifiers, ULMFit and FinBERT on 5 different configurations. 100 training examples is too low for all of the models. However, once the training size becomes 250, ULMFit and FinBERT starts to successfully differentiate between labels, with an accuracy as high as 80% for FinBERT. All of the methods consistently get better with more data, but ULMFit and FinBERT does better with 250 examples than LSTM classifiers do with the whole dataset. This shows the effectiveness of language model pre-training. Our model outperforms state-of-the-art models for both MSE and R2. Since we don’t have access to that we report the results on 10-Fold cross validation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. The question is related to the given table titled 'Table 3. Experimental Results on FiQA Sentiment Dataset'.\n\nQuestion: Which model has the lowest MSE and the highest R2?\n" }, { "role": "assistant", "content": "By comparing the MSE and R2 values in the table, FinBERT has the lowest MSE (0.07) and the highest R2 (0.55). So, the answer is {\"answer\": \"FinBERT\"}" } ], "images": [ "scigen/train/2355.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. Experimental Results on FiQA Sentiment Dataset" ], "reasoning": "By comparing the MSE and R2 values in the table, FinBERT has the lowest MSE (0.07) and the highest R2 (0.55).", "question": "Which model has the lowest MSE and the highest R2?", "answer": "So, the answer is {\"answer\": \"FinBERT\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence FinBERT has an R2 value of 0.55. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nFinBERT has an R2 value of 0.55.\n" }, { "role": "assistant", "content": "The table shows that FinBERT has an R2 value of 0.55, which supports the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2355.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. Experimental Results on FiQA Sentiment Dataset" ], "reasoning": "The table shows that FinBERT has an R2 value of 0.55, which supports the statement.", "statement": "FinBERT has an R2 value of 0.55.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4. Performance with different pre-training strategies'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The FinBERT-task model, which underwent further pre-training on the classification training set, achieved the highest F1 score, while the FinBERT-domain model, pre-trained on the domain corpus, had the lowest loss.We first measure the effect of further pre-training on the performance of the classifier. We compare three models: 1) No further pre-training (denoted by Vanilla BERT), 2) Further pre-training on classification training set (denoted by FinBERT-task), 3) Further pre-training on domain corpus, TRC2-financial (denoted by FinBERT-domain). Models are evaluated with loss, accuracy and macro average F1 scores on the test dataset." } ], "images": [ "scigen/train/2356.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4. Performance with different pre-training strategies" ], "reasoning": "The FinBERT-task model, which underwent further pre-training on the classification training set, achieved the highest F1 score, while the FinBERT-domain model, pre-trained on the domain corpus, had the lowest loss.", "description": "We first measure the effect of further pre-training on the performance of the classifier. We compare three models: 1) No further pre-training (denoted by Vanilla BERT), 2) Further pre-training on classification training set (denoted by FinBERT-task), 3) Further pre-training on domain corpus, TRC2-financial (denoted by FinBERT-domain). Models are evaluated with loss, accuracy and macro average F1 scores on the test dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 4. Performance with different pre-training strategies' to sequentially solve the question presented below. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhich model achieved the highest F1 score and what was its value?\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all three models, we see that FinBERT-task has the highest F1 score of 0.85. So, the answer is {\"answer\": \"FinBERT-task with 0.85\"}." } ], "images": [ "scigen/train/2356.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4. Performance with different pre-training strategies" ], "reasoning": "By comparing the F1 scores of all three models, we see that FinBERT-task has the highest F1 score of 0.85.", "question": "Which model achieved the highest F1 score and what was its value?", "answer": "So, the answer is {\"answer\": \"FinBERT-task with 0.85\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The FinBERT-domain model achieved the highest accuracy among the three models. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe FinBERT-domain model achieved the highest accuracy among the three models.\n" }, { "role": "assistant", "content": "The table shows that both FinBERT-task and FinBERT-domain models have the same accuracy of 0.86, which is higher than Vanilla BERT's accuracy of 0.85. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2356.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4. Performance with different pre-training strategies" ], "reasoning": "The table shows that both FinBERT-task and FinBERT-domain models have the same accuracy of 0.86, which is higher than Vanilla BERT's accuracy of 0.85.", "statement": "The FinBERT-domain model achieved the highest accuracy among the three models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7. Performance on starting training from different layers'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Fine-tuning only the classification layer is not effective, but fine-tuning up to Layer-9 achieves similar performance to fine-tuning the whole model with less training time.Fine-tuning only the classification layer does not achieve close performance to fine-tuning other layers. However fine-tuning only the last layer handily outperforms the state-of-the-art machine learning methods like HSC. After Layer-9, the performance becomes virtually the same, only to be outperformed by fine-tuning the whole model. This result shows that in order to utilize BERT, an expensive training of the whole model is not mandatory. A fair trade-off can be made for much less training time with a small decrease in model performance." } ], "images": [ "scigen/train/2357.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7. Performance on starting training from different layers" ], "reasoning": "Fine-tuning only the classification layer is not effective, but fine-tuning up to Layer-9 achieves similar performance to fine-tuning the whole model with less training time.", "description": "Fine-tuning only the classification layer does not achieve close performance to fine-tuning other layers. However fine-tuning only the last layer handily outperforms the state-of-the-art machine learning methods like HSC. After Layer-9, the performance becomes virtually the same, only to be outperformed by fine-tuning the whole model. This result shows that in order to utilize BERT, an expensive training of the whole model is not mandatory. A fair trade-off can be made for much less training time with a small decrease in model performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nTable title: Table 7. Performance on starting training from different layers\n\nQuestion: Which layer achieves the highest accuracy with the least training time?\n" }, { "role": "assistant", "content": "By examining the table, Layer-9 achieves an accuracy of 0.84 with a training time of 172 seconds, which is the highest accuracy with the least training time. So, the answer is {\"answer\": \"Layer-9\"}." } ], "images": [ "scigen/train/2357.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7. Performance on starting training from different layers" ], "reasoning": "By examining the table, Layer-9 achieves an accuracy of 0.84 with a training time of 172 seconds, which is the highest accuracy with the least training time.", "question": "Which layer achieves the highest accuracy with the least training time?", "answer": "So, the answer is {\"answer\": \"Layer-9\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nFine-tuning only the classification layer achieves the highest accuracy.\n" }, { "role": "assistant", "content": "The table shows that fine-tuning only the classification layer results in an accuracy of 0.52, which is lower than the accuracy achieved by fine-tuning other layers. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2357.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7. Performance on starting training from different layers" ], "reasoning": "The table shows that fine-tuning only the classification layer results in an accuracy of 0.52, which is lower than the accuracy achieved by fine-tuning other layers.", "statement": "Fine-tuning only the classification layer achieves the highest accuracy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: ser for different data augmentation techniques for sub-meetings of length 50 segments.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The use of data augmentation (daug) reduces the error rate for 'none' and 'meeting' randomization, but increases it for 'global' randomization.The use of random sub-sequences (sub-meetings) (Sec. For the dev set (used as the validation set), this augmentation technique is applied exclusively. Per original meeting, which dictates the label sequence y1:N, 5000 sub-meetings with 50 segments are generated and augmented using the techniques of Sec. Full meetings of the eval set are split into sub-meetings with at most 50 segments. The trend is different after applying \\glsdaug (\\nth2 col. Using none achieves an \\glsser of 15.25%. The result for meeting is reduced to 13.57%. This shows that \\glsdaug generates fairly typical data. Sec. The number of speaker groups available from global (∼C1554) is larger than the number of sub-meetings. Thus, \\glsdaug does not increase the number of speaker groups seen by the \\glsdnc model when using global." } ], "images": [ "scigen/train/2358.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: ser for different data augmentation techniques for sub-meetings of length 50 segments." ], "reasoning": "The use of data augmentation (daug) reduces the error rate for 'none' and 'meeting' randomization, but increases it for 'global' randomization.", "description": "The use of random sub-sequences (sub-meetings) (Sec. For the dev set (used as the validation set), this augmentation technique is applied exclusively. Per original meeting, which dictates the label sequence y1:N, 5000 sub-meetings with 50 segments are generated and augmented using the techniques of Sec. Full meetings of the eval set are split into sub-meetings with at most 50 segments. The trend is different after applying \\glsdaug (\\nth2 col. Using none achieves an \\glsser of 15.25%. The result for meeting is reduced to 13.57%. This shows that \\glsdaug generates fairly typical data. Sec. The number of speaker groups available from global (∼C1554) is larger than the number of sub-meetings. Thus, \\glsdaug does not increase the number of speaker groups seen by the \\glsdnc model when using global.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nTable title: Table 2: ser for different data augmentation techniques for sub-meetings of length 50 segments.\n\nQuestion: What is the error rate for 'meeting' randomization with data augmentation?\n" }, { "role": "assistant", "content": "The table shows the error rates for different randomization techniques with and without data augmentation. For 'meeting' randomization with data augmentation, the error rate is 13.57%. So, the answer is {\"answer\": \"13.57\"}." } ], "images": [ "scigen/train/2358.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: ser for different data augmentation techniques for sub-meetings of length 50 segments." ], "reasoning": "The table shows the error rates for different randomization techniques with and without data augmentation. For 'meeting' randomization with data augmentation, the error rate is 13.57%.", "question": "What is the error rate for 'meeting' randomization with data augmentation?", "answer": "So, the answer is {\"answer\": \"13.57\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nData augmentation increases the error rate for 'global' randomization.\n" }, { "role": "assistant", "content": "The table shows that the error rate for 'global' randomization increases from 14.47% without data augmentation to 19.80% with data augmentation. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2358.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: ser for different data augmentation techniques for sub-meetings of length 50 segments." ], "reasoning": "The table shows that the error rate for 'global' randomization increases from 14.47% without data augmentation to 19.80% with data augmentation.", "statement": "Data augmentation increases the error rate for 'global' randomization.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Comparison of dnc vs. sc at different cl stages. Finetuned dnc models are initialised from the corresponding models using full data augmentation.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "For meeting lengths above 50, 104 sub-meetings are chosen and modified using augmentation techniques. The finetuned dnc model for the full meeting length achieves a significant reduction in error rate compared to SC.For meeting lengths above 50, per original meeting, 104 sub-meetings are chosen and modified using the augmentation techniques. For maximum length 200, applying the two best-performing techniques of Sec. The latter technique will be continued for the later stages of the \\glscl. Moving to a maximum sub-meeting length of 500 results in an \\glsser of 17.73% and for the full sequence length of 20.65%. During finetuning, only \\glsdaug is used. Although the \\glsser does not always improve, the \\glsser on the dev set was reduced in all cases. This is why the finetuned models should be compared with \\glssc. For all meeting lengths the \\glsdnc model outperforms \\glssc. The finetuned \\glsdnc model for the full meeting length achieves an \\glsser of 16.92%, which reduces \\glsser over \\glssc by 29.4% relative. The \\glsser of a \\glsdnc model trained with meeting and \\glsdaug, but without \\glscl, is only 34.48% after finetuning." } ], "images": [ "scigen/train/2359.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of dnc vs. sc at different cl stages. Finetuned dnc models are initialised from the corresponding models using full data augmentation." ], "reasoning": "For meeting lengths above 50, 104 sub-meetings are chosen and modified using augmentation techniques. The finetuned dnc model for the full meeting length achieves a significant reduction in error rate compared to SC.", "description": "For meeting lengths above 50, per original meeting, 104 sub-meetings are chosen and modified using the augmentation techniques. For maximum length 200, applying the two best-performing techniques of Sec. The latter technique will be continued for the later stages of the \\glscl. Moving to a maximum sub-meeting length of 500 results in an \\glsser of 17.73% and for the full sequence length of 20.65%. During finetuning, only \\glsdaug is used. Although the \\glsser does not always improve, the \\glsser on the dev set was reduced in all cases. This is why the finetuned models should be compared with \\glssc. For all meeting lengths the \\glsdnc model outperforms \\glssc. The finetuned \\glsdnc model for the full meeting length achieves an \\glsser of 16.92%, which reduces \\glsser over \\glssc by 29.4% relative. The \\glsser of a \\glsdnc model trained with meeting and \\glsdaug, but without \\glscl, is only 34.48% after finetuning.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Comparison of dnc vs. sc at different cl stages. Finetuned dnc models are initialised from the corresponding models using full data augmentation.', unfold the question presented below, detailing every step of your calculation. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the error rate reduction achieved by the finetuned dnc model for the full meeting length compared to SC?\n" }, { "role": "assistant", "content": "The table shows that the error rate for the finetuned dnc model for the full meeting length is 16.92%, while for SC it is 23.95%. The description states that this reduction is 29.4% relative. So, the answer is {\"answer\": \"29.4%\"}." } ], "images": [ "scigen/train/2359.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of dnc vs. sc at different cl stages. Finetuned dnc models are initialised from the corresponding models using full data augmentation." ], "reasoning": "The table shows that the error rate for the finetuned dnc model for the full meeting length is 16.92%, while for SC it is 23.95%. The description states that this reduction is 29.4% relative.", "question": "What is the error rate reduction achieved by the finetuned dnc model for the full meeting length compared to SC?", "answer": "So, the answer is {\"answer\": \"29.4%\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe dnc finetune model achieves a lower error rate than SC for all meeting lengths.\n" }, { "role": "assistant", "content": "The table shows that for all meeting lengths (050, 200, 500, and all), the dnc finetune model has lower error rates (13.90%, 16.75%, 18.39%, and 16.92%) compared to SC (15.89%, 22.38%, 23.56%, and 23.95%). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2359.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of dnc vs. sc at different cl stages. Finetuned dnc models are initialised from the corresponding models using full data augmentation." ], "reasoning": "The table shows that for all meeting lengths (050, 200, 500, and all), the dnc finetune model has lower error rates (13.90%, 16.75%, 18.39%, and 16.92%) compared to SC (15.89%, 22.38%, 23.56%, and 23.95%).", "statement": "The dnc finetune model achieves a lower error rate than SC for all meeting lengths.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Comparative results on STS-B dataset with different tokenizers', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Despite its issues, the WordPiece tokenizer outperforms whitespace and character n-gram tokenizers due to the vocabulary overlap between the STS-B dataset and BERT vocabulary.Even though wordPiece tokenizer has the issues stated earlier, it is still performing better than whitespace and character n-gram tokenizer. This is primarily because of the vocabulary overlap between STS-B dataset and BERT vocabulary." } ], "images": [ "scigen/train/2360.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparative results on STS-B dataset with different tokenizers" ], "reasoning": "Despite its issues, the WordPiece tokenizer outperforms whitespace and character n-gram tokenizers due to the vocabulary overlap between the STS-B dataset and BERT vocabulary.", "description": "Even though wordPiece tokenizer has the issues stated earlier, it is still performing better than whitespace and character n-gram tokenizer. This is primarily because of the vocabulary overlap between STS-B dataset and BERT vocabulary.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 2: Comparative results on STS-B dataset with different tokenizers' in mind, break down and resolve the question below step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nAt 10% error, which tokenizer performs the best?\n" }, { "role": "assistant", "content": "To determine the best-performing tokenizer at 10% error, compare the values in the 10% error row. WordPiece has a value of 0.60, WhiteSpce has 0.41, and N-gram has 0.46. Therefore, WordPiece performs the best. So, the answer is {\"answer\": \"WordPiece\"}." } ], "images": [ "scigen/train/2360.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparative results on STS-B dataset with different tokenizers" ], "reasoning": "To determine the best-performing tokenizer at 10% error, compare the values in the 10% error row. WordPiece has a value of 0.60, WhiteSpce has 0.41, and N-gram has 0.46. Therefore, WordPiece performs the best.", "question": "At 10% error, which tokenizer performs the best?", "answer": "So, the answer is {\"answer\": \"WordPiece\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe WordPiece tokenizer performs better than the WhiteSpce tokenizer at all error levels.\n" }, { "role": "assistant", "content": "To verify this statement, compare the values of WordPiece and WhiteSpce at each error level. At 0% error, WordPiece (0.89) is greater than WhiteSpce (0.69). At 5% error, WordPiece (0.78) is greater than WhiteSpce (0.59). At 10% error, WordPiece (0.60) is greater than WhiteSpce (0.41). At 15% error, WordPiece (0.45) is greater than WhiteSpce (0.33). At 20% error, WordPiece (0.35) is greater than WhiteSpce (0.22). Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2360.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparative results on STS-B dataset with different tokenizers" ], "reasoning": "To verify this statement, compare the values of WordPiece and WhiteSpce at each error level. At 0% error, WordPiece (0.89) is greater than WhiteSpce (0.69). At 5% error, WordPiece (0.78) is greater than WhiteSpce (0.59). At 10% error, WordPiece (0.60) is greater than WhiteSpce (0.41). At 15% error, WordPiece (0.45) is greater than WhiteSpce (0.33). At 20% error, WordPiece (0.35) is greater than WhiteSpce (0.22). Therefore, the statement is supported.", "statement": "The WordPiece tokenizer performs better than the WhiteSpce tokenizer at all error levels.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 7: Assessing influence by code'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "CPC showed the highest classification performance overall, but USPC performed slightly better for GOCS data, prompting a quantitative analysis with a consistent dimensionality of 128 for all classification codes.As a result, CPC, the most subdivided classification, showed the highest classification performance. However, the performance of USPC was slightly higher than that of CPC for geostationary orbit complex satellite (GOCS) data. Therefore, we performed a quantitative analysis to investigate it. For a fair comparison, the dimensionality of the density layer (after the graph embedding layer) is 128 for all classification codes." } ], "images": [ "scigen/train/2361.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Assessing influence by code" ], "reasoning": "CPC showed the highest classification performance overall, but USPC performed slightly better for GOCS data, prompting a quantitative analysis with a consistent dimensionality of 128 for all classification codes.", "description": "As a result, CPC, the most subdivided classification, showed the highest classification performance. However, the performance of USPC was slightly higher than that of CPC for geostationary orbit complex satellite (GOCS) data. Therefore, we performed a quantitative analysis to investigate it. For a fair comparison, the dimensionality of the density layer (after the graph embedding layer) is 128 for all classification codes.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 7: Assessing influence by code', solve the following question step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich classification code showed the highest F1 score for the GOCS dataset?\n" }, { "role": "assistant", "content": "To find the highest F1 score for the GOCS dataset, compare the F1 scores across all classification codes listed in the table for the GOCS row. So, the answer is {\"answer\": \"TRF+DIFF\"}." } ], "images": [ "scigen/train/2361.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Assessing influence by code" ], "reasoning": "To find the highest F1 score for the GOCS dataset, compare the F1 scores across all classification codes listed in the table for the GOCS row.", "question": "Which classification code showed the highest F1 score for the GOCS dataset?", "answer": "So, the answer is {\"answer\": \"TRF+DIFF\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe text+uspc classification code showed the highest AP score for the MPUART dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the AP scores for the MPUART dataset across all classification codes listed in the table. The highest AP score for MPUART is 0.6552, which belongs to TRF+DIFF, not text+uspc. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2361.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Assessing influence by code" ], "reasoning": "To verify this statement, compare the AP scores for the MPUART dataset across all classification codes listed in the table. The highest AP score for MPUART is 0.6552, which belongs to TRF+DIFF, not text+uspc.", "statement": "The text+uspc classification code showed the highest AP score for the MPUART dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 6: SNLI hyperparameters.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The model uses Adam optimizer with a learning rate of 0.0001, 300D fixed Glove word embeddings, and trains for 100 epochs with a batch size of 64.Our hyperparameters are taken from Parikh et al. Word embeddings are projected to 200 dimensions with a trained linear layer. Unknown words are mapped to 100 unknown word classes based on the MD5 hash function, just as in Parikh et al. We train for 100 epochs, evaluate every 1000 updates, and select the best model based on validation loss." } ], "images": [ "scigen/train/2362.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: SNLI hyperparameters." ], "reasoning": "The model uses Adam optimizer with a learning rate of 0.0001, 300D fixed Glove word embeddings, and trains for 100 epochs with a batch size of 64.", "description": "Our hyperparameters are taken from Parikh et al. Word embeddings are projected to 200 dimensions with a trained linear layer. Unknown words are mapped to 100 unknown word classes based on the MD5 hash function, just as in Parikh et al. We train for 100 epochs, evaluate every 1000 updates, and select the best model based on validation loss.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 6: SNLI hyperparameters.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. \n\nQuestion: What is the hidden size used in the model and how many epochs is the model trained for?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The hidden size used in the model is 200, as stated in the table. The model is trained for 100 epochs, as mentioned in the description. So, the answer is {\"answer\": \"200 and 100\"}." } ], "images": [ "scigen/train/2362.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: SNLI hyperparameters." ], "reasoning": "The hidden size used in the model is 200, as stated in the table. The model is trained for 100 epochs, as mentioned in the description.", "question": "What is the hidden size used in the model and how many epochs is the model trained for?", "answer": "So, the answer is {\"answer\": \"200 and 100\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The model uses a batch size of 32., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model uses a batch size of 32.\n" }, { "role": "assistant", "content": "The table states that the batch size used in the model is 64, not 32. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2362.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: SNLI hyperparameters." ], "reasoning": "The table states that the batch size used in the model is 64, not 32.", "statement": "The model uses a batch size of 32.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: SNLI results (accuracy).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The HardKuma model achieved 8.5% non-zero attention with a target rate of 10%, and further explorations with HardKuma attention are planned for future work.With a target rate of 10%, the HardKuma model achieved 8.5% non-zero attention. We leave further explorations with HardKuma attention for future work." } ], "images": [ "scigen/train/2363.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: SNLI results (accuracy)." ], "reasoning": "The HardKuma model achieved 8.5% non-zero attention with a target rate of 10%, and further explorations with HardKuma attention are planned for future work.", "description": "With a target rate of 10%, the HardKuma model achieved 8.5% non-zero attention. We leave further explorations with HardKuma attention for future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: SNLI results (accuracy).'. Solve the following question based on the table.\n\nQuestion: What is the test accuracy of the DA model with HardKuma attention?\nLet's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the test accuracy of various models, including the DA model with HardKuma attention, which has a test accuracy of 85.5. So, the answer is {\"answer\": \"85.5\"}." } ], "images": [ "scigen/train/2363.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: SNLI results (accuracy)." ], "reasoning": "The table shows the test accuracy of various models, including the DA model with HardKuma attention, which has a test accuracy of 85.5.", "question": "What is the test accuracy of the DA model with HardKuma attention?", "answer": "So, the answer is {\"answer\": \"85.5\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe DA model with HardKuma attention has a higher test accuracy than the LSTM model.\n" }, { "role": "assistant", "content": "The table shows that the DA model with HardKuma attention has a test accuracy of 85.5, while the LSTM model has a test accuracy of 80.6, indicating that the DA model with HardKuma attention has a higher test accuracy. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2363.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: SNLI results (accuracy)." ], "reasoning": "The table shows that the DA model with HardKuma attention has a test accuracy of 85.5, while the LSTM model has a test accuracy of 80.6, indicating that the DA model with HardKuma attention has a higher test accuracy.", "statement": "The DA model with HardKuma attention has a higher test accuracy than the LSTM model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Beer hyperparameters.'.\n" }, { "role": "assistant", "content": "The model uses Adam optimizer with a learning rate of 0.0004, 200D fixed word embeddings, and trains for 100 epochs, selecting the best models based on validation loss.Our hyperparameters are taken from Lei et al. The pre-trained word embeddings and data sets are available online at http://people.csail.mit.edu/taolei/beer/. We train for 100 epochs and select the best models based on validation loss. For the MSE trade-off experiments on all aspects combined, we train for a maximum of 50 epochs." } ], "images": [ "scigen/train/2364.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Beer hyperparameters." ], "reasoning": "The model uses Adam optimizer with a learning rate of 0.0004, 200D fixed word embeddings, and trains for 100 epochs, selecting the best models based on validation loss.", "description": "Our hyperparameters are taken from Lei et al. The pre-trained word embeddings and data sets are available online at http://people.csail.mit.edu/taolei/beer/. We train for 100 epochs and select the best models based on validation loss. For the MSE trade-off experiments on all aspects combined, we train for a maximum of 50 epochs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Beer hyperparameters.\nQuestion: What is the hidden size and batch size used in the model?\nGive a detailed response to the above question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table lists the hidden size as 200 and the batch size as 256 under the Adam optimizer settings. So, the answer is {\"answer\": \"200, 256\"}" } ], "images": [ "scigen/train/2364.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Beer hyperparameters." ], "reasoning": "The table lists the hidden size as 200 and the batch size as 256 under the Adam optimizer settings.", "question": "What is the hidden size and batch size used in the model?", "answer": "So, the answer is {\"answer\": \"200, 256\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The model uses a learning rate of 0.0005., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe model uses a learning rate of 0.0005.\n" }, { "role": "assistant", "content": "The table specifies that the learning rate used in the model is 0.0004, not 0.0005. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2364.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Beer hyperparameters." ], "reasoning": "The table specifies that the learning rate used in the model is 0.0004, not 0.0005.", "statement": "The model uses a learning rate of 0.0005.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: SST hyperparameters.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The PyTorch bidirectional LSTM module is used for encoding sentences, with dropout applied to embeddings and the input of the output layer, followed by a linear layer and softmax for prediction.For sentiment classification we make use of the PyTorch bidirectional LSTM module for encoding sentences, for both the rationale extractor and the classifier. The BiLSTM final states are concatenated, after which a linear layer followed by a softmax produces the prediction. We apply dropout to the embeddings and to the input of the output layer." } ], "images": [ "scigen/train/2365.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: SST hyperparameters." ], "reasoning": "The PyTorch bidirectional LSTM module is used for encoding sentences, with dropout applied to embeddings and the input of the output layer, followed by a linear layer and softmax for prediction.", "description": "For sentiment classification we make use of the PyTorch bidirectional LSTM module for encoding sentences, for both the rationale extractor and the classifier. The BiLSTM final states are concatenated, after which a linear layer followed by a softmax produces the prediction. We apply dropout to the embeddings and to the input of the output layer.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 5: SST hyperparameters.'. Solve the following question based on the table.\n\nQuestion: What is the learning rate used for the Adam optimizer in the sentiment classification model?\nLet's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table specifies the learning rate for the Adam optimizer, which is used in the sentiment classification model described. So, the answer is {\"answer\": \"0.0002\"}" } ], "images": [ "scigen/train/2365.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: SST hyperparameters." ], "reasoning": "The table specifies the learning rate for the Adam optimizer, which is used in the sentiment classification model described.", "question": "What is the learning rate used for the Adam optimizer in the sentiment classification model?", "answer": "So, the answer is {\"answer\": \"0.0002\"}" }, { "messages": [ { "role": "user", "content": "The sentiment classification model uses a batch size of 50. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe sentiment classification model uses a batch size of 50.\n" }, { "role": "assistant", "content": "The table lists the batch size used in the model, which is 25, not 50. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2365.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: SST hyperparameters." ], "reasoning": "The table lists the batch size used in the model, which is 25, not 50.", "statement": "The sentiment classification model uses a batch size of 50.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Performance of our approach for English without clustering (k=1) and with the optimal number of cluster on the EVALution datasets (k=30) and on the combined datasets (k=25).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Our approach consistently outperforms the baseline across various configurations, with neighbor regularization yielding slightly better results than asymmetric regularization.S4SS3SSS0Px3 Discussion of Results. Similarly to the first experiment, our approach consistently improves results robustly across various configurations. As we change the number of clusters, types of embeddings, the size of the training data and type of relations used for negative sampling, results using our method stay superior to those of the baseline. The regularizers without re-projection (xΦ) obtain lower results in most configurations as compared to re-projected versions (xΦΦ). Overall, the neighbor regularization yields slightly better results in comparison to the asymmetric regularization. We attribute this to the fact that some synonyms z are close to the original hyponym x, while others can be distant. Thus, neighbor regularization is able to safeguard the model during training from more errors. This is also a likely reason why the performance of both regularizers is similar: the asymmetric regularization makes sure that a re-projected vector does not belong to a semantic neighborhood of the hyponym. Yet, this is exactly what neighbor regularization achieves. Note, however that neighbor regularization requires explicit negative examples, while asymmetric regularization does not." } ], "images": [ "scigen/train/2366.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of our approach for English without clustering (k=1) and with the optimal number of cluster on the EVALution datasets (k=30) and on the combined datasets (k=25)." ], "reasoning": "Our approach consistently outperforms the baseline across various configurations, with neighbor regularization yielding slightly better results than asymmetric regularization.", "description": "S4SS3SSS0Px3 Discussion of Results. Similarly to the first experiment, our approach consistently improves results robustly across various configurations. As we change the number of clusters, types of embeddings, the size of the training data and type of relations used for negative sampling, results using our method stay superior to those of the baseline. The regularizers without re-projection (xΦ) obtain lower results in most configurations as compared to re-projected versions (xΦΦ). Overall, the neighbor regularization yields slightly better results in comparison to the asymmetric regularization. We attribute this to the fact that some synonyms z are close to the original hyponym x, while others can be distant. Thus, neighbor regularization is able to safeguard the model during training from more errors. This is also a likely reason why the performance of both regularizers is similar: the asymmetric regularization makes sure that a re-projected vector does not belong to a semantic neighborhood of the hyponym. Yet, this is exactly what neighbor regularization achieves. Note, however that neighbor regularization requires explicit negative examples, while asymmetric regularization does not.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Performance of our approach for English without clustering (k=1) and with the optimal number of cluster on the EVALution datasets (k=30) and on the combined datasets (k=25).\nQuestion: Which model configuration achieves the highest EVALution hit@10 score?\nGive a detailed response to the above question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest EVALution hit@10 score, we need to compare the values in the EVALution hit@10 column across all model configurations. The highest value is 0.370, achieved by Neighbor Reg. xΦΦ with k=30. So, the answer is {\"answer\": \"Neighbor Reg. xΦΦ with k=30\"}" } ], "images": [ "scigen/train/2366.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of our approach for English without clustering (k=1) and with the optimal number of cluster on the EVALution datasets (k=30) and on the combined datasets (k=25)." ], "reasoning": "To find the highest EVALution hit@10 score, we need to compare the values in the EVALution hit@10 column across all model configurations. The highest value is 0.370, achieved by Neighbor Reg. xΦΦ with k=30.", "question": "Which model configuration achieves the highest EVALution hit@10 score?", "answer": "So, the answer is {\"answer\": \"Neighbor Reg. xΦΦ with k=30\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The baseline model achieves the highest EVALution AUC score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe baseline model achieves the highest EVALution AUC score.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the EVALution AUC scores across all model configurations. The highest EVALution AUC score is 3.276, achieved by Neighbor Reg. xΦΦ with k=30, not the baseline model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2366.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of our approach for English without clustering (k=1) and with the optimal number of cluster on the EVALution datasets (k=30) and on the combined datasets (k=25)." ], "reasoning": "To verify this statement, we need to compare the EVALution AUC scores across all model configurations. The highest EVALution AUC score is 3.276, achieved by Neighbor Reg. xΦΦ with k=30, not the baseline model.", "statement": "The baseline model achieves the highest EVALution AUC score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: Performance of our approach for Russian for k=20 clusters compared to [Fu et al.2014].', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The neighbor regularization approach with re-projection (xΦΦ) achieves the highest performance across most metrics, except for hit@1 where it is slightly lower than the asymmetric regularization approach without re-projection.the baseline approach, the asymmetric regularization approach, and the neighbor regularization approach. The method reaches optimal performance for k=20 clusters. Our approach based on the regularization using synonyms as negative samples outperform the baseline (all differences between the baseline and our models are significant with respect to the t-test). According to all metrics, but hit@1 for which results are comparable to xΦ, the re-projection (xΦΦ) improves results." } ], "images": [ "scigen/train/2367.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance of our approach for Russian for k=20 clusters compared to [Fu et al.2014]." ], "reasoning": "The neighbor regularization approach with re-projection (xΦΦ) achieves the highest performance across most metrics, except for hit@1 where it is slightly lower than the asymmetric regularization approach without re-projection.", "description": "the baseline approach, the asymmetric regularization approach, and the neighbor regularization approach. The method reaches optimal performance for k=20 clusters. Our approach based on the regularization using synonyms as negative samples outperform the baseline (all differences between the baseline and our models are significant with respect to the t-test). According to all metrics, but hit@1 for which results are comparable to xΦ, the re-projection (xΦΦ) improves results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model and configuration achieve the highest AUC score?\nSolve the above question based on the table titled 'Table 1: Performance of our approach for Russian for k=20 clusters compared to [Fu et al.2014].'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest AUC score, compare the AUC values for all models and configurations. The highest AUC score is 2.768, achieved by the neighbor regularization approach with re-projection (xΦΦ). So, the answer is {\"answer\": \"Neighbor Reg. xΦΦ\"}." } ], "images": [ "scigen/train/2367.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance of our approach for Russian for k=20 clusters compared to [Fu et al.2014]." ], "reasoning": "To find the highest AUC score, compare the AUC values for all models and configurations. The highest AUC score is 2.768, achieved by the neighbor regularization approach with re-projection (xΦΦ).", "question": "Which model and configuration achieve the highest AUC score?", "answer": "So, the answer is {\"answer\": \"Neighbor Reg. xΦΦ\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The baseline model has the lowest hit@10 score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe baseline model has the lowest hit@10 score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the hit@10 scores for all models. The baseline model has a hit@10 score of 0.323, which is indeed the lowest among all models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2367.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance of our approach for Russian for k=20 clusters compared to [Fu et al.2014]." ], "reasoning": "To verify this statement, compare the hit@10 scores for all models. The baseline model has a hit@10 score of 0.323, which is indeed the lowest among all models.", "statement": "The baseline model has the lowest hit@10 score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 6: Evaluation of dialog utterance prediction with ranking models using hits@1 in four settings: conditioned on the speakers persona (”self persona”), the dialogue partner’s persona (”their persona”), both or none. The personas are either the original source given to Turkers to condition the dialogue, or the rewritten personas that do not have word overlap, explaining the poor performance of IR in that case.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Revised personas improve model performance by forcing the model to learn semantic similarities rather than relying on simple word overlap, especially for Profile Memory networks.Revised Personas. Revised personas are much harder to use. We do however still see some gain for the Profile Memory networks compared to none (0.354 vs. 0.318 hits@1). Training on revised personas helps, both for test examples that are in original form or revised form, likely due to the model be forced to learn more than simple word overlap, forcing the model to generalize more (i.e., learn semantic similarity of differing phrases). Their Persona. Using “Their persona” has less impact on this dataset. We believe this is because most speakers tend to focus on themselves when it comes to their interests. It would be interesting how often this is the case in other datasets. Certainly this is skewed by the particular instructions one could give to the crowdworkers. For example if we gave the instructions “try not to talk about yourself, but about the other’s interests’ likely these metrics would change. In particular we give results for conditioning on the other person’s persona (”Their”) instead of one’s own (”Self”) or the combination (”Both”). In particular, we see that, knowing the other’s persona does not help precision on this data with these models. Revised personas give improved results, perhaps because the models are forced to learn more than trivial word overlap (i.e. semantic similarity of differing phrases)." } ], "images": [ "scigen/train/2368.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Evaluation of dialog utterance prediction with ranking models using hits@1 in four settings: conditioned on the speakers persona (”self persona”), the dialogue partner’s persona (”their persona”), both or none. The personas are either the original source given to Turkers to condition the dialogue, or the rewritten personas that do not have word overlap, explaining the poor performance of IR in that case." ], "reasoning": "Revised personas improve model performance by forcing the model to learn semantic similarities rather than relying on simple word overlap, especially for Profile Memory networks.", "description": "Revised Personas. Revised personas are much harder to use. We do however still see some gain for the Profile Memory networks compared to none (0.354 vs. 0.318 hits@1). Training on revised personas helps, both for test examples that are in original form or revised form, likely due to the model be forced to learn more than simple word overlap, forcing the model to generalize more (i.e., learn semantic similarity of differing phrases). Their Persona. Using “Their persona” has less impact on this dataset. We believe this is because most speakers tend to focus on themselves when it comes to their interests. It would be interesting how often this is the case in other datasets. Certainly this is skewed by the particular instructions one could give to the crowdworkers. For example if we gave the instructions “try not to talk about yourself, but about the other’s interests’ likely these metrics would change. In particular we give results for conditioning on the other person’s persona (”Their”) instead of one’s own (”Self”) or the combination (”Both”). In particular, we see that, knowing the other’s persona does not help precision on this data with these models. Revised personas give improved results, perhaps because the models are forced to learn more than trivial word overlap (i.e. semantic similarity of differing phrases).", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 6: Evaluation of dialog utterance prediction with ranking models using hits@1 in four settings: conditioned on the speakers persona (”self persona”), the dialogue partner’s persona (”their persona”), both or none. The personas are either the original source given to Turkers to condition the dialogue, or the rewritten personas that do not have word overlap, explaining the poor performance of IR in that case.' and proceed to solve the following question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the hits@1 score for Profile Memory when trained on revised personas with no persona rewrite?\n" }, { "role": "assistant", "content": "To find the hits@1 score for Profile Memory when trained on revised personas with no persona rewrite, locate the 'Profile Memory' row under 'Training on revised personas' and find the value under the 'No Persona Rewrite' column. So, the answer is {\"answer\": \"0.318\"}." } ], "images": [ "scigen/train/2368.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Evaluation of dialog utterance prediction with ranking models using hits@1 in four settings: conditioned on the speakers persona (”self persona”), the dialogue partner’s persona (”their persona”), both or none. The personas are either the original source given to Turkers to condition the dialogue, or the rewritten personas that do not have word overlap, explaining the poor performance of IR in that case." ], "reasoning": "To find the hits@1 score for Profile Memory when trained on revised personas with no persona rewrite, locate the 'Profile Memory' row under 'Training on revised personas' and find the value under the 'No Persona Rewrite' column.", "question": "What is the hits@1 score for Profile Memory when trained on revised personas with no persona rewrite?", "answer": "So, the answer is {\"answer\": \"0.318\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTraining on revised personas with both personas rewrite results in a hits@1 score of 0.330 for KV Profile Memory.\n" }, { "role": "assistant", "content": "To verify this statement, locate the 'KV Profile Memory' row under 'Training on revised personas' and find the value under the 'Both Personas Rewrite' column. The value is 0.330, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2368.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Evaluation of dialog utterance prediction with ranking models using hits@1 in four settings: conditioned on the speakers persona (”self persona”), the dialogue partner’s persona (”their persona”), both or none. The personas are either the original source given to Turkers to condition the dialogue, or the rewritten personas that do not have word overlap, explaining the poor performance of IR in that case." ], "reasoning": "To verify this statement, locate the 'KV Profile Memory' row under 'Training on revised personas' and find the value under the 'Both Personas Rewrite' column. The value is 0.330, which matches the statement.", "statement": "Training on revised personas with both personas rewrite results in a hits@1 score of 0.330 for KV Profile Memory.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Evaluation of dialog utterance prediction with generative models in four settings: conditioned on the speakers persona (“self persona”), the dialogue partner’s persona (“their persona”), both or none. The personas are either the original source given to Turkers to condition the dialogue, or the revised personas that do not have word overlap. In the “no persona” setting, the models are equivalent, so we only report once.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The description highlights that focusing on 'Their Persona' has less impact on precision in this dataset, as speakers tend to talk about themselves. Revised personas improve results by forcing models to learn beyond trivial word overlap.Their Persona. Using “Their persona” has less impact on this dataset. We believe this is because most speakers tend to focus on themselves when it comes to their interests. It would be interesting how often this is the case in other datasets. Certainly this is skewed by the particular instructions one could give to the crowdworkers. For example if we gave the instructions “try not to talk about yourself, but about the other’s interests’ likely these metrics would change. In particular we give results for conditioning on the other person’s persona (”Their”) instead of one’s own (”Self”) or the combination (”Both”). In particular, we see that, knowing the other’s persona does not help precision on this data with these models. Revised personas give improved results, perhaps because the models are forced to learn more than trivial word overlap (i.e. semantic similarity of differing phrases)." } ], "images": [ "scigen/train/2369.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Evaluation of dialog utterance prediction with generative models in four settings: conditioned on the speakers persona (“self persona”), the dialogue partner’s persona (“their persona”), both or none. The personas are either the original source given to Turkers to condition the dialogue, or the revised personas that do not have word overlap. In the “no persona” setting, the models are equivalent, so we only report once." ], "reasoning": "The description highlights that focusing on 'Their Persona' has less impact on precision in this dataset, as speakers tend to talk about themselves. Revised personas improve results by forcing models to learn beyond trivial word overlap.", "description": "Their Persona. Using “Their persona” has less impact on this dataset. We believe this is because most speakers tend to focus on themselves when it comes to their interests. It would be interesting how often this is the case in other datasets. Certainly this is skewed by the particular instructions one could give to the crowdworkers. For example if we gave the instructions “try not to talk about yourself, but about the other’s interests’ likely these metrics would change. In particular we give results for conditioning on the other person’s persona (”Their”) instead of one’s own (”Self”) or the combination (”Both”). In particular, we see that, knowing the other’s persona does not help precision on this data with these models. Revised personas give improved results, perhaps because the models are forced to learn more than trivial word overlap (i.e. semantic similarity of differing phrases).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 5: Evaluation of dialog utterance prediction with generative models in four settings: conditioned on the speakers persona (“self persona”), the dialogue partner’s persona (“their persona”), both or none. The personas are either the original source given to Turkers to condition the dialogue, or the revised personas that do not have word overlap. In the “no persona” setting, the models are equivalent, so we only report once.', answer the following question. Give detailed solution consisting of each step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich method and persona combination had the lowest Original ppl?\n" }, { "role": "assistant", "content": "To find the lowest Original ppl, we need to compare the Original ppl values across all method and persona combinations. The lowest value is 34.54, which corresponds to the 'Self Persona' with 'Profile Memory' method. So, the answer is {\"answer\": \"Self Persona with Profile Memory\"}." } ], "images": [ "scigen/train/2369.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Evaluation of dialog utterance prediction with generative models in four settings: conditioned on the speakers persona (“self persona”), the dialogue partner’s persona (“their persona”), both or none. The personas are either the original source given to Turkers to condition the dialogue, or the revised personas that do not have word overlap. In the “no persona” setting, the models are equivalent, so we only report once." ], "reasoning": "To find the lowest Original ppl, we need to compare the Original ppl values across all method and persona combinations. The lowest value is 34.54, which corresponds to the 'Self Persona' with 'Profile Memory' method.", "question": "Which method and persona combination had the lowest Original ppl?", "answer": "So, the answer is {\"answer\": \"Self Persona with Profile Memory\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe 'Their Persona' with 'Seq2Seq' method had the highest Original hits@1.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the Original hits@1 values for all persona and method combinations. The highest Original hits@1 is 0.125, which corresponds to 'Self Persona' with 'Profile Memory' method, not 'Their Persona' with 'Seq2Seq'. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2369.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Evaluation of dialog utterance prediction with generative models in four settings: conditioned on the speakers persona (“self persona”), the dialogue partner’s persona (“their persona”), both or none. The personas are either the original source given to Turkers to condition the dialogue, or the revised personas that do not have word overlap. In the “no persona” setting, the models are equivalent, so we only report once." ], "reasoning": "To verify this statement, we need to compare the Original hits@1 values for all persona and method combinations. The highest Original hits@1 is 0.125, which corresponds to 'Self Persona' with 'Profile Memory' method, not 'Their Persona' with 'Seq2Seq'.", "statement": "The 'Their Persona' with 'Seq2Seq' method had the highest Original hits@1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 13: Profile Prediction. Error rates are given for predicting either the persona of speaker 0 (Profile 0) or of speaker 1 (Profile 1) given the dialogue utterances of speaker 0 (PERSON 0) or speaker 1 (PERSON 1). This is shown for dialogues between humans (PERSON 0) and either the KV Profile Memory model (“KV Profile”) which conditions on its own profile, or the KV Memory model (“KV w/o Profile”) which does not.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The study shows that predicting a human's profile from their dialogue is highly accurate, while predicting the model's profile from the human's dialogue is less accurate.In this section we conduct a preliminary study of the ability to predict the persona of a speaker given a set of dialogue utterances. We tested the ability to predict the profile information of the two speakers from the dialogue utterances of each speaker, considering all four combinations. We employ the same IR baseline model used in the main paper to predict profiles: it ranks profile candidates, either at the entire profile level ( considering all the sentences that make up the profile as a bag) or at the sentence level (each sentence individually). We consider 100 negative profile candidates for each positive profile, and compute the error rate of predicting the true profile averaged over all dialogues and candidates. The results indicate the following: It is possible to predict the humans profile from their dialogue utterances (PERSON 0, Profile 0) with high accuracy at both the profile and sentence level, independent of the model they speaking to. Similarly the model’s profile can be predicted with high accuracy from its utterances (PERSON 1, Profile 1) when it is conditioned on the profile, otherwise this is chance level (w/o Profile). It is possible to predict the model’s profile from the human’s dialogue, but with a lower accuracy (PERSON 0, Profile 1) as long as the model is conditioned on its own profile. This indicates the human responds to the model’s utterances and pays attention to the model’s interests. Similarly, the human’s profile can be predicted from the model’s dialogue, but with lower accuracy. Interestingly, the model without profile conditioning is better at this, perhaps because it does not concentrate on talking about itself, and pays more attention to responding to the human’s interests. There appears to be a tradeoff that needs to be explored and understood here." } ], "images": [ "scigen/train/2370.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 13: Profile Prediction. Error rates are given for predicting either the persona of speaker 0 (Profile 0) or of speaker 1 (Profile 1) given the dialogue utterances of speaker 0 (PERSON 0) or speaker 1 (PERSON 1). This is shown for dialogues between humans (PERSON 0) and either the KV Profile Memory model (“KV Profile”) which conditions on its own profile, or the KV Memory model (“KV w/o Profile”) which does not." ], "reasoning": "The study shows that predicting a human's profile from their dialogue is highly accurate, while predicting the model's profile from the human's dialogue is less accurate.", "description": "In this section we conduct a preliminary study of the ability to predict the persona of a speaker given a set of dialogue utterances. We tested the ability to predict the profile information of the two speakers from the dialogue utterances of each speaker, considering all four combinations. We employ the same IR baseline model used in the main paper to predict profiles: it ranks profile candidates, either at the entire profile level ( considering all the sentences that make up the profile as a bag) or at the sentence level (each sentence individually). We consider 100 negative profile candidates for each positive profile, and compute the error rate of predicting the true profile averaged over all dialogues and candidates. The results indicate the following: It is possible to predict the humans profile from their dialogue utterances (PERSON 0, Profile 0) with high accuracy at both the profile and sentence level, independent of the model they speaking to. Similarly the model’s profile can be predicted with high accuracy from its utterances (PERSON 1, Profile 1) when it is conditioned on the profile, otherwise this is chance level (w/o Profile). It is possible to predict the model’s profile from the human’s dialogue, but with a lower accuracy (PERSON 0, Profile 1) as long as the model is conditioned on its own profile. This indicates the human responds to the model’s utterances and pays attention to the model’s interests. Similarly, the human’s profile can be predicted from the model’s dialogue, but with lower accuracy. Interestingly, the model without profile conditioning is better at this, perhaps because it does not concentrate on talking about itself, and pays more attention to responding to the human’s interests. There appears to be a tradeoff that needs to be explored and understood here.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 13: Profile Prediction. Error rates are given for predicting either the persona of speaker 0 (Profile 0) or of speaker 1 (Profile 1) given the dialogue utterances of speaker 0 (PERSON 0) or speaker 1 (PERSON 1). This is shown for dialogues between humans (PERSON 0) and either the KV Profile Memory model (“KV Profile”) which conditions on its own profile, or the KV Memory model (“KV w/o Profile”) which does not.' and proceed to solve the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the error rate for predicting PERSON 0's Profile 0 at the profile level without profile conditioning?\n" }, { "role": "assistant", "content": "To find the error rate for predicting PERSON 0's Profile 0 at the profile level without profile conditioning, look at the row for PERSON 0 and Profile 0, and then find the value under 'Profile Level KV w/o Profile'. So, the answer is {\"answer\": \"0.017\"}." } ], "images": [ "scigen/train/2370.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 13: Profile Prediction. Error rates are given for predicting either the persona of speaker 0 (Profile 0) or of speaker 1 (Profile 1) given the dialogue utterances of speaker 0 (PERSON 0) or speaker 1 (PERSON 1). This is shown for dialogues between humans (PERSON 0) and either the KV Profile Memory model (“KV Profile”) which conditions on its own profile, or the KV Memory model (“KV w/o Profile”) which does not." ], "reasoning": "To find the error rate for predicting PERSON 0's Profile 0 at the profile level without profile conditioning, look at the row for PERSON 0 and Profile 0, and then find the value under 'Profile Level KV w/o Profile'.", "question": "What is the error rate for predicting PERSON 0's Profile 0 at the profile level without profile conditioning?", "answer": "So, the answer is {\"answer\": \"0.017\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe error rate for predicting PERSON 1's Profile 1 at the sentence level without profile conditioning is higher than with profile conditioning.\n" }, { "role": "assistant", "content": "To verify this statement, compare the error rates for PERSON 1's Profile 1 at the sentence level with and without profile conditioning. The error rate without profile conditioning (0.530) is higher than with profile conditioning (0.246). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2370.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 13: Profile Prediction. Error rates are given for predicting either the persona of speaker 0 (Profile 0) or of speaker 1 (Profile 1) given the dialogue utterances of speaker 0 (PERSON 0) or speaker 1 (PERSON 1). This is shown for dialogues between humans (PERSON 0) and either the KV Profile Memory model (“KV Profile”) which conditions on its own profile, or the KV Memory model (“KV w/o Profile”) which does not." ], "reasoning": "To verify this statement, compare the error rates for PERSON 1's Profile 1 at the sentence level with and without profile conditioning. The error rate without profile conditioning (0.530) is higher than with profile conditioning (0.246).", "statement": "The error rate for predicting PERSON 1's Profile 1 at the sentence level without profile conditioning is higher than with profile conditioning.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 14: Profile Prediction By Dialog Length. Error rates are given for predicting either the persona of speaker 0 (Profile 0) or of speaker 1 (Profile 1) given the dialogue utterances of speaker 0 (PERSON 0) or speaker 1 (PERSON 1). This is shown for dialogues between humans (PERSON 0) and the KV Profile Memory model averaged over the first N dialogue utterances from 100 conversations (where N is the “Dialogue Length”). The results show the accuracy of predicting the persona improves in all cases as dialogue length increases.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The study focuses on next utterance prediction using profile information and explores profile prediction error rates as dialogue length increases.Two tasks could naturally be considered using PersonaChat: (1) next utterance prediction during dialogue, and (2) profile prediction given dialogue history. The main study of this work has been Task 1, where we have shown the use of profile information. Task 2, however, can be used to extract such information. They show ( Combining Task 1 and Task 2 into a full system is an exciting area of future research. We also study the performance of profile prediction as the dialogue progresses, by computing error rates for dialogue lengths 1 to 8 (the longest length we consider in this work)." } ], "images": [ "scigen/train/2371.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 14: Profile Prediction By Dialog Length. Error rates are given for predicting either the persona of speaker 0 (Profile 0) or of speaker 1 (Profile 1) given the dialogue utterances of speaker 0 (PERSON 0) or speaker 1 (PERSON 1). This is shown for dialogues between humans (PERSON 0) and the KV Profile Memory model averaged over the first N dialogue utterances from 100 conversations (where N is the “Dialogue Length”). The results show the accuracy of predicting the persona improves in all cases as dialogue length increases." ], "reasoning": "The study focuses on next utterance prediction using profile information and explores profile prediction error rates as dialogue length increases.", "description": "Two tasks could naturally be considered using PersonaChat: (1) next utterance prediction during dialogue, and (2) profile prediction given dialogue history. The main study of this work has been Task 1, where we have shown the use of profile information. Task 2, however, can be used to extract such information. They show ( Combining Task 1 and Task 2 into a full system is an exciting area of future research. We also study the performance of profile prediction as the dialogue progresses, by computing error rates for dialogue lengths 1 to 8 (the longest length we consider in this work).", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 14: Profile Prediction By Dialog Length. Error rates are given for predicting either the persona of speaker 0 (Profile 0) or of speaker 1 (Profile 1) given the dialogue utterances of speaker 0 (PERSON 0) or speaker 1 (PERSON 1). This is shown for dialogues between humans (PERSON 0) and the KV Profile Memory model averaged over the first N dialogue utterances from 100 conversations (where N is the “Dialogue Length”). The results show the accuracy of predicting the persona improves in all cases as dialogue length increases.' in mind, break down and resolve the question below step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the error rate for PERSON 1 with Profile 1 at Dialogue Length 5?\n" }, { "role": "assistant", "content": "To find the error rate for PERSON 1 with Profile 1 at Dialogue Length 5, locate the row for PERSON 1 with Profile 1 and then find the value under Dialogue Length 5. So, the answer is {\"answer\": \"0.45\"}." } ], "images": [ "scigen/train/2371.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 14: Profile Prediction By Dialog Length. Error rates are given for predicting either the persona of speaker 0 (Profile 0) or of speaker 1 (Profile 1) given the dialogue utterances of speaker 0 (PERSON 0) or speaker 1 (PERSON 1). This is shown for dialogues between humans (PERSON 0) and the KV Profile Memory model averaged over the first N dialogue utterances from 100 conversations (where N is the “Dialogue Length”). The results show the accuracy of predicting the persona improves in all cases as dialogue length increases." ], "reasoning": "To find the error rate for PERSON 1 with Profile 1 at Dialogue Length 5, locate the row for PERSON 1 with Profile 1 and then find the value under Dialogue Length 5.", "question": "What is the error rate for PERSON 1 with Profile 1 at Dialogue Length 5?", "answer": "So, the answer is {\"answer\": \"0.45\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe error rate for PERSON 0 with Profile 0 decreases as the dialogue length increases.\n" }, { "role": "assistant", "content": "By examining the row for PERSON 0 with Profile 0, we see that the error rate decreases from 0.76 at Dialogue Length 1 to 0.17 at Dialogue Length 8. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2371.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 14: Profile Prediction By Dialog Length. Error rates are given for predicting either the persona of speaker 0 (Profile 0) or of speaker 1 (Profile 1) given the dialogue utterances of speaker 0 (PERSON 0) or speaker 1 (PERSON 1). This is shown for dialogues between humans (PERSON 0) and the KV Profile Memory model averaged over the first N dialogue utterances from 100 conversations (where N is the “Dialogue Length”). The results show the accuracy of predicting the persona improves in all cases as dialogue length increases." ], "reasoning": "By examining the row for PERSON 0 with Profile 0, we see that the error rate decreases from 0.76 at Dialogue Length 1 to 0.17 at Dialogue Length 8.", "statement": "The error rate for PERSON 0 with Profile 0 decreases as the dialogue length increases.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Metric values of LCC, SRCC, and F1 (larger is better) obtained on the test data corrupted by noise using the baseline method and two proposed approaches.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The experiments were repeated eight times to mitigate bias, and the average values show that the LC_Att method outperforms the baseline in LCC, SRCC, and F1 scores.The results show that the use of our approaches can yield better performance than the baseline method. Since various natural sounds from MUSAN [musan15] were used as noise to mix with clean signals, these noise signals might take place at different time and have various effects in frequency domain. To mitigate possible bias on LCC, SRCC, and F1 values, the experiments were repeated for eight times and the average values are used as the final results. To further compare the structure using an attention model and not using, the third approach L_1DCNN was also conducted. It has the same structure as L_ATT, but without the attention layer." } ], "images": [ "scigen/train/2372.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Metric values of LCC, SRCC, and F1 (larger is better) obtained on the test data corrupted by noise using the baseline method and two proposed approaches." ], "reasoning": "The experiments were repeated eight times to mitigate bias, and the average values show that the LC_Att method outperforms the baseline in LCC, SRCC, and F1 scores.", "description": "The results show that the use of our approaches can yield better performance than the baseline method. Since various natural sounds from MUSAN [musan15] were used as noise to mix with clean signals, these noise signals might take place at different time and have various effects in frequency domain. To mitigate possible bias on LCC, SRCC, and F1 values, the experiments were repeated for eight times and the average values are used as the final results. To further compare the structure using an attention model and not using, the third approach L_1DCNN was also conducted. It has the same structure as L_ATT, but without the attention layer.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Metric values of LCC, SRCC, and F1 (larger is better) obtained on the test data corrupted by noise using the baseline method and two proposed approaches.\nQuestion: Which method has the highest F1 score and what is its value?\nSolve the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the F1 scores of all methods in the table, LC_Att has the highest F1 score of 0.848. So, the answer is {\"answer\": \"LC_Att, 0.848\"}." } ], "images": [ "scigen/train/2372.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Metric values of LCC, SRCC, and F1 (larger is better) obtained on the test data corrupted by noise using the baseline method and two proposed approaches." ], "reasoning": "By comparing the F1 scores of all methods in the table, LC_Att has the highest F1 score of 0.848.", "question": "Which method has the highest F1 score and what is its value?", "answer": "So, the answer is {\"answer\": \"LC_Att, 0.848\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The L_1DCNN method has a higher Precision score than the Baseline1 method. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe L_1DCNN method has a higher Precision score than the Baseline1 method.\n" }, { "role": "assistant", "content": "The table shows that the Precision score for L_1DCNN is 0.926, which is higher than the Baseline1 method's Precision score of 0.728. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2372.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Metric values of LCC, SRCC, and F1 (larger is better) obtained on the test data corrupted by noise using the baseline method and two proposed approaches." ], "reasoning": "The table shows that the Precision score for L_1DCNN is 0.926, which is higher than the Baseline1 method's Precision score of 0.728.", "statement": "The L_1DCNN method has a higher Precision score than the Baseline1 method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Pseudo scores defined in terms of SNR.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The SNR value of a recording can be set by mixing the original recording with noise signals, and using a set of scores as assessment targets can mitigate the impact of noise-corrupted target values.This is to mimic the definition of scores used for MOS, but does not require human assessors’ to mark each recording as the SNR value of a recording can be set when precisely mixing the original recording with noise signals. In addition, using a set of scores as assessment target might be able to mitigate the impact caused by the use of noise corrupted target values e.g., using the estimated PESQ values as targets in [fu2018]. This is also to compare with the baseline method (Baseline1)[fu2018], which used the same way to compute the utterance-level score." } ], "images": [ "scigen/train/2373.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Pseudo scores defined in terms of SNR." ], "reasoning": "The SNR value of a recording can be set by mixing the original recording with noise signals, and using a set of scores as assessment targets can mitigate the impact of noise-corrupted target values.", "description": "This is to mimic the definition of scores used for MOS, but does not require human assessors’ to mark each recording as the SNR value of a recording can be set when precisely mixing the original recording with noise signals. In addition, using a set of scores as assessment target might be able to mitigate the impact caused by the use of noise corrupted target values e.g., using the estimated PESQ values as targets in [fu2018]. This is also to compare with the baseline method (Baseline1)[fu2018], which used the same way to compute the utterance-level score.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: Pseudo scores defined in terms of SNR.'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the pseudo score for an SNR value of 10 dB?\n" }, { "role": "assistant", "content": "The table lists the pseudo scores corresponding to different SNR values. For an SNR value of 10 dB, the pseudo score is listed as 5. So, the answer is {\"answer\": \"5\"}" } ], "images": [ "scigen/train/2373.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Pseudo scores defined in terms of SNR." ], "reasoning": "The table lists the pseudo scores corresponding to different SNR values. For an SNR value of 10 dB, the pseudo score is listed as 5.", "question": "What is the pseudo score for an SNR value of 10 dB?", "answer": "So, the answer is {\"answer\": \"5\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The pseudo score for an SNR value of -5 dB is 2., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe pseudo score for an SNR value of -5 dB is 2.\n" }, { "role": "assistant", "content": "The table shows the pseudo score corresponding to an SNR value of -5 dB as 2. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2373.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Pseudo scores defined in terms of SNR." ], "reasoning": "The table shows the pseudo score corresponding to an SNR value of -5 dB as 2.", "statement": "The pseudo score for an SNR value of -5 dB is 2.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 6: Ablation study. (Trm: Transformer)', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The bidirectional nature of BERT and its additional parameters contribute to performance gains, with BERTsm showing good results and BERTl2r slightly hurting performance.There are several possible factors that could contribute to the performance gain: additional parameters of BERT, extra data (pretraining corpus) of BERT, and the bidirectional nature. To better understand the key contributions of our method, we conduct an ablation study described in the following. We finetune 2 extra teachers: BERTsm and BERTl2r. For BERTsm, we use a smaller BERT (6 layers) for C-MLM finetuning, which has approximately the same number of parameters as Transformer-base. For BERTl2r, we use the full BERT model but finetune it using left-to-right LM as in the conventional Seq2Seq model. Next, we apply the proposed KD method to train the Transformer on En-Vi and De-En MT tasks. BERTsm still works well though the full BERT provides further improvement. On the other hand, BERTl2r slightly hurts the performance. We hypothesize that it generates noisy learning targets for the student, hence the performance drop. Empirically, we show that the bidirectional knowledge could be more important than the extra parameters, while the pre-trained weights remain useful for more stable C-MLM training." } ], "images": [ "scigen/train/2374.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Ablation study. (Trm: Transformer)" ], "reasoning": "The bidirectional nature of BERT and its additional parameters contribute to performance gains, with BERTsm showing good results and BERTl2r slightly hurting performance.", "description": "There are several possible factors that could contribute to the performance gain: additional parameters of BERT, extra data (pretraining corpus) of BERT, and the bidirectional nature. To better understand the key contributions of our method, we conduct an ablation study described in the following. We finetune 2 extra teachers: BERTsm and BERTl2r. For BERTsm, we use a smaller BERT (6 layers) for C-MLM finetuning, which has approximately the same number of parameters as Transformer-base. For BERTl2r, we use the full BERT model but finetune it using left-to-right LM as in the conventional Seq2Seq model. Next, we apply the proposed KD method to train the Transformer on En-Vi and De-En MT tasks. BERTsm still works well though the full BERT provides further improvement. On the other hand, BERTl2r slightly hurts the performance. We hypothesize that it generates noisy learning targets for the student, hence the performance drop. Empirically, we show that the bidirectional knowledge could be more important than the extra parameters, while the pre-trained weights remain useful for more stable C-MLM training.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 6: Ablation study. (Trm: Transformer)', solve the subsequent question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich method achieved the highest score on the De-En (dev) task?\n" }, { "role": "assistant", "content": "By comparing the scores in the De-En (dev) column, the method 'Trm + BERT' achieved the highest score of 36.93. So, the answer is {\"answer\": \"Trm + BERT\"}." } ], "images": [ "scigen/train/2374.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Ablation study. (Trm: Transformer)" ], "reasoning": "By comparing the scores in the De-En (dev) column, the method 'Trm + BERT' achieved the highest score of 36.93.", "question": "Which method achieved the highest score on the De-En (dev) task?", "answer": "So, the answer is {\"answer\": \"Trm + BERT\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Transformer (base) method achieved the highest score on the En-Vi (tst2012) task. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Transformer (base) method achieved the highest score on the En-Vi (tst2012) task.\n" }, { "role": "assistant", "content": "The table shows that the highest score on the En-Vi (tst2012) task is 27.85, achieved by the 'Trm + BERT' method, not the Transformer (base) method. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2374.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Ablation study. (Trm: Transformer)" ], "reasoning": "The table shows that the highest score on the En-Vi (tst2012) task is 27.85, achieved by the 'Trm + BERT' method, not the Transformer (base) method.", "statement": "The Transformer (base) method achieved the highest score on the En-Vi (tst2012) task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: Full set of attribute prediction task metrics. We evaluate F1, Precision and Recall for all the tasks. All the metrics are computed as macro-average.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The classifier Φ is trained using binary cross-entropy loss and early stopping based on the multi-label F1-measure, with a learning rate scheduler applied to prevent overfitting.We consider the CompGuessWhat?! splits as reference for our experimental evaluation. We generate an hidden state for every successful dialogue and we use the classifier Φ to predict the target object attributes. We train the classifier Φ by minimising the binary cross-entropy loss computed between the model prediction and the reference set of attributes. To prevent overfitting, we perform early stopping on the validation set using the multi-label F1-measure (with threshold 0.75) as reference metric and we apply a learning rate scheduler to gradually reduce the learning rate. The model training has been implemented using AllenNLP Gardner et al." } ], "images": [ "scigen/train/2375.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Full set of attribute prediction task metrics. We evaluate F1, Precision and Recall for all the tasks. All the metrics are computed as macro-average." ], "reasoning": "The classifier Φ is trained using binary cross-entropy loss and early stopping based on the multi-label F1-measure, with a learning rate scheduler applied to prevent overfitting.", "description": "We consider the CompGuessWhat?! splits as reference for our experimental evaluation. We generate an hidden state for every successful dialogue and we use the classifier Φ to predict the target object attributes. We train the classifier Φ by minimising the binary cross-entropy loss computed between the model prediction and the reference set of attributes. To prevent overfitting, we perform early stopping on the validation set using the multi-label F1-measure (with threshold 0.75) as reference metric and we apply a learning rate scheduler to gradually reduce the learning rate. The model training has been implemented using AllenNLP Gardner et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model has the highest Abstract+situated F1 score?\nSolve the above question based on the table titled 'Table 5: Full set of attribute prediction task metrics. We evaluate F1, Precision and Recall for all the tasks. All the metrics are computed as macro-average.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest Abstract+situated F1 score, compare the Abstract+situated F1 scores of all models listed in the table. The highest score is 60.1, achieved by the GDSE-SL model. So, the answer is {\"answer\": \"GDSE-SL\"}" } ], "images": [ "scigen/train/2375.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Full set of attribute prediction task metrics. We evaluate F1, Precision and Recall for all the tasks. All the metrics are computed as macro-average." ], "reasoning": "To find the model with the highest Abstract+situated F1 score, compare the Abstract+situated F1 scores of all models listed in the table. The highest score is 60.1, achieved by the GDSE-SL model.", "question": "Which model has the highest Abstract+situated F1 score?", "answer": "So, the answer is {\"answer\": \"GDSE-SL\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe GDSE-CL model has a higher Abstract Precision than the DeVries-SL model.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Abstract Precision values of the GDSE-CL model and the DeVries-SL model. The GDSE-CL model has an Abstract Precision of 59.3, while the DeVries-SL model has an Abstract Precision of 46.2. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2375.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Full set of attribute prediction task metrics. We evaluate F1, Precision and Recall for all the tasks. All the metrics are computed as macro-average." ], "reasoning": "To verify this statement, compare the Abstract Precision values of the GDSE-CL model and the DeVries-SL model. The GDSE-CL model has an Abstract Precision of 59.3, while the DeVries-SL model has an Abstract Precision of 46.2.", "statement": "The GDSE-CL model has a higher Abstract Precision than the DeVries-SL model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 1: Results for state-of-the-art models on the CompGuessWhat?! suite of evaluation tasks. We assess model quality in terms of gameplay accuracy, the attribute prediction quality, measured in terms of F1 for the abstract (A-F1), situated (S-F1), abstract+situated (AS-F1) and location (L-F1) prediction scenario, as well as zero-shot learning gameplay. The final score GroLLA is a macro-average of the individual scores. We use the models GloVe, ResNet and GDSE-*-text only as a baseline for the attribute prediction tasks.'.\n" }, { "role": "assistant", "content": "GDSE-CL and GDSE-SL models have the highest guesser accuracy, indicating better representations than unimodal baselines, but do not show an advantage in the AP task.Guesser accuracy We evaluate the GDSE and DeVries models in gameplay mode using the set of reference games provided in CompGuessWhat?!. dataset de Vries et al. With the AP task, we try to answer the following question: “Do the representations associated with the target object encoding provide useful information that can be exploited to predict the object attributes correctly?” We assume that, due to the nature of the CompGuessWhat?! games, the final dialogue state representation should encode relevant features of the target object. So, a high gameplay accuracy should correlate with a high AP score. test games. As the average best model performance was only 44.27, far from ceiling, our hypothesis is only partially supported. In particular, the models having the highest guesser accuracy, GDSE-CL and GDSE-SL, seem to learn better representations than unimodal baselines GloVe and ResNet, confirming the importance of multi-modal training for this task. There is also a gap in performance between the GDSE and DeVries models. This might be related to the multi-task learning strategy used by GDSE models that favours the emergence of more expressive representations than the ones learned by DeVries models which are trained in isolation. By comparing the enhanced versions GDSE-CL and DeVries-RL with the less sophisticated ones, GDSE-SL and DeVries-SL, respectively, we observe that, despite their higher guesser accuracy, these models do not have any advantage in terms of the AP task. We believe that this is because the Collaborative training strategy (for GDSE-CL) and Reinforcement Learning (for DeVries-RL) are optimising end-goal performance while sacrificing the expressiveness of the representations. Finding a way to encode task-specific representations and generalise them to learn abstract representations becomes an important research direction to improve on this task. would result in a successful gameplay." } ], "images": [ "scigen/train/2376.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results for state-of-the-art models on the CompGuessWhat?! suite of evaluation tasks. We assess model quality in terms of gameplay accuracy, the attribute prediction quality, measured in terms of F1 for the abstract (A-F1), situated (S-F1), abstract+situated (AS-F1) and location (L-F1) prediction scenario, as well as zero-shot learning gameplay. The final score GroLLA is a macro-average of the individual scores. We use the models GloVe, ResNet and GDSE-*-text only as a baseline for the attribute prediction tasks." ], "reasoning": "GDSE-CL and GDSE-SL models have the highest guesser accuracy, indicating better representations than unimodal baselines, but do not show an advantage in the AP task.", "description": "Guesser accuracy We evaluate the GDSE and DeVries models in gameplay mode using the set of reference games provided in CompGuessWhat?!. dataset de Vries et al. With the AP task, we try to answer the following question: “Do the representations associated with the target object encoding provide useful information that can be exploited to predict the object attributes correctly?” We assume that, due to the nature of the CompGuessWhat?! games, the final dialogue state representation should encode relevant features of the target object. So, a high gameplay accuracy should correlate with a high AP score. test games. As the average best model performance was only 44.27, far from ceiling, our hypothesis is only partially supported. In particular, the models having the highest guesser accuracy, GDSE-CL and GDSE-SL, seem to learn better representations than unimodal baselines GloVe and ResNet, confirming the importance of multi-modal training for this task. There is also a gap in performance between the GDSE and DeVries models. This might be related to the multi-task learning strategy used by GDSE models that favours the emergence of more expressive representations than the ones learned by DeVries models which are trained in isolation. By comparing the enhanced versions GDSE-CL and DeVries-RL with the less sophisticated ones, GDSE-SL and DeVries-SL, respectively, we observe that, despite their higher guesser accuracy, these models do not have any advantage in terms of the AP task. We believe that this is because the Collaborative training strategy (for GDSE-CL) and Reinforcement Learning (for DeVries-RL) are optimising end-goal performance while sacrificing the expressiveness of the representations. Finding a way to encode task-specific representations and generalise them to learn abstract representations becomes an important research direction to improve on this task. would result in a successful gameplay.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 1: Results for state-of-the-art models on the CompGuessWhat?! suite of evaluation tasks. We assess model quality in terms of gameplay accuracy, the attribute prediction quality, measured in terms of F1 for the abstract (A-F1), situated (S-F1), abstract+situated (AS-F1) and location (L-F1) prediction scenario, as well as zero-shot learning gameplay. The final score GroLLA is a macro-average of the individual scores. We use the models GloVe, ResNet and GDSE-*-text only as a baseline for the attribute prediction tasks.' to sequentially solve the question presented below. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model has the highest gameplay accuracy?\n" }, { "role": "assistant", "content": "By examining the 'Gameplay Accuracy' column, we see that the GDSE-CL model has the highest value at 59.8%. So, the answer is {\"answer\": \"GDSE-CL\"}" } ], "images": [ "scigen/train/2376.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results for state-of-the-art models on the CompGuessWhat?! suite of evaluation tasks. We assess model quality in terms of gameplay accuracy, the attribute prediction quality, measured in terms of F1 for the abstract (A-F1), situated (S-F1), abstract+situated (AS-F1) and location (L-F1) prediction scenario, as well as zero-shot learning gameplay. The final score GroLLA is a macro-average of the individual scores. We use the models GloVe, ResNet and GDSE-*-text only as a baseline for the attribute prediction tasks." ], "reasoning": "By examining the 'Gameplay Accuracy' column, we see that the GDSE-CL model has the highest value at 59.8%.", "question": "Which model has the highest gameplay accuracy?", "answer": "So, the answer is {\"answer\": \"GDSE-CL\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe GDSE-SL model has a higher Attribute Prediction A-F1 score than the GDSE-CL model.\n" }, { "role": "assistant", "content": "The table shows that the GDSE-SL model has an Attribute Prediction A-F1 score of 59.9, while the GDSE-CL model has a score of 59.5. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2376.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results for state-of-the-art models on the CompGuessWhat?! suite of evaluation tasks. We assess model quality in terms of gameplay accuracy, the attribute prediction quality, measured in terms of F1 for the abstract (A-F1), situated (S-F1), abstract+situated (AS-F1) and location (L-F1) prediction scenario, as well as zero-shot learning gameplay. The final score GroLLA is a macro-average of the individual scores. We use the models GloVe, ResNet and GDSE-*-text only as a baseline for the attribute prediction tasks." ], "reasoning": "The table shows that the GDSE-SL model has an Attribute Prediction A-F1 score of 59.9, while the GDSE-CL model has a score of 59.5.", "statement": "The GDSE-SL model has a higher Attribute Prediction A-F1 score than the GDSE-CL model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: Hyperparameters used in the experiments.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The BiLSTM parameters are initialized with random orthonormal matrices, other parameters follow Glorot initialization, and dropout is applied.We initialize all the parameter matrices in BiLSTMs with random orthonormal matrices Saxe et al. Other parameters are initialized following \\newciteglorot:10. We apply dropout Srivastava et al." } ], "images": [ "scigen/train/2377.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Hyperparameters used in the experiments." ], "reasoning": "The BiLSTM parameters are initialized with random orthonormal matrices, other parameters follow Glorot initialization, and dropout is applied.", "description": "We initialize all the parameter matrices in BiLSTMs with random orthonormal matrices Saxe et al. Other parameters are initialized following \\newciteglorot:10. We apply dropout Srivastava et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 5: Hyperparameters used in the experiments.' to sequentially solve the question presented below. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhat is the learning rate used for the Adam optimization in the BiLSTM model?\n" }, { "role": "assistant", "content": "The table lists the learning rate as 0.001, which is used for the Adam optimization in the BiLSTM model. So, the answer is {\"answer\": \"0.001\"}." } ], "images": [ "scigen/train/2377.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Hyperparameters used in the experiments." ], "reasoning": "The table lists the learning rate as 0.001, which is used for the Adam optimization in the BiLSTM model.", "question": "What is the learning rate used for the Adam optimization in the BiLSTM model?", "answer": "So, the answer is {\"answer\": \"0.001\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The BiLSTM model uses a mini-batch size of 16., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe BiLSTM model uses a mini-batch size of 16.\n" }, { "role": "assistant", "content": "The table specifies that the mini-batch size used in the BiLSTM model is 8, not 16. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2377.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Hyperparameters used in the experiments." ], "reasoning": "The table specifies that the mini-batch size used in the BiLSTM model is 8, not 16.", "statement": "The BiLSTM model uses a mini-batch size of 16.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: BLEU scores of non code-switched (original) input on En-Ru test sets.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Our method shows slight improvements over the baseline in translating non code-switched sentences, attributed to augmented data and the pointer network.Translating non Code-Switched Sentences. Our method preserves its strength on translating non code-switched sentences. In addition, on some test sets, our method is slightly better than the baseline when translating non code-switched source sentences. This can be explained from two aspects: First, the augmented data make the model more robust to perturbed inputs; Second, the pointer network makes the model better by copying certain source-side words Gulcehre et al." } ], "images": [ "scigen/train/2378.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: BLEU scores of non code-switched (original) input on En-Ru test sets." ], "reasoning": "Our method shows slight improvements over the baseline in translating non code-switched sentences, attributed to augmented data and the pointer network.", "description": "Translating non Code-Switched Sentences. Our method preserves its strength on translating non code-switched sentences. In addition, on some test sets, our method is slightly better than the baseline when translating non code-switched source sentences. This can be explained from two aspects: First, the augmented data make the model more robust to perturbed inputs; Second, the pointer network makes the model better by copying certain source-side words Gulcehre et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 5: BLEU scores of non code-switched (original) input on En-Ru test sets.\nQuestion: On which test set does our method perform better than the baseline by the largest margin?\n" }, { "role": "assistant", "content": "Compare the scores of 'Ours' and 'Baseline' across all test sets and find the largest difference. So, the answer is {\"answer\": \"news16\"}." } ], "images": [ "scigen/train/2378.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: BLEU scores of non code-switched (original) input on En-Ru test sets." ], "reasoning": "Compare the scores of 'Ours' and 'Baseline' across all test sets and find the largest difference.", "question": "On which test set does our method perform better than the baseline by the largest margin?", "answer": "So, the answer is {\"answer\": \"news16\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Our method performs worse than the baseline on the news17 test set. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nOur method performs worse than the baseline on the news17 test set.\n" }, { "role": "assistant", "content": "The table shows that our method has a score of 36.54 on news17, which is slightly lower than the baseline score of 36.57. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2378.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: BLEU scores of non code-switched (original) input on En-Ru test sets." ], "reasoning": "The table shows that our method has a score of 36.54 on news17, which is slightly lower than the baseline score of 36.57.", "statement": "Our method performs worse than the baseline on the news17 test set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: Comparison with other approaches on DSTC2.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The proposed approach by Mrksic et al. can handle slots with unbounded value sets and transfer learning to new slots and domains, as demonstrated by their results.We present 3 results from the work of Mrksic et al. However, the advantage of the proposed approach is ability to handle slots with unbounded value sets and transfer learning to new slots and domains." } ], "images": [ "scigen/train/2379.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison with other approaches on DSTC2." ], "reasoning": "The proposed approach by Mrksic et al. can handle slots with unbounded value sets and transfer learning to new slots and domains, as demonstrated by their results.", "description": "We present 3 results from the work of Mrksic et al. However, the advantage of the proposed approach is ability to handle slots with unbounded value sets and transfer learning to new slots and domains.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Comparison with other approaches on DSTC2.\nQuestion: Which model has the highest Joint Goal Accuracy and what is its value?\nSolve the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the model with the highest Joint Goal Accuracy, compare the Joint Goal Acc. values of all models listed in the table. The Neural Belief Tracker (NBT-CNN) has the highest value of 0.734. So, the answer is {\"answer\": \"Neural Belief Tracker (NBT-CNN)\"}." } ], "images": [ "scigen/train/2379.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison with other approaches on DSTC2." ], "reasoning": "To find the model with the highest Joint Goal Accuracy, compare the Joint Goal Acc. values of all models listed in the table. The Neural Belief Tracker (NBT-CNN) has the highest value of 0.734.", "question": "Which model has the highest Joint Goal Accuracy and what is its value?", "answer": "So, the answer is {\"answer\": \"Neural Belief Tracker (NBT-CNN)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Scalable Multi-domain DST model has a higher Joint Goal Accuracy than the Delexicalization-based Model.\n" }, { "role": "assistant", "content": "Compare the Joint Goal Acc. values of the Scalable Multi-domain DST model (0.703) and the Delexicalization-based Model (0.691). The Scalable Multi-domain DST model has a higher value. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2379.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison with other approaches on DSTC2." ], "reasoning": "Compare the Joint Goal Acc. values of the Scalable Multi-domain DST model (0.703) and the Delexicalization-based Model (0.691). The Scalable Multi-domain DST model has a higher value.", "statement": "The Scalable Multi-domain DST model has a higher Joint Goal Accuracy than the Delexicalization-based Model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Joint goal accuracy results on different datasets to show the effect of slot level parameter sharing and comparison of Convolutional vs GRU based feature extraction.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The GRU model with weight sharing resulted in better performance for DSTC2 and simulated restaurant domain conversations, while results were mixed for the movie domain.We present two groups of experimental results. The first set of experiments compare the impact of model architectures and weight sharing on the three datasets. The two results columns in this table show results with no weight sharing between slots (i.e., weights are trained per slot type) and with weight sharing across all slots in each domain. For each dataset, results with CNN and GRU for representing system and user utterances is also presented. For both DSTC2 and simulated restaurant domain conversations, the GRU model with weight sharing resulted in better performance. The results are mixed with the movie domain conversations, but the movie dataset is also much smaller than the other two. In the rest of the experiments, we present results with weight sharing and GRUs." } ], "images": [ "scigen/train/2380.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Joint goal accuracy results on different datasets to show the effect of slot level parameter sharing and comparison of Convolutional vs GRU based feature extraction." ], "reasoning": "The GRU model with weight sharing resulted in better performance for DSTC2 and simulated restaurant domain conversations, while results were mixed for the movie domain.", "description": "We present two groups of experimental results. The first set of experiments compare the impact of model architectures and weight sharing on the three datasets. The two results columns in this table show results with no weight sharing between slots (i.e., weights are trained per slot type) and with weight sharing across all slots in each domain. For each dataset, results with CNN and GRU for representing system and user utterances is also presented. For both DSTC2 and simulated restaurant domain conversations, the GRU model with weight sharing resulted in better performance. The results are mixed with the movie domain conversations, but the movie dataset is also much smaller than the other two. In the rest of the experiments, we present results with weight sharing and GRUs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: Joint goal accuracy results on different datasets to show the effect of slot level parameter sharing and comparison of Convolutional vs GRU based feature extraction.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model and weight sharing combination resulted in the highest performance for the simulated restaurant domain?\n" }, { "role": "assistant", "content": "For the simulated restaurant domain (Sim-R), the GRU model with weight sharing resulted in a performance of 0.937, which is the highest for this domain. So, the answer is {\"answer\": \"GRU with weight sharing\"}" } ], "images": [ "scigen/train/2380.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Joint goal accuracy results on different datasets to show the effect of slot level parameter sharing and comparison of Convolutional vs GRU based feature extraction." ], "reasoning": "For the simulated restaurant domain (Sim-R), the GRU model with weight sharing resulted in a performance of 0.937, which is the highest for this domain.", "question": "Which model and weight sharing combination resulted in the highest performance for the simulated restaurant domain?", "answer": "So, the answer is {\"answer\": \"GRU with weight sharing\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe CNN model with weight sharing resulted in the highest performance for the simulated movie domain.\n" }, { "role": "assistant", "content": "For the simulated movie domain (Sim-M), the CNN model with weight sharing resulted in a performance of 0.959, which is not the highest. The highest performance was achieved by the CNN model without weight sharing, with a performance of 0.968. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2380.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Joint goal accuracy results on different datasets to show the effect of slot level parameter sharing and comparison of Convolutional vs GRU based feature extraction." ], "reasoning": "For the simulated movie domain (Sim-M), the CNN model with weight sharing resulted in a performance of 0.959, which is not the highest. The highest performance was achieved by the CNN model without weight sharing, with a performance of 0.968.", "statement": "The CNN model with weight sharing resulted in the highest performance for the simulated movie domain.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 7: Performances of C5.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The integrated training set consists of 2,983 job-related tweets and 3,736 not job-related tweets. The C5 classifier was evaluated for its ability to identify job-related tweets with a precision of 0.83, recall of 0.97, and F1 score of 0.89.Aggregating separate parts of human-labeled data (Part-1 to Part-4), we obtained an integrated training set with 2,983 job-related tweets and 3,736 not job-related tweets and trained C5 upon it. We proposed an end-to-end evaluation: to what degree can our trained automatic classifiers (C1, C2, C3 and C5) identify job-related tweets in the real world? We introduced the estimated effective recall under the assumption that for each model, the error rates in our test samples (1,600 tweets) are proportional to the actual error rates found in the entire one-year data set which resembles the real world. We labeled the entire data set using each classifier and defined the estimated effective recall ^R for each classifier as ^R = Y⋅Nt⋅RY⋅Nt⋅R+N⋅Yt⋅(1−R)" } ], "images": [ "scigen/train/2381.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Performances of C5." ], "reasoning": "The integrated training set consists of 2,983 job-related tweets and 3,736 not job-related tweets. The C5 classifier was evaluated for its ability to identify job-related tweets with a precision of 0.83, recall of 0.97, and F1 score of 0.89.", "description": "Aggregating separate parts of human-labeled data (Part-1 to Part-4), we obtained an integrated training set with 2,983 job-related tweets and 3,736 not job-related tweets and trained C5 upon it. We proposed an end-to-end evaluation: to what degree can our trained automatic classifiers (C1, C2, C3 and C5) identify job-related tweets in the real world? We introduced the estimated effective recall under the assumption that for each model, the error rates in our test samples (1,600 tweets) are proportional to the actual error rates found in the entire one-year data set which resembles the real world. We labeled the entire data set using each classifier and defined the estimated effective recall ^R for each classifier as ^R = Y⋅Nt⋅RY⋅Nt⋅R+N⋅Yt⋅(1−R)", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 7: Performances of C5.', answer the question below by showing each progressive step toward the answer. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the F1 score for the C5 classifier when identifying not job-related tweets?\n" }, { "role": "assistant", "content": "The table shows the performance metrics for the C5 classifier. For not job-related tweets, the F1 score is listed in the row corresponding to 'notjob' under the F1 column. So, the answer is {\"answer\": \"0.92\"}." } ], "images": [ "scigen/train/2381.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Performances of C5." ], "reasoning": "The table shows the performance metrics for the C5 classifier. For not job-related tweets, the F1 score is listed in the row corresponding to 'notjob' under the F1 column.", "question": "What is the F1 score for the C5 classifier when identifying not job-related tweets?", "answer": "So, the answer is {\"answer\": \"0.92\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe C5 classifier has a higher precision for identifying job-related tweets than not job-related tweets.\n" }, { "role": "assistant", "content": "The table shows that the precision (P) for job-related tweets is 0.83, while the precision for not job-related tweets is 0.98. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2381.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Performances of C5." ], "reasoning": "The table shows that the precision (P) for job-related tweets is 0.83, while the precision for not job-related tweets is 0.98. Therefore, the statement is incorrect.", "statement": "The C5 classifier has a higher precision for identifying job-related tweets than not job-related tweets.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Crowdsourced validations of samples identified by models C0, C1, C2 and C3.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The estimated effective recall for each classifier is calculated based on the error rates in the test samples and the entire data set.We proposed an end-to-end evaluation: to what degree can our trained automatic classifiers (C1, C2, C3 and C5) identify job-related tweets in the real world? We introduced the estimated effective recall under the assumption that for each model, the error rates in our test samples (1,600 tweets) are proportional to the actual error rates found in the entire one-year data set which resembles the real world. We labeled the entire data set using each classifier and defined the estimated effective recall ^R for each classifier as ^R = Y⋅Nt⋅RY⋅Nt⋅R+N⋅Yt⋅(1−R)" } ], "images": [ "scigen/train/2382.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Crowdsourced validations of samples identified by models C0, C1, C2 and C3." ], "reasoning": "The estimated effective recall for each classifier is calculated based on the error rates in the test samples and the entire data set.", "description": "We proposed an end-to-end evaluation: to what degree can our trained automatic classifiers (C1, C2, C3 and C5) identify job-related tweets in the real world? We introduced the estimated effective recall under the assumption that for each model, the error rates in our test samples (1,600 tweets) are proportional to the actual error rates found in the entire one-year data set which resembles the real world. We labeled the entire data set using each classifier and defined the estimated effective recall ^R for each classifier as ^R = Y⋅Nt⋅RY⋅Nt⋅R+N⋅Yt⋅(1−R)", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 5: Crowdsourced validations of samples identified by models C0, C1, C2 and C3.', solve the following question step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich classifier has the highest F1 score for identifying job-related tweets?\n" }, { "role": "assistant", "content": "To find the classifier with the highest F1 score for identifying job-related tweets, we need to compare the F1 scores for the 'job' class across all classifiers. C3 has the highest F1 score of 0.89 for the 'job' class. So, the answer is {\"answer\": \"C3\"}" } ], "images": [ "scigen/train/2382.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Crowdsourced validations of samples identified by models C0, C1, C2 and C3." ], "reasoning": "To find the classifier with the highest F1 score for identifying job-related tweets, we need to compare the F1 scores for the 'job' class across all classifiers. C3 has the highest F1 score of 0.89 for the 'job' class.", "question": "Which classifier has the highest F1 score for identifying job-related tweets?", "answer": "So, the answer is {\"answer\": \"C3\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nClassifier C1 has a higher precision for identifying job-related tweets than classifier C2.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the precision (P) values for the 'job' class of classifiers C1 and C2. C1 has a precision of 0.79, while C2 has a precision of 0.82. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2382.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Crowdsourced validations of samples identified by models C0, C1, C2 and C3." ], "reasoning": "To verify this statement, we compare the precision (P) values for the 'job' class of classifiers C1 and C2. C1 has a precision of 0.79, while C2 has a precision of 0.82. Therefore, the statement is refuted.", "statement": "Classifier C1 has a higher precision for identifying job-related tweets than classifier C2.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: Comparison of our method against Glove and CBOW with respect to results obtained from the sentence-level classification task. Avg. denotes the arithmetic mean of the obtained f-scores across 4 independent runs. The column ‘Time’ shows the average training time in minutes.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Our method outperforms GloVe in all settings, and CBOW when using CNN with larger vectors, but CBOW outperforms our method with RNN for smaller vectors.Our method clearly outperforms GloVe in all the settings with a large margin. The poor performance of GloVe is particularly surprising given that it showed a relatively better performance than the other two methods in the intrinsic evaluations (specially the analogy task). Concerning the CBOW model, vectors built by our method could outperform the CBOW baseline when using the CNN model, provided that we use vectors of the larger dimenionality of 1000 (which consequently comes at the expense of a longer training time). Regarding the RNN model, the CBOW model outperforms our method particularly for vectors of low dimesnionality. As we increase the size of vectors obtained by our model, the performance gap between the two methods decreases." } ], "images": [ "scigen/train/2383.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of our method against Glove and CBOW with respect to results obtained from the sentence-level classification task. Avg. denotes the arithmetic mean of the obtained f-scores across 4 independent runs. The column ‘Time’ shows the average training time in minutes." ], "reasoning": "Our method outperforms GloVe in all settings, and CBOW when using CNN with larger vectors, but CBOW outperforms our method with RNN for smaller vectors.", "description": "Our method clearly outperforms GloVe in all the settings with a large margin. The poor performance of GloVe is particularly surprising given that it showed a relatively better performance than the other two methods in the intrinsic evaluations (specially the analogy task). Concerning the CBOW model, vectors built by our method could outperform the CBOW baseline when using the CNN model, provided that we use vectors of the larger dimenionality of 1000 (which consequently comes at the expense of a longer training time). Regarding the RNN model, the CBOW model outperforms our method particularly for vectors of low dimesnionality. As we increase the size of vectors obtained by our model, the performance gap between the two methods decreases.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 4: Comparison of our method against Glove and CBOW with respect to results obtained from the sentence-level classification task. Avg. denotes the arithmetic mean of the obtained f-scores across 4 independent runs. The column ‘Time’ shows the average training time in minutes.\nQuestion: Which method has the highest average score using the CNN model?\n" }, { "role": "assistant", "content": "By comparing the CNN avg scores in the table, Hash m=1000 has the highest score of 86.54. So, the answer is {\"answer\": \"Hash m=1000\"}" } ], "images": [ "scigen/train/2383.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of our method against Glove and CBOW with respect to results obtained from the sentence-level classification task. Avg. denotes the arithmetic mean of the obtained f-scores across 4 independent runs. The column ‘Time’ shows the average training time in minutes." ], "reasoning": "By comparing the CNN avg scores in the table, Hash m=1000 has the highest score of 86.54.", "question": "Which method has the highest average score using the CNN model?", "answer": "So, the answer is {\"answer\": \"Hash m=1000\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nGloVe has the highest average score using the RNN model.\n" }, { "role": "assistant", "content": "The table shows that CBOW has the highest RNN avg score of 80.27, not GloVe. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2383.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of our method against Glove and CBOW with respect to results obtained from the sentence-level classification task. Avg. denotes the arithmetic mean of the obtained f-scores across 4 independent runs. The column ‘Time’ shows the average training time in minutes." ], "reasoning": "The table shows that CBOW has the highest RNN avg score of 80.27, not GloVe.", "statement": "GloVe has the highest average score using the RNN model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Performance of our method in semantic similarity and relatedness tasks and its comparison to baselines. The last two columns report arithmetic and geometric mean of the observed performances (i.e., the harmonic mean of ρ and r) obtained across tasks. For the hash-based method, we report results for both unweighted and PPMI-weighted vectors of various dimensionality. We use Goodman and Kruskal’s γ and Pearson’s r for estimating similarities for unweighted and PPMI weighted vectors build by our method, respectively. For baselines, we use cosine as the similarity estimator.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Increasing the dimension of word vectors improves performance up to m=10,000 but increases similarity computation cost; however, higher dimensional models can be compressed without performance loss.For our method, we build word vectors of dimension m= 500, 1000, 2000, and 4000, and report results obtained from both the unweighted vectors—with γ (Eq. (Eq. Obviously, increasing the dimension of our vector does not affect the computational complexity (nor the required training time) for building a model; however, it increases the cost of similarity computation. Figure LABEL: dimensionAvgPerformance plots changes in the method’s performance (i.e., the arithmetic average of performances across tasks in our experiments) when the dimensionality of our model is increased— the evaluation setup (i.e., the input corpus and context window size) remains the same as those reported earlier. As shown, up to m=10,000, the performance mostly improves. We admit that increasing dimensionality is not desirable due to the cost that it imposes in the subsequent process (e.g., estimating pairwise similarities). However, we notice that these models of higher dimensionality (e.g., m=2000) can be compressed (e.g., to m=250) using matrix factorization techniques (e.g., PCA) without hurting task-based performances. Since our models are already of low-rank, factorization (e.g., computing principal components) can be done considerably fast." } ], "images": [ "scigen/train/2384.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance of our method in semantic similarity and relatedness tasks and its comparison to baselines. The last two columns report arithmetic and geometric mean of the observed performances (i.e., the harmonic mean of ρ and r) obtained across tasks. For the hash-based method, we report results for both unweighted and PPMI-weighted vectors of various dimensionality. We use Goodman and Kruskal’s γ and Pearson’s r for estimating similarities for unweighted and PPMI weighted vectors build by our method, respectively. For baselines, we use cosine as the similarity estimator." ], "reasoning": "Increasing the dimension of word vectors improves performance up to m=10,000 but increases similarity computation cost; however, higher dimensional models can be compressed without performance loss.", "description": "For our method, we build word vectors of dimension m= 500, 1000, 2000, and 4000, and report results obtained from both the unweighted vectors—with γ (Eq. (Eq. Obviously, increasing the dimension of our vector does not affect the computational complexity (nor the required training time) for building a model; however, it increases the cost of similarity computation. Figure LABEL: dimensionAvgPerformance plots changes in the method’s performance (i.e., the arithmetic average of performances across tasks in our experiments) when the dimensionality of our model is increased— the evaluation setup (i.e., the input corpus and context window size) remains the same as those reported earlier. As shown, up to m=10,000, the performance mostly improves. We admit that increasing dimensionality is not desirable due to the cost that it imposes in the subsequent process (e.g., estimating pairwise similarities). However, we notice that these models of higher dimensionality (e.g., m=2000) can be compressed (e.g., to m=250) using matrix factorization techniques (e.g., PCA) without hurting task-based performances. Since our models are already of low-rank, factorization (e.g., computing principal components) can be done considerably fast.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 1: Performance of our method in semantic similarity and relatedness tasks and its comparison to baselines. The last two columns report arithmetic and geometric mean of the observed performances (i.e., the harmonic mean of ρ and r) obtained across tasks. For the hash-based method, we report results for both unweighted and PPMI-weighted vectors of various dimensionality. We use Goodman and Kruskal’s γ and Pearson’s r for estimating similarities for unweighted and PPMI weighted vectors build by our method, respectively. For baselines, we use cosine as the similarity estimator.'. Solve the following question based on the table.\n\nQuestion: What is the mean arithmetic performance for the PPMI vector set with dimension 2000?\nLet's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the mean arithmetic performance (Mean A) for the PPMI vector set with dimension 2000 as 0.609. So, the answer is {\"answer\": \"0.609\"}" } ], "images": [ "scigen/train/2384.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance of our method in semantic similarity and relatedness tasks and its comparison to baselines. The last two columns report arithmetic and geometric mean of the observed performances (i.e., the harmonic mean of ρ and r) obtained across tasks. For the hash-based method, we report results for both unweighted and PPMI-weighted vectors of various dimensionality. We use Goodman and Kruskal’s γ and Pearson’s r for estimating similarities for unweighted and PPMI weighted vectors build by our method, respectively. For baselines, we use cosine as the similarity estimator." ], "reasoning": "The table shows the mean arithmetic performance (Mean A) for the PPMI vector set with dimension 2000 as 0.609.", "question": "What is the mean arithmetic performance for the PPMI vector set with dimension 2000?", "answer": "So, the answer is {\"answer\": \"0.609\"}" }, { "messages": [ { "role": "user", "content": "The GloVe vector set has the highest geometric mean performance among all vector sets. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe GloVe vector set has the highest geometric mean performance among all vector sets.\n" }, { "role": "assistant", "content": "The table shows that the GloVe vector set has a geometric mean (Mean G) of 0.576, which is not the highest. The highest geometric mean is 0.587 for the PPMI vector set with dimension 4000. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2384.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance of our method in semantic similarity and relatedness tasks and its comparison to baselines. The last two columns report arithmetic and geometric mean of the observed performances (i.e., the harmonic mean of ρ and r) obtained across tasks. For the hash-based method, we report results for both unweighted and PPMI-weighted vectors of various dimensionality. We use Goodman and Kruskal’s γ and Pearson’s r for estimating similarities for unweighted and PPMI weighted vectors build by our method, respectively. For baselines, we use cosine as the similarity estimator." ], "reasoning": "The table shows that the GloVe vector set has a geometric mean (Mean G) of 0.576, which is not the highest. The highest geometric mean is 0.587 for the PPMI vector set with dimension 4000.", "statement": "The GloVe vector set has the highest geometric mean performance among all vector sets.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Differences in Spearman’s ρs obtained by different methods; Hash denotes our method.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Our hash-based PPMI-weighted vectors perform comparably to word2vec’s CBOW and GloVe models for large values of m, particularly in the RW and Wsr tests, while CBOW outperforms in the Men test.Concerning our model, disregarding m, our unweighted and PPMI-weighted models outperform their counterpart classic high-dimensional models, as well as the random indexing method. Moreover, for large values of m (e.g., m=1000), our hash-based PPMI-weighted vectors (for most tasks) are a match for the word2vec’s CBOW and GloVe models, particularly when considering the statistical significance of the difference between the observed results from these models, as it is described by \\newcite[][see Table 2]rastogi-vandurme-arora:2015: NAACL-HLT and their proposed Minimum Required Difference for Significance (MRDS) measure. Here, we use MRDS to filter out insignificant comparative gains between our method and the baselines. Based on MRDS values in \\newciterastogi-vandurme-arora:2015: NAACL-HLT, while our method lags behind CBOW and GloVe in \\NAT@swafalse\\NAT@partrue\\NAT@fullfalse\\NAT@citetpHill2015 SL, it certainly performs better than its neural counterparts in the RW and Wsr tests. Similarly, MRDS suggests that in the Men test, the CBOW clearly outperforms all the other methods, whereas the difference in the obtained ρs by our method and GloVe is insignificant. By the same token, GloVe and CBOW both perform equally well over most of the tests except MEN and RW, for which CBOW and GloVe perform better than the other, respectively." } ], "images": [ "scigen/train/2385.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Differences in Spearman’s ρs obtained by different methods; Hash denotes our method." ], "reasoning": "Our hash-based PPMI-weighted vectors perform comparably to word2vec’s CBOW and GloVe models for large values of m, particularly in the RW and Wsr tests, while CBOW outperforms in the Men test.", "description": "Concerning our model, disregarding m, our unweighted and PPMI-weighted models outperform their counterpart classic high-dimensional models, as well as the random indexing method. Moreover, for large values of m (e.g., m=1000), our hash-based PPMI-weighted vectors (for most tasks) are a match for the word2vec’s CBOW and GloVe models, particularly when considering the statistical significance of the difference between the observed results from these models, as it is described by \\newcite[][see Table 2]rastogi-vandurme-arora:2015: NAACL-HLT and their proposed Minimum Required Difference for Significance (MRDS) measure. Here, we use MRDS to filter out insignificant comparative gains between our method and the baselines. Based on MRDS values in \\newciterastogi-vandurme-arora:2015: NAACL-HLT, while our method lags behind CBOW and GloVe in \\NAT@swafalse\\NAT@partrue\\NAT@fullfalse\\NAT@citetpHill2015 SL, it certainly performs better than its neural counterparts in the RW and Wsr tests. Similarly, MRDS suggests that in the Men test, the CBOW clearly outperforms all the other methods, whereas the difference in the obtained ρs by our method and GloVe is insignificant. By the same token, GloVe and CBOW both perform equally well over most of the tests except MEN and RW, for which CBOW and GloVe perform better than the other, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 2: Differences in Spearman’s ρs obtained by different methods; Hash denotes our method.\nQuestion: Which model performs better in the RW test according to the table and description?\n" }, { "role": "assistant", "content": "The table shows the difference in performance between models. The description states that our method performs better than its neural counterparts in the RW test. The table shows a positive value for 'Hash - CBOW' and 'Hash - GloVe' in the RW column, indicating that the hash-based model outperforms both CBOW and GloVe in the RW test. So, the answer is {\"answer\": \"Hash-based model\"}." } ], "images": [ "scigen/train/2385.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Differences in Spearman’s ρs obtained by different methods; Hash denotes our method." ], "reasoning": "The table shows the difference in performance between models. The description states that our method performs better than its neural counterparts in the RW test. The table shows a positive value for 'Hash - CBOW' and 'Hash - GloVe' in the RW column, indicating that the hash-based model outperforms both CBOW and GloVe in the RW test.", "question": "Which model performs better in the RW test according to the table and description?", "answer": "So, the answer is {\"answer\": \"Hash-based model\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe CBOW model outperforms the GloVe model in the MEN test.\n" }, { "role": "assistant", "content": "The description mentions that in the Men test, the CBOW clearly outperforms all other methods. The table shows a positive value for 'CBOW - GloVe' in the MEN column, indicating that CBOW performs better than GloVe in the MEN test. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2385.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Differences in Spearman’s ρs obtained by different methods; Hash denotes our method." ], "reasoning": "The description mentions that in the Men test, the CBOW clearly outperforms all other methods. The table shows a positive value for 'CBOW - GloVe' in the MEN column, indicating that CBOW performs better than GloVe in the MEN test.", "statement": "The CBOW model outperforms the GloVe model in the MEN test.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Classifier performance on predicting appraisal dimensions.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Experiment 1 aims to predict appraisal dimensions from text, with certainty achieving the highest F1 score (85%) and control the lowest (55%), resulting in a 75 micro average F1 score.Experiment 1 (Appraisal Prediction, T→A) aims at understanding how well appraisal dimensions can be predicted from text. Emotion classification is an established task, and one might have some intuition on the expected performance with a given data set, but the prediction of appraisal dimensions has never been performed before. The prediction of certainty works best (85%F1) followed by attention (82%F1). The lowest performance is seen for control (55%F1) and circumstance (61%F1). These results are only partially in line with the inter-annotator agreement scores. We obtain a 75 micro average F1 score." } ], "images": [ "scigen/train/2386.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Classifier performance on predicting appraisal dimensions." ], "reasoning": "Experiment 1 aims to predict appraisal dimensions from text, with certainty achieving the highest F1 score (85%) and control the lowest (55%), resulting in a 75 micro average F1 score.", "description": "Experiment 1 (Appraisal Prediction, T→A) aims at understanding how well appraisal dimensions can be predicted from text. Emotion classification is an established task, and one might have some intuition on the expected performance with a given data set, but the prediction of appraisal dimensions has never been performed before. The prediction of certainty works best (85%F1) followed by attention (82%F1). The lowest performance is seen for control (55%F1) and circumstance (61%F1). These results are only partially in line with the inter-annotator agreement scores. We obtain a 75 micro average F1 score.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 4: Classifier performance on predicting appraisal dimensions.', unfold the question presented below, detailing every step of your calculation. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich appraisal dimension has the highest precision score in Experiment 1?\n" }, { "role": "assistant", "content": "The table shows the precision scores for each appraisal dimension. Certainty has the highest precision score of 84. So, the answer is {\"answer\": \"Certainty\"}" } ], "images": [ "scigen/train/2386.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Classifier performance on predicting appraisal dimensions." ], "reasoning": "The table shows the precision scores for each appraisal dimension. Certainty has the highest precision score of 84.", "question": "Which appraisal dimension has the highest precision score in Experiment 1?", "answer": "So, the answer is {\"answer\": \"Certainty\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe prediction of effort has the lowest F1 score in Experiment 1.\n" }, { "role": "assistant", "content": "The table shows that the prediction of control has the lowest F1 score (55%), not effort, which has an F1 score of 68. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2386.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Classifier performance on predicting appraisal dimensions." ], "reasoning": "The table shows that the prediction of control has the lowest F1 score (55%), not effort, which has an F1 score of 68.", "statement": "The prediction of effort has the lowest F1 score in Experiment 1.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: The locations of emotions along appraisal PCA dimensions, as published by Smith1985, Table 6. The top part corresponds to those emotions we consider in our work here.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The study focuses on modeling cognitive components of emotions, highlighting differences in attention and situational control between their study and \newciteSmith1985.We argue in this paper that this makes appraisals particularly useful for natural language processing, because they both provide a framework for research and represent a way of enriching existing data. As a matter of fact, few dimensions are sufficient to explain emotions based on cognitive appraisal. \\newciteSmith1985 explain 15 emotions based on pleasantness (polarity), self-other responsibility/control (for initiating the situation), certainty (about what is going on), attention (whether the emotion stimulus is worth attending), anticipated effort (the amount of physical or mental activation before the stimulus), and situational control (the ability to cope with the situation). In this paper, we focus on modelling the cognitive components described by \\newciteSmith1985. Results are consistent in most cases. These two are also the emotions that annotators associated with control more than others. Attention is prominent for events that elicited anger and which were under the control of others, as suggested by the low situational control. The highest situational control, on the contrary, appears with the data points labeled as fear, also characterized by a strong feeling of uncertainty and anticipated effort. There are also dissimilarities between the two tables, like the level of attention, reaching the lowest score for disgust in their study and not in ours. They also find that situational control is a stronger indicator for shame than for guilt, while effort is more marked in our sadness-related events than theirs." } ], "images": [ "scigen/train/2387.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The locations of emotions along appraisal PCA dimensions, as published by Smith1985, Table 6. The top part corresponds to those emotions we consider in our work here." ], "reasoning": "The study focuses on modeling cognitive components of emotions, highlighting differences in attention and situational control between their study and \newciteSmith1985.", "description": "We argue in this paper that this makes appraisals particularly useful for natural language processing, because they both provide a framework for research and represent a way of enriching existing data. As a matter of fact, few dimensions are sufficient to explain emotions based on cognitive appraisal. \\newciteSmith1985 explain 15 emotions based on pleasantness (polarity), self-other responsibility/control (for initiating the situation), certainty (about what is going on), attention (whether the emotion stimulus is worth attending), anticipated effort (the amount of physical or mental activation before the stimulus), and situational control (the ability to cope with the situation). In this paper, we focus on modelling the cognitive components described by \\newciteSmith1985. Results are consistent in most cases. These two are also the emotions that annotators associated with control more than others. Attention is prominent for events that elicited anger and which were under the control of others, as suggested by the low situational control. The highest situational control, on the contrary, appears with the data points labeled as fear, also characterized by a strong feeling of uncertainty and anticipated effort. There are also dissimilarities between the two tables, like the level of attention, reaching the lowest score for disgust in their study and not in ours. They also find that situational control is a stronger indicator for shame than for guilt, while effort is more marked in our sadness-related events than theirs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 1: The locations of emotions along appraisal PCA dimensions, as published by Smith1985, Table 6. The top part corresponds to those emotions we consider in our work here.', solve the following question step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich emotion is characterized by the highest level of situational control and what are its associated levels of uncertainty and effort?\n" }, { "role": "assistant", "content": "The table shows that 'Fear' has the highest situational control (0.59). The associated levels of uncertainty and effort for 'Fear' are 0.73 and 0.63, respectively. So, the answer is {\"answer\": \"Fear\"}." } ], "images": [ "scigen/train/2387.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The locations of emotions along appraisal PCA dimensions, as published by Smith1985, Table 6. The top part corresponds to those emotions we consider in our work here." ], "reasoning": "The table shows that 'Fear' has the highest situational control (0.59). The associated levels of uncertainty and effort for 'Fear' are 0.73 and 0.63, respectively.", "question": "Which emotion is characterized by the highest level of situational control and what are its associated levels of uncertainty and effort?", "answer": "So, the answer is {\"answer\": \"Fear\"}." }, { "messages": [ { "role": "user", "content": "Disgust has the lowest level of attention in the study. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nDisgust has the lowest level of attention in the study.\n" }, { "role": "assistant", "content": "The table shows that 'Boredom' has the lowest level of attention (-1.27), not 'Disgust' which has an attention level of -0.96. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2387.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The locations of emotions along appraisal PCA dimensions, as published by Smith1985, Table 6. The top part corresponds to those emotions we consider in our work here." ], "reasoning": "The table shows that 'Boredom' has the lowest level of attention (-1.27), not 'Disgust' which has an attention level of -0.96.", "statement": "Disgust has the lowest level of attention in the study.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 5: Comparison of the Text-to-Emotion baseline (T→E) with the performance of first prediction appraisal followed by emotion analysis (T→A,A→E) and the multi-task setting (T→A/E). The oracle consists of an ensemble of T→A,A→E and T→E, which is informed about which model is more likely to make the correct prediction.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The multi-task learning model does not outperform the text-only model, but an oracle ensemble shows a significant improvement over both the text-based and pipeline models.Finally, Experiment 3 (Multi-Task Learning, T→A/E and Oracle Ensemble) evaluates if the model which learns appraisal and emotion jointly performs better than the pipeline model. We see that the multi-task learning model cannot improve over the text-only setting. The question remains if the real-world pipeline (T→A, A→E) learns the same things as the text-based approach (T→E). To evaluate this, we design an ensemble, in which an oracle predicts which of the two models will obtain the correct result. In this experiment, we therefore accept a prediction as true positive if either the pipeline or the text-based prediction is correct. We see a clear improvement of 10pp over the text-based model and by 22 points over the pipeline. Therefore, we conclude that the model that is informed about the appraisal has the potential to contribute to the correct emotion labelling of a textual instance." } ], "images": [ "scigen/train/2388.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison of the Text-to-Emotion baseline (T→E) with the performance of first prediction appraisal followed by emotion analysis (T→A,A→E) and the multi-task setting (T→A/E). The oracle consists of an ensemble of T→A,A→E and T→E, which is informed about which model is more likely to make the correct prediction." ], "reasoning": "The multi-task learning model does not outperform the text-only model, but an oracle ensemble shows a significant improvement over both the text-based and pipeline models.", "description": "Finally, Experiment 3 (Multi-Task Learning, T→A/E and Oracle Ensemble) evaluates if the model which learns appraisal and emotion jointly performs better than the pipeline model. We see that the multi-task learning model cannot improve over the text-only setting. The question remains if the real-world pipeline (T→A, A→E) learns the same things as the text-based approach (T→E). To evaluate this, we design an ensemble, in which an oracle predicts which of the two models will obtain the correct result. In this experiment, we therefore accept a prediction as true positive if either the pipeline or the text-based prediction is correct. We see a clear improvement of 10pp over the text-based model and by 22 points over the pipeline. Therefore, we conclude that the model that is informed about the appraisal has the potential to contribute to the correct emotion labelling of a textual instance.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 5: Comparison of the Text-to-Emotion baseline (T→E) with the performance of first prediction appraisal followed by emotion analysis (T→A,A→E) and the multi-task setting (T→A/E). The oracle consists of an ensemble of T→A,A→E and T→E, which is informed about which model is more likely to make the correct prediction.'. Solve the following question based on the table.\n\nQuestion: What is the F1 score improvement of the Oracle Ensemble over the pipeline model for the emotion 'Joy'?\nLet's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The F1 score for 'Joy' in the pipeline model (T→A,A→E) is 64, and the F1 score for 'Joy' in the Oracle Ensemble is 80. The improvement is 80 - 64 = 16 points. So, the answer is {\"answer\": \"16\"}" } ], "images": [ "scigen/train/2388.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison of the Text-to-Emotion baseline (T→E) with the performance of first prediction appraisal followed by emotion analysis (T→A,A→E) and the multi-task setting (T→A/E). The oracle consists of an ensemble of T→A,A→E and T→E, which is informed about which model is more likely to make the correct prediction." ], "reasoning": "The F1 score for 'Joy' in the pipeline model (T→A,A→E) is 64, and the F1 score for 'Joy' in the Oracle Ensemble is 80. The improvement is 80 - 64 = 16 points.", "question": "What is the F1 score improvement of the Oracle Ensemble over the pipeline model for the emotion 'Joy'?", "answer": "So, the answer is {\"answer\": \"16\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Oracle Ensemble has a higher precision than the text-based model for the emotion 'Fear'.\n" }, { "role": "assistant", "content": "The precision for 'Fear' in the text-based model (T→E) is 69, while the precision for 'Fear' in the Oracle Ensemble is 76. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2388.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison of the Text-to-Emotion baseline (T→E) with the performance of first prediction appraisal followed by emotion analysis (T→A,A→E) and the multi-task setting (T→A/E). The oracle consists of an ensemble of T→A,A→E and T→E, which is informed about which model is more likely to make the correct prediction." ], "reasoning": "The precision for 'Fear' in the text-based model (T→E) is 69, while the precision for 'Fear' in the Oracle Ensemble is 76. Therefore, the statement is supported.", "statement": "The Oracle Ensemble has a higher precision than the text-based model for the emotion 'Fear'.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Ablation analysis of our model with respect to various Granular features on VisDial-v1.0 in validation dataset.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Increasing the number of granular objects improves performance, but the improvement is not significant beyond K=128. Therefore, K=64 is selected for both image and question attention maps.We conduct an experiment on various granular features for image and question attention map. We start with K=8,32,64,128 granular per image. Further increasing K=128 it improves but not significant. So we select number of granular object is 64 in case of image and also in question. From the distribution we can observe that feature are are correlated and lies between 0.2 to 0.6." } ], "images": [ "scigen/train/2389.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation analysis of our model with respect to various Granular features on VisDial-v1.0 in validation dataset." ], "reasoning": "Increasing the number of granular objects improves performance, but the improvement is not significant beyond K=128. Therefore, K=64 is selected for both image and question attention maps.", "description": "We conduct an experiment on various granular features for image and question attention map. We start with K=8,32,64,128 granular per image. Further increasing K=128 it improves but not significant. So we select number of granular object is 64 in case of image and also in question. From the distribution we can observe that feature are are correlated and lies between 0.2 to 0.6.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: Ablation analysis of our model with respect to various Granular features on VisDial-v1.0 in validation dataset.'. Solve the following question based on the table.\n\nQuestion: Which model and granular object combination achieved the highest MRR?\nLet's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the MRR column, the highest value is 0.6234, which corresponds to the GMA model with K=128. So, the answer is {\"answer\": \"GMA (K=128)\"}" } ], "images": [ "scigen/train/2389.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation analysis of our model with respect to various Granular features on VisDial-v1.0 in validation dataset." ], "reasoning": "By examining the MRR column, the highest value is 0.6234, which corresponds to the GMA model with K=128.", "question": "Which model and granular object combination achieved the highest MRR?", "answer": "So, the answer is {\"answer\": \"GMA (K=128)\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe GIA model with K=64 has a higher Mean score than the GTA model with K=64.\n" }, { "role": "assistant", "content": "The Mean score for GIA (K=64) is 4.68, while the Mean score for GTA (K=64) is 4.81. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2389.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation analysis of our model with respect to various Granular features on VisDial-v1.0 in validation dataset." ], "reasoning": "The Mean score for GIA (K=64) is 4.68, while the Mean score for GTA (K=64) is 4.81. Therefore, the statement is refuted.", "statement": "The GIA model with K=64 has a higher Mean score than the GTA model with K=64.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: Ablation analysis of our model on VisDial-v1.0 in test-std dataset.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The GMA(MCB-att) model outperforms all other models, showing significant improvements in R@1, R@5, R@10, MRR, and mean scores over the baseline SAN model.Each row provides results for one of the variations. The first block provides scores of our implementation for traditional methods such as stack attention method which is our baseline, History-Conditioned Image Attentive Encoder (HCIAE) based attention method. The second block provides scores for our proposed Granular Image (GIA) and Text(GTA) attention model and the third block provides scores for different variant of our proposed multimodal attention model GMA. It is apparent that our best variant (GMA(MCB-att)) outperforms all the other variants achieving an improvement of 3.57% in R@1 score, 4.22% in R@5 score, 3.77% in R@10 score, 0.0899% in MRR and 2.27% in mean over the baseline variant." } ], "images": [ "scigen/train/2390.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Ablation analysis of our model on VisDial-v1.0 in test-std dataset." ], "reasoning": "The GMA(MCB-att) model outperforms all other models, showing significant improvements in R@1, R@5, R@10, MRR, and mean scores over the baseline SAN model.", "description": "Each row provides results for one of the variations. The first block provides scores of our implementation for traditional methods such as stack attention method which is our baseline, History-Conditioned Image Attentive Encoder (HCIAE) based attention method. The second block provides scores for our proposed Granular Image (GIA) and Text(GTA) attention model and the third block provides scores for different variant of our proposed multimodal attention model GMA. It is apparent that our best variant (GMA(MCB-att)) outperforms all the other variants achieving an improvement of 3.57% in R@1 score, 4.22% in R@5 score, 3.77% in R@10 score, 0.0899% in MRR and 2.27% in mean over the baseline variant.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: Table 1: Ablation analysis of our model on VisDial-v1.0 in test-std dataset.\nQuestion: Which model shows the highest improvement in R@1 score compared to the baseline SAN model?\n" }, { "role": "assistant", "content": "The table shows that the GMA(MCB-att) model has the highest R@1 score of 45.66, which is an improvement over the SAN model's R@1 score of 40.93. So, the answer is {\"answer\": \"GMA(MCB-att)\"}" } ], "images": [ "scigen/train/2390.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Ablation analysis of our model on VisDial-v1.0 in test-std dataset." ], "reasoning": "The table shows that the GMA(MCB-att) model has the highest R@1 score of 45.66, which is an improvement over the SAN model's R@1 score of 40.93.", "question": "Which model shows the highest improvement in R@1 score compared to the baseline SAN model?", "answer": "So, the answer is {\"answer\": \"GMA(MCB-att)\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe GMA(MCB-att) model has the highest MRR score.\n" }, { "role": "assistant", "content": "The table indicates that the GMA(MCB-att) model has an MRR score of 0.6234, which is the highest among all the models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2390.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Ablation analysis of our model on VisDial-v1.0 in test-std dataset." ], "reasoning": "The table indicates that the GMA(MCB-att) model has an MRR score of 0.6234, which is the highest among all the models listed.", "statement": "The GMA(MCB-att) model has the highest MRR score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Results for transfer learning across views'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "CorrNet(123) performs similarly to MAE when using SGD, but outperforms MAE when using RMSProp.At the face of it, it may seem that both CorrNet and MAE differ only in their objective functions. Specifically, if we remove the last correlation term from the objective function of CorrNet then it would become equivalent to MAE. To verify this, we conducted experiments using both MAE and CorrNet without the last term (say CorrNet(123)). When using SGD to train the networks, we found that the performance is almost similar. However, when we use some advanced optimization technique like RMSProp, CorrNet(123) starts performing better than MAE." } ], "images": [ "scigen/train/2391.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results for transfer learning across views" ], "reasoning": "CorrNet(123) performs similarly to MAE when using SGD, but outperforms MAE when using RMSProp.", "description": "At the face of it, it may seem that both CorrNet and MAE differ only in their objective functions. Specifically, if we remove the last correlation term from the objective function of CorrNet then it would become equivalent to MAE. To verify this, we conducted experiments using both MAE and CorrNet without the last term (say CorrNet(123)). When using SGD to train the networks, we found that the performance is almost similar. However, when we use some advanced optimization technique like RMSProp, CorrNet(123) starts performing better than MAE.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 5: Results for transfer learning across views', and sequentially solve the question, writing out each step of your reasoning process. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model and optimization combination achieved the highest Right to Left performance?\n" }, { "role": "assistant", "content": "By comparing the Right to Left performance values in the table, CorrNet(123) with RMSProp has the highest value of 72.13. So, the answer is {\"answer\": \"CorrNet(123) with RMSProp\"}." } ], "images": [ "scigen/train/2391.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results for transfer learning across views" ], "reasoning": "By comparing the Right to Left performance values in the table, CorrNet(123) with RMSProp has the highest value of 72.13.", "question": "Which model and optimization combination achieved the highest Right to Left performance?", "answer": "So, the answer is {\"answer\": \"CorrNet(123) with RMSProp\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis MAE performs better than CorrNet(123) when using RMSProp. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nMAE performs better than CorrNet(123) when using RMSProp.\n" }, { "role": "assistant", "content": "The table shows that CorrNet(123) with RMSProp has higher Left to Right and Right to Left performance values (67.82 and 72.13) compared to MAE with RMSProp (64.14 and 68.88). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2391.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results for transfer learning across views" ], "reasoning": "The table shows that CorrNet(123) with RMSProp has higher Left to Right and Right to Left performance values (67.82 and 72.13) compared to MAE with RMSProp (64.14 and 68.88).", "statement": "MAE performs better than CorrNet(123) when using RMSProp.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Transfer learning accuracy using the representations learned using different models trained with 10000 instances from the MNIST dataset.'.\n" }, { "role": "assistant", "content": "CorrNet outperforms other models in both Left to Right and Right to Left tasks, even when the data for learning common representation is reduced to 10000 images.Single view corresponds to the classifier trained and tested on same view. This is the upper bound for the performance of any transfer learning algorithm. Once again, we see that CorrNet performs significantly better than the other models. To verify that this holds even when we decrease the data for learning common representation to 10000 images. The results as reported in" } ], "images": [ "scigen/train/2392.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Transfer learning accuracy using the representations learned using different models trained with 10000 instances from the MNIST dataset." ], "reasoning": "CorrNet outperforms other models in both Left to Right and Right to Left tasks, even when the data for learning common representation is reduced to 10000 images.", "description": "Single view corresponds to the classifier trained and tested on same view. This is the upper bound for the performance of any transfer learning algorithm. Once again, we see that CorrNet performs significantly better than the other models. To verify that this holds even when we decrease the data for learning common representation to 10000 images. The results as reported in", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 4: Transfer learning accuracy using the representations learned using different models trained with 10000 instances from the MNIST dataset.', unfold the question presented below, detailing every step of your calculation. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model performs the best in the Right to Left task?\n" }, { "role": "assistant", "content": "The table shows the performance of different models in the Right to Left task, with Single view having the highest score of 80.06. So, the answer is {\"answer\": \"Single view\"}" } ], "images": [ "scigen/train/2392.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Transfer learning accuracy using the representations learned using different models trained with 10000 instances from the MNIST dataset." ], "reasoning": "The table shows the performance of different models in the Right to Left task, with Single view having the highest score of 80.06.", "question": "Which model performs the best in the Right to Left task?", "answer": "So, the answer is {\"answer\": \"Single view\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim CorrNet performs better than MAE in the Left to Right task., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nCorrNet performs better than MAE in the Left to Right task.\n" }, { "role": "assistant", "content": "The table shows that CorrNet has a score of 76.6 in the Left to Right task, which is higher than MAE's score of 68.69. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2392.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Transfer learning accuracy using the representations learned using different models trained with 10000 instances from the MNIST dataset." ], "reasoning": "The table shows that CorrNet has a score of 76.6 in the Left to Right task, which is higher than MAE's score of 68.69.", "statement": "CorrNet performs better than MAE in the Left to Right task.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 6: Comparison of the performance of transfer learning with representations learned using different loss functions.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The sixth row shows the results for the loss function JZ(θ)=L1+L2+L3+L4, which is the same as that used in CorrNet.We then repeated the transfer learning experiments using common representations learned from each of these models. For example, the sixth row in the table shows the results when the following loss function is used for learning the common representations. JZ(θ)=L1+L2+L3+L4 which is the same as that used in CorrNet." } ], "images": [ "scigen/train/2393.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Comparison of the performance of transfer learning with representations learned using different loss functions." ], "reasoning": "The sixth row shows the results for the loss function JZ(θ)=L1+L2+L3+L4, which is the same as that used in CorrNet.", "description": "We then repeated the transfer learning experiments using common representations learned from each of these models. For example, the sixth row in the table shows the results when the following loss function is used for learning the common representations. JZ(θ)=L1+L2+L3+L4 which is the same as that used in CorrNet.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 6: Comparison of the performance of transfer learning with representations learned using different loss functions.\n\nQuestion: What is the Left to Right score when using the loss function JZ(θ)=L1+L2+L3+L4?\n" }, { "role": "assistant", "content": "The sixth row corresponds to the loss function JZ(θ)=L1+L2+L3+L4, and the Left to Right score in this row is 77.05. So, the answer is {\"answer\": \"77.05\"}" } ], "images": [ "scigen/train/2393.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Comparison of the performance of transfer learning with representations learned using different loss functions." ], "reasoning": "The sixth row corresponds to the loss function JZ(θ)=L1+L2+L3+L4, and the Left to Right score in this row is 77.05.", "question": "What is the Left to Right score when using the loss function JZ(θ)=L1+L2+L3+L4?", "answer": "So, the answer is {\"answer\": \"77.05\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Right to Left score for the loss function L2+L3+L4 is 80.57.\n" }, { "role": "assistant", "content": "The table shows that the Right to Left score for the loss function L2+L3+L4 is indeed 80.57. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2393.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Comparison of the performance of transfer learning with representations learned using different loss functions." ], "reasoning": "The table shows that the Right to Left score for the loss function L2+L3+L4 is indeed 80.57.", "statement": "The Right to Left score for the loss function L2+L3+L4 is 80.57.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 8: Cross-lingual classification accuracy for 3 language pairs, with 1000 labeled examples.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "CorrNet outperforms other methods in most language pairs, except for Hermann and Blunsom in the EN → DE and DE → EN tasks.They were obtained using 1000 RCV training examples. We report results in both directions, i.e. language X to Y and vice versa. The best performing method in all the pairs except one is CorrNet. In particular, CorrNet often outperforms the approach of Klementiev et al. They proposed two neural network architectures for learning word and document representations using sentence-aligned data only. Instead of an autoencoder paradigm, they propose a margin-based objective that aims to make the representation of aligned sentences closer than non-aligned sentences. While their trained embeddings are not publicly available, they report results for the EN/DE classification experiments, with representations of the same size as here (D=40) and trained on 500K EN/DE sentence pairs. Their best model in that setting reaches accuracies of 83.7% and 71.4% respectively for the EN → DE and DE → EN tasks. One clear advantage of our model is that unlike their model, it can use additional monolingual data. Indeed, when we train CorrNet with 500k EN/DE sentence pairs, plus monolingual RCV documents (which come at no additional cost), we get accuracies of 87.9% (EN → DE) and 76.7% (DE → EN), still improving on their best model. If we do not use the monolingual data, CorrNet’s performance is worse but still competitive at 86.1% for EN → DE and 68.8% for DE → EN." } ], "images": [ "scigen/train/2394.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Cross-lingual classification accuracy for 3 language pairs, with 1000 labeled examples." ], "reasoning": "CorrNet outperforms other methods in most language pairs, except for Hermann and Blunsom in the EN → DE and DE → EN tasks.", "description": "They were obtained using 1000 RCV training examples. We report results in both directions, i.e. language X to Y and vice versa. The best performing method in all the pairs except one is CorrNet. In particular, CorrNet often outperforms the approach of Klementiev et al. They proposed two neural network architectures for learning word and document representations using sentence-aligned data only. Instead of an autoencoder paradigm, they propose a margin-based objective that aims to make the representation of aligned sentences closer than non-aligned sentences. While their trained embeddings are not publicly available, they report results for the EN/DE classification experiments, with representations of the same size as here (D=40) and trained on 500K EN/DE sentence pairs. Their best model in that setting reaches accuracies of 83.7% and 71.4% respectively for the EN → DE and DE → EN tasks. One clear advantage of our model is that unlike their model, it can use additional monolingual data. Indeed, when we train CorrNet with 500k EN/DE sentence pairs, plus monolingual RCV documents (which come at no additional cost), we get accuracies of 87.9% (EN → DE) and 76.7% (DE → EN), still improving on their best model. If we do not use the monolingual data, CorrNet’s performance is worse but still competitive at 86.1% for EN → DE and 68.8% for DE → EN.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 8: Cross-lingual classification accuracy for 3 language pairs, with 1000 labeled examples.' in mind, break down and resolve the question below step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich method has the highest accuracy for the EN → DE task?\n" }, { "role": "assistant", "content": "By comparing the accuracies for the EN → DE task in the table, Hermann and Blunsom have the highest accuracy at 88.1%. So, the answer is {\"answer\": \"Hermann and Blunsom\"}" } ], "images": [ "scigen/train/2394.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Cross-lingual classification accuracy for 3 language pairs, with 1000 labeled examples." ], "reasoning": "By comparing the accuracies for the EN → DE task in the table, Hermann and Blunsom have the highest accuracy at 88.1%.", "question": "Which method has the highest accuracy for the EN → DE task?", "answer": "So, the answer is {\"answer\": \"Hermann and Blunsom\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nCorrNet has the highest accuracy for the EN → FR task.\n" }, { "role": "assistant", "content": "By examining the table, CorrNet has an accuracy of 84.6% for the EN → FR task, which is higher than the other methods listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2394.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Cross-lingual classification accuracy for 3 language pairs, with 1000 labeled examples." ], "reasoning": "By examining the table, CorrNet has an accuracy of 84.6% for the EN → FR task, which is higher than the other methods listed.", "statement": "CorrNet has the highest accuracy for the EN → FR task.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 10: Cross-lingual classification accuracy for 3 different pairs of languages, when merging the bag-of-words for different numbers of sentences. These results are based on 1000 labeled examples.'.\n" }, { "role": "assistant", "content": "Merging multiple sentences into single bags-of-words can still yield good word embeddings, indicating that exact sentence-level alignment is not essential for good performance.The excellent performance of CorrNet suggests that merging several sentences into single bags-of-words can still yield good word embeddings. In other words, not only we do not need to rely on word-level alignments, but exact sentence-level alignment is also not essential to reach good performances. We experimented with the merging of 5, 25 and 50 adjacent sentences into a single bag-of-words. They suggest that merging several sentences into single bags-of-words does not necessarily impact the quality of the word embeddings. Thus they confirm that exact sentence-level alignment is not essential to reach good performances as well." } ], "images": [ "scigen/train/2395.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Cross-lingual classification accuracy for 3 different pairs of languages, when merging the bag-of-words for different numbers of sentences. These results are based on 1000 labeled examples." ], "reasoning": "Merging multiple sentences into single bags-of-words can still yield good word embeddings, indicating that exact sentence-level alignment is not essential for good performance.", "description": "The excellent performance of CorrNet suggests that merging several sentences into single bags-of-words can still yield good word embeddings. In other words, not only we do not need to rely on word-level alignments, but exact sentence-level alignment is also not essential to reach good performances. We experimented with the merging of 5, 25 and 50 adjacent sentences into a single bag-of-words. They suggest that merging several sentences into single bags-of-words does not necessarily impact the quality of the word embeddings. Thus they confirm that exact sentence-level alignment is not essential to reach good performances as well.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 10: Cross-lingual classification accuracy for 3 different pairs of languages, when merging the bag-of-words for different numbers of sentences. These results are based on 1000 labeled examples.\nQuestion: What is the performance of CorrNet when merging 25 sentences for the EN to FR translation?\nGive a detailed response to the above question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the performance of CorrNet when merging 25 sentences for the EN to FR translation, look at the row labeled 'CorrNet' with 25 sentences and find the value under the 'EN → FR' column. So, the answer is {\"answer\": \"78.1\"}" } ], "images": [ "scigen/train/2395.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Cross-lingual classification accuracy for 3 different pairs of languages, when merging the bag-of-words for different numbers of sentences. These results are based on 1000 labeled examples." ], "reasoning": "To find the performance of CorrNet when merging 25 sentences for the EN to FR translation, look at the row labeled 'CorrNet' with 25 sentences and find the value under the 'EN → FR' column.", "question": "What is the performance of CorrNet when merging 25 sentences for the EN to FR translation?", "answer": "So, the answer is {\"answer\": \"78.1\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nMerging 50 sentences into single bags-of-words yields the highest performance for EN to DE translation.\n" }, { "role": "assistant", "content": "To verify this statement, compare the performance values for EN to DE translation across different numbers of merged sentences (5, 25, and 50). The value for 50 sentences (90.2) is higher than the values for 5 sentences (91.75) and 25 sentences (88.0). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2395.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Cross-lingual classification accuracy for 3 different pairs of languages, when merging the bag-of-words for different numbers of sentences. These results are based on 1000 labeled examples." ], "reasoning": "To verify this statement, compare the performance values for EN to DE translation across different numbers of merged sentences (5, 25, and 50). The value for 50 sentences (90.2) is higher than the values for 5 sentences (91.75) and 25 sentences (88.0).", "statement": "Merging 50 sentences into single bags-of-words yields the highest performance for EN to DE translation.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 11: Performance on NEWS 2010 En-Hi Transliteration Mining Dataset'.\n" }, { "role": "assistant", "content": "The table compares the F1-measure performance of different models on the NEWS 2010 transliteration mining task, with CorrNet achieving the highest score.For testing we consider the standard NEWS 2010 transliteration mining test set ( This test set contains approximately 1000 Wikipedia English Hindi title pairs. The original task definition is as follows. For a given English title containing T1 words and the corresponding Hindi title containing T2 words identify all pairs which form a transliteration pair. Specifically, for each title pair, consider all T1×T2 word pairs and identify the correct transliteration pairs. In all, the test set contains 5468 word pairs out of which 982 are transliteration pairs. For every word pair (xi,yi) we obtain a 50 dimensional common representation for xi and yi using the trained CorrNet. We then calculate the correlation between the representations of xi and yi. If the correlation is above a threshold we mark the word pair as equivalent. This threshold is tuned using an additional 1000 pairs which were provided as training data for the NEWS 2010 transliteration mining task. Note that our aim is not to achieve state of the art performance on this task but to compare the quality of the shared representations learned using different CRL methods considered in this paper." } ], "images": [ "scigen/train/2396.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Performance on NEWS 2010 En-Hi Transliteration Mining Dataset" ], "reasoning": "The table compares the F1-measure performance of different models on the NEWS 2010 transliteration mining task, with CorrNet achieving the highest score.", "description": "For testing we consider the standard NEWS 2010 transliteration mining test set ( This test set contains approximately 1000 Wikipedia English Hindi title pairs. The original task definition is as follows. For a given English title containing T1 words and the corresponding Hindi title containing T2 words identify all pairs which form a transliteration pair. Specifically, for each title pair, consider all T1×T2 word pairs and identify the correct transliteration pairs. In all, the test set contains 5468 word pairs out of which 982 are transliteration pairs. For every word pair (xi,yi) we obtain a 50 dimensional common representation for xi and yi using the trained CorrNet. We then calculate the correlation between the representations of xi and yi. If the correlation is above a threshold we mark the word pair as equivalent. This threshold is tuned using an additional 1000 pairs which were provided as training data for the NEWS 2010 transliteration mining task. Note that our aim is not to achieve state of the art performance on this task but to compare the quality of the shared representations learned using different CRL methods considered in this paper.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 11: Performance on NEWS 2010 En-Hi Transliteration Mining Dataset', solve the following question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model achieved the highest F1-measure (%) in the transliteration mining task?\n" }, { "role": "assistant", "content": "By comparing the F1-measure (%) values in the table, we see that CorrNet has the highest score of 81.56%. So, the answer is {\"answer\": \"CorrNet\"}" } ], "images": [ "scigen/train/2396.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Performance on NEWS 2010 En-Hi Transliteration Mining Dataset" ], "reasoning": "By comparing the F1-measure (%) values in the table, we see that CorrNet has the highest score of 81.56%.", "question": "Which model achieved the highest F1-measure (%) in the transliteration mining task?", "answer": "So, the answer is {\"answer\": \"CorrNet\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis KCCA achieved an F1-measure of 49.68% in the transliteration mining task. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nKCCA achieved an F1-measure of 49.68% in the transliteration mining task.\n" }, { "role": "assistant", "content": "The table shows that KCCA achieved an F1-measure of 42.36%, not 49.68%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2396.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Performance on NEWS 2010 En-Hi Transliteration Mining Dataset" ], "reasoning": "The table shows that KCCA achieved an F1-measure of 42.36%, not 49.68%.", "statement": "KCCA achieved an F1-measure of 49.68% in the transliteration mining task.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 12: Spearman’s correlation for bigram similarity dataset. Results for other models are taken from Wang et al. (2015)'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "CorrNet outperforms the previous state-of-the-art model DCCAE on average score, achieving the best results with CorrNet-500-384.Following Wang et al. , we fix the dimensionality of the vectors at L = 384. Other hyperparameters are tuned using the tuning data. CorrNet performs better than the previous state-of-the-art (DCCAE) on average score. Best results are obtained using CorrNet-500-384. This experiment suggests that apart from multiview applications such as (i) transfer learning (ii) reconstructing missing view and (iii) matching items across views, CorrNet can also be employed to exploit multiview data to improve the performance of a single view task (such as monolingual bigram similarity)." } ], "images": [ "scigen/train/2397.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 12: Spearman’s correlation for bigram similarity dataset. Results for other models are taken from Wang et al. (2015)" ], "reasoning": "CorrNet outperforms the previous state-of-the-art model DCCAE on average score, achieving the best results with CorrNet-500-384.", "description": "Following Wang et al. , we fix the dimensionality of the vectors at L = 384. Other hyperparameters are tuned using the tuning data. CorrNet performs better than the previous state-of-the-art (DCCAE) on average score. Best results are obtained using CorrNet-500-384. This experiment suggests that apart from multiview applications such as (i) transfer learning (ii) reconstructing missing view and (iii) matching items across views, CorrNet can also be employed to exploit multiview data to improve the performance of a single view task (such as monolingual bigram similarity).", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: Table 12: Spearman’s correlation for bigram similarity dataset. Results for other models are taken from Wang et al. (2015)\nQuestion: Which model has the highest average score and what is that score?\n" }, { "role": "assistant", "content": "By comparing the average scores of all models listed in the table, CorrNet has the highest average score of 46.8. So, the answer is {\"answer\": \"CorrNet, 46.8\"}." } ], "images": [ "scigen/train/2397.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 12: Spearman’s correlation for bigram similarity dataset. Results for other models are taken from Wang et al. (2015)" ], "reasoning": "By comparing the average scores of all models listed in the table, CorrNet has the highest average score of 46.8.", "question": "Which model has the highest average score and what is that score?", "answer": "So, the answer is {\"answer\": \"CorrNet, 46.8\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nCorrNet achieves the highest average score among all models.\n" }, { "role": "assistant", "content": "The table shows that CorrNet has an average score of 46.8, which is higher than the average scores of all other models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2397.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 12: Spearman’s correlation for bigram similarity dataset. Results for other models are taken from Wang et al. (2015)" ], "reasoning": "The table shows that CorrNet has an average score of 46.8, which is higher than the average scores of all other models listed.", "statement": "CorrNet achieves the highest average score among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Languages included in AlloVera'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "AlloVera provides mappings between phonemic and broad phonetic representations for 14 languages, with several languages currently in alpha and beta states.This resource consists of mappings between phonemic and broad phonetic representations for 14 languages with diverse phonologies. Languages were chosen based on three conditions: There is significant annotated speech data available for the language variety There is a description of the phonology of the language variety including allophonic rules Additionally, there are currently several languages in alpha and beta states. Current alpha languages are Bengali (Indian; ben), Sundanese (sun), Swahili (swa), Portuguese (por), Cantonese (yue), Haitian (hat), and Zulu (zul). Current beta languages are Nepali (nep), Bengali (Bangladesh; ben), Korean (kor), Mongolian (mon), Greek (tsd), and Catalan (cat). We view AlloVera as an open-ended project which will continue expanding in the future." } ], "images": [ "scigen/train/2398.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Languages included in AlloVera" ], "reasoning": "AlloVera provides mappings between phonemic and broad phonetic representations for 14 languages, with several languages currently in alpha and beta states.", "description": "This resource consists of mappings between phonemic and broad phonetic representations for 14 languages with diverse phonologies. Languages were chosen based on three conditions: There is significant annotated speech data available for the language variety There is a description of the phonology of the language variety including allophonic rules Additionally, there are currently several languages in alpha and beta states. Current alpha languages are Bengali (Indian; ben), Sundanese (sun), Swahili (swa), Portuguese (por), Cantonese (yue), Haitian (hat), and Zulu (zul). Current beta languages are Nepali (nep), Bengali (Bangladesh; ben), Korean (kor), Mongolian (mon), Greek (tsd), and Catalan (cat). We view AlloVera as an open-ended project which will continue expanding in the future.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 1: Languages included in AlloVera', and sequentially solve the question, writing out each step of your reasoning process. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich language has the highest number of phones and how many does it have?\n" }, { "role": "assistant", "content": "To find the language with the highest number of phones, look at the 'Phones' column and identify the highest value, then find the corresponding language. So, the answer is {\"answer\": \"Russian, 62\"}." } ], "images": [ "scigen/train/2398.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Languages included in AlloVera" ], "reasoning": "To find the language with the highest number of phones, look at the 'Phones' column and identify the highest value, then find the corresponding language.", "question": "Which language has the highest number of phones and how many does it have?", "answer": "So, the answer is {\"answer\": \"Russian, 62\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The language with the highest number of phonemes is Amharic., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe language with the highest number of phonemes is Amharic.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'Phonemes' column and identify the highest value, then check if it corresponds to Amharic. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2398.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Languages included in AlloVera" ], "reasoning": "To verify this statement, look at the 'Phonemes' column and identify the highest value, then check if it corresponds to Amharic.", "statement": "The language with the highest number of phonemes is Amharic.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Three models’ phoneme error rates on 11 languages. The top half shows the results when training with full datasets. The bottom half shows the low-resource results in which only 10k utterances are used for training from each dataset.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Allosaurus improves the private phoneme model by 0.6% under full condition and 2.0% under low resource condition, making it valuable for low resource languages.The row tagged with Full means that the whole training set was used to train the multilingual model. In contrast, the row with tag Low is trained under a low resource condition in which we only select 10k utterances from each training corpus. This low resource condition is useful when building speech recognizers for new languages since training sets of most new languages are very limited. the average PER of the shared phoneme model is 73.8% and the private phoneme model has 25.6% PER in the full training condition. During the evaluation process, we find that the performance of the shared phoneme model decreases significantly when increasing the number of training languages. This can be explained by the fact that phoneme assignment schemes are different across languages. Therefore, adding more languages can confuse the model, leading it to assign incorrect phonemes. In contrast, AlloVera provides a consistent assignment across languages by using allophone inventories. Comparing Allosaurus and the private phoneme model, we find that Allosaurus further improves from the private phoneme model by 0.6% under the full condition and 2.0% under the low resource condition. While the improvement is relatively limited in the full training case, it suggests AlloVera would be valuable for creating speech recognition models for low resource languages." } ], "images": [ "scigen/train/2399.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Three models’ phoneme error rates on 11 languages. The top half shows the results when training with full datasets. The bottom half shows the low-resource results in which only 10k utterances are used for training from each dataset." ], "reasoning": "Allosaurus improves the private phoneme model by 0.6% under full condition and 2.0% under low resource condition, making it valuable for low resource languages.", "description": "The row tagged with Full means that the whole training set was used to train the multilingual model. In contrast, the row with tag Low is trained under a low resource condition in which we only select 10k utterances from each training corpus. This low resource condition is useful when building speech recognizers for new languages since training sets of most new languages are very limited. the average PER of the shared phoneme model is 73.8% and the private phoneme model has 25.6% PER in the full training condition. During the evaluation process, we find that the performance of the shared phoneme model decreases significantly when increasing the number of training languages. This can be explained by the fact that phoneme assignment schemes are different across languages. Therefore, adding more languages can confuse the model, leading it to assign incorrect phonemes. In contrast, AlloVera provides a consistent assignment across languages by using allophone inventories. Comparing Allosaurus and the private phoneme model, we find that Allosaurus further improves from the private phoneme model by 0.6% under the full condition and 2.0% under the low resource condition. While the improvement is relatively limited in the full training case, it suggests AlloVera would be valuable for creating speech recognition models for low resource languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the average PER of the Allosaurus model under the low resource condition?\nSolve the above question based on the table titled 'Table 3: Three models’ phoneme error rates on 11 languages. The top half shows the results when training with full datasets. The bottom half shows the low-resource results in which only 10k utterances are used for training from each dataset.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the PER for Allosaurus under the low resource condition as 41.8%. So, the answer is {\"answer\": \"41.8%\"}" } ], "images": [ "scigen/train/2399.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Three models’ phoneme error rates on 11 languages. The top half shows the results when training with full datasets. The bottom half shows the low-resource results in which only 10k utterances are used for training from each dataset." ], "reasoning": "The table shows the PER for Allosaurus under the low resource condition as 41.8%.", "question": "What is the average PER of the Allosaurus model under the low resource condition?", "answer": "So, the answer is {\"answer\": \"41.8%\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The private phoneme model has a lower average PER than the shared phoneme model under the full training condition., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe private phoneme model has a lower average PER than the shared phoneme model under the full training condition.\n" }, { "role": "assistant", "content": "The table shows that the private phoneme model has an average PER of 25.6% under the full training condition, which is lower than the shared phoneme model's average PER of 73.8%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2399.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Three models’ phoneme error rates on 11 languages. The top half shows the results when training with full datasets. The bottom half shows the low-resource results in which only 10k utterances are used for training from each dataset." ], "reasoning": "The table shows that the private phoneme model has an average PER of 25.6% under the full training condition, which is lower than the shared phoneme model's average PER of 73.8%.", "statement": "The private phoneme model has a lower average PER than the shared phoneme model under the full training condition.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: Comparisons of phone error rates in two unseen languages', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Allosaurus and AlloVera significantly outperform both the shared and private phoneme models in terms of PER for Inuktitut and Tusom.AlloVera gives Allosaurus another important capability—the ability to generate phones from the universal phone inventory. The universal phone inventory consists of all allophones in AlloVera. In contrast, the shared phoneme model could only generate inconsistent universal phonemes and the private phoneme model could only generate language-specific phonemes. The table suggests that Allosaurus and AlloVera improve the performance over both the shared phoneme model and the private phoneme model substantially." } ], "images": [ "scigen/train/2400.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparisons of phone error rates in two unseen languages" ], "reasoning": "Allosaurus and AlloVera significantly outperform both the shared and private phoneme models in terms of PER for Inuktitut and Tusom.", "description": "AlloVera gives Allosaurus another important capability—the ability to generate phones from the universal phone inventory. The universal phone inventory consists of all allophones in AlloVera. In contrast, the shared phoneme model could only generate inconsistent universal phonemes and the private phoneme model could only generate language-specific phonemes. The table suggests that Allosaurus and AlloVera improve the performance over both the shared phoneme model and the private phoneme model substantially.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 5: Comparisons of phone error rates in two unseen languages', solve this question and give detail solutions. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhat is the PER improvement of Allosaurus over the Shared Phoneme model for Inuktitut?\n" }, { "role": "assistant", "content": "The PER for the Shared Phoneme model in Inuktitut is 94.1, and for Allosaurus, it is 73.1. The improvement is 94.1 - 73.1. So, the answer is {\"answer\": \"21\"}." } ], "images": [ "scigen/train/2400.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparisons of phone error rates in two unseen languages" ], "reasoning": "The PER for the Shared Phoneme model in Inuktitut is 94.1, and for Allosaurus, it is 73.1. The improvement is 94.1 - 73.1.", "question": "What is the PER improvement of Allosaurus over the Shared Phoneme model for Inuktitut?", "answer": "So, the answer is {\"answer\": \"21\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Private Phoneme model has a lower PER than the Shared Phoneme model for both Inuktitut and Tusom.\n" }, { "role": "assistant", "content": "The table shows that the Private Phoneme model has a PER of 86.2 for Inuktitut and 85.8 for Tusom, both of which are lower than the Shared Phoneme model's PER of 94.1 for Inuktitut and 93.5 for Tusom. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2400.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparisons of phone error rates in two unseen languages" ], "reasoning": "The table shows that the Private Phoneme model has a PER of 86.2 for Inuktitut and 85.8 for Tusom, both of which are lower than the Shared Phoneme model's PER of 94.1 for Inuktitut and 93.5 for Tusom.", "statement": "The Private Phoneme model has a lower PER than the Shared Phoneme model for both Inuktitut and Tusom.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6: Comparison of the results of our approaches with maximum entropy based method', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Both FDNN and CNN approaches have higher F1 scores compared to the baseline method by Brychcín et al.The results show that both proposed approaches significantly outperform this baseline approach that uses several features with ME classifier." } ], "images": [ "scigen/train/2401.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Comparison of the results of our approaches with maximum entropy based method" ], "reasoning": "Both FDNN and CNN approaches have higher F1 scores compared to the baseline method by Brychcín et al.", "description": "The results show that both proposed approaches significantly outperform this baseline approach that uses several features with ME classifier.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 6: Comparison of the results of our approaches with maximum entropy based method\nQuestion: Which method has the highest recall and what is its F1 score?\n" }, { "role": "assistant", "content": "To find the method with the highest recall, compare the recall values of Brychcín et al., FDNN, and CNN. FDNN has the highest recall of 83.6. The F1 score for FDNN is 83.9. So, the answer is {\"answer\": \"83.9\"}." } ], "images": [ "scigen/train/2401.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Comparison of the results of our approaches with maximum entropy based method" ], "reasoning": "To find the method with the highest recall, compare the recall values of Brychcín et al., FDNN, and CNN. FDNN has the highest recall of 83.6. The F1 score for FDNN is 83.9.", "question": "Which method has the highest recall and what is its F1 score?", "answer": "So, the answer is {\"answer\": \"83.9\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The CNN method has the highest precision among the listed methods., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe CNN method has the highest precision among the listed methods.\n" }, { "role": "assistant", "content": "To verify this statement, compare the precision values of Brychcín et al., FDNN, and CNN. Brychcín et al. has the highest precision of 89.0, not CNN. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2401.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Comparison of the results of our approaches with maximum entropy based method" ], "reasoning": "To verify this statement, compare the precision values of Brychcín et al., FDNN, and CNN. Brychcín et al. has the highest precision of 89.0, not CNN.", "statement": "The CNN method has the highest precision among the listed methods.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: F-measure of FDNN with different numbers of words in the dictionary', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The performance of the FDNN improves as the dictionary size increases up to 20,000 words, beyond which there is no significant improvement.The third experiment studies the influence of the dictionary size on the performance of the FDNN. This table shows that the previously chosen 20,000 words is a reasonable choice and further increasing the number does not bring any significant improvement." } ], "images": [ "scigen/train/2402.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: F-measure of FDNN with different numbers of words in the dictionary" ], "reasoning": "The performance of the FDNN improves as the dictionary size increases up to 20,000 words, beyond which there is no significant improvement.", "description": "The third experiment studies the influence of the dictionary size on the performance of the FDNN. This table shows that the previously chosen 20,000 words is a reasonable choice and further increasing the number does not bring any significant improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: F-measure of FDNN with different numbers of words in the dictionary', answer the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the F1 score when the dictionary size is 10,000 words, and how does it compare to the F1 score when the dictionary size is 30,000 words?\n" }, { "role": "assistant", "content": "The F1 score for 10,000 words is 82.9%, and for 30,000 words, it is 83.9%. The increase in F1 score from 10,000 to 30,000 words is only 1.0%, indicating minimal improvement. So, the answer is {\"answer\": \"1.0%\"}." } ], "images": [ "scigen/train/2402.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: F-measure of FDNN with different numbers of words in the dictionary" ], "reasoning": "The F1 score for 10,000 words is 82.9%, and for 30,000 words, it is 83.9%. The increase in F1 score from 10,000 to 30,000 words is only 1.0%, indicating minimal improvement.", "question": "What is the F1 score when the dictionary size is 10,000 words, and how does it compare to the F1 score when the dictionary size is 30,000 words?", "answer": "So, the answer is {\"answer\": \"1.0%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nIncreasing the dictionary size from 20,000 to 30,000 words significantly improves the F1 score.\n" }, { "role": "assistant", "content": "The F1 score for 20,000 words is 83.8%, and for 30,000 words, it is 83.9%. The increase is only 0.1%, which is not significant. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2402.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: F-measure of FDNN with different numbers of words in the dictionary" ], "reasoning": "The F1 score for 20,000 words is 83.8%, and for 30,000 words, it is 83.9%. The increase is only 0.1%, which is not significant.", "statement": "Increasing the dictionary size from 20,000 to 30,000 words significantly improves the F1 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: F-measure of CNN with different numbers of convolutional kernels', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The experiment investigates the effect of the number of convolutional kernels on classification scores, using 40 kernels for further experimentation as it falls within the suitable range of 20 to 64.In this experiment, we will show the impact of the number of convolutional kernels in our network on the classification score. 400 words are used for document representation (L=400) and the embedding vector size is 200. All values from interval [20; 64] are suitable for our goal and therefore we chose the value of 40 for further experimentation." } ], "images": [ "scigen/train/2403.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: F-measure of CNN with different numbers of convolutional kernels" ], "reasoning": "The experiment investigates the effect of the number of convolutional kernels on classification scores, using 40 kernels for further experimentation as it falls within the suitable range of 20 to 64.", "description": "In this experiment, we will show the impact of the number of convolutional kernels in our network on the classification score. 400 words are used for document representation (L=400) and the embedding vector size is 200. All values from interval [20; 64] are suitable for our goal and therefore we chose the value of 40 for further experimentation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 4: F-measure of CNN with different numbers of convolutional kernels', and sequentially solve the question, writing out each step of your reasoning process. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the F1 score when using 40 convolutional kernels?\n" }, { "role": "assistant", "content": "The table shows the F1 scores for different numbers of convolutional kernels. For 40 kernels, the F1 score is listed as 84.1%. So, the answer is {\"answer\": \"84.1%\"}." } ], "images": [ "scigen/train/2403.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: F-measure of CNN with different numbers of convolutional kernels" ], "reasoning": "The table shows the F1 scores for different numbers of convolutional kernels. For 40 kernels, the F1 score is listed as 84.1%.", "question": "What is the F1 score when using 40 convolutional kernels?", "answer": "So, the answer is {\"answer\": \"84.1%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The F1 score for 36 convolutional kernels is higher than for 44 kernels. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe F1 score for 36 convolutional kernels is higher than for 44 kernels.\n" }, { "role": "assistant", "content": "According to the table, the F1 score for 36 kernels is 84.1%, and for 44 kernels, it is also 84.1%. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2403.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: F-measure of CNN with different numbers of convolutional kernels" ], "reasoning": "According to the table, the F1 score for 36 kernels is 84.1%, and for 44 kernels, it is also 84.1%. Therefore, the statement is refuted.", "statement": "The F1 score for 36 convolutional kernels is higher than for 44 kernels.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: F-measure of CNN with different word numbers and different embedding sizes [%]', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Using 400 words was a reasonable choice, but longer embedding vectors improve performance, though they significantly increase computation time.Finally, we tested our network with different numbers of input words and with varying size of the embedding vectors. We can conclude that the 400 words that we chose at the beginning was a reasonable choice. However, it is beneficial to use longer embedding vectors. It must be noted that the further increasing of the embedding size has a strong impact on the computation time and might be not practical for real-world applications." } ], "images": [ "scigen/train/2404.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: F-measure of CNN with different word numbers and different embedding sizes [%]" ], "reasoning": "Using 400 words was a reasonable choice, but longer embedding vectors improve performance, though they significantly increase computation time.", "description": "Finally, we tested our network with different numbers of input words and with varying size of the embedding vectors. We can conclude that the 400 words that we chose at the beginning was a reasonable choice. However, it is beneficial to use longer embedding vectors. It must be noted that the further increasing of the embedding size has a strong impact on the computation time and might be not practical for real-world applications.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 5: F-measure of CNN with different word numbers and different embedding sizes [%]\nQuestion: What is the highest accuracy achieved with an embedding length of 300?\n" }, { "role": "assistant", "content": "To find the highest accuracy for an embedding length of 300, look at the column for 300 and identify the maximum value. So, the answer is {\"answer\": \"84.5\"}." } ], "images": [ "scigen/train/2404.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: F-measure of CNN with different word numbers and different embedding sizes [%]" ], "reasoning": "To find the highest accuracy for an embedding length of 300, look at the column for 300 and identify the maximum value.", "question": "What is the highest accuracy achieved with an embedding length of 300?", "answer": "So, the answer is {\"answer\": \"84.5\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Using 450 words with an embedding length of 500 achieves the highest accuracy. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nUsing 450 words with an embedding length of 500 achieves the highest accuracy.\n" }, { "role": "assistant", "content": "Check the accuracy for 450 words with an embedding length of 500 and compare it with other values in the table. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2404.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: F-measure of CNN with different word numbers and different embedding sizes [%]" ], "reasoning": "Check the accuracy for 450 words with an embedding length of 500 and compare it with other values in the table.", "statement": "Using 450 words with an embedding length of 500 achieves the highest accuracy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: F1 scores (%) of domain, intent and slot classification tasks modelled by character-level and word-level models. We present the results for various sizes of training data and compare the performance. For each data size, the best result is highlighted in bold for each task.'.\n" }, { "role": "assistant", "content": "The character-level model shows consistent improvement in F1 scores for domain, intent, and slot classification as the training size increases, outperforming the word-level model.Tab. For example, when using 10K training utterances, the character-level model achieves a relative improvement of 6.62%, 11.14% and 11.95% in F1 scores of domain, intent and slot classification respectively compared to the word-level model." } ], "images": [ "scigen/train/2405.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: F1 scores (%) of domain, intent and slot classification tasks modelled by character-level and word-level models. We present the results for various sizes of training data and compare the performance. For each data size, the best result is highlighted in bold for each task." ], "reasoning": "The character-level model shows consistent improvement in F1 scores for domain, intent, and slot classification as the training size increases, outperforming the word-level model.", "description": "Tab. For example, when using 10K training utterances, the character-level model achieves a relative improvement of 6.62%, 11.14% and 11.95% in F1 scores of domain, intent and slot classification respectively compared to the word-level model.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 1: F1 scores (%) of domain, intent and slot classification tasks modelled by character-level and word-level models. We present the results for various sizes of training data and compare the performance. For each data size, the best result is highlighted in bold for each task.', solve this question and give detail solutions. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhat is the F1 score improvement for the character-level model in slot classification when using 40K training utterances compared to the word-level model?\n" }, { "role": "assistant", "content": "The F1 score for the character-level model in slot classification with 40K training utterances is 77.46, while for the word-level model it is 74.72. The improvement is 77.46 - 74.72 = 2.74. So, the answer is {\"answer\": \"2.74\"}" } ], "images": [ "scigen/train/2405.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: F1 scores (%) of domain, intent and slot classification tasks modelled by character-level and word-level models. We present the results for various sizes of training data and compare the performance. For each data size, the best result is highlighted in bold for each task." ], "reasoning": "The F1 score for the character-level model in slot classification with 40K training utterances is 77.46, while for the word-level model it is 74.72. The improvement is 77.46 - 74.72 = 2.74.", "question": "What is the F1 score improvement for the character-level model in slot classification when using 40K training utterances compared to the word-level model?", "answer": "So, the answer is {\"answer\": \"2.74\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe character-level model achieves an F1 score of 88.26 in domain classification with 160K training utterances.\n" }, { "role": "assistant", "content": "The table shows that with 160K training utterances, the character-level model achieves an F1 score of 88.26 in domain classification. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2405.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: F1 scores (%) of domain, intent and slot classification tasks modelled by character-level and word-level models. We present the results for various sizes of training data and compare the performance. For each data size, the best result is highlighted in bold for each task." ], "reasoning": "The table shows that with 160K training utterances, the character-level model achieves an F1 score of 88.26 in domain classification.", "statement": "The character-level model achieves an F1 score of 88.26 in domain classification with 160K training utterances.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: F1 scores (%) of domain, intent and slot classification tasks modelled by single-task vs. multi-task models. We present the results for various sizes of training data and compare the performance. For each data size, the best result is highlighted in bold for each task.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Multi-task learning improves the performance of domain, intent, and slot classification tasks compared to single-task models, especially as the training size increases.Multi-task Learning (MTL) facilitates implicit knowledge transfer among the jointly-modeled tasks via parameter sharing, which is especially beneficial in low-resource settings. Such joint modeling techniques generally improve the performance of each learning task of the model, especially the ones with higher complexity. In this experiment, we created single-task models by separating domain, intent and slot classification tasks and training on the task specific data. We then compared single-task models with the model where MTL is used. We present the experiment results where we compare single and multi task models in Tab." } ], "images": [ "scigen/train/2406.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: F1 scores (%) of domain, intent and slot classification tasks modelled by single-task vs. multi-task models. We present the results for various sizes of training data and compare the performance. For each data size, the best result is highlighted in bold for each task." ], "reasoning": "Multi-task learning improves the performance of domain, intent, and slot classification tasks compared to single-task models, especially as the training size increases.", "description": "Multi-task Learning (MTL) facilitates implicit knowledge transfer among the jointly-modeled tasks via parameter sharing, which is especially beneficial in low-resource settings. Such joint modeling techniques generally improve the performance of each learning task of the model, especially the ones with higher complexity. In this experiment, we created single-task models by separating domain, intent and slot classification tasks and training on the task specific data. We then compared single-task models with the model where MTL is used. We present the experiment results where we compare single and multi task models in Tab.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: F1 scores (%) of domain, intent and slot classification tasks modelled by single-task vs. multi-task models. We present the results for various sizes of training data and compare the performance. For each data size, the best result is highlighted in bold for each task.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. \n\nQuestion: At what training size does the multi-task model for domain classification outperform the single-task model by the largest margin?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the training size where the multi-task model for domain classification outperforms the single-task model by the largest margin, we need to compare the differences in their performance across all training sizes. The largest difference is at 40K, where the multi-task model scores 86.25 and the single-task model scores 86.11, a difference of 0.14. So, the answer is {\"answer\": \"40K\"}." } ], "images": [ "scigen/train/2406.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: F1 scores (%) of domain, intent and slot classification tasks modelled by single-task vs. multi-task models. We present the results for various sizes of training data and compare the performance. For each data size, the best result is highlighted in bold for each task." ], "reasoning": "To find the training size where the multi-task model for domain classification outperforms the single-task model by the largest margin, we need to compare the differences in their performance across all training sizes. The largest difference is at 40K, where the multi-task model scores 86.25 and the single-task model scores 86.11, a difference of 0.14.", "question": "At what training size does the multi-task model for domain classification outperform the single-task model by the largest margin?", "answer": "So, the answer is {\"answer\": \"40K\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The multi-task model for slot classification always performs better than the single-task model for slot classification. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe multi-task model for slot classification always performs better than the single-task model for slot classification.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the performance of the multi-task model and the single-task model for slot classification across all training sizes. At 10K, the multi-task model scores 69.55, while the single-task model scores 68.20. At 20K, the multi-task model scores 72.96, while the single-task model scores 72.02. At 40K, the multi-task model scores 77.46, while the single-task model scores 76.03. At 80K, the multi-task model scores 80.23, while the single-task model scores 81.57. At 160K, the multi-task model scores 81.58, while the single-task model scores 78.34. The multi-task model does not always perform better, as seen at 80K. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2406.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: F1 scores (%) of domain, intent and slot classification tasks modelled by single-task vs. multi-task models. We present the results for various sizes of training data and compare the performance. For each data size, the best result is highlighted in bold for each task." ], "reasoning": "To verify this statement, we need to compare the performance of the multi-task model and the single-task model for slot classification across all training sizes. At 10K, the multi-task model scores 69.55, while the single-task model scores 68.20. At 20K, the multi-task model scores 72.96, while the single-task model scores 72.02. At 40K, the multi-task model scores 77.46, while the single-task model scores 76.03. At 80K, the multi-task model scores 80.23, while the single-task model scores 81.57. At 160K, the multi-task model scores 81.58, while the single-task model scores 78.34. The multi-task model does not always perform better, as seen at 80K.", "statement": "The multi-task model for slot classification always performs better than the single-task model for slot classification.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: F1 scores (%) of domain, intent and slot classification tasks modelled by random vs. fastText-init models. We present the results for various sizes of training data and compare the performance. For each data size, the best result is highlighted in bold for each task.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The FastText-init model consistently outperforms the randomly initialized model across all NLU tasks and training sizes, with the most significant improvements observed in the slot classification task.We present the comparison of the random vs FastText-init models in Tab. The FastText-init model performs better than the randomly initialized model for all NLU tasks and on all sizes of training data. We observe the biggest relative gains in the slot classification task which relies on robust word-embeddings, and benefits the most from pre-training even when trained with large amounts of data. This also shows that using character embeddings does not prevent us from taking advantage of the rich literature on word embeddings and using existing word embeddings to improve model performance." } ], "images": [ "scigen/train/2407.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: F1 scores (%) of domain, intent and slot classification tasks modelled by random vs. fastText-init models. We present the results for various sizes of training data and compare the performance. For each data size, the best result is highlighted in bold for each task." ], "reasoning": "The FastText-init model consistently outperforms the randomly initialized model across all NLU tasks and training sizes, with the most significant improvements observed in the slot classification task.", "description": "We present the comparison of the random vs FastText-init models in Tab. The FastText-init model performs better than the randomly initialized model for all NLU tasks and on all sizes of training data. We observe the biggest relative gains in the slot classification task which relies on robust word-embeddings, and benefits the most from pre-training even when trained with large amounts of data. This also shows that using character embeddings does not prevent us from taking advantage of the rich literature on word embeddings and using existing word embeddings to improve model performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: F1 scores (%) of domain, intent and slot classification tasks modelled by random vs. fastText-init models. We present the results for various sizes of training data and compare the performance. For each data size, the best result is highlighted in bold for each task.', and sequentially solve the question, writing out each step of your reasoning process. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model shows the highest performance in the slot classification task with a training size of 160K?\n" }, { "role": "assistant", "content": "The table shows that the FastText-init model achieves a score of 82.51 in the slot classification task with a training size of 160K, which is higher than the randomly initialized model's score of 81.58. So, the answer is {\"answer\": \"FastText-init model\"}." } ], "images": [ "scigen/train/2407.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: F1 scores (%) of domain, intent and slot classification tasks modelled by random vs. fastText-init models. We present the results for various sizes of training data and compare the performance. For each data size, the best result is highlighted in bold for each task." ], "reasoning": "The table shows that the FastText-init model achieves a score of 82.51 in the slot classification task with a training size of 160K, which is higher than the randomly initialized model's score of 81.58.", "question": "Which model shows the highest performance in the slot classification task with a training size of 160K?", "answer": "So, the answer is {\"answer\": \"FastText-init model\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe FastText-init model performs better than the randomly initialized model in the domain classification task with a training size of 40K.\n" }, { "role": "assistant", "content": "The table shows that with a training size of 40K, the FastText-init model achieves a score of 87.41 in the domain classification task, while the randomly initialized model achieves a score of 86.25. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2407.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: F1 scores (%) of domain, intent and slot classification tasks modelled by random vs. fastText-init models. We present the results for various sizes of training data and compare the performance. For each data size, the best result is highlighted in bold for each task." ], "reasoning": "The table shows that with a training size of 40K, the FastText-init model achieves a score of 87.41 in the domain classification task, while the randomly initialized model achieves a score of 86.25.", "statement": "The FastText-init model performs better than the randomly initialized model in the domain classification task with a training size of 40K.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Unlabeled parsing F1 on the MultiNLI development set for models trained on AllNLI. F1 wrt. shows F1 with respect to strictly right- and left-branching (LB/RB) trees and with respect to the Stanford Parser (SP) trees supplied with the corpus; The evaluations of SPINN, RL-SPINN, and ST-Gumbel are from Williams et al. (2018a). SPINN is a supervised parsing model, and the others are latent tree models. Median F1 of each model trained with 5 different random seeds is reported.'.\n" }, { "role": "assistant", "content": "PRPN demonstrates significant syntactic knowledge, aligning well with Penn Treebank grammar.We conclude that PRPN does acquire some substantial knowledge of syntax, and that this knowledge agrees with Penn Treebank (PTB) grammar significantly better than chance." } ], "images": [ "scigen/train/2408.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Unlabeled parsing F1 on the MultiNLI development set for models trained on AllNLI. F1 wrt. shows F1 with respect to strictly right- and left-branching (LB/RB) trees and with respect to the Stanford Parser (SP) trees supplied with the corpus; The evaluations of SPINN, RL-SPINN, and ST-Gumbel are from Williams et al. (2018a). SPINN is a supervised parsing model, and the others are latent tree models. Median F1 of each model trained with 5 different random seeds is reported." ], "reasoning": "PRPN demonstrates significant syntactic knowledge, aligning well with Penn Treebank grammar.", "description": "We conclude that PRPN does acquire some substantial knowledge of syntax, and that this knowledge agrees with Penn Treebank (PTB) grammar significantly better than chance.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: Unlabeled parsing F1 on the MultiNLI development set for models trained on AllNLI. F1 wrt. shows F1 with respect to strictly right- and left-branching (LB/RB) trees and with respect to the Stanford Parser (SP) trees supplied with the corpus; The evaluations of SPINN, RL-SPINN, and ST-Gumbel are from Williams et al. (2018a). SPINN is a supervised parsing model, and the others are latent tree models. Median F1 of each model trained with 5 different random seeds is reported.'. Solve the following question based on the table.\n\nQuestion: Which model has the highest F1 score with respect to LB and what is its depth?\nLet's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the F1 scores with respect to LB, the highest score is 99.1, achieved by the 300D RL-SPINN model, which has a depth of 8.6. So, the answer is {\"answer\": \"8.6\"}." } ], "images": [ "scigen/train/2408.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Unlabeled parsing F1 on the MultiNLI development set for models trained on AllNLI. F1 wrt. shows F1 with respect to strictly right- and left-branching (LB/RB) trees and with respect to the Stanford Parser (SP) trees supplied with the corpus; The evaluations of SPINN, RL-SPINN, and ST-Gumbel are from Williams et al. (2018a). SPINN is a supervised parsing model, and the others are latent tree models. Median F1 of each model trained with 5 different random seeds is reported." ], "reasoning": "By examining the F1 scores with respect to LB, the highest score is 99.1, achieved by the 300D RL-SPINN model, which has a depth of 8.6.", "question": "Which model has the highest F1 score with respect to LB and what is its depth?", "answer": "So, the answer is {\"answer\": \"8.6\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence PRPN-UP with LM stopping criterion has the highest F1 score with respect to SP. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nPRPN-UP with LM stopping criterion has the highest F1 score with respect to SP.\n" }, { "role": "assistant", "content": "The table shows that PRPN-UP with LM stopping criterion has an F1 score of 48.6 with respect to SP, which is the highest among all models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2408.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Unlabeled parsing F1 on the MultiNLI development set for models trained on AllNLI. F1 wrt. shows F1 with respect to strictly right- and left-branching (LB/RB) trees and with respect to the Stanford Parser (SP) trees supplied with the corpus; The evaluations of SPINN, RL-SPINN, and ST-Gumbel are from Williams et al. (2018a). SPINN is a supervised parsing model, and the others are latent tree models. Median F1 of each model trained with 5 different random seeds is reported." ], "reasoning": "The table shows that PRPN-UP with LM stopping criterion has an F1 score of 48.6 with respect to SP, which is the highest among all models listed.", "statement": "PRPN-UP with LM stopping criterion has the highest F1 score with respect to SP.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: The performance results for the two employed document representations', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Using a Naive Bayes model with Bag of Words representation resulted in lower precision, recall, and F1-measure compared to using a RandomForest model with Word2Vec representation.For the bag of word model, we represented each document by 5000 terms as features. We used 80 percent of randomly selected labeled dataset for training and its remaining as the test set. We applied a Naive Bayes model over the training set for classification. In the next experiment we used the document representation based on word2vec in our classification. We used word2vec for pretraining and extracting word vectors from a set of timelines of 7237 twitter users. Using the average vectors as described in the previous section we ended up with representing each document by a vector of length 200. Again we used 80 percent of labeled data for training and the remaining for testing. RandomForest model is used for classification. Beside these improvements we may also note that by using pre training we achieved the higher performance by using a highly dense vector representation of only 200 features." } ], "images": [ "scigen/train/2409.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The performance results for the two employed document representations" ], "reasoning": "Using a Naive Bayes model with Bag of Words representation resulted in lower precision, recall, and F1-measure compared to using a RandomForest model with Word2Vec representation.", "description": "For the bag of word model, we represented each document by 5000 terms as features. We used 80 percent of randomly selected labeled dataset for training and its remaining as the test set. We applied a Naive Bayes model over the training set for classification. In the next experiment we used the document representation based on word2vec in our classification. We used word2vec for pretraining and extracting word vectors from a set of timelines of 7237 twitter users. Using the average vectors as described in the previous section we ended up with representing each document by a vector of length 200. Again we used 80 percent of labeled data for training and the remaining for testing. RandomForest model is used for classification. Beside these improvements we may also note that by using pre training we achieved the higher performance by using a highly dense vector representation of only 200 features.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nTable title: Table 1: The performance results for the two employed document representations\n\nQuestion: Which document representation method achieved a higher F1-measure?\n" }, { "role": "assistant", "content": "The table shows that the F1-measure for Bag of Words is 0.74, while for Word2Vec it is 0.79. Therefore, Word2Vec achieved a higher F1-measure. So, the answer is {\"answer\": \"Word2Vec\"}." } ], "images": [ "scigen/train/2409.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The performance results for the two employed document representations" ], "reasoning": "The table shows that the F1-measure for Bag of Words is 0.74, while for Word2Vec it is 0.79. Therefore, Word2Vec achieved a higher F1-measure.", "question": "Which document representation method achieved a higher F1-measure?", "answer": "So, the answer is {\"answer\": \"Word2Vec\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Bag of Words model achieved a higher precision than the Word2Vec model.\n" }, { "role": "assistant", "content": "The table shows that the precision for Bag of Words is 0.69, while for Word2Vec it is 0.76. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2409.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The performance results for the two employed document representations" ], "reasoning": "The table shows that the precision for Bag of Words is 0.69, while for Word2Vec it is 0.76. Therefore, the statement is refuted.", "statement": "The Bag of Words model achieved a higher precision than the Word2Vec model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Conversational QA results on CoQA and QuAC, where (N-ctx) refers to using previous N QA pairs (%).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "FlowDeltaQA improves over FlowQA on both CoQA and QuAC datasets, and BERT-FlowDelta outperforms published models on the QuAC leaderboard as of April 24, 2019.It can be found that FlowDeltaQA yields substantial improvement over FlowQA on both datasets (+ 0.9 % F1 on both CoQA and QuAC), showing the usefulness of explicitly modeling the information gain in the Flow layer. Furthermore, BERT-FlowDelta outperforms the published models on QuAC leaderboard on Apr 24, 2019. Specifically, while BERT-FlowDelta achieves slightly worse HEQ-Q score on QuAC to the HAM model qu2019attentive, we outperform HAM in HEQ-D metrics, showing the superiority of our model in modeling whole dialogue. Note that FlowDelta actually introduced few additional parameters compared to Flow, since it only augments the input dimension of GRU. The consistent improvement from both data demonstrates the generalization capability of applying the proposed mechanism to various models." } ], "images": [ "scigen/train/2410.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Conversational QA results on CoQA and QuAC, where (N-ctx) refers to using previous N QA pairs (%)." ], "reasoning": "FlowDeltaQA improves over FlowQA on both CoQA and QuAC datasets, and BERT-FlowDelta outperforms published models on the QuAC leaderboard as of April 24, 2019.", "description": "It can be found that FlowDeltaQA yields substantial improvement over FlowQA on both datasets (+ 0.9 % F1 on both CoQA and QuAC), showing the usefulness of explicitly modeling the information gain in the Flow layer. Furthermore, BERT-FlowDelta outperforms the published models on QuAC leaderboard on Apr 24, 2019. Specifically, while BERT-FlowDelta achieves slightly worse HEQ-Q score on QuAC to the HAM model qu2019attentive, we outperform HAM in HEQ-D metrics, showing the superiority of our model in modeling whole dialogue. Note that FlowDelta actually introduced few additional parameters compared to Flow, since it only augments the input dimension of GRU. The consistent improvement from both data demonstrates the generalization capability of applying the proposed mechanism to various models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1: Conversational QA results on CoQA and QuAC, where (N-ctx) refers to using previous N QA pairs (%).', solve the subsequent question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model achieved the highest F1 score on the CoQA Dev dataset?\n" }, { "role": "assistant", "content": "By examining the CoQA Dev F1 scores in the table, the Human model achieved the highest F1 score of 89.8. So, the answer is {\"answer\": \"Human\"}" } ], "images": [ "scigen/train/2410.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Conversational QA results on CoQA and QuAC, where (N-ctx) refers to using previous N QA pairs (%)." ], "reasoning": "By examining the CoQA Dev F1 scores in the table, the Human model achieved the highest F1 score of 89.8.", "question": "Which model achieved the highest F1 score on the CoQA Dev dataset?", "answer": "So, the answer is {\"answer\": \"Human\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: FlowDeltaQA has a higher F1 score than FlowQA on the CoQA Test dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nFlowDeltaQA has a higher F1 score than FlowQA on the CoQA Test dataset.\n" }, { "role": "assistant", "content": "The table shows that FlowDeltaQA has an F1 score of 77.6 on the CoQA Test dataset, which is higher than FlowQA's F1 score of 75.0. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2410.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Conversational QA results on CoQA and QuAC, where (N-ctx) refers to using previous N QA pairs (%)." ], "reasoning": "The table shows that FlowDeltaQA has an F1 score of 77.6 on the CoQA Test dataset, which is higher than FlowQA's F1 score of 75.0.", "statement": "FlowDeltaQA has a higher F1 score than FlowQA on the CoQA Test dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: The ablation study of BERT-FlowDelta (%).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "FlowDelta improves performance more on QuAC due to frequent topic shifts, while vanilla BERT performs well on CoQA with long-term dialog history.It is interesting that the proposed inFlowDelta and exFlowDelta boost the performance more on QuAC. As yatskar2018qualitative mentioned, the topics in a dialogue shift more frequently on QuAC than on CoQA, and we can see vanilla BERT also performs well on CoQA in the ablation of Flow which provides long term dialog history information. Therefore, we can conclude that while FlowDelta improves the ability to grasp information gain in the dialog, it bring less performance improvement in the setting we do not need much contexts to answer the question." } ], "images": [ "scigen/train/2411.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The ablation study of BERT-FlowDelta (%)." ], "reasoning": "FlowDelta improves performance more on QuAC due to frequent topic shifts, while vanilla BERT performs well on CoQA with long-term dialog history.", "description": "It is interesting that the proposed inFlowDelta and exFlowDelta boost the performance more on QuAC. As yatskar2018qualitative mentioned, the topics in a dialogue shift more frequently on QuAC than on CoQA, and we can see vanilla BERT also performs well on CoQA in the ablation of Flow which provides long term dialog history information. Therefore, we can conclude that while FlowDelta improves the ability to grasp information gain in the dialog, it bring less performance improvement in the setting we do not need much contexts to answer the question.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: 'Table 2: The ablation study of BERT-FlowDelta (%).'\nQuestion: Which model has the highest F1 score on QuAC?\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all models on QuAC, BERT-FlowDelta has the highest score of 66.1. So, the answer is {\"answer\": \"BERT-FlowDelta\"}" } ], "images": [ "scigen/train/2411.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The ablation study of BERT-FlowDelta (%)." ], "reasoning": "By comparing the F1 scores of all models on QuAC, BERT-FlowDelta has the highest score of 66.1.", "question": "Which model has the highest F1 score on QuAC?", "answer": "So, the answer is {\"answer\": \"BERT-FlowDelta\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nBERT-FlowDelta has the highest F1 score on CoQA.\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all models on CoQA, BERT-FlowDelta has the highest score of 79.4. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2411.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The ablation study of BERT-FlowDelta (%)." ], "reasoning": "By comparing the F1 scores of all models on CoQA, BERT-FlowDelta has the highest score of 79.4.", "statement": "BERT-FlowDelta has the highest F1 score on CoQA.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Dialogue accuracy for SCONE test (%).'.\n" }, { "role": "assistant", "content": "Our model, FlowDeltaQA, outperforms FlowQA in the Scene and Tangrams domains, but shows no improvement in the Alchemy domain.Our model outperforms FlowQA and achieves the state-of-the-art in Scene and Tangrams domains. The small performance drop in Alchemy aligns well with the statement in the ablation study. Because experiments show that removing Flow affects performance in Alchemy less when comparing between FlowQA and FusionNet huang2017fusionnet (same models except Flow), we claim that the previous dialogue history is less important in this domain. Thus replaying Flow with FlowDelta does not bring any improvement in the Alchemy domain." } ], "images": [ "scigen/train/2412.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Dialogue accuracy for SCONE test (%)." ], "reasoning": "Our model, FlowDeltaQA, outperforms FlowQA in the Scene and Tangrams domains, but shows no improvement in the Alchemy domain.", "description": "Our model outperforms FlowQA and achieves the state-of-the-art in Scene and Tangrams domains. The small performance drop in Alchemy aligns well with the statement in the ablation study. Because experiments show that removing Flow affects performance in Alchemy less when comparing between FlowQA and FusionNet huang2017fusionnet (same models except Flow), we claim that the previous dialogue history is less important in this domain. Thus replaying Flow with FlowDelta does not bring any improvement in the Alchemy domain.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: Dialogue accuracy for SCONE test (%).' and methodically tackle the question that follows. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model achieves the highest score in the Scene domain and how does it compare to FlowQA in the Alchemy domain?\n" }, { "role": "assistant", "content": "FlowDeltaQA achieves the highest score in the Scene domain with 75.1 points. Comparing this to FlowQA in the Alchemy domain, FlowQA scores 76.4 points. So, the answer is {\"answer\": \"FlowDeltaQA\"}." } ], "images": [ "scigen/train/2412.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Dialogue accuracy for SCONE test (%)." ], "reasoning": "FlowDeltaQA achieves the highest score in the Scene domain with 75.1 points. Comparing this to FlowQA in the Alchemy domain, FlowQA scores 76.4 points.", "question": "Which model achieves the highest score in the Scene domain and how does it compare to FlowQA in the Alchemy domain?", "answer": "So, the answer is {\"answer\": \"FlowDeltaQA\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: FlowDeltaQA achieves the highest score in the Tangrams domain., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nFlowDeltaQA achieves the highest score in the Tangrams domain.\n" }, { "role": "assistant", "content": "The table shows that FlowDeltaQA scores 72.5 in the Tangrams domain, while FlowQA scores 72.3, making FlowDeltaQA the highest scoring model in this domain. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2412.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Dialogue accuracy for SCONE test (%)." ], "reasoning": "The table shows that FlowDeltaQA scores 72.5 in the Tangrams domain, while FlowQA scores 72.3, making FlowDeltaQA the highest scoring model in this domain.", "statement": "FlowDeltaQA achieves the highest score in the Tangrams domain.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: CoQA results of different variants of flow interaction. All models are provided with previous 1 gold answer.'.\n" }, { "role": "assistant", "content": "Using longer dialog history (SkipDelta) improves performance, but adding too much history (DoubleDelta) does not.The three variants are: SkipDelta: ht−1−ht−3 DoubleDelta: [ht−1−ht−2;ht−2−ht−3] Hadamard Product: ht−1∗ht−2 The reason to use SkipDelta and DoubleDelta is because we want to see if there is any benefit to incorporate longer (or more) dialog history. Experiment results show while using longer dialog history (i.e., SkipDelta) helps, adding too many dialog history (i.e., DoubleDelta) does not give any improvement." } ], "images": [ "scigen/train/2413.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: CoQA results of different variants of flow interaction. All models are provided with previous 1 gold answer." ], "reasoning": "Using longer dialog history (SkipDelta) improves performance, but adding too much history (DoubleDelta) does not.", "description": "The three variants are: SkipDelta: ht−1−ht−3 DoubleDelta: [ht−1−ht−2;ht−2−ht−3] Hadamard Product: ht−1∗ht−2 The reason to use SkipDelta and DoubleDelta is because we want to see if there is any benefit to incorporate longer (or more) dialog history. Experiment results show while using longer dialog history (i.e., SkipDelta) helps, adding too many dialog history (i.e., DoubleDelta) does not give any improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 4: CoQA results of different variants of flow interaction. All models are provided with previous 1 gold answer.', unfold the question presented below, detailing every step of your calculation. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model achieved the highest F1 score?\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all models in the table, FlowDeltaQA has the highest score of 77.6. So, the answer is {\"answer\": \"FlowDeltaQA\"}." } ], "images": [ "scigen/train/2413.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: CoQA results of different variants of flow interaction. All models are provided with previous 1 gold answer." ], "reasoning": "By comparing the F1 scores of all models in the table, FlowDeltaQA has the highest score of 77.6.", "question": "Which model achieved the highest F1 score?", "answer": "So, the answer is {\"answer\": \"FlowDeltaQA\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis FlowDeltaQA (Hadamard Product) achieved the highest F1 score. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nFlowDeltaQA (Hadamard Product) achieved the highest F1 score.\n" }, { "role": "assistant", "content": "The table shows that FlowDeltaQA achieved the highest F1 score of 77.6, while FlowDeltaQA (Hadamard Product) achieved 77.2. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2413.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: CoQA results of different variants of flow interaction. All models are provided with previous 1 gold answer." ], "reasoning": "The table shows that FlowDeltaQA achieved the highest F1 score of 77.6, while FlowDeltaQA (Hadamard Product) achieved 77.2.", "statement": "FlowDeltaQA (Hadamard Product) achieved the highest F1 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3.2: Information retrieval RCV1 results. The best binary results for each code dimensionality are highlighted.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The doctoral dissertation discusses the importance of feature engineering in traditional machine learning and presents results for Paragraph Vector and Binary Paragraph Vector models with different code sizes.AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted. Results for both Paragraph Vector and Binary Paragraph Vector for different code sizes are reported in Tab." } ], "images": [ "scigen/train/2414.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3.2: Information retrieval RCV1 results. The best binary results for each code dimensionality are highlighted." ], "reasoning": "The doctoral dissertation discusses the importance of feature engineering in traditional machine learning and presents results for Paragraph Vector and Binary Paragraph Vector models with different code sizes.", "description": "AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted. Results for both Paragraph Vector and Binary Paragraph Vector for different code sizes are reported in Tab.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3.2: Information retrieval RCV1 results. The best binary results for each code dimensionality are highlighted.', unfold the steps to solve the question given next. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nQuestion: What is the highest MAP value for the Binary PV-DBOW model with code dimensionality of 128?\n" }, { "role": "assistant", "content": "To find the highest MAP value for the Binary PV-DBOW model with code dimensionality of 128, look at the rows where the code dimensionality is 128 and the model is Binary PV-DBOW. The MAP values are 0.22 and 0.24. The highest value is 0.24. So, the answer is {\"answer\": \"0.24\"}." } ], "images": [ "scigen/train/2414.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3.2: Information retrieval RCV1 results. The best binary results for each code dimensionality are highlighted." ], "reasoning": "To find the highest MAP value for the Binary PV-DBOW model with code dimensionality of 128, look at the rows where the code dimensionality is 128 and the model is Binary PV-DBOW. The MAP values are 0.22 and 0.24. The highest value is 0.24.", "question": "What is the highest MAP value for the Binary PV-DBOW model with code dimensionality of 128?", "answer": "So, the answer is {\"answer\": \"0.24\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe highest NDCG@10 value for the PV-DBOW model with code dimensionality of 64 is 0.79.\n" }, { "role": "assistant", "content": "To verify this statement, look at the rows where the code dimensionality is 64 and the model is PV-DBOW. The NDCG@10 values are 0.77 and 0.79. The highest value is indeed 0.79. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2414.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3.2: Information retrieval RCV1 results. The best binary results for each code dimensionality are highlighted." ], "reasoning": "To verify this statement, look at the rows where the code dimensionality is 64 and the model is PV-DBOW. The NDCG@10 values are 0.77 and 0.79. The highest value is indeed 0.79.", "statement": "The highest NDCG@10 value for the PV-DBOW model with code dimensionality of 64 is 0.79.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3.1: Information retrieval 20 Newsgroups results. The best binary results for each code dimensionality are highlighted.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The doctoral dissertation focuses on vector representations of text data in deep learning, highlighting the challenges of feature engineering in traditional machine learning systems.AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted. Both Paragraph Vector and Binary Paragraph Vector results for different code sizes are reported in Tab." } ], "images": [ "scigen/train/2415.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3.1: Information retrieval 20 Newsgroups results. The best binary results for each code dimensionality are highlighted." ], "reasoning": "The doctoral dissertation focuses on vector representations of text data in deep learning, highlighting the challenges of feature engineering in traditional machine learning systems.", "description": "AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted. Both Paragraph Vector and Binary Paragraph Vector results for different code sizes are reported in Tab.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nTable title: Table 3.1: Information retrieval 20 Newsgroups results. The best binary results for each code dimensionality are highlighted.\n\nQuestion: What is the highest MAP score for the PV-DBOW model with code dimensionality of 128?\n" }, { "role": "assistant", "content": "To find the highest MAP score for the PV-DBOW model with code dimensionality of 128, look at the rows where the code dimensionality is 128 and the model is PV-DBOW. The highest MAP score among these rows is 0.45. So, the answer is {\"answer\": \"0.45\"}." } ], "images": [ "scigen/train/2415.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3.1: Information retrieval 20 Newsgroups results. The best binary results for each code dimensionality are highlighted." ], "reasoning": "To find the highest MAP score for the PV-DBOW model with code dimensionality of 128, look at the rows where the code dimensionality is 128 and the model is PV-DBOW. The highest MAP score among these rows is 0.45.", "question": "What is the highest MAP score for the PV-DBOW model with code dimensionality of 128?", "answer": "So, the answer is {\"answer\": \"0.45\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Binary PV-DBOW model with code dimensionality of 64 and inclusion of bigrams has a MAP score of 0.36. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Binary PV-DBOW model with code dimensionality of 64 and inclusion of bigrams has a MAP score of 0.36.\n" }, { "role": "assistant", "content": "The table shows that the Binary PV-DBOW model with code dimensionality of 64 and inclusion of bigrams has a MAP score of 0.36. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2415.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3.1: Information retrieval 20 Newsgroups results. The best binary results for each code dimensionality are highlighted." ], "reasoning": "The table shows that the Binary PV-DBOW model with code dimensionality of 64 and inclusion of bigrams has a MAP score of 0.36.", "statement": "The Binary PV-DBOW model with code dimensionality of 64 and inclusion of bigrams has a MAP score of 0.36.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3.3: Information retrieval results for English Wikipedia. The best binary results for each code dimensionality are highlighted.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table reports the results for Paragraph Vector and Binary Paragraph Vector models with different code sizes, including metrics like MAP and NDCG@10.AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted. Results for Paragraph Vector and Binary Paragraph Vector for different code sizes are reported in Tab." } ], "images": [ "scigen/train/2416.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3.3: Information retrieval results for English Wikipedia. The best binary results for each code dimensionality are highlighted." ], "reasoning": "The table reports the results for Paragraph Vector and Binary Paragraph Vector models with different code sizes, including metrics like MAP and NDCG@10.", "description": "AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted. Results for Paragraph Vector and Binary Paragraph Vector for different code sizes are reported in Tab.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3.3: Information retrieval results for English Wikipedia. The best binary results for each code dimensionality are highlighted.', and sequentially solve the question, writing out each step of your reasoning process. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model and code dimensionality combination achieved the highest NDCG@10 score?\n" }, { "role": "assistant", "content": "To find the highest NDCG@10 score, look through the NDCG@10 column and identify the highest value, which is 0.61. The corresponding model and code dimensionality are PV-DBOW with a code dimensionality of 300 and bigrams included. So, the answer is {\"answer\": \"PV-DBOW with 300 dimensionality and bigrams included\"}." } ], "images": [ "scigen/train/2416.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3.3: Information retrieval results for English Wikipedia. The best binary results for each code dimensionality are highlighted." ], "reasoning": "To find the highest NDCG@10 score, look through the NDCG@10 column and identify the highest value, which is 0.61. The corresponding model and code dimensionality are PV-DBOW with a code dimensionality of 300 and bigrams included.", "question": "Which model and code dimensionality combination achieved the highest NDCG@10 score?", "answer": "So, the answer is {\"answer\": \"PV-DBOW with 300 dimensionality and bigrams included\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Binary PV-DBOW model with a code dimensionality of 128 and bigrams included achieved an NDCG@10 score of 0.49.\n" }, { "role": "assistant", "content": "The table shows that the Binary PV-DBOW model with a code dimensionality of 128 and bigrams included has an NDCG@10 score of 0.49. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2416.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3.3: Information retrieval results for English Wikipedia. The best binary results for each code dimensionality are highlighted." ], "reasoning": "The table shows that the Binary PV-DBOW model with a code dimensionality of 128 and bigrams included has an NDCG@10 score of 0.49.", "statement": "The Binary PV-DBOW model with a code dimensionality of 128 and bigrams included achieved an NDCG@10 score of 0.49.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3.4: Comparison of performance of different binary units for 32 bit model trained on the 20 Newsgroups dataset.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The table compares different binarization approaches in terms of MAP and NDCG@10, showing that Krizhevsky & Hinton binarization achieves the highest scores.AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted. In Tab. In case of the former, we tried the approaches proposed by Salakhutdinov and Krizhevsky. In case of the latter, we examined straight-through gradient estimator in both described variants Model hyperparameters were selected separately for each binarization approach using hold-out validation set. Standard deviation of the Gaussian noise added to the pre-activation signal in the Salakhutdinov’s binarization is 0.4. Learning rate for that case is 1.0. Learning rates for BSN have higher values. They are 1.5 for Eq. In contrast to the Krizhevsky’s binarization, dropout does not improve the results of the Salakhutdinov’s binarization. Neither in case of BSNs. In variants that use slope annealing we increase the slope hiperparameter by 0.1 in each epoch. As can be concluded from the table Tab." } ], "images": [ "scigen/train/2417.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3.4: Comparison of performance of different binary units for 32 bit model trained on the 20 Newsgroups dataset." ], "reasoning": "The table compares different binarization approaches in terms of MAP and NDCG@10, showing that Krizhevsky & Hinton binarization achieves the highest scores.", "description": "AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted. In Tab. In case of the former, we tried the approaches proposed by Salakhutdinov and Krizhevsky. In case of the latter, we examined straight-through gradient estimator in both described variants Model hyperparameters were selected separately for each binarization approach using hold-out validation set. Standard deviation of the Gaussian noise added to the pre-activation signal in the Salakhutdinov’s binarization is 0.4. Learning rate for that case is 1.0. Learning rates for BSN have higher values. They are 1.5 for Eq. In contrast to the Krizhevsky’s binarization, dropout does not improve the results of the Salakhutdinov’s binarization. Neither in case of BSNs. In variants that use slope annealing we increase the slope hiperparameter by 0.1 in each epoch. As can be concluded from the table Tab.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3.4: Comparison of performance of different binary units for 32 bit model trained on the 20 Newsgroups dataset.\nQuestion: Which binarization approach achieved the highest NDCG@10 score?\nSolve the above question based on the table information. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the NDCG@10 column in the table, we see that the Krizhevsky & Hinton binarization approach has the highest score of 0.54. So, the answer is {\"answer\": \"Krizhevsky & Hinton binarization\"}." } ], "images": [ "scigen/train/2417.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3.4: Comparison of performance of different binary units for 32 bit model trained on the 20 Newsgroups dataset." ], "reasoning": "By examining the NDCG@10 column in the table, we see that the Krizhevsky & Hinton binarization approach has the highest score of 0.54.", "question": "Which binarization approach achieved the highest NDCG@10 score?", "answer": "So, the answer is {\"answer\": \"Krizhevsky & Hinton binarization\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe BSN with Eq. 3.5 straight-through gradient estimator achieved a higher MAP score than the Krizhevsky & Hinton binarization.\n" }, { "role": "assistant", "content": "The table shows that the Krizhevsky & Hinton binarization has a MAP score of 0.32, while the BSN with Eq. 3.5 straight-through gradient estimator has a MAP score of 0.25, which is lower. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2417.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3.4: Comparison of performance of different binary units for 32 bit model trained on the 20 Newsgroups dataset." ], "reasoning": "The table shows that the Krizhevsky & Hinton binarization has a MAP score of 0.32, while the BSN with Eq. 3.5 straight-through gradient estimator has a MAP score of 0.25, which is lower.", "statement": "The BSN with Eq. 3.5 straight-through gradient estimator achieved a higher MAP score than the Krizhevsky & Hinton binarization.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3.5: Information retrieval results for 32-bit binary codes constructed by first inferring 32d or 300d real-valued paragraph vectors and then employing another unsupervised model or hashing algorithm for binarization. Paragraph vectors were inferred using PV-DBOW with bigrams.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Feature engineering in traditional machine learning is time-consuming and crucial for performance, but deep learning with vector representations of text data can alleviate this bottleneck.AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted." } ], "images": [ "scigen/train/2418.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3.5: Information retrieval results for 32-bit binary codes constructed by first inferring 32d or 300d real-valued paragraph vectors and then employing another unsupervised model or hashing algorithm for binarization. Paragraph vectors were inferred using PV-DBOW with bigrams." ], "reasoning": "Feature engineering in traditional machine learning is time-consuming and crucial for performance, but deep learning with vector representations of text data can alleviate this bottleneck.", "description": "AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 3.5: Information retrieval results for 32-bit binary codes constructed by first inferring 32d or 300d real-valued paragraph vectors and then employing another unsupervised model or hashing algorithm for binarization. Paragraph vectors were inferred using PV-DBOW with bigrams.\nQuestion: Which binarization model with an embedding size of 300 has the highest 20 Newsgroups NDCG@10 score?\n" }, { "role": "assistant", "content": "From the table, we compare the 20 Newsgroups NDCG@10 scores for models with an embedding size of 300. The highest score is 0.58, achieved by Iterative quantization. So, the answer is {\"answer\": \"Iterative quantization\"}." } ], "images": [ "scigen/train/2418.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3.5: Information retrieval results for 32-bit binary codes constructed by first inferring 32d or 300d real-valued paragraph vectors and then employing another unsupervised model or hashing algorithm for binarization. Paragraph vectors were inferred using PV-DBOW with bigrams." ], "reasoning": "From the table, we compare the 20 Newsgroups NDCG@10 scores for models with an embedding size of 300. The highest score is 0.58, achieved by Iterative quantization.", "question": "Which binarization model with an embedding size of 300 has the highest 20 Newsgroups NDCG@10 score?", "answer": "So, the answer is {\"answer\": \"Iterative quantization\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Random hyperplane projection (SimHash) model with an embedding size of 300 has the lowest RCV1 MAP score. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Random hyperplane projection (SimHash) model with an embedding size of 300 has the lowest RCV1 MAP score.\n" }, { "role": "assistant", "content": "From the table, the RCV1 MAP scores for models with an embedding size of 300 are compared. The Random hyperplane projection (SimHash) model has the lowest score of 0.17. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2418.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3.5: Information retrieval results for 32-bit binary codes constructed by first inferring 32d or 300d real-valued paragraph vectors and then employing another unsupervised model or hashing algorithm for binarization. Paragraph vectors were inferred using PV-DBOW with bigrams." ], "reasoning": "From the table, the RCV1 MAP scores for models with an embedding size of 300 are compared. The Random hyperplane projection (SimHash) model has the lowest score of 0.17.", "statement": "The Random hyperplane projection (SimHash) model with an embedding size of 300 has the lowest RCV1 MAP score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3.6: Information retrieval results for transfer learning for 128-dimensional binary codes.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Feature engineering is a critical and time-consuming task in traditional machine learning, and poor feature selection can lead to suboptimal performance.AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted." } ], "images": [ "scigen/train/2419.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3.6: Information retrieval results for transfer learning for 128-dimensional binary codes." ], "reasoning": "Feature engineering is a critical and time-consuming task in traditional machine learning, and poor feature selection can lead to suboptimal performance.", "description": "AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 3.6: Information retrieval results for transfer learning for 128-dimensional binary codes.', solve the subsequent question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhich dataset has a higher NDCG@10 score?\n" }, { "role": "assistant", "content": "The table shows the NDCG@10 scores for two datasets: 20 Newsgroups and RCV1. Comparing the scores, RCV1 has a higher NDCG@10 score of 0.66 compared to 0.51 for 20 Newsgroups. So, the answer is {\"answer\": \"RCV1\"}." } ], "images": [ "scigen/train/2419.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3.6: Information retrieval results for transfer learning for 128-dimensional binary codes." ], "reasoning": "The table shows the NDCG@10 scores for two datasets: 20 Newsgroups and RCV1. Comparing the scores, RCV1 has a higher NDCG@10 score of 0.66 compared to 0.51 for 20 Newsgroups.", "question": "Which dataset has a higher NDCG@10 score?", "answer": "So, the answer is {\"answer\": \"RCV1\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe MAP score for the 20 Newsgroups dataset is higher than that for the RCV1 dataset.\n" }, { "role": "assistant", "content": "The table shows the MAP scores for both datasets. The MAP score for 20 Newsgroups is 0.19, while the MAP score for RCV1 is 0.18. Therefore, the statement is correct. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2419.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3.6: Information retrieval results for transfer learning for 128-dimensional binary codes." ], "reasoning": "The table shows the MAP scores for both datasets. The MAP score for 20 Newsgroups is 0.19, while the MAP score for RCV1 is 0.18. Therefore, the statement is correct.", "statement": "The MAP score for the 20 Newsgroups dataset is higher than that for the RCV1 dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3.7: Information retrieval results for the Real-Binary PV-DBOW model. Real valued representations have 300 dimensions. (A) Binary codes are used for selecting documents within a given Hamming distance to the query and real-valued representations are used for ranking. (B) For comparison, variant A was repeated with binary codes inferred using plain Binary PV-DBOW and a real-valued representation inferred using original PV-DBOW model.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The doctoral dissertation evaluates the Real-Binary PV-DBOW model using NDCG@10 on text datasets like 20 Newsgroups, RCV1, and English Wikipedia, highlighting the importance of feature engineering in machine learning.AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted. We evaluated the Real-Binary PV-DBOW using the same text datasets as in the case of Binary PV-DBOW, namely 20 Newsgroups, RCV1 and English Wikipedia. The only metric that we report in this evaluation is NDCG@10. We do not report MAP, because MAP is estimated using the whole recall range and the inherent trait of the filtering approach is that high recall levels may not be accessible. In Tab." } ], "images": [ "scigen/train/2420.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3.7: Information retrieval results for the Real-Binary PV-DBOW model. Real valued representations have 300 dimensions. (A) Binary codes are used for selecting documents within a given Hamming distance to the query and real-valued representations are used for ranking. (B) For comparison, variant A was repeated with binary codes inferred using plain Binary PV-DBOW and a real-valued representation inferred using original PV-DBOW model." ], "reasoning": "The doctoral dissertation evaluates the Real-Binary PV-DBOW model using NDCG@10 on text datasets like 20 Newsgroups, RCV1, and English Wikipedia, highlighting the importance of feature engineering in machine learning.", "description": "AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted. We evaluated the Real-Binary PV-DBOW using the same text datasets as in the case of Binary PV-DBOW, namely 20 Newsgroups, RCV1 and English Wikipedia. The only metric that we report in this evaluation is NDCG@10. We do not report MAP, because MAP is estimated using the whole recall range and the inherent trait of the filtering approach is that high recall levels may not be accessible. In Tab.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 3.7: Information retrieval results for the Real-Binary PV-DBOW model. Real valued representations have 300 dimensions. (A) Binary codes are used for selecting documents within a given Hamming distance to the query and real-valued representations are used for ranking. (B) For comparison, variant A was repeated with binary codes inferred using plain Binary PV-DBOW and a real-valued representation inferred using original PV-DBOW model.'.\n\nQuestion: What is the NDCG@10 score for the 20 Newsgroups dataset with a code size of 24 and radius of 2?\n" }, { "role": "assistant", "content": "To find the NDCG@10 score for the 20 Newsgroups dataset with a code size of 24 and radius of 2, locate the row with code size 24 and radius 2, then find the corresponding NDCG@10 score under the 20 Newsgroups column. So, the answer is {\"answer\": \"0.79\"}." } ], "images": [ "scigen/train/2420.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3.7: Information retrieval results for the Real-Binary PV-DBOW model. Real valued representations have 300 dimensions. (A) Binary codes are used for selecting documents within a given Hamming distance to the query and real-valued representations are used for ranking. (B) For comparison, variant A was repeated with binary codes inferred using plain Binary PV-DBOW and a real-valued representation inferred using original PV-DBOW model." ], "reasoning": "To find the NDCG@10 score for the 20 Newsgroups dataset with a code size of 24 and radius of 2, locate the row with code size 24 and radius 2, then find the corresponding NDCG@10 score under the 20 Newsgroups column.", "question": "What is the NDCG@10 score for the 20 Newsgroups dataset with a code size of 24 and radius of 2?", "answer": "So, the answer is {\"answer\": \"0.79\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe NDCG@10 score for the English Wikipedia dataset with a code size of 20 and radius of 3 is 0.42.\n" }, { "role": "assistant", "content": "To verify this statement, locate the row with code size 20 and radius 3, then check the NDCG@10 score under the English Wikipedia column. The score is indeed 0.42. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2420.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3.7: Information retrieval results for the Real-Binary PV-DBOW model. Real valued representations have 300 dimensions. (A) Binary codes are used for selecting documents within a given Hamming distance to the query and real-valued representations are used for ranking. (B) For comparison, variant A was repeated with binary codes inferred using plain Binary PV-DBOW and a real-valued representation inferred using original PV-DBOW model." ], "reasoning": "To verify this statement, locate the row with code size 20 and radius 3, then check the NDCG@10 score under the English Wikipedia column. The score is indeed 0.42.", "statement": "The NDCG@10 score for the English Wikipedia dataset with a code size of 20 and radius of 3 is 0.42.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4.1: Marginal probabilities and nearest neighbors for selected words from the vocabulary. Codes learned with a 3-sense, 300-dimensional Disambiguated Skip-gram model trained with different values of the entropy cost γ. The neighbors are selected are presented only for senses with a marginal probability higher than 0.05. (Part I)', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The doctoral dissertation by Karol Grzegorczyk at AGH University of Science and Technology focuses on vector representations of text data in deep learning, highlighting the challenges of feature engineering in traditional machine learning systems.AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted." } ], "images": [ "scigen/train/2421.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4.1: Marginal probabilities and nearest neighbors for selected words from the vocabulary. Codes learned with a 3-sense, 300-dimensional Disambiguated Skip-gram model trained with different values of the entropy cost γ. The neighbors are selected are presented only for senses with a marginal probability higher than 0.05. (Part I)" ], "reasoning": "The doctoral dissertation by Karol Grzegorczyk at AGH University of Science and Technology focuses on vector representations of text data in deep learning, highlighting the challenges of feature engineering in traditional machine learning systems.", "description": "AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 4.1: Marginal probabilities and nearest neighbors for selected words from the vocabulary. Codes learned with a 3-sense, 300-dimensional Disambiguated Skip-gram model trained with different values of the entropy cost γ. The neighbors are selected are presented only for senses with a marginal probability higher than 0.05. (Part I)\nQuestion: What is the Sense 2 Nearest Neighbors for the word 'rock' with γ value of 0.75?\n" }, { "role": "assistant", "content": "To find the Sense 2 Nearest Neighbors for the word 'rock' with γ value of 0.75, locate the row where 'rock' has a γ value of 0.75 and then check the Sense 2 Nearest Neighbors column. So, the answer is {\"answer\": \"basalt boulders quartzite\"}" } ], "images": [ "scigen/train/2421.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4.1: Marginal probabilities and nearest neighbors for selected words from the vocabulary. Codes learned with a 3-sense, 300-dimensional Disambiguated Skip-gram model trained with different values of the entropy cost γ. The neighbors are selected are presented only for senses with a marginal probability higher than 0.05. (Part I)" ], "reasoning": "To find the Sense 2 Nearest Neighbors for the word 'rock' with γ value of 0.75, locate the row where 'rock' has a γ value of 0.75 and then check the Sense 2 Nearest Neighbors column.", "question": "What is the Sense 2 Nearest Neighbors for the word 'rock' with γ value of 0.75?", "answer": "So, the answer is {\"answer\": \"basalt boulders quartzite\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe word 'apple' with γ value of 0.5 has 'macintosh blackberry' as its Sense 2 Nearest Neighbors.\n" }, { "role": "assistant", "content": "Check the table for the word 'apple' with γ value of 0.5 and verify the Sense 2 Nearest Neighbors column. The table shows 'macintosh blackberry' as the Sense 2 Nearest Neighbors for 'apple' with γ value of 0.5. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2421.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4.1: Marginal probabilities and nearest neighbors for selected words from the vocabulary. Codes learned with a 3-sense, 300-dimensional Disambiguated Skip-gram model trained with different values of the entropy cost γ. The neighbors are selected are presented only for senses with a marginal probability higher than 0.05. (Part I)" ], "reasoning": "Check the table for the word 'apple' with γ value of 0.5 and verify the Sense 2 Nearest Neighbors column. The table shows 'macintosh blackberry' as the Sense 2 Nearest Neighbors for 'apple' with γ value of 0.5.", "statement": "The word 'apple' with γ value of 0.5 has 'macintosh blackberry' as its Sense 2 Nearest Neighbors.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4.2: Marginal probabilities and nearest neighbors for selected words from the vocabulary. Codes learned with a 3-sense, 300-dimensional Disambiguated Skip-gram model trained with different values of the entropy cost γ. The neighbors are selected are presented only for senses with a marginal probability higher than 0.05. (Part II)'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Feature engineering is a critical and time-consuming task in traditional machine learning, and the model accurately discovers different senses of words based on their context.AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted. and Tab. In most cases our model discovers senses accurately. For example, in the case of ‘fox’, the first sense for the model without the entropy cost is a broadcasting company, the second is an animal and the third is a family name. In the case of ‘mouse’, the first sense is a cartoon character, the second is a pointing device and the third is an animal. We should note here that most of the words from Tab. In those cases, one of the senses is usually split by our model into two. However, the split is not random but corresponds to different contexts in which those senses occur. For example both sense 1 and sense 3 of word ‘plant’ in Tab. However, sense 1 is a agricultural factory while sense 3 is a petrochemical factory." } ], "images": [ "scigen/train/2422.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4.2: Marginal probabilities and nearest neighbors for selected words from the vocabulary. Codes learned with a 3-sense, 300-dimensional Disambiguated Skip-gram model trained with different values of the entropy cost γ. The neighbors are selected are presented only for senses with a marginal probability higher than 0.05. (Part II)" ], "reasoning": "Feature engineering is a critical and time-consuming task in traditional machine learning, and the model accurately discovers different senses of words based on their context.", "description": "AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted. and Tab. In most cases our model discovers senses accurately. For example, in the case of ‘fox’, the first sense for the model without the entropy cost is a broadcasting company, the second is an animal and the third is a family name. In the case of ‘mouse’, the first sense is a cartoon character, the second is a pointing device and the third is an animal. We should note here that most of the words from Tab. In those cases, one of the senses is usually split by our model into two. However, the split is not random but corresponds to different contexts in which those senses occur. For example both sense 1 and sense 3 of word ‘plant’ in Tab. However, sense 1 is a agricultural factory while sense 3 is a petrochemical factory.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4.2: Marginal probabilities and nearest neighbors for selected words from the vocabulary. Codes learned with a 3-sense, 300-dimensional Disambiguated Skip-gram model trained with different values of the entropy cost γ. The neighbors are selected are presented only for senses with a marginal probability higher than 0.05. (Part II)', solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the probability of the first sense of the word 'mouse' when the nearest neighbors are 'rabbit', 'goofy', and 'cat'?\n" }, { "role": "assistant", "content": "From the table, the word 'mouse' with the nearest neighbors 'rabbit', 'goofy', and 'cat' has a first sense probability (Sense 1 P) of 0.49. So, the answer is {\"answer\": \"0.49\"}" } ], "images": [ "scigen/train/2422.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4.2: Marginal probabilities and nearest neighbors for selected words from the vocabulary. Codes learned with a 3-sense, 300-dimensional Disambiguated Skip-gram model trained with different values of the entropy cost γ. The neighbors are selected are presented only for senses with a marginal probability higher than 0.05. (Part II)" ], "reasoning": "From the table, the word 'mouse' with the nearest neighbors 'rabbit', 'goofy', and 'cat' has a first sense probability (Sense 1 P) of 0.49.", "question": "What is the probability of the first sense of the word 'mouse' when the nearest neighbors are 'rabbit', 'goofy', and 'cat'?", "answer": "So, the answer is {\"answer\": \"0.49\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The word 'light' has a sense with the nearest neighbors 'sunlight', 'illumination', and 'ibo'., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe word 'light' has a sense with the nearest neighbors 'sunlight', 'illumination', and 'ibo'.\n" }, { "role": "assistant", "content": "The table shows that the word 'light' has a sense with the nearest neighbors 'shade', 'reflection', and 'ibo', but not 'sunlight' and 'illumination' together with 'ibo'. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2422.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4.2: Marginal probabilities and nearest neighbors for selected words from the vocabulary. Codes learned with a 3-sense, 300-dimensional Disambiguated Skip-gram model trained with different values of the entropy cost γ. The neighbors are selected are presented only for senses with a marginal probability higher than 0.05. (Part II)" ], "reasoning": "The table shows that the word 'light' has a sense with the nearest neighbors 'shade', 'reflection', and 'ibo', but not 'sunlight' and 'illumination' together with 'ibo'.", "statement": "The word 'light' has a sense with the nearest neighbors 'sunlight', 'illumination', and 'ibo'.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4.3: Average number of senses per word with marginal probability p≥0.05, learned by Disambiguated Skip-gram models with different values of the entropy cost γ.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The average number of active senses decreases as the entropy cost (γ) increases, with a strong γ=1.0 resulting in an average of 2.5 active senses.AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted. we present histograms of marginal probabilities of senses of all words from the vocabulary. As depicted, in the case there is no entropy cost (black curve) marginal probabilites are distributed quite evenly: there are very few word senses with probabilities close to 0.0 or 1.0. For higher values of the entropy cost, marginal probability distributions are more polarized, i.e. there are more word senses with probabilities close to 0.0 or 1.0. Assuming that we prune senses having probabilities smaller than 0.05, we get on average 4.7 active senses for model without a penalty and as low as 2.5 senses on average for model with a strong γ=1.0 entropy cost ( Tab." } ], "images": [ "scigen/train/2423.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4.3: Average number of senses per word with marginal probability p≥0.05, learned by Disambiguated Skip-gram models with different values of the entropy cost γ." ], "reasoning": "The average number of active senses decreases as the entropy cost (γ) increases, with a strong γ=1.0 resulting in an average of 2.5 active senses.", "description": "AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted. we present histograms of marginal probabilities of senses of all words from the vocabulary. As depicted, in the case there is no entropy cost (black curve) marginal probabilites are distributed quite evenly: there are very few word senses with probabilities close to 0.0 or 1.0. For higher values of the entropy cost, marginal probability distributions are more polarized, i.e. there are more word senses with probabilities close to 0.0 or 1.0. Assuming that we prune senses having probabilities smaller than 0.05, we get on average 4.7 active senses for model without a penalty and as low as 2.5 senses on average for model with a strong γ=1.0 entropy cost ( Tab.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 4.3: Average number of senses per word with marginal probability p≥0.05, learned by Disambiguated Skip-gram models with different values of the entropy cost γ.' and methodically tackle the question that follows. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the average number of active senses when the entropy cost γ is 0.5?\n" }, { "role": "assistant", "content": "The table shows the average sense number for different values of γ. For γ=0.5, the average sense number is 3.2. So, the answer is {\"answer\": \"3.2\"}." } ], "images": [ "scigen/train/2423.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4.3: Average number of senses per word with marginal probability p≥0.05, learned by Disambiguated Skip-gram models with different values of the entropy cost γ." ], "reasoning": "The table shows the average sense number for different values of γ. For γ=0.5, the average sense number is 3.2.", "question": "What is the average number of active senses when the entropy cost γ is 0.5?", "answer": "So, the answer is {\"answer\": \"3.2\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe average number of active senses is highest when the entropy cost γ is 0.0.\n" }, { "role": "assistant", "content": "The table indicates that the average sense number is 4.7 when γ=0.0, which is the highest value compared to other γ values. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2423.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4.3: Average number of senses per word with marginal probability p≥0.05, learned by Disambiguated Skip-gram models with different values of the entropy cost γ." ], "reasoning": "The table indicates that the average sense number is 4.7 when γ=0.0, which is the highest value compared to other γ values.", "statement": "The average number of active senses is highest when the entropy cost γ is 0.0.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4.4: Adjusted rand index for the Disambiguated Skip-gram model with different dimensionalities, sense numbers and different regularization terms, evaluated on different test datasets. The best results for each dataset and for each dimensionality are highlighted.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The doctoral dissertation discusses the importance of vector representations of text data in deep learning, highlighting the challenges of feature engineering in traditional machine learning systems.AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted. -A1S1SS5: SemEval-2007, SemEval-2010, SemEval-2013 and Wikipedia Word-sense Induction (WWSI). To disambiguate words from those datasets we first calculate vector representations of associated contexts by averaging all sense embeddings for all context words: ¯cw=(k⋅#Cw)−1∑c∈Cwk∑s=1vcs. (4.17) Then, we select a sense of the center word whose vector representation has the smallest cosine distance to the context representation: sw=argmaxjcos(vwj,¯cw). Results for different dimensionalities, number of senses and different model regularizers are presented in Tab." } ], "images": [ "scigen/train/2424.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4.4: Adjusted rand index for the Disambiguated Skip-gram model with different dimensionalities, sense numbers and different regularization terms, evaluated on different test datasets. The best results for each dataset and for each dimensionality are highlighted." ], "reasoning": "The doctoral dissertation discusses the importance of vector representations of text data in deep learning, highlighting the challenges of feature engineering in traditional machine learning systems.", "description": "AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted. -A1S1SS5: SemEval-2007, SemEval-2010, SemEval-2013 and Wikipedia Word-sense Induction (WWSI). To disambiguate words from those datasets we first calculate vector representations of associated contexts by averaging all sense embeddings for all context words: ¯cw=(k⋅#Cw)−1∑c∈Cwk∑s=1vcs. (4.17) Then, we select a sense of the center word whose vector representation has the smallest cosine distance to the context representation: sw=argmaxjcos(vwj,¯cw). Results for different dimensionalities, number of senses and different model regularizers are presented in Tab.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 4.4: Adjusted rand index for the Disambiguated Skip-gram model with different dimensionalities, sense numbers and different regularization terms, evaluated on different test datasets. The best results for each dataset and for each dimensionality are highlighted.'.\n\nQuestion: What is the highest score achieved in the SemEval-2010 dataset for a model with 300 dimensions and 5 senses?\n" }, { "role": "assistant", "content": "To find the highest score in the SemEval-2010 dataset for a model with 300 dimensions and 5 senses, look at the 'SemEval-2010' column for rows where 'Dim.' is 300 and 'Sense num.' is 5. The highest score is 0.121. So, the answer is {\"answer\": \"0.121\"}." } ], "images": [ "scigen/train/2424.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4.4: Adjusted rand index for the Disambiguated Skip-gram model with different dimensionalities, sense numbers and different regularization terms, evaluated on different test datasets. The best results for each dataset and for each dimensionality are highlighted." ], "reasoning": "To find the highest score in the SemEval-2010 dataset for a model with 300 dimensions and 5 senses, look at the 'SemEval-2010' column for rows where 'Dim.' is 300 and 'Sense num.' is 5. The highest score is 0.121.", "question": "What is the highest score achieved in the SemEval-2010 dataset for a model with 300 dimensions and 5 senses?", "answer": "So, the answer is {\"answer\": \"0.121\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model with 50 dimensions and 3 senses achieved the highest score in the WWSI dataset.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'WWSI' column for rows where 'Dim.' is 50 and 'Sense num.' is 3. The highest score in this subset is 0.28. However, the highest score in the entire 'WWSI' column is 0.296, achieved by a model with 300 dimensions and 5 senses. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2424.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4.4: Adjusted rand index for the Disambiguated Skip-gram model with different dimensionalities, sense numbers and different regularization terms, evaluated on different test datasets. The best results for each dataset and for each dimensionality are highlighted." ], "reasoning": "To verify this statement, look at the 'WWSI' column for rows where 'Dim.' is 50 and 'Sense num.' is 3. The highest score in this subset is 0.28. However, the highest score in the entire 'WWSI' column is 0.296, achieved by a model with 300 dimensions and 5 senses. Therefore, the statement is refuted.", "statement": "The model with 50 dimensions and 3 senses achieved the highest score in the WWSI dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4.5: Adjusted rand index for different 300-dimensional multi-sense word embedding models. Disambiguated Skip-gram was trained with 5 senses, without neither entropy cost nor parallel penalty. MSSG and MPSG were trained with 3 senses. AdaGram was trained with α=0.15. Results for all models except Disambiguated Skip-gram are taken from [Bartunov et al., 2016].'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Feature engineering is a bottleneck in machine learning, but using vector representations of text data in deep learning can improve performance.AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted. The best results for each dataset and for each dimensionality are highlighted. In most cases adding a small entropy cost or parallel penalty in fine-tuning has a positive effect. Comparison with other multi-sense word embedding models is presented in Tab." } ], "images": [ "scigen/train/2425.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4.5: Adjusted rand index for different 300-dimensional multi-sense word embedding models. Disambiguated Skip-gram was trained with 5 senses, without neither entropy cost nor parallel penalty. MSSG and MPSG were trained with 3 senses. AdaGram was trained with α=0.15. Results for all models except Disambiguated Skip-gram are taken from [Bartunov et al., 2016]." ], "reasoning": "Feature engineering is a bottleneck in machine learning, but using vector representations of text data in deep learning can improve performance.", "description": "AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted. The best results for each dataset and for each dimensionality are highlighted. In most cases adding a small entropy cost or parallel penalty in fine-tuning has a positive effect. Comparison with other multi-sense word embedding models is presented in Tab.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 4.5: Adjusted rand index for different 300-dimensional multi-sense word embedding models. Disambiguated Skip-gram was trained with 5 senses, without neither entropy cost nor parallel penalty. MSSG and MPSG were trained with 3 senses. AdaGram was trained with α=0.15. Results for all models except Disambiguated Skip-gram are taken from [Bartunov et al., 2016].', answer the question below by showing each progressive step toward the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieved the highest score in the SemEval-2010 dataset?\n" }, { "role": "assistant", "content": "By examining the SemEval-2010 column, the Disambiguated Skip-gram model has the highest score of 0.117. So, the answer is {\"answer\": \"Disambiguated Skip-gram\"}." } ], "images": [ "scigen/train/2425.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4.5: Adjusted rand index for different 300-dimensional multi-sense word embedding models. Disambiguated Skip-gram was trained with 5 senses, without neither entropy cost nor parallel penalty. MSSG and MPSG were trained with 3 senses. AdaGram was trained with α=0.15. Results for all models except Disambiguated Skip-gram are taken from [Bartunov et al., 2016]." ], "reasoning": "By examining the SemEval-2010 column, the Disambiguated Skip-gram model has the highest score of 0.117.", "question": "Which model achieved the highest score in the SemEval-2010 dataset?", "answer": "So, the answer is {\"answer\": \"Disambiguated Skip-gram\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The AdaGram model achieved the highest score in the WWWSI dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe AdaGram model achieved the highest score in the WWWSI dataset.\n" }, { "role": "assistant", "content": "By examining the WWWSI column, the Disambiguated Skip-gram model has the highest score of 0.292, not AdaGram. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2425.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4.5: Adjusted rand index for different 300-dimensional multi-sense word embedding models. Disambiguated Skip-gram was trained with 5 senses, without neither entropy cost nor parallel penalty. MSSG and MPSG were trained with 3 senses. AdaGram was trained with α=0.15. Results for all models except Disambiguated Skip-gram are taken from [Bartunov et al., 2016]." ], "reasoning": "By examining the WWWSI column, the Disambiguated Skip-gram model has the highest score of 0.292, not AdaGram.", "statement": "The AdaGram model achieved the highest score in the WWWSI dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4.6: Spearman’s correlation coefficient multiplied by 100 for different 300-dimensional models evaluated on the SCWS dataset. Disambiguated Skip-gram, MSSG and MPSG were trained with 3 senses. AdaGram was trained with α=0.15. Metrics definitions and results for all models except Disambiguated Skip-gram taken from [Bartunov et al., 2016].'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Feature engineering is a critical and time-consuming task in traditional machine learning, and poorly selected features can lead to poor performance.AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted." } ], "images": [ "scigen/train/2426.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4.6: Spearman’s correlation coefficient multiplied by 100 for different 300-dimensional models evaluated on the SCWS dataset. Disambiguated Skip-gram, MSSG and MPSG were trained with 3 senses. AdaGram was trained with α=0.15. Metrics definitions and results for all models except Disambiguated Skip-gram taken from [Bartunov et al., 2016]." ], "reasoning": "Feature engineering is a critical and time-consuming task in traditional machine learning, and poorly selected features can lead to poor performance.", "description": "AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nTable title: Table 4.6: Spearman’s correlation coefficient multiplied by 100 for different 300-dimensional models evaluated on the SCWS dataset. Disambiguated Skip-gram, MSSG and MPSG were trained with 3 senses. AdaGram was trained with α=0.15. Metrics definitions and results for all models except Disambiguated Skip-gram taken from [Bartunov et al., 2016].\n\nQuestion: Which model has the highest avgSimC score?\n" }, { "role": "assistant", "content": "By examining the 'avgSimC' column, the model with the highest score is MSSG with a score of 69.3. So, the answer is {\"answer\": \"MSSG\"}." } ], "images": [ "scigen/train/2426.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4.6: Spearman’s correlation coefficient multiplied by 100 for different 300-dimensional models evaluated on the SCWS dataset. Disambiguated Skip-gram, MSSG and MPSG were trained with 3 senses. AdaGram was trained with α=0.15. Metrics definitions and results for all models except Disambiguated Skip-gram taken from [Bartunov et al., 2016]." ], "reasoning": "By examining the 'avgSimC' column, the model with the highest score is MSSG with a score of 69.3.", "question": "Which model has the highest avgSimC score?", "answer": "So, the answer is {\"answer\": \"MSSG\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Skip-gram model has the highest maxSimC score.\n" }, { "role": "assistant", "content": "By examining the 'maxSimC' column, the Skip-gram model has the highest score of 65.2. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2426.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4.6: Spearman’s correlation coefficient multiplied by 100 for different 300-dimensional models evaluated on the SCWS dataset. Disambiguated Skip-gram, MSSG and MPSG were trained with 3 senses. AdaGram was trained with α=0.15. Metrics definitions and results for all models except Disambiguated Skip-gram taken from [Bartunov et al., 2016]." ], "reasoning": "By examining the 'maxSimC' column, the Skip-gram model has the highest score of 65.2.", "statement": "The Skip-gram model has the highest maxSimC score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table B.1: Results for the 20 Newsgroups dataset represented using 2000-dimensional bag-of-senses model compared against bag-of-words model. Both models were compressed to 32 dimensions using the same deep autoencoder.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Feature engineering is a critical and time-consuming task in traditional machine learning, and poorly selected features can lead to poor performance.AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted." } ], "images": [ "scigen/train/2428.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table B.1: Results for the 20 Newsgroups dataset represented using 2000-dimensional bag-of-senses model compared against bag-of-words model. Both models were compressed to 32 dimensions using the same deep autoencoder." ], "reasoning": "Feature engineering is a critical and time-consuming task in traditional machine learning, and poorly selected features can lead to poor performance.", "description": "AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nTable title: Table B.1: Results for the 20 Newsgroups dataset represented using 2000-dimensional bag-of-senses model compared against bag-of-words model. Both models were compressed to 32 dimensions using the same deep autoencoder.\n\nQuestion: Which model has a higher MAP score?\n" }, { "role": "assistant", "content": "The table shows the MAP scores for two models: bag-of-words and bag-of-senses. The bag-of-words model has a MAP score of 0.36, while the bag-of-senses model has a MAP score of 0.38. So, the answer is {\"answer\": \"bag-of-senses\"}." } ], "images": [ "scigen/train/2428.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table B.1: Results for the 20 Newsgroups dataset represented using 2000-dimensional bag-of-senses model compared against bag-of-words model. Both models were compressed to 32 dimensions using the same deep autoencoder." ], "reasoning": "The table shows the MAP scores for two models: bag-of-words and bag-of-senses. The bag-of-words model has a MAP score of 0.36, while the bag-of-senses model has a MAP score of 0.38.", "question": "Which model has a higher MAP score?", "answer": "So, the answer is {\"answer\": \"bag-of-senses\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The bag-of-words model has a higher NDCG@10 score than the bag-of-senses model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe bag-of-words model has a higher NDCG@10 score than the bag-of-senses model.\n" }, { "role": "assistant", "content": "The table shows that both the bag-of-words and bag-of-senses models have the same NDCG@10 score of 0.64. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2428.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table B.1: Results for the 20 Newsgroups dataset represented using 2000-dimensional bag-of-senses model compared against bag-of-words model. Both models were compressed to 32 dimensions using the same deep autoencoder." ], "reasoning": "The table shows that both the bag-of-words and bag-of-senses models have the same NDCG@10 score of 0.64.", "statement": "The bag-of-words model has a higher NDCG@10 score than the bag-of-senses model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table B.2: Results for the RCV1 dataset represented using 2000-dimensional bag-of-senses model compared against bag-of-words model. Both models were compressed to 128 dimensions using the same deep autoencoder.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Feature engineering in traditional machine learning is time-consuming and crucial for performance, but deep learning with vector representations of text data can alleviate this bottleneck.AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted." } ], "images": [ "scigen/train/2429.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table B.2: Results for the RCV1 dataset represented using 2000-dimensional bag-of-senses model compared against bag-of-words model. Both models were compressed to 128 dimensions using the same deep autoencoder." ], "reasoning": "Feature engineering in traditional machine learning is time-consuming and crucial for performance, but deep learning with vector representations of text data can alleviate this bottleneck.", "description": "AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table B.2: Results for the RCV1 dataset represented using 2000-dimensional bag-of-senses model compared against bag-of-words model. Both models were compressed to 128 dimensions using the same deep autoencoder.', solve the subsequent question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich model has a higher MAP score?\n" }, { "role": "assistant", "content": "The table shows the MAP scores for two models: bag-of-words and bag-of-senses. The bag-of-words model has a MAP score of 0.22, while the bag-of-senses model has a MAP score of 0.23. So, the answer is {\"answer\": \"bag-of-senses\"}." } ], "images": [ "scigen/train/2429.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table B.2: Results for the RCV1 dataset represented using 2000-dimensional bag-of-senses model compared against bag-of-words model. Both models were compressed to 128 dimensions using the same deep autoencoder." ], "reasoning": "The table shows the MAP scores for two models: bag-of-words and bag-of-senses. The bag-of-words model has a MAP score of 0.22, while the bag-of-senses model has a MAP score of 0.23.", "question": "Which model has a higher MAP score?", "answer": "So, the answer is {\"answer\": \"bag-of-senses\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The bag-of-words model has a higher NDCG@10 score than the bag-of-senses model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe bag-of-words model has a higher NDCG@10 score than the bag-of-senses model.\n" }, { "role": "assistant", "content": "The table shows that the bag-of-words model has an NDCG@10 score of 0.74, while the bag-of-senses model has an NDCG@10 score of 0.72. Therefore, the statement is supported by the table. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2429.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table B.2: Results for the RCV1 dataset represented using 2000-dimensional bag-of-senses model compared against bag-of-words model. Both models were compressed to 128 dimensions using the same deep autoencoder." ], "reasoning": "The table shows that the bag-of-words model has an NDCG@10 score of 0.74, while the bag-of-senses model has an NDCG@10 score of 0.72. Therefore, the statement is supported by the table.", "statement": "The bag-of-words model has a higher NDCG@10 score than the bag-of-senses model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table B.4: Results for the RCV1 dataset represented using 2000-dimensional TF-IDF-MR model and compressed to 128 dimensions using deep autoencoder.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Feature engineering is a critical and time-consuming task in traditional machine learning, and poor feature selection can lead to suboptimal performance.AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted." } ], "images": [ "scigen/train/2431.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table B.4: Results for the RCV1 dataset represented using 2000-dimensional TF-IDF-MR model and compressed to 128 dimensions using deep autoencoder." ], "reasoning": "Feature engineering is a critical and time-consuming task in traditional machine learning, and poor feature selection can lead to suboptimal performance.", "description": "AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table B.4: Results for the RCV1 dataset represented using 2000-dimensional TF-IDF-MR model and compressed to 128 dimensions using deep autoencoder.', unfold the steps to solve the question given next. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which model has the highest MAP score?\n" }, { "role": "assistant", "content": "The table shows the MAP scores for two models: bag-of-words with a MAP of 0.22 and TF-IDF-MR with a MAP of 0.23. The highest MAP score is 0.23. So, the answer is {\"answer\": \"TF-IDF-MR\"}." } ], "images": [ "scigen/train/2431.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table B.4: Results for the RCV1 dataset represented using 2000-dimensional TF-IDF-MR model and compressed to 128 dimensions using deep autoencoder." ], "reasoning": "The table shows the MAP scores for two models: bag-of-words with a MAP of 0.22 and TF-IDF-MR with a MAP of 0.23. The highest MAP score is 0.23.", "question": "Which model has the highest MAP score?", "answer": "So, the answer is {\"answer\": \"TF-IDF-MR\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe bag-of-words model has a higher MAP score than the TF-IDF-MR model.\n" }, { "role": "assistant", "content": "The table shows that the bag-of-words model has a MAP score of 0.22, while the TF-IDF-MR model has a MAP score of 0.23. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2431.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table B.4: Results for the RCV1 dataset represented using 2000-dimensional TF-IDF-MR model and compressed to 128 dimensions using deep autoencoder." ], "reasoning": "The table shows that the bag-of-words model has a MAP score of 0.22, while the TF-IDF-MR model has a MAP score of 0.23. Therefore, the statement is incorrect.", "statement": "The bag-of-words model has a higher MAP score than the TF-IDF-MR model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table B.5: Classification accuracy.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The dissertation explores the use of Paragraph Vector models to predict gender and age group of blog post authors, achieving higher accuracy with handcrafted features compared to document embeddings.AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted. As a part of this dissertation we wanted to employ the Paragraph Vector models to predict some basic personal information of authors of blog posts based solely on their content. The posts are gender and age annotated. 43% of the authors are teenagers, 42% are in their 20s and the rest are in their 30s. Both sexes are equally represented. We wanted to predict gender and an age group. We started with generation of 300-dimensional document embeddings using Paragraph Vector. Then we trained logistic regression classifier to correctly predict gender and age. Results are presented in Tab." } ], "images": [ "scigen/train/2432.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table B.5: Classification accuracy." ], "reasoning": "The dissertation explores the use of Paragraph Vector models to predict gender and age group of blog post authors, achieving higher accuracy with handcrafted features compared to document embeddings.", "description": "AGH University of Science and Technology Faculty of Computer Science, Electronics and Telecommunications Department of Computer Science Doctoral dissertation Vector representations of text data in deep learning Karol Grzegorczyk Advisor: Professor Witold Dzwinel, PhD, DSc Co-advisor: Marcin Kurdziel, PhD Kraków, 2018 A2S1SS3 -Ch4F2sf4 Users of traditional machine learning systems needed to carefully design or select features, and to do that they needed to deeply understand their data. Feature engineering was, and sometimes still is, one of the most time-consuming, daunting and tedious tasks in a machine learning pipeline. Training a state-of-the-art learning algorithm with data represented by a poorly selected set of features most often leads to poor overall performance. Engineering of features is therefore a bottleneck on a way to achieve satisfying results. Some researchers go even further and suggest that we cannot talk about true artificial intelligence (AI) when features are handcrafted. As a part of this dissertation we wanted to employ the Paragraph Vector models to predict some basic personal information of authors of blog posts based solely on their content. The posts are gender and age annotated. 43% of the authors are teenagers, 42% are in their 20s and the rest are in their 30s. Both sexes are equally represented. We wanted to predict gender and an age group. We started with generation of 300-dimensional document embeddings using Paragraph Vector. Then we trained logistic regression classifier to correctly predict gender and age. Results are presented in Tab.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table B.5: Classification accuracy.', answer the question below by showing each progressive step toward the answer. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the accuracy of predicting age using document embeddings?\n" }, { "role": "assistant", "content": "The table shows the accuracy of different methods for predicting gender and age. The accuracy for predicting age using document embeddings is listed in the last row under the 'Age' column. So, the answer is {\"answer\": \"0.61\"}." } ], "images": [ "scigen/train/2432.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table B.5: Classification accuracy." ], "reasoning": "The table shows the accuracy of different methods for predicting gender and age. The accuracy for predicting age using document embeddings is listed in the last row under the 'Age' column.", "question": "What is the accuracy of predicting age using document embeddings?", "answer": "So, the answer is {\"answer\": \"0.61\"}." }, { "messages": [ { "role": "user", "content": "The accuracy of predicting gender using handcrafted features is higher than using document embeddings. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe accuracy of predicting gender using handcrafted features is higher than using document embeddings.\n" }, { "role": "assistant", "content": "The table shows that the accuracy of predicting gender using handcrafted features is 0.73, while using document embeddings it is 0.65, indicating that the statement is true. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2432.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table B.5: Classification accuracy." ], "reasoning": "The table shows that the accuracy of predicting gender using handcrafted features is 0.73, while using document embeddings it is 0.65, indicating that the statement is true.", "statement": "The accuracy of predicting gender using handcrafted features is higher than using document embeddings.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 10: Results for subtask A: Phrase-Level Polarity. The systems are ordered by their score on the Twitter2015 test dataset; the rankings on the individual datasets are indicated with a subscript.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The systems are ranked based on their scores on the Twitter2015-test dataset, with other rankings being secondary.There is an index for each result showing the relative rank of that result within the respective column. The participating systems are ranked by their score on the Twitter2015-test dataset, which is the official ranking for subtask A; all remaining rankings are secondary." } ], "images": [ "scigen/train/2433.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Results for subtask A: Phrase-Level Polarity. The systems are ordered by their score on the Twitter2015 test dataset; the rankings on the individual datasets are indicated with a subscript." ], "reasoning": "The systems are ranked based on their scores on the Twitter2015-test dataset, with other rankings being secondary.", "description": "There is an index for each result showing the relative rank of that result within the respective column. The participating systems are ranked by their score on the Twitter2015-test dataset, which is the official ranking for subtask A; all remaining rankings are secondary.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 10: Results for subtask A: Phrase-Level Polarity. The systems are ordered by their score on the Twitter2015 test dataset; the rankings on the individual datasets are indicated with a subscript.', answer the following question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich system has the highest score in the 2015 Official Tweet ranking?\n" }, { "role": "assistant", "content": "To find the system with the highest score in the 2015 Official Tweet ranking, look at the '2015: Official Tweet' column and identify the highest value, then find the corresponding system. So, the answer is {\"answer\": \"unitn\"}." } ], "images": [ "scigen/train/2433.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Results for subtask A: Phrase-Level Polarity. The systems are ordered by their score on the Twitter2015 test dataset; the rankings on the individual datasets are indicated with a subscript." ], "reasoning": "To find the system with the highest score in the 2015 Official Tweet ranking, look at the '2015: Official Tweet' column and identify the highest value, then find the corresponding system.", "question": "Which system has the highest score in the 2015 Official Tweet ranking?", "answer": "So, the answer is {\"answer\": \"unitn\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The system 'IOA' has a higher score in the 2014 Progress Tweet sarcasm than in the 2014 Progress Tweet Journal., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe system 'IOA' has a higher score in the 2014 Progress Tweet sarcasm than in the 2014 Progress Tweet Journal.\n" }, { "role": "assistant", "content": "To verify this statement, compare the scores of 'IOA' in the '2014: Progress Tweet sarcasm' and '2014: Progress Tweet Journal' columns. The scores are 71.586 and 85.611 respectively, showing that the score in the Journal is higher. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2433.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Results for subtask A: Phrase-Level Polarity. The systems are ordered by their score on the Twitter2015 test dataset; the rankings on the individual datasets are indicated with a subscript." ], "reasoning": "To verify this statement, compare the scores of 'IOA' in the '2014: Progress Tweet sarcasm' and '2014: Progress Tweet Journal' columns. The scores are 71.586 and 85.611 respectively, showing that the score in the Journal is higher.", "statement": "The system 'IOA' has a higher score in the 2014 Progress Tweet sarcasm than in the 2014 Progress Tweet Journal.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: Comparison between our work and others. Note that the classification accuracy for separate WAE models is not a valid measure since each model is only trained on a single class. Hence the accuracy should be 100% in theory. Models with (s) in their names, are trained on a small subset of MNLI with 40960 raining samples for four classes'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The GMM WAE model achieves the highest diversity (D-1 and D-2) and entropy scores, but has lower perplexity and classification accuracy compared to separate WAEs.Style Accuracy : We follow the approach of the previous work hu2017toward, shen2017style, fu2018style, john2018disentangled and separately train a convolutional neural network (CNN) to classify sentences kim2014convolutional based on their classes. We use this classifier to classify sentences generated with our approach and compare our results with a separate WAE trained only on a specified class of a dataset. We also ran our experiments with separate VAEs and the results are very close to separate WAEs. The classification accuracy of the classifier over the original MNLI dataset is 98%. Using the WAE with a GM prior lowers the accuracy of the classifier. This is expected because we use a shared decoder over multiple class distributions, but the classifier still is easily able to identify different classes. Perplexity: We use the Kneser-Ney language model kneser1995improved to evaluate the fluency of our sampled sentences. We measure the empirical distribution of trigrams in a corpus, and compute the log-likelihood of a sentence. We train the language model on the original dataset and evaluate the fluency of our sampled sentences. Our model achieves acceptable results compared to separate WAEs trained on only one class of sentences. This is again because we are sharing the decoder for all of the GM priors and thus, the decoder sometimes uses rare words to generate a sentence for a specified class. These rare words might be common words in another class. Since the language model has not seen such a combination of words, it evaluates these sentences with a lower accuracy. Note that smaller values correspond to more fluent sentences. Diversity: We use distinct diversity metrics by computing the percentage of distinct unigrams and bigrams following the work of li2015diversity and bahuleyan2017variational. Our model outperforms all of the baselines and previous models in terms of sentence diversity. This is because the decoder learns on a more diverse set of sentences when it is trained over multiple classes. For the question generation task, since the dataset is very small, neither of the models are successful at generating diverse sentences and they tend to generate the same set of question over and over. However, WAE with GM prior generates twice more diverse sentences compared to separate WAEs." } ], "images": [ "scigen/train/2435.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison between our work and others. Note that the classification accuracy for separate WAE models is not a valid measure since each model is only trained on a single class. Hence the accuracy should be 100% in theory. Models with (s) in their names, are trained on a small subset of MNLI with 40960 raining samples for four classes" ], "reasoning": "The GMM WAE model achieves the highest diversity (D-1 and D-2) and entropy scores, but has lower perplexity and classification accuracy compared to separate WAEs.", "description": "Style Accuracy : We follow the approach of the previous work hu2017toward, shen2017style, fu2018style, john2018disentangled and separately train a convolutional neural network (CNN) to classify sentences kim2014convolutional based on their classes. We use this classifier to classify sentences generated with our approach and compare our results with a separate WAE trained only on a specified class of a dataset. We also ran our experiments with separate VAEs and the results are very close to separate WAEs. The classification accuracy of the classifier over the original MNLI dataset is 98%. Using the WAE with a GM prior lowers the accuracy of the classifier. This is expected because we use a shared decoder over multiple class distributions, but the classifier still is easily able to identify different classes. Perplexity: We use the Kneser-Ney language model kneser1995improved to evaluate the fluency of our sampled sentences. We measure the empirical distribution of trigrams in a corpus, and compute the log-likelihood of a sentence. We train the language model on the original dataset and evaluate the fluency of our sampled sentences. Our model achieves acceptable results compared to separate WAEs trained on only one class of sentences. This is again because we are sharing the decoder for all of the GM priors and thus, the decoder sometimes uses rare words to generate a sentence for a specified class. These rare words might be common words in another class. Since the language model has not seen such a combination of words, it evaluates these sentences with a lower accuracy. Note that smaller values correspond to more fluent sentences. Diversity: We use distinct diversity metrics by computing the percentage of distinct unigrams and bigrams following the work of li2015diversity and bahuleyan2017variational. Our model outperforms all of the baselines and previous models in terms of sentence diversity. This is because the decoder learns on a more diverse set of sentences when it is trained over multiple classes. For the question generation task, since the dataset is very small, neither of the models are successful at generating diverse sentences and they tend to generate the same set of question over and over. However, WAE with GM prior generates twice more diverse sentences compared to separate WAEs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 1: Comparison between our work and others. Note that the classification accuracy for separate WAE models is not a valid measure since each model is only trained on a single class. Hence the accuracy should be 100% in theory. Models with (s) in their names, are trained on a small subset of MNLI with 40960 raining samples for four classes' to sequentially solve the question presented below. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich model achieves the highest classification accuracy when trained on MNLI?\n" }, { "role": "assistant", "content": "The table shows that the separate WAE achieves the highest classification accuracy of 97.9% when trained on MNLI. So, the answer is {\"answer\": \"separate WAE\"}" } ], "images": [ "scigen/train/2435.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison between our work and others. Note that the classification accuracy for separate WAE models is not a valid measure since each model is only trained on a single class. Hence the accuracy should be 100% in theory. Models with (s) in their names, are trained on a small subset of MNLI with 40960 raining samples for four classes" ], "reasoning": "The table shows that the separate WAE achieves the highest classification accuracy of 97.9% when trained on MNLI.", "question": "Which model achieves the highest classification accuracy when trained on MNLI?", "answer": "So, the answer is {\"answer\": \"separate WAE\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The GMM WAE model achieves the highest perplexity score when trained on MNLI., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe GMM WAE model achieves the highest perplexity score when trained on MNLI.\n" }, { "role": "assistant", "content": "The table shows that the GMM WAE model has a perplexity score of 98.4 when trained on MNLI, which is not the highest. The disentangled VAE achieves the highest perplexity score of 73.81. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2435.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison between our work and others. Note that the classification accuracy for separate WAE models is not a valid measure since each model is only trained on a single class. Hence the accuracy should be 100% in theory. Models with (s) in their names, are trained on a small subset of MNLI with 40960 raining samples for four classes" ], "reasoning": "The table shows that the GMM WAE model has a perplexity score of 98.4 when trained on MNLI, which is not the highest. The disentangled VAE achieves the highest perplexity score of 73.81.", "statement": "The GMM WAE model achieves the highest perplexity score when trained on MNLI.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: LMs performance on the PTB test set.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The LSRC model with an embedding size of 100 outperforms RNN and LSTM while reducing the number of parameters by approximately 29% and 17%, respectively. Increasing the embedding size to 200 further improves performance with a comparable number of parameters to LSTM.As a first observation, we can clearly see that the proposed approach outperforms all other models for all configurations, in particular, RNN and LSTM. More particularly, we can conclude that LSRC, with an embedding size of 100, achieves a better performance than all other models while reducing the number of parameters by ≈29% and ≈17% compared to RNN and LSTM, respectively. Increasing the embedding size to 200, which is used by the other models, improves significantly the performance with a resulting NoP comparable to LSTM. The significance of the improvements obtained here over LSTM were confirmed through a statistical significance t-test, which led to p-values ≤10−10 for a significance level of 5% and 0.01%, respectively. In fact, the additional layer bridges the gap between the LSRC models with an embedding size of 100 and 200, respectively." } ], "images": [ "scigen/train/2436.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: LMs performance on the PTB test set." ], "reasoning": "The LSRC model with an embedding size of 100 outperforms RNN and LSTM while reducing the number of parameters by approximately 29% and 17%, respectively. Increasing the embedding size to 200 further improves performance with a comparable number of parameters to LSTM.", "description": "As a first observation, we can clearly see that the proposed approach outperforms all other models for all configurations, in particular, RNN and LSTM. More particularly, we can conclude that LSRC, with an embedding size of 100, achieves a better performance than all other models while reducing the number of parameters by ≈29% and ≈17% compared to RNN and LSTM, respectively. Increasing the embedding size to 200, which is used by the other models, improves significantly the performance with a resulting NoP comparable to LSTM. The significance of the improvements obtained here over LSTM were confirmed through a statistical significance t-test, which led to p-values ≤10−10 for a significance level of 5% and 0.01%, respectively. In fact, the additional layer bridges the gap between the LSRC models with an embedding size of 100 and 200, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 2: LMs performance on the PTB test set.' and proceed to solve the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the number of parameters (NoP) for the LSRC model with an embedding size of 100, and how does it compare to the RNN model?\n" }, { "role": "assistant", "content": "The table shows that the LSRC model with an embedding size of 100 has 5.81M parameters. The RNN model has 8.16M parameters. Therefore, the LSRC model with an embedding size of 100 has fewer parameters compared to the RNN model. So, the answer is {\"answer\": \"5.81M\"}." } ], "images": [ "scigen/train/2436.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: LMs performance on the PTB test set." ], "reasoning": "The table shows that the LSRC model with an embedding size of 100 has 5.81M parameters. The RNN model has 8.16M parameters. Therefore, the LSRC model with an embedding size of 100 has fewer parameters compared to the RNN model.", "question": "What is the number of parameters (NoP) for the LSRC model with an embedding size of 100, and how does it compare to the RNN model?", "answer": "So, the answer is {\"answer\": \"5.81M\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe LSRC model with an embedding size of 200 has fewer parameters than the LSTM model.\n" }, { "role": "assistant", "content": "The table shows that the LSRC model with an embedding size of 200 has 7.0M parameters, while the LSTM model has 6.96M parameters. Therefore, the LSRC model with an embedding size of 200 does not have fewer parameters than the LSTM model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2436.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: LMs performance on the PTB test set." ], "reasoning": "The table shows that the LSRC model with an embedding size of 200 has 7.0M parameters, while the LSTM model has 6.96M parameters. Therefore, the LSRC model with an embedding size of 200 does not have fewer parameters than the LSTM model.", "statement": "The LSRC model with an embedding size of 200 has fewer parameters than the LSTM model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: LMs performance on the LTCB test set.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The LSRC model outperforms all other models, including LSTM, with a negligible increase in parameters for single-layer results and maintains this improvement for deep models while reducing parameters and speeding up training time.The LTCB experiments use the same PTB setup with minor modifications. More precisely, these results were obtained without use of momentum or weight decay (due to the long training time required for this corpus), the mini-batch size was set to 400, the learning rate was set to 0.4 and the BPTT step was fixed at 5. The FFNN and FOFE architectures use 2 hidden layers of size 600, whereas RNN, LSTM and LSRC have a single hidden layer of size 600. Moreover, the word embedding size was set to 200 for all models except RNN, which was set to 600. We also report results for an LSTM with 2 recurrent layers as well as for LSRC with an additional non-recurrent layer. In particular, we can see that the proposed LSRC model largely outperforms all other models. In particular, LSRC clearly outperforms LSTM with a negligible increase in the number of parameters (resulting from the additional 200×200=0.04M local connection weights Ucl) for the single layer results. We can also see that this improvement is maintained for deep models (2 hidden layers), where the LSRC model achieves a slightly better performance while reducing the number of parameters by ≈2.5M and speeding up the training time by ≈20% compared to deep LSTM." } ], "images": [ "scigen/train/2437.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: LMs performance on the LTCB test set." ], "reasoning": "The LSRC model outperforms all other models, including LSTM, with a negligible increase in parameters for single-layer results and maintains this improvement for deep models while reducing parameters and speeding up training time.", "description": "The LTCB experiments use the same PTB setup with minor modifications. More precisely, these results were obtained without use of momentum or weight decay (due to the long training time required for this corpus), the mini-batch size was set to 400, the learning rate was set to 0.4 and the BPTT step was fixed at 5. The FFNN and FOFE architectures use 2 hidden layers of size 600, whereas RNN, LSTM and LSRC have a single hidden layer of size 600. Moreover, the word embedding size was set to 200 for all models except RNN, which was set to 600. We also report results for an LSTM with 2 recurrent layers as well as for LSRC with an additional non-recurrent layer. In particular, we can see that the proposed LSRC model largely outperforms all other models. In particular, LSRC clearly outperforms LSTM with a negligible increase in the number of parameters (resulting from the additional 200×200=0.04M local connection weights Ucl) for the single layer results. We can also see that this improvement is maintained for deep models (2 hidden layers), where the LSRC model achieves a slightly better performance while reducing the number of parameters by ≈2.5M and speeding up the training time by ≈20% compared to deep LSTM.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: LMs performance on the LTCB test set.\nQuestion: Which model has the lowest number of parameters and what is its performance for model 1?\nGive a detailed response to the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the FOFE model has the lowest number of parameters (64.84M) and its performance for model 1 is 112. So, the answer is {\"answer\": \"FOFE with 112\"}." } ], "images": [ "scigen/train/2437.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: LMs performance on the LTCB test set." ], "reasoning": "The table shows that the FOFE model has the lowest number of parameters (64.84M) and its performance for model 1 is 112.", "question": "Which model has the lowest number of parameters and what is its performance for model 1?", "answer": "So, the answer is {\"answer\": \"FOFE with 112\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe LSRC model with a single hidden layer has fewer parameters than the LSTM model with a single hidden layer.\n" }, { "role": "assistant", "content": "The table shows that the LSRC model with a single hidden layer has 65.96M parameters, while the LSTM model with a single hidden layer has 65.92M parameters. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2437.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: LMs performance on the LTCB test set." ], "reasoning": "The table shows that the LSRC model with a single hidden layer has 65.96M parameters, while the LSTM model with a single hidden layer has 65.92M parameters. Therefore, the statement is refuted.", "statement": "The LSRC model with a single hidden layer has fewer parameters than the LSTM model with a single hidden layer.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Ten most frequent first words in questions in the PrivacyQA dataset. We observe high lexical diversity in prefixes with 35 unique first word types and 131 unique combinations of first and second words.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Crowdworkers independently posed an average of 49.94 unique questions per policy, with questions averaging 8.4 words in length, and answers typically containing around 100 words of evidence from privacy policies that are about 3000 words long.We also observe low redundancy in the questions posed by crowdworkers over each policy, with each policy receiving ~49.94 unique questions despite crowdworkers independently posing questions. Questions are on average 8.4 words long. As declining to answer a question can be a legally sound response but is seldom practically useful, answers to questions where a minority of experts abstain to answer are filtered from the dataset. Privacy policies are ~3000 words long on average. The answers to the question asked by the users typically have ~100 words of evidence in the privacy policy document." } ], "images": [ "scigen/train/2438.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ten most frequent first words in questions in the PrivacyQA dataset. We observe high lexical diversity in prefixes with 35 unique first word types and 131 unique combinations of first and second words." ], "reasoning": "Crowdworkers independently posed an average of 49.94 unique questions per policy, with questions averaging 8.4 words in length, and answers typically containing around 100 words of evidence from privacy policies that are about 3000 words long.", "description": "We also observe low redundancy in the questions posed by crowdworkers over each policy, with each policy receiving ~49.94 unique questions despite crowdworkers independently posing questions. Questions are on average 8.4 words long. As declining to answer a question can be a legally sound response but is seldom practically useful, answers to questions where a minority of experts abstain to answer are filtered from the dataset. Privacy policies are ~3000 words long on average. The answers to the question asked by the users typically have ~100 words of evidence in the privacy policy document.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Ten most frequent first words in questions in the PrivacyQA dataset. We observe high lexical diversity in prefixes with 35 unique first word types and 131 unique combinations of first and second words.' to sequentially solve the question presented below. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhat is the average length of questions posed by crowdworkers and the average length of privacy policies?\n" }, { "role": "assistant", "content": "The description states that questions are on average 8.4 words long and privacy policies are ~3000 words long. So, the answer is {\"answer\": \"8.4 words for questions, 3000 words for privacy policies\"}." } ], "images": [ "scigen/train/2438.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ten most frequent first words in questions in the PrivacyQA dataset. We observe high lexical diversity in prefixes with 35 unique first word types and 131 unique combinations of first and second words." ], "reasoning": "The description states that questions are on average 8.4 words long and privacy policies are ~3000 words long.", "question": "What is the average length of questions posed by crowdworkers and the average length of privacy policies?", "answer": "So, the answer is {\"answer\": \"8.4 words for questions, 3000 words for privacy policies\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe answers to questions asked by users typically have around 100 words of evidence in the privacy policy document.\n" }, { "role": "assistant", "content": "The description explicitly states that the answers to the questions asked by the users typically have ~100 words of evidence in the privacy policy document. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2438.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ten most frequent first words in questions in the PrivacyQA dataset. We observe high lexical diversity in prefixes with 35 unique first word types and 131 unique combinations of first and second words." ], "reasoning": "The description explicitly states that the answers to the questions asked by the users typically have ~100 words of evidence in the privacy policy document.", "statement": "The answers to questions asked by users typically have around 100 words of evidence in the privacy policy document.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 7: Stratification of classifier performance by OPP-115 category of questions.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The BERT-U model shows significant room for improvement compared to human performance, especially in first party, third party, and data retention categories.We compare our best performing BERT variant against the NA model and human performance. We observe significant room for improvement across all categories of questions but especially for first party, third party and data retention categories." } ], "images": [ "scigen/train/2439.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Stratification of classifier performance by OPP-115 category of questions." ], "reasoning": "The BERT-U model shows significant room for improvement compared to human performance, especially in first party, third party, and data retention categories.", "description": "We compare our best performing BERT variant against the NA model and human performance. We observe significant room for improvement across all categories of questions but especially for first party, third party and data retention categories.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 7: Stratification of classifier performance by OPP-115 category of questions.\nQuestion: Which privacy practice category has the largest performance gap between BERT-U and human performance?\nGive a detailed response to the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the largest performance gap, subtract the BERT-U score from the human score for each category and identify the maximum difference. So, the answer is {\"answer\": \"Data Security\"}." } ], "images": [ "scigen/train/2439.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Stratification of classifier performance by OPP-115 category of questions." ], "reasoning": "To find the largest performance gap, subtract the BERT-U score from the human score for each category and identify the maximum difference.", "question": "Which privacy practice category has the largest performance gap between BERT-U and human performance?", "answer": "So, the answer is {\"answer\": \"Data Security\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe BERT-U model performs better than humans in the 'Other' category.\n" }, { "role": "assistant", "content": "The table shows that the BERT-U score for the 'Other' category is 0.45, while the human score is 0.72, indicating that the BERT-U model does not perform better than humans in this category. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2439.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Stratification of classifier performance by OPP-115 category of questions." ], "reasoning": "The table shows that the BERT-U score for the 'Other' category is 0.45, while the human score is 0.72, indicating that the BERT-U model does not perform better than humans in this category.", "statement": "The BERT-U model performs better than humans in the 'Other' category.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Result of gender direction relation tasks'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Hard-GloVe has the least bias-by-projection, while our method is the best-performing post-processing method on SemBias.We could see that, while Hard-GloVe has the least bias-by-projection among all methods, our method is the runner-up and outperforms the word-vector-learning method GN-GloVe. It is observed that our method is the best-performing post-processing method on SemBias." } ], "images": [ "scigen/train/2440.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Result of gender direction relation tasks" ], "reasoning": "Hard-GloVe has the least bias-by-projection, while our method is the best-performing post-processing method on SemBias.", "description": "We could see that, while Hard-GloVe has the least bias-by-projection among all methods, our method is the runner-up and outperforms the word-vector-learning method GN-GloVe. It is observed that our method is the best-performing post-processing method on SemBias.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 1: Result of gender direction relation tasks', unfold the steps to solve the question given next. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nQuestion: Which method has the highest SemBias score among post-processing methods?\n" }, { "role": "assistant", "content": "To find the highest SemBias score among post-processing methods, compare the SemBias scores of Hard-GloVe, GP-GloVe, and HSR-GloVe. HSR-GloVe has the highest score of 0.8591. So, the answer is {\"answer\": \"HSR-GloVe\"}" } ], "images": [ "scigen/train/2440.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Result of gender direction relation tasks" ], "reasoning": "To find the highest SemBias score among post-processing methods, compare the SemBias scores of Hard-GloVe, GP-GloVe, and HSR-GloVe. HSR-GloVe has the highest score of 0.8591.", "question": "Which method has the highest SemBias score among post-processing methods?", "answer": "So, the answer is {\"answer\": \"HSR-GloVe\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nGN-GloVe has the highest SemBias score among all methods.\n" }, { "role": "assistant", "content": "The table shows that GN-GloVe has a SemBias score of 0.9773, which is higher than all other methods listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2440.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Result of gender direction relation tasks" ], "reasoning": "The table shows that GN-GloVe has a SemBias score of 0.9773, which is higher than all other methods listed.", "statement": "GN-GloVe has the highest SemBias score among all methods.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Result of gender-biased word relation tasks'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "HSR-GloVe outperforms all other methods in reducing the Pearson correlation coefficient, achieving a 17.02% reduction compared to the original GloVe embedding.Clustering male- and female-biased words (GBWR-Clustering) This task takes the top 500 male-biased words and the top 500 female-biased words according to the original embedding and clusters them into two clusters using k-means [lloyd1982least]. We could see that HSR-GloVe is the runner-up among all post-processing methods. Specifically, HSR brings about 6.72% reduction of the correlation coefficient compared to the runner-up method, and it achieves 16.89% reduction compared to the original GloVe embedding. Again, our proposed HSR-GloVe has the lowest Pearson correlation coefficient and outperforms all other methods. Moreover, our method further reduces the correlation coefficient by 4.98% compared to the runner-up method, and it results in 17.02% reduction compared to the original GloVe embedding. the p-values of the three tasks for each algorithm are reported in the appendix. HSR-GloVe and Hard-GloVe have the best performance that only one of the three p-values is significant." } ], "images": [ "scigen/train/2441.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Result of gender-biased word relation tasks" ], "reasoning": "HSR-GloVe outperforms all other methods in reducing the Pearson correlation coefficient, achieving a 17.02% reduction compared to the original GloVe embedding.", "description": "Clustering male- and female-biased words (GBWR-Clustering) This task takes the top 500 male-biased words and the top 500 female-biased words according to the original embedding and clusters them into two clusters using k-means [lloyd1982least]. We could see that HSR-GloVe is the runner-up among all post-processing methods. Specifically, HSR brings about 6.72% reduction of the correlation coefficient compared to the runner-up method, and it achieves 16.89% reduction compared to the original GloVe embedding. Again, our proposed HSR-GloVe has the lowest Pearson correlation coefficient and outperforms all other methods. Moreover, our method further reduces the correlation coefficient by 4.98% compared to the runner-up method, and it results in 17.02% reduction compared to the original GloVe embedding. the p-values of the three tasks for each algorithm are reported in the appendix. HSR-GloVe and Hard-GloVe have the best performance that only one of the three p-values is significant.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Result of gender-biased word relation tasks\nQuestion: Which method achieves the lowest Pearson correlation coefficient in GBWR-Correlation?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that Post-Processing HSR-GloVe has the lowest Pearson correlation coefficient in GBWR-Correlation with a value of 0.6422. So, the answer is {\"answer\": \"Post-Processing HSR-GloVe\"}." } ], "images": [ "scigen/train/2441.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Result of gender-biased word relation tasks" ], "reasoning": "The table shows that Post-Processing HSR-GloVe has the lowest Pearson correlation coefficient in GBWR-Correlation with a value of 0.6422.", "question": "Which method achieves the lowest Pearson correlation coefficient in GBWR-Correlation?", "answer": "So, the answer is {\"answer\": \"Post-Processing HSR-GloVe\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nPost-Processing GP-GloVe has the highest Pearson correlation coefficient in GBWR-Correlation.\n" }, { "role": "assistant", "content": "The table shows that the highest Pearson correlation coefficient in GBWR-Correlation is 0.7727, which belongs to GloVe, not Post-Processing GP-GloVe. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2441.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Result of gender-biased word relation tasks" ], "reasoning": "The table shows that the highest Pearson correlation coefficient in GBWR-Correlation is 0.7727, which belongs to GloVe, not Post-Processing GP-GloVe.", "statement": "Post-Processing GP-GloVe has the highest Pearson correlation coefficient in GBWR-Correlation.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Average Pearson correlation coefficient of semantic textual similarity tasks each year'.\n" }, { "role": "assistant", "content": "HSR-GloVe shows the highest performance improvement in the SICK task and an average improvement of 14.19% over 20 STS tasks compared to prior best post-processing methods.The semantic textual similarity (STS) tasks examine the sentence-level effectiveness of word embeddings by measuring the degree of semantic equivalence between two texts [Agirre2012]. The STS tasks we employ include 20 tasks from 2012 SemEval Semantic Related task (SICK) and SemEval STS tasks from 2012 to 2015 [Marco2014, Agirre2012, Agirre2013, Agirre2014, Agirre2015]. For each task, we average the embedding of words in the sentence as the sentence embedding, and record the Pearson correlation coefficient of the estimated rankings of sentence similarity against the human rankings. For the SICK task, HSR-GloVe performs the best among all post-processing methods. On average, HSR-GloVe achieves an improvement of 14.19% over the 20 STS tasks with respect to the prior best post-processing methods, and it attains 14.71% improvement with respect to the original embedding." } ], "images": [ "scigen/train/2442.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Average Pearson correlation coefficient of semantic textual similarity tasks each year" ], "reasoning": "HSR-GloVe shows the highest performance improvement in the SICK task and an average improvement of 14.19% over 20 STS tasks compared to prior best post-processing methods.", "description": "The semantic textual similarity (STS) tasks examine the sentence-level effectiveness of word embeddings by measuring the degree of semantic equivalence between two texts [Agirre2012]. The STS tasks we employ include 20 tasks from 2012 SemEval Semantic Related task (SICK) and SemEval STS tasks from 2012 to 2015 [Marco2014, Agirre2012, Agirre2013, Agirre2014, Agirre2015]. For each task, we average the embedding of words in the sentence as the sentence embedding, and record the Pearson correlation coefficient of the estimated rankings of sentence similarity against the human rankings. For the SICK task, HSR-GloVe performs the best among all post-processing methods. On average, HSR-GloVe achieves an improvement of 14.19% over the 20 STS tasks with respect to the prior best post-processing methods, and it attains 14.71% improvement with respect to the original embedding.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 4: Average Pearson correlation coefficient of semantic textual similarity tasks each year', solve the following question step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method achieved the highest Pearson correlation coefficient in the STS-2015 task?\n" }, { "role": "assistant", "content": "By comparing the Pearson correlation coefficients for the STS-2015 task across all methods, HSR-GloVe has the highest value of 61.36. So, the answer is {\"answer\": \"HSR-GloVe\"}" } ], "images": [ "scigen/train/2442.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Average Pearson correlation coefficient of semantic textual similarity tasks each year" ], "reasoning": "By comparing the Pearson correlation coefficients for the STS-2015 task across all methods, HSR-GloVe has the highest value of 61.36.", "question": "Which method achieved the highest Pearson correlation coefficient in the STS-2015 task?", "answer": "So, the answer is {\"answer\": \"HSR-GloVe\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nGP-GloVe achieved the lowest Pearson correlation coefficient in the STS-2013 task.\n" }, { "role": "assistant", "content": "By comparing the Pearson correlation coefficients for the STS-2013 task, GP-GloVe has the lowest value of 43.16. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2442.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Average Pearson correlation coefficient of semantic textual similarity tasks each year" ], "reasoning": "By comparing the Pearson correlation coefficients for the STS-2013 task, GP-GloVe has the lowest value of 43.16.", "statement": "GP-GloVe achieved the lowest Pearson correlation coefficient in the STS-2013 task.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: Average F1 score of coreference resolution tasks', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "HSR-GloVe achieves the lowest WinoBias-Diff, significantly reducing bias compared to Hard-GloVe and GloVe, while maintaining comparable performance on the OntoNotes 5.0 benchmark.The results of GloVe, Hard-GloVe, and GN-GloVe are collected from \\citeauthorzhao2018learning \\shortcitezhao2018learning. From the table, we could see that HSR-GloVe performs the best on the WinoBias task by achieving the lowest difference (WinoBias-Diff) between PRO and ANTI datasets. Specifically, HSR-GloVe reduces WinoBias-Diff by 87.26% compared to the runner-up method Hard-GloVe and by 93.38% compared to the original GloVe embedding. Furthermore, HSR-GloVe has an on-par performance on the OntoNotes 5.0 benchmark dataset compared to other baselines." } ], "images": [ "scigen/train/2443.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Average F1 score of coreference resolution tasks" ], "reasoning": "HSR-GloVe achieves the lowest WinoBias-Diff, significantly reducing bias compared to Hard-GloVe and GloVe, while maintaining comparable performance on the OntoNotes 5.0 benchmark.", "description": "The results of GloVe, Hard-GloVe, and GN-GloVe are collected from \\citeauthorzhao2018learning \\shortcitezhao2018learning. From the table, we could see that HSR-GloVe performs the best on the WinoBias task by achieving the lowest difference (WinoBias-Diff) between PRO and ANTI datasets. Specifically, HSR-GloVe reduces WinoBias-Diff by 87.26% compared to the runner-up method Hard-GloVe and by 93.38% compared to the original GloVe embedding. Furthermore, HSR-GloVe has an on-par performance on the OntoNotes 5.0 benchmark dataset compared to other baselines.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 5: Average F1 score of coreference resolution tasks', and tackle the question below in a sequential manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method achieves the lowest WinoBias-Diff and how much does it reduce the bias compared to the original GloVe embedding?\n" }, { "role": "assistant", "content": "HSR-GloVe achieves the lowest WinoBias-Diff of 2.0. Compared to the original GloVe embedding's WinoBias-Diff of 30.2, HSR-GloVe reduces the bias by 93.38%. So, the answer is {\"answer\": \"HSR-GloVe, 93.38%\"}." } ], "images": [ "scigen/train/2443.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Average F1 score of coreference resolution tasks" ], "reasoning": "HSR-GloVe achieves the lowest WinoBias-Diff of 2.0. Compared to the original GloVe embedding's WinoBias-Diff of 30.2, HSR-GloVe reduces the bias by 93.38%.", "question": "Which method achieves the lowest WinoBias-Diff and how much does it reduce the bias compared to the original GloVe embedding?", "answer": "So, the answer is {\"answer\": \"HSR-GloVe, 93.38%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence HSR-GloVe performs worse than Hard-GloVe on the WinoBias-Diff metric. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nHSR-GloVe performs worse than Hard-GloVe on the WinoBias-Diff metric.\n" }, { "role": "assistant", "content": "The table shows that HSR-GloVe has a WinoBias-Diff of 2.0, which is lower than Hard-GloVe's WinoBias-Diff of 15.7, indicating that HSR-GloVe performs better. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2443.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Average F1 score of coreference resolution tasks" ], "reasoning": "The table shows that HSR-GloVe has a WinoBias-Diff of 2.0, which is lower than Hard-GloVe's WinoBias-Diff of 15.7, indicating that HSR-GloVe performs better.", "statement": "HSR-GloVe performs worse than Hard-GloVe on the WinoBias-Diff metric.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5: Ablation Experiment BLEU Results. The rows of the Lattice LSTM and the Lattice Transformer represent the 1-best hypothesis fine-tuning, and the BLEUs are evaluated on 1-best inputs and on lattice inputs for the others. The colored BLEU values come from Table 2 and 3.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The ablation study shows that adding marginal scores in both encoder and decoder improves performance, but the most complicated lattice transformer does not perform better on smaller data sizes.Model Ablation Study We conduct an ablation study to examine the effectiveness of every module in the lattice transformer. We gradually add one module from a standard transformer model to our most complicated lattice transformer. Furthermore, the superimposed application of marginal scores in both encoder and decoder can gain an additional promotion, compared to individual applications. However, the use of forward and backward scores has no significantly extra rewards in this situation. Perhaps due to overfitting, the most complicated lattice transformer on the Callhome of smaller data size cannot achieve better BLEUs than simpler models." } ], "images": [ "scigen/train/2444.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ablation Experiment BLEU Results. The rows of the Lattice LSTM and the Lattice Transformer represent the 1-best hypothesis fine-tuning, and the BLEUs are evaluated on 1-best inputs and on lattice inputs for the others. The colored BLEU values come from Table 2 and 3." ], "reasoning": "The ablation study shows that adding marginal scores in both encoder and decoder improves performance, but the most complicated lattice transformer does not perform better on smaller data sizes.", "description": "Model Ablation Study We conduct an ablation study to examine the effectiveness of every module in the lattice transformer. We gradually add one module from a standard transformer model to our most complicated lattice transformer. Furthermore, the superimposed application of marginal scores in both encoder and decoder can gain an additional promotion, compared to individual applications. However, the use of forward and backward scores has no significantly extra rewards in this situation. Perhaps due to overfitting, the most complicated lattice transformer on the Callhome of smaller data size cannot achieve better BLEUs than simpler models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: Ablation Experiment BLEU Results. The rows of the Lattice LSTM and the Lattice Transformer represent the 1-best hypothesis fine-tuning, and the BLEUs are evaluated on 1-best inputs and on lattice inputs for the others. The colored BLEU values come from Table 2 and 3.', solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model achieved the highest BLEU score on the Fisher dev2 dataset?\n" }, { "role": "assistant", "content": "By examining the table, the model with '+ marginal scores in encoder and decoder, and forward/backward scores only in encoder self-attention layer 0 and layer 1' achieved the highest BLEU score of 39.6 on the Fisher dev2 dataset. So, the answer is {\"answer\": \"39.6\"}." } ], "images": [ "scigen/train/2444.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ablation Experiment BLEU Results. The rows of the Lattice LSTM and the Lattice Transformer represent the 1-best hypothesis fine-tuning, and the BLEUs are evaluated on 1-best inputs and on lattice inputs for the others. The colored BLEU values come from Table 2 and 3." ], "reasoning": "By examining the table, the model with '+ marginal scores in encoder and decoder, and forward/backward scores only in encoder self-attention layer 0 and layer 1' achieved the highest BLEU score of 39.6 on the Fisher dev2 dataset.", "question": "Which model achieved the highest BLEU score on the Fisher dev2 dataset?", "answer": "So, the answer is {\"answer\": \"39.6\"}." }, { "messages": [ { "role": "user", "content": "The model with the highest BLEU score on the Fisher test dataset also achieved the highest score on the Callhome evltest dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe model with the highest BLEU score on the Fisher test dataset also achieved the highest score on the Callhome evltest dataset.\n" }, { "role": "assistant", "content": "The table shows that the model with '+ marginal scores in encoder and decoder, and forward/backward scores only in encoder self-attention layer 0 and layer 1' achieved the highest BLEU score of 39.1 on the Fisher test dataset, but the highest score on the Callhome evltest dataset is 14.9, achieved by the same model. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2444.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ablation Experiment BLEU Results. The rows of the Lattice LSTM and the Lattice Transformer represent the 1-best hypothesis fine-tuning, and the BLEUs are evaluated on 1-best inputs and on lattice inputs for the others. The colored BLEU values come from Table 2 and 3." ], "reasoning": "The table shows that the model with '+ marginal scores in encoder and decoder, and forward/backward scores only in encoder self-attention layer 0 and layer 1' achieved the highest BLEU score of 39.1 on the Fisher test dataset, but the highest score on the Callhome evltest dataset is 14.9, achieved by the same model. Therefore, the statement is supported.", "statement": "The model with the highest BLEU score on the Fisher test dataset also achieved the highest score on the Callhome evltest dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 6: BLEU on WMT 2017 Chinese English'.\n" }, { "role": "assistant", "content": "The lattice transformer models trained with word lattice corpus, either from scratch or fine-tuned, outperform the standard transformer models on the test2017 dataset.4.2.2 Results For our lattice transformer, we have three models trained for comparison. First we use the 32K BPE Chinese corpus alone to train our lattice transformer, which is equivalent to the standard transformer with relative positional embeddings. Secondly, we train another lattice transformer with the word lattice corpus from scratch. In addition, we follow the convention of the speech translation task in previous experiments by fine-tuning the first model with word lattice corpus. For each setting, the model evaluated on test 2017 dataset is selected from the best model performed on the dev2017 data. The fine-tuning of Lattice Model 3 starts from the best checkpoint of Lattice Model 1. Notice that the first two results of transformer-base and -big are directly copied from the relevant references. From the result, we can see that our Model 1 can be comparable with the vanilla transformer-big model in a base setting, and significantly better than the transformer-base model. We also validate the argument that training from scratch can also achieve a better result than most baselines. Empirically, we find an interesting phenomena that training from scratch converges faster than other settings." } ], "images": [ "scigen/train/2445.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: BLEU on WMT 2017 Chinese English" ], "reasoning": "The lattice transformer models trained with word lattice corpus, either from scratch or fine-tuned, outperform the standard transformer models on the test2017 dataset.", "description": "4.2.2 Results For our lattice transformer, we have three models trained for comparison. First we use the 32K BPE Chinese corpus alone to train our lattice transformer, which is equivalent to the standard transformer with relative positional embeddings. Secondly, we train another lattice transformer with the word lattice corpus from scratch. In addition, we follow the convention of the speech translation task in previous experiments by fine-tuning the first model with word lattice corpus. For each setting, the model evaluated on test 2017 dataset is selected from the best model performed on the dev2017 data. The fine-tuning of Lattice Model 3 starts from the best checkpoint of Lattice Model 1. Notice that the first two results of transformer-base and -big are directly copied from the relevant references. From the result, we can see that our Model 1 can be comparable with the vanilla transformer-big model in a base setting, and significantly better than the transformer-base model. We also validate the argument that training from scratch can also achieve a better result than most baselines. Empirically, we find an interesting phenomena that training from scratch converges faster than other settings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nTable title: Table 6: BLEU on WMT 2017 Chinese English\n\nQuestion: Which model achieved the highest score on the test2017 dataset?\n" }, { "role": "assistant", "content": "By comparing the scores in the 'test2017' column, the '3. Lattice Transformer with fine-tuning' model has the highest score of 24.81. So, the answer is {\"answer\": \"3. Lattice Transformer with fine-tuning\"}." } ], "images": [ "scigen/train/2445.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: BLEU on WMT 2017 Chinese English" ], "reasoning": "By comparing the scores in the 'test2017' column, the '3. Lattice Transformer with fine-tuning' model has the highest score of 24.81.", "question": "Which model achieved the highest score on the test2017 dataset?", "answer": "So, the answer is {\"answer\": \"3. Lattice Transformer with fine-tuning\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Transformer with BPE 32K achieved a higher score than the Transformer-big Hassan et al. (2018)., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Transformer with BPE 32K achieved a higher score than the Transformer-big Hassan et al. (2018).\n" }, { "role": "assistant", "content": "The score of the Transformer with BPE 32K is 24.26, which is higher than the Transformer-big Hassan et al. (2018) score of 24.20. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2445.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: BLEU on WMT 2017 Chinese English" ], "reasoning": "The score of the Transformer with BPE 32K is 24.26, which is higher than the Transformer-big Hassan et al. (2018) score of 24.20.", "statement": "The Transformer with BPE 32K achieved a higher score than the Transformer-big Hassan et al. (2018).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Ranking experiments on CACM'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The affect enriched paragram embeddings report the best NDCG and MAP, outperforming non-affect enriched paragram embeddings by 0.02 in both metrics.We observe that usage of term embeddings generally causes steadily higher NDCG and MAP. In particular the paragaram embeddings models report the best results, with affect enriched paragram embeddings reporting both best NDCG and MAP, 0.02 better than non affect enriched paragram embeddings results in both NDCG and MAP." } ], "images": [ "scigen/train/2446.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Ranking experiments on CACM" ], "reasoning": "The affect enriched paragram embeddings report the best NDCG and MAP, outperforming non-affect enriched paragram embeddings by 0.02 in both metrics.", "description": "We observe that usage of term embeddings generally causes steadily higher NDCG and MAP. In particular the paragaram embeddings models report the best results, with affect enriched paragram embeddings reporting both best NDCG and MAP, 0.02 better than non affect enriched paragram embeddings results in both NDCG and MAP.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 4: Ranking experiments on CACM\nQuestion: Which model reports the highest NDCG and MAP scores in the ranking experiments on CACM?\n" }, { "role": "assistant", "content": "By examining the table, we see that the 'paragram-counterfit-affect' model reports the highest NDCG score of 0.5860 and the highest MAP score of 0.3996. So, the answer is {\"answer\": \"paragram-counterfit-affect\"}." } ], "images": [ "scigen/train/2446.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Ranking experiments on CACM" ], "reasoning": "By examining the table, we see that the 'paragram-counterfit-affect' model reports the highest NDCG score of 0.5860 and the highest MAP score of 0.3996.", "question": "Which model reports the highest NDCG and MAP scores in the ranking experiments on CACM?", "answer": "So, the answer is {\"answer\": \"paragram-counterfit-affect\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe GloVe-Twitter-25 model reports an NDCG score higher than 0.51.\n" }, { "role": "assistant", "content": "The table shows that the GloVe-Twitter-25 model has an NDCG score of 0.5309, which is higher than 0.51. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2446.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Ranking experiments on CACM" ], "reasoning": "The table shows that the GloVe-Twitter-25 model has an NDCG score of 0.5309, which is higher than 0.51.", "statement": "The GloVe-Twitter-25 model reports an NDCG score higher than 0.51.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: Mean affect scores on some common IR datasets', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The table shows the mean affect scores for formality, politeness, and frustration for datasets used in information retrieval experiments, including TREC core 2018 and 2017 tracks.In order to assess the potential applicability of Aff2Vec embeddings in the context of information retrieval, we run preliminary evaluation of the amount of formality, politeness and frustration contained in common text collections used in information retrieval experiments. For this purpose we leverage the affect scoring algorithm that is used for building Aff2Vec embeddings. We extract mean affect scores for formality, politeness and frustration on each dataset. Such an evaluation involves two collection of news: the datasets from TREC core 2018 track, Washington Post articles, and TREC core 2017 track, New York Times articles." } ], "images": [ "scigen/train/2447.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Mean affect scores on some common IR datasets" ], "reasoning": "The table shows the mean affect scores for formality, politeness, and frustration for datasets used in information retrieval experiments, including TREC core 2018 and 2017 tracks.", "description": "In order to assess the potential applicability of Aff2Vec embeddings in the context of information retrieval, we run preliminary evaluation of the amount of formality, politeness and frustration contained in common text collections used in information retrieval experiments. For this purpose we leverage the affect scoring algorithm that is used for building Aff2Vec embeddings. We extract mean affect scores for formality, politeness and frustration on each dataset. Such an evaluation involves two collection of news: the datasets from TREC core 2018 track, Washington Post articles, and TREC core 2017 track, New York Times articles.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Mean affect scores on some common IR datasets\nQuestion: Which dataset has the highest mean affect score for politeness?\nSolve the above question based on the table information. Let's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the dataset with the highest mean affect score for politeness, compare the politeness scores of all datasets in the table. The WP dataset has the highest politeness score of 0.7456. So, the answer is {\"answer\": \"WP\"}" } ], "images": [ "scigen/train/2447.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Mean affect scores on some common IR datasets" ], "reasoning": "To find the dataset with the highest mean affect score for politeness, compare the politeness scores of all datasets in the table. The WP dataset has the highest politeness score of 0.7456.", "question": "Which dataset has the highest mean affect score for politeness?", "answer": "So, the answer is {\"answer\": \"WP\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe NYT dataset has the lowest mean affect score for frustration.\n" }, { "role": "assistant", "content": "To verify this statement, compare the frustration scores of all datasets in the table. The NYT dataset has a frustration score of 0.6248, which is not the lowest. The CACM dataset has the lowest frustration score of 0.3511. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2447.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Mean affect scores on some common IR datasets" ], "reasoning": "To verify this statement, compare the frustration scores of all datasets in the table. The NYT dataset has a frustration score of 0.6248, which is not the lowest. The CACM dataset has the lowest frustration score of 0.3511.", "statement": "The NYT dataset has the lowest mean affect score for frustration.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Ranking experiments on NYT', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Classic BM25 and query likelihood models generally outperform term embeddings in NDCG, except for affect enriched models, with GloVe retrofitted affect enriched embedding being the top performer. None of the term embedding models outperform BM25 in mean average precision.We observe that usage of term embeddings doesn’t give benefits in many cases, classic BM25 and query likelihood retrieval models provide better NDCG than almost all the models except the affect enriched ones. A GloVe retrofitted affect enriched embedding model is the top performing one for NDCG measure. On the other hand none of the term embedding ranking could outperform BM25 on the mean average precision measure." } ], "images": [ "scigen/train/2448.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ranking experiments on NYT" ], "reasoning": "Classic BM25 and query likelihood models generally outperform term embeddings in NDCG, except for affect enriched models, with GloVe retrofitted affect enriched embedding being the top performer. None of the term embedding models outperform BM25 in mean average precision.", "description": "We observe that usage of term embeddings doesn’t give benefits in many cases, classic BM25 and query likelihood retrieval models provide better NDCG than almost all the models except the affect enriched ones. A GloVe retrofitted affect enriched embedding model is the top performing one for NDCG measure. On the other hand none of the term embedding ranking could outperform BM25 on the mean average precision measure.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Ranking experiments on NYT' to sequentially solve the question presented below. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich model has the highest NDCG score in the ranking experiments on the NYT model?\n" }, { "role": "assistant", "content": "To find the model with the highest NDCG score, we look at the 'Ranking experiments on NYT NDCG' column and identify the highest value, which is 0.4693 for the GloVe-retrofitted-affect-555 model. So, the answer is {\"answer\": \"GloVe-retrofitted-affect-555\"}." } ], "images": [ "scigen/train/2448.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ranking experiments on NYT" ], "reasoning": "To find the model with the highest NDCG score, we look at the 'Ranking experiments on NYT NDCG' column and identify the highest value, which is 0.4693 for the GloVe-retrofitted-affect-555 model.", "question": "Which model has the highest NDCG score in the ranking experiments on the NYT model?", "answer": "So, the answer is {\"answer\": \"GloVe-retrofitted-affect-555\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The GloVe-retrofitted-affect-555 model has the highest mean average precision (MAP) score., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe GloVe-retrofitted-affect-555 model has the highest mean average precision (MAP) score.\n" }, { "role": "assistant", "content": "To verify this statement, we check the 'Ranking experiments on NYT MAP' column. The highest MAP score is 0.1977, which belongs to the BM25 model, not the GloVe-retrofitted-affect-555 model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2448.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ranking experiments on NYT" ], "reasoning": "To verify this statement, we check the 'Ranking experiments on NYT MAP' column. The highest MAP score is 0.1977, which belongs to the BM25 model, not the GloVe-retrofitted-affect-555 model.", "statement": "The GloVe-retrofitted-affect-555 model has the highest mean average precision (MAP) score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Query expansion experiments on NYT'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Classic BM25 and query likelihood models perform well in NDCG, but the GloVe retrofitted affect enriched embedding model outperforms all in both NDCG and MAP.We observe that classic BM25 and query likelihood retrieval models provide better NDCG than almost all the models except some of the affect enriched ones. This is in line with what we observed for the ranking task on the same dataset. A GloVe retrofitted affect enriched embedding model is the top performing one for both NDCG and MAP." } ], "images": [ "scigen/train/2449.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Query expansion experiments on NYT" ], "reasoning": "Classic BM25 and query likelihood models perform well in NDCG, but the GloVe retrofitted affect enriched embedding model outperforms all in both NDCG and MAP.", "description": "We observe that classic BM25 and query likelihood retrieval models provide better NDCG than almost all the models except some of the affect enriched ones. This is in line with what we observed for the ranking task on the same dataset. A GloVe retrofitted affect enriched embedding model is the top performing one for both NDCG and MAP.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Query expansion experiments on NYT\nQuestion: Which model has the highest NDCG score and what is its MAP score?\nSolve the above question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows that the GloVe-retrofitted-affect-555 model has the highest NDCG score of 0.4421. Its corresponding MAP score is 0.2021. So, the answer is {\"answer\": \"GloVe-retrofitted-affect-555 with MAP score of 0.2021\"}" } ], "images": [ "scigen/train/2449.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Query expansion experiments on NYT" ], "reasoning": "The table shows that the GloVe-retrofitted-affect-555 model has the highest NDCG score of 0.4421. Its corresponding MAP score is 0.2021.", "question": "Which model has the highest NDCG score and what is its MAP score?", "answer": "So, the answer is {\"answer\": \"GloVe-retrofitted-affect-555 with MAP score of 0.2021\"}" }, { "messages": [ { "role": "user", "content": "The GloVe-retrofitted-affect-555 model has the highest MAP score. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe GloVe-retrofitted-affect-555 model has the highest MAP score.\n" }, { "role": "assistant", "content": "The table shows that the GloVe-retrofitted-affect-555 model has the highest NDCG score but not the highest MAP score. The highest MAP score is 0.2021, which is not the highest among all models. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2449.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Query expansion experiments on NYT" ], "reasoning": "The table shows that the GloVe-retrofitted-affect-555 model has the highest NDCG score but not the highest MAP score. The highest MAP score is 0.2021, which is not the highest among all models.", "statement": "The GloVe-retrofitted-affect-555 model has the highest MAP score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: Query expansion experiments on CACM'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The w2v-GoogleNews-300 model outperformed all others in NDCG and MAP, while the best enriched model was the w2v-retrofitted model, and the best affect enriched model was the GloVe-retrofitted one.We observe that usage of term embeddings generally causes steadily higher NDCG and MAP. While we expected best results with Aff2Vec models it turned out ”vanilla” word2vec model trained on Google News corpus outperformed all the others in NDCG and MAP. On the other hand the best performing enriched model is a retrofitted word2vec model whereas among affect enriched models the GloVe retrofitted one provides the best results." } ], "images": [ "scigen/train/2450.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Query expansion experiments on CACM" ], "reasoning": "The w2v-GoogleNews-300 model outperformed all others in NDCG and MAP, while the best enriched model was the w2v-retrofitted model, and the best affect enriched model was the GloVe-retrofitted one.", "description": "We observe that usage of term embeddings generally causes steadily higher NDCG and MAP. While we expected best results with Aff2Vec models it turned out ”vanilla” word2vec model trained on Google News corpus outperformed all the others in NDCG and MAP. On the other hand the best performing enriched model is a retrofitted word2vec model whereas among affect enriched models the GloVe retrofitted one provides the best results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 5: Query expansion experiments on CACM'. The final result should be presented in the JSON format of {\"answer\": \"\"}. \n\nQuestion: Which model achieved the highest NDCG and MAP scores among all models?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows that the w2v-GoogleNews-300 model has the highest NDCG score of 0.4824 and the highest MAP score of 0.2828 among all models. So, the answer is {\"answer\": \"w2v-GoogleNews-300\"}." } ], "images": [ "scigen/train/2450.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Query expansion experiments on CACM" ], "reasoning": "The table shows that the w2v-GoogleNews-300 model has the highest NDCG score of 0.4824 and the highest MAP score of 0.2828 among all models.", "question": "Which model achieved the highest NDCG and MAP scores among all models?", "answer": "So, the answer is {\"answer\": \"w2v-GoogleNews-300\"}." }, { "messages": [ { "role": "user", "content": "The GloVe-retrofitted model achieved the highest NDCG score among enriched models. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe GloVe-retrofitted model achieved the highest NDCG score among enriched models.\n" }, { "role": "assistant", "content": "The table shows that the w2v-retrofitted model has the highest NDCG score of 0.4738 among enriched models, not the GloVe-retrofitted model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2450.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Query expansion experiments on CACM" ], "reasoning": "The table shows that the w2v-retrofitted model has the highest NDCG score of 0.4738 among enriched models, not the GloVe-retrofitted model.", "statement": "The GloVe-retrofitted model achieved the highest NDCG score among enriched models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 5: The performance (in %) of our model on the test set for subtask 1 (binary classification). The subscripts denote our official ranking.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Our system, using a simple MLP neural network architecture, achieved high performance, ranking third in accuracy and fifth in F1 out of forty-four teams.By using a simple MLP neural network architecture, our system achieves a high performance which is ranked third and fifth out of forty-four teams using accuracy and F1 metrics, respectively." } ], "images": [ "scigen/train/2451.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The performance (in %) of our model on the test set for subtask 1 (binary classification). The subscripts denote our official ranking." ], "reasoning": "Our system, using a simple MLP neural network architecture, achieved high performance, ranking third in accuracy and fifth in F1 out of forty-four teams.", "description": "By using a simple MLP neural network architecture, our system achieves a high performance which is ranked third and fifth out of forty-four teams using accuracy and F1 metrics, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: Table 5: The performance (in %) of our model on the test set for subtask 1 (binary classification). The subscripts denote our official ranking.\nQuestion: What is the F1 score achieved by the system?\n" }, { "role": "assistant", "content": "The table shows the F1 score as 64.765, which is the value achieved by the system. So, the answer is {\"answer\": \"64.765\"}" } ], "images": [ "scigen/train/2451.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The performance (in %) of our model on the test set for subtask 1 (binary classification). The subscripts denote our official ranking." ], "reasoning": "The table shows the F1 score as 64.765, which is the value achieved by the system.", "question": "What is the F1 score achieved by the system?", "answer": "So, the answer is {\"answer\": \"64.765\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The system achieved an accuracy of over 70%., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe system achieved an accuracy of over 70%.\n" }, { "role": "assistant", "content": "The table shows that the accuracy achieved by the system is 70.153, which is over 70%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2451.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The performance (in %) of our model on the test set for subtask 1 (binary classification). The subscripts denote our official ranking." ], "reasoning": "The table shows that the accuracy achieved by the system is 70.153, which is over 70%.", "statement": "The system achieved an accuracy of over 70%.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: Results on CNN-DailyMail abstractive summarization.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The proposed method, evaluated on the CNN-DailyMail dataset, achieves better results on Rouge-1 and Rouge-2 metrics with fewer parameters in the Standard model and outperforms all previous models with the Deep model.We evaluated the proposed method on the task of abstractive summarization. We test the method’s ability to process long documents on the CNN-DailyMail dataset. We encode an article of up to 400 sub-words and we generate a summarization composed from multiple sentences. Our Standard model is able to achieve better results on the Rouge-1 and Rouge-2 metrics than previous methods. In addition, the Standard model is using significantly less parameters, approximately 30M parameters less. The Deep model uses more layers to closely match the number of parameters and is able to outperform all previous models. This shows that our method is able to encode long sequences successfully without having the need to have access to all context." } ], "images": [ "scigen/train/2452.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on CNN-DailyMail abstractive summarization." ], "reasoning": "The proposed method, evaluated on the CNN-DailyMail dataset, achieves better results on Rouge-1 and Rouge-2 metrics with fewer parameters in the Standard model and outperforms all previous models with the Deep model.", "description": "We evaluated the proposed method on the task of abstractive summarization. We test the method’s ability to process long documents on the CNN-DailyMail dataset. We encode an article of up to 400 sub-words and we generate a summarization composed from multiple sentences. Our Standard model is able to achieve better results on the Rouge-1 and Rouge-2 metrics than previous methods. In addition, the Standard model is using significantly less parameters, approximately 30M parameters less. The Deep model uses more layers to closely match the number of parameters and is able to outperform all previous models. This shows that our method is able to encode long sequences successfully without having the need to have access to all context.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Results on CNN-DailyMail abstractive summarization.', answer the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model achieves the highest Rouge-1 score and how many parameters does it use?\n" }, { "role": "assistant", "content": "The table shows that the TaLK Convolution (Deep) model achieves the highest Rouge-1 score of 40.59 and uses 83M parameters. So, the answer is {\"answer\": \"TaLK Convolution (Deep) with 83M parameters\"}" } ], "images": [ "scigen/train/2452.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on CNN-DailyMail abstractive summarization." ], "reasoning": "The table shows that the TaLK Convolution (Deep) model achieves the highest Rouge-1 score of 40.59 and uses 83M parameters.", "question": "Which model achieves the highest Rouge-1 score and how many parameters does it use?", "answer": "So, the answer is {\"answer\": \"TaLK Convolution (Deep) with 83M parameters\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The TaLK Convolution (Standard) model uses the least number of parameters among all models., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe TaLK Convolution (Standard) model uses the least number of parameters among all models.\n" }, { "role": "assistant", "content": "The table shows that the TaLK Convolution (Standard) model uses 59M parameters, which is the least among all listed models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2452.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on CNN-DailyMail abstractive summarization." ], "reasoning": "The table shows that the TaLK Convolution (Standard) model uses 59M parameters, which is the least among all listed models.", "statement": "The TaLK Convolution (Standard) model uses the least number of parameters among all models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Machine translation accuracy in terms of BLEU for WMT En-De and WMT En-Fr on newstest2014.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our model matches the state-of-the-art score on WMT En-Fr and is only 0.1 BLEU points behind the state-of-the-art on WMT En-De, while using the least number of parameters.We demonstrate the effectiveness of our model in the WMT En-De and WMT En-Fr translation benchmarks. Specifically, our method was able to match the state-of-the-art score on WMT En-Fr, a benchmark dataset that is considered indicative for the effectiveness of a method due to the large number of training examples (36M) it contains. Additionally for WMT En-De, our method is only 0.1 BLEU points behind the current state-of-the-art score. It is important to underline, however, that our method uses the least number of parameters compared to the other counterpart methods." } ], "images": [ "scigen/train/2453.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Machine translation accuracy in terms of BLEU for WMT En-De and WMT En-Fr on newstest2014." ], "reasoning": "Our model matches the state-of-the-art score on WMT En-Fr and is only 0.1 BLEU points behind the state-of-the-art on WMT En-De, while using the least number of parameters.", "description": "We demonstrate the effectiveness of our model in the WMT En-De and WMT En-Fr translation benchmarks. Specifically, our method was able to match the state-of-the-art score on WMT En-Fr, a benchmark dataset that is considered indicative for the effectiveness of a method due to the large number of training examples (36M) it contains. Additionally for WMT En-De, our method is only 0.1 BLEU points behind the current state-of-the-art score. It is important to underline, however, that our method uses the least number of parameters compared to the other counterpart methods.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Machine translation accuracy in terms of BLEU for WMT En-De and WMT En-Fr on newstest2014.\nQuestion: Which model uses the least number of parameters and what is its WMT En-De score?\nGive a detailed response to the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that 'TaLK Convolution (Ours)' uses the least number of parameters (209M) and has a WMT En-De score of 29.6. So, the answer is {\"answer\": \"TaLK Convolution (Ours), 29.6\"}." } ], "images": [ "scigen/train/2453.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Machine translation accuracy in terms of BLEU for WMT En-De and WMT En-Fr on newstest2014." ], "reasoning": "The table shows that 'TaLK Convolution (Ours)' uses the least number of parameters (209M) and has a WMT En-De score of 29.6.", "question": "Which model uses the least number of parameters and what is its WMT En-De score?", "answer": "So, the answer is {\"answer\": \"TaLK Convolution (Ours), 29.6\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe TaLK Convolution model has the highest WMT En-Fr score.\n" }, { "role": "assistant", "content": "The table shows that the TaLK Convolution model has a WMT En-Fr score of 43.2, which is matched by two other models (DBLP:journals/corr/abs-1806-00187 and wu2019pay). Therefore, it does not have the highest score. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2453.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Machine translation accuracy in terms of BLEU for WMT En-De and WMT En-Fr on newstest2014." ], "reasoning": "The table shows that the TaLK Convolution model has a WMT En-Fr score of 43.2, which is matched by two other models (DBLP:journals/corr/abs-1806-00187 and wu2019pay). Therefore, it does not have the highest score.", "statement": "The TaLK Convolution model has the highest WMT En-Fr score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Test perplexity on WikiText-103. We used adaptive inputs similar to DBLP:journals/corr/abs-1809-10853 and show that our method yields better perplexity than dynamic convolutions and comparative performance with self-attention.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "We evaluated our method on the WikiText-103 dataset, comparing it to recent methods and found that our method uses fewer parameters while achieving comparative performance.We evaluated our method on the task of language modeling. We considered the WikiText-103 benchmark dataset and we compared against recent methods in the literature. Particularly, we followed the setup that was implemented in the adaptive inputs baseline (DBLP:journals/corr/abs-1809-10853). This work suggest the use of self-attention with adaptive input representations. We substituted the self-attention module with our method. In order to assimilate the number of parameters used in their experiments, we increased the number of layers by one. In addition, we get comparative performance with self-attention. Moreover, we use less number of parameters than the best comparison methods." } ], "images": [ "scigen/train/2454.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Test perplexity on WikiText-103. We used adaptive inputs similar to DBLP:journals/corr/abs-1809-10853 and show that our method yields better perplexity than dynamic convolutions and comparative performance with self-attention." ], "reasoning": "We evaluated our method on the WikiText-103 dataset, comparing it to recent methods and found that our method uses fewer parameters while achieving comparative performance.", "description": "We evaluated our method on the task of language modeling. We considered the WikiText-103 benchmark dataset and we compared against recent methods in the literature. Particularly, we followed the setup that was implemented in the adaptive inputs baseline (DBLP:journals/corr/abs-1809-10853). This work suggest the use of self-attention with adaptive input representations. We substituted the self-attention module with our method. In order to assimilate the number of parameters used in their experiments, we increased the number of layers by one. In addition, we get comparative performance with self-attention. Moreover, we use less number of parameters than the best comparison methods.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 5: Test perplexity on WikiText-103. We used adaptive inputs similar to DBLP:journals/corr/abs-1809-10853 and show that our method yields better perplexity than dynamic convolutions and comparative performance with self-attention.', unfold the steps to solve the question given next. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nQuestion: Which method uses fewer parameters than DBLP:journals/corr/abs-1809-10853 and achieves a test score of 23.3?\n" }, { "role": "assistant", "content": "The table shows that the method 'TaLK Convolution (Ours)' uses 240M parameters and achieves a test score of 23.3, which is fewer parameters than the 247M used by DBLP:journals/corr/abs-1809-10853. So, the answer is {\"answer\": \"TaLK Convolution (Ours)\"}." } ], "images": [ "scigen/train/2454.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Test perplexity on WikiText-103. We used adaptive inputs similar to DBLP:journals/corr/abs-1809-10853 and show that our method yields better perplexity than dynamic convolutions and comparative performance with self-attention." ], "reasoning": "The table shows that the method 'TaLK Convolution (Ours)' uses 240M parameters and achieves a test score of 23.3, which is fewer parameters than the 247M used by DBLP:journals/corr/abs-1809-10853.", "question": "Which method uses fewer parameters than DBLP:journals/corr/abs-1809-10853 and achieves a test score of 23.3?", "answer": "So, the answer is {\"answer\": \"TaLK Convolution (Ours)\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe method DBLP:journals/corr/abs-1809-10853 has the lowest test score.\n" }, { "role": "assistant", "content": "The table shows that DBLP:journals/corr/abs-1809-10853 has a test score of 20.5, which is the lowest among all listed methods. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2454.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Test perplexity on WikiText-103. We used adaptive inputs similar to DBLP:journals/corr/abs-1809-10853 and show that our method yields better perplexity than dynamic convolutions and comparative performance with self-attention." ], "reasoning": "The table shows that DBLP:journals/corr/abs-1809-10853 has a test score of 20.5, which is the lowest among all listed methods.", "statement": "The method DBLP:journals/corr/abs-1809-10853 has the lowest test score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1. Model architecture details.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The model uses a series of convolutional layers to extract features from the input text vectors, followed by an LSTM layer to capture temporal information, and finally employs dueling Q networks to calculate Q values for actions.As stated before, the input of the model is a list of five vectors carrying the GloVe and language model values of the corresponding words in the focus window. Thus, a 2D vector is fed to the model per each window, which is a raw representation of perceived words. To elicit useful features, a set of convolutional layers map the 2D input vector into a 1D feature vector, which can be used in the following layers of the DRL model to learn the Q values for the actions in the textual environment. The implemented convolutional encoder consists of four 2D convolutional layers. The sequential nature of the input text vector representation creates a need for memorizing the previously observed words and, as intended, determining the Q values of each action by integrating the information perceived throughout the revealed text. Consequently, a recurrent (LSTM) layer is utilized to capture and learn the temporal information in the extracted feature vectors. Dueling Q networks calculate the Q values of each action by independently calculating the state value and action advantage values. The Q value can be calculated by adding up the state value and advantage value. The benefit of dueling models is the detachment of prediction of the current state value from the value of taking a specific action, which is much useful in the current study as the careful judgment between the selection of a readability level in the current state or moving further through text is crucial. Each value (state value or advantage value) is calculated using separate dense layers, and the model takes a two-stream architecture after the LSTM layer. The products of these two streams are added up to form the Q values, which indicates the value of each readability level or move action." } ], "images": [ "scigen/train/2455.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1. Model architecture details." ], "reasoning": "The model uses a series of convolutional layers to extract features from the input text vectors, followed by an LSTM layer to capture temporal information, and finally employs dueling Q networks to calculate Q values for actions.", "description": "As stated before, the input of the model is a list of five vectors carrying the GloVe and language model values of the corresponding words in the focus window. Thus, a 2D vector is fed to the model per each window, which is a raw representation of perceived words. To elicit useful features, a set of convolutional layers map the 2D input vector into a 1D feature vector, which can be used in the following layers of the DRL model to learn the Q values for the actions in the textual environment. The implemented convolutional encoder consists of four 2D convolutional layers. The sequential nature of the input text vector representation creates a need for memorizing the previously observed words and, as intended, determining the Q values of each action by integrating the information perceived throughout the revealed text. Consequently, a recurrent (LSTM) layer is utilized to capture and learn the temporal information in the extracted feature vectors. Dueling Q networks calculate the Q values of each action by independently calculating the state value and action advantage values. The Q value can be calculated by adding up the state value and advantage value. The benefit of dueling models is the detachment of prediction of the current state value from the value of taking a specific action, which is much useful in the current study as the careful judgment between the selection of a readability level in the current state or moving further through text is crucial. Each value (state value or advantage value) is calculated using separate dense layers, and the model takes a two-stream architecture after the LSTM layer. The products of these two streams are added up to form the Q values, which indicates the value of each readability level or move action.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1. Model architecture details.\nQuestion: What is the width of the first dense layer in the value stream?\nSolve the above question based on the table information. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table lists the properties of the model, including the width of the dense layers in the value stream. The first dense layer in the value stream has a width of 256. So, the answer is {\"answer\": \"256\"}" } ], "images": [ "scigen/train/2455.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1. Model architecture details." ], "reasoning": "The table lists the properties of the model, including the width of the dense layers in the value stream. The first dense layer in the value stream has a width of 256.", "question": "What is the width of the first dense layer in the value stream?", "answer": "So, the answer is {\"answer\": \"256\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model uses 525 filters in the fourth convolutional layer.\n" }, { "role": "assistant", "content": "The table specifies the number of filters used in each convolutional layer. The fourth convolutional layer uses 525 filters. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2455.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1. Model architecture details." ], "reasoning": "The table specifies the number of filters used in each convolutional layer. The fourth convolutional layer uses 525 filters.", "statement": "The model uses 525 filters in the fourth convolutional layer.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2. Hyperparameters of the proposed deep reinforcement learning text readability assessment model.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Deep reinforcement learning models use stored past interactions to improve training efficiency and prevent forgetting.Deep reinforcement learning models demand numerous interactions with their environment to be sufficiently trained. The previous interactions of the DRL model are stored in [current-state, action, reward, next-state] tuples, which are fed multiple times to the DRL model during its training process. The reintroduction of previous experiences to the model increases the efficiency of data usage and prevents the model from forgetting older experiences." } ], "images": [ "scigen/train/2456.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2. Hyperparameters of the proposed deep reinforcement learning text readability assessment model." ], "reasoning": "Deep reinforcement learning models use stored past interactions to improve training efficiency and prevent forgetting.", "description": "Deep reinforcement learning models demand numerous interactions with their environment to be sufficiently trained. The previous interactions of the DRL model are stored in [current-state, action, reward, next-state] tuples, which are fed multiple times to the DRL model during its training process. The reintroduction of previous experiences to the model increases the efficiency of data usage and prevents the model from forgetting older experiences.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 2. Hyperparameters of the proposed deep reinforcement learning text readability assessment model.'.\n\nQuestion: What is the frequency of target network updates in the DRL model?\n" }, { "role": "assistant", "content": "The table lists the target network update frequency as one of the hyperparameters, which is 5 episodes. So, the answer is {\"answer\": \"5 episodes\"}" } ], "images": [ "scigen/train/2456.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2. Hyperparameters of the proposed deep reinforcement learning text readability assessment model." ], "reasoning": "The table lists the target network update frequency as one of the hyperparameters, which is 5 episodes.", "question": "What is the frequency of target network updates in the DRL model?", "answer": "So, the answer is {\"answer\": \"5 episodes\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe DRL model uses an experience buffer size of 2000.\n" }, { "role": "assistant", "content": "The table shows that the experience buffer size is 1000, not 2000. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2456.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2. Hyperparameters of the proposed deep reinforcement learning text readability assessment model." ], "reasoning": "The table shows that the experience buffer size is 1000, not 2000.", "statement": "The DRL model uses an experience buffer size of 2000.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3. Datasets information.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The Weebit dataset, with over ten thousand texts, was balanced to 3145 texts across five readability levels, while the Cambridge dataset has 331 texts and the Persian dataset has 1731 texts across three classes.Three datasets are used to evaluate three different aspects of the proposed model. Weebit dataset is gathered from articles in the Weekly Reader magazine and the BBC-Bitesize website, which is targeted at readers of various ages. Weebit dataset comprises texts from five different readability levels arranged by age (8-9, 9-10, 10-12, 11-14, 14-16). The total number of texts in the dataset is over ten thousand texts. Nonetheless, due to the significant imbalance in the dataset, some texts are randomly removed from some classes. Prior to this study, distinct models have been used to assess the readability of English texts for second language readers. Since the DRL model eliminates the need for specific feature engineering for different types of text readability assessment, the proposed model can be applied to second language datasets without any modifications. This dataset contains texts from the reading section of Cambridge English Exams, which is targeted for students at five readability levels (A2 to C2) of the Common European Framework of Reference (CEFR). This dataset contains 331 texts, which makes it a small dataset in comparison to the Weebit dataset. The automated feature extraction ability of the proposed model has also given the model the ability to be readily applied to other languages. As GloVe and language models are language-specific, the only necessary change is the use of the GloVe and frequency language model of the target language. These features are readily and freely available on the internet for many languages. This dataset includes near four thousand texts, which are split into three balanced classes." } ], "images": [ "scigen/train/2457.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. Datasets information." ], "reasoning": "The Weebit dataset, with over ten thousand texts, was balanced to 3145 texts across five readability levels, while the Cambridge dataset has 331 texts and the Persian dataset has 1731 texts across three classes.", "description": "Three datasets are used to evaluate three different aspects of the proposed model. Weebit dataset is gathered from articles in the Weekly Reader magazine and the BBC-Bitesize website, which is targeted at readers of various ages. Weebit dataset comprises texts from five different readability levels arranged by age (8-9, 9-10, 10-12, 11-14, 14-16). The total number of texts in the dataset is over ten thousand texts. Nonetheless, due to the significant imbalance in the dataset, some texts are randomly removed from some classes. Prior to this study, distinct models have been used to assess the readability of English texts for second language readers. Since the DRL model eliminates the need for specific feature engineering for different types of text readability assessment, the proposed model can be applied to second language datasets without any modifications. This dataset contains texts from the reading section of Cambridge English Exams, which is targeted for students at five readability levels (A2 to C2) of the Common European Framework of Reference (CEFR). This dataset contains 331 texts, which makes it a small dataset in comparison to the Weebit dataset. The automated feature extraction ability of the proposed model has also given the model the ability to be readily applied to other languages. As GloVe and language models are language-specific, the only necessary change is the use of the GloVe and frequency language model of the target language. These features are readily and freely available on the internet for many languages. This dataset includes near four thousand texts, which are split into three balanced classes.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3. Datasets information.' and methodically tackle the question that follows. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich dataset has the highest average number of words per text?\n" }, { "role": "assistant", "content": "To find the dataset with the highest average number of words per text, compare the '# words/text' column for each dataset. The Cambridge dataset has 519 words per text, which is the highest. So, the answer is {\"answer\": \"Cambridge\"}." } ], "images": [ "scigen/train/2457.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. Datasets information." ], "reasoning": "To find the dataset with the highest average number of words per text, compare the '# words/text' column for each dataset. The Cambridge dataset has 519 words per text, which is the highest.", "question": "Which dataset has the highest average number of words per text?", "answer": "So, the answer is {\"answer\": \"Cambridge\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Persian dataset has more texts than the Cambridge dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Persian dataset has more texts than the Cambridge dataset.\n" }, { "role": "assistant", "content": "The table shows that the Persian dataset has 1731 texts, while the Cambridge dataset has 331 texts. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2457.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. Datasets information." ], "reasoning": "The table shows that the Persian dataset has 1731 texts, while the Cambridge dataset has 331 texts. Therefore, the statement is supported.", "statement": "The Persian dataset has more texts than the Cambridge dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Experimental results (Pearson’s r × 100) on textual similarity tasks. Many results are collected from [53], DAN [22] and [54] (GRAN) except for tfidf-GloVe.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "P-SIF + PSL outperforms PSL + WR on all datasets, and partitioning through GMM generates better performance compared to k-svd for some STS tasks.Results and Analysis We denote our embeddings by P-SIF + PSL (+ PSL denotes using the PSL word vectors). We report the average results for the STS tasks. The detailed results on each sub-dataset are in the Supplementary material 5. We observe that P-SIF + PSL outperforms PSL + WR on all datasets, thus supporting the usefulness of our partitioned averaging. We observe that partitioning through overlapping clustering algorithms such as GMM generates a better performance compared to partitioning through sparse dictionary algorithms such as k-svd for some Semantic Textual Similarity (STS) task datasets. The main reason for this peculiar observation is related to the fact that some STS datasets contain documents which are single sentences of a maximum length of 40 words. As discussed earlier (sparse dictionary learning vs. overlapping clustering), for single sentence documents with a small number of topics, overlapping clustering optimizes better than sparse dictionary learning. Therefore, we use GMM for partitioning words into suitable clusters for some STS tasks. But both k-svd and GMM outperform simple averaging (SIF) by significant margins on most STS tasks. We also report qualitative results with real examples in the Supplementary 5." } ], "images": [ "scigen/train/2458.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Experimental results (Pearson’s r × 100) on textual similarity tasks. Many results are collected from [53], DAN [22] and [54] (GRAN) except for tfidf-GloVe." ], "reasoning": "P-SIF + PSL outperforms PSL + WR on all datasets, and partitioning through GMM generates better performance compared to k-svd for some STS tasks.", "description": "Results and Analysis We denote our embeddings by P-SIF + PSL (+ PSL denotes using the PSL word vectors). We report the average results for the STS tasks. The detailed results on each sub-dataset are in the Supplementary material 5. We observe that P-SIF + PSL outperforms PSL + WR on all datasets, thus supporting the usefulness of our partitioned averaging. We observe that partitioning through overlapping clustering algorithms such as GMM generates a better performance compared to partitioning through sparse dictionary algorithms such as k-svd for some Semantic Textual Similarity (STS) task datasets. The main reason for this peculiar observation is related to the fact that some STS datasets contain documents which are single sentences of a maximum length of 40 words. As discussed earlier (sparse dictionary learning vs. overlapping clustering), for single sentence documents with a small number of topics, overlapping clustering optimizes better than sparse dictionary learning. Therefore, we use GMM for partitioning words into suitable clusters for some STS tasks. But both k-svd and GMM outperform simple averaging (SIF) by significant margins on most STS tasks. We also report qualitative results with real examples in the Supplementary 5.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 1: Experimental results (Pearson’s r × 100) on textual similarity tasks. Many results are collected from [53], DAN [22] and [54] (GRAN) except for tfidf-GloVe.' to sequentially solve the question presented below. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich method achieved the highest score on the STS'15 dataset?\n" }, { "role": "assistant", "content": "By examining the table, we see that the highest score on the STS'15 dataset is achieved by the PSIF + PSL method with a score of 77.3. So, the answer is {\"answer\": \"PSIF + PSL\"}" } ], "images": [ "scigen/train/2458.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Experimental results (Pearson’s r × 100) on textual similarity tasks. Many results are collected from [53], DAN [22] and [54] (GRAN) except for tfidf-GloVe." ], "reasoning": "By examining the table, we see that the highest score on the STS'15 dataset is achieved by the PSIF + PSL method with a score of 77.3.", "question": "Which method achieved the highest score on the STS'15 dataset?", "answer": "So, the answer is {\"answer\": \"PSIF + PSL\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe GRAN method outperformed the LSTM (o.g.) method on the STS'14 dataset.\n" }, { "role": "assistant", "content": "The table shows that on the STS'14 dataset, GRAN scored 75.9 while LSTM (o.g.) scored 51.5, indicating that GRAN outperformed LSTM (o.g.). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2458.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Experimental results (Pearson’s r × 100) on textual similarity tasks. Many results are collected from [53], DAN [22] and [54] (GRAN) except for tfidf-GloVe." ], "reasoning": "The table shows that on the STS'14 dataset, GRAN scored 75.9 while LSTM (o.g.) scored 51.5, indicating that GRAN outperformed LSTM (o.g.).", "statement": "The GRAN method outperformed the LSTM (o.g.) method on the STS'14 dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: P-SIF comparison with the recent embedding techniques on various STS tasks. Baselines taken from [10], [47], [7], [12], [55] and [15]'.\n" }, { "role": "assistant", "content": "P-SIF + PSL outperforms PSL + WR on all datasets, and GMM partitioning generates better performance compared to k-svd for some STS tasks.Results and Analysis We denote our embeddings by P-SIF + PSL (+ PSL denotes using the PSL word vectors). We report the average results for the STS tasks. The detailed results on each sub-dataset are in the Supplementary material 5. We observe that P-SIF + PSL outperforms PSL + WR on all datasets, thus supporting the usefulness of our partitioned averaging. We observe that partitioning through overlapping clustering algorithms such as GMM generates a better performance compared to partitioning through sparse dictionary algorithms such as k-svd for some Semantic Textual Similarity (STS) task datasets. The main reason for this peculiar observation is related to the fact that some STS datasets contain documents which are single sentences of a maximum length of 40 words. As discussed earlier (sparse dictionary learning vs. overlapping clustering), for single sentence documents with a small number of topics, overlapping clustering optimizes better than sparse dictionary learning. Therefore, we use GMM for partitioning words into suitable clusters for some STS tasks. But both k-svd and GMM outperform simple averaging (SIF) by significant margins on most STS tasks. We also report qualitative results with real examples in the Supplementary 5." } ], "images": [ "scigen/train/2459.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: P-SIF comparison with the recent embedding techniques on various STS tasks. Baselines taken from [10], [47], [7], [12], [55] and [15]" ], "reasoning": "P-SIF + PSL outperforms PSL + WR on all datasets, and GMM partitioning generates better performance compared to k-svd for some STS tasks.", "description": "Results and Analysis We denote our embeddings by P-SIF + PSL (+ PSL denotes using the PSL word vectors). We report the average results for the STS tasks. The detailed results on each sub-dataset are in the Supplementary material 5. We observe that P-SIF + PSL outperforms PSL + WR on all datasets, thus supporting the usefulness of our partitioned averaging. We observe that partitioning through overlapping clustering algorithms such as GMM generates a better performance compared to partitioning through sparse dictionary algorithms such as k-svd for some Semantic Textual Similarity (STS) task datasets. The main reason for this peculiar observation is related to the fact that some STS datasets contain documents which are single sentences of a maximum length of 40 words. As discussed earlier (sparse dictionary learning vs. overlapping clustering), for single sentence documents with a small number of topics, overlapping clustering optimizes better than sparse dictionary learning. Therefore, we use GMM for partitioning words into suitable clusters for some STS tasks. But both k-svd and GMM outperform simple averaging (SIF) by significant margins on most STS tasks. We also report qualitative results with real examples in the Supplementary 5.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: P-SIF comparison with the recent embedding techniques on various STS tasks. Baselines taken from [10], [47], [7], [12], [55] and [15]', solve the subsequent question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich method has the highest average score across all STS tasks?\n" }, { "role": "assistant", "content": "To find the method with the highest average score, we need to compare the average scores of all methods listed in the table. The highest average score is 71.4, achieved by u-SIF + PSL. So, the answer is {\"answer\": \"u-SIF + PSL\"}" } ], "images": [ "scigen/train/2459.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: P-SIF comparison with the recent embedding techniques on various STS tasks. Baselines taken from [10], [47], [7], [12], [55] and [15]" ], "reasoning": "To find the method with the highest average score, we need to compare the average scores of all methods listed in the table. The highest average score is 71.4, achieved by u-SIF + PSL.", "question": "Which method has the highest average score across all STS tasks?", "answer": "So, the answer is {\"answer\": \"u-SIF + PSL\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis P-SIF + PSL has the highest score in STS 15. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nP-SIF + PSL has the highest score in STS 15.\n" }, { "role": "assistant", "content": "To verify this statement, we need to check the scores for STS 15. P-SIF + PSL has a score of 77.6, which is the highest among all methods for STS 15. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2459.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: P-SIF comparison with the recent embedding techniques on various STS tasks. Baselines taken from [10], [47], [7], [12], [55] and [15]" ], "reasoning": "To verify this statement, we need to check the scores for STS 15. P-SIF + PSL has a score of 77.6, which is the highest among all methods for STS 15.", "statement": "P-SIF + PSL has the highest score in STS 15.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Multi-class classification performance on 20NewsGroups.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "P-SIF (Doc2VecC) achieves the highest accuracy, precision, recall, and F-measure among the models, indicating its superior performance.We observe that the dictionary learns more diverse and non-redundant topics compared to overlapping clustering (SCDV) since we require only 40 partitions rather than 60 partitions in SCDV to obtain the best performance. Simple tf-idf weighted averaging-based document representations do not show significant improvement in performance by increasing word vector dimensions. We achieve a <0.4% improvement in the accuracy when the word-vector dimensions increase from 200 to 500 on 20NewsGroup. We observe that increasing the word-vectors’ dimensions beyond 500 does not improve SIF and P-SIF ’s performances. On 20NewsGroup, we require only 20 partitions instead of 40 with Doc2VecC-initialized word vectors. This shows that better word vector representations help in learning more diverse and non-redundant partitions. We also report our results (micro-F1) on each of the 20 classes of 20NewsGroup in the Supplementary material 5. Additionally, we empirically show that our proposed embedding P-SIF outperforms the word mover distance Overall, P-SIF outperforms most methods on several datasets by a significant margin." } ], "images": [ "scigen/train/2460.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Multi-class classification performance on 20NewsGroups." ], "reasoning": "P-SIF (Doc2VecC) achieves the highest accuracy, precision, recall, and F-measure among the models, indicating its superior performance.", "description": "We observe that the dictionary learns more diverse and non-redundant topics compared to overlapping clustering (SCDV) since we require only 40 partitions rather than 60 partitions in SCDV to obtain the best performance. Simple tf-idf weighted averaging-based document representations do not show significant improvement in performance by increasing word vector dimensions. We achieve a <0.4% improvement in the accuracy when the word-vector dimensions increase from 200 to 500 on 20NewsGroup. We observe that increasing the word-vectors’ dimensions beyond 500 does not improve SIF and P-SIF ’s performances. On 20NewsGroup, we require only 20 partitions instead of 40 with Doc2VecC-initialized word vectors. This shows that better word vector representations help in learning more diverse and non-redundant partitions. We also report our results (micro-F1) on each of the 20 classes of 20NewsGroup in the Supplementary material 5. Additionally, we empirically show that our proposed embedding P-SIF outperforms the word mover distance Overall, P-SIF outperforms most methods on several datasets by a significant margin.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: Multi-class classification performance on 20NewsGroups.', solve this question and give detail solutions. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich model achieves the highest F-measure score?\n" }, { "role": "assistant", "content": "The table shows that P-SIF (Doc2VecC) has the highest F-measure score of 86.0, which is the highest among all listed models. So, the answer is {\"answer\": \"P-SIF (Doc2VecC)\"}." } ], "images": [ "scigen/train/2460.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Multi-class classification performance on 20NewsGroups." ], "reasoning": "The table shows that P-SIF (Doc2VecC) has the highest F-measure score of 86.0, which is the highest among all listed models.", "question": "Which model achieves the highest F-measure score?", "answer": "So, the answer is {\"answer\": \"P-SIF (Doc2VecC)\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe SCDV model has a higher precision than the BERT (pr) model.\n" }, { "role": "assistant", "content": "The table shows that the precision of the SCDV model is 84.6, while the precision of the BERT (pr) model is 84.9. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2460.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Multi-class classification performance on 20NewsGroups." ], "reasoning": "The table shows that the precision of the SCDV model is 84.6, while the precision of the BERT (pr) model is 84.9. Therefore, the statement is incorrect.", "statement": "The SCDV model has a higher precision than the BERT (pr) model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Performance on multi-label classification on Reuters.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "P-SIF with Doc2VecC-initialized word vectors requires fewer partitions and achieves high performance, outperforming other methods on several datasets.We observe that the dictionary learns more diverse and non-redundant topics compared to overlapping clustering (SCDV) since we require only 40 partitions rather than 60 partitions in SCDV to obtain the best performance. Simple tf-idf weighted averaging-based document representations do not show significant improvement in performance by increasing word vector dimensions. We achieve a <0.4% improvement in the accuracy when the word-vector dimensions increase from 200 to 500 on 20NewsGroup. We observe that increasing the word-vectors’ dimensions beyond 500 does not improve SIF and P-SIF ’s performances. On 20NewsGroup, we require only 20 partitions instead of 40 with Doc2VecC-initialized word vectors. This shows that better word vector representations help in learning more diverse and non-redundant partitions. We also report our results (micro-F1) on each of the 20 classes of 20NewsGroup in the Supplementary material 5. Additionally, we empirically show that our proposed embedding P-SIF outperforms the word mover distance Overall, P-SIF outperforms most methods on several datasets by a significant margin." } ], "images": [ "scigen/train/2461.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Performance on multi-label classification on Reuters." ], "reasoning": "P-SIF with Doc2VecC-initialized word vectors requires fewer partitions and achieves high performance, outperforming other methods on several datasets.", "description": "We observe that the dictionary learns more diverse and non-redundant topics compared to overlapping clustering (SCDV) since we require only 40 partitions rather than 60 partitions in SCDV to obtain the best performance. Simple tf-idf weighted averaging-based document representations do not show significant improvement in performance by increasing word vector dimensions. We achieve a <0.4% improvement in the accuracy when the word-vector dimensions increase from 200 to 500 on 20NewsGroup. We observe that increasing the word-vectors’ dimensions beyond 500 does not improve SIF and P-SIF ’s performances. On 20NewsGroup, we require only 20 partitions instead of 40 with Doc2VecC-initialized word vectors. This shows that better word vector representations help in learning more diverse and non-redundant partitions. We also report our results (micro-F1) on each of the 20 classes of 20NewsGroup in the Supplementary material 5. Additionally, we empirically show that our proposed embedding P-SIF outperforms the word mover distance Overall, P-SIF outperforms most methods on several datasets by a significant margin.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Performance on multi-label classification on Reuters.\nQuestion: Which model has the highest F1 score and what is its value?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the F1 column, we see that P-SIF (Doc2VecC) has the highest F1 score of 82.87. So, the answer is {\"answer\": \"P-SIF (Doc2VecC) with 82.87\"}" } ], "images": [ "scigen/train/2461.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Performance on multi-label classification on Reuters." ], "reasoning": "By examining the F1 column, we see that P-SIF (Doc2VecC) has the highest F1 score of 82.87.", "question": "Which model has the highest F1 score and what is its value?", "answer": "So, the answer is {\"answer\": \"P-SIF (Doc2VecC) with 82.87\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nSIF has a higher F1 score than PV-DBoW.\n" }, { "role": "assistant", "content": "The F1 score for SIF is 81.29, while the F1 score for PV-DBoW is 71.24. Therefore, SIF has a higher F1 score than PV-DBoW. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2461.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Performance on multi-label classification on Reuters." ], "reasoning": "The F1 score for SIF is 81.29, while the F1 score for PV-DBoW is 71.24. Therefore, SIF has a higher F1 score than PV-DBoW.", "statement": "SIF has a higher F1 score than PV-DBoW.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 8: Experimental results (Pearson’s r × 100) on textual similarity tasks on STS 16. The highest score in each row is in bold.'.\n" }, { "role": "assistant", "content": "Our method outperforms state-of-the-art supervised methods on the STS16 task and performs significantly better than all unsupervised embedding methods.In this supplementary section, we present the details of the STS tasks for each year. Note that tasks with the same name in different years are different tasks in reality. Our method performs significantly better in comparison to all unsupervised embedding methods. In addition, P-SIF is very close to the best performance by supervised methods on the rest of the datasets. Our method was also able to outperform state of the art supervised averaging based Gated Recurrent Averaging Network (GRAN) Our results also outperform state of the art methods on many recent supervised embedding methods on the STS 16 task (" } ], "images": [ "scigen/train/2462.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Experimental results (Pearson’s r × 100) on textual similarity tasks on STS 16. The highest score in each row is in bold." ], "reasoning": "Our method outperforms state-of-the-art supervised methods on the STS16 task and performs significantly better than all unsupervised embedding methods.", "description": "In this supplementary section, we present the details of the STS tasks for each year. Note that tasks with the same name in different years are different tasks in reality. Our method performs significantly better in comparison to all unsupervised embedding methods. In addition, P-SIF is very close to the best performance by supervised methods on the rest of the datasets. Our method was also able to outperform state of the art supervised averaging based Gated Recurrent Averaging Network (GRAN) Our results also outperform state of the art methods on many recent supervised embedding methods on the STS 16 task (", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 8: Experimental results (Pearson’s r × 100) on textual similarity tasks on STS 16. The highest score in each row is in bold.', answer the question below by showing each progressive step toward the answer. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich method achieved the highest score on the plagiarism task?\n" }, { "role": "assistant", "content": "To find the highest score on the plagiarism task, look at the 'plagiarism' row and identify the highest value across all columns. So, the answer is {\"answer\": \"81.6\"}" } ], "images": [ "scigen/train/2462.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Experimental results (Pearson’s r × 100) on textual similarity tasks on STS 16. The highest score in each row is in bold." ], "reasoning": "To find the highest score on the plagiarism task, look at the 'plagiarism' row and identify the highest value across all columns.", "question": "Which method achieved the highest score on the plagiarism task?", "answer": "So, the answer is {\"answer\": \"81.6\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe highest score on the post editing task was achieved by the PSL method.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'post editing' row and identify the highest value. The highest score is 83.7, which corresponds to the PSL method. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2462.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Experimental results (Pearson’s r × 100) on textual similarity tasks on STS 16. The highest score in each row is in bold." ], "reasoning": "To verify this statement, look at the 'post editing' row and identify the highest value. The highest score is 83.7, which corresponds to the PSL method.", "statement": "The highest score on the post editing task was achieved by the PSL method.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 12: STS 2012 MSRVid examples where the P-SIF score were far away from the ground truth, whereas the SIF scores were closer to the actual ground truth', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The table compares SIF and P-SIF embedding similarity scores against normalized ground truth scores, showing their respective errors and differences.MSRvid dataset where SIF assigns a similarity score closer to the ground truth than P-SIF. GT: represents the given ground truth similarity score in a range of 0-5. NGT : represents the normalized ground truth similarity score. NGT is obtained by dividing the GT score by 5 so that it is in a range of 0-1. SIFsc : represents the SIF embedding similarity score in a range of 0-1. P-SIFsc: represents the P-SIF embedding similarity score in a range of 0-5. SIFerr: represents absolute error ∥SIFsc−NGT∥ between normalized ground truth similarity score and the SIF embedding similarity score. P-SIFerr: represents the absolute error ∥\\textscP−SIFsc−NGT∥ between the ground truth similarity score and the P-SIF embedding similarity score. Differr: represents absolute difference between SIFerr and P-SIFerr. Examples where P-SIF performs better Differr = Examples where SIF performs better Differr = Relerr: represents relative difference between SIFerr and P-SIFerr." } ], "images": [ "scigen/train/2463.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 12: STS 2012 MSRVid examples where the P-SIF score were far away from the ground truth, whereas the SIF scores were closer to the actual ground truth" ], "reasoning": "The table compares SIF and P-SIF embedding similarity scores against normalized ground truth scores, showing their respective errors and differences.", "description": "MSRvid dataset where SIF assigns a similarity score closer to the ground truth than P-SIF. GT: represents the given ground truth similarity score in a range of 0-5. NGT : represents the normalized ground truth similarity score. NGT is obtained by dividing the GT score by 5 so that it is in a range of 0-1. SIFsc : represents the SIF embedding similarity score in a range of 0-1. P-SIFsc: represents the P-SIF embedding similarity score in a range of 0-5. SIFerr: represents absolute error ∥SIFsc−NGT∥ between normalized ground truth similarity score and the SIF embedding similarity score. P-SIFerr: represents the absolute error ∥\\textscP−SIFsc−NGT∥ between the ground truth similarity score and the P-SIF embedding similarity score. Differr: represents absolute difference between SIFerr and P-SIFerr. Examples where P-SIF performs better Differr = Examples where SIF performs better Differr = Relerr: represents relative difference between SIFerr and P-SIFerr.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 12: STS 2012 MSRVid examples where the P-SIF score were far away from the ground truth, whereas the SIF scores were closer to the actual ground truth', and tackle the question below in a sequential manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich example has the highest relative error (Relerr) between SIFerr and P-SIFerr?\n" }, { "role": "assistant", "content": "To find the example with the highest relative error, we need to look at the 'Relerr' column and identify the maximum value. The highest relative error is 0.9892, which corresponds to the example where 'An animal is biting a persons finger' and 'A slow loris is biting a persons finger'. So, the answer is {\"answer\": \"An animal is biting a persons finger and A slow loris is biting a persons finger\"}." } ], "images": [ "scigen/train/2463.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 12: STS 2012 MSRVid examples where the P-SIF score were far away from the ground truth, whereas the SIF scores were closer to the actual ground truth" ], "reasoning": "To find the example with the highest relative error, we need to look at the 'Relerr' column and identify the maximum value. The highest relative error is 0.9892, which corresponds to the example where 'An animal is biting a persons finger' and 'A slow loris is biting a persons finger'.", "question": "Which example has the highest relative error (Relerr) between SIFerr and P-SIFerr?", "answer": "So, the answer is {\"answer\": \"An animal is biting a persons finger and A slow loris is biting a persons finger\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe SIF embedding similarity score (SIFsc) for the example 'The lady sliced a tomato' and 'Someone is cutting a tomato' is higher than the P-SIF embedding similarity score (P-SIFsc).\n" }, { "role": "assistant", "content": "The table shows that for the example 'The lady sliced a tomato' and 'Someone is cutting a tomato', the SIFsc is 0.693 and the P-SIFsc is 0.5591. Since 0.693 is indeed higher than 0.5591, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2463.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 12: STS 2012 MSRVid examples where the P-SIF score were far away from the ground truth, whereas the SIF scores were closer to the actual ground truth" ], "reasoning": "The table shows that for the example 'The lady sliced a tomato' and 'Someone is cutting a tomato', the SIFsc is 0.693 and the P-SIFsc is 0.5591. Since 0.693 is indeed higher than 0.5591, the statement is supported.", "statement": "The SIF embedding similarity score (SIFsc) for the example 'The lady sliced a tomato' and 'Someone is cutting a tomato' is higher than the P-SIF embedding similarity score (P-SIFsc).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 13: STS 2012 MSRVid example where the P-SIF scores were closer to the ground truth, whereas SIF scores were more away from the ground truth'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The table compares the performance of SIF and P-SIF embedding similarity scores against normalized ground truth similarity scores, showing the absolute and relative errors for each method.MSRvid dataset where SIF assigns a similarity score closer to the ground truth than P-SIF. GT: represents the given ground truth similarity score in a range of 0-5. NGT : represents the normalized ground truth similarity score. NGT is obtained by dividing the GT score by 5 so that it is in a range of 0-1. SIFsc : represents the SIF embedding similarity score in a range of 0-1. P-SIFsc: represents the P-SIF embedding similarity score in a range of 0-5. SIFerr: represents absolute error ∥SIFsc−NGT∥ between normalized ground truth similarity score and the SIF embedding similarity score. P-SIFerr: represents the absolute error ∥\\textscP−SIFsc−NGT∥ between the ground truth similarity score and the P-SIF embedding similarity score. Differr: represents absolute difference between SIFerr and P-SIFerr. Examples where P-SIF performs better Differr = Examples where SIF performs better Differr = Relerr: represents relative difference between SIFerr and P-SIFerr." } ], "images": [ "scigen/train/2464.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 13: STS 2012 MSRVid example where the P-SIF scores were closer to the ground truth, whereas SIF scores were more away from the ground truth" ], "reasoning": "The table compares the performance of SIF and P-SIF embedding similarity scores against normalized ground truth similarity scores, showing the absolute and relative errors for each method.", "description": "MSRvid dataset where SIF assigns a similarity score closer to the ground truth than P-SIF. GT: represents the given ground truth similarity score in a range of 0-5. NGT : represents the normalized ground truth similarity score. NGT is obtained by dividing the GT score by 5 so that it is in a range of 0-1. SIFsc : represents the SIF embedding similarity score in a range of 0-1. P-SIFsc: represents the P-SIF embedding similarity score in a range of 0-5. SIFerr: represents absolute error ∥SIFsc−NGT∥ between normalized ground truth similarity score and the SIF embedding similarity score. P-SIFerr: represents the absolute error ∥\\textscP−SIFsc−NGT∥ between the ground truth similarity score and the P-SIF embedding similarity score. Differr: represents absolute difference between SIFerr and P-SIFerr. Examples where P-SIF performs better Differr = Examples where SIF performs better Differr = Relerr: represents relative difference between SIFerr and P-SIFerr.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 13: STS 2012 MSRVid example where the P-SIF scores were closer to the ground truth, whereas SIF scores were more away from the ground truth', solve the subsequent question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich example has the highest SIF embedding similarity score (SIFsc)?\n" }, { "role": "assistant", "content": "To find the highest SIF embedding similarity score, we need to look at the SIFsc column and identify the maximum value. The highest SIFsc value is 0.8157 for the example 'The missile exploded. - A rocket exploded.' So, the answer is {\"answer\": \"0.8157\"}." } ], "images": [ "scigen/train/2464.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 13: STS 2012 MSRVid example where the P-SIF scores were closer to the ground truth, whereas SIF scores were more away from the ground truth" ], "reasoning": "To find the highest SIF embedding similarity score, we need to look at the SIFsc column and identify the maximum value. The highest SIFsc value is 0.8157 for the example 'The missile exploded. - A rocket exploded.'", "question": "Which example has the highest SIF embedding similarity score (SIFsc)?", "answer": "So, the answer is {\"answer\": \"0.8157\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe example 'A man is riding a horse. - A girl is riding a horse.' has a higher SIF embedding similarity score than the example 'A woman is peeling an apple. - A woman is peeling an apple.'\n" }, { "role": "assistant", "content": "To verify this statement, we compare the SIFsc values of the two examples. The SIFsc for 'A man is riding a horse. - A girl is riding a horse.' is 0.6933, while the SIFsc for 'A woman is peeling an apple. - A woman is peeling an apple.' is 0.6938. Since 0.6933 is less than 0.6938, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2464.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 13: STS 2012 MSRVid example where the P-SIF scores were closer to the ground truth, whereas SIF scores were more away from the ground truth" ], "reasoning": "To verify this statement, we compare the SIFsc values of the two examples. The SIFsc for 'A man is riding a horse. - A girl is riding a horse.' is 0.6933, while the SIFsc for 'A woman is peeling an apple. - A woman is peeling an apple.' is 0.6938. Since 0.6933 is less than 0.6938, the statement is refuted.", "statement": "The example 'A man is riding a horse. - A girl is riding a horse.' has a higher SIF embedding similarity score than the example 'A woman is peeling an apple. - A woman is peeling an apple.'", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Number of entities and triplets in the the OOKB datasets. The numbers of triplets include negative triplets.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Nine datasets were created by selecting OOKB entities from WordNet11 test triplets in three ways (Head, Tail, Both) and filtering them based on their connections with training entities.In total, nine datasets were constructed with different numbers and positions of OOKB entities sampled from the test set. The process consists of two steps: choosing OOKB entities and filtering and splitting triplets. Choosing OOKB entities. To choose the OOKB entities, we first selected N=1,000, 3,000, and 5,000 triplets from the WordNet11 test file. For each of these three sets, we chose the initial candidates for the OOKB entities (denoted by I) in three different ways (thereby yielding nine datasets in total); these settings are called Head, Tail, and Both. In the Head setting, all head entities in the N triplets are regarded as candidate OOKB entities. The Tail setting is similar, but with the tail entities regarded as candidates. In the Both setting, all entities appearing as either a head or tail are the candidates. The final OOKB entities are the entities e∈I that appear in a triplet (e,r,e′) or (e′,r,e) in the WordNet11 training set, with e′∉I. Note that the entities h,t∉I are contained in the knowledge bases we already have and not in the OOKB entities. This last process filters out candidate OOKB entities that do not have any connection with the training entities. Filtering and splitting triplets. Using the selected OOKB entities, the original training dataset was split into the training dataset and the auxiliary datasets for the OOKB entity problem. That is, triplets that did not contain the OOKB entities were placed in the OOKB training set, and triplets containing one OOKB entity and one non-OOKB entity were placed in the auxiliary set. Triplets that contained two OOKB entities were discarded. For the test triplets, we used the same first N triplets in the WordNet11 test file that we used in Step 1, with the exception that the triplets that did not contain any OOKB entities were removed. For the validation triplets, we simply removed the triplets containing OOKB entities from the WordNet11 validation set. We denote each of the nine datasets by {Head,Tail,Both}-{1,000,3,000,5,000}, respectively, where the first part represents the position of OOKB entities and the second part represents the number of triplets used for generating the OOKB entities." } ], "images": [ "scigen/train/2465.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Number of entities and triplets in the the OOKB datasets. The numbers of triplets include negative triplets." ], "reasoning": "Nine datasets were created by selecting OOKB entities from WordNet11 test triplets in three ways (Head, Tail, Both) and filtering them based on their connections with training entities.", "description": "In total, nine datasets were constructed with different numbers and positions of OOKB entities sampled from the test set. The process consists of two steps: choosing OOKB entities and filtering and splitting triplets. Choosing OOKB entities. To choose the OOKB entities, we first selected N=1,000, 3,000, and 5,000 triplets from the WordNet11 test file. For each of these three sets, we chose the initial candidates for the OOKB entities (denoted by I) in three different ways (thereby yielding nine datasets in total); these settings are called Head, Tail, and Both. In the Head setting, all head entities in the N triplets are regarded as candidate OOKB entities. The Tail setting is similar, but with the tail entities regarded as candidates. In the Both setting, all entities appearing as either a head or tail are the candidates. The final OOKB entities are the entities e∈I that appear in a triplet (e,r,e′) or (e′,r,e) in the WordNet11 training set, with e′∉I. Note that the entities h,t∉I are contained in the knowledge bases we already have and not in the OOKB entities. This last process filters out candidate OOKB entities that do not have any connection with the training entities. Filtering and splitting triplets. Using the selected OOKB entities, the original training dataset was split into the training dataset and the auxiliary datasets for the OOKB entity problem. That is, triplets that did not contain the OOKB entities were placed in the OOKB training set, and triplets containing one OOKB entity and one non-OOKB entity were placed in the auxiliary set. Triplets that contained two OOKB entities were discarded. For the test triplets, we used the same first N triplets in the WordNet11 test file that we used in Step 1, with the exception that the triplets that did not contain any OOKB entities were removed. For the validation triplets, we simply removed the triplets containing OOKB entities from the WordNet11 validation set. We denote each of the nine datasets by {Head,Tail,Both}-{1,000,3,000,5,000}, respectively, where the first part represents the position of OOKB entities and the second part represents the number of triplets used for generating the OOKB entities.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: Number of entities and triplets in the the OOKB datasets. The numbers of triplets include negative triplets.', answer the following question. Give detailed solution consisting of each step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich dataset has the highest number of auxiliary triplets?\n" }, { "role": "assistant", "content": "To find the dataset with the highest number of auxiliary triplets, compare the values in the 'Auxiliary triplets' row for all datasets. The highest value is 48,425 in the Both-5,000 dataset. So, the answer is {\"answer\": \"Both-5,000\"}." } ], "images": [ "scigen/train/2465.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Number of entities and triplets in the the OOKB datasets. The numbers of triplets include negative triplets." ], "reasoning": "To find the dataset with the highest number of auxiliary triplets, compare the values in the 'Auxiliary triplets' row for all datasets. The highest value is 48,425 in the Both-5,000 dataset.", "question": "Which dataset has the highest number of auxiliary triplets?", "answer": "So, the answer is {\"answer\": \"Both-5,000\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe dataset with the highest number of OOKB entities is Tail-5,000.\n" }, { "role": "assistant", "content": "To verify this statement, compare the values in the 'OOKB entities' row for all datasets. The highest value is 4,963 in the Both-5,000 dataset, not Tail-5,000. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2465.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Number of entities and triplets in the the OOKB datasets. The numbers of triplets include negative triplets." ], "reasoning": "To verify this statement, compare the values in the 'OOKB entities' row for all datasets. The highest value is 4,963 in the Both-5,000 dataset, not Tail-5,000.", "statement": "The dataset with the highest number of OOKB entities is Tail-5,000.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: Results of the OOKB experiment: accuracy of the simple baseline and proposed models. Bold and underlined figures are respectively the best and second best scores for each dataset.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Graph-NN with average pooling outperforms all other methods on all datasets, while Graph-NN with max pooling shows good accuracy but is outperformed by the baseline model with average pooling in some settings.The column labeled “pooling” indicates which pooling function was used. As the table shows, our model outperforms the baselines considerably. In particular, Graph-NN with average pooling outperforms the other methods on all datasets. Graph-NN with max pooling also shows good accuracy, but in several settings, such as Tail-3000, it was outperformed by the baseline model with average pooling." } ], "images": [ "scigen/train/2466.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of the OOKB experiment: accuracy of the simple baseline and proposed models. Bold and underlined figures are respectively the best and second best scores for each dataset." ], "reasoning": "Graph-NN with average pooling outperforms all other methods on all datasets, while Graph-NN with max pooling shows good accuracy but is outperformed by the baseline model with average pooling in some settings.", "description": "The column labeled “pooling” indicates which pooling function was used. As the table shows, our model outperforms the baselines considerably. In particular, Graph-NN with average pooling outperforms the other methods on all datasets. Graph-NN with max pooling also shows good accuracy, but in several settings, such as Tail-3000, it was outperformed by the baseline model with average pooling.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 4: Results of the OOKB experiment: accuracy of the simple baseline and proposed models. Bold and underlined figures are respectively the best and second best scores for each dataset.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: Which pooling method in the proposed model achieves the highest accuracy on the Head 1,000 dataset?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the highest accuracy on the Head 1,000 dataset, look at the 'Head 1,000' column. The proposed model with average pooling achieves the highest accuracy of 87.3. So, the answer is {\"answer\": \"87.3\"}" } ], "images": [ "scigen/train/2466.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of the OOKB experiment: accuracy of the simple baseline and proposed models. Bold and underlined figures are respectively the best and second best scores for each dataset." ], "reasoning": "To find the highest accuracy on the Head 1,000 dataset, look at the 'Head 1,000' column. The proposed model with average pooling achieves the highest accuracy of 87.3.", "question": "Which pooling method in the proposed model achieves the highest accuracy on the Head 1,000 dataset?", "answer": "So, the answer is {\"answer\": \"87.3\"}" }, { "messages": [ { "role": "user", "content": "The proposed model with max pooling outperforms the baseline model with average pooling on the Tail 3,000 dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe proposed model with max pooling outperforms the baseline model with average pooling on the Tail 3,000 dataset.\n" }, { "role": "assistant", "content": "The proposed model with max pooling has an accuracy of 63.1 on the Tail 3,000 dataset, while the baseline model with average pooling has an accuracy of 63.9. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2466.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of the OOKB experiment: accuracy of the simple baseline and proposed models. Bold and underlined figures are respectively the best and second best scores for each dataset." ], "reasoning": "The proposed model with max pooling has an accuracy of 63.1 on the Tail 3,000 dataset, while the baseline model with average pooling has an accuracy of 63.9. Therefore, the statement is refuted.", "statement": "The proposed model with max pooling outperforms the baseline model with average pooling on the Tail 3,000 dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: GAT settings results on development set.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The GAT model achieves the highest BLEU score with 2 layers, and performance decreases as the number of layers increases.In particular, we vary the value of l in the set {1, 2, 3, 4, 5} and measure the corresponding BLEU scores. The structural-aware model equipped with GAT achieves the best performance when l is 2, which justifies the selection on the number of layers in the experimental setting section. Moreover, a dropping trend on both metrics is present as l increases. For a larger l, the GAT module becomes more difficult to train due to larger amounts of parameters. One intuitive reason is that each layer of the GAT module aggregates the direct neighbor information of a node. After 2 layers, each node can obtain sufficient information, and further more layers can bring noise. We make comparison with multi-head attention, varying the heads in the set {1, 2, 3, 4, 5} and checking the corresponding BLEU scores. Theoretically, the larger the number of heads, the better the performance of the model. We thus choose the head to be 4 for the remaining experiments." } ], "images": [ "scigen/train/2467.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: GAT settings results on development set." ], "reasoning": "The GAT model achieves the highest BLEU score with 2 layers, and performance decreases as the number of layers increases.", "description": "In particular, we vary the value of l in the set {1, 2, 3, 4, 5} and measure the corresponding BLEU scores. The structural-aware model equipped with GAT achieves the best performance when l is 2, which justifies the selection on the number of layers in the experimental setting section. Moreover, a dropping trend on both metrics is present as l increases. For a larger l, the GAT module becomes more difficult to train due to larger amounts of parameters. One intuitive reason is that each layer of the GAT module aggregates the direct neighbor information of a node. After 2 layers, each node can obtain sufficient information, and further more layers can bring noise. We make comparison with multi-head attention, varying the heads in the set {1, 2, 3, 4, 5} and checking the corresponding BLEU scores. Theoretically, the larger the number of heads, the better the performance of the model. We thus choose the head to be 4 for the remaining experiments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: GAT settings results on development set.' and methodically tackle the question that follows. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the BLEU score when the number of layers is 4, and how does it compare to the BLEU score when the number of layers is 2?\n" }, { "role": "assistant", "content": "The BLEU score for 4 layers is 49.01, which is lower than the BLEU score of 50.04 for 2 layers, indicating a decrease in performance with more layers. So, the answer is {\"answer\": \"49.01\"}" } ], "images": [ "scigen/train/2467.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: GAT settings results on development set." ], "reasoning": "The BLEU score for 4 layers is 49.01, which is lower than the BLEU score of 50.04 for 2 layers, indicating a decrease in performance with more layers.", "question": "What is the BLEU score when the number of layers is 4, and how does it compare to the BLEU score when the number of layers is 2?", "answer": "So, the answer is {\"answer\": \"49.01\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The BLEU score decreases as the number of layers increases from 2 to 5. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe BLEU score decreases as the number of layers increases from 2 to 5.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score is highest at 2 layers (50.04) and decreases as the number of layers increases to 3 (49.72), 4 (49.01), and 5 (48.54). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2467.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: GAT settings results on development set." ], "reasoning": "The table shows that the BLEU score is highest at 2 layers (50.04) and decreases as the number of layers increases to 3 (49.72), 4 (49.01), and 5 (48.54).", "statement": "The BLEU score decreases as the number of layers increases from 2 to 5.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: GAT settings results on development set.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The GAT model performs best with 2 layers, and increasing the number of layers leads to a drop in performance. Multi-head attention performs best with 4 heads.In particular, we vary the value of l in the set {1, 2, 3, 4, 5} and measure the corresponding BLEU scores. The structural-aware model equipped with GAT achieves the best performance when l is 2, which justifies the selection on the number of layers in the experimental setting section. Moreover, a dropping trend on both metrics is present as l increases. For a larger l, the GAT module becomes more difficult to train due to larger amounts of parameters. One intuitive reason is that each layer of the GAT module aggregates the direct neighbor information of a node. After 2 layers, each node can obtain sufficient information, and further more layers can bring noise. We make comparison with multi-head attention, varying the heads in the set {1, 2, 3, 4, 5} and checking the corresponding BLEU scores. Theoretically, the larger the number of heads, the better the performance of the model. We thus choose the head to be 4 for the remaining experiments." } ], "images": [ "scigen/train/2468.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: GAT settings results on development set." ], "reasoning": "The GAT model performs best with 2 layers, and increasing the number of layers leads to a drop in performance. Multi-head attention performs best with 4 heads.", "description": "In particular, we vary the value of l in the set {1, 2, 3, 4, 5} and measure the corresponding BLEU scores. The structural-aware model equipped with GAT achieves the best performance when l is 2, which justifies the selection on the number of layers in the experimental setting section. Moreover, a dropping trend on both metrics is present as l increases. For a larger l, the GAT module becomes more difficult to train due to larger amounts of parameters. One intuitive reason is that each layer of the GAT module aggregates the direct neighbor information of a node. After 2 layers, each node can obtain sufficient information, and further more layers can bring noise. We make comparison with multi-head attention, varying the heads in the set {1, 2, 3, 4, 5} and checking the corresponding BLEU scores. Theoretically, the larger the number of heads, the better the performance of the model. We thus choose the head to be 4 for the remaining experiments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: GAT settings results on development set.', answer the question below by showing each progressive step toward the answer. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the BLEU score when the number of heads is set to 4, and why was this number chosen for the remaining experiments?\n" }, { "role": "assistant", "content": "The BLEU score for 4 heads is 50.04, and this number was chosen because theoretically, the larger the number of heads, the better the performance, and 4 heads provided the highest BLEU score. So, the answer is {\"answer\": \"50.04\"}" } ], "images": [ "scigen/train/2468.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: GAT settings results on development set." ], "reasoning": "The BLEU score for 4 heads is 50.04, and this number was chosen because theoretically, the larger the number of heads, the better the performance, and 4 heads provided the highest BLEU score.", "question": "What is the BLEU score when the number of heads is set to 4, and why was this number chosen for the remaining experiments?", "answer": "So, the answer is {\"answer\": \"50.04\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe BLEU score decreases as the number of heads increases.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score increases as the number of heads increases from 1 to 4 and then remains constant at 50.04 for 4 and 5 heads. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2468.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: GAT settings results on development set." ], "reasoning": "The table shows that the BLEU score increases as the number of heads increases from 1 to 4 and then remains constant at 50.04 for 4 and 5 heads.", "statement": "The BLEU score decreases as the number of heads increases.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Results on the sentence-level dataset.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The combination of GAT encoder and decoder significantly improves performance, achieving a new state-of-the-art exact F1 score of 79.94%.We use the same setup as the document-level structure-aware model. As shown, both the GAT encoder and decoder can bring better results (i.e., 0.37% and 1.29% by the GAT encoder and decoder, respectively), and their combination can give further improvements (i.e., 0.80% over the GAT-decoder) significantly, which are consistent with the findings of the document-level parsing. Finally, the sentence-level performance reaches 79.94%, a new state-of-the-art score. The results demonstrate that our model is also applicable to sentence-level DRTS parsing." } ], "images": [ "scigen/train/2469.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on the sentence-level dataset." ], "reasoning": "The combination of GAT encoder and decoder significantly improves performance, achieving a new state-of-the-art exact F1 score of 79.94%.", "description": "We use the same setup as the document-level structure-aware model. As shown, both the GAT encoder and decoder can bring better results (i.e., 0.37% and 1.29% by the GAT encoder and decoder, respectively), and their combination can give further improvements (i.e., 0.80% over the GAT-decoder) significantly, which are consistent with the findings of the document-level parsing. Finally, the sentence-level performance reaches 79.94%, a new state-of-the-art score. The results demonstrate that our model is also applicable to sentence-level DRTS parsing.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: Results on the sentence-level dataset.', answer the following question. Give detailed solution consisting of each step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the exact F1 score improvement when using the GAT encoder and decoder combination over the GAT decoder alone?\n" }, { "role": "assistant", "content": "The exact F1 score for the GAT decoder is 79.14%, and for the GAT encoder and decoder combination, it is 79.94%. The improvement is 79.94% - 79.14% = 0.80%. So, the answer is {\"answer\": \"0.80%\"}" } ], "images": [ "scigen/train/2469.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on the sentence-level dataset." ], "reasoning": "The exact F1 score for the GAT decoder is 79.14%, and for the GAT encoder and decoder combination, it is 79.94%. The improvement is 79.94% - 79.14% = 0.80%.", "question": "What is the exact F1 score improvement when using the GAT encoder and decoder combination over the GAT decoder alone?", "answer": "So, the answer is {\"answer\": \"0.80%\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The GAT encoder alone achieves a higher exact F1 score than the Liu:19 model., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe GAT encoder alone achieves a higher exact F1 score than the Liu:19 model.\n" }, { "role": "assistant", "content": "The exact F1 score for the GAT encoder is 78.22%, while the exact F1 score for the Liu:19 model is 77.85%. Therefore, the GAT encoder alone achieves a higher exact F1 score. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2469.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on the sentence-level dataset." ], "reasoning": "The exact F1 score for the GAT encoder is 78.22%, while the exact F1 score for the Liu:19 model is 77.85%. Therefore, the GAT encoder alone achieves a higher exact F1 score.", "statement": "The GAT encoder alone achieves a higher exact F1 score than the Liu:19 model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Exact Match (EM) and F1 scores on SQuAD 1.1 and 2.0. The gap between humans and the best tested model is much larger on SQuAD 2.0, suggesting there is a great deal of room for model improvement.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The gap between human and machine performance is larger on SQuAD 2.0 compared to SQuAD 1.1, indicating that SQuAD 2.0 is a more challenging dataset for existing models.Following Rajpurkar et al. Note that a baseline that always abstains gets 48.9 test F1; existing models are closer to this baseline than they are to human performance. Therefore, we see significant room for model improvement on this task. We also compare with reported test numbers for analogous model architectures on SQuAD 1.1. There is a much larger gap between humans and machines on SQuAD 2.0 compared to SQuAD 1.1, which confirms that SQuAD 2.0 is a much harder dataset for existing models." } ], "images": [ "scigen/train/2470.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Exact Match (EM) and F1 scores on SQuAD 1.1 and 2.0. The gap between humans and the best tested model is much larger on SQuAD 2.0, suggesting there is a great deal of room for model improvement." ], "reasoning": "The gap between human and machine performance is larger on SQuAD 2.0 compared to SQuAD 1.1, indicating that SQuAD 2.0 is a more challenging dataset for existing models.", "description": "Following Rajpurkar et al. Note that a baseline that always abstains gets 48.9 test F1; existing models are closer to this baseline than they are to human performance. Therefore, we see significant room for model improvement on this task. We also compare with reported test numbers for analogous model architectures on SQuAD 1.1. There is a much larger gap between humans and machines on SQuAD 2.0 compared to SQuAD 1.1, which confirms that SQuAD 2.0 is a much harder dataset for existing models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Exact Match (EM) and F1 scores on SQuAD 1.1 and 2.0. The gap between humans and the best tested model is much larger on SQuAD 2.0, suggesting there is a great deal of room for model improvement.\nQuestion: Which system has the highest SQuAD 2.0 test F1 score?\nSolve the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the SQuAD 2.0 test F1 scores in the table, we see that the Human system has the highest score of 89.5. So, the answer is {\"answer\": \"Human\"}" } ], "images": [ "scigen/train/2470.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Exact Match (EM) and F1 scores on SQuAD 1.1 and 2.0. The gap between humans and the best tested model is much larger on SQuAD 2.0, suggesting there is a great deal of room for model improvement." ], "reasoning": "By comparing the SQuAD 2.0 test F1 scores in the table, we see that the Human system has the highest score of 89.5.", "question": "Which system has the highest SQuAD 2.0 test F1 score?", "answer": "So, the answer is {\"answer\": \"Human\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nDocQA + ELMo has a higher SQuAD 2.0 dev EM score than the Human system.\n" }, { "role": "assistant", "content": "The table shows that DocQA + ELMo has a SQuAD 2.0 dev EM score of 67.6, while the Human system has a score of 86.3. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2470.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Exact Match (EM) and F1 scores on SQuAD 1.1 and 2.0. The gap between humans and the best tested model is much larger on SQuAD 2.0, suggesting there is a great deal of room for model improvement." ], "reasoning": "The table shows that DocQA + ELMo has a SQuAD 2.0 dev EM score of 67.6, while the Human system has a score of 86.3. Therefore, the statement is refuted.", "statement": "DocQA + ELMo has a higher SQuAD 2.0 dev EM score than the Human system.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: Exact Match (EM) and F1 scores on the SQuAD 2.0 development set, compared with SQuAD 1.1 with two types of automatically generated negative examples. SQuAD 2.0 is more challenging for current models.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Automatically generated negative examples are easier for models to detect, as evidenced by the higher scores on SQuAD 1.1 with TfIdf and RuleBased examples compared to SQuAD 2.0.Next, we investigated whether automatic ways of generating negative examples can also yield a challenging dataset. We trained and tested all three model architectures on SQuAD 1.1 augmented with either TfIdf or RuleBased examples. To ensure a fair comparison with SQuAD 2.0, we generated training data by applying TfIdf or RuleBased only to the 285 articles for which SQuAD 2.0 has unanswerable questions. We tested on the articles and answerable questions in the SQuAD 2.0 development set, adding unanswerable questions in a roughly one-to-one ratio with answerable ones. The highest score on SQuAD 2.0 is 15.4 F1 points lower than the highest score on either of the other two datasets, suggesting that automatically generated negative examples are much easier for existing models to detect." } ], "images": [ "scigen/train/2471.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Exact Match (EM) and F1 scores on the SQuAD 2.0 development set, compared with SQuAD 1.1 with two types of automatically generated negative examples. SQuAD 2.0 is more challenging for current models." ], "reasoning": "Automatically generated negative examples are easier for models to detect, as evidenced by the higher scores on SQuAD 1.1 with TfIdf and RuleBased examples compared to SQuAD 2.0.", "description": "Next, we investigated whether automatic ways of generating negative examples can also yield a challenging dataset. We trained and tested all three model architectures on SQuAD 1.1 augmented with either TfIdf or RuleBased examples. To ensure a fair comparison with SQuAD 2.0, we generated training data by applying TfIdf or RuleBased only to the 285 articles for which SQuAD 2.0 has unanswerable questions. We tested on the articles and answerable questions in the SQuAD 2.0 development set, adding unanswerable questions in a roughly one-to-one ratio with answerable ones. The highest score on SQuAD 2.0 is 15.4 F1 points lower than the highest score on either of the other two datasets, suggesting that automatically generated negative examples are much easier for existing models to detect.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 4: Exact Match (EM) and F1 scores on the SQuAD 2.0 development set, compared with SQuAD 1.1 with two types of automatically generated negative examples. SQuAD 2.0 is more challenging for current models.' to sequentially solve the question presented below. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich system achieved the highest F1 score on the SQuAD 2.0 development set?\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all systems on the SQuAD 2.0 development set, DocQA + ELMo achieved the highest score of 67.6. So, the answer is {\"answer\": \"DocQA + ELMo\"}." } ], "images": [ "scigen/train/2471.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Exact Match (EM) and F1 scores on the SQuAD 2.0 development set, compared with SQuAD 1.1 with two types of automatically generated negative examples. SQuAD 2.0 is more challenging for current models." ], "reasoning": "By comparing the F1 scores of all systems on the SQuAD 2.0 development set, DocQA + ELMo achieved the highest score of 67.6.", "question": "Which system achieved the highest F1 score on the SQuAD 2.0 development set?", "answer": "So, the answer is {\"answer\": \"DocQA + ELMo\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence DocQA + ELMo achieved the highest EM score on SQuAD 1.1 with RuleBased examples. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nDocQA + ELMo achieved the highest EM score on SQuAD 1.1 with RuleBased examples.\n" }, { "role": "assistant", "content": "By comparing the EM scores of all systems on SQuAD 1.1 with RuleBased examples, DocQA + ELMo achieved the highest score of 85.7. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2471.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Exact Match (EM) and F1 scores on the SQuAD 2.0 development set, compared with SQuAD 1.1 with two types of automatically generated negative examples. SQuAD 2.0 is more challenging for current models." ], "reasoning": "By comparing the EM scores of all systems on SQuAD 1.1 with RuleBased examples, DocQA + ELMo achieved the highest score of 85.7.", "statement": "DocQA + ELMo achieved the highest EM score on SQuAD 1.1 with RuleBased examples.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Exact match (EM) and F1 scores between system predictions and plausible answers, in cases where the system made a false positive error.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows the exact match (EM) and F1 scores for different systems and humans on false positive errors, indicating that plausible answers are effective distractors.For each system, we first isolated their false positive errors—cases where they predicted an answer to an unanswerable question—on the development set. Within this set of examples, we measured exact match and F1 scores between the system predictions and plausible answers. Plausible answers account for roughly half of the false positive errors made by each of the computer systems, as well as by human answerers. We conclude that the plausible answers in our dataset do indeed serve their purpose of being distracting spans that could be mistaken for the correct answer." } ], "images": [ "scigen/train/2472.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Exact match (EM) and F1 scores between system predictions and plausible answers, in cases where the system made a false positive error." ], "reasoning": "The table shows the exact match (EM) and F1 scores for different systems and humans on false positive errors, indicating that plausible answers are effective distractors.", "description": "For each system, we first isolated their false positive errors—cases where they predicted an answer to an unanswerable question—on the development set. Within this set of examples, we measured exact match and F1 scores between the system predictions and plausible answers. Plausible answers account for roughly half of the false positive errors made by each of the computer systems, as well as by human answerers. We conclude that the plausible answers in our dataset do indeed serve their purpose of being distracting spans that could be mistaken for the correct answer.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 5: Exact match (EM) and F1 scores between system predictions and plausible answers, in cases where the system made a false positive error.', answer the question below by showing each progressive step toward the answer. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich system had the highest exact match (EM) score?\n" }, { "role": "assistant", "content": "To find the system with the highest EM score, compare the EM scores of BNA, DocQA, DocQA+ELMo, and Human. DocQA has the highest EM score of 55.0. So, the answer is {\"answer\": \"DocQA\"}." } ], "images": [ "scigen/train/2472.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Exact match (EM) and F1 scores between system predictions and plausible answers, in cases where the system made a false positive error." ], "reasoning": "To find the system with the highest EM score, compare the EM scores of BNA, DocQA, DocQA+ELMo, and Human. DocQA has the highest EM score of 55.0.", "question": "Which system had the highest exact match (EM) score?", "answer": "So, the answer is {\"answer\": \"DocQA\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nHumans had the lowest F1 score among all systems.\n" }, { "role": "assistant", "content": "To verify this statement, compare the F1 scores of BNA, DocQA, DocQA+ELMo, and Human. Humans have an F1 score of 60.6, which is indeed the lowest. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2472.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Exact match (EM) and F1 scores between system predictions and plausible answers, in cases where the system made a false positive error." ], "reasoning": "To verify this statement, compare the F1 scores of BNA, DocQA, DocQA+ELMo, and Human. Humans have an F1 score of 60.6, which is indeed the lowest.", "statement": "Humans had the lowest F1 score among all systems.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Numbers of propositions with different sentence lengths and different numbers of arguments on the English test set.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The model outperforms the baseline except for sentences between 50 and 59 words, which is a small subset of 391 sentences.Our model consistently outperforms the baseline model, except on sentences of between 50 and 59 words. Note that the subset is small (only 391 sentences), so the effect may be random." } ], "images": [ "scigen/train/2473.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Numbers of propositions with different sentence lengths and different numbers of arguments on the English test set." ], "reasoning": "The model outperforms the baseline except for sentences between 50 and 59 words, which is a small subset of 391 sentences.", "description": "Our model consistently outperforms the baseline model, except on sentences of between 50 and 59 words. Note that the subset is small (only 391 sentences), so the effect may be random.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Numbers of propositions with different sentence lengths and different numbers of arguments on the English test set.\nQuestion: What is the number of sentence propositions for the length range where the model does not outperform the baseline?\nGive a detailed response to the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The description states that the model does not outperform the baseline for sentences between 50 and 59 words. The table shows that there are 391 sentence propositions in this length range. So, the answer is {\"answer\": \"391\"}" } ], "images": [ "scigen/train/2473.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Numbers of propositions with different sentence lengths and different numbers of arguments on the English test set." ], "reasoning": "The description states that the model does not outperform the baseline for sentences between 50 and 59 words. The table shows that there are 391 sentence propositions in this length range.", "question": "What is the number of sentence propositions for the length range where the model does not outperform the baseline?", "answer": "So, the answer is {\"answer\": \"391\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model outperforms the baseline model on sentences of 30 to 39 words.\n" }, { "role": "assistant", "content": "The description states that the model consistently outperforms the baseline model except on sentences of 50 to 59 words. Therefore, it supports that the model outperforms the baseline on sentences of 30 to 39 words. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2473.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Numbers of propositions with different sentence lengths and different numbers of arguments on the English test set." ], "reasoning": "The description states that the model consistently outperforms the baseline model except on sentences of 50 to 59 words. Therefore, it supports that the model outperforms the baseline on sentences of 30 to 39 words.", "statement": "The model outperforms the baseline model on sentences of 30 to 39 words.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 1: The performance of our model (w/o global node) with different discount coefficients η of loss L∗(θ) on the English development set. EM denotes the ratio of exact match on propositions.'.\n" }, { "role": "assistant", "content": "The model achieves the best performance with η equals to 0 and 2 refinement iterations, resulting in an F1 score of 89.92%.The model achieves the best performance when η equals to 0. It implies that adding uniqueness constraint on loss actually hurts the performance. Thus, we use the loss L∗(θ) with η equals to 0 in the rest of the experiments, which is equivalent to the loss L(θ) in Eq We also observe that the model with 2 refinement iterations performs the best (89.92% F1)." } ], "images": [ "scigen/train/2474.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The performance of our model (w/o global node) with different discount coefficients η of loss L∗(θ) on the English development set. EM denotes the ratio of exact match on propositions." ], "reasoning": "The model achieves the best performance with η equals to 0 and 2 refinement iterations, resulting in an F1 score of 89.92%.", "description": "The model achieves the best performance when η equals to 0. It implies that adding uniqueness constraint on loss actually hurts the performance. Thus, we use the loss L∗(θ) with η equals to 0 in the rest of the experiments, which is equivalent to the loss L(θ) in Eq We also observe that the model with 2 refinement iterations performs the best (89.92% F1).", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 1: The performance of our model (w/o global node) with different discount coefficients η of loss L∗(θ) on the English development set. EM denotes the ratio of exact match on propositions.' and proceed to solve the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the F1 score when η equals to 0 and the number of iterations is 2?\n" }, { "role": "assistant", "content": "To find the F1 score, look at the row where η equals to 0 and the number of iterations is 2. The F1 score in this row is 89.92%. So, the answer is {\"answer\": \"89.92%\"}" } ], "images": [ "scigen/train/2474.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The performance of our model (w/o global node) with different discount coefficients η of loss L∗(θ) on the English development set. EM denotes the ratio of exact match on propositions." ], "reasoning": "To find the F1 score, look at the row where η equals to 0 and the number of iterations is 2. The F1 score in this row is 89.92%.", "question": "What is the F1 score when η equals to 0 and the number of iterations is 2?", "answer": "So, the answer is {\"answer\": \"89.92%\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model achieves the best performance with η equals to 0 and 2 refinement iterations.\n" }, { "role": "assistant", "content": "The table shows that the highest F1 score of 89.92% is achieved when η equals to 0 and the number of iterations is 2. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2474.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The performance of our model (w/o global node) with different discount coefficients η of loss L∗(θ) on the English development set. EM denotes the ratio of exact match on propositions." ], "reasoning": "The table shows that the highest F1 score of 89.92% is achieved when η equals to 0 and the number of iterations is 2.", "statement": "The model achieves the best performance with η equals to 0 and 2 refinement iterations.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Numbers of propositions with different sentence lengths and different numbers of arguments on the English test set.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The model outperforms the baseline except for sentences between 50 and 59 words, which is a small subset of 391 sentences, possibly indicating a random effect.Our model consistently outperforms the baseline model, except on sentences of between 50 and 59 words. Note that the subset is small (only 391 sentences), so the effect may be random." } ], "images": [ "scigen/train/2475.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Numbers of propositions with different sentence lengths and different numbers of arguments on the English test set." ], "reasoning": "The model outperforms the baseline except for sentences between 50 and 59 words, which is a small subset of 391 sentences, possibly indicating a random effect.", "description": "Our model consistently outperforms the baseline model, except on sentences of between 50 and 59 words. Note that the subset is small (only 391 sentences), so the effect may be random.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 4: Numbers of propositions with different sentence lengths and different numbers of arguments on the English test set.\nQuestion: What is the total number of propositions for arguments ranging from 1 to 3?\n" }, { "role": "assistant", "content": "To find the total number of propositions for arguments ranging from 1 to 3, sum the values in the '# of Props' column for rows where '# of Args' is 1, 2, and 3: 2,591 + 4,497 + 2,189. So, the answer is {\"answer\": \"9,277\"}." } ], "images": [ "scigen/train/2475.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Numbers of propositions with different sentence lengths and different numbers of arguments on the English test set." ], "reasoning": "To find the total number of propositions for arguments ranging from 1 to 3, sum the values in the '# of Props' column for rows where '# of Args' is 1, 2, and 3: 2,591 + 4,497 + 2,189.", "question": "What is the total number of propositions for arguments ranging from 1 to 3?", "answer": "So, the answer is {\"answer\": \"9,277\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: There are fewer than 50 propositions for arguments with 6 or more arguments., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThere are fewer than 50 propositions for arguments with 6 or more arguments.\n" }, { "role": "assistant", "content": "The table shows that there are 39 propositions for 6 arguments and 7 propositions for 7 arguments, totaling 46 propositions for arguments with 6 or more arguments. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2475.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Numbers of propositions with different sentence lengths and different numbers of arguments on the English test set." ], "reasoning": "The table shows that there are 39 propositions for 6 arguments and 7 propositions for 7 arguments, totaling 46 propositions for arguments with 6 or more arguments.", "statement": "There are fewer than 50 propositions for arguments with 6 or more arguments.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5: Labeled F1 score (including senses) for all languages on the CoNLL-2009 in-domain test sets. For previous best result, Japanese (Ja) is from watanabe-etal-2010-structured, Catalan (Ca) is from zhao2009multilingual, Spanish (Es) and German (De) are from roth2016neural, Czech (Cz) is from henderson-etal-2013-multilingual, Chinese (Zh) is from cai2018full and English (En) is from li2019dependency.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The refinement method shows significant improvements on both the smallest (Japanese) and the largest (English) datasets, indicating its effectiveness is not dependent on dataset size.Interestingly, the effectiveness of the refinement method does not seem to be dependent on the dataset size: the improvements on the smallest (Japanese) and the largest datasets (English) are among the largest." } ], "images": [ "scigen/train/2476.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Labeled F1 score (including senses) for all languages on the CoNLL-2009 in-domain test sets. For previous best result, Japanese (Ja) is from watanabe-etal-2010-structured, Catalan (Ca) is from zhao2009multilingual, Spanish (Es) and German (De) are from roth2016neural, Czech (Cz) is from henderson-etal-2013-multilingual, Chinese (Zh) is from cai2018full and English (En) is from li2019dependency." ], "reasoning": "The refinement method shows significant improvements on both the smallest (Japanese) and the largest (English) datasets, indicating its effectiveness is not dependent on dataset size.", "description": "Interestingly, the effectiveness of the refinement method does not seem to be dependent on the dataset size: the improvements on the smallest (Japanese) and the largest datasets (English) are among the largest.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: Labeled F1 score (including senses) for all languages on the CoNLL-2009 in-domain test sets. For previous best result, Japanese (Ja) is from watanabe-etal-2010-structured, Catalan (Ca) is from zhao2009multilingual, Spanish (Es) and German (De) are from roth2016neural, Czech (Cz) is from henderson-etal-2013-multilingual, Chinese (Zh) is from cai2018full and English (En) is from li2019dependency.'.\n\nQuestion: Which model shows the highest average score across all datasets?\n" }, { "role": "assistant", "content": "By comparing the average scores of all models, CapsuleNet SRL (This Work) has the highest average score of 83.07. So, the answer is {\"answer\": \"CapsuleNet SRL (This Work)\"}." } ], "images": [ "scigen/train/2476.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Labeled F1 score (including senses) for all languages on the CoNLL-2009 in-domain test sets. For previous best result, Japanese (Ja) is from watanabe-etal-2010-structured, Catalan (Ca) is from zhao2009multilingual, Spanish (Es) and German (De) are from roth2016neural, Czech (Cz) is from henderson-etal-2013-multilingual, Chinese (Zh) is from cai2018full and English (En) is from li2019dependency." ], "reasoning": "By comparing the average scores of all models, CapsuleNet SRL (This Work) has the highest average score of 83.07.", "question": "Which model shows the highest average score across all datasets?", "answer": "So, the answer is {\"answer\": \"CapsuleNet SRL (This Work)\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe CapsuleNet SRL model shows the highest score for the English dataset.\n" }, { "role": "assistant", "content": "The table shows that CapsuleNet SRL (This Work) has a score of 91.06 for the English dataset, which is higher than the scores of the other models for the same dataset. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2476.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Labeled F1 score (including senses) for all languages on the CoNLL-2009 in-domain test sets. For previous best result, Japanese (Ja) is from watanabe-etal-2010-structured, Catalan (Ca) is from zhao2009multilingual, Spanish (Es) and German (De) are from roth2016neural, Czech (Cz) is from henderson-etal-2013-multilingual, Chinese (Zh) is from cai2018full and English (En) is from li2019dependency." ], "reasoning": "The table shows that CapsuleNet SRL (This Work) has a score of 91.06 for the English dataset, which is higher than the scores of the other models for the same dataset.", "statement": "The CapsuleNet SRL model shows the highest score for the English dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: Multi-source cross-domain polarity classification accuracy rates (in %) of our transductive approaches versus a state-of-the-art (sota) baseline based on string kernels Giménez-Pérez et al. (2017), as well as SST Bollegala et al. (2013) and KE-Meta Franco-Salvador et al. (2015). The best accuracy rates are highlighted in bold. The marker * indicates that the performance is significantly better than the best baseline string kernel according to a paired McNemar’s test performed at a significance level of 0.01.'.\n" }, { "role": "assistant", "content": "The transductive kernel classifier (TKC) improves accuracy rates by more than 1.5% over the best baseline in all domains, with statistically significant improvements.Results in multi-source setting. Both the transductive presence bits string kernel (¨K0/1) and the transductive intersection kernel (¨K∩) obtain better results than their original counterparts. When we employ the transductive kernel classifier (TKC), we obtain even better results. On all domains, the accuracy rates yielded by the transductive classifier are more than 1.5% better than the best baseline. For example, on the Books domain the accuracy of the transductive classifier based on the presence bits kernel (84.1%) is 2.1% above the best baseline (82.0%) represented by the intersection string kernel. Remarkably, the improvements brought by our transductive string kernel approach are statistically significant in all domains." } ], "images": [ "scigen/train/2477.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Multi-source cross-domain polarity classification accuracy rates (in %) of our transductive approaches versus a state-of-the-art (sota) baseline based on string kernels Giménez-Pérez et al. (2017), as well as SST Bollegala et al. (2013) and KE-Meta Franco-Salvador et al. (2015). The best accuracy rates are highlighted in bold. The marker * indicates that the performance is significantly better than the best baseline string kernel according to a paired McNemar’s test performed at a significance level of 0.01." ], "reasoning": "The transductive kernel classifier (TKC) improves accuracy rates by more than 1.5% over the best baseline in all domains, with statistically significant improvements.", "description": "Results in multi-source setting. Both the transductive presence bits string kernel (¨K0/1) and the transductive intersection kernel (¨K∩) obtain better results than their original counterparts. When we employ the transductive kernel classifier (TKC), we obtain even better results. On all domains, the accuracy rates yielded by the transductive classifier are more than 1.5% better than the best baseline. For example, on the Books domain the accuracy of the transductive classifier based on the presence bits kernel (84.1%) is 2.1% above the best baseline (82.0%) represented by the intersection string kernel. Remarkably, the improvements brought by our transductive string kernel approach are statistically significant in all domains.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 1: Multi-source cross-domain polarity classification accuracy rates (in %) of our transductive approaches versus a state-of-the-art (sota) baseline based on string kernels Giménez-Pérez et al. (2017), as well as SST Bollegala et al. (2013) and KE-Meta Franco-Salvador et al. (2015). The best accuracy rates are highlighted in bold. The marker * indicates that the performance is significantly better than the best baseline string kernel according to a paired McNemar’s test performed at a significance level of 0.01.', solve this question and give detail solutions. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the accuracy rate of the transductive classifier based on the intersection kernel for the BDE→K domain?\n" }, { "role": "assistant", "content": "The table shows the accuracy rates for different methods across various domains. For the BDE→K domain, the accuracy rate of the transductive classifier based on the intersection kernel (¨K∩ + TKC) is highlighted. So, the answer is {\"answer\": \"87.1%\"}." } ], "images": [ "scigen/train/2477.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Multi-source cross-domain polarity classification accuracy rates (in %) of our transductive approaches versus a state-of-the-art (sota) baseline based on string kernels Giménez-Pérez et al. (2017), as well as SST Bollegala et al. (2013) and KE-Meta Franco-Salvador et al. (2015). The best accuracy rates are highlighted in bold. The marker * indicates that the performance is significantly better than the best baseline string kernel according to a paired McNemar’s test performed at a significance level of 0.01." ], "reasoning": "The table shows the accuracy rates for different methods across various domains. For the BDE→K domain, the accuracy rate of the transductive classifier based on the intersection kernel (¨K∩ + TKC) is highlighted.", "question": "What is the accuracy rate of the transductive classifier based on the intersection kernel for the BDE→K domain?", "answer": "So, the answer is {\"answer\": \"87.1%\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe transductive classifier based on the presence bits kernel achieves an accuracy rate of 84.1% in the DEK→B domain.\n" }, { "role": "assistant", "content": "The table shows that the accuracy rate of the transductive classifier based on the presence bits kernel (¨K0/1 + TKC) in the DEK→B domain is 84.1%, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2477.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Multi-source cross-domain polarity classification accuracy rates (in %) of our transductive approaches versus a state-of-the-art (sota) baseline based on string kernels Giménez-Pérez et al. (2017), as well as SST Bollegala et al. (2013) and KE-Meta Franco-Salvador et al. (2015). The best accuracy rates are highlighted in bold. The marker * indicates that the performance is significantly better than the best baseline string kernel according to a paired McNemar’s test performed at a significance level of 0.01." ], "reasoning": "The table shows that the accuracy rate of the transductive classifier based on the presence bits kernel (¨K0/1 + TKC) in the DEK→B domain is 84.1%, which matches the statement.", "statement": "The transductive classifier based on the presence bits kernel achieves an accuracy rate of 84.1% in the DEK→B domain.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Single-source cross-domain polarity classification accuracy rates (in %) of our transductive approaches versus a state-of-the-art (sota) baseline based on string kernels Giménez-Pérez et al. (2017), as well as SFA Pan et al. (2010), KMM Huang et al. (2007), CORAL Sun et al. (2016) and TR-TrAdaBoost Huang et al. (2017). The best accuracy rates are highlighted in bold. The marker * indicates that the performance is significantly better than the best baseline string kernel according to a paired McNemar’s test performed at a significance level of 0.01.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The transductive string kernels significantly outperform the best baseline string kernel in all cases, with the highest improvements observed when the source domain is Books reviews and the target domain is Kitchen reviews.Results in single-source setting. We considered all possible combinations of source and target domains in this experiment, and we improve the results in each and every case. Without exception, the accuracy rates reached by the transductive string kernels are significantly better than the best baseline string kernel Giménez-Pérez et al. , according to the McNemar’s test performed at a confidence level of 0.01. The highest improvements (above 2.7%) are obtained when the source domain contains Books reviews and the target domain contains Kitchen reviews. As in the multi-source setting, we obtain much better results when the transductive classifier is employed for the learning task. In all cases, the accuracy rates of the transductive classifier are more than 2% better than the best baseline string kernel. Remarkably, in four cases (E→B, E→D, B→K and D→K) our improvements are greater than 4%. The improvements brought by our transductive classifier based on string kernels are statistically significant in each and every case. In comparison with SFA Pan et al. With respect to KMM Huang et al. Remarkably, we surpass the other state-of-the-art approaches Sun et al." } ], "images": [ "scigen/train/2478.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Single-source cross-domain polarity classification accuracy rates (in %) of our transductive approaches versus a state-of-the-art (sota) baseline based on string kernels Giménez-Pérez et al. (2017), as well as SFA Pan et al. (2010), KMM Huang et al. (2007), CORAL Sun et al. (2016) and TR-TrAdaBoost Huang et al. (2017). The best accuracy rates are highlighted in bold. The marker * indicates that the performance is significantly better than the best baseline string kernel according to a paired McNemar’s test performed at a significance level of 0.01." ], "reasoning": "The transductive string kernels significantly outperform the best baseline string kernel in all cases, with the highest improvements observed when the source domain is Books reviews and the target domain is Kitchen reviews.", "description": "Results in single-source setting. We considered all possible combinations of source and target domains in this experiment, and we improve the results in each and every case. Without exception, the accuracy rates reached by the transductive string kernels are significantly better than the best baseline string kernel Giménez-Pérez et al. , according to the McNemar’s test performed at a confidence level of 0.01. The highest improvements (above 2.7%) are obtained when the source domain contains Books reviews and the target domain contains Kitchen reviews. As in the multi-source setting, we obtain much better results when the transductive classifier is employed for the learning task. In all cases, the accuracy rates of the transductive classifier are more than 2% better than the best baseline string kernel. Remarkably, in four cases (E→B, E→D, B→K and D→K) our improvements are greater than 4%. The improvements brought by our transductive classifier based on string kernels are statistically significant in each and every case. In comparison with SFA Pan et al. With respect to KMM Huang et al. Remarkably, we surpass the other state-of-the-art approaches Sun et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: Single-source cross-domain polarity classification accuracy rates (in %) of our transductive approaches versus a state-of-the-art (sota) baseline based on string kernels Giménez-Pérez et al. (2017), as well as SFA Pan et al. (2010), KMM Huang et al. (2007), CORAL Sun et al. (2016) and TR-TrAdaBoost Huang et al. (2017). The best accuracy rates are highlighted in bold. The marker * indicates that the performance is significantly better than the best baseline string kernel according to a paired McNemar’s test performed at a significance level of 0.01.'.\n\nQuestion: What is the accuracy rate for the method 'K∩ + TKC' when the source domain is Books reviews and the target domain is Kitchen reviews?\n" }, { "role": "assistant", "content": "To find the accuracy rate for the method 'K∩ + TKC' when the source domain is Books reviews and the target domain is Kitchen reviews, locate the row labeled 'K∩ + TKC' and the column labeled 'B→K'. The intersection of this row and column gives the accuracy rate. So, the answer is {\"answer\": \"84.2\"}." } ], "images": [ "scigen/train/2478.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Single-source cross-domain polarity classification accuracy rates (in %) of our transductive approaches versus a state-of-the-art (sota) baseline based on string kernels Giménez-Pérez et al. (2017), as well as SFA Pan et al. (2010), KMM Huang et al. (2007), CORAL Sun et al. (2016) and TR-TrAdaBoost Huang et al. (2017). The best accuracy rates are highlighted in bold. The marker * indicates that the performance is significantly better than the best baseline string kernel according to a paired McNemar’s test performed at a significance level of 0.01." ], "reasoning": "To find the accuracy rate for the method 'K∩ + TKC' when the source domain is Books reviews and the target domain is Kitchen reviews, locate the row labeled 'K∩ + TKC' and the column labeled 'B→K'. The intersection of this row and column gives the accuracy rate.", "question": "What is the accuracy rate for the method 'K∩ + TKC' when the source domain is Books reviews and the target domain is Kitchen reviews?", "answer": "So, the answer is {\"answer\": \"84.2\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe method 'K∩ + TKC' achieves an accuracy rate of 87.0 when the source domain is Electronics reviews and the target domain is Kitchen reviews.\n" }, { "role": "assistant", "content": "To verify this statement, locate the row labeled 'K∩ + TKC' and the column labeled 'E→K'. The intersection of this row and column shows the accuracy rate for this method and domain combination. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2478.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Single-source cross-domain polarity classification accuracy rates (in %) of our transductive approaches versus a state-of-the-art (sota) baseline based on string kernels Giménez-Pérez et al. (2017), as well as SFA Pan et al. (2010), KMM Huang et al. (2007), CORAL Sun et al. (2016) and TR-TrAdaBoost Huang et al. (2017). The best accuracy rates are highlighted in bold. The marker * indicates that the performance is significantly better than the best baseline string kernel according to a paired McNemar’s test performed at a significance level of 0.01." ], "reasoning": "To verify this statement, locate the row labeled 'K∩ + TKC' and the column labeled 'E→K'. The intersection of this row and column shows the accuracy rate for this method and domain combination.", "statement": "The method 'K∩ + TKC' achieves an accuracy rate of 87.0 when the source domain is Electronics reviews and the target domain is Kitchen reviews.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Arabic dialect identification accuracy rates (in %) of our adapted string kernels versus the 2017 ADI Shared Task winner (sota) Ionescu and Butnaru (2017) and the first runner up Malmasi and Zampieri (2017). The best accuracy rates are highlighted in bold. The marker * indicates that the performance is significantly better than Ionescu and Butnaru (2017) according to a paired McNemar’s test performed at a significance level of 0.01.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The domain-adapted sum of kernels shows significant improvements over the state-of-the-art, with the highest accuracy of 66.73% on the development set and 78.35% on the test set.Results. The domain-adapted sum of kernels obtains improvements above 0.8% over the state-of-the-art sum of kernels Ionescu and Butnaru The improvement on the development set (from 64.17% to 65.42%) is statistically significant. Nevertheless, we obtain higher and significant improvements when we employ the transductive classifier. Our best accuracy is 66.73% (2.56% above the baseline) on the development set and 78.35% (2.08% above the baseline) on the test set. The results show that our domain adaptation framework based on string kernels attains the best performance on the ADI Shared Task data set, and the improvements over the state-of-the-art are statistically significant, according to the McNemar’s test." } ], "images": [ "scigen/train/2479.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Arabic dialect identification accuracy rates (in %) of our adapted string kernels versus the 2017 ADI Shared Task winner (sota) Ionescu and Butnaru (2017) and the first runner up Malmasi and Zampieri (2017). The best accuracy rates are highlighted in bold. The marker * indicates that the performance is significantly better than Ionescu and Butnaru (2017) according to a paired McNemar’s test performed at a significance level of 0.01." ], "reasoning": "The domain-adapted sum of kernels shows significant improvements over the state-of-the-art, with the highest accuracy of 66.73% on the development set and 78.35% on the test set.", "description": "Results. The domain-adapted sum of kernels obtains improvements above 0.8% over the state-of-the-art sum of kernels Ionescu and Butnaru The improvement on the development set (from 64.17% to 65.42%) is statistically significant. Nevertheless, we obtain higher and significant improvements when we employ the transductive classifier. Our best accuracy is 66.73% (2.56% above the baseline) on the development set and 78.35% (2.08% above the baseline) on the test set. The results show that our domain adaptation framework based on string kernels attains the best performance on the ADI Shared Task data set, and the improvements over the state-of-the-art are statistically significant, according to the McNemar’s test.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: Arabic dialect identification accuracy rates (in %) of our adapted string kernels versus the 2017 ADI Shared Task winner (sota) Ionescu and Butnaru (2017) and the first runner up Malmasi and Zampieri (2017). The best accuracy rates are highlighted in bold. The marker * indicates that the performance is significantly better than Ionescu and Butnaru (2017) according to a paired McNemar’s test performed at a significance level of 0.01.' to sequentially solve the question presented below. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the highest accuracy achieved on the test set and which method achieved it?\n" }, { "role": "assistant", "content": "The highest accuracy on the test set is 78.35%, achieved by the method 'K0/1+ K∩+ KLRD+ Kivec + TKC'. So, the answer is {\"answer\": \"78.35% by K0/1+ K∩+ KLRD+ Kivec + TKC\"}." } ], "images": [ "scigen/train/2479.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Arabic dialect identification accuracy rates (in %) of our adapted string kernels versus the 2017 ADI Shared Task winner (sota) Ionescu and Butnaru (2017) and the first runner up Malmasi and Zampieri (2017). The best accuracy rates are highlighted in bold. The marker * indicates that the performance is significantly better than Ionescu and Butnaru (2017) according to a paired McNemar’s test performed at a significance level of 0.01." ], "reasoning": "The highest accuracy on the test set is 78.35%, achieved by the method 'K0/1+ K∩+ KLRD+ Kivec + TKC'.", "question": "What is the highest accuracy achieved on the test set and which method achieved it?", "answer": "So, the answer is {\"answer\": \"78.35% by K0/1+ K∩+ KLRD+ Kivec + TKC\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The method 'K0/1+ K∩+ KLRD+ Kivec + TKC' achieved the highest accuracy on the development set. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe method 'K0/1+ K∩+ KLRD+ Kivec + TKC' achieved the highest accuracy on the development set.\n" }, { "role": "assistant", "content": "The table shows that the method 'K0/1+ K∩+ KLRD+ Kivec + TKC' achieved the highest accuracy on the development set with 66.73%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2479.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Arabic dialect identification accuracy rates (in %) of our adapted string kernels versus the 2017 ADI Shared Task winner (sota) Ionescu and Butnaru (2017) and the first runner up Malmasi and Zampieri (2017). The best accuracy rates are highlighted in bold. The marker * indicates that the performance is significantly better than Ionescu and Butnaru (2017) according to a paired McNemar’s test performed at a significance level of 0.01." ], "reasoning": "The table shows that the method 'K0/1+ K∩+ KLRD+ Kivec + TKC' achieved the highest accuracy on the development set with 66.73%.", "statement": "The method 'K0/1+ K∩+ KLRD+ Kivec + TKC' achieved the highest accuracy on the development set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: Performance measured in Pearson’s r×100, on the SemEval 2017 STS task on the es-es, ar-ar, en-es, en-ar, and en-tr datasets.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "BGT has the best performance across all datasets, with significant gains in the cross-lingual setting due to its ability to strip away language-specific information.From these results, we see that BGT has the best performance across all datasets, however the performance is significantly stronger than the BilingualTrans and BGT w/o Prior baselines in the cross-lingual setting. Since BGT w/o LangVars also has significantly better performance on these tasks, most of this gain seems to be due to the prior have a regularizing effect. However, BGT outperforms BGT w/o LangVars overall, and we hypothesize that the gap in performance between these two models is due to BGT being able to strip away the language-specific information in the representations with its language-specific variables, allowing for the semantics of the sentences to be more directly compared." } ], "images": [ "scigen/train/2480.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance measured in Pearson’s r×100, on the SemEval 2017 STS task on the es-es, ar-ar, en-es, en-ar, and en-tr datasets." ], "reasoning": "BGT has the best performance across all datasets, with significant gains in the cross-lingual setting due to its ability to strip away language-specific information.", "description": "From these results, we see that BGT has the best performance across all datasets, however the performance is significantly stronger than the BilingualTrans and BGT w/o Prior baselines in the cross-lingual setting. Since BGT w/o LangVars also has significantly better performance on these tasks, most of this gain seems to be due to the prior have a regularizing effect. However, BGT outperforms BGT w/o LangVars overall, and we hypothesize that the gap in performance between these two models is due to BGT being able to strip away the language-specific information in the representations with its language-specific variables, allowing for the semantics of the sentences to be more directly compared.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 4: Performance measured in Pearson’s r×100, on the SemEval 2017 STS task on the es-es, ar-ar, en-es, en-ar, and en-tr datasets.', answer the question below by showing each progressive step toward the answer. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model has the highest performance in the en-es dataset and what is the score?\n" }, { "role": "assistant", "content": "The en-es column shows the performance scores for each model. BGT has the highest score in this column. So, the answer is {\"answer\": \"BGT with 75.6\"}." } ], "images": [ "scigen/train/2480.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance measured in Pearson’s r×100, on the SemEval 2017 STS task on the es-es, ar-ar, en-es, en-ar, and en-tr datasets." ], "reasoning": "The en-es column shows the performance scores for each model. BGT has the highest score in this column.", "question": "Which model has the highest performance in the en-es dataset and what is the score?", "answer": "So, the answer is {\"answer\": \"BGT with 75.6\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nBGT w/o Prior has the best performance in the es-es dataset.\n" }, { "role": "assistant", "content": "The es-es column shows the performance scores for each model. BGT has the highest score, not BGT w/o Prior. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2480.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance measured in Pearson’s r×100, on the SemEval 2017 STS task on the es-es, ar-ar, en-es, en-ar, and en-tr datasets." ], "reasoning": "The es-es column shows the performance scores for each model. BGT has the highest score, not BGT w/o Prior.", "statement": "BGT w/o Prior has the best performance in the es-es dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 7: System-wise evaluation on TREC-QA and WikiQA using AVA model, A2((r,q∘t),(t,q∘r)).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Setting the right threshold on the dev. set results in an error of 0 on P@1, while MAP is harder to predict. AVA's error ranges from 2 to 4.1 points on TREC-QA test set and reaches 10% on WikiQA test set, with low std. dev.We note that (i) setting the right threshold on the dev. set, the error on P@1 is 0; (ii) this is not the case for MAP, which is a much harder value to predict as it requires to estimate an entire ranking; (iii) on the TREC-QA test set, AVA has an error ranging from 2 to 4.1 points on any measure; (iv) on the WikiQA test set, the error is higher, reaching 10%, probably due to a larger complexity of the questions; (v) the std. dev. is low, suggesting that AVA can be used to estimate system performance." } ], "images": [ "scigen/train/2481.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: System-wise evaluation on TREC-QA and WikiQA using AVA model, A2((r,q∘t),(t,q∘r))." ], "reasoning": "Setting the right threshold on the dev. set results in an error of 0 on P@1, while MAP is harder to predict. AVA's error ranges from 2 to 4.1 points on TREC-QA test set and reaches 10% on WikiQA test set, with low std. dev.", "description": "We note that (i) setting the right threshold on the dev. set, the error on P@1 is 0; (ii) this is not the case for MAP, which is a much harder value to predict as it requires to estimate an entire ranking; (iii) on the TREC-QA test set, AVA has an error ranging from 2 to 4.1 points on any measure; (iv) on the WikiQA test set, the error is higher, reaching 10%, probably due to a larger complexity of the questions; (v) the std. dev. is low, suggesting that AVA can be used to estimate system performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: Table 7: System-wise evaluation on TREC-QA and WikiQA using AVA model, A2((r,q∘t),(t,q∘r)).\nQuestion: What is the RMSE ± σ for MAP on the TREC-QA-Dev set?\n" }, { "role": "assistant", "content": "To find the RMSE ± σ for MAP on the TREC-QA-Dev set, locate the row corresponding to TREC-QA-Dev and MAP, then read the value under the RMSE ± σ column. So, the answer is {\"answer\": \"0.040 ± 0.019\"}." } ], "images": [ "scigen/train/2481.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: System-wise evaluation on TREC-QA and WikiQA using AVA model, A2((r,q∘t),(t,q∘r))." ], "reasoning": "To find the RMSE ± σ for MAP on the TREC-QA-Dev set, locate the row corresponding to TREC-QA-Dev and MAP, then read the value under the RMSE ± σ column.", "question": "What is the RMSE ± σ for MAP on the TREC-QA-Dev set?", "answer": "So, the answer is {\"answer\": \"0.040 ± 0.019\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The error on P@1 for the TREC-QA-Test set is 0.034 ± 0.018., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe error on P@1 for the TREC-QA-Test set is 0.034 ± 0.018.\n" }, { "role": "assistant", "content": "The table shows that the RMSE ± σ for P@1 on the TREC-QA-Test set is indeed 0.034 ± 0.018. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2481.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: System-wise evaluation on TREC-QA and WikiQA using AVA model, A2((r,q∘t),(t,q∘r))." ], "reasoning": "The table shows that the RMSE ± σ for P@1 on the TREC-QA-Test set is indeed 0.034 ± 0.018.", "statement": "The error on P@1 for the TREC-QA-Test set is 0.034 ± 0.018.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 9: Details of system-wise Evaluation on ADS benchmark dataset'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The ADS dataset evaluation shows that the evaluated systems have close accuracy, with a low RMSE of 3.1% and a standard deviation of less than 0.02, indicating an error probability of less than 7% with over 95% confidence.6.5.2 Results on ADS We use ADS dataset in this evaluation. The task is more challenging as AVA only receives one best answer for a system selected from different candidate pools. There was also no control of the sources for the candidates. We note a lower correlation due to the fact that the 8 evaluated systems have very close Accuracy. On the other hand, the RMSE is rather low 3.1% and the std. dev. is also acceptable <0.02, suggesting an error less than 7% with a probability > 95%." } ], "images": [ "scigen/train/2482.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Details of system-wise Evaluation on ADS benchmark dataset" ], "reasoning": "The ADS dataset evaluation shows that the evaluated systems have close accuracy, with a low RMSE of 3.1% and a standard deviation of less than 0.02, indicating an error probability of less than 7% with over 95% confidence.", "description": "6.5.2 Results on ADS We use ADS dataset in this evaluation. The task is more challenging as AVA only receives one best answer for a system selected from different candidate pools. There was also no control of the sources for the candidates. We note a lower correlation due to the fact that the 8 evaluated systems have very close Accuracy. On the other hand, the RMSE is rather low 3.1% and the std. dev. is also acceptable <0.02, suggesting an error less than 7% with a probability > 95%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the Kendall τ value for the AVA evaluator on the Test (80%) split?\nSolve the above question based on the table titled 'Table 9: Details of system-wise Evaluation on ADS benchmark dataset'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the Kendall τ value for the AVA evaluator on the Test (80%) split, locate the row corresponding to 'Test (80%)' and 'AVA' evaluator, then find the value under the 'Kendall τ' column. So, the answer is {\"answer\": \"0.643\"}." } ], "images": [ "scigen/train/2482.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Details of system-wise Evaluation on ADS benchmark dataset" ], "reasoning": "To find the Kendall τ value for the AVA evaluator on the Test (80%) split, locate the row corresponding to 'Test (80%)' and 'AVA' evaluator, then find the value under the 'Kendall τ' column.", "question": "What is the Kendall τ value for the AVA evaluator on the Test (80%) split?", "answer": "So, the answer is {\"answer\": \"0.643\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe RMSE for the ADS evaluator on the Dev (20%) split is 0.0350 ± 0.019.\n" }, { "role": "assistant", "content": "To verify this statement, locate the row corresponding to 'Dev (20%)' and 'ADS' evaluator, then find the value under the 'RMSE ± σ' column. The value is 0.0198 ± 0.012, which does not match the statement. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2482.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Details of system-wise Evaluation on ADS benchmark dataset" ], "reasoning": "To verify this statement, locate the row corresponding to 'Dev (20%)' and 'ADS' evaluator, then find the value under the 'RMSE ± σ' column. The value is 0.0198 ± 0.012, which does not match the statement.", "statement": "The RMSE for the ADS evaluator on the Dev (20%) split is 0.0350 ± 0.019.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Label-wise F1 scores for the CoNLL-05 and CoNLL-12 development sets.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Under low training data conditions, constrained models improved primarily on frequent labels like A0-A2, but struggled with AM-LOC and AM-NEG. With the CoNLL-12 training set, performance was on par with the baseline, indicating saturation on larger datasets.Under low training data conditions, our constrained models gained improvements primarily from the frequent labels, \\eg, A0-A2. On CoNLL-05 dataset, we found the location modifier (AM-LOC) posed challenges to our constrained models which significantly performed worse than the baseline. Another challenge is the negation modifier (AM-NEG), where our models underperformed on both datasets, particularly with small training data. When using the CoNLL-12 training set, our models performed on par with the baseline even on frequent labels, confirming that the performance of soft-structured learning is nearly saturated on the larger, cleaner dataset." } ], "images": [ "scigen/train/2483.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Label-wise F1 scores for the CoNLL-05 and CoNLL-12 development sets." ], "reasoning": "Under low training data conditions, constrained models improved primarily on frequent labels like A0-A2, but struggled with AM-LOC and AM-NEG. With the CoNLL-12 training set, performance was on par with the baseline, indicating saturation on larger datasets.", "description": "Under low training data conditions, our constrained models gained improvements primarily from the frequent labels, \\eg, A0-A2. On CoNLL-05 dataset, we found the location modifier (AM-LOC) posed challenges to our constrained models which significantly performed worse than the baseline. Another challenge is the negation modifier (AM-NEG), where our models underperformed on both datasets, particularly with small training data. When using the CoNLL-12 training set, our models performed on par with the baseline even on frequent labels, confirming that the performance of soft-structured learning is nearly saturated on the larger, cleaner dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}. The question is related to the given table titled 'Table 7: Label-wise F1 scores for the CoNLL-05 and CoNLL-12 development sets.'.\n\nQuestion: Which label posed challenges to the constrained models on the CoNLL-05 dataset, and how did the models perform on this label with 3% RoBERTa2?\n" }, { "role": "assistant", "content": "The description states that the location modifier (AM-LOC) posed challenges to the constrained models on the CoNLL-05 dataset. Looking at the table, the performance of the models on AM-LOC with CoNLL-05 3% RoBERTa2 is 53.08. So, the answer is {\"answer\": \"AM-LOC, 53.08\"}." } ], "images": [ "scigen/train/2483.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Label-wise F1 scores for the CoNLL-05 and CoNLL-12 development sets." ], "reasoning": "The description states that the location modifier (AM-LOC) posed challenges to the constrained models on the CoNLL-05 dataset. Looking at the table, the performance of the models on AM-LOC with CoNLL-05 3% RoBERTa2 is 53.08.", "question": "Which label posed challenges to the constrained models on the CoNLL-05 dataset, and how did the models perform on this label with 3% RoBERTa2?", "answer": "So, the answer is {\"answer\": \"AM-LOC, 53.08\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The constrained models performed better on the AM-NEG label with the CoNLL-12 100% +U,F,O dataset compared to the CoNLL-05 100% +U,F,O dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe constrained models performed better on the AM-NEG label with the CoNLL-12 100% +U,F,O dataset compared to the CoNLL-05 100% +U,F,O dataset.\n" }, { "role": "assistant", "content": "The table shows that the performance on AM-NEG with CoNLL-12 100% +U,F,O is 95.83, while with CoNLL-05 100% +U,F,O it is 94.06. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2483.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Label-wise F1 scores for the CoNLL-05 and CoNLL-12 development sets." ], "reasoning": "The table shows that the performance on AM-NEG with CoNLL-12 100% +U,F,O is 95.83, while with CoNLL-05 100% +U,F,O it is 94.06. Therefore, the statement is supported.", "statement": "The constrained models performed better on the AM-NEG label with the CoNLL-12 100% +U,F,O dataset compared to the CoNLL-05 100% +U,F,O dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Ablation tests on CoNLL-05.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "As models become more constrained, precision improves, but one class of constraints does not necessarily reduce the violation rate for others. Combining all three constraints balances precision, recall, and constraint violation.Constraint Ablations We see that as models become more constrained, precision improves. Furthermore, one class of constraints do not necessarily reduce the violation rate for the others. Combining all three constraints offers a balance between precision, recall, and constraint violation." } ], "images": [ "scigen/train/2484.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Ablation tests on CoNLL-05." ], "reasoning": "As models become more constrained, precision improves, but one class of constraints does not necessarily reduce the violation rate for others. Combining all three constraints balances precision, recall, and constraint violation.", "description": "Constraint Ablations We see that as models become more constrained, precision improves. Furthermore, one class of constraints do not necessarily reduce the violation rate for the others. Combining all three constraints offers a balance between precision, recall, and constraint violation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 6: Ablation tests on CoNLL-05.', and sequentially solve the question, writing out each step of your reasoning process. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model and constraint combination achieves the highest precision on the CoNLL-05 (100%, 36k) dataset?\n" }, { "role": "assistant", "content": "To find the highest precision, we look at the 'CoNLL-05 (100%, 36k) P' column. The highest value in this column is 88.08, achieved by the RoBERTa2 model with the +U,F constraint. So, the answer is {\"answer\": \"RoBERTa2 +U,F\"}." } ], "images": [ "scigen/train/2484.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Ablation tests on CoNLL-05." ], "reasoning": "To find the highest precision, we look at the 'CoNLL-05 (100%, 36k) P' column. The highest value in this column is 88.08, achieved by the RoBERTa2 model with the +U,F constraint.", "question": "Which model and constraint combination achieves the highest precision on the CoNLL-05 (100%, 36k) dataset?", "answer": "So, the answer is {\"answer\": \"RoBERTa2 +U,F\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe RoBERTa2 model with the +U,F,O constraint achieves the highest F1 score on the CoNLL-05 (100%, 36k) dataset.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the 'CoNLL-05 (100%, 36k) F1' column. The highest F1 score is 88.07, achieved by the RoBERTa2 model with the +U,F constraint, not the +U,F,O constraint. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2484.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Ablation tests on CoNLL-05." ], "reasoning": "To verify this statement, we look at the 'CoNLL-05 (100%, 36k) F1' column. The highest F1 score is 88.07, achieved by the RoBERTa2 model with the +U,F constraint, not the +U,F,O constraint.", "statement": "The RoBERTa2 model with the +U,F,O constraint achieves the highest F1 score on the CoNLL-05 (100%, 36k) dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: The RNN encoder results for cQA tasks (bold is best).'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The attention model consistently outperforms the RNN without attention, particularly in task C, where data imbalance leads to high precision but low recall.We observe that the attention model always gets better results than the RNN without attention, especially for task C. However, the RNN model achieves a very low F1 score. For task B, it is even worse than the random baseline. We believe the reason is because for task B, there are only 2,670 pairs for training which is very limited training for a reasonable neural network. For task C, we believe the problem is highly imbalanced data. Since the related comments did not directly comment on the original question, more than 90% of the comments are labeled as irrelevant to the original question. The low F1 (with high precision and low recall) means our system tends to label most comments as irrelevant. In the following section, we investigate methods to address these issues." } ], "images": [ "scigen/train/2485.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The RNN encoder results for cQA tasks (bold is best)." ], "reasoning": "The attention model consistently outperforms the RNN without attention, particularly in task C, where data imbalance leads to high precision but low recall.", "description": "We observe that the attention model always gets better results than the RNN without attention, especially for task C. However, the RNN model achieves a very low F1 score. For task B, it is even worse than the random baseline. We believe the reason is because for task B, there are only 2,670 pairs for training which is very limited training for a reasonable neural network. For task C, we believe the problem is highly imbalanced data. Since the related comments did not directly comment on the original question, more than 90% of the comments are labeled as irrelevant to the original question. The low F1 (with high precision and low recall) means our system tends to label most comments as irrelevant. In the following section, we investigate methods to address these issues.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: The RNN encoder results for cQA tasks (bold is best).', and sequentially solve the question, writing out each step of your reasoning process. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieves the highest F1 score for Task A?\n" }, { "role": "assistant", "content": "By examining the F1 scores for Task A across all models, the attention model has the highest F1 score of 0.6323. So, the answer is {\"answer\": \"0.6323\"}." } ], "images": [ "scigen/train/2485.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The RNN encoder results for cQA tasks (bold is best)." ], "reasoning": "By examining the F1 scores for Task A across all models, the attention model has the highest F1 score of 0.6323.", "question": "Which model achieves the highest F1 score for Task A?", "answer": "So, the answer is {\"answer\": \"0.6323\"}." }, { "messages": [ { "role": "user", "content": "The Seq LSTM model performs better than the Random model in Task B MAP. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Seq LSTM model performs better than the Random model in Task B MAP.\n" }, { "role": "assistant", "content": "The Seq LSTM model has a Task B MAP score of 0.5620, which is higher than the Random model's score of 0.5595. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2485.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The RNN encoder results for cQA tasks (bold is best)." ], "reasoning": "The Seq LSTM model has a Task B MAP score of 0.5620, which is higher than the Random model's score of 0.5595.", "statement": "The Seq LSTM model performs better than the Random model in Task B MAP.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: cQA task results with augmented features (bold is best).'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Incorporating a one hot vector of the original IR ranking into the FNN classifier improves the performance for tasks B and C, while task A shows a slight degradation in F1 score but an improvement in MAP.To further enhance the system, we incorporate a one hot vector of the original IR ranking as an additional feature into the FNN classifier. In comparing the models with and without augmented features, we can see large improvement for task B and C. The F1 score for task A degrades slightly but MAP improves. This might be because task A already had a substantial amount of training data." } ], "images": [ "scigen/train/2486.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: cQA task results with augmented features (bold is best)." ], "reasoning": "Incorporating a one hot vector of the original IR ranking into the FNN classifier improves the performance for tasks B and C, while task A shows a slight degradation in F1 score but an improvement in MAP.", "description": "To further enhance the system, we incorporate a one hot vector of the original IR ranking as an additional feature into the FNN classifier. In comparing the models with and without augmented features, we can see large improvement for task B and C. The F1 score for task A degrades slightly but MAP improves. This might be because task A already had a substantial amount of training data.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: cQA task results with augmented features (bold is best).'. Solve the following question based on the table.\n\nQuestion: How does the F1 score for Task B change when augmented features are added?\nLet's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "Compare the F1 scores for Task B with and without augmented features. The F1 score for Task B without augmented features is 0.4334, and with augmented features, it is 0.5382. So, the answer is {\"answer\": \"0.5382\"}" } ], "images": [ "scigen/train/2486.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: cQA task results with augmented features (bold is best)." ], "reasoning": "Compare the F1 scores for Task B with and without augmented features. The F1 score for Task B without augmented features is 0.4334, and with augmented features, it is 0.5382.", "question": "How does the F1 score for Task B change when augmented features are added?", "answer": "So, the answer is {\"answer\": \"0.5382\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe MAP score for Task C improves with the addition of augmented features.\n" }, { "role": "assistant", "content": "Compare the MAP scores for Task C with and without augmented features. The MAP score for Task C without augmented features is 0.2837, and with augmented features, it is 0.3236. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2486.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: cQA task results with augmented features (bold is best)." ], "reasoning": "Compare the MAP scores for Task C with and without augmented features. The MAP score for Task C without augmented features is 0.2837, and with augmented features, it is 0.3236.", "statement": "The MAP score for Task C improves with the addition of augmented features.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: Compared with other systems (bold is best).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Our system outperforms a strong feature-rich based system in Task A with sufficient training data, performs worse than the IR system in Task B with limited data, and achieves comparable results in Task C.Since the two baseline models did not use any additional data, in this table our system was also restricted to the provided training data. For task A, we can see that if there is enough training data our single system already performs better than a very strong feature-rich based system. For task B, since only limited training data is given, both feature-rich based system and our system are worse than the IR system. For task C, our system also got comparable results with the feature-rich based system. This implies that our system is complimentary with the IR system." } ], "images": [ "scigen/train/2487.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Compared with other systems (bold is best)." ], "reasoning": "Our system outperforms a strong feature-rich based system in Task A with sufficient training data, performs worse than the IR system in Task B with limited data, and achieves comparable results in Task C.", "description": "Since the two baseline models did not use any additional data, in this table our system was also restricted to the provided training data. For task A, we can see that if there is enough training data our single system already performs better than a very strong feature-rich based system. For task B, since only limited training data is given, both feature-rich based system and our system are worse than the IR system. For task C, our system also got comparable results with the feature-rich based system. This implies that our system is complimentary with the IR system.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: Compared with other systems (bold is best).', answer the following question. Give detailed solution consisting of each step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieved the highest MAP score in Task B?\n" }, { "role": "assistant", "content": "By comparing the MAP scores for Task B, the highest score is 0.717, achieved by the Attention & IR model. So, the answer is {\"answer\": \"Attention & IR\"}" } ], "images": [ "scigen/train/2487.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Compared with other systems (bold is best)." ], "reasoning": "By comparing the MAP scores for Task B, the highest score is 0.717, achieved by the Attention & IR model.", "question": "Which model achieved the highest MAP score in Task B?", "answer": "So, the answer is {\"answer\": \"Attention & IR\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Attention model performed better than the Feature-Rich & IR model in Task C.\n" }, { "role": "assistant", "content": "The MAP score for the Attention model in Task C is 0.324, while the MAP score for the Feature-Rich & IR model is 0.339. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2487.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Compared with other systems (bold is best)." ], "reasoning": "The MAP score for the Attention model in Task C is 0.324, while the MAP score for the Feature-Rich & IR model is 0.339. Therefore, the statement is refuted.", "statement": "The Attention model performed better than the Feature-Rich & IR model in Task C.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Performance of different style transfer methods on standard benchmarks.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The method achieved comparable performance with two baselines and outperformed them on Amazon, which has a larger vocabulary size.Since the trade-off between the transfer ability and content preservation can be controlled, it is hard to select one balanced point to fully characterize the performance of our method. As a complement, we suggest to use Mean as an overall performance measure, which is more stable than G-Score as observed in Fig. As we can see, our method achieved comparable performance with two baselines while averagely outperformed them on Amazon, the benchmark with a larger vocabulary size. For an illustrative comparison, we further provide some generated samples from each method in Appendix A." } ], "images": [ "scigen/train/2488.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of different style transfer methods on standard benchmarks." ], "reasoning": "The method achieved comparable performance with two baselines and outperformed them on Amazon, which has a larger vocabulary size.", "description": "Since the trade-off between the transfer ability and content preservation can be controlled, it is hard to select one balanced point to fully characterize the performance of our method. As a complement, we suggest to use Mean as an overall performance measure, which is more stable than G-Score as observed in Fig. As we can see, our method achieved comparable performance with two baselines while averagely outperformed them on Amazon, the benchmark with a larger vocabulary size. For an illustrative comparison, we further provide some generated samples from each method in Appendix A.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Performance of different style transfer methods on standard benchmarks.', answer the following question. Give detailed solution consisting of each step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model achieved the highest Amazon Mean score?\n" }, { "role": "assistant", "content": "By comparing the Amazon Mean scores of all models, the NS-BiGRU model achieved the highest score of 49.64. So, the answer is {\"answer\": \"NS-BiGRU\"}" } ], "images": [ "scigen/train/2488.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of different style transfer methods on standard benchmarks." ], "reasoning": "By comparing the Amazon Mean scores of all models, the NS-BiGRU model achieved the highest score of 49.64.", "question": "Which model achieved the highest Amazon Mean score?", "answer": "So, the answer is {\"answer\": \"NS-BiGRU\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe NS-GRU model achieved the highest Yelp G-Score.\n" }, { "role": "assistant", "content": "By comparing the Yelp G-Scores of all models, the Style-Embedding model achieved the highest score of 46.88, not the NS-GRU model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2488.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of different style transfer methods on standard benchmarks." ], "reasoning": "By comparing the Yelp G-Scores of all models, the Style-Embedding model achieved the highest score of 46.88, not the NS-GRU model.", "statement": "The NS-GRU model achieved the highest Yelp G-Score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Accuracy on the Gigaword corpus in terms of the full-length ROUGE-1 (RG-1), ROUGE-2 (RG-2), and ROUGE-L (RG-L). Best performance on each score is displayed in boldface.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The Reinforced-Topic-ConvS2S model, which combines topic-aware and reinforcement learning, achieves the highest scores in RG-1, RG-2, and RG-L metrics, demonstrating its superior performance in text summarization.We demonstrate the effectiveness of our proposed model via a step-by-step justification. First, the basic ConvS2S structure with topic-aware model or reinforcement learning is tested, respectively. Then we combine the two to show the performance of our Reinforced-Topic-ConvS2S model. The ABS and ABS+ models are attention-based neural models for text summarization. The RAS-Elman model introduces a conditional RNN, in which the conditioner is provided by a convolutional attention-based encoder. The words-lvt5k-1sent model is also a RNN-based attention model which implements a large-vocabulary trick. Besides, RNN+MRT employs the minimum risk training strategy which directly optimizes model parameters in sentence level with respect to the evaluation metrics. SEASS (beam) extends the sequence-to-sequence framework with a selective encoding model. The results have demonstrated that both the topic-aware module and the reinforcement learning process can improve the accuracy on text summarization. Moreover, our proposed model exhibits best scores of RG-1, RG-2 and RG-L." } ], "images": [ "scigen/train/2489.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracy on the Gigaword corpus in terms of the full-length ROUGE-1 (RG-1), ROUGE-2 (RG-2), and ROUGE-L (RG-L). Best performance on each score is displayed in boldface." ], "reasoning": "The Reinforced-Topic-ConvS2S model, which combines topic-aware and reinforcement learning, achieves the highest scores in RG-1, RG-2, and RG-L metrics, demonstrating its superior performance in text summarization.", "description": "We demonstrate the effectiveness of our proposed model via a step-by-step justification. First, the basic ConvS2S structure with topic-aware model or reinforcement learning is tested, respectively. Then we combine the two to show the performance of our Reinforced-Topic-ConvS2S model. The ABS and ABS+ models are attention-based neural models for text summarization. The RAS-Elman model introduces a conditional RNN, in which the conditioner is provided by a convolutional attention-based encoder. The words-lvt5k-1sent model is also a RNN-based attention model which implements a large-vocabulary trick. Besides, RNN+MRT employs the minimum risk training strategy which directly optimizes model parameters in sentence level with respect to the evaluation metrics. SEASS (beam) extends the sequence-to-sequence framework with a selective encoding model. The results have demonstrated that both the topic-aware module and the reinforcement learning process can improve the accuracy on text summarization. Moreover, our proposed model exhibits best scores of RG-1, RG-2 and RG-L.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 2: Accuracy on the Gigaword corpus in terms of the full-length ROUGE-1 (RG-1), ROUGE-2 (RG-2), and ROUGE-L (RG-L). Best performance on each score is displayed in boldface.\nQuestion: Which model achieves the highest score in the RG-2 metric?\n" }, { "role": "assistant", "content": "By examining the table, the highest score in the RG-2 metric is 18.29, which is achieved by the Reinforced-Topic-ConvS2S model. So, the answer is {\"answer\": \"Reinforced-Topic-ConvS2S\"}" } ], "images": [ "scigen/train/2489.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracy on the Gigaword corpus in terms of the full-length ROUGE-1 (RG-1), ROUGE-2 (RG-2), and ROUGE-L (RG-L). Best performance on each score is displayed in boldface." ], "reasoning": "By examining the table, the highest score in the RG-2 metric is 18.29, which is achieved by the Reinforced-Topic-ConvS2S model.", "question": "Which model achieves the highest score in the RG-2 metric?", "answer": "So, the answer is {\"answer\": \"Reinforced-Topic-ConvS2S\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The ABS+ model outperforms the RAS-Elman model in the RG-1 metric., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe ABS+ model outperforms the RAS-Elman model in the RG-1 metric.\n" }, { "role": "assistant", "content": "The table shows that the ABS+ model has an RG-1 score of 29.76, while the RAS-Elman model has an RG-1 score of 33.78. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2489.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracy on the Gigaword corpus in terms of the full-length ROUGE-1 (RG-1), ROUGE-2 (RG-2), and ROUGE-L (RG-L). Best performance on each score is displayed in boldface." ], "reasoning": "The table shows that the ABS+ model has an RG-1 score of 29.76, while the RAS-Elman model has an RG-1 score of 33.78. Therefore, the statement is refuted.", "statement": "The ABS+ model outperforms the RAS-Elman model in the RG-1 metric.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Accuracy on the internal test set of Gigaword corpus in terms of the full-length RG-1, RG-2, and RG-L. Best performance on each score is displayed in boldface.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The Reinforced-Topic-ConvS2S model achieves the highest scores in all three ROUGE metrics (RG-1, RG-2, RG-L).Again, our proposed model achieves the best performance in terms of all the three ROUGE scores." } ], "images": [ "scigen/train/2490.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracy on the internal test set of Gigaword corpus in terms of the full-length RG-1, RG-2, and RG-L. Best performance on each score is displayed in boldface." ], "reasoning": "The Reinforced-Topic-ConvS2S model achieves the highest scores in all three ROUGE metrics (RG-1, RG-2, RG-L).", "description": "Again, our proposed model achieves the best performance in terms of all the three ROUGE scores.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: Accuracy on the internal test set of Gigaword corpus in terms of the full-length RG-1, RG-2, and RG-L. Best performance on each score is displayed in boldface.', and sequentially solve the question, writing out each step of your reasoning process. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieves the highest RG-2 score and what is that score?\n" }, { "role": "assistant", "content": "By comparing the RG-2 scores of all models, the Reinforced-Topic-ConvS2S model has the highest RG-2 score of 24.83. So, the answer is {\"answer\": \"Reinforced-Topic-ConvS2S\"}." } ], "images": [ "scigen/train/2490.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracy on the internal test set of Gigaword corpus in terms of the full-length RG-1, RG-2, and RG-L. Best performance on each score is displayed in boldface." ], "reasoning": "By comparing the RG-2 scores of all models, the Reinforced-Topic-ConvS2S model has the highest RG-2 score of 24.83.", "question": "Which model achieves the highest RG-2 score and what is that score?", "answer": "So, the answer is {\"answer\": \"Reinforced-Topic-ConvS2S\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe SEASS (beam) model achieves the highest RG-1 score.\n" }, { "role": "assistant", "content": "The table shows that the highest RG-1 score is 46.92, achieved by the Reinforced-Topic-ConvS2S model, not the SEASS (beam) model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2490.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracy on the internal test set of Gigaword corpus in terms of the full-length RG-1, RG-2, and RG-L. Best performance on each score is displayed in boldface." ], "reasoning": "The table shows that the highest RG-1 score is 46.92, achieved by the Reinforced-Topic-ConvS2S model, not the SEASS (beam) model.", "statement": "The SEASS (beam) model achieves the highest RG-1 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Accuracy on the DUC-2004 dataset in terms of the recall-only RG-1, RG-2, and RG-L. Best performance on each score is displayed in boldface.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Models are trained on the Gigaword corpus and evaluated on the DUC-2004 dataset due to their similarity.Since the DUC-2004 dataset is an evaluation-only dataset, we train the models on the Gigaword corpus first and then evaluate their performance on the DUC dataset. Due to the similarity of the two datasets, we do not provide qualitative summarization examples in this experiment." } ], "images": [ "scigen/train/2491.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Accuracy on the DUC-2004 dataset in terms of the recall-only RG-1, RG-2, and RG-L. Best performance on each score is displayed in boldface." ], "reasoning": "Models are trained on the Gigaword corpus and evaluated on the DUC-2004 dataset due to their similarity.", "description": "Since the DUC-2004 dataset is an evaluation-only dataset, we train the models on the Gigaword corpus first and then evaluate their performance on the DUC dataset. Due to the similarity of the two datasets, we do not provide qualitative summarization examples in this experiment.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model achieved the highest RG-1 score and what was the score?\nSolve the above question based on the table titled 'Table 5: Accuracy on the DUC-2004 dataset in terms of the recall-only RG-1, RG-2, and RG-L. Best performance on each score is displayed in boldface.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest RG-1 score, look at the RG-1 column and identify the highest value. The model with the highest RG-1 score is Reinforced-Topic-ConvS2S with a score of 31.15. So, the answer is {\"answer\": \"Reinforced-Topic-ConvS2S with 31.15\"}" } ], "images": [ "scigen/train/2491.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Accuracy on the DUC-2004 dataset in terms of the recall-only RG-1, RG-2, and RG-L. Best performance on each score is displayed in boldface." ], "reasoning": "To find the model with the highest RG-1 score, look at the RG-1 column and identify the highest value. The model with the highest RG-1 score is Reinforced-Topic-ConvS2S with a score of 31.15.", "question": "Which model achieved the highest RG-1 score and what was the score?", "answer": "So, the answer is {\"answer\": \"Reinforced-Topic-ConvS2S with 31.15\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The RNN+MLE model achieved an RG-2 score higher than 9., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe RNN+MLE model achieved an RG-2 score higher than 9.\n" }, { "role": "assistant", "content": "Check the RG-2 score for the RNN+MLE model in the table. The RG-2 score for RNN+MLE is 8.60, which is not higher than 9. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2491.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Accuracy on the DUC-2004 dataset in terms of the recall-only RG-1, RG-2, and RG-L. Best performance on each score is displayed in boldface." ], "reasoning": "Check the RG-2 score for the RNN+MLE model in the table. The RG-2 score for RNN+MLE is 8.60, which is not higher than 9.", "statement": "The RNN+MLE model achieved an RG-2 score higher than 9.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 6: Accuracy on the LCSTS dataset in terms of the full-length RG-1, RG-2, and RG-L. In last three rows, the word-level ROUGE scores are presented on the left and the character-level on the right.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The Reinforced-Topic-ConvS2S model outperforms other methods in character-based ROUGE metrics and achieves the best RG-1 and RG-L scores in word-based metrics, but not RG-2.Since not all previous work explicitly mentioned whether word-based or character-based ROUGE metrics were reported, we evaluate our proposed model with both metrics in order to obtain a comprehensive comparison. We can also observe that the character-based results of our Reinforced-Topic-ConvS2S model outperforms every other method. Regarding to word-based ROUGE scores, our model obtains the best performance in terms of RG-1 and RG-L metrics. However, our best model does not achieve a good RG-2 score as its RG-1 and RG-L scores. We suspect that it may be partly caused by the biased probability generation mechanism that influences word order, which requires further studies." } ], "images": [ "scigen/train/2492.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Accuracy on the LCSTS dataset in terms of the full-length RG-1, RG-2, and RG-L. In last three rows, the word-level ROUGE scores are presented on the left and the character-level on the right." ], "reasoning": "The Reinforced-Topic-ConvS2S model outperforms other methods in character-based ROUGE metrics and achieves the best RG-1 and RG-L scores in word-based metrics, but not RG-2.", "description": "Since not all previous work explicitly mentioned whether word-based or character-based ROUGE metrics were reported, we evaluate our proposed model with both metrics in order to obtain a comprehensive comparison. We can also observe that the character-based results of our Reinforced-Topic-ConvS2S model outperforms every other method. Regarding to word-based ROUGE scores, our model obtains the best performance in terms of RG-1 and RG-L metrics. However, our best model does not achieve a good RG-2 score as its RG-1 and RG-L scores. We suspect that it may be partly caused by the biased probability generation mechanism that influences word order, which requires further studies.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: Accuracy on the LCSTS dataset in terms of the full-length RG-1, RG-2, and RG-L. In last three rows, the word-level ROUGE scores are presented on the left and the character-level on the right.\nQuestion: Which model achieves the highest RG-1 score in word-based preprocessing?\nSolve the above question based on the table information. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest RG-1 score in word-based preprocessing, we look at the RG-1 column under word-based preprocessing. The highest score is 45.12, achieved by the Reinforced-Topic-ConvS2S model. So, the answer is {\"answer\": \"Reinforced-Topic-ConvS2S\"}." } ], "images": [ "scigen/train/2492.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Accuracy on the LCSTS dataset in terms of the full-length RG-1, RG-2, and RG-L. In last three rows, the word-level ROUGE scores are presented on the left and the character-level on the right." ], "reasoning": "To find the model with the highest RG-1 score in word-based preprocessing, we look at the RG-1 column under word-based preprocessing. The highest score is 45.12, achieved by the Reinforced-Topic-ConvS2S model.", "question": "Which model achieves the highest RG-1 score in word-based preprocessing?", "answer": "So, the answer is {\"answer\": \"Reinforced-Topic-ConvS2S\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Reinforced-Topic-ConvS2S model achieves the highest RG-2 score in character-based preprocessing.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the RG-2 column under character-based preprocessing. The highest RG-2 score is 25.20, achieved by the RNN+MRT model, not the Reinforced-Topic-ConvS2S model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2492.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Accuracy on the LCSTS dataset in terms of the full-length RG-1, RG-2, and RG-L. In last three rows, the word-level ROUGE scores are presented on the left and the character-level on the right." ], "reasoning": "To verify this statement, we look at the RG-2 column under character-based preprocessing. The highest RG-2 score is 25.20, achieved by the RNN+MRT model, not the Reinforced-Topic-ConvS2S model.", "statement": "The Reinforced-Topic-ConvS2S model achieves the highest RG-2 score in character-based preprocessing.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Intrinsic evaluation of pronoun gender classification over our new test sets.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "We evaluate NMT systems on pronoun gender translation using a new dataset, and assess BERT's pronoun gender knowledge by extending the masked LM approach.We take as our NMT baseline a Transformer Vaswani et al. We set input dimension to 512 and use 6 layers with 8 attention heads and hidden dimension 2048, dropout probabilities of 0.1 and learning rate 2.0. We train this model on WMT’13 Spanish→English data To evaluate how well these NMT systems model pronoun gender, we use them to translate the Spanish text from our new dataset into English. For each target Spanish pronoun, we use an implementation of Vogel et al. All context sentences precede the one containing the pronoun (no lookahead). We initialize our LM experiments from a BERTBASE model (110M parameters; see Devlin et al. To assess how much this model knows about pronoun gender out-of-the-box, we extend the masked LM approach from the original BERT work, inserting a mask token in each dropped subject position (see To generate gender predictions, we deduce masculine if él appears before ella in the list of mask fills and vice versa; we make no gender prediction where neither pronoun appears in the top ten mask fills. We note that the performance we see here is remarkably similar to human ratings of quality for the dataset." } ], "images": [ "scigen/train/2493.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Intrinsic evaluation of pronoun gender classification over our new test sets." ], "reasoning": "We evaluate NMT systems on pronoun gender translation using a new dataset, and assess BERT's pronoun gender knowledge by extending the masked LM approach.", "description": "We take as our NMT baseline a Transformer Vaswani et al. We set input dimension to 512 and use 6 layers with 8 attention heads and hidden dimension 2048, dropout probabilities of 0.1 and learning rate 2.0. We train this model on WMT’13 Spanish→English data To evaluate how well these NMT systems model pronoun gender, we use them to translate the Spanish text from our new dataset into English. For each target Spanish pronoun, we use an implementation of Vogel et al. All context sentences precede the one containing the pronoun (no lookahead). We initialize our LM experiments from a BERTBASE model (110M parameters; see Devlin et al. To assess how much this model knows about pronoun gender out-of-the-box, we extend the masked LM approach from the original BERT work, inserting a mask token in each dropped subject position (see To generate gender predictions, we deduce masculine if él appears before ella in the list of mask fills and vice versa; we make no gender prediction where neither pronoun appears in the top ten mask fills. We note that the performance we see here is remarkably similar to human ratings of quality for the dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 6: Intrinsic evaluation of pronoun gender classification over our new test sets.', unfold the steps to solve the question given next. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which model has the highest Masculine F1 score for Prodrops?\n" }, { "role": "assistant", "content": "To find the model with the highest Masculine F1 score for Prodrops, we look at the 'Masculine F1' column under the 'Prodrop' section. The highest value is 92.7, which corresponds to the 'BERT (Contexts) + Data' model. So, the answer is {\"answer\": \"BERT (Contexts) + Data\"}." } ], "images": [ "scigen/train/2493.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Intrinsic evaluation of pronoun gender classification over our new test sets." ], "reasoning": "To find the model with the highest Masculine F1 score for Prodrops, we look at the 'Masculine F1' column under the 'Prodrop' section. The highest value is 92.7, which corresponds to the 'BERT (Contexts) + Data' model.", "question": "Which model has the highest Masculine F1 score for Prodrops?", "answer": "So, the answer is {\"answer\": \"BERT (Contexts) + Data\"}." }, { "messages": [ { "role": "user", "content": "The Baseline MT (Sentences) model has a higher Masculine P score for Possessives than for Prodrops. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Baseline MT (Sentences) model has a higher Masculine P score for Possessives than for Prodrops.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the 'Masculine P' scores for the Baseline MT (Sentences) model in both the 'Prodrop' and 'Possessives' sections. The score for Possessives is 58.2, and for Prodrops, it is 56.2. Since 58.2 is higher than 56.2, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2493.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Intrinsic evaluation of pronoun gender classification over our new test sets." ], "reasoning": "To verify this statement, we compare the 'Masculine P' scores for the Baseline MT (Sentences) model in both the 'Prodrop' and 'Possessives' sections. The score for Possessives is 58.2, and for Prodrops, it is 56.2. Since 58.2 is higher than 56.2, the statement is supported.", "statement": "The Baseline MT (Sentences) model has a higher Masculine P score for Possessives than for Prodrops.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 7: Even accounting for difference in the raw number of pronouns by gender, prodrop is more likely for masculine entities than feminine entities in WMT’13 (development).'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The table shows a higher frequency and rate of prodrop for masculine pronouns compared to feminine pronouns, indicating a bias towards over-using masculine pronouns.Performance is weak overall but especially on feminine examples of prodrop. The tendency is for masculine pronouns to be over-used: feminine pronouns are used by Baseline MT (Sentences) less than one time in five when they should be. Specifically, we counted the number of times the English pronouns he and she occurred (first column). Not surprisingly, there was an over-representation of masculine pronouns; for each feminine example in the data, there was four masculine examples. Next, we looked in the aligned Spanish sentences for the corresponding pronouns él and ella and counted a case of prodrop each time they were missing (second column). Even accounting for differences in raw frequency by gender, there is a difference in which prodrop is more frequent for masculine entities. This is the first time this difference has been reported and future work should control for the prior it induces." } ], "images": [ "scigen/train/2494.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Even accounting for difference in the raw number of pronouns by gender, prodrop is more likely for masculine entities than feminine entities in WMT’13 (development)." ], "reasoning": "The table shows a higher frequency and rate of prodrop for masculine pronouns compared to feminine pronouns, indicating a bias towards over-using masculine pronouns.", "description": "Performance is weak overall but especially on feminine examples of prodrop. The tendency is for masculine pronouns to be over-used: feminine pronouns are used by Baseline MT (Sentences) less than one time in five when they should be. Specifically, we counted the number of times the English pronouns he and she occurred (first column). Not surprisingly, there was an over-representation of masculine pronouns; for each feminine example in the data, there was four masculine examples. Next, we looked in the aligned Spanish sentences for the corresponding pronouns él and ella and counted a case of prodrop each time they were missing (second column). Even accounting for differences in raw frequency by gender, there is a difference in which prodrop is more frequent for masculine entities. This is the first time this difference has been reported and future work should control for the prior it induces.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 7: Even accounting for difference in the raw number of pronouns by gender, prodrop is more likely for masculine entities than feminine entities in WMT’13 (development).', answer the question below by showing each progressive step toward the answer. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the difference in the prodrop rate between masculine and feminine pronouns?\n" }, { "role": "assistant", "content": "The table shows that the prodrop rate for 'he' is 90.7% and for 'she' is 79.9%. The difference is calculated as 90.7% - 79.9%. So, the answer is {\"answer\": \"10.8%\"}." } ], "images": [ "scigen/train/2494.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Even accounting for difference in the raw number of pronouns by gender, prodrop is more likely for masculine entities than feminine entities in WMT’13 (development)." ], "reasoning": "The table shows that the prodrop rate for 'he' is 90.7% and for 'she' is 79.9%. The difference is calculated as 90.7% - 79.9%.", "question": "What is the difference in the prodrop rate between masculine and feminine pronouns?", "answer": "So, the answer is {\"answer\": \"10.8%\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The prodrop rate for 'he' is higher than for 'she'., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe prodrop rate for 'he' is higher than for 'she'.\n" }, { "role": "assistant", "content": "The table shows that the prodrop rate for 'he' is 90.7% and for 'she' is 79.9%, indicating that the rate for 'he' is indeed higher. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2494.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Even accounting for difference in the raw number of pronouns by gender, prodrop is more likely for masculine entities than feminine entities in WMT’13 (development)." ], "reasoning": "The table shows that the prodrop rate for 'he' is 90.7% and for 'she' is 79.9%, indicating that the rate for 'he' is indeed higher.", "statement": "The prodrop rate for 'he' is higher than for 'she'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 9: BLEU score and pronoun accuracy by gender on the WMT’13 Spanish-to-English test set.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The Baseline MT + Gender Tags model shows the best performance on feminine pronouns with an 8.8% F1 score improvement compared to Baseline MT, primarily due to improved recall while maintaining precision.Masculine performance is uniformly strong and our new model, Baseline MT + Gender Tags, gives the best performance on feminine pronouns, achieving a 8.8% F1 score improvement compared to Baseline MT. As for the contextual MT models analyzed in the previous section, the improvement is from addressing the low recall over feminine pronouns in sentence-level translation (while retaining precision). We manually reviewed error cases and noted the major class was from domain shift: where antecedents were close to pronouns in Wikipedia, there were more frequent cases in WMT of antecedents being outside the BERT context window, and these were misclassified. Future work could look at methods for learning pre-trained language models from extended document context." } ], "images": [ "scigen/train/2495.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: BLEU score and pronoun accuracy by gender on the WMT’13 Spanish-to-English test set." ], "reasoning": "The Baseline MT + Gender Tags model shows the best performance on feminine pronouns with an 8.8% F1 score improvement compared to Baseline MT, primarily due to improved recall while maintaining precision.", "description": "Masculine performance is uniformly strong and our new model, Baseline MT + Gender Tags, gives the best performance on feminine pronouns, achieving a 8.8% F1 score improvement compared to Baseline MT. As for the contextual MT models analyzed in the previous section, the improvement is from addressing the low recall over feminine pronouns in sentence-level translation (while retaining precision). We manually reviewed error cases and noted the major class was from domain shift: where antecedents were close to pronouns in Wikipedia, there were more frequent cases in WMT of antecedents being outside the BERT context window, and these were misclassified. Future work could look at methods for learning pre-trained language models from extended document context.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 9: BLEU score and pronoun accuracy by gender on the WMT’13 Spanish-to-English test set.', answer the following question. Give detailed solution consisting of each step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model achieved the highest F1 score for feminine pronouns?\n" }, { "role": "assistant", "content": "By comparing the F1 scores for feminine pronouns across all models, the Baseline MT + Gender Tags model has the highest F1 score of 71.8. So, the answer is {\"answer\": \"Baseline MT + Gender Tags\"}." } ], "images": [ "scigen/train/2495.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: BLEU score and pronoun accuracy by gender on the WMT’13 Spanish-to-English test set." ], "reasoning": "By comparing the F1 scores for feminine pronouns across all models, the Baseline MT + Gender Tags model has the highest F1 score of 71.8.", "question": "Which model achieved the highest F1 score for feminine pronouns?", "answer": "So, the answer is {\"answer\": \"Baseline MT + Gender Tags\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Context MT model has the highest BLEU score.\n" }, { "role": "assistant", "content": "The table shows that the Baseline MT + Gender Tags model has the highest BLEU score of 34.12, while the Context MT model has a BLEU score of 33.99. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2495.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: BLEU score and pronoun accuracy by gender on the WMT’13 Spanish-to-English test set." ], "reasoning": "The table shows that the Baseline MT + Gender Tags model has the highest BLEU score of 34.12, while the Context MT model has a BLEU score of 33.99.", "statement": "The Context MT model has the highest BLEU score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 0.B.1: Pretraining validation losses of VisualBERT and ViLBERT. MLM points to masked language modelling object and MIM points masked image modelling objective. (Left) Losses on different same size pretraining datasets when pretrained for the same number of iterations. COCO and CC achieve the lowest and highest losses respectively. (Right) Losses on different sized subsets of CC. Adding more data consistently improves validation performance when trained for the same number of epochs. The validation set for all CC datasets are the same.'.\n" }, { "role": "assistant", "content": "Both models achieve the lowest MLM loss using COCO, indicating less noisy and superior quality annotations.Performance. In this section, we see how well the models perform on pretraining objectives for different pretraining datasets. It is important to note here that models sees equal number of training samples for each of the pretraining datasets and was trained for the same number of training iterations (88K). We observe that both models achieve ∼ lowest MLM loss using COCO. We hypothesize that this lower pretraining loss points to less noisy and superior quality annotations. Pretraining Dataset Size. We observe that increasing dataset size improves validation performance consistently. Here we increase dataset size for CC in subsets of 10%, 50% and 100%. Note that the number of epochs in these set of experiments is kept constant (30 Epochs) so as to not overfit smaller size datasets. We see the difference in CC 10% on left and right due to difference in the number of iterations of training (88K on left vs 12K on right), indicating longer pretraining is helpful." } ], "images": [ "scigen/train/2496.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 0.B.1: Pretraining validation losses of VisualBERT and ViLBERT. MLM points to masked language modelling object and MIM points masked image modelling objective. (Left) Losses on different same size pretraining datasets when pretrained for the same number of iterations. COCO and CC achieve the lowest and highest losses respectively. (Right) Losses on different sized subsets of CC. Adding more data consistently improves validation performance when trained for the same number of epochs. The validation set for all CC datasets are the same." ], "reasoning": "Both models achieve the lowest MLM loss using COCO, indicating less noisy and superior quality annotations.", "description": "Performance. In this section, we see how well the models perform on pretraining objectives for different pretraining datasets. It is important to note here that models sees equal number of training samples for each of the pretraining datasets and was trained for the same number of training iterations (88K). We observe that both models achieve ∼ lowest MLM loss using COCO. We hypothesize that this lower pretraining loss points to less noisy and superior quality annotations. Pretraining Dataset Size. We observe that increasing dataset size improves validation performance consistently. Here we increase dataset size for CC in subsets of 10%, 50% and 100%. Note that the number of epochs in these set of experiments is kept constant (30 Epochs) so as to not overfit smaller size datasets. We see the difference in CC 10% on left and right due to difference in the number of iterations of training (88K on left vs 12K on right), indicating longer pretraining is helpful.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: 'Table 0.B.1: Pretraining validation losses of VisualBERT and ViLBERT. MLM points to masked language modelling object and MIM points masked image modelling objective. (Left) Losses on different same size pretraining datasets when pretrained for the same number of iterations. COCO and CC achieve the lowest and highest losses respectively. (Right) Losses on different sized subsets of CC. Adding more data consistently improves validation performance when trained for the same number of epochs. The validation set for all CC datasets are the same.'\nQuestion: Which model achieved the lowest loss on the COCO dataset?\n" }, { "role": "assistant", "content": "By comparing the losses for the COCO dataset, we see that ViLBERT(MIM) achieved the lowest loss with a value of 0.19. So, the answer is {\"answer\": \"ViLBERT(MIM)\"}" } ], "images": [ "scigen/train/2496.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 0.B.1: Pretraining validation losses of VisualBERT and ViLBERT. MLM points to masked language modelling object and MIM points masked image modelling objective. (Left) Losses on different same size pretraining datasets when pretrained for the same number of iterations. COCO and CC achieve the lowest and highest losses respectively. (Right) Losses on different sized subsets of CC. Adding more data consistently improves validation performance when trained for the same number of epochs. The validation set for all CC datasets are the same." ], "reasoning": "By comparing the losses for the COCO dataset, we see that ViLBERT(MIM) achieved the lowest loss with a value of 0.19.", "question": "Which model achieved the lowest loss on the COCO dataset?", "answer": "So, the answer is {\"answer\": \"ViLBERT(MIM)\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nViLBERT(MIM) achieved the highest loss on the COCO dataset.\n" }, { "role": "assistant", "content": "The table shows that ViLBERT(MIM) achieved the lowest loss on the COCO dataset with a value of 0.19, not the highest. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2496.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 0.B.1: Pretraining validation losses of VisualBERT and ViLBERT. MLM points to masked language modelling object and MIM points masked image modelling objective. (Left) Losses on different same size pretraining datasets when pretrained for the same number of iterations. COCO and CC achieve the lowest and highest losses respectively. (Right) Losses on different sized subsets of CC. Adding more data consistently improves validation performance when trained for the same number of epochs. The validation set for all CC datasets are the same." ], "reasoning": "The table shows that ViLBERT(MIM) achieved the lowest loss on the COCO dataset with a value of 0.19, not the highest.", "statement": "ViLBERT(MIM) achieved the highest loss on the COCO dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 6: Hyper-parameters tuned separately for each stop consonant model.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The hyper-parameters for each model are trained using development data, and if the network has more than three convolutional layers, the time domain kernel size beyond the third layer is set equal to w3.The hyper-parameters for each model are trained by using the development data. These hyper-parameters include number of convolutional layers, time domain kernel size for each layer, learning rate for gradient descent optimization, batch size, and dropout rate. If the network has more than three convolutional layers, the time domain kernel size beyond the third layer is set equal to w3." } ], "images": [ "scigen/train/2497.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Hyper-parameters tuned separately for each stop consonant model." ], "reasoning": "The hyper-parameters for each model are trained using development data, and if the network has more than three convolutional layers, the time domain kernel size beyond the third layer is set equal to w3.", "description": "The hyper-parameters for each model are trained by using the development data. These hyper-parameters include number of convolutional layers, time domain kernel size for each layer, learning rate for gradient descent optimization, batch size, and dropout rate. If the network has more than three convolutional layers, the time domain kernel size beyond the third layer is set equal to w3.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nTable title: Table 6: Hyper-parameters tuned separately for each stop consonant model.\n\nQuestion: What is the dropout rate for the model with the highest number of convolutional layers?\n" }, { "role": "assistant", "content": "The model with the highest number of convolutional layers is tA/7 with 7 layers. The dropout rate for this model is 17%. So, the answer is {\"answer\": \"17%\"}." } ], "images": [ "scigen/train/2497.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Hyper-parameters tuned separately for each stop consonant model." ], "reasoning": "The model with the highest number of convolutional layers is tA/7 with 7 layers. The dropout rate for this model is 17%.", "question": "What is the dropout rate for the model with the highest number of convolutional layers?", "answer": "So, the answer is {\"answer\": \"17%\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The model with the smallest batch size has a dropout rate of 8%., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model with the smallest batch size has a dropout rate of 8%.\n" }, { "role": "assistant", "content": "The model with the smallest batch size is tA/7 with a batch size of 4. The dropout rate for this model is 17%, not 8%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2497.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Hyper-parameters tuned separately for each stop consonant model." ], "reasoning": "The model with the smallest batch size is tA/7 with a batch size of 4. The dropout rate for this model is 17%, not 8%.", "statement": "The model with the smallest batch size has a dropout rate of 8%.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Linguistic constructs addressed by DisSim.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table lists various types of clausal and phrasal disembedding along with the number of rules for each type.Subtask 1: Sentence Splitting and Rephrasing. The decomposed text spans, as well as the remaining text span are then transformed into new stand-alone sentences. In order to ensure that the resulting simplified output is grammatically sound, some of the extracted text spans are combined with their corresponding referents from the main sentence or appended to a simple phrase (e.g. “This is”). In that way, the simplification rules encode both the splitting points and rephrasing procedure for reconstructing proper sentences. Both coordinate and subordinate clauses, as well as various types of phrasal elements are addressed by our TS approach." } ], "images": [ "scigen/train/2498.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Linguistic constructs addressed by DisSim." ], "reasoning": "The table lists various types of clausal and phrasal disembedding along with the number of rules for each type.", "description": "Subtask 1: Sentence Splitting and Rephrasing. The decomposed text spans, as well as the remaining text span are then transformed into new stand-alone sentences. In order to ensure that the resulting simplified output is grammatically sound, some of the extracted text spans are combined with their corresponding referents from the main sentence or appended to a simple phrase (e.g. “This is”). In that way, the simplification rules encode both the splitting points and rephrasing procedure for reconstructing proper sentences. Both coordinate and subordinate clauses, as well as various types of phrasal elements are addressed by our TS approach.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 1: Linguistic constructs addressed by DisSim.', and sequentially solve the question, writing out each step of your reasoning process. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nHow many rules are there for disembedding relative clauses (defining) and coordinate noun phrases (NPs) combined?\n" }, { "role": "assistant", "content": "The table shows that there are 5 rules for disembedding relative clauses (defining) and 2 rules for disembedding coordinate noun phrases (NPs). Adding these together gives a total of 7 rules. So, the answer is {\"answer\": \"7\"}" } ], "images": [ "scigen/train/2498.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Linguistic constructs addressed by DisSim." ], "reasoning": "The table shows that there are 5 rules for disembedding relative clauses (defining) and 2 rules for disembedding coordinate noun phrases (NPs). Adding these together gives a total of 7 rules.", "question": "How many rules are there for disembedding relative clauses (defining) and coordinate noun phrases (NPs) combined?", "answer": "So, the answer is {\"answer\": \"7\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: There are more rules for disembedding adverbial clauses than for disembedding prepositional phrases (PPs)., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThere are more rules for disembedding adverbial clauses than for disembedding prepositional phrases (PPs).\n" }, { "role": "assistant", "content": "The table shows that there are 6 rules for disembedding adverbial clauses and 3 rules for disembedding prepositional phrases (PPs). Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2498.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Linguistic constructs addressed by DisSim." ], "reasoning": "The table shows that there are 6 rules for disembedding adverbial clauses and 3 rules for disembedding prepositional phrases (PPs). Therefore, the statement is supported.", "statement": "There are more rules for disembedding adverbial clauses than for disembedding prepositional phrases (PPs).", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Impact of training regime and vocabulary overlap on agreement accuracy (FR→IT).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Pre-training reduces negative interference and improves accuracy on Original sentences in the no-overlap setup, while eliminating vocabulary overlap leads to better agreement scores.Pre-training considerably reduces the negative interference effect observed in joint training, and even leads to a higher accuracy on Original sentences in the no-overlap setup (83.2 vs 79.8). Eliminating vocabulary overlap (None) also leads to better agreement scores in most cases. The best gain overall is obtained by the jointly trained model with no overlap (85.7 vs 79.8) in the Original sentences, whereas no gain is observed in the Nonce sentences." } ], "images": [ "scigen/train/2499.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Impact of training regime and vocabulary overlap on agreement accuracy (FR→IT)." ], "reasoning": "Pre-training reduces negative interference and improves accuracy on Original sentences in the no-overlap setup, while eliminating vocabulary overlap leads to better agreement scores.", "description": "Pre-training considerably reduces the negative interference effect observed in joint training, and even leads to a higher accuracy on Original sentences in the no-overlap setup (83.2 vs 79.8). Eliminating vocabulary overlap (None) also leads to better agreement scores in most cases. The best gain overall is obtained by the jointly trained model with no overlap (85.7 vs 79.8) in the Original sentences, whereas no gain is observed in the Nonce sentences.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. The question is related to the given table titled 'Table 1: Impact of training regime and vocabulary overlap on agreement accuracy (FR→IT).'.\n\nQuestion: What is the accuracy of the jointly trained model with no overlap on Original sentences compared to the IT small model?\n" }, { "role": "assistant", "content": "The table shows that the accuracy of the jointly trained model with no overlap on Original sentences is 85.7, while the IT small model has an accuracy of 79.8. So, the answer is {\"answer\": \"85.7\"}." } ], "images": [ "scigen/train/2499.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Impact of training regime and vocabulary overlap on agreement accuracy (FR→IT)." ], "reasoning": "The table shows that the accuracy of the jointly trained model with no overlap on Original sentences is 85.7, while the IT small model has an accuracy of 79.8.", "question": "What is the accuracy of the jointly trained model with no overlap on Original sentences compared to the IT small model?", "answer": "So, the answer is {\"answer\": \"85.7\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe jointly trained model with no overlap has the highest accuracy on Original sentences.\n" }, { "role": "assistant", "content": "The table indicates that the jointly trained model with no overlap has an accuracy of 85.7 on Original sentences, which is higher than any other model listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2499.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Impact of training regime and vocabulary overlap on agreement accuracy (FR→IT)." ], "reasoning": "The table indicates that the jointly trained model with no overlap has an accuracy of 85.7 on Original sentences, which is higher than any other model listed.", "statement": "The jointly trained model with no overlap has the highest accuracy on Original sentences.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 8: Comparison in generalization performance of benchmark NLP systems with our (NN) approach on POS, chunking, NER and SRL tasks. We report results with both the word-level log-likelihood (WLL) and the sentence-level log-likelihood (SLL). We report with (LMn) performance of the networks trained from the language model embeddings (Table 7). Generalization performance is reported in per-word accuracy (PWA) for POS and F1 score for other tasks.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Larger language models improve performance, indicating potential benefits from even bigger unlabeled datasets.It is worth mentioning the larger language model led to even better performance. This suggests that we could still take advantage of even bigger unlabeled datasets." } ], "images": [ "scigen/train/2500.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Comparison in generalization performance of benchmark NLP systems with our (NN) approach on POS, chunking, NER and SRL tasks. We report results with both the word-level log-likelihood (WLL) and the sentence-level log-likelihood (SLL). We report with (LMn) performance of the networks trained from the language model embeddings (Table 7). Generalization performance is reported in per-word accuracy (PWA) for POS and F1 score for other tasks." ], "reasoning": "Larger language models improve performance, indicating potential benefits from even bigger unlabeled datasets.", "description": "It is worth mentioning the larger language model led to even better performance. This suggests that we could still take advantage of even bigger unlabeled datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 8: Comparison in generalization performance of benchmark NLP systems with our (NN) approach on POS, chunking, NER and SRL tasks. We report results with both the word-level log-likelihood (WLL) and the sentence-level log-likelihood (SLL). We report with (LMn) performance of the networks trained from the language model embeddings (Table 7). Generalization performance is reported in per-word accuracy (PWA) for POS and F1 score for other tasks.', and sequentially solve the question, writing out each step of your reasoning process. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich approach achieved the highest F1 score in the CHUNK task?\n" }, { "role": "assistant", "content": "By comparing the F1 scores in the CHUNK column, the highest score is 94.29 achieved by the Benchmark Systems approach. So, the answer is {\"answer\": \"Benchmark Systems\"}" } ], "images": [ "scigen/train/2500.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Comparison in generalization performance of benchmark NLP systems with our (NN) approach on POS, chunking, NER and SRL tasks. We report results with both the word-level log-likelihood (WLL) and the sentence-level log-likelihood (SLL). We report with (LMn) performance of the networks trained from the language model embeddings (Table 7). Generalization performance is reported in per-word accuracy (PWA) for POS and F1 score for other tasks." ], "reasoning": "By comparing the F1 scores in the CHUNK column, the highest score is 94.29 achieved by the Benchmark Systems approach.", "question": "Which approach achieved the highest F1 score in the CHUNK task?", "answer": "So, the answer is {\"answer\": \"Benchmark Systems\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis NN+SLL+LM2 achieved the highest F1 score in the NER task. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nNN+SLL+LM2 achieved the highest F1 score in the NER task.\n" }, { "role": "assistant", "content": "By comparing the F1 scores in the NER column, NN+SLL+LM2 has a score of 88.67, which is not the highest. The highest is 89.31 by Benchmark Systems. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2500.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Comparison in generalization performance of benchmark NLP systems with our (NN) approach on POS, chunking, NER and SRL tasks. We report results with both the word-level log-likelihood (WLL) and the sentence-level log-likelihood (SLL). We report with (LMn) performance of the networks trained from the language model embeddings (Table 7). Generalization performance is reported in per-word accuracy (PWA) for POS and F1 score for other tasks." ], "reasoning": "By comparing the F1 scores in the NER column, NN+SLL+LM2 has a score of 88.67, which is not the highest. The highest is 89.31 by Benchmark Systems.", "statement": "NN+SLL+LM2 achieved the highest F1 score in the NER task.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 11: Comparison in generalization performance for POS, CHUNK and NER tasks of the networks obtained using by combining ten training runs with different initialization.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Voting ensemble slightly improves performance over the average network, but joined ensemble does not improve on simple voting.Because neural networks are nonconvex, training runs with different initial parameters usually give different solutions. Voting the ten network outputs on a per tag basis (“voting ensemble”) leads to a small improvement over the average network performance. The parameters of the combining layers were then trained on the existing training set, while keeping the ten networks fixed (“joined ensemble”). This approach did not improve on simple voting. We can also observe that the performance variability among the ten networks is not very large." } ], "images": [ "scigen/train/2501.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Comparison in generalization performance for POS, CHUNK and NER tasks of the networks obtained using by combining ten training runs with different initialization." ], "reasoning": "Voting ensemble slightly improves performance over the average network, but joined ensemble does not improve on simple voting.", "description": "Because neural networks are nonconvex, training runs with different initial parameters usually give different solutions. Voting the ten network outputs on a per tag basis (“voting ensemble”) leads to a small improvement over the average network performance. The parameters of the combining layers were then trained on the existing training set, while keeping the ten networks fixed (“joined ensemble”). This approach did not improve on simple voting. We can also observe that the performance variability among the ten networks is not very large.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 11: Comparison in generalization performance for POS, CHUNK and NER tasks of the networks obtained using by combining ten training runs with different initialization.', and sequentially solve the question, writing out each step of your reasoning process. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the NER performance of the voting ensemble compared to the joined ensemble?\n" }, { "role": "assistant", "content": "The NER performance of the voting ensemble is 89.70, while the joined ensemble has a performance of 89.67. So, the answer is {\"answer\": \"89.70\"}." } ], "images": [ "scigen/train/2501.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Comparison in generalization performance for POS, CHUNK and NER tasks of the networks obtained using by combining ten training runs with different initialization." ], "reasoning": "The NER performance of the voting ensemble is 89.70, while the joined ensemble has a performance of 89.67.", "question": "What is the NER performance of the voting ensemble compared to the joined ensemble?", "answer": "So, the answer is {\"answer\": \"89.70\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe mean performance of the NN+SLL+LM2+POS approach in CHUNK is better than the benchmark system.\n" }, { "role": "assistant", "content": "The mean performance of the NN+SLL+LM2+POS approach in CHUNK is 94.17, which is lower than the benchmark system's performance of 94.29. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2501.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Comparison in generalization performance for POS, CHUNK and NER tasks of the networks obtained using by combining ten training runs with different initialization." ], "reasoning": "The mean performance of the NN+SLL+LM2+POS approach in CHUNK is 94.17, which is lower than the benchmark system's performance of 94.29.", "statement": "The mean performance of the NN+SLL+LM2+POS approach in CHUNK is better than the benchmark system.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 13: Generalization performance of our neural network architecture trained with our language model (LM2) word embeddings, and with the word representations derived from the Brown Clusters. As before, all networks are fed with a capitalization feature. Additionally, POS is using a word suffix of size 2 feature, CHUNK is fed with POS, NER uses the CoNLL 2003 gazetteer, and SRL is fed with levels 1–5 of the Charniak parse tree, as well as a verb position feature. We report performance with both convex and non-convex architectures (300 hidden units for all tasks, with an additional 500 hidden units layer for SRL). We also provide results for Brown Clusters induced with a 130K word dictionary, as well as Brown Clusters induced with all words of the given tasks.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our embeddings perform at least as well as Brown Clusters, with non-convex setups generally outperforming convex setups. Fine-tuning embeddings for each task provides an additional performance boost.Our embeddings perform at least as well as Brown Clusters. Results are more mitigated in a convex setup. For most task, going non-convex is better for both word representation types. In general, “fine-tuning” our embeddings for each task also gives an extra boost." } ], "images": [ "scigen/train/2502.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 13: Generalization performance of our neural network architecture trained with our language model (LM2) word embeddings, and with the word representations derived from the Brown Clusters. As before, all networks are fed with a capitalization feature. Additionally, POS is using a word suffix of size 2 feature, CHUNK is fed with POS, NER uses the CoNLL 2003 gazetteer, and SRL is fed with levels 1–5 of the Charniak parse tree, as well as a verb position feature. We report performance with both convex and non-convex architectures (300 hidden units for all tasks, with an additional 500 hidden units layer for SRL). We also provide results for Brown Clusters induced with a 130K word dictionary, as well as Brown Clusters induced with all words of the given tasks." ], "reasoning": "Our embeddings perform at least as well as Brown Clusters, with non-convex setups generally outperforming convex setups. Fine-tuning embeddings for each task provides an additional performance boost.", "description": "Our embeddings perform at least as well as Brown Clusters. Results are more mitigated in a convex setup. For most task, going non-convex is better for both word representation types. In general, “fine-tuning” our embeddings for each task also gives an extra boost.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 13: Generalization performance of our neural network architecture trained with our language model (LM2) word embeddings, and with the word representations derived from the Brown Clusters. As before, all networks are fed with a capitalization feature. Additionally, POS is using a word suffix of size 2 feature, CHUNK is fed with POS, NER uses the CoNLL 2003 gazetteer, and SRL is fed with levels 1–5 of the Charniak parse tree, as well as a verb position feature. We report performance with both convex and non-convex architectures (300 hidden units for all tasks, with an additional 500 hidden units layer for SRL). We also provide results for Brown Clusters induced with a 130K word dictionary, as well as Brown Clusters induced with all words of the given tasks.', unfold the question presented below, detailing every step of your calculation. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich approach has the highest score for the POS task in a non-convex setup?\n" }, { "role": "assistant", "content": "To find the highest score for the POS task in a non-convex setup, compare the POS scores under the 'Non-convex Approach' column. The highest score is 97.29 by LM2 (non-linear NN). So, the answer is {\"answer\": \"LM2 (non-linear NN)\"}." } ], "images": [ "scigen/train/2502.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 13: Generalization performance of our neural network architecture trained with our language model (LM2) word embeddings, and with the word representations derived from the Brown Clusters. As before, all networks are fed with a capitalization feature. Additionally, POS is using a word suffix of size 2 feature, CHUNK is fed with POS, NER uses the CoNLL 2003 gazetteer, and SRL is fed with levels 1–5 of the Charniak parse tree, as well as a verb position feature. We report performance with both convex and non-convex architectures (300 hidden units for all tasks, with an additional 500 hidden units layer for SRL). We also provide results for Brown Clusters induced with a 130K word dictionary, as well as Brown Clusters induced with all words of the given tasks." ], "reasoning": "To find the highest score for the POS task in a non-convex setup, compare the POS scores under the 'Non-convex Approach' column. The highest score is 97.29 by LM2 (non-linear NN).", "question": "Which approach has the highest score for the POS task in a non-convex setup?", "answer": "So, the answer is {\"answer\": \"LM2 (non-linear NN)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nBrown Clusters (non-linear NN, 130K words) has the highest score for the NER task in a non-convex setup.\n" }, { "role": "assistant", "content": "To verify this statement, compare the NER scores under the 'Non-convex Approach' column. The highest score is 89.52 by LM2 (non-linear NN), not Brown Clusters (non-linear NN, 130K words) which has a score of 87.15. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2502.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 13: Generalization performance of our neural network architecture trained with our language model (LM2) word embeddings, and with the word representations derived from the Brown Clusters. As before, all networks are fed with a capitalization feature. Additionally, POS is using a word suffix of size 2 feature, CHUNK is fed with POS, NER uses the CoNLL 2003 gazetteer, and SRL is fed with levels 1–5 of the Charniak parse tree, as well as a verb position feature. We report performance with both convex and non-convex architectures (300 hidden units for all tasks, with an additional 500 hidden units layer for SRL). We also provide results for Brown Clusters induced with a 130K word dictionary, as well as Brown Clusters induced with all words of the given tasks." ], "reasoning": "To verify this statement, compare the NER scores under the 'Non-convex Approach' column. The highest score is 89.52 by LM2 (non-linear NN), not Brown Clusters (non-linear NN, 130K words) which has a score of 87.15.", "statement": "Brown Clusters (non-linear NN, 130K words) has the highest score for the NER task in a non-convex setup.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 16: Runtime speed and memory consumption comparison between state-of-the-art systems and our approach (SENNA). We give the runtime in seconds for running both the POS and SRL taggers on their respective testing sets. Memory usage is reported in megabytes.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Our neural networks for POS and SRL tagging run significantly faster and require much less memory compared to other taggers.All programs were run on a single 3GHz Intel core. The POS taggers were run with Sun Java 1.6 with a large enough memory allocation to reach their top tagging speed. The beam size of the Shen tagger was set to 3 as recommended in the paper. Regardless of implementation differences, it is clear that our neural networks run considerably faster. They also require much less memory. Our POS and SRL tagger runs in 32MB and 120MB of RAM respectively. The Shen and Toutanova taggers slow down significantly when the Java machine is given less than 2.2GB and 800MB of RAM respectively, while the Koomen tagger requires at least 3GB of RAM." } ], "images": [ "scigen/train/2503.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 16: Runtime speed and memory consumption comparison between state-of-the-art systems and our approach (SENNA). We give the runtime in seconds for running both the POS and SRL taggers on their respective testing sets. Memory usage is reported in megabytes." ], "reasoning": "Our neural networks for POS and SRL tagging run significantly faster and require much less memory compared to other taggers.", "description": "All programs were run on a single 3GHz Intel core. The POS taggers were run with Sun Java 1.6 with a large enough memory allocation to reach their top tagging speed. The beam size of the Shen tagger was set to 3 as recommended in the paper. Regardless of implementation differences, it is clear that our neural networks run considerably faster. They also require much less memory. Our POS and SRL tagger runs in 32MB and 120MB of RAM respectively. The Shen and Toutanova taggers slow down significantly when the Java machine is given less than 2.2GB and 800MB of RAM respectively, while the Koomen tagger requires at least 3GB of RAM.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 16: Runtime speed and memory consumption comparison between state-of-the-art systems and our approach (SENNA). We give the runtime in seconds for running both the POS and SRL taggers on their respective testing sets. Memory usage is reported in megabytes.', solve the subsequent question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich POS system requires the least amount of RAM and how much time does it take?\n" }, { "role": "assistant", "content": "From the table, the POS system with the least RAM requirement is SENNA with 32MB, and it takes 4 seconds. So, the answer is {\"answer\": \"SENNA, 4 seconds\"}" } ], "images": [ "scigen/train/2503.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 16: Runtime speed and memory consumption comparison between state-of-the-art systems and our approach (SENNA). We give the runtime in seconds for running both the POS and SRL taggers on their respective testing sets. Memory usage is reported in megabytes." ], "reasoning": "From the table, the POS system with the least RAM requirement is SENNA with 32MB, and it takes 4 seconds.", "question": "Which POS system requires the least amount of RAM and how much time does it take?", "answer": "So, the answer is {\"answer\": \"SENNA, 4 seconds\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Koomen et al. (2005) SRL system requires less than 3GB of RAM.\n" }, { "role": "assistant", "content": "The table shows that the Koomen et al. (2005) SRL system requires 3400MB of RAM, which is more than 3GB. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2503.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 16: Runtime speed and memory consumption comparison between state-of-the-art systems and our approach (SENNA). We give the runtime in seconds for running both the POS and SRL taggers on their respective testing sets. Memory usage is reported in megabytes." ], "reasoning": "The table shows that the Koomen et al. (2005) SRL system requires 3400MB of RAM, which is more than 3GB.", "statement": "The Koomen et al. (2005) SRL system requires less than 3GB of RAM.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Classification Performance on the NYT Dataset (2,735 categories).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "ODPCV+WV outperforms all other methods in terms of precision at k, demonstrating the effectiveness of using both category and word vectors for text classification.Again, the performance of ODPCV+WV outperforms ODP, PV, CNN, ODPCV and ODPWV over 28%, 119%, 216%, 12%, and 10% in terms of precision at k on average, respectively. We also observe that both ODPCV and ODPWV outperform ODP. These results clearly demonstrate that both category and word vectors are effective at text classification. Specifically, ODPCV+WV, which utilizes both category and word vectors, achieves the best performance in all experiments. We also perform the t-test for the classification results, and find that ODPCV+WV results are statistically significant with p \\textless 0.01." } ], "images": [ "scigen/train/2504.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Classification Performance on the NYT Dataset (2,735 categories)." ], "reasoning": "ODPCV+WV outperforms all other methods in terms of precision at k, demonstrating the effectiveness of using both category and word vectors for text classification.", "description": "Again, the performance of ODPCV+WV outperforms ODP, PV, CNN, ODPCV and ODPWV over 28%, 119%, 216%, 12%, and 10% in terms of precision at k on average, respectively. We also observe that both ODPCV and ODPWV outperform ODP. These results clearly demonstrate that both category and word vectors are effective at text classification. Specifically, ODPCV+WV, which utilizes both category and word vectors, achieves the best performance in all experiments. We also perform the t-test for the classification results, and find that ODPCV+WV results are statistically significant with p \\textless 0.01.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 5: Classification Performance on the NYT Dataset (2,735 categories).', solve this question and give detail solutions. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich method achieves the highest precision at k=3?\n" }, { "role": "assistant", "content": "By examining the table, ODPCV+WV has the highest precision at k=3 with a value of 0.583. So, the answer is {\"answer\": \"ODPCV+WV\"}" } ], "images": [ "scigen/train/2504.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Classification Performance on the NYT Dataset (2,735 categories)." ], "reasoning": "By examining the table, ODPCV+WV has the highest precision at k=3 with a value of 0.583.", "question": "Which method achieves the highest precision at k=3?", "answer": "So, the answer is {\"answer\": \"ODPCV+WV\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nODPCV+WV achieves the highest precision at k=1.\n" }, { "role": "assistant", "content": "The table shows that ODPCV+WV has a precision of 0.692 at k=1, which is higher than any other method. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2504.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Classification Performance on the NYT Dataset (2,735 categories)." ], "reasoning": "The table shows that ODPCV+WV has a precision of 0.692 at k=1, which is higher than any other method.", "statement": "ODPCV+WV achieves the highest precision at k=1.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Comparison of Category Vector Generations on the ODP Dataset (2,735 categories)', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "ODPCV(Algebra) outperforms ODPCV(Embedding) in terms of Precision, Recall, and F1-score on the ODP dataset, leading to its adoption in further experiments.We first compare the two methods to generate category vectors with the ODP dataset (2,735 categories). Unexpectedly, we observe that a simple ODPCV(Algebra) clearly outperforms a relatively elaborate ODPCV(Embedding). Thus, we adopt ODPCV(Algebra) in the remaining experiments, which is simply denoted by ODPCV." } ], "images": [ "scigen/train/2505.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of Category Vector Generations on the ODP Dataset (2,735 categories)" ], "reasoning": "ODPCV(Algebra) outperforms ODPCV(Embedding) in terms of Precision, Recall, and F1-score on the ODP dataset, leading to its adoption in further experiments.", "description": "We first compare the two methods to generate category vectors with the ODP dataset (2,735 categories). Unexpectedly, we observe that a simple ODPCV(Algebra) clearly outperforms a relatively elaborate ODPCV(Embedding). Thus, we adopt ODPCV(Algebra) in the remaining experiments, which is simply denoted by ODPCV.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Comparison of Category Vector Generations on the ODP Dataset (2,735 categories)', answer the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method has a higher F1-score, and what is the value?\n" }, { "role": "assistant", "content": "By comparing the F1-scores of both methods, ODPCV(Algebra) has a higher F1-score of 0.453 compared to ODPCV(Embedding)'s 0.230. So, the answer is {\"answer\": \"ODPCV(Algebra), 0.453\"}." } ], "images": [ "scigen/train/2505.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of Category Vector Generations on the ODP Dataset (2,735 categories)" ], "reasoning": "By comparing the F1-scores of both methods, ODPCV(Algebra) has a higher F1-score of 0.453 compared to ODPCV(Embedding)'s 0.230.", "question": "Which method has a higher F1-score, and what is the value?", "answer": "So, the answer is {\"answer\": \"ODPCV(Algebra), 0.453\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nODPCV(Embedding) has a higher Precision than ODPCV(Algebra).\n" }, { "role": "assistant", "content": "The table shows that ODPCV(Algebra) has a Precision of 0.449, which is higher than ODPCV(Embedding)'s Precision of 0.278. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2505.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of Category Vector Generations on the ODP Dataset (2,735 categories)" ], "reasoning": "The table shows that ODPCV(Algebra) has a Precision of 0.449, which is higher than ODPCV(Embedding)'s Precision of 0.278.", "statement": "ODPCV(Embedding) has a higher Precision than ODPCV(Algebra).", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled '(a) large-scale (2,735 categories)', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "ODPCV+WV outperforms all other methods and baselines in terms of precision, recall, and F1-score, with improvements over ODP by 9%, 12%, and 10% respectively.We observe that ODPCV+WV outperforms all the other proposed methods, as well as the baselines. ODPCV+WV performs better than ODP over 9%, 12%, and 10% on average in terms of precision, recall, and F1-score, respectively. This can be explained by the fact the distribution of webpages is skewed toward a few categories in the original ODP Actually, we observe that 73% of ODP categories contain fewer than five webpages. We also compare the performance of CNN with the ODP baseline on the ODP test dataset with 13 target categories." } ], "images": [ "scigen/train/2506.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) large-scale (2,735 categories)" ], "reasoning": "ODPCV+WV outperforms all other methods and baselines in terms of precision, recall, and F1-score, with improvements over ODP by 9%, 12%, and 10% respectively.", "description": "We observe that ODPCV+WV outperforms all the other proposed methods, as well as the baselines. ODPCV+WV performs better than ODP over 9%, 12%, and 10% on average in terms of precision, recall, and F1-score, respectively. This can be explained by the fact the distribution of webpages is skewed toward a few categories in the original ODP Actually, we observe that 73% of ODP categories contain fewer than five webpages. We also compare the performance of CNN with the ODP baseline on the ODP test dataset with 13 target categories.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of '(a) large-scale (2,735 categories)', unfold the question presented below, detailing every step of your calculation. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nHow much better does ODPCV+WV perform compared to ODP in terms of F1-score?\n" }, { "role": "assistant", "content": "ODPCV+WV has an F1-score of 0.481, while ODP has an F1-score of 0.436. The difference is 0.481 - 0.436 = 0.045, which is approximately 10%. So, the answer is {\"answer\": \"10%\"}." } ], "images": [ "scigen/train/2506.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) large-scale (2,735 categories)" ], "reasoning": "ODPCV+WV has an F1-score of 0.481, while ODP has an F1-score of 0.436. The difference is 0.481 - 0.436 = 0.045, which is approximately 10%.", "question": "How much better does ODPCV+WV perform compared to ODP in terms of F1-score?", "answer": "So, the answer is {\"answer\": \"10%\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nODPCV+WV has the highest precision among all methods.\n" }, { "role": "assistant", "content": "The table shows that ODPCV+WV has a precision of 0.468, which is higher than the precision of all other methods listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2506.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) large-scale (2,735 categories)" ], "reasoning": "The table shows that ODPCV+WV has a precision of 0.468, which is higher than the precision of all other methods listed.", "statement": "ODPCV+WV has the highest precision among all methods.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named '(b) moderate-scale (13 categories)'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "ODPCV+WV outperforms ODP by over 9%, 12%, and 10% in precision, recall, and F1-score, respectively, due to the skewed distribution of webpages in ODP.We observe that ODPCV+WV outperforms all the other proposed methods, as well as the baselines. ODPCV+WV performs better than ODP over 9%, 12%, and 10% on average in terms of precision, recall, and F1-score, respectively. This can be explained by the fact the distribution of webpages is skewed toward a few categories in the original ODP Actually, we observe that 73% of ODP categories contain fewer than five webpages. We also compare the performance of CNN with the ODP baseline on the ODP test dataset with 13 target categories." } ], "images": [ "scigen/train/2507.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) moderate-scale (13 categories)" ], "reasoning": "ODPCV+WV outperforms ODP by over 9%, 12%, and 10% in precision, recall, and F1-score, respectively, due to the skewed distribution of webpages in ODP.", "description": "We observe that ODPCV+WV outperforms all the other proposed methods, as well as the baselines. ODPCV+WV performs better than ODP over 9%, 12%, and 10% on average in terms of precision, recall, and F1-score, respectively. This can be explained by the fact the distribution of webpages is skewed toward a few categories in the original ODP Actually, we observe that 73% of ODP categories contain fewer than five webpages. We also compare the performance of CNN with the ODP baseline on the ODP test dataset with 13 target categories.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of '(b) moderate-scale (13 categories)'. Solve the following question based on the table.\n\nQuestion: By what percentage does ODPCV+WV outperform ODP in terms of F1-score?\nLet's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The description states that ODPCV+WV performs better than ODP by 10% on average in terms of F1-score. So, the answer is {\"answer\": \"10%\"}." } ], "images": [ "scigen/train/2507.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) moderate-scale (13 categories)" ], "reasoning": "The description states that ODPCV+WV performs better than ODP by 10% on average in terms of F1-score.", "question": "By what percentage does ODPCV+WV outperform ODP in terms of F1-score?", "answer": "So, the answer is {\"answer\": \"10%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nODP has a higher recall than CNN.\n" }, { "role": "assistant", "content": "The table shows that ODP has a recall of 0.707, while CNN has a recall of 0.661, indicating that ODP has a higher recall. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2507.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) moderate-scale (13 categories)" ], "reasoning": "The table shows that ODP has a recall of 0.707, while CNN has a recall of 0.661, indicating that ODP has a higher recall.", "statement": "ODP has a higher recall than CNN.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Comparing pre-trained GloVe and dLCE word embeddings.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The dLCE word embeddings outperform the pre-trained GloVe word embeddings by approximately .01 F1 for both the pattern-based and combined AntSynNET models in adjective and verb pairs, and by around .01 and .04 F1 in noun pairs for the pattern-based and combined models, respectively.The table shows that the dLCE word embeddings are better than the pre-trained GloVe word embeddings, by around .01 F1 for the pattern-based AntSynNET model and the combined AntSynNET model regarding adjective and verb pairs. Regarding noun pairs, the improvements of the dLCE word embeddings over pre-trained GloVe word embeddings achieve around .01 and .04 F1 for the pattern-based model and the combined model, respectively." } ], "images": [ "scigen/train/2508.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparing pre-trained GloVe and dLCE word embeddings." ], "reasoning": "The dLCE word embeddings outperform the pre-trained GloVe word embeddings by approximately .01 F1 for both the pattern-based and combined AntSynNET models in adjective and verb pairs, and by around .01 and .04 F1 in noun pairs for the pattern-based and combined models, respectively.", "description": "The table shows that the dLCE word embeddings are better than the pre-trained GloVe word embeddings, by around .01 F1 for the pattern-based AntSynNET model and the combined AntSynNET model regarding adjective and verb pairs. Regarding noun pairs, the improvements of the dLCE word embeddings over pre-trained GloVe word embeddings achieve around .01 and .04 F1 for the pattern-based model and the combined model, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 5: Comparing pre-trained GloVe and dLCE word embeddings.' and methodically tackle the question that follows. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the difference in F1 score for noun pairs between the dLCE and GloVe word embeddings in the combined model?\n" }, { "role": "assistant", "content": "The F1 score for noun pairs using GloVe in the combined model is 0.817, while for dLCE it is 0.855. The difference is 0.855 - 0.817 = 0.038. So, the answer is {\"answer\": \"0.038\"}" } ], "images": [ "scigen/train/2508.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparing pre-trained GloVe and dLCE word embeddings." ], "reasoning": "The F1 score for noun pairs using GloVe in the combined model is 0.817, while for dLCE it is 0.855. The difference is 0.855 - 0.817 = 0.038.", "question": "What is the difference in F1 score for noun pairs between the dLCE and GloVe word embeddings in the combined model?", "answer": "So, the answer is {\"answer\": \"0.038\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe dLCE word embeddings improve the F1 score for verb pairs by more than 0.01 in the pattern-based model.\n" }, { "role": "assistant", "content": "The F1 score for verb pairs using GloVe in the pattern-based model is 0.772, while for dLCE it is 0.784. The difference is 0.784 - 0.772 = 0.012, which is more than 0.01. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2508.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparing pre-trained GloVe and dLCE word embeddings." ], "reasoning": "The F1 score for verb pairs using GloVe in the pattern-based model is 0.772, while for dLCE it is 0.784. The difference is 0.784 - 0.772 = 0.012, which is more than 0.01.", "statement": "The dLCE word embeddings improve the F1 score for verb pairs by more than 0.01 in the pattern-based model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Performance of the AntSynNET models in comparison to the baseline models.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The combined AntSynNET model shows significant improvements in F1 scores for adjectives and nouns compared to the baselines, with a notable increase of >.06 for adjectives and .37 for nouns compared to the SP baseline.Concerning adjectives, the two proposed models significantly outperform the two baselines: The performance of the baselines is around .72 for F1, and the corresponding results for the combined AntSynNET model achieve an improvement of >.06. Regarding nouns, the improvement of the new methods is just .02 F1 in comparison to the R&SiW baseline, but we achieve a much better performance in comparison to the SP baseline, an increase of .37 F1. Regarding verbs, we do not outperform the more advanced R&SiW baseline in terms of the F1 score, but we obtain higher recall scores. In comparison to the SP baseline, our models still show a clear F1 improvement." } ], "images": [ "scigen/train/2509.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of the AntSynNET models in comparison to the baseline models." ], "reasoning": "The combined AntSynNET model shows significant improvements in F1 scores for adjectives and nouns compared to the baselines, with a notable increase of >.06 for adjectives and .37 for nouns compared to the SP baseline.", "description": "Concerning adjectives, the two proposed models significantly outperform the two baselines: The performance of the baselines is around .72 for F1, and the corresponding results for the combined AntSynNET model achieve an improvement of >.06. Regarding nouns, the improvement of the new methods is just .02 F1 in comparison to the R&SiW baseline, but we achieve a much better performance in comparison to the SP baseline, an increase of .37 F1. Regarding verbs, we do not outperform the more advanced R&SiW baseline in terms of the F1 score, but we obtain higher recall scores. In comparison to the SP baseline, our models still show a clear F1 improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 3: Performance of the AntSynNET models in comparison to the baseline models.\n\nQuestion: What is the F1 score improvement for nouns when comparing the combined AntSynNET model to the SP baseline?\n" }, { "role": "assistant", "content": "The F1 score for nouns in the SP baseline is 0.482, while the combined AntSynNET model achieves an F1 score of 0.855. The improvement is 0.855 - 0.482 = 0.373. So, the answer is {\"answer\": \"0.373\"}." } ], "images": [ "scigen/train/2509.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of the AntSynNET models in comparison to the baseline models." ], "reasoning": "The F1 score for nouns in the SP baseline is 0.482, while the combined AntSynNET model achieves an F1 score of 0.855. The improvement is 0.855 - 0.482 = 0.373.", "question": "What is the F1 score improvement for nouns when comparing the combined AntSynNET model to the SP baseline?", "answer": "So, the answer is {\"answer\": \"0.373\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe combined AntSynNET model achieves an F1 score of 0.784 for adjectives.\n" }, { "role": "assistant", "content": "According to the table, the combined AntSynNET model has an F1 score of 0.784 for adjectives. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2509.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of the AntSynNET models in comparison to the baseline models." ], "reasoning": "According to the table, the combined AntSynNET model has an F1 score of 0.784 for adjectives.", "statement": "The combined AntSynNET model achieves an F1 score of 0.784 for adjectives.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Comparing the novel distance feature with Schwarz et al.’s direction feature, across word classes.'.\n" }, { "role": "assistant", "content": "The distance feature consistently improves model performance more than the direction feature across all word classes, as evidenced by higher F1 scores.The distance feature enhances the performance of our proposed models more effectively than the direction feature does, across all word classes." } ], "images": [ "scigen/train/2510.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparing the novel distance feature with Schwarz et al.’s direction feature, across word classes." ], "reasoning": "The distance feature consistently improves model performance more than the direction feature across all word classes, as evidenced by higher F1 scores.", "description": "The distance feature enhances the performance of our proposed models more effectively than the direction feature does, across all word classes.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 4: Comparing the novel distance feature with Schwarz et al.’s direction feature, across word classes.\nQuestion: Which feature and model combination achieves the highest F1 score for nouns?\n" }, { "role": "assistant", "content": "To find the highest F1 score for nouns, compare the F1 scores for nouns across all feature and model combinations. The highest F1 score for nouns is 0.855, achieved by the Distance feature with the Combined model. So, the answer is {\"answer\": \"Distance Combined\"}." } ], "images": [ "scigen/train/2510.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparing the novel distance feature with Schwarz et al.’s direction feature, across word classes." ], "reasoning": "To find the highest F1 score for nouns, compare the F1 scores for nouns across all feature and model combinations. The highest F1 score for nouns is 0.855, achieved by the Distance feature with the Combined model.", "question": "Which feature and model combination achieves the highest F1 score for nouns?", "answer": "So, the answer is {\"answer\": \"Distance Combined\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe direction feature with the combined model achieves the highest F1 score for adjectives.\n" }, { "role": "assistant", "content": "To verify this statement, compare the F1 scores for adjectives across all feature and model combinations. The highest F1 score for adjectives is 0.784, achieved by the Distance feature with the Combined model, not the Direction feature. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2510.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparing the novel distance feature with Schwarz et al.’s direction feature, across word classes." ], "reasoning": "To verify this statement, compare the F1 scores for adjectives across all feature and model combinations. The highest F1 score for adjectives is 0.784, achieved by the Distance feature with the Combined model, not the Direction feature.", "statement": "The direction feature with the combined model achieves the highest F1 score for adjectives.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Language model perplexities on validation and test sets of Penn Treebank.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Behavior gating improves the performance of language models, with LSTM-Medium achieving similar results to LSTM-Large despite having fewer parameters.Although the proposed model is motivated and targeted towards behavior related datasets, the hypothesis should theoretically extend towards any human generated corpora. To assess this, we also train models on a non-behavior-rich database, the Penn Tree Bank Corpus. We experiment with both the medium and large architectures proposed by Zaremba et al. All language models showed an improvement in perplexity through the addition of behavior gates. It can also be observed that LSTM-Medium with behavior gating gives similar performance to baseline LSTM-Large even though the latter has more than three times the number of parameters." } ], "images": [ "scigen/train/2511.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Language model perplexities on validation and test sets of Penn Treebank." ], "reasoning": "Behavior gating improves the performance of language models, with LSTM-Medium achieving similar results to LSTM-Large despite having fewer parameters.", "description": "Although the proposed model is motivated and targeted towards behavior related datasets, the hypothesis should theoretically extend towards any human generated corpora. To assess this, we also train models on a non-behavior-rich database, the Penn Tree Bank Corpus. We experiment with both the medium and large architectures proposed by Zaremba et al. All language models showed an improvement in perplexity through the addition of behavior gates. It can also be observed that LSTM-Medium with behavior gating gives similar performance to baseline LSTM-Large even though the latter has more than three times the number of parameters.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the test perplexity of the LSTM-Medium model with behavior gating?\nSolve the above question based on the table titled 'Table 2: Language model perplexities on validation and test sets of Penn Treebank.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the test perplexity of the LSTM-Medium model with behavior gating as 78.75. So, the answer is {\"answer\": \"78.75\"}" } ], "images": [ "scigen/train/2511.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Language model perplexities on validation and test sets of Penn Treebank." ], "reasoning": "The table shows the test perplexity of the LSTM-Medium model with behavior gating as 78.75.", "question": "What is the test perplexity of the LSTM-Medium model with behavior gating?", "answer": "So, the answer is {\"answer\": \"78.75\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The LSTM-Large model with behavior gating has fewer parameters than the LSTM-Medium model., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe LSTM-Large model with behavior gating has fewer parameters than the LSTM-Medium model.\n" }, { "role": "assistant", "content": "The table shows that the LSTM-Large model with behavior gating has 67M parameters, while the LSTM-Medium model has 20M parameters, refuting the statement. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2511.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Language model perplexities on validation and test sets of Penn Treebank." ], "reasoning": "The table shows that the LSTM-Large model with behavior gating has 67M parameters, while the LSTM-Medium model has 20M parameters, refuting the statement.", "statement": "The LSTM-Large model with behavior gating has fewer parameters than the LSTM-Medium model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Language model test perplexities on Couples Therapy and Cancer Couples Interaction Dataset.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The behavior gated language models show a 2.43% relative improvement in perplexity over the vanilla LSTM model on the Couple's Therapy Corpus.Couple ’s Therapy Corpus We utilize the Couple’s Therapy Corpus as an in-domain experimental corpus since our behavior classification model is also trained on the same. The RNNLM architecture is similar to Zaremba et al. We find that the behavior gated language models yield lower perplexity compared to vanilla LSTM language model. A relative improvement of 2.43% is obtained with behavior gating on the couple’s data." } ], "images": [ "scigen/train/2512.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Language model test perplexities on Couples Therapy and Cancer Couples Interaction Dataset." ], "reasoning": "The behavior gated language models show a 2.43% relative improvement in perplexity over the vanilla LSTM model on the Couple's Therapy Corpus.", "description": "Couple ’s Therapy Corpus We utilize the Couple’s Therapy Corpus as an in-domain experimental corpus since our behavior classification model is also trained on the same. The RNNLM architecture is similar to Zaremba et al. We find that the behavior gated language models yield lower perplexity compared to vanilla LSTM language model. A relative improvement of 2.43% is obtained with behavior gating on the couple’s data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 1: Language model test perplexities on Couples Therapy and Cancer Couples Interaction Dataset.', and sequentially solve the question, writing out each step of your reasoning process. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the perplexity improvement in the Couple's Therapy Corpus when using behavior gating compared to the LSTM model?\n" }, { "role": "assistant", "content": "The perplexity for the LSTM model on the Couple's Therapy Corpus is 66.32, and with behavior gating, it is 64.71. The relative improvement is calculated as (66.32 - 64.71) / 66.32 * 100, which is approximately 2.43%. So, the answer is {\"answer\": \"2.43%\"}." } ], "images": [ "scigen/train/2512.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Language model test perplexities on Couples Therapy and Cancer Couples Interaction Dataset." ], "reasoning": "The perplexity for the LSTM model on the Couple's Therapy Corpus is 66.32, and with behavior gating, it is 64.71. The relative improvement is calculated as (66.32 - 64.71) / 66.32 * 100, which is approximately 2.43%.", "question": "What is the perplexity improvement in the Couple's Therapy Corpus when using behavior gating compared to the LSTM model?", "answer": "So, the answer is {\"answer\": \"2.43%\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe behavior gated model has a lower perplexity than the LSTM model on the Cancer dataset.\n" }, { "role": "assistant", "content": "The table shows that the perplexity for the LSTM model on the Cancer dataset is 159.65, while with behavior gating, it is 148.78. Since 148.78 is lower than 159.65, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2512.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Language model test perplexities on Couples Therapy and Cancer Couples Interaction Dataset." ], "reasoning": "The table shows that the perplexity for the LSTM model on the Cancer dataset is 159.65, while with behavior gating, it is 148.78. Since 148.78 is lower than 159.65, the statement is supported.", "statement": "The behavior gated model has a lower perplexity than the LSTM model on the Cancer dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Speaker identification and verification results on TDNN+Para and CNN+Para with different γ value, the noise type is Babble and SNR level is 0 dB.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Increasing the weight value γ from 0 to 1 improves the accuracies and lowers the equal error rate, with TDNN peaking at γ = 0.6 and CNN peaking at γ = 0.8.To further test and compare the performance of parallel scenario (Para), the weight value γ in para scenario is tuned from 0 to 1. The different γ value is tested on Babble noise type and SNR value is equals to Results show that with the increase of γ, the accuracies become higher and the equal error rate lower down. For TDNN model, it reaches a peak when γ is equals to 0.6, and when γ =0.8, CNN model reaches its peak. This results shows that the frequency attention in both TDNN and CNN architectures contributes more to the recognition results, it also shows a possible reason why the results of time attention first (T-F) is worse than that of frequency attention first (F-T)." } ], "images": [ "scigen/train/2513.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Speaker identification and verification results on TDNN+Para and CNN+Para with different γ value, the noise type is Babble and SNR level is 0 dB." ], "reasoning": "Increasing the weight value γ from 0 to 1 improves the accuracies and lowers the equal error rate, with TDNN peaking at γ = 0.6 and CNN peaking at γ = 0.8.", "description": "To further test and compare the performance of parallel scenario (Para), the weight value γ in para scenario is tuned from 0 to 1. The different γ value is tested on Babble noise type and SNR value is equals to Results show that with the increase of γ, the accuracies become higher and the equal error rate lower down. For TDNN model, it reaches a peak when γ is equals to 0.6, and when γ =0.8, CNN model reaches its peak. This results shows that the frequency attention in both TDNN and CNN architectures contributes more to the recognition results, it also shows a possible reason why the results of time attention first (T-F) is worse than that of frequency attention first (F-T).", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Speaker identification and verification results on TDNN+Para and CNN+Para with different γ value, the noise type is Babble and SNR level is 0 dB.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. \n\nQuestion: At what γ value does the CNN+Para model achieve its highest Top1 accuracy?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows the Top1 accuracy for different γ values. The highest Top1 accuracy for the CNN+Para model is 72.2%, which occurs at γ = 0.8. So, the answer is {\"answer\": \"0.8\"}" } ], "images": [ "scigen/train/2513.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Speaker identification and verification results on TDNN+Para and CNN+Para with different γ value, the noise type is Babble and SNR level is 0 dB." ], "reasoning": "The table shows the Top1 accuracy for different γ values. The highest Top1 accuracy for the CNN+Para model is 72.2%, which occurs at γ = 0.8.", "question": "At what γ value does the CNN+Para model achieve its highest Top1 accuracy?", "answer": "So, the answer is {\"answer\": \"0.8\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe TDNN+Para model achieves its lowest EER at γ = 0.6.\n" }, { "role": "assistant", "content": "The table shows the EER values for the TDNN+Para model at different γ values. The lowest EER for the TDNN+Para model is 26.59%, which occurs at γ = 0.6. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2513.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Speaker identification and verification results on TDNN+Para and CNN+Para with different γ value, the noise type is Babble and SNR level is 0 dB." ], "reasoning": "The table shows the EER values for the TDNN+Para model at different γ values. The lowest EER for the TDNN+Para model is 26.59%, which occurs at γ = 0.6.", "statement": "The TDNN+Para model achieves its lowest EER at γ = 0.6.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Performances of models trained with only root labels (the first two rows) and models that use both root and leaf labels (the last two rows).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The table compares the performance of RNTN and S-LSTM models using root labels and both root and leaf labels, showing that S-LSTM outperforms RNTN in both settings.More real-life settings We further compare S-LSTM with RNTN in two more experimental settings. ROOT LBLS besides the model names stands for root labels; that is, only the gold labels of the sentence level are used to train the model. Also, annotating all phrases is expensive. However, these should not be regarded as comments on the value of the Sentiment Tree Bank. (LEAFLBLS along the side of the model names stands for leaf labels), and so there is no out-of-vocabulary concern. Using real sentiment lexicons is expected to have a performance between the two settings here." } ], "images": [ "scigen/train/2514.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performances of models trained with only root labels (the first two rows) and models that use both root and leaf labels (the last two rows)." ], "reasoning": "The table compares the performance of RNTN and S-LSTM models using root labels and both root and leaf labels, showing that S-LSTM outperforms RNTN in both settings.", "description": "More real-life settings We further compare S-LSTM with RNTN in two more experimental settings. ROOT LBLS besides the model names stands for root labels; that is, only the gold labels of the sentence level are used to train the model. Also, annotating all phrases is expensive. However, these should not be regarded as comments on the value of the Sentiment Tree Bank. (LEAFLBLS along the side of the model names stands for leaf labels), and so there is no out-of-vocabulary concern. Using real sentiment lexicons is expected to have a performance between the two settings here.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Performances of models trained with only root labels (the first two rows) and models that use both root and leaf labels (the last two rows).' to sequentially solve the question presented below. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich model has the highest performance when using both root and leaf labels?\n" }, { "role": "assistant", "content": "The table shows the performance of models using root and leaf labels. The S-LSTM model with root and leaf labels has a performance score of 44.1, which is higher than the RNTN model's score of 34.9. So, the answer is {\"answer\": \"S-LSTM\"}." } ], "images": [ "scigen/train/2514.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performances of models trained with only root labels (the first two rows) and models that use both root and leaf labels (the last two rows)." ], "reasoning": "The table shows the performance of models using root and leaf labels. The S-LSTM model with root and leaf labels has a performance score of 44.1, which is higher than the RNTN model's score of 34.9.", "question": "Which model has the highest performance when using both root and leaf labels?", "answer": "So, the answer is {\"answer\": \"S-LSTM\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence RNTN performs better than S-LSTM when using only root labels. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRNTN performs better than S-LSTM when using only root labels.\n" }, { "role": "assistant", "content": "The table shows that the RNTN model with root labels has a performance score of 29.1, while the S-LSTM model with root labels has a higher score of 43.5. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2514.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performances of models trained with only root labels (the first two rows) and models that use both root and leaf labels (the last two rows)." ], "reasoning": "The table shows that the RNTN model with root labels has a performance score of 29.1, while the S-LSTM model with root labels has a higher score of 43.5. Therefore, the statement is refuted.", "statement": "RNTN performs better than S-LSTM when using only root labels.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Performances (accuracies) of different models on the test set of Stanford Sentiment Tree Bank, at the sentence level (roots) and the phrase level. † shows the performance are statistically significantly better (p<0.05) than the corresponding models.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The S-LSTM model achieved the highest accuracy in both sentence-level and phrase-level sentiment prediction, with hyper-parameters tuned for hidden unit size, batch size, and learning rate.We present the results on 5-category sentiment prediction at both the sentence level (i.e., the ROOTS column in the table) and for all phrases including roots (the PHRASES column) As described earlier, we refer to recursive neural networks to as RvNN to avoid confusion with recurrent neural networks. RNTN is different from RvNN in that when merging two nodes to obtain the hidden vector of their parent, tensor is used to obtain the second-degree polynomial interactions. The S-LSTM results reported here were obtained by setting the size of the hidden units to be 100, batch size to be 10, and learning rate to be 0.1. In our experiments, we only tuned these hyper-parameters, and we feel that more finer tuning, such as discriminating the classification weights between the leaves (word embedding) and other nodes, using different numbers of hidden units for the memory blocks (e.g., for the hidden layers of words), or different initializations of word embedding, may further improve the performances reported here." } ], "images": [ "scigen/train/2515.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performances (accuracies) of different models on the test set of Stanford Sentiment Tree Bank, at the sentence level (roots) and the phrase level. † shows the performance are statistically significantly better (p<0.05) than the corresponding models." ], "reasoning": "The S-LSTM model achieved the highest accuracy in both sentence-level and phrase-level sentiment prediction, with hyper-parameters tuned for hidden unit size, batch size, and learning rate.", "description": "We present the results on 5-category sentiment prediction at both the sentence level (i.e., the ROOTS column in the table) and for all phrases including roots (the PHRASES column) As described earlier, we refer to recursive neural networks to as RvNN to avoid confusion with recurrent neural networks. RNTN is different from RvNN in that when merging two nodes to obtain the hidden vector of their parent, tensor is used to obtain the second-degree polynomial interactions. The S-LSTM results reported here were obtained by setting the size of the hidden units to be 100, batch size to be 10, and learning rate to be 0.1. In our experiments, we only tuned these hyper-parameters, and we feel that more finer tuning, such as discriminating the classification weights between the leaves (word embedding) and other nodes, using different numbers of hidden units for the memory blocks (e.g., for the hidden layers of words), or different initializations of word embedding, may further improve the performances reported here.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: Performances (accuracies) of different models on the test set of Stanford Sentiment Tree Bank, at the sentence level (roots) and the phrase level. † shows the performance are statistically significantly better (p<0.05) than the corresponding models.', answer the question below by showing each progressive step toward the answer. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model achieved the highest accuracy in both the ROOTS and PHRASES columns?\n" }, { "role": "assistant", "content": "By comparing the values in both the ROOTS and PHRASES columns, the S-LSTM model has the highest accuracy with 48.0 in ROOTS and 81.9 in PHRASES. So, the answer is {\"answer\": \"S-LSTM\"}." } ], "images": [ "scigen/train/2515.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performances (accuracies) of different models on the test set of Stanford Sentiment Tree Bank, at the sentence level (roots) and the phrase level. † shows the performance are statistically significantly better (p<0.05) than the corresponding models." ], "reasoning": "By comparing the values in both the ROOTS and PHRASES columns, the S-LSTM model has the highest accuracy with 48.0 in ROOTS and 81.9 in PHRASES.", "question": "Which model achieved the highest accuracy in both the ROOTS and PHRASES columns?", "answer": "So, the answer is {\"answer\": \"S-LSTM\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe RNTN model achieved higher accuracy than the RvNN model in both ROOTS and PHRASES columns.\n" }, { "role": "assistant", "content": "By comparing the values, RNTN has 45.7 in ROOTS and 80.7 in PHRASES, while RvNN has 43.2 in ROOTS and 79.0 in PHRASES, indicating that RNTN achieved higher accuracy in both columns. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2515.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performances (accuracies) of different models on the test set of Stanford Sentiment Tree Bank, at the sentence level (roots) and the phrase level. † shows the performance are statistically significantly better (p<0.05) than the corresponding models." ], "reasoning": "By comparing the values, RNTN has 45.7 in ROOTS and 80.7 in PHRASES, while RvNN has 43.2 in ROOTS and 79.0 in PHRASES, indicating that RNTN achieved higher accuracy in both columns.", "statement": "The RNTN model achieved higher accuracy than the RvNN model in both ROOTS and PHRASES columns.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Performances of models that do not use the given sentence structures. S-LSTM-LR is a degenerated version of S-LSTM that reads input words from left to right, and S-LSTM-RR reads words from right to left.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The S-LSTM model outperforms both the left and right recursive S-LSTM models when using only root labels and when using both root and leaf labels.In the case of using only root labels, the left recursive S-LSTM and right recursive S-LSTM have similar performance (40.2 and 40.3, respectively), both inferior to S-LSTM (43.5). When using gold leaf labels, the gaps are smaller, but still, using the parse structure are better. Note that in real applications, where there is no out-of-vocabulary issue (i.e., some leafs are not seen in the sentiment dictionaries), the difference between S-LSTM and the recursive version without using the structures are expected to be between the gaps we observed here." } ], "images": [ "scigen/train/2516.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performances of models that do not use the given sentence structures. S-LSTM-LR is a degenerated version of S-LSTM that reads input words from left to right, and S-LSTM-RR reads words from right to left." ], "reasoning": "The S-LSTM model outperforms both the left and right recursive S-LSTM models when using only root labels and when using both root and leaf labels.", "description": "In the case of using only root labels, the left recursive S-LSTM and right recursive S-LSTM have similar performance (40.2 and 40.3, respectively), both inferior to S-LSTM (43.5). When using gold leaf labels, the gaps are smaller, but still, using the parse structure are better. Note that in real applications, where there is no out-of-vocabulary issue (i.e., some leafs are not seen in the sentiment dictionaries), the difference between S-LSTM and the recursive version without using the structures are expected to be between the gaps we observed here.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Performances of models that do not use the given sentence structures. S-LSTM-LR is a degenerated version of S-LSTM that reads input words from left to right, and S-LSTM-RR reads words from right to left.'. Output the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: Which model has the highest performance when using both root and leaf labels?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By comparing the performance scores in the table, the S-LSTM model with root and leaf labels has the highest score of 44.1. So, the answer is {\"answer\": \"S-LSTM (Root + Leaf Lbls)\"}" } ], "images": [ "scigen/train/2516.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performances of models that do not use the given sentence structures. S-LSTM-LR is a degenerated version of S-LSTM that reads input words from left to right, and S-LSTM-RR reads words from right to left." ], "reasoning": "By comparing the performance scores in the table, the S-LSTM model with root and leaf labels has the highest score of 44.1.", "question": "Which model has the highest performance when using both root and leaf labels?", "answer": "So, the answer is {\"answer\": \"S-LSTM (Root + Leaf Lbls)\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The S-LSTM-LR model with root labels has a performance score of 40.2. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe S-LSTM-LR model with root labels has a performance score of 40.2.\n" }, { "role": "assistant", "content": "The table shows that the S-LSTM-LR model with root labels indeed has a performance score of 40.2. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2516.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performances of models that do not use the given sentence structures. S-LSTM-LR is a degenerated version of S-LSTM that reads input words from left to right, and S-LSTM-RR reads words from right to left." ], "reasoning": "The table shows that the S-LSTM-LR model with root labels indeed has a performance score of 40.2.", "statement": "The S-LSTM-LR model with root labels has a performance score of 40.2.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Comparisons between baselines and best adversaries. All metrics reported in this table are accuracies.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The trained baseline significantly outperforms the most frequent class baseline, and the attacker surpasses the most frequent class baseline overall, especially for age prediction.First of all, we observe that apart from gender on the German dataset, the trained baseline outperforms the most frequent class baseline by a wide margin (8 to 25 absolute difference). Second of all, the attacker is able to outperform the most frequent class baseline overall, even in the raw setting. In more details, for age, the adversary is well over the baseline in all cases except US. On the other hand, gender seems harder to predict: the adversary outperforms the most frequent class baseline only in the +demo setting." } ], "images": [ "scigen/train/2517.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparisons between baselines and best adversaries. All metrics reported in this table are accuracies." ], "reasoning": "The trained baseline significantly outperforms the most frequent class baseline, and the attacker surpasses the most frequent class baseline overall, especially for age prediction.", "description": "First of all, we observe that apart from gender on the German dataset, the trained baseline outperforms the most frequent class baseline by a wide margin (8 to 25 absolute difference). Second of all, the attacker is able to outperform the most frequent class baseline overall, even in the raw setting. In more details, for age, the adversary is well over the baseline in all cases except US. On the other hand, gender seems harder to predict: the adversary outperforms the most frequent class baseline only in the +demo setting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Comparisons between baselines and best adversaries. All metrics reported in this table are accuracies.', answer the question below by showing each progressive step toward the answer. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich dataset shows the highest performance for age prediction in the +demo setting?\n" }, { "role": "assistant", "content": "To find the highest performance for age prediction in the +demo setting, we look at the 'Best adversaries' column under '+demo' for 'Age'. The highest value is 81.3 for TP (US). So, the answer is {\"answer\": \"TP (US)\"}." } ], "images": [ "scigen/train/2517.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparisons between baselines and best adversaries. All metrics reported in this table are accuracies." ], "reasoning": "To find the highest performance for age prediction in the +demo setting, we look at the 'Best adversaries' column under '+demo' for 'Age'. The highest value is 81.3 for TP (US).", "question": "Which dataset shows the highest performance for age prediction in the +demo setting?", "answer": "So, the answer is {\"answer\": \"TP (US)\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The trained baseline for gender prediction in TP (France) is higher than the best adversaries in the +demo setting. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe trained baseline for gender prediction in TP (France) is higher than the best adversaries in the +demo setting.\n" }, { "role": "assistant", "content": "The trained baseline for gender prediction in TP (France) is 69.0, while the best adversaries in the +demo setting is 61.0. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2517.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparisons between baselines and best adversaries. All metrics reported in this table are accuracies." ], "reasoning": "The trained baseline for gender prediction in TP (France) is 69.0, while the best adversaries in the +demo setting is 61.0. Therefore, the statement is refuted.", "statement": "The trained baseline for gender prediction in TP (France) is higher than the best adversaries in the +demo setting.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 7: Human evaluation results of summaries for 100 randomly sampled articles in CNN/DailyMail test set. *p-value<0.05.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Our model's score is statistically significant compared to UniLM with a p-value smaller than 0.05, and it is comparable in terms of informativeness. Without the knowledge graph component, the Seq2Seq model performs worse in factual correctness and informativeness.We conduct a statistical test and find that compared with UniLM, our model ’s score is statistically significant with p-value smaller than 0.05 under paired t-test. In terms of informativeness, our model is comparable with UniLM. Finally, without the knowledge graph component, the Seq2Seq model generates summaries with both less factual correctness and informativeness." } ], "images": [ "scigen/train/2518.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Human evaluation results of summaries for 100 randomly sampled articles in CNN/DailyMail test set. *p-value<0.05." ], "reasoning": "Our model's score is statistically significant compared to UniLM with a p-value smaller than 0.05, and it is comparable in terms of informativeness. Without the knowledge graph component, the Seq2Seq model performs worse in factual correctness and informativeness.", "description": "We conduct a statistical test and find that compared with UniLM, our model ’s score is statistically significant with p-value smaller than 0.05 under paired t-test. In terms of informativeness, our model is comparable with UniLM. Finally, without the knowledge graph component, the Seq2Seq model generates summaries with both less factual correctness and informativeness.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 7: Human evaluation results of summaries for 100 randomly sampled articles in CNN/DailyMail test set. *p-value<0.05.' to sequentially solve the question presented below. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhich model has the highest factual score and how does its informativeness compare to UniLM?\n" }, { "role": "assistant", "content": "The model with the highest factual score is FASum with a score of 2.79. Its informativeness score is 2.36, which is slightly lower than UniLM's informativeness score of 2.45. So, the answer is {\"answer\": \"FASum\"}" } ], "images": [ "scigen/train/2518.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Human evaluation results of summaries for 100 randomly sampled articles in CNN/DailyMail test set. *p-value<0.05." ], "reasoning": "The model with the highest factual score is FASum with a score of 2.79. Its informativeness score is 2.36, which is slightly lower than UniLM's informativeness score of 2.45.", "question": "Which model has the highest factual score and how does its informativeness compare to UniLM?", "answer": "So, the answer is {\"answer\": \"FASum\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Seq2Seq model generates summaries with the highest factual correctness.\n" }, { "role": "assistant", "content": "The table shows that the Seq2Seq model has a factual score of 2.57, which is lower than FASum's score of 2.79, UniLM's score of 2.65, and higher than BottomUp's score of 2.32. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2518.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Human evaluation results of summaries for 100 randomly sampled articles in CNN/DailyMail test set. *p-value<0.05." ], "reasoning": "The table shows that the Seq2Seq model has a factual score of 2.57, which is lower than FASum's score of 2.79, UniLM's score of 2.65, and higher than BottomUp's score of 2.32.", "statement": "The Seq2Seq model generates summaries with the highest factual correctness.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: Factual correctness score and ROUGE scores on CNN/DailyMail and XSum test set. *p-value<0.05.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "FASum has a factual correctness score that is 1.2% higher than UniLM and 4.5% higher than BottomUp on CNN/DailyMail, with statistical significance (p < 0.05).In CNN/DailyMail, FASum is 1.2% higher than UniLM and 4.5% higher than BottomUp in factual correctness score. Statistical test shows that the lead is statistically significant with p-value smaller than 0.05." } ], "images": [ "scigen/train/2519.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Factual correctness score and ROUGE scores on CNN/DailyMail and XSum test set. *p-value<0.05." ], "reasoning": "FASum has a factual correctness score that is 1.2% higher than UniLM and 4.5% higher than BottomUp on CNN/DailyMail, with statistical significance (p < 0.05).", "description": "In CNN/DailyMail, FASum is 1.2% higher than UniLM and 4.5% higher than BottomUp in factual correctness score. Statistical test shows that the lead is statistically significant with p-value smaller than 0.05.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 4: Factual correctness score and ROUGE scores on CNN/DailyMail and XSum test set. *p-value<0.05.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: Which model has the highest factual correctness score on CNN/DailyMail?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By comparing the factual correctness scores of all models on CNN/DailyMail, FASum has the highest score of 88.4. So, the answer is {\"answer\": \"FASum\"}" } ], "images": [ "scigen/train/2519.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Factual correctness score and ROUGE scores on CNN/DailyMail and XSum test set. *p-value<0.05." ], "reasoning": "By comparing the factual correctness scores of all models on CNN/DailyMail, FASum has the highest score of 88.4.", "question": "Which model has the highest factual correctness score on CNN/DailyMail?", "answer": "So, the answer is {\"answer\": \"FASum\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nFASum has the highest ROUGE-1 score on CNN/DailyMail.\n" }, { "role": "assistant", "content": "The table shows that UniLM has the highest ROUGE-1 score of 43.33 on CNN/DailyMail, not FASum. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2519.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Factual correctness score and ROUGE scores on CNN/DailyMail and XSum test set. *p-value<0.05." ], "reasoning": "The table shows that UniLM has the highest ROUGE-1 score of 43.33 on CNN/DailyMail, not FASum.", "statement": "FASum has the highest ROUGE-1 score on CNN/DailyMail.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 6: Average relation matching rate (RMR) between the summary and the article in CNN/DailyMail test set. The definition of this metric is in Equation 7.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "FASum achieves the highest precision of correct hits under both RMR1 and RMR2 measures, with significant improvements over Seq2Seq: 13.6% in RMR1 and 17.3% in RMR2.As shown, FASum achieves the highest precision of correct hits under both measures. And there is a considerable boost from the knowledge graph component (FASum vs Seq2Seq): 13.6% in RMR1 and 17.3% in RMR2." } ], "images": [ "scigen/train/2520.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Average relation matching rate (RMR) between the summary and the article in CNN/DailyMail test set. The definition of this metric is in Equation 7." ], "reasoning": "FASum achieves the highest precision of correct hits under both RMR1 and RMR2 measures, with significant improvements over Seq2Seq: 13.6% in RMR1 and 17.3% in RMR2.", "description": "As shown, FASum achieves the highest precision of correct hits under both measures. And there is a considerable boost from the knowledge graph component (FASum vs Seq2Seq): 13.6% in RMR1 and 17.3% in RMR2.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: Average relation matching rate (RMR) between the summary and the article in CNN/DailyMail test set. The definition of this metric is in Equation 7.\nQuestion: Which model shows the greatest improvement in precision from Seq2Seq to another model in RMR2?\nSolve the above question based on the table information. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "Seq2Seq has a precision of 31.3% in RMR2. The greatest improvement is seen in the FASum model, which has a precision of 48.6%, resulting in an improvement of 17.3%. So, the answer is {\"answer\": \"FASum\"}" } ], "images": [ "scigen/train/2520.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Average relation matching rate (RMR) between the summary and the article in CNN/DailyMail test set. The definition of this metric is in Equation 7." ], "reasoning": "Seq2Seq has a precision of 31.3% in RMR2. The greatest improvement is seen in the FASum model, which has a precision of 48.6%, resulting in an improvement of 17.3%.", "question": "Which model shows the greatest improvement in precision from Seq2Seq to another model in RMR2?", "answer": "So, the answer is {\"answer\": \"FASum\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Corrected by FC achieves the highest precision in RMR1., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nCorrected by FC achieves the highest precision in RMR1.\n" }, { "role": "assistant", "content": "The table shows that Corrected by FC has a precision of 68.6% in RMR1, which is higher than all other models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2520.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Average relation matching rate (RMR) between the summary and the article in CNN/DailyMail test set. The definition of this metric is in Equation 7." ], "reasoning": "The table shows that Corrected by FC has a precision of 68.6% in RMR1, which is higher than all other models listed.", "statement": "Corrected by FC achieves the highest precision in RMR1.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 5: Ablation study with full training set by freezing ( ) model parameters for generator or parser during learning. Darker indicates higher values.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Freezing the parameters of either the parser or generator in DIM results in degraded performance, highlighting the importance of jointly optimizing both tasks.Ablation Study. We conduct ablation studies by training DIM with the parameters of parser or generator frozen. As anticipated, for both of parsing and generation, when the dual model is frozen, the performance of the primal model degrades. This again demonstrates DIM’s effectiveness of jointly optimizing both tasks. Intuitively, jointly updating both the primal and dual models allows a better learned dual model to provide high-quality learning signals, leading to an improved lower bound for the primal. As a result, freezing parameters of the dual model has a negative impact on the learning signal quality, which affects primal model learning." } ], "images": [ "scigen/train/2521.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ablation study with full training set by freezing ( ) model parameters for generator or parser during learning. Darker indicates higher values." ], "reasoning": "Freezing the parameters of either the parser or generator in DIM results in degraded performance, highlighting the importance of jointly optimizing both tasks.", "description": "Ablation Study. We conduct ablation studies by training DIM with the parameters of parser or generator frozen. As anticipated, for both of parsing and generation, when the dual model is frozen, the performance of the primal model degrades. This again demonstrates DIM’s effectiveness of jointly optimizing both tasks. Intuitively, jointly updating both the primal and dual models allows a better learned dual model to provide high-quality learning signals, leading to an improved lower bound for the primal. As a result, freezing parameters of the dual model has a negative impact on the learning signal quality, which affects primal model learning.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 5: Ablation study with full training set by freezing ( ) model parameters for generator or parser during learning. Darker indicates higher values.' to sequentially solve the question presented below. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhat is the performance of DIM on the CoNaLa dataset when the generator parameters are frozen?\n" }, { "role": "assistant", "content": "The table shows the performance of DIM on the CoNaLa dataset under different conditions. When the generator parameters are frozen, the performance of DIM is 24.8. So, the answer is {\"answer\": \"24.8\"}." } ], "images": [ "scigen/train/2521.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ablation study with full training set by freezing ( ) model parameters for generator or parser during learning. Darker indicates higher values." ], "reasoning": "The table shows the performance of DIM on the CoNaLa dataset under different conditions. When the generator parameters are frozen, the performance of DIM is 24.8.", "question": "What is the performance of DIM on the CoNaLa dataset when the generator parameters are frozen?", "answer": "So, the answer is {\"answer\": \"24.8\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nFreezing the generator parameters results in the highest performance for DIM on the Django dataset.\n" }, { "role": "assistant", "content": "The table shows that the performance of DIM on the Django dataset is 70.8 when the generator parameters are frozen. This is higher than the performance when the parser parameters are frozen (66.5) or when both are optimized (68.1). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2521.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ablation study with full training set by freezing ( ) model parameters for generator or parser during learning. Darker indicates higher values." ], "reasoning": "The table shows that the performance of DIM on the Django dataset is 70.8 when the generator parameters are frozen. This is higher than the performance when the parser parameters are frozen (66.5) or when both are optimized (68.1).", "statement": "Freezing the generator parameters results in the highest performance for DIM on the Django dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: Average annotation time per text (in minutes) and speed-up of our compared to previous annotations.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Annotators in the Suggestion group consistently speed up their annotation times, achieving higher average speed-ups compared to the Standard group in both MeD and TeD phases.We note that annotators in the Suggestion group (A1–A3) always speed up compared to previous annotations, whereas some of the annotators in the Standard group (A4–A5) slow down. Furthermore, on average, annotators in the Suggestion group exhibit a higher speed-up of annotation time: A1–A3 have a speed-up of 35 % compared to only 21 % for A4–A5 in MeD, and 20 % compared to only 11 % in TeD. Thus, suggestions make the annotation of epistemic activities more efficient." } ], "images": [ "scigen/train/2522.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Average annotation time per text (in minutes) and speed-up of our compared to previous annotations." ], "reasoning": "Annotators in the Suggestion group consistently speed up their annotation times, achieving higher average speed-ups compared to the Standard group in both MeD and TeD phases.", "description": "We note that annotators in the Suggestion group (A1–A3) always speed up compared to previous annotations, whereas some of the annotators in the Standard group (A4–A5) slow down. Furthermore, on average, annotators in the Suggestion group exhibit a higher speed-up of annotation time: A1–A3 have a speed-up of 35 % compared to only 21 % for A4–A5 in MeD, and 20 % compared to only 11 % in TeD. Thus, suggestions make the annotation of epistemic activities more efficient.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: 'Table 5: Average annotation time per text (in minutes) and speed-up of our compared to previous annotations.'\nQuestion: What is the average speed-up percentage for annotators in the Suggestion group during the MeD phase?\n" }, { "role": "assistant", "content": "To find the average speed-up percentage for annotators in the Suggestion group during the MeD phase, we need to average the speed-up percentages of A1, A2, and A3. The percentages are 54%, 25%, and 29%. The average is (54 + 25 + 29) / 3 = 36%. So, the answer is {\"answer\": \"36%\"}" } ], "images": [ "scigen/train/2522.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Average annotation time per text (in minutes) and speed-up of our compared to previous annotations." ], "reasoning": "To find the average speed-up percentage for annotators in the Suggestion group during the MeD phase, we need to average the speed-up percentages of A1, A2, and A3. The percentages are 54%, 25%, and 29%. The average is (54 + 25 + 29) / 3 = 36%.", "question": "What is the average speed-up percentage for annotators in the Suggestion group during the MeD phase?", "answer": "So, the answer is {\"answer\": \"36%\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nAnnotators in the Standard group always speed up their annotation times.\n" }, { "role": "assistant", "content": "The table shows that in the MeD phase, Standard A4 has a speed-up of 36%, but Standard A5 has a speed-up of -6%, indicating a slowdown. In the TeD phase, Standard A4 has a speed-up of -8%, indicating a slowdown, while Standard A5 has a speed-up of 31%. Therefore, not all annotators in the Standard group always speed up. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2522.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Average annotation time per text (in minutes) and speed-up of our compared to previous annotations." ], "reasoning": "The table shows that in the MeD phase, Standard A4 has a speed-up of 36%, but Standard A5 has a speed-up of -6%, indicating a slowdown. In the TeD phase, Standard A4 has a speed-up of -8%, indicating a slowdown, while Standard A5 has a speed-up of 31%. Therefore, not all annotators in the Standard group always speed up.", "statement": "Annotators in the Standard group always speed up their annotation times.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Intra-annotator agreement (in terms of Krippendorff’s αU) on double-annotated texts.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The intra-annotator agreement for annotators with suggestions (A1–A3) is similar to that without suggestions (A4–A5), indicating that suggestions do not significantly affect annotation decisions.In O2, we mixed 100 new texts with 50 texts the annotators saw previously during S1 or O1, but we did not inform the annotators about this setup. Even a single annotator shows annotation discrepancies instead of perfect consistency, evidencing the difficulty of annotating epistemic activities. Since the intra-annotator agreement for annotators with suggestions (A1–A3) is similar to that without (A4–A5), we conclude that suggestions do not considerably change annotators’ annotation decisions." } ], "images": [ "scigen/train/2523.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Intra-annotator agreement (in terms of Krippendorff’s αU) on double-annotated texts." ], "reasoning": "The intra-annotator agreement for annotators with suggestions (A1–A3) is similar to that without suggestions (A4–A5), indicating that suggestions do not significantly affect annotation decisions.", "description": "In O2, we mixed 100 new texts with 50 texts the annotators saw previously during S1 or O1, but we did not inform the annotators about this setup. Even a single annotator shows annotation discrepancies instead of perfect consistency, evidencing the difficulty of annotating epistemic activities. Since the intra-annotator agreement for annotators with suggestions (A1–A3) is similar to that without (A4–A5), we conclude that suggestions do not considerably change annotators’ annotation decisions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Intra-annotator agreement (in terms of Krippendorff’s αU) on double-annotated texts.\nQuestion: What is the average intra-annotator agreement for MeD across all annotators?\nGive a detailed response to the above question. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the average intra-annotator agreement for MeD, we look at the values for MeD across all annotators (A1 to A5) and then calculate the average. So, the answer is {\"answer\": \"0.77\"}." } ], "images": [ "scigen/train/2523.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Intra-annotator agreement (in terms of Krippendorff’s αU) on double-annotated texts." ], "reasoning": "To find the average intra-annotator agreement for MeD, we look at the values for MeD across all annotators (A1 to A5) and then calculate the average.", "question": "What is the average intra-annotator agreement for MeD across all annotators?", "answer": "So, the answer is {\"answer\": \"0.77\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nAnnotator A2 had the lowest intra-annotator agreement for TeD.\n" }, { "role": "assistant", "content": "By comparing the TeD values for all annotators, we see that A2 has the lowest value (0.64). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2523.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Intra-annotator agreement (in terms of Krippendorff’s αU) on double-annotated texts." ], "reasoning": "By comparing the TeD values for all annotators, we see that A2 has the lowest value (0.64).", "statement": "Annotator A2 had the lowest intra-annotator agreement for TeD.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Comparison with the current state-of-the-art approaches on the CoNLL 2012 test sets. NN Mention Ranker and NN Cluster Ranker are contributions of this work.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The mention-ranking model outperforms previous systems due to its deeper neural network, pretrained word embeddings, and sophisticated pretraining.Our mention-ranking model surpasses all previous systems. We attribute its improvement over the neural mention ranker from Wiseman et al. (2015) to our model using a deeper neural network, pretrained word embeddings, and more sophisticated pretraining." } ], "images": [ "scigen/train/2524.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison with the current state-of-the-art approaches on the CoNLL 2012 test sets. NN Mention Ranker and NN Cluster Ranker are contributions of this work." ], "reasoning": "The mention-ranking model outperforms previous systems due to its deeper neural network, pretrained word embeddings, and sophisticated pretraining.", "description": "Our mention-ranking model surpasses all previous systems. We attribute its improvement over the neural mention ranker from Wiseman et al. (2015) to our model using a deeper neural network, pretrained word embeddings, and more sophisticated pretraining.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 5: Comparison with the current state-of-the-art approaches on the CoNLL 2012 test sets. NN Mention Ranker and NN Cluster Ranker are contributions of this work.'. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest average F1 score on the CoNLL 2012 English Test Data?\n" }, { "role": "assistant", "content": "To find the model with the highest average F1 score on the CoNLL 2012 English Test Data, look at the 'Avg. F1' column for the CoNLL 2012 English Test Data. The NN Cluster Ranker has the highest score of 65.29. So, the answer is {\"answer\": \"NN Cluster Ranker\"}" } ], "images": [ "scigen/train/2524.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison with the current state-of-the-art approaches on the CoNLL 2012 test sets. NN Mention Ranker and NN Cluster Ranker are contributions of this work." ], "reasoning": "To find the model with the highest average F1 score on the CoNLL 2012 English Test Data, look at the 'Avg. F1' column for the CoNLL 2012 English Test Data. The NN Cluster Ranker has the highest score of 65.29.", "question": "Which model has the highest average F1 score on the CoNLL 2012 English Test Data?", "answer": "So, the answer is {\"answer\": \"NN Cluster Ranker\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe NN Cluster Ranker has the highest precision score in the MUC metric for the CoNLL 2012 English Test Data.\n" }, { "role": "assistant", "content": "To verify this statement, check the 'MUC Prec.' column for the CoNLL 2012 English Test Data. The NN Cluster Ranker has a precision score of 78.93, which is the highest among the listed models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2524.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison with the current state-of-the-art approaches on the CoNLL 2012 test sets. NN Mention Ranker and NN Cluster Ranker are contributions of this work." ], "reasoning": "To verify this statement, check the 'MUC Prec.' column for the CoNLL 2012 English Test Data. The NN Cluster Ranker has a precision score of 78.93, which is the highest among the listed models.", "statement": "The NN Cluster Ranker has the highest precision score in the MUC metric for the CoNLL 2012 English Test Data.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: P@K performance of different cross-lingual embedding models in the bilingual dictionary induction task.'.\n" }, { "role": "assistant", "content": "The impact of Meemi is more apparent when used with orthogonal base models, while Meemiw is not particularly beneficial except for English-Farsi. Unsupervised models perform well for closely-related languages but underperform for distant languages.Not surprisingly, the impact of Meemi is more apparent when used in combination with the orthogonal base models. On the other hand, using the weighted version of Meemi (i.e., Meemiw) does not seem to be particularly beneficial on this task, with the only exception of English-Farsi. In general, the performance of unsupervised models (i.e., VecMapuns and MUSEuns) is competitive in closely-related languages such as English-Spanish or English-German but they considerably under-perform for distant languages, especially English-Finnish and English-Russian. Finally, the results obtained by the multilingual model that includes all seven languages considered, i.e., Meemi-multi (VecMaportho), improve over the base orthogonal model, but they do not improve over the results of our bilingual model." } ], "images": [ "scigen/train/2525.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: P@K performance of different cross-lingual embedding models in the bilingual dictionary induction task." ], "reasoning": "The impact of Meemi is more apparent when used with orthogonal base models, while Meemiw is not particularly beneficial except for English-Farsi. Unsupervised models perform well for closely-related languages but underperform for distant languages.", "description": "Not surprisingly, the impact of Meemi is more apparent when used in combination with the orthogonal base models. On the other hand, using the weighted version of Meemi (i.e., Meemiw) does not seem to be particularly beneficial on this task, with the only exception of English-Farsi. In general, the performance of unsupervised models (i.e., VecMapuns and MUSEuns) is competitive in closely-related languages such as English-Spanish or English-German but they considerably under-perform for distant languages, especially English-Finnish and English-Russian. Finally, the results obtained by the multilingual model that includes all seven languages considered, i.e., Meemi-multi (VecMaportho), improve over the base orthogonal model, but they do not improve over the results of our bilingual model.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 1: P@K performance of different cross-lingual embedding models in the bilingual dictionary induction task.' in mind, break down and resolve the question below step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhich model has the highest P@10 score for English-Farsi?\n" }, { "role": "assistant", "content": "To find the model with the highest P@10 score for English-Farsi, look at the column 'English-Farsi P@10' and identify the highest value. The highest value is 38.5, achieved by Meemi (VecMapmultistep). So, the answer is {\"answer\": \"Meemi (VecMapmultistep)\"}." } ], "images": [ "scigen/train/2525.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: P@K performance of different cross-lingual embedding models in the bilingual dictionary induction task." ], "reasoning": "To find the model with the highest P@10 score for English-Farsi, look at the column 'English-Farsi P@10' and identify the highest value. The highest value is 38.5, achieved by Meemi (VecMapmultistep).", "question": "Which model has the highest P@10 score for English-Farsi?", "answer": "So, the answer is {\"answer\": \"Meemi (VecMapmultistep)\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nMeemiw (VecMaportho) has the highest P@1 score for English-Finnish.\n" }, { "role": "assistant", "content": "To verify this statement, check the 'English-Finnish P@1' column. The highest P@1 score is 24.8, achieved by Meemi (VecMaportho), not Meemiw (VecMaportho). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2525.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: P@K performance of different cross-lingual embedding models in the bilingual dictionary induction task." ], "reasoning": "To verify this statement, check the 'English-Finnish P@1' column. The highest P@1 score is 24.8, achieved by Meemi (VecMaportho), not Meemiw (VecMaportho).", "statement": "Meemiw (VecMaportho) has the highest P@1 score for English-Finnish.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Cross-lingual word similarity results in terms of Pearson (r) and Spearman (ρ) correlation. Languages codes: English-EN, Spanish-ES, Italian-IT, German-DE, and Farsi-FA.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The Meemi transformation generally outperforms base models and their unsupervised variants, especially in distant languages like Farsi, where it surpasses the best VecMap model by nearly four percentage points.Except in a few cases for the VecMapmultistep model, our Meemi transformation proves superior to the base models, and to all their unsupervised variants. In Farsi, which is the most distant language with a different alphabet, the results are lower overall, but in this case our Meemi transformation proves essential, outperforming the best VecMap model by almost four percentage points (from 36.5% to 40.4%). Similarly as in the bilingual dictionary induction task, the weighted version of Meemi proves robust only on English-Farsi, which suggests that this weighting scheme is most useful for distant languages, as in this case the Farsi monolingual space (which is learned from a smaller corpus and hence, as we will see in the next section, has a lower quality) gets closer to the English monolingual space. As far as the multilingual model is concerned, it proves beneficial in all cases with respect to the orthogonal version of VecMap, as well as compared to the bilingual variant of Meemi." } ], "images": [ "scigen/train/2526.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Cross-lingual word similarity results in terms of Pearson (r) and Spearman (ρ) correlation. Languages codes: English-EN, Spanish-ES, Italian-IT, German-DE, and Farsi-FA." ], "reasoning": "The Meemi transformation generally outperforms base models and their unsupervised variants, especially in distant languages like Farsi, where it surpasses the best VecMap model by nearly four percentage points.", "description": "Except in a few cases for the VecMapmultistep model, our Meemi transformation proves superior to the base models, and to all their unsupervised variants. In Farsi, which is the most distant language with a different alphabet, the results are lower overall, but in this case our Meemi transformation proves essential, outperforming the best VecMap model by almost four percentage points (from 36.5% to 40.4%). Similarly as in the bilingual dictionary induction task, the weighted version of Meemi proves robust only on English-Farsi, which suggests that this weighting scheme is most useful for distant languages, as in this case the Farsi monolingual space (which is learned from a smaller corpus and hence, as we will see in the next section, has a lower quality) gets closer to the English monolingual space. As far as the multilingual model is concerned, it proves beneficial in all cases with respect to the orthogonal version of VecMap, as well as compared to the bilingual variant of Meemi.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model achieves the highest score for EN-FA in the table?\nSolve the above question based on the table titled 'Table 2: Cross-lingual word similarity results in terms of Pearson (r) and Spearman (ρ) correlation. Languages codes: English-EN, Spanish-ES, Italian-IT, German-DE, and Farsi-FA.'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest score for EN-FA, we need to look at the EN-FA column in the table and identify the highest value. The highest score is 40.8, achieved by the Meemiw (VecMapmultistep) model. So, the answer is {\"answer\": \"Meemiw (VecMapmultistep)\"}" } ], "images": [ "scigen/train/2526.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Cross-lingual word similarity results in terms of Pearson (r) and Spearman (ρ) correlation. Languages codes: English-EN, Spanish-ES, Italian-IT, German-DE, and Farsi-FA." ], "reasoning": "To find the highest score for EN-FA, we need to look at the EN-FA column in the table and identify the highest value. The highest score is 40.8, achieved by the Meemiw (VecMapmultistep) model.", "question": "Which model achieves the highest score for EN-FA in the table?", "answer": "So, the answer is {\"answer\": \"Meemiw (VecMapmultistep)\"}" }, { "messages": [ { "role": "user", "content": "The Meemiw (VecMapmultistep) model achieves the highest score for EN-FA. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Meemiw (VecMapmultistep) model achieves the highest score for EN-FA.\n" }, { "role": "assistant", "content": "The table shows that the highest score for EN-FA is 40.8, which is achieved by the Meemiw (VecMapmultistep) model. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2526.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Cross-lingual word similarity results in terms of Pearson (r) and Spearman (ρ) correlation. Languages codes: English-EN, Spanish-ES, Italian-IT, German-DE, and Farsi-FA." ], "reasoning": "The table shows that the highest score for EN-FA is 40.8, which is achieved by the Meemiw (VecMapmultistep) model.", "statement": "The Meemiw (VecMapmultistep) model achieves the highest score for EN-FA.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Monolingual word similarity results in terms of Pearson (r) and Spearman (ρ) correlation.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The multilingual model excels in English, Spanish, and Italian, outperforming the base VecMaportho model in other languages, except for German where the multi-step framework is most effective.In this task our multilingual model representing seven languages in a single space clearly stands out, obtaining the best overall results for English, Spanish and Italian, and improving over the base VecMaportho model on the rest. With the exception of German, where the multi-step framework of \\citeauthorartetxe2018generalizing \\citeyearartetxe2018generalizing proves most effective, the plain Meemi transformation improves over the base models, for both VecMap and MUSE." } ], "images": [ "scigen/train/2527.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Monolingual word similarity results in terms of Pearson (r) and Spearman (ρ) correlation." ], "reasoning": "The multilingual model excels in English, Spanish, and Italian, outperforming the base VecMaportho model in other languages, except for German where the multi-step framework is most effective.", "description": "In this task our multilingual model representing seven languages in a single space clearly stands out, obtaining the best overall results for English, Spanish and Italian, and improving over the base VecMaportho model on the rest. With the exception of German, where the multi-step framework of \\citeauthorartetxe2018generalizing \\citeyearartetxe2018generalizing proves most effective, the plain Meemi transformation improves over the base models, for both VecMap and MUSE.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: Monolingual word similarity results in terms of Pearson (r) and Spearman (ρ) correlation.', answer the following question. Give detailed solution consisting of each step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model achieves the highest score for English r and what is the score?\n" }, { "role": "assistant", "content": "To find the highest score for English r, we need to compare the scores of all models listed under the English r column. The highest score is 75.1 achieved by the Meemi-multi (VecMaportho) model. So, the answer is {\"answer\": \"75.1\"}." } ], "images": [ "scigen/train/2527.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Monolingual word similarity results in terms of Pearson (r) and Spearman (ρ) correlation." ], "reasoning": "To find the highest score for English r, we need to compare the scores of all models listed under the English r column. The highest score is 75.1 achieved by the Meemi-multi (VecMaportho) model.", "question": "Which model achieves the highest score for English r and what is the score?", "answer": "So, the answer is {\"answer\": \"75.1\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Meemi (MUSE) model achieves the highest score for German ρ.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the German ρ scores of all models. The highest score for German ρ is 71.2, achieved by the Meemi (VecMapmultistep) model, not the Meemi (MUSE) model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2527.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Monolingual word similarity results in terms of Pearson (r) and Spearman (ρ) correlation." ], "reasoning": "To verify this statement, we need to compare the German ρ scores of all models. The highest score for German ρ is 71.2, achieved by the Meemi (VecMapmultistep) model, not the Meemi (MUSE) model.", "statement": "The Meemi (MUSE) model achieves the highest score for German ρ.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: Accuracy on the XNLI task using different cross-lingual embeddings as features.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The unsupervised VecMap model and multilingual Meemi outperform the orthogonal bilingual mapping baseline for both Spanish and German, suggesting that a unified vector space is better than pairwise alignments.The main conclusion in light of these results is the remarkable performance of the unsupervised VecMap model and, most notably, multilingual Meemi for both Spanish and German, clearly outperforming the orthogonal bilingual mapping baseline. Our results are encouraging for two reasons. First, they suggest that, at least for this task, collapsing several languages into a unified vector space is better than performing pairwise alignments. And second, the inherent benefit of having one single model accounting for an arbitrary number of languages." } ], "images": [ "scigen/train/2528.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Accuracy on the XNLI task using different cross-lingual embeddings as features." ], "reasoning": "The unsupervised VecMap model and multilingual Meemi outperform the orthogonal bilingual mapping baseline for both Spanish and German, suggesting that a unified vector space is better than pairwise alignments.", "description": "The main conclusion in light of these results is the remarkable performance of the unsupervised VecMap model and, most notably, multilingual Meemi for both Spanish and German, clearly outperforming the orthogonal bilingual mapping baseline. Our results are encouraging for two reasons. First, they suggest that, at least for this task, collapsing several languages into a unified vector space is better than performing pairwise alignments. And second, the inherent benefit of having one single model accounting for an arbitrary number of languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Accuracy on the XNLI task using different cross-lingual embeddings as features.\nQuestion: Which model achieved the highest score for the EN-ES language pair?\nSolve the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By examining the table, the highest score for the EN-ES language pair is found in the Meemi-multi (VecMaportho) model with a score of 46.6. So, the answer is {\"answer\": \"Meemi-multi (VecMaportho)\"}." } ], "images": [ "scigen/train/2528.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Accuracy on the XNLI task using different cross-lingual embeddings as features." ], "reasoning": "By examining the table, the highest score for the EN-ES language pair is found in the Meemi-multi (VecMaportho) model with a score of 46.6.", "question": "Which model achieved the highest score for the EN-ES language pair?", "answer": "So, the answer is {\"answer\": \"Meemi-multi (VecMaportho)\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The VecMapmultistep model outperformed the VecMapuns model for the EN-DE language pair., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe VecMapmultistep model outperformed the VecMapuns model for the EN-DE language pair.\n" }, { "role": "assistant", "content": "The table shows that the VecMapuns model scored 44.4 for the EN-DE language pair, while the VecMapmultistep model scored 37.7, indicating that VecMapuns outperformed VecMapmultistep. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2528.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Accuracy on the XNLI task using different cross-lingual embeddings as features." ], "reasoning": "The table shows that the VecMapuns model scored 44.4 for the EN-DE language pair, while the VecMapmultistep model scored 37.7, indicating that VecMapuns outperformed VecMapmultistep.", "statement": "The VecMapmultistep model outperformed the VecMapuns model for the EN-DE language pair.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2. Entity Classification performance with and without augmented features.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Token level features can be obtained using off-the-shelf NLP tools, and while state-of-the-art methods like TACRED perform well, relations are often not mentioned in text. The UDBMS dataset has over 500k nodes and only 28k edges.The important thing to note is that these token level features can be obtained cheaply, using off-the-shelf NLP tools to deliver linguistic features such as POS tags, or using existing rule based systems to deliver task or domain specific type tags. The state of the art methods like TACRED perform reasonably well, relations are often not mentioned in text. The UDBMS dataset based on the person data in DBPedia has over 500k nodes and only 28k edges." } ], "images": [ "scigen/train/2529.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2. Entity Classification performance with and without augmented features." ], "reasoning": "Token level features can be obtained using off-the-shelf NLP tools, and while state-of-the-art methods like TACRED perform well, relations are often not mentioned in text. The UDBMS dataset has over 500k nodes and only 28k edges.", "description": "The important thing to note is that these token level features can be obtained cheaply, using off-the-shelf NLP tools to deliver linguistic features such as POS tags, or using existing rule based systems to deliver task or domain specific type tags. The state of the art methods like TACRED perform reasonably well, relations are often not mentioned in text. The UDBMS dataset based on the person data in DBPedia has over 500k nodes and only 28k edges.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2. Entity Classification performance with and without augmented features.', answer the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model has the highest Macro F1 score for the Elected Reps dataset?\n" }, { "role": "assistant", "content": "To find the model with the highest Macro F1 score for the Elected Reps dataset, look at the 'Macro F1' column for the Elected Reps dataset. The highest score is 0.989, achieved by the NFGEG+ model. So, the answer is {\"answer\": \"NFGEG+\"}." } ], "images": [ "scigen/train/2529.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2. Entity Classification performance with and without augmented features." ], "reasoning": "To find the model with the highest Macro F1 score for the Elected Reps dataset, look at the 'Macro F1' column for the Elected Reps dataset. The highest score is 0.989, achieved by the NFGEG+ model.", "question": "Which model has the highest Macro F1 score for the Elected Reps dataset?", "answer": "So, the answer is {\"answer\": \"NFGEG+\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe NFGEG model has a higher Micro F1 score for the OntoNotes dataset than the NFGEG+ model.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Micro F1 scores for the OntoNotes dataset. The NFGEG model has a Micro F1 score of 0.617, while the NFGEG+ model has a Micro F1 score of 0.672. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2529.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2. Entity Classification performance with and without augmented features." ], "reasoning": "To verify this statement, compare the Micro F1 scores for the OntoNotes dataset. The NFGEG model has a Micro F1 score of 0.617, while the NFGEG+ model has a Micro F1 score of 0.672. Therefore, the statement is incorrect.", "statement": "The NFGEG model has a higher Micro F1 score for the OntoNotes dataset than the NFGEG+ model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 6: Out of 110 pivot translation tasks, how many failed the language identification check?', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The Emb model failed 31 out of 110 translation directions, indicating that models using language embeddings may have a more 'fuzzy' representation and are more prone to copying.For all models except Emb only ru→kk→ru fi→lt→fi, and zh→gu→zh failed this test, but for the Emb model 31 of the 110 translation directions failed (see This result indicates that models which use language embeddings may have a more \"fuzzy\" representation of the output task, and are much more prone to copying than other approaches to multi-lingual MT." } ], "images": [ "scigen/train/2530.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Out of 110 pivot translation tasks, how many failed the language identification check?" ], "reasoning": "The Emb model failed 31 out of 110 translation directions, indicating that models using language embeddings may have a more 'fuzzy' representation and are more prone to copying.", "description": "For all models except Emb only ru→kk→ru fi→lt→fi, and zh→gu→zh failed this test, but for the Emb model 31 of the 110 translation directions failed (see This result indicates that models which use language embeddings may have a more \"fuzzy\" representation of the output task, and are much more prone to copying than other approaches to multi-lingual MT.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 6: Out of 110 pivot translation tasks, how many failed the language identification check?', unfold the question presented below, detailing every step of your calculation. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model had the highest number of failed pivot tasks?\n" }, { "role": "assistant", "content": "By examining the table, we see that the Emb model had 31 failed pivot tasks, which is the highest number compared to the other models. So, the answer is {\"answer\": \"Emb\"}" } ], "images": [ "scigen/train/2530.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Out of 110 pivot translation tasks, how many failed the language identification check?" ], "reasoning": "By examining the table, we see that the Emb model had 31 failed pivot tasks, which is the highest number compared to the other models.", "question": "Which model had the highest number of failed pivot tasks?", "answer": "So, the answer is {\"answer\": \"Emb\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Dec model failed more pivot tasks than the Prepend model. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Dec model failed more pivot tasks than the Prepend model.\n" }, { "role": "assistant", "content": "The table shows that the Dec model failed 1 pivot task, while the Prepend model failed 3 pivot tasks. Therefore, the statement is incorrect. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2530.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Out of 110 pivot translation tasks, how many failed the language identification check?" ], "reasoning": "The table shows that the Dec model failed 1 pivot task, while the Prepend model failed 3 pivot tasks. Therefore, the statement is incorrect.", "statement": "The Dec model failed more pivot tasks than the Prepend model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: XNLI dev accuracy for English, Russian, Chinese and Urdu (Δ for average) for BERT-BASE models trained either on Wikipedia or CommonCrawl. The additional data provided by our pipeline alleviates the lack of resources in most languages and enables representation learning for low-resource languages such as Urdu.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Training BERT models on CommonCrawl data improves performance across all evaluated languages compared to Wikipedia data, especially for low-resource languages like Urdu.We also train BERT models on the English (en), Russian (ru), Chinese (zh) and Urdu (ur) languages, using either the Wikipedia corpora or our new CommonCrawl datasets. For these languages, we use respectively 16G, 5G, 1.1G and 106M of raw Wikipedia data (full datasets), and we cap the head CommonCrawl data to 21G, 21G, 17G, 2.2G for English, Russian, Chinese and Urdu. That is, we consider roughly the same amount of data for English, but increase the amount of data for Russian, Chinese and Urdu. We train a BERT-BASE architecture [devlin2018bert] on each of these corpora, without next sentence prediction (NSP) as in [lample2019cross]. For better comparison, we early-stop all our models after two days of training on 16 Volta32 GPUs, and use the exact same number of steps for each model. We evaluate each model on the XNLI [conneau2018xnli] corpus by using the training data in each language. With the same amount of data for English, the BERT-BASE model trained on our corpus outperforms the one trained on the Wikipedia. For low-resource languages like Urdu (ur), the Wikipedia dataset being too small, the model pretrained on Wikipedia obtains similar performance than a randomly initialized model. Using our corpus instead, we obtain a 7 points improvement in accuracy, which demonstrates how our filtered corpus can enable language model pretraining for low-resource languages." } ], "images": [ "scigen/train/2531.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: XNLI dev accuracy for English, Russian, Chinese and Urdu (Δ for average) for BERT-BASE models trained either on Wikipedia or CommonCrawl. The additional data provided by our pipeline alleviates the lack of resources in most languages and enables representation learning for low-resource languages such as Urdu." ], "reasoning": "Training BERT models on CommonCrawl data improves performance across all evaluated languages compared to Wikipedia data, especially for low-resource languages like Urdu.", "description": "We also train BERT models on the English (en), Russian (ru), Chinese (zh) and Urdu (ur) languages, using either the Wikipedia corpora or our new CommonCrawl datasets. For these languages, we use respectively 16G, 5G, 1.1G and 106M of raw Wikipedia data (full datasets), and we cap the head CommonCrawl data to 21G, 21G, 17G, 2.2G for English, Russian, Chinese and Urdu. That is, we consider roughly the same amount of data for English, but increase the amount of data for Russian, Chinese and Urdu. We train a BERT-BASE architecture [devlin2018bert] on each of these corpora, without next sentence prediction (NSP) as in [lample2019cross]. For better comparison, we early-stop all our models after two days of training on 16 Volta32 GPUs, and use the exact same number of steps for each model. We evaluate each model on the XNLI [conneau2018xnli] corpus by using the training data in each language. With the same amount of data for English, the BERT-BASE model trained on our corpus outperforms the one trained on the Wikipedia. For low-resource languages like Urdu (ur), the Wikipedia dataset being too small, the model pretrained on Wikipedia obtains similar performance than a randomly initialized model. Using our corpus instead, we obtain a 7 points improvement in accuracy, which demonstrates how our filtered corpus can enable language model pretraining for low-resource languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: XNLI dev accuracy for English, Russian, Chinese and Urdu (Δ for average) for BERT-BASE models trained either on Wikipedia or CommonCrawl. The additional data provided by our pipeline alleviates the lack of resources in most languages and enables representation learning for low-resource languages such as Urdu.', solve this question and give detail solutions. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhat is the accuracy improvement for the BERT-BASE model trained on CommonCrawl data compared to Wikipedia data for the Urdu language?\n" }, { "role": "assistant", "content": "The table shows the accuracy for Urdu as 57.3 for Wikipedia and 64.3 for CommonCrawl. The improvement is calculated as 64.3 - 57.3. So, the answer is {\"answer\": \"7.0\"}." } ], "images": [ "scigen/train/2531.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: XNLI dev accuracy for English, Russian, Chinese and Urdu (Δ for average) for BERT-BASE models trained either on Wikipedia or CommonCrawl. The additional data provided by our pipeline alleviates the lack of resources in most languages and enables representation learning for low-resource languages such as Urdu." ], "reasoning": "The table shows the accuracy for Urdu as 57.3 for Wikipedia and 64.3 for CommonCrawl. The improvement is calculated as 64.3 - 57.3.", "question": "What is the accuracy improvement for the BERT-BASE model trained on CommonCrawl data compared to Wikipedia data for the Urdu language?", "answer": "So, the answer is {\"answer\": \"7.0\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe BERT-BASE model trained on CommonCrawl data outperforms the one trained on Wikipedia data for the Russian language.\n" }, { "role": "assistant", "content": "The table shows the accuracy for Russian as 73.3 for Wikipedia and 76.4 for CommonCrawl, indicating better performance for the CommonCrawl data. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2531.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: XNLI dev accuracy for English, Russian, Chinese and Urdu (Δ for average) for BERT-BASE models trained either on Wikipedia or CommonCrawl. The additional data provided by our pipeline alleviates the lack of resources in most languages and enables representation learning for low-resource languages such as Urdu." ], "reasoning": "The table shows the accuracy for Russian as 73.3 for Wikipedia and 76.4 for CommonCrawl, indicating better performance for the CommonCrawl data.", "statement": "The BERT-BASE model trained on CommonCrawl data outperforms the one trained on Wikipedia data for the Russian language.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Accuracy and precision at K (P@K in the table) analysis overall and per query type. Avg. |Cq| indicates the average number of candidates with one standard deviation.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The model performs best on 'member_of_political_party', 'record_label', and 'publisher' queries, but struggles with 'place_of_birth', 'place_of_death', and 'inception' queries.In this section we provide an error analysis for our best single model predictions. First of all, we look at which type of questions our model performs well or poorly. There are more than 150 query types in the validation set but we filtered the three with the best and with the worst accuracy that have at least 50 supporting documents and at least 5 candidates. We observe that questions regarding places (birth and death) are considered harder for Entity-GCN. We then inspect samples where our model fails while assigning highest likelihood and noticed two principal sources of failure i) a mismatch between what is written in Wikipedia and what is annotated in Wikidata, and ii) a different degree of granularity (e.g., born in “London” vs “UK” could be considered both correct by a human but not when measuring accuracy)." } ], "images": [ "scigen/train/2532.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Accuracy and precision at K (P@K in the table) analysis overall and per query type. Avg. |Cq| indicates the average number of candidates with one standard deviation." ], "reasoning": "The model performs best on 'member_of_political_party', 'record_label', and 'publisher' queries, but struggles with 'place_of_birth', 'place_of_death', and 'inception' queries.", "description": "In this section we provide an error analysis for our best single model predictions. First of all, we look at which type of questions our model performs well or poorly. There are more than 150 query types in the validation set but we filtered the three with the best and with the worst accuracy that have at least 50 supporting documents and at least 5 candidates. We observe that questions regarding places (birth and death) are considered harder for Entity-GCN. We then inspect samples where our model fails while assigning highest likelihood and noticed two principal sources of failure i) a mismatch between what is written in Wikipedia and what is annotated in Wikidata, and ii) a different degree of granularity (e.g., born in “London” vs “UK” could be considered both correct by a human but not when measuring accuracy).", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: Accuracy and precision at K (P@K in the table) analysis overall and per query type. Avg. |Cq| indicates the average number of candidates with one standard deviation.', solve this question and give detail solutions. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich query type has the highest accuracy and how many supporting documents does it have?\n" }, { "role": "assistant", "content": "The query type 'member_of_political_party' has the highest accuracy of 85.5% and it has 70 supporting documents. So, the answer is {\"answer\": \"70\"}" } ], "images": [ "scigen/train/2532.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Accuracy and precision at K (P@K in the table) analysis overall and per query type. Avg. |Cq| indicates the average number of candidates with one standard deviation." ], "reasoning": "The query type 'member_of_political_party' has the highest accuracy of 85.5% and it has 70 supporting documents.", "question": "Which query type has the highest accuracy and how many supporting documents does it have?", "answer": "So, the answer is {\"answer\": \"70\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The model performs better on 'place_of_birth' queries than 'place_of_death' queries. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model performs better on 'place_of_birth' queries than 'place_of_death' queries.\n" }, { "role": "assistant", "content": "The accuracy for 'place_of_birth' queries is 51.0%, while for 'place_of_death' queries it is 50.0%. Therefore, the model performs slightly better on 'place_of_birth' queries. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2532.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Accuracy and precision at K (P@K in the table) analysis overall and per query type. Avg. |Cq| indicates the average number of candidates with one standard deviation." ], "reasoning": "The accuracy for 'place_of_birth' queries is 51.0%, while for 'place_of_death' queries it is 50.0%. Therefore, the model performs slightly better on 'place_of_birth' queries.", "statement": "The model performs better on 'place_of_birth' queries than 'place_of_death' queries.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Ablation study on WikiHop validation set. The full model is our Entity-GCN with all of its components and other rows indicate models trained without a component of interest. We also report baselines using GloVe instead of ELMo with and without R-GCN. For the full model we report mean±1std over 5 runs.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The R-GCN component significantly improves model performance, as evidenced by the higher scores compared to models without it.We perform two groups of ablation, one on the embedding layer, to study the effect of ELMo, and one on the edges, to study how different relations affect the overall model performance. and even above the Coref-GRU of Dhingra et al. That is, the R-GCN component pushes the model to perform above Coref-GRU still without accessing context, but rather by updating mention representations based on their relation to other ones. These results highlight the impact of our R-GCN component." } ], "images": [ "scigen/train/2533.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation study on WikiHop validation set. The full model is our Entity-GCN with all of its components and other rows indicate models trained without a component of interest. We also report baselines using GloVe instead of ELMo with and without R-GCN. For the full model we report mean±1std over 5 runs." ], "reasoning": "The R-GCN component significantly improves model performance, as evidenced by the higher scores compared to models without it.", "description": "We perform two groups of ablation, one on the embedding layer, to study the effect of ELMo, and one on the edges, to study how different relations affect the overall model performance. and even above the Coref-GRU of Dhingra et al. That is, the R-GCN component pushes the model to perform above Coref-GRU still without accessing context, but rather by updating mention representations based on their relation to other ones. These results highlight the impact of our R-GCN component.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Ablation study on WikiHop validation set. The full model is our Entity-GCN with all of its components and other rows indicate models trained without a component of interest. We also report baselines using GloVe instead of ELMo with and without R-GCN. For the full model we report mean±1std over 5 runs.\nQuestion: What is the unmasked score for the model without R-GCN?\nSolve the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table lists the unmasked score for the 'No R-GCN' model as 62.4, which is lower than the full models. So, the answer is {\"answer\": \"62.4\"}." } ], "images": [ "scigen/train/2533.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation study on WikiHop validation set. The full model is our Entity-GCN with all of its components and other rows indicate models trained without a component of interest. We also report baselines using GloVe instead of ELMo with and without R-GCN. For the full model we report mean±1std over 5 runs." ], "reasoning": "The table lists the unmasked score for the 'No R-GCN' model as 62.4, which is lower than the full models.", "question": "What is the unmasked score for the model without R-GCN?", "answer": "So, the answer is {\"answer\": \"62.4\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe model with induced edges has a higher masked score than the model without R-GCN.\n" }, { "role": "assistant", "content": "The table shows that the model with induced edges has a masked score of 56.4, while the model without R-GCN has a masked score of 63.2, indicating that the statement is false. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2533.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation study on WikiHop validation set. The full model is our Entity-GCN with all of its components and other rows indicate models trained without a component of interest. We also report baselines using GloVe instead of ELMo with and without R-GCN. For the full model we report mean±1std over 5 runs." ], "reasoning": "The table shows that the model with induced edges has a masked score of 56.4, while the model without R-GCN has a masked score of 63.2, indicating that the statement is false.", "statement": "The model with induced edges has a higher masked score than the model without R-GCN.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Performance on various models using full-length ROUGE metrics'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Adding an encoder for the query and allowing it to influence the outputs of the decoder improves performance, as seen in the higher ROUGE scores for Queryenc and Queryatt compared to Vanilla e-a-d.Effect of Query: Comparing rows 1 and 2 we observe that adding an encoder for the query and allowing it to influence the outputs of the decoder indeed improves the performance. This is expected as the query contains some keywords which could help in sharpening the focus of the summary." } ], "images": [ "scigen/train/2534.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance on various models using full-length ROUGE metrics" ], "reasoning": "Adding an encoder for the query and allowing it to influence the outputs of the decoder improves performance, as seen in the higher ROUGE scores for Queryenc and Queryatt compared to Vanilla e-a-d.", "description": "Effect of Query: Comparing rows 1 and 2 we observe that adding an encoder for the query and allowing it to influence the outputs of the decoder indeed improves the performance. This is expected as the query contains some keywords which could help in sharpening the focus of the summary.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Performance on various models using full-length ROUGE metrics', solve the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model shows the highest improvement in ROUGE-2 score compared to Vanilla e-a-d?\n" }, { "role": "assistant", "content": "To find the model with the highest improvement in ROUGE-2 score, compare the ROUGE-2 scores of all models with that of Vanilla e-a-d. SD2 has the highest ROUGE-2 score of 18.75, which is significantly higher than Vanilla e-a-d's 2.06. So, the answer is {\"answer\": \"SD2\"}." } ], "images": [ "scigen/train/2534.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance on various models using full-length ROUGE metrics" ], "reasoning": "To find the model with the highest improvement in ROUGE-2 score, compare the ROUGE-2 scores of all models with that of Vanilla e-a-d. SD2 has the highest ROUGE-2 score of 18.75, which is significantly higher than Vanilla e-a-d's 2.06.", "question": "Which model shows the highest improvement in ROUGE-2 score compared to Vanilla e-a-d?", "answer": "So, the answer is {\"answer\": \"SD2\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The model M1 has a higher ROUGE-1 score than the model B1. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model M1 has a higher ROUGE-1 score than the model B1.\n" }, { "role": "assistant", "content": "By comparing the ROUGE-1 scores of M1 and B1, we see that M1 has a score of 33.06, which is higher than B1's score of 23.18. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2534.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance on various models using full-length ROUGE metrics" ], "reasoning": "By comparing the ROUGE-1 scores of M1 and B1, we see that M1 has a score of 33.06, which is higher than B1's score of 23.18.", "statement": "The model M1 has a higher ROUGE-1 score than the model B1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: The multi-domain DST evaluation on MultiWOZ and its single restaurant domain. TRADE has the highest joint accuracy, which surpasses current state-of-the-art GCE model.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The performance difference between SpanPtr and the proposed model is due to the limitation of index-based copying, which affects the accuracy of slot filling when synonyms are used.For comparison with the performance on single-domain, the results on the restaurant domain of MultiWOZ are reported as well. The performance difference between SpanPtr and our model mainly comes from the limitation of index-based copying. For examples, if the true label for the price range slot is cheap, the relevant user utterance describing the restaurant may actually be, for example, economical, inexpensive, or cheaply." } ], "images": [ "scigen/train/2535.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The multi-domain DST evaluation on MultiWOZ and its single restaurant domain. TRADE has the highest joint accuracy, which surpasses current state-of-the-art GCE model." ], "reasoning": "The performance difference between SpanPtr and the proposed model is due to the limitation of index-based copying, which affects the accuracy of slot filling when synonyms are used.", "description": "For comparison with the performance on single-domain, the results on the restaurant domain of MultiWOZ are reported as well. The performance difference between SpanPtr and our model mainly comes from the limitation of index-based copying. For examples, if the true label for the price range slot is cheap, the relevant user utterance describing the restaurant may actually be, for example, economical, inexpensive, or cheaply.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: 'Table 2: The multi-domain DST evaluation on MultiWOZ and its single restaurant domain. TRADE has the highest joint accuracy, which surpasses current state-of-the-art GCE model.'\nQuestion: What is the performance difference between SpanPtr and TRADE on the MultiWOZ (Only Restaurant) Joint task?\n" }, { "role": "assistant", "content": "To find the performance difference, subtract the MultiWOZ (Only Restaurant) Joint score of SpanPtr from that of TRADE: 65.35 - 49.12. So, the answer is {\"answer\": \"16.23\"}." } ], "images": [ "scigen/train/2535.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The multi-domain DST evaluation on MultiWOZ and its single restaurant domain. TRADE has the highest joint accuracy, which surpasses current state-of-the-art GCE model." ], "reasoning": "To find the performance difference, subtract the MultiWOZ (Only Restaurant) Joint score of SpanPtr from that of TRADE: 65.35 - 49.12.", "question": "What is the performance difference between SpanPtr and TRADE on the MultiWOZ (Only Restaurant) Joint task?", "answer": "So, the answer is {\"answer\": \"16.23\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim TRADE outperforms all other models in the MultiWOZ Joint task., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTRADE outperforms all other models in the MultiWOZ Joint task.\n" }, { "role": "assistant", "content": "The table shows that TRADE has the highest score (48.62) in the MultiWOZ Joint task compared to other models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2535.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The multi-domain DST evaluation on MultiWOZ and its single restaurant domain. TRADE has the highest joint accuracy, which surpasses current state-of-the-art GCE model." ], "reasoning": "The table shows that TRADE has the highest score (48.62) in the MultiWOZ Joint task compared to other models.", "statement": "TRADE outperforms all other models in the MultiWOZ Joint task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: We run domain expansion experiments by excluding one domain and fine-tuning on that domain. The first row is the base model trained on the four domains. The second row is the results on the four domains after fine-tuning on 1% new domain data using three different strategies. One can find out that GEM outperforms Naive and EWC fine-tuning in terms of catastrophic forgetting on the four domains. Then, we evaluate the results on new domain for two cases: training from scratch and fine-tuning from the base model. Results show that fine-tuning from the base model usually achieves better results on the new domain compared to training from scratch.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The TRADE model, pre-trained on four domains, shows that GEM fine-tuning outperforms naive and EWC strategies in overcoming catastrophic forgetting and performs better than training from scratch on a single domain.Domain Expanding In this setting, the TRADE model is pre-trained on four domains and a held-out domain is reserved for domain expansion to perform fine-tuning. After fine-tuning on the new domain, we evaluate the performance of TRADE on 1) the four pre-trained domains and 2) the new domain. We experiment with different fine-tuning strategies. In general, GEM outperforms naive and EWC fine-tuning in terms of overcoming catastrophic forgetting. We also find that pre-training followed by fine-tuning outperforms training from scratch on the single domain." } ], "images": [ "scigen/train/2536.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: We run domain expansion experiments by excluding one domain and fine-tuning on that domain. The first row is the base model trained on the four domains. The second row is the results on the four domains after fine-tuning on 1% new domain data using three different strategies. One can find out that GEM outperforms Naive and EWC fine-tuning in terms of catastrophic forgetting on the four domains. Then, we evaluate the results on new domain for two cases: training from scratch and fine-tuning from the base model. Results show that fine-tuning from the base model usually achieves better results on the new domain compared to training from scratch." ], "reasoning": "The TRADE model, pre-trained on four domains, shows that GEM fine-tuning outperforms naive and EWC strategies in overcoming catastrophic forgetting and performs better than training from scratch on a single domain.", "description": "Domain Expanding In this setting, the TRADE model is pre-trained on four domains and a held-out domain is reserved for domain expansion to perform fine-tuning. After fine-tuning on the new domain, we evaluate the performance of TRADE on 1) the four pre-trained domains and 2) the new domain. We experiment with different fine-tuning strategies. In general, GEM outperforms naive and EWC fine-tuning in terms of overcoming catastrophic forgetting. We also find that pre-training followed by fine-tuning outperforms training from scratch on the single domain.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: We run domain expansion experiments by excluding one domain and fine-tuning on that domain. The first row is the base model trained on the four domains. The second row is the results on the four domains after fine-tuning on 1% new domain data using three different strategies. One can find out that GEM outperforms Naive and EWC fine-tuning in terms of catastrophic forgetting on the four domains. Then, we evaluate the results on new domain for two cases: training from scratch and fine-tuning from the base model. Results show that fine-tuning from the base model usually achieves better results on the new domain compared to training from scratch.', solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich fine-tuning strategy on 1% new domain achieves the highest Joint Except Restaurant score on the new domain?\n" }, { "role": "assistant", "content": "To find the highest Joint Except Restaurant score on the new domain, we look at the 'Evaluation on New Domain' section and compare the 'Joint Except Restaurant' scores for different fine-tuning strategies. GEM achieves the highest score of 39.24. So, the answer is {\"answer\": \"GEM\"}." } ], "images": [ "scigen/train/2536.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: We run domain expansion experiments by excluding one domain and fine-tuning on that domain. The first row is the base model trained on the four domains. The second row is the results on the four domains after fine-tuning on 1% new domain data using three different strategies. One can find out that GEM outperforms Naive and EWC fine-tuning in terms of catastrophic forgetting on the four domains. Then, we evaluate the results on new domain for two cases: training from scratch and fine-tuning from the base model. Results show that fine-tuning from the base model usually achieves better results on the new domain compared to training from scratch." ], "reasoning": "To find the highest Joint Except Restaurant score on the new domain, we look at the 'Evaluation on New Domain' section and compare the 'Joint Except Restaurant' scores for different fine-tuning strategies. GEM achieves the highest score of 39.24.", "question": "Which fine-tuning strategy on 1% new domain achieves the highest Joint Except Restaurant score on the new domain?", "answer": "So, the answer is {\"answer\": \"GEM\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nFine-tuning BM on 1% new domain using GEM achieves a higher Slot Except Train score on the new domain compared to naive fine-tuning.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the 'Slot Except Train' scores on the new domain for GEM and naive fine-tuning strategies. GEM has a score of 54.31, while naive has a score of 59.83. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2536.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: We run domain expansion experiments by excluding one domain and fine-tuning on that domain. The first row is the base model trained on the four domains. The second row is the results on the four domains after fine-tuning on 1% new domain data using three different strategies. One can find out that GEM outperforms Naive and EWC fine-tuning in terms of catastrophic forgetting on the four domains. Then, we evaluate the results on new domain for two cases: training from scratch and fine-tuning from the base model. Results show that fine-tuning from the base model usually achieves better results on the new domain compared to training from scratch." ], "reasoning": "To verify this statement, we compare the 'Slot Except Train' scores on the new domain for GEM and naive fine-tuning strategies. GEM has a score of 54.31, while naive has a score of 59.83. Therefore, the statement is refuted.", "statement": "Fine-tuning BM on 1% new domain using GEM achieves a higher Slot Except Train score on the new domain compared to naive fine-tuning.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Zero-shot experiments on an unseen domain. In taxi domain, our model achieves 60.58% joint goal accuracy without training on any samples from taxi domain. Trained Single column is the results achieved by training on 100% single-domain data as a reference.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Zero-shot experiments achieve around 50 to 65% slot accuracy without in-domain samples, with the taxi domain performing well due to shared slot values with the training domain.S4SS3SSS0Px2 Zero-shot We run zero-shot experiments by excluding one domain from the training set. Although performances on the other zero-shot domains are not especially promising, they still achieve around 50 to 65% slot accuracy without using any in-domain samples. The reason why the zero-shot performance on the taxi domain is high is because all four slots share similar values with the corresponding slots in the train domain." } ], "images": [ "scigen/train/2537.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Zero-shot experiments on an unseen domain. In taxi domain, our model achieves 60.58% joint goal accuracy without training on any samples from taxi domain. Trained Single column is the results achieved by training on 100% single-domain data as a reference." ], "reasoning": "Zero-shot experiments achieve around 50 to 65% slot accuracy without in-domain samples, with the taxi domain performing well due to shared slot values with the training domain.", "description": "S4SS3SSS0Px2 Zero-shot We run zero-shot experiments by excluding one domain from the training set. Although performances on the other zero-shot domains are not especially promising, they still achieve around 50 to 65% slot accuracy without using any in-domain samples. The reason why the zero-shot performance on the taxi domain is high is because all four slots share similar values with the corresponding slots in the train domain.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: Table 4: Zero-shot experiments on an unseen domain. In taxi domain, our model achieves 60.58% joint goal accuracy without training on any samples from taxi domain. Trained Single column is the results achieved by training on 100% single-domain data as a reference.\nQuestion: Which domain has the highest zero-shot joint accuracy?\n" }, { "role": "assistant", "content": "By comparing the zero-shot joint accuracy values in the table, the taxi domain has the highest zero-shot joint accuracy at 60.58. So, the answer is {\"answer\": \"Taxi\"}." } ], "images": [ "scigen/train/2537.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Zero-shot experiments on an unseen domain. In taxi domain, our model achieves 60.58% joint goal accuracy without training on any samples from taxi domain. Trained Single column is the results achieved by training on 100% single-domain data as a reference." ], "reasoning": "By comparing the zero-shot joint accuracy values in the table, the taxi domain has the highest zero-shot joint accuracy at 60.58.", "question": "Which domain has the highest zero-shot joint accuracy?", "answer": "So, the answer is {\"answer\": \"Taxi\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe zero-shot slot accuracy for the hotel domain is higher than that for the restaurant domain.\n" }, { "role": "assistant", "content": "The table shows that the zero-shot slot accuracy for the hotel domain is 65.32, which is higher than the 53.43 for the restaurant domain. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2537.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Zero-shot experiments on an unseen domain. In taxi domain, our model achieves 60.58% joint goal accuracy without training on any samples from taxi domain. Trained Single column is the results achieved by training on 100% single-domain data as a reference." ], "reasoning": "The table shows that the zero-shot slot accuracy for the hotel domain is 65.32, which is higher than the 53.43 for the restaurant domain.", "statement": "The zero-shot slot accuracy for the hotel domain is higher than that for the restaurant domain.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Character Recognition accuracy on 100 random frames.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The table shows the precision, recall, and F1-score for character recognition before and after applying the spatio-temporal filter, with all metrics decreasing after the filter is applied.We evaluate the performance of the different video modules used in the observe branch. As datasets in video story question answering do not generally contain annotations on characters, places, objects relationships or actions, accuracy results are difficult to obtain. Even so, we report accuracy for the Character Recognition For the Character Recognition, we randomly annotate a subset of 100 video frames and measure accuracy as the number of correct predictions before and after applying the spatio-temporal filter. For the Place Classification, we report accuracy before and after the temporal filter on the KnowIT VQA test set according to the labels obtained from the scripts. Additionally, visual results with the predictions from the four modules (Character Recognition, Place Classification, Object Relation Detection, and Action Recognition) are in Fig. Finally, the list of characters and places can be found in Tables" } ], "images": [ "scigen/train/2538.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Character Recognition accuracy on 100 random frames." ], "reasoning": "The table shows the precision, recall, and F1-score for character recognition before and after applying the spatio-temporal filter, with all metrics decreasing after the filter is applied.", "description": "We evaluate the performance of the different video modules used in the observe branch. As datasets in video story question answering do not generally contain annotations on characters, places, objects relationships or actions, accuracy results are difficult to obtain. Even so, we report accuracy for the Character Recognition For the Character Recognition, we randomly annotate a subset of 100 video frames and measure accuracy as the number of correct predictions before and after applying the spatio-temporal filter. For the Place Classification, we report accuracy before and after the temporal filter on the KnowIT VQA test set according to the labels obtained from the scripts. Additionally, visual results with the predictions from the four modules (Character Recognition, Place Classification, Object Relation Detection, and Action Recognition) are in Fig. Finally, the list of characters and places can be found in Tables", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: 'Table 6: Character Recognition accuracy on 100 random frames.'\nQuestion: What is the difference in F1-score for character recognition before and after applying the spatio-temporal filter?\n" }, { "role": "assistant", "content": "The F1-score before applying the filter is 87.96, and after applying the filter, it is 74.86. The difference is calculated as 87.96 - 74.86. So, the answer is {\"answer\": \"13.10\"}" } ], "images": [ "scigen/train/2538.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Character Recognition accuracy on 100 random frames." ], "reasoning": "The F1-score before applying the filter is 87.96, and after applying the filter, it is 74.86. The difference is calculated as 87.96 - 74.86.", "question": "What is the difference in F1-score for character recognition before and after applying the spatio-temporal filter?", "answer": "So, the answer is {\"answer\": \"13.10\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe precision for character recognition increased after applying the spatio-temporal filter.\n" }, { "role": "assistant", "content": "The table shows that the precision before applying the filter is 86.11, and after applying the filter, it is 75.76. Since 75.76 is less than 86.11, the precision did not increase. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2538.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Character Recognition accuracy on 100 random frames." ], "reasoning": "The table shows that the precision before applying the filter is 86.11, and after applying the filter, it is 75.76. Since 75.76 is less than 86.11, the precision did not increase.", "statement": "The precision for character recognition increased after applying the spatio-temporal filter.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Evaluation on KnowIT VQA test set.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "ROLL outperforms previous methods in all question categories, with a 6.3% improvement in overall accuracy compared to the best performing ROCK method.Evaluation on KnowIT VQA We compare ROLL against the latest reported results on the KnowIT VQA dataset: TVQA and four different models in ROCK. ROCK reports results using four different visual representations: ResNet features, visual concepts, list of characters, and generated captions. Main findings are summarised as: Overall, ROLL outperforms previous methods in all the question categories by a large margin, with 6.3% improvement on the overall accuracy with respect to the best performing ROCK. When comparing the visual representations, our proposed video descriptions contain more semantic information than previous methods, improving visual-based questions by at least 6.4%. Specially, the boost in performance in visual-based questions with respect to standard captioning (ROCK Captions) or visual concepts (ROCK Concepts, TVQA) validates our unsupervised video descriptions as the best representation for this task. Additional evidence of the superior performance of our proposed unsupervised descriptions is shown when ROLL uses human annotations as external knowledge. Although the overall performance is lower because ROLL-human is not optimised to exploit this kind of information, on the visual-based questions our method improves best previous work by 5.4%. As the same source of knowledge is used, the superior performance can only be due to the contribution of our proposed visual representations. On the knowledge-based samples, our method based on plot summaries outperforms task-specific human annotations by 6.7%, even when less annotations are required. This implies that the proposed slicing mechanism in the recall branch successfully extracts the relevant information from the long documents provided as external knowledge. When compared against human performance, ROLL is 18% behind masters accuracy (humans that have watched the show) and it is closer to rookies non-knowledge accuracy (humans that have never watched the show). This shows how challenging this task is, with still plenty room for improvement." } ], "images": [ "scigen/train/2539.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Evaluation on KnowIT VQA test set." ], "reasoning": "ROLL outperforms previous methods in all question categories, with a 6.3% improvement in overall accuracy compared to the best performing ROCK method.", "description": "Evaluation on KnowIT VQA We compare ROLL against the latest reported results on the KnowIT VQA dataset: TVQA and four different models in ROCK. ROCK reports results using four different visual representations: ResNet features, visual concepts, list of characters, and generated captions. Main findings are summarised as: Overall, ROLL outperforms previous methods in all the question categories by a large margin, with 6.3% improvement on the overall accuracy with respect to the best performing ROCK. When comparing the visual representations, our proposed video descriptions contain more semantic information than previous methods, improving visual-based questions by at least 6.4%. Specially, the boost in performance in visual-based questions with respect to standard captioning (ROCK Captions) or visual concepts (ROCK Concepts, TVQA) validates our unsupervised video descriptions as the best representation for this task. Additional evidence of the superior performance of our proposed unsupervised descriptions is shown when ROLL uses human annotations as external knowledge. Although the overall performance is lower because ROLL-human is not optimised to exploit this kind of information, on the visual-based questions our method improves best previous work by 5.4%. As the same source of knowledge is used, the superior performance can only be due to the contribution of our proposed visual representations. On the knowledge-based samples, our method based on plot summaries outperforms task-specific human annotations by 6.7%, even when less annotations are required. This implies that the proposed slicing mechanism in the recall branch successfully extracts the relevant information from the long documents provided as external knowledge. When compared against human performance, ROLL is 18% behind masters accuracy (humans that have watched the show) and it is closer to rookies non-knowledge accuracy (humans that have never watched the show). This shows how challenging this task is, with still plenty room for improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: Evaluation on KnowIT VQA test set.'.\n\nQuestion: Which method has the highest accuracy in visual-based questions?\n" }, { "role": "assistant", "content": "By comparing the 'Accuracy Vis.' column, ROLL has the highest accuracy in visual-based questions with a value of 0.718. So, the answer is {\"answer\": \"ROLL\"}" } ], "images": [ "scigen/train/2539.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Evaluation on KnowIT VQA test set." ], "reasoning": "By comparing the 'Accuracy Vis.' column, ROLL has the highest accuracy in visual-based questions with a value of 0.718.", "question": "Which method has the highest accuracy in visual-based questions?", "answer": "So, the answer is {\"answer\": \"ROLL\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nROLL-human has the highest accuracy in text-based questions.\n" }, { "role": "assistant", "content": "By comparing the 'Accuracy Text.' column, ROLL-human has an accuracy of 0.754, which is the highest among all methods. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2539.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Evaluation on KnowIT VQA test set." ], "reasoning": "By comparing the 'Accuracy Text.' column, ROLL-human has an accuracy of 0.754, which is the highest among all methods.", "statement": "ROLL-human has the highest accuracy in text-based questions.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: ROLL ablation study.'.\n" }, { "role": "assistant", "content": "The full model combining the three branches performs the best overall, while the observe branch alone performs the worst.Ablation study We perform an ablation study to measure the contribution of each branch. When a single branch is used, the observe branch gets the worst overall accuracy and the recall branch performs the best. This is consistent with the types of questions in the dataset, with 22% being visual-based and 63% being knowledge-based. The read branch gets the best performance in the text-based questions (i.e., about the subtitles), and the recall branch gets the best accuracy in the knwoledge-based questions (i.e., about the storyline). When the observe branch is combined with other branches it consistently contributes to improve the results. Again, this result strongly suggests that the generated scene descriptions do contain meaningful information for the task. The full model combining the three branches performs the best. Fusion Methods Comparison We also study the performance of our proposed MW mechanism and compare it against several fusion methods. Given the three prediction scores from each of the branches, Average and Maximum compute the average and maximum score, respectively. The Self-att method implements a self-attention mechanism based on the Transformer outputs, and the QA-att mechanism attends each of the modality predictions based on the BERT representation of the question and candidate answers. The FC w/o MW predicts the answer scores by concatenating the scores of the three branches and feeding them into a linear layer, and FC w/ MW builds our proposed MW mechanism on top. The results show that most of the methods fail at properly fusing the information from the three branches, This is probably because the fusion of the different modalities incurs in information loss. Our MW mechanism, in contrast, successfully balances the contribution from the three branches. Fig. When the MW is not used (βω=1) the model obtains the worst performance. Likewise, when the loss contribution from the final prediction is too weak (βω<0.6), the model is not able to fuse the information correctly." } ], "images": [ "scigen/train/2540.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: ROLL ablation study." ], "reasoning": "The full model combining the three branches performs the best overall, while the observe branch alone performs the worst.", "description": "Ablation study We perform an ablation study to measure the contribution of each branch. When a single branch is used, the observe branch gets the worst overall accuracy and the recall branch performs the best. This is consistent with the types of questions in the dataset, with 22% being visual-based and 63% being knowledge-based. The read branch gets the best performance in the text-based questions (i.e., about the subtitles), and the recall branch gets the best accuracy in the knwoledge-based questions (i.e., about the storyline). When the observe branch is combined with other branches it consistently contributes to improve the results. Again, this result strongly suggests that the generated scene descriptions do contain meaningful information for the task. The full model combining the three branches performs the best. Fusion Methods Comparison We also study the performance of our proposed MW mechanism and compare it against several fusion methods. Given the three prediction scores from each of the branches, Average and Maximum compute the average and maximum score, respectively. The Self-att method implements a self-attention mechanism based on the Transformer outputs, and the QA-att mechanism attends each of the modality predictions based on the BERT representation of the question and candidate answers. The FC w/o MW predicts the answer scores by concatenating the scores of the three branches and feeding them into a linear layer, and FC w/ MW builds our proposed MW mechanism on top. The results show that most of the methods fail at properly fusing the information from the three branches, This is probably because the fusion of the different modalities incurs in information loss. Our MW mechanism, in contrast, successfully balances the contribution from the three branches. Fig. When the MW is not used (βω=1) the model obtains the worst performance. Likewise, when the loss contribution from the final prediction is too weak (βω<0.6), the model is not able to fuse the information correctly.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 4: ROLL ablation study.', unfold the steps to solve the question given next. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nQuestion: Which branch combination achieves the highest overall accuracy?\n" }, { "role": "assistant", "content": "The table shows the overall accuracy (All) for each branch and combination. The Full Model has the highest overall accuracy with a score of 0.715. So, the answer is {\"answer\": \"Full Model\"}" } ], "images": [ "scigen/train/2540.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: ROLL ablation study." ], "reasoning": "The table shows the overall accuracy (All) for each branch and combination. The Full Model has the highest overall accuracy with a score of 0.715.", "question": "Which branch combination achieves the highest overall accuracy?", "answer": "So, the answer is {\"answer\": \"Full Model\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The observe branch alone achieves the highest accuracy in knowledge-based questions., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe observe branch alone achieves the highest accuracy in knowledge-based questions.\n" }, { "role": "assistant", "content": "The table shows that the recall branch achieves the highest accuracy in knowledge-based questions with a score of 0.725, not the observe branch. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2540.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: ROLL ablation study." ], "reasoning": "The table shows that the recall branch achieves the highest accuracy in knowledge-based questions with a score of 0.725, not the observe branch.", "statement": "The observe branch alone achieves the highest accuracy in knowledge-based questions.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: ROLL ablation study.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The ablation study shows that the recall branch performs best overall, while the observe branch improves results when combined with others. The MW mechanism outperforms other fusion methods by effectively balancing contributions from all branches.Ablation study We perform an ablation study to measure the contribution of each branch. When a single branch is used, the observe branch gets the worst overall accuracy and the recall branch performs the best. This is consistent with the types of questions in the dataset, with 22% being visual-based and 63% being knowledge-based. The read branch gets the best performance in the text-based questions (i.e., about the subtitles), and the recall branch gets the best accuracy in the knwoledge-based questions (i.e., about the storyline). When the observe branch is combined with other branches it consistently contributes to improve the results. Again, this result strongly suggests that the generated scene descriptions do contain meaningful information for the task. The full model combining the three branches performs the best. Fusion Methods Comparison We also study the performance of our proposed MW mechanism and compare it against several fusion methods. Given the three prediction scores from each of the branches, Average and Maximum compute the average and maximum score, respectively. The Self-att method implements a self-attention mechanism based on the Transformer outputs, and the QA-att mechanism attends each of the modality predictions based on the BERT representation of the question and candidate answers. The FC w/o MW predicts the answer scores by concatenating the scores of the three branches and feeding them into a linear layer, and FC w/ MW builds our proposed MW mechanism on top. The results show that most of the methods fail at properly fusing the information from the three branches, This is probably because the fusion of the different modalities incurs in information loss. Our MW mechanism, in contrast, successfully balances the contribution from the three branches. Fig. When the MW is not used (βω=1) the model obtains the worst performance. Likewise, when the loss contribution from the final prediction is too weak (βω<0.6), the model is not able to fuse the information correctly." } ], "images": [ "scigen/train/2541.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: ROLL ablation study." ], "reasoning": "The ablation study shows that the recall branch performs best overall, while the observe branch improves results when combined with others. The MW mechanism outperforms other fusion methods by effectively balancing contributions from all branches.", "description": "Ablation study We perform an ablation study to measure the contribution of each branch. When a single branch is used, the observe branch gets the worst overall accuracy and the recall branch performs the best. This is consistent with the types of questions in the dataset, with 22% being visual-based and 63% being knowledge-based. The read branch gets the best performance in the text-based questions (i.e., about the subtitles), and the recall branch gets the best accuracy in the knwoledge-based questions (i.e., about the storyline). When the observe branch is combined with other branches it consistently contributes to improve the results. Again, this result strongly suggests that the generated scene descriptions do contain meaningful information for the task. The full model combining the three branches performs the best. Fusion Methods Comparison We also study the performance of our proposed MW mechanism and compare it against several fusion methods. Given the three prediction scores from each of the branches, Average and Maximum compute the average and maximum score, respectively. The Self-att method implements a self-attention mechanism based on the Transformer outputs, and the QA-att mechanism attends each of the modality predictions based on the BERT representation of the question and candidate answers. The FC w/o MW predicts the answer scores by concatenating the scores of the three branches and feeding them into a linear layer, and FC w/ MW builds our proposed MW mechanism on top. The results show that most of the methods fail at properly fusing the information from the three branches, This is probably because the fusion of the different modalities incurs in information loss. Our MW mechanism, in contrast, successfully balances the contribution from the three branches. Fig. When the MW is not used (βω=1) the model obtains the worst performance. Likewise, when the loss contribution from the final prediction is too weak (βω<0.6), the model is not able to fuse the information correctly.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 4: ROLL ablation study.', solve the subsequent question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhich method achieves the highest overall accuracy in knowledge-based questions?\n" }, { "role": "assistant", "content": "To find the method with the highest overall accuracy in knowledge-based questions, look at the 'Know.' column in the table. The highest value in this column is 0.713, which corresponds to the 'FC w/ MW' method. So, the answer is {\"answer\": \"FC w/ MW\"}" } ], "images": [ "scigen/train/2541.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: ROLL ablation study." ], "reasoning": "To find the method with the highest overall accuracy in knowledge-based questions, look at the 'Know.' column in the table. The highest value in this column is 0.713, which corresponds to the 'FC w/ MW' method.", "question": "Which method achieves the highest overall accuracy in knowledge-based questions?", "answer": "So, the answer is {\"answer\": \"FC w/ MW\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Self-att method achieves the highest accuracy in text-based questions.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'Text.' column in the table. The highest value in this column is 0.761, which corresponds to the 'Self-att' method. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2541.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: ROLL ablation study." ], "reasoning": "To verify this statement, look at the 'Text.' column in the table. The highest value in this column is 0.761, which corresponds to the 'Self-att' method.", "statement": "The Self-att method achieves the highest accuracy in text-based questions.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 6: Character Recognition accuracy on 100 random frames.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows the accuracy of Character Recognition before and after applying the spatio-temporal filter, with improvements noted in Prec@1.We evaluate the performance of the different video modules used in the observe branch. As datasets in video story question answering do not generally contain annotations on characters, places, objects relationships or actions, accuracy results are difficult to obtain. Even so, we report accuracy for the Character Recognition For the Character Recognition, we randomly annotate a subset of 100 video frames and measure accuracy as the number of correct predictions before and after applying the spatio-temporal filter. For the Place Classification, we report accuracy before and after the temporal filter on the KnowIT VQA test set according to the labels obtained from the scripts. Additionally, visual results with the predictions from the four modules (Character Recognition, Place Classification, Object Relation Detection, and Action Recognition) are in Fig. Finally, the list of characters and places can be found in Tables" } ], "images": [ "scigen/train/2542.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Character Recognition accuracy on 100 random frames." ], "reasoning": "The table shows the accuracy of Character Recognition before and after applying the spatio-temporal filter, with improvements noted in Prec@1.", "description": "We evaluate the performance of the different video modules used in the observe branch. As datasets in video story question answering do not generally contain annotations on characters, places, objects relationships or actions, accuracy results are difficult to obtain. Even so, we report accuracy for the Character Recognition For the Character Recognition, we randomly annotate a subset of 100 video frames and measure accuracy as the number of correct predictions before and after applying the spatio-temporal filter. For the Place Classification, we report accuracy before and after the temporal filter on the KnowIT VQA test set according to the labels obtained from the scripts. Additionally, visual results with the predictions from the four modules (Character Recognition, Place Classification, Object Relation Detection, and Action Recognition) are in Fig. Finally, the list of characters and places can be found in Tables", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 6: Character Recognition accuracy on 100 random frames.', unfold the steps to solve the question given next. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nQuestion: What is the improvement in Prec@1 accuracy for Character Recognition after applying the spatio-temporal filter?\n" }, { "role": "assistant", "content": "The table shows Prec@1 accuracy before the filter as 63.13 and after the filter as 66.43. The improvement is calculated as 66.43 - 63.13. So, the answer is {\"answer\": \"3.3\"}" } ], "images": [ "scigen/train/2542.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Character Recognition accuracy on 100 random frames." ], "reasoning": "The table shows Prec@1 accuracy before the filter as 63.13 and after the filter as 66.43. The improvement is calculated as 66.43 - 63.13.", "question": "What is the improvement in Prec@1 accuracy for Character Recognition after applying the spatio-temporal filter?", "answer": "So, the answer is {\"answer\": \"3.3\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Prec@5 accuracy for Character Recognition after applying the spatio-temporal filter is 84.87., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Prec@5 accuracy for Character Recognition after applying the spatio-temporal filter is 84.87.\n" }, { "role": "assistant", "content": "The table shows Prec@5 accuracy only before the filter, which is 84.87. There is no data for Prec@5 after the filter. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2542.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Character Recognition accuracy on 100 random frames." ], "reasoning": "The table shows Prec@5 accuracy only before the filter, which is 84.87. There is no data for Prec@5 after the filter.", "statement": "The Prec@5 accuracy for Character Recognition after applying the spatio-temporal filter is 84.87.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 6: Average speaker-independent cluster (clust.), speaker (spk.), and gender (gndr) purity for BayesSegMinDur on the three datasets.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "A speaker-independent system should have high cluster purity and low speaker and gender purities, with specific percentages indicating perfect balance or purity.Average cluster purity is as defined before. Average speaker purity is similarly defined, but instead of considering the mapped ground truth label of a segmented token, it considers the speaker who produced it: speaker purity is 100% if every cluster contains tokens from a single speaker, while it is 1/12=8.3% if all clusters are completely speaker balanced for the English sets and 1/24=4.2% for Xitsonga. Average gender purity is similarly defined: it is 100% if every cluster contains tokens from a single gender, while 1/2=50% indicates a perfectly gender-balanced cluster. Ideally, a speaker-independent system should have high cluster purity and low speaker and gender purities." } ], "images": [ "scigen/train/2543.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Average speaker-independent cluster (clust.), speaker (spk.), and gender (gndr) purity for BayesSegMinDur on the three datasets." ], "reasoning": "A speaker-independent system should have high cluster purity and low speaker and gender purities, with specific percentages indicating perfect balance or purity.", "description": "Average cluster purity is as defined before. Average speaker purity is similarly defined, but instead of considering the mapped ground truth label of a segmented token, it considers the speaker who produced it: speaker purity is 100% if every cluster contains tokens from a single speaker, while it is 1/12=8.3% if all clusters are completely speaker balanced for the English sets and 1/24=4.2% for Xitsonga. Average gender purity is similarly defined: it is 100% if every cluster contains tokens from a single gender, while 1/2=50% indicates a perfectly gender-balanced cluster. Ideally, a speaker-independent system should have high cluster purity and low speaker and gender purities.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 6: Average speaker-independent cluster (clust.), speaker (spk.), and gender (gndr) purity for BayesSegMinDur on the three datasets.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich embedding method has the highest cluster purity for the Xitsonga dataset?\n" }, { "role": "assistant", "content": "To find the highest cluster purity for the Xitsonga dataset, compare the values in the Xitsonga (%) Clust. column for both embedding methods. The highest value is 33.1% for cAE. So, the answer is {\"answer\": \"cAE\"}." } ], "images": [ "scigen/train/2543.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Average speaker-independent cluster (clust.), speaker (spk.), and gender (gndr) purity for BayesSegMinDur on the three datasets." ], "reasoning": "To find the highest cluster purity for the Xitsonga dataset, compare the values in the Xitsonga (%) Clust. column for both embedding methods. The highest value is 33.1% for cAE.", "question": "Which embedding method has the highest cluster purity for the Xitsonga dataset?", "answer": "So, the answer is {\"answer\": \"cAE\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The MFCC embedding method has a higher speaker purity than the cAE method for the English1 dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe MFCC embedding method has a higher speaker purity than the cAE method for the English1 dataset.\n" }, { "role": "assistant", "content": "Compare the English1 (%) Spk. values for MFCC and cAE. MFCC has 56.7% and cAE has 37.9%, so MFCC has a higher speaker purity. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2543.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Average speaker-independent cluster (clust.), speaker (spk.), and gender (gndr) purity for BayesSegMinDur on the three datasets." ], "reasoning": "Compare the English1 (%) Spk. values for MFCC and cAE. MFCC has 56.7% and cAE has 37.9%, so MFCC has a higher speaker purity.", "statement": "The MFCC embedding method has a higher speaker purity than the cAE method for the English1 dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled '(e) Subset-3, n≥650'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The BLEU scores show that Reference-HT and Reference-PE have higher scores compared to Reference-WMT, highlighting the importance of human evaluation.BLEU scores against single or multiple references. WMT is Reference-WMT, PE is Reference-PE, HT is Reference-HT. Scoring based on sacreBLEU v1.2.3, with signature BLEU+case.mixed+numrefs.1+smooth.exp+tok.13a+version.1.2.3 for refs=1. Signature changes to numrefs.2 and numrefs.3 for refs=2 and refs=3, respectively. Note how different scores for Reference-WMT and Reference-PE are compared to Reference-HT and This emphasizes the need for human evaluation." } ], "images": [ "scigen/train/2544.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(e) Subset-3, n≥650" ], "reasoning": "The BLEU scores show that Reference-HT and Reference-PE have higher scores compared to Reference-WMT, highlighting the importance of human evaluation.", "description": "BLEU scores against single or multiple references. WMT is Reference-WMT, PE is Reference-PE, HT is Reference-HT. Scoring based on sacreBLEU v1.2.3, with signature BLEU+case.mixed+numrefs.1+smooth.exp+tok.13a+version.1.2.3 for refs=1. Signature changes to numrefs.2 and numrefs.3 for refs=2 and refs=3, respectively. Note how different scores for Reference-WMT and Reference-PE are compared to Reference-HT and This emphasizes the need for human evaluation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title '(e) Subset-3, n≥650', unfold the steps to solve the question given next. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nQuestion: What is the difference in Ave % between the highest and lowest scoring systems?\n" }, { "role": "assistant", "content": "The highest Ave % is 67.4 for Reference-HT and the lowest Ave % is 54.4 for Online-B-1710. The difference is 67.4 - 54.4. So, the answer is {\"answer\": \"13.0\"}." } ], "images": [ "scigen/train/2544.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(e) Subset-3, n≥650" ], "reasoning": "The highest Ave % is 67.4 for Reference-HT and the lowest Ave % is 54.4 for Online-B-1710. The difference is 67.4 - 54.4.", "question": "What is the difference in Ave % between the highest and lowest scoring systems?", "answer": "So, the answer is {\"answer\": \"13.0\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Combo-6 has a higher Ave % than Combo-4. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nCombo-6 has a higher Ave % than Combo-4.\n" }, { "role": "assistant", "content": "The table shows that Combo-6 has an Ave % of 65.3, while Combo-4 has an Ave % of 64.9, indicating that Combo-6 has a higher Ave %. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2544.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(e) Subset-3, n≥650" ], "reasoning": "The table shows that Combo-6 has an Ave % of 65.3, while Combo-4 has an Ave % of 64.9, indicating that Combo-6 has a higher Ave %.", "statement": "Combo-6 has a higher Ave % than Combo-4.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: System combination results on the WMT 2017 Chinese-English test set'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The most helpful scoring features for system combination were SYSScore, LMScore, R2Lscore, R2LSV, and E2ZSV, despite the systems focusing on similar features.We experiment with system combination of n-best lists generated from various systems discussed above with 8 hypothesis from each system. We have experimented with many configurations and features for systems combination, we found out that the most helpful scoring features are: SYSScore, LMScore, R2Lscore, R2LSV and E2ZSV. This is quite surprising since the combined systems were focusing on modeling similar features. This may be due to the fact that the models are learning complimentary features, so they have extra capacity for complementing each other." } ], "images": [ "scigen/train/2545.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: System combination results on the WMT 2017 Chinese-English test set" ], "reasoning": "The most helpful scoring features for system combination were SYSScore, LMScore, R2Lscore, R2LSV, and E2ZSV, despite the systems focusing on similar features.", "description": "We experiment with system combination of n-best lists generated from various systems discussed above with 8 hypothesis from each system. We have experimented with many configurations and features for systems combination, we found out that the most helpful scoring features are: SYSScore, LMScore, R2Lscore, R2LSV and E2ZSV. This is quite surprising since the combined systems were focusing on modeling similar features. This may be due to the fact that the models are learning complimentary features, so they have extra capacity for complementing each other.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 3: System combination results on the WMT 2017 Chinese-English test set\n\nQuestion: Which system combination achieved the highest BLEU score?\n" }, { "role": "assistant", "content": "By examining the BLEU scores for each system combination, Combo-4 achieved the highest BLEU score of 28.46. So, the answer is {\"answer\": \"Combo-4\"}." } ], "images": [ "scigen/train/2545.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: System combination results on the WMT 2017 Chinese-English test set" ], "reasoning": "By examining the BLEU scores for each system combination, Combo-4 achieved the highest BLEU score of 28.46.", "question": "Which system combination achieved the highest BLEU score?", "answer": "So, the answer is {\"answer\": \"Combo-4\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Combo-2 achieved a BLEU score higher than 28.00. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nCombo-2 achieved a BLEU score higher than 28.00.\n" }, { "role": "assistant", "content": "The table shows that Combo-2 achieved a BLEU score of 27.92, which is not higher than 28.00. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2545.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: System combination results on the WMT 2017 Chinese-English test set" ], "reasoning": "The table shows that Combo-2 achieved a BLEU score of 27.92, which is not higher than 28.00.", "statement": "Combo-2 achieved a BLEU score higher than 28.00.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title '(a) Subset-1, n≥609', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The BLEU scores show significant differences between Reference-WMT, Reference-PE, and Reference-HT, highlighting the importance of human evaluation.BLEU scores against single or multiple references. WMT is Reference-WMT, PE is Reference-PE, HT is Reference-HT. Scoring based on sacreBLEU v1.2.3, with signature BLEU+case.mixed+numrefs.1+smooth.exp+tok.13a+version.1.2.3 for refs=1. Signature changes to numrefs.2 and numrefs.3 for refs=2 and refs=3, respectively. Note how different scores for Reference-WMT and Reference-PE are compared to Reference-HT and This emphasizes the need for human evaluation." } ], "images": [ "scigen/train/2546.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) Subset-1, n≥609" ], "reasoning": "The BLEU scores show significant differences between Reference-WMT, Reference-PE, and Reference-HT, highlighting the importance of human evaluation.", "description": "BLEU scores against single or multiple references. WMT is Reference-WMT, PE is Reference-PE, HT is Reference-HT. Scoring based on sacreBLEU v1.2.3, with signature BLEU+case.mixed+numrefs.1+smooth.exp+tok.13a+version.1.2.3 for refs=1. Signature changes to numrefs.2 and numrefs.3 for refs=2 and refs=3, respectively. Note how different scores for Reference-WMT and Reference-PE are compared to Reference-HT and This emphasizes the need for human evaluation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about '(a) Subset-1, n≥609'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. \n\nQuestion: Which system has the highest BLEU score and what is its Ave %?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By examining the table, the system with the highest BLEU score is Combo-6, which has an Ave % of 69.9. So, the answer is {\"answer\": \"Combo-6 with 69.9\"}." } ], "images": [ "scigen/train/2546.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) Subset-1, n≥609" ], "reasoning": "By examining the table, the system with the highest BLEU score is Combo-6, which has an Ave % of 69.9.", "question": "Which system has the highest BLEU score and what is its Ave %?", "answer": "So, the answer is {\"answer\": \"Combo-6 with 69.9\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nReference-HT has a higher BLEU score than Reference-PE.\n" }, { "role": "assistant", "content": "According to the table, Reference-HT has an Ave % of 68.6, while Reference-PE has an Ave % of 67.6, indicating that Reference-HT has a higher BLEU score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2546.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) Subset-1, n≥609" ], "reasoning": "According to the table, Reference-HT has an Ave % of 68.6, while Reference-PE has an Ave % of 67.6, indicating that Reference-HT has a higher BLEU score.", "statement": "Reference-HT has a higher BLEU score than Reference-PE.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title '(b) Subset-1, second iteration'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The table shows BLEU scores for different systems, highlighting the differences between Reference-WMT, Reference-PE, and Reference-HT, emphasizing the need for human evaluation.BLEU scores against single or multiple references. WMT is Reference-WMT, PE is Reference-PE, HT is Reference-HT. Scoring based on sacreBLEU v1.2.3, with signature BLEU+case.mixed+numrefs.1+smooth.exp+tok.13a+version.1.2.3 for refs=1. Signature changes to numrefs.2 and numrefs.3 for refs=2 and refs=3, respectively. Note how different scores for Reference-WMT and Reference-PE are compared to Reference-HT and This emphasizes the need for human evaluation." } ], "images": [ "scigen/train/2547.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) Subset-1, second iteration" ], "reasoning": "The table shows BLEU scores for different systems, highlighting the differences between Reference-WMT, Reference-PE, and Reference-HT, emphasizing the need for human evaluation.", "description": "BLEU scores against single or multiple references. WMT is Reference-WMT, PE is Reference-PE, HT is Reference-HT. Scoring based on sacreBLEU v1.2.3, with signature BLEU+case.mixed+numrefs.1+smooth.exp+tok.13a+version.1.2.3 for refs=1. Signature changes to numrefs.2 and numrefs.3 for refs=2 and refs=3, respectively. Note how different scores for Reference-WMT and Reference-PE are compared to Reference-HT and This emphasizes the need for human evaluation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of '(b) Subset-1, second iteration'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich system has the highest BLEU score and what is the score?\n" }, { "role": "assistant", "content": "By examining the 'Ave %' column, the highest BLEU score is 68.6, which is achieved by both Reference-HT and Combo-6. So, the answer is {\"answer\": \"Reference-HT and Combo-6 with 68.6\"}." } ], "images": [ "scigen/train/2547.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) Subset-1, second iteration" ], "reasoning": "By examining the 'Ave %' column, the highest BLEU score is 68.6, which is achieved by both Reference-HT and Combo-6.", "question": "Which system has the highest BLEU score and what is the score?", "answer": "So, the answer is {\"answer\": \"Reference-HT and Combo-6 with 68.6\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Reference-WMT has a higher BLEU score than Sogou. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nReference-WMT has a higher BLEU score than Sogou.\n" }, { "role": "assistant", "content": "The table shows that Reference-WMT has a BLEU score of 62.1, while Sogou has a BLEU score of 61.9, indicating that Reference-WMT has a higher score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2547.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) Subset-1, second iteration" ], "reasoning": "The table shows that Reference-WMT has a BLEU score of 62.1, while Sogou has a BLEU score of 61.9, indicating that Reference-WMT has a higher score.", "statement": "Reference-WMT has a higher BLEU score than Sogou.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled '(c) Subset-1, third iteration'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table shows BLEU scores for different systems, highlighting the differences between Reference-WMT, Reference-PE, and Reference-HT, emphasizing the need for human evaluation.BLEU scores against single or multiple references. WMT is Reference-WMT, PE is Reference-PE, HT is Reference-HT. Scoring based on sacreBLEU v1.2.3, with signature BLEU+case.mixed+numrefs.1+smooth.exp+tok.13a+version.1.2.3 for refs=1. Signature changes to numrefs.2 and numrefs.3 for refs=2 and refs=3, respectively. Note how different scores for Reference-WMT and Reference-PE are compared to Reference-HT and This emphasizes the need for human evaluation." } ], "images": [ "scigen/train/2548.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(c) Subset-1, third iteration" ], "reasoning": "The table shows BLEU scores for different systems, highlighting the differences between Reference-WMT, Reference-PE, and Reference-HT, emphasizing the need for human evaluation.", "description": "BLEU scores against single or multiple references. WMT is Reference-WMT, PE is Reference-PE, HT is Reference-HT. Scoring based on sacreBLEU v1.2.3, with signature BLEU+case.mixed+numrefs.1+smooth.exp+tok.13a+version.1.2.3 for refs=1. Signature changes to numrefs.2 and numrefs.3 for refs=2 and refs=3, respectively. Note how different scores for Reference-WMT and Reference-PE are compared to Reference-HT and This emphasizes the need for human evaluation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: (c) Subset-1, third iteration\nQuestion: Which system has the highest BLEU score and what is its score?\nGive a detailed response to the above question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the system with the highest BLEU score, look at the 'Ave %' column and identify the highest value. The highest value is 68.5, which corresponds to the 'Reference-HT' system. So, the answer is {\"answer\": \"Reference-HT with 68.5\"}." } ], "images": [ "scigen/train/2548.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(c) Subset-1, third iteration" ], "reasoning": "To find the system with the highest BLEU score, look at the 'Ave %' column and identify the highest value. The highest value is 68.5, which corresponds to the 'Reference-HT' system.", "question": "Which system has the highest BLEU score and what is its score?", "answer": "So, the answer is {\"answer\": \"Reference-HT with 68.5\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Reference-WMT has a higher BLEU score than Reference-PE., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nReference-WMT has a higher BLEU score than Reference-PE.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Ave %' scores of Reference-WMT and Reference-PE. Reference-WMT has a score of 62.0, while Reference-PE has a score of 66.8. Since 62.0 is less than 66.8, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2548.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(c) Subset-1, third iteration" ], "reasoning": "To verify this statement, compare the 'Ave %' scores of Reference-WMT and Reference-PE. Reference-WMT has a score of 62.0, while Reference-PE has a score of 66.8. Since 62.0 is less than 66.8, the statement is refuted.", "statement": "Reference-WMT has a higher BLEU score than Reference-PE.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title '(d) Subset-2, n≥607', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The BLEU scores show significant differences between Reference-WMT, Reference-PE, and Reference-HT, highlighting the importance of human evaluation.BLEU scores against single or multiple references. WMT is Reference-WMT, PE is Reference-PE, HT is Reference-HT. Scoring based on sacreBLEU v1.2.3, with signature BLEU+case.mixed+numrefs.1+smooth.exp+tok.13a+version.1.2.3 for refs=1. Signature changes to numrefs.2 and numrefs.3 for refs=2 and refs=3, respectively. Note how different scores for Reference-WMT and Reference-PE are compared to Reference-HT and This emphasizes the need for human evaluation." } ], "images": [ "scigen/train/2549.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(d) Subset-2, n≥607" ], "reasoning": "The BLEU scores show significant differences between Reference-WMT, Reference-PE, and Reference-HT, highlighting the importance of human evaluation.", "description": "BLEU scores against single or multiple references. WMT is Reference-WMT, PE is Reference-PE, HT is Reference-HT. Scoring based on sacreBLEU v1.2.3, with signature BLEU+case.mixed+numrefs.1+smooth.exp+tok.13a+version.1.2.3 for refs=1. Signature changes to numrefs.2 and numrefs.3 for refs=2 and refs=3, respectively. Note how different scores for Reference-WMT and Reference-PE are compared to Reference-HT and This emphasizes the need for human evaluation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of '(d) Subset-2, n≥607', answer the question below by showing each progressive step toward the answer. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the average percentage score for the system with the highest Ave z score?\n" }, { "role": "assistant", "content": "To find the system with the highest Ave z score, look for the highest value in the Ave z column. The highest Ave z score is 0.353, which corresponds to the system Online-B-1710. The average percentage score for this system is 55.7. So, the answer is {\"answer\": \"55.7\"}." } ], "images": [ "scigen/train/2549.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(d) Subset-2, n≥607" ], "reasoning": "To find the system with the highest Ave z score, look for the highest value in the Ave z column. The highest Ave z score is 0.353, which corresponds to the system Online-B-1710. The average percentage score for this system is 55.7.", "question": "What is the average percentage score for the system with the highest Ave z score?", "answer": "So, the answer is {\"answer\": \"55.7\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe system Combo-5 has a higher average percentage score than Reference-PE.\n" }, { "role": "assistant", "content": "According to the table, Combo-5 has an average percentage score of 68.2, while Reference-PE has an average percentage score of 64.8. Therefore, Combo-5 has a higher average percentage score than Reference-PE. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2549.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(d) Subset-2, n≥607" ], "reasoning": "According to the table, Combo-5 has an average percentage score of 68.2, while Reference-PE has an average percentage score of 64.8. Therefore, Combo-5 has a higher average percentage score than Reference-PE.", "statement": "The system Combo-5 has a higher average percentage score than Reference-PE.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is '(f) Subset-4, n≥649'.\n" }, { "role": "assistant", "content": "The BLEU scores show that Reference-HT has the highest score, indicating the need for human evaluation compared to Reference-WMT and Reference-PE.BLEU scores against single or multiple references. WMT is Reference-WMT, PE is Reference-PE, HT is Reference-HT. Scoring based on sacreBLEU v1.2.3, with signature BLEU+case.mixed+numrefs.1+smooth.exp+tok.13a+version.1.2.3 for refs=1. Signature changes to numrefs.2 and numrefs.3 for refs=2 and refs=3, respectively. Note how different scores for Reference-WMT and Reference-PE are compared to Reference-HT and This emphasizes the need for human evaluation." } ], "images": [ "scigen/train/2550.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(f) Subset-4, n≥649" ], "reasoning": "The BLEU scores show that Reference-HT has the highest score, indicating the need for human evaluation compared to Reference-WMT and Reference-PE.", "description": "BLEU scores against single or multiple references. WMT is Reference-WMT, PE is Reference-PE, HT is Reference-HT. Scoring based on sacreBLEU v1.2.3, with signature BLEU+case.mixed+numrefs.1+smooth.exp+tok.13a+version.1.2.3 for refs=1. Signature changes to numrefs.2 and numrefs.3 for refs=2 and refs=3, respectively. Note how different scores for Reference-WMT and Reference-PE are compared to Reference-HT and This emphasizes the need for human evaluation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: (f) Subset-4, n≥649\nQuestion: What is the BLEU score for the system with the highest Ave z score?\nSolve the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the BLEU score for the system with the highest Ave z score, identify the system with the highest Ave z score from the table, which is Reference-HT with an Ave z score of 0.254. The corresponding BLEU score (Ave %) is 66.6. So, the answer is {\"answer\": \"66.6\"}." } ], "images": [ "scigen/train/2550.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(f) Subset-4, n≥649" ], "reasoning": "To find the BLEU score for the system with the highest Ave z score, identify the system with the highest Ave z score from the table, which is Reference-HT with an Ave z score of 0.254. The corresponding BLEU score (Ave %) is 66.6.", "question": "What is the BLEU score for the system with the highest Ave z score?", "answer": "So, the answer is {\"answer\": \"66.6\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nCombo-6 has a higher BLEU score than Reference-PE.\n" }, { "role": "assistant", "content": "To verify this statement, compare the BLEU scores (Ave %) of Combo-6 and Reference-PE from the table. Combo-6 has a BLEU score of 65.2, while Reference-PE has a BLEU score of 63.4. Therefore, Combo-6 has a higher BLEU score than Reference-PE. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2550.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(f) Subset-4, n≥649" ], "reasoning": "To verify this statement, compare the BLEU scores (Ave %) of Combo-6 and Reference-PE from the table. Combo-6 has a BLEU score of 65.2, while Reference-PE has a BLEU score of 63.4. Therefore, Combo-6 has a higher BLEU score than Reference-PE.", "statement": "Combo-6 has a higher BLEU score than Reference-PE.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title '(g) Meta-1, n≥1,827'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The table shows BLEU scores for different systems, highlighting the differences between Reference-WMT, Reference-PE, and Reference-HT, emphasizing the need for human evaluation.BLEU scores against single or multiple references. WMT is Reference-WMT, PE is Reference-PE, HT is Reference-HT. Scoring based on sacreBLEU v1.2.3, with signature BLEU+case.mixed+numrefs.1+smooth.exp+tok.13a+version.1.2.3 for refs=1. Signature changes to numrefs.2 and numrefs.3 for refs=2 and refs=3, respectively. Note how different scores for Reference-WMT and Reference-PE are compared to Reference-HT and This emphasizes the need for human evaluation." } ], "images": [ "scigen/train/2551.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(g) Meta-1, n≥1,827" ], "reasoning": "The table shows BLEU scores for different systems, highlighting the differences between Reference-WMT, Reference-PE, and Reference-HT, emphasizing the need for human evaluation.", "description": "BLEU scores against single or multiple references. WMT is Reference-WMT, PE is Reference-PE, HT is Reference-HT. Scoring based on sacreBLEU v1.2.3, with signature BLEU+case.mixed+numrefs.1+smooth.exp+tok.13a+version.1.2.3 for refs=1. Signature changes to numrefs.2 and numrefs.3 for refs=2 and refs=3, respectively. Note how different scores for Reference-WMT and Reference-PE are compared to Reference-HT and This emphasizes the need for human evaluation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: (g) Meta-1, n≥1,827\nQuestion: Which system has the highest BLEU score and what is the score?\n" }, { "role": "assistant", "content": "By examining the 'Ave %' column, the system with the highest BLEU score is Combo-6 with a score of 69.0. So, the answer is {\"answer\": \"Combo-6 with 69.0\"}." } ], "images": [ "scigen/train/2551.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(g) Meta-1, n≥1,827" ], "reasoning": "By examining the 'Ave %' column, the system with the highest BLEU score is Combo-6 with a score of 69.0.", "question": "Which system has the highest BLEU score and what is the score?", "answer": "So, the answer is {\"answer\": \"Combo-6 with 69.0\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nReference-PE has a higher BLEU score than Reference-WMT.\n" }, { "role": "assistant", "content": "Reference-PE has an 'Ave %' score of 67.3, while Reference-WMT has an 'Ave %' score of 62.1, indicating that Reference-PE has a higher BLEU score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2551.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(g) Meta-1, n≥1,827" ], "reasoning": "Reference-PE has an 'Ave %' score of 67.3, while Reference-WMT has an 'Ave %' score of 62.1, indicating that Reference-PE has a higher BLEU score.", "statement": "Reference-PE has a higher BLEU score than Reference-WMT.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Error distribution, as fraction of sentences that contain specific error categories.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The four major error types in machine translation are Missing Words, Incorrect Words, Ungrammatical, and Named Entity, each accounting for roughly 5% of errors, indicating room for improvement through various approaches.The four major error types are Missing words, Incorrect Words, Ungrammatical, and Named Entity. Each accounts for roughly 5% of errors. This indicates that there is still room to improve machine translation quality via various approaches, such as modeling Missing Words [tu2016modeling, AttentionFertility], integration of high quality data for named-entity translation, as well as domain and topic adaptation for the issues of incorrect words and ungrammaticality." } ], "images": [ "scigen/train/2552.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Error distribution, as fraction of sentences that contain specific error categories." ], "reasoning": "The four major error types in machine translation are Missing Words, Incorrect Words, Ungrammatical, and Named Entity, each accounting for roughly 5% of errors, indicating room for improvement through various approaches.", "description": "The four major error types are Missing words, Incorrect Words, Ungrammatical, and Named Entity. Each accounts for roughly 5% of errors. This indicates that there is still room to improve machine translation quality via various approaches, such as modeling Missing Words [tu2016modeling, AttentionFertility], integration of high quality data for named-entity translation, as well as domain and topic adaptation for the issues of incorrect words and ungrammaticality.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 7: Error distribution, as fraction of sentences that contain specific error categories.\nQuestion: Which error type has the highest fraction percentage and what is its value?\nGive a detailed response to the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, the error type with the highest fraction percentage is Incorrect Words with a value of 7.64%. So, the answer is {\"answer\": \"Incorrect Words, 7.64%\"}." } ], "images": [ "scigen/train/2552.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Error distribution, as fraction of sentences that contain specific error categories." ], "reasoning": "By examining the table, the error type with the highest fraction percentage is Incorrect Words with a value of 7.64%.", "question": "Which error type has the highest fraction percentage and what is its value?", "answer": "So, the answer is {\"answer\": \"Incorrect Words, 7.64%\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Named Entity errors account for more than 5% of the total errors., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nNamed Entity errors account for more than 5% of the total errors.\n" }, { "role": "assistant", "content": "The table shows that Named Entity errors account for 4.38% of the total errors, which is less than 5%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2552.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Error distribution, as fraction of sentences that contain specific error categories." ], "reasoning": "The table shows that Named Entity errors account for 4.38% of the total errors, which is less than 5%.", "statement": "Named Entity errors account for more than 5% of the total errors.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Reconstruction BLEU scores of MAD and MACD in different languages for the WMT unsupervised tasks. Lower BLEU means more diverse.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table compares the reconstruction BLEU scores for MAD and MACD methods across WMT En-Fr, En-De, and En-Ro tasks, showing that MAD generally achieves higher scores than MACD.Having argued that cross-translation creates extra information for the supervised MT model to leverage on, we hypothesize that such extra information can be measurable by the diversity of the generated data. To measure this, we compute the reconstruction BLEU and compare the scores for MAD and MACD (both 2nd level, n=2) in the WMT En-Fr, En-De and En-Ro tasks. The scores are obtained by using the first agent to translate monolingual data in language s to t and then the second agent to translate those translations back to language s. After that, a BLEU score is measured by comparing the reconstructed text with the original text. In MAD, the first and second agents are identical, while they are distinct for MACD." } ], "images": [ "scigen/train/2553.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Reconstruction BLEU scores of MAD and MACD in different languages for the WMT unsupervised tasks. Lower BLEU means more diverse." ], "reasoning": "The table compares the reconstruction BLEU scores for MAD and MACD methods across WMT En-Fr, En-De, and En-Ro tasks, showing that MAD generally achieves higher scores than MACD.", "description": "Having argued that cross-translation creates extra information for the supervised MT model to leverage on, we hypothesize that such extra information can be measurable by the diversity of the generated data. To measure this, we compute the reconstruction BLEU and compare the scores for MAD and MACD (both 2nd level, n=2) in the WMT En-Fr, En-De and En-Ro tasks. The scores are obtained by using the first agent to translate monolingual data in language s to t and then the second agent to translate those translations back to language s. After that, a BLEU score is measured by comparing the reconstructed text with the original text. In MAD, the first and second agents are identical, while they are distinct for MACD.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 4: Reconstruction BLEU scores of MAD and MACD in different languages for the WMT unsupervised tasks. Lower BLEU means more diverse.' and methodically tackle the question that follows. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method achieves a higher reconstruction BLEU score for the En-Fr task?\n" }, { "role": "assistant", "content": "The table shows the reconstruction BLEU scores for both MAD and MACD methods. For the En-Fr task, MAD has a score of 76.5, while MACD has a score of 63.1. So, the answer is {\"answer\": \"MAD\"}." } ], "images": [ "scigen/train/2553.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Reconstruction BLEU scores of MAD and MACD in different languages for the WMT unsupervised tasks. Lower BLEU means more diverse." ], "reasoning": "The table shows the reconstruction BLEU scores for both MAD and MACD methods. For the En-Fr task, MAD has a score of 76.5, while MACD has a score of 63.1.", "question": "Which method achieves a higher reconstruction BLEU score for the En-Fr task?", "answer": "So, the answer is {\"answer\": \"MAD\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: MACD achieves a higher reconstruction BLEU score than MAD for the En-De task., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nMACD achieves a higher reconstruction BLEU score than MAD for the En-De task.\n" }, { "role": "assistant", "content": "The table shows that for the En-De task, MAD has a reconstruction BLEU score of 75.1, while MACD has a score of 60.7. Therefore, MAD achieves a higher score than MACD. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2553.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Reconstruction BLEU scores of MAD and MACD in different languages for the WMT unsupervised tasks. Lower BLEU means more diverse." ], "reasoning": "The table shows that for the En-De task, MAD has a reconstruction BLEU score of 75.1, while MACD has a score of 60.7. Therefore, MAD achieves a higher score than MACD.", "statement": "MACD achieves a higher reconstruction BLEU score than MAD for the En-De task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: BLEU scores on the unsupervised IWSLT’13 English-French (En-Fr) and IWSLT’14 English-German (En-De) tasks with varying level k and diversification multiplier n.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Increasing the MACD level to n=3 improves performance by 1 BLEU, but further increases do not yield additional benefits.As shown, 2nd level MACD with n=2 improves the performance in all four tasks by 2-3 BLEU compared to the baseline. Furthermore, increasing the multiplier to n=3 adds another 1 BLEU to the results. However, the experiments show that increasing it to n=4 does not improve the translation quality further. Furthermore, applying the 3rd level MACD seems to reduce the performance slightly, perhaps because the long daisy chain translation process induces more noises and deteriorates the sample quality. Thus, scaling up MACD’s synthetic parallel data may not always be beneficial, rather there is a saturation point that can be found through hyper-parameter tuning." } ], "images": [ "scigen/train/2554.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: BLEU scores on the unsupervised IWSLT’13 English-French (En-Fr) and IWSLT’14 English-German (En-De) tasks with varying level k and diversification multiplier n." ], "reasoning": "Increasing the MACD level to n=3 improves performance by 1 BLEU, but further increases do not yield additional benefits.", "description": "As shown, 2nd level MACD with n=2 improves the performance in all four tasks by 2-3 BLEU compared to the baseline. Furthermore, increasing the multiplier to n=3 adds another 1 BLEU to the results. However, the experiments show that increasing it to n=4 does not improve the translation quality further. Furthermore, applying the 3rd level MACD seems to reduce the performance slightly, perhaps because the long daisy chain translation process induces more noises and deteriorates the sample quality. Thus, scaling up MACD’s synthetic parallel data may not always be beneficial, rather there is a saturation point that can be found through hyper-parameter tuning.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: BLEU scores on the unsupervised IWSLT’13 English-French (En-Fr) and IWSLT’14 English-German (En-De) tasks with varying level k and diversification multiplier n.', and tackle the question below in a sequential manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the BLEU score for En-Fr when using MACD with k=2 and n=3?\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for different translation tasks using various MACD configurations. For En-Fr with MACD (k=2, n=3), the BLEU score is 32.8. So, the answer is {\"answer\": \"32.8\"}." } ], "images": [ "scigen/train/2554.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: BLEU scores on the unsupervised IWSLT’13 English-French (En-Fr) and IWSLT’14 English-German (En-De) tasks with varying level k and diversification multiplier n." ], "reasoning": "The table shows the BLEU scores for different translation tasks using various MACD configurations. For En-Fr with MACD (k=2, n=3), the BLEU score is 32.8.", "question": "What is the BLEU score for En-Fr when using MACD with k=2 and n=3?", "answer": "So, the answer is {\"answer\": \"32.8\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nIncreasing MACD to n=4 improves the translation quality for En-De.\n" }, { "role": "assistant", "content": "The table shows that increasing MACD to n=4 does not improve the BLEU score for En-De compared to n=3. Both have a BLEU score of 19.1. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2554.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: BLEU scores on the unsupervised IWSLT’13 English-French (En-Fr) and IWSLT’14 English-German (En-De) tasks with varying level k and diversification multiplier n." ], "reasoning": "The table shows that increasing MACD to n=4 does not improve the BLEU score for En-De compared to n=3. Both have a BLEU score of 19.1.", "statement": "Increasing MACD to n=4 improves the translation quality for En-De.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: BLEU comparison of MACD vs. no cross-translation variants in the WMT’14 English-French (En-Fr), WMT’16 English-German (En-De) and English-Romanian (En-Ro) tasks.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The MACD (2nd level, n=2) variant outperforms other methods by using cross-translation to generate new information, improving overall MT performance.In particular, the MAD (1st level, n=1) variant fails to improve as the final supervised model is trained on the same synthetic data that the UMT agent is already trained on. The variant MAD (1st level, n=2) is in fact inspired from nguyen2019data, which improves supervised and semi-supervised MT. However, it fails to impress in the unsupervised setup, possibly due to the lack of supervised agents. The variant MAD (2nd level, n=2) also fails because the 2nd level synthetic data is already optimized during back-translation training of the UMT agents, leaving the supervised model with no extra information to exploit. On the other hand, cross-translation enables MACD to translate the second-level data by an agent other than the first-level agent. In this strategy, the second agent produces targets that the first agent is not aware of, while the second agent receives as input the sources that are foreign to it. This process creates corrupted but new information, which the final supervised MT model can take advantage of to improve the overall MT performance." } ], "images": [ "scigen/train/2555.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: BLEU comparison of MACD vs. no cross-translation variants in the WMT’14 English-French (En-Fr), WMT’16 English-German (En-De) and English-Romanian (En-Ro) tasks." ], "reasoning": "The MACD (2nd level, n=2) variant outperforms other methods by using cross-translation to generate new information, improving overall MT performance.", "description": "In particular, the MAD (1st level, n=1) variant fails to improve as the final supervised model is trained on the same synthetic data that the UMT agent is already trained on. The variant MAD (1st level, n=2) is in fact inspired from nguyen2019data, which improves supervised and semi-supervised MT. However, it fails to impress in the unsupervised setup, possibly due to the lack of supervised agents. The variant MAD (2nd level, n=2) also fails because the 2nd level synthetic data is already optimized during back-translation training of the UMT agents, leaving the supervised model with no extra information to exploit. On the other hand, cross-translation enables MACD to translate the second-level data by an agent other than the first-level agent. In this strategy, the second agent produces targets that the first agent is not aware of, while the second agent receives as input the sources that are foreign to it. This process creates corrupted but new information, which the final supervised MT model can take advantage of to improve the overall MT performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 3: BLEU comparison of MACD vs. no cross-translation variants in the WMT’14 English-French (En-Fr), WMT’16 English-German (En-De) and English-Romanian (En-Ro) tasks.', and tackle the question below in a sequential manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method achieves the highest score for En-Ro translation?\n" }, { "role": "assistant", "content": "By comparing the En-Ro scores across all methods, MACD (2nd level, n=2) has the highest score of 18.3. So, the answer is {\"answer\": \"MACD (2nd level, n=2)\"}." } ], "images": [ "scigen/train/2555.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: BLEU comparison of MACD vs. no cross-translation variants in the WMT’14 English-French (En-Fr), WMT’16 English-German (En-De) and English-Romanian (En-Ro) tasks." ], "reasoning": "By comparing the En-Ro scores across all methods, MACD (2nd level, n=2) has the highest score of 18.3.", "question": "Which method achieves the highest score for En-Ro translation?", "answer": "So, the answer is {\"answer\": \"MACD (2nd level, n=2)\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis MAD (1st level, n=2) achieves a higher score than the baseline for En-De translation. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nMAD (1st level, n=2) achieves a higher score than the baseline for En-De translation.\n" }, { "role": "assistant", "content": "The table shows that MAD (1st level, n=2) has a score of 17.9 for En-De, which is higher than the baseline score of 14.5. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2555.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: BLEU comparison of MACD vs. no cross-translation variants in the WMT’14 English-French (En-Fr), WMT’16 English-German (En-De) and English-Romanian (En-Ro) tasks." ], "reasoning": "The table shows that MAD (1st level, n=2) has a score of 17.9 for En-De, which is higher than the baseline score of 14.5.", "statement": "MAD (1st level, n=2) achieves a higher score than the baseline for En-De translation.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 6: BLEU comparison of MACD vs. an ensemble of UMT agents and ensemble knowledge distillation (ensemble_distill_freitag2017) on WMT’14 En-Fr, WMT’16 En-De and En-Ro translation tasks.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "MACD outperforms both a simple ensemble of UMT models and ensemble knowledge distillation in unsupervised tasks, while requiring less memory and computation during inference.Since MACD utilizes outputs from multiple UMT agents to improve the baseline system, it is interesting to see how does it perform compared to a simple ensemble of UMT models and ensemble knowledge distillation (ensemble_distill_freitag2017). To perform ensembling, we average the probabilities of 2 UMT agents at each decoding step. For ensemble distillation, we generate synthetic parallel data from an ensemble of UMT agents, which is then used to train the supervised model. The results show that MACD is capable of leveraging the potentials of multiple UMT agents better than how an ensemble of agents does. This is in contrast to data diversification (nguyen2019data), which is shown to mimic and perform similarly to model ensembling. More importantly, during inference, an ensemble of models requires more memory and computations to store and execute multiple models while MACD needs only one model. Meanwhile, ensemble knowledge distillation (ensemble_distill_freitag2017), which performs well with supervised agents, performs poorly in unsupervised tasks, perhaps because the UMT agents are not capable enough for the method intended for supervised learning. Further inspection in the Appendix suggests that many samples in the ensemble translations contain incomprehensible repetitions." } ], "images": [ "scigen/train/2556.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: BLEU comparison of MACD vs. an ensemble of UMT agents and ensemble knowledge distillation (ensemble_distill_freitag2017) on WMT’14 En-Fr, WMT’16 En-De and En-Ro translation tasks." ], "reasoning": "MACD outperforms both a simple ensemble of UMT models and ensemble knowledge distillation in unsupervised tasks, while requiring less memory and computation during inference.", "description": "Since MACD utilizes outputs from multiple UMT agents to improve the baseline system, it is interesting to see how does it perform compared to a simple ensemble of UMT models and ensemble knowledge distillation (ensemble_distill_freitag2017). To perform ensembling, we average the probabilities of 2 UMT agents at each decoding step. For ensemble distillation, we generate synthetic parallel data from an ensemble of UMT agents, which is then used to train the supervised model. The results show that MACD is capable of leveraging the potentials of multiple UMT agents better than how an ensemble of agents does. This is in contrast to data diversification (nguyen2019data), which is shown to mimic and perform similarly to model ensembling. More importantly, during inference, an ensemble of models requires more memory and computations to store and execute multiple models while MACD needs only one model. Meanwhile, ensemble knowledge distillation (ensemble_distill_freitag2017), which performs well with supervised agents, performs poorly in unsupervised tasks, perhaps because the UMT agents are not capable enough for the method intended for supervised learning. Further inspection in the Appendix suggests that many samples in the ensemble translations contain incomprehensible repetitions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: 'Table 6: BLEU comparison of MACD vs. an ensemble of UMT agents and ensemble knowledge distillation (ensemble_distill_freitag2017) on WMT’14 En-Fr, WMT’16 En-De and En-Ro translation tasks.'\nQuestion: Which method achieves the highest score for the En-Fr translation task?\n" }, { "role": "assistant", "content": "By comparing the En-Fr scores in the table, MACD with NMT (2nd level, n=2) achieves the highest score of 26.5. So, the answer is {\"answer\": \"MACD with NMT (2nd level, n=2)\"}." } ], "images": [ "scigen/train/2556.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: BLEU comparison of MACD vs. an ensemble of UMT agents and ensemble knowledge distillation (ensemble_distill_freitag2017) on WMT’14 En-Fr, WMT’16 En-De and En-Ro translation tasks." ], "reasoning": "By comparing the En-Fr scores in the table, MACD with NMT (2nd level, n=2) achieves the highest score of 26.5.", "question": "Which method achieves the highest score for the En-Fr translation task?", "answer": "So, the answer is {\"answer\": \"MACD with NMT (2nd level, n=2)\"}." }, { "messages": [ { "role": "user", "content": "Ensemble knowledge distillation performs better than the baseline NMT for the En-De translation task. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nEnsemble knowledge distillation performs better than the baseline NMT for the En-De translation task.\n" }, { "role": "assistant", "content": "The table shows that the baseline NMT has a score of 14.5 for En-De, while ensemble knowledge distillation has a score of 3.5, which is lower. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2556.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: BLEU comparison of MACD vs. an ensemble of UMT agents and ensemble knowledge distillation (ensemble_distill_freitag2017) on WMT’14 En-Fr, WMT’16 En-De and En-Ro translation tasks." ], "reasoning": "The table shows that the baseline NMT has a score of 14.5 for En-De, while ensemble knowledge distillation has a score of 3.5, which is lower.", "statement": "Ensemble knowledge distillation performs better than the baseline NMT for the En-De translation task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Multi-Domain Joint Training Results'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "MALA models, particularly MALA-S3, outperform other models in both Entity-F1 and BLEU scores on MultiWOZ and SMD datasets, benefiting from stage-I and stage-II joint learning.MALA improves dialogue task completion (measured by Entity-F1) while maintaining a high quality of language generation (measured by BLEU). For example, MALA-S3 (76.8) outperforms LaRL (71.3) by 7.71% under Entity-F1 on MultiWOZ, and has the highest BLEU score. Meanwhile, we also find that MALA benefits much from stage-I and stage-II in the joint learning setting. For example, MALA-S1 and MALA-S2 achieve 9.25% and 10.43% improvements over LIDM under Entity-F1 on SMD. This is largely because that, having complete dialogue state annotations, MALA can learn semantic latent actions in each domain at stage-I, and the action alignment at stage-II reduce action space for learning dialogue policy more effectively by finding shared actions across domains. We further find that LIDM and LaRL perform worse than Sequicity on SMD. The reason is that system utterances on SMD have shorter length and various expressions, making it challenging to capture underlying intentions merely based on surface realization. MALA overcomes this challenge by considering dialogue state transitions beyond surface realization in semantic latent action learning." } ], "images": [ "scigen/train/2557.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Multi-Domain Joint Training Results" ], "reasoning": "MALA models, particularly MALA-S3, outperform other models in both Entity-F1 and BLEU scores on MultiWOZ and SMD datasets, benefiting from stage-I and stage-II joint learning.", "description": "MALA improves dialogue task completion (measured by Entity-F1) while maintaining a high quality of language generation (measured by BLEU). For example, MALA-S3 (76.8) outperforms LaRL (71.3) by 7.71% under Entity-F1 on MultiWOZ, and has the highest BLEU score. Meanwhile, we also find that MALA benefits much from stage-I and stage-II in the joint learning setting. For example, MALA-S1 and MALA-S2 achieve 9.25% and 10.43% improvements over LIDM under Entity-F1 on SMD. This is largely because that, having complete dialogue state annotations, MALA can learn semantic latent actions in each domain at stage-I, and the action alignment at stage-II reduce action space for learning dialogue policy more effectively by finding shared actions across domains. We further find that LIDM and LaRL perform worse than Sequicity on SMD. The reason is that system utterances on SMD have shorter length and various expressions, making it challenging to capture underlying intentions merely based on surface realization. MALA overcomes this challenge by considering dialogue state transitions beyond surface realization in semantic latent action learning.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Multi-Domain Joint Training Results', solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest SMD Entity-F1 score and what is its value?\n" }, { "role": "assistant", "content": "By examining the SMD Entity-F1 column, MALA-S3 has the highest score of 85.2. So, the answer is {\"answer\": \"MALA-S3 with 85.2\"}" } ], "images": [ "scigen/train/2557.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Multi-Domain Joint Training Results" ], "reasoning": "By examining the SMD Entity-F1 column, MALA-S3 has the highest score of 85.2.", "question": "Which model has the highest SMD Entity-F1 score and what is its value?", "answer": "So, the answer is {\"answer\": \"MALA-S3 with 85.2\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nMALA-S3 has the highest BLEU score on MultiWOZ.\n" }, { "role": "assistant", "content": "The table shows that MALA-S3 has a BLEU score of 20.1 on MultiWOZ, which is the highest among the listed models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2557.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Multi-Domain Joint Training Results" ], "reasoning": "The table shows that MALA-S3 has a BLEU score of 20.1 on MultiWOZ, which is the highest among the listed models.", "statement": "MALA-S3 has the highest BLEU score on MultiWOZ.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Ablation studies for the proposed architecture.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The experiments with new tag schemes and removing transition features show that capturing domain constraints in entity changes over time is crucial for the model's performance.Discrete Structural Constraints We experiment with two new tag schemes: (i) tag1:OA=OB, and (ii) tag2:OA =E=OB. Additionally, we experiment with removing the transition features from our CRF layer, though we still use structural constraints. Taken together, these results show that carefully capturing the domain constraints in how entities change over time is an important factor in our model." } ], "images": [ "scigen/train/2558.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation studies for the proposed architecture." ], "reasoning": "The experiments with new tag schemes and removing transition features show that capturing domain constraints in entity changes over time is crucial for the model's performance.", "description": "Discrete Structural Constraints We experiment with two new tag schemes: (i) tag1:OA=OB, and (ii) tag2:OA =E=OB. Additionally, we experiment with removing the transition features from our CRF layer, though we still use structural constraints. Taken together, these results show that carefully capturing the domain constraints in how entities change over time is an important factor in our model.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: Ablation studies for the proposed architecture.'. Solve the following question based on the table.\n\nQuestion: Which model has the highest score in the C-1 column?\nLet's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest score in the C-1 column, compare the values in the C-1 column for all models. The highest score is 72.27, which corresponds to the NCET model. So, the answer is {\"answer\": \"NCET\"}." } ], "images": [ "scigen/train/2558.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation studies for the proposed architecture." ], "reasoning": "To find the highest score in the C-1 column, compare the values in the C-1 column for all models. The highest score is 72.27, which corresponds to the NCET model.", "question": "Which model has the highest score in the C-1 column?", "answer": "So, the answer is {\"answer\": \"NCET\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The model with Tag Set 2 has a higher Mac. score than the model with Tag Set 1. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe model with Tag Set 2 has a higher Mac. score than the model with Tag Set 1.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Mac. scores of Tag Set 2 and Tag Set 1. Tag Set 2 has a Mac. score of 51.18, while Tag Set 1 has a Mac. score of 51.61. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2558.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation studies for the proposed architecture." ], "reasoning": "To verify this statement, compare the Mac. scores of Tag Set 2 and Tag Set 1. Tag Set 2 has a Mac. score of 51.18, while Tag Set 1 has a Mac. score of 51.61. Therefore, the statement is refuted.", "statement": "The model with Tag Set 2 has a higher Mac. score than the model with Tag Set 1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Results on the sentence-level (Task-1) and document-level (Task-2) evaluation task of the ProPara dataset on the test set. Our proposed CRF-based model achieves state of the art results on both the tasks compared to the previous work in Das et al. (2019). Incorporating ELMo further improves the performance for the state tracking module, as we see from the gains in Cat-1 and Cat-2.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The performance gains in Cat-1 and Cat-2 are attributed to the structural constraints imposed by the CRF layer, with further improvements using frozen ELMo embedding.The performance gain is attributed to the gains in Cat-1 and Cat-2 (7.69% and 4.57% absolute), owing to the structural constraints imposed by the CRF layer. The gain in Cat-3 is relatively lower as it is the only sub-task involving location tracking. Additionally, using the frozen ELMo embedding the performance further improves with major improvements in Cat-1 and Cat-2. What are the inputs to the process? (ii) What are the outputs of the process? (iii) What conversions occur, when and where? (iv) What movements occur, when and where? We achieve state of the art results with a F1 of 58.6." } ], "images": [ "scigen/train/2559.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on the sentence-level (Task-1) and document-level (Task-2) evaluation task of the ProPara dataset on the test set. Our proposed CRF-based model achieves state of the art results on both the tasks compared to the previous work in Das et al. (2019). Incorporating ELMo further improves the performance for the state tracking module, as we see from the gains in Cat-1 and Cat-2." ], "reasoning": "The performance gains in Cat-1 and Cat-2 are attributed to the structural constraints imposed by the CRF layer, with further improvements using frozen ELMo embedding.", "description": "The performance gain is attributed to the gains in Cat-1 and Cat-2 (7.69% and 4.57% absolute), owing to the structural constraints imposed by the CRF layer. The gain in Cat-3 is relatively lower as it is the only sub-task involving location tracking. Additionally, using the frozen ELMo embedding the performance further improves with major improvements in Cat-1 and Cat-2. What are the inputs to the process? (ii) What are the outputs of the process? (iii) What conversions occur, when and where? (iv) What movements occur, when and where? We achieve state of the art results with a F1 of 58.6.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2: Results on the sentence-level (Task-1) and document-level (Task-2) evaluation task of the ProPara dataset on the test set. Our proposed CRF-based model achieves state of the art results on both the tasks compared to the previous work in Das et al. (2019). Incorporating ELMo further improves the performance for the state tracking module, as we see from the gains in Cat-1 and Cat-2.', unfold the steps to solve the question given next. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nQuestion: What is the Task-2 Recall for the model 'This work: NCET + ELMo'?\n" }, { "role": "assistant", "content": "To find the Task-2 Recall for the model 'This work: NCET + ELMo', locate the corresponding row and column in the table. Thus, the answer is {\"answer\": \"58.5\"}." } ], "images": [ "scigen/train/2559.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on the sentence-level (Task-1) and document-level (Task-2) evaluation task of the ProPara dataset on the test set. Our proposed CRF-based model achieves state of the art results on both the tasks compared to the previous work in Das et al. (2019). Incorporating ELMo further improves the performance for the state tracking module, as we see from the gains in Cat-1 and Cat-2." ], "reasoning": "To find the Task-2 Recall for the model 'This work: NCET + ELMo', locate the corresponding row and column in the table.", "question": "What is the Task-2 Recall for the model 'This work: NCET + ELMo'?", "answer": "Thus, the answer is {\"answer\": \"58.5\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model 'ProGlobal Dalvi et al. (2018)' has the highest Task-2 Precision.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Task-2 Precision values for all models in the table. The highest value is 67.1, which belongs to 'This work: NCET + ELMo'. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2559.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on the sentence-level (Task-1) and document-level (Task-2) evaluation task of the ProPara dataset on the test set. Our proposed CRF-based model achieves state of the art results on both the tasks compared to the previous work in Das et al. (2019). Incorporating ELMo further improves the performance for the state tracking module, as we see from the gains in Cat-1 and Cat-2." ], "reasoning": "To verify this statement, compare the Task-2 Precision values for all models in the table. The highest value is 67.1, which belongs to 'This work: NCET + ELMo'.", "statement": "The model 'ProGlobal Dalvi et al. (2018)' has the highest Task-2 Precision.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: 50 Most Frequently Tweeted Hashtags: A table of the most frequently tweeted hashtags (#) from all collected breast cancer tweets (left) and from sampled breast cancer patients (right). The relative computed average happiness havg for each tag is colored relative to the group average (blue- negative, orange - positive). This version is sorted by computed word happiness.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Many patients and survivors expressed negative views on the proposed legislation to repeal the Affordable Care Act, with concerns about insurance premiums, costs, and the pre-existing condition mandate.Numerous patients and survivors in our sample had identified their condition in reference to the American healthcare regulation debate. Many sampled views of the proposed legislation were very negative, since repealing the Affordable Care Act without replacement could leave many uninsured. Other tweets mentioned worries regarding insurance premiums and costs for patients and survivors’ continued screening. In particular the pre-existing condition mandate was a chief concern of patients/survivors future coverage. Hashtags (#) are terms that categorize topics within posts. Each entry contains the tweet frequency, number of distinct profiles, and the relative happiness score (havg) for comparisons. Political terms were prevalent in both distributions describing the Affordable Care Act (#aca, #obamacare, #saveaca, #pretectourcare) and the newly introduced American Healthcare Act (#ahca, #trumpcare). A visual representation of these hashtags are displayed using a word-cloud in the Appendix (Figure A4)." } ], "images": [ "scigen/train/2560.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: 50 Most Frequently Tweeted Hashtags: A table of the most frequently tweeted hashtags (#) from all collected breast cancer tweets (left) and from sampled breast cancer patients (right). The relative computed average happiness havg for each tag is colored relative to the group average (blue- negative, orange - positive). This version is sorted by computed word happiness." ], "reasoning": "Many patients and survivors expressed negative views on the proposed legislation to repeal the Affordable Care Act, with concerns about insurance premiums, costs, and the pre-existing condition mandate.", "description": "Numerous patients and survivors in our sample had identified their condition in reference to the American healthcare regulation debate. Many sampled views of the proposed legislation were very negative, since repealing the Affordable Care Act without replacement could leave many uninsured. Other tweets mentioned worries regarding insurance premiums and costs for patients and survivors’ continued screening. In particular the pre-existing condition mandate was a chief concern of patients/survivors future coverage. Hashtags (#) are terms that categorize topics within posts. Each entry contains the tweet frequency, number of distinct profiles, and the relative happiness score (havg) for comparisons. Political terms were prevalent in both distributions describing the Affordable Care Act (#aca, #obamacare, #saveaca, #pretectourcare) and the newly introduced American Healthcare Act (#ahca, #trumpcare). A visual representation of these hashtags are displayed using a word-cloud in the Appendix (Figure A4).", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 4: 50 Most Frequently Tweeted Hashtags: A table of the most frequently tweeted hashtags (#) from all collected breast cancer tweets (left) and from sampled breast cancer patients (right). The relative computed average happiness havg for each tag is colored relative to the group average (blue- negative, orange - positive). This version is sorted by computed word happiness.' and proceed to solve the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich hashtag related to the Affordable Care Act has the highest number of tweets?\n" }, { "role": "assistant", "content": "To find the hashtag related to the Affordable Care Act with the highest number of tweets, look at the hashtags #aca, #obamacare, #saveaca, and #protectourcare in the table. Compare their tweet counts. So, the answer is {\"answer\": \"#aca\"}." } ], "images": [ "scigen/train/2560.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: 50 Most Frequently Tweeted Hashtags: A table of the most frequently tweeted hashtags (#) from all collected breast cancer tweets (left) and from sampled breast cancer patients (right). The relative computed average happiness havg for each tag is colored relative to the group average (blue- negative, orange - positive). This version is sorted by computed word happiness." ], "reasoning": "To find the hashtag related to the Affordable Care Act with the highest number of tweets, look at the hashtags #aca, #obamacare, #saveaca, and #protectourcare in the table. Compare their tweet counts.", "question": "Which hashtag related to the Affordable Care Act has the highest number of tweets?", "answer": "So, the answer is {\"answer\": \"#aca\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The hashtag #breastcancerawarenessmonth has the highest number of tweets among all hashtags., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe hashtag #breastcancerawarenessmonth has the highest number of tweets among all hashtags.\n" }, { "role": "assistant", "content": "To verify this statement, compare the number of tweets for #breastcancerawarenessmonth with the tweet counts of all other hashtags in the table. #breastcancerawarenessmonth has 20,961 tweets, which is not the highest. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2560.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: 50 Most Frequently Tweeted Hashtags: A table of the most frequently tweeted hashtags (#) from all collected breast cancer tweets (left) and from sampled breast cancer patients (right). The relative computed average happiness havg for each tag is colored relative to the group average (blue- negative, orange - positive). This version is sorted by computed word happiness." ], "reasoning": "To verify this statement, compare the number of tweets for #breastcancerawarenessmonth with the tweet counts of all other hashtags in the table. #breastcancerawarenessmonth has 20,961 tweets, which is not the highest.", "statement": "The hashtag #breastcancerawarenessmonth has the highest number of tweets among all hashtags.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: 50 Most Frequently Tweeted Hashtags: A table of the most frequently tweeted hashtags (#) from all collected breast cancer tweets (left) and from sampled breast cancer patients (right). The relative computed average happiness havg for each tag is colored relative to the group average (blue- negative, orange - positive). This version is sorted by computed word happiness.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The table shows the frequency, number of distinct profiles, and relative happiness score for various hashtags used by breast cancer patients and survivors. Political terms related to healthcare regulation, such as #aca, #obamacare, and #trumpcare, are prevalent.Numerous patients and survivors in our sample had identified their condition in reference to the American healthcare regulation debate. Many sampled views of the proposed legislation were very negative, since repealing the Affordable Care Act without replacement could leave many uninsured. Other tweets mentioned worries regarding insurance premiums and costs for patients and survivors’ continued screening. In particular the pre-existing condition mandate was a chief concern of patients/survivors future coverage. Hashtags (#) are terms that categorize topics within posts. Each entry contains the tweet frequency, number of distinct profiles, and the relative happiness score (havg) for comparisons. Political terms were prevalent in both distributions describing the Affordable Care Act (#aca, #obamacare, #saveaca, #pretectourcare) and the newly introduced American Healthcare Act (#ahca, #trumpcare). A visual representation of these hashtags are displayed using a word-cloud in the Appendix (Figure A4)." } ], "images": [ "scigen/train/2561.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: 50 Most Frequently Tweeted Hashtags: A table of the most frequently tweeted hashtags (#) from all collected breast cancer tweets (left) and from sampled breast cancer patients (right). The relative computed average happiness havg for each tag is colored relative to the group average (blue- negative, orange - positive). This version is sorted by computed word happiness." ], "reasoning": "The table shows the frequency, number of distinct profiles, and relative happiness score for various hashtags used by breast cancer patients and survivors. Political terms related to healthcare regulation, such as #aca, #obamacare, and #trumpcare, are prevalent.", "description": "Numerous patients and survivors in our sample had identified their condition in reference to the American healthcare regulation debate. Many sampled views of the proposed legislation were very negative, since repealing the Affordable Care Act without replacement could leave many uninsured. Other tweets mentioned worries regarding insurance premiums and costs for patients and survivors’ continued screening. In particular the pre-existing condition mandate was a chief concern of patients/survivors future coverage. Hashtags (#) are terms that categorize topics within posts. Each entry contains the tweet frequency, number of distinct profiles, and the relative happiness score (havg) for comparisons. Political terms were prevalent in both distributions describing the Affordable Care Act (#aca, #obamacare, #saveaca, #pretectourcare) and the newly introduced American Healthcare Act (#ahca, #trumpcare). A visual representation of these hashtags are displayed using a word-cloud in the Appendix (Figure A4).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: 50 Most Frequently Tweeted Hashtags: A table of the most frequently tweeted hashtags (#) from all collected breast cancer tweets (left) and from sampled breast cancer patients (right). The relative computed average happiness havg for each tag is colored relative to the group average (blue- negative, orange - positive). This version is sorted by computed word happiness.\nQuestion: Which hashtag related to healthcare regulation has the highest number of tweets?\nSolve the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the hashtag related to healthcare regulation with the highest number of tweets, look for hashtags like #aca, #obamacare, #ahca, and #trumpcare in the table and compare their tweet counts. So, the answer is {\"answer\": \"#aca\"}." } ], "images": [ "scigen/train/2561.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: 50 Most Frequently Tweeted Hashtags: A table of the most frequently tweeted hashtags (#) from all collected breast cancer tweets (left) and from sampled breast cancer patients (right). The relative computed average happiness havg for each tag is colored relative to the group average (blue- negative, orange - positive). This version is sorted by computed word happiness." ], "reasoning": "To find the hashtag related to healthcare regulation with the highest number of tweets, look for hashtags like #aca, #obamacare, #ahca, and #trumpcare in the table and compare their tweet counts.", "question": "Which hashtag related to healthcare regulation has the highest number of tweets?", "answer": "So, the answer is {\"answer\": \"#aca\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The hashtag #cancer has the highest number of tweets among all hashtags listed., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe hashtag #cancer has the highest number of tweets among all hashtags listed.\n" }, { "role": "assistant", "content": "To verify this statement, compare the tweet counts of all hashtags in the table. The hashtag #cancer has 2,063 tweets, which is the highest among all listed hashtags. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2561.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: 50 Most Frequently Tweeted Hashtags: A table of the most frequently tweeted hashtags (#) from all collected breast cancer tweets (left) and from sampled breast cancer patients (right). The relative computed average happiness havg for each tag is colored relative to the group average (blue- negative, orange - positive). This version is sorted by computed word happiness." ], "reasoning": "To verify this statement, compare the tweet counts of all hashtags in the table. The hashtag #cancer has 2,063 tweets, which is the highest among all listed hashtags.", "statement": "The hashtag #cancer has the highest number of tweets among all hashtags listed.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Accuracy rate and p-value obtained for the classification substaks. Only the best results are shown among all considered classifiers. We considered the unweighted version of the Markov networks to extract motifs.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The distinction between philosophy books and the other two classes yielded better discrimination, while the distinction between children and investigative texts was not significant.In the first experiment, we evaluated if patterns of semantic changes are able to distinguish between children, philosophy or investigative books. We considered problems with two or three classes. In this case, weights were disregarded after the construction of the Markov networks (unweighted version). Considering subtasks encompassing only two classes, only the distinction between children and investigative texts were not significant, with a low accuracy rate. The distinction philosophy books and the other two classes, however, yielded a much better discrimination. These results were found to be significant. When all three classes are discriminated, a low accuracy rate was found (43.8%), even though this still represents a significant result. The low accuracy rate found using the proposed approach is a consequence of a regular behavior found in the Markov chains. In other words, in most of the books, all communities were found to be connected to each other, hampering thus the discriminability of different types of books." } ], "images": [ "scigen/train/2562.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Accuracy rate and p-value obtained for the classification substaks. Only the best results are shown among all considered classifiers. We considered the unweighted version of the Markov networks to extract motifs." ], "reasoning": "The distinction between philosophy books and the other two classes yielded better discrimination, while the distinction between children and investigative texts was not significant.", "description": "In the first experiment, we evaluated if patterns of semantic changes are able to distinguish between children, philosophy or investigative books. We considered problems with two or three classes. In this case, weights were disregarded after the construction of the Markov networks (unweighted version). Considering subtasks encompassing only two classes, only the distinction between children and investigative texts were not significant, with a low accuracy rate. The distinction philosophy books and the other two classes, however, yielded a much better discrimination. These results were found to be significant. When all three classes are discriminated, a low accuracy rate was found (43.8%), even though this still represents a significant result. The low accuracy rate found using the proposed approach is a consequence of a regular behavior found in the Markov chains. In other words, in most of the books, all communities were found to be connected to each other, hampering thus the discriminability of different types of books.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat was the accuracy rate for distinguishing between children and philosophy books?\nSolve the above question based on the table titled 'Table 1: Accuracy rate and p-value obtained for the classification substaks. Only the best results are shown among all considered classifiers. We considered the unweighted version of the Markov networks to extract motifs.'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the accuracy rates for different subtasks. For the subtask 'children × philosophy', the accuracy rate is listed. So, the answer is {\"answer\": \"71.6%\"}." } ], "images": [ "scigen/train/2562.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Accuracy rate and p-value obtained for the classification substaks. Only the best results are shown among all considered classifiers. We considered the unweighted version of the Markov networks to extract motifs." ], "reasoning": "The table shows the accuracy rates for different subtasks. For the subtask 'children × philosophy', the accuracy rate is listed.", "question": "What was the accuracy rate for distinguishing between children and philosophy books?", "answer": "So, the answer is {\"answer\": \"71.6%\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe accuracy rate for distinguishing between children and investigative books was higher than 50%.\n" }, { "role": "assistant", "content": "The table shows the accuracy rate for the subtask 'children × investigative' as 50.8%, which is higher than 50%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2562.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Accuracy rate and p-value obtained for the classification substaks. Only the best results are shown among all considered classifiers. We considered the unweighted version of the Markov networks to extract motifs." ], "reasoning": "The table shows the accuracy rate for the subtask 'children × investigative' as 50.8%, which is higher than 50%.", "statement": "The accuracy rate for distinguishing between children and investigative books was higher than 50%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Accuracy rate and p-value obtained for the classification substaks. Only the best results are shown among all considered classifiers and thresholds. We considered the simplified unweighted version of the Markov networks to extract motifs.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Removing the weakest edges before extracting motifs and applying a threshold between 0.01 and 0.20 significantly improved accuracy rates, with a high discrimination rate of 91.6% for investigative and philosophy books.Given the low accuracy rates obtained with the unweighted strategy, we analyzed if the simplified unweighted version was able to provide a better characterization. In this case, the weakest edges were removed before the extraction of motifs. We considered the thresholding ranging between 0.01 and 0.20. The main idea here is to remove less important links between communities. All obtained results turned out to be significant. All previous accuracy rates were improved. Interestingly, a high discrimination rate (91.6%) was obtained when discriminating investigative and philosophy books. These results suggest that the threshold is an important pre-processing step here, given that it can boost the performance of the classification by a large margin." } ], "images": [ "scigen/train/2563.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracy rate and p-value obtained for the classification substaks. Only the best results are shown among all considered classifiers and thresholds. We considered the simplified unweighted version of the Markov networks to extract motifs." ], "reasoning": "Removing the weakest edges before extracting motifs and applying a threshold between 0.01 and 0.20 significantly improved accuracy rates, with a high discrimination rate of 91.6% for investigative and philosophy books.", "description": "Given the low accuracy rates obtained with the unweighted strategy, we analyzed if the simplified unweighted version was able to provide a better characterization. In this case, the weakest edges were removed before the extraction of motifs. We considered the thresholding ranging between 0.01 and 0.20. The main idea here is to remove less important links between communities. All obtained results turned out to be significant. All previous accuracy rates were improved. Interestingly, a high discrimination rate (91.6%) was obtained when discriminating investigative and philosophy books. These results suggest that the threshold is an important pre-processing step here, given that it can boost the performance of the classification by a large margin.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: Accuracy rate and p-value obtained for the classification substaks. Only the best results are shown among all considered classifiers and thresholds. We considered the simplified unweighted version of the Markov networks to extract motifs.'. Output the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: What was the accuracy rate for the subtask with the highest discrimination rate?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows that the highest discrimination rate (91.6%) was obtained for the subtask 'investigative × philosophy'. So, the answer is {\"answer\": \"91.6%\"}." } ], "images": [ "scigen/train/2563.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracy rate and p-value obtained for the classification substaks. Only the best results are shown among all considered classifiers and thresholds. We considered the simplified unweighted version of the Markov networks to extract motifs." ], "reasoning": "The table shows that the highest discrimination rate (91.6%) was obtained for the subtask 'investigative × philosophy'.", "question": "What was the accuracy rate for the subtask with the highest discrimination rate?", "answer": "So, the answer is {\"answer\": \"91.6%\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe subtask 'children × philosophy' had an accuracy rate of 81.0%.\n" }, { "role": "assistant", "content": "The table shows that the subtask 'children × philosophy' indeed had an accuracy rate of 81.0%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2563.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracy rate and p-value obtained for the classification substaks. Only the best results are shown among all considered classifiers and thresholds. We considered the simplified unweighted version of the Markov networks to extract motifs." ], "reasoning": "The table shows that the subtask 'children × philosophy' indeed had an accuracy rate of 81.0%.", "statement": "The subtask 'children × philosophy' had an accuracy rate of 81.0%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table II: ROUGE F1 score evaluation with various control codes, in the form of [importance, diversity, position]. * denotes the results from corresponding paper.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Code [0,0,1] dynamically learns positional features beyond the first 3 sentences, isolating diversity and importance features, while focusing only on importance leads to the worst performance.(1) Summary generated from code [0,0,1] is similar to LEAD-3 but can dynamically learn the positional features not limited to the first 3 sentences, while isolating out diversity and importance features. (2) Only focusing on the importance sub-aspect leads to the worst performance, but performance can be improved when considering other sub-aspects. (3) Focusing on the diversity sub-aspect alone (i.e. Code [0,1,0]) can generate results comparable to strong baselines such as SummaRuNNer." } ], "images": [ "scigen/train/2564.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table II: ROUGE F1 score evaluation with various control codes, in the form of [importance, diversity, position]. * denotes the results from corresponding paper." ], "reasoning": "Code [0,0,1] dynamically learns positional features beyond the first 3 sentences, isolating diversity and importance features, while focusing only on importance leads to the worst performance.", "description": "(1) Summary generated from code [0,0,1] is similar to LEAD-3 but can dynamically learn the positional features not limited to the first 3 sentences, while isolating out diversity and importance features. (2) Only focusing on the importance sub-aspect leads to the worst performance, but performance can be improved when considering other sub-aspects. (3) Focusing on the diversity sub-aspect alone (i.e. Code [0,1,0]) can generate results comparable to strong baselines such as SummaRuNNer.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table II: ROUGE F1 score evaluation with various control codes, in the form of [importance, diversity, position]. * denotes the results from corresponding paper.', and sequentially solve the question, writing out each step of your reasoning process. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich code configuration achieves the highest ROUGE-2 score?\n" }, { "role": "assistant", "content": "By examining the table, the highest ROUGE-2 score is achieved by BertEXT* with a score of 20.24. So, the answer is {\"answer\": \"BertEXT*\"}." } ], "images": [ "scigen/train/2564.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table II: ROUGE F1 score evaluation with various control codes, in the form of [importance, diversity, position]. * denotes the results from corresponding paper." ], "reasoning": "By examining the table, the highest ROUGE-2 score is achieved by BertEXT* with a score of 20.24.", "question": "Which code configuration achieves the highest ROUGE-2 score?", "answer": "So, the answer is {\"answer\": \"BertEXT*\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nCode [0,1,0] generates results comparable to SummaRuNNer.\n" }, { "role": "assistant", "content": "The table shows that Code [0,1,0] has ROUGE-1 and ROUGE-2 scores of 39.18 and 17.11, respectively, while SummaRuNNer has scores of 39.60 and 16.20, indicating comparable performance. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2564.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table II: ROUGE F1 score evaluation with various control codes, in the form of [importance, diversity, position]. * denotes the results from corresponding paper." ], "reasoning": "The table shows that Code [0,1,0] has ROUGE-1 and ROUGE-2 scores of 39.18 and 17.11, respectively, while SummaRuNNer has scores of 39.60 and 16.20, indicating comparable performance.", "statement": "Code [0,1,0] generates results comparable to SummaRuNNer.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table I: ROUGE and Human evaluation scores of oracle summaries built on BertScore and ROUGE.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The effectiveness of oracle summaries generated through semantic similarity was evaluated using ROUGE scores and human evaluation experiments, showing differences from traditional n-gram overlap methods.We observed that the oracle summaries generated through semantic similarity is different from those chosen from traditional n-gram overlap. To further evaluate the effectiveness of this oracle construction approach, we conducted two assessments. First, we calculated their ROUGE scores with the gold summary. Next, we conducted 2 human evaluation experiments. The evaluation score represents the likelihood of a higher ranking, and is normalized to range [0,1]. For each sentence of gold summary, questions were related to key information like event and name entities. Then human annotators were asked to answer these questions given an oracle summary. The accuracy of answers is regarded as the evaluation score. Moreover, we constructed some extended questions in which the answer can only be answered with comprehension of the full summary." } ], "images": [ "scigen/train/2565.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table I: ROUGE and Human evaluation scores of oracle summaries built on BertScore and ROUGE." ], "reasoning": "The effectiveness of oracle summaries generated through semantic similarity was evaluated using ROUGE scores and human evaluation experiments, showing differences from traditional n-gram overlap methods.", "description": "We observed that the oracle summaries generated through semantic similarity is different from those chosen from traditional n-gram overlap. To further evaluate the effectiveness of this oracle construction approach, we conducted two assessments. First, we calculated their ROUGE scores with the gold summary. Next, we conducted 2 human evaluation experiments. The evaluation score represents the likelihood of a higher ranking, and is normalized to range [0,1]. For each sentence of gold summary, questions were related to key information like event and name entities. Then human annotators were asked to answer these questions given an oracle summary. The accuracy of answers is regarded as the evaluation score. Moreover, we constructed some extended questions in which the answer can only be answered with comprehension of the full summary.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table I: ROUGE and Human evaluation scores of oracle summaries built on BertScore and ROUGE.\nQuestion: What is the ROUGE-1 F1 Score for the BertScore Oracle?\nGive a detailed response to the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the ROUGE-1 F1 Score for the BertScore Oracle, look at the intersection of the 'BertScore Oracle' row and the 'ROUGE-1 F1 Score' column. So, the answer is {\"answer\": \"50.56\"}." } ], "images": [ "scigen/train/2565.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table I: ROUGE and Human evaluation scores of oracle summaries built on BertScore and ROUGE." ], "reasoning": "To find the ROUGE-1 F1 Score for the BertScore Oracle, look at the intersection of the 'BertScore Oracle' row and the 'ROUGE-1 F1 Score' column.", "question": "What is the ROUGE-1 F1 Score for the BertScore Oracle?", "answer": "So, the answer is {\"answer\": \"50.56\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe ROUGE-2 F1 Score for the ROUGE Oracle is higher than that for the BertScore Oracle.\n" }, { "role": "assistant", "content": "Compare the ROUGE-2 F1 Scores for both the ROUGE Oracle and the BertScore Oracle. The ROUGE Oracle has a score of 31.08, which is higher than the BertScore Oracle's score of 29.41. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2565.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table I: ROUGE and Human evaluation scores of oracle summaries built on BertScore and ROUGE." ], "reasoning": "Compare the ROUGE-2 F1 Scores for both the ROUGE Oracle and the BertScore Oracle. The ROUGE Oracle has a score of 31.08, which is higher than the BertScore Oracle's score of 29.41.", "statement": "The ROUGE-2 F1 Score for the ROUGE Oracle is higher than that for the BertScore Oracle.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table I: ROUGE and Human evaluation scores of oracle summaries built on BertScore and ROUGE.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The effectiveness of oracle summaries generated through semantic similarity and traditional n-gram overlap was evaluated using ROUGE scores and human evaluation experiments, focusing on accuracy in answering questions related to key information and comprehension of the full summary.We observed that the oracle summaries generated through semantic similarity is different from those chosen from traditional n-gram overlap. To further evaluate the effectiveness of this oracle construction approach, we conducted two assessments. First, we calculated their ROUGE scores with the gold summary. Next, we conducted 2 human evaluation experiments. The evaluation score represents the likelihood of a higher ranking, and is normalized to range [0,1]. For each sentence of gold summary, questions were related to key information like event and name entities. Then human annotators were asked to answer these questions given an oracle summary. The accuracy of answers is regarded as the evaluation score. Moreover, we constructed some extended questions in which the answer can only be answered with comprehension of the full summary." } ], "images": [ "scigen/train/2566.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table I: ROUGE and Human evaluation scores of oracle summaries built on BertScore and ROUGE." ], "reasoning": "The effectiveness of oracle summaries generated through semantic similarity and traditional n-gram overlap was evaluated using ROUGE scores and human evaluation experiments, focusing on accuracy in answering questions related to key information and comprehension of the full summary.", "description": "We observed that the oracle summaries generated through semantic similarity is different from those chosen from traditional n-gram overlap. To further evaluate the effectiveness of this oracle construction approach, we conducted two assessments. First, we calculated their ROUGE scores with the gold summary. Next, we conducted 2 human evaluation experiments. The evaluation score represents the likelihood of a higher ranking, and is normalized to range [0,1]. For each sentence of gold summary, questions were related to key information like event and name entities. Then human annotators were asked to answer these questions given an oracle summary. The accuracy of answers is regarded as the evaluation score. Moreover, we constructed some extended questions in which the answer can only be answered with comprehension of the full summary.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table I: ROUGE and Human evaluation scores of oracle summaries built on BertScore and ROUGE.\nQuestion: Which candidate had the highest accuracy score for entity and event questions?\nSolve the above question based on the table information. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the candidate with the highest accuracy score for entity and event questions, compare the scores of Gold Summaries, ROUGE Candidates, and BertScore Candidates under the 'Entity and Event Questions' section. The highest score is 0.95 for Gold Summaries. So, the answer is {\"answer\": \"Gold Summaries\"}." } ], "images": [ "scigen/train/2566.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table I: ROUGE and Human evaluation scores of oracle summaries built on BertScore and ROUGE." ], "reasoning": "To find the candidate with the highest accuracy score for entity and event questions, compare the scores of Gold Summaries, ROUGE Candidates, and BertScore Candidates under the 'Entity and Event Questions' section. The highest score is 0.95 for Gold Summaries.", "question": "Which candidate had the highest accuracy score for entity and event questions?", "answer": "So, the answer is {\"answer\": \"Gold Summaries\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nBertScore Candidates had a higher accuracy score than ROUGE Candidates for extended questions.\n" }, { "role": "assistant", "content": "To verify this statement, compare the accuracy scores of BertScore Candidates and ROUGE Candidates under the 'Extended Questions' section. BertScore Candidates have a score of 0.70, which is higher than the ROUGE Candidates' score of 0.52. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2566.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table I: ROUGE and Human evaluation scores of oracle summaries built on BertScore and ROUGE." ], "reasoning": "To verify this statement, compare the accuracy scores of BertScore Candidates and ROUGE Candidates under the 'Extended Questions' section. BertScore Candidates have a score of 0.70, which is higher than the ROUGE Candidates' score of 0.52.", "statement": "BertScore Candidates had a higher accuracy score than ROUGE Candidates for extended questions.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table V: Inference scores on AMI corpus from baselines and our model with control codes, in the form of [importance, diversity, position]. * denotes the results from corresponding paper.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The experiment showed that semantic-related control codes are more effective than position-based codes in generating balanced meeting summaries.We also conducted an inference experiment on a less position-biased corpus. Different from news summarization, the meeting summaries are usually more abstractive with keywords extracted from the whole conversation. Not surprisingly, summaries under position code do not perform well as the corpus is more balanced. This shows the effectiveness of semantic-related control codes and the generality of our model." } ], "images": [ "scigen/train/2567.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table V: Inference scores on AMI corpus from baselines and our model with control codes, in the form of [importance, diversity, position]. * denotes the results from corresponding paper." ], "reasoning": "The experiment showed that semantic-related control codes are more effective than position-based codes in generating balanced meeting summaries.", "description": "We also conducted an inference experiment on a less position-biased corpus. Different from news summarization, the meeting summaries are usually more abstractive with keywords extracted from the whole conversation. Not surprisingly, summaries under position code do not perform well as the corpus is more balanced. This shows the effectiveness of semantic-related control codes and the generality of our model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table V: Inference scores on AMI corpus from baselines and our model with control codes, in the form of [importance, diversity, position]. * denotes the results from corresponding paper.'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: Which code achieved the highest R-1 F1 score in the experiment?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By comparing the R-1 F1 scores of the different codes, we see that Code [1,0,0] achieved the highest score of 34.81. So, the answer is {\"answer\": \"Code [1,0,0]\"}." } ], "images": [ "scigen/train/2567.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table V: Inference scores on AMI corpus from baselines and our model with control codes, in the form of [importance, diversity, position]. * denotes the results from corresponding paper." ], "reasoning": "By comparing the R-1 F1 scores of the different codes, we see that Code [1,0,0] achieved the highest score of 34.81.", "question": "Which code achieved the highest R-1 F1 score in the experiment?", "answer": "So, the answer is {\"answer\": \"Code [1,0,0]\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe baseline model achieved a higher R-2 Recall than Code [0,1,0].\n" }, { "role": "assistant", "content": "The table shows that the baseline model has an R-2 Recall of 6.10, while Code [0,1,0] has an R-2 Recall of 4.62, indicating that the statement is true. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2567.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table V: Inference scores on AMI corpus from baselines and our model with control codes, in the form of [importance, diversity, position]. * denotes the results from corresponding paper." ], "reasoning": "The table shows that the baseline model has an R-2 Recall of 6.10, while Code [0,1,0] has an R-2 Recall of 4.62, indicating that the statement is true.", "statement": "The baseline model achieved a higher R-2 Recall than Code [0,1,0].", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 7: Cosine Distance between conference vectors and mean class vectors of languages.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The cosine distance analysis shows that ACL is closest to category 5 languages and farthest from category 2, with a steep rise in distance from category 4 to category 3. Similar trends are observed for other ACL-related conferences.To quantify this analysis, we compute the cosine distance between the conference vector and the mean of the vector each category of the taxonomy. and we see a very similar trend that while ACL is an at average distance of 0.291 from category 5 languages, its almost more than double far away from category 2. There is also a very steep rise in distance of the ACL vector from category 4 to category 3. In fact, similar trends are visible for other ACL related conferences including EACL, NAACL, EMNLP and TACL. The trend for category 0 languages seems somewhat different than the usual trend is this table, probably because of the large number of languages in this category as well as the sparsity in papers." } ], "images": [ "scigen/train/2568.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Cosine Distance between conference vectors and mean class vectors of languages." ], "reasoning": "The cosine distance analysis shows that ACL is closest to category 5 languages and farthest from category 2, with a steep rise in distance from category 4 to category 3. Similar trends are observed for other ACL-related conferences.", "description": "To quantify this analysis, we compute the cosine distance between the conference vector and the mean of the vector each category of the taxonomy. and we see a very similar trend that while ACL is an at average distance of 0.291 from category 5 languages, its almost more than double far away from category 2. There is also a very steep rise in distance of the ACL vector from category 4 to category 3. In fact, similar trends are visible for other ACL related conferences including EACL, NAACL, EMNLP and TACL. The trend for category 0 languages seems somewhat different than the usual trend is this table, probably because of the large number of languages in this category as well as the sparsity in papers.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 7: Cosine Distance between conference vectors and mean class vectors of languages.\nQuestion: What is the cosine distance between the ACL vector and category 2 languages, and how does it compare to the distance from category 5 languages?\n" }, { "role": "assistant", "content": "The table shows that the cosine distance between ACL and category 2 languages is 0.60, while the distance from category 5 languages is 0.29. This indicates that ACL is more than double the distance from category 2 compared to category 5. So, the answer is {\"answer\": \"0.60\"}" } ], "images": [ "scigen/train/2568.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Cosine Distance between conference vectors and mean class vectors of languages." ], "reasoning": "The table shows that the cosine distance between ACL and category 2 languages is 0.60, while the distance from category 5 languages is 0.29. This indicates that ACL is more than double the distance from category 2 compared to category 5.", "question": "What is the cosine distance between the ACL vector and category 2 languages, and how does it compare to the distance from category 5 languages?", "answer": "So, the answer is {\"answer\": \"0.60\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe cosine distance between ACL and category 3 languages is less than the distance from category 4 languages.\n" }, { "role": "assistant", "content": "The table shows that the cosine distance between ACL and category 3 languages is 0.42, while the distance from category 4 languages is 0.34. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2568.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Cosine Distance between conference vectors and mean class vectors of languages." ], "reasoning": "The table shows that the cosine distance between ACL and category 3 languages is 0.42, while the distance from category 4 languages is 0.34. Therefore, the statement is refuted.", "statement": "The cosine distance between ACL and category 3 languages is less than the distance from category 4 languages.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: Class-wise (1/MRR) for each conference.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "LREC and WS have been the most inclusive conferences across all categories, while conferences like CONLL, TACL, and SEMEVAL have seen a more significant dip in inclusivity.The smaller the inverse MRR value, the more inclusive that conference is to that language class. All-Inclusive Looking at the combined trends, both the entropy plots and the MRR figures suggest that LREC and WS have been the most inclusive across all categories and have been continuing to do so over the years. A ray of hope With regards to the proceedings of ACL, EMNLP, NAACL, LREC, we note a marked spike in entropy in the 2010s, which is absent in other conferences. This might be due to the increased buzz surrounding cross-lingual techniques. The later the merrier An interesting point to note is that conferences which started later have taken lessons from past in matters of language inclusion. While the earlier established conferences have continued to maintain interest in a particular underlying theme of research which may or may not favour multilingual systems. This can be observed in : COLING, ACL, EACL, EMNLP ( order of their start dates).Falling off the radar While the dip in ranks is more forgiving for conferences such as LREC, WS , it is more stark in CONLL, TACL, SEMEVAL." } ], "images": [ "scigen/train/2569.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Class-wise (1/MRR) for each conference." ], "reasoning": "LREC and WS have been the most inclusive conferences across all categories, while conferences like CONLL, TACL, and SEMEVAL have seen a more significant dip in inclusivity.", "description": "The smaller the inverse MRR value, the more inclusive that conference is to that language class. All-Inclusive Looking at the combined trends, both the entropy plots and the MRR figures suggest that LREC and WS have been the most inclusive across all categories and have been continuing to do so over the years. A ray of hope With regards to the proceedings of ACL, EMNLP, NAACL, LREC, we note a marked spike in entropy in the 2010s, which is absent in other conferences. This might be due to the increased buzz surrounding cross-lingual techniques. The later the merrier An interesting point to note is that conferences which started later have taken lessons from past in matters of language inclusion. While the earlier established conferences have continued to maintain interest in a particular underlying theme of research which may or may not favour multilingual systems. This can be observed in : COLING, ACL, EACL, EMNLP ( order of their start dates).Falling off the radar While the dip in ranks is more forgiving for conferences such as LREC, WS , it is more stark in CONLL, TACL, SEMEVAL.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Class-wise (1/MRR) for each conference.', solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich conference has the lowest inverse MRR value in class 3, indicating the highest inclusivity for that class?\n" }, { "role": "assistant", "content": "To determine the conference with the highest inclusivity for class 3, we need to find the conference with the lowest value in the column labeled '3'. The lowest value in this column is 45, which corresponds to the LREC conference. So, the answer is {\"answer\": \"LREC\"}." } ], "images": [ "scigen/train/2569.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Class-wise (1/MRR) for each conference." ], "reasoning": "To determine the conference with the highest inclusivity for class 3, we need to find the conference with the lowest value in the column labeled '3'. The lowest value in this column is 45, which corresponds to the LREC conference.", "question": "Which conference has the lowest inverse MRR value in class 3, indicating the highest inclusivity for that class?", "answer": "So, the answer is {\"answer\": \"LREC\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nLREC has been the most inclusive conference across all categories.\n" }, { "role": "assistant", "content": "The description states that LREC has been one of the most inclusive conferences across all categories, and the table shows that LREC has relatively low inverse MRR values across multiple classes, supporting this statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2569.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Class-wise (1/MRR) for each conference." ], "reasoning": "The description states that LREC has been one of the most inclusive conferences across all categories, and the table shows that LREC has relatively low inverse MRR values across multiple classes, supporting this statement.", "statement": "LREC has been the most inclusive conference across all categories.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: The performance for BoW sub-model orthogonality on CWB and WOB. The means and standard deviation here are averaged over five random runs.'.\n" }, { "role": "assistant", "content": "The BoW sub-model orthogonality improves Acc_hr on both Bal and stress-test datasets for CWB and WOB, while only slightly reducing overall Acc.BoW Sub-Model Orthogonality (Higher Level Model Debiasing). Here, we also show that the improvement trend holds regardless of minor hyper-parameter changes in the model (number of layers). On both CWB and WOB, the model shows a large improvement on Acc_hr for both Bal and stress-test datasets. We achieve close or higher Acc on all the bias testing sets and the overall Acc is only 1.4%/1.3% lower than the baseline, showing that adding a BoW sub-model orthogonality will only slightly hurt the model. In conclusion, this approach significantly robustifies the model against CWB and WOB while maintaining competitive overall performance. In comparison to the debiasing embeddings results, we can see that instead of regularizing the content in the word embeddings, regularizing the model’s compositionality at the upper interaction level is a more promising direction for debiasing lexical biases. We have also tried combining this method with the data-level debiasing approach above but get no further improvement." } ], "images": [ "scigen/train/2570.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The performance for BoW sub-model orthogonality on CWB and WOB. The means and standard deviation here are averaged over five random runs." ], "reasoning": "The BoW sub-model orthogonality improves Acc_hr on both Bal and stress-test datasets for CWB and WOB, while only slightly reducing overall Acc.", "description": "BoW Sub-Model Orthogonality (Higher Level Model Debiasing). Here, we also show that the improvement trend holds regardless of minor hyper-parameter changes in the model (number of layers). On both CWB and WOB, the model shows a large improvement on Acc_hr for both Bal and stress-test datasets. We achieve close or higher Acc on all the bias testing sets and the overall Acc is only 1.4%/1.3% lower than the baseline, showing that adding a BoW sub-model orthogonality will only slightly hurt the model. In conclusion, this approach significantly robustifies the model against CWB and WOB while maintaining competitive overall performance. In comparison to the debiasing embeddings results, we can see that instead of regularizing the content in the word embeddings, regularizing the model’s compositionality at the upper interaction level is a more promising direction for debiasing lexical biases. We have also tried combining this method with the data-level debiasing approach above but get no further improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the Acc_hr for the CWB Stress dataset when using the BoW sub-model?\nSolve the above question based on the table titled 'Table 4: The performance for BoW sub-model orthogonality on CWB and WOB. The means and standard deviation here are averaged over five random runs.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the Acc_hr for the CWB Stress dataset when using the BoW sub-model is 48.0±1.44. So, the answer is {\"answer\": \"48.0±1.44\"}" } ], "images": [ "scigen/train/2570.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The performance for BoW sub-model orthogonality on CWB and WOB. The means and standard deviation here are averaged over five random runs." ], "reasoning": "The table shows that the Acc_hr for the CWB Stress dataset when using the BoW sub-model is 48.0±1.44.", "question": "What is the Acc_hr for the CWB Stress dataset when using the BoW sub-model?", "answer": "So, the answer is {\"answer\": \"48.0±1.44\"}" }, { "messages": [ { "role": "user", "content": "The model with BoW sub-model orthogonality has a higher Acc_hr on the WOB Stress dataset compared to the baseline. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe model with BoW sub-model orthogonality has a higher Acc_hr on the WOB Stress dataset compared to the baseline.\n" }, { "role": "assistant", "content": "The table shows that the Acc_hr for the WOB Stress dataset is 46.6±4.64 with the BoW sub-model, which is higher than the baseline Acc_hr of 37.6±9.63. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2570.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The performance for BoW sub-model orthogonality on CWB and WOB. The means and standard deviation here are averaged over five random runs." ], "reasoning": "The table shows that the Acc_hr for the WOB Stress dataset is 46.6±4.64 with the BoW sub-model, which is higher than the baseline Acc_hr of 37.6±9.63.", "statement": "The model with BoW sub-model orthogonality has a higher Acc_hr on the WOB Stress dataset compared to the baseline.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: The performance for reducing the CWB via data enhancement/augmentation. The numbers each representing the result after adding 500/20,000/50,000 additional data.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows the performance of different training methods on various datasets, highlighting the impact of data augmentation and enhancement on accuracy and bias.Since we observe similar performance for CWB and WOB, we leave the results for WOB in Appendix. On every dataset, there’s a significant gap between Acc and Acc_hr, showing the baseline has both strong CWB bias and strong WOB bias. For the data augmentation/enhancement experiments, we report results after adding 500/20,000/50,000 additional samples. We demonstrate the effect of adding a small portion of data for the 500 case and the limitation of this method using the 20,000 and 50,000 cases. We use “+origin” to denote the results from data enhancement using the original dataset and use “+synthetic” to denote the results from data augmentation by generating new synthetic data similar to NLI stress tests." } ], "images": [ "scigen/train/2571.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The performance for reducing the CWB via data enhancement/augmentation. The numbers each representing the result after adding 500/20,000/50,000 additional data." ], "reasoning": "The table shows the performance of different training methods on various datasets, highlighting the impact of data augmentation and enhancement on accuracy and bias.", "description": "Since we observe similar performance for CWB and WOB, we leave the results for WOB in Appendix. On every dataset, there’s a significant gap between Acc and Acc_hr, showing the baseline has both strong CWB bias and strong WOB bias. For the data augmentation/enhancement experiments, we report results after adding 500/20,000/50,000 additional samples. We demonstrate the effect of adding a small portion of data for the 500 case and the limitation of this method using the 20,000 and 50,000 cases. We use “+origin” to denote the results from data enhancement using the original dataset and use “+synthetic” to denote the results from data augmentation by generating new synthetic data similar to NLI stress tests.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: The performance for reducing the CWB via data enhancement/augmentation. The numbers each representing the result after adding 500/20,000/50,000 additional data.', solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the highest Stress* Acc achieved after data augmentation?\n" }, { "role": "assistant", "content": "To find the highest Stress* Acc, compare the Stress* Acc values for the baseline, +origin, and +synthetic methods. The highest value is 68.5 achieved by +synthetic. So, the answer is {\"answer\": \"68.5\"}." } ], "images": [ "scigen/train/2571.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The performance for reducing the CWB via data enhancement/augmentation. The numbers each representing the result after adding 500/20,000/50,000 additional data." ], "reasoning": "To find the highest Stress* Acc, compare the Stress* Acc values for the baseline, +origin, and +synthetic methods. The highest value is 68.5 achieved by +synthetic.", "question": "What is the highest Stress* Acc achieved after data augmentation?", "answer": "So, the answer is {\"answer\": \"68.5\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Data augmentation using synthetic data significantly improves the Stress* Acc_hr compared to the baseline. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nData augmentation using synthetic data significantly improves the Stress* Acc_hr compared to the baseline.\n" }, { "role": "assistant", "content": "The Stress* Acc_hr for the baseline is 38.7, while for +synthetic it is 68.4, showing a significant improvement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2571.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The performance for reducing the CWB via data enhancement/augmentation. The numbers each representing the result after adding 500/20,000/50,000 additional data." ], "reasoning": "The Stress* Acc_hr for the baseline is 38.7, while for +synthetic it is 68.4, showing a significant improvement.", "statement": "Data augmentation using synthetic data significantly improves the Stress* Acc_hr compared to the baseline.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: The performance for debiasing the embeddings on CWB and WOB.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Training the whole model with the debias network from a pre-trained baseline improves stability, but no method significantly improved Acc_hr metrics, with emb_basic showing only small improvements.Debiasing Embeddings (Lower Level Model Debiasing). Empirically, we observe that training the whole model with the debias network from a pre-trained baseline can significantly improve the stability of results, so we perform our experiments from one baseline with average performance for fair comparisons. The multi-task coefficient λ controls the trade-off between high accuracy and little bias. Here we report the results with λ=1, which we find is one good balance point. From both tables, none of the methods achieved a significant improvement on the Acc_hr metrics. The best results come from the emb_basic approach, but even this method only achieves small improvement on the Acc_hr metric for CWB but does worse on WOB and has a comparable loss on overall Acc. We do not observe any significantly larger improvements with smaller or larger λ. We also tried other techniques to further stabilize the training (e.g., freezing the main model when training, using different optimization algorithms), but we observe no significant improvement." } ], "images": [ "scigen/train/2572.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The performance for debiasing the embeddings on CWB and WOB." ], "reasoning": "Training the whole model with the debias network from a pre-trained baseline improves stability, but no method significantly improved Acc_hr metrics, with emb_basic showing only small improvements.", "description": "Debiasing Embeddings (Lower Level Model Debiasing). Empirically, we observe that training the whole model with the debias network from a pre-trained baseline can significantly improve the stability of results, so we perform our experiments from one baseline with average performance for fair comparisons. The multi-task coefficient λ controls the trade-off between high accuracy and little bias. Here we report the results with λ=1, which we find is one good balance point. From both tables, none of the methods achieved a significant improvement on the Acc_hr metrics. The best results come from the emb_basic approach, but even this method only achieves small improvement on the Acc_hr metric for CWB but does worse on WOB and has a comparable loss on overall Acc. We do not observe any significantly larger improvements with smaller or larger λ. We also tried other techniques to further stabilize the training (e.g., freezing the main model when training, using different optimization algorithms), but we observe no significant improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. The question is related to the given table titled 'Table 3: The performance for debiasing the embeddings on CWB and WOB.'.\n\nQuestion: Which model achieved the highest Acc_hr metric for CWB Stress*?\n" }, { "role": "assistant", "content": "To find the highest Acc_hr metric for CWB Stress*, compare the Acc_hr values under the CWB Stress* column for all models. The highest value is 50.2, achieved by the emb_basic model. So, the answer is {\"answer\": \"emb_basic\"}." } ], "images": [ "scigen/train/2572.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The performance for debiasing the embeddings on CWB and WOB." ], "reasoning": "To find the highest Acc_hr metric for CWB Stress*, compare the Acc_hr values under the CWB Stress* column for all models. The highest value is 50.2, achieved by the emb_basic model.", "question": "Which model achieved the highest Acc_hr metric for CWB Stress*?", "answer": "So, the answer is {\"answer\": \"emb_basic\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe emb_basic model achieved the highest overall accuracy on the MNLI dataset.\n" }, { "role": "assistant", "content": "The table shows the overall accuracy (Acc) for the MNLI dataset for each model. The baseline model has the highest accuracy at 70.0, while emb_basic has 67.8. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2572.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The performance for debiasing the embeddings on CWB and WOB." ], "reasoning": "The table shows the overall accuracy (Acc) for the MNLI dataset for each model. The baseline model has the highest accuracy at 70.0, while emb_basic has 67.8. Therefore, the statement is refuted.", "statement": "The emb_basic model achieved the highest overall accuracy on the MNLI dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 7: The performance of BERT for reducing the CWB via data enhancement/augmentation. The numbers each representing the result after adding 500/20,000/50,000 additional data.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "BERT shows high performance on the WOB Bal dataset but performs poorly on Stress, indicating significant WOB bias.The data augmentation/enhancement results for BERT-base Devlin et al. As for WOB, the gap between Acc and Acc_hr for Bal is much smaller, however, the performance on Stress is very poor. Therefore, we assume that even though BERT achieves a high score on the WOB Bal dataset, BERT is just overfitting the dataset in another different way, i.e., there is still significant WOB bias in BERT. In conclusion, in our experiment, BERT still shows significant CWB and WOB." } ], "images": [ "scigen/train/2573.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: The performance of BERT for reducing the CWB via data enhancement/augmentation. The numbers each representing the result after adding 500/20,000/50,000 additional data." ], "reasoning": "BERT shows high performance on the WOB Bal dataset but performs poorly on Stress, indicating significant WOB bias.", "description": "The data augmentation/enhancement results for BERT-base Devlin et al. As for WOB, the gap between Acc and Acc_hr for Bal is much smaller, however, the performance on Stress is very poor. Therefore, we assume that even though BERT achieves a high score on the WOB Bal dataset, BERT is just overfitting the dataset in another different way, i.e., there is still significant WOB bias in BERT. In conclusion, in our experiment, BERT still shows significant CWB and WOB.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 7: The performance of BERT for reducing the CWB via data enhancement/augmentation. The numbers each representing the result after adding 500/20,000/50,000 additional data.', unfold the question presented below, detailing every step of your calculation. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the Stress* Acc_hr for the baseline and + synthetic training/testing conditions?\n" }, { "role": "assistant", "content": "The Stress* Acc_hr for the baseline is 41.9, and for the + synthetic condition, it ranges from 81.9 to 83.0. So, the answer is {\"answer\": \"41.9 and 81.9-83.0\"}." } ], "images": [ "scigen/train/2573.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: The performance of BERT for reducing the CWB via data enhancement/augmentation. The numbers each representing the result after adding 500/20,000/50,000 additional data." ], "reasoning": "The Stress* Acc_hr for the baseline is 41.9, and for the + synthetic condition, it ranges from 81.9 to 83.0.", "question": "What is the Stress* Acc_hr for the baseline and + synthetic training/testing conditions?", "answer": "So, the answer is {\"answer\": \"41.9 and 81.9-83.0\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nBERT's performance on the Stress* Acc_hr improves significantly with synthetic data.\n" }, { "role": "assistant", "content": "The Stress* Acc_hr for the baseline is 41.9, while with synthetic data, it ranges from 81.9 to 83.0, indicating a significant improvement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2573.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: The performance of BERT for reducing the CWB via data enhancement/augmentation. The numbers each representing the result after adding 500/20,000/50,000 additional data." ], "reasoning": "The Stress* Acc_hr for the baseline is 41.9, while with synthetic data, it ranges from 81.9 to 83.0, indicating a significant improvement.", "statement": "BERT's performance on the Stress* Acc_hr improves significantly with synthetic data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 8: The performance of BERT for reducing the WOB via data enhancement/augmentation. The numbers each representing the result after adding 500/20,000/50,000 additional data.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "BERT shows high performance on the WOB Bal dataset but performs poorly on Stress, indicating overfitting and significant WOB bias.The data augmentation/enhancement results for BERT-base Devlin et al. As for WOB, the gap between Acc and Acc_hr for Bal is much smaller, however, the performance on Stress is very poor. Therefore, we assume that even though BERT achieves a high score on the WOB Bal dataset, BERT is just overfitting the dataset in another different way, i.e., there is still significant WOB bias in BERT. In conclusion, in our experiment, BERT still shows significant CWB and WOB." } ], "images": [ "scigen/train/2574.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: The performance of BERT for reducing the WOB via data enhancement/augmentation. The numbers each representing the result after adding 500/20,000/50,000 additional data." ], "reasoning": "BERT shows high performance on the WOB Bal dataset but performs poorly on Stress, indicating overfitting and significant WOB bias.", "description": "The data augmentation/enhancement results for BERT-base Devlin et al. As for WOB, the gap between Acc and Acc_hr for Bal is much smaller, however, the performance on Stress is very poor. Therefore, we assume that even though BERT achieves a high score on the WOB Bal dataset, BERT is just overfitting the dataset in another different way, i.e., there is still significant WOB bias in BERT. In conclusion, in our experiment, BERT still shows significant CWB and WOB.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 8: The performance of BERT for reducing the WOB via data enhancement/augmentation. The numbers each representing the result after adding 500/20,000/50,000 additional data.\nQuestion: What is the Stress Acc_hr for the baseline and + synthetic models?\nGive a detailed response to the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The Stress Acc_hr for the baseline model is 6.49, and for the + synthetic model, it is 83.8. So, the answer is {\"answer\": \"6.49 and 83.8\"}." } ], "images": [ "scigen/train/2574.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: The performance of BERT for reducing the WOB via data enhancement/augmentation. The numbers each representing the result after adding 500/20,000/50,000 additional data." ], "reasoning": "The Stress Acc_hr for the baseline model is 6.49, and for the + synthetic model, it is 83.8.", "question": "What is the Stress Acc_hr for the baseline and + synthetic models?", "answer": "So, the answer is {\"answer\": \"6.49 and 83.8\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe + synthetic model has a higher Stress Acc_hr than the baseline model.\n" }, { "role": "assistant", "content": "The table shows that the Stress Acc_hr for the baseline model is 6.49, while for the + synthetic model, it is 83.8, indicating a higher performance for the + synthetic model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2574.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: The performance of BERT for reducing the WOB via data enhancement/augmentation. The numbers each representing the result after adding 500/20,000/50,000 additional data." ], "reasoning": "The table shows that the Stress Acc_hr for the baseline model is 6.49, while for the + synthetic model, it is 83.8, indicating a higher performance for the + synthetic model.", "statement": "The + synthetic model has a higher Stress Acc_hr than the baseline model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: The WER (%) performance of unadapted, T/S learning adapted LSTM acoustic models for robust ASR on the real noisy channel 5 test set of CHiME-3.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The T/S learning model achieves significant improvements in WER on noisy test data compared to the unadapted model, with relative improvements of 40.05% and 41.45% for 'clean-noisy' and 'clean-noisy & clean-clean' adaptations respectively.The unadapted Cortana model has 6.96% WER on the clean test set. After T/S learning with both the “clean-noisy” and “clean-clean” parallel data, the student model has 6.99% WER on the clean test. As the focus of this study is to improve T/S adaptation on noisy test data, we will only report results with the CHiME-3 real noisy channel 5 test set. The T/S learning achieves 13.88% and 13.56% average WERs when adapted to “clean-noisy” and “clean-noisy & clean-clean” respectively, which are 40.05% and 41.45% relative improvements over the unadapted clean model. Note that our experimental setup does not achieve the state-of-the-art performance on CHiME-3 dataset (e.g., we did not perform beamforming, sequence training or use RNN LM for decoding.) since our goal is to simply verify the effectiveness of adversarial T/S learning in achieving condition-robust unsupervised adaptation." } ], "images": [ "scigen/train/2575.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The WER (%) performance of unadapted, T/S learning adapted LSTM acoustic models for robust ASR on the real noisy channel 5 test set of CHiME-3." ], "reasoning": "The T/S learning model achieves significant improvements in WER on noisy test data compared to the unadapted model, with relative improvements of 40.05% and 41.45% for 'clean-noisy' and 'clean-noisy & clean-clean' adaptations respectively.", "description": "The unadapted Cortana model has 6.96% WER on the clean test set. After T/S learning with both the “clean-noisy” and “clean-clean” parallel data, the student model has 6.99% WER on the clean test. As the focus of this study is to improve T/S adaptation on noisy test data, we will only report results with the CHiME-3 real noisy channel 5 test set. The T/S learning achieves 13.88% and 13.56% average WERs when adapted to “clean-noisy” and “clean-noisy & clean-clean” respectively, which are 40.05% and 41.45% relative improvements over the unadapted clean model. Note that our experimental setup does not achieve the state-of-the-art performance on CHiME-3 dataset (e.g., we did not perform beamforming, sequence training or use RNN LM for decoding.) since our goal is to simply verify the effectiveness of adversarial T/S learning in achieving condition-robust unsupervised adaptation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the average WER improvement percentage of the T/S model adapted with 'clean-noisy & clean-clean' data over the unadapted model?\nSolve the above question based on the table titled 'Table 1: The WER (%) performance of unadapted, T/S learning adapted LSTM acoustic models for robust ASR on the real noisy channel 5 test set of CHiME-3.'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The unadapted model has an average WER of 23.16%. The T/S model adapted with 'clean-noisy & clean-clean' data has an average WER of 13.56%. The improvement percentage is calculated as ((23.16 - 13.56) / 23.16) * 100. So, the answer is {\"answer\": \"41.45%\"}." } ], "images": [ "scigen/train/2575.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The WER (%) performance of unadapted, T/S learning adapted LSTM acoustic models for robust ASR on the real noisy channel 5 test set of CHiME-3." ], "reasoning": "The unadapted model has an average WER of 23.16%. The T/S model adapted with 'clean-noisy & clean-clean' data has an average WER of 13.56%. The improvement percentage is calculated as ((23.16 - 13.56) / 23.16) * 100.", "question": "What is the average WER improvement percentage of the T/S model adapted with 'clean-noisy & clean-clean' data over the unadapted model?", "answer": "So, the answer is {\"answer\": \"41.45%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The T/S model adapted with 'clean-noisy' data achieves a lower WER on the PED test set compared to the unadapted model. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe T/S model adapted with 'clean-noisy' data achieves a lower WER on the PED test set compared to the unadapted model.\n" }, { "role": "assistant", "content": "The table shows that the unadapted model has a WER of 18.53% on the PED test set, while the T/S model adapted with 'clean-noisy' data has a WER of 11.27%, which is lower. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2575.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The WER (%) performance of unadapted, T/S learning adapted LSTM acoustic models for robust ASR on the real noisy channel 5 test set of CHiME-3." ], "reasoning": "The table shows that the unadapted model has a WER of 18.53% on the PED test set, while the T/S model adapted with 'clean-noisy' data has a WER of 11.27%, which is lower.", "statement": "The T/S model adapted with 'clean-noisy' data achieves a lower WER on the PED test set compared to the unadapted model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: The WER (%) performance of adversarial T/S learning adapted LSTM acoustic models for robust ASR on the real noisy channel 5 test set of CHiME-3. The adaptation data consists of “clean-noisy” and “clean-clean”.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Speaker-robust unsupervised adaptation achieves better WER improvement than environment-robust methods, and MFA T/S learning, which uses two condition classifiers, achieves the lowest WER.To achieve environment-robust unsupervised adaptation, the condition classifier DNN Mc is designed to predict the posteriors of different environments at the output layer. As the adaptation data comes from both the clean and noisy environments, we first use an Mc with 2 output units to predict these two environments. The Nh and λ are fixed at 4 and 5.0 respectively in all our experiments. To achieve speaker-robust unsupervised adaptation, Mc is designed to predict the posteriors of different speaker identities at the output layer. The 7138 simulated and 1999 real noisy utterances in CHiME-3 training set are dictated by 83 and 4 different speakers respectively and the 9137 clean utterances are read by the same speakers. In speaker-robust adversarial T/S adaptation, an Mc with 87 output units are used to predict the posteriors of the 87 speakers. Larger WER improvement is achieved by speaker-robust unsupervised adaptation than the environment-robust methods. This is because T/S learning itself is able to reduce the environment variability through directly teaching the noisy student model with the senone posteriors from the clean data, which limits the space of improvement that environment-robust adversarial T/S learning can obtain. Speaker and environment robustness can be achieved simultaneously in unsupervised adaptation through MFA T/S learning, in which we need two condition classifiers: M1c predicts the posteriors of 87 speakers and M2c predicts the posteriors of 1 clean and 5 noisy environments in the adaptation data. The MFA T/S achieves lower WER than all the unifactorial adversarial T/S systems because it addresses the variations caused by all kinds of factors." } ], "images": [ "scigen/train/2576.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The WER (%) performance of adversarial T/S learning adapted LSTM acoustic models for robust ASR on the real noisy channel 5 test set of CHiME-3. The adaptation data consists of “clean-noisy” and “clean-clean”." ], "reasoning": "Speaker-robust unsupervised adaptation achieves better WER improvement than environment-robust methods, and MFA T/S learning, which uses two condition classifiers, achieves the lowest WER.", "description": "To achieve environment-robust unsupervised adaptation, the condition classifier DNN Mc is designed to predict the posteriors of different environments at the output layer. As the adaptation data comes from both the clean and noisy environments, we first use an Mc with 2 output units to predict these two environments. The Nh and λ are fixed at 4 and 5.0 respectively in all our experiments. To achieve speaker-robust unsupervised adaptation, Mc is designed to predict the posteriors of different speaker identities at the output layer. The 7138 simulated and 1999 real noisy utterances in CHiME-3 training set are dictated by 83 and 4 different speakers respectively and the 9137 clean utterances are read by the same speakers. In speaker-robust adversarial T/S adaptation, an Mc with 87 output units are used to predict the posteriors of the 87 speakers. Larger WER improvement is achieved by speaker-robust unsupervised adaptation than the environment-robust methods. This is because T/S learning itself is able to reduce the environment variability through directly teaching the noisy student model with the senone posteriors from the clean data, which limits the space of improvement that environment-robust adversarial T/S learning can obtain. Speaker and environment robustness can be achieved simultaneously in unsupervised adaptation through MFA T/S learning, in which we need two condition classifiers: M1c predicts the posteriors of 87 speakers and M2c predicts the posteriors of 1 clean and 5 noisy environments in the adaptation data. The MFA T/S achieves lower WER than all the unifactorial adversarial T/S systems because it addresses the variations caused by all kinds of factors.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nTable title: Table 2: The WER (%) performance of adversarial T/S learning adapted LSTM acoustic models for robust ASR on the real noisy channel 5 test set of CHiME-3. The adaptation data consists of “clean-noisy” and “clean-clean”.\n\nQuestion: What is the average WER for the adversarial T/S system that uses 87 speakers and 6 environments?\n" }, { "role": "assistant", "content": "The table lists the average WER for different adversarial T/S systems. The system with 87 speakers and 6 environments has an average WER of 12.83. So, the answer is {\"answer\": \"12.83\"}." } ], "images": [ "scigen/train/2576.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The WER (%) performance of adversarial T/S learning adapted LSTM acoustic models for robust ASR on the real noisy channel 5 test set of CHiME-3. The adaptation data consists of “clean-noisy” and “clean-clean”." ], "reasoning": "The table lists the average WER for different adversarial T/S systems. The system with 87 speakers and 6 environments has an average WER of 12.83.", "question": "What is the average WER for the adversarial T/S system that uses 87 speakers and 6 environments?", "answer": "So, the answer is {\"answer\": \"12.83\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe adversarial T/S system with 87 speakers has a higher average WER than the system with 2 environments.\n" }, { "role": "assistant", "content": "The table shows that the average WER for the system with 87 speakers is 12.90, while the system with 2 environments has an average WER of 13.15. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2576.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The WER (%) performance of adversarial T/S learning adapted LSTM acoustic models for robust ASR on the real noisy channel 5 test set of CHiME-3. The adaptation data consists of “clean-noisy” and “clean-clean”." ], "reasoning": "The table shows that the average WER for the system with 87 speakers is 12.90, while the system with 2 environments has an average WER of 13.15. Therefore, the statement is refuted.", "statement": "The adversarial T/S system with 87 speakers has a higher average WER than the system with 2 environments.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: BLEU scores of NMT models trained on bitext data mined from aligned documents on TED Talk test sets. Volume given as number of distinct aligned sentence pairs.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "European languages generally yield higher quality sentences than non-European languages, regardless of resource levels.the number of distinct aligned sentence pairs (reported in millions). Based on these results, it appears that European languages yield higher quality sentences than non-European regardless of the resource level of the direction. Additionally, documents aligned across high-resource directions yield enough high-quality aligned data to learn high-quality models. While these bleu scores should be taken in context of the volume of aligned bitexts, one can get an intuition as to the quality of the underlying URL-aligned documents the sentences were mined from from the resultant test-set BLEU scores." } ], "images": [ "scigen/train/2577.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: BLEU scores of NMT models trained on bitext data mined from aligned documents on TED Talk test sets. Volume given as number of distinct aligned sentence pairs." ], "reasoning": "European languages generally yield higher quality sentences than non-European languages, regardless of resource levels.", "description": "the number of distinct aligned sentence pairs (reported in millions). Based on these results, it appears that European languages yield higher quality sentences than non-European regardless of the resource level of the direction. Additionally, documents aligned across high-resource directions yield enough high-quality aligned data to learn high-quality models. While these bleu scores should be taken in context of the volume of aligned bitexts, one can get an intuition as to the quality of the underlying URL-aligned documents the sentences were mined from from the resultant test-set BLEU scores.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: BLEU scores of NMT models trained on bitext data mined from aligned documents on TED Talk test sets. Volume given as number of distinct aligned sentence pairs.', unfold the steps to solve the question given next. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nQuestion: Which language has the highest BLEU score for translations from English to another language?\n" }, { "role": "assistant", "content": "To find the language with the highest BLEU score for translations from English to another language, we need to look at the 'BLEU En-x' column and identify the highest value. So, the answer is {\"answer\": \"Macedonian\"}" } ], "images": [ "scigen/train/2577.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: BLEU scores of NMT models trained on bitext data mined from aligned documents on TED Talk test sets. Volume given as number of distinct aligned sentence pairs." ], "reasoning": "To find the language with the highest BLEU score for translations from English to another language, we need to look at the 'BLEU En-x' column and identify the highest value.", "question": "Which language has the highest BLEU score for translations from English to another language?", "answer": "So, the answer is {\"answer\": \"Macedonian\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nKazakh has a higher BLEU score for translations from another language to English than from English to another language.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for Kazakh translations from another language to English (x-En) is 6.2, while the BLEU score for translations from English to another language (En-x) is 3.4. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2577.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: BLEU scores of NMT models trained on bitext data mined from aligned documents on TED Talk test sets. Volume given as number of distinct aligned sentence pairs." ], "reasoning": "The table shows that the BLEU score for Kazakh translations from another language to English (x-En) is 6.2, while the BLEU score for translations from English to another language (En-x) is 3.4. Therefore, the statement is supported.", "statement": "Kazakh has a higher BLEU score for translations from another language to English than from English to another language.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: BLEU scores of NMT models trained on bitext data mined from aligned documents on TED Talk test sets. Volume given as number of distinct aligned sentence pairs.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "European languages generally yield higher quality sentences than non-European languages, and high-resource directions provide sufficient high-quality aligned data for learning effective models.the number of distinct aligned sentence pairs (reported in millions). Based on these results, it appears that European languages yield higher quality sentences than non-European regardless of the resource level of the direction. Additionally, documents aligned across high-resource directions yield enough high-quality aligned data to learn high-quality models. While these bleu scores should be taken in context of the volume of aligned bitexts, one can get an intuition as to the quality of the underlying URL-aligned documents the sentences were mined from from the resultant test-set BLEU scores." } ], "images": [ "scigen/train/2578.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: BLEU scores of NMT models trained on bitext data mined from aligned documents on TED Talk test sets. Volume given as number of distinct aligned sentence pairs." ], "reasoning": "European languages generally yield higher quality sentences than non-European languages, and high-resource directions provide sufficient high-quality aligned data for learning effective models.", "description": "the number of distinct aligned sentence pairs (reported in millions). Based on these results, it appears that European languages yield higher quality sentences than non-European regardless of the resource level of the direction. Additionally, documents aligned across high-resource directions yield enough high-quality aligned data to learn high-quality models. While these bleu scores should be taken in context of the volume of aligned bitexts, one can get an intuition as to the quality of the underlying URL-aligned documents the sentences were mined from from the resultant test-set BLEU scores.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 3: BLEU scores of NMT models trained on bitext data mined from aligned documents on TED Talk test sets. Volume given as number of distinct aligned sentence pairs.', solve the following question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich language has the highest BLEU score for translations from English to another language?\n" }, { "role": "assistant", "content": "To find the language with the highest BLEU score for translations from English to another language, we need to look at the 'BLEU En→x' column and identify the highest value. So, the answer is {\"answer\": \"Norwegian\"}." } ], "images": [ "scigen/train/2578.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: BLEU scores of NMT models trained on bitext data mined from aligned documents on TED Talk test sets. Volume given as number of distinct aligned sentence pairs." ], "reasoning": "To find the language with the highest BLEU score for translations from English to another language, we need to look at the 'BLEU En→x' column and identify the highest value.", "question": "Which language has the highest BLEU score for translations from English to another language?", "answer": "So, the answer is {\"answer\": \"Norwegian\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nFrench has a higher BLEU score for translations from English to French than from French to English.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for translations from English to French (36.2) is higher than the BLEU score for translations from French to English (34.2). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2578.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: BLEU scores of NMT models trained on bitext data mined from aligned documents on TED Talk test sets. Volume given as number of distinct aligned sentence pairs." ], "reasoning": "The table shows that the BLEU score for translations from English to French (36.2) is higher than the BLEU score for translations from French to English (34.2).", "statement": "French has a higher BLEU score for translations from English to French than from French to English.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: BLEU scores of NMT models trained on bitext data mined from aligned documents on TED Talk test sets. Volume given as number of distinct aligned sentence pairs.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "European languages generally yield higher quality sentences than non-European languages, and high-resource directions provide enough data to learn high-quality models.the number of distinct aligned sentence pairs (reported in millions). Based on these results, it appears that European languages yield higher quality sentences than non-European regardless of the resource level of the direction. Additionally, documents aligned across high-resource directions yield enough high-quality aligned data to learn high-quality models. While these bleu scores should be taken in context of the volume of aligned bitexts, one can get an intuition as to the quality of the underlying URL-aligned documents the sentences were mined from from the resultant test-set BLEU scores." } ], "images": [ "scigen/train/2579.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: BLEU scores of NMT models trained on bitext data mined from aligned documents on TED Talk test sets. Volume given as number of distinct aligned sentence pairs." ], "reasoning": "European languages generally yield higher quality sentences than non-European languages, and high-resource directions provide enough data to learn high-quality models.", "description": "the number of distinct aligned sentence pairs (reported in millions). Based on these results, it appears that European languages yield higher quality sentences than non-European regardless of the resource level of the direction. Additionally, documents aligned across high-resource directions yield enough high-quality aligned data to learn high-quality models. While these bleu scores should be taken in context of the volume of aligned bitexts, one can get an intuition as to the quality of the underlying URL-aligned documents the sentences were mined from from the resultant test-set BLEU scores.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: BLEU scores of NMT models trained on bitext data mined from aligned documents on TED Talk test sets. Volume given as number of distinct aligned sentence pairs.', unfold the question presented below, detailing every step of your calculation. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich language has the highest BLEU score for translations from English to another language?\n" }, { "role": "assistant", "content": "To find the language with the highest BLEU score for translations from English to another language, we need to look at the 'BLEU En→x' column and identify the highest value. So, the answer is {\"answer\": \"Vietnamese\"}" } ], "images": [ "scigen/train/2579.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: BLEU scores of NMT models trained on bitext data mined from aligned documents on TED Talk test sets. Volume given as number of distinct aligned sentence pairs." ], "reasoning": "To find the language with the highest BLEU score for translations from English to another language, we need to look at the 'BLEU En→x' column and identify the highest value.", "question": "Which language has the highest BLEU score for translations from English to another language?", "answer": "So, the answer is {\"answer\": \"Vietnamese\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nGreek has a higher BLEU score for translations from another language to English than from English to another language.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the BLEU scores for Greek in both directions. The BLEU score for En→x is 22.4, and for x→En is 21.8. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2579.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: BLEU scores of NMT models trained on bitext data mined from aligned documents on TED Talk test sets. Volume given as number of distinct aligned sentence pairs." ], "reasoning": "To verify this statement, we need to compare the BLEU scores for Greek in both directions. The BLEU score for En→x is 22.4, and for x→En is 21.8.", "statement": "Greek has a higher BLEU score for translations from another language to English than from English to another language.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Content-based Document Alignment Recall'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "SAE outperforms DDE in all resource categories, likely due to better handling of long documents and less influence from noisy content.Comparing DDE which directly applies LASER to the entirety of the document content, we see that performance is significantly lower than SAE which averages the individual sentence embeddings. We suspect this may be the case for two reasons (1) sentence encoders may suffer at representing the semantic meaning of long documents (2) there may be noisy boiler plate content at the beginning of each web document that is less useful semantically but dominates the representation. Intuitively, higher-resource directions appear to align better than lower-resource directions. We believe this is a byproduct of the data LASER was trained on which is predominantly high-resource." } ], "images": [ "scigen/train/2580.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Content-based Document Alignment Recall" ], "reasoning": "SAE outperforms DDE in all resource categories, likely due to better handling of long documents and less influence from noisy content.", "description": "Comparing DDE which directly applies LASER to the entirety of the document content, we see that performance is significantly lower than SAE which averages the individual sentence embeddings. We suspect this may be the case for two reasons (1) sentence encoders may suffer at representing the semantic meaning of long documents (2) there may be noisy boiler plate content at the beginning of each web document that is less useful semantically but dominates the representation. Intuitively, higher-resource directions appear to align better than lower-resource directions. We believe this is a byproduct of the data LASER was trained on which is predominantly high-resource.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Content-based Document Alignment Recall\nQuestion: Which method has the highest similarity score in the high-resource category?\nGive a detailed response to the above question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the similarity scores in the high-resource category, SAE has a score of 0.68, which is higher than DDE's score of 0.24. So, the answer is {\"answer\": \"SAE\"}." } ], "images": [ "scigen/train/2580.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Content-based Document Alignment Recall" ], "reasoning": "By comparing the similarity scores in the high-resource category, SAE has a score of 0.68, which is higher than DDE's score of 0.24.", "question": "Which method has the highest similarity score in the high-resource category?", "answer": "So, the answer is {\"answer\": \"SAE\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nDDE performs better than SAE in the low-resource category.\n" }, { "role": "assistant", "content": "The table shows that in the low-resource category, DDE has a similarity score of 0.08, while SAE has a higher score of 0.27. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2580.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Content-based Document Alignment Recall" ], "reasoning": "The table shows that in the low-resource category, DDE has a similarity score of 0.08, while SAE has a higher score of 0.27.", "statement": "DDE performs better than SAE in the low-resource category.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: Precision@k accuracies for Neural Sieves and state-of-the-art systems. Note that even without using the referring expression, Sieve I is able to reduce the number of candidate boxes to 3 for 86.6% of the instances. When we further predict the type of objects with Sieve II, the number of candidate boxes is reduced to 2 for 84.2% of the instances.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Combining both sieves into a pipeline increases the top-2 prediction accuracy to 84.2% and the top-3 prediction accuracy to 95.3%.The referred object is in the top-2 candidates selected by Sieve I 71.2% of the time and in the top-3 predictions 86.6% of the time. Combining both sieves into a pipeline, these numbers further increase to 84.2% for top-2 predictions and to 95.3% for top-3 predictions. Considering the simplicity of Neural Sieve approach, these are surprising results: two simple neural network systems, the first one ignoring the referring expression, the second predicting only object type, are able to reduce the number of candidate boxes down to 2 on 84.2% of instances." } ], "images": [ "scigen/train/2581.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Precision@k accuracies for Neural Sieves and state-of-the-art systems. Note that even without using the referring expression, Sieve I is able to reduce the number of candidate boxes to 3 for 86.6% of the instances. When we further predict the type of objects with Sieve II, the number of candidate boxes is reduced to 2 for 84.2% of the instances." ], "reasoning": "Combining both sieves into a pipeline increases the top-2 prediction accuracy to 84.2% and the top-3 prediction accuracy to 95.3%.", "description": "The referred object is in the top-2 candidates selected by Sieve I 71.2% of the time and in the top-3 predictions 86.6% of the time. Combining both sieves into a pipeline, these numbers further increase to 84.2% for top-2 predictions and to 95.3% for top-3 predictions. Considering the simplicity of Neural Sieve approach, these are surprising results: two simple neural network systems, the first one ignoring the referring expression, the second predicting only object type, are able to reduce the number of candidate boxes down to 2 on 84.2% of instances.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Precision@k accuracies for Neural Sieves and state-of-the-art systems. Note that even without using the referring expression, Sieve I is able to reduce the number of candidate boxes to 3 for 86.6% of the instances. When we further predict the type of objects with Sieve II, the number of candidate boxes is reduced to 2 for 84.2% of the instances.\nQuestion: What is the accuracy of Neural Sieve I + II for precision@2?\nSolve the above question based on the table information. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the accuracy of Neural Sieve I + II for precision@2 as 0.842. So, the answer is {\"answer\": \"0.842\"}." } ], "images": [ "scigen/train/2581.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Precision@k accuracies for Neural Sieves and state-of-the-art systems. Note that even without using the referring expression, Sieve I is able to reduce the number of candidate boxes to 3 for 86.6% of the instances. When we further predict the type of objects with Sieve II, the number of candidate boxes is reduced to 2 for 84.2% of the instances." ], "reasoning": "The table shows the accuracy of Neural Sieve I + II for precision@2 as 0.842.", "question": "What is the accuracy of Neural Sieve I + II for precision@2?", "answer": "So, the answer is {\"answer\": \"0.842\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe accuracy of Neural Sieve I for precision@3 is higher than that of LSTM+CNN-MIL for precision@1.\n" }, { "role": "assistant", "content": "The table shows that the accuracy of Neural Sieve I for precision@3 is 0.866, while the accuracy of LSTM+CNN-MIL for precision@1 is 0.684. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2581.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Precision@k accuracies for Neural Sieves and state-of-the-art systems. Note that even without using the referring expression, Sieve I is able to reduce the number of candidate boxes to 3 for 86.6% of the instances. When we further predict the type of objects with Sieve II, the number of candidate boxes is reduced to 2 for 84.2% of the instances." ], "reasoning": "The table shows that the accuracy of Neural Sieve I for precision@3 is 0.866, while the accuracy of LSTM+CNN-MIL for precision@1 is 0.684. Therefore, the statement is supported.", "statement": "The accuracy of Neural Sieve I for precision@3 is higher than that of LSTM+CNN-MIL for precision@1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Best hyper parameters for neural methods. Nl: number of layers, HU: hidden units size, Dd: dropout rate across dimensions, Dwe: dropout rate of word embeddings, BS: batch size. * Hidden units size is fixed to word embedding dimensionality, + Nl, HU are fixed from the pre-trained model. Dropout rate fixed as suggested by Devlin et al. (2018).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The model was trained with a dropout rate of 0.1 and a learning rate of 5e-5, and early-stopping was used after the fourth epoch, resulting in an average of eight to nine epochs over five runs.Concerning bert, we set the dropout rate and learning rate to 0.1 and 5e-5, respectively, as suggested by Devlin et al. Finally, we noticed that the model did not converge in the fourth epoch, as suggested by Devlin et al. Thus we used early-stopping with no patience and trained the model for eight to nine epochs on average among the five runs." } ], "images": [ "scigen/train/2582.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Best hyper parameters for neural methods. Nl: number of layers, HU: hidden units size, Dd: dropout rate across dimensions, Dwe: dropout rate of word embeddings, BS: batch size. * Hidden units size is fixed to word embedding dimensionality, + Nl, HU are fixed from the pre-trained model. Dropout rate fixed as suggested by Devlin et al. (2018)." ], "reasoning": "The model was trained with a dropout rate of 0.1 and a learning rate of 5e-5, and early-stopping was used after the fourth epoch, resulting in an average of eight to nine epochs over five runs.", "description": "Concerning bert, we set the dropout rate and learning rate to 0.1 and 5e-5, respectively, as suggested by Devlin et al. Finally, we noticed that the model did not converge in the fourth epoch, as suggested by Devlin et al. Thus we used early-stopping with no patience and trained the model for eight to nine epochs on average among the five runs.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 5: Best hyper parameters for neural methods. Nl: number of layers, HU: hidden units size, Dd: dropout rate across dimensions, Dwe: dropout rate of word embeddings, BS: batch size. * Hidden units size is fixed to word embedding dimensionality, + Nl, HU are fixed from the pre-trained model. Dropout rate fixed as suggested by Devlin et al. (2018).\nQuestion: What is the HUe value for the bert-base model?\n" }, { "role": "assistant", "content": "The table lists the HUe value for each model under the 'HUe' row. For the bert-base model, the HUe value is 768. So, the answer is {\"answer\": \"768\"}." } ], "images": [ "scigen/train/2582.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Best hyper parameters for neural methods. Nl: number of layers, HU: hidden units size, Dd: dropout rate across dimensions, Dwe: dropout rate of word embeddings, BS: batch size. * Hidden units size is fixed to word embedding dimensionality, + Nl, HU are fixed from the pre-trained model. Dropout rate fixed as suggested by Devlin et al. (2018)." ], "reasoning": "The table lists the HUe value for each model under the 'HUe' row. For the bert-base model, the HUe value is 768.", "question": "What is the HUe value for the bert-base model?", "answer": "So, the answer is {\"answer\": \"768\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The bert-base model has a Dwee value of 0.01. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe bert-base model has a Dwee value of 0.01.\n" }, { "role": "assistant", "content": "The table lists the Dwee values for each model under the 'Dwee' row. For the bert-base model, the Dwee value is 0.00, not 0.01. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2582.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Best hyper parameters for neural methods. Nl: number of layers, HU: hidden units size, Dd: dropout rate across dimensions, Dwe: dropout rate of word embeddings, BS: batch size. * Hidden units size is fixed to word embedding dimensionality, + Nl, HU are fixed from the pre-trained model. Dropout rate fixed as suggested by Devlin et al. (2018)." ], "reasoning": "The table lists the Dwee values for each model under the 'Dwee' row. For the bert-base model, the Dwee value is 0.00, not 0.01.", "statement": "The bert-base model has a Dwee value of 0.01.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Results on eurlex57k for all, frequent, few-shot, zero-shot labels. Starred methods use the first 512 document tokens; all other methods use full documents. Unless otherwise stated, glove embeddings are used.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "bigru-lwan outperforms cnn-lwan in all, frequent, and few-shot labels, indicating the main weakness of cnn-lwan is its vanilla cnn encoder.The naive baselines are weak, as expected. Interestingly, for all, frequent, and even few-shot labels, the generic bigru-att performs better than cnn-lwan, which was designed for lmtc. han also performs better than cnn-lwan for all and frequent labels. However, replacing the cnn encoder of cnn-lwan with a bigru (bigru-lwan) leads to the best results, indicating that the main weakness of cnn-lwan is its vanilla cnn encoder. Exact Match also performs better on zero-shot labels, for the same reason (i.e., the prior knowledge is intact). bigru-lwan, however, is still the best method in few-shot learning. Given that H+R contains enough information and is shorter than 500 tokens in 83% of our dataset’s documents, we also apply bert to the first 512 tokens of each document (truncated to bert ’s max. length), comparing to bigru-lwan also operating on the first 512 tokens. It fails, however, in zero-shot learning, since it does not have a component that exploits prior knowledge as is (i.e., all the components are fine-tuned on training data)." } ], "images": [ "scigen/train/2583.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on eurlex57k for all, frequent, few-shot, zero-shot labels. Starred methods use the first 512 document tokens; all other methods use full documents. Unless otherwise stated, glove embeddings are used." ], "reasoning": "bigru-lwan outperforms cnn-lwan in all, frequent, and few-shot labels, indicating the main weakness of cnn-lwan is its vanilla cnn encoder.", "description": "The naive baselines are weak, as expected. Interestingly, for all, frequent, and even few-shot labels, the generic bigru-att performs better than cnn-lwan, which was designed for lmtc. han also performs better than cnn-lwan for all and frequent labels. However, replacing the cnn encoder of cnn-lwan with a bigru (bigru-lwan) leads to the best results, indicating that the main weakness of cnn-lwan is its vanilla cnn encoder. Exact Match also performs better on zero-shot labels, for the same reason (i.e., the prior knowledge is intact). bigru-lwan, however, is still the best method in few-shot learning. Given that H+R contains enough information and is shorter than 500 tokens in 83% of our dataset’s documents, we also apply bert to the first 512 tokens of each document (truncated to bert ’s max. length), comparing to bigru-lwan also operating on the first 512 tokens. It fails, however, in zero-shot learning, since it does not have a component that exploits prior knowledge as is (i.e., all the components are fine-tuned on training data).", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: Results on eurlex57k for all, frequent, few-shot, zero-shot labels. Starred methods use the first 512 document tokens; all other methods use full documents. Unless otherwise stated, glove embeddings are used.' and methodically tackle the question that follows. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method performs best for zero-shot labels in terms of nDCG@5?\n" }, { "role": "assistant", "content": "The table shows that zero-bigru-lwan has the highest nDCG@5 score for zero-shot labels, with a value of 0.345. So, the answer is {\"answer\": \"zero-bigru-lwan\"}" } ], "images": [ "scigen/train/2583.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on eurlex57k for all, frequent, few-shot, zero-shot labels. Starred methods use the first 512 document tokens; all other methods use full documents. Unless otherwise stated, glove embeddings are used." ], "reasoning": "The table shows that zero-bigru-lwan has the highest nDCG@5 score for zero-shot labels, with a value of 0.345.", "question": "Which method performs best for zero-shot labels in terms of nDCG@5?", "answer": "So, the answer is {\"answer\": \"zero-bigru-lwan\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nbigru-lwan performs better than cnn-lwan for all, frequent, and few-shot labels.\n" }, { "role": "assistant", "content": "The table shows that bigru-lwan has higher scores than cnn-lwan for all labels (RP@5, nDCG@5, Micro-F1), frequent labels (RP@5, nDCG@5), and few-shot labels (RP@5, nDCG@5). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2583.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on eurlex57k for all, frequent, few-shot, zero-shot labels. Starred methods use the first 512 document tokens; all other methods use full documents. Unless otherwise stated, glove embeddings are used." ], "reasoning": "The table shows that bigru-lwan has higher scores than cnn-lwan for all labels (RP@5, nDCG@5, Micro-F1), frequent labels (RP@5, nDCG@5), and few-shot labels (RP@5, nDCG@5).", "statement": "bigru-lwan performs better than cnn-lwan for all, frequent, and few-shot labels.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3. RQ1: Automatic evaluation comparison. Significant differences are with respect to MRFN.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "SRS outperforms both multi-turn response selection models and visual Q&A models, showing significant improvements in MAP, R10@1, and R10@2 over the state-of-the-art models MRFN and PSAC.First, the performance of the multi-turn response selection models is generally consistent with their performances on text response selection datasets. SMN ( , obtains the worst performance on both sticker response and text response selection. MRFN is the state-of-the-art text response selection model and achieves the best performance among baselines in our task as well. Second, VQA models perform generally worse than multi-turn response selection models, since the interaction between multi-turn utterances and sticker is important, which is not taken into account by VQA models. Finally, SRS achieves the best performance with 3.36%, 5.92% and 3.72% improvements in MAP, R10@1 and R10@2 respectively, over the state-of-the-art multi-turn selection model, i.e. ,MRFN, and with 6.80%, 10.69% and 8.74% significant increases over the state-of-the-art visual dialog model, PSAC. This proves the superiority of our model." } ], "images": [ "scigen/train/2584.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. RQ1: Automatic evaluation comparison. Significant differences are with respect to MRFN." ], "reasoning": "SRS outperforms both multi-turn response selection models and visual Q&A models, showing significant improvements in MAP, R10@1, and R10@2 over the state-of-the-art models MRFN and PSAC.", "description": "First, the performance of the multi-turn response selection models is generally consistent with their performances on text response selection datasets. SMN ( , obtains the worst performance on both sticker response and text response selection. MRFN is the state-of-the-art text response selection model and achieves the best performance among baselines in our task as well. Second, VQA models perform generally worse than multi-turn response selection models, since the interaction between multi-turn utterances and sticker is important, which is not taken into account by VQA models. Finally, SRS achieves the best performance with 3.36%, 5.92% and 3.72% improvements in MAP, R10@1 and R10@2 respectively, over the state-of-the-art multi-turn selection model, i.e. ,MRFN, and with 6.80%, 10.69% and 8.74% significant increases over the state-of-the-art visual dialog model, PSAC. This proves the superiority of our model.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 3. RQ1: Automatic evaluation comparison. Significant differences are with respect to MRFN.', solve this question and give detail solutions. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich model achieves the highest R10@2 score, and what is the value?\n" }, { "role": "assistant", "content": "By comparing the R10@2 scores of all models in the table, SRS achieves the highest R10@2 score with a value of 0.703. So, the answer is {\"answer\": \"SRS with 0.703\"}" } ], "images": [ "scigen/train/2584.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. RQ1: Automatic evaluation comparison. Significant differences are with respect to MRFN." ], "reasoning": "By comparing the R10@2 scores of all models in the table, SRS achieves the highest R10@2 score with a value of 0.703.", "question": "Which model achieves the highest R10@2 score, and what is the value?", "answer": "So, the answer is {\"answer\": \"SRS with 0.703\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nMRFN achieves the highest MAP score among all models.\n" }, { "role": "assistant", "content": "The table shows that SRS achieves the highest MAP score of 0.709, while MRFN has a MAP score of 0.684. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2584.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. RQ1: Automatic evaluation comparison. Significant differences are with respect to MRFN." ], "reasoning": "The table shows that SRS achieves the highest MAP score of 0.709, while MRFN has a MAP score of 0.684.", "statement": "MRFN achieves the highest MAP score among all models.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4. RQ2: Evaluation of different ablation models.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The fusion RNN and deep interaction network significantly improve the performance of SRS, while the sticker classification speeds up the training process.For research question RQ2, we conduct ablation tests on the use of the pre-trained Inception-v3 model, the sticker classification loss, the deep interaction network and the fusion RNN respectively. The performances of all ablation models are worse than that of SRS under all metrics, which demonstrates the necessity of each component in SRS. We also find that the sticker classification makes the least contribution to the overall performance. But this additional task can speed up the training process, and help our model to converge quickly. We use 19 hours to train the SRS until convergence, and we use 30 hours for training SRS w/o Classify. The fusion RNN brings a significant contribution, improving the MAP and R10@1 scores by 4.43% and 7.08%, respectively. Besides, the deep interaction network also plays an important part. Without this module, the interaction between the sticker and utterance are hindered, leading to a 6.88% drop in R10@1." } ], "images": [ "scigen/train/2585.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4. RQ2: Evaluation of different ablation models." ], "reasoning": "The fusion RNN and deep interaction network significantly improve the performance of SRS, while the sticker classification speeds up the training process.", "description": "For research question RQ2, we conduct ablation tests on the use of the pre-trained Inception-v3 model, the sticker classification loss, the deep interaction network and the fusion RNN respectively. The performances of all ablation models are worse than that of SRS under all metrics, which demonstrates the necessity of each component in SRS. We also find that the sticker classification makes the least contribution to the overall performance. But this additional task can speed up the training process, and help our model to converge quickly. We use 19 hours to train the SRS until convergence, and we use 30 hours for training SRS w/o Classify. The fusion RNN brings a significant contribution, improving the MAP and R10@1 scores by 4.43% and 7.08%, respectively. Besides, the deep interaction network also plays an important part. Without this module, the interaction between the sticker and utterance are hindered, leading to a 6.88% drop in R10@1.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4. RQ2: Evaluation of different ablation models.', solve this question and give detail solutions. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nHow much does the fusion RNN improve the R10@1 score?\n" }, { "role": "assistant", "content": "The R10@1 score for SRS is 0.590, and for SRS w/o FR it is 0.551. The improvement is 0.590 - 0.551 = 0.039 or 3.9%. So, the answer is {\"answer\": \"3.9%\"}" } ], "images": [ "scigen/train/2585.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4. RQ2: Evaluation of different ablation models." ], "reasoning": "The R10@1 score for SRS is 0.590, and for SRS w/o FR it is 0.551. The improvement is 0.590 - 0.551 = 0.039 or 3.9%.", "question": "How much does the fusion RNN improve the R10@1 score?", "answer": "So, the answer is {\"answer\": \"3.9%\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe deep interaction network improves the R10@1 score by 6.88%.\n" }, { "role": "assistant", "content": "The R10@1 score for SRS is 0.590, and for SRS w/o DIN it is 0.552. The difference is 0.590 - 0.552 = 0.038 or 3.8%, not 6.88%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2585.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4. RQ2: Evaluation of different ablation models." ], "reasoning": "The R10@1 score for SRS is 0.590, and for SRS w/o DIN it is 0.552. The difference is 0.590 - 0.552 = 0.038 or 3.8%, not 6.88%.", "statement": "The deep interaction network improves the R10@1 score by 6.88%.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: BLEU scores on Paracrawl and WMT En-Fr datasets with uniform, heuristic and learned curricula.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Uniform (6-bins) performs better than the standard NMT baseline, likely due to more homogeneous mini-batches.Uniform baselines consist of: Uniform – standard NMT training; Uniform (6-bins) – uniform sampling over all bins; and Uniform (bookends) – uniform sampling over just the best and worst bins. Surprisingly, 6-bins performs better than the standard NMT baseline. We hypothesize that this can be attributed to more homogeneous mini-batches. Our exploration strategy for Q-learning (see This mimics the gradual annealing of the online curriculum, so one possibility is that the agent is simply choosing the cleanest bin whenever it can, and its good performance comes from the enforced period of exploration." } ], "images": [ "scigen/train/2586.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: BLEU scores on Paracrawl and WMT En-Fr datasets with uniform, heuristic and learned curricula." ], "reasoning": "Uniform (6-bins) performs better than the standard NMT baseline, likely due to more homogeneous mini-batches.", "description": "Uniform baselines consist of: Uniform – standard NMT training; Uniform (6-bins) – uniform sampling over all bins; and Uniform (bookends) – uniform sampling over just the best and worst bins. Surprisingly, 6-bins performs better than the standard NMT baseline. We hypothesize that this can be attributed to more homogeneous mini-batches. Our exploration strategy for Q-learning (see This mimics the gradual annealing of the online curriculum, so one possibility is that the agent is simply choosing the cleanest bin whenever it can, and its good performance comes from the enforced period of exploration.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nTable title: Table 1: BLEU scores on Paracrawl and WMT En-Fr datasets with uniform, heuristic and learned curricula.\n\nQuestion: Which uniform baseline performs the best on the Paracrawl dataset?\n" }, { "role": "assistant", "content": "To determine the best performing uniform baseline on the Paracrawl dataset, compare the scores of Uniform, Uniform (6-bins), and Uniform (bookends). Uniform (bookends) has the highest score of 35.0. So, the answer is {\"answer\": \"Uniform (bookends)\"}" } ], "images": [ "scigen/train/2586.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: BLEU scores on Paracrawl and WMT En-Fr datasets with uniform, heuristic and learned curricula." ], "reasoning": "To determine the best performing uniform baseline on the Paracrawl dataset, compare the scores of Uniform, Uniform (6-bins), and Uniform (bookends). Uniform (bookends) has the highest score of 35.0.", "question": "Which uniform baseline performs the best on the Paracrawl dataset?", "answer": "So, the answer is {\"answer\": \"Uniform (bookends)\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Q-learning (6-bins) method performs better than the Online method on the WMT dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Q-learning (6-bins) method performs better than the Online method on the WMT dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the scores of Q-learning (6-bins) and Online on the WMT dataset. Q-learning (6-bins) has a score of 38.4, while Online has a score of 37.7. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2586.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: BLEU scores on Paracrawl and WMT En-Fr datasets with uniform, heuristic and learned curricula." ], "reasoning": "To verify this statement, compare the scores of Q-learning (6-bins) and Online on the WMT dataset. Q-learning (6-bins) has a score of 38.4, while Online has a score of 37.7.", "statement": "The Q-learning (6-bins) method performs better than the Online method on the WMT dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: BLEU scores on ablation experiments with fixed rewards or observations.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Ablation experiments show that both the chosen reward and observation settings outperform fixed settings, indicating their importance in the curriculum learning task.Task-specific reward and observation engineering is critical when building an RL model. We performed ablation experiments to determine if the rewards and observations we have chosen contain information which aids us in the curriculum learning task. The fixed reward experiments were conducted by replacing the default delta-perplexity based reward with a static reward which returns a reward of one when the cleanest bin was selected and zero otherwise. The fixed observation experiments used a static vector of ones as input at all time steps. Both settings perform worse than our default setup, indicating that both the chosen observation and reward provide information to help with this task." } ], "images": [ "scigen/train/2587.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: BLEU scores on ablation experiments with fixed rewards or observations." ], "reasoning": "Ablation experiments show that both the chosen reward and observation settings outperform fixed settings, indicating their importance in the curriculum learning task.", "description": "Task-specific reward and observation engineering is critical when building an RL model. We performed ablation experiments to determine if the rewards and observations we have chosen contain information which aids us in the curriculum learning task. The fixed reward experiments were conducted by replacing the default delta-perplexity based reward with a static reward which returns a reward of one when the cleanest bin was selected and zero otherwise. The fixed observation experiments used a static vector of ones as input at all time steps. Both settings perform worse than our default setup, indicating that both the chosen observation and reward provide information to help with this task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: BLEU scores on ablation experiments with fixed rewards or observations.\nQuestion: What is the performance difference between the default and fixed reward settings when using the default observation?\nSolve the above question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows that the performance with the default reward and default observation is 37.5, while the performance with the fixed reward and default observation is 32.5. The difference is 37.5 - 32.5. So, the answer is {\"answer\": \"5.0\"}" } ], "images": [ "scigen/train/2587.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: BLEU scores on ablation experiments with fixed rewards or observations." ], "reasoning": "The table shows that the performance with the default reward and default observation is 37.5, while the performance with the fixed reward and default observation is 32.5. The difference is 37.5 - 32.5.", "question": "What is the performance difference between the default and fixed reward settings when using the default observation?", "answer": "So, the answer is {\"answer\": \"5.0\"}" }, { "messages": [ { "role": "user", "content": "The default reward and observation setting performs better than the fixed reward and observation setting. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe default reward and observation setting performs better than the fixed reward and observation setting.\n" }, { "role": "assistant", "content": "The table shows that the default reward and observation setting has a performance of 37.5, while the fixed reward and observation setting has a performance of 32.5, indicating that the default setting performs better. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2587.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: BLEU scores on ablation experiments with fixed rewards or observations." ], "reasoning": "The table shows that the default reward and observation setting has a performance of 37.5, while the fixed reward and observation setting has a performance of 32.5, indicating that the default setting performs better.", "statement": "The default reward and observation setting performs better than the fixed reward and observation setting.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2. Attack success rates on Chinese BERT-based classifier for two datasets.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The table shows the performance of char-level adversarial attacks on BERT-based classifiers for THUCTC and Wechat datasets in both targeted and untargeted scenarios, comparing various metrics.Results. We perform our char-level adversarial attack on BERT-based classifiers for two datasets in both targeted and untargeted attack scenarios." } ], "images": [ "scigen/train/2588.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2. Attack success rates on Chinese BERT-based classifier for two datasets." ], "reasoning": "The table shows the performance of char-level adversarial attacks on BERT-based classifiers for THUCTC and Wechat datasets in both targeted and untargeted scenarios, comparing various metrics.", "description": "Results. We perform our char-level adversarial attack on BERT-based classifiers for two datasets in both targeted and untargeted attack scenarios.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2. Attack success rates on Chinese BERT-based classifier for two datasets.', solve this question and give detail solutions. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhat is the highest edit distance achieved in the AdvChar (targeted) attack scenario for the Wechat dataset?\n" }, { "role": "assistant", "content": "To find the highest edit distance for the Wechat dataset in the AdvChar (targeted) attack scenario, look at the 'edit distance' rows under the Wechat dataset and compare the values in the AdvChar (targeted) columns. So, the answer is {\"answer\": \"12.58\"}." } ], "images": [ "scigen/train/2588.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2. Attack success rates on Chinese BERT-based classifier for two datasets." ], "reasoning": "To find the highest edit distance for the Wechat dataset in the AdvChar (targeted) attack scenario, look at the 'edit distance' rows under the Wechat dataset and compare the values in the AdvChar (targeted) columns.", "question": "What is the highest edit distance achieved in the AdvChar (targeted) attack scenario for the Wechat dataset?", "answer": "So, the answer is {\"answer\": \"12.58\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe original accuracy for the THUCTC dataset is higher than that for the Wechat dataset.\n" }, { "role": "assistant", "content": "The table shows that the original accuracy for the THUCTC dataset is 0.918, while for the Wechat dataset it is 0.886, indicating that the original accuracy for THUCTC is indeed higher. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2588.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2. Attack success rates on Chinese BERT-based classifier for two datasets." ], "reasoning": "The table shows that the original accuracy for the THUCTC dataset is 0.918, while for the Wechat dataset it is 0.886, indicating that the original accuracy for THUCTC is indeed higher.", "statement": "The original accuracy for the THUCTC dataset is higher than that for the Wechat dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3. Classifier accuracy after transerability-based blackbox attack on other BERT-based classifiers.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Targeted adversarial examples significantly reduce the performance of blackbox models, with the Wechat dataset showing a drop from 88.2% to 14.3%.We find the adversarial text can still substantially affect the accuracy of blackbox models. In addition, the targeted attack success rate turns out to be stronger than the untargeted attack. Particularly, the targeted adversarial examples on the Wechat dataset can make the blackbox BERT classifier performance drop from 88.2\\% to 14.3\\%." } ], "images": [ "scigen/train/2589.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. Classifier accuracy after transerability-based blackbox attack on other BERT-based classifiers." ], "reasoning": "Targeted adversarial examples significantly reduce the performance of blackbox models, with the Wechat dataset showing a drop from 88.2% to 14.3%.", "description": "We find the adversarial text can still substantially affect the accuracy of blackbox models. In addition, the targeted attack success rate turns out to be stronger than the untargeted attack. Particularly, the targeted adversarial examples on the Wechat dataset can make the blackbox BERT classifier performance drop from 88.2\\% to 14.3\\%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3. Classifier accuracy after transerability-based blackbox attack on other BERT-based classifiers.', unfold the steps to solve the question given next. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: What is the difference in the AdvChar (target) success rate between the THUCTC and Wechat datasets?\n" }, { "role": "assistant", "content": "The AdvChar (target) success rate for THUCTC is 0.491, and for Wechat, it is 0.143. The difference is calculated as 0.491 - 0.143. So, the answer is {\"answer\": \"0.348\"}" } ], "images": [ "scigen/train/2589.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. Classifier accuracy after transerability-based blackbox attack on other BERT-based classifiers." ], "reasoning": "The AdvChar (target) success rate for THUCTC is 0.491, and for Wechat, it is 0.143. The difference is calculated as 0.491 - 0.143.", "question": "What is the difference in the AdvChar (target) success rate between the THUCTC and Wechat datasets?", "answer": "So, the answer is {\"answer\": \"0.348\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The untargeted attack success rate on the Wechat dataset is higher than the targeted attack success rate on the THUCTC dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe untargeted attack success rate on the Wechat dataset is higher than the targeted attack success rate on the THUCTC dataset.\n" }, { "role": "assistant", "content": "The table shows that the AdvChar (untarget) success rate for Wechat is 0.552, and the AdvChar (target) success rate for THUCTC is 0.491. Since 0.552 is greater than 0.491, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2589.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. Classifier accuracy after transerability-based blackbox attack on other BERT-based classifiers." ], "reasoning": "The table shows that the AdvChar (untarget) success rate for Wechat is 0.552, and the AdvChar (target) success rate for THUCTC is 0.491. Since 0.552 is greater than 0.491, the statement is supported.", "statement": "The untargeted attack success rate on the Wechat dataset is higher than the targeted attack success rate on the THUCTC dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4. Human performances compared with BERT classifiers in original dataset and adversarial dataset.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Human performance on adversarial data is only slightly lower than on clean data, while BERT classifiers experience a significant drop from 82% to 0%.We find that our adversarial text barely impacts the human perception, since the human performance on adversarial data is only 4\\% lower than the clean data, in contrast to the huge performance drop of BERT classifiers from 82\\% to 0\\%." } ], "images": [ "scigen/train/2590.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4. Human performances compared with BERT classifiers in original dataset and adversarial dataset." ], "reasoning": "Human performance on adversarial data is only slightly lower than on clean data, while BERT classifiers experience a significant drop from 82% to 0%.", "description": "We find that our adversarial text barely impacts the human perception, since the human performance on adversarial data is only 4\\% lower than the clean data, in contrast to the huge performance drop of BERT classifiers from 82\\% to 0\\%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 4. Human performances compared with BERT classifiers in original dataset and adversarial dataset.' and methodically tackle the question that follows. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the performance drop percentage for BERT classifiers when moving from clean to adversarial data?\n" }, { "role": "assistant", "content": "The performance of BERT classifiers on clean data is 82%, and on adversarial data, it is 0%. The drop in performance is calculated as 82% - 0% = 82%. So, the answer is {\"answer\": \"82%\"}" } ], "images": [ "scigen/train/2590.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4. Human performances compared with BERT classifiers in original dataset and adversarial dataset." ], "reasoning": "The performance of BERT classifiers on clean data is 82%, and on adversarial data, it is 0%. The drop in performance is calculated as 82% - 0% = 82%.", "question": "What is the performance drop percentage for BERT classifiers when moving from clean to adversarial data?", "answer": "So, the answer is {\"answer\": \"82%\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nHuman performance on adversarial data is significantly lower than on clean data.\n" }, { "role": "assistant", "content": "The table shows that human performance on clean data is 0.84 and on adversarial data is 0.80, which is only a slight decrease of 0.04, not significant. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2590.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4. Human performances compared with BERT classifiers in original dataset and adversarial dataset." ], "reasoning": "The table shows that human performance on clean data is 0.84 and on adversarial data is 0.80, which is only a slight decrease of 0.04, not significant.", "statement": "Human performance on adversarial data is significantly lower than on clean data.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5. Untargeted attack success rates on Chinese BERT-based classifier for THUCTC dataset. “target” and “untarget” calculate the targeted attack success rate (equation 2) and the untargeted attack success rate (equation 3). “#/chars” counts the number charcters are modified in average.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Switching from l_{2} norm to l_{1} norm in the attack loss function decreases the overall attack success rates but results in fewer word changes.In the main experiment, we use l_{2} norm for our attack loss function (equation 7). However, because l_{1} norm is known for good at feature selection and generating sparse features, we conduct the following experiments by set l_{p} to l_{1} and make an comparison with l_{2} norm. We find the overall attack success rates decrease when switching to l_{1} norm. However, given the same set of constants c and \\kappa, we find the l_{1} attack does change less words." } ], "images": [ "scigen/train/2591.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5. Untargeted attack success rates on Chinese BERT-based classifier for THUCTC dataset. “target” and “untarget” calculate the targeted attack success rate (equation 2) and the untargeted attack success rate (equation 3). “#/chars” counts the number charcters are modified in average." ], "reasoning": "Switching from l_{2} norm to l_{1} norm in the attack loss function decreases the overall attack success rates but results in fewer word changes.", "description": "In the main experiment, we use l_{2} norm for our attack loss function (equation 7). However, because l_{1} norm is known for good at feature selection and generating sparse features, we conduct the following experiments by set l_{p} to l_{1} and make an comparison with l_{2} norm. We find the overall attack success rates decrease when switching to l_{1} norm. However, given the same set of constants c and \\kappa, we find the l_{1} attack does change less words.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5. Untargeted attack success rates on Chinese BERT-based classifier for THUCTC dataset. “target” and “untarget” calculate the targeted attack success rate (equation 2) and the untargeted attack success rate (equation 3). “#/chars” counts the number charcters are modified in average.', solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the attack success rate for AdvChar (l_{1} untargeted) 20/20?\n" }, { "role": "assistant", "content": "The table shows the attack success rates for different configurations. For AdvChar (l_{1} untargeted) 20/20, the success rate is 0.995. So, the answer is {\"answer\": \"0.995\"}." } ], "images": [ "scigen/train/2591.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5. Untargeted attack success rates on Chinese BERT-based classifier for THUCTC dataset. “target” and “untarget” calculate the targeted attack success rate (equation 2) and the untargeted attack success rate (equation 3). “#/chars” counts the number charcters are modified in average." ], "reasoning": "The table shows the attack success rates for different configurations. For AdvChar (l_{1} untargeted) 20/20, the success rate is 0.995.", "question": "What is the attack success rate for AdvChar (l_{1} untargeted) 20/20?", "answer": "So, the answer is {\"answer\": \"0.995\"}." }, { "messages": [ { "role": "user", "content": "The attack success rate for AdvChar (l_{2} untargeted) 10/10 is higher than for AdvChar (l_{1} untargeted) 10/10. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe attack success rate for AdvChar (l_{2} untargeted) 10/10 is higher than for AdvChar (l_{1} untargeted) 10/10.\n" }, { "role": "assistant", "content": "The table shows that the attack success rate for AdvChar (l_{2} untargeted) 10/10 is 1.000, while for AdvChar (l_{1} untargeted) 10/10, it is 0.983. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2591.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5. Untargeted attack success rates on Chinese BERT-based classifier for THUCTC dataset. “target” and “untarget” calculate the targeted attack success rate (equation 2) and the untargeted attack success rate (equation 3). “#/chars” counts the number charcters are modified in average." ], "reasoning": "The table shows that the attack success rate for AdvChar (l_{2} untargeted) 10/10 is 1.000, while for AdvChar (l_{1} untargeted) 10/10, it is 0.983.", "statement": "The attack success rate for AdvChar (l_{2} untargeted) 10/10 is higher than for AdvChar (l_{1} untargeted) 10/10.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 6. Targeted attack success rates on Chinese BERT-based classifier for THUCTC dataset. “target” and “untarget” calculate the targeted attack success rate (equation 2) and the untargeted attack success rate (equation 3). “#/chars” counts the number charcters are modified in average.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Switching from l_{2} norm to l_{1} norm in the attack loss function decreases overall attack success rates but results in fewer word changes.In the main experiment, we use l_{2} norm for our attack loss function (equation 7). However, because l_{1} norm is known for good at feature selection and generating sparse features, we conduct the following experiments by set l_{p} to l_{1} and make an comparison with l_{2} norm. We find the overall attack success rates decrease when switching to l_{1} norm. However, given the same set of constants c and \\kappa, we find the l_{1} attack does change less words." } ], "images": [ "scigen/train/2592.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6. Targeted attack success rates on Chinese BERT-based classifier for THUCTC dataset. “target” and “untarget” calculate the targeted attack success rate (equation 2) and the untargeted attack success rate (equation 3). “#/chars” counts the number charcters are modified in average." ], "reasoning": "Switching from l_{2} norm to l_{1} norm in the attack loss function decreases overall attack success rates but results in fewer word changes.", "description": "In the main experiment, we use l_{2} norm for our attack loss function (equation 7). However, because l_{1} norm is known for good at feature selection and generating sparse features, we conduct the following experiments by set l_{p} to l_{1} and make an comparison with l_{2} norm. We find the overall attack success rates decrease when switching to l_{1} norm. However, given the same set of constants c and \\kappa, we find the l_{1} attack does change less words.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 6. Targeted attack success rates on Chinese BERT-based classifier for THUCTC dataset. “target” and “untarget” calculate the targeted attack success rate (equation 2) and the untargeted attack success rate (equation 3). “#/chars” counts the number charcters are modified in average.\nQuestion: What is the attack success rate for AdvChar (l_{1} targeted) with 30/30 c/k?\n" }, { "role": "assistant", "content": "To find the attack success rate for AdvChar (l_{1} targeted) with 30/30 c/k, look at the corresponding cell in the table. The value is 0.898. So, the answer is {\"answer\": \"0.898\"}." } ], "images": [ "scigen/train/2592.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6. Targeted attack success rates on Chinese BERT-based classifier for THUCTC dataset. “target” and “untarget” calculate the targeted attack success rate (equation 2) and the untargeted attack success rate (equation 3). “#/chars” counts the number charcters are modified in average." ], "reasoning": "To find the attack success rate for AdvChar (l_{1} targeted) with 30/30 c/k, look at the corresponding cell in the table. The value is 0.898.", "question": "What is the attack success rate for AdvChar (l_{1} targeted) with 30/30 c/k?", "answer": "So, the answer is {\"answer\": \"0.898\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe attack success rate for AdvChar (l_{2} targeted) with 5/5 c/k is higher than with 10/10 c/k.\n" }, { "role": "assistant", "content": "The table shows that the attack success rate for AdvChar (l_{2} targeted) with 5/5 c/k is 0.945, while with 10/10 c/k it is also 0.945. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2592.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6. Targeted attack success rates on Chinese BERT-based classifier for THUCTC dataset. “target” and “untarget” calculate the targeted attack success rate (equation 2) and the untargeted attack success rate (equation 3). “#/chars” counts the number charcters are modified in average." ], "reasoning": "The table shows that the attack success rate for AdvChar (l_{2} targeted) with 5/5 c/k is 0.945, while with 10/10 c/k it is also 0.945. Therefore, the statement is refuted.", "statement": "The attack success rate for AdvChar (l_{2} targeted) with 5/5 c/k is higher than with 10/10 c/k.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 7. Attack success rates on Chinese BERT-based classifier for two datasets. “target” and “untarget” calculate the targeted attack success rate (equation 2) and the untargeted attack success rate (equation 3). “#/chars” counts the number charcters are modified in average.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Different targeted attack strategies impact the targeted attack success rate on the THUCTC dataset, with strategy 1 achieving higher success rates than strategy 2.As we have achieved 100\\% attack success rate in the untargeted attack scenario, we now focus on the targeted attack scenario and see which factor contributes to the targeted attack success rate. It is straightfoward to think different targeted attack strategy will impact the targeted attack success rate, because maybe some classes look ”farther” than semantic closer classes. So we tried two strategies on THUCTC dataset: 1) as used in the main paper, we set the targeted false class as “entertainment news”. 2) we enumerate all the classes and set the targeted false class to be numerically the next class. We do find choosing different attack strategy will impact the attack success rate." } ], "images": [ "scigen/train/2593.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7. Attack success rates on Chinese BERT-based classifier for two datasets. “target” and “untarget” calculate the targeted attack success rate (equation 2) and the untargeted attack success rate (equation 3). “#/chars” counts the number charcters are modified in average." ], "reasoning": "Different targeted attack strategies impact the targeted attack success rate on the THUCTC dataset, with strategy 1 achieving higher success rates than strategy 2.", "description": "As we have achieved 100\\% attack success rate in the untargeted attack scenario, we now focus on the targeted attack scenario and see which factor contributes to the targeted attack success rate. It is straightfoward to think different targeted attack strategy will impact the targeted attack success rate, because maybe some classes look ”farther” than semantic closer classes. So we tried two strategies on THUCTC dataset: 1) as used in the main paper, we set the targeted false class as “entertainment news”. 2) we enumerate all the classes and set the targeted false class to be numerically the next class. We do find choosing different attack strategy will impact the attack success rate.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 7. Attack success rates on Chinese BERT-based classifier for two datasets. “target” and “untarget” calculate the targeted attack success rate (equation 2) and the untargeted attack success rate (equation 3). “#/chars” counts the number charcters are modified in average.', and tackle the question below in a sequential manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the targeted attack success rate for strategy 1 on the THUCTC dataset?\n" }, { "role": "assistant", "content": "The table shows the targeted attack success rate for strategy 1 under the column 'AdvChar (targeted c/kappa=10/10) strategy 1' and the row labeled 'target'. So, the answer is {\"answer\": \"0.945\"}." } ], "images": [ "scigen/train/2593.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7. Attack success rates on Chinese BERT-based classifier for two datasets. “target” and “untarget” calculate the targeted attack success rate (equation 2) and the untargeted attack success rate (equation 3). “#/chars” counts the number charcters are modified in average." ], "reasoning": "The table shows the targeted attack success rate for strategy 1 under the column 'AdvChar (targeted c/kappa=10/10) strategy 1' and the row labeled 'target'.", "question": "What is the targeted attack success rate for strategy 1 on the THUCTC dataset?", "answer": "So, the answer is {\"answer\": \"0.945\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe untargeted attack success rate for strategy 2 is higher than the baseline untargeted attack success rate.\n" }, { "role": "assistant", "content": "The table shows the untargeted attack success rate for strategy 2 under the column 'AdvChar (targeted c/kappa=10/10) strategy 2' and the row labeled 'untarget', which is 0.913. The baseline untargeted attack success rate is 0.040. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2593.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7. Attack success rates on Chinese BERT-based classifier for two datasets. “target” and “untarget” calculate the targeted attack success rate (equation 2) and the untargeted attack success rate (equation 3). “#/chars” counts the number charcters are modified in average." ], "reasoning": "The table shows the untargeted attack success rate for strategy 2 under the column 'AdvChar (targeted c/kappa=10/10) strategy 2' and the row labeled 'untarget', which is 0.913. The baseline untargeted attack success rate is 0.040.", "statement": "The untargeted attack success rate for strategy 2 is higher than the baseline untargeted attack success rate.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 6: Comparison of different channels'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Channel two performs best on conversation data, while channel three excels on QA data due to differences in prior knowledge generation.We can see that channel two is the most powerful one on the conversation data, while channel three is the best one on the QA data. This is because the prior knowledge in the conversation data is automatically generated rather than obtained from meta-data like that in the QA data. The automatically generated prior knowledge contains noise which hurts the performance of channel three. The full model outperforms all single channels consistently, demonstrating that matching with multiple channels could leverage the three types of features and sufficiently model the semantic relations in text pairs." } ], "images": [ "scigen/train/2594.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Comparison of different channels" ], "reasoning": "Channel two performs best on conversation data, while channel three excels on QA data due to differences in prior knowledge generation.", "description": "We can see that channel two is the most powerful one on the conversation data, while channel three is the best one on the QA data. This is because the prior knowledge in the conversation data is automatically generated rather than obtained from meta-data like that in the QA data. The automatically generated prior knowledge contains noise which hurts the performance of channel three. The full model outperforms all single channels consistently, demonstrating that matching with multiple channels could leverage the three types of features and sufficiently model the semantic relations in text pairs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 6: Comparison of different channels'.\n\nQuestion: Which channel has the highest accuracy on the QA data and why?\n" }, { "role": "assistant", "content": "Channel three (only M3) has the highest accuracy on the QA data with a score of 0.738 because the prior knowledge in the QA data is obtained from meta-data, reducing noise. So, the answer is {\"answer\": \"only M3\"}" } ], "images": [ "scigen/train/2594.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Comparison of different channels" ], "reasoning": "Channel three (only M3) has the highest accuracy on the QA data with a score of 0.738 because the prior knowledge in the QA data is obtained from meta-data, reducing noise.", "question": "Which channel has the highest accuracy on the QA data and why?", "answer": "So, the answer is {\"answer\": \"only M3\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe full model KEHNN outperforms all single channels on both conversation and QA data.\n" }, { "role": "assistant", "content": "The table shows that KEHNN has the highest scores across all conversation metrics and the highest accuracy on QA data, confirming the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2594.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Comparison of different channels" ], "reasoning": "The table shows that KEHNN has the highest scores across all conversation metrics and the highest accuracy on QA data, confirming the statement.", "statement": "The full model KEHNN outperforms all single channels on both conversation and QA data.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Evaluation results on answer selection', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "KEHNN outperforms all other methods due to leveraging both sequential information and prior knowledge, with the improvement being statistically significant.Models that match text pairs by interaction representations like Arc2 and MatchPyramid are not better than models that perform matching with sentence embeddings like Arc1. This is because the training data is small and we fixed the word embedding in learning. LSTM based models in general performs better than CNN based models, because they can capture sequential information in sentences. KEHNN leverages both the sequential information and the prior knowledge from categories in matching by a CNN with multiple channels. Therefore, it outperforms all other methods, and the improvement is statistically significant (t-test with p-value ≤0.05). It is worthy to note that the gap between different methods is not big. This is because answers labeled as ”potential” only cover 10% of the data and are hard to predict." } ], "images": [ "scigen/train/2595.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Evaluation results on answer selection" ], "reasoning": "KEHNN outperforms all other methods due to leveraging both sequential information and prior knowledge, with the improvement being statistically significant.", "description": "Models that match text pairs by interaction representations like Arc2 and MatchPyramid are not better than models that perform matching with sentence embeddings like Arc1. This is because the training data is small and we fixed the word embedding in learning. LSTM based models in general performs better than CNN based models, because they can capture sequential information in sentences. KEHNN leverages both the sequential information and the prior knowledge from categories in matching by a CNN with multiple channels. Therefore, it outperforms all other methods, and the improvement is statistically significant (t-test with p-value ≤0.05). It is worthy to note that the gap between different methods is not big. This is because answers labeled as ”potential” only cover 10% of the data and are hard to predict.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: Evaluation results on answer selection', and sequentially solve the question, writing out each step of your reasoning process. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model has the highest accuracy and why does it outperform other models?\n" }, { "role": "assistant", "content": "KEHNN has the highest accuracy at 0.748. It outperforms other models because it leverages both sequential information and prior knowledge from categories in matching by a CNN with multiple channels. So, the answer is {\"answer\": \"KEHNN\"}." } ], "images": [ "scigen/train/2595.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Evaluation results on answer selection" ], "reasoning": "KEHNN has the highest accuracy at 0.748. It outperforms other models because it leverages both sequential information and prior knowledge from categories in matching by a CNN with multiple channels.", "question": "Which model has the highest accuracy and why does it outperform other models?", "answer": "So, the answer is {\"answer\": \"KEHNN\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Arc2 has a higher accuracy than Arc1., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nArc2 has a higher accuracy than Arc1.\n" }, { "role": "assistant", "content": "The table shows that both Arc1 and Arc2 have the same accuracy of 0.715. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2595.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Evaluation results on answer selection" ], "reasoning": "The table shows that both Arc1 and Arc2 have the same accuracy of 0.715.", "statement": "Arc2 has a higher accuracy than Arc1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4: Evaluation results on response selection', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our method outperforms baseline models on all metrics, with statistically significant improvements, and LSTM-based models perform better than CNN-based models.Our method outperforms baseline models on all metrics, and the improvement is statistically significant (t-test with p-value ≤0.01). In the data set, as the training data becomes large and we updated word embedding in learning, Arc2 and MatchPyraimd are much better than Arc1. LSTM based models perform better than CNN based models, which is consistent with the results in the QA task." } ], "images": [ "scigen/train/2596.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Evaluation results on response selection" ], "reasoning": "Our method outperforms baseline models on all metrics, with statistically significant improvements, and LSTM-based models perform better than CNN-based models.", "description": "Our method outperforms baseline models on all metrics, and the improvement is statistically significant (t-test with p-value ≤0.01). In the data set, as the training data becomes large and we updated word embedding in learning, Arc2 and MatchPyraimd are much better than Arc1. LSTM based models perform better than CNN based models, which is consistent with the results in the QA task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Evaluation results on response selection\nQuestion: Which model has the highest R10@5 score and how does it compare to the Arc2 model?\nSolve the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The KEHNN model has the highest R10@5 score of 0.819. Comparing it to the Arc2 model, which has an R10@5 score of 0.777, KEHNN performs better. So, the answer is {\"answer\": \"KEHNN\"}." } ], "images": [ "scigen/train/2596.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Evaluation results on response selection" ], "reasoning": "The KEHNN model has the highest R10@5 score of 0.819. Comparing it to the Arc2 model, which has an R10@5 score of 0.777, KEHNN performs better.", "question": "Which model has the highest R10@5 score and how does it compare to the Arc2 model?", "answer": "So, the answer is {\"answer\": \"KEHNN\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Arc2 outperforms Arc1 on all metrics. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nArc2 outperforms Arc1 on all metrics.\n" }, { "role": "assistant", "content": "Comparing the metrics for Arc2 and Arc1, Arc2 has higher scores in R2@1 (0.736 vs. 0.665), R10@1 (0.380 vs. 0.221), R10@2 (0.534 vs. 0.360), and R10@5 (0.777 vs. 0.684). Therefore, Arc2 outperforms Arc1 on all metrics. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2596.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Evaluation results on response selection" ], "reasoning": "Comparing the metrics for Arc2 and Arc1, Arc2 has higher scores in R2@1 (0.736 vs. 0.665), R10@1 (0.380 vs. 0.221), R10@2 (0.534 vs. 0.360), and R10@5 (0.777 vs. 0.684). Therefore, Arc2 outperforms Arc1 on all metrics.", "statement": "Arc2 outperforms Arc1 on all metrics.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled '(a) QA dataset'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "KEHNN performs comparably well with MV-LSTM on short text but significantly improves matching accuracy on long text, verifying that multiple channels and prior knowledge enhance accuracy.We compared our model with 2 typical matching models: LSTM and MV-LSTM. We binned the text pairs into 4 buckets, according to the length of the concatenation of the two pieces of text. #Pair represents the number of pairs that fall into the bucket. From the results, we can see that on relatively short text (i.e., length in [0,30) ), KEHNN performs comparably well with MV-LSTM, while on long text, KEHNN significantly improves the matching accuracy. The results verified our claim that matching with multiple channels and prior knowledge can enhance accuracy on long text. Note that on the Ubuntu data, all models perform worse on short text than them on long text. This is because we ignored context for short message-response pairs, while long pairs are usually independent with context and have complete semantics." } ], "images": [ "scigen/train/2597.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) QA dataset" ], "reasoning": "KEHNN performs comparably well with MV-LSTM on short text but significantly improves matching accuracy on long text, verifying that multiple channels and prior knowledge enhance accuracy.", "description": "We compared our model with 2 typical matching models: LSTM and MV-LSTM. We binned the text pairs into 4 buckets, according to the length of the concatenation of the two pieces of text. #Pair represents the number of pairs that fall into the bucket. From the results, we can see that on relatively short text (i.e., length in [0,30) ), KEHNN performs comparably well with MV-LSTM, while on long text, KEHNN significantly improves the matching accuracy. The results verified our claim that matching with multiple channels and prior knowledge can enhance accuracy on long text. Note that on the Ubuntu data, all models perform worse on short text than them on long text. This is because we ignored context for short message-response pairs, while long pairs are usually independent with context and have complete semantics.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of '(a) QA dataset'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model has the highest accuracy for text pairs with a length in [60,90)?\n" }, { "role": "assistant", "content": "To find the model with the highest accuracy for text pairs with a length in [60,90), compare the accuracy values in the [60,90) column. KEHNN has the highest accuracy of 0.765. So, the answer is {\"answer\": \"KEHNN\"}." } ], "images": [ "scigen/train/2597.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) QA dataset" ], "reasoning": "To find the model with the highest accuracy for text pairs with a length in [60,90), compare the accuracy values in the [60,90) column. KEHNN has the highest accuracy of 0.765.", "question": "Which model has the highest accuracy for text pairs with a length in [60,90)?", "answer": "So, the answer is {\"answer\": \"KEHNN\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nKEHNN has the lowest accuracy for text pairs with a length in [0,30).\n" }, { "role": "assistant", "content": "To verify this statement, compare the accuracy values in the [0,30) column. KEHNN has an accuracy of 0.792, which is higher than both LSTM (0.768) and MV-LSTM (0.788). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2597.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) QA dataset" ], "reasoning": "To verify this statement, compare the accuracy values in the [0,30) column. KEHNN has an accuracy of 0.792, which is higher than both LSTM (0.768) and MV-LSTM (0.788).", "statement": "KEHNN has the lowest accuracy for text pairs with a length in [0,30).", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption '(b) Ubuntu dataset'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "KEHNN performs comparably well with MV-LSTM on short text but significantly improves matching accuracy on long text, verifying that multiple channels and prior knowledge enhance accuracy.We compared our model with 2 typical matching models: LSTM and MV-LSTM. We binned the text pairs into 4 buckets, according to the length of the concatenation of the two pieces of text. #Pair represents the number of pairs that fall into the bucket. From the results, we can see that on relatively short text (i.e., length in [0,30) ), KEHNN performs comparably well with MV-LSTM, while on long text, KEHNN significantly improves the matching accuracy. The results verified our claim that matching with multiple channels and prior knowledge can enhance accuracy on long text. Note that on the Ubuntu data, all models perform worse on short text than them on long text. This is because we ignored context for short message-response pairs, while long pairs are usually independent with context and have complete semantics." } ], "images": [ "scigen/train/2598.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) Ubuntu dataset" ], "reasoning": "KEHNN performs comparably well with MV-LSTM on short text but significantly improves matching accuracy on long text, verifying that multiple channels and prior knowledge enhance accuracy.", "description": "We compared our model with 2 typical matching models: LSTM and MV-LSTM. We binned the text pairs into 4 buckets, according to the length of the concatenation of the two pieces of text. #Pair represents the number of pairs that fall into the bucket. From the results, we can see that on relatively short text (i.e., length in [0,30) ), KEHNN performs comparably well with MV-LSTM, while on long text, KEHNN significantly improves the matching accuracy. The results verified our claim that matching with multiple channels and prior knowledge can enhance accuracy on long text. Note that on the Ubuntu data, all models perform worse on short text than them on long text. This is because we ignored context for short message-response pairs, while long pairs are usually independent with context and have complete semantics.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to '(b) Ubuntu dataset' to sequentially solve the question presented below. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich model has the highest matching accuracy for text pairs with lengths in the range [60,90)?\n" }, { "role": "assistant", "content": "From the table, we see that for the length range [60,90), KEHNN has a matching accuracy of 0.785, which is higher than both LSTM (0.732) and MV-LSTM (0.725). So, the answer is {\"answer\": \"KEHNN\"}" } ], "images": [ "scigen/train/2598.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) Ubuntu dataset" ], "reasoning": "From the table, we see that for the length range [60,90), KEHNN has a matching accuracy of 0.785, which is higher than both LSTM (0.732) and MV-LSTM (0.725).", "question": "Which model has the highest matching accuracy for text pairs with lengths in the range [60,90)?", "answer": "So, the answer is {\"answer\": \"KEHNN\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nKEHNN performs worse than LSTM on text pairs with lengths in the range [30,60).\n" }, { "role": "assistant", "content": "The table shows that for the length range [30,60), KEHNN has a matching accuracy of 0.774, which is higher than LSTM's accuracy of 0.748. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2598.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) Ubuntu dataset" ], "reasoning": "The table shows that for the length range [30,60), KEHNN has a matching accuracy of 0.774, which is higher than LSTM's accuracy of 0.748.", "statement": "KEHNN performs worse than LSTM on text pairs with lengths in the range [30,60).", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Evaluation on Hi.'.\n" }, { "role": "assistant", "content": "The context model shows an accuracy increase of 0.67% over the word model, and precision is consistently higher than recall across all models.Using the context model, accuracy increases by 0.67%. In both the Tables, we see that precision has been much higher than recall." } ], "images": [ "scigen/train/2599.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Evaluation on Hi." ], "reasoning": "The context model shows an accuracy increase of 0.67% over the word model, and precision is consistently higher than recall across all models.", "description": "Using the context model, accuracy increases by 0.67%. In both the Tables, we see that precision has been much higher than recall.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 3: Evaluation on Hi.\nQuestion: What is the difference in recall between the baseline and context models?\n" }, { "role": "assistant", "content": "The recall for the baseline model is 88.01%, and for the context model, it is 93.03%. The difference is 93.03% - 88.01% = 5.02%. So, the answer is {\"answer\": \"5.02%\"}." } ], "images": [ "scigen/train/2599.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Evaluation on Hi." ], "reasoning": "The recall for the baseline model is 88.01%, and for the context model, it is 93.03%. The difference is 93.03% - 88.01% = 5.02%.", "question": "What is the difference in recall between the baseline and context models?", "answer": "So, the answer is {\"answer\": \"5.02%\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The context model has the highest precision among all models., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe context model has the highest precision among all models.\n" }, { "role": "assistant", "content": "The table shows that the precision for the context model is 93.62%, which is higher than the precision for both the baseline model (88.57%) and the word model (93.54%). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2599.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Evaluation on Hi." ], "reasoning": "The table shows that the precision for the context model is 93.62%, which is higher than the precision for both the baseline model (88.57%) and the word model (93.54%).", "statement": "The context model has the highest precision among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: Data distribution.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows the number of instances and corresponding values for training, development, and testing datasets for Bengali-English (Bn-En) and Hindi-English (Hi-En) language pairs, with specific metrics for each dataset.We wanted to test our approach on two different language pairs, which were Bengali-English (Bn-En) and Hindi-English (Hi-En). For Bn-En, we used the data prepared in Mandal et al. The number of instances of each type we selected for our experiments was 6000. All indic tagged tokens were used instead of just unique ones of respective languages. The whole model was compiled using loss as binary cross-entropy, optimization function used was adam Kingma and Ba The batch size was set to 64, and number of epochs was set to 30. Other parameters were kept at default. The training accuracy and loss graphs for both Bn and Hi are shown below. As the MNN model produces a sigmoid output, to convert the model into a classifier, we decided to use a threshold based technique identical to the one used in Mandal et al. For this the development data was used, threshold for Bn was calculated to be θ ≤ 0.95, while threshold for Hi was calculated to be θ ≤ 0.89. Using these, the accuracies on the development data was 93.6% and 92.87% for Bn and Hi respectively. The targets here were the actual language labels (0 for the Bn/Hi and 1 for En). The hyper-parameters which we set mostly follow Yang et al. Both the models (Bn-En & Hi-En) had identical parameters. L2 regularization λ was set at 1e-8. Learning rate η was set to 0.015. Batch size was kept at 16 and number of epochs was set to 210. Mini-batch stochastic gradient descent (SGD) with decaying learning rate (0.05) was used to update the parameters. All the other parameters were kept at default values. This setting was finalized post multiple experiments on the development data. Final accuracy scores on the development data was 93.91% and 93.11% for Bn and Hi respectively. For comparison purposes, we decided to use the character encoding architecture described in Mandal et al. The thresholds for the baseline models calculated on the development data was found to be θ ≤ 0.91 and θ ≤ 0.90 for Bn and Hi respectively. The results (in %) for each of the language pairs are shown below." } ], "images": [ "scigen/train/2600.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Data distribution." ], "reasoning": "The table shows the number of instances and corresponding values for training, development, and testing datasets for Bengali-English (Bn-En) and Hindi-English (Hi-En) language pairs, with specific metrics for each dataset.", "description": "We wanted to test our approach on two different language pairs, which were Bengali-English (Bn-En) and Hindi-English (Hi-En). For Bn-En, we used the data prepared in Mandal et al. The number of instances of each type we selected for our experiments was 6000. All indic tagged tokens were used instead of just unique ones of respective languages. The whole model was compiled using loss as binary cross-entropy, optimization function used was adam Kingma and Ba The batch size was set to 64, and number of epochs was set to 30. Other parameters were kept at default. The training accuracy and loss graphs for both Bn and Hi are shown below. As the MNN model produces a sigmoid output, to convert the model into a classifier, we decided to use a threshold based technique identical to the one used in Mandal et al. For this the development data was used, threshold for Bn was calculated to be θ ≤ 0.95, while threshold for Hi was calculated to be θ ≤ 0.89. Using these, the accuracies on the development data was 93.6% and 92.87% for Bn and Hi respectively. The targets here were the actual language labels (0 for the Bn/Hi and 1 for En). The hyper-parameters which we set mostly follow Yang et al. Both the models (Bn-En & Hi-En) had identical parameters. L2 regularization λ was set at 1e-8. Learning rate η was set to 0.015. Batch size was kept at 16 and number of epochs was set to 210. Mini-batch stochastic gradient descent (SGD) with decaying learning rate (0.05) was used to update the parameters. All the other parameters were kept at default values. This setting was finalized post multiple experiments on the development data. Final accuracy scores on the development data was 93.91% and 93.11% for Bn and Hi respectively. For comparison purposes, we decided to use the character encoding architecture described in Mandal et al. The thresholds for the baseline models calculated on the development data was found to be θ ≤ 0.91 and θ ≤ 0.90 for Bn and Hi respectively. The results (in %) for each of the language pairs are shown below.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 1: Data distribution.'. Solve the following question based on the table.\n\nQuestion: What is the total number of instances used for training and testing in the Bn-En language pair?\nLet's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the total number of instances used for training and testing in the Bn-En language pair, sum the instances from the 'Train' and 'Test' columns for Bn. The training instances are 3000 and the testing instances are 2000, so the total is 3000 + 2000 = 5000. So, the answer is {\"answer\": \"5000\"}" } ], "images": [ "scigen/train/2600.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Data distribution." ], "reasoning": "To find the total number of instances used for training and testing in the Bn-En language pair, sum the instances from the 'Train' and 'Test' columns for Bn. The training instances are 3000 and the testing instances are 2000, so the total is 3000 + 2000 = 5000.", "question": "What is the total number of instances used for training and testing in the Bn-En language pair?", "answer": "So, the answer is {\"answer\": \"5000\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The number of instances used for development in the Hi-En language pair is 1000., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe number of instances used for development in the Hi-En language pair is 1000.\n" }, { "role": "assistant", "content": "The table shows that the number of instances used for development in the Hi-En language pair is indeed 1000, which matches the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2600.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Data distribution." ], "reasoning": "The table shows that the number of instances used for development in the Hi-En language pair is indeed 1000, which matches the statement.", "statement": "The number of instances used for development in the Hi-En language pair is 1000.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Evaluation on Bn.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The context model shows a slight improvement of 0.41% in accuracy over the word model.From word to context model, though not much, but still an improvement is seen (0.41%)." } ], "images": [ "scigen/train/2601.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Evaluation on Bn." ], "reasoning": "The context model shows a slight improvement of 0.41% in accuracy over the word model.", "description": "From word to context model, though not much, but still an improvement is seen (0.41%).", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 2: Evaluation on Bn.\nQuestion: What is the difference in accuracy between the baseline and the context model?\n" }, { "role": "assistant", "content": "The accuracy of the baseline is 88.32%, and the accuracy of the context model is 93.28%. The difference is 93.28% - 88.32% = 4.96%. So, the answer is {\"answer\": \"4.96%\"}" } ], "images": [ "scigen/train/2601.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Evaluation on Bn." ], "reasoning": "The accuracy of the baseline is 88.32%, and the accuracy of the context model is 93.28%. The difference is 93.28% - 88.32% = 4.96%.", "question": "What is the difference in accuracy between the baseline and the context model?", "answer": "So, the answer is {\"answer\": \"4.96%\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe precision of the word model and the context model is the same.\n" }, { "role": "assistant", "content": "The table shows that both the word model and the context model have a precision of 94.33%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2601.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Evaluation on Bn." ], "reasoning": "The table shows that both the word model and the context model have a precision of 94.33%.", "statement": "The precision of the word model and the context model is the same.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Spearman ρ correlation of different sentence and word embeddings on the Usim dataset using different context window sizes (cw). For BERT and ELMo, top refers to the top layer, and av refers to the average of layers (3 for ELMo, and the last 4 for BERT).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "BERT embeddings for the target word provide the best performance, while context2vec sentence representations are the next best but with much lower correlation. The SIF approach for sentence representation outperforms simple GloVe averages and is comparable to USE.We observe that target word BERT embeddings give best performance in this task. Selecting a context window around (or including) the target word does not always help, on the contrary it can harm the models. Context2vec sentence representations are the next best performing representation, after BERT, but their correlation is much lower. The simple GloVe-based SIF approach for sentence representation, which consists in applying dimensionality reduction to a weighted average of GloVe vectors of the words in a sentence, is much superior to the simple average of GloVe vectors and even better than doc2vec sentence representations, obtaining a correlation comparable to that of USE." } ], "images": [ "scigen/train/2602.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Spearman ρ correlation of different sentence and word embeddings on the Usim dataset using different context window sizes (cw). For BERT and ELMo, top refers to the top layer, and av refers to the average of layers (3 for ELMo, and the last 4 for BERT)." ], "reasoning": "BERT embeddings for the target word provide the best performance, while context2vec sentence representations are the next best but with much lower correlation. The SIF approach for sentence representation outperforms simple GloVe averages and is comparable to USE.", "description": "We observe that target word BERT embeddings give best performance in this task. Selecting a context window around (or including) the target word does not always help, on the contrary it can harm the models. Context2vec sentence representations are the next best performing representation, after BERT, but their correlation is much lower. The simple GloVe-based SIF approach for sentence representation, which consists in applying dimensionality reduction to a weighted average of GloVe vectors of the words in a sentence, is much superior to the simple average of GloVe vectors and even better than doc2vec sentence representations, obtaining a correlation comparable to that of USE.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Spearman ρ correlation of different sentence and word embeddings on the Usim dataset using different context window sizes (cw). For BERT and ELMo, top refers to the top layer, and av refers to the average of layers (3 for ELMo, and the last 4 for BERT).', solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich embedding method achieves the highest correlation for the target word context?\n" }, { "role": "assistant", "content": "The table shows that BERT av 4 achieves the highest correlation of 0.518 for the target word context. So, the answer is {\"answer\": \"BERT av 4\"}." } ], "images": [ "scigen/train/2602.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Spearman ρ correlation of different sentence and word embeddings on the Usim dataset using different context window sizes (cw). For BERT and ELMo, top refers to the top layer, and av refers to the average of layers (3 for ELMo, and the last 4 for BERT)." ], "reasoning": "The table shows that BERT av 4 achieves the highest correlation of 0.518 for the target word context.", "question": "Which embedding method achieves the highest correlation for the target word context?", "answer": "So, the answer is {\"answer\": \"BERT av 4\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe simple average of GloVe vectors achieves a higher correlation than doc2vec sentence representations.\n" }, { "role": "assistant", "content": "The table shows that the simple average of GloVe vectors has a correlation of 0.142, while doc2vec sentence representations have a correlation of 0.124. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2602.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Spearman ρ correlation of different sentence and word embeddings on the Usim dataset using different context window sizes (cw). For BERT and ELMo, top refers to the top layer, and av refers to the average of layers (3 for ELMo, and the last 4 for BERT)." ], "reasoning": "The table shows that the simple average of GloVe vectors has a correlation of 0.142, while doc2vec sentence representations have a correlation of 0.124. Therefore, the statement is supported.", "statement": "The simple average of GloVe vectors achieves a higher correlation than doc2vec sentence representations.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: BLEU and TER scores on the NIST Chinese-English translation tasks. The BLEU scores are case-insensitive. Avg means the average scores on all test sets. “‡”: statistically better than RNNSearch* (p < 0.01). Higher BLEU (or lower TER) scores indicate better translation quality.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "All three proposed NMT models with bridging methods outperform the SMT (cdec) and NMT (RNNSearch*) baseline systems in translation accuracy across all test sets.Clearly, every one of the three NMT models we proposed, with some bridging method, improve the translation accuracy over all test sets in comparison to the SMT (cdec) and NMT (RNNSearch*) baseline systems." } ], "images": [ "scigen/train/2604.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: BLEU and TER scores on the NIST Chinese-English translation tasks. The BLEU scores are case-insensitive. Avg means the average scores on all test sets. “‡”: statistically better than RNNSearch* (p < 0.01). Higher BLEU (or lower TER) scores indicate better translation quality." ], "reasoning": "All three proposed NMT models with bridging methods outperform the SMT (cdec) and NMT (RNNSearch*) baseline systems in translation accuracy across all test sets.", "description": "Clearly, every one of the three NMT models we proposed, with some bridging method, improve the translation accuracy over all test sets in comparison to the SMT (cdec) and NMT (RNNSearch*) baseline systems.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 1: BLEU and TER scores on the NIST Chinese-English translation tasks. The BLEU scores are case-insensitive. Avg means the average scores on all test sets. “‡”: statistically better than RNNSearch* (p < 0.01). Higher BLEU (or lower TER) scores indicate better translation quality.' and methodically tackle the question that follows. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich NMT model with a bridging method has the highest average BLEU score?\n" }, { "role": "assistant", "content": "To find the NMT model with the highest average BLEU score, compare the average BLEU scores of the Source bridging, Target bridging, and Direct bridging models. The Direct bridging model has the highest average BLEU score of 36.62. So, the answer is {\"answer\": \"Direct bridging\"}." } ], "images": [ "scigen/train/2604.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: BLEU and TER scores on the NIST Chinese-English translation tasks. The BLEU scores are case-insensitive. Avg means the average scores on all test sets. “‡”: statistically better than RNNSearch* (p < 0.01). Higher BLEU (or lower TER) scores indicate better translation quality." ], "reasoning": "To find the NMT model with the highest average BLEU score, compare the average BLEU scores of the Source bridging, Target bridging, and Direct bridging models. The Direct bridging model has the highest average BLEU score of 36.62.", "question": "Which NMT model with a bridging method has the highest average BLEU score?", "answer": "So, the answer is {\"answer\": \"Direct bridging\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The Source bridging model has the highest TER score on the NIST02 test set., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Source bridging model has the highest TER score on the NIST02 test set.\n" }, { "role": "assistant", "content": "To verify this statement, compare the TER scores of all models on the NIST02 test set. The Source bridging model has a TER score of 56.25, which is the highest among all models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2604.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: BLEU and TER scores on the NIST Chinese-English translation tasks. The BLEU scores are case-insensitive. Avg means the average scores on all test sets. “‡”: statistically better than RNNSearch* (p < 0.01). Higher BLEU (or lower TER) scores indicate better translation quality." ], "reasoning": "To verify this statement, compare the TER scores of all models on the NIST02 test set. The Source bridging model has a TER score of 56.25, which is the highest among all models.", "statement": "The Source bridging model has the highest TER score on the NIST02 test set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Percentage of target side eos translated from source side eos on the development set.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The target side eos is crucial for controlling translation length, and aligning it with the source eos improves translation quality, as shown by the higher percentage for Direct bridging.Better eos translation As a special symbol marking the end of sentence , target side eos has a critical impact on controlling the length of the generated translation. A target eos is a correct translation when is aligned with the source eos. It indicates that our model improved with bridging substantially achieves better translation of source eos." } ], "images": [ "scigen/train/2605.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Percentage of target side eos translated from source side eos on the development set." ], "reasoning": "The target side eos is crucial for controlling translation length, and aligning it with the source eos improves translation quality, as shown by the higher percentage for Direct bridging.", "description": "Better eos translation As a special symbol marking the end of sentence , target side eos has a critical impact on controlling the length of the generated translation. A target eos is a correct translation when is aligned with the source eos. It indicates that our model improved with bridging substantially achieves better translation of source eos.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Percentage of target side eos translated from source side eos on the development set.', and sequentially solve the question, writing out each step of your reasoning process. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich system achieved a higher percentage in translation quality?\n" }, { "role": "assistant", "content": "The table shows the percentages for two systems: RNNSearch* and Direct bridging. Direct bridging has a higher percentage (81.30%) compared to RNNSearch* (49.82%). So, the answer is {\"answer\": \"Direct bridging\"}." } ], "images": [ "scigen/train/2605.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Percentage of target side eos translated from source side eos on the development set." ], "reasoning": "The table shows the percentages for two systems: RNNSearch* and Direct bridging. Direct bridging has a higher percentage (81.30%) compared to RNNSearch* (49.82%).", "question": "Which system achieved a higher percentage in translation quality?", "answer": "So, the answer is {\"answer\": \"Direct bridging\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nRNNSearch* achieved a higher percentage than Direct bridging.\n" }, { "role": "assistant", "content": "The table shows that RNNSearch* has a percentage of 49.82%, while Direct bridging has a higher percentage of 81.30%. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2605.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Percentage of target side eos translated from source side eos on the development set." ], "reasoning": "The table shows that RNNSearch* has a percentage of 49.82%, while Direct bridging has a higher percentage of 81.30%. Therefore, the statement is incorrect.", "statement": "RNNSearch* achieved a higher percentage than Direct bridging.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Ratios of over translation (ROT) on test sets. NN stands for nouns excluding proper nouns and temporal nouns, NR for proper nouns, DT for determiners, and CD for cardinal numbers.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Both systems have higher over translation with proper nouns and other nouns, but the direct bridging model reduces the over translation issue by 15%.These data indicate that both systems have higher over translation with proper nouns (NR) and other nouns (NN) than with other POS tags, which is consistent with the results in Li et al. The likely reason is that these two POS tags usually have more unknown words, which are words that tend to be over translated. Importantly, these data also show that our direct bridging model alleviates the over translation issue by 15%, as ROT drops from 5.28% to 4.49%." } ], "images": [ "scigen/train/2606.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ratios of over translation (ROT) on test sets. NN stands for nouns excluding proper nouns and temporal nouns, NR for proper nouns, DT for determiners, and CD for cardinal numbers." ], "reasoning": "Both systems have higher over translation with proper nouns and other nouns, but the direct bridging model reduces the over translation issue by 15%.", "description": "These data indicate that both systems have higher over translation with proper nouns (NR) and other nouns (NN) than with other POS tags, which is consistent with the results in Li et al. The likely reason is that these two POS tags usually have more unknown words, which are words that tend to be over translated. Importantly, these data also show that our direct bridging model alleviates the over translation issue by 15%, as ROT drops from 5.28% to 4.49%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 5: Ratios of over translation (ROT) on test sets. NN stands for nouns excluding proper nouns and temporal nouns, NR for proper nouns, DT for determiners, and CD for cardinal numbers.' to sequentially solve the question presented below. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the reduction in ROT(%) for the NN POS tag when using the direct bridging model compared to RNNSearch*?\n" }, { "role": "assistant", "content": "The ROT(%) for NN in RNNSearch* is 8.63%, and for the direct bridging model, it is 7.56%. The reduction is 8.63% - 7.56%. So, the answer is {\"answer\": \"1.07%\"}" } ], "images": [ "scigen/train/2606.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ratios of over translation (ROT) on test sets. NN stands for nouns excluding proper nouns and temporal nouns, NR for proper nouns, DT for determiners, and CD for cardinal numbers." ], "reasoning": "The ROT(%) for NN in RNNSearch* is 8.63%, and for the direct bridging model, it is 7.56%. The reduction is 8.63% - 7.56%.", "question": "What is the reduction in ROT(%) for the NN POS tag when using the direct bridging model compared to RNNSearch*?", "answer": "So, the answer is {\"answer\": \"1.07%\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe ROT(%) for NR POS tag is higher in the direct bridging model compared to RNNSearch*.\n" }, { "role": "assistant", "content": "The ROT(%) for NR in RNNSearch* is 12.92%, and for the direct bridging model, it is 10.88%. The ROT(%) for NR is lower in the direct bridging model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2606.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ratios of over translation (ROT) on test sets. NN stands for nouns excluding proper nouns and temporal nouns, NR for proper nouns, DT for determiners, and CD for cardinal numbers." ], "reasoning": "The ROT(%) for NR in RNNSearch* is 12.92%, and for the direct bridging model, it is 10.88%. The ROT(%) for NR is lower in the direct bridging model.", "statement": "The ROT(%) for NR POS tag is higher in the direct bridging model compared to RNNSearch*.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'TABLE II: Averages and 95% conf. intervals of c-stats of all five folds. Top most row represents the scores of the baseline with only structured features. Others are combinations of two multimodal ensembles with different vectorizations.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The proposed framework, integrating three types of clinical notes with a structured dataset, improves performance by 0.0211 compared to the structured-only model and consistently outperforms the baseline by more than a 0.01 margin within a 95% confidence interval.This framework successfully integrates three types of clinical notes with structured dataset, improving 0.0211 compared to the structured only model. Moreover, 95% confidence interval of the proposed framework indicates that our framework consistently outperforms the baseline by more than 0.01 margin." } ], "images": [ "scigen/train/2607.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Averages and 95% conf. intervals of c-stats of all five folds. Top most row represents the scores of the baseline with only structured features. Others are combinations of two multimodal ensembles with different vectorizations." ], "reasoning": "The proposed framework, integrating three types of clinical notes with a structured dataset, improves performance by 0.0211 compared to the structured-only model and consistently outperforms the baseline by more than a 0.01 margin within a 95% confidence interval.", "description": "This framework successfully integrates three types of clinical notes with structured dataset, improving 0.0211 compared to the structured only model. Moreover, 95% confidence interval of the proposed framework indicates that our framework consistently outperforms the baseline by more than 0.01 margin.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'TABLE II: Averages and 95% conf. intervals of c-stats of all five folds. Top most row represents the scores of the baseline with only structured features. Others are combinations of two multimodal ensembles with different vectorizations.', solve this question and give detail solutions. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhat is the average c-stat of the method that shows the highest improvement in Delta?\n" }, { "role": "assistant", "content": "The method with the highest improvement in Delta is TFIDF-LDA (Avg.Sig.) with a Delta of 0.0211. The average c-stat for this method is 0.6734. So, the answer is {\"answer\": \"0.6734\"}." } ], "images": [ "scigen/train/2607.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Averages and 95% conf. intervals of c-stats of all five folds. Top most row represents the scores of the baseline with only structured features. Others are combinations of two multimodal ensembles with different vectorizations." ], "reasoning": "The method with the highest improvement in Delta is TFIDF-LDA (Avg.Sig.) with a Delta of 0.0211. The average c-stat for this method is 0.6734.", "question": "What is the average c-stat of the method that shows the highest improvement in Delta?", "answer": "So, the answer is {\"answer\": \"0.6734\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The TFIDF-LDA (Concat) method has a higher average c-stat than the Avg.W2V (Concat) method. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe TFIDF-LDA (Concat) method has a higher average c-stat than the Avg.W2V (Concat) method.\n" }, { "role": "assistant", "content": "The average c-stat for TFIDF-LDA (Concat) is 0.6669, while the average c-stat for Avg.W2V (Concat) is 0.6561. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2607.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Averages and 95% conf. intervals of c-stats of all five folds. Top most row represents the scores of the baseline with only structured features. Others are combinations of two multimodal ensembles with different vectorizations." ], "reasoning": "The average c-stat for TFIDF-LDA (Concat) is 0.6669, while the average c-stat for Avg.W2V (Concat) is 0.6561. Therefore, the statement is supported.", "statement": "The TFIDF-LDA (Concat) method has a higher average c-stat than the Avg.W2V (Concat) method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Performance comparison of language models. Models marked with† adopted adaptive softmax and the vanilla LSTMs, which has less softmax parameters. Models marked with♯ employed sampled softmax LSTMs w. projection, which results in less RNN parameters w.r.t. the size of hidden states.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The models use a dropout ratio of 0.5, unroll RNNs for 20 steps without resetting LSTM states, and have a batch size of 128. For models without official parameter numbers, estimates are made assuming vanilla LSTM.The layer-wise dropout ratio is set to 0.5, the RNNs are unrolled for 20 steps without resetting the LSTM states, and the batch size is set to 128. For models without official reported parameter numbers, we estimate their values (marked with†) by assuming they adopted the vanilla LSTM. Note that, for models 3, 5, 6, 7, 8, and 9, PPL refers to the averaged perplexity of the forward and the backward LMs." } ], "images": [ "scigen/train/2608.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance comparison of language models. Models marked with† adopted adaptive softmax and the vanilla LSTMs, which has less softmax parameters. Models marked with♯ employed sampled softmax LSTMs w. projection, which results in less RNN parameters w.r.t. the size of hidden states." ], "reasoning": "The models use a dropout ratio of 0.5, unroll RNNs for 20 steps without resetting LSTM states, and have a batch size of 128. For models without official parameter numbers, estimates are made assuming vanilla LSTM.", "description": "The layer-wise dropout ratio is set to 0.5, the RNNs are unrolled for 20 steps without resetting the LSTM states, and the batch size is set to 128. For models without official reported parameter numbers, we estimate their values (marked with†) by assuming they adopted the vanilla LSTM. Note that, for models 3, 5, 6, 7, 8, and 9, PPL refers to the averaged perplexity of the forward and the backward LMs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 1: Performance comparison of language models. Models marked with† adopted adaptive softmax and the vanilla LSTMs, which has less softmax parameters. Models marked with♯ employed sampled softmax LSTMs w. projection, which results in less RNN parameters w.r.t. the size of hidden states.', solve the following question step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the lowest perplexity (PPL) and what are its parameter numbers for RNN and Others?\n" }, { "role": "assistant", "content": "To find the model with the lowest perplexity, we look at the PPL column and identify the model with the smallest value. Then, we check the corresponding parameter numbers for RNN and Others for that model. The model with the lowest perplexity is 'CNN-8192-1024 Józefowicz et al. (2016)' with parameter numbers 15.1‡ for RNN and 89‡ for Others. So, the answer is {\"answer\": \"CNN-8192-1024 Józefowicz et al. (2016)\"}." } ], "images": [ "scigen/train/2608.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance comparison of language models. Models marked with† adopted adaptive softmax and the vanilla LSTMs, which has less softmax parameters. Models marked with♯ employed sampled softmax LSTMs w. projection, which results in less RNN parameters w.r.t. the size of hidden states." ], "reasoning": "To find the model with the lowest perplexity, we look at the PPL column and identify the model with the smallest value. Then, we check the corresponding parameter numbers for RNN and Others for that model.", "question": "Which model has the lowest perplexity (PPL) and what are its parameter numbers for RNN and Others?", "answer": "The model with the lowest perplexity is 'CNN-8192-1024 Józefowicz et al. (2016)' with parameter numbers 15.1‡ for RNN and 89‡ for Others. So, the answer is {\"answer\": \"CNN-8192-1024 Józefowicz et al. (2016)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe model 'LD-Net with Layer-wise Dropout' has a higher perplexity than 'vanilla LSTM'.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the PPL values of 'LD-Net with Layer-wise Dropout' and 'vanilla LSTM'. 'LD-Net with Layer-wise Dropout' has a PPL of 50.06, while 'vanilla LSTM' has a PPL of 48.85. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2608.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance comparison of language models. Models marked with† adopted adaptive softmax and the vanilla LSTMs, which has less softmax parameters. Models marked with♯ employed sampled softmax LSTMs w. projection, which results in less RNN parameters w.r.t. the size of hidden states." ], "reasoning": "To verify this statement, we compare the PPL values of 'LD-Net with Layer-wise Dropout' and 'vanilla LSTM'. 'LD-Net with Layer-wise Dropout' has a PPL of 50.06, while 'vanilla LSTM' has a PPL of 48.85.", "statement": "The model 'LD-Net with Layer-wise Dropout' has a higher perplexity than 'vanilla LSTM'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: Speed comparison in the CoNLL03 NER task. We can observe that LD-Net (9, pruned) achieved about 5 times speed up on the wall-clock time over LD-Net (9, origin).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The decode speed of 166K words/s on the training set is considered sufficient for most real-world applications despite the large margin between actual speed-up and FLOPs speed-up.Specifically, due to the limited size of CoNLL03 test set, we measure such speeds on the training set. Although there is still a large margin between the actual speed-up and the FLOPs speed-up, we think the resulting decode speed (166K words/s) is sufficient for most real-world applications." } ], "images": [ "scigen/train/2609.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Speed comparison in the CoNLL03 NER task. We can observe that LD-Net (9, pruned) achieved about 5 times speed up on the wall-clock time over LD-Net (9, origin)." ], "reasoning": "The decode speed of 166K words/s on the training set is considered sufficient for most real-world applications despite the large margin between actual speed-up and FLOPs speed-up.", "description": "Specifically, due to the limited size of CoNLL03 test set, we measure such speeds on the training set. Although there is still a large margin between the actual speed-up and the FLOPs speed-up, we think the resulting decode speed (166K words/s) is sufficient for most real-world applications.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 4: Speed comparison in the CoNLL03 NER task. We can observe that LD-Net (9, pruned) achieved about 5 times speed up on the wall-clock time over LD-Net (9, origin).', solve the following question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the batch size and peak RAM for the network with the highest speed in words per second?\n" }, { "role": "assistant", "content": "The network with the highest speed in words per second is LD-Net (9, pruned) with a speed of 166K words/s. The batch size for this network is 500 and the peak RAM is 8Gb. So, the answer is {\"answer\": \"500, 8Gb\"}." } ], "images": [ "scigen/train/2609.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Speed comparison in the CoNLL03 NER task. We can observe that LD-Net (9, pruned) achieved about 5 times speed up on the wall-clock time over LD-Net (9, origin)." ], "reasoning": "The network with the highest speed in words per second is LD-Net (9, pruned) with a speed of 166K words/s. The batch size for this network is 500 and the peak RAM is 8Gb.", "question": "What is the batch size and peak RAM for the network with the highest speed in words per second?", "answer": "So, the answer is {\"answer\": \"500, 8Gb\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe LD-Net (9, pruned) network with a batch size of 500 has the highest speed in sentences per second.\n" }, { "role": "assistant", "content": "The table shows that the LD-Net (9, pruned) network with a batch size of 500 has a speed of 2.9 sentences per second, which is the highest speed in sentences per second listed in the table. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2609.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Speed comparison in the CoNLL03 NER task. We can observe that LD-Net (9, pruned) achieved about 5 times speed up on the wall-clock time over LD-Net (9, origin)." ], "reasoning": "The table shows that the LD-Net (9, pruned) network with a batch size of 500 has a speed of 2.9 sentences per second, which is the highest speed in sentences per second listed in the table.", "statement": "The LD-Net (9, pruned) network with a batch size of 500 has the highest speed in sentences per second.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 5. Evaluation results for the test corpus CPerv.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The experiment investigates how text length affects AV methods by truncating documents to various lengths and applying top-performing methods to observe their robustness.The Effect of Limited Text Length: In our third experiment, we investigate the question how text lengths affect the results of the examined AV methods. The motivation behind this experiment is based on the observation of Stamatatos et al. that text length is an important issue, which has not been thoroughly studied within authorship verification research. The corpus is suitable for this purpose, as it comprises a large number of verification problems, where more than 90% of all documents have sufficient text lengths (≥2,000 characters). This allows a stepwise truncation and by this to analyze the effect between the text lengths and the recognition results. Although these results are quite impressive, it should be noted that a large fraction of the documents comprises thousands of words. Thus, the methods can learn precise representations based on a large variety of features, which in turn enable a good determination of (dis)similarities between known/unknown documents. To investigate this issue in more detail, we constructed four versions of the test corpus and equalized the unknown document lengths to 250, 500, 1000, and 2000 characters. Then, we applied the top performing AV methods with a c@1 value >0.9 on the four corpora. Here, we reused the same models and hyperparameters (including the decision criteria θ and θM) that were determined on the training corpus. The intention behind this was to observe the robustness of the trained AV models, given the fact that during training they were confronted with longer documents." } ], "images": [ "scigen/train/2610.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5. Evaluation results for the test corpus CPerv." ], "reasoning": "The experiment investigates how text length affects AV methods by truncating documents to various lengths and applying top-performing methods to observe their robustness.", "description": "The Effect of Limited Text Length: In our third experiment, we investigate the question how text lengths affect the results of the examined AV methods. The motivation behind this experiment is based on the observation of Stamatatos et al. that text length is an important issue, which has not been thoroughly studied within authorship verification research. The corpus is suitable for this purpose, as it comprises a large number of verification problems, where more than 90% of all documents have sufficient text lengths (≥2,000 characters). This allows a stepwise truncation and by this to analyze the effect between the text lengths and the recognition results. Although these results are quite impressive, it should be noted that a large fraction of the documents comprises thousands of words. Thus, the methods can learn precise representations based on a large variety of features, which in turn enable a good determination of (dis)similarities between known/unknown documents. To investigate this issue in more detail, we constructed four versions of the test corpus and equalized the unknown document lengths to 250, 500, 1000, and 2000 characters. Then, we applied the top performing AV methods with a c@1 value >0.9 on the four corpora. Here, we reused the same models and hyperparameters (including the decision criteria θ and θM) that were determined on the training corpus. The intention behind this was to observe the robustness of the trained AV models, given the fact that during training they were confronted with longer documents.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 5. Evaluation results for the test corpus CPerv.', solve the subsequent question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhich AV method has the highest AUC value and what is its c@1 value?\n" }, { "role": "assistant", "content": "From the table, Caravel and GLAD both have the highest AUC value of 0.995. Caravel has a c@1 value of 0.973 and GLAD has a c@1 value of 0.970. So, the answer is {\"answer\": \"Caravel with c@1 value of 0.973\"}" } ], "images": [ "scigen/train/2610.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5. Evaluation results for the test corpus CPerv." ], "reasoning": "From the table, Caravel and GLAD both have the highest AUC value of 0.995. Caravel has a c@1 value of 0.973 and GLAD has a c@1 value of 0.970.", "question": "Which AV method has the highest AUC value and what is its c@1 value?", "answer": "So, the answer is {\"answer\": \"Caravel with c@1 value of 0.973\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nNNCD has the highest true positive (TP) count among all AV methods.\n" }, { "role": "assistant", "content": "From the table, NNCD has a TP count of 325, which is the highest among all listed AV methods. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2610.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5. Evaluation results for the test corpus CPerv." ], "reasoning": "From the table, NNCD has a TP count of 325, which is the highest among all listed AV methods.", "statement": "NNCD has the highest true positive (TP) count among all AV methods.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4. Evaluation results for the test corpus CReddit.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "All examined AV methods are influenced by topical bias, with the highest c@1 and AUC results being close to random guessing, indicating none are robust against topical influence.In the following we show to which extent these assumptions hold. All examined AV methods (with no exception) are fooled by the topical bias in the corpus. Here, the highest achieved results in terms of c@1 and AUC are very close to random guessing. A closer look at the confusion matrix outcomes reveals that some methods, for example ImpGI and OCCAV, perform almost entirely inverse to each other, where the former predicts nothing but Y and the latter nothing but N (except 1 Y). Moreover, we can assume that the lower c@1 is, the stronger is the focus of the respective AV method on the topic of the documents. Overall, the results of this experiment suggest that none of the examined AV methods is robust against topical influence." } ], "images": [ "scigen/train/2611.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4. Evaluation results for the test corpus CReddit." ], "reasoning": "All examined AV methods are influenced by topical bias, with the highest c@1 and AUC results being close to random guessing, indicating none are robust against topical influence.", "description": "In the following we show to which extent these assumptions hold. All examined AV methods (with no exception) are fooled by the topical bias in the corpus. Here, the highest achieved results in terms of c@1 and AUC are very close to random guessing. A closer look at the confusion matrix outcomes reveals that some methods, for example ImpGI and OCCAV, perform almost entirely inverse to each other, where the former predicts nothing but Y and the latter nothing but N (except 1 Y). Moreover, we can assume that the lower c@1 is, the stronger is the focus of the respective AV method on the topic of the documents. Overall, the results of this experiment suggest that none of the examined AV methods is robust against topical influence.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4. Evaluation results for the test corpus CReddit.\nQuestion: Which AV method has the highest AUC and what does this imply about its performance?\nGive a detailed response to the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that 'Unmasking' has the highest AUC of 0.559. According to the description, higher AUC values close to random guessing imply that the method is not robust against topical influence. So, the answer is {\"answer\": \"Unmasking\"}." } ], "images": [ "scigen/train/2611.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4. Evaluation results for the test corpus CReddit." ], "reasoning": "The table shows that 'Unmasking' has the highest AUC of 0.559. According to the description, higher AUC values close to random guessing imply that the method is not robust against topical influence.", "question": "Which AV method has the highest AUC and what does this imply about its performance?", "answer": "So, the answer is {\"answer\": \"Unmasking\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence ImpGI predicts nothing but Y. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nImpGI predicts nothing but Y.\n" }, { "role": "assistant", "content": "The table shows that ImpGI has 30 TP and 0 TN, indicating it predicts only Y, which supports the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2611.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4. Evaluation results for the test corpus CReddit." ], "reasoning": "The table shows that ImpGI has 30 TP and 0 TN, indicating it predicts only Y, which supports the statement.", "statement": "ImpGI predicts nothing but Y.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Evaluating the output with word-level BLEU, multi-source models significantly improve upon the baselines on higher-resource settings (Spanish). On Ainu, the best model performs on par with the very competitive baseline.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "In the Ainu dataset, the multi-source model with shared attention mechanism performs similarly to the speech-only model, while in the CALLHOME dataset, the multi-source model with tied attention mechanism achieves significantly higher BLEU scores.A higher BLEU almost always translates to lower WER. We only report results on Ainu and Spanish as Mboshi does not have standardized word segmentation rules. The BLEU scores reinforce our previous analysis. In the Ainu dataset, where a translation-only model is impossible to train (as outlined by the BLEU score of about 5.9), the multi-source model with shared attention mechanism performs on par with the speech-only model, as does the coupled ensemble model. In the CALLHOME dataset, our multi-source model with the tied attention mechanism achieves significantly higher BLEU scores than the translation-only or the coupled ensemble models. In addition, our best model’s WER on the CALLHOME test set is 53.0, outperforming LatticeTM that achieved a WER of 56.2, despite the fact that our model operates in a harder setting, trained and tested directly on speech." } ], "images": [ "scigen/train/2612.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Evaluating the output with word-level BLEU, multi-source models significantly improve upon the baselines on higher-resource settings (Spanish). On Ainu, the best model performs on par with the very competitive baseline." ], "reasoning": "In the Ainu dataset, the multi-source model with shared attention mechanism performs similarly to the speech-only model, while in the CALLHOME dataset, the multi-source model with tied attention mechanism achieves significantly higher BLEU scores.", "description": "A higher BLEU almost always translates to lower WER. We only report results on Ainu and Spanish as Mboshi does not have standardized word segmentation rules. The BLEU scores reinforce our previous analysis. In the Ainu dataset, where a translation-only model is impossible to train (as outlined by the BLEU score of about 5.9), the multi-source model with shared attention mechanism performs on par with the speech-only model, as does the coupled ensemble model. In the CALLHOME dataset, our multi-source model with the tied attention mechanism achieves significantly higher BLEU scores than the translation-only or the coupled ensemble models. In addition, our best model’s WER on the CALLHOME test set is 53.0, outperforming LatticeTM that achieved a WER of 56.2, despite the fact that our model operates in a harder setting, trained and tested directly on speech.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 2: Evaluating the output with word-level BLEU, multi-source models significantly improve upon the baselines on higher-resource settings (Spanish). On Ainu, the best model performs on par with the very competitive baseline.' in mind, break down and resolve the question below step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich model achieves the highest BLEU score in the Spanish dataset?\n" }, { "role": "assistant", "content": "By examining the table, the highest BLEU score in the Spanish dataset is achieved by the multi-source model with tied attention mechanism, which has a BLEU score of 20.82. So, the answer is {\"answer\": \"multi-source model with tied attention mechanism\"}." } ], "images": [ "scigen/train/2612.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Evaluating the output with word-level BLEU, multi-source models significantly improve upon the baselines on higher-resource settings (Spanish). On Ainu, the best model performs on par with the very competitive baseline." ], "reasoning": "By examining the table, the highest BLEU score in the Spanish dataset is achieved by the multi-source model with tied attention mechanism, which has a BLEU score of 20.82.", "question": "Which model achieves the highest BLEU score in the Spanish dataset?", "answer": "So, the answer is {\"answer\": \"multi-source model with tied attention mechanism\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe multi-source model with shared attention mechanism achieves the highest BLEU score in the Ainu dataset.\n" }, { "role": "assistant", "content": "The table shows that the multi-source model with shared attention mechanism has a BLEU score of 28.57 in the Ainu dataset, which is slightly lower than the speech-only model's BLEU score of 28.92. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2612.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Evaluating the output with word-level BLEU, multi-source models significantly improve upon the baselines on higher-resource settings (Spanish). On Ainu, the best model performs on par with the very competitive baseline." ], "reasoning": "The table shows that the multi-source model with shared attention mechanism has a BLEU score of 28.57 in the Ainu dataset, which is slightly lower than the speech-only model's BLEU score of 28.92.", "statement": "The multi-source model with shared attention mechanism achieves the highest BLEU score in the Ainu dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Multi-Source models achieve lower Character Error Rates (CER) on all three target languages, even in extremely low resource settings (Ainu, Mboshi). In Spanish, we observe an 8.4% reduction in CER.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "In Ainu and Mboshi, the speech-only baselines are competitive due to the small number of speakers, while translation tasks are harder with poor performance in translation-only models.In the two extremely low-resource datasets, Ainu and Mboshi, we find that the speech-only baselines are quite competitive. The very small number of speakers (one and three, respectively) makes the speech transcription task easier. In contrast, the translation task is much harder with so little data, as is also confirmed by the poor performance of the translation-only models in Ainu and Mboshi." } ], "images": [ "scigen/train/2613.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Multi-Source models achieve lower Character Error Rates (CER) on all three target languages, even in extremely low resource settings (Ainu, Mboshi). In Spanish, we observe an 8.4% reduction in CER." ], "reasoning": "In Ainu and Mboshi, the speech-only baselines are competitive due to the small number of speakers, while translation tasks are harder with poor performance in translation-only models.", "description": "In the two extremely low-resource datasets, Ainu and Mboshi, we find that the speech-only baselines are quite competitive. The very small number of speakers (one and three, respectively) makes the speech transcription task easier. In contrast, the translation task is much harder with so little data, as is also confirmed by the poor performance of the translation-only models in Ainu and Mboshi.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 1: Multi-Source models achieve lower Character Error Rates (CER) on all three target languages, even in extremely low resource settings (Ainu, Mboshi). In Spanish, we observe an 8.4% reduction in CER.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. \n\nQuestion: Which transcription method has the lowest CER for Mboshi?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By comparing the CER values for Mboshi across all transcription methods, the lowest CER is found in the '+shared' method with a CER of 28.6. So, the answer is {\"answer\": \"28.6\"}." } ], "images": [ "scigen/train/2613.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Multi-Source models achieve lower Character Error Rates (CER) on all three target languages, even in extremely low resource settings (Ainu, Mboshi). In Spanish, we observe an 8.4% reduction in CER." ], "reasoning": "By comparing the CER values for Mboshi across all transcription methods, the lowest CER is found in the '+shared' method with a CER of 28.6.", "question": "Which transcription method has the lowest CER for Mboshi?", "answer": "So, the answer is {\"answer\": \"28.6\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The coupled ensemble method has the lowest CER for Ainu., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe coupled ensemble method has the lowest CER for Ainu.\n" }, { "role": "assistant", "content": "By comparing the CER values for Ainu across all transcription methods, the coupled ensemble method has the lowest CER of 40.6. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2613.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Multi-Source models achieve lower Character Error Rates (CER) on all three target languages, even in extremely low resource settings (Ainu, Mboshi). In Spanish, we observe an 8.4% reduction in CER." ], "reasoning": "By comparing the CER values for Ainu across all transcription methods, the coupled ensemble method has the lowest CER of 40.6.", "statement": "The coupled ensemble method has the lowest CER for Ainu.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Saliency selection results. Random: randomly select K characters for three times and use the average Jaccard values. tf-idf: directly select K characters in terms of tf-idf, without SC.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "tfidf-SSal achieves the highest overlap of human-selected salient characters, outperforming Random and tf-idf, while naive-TopK performs worse than tf-idf on Wujue.tfidf-SSal achieves about 50% overlap of human-selected salient characters, which is notably higher than Random and tf-idf. With only attention values, naive-TopK performs worse than tf-idf on Wujue. Combining tf-idf and SSal makes notable improvement." } ], "images": [ "scigen/train/2614.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Saliency selection results. Random: randomly select K characters for three times and use the average Jaccard values. tf-idf: directly select K characters in terms of tf-idf, without SC." ], "reasoning": "tfidf-SSal achieves the highest overlap of human-selected salient characters, outperforming Random and tf-idf, while naive-TopK performs worse than tf-idf on Wujue.", "description": "tfidf-SSal achieves about 50% overlap of human-selected salient characters, which is notably higher than Random and tf-idf. With only attention values, naive-TopK performs worse than tf-idf on Wujue. Combining tf-idf and SSal makes notable improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Saliency selection results. Random: randomly select K characters for three times and use the average Jaccard values. tf-idf: directly select K characters in terms of tf-idf, without SC.\nQuestion: Which model achieves the highest overlap of human-selected salient characters on Wujue?\nGive a detailed response to the above question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the values in the Wujue column, tfidf-SSal SC has the highest value of 0.525, indicating the highest overlap. So, the answer is {\"answer\": \"tfidf-SSal SC\"}." } ], "images": [ "scigen/train/2614.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Saliency selection results. Random: randomly select K characters for three times and use the average Jaccard values. tf-idf: directly select K characters in terms of tf-idf, without SC." ], "reasoning": "By comparing the values in the Wujue column, tfidf-SSal SC has the highest value of 0.525, indicating the highest overlap.", "question": "Which model achieves the highest overlap of human-selected salient characters on Wujue?", "answer": "So, the answer is {\"answer\": \"tfidf-SSal SC\"}." }, { "messages": [ { "role": "user", "content": "naive-TopK SC performs better than tf-idf on Qijue. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nnaive-TopK SC performs better than tf-idf on Qijue.\n" }, { "role": "assistant", "content": "The table shows that naive-TopK SC has a value of 0.415 on Qijue, while tf-idf has a value of 0.378, indicating that naive-TopK SC performs better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2614.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Saliency selection results. Random: randomly select K characters for three times and use the average Jaccard values. tf-idf: directly select K characters in terms of tf-idf, without SC." ], "reasoning": "The table shows that naive-TopK SC has a value of 0.415 on Qijue, while tf-idf has a value of 0.378, indicating that naive-TopK SC performs better.", "statement": "naive-TopK SC performs better than tf-idf on Qijue.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Human evaluation results. Diacritics * (p<0.05) and ** (p<0.01) indicates SC models significantly outperform the three baselines; + (p<0.05) and ++ (p<0.01) indicates Human is significantly better than all the five models. The Intraclass Correlation Coefficient of the four groups of scores is 0.596, which indicates an acceptable inter-annotator agreement.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Style-SC achieves better results than SC on most criteria, especially on Coherence, where it matches Human for Wujue and improves significantly over SC.SC and Style-SC achieve better results than other models and get close to Human, though there is still a gap. Especially on Coherence, our Style-SC gets the same score as Human for Wujue. Moreover, Style-SC makes a considerable improvement on Coherence compared to SC (+0.05 for Wujue and +0.15 for Qijue), which demonstrates that consistent style can actually enhance the coherence, though it’s not easy to predict an appropriate style automatically. Interestingly, Style-SC outperforms SC on most criteria, except for Poeticness. We believe this is mainly because that style control forces the model to always generate some style-related words, which limits the imagination and thus hurts the Poeticness." } ], "images": [ "scigen/train/2615.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Human evaluation results. Diacritics * (p<0.05) and ** (p<0.01) indicates SC models significantly outperform the three baselines; + (p<0.05) and ++ (p<0.01) indicates Human is significantly better than all the five models. The Intraclass Correlation Coefficient of the four groups of scores is 0.596, which indicates an acceptable inter-annotator agreement." ], "reasoning": "Style-SC achieves better results than SC on most criteria, especially on Coherence, where it matches Human for Wujue and improves significantly over SC.", "description": "SC and Style-SC achieve better results than other models and get close to Human, though there is still a gap. Especially on Coherence, our Style-SC gets the same score as Human for Wujue. Moreover, Style-SC makes a considerable improvement on Coherence compared to SC (+0.05 for Wujue and +0.15 for Qijue), which demonstrates that consistent style can actually enhance the coherence, though it’s not easy to predict an appropriate style automatically. Interestingly, Style-SC outperforms SC on most criteria, except for Poeticness. We believe this is mainly because that style control forces the model to always generate some style-related words, which limits the imagination and thus hurts the Poeticness.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Human evaluation results. Diacritics * (p<0.05) and ** (p<0.01) indicates SC models significantly outperform the three baselines; + (p<0.05) and ++ (p<0.01) indicates Human is significantly better than all the five models. The Intraclass Correlation Coefficient of the four groups of scores is 0.596, which indicates an acceptable inter-annotator agreement.' to sequentially solve the question presented below. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nHow does Style-SC's performance on Coherence for Wujue compare to Human and SC?\n" }, { "role": "assistant", "content": "Style-SC achieves the same score as Human (3.90) on Coherence for Wujue and improves by 0.05 over SC (3.85). So, the answer is {\"answer\": \"3.90\"}." } ], "images": [ "scigen/train/2615.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Human evaluation results. Diacritics * (p<0.05) and ** (p<0.01) indicates SC models significantly outperform the three baselines; + (p<0.05) and ++ (p<0.01) indicates Human is significantly better than all the five models. The Intraclass Correlation Coefficient of the four groups of scores is 0.596, which indicates an acceptable inter-annotator agreement." ], "reasoning": "Style-SC achieves the same score as Human (3.90) on Coherence for Wujue and improves by 0.05 over SC (3.85).", "question": "How does Style-SC's performance on Coherence for Wujue compare to Human and SC?", "answer": "So, the answer is {\"answer\": \"3.90\"}." }, { "messages": [ { "role": "user", "content": "Style-SC outperforms SC on Poeticness for Wujue. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nStyle-SC outperforms SC on Poeticness for Wujue.\n" }, { "role": "assistant", "content": "The table shows that SC has a Poeticness score of 3.74 for Wujue, while Style-SC has a lower score of 3.61, indicating that SC outperforms Style-SC on Poeticness for Wujue. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2615.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Human evaluation results. Diacritics * (p<0.05) and ** (p<0.01) indicates SC models significantly outperform the three baselines; + (p<0.05) and ++ (p<0.01) indicates Human is significantly better than all the five models. The Intraclass Correlation Coefficient of the four groups of scores is 0.596, which indicates an acceptable inter-annotator agreement." ], "reasoning": "The table shows that SC has a Poeticness score of 3.74 for Wujue, while Style-SC has a lower score of 3.61, indicating that SC outperforms Style-SC on Poeticness for Wujue.", "statement": "Style-SC outperforms SC on Poeticness for Wujue.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 7: Zero-shot evaluation by following Davison et al. (2019).'.\n" }, { "role": "assistant", "content": "The GLM (BERT-large) model, trained on a new corpus, significantly outperforms both the BERT-large model by Davison et al. and our implementation in terms of F1 score.To explore whether GLM can indeed learn the structured information from raw text, we conduct a zero-shot evaluation on CKBC by following Davison et al. For fair comparison, we re-train the GLM (BERT-large) on a new corpus in which all raw texts containing the CKBC testing pairs are discarded. Our augmented pre-trained language model significantly outperforms its baseline, which demonstrates our model’s capability of retaining structured information in a language model." } ], "images": [ "scigen/train/2616.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Zero-shot evaluation by following Davison et al. (2019)." ], "reasoning": "The GLM (BERT-large) model, trained on a new corpus, significantly outperforms both the BERT-large model by Davison et al. and our implementation in terms of F1 score.", "description": "To explore whether GLM can indeed learn the structured information from raw text, we conduct a zero-shot evaluation on CKBC by following Davison et al. For fair comparison, we re-train the GLM (BERT-large) on a new corpus in which all raw texts containing the CKBC testing pairs are discarded. Our augmented pre-trained language model significantly outperforms its baseline, which demonstrates our model’s capability of retaining structured information in a language model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 7: Zero-shot evaluation by following Davison et al. (2019).', unfold the question presented below, detailing every step of your calculation. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieved the highest F1 score?\n" }, { "role": "assistant", "content": "By comparing the F1 scores in the table, the GLM (BERT-large) model has the highest F1 score of 83.4. So, the answer is {\"answer\": \"GLM (BERT-large)\"}." } ], "images": [ "scigen/train/2616.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Zero-shot evaluation by following Davison et al. (2019)." ], "reasoning": "By comparing the F1 scores in the table, the GLM (BERT-large) model has the highest F1 score of 83.4.", "question": "Which model achieved the highest F1 score?", "answer": "So, the answer is {\"answer\": \"GLM (BERT-large)\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The BERT-large model by Davison et al. achieved a higher F1 score than our implementation. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe BERT-large model by Davison et al. achieved a higher F1 score than our implementation.\n" }, { "role": "assistant", "content": "The table shows that the BERT-large model by Davison et al. has an F1 score of 78.8, while our implementation has an F1 score of 77.1, indicating that the statement is true. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2616.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Zero-shot evaluation by following Davison et al. (2019)." ], "reasoning": "The table shows that the BERT-large model by Davison et al. has an F1 score of 78.8, while our implementation has an F1 score of 77.1, indicating that the statement is true.", "statement": "The BERT-large model by Davison et al. achieved a higher F1 score than our implementation.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Results on CommonsenseQA for single models.“-” denotes unavailable result, and underlined score is the previous best. ∗IR stands for information retrieval. †GLM is initialized with RoBERTa-large and falls into the last group.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "GLM based on RoBERTa-large achieves a new state-of-the-art result with a 0.8% improvement over RoBERTa+KE and outperforms RoBERTa-large by 2.0%.A brief introduction to each approach without reference can be found on the leaderboard. Compared to the previous best model RoBERTa+KE which is also trained with an extra in-domain corpus (i.e., OMCS) and uses retrieval during finetuning, our approach achieves 0.8% absolute improvement to deliver a new state-of-the-art result. In addition, GLM based on RoBERTa-large outperforms its respective baseline RoBERTa-large by 2.0%." } ], "images": [ "scigen/train/2617.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on CommonsenseQA for single models.“-” denotes unavailable result, and underlined score is the previous best. ∗IR stands for information retrieval. †GLM is initialized with RoBERTa-large and falls into the last group." ], "reasoning": "GLM based on RoBERTa-large achieves a new state-of-the-art result with a 0.8% improvement over RoBERTa+KE and outperforms RoBERTa-large by 2.0%.", "description": "A brief introduction to each approach without reference can be found on the leaderboard. Compared to the previous best model RoBERTa+KE which is also trained with an extra in-domain corpus (i.e., OMCS) and uses retrieval during finetuning, our approach achieves 0.8% absolute improvement to deliver a new state-of-the-art result. In addition, GLM based on RoBERTa-large outperforms its respective baseline RoBERTa-large by 2.0%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: Results on CommonsenseQA for single models.“-” denotes unavailable result, and underlined score is the previous best. ∗IR stands for information retrieval. †GLM is initialized with RoBERTa-large and falls into the last group.', solve the subsequent question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model achieved the highest test score among those with further self-supervision tasks?\n" }, { "role": "assistant", "content": "Among the models with further self-supervision tasks, GLM (RoBERTa) achieved the highest test score of 74.1. Thus, the answer is {\"answer\": \"GLM (RoBERTa)\"}." } ], "images": [ "scigen/train/2617.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on CommonsenseQA for single models.“-” denotes unavailable result, and underlined score is the previous best. ∗IR stands for information retrieval. †GLM is initialized with RoBERTa-large and falls into the last group." ], "reasoning": "Among the models with further self-supervision tasks, GLM (RoBERTa) achieved the highest test score of 74.1.", "question": "Which model achieved the highest test score among those with further self-supervision tasks?", "answer": "Thus, the answer is {\"answer\": \"GLM (RoBERTa)\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis RoBERTa+KE achieved a higher test score than FreeLB-RoBERTa. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nRoBERTa+KE achieved a higher test score than FreeLB-RoBERTa.\n" }, { "role": "assistant", "content": "The table shows that RoBERTa+KE has a test score of 73.3, while FreeLB-RoBERTa has a test score of 72.2, indicating that RoBERTa+KE achieved a higher test score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2617.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on CommonsenseQA for single models.“-” denotes unavailable result, and underlined score is the previous best. ∗IR stands for information retrieval. †GLM is initialized with RoBERTa-large and falls into the last group." ], "reasoning": "The table shows that RoBERTa+KE has a test score of 73.3, while FreeLB-RoBERTa has a test score of 72.2, indicating that RoBERTa+KE achieved a higher test score.", "statement": "RoBERTa+KE achieved a higher test score than FreeLB-RoBERTa.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: Test accuracy on WN11 triple classification task.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Our approach, GLM (BERT-base), achieves the highest test accuracy of 94.0%, outperforming other methods and improving state-of-the-art accuracy by 0.5%.Consistent with the results on WN18RR, our approach outperforms translation-based graph embedding models and convolution-based methods, and improves state-of-the-art accuracy by 0.5%." } ], "images": [ "scigen/train/2618.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Test accuracy on WN11 triple classification task." ], "reasoning": "Our approach, GLM (BERT-base), achieves the highest test accuracy of 94.0%, outperforming other methods and improving state-of-the-art accuracy by 0.5%.", "description": "Consistent with the results on WN18RR, our approach outperforms translation-based graph embedding models and convolution-based methods, and improves state-of-the-art accuracy by 0.5%.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 5: Test accuracy on WN11 triple classification task.\nQuestion: Which method achieves the highest test accuracy and by how much does it improve the state-of-the-art accuracy?\n" }, { "role": "assistant", "content": "The table shows that GLM (BERT-base) achieves the highest test accuracy of 94.0%. The description states that this improves the state-of-the-art accuracy by 0.5%. So, the answer is {\"answer\": \"GLM (BERT-base), 0.5%\"}." } ], "images": [ "scigen/train/2618.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Test accuracy on WN11 triple classification task." ], "reasoning": "The table shows that GLM (BERT-base) achieves the highest test accuracy of 94.0%. The description states that this improves the state-of-the-art accuracy by 0.5%.", "question": "Which method achieves the highest test accuracy and by how much does it improve the state-of-the-art accuracy?", "answer": "So, the answer is {\"answer\": \"GLM (BERT-base), 0.5%\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The NTN method achieves a test accuracy higher than 87.0%., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe NTN method achieves a test accuracy higher than 87.0%.\n" }, { "role": "assistant", "content": "The table shows that the NTN method achieves a test accuracy of 86.2%, which is lower than 87.0%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2618.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Test accuracy on WN11 triple classification task." ], "reasoning": "The table shows that the NTN method achieves a test accuracy of 86.2%, which is lower than 87.0%.", "statement": "The NTN method achieves a test accuracy higher than 87.0%.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 6: Test accuracy on CKBC triple classification task..'.\n" }, { "role": "assistant", "content": "The performance of the CKBC task drops with more data due to lower quality additional triples and increased false negative examples from negative sampling.Finally, we evaluate our approach on the CKBC task, which should directly benefit from commonsense knowledge. In addition, we remove raw sentences belonging to CKBC’s test set from our GLM training corpora to avoid data leakage. There are two possible reasons why performance actually drops with more data: 1) the training triples are sorted w.r.t annotated confidence, so additional triples have a lower quality and may introduce noise; 2) more negative sampling must be done with more training triples, which introduces more false negative examples (from 1.25% to 2.42%)." } ], "images": [ "scigen/train/2619.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Test accuracy on CKBC triple classification task.." ], "reasoning": "The performance of the CKBC task drops with more data due to lower quality additional triples and increased false negative examples from negative sampling.", "description": "Finally, we evaluate our approach on the CKBC task, which should directly benefit from commonsense knowledge. In addition, we remove raw sentences belonging to CKBC’s test set from our GLM training corpora to avoid data leakage. There are two possible reasons why performance actually drops with more data: 1) the training triples are sorted w.r.t annotated confidence, so additional triples have a lower quality and may introduce noise; 2) more negative sampling must be done with more training triples, which introduces more false negative examples (from 1.25% to 2.42%).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 6: Test accuracy on CKBC triple classification task..', solve the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich method achieved the highest score on the Dev2 set, and what was the score?\n" }, { "role": "assistant", "content": "By examining the table, we see that the GLM (RoBERTa-large) method achieved the highest score on the Dev2 set with a score of 94.7. So, the answer is {\"answer\": \"GLM (RoBERTa-large), 94.7\"}." } ], "images": [ "scigen/train/2619.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Test accuracy on CKBC triple classification task.." ], "reasoning": "By examining the table, we see that the GLM (RoBERTa-large) method achieved the highest score on the Dev2 set with a score of 94.7.", "question": "Which method achieved the highest score on the Dev2 set, and what was the score?", "answer": "So, the answer is {\"answer\": \"GLM (RoBERTa-large), 94.7\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The performance of the GLM (RoBERTa-large) method on the Test set is higher than 94.5. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe performance of the GLM (RoBERTa-large) method on the Test set is higher than 94.5.\n" }, { "role": "assistant", "content": "The table shows that the GLM (RoBERTa-large) method achieved a score of 94.6 on the Test set, which is higher than 94.5. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2619.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Test accuracy on CKBC triple classification task.." ], "reasoning": "The table shows that the GLM (RoBERTa-large) method achieved a score of 94.6 on the Test set, which is higher than 94.5.", "statement": "The performance of the GLM (RoBERTa-large) method on the Test set is higher than 94.5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Ablation study of MuRel. Experimental validation of the pairwise module and the iterative processing on the VQA 2.0 val, VQA-CP v2 and TDIUC datasets.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Adding the pairwise module and the iterative process to MuRel significantly improves accuracy across all datasets, with the complete MuRel network achieving the highest performance., we compare three ablated instances of MuRel to its complete form. First, we validate the benefits of the pairwise module. Adding it to a vanilla MuRel without iterative process leads to higher accuracy on every datasets. In fact, between line 1 and 2, we report a gain of +0.44 on VQA 2.0, +0.24 on VQA CP v2 and +0.36 on TDIUC. Secondly, we validate the interest of the iterative process. Between line 1 et 3, we report a gain of +0.59 on VQA 2.0, +0.49 on VQA CP v2 and +0.42 on TDIUC. Notably, this modification does not add any parameters, because we iterate over a single MuRel cell. Unsharing the weights by using a different MuRel cell for each step gives similar results. Finally, the pairwise module and the iterative process are added to create the complete MuRel network. This instance (in line 4) reaches the highest accuracy on the three datasets. Interestingly, the gains provided by the combination of the two methods are sometimes larger than those of each one separately. For instance, we report a gain of +1.01 on VQA 2.0 between line 1 and 4. This attests to the complementary of the two modules." } ], "images": [ "scigen/train/2620.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ablation study of MuRel. Experimental validation of the pairwise module and the iterative processing on the VQA 2.0 val, VQA-CP v2 and TDIUC datasets." ], "reasoning": "Adding the pairwise module and the iterative process to MuRel significantly improves accuracy across all datasets, with the complete MuRel network achieving the highest performance.", "description": ", we compare three ablated instances of MuRel to its complete form. First, we validate the benefits of the pairwise module. Adding it to a vanilla MuRel without iterative process leads to higher accuracy on every datasets. In fact, between line 1 and 2, we report a gain of +0.44 on VQA 2.0, +0.24 on VQA CP v2 and +0.36 on TDIUC. Secondly, we validate the interest of the iterative process. Between line 1 et 3, we report a gain of +0.59 on VQA 2.0, +0.49 on VQA CP v2 and +0.42 on TDIUC. Notably, this modification does not add any parameters, because we iterate over a single MuRel cell. Unsharing the weights by using a different MuRel cell for each step gives similar results. Finally, the pairwise module and the iterative process are added to create the complete MuRel network. This instance (in line 4) reaches the highest accuracy on the three datasets. Interestingly, the gains provided by the combination of the two methods are sometimes larger than those of each one separately. For instance, we report a gain of +1.01 on VQA 2.0 between line 1 and 4. This attests to the complementary of the two modules.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Ablation study of MuRel. Experimental validation of the pairwise module and the iterative processing on the VQA 2.0 val, VQA-CP v2 and TDIUC datasets.' to sequentially solve the question presented below. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhat is the accuracy gain on VQA 2.0 when both the pairwise module and iterative process are added to MuRel compared to the vanilla MuRel?\n" }, { "role": "assistant", "content": "The accuracy on VQA 2.0 for the vanilla MuRel (line 1) is 64.13, and for the complete MuRel (line 4) it is 65.14. The gain is 65.14 - 64.13 = 1.01. So, the answer is {\"answer\": \"1.01\"}" } ], "images": [ "scigen/train/2620.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ablation study of MuRel. Experimental validation of the pairwise module and the iterative processing on the VQA 2.0 val, VQA-CP v2 and TDIUC datasets." ], "reasoning": "The accuracy on VQA 2.0 for the vanilla MuRel (line 1) is 64.13, and for the complete MuRel (line 4) it is 65.14. The gain is 65.14 - 64.13 = 1.01.", "question": "What is the accuracy gain on VQA 2.0 when both the pairwise module and iterative process are added to MuRel compared to the vanilla MuRel?", "answer": "So, the answer is {\"answer\": \"1.01\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe iterative process alone provides a higher accuracy gain on VQA CP v2 than the pairwise module alone.\n" }, { "role": "assistant", "content": "The accuracy gain on VQA CP v2 from adding the iterative process (line 3) compared to the vanilla MuRel (line 1) is 39.37 - 38.88 = 0.49. The gain from adding the pairwise module (line 2) is 39.12 - 38.88 = 0.24. Since 0.49 > 0.24, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2620.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ablation study of MuRel. Experimental validation of the pairwise module and the iterative processing on the VQA 2.0 val, VQA-CP v2 and TDIUC datasets." ], "reasoning": "The accuracy gain on VQA CP v2 from adding the iterative process (line 3) compared to the vanilla MuRel (line 1) is 39.37 - 38.88 = 0.49. The gain from adding the pairwise module (line 2) is 39.12 - 38.88 = 0.24. Since 0.49 > 0.24, the statement is supported.", "statement": "The iterative process alone provides a higher accuracy gain on VQA CP v2 than the pairwise module alone.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: Comparing MuRel to Attention. Comparison of the MuRel strategy against a strong Attention-based model on the VQA 2.0 val, VQA-CP v2 and TDIUC datasets. Both models have an equivalent number of parameters (∼60 million) and are trained on the same features following the same experimental setup.'.\n" }, { "role": "assistant", "content": "MuRel outperforms the attention-based baseline on VQA 2.0, VQA CP v2, and TDIUC datasets, demonstrating its superior ability to model interactions between questions and images.The goal of this experiments is to compare our approach with strong baselines for real VQA in controlled conditions. In addition to using the same bottom-up features, which are crucial for fair comparisons, we also dimension the attention-based baseline to have an equivalent amount of learned parameters than MuRel (∼60 millions including those from the GRU encoder). Also, we train it following the same experimental setup to insure competitiveness. MuRel reaches a higher accuracy on the three datasets. We report a significant gain of +1.70 on VQA 2.0 and +1.50 on VQA CP v2. Not only these results validate the ability of MuRel to better model interactions between the question and the image, but also to generalize when the distribution of the answers per question are completely different between the training and validation set as in VQA CP v2. A gain of +1.24 on TDIUC demonstrates the richer modeling capacity of MuRel in a fine-grained context of 12 well delimited question types." } ], "images": [ "scigen/train/2621.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparing MuRel to Attention. Comparison of the MuRel strategy against a strong Attention-based model on the VQA 2.0 val, VQA-CP v2 and TDIUC datasets. Both models have an equivalent number of parameters (∼60 million) and are trained on the same features following the same experimental setup." ], "reasoning": "MuRel outperforms the attention-based baseline on VQA 2.0, VQA CP v2, and TDIUC datasets, demonstrating its superior ability to model interactions between questions and images.", "description": "The goal of this experiments is to compare our approach with strong baselines for real VQA in controlled conditions. In addition to using the same bottom-up features, which are crucial for fair comparisons, we also dimension the attention-based baseline to have an equivalent amount of learned parameters than MuRel (∼60 millions including those from the GRU encoder). Also, we train it following the same experimental setup to insure competitiveness. MuRel reaches a higher accuracy on the three datasets. We report a significant gain of +1.70 on VQA 2.0 and +1.50 on VQA CP v2. Not only these results validate the ability of MuRel to better model interactions between the question and the image, but also to generalize when the distribution of the answers per question are completely different between the training and validation set as in VQA CP v2. A gain of +1.24 on TDIUC demonstrates the richer modeling capacity of MuRel in a fine-grained context of 12 well delimited question types.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Comparing MuRel to Attention. Comparison of the MuRel strategy against a strong Attention-based model on the VQA 2.0 val, VQA-CP v2 and TDIUC datasets. Both models have an equivalent number of parameters (∼60 million) and are trained on the same features following the same experimental setup.\nQuestion: What is the accuracy gain of MuRel over the attention baseline on the VQA CP v2 dataset?\nSolve the above question based on the table information. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The accuracy of MuRel on VQA CP v2 is 39.54, while the attention baseline is 38.04. The gain is calculated as 39.54 - 38.04. So, the answer is {\"answer\": \"1.50\"}" } ], "images": [ "scigen/train/2621.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparing MuRel to Attention. Comparison of the MuRel strategy against a strong Attention-based model on the VQA 2.0 val, VQA-CP v2 and TDIUC datasets. Both models have an equivalent number of parameters (∼60 million) and are trained on the same features following the same experimental setup." ], "reasoning": "The accuracy of MuRel on VQA CP v2 is 39.54, while the attention baseline is 38.04. The gain is calculated as 39.54 - 38.04.", "question": "What is the accuracy gain of MuRel over the attention baseline on the VQA CP v2 dataset?", "answer": "So, the answer is {\"answer\": \"1.50\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nMuRel has a higher accuracy than the attention baseline on the TDIUC dataset.\n" }, { "role": "assistant", "content": "The table shows that MuRel has an accuracy of 88.20 on TDIUC, while the attention baseline has an accuracy of 86.96. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2621.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparing MuRel to Attention. Comparison of the MuRel strategy against a strong Attention-based model on the VQA 2.0 val, VQA-CP v2 and TDIUC datasets. Both models have an equivalent number of parameters (∼60 million) and are trained on the same features following the same experimental setup." ], "reasoning": "The table shows that MuRel has an accuracy of 88.20 on TDIUC, while the attention baseline has an accuracy of 86.96.", "statement": "MuRel has a higher accuracy than the attention baseline on the TDIUC dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: State-of-the-art comparison on the VQA 2.0 dataset. Results on test-dev and test-std splits. All these models were trained on the same training set (VQA 2.0 train+val), using the Bottom-up features provided by [3]. No ensembling methods have been used. † have been trained by [6].'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "VQA models benefit from retaining local information in multimodal vectors, and spatial-semantic pairwise modeling between all possible pairs of regions shows promise. Despite not extensively tuning hyperparameters, the model's overall score on the test-dev split is competitive with state-of-the-art methods.S4SS3SSS0Px1 Vqa 2.0 This tends to indicate that VQA models can benefit from retaining local information in mulitmodal vectors instead of scalar coefficients. This shows the interest of our spatial-semantic pairwise modeling between all possible pairs of regions. Finally, even though we did not extensively tune the hyperparameters of our model, our overall score on the test-dev split is highly competitive with state-of-the-art methods. Please note that they improve their overall scores up to 70.01% when they include multiple types of visual features and more training data." } ], "images": [ "scigen/train/2622.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: State-of-the-art comparison on the VQA 2.0 dataset. Results on test-dev and test-std splits. All these models were trained on the same training set (VQA 2.0 train+val), using the Bottom-up features provided by [3]. No ensembling methods have been used. † have been trained by [6]." ], "reasoning": "VQA models benefit from retaining local information in multimodal vectors, and spatial-semantic pairwise modeling between all possible pairs of regions shows promise. Despite not extensively tuning hyperparameters, the model's overall score on the test-dev split is competitive with state-of-the-art methods.", "description": "S4SS3SSS0Px1 Vqa 2.0 This tends to indicate that VQA models can benefit from retaining local information in mulitmodal vectors instead of scalar coefficients. This shows the interest of our spatial-semantic pairwise modeling between all possible pairs of regions. Finally, even though we did not extensively tune the hyperparameters of our model, our overall score on the test-dev split is highly competitive with state-of-the-art methods. Please note that they improve their overall scores up to 70.01% when they include multiple types of visual features and more training data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: State-of-the-art comparison on the VQA 2.0 dataset. Results on test-dev and test-std splits. All these models were trained on the same training set (VQA 2.0 train+val), using the Bottom-up features provided by [3]. No ensembling methods have been used. † have been trained by [6].' and methodically tackle the question that follows. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model has the highest score on the test-dev All split?\n" }, { "role": "assistant", "content": "To find the model with the highest score on the test-dev All split, we need to compare the scores in the 'test-dev All' column. The highest score in this column is 68.09, achieved by the Counter model. So, the answer is {\"answer\": \"Counter\"}" } ], "images": [ "scigen/train/2622.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: State-of-the-art comparison on the VQA 2.0 dataset. Results on test-dev and test-std splits. All these models were trained on the same training set (VQA 2.0 train+val), using the Bottom-up features provided by [3]. No ensembling methods have been used. † have been trained by [6]." ], "reasoning": "To find the model with the highest score on the test-dev All split, we need to compare the scores in the 'test-dev All' column. The highest score in this column is 68.09, achieved by the Counter model.", "question": "Which model has the highest score on the test-dev All split?", "answer": "So, the answer is {\"answer\": \"Counter\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Counter model has the highest score on the test-std All split.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the scores in the 'test-std All' column. The highest score in this column is 68.41, achieved by both the Counter and MuRel models. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2622.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: State-of-the-art comparison on the VQA 2.0 dataset. Results on test-dev and test-std splits. All these models were trained on the same training set (VQA 2.0 train+val), using the Bottom-up features provided by [3]. No ensembling methods have been used. † have been trained by [6]." ], "reasoning": "To verify this statement, we need to compare the scores in the 'test-std All' column. The highest score in this column is 68.41, achieved by both the Counter and MuRel models. Therefore, the statement is supported.", "statement": "The Counter model has the highest score on the test-std All split.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4: State-of-the-art comparison on the TDIUC dataset. * trained by [18].', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "MuRel outperforms previous models in Positional reasoning, Counting, and Scene recognition tasks due to its pairwise structure and per-region context modeling.One of the core aspect of VQA models lies in their ability to address different tasks. The TDIUC dataset enables a detailed analysis of the strengths and limitations of a model by evaluating its performance on different types of question. Interestingly, we improve over this model even though it uses a combination of Bottom-up and fixed-grid features, as well as a supervision on the question types (hence its 100% result on the Absurd task). MuRel notably surpasses all previous methods on the Positional reasoning (+5.9 over MCB), Counting (+8.53 over QTA) questions. These improvements are likely due to the pairwise structure induced within the MuRel cell, which makes the answer prediction depend on the spatial and semantic relations between regions. The effectiveness of our per-region context modelling is also demonstrated by our the improvement on Scene recognition questions. For these questions, representing the image as a collection of independent objects shows lower performance than replacing each of them in its spatial and semantic context. Interestingly, our results on the harmonic mean of per-type accuracies (H-MPT) are lower than state-of-the-art. For MuRel, this harmonic metric is significantly harmed by our low score of 21.43% on the Utility and Affordances task. As these questions concern the possible usages of objects present in the scene (such as Can you eat the yellow object?), and are not directly related to the visual understanding of the scene." } ], "images": [ "scigen/train/2623.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: State-of-the-art comparison on the TDIUC dataset. * trained by [18]." ], "reasoning": "MuRel outperforms previous models in Positional reasoning, Counting, and Scene recognition tasks due to its pairwise structure and per-region context modeling.", "description": "One of the core aspect of VQA models lies in their ability to address different tasks. The TDIUC dataset enables a detailed analysis of the strengths and limitations of a model by evaluating its performance on different types of question. Interestingly, we improve over this model even though it uses a combination of Bottom-up and fixed-grid features, as well as a supervision on the question types (hence its 100% result on the Absurd task). MuRel notably surpasses all previous methods on the Positional reasoning (+5.9 over MCB), Counting (+8.53 over QTA) questions. These improvements are likely due to the pairwise structure induced within the MuRel cell, which makes the answer prediction depend on the spatial and semantic relations between regions. The effectiveness of our per-region context modelling is also demonstrated by our the improvement on Scene recognition questions. For these questions, representing the image as a collection of independent objects shows lower performance than replacing each of them in its spatial and semantic context. Interestingly, our results on the harmonic mean of per-type accuracies (H-MPT) are lower than state-of-the-art. For MuRel, this harmonic metric is significantly harmed by our low score of 21.43% on the Utility and Affordances task. As these questions concern the possible usages of objects present in the scene (such as Can you eat the yellow object?), and are not directly related to the visual understanding of the scene.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 4: State-of-the-art comparison on the TDIUC dataset. * trained by [18].\nQuestion: Which model has the highest score in the Positional reasoning task?\n" }, { "role": "assistant", "content": "By comparing the scores in the Positional reasoning row, MuRel has the highest score of 41.19. So, the answer is {\"answer\": \"MuRel\"}" } ], "images": [ "scigen/train/2623.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: State-of-the-art comparison on the TDIUC dataset. * trained by [18]." ], "reasoning": "By comparing the scores in the Positional reasoning row, MuRel has the highest score of 41.19.", "question": "Which model has the highest score in the Positional reasoning task?", "answer": "So, the answer is {\"answer\": \"MuRel\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis MuRel has the lowest score in the Utility and Affordances task. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nMuRel has the lowest score in the Utility and Affordances task.\n" }, { "role": "assistant", "content": "The table shows that MuRel has a score of 21.43 in the Utility and Affordances task, which is lower than the scores of RAU, MCB, and QTA. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2623.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: State-of-the-art comparison on the TDIUC dataset. * trained by [18]." ], "reasoning": "The table shows that MuRel has a score of 21.43 in the Utility and Affordances task, which is lower than the scores of RAU, MCB, and QTA.", "statement": "MuRel has the lowest score in the Utility and Affordances task.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: State-of-the-art comparison on the VQA-CP v2 dataset. The Attention model was trained by us using the Bottom-up features.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "MuRel outperforms other models in the 'Other' category and overall, despite the distribution mismatch between train and val splits.S4SS3SSS0Px3 VQA-CP v2 This dataset has been proposed to evaluate and reduce the question-oriented bias in VQA models. In particular, the distributions of answers with respect to question types differ from train to val splits. No questions. However, since both methods do not use the Bottom-up features, the fairness of the comparison can be questioned. We observe that MuRel provides a substantial gain over this strong attention baseline. Given the distribution mismatch between train and val splits, models that only focus on linguistic biases to answer the question are systematically penalized on their val scores. This property of VQA-CP v2 implies that the pairwise iterative structure of MuRel is less prone to question-based overfitting than classical attention architectures." } ], "images": [ "scigen/train/2624.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: State-of-the-art comparison on the VQA-CP v2 dataset. The Attention model was trained by us using the Bottom-up features." ], "reasoning": "MuRel outperforms other models in the 'Other' category and overall, despite the distribution mismatch between train and val splits.", "description": "S4SS3SSS0Px3 VQA-CP v2 This dataset has been proposed to evaluate and reduce the question-oriented bias in VQA models. In particular, the distributions of answers with respect to question types differ from train to val splits. No questions. However, since both methods do not use the Bottom-up features, the fairness of the comparison can be questioned. We observe that MuRel provides a substantial gain over this strong attention baseline. Given the distribution mismatch between train and val splits, models that only focus on linguistic biases to answer the question are systematically penalized on their val scores. This property of VQA-CP v2 implies that the pairwise iterative structure of MuRel is less prone to question-based overfitting than classical attention architectures.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 5: State-of-the-art comparison on the VQA-CP v2 dataset. The Attention model was trained by us using the Bottom-up features.' and methodically tackle the question that follows. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model performs best in the 'Other' category?\n" }, { "role": "assistant", "content": "By comparing the 'Other' category scores across all models, MuRel has the highest score of 45.04. So, the answer is {\"answer\": \"MuRel\"}" } ], "images": [ "scigen/train/2624.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: State-of-the-art comparison on the VQA-CP v2 dataset. The Attention model was trained by us using the Bottom-up features." ], "reasoning": "By comparing the 'Other' category scores across all models, MuRel has the highest score of 45.04.", "question": "Which model performs best in the 'Other' category?", "answer": "So, the answer is {\"answer\": \"MuRel\"}" }, { "messages": [ { "role": "user", "content": "GVQA performs best in the 'Yes/No' category. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nGVQA performs best in the 'Yes/No' category.\n" }, { "role": "assistant", "content": "By comparing the 'Yes/No' category scores across all models, GVQA has the highest score of 57.99. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2624.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: State-of-the-art comparison on the VQA-CP v2 dataset. The Attention model was trained by us using the Bottom-up features." ], "reasoning": "By comparing the 'Yes/No' category scores across all models, GVQA has the highest score of 57.99.", "statement": "GVQA performs best in the 'Yes/No' category.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: Overall accuracy for the original model and the agent, split by action type.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "MMA\textscAnswerOnly is more accurate in both abstaining and answering compared to BERTWWM, resulting in higher precision and recall for the NQ evaluation metric.We find that MMA\\textscAnswerOnly is a slightly more accurate abstainer, but that by and large, its tendency to answer or abstain matches that of BERTWWM. However, it is a considerably more accurate answerer – even though it answers 38 times fewer than BERTWWM, it increases the absolute number of correct answers given to by 83 and decreases the number of incorrect answers given by 121, explaining both the higher precision and recall for the NQ evaluation metric." } ], "images": [ "scigen/train/2625.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Overall accuracy for the original model and the agent, split by action type." ], "reasoning": "MMA\textscAnswerOnly is more accurate in both abstaining and answering compared to BERTWWM, resulting in higher precision and recall for the NQ evaluation metric.", "description": "We find that MMA\\textscAnswerOnly is a slightly more accurate abstainer, but that by and large, its tendency to answer or abstain matches that of BERTWWM. However, it is a considerably more accurate answerer – even though it answers 38 times fewer than BERTWWM, it increases the absolute number of correct answers given to by 83 and decreases the number of incorrect answers given by 121, explaining both the higher precision and recall for the NQ evaluation metric.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 5: Overall accuracy for the original model and the agent, split by action type.', solve this question and give detail solutions. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nHow many more correct answers does MMA\textscAnswerOnly provide compared to BERTWWM?\n" }, { "role": "assistant", "content": "The table shows that MMA\textscAnswerOnly provides 2977 correct answers while BERTWWM provides 3015 correct answers. The difference is 83 more correct answers for MMA\textscAnswerOnly. So, the answer is {\"answer\": \"83\"}." } ], "images": [ "scigen/train/2625.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Overall accuracy for the original model and the agent, split by action type." ], "reasoning": "The table shows that MMA\textscAnswerOnly provides 2977 correct answers while BERTWWM provides 3015 correct answers. The difference is 83 more correct answers for MMA\textscAnswerOnly.", "question": "How many more correct answers does MMA\textscAnswerOnly provide compared to BERTWWM?", "answer": "So, the answer is {\"answer\": \"83\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nMMA\textscAnswerOnly has a higher percentage of correct answers compared to BERTWWM.\n" }, { "role": "assistant", "content": "The table shows that MMA\textscAnswerOnly has a correct answer percentage of 63.12%, while BERTWWM has a correct answer percentage of 59.57%. Therefore, MMA\textscAnswerOnly has a higher percentage of correct answers. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2625.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Overall accuracy for the original model and the agent, split by action type." ], "reasoning": "The table shows that MMA\textscAnswerOnly has a correct answer percentage of 63.12%, while BERTWWM has a correct answer percentage of 59.57%. Therefore, MMA\textscAnswerOnly has a higher percentage of correct answers.", "statement": "MMA\textscAnswerOnly has a higher percentage of correct answers compared to BERTWWM.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: A boostrap evaluation to fairly compare original NQ annotators, human meta-answerers, and the baseline QA system. In the context with the richest interaction, the best human meta-answerer can improve over both the average NQ annotator and the baseline QA system, mostly through improving recall.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The best human meta-answerer outperforms the average NQ annotator in RewriteQues F1 with less information, and all humans improve from AnswerOnly to Context.Rather than rely on a deficient gold set, bootstrap sample from the remaining four annotations to get back to five annotations. But it would unfair to only evaluate NQ annotators using this, hence we evaluate our humans and the baseline bert QA system using the same metric (a bootstrap sample to get five annotations). This is a fair comparison between the NQ annotator, our human annotators, and the baseline QA system. All humans improve between AnswerOnly and Context. The better human meta-answerers improve over the baseline system. The best human (consistent across all conditions) was −7.9 worse on F1 in the AnswerOnly, the best human meta-answerer has a higher F1 than the average NQ annotator in RewriteQues with substantially less information." } ], "images": [ "scigen/train/2626.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: A boostrap evaluation to fairly compare original NQ annotators, human meta-answerers, and the baseline QA system. In the context with the richest interaction, the best human meta-answerer can improve over both the average NQ annotator and the baseline QA system, mostly through improving recall." ], "reasoning": "The best human meta-answerer outperforms the average NQ annotator in RewriteQues F1 with less information, and all humans improve from AnswerOnly to Context.", "description": "Rather than rely on a deficient gold set, bootstrap sample from the remaining four annotations to get back to five annotations. But it would unfair to only evaluate NQ annotators using this, hence we evaluate our humans and the baseline bert QA system using the same metric (a bootstrap sample to get five annotations). This is a fair comparison between the NQ annotator, our human annotators, and the baseline QA system. All humans improve between AnswerOnly and Context. The better human meta-answerers improve over the baseline system. The best human (consistent across all conditions) was −7.9 worse on F1 in the AnswerOnly, the best human meta-answerer has a higher F1 than the average NQ annotator in RewriteQues with substantially less information.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 1: A boostrap evaluation to fairly compare original NQ annotators, human meta-answerers, and the baseline QA system. In the context with the richest interaction, the best human meta-answerer can improve over both the average NQ annotator and the baseline QA system, mostly through improving recall.', solve this question and give detail solutions. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the F1 score difference between the best human and the NQ annotator in the RewriteQues task?\n" }, { "role": "assistant", "content": "The F1 score for the best human in RewriteQues is 63.3, and for the NQ annotator, it is 50.9. The difference is 63.3 - 50.9. So, the answer is {\"answer\": \"12.4\"}." } ], "images": [ "scigen/train/2626.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: A boostrap evaluation to fairly compare original NQ annotators, human meta-answerers, and the baseline QA system. In the context with the richest interaction, the best human meta-answerer can improve over both the average NQ annotator and the baseline QA system, mostly through improving recall." ], "reasoning": "The F1 score for the best human in RewriteQues is 63.3, and for the NQ annotator, it is 50.9. The difference is 63.3 - 50.9.", "question": "What is the F1 score difference between the best human and the NQ annotator in the RewriteQues task?", "answer": "So, the answer is {\"answer\": \"12.4\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The best human has a higher F1 score than the NQ annotator in the Context task., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe best human has a higher F1 score than the NQ annotator in the Context task.\n" }, { "role": "assistant", "content": "The table shows that the best human has an F1 score of 59.1 in the Context task, while the NQ annotator has an F1 score of 57.2. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2626.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: A boostrap evaluation to fairly compare original NQ annotators, human meta-answerers, and the baseline QA system. In the context with the richest interaction, the best human meta-answerer can improve over both the average NQ annotator and the baseline QA system, mostly through improving recall." ], "reasoning": "The table shows that the best human has an F1 score of 59.1 in the Context task, while the NQ annotator has an F1 score of 57.2.", "statement": "The best human has a higher F1 score than the NQ annotator in the Context task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Results on the Short Answer task of the Natural Questions dataset.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The MMA systems outperform MMABase and BERTWWM, with MMA\\textscAnswerOnly achieving the highest F1 score on the test set.We take as reference Alberti et al. ’s QA model trained on NQ and refer to it as BERT. Adding whole word masking The baseline, MMABase, is worse than BERTWWM. This shows that a naïve approach to encode evidential information is not comparable with the original answer context. The more careful handling of evidence, embeddings, and multiple losses lead the full MMA systems to outperforming MMABase by more than 6 F1 points on test. They also improve over BERTWWM by about 2 F1 points. This is counter intuitive, from a human perspective, as humans are better with more information. Together with the additional finding that this is flipped on dev—MMA\\textscContext has higher precision and F1 than MMA\\textscAnswerOnly—indicates that more work is needed to properly model context." } ], "images": [ "scigen/train/2627.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on the Short Answer task of the Natural Questions dataset." ], "reasoning": "The MMA systems outperform MMABase and BERTWWM, with MMA\\textscAnswerOnly achieving the highest F1 score on the test set.", "description": "We take as reference Alberti et al. ’s QA model trained on NQ and refer to it as BERT. Adding whole word masking The baseline, MMABase, is worse than BERTWWM. This shows that a naïve approach to encode evidential information is not comparable with the original answer context. The more careful handling of evidence, embeddings, and multiple losses lead the full MMA systems to outperforming MMABase by more than 6 F1 points on test. They also improve over BERTWWM by about 2 F1 points. This is counter intuitive, from a human perspective, as humans are better with more information. Together with the additional finding that this is flipped on dev—MMA\\textscContext has higher precision and F1 than MMA\\textscAnswerOnly—indicates that more work is needed to properly model context.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich system has the highest Short Answer Test Span F1 score?\nSolve the above question based on the table titled 'Table 4: Results on the Short Answer task of the Natural Questions dataset.'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the Short Answer Test Span F1 scores in the table, MMA\\textscAnswerOnly has the highest score of 58.9. So, the answer is {\"answer\": \"MMA\\textscAnswerOnly\"}" } ], "images": [ "scigen/train/2627.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on the Short Answer task of the Natural Questions dataset." ], "reasoning": "By comparing the Short Answer Test Span F1 scores in the table, MMA\\textscAnswerOnly has the highest score of 58.9.", "question": "Which system has the highest Short Answer Test Span F1 score?", "answer": "So, the answer is {\"answer\": \"MMA\\textscAnswerOnly\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nMMABase has a higher Short Answer Test Span F1 score than BERTWWM.\n" }, { "role": "assistant", "content": "The table shows that MMABase has a Short Answer Test Span F1 score of 53.9, while BERTWWM has a score of 56.6, indicating that the statement is false. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2627.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on the Short Answer task of the Natural Questions dataset." ], "reasoning": "The table shows that MMABase has a Short Answer Test Span F1 score of 53.9, while BERTWWM has a score of 56.6, indicating that the statement is false.", "statement": "MMABase has a higher Short Answer Test Span F1 score than BERTWWM.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Comparison of Lithium EDL and Google Cloud NL API'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Our system disambiguated significantly more entities than Google NL and OpenCalais, with at least 6080 correct disambiguations, implying Google NL missed over 2600 entities and OpenCalais missed at least 3500 entities.An entity from Google NL is considered to be disambiguated if it was associated with a Wikipedia link. Column Both shows the numbers of entities that were disambiguated by both systems. Most entities disambiguated by Google NL were also disambiguated by our system. In addition, our system disambiguated several more entities. Based on the the precision of our system, we can estimate that at least 6080 disambiguated entities from our system are correct. This implies that Google NL missed more than 2600 entities that were correctly disambiguated by our system. Thus, our system correctly disambiguated at least 75% more entities than Google NL. Every entity from OpenCalais API is considered to be disambiguated. However, since OpenCalais entity does not link the disambiguated entities to Wikipedia or Freebase but to their own proprietary KB, we cannot determine which entities were discovered by both the systems. Nevertheless, based on the precision of our system, at least 3500 entities that were correctly disambiguated by our system, were missed by OpenCalais, which is significantly more than the number of entities they detected." } ], "images": [ "scigen/train/2628.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of Lithium EDL and Google Cloud NL API" ], "reasoning": "Our system disambiguated significantly more entities than Google NL and OpenCalais, with at least 6080 correct disambiguations, implying Google NL missed over 2600 entities and OpenCalais missed at least 3500 entities.", "description": "An entity from Google NL is considered to be disambiguated if it was associated with a Wikipedia link. Column Both shows the numbers of entities that were disambiguated by both systems. Most entities disambiguated by Google NL were also disambiguated by our system. In addition, our system disambiguated several more entities. Based on the the precision of our system, we can estimate that at least 6080 disambiguated entities from our system are correct. This implies that Google NL missed more than 2600 entities that were correctly disambiguated by our system. Thus, our system correctly disambiguated at least 75% more entities than Google NL. Every entity from OpenCalais API is considered to be disambiguated. However, since OpenCalais entity does not link the disambiguated entities to Wikipedia or Freebase but to their own proprietary KB, we cannot determine which entities were discovered by both the systems. Nevertheless, based on the precision of our system, at least 3500 entities that were correctly disambiguated by our system, were missed by OpenCalais, which is significantly more than the number of entities they detected.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. The question is related to the given table titled 'Table 4: Comparison of Lithium EDL and Google Cloud NL API'.\n\nQuestion: How many more entities did our system disambiguate compared to Google NL for all languages combined?\n" }, { "role": "assistant", "content": "The table shows that our system (Lithium) disambiguated 9589 entities, while Google NL disambiguated 3454 entities. The difference is 9589 - 3454. So, the answer is {\"answer\": \"6135\"}" } ], "images": [ "scigen/train/2628.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of Lithium EDL and Google Cloud NL API" ], "reasoning": "The table shows that our system (Lithium) disambiguated 9589 entities, while Google NL disambiguated 3454 entities. The difference is 9589 - 3454.", "question": "How many more entities did our system disambiguate compared to Google NL for all languages combined?", "answer": "So, the answer is {\"answer\": \"6135\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nGoogle NL disambiguated more entities than our system in Spanish.\n" }, { "role": "assistant", "content": "The table shows that our system disambiguated 2410 entities in Spanish, while Google NL disambiguated 1152 entities in Spanish, which is less than our system. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2628.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of Lithium EDL and Google Cloud NL API" ], "reasoning": "The table shows that our system disambiguated 2410 entities in Spanish, while Google NL disambiguated 1152 entities in Spanish, which is less than our system.", "statement": "Google NL disambiguated more entities than our system in Spanish.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Precision, recall, f-score and accuracy for different features and feature sets on our test set (English only)', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Mention-Entity-Cooccurr has the highest individual precision and f-score among all individual features, indicating its significant impact on the algorithm's performance.We compute the performance metrics for individual features as well as for various feature sets on our English language test set to assess their impact. As evident from the results, Mention-Entity-Cooccurr has the biggest impact on the performance of the algorithm among all individual features as it has the highest individual precision and f-score." } ], "images": [ "scigen/train/2629.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Precision, recall, f-score and accuracy for different features and feature sets on our test set (English only)" ], "reasoning": "Mention-Entity-Cooccurr has the highest individual precision and f-score among all individual features, indicating its significant impact on the algorithm's performance.", "description": "We compute the performance metrics for individual features as well as for various feature sets on our English language test set to assess their impact. As evident from the results, Mention-Entity-Cooccurr has the biggest impact on the performance of the algorithm among all individual features as it has the highest individual precision and f-score.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Precision, recall, f-score and accuracy for different features and feature sets on our test set (English only)', answer the following question. Give detailed solution consisting of each step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich individual feature has the highest precision and f-score?\n" }, { "role": "assistant", "content": "By examining the table, Mention-Entity-Cooccurr (context independent) has the highest precision (0.65) and f-score (0.70) among all individual features. So, the answer is {\"answer\": \"Mention-Entity-Cooccurr\"}" } ], "images": [ "scigen/train/2629.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Precision, recall, f-score and accuracy for different features and feature sets on our test set (English only)" ], "reasoning": "By examining the table, Mention-Entity-Cooccurr (context independent) has the highest precision (0.65) and f-score (0.70) among all individual features.", "question": "Which individual feature has the highest precision and f-score?", "answer": "So, the answer is {\"answer\": \"Mention-Entity-Cooccurr\"}" }, { "messages": [ { "role": "user", "content": "Entity-Entity-Cooccurr (context dependent) has the highest recall among all features. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nEntity-Entity-Cooccurr (context dependent) has the highest recall among all features.\n" }, { "role": "assistant", "content": "The table shows that Entity-Entity-Cooccurr (context dependent) has a recall of 0.91, but Combined Context dependent features have a higher recall of 0.92. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2629.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Precision, recall, f-score and accuracy for different features and feature sets on our test set (English only)" ], "reasoning": "The table shows that Entity-Entity-Cooccurr (context dependent) has a recall of 0.91, but Combined Context dependent features have a higher recall of 0.92.", "statement": "Entity-Entity-Cooccurr (context dependent) has the highest recall among all features.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Precision, recall, f-score and accuracy across various languages'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The algorithm's performance for smaller test datasets in languages like French and Spanish is comparable to that for the English dataset.We note that the test datasets for these languages are smaller. However, the algorithm’s performance is comparable to that for the English dataset." } ], "images": [ "scigen/train/2630.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Precision, recall, f-score and accuracy across various languages" ], "reasoning": "The algorithm's performance for smaller test datasets in languages like French and Spanish is comparable to that for the English dataset.", "description": "We note that the test datasets for these languages are smaller. However, the algorithm’s performance is comparable to that for the English dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: Precision, recall, f-score and accuracy across various languages', and sequentially solve the question, writing out each step of your reasoning process. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich language has the highest F-score and what is its value?\n" }, { "role": "assistant", "content": "To find the language with the highest F-score, compare the F-scores of all languages listed in the table. Japanese has the highest F-score of 0.80. So, the answer is {\"answer\": \"Japanese, 0.80\"}." } ], "images": [ "scigen/train/2630.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Precision, recall, f-score and accuracy across various languages" ], "reasoning": "To find the language with the highest F-score, compare the F-scores of all languages listed in the table. Japanese has the highest F-score of 0.80.", "question": "Which language has the highest F-score and what is its value?", "answer": "So, the answer is {\"answer\": \"Japanese, 0.80\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The algorithm has the highest accuracy for the German dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe algorithm has the highest accuracy for the German dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the accuracy values for all languages listed in the table. The highest accuracy is for the Japanese dataset with a value of 0.74, not the German dataset. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2630.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Precision, recall, f-score and accuracy across various languages" ], "reasoning": "To verify this statement, compare the accuracy values for all languages listed in the table. The highest accuracy is for the Japanese dataset with a value of 0.74, not the German dataset.", "statement": "The algorithm has the highest accuracy for the German dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Comparison of Lithium EDL and Google Cloud NL API', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Our system disambiguated significantly more entities than Google NL and OpenCalais, with at least 6080 correct disambiguations missed by Google NL and 3500 by OpenCalais.An entity from Google NL is considered to be disambiguated if it was associated with a Wikipedia link. Column Both shows the numbers of entities that were disambiguated by both systems. Most entities disambiguated by Google NL were also disambiguated by our system. In addition, our system disambiguated several more entities. Based on the the precision of our system, we can estimate that at least 6080 disambiguated entities from our system are correct. This implies that Google NL missed more than 2600 entities that were correctly disambiguated by our system. Thus, our system correctly disambiguated at least 75% more entities than Google NL. Every entity from OpenCalais API is considered to be disambiguated. However, since OpenCalais entity does not link the disambiguated entities to Wikipedia or Freebase but to their own proprietary KB, we cannot determine which entities were discovered by both the systems. Nevertheless, based on the precision of our system, at least 3500 entities that were correctly disambiguated by our system, were missed by OpenCalais, which is significantly more than the number of entities they detected." } ], "images": [ "scigen/train/2631.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of Lithium EDL and Google Cloud NL API" ], "reasoning": "Our system disambiguated significantly more entities than Google NL and OpenCalais, with at least 6080 correct disambiguations missed by Google NL and 3500 by OpenCalais.", "description": "An entity from Google NL is considered to be disambiguated if it was associated with a Wikipedia link. Column Both shows the numbers of entities that were disambiguated by both systems. Most entities disambiguated by Google NL were also disambiguated by our system. In addition, our system disambiguated several more entities. Based on the the precision of our system, we can estimate that at least 6080 disambiguated entities from our system are correct. This implies that Google NL missed more than 2600 entities that were correctly disambiguated by our system. Thus, our system correctly disambiguated at least 75% more entities than Google NL. Every entity from OpenCalais API is considered to be disambiguated. However, since OpenCalais entity does not link the disambiguated entities to Wikipedia or Freebase but to their own proprietary KB, we cannot determine which entities were discovered by both the systems. Nevertheless, based on the precision of our system, at least 3500 entities that were correctly disambiguated by our system, were missed by OpenCalais, which is significantly more than the number of entities they detected.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: Comparison of Lithium EDL and Google Cloud NL API', solve this question and give detail solutions. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nHow many more entities did Lithium disambiguate compared to OpenCalais for all languages combined?\n" }, { "role": "assistant", "content": "To find the difference, subtract the total number of entities disambiguated by OpenCalais from the total number disambiguated by Lithium: 11299 - 3341. So, the answer is {\"answer\": \"7958\"}." } ], "images": [ "scigen/train/2631.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of Lithium EDL and Google Cloud NL API" ], "reasoning": "To find the difference, subtract the total number of entities disambiguated by OpenCalais from the total number disambiguated by Lithium: 11299 - 3341.", "question": "How many more entities did Lithium disambiguate compared to OpenCalais for all languages combined?", "answer": "So, the answer is {\"answer\": \"7958\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nLithium disambiguated more entities in Spanish than OpenCalais did in English.\n" }, { "role": "assistant", "content": "Compare the number of entities disambiguated by Lithium in Spanish (2410) with the number disambiguated by OpenCalais in English (1295). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2631.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of Lithium EDL and Google Cloud NL API" ], "reasoning": "Compare the number of entities disambiguated by Lithium in Spanish (2410) with the number disambiguated by OpenCalais in English (1295).", "statement": "Lithium disambiguated more entities in Spanish than OpenCalais did in English.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title '(c) Cascading with HEQ'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The proposed method aims to improve speech feature vector representations using building-blocks, achieving varying degrees of noise robustness, with NMF_plain+HEQ showing the highest improvement in recognition accuracy.During MFCC extraction, usually 23 LMFB coefficients are converted into 13 dimensional MFCCs through DCT. But this technique fails in high noise conditions. However, the objective of HLDA is not to achieve robustness, but class-separability. Here a method is proposed which aims at finding representations of speech feature vectors using building-blocks. Average values shown are taken over SNR levels It can be observed that HLDA can be used to achieve noise robustness in low noise levels only, and fails at high noise levels (SNR 5 to −5 dB). It can be seen that this method hardly gives an improvement over the baseline at different dictionary sizes. NMF_plain gives 1.86% absolute improvement in recognition accuracy, NMF_plain cascaded with HLDA gives 5.09%, NMF_robustW gives 5.96%, NMF_robustW+HLDA gives 8.6%. NMF_plain+HEQ gives 13.69% and NMF_robustW gives 13.48% improvement." } ], "images": [ "scigen/train/2632.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(c) Cascading with HEQ" ], "reasoning": "The proposed method aims to improve speech feature vector representations using building-blocks, achieving varying degrees of noise robustness, with NMF_plain+HEQ showing the highest improvement in recognition accuracy.", "description": "During MFCC extraction, usually 23 LMFB coefficients are converted into 13 dimensional MFCCs through DCT. But this technique fails in high noise conditions. However, the objective of HLDA is not to achieve robustness, but class-separability. Here a method is proposed which aims at finding representations of speech feature vectors using building-blocks. Average values shown are taken over SNR levels It can be observed that HLDA can be used to achieve noise robustness in low noise levels only, and fails at high noise levels (SNR 5 to −5 dB). It can be seen that this method hardly gives an improvement over the baseline at different dictionary sizes. NMF_plain gives 1.86% absolute improvement in recognition accuracy, NMF_plain cascaded with HLDA gives 5.09%, NMF_robustW gives 5.96%, NMF_robustW+HLDA gives 8.6%. NMF_plain+HEQ gives 13.69% and NMF_robustW gives 13.48% improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title '(c) Cascading with HEQ', unfold the steps to solve the question given next. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nQuestion: Which method shows the highest average recognition accuracy across different noise levels?\n" }, { "role": "assistant", "content": "By comparing the average values in the table, NMF_robustW+HEQ has the highest average recognition accuracy of 82.99. So, the answer is {\"answer\": \"NMF_robustW+HEQ\"}." } ], "images": [ "scigen/train/2632.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(c) Cascading with HEQ" ], "reasoning": "By comparing the average values in the table, NMF_robustW+HEQ has the highest average recognition accuracy of 82.99.", "question": "Which method shows the highest average recognition accuracy across different noise levels?", "answer": "So, the answer is {\"answer\": \"NMF_robustW+HEQ\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nNMF_plain+HEQ performs better than HEQ at SNR 0.\n" }, { "role": "assistant", "content": "At SNR 0, HEQ has a recognition accuracy of 46.66, while NMF_plain+HEQ has a recognition accuracy of 51.63, which is higher. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2632.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(c) Cascading with HEQ" ], "reasoning": "At SNR 0, HEQ has a recognition accuracy of 46.66, while NMF_plain+HEQ has a recognition accuracy of 51.63, which is higher.", "statement": "NMF_plain+HEQ performs better than HEQ at SNR 0.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title '(a) Individual methods'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "HLDA aims at class-separability and fails at high noise levels, while NMF_plain and NMF_robustW methods show varying improvements in recognition accuracy under different noise conditions.During MFCC extraction, usually 23 LMFB coefficients are converted into 13 dimensional MFCCs through DCT. But this technique fails in high noise conditions. However, the objective of HLDA is not to achieve robustness, but class-separability. Here a method is proposed which aims at finding representations of speech feature vectors using building-blocks. Average values shown are taken over SNR levels It can be observed that HLDA can be used to achieve noise robustness in low noise levels only, and fails at high noise levels (SNR 5 to −5 dB). It can be seen that this method hardly gives an improvement over the baseline at different dictionary sizes. NMF_plain gives 1.86% absolute improvement in recognition accuracy, NMF_plain cascaded with HLDA gives 5.09%, NMF_robustW gives 5.96%, NMF_robustW+HLDA gives 8.6%. NMF_plain+HEQ gives 13.69% and NMF_robustW gives 13.48% improvement." } ], "images": [ "scigen/train/2633.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) Individual methods" ], "reasoning": "HLDA aims at class-separability and fails at high noise levels, while NMF_plain and NMF_robustW methods show varying improvements in recognition accuracy under different noise conditions.", "description": "During MFCC extraction, usually 23 LMFB coefficients are converted into 13 dimensional MFCCs through DCT. But this technique fails in high noise conditions. However, the objective of HLDA is not to achieve robustness, but class-separability. Here a method is proposed which aims at finding representations of speech feature vectors using building-blocks. Average values shown are taken over SNR levels It can be observed that HLDA can be used to achieve noise robustness in low noise levels only, and fails at high noise levels (SNR 5 to −5 dB). It can be seen that this method hardly gives an improvement over the baseline at different dictionary sizes. NMF_plain gives 1.86% absolute improvement in recognition accuracy, NMF_plain cascaded with HLDA gives 5.09%, NMF_robustW gives 5.96%, NMF_robustW+HLDA gives 8.6%. NMF_plain+HEQ gives 13.69% and NMF_robustW gives 13.48% improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled '(a) Individual methods' and methodically tackle the question that follows. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the average recognition accuracy improvement of NMF_robustW over the baseline?\n" }, { "role": "assistant", "content": "To find the average recognition accuracy improvement of NMF_robustW over the baseline, subtract the average baseline accuracy from the average NMF_robustW accuracy: 75.47 - 69.51. So, the answer is {\"answer\": \"5.96\"}." } ], "images": [ "scigen/train/2633.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) Individual methods" ], "reasoning": "To find the average recognition accuracy improvement of NMF_robustW over the baseline, subtract the average baseline accuracy from the average NMF_robustW accuracy: 75.47 - 69.51.", "question": "What is the average recognition accuracy improvement of NMF_robustW over the baseline?", "answer": "So, the answer is {\"answer\": \"5.96\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim NMF_plain performs better than the baseline at SNR 10., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nNMF_plain performs better than the baseline at SNR 10.\n" }, { "role": "assistant", "content": "According to the table, at SNR 10, NMF_plain has a recognition accuracy of 82.73, which is higher than the baseline accuracy of 80.62. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2633.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) Individual methods" ], "reasoning": "According to the table, at SNR 10, NMF_plain has a recognition accuracy of 82.73, which is higher than the baseline accuracy of 80.62.", "statement": "NMF_plain performs better than the baseline at SNR 10.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled '(b) Cascading with HLDA'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "HLDA achieves noise robustness at low noise levels but fails at high noise levels, while NMF_plain and NMF_robustW methods show varying improvements in recognition accuracy.During MFCC extraction, usually 23 LMFB coefficients are converted into 13 dimensional MFCCs through DCT. But this technique fails in high noise conditions. However, the objective of HLDA is not to achieve robustness, but class-separability. Here a method is proposed which aims at finding representations of speech feature vectors using building-blocks. Average values shown are taken over SNR levels It can be observed that HLDA can be used to achieve noise robustness in low noise levels only, and fails at high noise levels (SNR 5 to −5 dB). It can be seen that this method hardly gives an improvement over the baseline at different dictionary sizes. NMF_plain gives 1.86% absolute improvement in recognition accuracy, NMF_plain cascaded with HLDA gives 5.09%, NMF_robustW gives 5.96%, NMF_robustW+HLDA gives 8.6%. NMF_plain+HEQ gives 13.69% and NMF_robustW gives 13.48% improvement." } ], "images": [ "scigen/train/2634.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) Cascading with HLDA" ], "reasoning": "HLDA achieves noise robustness at low noise levels but fails at high noise levels, while NMF_plain and NMF_robustW methods show varying improvements in recognition accuracy.", "description": "During MFCC extraction, usually 23 LMFB coefficients are converted into 13 dimensional MFCCs through DCT. But this technique fails in high noise conditions. However, the objective of HLDA is not to achieve robustness, but class-separability. Here a method is proposed which aims at finding representations of speech feature vectors using building-blocks. Average values shown are taken over SNR levels It can be observed that HLDA can be used to achieve noise robustness in low noise levels only, and fails at high noise levels (SNR 5 to −5 dB). It can be seen that this method hardly gives an improvement over the baseline at different dictionary sizes. NMF_plain gives 1.86% absolute improvement in recognition accuracy, NMF_plain cascaded with HLDA gives 5.09%, NMF_robustW gives 5.96%, NMF_robustW+HLDA gives 8.6%. NMF_plain+HEQ gives 13.69% and NMF_robustW gives 13.48% improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to '(b) Cascading with HLDA' to sequentially solve the question presented below. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhich method shows the highest average recognition accuracy across all noise levels?\n" }, { "role": "assistant", "content": "By comparing the average values in the table, HLDA has an average of 69.30, NMF_plain + HLDA has 74.60, and NMF_robustW + HLDA has 78.11. Therefore, NMF_robustW + HLDA shows the highest average recognition accuracy. So, the answer is {\"answer\": \"NMF_robustW + HLDA\"}." } ], "images": [ "scigen/train/2634.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) Cascading with HLDA" ], "reasoning": "By comparing the average values in the table, HLDA has an average of 69.30, NMF_plain + HLDA has 74.60, and NMF_robustW + HLDA has 78.11. Therefore, NMF_robustW + HLDA shows the highest average recognition accuracy.", "question": "Which method shows the highest average recognition accuracy across all noise levels?", "answer": "So, the answer is {\"answer\": \"NMF_robustW + HLDA\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim HLDA performs better than NMF_plain + HLDA at SNR 10., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nHLDA performs better than NMF_plain + HLDA at SNR 10.\n" }, { "role": "assistant", "content": "At SNR 10, HLDA has a recognition accuracy of 82.08, while NMF_plain + HLDA has 87.14. Therefore, HLDA does not perform better than NMF_plain + HLDA at SNR 10. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2634.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) Cascading with HLDA" ], "reasoning": "At SNR 10, HLDA has a recognition accuracy of 82.08, while NMF_plain + HLDA has 87.14. Therefore, HLDA does not perform better than NMF_plain + HLDA at SNR 10.", "statement": "HLDA performs better than NMF_plain + HLDA at SNR 10.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Human evaluation results of zero-shot English-Chinese question generation. “*” indicates the improvements are significant at p<0.05. Same shorthands apply as in Table 3.'.\n" }, { "role": "assistant", "content": "In the zero-shot English-Chinese question generation experiments, Xlm and Pipeline (Xlm) are used as baselines, with Pipeline (Xlm) translating En-En-QG results to Chinese.In the zero-shot English-Chinese question generation experiments, we use Xlm and Pipeline (Xlm) as our baselines. Pipeline (Xlm) is a pipeline method that uses En-En-QG with Xlm to generate questions, and then translates the results to Chinese. Because there are no annotations for En-Zh-QG, we perform human evaluation studies for this setting." } ], "images": [ "scigen/train/2635.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Human evaluation results of zero-shot English-Chinese question generation. “*” indicates the improvements are significant at p<0.05. Same shorthands apply as in Table 3." ], "reasoning": "In the zero-shot English-Chinese question generation experiments, Xlm and Pipeline (Xlm) are used as baselines, with Pipeline (Xlm) translating En-En-QG results to Chinese.", "description": "In the zero-shot English-Chinese question generation experiments, we use Xlm and Pipeline (Xlm) as our baselines. Pipeline (Xlm) is a pipeline method that uses En-En-QG with Xlm to generate questions, and then translates the results to Chinese. Because there are no annotations for En-Zh-QG, we perform human evaluation studies for this setting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 4: Human evaluation results of zero-shot English-Chinese question generation. “*” indicates the improvements are significant at p<0.05. Same shorthands apply as in Table 3.', answer the question below by showing each progressive step toward the answer. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model has the highest score in the Corr column?\n" }, { "role": "assistant", "content": "To find the model with the highest score in the Corr column, compare the values in the Corr column for all models. Xnlg has the highest score of 0.53. So, the answer is {\"answer\": \"Xnlg\"}" } ], "images": [ "scigen/train/2635.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Human evaluation results of zero-shot English-Chinese question generation. “*” indicates the improvements are significant at p<0.05. Same shorthands apply as in Table 3." ], "reasoning": "To find the model with the highest score in the Corr column, compare the values in the Corr column for all models. Xnlg has the highest score of 0.53.", "question": "Which model has the highest score in the Corr column?", "answer": "So, the answer is {\"answer\": \"Xnlg\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence Pipeline (Xlm) has a higher Flu score than Xnlg. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nPipeline (Xlm) has a higher Flu score than Xnlg.\n" }, { "role": "assistant", "content": "The table shows that Pipeline (Xlm) has a Flu score of 0.86, while Xnlg has a Flu score of 0.95. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2635.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Human evaluation results of zero-shot English-Chinese question generation. “*” indicates the improvements are significant at p<0.05. Same shorthands apply as in Table 3." ], "reasoning": "The table shows that Pipeline (Xlm) has a Flu score of 0.86, while Xnlg has a Flu score of 0.95. Therefore, the statement is refuted.", "statement": "Pipeline (Xlm) has a higher Flu score than Xnlg.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 9: Ablations for pre-training objectives, where models are evaluated on zero-shot Chinese-Chinese question generation. Same shorthands apply as in Table 1.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Combining DAE and XAE objectives improves cross-lingual NLG performance and reduces spurious correlations.We observe that our model greatly benefits from the DAE objective for the zero-shot Chinese question generation task. The results also demonstrate that combining DAE and XAE can alleviate the spurious correlation issue and improves cross-lingual NLG." } ], "images": [ "scigen/train/2636.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Ablations for pre-training objectives, where models are evaluated on zero-shot Chinese-Chinese question generation. Same shorthands apply as in Table 1." ], "reasoning": "Combining DAE and XAE objectives improves cross-lingual NLG performance and reduces spurious correlations.", "description": "We observe that our model greatly benefits from the DAE objective for the zero-shot Chinese question generation task. The results also demonstrate that combining DAE and XAE can alleviate the spurious correlation issue and improves cross-lingual NLG.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 9: Ablations for pre-training objectives, where models are evaluated on zero-shot Chinese-Chinese question generation. Same shorthands apply as in Table 1.', unfold the steps to solve the question given next. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nQuestion: Which model has the highest score in the RG-L metric?\n" }, { "role": "assistant", "content": "By comparing the RG-L scores of all models in the table, we see that the Xnlg model has the highest score of 34.93. So, the answer is {\"answer\": \"Xnlg\"}" } ], "images": [ "scigen/train/2636.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Ablations for pre-training objectives, where models are evaluated on zero-shot Chinese-Chinese question generation. Same shorthands apply as in Table 1." ], "reasoning": "By comparing the RG-L scores of all models in the table, we see that the Xnlg model has the highest score of 34.93.", "question": "Which model has the highest score in the RG-L metric?", "answer": "So, the answer is {\"answer\": \"Xnlg\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Xlm model has the lowest score in the BL-4 metric.\n" }, { "role": "assistant", "content": "By examining the BL-4 scores in the table, we see that the Xlm model has the lowest score of 0.25. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2636.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Ablations for pre-training objectives, where models are evaluated on zero-shot Chinese-Chinese question generation. Same shorthands apply as in Table 1." ], "reasoning": "By examining the BL-4 scores in the table, we see that the Xlm model has the lowest score of 0.25.", "statement": "The Xlm model has the lowest score in the BL-4 metric.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 10: Effects of different fine-tuning strategies. Dec, Enc and ET represent fine-tuning the parameters of the decoder, the encoder, and the Transformer layers of the encoder, respectively. Same shorthands apply as in Table 1.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Fine-tuning encoder parameters improves performance for both English and Chinese QG, while fine-tuning all parameters achieves the best score for English but drops for Chinese QG.It can be observed that fine-tuning encoder parameters, our model obtain an impressive performance for both English and Chinese QG, which shows the strong cross-lingual transfer ability of our model. When fine-tuning all the parameters, the model achieves the best score for English QG, but it suffers a performance drop when evaluating on Chinese QG. We find that fine-tuning decoder hurts cross-lingual decoding, and the model learns to only decode English words. For only fine-tuning decoder, the performance degrades by a large margin for both languages because of the underfitting issue, which indicates the necessity of fine-tuning encoder." } ], "images": [ "scigen/train/2637.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Effects of different fine-tuning strategies. Dec, Enc and ET represent fine-tuning the parameters of the decoder, the encoder, and the Transformer layers of the encoder, respectively. Same shorthands apply as in Table 1." ], "reasoning": "Fine-tuning encoder parameters improves performance for both English and Chinese QG, while fine-tuning all parameters achieves the best score for English but drops for Chinese QG.", "description": "It can be observed that fine-tuning encoder parameters, our model obtain an impressive performance for both English and Chinese QG, which shows the strong cross-lingual transfer ability of our model. When fine-tuning all the parameters, the model achieves the best score for English QG, but it suffers a performance drop when evaluating on Chinese QG. We find that fine-tuning decoder hurts cross-lingual decoding, and the model learns to only decode English words. For only fine-tuning decoder, the performance degrades by a large margin for both languages because of the underfitting issue, which indicates the necessity of fine-tuning encoder.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 10: Effects of different fine-tuning strategies. Dec, Enc and ET represent fine-tuning the parameters of the decoder, the encoder, and the Transformer layers of the encoder, respectively. Same shorthands apply as in Table 1.', solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the performance score for English QG when only the encoder is fine-tuned?\n" }, { "role": "assistant", "content": "The table shows that when only the encoder is fine-tuned, the performance score for English QG under the RG-L metric is 48.78. So, the answer is {\"answer\": \"48.78\"}." } ], "images": [ "scigen/train/2637.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Effects of different fine-tuning strategies. Dec, Enc and ET represent fine-tuning the parameters of the decoder, the encoder, and the Transformer layers of the encoder, respectively. Same shorthands apply as in Table 1." ], "reasoning": "The table shows that when only the encoder is fine-tuned, the performance score for English QG under the RG-L metric is 48.78.", "question": "What is the performance score for English QG when only the encoder is fine-tuned?", "answer": "So, the answer is {\"answer\": \"48.78\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nFine-tuning only the decoder results in the highest performance for English QG.\n" }, { "role": "assistant", "content": "The table shows that fine-tuning only the decoder results in lower performance scores for English QG compared to fine-tuning the encoder or all parameters. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2637.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Effects of different fine-tuning strategies. Dec, Enc and ET represent fine-tuning the parameters of the decoder, the encoder, and the Transformer layers of the encoder, respectively. Same shorthands apply as in Table 1." ], "reasoning": "The table shows that fine-tuning only the decoder results in lower performance scores for English QG compared to fine-tuning the encoder or all parameters.", "statement": "Fine-tuning only the decoder results in the highest performance for English QG.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Percentage of correctly classified strings of various RNNs, trained with and without noise, on D2 language (averaged over 10 trials), with mean and best accuracy measurements reported for each model. The test set used for all experiments had short samples mixed with long ones up to length T=102, containing both positive and negative sample strings.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Adding noise to the hidden weights of RNNs improves generalization and forecasting horizon, despite slowing down model convergence.Adding Noise to the Hidden Weights: Noise plays a crucial role in regularizing RNNs. Besides regularization, incorporating it has also been shown to improve the model’s forecasting horizon yielding better generalization [mali2019neural, mali2019sibling, noh2017regularizing, jim1996analysis, goodfellow2016deep]. Motivated by this result, we modify the state update equation further as follows: ^zt−1=zt−1+Ps0t−1+ϵ,∀i,ϵ[i]∼N(μ,σ2) (9) where μ (mean) and σ2 (variance) are user-set meta-parameters to control the strength of the additive Gaussian regularization noise. Based on our experiments, we found that injecting a small amount of this type of noise into the state improve the RNN recognition model’s generalization even though it does slow down model convergence." } ], "images": [ "scigen/train/2638.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Percentage of correctly classified strings of various RNNs, trained with and without noise, on D2 language (averaged over 10 trials), with mean and best accuracy measurements reported for each model. The test set used for all experiments had short samples mixed with long ones up to length T=102, containing both positive and negative sample strings." ], "reasoning": "Adding noise to the hidden weights of RNNs improves generalization and forecasting horizon, despite slowing down model convergence.", "description": "Adding Noise to the Hidden Weights: Noise plays a crucial role in regularizing RNNs. Besides regularization, incorporating it has also been shown to improve the model’s forecasting horizon yielding better generalization [mali2019neural, mali2019sibling, noh2017regularizing, jim1996analysis, goodfellow2016deep]. Motivated by this result, we modify the state update equation further as follows: ^zt−1=zt−1+Ps0t−1+ϵ,∀i,ϵ[i]∼N(μ,σ2) (9) where μ (mean) and σ2 (variance) are user-set meta-parameters to control the strength of the additive Gaussian regularization noise. Based on our experiments, we found that injecting a small amount of this type of noise into the state improve the RNN recognition model’s generalization even though it does slow down model convergence.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Percentage of correctly classified strings of various RNNs, trained with and without noise, on D2 language (averaged over 10 trials), with mean and best accuracy measurements reported for each model. The test set used for all experiments had short samples mixed with long ones up to length T=102, containing both positive and negative sample strings.\nQuestion: Which RNN model has the highest 'WithNoise Best' score?\nSolve the above question based on the table information. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the RNN model with the highest 'WithNoise Best' score, we compare the values in the 'WithNoise Best' column. The highest value is 100, which is achieved by both DiffStk-RNN (Ours) and DiffStk-MRNN (Ours). So, the answer is {\"answer\": \"DiffStk-RNN (Ours), DiffStk-MRNN (Ours)\"}." } ], "images": [ "scigen/train/2638.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Percentage of correctly classified strings of various RNNs, trained with and without noise, on D2 language (averaged over 10 trials), with mean and best accuracy measurements reported for each model. The test set used for all experiments had short samples mixed with long ones up to length T=102, containing both positive and negative sample strings." ], "reasoning": "To find the RNN model with the highest 'WithNoise Best' score, we compare the values in the 'WithNoise Best' column. The highest value is 100, which is achieved by both DiffStk-RNN (Ours) and DiffStk-MRNN (Ours).", "question": "Which RNN model has the highest 'WithNoise Best' score?", "answer": "So, the answer is {\"answer\": \"DiffStk-RNN (Ours), DiffStk-MRNN (Ours)\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nDiffStk-MIRNN (Ours) has a higher 'WithNoise Mean' score than LSTM.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the 'WithNoise Mean' scores of DiffStk-MIRNN (Ours) and LSTM. DiffStk-MIRNN (Ours) has a score of 97.25, while LSTM has a score of 1.40. Therefore, the statement is correct. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2638.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Percentage of correctly classified strings of various RNNs, trained with and without noise, on D2 language (averaged over 10 trials), with mean and best accuracy measurements reported for each model. The test set used for all experiments had short samples mixed with long ones up to length T=102, containing both positive and negative sample strings." ], "reasoning": "To verify this statement, we compare the 'WithNoise Mean' scores of DiffStk-MIRNN (Ours) and LSTM. DiffStk-MIRNN (Ours) has a score of 97.25, while LSTM has a score of 1.40. Therefore, the statement is correct.", "statement": "DiffStk-MIRNN (Ours) has a higher 'WithNoise Mean' score than LSTM.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Percentage of correctly classified strings of various RNNs with and without noise as well as carry forwarding state on D2 language (averaged over 10 trials). We report the mean and best accuracy for each of the models. The test set had short samples mixed with long ones up to length T=102, with both positive and negative samples, for all experiments', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The proposed carry forward state update helps stabilize learning in RNNs by using a counter to trigger updates, improving memory retention over longer sequences.Carry Forward State Update: Whenever there exists a symbol that leads to more than one No-OP operation on the stack, we propose carrying forward the previous hidden state. Empirically, we found that this helps whenever the RNN encounters too many No-OPs in the input strings. This can be considered as approximation of a “reject” state, which have been shown to stabilize the learning process of higher order RNNs [das1993using, nndpa1998sun]. We keep a counter which indicates how many No-OPs the network has seen so far, which is used to trigger the update equation. With this counter-based trigger integrated, the full hidden state update equation can then be written as follows: zt= utf1(Uxt+R^zt−1)+(1−ut)^zt−1,ut={ut=0,andct>1if NoOPut=1,otherwise states work better than models that do not. We report accuracy over 10 trials on a D2 grammar. Observe that our approach consistently demonstrates improved memory retention over longer pattern sequence lengths." } ], "images": [ "scigen/train/2639.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Percentage of correctly classified strings of various RNNs with and without noise as well as carry forwarding state on D2 language (averaged over 10 trials). We report the mean and best accuracy for each of the models. The test set had short samples mixed with long ones up to length T=102, with both positive and negative samples, for all experiments" ], "reasoning": "The proposed carry forward state update helps stabilize learning in RNNs by using a counter to trigger updates, improving memory retention over longer sequences.", "description": "Carry Forward State Update: Whenever there exists a symbol that leads to more than one No-OP operation on the stack, we propose carrying forward the previous hidden state. Empirically, we found that this helps whenever the RNN encounters too many No-OPs in the input strings. This can be considered as approximation of a “reject” state, which have been shown to stabilize the learning process of higher order RNNs [das1993using, nndpa1998sun]. We keep a counter which indicates how many No-OPs the network has seen so far, which is used to trigger the update equation. With this counter-based trigger integrated, the full hidden state update equation can then be written as follows: zt= utf1(Uxt+R^zt−1)+(1−ut)^zt−1,ut={ut=0,andct>1if NoOPut=1,otherwise states work better than models that do not. We report accuracy over 10 trials on a D2 grammar. Observe that our approach consistently demonstrates improved memory retention over longer pattern sequence lengths.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: Percentage of correctly classified strings of various RNNs with and without noise as well as carry forwarding state on D2 language (averaged over 10 trials). We report the mean and best accuracy for each of the models. The test set had short samples mixed with long ones up to length T=102, with both positive and negative samples, for all experiments', solve this question and give detail solutions. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich RNN model has the highest Wchanges Mean value?\n" }, { "role": "assistant", "content": "To find the RNN model with the highest Wchanges Mean value, compare the Wchanges Mean values for all models listed in the table. So, the answer is {\"answer\": \"DiffStk-MRNN (Ours)\"}." } ], "images": [ "scigen/train/2639.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Percentage of correctly classified strings of various RNNs with and without noise as well as carry forwarding state on D2 language (averaged over 10 trials). We report the mean and best accuracy for each of the models. The test set had short samples mixed with long ones up to length T=102, with both positive and negative samples, for all experiments" ], "reasoning": "To find the RNN model with the highest Wchanges Mean value, compare the Wchanges Mean values for all models listed in the table.", "question": "Which RNN model has the highest Wchanges Mean value?", "answer": "So, the answer is {\"answer\": \"DiffStk-MRNN (Ours)\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe DiffStk-MRNN (Ours) model has the highest W/ochanges Mean value.\n" }, { "role": "assistant", "content": "The table shows that the highest W/ochanges Mean value is 97.58, which belongs to the DiffStk-RNN (Ours) model, not the DiffStk-MRNN (Ours) model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2639.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Percentage of correctly classified strings of various RNNs with and without noise as well as carry forwarding state on D2 language (averaged over 10 trials). We report the mean and best accuracy for each of the models. The test set had short samples mixed with long ones up to length T=102, with both positive and negative samples, for all experiments" ], "reasoning": "The table shows that the highest W/ochanges Mean value is 97.58, which belongs to the DiffStk-RNN (Ours) model, not the DiffStk-MRNN (Ours) model.", "statement": "The DiffStk-MRNN (Ours) model has the highest W/ochanges Mean value.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: GeoQuery results.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "ScanneR shows performance improvements over symbolic systems and neural models when using comparable data sources for training, but jia2016data achieves better results with synthetic data.The first block contains symbolic systems, whereas neural models are presented in the second block. We report accuracy which is defined as the proportion of the utterance that are correctly parsed to their gold standard logical forms. ScanneR yields performance improvements over these systems when using comparable data sources for training. \\newcitejia2016data achieve better results with synthetic data that expands GeoQuery; we could adopt their approach to improve model performance, however, we leave this to future work." } ], "images": [ "scigen/train/2640.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: GeoQuery results." ], "reasoning": "ScanneR shows performance improvements over symbolic systems and neural models when using comparable data sources for training, but jia2016data achieves better results with synthetic data.", "description": "The first block contains symbolic systems, whereas neural models are presented in the second block. We report accuracy which is defined as the proportion of the utterance that are correctly parsed to their gold standard logical forms. ScanneR yields performance improvements over these systems when using comparable data sources for training. \\newcitejia2016data achieve better results with synthetic data that expands GeoQuery; we could adopt their approach to improve model performance, however, we leave this to future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 5: GeoQuery results.', unfold the question presented below, detailing every step of your calculation. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model achieves the highest accuracy according to the table?\n" }, { "role": "assistant", "content": "To find the model with the highest accuracy, we need to compare the accuracy values of all models listed in the table. The model with the highest accuracy is liang2011learning with an accuracy of 91.1. So, the answer is {\"answer\": \"liang2011learning\"}." } ], "images": [ "scigen/train/2640.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: GeoQuery results." ], "reasoning": "To find the model with the highest accuracy, we need to compare the accuracy values of all models listed in the table. The model with the highest accuracy is liang2011learning with an accuracy of 91.1.", "question": "Which model achieves the highest accuracy according to the table?", "answer": "So, the answer is {\"answer\": \"liang2011learning\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nScanneR achieves the highest accuracy among all models listed in the table.\n" }, { "role": "assistant", "content": "The table lists the accuracy of various models. ScanneR has an accuracy of 86.7, while liang2011learning has the highest accuracy of 91.1. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2640.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: GeoQuery results." ], "reasoning": "The table lists the accuracy of various models. ScanneR has an accuracy of 86.7, while liang2011learning has the highest accuracy of 91.1. Therefore, the statement is refuted.", "statement": "ScanneR achieves the highest accuracy among all models listed in the table.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: GraphQuestions results. Numbers for comparison systems are from su2016generating.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "ScanneR achieves the highest F1 score of 17.02, surpassing the neural baseline model and three symbolic systems, with a gain of 4.23 F1 points over the best previously reported model.We report F1 for ScanneR, the neural baseline model, and three symbolic systems presented in \\newcitesu2016generating. ScanneR achieves a new state of the art on this dataset with a gain of 4.23 F1 points over the best previously reported model." } ], "images": [ "scigen/train/2641.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: GraphQuestions results. Numbers for comparison systems are from su2016generating." ], "reasoning": "ScanneR achieves the highest F1 score of 17.02, surpassing the neural baseline model and three symbolic systems, with a gain of 4.23 F1 points over the best previously reported model.", "description": "We report F1 for ScanneR, the neural baseline model, and three symbolic systems presented in \\newcitesu2016generating. ScanneR achieves a new state of the art on this dataset with a gain of 4.23 F1 points over the best previously reported model.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: GraphQuestions results. Numbers for comparison systems are from su2016generating.', solve this question and give detail solutions. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhat is the F1 score difference between the ScanneR model and the parasempre Berant and Liang (2014) model?\n" }, { "role": "assistant", "content": "The F1 score of the ScanneR model is 17.02, and the F1 score of the parasempre Berant and Liang (2014) model is 12.79. The difference is calculated as 17.02 - 12.79. So, the answer is {\"answer\": \"4.23\"}" } ], "images": [ "scigen/train/2641.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: GraphQuestions results. Numbers for comparison systems are from su2016generating." ], "reasoning": "The F1 score of the ScanneR model is 17.02, and the F1 score of the parasempre Berant and Liang (2014) model is 12.79. The difference is calculated as 17.02 - 12.79.", "question": "What is the F1 score difference between the ScanneR model and the parasempre Berant and Liang (2014) model?", "answer": "So, the answer is {\"answer\": \"4.23\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe neural baseline model has a higher F1 score than the parasempre Berant and Liang (2014) model.\n" }, { "role": "assistant", "content": "The F1 score of the neural baseline model is 16.24, which is higher than the F1 score of the parasempre Berant and Liang (2014) model, which is 12.79. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2641.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: GraphQuestions results. Numbers for comparison systems are from su2016generating." ], "reasoning": "The F1 score of the neural baseline model is 16.24, which is higher than the F1 score of the parasempre Berant and Liang (2014) model, which is 12.79.", "statement": "The neural baseline model has a higher F1 score than the parasempre Berant and Liang (2014) model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 6: SacreBLEU scores for the data selection experiments. Highest scores per column are marked in bold.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "All selection methods outperformed random selection in BLEU scores, with Domain-Finetune performing the best, highlighting the effectiveness of fine-tuning large pretrained models for data selection.We can see that all selection methods performed much better in terms of BLEU than random selection. It is also nice to see that all selection methods performed better than using all the available data or the oracle-selected data when averaged across all domains, showing again that more data is not necessarily better in multi-domain scenarios and that data selection is a useful approach. Regarding a comparison of the data selection methods, Moore-Lewis performed better than Domain-Cosine, while Domain-Finetune performed best, showing the benefit of fine-tuning large pretrained models for the data selection task. Using the positively-labeled examples alone (Domain-Finetune-Positive) performed worse than using the top 500k examples but better than Domain-Cosine, while not requiring to determine the number of selected sentences." } ], "images": [ "scigen/train/2642.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: SacreBLEU scores for the data selection experiments. Highest scores per column are marked in bold." ], "reasoning": "All selection methods outperformed random selection in BLEU scores, with Domain-Finetune performing the best, highlighting the effectiveness of fine-tuning large pretrained models for data selection.", "description": "We can see that all selection methods performed much better in terms of BLEU than random selection. It is also nice to see that all selection methods performed better than using all the available data or the oracle-selected data when averaged across all domains, showing again that more data is not necessarily better in multi-domain scenarios and that data selection is a useful approach. Regarding a comparison of the data selection methods, Moore-Lewis performed better than Domain-Cosine, while Domain-Finetune performed best, showing the benefit of fine-tuning large pretrained models for the data selection task. Using the positively-labeled examples alone (Domain-Finetune-Positive) performed worse than using the top 500k examples but better than Domain-Cosine, while not requiring to determine the number of selected sentences.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 6: SacreBLEU scores for the data selection experiments. Highest scores per column are marked in bold.', answer the following question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich selection method performed best on average across all domains?\n" }, { "role": "assistant", "content": "By comparing the average BLEU scores of all selection methods, Domain-Finetune-Top-500k has the highest average score of 41.26. So, the answer is {\"answer\": \"Domain-Finetune-Top-500k\"}" } ], "images": [ "scigen/train/2642.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: SacreBLEU scores for the data selection experiments. Highest scores per column are marked in bold." ], "reasoning": "By comparing the average BLEU scores of all selection methods, Domain-Finetune-Top-500k has the highest average score of 41.26.", "question": "Which selection method performed best on average across all domains?", "answer": "So, the answer is {\"answer\": \"Domain-Finetune-Top-500k\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nDomain-Cosine-Top-500k performed better than Moore-Lewis-Top-500k on average.\n" }, { "role": "assistant", "content": "The average BLEU score for Domain-Cosine-Top-500k is 40.46, while for Moore-Lewis-Top-500k it is 40.88, indicating that Moore-Lewis-Top-500k performed better. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2642.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: SacreBLEU scores for the data selection experiments. Highest scores per column are marked in bold." ], "reasoning": "The average BLEU score for Domain-Cosine-Top-500k is 40.46, while for Moore-Lewis-Top-500k it is 40.88, indicating that Moore-Lewis-Top-500k performed better.", "statement": "Domain-Cosine-Top-500k performed better than Moore-Lewis-Top-500k on average.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: Ablation analysis showing precision (p) recall (r) and F1 for the binary classification accuracy on a held-out set, with and without pre-ranking.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Using pre-ranking for negative sampling improves F1 scores across all domains by reducing the number of in-domain examples drawn as negative samples.Negative Sampling with Pre-ranking One problem that may rise when randomly sampling negative examples is that unlabeled in-domain sentences from the general-domain data may be sampled as negative examples – deteriorating the classifier performance. To alleviate this issue, we perform a biased sampling of negative examples. We first rank the general-domain data using the Domain-Cosine method, and then sample negative examples under a certain threshold in the ranking (in our experiments we sampled from the bottom two-thirds). When not using pre-ranking, as the training data for the domain is larger, the precision is lower – since more in-domain examples are drawn as negative samples. Using pre-ranking indeed alleviates this issue, achieving higher F1 scores in all cases." } ], "images": [ "scigen/train/2643.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ablation analysis showing precision (p) recall (r) and F1 for the binary classification accuracy on a held-out set, with and without pre-ranking." ], "reasoning": "Using pre-ranking for negative sampling improves F1 scores across all domains by reducing the number of in-domain examples drawn as negative samples.", "description": "Negative Sampling with Pre-ranking One problem that may rise when randomly sampling negative examples is that unlabeled in-domain sentences from the general-domain data may be sampled as negative examples – deteriorating the classifier performance. To alleviate this issue, we perform a biased sampling of negative examples. We first rank the general-domain data using the Domain-Cosine method, and then sample negative examples under a certain threshold in the ranking (in our experiments we sampled from the bottom two-thirds). When not using pre-ranking, as the training data for the domain is larger, the precision is lower – since more in-domain examples are drawn as negative samples. Using pre-ranking indeed alleviates this issue, achieving higher F1 scores in all cases.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 5: Ablation analysis showing precision (p) recall (r) and F1 for the binary classification accuracy on a held-out set, with and without pre-ranking.' and methodically tackle the question that follows. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich domain shows the highest precision improvement when using pre-ranking?\n" }, { "role": "assistant", "content": "To find the domain with the highest precision improvement, subtract the precision without pre-ranking from the precision with pre-ranking for each domain. The highest difference indicates the greatest improvement. So, the answer is {\"answer\": \"Subtitles\"}." } ], "images": [ "scigen/train/2643.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ablation analysis showing precision (p) recall (r) and F1 for the binary classification accuracy on a held-out set, with and without pre-ranking." ], "reasoning": "To find the domain with the highest precision improvement, subtract the precision without pre-ranking from the precision with pre-ranking for each domain. The highest difference indicates the greatest improvement.", "question": "Which domain shows the highest precision improvement when using pre-ranking?", "answer": "So, the answer is {\"answer\": \"Subtitles\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nUsing pre-ranking improves the recall for the Medical domain.\n" }, { "role": "assistant", "content": "The table shows that the recall for the Medical domain without pre-ranking is 0.916, and with pre-ranking, it is 0.92. Since 0.92 is higher than 0.916, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2643.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ablation analysis showing precision (p) recall (r) and F1 for the binary classification accuracy on a held-out set, with and without pre-ranking." ], "reasoning": "The table shows that the recall for the Medical domain without pre-ranking is 0.916, and with pre-ranking, it is 0.92. Since 0.92 is higher than 0.916, the statement is supported.", "statement": "Using pre-ranking improves the recall for the Medical domain.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 7: Precision (p) and recall (r) for data selection of 500k sentences with respect to the oracle selection.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The Domain-Finetune method achieved the highest domain recall with a minimum of 97.5, outperforming Moore-Lewis and Domain-Cosine, which scored 89.4 and 78.8 respectively.We perform an analysis on the selected datasets, where we measure the precision and recall of sentence selection with respect to the oracle selection. As also reflected in the BLEU scores, the Domain-Finetune method resulted in the highest domain recall with a minimum of 97.5, while Moore-Lewis and Domain-Cosine scored 89.4 and 78.8 respectively. We find these results very appealing given that only 2000 in-domain sentences were used for selection for each domain out of 1.45 million sentences. Also note that we used DistilBERT in these experiments: we believe that using larger, non-distilled models may result in even better selection performance (although at the price of larger computational requirements)." } ], "images": [ "scigen/train/2644.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Precision (p) and recall (r) for data selection of 500k sentences with respect to the oracle selection." ], "reasoning": "The Domain-Finetune method achieved the highest domain recall with a minimum of 97.5, outperforming Moore-Lewis and Domain-Cosine, which scored 89.4 and 78.8 respectively.", "description": "We perform an analysis on the selected datasets, where we measure the precision and recall of sentence selection with respect to the oracle selection. As also reflected in the BLEU scores, the Domain-Finetune method resulted in the highest domain recall with a minimum of 97.5, while Moore-Lewis and Domain-Cosine scored 89.4 and 78.8 respectively. We find these results very appealing given that only 2000 in-domain sentences were used for selection for each domain out of 1.45 million sentences. Also note that we used DistilBERT in these experiments: we believe that using larger, non-distilled models may result in even better selection performance (although at the price of larger computational requirements).", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 7: Precision (p) and recall (r) for data selection of 500k sentences with respect to the oracle selection.', and sequentially solve the question, writing out each step of your reasoning process. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method achieved the highest average precision across all domains?\n" }, { "role": "assistant", "content": "To determine the method with the highest average precision, we compare the average precision scores of Moore-Lewis, Domain-Cosine, and Domain-Finetune. The average precision scores are 0.6, 0.578, and 0.63 respectively. So, the answer is {\"answer\": \"Domain-Finetune\"}." } ], "images": [ "scigen/train/2644.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Precision (p) and recall (r) for data selection of 500k sentences with respect to the oracle selection." ], "reasoning": "To determine the method with the highest average precision, we compare the average precision scores of Moore-Lewis, Domain-Cosine, and Domain-Finetune. The average precision scores are 0.6, 0.578, and 0.63 respectively.", "question": "Which method achieved the highest average precision across all domains?", "answer": "So, the answer is {\"answer\": \"Domain-Finetune\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The Domain-Cosine method achieved the highest recall in the Koran domain., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Domain-Cosine method achieved the highest recall in the Koran domain.\n" }, { "role": "assistant", "content": "The table shows the recall scores for the Koran domain: Moore-Lewis (0.985), Domain-Cosine (0.989), and Domain-Finetune (0.998). The highest recall score is 0.998, achieved by Domain-Finetune. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2644.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Precision (p) and recall (r) for data selection of 500k sentences with respect to the oracle selection." ], "reasoning": "The table shows the recall scores for the Koran domain: Moore-Lewis (0.985), Domain-Cosine (0.989), and Domain-Finetune (0.998). The highest recall score is 0.998, achieved by Domain-Finetune.", "statement": "The Domain-Cosine method achieved the highest recall in the Koran domain.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Sequence labeling results. p means dropout rate, w means window size. NER and Chunking are evaluated by F1 score while POS is evaluated by accuracy. Table shows the best results and their corresponding hyperparameters.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "DropAttention(c) and DropAttention(e) significantly improve performance in POS, NER, and Chunking tasks, with DropAttention(e) achieving the highest BLEU score of 28.32 with p=0.2 and w=2.It shows that both DropAttention(c) and DropAttention(e) can obtain significant improvements. Our model achieve 0.29 accuracy, 0.40 F1 score, 1.76 F1 score improvements in POS, NER and Chunking respectively. And we find that larger dropout rate and larger window size are generally preferred. The results show that DropAttention can generally obtain higher performance compared with baseline without DropAttention. With DropAttention(e) of p=0.2,w=2, the model can outperform the baseline by a large margin, reaching a BLEU score of 28.32. For DropAttention(c), the model also reaches the best BLEU score with p=0.2,w=2." } ], "images": [ "scigen/train/2645.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Sequence labeling results. p means dropout rate, w means window size. NER and Chunking are evaluated by F1 score while POS is evaluated by accuracy. Table shows the best results and their corresponding hyperparameters." ], "reasoning": "DropAttention(c) and DropAttention(e) significantly improve performance in POS, NER, and Chunking tasks, with DropAttention(e) achieving the highest BLEU score of 28.32 with p=0.2 and w=2.", "description": "It shows that both DropAttention(c) and DropAttention(e) can obtain significant improvements. Our model achieve 0.29 accuracy, 0.40 F1 score, 1.76 F1 score improvements in POS, NER and Chunking respectively. And we find that larger dropout rate and larger window size are generally preferred. The results show that DropAttention can generally obtain higher performance compared with baseline without DropAttention. With DropAttention(e) of p=0.2,w=2, the model can outperform the baseline by a large margin, reaching a BLEU score of 28.32. For DropAttention(c), the model also reaches the best BLEU score with p=0.2,w=2.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 5: Sequence labeling results. p means dropout rate, w means window size. NER and Chunking are evaluated by F1 score while POS is evaluated by accuracy. Table shows the best results and their corresponding hyperparameters.' and proceed to solve the following question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the highest accuracy achieved in the POS task and which model configuration achieved it?\n" }, { "role": "assistant", "content": "The highest accuracy in the POS task is 96.21, achieved by the DropAttention(c) model with p=0.2 and w=2. So, the answer is {\"answer\": \"96.21, DropAttention(c) with p=0.2 and w=2\"}." } ], "images": [ "scigen/train/2645.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Sequence labeling results. p means dropout rate, w means window size. NER and Chunking are evaluated by F1 score while POS is evaluated by accuracy. Table shows the best results and their corresponding hyperparameters." ], "reasoning": "The highest accuracy in the POS task is 96.21, achieved by the DropAttention(c) model with p=0.2 and w=2.", "question": "What is the highest accuracy achieved in the POS task and which model configuration achieved it?", "answer": "So, the answer is {\"answer\": \"96.21, DropAttention(c) with p=0.2 and w=2\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The model without DropAttention achieves the highest accuracy in the POS task., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model without DropAttention achieves the highest accuracy in the POS task.\n" }, { "role": "assistant", "content": "The table shows that the highest accuracy in the POS task is achieved by DropAttention(c) with p=0.2 and w=2, not by the model without DropAttention. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2645.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Sequence labeling results. p means dropout rate, w means window size. NER and Chunking are evaluated by F1 score while POS is evaluated by accuracy. Table shows the best results and their corresponding hyperparameters." ], "reasoning": "The table shows that the highest accuracy in the POS task is achieved by DropAttention(c) with p=0.2 and w=2, not by the model without DropAttention.", "statement": "The model without DropAttention achieves the highest accuracy in the POS task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Text classification, in percentage. p represents dropout rate, w represents window size. The column of “Norm?” indicates the results of normalized rescaling or traditional rescaling 1−p. We only represents the best results in the table and their corresponding hyperparameters.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "DropAttention models, especially with normalization, show improved accuracy across various datasets compared to models without DropAttention.Detail model configurations are given in Appendix. We use accuracy as evaluation metrics. It shows that DropAttentions can significantly improve performance on a wide range of datasets of small, medium and large scale." } ], "images": [ "scigen/train/2646.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Text classification, in percentage. p represents dropout rate, w represents window size. The column of “Norm?” indicates the results of normalized rescaling or traditional rescaling 1−p. We only represents the best results in the table and their corresponding hyperparameters." ], "reasoning": "DropAttention models, especially with normalization, show improved accuracy across various datasets compared to models without DropAttention.", "description": "Detail model configurations are given in Appendix. We use accuracy as evaluation metrics. It shows that DropAttentions can significantly improve performance on a wide range of datasets of small, medium and large scale.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: Text classification, in percentage. p represents dropout rate, w represents window size. The column of “Norm?” indicates the results of normalized rescaling or traditional rescaling 1−p. We only represents the best results in the table and their corresponding hyperparameters.', answer the following question. Give detailed solution consisting of each step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model configuration achieved the highest accuracy on the SUBJ dataset?\n" }, { "role": "assistant", "content": "By comparing the accuracies on the SUBJ dataset across all model configurations, the highest accuracy is 94.10 achieved by DropAttention(c) with normalization. So, the answer is {\"answer\": \"DropAttention(c) with normalization\"}" } ], "images": [ "scigen/train/2646.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Text classification, in percentage. p represents dropout rate, w represents window size. The column of “Norm?” indicates the results of normalized rescaling or traditional rescaling 1−p. We only represents the best results in the table and their corresponding hyperparameters." ], "reasoning": "By comparing the accuracies on the SUBJ dataset across all model configurations, the highest accuracy is 94.10 achieved by DropAttention(c) with normalization.", "question": "Which model configuration achieved the highest accuracy on the SUBJ dataset?", "answer": "So, the answer is {\"answer\": \"DropAttention(c) with normalization\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis DropAttention(e) without normalization achieved the highest accuracy on the CR dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nDropAttention(e) without normalization achieved the highest accuracy on the CR dataset.\n" }, { "role": "assistant", "content": "The table shows that DropAttention(c) with normalization achieved the highest accuracy on the CR dataset with 82.75, not DropAttention(e) without normalization. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2646.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Text classification, in percentage. p represents dropout rate, w represents window size. The column of “Norm?” indicates the results of normalized rescaling or traditional rescaling 1−p. We only represents the best results in the table and their corresponding hyperparameters." ], "reasoning": "The table shows that DropAttention(c) with normalization achieved the highest accuracy on the CR dataset with 82.75, not DropAttention(e) without normalization.", "statement": "DropAttention(e) without normalization achieved the highest accuracy on the CR dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: The sizes of the sequence labeling datasets in our experiments, in terms of the number of tokens.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "We evaluated DropAttention on sequence labeling using benchmark datasets including PTB POS tagging, CoNLL 2000 chunking, and CoNLL 2003 English NER.We also evaluate the effectiveness of DropAttention on sequence labeling. We conducted experiments by following the same settings as yang2016multi. We use the following benchmark datasets in our experiments: Penn Treebank (PTB) POS tagging, CoNLL 2000 chunking, CoNLL 2003 English NER." } ], "images": [ "scigen/train/2647.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The sizes of the sequence labeling datasets in our experiments, in terms of the number of tokens." ], "reasoning": "We evaluated DropAttention on sequence labeling using benchmark datasets including PTB POS tagging, CoNLL 2000 chunking, and CoNLL 2003 English NER.", "description": "We also evaluate the effectiveness of DropAttention on sequence labeling. We conducted experiments by following the same settings as yang2016multi. We use the following benchmark datasets in our experiments: Penn Treebank (PTB) POS tagging, CoNLL 2000 chunking, CoNLL 2003 English NER.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 4: The sizes of the sequence labeling datasets in our experiments, in terms of the number of tokens.' and proceed to solve the following question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the total number of training instances across all datasets?\n" }, { "role": "assistant", "content": "To find the total number of training instances, sum the training instances for each dataset: 211,727 (CoNLL 2000) + 204,567 (CoNLL 2003) + 912,344 (PTB). So, the answer is {\"answer\": \"1,328,638\"}." } ], "images": [ "scigen/train/2647.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The sizes of the sequence labeling datasets in our experiments, in terms of the number of tokens." ], "reasoning": "To find the total number of training instances, sum the training instances for each dataset: 211,727 (CoNLL 2000) + 204,567 (CoNLL 2003) + 912,344 (PTB).", "question": "What is the total number of training instances across all datasets?", "answer": "So, the answer is {\"answer\": \"1,328,638\"}." }, { "messages": [ { "role": "user", "content": "The CoNLL 2003 dataset has more training instances than the CoNLL 2000 dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe CoNLL 2003 dataset has more training instances than the CoNLL 2000 dataset.\n" }, { "role": "assistant", "content": "The table shows that CoNLL 2003 has 204,567 training instances, while CoNLL 2000 has 211,727 training instances. Therefore, CoNLL 2003 has fewer training instances than CoNLL 2000. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2647.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The sizes of the sequence labeling datasets in our experiments, in terms of the number of tokens." ], "reasoning": "The table shows that CoNLL 2003 has 204,567 training instances, while CoNLL 2000 has 211,727 training instances. Therefore, CoNLL 2003 has fewer training instances than CoNLL 2000.", "statement": "The CoNLL 2003 dataset has more training instances than the CoNLL 2000 dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Sequence labeling results. p means dropout rate, w means window size. NER and Chunking are evaluated by F1 score while POS is evaluated by accuracy. Table shows the best results and their corresponding hyperparameters.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Both DropAttention(c) and DropAttention(e) achieve significant improvements, with DropAttention(e) reaching a BLEU score of 28.32 and DropAttention(c) reaching 28.04, both with p=0.2 and w=2.It shows that both DropAttention(c) and DropAttention(e) can obtain significant improvements. Our model achieve 0.29 accuracy, 0.40 F1 score, 1.76 F1 score improvements in POS, NER and Chunking respectively. And we find that larger dropout rate and larger window size are generally preferred. The results show that DropAttention can generally obtain higher performance compared with baseline without DropAttention. With DropAttention(e) of p=0.2,w=2, the model can outperform the baseline by a large margin, reaching a BLEU score of 28.32. For DropAttention(c), the model also reaches the best BLEU score with p=0.2,w=2." } ], "images": [ "scigen/train/2648.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Sequence labeling results. p means dropout rate, w means window size. NER and Chunking are evaluated by F1 score while POS is evaluated by accuracy. Table shows the best results and their corresponding hyperparameters." ], "reasoning": "Both DropAttention(c) and DropAttention(e) achieve significant improvements, with DropAttention(e) reaching a BLEU score of 28.32 and DropAttention(c) reaching 28.04, both with p=0.2 and w=2.", "description": "It shows that both DropAttention(c) and DropAttention(e) can obtain significant improvements. Our model achieve 0.29 accuracy, 0.40 F1 score, 1.76 F1 score improvements in POS, NER and Chunking respectively. And we find that larger dropout rate and larger window size are generally preferred. The results show that DropAttention can generally obtain higher performance compared with baseline without DropAttention. With DropAttention(e) of p=0.2,w=2, the model can outperform the baseline by a large margin, reaching a BLEU score of 28.32. For DropAttention(c), the model also reaches the best BLEU score with p=0.2,w=2.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Sequence labeling results. p means dropout rate, w means window size. NER and Chunking are evaluated by F1 score while POS is evaluated by accuracy. Table shows the best results and their corresponding hyperparameters.\nQuestion: What is the BLEU score achieved by DropAttention(c) with p=0.2 and w=2?\nSolve the above question based on the table information. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that DropAttention(c) with p=0.2 and w=2 achieves a BLEU score of 28.04. So, the answer is {\"answer\": \"28.04\"}." } ], "images": [ "scigen/train/2648.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Sequence labeling results. p means dropout rate, w means window size. NER and Chunking are evaluated by F1 score while POS is evaluated by accuracy. Table shows the best results and their corresponding hyperparameters." ], "reasoning": "The table shows that DropAttention(c) with p=0.2 and w=2 achieves a BLEU score of 28.04.", "question": "What is the BLEU score achieved by DropAttention(c) with p=0.2 and w=2?", "answer": "So, the answer is {\"answer\": \"28.04\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nDropAttention(e) with p=0.2 and w=2 achieves a BLEU score of 28.32.\n" }, { "role": "assistant", "content": "The table shows that DropAttention(e) with p=0.2 and w=2 achieves a BLEU score of 28.32. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2648.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Sequence labeling results. p means dropout rate, w means window size. NER and Chunking are evaluated by F1 score while POS is evaluated by accuracy. Table shows the best results and their corresponding hyperparameters." ], "reasoning": "The table shows that DropAttention(e) with p=0.2 and w=2 achieves a BLEU score of 28.32.", "statement": "DropAttention(e) with p=0.2 and w=2 achieves a BLEU score of 28.32.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 7: SNLI best results and the corresponding hyperparameters.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Experiments show that DropAttention(c) and DropAttention(e) significantly improve performance on classification and machine translation tasks compared to standard Dropout.For full results with different hyperparameters please refer to Appendix. Experiments show that DropAttention(c) and DropAttention(e) can significantly improve performances. We also explore the effect of DropAttention combining with standard Dropout. We conduct experiments on classification tasks and machine translation tasks. We choose AG’s News as classification dataset and WMT’ 16 En-De as Machine Translation dataset. It implys that DropAttention is complementary to stardard Dropout." } ], "images": [ "scigen/train/2649.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: SNLI best results and the corresponding hyperparameters." ], "reasoning": "Experiments show that DropAttention(c) and DropAttention(e) significantly improve performance on classification and machine translation tasks compared to standard Dropout.", "description": "For full results with different hyperparameters please refer to Appendix. Experiments show that DropAttention(c) and DropAttention(e) can significantly improve performances. We also explore the effect of DropAttention combining with standard Dropout. We conduct experiments on classification tasks and machine translation tasks. We choose AG’s News as classification dataset and WMT’ 16 En-De as Machine Translation dataset. It implys that DropAttention is complementary to stardard Dropout.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 7: SNLI best results and the corresponding hyperparameters.', solve this question and give detail solutions. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the SNLI score for DropAttention(c) with p=0.5 and w=1?\n" }, { "role": "assistant", "content": "The table shows the SNLI scores for different configurations. For DropAttention(c) with p=0.5 and w=1, the SNLI score is 84.38. So, the answer is {\"answer\": \"84.38\"}." } ], "images": [ "scigen/train/2649.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: SNLI best results and the corresponding hyperparameters." ], "reasoning": "The table shows the SNLI scores for different configurations. For DropAttention(c) with p=0.5 and w=1, the SNLI score is 84.38.", "question": "What is the SNLI score for DropAttention(c) with p=0.5 and w=1?", "answer": "So, the answer is {\"answer\": \"84.38\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nDropAttention(e) has a higher SNLI score than DropAttention(c).\n" }, { "role": "assistant", "content": "The table shows that DropAttention(e) has an SNLI score of 84.48, while DropAttention(c) has an SNLI score of 84.38. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2649.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: SNLI best results and the corresponding hyperparameters." ], "reasoning": "The table shows that DropAttention(e) has an SNLI score of 84.48, while DropAttention(c) has an SNLI score of 84.38.", "statement": "DropAttention(e) has a higher SNLI score than DropAttention(c).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 7: SNLI best results and the corresponding hyperparameters.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Experiments show that DropAttention and its combination with standard Dropout significantly improve performance on classification and machine translation tasks.For full results with different hyperparameters please refer to Appendix. Experiments show that DropAttention(c) and DropAttention(e) can significantly improve performances. We also explore the effect of DropAttention combining with standard Dropout. We conduct experiments on classification tasks and machine translation tasks. We choose AG’s News as classification dataset and WMT’ 16 En-De as Machine Translation dataset. It implys that DropAttention is complementary to stardard Dropout." } ], "images": [ "scigen/train/2650.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: SNLI best results and the corresponding hyperparameters." ], "reasoning": "Experiments show that DropAttention and its combination with standard Dropout significantly improve performance on classification and machine translation tasks.", "description": "For full results with different hyperparameters please refer to Appendix. Experiments show that DropAttention(c) and DropAttention(e) can significantly improve performances. We also explore the effect of DropAttention combining with standard Dropout. We conduct experiments on classification tasks and machine translation tasks. We choose AG’s News as classification dataset and WMT’ 16 En-De as Machine Translation dataset. It implys that DropAttention is complementary to stardard Dropout.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 7: SNLI best results and the corresponding hyperparameters.', answer the question below by showing each progressive step toward the answer. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the classification performance when combining DropAttention with standard Dropout?\n" }, { "role": "assistant", "content": "The table shows the classification performance for different configurations. The combination of DropAttention with standard Dropout achieves a classification score of 88.70. So, the answer is {\"answer\": \"88.70\"}." } ], "images": [ "scigen/train/2650.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: SNLI best results and the corresponding hyperparameters." ], "reasoning": "The table shows the classification performance for different configurations. The combination of DropAttention with standard Dropout achieves a classification score of 88.70.", "question": "What is the classification performance when combining DropAttention with standard Dropout?", "answer": "So, the answer is {\"answer\": \"88.70\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nDropAttention alone improves the machine translation performance more than standard Dropout.\n" }, { "role": "assistant", "content": "The table shows that DropAttention alone improves the MT score to 26.3, while standard Dropout improves it to 27.3. Therefore, DropAttention alone does not improve the MT performance more than standard Dropout. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2650.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: SNLI best results and the corresponding hyperparameters." ], "reasoning": "The table shows that DropAttention alone improves the MT score to 26.3, while standard Dropout improves it to 27.3. Therefore, DropAttention alone does not improve the MT performance more than standard Dropout.", "statement": "DropAttention alone improves the machine translation performance more than standard Dropout.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Topic coherence scores (npmi)', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "lda and cluster models show high topic coherence across both apnews and bnc datasets, while ctm and ntm models have low coherence.We first compute topic coherence for all 5 topic models over apnews and bnc using npmi Lau et al. We see that lda and cluster perform consistently well across both datasets. hca performs well over apnews but poorly over bnc. Both ctm and ntm topics appear to have low coherence over the two datasets." } ], "images": [ "scigen/train/2651.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Topic coherence scores (npmi)" ], "reasoning": "lda and cluster models show high topic coherence across both apnews and bnc datasets, while ctm and ntm models have low coherence.", "description": "We first compute topic coherence for all 5 topic models over apnews and bnc using npmi Lau et al. We see that lda and cluster perform consistently well across both datasets. hca performs well over apnews but poorly over bnc. Both ctm and ntm topics appear to have low coherence over the two datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 2: Topic coherence scores (npmi)', unfold the question presented below, detailing every step of your calculation. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest topic coherence over the apnews dataset?\n" }, { "role": "assistant", "content": "By comparing the topic coherence values for all models over the apnews dataset, the cluster model has the highest value of 0.18. So, the answer is {\"answer\": \"cluster\"}." } ], "images": [ "scigen/train/2651.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Topic coherence scores (npmi)" ], "reasoning": "By comparing the topic coherence values for all models over the apnews dataset, the cluster model has the highest value of 0.18.", "question": "Which model has the highest topic coherence over the apnews dataset?", "answer": "So, the answer is {\"answer\": \"cluster\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe hca model performs better than the ntm model over the apnews dataset.\n" }, { "role": "assistant", "content": "The table shows that the hca model has a topic coherence of 0.14 over the apnews dataset, while the ntm model has a coherence of 0.10, indicating that hca performs better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2651.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Topic coherence scores (npmi)" ], "reasoning": "The table shows that the hca model has a topic coherence of 0.14 over the apnews dataset, while the ntm model has a coherence of 0.10, indicating that hca performs better.", "statement": "The hca model performs better than the ntm model over the apnews dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Comparison with query embedding baselines.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The RQRF model outperforms other models in MAP, MRR, and NDCG metrics, with statistical significance indicated for α=0.01.Offline Evaluation Apart from NLL, we also employ commonly-used ranking metrics, including mean average precision (MAP), mean reciprocal rank (MRR) and normalized discounted cumulative gain (NDCG) to evaluate RQRF. We compare our query embedding model with various baselines, including BM25, PM(non-linear projection),CLSM[DBLP:journals/corr/abs-1812-00158],BIDAF[seo2016bidirectional]. Statistical significance of observed differences between the performance of two runs are tested using a two-tailed paired t-test and is denoted using {}^{\\blacktriangle}(or {}^{\\blacktriangledown}) for strong significance for \\alpha=0.01." } ], "images": [ "scigen/train/2652.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison with query embedding baselines." ], "reasoning": "The RQRF model outperforms other models in MAP, MRR, and NDCG metrics, with statistical significance indicated for α=0.01.", "description": "Offline Evaluation Apart from NLL, we also employ commonly-used ranking metrics, including mean average precision (MAP), mean reciprocal rank (MRR) and normalized discounted cumulative gain (NDCG) to evaluate RQRF. We compare our query embedding model with various baselines, including BM25, PM(non-linear projection),CLSM[DBLP:journals/corr/abs-1812-00158],BIDAF[seo2016bidirectional]. Statistical significance of observed differences between the performance of two runs are tested using a two-tailed paired t-test and is denoted using {}^{\\blacktriangle}(or {}^{\\blacktriangledown}) for strong significance for \\alpha=0.01.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Comparison with query embedding baselines.', solve the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest MAP score and what is the value?\n" }, { "role": "assistant", "content": "By comparing the MAP scores of all models, RQRF has the highest MAP score of 0.207. So, the answer is {\"answer\": \"RQRF with 0.207\"}." } ], "images": [ "scigen/train/2652.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison with query embedding baselines." ], "reasoning": "By comparing the MAP scores of all models, RQRF has the highest MAP score of 0.207.", "question": "Which model has the highest MAP score and what is the value?", "answer": "So, the answer is {\"answer\": \"RQRF with 0.207\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The PM model has a higher NLL score than the BIDAF model. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe PM model has a higher NLL score than the BIDAF model.\n" }, { "role": "assistant", "content": "The table shows that the NLL score for PM is 0.662, while the NLL score for BIDAF is 0.623, indicating that PM has a higher NLL score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2652.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison with query embedding baselines." ], "reasoning": "The table shows that the NLL score for PM is 0.662, while the NLL score for BIDAF is 0.623, indicating that PM has a higher NLL score.", "statement": "The PM model has a higher NLL score than the BIDAF model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Performance of different ablation models.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The RQRF model outperforms its ablation models, with the convolutional layer contributing the most to its performance.RQRF-CNN, RQRF-Attention, RQRF-MLP represent RQRF without CNN, attention, and MLP module, respectively. The performances of all ablation models are worse than that of RQRF, which demonstrates the preeminence of RQRF. Specifically, the module that makes the greatest contribution to RQRF is the convolutional layer, without which MAP, MRR, and NDCG drops by 15.9%, 10%, and 23.3%, respectively." } ], "images": [ "scigen/train/2653.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of different ablation models." ], "reasoning": "The RQRF model outperforms its ablation models, with the convolutional layer contributing the most to its performance.", "description": "RQRF-CNN, RQRF-Attention, RQRF-MLP represent RQRF without CNN, attention, and MLP module, respectively. The performances of all ablation models are worse than that of RQRF, which demonstrates the preeminence of RQRF. Specifically, the module that makes the greatest contribution to RQRF is the convolutional layer, without which MAP, MRR, and NDCG drops by 15.9%, 10%, and 23.3%, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 3: Performance of different ablation models.\nQuestion: Which module's removal causes the greatest drop in NDCG for the RQRF model?\n" }, { "role": "assistant", "content": "The table shows that the NDCG drops from 0.253 to 0.194 when the CNN module is removed, which is a 23.3% decrease, the largest drop among the modules. So, the answer is {\"answer\": \"CNN\"}" } ], "images": [ "scigen/train/2653.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of different ablation models." ], "reasoning": "The table shows that the NDCG drops from 0.253 to 0.194 when the CNN module is removed, which is a 23.3% decrease, the largest drop among the modules.", "question": "Which module's removal causes the greatest drop in NDCG for the RQRF model?", "answer": "So, the answer is {\"answer\": \"CNN\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe removal of the attention module causes the greatest drop in MAP for the RQRF model.\n" }, { "role": "assistant", "content": "The table shows that the MAP drops from 0.207 to 0.174 when the CNN module is removed, which is a 15.9% decrease, the largest drop among the modules. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2653.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of different ablation models." ], "reasoning": "The table shows that the MAP drops from 0.207 to 0.174 when the CNN module is removed, which is a 15.9% decrease, the largest drop among the modules.", "statement": "The removal of the attention module causes the greatest drop in MAP for the RQRF model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Results for the SVMRank models for three different combinations of features. “Text Only Features” are only the text similarity features. “Feature Selection” is the set of features found after running a forward feature selection algorithm. A “*” indicates statistical significance between the two models.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Models incorporating additional features such as age, citation impact, and number of references outperform those using only text similarity features in terms of correlation measures and NDCG.Next we test three different feature sets for our supervised learning-to-rank models. The model using only the text similarity features performs poorly: NDCG stays at baseline and the correlation measures are low. Models that incorporate information about the age, number of times a cited document was referenced, and the citation impact of that document in addition to the text similarity features significantly outperformed models that used only text similarity features τap=0.35. Because τap takes into account the position in the ranking of the errors, this indicates that the All Features model was able to better correctly place highly ranked documents above lower ranked ones. Similarly, because Kendall’s τ is an overall measure of correlation that does not take into account the position of errors, the higher value here means that more rankings were correctly placed. Interestingly, feature selection (which is optimized for NDCG) does not outperform the model using all of the features in terms of our correlation measures. The features chosen during forward feature selection are (1) the citation impact, (2) number of mentions in the full text, (3) text similarity between the annotated document’s title and the referenced document’s abstract, (4) the text similarity between the annotated document’s discussion/conclusion section and the referenced document’s title. The models trained on the text similarity judgments perform worse than the models trained on the annotated data. However, in terms of both NDCG and the correlation measures, they perform significantly better than the random baseline." } ], "images": [ "scigen/train/2654.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results for the SVMRank models for three different combinations of features. “Text Only Features” are only the text similarity features. “Feature Selection” is the set of features found after running a forward feature selection algorithm. A “*” indicates statistical significance between the two models." ], "reasoning": "Models incorporating additional features such as age, citation impact, and number of references outperform those using only text similarity features in terms of correlation measures and NDCG.", "description": "Next we test three different feature sets for our supervised learning-to-rank models. The model using only the text similarity features performs poorly: NDCG stays at baseline and the correlation measures are low. Models that incorporate information about the age, number of times a cited document was referenced, and the citation impact of that document in addition to the text similarity features significantly outperformed models that used only text similarity features τap=0.35. Because τap takes into account the position in the ranking of the errors, this indicates that the All Features model was able to better correctly place highly ranked documents above lower ranked ones. Similarly, because Kendall’s τ is an overall measure of correlation that does not take into account the position of errors, the higher value here means that more rankings were correctly placed. Interestingly, feature selection (which is optimized for NDCG) does not outperform the model using all of the features in terms of our correlation measures. The features chosen during forward feature selection are (1) the citation impact, (2) number of mentions in the full text, (3) text similarity between the annotated document’s title and the referenced document’s abstract, (4) the text similarity between the annotated document’s discussion/conclusion section and the referenced document’s title. The models trained on the text similarity judgments perform worse than the models trained on the annotated data. However, in terms of both NDCG and the correlation measures, they perform significantly better than the random baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Results for the SVMRank models for three different combinations of features. “Text Only Features” are only the text similarity features. “Feature Selection” is the set of features found after running a forward feature selection algorithm. A “*” indicates statistical significance between the two models.', unfold the question presented below, detailing every step of your calculation. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model has the highest Annotated Model τ value?\n" }, { "role": "assistant", "content": "By comparing the Annotated Model τ values in the table, the Feature Selection model has the highest value of 0.26. So, the answer is {\"answer\": \"Feature Selection\"}." } ], "images": [ "scigen/train/2654.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results for the SVMRank models for three different combinations of features. “Text Only Features” are only the text similarity features. “Feature Selection” is the set of features found after running a forward feature selection algorithm. A “*” indicates statistical significance between the two models." ], "reasoning": "By comparing the Annotated Model τ values in the table, the Feature Selection model has the highest value of 0.26.", "question": "Which model has the highest Annotated Model τ value?", "answer": "So, the answer is {\"answer\": \"Feature Selection\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe All Features model has the highest Annotated Model τap value.\n" }, { "role": "assistant", "content": "By comparing the Annotated Model τap values in the table, the All Features model has the highest value of 0.35. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2654.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results for the SVMRank models for three different combinations of features. “Text Only Features” are only the text similarity features. “Feature Selection” is the set of features found after running a forward feature selection algorithm. A “*” indicates statistical significance between the two models." ], "reasoning": "By comparing the Annotated Model τap values in the table, the All Features model has the highest value of 0.35.", "statement": "The All Features model has the highest Annotated Model τap value.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Results for the citation baselines. The number of times a citation is mentioned in the document is a better indicator of rank than the citation impact.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The number of times a document is mentioned in the annotated document is significantly better than the random baseline or the citation impact, showing a negative correlation with citation impact.We first compare our gold standard to the baselines. A random baseline is provided for reference. Because all of the documents that we rank are relevant, NDCG will be fairly high simply by chance. We find that the number of times a document is mentioned in the annotated document is significantly better than the random baseline or the citation impact. The more times a document is mentioned in a paper, the more likely the author was to annotate it as important. Interestingly, we see a negative correlation with the citation impact. The more times a document is mentioned in the literature, the less likely it is to be important." } ], "images": [ "scigen/train/2655.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results for the citation baselines. The number of times a citation is mentioned in the document is a better indicator of rank than the citation impact." ], "reasoning": "The number of times a document is mentioned in the annotated document is significantly better than the random baseline or the citation impact, showing a negative correlation with citation impact.", "description": "We first compare our gold standard to the baselines. A random baseline is provided for reference. Because all of the documents that we rank are relevant, NDCG will be fairly high simply by chance. We find that the number of times a document is mentioned in the annotated document is significantly better than the random baseline or the citation impact. The more times a document is mentioned in a paper, the more likely the author was to annotate it as important. Interestingly, we see a negative correlation with the citation impact. The more times a document is mentioned in the literature, the less likely it is to be important.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 1: Results for the citation baselines. The number of times a citation is mentioned in the document is a better indicator of rank than the citation impact.' to sequentially solve the question presented below. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhich metric shows the highest NDCG value?\n" }, { "role": "assistant", "content": "By comparing the NDCG values in the table, the '# times referenced in document' has the highest NDCG value of 0.77. So, the answer is {\"answer\": \"# times referenced in document\"}." } ], "images": [ "scigen/train/2655.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results for the citation baselines. The number of times a citation is mentioned in the document is a better indicator of rank than the citation impact." ], "reasoning": "By comparing the NDCG values in the table, the '# times referenced in document' has the highest NDCG value of 0.77.", "question": "Which metric shows the highest NDCG value?", "answer": "So, the answer is {\"answer\": \"# times referenced in document\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Citation impact has a positive Kendall's τ value. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nCitation impact has a positive Kendall's τ value.\n" }, { "role": "assistant", "content": "The table shows that the Kendall's τ value for Citation Impact is -0.10, which is negative. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2655.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results for the citation baselines. The number of times a citation is mentioned in the document is a better indicator of rank than the citation impact." ], "reasoning": "The table shows that the Kendall's τ value for Citation Impact is -0.10, which is negative.", "statement": "Citation impact has a positive Kendall's τ value.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Results for ranking by each individual feature value. Similarity features are text similarity features. The first parameter is the section of text in the annotated document, the second parameter is the section of text in the referenced document. Here, “a” means abstract, “t” means title, “f” means full text, “c” means the 10 word window around a citation, “d” means the discussion/conclusion sections, and “cd” means 10 word windows around citations in the discussion/conclusion section. Age is the age of the referenced document, MentionCount is the number of times the annotated document mentions the referenced document in text, and CitationImpact is the number of documents that have cited the referenced document in the literature.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The importance of a citation is indicated by its frequency of mention in the text and its recency, with newer citations being more crucial.We also keep track of the number of times a citation is mentioned in both the full text and discussion/conclusion sections. We hypothesize that if a citation is mentioned multiple times, it is more important than citations that are mentioned only once. Further, citations that appear in the discussion/conclusion sections are more likely to be crucial to understanding the results. We normalize the counts of the citations by the total number of citations in that section. The features are normalized within each document so that each of citation features is on a scale from 0 to 1, and are evenly distributed within that range. This is done because some of the features (such as years since citation) are unbounded. The best performing text similarity feature is the similarity between the abstract of the annotated document and the abstract of the cited document. However, the number of times that a cited document is mentioned in the text of the annotated document are also high-scoring features, especially in the τap correlation coefficient. These results indicate that text similarity alone may not be a good measure for judging the rank of a document. We found that authors rank the references they cite substantially differently from rankings based on text-similarity. Our results show that decomposing a document into a set of features that is able to capture that difference is key. The more often a citation is mentioned in the text, the more likely it is to be important. This feature is often overlooked in article citation recommendation. We also found that recency is important: the age of the citation is negatively correlated with the rank. Newer citations are more likely to be directly important than older, more foundational citations. Additionally, the number of times a document is cited in the literature is negatively correlated with rank. This is likely due to highly cited documents being more foundational works; they may be older papers that are important to the field but not directly influential to the new work." } ], "images": [ "scigen/train/2656.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results for ranking by each individual feature value. Similarity features are text similarity features. The first parameter is the section of text in the annotated document, the second parameter is the section of text in the referenced document. Here, “a” means abstract, “t” means title, “f” means full text, “c” means the 10 word window around a citation, “d” means the discussion/conclusion sections, and “cd” means 10 word windows around citations in the discussion/conclusion section. Age is the age of the referenced document, MentionCount is the number of times the annotated document mentions the referenced document in text, and CitationImpact is the number of documents that have cited the referenced document in the literature." ], "reasoning": "The importance of a citation is indicated by its frequency of mention in the text and its recency, with newer citations being more crucial.", "description": "We also keep track of the number of times a citation is mentioned in both the full text and discussion/conclusion sections. We hypothesize that if a citation is mentioned multiple times, it is more important than citations that are mentioned only once. Further, citations that appear in the discussion/conclusion sections are more likely to be crucial to understanding the results. We normalize the counts of the citations by the total number of citations in that section. The features are normalized within each document so that each of citation features is on a scale from 0 to 1, and are evenly distributed within that range. This is done because some of the features (such as years since citation) are unbounded. The best performing text similarity feature is the similarity between the abstract of the annotated document and the abstract of the cited document. However, the number of times that a cited document is mentioned in the text of the annotated document are also high-scoring features, especially in the τap correlation coefficient. These results indicate that text similarity alone may not be a good measure for judging the rank of a document. We found that authors rank the references they cite substantially differently from rankings based on text-similarity. Our results show that decomposing a document into a set of features that is able to capture that difference is key. The more often a citation is mentioned in the text, the more likely it is to be important. This feature is often overlooked in article citation recommendation. We also found that recency is important: the age of the citation is negatively correlated with the rank. Newer citations are more likely to be directly important than older, more foundational citations. Additionally, the number of times a document is cited in the literature is negatively correlated with rank. This is likely due to highly cited documents being more foundational works; they may be older papers that are important to the field but not directly influential to the new work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: Table 2: Results for ranking by each individual feature value. Similarity features are text similarity features. The first parameter is the section of text in the annotated document, the second parameter is the section of text in the referenced document. Here, “a” means abstract, “t” means title, “f” means full text, “c” means the 10 word window around a citation, “d” means the discussion/conclusion sections, and “cd” means 10 word windows around citations in the discussion/conclusion section. Age is the age of the referenced document, MentionCount is the number of times the annotated document mentions the referenced document in text, and CitationImpact is the number of documents that have cited the referenced document in the literature.\nQuestion: Which feature has the highest τap correlation coefficient?\n" }, { "role": "assistant", "content": "By examining the τap column, the feature 'Similarity(f,t)' has the highest value of 0.31. So, the answer is {\"answer\": \"Similarity(f,t)\"}." } ], "images": [ "scigen/train/2656.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results for ranking by each individual feature value. Similarity features are text similarity features. The first parameter is the section of text in the annotated document, the second parameter is the section of text in the referenced document. Here, “a” means abstract, “t” means title, “f” means full text, “c” means the 10 word window around a citation, “d” means the discussion/conclusion sections, and “cd” means 10 word windows around citations in the discussion/conclusion section. Age is the age of the referenced document, MentionCount is the number of times the annotated document mentions the referenced document in text, and CitationImpact is the number of documents that have cited the referenced document in the literature." ], "reasoning": "By examining the τap column, the feature 'Similarity(f,t)' has the highest value of 0.31.", "question": "Which feature has the highest τap correlation coefficient?", "answer": "So, the answer is {\"answer\": \"Similarity(f,t)\"}." }, { "messages": [ { "role": "user", "content": "The feature 'Age' has a positive correlation with rank. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe feature 'Age' has a positive correlation with rank.\n" }, { "role": "assistant", "content": "The table shows that the 'Age' feature has a Kendall’s τ value of -0.12, indicating a negative correlation with rank. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2656.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results for ranking by each individual feature value. Similarity features are text similarity features. The first parameter is the section of text in the annotated document, the second parameter is the section of text in the referenced document. Here, “a” means abstract, “t” means title, “f” means full text, “c” means the 10 word window around a citation, “d” means the discussion/conclusion sections, and “cd” means 10 word windows around citations in the discussion/conclusion section. Age is the age of the referenced document, MentionCount is the number of times the annotated document mentions the referenced document in text, and CitationImpact is the number of documents that have cited the referenced document in the literature." ], "reasoning": "The table shows that the 'Age' feature has a Kendall’s τ value of -0.12, indicating a negative correlation with rank.", "statement": "The feature 'Age' has a positive correlation with rank.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Results for the model trained using the Google Scholar Related Articles ranking. We find that building a model using Google Scholar’s Related Articles ranking to predict our authors’ rankings performs poorly compared to the other models.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Google Scholar's rankings are similar to the text-only features model, but differ substantially from the authors' rankings.Next we compare our model to Google Scholar ’s rankings. Using the ranking collected from Google Scholar, we build a training set to try to predict our authors’ rankings. We find that Google Scholar performs similarly to the text-only features model. This indicates that the rankings we obtained from the authors are substantially different than the rankings that Google Scholar provides." } ], "images": [ "scigen/train/2657.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results for the model trained using the Google Scholar Related Articles ranking. We find that building a model using Google Scholar’s Related Articles ranking to predict our authors’ rankings performs poorly compared to the other models." ], "reasoning": "Google Scholar's rankings are similar to the text-only features model, but differ substantially from the authors' rankings.", "description": "Next we compare our model to Google Scholar ’s rankings. Using the ranking collected from Google Scholar, we build a training set to try to predict our authors’ rankings. We find that Google Scholar performs similarly to the text-only features model. This indicates that the rankings we obtained from the authors are substantially different than the rankings that Google Scholar provides.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: Results for the model trained using the Google Scholar Related Articles ranking. We find that building a model using Google Scholar’s Related Articles ranking to predict our authors’ rankings performs poorly compared to the other models.', answer the following question. Give detailed solution consisting of each step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the NDCG score for the Google Scholar training set when tested on Author Annotations?\n" }, { "role": "assistant", "content": "The table shows the NDCG score for the Google Scholar training set tested on Author Annotations as 0.77. So, the answer is {\"answer\": \"0.77\"}." } ], "images": [ "scigen/train/2657.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results for the model trained using the Google Scholar Related Articles ranking. We find that building a model using Google Scholar’s Related Articles ranking to predict our authors’ rankings performs poorly compared to the other models." ], "reasoning": "The table shows the NDCG score for the Google Scholar training set tested on Author Annotations as 0.77.", "question": "What is the NDCG score for the Google Scholar training set when tested on Author Annotations?", "answer": "So, the answer is {\"answer\": \"0.77\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Kendall's τ score for the Author Annotations training set is higher than that for the Google Scholar training set.\n" }, { "role": "assistant", "content": "The table shows that the Kendall's τ score for the Author Annotations training set is 0.27, which is higher than the 0.16 score for the Google Scholar training set. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2657.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results for the model trained using the Google Scholar Related Articles ranking. We find that building a model using Google Scholar’s Related Articles ranking to predict our authors’ rankings performs poorly compared to the other models." ], "reasoning": "The table shows that the Kendall's τ score for the Author Annotations training set is 0.27, which is higher than the 0.16 score for the Google Scholar training set.", "statement": "The Kendall's τ score for the Author Annotations training set is higher than that for the Google Scholar training set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: Results of three models’ phoneme error rate performance on 11 languages. The top-half shows the results trained with all training datasets. The bottom-half shows the low-resource results in which only 1k utterances are used for training from each dataset.'.\n" }, { "role": "assistant", "content": "The private phoneme model and Allosaurus model outperform the shared phoneme model with the full training set, and our model outperforms the private phoneme model by 2.0% when training data is limited.The top half of the table summarizes the performance when trained with the full training set. The results suggests both the private phoneme model and the Allosaurus model outperforms the shared phoneme model significantly. The results of the shared phoneme model can be explained by the disagreement of phoneme assignments across languages. In contrast, the private phoneme model handles this issue by using language specific phoneme layers. Our model also circumvents this issue by introducing the language-specific allophone layers. Unsurprisingly, limiting the amount of training data hurts accuracy across the board. While the private phoneme model and our model achieve similar results when using the full training set, our model outperforms the private phoneme model by 2.0% when training data is limited. This suggests that our model is better at sharing parameters across languages by using prior phonetic knowledge in this case, likely due to the fact that the private phoneme model needs to learn each phoneme predictor from scratch, while our model already has phone-phoneme mapping knowledge seeded by linguistically motivated annotations." } ], "images": [ "scigen/train/2658.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results of three models’ phoneme error rate performance on 11 languages. The top-half shows the results trained with all training datasets. The bottom-half shows the low-resource results in which only 1k utterances are used for training from each dataset." ], "reasoning": "The private phoneme model and Allosaurus model outperform the shared phoneme model with the full training set, and our model outperforms the private phoneme model by 2.0% when training data is limited.", "description": "The top half of the table summarizes the performance when trained with the full training set. The results suggests both the private phoneme model and the Allosaurus model outperforms the shared phoneme model significantly. The results of the shared phoneme model can be explained by the disagreement of phoneme assignments across languages. In contrast, the private phoneme model handles this issue by using language specific phoneme layers. Our model also circumvents this issue by introducing the language-specific allophone layers. Unsurprisingly, limiting the amount of training data hurts accuracy across the board. While the private phoneme model and our model achieve similar results when using the full training set, our model outperforms the private phoneme model by 2.0% when training data is limited. This suggests that our model is better at sharing parameters across languages by using prior phonetic knowledge in this case, likely due to the fact that the private phoneme model needs to learn each phoneme predictor from scratch, while our model already has phone-phoneme mapping knowledge seeded by linguistically motivated annotations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Results of three models’ phoneme error rate performance on 11 languages. The top-half shows the results trained with all training datasets. The bottom-half shows the low-resource results in which only 1k utterances are used for training from each dataset.', answer the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the average PER for the Allosaurus model when trained with the full training set?\n" }, { "role": "assistant", "content": "To find the average PER for the Allosaurus model when trained with the full training set, look at the row labeled 'Full' under 'Allosaurus PER' and find the value in the 'Average' column. So, the answer is {\"answer\": \"25.0\"}" } ], "images": [ "scigen/train/2658.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results of three models’ phoneme error rate performance on 11 languages. The top-half shows the results trained with all training datasets. The bottom-half shows the low-resource results in which only 1k utterances are used for training from each dataset." ], "reasoning": "To find the average PER for the Allosaurus model when trained with the full training set, look at the row labeled 'Full' under 'Allosaurus PER' and find the value in the 'Average' column.", "question": "What is the average PER for the Allosaurus model when trained with the full training set?", "answer": "So, the answer is {\"answer\": \"25.0\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The private phoneme model has a lower average PER than the Allosaurus model when trained with the full training set. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe private phoneme model has a lower average PER than the Allosaurus model when trained with the full training set.\n" }, { "role": "assistant", "content": "The average PER for the private phoneme model when trained with the full training set is 25.6, while the average PER for the Allosaurus model is 25.0. Since 25.6 is higher than 25.0, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2658.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results of three models’ phoneme error rate performance on 11 languages. The top-half shows the results trained with all training datasets. The bottom-half shows the low-resource results in which only 1k utterances are used for training from each dataset." ], "reasoning": "The average PER for the private phoneme model when trained with the full training set is 25.6, while the average PER for the Allosaurus model is 25.0. Since 25.6 is higher than 25.0, the statement is refuted.", "statement": "The private phoneme model has a lower average PER than the Allosaurus model when trained with the full training set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Ablation experimental results on the whole test data.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Incorporating global information significantly boosts the F-score compared to using only local information, while local representations provide strong semantic understanding.In each ablation experiment, we retrain the model with only one type of representations of candidates utilized as inputs. To minimize the external influence, we simply employ experimental Setting 1 (gold parse and gold AZP) to perform the experiments. We can observe that incorporating the global information significantly boosts the F-score comparing with the model utilize only the local information. Meanwhile, local representations of candidate antecedents provide the system with a strong ability to reveal the semantics of the phrases. Consequently, when the local information is ablated (i.e., without utilizing l(npi) when computing the formula 2), F-score drops by 0.7% comparing with the full system." } ], "images": [ "scigen/train/2659.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation experimental results on the whole test data." ], "reasoning": "Incorporating global information significantly boosts the F-score compared to using only local information, while local representations provide strong semantic understanding.", "description": "In each ablation experiment, we retrain the model with only one type of representations of candidates utilized as inputs. To minimize the external influence, we simply employ experimental Setting 1 (gold parse and gold AZP) to perform the experiments. We can observe that incorporating the global information significantly boosts the F-score comparing with the model utilize only the local information. Meanwhile, local representations of candidate antecedents provide the system with a strong ability to reveal the semantics of the phrases. Consequently, when the local information is ablated (i.e., without utilizing l(npi) when computing the formula 2), F-score drops by 0.7% comparing with the full system.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: Ablation experimental results on the whole test data.'. Solve the following question based on the table.\n\nQuestion: What is the F-score when only global information is used?\nLet's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the F-score for different types of information used. The F-score for 'Global information only' is 52.9. So, the answer is {\"answer\": \"52.9\"}" } ], "images": [ "scigen/train/2659.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation experimental results on the whole test data." ], "reasoning": "The table shows the F-score for different types of information used. The F-score for 'Global information only' is 52.9.", "question": "What is the F-score when only global information is used?", "answer": "So, the answer is {\"answer\": \"52.9\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The F-score drops by 0.7% when local information is ablated. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe F-score drops by 0.7% when local information is ablated.\n" }, { "role": "assistant", "content": "The description states that the F-score drops by 0.7% when local information is ablated. The table shows the F-score for the full system is 53.6 and for global information only is 52.9, which is a drop of 0.7%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2659.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation experimental results on the whole test data." ], "reasoning": "The description states that the F-score drops by 0.7% when local information is ablated. The table shows the F-score for the full system is 53.6 and for global information only is 52.9, which is a drop of 0.7%.", "statement": "The F-score drops by 0.7% when local information is ablated.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Experimental results on the test data. ZPSNN represents our approach. †∗ indicate that our approach is statistical significant over the baseline system (using t-test, with p<0.05).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Our proposed approach outperforms the baseline system in all three experimental settings, achieving higher F-scores by 1.4%, 1.9%, and 2.5%, respectively.We employ \\citeauthorchen2016 \\shortcitechen2016’s system as the baseline, which is the state-of-the-art Chinese ZP resolution system. To evaluate our proposed approach, following \\citeauthorchen2016 \\shortcitechen2016, three experimental settings are designed. In Setting 1, we assume that gold AZPs and gold syntactic parse trees are available (obtained from the CoNLL-2012 shared task dataset). Finally, in Setting 3, we employ system AZP and system syntactic parse trees that obtained by Berkeley parser, which is the state-of-the-art parsing model. As we can see, our approach significantly outperforms the baseline system under three experimental settings by 1.4%, 1.9%, 2.5% in terms of overall F-score, respectively. In per-source results, for Setting 1 and 2, ZPSNN beats the baseline system in four of the six sources of data, especial on the source BC, which has the most AZPs in six sources. Moreover, in Setting 3, our system achieves higher performance than the baseline system in all the six sources of data. All these approve that our proposed approach achieves a considerable improvement in Chinese ZP resolution." } ], "images": [ "scigen/train/2660.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Experimental results on the test data. ZPSNN represents our approach. †∗ indicate that our approach is statistical significant over the baseline system (using t-test, with p<0.05)." ], "reasoning": "Our proposed approach outperforms the baseline system in all three experimental settings, achieving higher F-scores by 1.4%, 1.9%, and 2.5%, respectively.", "description": "We employ \\citeauthorchen2016 \\shortcitechen2016’s system as the baseline, which is the state-of-the-art Chinese ZP resolution system. To evaluate our proposed approach, following \\citeauthorchen2016 \\shortcitechen2016, three experimental settings are designed. In Setting 1, we assume that gold AZPs and gold syntactic parse trees are available (obtained from the CoNLL-2012 shared task dataset). Finally, in Setting 3, we employ system AZP and system syntactic parse trees that obtained by Berkeley parser, which is the state-of-the-art parsing model. As we can see, our approach significantly outperforms the baseline system under three experimental settings by 1.4%, 1.9%, 2.5% in terms of overall F-score, respectively. In per-source results, for Setting 1 and 2, ZPSNN beats the baseline system in four of the six sources of data, especial on the source BC, which has the most AZPs in six sources. Moreover, in Setting 3, our system achieves higher performance than the baseline system in all the six sources of data. All these approve that our proposed approach achieves a considerable improvement in Chinese ZP resolution.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Experimental results on the test data. ZPSNN represents our approach. †∗ indicate that our approach is statistical significant over the baseline system (using t-test, with p<0.05).\nQuestion: What is the F-score improvement of ZPSNN over the baseline system in Setting 3 for the source BC?\nGive a detailed response to the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "In Setting 3, the baseline system has an F-score of 17.3 for the source BC, while ZPSNN has an F-score of 20.6. The improvement is calculated as 20.6 - 17.3. So, the answer is {\"answer\": \"3.3\"}" } ], "images": [ "scigen/train/2660.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Experimental results on the test data. ZPSNN represents our approach. †∗ indicate that our approach is statistical significant over the baseline system (using t-test, with p<0.05)." ], "reasoning": "In Setting 3, the baseline system has an F-score of 17.3 for the source BC, while ZPSNN has an F-score of 20.6. The improvement is calculated as 20.6 - 17.3.", "question": "What is the F-score improvement of ZPSNN over the baseline system in Setting 3 for the source BC?", "answer": "So, the answer is {\"answer\": \"3.3\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nZPSNN achieves higher performance than the baseline system in all sources of data in Setting 3.\n" }, { "role": "assistant", "content": "In Setting 3, ZPSNN outperforms the baseline system in all six sources of data as shown by the higher F-scores in each source. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2660.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Experimental results on the test data. ZPSNN represents our approach. †∗ indicate that our approach is statistical significant over the baseline system (using t-test, with p<0.05)." ], "reasoning": "In Setting 3, ZPSNN outperforms the baseline system in all six sources of data as shown by the higher F-scores in each source.", "statement": "ZPSNN achieves higher performance than the baseline system in all sources of data in Setting 3.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 7: Feature Sets and Data Sets. The results of our best classifier trained on different subsets of features and data. Features are grouped by type and by magnitude of their Pearson correlation with the train labels (no features had an absolute correlation greater than 0.5); data is separated by the proportion of annotators who agreed. Our best score (corresponding to our best non-neural model) is shown in bold.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Higher agreement data results in better performance, and LIWC features consistently outperform others, indicating the importance of feature quality over quantity.First, we see a tradeoff between data size and data quality, where lower-agreement data (which can be seen as lower-quality) results in worse performance, but the larger 80% agreement data consistently outperforms the smaller perfect agreement data. Additionally, LIWC features consistently perform well while syntactic features consistently do not, and we see a trend towards the quality of features over their quantity; those with the highest Pearson correlation with the train set (which all happen to be LIWC features) outperform sets with lower correlations, which in turn outperform the set of all features. This suggests that stress detection is a highly lexical problem, and in particular, resources developed with psychological applications in mind, like LIWC, are very helpful." } ], "images": [ "scigen/train/2661.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Feature Sets and Data Sets. The results of our best classifier trained on different subsets of features and data. Features are grouped by type and by magnitude of their Pearson correlation with the train labels (no features had an absolute correlation greater than 0.5); data is separated by the proportion of annotators who agreed. Our best score (corresponding to our best non-neural model) is shown in bold." ], "reasoning": "Higher agreement data results in better performance, and LIWC features consistently outperform others, indicating the importance of feature quality over quantity.", "description": "First, we see a tradeoff between data size and data quality, where lower-agreement data (which can be seen as lower-quality) results in worse performance, but the larger 80% agreement data consistently outperforms the smaller perfect agreement data. Additionally, LIWC features consistently perform well while syntactic features consistently do not, and we see a trend towards the quality of features over their quantity; those with the highest Pearson correlation with the train set (which all happen to be LIWC features) outperform sets with lower correlations, which in turn outperform the set of all features. This suggests that stress detection is a highly lexical problem, and in particular, resources developed with psychological applications in mind, like LIWC, are very helpful.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 7: Feature Sets and Data Sets. The results of our best classifier trained on different subsets of features and data. Features are grouped by type and by magnitude of their Pearson correlation with the train labels (no features had an absolute correlation greater than 0.5); data is separated by the proportion of annotators who agreed. Our best score (corresponding to our best non-neural model) is shown in bold.\nQuestion: Which feature set has the highest performance for the 80% agreement threshold?\n" }, { "role": "assistant", "content": "By examining the table, the highest performance for the 80% agreement threshold is found in the row with the value 79.80, which corresponds to the feature set with |r| ≥ 0.4. So, the answer is {\"answer\": \"|r| ≥ 0.4\"}" } ], "images": [ "scigen/train/2661.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Feature Sets and Data Sets. The results of our best classifier trained on different subsets of features and data. Features are grouped by type and by magnitude of their Pearson correlation with the train labels (no features had an absolute correlation greater than 0.5); data is separated by the proportion of annotators who agreed. Our best score (corresponding to our best non-neural model) is shown in bold." ], "reasoning": "By examining the table, the highest performance for the 80% agreement threshold is found in the row with the value 79.80, which corresponds to the feature set with |r| ≥ 0.4.", "question": "Which feature set has the highest performance for the 80% agreement threshold?", "answer": "So, the answer is {\"answer\": \"|r| ≥ 0.4\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSyntactic features perform better than lexical features for the 60% agreement threshold.\n" }, { "role": "assistant", "content": "The table shows that for the 60% agreement threshold, syntactic features have a performance of 74.63, while lexical features have a performance of 77.92. Therefore, syntactic features do not perform better than lexical features. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2661.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Feature Sets and Data Sets. The results of our best classifier trained on different subsets of features and data. Features are grouped by type and by magnitude of their Pearson correlation with the train labels (no features had an absolute correlation greater than 0.5); data is separated by the proportion of annotators who agreed. Our best score (corresponding to our best non-neural model) is shown in bold." ], "reasoning": "The table shows that for the 60% agreement threshold, syntactic features have a performance of 74.63, while lexical features have a performance of 77.92. Therefore, syntactic features do not perform better than lexical features.", "statement": "Syntactic features perform better than lexical features for the 60% agreement threshold.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 6: Supervised Results. Precision (P), recall (R), and F1-score (F) for our supervised models. Our best model achieves 79.80 F1-score on our test set, comparable to the state-of-the-art pretrained BERT-base model. In this table, “features” always refers to our best-performing feature set (≥ 0.4 absolute Pearson’s r). Models marked with a * show a significant improvement over the majority baseline (approximate randomization test, p<0.01).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The best model is a logistic regression classifier with domain Word2Vec embeddings and high-correlation features, achieving an F-score of 79.8, outperforming other baselines.Our best model is a logistic regression classifier with Word2Vec embeddings trained on our unlabeled corpus, high-correlation features (≥ 0.4 absolute Pearson’s r), and high-agreement data (at least 4/5 annotators agreed); this model achieves an F-score of 79.8 on our test set, a significant improvement over the majority baseline, the n-gram baseline, and the pre-trained embedding model, (all by the approximate randomization test, p<0.01). The high-correlation features used by this model are LIWC’s clout, tone, and “I” pronoun features, and we investigate the use of these features in the other model types. Particularly, we apply different architectures (GRNN and CNN) and different input representations (pretrained Word2Vec, domain-specific BERT)." } ], "images": [ "scigen/train/2662.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Supervised Results. Precision (P), recall (R), and F1-score (F) for our supervised models. Our best model achieves 79.80 F1-score on our test set, comparable to the state-of-the-art pretrained BERT-base model. In this table, “features” always refers to our best-performing feature set (≥ 0.4 absolute Pearson’s r). Models marked with a * show a significant improvement over the majority baseline (approximate randomization test, p<0.01)." ], "reasoning": "The best model is a logistic regression classifier with domain Word2Vec embeddings and high-correlation features, achieving an F-score of 79.8, outperforming other baselines.", "description": "Our best model is a logistic regression classifier with Word2Vec embeddings trained on our unlabeled corpus, high-correlation features (≥ 0.4 absolute Pearson’s r), and high-agreement data (at least 4/5 annotators agreed); this model achieves an F-score of 79.8 on our test set, a significant improvement over the majority baseline, the n-gram baseline, and the pre-trained embedding model, (all by the approximate randomization test, p<0.01). The high-correlation features used by this model are LIWC’s clout, tone, and “I” pronoun features, and we investigate the use of these features in the other model types. Particularly, we apply different architectures (GRNN and CNN) and different input representations (pretrained Word2Vec, domain-specific BERT).", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 6: Supervised Results. Precision (P), recall (R), and F1-score (F) for our supervised models. Our best model achieves 79.80 F1-score on our test set, comparable to the state-of-the-art pretrained BERT-base model. In this table, “features” always refers to our best-performing feature set (≥ 0.4 absolute Pearson’s r). Models marked with a * show a significant improvement over the majority baseline (approximate randomization test, p<0.01).'.\n\nQuestion: Which model achieves the highest F-score and what is its value?\n" }, { "role": "assistant", "content": "The table lists various models and their performance metrics. The model with the highest F-score is BERT-base with an F-score of 0.8065. So, the answer is {\"answer\": \"BERT-base\"}." } ], "images": [ "scigen/train/2662.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Supervised Results. Precision (P), recall (R), and F1-score (F) for our supervised models. Our best model achieves 79.80 F1-score on our test set, comparable to the state-of-the-art pretrained BERT-base model. In this table, “features” always refers to our best-performing feature set (≥ 0.4 absolute Pearson’s r). Models marked with a * show a significant improvement over the majority baseline (approximate randomization test, p<0.01)." ], "reasoning": "The table lists various models and their performance metrics. The model with the highest F-score is BERT-base with an F-score of 0.8065.", "question": "Which model achieves the highest F-score and what is its value?", "answer": "So, the answer is {\"answer\": \"BERT-base\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The CNN model with features achieves an F-score higher than 0.72. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe CNN model with features achieves an F-score higher than 0.72.\n" }, { "role": "assistant", "content": "The table shows that the CNN model with features has an F-score of 0.7182, which is not higher than 0.72. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2662.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Supervised Results. Precision (P), recall (R), and F1-score (F) for our supervised models. Our best model achieves 79.80 F1-score on our test set, comparable to the state-of-the-art pretrained BERT-base model. In this table, “features” always refers to our best-performing feature set (≥ 0.4 absolute Pearson’s r). Models marked with a * show a significant improvement over the majority baseline (approximate randomization test, p<0.01)." ], "reasoning": "The table shows that the CNN model with features has an F-score of 0.7182, which is not higher than 0.72.", "statement": "The CNN model with features achieves an F-score higher than 0.72.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: Evaluation metrics on development dataset'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The neural network model achieved an f1-score of 0.998 for language classification and 0.578 for exact full entities matching in Multilingual Named Entity Recognition, outperforming the CRF baseline.On the prediction stage result, labels were received by voice classifier. After this, we transform word predictions to spans markup. For proposed neural network architecture the evaluation of the training stage was produced on development dataset. For development set, we obtained the following scores: language classification quality (f1-score): 0.998 and Multilingual Named Entity Recognition quality (f1-score): 0.70 for exact word level matching and 0.578 for exact full entities matching. Also we train model without language classification, which resulted in f1-score equal to 0.66 . This confirms the impact of language classification. Our model significantly outperforms the CRF baseline." } ], "images": [ "scigen/train/2663.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Evaluation metrics on development dataset" ], "reasoning": "The neural network model achieved an f1-score of 0.998 for language classification and 0.578 for exact full entities matching in Multilingual Named Entity Recognition, outperforming the CRF baseline.", "description": "On the prediction stage result, labels were received by voice classifier. After this, we transform word predictions to spans markup. For proposed neural network architecture the evaluation of the training stage was produced on development dataset. For development set, we obtained the following scores: language classification quality (f1-score): 0.998 and Multilingual Named Entity Recognition quality (f1-score): 0.70 for exact word level matching and 0.578 for exact full entities matching. Also we train model without language classification, which resulted in f1-score equal to 0.66 . This confirms the impact of language classification. Our model significantly outperforms the CRF baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 1: Evaluation metrics on development dataset'\nQuestion: What is the f1-score for the label with the highest recall?\n" }, { "role": "assistant", "content": "The label with the highest recall is LOC with a recall of 0.872. The corresponding f1-score for LOC is 0.744. So, the answer is {\"answer\": \"0.744\"}" } ], "images": [ "scigen/train/2663.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Evaluation metrics on development dataset" ], "reasoning": "The label with the highest recall is LOC with a recall of 0.872. The corresponding f1-score for LOC is 0.744.", "question": "What is the f1-score for the label with the highest recall?", "answer": "So, the answer is {\"answer\": \"0.744\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe neural network model achieved an f1-score of 0.578 for exact full entities matching.\n" }, { "role": "assistant", "content": "The description states that the model achieved an f1-score of 0.578 for exact full entities matching, which matches the information provided. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2663.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Evaluation metrics on development dataset" ], "reasoning": "The description states that the model achieved an f1-score of 0.578 for exact full entities matching, which matches the information provided.", "statement": "The neural network model achieved an f1-score of 0.578 for exact full entities matching.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 14: Ablation study: impact of hops in BoSsNet encoder'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Multi-hop encoder improves performance in bAbI tasks 3 and 5, and shows substantial improvements in CamRest BLEU and entity F1 metrics.Multi-hops in encoder helps in bAbI task 3 and 5, as they require inferencing over the KB tuples (sorting restaurants by rating) to recommend a restaurant. We also see substantial improvements on CamRest in both BLEU and entity F1 metric." } ], "images": [ "scigen/train/2664.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 14: Ablation study: impact of hops in BoSsNet encoder" ], "reasoning": "Multi-hop encoder improves performance in bAbI tasks 3 and 5, and shows substantial improvements in CamRest BLEU and entity F1 metrics.", "description": "Multi-hops in encoder helps in bAbI task 3 and 5, as they require inferencing over the KB tuples (sorting restaurants by rating) to recommend a restaurant. We also see substantial improvements on CamRest in both BLEU and entity F1 metric.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 14: Ablation study: impact of hops in BoSsNet encoder', solve the subsequent question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nHow does the performance of BoSsNet with Multi-Hop Encoder compare to BoSsNet with 1-Hop Encoder in bAbI Dialog Tasks T3 and CamRest BLEU?\n" }, { "role": "assistant", "content": "BoSsNet with Multi-Hop Encoder has a higher score in bAbI Dialog Tasks T3 (95.2) compared to BoSsNet with 1-Hop Encoder (92.3). Similarly, it has a higher CamRest BLEU score (15.2) compared to BoSsNet with 1-Hop Encoder (10.5). So, the answer is {\"answer\": \"BoSsNet with Multi-Hop Encoder performs better in both bAbI Dialog Tasks T3 and CamRest BLEU.\"}." } ], "images": [ "scigen/train/2664.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 14: Ablation study: impact of hops in BoSsNet encoder" ], "reasoning": "BoSsNet with Multi-Hop Encoder has a higher score in bAbI Dialog Tasks T3 (95.2) compared to BoSsNet with 1-Hop Encoder (92.3). Similarly, it has a higher CamRest BLEU score (15.2) compared to BoSsNet with 1-Hop Encoder (10.5).", "question": "How does the performance of BoSsNet with Multi-Hop Encoder compare to BoSsNet with 1-Hop Encoder in bAbI Dialog Tasks T3 and CamRest BLEU?", "answer": "So, the answer is {\"answer\": \"BoSsNet with Multi-Hop Encoder performs better in both bAbI Dialog Tasks T3 and CamRest BLEU.\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nBoSsNet with Multi-Hop Encoder performs worse than BoSsNet with 1-Hop Encoder in CamRest entity F1 metric.\n" }, { "role": "assistant", "content": "The table shows that BoSsNet with Multi-Hop Encoder has a CamRest entity F1 score of 43.1, which is higher than the score of 36.9 for BoSsNet with 1-Hop Encoder. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2664.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 14: Ablation study: impact of hops in BoSsNet encoder" ], "reasoning": "The table shows that BoSsNet with Multi-Hop Encoder has a CamRest entity F1 score of 43.1, which is higher than the score of 36.9 for BoSsNet with 1-Hop Encoder.", "statement": "BoSsNet with Multi-Hop Encoder performs worse than BoSsNet with 1-Hop Encoder in CamRest entity F1 metric.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Performance of BoSsNet and baselines on the CamRest and SMD datasets', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "BoSsNet achieves the highest scores in both Entity F1 and BLEU on CamRest, and the highest Entity F1 on SMD, but its BLEU score on SMD is lower due to less lexical overlap with the gold response.BoSsNet greatly outperforms other models in both Entity F1 metric and BLEU scores on CamRest. On SMD, BoSsNet achieves the best only in Entity F1. On further analysis of the generated responses we observe that BoSsNet responses often convey the necessary entity information from the KB. However, they consist of meaningful phrases with little lexical overlap with the gold response, reducing the BLEU scores. We investigate this further in our human evaluation." } ], "images": [ "scigen/train/2665.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of BoSsNet and baselines on the CamRest and SMD datasets" ], "reasoning": "BoSsNet achieves the highest scores in both Entity F1 and BLEU on CamRest, and the highest Entity F1 on SMD, but its BLEU score on SMD is lower due to less lexical overlap with the gold response.", "description": "BoSsNet greatly outperforms other models in both Entity F1 metric and BLEU scores on CamRest. On SMD, BoSsNet achieves the best only in Entity F1. On further analysis of the generated responses we observe that BoSsNet responses often convey the necessary entity information from the KB. However, they consist of meaningful phrases with little lexical overlap with the gold response, reducing the BLEU scores. We investigate this further in our human evaluation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Performance of BoSsNet and baselines on the CamRest and SMD datasets\nQuestion: Which model has the highest BLEU score on CamRest and what is its score?\nGive a detailed response to the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest BLEU score on CamRest, look at the CamRest BLEU column and identify the highest value. The highest value is 15.2, which corresponds to BoSsNet. So, the answer is {\"answer\": \"BoSsNet, 15.2\"}." } ], "images": [ "scigen/train/2665.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of BoSsNet and baselines on the CamRest and SMD datasets" ], "reasoning": "To find the model with the highest BLEU score on CamRest, look at the CamRest BLEU column and identify the highest value. The highest value is 15.2, which corresponds to BoSsNet.", "question": "Which model has the highest BLEU score on CamRest and what is its score?", "answer": "So, the answer is {\"answer\": \"BoSsNet, 15.2\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nBoSsNet has the highest Entity F1 score on both CamRest and SMD.\n" }, { "role": "assistant", "content": "The table shows that BoSsNet has the highest Entity F1 score on CamRest with 43.1 and on SMD with 35.9, confirming the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2665.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of BoSsNet and baselines on the CamRest and SMD datasets" ], "reasoning": "The table shows that BoSsNet has the highest Entity F1 score on CamRest with 43.1 and on SMD with 35.9, confirming the statement.", "statement": "BoSsNet has the highest Entity F1 score on both CamRest and SMD.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: AMT Evaluations on CamRest and SMD'.\n" }, { "role": "assistant", "content": "BoSsNet shows the best performance on Camrest with 77 useful judgments out of 100 and has the highest average grammatical correctness score of 2.28.BoSsNet shows the best performance on Camrest, and is judged useful 77 times out of 100. Also, it has the highest average grammatical correctness score of 2.28 (very close to Seq2Seq and Mem2Seq). BoSsNet performs on par with Mem2Seq and Seq2Seq in its ability to relay appropriate information to solve SMD dialog tasks, and has a slightly higher grammaticality score." } ], "images": [ "scigen/train/2666.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: AMT Evaluations on CamRest and SMD" ], "reasoning": "BoSsNet shows the best performance on Camrest with 77 useful judgments out of 100 and has the highest average grammatical correctness score of 2.28.", "description": "BoSsNet shows the best performance on Camrest, and is judged useful 77 times out of 100. Also, it has the highest average grammatical correctness score of 2.28 (very close to Seq2Seq and Mem2Seq). BoSsNet performs on par with Mem2Seq and Seq2Seq in its ability to relay appropriate information to solve SMD dialog tasks, and has a slightly higher grammaticality score.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: AMT Evaluations on CamRest and SMD', solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest average grammatical correctness score on CamRest?\n" }, { "role": "assistant", "content": "The table shows the CamRest Grammar scores for each model. BoSsNet has the highest score of 2.28. So, the answer is {\"answer\": \"BoSsNet\"}." } ], "images": [ "scigen/train/2666.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: AMT Evaluations on CamRest and SMD" ], "reasoning": "The table shows the CamRest Grammar scores for each model. BoSsNet has the highest score of 2.28.", "question": "Which model has the highest average grammatical correctness score on CamRest?", "answer": "So, the answer is {\"answer\": \"BoSsNet\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nBoSsNet has the highest average grammatical correctness score on SMD.\n" }, { "role": "assistant", "content": "The table shows the SMD Grammar scores for each model. BoSsNet has the highest score of 2.5. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2666.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: AMT Evaluations on CamRest and SMD" ], "reasoning": "The table shows the SMD Grammar scores for each model. BoSsNet has the highest score of 2.5.", "statement": "BoSsNet has the highest average grammatical correctness score on SMD.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5: AMT Evaluations on CamRest and SMD (50% unseen) KA datasets'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "BoSsNet outperforms other models on both CamRest and SMD datasets, being labeled successful twice as often as the next best model.BoSsNet again outperforms the baselines and is labeled successful twice more often than the next best model on both Camrest and SMD. Seq2Seq appears to produce better sentence structures on the SMD dataset, primarily because it does not attempt to learn inference on the KB, allowing it to solely focus on learning the language model better." } ], "images": [ "scigen/train/2667.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: AMT Evaluations on CamRest and SMD (50% unseen) KA datasets" ], "reasoning": "BoSsNet outperforms other models on both CamRest and SMD datasets, being labeled successful twice as often as the next best model.", "description": "BoSsNet again outperforms the baselines and is labeled successful twice more often than the next best model on both Camrest and SMD. Seq2Seq appears to produce better sentence structures on the SMD dataset, primarily because it does not attempt to learn inference on the KB, allowing it to solely focus on learning the language model better.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 5: AMT Evaluations on CamRest and SMD (50% unseen) KA datasets\n\nQuestion: Which model has the highest CamRest Info score and what is the score?\n" }, { "role": "assistant", "content": "To find the model with the highest CamRest Info score, compare the CamRest Info scores of all models. BoSsNet has the highest score of 80. So, the answer is {\"answer\": \"BoSsNet with a score of 80\"}." } ], "images": [ "scigen/train/2667.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: AMT Evaluations on CamRest and SMD (50% unseen) KA datasets" ], "reasoning": "To find the model with the highest CamRest Info score, compare the CamRest Info scores of all models. BoSsNet has the highest score of 80.", "question": "Which model has the highest CamRest Info score and what is the score?", "answer": "So, the answer is {\"answer\": \"BoSsNet with a score of 80\"}." }, { "messages": [ { "role": "user", "content": "Seq2Seq+Copy has the lowest SMD Grammar score. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSeq2Seq+Copy has the lowest SMD Grammar score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the SMD Grammar scores of all models. Seq2Seq+Copy has the lowest score of 1.04. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2667.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: AMT Evaluations on CamRest and SMD (50% unseen) KA datasets" ], "reasoning": "To verify this statement, compare the SMD Grammar scores of all models. Seq2Seq+Copy has the lowest score of 1.04.", "statement": "Seq2Seq+Copy has the lowest SMD Grammar score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: MOS similarity scores - English and Spanish.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The highest similarity scores were for pairs 3−3en and 6−6en, indicating better performance for Spanish to English cross-lingual transfer.Similarity: A similarity experiment (Test 3) examined to what extent speech in a different language can be identified as being from the same speaker. 84 pairs of sentences, with one sentence in English and one in Spanish, using seven different voice combinations Listeners were asked to rate the voice similarity for each pair of sentences on a scale from (1) Very Different to (5) Very Similar. The higher the score the higher the perceived similarity between pairs of voices in the two languages. Matched voices with cross-lingual transfer scored higher. Pairs 3−3en and 6−6en scored highest, showing the model was performing better for Spanish to English cross-lingual transfer than for English to Spanish. The two mixed voice combinations were rated lower than the same-voice combinations and the difference was statistically significant (p<0.001). The bilingual speaker was perceived as less similar than some combinations, probably because the original recordings in each language had slightly different style requirements." } ], "images": [ "scigen/train/2668.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: MOS similarity scores - English and Spanish." ], "reasoning": "The highest similarity scores were for pairs 3−3en and 6−6en, indicating better performance for Spanish to English cross-lingual transfer.", "description": "Similarity: A similarity experiment (Test 3) examined to what extent speech in a different language can be identified as being from the same speaker. 84 pairs of sentences, with one sentence in English and one in Spanish, using seven different voice combinations Listeners were asked to rate the voice similarity for each pair of sentences on a scale from (1) Very Different to (5) Very Similar. The higher the score the higher the perceived similarity between pairs of voices in the two languages. Matched voices with cross-lingual transfer scored higher. Pairs 3−3en and 6−6en scored highest, showing the model was performing better for Spanish to English cross-lingual transfer than for English to Spanish. The two mixed voice combinations were rated lower than the same-voice combinations and the difference was statistically significant (p<0.001). The bilingual speaker was perceived as less similar than some combinations, probably because the original recordings in each language had slightly different style requirements.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: Table 4: MOS similarity scores - English and Spanish.\nQuestion: Which voice ID pair had the highest MOS score?\n" }, { "role": "assistant", "content": "By examining the MOS scores in the table, the highest score is 3.99 for the voice ID pair 3 en−3. So, the answer is {\"answer\": \"3 en−3\"}" } ], "images": [ "scigen/train/2668.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: MOS similarity scores - English and Spanish." ], "reasoning": "By examining the MOS scores in the table, the highest score is 3.99 for the voice ID pair 3 en−3.", "question": "Which voice ID pair had the highest MOS score?", "answer": "So, the answer is {\"answer\": \"3 en−3\"}" }, { "messages": [ { "role": "user", "content": "The voice ID pair 1−3 es had a higher MOS score than 0−2 (refen−refes). Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe voice ID pair 1−3 es had a higher MOS score than 0−2 (refen−refes).\n" }, { "role": "assistant", "content": "The table shows that the MOS score for 1−3 es is 2.50, which is lower than the MOS score for 0−2 (refen−refes) which is 3.05. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2668.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: MOS similarity scores - English and Spanish." ], "reasoning": "The table shows that the MOS score for 1−3 es is 2.50, which is lower than the MOS score for 0−2 (refen−refes) which is 3.05.", "statement": "The voice ID pair 1−3 es had a higher MOS score than 0−2 (refen−refes).", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: The question-answering accuracy for the “Hasty Student” who tries to answer questions without looking at the story.'.\n" }, { "role": "assistant", "content": "Choosing the longest answer performs better than random, while the most distinct answers perform worse than random on all features.Notably, always choosing the longest answer performs better (25.3%) than random (20%). We see that the answer most similar to others is likely to be correct when the representations are generic and try to capture the semantics of the sentence (Word2Vec, SkipThoughts). The most distinct answers performs worse than random on all features. Especially, TF-IDF performs worse than random since words in the question rarely appear in the answer." } ], "images": [ "scigen/train/2669.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The question-answering accuracy for the “Hasty Student” who tries to answer questions without looking at the story." ], "reasoning": "Choosing the longest answer performs better than random, while the most distinct answers perform worse than random on all features.", "description": "Notably, always choosing the longest answer performs better (25.3%) than random (20%). We see that the answer most similar to others is likely to be correct when the representations are generic and try to capture the semantics of the sentence (Word2Vec, SkipThoughts). The most distinct answers performs worse than random on all features. Especially, TF-IDF performs worse than random since words in the question rarely appear in the answer.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 4: The question-answering accuracy for the “Hasty Student” who tries to answer questions without looking at the story.' in mind, break down and resolve the question below step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhat is the performance percentage of the most similar answers within answers for the shortest answer length?\n" }, { "role": "assistant", "content": "To find the performance percentage of the most similar answers within answers for the shortest answer length, look at the 'Within answers' row under the 'shortest' column. So, the answer is {\"answer\": \"28.14\"}" } ], "images": [ "scigen/train/2669.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The question-answering accuracy for the “Hasty Student” who tries to answer questions without looking at the story." ], "reasoning": "To find the performance percentage of the most similar answers within answers for the shortest answer length, look at the 'Within answers' row under the 'shortest' column.", "question": "What is the performance percentage of the most similar answers within answers for the shortest answer length?", "answer": "So, the answer is {\"answer\": \"28.14\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe performance of the most distinct answers within answers for the longest answer length is better than random.\n" }, { "role": "assistant", "content": "The table shows that the performance of the most distinct answers within answers for the longest answer length is 19.92%, which is worse than random (20%). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2669.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The question-answering accuracy for the “Hasty Student” who tries to answer questions without looking at the story." ], "reasoning": "The table shows that the performance of the most distinct answers within answers for the longest answer length is 19.92%, which is worse than random (20%).", "statement": "The performance of the most distinct answers within answers for the longest answer length is better than random.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: Accuracy for Text-based QA. Top: results for the Searching student with cosine similarity; Middle: Convnet SSCB; and Bottom: the modified Memory Network.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "SSCB Fusion achieves the highest accuracy for plots, while MemN2N performs best for scripts, indicating the effectiveness of different methods for various sources.Cosine similarity with window. Using the plots to answer questions outperforms other sources (subtitles, scripts, and DVS) as the QAs were collected using plots and annotators often reproduce words from the plot. SkipThoughts perform much worse than both TF-IDF and Word2Vec which are closer. We suspect that while SkipThoughts are good at capturing the overall semantics of a sentence, proper nouns – names, places – are often hard to distinguish. Fig. TF-IDF and Word2Vec perform considerably well , however, we see a larger difference between the two for “Who” and “Why” questions. “Who” questions require distinguishing between names, and “Why” answers are typically long, and mean pooling destroys semantics. In fact Word2Vec performs best on “Where” questions that may use synonyms to indicate places. SkipThoughts perform best on “Why” questions where sentence semantics help improve answering. SSCB. Here, we present additional results combining all text representations (SSCB fusion) via our CNN. We split the train set into 90% train / 10% dev, such that all questions and answers of the same movie are in the same split, train our model on train and monitor performance on dev. Both val and test sets are held out. During training, we also create several model replicas and pick the ones with the best validation performance. Ignoring the case of plots, the accuracy is capped at about 30% for most modalities showing the difficulty of our dataset. The original MemN2N which trains the word embeddings along with the answering modules overfits heavily on our dataset leading to near random performance on val (∼20%). However, our modifications help in restraining the learning process. Using plot synopses, we see a performance closer to SSCB with Word2Vec features. However, in the case of longer stories, the attention mechanism in the network is able to sift through thousands of story sentences and performs well on DVS, subtitles and scripts. This shows that complex three-way scoring functions are needed to tackle such QA sources. In terms of story sources, the MemN2N performs best with scripts which contain the most information (descriptions, dialogs and speaker information)." } ], "images": [ "scigen/train/2670.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Accuracy for Text-based QA. Top: results for the Searching student with cosine similarity; Middle: Convnet SSCB; and Bottom: the modified Memory Network." ], "reasoning": "SSCB Fusion achieves the highest accuracy for plots, while MemN2N performs best for scripts, indicating the effectiveness of different methods for various sources.", "description": "Cosine similarity with window. Using the plots to answer questions outperforms other sources (subtitles, scripts, and DVS) as the QAs were collected using plots and annotators often reproduce words from the plot. SkipThoughts perform much worse than both TF-IDF and Word2Vec which are closer. We suspect that while SkipThoughts are good at capturing the overall semantics of a sentence, proper nouns – names, places – are often hard to distinguish. Fig. TF-IDF and Word2Vec perform considerably well , however, we see a larger difference between the two for “Who” and “Why” questions. “Who” questions require distinguishing between names, and “Why” answers are typically long, and mean pooling destroys semantics. In fact Word2Vec performs best on “Where” questions that may use synonyms to indicate places. SkipThoughts perform best on “Why” questions where sentence semantics help improve answering. SSCB. Here, we present additional results combining all text representations (SSCB fusion) via our CNN. We split the train set into 90% train / 10% dev, such that all questions and answers of the same movie are in the same split, train our model on train and monitor performance on dev. Both val and test sets are held out. During training, we also create several model replicas and pick the ones with the best validation performance. Ignoring the case of plots, the accuracy is capped at about 30% for most modalities showing the difficulty of our dataset. The original MemN2N which trains the word embeddings along with the answering modules overfits heavily on our dataset leading to near random performance on val (∼20%). However, our modifications help in restraining the learning process. Using plot synopses, we see a performance closer to SSCB with Word2Vec features. However, in the case of longer stories, the attention mechanism in the network is able to sift through thousands of story sentences and performs well on DVS, subtitles and scripts. This shows that complex three-way scoring functions are needed to tackle such QA sources. In terms of story sources, the MemN2N performs best with scripts which contain the most information (descriptions, dialogs and speaker information).", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 5: Accuracy for Text-based QA. Top: results for the Searching student with cosine similarity; Middle: Convnet SSCB; and Bottom: the modified Memory Network.'\nQuestion: Which method performs best for scripts?\n" }, { "role": "assistant", "content": "By comparing the accuracy values for scripts across all methods, MemN2N (w2v, linproj) has the highest accuracy at 42.3. So, the answer is {\"answer\": \"MemN2N (w2v, linproj)\"}." } ], "images": [ "scigen/train/2670.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Accuracy for Text-based QA. Top: results for the Searching student with cosine similarity; Middle: Convnet SSCB; and Bottom: the modified Memory Network." ], "reasoning": "By comparing the accuracy values for scripts across all methods, MemN2N (w2v, linproj) has the highest accuracy at 42.3.", "question": "Which method performs best for scripts?", "answer": "So, the answer is {\"answer\": \"MemN2N (w2v, linproj)\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nSSCB TFIDF performs better than Cosine Word2Vec for subtitles.\n" }, { "role": "assistant", "content": "The table shows that SSCB TFIDF has an accuracy of 27.6 for subtitles, while Cosine Word2Vec has an accuracy of 24.5, indicating that SSCB TFIDF performs better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2670.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Accuracy for Text-based QA. Top: results for the Searching student with cosine similarity; Middle: Convnet SSCB; and Bottom: the modified Memory Network." ], "reasoning": "The table shows that SSCB TFIDF has an accuracy of 27.6 for subtitles, while Cosine Word2Vec has an accuracy of 24.5, indicating that SSCB TFIDF performs better.", "statement": "SSCB TFIDF performs better than Cosine Word2Vec for subtitles.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Accuracy for Video-based QA and late fusion of Subtitle and Video scores.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Combining video and subtitles through late fusion yields better performance for the MemN2N model compared to the SSCB model.We see that learning to answer questions using video is still a hard problem with performance close to random. As visual information alone is insufficient, we also perform and experiment combining video and dialog (subtitles) through late fusion. We train the SSCB model with the visual-text embedding for subtitles and see that it yields poor performance (22.3%) compared to the fusion of all text features (27.7%). For the memory network, we answer subtitles as before using Word2Vec." } ], "images": [ "scigen/train/2671.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Accuracy for Video-based QA and late fusion of Subtitle and Video scores." ], "reasoning": "Combining video and subtitles through late fusion yields better performance for the MemN2N model compared to the SSCB model.", "description": "We see that learning to answer questions using video is still a hard problem with performance close to random. As visual information alone is insufficient, we also perform and experiment combining video and dialog (subtitles) through late fusion. We train the SSCB model with the visual-text embedding for subtitles and see that it yields poor performance (22.3%) compared to the fusion of all text features (27.7%). For the memory network, we answer subtitles as before using Word2Vec.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 6: Accuracy for Video-based QA and late fusion of Subtitle and Video scores.', solve the subsequent question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhat is the performance of the MemN2N model when using both video and subtitles?\n" }, { "role": "assistant", "content": "The table shows the performance of different models using various inputs. For the MemN2N model, the performance when using both video and subtitles is 34.2%. So, the answer is {\"answer\": \"34.2%\"}." } ], "images": [ "scigen/train/2671.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Accuracy for Video-based QA and late fusion of Subtitle and Video scores." ], "reasoning": "The table shows the performance of different models using various inputs. For the MemN2N model, the performance when using both video and subtitles is 34.2%.", "question": "What is the performance of the MemN2N model when using both video and subtitles?", "answer": "So, the answer is {\"answer\": \"34.2%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The SSCB model performs better with subtitles alone than with video alone. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe SSCB model performs better with subtitles alone than with video alone.\n" }, { "role": "assistant", "content": "The table shows that the SSCB model has a performance of 22.3% with subtitles alone and 21.6% with video alone, indicating better performance with subtitles. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2671.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Accuracy for Video-based QA and late fusion of Subtitle and Video scores." ], "reasoning": "The table shows that the SSCB model has a performance of 22.3% with subtitles alone and 21.6% with video alone, indicating better performance with subtitles.", "statement": "The SSCB model performs better with subtitles alone than with video alone.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 6: Comparisons of the results produced by MODEL-DP-L from lexical resources vs. MODEL-DP along with the baselines for the reduced dataset.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "MODEL-DP outperforms other models across all datasets, likely due to the sparsity of lexical resources used in MODEL-DP-L.Even though the quality of the hyponym-hypernym pairs from lexical resources is better compared to the pairs extracted using Hearst patterns, the coverage of target words is very low. Therefore, for a fair comparison, we prepare a reduced version of the three gold standard datasets (RD-RL, RD++-RL, FD-RL), where all the target words are present in lexical resources as well as hyponym-hypernym pairs extracted using Hearst patterns. RD-RL, RD++-RL, and FD-RL contain 74, 131, 380 target words, respectively. MODEL-DP-L uses the same compositionality score metric as MODEL-DP but in the case of MODEL-DP-L, the Poincaré embedding is learned using the hyponym-hypernym pairs extracted only from WordNet and ConceptNet combined. We see that even though MODEL-DP-L performs better than the baselines for two of the datasets, MODEL-DP gives the best result. We attribute this to the relative sparsity of lexical resources, which are seemingly not sufficient for training reliable Poincaré embeddings." } ], "images": [ "scigen/train/2672.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Comparisons of the results produced by MODEL-DP-L from lexical resources vs. MODEL-DP along with the baselines for the reduced dataset." ], "reasoning": "MODEL-DP outperforms other models across all datasets, likely due to the sparsity of lexical resources used in MODEL-DP-L.", "description": "Even though the quality of the hyponym-hypernym pairs from lexical resources is better compared to the pairs extracted using Hearst patterns, the coverage of target words is very low. Therefore, for a fair comparison, we prepare a reduced version of the three gold standard datasets (RD-RL, RD++-RL, FD-RL), where all the target words are present in lexical resources as well as hyponym-hypernym pairs extracted using Hearst patterns. RD-RL, RD++-RL, and FD-RL contain 74, 131, 380 target words, respectively. MODEL-DP-L uses the same compositionality score metric as MODEL-DP but in the case of MODEL-DP-L, the Poincaré embedding is learned using the hyponym-hypernym pairs extracted only from WordNet and ConceptNet combined. We see that even though MODEL-DP-L performs better than the baselines for two of the datasets, MODEL-DP gives the best result. We attribute this to the relative sparsity of lexical resources, which are seemingly not sufficient for training reliable Poincaré embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 6: Comparisons of the results produced by MODEL-DP-L from lexical resources vs. MODEL-DP along with the baselines for the reduced dataset.' and methodically tackle the question that follows. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model performs best on the RD-RL dataset?\n" }, { "role": "assistant", "content": "To determine the best-performing model on the RD-RL dataset, compare the scores of W2V-CBOW, MODEL-DP-L, and MODEL-DP in the RD-RL column. MODEL-DP has the highest score of 0.8288. So, the answer is {\"answer\": \"MODEL-DP\"}" } ], "images": [ "scigen/train/2672.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Comparisons of the results produced by MODEL-DP-L from lexical resources vs. MODEL-DP along with the baselines for the reduced dataset." ], "reasoning": "To determine the best-performing model on the RD-RL dataset, compare the scores of W2V-CBOW, MODEL-DP-L, and MODEL-DP in the RD-RL column. MODEL-DP has the highest score of 0.8288.", "question": "Which model performs best on the RD-RL dataset?", "answer": "So, the answer is {\"answer\": \"MODEL-DP\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nMODEL-DP-L performs better than W2V-CBOW on the FD-RL dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the scores of MODEL-DP-L and W2V-CBOW in the FD-RL column. MODEL-DP-L has a score of 0.4179, which is lower than W2V-CBOW's score of 0.4198. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2672.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Comparisons of the results produced by MODEL-DP-L from lexical resources vs. MODEL-DP along with the baselines for the reduced dataset." ], "reasoning": "To verify this statement, compare the scores of MODEL-DP-L and W2V-CBOW in the FD-RL column. MODEL-DP-L has a score of 0.4179, which is lower than W2V-CBOW's score of 0.4198.", "statement": "MODEL-DP-L performs better than W2V-CBOW on the FD-RL dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 7: Mean (μ) and Standard Deviation (σ) of Spearman’s rank correlation (ρ) of the supervised approach for FD-R and FD-F datasets over 25 random splits. We compare best baseline model (CBOW - 750 and 50 dimension) and our model (MODEL-DP-S) using both 750 and 50 dimension of CBOW vectors.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Kernel Ridge regression gives the best performance, followed by PLS regression for both FD-R and FD-F datasets, and our approach outperforms the baseline results by a large margin.Among all the regression models (respective to the best choice of the hyperparameters), Kernel Ridge regression gives the best performance while PLS regression is the second best for both the FD-R and FD-F dataset. The performance of these two best regression models for the baseline and our model (for α=0.4) In the same table, we also report the results of the evaluation on FD-F dataset using a fallback strategy for the supervised setup: here, we use a 50-dimensional zero vector of the target word or compound for which the Poincaré embedding is absent. We observe that for both the datasets (reduced and full) our approach outperforms the baseline results by a large margin. As discussed earlier, the CBOW vectors used for experiments consist of 750 dimensions. Since the number of data points in the training set is small, we also experiment with CBOW vector dimension of 50 (MODEL-DPS-50) in the supervised setup to avoid overfitting due to a large number of parameters." } ], "images": [ "scigen/train/2673.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Mean (μ) and Standard Deviation (σ) of Spearman’s rank correlation (ρ) of the supervised approach for FD-R and FD-F datasets over 25 random splits. We compare best baseline model (CBOW - 750 and 50 dimension) and our model (MODEL-DP-S) using both 750 and 50 dimension of CBOW vectors." ], "reasoning": "Kernel Ridge regression gives the best performance, followed by PLS regression for both FD-R and FD-F datasets, and our approach outperforms the baseline results by a large margin.", "description": "Among all the regression models (respective to the best choice of the hyperparameters), Kernel Ridge regression gives the best performance while PLS regression is the second best for both the FD-R and FD-F dataset. The performance of these two best regression models for the baseline and our model (for α=0.4) In the same table, we also report the results of the evaluation on FD-F dataset using a fallback strategy for the supervised setup: here, we use a 50-dimensional zero vector of the target word or compound for which the Poincaré embedding is absent. We observe that for both the datasets (reduced and full) our approach outperforms the baseline results by a large margin. As discussed earlier, the CBOW vectors used for experiments consist of 750 dimensions. Since the number of data points in the training set is small, we also experiment with CBOW vector dimension of 50 (MODEL-DPS-50) in the supervised setup to avoid overfitting due to a large number of parameters.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: 'Table 7: Mean (μ) and Standard Deviation (σ) of Spearman’s rank correlation (ρ) of the supervised approach for FD-R and FD-F datasets over 25 random splits. We compare best baseline model (CBOW - 750 and 50 dimension) and our model (MODEL-DP-S) using both 750 and 50 dimension of CBOW vectors.'\nQuestion: What is the performance of Kernel Ridge regression for the FD-F dataset with CBOW-S (750) and α=0.4?\n" }, { "role": "assistant", "content": "To find the performance of Kernel Ridge regression for the FD-F dataset with CBOW-S (750) and α=0.4, look at the FD-F Kernel Regression column under CBOW-S (750) and α=0.4. The value is 0.4083. So, the answer is {\"answer\": \"0.4083\"}." } ], "images": [ "scigen/train/2673.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Mean (μ) and Standard Deviation (σ) of Spearman’s rank correlation (ρ) of the supervised approach for FD-R and FD-F datasets over 25 random splits. We compare best baseline model (CBOW - 750 and 50 dimension) and our model (MODEL-DP-S) using both 750 and 50 dimension of CBOW vectors." ], "reasoning": "To find the performance of Kernel Ridge regression for the FD-F dataset with CBOW-S (750) and α=0.4, look at the FD-F Kernel Regression column under CBOW-S (750) and α=0.4. The value is 0.4083.", "question": "What is the performance of Kernel Ridge regression for the FD-F dataset with CBOW-S (750) and α=0.4?", "answer": "So, the answer is {\"answer\": \"0.4083\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe performance of PLS regression for the FD-R dataset with CBOW-S (50) and α=0.4 is 0.4372.\n" }, { "role": "assistant", "content": "To verify this statement, look at the FD-R PLS Regression column under CBOW-S (50) and α=0.4. The value is 0.4372, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2673.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Mean (μ) and Standard Deviation (σ) of Spearman’s rank correlation (ρ) of the supervised approach for FD-R and FD-F datasets over 25 random splits. We compare best baseline model (CBOW - 750 and 50 dimension) and our model (MODEL-DP-S) using both 750 and 50 dimension of CBOW vectors." ], "reasoning": "To verify this statement, look at the FD-R PLS Regression column under CBOW-S (50) and α=0.4. The value is 0.4372, which matches the statement.", "statement": "The performance of PLS regression for the FD-R dataset with CBOW-S (50) and α=0.4 is 0.4372.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 6: Evaluation of the proposed methods on language modeling. The number of parameter for all models is 47M.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Incorporating residual connections into a neural LM showed improved perplexity compared to traditional LSTM models, but this improvement does not necessarily translate to better performance in NMT tasks.To examine whether language modeling (LM) can benefit from the proposed method, we incorporate the residual connections into a neural LM. We use the same setting as Daniluk et al. Experiments using 4-gram LSTM for NMT showed poor performance (13.9 BLEU points for English-Chinese) which can be attributed to the difference between the LM and NMT tasks. Both tasks predict one word at a time conditioned over previous words, however, in NMT the previous target-word-inputs are not given, they have to be generated by the decoder. Thus, the output could be conditioned over previous erroneous predictions affecting in higher proportion the 4-gram LSTM model. This shows that even if a model improves language modeling, it does not necessarily improve machine translation." } ], "images": [ "scigen/train/2674.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Evaluation of the proposed methods on language modeling. The number of parameter for all models is 47M." ], "reasoning": "Incorporating residual connections into a neural LM showed improved perplexity compared to traditional LSTM models, but this improvement does not necessarily translate to better performance in NMT tasks.", "description": "To examine whether language modeling (LM) can benefit from the proposed method, we incorporate the residual connections into a neural LM. We use the same setting as Daniluk et al. Experiments using 4-gram LSTM for NMT showed poor performance (13.9 BLEU points for English-Chinese) which can be attributed to the difference between the LM and NMT tasks. Both tasks predict one word at a time conditioned over previous words, however, in NMT the previous target-word-inputs are not given, they have to be generated by the decoder. Thus, the output could be conditioned over previous erroneous predictions affecting in higher proportion the 4-gram LSTM model. This shows that even if a model improves language modeling, it does not necessarily improve machine translation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 6: Evaluation of the proposed methods on language modeling. The number of parameter for all models is 47M.' and methodically tackle the question that follows. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich system has the lowest perplexity and what is its value?\n" }, { "role": "assistant", "content": "By examining the table, the system with the lowest perplexity is 'LSTM + 4-gram Daniluk et al. (2016)' with a perplexity value of 75.9. So, the answer is {\"answer\": \"LSTM + 4-gram Daniluk et al. (2016), 75.9\"}." } ], "images": [ "scigen/train/2674.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Evaluation of the proposed methods on language modeling. The number of parameter for all models is 47M." ], "reasoning": "By examining the table, the system with the lowest perplexity is 'LSTM + 4-gram Daniluk et al. (2016)' with a perplexity value of 75.9.", "question": "Which system has the lowest perplexity and what is its value?", "answer": "So, the answer is {\"answer\": \"LSTM + 4-gram Daniluk et al. (2016), 75.9\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The LSTM + Attention Daniluk et al. (2016) system has a perplexity of 82.0. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe LSTM + Attention Daniluk et al. (2016) system has a perplexity of 82.0.\n" }, { "role": "assistant", "content": "The table shows that the LSTM + Attention Daniluk et al. (2016) system indeed has a perplexity of 82.0. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2674.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Evaluation of the proposed methods on language modeling. The number of parameter for all models is 47M." ], "reasoning": "The table shows that the LSTM + Attention Daniluk et al. (2016) system indeed has a perplexity of 82.0.", "statement": "The LSTM + Attention Daniluk et al. (2016) system has a perplexity of 82.0.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: BLEU scores for two scoring variants of the attention function of the proposed decoder.'.\n" }, { "role": "assistant", "content": "The content matching function, which relies solely on word embeddings, outperforms the content+scope function, which also considers the hidden representation of the current prediction.We now examine the two scoring functions that can be used for the self-attentive residual connections model presented in Eq. the best option is the content matching function, which depends only on the word embeddings. The content+scope function, which depends additionally on the hidden representation of the current prediction is better than the baseline but scores lower than content." } ], "images": [ "scigen/train/2675.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: BLEU scores for two scoring variants of the attention function of the proposed decoder." ], "reasoning": "The content matching function, which relies solely on word embeddings, outperforms the content+scope function, which also considers the hidden representation of the current prediction.", "description": "We now examine the two scoring functions that can be used for the self-attentive residual connections model presented in Eq. the best option is the content matching function, which depends only on the word embeddings. The content+scope function, which depends additionally on the hidden representation of the current prediction is better than the baseline but scores lower than content.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich attention function has the highest BLEU score for En-Zh translation?\nSolve the above question based on the table titled 'Table 4: BLEU scores for two scoring variants of the attention function of the proposed decoder.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for two attention functions, Content and Content+Scope, for En-Zh and Es-En translations. By comparing the BLEU scores for En-Zh, we see that Content has a higher score (24.0) than Content+Scope (23.1). So, the answer is {\"answer\": \"Content\"}." } ], "images": [ "scigen/train/2675.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: BLEU scores for two scoring variants of the attention function of the proposed decoder." ], "reasoning": "The table shows the BLEU scores for two attention functions, Content and Content+Scope, for En-Zh and Es-En translations. By comparing the BLEU scores for En-Zh, we see that Content has a higher score (24.0) than Content+Scope (23.1).", "question": "Which attention function has the highest BLEU score for En-Zh translation?", "answer": "So, the answer is {\"answer\": \"Content\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Content+Scope function has a higher BLEU score for Es-En translation than the Content function.\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for Es-En translation. The Content function has a BLEU score of 26.3, while the Content+Scope function has a BLEU score of 25.6. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2675.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: BLEU scores for two scoring variants of the attention function of the proposed decoder." ], "reasoning": "The table shows the BLEU scores for Es-En translation. The Content function has a BLEU score of 26.3, while the Content+Scope function has a BLEU score of 25.6. Therefore, the statement is incorrect.", "statement": "The Content+Scope function has a higher BLEU score for Es-En translation than the Content function.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Performance of the three systems'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The baseline model learns quickly but saturates due to untrained dialog policy, while RL has high performance but slow convergence, and hybrid-RL converges faster by exploiting state tracking information.The horizontal axis is the total number of interaction between the agent and either the user or the database. The baseline model has the fastest learning speed but its performance saturated quickly because the dialog policy was not trained together with the state tracker. So the dialog policy is not aware of the uncertainty in slot-filling and the slot-filler does not distinguish between the consequences of different wrong labels (e.g classify yes to no versus to unknown). On the other hand, although RL reaches high performance at the end of the training, it struggles in the early stages and suffers from slow convergence. This is due to that fact that correct slot-filling is a prerequisite for winning 20Q, while the reward signal has a long delayed horizon in the RL approach. Finally, the hybrid-RL approach is able to converge to the optimal solution much faster than RL due to the fact that it efficiently exploits the information in the state tracking label." } ], "images": [ "scigen/train/2676.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of the three systems" ], "reasoning": "The baseline model learns quickly but saturates due to untrained dialog policy, while RL has high performance but slow convergence, and hybrid-RL converges faster by exploiting state tracking information.", "description": "The horizontal axis is the total number of interaction between the agent and either the user or the database. The baseline model has the fastest learning speed but its performance saturated quickly because the dialog policy was not trained together with the state tracker. So the dialog policy is not aware of the uncertainty in slot-filling and the slot-filler does not distinguish between the consequences of different wrong labels (e.g classify yes to no versus to unknown). On the other hand, although RL reaches high performance at the end of the training, it struggles in the early stages and suffers from slow convergence. This is due to that fact that correct slot-filling is a prerequisite for winning 20Q, while the reward signal has a long delayed horizon in the RL approach. Finally, the hybrid-RL approach is able to converge to the optimal solution much faster than RL due to the fact that it efficiently exploits the information in the state tracking label.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: Performance of the three systems', answer the following question. Give detailed solution consisting of each step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest win rate and what is its average turn?\n" }, { "role": "assistant", "content": "The table shows that the Hybrid-RL model has the highest win rate of 90.5% and an average turn of 19.22. So, the answer is {\"answer\": \"Hybrid-RL with 19.22 average turn\"}." } ], "images": [ "scigen/train/2676.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of the three systems" ], "reasoning": "The table shows that the Hybrid-RL model has the highest win rate of 90.5% and an average turn of 19.22.", "question": "Which model has the highest win rate and what is its average turn?", "answer": "So, the answer is {\"answer\": \"Hybrid-RL with 19.22 average turn\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The RL model has a higher win rate than the baseline model but a lower average turn., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe RL model has a higher win rate than the baseline model but a lower average turn.\n" }, { "role": "assistant", "content": "The table shows that the RL model has a win rate of 85.6% and an average turn of 21.6, while the baseline model has a win rate of 68.5% and an average turn of 12.2. Therefore, the RL model has a higher win rate but a higher average turn compared to the baseline model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2676.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of the three systems" ], "reasoning": "The table shows that the RL model has a win rate of 85.6% and an average turn of 21.6, while the baseline model has a win rate of 68.5% and an average turn of 12.2. Therefore, the RL model has a higher win rate but a higher average turn compared to the baseline model.", "statement": "The RL model has a higher win rate than the baseline model but a lower average turn.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Ablation analysis on the CDR development set, in terms of F1-score (%), for intra- (Intra) and inter-sentence (Inter) pairs.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "All dependency types improve inter-sentence RE and overall performance, but self-node and adjacent sentence edges slightly harm intra-sentence relations, and coreference does not affect intra-sentence pairs.We perform ablation analysis on the CDR dataset by separating the development set to intra- and inter-sentence pairs (approximately 70% and 30% of pairs, respectively). In general, all dependency types have positive effects on inter-sentence RE and the overall performance, although self-node and adjacent sentence edges slightly harm the performance of intra-sentence relations. Additionally, coreference does not affect intra-sentence pairs." } ], "images": [ "scigen/train/2677.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation analysis on the CDR development set, in terms of F1-score (%), for intra- (Intra) and inter-sentence (Inter) pairs." ], "reasoning": "All dependency types improve inter-sentence RE and overall performance, but self-node and adjacent sentence edges slightly harm intra-sentence relations, and coreference does not affect intra-sentence pairs.", "description": "We perform ablation analysis on the CDR dataset by separating the development set to intra- and inter-sentence pairs (approximately 70% and 30% of pairs, respectively). In general, all dependency types have positive effects on inter-sentence RE and the overall performance, although self-node and adjacent sentence edges slightly harm the performance of intra-sentence relations. Additionally, coreference does not affect intra-sentence pairs.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: Table 3: Ablation analysis on the CDR development set, in terms of F1-score (%), for intra- (Intra) and inter-sentence (Inter) pairs.\nQuestion: Which dependency type has the highest intra-sentence performance?\n" }, { "role": "assistant", "content": "By comparing the intra-sentence performance scores, the highest value is 63.99, which corresponds to the 'Adjacent sentence' dependency type. So, the answer is {\"answer\": \"Adjacent sentence\"}" } ], "images": [ "scigen/train/2677.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation analysis on the CDR development set, in terms of F1-score (%), for intra- (Intra) and inter-sentence (Inter) pairs." ], "reasoning": "By comparing the intra-sentence performance scores, the highest value is 63.99, which corresponds to the 'Adjacent sentence' dependency type.", "question": "Which dependency type has the highest intra-sentence performance?", "answer": "So, the answer is {\"answer\": \"Adjacent sentence\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Coreference dependency type has a positive effect on intra-sentence pairs. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nCoreference dependency type has a positive effect on intra-sentence pairs.\n" }, { "role": "assistant", "content": "The description states that coreference does not affect intra-sentence pairs, and the table shows that the intra-sentence score for coreference is 63.27, which is not the highest. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2677.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation analysis on the CDR development set, in terms of F1-score (%), for intra- (Intra) and inter-sentence (Inter) pairs." ], "reasoning": "The description states that coreference does not affect intra-sentence pairs, and the table shows that the intra-sentence score for coreference is 63.27, which is not the highest.", "statement": "Coreference dependency type has a positive effect on intra-sentence pairs.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Performance on the CDR and CHR test sets in comparison with the state-of-the-art.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "GCNN outperforms baseline models in both datasets but is 1.6 percentage points lower than gu2017 in the CDR dataset.We report the performance of state-of-the-art models without any additional enhancements, such as joint training with NER, model ensembling and heuristic rules, to avoid any effects from the enhancements in the comparison. We observe that the GCNN outperforms the baseline models (CNN-RE/RNN-RE) in both datasets. However, in the CDR dataset, the performance of GCNN is 1.6 percentage points lower than the best performing system of gu2017. In fact, gu2017 incorporates two separate neural and feature-based models for intra- and inter-sentence pairs, respectively, whereas we utilize a single model for both pairs. Additionally, GCNN performs comparably to the second state-of-the-art neural model Li2018, which requires a two-step process for mention aggregation unlike our unified approach." } ], "images": [ "scigen/train/2678.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance on the CDR and CHR test sets in comparison with the state-of-the-art." ], "reasoning": "GCNN outperforms baseline models in both datasets but is 1.6 percentage points lower than gu2017 in the CDR dataset.", "description": "We report the performance of state-of-the-art models without any additional enhancements, such as joint training with NER, model ensembling and heuristic rules, to avoid any effects from the enhancements in the comparison. We observe that the GCNN outperforms the baseline models (CNN-RE/RNN-RE) in both datasets. However, in the CDR dataset, the performance of GCNN is 1.6 percentage points lower than the best performing system of gu2017. In fact, gu2017 incorporates two separate neural and feature-based models for intra- and inter-sentence pairs, respectively, whereas we utilize a single model for both pairs. Additionally, GCNN performs comparably to the second state-of-the-art neural model Li2018, which requires a two-step process for mention aggregation unlike our unified approach.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Performance on the CDR and CHR test sets in comparison with the state-of-the-art.', answer the following question. Give detailed solution consisting of each step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nHow does the performance of GCNN compare to the best performing system in the CDR dataset?\n" }, { "role": "assistant", "content": "The table shows that GCNN has an F1 score of 58.6% in the CDR dataset, while gu2017 has an F1 score of 60.2%. The description states that GCNN is 1.6 percentage points lower than gu2017. So, the answer is {\"answer\": \"1.6 percentage points lower\"}" } ], "images": [ "scigen/train/2678.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance on the CDR and CHR test sets in comparison with the state-of-the-art." ], "reasoning": "The table shows that GCNN has an F1 score of 58.6% in the CDR dataset, while gu2017 has an F1 score of 60.2%. The description states that GCNN is 1.6 percentage points lower than gu2017.", "question": "How does the performance of GCNN compare to the best performing system in the CDR dataset?", "answer": "So, the answer is {\"answer\": \"1.6 percentage points lower\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis GCNN has the highest F1 score in the CHR dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nGCNN has the highest F1 score in the CHR dataset.\n" }, { "role": "assistant", "content": "The table shows that GCNN has an F1 score of 87.5% in the CHR dataset, which is higher than the F1 scores of CNN-RE (84.1%) and RNN-RE (86.4%). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2678.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance on the CDR and CHR test sets in comparison with the state-of-the-art." ], "reasoning": "The table shows that GCNN has an F1 score of 87.5% in the CHR dataset, which is higher than the F1 scores of CNN-RE (84.1%) and RNN-RE (86.4%).", "statement": "GCNN has the highest F1 score in the CHR dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 7: Evaluation results for Question 1.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Humans performed decently in identifying merge classes but struggled significantly with non-merge cases, indicating difficulty in merging topics.We observe that humans performed decently with majority voting accuracy of 0.75 for identification of merge classes. For the non-merge cases, humans performed very poorly (worst performance for the neighborhood class). These results indicate that the task of merging topics is difficult for Quora users as they seem to be confused by the non-merge cases in which they performed very poorly. Since the number of participants answering Question 1 for all the questions is 3, the micro-averaging and macro-averaging yields the same result. Overall, we observe that the regular Quora users find it difficult to judge if two given topics should merge or not, and in deciding the direction of merge." } ], "images": [ "scigen/train/2679.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Evaluation results for Question 1." ], "reasoning": "Humans performed decently in identifying merge classes but struggled significantly with non-merge cases, indicating difficulty in merging topics.", "description": "We observe that humans performed decently with majority voting accuracy of 0.75 for identification of merge classes. For the non-merge cases, humans performed very poorly (worst performance for the neighborhood class). These results indicate that the task of merging topics is difficult for Quora users as they seem to be confused by the non-merge cases in which they performed very poorly. Since the number of participants answering Question 1 for all the questions is 3, the micro-averaging and macro-averaging yields the same result. Overall, we observe that the regular Quora users find it difficult to judge if two given topics should merge or not, and in deciding the direction of merge.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 7: Evaluation results for Question 1.\n\nQuestion: What was the majority voting accuracy for Question 1?\n" }, { "role": "assistant", "content": "The table shows the majority voting accuracy for Question 1 as 0.54. So, the answer is {\"answer\": \"0.54\"}." } ], "images": [ "scigen/train/2679.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Evaluation results for Question 1." ], "reasoning": "The table shows the majority voting accuracy for Question 1 as 0.54.", "question": "What was the majority voting accuracy for Question 1?", "answer": "So, the answer is {\"answer\": \"0.54\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nHumans performed better on Question 2 than on Question 1 according to the majority voting accuracy.\n" }, { "role": "assistant", "content": "The table shows that the majority voting accuracy for Question 1 is 0.54 and for Question 2 is 0.40, indicating that humans performed better on Question 1. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2679.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Evaluation results for Question 1." ], "reasoning": "The table shows that the majority voting accuracy for Question 1 is 0.54 and for Question 2 is 0.40, indicating that humans performed better on Question 1.", "statement": "Humans performed better on Question 2 than on Question 1 according to the majority voting accuracy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Results of ablation experiments.'.\n" }, { "role": "assistant", "content": "The combination of question content and ontology features produces results close to the best, with the presence of topic name in the question text being the most discriminatory feature.Ablation experiments: To understand the importance of individual feature types, we perform ablation experiments by considering different combination of feature types for the classification. The combination of question content and the ontology feature types seems to produce results, very close to the best results, obtained by taking all features. Feature importance The most discriminatory feature is the presence of topic name in the question text of the other topic. Also Quora topic ontology features have higher ranks." } ], "images": [ "scigen/train/2680.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of ablation experiments." ], "reasoning": "The combination of question content and ontology features produces results close to the best, with the presence of topic name in the question text being the most discriminatory feature.", "description": "Ablation experiments: To understand the importance of individual feature types, we perform ablation experiments by considering different combination of feature types for the classification. The combination of question content and the ontology feature types seems to produce results, very close to the best results, obtained by taking all features. Feature importance The most discriminatory feature is the presence of topic name in the question text of the other topic. Also Quora topic ontology features have higher ranks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 4: Results of ablation experiments.', unfold the question presented below, detailing every step of your calculation. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich feature combination has the highest precision?\n" }, { "role": "assistant", "content": "By examining the table, the feature combination 'Question Content + External' has the highest precision value of 0.937. So, the answer is {\"answer\": \"Question Content + External\"}." } ], "images": [ "scigen/train/2680.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of ablation experiments." ], "reasoning": "By examining the table, the feature combination 'Question Content + External' has the highest precision value of 0.937.", "question": "Which feature combination has the highest precision?", "answer": "So, the answer is {\"answer\": \"Question Content + External\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The combination of Ontology and External features has the lowest recall., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe combination of Ontology and External features has the lowest recall.\n" }, { "role": "assistant", "content": "The table shows that the combination of Ontology and External features has a recall value of 0.056, which is the lowest among all combinations. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2680.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of ablation experiments." ], "reasoning": "The table shows that the combination of Ontology and External features has a recall value of 0.056, which is the lowest among all combinations.", "statement": "The combination of Ontology and External features has the lowest recall.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 6: Results for predicting the direction of topic merge. For the two-step approach the best five results are only shown.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The Support Vector Machine classifier achieves the highest accuracy at 88.857%, with high precision and recall, slightly outperforming Random Forest and Naive Bayes classifiers.Predicting the direction of topic merge: We use the above discriminating factors in a standard classification framework to predict the direction of topic merge. We use Support Vector Machine (SVM), Random Forest and Naive Bayes classifiers available in Weka Toolkit for classification. We achieve 88.857% accuracy with high precision and recall. From the table, we can observe that SVM achieves a slightly better performance than the other classifiers." } ], "images": [ "scigen/train/2681.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results for predicting the direction of topic merge. For the two-step approach the best five results are only shown." ], "reasoning": "The Support Vector Machine classifier achieves the highest accuracy at 88.857%, with high precision and recall, slightly outperforming Random Forest and Naive Bayes classifiers.", "description": "Predicting the direction of topic merge: We use the above discriminating factors in a standard classification framework to predict the direction of topic merge. We use Support Vector Machine (SVM), Random Forest and Naive Bayes classifiers available in Weka Toolkit for classification. We achieve 88.857% accuracy with high precision and recall. From the table, we can observe that SVM achieves a slightly better performance than the other classifiers.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 6: Results for predicting the direction of topic merge. For the two-step approach the best five results are only shown.', solve this question and give detail solutions. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich classifier has the highest F-Score and what is its value?\n" }, { "role": "assistant", "content": "To find the classifier with the highest F-Score, compare the F-Score values of all classifiers. The Support Vector Machine has the highest F-Score of 0.898. So, the answer is {\"answer\": \"Support Vector Machine with 0.898\"}." } ], "images": [ "scigen/train/2681.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results for predicting the direction of topic merge. For the two-step approach the best five results are only shown." ], "reasoning": "To find the classifier with the highest F-Score, compare the F-Score values of all classifiers. The Support Vector Machine has the highest F-Score of 0.898.", "question": "Which classifier has the highest F-Score and what is its value?", "answer": "So, the answer is {\"answer\": \"Support Vector Machine with 0.898\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The Naive Bayes classifier has the lowest accuracy among the three classifiers., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Naive Bayes classifier has the lowest accuracy among the three classifiers.\n" }, { "role": "assistant", "content": "By comparing the accuracy values of all classifiers, it is evident that Naive Bayes has the lowest accuracy at 87.546%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2681.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results for predicting the direction of topic merge. For the two-step approach the best five results are only shown." ], "reasoning": "By comparing the accuracy values of all classifiers, it is evident that Naive Bayes has the lowest accuracy at 87.546%.", "statement": "The Naive Bayes classifier has the lowest accuracy among the three classifiers.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 7: Evaluation results for Question 1.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Humans performed well in identifying merge cases with a majority voting accuracy of 0.75, but struggled significantly with non-merge cases, especially the neighborhood class.We observe that humans performed decently with majority voting accuracy of 0.75 for identification of merge classes. For the non-merge cases, humans performed very poorly (worst performance for the neighborhood class). These results indicate that the task of merging topics is difficult for Quora users as they seem to be confused by the non-merge cases in which they performed very poorly. Since the number of participants answering Question 1 for all the questions is 3, the micro-averaging and macro-averaging yields the same result. Overall, we observe that the regular Quora users find it difficult to judge if two given topics should merge or not, and in deciding the direction of merge." } ], "images": [ "scigen/train/2682.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Evaluation results for Question 1." ], "reasoning": "Humans performed well in identifying merge cases with a majority voting accuracy of 0.75, but struggled significantly with non-merge cases, especially the neighborhood class.", "description": "We observe that humans performed decently with majority voting accuracy of 0.75 for identification of merge classes. For the non-merge cases, humans performed very poorly (worst performance for the neighborhood class). These results indicate that the task of merging topics is difficult for Quora users as they seem to be confused by the non-merge cases in which they performed very poorly. Since the number of participants answering Question 1 for all the questions is 3, the micro-averaging and macro-averaging yields the same result. Overall, we observe that the regular Quora users find it difficult to judge if two given topics should merge or not, and in deciding the direction of merge.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 7: Evaluation results for Question 1.', answer the question below by showing each progressive step toward the answer. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the accuracy of humans in identifying the neighbor class using majority voting?\n" }, { "role": "assistant", "content": "The table shows the accuracy of humans in identifying different classes using various evaluation methods. For the neighbor class, the majority voting accuracy is listed. So, the answer is {\"answer\": \"0.31\"}." } ], "images": [ "scigen/train/2682.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Evaluation results for Question 1." ], "reasoning": "The table shows the accuracy of humans in identifying different classes using various evaluation methods. For the neighbor class, the majority voting accuracy is listed.", "question": "What is the accuracy of humans in identifying the neighbor class using majority voting?", "answer": "So, the answer is {\"answer\": \"0.31\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nHumans performed better in identifying merge cases than unmerge cases using majority voting.\n" }, { "role": "assistant", "content": "The table shows that the majority voting accuracy for merge cases is 0.75, while for unmerge cases it is 0.35, indicating better performance in identifying merge cases. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2682.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Evaluation results for Question 1." ], "reasoning": "The table shows that the majority voting accuracy for merge cases is 0.75, while for unmerge cases it is 0.35, indicating better performance in identifying merge cases.", "statement": "Humans performed better in identifying merge cases than unmerge cases using majority voting.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 8: The accuracy (ACC), expected wins (EW), and Quizbowl score (Score) of each buzzer on the validation set. Both mlp and rnn outperform the static threshold baseline by a large margin, but there is still a considerable gap from the optimal buzzer.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Both mlp and rnn models significantly outperform the static threshold baseline, but rnn still lags behind the optimal buzzer.Both mlp and rnn buzzers outperform the static threshold baseline by a large margin, but there is still a considerable gap between rnn and the optimal buzzer." } ], "images": [ "scigen/train/2683.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: The accuracy (ACC), expected wins (EW), and Quizbowl score (Score) of each buzzer on the validation set. Both mlp and rnn outperform the static threshold baseline by a large margin, but there is still a considerable gap from the optimal buzzer." ], "reasoning": "Both mlp and rnn models significantly outperform the static threshold baseline, but rnn still lags behind the optimal buzzer.", "description": "Both mlp and rnn buzzers outperform the static threshold baseline by a large margin, but there is still a considerable gap between rnn and the optimal buzzer.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: Table 8: The accuracy (ACC), expected wins (EW), and Quizbowl score (Score) of each buzzer on the validation set. Both mlp and rnn outperform the static threshold baseline by a large margin, but there is still a considerable gap from the optimal buzzer.\nQuestion: Which model has the highest score and how does it compare to the rnn model?\n" }, { "role": "assistant", "content": "The optimal model has the highest score of 2.19, which is higher than the rnn model's score of -1.01. So, the answer is {\"answer\": \"Optimal\"}." } ], "images": [ "scigen/train/2683.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: The accuracy (ACC), expected wins (EW), and Quizbowl score (Score) of each buzzer on the validation set. Both mlp and rnn outperform the static threshold baseline by a large margin, but there is still a considerable gap from the optimal buzzer." ], "reasoning": "The optimal model has the highest score of 2.19, which is higher than the rnn model's score of -1.01.", "question": "Which model has the highest score and how does it compare to the rnn model?", "answer": "So, the answer is {\"answer\": \"Optimal\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe mlp model has a higher score than the threshold model.\n" }, { "role": "assistant", "content": "The table shows that the mlp model has a score of -2.31, which is higher than the threshold model's score of -9.98. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2683.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: The accuracy (ACC), expected wins (EW), and Quizbowl score (Score) of each buzzer on the validation set. Both mlp and rnn outperform the static threshold baseline by a large margin, but there is still a considerable gap from the optimal buzzer." ], "reasoning": "The table shows that the mlp model has a score of -2.31, which is higher than the threshold model's score of -9.98.", "statement": "The mlp model has a higher score than the threshold model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 6: Best model is bolded. Each model is run five times with different random initializations. For the accuracy metrics we show the top score, mean score, standard deviation of scores. Expected win scores and training time are from the top model. The rnn models achieve the best results, but compared to dan models take significantly longer to train for 2.3% absolute improvement. Using Wikipedia as training data often helps, but is not a universal trend.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Using Wikipedia as additional training data improves model accuracy, especially towards the end of questions where giveaway clues are present.Although Wikipedia text is of a different distribution than questions, using it as additional training data is better than too little training data for specific answers. For example, there are zero training questions about Rocket, but a test question refers to Konstantin Tsiolkovksy’s development of rocket theory. However, this fact is mentioned on the Wikipedia page on rockets so with Wikipedia as training data answering this question is plausible. Each Quizbowl answer is paired with a Wikipedia page so we use the text of that page as additional training data. This section shows how we incorporate Wikipedia as training data in each model. We report an our primary version of ew (eager), and an alternative version of ew (stable). All models struggle with answering at the start of the question with the best score achieving only about 11% accuracy. This is unsurprising as the first sentence contains the most difficult clue, and they are difficult for even the best human players. The story is quite different at the end of the question since nearly all questions have giveaway clues. These clues are specifically designed to make the question extremely easy to answer so accuracy here should be near perfect. Despite this the best model achieves only 61% accuracy so there is much room for improvement. The ew scores for all models fall in between, but trend closer towards end accuracies. The difference in eager versus stable ew scores indicates that many although models can answer early correctly they often change their answer." } ], "images": [ "scigen/train/2684.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Best model is bolded. Each model is run five times with different random initializations. For the accuracy metrics we show the top score, mean score, standard deviation of scores. Expected win scores and training time are from the top model. The rnn models achieve the best results, but compared to dan models take significantly longer to train for 2.3% absolute improvement. Using Wikipedia as training data often helps, but is not a universal trend." ], "reasoning": "Using Wikipedia as additional training data improves model accuracy, especially towards the end of questions where giveaway clues are present.", "description": "Although Wikipedia text is of a different distribution than questions, using it as additional training data is better than too little training data for specific answers. For example, there are zero training questions about Rocket, but a test question refers to Konstantin Tsiolkovksy’s development of rocket theory. However, this fact is mentioned on the Wikipedia page on rockets so with Wikipedia as training data answering this question is plausible. Each Quizbowl answer is paired with a Wikipedia page so we use the text of that page as additional training data. This section shows how we incorporate Wikipedia as training data in each model. We report an our primary version of ew (eager), and an alternative version of ew (stable). All models struggle with answering at the start of the question with the best score achieving only about 11% accuracy. This is unsurprising as the first sentence contains the most difficult clue, and they are difficult for even the best human players. The story is quite different at the end of the question since nearly all questions have giveaway clues. These clues are specifically designed to make the question extremely easy to answer so accuracy here should be near perfect. Despite this the best model achieves only 61% accuracy so there is much room for improvement. The ew scores for all models fall in between, but trend closer towards end accuracies. The difference in eager versus stable ew scores indicates that many although models can answer early correctly they often change their answer.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: Best model is bolded. Each model is run five times with different random initializations. For the accuracy metrics we show the top score, mean score, standard deviation of scores. Expected win scores and training time are from the top model. The rnn models achieve the best results, but compared to dan models take significantly longer to train for 2.3% absolute improvement. Using Wikipedia as training data often helps, but is not a universal trend.\nQuestion: Which model achieved the highest accuracy at the end of the question when using Wikipedia as training data?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the 'Accuracy (%) End' values for models using Wikipedia, the rnn model with Wikipedia achieved the highest accuracy of 61.0%. So, the answer is {\"answer\": \"rnn\"}" } ], "images": [ "scigen/train/2684.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Best model is bolded. Each model is run five times with different random initializations. For the accuracy metrics we show the top score, mean score, standard deviation of scores. Expected win scores and training time are from the top model. The rnn models achieve the best results, but compared to dan models take significantly longer to train for 2.3% absolute improvement. Using Wikipedia as training data often helps, but is not a universal trend." ], "reasoning": "By comparing the 'Accuracy (%) End' values for models using Wikipedia, the rnn model with Wikipedia achieved the highest accuracy of 61.0%.", "question": "Which model achieved the highest accuracy at the end of the question when using Wikipedia as training data?", "answer": "So, the answer is {\"answer\": \"rnn\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe ir model without Wikipedia achieved higher accuracy at the start of the question than the dan model without Wikipedia.\n" }, { "role": "assistant", "content": "The table shows that the ir model without Wikipedia has a 'Top' accuracy of 6.37% at the start, while the dan model without Wikipedia has a 'Top' accuracy of 8.00%. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2684.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Best model is bolded. Each model is run five times with different random initializations. For the accuracy metrics we show the top score, mean score, standard deviation of scores. Expected win scores and training time are from the top model. The rnn models achieve the best results, but compared to dan models take significantly longer to train for 2.3% absolute improvement. Using Wikipedia as training data often helps, but is not a universal trend." ], "reasoning": "The table shows that the ir model without Wikipedia has a 'Top' accuracy of 6.37% at the start, while the dan model without Wikipedia has a 'Top' accuracy of 8.00%. Therefore, the statement is refuted.", "statement": "The ir model without Wikipedia achieved higher accuracy at the start of the question than the dan model without Wikipedia.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4: Average WikiAnn NER F1 scores on the development sets of 265 languages with shared vocabularies of different size.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The shared BPE vocabulary and embeddings allow training a single NER model for 265 languages, with a maximum of 3000 instances per language to reduce training time and disparity between dataset sizes.The shared BPE vocabulary and corresponding byte-pair embeddings allow training a single NER model for all 265 languages. To do so, we first encode WikiAnn in all languages using the shared BPE vocabulary and then train a single multilingual NER model in the same fashion as a monolingual model. As the vocabulary size has a large effect on the distribution of BPE symbol lengths To reduce the disparity between dataset sizes of different languages, and to keep training time short, we limit training data to a maximum of 3000 instances per language. without further finetuning -finetune show a strong improvement in low-resource languages (89.7 vs. 81.9 with +someshape), while performance degrades drastically on high-resource languages. In multilingual training, certain aspects of the task at hand, such as tag distribution and BIO constraints have to be learned only once, while they have to be separately learned on each language in monolingual training. Furthermore, multilingual training may prevent overfitting to biases in small monolingual datasets, such as a skewed tag distributions. Before training, distinct clusters of subword embeddings from the same language are visible. After training, some of these clusters are more spread out and show more overlap, which indicates that some embeddings from different languages appear to have moved “closer together”, as one would expect embeddings of semantically-related words to do. However, the overall structure of the embedding space remains largely unchanged. The model maintains language-specific subspaces and does not appear to create an interlingual semantic space which could facilitate cross-lingual transfer." } ], "images": [ "scigen/train/2685.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Average WikiAnn NER F1 scores on the development sets of 265 languages with shared vocabularies of different size." ], "reasoning": "The shared BPE vocabulary and embeddings allow training a single NER model for 265 languages, with a maximum of 3000 instances per language to reduce training time and disparity between dataset sizes.", "description": "The shared BPE vocabulary and corresponding byte-pair embeddings allow training a single NER model for all 265 languages. To do so, we first encode WikiAnn in all languages using the shared BPE vocabulary and then train a single multilingual NER model in the same fashion as a monolingual model. As the vocabulary size has a large effect on the distribution of BPE symbol lengths To reduce the disparity between dataset sizes of different languages, and to keep training time short, we limit training data to a maximum of 3000 instances per language. without further finetuning -finetune show a strong improvement in low-resource languages (89.7 vs. 81.9 with +someshape), while performance degrades drastically on high-resource languages. In multilingual training, certain aspects of the task at hand, such as tag distribution and BIO constraints have to be learned only once, while they have to be separately learned on each language in monolingual training. Furthermore, multilingual training may prevent overfitting to biases in small monolingual datasets, such as a skewed tag distributions. Before training, distinct clusters of subword embeddings from the same language are visible. After training, some of these clusters are more spread out and show more overlap, which indicates that some embeddings from different languages appear to have moved “closer together”, as one would expect embeddings of semantically-related words to do. However, the overall structure of the embedding space remains largely unchanged. The model maintains language-specific subspaces and does not appear to create an interlingual semantic space which could facilitate cross-lingual transfer.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Average WikiAnn NER F1 scores on the development sets of 265 languages with shared vocabularies of different size.\nQuestion: What is the Dev. F1 score for a BPE vocabulary size of 320k?\nSolve the above question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows the Dev. F1 scores for different BPE vocabulary sizes. For a BPE vocabulary size of 320k, the Dev. F1 score is 88.7. So, the answer is {\"answer\": \"88.7\"}." } ], "images": [ "scigen/train/2685.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Average WikiAnn NER F1 scores on the development sets of 265 languages with shared vocabularies of different size." ], "reasoning": "The table shows the Dev. F1 scores for different BPE vocabulary sizes. For a BPE vocabulary size of 320k, the Dev. F1 score is 88.7.", "question": "What is the Dev. F1 score for a BPE vocabulary size of 320k?", "answer": "So, the answer is {\"answer\": \"88.7\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Dev. F1 score increases as the BPE vocabulary size increases.\n" }, { "role": "assistant", "content": "The table shows Dev. F1 scores for BPE vocabulary sizes of 100k, 320k, and 1000k. The scores are 87.1, 88.7, and 89.3 respectively, indicating an increase in Dev. F1 score with larger BPE vocabulary sizes. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2685.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Average WikiAnn NER F1 scores on the development sets of 265 languages with shared vocabularies of different size." ], "reasoning": "The table shows Dev. F1 scores for BPE vocabulary sizes of 100k, 320k, and 1000k. The scores are 87.1, 88.7, and 89.3 respectively, indicating an increase in Dev. F1 score with larger BPE vocabulary sizes.", "statement": "The Dev. F1 score increases as the BPE vocabulary size increases.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: NER results on WikiAnn. The first row shows macro-averaged F1 scores (%) for all 265 languages in the Intersect. 1 setting. Rows two to four break down scores for 188 low-resource languages (<10k instances), 48 medium-resource languages (10k to 100k instances), and 29 high-resource languages (>100k instances).', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "FastText performs worse than Pan et al. by 4.1 F1 points on average, while BPEmb combined with character embeddings achieves the highest scores for medium- and high-resource languages.In this section, we evaluate FastText and BPEmb on NER in 265 languages. As baseline, we compare to Pan et al. Averaged over all languages, FastText performs 4.1 F1 points worse than this baseline. BPEmb is on par overall, with higher scores for medium- and high-resource languages, but a worse F1 score on low-resource languages. BPEmb combined with character embeddings (+char) yields the overall highest scores for medium- and high-resource languages among monolingual methods. Word shape. that on the one hand reduces vocabulary size, but on the other loses information in writing systems with a distinction between upper and lower case letters. For example, Magnus is mapped to the word shape Aa and G.M. to A.A. These results show that multilingual training followed by monolingual finetuning is an effective method for low-resource sequence tagging." } ], "images": [ "scigen/train/2686.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: NER results on WikiAnn. The first row shows macro-averaged F1 scores (%) for all 265 languages in the Intersect. 1 setting. Rows two to four break down scores for 188 low-resource languages (<10k instances), 48 medium-resource languages (10k to 100k instances), and 29 high-resource languages (>100k instances)." ], "reasoning": "FastText performs worse than Pan et al. by 4.1 F1 points on average, while BPEmb combined with character embeddings achieves the highest scores for medium- and high-resource languages.", "description": "In this section, we evaluate FastText and BPEmb on NER in 265 languages. As baseline, we compare to Pan et al. Averaged over all languages, FastText performs 4.1 F1 points worse than this baseline. BPEmb is on par overall, with higher scores for medium- and high-resource languages, but a worse F1 score on low-resource languages. BPEmb combined with character embeddings (+char) yields the overall highest scores for medium- and high-resource languages among monolingual methods. Word shape. that on the one hand reduces vocabulary size, but on the other loses information in writing systems with a distinction between upper and lower case letters. For example, Magnus is mapped to the word shape Aa and G.M. to A.A. These results show that multilingual training followed by monolingual finetuning is an effective method for low-resource sequence tagging.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 3: NER results on WikiAnn. The first row shows macro-averaged F1 scores (%) for all 265 languages in the Intersect. 1 setting. Rows two to four break down scores for 188 low-resource languages (<10k instances), 48 medium-resource languages (10k to 100k instances), and 29 high-resource languages (>100k instances).\nQuestion: Which method achieves the highest F1 score for medium-resource languages?\n" }, { "role": "assistant", "content": "The table shows that BPEmb combined with character embeddings (+char) achieves the highest F1 score for medium-resource languages with a score of 94.1. So, the answer is {\"answer\": \"BPEmb +char\"}." } ], "images": [ "scigen/train/2686.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: NER results on WikiAnn. The first row shows macro-averaged F1 scores (%) for all 265 languages in the Intersect. 1 setting. Rows two to four break down scores for 188 low-resource languages (<10k instances), 48 medium-resource languages (10k to 100k instances), and 29 high-resource languages (>100k instances)." ], "reasoning": "The table shows that BPEmb combined with character embeddings (+char) achieves the highest F1 score for medium-resource languages with a score of 94.1.", "question": "Which method achieves the highest F1 score for medium-resource languages?", "answer": "So, the answer is {\"answer\": \"BPEmb +char\"}." }, { "messages": [ { "role": "user", "content": "BPEmb combined with character embeddings achieves the highest F1 score for low-resource languages. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nBPEmb combined with character embeddings achieves the highest F1 score for low-resource languages.\n" }, { "role": "assistant", "content": "The table shows that BPEmb combined with character embeddings (+char) achieves an F1 score of 81.4 for low-resource languages, but MultiBPEmb+char+finetune achieves a higher score of 90.4. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2686.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: NER results on WikiAnn. The first row shows macro-averaged F1 scores (%) for all 265 languages in the Intersect. 1 setting. Rows two to four break down scores for 188 low-resource languages (<10k instances), 48 medium-resource languages (10k to 100k instances), and 29 high-resource languages (>100k instances)." ], "reasoning": "The table shows that BPEmb combined with character embeddings (+char) achieves an F1 score of 81.4 for low-resource languages, but MultiBPEmb+char+finetune achieves a higher score of 90.4.", "statement": "BPEmb combined with character embeddings achieves the highest F1 score for low-resource languages.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: NER F1 scores for the 101 WikiAnn languages supported by all evaluated methods.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Multilingual BERT combined with monolingual BPEmb and character embeddings achieves strong results, but MultiBPEmb, trained on multilingual data and finetuned for each language, performs best overall.Multilingual BERT outperforms the Pan17 baseline and shows strong results in comparison to monolingual BPEmb. The combination of multilingual BERT, monolingual BPEmb, and character embeddings is best overall (92.0) among models trained only on monolingual NER data. However, this ensemble of contextual and non-contextual subword embeddings is inferior to MultiBPEmb (93.2), which was first trained on multilingual data from all languages collectively, and then separately finetuned to each language." } ], "images": [ "scigen/train/2687.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: NER F1 scores for the 101 WikiAnn languages supported by all evaluated methods." ], "reasoning": "Multilingual BERT combined with monolingual BPEmb and character embeddings achieves strong results, but MultiBPEmb, trained on multilingual data and finetuned for each language, performs best overall.", "description": "Multilingual BERT outperforms the Pan17 baseline and shows strong results in comparison to monolingual BPEmb. The combination of multilingual BERT, monolingual BPEmb, and character embeddings is best overall (92.0) among models trained only on monolingual NER data. However, this ensemble of contextual and non-contextual subword embeddings is inferior to MultiBPEmb (93.2), which was first trained on multilingual data from all languages collectively, and then separately finetuned to each language.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: 'Table 5: NER F1 scores for the 101 WikiAnn languages supported by all evaluated methods.'\nQuestion: Which model achieves the highest score for Med-res. ∩ BERT?\n" }, { "role": "assistant", "content": "The table shows that for Med-res. ∩ BERT, the highest score is achieved by MultiBPEmb + char + finetune with a score of 95.1. So, the answer is {\"answer\": \"MultiBPEmb + char + finetune\"}" } ], "images": [ "scigen/train/2687.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: NER F1 scores for the 101 WikiAnn languages supported by all evaluated methods." ], "reasoning": "The table shows that for Med-res. ∩ BERT, the highest score is achieved by MultiBPEmb + char + finetune with a score of 95.1.", "question": "Which model achieves the highest score for Med-res. ∩ BERT?", "answer": "So, the answer is {\"answer\": \"MultiBPEmb + char + finetune\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: BERT + char achieves the highest score for High-res. ∩ BERT., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nBERT + char achieves the highest score for High-res. ∩ BERT.\n" }, { "role": "assistant", "content": "The table shows that for High-res. ∩ BERT, the highest score is achieved by BPEmb + char with a score of 93.6, not BERT + char. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2687.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: NER F1 scores for the 101 WikiAnn languages supported by all evaluated methods." ], "reasoning": "The table shows that for High-res. ∩ BERT, the highest score is achieved by BPEmb + char with a score of 93.6, not BERT + char.", "statement": "BERT + char achieves the highest score for High-res. ∩ BERT.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: BLEU scores obtained on the newstest2013 test set. Sentences is the number of sentences used to train the SMT systems. The Europarl row is the baseline SMT system trained on 500k sentences pairs from the Europarl corpus.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The BLEU score improves significantly when using the full extracted parallel corpus compared to using only the top 500k sentence pairs, indicating the quality of the top-ranked sentence pairs.To train the phrase-based translation systems Koehn et al. we use the Moses toolkit. We train two additional SMT systems by augmenting the training set with the extracted sentence pairs from the BiRNN and baseline models. Each system uses newstest2012 as tuning set and is evaluated on the English-French translation quality on newstest2013. Since both extracted parallel corpus are not on the same order of magnitude, we sorted the sentence pairs by similarity score in descending order and used the top 500k sentence pairs to train new SMT systems with training sets of equal size. When using the full extracted parallel corpus, we see that our approach improves the BLEU score over the baseline SMT system trained solely on the 500k sentence pairs of the Europarl corpus by 4.7 and about 0.8 for the system trained with the extra sentence pairs extracted with the baseline model. When we only use the top 500k sentence pairs with higher similarity score, our approach is on par with the baseline system. Thus, it confirms the quality of the top 500k ranked sentence pairs, such that we could lower the decision threshold value to extract more high-quality parallel sentences. Given the out-of-domain nature of our Wikipedia articles with respect to our training set, those results are encouraging because they show that our approach should adapt well on comparable corpora with a lower degree of comparability." } ], "images": [ "scigen/train/2688.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: BLEU scores obtained on the newstest2013 test set. Sentences is the number of sentences used to train the SMT systems. The Europarl row is the baseline SMT system trained on 500k sentences pairs from the Europarl corpus." ], "reasoning": "The BLEU score improves significantly when using the full extracted parallel corpus compared to using only the top 500k sentence pairs, indicating the quality of the top-ranked sentence pairs.", "description": "To train the phrase-based translation systems Koehn et al. we use the Moses toolkit. We train two additional SMT systems by augmenting the training set with the extracted sentence pairs from the BiRNN and baseline models. Each system uses newstest2012 as tuning set and is evaluated on the English-French translation quality on newstest2013. Since both extracted parallel corpus are not on the same order of magnitude, we sorted the sentence pairs by similarity score in descending order and used the top 500k sentence pairs to train new SMT systems with training sets of equal size. When using the full extracted parallel corpus, we see that our approach improves the BLEU score over the baseline SMT system trained solely on the 500k sentence pairs of the Europarl corpus by 4.7 and about 0.8 for the system trained with the extra sentence pairs extracted with the baseline model. When we only use the top 500k sentence pairs with higher similarity score, our approach is on par with the baseline system. Thus, it confirms the quality of the top 500k ranked sentence pairs, such that we could lower the decision threshold value to extract more high-quality parallel sentences. Given the out-of-domain nature of our Wikipedia articles with respect to our training set, those results are encouraging because they show that our approach should adapt well on comparable corpora with a lower degree of comparability.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: BLEU scores obtained on the newstest2013 test set. Sentences is the number of sentences used to train the SMT systems. The Europarl row is the baseline SMT system trained on 500k sentences pairs from the Europarl corpus.', unfold the steps to solve the question given next. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nQuestion: What is the BLEU score improvement when using the full extracted parallel corpus with the BiRNN model compared to the Europarl baseline?\n" }, { "role": "assistant", "content": "The BLEU score for the Europarl baseline is 21.5. When using the full extracted parallel corpus with the BiRNN model, the BLEU score is 26.2, which is an improvement of 4.7. So, the answer is {\"answer\": \"4.7\"}." } ], "images": [ "scigen/train/2688.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: BLEU scores obtained on the newstest2013 test set. Sentences is the number of sentences used to train the SMT systems. The Europarl row is the baseline SMT system trained on 500k sentences pairs from the Europarl corpus." ], "reasoning": "The BLEU score for the Europarl baseline is 21.5. When using the full extracted parallel corpus with the BiRNN model, the BLEU score is 26.2, which is an improvement of 4.7.", "question": "What is the BLEU score improvement when using the full extracted parallel corpus with the BiRNN model compared to the Europarl baseline?", "answer": "So, the answer is {\"answer\": \"4.7\"}." }, { "messages": [ { "role": "user", "content": "Using the top 500k sentence pairs with the BiRNN model results in a BLEU score of 25.0. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nUsing the top 500k sentence pairs with the BiRNN model results in a BLEU score of 25.0.\n" }, { "role": "assistant", "content": "The table shows that using the top 500k sentence pairs with the BiRNN model results in a BLEU score of 25.0. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2688.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: BLEU scores obtained on the newstest2013 test set. Sentences is the number of sentences used to train the SMT systems. The Europarl row is the baseline SMT system trained on 500k sentences pairs from the Europarl corpus." ], "reasoning": "The table shows that using the top 500k sentence pairs with the BiRNN model results in a BLEU score of 25.0.", "statement": "Using the top 500k sentence pairs with the BiRNN model results in a BLEU score of 25.0.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: Precision, recall and F1 scores at decision threshold value ρ maximizing the F1 score on our test set.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The BiRNN model consistently outperforms the baseline model in precision, recall, and F1 scores across different noise ratios, demonstrating significant improvements in parallel sentence extraction.In this section we measure the precision, recall and F1 scores to compare both methods. In order to compare the feasibility of our approach on different degrees of comparability of comparable corpora, we insert noisy non-parallel sentences into the test set by substituting a defined number of target sentences with external target sentences from the held-out sentence pairs of the newstest2012 corpus. For example, with a noise ratio of 60%, 600 out of the 1,000 sentence pairs are not parallel, such that only 0.04% of the sentence pairs in the Cartesian product are truly parallel. We see that we are able to consistently outperform by a significant margin the results obtained with the baseline model. Our approach has a F1 improvement over the baseline model of 9.61% and 19.61% on the test set with a noise ratio of 0% and 90%, respectively. The objective of parallel sentence extraction is to increase the size of existing parallel corpora and to broaden the covered domains in order to improve the generalization of machine translation systems. To justify the utility of our proposed approach, we extract parallel sentences from interlanguage linked English-French Wikipedia articles and evaluate their quality by measuring the BLEU scores on SMT systems. We use these values as a rough estimate to represent the degree of comparability present in Wikipedia article pairs." } ], "images": [ "scigen/train/2689.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Precision, recall and F1 scores at decision threshold value ρ maximizing the F1 score on our test set." ], "reasoning": "The BiRNN model consistently outperforms the baseline model in precision, recall, and F1 scores across different noise ratios, demonstrating significant improvements in parallel sentence extraction.", "description": "In this section we measure the precision, recall and F1 scores to compare both methods. In order to compare the feasibility of our approach on different degrees of comparability of comparable corpora, we insert noisy non-parallel sentences into the test set by substituting a defined number of target sentences with external target sentences from the held-out sentence pairs of the newstest2012 corpus. For example, with a noise ratio of 60%, 600 out of the 1,000 sentence pairs are not parallel, such that only 0.04% of the sentence pairs in the Cartesian product are truly parallel. We see that we are able to consistently outperform by a significant margin the results obtained with the baseline model. Our approach has a F1 improvement over the baseline model of 9.61% and 19.61% on the test set with a noise ratio of 0% and 90%, respectively. The objective of parallel sentence extraction is to increase the size of existing parallel corpora and to broaden the covered domains in order to improve the generalization of machine translation systems. To justify the utility of our proposed approach, we extract parallel sentences from interlanguage linked English-French Wikipedia articles and evaluate their quality by measuring the BLEU scores on SMT systems. We use these values as a rough estimate to represent the degree of comparability present in Wikipedia article pairs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: Table 1: Precision, recall and F1 scores at decision threshold value ρ maximizing the F1 score on our test set.\nQuestion: What is the precision of the BiRNN model at a noise ratio of 0%?\n" }, { "role": "assistant", "content": "To find the precision of the BiRNN model at a noise ratio of 0%, look at the column labeled 'Noise ratio = 0% P (%)' and find the value corresponding to the BiRNN model. So, the answer is {\"answer\": \"83.0\"}." } ], "images": [ "scigen/train/2689.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Precision, recall and F1 scores at decision threshold value ρ maximizing the F1 score on our test set." ], "reasoning": "To find the precision of the BiRNN model at a noise ratio of 0%, look at the column labeled 'Noise ratio = 0% P (%)' and find the value corresponding to the BiRNN model.", "question": "What is the precision of the BiRNN model at a noise ratio of 0%?", "answer": "So, the answer is {\"answer\": \"83.0\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The baseline model has a higher F1 score than the BiRNN model at a noise ratio of 90%. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe baseline model has a higher F1 score than the BiRNN model at a noise ratio of 90%.\n" }, { "role": "assistant", "content": "To verify this statement, compare the F1 scores of the baseline model and the BiRNN model at a noise ratio of 90%. The BiRNN model has an F1 score of 66.7%, while the baseline model has an F1 score of 47.1%. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2689.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Precision, recall and F1 scores at decision threshold value ρ maximizing the F1 score on our test set." ], "reasoning": "To verify this statement, compare the F1 scores of the baseline model and the BiRNN model at a noise ratio of 90%. The BiRNN model has an F1 score of 66.7%, while the baseline model has an F1 score of 47.1%. Therefore, the statement is refuted.", "statement": "The baseline model has a higher F1 score than the BiRNN model at a noise ratio of 90%.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Effect of ablating gedge and gkb from XPAD (test set).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Removing gkb results in a significant drop in recall, while removing gedge score decreases recall slightly but increases precision.Removing gkb, results in a substantial (9 points) drop in recall, since the action dependency priors uses background knowledge to help the model discover dependency links that the model could not infer from the current paragraph. Further, removing gedge score results in around 2 points drop in recall and a substantial increase in precision (F1 is still lower). This is because the gedge score encourages the model to predict state changes that result in more action dependency links, often at the cost of a drop in precision. Thus, the gedge and gkb scores together help XPAD discover meaningful action dependency links." } ], "images": [ "scigen/train/2690.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Effect of ablating gedge and gkb from XPAD (test set)." ], "reasoning": "Removing gkb results in a significant drop in recall, while removing gedge score decreases recall slightly but increases precision.", "description": "Removing gkb, results in a substantial (9 points) drop in recall, since the action dependency priors uses background knowledge to help the model discover dependency links that the model could not infer from the current paragraph. Further, removing gedge score results in around 2 points drop in recall and a substantial increase in precision (F1 is still lower). This is because the gedge score encourages the model to predict state changes that result in more action dependency links, often at the cost of a drop in precision. Thus, the gedge and gkb scores together help XPAD discover meaningful action dependency links.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Effect of ablating gedge and gkb from XPAD (test set).\nQuestion: What is the recall score for the XPAD model without gkb and gedge?\nSolve the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows the recall score for the XPAD model without gkb and gedge as 21.3. So, the answer is {\"answer\": \"21.3\"}." } ], "images": [ "scigen/train/2690.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Effect of ablating gedge and gkb from XPAD (test set)." ], "reasoning": "The table shows the recall score for the XPAD model without gkb and gedge as 21.3.", "question": "What is the recall score for the XPAD model without gkb and gedge?", "answer": "So, the answer is {\"answer\": \"21.3\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Removing gkb results in a 9 points drop in recall., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRemoving gkb results in a 9 points drop in recall.\n" }, { "role": "assistant", "content": "The table shows that removing gkb results in a drop in recall from 32.9 to 23.1, which is a 9.8 points drop. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2690.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Effect of ablating gedge and gkb from XPAD (test set)." ], "reasoning": "The table shows that removing gkb results in a drop in recall from 32.9 to 23.1, which is a 9.8 points drop.", "statement": "Removing gkb results in a 9 points drop in recall.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Results on the dependency task (test set).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "XPAD outperforms ProGlobal and ProStruct by more than 3 points F1 and has higher precision than ProGlobal with similar recall, indicating the effectiveness of its dependency-aware decoder.XPAD significantly outperforms the strongest baselines, ProGlobal and ProStruct, by more than 3 points F1. XPAD has much higher precision than ProGlobal with similar recall, suggesting that XPAD’s dependency-aware decoder helps it select more accurate dependencies. Compared with ProStruct, it yields more than 11.6 points improvement on recall. As XPAD adds a novel dependency layer on top of the ProStruct architecture, we note that all these gains come exclusively from the dependency layer." } ], "images": [ "scigen/train/2691.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results on the dependency task (test set)." ], "reasoning": "XPAD outperforms ProGlobal and ProStruct by more than 3 points F1 and has higher precision than ProGlobal with similar recall, indicating the effectiveness of its dependency-aware decoder.", "description": "XPAD significantly outperforms the strongest baselines, ProGlobal and ProStruct, by more than 3 points F1. XPAD has much higher precision than ProGlobal with similar recall, suggesting that XPAD’s dependency-aware decoder helps it select more accurate dependencies. Compared with ProStruct, it yields more than 11.6 points improvement on recall. As XPAD adds a novel dependency layer on top of the ProStruct architecture, we note that all these gains come exclusively from the dependency layer.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 1: Results on the dependency task (test set).'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: How much higher is XPAD's recall compared to ProStruct?\n\nYour Solution:\n" }, { "role": "assistant", "content": "XPAD has a recall of 32.9, while ProStruct has a recall of 21.3. The difference is 32.9 - 21.3 = 11.6 points. So, the answer is {\"answer\": \"11.6\"}" } ], "images": [ "scigen/train/2691.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results on the dependency task (test set)." ], "reasoning": "XPAD has a recall of 32.9, while ProStruct has a recall of 21.3. The difference is 32.9 - 21.3 = 11.6 points.", "question": "How much higher is XPAD's recall compared to ProStruct?", "answer": "So, the answer is {\"answer\": \"11.6\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nXPAD has a higher F1 score than ProGlobal by more than 3 points.\n" }, { "role": "assistant", "content": "XPAD has an F1 score of 43.0, while ProGlobal has an F1 score of 39.9. The difference is 43.0 - 39.9 = 3.1 points, which is more than 3 points. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2691.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results on the dependency task (test set)." ], "reasoning": "XPAD has an F1 score of 43.0, while ProGlobal has an F1 score of 39.9. The difference is 43.0 - 39.9 = 3.1 points, which is more than 3 points.", "statement": "XPAD has a higher F1 score than ProGlobal by more than 3 points.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Results on the state-change task (test set), comparing with the best published prior result.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Encouraging purposefulness in action prediction improves both the explainability and accuracy of action sequences, as evidenced by the higher F1 score of XPAD compared to ProStruct.This illustrates that encouraging purposefulness in action prediction not only produces more explainable action sequences, but can improve the accuracy of those action predictions themselves." } ], "images": [ "scigen/train/2692.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on the state-change task (test set), comparing with the best published prior result." ], "reasoning": "Encouraging purposefulness in action prediction improves both the explainability and accuracy of action sequences, as evidenced by the higher F1 score of XPAD compared to ProStruct.", "description": "This illustrates that encouraging purposefulness in action prediction not only produces more explainable action sequences, but can improve the accuracy of those action predictions themselves.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 3: Results on the state-change task (test set), comparing with the best published prior result.', solve the subsequent question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich model has the highest F1 score and what is the value?\n" }, { "role": "assistant", "content": "By comparing the F1 scores of both models, XPAD has a higher F1 score of 55.2 compared to ProStruct's 54.5. So, the answer is {\"answer\": \"XPAD with 55.2\"}." } ], "images": [ "scigen/train/2692.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on the state-change task (test set), comparing with the best published prior result." ], "reasoning": "By comparing the F1 scores of both models, XPAD has a higher F1 score of 55.2 compared to ProStruct's 54.5.", "question": "Which model has the highest F1 score and what is the value?", "answer": "So, the answer is {\"answer\": \"XPAD with 55.2\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence ProStruct has a higher recall than XPAD. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nProStruct has a higher recall than XPAD.\n" }, { "role": "assistant", "content": "The table shows that ProStruct has a recall of 43.0, while XPAD has a recall of 45.3, indicating that XPAD has a higher recall. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2692.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on the state-change task (test set), comparing with the best published prior result." ], "reasoning": "The table shows that ProStruct has a recall of 43.0, while XPAD has a recall of 45.3, indicating that XPAD has a higher recall.", "statement": "ProStruct has a higher recall than XPAD.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Overall Recall on News Content Taxonomy dataset'.\n" }, { "role": "assistant", "content": "DocTag2Vec incremental training maintains high precision with minimal sacrifice, and adding more learners beyond 10 does not significantly improve precision.For NCT dataset, we also train the DocTag2Vec incrementally, i.e., each time we only feed 100 documents to DocTag2Vec and let it run SGD, and we keep doing so until all training samples are presented. Also, the incremental training does not sacrifice too much precision, which makes DocTag2Vec even appealing. It is not difficult to see that after b=10, adding more learners does not give significant improvement on precision. For nearest neighbor search, k′=5 would suffice." } ], "images": [ "scigen/train/2693.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Overall Recall on News Content Taxonomy dataset" ], "reasoning": "DocTag2Vec incremental training maintains high precision with minimal sacrifice, and adding more learners beyond 10 does not significantly improve precision.", "description": "For NCT dataset, we also train the DocTag2Vec incrementally, i.e., each time we only feed 100 documents to DocTag2Vec and let it run SGD, and we keep doing so until all training samples are presented. Also, the incremental training does not sacrifice too much precision, which makes DocTag2Vec even appealing. It is not difficult to see that after b=10, adding more learners does not give significant improvement on precision. For nearest neighbor search, k′=5 would suffice.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Overall Recall on News Content Taxonomy dataset', answer the following question. Give detailed solution consisting of each step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the precision of DocTag2Vec (incremental) for NCT (specific tags)?\n" }, { "role": "assistant", "content": "The table shows the precision values for different models and tag types. For NCT (specific tags), the precision of DocTag2Vec (incremental) is 0.7678. So, the answer is {\"answer\": \"0.7678\"}." } ], "images": [ "scigen/train/2693.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Overall Recall on News Content Taxonomy dataset" ], "reasoning": "The table shows the precision values for different models and tag types. For NCT (specific tags), the precision of DocTag2Vec (incremental) is 0.7678.", "question": "What is the precision of DocTag2Vec (incremental) for NCT (specific tags)?", "answer": "So, the answer is {\"answer\": \"0.7678\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nDocTag2Vec (incremental) has a higher precision than Doc2Vec for NCT (all tags).\n" }, { "role": "assistant", "content": "The table shows that the precision of DocTag2Vec (incremental) for NCT (all tags) is 0.6173, while the precision of Doc2Vec for NCT (all tags) is 0.6389. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2693.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Overall Recall on News Content Taxonomy dataset" ], "reasoning": "The table shows that the precision of DocTag2Vec (incremental) for NCT (all tags) is 0.6173, while the precision of Doc2Vec for NCT (all tags) is 0.6389. Therefore, the statement is refuted.", "statement": "DocTag2Vec (incremental) has a higher precision than Doc2Vec for NCT (all tags).", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: Evaluation of forgetting curve models. Pimsleur and Leitner are previous methods of modelling the forgetting curve.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "By including complexity as a parameter in the C-HLR+ model, the performance improved, supporting the hypothesis that complex words are forgotten faster.By modifying the loss function to include complexity as a parameter in the C-HLR+ model, we considerably improved the performance of our model. This was in line with our hypothesis that more complex words are forgotten faster and thus are an important feature in modelling the forgetting curve." } ], "images": [ "scigen/train/2694.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Evaluation of forgetting curve models. Pimsleur and Leitner are previous methods of modelling the forgetting curve." ], "reasoning": "By including complexity as a parameter in the C-HLR+ model, the performance improved, supporting the hypothesis that complex words are forgotten faster.", "description": "By modifying the loss function to include complexity as a parameter in the C-HLR+ model, we considerably improved the performance of our model. This was in line with our hypothesis that more complex words are forgotten faster and thus are an important feature in modelling the forgetting curve.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 1: Evaluation of forgetting curve models. Pimsleur and Leitner are previous methods of modelling the forgetting curve.\nQuestion: Which model showed the greatest improvement in performance after including complexity as a parameter?\n" }, { "role": "assistant", "content": "The table shows that the C-HLR+ model has a lower MAE (0.109) compared to the HLR+ model (0.129), indicating an improvement in performance. So, the answer is {\"answer\": \"C-HLR+\"}." } ], "images": [ "scigen/train/2694.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Evaluation of forgetting curve models. Pimsleur and Leitner are previous methods of modelling the forgetting curve." ], "reasoning": "The table shows that the C-HLR+ model has a lower MAE (0.109) compared to the HLR+ model (0.129), indicating an improvement in performance.", "question": "Which model showed the greatest improvement in performance after including complexity as a parameter?", "answer": "So, the answer is {\"answer\": \"C-HLR+\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The CN-HLR+ model has the lowest MAE among all models. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe CN-HLR+ model has the lowest MAE among all models.\n" }, { "role": "assistant", "content": "The table shows that both the N-HLR+ and CN-HLR+ models have the same lowest MAE of 0.105. Thus, the answer is {\"answer\": \"not enough info\"}." } ], "images": [ "scigen/train/2694.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Evaluation of forgetting curve models. Pimsleur and Leitner are previous methods of modelling the forgetting curve." ], "reasoning": "The table shows that both the N-HLR+ and CN-HLR+ models have the same lowest MAE of 0.105.", "statement": "The CN-HLR+ model has the lowest MAE among all models.", "answer": "Thus, the answer is {\"answer\": \"not enough info\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Evaluation of forgetting curve models. Pimsleur and Leitner are previous methods of modelling the forgetting curve.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "By including complexity as a parameter in the C-HLR+ model, the performance improved, supporting the hypothesis that complex words are forgotten faster.By modifying the loss function to include complexity as a parameter in the C-HLR+ model, we considerably improved the performance of our model. This was in line with our hypothesis that more complex words are forgotten faster and thus are an important feature in modelling the forgetting curve." } ], "images": [ "scigen/train/2695.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Evaluation of forgetting curve models. Pimsleur and Leitner are previous methods of modelling the forgetting curve." ], "reasoning": "By including complexity as a parameter in the C-HLR+ model, the performance improved, supporting the hypothesis that complex words are forgotten faster.", "description": "By modifying the loss function to include complexity as a parameter in the C-HLR+ model, we considerably improved the performance of our model. This was in line with our hypothesis that more complex words are forgotten faster and thus are an important feature in modelling the forgetting curve.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Evaluation of forgetting curve models. Pimsleur and Leitner are previous methods of modelling the forgetting curve.\nQuestion: Which model has the lowest Mean Absolute Error (MAE) and how does it compare to the Pimsleur model?\nSolve the above question based on the table information. Let's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the HLR-lex model has the lowest MAE of 0.130, while the Pimsleur model has an MAE of 0.396. Comparing these values shows that the HLR-lex model performs significantly better. So, the answer is {\"answer\": \"HLR-lex\"}." } ], "images": [ "scigen/train/2695.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Evaluation of forgetting curve models. Pimsleur and Leitner are previous methods of modelling the forgetting curve." ], "reasoning": "The table shows that the HLR-lex model has the lowest MAE of 0.130, while the Pimsleur model has an MAE of 0.396. Comparing these values shows that the HLR-lex model performs significantly better.", "question": "Which model has the lowest Mean Absolute Error (MAE) and how does it compare to the Pimsleur model?", "answer": "So, the answer is {\"answer\": \"HLR-lex\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe HLR model has a lower MAE than the Linear Regression model.\n" }, { "role": "assistant", "content": "The table shows that the HLR model has an MAE of 0.195, while the Linear Regression model has an MAE of 0.196. Therefore, the HLR model has a slightly lower MAE. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2695.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Evaluation of forgetting curve models. Pimsleur and Leitner are previous methods of modelling the forgetting curve." ], "reasoning": "The table shows that the HLR model has an MAE of 0.195, while the Linear Regression model has an MAE of 0.196. Therefore, the HLR model has a slightly lower MAE.", "statement": "The HLR model has a lower MAE than the Linear Regression model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 7: Effect of number of attention heads on scaling on 8.3 billion of parameters with 8-way model parallelism.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Increasing the number of attention heads from 16 to 32 in an 8.3 billion parameter model with 8-way model parallelism results in a decrease in scaling efficiency from 82% to 77%.This section studies the effect of attention heads on model parallel scaling. To this end, we consider the 8.3 billion parameter configuration with 8-way model parallelism and vary the number of heads from 16 to 32. As the number of attention heads increases, some of the GEMMS inside the self-attention layer become smaller and also the number of elements in the self attention softmax increases. This results in a slight decrease in scaling efficiency. Future research should be wary of this hyperparameter to design large transformer models that balance model speed and model accuracy." } ], "images": [ "scigen/train/2696.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Effect of number of attention heads on scaling on 8.3 billion of parameters with 8-way model parallelism." ], "reasoning": "Increasing the number of attention heads from 16 to 32 in an 8.3 billion parameter model with 8-way model parallelism results in a decrease in scaling efficiency from 82% to 77%.", "description": "This section studies the effect of attention heads on model parallel scaling. To this end, we consider the 8.3 billion parameter configuration with 8-way model parallelism and vary the number of heads from 16 to 32. As the number of attention heads increases, some of the GEMMS inside the self-attention layer become smaller and also the number of elements in the self attention softmax increases. This results in a slight decrease in scaling efficiency. Future research should be wary of this hyperparameter to design large transformer models that balance model speed and model accuracy.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 7: Effect of number of attention heads on scaling on 8.3 billion of parameters with 8-way model parallelism.', solve the following question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the scaling efficiency when the number of attention heads is 24?\n" }, { "role": "assistant", "content": "The table shows the scaling efficiency for different numbers of attention heads. When the number of attention heads is 24, the scaling efficiency is 80%. So, the answer is {\"answer\": \"80%\"}." } ], "images": [ "scigen/train/2696.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Effect of number of attention heads on scaling on 8.3 billion of parameters with 8-way model parallelism." ], "reasoning": "The table shows the scaling efficiency for different numbers of attention heads. When the number of attention heads is 24, the scaling efficiency is 80%.", "question": "What is the scaling efficiency when the number of attention heads is 24?", "answer": "So, the answer is {\"answer\": \"80%\"}." }, { "messages": [ { "role": "user", "content": "Increasing the number of attention heads to 32 results in a scaling efficiency of 77%. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nIncreasing the number of attention heads to 32 results in a scaling efficiency of 77%.\n" }, { "role": "assistant", "content": "The table shows that when the number of attention heads is increased to 32, the scaling efficiency is 77%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2696.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Effect of number of attention heads on scaling on 8.3 billion of parameters with 8-way model parallelism." ], "reasoning": "The table shows that when the number of attention heads is increased to 32, the scaling efficiency is 77%.", "statement": "Increasing the number of attention heads to 32 results in a scaling efficiency of 77%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 8: Speedup obtained for the 1.2 billion parameters model using model parallelism while keeping the batch size constant.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Training a model with 1.2 billion parameters using model parallelism on two GPUs results in a 64% speedup, but further increasing the number of GPUs shows diminishing returns.Our model parallelism is primarily designed to enable training models larger than what can fit in the memory of a single GPU, but it can also accelerate the training of smaller models without increasing the batch size. To measure this acceleration we train a model with a fixed 1.2 billion parameters. We use a fixed batch size of 8 samples per iteration and increase the number of GPUs using model parallelism. Using two GPUs makes training 64% faster. Above that we see diminishing returns as the per-GPU computation decreases and the memory bandwidth and communication overheads begin to dominate." } ], "images": [ "scigen/train/2697.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Speedup obtained for the 1.2 billion parameters model using model parallelism while keeping the batch size constant." ], "reasoning": "Training a model with 1.2 billion parameters using model parallelism on two GPUs results in a 64% speedup, but further increasing the number of GPUs shows diminishing returns.", "description": "Our model parallelism is primarily designed to enable training models larger than what can fit in the memory of a single GPU, but it can also accelerate the training of smaller models without increasing the batch size. To measure this acceleration we train a model with a fixed 1.2 billion parameters. We use a fixed batch size of 8 samples per iteration and increase the number of GPUs using model parallelism. Using two GPUs makes training 64% faster. Above that we see diminishing returns as the per-GPU computation decreases and the memory bandwidth and communication overheads begin to dominate.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 8: Speedup obtained for the 1.2 billion parameters model using model parallelism while keeping the batch size constant.' and methodically tackle the question that follows. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the speedup when using four GPUs for training the model?\n" }, { "role": "assistant", "content": "The table shows the speedup values for different numbers of GPUs. For four GPUs, the speedup is listed as 2.34. So, the answer is {\"answer\": \"2.34\"}." } ], "images": [ "scigen/train/2697.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Speedup obtained for the 1.2 billion parameters model using model parallelism while keeping the batch size constant." ], "reasoning": "The table shows the speedup values for different numbers of GPUs. For four GPUs, the speedup is listed as 2.34.", "question": "What is the speedup when using four GPUs for training the model?", "answer": "So, the answer is {\"answer\": \"2.34\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nUsing eight GPUs results in a speedup of 3.0.\n" }, { "role": "assistant", "content": "The table shows the speedup for eight GPUs as 2.98, not 3.0. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2697.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Speedup obtained for the 1.2 billion parameters model using model parallelism while keeping the batch size constant." ], "reasoning": "The table shows the speedup for eight GPUs as 2.98, not 3.0.", "statement": "Using eight GPUs results in a speedup of 3.0.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 1: Accuracy Scores of all models. Performances of baselines are cited from [14]'.\n" }, { "role": "assistant", "content": "Proposed models like Inverse-PDN and Expo-PDN significantly outperform baselines such as Majority, NBOW, and LSTM in both Restaurant and Laptop tasks.The Baselines Majority, NBOW and LSTM do not use aspect information for the task at all. Proposed models significantly outperform them." } ], "images": [ "scigen/train/2698.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Accuracy Scores of all models. Performances of baselines are cited from [14]" ], "reasoning": "Proposed models like Inverse-PDN and Expo-PDN significantly outperform baselines such as Majority, NBOW, and LSTM in both Restaurant and Laptop tasks.", "description": "The Baselines Majority, NBOW and LSTM do not use aspect information for the task at all. Proposed models significantly outperform them.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}. The question is related to the given table titled 'Table 1: Accuracy Scores of all models. Performances of baselines are cited from [14]'.\n\nQuestion: Which model has the highest score for the Restaurant task and how does it compare to the Majority model?\n" }, { "role": "assistant", "content": "The Inverse-PDN model has the highest score for the Restaurant task with 78.9 points. The Majority model has a score of 65.00 points. Therefore, Inverse-PDN outperforms Majority by 13.9 points. So, the answer is {\"answer\": \"Inverse-PDN\"}." } ], "images": [ "scigen/train/2698.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Accuracy Scores of all models. Performances of baselines are cited from [14]" ], "reasoning": "The Inverse-PDN model has the highest score for the Restaurant task with 78.9 points. The Majority model has a score of 65.00 points. Therefore, Inverse-PDN outperforms Majority by 13.9 points.", "question": "Which model has the highest score for the Restaurant task and how does it compare to the Majority model?", "answer": "So, the answer is {\"answer\": \"Inverse-PDN\"}." }, { "messages": [ { "role": "user", "content": "The NBOW model outperforms the LSTM model in the Laptop task. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe NBOW model outperforms the LSTM model in the Laptop task.\n" }, { "role": "assistant", "content": "The table shows that the NBOW model has a score of 58.62 for the Laptop task, while the LSTM model has a score of 61.75. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2698.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Accuracy Scores of all models. Performances of baselines are cited from [14]" ], "reasoning": "The table shows that the NBOW model has a score of 58.62 for the Laptop task, while the LSTM model has a score of 61.75. Therefore, the statement is refuted.", "statement": "The NBOW model outperforms the LSTM model in the Laptop task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: MT ablation study on {Japanese, French}-to-English translation showing the development set BLEU score. The ablations are 1) removing beam search. 2) using manually translated questions without oversampling them. 3) not using manually translated questions.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Beam search improves BLEU scores except for Japanese-to-English question translations, and manually translated questions are crucial as their absence significantly reduces BLEU scores.As a standard practice, beam search is effective in improving the BLEU scores, except for the Japanese-to-English question translations. More importantly, the use of the small amount of manually translated questions is crucial. Without using the question translations as shown in the row of “w/o questions”, the BLEU scores are almost halved. Our oversampling technique for the question translations is effective in better translating the question sentences." } ], "images": [ "scigen/train/2699.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: MT ablation study on {Japanese, French}-to-English translation showing the development set BLEU score. The ablations are 1) removing beam search. 2) using manually translated questions without oversampling them. 3) not using manually translated questions." ], "reasoning": "Beam search improves BLEU scores except for Japanese-to-English question translations, and manually translated questions are crucial as their absence significantly reduces BLEU scores.", "description": "As a standard practice, beam search is effective in improving the BLEU scores, except for the Japanese-to-English question translations. More importantly, the use of the small amount of manually translated questions is crucial. Without using the question translations as shown in the row of “w/o questions”, the BLEU scores are almost halved. Our oversampling technique for the question translations is effective in better translating the question sentences.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: MT ablation study on {Japanese, French}-to-English translation showing the development set BLEU score. The ablations are 1) removing beam search. 2) using manually translated questions without oversampling them. 3) not using manually translated questions.' to sequentially solve the question presented below. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhat is the BLEU score for Ja-En Question translations without using beam search?\n" }, { "role": "assistant", "content": "To find the BLEU score for Ja-En Question translations without using beam search, locate the row labeled 'w/o beam search' and the column labeled 'Ja-En Question'. The score is 23.06. So, the answer is {\"answer\": \"23.06\"}." } ], "images": [ "scigen/train/2699.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: MT ablation study on {Japanese, French}-to-English translation showing the development set BLEU score. The ablations are 1) removing beam search. 2) using manually translated questions without oversampling them. 3) not using manually translated questions." ], "reasoning": "To find the BLEU score for Ja-En Question translations without using beam search, locate the row labeled 'w/o beam search' and the column labeled 'Ja-En Question'. The score is 23.06.", "question": "What is the BLEU score for Ja-En Question translations without using beam search?", "answer": "So, the answer is {\"answer\": \"23.06\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The BLEU score for Fr-En Wiki translations without using questions is higher than 41. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe BLEU score for Fr-En Wiki translations without using questions is higher than 41.\n" }, { "role": "assistant", "content": "To verify this statement, locate the row labeled 'w/o questions' and the column labeled 'Fr-En Wiki'. The BLEU score is 41.37, which is higher than 41. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2699.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: MT ablation study on {Japanese, French}-to-English translation showing the development set BLEU score. The ablations are 1) removing beam search. 2) using manually translated questions without oversampling them. 3) not using manually translated questions." ], "reasoning": "To verify this statement, locate the row labeled 'w/o questions' and the column labeled 'Fr-En Wiki'. The BLEU score is 41.37, which is higher than 41.", "statement": "The BLEU score for Fr-En Wiki translations without using questions is higher than 41.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: Annotator agreement.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The Kappa agreement for Spatial Indicators is consistently high across annotation rounds, while the F1 agreement for spatial roles improved significantly in the second round due to the complexity of identifying spatial roles compared to spatial prepositions.The Kappa (κ) agreement between the two annotators has been calculated for Spatial Indicator (as this is a binary classification task) whereas we report the overall F1 agreement for annotating the spatial role labels (as this is a role identification task). The Kappa agreement is high for Spatial Indicators in both annotation rounds. The F1 agreements for the 4 spatial roles are fairly low in the first round with much improvement in the second round. This is mainly because it is relatively easy and unambiguous to locate a spatial preposition in a sentence compared to identifying the spatial roles. All conflicts were reconciled with an NLP expert (K.R.) following each round of annotation. The moderate agreement rate for Trajector and Diagnosis roles was likely due to ambiguity in distinguishing the two roles in a sentence, especially when the language pattern is different from the usual. Consider the examples below: Probably scarring in the left apex, although difficult to exclude a cavitary lesion. There are irregular opacities in the left lung apex, that could represent a cavitary lesion in the left lung apex. In the first example, ‘scarring’ was annotated as a Trajector after reconciliation as its spatial location (‘left apex’) is described directly, although there is a higher chance of annotating it as a Diagnosis since most of the probable diagnoses terms are usually preceded by a Hedge term (‘Probably’ in this case). Similarly, ‘cavitary lesion’ is indirectly connected to the same body location (‘left apex’) and has been interpreted as an additional finding. So, ‘cavitary lesion’ was also annotated as a Trajector and not as a Diagnosis. In the second example, ‘cavitary lesion’ was annotated as a Diagnosis in context to the first ‘in’ in the sentence, whereas the same term ‘cavitary lesion’ was annotated as a Trajector when its role was identified in context to the second ‘in’. As previously noted, this difference where the same term can be both a Trajector and Diagnosis in different sentences is a consequence of focusing on explicitly representing the spatial language as described as well as the natural ambiguity between a finding and diagnosis in radiology. As a result, some downstream processing or interpretation is still required, which we leave to future work." } ], "images": [ "scigen/train/2700.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Annotator agreement." ], "reasoning": "The Kappa agreement for Spatial Indicators is consistently high across annotation rounds, while the F1 agreement for spatial roles improved significantly in the second round due to the complexity of identifying spatial roles compared to spatial prepositions.", "description": "The Kappa (κ) agreement between the two annotators has been calculated for Spatial Indicator (as this is a binary classification task) whereas we report the overall F1 agreement for annotating the spatial role labels (as this is a role identification task). The Kappa agreement is high for Spatial Indicators in both annotation rounds. The F1 agreements for the 4 spatial roles are fairly low in the first round with much improvement in the second round. This is mainly because it is relatively easy and unambiguous to locate a spatial preposition in a sentence compared to identifying the spatial roles. All conflicts were reconciled with an NLP expert (K.R.) following each round of annotation. The moderate agreement rate for Trajector and Diagnosis roles was likely due to ambiguity in distinguishing the two roles in a sentence, especially when the language pattern is different from the usual. Consider the examples below: Probably scarring in the left apex, although difficult to exclude a cavitary lesion. There are irregular opacities in the left lung apex, that could represent a cavitary lesion in the left lung apex. In the first example, ‘scarring’ was annotated as a Trajector after reconciliation as its spatial location (‘left apex’) is described directly, although there is a higher chance of annotating it as a Diagnosis since most of the probable diagnoses terms are usually preceded by a Hedge term (‘Probably’ in this case). Similarly, ‘cavitary lesion’ is indirectly connected to the same body location (‘left apex’) and has been interpreted as an additional finding. So, ‘cavitary lesion’ was also annotated as a Trajector and not as a Diagnosis. In the second example, ‘cavitary lesion’ was annotated as a Diagnosis in context to the first ‘in’ in the sentence, whereas the same term ‘cavitary lesion’ was annotated as a Trajector when its role was identified in context to the second ‘in’. As previously noted, this difference where the same term can be both a Trajector and Diagnosis in different sentences is a consequence of focusing on explicitly representing the spatial language as described as well as the natural ambiguity between a finding and diagnosis in radiology. As a result, some downstream processing or interpretation is still required, which we leave to future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Annotator agreement.\nQuestion: What was the Kappa agreement for Spatial Indicators in the first 500 reports?\nGive a detailed response to the above question. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the Kappa agreement for Spatial Indicators in the first 500 reports as 0.88. So, the answer is {\"answer\": \"0.88\"}." } ], "images": [ "scigen/train/2700.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Annotator agreement." ], "reasoning": "The table shows the Kappa agreement for Spatial Indicators in the first 500 reports as 0.88.", "question": "What was the Kappa agreement for Spatial Indicators in the first 500 reports?", "answer": "So, the answer is {\"answer\": \"0.88\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe F1 agreement for the Diagnosis role improved from the first 500 reports to the remaining 1500 reports.\n" }, { "role": "assistant", "content": "The table shows that the Overall F1 Diagnosis improved from 0.25 in the first 500 reports to 0.62 in the remaining 1500 reports. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2700.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Annotator agreement." ], "reasoning": "The table shows that the Overall F1 Diagnosis improved from 0.25 in the first 500 reports to 0.62 in the remaining 1500 reports.", "statement": "The F1 agreement for the Diagnosis role improved from the first 500 reports to the remaining 1500 reports.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: Classification results for BLESS and ENTAILMENT in terms of accuracy.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "HyperVec achieves the highest BLESS score and matches the highest ENTAILMENT score, slightly outperforming Tuan et al.'s method.HyperVec slightly outperforms the method of Tuan et al. In comparison to the method of Yu et al." } ], "images": [ "scigen/train/2701.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Classification results for BLESS and ENTAILMENT in terms of accuracy." ], "reasoning": "HyperVec achieves the highest BLESS score and matches the highest ENTAILMENT score, slightly outperforming Tuan et al.'s method.", "description": "HyperVec slightly outperforms the method of Tuan et al. In comparison to the method of Yu et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 4: Classification results for BLESS and ENTAILMENT in terms of accuracy.', solve the subsequent question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model has the highest BLESS score and matches the highest ENTAILMENT score?\n" }, { "role": "assistant", "content": "By comparing the BLESS and ENTAILMENT scores of all models, HyperVec has the highest BLESS score of 0.94 and matches the highest ENTAILMENT score of 0.91. So, the answer is {\"answer\": \"HyperVec\"}" } ], "images": [ "scigen/train/2701.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Classification results for BLESS and ENTAILMENT in terms of accuracy." ], "reasoning": "By comparing the BLESS and ENTAILMENT scores of all models, HyperVec has the highest BLESS score of 0.94 and matches the highest ENTAILMENT score of 0.91.", "question": "Which model has the highest BLESS score and matches the highest ENTAILMENT score?", "answer": "So, the answer is {\"answer\": \"HyperVec\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The method of Tuan et al. has the highest BLESS score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe method of Tuan et al. has the highest BLESS score.\n" }, { "role": "assistant", "content": "The table shows that HyperVec has the highest BLESS score of 0.94, while Tuan et al.'s method has a BLESS score of 0.93. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2701.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Classification results for BLESS and ENTAILMENT in terms of accuracy." ], "reasoning": "The table shows that HyperVec has the highest BLESS score of 0.94, while Tuan et al.'s method has a BLESS score of 0.93.", "statement": "The method of Tuan et al. has the highest BLESS score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Details of the semantic relations and the number of instances in each dataset.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table shows the number of instances and total counts for various relations across different datasets, evaluated using the Average Precision (AP) ranking measure.Ranking Retrieval Shwartz et al. The Average Precision (AP) ranking measure is used to evaluate the performance of the measures." } ], "images": [ "scigen/train/2702.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Details of the semantic relations and the number of instances in each dataset." ], "reasoning": "The table shows the number of instances and total counts for various relations across different datasets, evaluated using the Average Precision (AP) ranking measure.", "description": "Ranking Retrieval Shwartz et al. The Average Precision (AP) ranking measure is used to evaluate the performance of the measures.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Details of the semantic relations and the number of instances in each dataset.', solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich dataset has the highest number of instances for the 'event' relation?\n" }, { "role": "assistant", "content": "To find the dataset with the highest number of instances for the 'event' relation, look at the 'Relation' column for 'event' and then compare the '#Instance' values. The dataset with the highest value is BLESS with 3,824 instances. So, the answer is {\"answer\": \"BLESS\"}." } ], "images": [ "scigen/train/2702.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Details of the semantic relations and the number of instances in each dataset." ], "reasoning": "To find the dataset with the highest number of instances for the 'event' relation, look at the 'Relation' column for 'event' and then compare the '#Instance' values. The dataset with the highest value is BLESS with 3,824 instances.", "question": "Which dataset has the highest number of instances for the 'event' relation?", "answer": "So, the answer is {\"answer\": \"BLESS\"}." }, { "messages": [ { "role": "user", "content": "The Weeds dataset has more instances for the 'hypernymy' relation than the Lenci&Benotto dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Weeds dataset has more instances for the 'hypernymy' relation than the Lenci&Benotto dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the '#Instance' values for the 'hypernymy' relation in the Weeds and Lenci&Benotto datasets. Weeds has 1,469 instances, while Lenci&Benotto has 1,933 instances. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2702.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Details of the semantic relations and the number of instances in each dataset." ], "reasoning": "To verify this statement, compare the '#Instance' values for the 'hypernymy' relation in the Weeds and Lenci&Benotto datasets. Weeds has 1,469 instances, while Lenci&Benotto has 1,933 instances. Therefore, the statement is refuted.", "statement": "The Weeds dataset has more instances for the 'hypernymy' relation than the Lenci&Benotto dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Accuracy for hypernymy directionality.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "HyperVec achieves the highest scores across all three tasks, while the SGNS model fails to perform adequately on any task.HyperVec outperforms all other methods on all three tasks. In addition we see again that an unmodified SGNS model cannot solve any of the three tasks." } ], "images": [ "scigen/train/2703.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracy for hypernymy directionality." ], "reasoning": "HyperVec achieves the highest scores across all three tasks, while the SGNS model fails to perform adequately on any task.", "description": "HyperVec outperforms all other methods on all three tasks. In addition we see again that an unmodified SGNS model cannot solve any of the three tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: Table 3: Accuracy for hypernymy directionality.\nQuestion: Which method has the highest score on the BLESS task and what is the score?\n" }, { "role": "assistant", "content": "By examining the BLESS column, HyperVec has the highest score of 0.92. So, the answer is {\"answer\": \"HyperVec with 0.92\"}." } ], "images": [ "scigen/train/2703.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracy for hypernymy directionality." ], "reasoning": "By examining the BLESS column, HyperVec has the highest score of 0.92.", "question": "Which method has the highest score on the BLESS task and what is the score?", "answer": "So, the answer is {\"answer\": \"HyperVec with 0.92\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSGNS outperforms HyperVec on the WBLESS task.\n" }, { "role": "assistant", "content": "The table shows that HyperVec has a score of 0.87 on the WBLESS task, while SGNS has a score of 0.48, indicating that HyperVec outperforms SGNS. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2703.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracy for hypernymy directionality." ], "reasoning": "The table shows that HyperVec has a score of 0.87 on the WBLESS task, while SGNS has a score of 0.48, indicating that HyperVec outperforms SGNS.", "statement": "SGNS outperforms HyperVec on the WBLESS task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Results (ρ) of HyperScore and state-of-the-art measures and word embedding models on graded lexical entailment.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "HyperScore outperforms the previously best word embedding model PARAGRAM by 0.22 and the best measure FR by 0.27 due to its ability to differentiate between hypernym–hyponym pairs and vice versa.HyperScore outperforms even the previously best word embedding model PARAGRAM by .22, and the previously best measures FR by .27. The reason that HyperVec outperforms all other models is that the hierarchy between hypernym and hypornym within HyperVec differentiates hyponym–hypernym pairs from hypernym–hyponym pairs. For example, the HyperScore for the pairs duck–animal and animal–duck are 3.02 and 0.30, respectively. Thus, the magnitude proportion of the hypernym–hyponym pair duck–animal is larger than that for the pair animal–duck." } ], "images": [ "scigen/train/2704.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results (ρ) of HyperScore and state-of-the-art measures and word embedding models on graded lexical entailment." ], "reasoning": "HyperScore outperforms the previously best word embedding model PARAGRAM by 0.22 and the best measure FR by 0.27 due to its ability to differentiate between hypernym–hyponym pairs and vice versa.", "description": "HyperScore outperforms even the previously best word embedding model PARAGRAM by .22, and the previously best measures FR by .27. The reason that HyperVec outperforms all other models is that the hierarchy between hypernym and hypornym within HyperVec differentiates hyponym–hypernym pairs from hypernym–hyponym pairs. For example, the HyperScore for the pairs duck–animal and animal–duck are 3.02 and 0.30, respectively. Thus, the magnitude proportion of the hypernym–hyponym pair duck–animal is larger than that for the pair animal–duck.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 5: Results (ρ) of HyperScore and state-of-the-art measures and word embedding models on graded lexical entailment.' and proceed to solve the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nBy how much does HyperScore outperform the previously best word embedding model and the best measure?\n" }, { "role": "assistant", "content": "HyperScore outperforms PARAGRAM by 0.22 (0.540 - 0.320) and FR by 0.27 (0.540 - 0.279). So, the answer is {\"answer\": \"0.22 and 0.27\"}." } ], "images": [ "scigen/train/2704.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results (ρ) of HyperScore and state-of-the-art measures and word embedding models on graded lexical entailment." ], "reasoning": "HyperScore outperforms PARAGRAM by 0.22 (0.540 - 0.320) and FR by 0.27 (0.540 - 0.279).", "question": "By how much does HyperScore outperform the previously best word embedding model and the best measure?", "answer": "So, the answer is {\"answer\": \"0.22 and 0.27\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nHyperScore has a higher ρ value than PARAGRAM and FR.\n" }, { "role": "assistant", "content": "The table shows that HyperScore has a ρ value of 0.540, which is higher than PARAGRAM's 0.320 and FR's 0.279. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2704.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results (ρ) of HyperScore and state-of-the-art measures and word embedding models on graded lexical entailment." ], "reasoning": "The table shows that HyperScore has a ρ value of 0.540, which is higher than PARAGRAM's 0.320 and FR's 0.279.", "statement": "HyperScore has a higher ρ value than PARAGRAM and FR.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 6: AP results across languages, comparing SGNS and the projected representations.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Mapping embeddings into the English HyperVec space increases similarity scores for hypernymy pairs in both German and Italian.We clearly see that for both languages the default SGNS embeddings do not provide higher similarity scores for hypernymy pairs (except for Italian Hyp/Ant), but both languages provide higher scores when we map the embeddings into the English HyperVec space." } ], "images": [ "scigen/train/2705.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: AP results across languages, comparing SGNS and the projected representations." ], "reasoning": "Mapping embeddings into the English HyperVec space increases similarity scores for hypernymy pairs in both German and Italian.", "description": "We clearly see that for both languages the default SGNS embeddings do not provide higher similarity scores for hypernymy pairs (except for Italian Hyp/Ant), but both languages provide higher scores when we map the embeddings into the English HyperVec space.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 6: AP results across languages, comparing SGNS and the projected representations.', unfold the steps to solve the question given next. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nQuestion: What is the similarity score for Italian Hyp/All when mapped into the English HyperVec space?\n" }, { "role": "assistant", "content": "The table shows that the similarity score for Italian Hyp/All when mapped into the English HyperVec space is 0.44. So, the answer is {\"answer\": \"0.44\"}." } ], "images": [ "scigen/train/2705.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: AP results across languages, comparing SGNS and the projected representations." ], "reasoning": "The table shows that the similarity score for Italian Hyp/All when mapped into the English HyperVec space is 0.44.", "question": "What is the similarity score for Italian Hyp/All when mapped into the English HyperVec space?", "answer": "So, the answer is {\"answer\": \"0.44\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The similarity score for German Hyp/Syn is higher when mapped into the English HyperVec space. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe similarity score for German Hyp/Syn is higher when mapped into the English HyperVec space.\n" }, { "role": "assistant", "content": "The table shows that the similarity score for German Hyp/Syn is 0.48 with DE-SGNS and 0.65 when mapped into the English HyperVec space, indicating an increase. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2705.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: AP results across languages, comparing SGNS and the projected representations." ], "reasoning": "The table shows that the similarity score for German Hyp/Syn is 0.48 with DE-SGNS and 0.65 when mapped into the English HyperVec space, indicating an increase.", "statement": "The similarity score for German Hyp/Syn is higher when mapped into the English HyperVec space.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: Pearson correlation for inter-annotator agreement on the SemEval-2007 Affective Text Corpus (Strapparava and Mihalcea, 2007).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Trained annotators have a simple average agreement of 53.67, while the frequency-based average is 43, indicating that untrained respondents would likely show even lower agreement.None of the systems participating to SemEval-2007 reached an accuracy similar to that of sentiment analysis methods. This is possibly the reason why it is hard to find literature about alternative approaches to those used in the Affective Text task, and why a similar task was not proposed in successive editions of SemEval. If trained annotators agree with a simple average r of 53.67 (the frequency-based average r is 43), it is plausible that untrained respondents would show an even lower level of agreement." } ], "images": [ "scigen/train/2706.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Pearson correlation for inter-annotator agreement on the SemEval-2007 Affective Text Corpus (Strapparava and Mihalcea, 2007)." ], "reasoning": "Trained annotators have a simple average agreement of 53.67, while the frequency-based average is 43, indicating that untrained respondents would likely show even lower agreement.", "description": "None of the systems participating to SemEval-2007 reached an accuracy similar to that of sentiment analysis methods. This is possibly the reason why it is hard to find literature about alternative approaches to those used in the Affective Text task, and why a similar task was not proposed in successive editions of SemEval. If trained annotators agree with a simple average r of 53.67 (the frequency-based average r is 43), it is plausible that untrained respondents would show an even lower level of agreement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1: Pearson correlation for inter-annotator agreement on the SemEval-2007 Affective Text Corpus (Strapparava and Mihalcea, 2007).', solve the subsequent question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the simple average agreement score of trained annotators?\n" }, { "role": "assistant", "content": "The table shows the simple average agreement score as 53.67. So, the answer is {\"answer\": \"53.67\"}." } ], "images": [ "scigen/train/2706.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Pearson correlation for inter-annotator agreement on the SemEval-2007 Affective Text Corpus (Strapparava and Mihalcea, 2007)." ], "reasoning": "The table shows the simple average agreement score as 53.67.", "question": "What is the simple average agreement score of trained annotators?", "answer": "So, the answer is {\"answer\": \"53.67\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe frequency-based average agreement score is higher than the simple average agreement score.\n" }, { "role": "assistant", "content": "The table shows that the frequency-based average agreement score is 43, which is lower than the simple average agreement score of 53.67. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2706.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Pearson correlation for inter-annotator agreement on the SemEval-2007 Affective Text Corpus (Strapparava and Mihalcea, 2007)." ], "reasoning": "The table shows that the frequency-based average agreement score is 43, which is lower than the simple average agreement score of 53.67.", "statement": "The frequency-based average agreement score is higher than the simple average agreement score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Pearson correlation for inter-annotator agreement on the SemEval-2007 Affective Text Corpus (Strapparava and Mihalcea, 2007).', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The low accuracy of systems in SemEval-2007's Affective Text task, with an average r of 53.67, suggests that untrained respondents would likely show even lower agreement levels.None of the systems participating to SemEval-2007 reached an accuracy similar to that of sentiment analysis methods. This is possibly the reason why it is hard to find literature about alternative approaches to those used in the Affective Text task, and why a similar task was not proposed in successive editions of SemEval. If trained annotators agree with a simple average r of 53.67 (the frequency-based average r is 43), it is plausible that untrained respondents would show an even lower level of agreement." } ], "images": [ "scigen/train/2707.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Pearson correlation for inter-annotator agreement on the SemEval-2007 Affective Text Corpus (Strapparava and Mihalcea, 2007)." ], "reasoning": "The low accuracy of systems in SemEval-2007's Affective Text task, with an average r of 53.67, suggests that untrained respondents would likely show even lower agreement levels.", "description": "None of the systems participating to SemEval-2007 reached an accuracy similar to that of sentiment analysis methods. This is possibly the reason why it is hard to find literature about alternative approaches to those used in the Affective Text task, and why a similar task was not proposed in successive editions of SemEval. If trained annotators agree with a simple average r of 53.67 (the frequency-based average r is 43), it is plausible that untrained respondents would show an even lower level of agreement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 1: Pearson correlation for inter-annotator agreement on the SemEval-2007 Affective Text Corpus (Strapparava and Mihalcea, 2007).', solve the following question step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich emotion had the highest average r score in the SemEval-2007 Affective Text task?\n" }, { "role": "assistant", "content": "By comparing the average r scores for each emotion in the table, sadness had the highest score at 68.19. So, the answer is {\"answer\": \"sadness\"}." } ], "images": [ "scigen/train/2707.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Pearson correlation for inter-annotator agreement on the SemEval-2007 Affective Text Corpus (Strapparava and Mihalcea, 2007)." ], "reasoning": "By comparing the average r scores for each emotion in the table, sadness had the highest score at 68.19.", "question": "Which emotion had the highest average r score in the SemEval-2007 Affective Text task?", "answer": "So, the answer is {\"answer\": \"sadness\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe average r score for joy was higher than that for fear in the SemEval-2007 Affective Text task.\n" }, { "role": "assistant", "content": "The table shows that the average r score for joy is 59.91, while the score for fear is 63.81, indicating that joy's score is lower. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2707.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Pearson correlation for inter-annotator agreement on the SemEval-2007 Affective Text Corpus (Strapparava and Mihalcea, 2007)." ], "reasoning": "The table shows that the average r score for joy is 59.91, while the score for fear is 63.81, indicating that joy's score is lower.", "statement": "The average r score for joy was higher than that for fear in the SemEval-2007 Affective Text task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 6: Accuracy results of models with BERT transferring to new target datasets. All models are trained on SNLI and tested on the target datasets. Δ are absolute differences between our methods and the CE loss baseline.'.\n" }, { "role": "assistant", "content": "The proposed models show substantial improvement over the baseline on all datasets except MNLI and MNLI-M, where no improvement is expected due to similar biases.As shown in prior work (belinkov-etal-2019-dont), the MNLI datasets have very similar biases to SNLI, which the models are trained on, so we do not expect any improvement in the relative performance of our models and the baseline for MNLI and MNLI-M. On all the remaining datasets, our proposed models perform better than the baseline, showing a substantial improvement in generalization by using our debasing techniques." } ], "images": [ "scigen/train/2708.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Accuracy results of models with BERT transferring to new target datasets. All models are trained on SNLI and tested on the target datasets. Δ are absolute differences between our methods and the CE loss baseline." ], "reasoning": "The proposed models show substantial improvement over the baseline on all datasets except MNLI and MNLI-M, where no improvement is expected due to similar biases.", "description": "As shown in prior work (belinkov-etal-2019-dont), the MNLI datasets have very similar biases to SNLI, which the models are trained on, so we do not expect any improvement in the relative performance of our models and the baseline for MNLI and MNLI-M. On all the remaining datasets, our proposed models perform better than the baseline, showing a substantial improvement in generalization by using our debasing techniques.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: Accuracy results of models with BERT transferring to new target datasets. All models are trained on SNLI and tested on the target datasets. Δ are absolute differences between our methods and the CE loss baseline.\nQuestion: Which dataset shows the highest improvement in performance using the proposed models compared to the baseline?\nSolve the above question based on the table information. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the Δ values for each dataset, SCITAIL shows the highest improvement with a Δ of +3.8. So, the answer is {\"answer\": \"SCITAIL\"}" } ], "images": [ "scigen/train/2708.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Accuracy results of models with BERT transferring to new target datasets. All models are trained on SNLI and tested on the target datasets. Δ are absolute differences between our methods and the CE loss baseline." ], "reasoning": "By comparing the Δ values for each dataset, SCITAIL shows the highest improvement with a Δ of +3.8.", "question": "Which dataset shows the highest improvement in performance using the proposed models compared to the baseline?", "answer": "So, the answer is {\"answer\": \"SCITAIL\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe proposed models perform better than the baseline on the QQP dataset.\n" }, { "role": "assistant", "content": "The table shows that the Δ value for QQP is +1.5, indicating an improvement in performance using the proposed models compared to the baseline. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2708.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Accuracy results of models with BERT transferring to new target datasets. All models are trained on SNLI and tested on the target datasets. Δ are absolute differences between our methods and the CE loss baseline." ], "reasoning": "The table shows that the Δ value for QQP is +1.5, indicating an improvement in performance using the proposed models compared to the baseline.", "statement": "The proposed models perform better than the baseline on the QQP dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Results on FEVER development and symmetric test set. Δ are absolute differences with CE loss.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The proposed debiasing methods, PoE and DFL, significantly improve the baseline performance by 9.8 and 7.5 points respectively, outperforming Schuster et al.'s prior work.S4SS1SSS0Px4 Results: Our proposed debiasing methods, PoE and DFL, are highly effective, boosting the performance of the baseline by 9.8 and 7.5 points respectively, significantly surpassing the prior work of Schuster et al." } ], "images": [ "scigen/train/2709.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results on FEVER development and symmetric test set. Δ are absolute differences with CE loss." ], "reasoning": "The proposed debiasing methods, PoE and DFL, significantly improve the baseline performance by 9.8 and 7.5 points respectively, outperforming Schuster et al.'s prior work.", "description": "S4SS1SSS0Px4 Results: Our proposed debiasing methods, PoE and DFL, are highly effective, boosting the performance of the baseline by 9.8 and 7.5 points respectively, significantly surpassing the prior work of Schuster et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 1: Results on FEVER development and symmetric test set. Δ are absolute differences with CE loss.', answer the following question. Give detailed solution consisting of each step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nHow much did the PoE method improve the baseline performance compared to Schuster et al.'s work?\n" }, { "role": "assistant", "content": "The PoE method improved the baseline performance by 9.8 points, while Schuster et al.'s work improved it by 5.1 points. The difference in improvement is 9.8 - 5.1. So, the answer is {\"answer\": \"4.7\"}" } ], "images": [ "scigen/train/2709.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results on FEVER development and symmetric test set. Δ are absolute differences with CE loss." ], "reasoning": "The PoE method improved the baseline performance by 9.8 points, while Schuster et al.'s work improved it by 5.1 points. The difference in improvement is 9.8 - 5.1.", "question": "How much did the PoE method improve the baseline performance compared to Schuster et al.'s work?", "answer": "So, the answer is {\"answer\": \"4.7\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe DFL method improved the baseline performance by more than 8 points.\n" }, { "role": "assistant", "content": "According to the table, the DFL method improved the baseline performance by 7.5 points, which is less than 8 points. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2709.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results on FEVER development and symmetric test set. Δ are absolute differences with CE loss." ], "reasoning": "According to the table, the DFL method improved the baseline performance by 7.5 points, which is less than 8 points.", "statement": "The DFL method improved the baseline performance by more than 8 points.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Results on the SNLI test, hard set, and differences with CE loss. *: results from Belinkov et al. (2019).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "DFL and PoE models show significant improvements in both BERT and InferSent tests, with PoE setting a new state-of-the-art compared to prior work.With InferSent, DFL and PoE result in 4.1 and 4.8 points gain. With BERT, DFL and PoE improve the results by 2.5 and 1.6 absolute points. Compared to the prior work of Belinkov et al. (AdvCls), our PoE model obtains a 7.4 points gain, setting a new state-of-the-art." } ], "images": [ "scigen/train/2710.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on the SNLI test, hard set, and differences with CE loss. *: results from Belinkov et al. (2019)." ], "reasoning": "DFL and PoE models show significant improvements in both BERT and InferSent tests, with PoE setting a new state-of-the-art compared to prior work.", "description": "With InferSent, DFL and PoE result in 4.1 and 4.8 points gain. With BERT, DFL and PoE improve the results by 2.5 and 1.6 absolute points. Compared to the prior work of Belinkov et al. (AdvCls), our PoE model obtains a 7.4 points gain, setting a new state-of-the-art.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Results on the SNLI test, hard set, and differences with CE loss. *: results from Belinkov et al. (2019).\nQuestion: What is the absolute points gain of the PoE model with InferSent?\nSolve the above question based on the table information. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the InferSent Δ for PoE as 4.8, indicating the absolute points gain. So, the answer is {\"answer\": \"4.8\"}" } ], "images": [ "scigen/train/2710.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on the SNLI test, hard set, and differences with CE loss. *: results from Belinkov et al. (2019)." ], "reasoning": "The table shows the InferSent Δ for PoE as 4.8, indicating the absolute points gain.", "question": "What is the absolute points gain of the PoE model with InferSent?", "answer": "So, the answer is {\"answer\": \"4.8\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe PoE model improves the results by 4.8 absolute points with InferSent.\n" }, { "role": "assistant", "content": "The table shows that the PoE model has an InferSent Δ of 4.8, confirming the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2710.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on the SNLI test, hard set, and differences with CE loss. *: results from Belinkov et al. (2019)." ], "reasoning": "The table shows that the PoE model has an InferSent Δ of 4.8, confirming the statement.", "statement": "The PoE model improves the results by 4.8 absolute points with InferSent.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 5: Results on MNLI mismatched dev set, MNLI mismatched hard set, and HANS when training independently to debias against either hypothesis artifacts (♣) or syntactic biases (♥), compared with jointly training to debias against both bias types. Δ: differences with baseline CE loss.'.\n" }, { "role": "assistant", "content": "Models trained to be robust to hypothesis biases do not generalize to HANS, while models trained on HANS show slight improvement on MNLI mismatched hard dev set. Jointly debiased models improve on both datasets.Models trained to be robust to hypothesis biases (♣) do not generalize to HANS. On the other hand, models trained to be robust on HANS (♥) use a powerful bias-only model resulting in a slight improvement on MNLI mismatched hard dev set. We expect a slight degradation when debiasing for both biases since models need to select samples accommodating both debiasing needs. The jointly debiased models successfully obtain improvements on both datasets, which are close to the improvements on each dataset by the individually debiased models." } ], "images": [ "scigen/train/2711.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on MNLI mismatched dev set, MNLI mismatched hard set, and HANS when training independently to debias against either hypothesis artifacts (♣) or syntactic biases (♥), compared with jointly training to debias against both bias types. Δ: differences with baseline CE loss." ], "reasoning": "Models trained to be robust to hypothesis biases do not generalize to HANS, while models trained on HANS show slight improvement on MNLI mismatched hard dev set. Jointly debiased models improve on both datasets.", "description": "Models trained to be robust to hypothesis biases (♣) do not generalize to HANS. On the other hand, models trained to be robust on HANS (♥) use a powerful bias-only model resulting in a slight improvement on MNLI mismatched hard dev set. We expect a slight degradation when debiasing for both biases since models need to select samples accommodating both debiasing needs. The jointly debiased models successfully obtain improvements on both datasets, which are close to the improvements on each dataset by the individually debiased models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Results on MNLI mismatched dev set, MNLI mismatched hard set, and HANS when training independently to debias against either hypothesis artifacts (♣) or syntactic biases (♥), compared with jointly training to debias against both bias types. Δ: differences with baseline CE loss.\nQuestion: Which model shows the highest improvement on the HANS dataset?\nGive a detailed response to the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest improvement on the HANS dataset, look at the Δ column for HANS. The DFL♥ model shows the highest improvement with a Δ of +7.4. So, the answer is {\"answer\": \"DFL♥\"}." } ], "images": [ "scigen/train/2711.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on MNLI mismatched dev set, MNLI mismatched hard set, and HANS when training independently to debias against either hypothesis artifacts (♣) or syntactic biases (♥), compared with jointly training to debias against both bias types. Δ: differences with baseline CE loss." ], "reasoning": "To find the model with the highest improvement on the HANS dataset, look at the Δ column for HANS. The DFL♥ model shows the highest improvement with a Δ of +7.4.", "question": "Which model shows the highest improvement on the HANS dataset?", "answer": "So, the answer is {\"answer\": \"DFL♥\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The PoE♣ model shows a higher improvement on the MNLI mismatched hard dev set compared to the PoE♥ model. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe PoE♣ model shows a higher improvement on the MNLI mismatched hard dev set compared to the PoE♥ model.\n" }, { "role": "assistant", "content": "The PoE♣ model has a Δ of +1.7 on the MNLI mismatched hard dev set, while the PoE♥ model has a Δ of +0.4. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2711.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on MNLI mismatched dev set, MNLI mismatched hard set, and HANS when training independently to debias against either hypothesis artifacts (♣) or syntactic biases (♥), compared with jointly training to debias against both bias types. Δ: differences with baseline CE loss." ], "reasoning": "The PoE♣ model has a Δ of +1.7 on the MNLI mismatched hard dev set, while the PoE♥ model has a Δ of +0.4. Therefore, the statement is supported.", "statement": "The PoE♣ model shows a higher improvement on the MNLI mismatched hard dev set compared to the PoE♥ model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 9: Accuracy results of models with InferSent transferring to new target datasets. All models are trained on SNLI and tested on the target datasets. M1 and M2 are our re-implementation of belinkov-etal-2019-dont. Δ are relative differences in percentage with respect to CE loss.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The DFL and PoE models outperform the baseline in most datasets, with DFL showing a 2.57% improvement and PoE showing a 3.39% improvement in relative generalization performance.As used in prior work to adjust the learning-rate of the bias-only and baseline models (belinkov-etal-2019-dont), we introduce a hyperparameter β for the bias-only model to modulate the loss of the bias-only model in ensembling. We sweep hyper-parameters γ,α over {0.02,0.05,0.1,0.6,2.0,4.0,5.0} and β over {0.05,0.2,0.4,0.8,1.0}. The DFL model outperforms the baseline in 10 out of 12 datasets, while the PoE model outperforms the baseline in 9 datasets and does equally well on the DPR dataset. As shown in prior work (belinkov-etal-2019-dont), the MNLI dataset has very similar biases to SNLI, which the models are trained on, so we do not expect any improvement in the relative performance of our models and the baseline for MNLI dataset. Interestingly, our methods obtain improvement on MNLI-M, in which the test data differs from training distribution. Our proposed debiasing methods, PoE and DFL, are highly effective, boosting the relative generalization performance of the baseline by 3.39% and 2.57% respectively, significantly surpassing the prior work of belinkov-etal-2019-dont. Compared to M1 and M2, our methods outperform them on 9 datasets, while they do better on two datasets of SPR and FN+, and slightly better on the DPR dataset. However, note that DPR is a very small dataset and all models perform close to random-chance on this dataset." } ], "images": [ "scigen/train/2712.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Accuracy results of models with InferSent transferring to new target datasets. All models are trained on SNLI and tested on the target datasets. M1 and M2 are our re-implementation of belinkov-etal-2019-dont. Δ are relative differences in percentage with respect to CE loss." ], "reasoning": "The DFL and PoE models outperform the baseline in most datasets, with DFL showing a 2.57% improvement and PoE showing a 3.39% improvement in relative generalization performance.", "description": "As used in prior work to adjust the learning-rate of the bias-only and baseline models (belinkov-etal-2019-dont), we introduce a hyperparameter β for the bias-only model to modulate the loss of the bias-only model in ensembling. We sweep hyper-parameters γ,α over {0.02,0.05,0.1,0.6,2.0,4.0,5.0} and β over {0.05,0.2,0.4,0.8,1.0}. The DFL model outperforms the baseline in 10 out of 12 datasets, while the PoE model outperforms the baseline in 9 datasets and does equally well on the DPR dataset. As shown in prior work (belinkov-etal-2019-dont), the MNLI dataset has very similar biases to SNLI, which the models are trained on, so we do not expect any improvement in the relative performance of our models and the baseline for MNLI dataset. Interestingly, our methods obtain improvement on MNLI-M, in which the test data differs from training distribution. Our proposed debiasing methods, PoE and DFL, are highly effective, boosting the relative generalization performance of the baseline by 3.39% and 2.57% respectively, significantly surpassing the prior work of belinkov-etal-2019-dont. Compared to M1 and M2, our methods outperform them on 9 datasets, while they do better on two datasets of SPR and FN+, and slightly better on the DPR dataset. However, note that DPR is a very small dataset and all models perform close to random-chance on this dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 9: Accuracy results of models with InferSent transferring to new target datasets. All models are trained on SNLI and tested on the target datasets. M1 and M2 are our re-implementation of belinkov-etal-2019-dont. Δ are relative differences in percentage with respect to CE loss.', and sequentially solve the question, writing out each step of your reasoning process. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model shows the highest improvement in relative generalization performance compared to the baseline?\n" }, { "role": "assistant", "content": "The table shows the relative improvement percentages for DFL and PoE models compared to the baseline. PoE has a higher improvement percentage (3.39%) compared to DFL (2.57%). So, the answer is {\"answer\": \"PoE\"}." } ], "images": [ "scigen/train/2712.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Accuracy results of models with InferSent transferring to new target datasets. All models are trained on SNLI and tested on the target datasets. M1 and M2 are our re-implementation of belinkov-etal-2019-dont. Δ are relative differences in percentage with respect to CE loss." ], "reasoning": "The table shows the relative improvement percentages for DFL and PoE models compared to the baseline. PoE has a higher improvement percentage (3.39%) compared to DFL (2.57%).", "question": "Which model shows the highest improvement in relative generalization performance compared to the baseline?", "answer": "So, the answer is {\"answer\": \"PoE\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe DFL model outperforms the baseline in 10 out of 12 datasets.\n" }, { "role": "assistant", "content": "The description states that the DFL model outperforms the baseline in 10 out of 12 datasets, which is consistent with the table showing DFL outperforming the baseline in most datasets. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2712.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Accuracy results of models with InferSent transferring to new target datasets. All models are trained on SNLI and tested on the target datasets. M1 and M2 are our re-implementation of belinkov-etal-2019-dont. Δ are relative differences in percentage with respect to CE loss." ], "reasoning": "The description states that the DFL model outperforms the baseline in 10 out of 12 datasets, which is consistent with the table showing DFL outperforming the baseline in most datasets.", "statement": "The DFL model outperforms the baseline in 10 out of 12 datasets.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Macro-averaged scores for ICO span prediction at both the token and clinical entity level.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The model prioritizes high recall over precision, achieving 0.87 recall at the clinical concept level.Since these spans will serve as inputs to downstream models in the pipeline, high recall at the expense of precision is preferable; we will allow subsequent classifiers to discard spurious spans. We achieve 0.87 recall at the clinical concept level." } ], "images": [ "scigen/train/2713.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Macro-averaged scores for ICO span prediction at both the token and clinical entity level." ], "reasoning": "The model prioritizes high recall over precision, achieving 0.87 recall at the clinical concept level.", "description": "Since these spans will serve as inputs to downstream models in the pipeline, high recall at the expense of precision is preferable; we will allow subsequent classifiers to discard spurious spans. We achieve 0.87 recall at the clinical concept level.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 1: Macro-averaged scores for ICO span prediction at both the token and clinical entity level.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. \n\nQuestion: What is the recall for tokens and how does it compare to the recall for clinical entities?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The recall for tokens is 0.72, while the recall for clinical entities is 0.87. Comparing these values shows that the recall for clinical entities is higher. So, the answer is {\"answer\": \"0.87\"}" } ], "images": [ "scigen/train/2713.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Macro-averaged scores for ICO span prediction at both the token and clinical entity level." ], "reasoning": "The recall for tokens is 0.72, while the recall for clinical entities is 0.87. Comparing these values shows that the recall for clinical entities is higher.", "question": "What is the recall for tokens and how does it compare to the recall for clinical entities?", "answer": "So, the answer is {\"answer\": \"0.87\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe precision for clinical entities is higher than the precision for tokens.\n" }, { "role": "assistant", "content": "The table shows that the precision for clinical entities is 0.55, while the precision for tokens is 0.56. Therefore, the precision for clinical entities is not higher than the precision for tokens. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2713.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Macro-averaged scores for ICO span prediction at both the token and clinical entity level." ], "reasoning": "The table shows that the precision for clinical entities is 0.55, while the precision for tokens is 0.56. Therefore, the precision for clinical entities is not higher than the precision for tokens.", "statement": "The precision for clinical entities is higher than the precision for tokens.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: Performance for predicting an article’s exact MeSH terms using the rule-base system, run on both the automatically extracted spans and the expert-provided test spans.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The performance of the approach is evaluated under both strict and relaxed equivalence criteria, considering precision and recall for extracted and expert spans.To better characterize the performance of our approach, we therefore also consider relaxing the equivalence criteria to include matching immediate parents or children in the MeSH hierarchy." } ], "images": [ "scigen/train/2714.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance for predicting an article’s exact MeSH terms using the rule-base system, run on both the automatically extracted spans and the expert-provided test spans." ], "reasoning": "The performance of the approach is evaluated under both strict and relaxed equivalence criteria, considering precision and recall for extracted and expert spans.", "description": "To better characterize the performance of our approach, we therefore also consider relaxing the equivalence criteria to include matching immediate parents or children in the MeSH hierarchy.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: Performance for predicting an article’s exact MeSH terms using the rule-base system, run on both the automatically extracted spans and the expert-provided test spans.', solve this question and give detail solutions. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhat is the difference in precision between extracted spans under strict and relaxed criteria?\n" }, { "role": "assistant", "content": "To find the difference in precision between extracted spans under strict and relaxed criteria, subtract the precision value under strict criteria (0.26) from the precision value under relaxed criteria (0.32). So, the answer is {\"answer\": \"0.06\"}." } ], "images": [ "scigen/train/2714.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance for predicting an article’s exact MeSH terms using the rule-base system, run on both the automatically extracted spans and the expert-provided test spans." ], "reasoning": "To find the difference in precision between extracted spans under strict and relaxed criteria, subtract the precision value under strict criteria (0.26) from the precision value under relaxed criteria (0.32).", "question": "What is the difference in precision between extracted spans under strict and relaxed criteria?", "answer": "So, the answer is {\"answer\": \"0.06\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nExpert spans have higher recall under relaxed criteria compared to strict criteria.\n" }, { "role": "assistant", "content": "The table shows that the recall for expert spans is 0.26 under strict criteria and 0.34 under relaxed criteria, indicating an increase. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2714.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance for predicting an article’s exact MeSH terms using the rule-base system, run on both the automatically extracted spans and the expert-provided test spans." ], "reasoning": "The table shows that the recall for expert spans is 0.26 under strict criteria and 0.34 under relaxed criteria, indicating an increase.", "statement": "Expert spans have higher recall under relaxed criteria compared to strict criteria.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: For our complexity, ambiguity, and unseen prevalence metrics, we slice the validation set using the median metric value, and compute the average error rates for BERT and random on each slice. We show that the gap between BERT and random errors is larger on the slice above the median than below it in 19 out of 21 cases, in absolute (Abs.) and relative (Rel.) terms.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The accuracy gap between BERT and random embeddings is larger for inputs with high metric values, supporting the hypothesis that contextual embeddings improve accuracy.we show that for each of the metrics defined above, the accuracy gap between BERT and random embeddings is larger on inputs for which the metrics are large. In particular, we split each of the task validation sets into two halves, with points with metric values below the median in one half, and above the median in the other. We see that in 19 out of 21 cases, the accuracy gap between BERT and random embeddings is larger on the slice of the validation set corresponding to large metric values, validating our hypothesis that contextual embeddings provide important boosts in accuracy on these points." } ], "images": [ "scigen/train/2715.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: For our complexity, ambiguity, and unseen prevalence metrics, we slice the validation set using the median metric value, and compute the average error rates for BERT and random on each slice. We show that the gap between BERT and random errors is larger on the slice above the median than below it in 19 out of 21 cases, in absolute (Abs.) and relative (Rel.) terms." ], "reasoning": "The accuracy gap between BERT and random embeddings is larger for inputs with high metric values, supporting the hypothesis that contextual embeddings improve accuracy.", "description": "we show that for each of the metrics defined above, the accuracy gap between BERT and random embeddings is larger on inputs for which the metrics are large. In particular, we split each of the task validation sets into two halves, with points with metric values below the median in one half, and above the median in the other. We see that in 19 out of 21 cases, the accuracy gap between BERT and random embeddings is larger on the slice of the validation set corresponding to large metric values, validating our hypothesis that contextual embeddings provide important boosts in accuracy on these points.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 2: For our complexity, ambiguity, and unseen prevalence metrics, we slice the validation set using the median metric value, and compute the average error rates for BERT and random on each slice. We show that the gap between BERT and random errors is larger on the slice above the median than below it in 19 out of 21 cases, in absolute (Abs.) and relative (Rel.) terms.' in mind, break down and resolve the question below step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich task has the highest relative ambiguity value?\n" }, { "role": "assistant", "content": "By examining the 'Ambiguity Rel.' column, we see that the highest value is 12.4, which corresponds to the Sent. (MPQA) task. So, the answer is {\"answer\": \"Sent. (MPQA)\"}" } ], "images": [ "scigen/train/2715.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: For our complexity, ambiguity, and unseen prevalence metrics, we slice the validation set using the median metric value, and compute the average error rates for BERT and random on each slice. We show that the gap between BERT and random errors is larger on the slice above the median than below it in 19 out of 21 cases, in absolute (Abs.) and relative (Rel.) terms." ], "reasoning": "By examining the 'Ambiguity Rel.' column, we see that the highest value is 12.4, which corresponds to the Sent. (MPQA) task.", "question": "Which task has the highest relative ambiguity value?", "answer": "So, the answer is {\"answer\": \"Sent. (MPQA)\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Sent. (SST) task has a higher absolute complexity value than the Sent. (TREC) task.\n" }, { "role": "assistant", "content": "The table shows that the Sent. (SST) task has an absolute complexity value of +7.4, while the Sent. (TREC) task has an absolute complexity value of +5.1. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2715.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: For our complexity, ambiguity, and unseen prevalence metrics, we slice the validation set using the median metric value, and compute the average error rates for BERT and random on each slice. We show that the gap between BERT and random errors is larger on the slice above the median than below it in 19 out of 21 cases, in absolute (Abs.) and relative (Rel.) terms." ], "reasoning": "The table shows that the Sent. (SST) task has an absolute complexity value of +7.4, while the Sent. (TREC) task has an absolute complexity value of +5.1. Therefore, the statement is supported.", "statement": "The Sent. (SST) task has a higher absolute complexity value than the Sent. (TREC) task.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: Performance and sample complexity of random (R) and GloVe (G) relative to BERT (B) for NER, sentiment analysis (Sent.), and language understanding (GLUE) tasks. Second column shows BERT accuracy; third/fourth columns show the accuracy gap between BERT and random/GloVe; fifth/sixth columns show sample complexity ratios, the largest n∈{1,4,16,64,256} for which BERT outperforms random/GloVe when trained on n-times less data. We observe that non-contextual embeddings can often (1) perform within 10% absolute accuracy of the contextual embeddings, and (2) match the performance of contextual embeddings which are trained on 1x-16x less data. This sheds light on a tradeoff between the upfront cost of labeling training data and the inference-time computational cost of the embeddings.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The amount of downstream training data significantly impacts the performance of contextual versus non-contextual embeddings, suggesting that labeling more data can lead to efficiency gains in real-world tasks.We show that the amount of downstream training data is a critical factor in determining the relative performance of contextual vs. non-contextual embeddings. In light of this, ML practitioners may find that for certain real-world tasks the large gains in efficiency are well worth the cost of labeling more data." } ], "images": [ "scigen/train/2716.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance and sample complexity of random (R) and GloVe (G) relative to BERT (B) for NER, sentiment analysis (Sent.), and language understanding (GLUE) tasks. Second column shows BERT accuracy; third/fourth columns show the accuracy gap between BERT and random/GloVe; fifth/sixth columns show sample complexity ratios, the largest n∈{1,4,16,64,256} for which BERT outperforms random/GloVe when trained on n-times less data. We observe that non-contextual embeddings can often (1) perform within 10% absolute accuracy of the contextual embeddings, and (2) match the performance of contextual embeddings which are trained on 1x-16x less data. This sheds light on a tradeoff between the upfront cost of labeling training data and the inference-time computational cost of the embeddings." ], "reasoning": "The amount of downstream training data significantly impacts the performance of contextual versus non-contextual embeddings, suggesting that labeling more data can lead to efficiency gains in real-world tasks.", "description": "We show that the amount of downstream training data is a critical factor in determining the relative performance of contextual vs. non-contextual embeddings. In light of this, ML practitioners may find that for certain real-world tasks the large gains in efficiency are well worth the cost of labeling more data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 1: Performance and sample complexity of random (R) and GloVe (G) relative to BERT (B) for NER, sentiment analysis (Sent.), and language understanding (GLUE) tasks. Second column shows BERT accuracy; third/fourth columns show the accuracy gap between BERT and random/GloVe; fifth/sixth columns show sample complexity ratios, the largest n∈{1,4,16,64,256} for which BERT outperforms random/GloVe when trained on n-times less data. We observe that non-contextual embeddings can often (1) perform within 10% absolute accuracy of the contextual embeddings, and (2) match the performance of contextual embeddings which are trained on 1x-16x less data. This sheds light on a tradeoff between the upfront cost of labeling training data and the inference-time computational cost of the embeddings.', and tackle the question below in a sequential manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich task has the highest sample complexity ratio for R/B?\n" }, { "role": "assistant", "content": "To find the task with the highest sample complexity ratio for R/B, we need to look at the 'Sample complexity ratio' column for R/B and identify the maximum value. The highest value is 256, which corresponds to the tasks SUBJ, SST, MR, and SST (GLUE). So, the answer is {\"answer\": \"SUBJ, SST, MR, SST (GLUE)\"}" } ], "images": [ "scigen/train/2716.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance and sample complexity of random (R) and GloVe (G) relative to BERT (B) for NER, sentiment analysis (Sent.), and language understanding (GLUE) tasks. Second column shows BERT accuracy; third/fourth columns show the accuracy gap between BERT and random/GloVe; fifth/sixth columns show sample complexity ratios, the largest n∈{1,4,16,64,256} for which BERT outperforms random/GloVe when trained on n-times less data. We observe that non-contextual embeddings can often (1) perform within 10% absolute accuracy of the contextual embeddings, and (2) match the performance of contextual embeddings which are trained on 1x-16x less data. This sheds light on a tradeoff between the upfront cost of labeling training data and the inference-time computational cost of the embeddings." ], "reasoning": "To find the task with the highest sample complexity ratio for R/B, we need to look at the 'Sample complexity ratio' column for R/B and identify the maximum value. The highest value is 256, which corresponds to the tasks SUBJ, SST, MR, and SST (GLUE).", "question": "Which task has the highest sample complexity ratio for R/B?", "answer": "So, the answer is {\"answer\": \"SUBJ, SST, MR, SST (GLUE)\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The task with the highest performance gap in B is CoNLL. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe task with the highest performance gap in B is CoNLL.\n" }, { "role": "assistant", "content": "To verify this statement, we need to look at the 'Performance gap' column for B and identify the maximum value. The highest value is 97.7, which corresponds to the task SUBJ, not CoNLL. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2716.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance and sample complexity of random (R) and GloVe (G) relative to BERT (B) for NER, sentiment analysis (Sent.), and language understanding (GLUE) tasks. Second column shows BERT accuracy; third/fourth columns show the accuracy gap between BERT and random/GloVe; fifth/sixth columns show sample complexity ratios, the largest n∈{1,4,16,64,256} for which BERT outperforms random/GloVe when trained on n-times less data. We observe that non-contextual embeddings can often (1) perform within 10% absolute accuracy of the contextual embeddings, and (2) match the performance of contextual embeddings which are trained on 1x-16x less data. This sheds light on a tradeoff between the upfront cost of labeling training data and the inference-time computational cost of the embeddings." ], "reasoning": "To verify this statement, we need to look at the 'Performance gap' column for B and identify the maximum value. The highest value is 97.7, which corresponds to the task SUBJ, not CoNLL.", "statement": "The task with the highest performance gap in B is CoNLL.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: The performance (Matthews correlation coefficients) of BERT, random, and GloVe embeddings across the four linguistic categories defined by the GLUE diagnostic task: lexical semantics (LS), predicate-argument structure (PAS), logic (L), and knowledge and common sense (KCS). We also include the overall diagnostic performance.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The GLUE diagnostic task evaluates models on linguistic phenomena using sentence pairs classified as entailment, contradiction, or neutral, with BERT showing the highest overall performance.GLUE Diagnostic Results The GLUE diagnostic task facilitates a fine-grained analysis of a model’s strengths and weaknesses in terms of how well the model handles different linguistic phenomena. The task consists of 550 sentence pairs which are classified as entailment, contradiction, or neutral. The GLUE team curated the sentence pairs to represent over 20 linguistic phenomena, which are grouped in four top-level categories: lexical semantics (LS), predicate-argument structure (PAS), logic (L), and knowledge and common sense (KCS). We follow the standard procedure and use the model trained on the MNLI dataset (using the random, GloVe, or BERT embeddings) to evaluate performance on the diagnostic task." } ], "images": [ "scigen/train/2717.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The performance (Matthews correlation coefficients) of BERT, random, and GloVe embeddings across the four linguistic categories defined by the GLUE diagnostic task: lexical semantics (LS), predicate-argument structure (PAS), logic (L), and knowledge and common sense (KCS). We also include the overall diagnostic performance." ], "reasoning": "The GLUE diagnostic task evaluates models on linguistic phenomena using sentence pairs classified as entailment, contradiction, or neutral, with BERT showing the highest overall performance.", "description": "GLUE Diagnostic Results The GLUE diagnostic task facilitates a fine-grained analysis of a model’s strengths and weaknesses in terms of how well the model handles different linguistic phenomena. The task consists of 550 sentence pairs which are classified as entailment, contradiction, or neutral. The GLUE team curated the sentence pairs to represent over 20 linguistic phenomena, which are grouped in four top-level categories: lexical semantics (LS), predicate-argument structure (PAS), logic (L), and knowledge and common sense (KCS). We follow the standard procedure and use the model trained on the MNLI dataset (using the random, GloVe, or BERT embeddings) to evaluate performance on the diagnostic task.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: The performance (Matthews correlation coefficients) of BERT, random, and GloVe embeddings across the four linguistic categories defined by the GLUE diagnostic task: lexical semantics (LS), predicate-argument structure (PAS), logic (L), and knowledge and common sense (KCS). We also include the overall diagnostic performance.'. Solve the following question based on the table.\n\nQuestion: Which model performed best overall in the GLUE diagnostic task?\nLet's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the overall performance scores for BERT, Random, and GloVe embeddings. BERT has the highest overall score of 0.500. So, the answer is {\"answer\": \"BERT\"}." } ], "images": [ "scigen/train/2717.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The performance (Matthews correlation coefficients) of BERT, random, and GloVe embeddings across the four linguistic categories defined by the GLUE diagnostic task: lexical semantics (LS), predicate-argument structure (PAS), logic (L), and knowledge and common sense (KCS). We also include the overall diagnostic performance." ], "reasoning": "The table shows the overall performance scores for BERT, Random, and GloVe embeddings. BERT has the highest overall score of 0.500.", "question": "Which model performed best overall in the GLUE diagnostic task?", "answer": "So, the answer is {\"answer\": \"BERT\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Random model outperformed the GloVe model in the KCS category.\n" }, { "role": "assistant", "content": "The table shows that the Random model has a score of 0.17 in the KCS category, while the GloVe model has a score of 0.13. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2717.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The performance (Matthews correlation coefficients) of BERT, random, and GloVe embeddings across the four linguistic categories defined by the GLUE diagnostic task: lexical semantics (LS), predicate-argument structure (PAS), logic (L), and knowledge and common sense (KCS). We also include the overall diagnostic performance." ], "reasoning": "The table shows that the Random model has a score of 0.17 in the KCS category, while the GloVe model has a score of 0.13.", "statement": "The Random model outperformed the GloVe model in the KCS category.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: For our complexity, ambiguity, and unseen prevalence metrics, we slice the validation set using the median metric value, and compute the average error rates for GloVe and BERT on each slice. We show that the gap between GloVe and BERT errors is larger above than below the median in 11 out of 14 of the complexity and ambiguity results both in absolute (Abs.) and relative (Rel.) terms; however, on the unseen metrics, this only holds for 2 out of 7 cases, which suggests that GloVe embeddings are able to relatively effectively deal with unseen words.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The gap between BERT and GloVe performance is larger for validation slices above the median for complexity and ambiguity metrics, but not for unseen metrics.We can see that for 11 out of 14 cases for the complexity and ambiguity metrics, the gap between contextual (BERT) and non-contextual (GloVe) performance is larger for the validation slices above the median than below; this aligns with our results comparing random and BERT embeddings. Interestingly, this is only the case for 2 out of 7 of the cases for the unseen metrics. This is likely because both GloVe and BERT embeddings are able to leverage pretrained semantic information about unseen words to make accurate predictions for them, and thus perform relatively similarly to one another on unseen words." } ], "images": [ "scigen/train/2718.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: For our complexity, ambiguity, and unseen prevalence metrics, we slice the validation set using the median metric value, and compute the average error rates for GloVe and BERT on each slice. We show that the gap between GloVe and BERT errors is larger above than below the median in 11 out of 14 of the complexity and ambiguity results both in absolute (Abs.) and relative (Rel.) terms; however, on the unseen metrics, this only holds for 2 out of 7 cases, which suggests that GloVe embeddings are able to relatively effectively deal with unseen words." ], "reasoning": "The gap between BERT and GloVe performance is larger for validation slices above the median for complexity and ambiguity metrics, but not for unseen metrics.", "description": "We can see that for 11 out of 14 cases for the complexity and ambiguity metrics, the gap between contextual (BERT) and non-contextual (GloVe) performance is larger for the validation slices above the median than below; this aligns with our results comparing random and BERT embeddings. Interestingly, this is only the case for 2 out of 7 of the cases for the unseen metrics. This is likely because both GloVe and BERT embeddings are able to leverage pretrained semantic information about unseen words to make accurate predictions for them, and thus perform relatively similarly to one another on unseen words.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: Table 4: For our complexity, ambiguity, and unseen prevalence metrics, we slice the validation set using the median metric value, and compute the average error rates for GloVe and BERT on each slice. We show that the gap between GloVe and BERT errors is larger above than below the median in 11 out of 14 of the complexity and ambiguity results both in absolute (Abs.) and relative (Rel.) terms; however, on the unseen metrics, this only holds for 2 out of 7 cases, which suggests that GloVe embeddings are able to relatively effectively deal with unseen words.\nQuestion: For how many cases is the gap between BERT and GloVe performance larger for validation slices above the median for the unseen metrics?\n" }, { "role": "assistant", "content": "The description states that this is the case for 2 out of 7 cases for the unseen metrics. So, the answer is {\"answer\": \"2\"}" } ], "images": [ "scigen/train/2718.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: For our complexity, ambiguity, and unseen prevalence metrics, we slice the validation set using the median metric value, and compute the average error rates for GloVe and BERT on each slice. We show that the gap between GloVe and BERT errors is larger above than below the median in 11 out of 14 of the complexity and ambiguity results both in absolute (Abs.) and relative (Rel.) terms; however, on the unseen metrics, this only holds for 2 out of 7 cases, which suggests that GloVe embeddings are able to relatively effectively deal with unseen words." ], "reasoning": "The description states that this is the case for 2 out of 7 cases for the unseen metrics.", "question": "For how many cases is the gap between BERT and GloVe performance larger for validation slices above the median for the unseen metrics?", "answer": "So, the answer is {\"answer\": \"2\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The gap between BERT and GloVe performance is larger for validation slices above the median for the complexity and ambiguity metrics in more than 10 cases. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe gap between BERT and GloVe performance is larger for validation slices above the median for the complexity and ambiguity metrics in more than 10 cases.\n" }, { "role": "assistant", "content": "The description states that for 11 out of 14 cases for the complexity and ambiguity metrics, the gap is larger for validation slices above the median. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2718.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: For our complexity, ambiguity, and unseen prevalence metrics, we slice the validation set using the median metric value, and compute the average error rates for GloVe and BERT on each slice. We show that the gap between GloVe and BERT errors is larger above than below the median in 11 out of 14 of the complexity and ambiguity results both in absolute (Abs.) and relative (Rel.) terms; however, on the unseen metrics, this only holds for 2 out of 7 cases, which suggests that GloVe embeddings are able to relatively effectively deal with unseen words." ], "reasoning": "The description states that for 11 out of 14 cases for the complexity and ambiguity metrics, the gap is larger for validation slices above the median.", "statement": "The gap between BERT and GloVe performance is larger for validation slices above the median for the complexity and ambiguity metrics in more than 10 cases.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Top-1 validation accuracy of ImageNet/ResNet-50 training at the batch size of 16K (90 epochs). The performance of momentum was reported by (Goyal et al., 2017). + means adding the learning rate scheme of Goyal et al. (2017) to the optimizer: (1) 5-epoch warmup to stablize the initial stage; and (2) multiply the learning rate by 0.1 at 30th, 60th, and 80th epoch. The target accuracy is around 0.763 (Goyal et al., 2017). All the adaptive solvers were comprehensively tuned. The tuning information was in the Appendix.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Lamb achieves the highest accuracy of 77.11% at a batch size of 2K, outperforming Lars and other optimizers.Before our study, we did not find any paper reporting a state-of-the-art accuracy achieved by Adam, Adagrad, or AdamW optimizer. In our experiments, even with comprehensive hyper-parameter tuning, Adagrad/Adam/AdamW (with batch size 16K) only achieves 55.38%/66.04%/67.27% top-1 accuracy. After adding learning rate scheme of Goyal et al. However, they are still much lower than 76.3%. The details of the tuning information are in the Appendix. Beyond a batch size of 8K, Lamb’s accuracy is higher than the momentum. Lamb’s accuracy is also slightly better than Lars. At a batch size of 32K, Lamb achieves 76.4% top-1 accuracy while Lars achieves 76.3%. At a batch size of 2K, Lamb is able to achieve 77.11% top-1 accuracy while Lars achieves 76.6%." } ], "images": [ "scigen/train/2719.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Top-1 validation accuracy of ImageNet/ResNet-50 training at the batch size of 16K (90 epochs). The performance of momentum was reported by (Goyal et al., 2017). + means adding the learning rate scheme of Goyal et al. (2017) to the optimizer: (1) 5-epoch warmup to stablize the initial stage; and (2) multiply the learning rate by 0.1 at 30th, 60th, and 80th epoch. The target accuracy is around 0.763 (Goyal et al., 2017). All the adaptive solvers were comprehensively tuned. The tuning information was in the Appendix." ], "reasoning": "Lamb achieves the highest accuracy of 77.11% at a batch size of 2K, outperforming Lars and other optimizers.", "description": "Before our study, we did not find any paper reporting a state-of-the-art accuracy achieved by Adam, Adagrad, or AdamW optimizer. In our experiments, even with comprehensive hyper-parameter tuning, Adagrad/Adam/AdamW (with batch size 16K) only achieves 55.38%/66.04%/67.27% top-1 accuracy. After adding learning rate scheme of Goyal et al. However, they are still much lower than 76.3%. The details of the tuning information are in the Appendix. Beyond a batch size of 8K, Lamb’s accuracy is higher than the momentum. Lamb’s accuracy is also slightly better than Lars. At a batch size of 32K, Lamb achieves 76.4% top-1 accuracy while Lars achieves 76.3%. At a batch size of 2K, Lamb is able to achieve 77.11% top-1 accuracy while Lars achieves 76.6%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nTable title: Table 3: Top-1 validation accuracy of ImageNet/ResNet-50 training at the batch size of 16K (90 epochs). The performance of momentum was reported by (Goyal et al., 2017). + means adding the learning rate scheme of Goyal et al. (2017) to the optimizer: (1) 5-epoch warmup to stablize the initial stage; and (2) multiply the learning rate by 0.1 at 30th, 60th, and 80th epoch. The target accuracy is around 0.763 (Goyal et al., 2017). All the adaptive solvers were comprehensively tuned. The tuning information was in the Appendix.\n\nQuestion: Which optimizer achieves the highest accuracy at a batch size of 2K?\n" }, { "role": "assistant", "content": "The table shows that Lamb achieves an accuracy of 0.7666, which is higher than the accuracies of other optimizers. So, the answer is {\"answer\": \"lamb\"}." } ], "images": [ "scigen/train/2719.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Top-1 validation accuracy of ImageNet/ResNet-50 training at the batch size of 16K (90 epochs). The performance of momentum was reported by (Goyal et al., 2017). + means adding the learning rate scheme of Goyal et al. (2017) to the optimizer: (1) 5-epoch warmup to stablize the initial stage; and (2) multiply the learning rate by 0.1 at 30th, 60th, and 80th epoch. The target accuracy is around 0.763 (Goyal et al., 2017). All the adaptive solvers were comprehensively tuned. The tuning information was in the Appendix." ], "reasoning": "The table shows that Lamb achieves an accuracy of 0.7666, which is higher than the accuracies of other optimizers.", "question": "Which optimizer achieves the highest accuracy at a batch size of 2K?", "answer": "So, the answer is {\"answer\": \"lamb\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nAdam achieves a higher accuracy than Adagrad.\n" }, { "role": "assistant", "content": "The table shows that Adam achieves an accuracy of 0.6604, which is higher than Adagrad's accuracy of 0.5538. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2719.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Top-1 validation accuracy of ImageNet/ResNet-50 training at the batch size of 16K (90 epochs). The performance of momentum was reported by (Goyal et al., 2017). + means adding the learning rate scheme of Goyal et al. (2017) to the optimizer: (1) 5-epoch warmup to stablize the initial stage; and (2) multiply the learning rate by 0.1 at 30th, 60th, and 80th epoch. The target accuracy is around 0.763 (Goyal et al., 2017). All the adaptive solvers were comprehensively tuned. The tuning information was in the Appendix." ], "reasoning": "The table shows that Adam achieves an accuracy of 0.6604, which is higher than Adagrad's accuracy of 0.5538.", "statement": "Adam achieves a higher accuracy than Adagrad.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 1: We use the F1 score on SQuAD-v1 as the accuracy metric. The baseline F1 score is the score obtained by the pre-trained model (Bert-Large) provided on Bert’s public repository (as of February 1st, 2019). We use TPUv3s in our experiments. We use the same setting as the baseline: the first 9/10 of the total epochs used a sequence length of 128 and the last 1/10 of the total epochs used a sequence length of 512. All the experiments run the same number of epochs. Dev set means the test data. It is worth noting that we can achieve better results by manually tuning the hyperparameters. The data in this table is collected from the untuned version.'.\n" }, { "role": "assistant", "content": "By using the Lamb optimizer with a batch size of 32K, the training time for Bert was reduced from 3 days to around 100 minutes, achieving a 49.1 times speedup with 76.7% efficiency.We first discuss empirical results for speeding up Bert training. For this experiment, we use the same dataset as Devlin et al. The F1 score on SQuAD-v1 is used as the accuracy metric in our experiments. All our comparisons are with respect to the baseline Bert model by Devlin et al. To train Bert, Devlin et al. first train the model for 900k iterations using a sequence length of 128 and then switch to a sequence length of 512 for the last 100k iterations. This results in a training time of around 3 days on 16 TPUv3 chips. To ensure a fair comparison, we follow the same SQuAD fine-tune procedure of Devlin et al. As noted earlier, we could get even better results by changing the fine-tune configuration. For instance, by just slightly changing the learning rate in the fine-tune stage, we can obtain a higher F1 score of 91.688 for the batch size of 16K using Lamb. Below we describe two different training choices for training Bert and discuss the corresponding speedups. For the first choice, we maintain the same training procedure as the baseline except for changing the training optimizer to Lamb. We run with the same number of epochs as the baseline but with batch size scaled from 512 to 32K. The choice of 32K batch size (with sequence length 512) is mainly due to memory limits of TPU Pod. By using the Lamb optimizer, we are able to achieve a F1 score of 91.460 in 15625 iterations for a batch size of 32768 (14063 iterations for sequence length 128 and 1562 iterations for sequence length 512). With 32K batch size, we reduce Bert training time from 3 days to around 100 minutes. We achieved 49.1 times speedup by 64 times computational resources (76.7% efficiency). We consider the speedup is great because we use the synchronous data-parallelism. There is a communication overhead coming from transferring of the gradients over the interconnect. For ResNet-50, researchers are able to achieve 90% scaling efficiency because ResNet-50 has much fewer parameters (# parameters is equal to #gradients) than Bert (25 million versus 300 million)." } ], "images": [ "scigen/train/2720.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: We use the F1 score on SQuAD-v1 as the accuracy metric. The baseline F1 score is the score obtained by the pre-trained model (Bert-Large) provided on Bert’s public repository (as of February 1st, 2019). We use TPUv3s in our experiments. We use the same setting as the baseline: the first 9/10 of the total epochs used a sequence length of 128 and the last 1/10 of the total epochs used a sequence length of 512. All the experiments run the same number of epochs. Dev set means the test data. It is worth noting that we can achieve better results by manually tuning the hyperparameters. The data in this table is collected from the untuned version." ], "reasoning": "By using the Lamb optimizer with a batch size of 32K, the training time for Bert was reduced from 3 days to around 100 minutes, achieving a 49.1 times speedup with 76.7% efficiency.", "description": "We first discuss empirical results for speeding up Bert training. For this experiment, we use the same dataset as Devlin et al. The F1 score on SQuAD-v1 is used as the accuracy metric in our experiments. All our comparisons are with respect to the baseline Bert model by Devlin et al. To train Bert, Devlin et al. first train the model for 900k iterations using a sequence length of 128 and then switch to a sequence length of 512 for the last 100k iterations. This results in a training time of around 3 days on 16 TPUv3 chips. To ensure a fair comparison, we follow the same SQuAD fine-tune procedure of Devlin et al. As noted earlier, we could get even better results by changing the fine-tune configuration. For instance, by just slightly changing the learning rate in the fine-tune stage, we can obtain a higher F1 score of 91.688 for the batch size of 16K using Lamb. Below we describe two different training choices for training Bert and discuss the corresponding speedups. For the first choice, we maintain the same training procedure as the baseline except for changing the training optimizer to Lamb. We run with the same number of epochs as the baseline but with batch size scaled from 512 to 32K. The choice of 32K batch size (with sequence length 512) is mainly due to memory limits of TPU Pod. By using the Lamb optimizer, we are able to achieve a F1 score of 91.460 in 15625 iterations for a batch size of 32768 (14063 iterations for sequence length 128 and 1562 iterations for sequence length 512). With 32K batch size, we reduce Bert training time from 3 days to around 100 minutes. We achieved 49.1 times speedup by 64 times computational resources (76.7% efficiency). We consider the speedup is great because we use the synchronous data-parallelism. There is a communication overhead coming from transferring of the gradients over the interconnect. For ResNet-50, researchers are able to achieve 90% scaling efficiency because ResNet-50 has much fewer parameters (# parameters is equal to #gradients) than Bert (25 million versus 300 million).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: We use the F1 score on SQuAD-v1 as the accuracy metric. The baseline F1 score is the score obtained by the pre-trained model (Bert-Large) provided on Bert’s public repository (as of February 1st, 2019). We use TPUv3s in our experiments. We use the same setting as the baseline: the first 9/10 of the total epochs used a sequence length of 128 and the last 1/10 of the total epochs used a sequence length of 512. All the experiments run the same number of epochs. Dev set means the test data. It is worth noting that we can achieve better results by manually tuning the hyperparameters. The data in this table is collected from the untuned version.\nQuestion: What is the F1 score on the dev set when using a batch size of 32K and 1024 TPUs?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows that with a batch size of 32K and 1024 TPUs, the F1 score on the dev set is 91.475. So, the answer is {\"answer\": \"91.475\"}." } ], "images": [ "scigen/train/2720.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: We use the F1 score on SQuAD-v1 as the accuracy metric. The baseline F1 score is the score obtained by the pre-trained model (Bert-Large) provided on Bert’s public repository (as of February 1st, 2019). We use TPUv3s in our experiments. We use the same setting as the baseline: the first 9/10 of the total epochs used a sequence length of 128 and the last 1/10 of the total epochs used a sequence length of 512. All the experiments run the same number of epochs. Dev set means the test data. It is worth noting that we can achieve better results by manually tuning the hyperparameters. The data in this table is collected from the untuned version." ], "reasoning": "The table shows that with a batch size of 32K and 1024 TPUs, the F1 score on the dev set is 91.475.", "question": "What is the F1 score on the dev set when using a batch size of 32K and 1024 TPUs?", "answer": "So, the answer is {\"answer\": \"91.475\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nUsing a batch size of 8k and 256 TPUs, the F1 score on the dev set is higher than 91.3.\n" }, { "role": "assistant", "content": "The table shows that with a batch size of 8k and 256 TPUs, the F1 score on the dev set is 91.263, which is not higher than 91.3. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2720.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: We use the F1 score on SQuAD-v1 as the accuracy metric. The baseline F1 score is the score obtained by the pre-trained model (Bert-Large) provided on Bert’s public repository (as of February 1st, 2019). We use TPUv3s in our experiments. We use the same setting as the baseline: the first 9/10 of the total epochs used a sequence length of 128 and the last 1/10 of the total epochs used a sequence length of 512. All the experiments run the same number of epochs. Dev set means the test data. It is worth noting that we can achieve better results by manually tuning the hyperparameters. The data in this table is collected from the untuned version." ], "reasoning": "The table shows that with a batch size of 8k and 256 TPUs, the F1 score on the dev set is 91.263, which is not higher than 91.3.", "statement": "Using a batch size of 8k and 256 TPUs, the F1 score on the dev set is higher than 91.3.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Lamb achieves a higher performance (F1 score) than Lars for all the batch sizes. The baseline achieves a F1 score of 90.390. Thus, Lars stops scaling at the batch size of 16K.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "AdamW optimizer fails to achieve the target F1 score in large-batch Bert training, unlike Lars and Lamb optimizers which perform well up to a batch size of 32K.To ensure that our approach is compared to a solid baseline for the Bert training, we tried three different strategies for tuning AdamW: (1) AdamW with default hyperparameters (see Devlin et al. (2) AdamW with the same hyperparameters as Lamb, and (3) AdamW with tuned hyperparameters. AdamW stops scaling at the batch size of 16K because it is not able to achieve the target F1 score (88.1 vs 90.4). The tuning information of AdamW is shown in the Appendix. For 64K/32K mixed-batch training, even after extensive tuning of the hyperparameters, we fail to get any reasonable result with AdamW optimizer. We conclude that AdamW does not work well in large-batch Bert training or is at least hard to tune." } ], "images": [ "scigen/train/2721.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Lamb achieves a higher performance (F1 score) than Lars for all the batch sizes. The baseline achieves a F1 score of 90.390. Thus, Lars stops scaling at the batch size of 16K." ], "reasoning": "AdamW optimizer fails to achieve the target F1 score in large-batch Bert training, unlike Lars and Lamb optimizers which perform well up to a batch size of 32K.", "description": "To ensure that our approach is compared to a solid baseline for the Bert training, we tried three different strategies for tuning AdamW: (1) AdamW with default hyperparameters (see Devlin et al. (2) AdamW with the same hyperparameters as Lamb, and (3) AdamW with tuned hyperparameters. AdamW stops scaling at the batch size of 16K because it is not able to achieve the target F1 score (88.1 vs 90.4). The tuning information of AdamW is shown in the Appendix. For 64K/32K mixed-batch training, even after extensive tuning of the hyperparameters, we fail to get any reasonable result with AdamW optimizer. We conclude that AdamW does not work well in large-batch Bert training or is at least hard to tune.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 2: Lamb achieves a higher performance (F1 score) than Lars for all the batch sizes. The baseline achieves a F1 score of 90.390. Thus, Lars stops scaling at the batch size of 16K.' and proceed to solve the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the F1 score achieved by the Lamb optimizer at a batch size of 16K?\n" }, { "role": "assistant", "content": "The table shows the F1 scores for different batch sizes for both Lars and Lamb optimizers. For the Lamb optimizer at a batch size of 16K, the F1 score is 91.345. So, the answer is {\"answer\": \"91.345\"}." } ], "images": [ "scigen/train/2721.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Lamb achieves a higher performance (F1 score) than Lars for all the batch sizes. The baseline achieves a F1 score of 90.390. Thus, Lars stops scaling at the batch size of 16K." ], "reasoning": "The table shows the F1 scores for different batch sizes for both Lars and Lamb optimizers. For the Lamb optimizer at a batch size of 16K, the F1 score is 91.345.", "question": "What is the F1 score achieved by the Lamb optimizer at a batch size of 16K?", "answer": "So, the answer is {\"answer\": \"91.345\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Lars optimizer achieves a higher F1 score than the Lamb optimizer at a batch size of 512., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Lars optimizer achieves a higher F1 score than the Lamb optimizer at a batch size of 512.\n" }, { "role": "assistant", "content": "The table shows that at a batch size of 512, the F1 score for Lars is 90.717 and for Lamb is 91.752. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2721.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Lamb achieves a higher performance (F1 score) than Lars for all the batch sizes. The baseline achieves a F1 score of 90.390. Thus, Lars stops scaling at the batch size of 16K." ], "reasoning": "The table shows that at a batch size of 512, the F1 score for Lars is 90.717 and for Lamb is 91.752. Therefore, the statement is refuted.", "statement": "The Lars optimizer achieves a higher F1 score than the Lamb optimizer at a batch size of 512.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 6: CIFAR-10 training with DavidNet (batch size = 512). All of them run 24 epochs and finish the training under one minute on one cloud TPU. We make sure all the solvers are carefully tuned. The learning rate tuning space of Adam, AdamW, Adagrad and LAMB is {0.0001, 0.0002, 0.0004, 0.0006, 0.0008, 0.001, 0.002, 0.004, 0.006, 0.008, 0.01, 0.02, 0.04, 0.06, 0.08, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 2, 4, 6, 8, 10, 15, 20, 25, 30, 35, 40, 45, 50}. The momentum optimizer was tuned by the baseline implementer. The weight decay term of AdamW was tuned by {0.0001, 0.001, 0.01, 0.1, 1.0}.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "DavidNet achieves 94.06% accuracy in 24 epochs using momentum SGD on GPUs, while Lamb optimizer on TPUs achieves 94.08% accuracy in 24 epochs.According to DAWNBench, DavidNet (a custom 9-layer Residual ConvNet) is the fastest model for CIFAR-10 dataset (as of April 1st, 2019) The baseline uses the momentum SGD optimizer. The PyTorch implementation (momentum SGD optimizer) on GPUs was reported on Standford DAWNBench’s website, which achieves 94.06% in 24 epochs. We use the implementation of TensorFlow on TPUs. Lamb optimizer is able to achieve 94.08% test accuracy in 24 epochs, which is better than other adaptive optimizers and momentum SGD." } ], "images": [ "scigen/train/2722.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: CIFAR-10 training with DavidNet (batch size = 512). All of them run 24 epochs and finish the training under one minute on one cloud TPU. We make sure all the solvers are carefully tuned. The learning rate tuning space of Adam, AdamW, Adagrad and LAMB is {0.0001, 0.0002, 0.0004, 0.0006, 0.0008, 0.001, 0.002, 0.004, 0.006, 0.008, 0.01, 0.02, 0.04, 0.06, 0.08, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 2, 4, 6, 8, 10, 15, 20, 25, 30, 35, 40, 45, 50}. The momentum optimizer was tuned by the baseline implementer. The weight decay term of AdamW was tuned by {0.0001, 0.001, 0.01, 0.1, 1.0}." ], "reasoning": "DavidNet achieves 94.06% accuracy in 24 epochs using momentum SGD on GPUs, while Lamb optimizer on TPUs achieves 94.08% accuracy in 24 epochs.", "description": "According to DAWNBench, DavidNet (a custom 9-layer Residual ConvNet) is the fastest model for CIFAR-10 dataset (as of April 1st, 2019) The baseline uses the momentum SGD optimizer. The PyTorch implementation (momentum SGD optimizer) on GPUs was reported on Standford DAWNBench’s website, which achieves 94.06% in 24 epochs. We use the implementation of TensorFlow on TPUs. Lamb optimizer is able to achieve 94.08% test accuracy in 24 epochs, which is better than other adaptive optimizers and momentum SGD.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 6: CIFAR-10 training with DavidNet (batch size = 512). All of them run 24 epochs and finish the training under one minute on one cloud TPU. We make sure all the solvers are carefully tuned. The learning rate tuning space of Adam, AdamW, Adagrad and LAMB is {0.0001, 0.0002, 0.0004, 0.0006, 0.0008, 0.001, 0.002, 0.004, 0.006, 0.008, 0.01, 0.02, 0.04, 0.06, 0.08, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 2, 4, 6, 8, 10, 15, 20, 25, 30, 35, 40, 45, 50}. The momentum optimizer was tuned by the baseline implementer. The weight decay term of AdamW was tuned by {0.0001, 0.001, 0.01, 0.1, 1.0}.', unfold the steps to solve the question given next. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nQuestion: Which optimizer achieves the highest test accuracy according to the table?\n" }, { "role": "assistant", "content": "The table lists the test accuracies for different optimizers. By comparing the values, Lamb optimizer achieves the highest test accuracy of 0.9408. So, the answer is {\"answer\": \"Lamb\"}" } ], "images": [ "scigen/train/2722.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: CIFAR-10 training with DavidNet (batch size = 512). All of them run 24 epochs and finish the training under one minute on one cloud TPU. We make sure all the solvers are carefully tuned. The learning rate tuning space of Adam, AdamW, Adagrad and LAMB is {0.0001, 0.0002, 0.0004, 0.0006, 0.0008, 0.001, 0.002, 0.004, 0.006, 0.008, 0.01, 0.02, 0.04, 0.06, 0.08, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 2, 4, 6, 8, 10, 15, 20, 25, 30, 35, 40, 45, 50}. The momentum optimizer was tuned by the baseline implementer. The weight decay term of AdamW was tuned by {0.0001, 0.001, 0.01, 0.1, 1.0}." ], "reasoning": "The table lists the test accuracies for different optimizers. By comparing the values, Lamb optimizer achieves the highest test accuracy of 0.9408.", "question": "Which optimizer achieves the highest test accuracy according to the table?", "answer": "So, the answer is {\"answer\": \"Lamb\"}" }, { "messages": [ { "role": "user", "content": "The Adam optimizer achieves a test accuracy of 0.9271. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Adam optimizer achieves a test accuracy of 0.9271.\n" }, { "role": "assistant", "content": "According to the table, the test accuracy for the Adam optimizer is 0.9225, not 0.9271. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2722.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: CIFAR-10 training with DavidNet (batch size = 512). All of them run 24 epochs and finish the training under one minute on one cloud TPU. We make sure all the solvers are carefully tuned. The learning rate tuning space of Adam, AdamW, Adagrad and LAMB is {0.0001, 0.0002, 0.0004, 0.0006, 0.0008, 0.001, 0.002, 0.004, 0.006, 0.008, 0.01, 0.02, 0.04, 0.06, 0.08, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 2, 4, 6, 8, 10, 15, 20, 25, 30, 35, 40, 45, 50}. The momentum optimizer was tuned by the baseline implementer. The weight decay term of AdamW was tuned by {0.0001, 0.001, 0.01, 0.1, 1.0}." ], "reasoning": "According to the table, the test accuracy for the Adam optimizer is 0.9225, not 0.9271.", "statement": "The Adam optimizer achieves a test accuracy of 0.9271.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 7: Test Accuracy by MNIST training with LeNet (30 epochs for Batch Size = 1024). The tuning space of learning rate for all the optimizers is {0.0001, 0.001, 0.01, 0.1}. We use the same learning rate warmup and decay schedule for all of them.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "DavidNet achieves 94.06% accuracy in 24 epochs using momentum SGD on GPUs, while Lamb optimizer on TPUs achieves 94.08% accuracy in 24 epochs.According to DAWNBench, DavidNet (a custom 9-layer Residual ConvNet) is the fastest model for CIFAR-10 dataset (as of April 1st, 2019) The baseline uses the momentum SGD optimizer. The PyTorch implementation (momentum SGD optimizer) on GPUs was reported on Standford DAWNBench’s website, which achieves 94.06% in 24 epochs. We use the implementation of TensorFlow on TPUs. Lamb optimizer is able to achieve 94.08% test accuracy in 24 epochs, which is better than other adaptive optimizers and momentum SGD." } ], "images": [ "scigen/train/2723.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Test Accuracy by MNIST training with LeNet (30 epochs for Batch Size = 1024). The tuning space of learning rate for all the optimizers is {0.0001, 0.001, 0.01, 0.1}. We use the same learning rate warmup and decay schedule for all of them." ], "reasoning": "DavidNet achieves 94.06% accuracy in 24 epochs using momentum SGD on GPUs, while Lamb optimizer on TPUs achieves 94.08% accuracy in 24 epochs.", "description": "According to DAWNBench, DavidNet (a custom 9-layer Residual ConvNet) is the fastest model for CIFAR-10 dataset (as of April 1st, 2019) The baseline uses the momentum SGD optimizer. The PyTorch implementation (momentum SGD optimizer) on GPUs was reported on Standford DAWNBench’s website, which achieves 94.06% in 24 epochs. We use the implementation of TensorFlow on TPUs. Lamb optimizer is able to achieve 94.08% test accuracy in 24 epochs, which is better than other adaptive optimizers and momentum SGD.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 7: Test Accuracy by MNIST training with LeNet (30 epochs for Batch Size = 1024). The tuning space of learning rate for all the optimizers is {0.0001, 0.001, 0.01, 0.1}. We use the same learning rate warmup and decay schedule for all of them.', solve the following question step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich optimizer achieved the highest average accuracy over 5 runs?\n" }, { "role": "assistant", "content": "The table shows the average accuracy over 5 runs for different optimizers. By comparing the values, Lamb optimizer has the highest average accuracy of 0.9945. So, the answer is {\"answer\": \"Lamb\"}" } ], "images": [ "scigen/train/2723.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Test Accuracy by MNIST training with LeNet (30 epochs for Batch Size = 1024). The tuning space of learning rate for all the optimizers is {0.0001, 0.001, 0.01, 0.1}. We use the same learning rate warmup and decay schedule for all of them." ], "reasoning": "The table shows the average accuracy over 5 runs for different optimizers. By comparing the values, Lamb optimizer has the highest average accuracy of 0.9945.", "question": "Which optimizer achieved the highest average accuracy over 5 runs?", "answer": "So, the answer is {\"answer\": \"Lamb\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nAdamW optimizer achieved an average accuracy of 0.9941 over 5 runs.\n" }, { "role": "assistant", "content": "The table lists the average accuracy for AdamW optimizer as 0.9941 over 5 runs, which matches the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2723.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Test Accuracy by MNIST training with LeNet (30 epochs for Batch Size = 1024). The tuning space of learning rate for all the optimizers is {0.0001, 0.001, 0.01, 0.1}. We use the same learning rate warmup and decay schedule for all of them." ], "reasoning": "The table lists the average accuracy for AdamW optimizer as 0.9941 over 5 runs, which matches the statement.", "statement": "AdamW optimizer achieved an average accuracy of 0.9941 over 5 runs.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 8: AdamW stops scaling at the batch size of 16K. The target F1 score is 90.5. Lamb achieves a F1 score of 91.345. The table shows the tuning information of AdamW. In this table, we report the best F1 score we observed from our experiments.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "AdamW stops scaling at the batch size of 16K, achieving a maximum F1 score of 88.540, while Lamb achieves a higher F1 score of 91.345.AdamW stops scaling at the batch size of 16K. The target F1 score is 90.5. Lamb achieves a F1 score of 91.345." } ], "images": [ "scigen/train/2724.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: AdamW stops scaling at the batch size of 16K. The target F1 score is 90.5. Lamb achieves a F1 score of 91.345. The table shows the tuning information of AdamW. In this table, we report the best F1 score we observed from our experiments." ], "reasoning": "AdamW stops scaling at the batch size of 16K, achieving a maximum F1 score of 88.540, while Lamb achieves a higher F1 score of 91.345.", "description": "AdamW stops scaling at the batch size of 16K. The target F1 score is 90.5. Lamb achieves a F1 score of 91.345.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 8: AdamW stops scaling at the batch size of 16K. The target F1 score is 90.5. Lamb achieves a F1 score of 91.345. The table shows the tuning information of AdamW. In this table, we report the best F1 score we observed from our experiments.\nQuestion: What is the highest F1 score achieved by AdamW at a batch size of 16K?\nGive a detailed response to the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest F1 score achieved by AdamW at a batch size of 16K, look at the 'F1 score on dev set' column for AdamW with a batch size of 16K. The highest F1 score listed is 88.540. So, the answer is {\"answer\": \"88.540\"}." } ], "images": [ "scigen/train/2724.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: AdamW stops scaling at the batch size of 16K. The target F1 score is 90.5. Lamb achieves a F1 score of 91.345. The table shows the tuning information of AdamW. In this table, we report the best F1 score we observed from our experiments." ], "reasoning": "To find the highest F1 score achieved by AdamW at a batch size of 16K, look at the 'F1 score on dev set' column for AdamW with a batch size of 16K. The highest F1 score listed is 88.540.", "question": "What is the highest F1 score achieved by AdamW at a batch size of 16K?", "answer": "So, the answer is {\"answer\": \"88.540\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: AdamW achieves a higher F1 score than Lamb., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nAdamW achieves a higher F1 score than Lamb.\n" }, { "role": "assistant", "content": "The table shows that the highest F1 score achieved by AdamW is 88.540, while the description states that Lamb achieves an F1 score of 91.345. Therefore, AdamW does not achieve a higher F1 score than Lamb. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2724.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: AdamW stops scaling at the batch size of 16K. The target F1 score is 90.5. Lamb achieves a F1 score of 91.345. The table shows the tuning information of AdamW. In this table, we report the best F1 score we observed from our experiments." ], "reasoning": "The table shows that the highest F1 score achieved by AdamW is 88.540, while the description states that Lamb achieves an F1 score of 91.345. Therefore, AdamW does not achieve a higher F1 score than Lamb.", "statement": "AdamW achieves a higher F1 score than Lamb.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 9: The accuracy information of tuning default AdaGrad optimizer for ImageNet training with ResNet-50 (batch size = 16384, 90 epochs, 7038 iterations).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The table shows the tuning information for the AdamW optimizer with various learning rates, highlighting the highest top-1 validation accuracy achieved.To make sure our baseline is solid, we carefully tuned the hyper-parameters. Table shows the tuning information of adding the learning rate scheme of Goyal et al. It is tricky to tune the AdamW optimizer since both the L2 regularization and weight decay have the effect on the performance. Thus we have four tuning sets." } ], "images": [ "scigen/train/2725.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: The accuracy information of tuning default AdaGrad optimizer for ImageNet training with ResNet-50 (batch size = 16384, 90 epochs, 7038 iterations)." ], "reasoning": "The table shows the tuning information for the AdamW optimizer with various learning rates, highlighting the highest top-1 validation accuracy achieved.", "description": "To make sure our baseline is solid, we carefully tuned the hyper-parameters. Table shows the tuning information of adding the learning rate scheme of Goyal et al. It is tricky to tune the AdamW optimizer since both the L2 regularization and weight decay have the effect on the performance. Thus we have four tuning sets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 9: The accuracy information of tuning default AdaGrad optimizer for ImageNet training with ResNet-50 (batch size = 16384, 90 epochs, 7038 iterations).\nQuestion: What is the highest top-1 validation accuracy achieved and at which learning rate?\nGive a detailed response to the above question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest top-1 validation accuracy, we need to look through the 'Top-1 Validation Accuracy' column and identify the maximum value, then find the corresponding learning rate. So, the answer is {\"answer\": \"0.553833 at learning rate 0.400\"}." } ], "images": [ "scigen/train/2725.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: The accuracy information of tuning default AdaGrad optimizer for ImageNet training with ResNet-50 (batch size = 16384, 90 epochs, 7038 iterations)." ], "reasoning": "To find the highest top-1 validation accuracy, we need to look through the 'Top-1 Validation Accuracy' column and identify the maximum value, then find the corresponding learning rate.", "question": "What is the highest top-1 validation accuracy achieved and at which learning rate?", "answer": "So, the answer is {\"answer\": \"0.553833 at learning rate 0.400\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe learning rate of 0.400 achieved the highest top-1 validation accuracy.\n" }, { "role": "assistant", "content": "By examining the 'Top-1 Validation Accuracy' column, the highest value is 0.553833, which corresponds to the learning rate of 0.400. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2725.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: The accuracy information of tuning default AdaGrad optimizer for ImageNet training with ResNet-50 (batch size = 16384, 90 epochs, 7038 iterations)." ], "reasoning": "By examining the 'Top-1 Validation Accuracy' column, the highest value is 0.553833, which corresponds to the learning rate of 0.400.", "statement": "The learning rate of 0.400 achieved the highest top-1 validation accuracy.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 10: The accuracy information of tuning AdaGrad optimizer for ImageNet training with ResNet-50 (batch size = 16384, 90 epochs, 7038 iterations). We use the learning rate recipe of (Goyal et al., 2017): (1) 5-epoch warmup to stablize the initial stage; and (2) multiply the learning rate by 0.1 at 30th, 60th, and 80th epoch. The target accuracy is around 0.763 (Goyal et al., 2017).'.\n" }, { "role": "assistant", "content": "The table shows the tuning information of adding the learning rate scheme of Goyal et al. The highest top-1 validation accuracy is achieved with a learning rate of 14.00.To make sure our baseline is solid, we carefully tuned the hyper-parameters. Table shows the tuning information of adding the learning rate scheme of Goyal et al. It is tricky to tune the AdamW optimizer since both the L2 regularization and weight decay have the effect on the performance. Thus we have four tuning sets." } ], "images": [ "scigen/train/2726.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: The accuracy information of tuning AdaGrad optimizer for ImageNet training with ResNet-50 (batch size = 16384, 90 epochs, 7038 iterations). We use the learning rate recipe of (Goyal et al., 2017): (1) 5-epoch warmup to stablize the initial stage; and (2) multiply the learning rate by 0.1 at 30th, 60th, and 80th epoch. The target accuracy is around 0.763 (Goyal et al., 2017)." ], "reasoning": "The table shows the tuning information of adding the learning rate scheme of Goyal et al. The highest top-1 validation accuracy is achieved with a learning rate of 14.00.", "description": "To make sure our baseline is solid, we carefully tuned the hyper-parameters. Table shows the tuning information of adding the learning rate scheme of Goyal et al. It is tricky to tune the AdamW optimizer since both the L2 regularization and weight decay have the effect on the performance. Thus we have four tuning sets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 10: The accuracy information of tuning AdaGrad optimizer for ImageNet training with ResNet-50 (batch size = 16384, 90 epochs, 7038 iterations). We use the learning rate recipe of (Goyal et al., 2017): (1) 5-epoch warmup to stablize the initial stage; and (2) multiply the learning rate by 0.1 at 30th, 60th, and 80th epoch. The target accuracy is around 0.763 (Goyal et al., 2017).' and proceed to solve the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat learning rate achieves the highest top-1 validation accuracy?\n" }, { "role": "assistant", "content": "By examining the table, we see that the highest top-1 validation accuracy is 0.72007245, which corresponds to a learning rate of 14.00. So, the answer is {\"answer\": \"14.00\"}." } ], "images": [ "scigen/train/2726.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: The accuracy information of tuning AdaGrad optimizer for ImageNet training with ResNet-50 (batch size = 16384, 90 epochs, 7038 iterations). We use the learning rate recipe of (Goyal et al., 2017): (1) 5-epoch warmup to stablize the initial stage; and (2) multiply the learning rate by 0.1 at 30th, 60th, and 80th epoch. The target accuracy is around 0.763 (Goyal et al., 2017)." ], "reasoning": "By examining the table, we see that the highest top-1 validation accuracy is 0.72007245, which corresponds to a learning rate of 14.00.", "question": "What learning rate achieves the highest top-1 validation accuracy?", "answer": "So, the answer is {\"answer\": \"14.00\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe learning rate of 0.100 achieves a top-1 validation accuracy of 0.13429768.\n" }, { "role": "assistant", "content": "The table shows that a learning rate of 0.100 corresponds to a top-1 validation accuracy of 0.13429768. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2726.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: The accuracy information of tuning AdaGrad optimizer for ImageNet training with ResNet-50 (batch size = 16384, 90 epochs, 7038 iterations). We use the learning rate recipe of (Goyal et al., 2017): (1) 5-epoch warmup to stablize the initial stage; and (2) multiply the learning rate by 0.1 at 30th, 60th, and 80th epoch. The target accuracy is around 0.763 (Goyal et al., 2017)." ], "reasoning": "The table shows that a learning rate of 0.100 corresponds to a top-1 validation accuracy of 0.13429768.", "statement": "The learning rate of 0.100 achieves a top-1 validation accuracy of 0.13429768.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 11: The accuracy information of tuning default Adam optimizer for ImageNet training with ResNet-50 (batch size = 16384, 90 epochs, 7038 iterations). The target accuracy is around 0.763 (Goyal et al., 2017).', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The table shows the tuning results of the learning rate for the AdamW optimizer, highlighting the highest top-1 validation accuracy achieved.To make sure our baseline is solid, we carefully tuned the hyper-parameters. Table shows the tuning information of adding the learning rate scheme of Goyal et al. It is tricky to tune the AdamW optimizer since both the L2 regularization and weight decay have the effect on the performance. Thus we have four tuning sets." } ], "images": [ "scigen/train/2727.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: The accuracy information of tuning default Adam optimizer for ImageNet training with ResNet-50 (batch size = 16384, 90 epochs, 7038 iterations). The target accuracy is around 0.763 (Goyal et al., 2017)." ], "reasoning": "The table shows the tuning results of the learning rate for the AdamW optimizer, highlighting the highest top-1 validation accuracy achieved.", "description": "To make sure our baseline is solid, we carefully tuned the hyper-parameters. Table shows the tuning information of adding the learning rate scheme of Goyal et al. It is tricky to tune the AdamW optimizer since both the L2 regularization and weight decay have the effect on the performance. Thus we have four tuning sets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 11: The accuracy information of tuning default Adam optimizer for ImageNet training with ResNet-50 (batch size = 16384, 90 epochs, 7038 iterations). The target accuracy is around 0.763 (Goyal et al., 2017).\nQuestion: What learning rate achieved the highest top-1 validation accuracy?\n" }, { "role": "assistant", "content": "To find the learning rate that achieved the highest top-1 validation accuracy, look for the highest value in the 'Top-1 Validation Accuracy' column and identify the corresponding learning rate. So, the answer is {\"answer\": \"0.001\"}" } ], "images": [ "scigen/train/2727.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: The accuracy information of tuning default Adam optimizer for ImageNet training with ResNet-50 (batch size = 16384, 90 epochs, 7038 iterations). The target accuracy is around 0.763 (Goyal et al., 2017)." ], "reasoning": "To find the learning rate that achieved the highest top-1 validation accuracy, look for the highest value in the 'Top-1 Validation Accuracy' column and identify the corresponding learning rate.", "question": "What learning rate achieved the highest top-1 validation accuracy?", "answer": "So, the answer is {\"answer\": \"0.001\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The learning rate of 0.0008 achieved the highest top-1 validation accuracy., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe learning rate of 0.0008 achieved the highest top-1 validation accuracy.\n" }, { "role": "assistant", "content": "The table shows that the highest top-1 validation accuracy is 0.6604, which corresponds to a learning rate of 0.001, not 0.0008. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2727.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: The accuracy information of tuning default Adam optimizer for ImageNet training with ResNet-50 (batch size = 16384, 90 epochs, 7038 iterations). The target accuracy is around 0.763 (Goyal et al., 2017)." ], "reasoning": "The table shows that the highest top-1 validation accuracy is 0.6604, which corresponds to a learning rate of 0.001, not 0.0008.", "statement": "The learning rate of 0.0008 achieved the highest top-1 validation accuracy.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: Number of annotators per instance and total instances annotated by Experts and the Crowd, and the number of overlapping instances by domain.'.\n" }, { "role": "assistant", "content": "Both experts and crowd workers annotated up to 500 arguments per domain, with crowd workers being paid between $0.45 and $0.65 per judgment to ensure $21 per hour.qualifications and pay?color= yellow!20todo : color=yellow!20crowd workers were all native English speakers paid $.45-$.65 per judgment (increased over time) to ensure $21 per hour. manually reviewed each crowd workers judgments to block those underperforming and used MACE and Krippendorff to assess annotators -LN For both groups, we used the same annotation guidelines and annotation interface, which we iteratively improved according to the feedback collected in each calibration round. The guidelines as well as our interface are available online. For each domain, up to 500 arguments were annotated by both experts and crowd workers.color=blue!20todo: color=blue!20Just to confirm: 1,2,3 in Experts the number of Experts that annotated those n’s. So in column 3, 625 Q&A arguments were annotated by 3 experts. Is this right? Yes! be in the Standard Splits section" } ], "images": [ "scigen/train/2728.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Number of annotators per instance and total instances annotated by Experts and the Crowd, and the number of overlapping instances by domain." ], "reasoning": "Both experts and crowd workers annotated up to 500 arguments per domain, with crowd workers being paid between $0.45 and $0.65 per judgment to ensure $21 per hour.", "description": "qualifications and pay?color= yellow!20todo : color=yellow!20crowd workers were all native English speakers paid $.45-$.65 per judgment (increased over time) to ensure $21 per hour. manually reviewed each crowd workers judgments to block those underperforming and used MACE and Krippendorff to assess annotators -LN For both groups, we used the same annotation guidelines and annotation interface, which we iteratively improved according to the feedback collected in each calibration round. The guidelines as well as our interface are available online. For each domain, up to 500 arguments were annotated by both experts and crowd workers.color=blue!20todo: color=blue!20Just to confirm: 1,2,3 in Experts the number of Experts that annotated those n’s. So in column 3, 625 Q&A arguments were annotated by 3 experts. Is this right? Yes! be in the Standard Splits section", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 1: Number of annotators per instance and total instances annotated by Experts and the Crowd, and the number of overlapping instances by domain.', solve this question and give detail solutions. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nHow many Q&A arguments were annotated by 1 expert?\n" }, { "role": "assistant", "content": "The table shows that 626 Q&A arguments were annotated by 1 expert. So, the answer is {\"answer\": \"626\"}" } ], "images": [ "scigen/train/2728.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Number of annotators per instance and total instances annotated by Experts and the Crowd, and the number of overlapping instances by domain." ], "reasoning": "The table shows that 626 Q&A arguments were annotated by 1 expert.", "question": "How many Q&A arguments were annotated by 1 expert?", "answer": "So, the answer is {\"answer\": \"626\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nCrowd workers annotated more Debate arguments than Q&A arguments.\n" }, { "role": "assistant", "content": "The table shows that crowd workers annotated 1,438 Debate arguments and 1,334 Q&A arguments, which means they annotated more Debate arguments. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2728.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Number of annotators per instance and total instances annotated by Experts and the Crowd, and the number of overlapping instances by domain." ], "reasoning": "The table shows that crowd workers annotated 1,438 Debate arguments and 1,334 Q&A arguments, which means they annotated more Debate arguments.", "statement": "Crowd workers annotated more Debate arguments than Q&A arguments.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Number of annotators per instance and total instances annotated by Experts and the Crowd, and the number of overlapping instances by domain.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table shows the distribution of annotated arguments across different domains and splits, with a total of 5,285 arguments annotated.qualifications and pay?color= yellow!20todo : color=yellow!20crowd workers were all native English speakers paid $.45-$.65 per judgment (increased over time) to ensure $21 per hour. manually reviewed each crowd workers judgments to block those underperforming and used MACE and Krippendorff to assess annotators -LN For both groups, we used the same annotation guidelines and annotation interface, which we iteratively improved according to the feedback collected in each calibration round. The guidelines as well as our interface are available online. For each domain, up to 500 arguments were annotated by both experts and crowd workers.color=blue!20todo: color=blue!20Just to confirm: 1,2,3 in Experts the number of Experts that annotated those n’s. So in column 3, 625 Q&A arguments were annotated by 3 experts. Is this right? Yes! be in the Standard Splits section" } ], "images": [ "scigen/train/2729.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Number of annotators per instance and total instances annotated by Experts and the Crowd, and the number of overlapping instances by domain." ], "reasoning": "The table shows the distribution of annotated arguments across different domains and splits, with a total of 5,285 arguments annotated.", "description": "qualifications and pay?color= yellow!20todo : color=yellow!20crowd workers were all native English speakers paid $.45-$.65 per judgment (increased over time) to ensure $21 per hour. manually reviewed each crowd workers judgments to block those underperforming and used MACE and Krippendorff to assess annotators -LN For both groups, we used the same annotation guidelines and annotation interface, which we iteratively improved according to the feedback collected in each calibration round. The guidelines as well as our interface are available online. For each domain, up to 500 arguments were annotated by both experts and crowd workers.color=blue!20todo: color=blue!20Just to confirm: 1,2,3 in Experts the number of Experts that annotated those n’s. So in column 3, 625 Q&A arguments were annotated by 3 experts. Is this right? Yes! be in the Standard Splits section", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 1: Number of annotators per instance and total instances annotated by Experts and the Crowd, and the number of overlapping instances by domain.'\nQuestion: How many arguments were annotated in the Debate domain for the Test split?\n" }, { "role": "assistant", "content": "To find the number of arguments annotated in the Debate domain for the Test split, look at the row labeled 'Debate' and the column labeled 'Test'. So, the answer is {\"answer\": \"538\"}" } ], "images": [ "scigen/train/2729.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Number of annotators per instance and total instances annotated by Experts and the Crowd, and the number of overlapping instances by domain." ], "reasoning": "To find the number of arguments annotated in the Debate domain for the Test split, look at the row labeled 'Debate' and the column labeled 'Test'.", "question": "How many arguments were annotated in the Debate domain for the Test split?", "answer": "So, the answer is {\"answer\": \"538\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe total number of annotated arguments in the Review domain is 1,100.\n" }, { "role": "assistant", "content": "The table shows that the total number of annotated arguments in the Review domain is indeed 1,100. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2729.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Number of annotators per instance and total instances annotated by Experts and the Crowd, and the number of overlapping instances by domain." ], "reasoning": "The table shows that the total number of annotated arguments in the Review domain is indeed 1,100.", "statement": "The total number of annotated arguments in the Review domain is 1,100.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Agreement between expert annotations from wachsmuth-etal-2017-computational and crowd-sourced annotations from two sources: AQCorpus (Ours) and wachsmuth-etal-2017-argumentation (TvsP) on 200 randomly sampled instances.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The inter-annotator agreement (IAA) between expert and crowd ratings varies across domains, with Q&A forums showing the highest agreement scores.Inter-annotator Agreements. In order to assess the quality of our crowd-sourced annotations and to test our simplified annotation guidelines, we employ the Dagstuhl-ArgQuality-Corpus-V2 (DS) We take “gold“ ratings from the original, author-produced annotations presented in \\newcitewachsmuth-etal-2017-computational. DS was presented in combination with the taxonomy of theory-based AQ described above and consists of 320 web debate arguments annotated with all 15 argument quality aspects. We randomly sample 200 arguments and crowd-source annotations on Appen.color= blue!20todo : color= blue!20how many workers were used per argument? is it the same as in the prior section? For each instance and AQ dimension, we obtain group votes by either averaging the workers’ votes (Mean) or applying MACEcolor=green!20todo: color =green!20why do we use MACE, and do we need to? We measure inter-annotator agreement (IAA) between the obtained group vote and the DS expert vote. Generally, the agreement scores of our crowd ratings are comparably high and often surpass the agreement scores reported by TvsP. We therefore conclude that our guidelines and user interface support the task, and confirm the suitability of our crowd annotators. For debate forums, Krippendorff’s α is up to .21 , while for the Q&A forums, the agreement is higher – up to .53. These results suggest that the difficulty of the task is highly dependent on the domain." } ], "images": [ "scigen/train/2730.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Agreement between expert annotations from wachsmuth-etal-2017-computational and crowd-sourced annotations from two sources: AQCorpus (Ours) and wachsmuth-etal-2017-argumentation (TvsP) on 200 randomly sampled instances." ], "reasoning": "The inter-annotator agreement (IAA) between expert and crowd ratings varies across domains, with Q&A forums showing the highest agreement scores.", "description": "Inter-annotator Agreements. In order to assess the quality of our crowd-sourced annotations and to test our simplified annotation guidelines, we employ the Dagstuhl-ArgQuality-Corpus-V2 (DS) We take “gold“ ratings from the original, author-produced annotations presented in \\newcitewachsmuth-etal-2017-computational. DS was presented in combination with the taxonomy of theory-based AQ described above and consists of 320 web debate arguments annotated with all 15 argument quality aspects. We randomly sample 200 arguments and crowd-source annotations on Appen.color= blue!20todo : color= blue!20how many workers were used per argument? is it the same as in the prior section? For each instance and AQ dimension, we obtain group votes by either averaging the workers’ votes (Mean) or applying MACEcolor=green!20todo: color =green!20why do we use MACE, and do we need to? We measure inter-annotator agreement (IAA) between the obtained group vote and the DS expert vote. Generally, the agreement scores of our crowd ratings are comparably high and often surpass the agreement scores reported by TvsP. We therefore conclude that our guidelines and user interface support the task, and confirm the suitability of our crowd annotators. For debate forums, Krippendorff’s α is up to .21 , while for the Q&A forums, the agreement is higher – up to .53. These results suggest that the difficulty of the task is highly dependent on the domain.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: Agreement between expert annotations from wachsmuth-etal-2017-computational and crowd-sourced annotations from two sources: AQCorpus (Ours) and wachsmuth-etal-2017-argumentation (TvsP) on 200 randomly sampled instances.', answer the following question. Give detailed solution consisting of each step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich domain has the highest inter-annotator agreement (IAA) for the 'Effec' dimension?\n" }, { "role": "assistant", "content": "By comparing the IAA scores for the 'Effec' dimension across all domains, the Q&A domain has the highest score of 0.52. So, the answer is {\"answer\": \"Q&A\"}." } ], "images": [ "scigen/train/2730.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Agreement between expert annotations from wachsmuth-etal-2017-computational and crowd-sourced annotations from two sources: AQCorpus (Ours) and wachsmuth-etal-2017-argumentation (TvsP) on 200 randomly sampled instances." ], "reasoning": "By comparing the IAA scores for the 'Effec' dimension across all domains, the Q&A domain has the highest score of 0.52.", "question": "Which domain has the highest inter-annotator agreement (IAA) for the 'Effec' dimension?", "answer": "So, the answer is {\"answer\": \"Q&A\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe inter-annotator agreement (IAA) for the 'Reas' dimension is highest in the Review domain.\n" }, { "role": "assistant", "content": "The table shows that the IAA for the 'Reas' dimension is 0.21 for Debate, 0.52 for Q&A, and 0.31 for Review. Therefore, the highest IAA for 'Reas' is in the Q&A domain, not the Review domain. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2730.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Agreement between expert annotations from wachsmuth-etal-2017-computational and crowd-sourced annotations from two sources: AQCorpus (Ours) and wachsmuth-etal-2017-argumentation (TvsP) on 200 randomly sampled instances." ], "reasoning": "The table shows that the IAA for the 'Reas' dimension is 0.21 for Debate, 0.52 for Q&A, and 0.31 for Review. Therefore, the highest IAA for 'Reas' is in the Q&A domain, not the Review domain.", "statement": "The inter-annotator agreement (IAA) for the 'Reas' dimension is highest in the Review domain.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 10: Pearson (r) and Spearman (ρ) correlation coefficients when predicting with our BERT MTFlat trained all-domain or in-domain on the Dagstuhl data. We correlate our prediction for each dimension with its associated true label when applying mean or majority aggregation on the annotations. Scores in bold indicate best performances per domain.'.\n" }, { "role": "assistant", "content": "The BERT MTFlat models, trained on in-domain or all-domain data, predict AQ dimension scores on the DS corpus, with performance generally dropping due to domain-transfer.For further validation, in addition to only predicting on our own corpus, AQCorpus, we, again, employ DScolor=blue!20todo: color= blue!20what is DS? I must have missed this and predict theory-based AQ scores on DS instances. In order to obtain a single gold score per annotation instance and dimension from DS, we apply two ways of aggregating the annotations provided by the authors: (1) majority vote, and (2) mean aggregation. As the BERT MTFlat modelscolor=yellow!20todo: color= yellow!20This is the problem here. have shown to perform best on many setups of the previous experiments, we reuse them – trained either on in-domain data or using data from all domains on AQCorpus – and predict AQ dimension scores on the whole DS corpus.color= yellow!20todo : color= yellow!20I need to rerun the experiments with the ST models because I employed the MT models and at this point in time, they are not discussed yet, right?color=green!20todo : color= green!20yes , I see the problem here. Generally, compared to the results on AQCorpus, the performance drops. This can be attributed to the domain-transfer. While DS also consist of web debate arguments, they consist of individual isolated arguments and not – as our corpus – of more complex texts. Training on our joint training set across all domains yields the highest correlation scores and generally, the annotations related to overall argument quality and reasonableness correlate best with our scores. To the best of our knowledge, we are the first to have a trained model in-place which is able to predict scores for fine-grained argument quality dimensions." } ], "images": [ "scigen/train/2731.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Pearson (r) and Spearman (ρ) correlation coefficients when predicting with our BERT MTFlat trained all-domain or in-domain on the Dagstuhl data. We correlate our prediction for each dimension with its associated true label when applying mean or majority aggregation on the annotations. Scores in bold indicate best performances per domain." ], "reasoning": "The BERT MTFlat models, trained on in-domain or all-domain data, predict AQ dimension scores on the DS corpus, with performance generally dropping due to domain-transfer.", "description": "For further validation, in addition to only predicting on our own corpus, AQCorpus, we, again, employ DScolor=blue!20todo: color= blue!20what is DS? I must have missed this and predict theory-based AQ scores on DS instances. In order to obtain a single gold score per annotation instance and dimension from DS, we apply two ways of aggregating the annotations provided by the authors: (1) majority vote, and (2) mean aggregation. As the BERT MTFlat modelscolor=yellow!20todo: color= yellow!20This is the problem here. have shown to perform best on many setups of the previous experiments, we reuse them – trained either on in-domain data or using data from all domains on AQCorpus – and predict AQ dimension scores on the whole DS corpus.color= yellow!20todo : color= yellow!20I need to rerun the experiments with the ST models because I employed the MT models and at this point in time, they are not discussed yet, right?color=green!20todo : color= green!20yes , I see the problem here. Generally, compared to the results on AQCorpus, the performance drops. This can be attributed to the domain-transfer. While DS also consist of web debate arguments, they consist of individual isolated arguments and not – as our corpus – of more complex texts. Training on our joint training set across all domains yields the highest correlation scores and generally, the annotations related to overall argument quality and reasonableness correlate best with our scores. To the best of our knowledge, we are the first to have a trained model in-place which is able to predict scores for fine-grained argument quality dimensions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 10: Pearson (r) and Spearman (ρ) correlation coefficients when predicting with our BERT MTFlat trained all-domain or in-domain on the Dagstuhl data. We correlate our prediction for each dimension with its associated true label when applying mean or majority aggregation on the annotations. Scores in bold indicate best performances per domain.', answer the question below by showing each progressive step toward the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich domain and dimension combination has the highest mean r correlation score?\n" }, { "role": "assistant", "content": "By examining the table, the highest mean r correlation score is found in the 'All' domain for the 'Overall' dimension with a score of 0.515. So, the answer is {\"answer\": \"All, Overall\"}" } ], "images": [ "scigen/train/2731.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Pearson (r) and Spearman (ρ) correlation coefficients when predicting with our BERT MTFlat trained all-domain or in-domain on the Dagstuhl data. We correlate our prediction for each dimension with its associated true label when applying mean or majority aggregation on the annotations. Scores in bold indicate best performances per domain." ], "reasoning": "By examining the table, the highest mean r correlation score is found in the 'All' domain for the 'Overall' dimension with a score of 0.515.", "question": "Which domain and dimension combination has the highest mean r correlation score?", "answer": "So, the answer is {\"answer\": \"All, Overall\"}" }, { "messages": [ { "role": "user", "content": "The 'Review forums' domain has the lowest majority r correlation score for the 'Effectiveness' dimension. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe 'Review forums' domain has the lowest majority r correlation score for the 'Effectiveness' dimension.\n" }, { "role": "assistant", "content": "By examining the table, the 'Review forums' domain has a majority r correlation score of 0.257 for the 'Effectiveness' dimension, which is the lowest among all domains and dimensions. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2731.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Pearson (r) and Spearman (ρ) correlation coefficients when predicting with our BERT MTFlat trained all-domain or in-domain on the Dagstuhl data. We correlate our prediction for each dimension with its associated true label when applying mean or majority aggregation on the annotations. Scores in bold indicate best performances per domain." ], "reasoning": "By examining the table, the 'Review forums' domain has a majority r correlation score of 0.257 for the 'Effectiveness' dimension, which is the lowest among all domains and dimensions.", "statement": "The 'Review forums' domain has the lowest majority r correlation score for the 'Effectiveness' dimension.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Tagging accuracy on various test set varieties (domains, languages and age groups; Tw=Twitter), using coarse POS [petrov:ea:2012]. OOV: out-of-vocabulary rate wrt Wsj.train. Accuracy is significantly correlated with OOV rate (ρ = -0.70).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Models trained on WSJ perform worse on data from younger age groups, confirming age bias. Performance drops significantly when applying a single language tagger to other languages.First, as is well known, we see that all taggers suffer when applied to other domains. However, models trained on WSJ fare worse on data from the younger age group, thus age is a covariate. This confirms the age bias reported in [hovy:soegaard:2015:age] for the same data but using different taggers. If we stretch the notion of variety to other languages, we see that performance unsurprisingly drops dramatically. Remember, we just apply an in-domain single language tagger to other languages, although only trained on WSJ here." } ], "images": [ "scigen/train/2732.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Tagging accuracy on various test set varieties (domains, languages and age groups; Tw=Twitter), using coarse POS [petrov:ea:2012]. OOV: out-of-vocabulary rate wrt Wsj.train. Accuracy is significantly correlated with OOV rate (ρ = -0.70)." ], "reasoning": "Models trained on WSJ perform worse on data from younger age groups, confirming age bias. Performance drops significantly when applying a single language tagger to other languages.", "description": "First, as is well known, we see that all taggers suffer when applied to other domains. However, models trained on WSJ fare worse on data from the younger age group, thus age is a covariate. This confirms the age bias reported in [hovy:soegaard:2015:age] for the same data but using different taggers. If we stretch the notion of variety to other languages, we see that performance unsurprisingly drops dramatically. Remember, we just apply an in-domain single language tagger to other languages, although only trained on WSJ here.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nTable title: Table 2: Tagging accuracy on various test set varieties (domains, languages and age groups; Tw=Twitter), using coarse POS [petrov:ea:2012]. OOV: out-of-vocabulary rate wrt Wsj.train. Accuracy is significantly correlated with OOV rate (ρ = -0.70).\n\nQuestion: Which sample has the highest TnT score among the different domains?\n" }, { "role": "assistant", "content": "To find the sample with the highest TnT score among the different domains, we look at the 'domain' rows and compare their TnT scores. The sample 'anwers' has the highest TnT score of 90.08. So, the answer is {\"answer\": \"anwers\"}" } ], "images": [ "scigen/train/2732.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Tagging accuracy on various test set varieties (domains, languages and age groups; Tw=Twitter), using coarse POS [petrov:ea:2012]. OOV: out-of-vocabulary rate wrt Wsj.train. Accuracy is significantly correlated with OOV rate (ρ = -0.70)." ], "reasoning": "To find the sample with the highest TnT score among the different domains, we look at the 'domain' rows and compare their TnT scores. The sample 'anwers' has the highest TnT score of 90.08.", "question": "Which sample has the highest TnT score among the different domains?", "answer": "So, the answer is {\"answer\": \"anwers\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe TnT score for the 'emails' sample is higher than the TnT score for the 'Tw (foster)' sample.\n" }, { "role": "assistant", "content": "The TnT score for the 'emails' sample is 91.03, while the TnT score for the 'Tw (foster)' sample is 90.25. Therefore, the statement is supported by the table. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2732.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Tagging accuracy on various test set varieties (domains, languages and age groups; Tw=Twitter), using coarse POS [petrov:ea:2012]. OOV: out-of-vocabulary rate wrt Wsj.train. Accuracy is significantly correlated with OOV rate (ρ = -0.70)." ], "reasoning": "The TnT score for the 'emails' sample is 91.03, while the TnT score for the 'Tw (foster)' sample is 90.25. Therefore, the statement is supported by the table.", "statement": "The TnT score for the 'emails' sample is higher than the TnT score for the 'Tw (foster)' sample.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Single-task learning (STL) and Multi-task (MTL) learning frameworks for the proposed approach. T: Text, V: Visual, A: Acoustic. Weighted accuracy as a metric is chosen due to unbalanced samples across various emotions and it is also in line with the other existing works Zadeh et al. (2018c).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The proposed framework uses bi-GRU networks and pair-wise inter-modal attention mechanisms to capture contextual information and joint-associations between multiple modalities for sentiment and emotion classification, achieving high F1-scores and accuracy values.In our proposed framework, we aim to leverage multi-modal and contextual information for predicting sentiment and emotion of an utterance simultaneously in a multi-task learning framework. As stated earlier, a video consists of a sequence of utterances and their semantics often have inter-dependencies on each other. We employ three bi-directional Gated Recurrent Unit (bi-GRU) network for capturing the contextual information (i.e., one for each modality). Subsequently, we introduce pair-wise inter-modal attention mechanism (i.e. visual-text, text-acoustic and acoustic-visual) to learn the joint-association between the multiple modalities & utterances. The objective is to emphasize on the contributing features by putting more attention to the respective utterance and neighboring utterances. Motivated by the residual skip connection He et al. the outputs of pair-wise attentions along with the representations of individual modalities are concatenated. Finally, the concatenated representation is shared across the two branches of our proposed network- corresponding to two tasks, i.e., sentiment and emotion classification for prediction (one for each task in the multi-task framework). Sentiment classification branch contains a softmax layer for final classification (i.e. positive & negative), whereas for emotion classification we use sigmoid layer. The shared representation will receive gradients of error from both the branches (sentiment & emotion) and accordingly adjust the weights of the models. Thus, the shared representations will not be biased to any particular task, and it will assist the model in achieving generalization for the multiple tasks. As stated earlier, our proposed approach requires at least two modalities to compute bi-modal attention. Hence, we experiment with bi-modal and tri-modal input combinations for the proposed approach i.e. taking text-visual, text-acoustic, acoustic-visual and text-visual-acoustic at a time. For completeness (i.e., uni-modal inputs), we also experiment with a variant of the proposed approach where we apply self-attention on the utterances of each modality separately. The self-attention unit utilizes the contextual information of the utterances (i.e., it receives u×d hidden representations), applies attention and forward it to the output layer for classification. In the single-task framework, we build separate systems for sentiment and emotion analysis, whereas in multi-task framework a joint-model is learned for both of these problems. For sentiment classification, our single-task framework reports an F1-score of 77.67% and accuracy value of 79.8% for the tri-modal inputs. Similarly, we obtain 77.71% F1-score and 60.88% weighted accuracy for emotion classification." } ], "images": [ "scigen/train/2733.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Single-task learning (STL) and Multi-task (MTL) learning frameworks for the proposed approach. T: Text, V: Visual, A: Acoustic. Weighted accuracy as a metric is chosen due to unbalanced samples across various emotions and it is also in line with the other existing works Zadeh et al. (2018c)." ], "reasoning": "The proposed framework uses bi-GRU networks and pair-wise inter-modal attention mechanisms to capture contextual information and joint-associations between multiple modalities for sentiment and emotion classification, achieving high F1-scores and accuracy values.", "description": "In our proposed framework, we aim to leverage multi-modal and contextual information for predicting sentiment and emotion of an utterance simultaneously in a multi-task learning framework. As stated earlier, a video consists of a sequence of utterances and their semantics often have inter-dependencies on each other. We employ three bi-directional Gated Recurrent Unit (bi-GRU) network for capturing the contextual information (i.e., one for each modality). Subsequently, we introduce pair-wise inter-modal attention mechanism (i.e. visual-text, text-acoustic and acoustic-visual) to learn the joint-association between the multiple modalities & utterances. The objective is to emphasize on the contributing features by putting more attention to the respective utterance and neighboring utterances. Motivated by the residual skip connection He et al. the outputs of pair-wise attentions along with the representations of individual modalities are concatenated. Finally, the concatenated representation is shared across the two branches of our proposed network- corresponding to two tasks, i.e., sentiment and emotion classification for prediction (one for each task in the multi-task framework). Sentiment classification branch contains a softmax layer for final classification (i.e. positive & negative), whereas for emotion classification we use sigmoid layer. The shared representation will receive gradients of error from both the branches (sentiment & emotion) and accordingly adjust the weights of the models. Thus, the shared representations will not be biased to any particular task, and it will assist the model in achieving generalization for the multiple tasks. As stated earlier, our proposed approach requires at least two modalities to compute bi-modal attention. Hence, we experiment with bi-modal and tri-modal input combinations for the proposed approach i.e. taking text-visual, text-acoustic, acoustic-visual and text-visual-acoustic at a time. For completeness (i.e., uni-modal inputs), we also experiment with a variant of the proposed approach where we apply self-attention on the utterances of each modality separately. The self-attention unit utilizes the contextual information of the utterances (i.e., it receives u×d hidden representations), applies attention and forward it to the output layer for classification. In the single-task framework, we build separate systems for sentiment and emotion analysis, whereas in multi-task framework a joint-model is learned for both of these problems. For sentiment classification, our single-task framework reports an F1-score of 77.67% and accuracy value of 79.8% for the tri-modal inputs. Similarly, we obtain 77.71% F1-score and 60.88% weighted accuracy for emotion classification.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Single-task learning (STL) and Multi-task (MTL) learning frameworks for the proposed approach. T: Text, V: Visual, A: Acoustic. Weighted accuracy as a metric is chosen due to unbalanced samples across various emotions and it is also in line with the other existing works Zadeh et al. (2018c).', answer the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the F1-score for emotion classification using the tri-modal input (T+A+V) in the multi-task learning framework?\n" }, { "role": "assistant", "content": "To find the F1-score for emotion classification using the tri-modal input (T+A+V) in the multi-task learning framework, locate the row for 'Emo' and 'MTL' and then find the F1-score under the column 'T+A+V'. So, the answer is {\"answer\": \"77.6\"}." } ], "images": [ "scigen/train/2733.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Single-task learning (STL) and Multi-task (MTL) learning frameworks for the proposed approach. T: Text, V: Visual, A: Acoustic. Weighted accuracy as a metric is chosen due to unbalanced samples across various emotions and it is also in line with the other existing works Zadeh et al. (2018c)." ], "reasoning": "To find the F1-score for emotion classification using the tri-modal input (T+A+V) in the multi-task learning framework, locate the row for 'Emo' and 'MTL' and then find the F1-score under the column 'T+A+V'.", "question": "What is the F1-score for emotion classification using the tri-modal input (T+A+V) in the multi-task learning framework?", "answer": "So, the answer is {\"answer\": \"77.6\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe single-task learning framework achieves a higher F1-score for sentiment classification using text and visual modalities compared to the multi-task learning framework.\n" }, { "role": "assistant", "content": "To verify this statement, compare the F1-scores for sentiment classification using text and visual modalities (T+V) in both the single-task (STL) and multi-task (MTL) learning frameworks. The F1-score for STL is 77.0, and for MTL it is 78.7. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2733.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Single-task learning (STL) and Multi-task (MTL) learning frameworks for the proposed approach. T: Text, V: Visual, A: Acoustic. Weighted accuracy as a metric is chosen due to unbalanced samples across various emotions and it is also in line with the other existing works Zadeh et al. (2018c)." ], "reasoning": "To verify this statement, compare the F1-scores for sentiment classification using text and visual modalities (T+V) in both the single-task (STL) and multi-task (MTL) learning frameworks. The F1-score for STL is 77.0, and for MTL it is 78.7.", "statement": "The single-task learning framework achieves a higher F1-score for sentiment classification using text and visual modalities compared to the multi-task learning framework.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Model hyperparameters'.\n" }, { "role": "assistant", "content": "Images were labeled into 8 emotion classes by at least 5 workers, and only those with majority agreement were kept. Submissions with images and high upvotes were collected from subreddits, leading to an imbalance in posts across emotion classes.Enriching an image-only dataset. To obtain the labels for these images, at least 5 workers from Amazon Mechanical Turks were hired to label them into 8 emotion classes: amusement, anger, awe, contentment, disgust, fear and sadness. We first crawl the images from this dataset while keeping only the ones where the majority of the workers agree that the image belongs to a specific emotion class. Then, for each image, we collect its title and description to use as textual data. Although these information are not provided in the dataset, they are still available from the image sharing website. We only keep the images with English title and description and the total of words in the title and description is more than 5. For each subreddit, we crawled 1000 submissions with the highest number of upvotes. We kept only posts containing image and discarded the rest. This created an imbalance in the number of posts between the emotion classes. For this reason, we collected submissions with at least 100 upvotes for two classes with the least amount of posts. It is worth noting that one post may contain several images. All the images are then converted to jpg format. For regularization, we used a dropout layer with a dropout probability of 0.25 right after the lookup table to reduce overfitting. We also followed this approach by adding a linear layer right before the last layer to map the concatenated vector (in the case of early fusion) and the pooled vector (in the case of joint fusion models) to a hidden vector space with a size of h. Regarding the hyperparameters, we tested different values of them on the validation set and select the ones that gave the best results. Our models were trained with a learning rate set to 0.01." } ], "images": [ "scigen/train/2734.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Model hyperparameters" ], "reasoning": "Images were labeled into 8 emotion classes by at least 5 workers, and only those with majority agreement were kept. Submissions with images and high upvotes were collected from subreddits, leading to an imbalance in posts across emotion classes.", "description": "Enriching an image-only dataset. To obtain the labels for these images, at least 5 workers from Amazon Mechanical Turks were hired to label them into 8 emotion classes: amusement, anger, awe, contentment, disgust, fear and sadness. We first crawl the images from this dataset while keeping only the ones where the majority of the workers agree that the image belongs to a specific emotion class. Then, for each image, we collect its title and description to use as textual data. Although these information are not provided in the dataset, they are still available from the image sharing website. We only keep the images with English title and description and the total of words in the title and description is more than 5. For each subreddit, we crawled 1000 submissions with the highest number of upvotes. We kept only posts containing image and discarded the rest. This created an imbalance in the number of posts between the emotion classes. For this reason, we collected submissions with at least 100 upvotes for two classes with the least amount of posts. It is worth noting that one post may contain several images. All the images are then converted to jpg format. For regularization, we used a dropout layer with a dropout probability of 0.25 right after the lookup table to reduce overfitting. We also followed this approach by adding a linear layer right before the last layer to map the concatenated vector (in the case of early fusion) and the pooled vector (in the case of joint fusion models) to a hidden vector space with a size of h. Regarding the hyperparameters, we tested different values of them on the validation set and select the ones that gave the best results. Our models were trained with a learning rate set to 0.01.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: Model hyperparameters', unfold the steps to solve the question given next. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nQuestion: Which emotion class has the highest number of posts and how many posts does it have?\n" }, { "role": "assistant", "content": "By examining the table, we see that the emotion class 'joy' has the highest number of posts, which is 1119. So, the answer is {\"answer\": \"joy, 1119\"}." } ], "images": [ "scigen/train/2734.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Model hyperparameters" ], "reasoning": "By examining the table, we see that the emotion class 'joy' has the highest number of posts, which is 1119.", "question": "Which emotion class has the highest number of posts and how many posts does it have?", "answer": "So, the answer is {\"answer\": \"joy, 1119\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The emotion class 'fear' has more posts than 'anger'. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe emotion class 'fear' has more posts than 'anger'.\n" }, { "role": "assistant", "content": "According to the table, 'fear' has 697 posts while 'anger' has 613 posts, indicating that 'fear' indeed has more posts than 'anger'. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2734.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Model hyperparameters" ], "reasoning": "According to the table, 'fear' has 697 posts while 'anger' has 613 posts, indicating that 'fear' indeed has more posts than 'anger'.", "statement": "The emotion class 'fear' has more posts than 'anger'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Model hyperparameters', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Images were labeled into 8 emotion classes by at least 5 workers, and only those with majority agreement were kept. Titles and descriptions were collected for each image, and only those with English text and more than 5 words were retained.Enriching an image-only dataset. To obtain the labels for these images, at least 5 workers from Amazon Mechanical Turks were hired to label them into 8 emotion classes: amusement, anger, awe, contentment, disgust, fear and sadness. We first crawl the images from this dataset while keeping only the ones where the majority of the workers agree that the image belongs to a specific emotion class. Then, for each image, we collect its title and description to use as textual data. Although these information are not provided in the dataset, they are still available from the image sharing website. We only keep the images with English title and description and the total of words in the title and description is more than 5. For each subreddit, we crawled 1000 submissions with the highest number of upvotes. We kept only posts containing image and discarded the rest. This created an imbalance in the number of posts between the emotion classes. For this reason, we collected submissions with at least 100 upvotes for two classes with the least amount of posts. It is worth noting that one post may contain several images. All the images are then converted to jpg format. For regularization, we used a dropout layer with a dropout probability of 0.25 right after the lookup table to reduce overfitting. We also followed this approach by adding a linear layer right before the last layer to map the concatenated vector (in the case of early fusion) and the pooled vector (in the case of joint fusion models) to a hidden vector space with a size of h. Regarding the hyperparameters, we tested different values of them on the validation set and select the ones that gave the best results. Our models were trained with a learning rate set to 0.01." } ], "images": [ "scigen/train/2735.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Model hyperparameters" ], "reasoning": "Images were labeled into 8 emotion classes by at least 5 workers, and only those with majority agreement were kept. Titles and descriptions were collected for each image, and only those with English text and more than 5 words were retained.", "description": "Enriching an image-only dataset. To obtain the labels for these images, at least 5 workers from Amazon Mechanical Turks were hired to label them into 8 emotion classes: amusement, anger, awe, contentment, disgust, fear and sadness. We first crawl the images from this dataset while keeping only the ones where the majority of the workers agree that the image belongs to a specific emotion class. Then, for each image, we collect its title and description to use as textual data. Although these information are not provided in the dataset, they are still available from the image sharing website. We only keep the images with English title and description and the total of words in the title and description is more than 5. For each subreddit, we crawled 1000 submissions with the highest number of upvotes. We kept only posts containing image and discarded the rest. This created an imbalance in the number of posts between the emotion classes. For this reason, we collected submissions with at least 100 upvotes for two classes with the least amount of posts. It is worth noting that one post may contain several images. All the images are then converted to jpg format. For regularization, we used a dropout layer with a dropout probability of 0.25 right after the lookup table to reduce overfitting. We also followed this approach by adding a linear layer right before the last layer to map the concatenated vector (in the case of early fusion) and the pooled vector (in the case of joint fusion models) to a hidden vector space with a size of h. Regarding the hyperparameters, we tested different values of them on the validation set and select the ones that gave the best results. Our models were trained with a learning rate set to 0.01.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: Model hyperparameters' in mind, break down and resolve the question below step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhich emotion class has the highest number of posts?\n" }, { "role": "assistant", "content": "By examining the table, we see that the 'contentment' class has the highest number of posts with 2389. So, the answer is {\"answer\": \"contentment\"}." } ], "images": [ "scigen/train/2735.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Model hyperparameters" ], "reasoning": "By examining the table, we see that the 'contentment' class has the highest number of posts with 2389.", "question": "Which emotion class has the highest number of posts?", "answer": "So, the answer is {\"answer\": \"contentment\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe total number of posts for the 'fear' emotion class is less than 500.\n" }, { "role": "assistant", "content": "The table shows that the 'fear' emotion class has 434 posts, which is indeed less than 500. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2735.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Model hyperparameters" ], "reasoning": "The table shows that the 'fear' emotion class has 434 posts, which is indeed less than 500.", "statement": "The total number of posts for the 'fear' emotion class is less than 500.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Performance of different fusion models and two baselines on two datasets'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The proposed techniques, especially common space fusion, outperform single modality and traditional fusion methods, demonstrating the benefits of combining visual and textual data.The results show that visual and textual classifiers have similar accuracy on the reddit dataset as their difference in accuracy is only 1%. In addition, all the fusion techniques have better accuracy than the baselines. The traditional fusion approaches improve the accuracy by at least 5%. This clearly demonstrates the benefits of combing visual and textual data. Our proposed techniques perform the best as they improves the accuracy by 8% and 2% in comparison with using single modality and early/late fusion, respectively. Among the proposed techniques, common space fusion has higher accuracy. However, the difference is small as it is only 0.6%." } ], "images": [ "scigen/train/2736.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance of different fusion models and two baselines on two datasets" ], "reasoning": "The proposed techniques, especially common space fusion, outperform single modality and traditional fusion methods, demonstrating the benefits of combining visual and textual data.", "description": "The results show that visual and textual classifiers have similar accuracy on the reddit dataset as their difference in accuracy is only 1%. In addition, all the fusion techniques have better accuracy than the baselines. The traditional fusion approaches improve the accuracy by at least 5%. This clearly demonstrates the benefits of combing visual and textual data. Our proposed techniques perform the best as they improves the accuracy by 8% and 2% in comparison with using single modality and early/late fusion, respectively. Among the proposed techniques, common space fusion has higher accuracy. However, the difference is small as it is only 0.6%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 4: Performance of different fusion models and two baselines on two datasets', and sequentially solve the question, writing out each step of your reasoning process. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the accuracy improvement of the common space fusion technique over the image-based classifier on the Reddit dataset?\n" }, { "role": "assistant", "content": "The accuracy of the common space fusion technique on the Reddit dataset is 86.92%, while the accuracy of the image-based classifier is 77.48%. The improvement is calculated as 86.92% - 77.48% = 9.44%. So, the answer is {\"answer\": \"9.44%\"}" } ], "images": [ "scigen/train/2736.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance of different fusion models and two baselines on two datasets" ], "reasoning": "The accuracy of the common space fusion technique on the Reddit dataset is 86.92%, while the accuracy of the image-based classifier is 77.48%. The improvement is calculated as 86.92% - 77.48% = 9.44%.", "question": "What is the accuracy improvement of the common space fusion technique over the image-based classifier on the Reddit dataset?", "answer": "So, the answer is {\"answer\": \"9.44%\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe joint fusion technique has higher accuracy than the early fusion technique on the Flickr dataset.\n" }, { "role": "assistant", "content": "The accuracy of the joint fusion technique on the Flickr dataset is 93.01%, while the accuracy of the early fusion technique is 92.69%. Since 93.01% is higher than 92.69%, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2736.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance of different fusion models and two baselines on two datasets" ], "reasoning": "The accuracy of the joint fusion technique on the Flickr dataset is 93.01%, while the accuracy of the early fusion technique is 92.69%. Since 93.01% is higher than 92.69%, the statement is supported.", "statement": "The joint fusion technique has higher accuracy than the early fusion technique on the Flickr dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5: Accuracies of joint and common space fusion when only one modality is available', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Common space fusion achieves more balanced accuracy between image and text inputs compared to joint fusion, especially for the reddit dataset, and outperforms single-input classifiers on the flickr dataset.Handling the absence of one modality The goal of joint and common space fusion is not only able to achieve higher accuracy by combining visual and textual signals but also able to provide correct classification when only either text or image is available. In this section, we analyze the performance of our proposed approaches when only one modality is used as input. only classifiers for comparison purposes. The results clearly demonstrate the benefits of creating a common space as the difference in accuracy between using only image or text as input for common space fusion is significantly lower than that of joint fusion. For the reddit dataset, the difference is only 0.8% for common space fusion while it is 34% for joint fusion. The discrepancy for common space fusion is lower as it considers image and text vector of a post as equally important. As a result, using either image or text to classify, we get similar accuracy. On the other hand, joint fusion considers textual information as more important. This makes the classification using text significantly better than using only image. Another key finding is that using only image or text as input, the common space fusion has better result on the flickr dataset than single-input classifier. We achieve a gain of 13% for image and 0.8% for text. As the common space contains information from two modalities, even when we use only one modality as input, we can also leverage information from other modality. However, by enforcing a common space from two modalities, we may lose information if one modality is less informative than the other. This happens with the reddit dataset where the titles are shorter and less descriptive than the descriptions of flickr images." } ], "images": [ "scigen/train/2737.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Accuracies of joint and common space fusion when only one modality is available" ], "reasoning": "Common space fusion achieves more balanced accuracy between image and text inputs compared to joint fusion, especially for the reddit dataset, and outperforms single-input classifiers on the flickr dataset.", "description": "Handling the absence of one modality The goal of joint and common space fusion is not only able to achieve higher accuracy by combining visual and textual signals but also able to provide correct classification when only either text or image is available. In this section, we analyze the performance of our proposed approaches when only one modality is used as input. only classifiers for comparison purposes. The results clearly demonstrate the benefits of creating a common space as the difference in accuracy between using only image or text as input for common space fusion is significantly lower than that of joint fusion. For the reddit dataset, the difference is only 0.8% for common space fusion while it is 34% for joint fusion. The discrepancy for common space fusion is lower as it considers image and text vector of a post as equally important. As a result, using either image or text to classify, we get similar accuracy. On the other hand, joint fusion considers textual information as more important. This makes the classification using text significantly better than using only image. Another key finding is that using only image or text as input, the common space fusion has better result on the flickr dataset than single-input classifier. We achieve a gain of 13% for image and 0.8% for text. As the common space contains information from two modalities, even when we use only one modality as input, we can also leverage information from other modality. However, by enforcing a common space from two modalities, we may lose information if one modality is less informative than the other. This happens with the reddit dataset where the titles are shorter and less descriptive than the descriptions of flickr images.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 5: Accuracies of joint and common space fusion when only one modality is available'. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the accuracy difference between using only image and only text as input for joint fusion on the reddit dataset?\n" }, { "role": "assistant", "content": "The table shows that the accuracy for joint fusion using reddit image is 35.83% and using reddit text is 69.47%. The difference is 69.47% - 35.83% = 33.64%. So, the answer is {\"answer\": \"33.64%\"}." } ], "images": [ "scigen/train/2737.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Accuracies of joint and common space fusion when only one modality is available" ], "reasoning": "The table shows that the accuracy for joint fusion using reddit image is 35.83% and using reddit text is 69.47%. The difference is 69.47% - 35.83% = 33.64%.", "question": "What is the accuracy difference between using only image and only text as input for joint fusion on the reddit dataset?", "answer": "So, the answer is {\"answer\": \"33.64%\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nCommon space fusion achieves higher accuracy than single-input classifiers for both image and text inputs on the flickr dataset.\n" }, { "role": "assistant", "content": "The table shows that for the flickr dataset, common space fusion achieves 69.03% for image and 89.14% for text, while single-input classifiers achieve 56.869% for image and 88.3% for text. Both values for common space fusion are higher than those for single-input classifiers. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2737.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Accuracies of joint and common space fusion when only one modality is available" ], "reasoning": "The table shows that for the flickr dataset, common space fusion achieves 69.03% for image and 89.14% for text, while single-input classifiers achieve 56.869% for image and 88.3% for text. Both values for common space fusion are higher than those for single-input classifiers.", "statement": "Common space fusion achieves higher accuracy than single-input classifiers for both image and text inputs on the flickr dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Results of domain adaptation on three tasks, where zh denotes the Weibo datasets (in Chinese), and en denotes the Twitter dataset (in English).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "FGKF achieves the best results in both F and ROOV, demonstrating its effectiveness for domain adaptation and generalization across tasks and languages.Compared with other methods, FGKF achieves the best results in both F and ROOV. Results demonstrate the effectiveness of our fine-grained knowledge fusion architecture for domain adaptation, and also show the significance of considering sample-level and element-level relevance discrepancies. To further verify the effectiveness of FGKF, we conduct experiments on POS and NER tasks, using F1-score as the evaluation criterion. In these tasks, FGKF achieves better results than other adaptation methods. Extra gain could be obtained by using pre-trained embedding. These results also verify the generalization of our method over different tasks and languages." } ], "images": [ "scigen/train/2738.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of domain adaptation on three tasks, where zh denotes the Weibo datasets (in Chinese), and en denotes the Twitter dataset (in English)." ], "reasoning": "FGKF achieves the best results in both F and ROOV, demonstrating its effectiveness for domain adaptation and generalization across tasks and languages.", "description": "Compared with other methods, FGKF achieves the best results in both F and ROOV. Results demonstrate the effectiveness of our fine-grained knowledge fusion architecture for domain adaptation, and also show the significance of considering sample-level and element-level relevance discrepancies. To further verify the effectiveness of FGKF, we conduct experiments on POS and NER tasks, using F1-score as the evaluation criterion. In these tasks, FGKF achieves better results than other adaptation methods. Extra gain could be obtained by using pre-trained embedding. These results also verify the generalization of our method over different tasks and languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 3: Results of domain adaptation on three tasks, where zh denotes the Weibo datasets (in Chinese), and en denotes the Twitter dataset (in English).\nQuestion: Which method achieves the highest F1-score in the NER task for the English language?\n" }, { "role": "assistant", "content": "To find the method with the highest F1-score in the NER task for the English language, look at the NER en column and identify the highest value. The highest F1-score is 62.88, achieved by the Pre-trained model. So, the answer is {\"answer\": \"Pre-trained model\"}." } ], "images": [ "scigen/train/2738.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of domain adaptation on three tasks, where zh denotes the Weibo datasets (in Chinese), and en denotes the Twitter dataset (in English)." ], "reasoning": "To find the method with the highest F1-score in the NER task for the English language, look at the NER en column and identify the highest value. The highest F1-score is 62.88, achieved by the Pre-trained model.", "question": "Which method achieves the highest F1-score in the NER task for the English language?", "answer": "So, the answer is {\"answer\": \"Pre-trained model\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis FGKF achieves the highest F1-score in the POS task for the Chinese language. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nFGKF achieves the highest F1-score in the POS task for the Chinese language.\n" }, { "role": "assistant", "content": "To verify this statement, look at the POS zh column and compare the F1-scores. The highest F1-score in the POS zh column is 96.36, achieved by the method '+ Pre-trained embedding', not FGKF. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2738.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of domain adaptation on three tasks, where zh denotes the Weibo datasets (in Chinese), and en denotes the Twitter dataset (in English)." ], "reasoning": "To verify this statement, look at the POS zh column and compare the F1-scores. The highest F1-score in the POS zh column is 96.36, achieved by the method '+ Pre-trained embedding', not FGKF.", "statement": "FGKF achieves the highest F1-score in the POS task for the Chinese language.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4: Results of baselines and fine-grained knowledge fusion methods on CWS.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "FGKF outperforms basicKD by considering multi-level relevance discrepancies, and sample-q methods perform better than domain-q methods, indicating better representation at the sample level.Compared with the basicKD method, FGKF behaves better (+1.1% F and +2.8% Roov v.s. basicKD on average), as it takes multi-level relevance discrepancies into account. The sample-q method performs better than the domain-q method, which shows the domain feature is better represented at the sample level, not at the domain level. As for the granularity of α, the performances of αelem is better than αsamp, showing the necessity of modeling element-level relevance. And there isn’t a distinct margin between αelem and αmulti as most of the multi-level domain relevance can be included by the element level. Results of FGKF with warm up indicate that starting from sub-optimal point is better than starting from scratch for the target model." } ], "images": [ "scigen/train/2739.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of baselines and fine-grained knowledge fusion methods on CWS." ], "reasoning": "FGKF outperforms basicKD by considering multi-level relevance discrepancies, and sample-q methods perform better than domain-q methods, indicating better representation at the sample level.", "description": "Compared with the basicKD method, FGKF behaves better (+1.1% F and +2.8% Roov v.s. basicKD on average), as it takes multi-level relevance discrepancies into account. The sample-q method performs better than the domain-q method, which shows the domain feature is better represented at the sample level, not at the domain level. As for the granularity of α, the performances of αelem is better than αsamp, showing the necessity of modeling element-level relevance. And there isn’t a distinct margin between αelem and αmulti as most of the multi-level domain relevance can be included by the element level. Results of FGKF with warm up indicate that starting from sub-optimal point is better than starting from scratch for the target model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Results of baselines and fine-grained knowledge fusion methods on CWS.', solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method shows the highest performance in Zhuxian F and 5% Weibo F?\n" }, { "role": "assistant", "content": "To find the highest performance in Zhuxian F and 5% Weibo F, we need to compare the values in the respective columns. FGKF has the highest Zhuxian F (95.01) and 5% Weibo F (90.45). So, the answer is {\"answer\": \"FGKF\"}" } ], "images": [ "scigen/train/2739.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of baselines and fine-grained knowledge fusion methods on CWS." ], "reasoning": "To find the highest performance in Zhuxian F and 5% Weibo F, we need to compare the values in the respective columns. FGKF has the highest Zhuxian F (95.01) and 5% Weibo F (90.45).", "question": "Which method shows the highest performance in Zhuxian F and 5% Weibo F?", "answer": "So, the answer is {\"answer\": \"FGKF\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe sample-q method performs better than the domain-q method in Zhuxian ROOV.\n" }, { "role": "assistant", "content": "Comparing the Zhuxian ROOV values for sample-q and domain-q methods, sample-q csamp (74.47), sample-q celem (74.52), and sample-q cmulti (75.56) all perform better than domain-q csamp (74.02), domain-q celem (74.75), and domain-q cmulti (74.96). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2739.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of baselines and fine-grained knowledge fusion methods on CWS." ], "reasoning": "Comparing the Zhuxian ROOV values for sample-q and domain-q methods, sample-q csamp (74.47), sample-q celem (74.52), and sample-q cmulti (75.56) all perform better than domain-q csamp (74.02), domain-q celem (74.75), and domain-q cmulti (74.96).", "statement": "The sample-q method performs better than the domain-q method in Zhuxian ROOV.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: Results of the strongly/weakly relevant elements on the Twitter test set.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The table classifies test data into strongly and weakly relevant categories based on their relevance to the source domain, using the average relevance score of the target training data as the threshold.To further probe into the experimental results of the fine-grained knowledge fusion, we classify the target test data (in element level) into two classes: strongly relevant and weakly relevant, based on their relevance degrees to the source domain. The partition threshold is according to the average relevance score of the target training data." } ], "images": [ "scigen/train/2740.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results of the strongly/weakly relevant elements on the Twitter test set." ], "reasoning": "The table classifies test data into strongly and weakly relevant categories based on their relevance to the source domain, using the average relevance score of the target training data as the threshold.", "description": "To further probe into the experimental results of the fine-grained knowledge fusion, we classify the target test data (in element level) into two classes: strongly relevant and weakly relevant, based on their relevance degrees to the source domain. The partition threshold is according to the average relevance score of the target training data.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 5: Results of the strongly/weakly relevant elements on the Twitter test set.\nQuestion: Which method has the highest score for NER Weak?\n" }, { "role": "assistant", "content": "To find the method with the highest score for NER Weak, compare the NER Weak scores across all methods. FGKF has the highest score of 57.92. So, the answer is {\"answer\": \"FGKF\"}." } ], "images": [ "scigen/train/2740.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results of the strongly/weakly relevant elements on the Twitter test set." ], "reasoning": "To find the method with the highest score for NER Weak, compare the NER Weak scores across all methods. FGKF has the highest score of 57.92.", "question": "Which method has the highest score for NER Weak?", "answer": "So, the answer is {\"answer\": \"FGKF\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The BasicKD method has a higher score for POS Strong than the Target only method. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe BasicKD method has a higher score for POS Strong than the Target only method.\n" }, { "role": "assistant", "content": "Comparing the POS Strong scores, BasicKD has a score of 91.92, which is higher than the Target only method's score of 86.46. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2740.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results of the strongly/weakly relevant elements on the Twitter test set." ], "reasoning": "Comparing the POS Strong scores, BasicKD has a score of 91.92, which is higher than the Target only method's score of 86.46.", "statement": "The BasicKD method has a higher score for POS Strong than the Target only method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 7: Ablation results of the Twitter test set.'.\n" }, { "role": "assistant", "content": "The ablation study on the Twitter dataset shows that the gradient block and multi-level knowledge fusion are crucial for FGKF, while embedding sharing and warm-up also contribute.We conduct the ablation study on Twitter dataset Results show the gradient block and the multi-level knowledge fusion are of vital importance to FGKF. The embedding sharing and warm up also make contributions." } ], "images": [ "scigen/train/2741.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Ablation results of the Twitter test set." ], "reasoning": "The ablation study on the Twitter dataset shows that the gradient block and multi-level knowledge fusion are crucial for FGKF, while embedding sharing and warm-up also contribute.", "description": "We conduct the ablation study on Twitter dataset Results show the gradient block and the multi-level knowledge fusion are of vital importance to FGKF. The embedding sharing and warm up also make contributions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 7: Ablation results of the Twitter test set.' and methodically tackle the question that follows. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method had the largest negative impact on NER F score when removed?\n" }, { "role": "assistant", "content": "By comparing the NER Δ values, the removal of the gradient block had the largest negative impact on the NER F score with a decrease of 4.98. So, the answer is {\"answer\": \"gradient block\"}" } ], "images": [ "scigen/train/2741.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Ablation results of the Twitter test set." ], "reasoning": "By comparing the NER Δ values, the removal of the gradient block had the largest negative impact on the NER F score with a decrease of 4.98.", "question": "Which method had the largest negative impact on NER F score when removed?", "answer": "So, the answer is {\"answer\": \"gradient block\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nRemoving the gradient block resulted in the largest decrease in POS F score.\n" }, { "role": "assistant", "content": "The table shows that removing the gradient block resulted in a decrease of 2.85 in POS F score, which is the largest decrease among all methods. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2741.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Ablation results of the Twitter test set." ], "reasoning": "The table shows that removing the gradient block resulted in a decrease of 2.85 in POS F score, which is the largest decrease among all methods.", "statement": "Removing the gradient block resulted in the largest decrease in POS F score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Official macro-averaged LAS F1 scores of MQuni and baselines from the CoNLL 2017 shared task on UD parsing (Zeman et al., 2017): http://universaldependencies.org/conll17/results-las.html. “All” refers to the averaged score over all 81 test sets, which is used as the main metric for ranking participating systems. Big: the averaged score over 55/81 test sets whose training treebanks are big and have development data available. PUD: the averaged score over 14/81 test sets that are additional parallel ones, produced separately and their domain may be different from their training data. Sma.: the averaged score over 8/81 test sets whose training treebanks are small, i.e., they lack development data and some of them have very little training data. Sur.: the averaged score over 4/81 remaining test sets for surprise languages. Here the subscript denotes the official rank out of 33 participating systems. R-S is the system rank where the 4 surprise language test sets are not taken into account.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Our parsing model jPTDP achieves a highest rank of 8th place for the PUD category, demonstrating its effectiveness in real practical applications with out-of-domain data, despite obtaining a very low averaged score over the 4 surprise language test sets.We obtain 1% absolute higher averaged scores than the baseline UDPipe 1.1 Specifically, we obtain a highest rank at 8th place for the PUD category, showing that our parsing model jPTDP is particularly good when it is applied to a real practical application in out-of-domain data. Unlike the baseline UDPipe 1.1 and others, for each surprise language, we simply train a joint model just on the sample data of few sentences with gold-standard annotation provided before the test phase, i.e., we utilize neither external resources nor a cross-lingual technique nor a delexicalized parser. So, it is not surprising that we obtain a very low averaged score over the 4 surprise language test sets. When the 4 surprise language test sets are not taken into account, we obtain a rank in top-10 participating systems." } ], "images": [ "scigen/train/2742.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Official macro-averaged LAS F1 scores of MQuni and baselines from the CoNLL 2017 shared task on UD parsing (Zeman et al., 2017): http://universaldependencies.org/conll17/results-las.html. “All” refers to the averaged score over all 81 test sets, which is used as the main metric for ranking participating systems. Big: the averaged score over 55/81 test sets whose training treebanks are big and have development data available. PUD: the averaged score over 14/81 test sets that are additional parallel ones, produced separately and their domain may be different from their training data. Sma.: the averaged score over 8/81 test sets whose training treebanks are small, i.e., they lack development data and some of them have very little training data. Sur.: the averaged score over 4/81 remaining test sets for surprise languages. Here the subscript denotes the official rank out of 33 participating systems. R-S is the system rank where the 4 surprise language test sets are not taken into account." ], "reasoning": "Our parsing model jPTDP achieves a highest rank of 8th place for the PUD category, demonstrating its effectiveness in real practical applications with out-of-domain data, despite obtaining a very low averaged score over the 4 surprise language test sets.", "description": "We obtain 1% absolute higher averaged scores than the baseline UDPipe 1.1 Specifically, we obtain a highest rank at 8th place for the PUD category, showing that our parsing model jPTDP is particularly good when it is applied to a real practical application in out-of-domain data. Unlike the baseline UDPipe 1.1 and others, for each surprise language, we simply train a joint model just on the sample data of few sentences with gold-standard annotation provided before the test phase, i.e., we utilize neither external resources nor a cross-lingual technique nor a delexicalized parser. So, it is not surprising that we obtain a very low averaged score over the 4 surprise language test sets. When the 4 surprise language test sets are not taken into account, we obtain a rank in top-10 participating systems.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2: Official macro-averaged LAS F1 scores of MQuni and baselines from the CoNLL 2017 shared task on UD parsing (Zeman et al., 2017): http://universaldependencies.org/conll17/results-las.html. “All” refers to the averaged score over all 81 test sets, which is used as the main metric for ranking participating systems. Big: the averaged score over 55/81 test sets whose training treebanks are big and have development data available. PUD: the averaged score over 14/81 test sets that are additional parallel ones, produced separately and their domain may be different from their training data. Sma.: the averaged score over 8/81 test sets whose training treebanks are small, i.e., they lack development data and some of them have very little training data. Sur.: the averaged score over 4/81 remaining test sets for surprise languages. Here the subscript denotes the official rank out of 33 participating systems. R-S is the system rank where the 4 surprise language test sets are not taken into account.', unfold the steps to solve the question given next. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nQuestion: What is the rank of UDPipe 1.2 in the PUD category?\n" }, { "role": "assistant", "content": "The table shows the rank of UDPipe 1.2 in the PUD category as 8th place. So, the answer is {\"answer\": \"8\"}" } ], "images": [ "scigen/train/2742.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Official macro-averaged LAS F1 scores of MQuni and baselines from the CoNLL 2017 shared task on UD parsing (Zeman et al., 2017): http://universaldependencies.org/conll17/results-las.html. “All” refers to the averaged score over all 81 test sets, which is used as the main metric for ranking participating systems. Big: the averaged score over 55/81 test sets whose training treebanks are big and have development data available. PUD: the averaged score over 14/81 test sets that are additional parallel ones, produced separately and their domain may be different from their training data. Sma.: the averaged score over 8/81 test sets whose training treebanks are small, i.e., they lack development data and some of them have very little training data. Sur.: the averaged score over 4/81 remaining test sets for surprise languages. Here the subscript denotes the official rank out of 33 participating systems. R-S is the system rank where the 4 surprise language test sets are not taken into account." ], "reasoning": "The table shows the rank of UDPipe 1.2 in the PUD category as 8th place.", "question": "What is the rank of UDPipe 1.2 in the PUD category?", "answer": "So, the answer is {\"answer\": \"8\"}" }, { "messages": [ { "role": "user", "content": "UDPipe 1.1 has a higher score than MQuuni in the 'Sur.' category. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nUDPipe 1.1 has a higher score than MQuuni in the 'Sur.' category.\n" }, { "role": "assistant", "content": "The table shows that UDPipe 1.1 has a score of 37.0711 in the 'Sur.' category, which is higher than MQuuni's score of 14.4828. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2742.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Official macro-averaged LAS F1 scores of MQuni and baselines from the CoNLL 2017 shared task on UD parsing (Zeman et al., 2017): http://universaldependencies.org/conll17/results-las.html. “All” refers to the averaged score over all 81 test sets, which is used as the main metric for ranking participating systems. Big: the averaged score over 55/81 test sets whose training treebanks are big and have development data available. PUD: the averaged score over 14/81 test sets that are additional parallel ones, produced separately and their domain may be different from their training data. Sma.: the averaged score over 8/81 test sets whose training treebanks are small, i.e., they lack development data and some of them have very little training data. Sur.: the averaged score over 4/81 remaining test sets for surprise languages. Here the subscript denotes the official rank out of 33 participating systems. R-S is the system rank where the 4 surprise language test sets are not taken into account." ], "reasoning": "The table shows that UDPipe 1.1 has a score of 37.0711 in the 'Sur.' category, which is higher than MQuuni's score of 14.4828.", "statement": "UDPipe 1.1 has a higher score than MQuuni in the 'Sur.' category.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: Universal POS tagging accuracies and LAS scores computed on all tokens (including punctuation) on test sets for 19 languages in UD v1.2. The language codes with ∙ refer to morphologically rich languages. Numbers (in the second top row) right below language codes are out-of-vocabulary rates. UDPipe is the trainable pipeline for processing CoNLL-U files (Straka et al., 2016). TnT denotes the second order HMM-based TnT tagger (Brants, 2000). CRF denotes the Conditional random fields-based tagger, presented in Plank et al. (2014). BiLSTM-aux refers to the state-of-the-art (SOTA) BiLSTM-based POS tagging model with an additional auxiliary loss for rare words (Plank et al., 2016). Note that the (old) language code for Hebrew “iw” is referred to as “he” as in Plank et al. (2016). [⊕]: Results are reported in Plank et al. (2016). Stack-prop refers to the SOTA Stack-propagation model for joint POS tagging and transition-based dependency parsing (Zhang and Weiss, 2016). ▽-Chars denotes the absolute accuracy decrease of our jPTDP, when the character-based representations of words are not taken into account. B’15 denotes the character-based stack LSTM model for transition-based dependency parsing (Ballesteros et al., 2015). PipelinePtag refers to a greedy version of the approach proposed by Alberti et al. (2015). RBGParser refers to the graph-based dependency parser with tensor decomposition, presented in Lei et al. (2014). [*]: Results are reported in Zhang and Weiss (2016).'.\n" }, { "role": "assistant", "content": "jPTDP outperforms Stack-propagation in morphologically rich languages by 1.7% in LAS due to its use of character-based word representations.with results reported in prior work, using the same experimental setup. One reason for our better LAS is probably because jPTDP uses character-based representations of words, while Stack-propagation uses feature representations for suffixes and prefixes which might not be as useful as character-based representations for capturing unknown words. So, our jPDTP is particularly good for morphologically rich languages, with 1.7% higher averaged LAS than Stack-propagation over these languages." } ], "images": [ "scigen/train/2743.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Universal POS tagging accuracies and LAS scores computed on all tokens (including punctuation) on test sets for 19 languages in UD v1.2. The language codes with ∙ refer to morphologically rich languages. Numbers (in the second top row) right below language codes are out-of-vocabulary rates. UDPipe is the trainable pipeline for processing CoNLL-U files (Straka et al., 2016). TnT denotes the second order HMM-based TnT tagger (Brants, 2000). CRF denotes the Conditional random fields-based tagger, presented in Plank et al. (2014). BiLSTM-aux refers to the state-of-the-art (SOTA) BiLSTM-based POS tagging model with an additional auxiliary loss for rare words (Plank et al., 2016). Note that the (old) language code for Hebrew “iw” is referred to as “he” as in Plank et al. (2016). [⊕]: Results are reported in Plank et al. (2016). Stack-prop refers to the SOTA Stack-propagation model for joint POS tagging and transition-based dependency parsing (Zhang and Weiss, 2016). ▽-Chars denotes the absolute accuracy decrease of our jPTDP, when the character-based representations of words are not taken into account. B’15 denotes the character-based stack LSTM model for transition-based dependency parsing (Ballesteros et al., 2015). PipelinePtag refers to a greedy version of the approach proposed by Alberti et al. (2015). RBGParser refers to the graph-based dependency parser with tensor decomposition, presented in Lei et al. (2014). [*]: Results are reported in Zhang and Weiss (2016)." ], "reasoning": "jPTDP outperforms Stack-propagation in morphologically rich languages by 1.7% in LAS due to its use of character-based word representations.", "description": "with results reported in prior work, using the same experimental setup. One reason for our better LAS is probably because jPTDP uses character-based representations of words, while Stack-propagation uses feature representations for suffixes and prefixes which might not be as useful as character-based representations for capturing unknown words. So, our jPDTP is particularly good for morphologically rich languages, with 1.7% higher averaged LAS than Stack-propagation over these languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Universal POS tagging accuracies and LAS scores computed on all tokens (including punctuation) on test sets for 19 languages in UD v1.2. The language codes with ∙ refer to morphologically rich languages. Numbers (in the second top row) right below language codes are out-of-vocabulary rates. UDPipe is the trainable pipeline for processing CoNLL-U files (Straka et al., 2016). TnT denotes the second order HMM-based TnT tagger (Brants, 2000). CRF denotes the Conditional random fields-based tagger, presented in Plank et al. (2014). BiLSTM-aux refers to the state-of-the-art (SOTA) BiLSTM-based POS tagging model with an additional auxiliary loss for rare words (Plank et al., 2016). Note that the (old) language code for Hebrew “iw” is referred to as “he” as in Plank et al. (2016). [⊕]: Results are reported in Plank et al. (2016). Stack-prop refers to the SOTA Stack-propagation model for joint POS tagging and transition-based dependency parsing (Zhang and Weiss, 2016). ▽-Chars denotes the absolute accuracy decrease of our jPTDP, when the character-based representations of words are not taken into account. B’15 denotes the character-based stack LSTM model for transition-based dependency parsing (Ballesteros et al., 2015). PipelinePtag refers to a greedy version of the approach proposed by Alberti et al. (2015). RBGParser refers to the graph-based dependency parser with tensor decomposition, presented in Lei et al. (2014). [*]: Results are reported in Zhang and Weiss (2016).', answer the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the average LAS score for Our jPTDP across all languages?\n" }, { "role": "assistant", "content": "To find the average LAS score for Our jPTDP, look at the 'AVG' column under 'Dependency parsing' for 'Our jPTDP'. The value is 79.6. So, the answer is {\"answer\": \"79.6\"}" } ], "images": [ "scigen/train/2743.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Universal POS tagging accuracies and LAS scores computed on all tokens (including punctuation) on test sets for 19 languages in UD v1.2. The language codes with ∙ refer to morphologically rich languages. Numbers (in the second top row) right below language codes are out-of-vocabulary rates. UDPipe is the trainable pipeline for processing CoNLL-U files (Straka et al., 2016). TnT denotes the second order HMM-based TnT tagger (Brants, 2000). CRF denotes the Conditional random fields-based tagger, presented in Plank et al. (2014). BiLSTM-aux refers to the state-of-the-art (SOTA) BiLSTM-based POS tagging model with an additional auxiliary loss for rare words (Plank et al., 2016). Note that the (old) language code for Hebrew “iw” is referred to as “he” as in Plank et al. (2016). [⊕]: Results are reported in Plank et al. (2016). Stack-prop refers to the SOTA Stack-propagation model for joint POS tagging and transition-based dependency parsing (Zhang and Weiss, 2016). ▽-Chars denotes the absolute accuracy decrease of our jPTDP, when the character-based representations of words are not taken into account. B’15 denotes the character-based stack LSTM model for transition-based dependency parsing (Ballesteros et al., 2015). PipelinePtag refers to a greedy version of the approach proposed by Alberti et al. (2015). RBGParser refers to the graph-based dependency parser with tensor decomposition, presented in Lei et al. (2014). [*]: Results are reported in Zhang and Weiss (2016)." ], "reasoning": "To find the average LAS score for Our jPTDP, look at the 'AVG' column under 'Dependency parsing' for 'Our jPTDP'. The value is 79.6.", "question": "What is the average LAS score for Our jPTDP across all languages?", "answer": "So, the answer is {\"answer\": \"79.6\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Our jPTDP has the highest LAS score for the Finnish language., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nOur jPTDP has the highest LAS score for the Finnish language.\n" }, { "role": "assistant", "content": "To verify this, compare the LAS scores for the Finnish language under 'Dependency parsing'. Our jPTDP has a score of 81.5, which is higher than the scores of other methods. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2743.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Universal POS tagging accuracies and LAS scores computed on all tokens (including punctuation) on test sets for 19 languages in UD v1.2. The language codes with ∙ refer to morphologically rich languages. Numbers (in the second top row) right below language codes are out-of-vocabulary rates. UDPipe is the trainable pipeline for processing CoNLL-U files (Straka et al., 2016). TnT denotes the second order HMM-based TnT tagger (Brants, 2000). CRF denotes the Conditional random fields-based tagger, presented in Plank et al. (2014). BiLSTM-aux refers to the state-of-the-art (SOTA) BiLSTM-based POS tagging model with an additional auxiliary loss for rare words (Plank et al., 2016). Note that the (old) language code for Hebrew “iw” is referred to as “he” as in Plank et al. (2016). [⊕]: Results are reported in Plank et al. (2016). Stack-prop refers to the SOTA Stack-propagation model for joint POS tagging and transition-based dependency parsing (Zhang and Weiss, 2016). ▽-Chars denotes the absolute accuracy decrease of our jPTDP, when the character-based representations of words are not taken into account. B’15 denotes the character-based stack LSTM model for transition-based dependency parsing (Ballesteros et al., 2015). PipelinePtag refers to a greedy version of the approach proposed by Alberti et al. (2015). RBGParser refers to the graph-based dependency parser with tensor decomposition, presented in Lei et al. (2014). [*]: Results are reported in Zhang and Weiss (2016)." ], "reasoning": "To verify this, compare the LAS scores for the Finnish language under 'Dependency parsing'. Our jPTDP has a score of 81.5, which is higher than the scores of other methods.", "statement": "Our jPTDP has the highest LAS score for the Finnish language.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Comparison with the state-of-the-art systems.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The two-layered attention network outperforms the best system of SemEval 2017 Task in both sub-tracks, reducing model complexity without compromising performance.The two-layered attention network alone performs better than the best system of SemEval 2017 Task for both the sub-track. This shows that the two-layered attention network helps to reduce overall model complexity without compromising the performance." } ], "images": [ "scigen/train/2744.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison with the state-of-the-art systems." ], "reasoning": "The two-layered attention network outperforms the best system of SemEval 2017 Task in both sub-tracks, reducing model complexity without compromising performance.", "description": "The two-layered attention network alone performs better than the best system of SemEval 2017 Task for both the sub-track. This shows that the two-layered attention network helps to reduce overall model complexity without compromising the performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 2: Comparison with the state-of-the-art systems.' in mind, break down and resolve the question below step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model has the highest score in the Microblog sub-track?\n" }, { "role": "assistant", "content": "By examining the Microblog sub-track scores, the highest score is 0.797 by Akhtar et al. So, the answer is {\"answer\": \"Akhtar et al.\"}" } ], "images": [ "scigen/train/2744.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison with the state-of-the-art systems." ], "reasoning": "By examining the Microblog sub-track scores, the highest score is 0.797 by Akhtar et al.", "question": "Which model has the highest score in the Microblog sub-track?", "answer": "So, the answer is {\"answer\": \"Akhtar et al.\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe proposed L3 model performs better than the proposed E1 model in the News sub-track.\n" }, { "role": "assistant", "content": "The table shows that the proposed L3 model has a score of 0.763, while the proposed E1 model has a score of 0.782 in the News sub-track. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2744.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison with the state-of-the-art systems." ], "reasoning": "The table shows that the proposed L3 model has a score of 0.763, while the proposed E1 model has a score of 0.782 in the News sub-track.", "statement": "The proposed L3 model performs better than the proposed E1 model in the News sub-track.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Test denotation accuracy for each domain comparing our proposed method DistillCombined with the three baselines. We also report the skyline Supervised.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The average denotation accuracy with beam width 1 is 70.6%, which is comparable to state-of-the-art methods, suggesting that the data normalization process does not alter task complexity.To reduce the variance in gradient estimation we use baseline b(x)=1|B|∑z∈BR(x,z) i.e. the average reward for the beam corresponding to the input instance x. See Note that the primary challenge with this weakly supervised training is the sparsity in reward signal given the large search space leading to only a few predictions having a non-zero reward. The average denotation accuracy (with beam width 1) of 70.6% which is comparable to state-of-the-art Jia and Liang This additionally suggests that data normalization process does not alter the task complexity." } ], "images": [ "scigen/train/2745.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Test denotation accuracy for each domain comparing our proposed method DistillCombined with the three baselines. We also report the skyline Supervised." ], "reasoning": "The average denotation accuracy with beam width 1 is 70.6%, which is comparable to state-of-the-art methods, suggesting that the data normalization process does not alter task complexity.", "description": "To reduce the variance in gradient estimation we use baseline b(x)=1|B|∑z∈BR(x,z) i.e. the average reward for the beam corresponding to the input instance x. See Note that the primary challenge with this weakly supervised training is the sparsity in reward signal given the large search space leading to only a few predictions having a non-zero reward. The average denotation accuracy (with beam width 1) of 70.6% which is comparable to state-of-the-art Jia and Liang This additionally suggests that data normalization process does not alter the task complexity.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 2: Test denotation accuracy for each domain comparing our proposed method DistillCombined with the three baselines. We also report the skyline Supervised.'. Solve the following question based on the table.\n\nQuestion: What is the highest accuracy achieved in the Distill-Combined method and in which domain?\nLet's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest accuracy in the Distill-Combined method, look at the Distill-Combined column and identify the highest value, which is 66.2 in the Recipes domain. So, the answer is {\"answer\": \"66.2\"}" } ], "images": [ "scigen/train/2745.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Test denotation accuracy for each domain comparing our proposed method DistillCombined with the three baselines. We also report the skyline Supervised." ], "reasoning": "To find the highest accuracy in the Distill-Combined method, look at the Distill-Combined column and identify the highest value, which is 66.2 in the Recipes domain.", "question": "What is the highest accuracy achieved in the Distill-Combined method and in which domain?", "answer": "So, the answer is {\"answer\": \"66.2\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Weak-Independent method achieved the highest accuracy in the Publications domain.\n" }, { "role": "assistant", "content": "The table shows that the highest accuracy in the Publications domain is 48.4, achieved by the Distill-Combined method, not the Weak-Independent method. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2745.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Test denotation accuracy for each domain comparing our proposed method DistillCombined with the three baselines. We also report the skyline Supervised." ], "reasoning": "The table shows that the highest accuracy in the Publications domain is 48.4, achieved by the Distill-Combined method, not the Weak-Independent method.", "statement": "The Weak-Independent method achieved the highest accuracy in the Publications domain.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Illustrative examples from final lexicon: highest/lowest ranking words for empathy and distress.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "High-empathy words often include named entities related to suffering, while high-distress words are typically adjectives and nouns describing abusive behavior.Our final lexicon consists of 9,356 word types (lower-cased, non-lemmatized, including named entities and spelling errors) each with associated empathy and distress ratings. High-empathy words contain many named entities that experience or cause suffering making a reader feel empathic (e.g., lukemia or lakota). This is likely because the Empathic Reactions corpus used news stories to evoke empathy in subjects who then referred to those named entities for expressing their feeling Low-empathy words, on the other hand, are often ones used for ridiculing, hence expressing a lack of empathy (joke, wacky). High-Distress words contain predominantly adjectives, nouns, and participles which can be used to characterize abusive behaviour (inhumane, mistreating) thus causing personal distress in readers when taking the perspective of the affected entity. Interestingly, low-distress words do not seem to display any clear pattern, making us suspect that personal distress should be addressed in terms of a unipolar rather than a bipolar scale." } ], "images": [ "scigen/train/2746.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Illustrative examples from final lexicon: highest/lowest ranking words for empathy and distress." ], "reasoning": "High-empathy words often include named entities related to suffering, while high-distress words are typically adjectives and nouns describing abusive behavior.", "description": "Our final lexicon consists of 9,356 word types (lower-cased, non-lemmatized, including named entities and spelling errors) each with associated empathy and distress ratings. High-empathy words contain many named entities that experience or cause suffering making a reader feel empathic (e.g., lukemia or lakota). This is likely because the Empathic Reactions corpus used news stories to evoke empathy in subjects who then referred to those named entities for expressing their feeling Low-empathy words, on the other hand, are often ones used for ridiculing, hence expressing a lack of empathy (joke, wacky). High-Distress words contain predominantly adjectives, nouns, and participles which can be used to characterize abusive behaviour (inhumane, mistreating) thus causing personal distress in readers when taking the perspective of the affected entity. Interestingly, low-distress words do not seem to display any clear pattern, making us suspect that personal distress should be addressed in terms of a unipolar rather than a bipolar scale.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Illustrative examples from final lexicon: highest/lowest ranking words for empathy and distress.', and sequentially solve the question, writing out each step of your reasoning process. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the empathy rating for the word 'dehumanizes'?\n" }, { "role": "assistant", "content": "The table lists 'dehumanizes' under High Distress with an empathy rating of 5.46. So, the answer is {\"answer\": \"5.46\"}." } ], "images": [ "scigen/train/2746.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Illustrative examples from final lexicon: highest/lowest ranking words for empathy and distress." ], "reasoning": "The table lists 'dehumanizes' under High Distress with an empathy rating of 5.46.", "question": "What is the empathy rating for the word 'dehumanizes'?", "answer": "So, the answer is {\"answer\": \"5.46\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe word 'healing' has a higher empathy rating than 'lakota'.\n" }, { "role": "assistant", "content": "The table shows that 'healing' has an empathy rating of 6.60, while 'lakota' has an empathy rating of 6.70. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2746.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Illustrative examples from final lexicon: highest/lowest ranking words for empathy and distress." ], "reasoning": "The table shows that 'healing' has an empathy rating of 6.60, while 'lakota' has an empathy rating of 6.70.", "statement": "The word 'healing' has a higher empathy rating than 'lakota'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Identification accuracy on the test data of SRE08 when the utterance length is 1s or 3s. Number of speakers is 1336'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The H-vectors model shows higher accuracy than the X-vectors and X-vectors+Attention models for both 1-second and 3-second utterance lengths, with longer utterances yielding better accuracy.Two different utterance lengths, 1 second and 3 seconds, are used in the experiments, respectively. The use of the H-vectors shows higher accuracy when using either 1-second or 3-second input length than the two baselines. When the length of input utterances is one second, the accuracy obtained using the H-vectors can reach 94.5%, with 4.4% improvement over X-vectors and 2.4% improvement over X-vectors+Attention, respectively. When the length of input utterances is three seconds, the accuracy obtained using the H-vectors can reach 98.5%, with about 3% improvement over X-vectors and about 2% improvement over X-vectors+Attention. The proposed approach is more robust than the two baselines when processed utterances are short. In addition, the accuracies obtained using 3-second utterances are better than those using 1-second utterances. As longer utterance contains more information relevant to a target speaker than those in short ones." } ], "images": [ "scigen/train/2747.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Identification accuracy on the test data of SRE08 when the utterance length is 1s or 3s. Number of speakers is 1336" ], "reasoning": "The H-vectors model shows higher accuracy than the X-vectors and X-vectors+Attention models for both 1-second and 3-second utterance lengths, with longer utterances yielding better accuracy.", "description": "Two different utterance lengths, 1 second and 3 seconds, are used in the experiments, respectively. The use of the H-vectors shows higher accuracy when using either 1-second or 3-second input length than the two baselines. When the length of input utterances is one second, the accuracy obtained using the H-vectors can reach 94.5%, with 4.4% improvement over X-vectors and 2.4% improvement over X-vectors+Attention, respectively. When the length of input utterances is three seconds, the accuracy obtained using the H-vectors can reach 98.5%, with about 3% improvement over X-vectors and about 2% improvement over X-vectors+Attention. The proposed approach is more robust than the two baselines when processed utterances are short. In addition, the accuracies obtained using 3-second utterances are better than those using 1-second utterances. As longer utterance contains more information relevant to a target speaker than those in short ones.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: Identification accuracy on the test data of SRE08 when the utterance length is 1s or 3s. Number of speakers is 1336', unfold the steps to solve the question given next. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nQuestion: What is the accuracy improvement of the H-vectors model over the X-vectors model for 3-second utterances?\n" }, { "role": "assistant", "content": "The accuracy of the H-vectors model for 3-second utterances is 98.5%, while the accuracy of the X-vectors model for 3-second utterances is 95.2%. The improvement is calculated as 98.5% - 95.2%. So, the answer is {\"answer\": \"3.3%\"}" } ], "images": [ "scigen/train/2747.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Identification accuracy on the test data of SRE08 when the utterance length is 1s or 3s. Number of speakers is 1336" ], "reasoning": "The accuracy of the H-vectors model for 3-second utterances is 98.5%, while the accuracy of the X-vectors model for 3-second utterances is 95.2%. The improvement is calculated as 98.5% - 95.2%.", "question": "What is the accuracy improvement of the H-vectors model over the X-vectors model for 3-second utterances?", "answer": "So, the answer is {\"answer\": \"3.3%\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe H-vectors model achieves an accuracy of 94.5% for 1-second utterances.\n" }, { "role": "assistant", "content": "The table shows that the H-vectors model has an accuracy of 94.5% for 1-second utterances. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2747.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Identification accuracy on the test data of SRE08 when the utterance length is 1s or 3s. Number of speakers is 1336" ], "reasoning": "The table shows that the H-vectors model has an accuracy of 94.5% for 1-second utterances.", "statement": "The H-vectors model achieves an accuracy of 94.5% for 1-second utterances.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Evaluation of LIME-based faithfulness scores for different numbers of interpretable units K using 15K training explanations. “Random” means the training explanations are randomly sampled from the train set, and “Filtered” means the models are trained using the remaining faithful explanations.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Training with faithful human explanation and enforcing local faithfulness achieves better results, with an average cosine similarity around 0.7.K. The influential objects recognized by LIME match objects that are linked to words in our explanations with an average cosine similarity around 0.7. This indicates that the explanations are faithfully making reference to visual segmentations that actually influenced the decision of the underlying VQA system. Also, we observe that training with faithful human explanation outperforms purely mimicking human explanations in terms of our faithful metric, and further enforcing the local faithfulness achieves a better result." } ], "images": [ "scigen/train/2748.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Evaluation of LIME-based faithfulness scores for different numbers of interpretable units K using 15K training explanations. “Random” means the training explanations are randomly sampled from the train set, and “Filtered” means the models are trained using the remaining faithful explanations." ], "reasoning": "Training with faithful human explanation and enforcing local faithfulness achieves better results, with an average cosine similarity around 0.7.", "description": "K. The influential objects recognized by LIME match objects that are linked to words in our explanations with an average cosine similarity around 0.7. This indicates that the explanations are faithfully making reference to visual segmentations that actually influenced the decision of the underlying VQA system. Also, we observe that training with faithful human explanation outperforms purely mimicking human explanations in terms of our faithful metric, and further enforcing the local faithfulness achieves a better result.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: Evaluation of LIME-based faithfulness scores for different numbers of interpretable units K using 15K training explanations. “Random” means the training explanations are randomly sampled from the train set, and “Filtered” means the models are trained using the remaining faithful explanations.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the cosine similarity for K=2 when using the 'Ours (Filtered+Lf)' method?\n" }, { "role": "assistant", "content": "The table shows the cosine similarity values for different methods and K values. For K=2 and the 'Ours (Filtered+Lf)' method, the value is 0.705. So, the answer is {\"answer\": \"0.705\"}." } ], "images": [ "scigen/train/2748.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Evaluation of LIME-based faithfulness scores for different numbers of interpretable units K using 15K training explanations. “Random” means the training explanations are randomly sampled from the train set, and “Filtered” means the models are trained using the remaining faithful explanations." ], "reasoning": "The table shows the cosine similarity values for different methods and K values. For K=2 and the 'Ours (Filtered+Lf)' method, the value is 0.705.", "question": "What is the cosine similarity for K=2 when using the 'Ours (Filtered+Lf)' method?", "answer": "So, the answer is {\"answer\": \"0.705\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe cosine similarity for K=1 using the 'Ours (Filtered)' method is higher than using the 'Ours (Random)' method.\n" }, { "role": "assistant", "content": "The table shows that for K=1, the 'Ours (Filtered)' method has a cosine similarity of 0.636, which is higher than the 0.588 cosine similarity for the 'Ours (Random)' method. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2748.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Evaluation of LIME-based faithfulness scores for different numbers of interpretable units K using 15K training explanations. “Random” means the training explanations are randomly sampled from the train set, and “Filtered” means the models are trained using the remaining faithful explanations." ], "reasoning": "The table shows that for K=1, the 'Ours (Filtered)' method has a cosine similarity of 0.636, which is higher than the 0.588 cosine similarity for the 'Ours (Random)' method.", "statement": "The cosine similarity for K=1 using the 'Ours (Filtered)' method is higher than using the 'Ours (Random)' method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Explanation evaluation results, the top half shows results using the entire train set and the bottom half shows results using about 15K explanations. F denotes whether to filter out the unfaithful training explanations. With F, the 15K explanations are the remaining explanation and without F, the 15K explanations are randomly sampled from train set. Ls, Lf denote the losses of the source identifier and the faithful gradient alignment, respectively. B-4, M, R-L, C and S are short hand for BLEU-4, METEOR, ROUGE-L, CIDEr and SPICE, respectively.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our approach outperforms the PJ-X model by using detailed image segmentation and linking segmented visual objects to specific words in textual explanations, reducing noisy attention.Similar to Park et al. BLEU-4 Papineni et al. et al. In particular, “Justification” denotes training on the entire or randomly sampled VQA-X dataset and “Explanation” denotes training only on the remaining faithful explanations. We outperform the current state-of-the-art PJ-X model Park et al. This indicates that constructing explanations that faithfully reflect the VQA process can actually generate explanations that match human explanations better than just training to directly match human explanations, possibly by avoiding over-fitting and focusing more on important aspects of the test images. We attribute this improvement to the following reasons. First, our approach uses detailed image segmentation which avoids focusing on background and is much more precise than bounding-box detection. Second, our visual explanation is focused by textual explanation where the segmented visual objects must be linked to specific words in the textual explanation. Therefore, the risk of attending to unnecessary objects in the images is significantly reduced. As a result, we filter out most of the noisy attention in a purely visual explanation like that in PJ-X." } ], "images": [ "scigen/train/2749.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Explanation evaluation results, the top half shows results using the entire train set and the bottom half shows results using about 15K explanations. F denotes whether to filter out the unfaithful training explanations. With F, the 15K explanations are the remaining explanation and without F, the 15K explanations are randomly sampled from train set. Ls, Lf denote the losses of the source identifier and the faithful gradient alignment, respectively. B-4, M, R-L, C and S are short hand for BLEU-4, METEOR, ROUGE-L, CIDEr and SPICE, respectively." ], "reasoning": "Our approach outperforms the PJ-X model by using detailed image segmentation and linking segmented visual objects to specific words in textual explanations, reducing noisy attention.", "description": "Similar to Park et al. BLEU-4 Papineni et al. et al. In particular, “Justification” denotes training on the entire or randomly sampled VQA-X dataset and “Explanation” denotes training only on the remaining faithful explanations. We outperform the current state-of-the-art PJ-X model Park et al. This indicates that constructing explanations that faithfully reflect the VQA process can actually generate explanations that match human explanations better than just training to directly match human explanations, possibly by avoiding over-fitting and focusing more on important aspects of the test images. We attribute this improvement to the following reasons. First, our approach uses detailed image segmentation which avoids focusing on background and is much more precise than bounding-box detection. Second, our visual explanation is focused by textual explanation where the segmented visual objects must be linked to specific words in the textual explanation. Therefore, the risk of attending to unnecessary objects in the images is significantly reduced. As a result, we filter out most of the noisy attention in a purely visual explanation like that in PJ-X.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: Explanation evaluation results, the top half shows results using the entire train set and the bottom half shows results using about 15K explanations. F denotes whether to filter out the unfaithful training explanations. With F, the 15K explanations are the remaining explanation and without F, the 15K explanations are randomly sampled from train set. Ls, Lf denote the losses of the source identifier and the faithful gradient alignment, respectively. B-4, M, R-L, C and S are short hand for BLEU-4, METEOR, ROUGE-L, CIDEr and SPICE, respectively.'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model achieved the highest Textual B-4 score and what was the score?\n" }, { "role": "assistant", "content": "By examining the table, the highest Textual B-4 score is 25.1, achieved by the 'Ours (Explanation)' model with 15K explanations. So, the answer is {\"answer\": \"25.1\"}." } ], "images": [ "scigen/train/2749.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Explanation evaluation results, the top half shows results using the entire train set and the bottom half shows results using about 15K explanations. F denotes whether to filter out the unfaithful training explanations. With F, the 15K explanations are the remaining explanation and without F, the 15K explanations are randomly sampled from train set. Ls, Lf denote the losses of the source identifier and the faithful gradient alignment, respectively. B-4, M, R-L, C and S are short hand for BLEU-4, METEOR, ROUGE-L, CIDEr and SPICE, respectively." ], "reasoning": "By examining the table, the highest Textual B-4 score is 25.1, achieved by the 'Ours (Explanation)' model with 15K explanations.", "question": "Which model achieved the highest Textual B-4 score and what was the score?", "answer": "So, the answer is {\"answer\": \"25.1\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe PJ-X model achieved a higher Textual C score than the 'Ours (Explanation)' model.\n" }, { "role": "assistant", "content": "The table shows that the PJ-X model has a Textual C score of 71.3, while the 'Ours (Explanation)' model has a higher score of 86.7. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2749.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Explanation evaluation results, the top half shows results using the entire train set and the bottom half shows results using about 15K explanations. F denotes whether to filter out the unfaithful training explanations. With F, the 15K explanations are the remaining explanation and without F, the 15K explanations are randomly sampled from train set. Ls, Lf denote the losses of the source identifier and the faithful gradient alignment, respectively. B-4, M, R-L, C and S are short hand for BLEU-4, METEOR, ROUGE-L, CIDEr and SPICE, respectively." ], "reasoning": "The table shows that the PJ-X model has a Textual C score of 71.3, while the 'Ours (Explanation)' model has a higher score of 86.7.", "statement": "The PJ-X model achieved a higher Textual C score than the 'Ours (Explanation)' model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: Accuracies of the worst CNN+translation classifiers when N=1. Accuracies less than CNN accuracies are highlighted in red.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Our model (CNN+MCFA) consistently outperforms the baselines (CNN+B1, CNN+B2) across all settings, with notable improvements in accuracy over the base CNN model, except for CNN+B1 on datasets other than SUBJ.We compare our model (CNN+MCFA) and the baselines discussed above (CNN+B1, CNN+B2). On all settings, our model outperforms the baselines. When N=10, the performance of our model increases over the performance when N=1, however the performance of CNN+B1 decreases when compared to the performance when N=1. On all data sets except SUBJ, the accuracy of CNN+B1 decreases from the base CNN accuracy, while the accuracy of our model always improves from the base CNN accuracy. This is resolved by CNN+B2 by applying L2 regularization, however the increase in performance is marginal." } ], "images": [ "scigen/train/2750.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracies of the worst CNN+translation classifiers when N=1. Accuracies less than CNN accuracies are highlighted in red." ], "reasoning": "Our model (CNN+MCFA) consistently outperforms the baselines (CNN+B1, CNN+B2) across all settings, with notable improvements in accuracy over the base CNN model, except for CNN+B1 on datasets other than SUBJ.", "description": "We compare our model (CNN+MCFA) and the baselines discussed above (CNN+B1, CNN+B2). On all settings, our model outperforms the baselines. When N=10, the performance of our model increases over the performance when N=1, however the performance of CNN+B1 decreases when compared to the performance when N=1. On all data sets except SUBJ, the accuracy of CNN+B1 decreases from the base CNN accuracy, while the accuracy of our model always improves from the base CNN accuracy. This is resolved by CNN+B2 by applying L2 regularization, however the increase in performance is marginal.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Accuracies of the worst CNN+translation classifiers when N=1. Accuracies less than CNN accuracies are highlighted in red.', answer the following question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model shows the highest accuracy on the TREC dataset?\n" }, { "role": "assistant", "content": "To determine the highest accuracy on the TREC dataset, compare the TREC column values for all models. CNN+MCFA has the highest accuracy of 94.2. So, the answer is {\"answer\": \"CNN+MCFA\"}." } ], "images": [ "scigen/train/2750.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracies of the worst CNN+translation classifiers when N=1. Accuracies less than CNN accuracies are highlighted in red." ], "reasoning": "To determine the highest accuracy on the TREC dataset, compare the TREC column values for all models. CNN+MCFA has the highest accuracy of 94.2.", "question": "Which model shows the highest accuracy on the TREC dataset?", "answer": "So, the answer is {\"answer\": \"CNN+MCFA\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nCNN+B1 has the lowest accuracy on the CR dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the CR column values for all models. CNN+B1 has an accuracy of 83.8, which is the lowest among the models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2750.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracies of the worst CNN+translation classifiers when N=1. Accuracies less than CNN accuracies are highlighted in red." ], "reasoning": "To verify this statement, compare the CR column values for all models. CNN+B1 has an accuracy of 83.8, which is the lowest among the models listed.", "statement": "CNN+B1 has the lowest accuracy on the CR dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Diagnostic intelligibility and naturalness test without using SUS sentences on the pathological test set'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The WRF model significantly improves intelligibility and naturalness rates compared to the baseline and PSF models, demonstrating the effectiveness of syntactic features.Around 10% of results synthesized by baseline model have intelligibility or naturalness issues. When the models are trained with the syntactic features, the troublesome issues are significantly improved. It shows that syntactic information is helpful to improve the pronunciation clarity and generalization capability. WRF still yields the best performance, in comparing with PSF or the baseline system as shown in the Table." } ], "images": [ "scigen/train/2751.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Diagnostic intelligibility and naturalness test without using SUS sentences on the pathological test set" ], "reasoning": "The WRF model significantly improves intelligibility and naturalness rates compared to the baseline and PSF models, demonstrating the effectiveness of syntactic features.", "description": "Around 10% of results synthesized by baseline model have intelligibility or naturalness issues. When the models are trained with the syntactic features, the troublesome issues are significantly improved. It shows that syntactic information is helpful to improve the pronunciation clarity and generalization capability. WRF still yields the best performance, in comparing with PSF or the baseline system as shown in the Table.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the intelligibility rate (%) when using the WRF model?\nSolve the above question based on the table titled 'Table 3: Diagnostic intelligibility and naturalness test without using SUS sentences on the pathological test set'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the intelligibility rate for different models. The WRF model has the highest intelligibility rate. So, the answer is {\"answer\": \"99.0\"}." } ], "images": [ "scigen/train/2751.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Diagnostic intelligibility and naturalness test without using SUS sentences on the pathological test set" ], "reasoning": "The table shows the intelligibility rate for different models. The WRF model has the highest intelligibility rate.", "question": "What is the intelligibility rate (%) when using the WRF model?", "answer": "So, the answer is {\"answer\": \"99.0\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe baseline model has a naturalness rate of 89.5%.\n" }, { "role": "assistant", "content": "The table shows the naturalness rate for the baseline model, which is 89.5%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2751.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Diagnostic intelligibility and naturalness test without using SUS sentences on the pathological test set" ], "reasoning": "The table shows the naturalness rate for the baseline model, which is 89.5%.", "statement": "The baseline model has a naturalness rate of 89.5%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: Subjective evaluations on different layers and orders. T: top-down, B: bottom-up, e.g. T5 refers to top-down 5 levels'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Different models show unique effects on different sentences, and sorting by CMOS scores and preferences leads to adopting T10 as phrase structure based features.The comparisons show most models achieve similar performance. But their good effect are performed on different sentences. For example, the comparison of T5 and T10 show a small CMOS score of 0.03. But the preferences on these two models are high and similar. It shows that the two features have their own unique effects on different sentences. So we infer that selection of different layers may lead to different performance on the same sentence. Finally we sort these models by CMOS scores and preference, then adopt T10 as phrase structure based features (PSF)." } ], "images": [ "scigen/train/2752.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Subjective evaluations on different layers and orders. T: top-down, B: bottom-up, e.g. T5 refers to top-down 5 levels" ], "reasoning": "Different models show unique effects on different sentences, and sorting by CMOS scores and preferences leads to adopting T10 as phrase structure based features.", "description": "The comparisons show most models achieve similar performance. But their good effect are performed on different sentences. For example, the comparison of T5 and T10 show a small CMOS score of 0.03. But the preferences on these two models are high and similar. It shows that the two features have their own unique effects on different sentences. So we infer that selection of different layers may lead to different performance on the same sentence. Finally we sort these models by CMOS scores and preference, then adopt T10 as phrase structure based features (PSF).", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: Subjective evaluations on different layers and orders. T: top-down, B: bottom-up, e.g. T5 refers to top-down 5 levels', answer the question below by showing each progressive step toward the answer. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the lowest CMOS score and what is its preference percentage for Feature B?\n" }, { "role": "assistant", "content": "By examining the table, T10 has the lowest CMOS score of 0.03. The preference percentage for Feature B for T10 is 39.6%. So, the answer is {\"answer\": \"T10, 39.6%\"}" } ], "images": [ "scigen/train/2752.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Subjective evaluations on different layers and orders. T: top-down, B: bottom-up, e.g. T5 refers to top-down 5 levels" ], "reasoning": "By examining the table, T10 has the lowest CMOS score of 0.03. The preference percentage for Feature B for T10 is 39.6%.", "question": "Which model has the lowest CMOS score and what is its preference percentage for Feature B?", "answer": "So, the answer is {\"answer\": \"T10, 39.6%\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nT5 has the highest preference percentage for Feature A.\n" }, { "role": "assistant", "content": "By examining the table, T5 has a preference percentage of 42.4% for Feature A, which is higher than any other model listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2752.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Subjective evaluations on different layers and orders. T: top-down, B: bottom-up, e.g. T5 refers to top-down 5 levels" ], "reasoning": "By examining the table, T5 has a preference percentage of 42.4% for Feature A, which is higher than any other model listed.", "statement": "T5 has the highest preference percentage for Feature A.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Subjective preference of models trained with different input features', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Syntactic feature-based models show larger improvements on the complex test set, indicating that syntactic information significantly enhances speech synthesis for complex sentences, mainly improving prosody.In these two test sets, syntactic feature based models have larger improvement on the complex test set. These results show that providing syntactic information can significantly help synthesize better speech, especially for sentences with more complex grammar and longer length. Our analysis of these test cases find that the improvement of the effect is mainly on the prosody." } ], "images": [ "scigen/train/2753.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Subjective preference of models trained with different input features" ], "reasoning": "Syntactic feature-based models show larger improvements on the complex test set, indicating that syntactic information significantly enhances speech synthesis for complex sentences, mainly improving prosody.", "description": "In these two test sets, syntactic feature based models have larger improvement on the complex test set. These results show that providing syntactic information can significantly help synthesize better speech, especially for sentences with more complex grammar and longer length. Our analysis of these test cases find that the improvement of the effect is mainly on the prosody.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Subjective preference of models trained with different input features\nQuestion: Which test set shows a higher CMOS value for the Preference (%) WRF model?\nGive a detailed response to the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To determine which test set shows a higher CMOS value for the Preference (%) WRF model, compare the CMOS values for the Common Test Set and the Complex Test Set under the Preference (%) WRF column. The Complex Test Set has a CMOS value of 0.400, which is higher than the Common Test Set's CMOS value of 0.230. So, the answer is {\"answer\": \"Complex Test Set\"}." } ], "images": [ "scigen/train/2753.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Subjective preference of models trained with different input features" ], "reasoning": "To determine which test set shows a higher CMOS value for the Preference (%) WRF model, compare the CMOS values for the Common Test Set and the Complex Test Set under the Preference (%) WRF column. The Complex Test Set has a CMOS value of 0.400, which is higher than the Common Test Set's CMOS value of 0.230.", "question": "Which test set shows a higher CMOS value for the Preference (%) WRF model?", "answer": "So, the answer is {\"answer\": \"Complex Test Set\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Preference (%) Neutral model has a higher percentage in the Complex Test Set compared to the Common Test Set.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Preference (%) Neutral values for the Complex Test Set and the Common Test Set. The Complex Test Set has a value of 70.0%, which is higher than the Common Test Set's value of 58.6%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2753.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Subjective preference of models trained with different input features" ], "reasoning": "To verify this statement, compare the Preference (%) Neutral values for the Complex Test Set and the Common Test Set. The Complex Test Set has a value of 70.0%, which is higher than the Common Test Set's value of 58.6%.", "statement": "The Preference (%) Neutral model has a higher percentage in the Complex Test Set compared to the Common Test Set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Ablation study of question answering performances in the development set of HotpotQA in the distractor setting. We use a DFGN with 2-layer fusion blocks as the origin model. The upper part is the model ablation results and the lower part is the dataset ablation results.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Each model component provides a 1% to 2% gain in QA performance, and using a 1-layer fusion block results in significant performance loss, highlighting the importance of multi-hop reasoning.To evaluate the performance of different components in our DFGN, we perform ablation study on both model components and dataset segments. Here we follow the experiment setting in \\newciteyang2018hotpotqa to perform the dataset ablation study, where we only use golden paragraphs or supporting facts as the input context. From the table we can see that each of our model components can provide from 1% to 2% relative gain over the QA performance. Particularly, using a 1-layer fusion block leads to an obvious performance loss, which implies the significance of performing multi-hop reasoning in HotpotQA. Besides, the dataset ablation results show that our model is not very sensitive to the noisy paragraphs comparing with the baseline model which can achieve a more than 5% performance gain in the “gold paragraphs only” and “supporting facts only” settings. Yang et al." } ], "images": [ "scigen/train/2754.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ablation study of question answering performances in the development set of HotpotQA in the distractor setting. We use a DFGN with 2-layer fusion blocks as the origin model. The upper part is the model ablation results and the lower part is the dataset ablation results." ], "reasoning": "Each model component provides a 1% to 2% gain in QA performance, and using a 1-layer fusion block results in significant performance loss, highlighting the importance of multi-hop reasoning.", "description": "To evaluate the performance of different components in our DFGN, we perform ablation study on both model components and dataset segments. Here we follow the experiment setting in \\newciteyang2018hotpotqa to perform the dataset ablation study, where we only use golden paragraphs or supporting facts as the input context. From the table we can see that each of our model components can provide from 1% to 2% relative gain over the QA performance. Particularly, using a 1-layer fusion block leads to an obvious performance loss, which implies the significance of performing multi-hop reasoning in HotpotQA. Besides, the dataset ablation results show that our model is not very sensitive to the noisy paragraphs comparing with the baseline model which can achieve a more than 5% performance gain in the “gold paragraphs only” and “supporting facts only” settings. Yang et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Ablation study of question answering performances in the development set of HotpotQA in the distractor setting. We use a DFGN with 2-layer fusion blocks as the origin model. The upper part is the model ablation results and the lower part is the dataset ablation results.', answer the question below by showing each progressive step toward the answer. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the F1 score when using only supporting facts as the input context?\n" }, { "role": "assistant", "content": "The table shows the F1 scores for different settings. For the 'supporting facts only' setting, the F1 score is 71.67. So, the answer is {\"answer\": \"71.67\"}" } ], "images": [ "scigen/train/2754.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ablation study of question answering performances in the development set of HotpotQA in the distractor setting. We use a DFGN with 2-layer fusion blocks as the origin model. The upper part is the model ablation results and the lower part is the dataset ablation results." ], "reasoning": "The table shows the F1 scores for different settings. For the 'supporting facts only' setting, the F1 score is 71.67.", "question": "What is the F1 score when using only supporting facts as the input context?", "answer": "So, the answer is {\"answer\": \"71.67\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nUsing a 1-layer fusion block leads to a performance gain in QA.\n" }, { "role": "assistant", "content": "The table shows that using a 1-layer fusion block results in an F1 score of 67.70, which is lower than the baseline DFGN (2-layer) F1 score of 69.23, indicating a performance loss rather than a gain. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2754.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ablation study of question answering performances in the development set of HotpotQA in the distractor setting. We use a DFGN with 2-layer fusion blocks as the origin model. The upper part is the model ablation results and the lower part is the dataset ablation results." ], "reasoning": "The table shows that using a 1-layer fusion block results in an F1 score of 67.70, which is lower than the baseline DFGN (2-layer) F1 score of 69.23, indicating a performance loss rather than a gain.", "statement": "Using a 1-layer fusion block leads to a performance gain in QA.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Performance comparison on the private test set of HotpotQA in the distractor setting. Our DFGN is the second best result on the leaderboard before submission (on March 1st). The baseline model is from yang2018hotpotqa and the results with ∗ is unpublished. DFGN(Ours)† refers to the same model with a revised entity graph, whose entities are recognized by a BERT NER model. Note that the result of DFGN(Ours)† is submitted to the leaderboard during the review process of our paper.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Our model achieves a 1.5% gain in the joint F1-score with a revised entity graph recognized by a BERT NER model.Besides, the answer performance and the joint performance of our model are competitive against state-of-the-art unpublished models. We also include the result of our model with a revised entity graph whose entities are recognized by a BERT NER model Devlin et al. We fine-tune the pre-trained BERT model on the dataset of the CoNLL’03 NER shared task Sang and De Meulder The results show that our model achieves a 1.5% gain in the joint F1-score with the entity graph built from a better entity recognizer." } ], "images": [ "scigen/train/2755.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance comparison on the private test set of HotpotQA in the distractor setting. Our DFGN is the second best result on the leaderboard before submission (on March 1st). The baseline model is from yang2018hotpotqa and the results with ∗ is unpublished. DFGN(Ours)† refers to the same model with a revised entity graph, whose entities are recognized by a BERT NER model. Note that the result of DFGN(Ours)† is submitted to the leaderboard during the review process of our paper." ], "reasoning": "Our model achieves a 1.5% gain in the joint F1-score with a revised entity graph recognized by a BERT NER model.", "description": "Besides, the answer performance and the joint performance of our model are competitive against state-of-the-art unpublished models. We also include the result of our model with a revised entity graph whose entities are recognized by a BERT NER model Devlin et al. We fine-tune the pre-trained BERT model on the dataset of the CoNLL’03 NER shared task Sang and De Meulder The results show that our model achieves a 1.5% gain in the joint F1-score with the entity graph built from a better entity recognizer.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: Performance comparison on the private test set of HotpotQA in the distractor setting. Our DFGN is the second best result on the leaderboard before submission (on March 1st). The baseline model is from yang2018hotpotqa and the results with ∗ is unpublished. DFGN(Ours)† refers to the same model with a revised entity graph, whose entities are recognized by a BERT NER model. Note that the result of DFGN(Ours)† is submitted to the leaderboard during the review process of our paper.', answer the question below by showing each progressive step toward the answer. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the joint F1-score of the DFGN model with a revised entity graph?\n" }, { "role": "assistant", "content": "The table shows the joint F1-score for different models. The DFGN model with a revised entity graph (DFGN(Ours)†) has a joint F1-score of 59.82. So, the answer is {\"answer\": \"59.82\"}" } ], "images": [ "scigen/train/2755.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance comparison on the private test set of HotpotQA in the distractor setting. Our DFGN is the second best result on the leaderboard before submission (on March 1st). The baseline model is from yang2018hotpotqa and the results with ∗ is unpublished. DFGN(Ours)† refers to the same model with a revised entity graph, whose entities are recognized by a BERT NER model. Note that the result of DFGN(Ours)† is submitted to the leaderboard during the review process of our paper." ], "reasoning": "The table shows the joint F1-score for different models. The DFGN model with a revised entity graph (DFGN(Ours)†) has a joint F1-score of 59.82.", "question": "What is the joint F1-score of the DFGN model with a revised entity graph?", "answer": "So, the answer is {\"answer\": \"59.82\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The QFE model has a higher joint F1-score than the GRN model., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe QFE model has a higher joint F1-score than the GRN model.\n" }, { "role": "assistant", "content": "The table shows that the QFE model has a joint F1-score of 59.61, while the GRN model has a joint F1-score of 58.47. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2755.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance comparison on the private test set of HotpotQA in the distractor setting. Our DFGN is the second best result on the leaderboard before submission (on March 1st). The baseline model is from yang2018hotpotqa and the results with ∗ is unpublished. DFGN(Ours)† refers to the same model with a revised entity graph, whose entities are recognized by a BERT NER model. Note that the result of DFGN(Ours)† is submitted to the leaderboard during the review process of our paper." ], "reasoning": "The table shows that the QFE model has a joint F1-score of 59.61, while the GRN model has a joint F1-score of 58.47. Therefore, the statement is supported.", "statement": "The QFE model has a higher joint F1-score than the GRN model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Results on GLUE test sets. Metrics differ per task (explained in Appendix A) but the best result is highlighted.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Reptile outperforms other models in most datasets, achieving the highest scores in MRPC and RTE.the experimental results on the GLUE test set. Generally, the meta-learning algorithms achieve better performance than the strong baseline models, with Reptile performing the best." } ], "images": [ "scigen/train/2756.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results on GLUE test sets. Metrics differ per task (explained in Appendix A) but the best result is highlighted." ], "reasoning": "Reptile outperforms other models in most datasets, achieving the highest scores in MRPC and RTE.", "description": "the experimental results on the GLUE test set. Generally, the meta-learning algorithms achieve better performance than the strong baseline models, with Reptile performing the best.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 1: Results on GLUE test sets. Metrics differ per task (explained in Appendix A) but the best result is highlighted.', solve the following question step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model achieved the highest score in the MRPC dataset?\n" }, { "role": "assistant", "content": "By comparing the MRPC scores of all models, Reptile has the highest score of 90.2/86.7. So, the answer is {\"answer\": \"Reptile\"}." } ], "images": [ "scigen/train/2756.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results on GLUE test sets. Metrics differ per task (explained in Appendix A) but the best result is highlighted." ], "reasoning": "By comparing the MRPC scores of all models, Reptile has the highest score of 90.2/86.7.", "question": "Which model achieved the highest score in the MRPC dataset?", "answer": "So, the answer is {\"answer\": \"Reptile\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nMAML achieved the highest score in the CoLA dataset.\n" }, { "role": "assistant", "content": "By comparing the CoLA scores of all models, MAML has the highest score of 53.4. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2756.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results on GLUE test sets. Metrics differ per task (explained in Appendix A) but the best result is highlighted." ], "reasoning": "By comparing the CoLA scores of all models, MAML has the highest score of 53.4.", "statement": "MAML achieved the highest score in the CoLA dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Effect of the number of update steps and the inner learning rate α.'.\n" }, { "role": "assistant", "content": "Setting the number of update steps k to 5 and adjusting the inner learning rate α can optimize performance, but larger α may degrade it.In this part, we test the effect of the number of update steps k and the learning rate in the inner learning loop. We find that setting k to 5 is the optimal strategy and more or fewer update steps may lead to worse performance. We also vary the inner learning rate α and investigate its impact. We can see that larger α may degrade the performance because the resulting gradients deviate a lot from normal ones. The above two ablations studies demonstrate the importance of making the meta-gradient informative." } ], "images": [ "scigen/train/2757.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Effect of the number of update steps and the inner learning rate α." ], "reasoning": "Setting the number of update steps k to 5 and adjusting the inner learning rate α can optimize performance, but larger α may degrade it.", "description": "In this part, we test the effect of the number of update steps k and the learning rate in the inner learning loop. We find that setting k to 5 is the optimal strategy and more or fewer update steps may lead to worse performance. We also vary the inner learning rate α and investigate its impact. We can see that larger α may degrade the performance because the resulting gradients deviate a lot from normal ones. The above two ablations studies demonstrate the importance of making the meta-gradient informative.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Effect of the number of update steps and the inner learning rate α.\nQuestion: What is the CoLA score when the number of update steps is set to 5 and the inner learning rate α is 1e-4?\nGive a detailed response to the above question. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the CoLA score, locate the row where #Upt is 5 and α is 1e-4, then check the corresponding CoLA column. So, the answer is {\"answer\": \"62.0\"}." } ], "images": [ "scigen/train/2757.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Effect of the number of update steps and the inner learning rate α." ], "reasoning": "To find the CoLA score, locate the row where #Upt is 5 and α is 1e-4, then check the corresponding CoLA column.", "question": "What is the CoLA score when the number of update steps is set to 5 and the inner learning rate α is 1e-4?", "answer": "So, the answer is {\"answer\": \"62.0\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe optimal strategy for the number of update steps is setting k to 5.\n" }, { "role": "assistant", "content": "The description states that setting k to 5 is the optimal strategy, and the table shows that the best performance metrics are achieved when #Upt is 5. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2757.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Effect of the number of update steps and the inner learning rate α." ], "reasoning": "The description states that setting k to 5 is the optimal strategy, and the table shows that the best performance metrics are achieved when #Upt is 5.", "statement": "The optimal strategy for the number of update steps is setting k to 5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Interaction between POS tagging and dependency parsing. “tag → parse” denotes that parsing leverages lexical information and “tag ← parse” denotes that tagging exploits syntactic information. The interactions can be disabled as shown in Eq. (10)-(12). The tagging and parsing results are evaluated on the Chinese Penn Treebank development set.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Disabling interactions between tagging and parsing reduces the quality of both tagging and parsing.We observe that disabling the interactions between tagging and parsing significantly deteriorates both tagging and parsing quality." } ], "images": [ "scigen/train/2758.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Interaction between POS tagging and dependency parsing. “tag → parse” denotes that parsing leverages lexical information and “tag ← parse” denotes that tagging exploits syntactic information. The interactions can be disabled as shown in Eq. (10)-(12). The tagging and parsing results are evaluated on the Chinese Penn Treebank development set." ], "reasoning": "Disabling interactions between tagging and parsing reduces the quality of both tagging and parsing.", "description": "We observe that disabling the interactions between tagging and parsing significantly deteriorates both tagging and parsing quality.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: Interaction between POS tagging and dependency parsing. “tag → parse” denotes that parsing leverages lexical information and “tag ← parse” denotes that tagging exploits syntactic information. The interactions can be disabled as shown in Eq. (10)-(12). The tagging and parsing results are evaluated on the Chinese Penn Treebank development set.', solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the POS score when both interactions between tagging and parsing are enabled?\n" }, { "role": "assistant", "content": "To find the POS score when both interactions are enabled, look at the row where both 'Interaction tag → parse' and 'Interaction tag ← parse' are marked with a check (√). So, the answer is {\"answer\": \"95.63\"}." } ], "images": [ "scigen/train/2758.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Interaction between POS tagging and dependency parsing. “tag → parse” denotes that parsing leverages lexical information and “tag ← parse” denotes that tagging exploits syntactic information. The interactions can be disabled as shown in Eq. (10)-(12). The tagging and parsing results are evaluated on the Chinese Penn Treebank development set." ], "reasoning": "To find the POS score when both interactions are enabled, look at the row where both 'Interaction tag → parse' and 'Interaction tag ← parse' are marked with a check (√).", "question": "What is the POS score when both interactions between tagging and parsing are enabled?", "answer": "So, the answer is {\"answer\": \"95.63\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The highest UAS score is achieved when both interactions between tagging and parsing are enabled. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe highest UAS score is achieved when both interactions between tagging and parsing are enabled.\n" }, { "role": "assistant", "content": "To verify this statement, compare the UAS scores across all rows and check if the highest score corresponds to the row where both interactions are enabled. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2758.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Interaction between POS tagging and dependency parsing. “tag → parse” denotes that parsing leverages lexical information and “tag ← parse” denotes that tagging exploits syntactic information. The interactions can be disabled as shown in Eq. (10)-(12). The tagging and parsing results are evaluated on the Chinese Penn Treebank development set." ], "reasoning": "To verify this statement, compare the UAS scores across all rows and check if the highest score corresponds to the row where both interactions are enabled.", "statement": "The highest UAS score is achieved when both interactions between tagging and parsing are enabled.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Final results on the datasets of PTB and CTB5.1, where the tagging accuracy being 100% denotes gold-standard POS tags are employed. We include the results of state-of-the-art previous transition-based parsers as well. In particular, Andor et al. (2016) use beam search in decoding and Bohnet and Nivre (2012) use a different method to produce dependency trees.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The joint model significantly improves POS tagging and dependency parsing compared to the pipeline model, but still falls short of the oracle performances using gold-standard POS tags.We include the pipeline performances as well. Our joint model brings significant improvements on both POS tagging (POS) and dependency parsing (LAS) compared with the pipeline model (the p-value is below 10−5 using pairwise t-test). In addition, we compare our joint model with the baseline parsing model using gold-standard POS tags, which can be treated as the oracle performances of our joint model. Although the joint model gives improved performances over the pipeline model, it still has large spaces to reach the oracle performances, which demonstrates the effectiveness of POS tags in dependency parsing. We compare our model with previous work as well. On the one hand, we compare our joint model with previous joint models. On the other hand, we compare our baseline model with state-of-the-art transition-based dependency parsing models. Typically, the PTB results are reported by using auto POS tags and the CTB5.1 results are reported by using gold-standard POS tags, respectively. Our baseline model produces strong enough results for both PTB and CTB5.1." } ], "images": [ "scigen/train/2759.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Final results on the datasets of PTB and CTB5.1, where the tagging accuracy being 100% denotes gold-standard POS tags are employed. We include the results of state-of-the-art previous transition-based parsers as well. In particular, Andor et al. (2016) use beam search in decoding and Bohnet and Nivre (2012) use a different method to produce dependency trees." ], "reasoning": "The joint model significantly improves POS tagging and dependency parsing compared to the pipeline model, but still falls short of the oracle performances using gold-standard POS tags.", "description": "We include the pipeline performances as well. Our joint model brings significant improvements on both POS tagging (POS) and dependency parsing (LAS) compared with the pipeline model (the p-value is below 10−5 using pairwise t-test). In addition, we compare our joint model with the baseline parsing model using gold-standard POS tags, which can be treated as the oracle performances of our joint model. Although the joint model gives improved performances over the pipeline model, it still has large spaces to reach the oracle performances, which demonstrates the effectiveness of POS tags in dependency parsing. We compare our model with previous work as well. On the one hand, we compare our joint model with previous joint models. On the other hand, we compare our baseline model with state-of-the-art transition-based dependency parsing models. Typically, the PTB results are reported by using auto POS tags and the CTB5.1 results are reported by using gold-standard POS tags, respectively. Our baseline model produces strong enough results for both PTB and CTB5.1.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: Final results on the datasets of PTB and CTB5.1, where the tagging accuracy being 100% denotes gold-standard POS tags are employed. We include the results of state-of-the-art previous transition-based parsers as well. In particular, Andor et al. (2016) use beam search in decoding and Bohnet and Nivre (2012) use a different method to produce dependency trees.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the PTB POS score for the joint model in this work?\n" }, { "role": "assistant", "content": "The table shows the PTB POS score for the joint model in this work as 97.54. So, the answer is {\"answer\": \"97.54\"}" } ], "images": [ "scigen/train/2759.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Final results on the datasets of PTB and CTB5.1, where the tagging accuracy being 100% denotes gold-standard POS tags are employed. We include the results of state-of-the-art previous transition-based parsers as well. In particular, Andor et al. (2016) use beam search in decoding and Bohnet and Nivre (2012) use a different method to produce dependency trees." ], "reasoning": "The table shows the PTB POS score for the joint model in this work as 97.54.", "question": "What is the PTB POS score for the joint model in this work?", "answer": "So, the answer is {\"answer\": \"97.54\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe joint model in this work has a higher CTB5 LAS score than the pipeline model.\n" }, { "role": "assistant", "content": "The table shows that the joint model in this work has a CTB5 LAS score of 81.39, while the pipeline model has a CTB5 LAS score of 79.93, indicating that the joint model has a higher score. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2759.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Final results on the datasets of PTB and CTB5.1, where the tagging accuracy being 100% denotes gold-standard POS tags are employed. We include the results of state-of-the-art previous transition-based parsers as well. In particular, Andor et al. (2016) use beam search in decoding and Bohnet and Nivre (2012) use a different method to produce dependency trees." ], "reasoning": "The table shows that the joint model in this work has a CTB5 LAS score of 81.39, while the pipeline model has a CTB5 LAS score of 79.93, indicating that the joint model has a higher score.", "statement": "The joint model in this work has a higher CTB5 LAS score than the pipeline model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Final dependency parsing results (LAS) on the UD dataset.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The joint model achieves the best results among transition-based models, significantly outperforming pipeline models, due to effective interaction between tagging and parsing.Joint models also achieves significantly better results in comparison with the pipeline models (p-value below 10−5), which is similar to our finding on CTB5.1. Besides, our joint model achieves the best-reported results among the transition-based models, even by using a greedy manner for decoding, which can be attributed to the effective exploration of the interaction between the tagging and parsing in our joint model, while no previous work has studied it under the neural setting to our knowledge. The work of Zhang and Weiss \\shortciteZhang:16 resembles our work most, which improve a feed-forward dependency parser by using POS tags in a pipeline way by stack-propagation. While our joint model benefits from the use of LSTM, and in addition, we find that directly using the resulting tags rather than the penultimate hidden representations of a tag classifier leads to better results." } ], "images": [ "scigen/train/2760.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Final dependency parsing results (LAS) on the UD dataset." ], "reasoning": "The joint model achieves the best results among transition-based models, significantly outperforming pipeline models, due to effective interaction between tagging and parsing.", "description": "Joint models also achieves significantly better results in comparison with the pipeline models (p-value below 10−5), which is similar to our finding on CTB5.1. Besides, our joint model achieves the best-reported results among the transition-based models, even by using a greedy manner for decoding, which can be attributed to the effective exploration of the interaction between the tagging and parsing in our joint model, while no previous work has studied it under the neural setting to our knowledge. The work of Zhang and Weiss \\shortciteZhang:16 resembles our work most, which improve a feed-forward dependency parser by using POS tags in a pipeline way by stack-propagation. While our joint model benefits from the use of LSTM, and in addition, we find that directly using the resulting tags rather than the penultimate hidden representations of a tag classifier leads to better results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: Final dependency parsing results (LAS) on the UD dataset.', answer the following question. Give detailed solution consisting of each step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the average score of the joint model across all languages?\n" }, { "role": "assistant", "content": "The table shows the scores of different models across various languages. The average score for the joint model is listed in the 'AVG' column. So, the answer is {\"answer\": \"82.0\"}" } ], "images": [ "scigen/train/2760.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Final dependency parsing results (LAS) on the UD dataset." ], "reasoning": "The table shows the scores of different models across various languages. The average score for the joint model is listed in the 'AVG' column.", "question": "What is the average score of the joint model across all languages?", "answer": "So, the answer is {\"answer\": \"82.0\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe joint model achieves the highest score in the English language.\n" }, { "role": "assistant", "content": "The table shows the scores of different models for the English language. The joint model has the highest score of 82.5 compared to other models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2760.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Final dependency parsing results (LAS) on the UD dataset." ], "reasoning": "The table shows the scores of different models for the English language. The joint model has the highest score of 82.5 compared to other models.", "statement": "The joint model achieves the highest score in the English language.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Results for unsupervised bilingual word translation using different approaches, all with monolingual training: translation (A) from Mandarin to English and (B) from English to Mandarin.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table shows that adding constraints and normalization significantly improves the MRR and P@10 for both Mandarin-English and English-Mandarin translations compared to the baseline.To analyze whether words with equivalent semantics in different languages are mapped together with the proposed approaches, we conducted experiments on unsupervised bilingual word translation. If the translated word of w is ranked as the r-th candidate, then the reciprocal rank is \\frac{1}{r}. The mean reciprocal rank (MRR) is used as an evaluation metric, which is the average of the reciprocal ranks; thus the MRR should be less than 1.0, and the closer to 1.0 the better. The proportion of correct translations that are in the top 10 candidate list (r\\leq 10) is also reported as “P@10” [Xing2015NormalizedWE]. In order to mitigate the degradation in performance caused by low-frequency words, we selected words only with a frequency greater than 80, resulting in about 200 vocabulary words in Mandarin and English respectively, and 55 bilingual pairs used for unsupervised bilingual word translation. We see performance for Mandarin-English translation (column (A)) in both MRR and P@10 that is worse than that in the reverse direction (column (B)). Row (i) demonstrates that the unconstrained baseline performs poorly, whereas additional constraints and normalization in rows (ii) and (iii) yield significantly improved MRR and P@10 compared with row (i). This suggests that constraints and normalization for CS language modeling indeed enhance semantic mapping." } ], "images": [ "scigen/train/2761.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results for unsupervised bilingual word translation using different approaches, all with monolingual training: translation (A) from Mandarin to English and (B) from English to Mandarin." ], "reasoning": "The table shows that adding constraints and normalization significantly improves the MRR and P@10 for both Mandarin-English and English-Mandarin translations compared to the baseline.", "description": "To analyze whether words with equivalent semantics in different languages are mapped together with the proposed approaches, we conducted experiments on unsupervised bilingual word translation. If the translated word of w is ranked as the r-th candidate, then the reciprocal rank is \\frac{1}{r}. The mean reciprocal rank (MRR) is used as an evaluation metric, which is the average of the reciprocal ranks; thus the MRR should be less than 1.0, and the closer to 1.0 the better. The proportion of correct translations that are in the top 10 candidate list (r\\leq 10) is also reported as “P@10” [Xing2015NormalizedWE]. In order to mitigate the degradation in performance caused by low-frequency words, we selected words only with a frequency greater than 80, resulting in about 200 vocabulary words in Mandarin and English respectively, and 55 bilingual pairs used for unsupervised bilingual word translation. We see performance for Mandarin-English translation (column (A)) in both MRR and P@10 that is worse than that in the reverse direction (column (B)). Row (i) demonstrates that the unconstrained baseline performs poorly, whereas additional constraints and normalization in rows (ii) and (iii) yield significantly improved MRR and P@10 compared with row (i). This suggests that constraints and normalization for CS language modeling indeed enhance semantic mapping.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Results for unsupervised bilingual word translation using different approaches, all with monolingual training: translation (A) from Mandarin to English and (B) from English to Mandarin.\nQuestion: What is the MRR for English to Mandarin translation when using SKLD + normalization?\nSolve the above question based on the table information. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the MRR for English to Mandarin translation using SKLD + normalization, look at row (iii) under column (B) English rightarrow Mandarin MRR. So, the answer is {\"answer\": \"0.1496\"}." } ], "images": [ "scigen/train/2761.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results for unsupervised bilingual word translation using different approaches, all with monolingual training: translation (A) from Mandarin to English and (B) from English to Mandarin." ], "reasoning": "To find the MRR for English to Mandarin translation using SKLD + normalization, look at row (iii) under column (B) English rightarrow Mandarin MRR.", "question": "What is the MRR for English to Mandarin translation when using SKLD + normalization?", "answer": "So, the answer is {\"answer\": \"0.1496\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe baseline approach has a higher P@10 for Mandarin to English translation compared to English to Mandarin translation.\n" }, { "role": "assistant", "content": "The table shows that the baseline approach has a P@10 of 5.4% for Mandarin to English translation and 20.0% for English to Mandarin translation, indicating that the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2761.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results for unsupervised bilingual word translation using different approaches, all with monolingual training: translation (A) from Mandarin to English and (B) from English to Mandarin." ], "reasoning": "The table shows that the baseline approach has a P@10 of 5.4% for Mandarin to English translation and 20.0% for English to Mandarin translation, indicating that the statement is incorrect.", "statement": "The baseline approach has a higher P@10 for Mandarin to English translation compared to English to Mandarin translation.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: ZH, EN, CS-PPL, CSP-PPL and overall perplexity on testing set'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Applying constraints and normalization to the language model improves performance in both monolingual and code-switching perplexity, with the best results achieved by combining SKLD constraint with normalization.Baselines in rows (a)(d)(g) represent the language model trained without constraints or normalization. , we observe that learning with pseudo-CS sentences indeed helps considerably in CS perplexity, which is reasonable because the LM has seen CS cases during training even though the training data is synthetic. However, comparing rows (b)(c) with (d) and (g) reveals that after applying additional constraints, the LM trained on monolingual data only is comparable or even better in terms of both monolingual (ZH and EN columns) and CS (CS-PPL and CSP-PPL columns) perplexity than LMs trained with pseudo-CS data. Whether using monolingual or pseudo-CS data for training, normalizing the output projection matrix generally improves language modeling. Even trained with monolingual data only, normalization also improves CSP-PPL, as shown in rows (a) and (j). Thus we conclude that the monolingual data in our corpus has a similar sentence structure, and normalization yields a similar latent space, aiding in switching between languages. After applying SKLD and normalization together, the CSP-PPL improves, yielding the best results in the monolingual data training case. The perplexity of CS points is reduced significantly when constraints are applied on the output projection matrix by minimizing SKLD or CD without degrading the performance on monolingual data. Rows (k)(n)(q) also show that combining the SKLD constraint with normalization results in the best performance on each kind of perplexity over only monolingual and pseudo-CS data. In addition to numerical analysis, we seek to determine if the overlapping level of embedding space is aligned with the perplexity results. We applied principal component analysis (PCA) on the output projection matrix, and then visualized the results on a 2-D plane. Fig. Fig. After synthesizing pseudo-CS data for training as shown in Fig. In Fig." } ], "images": [ "scigen/train/2762.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: ZH, EN, CS-PPL, CSP-PPL and overall perplexity on testing set" ], "reasoning": "Applying constraints and normalization to the language model improves performance in both monolingual and code-switching perplexity, with the best results achieved by combining SKLD constraint with normalization.", "description": "Baselines in rows (a)(d)(g) represent the language model trained without constraints or normalization. , we observe that learning with pseudo-CS sentences indeed helps considerably in CS perplexity, which is reasonable because the LM has seen CS cases during training even though the training data is synthetic. However, comparing rows (b)(c) with (d) and (g) reveals that after applying additional constraints, the LM trained on monolingual data only is comparable or even better in terms of both monolingual (ZH and EN columns) and CS (CS-PPL and CSP-PPL columns) perplexity than LMs trained with pseudo-CS data. Whether using monolingual or pseudo-CS data for training, normalizing the output projection matrix generally improves language modeling. Even trained with monolingual data only, normalization also improves CSP-PPL, as shown in rows (a) and (j). Thus we conclude that the monolingual data in our corpus has a similar sentence structure, and normalization yields a similar latent space, aiding in switching between languages. After applying SKLD and normalization together, the CSP-PPL improves, yielding the best results in the monolingual data training case. The perplexity of CS points is reduced significantly when constraints are applied on the output projection matrix by minimizing SKLD or CD without degrading the performance on monolingual data. Rows (k)(n)(q) also show that combining the SKLD constraint with normalization results in the best performance on each kind of perplexity over only monolingual and pseudo-CS data. In addition to numerical analysis, we seek to determine if the overlapping level of embedding space is aligned with the perplexity results. We applied principal component analysis (PCA) on the output projection matrix, and then visualized the results on a 2-D plane. Fig. Fig. After synthesizing pseudo-CS data for training as shown in Fig. In Fig.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: ZH, EN, CS-PPL, CSP-PPL and overall perplexity on testing set', answer the question below by showing each progressive step toward the answer. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the CS-PPL value for the monolingual data only model with normalization and SKLD constraint?\n" }, { "role": "assistant", "content": "To find the CS-PPL value for the monolingual data only model with normalization and SKLD constraint, locate the row labeled 'SKLD' under the 'Monolingual only' section with normalization. The CS-PPL value is 247.60. So, the answer is {\"answer\": \"247.60\"}" } ], "images": [ "scigen/train/2762.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: ZH, EN, CS-PPL, CSP-PPL and overall perplexity on testing set" ], "reasoning": "To find the CS-PPL value for the monolingual data only model with normalization and SKLD constraint, locate the row labeled 'SKLD' under the 'Monolingual only' section with normalization. The CS-PPL value is 247.60.", "question": "What is the CS-PPL value for the monolingual data only model with normalization and SKLD constraint?", "answer": "So, the answer is {\"answer\": \"247.60\"}" }, { "messages": [ { "role": "user", "content": "The language model trained with pseudo-CS data and word substitution without normalization has a higher CS-PPL than the model trained with monolingual data only and SKLD constraint without normalization. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe language model trained with pseudo-CS data and word substitution without normalization has a higher CS-PPL than the model trained with monolingual data only and SKLD constraint without normalization.\n" }, { "role": "assistant", "content": "The CS-PPL for the pseudo-CS data with word substitution without normalization (row e) is 298.24, while the CS-PPL for the monolingual data only with SKLD constraint without normalization (row b) is 319.71. Since 298.24 is lower than 319.71, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2762.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: ZH, EN, CS-PPL, CSP-PPL and overall perplexity on testing set" ], "reasoning": "The CS-PPL for the pseudo-CS data with word substitution without normalization (row e) is 298.24, while the CS-PPL for the monolingual data only with SKLD constraint without normalization (row b) is 319.71. Since 298.24 is lower than 319.71, the statement is refuted.", "statement": "The language model trained with pseudo-CS data and word substitution without normalization has a higher CS-PPL than the model trained with monolingual data only and SKLD constraint without normalization.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 10: Silver Set evaluation using DNA fusion.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Combining textual and image signals yields the highest performance in sarcasm detection across Instagram, Twitter, and another platform, confirming the importance of visual aspects.It is easy to see that fusing the textual and image signals together provides the best performance across all three sets, ranging from 74.2% in Instagram to 69.7% in Twitter. That confirms our hypothesis that the visual aspect plays a role in the detection of sarcasm." } ], "images": [ "scigen/train/2763.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Silver Set evaluation using DNA fusion." ], "reasoning": "Combining textual and image signals yields the highest performance in sarcasm detection across Instagram, Twitter, and another platform, confirming the importance of visual aspects.", "description": "It is easy to see that fusing the textual and image signals together provides the best performance across all three sets, ranging from 74.2% in Instagram to 69.7% in Twitter. That confirms our hypothesis that the visual aspect plays a role in the detection of sarcasm.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: 'Table 10: Silver Set evaluation using DNA fusion.'\nQuestion: What is the performance percentage for the 1-grams + AVR feature set on Twitter?\n" }, { "role": "assistant", "content": "The table shows the performance percentages for different feature sets across three platforms. For the 1-grams + AVR feature set on Twitter, the performance is 69.7%. So, the answer is {\"answer\": \"69.7%\"}." } ], "images": [ "scigen/train/2763.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Silver Set evaluation using DNA fusion." ], "reasoning": "The table shows the performance percentages for different feature sets across three platforms. For the 1-grams + AVR feature set on Twitter, the performance is 69.7%.", "question": "What is the performance percentage for the 1-grams + AVR feature set on Twitter?", "answer": "So, the answer is {\"answer\": \"69.7%\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The AVR only feature set performs better than the 1-grams feature set on Instagram., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe AVR only feature set performs better than the 1-grams feature set on Instagram.\n" }, { "role": "assistant", "content": "The table shows that the AVR only feature set has a performance of 73.8% on Instagram, while the 1-grams feature set has a performance of 71.0%. Therefore, the AVR only feature set performs better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2763.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Silver Set evaluation using DNA fusion." ], "reasoning": "The table shows that the AVR only feature set has a performance of 73.8% on Instagram, while the 1-grams feature set has a performance of 71.0%. Therefore, the AVR only feature set performs better.", "statement": "The AVR only feature set performs better than the 1-grams feature set on Instagram.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Average number of words, emojis and tags.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Twitter has the shortest posts and the fewest tags, while Instagram has the highest tag-to-word ratio and similar emoji-to-word ratio to Twitter.Due to its tight character limitation (which also accounts for the hashtags), Twitter is the platform with the shortest text and the lowest number of tags per post. While Tumblr posts are the longest, the average number of tags is similar to that of Instagram, which has in turn the highest tag-to-word ratio. Indeed, Instagram users seem to express heavily through hashtags, especially compared to Twitter users, whose posts have a similar average word count. Both platforms also have a similar emoji-to-word ratio, which is much lower on Tumblr. The fact that there is a character limitation for both Instagram and Twitter might justify the usage of emojis, which are compact representations of concepts and reactions that would be much more verbose if expressed in words." } ], "images": [ "scigen/train/2764.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Average number of words, emojis and tags." ], "reasoning": "Twitter has the shortest posts and the fewest tags, while Instagram has the highest tag-to-word ratio and similar emoji-to-word ratio to Twitter.", "description": "Due to its tight character limitation (which also accounts for the hashtags), Twitter is the platform with the shortest text and the lowest number of tags per post. While Tumblr posts are the longest, the average number of tags is similar to that of Instagram, which has in turn the highest tag-to-word ratio. Indeed, Instagram users seem to express heavily through hashtags, especially compared to Twitter users, whose posts have a similar average word count. Both platforms also have a similar emoji-to-word ratio, which is much lower on Tumblr. The fact that there is a character limitation for both Instagram and Twitter might justify the usage of emojis, which are compact representations of concepts and reactions that would be much more verbose if expressed in words.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: Average number of words, emojis and tags.' in mind, break down and resolve the question below step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich platform has the highest tag-to-word ratio?\n" }, { "role": "assistant", "content": "To find the highest tag-to-word ratio, compare the number of tags to the number of words for each platform. Instagram has 7.44 tags and 10.77 words, which is the highest ratio. So, the answer is {\"answer\": \"Instagram\"}." } ], "images": [ "scigen/train/2764.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Average number of words, emojis and tags." ], "reasoning": "To find the highest tag-to-word ratio, compare the number of tags to the number of words for each platform. Instagram has 7.44 tags and 10.77 words, which is the highest ratio.", "question": "Which platform has the highest tag-to-word ratio?", "answer": "So, the answer is {\"answer\": \"Instagram\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nTumblr has the highest number of tags per post.\n" }, { "role": "assistant", "content": "The table shows that Tumblr has 7.00 tags per post, while Instagram has 7.44 tags per post, making Tumblr not the highest. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2764.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Average number of words, emojis and tags." ], "reasoning": "The table shows that Tumblr has 7.00 tags per post, while Instagram has 7.44 tags per post, making Tumblr not the highest.", "statement": "Tumblr has the highest number of tags per post.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 7: Silver Set evaluation using SVM fusion.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Combining several feature classes yields the best performance, with the highest accuracy achieved by combining visual features with combination features.The top individual feature is n-gram ( 1- and 2-grams), roughly performing at close to 80% accuracy across all data sets. In fact, even though we use three disparate data sets, the performance figures for each feature are consistently the same as the the ranking of the features. This may suggest that users do not alter the way they use sarcasm across platforms, though the best way of testing this hypothesis would be to investigate whether models trained on one platform, e.g., Twitter, can approximate the performance found on the other platforms, e.g., Instagram, when models are trained on native data. Finally, merging several of the feature classes into one (combination) yields the best performance, exceeding 80% for all data sets. For all the three data sets, the model with n-grams + VFS outperformed the model solely trained on n-grams by a small margin. However, it was not better than using the combination features. When combining the visual features with the combination features, we achieve the highest performance in Instagram (82.3%) and Tumblr (81.0%). In Twitter, the fusion produces the second highest performance (80.0%) to the 80.5% yielded by combination features only. These results show that including simple, noisy image-related features can improve sarcasm detection, albeit by a small margin." } ], "images": [ "scigen/train/2765.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Silver Set evaluation using SVM fusion." ], "reasoning": "Combining several feature classes yields the best performance, with the highest accuracy achieved by combining visual features with combination features.", "description": "The top individual feature is n-gram ( 1- and 2-grams), roughly performing at close to 80% accuracy across all data sets. In fact, even though we use three disparate data sets, the performance figures for each feature are consistently the same as the the ranking of the features. This may suggest that users do not alter the way they use sarcasm across platforms, though the best way of testing this hypothesis would be to investigate whether models trained on one platform, e.g., Twitter, can approximate the performance found on the other platforms, e.g., Instagram, when models are trained on native data. Finally, merging several of the feature classes into one (combination) yields the best performance, exceeding 80% for all data sets. For all the three data sets, the model with n-grams + VFS outperformed the model solely trained on n-grams by a small margin. However, it was not better than using the combination features. When combining the visual features with the combination features, we achieve the highest performance in Instagram (82.3%) and Tumblr (81.0%). In Twitter, the fusion produces the second highest performance (80.0%) to the 80.5% yielded by combination features only. These results show that including simple, noisy image-related features can improve sarcasm detection, albeit by a small margin.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 7: Silver Set evaluation using SVM fusion.', answer the question below by showing each progressive step toward the answer. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich feature set achieves the highest performance in Instagram?\n" }, { "role": "assistant", "content": "By examining the table, the highest performance in Instagram (IG) is achieved by the 'combination + VSF' feature set with an accuracy of 82.3%. So, the answer is {\"answer\": \"combination + VSF\"}" } ], "images": [ "scigen/train/2765.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Silver Set evaluation using SVM fusion." ], "reasoning": "By examining the table, the highest performance in Instagram (IG) is achieved by the 'combination + VSF' feature set with an accuracy of 82.3%.", "question": "Which feature set achieves the highest performance in Instagram?", "answer": "So, the answer is {\"answer\": \"combination + VSF\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe n-gram feature set achieves the highest performance in Twitter.\n" }, { "role": "assistant", "content": "The table shows that the 'combination' feature set achieves the highest performance in Twitter (TW) with an accuracy of 80.5%, not the n-gram feature set. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2765.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Silver Set evaluation using SVM fusion." ], "reasoning": "The table shows that the 'combination' feature set achieves the highest performance in Twitter (TW) with an accuracy of 80.5%, not the n-gram feature set.", "statement": "The n-gram feature set achieves the highest performance in Twitter.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 6: BLEU score of transformer-large models trained with Scheduled DropHead applied on different MHA modules.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Scheduled DropHead is more effective on encoder-encoder and encoder-decoder attention modules, improving BLEU scores.We also analyze the effect of Scheduled DropHead on different types of attention heads. Specifically, we apply Scheduled DropHead separately on encoder-encoder attention, encoder-decoder attention, and decoder-decoder attention modules. We can see that the proposed approach is more effective when used on encoder-encoder self-attention and encoder-decoder attention modules, which is consistent with the finding of Michel et al." } ], "images": [ "scigen/train/2766.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: BLEU score of transformer-large models trained with Scheduled DropHead applied on different MHA modules." ], "reasoning": "Scheduled DropHead is more effective on encoder-encoder and encoder-decoder attention modules, improving BLEU scores.", "description": "We also analyze the effect of Scheduled DropHead on different types of attention heads. Specifically, we apply Scheduled DropHead separately on encoder-encoder attention, encoder-decoder attention, and decoder-decoder attention modules. We can see that the proposed approach is more effective when used on encoder-encoder self-attention and encoder-decoder attention modules, which is consistent with the finding of Michel et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 6: BLEU score of transformer-large models trained with Scheduled DropHead applied on different MHA modules.' and proceed to solve the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich Scheduled DropHead application yields the highest BLEU score?\n" }, { "role": "assistant", "content": "By comparing the BLEU scores for different Scheduled DropHead applications, the highest score is observed for the general Scheduled DropHead application. So, the answer is {\"answer\": \"29.4\"}" } ], "images": [ "scigen/train/2766.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: BLEU score of transformer-large models trained with Scheduled DropHead applied on different MHA modules." ], "reasoning": "By comparing the BLEU scores for different Scheduled DropHead applications, the highest score is observed for the general Scheduled DropHead application.", "question": "Which Scheduled DropHead application yields the highest BLEU score?", "answer": "So, the answer is {\"answer\": \"29.4\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Applying Scheduled DropHead on encoder-decoder attention yields a higher BLEU score than on decoder-decoder attention., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nApplying Scheduled DropHead on encoder-decoder attention yields a higher BLEU score than on decoder-decoder attention.\n" }, { "role": "assistant", "content": "The BLEU score for encoder-decoder Scheduled DropHead is 29.1, while for decoder-decoder Scheduled DropHead it is 28.7, indicating a higher score for encoder-decoder. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2766.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: BLEU score of transformer-large models trained with Scheduled DropHead applied on different MHA modules." ], "reasoning": "The BLEU score for encoder-decoder Scheduled DropHead is 29.1, while for decoder-decoder Scheduled DropHead it is 28.7, indicating a higher score for encoder-decoder.", "statement": "Applying Scheduled DropHead on encoder-decoder attention yields a higher BLEU score than on decoder-decoder attention.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Machine translation performance on WMT14 en-de and newstest2014 of compared models. * denotes statistically significance with p < 0.01 compared with all baselines.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The transformer-big model was modified to increase the number of attention heads from 16 to 32 without increasing the total number of parameters, and this variant is referred to as transformer-big (more heads).S4SS1SSS0Px2 Models We train the transformer-big Vaswani et al. To ensure fair comparisons, we compare our models against the result reported by Vaswani et al. In addition, as our approach is expected to be able to reduce excessive co-adaptation between multiple attention heads, we also experiment with a variant of the transformer model where we increase the number of attention heads in each layer from 16 to 32. We denote the modified transformer model as transformer-big (more heads). Note that we change the number of transformer heads by reducing the hidden dimension size of each attention heads, thus do not increase the number of total parameters in the resulting model." } ], "images": [ "scigen/train/2767.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Machine translation performance on WMT14 en-de and newstest2014 of compared models. * denotes statistically significance with p < 0.01 compared with all baselines." ], "reasoning": "The transformer-big model was modified to increase the number of attention heads from 16 to 32 without increasing the total number of parameters, and this variant is referred to as transformer-big (more heads).", "description": "S4SS1SSS0Px2 Models We train the transformer-big Vaswani et al. To ensure fair comparisons, we compare our models against the result reported by Vaswani et al. In addition, as our approach is expected to be able to reduce excessive co-adaptation between multiple attention heads, we also experiment with a variant of the transformer model where we increase the number of attention heads in each layer from 16 to 32. We denote the modified transformer model as transformer-big (more heads). Note that we change the number of transformer heads by reducing the hidden dimension size of each attention heads, thus do not increase the number of total parameters in the resulting model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 1: Machine translation performance on WMT14 en-de and newstest2014 of compared models. * denotes statistically significance with p < 0.01 compared with all baselines.', unfold the question presented below, detailing every step of your calculation. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the BLEU score of the transformer-big (more heads) model with Scheduled DropHead?\n" }, { "role": "assistant", "content": "The table lists the BLEU scores for various models and their variants. The BLEU score for the transformer-big (more heads) model with Scheduled DropHead is 29.6. So, the answer is {\"answer\": \"29.6\"}." } ], "images": [ "scigen/train/2767.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Machine translation performance on WMT14 en-de and newstest2014 of compared models. * denotes statistically significance with p < 0.01 compared with all baselines." ], "reasoning": "The table lists the BLEU scores for various models and their variants. The BLEU score for the transformer-big (more heads) model with Scheduled DropHead is 29.6.", "question": "What is the BLEU score of the transformer-big (more heads) model with Scheduled DropHead?", "answer": "So, the answer is {\"answer\": \"29.6\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The transformer-big model with DropHead has a BLEU score higher than 29., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe transformer-big model with DropHead has a BLEU score higher than 29.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for the transformer-big model with DropHead is 29.2, which is higher than 29. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2767.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Machine translation performance on WMT14 en-de and newstest2014 of compared models. * denotes statistically significance with p < 0.01 compared with all baselines." ], "reasoning": "The table shows that the BLEU score for the transformer-big model with DropHead is 29.2, which is higher than 29.", "statement": "The transformer-big model with DropHead has a BLEU score higher than 29.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Test set results (accuracy) of compared models on text classification benchmarks. The results in the first section are the results reported on their papers.‘–’ means not reported results. * means statistically significant improvement upon the corresponding baseline with p < 0.01.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Both the DropHead mechanism and the dropout rate schedule improve the performance of BERT-based text classification models, outperforming vanilla Dropout on self-attention layers.We can see that both the DropHead mechanism and the dropout rate schedule are able to improve the performance of strong BERT-based text classification models on a wide range of datasets and substantially outperform the variant where vanilla Dropout is applied on the self-attention layers. This further confirms the effectiveness of the proposed methods." } ], "images": [ "scigen/train/2768.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Test set results (accuracy) of compared models on text classification benchmarks. The results in the first section are the results reported on their papers.‘–’ means not reported results. * means statistically significant improvement upon the corresponding baseline with p < 0.01." ], "reasoning": "Both the DropHead mechanism and the dropout rate schedule improve the performance of BERT-based text classification models, outperforming vanilla Dropout on self-attention layers.", "description": "We can see that both the DropHead mechanism and the dropout rate schedule are able to improve the performance of strong BERT-based text classification models on a wide range of datasets and substantially outperform the variant where vanilla Dropout is applied on the self-attention layers. This further confirms the effectiveness of the proposed methods.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: Test set results (accuracy) of compared models on text classification benchmarks. The results in the first section are the results reported on their papers.‘–’ means not reported results. * means statistically significant improvement upon the corresponding baseline with p < 0.01.' to sequentially solve the question presented below. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich model and method combination achieved the highest score on the DBPedia dataset?\n" }, { "role": "assistant", "content": "By examining the DBPedia column, the highest score is 99.41, achieved by the BERT-base model with the Scheduled DropHead method. So, the answer is {\"answer\": \"BERT-base with Scheduled DropHead\"}" } ], "images": [ "scigen/train/2768.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Test set results (accuracy) of compared models on text classification benchmarks. The results in the first section are the results reported on their papers.‘–’ means not reported results. * means statistically significant improvement upon the corresponding baseline with p < 0.01." ], "reasoning": "By examining the DBPedia column, the highest score is 99.41, achieved by the BERT-base model with the Scheduled DropHead method.", "question": "Which model and method combination achieved the highest score on the DBPedia dataset?", "answer": "So, the answer is {\"answer\": \"BERT-base with Scheduled DropHead\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe DropHead mechanism outperforms Attention Dropout on the TREC dataset.\n" }, { "role": "assistant", "content": "The table shows that the DropHead mechanism achieved a score of 97.60 on the TREC dataset, while Attention Dropout achieved a score of 97.20, confirming that DropHead outperforms Attention Dropout. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2768.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Test set results (accuracy) of compared models on text classification benchmarks. The results in the first section are the results reported on their papers.‘–’ means not reported results. * means statistically significant improvement upon the corresponding baseline with p < 0.01." ], "reasoning": "The table shows that the DropHead mechanism achieved a score of 97.60 on the TREC dataset, while Attention Dropout achieved a score of 97.20, confirming that DropHead outperforms Attention Dropout.", "statement": "The DropHead mechanism outperforms Attention Dropout on the TREC dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: BLEU score of transformer-large models trained with different regularization methods.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Scheduled DropHead significantly improves model performance compared to standard dropout, which shows marginal improvement.We can see that applying standard dropout on the MHA modules only marginally improve the model’s performance. In contrast, the proposed Scheduled DropHead can yield significant improvement upon the baseline model and the model trained with standard dropout. We also find that the combination of the standard dropout and DropHead does not yield further improvement. This suggests that the Scheduled DropHead is more effective than the standard Dropout for the MHA mechanism." } ], "images": [ "scigen/train/2769.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: BLEU score of transformer-large models trained with different regularization methods." ], "reasoning": "Scheduled DropHead significantly improves model performance compared to standard dropout, which shows marginal improvement.", "description": "We can see that applying standard dropout on the MHA modules only marginally improve the model’s performance. In contrast, the proposed Scheduled DropHead can yield significant improvement upon the baseline model and the model trained with standard dropout. We also find that the combination of the standard dropout and DropHead does not yield further improvement. This suggests that the Scheduled DropHead is more effective than the standard Dropout for the MHA mechanism.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: 'Table 5: BLEU score of transformer-large models trained with different regularization methods.'\nQuestion: Which model configuration yields the highest BLEU score?\n" }, { "role": "assistant", "content": "The table shows that both the Scheduled DropHead and the Combination configurations yield the highest BLEU score of 29.4. So, the answer is {\"answer\": \"Scheduled DropHead and Combination\"}." } ], "images": [ "scigen/train/2769.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: BLEU score of transformer-large models trained with different regularization methods." ], "reasoning": "The table shows that both the Scheduled DropHead and the Combination configurations yield the highest BLEU score of 29.4.", "question": "Which model configuration yields the highest BLEU score?", "answer": "So, the answer is {\"answer\": \"Scheduled DropHead and Combination\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe combination of standard dropout and DropHead yields the highest BLEU score.\n" }, { "role": "assistant", "content": "The table shows that both the Scheduled DropHead and the Combination configurations yield the highest BLEU score of 29.4, supporting the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2769.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: BLEU score of transformer-large models trained with different regularization methods." ], "reasoning": "The table shows that both the Scheduled DropHead and the Combination configurations yield the highest BLEU score of 29.4, supporting the statement.", "statement": "The combination of standard dropout and DropHead yields the highest BLEU score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 7: BLEU score of transformer-large models trained with Scheduled DropHead with different dropout rate schedules.'.\n" }, { "role": "assistant", "content": "The proposed dropout rate schedule outperforms both curriculum and anti-curriculum dropout schedules, demonstrating its effectiveness over a constant dropout rate.S4SS4SSS0Px3 Comparison between different dropout rate schedules We then analyze the influence of different dropout rate schedules and compare the proposed dropout schedule with two variants. The first variant is that used in ScheduledDropPath Zoph et al. The second is the opposite of the former schedule which linearly decreases the dropout rate from the pre-defined dropout rate to 0. We denote the two compared dropout rate schedule as curriculum DropHead and anti-curriculum DropHead. We can see that both curriculum dropout rate schedule and anti-curriculum dropout schedule fails to improve the performance of the vanilla DropHead where the dropout rate is constant, which demonstrates the effectiveness of the proposed dropout rate schedule." } ], "images": [ "scigen/train/2770.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: BLEU score of transformer-large models trained with Scheduled DropHead with different dropout rate schedules." ], "reasoning": "The proposed dropout rate schedule outperforms both curriculum and anti-curriculum dropout schedules, demonstrating its effectiveness over a constant dropout rate.", "description": "S4SS4SSS0Px3 Comparison between different dropout rate schedules We then analyze the influence of different dropout rate schedules and compare the proposed dropout schedule with two variants. The first variant is that used in ScheduledDropPath Zoph et al. The second is the opposite of the former schedule which linearly decreases the dropout rate from the pre-defined dropout rate to 0. We denote the two compared dropout rate schedule as curriculum DropHead and anti-curriculum DropHead. We can see that both curriculum dropout rate schedule and anti-curriculum dropout schedule fails to improve the performance of the vanilla DropHead where the dropout rate is constant, which demonstrates the effectiveness of the proposed dropout rate schedule.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 7: BLEU score of transformer-large models trained with Scheduled DropHead with different dropout rate schedules.\nQuestion: Which dropout rate schedule achieved the highest BLEU score?\nSolve the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows the BLEU scores for different dropout rate schedules. The highest BLEU score is 29.4, achieved by the Scheduled DropHead. So, the answer is {\"answer\": \"Scheduled DropHead\"}" } ], "images": [ "scigen/train/2770.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: BLEU score of transformer-large models trained with Scheduled DropHead with different dropout rate schedules." ], "reasoning": "The table shows the BLEU scores for different dropout rate schedules. The highest BLEU score is 29.4, achieved by the Scheduled DropHead.", "question": "Which dropout rate schedule achieved the highest BLEU score?", "answer": "So, the answer is {\"answer\": \"Scheduled DropHead\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Curriculum DropHead schedule achieved a BLEU score higher than 29.2.\n" }, { "role": "assistant", "content": "The table shows that the Curriculum DropHead schedule achieved a BLEU score of 29.0, which is lower than 29.2. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2770.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: BLEU score of transformer-large models trained with Scheduled DropHead with different dropout rate schedules." ], "reasoning": "The table shows that the Curriculum DropHead schedule achieved a BLEU score of 29.0, which is lower than 29.2.", "statement": "The Curriculum DropHead schedule achieved a BLEU score higher than 29.2.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 8: Fairness test of the debiased Seq2Seq generative model. Green value indicates that the absolute value of difference drops compared with the original model, while red value indicates it rises.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Both debiasing models reduce gender and race biases significantly, with WER performing better than CDA, but WER may reduce response diversity.We can observe that first, for most of the cases, both of the two debiasing models reduce gender biases and race biases in terms of various measurements significantly. The differences between the two groups are controlled within a reasonable range and are not statistically significant anymore. Second, WER performs better than CDA in mitigating biases. However, a drawback of WER is, after sufficient training with the regularization term, the dialogue model tends to generate similar responses to two genders/races, which may debase the diversity of the generated responses. It reminds us that there may exist a trade-off between the performance and the fairness of a model. It’s important for us to find a balance according to specific situations." } ], "images": [ "scigen/train/2771.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Fairness test of the debiased Seq2Seq generative model. Green value indicates that the absolute value of difference drops compared with the original model, while red value indicates it rises." ], "reasoning": "Both debiasing models reduce gender and race biases significantly, with WER performing better than CDA, but WER may reduce response diversity.", "description": "We can observe that first, for most of the cases, both of the two debiasing models reduce gender biases and race biases in terms of various measurements significantly. The differences between the two groups are controlled within a reasonable range and are not statistically significant anymore. Second, WER performs better than CDA in mitigating biases. However, a drawback of WER is, after sufficient training with the regularization term, the dialogue model tends to generate similar responses to two genders/races, which may debase the diversity of the generated responses. It reminds us that there may exist a trade-off between the performance and the fairness of a model. It’s important for us to find a balance according to specific situations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 8: Fairness test of the debiased Seq2Seq generative model. Green value indicates that the absolute value of difference drops compared with the original model, while red value indicates it rises.\nQuestion: Which debiasing model shows a higher positive sentiment percentage for males in terms of gender bias?\nSolve the above question based on the table information. Let's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the Senti.Pos. (%) for males under Gender CDA and Gender WER, we see that Gender CDA has 1.885% while Gender WER has 1.821%. So, the answer is {\"answer\": \"Gender CDA\"}." } ], "images": [ "scigen/train/2771.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Fairness test of the debiased Seq2Seq generative model. Green value indicates that the absolute value of difference drops compared with the original model, while red value indicates it rises." ], "reasoning": "By comparing the Senti.Pos. (%) for males under Gender CDA and Gender WER, we see that Gender CDA has 1.885% while Gender WER has 1.821%.", "question": "Which debiasing model shows a higher positive sentiment percentage for males in terms of gender bias?", "answer": "So, the answer is {\"answer\": \"Gender CDA\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe WER model completely eliminates the difference in offense rate between genders.\n" }, { "role": "assistant", "content": "The table shows that the difference in offense rate between genders under the WER model is 0%, indicating that WER eliminates the difference. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2771.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Fairness test of the debiased Seq2Seq generative model. Green value indicates that the absolute value of difference drops compared with the original model, while red value indicates it rises." ], "reasoning": "The table shows that the difference in offense rate between genders under the WER model is 0%, indicating that WER eliminates the difference.", "statement": "The WER model completely eliminates the difference in offense rate between genders.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Results for task 3 model.999All values computed using contest-provided code. Due to time constraints imposed by the contest structure, performance was not evaluated for tasks 1 and 2 on the test and China data sets. Unfortunately, the random seed values for the models trained and evaluated on the test and China data sets were lost and so the (train and dev) and (test and China) results are from two different model runs.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The model shows high accuracy on the dev set due to class imbalance but generalizes poorly, with F1 scores dropping significantly from the train to the dev, test, and China data sets.However, I focus here on the results for semantic role labeling as this model was only evaluated on the test and China data sets for that particular task. The precision, recall, and F1 scores shown here are multiclass weighted averages computed with a Lab-provided script. The unweighted average accuracy of this model on the dev set is very high, 94%, due largely to class imbalance. The model correctly predicts that most words in each sentence are not one of the selected roles. However, the model appears to generalize poorly: the F1 score for the train data set is 0.82 but drops to 0.50, 0.52, and 0.39 for the dev, test, and China data sets, respectively. This is indicative of a model that is overfit to the training data." } ], "images": [ "scigen/train/2772.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results for task 3 model.999All values computed using contest-provided code. Due to time constraints imposed by the contest structure, performance was not evaluated for tasks 1 and 2 on the test and China data sets. Unfortunately, the random seed values for the models trained and evaluated on the test and China data sets were lost and so the (train and dev) and (test and China) results are from two different model runs." ], "reasoning": "The model shows high accuracy on the dev set due to class imbalance but generalizes poorly, with F1 scores dropping significantly from the train to the dev, test, and China data sets.", "description": "However, I focus here on the results for semantic role labeling as this model was only evaluated on the test and China data sets for that particular task. The precision, recall, and F1 scores shown here are multiclass weighted averages computed with a Lab-provided script. The unweighted average accuracy of this model on the dev set is very high, 94%, due largely to class imbalance. The model correctly predicts that most words in each sentence are not one of the selected roles. However, the model appears to generalize poorly: the F1 score for the train data set is 0.82 but drops to 0.50, 0.52, and 0.39 for the dev, test, and China data sets, respectively. This is indicative of a model that is overfit to the training data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Results for task 3 model.999All values computed using contest-provided code. Due to time constraints imposed by the contest structure, performance was not evaluated for tasks 1 and 2 on the test and China data sets. Unfortunately, the random seed values for the models trained and evaluated on the test and China data sets were lost and so the (train and dev) and (test and China) results are from two different model runs.', answer the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the F1 score for Task 3 on the China data set?\n" }, { "role": "assistant", "content": "The table shows the F1 scores for different tasks and data sets. For Task 3 on the China data set, the F1 score is 0.39. So, the answer is {\"answer\": \"0.39\"}" } ], "images": [ "scigen/train/2772.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results for task 3 model.999All values computed using contest-provided code. Due to time constraints imposed by the contest structure, performance was not evaluated for tasks 1 and 2 on the test and China data sets. Unfortunately, the random seed values for the models trained and evaluated on the test and China data sets were lost and so the (train and dev) and (test and China) results are from two different model runs." ], "reasoning": "The table shows the F1 scores for different tasks and data sets. For Task 3 on the China data set, the F1 score is 0.39.", "question": "What is the F1 score for Task 3 on the China data set?", "answer": "So, the answer is {\"answer\": \"0.39\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model has an F1 score of 0.50 for Task 2 on the dev data set.\n" }, { "role": "assistant", "content": "The table shows the F1 score for Task 2 on the dev data set as 0.66, not 0.50. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2772.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results for task 3 model.999All values computed using contest-provided code. Due to time constraints imposed by the contest structure, performance was not evaluated for tasks 1 and 2 on the test and China data sets. Unfortunately, the random seed values for the models trained and evaluated on the test and China data sets were lost and so the (train and dev) and (test and China) results are from two different model runs." ], "reasoning": "The table shows the F1 score for Task 2 on the dev data set as 0.66, not 0.50.", "statement": "The model has an F1 score of 0.50 for Task 2 on the dev data set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Results for task 1 and 2 model.888All values computed using contest-provided code. Precision and recall values were not provided by the contest organizers in the output of test and China data set evaluations and are therefore unavailable here. Unfortunately, the random seed values for the models trained and evaluated on the test and China data sets were lost and so the (train and dev) and (test and China) results are from two different model runs.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The multitask model for tasks 1 and 2 shows better performance on the test set compared to the dev set, indicating no overfitting, while the model for task 3 needs further investigation.The multitask model for tasks 1 and 2 is able to classify documents with 92% accuracy on the dev set with an F1 score of 0.80. This result on the test set is encouraging because it is actually above the corresponding value on the dev set, 0.80, and suggests that the model has not be overfit to the dev set through hyperparameter selection. The model for task 3 is also able to perform document and sentence classification. While test and China set Future work should determine whether this is due to the second model’s ability to overfit to these tasks and data sets or due to the inclusion of task 3 data in the model’s first GRU layer. One approach for exploring this is discussed in the paper’s final section." } ], "images": [ "scigen/train/2773.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results for task 1 and 2 model.888All values computed using contest-provided code. Precision and recall values were not provided by the contest organizers in the output of test and China data set evaluations and are therefore unavailable here. Unfortunately, the random seed values for the models trained and evaluated on the test and China data sets were lost and so the (train and dev) and (test and China) results are from two different model runs." ], "reasoning": "The multitask model for tasks 1 and 2 shows better performance on the test set compared to the dev set, indicating no overfitting, while the model for task 3 needs further investigation.", "description": "The multitask model for tasks 1 and 2 is able to classify documents with 92% accuracy on the dev set with an F1 score of 0.80. This result on the test set is encouraging because it is actually above the corresponding value on the dev set, 0.80, and suggests that the model has not be overfit to the dev set through hyperparameter selection. The model for task 3 is also able to perform document and sentence classification. While test and China set Future work should determine whether this is due to the second model’s ability to overfit to these tasks and data sets or due to the inclusion of task 3 data in the model’s first GRU layer. One approach for exploring this is discussed in the paper’s final section.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: Results for task 1 and 2 model.888All values computed using contest-provided code. Precision and recall values were not provided by the contest organizers in the output of test and China data set evaluations and are therefore unavailable here. Unfortunately, the random seed values for the models trained and evaluated on the test and China data sets were lost and so the (train and dev) and (test and China) results are from two different model runs.', solve this question and give detail solutions. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhat is the F1 score for Task 1 on the test set?\n" }, { "role": "assistant", "content": "The table shows the F1 score for Task 1 on the test set as 0.84, which is higher than the dev set score of 0.80. So, the answer is {\"answer\": \"0.84\"}" } ], "images": [ "scigen/train/2773.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results for task 1 and 2 model.888All values computed using contest-provided code. Precision and recall values were not provided by the contest organizers in the output of test and China data set evaluations and are therefore unavailable here. Unfortunately, the random seed values for the models trained and evaluated on the test and China data sets were lost and so the (train and dev) and (test and China) results are from two different model runs." ], "reasoning": "The table shows the F1 score for Task 1 on the test set as 0.84, which is higher than the dev set score of 0.80.", "question": "What is the F1 score for Task 1 on the test set?", "answer": "So, the answer is {\"answer\": \"0.84\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe precision for Task 2 on the dev set is higher than on the train set.\n" }, { "role": "assistant", "content": "The table shows that the precision for Task 2 on the dev set is 0.51, which is lower than the precision on the train set, which is 0.63. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2773.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results for task 1 and 2 model.888All values computed using contest-provided code. Precision and recall values were not provided by the contest organizers in the output of test and China data set evaluations and are therefore unavailable here. Unfortunately, the random seed values for the models trained and evaluated on the test and China data sets were lost and so the (train and dev) and (test and China) results are from two different model runs." ], "reasoning": "The table shows that the precision for Task 2 on the dev set is 0.51, which is lower than the precision on the train set, which is 0.63.", "statement": "The precision for Task 2 on the dev set is higher than on the train set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: Role-wise performance on task 3 dev set'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The model achieves F1 scores greater than 0.5 on triggers, participants, and places, but fails to label any locations correctly.These metrics are evaluated on the out-of-sample dev set. The model performs better on common role labels than less common labels; it achieves F1 scores greater than 0.5 on triggers, participants, and places. The model fails to label any locations correctly. This is probably due to the model’s failure to recognize the prepositions preceding locations as the B tokens in the location phrase. For example, “in front of the state secretariat” should be labeled “B-loc, I-loc, I-loc, I-loc, I-loc, I-loc.” Instead, the model predicts “O, I-loc, I-loc, I-loc, I-loc, I-loc.” Another example from the dev set reads “near a mosque” and should be labeled “B-loc, I-loc, I-loc.” The model instead predicts “B-fname, I-loc, I-loc,” where “fname” represents the role “facility name.”" } ], "images": [ "scigen/train/2774.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Role-wise performance on task 3 dev set" ], "reasoning": "The model achieves F1 scores greater than 0.5 on triggers, participants, and places, but fails to label any locations correctly.", "description": "These metrics are evaluated on the out-of-sample dev set. The model performs better on common role labels than less common labels; it achieves F1 scores greater than 0.5 on triggers, participants, and places. The model fails to label any locations correctly. This is probably due to the model’s failure to recognize the prepositions preceding locations as the B tokens in the location phrase. For example, “in front of the state secretariat” should be labeled “B-loc, I-loc, I-loc, I-loc, I-loc, I-loc.” Instead, the model predicts “O, I-loc, I-loc, I-loc, I-loc, I-loc.” Another example from the dev set reads “near a mosque” and should be labeled “B-loc, I-loc, I-loc.” The model instead predicts “B-fname, I-loc, I-loc,” where “fname” represents the role “facility name.”", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Role-wise performance on task 3 dev set\nQuestion: Which role label has the highest F1 score and what is the score?\nGive a detailed response to the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the F1 scores in the table, the highest F1 score is for the role label 'trigger' with a score of 0.76. So, the answer is {\"answer\": \"trigger, 0.76\"}" } ], "images": [ "scigen/train/2774.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Role-wise performance on task 3 dev set" ], "reasoning": "By examining the F1 scores in the table, the highest F1 score is for the role label 'trigger' with a score of 0.76.", "question": "Which role label has the highest F1 score and what is the score?", "answer": "So, the answer is {\"answer\": \"trigger, 0.76\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model achieves an F1 score greater than 0.5 for the role label 'organizer'.\n" }, { "role": "assistant", "content": "The table shows that the F1 score for 'organizer' is 0.47, which is less than 0.5. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2774.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Role-wise performance on task 3 dev set" ], "reasoning": "The table shows that the F1 score for 'organizer' is 0.47, which is less than 0.5.", "statement": "The model achieves an F1 score greater than 0.5 for the role label 'organizer'.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Comparison of human evaluation in terms of informativeness(Inf), coherence(Coh) and overall ranking. Lower is better.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The RNES model trained with coherence reward outperforms the model without coherence reward in informativeness, coherence, and overall quality, especially in coherence.We also conduct a qualitative evaluation to find out whether the introduction of coherence reward improves the coherence of the output summaries. We randomly sample 50 documents from the test set and ask three volunteers to evaluate the summaries extracted by RNES trained with or without coherence as the reward. They are asked to compare and rank the outputs of two models regarding three aspects: informativeness, coherence and overall quality. The better one will be given rank 1, while the other will be given rank 2 if it is worse. In some cases, if the two outputs are identical or have the same quality, the ranks could be tied, i.e., both of them are given rank 1. RNES model trained with coherence reward is better than RNES model without coherence reward in all three aspects, especially in the coherence. The result indicates that the introduction of coherence effectively improves the coherence of extracted summaries, as well as the overall quality. It is surprising that summaries produced by RNES with coherence are also more informative than RNES without coherence, indicating that ROUGE might not be the gold standard to evaluate informativeness as well." } ], "images": [ "scigen/train/2775.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of human evaluation in terms of informativeness(Inf), coherence(Coh) and overall ranking. Lower is better." ], "reasoning": "The RNES model trained with coherence reward outperforms the model without coherence reward in informativeness, coherence, and overall quality, especially in coherence.", "description": "We also conduct a qualitative evaluation to find out whether the introduction of coherence reward improves the coherence of the output summaries. We randomly sample 50 documents from the test set and ask three volunteers to evaluate the summaries extracted by RNES trained with or without coherence as the reward. They are asked to compare and rank the outputs of two models regarding three aspects: informativeness, coherence and overall quality. The better one will be given rank 1, while the other will be given rank 2 if it is worse. In some cases, if the two outputs are identical or have the same quality, the ranks could be tied, i.e., both of them are given rank 1. RNES model trained with coherence reward is better than RNES model without coherence reward in all three aspects, especially in the coherence. The result indicates that the introduction of coherence effectively improves the coherence of extracted summaries, as well as the overall quality. It is surprising that summaries produced by RNES with coherence are also more informative than RNES without coherence, indicating that ROUGE might not be the gold standard to evaluate informativeness as well.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 3: Comparison of human evaluation in terms of informativeness(Inf), coherence(Coh) and overall ranking. Lower is better.', solve the following question step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has a better overall quality score?\n" }, { "role": "assistant", "content": "The table shows the overall quality scores for both models. RNES w/o coherence has a score of 1.492, while RNES w/ coherence has a score of 1.209. The lower score indicates better quality. So, the answer is {\"answer\": \"RNES w/ coherence\"}" } ], "images": [ "scigen/train/2775.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of human evaluation in terms of informativeness(Inf), coherence(Coh) and overall ranking. Lower is better." ], "reasoning": "The table shows the overall quality scores for both models. RNES w/o coherence has a score of 1.492, while RNES w/ coherence has a score of 1.209. The lower score indicates better quality.", "question": "Which model has a better overall quality score?", "answer": "So, the answer is {\"answer\": \"RNES w/ coherence\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The RNES model trained with coherence reward has a higher coherence score than the model without coherence reward., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe RNES model trained with coherence reward has a higher coherence score than the model without coherence reward.\n" }, { "role": "assistant", "content": "The table shows that the coherence score for RNES w/o coherence is 1.325, while the coherence score for RNES w/ coherence is 1.092. A lower score indicates better coherence. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2775.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of human evaluation in terms of informativeness(Inf), coherence(Coh) and overall ranking. Lower is better." ], "reasoning": "The table shows that the coherence score for RNES w/o coherence is 1.325, while the coherence score for RNES w/ coherence is 1.092. A lower score indicates better coherence.", "statement": "The RNES model trained with coherence reward has a higher coherence score than the model without coherence reward.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Performance comparison on CNN/Daily Mail test set, evaluated with full-length F1 ROUGE scores (%). All scores of RNES are statistically significant using 95% confidence interval with respect to previous best models.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The RNES models, both with and without coherence, achieve higher ROUGE scores compared to other models, indicating superior summary quality.At test time our model produces summaries by beam search with beam size 10. We use full-length F1 of ROUGE-1, ROUGE-2, and ROUGE-L to evaluate our model. Our RNES models (with or without coherence as the reward) outperform current state-of-the-art models and NES by a large margin. The result indicates that the summaries extracted by RNES are of higher quality than summaries produced by previous works." } ], "images": [ "scigen/train/2776.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance comparison on CNN/Daily Mail test set, evaluated with full-length F1 ROUGE scores (%). All scores of RNES are statistically significant using 95% confidence interval with respect to previous best models." ], "reasoning": "The RNES models, both with and without coherence, achieve higher ROUGE scores compared to other models, indicating superior summary quality.", "description": "At test time our model produces summaries by beam search with beam size 10. We use full-length F1 of ROUGE-1, ROUGE-2, and ROUGE-L to evaluate our model. Our RNES models (with or without coherence as the reward) outperform current state-of-the-art models and NES by a large margin. The result indicates that the summaries extracted by RNES are of higher quality than summaries produced by previous works.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Performance comparison on CNN/Daily Mail test set, evaluated with full-length F1 ROUGE scores (%). All scores of RNES are statistically significant using 95% confidence interval with respect to previous best models.\nQuestion: Which model achieved the highest ROUGE-2 score?\nSolve the above question based on the table information. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the ROUGE-2 scores of all models in the table, the RNES w/o coherence model has the highest score of 18.87. So, the answer is {\"answer\": \"RNES w/o coherence\"}." } ], "images": [ "scigen/train/2776.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance comparison on CNN/Daily Mail test set, evaluated with full-length F1 ROUGE scores (%). All scores of RNES are statistically significant using 95% confidence interval with respect to previous best models." ], "reasoning": "By comparing the ROUGE-2 scores of all models in the table, the RNES w/o coherence model has the highest score of 18.87.", "question": "Which model achieved the highest ROUGE-2 score?", "answer": "So, the answer is {\"answer\": \"RNES w/o coherence\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe RNES w/ coherence model achieved the highest ROUGE-L score.\n" }, { "role": "assistant", "content": "The table shows that the RNES w/o coherence model has a ROUGE-L score of 37.75, which is higher than the RNES w/ coherence model's score of 37.41. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2776.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance comparison on CNN/Daily Mail test set, evaluated with full-length F1 ROUGE scores (%). All scores of RNES are statistically significant using 95% confidence interval with respect to previous best models." ], "reasoning": "The table shows that the RNES w/o coherence model has a ROUGE-L score of 37.75, which is higher than the RNES w/ coherence model's score of 37.41.", "statement": "The RNES w/ coherence model achieved the highest ROUGE-L score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'TABLE II: Evaluation values of NMI and EAR for each method'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The proposed method (A) showed higher NMI values for it after step 30 and was closer to the true data for both L and K compared to other methods.Fig. In both Ct and it, the NMI values tended to rise at the beginning. The NMI values of Ct were similar for methods (A), (B), and (C). In the NMI values for it, the proposed method (A) showed higher values than the other methods after step 30. We consider a major possible reason for the clustering results of spatial concepts. In online lexical acquisition, the word segmentation results cannot be obtained stably when training dataset is small. We consider that stable words can be obtained by further increasing the number of training steps. Fig. The average values of the estimated results of method (D) were L=18.9, K=13.1. True data was determined by a user based on teaching data. The experimental results show that the proposed method (A) was closer to the true data than other methods for both L and K." } ], "images": [ "scigen/train/2777.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Evaluation values of NMI and EAR for each method" ], "reasoning": "The proposed method (A) showed higher NMI values for it after step 30 and was closer to the true data for both L and K compared to other methods.", "description": "Fig. In both Ct and it, the NMI values tended to rise at the beginning. The NMI values of Ct were similar for methods (A), (B), and (C). In the NMI values for it, the proposed method (A) showed higher values than the other methods after step 30. We consider a major possible reason for the clustering results of spatial concepts. In online lexical acquisition, the word segmentation results cannot be obtained stably when training dataset is small. We consider that stable words can be obtained by further increasing the number of training steps. Fig. The average values of the estimated results of method (D) were L=18.9, K=13.1. True data was determined by a user based on teaching data. The experimental results show that the proposed method (A) was closer to the true data than other methods for both L and K.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: TABLE II: Evaluation values of NMI and EAR for each method\n\nQuestion: Which method had the highest NMI value for Ct?\n" }, { "role": "assistant", "content": "To find the method with the highest NMI value for Ct, compare the NMI Ct values for all methods. Method (D) SpCoA has the highest NMI Ct value of 0.805. So, the answer is {\"answer\": \"(D) SpCoA\"}." } ], "images": [ "scigen/train/2777.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Evaluation values of NMI and EAR for each method" ], "reasoning": "To find the method with the highest NMI value for Ct, compare the NMI Ct values for all methods. Method (D) SpCoA has the highest NMI Ct value of 0.805.", "question": "Which method had the highest NMI value for Ct?", "answer": "So, the answer is {\"answer\": \"(D) SpCoA\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nMethod (A) SpCoSLAM had the highest EAR K value.\n" }, { "role": "assistant", "content": "To verify this statement, compare the EAR K values for all methods. Method (A) SpCoSLAM has the highest EAR K value of 0.964. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2777.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Evaluation values of NMI and EAR for each method" ], "reasoning": "To verify this statement, compare the EAR K values for all methods. Method (A) SpCoSLAM has the highest EAR K value of 0.964.", "statement": "Method (A) SpCoSLAM had the highest EAR K value.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 7: Ablation results with base model crawled_ranked for different types of metadata'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Using all metadata together achieves the highest Macro F1 score, while topic tags alone contribute the most to performance improvement.S5SS2SSS0Px2 Metadata. Out of the different types of metadata, topic tags on their own contribute the most. This is likely because they offer highly complementary information to the claim text of evidence pages. Only using all metadata together achieves a higher Macro F1 at similar Micro F1 than using no metadata at all. To further investigate this, we split the test set into those instances for which no metadata is available vs. those for which metadata is available. We find that encoding metadata within the model hurts performance for domains where no metadata is available, but improves performance where it is. In practice, an ensemble of both types of models would be sensible, as well as exploring more involved methods of encoding metadata." } ], "images": [ "scigen/train/2778.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Ablation results with base model crawled_ranked for different types of metadata" ], "reasoning": "Using all metadata together achieves the highest Macro F1 score, while topic tags alone contribute the most to performance improvement.", "description": "S5SS2SSS0Px2 Metadata. Out of the different types of metadata, topic tags on their own contribute the most. This is likely because they offer highly complementary information to the claim text of evidence pages. Only using all metadata together achieves a higher Macro F1 at similar Micro F1 than using no metadata at all. To further investigate this, we split the test set into those instances for which no metadata is available vs. those for which metadata is available. We find that encoding metadata within the model hurts performance for domains where no metadata is available, but improves performance where it is. In practice, an ensemble of both types of models would be sensible, as well as exploring more involved methods of encoding metadata.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 7: Ablation results with base model crawled_ranked for different types of metadata', and tackle the question below in a sequential manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich metadata combination achieves the highest Macro F1 score?\n" }, { "role": "assistant", "content": "By examining the table, the combination of '+ Speaker + Tags' achieves the highest Macro F1 score of 0.492. So, the answer is {\"answer\": \"0.492\"}" } ], "images": [ "scigen/train/2778.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Ablation results with base model crawled_ranked for different types of metadata" ], "reasoning": "By examining the table, the combination of '+ Speaker + Tags' achieves the highest Macro F1 score of 0.492.", "question": "Which metadata combination achieves the highest Macro F1 score?", "answer": "So, the answer is {\"answer\": \"0.492\"}" }, { "messages": [ { "role": "user", "content": "Using no metadata achieves the highest Micro F1 score. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nUsing no metadata achieves the highest Micro F1 score.\n" }, { "role": "assistant", "content": "The table shows that using no metadata achieves a Micro F1 score of 0.627, which is the highest among all listed metadata combinations. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2778.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Ablation results with base model crawled_ranked for different types of metadata" ], "reasoning": "The table shows that using no metadata achieves a Micro F1 score of 0.627, which is the highest among all listed metadata combinations.", "statement": "Using no metadata achieves the highest Micro F1 score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 10: Number of instances, and labels per domain sorted by number of occurrences', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The dataset contains 34,918 claims after discarding instances with labels occurring fewer than 5 times. It is split into training (80%), development (10%), and testing (10%) parts in a label-stratified manner.This resulted in a total of 36,534 claims with their metadata. For the purposes of fact verification, we discarded instances with labels that occur fewer than 5 times, resulting in 34,918 claims. The dataset is split into a training part (80%) and a development and testing part (10% each) in a label-stratified manner. Note that the domains vary in the number of labels, ranging from 2 to 27. Labels include both straight-forward ratings of veracity (‘correct’, ‘incorrect’), but also labels that would be more difficult to map onto a veracity scale (e.g. ‘grass roots movement!’ , ‘misattributed’, ‘not the whole story’). We therefore do not postprocess label types across domains to map them onto the same scale, and rather treat them as is. Since not all fact checking websites use the same claim labels (see , training a claim veracity prediction model is not entirely straight-forward. One option would be to manually map those labels onto one another. However, since the sheer number of labels is rather large (165), and it is not always clear from the guidelines on fact checking websites how they can be mapped onto one another, we opt to learn how these labels relate to one another as part of the veracity prediction model. To do so, we employ the multi-task learning (MTL) approach inspired by collaborative filtering presented in Augenstein et al. (MTL with LEL– multitask learning with label embedding layer) that excels on pairwise sequence classification tasks with disparate label spaces. More concretely, each domain is modelled as its own task in a MTL architecture, and labels are projected into a fixed-length label embedding space. Predictions are then made by taking the dot product between the claim-evidence embeddings and the label embeddings. By doing so, the model implicitly learns how semantically close the labels are to one another, and can benefit from this knowledge when making predictions for individual tasks, which on their own might only have a small number of instances. When making predictions for individual domains/tasks, both at training and at test time, as well as when calculating the loss, a mask is applied such that the valid and invalid labels for that task are restricted to the set of known task labels." } ], "images": [ "scigen/train/2779.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Number of instances, and labels per domain sorted by number of occurrences" ], "reasoning": "The dataset contains 34,918 claims after discarding instances with labels occurring fewer than 5 times. It is split into training (80%), development (10%), and testing (10%) parts in a label-stratified manner.", "description": "This resulted in a total of 36,534 claims with their metadata. For the purposes of fact verification, we discarded instances with labels that occur fewer than 5 times, resulting in 34,918 claims. The dataset is split into a training part (80%) and a development and testing part (10% each) in a label-stratified manner. Note that the domains vary in the number of labels, ranging from 2 to 27. Labels include both straight-forward ratings of veracity (‘correct’, ‘incorrect’), but also labels that would be more difficult to map onto a veracity scale (e.g. ‘grass roots movement!’ , ‘misattributed’, ‘not the whole story’). We therefore do not postprocess label types across domains to map them onto the same scale, and rather treat them as is. Since not all fact checking websites use the same claim labels (see , training a claim veracity prediction model is not entirely straight-forward. One option would be to manually map those labels onto one another. However, since the sheer number of labels is rather large (165), and it is not always clear from the guidelines on fact checking websites how they can be mapped onto one another, we opt to learn how these labels relate to one another as part of the veracity prediction model. To do so, we employ the multi-task learning (MTL) approach inspired by collaborative filtering presented in Augenstein et al. (MTL with LEL– multitask learning with label embedding layer) that excels on pairwise sequence classification tasks with disparate label spaces. More concretely, each domain is modelled as its own task in a MTL architecture, and labels are projected into a fixed-length label embedding space. Predictions are then made by taking the dot product between the claim-evidence embeddings and the label embeddings. By doing so, the model implicitly learns how semantically close the labels are to one another, and can benefit from this knowledge when making predictions for individual tasks, which on their own might only have a small number of instances. When making predictions for individual domains/tasks, both at training and at test time, as well as when calculating the loss, a mask is applied such that the valid and invalid labels for that task are restricted to the set of known task labels.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 10: Number of instances, and labels per domain sorted by number of occurrences' in mind, break down and resolve the question below step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nHow many labels are there in the domain with the highest number of instances?\n" }, { "role": "assistant", "content": "The domain with the highest number of instances is 'pomt' with 15,390 instances. This domain has 9 labels. So, the answer is {\"answer\": \"9\"}" } ], "images": [ "scigen/train/2779.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Number of instances, and labels per domain sorted by number of occurrences" ], "reasoning": "The domain with the highest number of instances is 'pomt' with 15,390 instances. This domain has 9 labels.", "question": "How many labels are there in the domain with the highest number of instances?", "answer": "So, the answer is {\"answer\": \"9\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe domain 'tron' has the highest number of labels.\n" }, { "role": "assistant", "content": "The table shows that the domain 'tron' has 27 labels, which is the highest number of labels among all domains listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2779.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Number of instances, and labels per domain sorted by number of occurrences" ], "reasoning": "The table shows that the domain 'tron' has 27 labels, which is the highest number of labels among all domains listed.", "statement": "The domain 'tron' has the highest number of labels.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: Results on AGENDA test set. BS, MS and BT stands for BertScore, MoverScore and BLEURT, respectively.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "BART-Large achieves the highest BLEU score of 23.65, outperforming the previous state of the art by 5.6 points.We compare our approaches against several recent results reported on this dataset. It is worth mentioning that AGENDA graphs are incomplete regarding the target text. In contrast to WebNLG, this dataset is from scientific domain and the KGs and texts are extracted from the AI domain koncel-kedziorski-etal-2019-text. The models also have strong performance on this dataset. We believe that their capacity to generate fluent text helps when generating paper abstracts, even though they were not pretrained in the scientific domain. We note that an intermediate task-adaptive pretraining can enable the models to achieve better performance. Finally, note that BART has impressive performance with its large variant achieving a score of 23.65 BLEU points, 5.6 points higher than the previous state of the art." } ], "images": [ "scigen/train/2780.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on AGENDA test set. BS, MS and BT stands for BertScore, MoverScore and BLEURT, respectively." ], "reasoning": "BART-Large achieves the highest BLEU score of 23.65, outperforming the previous state of the art by 5.6 points.", "description": "We compare our approaches against several recent results reported on this dataset. It is worth mentioning that AGENDA graphs are incomplete regarding the target text. In contrast to WebNLG, this dataset is from scientific domain and the KGs and texts are extracted from the AI domain koncel-kedziorski-etal-2019-text. The models also have strong performance on this dataset. We believe that their capacity to generate fluent text helps when generating paper abstracts, even though they were not pretrained in the scientific domain. We note that an intermediate task-adaptive pretraining can enable the models to achieve better performance. Finally, note that BART has impressive performance with its large variant achieving a score of 23.65 BLEU points, 5.6 points higher than the previous state of the art.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Results on AGENDA test set. BS, MS and BT stands for BertScore, MoverScore and BLEURT, respectively.', answer the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model has the highest chrF++ score and what is that score?\n" }, { "role": "assistant", "content": "By examining the chrF++ column, BART-Large has the highest score of 50.44. So, the answer is {\"answer\": \"BART-Large\"}" } ], "images": [ "scigen/train/2780.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on AGENDA test set. BS, MS and BT stands for BertScore, MoverScore and BLEURT, respectively." ], "reasoning": "By examining the chrF++ column, BART-Large has the highest score of 50.44.", "question": "Which model has the highest chrF++ score and what is that score?", "answer": "So, the answer is {\"answer\": \"BART-Large\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe T5-Large model has the highest METEOR score.\n" }, { "role": "assistant", "content": "By examining the METEOR column, BART-Large has the highest score of 25.19, not T5-Large. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2780.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on AGENDA test set. BS, MS and BT stands for BertScore, MoverScore and BLEURT, respectively." ], "reasoning": "By examining the METEOR column, BART-Large has the highest score of 25.19, not T5-Large.", "statement": "The T5-Large model has the highest METEOR score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Results on AMR-to-text generation for the AMR17 test set. BS, MS and BT stands for BertScore, MoverScore and BLEURT, respectively.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "BART-Large achieves the highest BLEU score of 43.72, which is 15.9% higher than the previous state-of-the-art model using GPT-2.Note that we also report results from models that leverage PLMs. mager2020gpttoo and harkous2020text employ the GPT-2 model in their approaches. Note whereas GPT-2 is a Transformer-based decoder, our approaches employ BART and T5 that follow a Transformer-based encoder-decoder architecture. T5-Large achieves a considerable improvement of 5.48 BLEU and 1.2 METEOR scores over the previous state-of-the-art model that uses GPT2. BART achieves the new state-of-the-art BLEU score of 43.72, 15.9% higher. Note that the semantic metrics follow similar trends." } ], "images": [ "scigen/train/2781.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results on AMR-to-text generation for the AMR17 test set. BS, MS and BT stands for BertScore, MoverScore and BLEURT, respectively." ], "reasoning": "BART-Large achieves the highest BLEU score of 43.72, which is 15.9% higher than the previous state-of-the-art model using GPT-2.", "description": "Note that we also report results from models that leverage PLMs. mager2020gpttoo and harkous2020text employ the GPT-2 model in their approaches. Note whereas GPT-2 is a Transformer-based decoder, our approaches employ BART and T5 that follow a Transformer-based encoder-decoder architecture. T5-Large achieves a considerable improvement of 5.48 BLEU and 1.2 METEOR scores over the previous state-of-the-art model that uses GPT2. BART achieves the new state-of-the-art BLEU score of 43.72, 15.9% higher. Note that the semantic metrics follow similar trends.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 1: Results on AMR-to-text generation for the AMR17 test set. BS, MS and BT stands for BertScore, MoverScore and BLEURT, respectively.', unfold the steps to solve the question given next. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nQuestion: Which model achieves the highest BLEU score and by what percentage is it higher than the previous state-of-the-art model?\n" }, { "role": "assistant", "content": "BART-Large achieves the highest BLEU score of 43.72, which is 15.9% higher than the previous state-of-the-art model using GPT-2. So, the answer is {\"answer\": \"BART-Large, 15.9%\"}" } ], "images": [ "scigen/train/2781.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results on AMR-to-text generation for the AMR17 test set. BS, MS and BT stands for BertScore, MoverScore and BLEURT, respectively." ], "reasoning": "BART-Large achieves the highest BLEU score of 43.72, which is 15.9% higher than the previous state-of-the-art model using GPT-2.", "question": "Which model achieves the highest BLEU score and by what percentage is it higher than the previous state-of-the-art model?", "answer": "So, the answer is {\"answer\": \"BART-Large, 15.9%\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nBART-Large achieves a BLEU score of 43.72, which is 15.9% higher than the previous state-of-the-art model using GPT-2.\n" }, { "role": "assistant", "content": "The table shows that BART-Large achieves a BLEU score of 43.72, and the description confirms that this score is 15.9% higher than the previous state-of-the-art model using GPT-2. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2781.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results on AMR-to-text generation for the AMR17 test set. BS, MS and BT stands for BertScore, MoverScore and BLEURT, respectively." ], "reasoning": "The table shows that BART-Large achieves a BLEU score of 43.72, and the description confirms that this score is 15.9% higher than the previous state-of-the-art model using GPT-2.", "statement": "BART-Large achieves a BLEU score of 43.72, which is 15.9% higher than the previous state-of-the-art model using GPT-2.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Results on WebNLG test set. A, S and U stands for all, seen and unseen partitions of the WebNLG test sets, respectively.'.\n" }, { "role": "assistant", "content": "T5 achieves high BLEU scores on both seen and unseen datasets, outperforming BART by around 10 BLEU points.We compare the performance of our experiments with many state-of-the-art results reported on this dataset. Melbourne is one of the best competitors in the WebNLG challenge. moryossef-etal-2019-step and castro-ferreira- etal-2019-neural develop neural pipeline approaches with explicit intermediate steps in the generation process and achieve strong performance in the unseen dataset. On the other hand, fully end-to-end models ribeiro2020modeling; schmitt2020modeling have strong performance on seen dataset and usually perform poorly in unseen data. A very recent model zhao-etal-2020-bridging achieves the best performance in the seen test set. However, this model leverages additional information about the order that the triples are realized into the target text. T5 achieves 59.29 and 54.69 BLEU points on all seen and unseen datasets. Note that kale2020texttotext also employ T5 in this benchmark. BART performs much worse than T5 on this dataset, with around 10 BLEU points lower compared to T5 in all seen partition." } ], "images": [ "scigen/train/2782.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on WebNLG test set. A, S and U stands for all, seen and unseen partitions of the WebNLG test sets, respectively." ], "reasoning": "T5 achieves high BLEU scores on both seen and unseen datasets, outperforming BART by around 10 BLEU points.", "description": "We compare the performance of our experiments with many state-of-the-art results reported on this dataset. Melbourne is one of the best competitors in the WebNLG challenge. moryossef-etal-2019-step and castro-ferreira- etal-2019-neural develop neural pipeline approaches with explicit intermediate steps in the generation process and achieve strong performance in the unseen dataset. On the other hand, fully end-to-end models ribeiro2020modeling; schmitt2020modeling have strong performance on seen dataset and usually perform poorly in unseen data. A very recent model zhao-etal-2020-bridging achieves the best performance in the seen test set. However, this model leverages additional information about the order that the triples are realized into the target text. T5 achieves 59.29 and 54.69 BLEU points on all seen and unseen datasets. Note that kale2020texttotext also employ T5 in this benchmark. BART performs much worse than T5 on this dataset, with around 10 BLEU points lower compared to T5 in all seen partition.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: Results on WebNLG test set. A, S and U stands for all, seen and unseen partitions of the WebNLG test sets, respectively.', and sequentially solve the question, writing out each step of your reasoning process. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieves the highest BLEU score on the seen dataset?\n" }, { "role": "assistant", "content": "By examining the BLEU S column, the highest score is 64.42, achieved by zhao-etal-2020-bridging. So, the answer is {\"answer\": \"zhao-etal-2020-bridging\"}" } ], "images": [ "scigen/train/2782.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on WebNLG test set. A, S and U stands for all, seen and unseen partitions of the WebNLG test sets, respectively." ], "reasoning": "By examining the BLEU S column, the highest score is 64.42, achieved by zhao-etal-2020-bridging.", "question": "Which model achieves the highest BLEU score on the seen dataset?", "answer": "So, the answer is {\"answer\": \"zhao-etal-2020-bridging\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim T5-Large achieves the highest METEOR score on the unseen dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nT5-Large achieves the highest METEOR score on the unseen dataset.\n" }, { "role": "assistant", "content": "The highest METEOR U score is 46.00, achieved by both zhao-etal-2020-bridging and kale2020texttotext, not T5-Large. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2782.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on WebNLG test set. A, S and U stands for all, seen and unseen partitions of the WebNLG test sets, respectively." ], "reasoning": "The highest METEOR U score is 46.00, achieved by both zhao-etal-2020-bridging and kale2020texttotext, not T5-Large.", "statement": "T5-Large achieves the highest METEOR score on the unseen dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Impact of additional task-adaptive pretraining of BART-Large and T5-Large on AMR17 and AGENDA datasets. TSU and TSS refer to unsupervised and supervised pretraining on the additional task-specific data.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Unsupervised pretraining helps less than supervised pretraining but still brings gains, suggesting models learn graph handling and graph-to-text correspondence.Unsupervised pretraining helps less than supervised pretraining but also brings gains in most cases. This suggests that the performance increases after additional supervised pretraining do not only come from seeing more task-specific target texts but that the models learn how to handle graphs and the graph-to-text correspondence. Also note that we use the same number of datapoints for unsupervised and supervised experiments. It is probable that unsupervised pretraining on larger amounts of in-domain data would improve the models even more." } ], "images": [ "scigen/train/2783.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Impact of additional task-adaptive pretraining of BART-Large and T5-Large on AMR17 and AGENDA datasets. TSU and TSS refer to unsupervised and supervised pretraining on the additional task-specific data." ], "reasoning": "Unsupervised pretraining helps less than supervised pretraining but still brings gains, suggesting models learn graph handling and graph-to-text correspondence.", "description": "Unsupervised pretraining helps less than supervised pretraining but also brings gains in most cases. This suggests that the performance increases after additional supervised pretraining do not only come from seeing more task-specific target texts but that the models learn how to handle graphs and the graph-to-text correspondence. Also note that we use the same number of datapoints for unsupervised and supervised experiments. It is probable that unsupervised pretraining on larger amounts of in-domain data would improve the models even more.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 6: Impact of additional task-adaptive pretraining of BART-Large and T5-Large on AMR17 and AGENDA datasets. TSU and TSS refer to unsupervised and supervised pretraining on the additional task-specific data.\nQuestion: Which model has the highest AMR17 BLEU score after supervised pretraining?\n" }, { "role": "assistant", "content": "The table shows that T5-TSS has the highest AMR17 BLEU score of 48.85 after supervised pretraining. So, the answer is {\"answer\": \"T5-TSS\"}" } ], "images": [ "scigen/train/2783.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Impact of additional task-adaptive pretraining of BART-Large and T5-Large on AMR17 and AGENDA datasets. TSU and TSS refer to unsupervised and supervised pretraining on the additional task-specific data." ], "reasoning": "The table shows that T5-TSS has the highest AMR17 BLEU score of 48.85 after supervised pretraining.", "question": "Which model has the highest AMR17 BLEU score after supervised pretraining?", "answer": "So, the answer is {\"answer\": \"T5-TSS\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: BART-TSU has a higher AMR17 chrF++ score than T5-TSU., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nBART-TSU has a higher AMR17 chrF++ score than T5-TSU.\n" }, { "role": "assistant", "content": "The table shows that BART-TSU has an AMR17 chrF++ score of 71.14, while T5-TSU has a score of 71.19, indicating that BART-TSU does not have a higher score. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2783.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Impact of additional task-adaptive pretraining of BART-Large and T5-Large on AMR17 and AGENDA datasets. TSU and TSS refer to unsupervised and supervised pretraining on the additional task-specific data." ], "reasoning": "The table shows that BART-TSU has an AMR17 chrF++ score of 71.14, while T5-TSU has a score of 71.19, indicating that BART-TSU does not have a higher score.", "statement": "BART-TSU has a higher AMR17 chrF++ score than T5-TSU.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 7: Impact of cross-domain supervised pretraining for BART-Base on KG-to-text generation.'.\n" }, { "role": "assistant", "content": "Supervised pretraining generally improves performance in cross-domain settings, except when pretraining on the smaller WebNLG for the larger AGENDA.We also investigate how cross-domain pretraining affects the performance after fine-tuning. While the target texts in KGAIA and AGENDA share the domain of scientific abstracts , texts in WebNLG are more general. The graphs in all three datasets are KGs, but WebNLG graphs do not share any relations with the other KGs. Nevertheless, supervised pretraining increases the performance in the cross-domain setting in most of the cases. Only pretraining on the smaller WebNLG does not help for the larger AGENDA." } ], "images": [ "scigen/train/2784.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Impact of cross-domain supervised pretraining for BART-Base on KG-to-text generation." ], "reasoning": "Supervised pretraining generally improves performance in cross-domain settings, except when pretraining on the smaller WebNLG for the larger AGENDA.", "description": "We also investigate how cross-domain pretraining affects the performance after fine-tuning. While the target texts in KGAIA and AGENDA share the domain of scientific abstracts , texts in WebNLG are more general. The graphs in all three datasets are KGs, but WebNLG graphs do not share any relations with the other KGs. Nevertheless, supervised pretraining increases the performance in the cross-domain setting in most of the cases. Only pretraining on the smaller WebNLG does not help for the larger AGENDA.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 7: Impact of cross-domain supervised pretraining for BART-Base on KG-to-text generation.', and sequentially solve the question, writing out each step of your reasoning process. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the BLEU score when pretrained on KGAIA and evaluated on AGENDA?\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for different pretraining and evaluation combinations. For KGAIA pretrained and AGENDA evaluated, the BLEU score is 23.48. So, the answer is {\"answer\": \"23.48\"}" } ], "images": [ "scigen/train/2784.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Impact of cross-domain supervised pretraining for BART-Base on KG-to-text generation." ], "reasoning": "The table shows the BLEU scores for different pretraining and evaluation combinations. For KGAIA pretrained and AGENDA evaluated, the BLEU score is 23.48.", "question": "What is the BLEU score when pretrained on KGAIA and evaluated on AGENDA?", "answer": "So, the answer is {\"answer\": \"23.48\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nPretraining on KGAIA results in a higher BLEU score than pretraining on WebNLG when evaluated on AGENDA.\n" }, { "role": "assistant", "content": "The table shows that pretraining on KGAIA and evaluating on AGENDA results in a BLEU score of 23.48, while pretraining on WebNLG and evaluating on AGENDA results in a BLEU score of 21.98. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2784.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Impact of cross-domain supervised pretraining for BART-Base on KG-to-text generation." ], "reasoning": "The table shows that pretraining on KGAIA and evaluating on AGENDA results in a BLEU score of 23.48, while pretraining on WebNLG and evaluating on AGENDA results in a BLEU score of 21.98.", "statement": "Pretraining on KGAIA results in a higher BLEU score than pretraining on WebNLG when evaluated on AGENDA.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4: Test performance (F1) on BLESS in the RAND and OOD setups, using Glove and Word2vec.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Concat captures the typicality of each word in the relation, while Mult captures the similarity between the words, leading to consistent performance.Concat ⊕ Mult performed consistently well, intuitively because Concat captures the typicality of each word in the relation (e.g., if y is a typical hypernym) and Mult captures the similarity between the words (where Concat alone may suggest that animal is a hypernym of apple). To take a closer look at the gain from adding" } ], "images": [ "scigen/train/2785.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Test performance (F1) on BLESS in the RAND and OOD setups, using Glove and Word2vec." ], "reasoning": "Concat captures the typicality of each word in the relation, while Mult captures the similarity between the words, leading to consistent performance.", "description": "Concat ⊕ Mult performed consistently well, intuitively because Concat captures the typicality of each word in the relation (e.g., if y is a typical hypernym) and Mult captures the similarity between the words (where Concat alone may suggest that animal is a hypernym of apple). To take a closer look at the gain from adding", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: 'Table 4: Test performance (F1) on BLESS in the RAND and OOD setups, using Glove and Word2vec.'\nQuestion: Which combination of vector and classifier has the highest OOD Diff ⊕ Mult score?\n" }, { "role": "assistant", "content": "To find the highest OOD Diff ⊕ Mult score, look at the OOD Diff ⊕ Mult column and identify the highest value. The highest value is 81.5, which corresponds to GloVe with RBF classifier. So, the answer is {\"answer\": \"GloVe with RBF\"}" } ], "images": [ "scigen/train/2785.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Test performance (F1) on BLESS in the RAND and OOD setups, using Glove and Word2vec." ], "reasoning": "To find the highest OOD Diff ⊕ Mult score, look at the OOD Diff ⊕ Mult column and identify the highest value. The highest value is 81.5, which corresponds to GloVe with RBF classifier.", "question": "Which combination of vector and classifier has the highest OOD Diff ⊕ Mult score?", "answer": "So, the answer is {\"answer\": \"GloVe with RBF\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Word2vec with LIN classifier has a higher RAND Diff ⊕ Mult score than GloVe with LIN classifier., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWord2vec with LIN classifier has a higher RAND Diff ⊕ Mult score than GloVe with LIN classifier.\n" }, { "role": "assistant", "content": "Compare the RAND Diff ⊕ Mult scores for Word2vec with LIN classifier and GloVe with LIN classifier. Word2vec with LIN has a score of 87.7, while GloVe with LIN has a score of 87.7. Since both scores are equal, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2785.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Test performance (F1) on BLESS in the RAND and OOD setups, using Glove and Word2vec." ], "reasoning": "Compare the RAND Diff ⊕ Mult scores for Word2vec with LIN classifier and GloVe with LIN classifier. Word2vec with LIN has a score of 87.7, while GloVe with LIN has a score of 87.7. Since both scores are equal, the statement is refuted.", "statement": "Word2vec with LIN classifier has a higher RAND Diff ⊕ Mult score than GloVe with LIN classifier.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Best test performance (F1) across different datasets and evaluation setups, using Glove. The number in brackets indicates the performance gap between the best performing combination and base representation setups.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The performance of datasets varies due to differences in relations, class balance, and sources. Multiplicative features improve the performance of base representations.The results across the datasets vary substantially in some cases due to the differences between the datasets’ relations, class balance, and the source from which they were created. For instance, K&H+N is imbalanced between the number of instances across relations and domains. ROOT09 was designed to mitigate the lexical memorization issue by adding negative switched hyponym-hypernym pairs to the dataset, making it an inherently more difficult dataset. EVALution contains a richer set of semantic relations. Overall, the addition of multiplicative features improves upon the performance of the base representations." } ], "images": [ "scigen/train/2786.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Best test performance (F1) across different datasets and evaluation setups, using Glove. The number in brackets indicates the performance gap between the best performing combination and base representation setups." ], "reasoning": "The performance of datasets varies due to differences in relations, class balance, and sources. Multiplicative features improve the performance of base representations.", "description": "The results across the datasets vary substantially in some cases due to the differences between the datasets’ relations, class balance, and the source from which they were created. For instance, K&H+N is imbalanced between the number of instances across relations and domains. ROOT09 was designed to mitigate the lexical memorization issue by adding negative switched hyponym-hypernym pairs to the dataset, making it an inherently more difficult dataset. EVALution contains a richer set of semantic relations. Overall, the addition of multiplicative features improves upon the performance of the base representations.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 3: Best test performance (F1) across different datasets and evaluation setups, using Glove. The number in brackets indicates the performance gap between the best performing combination and base representation setups.\nQuestion: Which dataset has the highest score using Non-linear classifiers (RBF, MLP) Combination?\n" }, { "role": "assistant", "content": "To find the highest score using Non-linear classifiers (RBF, MLP) Combination, look at the last column of the table. The highest score is 98.6 for the K&H+N dataset. So, the answer is {\"answer\": \"K&H+N\"}" } ], "images": [ "scigen/train/2786.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Best test performance (F1) across different datasets and evaluation setups, using Glove. The number in brackets indicates the performance gap between the best performing combination and base representation setups." ], "reasoning": "To find the highest score using Non-linear classifiers (RBF, MLP) Combination, look at the last column of the table. The highest score is 98.6 for the K&H+N dataset.", "question": "Which dataset has the highest score using Non-linear classifiers (RBF, MLP) Combination?", "answer": "So, the answer is {\"answer\": \"K&H+N\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe ROOT09 dataset has the lowest score using Linear classifiers (LR, LIN) Combination.\n" }, { "role": "assistant", "content": "To verify this, compare the scores in the Linear classifiers (LR, LIN) Combination column for all datasets. The lowest score is 56.8 for the EVALution dataset, not ROOT09. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2786.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Best test performance (F1) across different datasets and evaluation setups, using Glove. The number in brackets indicates the performance gap between the best performing combination and base representation setups." ], "reasoning": "To verify this, compare the scores in the Linear classifiers (LR, LIN) Combination column for all datasets. The lowest score is 56.8 for the EVALution dataset, not ROOT09.", "statement": "The ROOT09 dataset has the lowest score using Linear classifiers (LR, LIN) Combination.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: BLEU scores of a supervised NMT system trained on Wikimatrix as well as the unique pairs collected by SS-NMT in the first (NMTinit), intermediate (NMTmid), final (NMTend) and all (NMTall) epochs of training. Evaluation performed on NT14 (en-{fr,de}) and NT13 (en-es) respectively.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "SS-NMT outperforms supervised systems across all tested languages, despite using less data initially, due to its ability to exclude false positives and improve data quality over epochs.For en-fr, the supervised system trained on Wikimatrix outperforms SS-NMT by 3-4 BLEU points, while the opposite is the case for en-de, where SS-NMT achieves 1-5 BLEU points more. For en-es, both approaches perform similarly. The variable performance of the two approaches may be due to the varying appropriateness of the extraction threshold θ in Wikimatrix. This yields the problem that for each language and corpus, a new optimal threshold needs to be found; a problem that SS-NMT overcomes by its use of two representation types that compliment each other during extraction without the need of a manually set threshold. The SS-NMT training outperforms all the supervised versions across all tested languages. Notably, the performance is 1–3 BLEU points above the supervised system trained on all extracted data, despite the fact that the SS-NMT system uses a much smaller amount of data in its first epochs. This suggests that the SS-NMT system is able to exclude previously accepted false positives at later epochs, while training directly on the entirety of its extracted data leads to a recurring visitation of the same erroneous samples. Further, the quality and quantity of the extracted data grows as training continues for all languages analysed. This can be observed by the fact that the concatenation of the data extracted across epochs (NMTall) is always outperformed by the last and thus largest epoch (NMTend), despite being much larger in size." } ], "images": [ "scigen/train/2787.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: BLEU scores of a supervised NMT system trained on Wikimatrix as well as the unique pairs collected by SS-NMT in the first (NMTinit), intermediate (NMTmid), final (NMTend) and all (NMTall) epochs of training. Evaluation performed on NT14 (en-{fr,de}) and NT13 (en-es) respectively." ], "reasoning": "SS-NMT outperforms supervised systems across all tested languages, despite using less data initially, due to its ability to exclude false positives and improve data quality over epochs.", "description": "For en-fr, the supervised system trained on Wikimatrix outperforms SS-NMT by 3-4 BLEU points, while the opposite is the case for en-de, where SS-NMT achieves 1-5 BLEU points more. For en-es, both approaches perform similarly. The variable performance of the two approaches may be due to the varying appropriateness of the extraction threshold θ in Wikimatrix. This yields the problem that for each language and corpus, a new optimal threshold needs to be found; a problem that SS-NMT overcomes by its use of two representation types that compliment each other during extraction without the need of a manually set threshold. The SS-NMT training outperforms all the supervised versions across all tested languages. Notably, the performance is 1–3 BLEU points above the supervised system trained on all extracted data, despite the fact that the SS-NMT system uses a much smaller amount of data in its first epochs. This suggests that the SS-NMT system is able to exclude previously accepted false positives at later epochs, while training directly on the entirety of its extracted data leads to a recurring visitation of the same erroneous samples. Further, the quality and quantity of the extracted data grows as training continues for all languages analysed. This can be observed by the fact that the concatenation of the data extracted across epochs (NMTall) is always outperformed by the last and thus largest epoch (NMTend), despite being much larger in size.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: BLEU scores of a supervised NMT system trained on Wikimatrix as well as the unique pairs collected by SS-NMT in the first (NMTinit), intermediate (NMTmid), final (NMTend) and all (NMTall) epochs of training. Evaluation performed on NT14 (en-{fr,de}) and NT13 (en-es) respectively.', unfold the steps to solve the question given next. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nQuestion: Which system achieves the highest BLEU score for en-fr?\n" }, { "role": "assistant", "content": "The table shows that the highest BLEU score for en-fr is achieved by Wikimatrix with a score of 33.50. So, the answer is {\"answer\": \"Wikimatrix\"}." } ], "images": [ "scigen/train/2787.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: BLEU scores of a supervised NMT system trained on Wikimatrix as well as the unique pairs collected by SS-NMT in the first (NMTinit), intermediate (NMTmid), final (NMTend) and all (NMTall) epochs of training. Evaluation performed on NT14 (en-{fr,de}) and NT13 (en-es) respectively." ], "reasoning": "The table shows that the highest BLEU score for en-fr is achieved by Wikimatrix with a score of 33.50.", "question": "Which system achieves the highest BLEU score for en-fr?", "answer": "So, the answer is {\"answer\": \"Wikimatrix\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nSS-NMT achieves the highest BLEU score for en-de.\n" }, { "role": "assistant", "content": "The table shows that the highest BLEU score for en-de is achieved by SS-NMT with a score of 18.44. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2787.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: BLEU scores of a supervised NMT system trained on Wikimatrix as well as the unique pairs collected by SS-NMT in the first (NMTinit), intermediate (NMTmid), final (NMTend) and all (NMTall) epochs of training. Evaluation performed on NT14 (en-{fr,de}) and NT13 (en-es) respectively." ], "reasoning": "The table shows that the highest BLEU score for en-de is achieved by SS-NMT with a score of 18.44.", "statement": "SS-NMT achieves the highest BLEU score for en-de.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: BLEU, TER and METEOR scores of the self-supervised NMT systems trained on the three en–{fr,de,es} comparable WPs and tested on NT13/NT14. We compare the performance with current state-of-the-art (SOTA) systems in supervised NMT (Edunov et al., ) / unsupervised NMT (Lample et al., 2018b) / unsupervised NMT+SMT (Artetxe et al., 2019).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "SS-NMT slightly outperforms the current SOTA in unsupervised NMT for en–fr by 3-4 BLEU points but underperforms for en–de by 3 BLEU points. However, when combined with statistical methods, the SOTA outperforms SS-NMT by large margins.SS-NMT is on par with the current SOTA in unsupervised NMT, slightly outperforming it by ∼3-4 BLEU points in en–fr while having a lower performance on en–de (∼3 BLEU). Notice that unsupervised systems such as Lample et al. However, once unsupervised NMT is combined with statistical methods, these outperform SS-NMT by large margins, i.e. ∼6 for en–fr and ∼5–9 BLEU for en– de. In a recent study by Wu et al. They achieve similar BLEU scores as SS-NMT for en2es (28.1) and es2en (27.6) on NT13; notably SS-NMT trains on comparable data only." } ], "images": [ "scigen/train/2788.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: BLEU, TER and METEOR scores of the self-supervised NMT systems trained on the three en–{fr,de,es} comparable WPs and tested on NT13/NT14. We compare the performance with current state-of-the-art (SOTA) systems in supervised NMT (Edunov et al., ) / unsupervised NMT (Lample et al., 2018b) / unsupervised NMT+SMT (Artetxe et al., 2019)." ], "reasoning": "SS-NMT slightly outperforms the current SOTA in unsupervised NMT for en–fr by 3-4 BLEU points but underperforms for en–de by 3 BLEU points. However, when combined with statistical methods, the SOTA outperforms SS-NMT by large margins.", "description": "SS-NMT is on par with the current SOTA in unsupervised NMT, slightly outperforming it by ∼3-4 BLEU points in en–fr while having a lower performance on en–de (∼3 BLEU). Notice that unsupervised systems such as Lample et al. However, once unsupervised NMT is combined with statistical methods, these outperform SS-NMT by large margins, i.e. ∼6 for en–fr and ∼5–9 BLEU for en– de. In a recent study by Wu et al. They achieve similar BLEU scores as SS-NMT for en2es (28.1) and es2en (27.6) on NT13; notably SS-NMT trains on comparable data only.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nTable title: Table 2: BLEU, TER and METEOR scores of the self-supervised NMT systems trained on the three en–{fr,de,es} comparable WPs and tested on NT13/NT14. We compare the performance with current state-of-the-art (SOTA) systems in supervised NMT (Edunov et al., ) / unsupervised NMT (Lample et al., 2018b) / unsupervised NMT+SMT (Artetxe et al., 2019).\n\nQuestion: How does SS-NMT's BLEU score for en–fr compare to the SOTA L1–L2 BLEU score?\n" }, { "role": "assistant", "content": "SS-NMT's BLEU score for en–fr is 29.48, while the SOTA L1–L2 BLEU score is 45.6. Therefore, the SOTA L1–L2 BLEU score is higher. So, the answer is {\"answer\": \"SOTA L1–L2 BLEU score is higher\"}." } ], "images": [ "scigen/train/2788.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: BLEU, TER and METEOR scores of the self-supervised NMT systems trained on the three en–{fr,de,es} comparable WPs and tested on NT13/NT14. We compare the performance with current state-of-the-art (SOTA) systems in supervised NMT (Edunov et al., ) / unsupervised NMT (Lample et al., 2018b) / unsupervised NMT+SMT (Artetxe et al., 2019)." ], "reasoning": "SS-NMT's BLEU score for en–fr is 29.48, while the SOTA L1–L2 BLEU score is 45.6. Therefore, the SOTA L1–L2 BLEU score is higher.", "question": "How does SS-NMT's BLEU score for en–fr compare to the SOTA L1–L2 BLEU score?", "answer": "So, the answer is {\"answer\": \"SOTA L1–L2 BLEU score is higher\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nSS-NMT outperforms the SOTA in BLEU score for en–fr.\n" }, { "role": "assistant", "content": "The table shows that SS-NMT's BLEU score for en–fr is 29.48, while the SOTA L1–L2 BLEU score is 45.6. Therefore, SS-NMT does not outperform the SOTA in BLEU score for en–fr. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2788.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: BLEU, TER and METEOR scores of the self-supervised NMT systems trained on the three en–{fr,de,es} comparable WPs and tested on NT13/NT14. We compare the performance with current state-of-the-art (SOTA) systems in supervised NMT (Edunov et al., ) / unsupervised NMT (Lample et al., 2018b) / unsupervised NMT+SMT (Artetxe et al., 2019)." ], "reasoning": "The table shows that SS-NMT's BLEU score for en–fr is 29.48, while the SOTA L1–L2 BLEU score is 45.6. Therefore, SS-NMT does not outperform the SOTA in BLEU score for en–fr.", "statement": "SS-NMT outperforms the SOTA in BLEU score for en–fr.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Compared to pipeline models, multitask models produce complexity controlled translations that better match human references (BLEU), that are simpler (SARI), and whose resulting complexity correlates better with the target grade level (PCC). Pipeline models are trained on Newsela Simplification data and MT parallel data from Newsela and OPUS. “Translate and Simplify” uses the exact same data in a multi-task model. The “All tasks” model uses all data available, including Newsla MT+Simplify examples.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Multitask models produce translations that better match human references and are simpler than baseline translations.Overall, results show that compared to pipeline models, multitask models produce complexity controlled translations that better match human references according to BLEU. SARI suggests that multitask translations are simpler than baseline translations, and their resulting complexity correlates better with reference grade levels according to PCC." } ], "images": [ "scigen/train/2789.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Compared to pipeline models, multitask models produce complexity controlled translations that better match human references (BLEU), that are simpler (SARI), and whose resulting complexity correlates better with the target grade level (PCC). Pipeline models are trained on Newsela Simplification data and MT parallel data from Newsela and OPUS. “Translate and Simplify” uses the exact same data in a multi-task model. The “All tasks” model uses all data available, including Newsla MT+Simplify examples." ], "reasoning": "Multitask models produce translations that better match human references and are simpler than baseline translations.", "description": "Overall, results show that compared to pipeline models, multitask models produce complexity controlled translations that better match human references according to BLEU. SARI suggests that multitask translations are simpler than baseline translations, and their resulting complexity correlates better with reference grade levels according to PCC.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 4: Compared to pipeline models, multitask models produce complexity controlled translations that better match human references (BLEU), that are simpler (SARI), and whose resulting complexity correlates better with the target grade level (PCC). Pipeline models are trained on Newsela Simplification data and MT parallel data from Newsela and OPUS. “Translate and Simplify” uses the exact same data in a multi-task model. The “All tasks” model uses all data available, including Newsla MT+Simplify examples.', solve the following question step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest SARI score?\n" }, { "role": "assistant", "content": "By comparing the SARI scores of all models, the 'All Tasks' model has the highest SARI score of 45.0. So, the answer is {\"answer\": \"All Tasks\"}." } ], "images": [ "scigen/train/2789.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Compared to pipeline models, multitask models produce complexity controlled translations that better match human references (BLEU), that are simpler (SARI), and whose resulting complexity correlates better with the target grade level (PCC). Pipeline models are trained on Newsela Simplification data and MT parallel data from Newsela and OPUS. “Translate and Simplify” uses the exact same data in a multi-task model. The “All tasks” model uses all data available, including Newsla MT+Simplify examples." ], "reasoning": "By comparing the SARI scores of all models, the 'All Tasks' model has the highest SARI score of 45.0.", "question": "Which model has the highest SARI score?", "answer": "So, the answer is {\"answer\": \"All Tasks\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The 'Translate and Simplify' multitask model has a higher BLEU score than the 'Translate then Simplify' pipeline model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe 'Translate and Simplify' multitask model has a higher BLEU score than the 'Translate then Simplify' pipeline model.\n" }, { "role": "assistant", "content": "The table shows that the 'Translate and Simplify' multitask model has a BLEU score of 22.51, which is higher than the 'Translate then Simplify' pipeline model's BLEU score of 21.98. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2789.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Compared to pipeline models, multitask models produce complexity controlled translations that better match human references (BLEU), that are simpler (SARI), and whose resulting complexity correlates better with the target grade level (PCC). Pipeline models are trained on Newsela Simplification data and MT parallel data from Newsela and OPUS. “Translate and Simplify” uses the exact same data in a multi-task model. The “All tasks” model uses all data available, including Newsla MT+Simplify examples." ], "reasoning": "The table shows that the 'Translate and Simplify' multitask model has a BLEU score of 22.51, which is higher than the 'Translate then Simplify' pipeline model's BLEU score of 21.98.", "statement": "The 'Translate and Simplify' multitask model has a higher BLEU score than the 'Translate then Simplify' pipeline model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6: Adjacency ARI accuracy within grade level given by Adjacency level for the system output with respect to the target grade: Multitask model is able to better capture the target grade than the pipeline model when the difference between the source and the target grade is greater than 3.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The table compares the adjacency scores of the 'Translate then Simplify' pipeline and multitask models across different source and target grade levels.We aim to better understand to what degree models simplify the input text: how often does the output complexity exactly matches that of the reference? Does this change depend on the distance between input and output complexity levels? We derive the reading grade levels from ARI SenterSmith1967 and conduct this analysis for the best pipeline (“Translate then Simplify”) and multitask models (“All Tasks”). These adjacency scores are broken down according to the distance between input and target grade levels." } ], "images": [ "scigen/train/2790.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Adjacency ARI accuracy within grade level given by Adjacency level for the system output with respect to the target grade: Multitask model is able to better capture the target grade than the pipeline model when the difference between the source and the target grade is greater than 3." ], "reasoning": "The table compares the adjacency scores of the 'Translate then Simplify' pipeline and multitask models across different source and target grade levels.", "description": "We aim to better understand to what degree models simplify the input text: how often does the output complexity exactly matches that of the reference? Does this change depend on the distance between input and output complexity levels? We derive the reading grade levels from ARI SenterSmith1967 and conduct this analysis for the best pipeline (“Translate then Simplify”) and multitask models (“All Tasks”). These adjacency scores are broken down according to the distance between input and target grade levels.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 6: Adjacency ARI accuracy within grade level given by Adjacency level for the system output with respect to the target grade: Multitask model is able to better capture the target grade than the pipeline model when the difference between the source and the target grade is greater than 3.' in mind, break down and resolve the question below step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich model has the highest adjacency score for Source Grade - Target Grade 3 at Adj. level 2?\n" }, { "role": "assistant", "content": "To find the highest adjacency score for Source Grade - Target Grade 3 at Adj. level 2, compare the scores of the Pipeline and Multitask models in the corresponding column and row. So, the answer is {\"answer\": \"Pipeline\"}." } ], "images": [ "scigen/train/2790.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Adjacency ARI accuracy within grade level given by Adjacency level for the system output with respect to the target grade: Multitask model is able to better capture the target grade than the pipeline model when the difference between the source and the target grade is greater than 3." ], "reasoning": "To find the highest adjacency score for Source Grade - Target Grade 3 at Adj. level 2, compare the scores of the Pipeline and Multitask models in the corresponding column and row.", "question": "Which model has the highest adjacency score for Source Grade - Target Grade 3 at Adj. level 2?", "answer": "So, the answer is {\"answer\": \"Pipeline\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Pipeline model has a higher adjacency score than the Multitask model for Source Grade - Target Grade 5 at Adj. level 1.\n" }, { "role": "assistant", "content": "Compare the adjacency scores of the Pipeline and Multitask models for Source Grade - Target Grade 5 at Adj. level 1. The Pipeline model has a score of 0.524, while the Multitask model has a score of 0.504. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2790.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Adjacency ARI accuracy within grade level given by Adjacency level for the system output with respect to the target grade: Multitask model is able to better capture the target grade than the pipeline model when the difference between the source and the target grade is greater than 3." ], "reasoning": "Compare the adjacency scores of the Pipeline and Multitask models for Source Grade - Target Grade 5 at Adj. level 1. The Pipeline model has a score of 0.524, while the Multitask model has a score of 0.504.", "statement": "The Pipeline model has a higher adjacency score than the Multitask model for Source Grade - Target Grade 5 at Adj. level 1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Data ablation experiments showing the impact of different types of training examples on multi-task model. The OPUS parallel corpus is essential to good performance. Simplification data (S) can be used for simplification supervision when joint translation and simplification examples (MT+S) are unavailable.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Using OPUS without Newsela MT data reduces the correlation score, highlighting the need for in-domain MT data. The performance difference between using joint translation and simplification examples versus simplification only is minimal, indicating the monolingual simplification dataset's effectiveness.OPUS improves BLEU and SARI performance across the board. However, using OPUS without any Newsela MT data (Row 4) hurts the correlation score, indicating the importance of in-domain MT data to control complexity. The difference between the performance when using joint translation and simplification (MT+S) examples (Row 2) vs. simplification only (S in Row 3) is small in terms of BLEU (+0.11) and PCC (0.012), indicating that the monolingual simplification dataset can provide simplification supervision when MT+Simplify data is unavailable. The overall best performance for the task is obtained by using all types of training examples. Example translations produced by our best multitask model." } ], "images": [ "scigen/train/2791.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Data ablation experiments showing the impact of different types of training examples on multi-task model. The OPUS parallel corpus is essential to good performance. Simplification data (S) can be used for simplification supervision when joint translation and simplification examples (MT+S) are unavailable." ], "reasoning": "Using OPUS without Newsela MT data reduces the correlation score, highlighting the need for in-domain MT data. The performance difference between using joint translation and simplification examples versus simplification only is minimal, indicating the monolingual simplification dataset's effectiveness.", "description": "OPUS improves BLEU and SARI performance across the board. However, using OPUS without any Newsela MT data (Row 4) hurts the correlation score, indicating the importance of in-domain MT data to control complexity. The difference between the performance when using joint translation and simplification (MT+S) examples (Row 2) vs. simplification only (S in Row 3) is small in terms of BLEU (+0.11) and PCC (0.012), indicating that the monolingual simplification dataset can provide simplification supervision when MT+Simplify data is unavailable. The overall best performance for the task is obtained by using all types of training examples. Example translations produced by our best multitask model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 7: Data ablation experiments showing the impact of different types of training examples on multi-task model. The OPUS parallel corpus is essential to good performance. Simplification data (S) can be used for simplification supervision when joint translation and simplification examples (MT+S) are unavailable.'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the BLEU score difference between using all types of training examples and using only OPUS MT data?\n" }, { "role": "assistant", "content": "The BLEU score for using all types of training examples (Row 1) is 22.75, and for using only OPUS MT data (Row 4) is 19.16. The difference is 22.75 - 19.16 = 3.59. So, the answer is {\"answer\": \"3.59\"}" } ], "images": [ "scigen/train/2791.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Data ablation experiments showing the impact of different types of training examples on multi-task model. The OPUS parallel corpus is essential to good performance. Simplification data (S) can be used for simplification supervision when joint translation and simplification examples (MT+S) are unavailable." ], "reasoning": "The BLEU score for using all types of training examples (Row 1) is 22.75, and for using only OPUS MT data (Row 4) is 19.16. The difference is 22.75 - 19.16 = 3.59.", "question": "What is the BLEU score difference between using all types of training examples and using only OPUS MT data?", "answer": "So, the answer is {\"answer\": \"3.59\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nUsing only OPUS MT data results in the lowest PCC score.\n" }, { "role": "assistant", "content": "The table shows that the PCC score for using only OPUS MT data (Row 4) is 0.468, which is lower than the PCC scores for all other rows. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2791.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Data ablation experiments showing the impact of different types of training examples on multi-task model. The OPUS parallel corpus is essential to good performance. Simplification data (S) can be used for simplification supervision when joint translation and simplification examples (MT+S) are unavailable." ], "reasoning": "The table shows that the PCC score for using only OPUS MT data (Row 4) is 0.468, which is lower than the PCC scores for all other rows.", "statement": "Using only OPUS MT data results in the lowest PCC score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 8: Evaluation on auxiliary tasks: Multitask models trained on both the translation and simplification dataset improves the performance for the task of English Simplification.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The standalone translation system performs better on the resource-rich MT task because it focuses solely on text translation, unlike the multi-task model which handles more diverse samples.By contrast, on the resource-rich MT task, the standalone translation system performs better. This can be explained by the fact that the standalone system is only responsible for text translation, while the multi-task model is exposed to samples of more diverse complexity levels during training which damage its ability to preserve complexity." } ], "images": [ "scigen/train/2792.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Evaluation on auxiliary tasks: Multitask models trained on both the translation and simplification dataset improves the performance for the task of English Simplification." ], "reasoning": "The standalone translation system performs better on the resource-rich MT task because it focuses solely on text translation, unlike the multi-task model which handles more diverse samples.", "description": "By contrast, on the resource-rich MT task, the standalone translation system performs better. This can be explained by the fact that the standalone system is only responsible for text translation, while the multi-task model is exposed to samples of more diverse complexity levels during training which damage its ability to preserve complexity.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 8: Evaluation on auxiliary tasks: Multitask models trained on both the translation and simplification dataset improves the performance for the task of English Simplification.', solve this question and give detail solutions. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich task has the highest BLEU score in the Machine Translation category?\n" }, { "role": "assistant", "content": "To find the highest BLEU score in the Machine Translation category, compare the BLEU scores for 'Translate', 'Translate and Simplify', and 'All Tasks'. The highest score is 29.09 for 'Translate'. So, the answer is {\"answer\": \"Translate\"}." } ], "images": [ "scigen/train/2792.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Evaluation on auxiliary tasks: Multitask models trained on both the translation and simplification dataset improves the performance for the task of English Simplification." ], "reasoning": "To find the highest BLEU score in the Machine Translation category, compare the BLEU scores for 'Translate', 'Translate and Simplify', and 'All Tasks'. The highest score is 29.09 for 'Translate'.", "question": "Which task has the highest BLEU score in the Machine Translation category?", "answer": "So, the answer is {\"answer\": \"Translate\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Translate task in Machine Translation has the highest PCC score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the PCC scores for 'Translate', 'Translate and Simplify', and 'All Tasks' in the Machine Translation category. The highest PCC score is 0.769 for 'Translate'. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2792.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Evaluation on auxiliary tasks: Multitask models trained on both the translation and simplification dataset improves the performance for the task of English Simplification." ], "reasoning": "To verify this statement, compare the PCC scores for 'Translate', 'Translate and Simplify', and 'All Tasks' in the Machine Translation category. The highest PCC score is 0.769 for 'Translate'.", "statement": "The Translate task in Machine Translation has the highest PCC score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled '(a) Subtask A'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Combining both BERT- and CNN-based encoders improves performance, with CNN better for Subtask A and BiSRU better for Subtask B.Specifically, we compare JESSI-A with the same model, but without the CNN-based encoder, without the BERT-based encoder, and with the CNN sentence encoder of the BERT-based encoder replaced with the BiSRU variant. We also compare JESSI-B with the same model, but without the CNN-based encoder, without the BERT-based encoder, without the domain adversarial training module, and with the BiSRU sentence encoder of the BERT-based encoder replaced with the CNN variant. The ablation studies show several observations. First, jointly combining both BERT- and CNN-based encoders help improve the performance on both subtasks. Second, the more effective sentence encoder for the BERT-based encoder (i.e., CNN versus BiSRU) differs for each subtask; the CNN variant is better for Subtask A, while the BiSRU variant is better for Subtask B. Finally, the domain adversarial training module is very crucial in achieving a significant increase in performance." } ], "images": [ "scigen/train/2793.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) Subtask A" ], "reasoning": "Combining both BERT- and CNN-based encoders improves performance, with CNN better for Subtask A and BiSRU better for Subtask B.", "description": "Specifically, we compare JESSI-A with the same model, but without the CNN-based encoder, without the BERT-based encoder, and with the CNN sentence encoder of the BERT-based encoder replaced with the BiSRU variant. We also compare JESSI-B with the same model, but without the CNN-based encoder, without the BERT-based encoder, without the domain adversarial training module, and with the BiSRU sentence encoder of the BERT-based encoder replaced with the CNN variant. The ablation studies show several observations. First, jointly combining both BERT- and CNN-based encoders help improve the performance on both subtasks. Second, the more effective sentence encoder for the BERT-based encoder (i.e., CNN versus BiSRU) differs for each subtask; the CNN variant is better for Subtask A, while the BiSRU variant is better for Subtask B. Finally, the domain adversarial training module is very crucial in achieving a significant increase in performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of '(a) Subtask A' and proceed to solve the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the F-Score of the model when the BERT-based encoder is replaced with the CNN variant?\n" }, { "role": "assistant", "content": "The table shows the F-Score for different model variations. The F-Score when the BERT-based encoder is replaced with the CNN variant is 83.89. So, the answer is {\"answer\": \"83.89\"}" } ], "images": [ "scigen/train/2793.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) Subtask A" ], "reasoning": "The table shows the F-Score for different model variations. The F-Score when the BERT-based encoder is replaced with the CNN variant is 83.89.", "question": "What is the F-Score of the model when the BERT-based encoder is replaced with the CNN variant?", "answer": "So, the answer is {\"answer\": \"83.89\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe model without the CNN-based encoder has a higher F-Score than the model with the BiSRU variant.\n" }, { "role": "assistant", "content": "The table shows that the F-Score for the model without the CNN-based encoder is 85.14, while the F-Score for the model with the BiSRU variant is 86.01. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2793.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) Subtask A" ], "reasoning": "The table shows that the F-Score for the model without the CNN-based encoder is 85.14, while the F-Score for the model with the BiSRU variant is 86.01. Therefore, the statement is refuted.", "statement": "The model without the CNN-based encoder has a higher F-Score than the model with the BiSRU variant.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title '(b) Subtask B', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Combining both BERT- and CNN-based encoders improves performance, with the CNN variant being better for Subtask A and the BiSRU variant better for Subtask B. The domain adversarial training module significantly boosts performance.Specifically, we compare JESSI-A with the same model, but without the CNN-based encoder, without the BERT-based encoder, and with the CNN sentence encoder of the BERT-based encoder replaced with the BiSRU variant. We also compare JESSI-B with the same model, but without the CNN-based encoder, without the BERT-based encoder, without the domain adversarial training module, and with the BiSRU sentence encoder of the BERT-based encoder replaced with the CNN variant. The ablation studies show several observations. First, jointly combining both BERT- and CNN-based encoders help improve the performance on both subtasks. Second, the more effective sentence encoder for the BERT-based encoder (i.e., CNN versus BiSRU) differs for each subtask; the CNN variant is better for Subtask A, while the BiSRU variant is better for Subtask B. Finally, the domain adversarial training module is very crucial in achieving a significant increase in performance." } ], "images": [ "scigen/train/2794.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) Subtask B" ], "reasoning": "Combining both BERT- and CNN-based encoders improves performance, with the CNN variant being better for Subtask A and the BiSRU variant better for Subtask B. The domain adversarial training module significantly boosts performance.", "description": "Specifically, we compare JESSI-A with the same model, but without the CNN-based encoder, without the BERT-based encoder, and with the CNN sentence encoder of the BERT-based encoder replaced with the BiSRU variant. We also compare JESSI-B with the same model, but without the CNN-based encoder, without the BERT-based encoder, without the domain adversarial training module, and with the BiSRU sentence encoder of the BERT-based encoder replaced with the CNN variant. The ablation studies show several observations. First, jointly combining both BERT- and CNN-based encoders help improve the performance on both subtasks. Second, the more effective sentence encoder for the BERT-based encoder (i.e., CNN versus BiSRU) differs for each subtask; the CNN variant is better for Subtask A, while the BiSRU variant is better for Subtask B. Finally, the domain adversarial training module is very crucial in achieving a significant increase in performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about '(b) Subtask B', solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the F-Score of the JESSI-B model without the domain adversarial training module?\n" }, { "role": "assistant", "content": "The table shows the F-Score for JESSI-B and its variants. The F-Score for JESSI-B without the domain adversarial training module is listed as 47.48. Thus, the answer is {\"answer\": \"47.48\"}." } ], "images": [ "scigen/train/2794.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) Subtask B" ], "reasoning": "The table shows the F-Score for JESSI-B and its variants. The F-Score for JESSI-B without the domain adversarial training module is listed as 47.48.", "question": "What is the F-Score of the JESSI-B model without the domain adversarial training module?", "answer": "Thus, the answer is {\"answer\": \"47.48\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The JESSI-B model with the CNN sentence encoder of the BERT-based encoder replaced with the BiSRU variant has an F-Score of 81.13., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe JESSI-B model with the CNN sentence encoder of the BERT-based encoder replaced with the BiSRU variant has an F-Score of 81.13.\n" }, { "role": "assistant", "content": "The table lists the F-Score for the JESSI-B model with the CNN sentence encoder of the BERT-based encoder replaced with the BiSRU variant as 81.13. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2794.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) Subtask B" ], "reasoning": "The table lists the F-Score for the JESSI-B model with the CNN sentence encoder of the BERT-based encoder replaced with the BiSRU variant as 81.13.", "statement": "The JESSI-B model with the CNN sentence encoder of the BERT-based encoder replaced with the BiSRU variant has an F-Score of 81.13.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named '(a) Subtask A'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "JESSI-A ranks second with an F-Score of 77.78%, while JESSI-B, which was not submitted, achieves an F-Score of 79.59% on the official test set.Overall, JESSI-A ranks second out of 33 official submissions with an F-Score of 77.78%. Although we were not able to submit JESSI-B during the submission phase, JESSI-B achieves an F-Score of 79.59% on the official test set. This performance is similar to the performance of the model that obtained sixth place in the competition. We emphasize that JESSI does not use any labeled and external data for Subtask B, and thus is just exposed to the hotels domain using the available unlabeled trial dataset, containing 808 data instances. We expect the model to perform better when additional data from the hotels domain." } ], "images": [ "scigen/train/2795.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) Subtask A" ], "reasoning": "JESSI-A ranks second with an F-Score of 77.78%, while JESSI-B, which was not submitted, achieves an F-Score of 79.59% on the official test set.", "description": "Overall, JESSI-A ranks second out of 33 official submissions with an F-Score of 77.78%. Although we were not able to submit JESSI-B during the submission phase, JESSI-B achieves an F-Score of 79.59% on the official test set. This performance is similar to the performance of the model that obtained sixth place in the competition. We emphasize that JESSI does not use any labeled and external data for Subtask B, and thus is just exposed to the hotels domain using the available unlabeled trial dataset, containing 808 data instances. We expect the model to perform better when additional data from the hotels domain.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about '(a) Subtask A', solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the F-Score of the model that ranks first, and how does it compare to JESSI-A?\n" }, { "role": "assistant", "content": "The table shows that the model OleNet ranks first with an F-Score of 78.12%. Comparing this to JESSI-A's F-Score of 77.78%, OleNet has a higher F-Score. So, the answer is {\"answer\": \"78.12\"}" } ], "images": [ "scigen/train/2795.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) Subtask A" ], "reasoning": "The table shows that the model OleNet ranks first with an F-Score of 78.12%. Comparing this to JESSI-A's F-Score of 77.78%, OleNet has a higher F-Score.", "question": "What is the F-Score of the model that ranks first, and how does it compare to JESSI-A?", "answer": "So, the answer is {\"answer\": \"78.12\"}" }, { "messages": [ { "role": "user", "content": "JESSI-A has a higher F-Score than OleNet. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nJESSI-A has a higher F-Score than OleNet.\n" }, { "role": "assistant", "content": "The table shows that JESSI-A has an F-Score of 77.78%, while OleNet has an F-Score of 78.12%. Therefore, JESSI-A does not have a higher F-Score than OleNet. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2795.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) Subtask A" ], "reasoning": "The table shows that JESSI-A has an F-Score of 77.78%, while OleNet has an F-Score of 78.12%. Therefore, JESSI-A does not have a higher F-Score than OleNet.", "statement": "JESSI-A has a higher F-Score than OleNet.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named '(b) Subtask B'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "JESSI-A ranks second with an F-Score of 77.78%, while JESSI-B, which was not submitted, achieves an F-Score of 79.59%, similar to the sixth place model.Overall, JESSI-A ranks second out of 33 official submissions with an F-Score of 77.78%. Although we were not able to submit JESSI-B during the submission phase, JESSI-B achieves an F-Score of 79.59% on the official test set. This performance is similar to the performance of the model that obtained sixth place in the competition. We emphasize that JESSI does not use any labeled and external data for Subtask B, and thus is just exposed to the hotels domain using the available unlabeled trial dataset, containing 808 data instances. We expect the model to perform better when additional data from the hotels domain." } ], "images": [ "scigen/train/2796.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) Subtask B" ], "reasoning": "JESSI-A ranks second with an F-Score of 77.78%, while JESSI-B, which was not submitted, achieves an F-Score of 79.59%, similar to the sixth place model.", "description": "Overall, JESSI-A ranks second out of 33 official submissions with an F-Score of 77.78%. Although we were not able to submit JESSI-B during the submission phase, JESSI-B achieves an F-Score of 79.59% on the official test set. This performance is similar to the performance of the model that obtained sixth place in the competition. We emphasize that JESSI does not use any labeled and external data for Subtask B, and thus is just exposed to the hotels domain using the available unlabeled trial dataset, containing 808 data instances. We expect the model to perform better when additional data from the hotels domain.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of '(b) Subtask B'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the F-Score of the model that ranks second in the competition?\n" }, { "role": "assistant", "content": "The table shows that the model OleNet ranks second with an F-Score of 85.79%. So, the answer is {\"answer\": \"85.79%\"}" } ], "images": [ "scigen/train/2796.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) Subtask B" ], "reasoning": "The table shows that the model OleNet ranks second with an F-Score of 85.79%.", "question": "What is the F-Score of the model that ranks second in the competition?", "answer": "So, the answer is {\"answer\": \"85.79%\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: JESSI-B ranks fourth in the competition., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nJESSI-B ranks fourth in the competition.\n" }, { "role": "assistant", "content": "The table shows that JESSI-B has an F-Score of 79.59%, but it does not provide a specific rank for JESSI-B. The description states that JESSI-B's performance is similar to the sixth place model, not fourth. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2796.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) Subtask B" ], "reasoning": "The table shows that JESSI-B has an F-Score of 79.59%, but it does not provide a specific rank for JESSI-B. The description states that JESSI-B's performance is similar to the sixth place model, not fourth.", "statement": "JESSI-B ranks fourth in the competition.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Attribute values for the texts of Section 4.2.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Graduate students extracted 24 personality attributes from texts and scored each text on these attributes using Figure Eight, resulting in a vector representation for each text.Having collected the data, we turned to extract commonsensical personality attributes from the texts. In service of that, two graduate students read the provided texts and extracted attributes that were considered appropriate as descriptors of the writers. In total, they highlighted 24 attributes, including adventurous, competitive, cynical, introvert, kind, passionate, pessimistic and rational, among others Then, we turned to scoring each text with a number in {0,1,…5} for each of these attributes, corresponding to the degree to which the text is affiliated with that attribute. We used Figure Eight (previously known as Crowd Flower) taking into account the necessary differences between human judgments when it comes to personality questions. Finally, the vector representation of each text was set to the vector whose coordinates correspond to the attributes, and the value in each entry is the average of the scores collected for this text-attribute pair. The author of the first text, for example, is most associated with attributes such as adventurous, friendly, kind, lightmineded, optimistic, passionate, and sincere. Naturally, the other authors that did not write about journeys, are not judged to be as adventurous as the author of the first text. Yet, the author of the fourth text, that discusses their personal journey in the career path, is also assigned a relatively high adventurous score." } ], "images": [ "scigen/train/2797.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Attribute values for the texts of Section 4.2." ], "reasoning": "Graduate students extracted 24 personality attributes from texts and scored each text on these attributes using Figure Eight, resulting in a vector representation for each text.", "description": "Having collected the data, we turned to extract commonsensical personality attributes from the texts. In service of that, two graduate students read the provided texts and extracted attributes that were considered appropriate as descriptors of the writers. In total, they highlighted 24 attributes, including adventurous, competitive, cynical, introvert, kind, passionate, pessimistic and rational, among others Then, we turned to scoring each text with a number in {0,1,…5} for each of these attributes, corresponding to the degree to which the text is affiliated with that attribute. We used Figure Eight (previously known as Crowd Flower) taking into account the necessary differences between human judgments when it comes to personality questions. Finally, the vector representation of each text was set to the vector whose coordinates correspond to the attributes, and the value in each entry is the average of the scores collected for this text-attribute pair. The author of the first text, for example, is most associated with attributes such as adventurous, friendly, kind, lightmineded, optimistic, passionate, and sincere. Naturally, the other authors that did not write about journeys, are not judged to be as adventurous as the author of the first text. Yet, the author of the fourth text, that discusses their personal journey in the career path, is also assigned a relatively high adventurous score.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 1: Attribute values for the texts of Section 4.2.'. Solve the following question based on the table.\n\nQuestion: Which text is most associated with the attribute 'adventurous'?\nLet's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The description mentions that the author of the first text is most associated with attributes such as adventurous, friendly, kind, light-minded, optimistic, passionate, and sincere. So, the answer is {\"answer\": \"Text #1\"}." } ], "images": [ "scigen/train/2797.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Attribute values for the texts of Section 4.2." ], "reasoning": "The description mentions that the author of the first text is most associated with attributes such as adventurous, friendly, kind, light-minded, optimistic, passionate, and sincere.", "question": "Which text is most associated with the attribute 'adventurous'?", "answer": "So, the answer is {\"answer\": \"Text #1\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe author of the fourth text is assigned a relatively high adventurous score.\n" }, { "role": "assistant", "content": "The description states that the author of the fourth text, which discusses their personal journey in the career path, is also assigned a relatively high adventurous score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2797.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Attribute values for the texts of Section 4.2." ], "reasoning": "The description states that the author of the fourth text, which discusses their personal journey in the career path, is also assigned a relatively high adventurous score.", "statement": "The author of the fourth text is assigned a relatively high adventurous score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 6: Experimental results of baseline methods on MatInf-C in terms of accuracy. †: Character-based models.'.\n" }, { "role": "assistant", "content": "DPCNN outperforms other baselines on MatInf-C-Topic and MatInf-C-Age, with an accuracy of 91.02 on the latter. MTF-S2S shows improved performance when trained on multiple tasks.Classification. On the tougher MatInf-C-Topic, language models prominently outperform other baselines. Among non-LM neural networks, DPCNN dpcnn, which has the deepest architecture and the most parameters, outperforms other baselines with a considerable margin. On MatInf-C-Age, which is a smaller dataset with fewer classes, DPCNN outperforms all other baselines including language models with an accuracy of 91.02. To analyze, this task has fewer training samples, which is in favor of a model with moderate parameter numbers instead of huge parameter numbers as in language models. Also, the task is relatively easier due to the class number, which makes the advantage of language models more trivial. For the multi-task baseline, MTF-S2S shows a satisfying performance on both MatInf-C-Age and MatInf-C-Topic, outperforming the same model which is only trained on the single task by 0.14 and 0.19 in terms of accuracy. Notably, BERT-of-Theseus theseus has a satisfying performance compressing the fine-tuned BERT to smaller models." } ], "images": [ "scigen/train/2798.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Experimental results of baseline methods on MatInf-C in terms of accuracy. †: Character-based models." ], "reasoning": "DPCNN outperforms other baselines on MatInf-C-Topic and MatInf-C-Age, with an accuracy of 91.02 on the latter. MTF-S2S shows improved performance when trained on multiple tasks.", "description": "Classification. On the tougher MatInf-C-Topic, language models prominently outperform other baselines. Among non-LM neural networks, DPCNN dpcnn, which has the deepest architecture and the most parameters, outperforms other baselines with a considerable margin. On MatInf-C-Age, which is a smaller dataset with fewer classes, DPCNN outperforms all other baselines including language models with an accuracy of 91.02. To analyze, this task has fewer training samples, which is in favor of a model with moderate parameter numbers instead of huge parameter numbers as in language models. Also, the task is relatively easier due to the class number, which makes the advantage of language models more trivial. For the multi-task baseline, MTF-S2S shows a satisfying performance on both MatInf-C-Age and MatInf-C-Topic, outperforming the same model which is only trained on the single task by 0.14 and 0.19 in terms of accuracy. Notably, BERT-of-Theseus theseus has a satisfying performance compressing the fine-tuned BERT to smaller models.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 6: Experimental results of baseline methods on MatInf-C in terms of accuracy. †: Character-based models.', solve this question and give detail solutions. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich method has the highest accuracy on the MatInf-C-Age dataset?\n" }, { "role": "assistant", "content": "By examining the AGE column in the table, DPCNN has the highest accuracy with a value of 91.02. So, the answer is {\"answer\": \"DPCNN\"}." } ], "images": [ "scigen/train/2798.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Experimental results of baseline methods on MatInf-C in terms of accuracy. †: Character-based models." ], "reasoning": "By examining the AGE column in the table, DPCNN has the highest accuracy with a value of 91.02.", "question": "Which method has the highest accuracy on the MatInf-C-Age dataset?", "answer": "So, the answer is {\"answer\": \"DPCNN\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nMTF-S2S outperforms all other baselines on the MatInf-C-Topic dataset.\n" }, { "role": "assistant", "content": "The table shows that MTF-S2S has an accuracy of 63.59 on MatInf-C-Topic, which is lower than the accuracy of BERT (66.95) and DPCNN (65.92). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2798.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Experimental results of baseline methods on MatInf-C in terms of accuracy. †: Character-based models." ], "reasoning": "The table shows that MTF-S2S has an accuracy of 63.59 on MatInf-C-Topic, which is lower than the accuracy of BERT (66.95) and DPCNN (65.92).", "statement": "MTF-S2S outperforms all other baselines on the MatInf-C-Topic dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: Comparison of summarization datasets. “#Token” indicates the average token numbers of a document and a summary for each dataset.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table provides information on various datasets, including their language, domain, number of documents, and token counts per document and summary.In MatInf-Summ, question description can be seen as an extended and specific version of the question itself, containing more detailed background information with respect to the question. Besides, the question itself is often a well-formed interrogative sentence rather than extracted phrases. Our task is to generate the question from the corresponding description (i.e., D\\rightarrow Q). Note that our task itself can support many meaningful real-world applications, e.g., generating an informative title for user-generated content (UGC). Also, there is only one public dataset for summarization in Chinese to date. Our dataset can be used to verify the effectiveness of existing models and eliminate the overfitting bias caused by evaluation on merely one dataset." } ], "images": [ "scigen/train/2799.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison of summarization datasets. “#Token” indicates the average token numbers of a document and a summary for each dataset." ], "reasoning": "The table provides information on various datasets, including their language, domain, number of documents, and token counts per document and summary.", "description": "In MatInf-Summ, question description can be seen as an extended and specific version of the question itself, containing more detailed background information with respect to the question. Besides, the question itself is often a well-formed interrogative sentence rather than extracted phrases. Our task is to generate the question from the corresponding description (i.e., D\\rightarrow Q). Note that our task itself can support many meaningful real-world applications, e.g., generating an informative title for user-generated content (UGC). Also, there is only one public dataset for summarization in Chinese to date. Our dataset can be used to verify the effectiveness of existing models and eliminate the overfitting bias caused by evaluation on merely one dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 5: Comparison of summarization datasets. “#Token” indicates the average token numbers of a document and a summary for each dataset.' in mind, break down and resolve the question below step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich dataset has the highest number of tokens per document?\n" }, { "role": "assistant", "content": "To find the dataset with the highest number of tokens per document, compare the values in the '# Token Doc.' column. The highest value is 6914, which corresponds to the arXiv dataset. So, the answer is {\"answer\": \"arXiv\"}." } ], "images": [ "scigen/train/2799.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison of summarization datasets. “#Token” indicates the average token numbers of a document and a summary for each dataset." ], "reasoning": "To find the dataset with the highest number of tokens per document, compare the values in the '# Token Doc.' column. The highest value is 6914, which corresponds to the arXiv dataset.", "question": "Which dataset has the highest number of tokens per document?", "answer": "So, the answer is {\"answer\": \"arXiv\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The dataset with the highest number of documents is Gigawords. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe dataset with the highest number of documents is Gigawords.\n" }, { "role": "assistant", "content": "To verify this statement, compare the values in the '# Doc' column. The highest value is 4.02M, which corresponds to the Gigawords dataset. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2799.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison of summarization datasets. “#Token” indicates the average token numbers of a document and a summary for each dataset." ], "reasoning": "To verify this statement, compare the values in the '# Doc' column. The highest value is 4.02M, which corresponds to the Gigawords dataset.", "statement": "The dataset with the highest number of documents is Gigawords.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 7: Experimental results of baseline methods on MatInf-QA.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Seq2Seq with Attention outperforms the retrieval-based baseline by 2.56 in ROUGE-L, and MTF-S2S outperforms its single-task version by 0.74 in ROUGE-L.Question Answering. The high scores of Best Passage (maximum possible performance) indicate that using training data as a document set is completely feasible. Seq2Seq with Attention outperforms the retrieval-based baseline by a margin of 2.56 in terms of ROUGE-L. It suggests that a generation-based neural network can effectively learn from multiple relevant samples and generalize. Besides, since we do the matching between each question and every entry within the same class in the training set, the inference of BERT Matching takes quite a long time. Similar to MS-MARCO msmarco, it is possible to use a search engine (e.g., Elastic Search) to pre-filter the documents and reduce the computational cost. Meanwhile, MTF-S2S is effective on QA task and outperforms its single-task version by 0.74 on ROUGE-L." } ], "images": [ "scigen/train/2800.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Experimental results of baseline methods on MatInf-QA." ], "reasoning": "Seq2Seq with Attention outperforms the retrieval-based baseline by 2.56 in ROUGE-L, and MTF-S2S outperforms its single-task version by 0.74 in ROUGE-L.", "description": "Question Answering. The high scores of Best Passage (maximum possible performance) indicate that using training data as a document set is completely feasible. Seq2Seq with Attention outperforms the retrieval-based baseline by a margin of 2.56 in terms of ROUGE-L. It suggests that a generation-based neural network can effectively learn from multiple relevant samples and generalize. Besides, since we do the matching between each question and every entry within the same class in the training set, the inference of BERT Matching takes quite a long time. Similar to MS-MARCO msmarco, it is possible to use a search engine (e.g., Elastic Search) to pre-filter the documents and reduce the computational cost. Meanwhile, MTF-S2S is effective on QA task and outperforms its single-task version by 0.74 on ROUGE-L.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 7: Experimental results of baseline methods on MatInf-QA.', answer the following question. Give detailed solution consisting of each step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich method has the highest ROUGE-L score among the non-upper bound methods?\n" }, { "role": "assistant", "content": "By comparing the ROUGE-L scores of all methods except the upper bound, MTF-S2S has the highest score of 14.26. So, the answer is {\"answer\": \"MTF-S2S\"}." } ], "images": [ "scigen/train/2800.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Experimental results of baseline methods on MatInf-QA." ], "reasoning": "By comparing the ROUGE-L scores of all methods except the upper bound, MTF-S2S has the highest score of 14.26.", "question": "Which method has the highest ROUGE-L score among the non-upper bound methods?", "answer": "So, the answer is {\"answer\": \"MTF-S2S\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Seq2Seq with Attention has a higher ROUGE-L score than BERT Matching., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSeq2Seq with Attention has a higher ROUGE-L score than BERT Matching.\n" }, { "role": "assistant", "content": "The table shows that Seq2Seq with Attention has a ROUGE-L score of 13.34, while BERT Matching has a score of 10.78. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2800.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Experimental results of baseline methods on MatInf-QA." ], "reasoning": "The table shows that Seq2Seq with Attention has a ROUGE-L score of 13.34, while BERT Matching has a score of 10.78.", "statement": "Seq2Seq with Attention has a higher ROUGE-L score than BERT Matching.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 8: Experimental results of baseline methods on CNN / DM cnndm, LCSTS lcsts, and MatInf-Summ.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "BertAbs significantly outperforms other models on MatInf-Summ due to its pretraining and BERT model capacity, while there is a notable difference in performance between extractive and abstractive methods on datasets of different genres.Summarization. By comparing the performance of two basic baselines, TextRank textrank and Seq2Seq+Att s2satt, we can see an obvious difference in performance between extractive and abstractive methods on datasets of different genres. BertAbs bertabs, the powerful BERT-based model, significantly outperforms all other baselines on MatInf-Summ thanks to its exploitation of pretraining and the capacity of a BERT model. For MTF-S2S, it outperforms the single-task counterpart by 4.73 on ROUGE-L." } ], "images": [ "scigen/train/2801.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Experimental results of baseline methods on CNN / DM cnndm, LCSTS lcsts, and MatInf-Summ." ], "reasoning": "BertAbs significantly outperforms other models on MatInf-Summ due to its pretraining and BERT model capacity, while there is a notable difference in performance between extractive and abstractive methods on datasets of different genres.", "description": "Summarization. By comparing the performance of two basic baselines, TextRank textrank and Seq2Seq+Att s2satt, we can see an obvious difference in performance between extractive and abstractive methods on datasets of different genres. BertAbs bertabs, the powerful BERT-based model, significantly outperforms all other baselines on MatInf-Summ thanks to its exploitation of pretraining and the capacity of a BERT model. For MTF-S2S, it outperforms the single-task counterpart by 4.73 on ROUGE-L.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 8: Experimental results of baseline methods on CNN / DM cnndm, LCSTS lcsts, and MatInf-Summ.' and proceed to solve the following question in a stepwise manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest ROUGE-L score on the MatInf-Summ dataset?\n" }, { "role": "assistant", "content": "By examining the MatInf-Summ R-L column, BertAbs has the highest score of 55.93. So, the answer is {\"answer\": \"BertAbs\"}" } ], "images": [ "scigen/train/2801.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Experimental results of baseline methods on CNN / DM cnndm, LCSTS lcsts, and MatInf-Summ." ], "reasoning": "By examining the MatInf-Summ R-L column, BertAbs has the highest score of 55.93.", "question": "Which model has the highest ROUGE-L score on the MatInf-Summ dataset?", "answer": "So, the answer is {\"answer\": \"BertAbs\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Seq2Seq+Att has a higher ROUGE-1 score than TextRank on the CNN/DM dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nSeq2Seq+Att has a higher ROUGE-1 score than TextRank on the CNN/DM dataset.\n" }, { "role": "assistant", "content": "By comparing the CNN/DM R-1 scores, TextRank has a score of 37.72, while Seq2Seq+Att has a score of 31.33, which is lower. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2801.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Experimental results of baseline methods on CNN / DM cnndm, LCSTS lcsts, and MatInf-Summ." ], "reasoning": "By comparing the CNN/DM R-1 scores, TextRank has a score of 37.72, while Seq2Seq+Att has a score of 31.33, which is lower.", "statement": "Seq2Seq+Att has a higher ROUGE-1 score than TextRank on the CNN/DM dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table VI: Precision, recall and F1 scores on the binary classification of pseudo-alignments on the held-out test set.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Greedy threshold search on context vectors outperforms machine learning models in F1 score for parallel sentence identification, especially for fr–en, but combined features improve overall performance in multilingual settings.Notice that a greedy threshold search is better than any of the machine learning counterparts when only context vectors are used, but differences are not significant. The greedy search on the context vector similarities gives a better F1 on the held-out test set than an ensemble of SVM and GB operating only the set of additional features with almost no knowledge of semantics. As we argued in the previous section, translations and non-translations are clearly differentiated by a cosine similarity of the context vectors for these languages pairs, as the difference between the mean similarities of translations and unrelated texts is much higher than its uncertainty (Δtr−ur=0.36±0.14 for de–en, and 0.41±0.14 for fr–en). This clear distinction in the similarities is translated into an F1=98.2% in the task of parallel sentence identification. By contrast, the set of complementary features depends on the language pair and shows a performance drop for de–en. For this reason, the results in the multilingual setting are always worse than in the bilingual one. This fact is inherited in the all scenario, where the classification for the joint corpus obtains F1=98.9%, which is lower than the one obtained for fr–en alone (F1=99.3%). Nevertheless, semantic and syntactic similarity features are complementary and the combination of all similarity measures slightly improves precision, recall and F1 in the multilingual setting. It is worth noting the high recall derived from the context vectors, which reaches 100% for fr–en and falls to 98.1% for the joint data, being still 6.5 points higher than for the comp features." } ], "images": [ "scigen/train/2802.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table VI: Precision, recall and F1 scores on the binary classification of pseudo-alignments on the held-out test set." ], "reasoning": "Greedy threshold search on context vectors outperforms machine learning models in F1 score for parallel sentence identification, especially for fr–en, but combined features improve overall performance in multilingual settings.", "description": "Notice that a greedy threshold search is better than any of the machine learning counterparts when only context vectors are used, but differences are not significant. The greedy search on the context vector similarities gives a better F1 on the held-out test set than an ensemble of SVM and GB operating only the set of additional features with almost no knowledge of semantics. As we argued in the previous section, translations and non-translations are clearly differentiated by a cosine similarity of the context vectors for these languages pairs, as the difference between the mean similarities of translations and unrelated texts is much higher than its uncertainty (Δtr−ur=0.36±0.14 for de–en, and 0.41±0.14 for fr–en). This clear distinction in the similarities is translated into an F1=98.2% in the task of parallel sentence identification. By contrast, the set of complementary features depends on the language pair and shows a performance drop for de–en. For this reason, the results in the multilingual setting are always worse than in the bilingual one. This fact is inherited in the all scenario, where the classification for the joint corpus obtains F1=98.9%, which is lower than the one obtained for fr–en alone (F1=99.3%). Nevertheless, semantic and syntactic similarity features are complementary and the combination of all similarity measures slightly improves precision, recall and F1 in the multilingual setting. It is worth noting the high recall derived from the context vectors, which reaches 100% for fr–en and falls to 98.1% for the joint data, being still 6.5 points higher than for the comp features.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table VI: Precision, recall and F1 scores on the binary classification of pseudo-alignments on the held-out test set.', solve the subsequent question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhat is the F1 score for the joint corpus when using an ensemble of all features?\n" }, { "role": "assistant", "content": "The table shows that the F1 score for the joint corpus using an ensemble of all features is 98.9%. So, the answer is {\"answer\": \"98.9%\"}" } ], "images": [ "scigen/train/2802.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table VI: Precision, recall and F1 scores on the binary classification of pseudo-alignments on the held-out test set." ], "reasoning": "The table shows that the F1 score for the joint corpus using an ensemble of all features is 98.9%.", "question": "What is the F1 score for the joint corpus when using an ensemble of all features?", "answer": "So, the answer is {\"answer\": \"98.9%\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The recall for fr–en using context vectors is higher than the recall for the joint corpus using all features. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe recall for fr–en using context vectors is higher than the recall for the joint corpus using all features.\n" }, { "role": "assistant", "content": "The table shows that the recall for fr–en using context vectors is 100.0%, while the recall for the joint corpus using all features is 99.1%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2802.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table VI: Precision, recall and F1 scores on the binary classification of pseudo-alignments on the held-out test set." ], "reasoning": "The table shows that the recall for fr–en using context vectors is 100.0%, while the recall for the joint corpus using all features is 99.1%.", "statement": "The recall for fr–en using context vectors is higher than the recall for the joint corpus using all features.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table V: Comparison of the Pearson correlation obtained by context vectors at different epochs of the training and word embeddings on the test set of the “Semantic Textual Similarity Task’’ at SemEval 2017.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The correlation increases with the number of iterations for all language pairs and systems, and higher-dimensional word embeddings outperform the 300-dimensional ones.Observing the evolution through training by taking a shot at four different points, the correlation increases with the number of iterations for all the language pairs and systems. In this fine-grained task, the internal representation improves in parallel to the translation quality. As before, the system with words is better than the one with lemmas with the only exception of ar–en. A reason could be the low initial similarity for semantically equivalent sentences (trad) for this pair with the S1-w system (0.26±0.10). The initial point seems to be relevant for the final performance; i.e. the relative improvement from epoch to epoch for all language pairs is very similar, but the final performance seems to be conditioned to the quality of the initial representations. The performance in the monolingual tracks is always higher than in the cross-language ones, and the difference at the end of the training is proportional to the difference at the beginning. The study of how a proper initialisation of the input word embeddings could alleviate this disparity deserves future research. We estimated 300 (WE-d300-nmt) and 1024 (WE-d1024-nmt) dimensional word embeddings with the same corpus used to train the NMT systems (adding monolingual corpora did not improve the results). When sentences belong to different languages, we translate them into en and use the embeddings estimated for en. As done with context vectors, the similarity between sentences is assessed by the cosine of the summed embeddings. Higher-dimensional word embeddings outperform the 300-dimensional ones in the task." } ], "images": [ "scigen/train/2803.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table V: Comparison of the Pearson correlation obtained by context vectors at different epochs of the training and word embeddings on the test set of the “Semantic Textual Similarity Task’’ at SemEval 2017." ], "reasoning": "The correlation increases with the number of iterations for all language pairs and systems, and higher-dimensional word embeddings outperform the 300-dimensional ones.", "description": "Observing the evolution through training by taking a shot at four different points, the correlation increases with the number of iterations for all the language pairs and systems. In this fine-grained task, the internal representation improves in parallel to the translation quality. As before, the system with words is better than the one with lemmas with the only exception of ar–en. A reason could be the low initial similarity for semantically equivalent sentences (trad) for this pair with the S1-w system (0.26±0.10). The initial point seems to be relevant for the final performance; i.e. the relative improvement from epoch to epoch for all language pairs is very similar, but the final performance seems to be conditioned to the quality of the initial representations. The performance in the monolingual tracks is always higher than in the cross-language ones, and the difference at the end of the training is proportional to the difference at the beginning. The study of how a proper initialisation of the input word embeddings could alleviate this disparity deserves future research. We estimated 300 (WE-d300-nmt) and 1024 (WE-d1024-nmt) dimensional word embeddings with the same corpus used to train the NMT systems (adding monolingual corpora did not improve the results). When sentences belong to different languages, we translate them into en and use the embeddings estimated for en. As done with context vectors, the similarity between sentences is assessed by the cosine of the summed embeddings. Higher-dimensional word embeddings outperform the 300-dimensional ones in the task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table V: Comparison of the Pearson correlation obtained by context vectors at different epochs of the training and word embeddings on the test set of the “Semantic Textual Similarity Task’’ at SemEval 2017.\nQuestion: What is the correlation value for the S1-w-2.0Ep system in track5?\nSolve the above question based on the table information. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the correlation value for the S1-w-2.0Ep system in track5, locate the row labeled 'S1-w-2.0Ep' and the column labeled 'track5'. The value at the intersection is 0.76. So, the answer is {\"answer\": \"0.76\"}." } ], "images": [ "scigen/train/2803.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table V: Comparison of the Pearson correlation obtained by context vectors at different epochs of the training and word embeddings on the test set of the “Semantic Textual Similarity Task’’ at SemEval 2017." ], "reasoning": "To find the correlation value for the S1-w-2.0Ep system in track5, locate the row labeled 'S1-w-2.0Ep' and the column labeled 'track5'. The value at the intersection is 0.76.", "question": "What is the correlation value for the S1-w-2.0Ep system in track5?", "answer": "So, the answer is {\"answer\": \"0.76\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The initial correlation value for the S1-l-0.1Ep system in track2 is higher than in track1., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe initial correlation value for the S1-l-0.1Ep system in track2 is higher than in track1.\n" }, { "role": "assistant", "content": "To verify this statement, compare the initial correlation values for the S1-l-0.1Ep system in track2 and track1. The value for track2 is 0.32 and for track1 is 0.32. Since they are equal, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2803.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table V: Comparison of the Pearson correlation obtained by context vectors at different epochs of the training and word embeddings on the test set of the “Semantic Textual Similarity Task’’ at SemEval 2017." ], "reasoning": "To verify this statement, compare the initial correlation values for the S1-l-0.1Ep system in track2 and track1. The value for track2 is 0.32 and for track1 is 0.32. Since they are equal, the statement is refuted.", "statement": "The initial correlation value for the S1-l-0.1Ep system in track2 is higher than in track1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Joint goal and slot accuracy of different models in the all-domain setting of MultiWOZ 2.1'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The MA-DST model achieves the highest performance in both joint goal accuracy and average slot accuracy, with each additional layer contributing to progressively higher accuracy.We compare the the accuracy of MA-DST with the TRADE baseline and four additional ablation variants of our model. Our full proposed MA-DST model achieves the highest performance on joint goal accuracy and average slot accuracy, surpassing the current state-of-the-art performance. Each of the additional layers of self and cross-attention contribute to progressively higher accuracy for both metrics." } ], "images": [ "scigen/train/2804.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Joint goal and slot accuracy of different models in the all-domain setting of MultiWOZ 2.1" ], "reasoning": "The MA-DST model achieves the highest performance in both joint goal accuracy and average slot accuracy, with each additional layer contributing to progressively higher accuracy.", "description": "We compare the the accuracy of MA-DST with the TRADE baseline and four additional ablation variants of our model. Our full proposed MA-DST model achieves the highest performance on joint goal accuracy and average slot accuracy, surpassing the current state-of-the-art performance. Each of the additional layers of self and cross-attention contribute to progressively higher accuracy for both metrics.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Joint goal and slot accuracy of different models in the all-domain setting of MultiWOZ 2.1', answer the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model achieves the highest Multi Domain Joint accuracy?\n" }, { "role": "assistant", "content": "The table lists the Multi Domain Joint accuracy for various models. The Ensemble model has the highest accuracy at 51.88. So, the answer is {\"answer\": \"Ensemble\"}" } ], "images": [ "scigen/train/2804.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Joint goal and slot accuracy of different models in the all-domain setting of MultiWOZ 2.1" ], "reasoning": "The table lists the Multi Domain Joint accuracy for various models. The Ensemble model has the highest accuracy at 51.88.", "question": "Which model achieves the highest Multi Domain Joint accuracy?", "answer": "So, the answer is {\"answer\": \"Ensemble\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Baseline (TRADE) model has the highest Multi Domain Slot accuracy. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Baseline (TRADE) model has the highest Multi Domain Slot accuracy.\n" }, { "role": "assistant", "content": "The table shows that the Baseline (TRADE) model has a Multi Domain Slot accuracy of 96.62, which is lower than the accuracies of the other models listed. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2804.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Joint goal and slot accuracy of different models in the all-domain setting of MultiWOZ 2.1" ], "reasoning": "The table shows that the Baseline (TRADE) model has a Multi Domain Slot accuracy of 96.62, which is lower than the accuracies of the other models listed.", "statement": "The Baseline (TRADE) model has the highest Multi Domain Slot accuracy.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: F1 Score and Average Slot Accuracy Domain Wise', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The model performs worst in terms of F1-Score for the Taxi domain, but achieves high average slot accuracy due to the prevalence of 'none' slots. Conversely, slots like restaurant-bookday and hotel-bookstay achieve over 99% accuracy.In this section we analyze the errors being made by the model on MultiWoz 2.1 dataset. In terms of F1-Score, the model performs worse for Taxi domain. The average slot accuracy for Taxi domain is high because a vast number of taxi domain’s slots are ‘‘none\" (i.e. not present in the dialog), which model easily identifies. These slots are difficult to predict for the model because, unlike categorical slots, these slots can take on a large number of possible values and are more likely to encounter unseen values. On the other end of spectrum, we have slots like restaurant-bookday, hotel-bookstay, hotel-bookday, and train-day, for which the model is able to achieve more than 99% in terms of average slot accuracy. As expected, most of the top-perfoming slots are categorical, i.e. they can take only a small number of different values from a pre-defined set." } ], "images": [ "scigen/train/2805.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: F1 Score and Average Slot Accuracy Domain Wise" ], "reasoning": "The model performs worst in terms of F1-Score for the Taxi domain, but achieves high average slot accuracy due to the prevalence of 'none' slots. Conversely, slots like restaurant-bookday and hotel-bookstay achieve over 99% accuracy.", "description": "In this section we analyze the errors being made by the model on MultiWoz 2.1 dataset. In terms of F1-Score, the model performs worse for Taxi domain. The average slot accuracy for Taxi domain is high because a vast number of taxi domain’s slots are ‘‘none\" (i.e. not present in the dialog), which model easily identifies. These slots are difficult to predict for the model because, unlike categorical slots, these slots can take on a large number of possible values and are more likely to encounter unseen values. On the other end of spectrum, we have slots like restaurant-bookday, hotel-bookstay, hotel-bookday, and train-day, for which the model is able to achieve more than 99% in terms of average slot accuracy. As expected, most of the top-perfoming slots are categorical, i.e. they can take only a small number of different values from a pre-defined set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 5: F1 Score and Average Slot Accuracy Domain Wise' and proceed to solve the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich domain has the lowest F1-Score and what is its slot accuracy?\n" }, { "role": "assistant", "content": "The Taxi domain has the lowest F1-Score of 0.71. Its slot accuracy is 97.87%. So, the answer is {\"answer\": \"Taxi with 97.87% slot accuracy\"}." } ], "images": [ "scigen/train/2805.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: F1 Score and Average Slot Accuracy Domain Wise" ], "reasoning": "The Taxi domain has the lowest F1-Score of 0.71. Its slot accuracy is 97.87%.", "question": "Which domain has the lowest F1-Score and what is its slot accuracy?", "answer": "So, the answer is {\"answer\": \"Taxi with 97.87% slot accuracy\"}." }, { "messages": [ { "role": "user", "content": "The model achieves over 99% slot accuracy for the hotel domain. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model achieves over 99% slot accuracy for the hotel domain.\n" }, { "role": "assistant", "content": "The table shows that the slot accuracy for the hotel domain is 97.11%, which is less than 99%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2805.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: F1 Score and Average Slot Accuracy Domain Wise" ], "reasoning": "The table shows that the slot accuracy for the hotel domain is 97.11%, which is less than 99%.", "statement": "The model achieves over 99% slot accuracy for the hotel domain.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 6: Davies-Bouldin (DB) index for different types of features used in TDNN training. The DB index is calculated on lattice-based features produced by the corresponding model.'.\n" }, { "role": "assistant", "content": "The adapted GMMD features (BN⊕i-vectors⊕GMMD) yield the best results with the smallest DB index values across all states, confirming that clusters are better for adapted features.(State 0, 1, 2). That confirms the fact that the clusters are better for adapted features. The acoustic model with the adapted GMMD features (BN⊕i-vectors⊕GMMD) shows the best result (the smallest value of DB index)." } ], "images": [ "scigen/train/2806.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Davies-Bouldin (DB) index for different types of features used in TDNN training. The DB index is calculated on lattice-based features produced by the corresponding model." ], "reasoning": "The adapted GMMD features (BN⊕i-vectors⊕GMMD) yield the best results with the smallest DB index values across all states, confirming that clusters are better for adapted features.", "description": "(State 0, 1, 2). That confirms the fact that the clusters are better for adapted features. The acoustic model with the adapted GMMD features (BN⊕i-vectors⊕GMMD) shows the best result (the smallest value of DB index).", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 6: Davies-Bouldin (DB) index for different types of features used in TDNN training. The DB index is calculated on lattice-based features produced by the corresponding model.' and methodically tackle the question that follows. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich feature set has the smallest DB index value in State 1?\n" }, { "role": "assistant", "content": "By comparing the DB index values for State 1 across all feature sets, the smallest value is found in the BN⊕i-vectors⊕GMMD feature set. So, the answer is {\"answer\": \"BN⊕i-vectors⊕GMMD\"}." } ], "images": [ "scigen/train/2806.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Davies-Bouldin (DB) index for different types of features used in TDNN training. The DB index is calculated on lattice-based features produced by the corresponding model." ], "reasoning": "By comparing the DB index values for State 1 across all feature sets, the smallest value is found in the BN⊕i-vectors⊕GMMD feature set.", "question": "Which feature set has the smallest DB index value in State 1?", "answer": "So, the answer is {\"answer\": \"BN⊕i-vectors⊕GMMD\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe MFCC feature set has the highest DB index value in State 0.\n" }, { "role": "assistant", "content": "By comparing the DB index values for State 0 across all feature sets, the highest value is found in the MFCC feature set. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2806.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Davies-Bouldin (DB) index for different types of features used in TDNN training. The DB index is calculated on lattice-based features produced by the corresponding model." ], "reasoning": "By comparing the DB index values for State 0 across all feature sets, the highest value is found in the MFCC feature set.", "statement": "The MFCC feature set has the highest DB index value in State 0.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Discourse sense classification results, measured by F1, in comparison with the most competitive systems from the shared task.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our system excelled on the blind test set for non-explicit relations but lagged in explicit relations, indicating a need for additional features.Evaluation was performed using the evaluation script provided by the conll16 task organizers. Our system was particularly competitive on the blind test set. (The best performance on non-explicit relations on the blind test set was from ttr, but this system did not attempt to classify explicit relations, and so did not obtain an overall score for all relations.) This suggests that our approach suffered less from overfitting to the dev data. On the other hand, our system’s performance on explicit relations was further behind the best systems, suggesting the need for additional features to handle this case." } ], "images": [ "scigen/train/2807.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Discourse sense classification results, measured by F1, in comparison with the most competitive systems from the shared task." ], "reasoning": "Our system excelled on the blind test set for non-explicit relations but lagged in explicit relations, indicating a need for additional features.", "description": "Evaluation was performed using the evaluation script provided by the conll16 task organizers. Our system was particularly competitive on the blind test set. (The best performance on non-explicit relations on the blind test set was from ttr, but this system did not attempt to classify explicit relations, and so did not obtain an overall score for all relations.) This suggests that our approach suffered less from overfitting to the dev data. On the other hand, our system’s performance on explicit relations was further behind the best systems, suggesting the need for additional features to handle this case.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 3: Discourse sense classification results, measured by F1, in comparison with the most competitive systems from the shared task.\n\nQuestion: Which system had the highest performance on the blind test set for non-explicit relations?\n" }, { "role": "assistant", "content": "By examining the 'Blind Non-expl.' column, we see that 'gtlnlp (this paper)' had the highest score of 0.368. So, the answer is {\"answer\": \"gtlnlp (this paper)\"}" } ], "images": [ "scigen/train/2807.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Discourse sense classification results, measured by F1, in comparison with the most competitive systems from the shared task." ], "reasoning": "By examining the 'Blind Non-expl.' column, we see that 'gtlnlp (this paper)' had the highest score of 0.368.", "question": "Which system had the highest performance on the blind test set for non-explicit relations?", "answer": "So, the answer is {\"answer\": \"gtlnlp (this paper)\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe system 'tbmihaylov' had the highest performance on the blind test set for explicit relations.\n" }, { "role": "assistant", "content": "By examining the 'Blind Explicit' column, we see that 'tbmihaylov' had the highest score of 0.782. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2807.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Discourse sense classification results, measured by F1, in comparison with the most competitive systems from the shared task." ], "reasoning": "By examining the 'Blind Explicit' column, we see that 'tbmihaylov' had the highest score of 0.782.", "statement": "The system 'tbmihaylov' had the highest performance on the blind test set for explicit relations.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: Hyperparameters selected by Spearmint from the provided ranges, for non-explicit discourse relations', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The algorithm samples hyperparameters to optimize cross-entropy performance, with only twenty samples due to time constraints.This algorithm samples from the space of hyperparameters, while trying to learn a function from hyperparameters to overall performance. We use the cross-entropy (and not the F1) as the overall performance objective; due to the time cost for training the model, we could run only twenty samples, which took several days. More samples may have yielded better performance, but this was not possible under the time constraints of the shared task." } ], "images": [ "scigen/train/2808.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Hyperparameters selected by Spearmint from the provided ranges, for non-explicit discourse relations" ], "reasoning": "The algorithm samples hyperparameters to optimize cross-entropy performance, with only twenty samples due to time constraints.", "description": "This algorithm samples from the space of hyperparameters, while trying to learn a function from hyperparameters to overall performance. We use the cross-entropy (and not the F1) as the overall performance objective; due to the time cost for training the model, we could run only twenty samples, which took several days. More samples may have yielded better performance, but this was not possible under the time constraints of the shared task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: Table 1: Hyperparameters selected by Spearmint from the provided ranges, for non-explicit discourse relations\nQuestion: What is the best number of hidden nodes for lstm3?\n" }, { "role": "assistant", "content": "The table lists the best number of hidden nodes for each hyperparameter. For lstm3, the best number of hidden nodes is 263. So, the answer is {\"answer\": \"263\"}" } ], "images": [ "scigen/train/2808.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Hyperparameters selected by Spearmint from the provided ranges, for non-explicit discourse relations" ], "reasoning": "The table lists the best number of hidden nodes for each hyperparameter. For lstm3, the best number of hidden nodes is 263.", "question": "What is the best number of hidden nodes for lstm3?", "answer": "So, the answer is {\"answer\": \"263\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The best number of hidden nodes for dense2 is 75. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe best number of hidden nodes for dense2 is 75.\n" }, { "role": "assistant", "content": "The table shows that the best number of hidden nodes for dense2 is 69, not 75. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2808.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Hyperparameters selected by Spearmint from the provided ranges, for non-explicit discourse relations" ], "reasoning": "The table shows that the best number of hidden nodes for dense2 is 69, not 75.", "statement": "The best number of hidden nodes for dense2 is 75.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Performance of MAP scores on the MATERIAL analysis set and Q1 queries.'.\n" }, { "role": "assistant", "content": "BERT outperforms other models in both the phrase query subset and the entire query set, despite being trained with single-word queries.In the table, we report MAP scores on the phrase query subset and the entire query set separately, to see how our model trained with single-word queries performs on query phrases. In the model training stage, QRANN model is the only model that is trained with the query phrases directly, all other models (including BERT) in this experiment will split a multi-word query or query phrase into multiple single-word queries. Surprisingly, the BERT MAP scores for the phrase query subset is the best compared with the performances of other approaches. It shows that BERT model can produce better relevance model for single-word queries and foreign-language sentence. The table also shows that BERT outperforms the other neural approaches over the entire query set." } ], "images": [ "scigen/train/2809.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of MAP scores on the MATERIAL analysis set and Q1 queries." ], "reasoning": "BERT outperforms other models in both the phrase query subset and the entire query set, despite being trained with single-word queries.", "description": "In the table, we report MAP scores on the phrase query subset and the entire query set separately, to see how our model trained with single-word queries performs on query phrases. In the model training stage, QRANN model is the only model that is trained with the query phrases directly, all other models (including BERT) in this experiment will split a multi-word query or query phrase into multiple single-word queries. Surprisingly, the BERT MAP scores for the phrase query subset is the best compared with the performances of other approaches. It shows that BERT model can produce better relevance model for single-word queries and foreign-language sentence. The table also shows that BERT outperforms the other neural approaches over the entire query set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 3: Performance of MAP scores on the MATERIAL analysis set and Q1 queries.', answer the question below by showing each progressive step toward the answer. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model has the highest MAP score for the entire query set?\n" }, { "role": "assistant", "content": "The table shows the MAP scores for different models on the entire query set. By comparing the scores, Prob. CLIR has the highest score of 61.2. So, the answer is {\"answer\": \"Prob. CLIR\"}" } ], "images": [ "scigen/train/2809.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of MAP scores on the MATERIAL analysis set and Q1 queries." ], "reasoning": "The table shows the MAP scores for different models on the entire query set. By comparing the scores, Prob. CLIR has the highest score of 61.2.", "question": "Which model has the highest MAP score for the entire query set?", "answer": "So, the answer is {\"answer\": \"Prob. CLIR\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nQRANN has the lowest MAP score for the entire query set.\n" }, { "role": "assistant", "content": "The table shows the MAP scores for different models on the entire query set. By comparing the scores, Dot-Product has the lowest score of 39.2, not QRANN. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2809.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of MAP scores on the MATERIAL analysis set and Q1 queries." ], "reasoning": "The table shows the MAP scores for different models on the entire query set. By comparing the scores, Dot-Product has the lowest score of 39.2, not QRANN.", "statement": "QRANN has the lowest MAP score for the entire query set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Performance of classification accuracy on the generated query-sentence pairs from the bitexts of the MATERIAL analysis set. The first column in the confusion matrix corresponds to the positive class (i.e., relevant query-sentence pair) while the second the column is the negative class.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The BERT model achieves the highest accuracy and significantly improves the classification of relevant query-sentence pairs while matching the performance of classifying irrelevant pairs.The QRANN and Dot-product models are trained using the same CLIR training data used to train BERT model described earlier. The CLIR BERT model achieves the best result compared to other two neural models. From the confusion matrix in the table, BERT significantly improves the performance of classifying relevant query-sentence pairs (i.e., true positives), while matching the performance of classifying irrelevant query-sentence pairs (i.e., true negatives)." } ], "images": [ "scigen/train/2810.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of classification accuracy on the generated query-sentence pairs from the bitexts of the MATERIAL analysis set. The first column in the confusion matrix corresponds to the positive class (i.e., relevant query-sentence pair) while the second the column is the negative class." ], "reasoning": "The BERT model achieves the highest accuracy and significantly improves the classification of relevant query-sentence pairs while matching the performance of classifying irrelevant pairs.", "description": "The QRANN and Dot-product models are trained using the same CLIR training data used to train BERT model described earlier. The CLIR BERT model achieves the best result compared to other two neural models. From the confusion matrix in the table, BERT significantly improves the performance of classifying relevant query-sentence pairs (i.e., true positives), while matching the performance of classifying irrelevant query-sentence pairs (i.e., true negatives).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Performance of classification accuracy on the generated query-sentence pairs from the bitexts of the MATERIAL analysis set. The first column in the confusion matrix corresponds to the positive class (i.e., relevant query-sentence pair) while the second the column is the negative class.', solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model achieves the highest accuracy and how does it perform in classifying relevant and irrelevant query-sentence pairs?\n" }, { "role": "assistant", "content": "The BERT model achieves the highest accuracy of 95.3%. It significantly improves the classification of relevant query-sentence pairs (true positives) with a confusion matrix value of 0.02 and matches the performance of classifying irrelevant pairs (true negatives) with a confusion matrix value of 0.98. So, the answer is {\"answer\": \"BERT\"}" } ], "images": [ "scigen/train/2810.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of classification accuracy on the generated query-sentence pairs from the bitexts of the MATERIAL analysis set. The first column in the confusion matrix corresponds to the positive class (i.e., relevant query-sentence pair) while the second the column is the negative class." ], "reasoning": "The BERT model achieves the highest accuracy of 95.3%. It significantly improves the classification of relevant query-sentence pairs (true positives) with a confusion matrix value of 0.02 and matches the performance of classifying irrelevant pairs (true negatives) with a confusion matrix value of 0.98.", "question": "Which model achieves the highest accuracy and how does it perform in classifying relevant and irrelevant query-sentence pairs?", "answer": "So, the answer is {\"answer\": \"BERT\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe QRANN model has the highest accuracy among the models.\n" }, { "role": "assistant", "content": "The table shows that the BERT model has the highest accuracy of 95.3%, while the QRANN model has an accuracy of 87.3%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2810.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of classification accuracy on the generated query-sentence pairs from the bitexts of the MATERIAL analysis set. The first column in the confusion matrix corresponds to the positive class (i.e., relevant query-sentence pair) while the second the column is the negative class." ], "reasoning": "The table shows that the BERT model has the highest accuracy of 95.3%, while the QRANN model has an accuracy of 87.3%.", "statement": "The QRANN model has the highest accuracy among the models.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: MQWV scores on the Lithuanian analysis and development sets and Q1 queries.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The BERT model performs best on speech sets compared to non-neural and other neural CLIR models, while its performance on text sets is comparable to non-neural systems and better than other neural systems.We compare BERT models with other CLIR models in terms of MQWV scores. The first row in the table shows the best results of non-neural CLIR models, which are probabilistic CLIR model and probabilistic occurrence model. In this table, we separate the results based on the type of source documents: text or speech. The results of the BERT model on the speech sets are the best, compared with the non-neural CLIR systems, QRANN and Dot-product models, while the results on the text sets are comparable to those from the non-neural systems, and better than the other neural systems." } ], "images": [ "scigen/train/2811.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: MQWV scores on the Lithuanian analysis and development sets and Q1 queries." ], "reasoning": "The BERT model performs best on speech sets compared to non-neural and other neural CLIR models, while its performance on text sets is comparable to non-neural systems and better than other neural systems.", "description": "We compare BERT models with other CLIR models in terms of MQWV scores. The first row in the table shows the best results of non-neural CLIR models, which are probabilistic CLIR model and probabilistic occurrence model. In this table, we separate the results based on the type of source documents: text or speech. The results of the BERT model on the speech sets are the best, compared with the non-neural CLIR systems, QRANN and Dot-product models, while the results on the text sets are comparable to those from the non-neural systems, and better than the other neural systems.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 4: MQWV scores on the Lithuanian analysis and development sets and Q1 queries.', solve the following question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest MQWV score on the Development Set Text?\n" }, { "role": "assistant", "content": "To find the highest MQWV score on the Development Set Text, compare the scores in the Development Set Text column. The highest score is 68.8, which belongs to the Best non-neural system. So, the answer is {\"answer\": \"Best non-neural system\"}" } ], "images": [ "scigen/train/2811.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: MQWV scores on the Lithuanian analysis and development sets and Q1 queries." ], "reasoning": "To find the highest MQWV score on the Development Set Text, compare the scores in the Development Set Text column. The highest score is 68.8, which belongs to the Best non-neural system.", "question": "Which model has the highest MQWV score on the Development Set Text?", "answer": "So, the answer is {\"answer\": \"Best non-neural system\"}" }, { "messages": [ { "role": "user", "content": "The BERT model outperforms all other models on the Analysis Set Speech. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe BERT model outperforms all other models on the Analysis Set Speech.\n" }, { "role": "assistant", "content": "To verify this statement, compare the MQWV scores of all models on the Analysis Set Speech. The BERT model has a score of 65.4, which is higher than Dot-Product (60.4) and QRANN (58.4), but lower than the Best non-neural system (63.3). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2811.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: MQWV scores on the Lithuanian analysis and development sets and Q1 queries." ], "reasoning": "To verify this statement, compare the MQWV scores of all models on the Analysis Set Speech. The BERT model has a score of 65.4, which is higher than Dot-Product (60.4) and QRANN (58.4), but lower than the Best non-neural system (63.3).", "statement": "The BERT model outperforms all other models on the Analysis Set Speech.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 5: Development set accuracy of varying models by the hidden size (denoted as H) and the number of LSTM layers (denoted as L). Note that the model in bold face is the best one.'.\n" }, { "role": "assistant", "content": "The model with a single layer and 64 hidden units shows the best performance, while the number of layers does not consistently affect accuracy.We observe that the bigger the hidden size is, the higher the accuracy is, as expected. The number of layers, however, is not the case. The model of a single layer with 64 hidden units shows the best performance." } ], "images": [ "scigen/train/2812.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Development set accuracy of varying models by the hidden size (denoted as H) and the number of LSTM layers (denoted as L). Note that the model in bold face is the best one." ], "reasoning": "The model with a single layer and 64 hidden units shows the best performance, while the number of layers does not consistently affect accuracy.", "description": "We observe that the bigger the hidden size is, the higher the accuracy is, as expected. The number of layers, however, is not the case. The model of a single layer with 64 hidden units shows the best performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 5: Development set accuracy of varying models by the hidden size (denoted as H) and the number of LSTM layers (denoted as L). Note that the model in bold face is the best one.', and sequentially solve the question, writing out each step of your reasoning process. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model configuration shows the highest accuracy?\n" }, { "role": "assistant", "content": "By examining the table, the highest accuracy is found in the model with 1 layer and 64 hidden units, which has an accuracy of 97.15±0.09. So, the answer is {\"answer\": \"97.15±0.09\"}." } ], "images": [ "scigen/train/2812.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Development set accuracy of varying models by the hidden size (denoted as H) and the number of LSTM layers (denoted as L). Note that the model in bold face is the best one." ], "reasoning": "By examining the table, the highest accuracy is found in the model with 1 layer and 64 hidden units, which has an accuracy of 97.15±0.09.", "question": "Which model configuration shows the highest accuracy?", "answer": "So, the answer is {\"answer\": \"97.15±0.09\"}." }, { "messages": [ { "role": "user", "content": "The model with 3 layers and 16 hidden units has the lowest accuracy. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model with 3 layers and 16 hidden units has the lowest accuracy.\n" }, { "role": "assistant", "content": "The table shows that the model with 3 layers and 16 hidden units has an accuracy of 85.30±4.50, which is the lowest among all configurations. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2812.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Development set accuracy of varying models by the hidden size (denoted as H) and the number of LSTM layers (denoted as L). Note that the model in bold face is the best one." ], "reasoning": "The table shows that the model with 3 layers and 16 hidden units has an accuracy of 85.30±4.50, which is the lowest among all configurations.", "statement": "The model with 3 layers and 16 hidden units has the lowest accuracy.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Test set accuracy of Chinese g2p systems'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our model outperforms all systems except Chinese BERT, showing comparable performance despite having fewer parameters.Results Our model slightly underperforms Chinese BERT and outperforms all the other systems by large margin. That our simple neural model shows comparable performance to the heavy BERT model, which has more than 102M parameters, tells us two things. One is that our model is simple but powerful enough. Another is that it is not too simple for the naïve majority vote to beat." } ], "images": [ "scigen/train/2813.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Test set accuracy of Chinese g2p systems" ], "reasoning": "Our model outperforms all systems except Chinese BERT, showing comparable performance despite having fewer parameters.", "description": "Results Our model slightly underperforms Chinese BERT and outperforms all the other systems by large margin. That our simple neural model shows comparable performance to the heavy BERT model, which has more than 102M parameters, tells us two things. One is that our model is simple but powerful enough. Another is that it is not too simple for the naïve majority vote to beat.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. The question is related to the given table titled 'Table 6: Test set accuracy of Chinese g2p systems'.\n\nQuestion: Which system has the second highest test accuracy?\n" }, { "role": "assistant", "content": "By examining the test accuracy values, Chinese BERT has the highest accuracy at 97.85, and our model has the second highest at 97.31. So, the answer is {\"answer\": \"Ours\"}." } ], "images": [ "scigen/train/2813.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Test set accuracy of Chinese g2p systems" ], "reasoning": "By examining the test accuracy values, Chinese BERT has the highest accuracy at 97.85, and our model has the second highest at 97.31.", "question": "Which system has the second highest test accuracy?", "answer": "So, the answer is {\"answer\": \"Ours\"}." }, { "messages": [ { "role": "user", "content": "The majority vote system has a higher test accuracy than the pypinyin system. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe majority vote system has a higher test accuracy than the pypinyin system.\n" }, { "role": "assistant", "content": "The table shows that the majority vote system has a test accuracy of 92.08, while the pypinyin system has a test accuracy of 86.13. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2813.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Test set accuracy of Chinese g2p systems" ], "reasoning": "The table shows that the majority vote system has a test accuracy of 92.08, while the pypinyin system has a test accuracy of 86.13.", "statement": "The majority vote system has a higher test accuracy than the pypinyin system.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Precision, recall and semantic F1-score on CoNLL-2009 English in-domain data and Chinese test set.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Our model with the argument pruning layer improves F1 scores for English and Chinese, and with BERT, it achieves the best results on both benchmarks.Our baseline is a modification to the model of Cai et al. For English, our baseline gives slightly weaker performance than the work of Li et al. Our model with the proposed argument pruning layer (+ AP) brings absolute improvements of 0.35% and 0.5% F1 on English and Chinese, respectively, which is on par with the best published scores. Moreover, we introduce deep enhanced representation based on the argument pruning. Our model utilizing BERT (+ AP + BERT) achieves the new best results on English and Chinese benchmarks." } ], "images": [ "scigen/train/2814.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Precision, recall and semantic F1-score on CoNLL-2009 English in-domain data and Chinese test set." ], "reasoning": "Our model with the argument pruning layer improves F1 scores for English and Chinese, and with BERT, it achieves the best results on both benchmarks.", "description": "Our baseline is a modification to the model of Cai et al. For English, our baseline gives slightly weaker performance than the work of Li et al. Our model with the proposed argument pruning layer (+ AP) brings absolute improvements of 0.35% and 0.5% F1 on English and Chinese, respectively, which is on par with the best published scores. Moreover, we introduce deep enhanced representation based on the argument pruning. Our model utilizing BERT (+ AP + BERT) achieves the new best results on English and Chinese benchmarks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Precision, recall and semantic F1-score on CoNLL-2009 English in-domain data and Chinese test set.' to sequentially solve the question presented below. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the English F1 score of the model that achieves the new best results on both English and Chinese benchmarks?\n" }, { "role": "assistant", "content": "The description states that the model utilizing BERT (+ AP + BERT) achieves the new best results on English and Chinese benchmarks. According to the table, the English F1 score for + AP + BERT is 90.86. So, the answer is {\"answer\": \"90.86\"}." } ], "images": [ "scigen/train/2814.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Precision, recall and semantic F1-score on CoNLL-2009 English in-domain data and Chinese test set." ], "reasoning": "The description states that the model utilizing BERT (+ AP + BERT) achieves the new best results on English and Chinese benchmarks. According to the table, the English F1 score for + AP + BERT is 90.86.", "question": "What is the English F1 score of the model that achieves the new best results on both English and Chinese benchmarks?", "answer": "So, the answer is {\"answer\": \"90.86\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model with the proposed argument pruning layer (+ AP) achieves an English F1 score of 89.66.\n" }, { "role": "assistant", "content": "The description mentions that the model with the proposed argument pruning layer (+ AP) brings improvements. According to the table, the English F1 score for + AP is 89.66. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2814.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Precision, recall and semantic F1-score on CoNLL-2009 English in-domain data and Chinese test set." ], "reasoning": "The description mentions that the model with the proposed argument pruning layer (+ AP) brings improvements. According to the table, the English F1 score for + AP is 89.66.", "statement": "The model with the proposed argument pruning layer (+ AP) achieves an English F1 score of 89.66.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Semantic F1-score on CoNLL-2009 in-domain test set. The first row is the best result of CoNLL-2009 shared task Hajič et al. (2009). The previously best published results of Catalan and Japanese is from Zhao et al. (2009a), Chinese from Cai et al. (2018), Czech from Marcheggiani et al. (2017), English from Li et al. (2019), German from Roth and Lapata (2016) and Spanish from Kasai et al. (2019).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Our baseline model outperforms previous methods significantly in Catalan, Czech, Japanese, and Spanish, and the use of BERT further enhances performance across all languages.So far, the best previously reported results of Catalan, Japanese and Spanish are still from CoNLL-2009 shared task. Compared with previous methods, our baseline yields strong performance on all datasets except German. Especially for Catalan, Czech, Japanese and Spanish, our baseline performs better than existing methods with a large margin of 3.5% F1 on average. Nevertheless, applying our argument pruning to the strong syntax-agnostic baseline can still boost the model performance, which demonstrates the effectiveness of proposed method. On the other hand, it indicates that syntax is generally beneficial to multiple languages, and can enhance the multilingual SRL performance with effective syntactic integration. The use of contextualized word representation further improves model performance, which overwhelmingly outperforms previously published best results and achieves the new state of the art in multilingual SRL for the first time. Furthermore, we find that ELMo promotes the overall performance of SRL model, but BERT gives more significant performance increase than ELMo on all languages, which suggests that BERT is better at enriching contextual information. More interestingly, we observe that the performance gains from the proposed argument pruning method or these deep enhanced representations are relatively marginal on Japanese, one possible reason is the relatively small size of its training set. This observation also indicates that ELMo or BERT is more suitable for learning on large annotated corpus." } ], "images": [ "scigen/train/2815.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Semantic F1-score on CoNLL-2009 in-domain test set. The first row is the best result of CoNLL-2009 shared task Hajič et al. (2009). The previously best published results of Catalan and Japanese is from Zhao et al. (2009a), Chinese from Cai et al. (2018), Czech from Marcheggiani et al. (2017), English from Li et al. (2019), German from Roth and Lapata (2016) and Spanish from Kasai et al. (2019)." ], "reasoning": "Our baseline model outperforms previous methods significantly in Catalan, Czech, Japanese, and Spanish, and the use of BERT further enhances performance across all languages.", "description": "So far, the best previously reported results of Catalan, Japanese and Spanish are still from CoNLL-2009 shared task. Compared with previous methods, our baseline yields strong performance on all datasets except German. Especially for Catalan, Czech, Japanese and Spanish, our baseline performs better than existing methods with a large margin of 3.5% F1 on average. Nevertheless, applying our argument pruning to the strong syntax-agnostic baseline can still boost the model performance, which demonstrates the effectiveness of proposed method. On the other hand, it indicates that syntax is generally beneficial to multiple languages, and can enhance the multilingual SRL performance with effective syntactic integration. The use of contextualized word representation further improves model performance, which overwhelmingly outperforms previously published best results and achieves the new state of the art in multilingual SRL for the first time. Furthermore, we find that ELMo promotes the overall performance of SRL model, but BERT gives more significant performance increase than ELMo on all languages, which suggests that BERT is better at enriching contextual information. More interestingly, we observe that the performance gains from the proposed argument pruning method or these deep enhanced representations are relatively marginal on Japanese, one possible reason is the relatively small size of its training set. This observation also indicates that ELMo or BERT is more suitable for learning on large annotated corpus.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: 'Table 3: Semantic F1-score on CoNLL-2009 in-domain test set. The first row is the best result of CoNLL-2009 shared task Hajič et al. (2009). The previously best published results of Catalan and Japanese is from Zhao et al. (2009a), Chinese from Cai et al. (2018), Czech from Marcheggiani et al. (2017), English from Li et al. (2019), German from Roth and Lapata (2016) and Spanish from Kasai et al. (2019).'\nQuestion: Which model achieves the highest F1 score for Czech?\n" }, { "role": "assistant", "content": "The table shows that the highest F1 score for Czech is achieved by the model '+ AP + BERT' with a score of 89.66. So, the answer is {\"answer\": \"89.66\"}" } ], "images": [ "scigen/train/2815.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Semantic F1-score on CoNLL-2009 in-domain test set. The first row is the best result of CoNLL-2009 shared task Hajič et al. (2009). The previously best published results of Catalan and Japanese is from Zhao et al. (2009a), Chinese from Cai et al. (2018), Czech from Marcheggiani et al. (2017), English from Li et al. (2019), German from Roth and Lapata (2016) and Spanish from Kasai et al. (2019)." ], "reasoning": "The table shows that the highest F1 score for Czech is achieved by the model '+ AP + BERT' with a score of 89.66.", "question": "Which model achieves the highest F1 score for Czech?", "answer": "So, the answer is {\"answer\": \"89.66\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe best previously published result for English is higher than 90.\n" }, { "role": "assistant", "content": "The table indicates that the best previously published result for English is 90.4, which is higher than 90. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2815.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Semantic F1-score on CoNLL-2009 in-domain test set. The first row is the best result of CoNLL-2009 shared task Hajič et al. (2009). The previously best published results of Catalan and Japanese is from Zhao et al. (2009a), Chinese from Cai et al. (2018), Czech from Marcheggiani et al. (2017), English from Li et al. (2019), German from Roth and Lapata (2016) and Spanish from Kasai et al. (2019)." ], "reasoning": "The table indicates that the best previously published result for English is 90.4, which is higher than 90.", "statement": "The best previously published result for English is higher than 90.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Results of full end-to-end model. PD denotes the accuracy of predicate disambiguation. + AP represents biaffine SRL+Argument Pruning Layer, while the last row indicates biaffine SRL+Argument Pruning Layer+BERT.'.\n" }, { "role": "assistant", "content": "Our model using argument pruning and BERT achieves the highest scores on five benchmarks, improving both predicate disambiguation and role labeling performance.Compared to the baseline, our full end-to-end model (Biaffine SRL) yields slightly higher precision of predicate disambiguation as a whole, which gives rise to a corresponding gain of semantic F1. What is more, our model (using argument pruning and BERT) reaches the highest scores on the five benchmarks. Besides, experiments indicate that argument pruning promotes role labeling performance while BERT significantly improves the performance of predicate disambiguation." } ], "images": [ "scigen/train/2816.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of full end-to-end model. PD denotes the accuracy of predicate disambiguation. + AP represents biaffine SRL+Argument Pruning Layer, while the last row indicates biaffine SRL+Argument Pruning Layer+BERT." ], "reasoning": "Our model using argument pruning and BERT achieves the highest scores on five benchmarks, improving both predicate disambiguation and role labeling performance.", "description": "Compared to the baseline, our full end-to-end model (Biaffine SRL) yields slightly higher precision of predicate disambiguation as a whole, which gives rise to a corresponding gain of semantic F1. What is more, our model (using argument pruning and BERT) reaches the highest scores on the five benchmarks. Besides, experiments indicate that argument pruning promotes role labeling performance while BERT significantly improves the performance of predicate disambiguation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Results of full end-to-end model. PD denotes the accuracy of predicate disambiguation. + AP represents biaffine SRL+Argument Pruning Layer, while the last row indicates biaffine SRL+Argument Pruning Layer+BERT.\nQuestion: Which model achieves the highest F1 score for English?\nSolve the above question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By examining the table, the highest F1 score for English is achieved by the model '+ AP + BERT' with a score of 91.00. So, the answer is {\"answer\": \"91.00\"}" } ], "images": [ "scigen/train/2816.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of full end-to-end model. PD denotes the accuracy of predicate disambiguation. + AP represents biaffine SRL+Argument Pruning Layer, while the last row indicates biaffine SRL+Argument Pruning Layer+BERT." ], "reasoning": "By examining the table, the highest F1 score for English is achieved by the model '+ AP + BERT' with a score of 91.00.", "question": "Which model achieves the highest F1 score for English?", "answer": "So, the answer is {\"answer\": \"91.00\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Biaffine SRL model with argument pruning and BERT achieves the highest F1 score for German. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Biaffine SRL model with argument pruning and BERT achieves the highest F1 score for German.\n" }, { "role": "assistant", "content": "The table shows that the highest F1 score for German is 81.14, achieved by the '+ AP + BERT' model, which supports the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2816.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of full end-to-end model. PD denotes the accuracy of predicate disambiguation. + AP represents biaffine SRL+Argument Pruning Layer, while the last row indicates biaffine SRL+Argument Pruning Layer+BERT." ], "reasoning": "The table shows that the highest F1 score for German is 81.14, achieved by the '+ AP + BERT' model, which supports the statement.", "statement": "The Biaffine SRL model with argument pruning and BERT achieves the highest F1 score for German.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Parameters leading to the higher accuracy during the optimisation task.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The PV-DBOW architecture achieved the highest accuracy of 25.78%, while the PV-DM architecture reached 18.08%. Embedding vectors with dimensions greater than 256 improve accuracy, and a low alpha parameter (<0.01) leads to poor accuracy.Regarding the optimisation, 1,920 different models were trained and evaluated. First, the dm parameter highly affects the accuracy. Indeed, the PV-DBOW architecture looks more precise with a highest accuracy of 25.78%, while the PV-DM reached only 18.08% of common MeSH terms in average between query and top-close documents. Then, embedding vectors having large number of dimensions (>256) seem to lead to a better accuracy, for PV-DBOW at least. Finally, when set too low (<0.01), the alpha parameter leads to poor accuracy. The best combination of parameters, obtained thanks to the PV-DBOW architecture, was selected. The best parameters regarding the PV-DM, but having the same vector_size value, were also kept (13.30% of accuracy). The concatenation of models is thus possible without dimensions reduction, this method being promoted by Mikolov and Lee [le2014distributed]." } ], "images": [ "scigen/train/2817.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Parameters leading to the higher accuracy during the optimisation task." ], "reasoning": "The PV-DBOW architecture achieved the highest accuracy of 25.78%, while the PV-DM architecture reached 18.08%. Embedding vectors with dimensions greater than 256 improve accuracy, and a low alpha parameter (<0.01) leads to poor accuracy.", "description": "Regarding the optimisation, 1,920 different models were trained and evaluated. First, the dm parameter highly affects the accuracy. Indeed, the PV-DBOW architecture looks more precise with a highest accuracy of 25.78%, while the PV-DM reached only 18.08% of common MeSH terms in average between query and top-close documents. Then, embedding vectors having large number of dimensions (>256) seem to lead to a better accuracy, for PV-DBOW at least. Finally, when set too low (<0.01), the alpha parameter leads to poor accuracy. The best combination of parameters, obtained thanks to the PV-DBOW architecture, was selected. The best parameters regarding the PV-DM, but having the same vector_size value, were also kept (13.30% of accuracy). The concatenation of models is thus possible without dimensions reduction, this method being promoted by Mikolov and Lee [le2014distributed].", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 1: Parameters leading to the higher accuracy during the optimisation task.' to sequentially solve the question presented below. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the alpha parameter value for the PV-DM architecture with a vector size of 512?\n" }, { "role": "assistant", "content": "The table shows two rows with vector_size of 512. The row with dm=1 corresponds to the PV-DM architecture. The alpha parameter value for this row is 0.1. So, the answer is {\"answer\": \"0.1\"}." } ], "images": [ "scigen/train/2817.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Parameters leading to the higher accuracy during the optimisation task." ], "reasoning": "The table shows two rows with vector_size of 512. The row with dm=1 corresponds to the PV-DM architecture. The alpha parameter value for this row is 0.1.", "question": "What is the alpha parameter value for the PV-DM architecture with a vector size of 512?", "answer": "So, the answer is {\"answer\": \"0.1\"}." }, { "messages": [ { "role": "user", "content": "The PV-DBOW architecture with a vector size of 512 has an alpha parameter of 0.01. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe PV-DBOW architecture with a vector size of 512 has an alpha parameter of 0.01.\n" }, { "role": "assistant", "content": "The table shows that the row with dm=0 corresponds to the PV-DBOW architecture. This row has a vector_size of 512 and an alpha parameter of 0.01. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2817.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Parameters leading to the higher accuracy during the optimisation task." ], "reasoning": "The table shows that the row with dm=0 corresponds to the PV-DBOW architecture. This row has a vector_size of 512 and an alpha parameter of 0.01.", "statement": "The PV-DBOW architecture with a vector size of 512 has an alpha parameter of 0.01.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Number of instances in each dataset split.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The datasets were split into train, test, and validation sets using four different methods: random, lexical-full, lexical-mod, and lexical-head.To better test this hypothesis, we evaluate on 4 different splits of the datasets to train, test, and validation sets: (1) random, in a 75:20:5 ratio, (2) lexical-full, in which the train, test, and validation sets each consists of a distinct vocabulary. The split was suggested by \\newcitelevy-EtAl:2015:NAACL-HLT, and it randomly assigns words to distinct sets, such that for example, including travel guide in the train set promises that fishing guide would not be included in the test set, and the models do not benefit from memorizing that the head guide is always annotated as topic. Given that the split discards many NCs, we experimented with two additional splits: (3) lexical-mod split, in which the w1 words are unique in each set, and (4) lexical-head split, in which the w2 words are unique in each set." } ], "images": [ "scigen/train/2818.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Number of instances in each dataset split." ], "reasoning": "The datasets were split into train, test, and validation sets using four different methods: random, lexical-full, lexical-mod, and lexical-head.", "description": "To better test this hypothesis, we evaluate on 4 different splits of the datasets to train, test, and validation sets: (1) random, in a 75:20:5 ratio, (2) lexical-full, in which the train, test, and validation sets each consists of a distinct vocabulary. The split was suggested by \\newcitelevy-EtAl:2015:NAACL-HLT, and it randomly assigns words to distinct sets, such that for example, including travel guide in the train set promises that fishing guide would not be included in the test set, and the models do not benefit from memorizing that the head guide is always annotated as topic. Given that the split discards many NCs, we experimented with two additional splits: (3) lexical-mod split, in which the w1 words are unique in each set, and (4) lexical-head split, in which the w2 words are unique in each set.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 2: Number of instances in each dataset split.' in mind, break down and resolve the question below step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhat is the total number of training samples in the Tratz-fine dataset across all splits?\n" }, { "role": "assistant", "content": "To find the total number of training samples in the Tratz-fine dataset, sum the training samples from all splits: 4,730 (Lex full) + 9,185 (Lex head) + 9,783 (Lex mod) + 14,369 (Rand). So, the answer is {\"answer\": \"38,067\"}" } ], "images": [ "scigen/train/2818.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Number of instances in each dataset split." ], "reasoning": "To find the total number of training samples in the Tratz-fine dataset, sum the training samples from all splits: 4,730 (Lex full) + 9,185 (Lex head) + 9,783 (Lex mod) + 14,369 (Rand).", "question": "What is the total number of training samples in the Tratz-fine dataset across all splits?", "answer": "So, the answer is {\"answer\": \"38,067\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Tratz-coarse dataset has more training samples in the Lex head split than in the Rand split.\n" }, { "role": "assistant", "content": "The table shows that the Tratz-coarse dataset has 9,214 training samples in the Lex head split and 14,093 training samples in the Rand split, which means the Rand split has more training samples. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2818.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Number of instances in each dataset split." ], "reasoning": "The table shows that the Tratz-coarse dataset has 9,214 training samples in the Lex head split and 14,093 training samples in the Rand split, which means the Rand split has more training samples.", "statement": "The Tratz-coarse dataset has more training samples in the Lex head split than in the Rand split.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Top: Oracle SPICE@k and distinct n-grams on the COCO image captioning task at B=20. While we report SPICE, we observe similar trends in other metrics (reported in the supplement). Bottom: Breakdown of results by difficulty class, highlighting the relative improvement over BS.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "DBS outperforms BS in terms of distinct n-grams and Oracle SPICE scores, especially for complex images.Our method produces significantly more distinct n-grams (almost 300% increase in the number of 4-grams produced) as compared to BS. We also note that our method tends to produce slightly longer captions compared to beam search on average. Moreover, on the PASCAL-50S test split we observe that DBS finds more likely top-1 solutions on average – DBS obtains a maximum log-probability of -6.53 as against -6.91 got by BS of same beam width. While the performance of DBS is guaranteed to be better than a BS of size \\nicefracBG, this experimental evidence suggests that using DBS as a replacement to BS leads to better or at least comparable performance. Results by Image Complexity. For example, at Oracle Spice@20, DBS achieves significant improvements over BS of 0.67, 0.91, and 1.13 for Simple, Average, and Complex images respectively. While DBS improves over BS in all settings, complex images benefit even more from diversity-inducing inference than simple images." } ], "images": [ "scigen/train/2819.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Top: Oracle SPICE@k and distinct n-grams on the COCO image captioning task at B=20. While we report SPICE, we observe similar trends in other metrics (reported in the supplement). Bottom: Breakdown of results by difficulty class, highlighting the relative improvement over BS." ], "reasoning": "DBS outperforms BS in terms of distinct n-grams and Oracle SPICE scores, especially for complex images.", "description": "Our method produces significantly more distinct n-grams (almost 300% increase in the number of 4-grams produced) as compared to BS. We also note that our method tends to produce slightly longer captions compared to beam search on average. Moreover, on the PASCAL-50S test split we observe that DBS finds more likely top-1 solutions on average – DBS obtains a maximum log-probability of -6.53 as against -6.91 got by BS of same beam width. While the performance of DBS is guaranteed to be better than a BS of size \\nicefracBG, this experimental evidence suggests that using DBS as a replacement to BS leads to better or at least comparable performance. Results by Image Complexity. For example, at Oracle Spice@20, DBS achieves significant improvements over BS of 0.67, 0.91, and 1.13 for Simple, Average, and Complex images respectively. While DBS improves over BS in all settings, complex images benefit even more from diversity-inducing inference than simple images.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Top: Oracle SPICE@k and distinct n-grams on the COCO image captioning task at B=20. While we report SPICE, we observe similar trends in other metrics (reported in the supplement). Bottom: Breakdown of results by difficulty class, highlighting the relative improvement over BS.\nQuestion: What is the Oracle SPICE@k=20 score for DBS on the PASCAL-50S dataset?\nSolve the above question based on the table information. Let's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the Oracle SPICE@k=20 score for DBS on the PASCAL-50S dataset, locate the row labeled 'PASCAL-50S' and 'DBS' and then find the value under the 'Oracle SPICE@k=20' column. So, the answer is {\"answer\": \"9.293\"}" } ], "images": [ "scigen/train/2819.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Top: Oracle SPICE@k and distinct n-grams on the COCO image captioning task at B=20. While we report SPICE, we observe similar trends in other metrics (reported in the supplement). Bottom: Breakdown of results by difficulty class, highlighting the relative improvement over BS." ], "reasoning": "To find the Oracle SPICE@k=20 score for DBS on the PASCAL-50S dataset, locate the row labeled 'PASCAL-50S' and 'DBS' and then find the value under the 'Oracle SPICE@k=20' column.", "question": "What is the Oracle SPICE@k=20 score for DBS on the PASCAL-50S dataset?", "answer": "So, the answer is {\"answer\": \"9.293\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nDBS achieves a higher SPICE score than BS on the COCO dataset.\n" }, { "role": "assistant", "content": "Compare the SPICE scores for DBS and BS on the COCO dataset. DBS has a SPICE score of 16.783, while BS has a SPICE score of 16.27. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2819.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Top: Oracle SPICE@k and distinct n-grams on the COCO image captioning task at B=20. While we report SPICE, we observe similar trends in other metrics (reported in the supplement). Bottom: Breakdown of results by difficulty class, highlighting the relative improvement over BS." ], "reasoning": "Compare the SPICE scores for DBS and BS on the COCO dataset. DBS has a SPICE score of 16.783, while BS has a SPICE score of 16.27.", "statement": "DBS achieves a higher SPICE score than BS on the COCO dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Top: Oracle SPICE@k and distinct n-grams on the VQG task at B=20. Bottom: Results by difficulty class, highlighting the relative improvement over BS.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "DBS significantly outperforms baseline methods in both SPICE and diversity metrics, especially in tasks with more complex images.We find DBS significantly outperforms the baseline methods on this task both on standard metrics (SPICE) and measure of diversity. We also observe that gap between DBS and the baseline methods is more pronounced than in the captioning task and attribute this to the increased variety of possible visually grounded questions compared to captions which often describe only a few major salient objects. The general trend that more complex images benefit more from diverse decoding also persists in this setting." } ], "images": [ "scigen/train/2820.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Top: Oracle SPICE@k and distinct n-grams on the VQG task at B=20. Bottom: Results by difficulty class, highlighting the relative improvement over BS." ], "reasoning": "DBS significantly outperforms baseline methods in both SPICE and diversity metrics, especially in tasks with more complex images.", "description": "We find DBS significantly outperforms the baseline methods on this task both on standard metrics (SPICE) and measure of diversity. We also observe that gap between DBS and the baseline methods is more pronounced than in the captioning task and attribute this to the increased variety of possible visually grounded questions compared to captions which often describe only a few major salient objects. The general trend that more complex images benefit more from diverse decoding also persists in this setting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 2: Top: Oracle SPICE@k and distinct n-grams on the VQG task at B=20. Bottom: Results by difficulty class, highlighting the relative improvement over BS.', unfold the question presented below, detailing every step of your calculation. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich method has the highest Oracle SPICE@k @20 score?\n" }, { "role": "assistant", "content": "To find the method with the highest Oracle SPICE@k @20 score, compare the values in the Oracle SPICE@k @20 column. DBS has the highest score of 27.94. So, the answer is {\"answer\": \"DBS\"}." } ], "images": [ "scigen/train/2820.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Top: Oracle SPICE@k and distinct n-grams on the VQG task at B=20. Bottom: Results by difficulty class, highlighting the relative improvement over BS." ], "reasoning": "To find the method with the highest Oracle SPICE@k @20 score, compare the values in the Oracle SPICE@k @20 column. DBS has the highest score of 27.94.", "question": "Which method has the highest Oracle SPICE@k @20 score?", "answer": "So, the answer is {\"answer\": \"DBS\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Li & Jurafsky (2016) method has the highest Distinct n-Grams 4 score. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nLi & Jurafsky (2016) method has the highest Distinct n-Grams 4 score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Distinct n-Grams 4 scores for all methods. DBS has the highest score of 12.24, not Li & Jurafsky (2016). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2820.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Top: Oracle SPICE@k and distinct n-grams on the VQG task at B=20. Bottom: Results by difficulty class, highlighting the relative improvement over BS." ], "reasoning": "To verify this statement, compare the Distinct n-Grams 4 scores for all methods. DBS has the highest score of 12.24, not Li & Jurafsky (2016).", "statement": "Li & Jurafsky (2016) method has the highest Distinct n-Grams 4 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: The performance of speech recognition and speech translation with different word latency in wait-k policy on the En-Zh Dev set and Test set.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The interactive learning model performs best when k=3, balancing both speech translation and recognition tasks.We then investigate the effect of word latency in wait-k policy on En-Zh language pairs. It indicates that the speech translation task can become easier if more source information from the same modality is given. However, as k increases, it will affect the performance of speech recognition task. If k→∞, this model degrades to the analogous two-stage model. Then the speech translation task can obtain the information from complete transcribed sentence, while speech recognition task can not utilize any information from translations. The interactive learning model has the best performance when k=3." } ], "images": [ "scigen/train/2821.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The performance of speech recognition and speech translation with different word latency in wait-k policy on the En-Zh Dev set and Test set." ], "reasoning": "The interactive learning model performs best when k=3, balancing both speech translation and recognition tasks.", "description": "We then investigate the effect of word latency in wait-k policy on En-Zh language pairs. It indicates that the speech translation task can become easier if more source information from the same modality is given. However, as k increases, it will affect the performance of speech recognition task. If k→∞, this model degrades to the analogous two-stage model. Then the speech translation task can obtain the information from complete transcribed sentence, while speech recognition task can not utilize any information from translations. The interactive learning model has the best performance when k=3.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: Table 3: The performance of speech recognition and speech translation with different word latency in wait-k policy on the En-Zh Dev set and Test set.\nQuestion: What is the Test BLEU score when k=3, and why is this significant?\n" }, { "role": "assistant", "content": "The table shows that the Test BLEU score is 21.68 when k=3, which is the highest score, indicating the best performance for the interactive learning model. So, the answer is {\"answer\": \"21.68\"}." } ], "images": [ "scigen/train/2821.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The performance of speech recognition and speech translation with different word latency in wait-k policy on the En-Zh Dev set and Test set." ], "reasoning": "The table shows that the Test BLEU score is 21.68 when k=3, which is the highest score, indicating the best performance for the interactive learning model.", "question": "What is the Test BLEU score when k=3, and why is this significant?", "answer": "So, the answer is {\"answer\": \"21.68\"}." }, { "messages": [ { "role": "user", "content": "The interactive learning model has the best performance when k=0. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe interactive learning model has the best performance when k=0.\n" }, { "role": "assistant", "content": "The table shows that the highest Test BLEU score is 21.68 when k=3, not k=0, indicating the best performance at k=3. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2821.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The performance of speech recognition and speech translation with different word latency in wait-k policy on the En-Zh Dev set and Test set." ], "reasoning": "The table shows that the highest Test BLEU score is 21.68 when k=3, not k=0, indicating the best performance at k=3.", "statement": "The interactive learning model has the best performance when k=0.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: The performance of speech recognition and speech translation under different hyper-parameters λ on the En-Zh Dev set and Test set.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "As λ increases, both recognition and translation quality improve, peaking at λ=0.3 for the best performance in the speech translation task.We investigate how much information from two tasks should be taken into consideration in the interactive attention sub-layer. If λ=0.0, the model degrades to traditional multi-task learning model which does not utilize any information from the other task. As shown in the table, as λ increases, both recognition quality and translation quality can be improved with information interacting. When λ=0.3, our interactive learning model achieves the best performance on the speech translation task. However, λ can not be too large, otherwise two tasks may interfere with each other and affect its own performance. Therefore, we use λ=0.3 for all experiments." } ], "images": [ "scigen/train/2822.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The performance of speech recognition and speech translation under different hyper-parameters λ on the En-Zh Dev set and Test set." ], "reasoning": "As λ increases, both recognition and translation quality improve, peaking at λ=0.3 for the best performance in the speech translation task.", "description": "We investigate how much information from two tasks should be taken into consideration in the interactive attention sub-layer. If λ=0.0, the model degrades to traditional multi-task learning model which does not utilize any information from the other task. As shown in the table, as λ increases, both recognition quality and translation quality can be improved with information interacting. When λ=0.3, our interactive learning model achieves the best performance on the speech translation task. However, λ can not be too large, otherwise two tasks may interfere with each other and affect its own performance. Therefore, we use λ=0.3 for all experiments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: Table 2: The performance of speech recognition and speech translation under different hyper-parameters λ on the En-Zh Dev set and Test set.\nQuestion: What is the Test BLEU score when λ=0.3?\n" }, { "role": "assistant", "content": "The table shows that the Test BLEU score for λ=0.3 is 21.01, which is the highest among all λ values. So, the answer is {\"answer\": \"21.01\"}" } ], "images": [ "scigen/train/2822.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The performance of speech recognition and speech translation under different hyper-parameters λ on the En-Zh Dev set and Test set." ], "reasoning": "The table shows that the Test BLEU score for λ=0.3 is 21.01, which is the highest among all λ values.", "question": "What is the Test BLEU score when λ=0.3?", "answer": "So, the answer is {\"answer\": \"21.01\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model achieves the lowest Test WER when λ=0.1.\n" }, { "role": "assistant", "content": "The table shows that the Test WER for λ=0.1 is 12.92, which is the lowest among all λ values. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2822.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The performance of speech recognition and speech translation under different hyper-parameters λ on the En-Zh Dev set and Test set." ], "reasoning": "The table shows that the Test WER for λ=0.1 is 12.92, which is the lowest among all λ values.", "statement": "The model achieves the lowest Test WER when λ=0.1.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 7: Comparison of F1-score for practicality judgment on USPTO + Chemical.AI-Real-1(%)'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The Siamese network has the highest F1-score for positive cases and a significantly higher F1-score for negative cases compared to other models, indicating its effectiveness in handling data imbalance.Obviously, Siamese network outperforms all the others, especially on the negative cases, which shows Siamese network could effectively handle the data imbalance issue." } ], "images": [ "scigen/train/2823.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Comparison of F1-score for practicality judgment on USPTO + Chemical.AI-Real-1(%)" ], "reasoning": "The Siamese network has the highest F1-score for positive cases and a significantly higher F1-score for negative cases compared to other models, indicating its effectiveness in handling data imbalance.", "description": "Obviously, Siamese network outperforms all the others, especially on the negative cases, which shows Siamese network could effectively handle the data imbalance issue.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 7: Comparison of F1-score for practicality judgment on USPTO + Chemical.AI-Real-1(%)', and tackle the question below in a sequential manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model has the highest F1-score for positive cases and how does it compare to its F1-score for negative cases?\n" }, { "role": "assistant", "content": "The Siamese network has the highest F1-score for positive cases at 99.02. For negative cases, its F1-score is 67.88, which is still higher compared to other models' F1-scores for negative cases. So, the answer is {\"answer\": \"Siamese\"}." } ], "images": [ "scigen/train/2823.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Comparison of F1-score for practicality judgment on USPTO + Chemical.AI-Real-1(%)" ], "reasoning": "The Siamese network has the highest F1-score for positive cases at 99.02. For negative cases, its F1-score is 67.88, which is still higher compared to other models' F1-scores for negative cases.", "question": "Which model has the highest F1-score for positive cases and how does it compare to its F1-score for negative cases?", "answer": "So, the answer is {\"answer\": \"Siamese\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The GRU model has a higher F1-score for positive cases than the BiLSTM model. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe GRU model has a higher F1-score for positive cases than the BiLSTM model.\n" }, { "role": "assistant", "content": "The table shows that the GRU model has an F1-score of 98.83 for positive cases, while the BiLSTM model has an F1-score of 98.96 for positive cases. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2823.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Comparison of F1-score for practicality judgment on USPTO + Chemical.AI-Real-1(%)" ], "reasoning": "The table shows that the GRU model has an F1-score of 98.83 for positive cases, while the BiLSTM model has an F1-score of 98.96 for positive cases. Therefore, the statement is refuted.", "statement": "The GRU model has a higher F1-score for positive cases than the BiLSTM model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 6: Ablation study for practicality judgment(F1 Score on Positive case / Negative case) (%)', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Removing either RSD or DLG Tokenization results in a performance drop, indicating their complementary importance in feature representation.We investigate the effect of different features in our model by removing them one by one. If we remove either RSD or DLG Tokenizaton, the performance drops. This result indicates that both features play an important and complementary role in the feature representation." } ], "images": [ "scigen/train/2824.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Ablation study for practicality judgment(F1 Score on Positive case / Negative case) (%)" ], "reasoning": "Removing either RSD or DLG Tokenization results in a performance drop, indicating their complementary importance in feature representation.", "description": "We investigate the effect of different features in our model by removing them one by one. If we remove either RSD or DLG Tokenizaton, the performance drops. This result indicates that both features play an important and complementary role in the feature representation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 6: Ablation study for practicality judgment(F1 Score on Positive case / Negative case) (%)' and proceed to solve the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the USPTO + Chemical.AI-Real-1 Recall for the Full feature set in the N case?\n" }, { "role": "assistant", "content": "To find the USPTO + Chemical.AI-Real-1 Recall for the Full feature set in the N case, locate the row labeled 'Full' and 'N' under the 'Case' column, then find the value under the 'USPTO + Chemical.AI-Real-1 Recall' column. So, the answer is {\"answer\": \"63.83\"}." } ], "images": [ "scigen/train/2824.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Ablation study for practicality judgment(F1 Score on Positive case / Negative case) (%)" ], "reasoning": "To find the USPTO + Chemical.AI-Real-1 Recall for the Full feature set in the N case, locate the row labeled 'Full' and 'N' under the 'Case' column, then find the value under the 'USPTO + Chemical.AI-Real-1 Recall' column.", "question": "What is the USPTO + Chemical.AI-Real-1 Recall for the Full feature set in the N case?", "answer": "So, the answer is {\"answer\": \"63.83\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe performance drops when either RSD or DLG Tokenization is removed.\n" }, { "role": "assistant", "content": "The table shows that removing either RSD or DLG Tokenization results in lower values in several performance metrics compared to the Full feature set, supporting the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2824.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Ablation study for practicality judgment(F1 Score on Positive case / Negative case) (%)" ], "reasoning": "The table shows that removing either RSD or DLG Tokenization results in lower values in several performance metrics compared to the Full feature set, supporting the statement.", "statement": "The performance drops when either RSD or DLG Tokenization is removed.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 8: Results for practicality judgment (%)'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The model achieves nearly 99% recognition for positive reactions and maintains a high negative F1-score of over 72%, with the rule-based negative dataset being larger and more diverse.Given input sequences, the model will output the reaction success probabilities. To evaluate the result, a threshold is required to distinguish from positive or negative predictions. We observe the positive reactions could be recognized essentially (nearly 99%). Though the proportion of positive and negative cases is over 30:1, our model also ensures a high negative F1-score more than 72%. Besides the rule-based negative dataset have a higher Negative-F1-score. From the statistics of the datasets, we know the rule-based negative dataset is much bigger than the Chemical. AI-Real-1, which not only alleviates the imbalance between positive and negative examples but also increases the diversity of negative examples." } ], "images": [ "scigen/train/2825.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Results for practicality judgment (%)" ], "reasoning": "The model achieves nearly 99% recognition for positive reactions and maintains a high negative F1-score of over 72%, with the rule-based negative dataset being larger and more diverse.", "description": "Given input sequences, the model will output the reaction success probabilities. To evaluate the result, a threshold is required to distinguish from positive or negative predictions. We observe the positive reactions could be recognized essentially (nearly 99%). Though the proportion of positive and negative cases is over 30:1, our model also ensures a high negative F1-score more than 72%. Besides the rule-based negative dataset have a higher Negative-F1-score. From the statistics of the datasets, we know the rule-based negative dataset is much bigger than the Chemical. AI-Real-1, which not only alleviates the imbalance between positive and negative examples but also increases the diversity of negative examples.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 8: Results for practicality judgment (%)', unfold the question presented below, detailing every step of your calculation. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the F1-score for the negative cases in the CJHIF + Chemical.AI-Real-1 dataset?\n" }, { "role": "assistant", "content": "To find the F1-score for the negative cases in the CJHIF + Chemical.AI-Real-1 dataset, look at the row where the 'Data' column is 'CJHIF + Chemical.AI-Real-1' and the 'Case' column is 'N'. The F1-score in this row is 72.24. So, the answer is {\"answer\": \"72.24\"}" } ], "images": [ "scigen/train/2825.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Results for practicality judgment (%)" ], "reasoning": "To find the F1-score for the negative cases in the CJHIF + Chemical.AI-Real-1 dataset, look at the row where the 'Data' column is 'CJHIF + Chemical.AI-Real-1' and the 'Case' column is 'N'. The F1-score in this row is 72.24.", "question": "What is the F1-score for the negative cases in the CJHIF + Chemical.AI-Real-1 dataset?", "answer": "So, the answer is {\"answer\": \"72.24\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model's accuracy for positive cases in the USPTO + Chemical.AI-Real-1 dataset is higher than 97%.\n" }, { "role": "assistant", "content": "To verify this statement, look at the row where the 'Data' column is 'USPTO + Chemical.AI-Real-1' and the 'Case' column is 'P'. The accuracy in this row is 97.92%, which is higher than 97%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2825.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Results for practicality judgment (%)" ], "reasoning": "To verify this statement, look at the row where the 'Data' column is 'USPTO + Chemical.AI-Real-1' and the 'Case' column is 'P'. The accuracy in this row is 97.92%, which is higher than 97%.", "statement": "The model's accuracy for positive cases in the USPTO + Chemical.AI-Real-1 dataset is higher than 97%.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Performance comparison between other models proposed in [12] and MDAM variants on the test set of PororoQA.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "MDAM (Lattn=2, Lm=1) achieves 41.41%, outperforming the Layered Memory Network by 2.38%, which achieves 39.03%.Our MDAM (Lattn=2, Lm=1) achieves 41.41% and shows the performance gain of 2.38% compared to the runner-up model, Layered Memory Network, which achieves 39.03%." } ], "images": [ "scigen/train/2826.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance comparison between other models proposed in [12] and MDAM variants on the test set of PororoQA." ], "reasoning": "MDAM (Lattn=2, Lm=1) achieves 41.41%, outperforming the Layered Memory Network by 2.38%, which achieves 39.03%.", "description": "Our MDAM (Lattn=2, Lm=1) achieves 41.41% and shows the performance gain of 2.38% compared to the runner-up model, Layered Memory Network, which achieves 39.03%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Performance comparison between other models proposed in [12] and MDAM variants on the test set of PororoQA.', answer the following question. Give detailed solution consisting of each step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the performance gain of MDAM compared to the Layered Memory Network?\n" }, { "role": "assistant", "content": "MDAM achieves 41.41% while the Layered Memory Network achieves 39.03%. The performance gain is calculated as 41.41% - 39.03% = 2.38%. So, the answer is {\"answer\": \"2.38%\"}" } ], "images": [ "scigen/train/2826.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance comparison between other models proposed in [12] and MDAM variants on the test set of PororoQA." ], "reasoning": "MDAM achieves 41.41% while the Layered Memory Network achieves 39.03%. The performance gain is calculated as 41.41% - 39.03% = 2.38%.", "question": "What is the performance gain of MDAM compared to the Layered Memory Network?", "answer": "So, the answer is {\"answer\": \"2.38%\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis MDAM achieves the highest test score among all methods. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nMDAM achieves the highest test score among all methods.\n" }, { "role": "assistant", "content": "The table shows that MDAM has a test score of 41.41%, which is higher than all other listed methods. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2826.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance comparison between other models proposed in [12] and MDAM variants on the test set of PororoQA." ], "reasoning": "The table shows that MDAM has a test score of 41.41%, which is higher than all other listed methods.", "statement": "MDAM achieves the highest test score among all methods.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Performance comparison between other models proposed in [12] and MDAM variants on the test set of PororoQA.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "MDAM (Lattn=2, Lm=1) achieves 41.41%, which is 2.38% higher than the Layered Memory Network at 39.03%.Our MDAM (Lattn=2, Lm=1) achieves 41.41% and shows the performance gain of 2.38% compared to the runner-up model, Layered Memory Network, which achieves 39.03%." } ], "images": [ "scigen/train/2827.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance comparison between other models proposed in [12] and MDAM variants on the test set of PororoQA." ], "reasoning": "MDAM (Lattn=2, Lm=1) achieves 41.41%, which is 2.38% higher than the Layered Memory Network at 39.03%.", "description": "Our MDAM (Lattn=2, Lm=1) achieves 41.41% and shows the performance gain of 2.38% compared to the runner-up model, Layered Memory Network, which achieves 39.03%.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 2: Performance comparison between other models proposed in [12] and MDAM variants on the test set of PororoQA.'. Solve the following question based on the table.\n\nQuestion: What is the performance gain of MDAM compared to LSTM V+Q?\nLet's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "MDAM achieves 48.9% while LSTM V+Q achieves 41.1%. The performance gain is calculated as 48.9% - 41.1% = 7.8%. So, the answer is {\"answer\": \"7.8%\"}." } ], "images": [ "scigen/train/2827.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance comparison between other models proposed in [12] and MDAM variants on the test set of PororoQA." ], "reasoning": "MDAM achieves 48.9% while LSTM V+Q achieves 41.1%. The performance gain is calculated as 48.9% - 41.1% = 7.8%.", "question": "What is the performance gain of MDAM compared to LSTM V+Q?", "answer": "So, the answer is {\"answer\": \"7.8%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis MDAM-FrameOnly achieves higher performance than MDAM-MulFusion. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nMDAM-FrameOnly achieves higher performance than MDAM-MulFusion.\n" }, { "role": "assistant", "content": "MDAM-FrameOnly achieves 42.1% while MDAM-MulFusion achieves 41.5%. Therefore, MDAM-FrameOnly has higher performance. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2827.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance comparison between other models proposed in [12] and MDAM variants on the test set of PororoQA." ], "reasoning": "MDAM-FrameOnly achieves 42.1% while MDAM-MulFusion achieves 41.5%. Therefore, MDAM-FrameOnly has higher performance.", "statement": "MDAM-FrameOnly achieves higher performance than MDAM-MulFusion.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4. Results on 1,000 samples and full training sets.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "MixMatchNL and MixDA outperform BERT-PT− in restaurant AE and ASC tasks using only 1,000 labeled examples, and achieve competitive results in laptop AE and ASC tasks with reduced training data.Low resource setting. In restaurant AE and ASC, MixMatchNL already outperforms BERT-PT−, which is trained with the full data, using only 1,000 labeled training examples, i.e., 50% and 28% of training examples respectively. MixDA also achieves similar good performance with only 1000 size training set on restaurant AE and even outperforms BERT-PT on full training data by 0.9% (77.79-76.9) on the restaurant ASC task. In laptop AE and ASC, MixMatchNL achieves results within 2.07% and 0.04% to BERT-PT− using 33% or 43% of training examples respectively. These results indicate that the proposed methods are able to significantly reduce the number of training labels required for opinion mining tasks." } ], "images": [ "scigen/train/2828.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4. Results on 1,000 samples and full training sets." ], "reasoning": "MixMatchNL and MixDA outperform BERT-PT− in restaurant AE and ASC tasks using only 1,000 labeled examples, and achieve competitive results in laptop AE and ASC tasks with reduced training data.", "description": "Low resource setting. In restaurant AE and ASC, MixMatchNL already outperforms BERT-PT−, which is trained with the full data, using only 1,000 labeled training examples, i.e., 50% and 28% of training examples respectively. MixDA also achieves similar good performance with only 1000 size training set on restaurant AE and even outperforms BERT-PT on full training data by 0.9% (77.79-76.9) on the restaurant ASC task. In laptop AE and ASC, MixMatchNL achieves results within 2.07% and 0.04% to BERT-PT− using 33% or 43% of training examples respectively. These results indicate that the proposed methods are able to significantly reduce the number of training labels required for opinion mining tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 4. Results on 1,000 samples and full training sets.', solve the following question step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nHow does MixDA perform on the ASC@full rest task compared to BERT-PT−?\n" }, { "role": "assistant", "content": "MixDA achieves a score of 79.22 on the ASC@full rest task, while BERT-PT− achieves a score of 75.45, indicating that MixDA outperforms BERT-PT− by 3.77 points. So, the answer is {\"answer\": \"79.22\"}." } ], "images": [ "scigen/train/2828.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4. Results on 1,000 samples and full training sets." ], "reasoning": "MixDA achieves a score of 79.22 on the ASC@full rest task, while BERT-PT− achieves a score of 75.45, indicating that MixDA outperforms BERT-PT− by 3.77 points.", "question": "How does MixDA perform on the ASC@full rest task compared to BERT-PT−?", "answer": "So, the answer is {\"answer\": \"79.22\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nMixMatchNL achieves the highest score on the AE@1000 laptop task.\n" }, { "role": "assistant", "content": "The table shows that MixMatchNL achieves a score of 81.48 on the AE@1000 laptop task, which is higher than any other method listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2828.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4. Results on 1,000 samples and full training sets." ], "reasoning": "The table shows that MixMatchNL achieves a score of 81.48 on the AE@1000 laptop task, which is higher than any other method listed.", "statement": "MixMatchNL achieves the highest score on the AE@1000 laptop task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7. Ablation analysis of MixMatchNL. We evaluate performance with F1 score for AE and MF1 for ASC.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Removing MixDA and unlabeled data degrades performance, with the largest impact seen in settings with less data.We analyze the effect of each component of MixMatchNL by ablation. We consider a few variants. First, we replace the MixDA component with regular DA’s (the “w/o. MixDA” row). Second, we disable the use of unlabeled data. The resulting method is equivalent to the BERT-FD baseline but with MixUpNL as regularization (the 3rd row). Third, to investigate if the guessed labels by pre-mature models harm the performance , we disable label guessing for the first 3 epochs (the 4th row). Results. The performance generally degrades as MixDA is removed (by up to 1.8% in Restaurant ASC@1000) and unlabeled data are removed (by up to 2.6%). The effectiveness of the two optimizations is similar among both AE and ASC tasks. As expected, both optimizations are more effective in the settings with less data (a total of 9.76% absolute improvement at size 1000 vs. 6.75% at full size). Finally, it is unclear whether discarding guessed labels from pre-mature models helps improve the performance (with only ∼1% difference overall)." } ], "images": [ "scigen/train/2829.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7. Ablation analysis of MixMatchNL. We evaluate performance with F1 score for AE and MF1 for ASC." ], "reasoning": "Removing MixDA and unlabeled data degrades performance, with the largest impact seen in settings with less data.", "description": "We analyze the effect of each component of MixMatchNL by ablation. We consider a few variants. First, we replace the MixDA component with regular DA’s (the “w/o. MixDA” row). Second, we disable the use of unlabeled data. The resulting method is equivalent to the BERT-FD baseline but with MixUpNL as regularization (the 3rd row). Third, to investigate if the guessed labels by pre-mature models harm the performance , we disable label guessing for the first 3 epochs (the 4th row). Results. The performance generally degrades as MixDA is removed (by up to 1.8% in Restaurant ASC@1000) and unlabeled data are removed (by up to 2.6%). The effectiveness of the two optimizations is similar among both AE and ASC tasks. As expected, both optimizations are more effective in the settings with less data (a total of 9.76% absolute improvement at size 1000 vs. 6.75% at full size). Finally, it is unclear whether discarding guessed labels from pre-mature models helps improve the performance (with only ∼1% difference overall).", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 7. Ablation analysis of MixMatchNL. We evaluate performance with F1 score for AE and MF1 for ASC.', solve this question and give detail solutions. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the performance difference in ASC@1000 rest between MixMatchNL and MixUpNL?\n" }, { "role": "assistant", "content": "To find the performance difference in ASC@1000 rest between MixMatchNL and MixUpNL, subtract the ASC@1000 rest score of MixUpNL (74.78) from that of MixMatchNL (77.40). So, the answer is {\"answer\": \"2.62\"}" } ], "images": [ "scigen/train/2829.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7. Ablation analysis of MixMatchNL. We evaluate performance with F1 score for AE and MF1 for ASC." ], "reasoning": "To find the performance difference in ASC@1000 rest between MixMatchNL and MixUpNL, subtract the ASC@1000 rest score of MixUpNL (74.78) from that of MixMatchNL (77.40).", "question": "What is the performance difference in ASC@1000 rest between MixMatchNL and MixUpNL?", "answer": "So, the answer is {\"answer\": \"2.62\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Disabling MixDA results in a performance drop in AE@full rest., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nDisabling MixDA results in a performance drop in AE@full rest.\n" }, { "role": "assistant", "content": "The table shows that the AE@full rest score for MixMatchNL is 79.65, while for w/o. MixDA it is 79.29, indicating a performance drop. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2829.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7. Ablation analysis of MixMatchNL. We evaluate performance with F1 score for AE and MF1 for ASC." ], "reasoning": "The table shows that the AE@full rest score for MixMatchNL is 79.65, while for w/o. MixDA it is 79.29, indicating a performance drop.", "statement": "Disabling MixDA results in a performance drop in AE@full rest.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 9. Most frequent new opinion tuples discovered by Snippext.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Snippext extracts more fine-grained opinions from reviews, resulting in a higher number of aspect-opinion tuples compared to the baseline pipeline.With these 4 models deployed, Snippext can extract 3.49M aspect-opinion tuples from the review corpus, compared to only 3.16M tuples extracted by the baseline pipeline. To better understand the coverage difference, we look into the aspect-opinion pairs extracted only by Snippext but not by the baseline pipeline. Observe that Snippext extracts more fine-grained opinions. For example, “hidden, fees” appears 198 times in Snippext’s extractions, out of 707 “fees” related extractions. In contrast, there are only 124 “fees” related extractions with the baseline method and the most frequent ones are “too many, fees”, which is less informative than “hidden fees” (and “hidden fees” are not extracted by the baseline method). As another example, there are only 95 baseline extractions about the price (i.e., contains “$” and a number) of an aspect. In comparison, Snippext extracts 21,738 (228× more) tuples about the price of an aspect (e.g., “$50, parking”)." } ], "images": [ "scigen/train/2830.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9. Most frequent new opinion tuples discovered by Snippext." ], "reasoning": "Snippext extracts more fine-grained opinions from reviews, resulting in a higher number of aspect-opinion tuples compared to the baseline pipeline.", "description": "With these 4 models deployed, Snippext can extract 3.49M aspect-opinion tuples from the review corpus, compared to only 3.16M tuples extracted by the baseline pipeline. To better understand the coverage difference, we look into the aspect-opinion pairs extracted only by Snippext but not by the baseline pipeline. Observe that Snippext extracts more fine-grained opinions. For example, “hidden, fees” appears 198 times in Snippext’s extractions, out of 707 “fees” related extractions. In contrast, there are only 124 “fees” related extractions with the baseline method and the most frequent ones are “too many, fees”, which is less informative than “hidden fees” (and “hidden fees” are not extracted by the baseline method). As another example, there are only 95 baseline extractions about the price (i.e., contains “$” and a number) of an aspect. In comparison, Snippext extracts 21,738 (228× more) tuples about the price of an aspect (e.g., “$50, parking”).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 9. Most frequent new opinion tuples discovered by Snippext.\nQuestion: How many more 'fees' related extractions does Snippext have compared to the baseline method?\nSolve the above question based on the table information. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "Snippext has 707 'fees' related extractions, while the baseline method has 124. The difference is 707 - 124. So, the answer is {\"answer\": \"583\"}" } ], "images": [ "scigen/train/2830.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9. Most frequent new opinion tuples discovered by Snippext." ], "reasoning": "Snippext has 707 'fees' related extractions, while the baseline method has 124. The difference is 707 - 124.", "question": "How many more 'fees' related extractions does Snippext have compared to the baseline method?", "answer": "So, the answer is {\"answer\": \"583\"}" }, { "messages": [ { "role": "user", "content": "Snippext extracts more tuples about the price of an aspect compared to the baseline method. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nSnippext extracts more tuples about the price of an aspect compared to the baseline method.\n" }, { "role": "assistant", "content": "The description states that Snippext extracts 21,738 tuples about the price of an aspect, while the baseline method extracts only 95. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2830.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9. Most frequent new opinion tuples discovered by Snippext." ], "reasoning": "The description states that Snippext extracts 21,738 tuples about the price of an aspect, while the baseline method extracts only 95.", "statement": "Snippext extracts more tuples about the price of an aspect compared to the baseline method.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: BLEU scores [%]. We conduct experiments with both low-resource and high-resource settings. Two bert-fused NMT models (Yang et al., 2020; Zhu et al., 2020) are considered as our baselines. Following (Gururangan et al., 2020), we also adopt pre-trained language models to downstream tasks by introducing a second phase of pre-training for Hictl on IWSLT or WMT parallel corpora, which denoted as Hictl∗. Results with ‡ are from our in-house implementation.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The proposed Hictl model improves BLEU scores across eight tasks, achieving new state-of-the-art results on IWSLT’14 En→De and IWSLT’17 En→{Fr, Zh} translations.We implemented standard Transformer (apply the base and big setting for IWSLT and WMT tasks respectively) as baseline. The proposed Hictl can improve the BLEU scores of the eight tasks by 1.93, 1.76, 2.7, 2.2, 1.43, 1.25, 1.87 and 1.67. Note we achieve new state-of-the-art results on IWSLT’14 En→De, IWSLT’17 En→{Fr, Zh} translations. These improvements show that mapping different languages into an universal representation space by using our Hictl to bridge the semantic discrepancy among cross-lingual sentence, is beneficial for both low-resource and high-resource translations." } ], "images": [ "scigen/train/2831.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: BLEU scores [%]. We conduct experiments with both low-resource and high-resource settings. Two bert-fused NMT models (Yang et al., 2020; Zhu et al., 2020) are considered as our baselines. Following (Gururangan et al., 2020), we also adopt pre-trained language models to downstream tasks by introducing a second phase of pre-training for Hictl on IWSLT or WMT parallel corpora, which denoted as Hictl∗. Results with ‡ are from our in-house implementation." ], "reasoning": "The proposed Hictl model improves BLEU scores across eight tasks, achieving new state-of-the-art results on IWSLT’14 En→De and IWSLT’17 En→{Fr, Zh} translations.", "description": "We implemented standard Transformer (apply the base and big setting for IWSLT and WMT tasks respectively) as baseline. The proposed Hictl can improve the BLEU scores of the eight tasks by 1.93, 1.76, 2.7, 2.2, 1.43, 1.25, 1.87 and 1.67. Note we achieve new state-of-the-art results on IWSLT’14 En→De, IWSLT’17 En→{Fr, Zh} translations. These improvements show that mapping different languages into an universal representation space by using our Hictl to bridge the semantic discrepancy among cross-lingual sentence, is beneficial for both low-resource and high-resource translations.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: Table 4: BLEU scores [%]. We conduct experiments with both low-resource and high-resource settings. Two bert-fused NMT models (Yang et al., 2020; Zhu et al., 2020) are considered as our baselines. Following (Gururangan et al., 2020), we also adopt pre-trained language models to downstream tasks by introducing a second phase of pre-training for Hictl on IWSLT or WMT parallel corpora, which denoted as Hictl∗. Results with ‡ are from our in-house implementation.\nQuestion: What is the BLEU score improvement of Hictl* over Transformer on the IWSLT'14 En→De task?\n" }, { "role": "assistant", "content": "The BLEU score for Transformer on IWSLT'14 En→De is 28.64, and for Hictl* it is 31.36. The improvement is 31.36 - 28.64 = 2.72. So, the answer is {\"answer\": \"2.72\"}" } ], "images": [ "scigen/train/2831.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: BLEU scores [%]. We conduct experiments with both low-resource and high-resource settings. Two bert-fused NMT models (Yang et al., 2020; Zhu et al., 2020) are considered as our baselines. Following (Gururangan et al., 2020), we also adopt pre-trained language models to downstream tasks by introducing a second phase of pre-training for Hictl on IWSLT or WMT parallel corpora, which denoted as Hictl∗. Results with ‡ are from our in-house implementation." ], "reasoning": "The BLEU score for Transformer on IWSLT'14 En→De is 28.64, and for Hictl* it is 31.36. The improvement is 31.36 - 28.64 = 2.72.", "question": "What is the BLEU score improvement of Hictl* over Transformer on the IWSLT'14 En→De task?", "answer": "So, the answer is {\"answer\": \"2.72\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nHictl* achieves a BLEU score of 30.86 on the WMT'14 En→De task.\n" }, { "role": "assistant", "content": "The table shows that Hictl* achieves a BLEU score of 30.86 on the WMT'14 En→De task. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2831.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: BLEU scores [%]. We conduct experiments with both low-resource and high-resource settings. Two bert-fused NMT models (Yang et al., 2020; Zhu et al., 2020) are considered as our baselines. Following (Gururangan et al., 2020), we also adopt pre-trained language models to downstream tasks by introducing a second phase of pre-training for Hictl on IWSLT or WMT parallel corpora, which denoted as Hictl∗. Results with ‡ are from our in-house implementation." ], "reasoning": "The table shows that Hictl* achieves a BLEU score of 30.86 on the WMT'14 En→De task.", "statement": "Hictl* achieves a BLEU score of 30.86 on the WMT'14 En→De task.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: GLUE dev results. Results with † and ‡ are from (Liu et al., 2019) and our in-house implementation respectively. We compare the performance of Hictl to BERTLarge, XLNetLarge (Yang et al., 2019), RoBERTa (Liu et al., 2019) and XLM-RLarge on the English GLUE benchmark.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Hictl achieves a 1.0% average accuracy improvement over XLM-RBase on all tasks and outperforms XLM-RLarge, reaching performance on par with XLNetLarge with fewer parameters.We can observe that Hictl achieves significantly improvements, by 1.0% accuracy on average, over XLM-RBase on all tasks. Note that Hictl also outperforms XLM-RLarge and reaches performance on par with XLNetLarge with less parameters. These results demonstrate the effectiveness of learning universal representations at both sentence and word level for many languages, which maintains strong capabilities on per-language downstream tasks as well." } ], "images": [ "scigen/train/2832.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: GLUE dev results. Results with † and ‡ are from (Liu et al., 2019) and our in-house implementation respectively. We compare the performance of Hictl to BERTLarge, XLNetLarge (Yang et al., 2019), RoBERTa (Liu et al., 2019) and XLM-RLarge on the English GLUE benchmark." ], "reasoning": "Hictl achieves a 1.0% average accuracy improvement over XLM-RBase on all tasks and outperforms XLM-RLarge, reaching performance on par with XLNetLarge with fewer parameters.", "description": "We can observe that Hictl achieves significantly improvements, by 1.0% accuracy on average, over XLM-RBase on all tasks. Note that Hictl also outperforms XLM-RLarge and reaches performance on par with XLNetLarge with less parameters. These results demonstrate the effectiveness of learning universal representations at both sentence and word level for many languages, which maintains strong capabilities on per-language downstream tasks as well.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 3: GLUE dev results. Results with † and ‡ are from (Liu et al., 2019) and our in-house implementation respectively. We compare the performance of Hictl to BERTLarge, XLNetLarge (Yang et al., 2019), RoBERTa (Liu et al., 2019) and XLM-RLarge on the English GLUE benchmark.\n\nQuestion: Which model has the highest average accuracy and how does Hictl compare to it?\n" }, { "role": "assistant", "content": "The table shows that RoBERTaLarge has the highest average accuracy of 92.0%. Hictl has an average accuracy of 91.9%, which is 0.1% lower than RoBERTaLarge. So, the answer is {\"answer\": \"RoBERTaLarge\"}." } ], "images": [ "scigen/train/2832.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: GLUE dev results. Results with † and ‡ are from (Liu et al., 2019) and our in-house implementation respectively. We compare the performance of Hictl to BERTLarge, XLNetLarge (Yang et al., 2019), RoBERTa (Liu et al., 2019) and XLM-RLarge on the English GLUE benchmark." ], "reasoning": "The table shows that RoBERTaLarge has the highest average accuracy of 92.0%. Hictl has an average accuracy of 91.9%, which is 0.1% lower than RoBERTaLarge.", "question": "Which model has the highest average accuracy and how does Hictl compare to it?", "answer": "So, the answer is {\"answer\": \"RoBERTaLarge\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nHictl has the highest average accuracy among all models.\n" }, { "role": "assistant", "content": "The table shows that RoBERTaLarge has the highest average accuracy of 92.0%, while Hictl has an average accuracy of 91.9%. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2832.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: GLUE dev results. Results with † and ‡ are from (Liu et al., 2019) and our in-house implementation respectively. We compare the performance of Hictl to BERTLarge, XLNetLarge (Yang et al., 2019), RoBERTa (Liu et al., 2019) and XLM-RLarge on the English GLUE benchmark." ], "reasoning": "The table shows that RoBERTaLarge has the highest average accuracy of 92.0%, while Hictl has an average accuracy of 91.9%. Therefore, the statement is refuted.", "statement": "Hictl has the highest average accuracy among all models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4.9: YiSi-1 scores (higher is better) computed using ess or esu BPE 5000 embeddings built by word2vec (w2v) for experiments on multilingual neural machine translation. The baseline is the original Transformer baseline for each language pair. Multilingual is the single multilingual system (trained on Inuktitut and Yupik data), and the remaining two columns show that system fine-tuned on a particular variety of Yupik.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The multilingual baseline model was trained on concatenated data from Inuktitut, St. Lawrence Island Yupik, and Central Alaskan Yup’ik, and fine-tuning on language-specific data improved performance for each language.We train joint BPE (vocabulary 5000) on Inuktitut, St. Lawrence Island Yupik, and Central Alaskan Yup’ik, downsampling the Inuktitut and upsampling St. Lawrence Island Yupik to match the size of Central Alaskan Yup’ik. We prepend a language tag (e.g. “ ”) to each source and target sentence in the three sub-corpora. Next we train a Transformer model (our “multilingual baseline”) on the concatenation of all available training data (with no sampling, 3 layer encoder, 3 layer decoder, 512 embedding size, early stopping on perplexity of the concatenated development data). For St. Lawrence Island Yupik and Central Alaskan Yup’ik, we then fine-tune the multilingual baseline on all language-specific training data (with early stopping based on perplexity on the language-specific development data). As expected, fine-tuning on language specific data boosts performance on that particular language (while the output on the other language appears to exhibit catastrophic forgetting (Kirkpatrick3521)), giving us our best performance. However, with BLEU scores in the single digits, it is clear that there is still a long way to go before the MT output may be genuinely useful (e.g. in post-editing or interactive translation) for these low-resource languages." } ], "images": [ "scigen/train/2833.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4.9: YiSi-1 scores (higher is better) computed using ess or esu BPE 5000 embeddings built by word2vec (w2v) for experiments on multilingual neural machine translation. The baseline is the original Transformer baseline for each language pair. Multilingual is the single multilingual system (trained on Inuktitut and Yupik data), and the remaining two columns show that system fine-tuned on a particular variety of Yupik." ], "reasoning": "The multilingual baseline model was trained on concatenated data from Inuktitut, St. Lawrence Island Yupik, and Central Alaskan Yup’ik, and fine-tuning on language-specific data improved performance for each language.", "description": "We train joint BPE (vocabulary 5000) on Inuktitut, St. Lawrence Island Yupik, and Central Alaskan Yup’ik, downsampling the Inuktitut and upsampling St. Lawrence Island Yupik to match the size of Central Alaskan Yup’ik. We prepend a language tag (e.g. “ ”) to each source and target sentence in the three sub-corpora. Next we train a Transformer model (our “multilingual baseline”) on the concatenation of all available training data (with no sampling, 3 layer encoder, 3 layer decoder, 512 embedding size, early stopping on perplexity of the concatenated development data). For St. Lawrence Island Yupik and Central Alaskan Yup’ik, we then fine-tune the multilingual baseline on all language-specific training data (with early stopping based on perplexity on the language-specific development data). As expected, fine-tuning on language specific data boosts performance on that particular language (while the output on the other language appears to exhibit catastrophic forgetting (Kirkpatrick3521)), giving us our best performance. However, with BLEU scores in the single digits, it is clear that there is still a long way to go before the MT output may be genuinely useful (e.g. in post-editing or interactive translation) for these low-resource languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 4.9: YiSi-1 scores (higher is better) computed using ess or esu BPE 5000 embeddings built by word2vec (w2v) for experiments on multilingual neural machine translation. The baseline is the original Transformer baseline for each language pair. Multilingual is the single multilingual system (trained on Inuktitut and Yupik data), and the remaining two columns show that system fine-tuned on a particular variety of Yupik.' and proceed to solve the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the BLEU score for the eng-ess translation after fine-tuning the multilingual baseline on ess-specific data?\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for different translation models. The score for eng-ess after fine-tuning on ess-specific data (ess-Ad. Multi.) is 30.1. So, the answer is {\"answer\": \"30.1\"}." } ], "images": [ "scigen/train/2833.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4.9: YiSi-1 scores (higher is better) computed using ess or esu BPE 5000 embeddings built by word2vec (w2v) for experiments on multilingual neural machine translation. The baseline is the original Transformer baseline for each language pair. Multilingual is the single multilingual system (trained on Inuktitut and Yupik data), and the remaining two columns show that system fine-tuned on a particular variety of Yupik." ], "reasoning": "The table shows the BLEU scores for different translation models. The score for eng-ess after fine-tuning on ess-specific data (ess-Ad. Multi.) is 30.1.", "question": "What is the BLEU score for the eng-ess translation after fine-tuning the multilingual baseline on ess-specific data?", "answer": "So, the answer is {\"answer\": \"30.1\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The multilingual baseline model achieved a higher BLEU score for eng-ess than the baseline model. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe multilingual baseline model achieved a higher BLEU score for eng-ess than the baseline model.\n" }, { "role": "assistant", "content": "The table shows that the multilingual baseline model has a BLEU score of 28.0 for eng-ess, which is higher than the baseline model's score of 26.9. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2833.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4.9: YiSi-1 scores (higher is better) computed using ess or esu BPE 5000 embeddings built by word2vec (w2v) for experiments on multilingual neural machine translation. The baseline is the original Transformer baseline for each language pair. Multilingual is the single multilingual system (trained on Inuktitut and Yupik data), and the remaining two columns show that system fine-tuned on a particular variety of Yupik." ], "reasoning": "The table shows that the multilingual baseline model has a BLEU score of 28.0 for eng-ess, which is higher than the baseline model's score of 26.9.", "statement": "The multilingual baseline model achieved a higher BLEU score for eng-ess than the baseline model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4.3: Results of Inuktitut-to-English NMT systems as evaluated by BLEU, chrF, YiSi-0 and YiSi-1 .'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Combining morphological analysis with BPE segmentation retains morphemes while segmenting unanalyzed words, improving manageability.We compared BPE of various vocabulary sizes to the morphological analysis described above. We think this is not due to a problem in the morphological analysis itself (e.g. identifying morphemes incorrectly), but that the process left unanalyzable words intact, whereas BPE manages to segment all words into more manageable pieces. We therefore also performed a preliminary attempt to combine them, in hopes of combining some of the benefits of true morphological analysis with the statistical advantages of BPE. Manual inspection of the results of this process suggest that morphemes identified in morphological analysis were typically left intact by BPE – that is to say, they were identified as units by BPE as well – and only unanalyzed words were further segmented." } ], "images": [ "scigen/train/2834.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4.3: Results of Inuktitut-to-English NMT systems as evaluated by BLEU, chrF, YiSi-0 and YiSi-1 ." ], "reasoning": "Combining morphological analysis with BPE segmentation retains morphemes while segmenting unanalyzed words, improving manageability.", "description": "We compared BPE of various vocabulary sizes to the morphological analysis described above. We think this is not due to a problem in the morphological analysis itself (e.g. identifying morphemes incorrectly), but that the process left unanalyzable words intact, whereas BPE manages to segment all words into more manageable pieces. We therefore also performed a preliminary attempt to combine them, in hopes of combining some of the benefits of true morphological analysis with the statistical advantages of BPE. Manual inspection of the results of this process suggest that morphemes identified in morphological analysis were typically left intact by BPE – that is to say, they were identified as units by BPE as well – and only unanalyzed words were further segmented.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4.3: Results of Inuktitut-to-English NMT systems as evaluated by BLEU, chrF, YiSi-0 and YiSi-1 .', answer the following question. Give detailed solution consisting of each step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the BLEU score for the Morph + 5000 BPE segmentation?\n" }, { "role": "assistant", "content": "The table lists the BLEU scores for different segmentation methods. The BLEU score for Morph + 5000 BPE segmentation is 26.6. So, the answer is {\"answer\": \"26.6\"}." } ], "images": [ "scigen/train/2834.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4.3: Results of Inuktitut-to-English NMT systems as evaluated by BLEU, chrF, YiSi-0 and YiSi-1 ." ], "reasoning": "The table lists the BLEU scores for different segmentation methods. The BLEU score for Morph + 5000 BPE segmentation is 26.6.", "question": "What is the BLEU score for the Morph + 5000 BPE segmentation?", "answer": "So, the answer is {\"answer\": \"26.6\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The highest YiSi-1 score is achieved by the 5000 BPE segmentation., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe highest YiSi-1 score is achieved by the 5000 BPE segmentation.\n" }, { "role": "assistant", "content": "The table shows the YiSi-1 scores for different segmentation methods. The highest YiSi-1 score is 70.8, achieved by the 5000 BPE segmentation. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2834.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4.3: Results of Inuktitut-to-English NMT systems as evaluated by BLEU, chrF, YiSi-0 and YiSi-1 ." ], "reasoning": "The table shows the YiSi-1 scores for different segmentation methods. The highest YiSi-1 score is 70.8, achieved by the 5000 BPE segmentation.", "statement": "The highest YiSi-1 score is achieved by the 5000 BPE segmentation.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4.6: BLEU scores of baseline and vocabulary experiments for Yupik–English and Guaraní–Spanish machine translation experiments. All BPE vocabularies in this table are of size 5000, learned separately.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The experiments compare RNN and Transformer models using BPE vocabularies of 5000, with additional experiments on Guaraní–Spanish using full-word and FST-segmented vocabularies, finding that the Transformer outperforms the RNN.We first compare RNN and Transformer translation models using BPE vocabularies of 5000. The size of 5000 was selected for consistency with other experiments and because it was among the highest performing vocabulary size on initial RNN experiments for several language pairs (not reported here). The RNN models were trained using OpenNMT (klein-etal-2017-opennmt) with default settings, and the Transformer models were trained using Sockeye (Sockeye:17) with a 3 layer encoder, 3 layer decoder, batch size 2048, optimized toward perplexity, and the remaining parameters set to defaults. For Guaraní–Spanish, we also experiment with full-word vocabularies, FST-segmented vocabulary (Guaraní side only; Spanish side BPE 5000), and an FST-segmented vocabulary with backoff to BPE (all Guaraní words left unsegmented by the FST were segmented by a BPE model learned for a BPE 5000 vocabulary on Guaraní; Spanish side BPE 5000). Yupik Language Experiments Our Yupik language experiments begin with baseline RNN and Transformer models. Finding that the Transformer strongly outperforms the RNN" } ], "images": [ "scigen/train/2835.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4.6: BLEU scores of baseline and vocabulary experiments for Yupik–English and Guaraní–Spanish machine translation experiments. All BPE vocabularies in this table are of size 5000, learned separately." ], "reasoning": "The experiments compare RNN and Transformer models using BPE vocabularies of 5000, with additional experiments on Guaraní–Spanish using full-word and FST-segmented vocabularies, finding that the Transformer outperforms the RNN.", "description": "We first compare RNN and Transformer translation models using BPE vocabularies of 5000. The size of 5000 was selected for consistency with other experiments and because it was among the highest performing vocabulary size on initial RNN experiments for several language pairs (not reported here). The RNN models were trained using OpenNMT (klein-etal-2017-opennmt) with default settings, and the Transformer models were trained using Sockeye (Sockeye:17) with a 3 layer encoder, 3 layer decoder, batch size 2048, optimized toward perplexity, and the remaining parameters set to defaults. For Guaraní–Spanish, we also experiment with full-word vocabularies, FST-segmented vocabulary (Guaraní side only; Spanish side BPE 5000), and an FST-segmented vocabulary with backoff to BPE (all Guaraní words left unsegmented by the FST were segmented by a BPE model learned for a BPE 5000 vocabulary on Guaraní; Spanish side BPE 5000). Yupik Language Experiments Our Yupik language experiments begin with baseline RNN and Transformer models. Finding that the Transformer strongly outperforms the RNN", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 4.6: BLEU scores of baseline and vocabulary experiments for Yupik–English and Guaraní–Spanish machine translation experiments. All BPE vocabularies in this table are of size 5000, learned separately.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: Which translation direction had the highest perplexity score using the Transformer BPE model?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the highest perplexity score using the Transformer BPE model, look at the 'Transf. BPE' column and identify the highest value. The highest value is 13.9 for the esu→eng translation direction. So, the answer is {\"answer\": \"esu→eng\"}." } ], "images": [ "scigen/train/2835.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4.6: BLEU scores of baseline and vocabulary experiments for Yupik–English and Guaraní–Spanish machine translation experiments. All BPE vocabularies in this table are of size 5000, learned separately." ], "reasoning": "To find the highest perplexity score using the Transformer BPE model, look at the 'Transf. BPE' column and identify the highest value. The highest value is 13.9 for the esu→eng translation direction.", "question": "Which translation direction had the highest perplexity score using the Transformer BPE model?", "answer": "So, the answer is {\"answer\": \"esu→eng\"}." }, { "messages": [ { "role": "user", "content": "The RNN BPE model had a lower perplexity score than the Transformer BPE model for the eng→ess translation direction. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe RNN BPE model had a lower perplexity score than the Transformer BPE model for the eng→ess translation direction.\n" }, { "role": "assistant", "content": "The table shows that for the eng→ess translation direction, the RNN BPE model had a perplexity score of 3.3, while the Transformer BPE model had a score of 4.4. Since 3.3 is lower than 4.4, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2835.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4.6: BLEU scores of baseline and vocabulary experiments for Yupik–English and Guaraní–Spanish machine translation experiments. All BPE vocabularies in this table are of size 5000, learned separately." ], "reasoning": "The table shows that for the eng→ess translation direction, the RNN BPE model had a perplexity score of 3.3, while the Transformer BPE model had a score of 4.4. Since 3.3 is lower than 4.4, the statement is supported.", "statement": "The RNN BPE model had a lower perplexity score than the Transformer BPE model for the eng→ess translation direction.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4.8: BLEU scores for experiments on multilingual neural machine translation. The baseline is the original Transformer baseline for each language pair. Multilingual is the single multilingual system (trained on Inuktitut and Yupik data), and the remaining two columns show that system fine-tuned on a particular variety of Yupik.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Experiments on machine translation between English, Inuktitut, and Yupik languages show that fine-tuning on language-specific data improves performance, but BLEU scores remain low.In this work, we take steps towards answering two relevant questions through experiments on machine translation between English, Inuktitut, and Yupik as well as Guaraní and Spanish. First, can we untangle the influences of small data and morphological complexity on the challenge of modelling these languages? Second, can we make use of higher-resource languages in the same language family to improve machine translation of lower-resource languages? We train joint BPE (vocabulary 5000) on Inuktitut, St. Lawrence Island Yupik, and Central Alaskan Yup’ik, downsampling the Inuktitut and upsampling St. Lawrence Island Yupik to match the size of Central Alaskan Yup’ik. We prepend a language tag (e.g. “ ”) to each source and target sentence in the three sub-corpora. Next we train a Transformer model (our “multilingual baseline”) on the concatenation of all available training data (with no sampling, 3 layer encoder, 3 layer decoder, 512 embedding size, early stopping on perplexity of the concatenated development data). For St. Lawrence Island Yupik and Central Alaskan Yup’ik, we then fine-tune the multilingual baseline on all language-specific training data (with early stopping based on perplexity on the language-specific development data). As expected, fine-tuning on language specific data boosts performance on that particular language (while the output on the other language appears to exhibit catastrophic forgetting (Kirkpatrick3521)), giving us our best performance. However, with BLEU scores in the single digits, it is clear that there is still a long way to go before the MT output may be genuinely useful (e.g. in post-editing or interactive translation) for these low-resource languages." } ], "images": [ "scigen/train/2836.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4.8: BLEU scores for experiments on multilingual neural machine translation. The baseline is the original Transformer baseline for each language pair. Multilingual is the single multilingual system (trained on Inuktitut and Yupik data), and the remaining two columns show that system fine-tuned on a particular variety of Yupik." ], "reasoning": "Experiments on machine translation between English, Inuktitut, and Yupik languages show that fine-tuning on language-specific data improves performance, but BLEU scores remain low.", "description": "In this work, we take steps towards answering two relevant questions through experiments on machine translation between English, Inuktitut, and Yupik as well as Guaraní and Spanish. First, can we untangle the influences of small data and morphological complexity on the challenge of modelling these languages? Second, can we make use of higher-resource languages in the same language family to improve machine translation of lower-resource languages? We train joint BPE (vocabulary 5000) on Inuktitut, St. Lawrence Island Yupik, and Central Alaskan Yup’ik, downsampling the Inuktitut and upsampling St. Lawrence Island Yupik to match the size of Central Alaskan Yup’ik. We prepend a language tag (e.g. “ ”) to each source and target sentence in the three sub-corpora. Next we train a Transformer model (our “multilingual baseline”) on the concatenation of all available training data (with no sampling, 3 layer encoder, 3 layer decoder, 512 embedding size, early stopping on perplexity of the concatenated development data). For St. Lawrence Island Yupik and Central Alaskan Yup’ik, we then fine-tune the multilingual baseline on all language-specific training data (with early stopping based on perplexity on the language-specific development data). As expected, fine-tuning on language specific data boosts performance on that particular language (while the output on the other language appears to exhibit catastrophic forgetting (Kirkpatrick3521)), giving us our best performance. However, with BLEU scores in the single digits, it is clear that there is still a long way to go before the MT output may be genuinely useful (e.g. in post-editing or interactive translation) for these low-resource languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 4.8: BLEU scores for experiments on multilingual neural machine translation. The baseline is the original Transformer baseline for each language pair. Multilingual is the single multilingual system (trained on Inuktitut and Yupik data), and the remaining two columns show that system fine-tuned on a particular variety of Yupik.' to sequentially solve the question presented below. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhat is the BLEU score for the eng-ess translation using the ess-Ad. Multi. model?\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for different models. For the eng-ess translation using the ess-Ad. Multi. model, the score is 6.5. So, the answer is {\"answer\": \"6.5\"}" } ], "images": [ "scigen/train/2836.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4.8: BLEU scores for experiments on multilingual neural machine translation. The baseline is the original Transformer baseline for each language pair. Multilingual is the single multilingual system (trained on Inuktitut and Yupik data), and the remaining two columns show that system fine-tuned on a particular variety of Yupik." ], "reasoning": "The table shows the BLEU scores for different models. For the eng-ess translation using the ess-Ad. Multi. model, the score is 6.5.", "question": "What is the BLEU score for the eng-ess translation using the ess-Ad. Multi. model?", "answer": "So, the answer is {\"answer\": \"6.5\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The multilingual model performs better than the baseline for both eng-ess and eng-esu translations., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe multilingual model performs better than the baseline for both eng-ess and eng-esu translations.\n" }, { "role": "assistant", "content": "The table shows that the multilingual model has higher BLEU scores than the baseline for both eng-ess (5.8 vs. 4.4) and eng-esu (5.7 vs. 5.3) translations. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2836.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4.8: BLEU scores for experiments on multilingual neural machine translation. The baseline is the original Transformer baseline for each language pair. Multilingual is the single multilingual system (trained on Inuktitut and Yupik data), and the remaining two columns show that system fine-tuned on a particular variety of Yupik." ], "reasoning": "The table shows that the multilingual model has higher BLEU scores than the baseline for both eng-ess (5.8 vs. 4.4) and eng-esu (5.7 vs. 5.3) translations.", "statement": "The multilingual model performs better than the baseline for both eng-ess and eng-esu translations.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 5.9: Word-level perplexity for setting 1 (all available data). V denotes vocabulary size for BPE operation'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The word-level perplexity shows that eng and spa are the easiest languages to model, while ess, esu, and iku are similarly hard to model, with grn being less difficult but still harder than eng and spa.After comparing different tokenization methods per language, we compared different languages to see which language is easier or harder to model. This line of inquiry has been pursued by several recent studies (cotterell-etal-2018-languages; mielke-etal-2019-kind; gerz-etal-2018-relation), where various languages are modeled using a state-of-the-art neural language model to compare relative difficulty of modelling a language with particular linguistic features. It should be noted that our data per language were not parallel so the comparison has to be drawn with caution. However, we still attempted the comparison here as comparing our models may provide insights for future studies given that we used the same or very similar RNN language models as the previous literature and that polysynthetic languages have not been discussed in this line of inquiry. all data setting and eng under the 2) New Testament setting. However, character-level perplexity may not be the right metric to use to compare different languages. The problem with the character-level measure is that it does not tell us much about real-life applications, where the difficulty of predicting an entire word might be more meaningful. More importantly, the character-level perplexity underestimates the difficulty of modelling polysynthetic languages as they tend to have longer, morphologically complex words. In fact, when we look at the word-level perplexity, the differences between polysynthetic languages and others become clearer. When considering the difficulty of predicting the next word in the languages than the next character, iku is no longer the easiest to model under any condition. The word-level measure clearly shows that eng, followed by spa, was the easiest to model. Comparisons of the word-level perplexity values suggest that ess, esu and iku are quite similarly hard to model while grn is less difficult even though it is still quite harder to model than language like eng and spa. This observation agrees with our previous observation about the descriptive statistics of the datasets." } ], "images": [ "scigen/train/2837.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5.9: Word-level perplexity for setting 1 (all available data). V denotes vocabulary size for BPE operation" ], "reasoning": "The word-level perplexity shows that eng and spa are the easiest languages to model, while ess, esu, and iku are similarly hard to model, with grn being less difficult but still harder than eng and spa.", "description": "After comparing different tokenization methods per language, we compared different languages to see which language is easier or harder to model. This line of inquiry has been pursued by several recent studies (cotterell-etal-2018-languages; mielke-etal-2019-kind; gerz-etal-2018-relation), where various languages are modeled using a state-of-the-art neural language model to compare relative difficulty of modelling a language with particular linguistic features. It should be noted that our data per language were not parallel so the comparison has to be drawn with caution. However, we still attempted the comparison here as comparing our models may provide insights for future studies given that we used the same or very similar RNN language models as the previous literature and that polysynthetic languages have not been discussed in this line of inquiry. all data setting and eng under the 2) New Testament setting. However, character-level perplexity may not be the right metric to use to compare different languages. The problem with the character-level measure is that it does not tell us much about real-life applications, where the difficulty of predicting an entire word might be more meaningful. More importantly, the character-level perplexity underestimates the difficulty of modelling polysynthetic languages as they tend to have longer, morphologically complex words. In fact, when we look at the word-level perplexity, the differences between polysynthetic languages and others become clearer. When considering the difficulty of predicting the next word in the languages than the next character, iku is no longer the easiest to model under any condition. The word-level measure clearly shows that eng, followed by spa, was the easiest to model. Comparisons of the word-level perplexity values suggest that ess, esu and iku are quite similarly hard to model while grn is less difficult even though it is still quite harder to model than language like eng and spa. This observation agrees with our previous observation about the descriptive statistics of the datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5.9: Word-level perplexity for setting 1 (all available data). V denotes vocabulary size for BPE operation\nQuestion: Which language has the lowest character-level perplexity?\nSolve the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By examining the 'Character' column in the table, we can see that 'eng' has the lowest character-level perplexity value of 47.83. So, the answer is {\"answer\": \"eng\"}." } ], "images": [ "scigen/train/2837.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5.9: Word-level perplexity for setting 1 (all available data). V denotes vocabulary size for BPE operation" ], "reasoning": "By examining the 'Character' column in the table, we can see that 'eng' has the lowest character-level perplexity value of 47.83.", "question": "Which language has the lowest character-level perplexity?", "answer": "So, the answer is {\"answer\": \"eng\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The language iku is the easiest to model at the character level., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe language iku is the easiest to model at the character level.\n" }, { "role": "assistant", "content": "The table shows that iku has a character-level perplexity of 1773.08, which is higher than eng's 47.83 and spa's 71.97, indicating that iku is not the easiest to model at the character level. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2837.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5.9: Word-level perplexity for setting 1 (all available data). V denotes vocabulary size for BPE operation" ], "reasoning": "The table shows that iku has a character-level perplexity of 1773.08, which is higher than eng's 47.83 and spa's 71.97, indicating that iku is not the easiest to model at the character level.", "statement": "The language iku is the easiest to model at the character level.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5.10: Word-level perplexity for setting 2 (New Testament only). V denotes vocabulary size for BPE operation', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The word-level perplexity shows that eng and spa are the easiest languages to model, while ess, esu, and iku are similarly hard to model, with grn being less difficult but still harder than eng and spa.After comparing different tokenization methods per language, we compared different languages to see which language is easier or harder to model. This line of inquiry has been pursued by several recent studies (cotterell-etal-2018-languages; mielke-etal-2019-kind; gerz-etal-2018-relation), where various languages are modeled using a state-of-the-art neural language model to compare relative difficulty of modelling a language with particular linguistic features. It should be noted that our data per language were not parallel so the comparison has to be drawn with caution. However, we still attempted the comparison here as comparing our models may provide insights for future studies given that we used the same or very similar RNN language models as the previous literature and that polysynthetic languages have not been discussed in this line of inquiry. all data setting and eng under the 2) New Testament setting. However, character-level perplexity may not be the right metric to use to compare different languages. The problem with the character-level measure is that it does not tell us much about real-life applications, where the difficulty of predicting an entire word might be more meaningful. More importantly, the character-level perplexity underestimates the difficulty of modelling polysynthetic languages as they tend to have longer, morphologically complex words. In fact, when we look at the word-level perplexity, the differences between polysynthetic languages and others become clearer. When considering the difficulty of predicting the next word in the languages than the next character, iku is no longer the easiest to model under any condition. The word-level measure clearly shows that eng, followed by spa, was the easiest to model. Comparisons of the word-level perplexity values suggest that ess, esu and iku are quite similarly hard to model while grn is less difficult even though it is still quite harder to model than language like eng and spa. This observation agrees with our previous observation about the descriptive statistics of the datasets." } ], "images": [ "scigen/train/2838.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5.10: Word-level perplexity for setting 2 (New Testament only). V denotes vocabulary size for BPE operation" ], "reasoning": "The word-level perplexity shows that eng and spa are the easiest languages to model, while ess, esu, and iku are similarly hard to model, with grn being less difficult but still harder than eng and spa.", "description": "After comparing different tokenization methods per language, we compared different languages to see which language is easier or harder to model. This line of inquiry has been pursued by several recent studies (cotterell-etal-2018-languages; mielke-etal-2019-kind; gerz-etal-2018-relation), where various languages are modeled using a state-of-the-art neural language model to compare relative difficulty of modelling a language with particular linguistic features. It should be noted that our data per language were not parallel so the comparison has to be drawn with caution. However, we still attempted the comparison here as comparing our models may provide insights for future studies given that we used the same or very similar RNN language models as the previous literature and that polysynthetic languages have not been discussed in this line of inquiry. all data setting and eng under the 2) New Testament setting. However, character-level perplexity may not be the right metric to use to compare different languages. The problem with the character-level measure is that it does not tell us much about real-life applications, where the difficulty of predicting an entire word might be more meaningful. More importantly, the character-level perplexity underestimates the difficulty of modelling polysynthetic languages as they tend to have longer, morphologically complex words. In fact, when we look at the word-level perplexity, the differences between polysynthetic languages and others become clearer. When considering the difficulty of predicting the next word in the languages than the next character, iku is no longer the easiest to model under any condition. The word-level measure clearly shows that eng, followed by spa, was the easiest to model. Comparisons of the word-level perplexity values suggest that ess, esu and iku are quite similarly hard to model while grn is less difficult even though it is still quite harder to model than language like eng and spa. This observation agrees with our previous observation about the descriptive statistics of the datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 5.10: Word-level perplexity for setting 2 (New Testament only). V denotes vocabulary size for BPE operation\n\nQuestion: Which language has the highest character-level perplexity?\n" }, { "role": "assistant", "content": "By examining the 'Character' column in the table, we see that esu has the highest character-level perplexity value of 3572.00. So, the answer is {\"answer\": \"esu\"}." } ], "images": [ "scigen/train/2838.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5.10: Word-level perplexity for setting 2 (New Testament only). V denotes vocabulary size for BPE operation" ], "reasoning": "By examining the 'Character' column in the table, we see that esu has the highest character-level perplexity value of 3572.00.", "question": "Which language has the highest character-level perplexity?", "answer": "So, the answer is {\"answer\": \"esu\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe language iku has a lower BPE (V=500) perplexity than the language ess.\n" }, { "role": "assistant", "content": "By comparing the BPE (V=500) column for both languages, iku has a perplexity of 14970.43, which is higher than ess's perplexity of 10993.00. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2838.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5.10: Word-level perplexity for setting 2 (New Testament only). V denotes vocabulary size for BPE operation" ], "reasoning": "By comparing the BPE (V=500) column for both languages, iku has a perplexity of 14970.43, which is higher than ess's perplexity of 10993.00.", "statement": "The language iku has a lower BPE (V=500) perplexity than the language ess.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6.1: Crow speech recognition'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "A pretrained model on Librispeech was adapted to Crow data, producing poor but some output.A pretrained English model trained on the Librispeech corpus was leveraged in an attempt to get any output at all from the Crow data. This pretrained model was then adapted to the available Crow data. While the results produced are very poor, the network was at least producing some output at this point." } ], "images": [ "scigen/train/2839.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6.1: Crow speech recognition" ], "reasoning": "A pretrained model on Librispeech was adapted to Crow data, producing poor but some output.", "description": "A pretrained English model trained on the Librispeech corpus was leveraged in an attempt to get any output at all from the Crow data. This pretrained model was then adapted to the available Crow data. While the results produced are very poor, the network was at least producing some output at this point.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nTable title: Table 6.1: Crow speech recognition\n\nQuestion: What is the Character Error Rate (CER) for the learning rate of 10^-4?\n" }, { "role": "assistant", "content": "The table shows the CER for different learning rates. For the learning rate of 10^-4, the CER is 85.76. So, the answer is {\"answer\": \"85.76\"}" } ], "images": [ "scigen/train/2839.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6.1: Crow speech recognition" ], "reasoning": "The table shows the CER for different learning rates. For the learning rate of 10^-4, the CER is 85.76.", "question": "What is the Character Error Rate (CER) for the learning rate of 10^-4?", "answer": "So, the answer is {\"answer\": \"85.76\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Word Error Rate (WER) is higher for the learning rate of 10^-4 compared to 10^-3., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Word Error Rate (WER) is higher for the learning rate of 10^-4 compared to 10^-3.\n" }, { "role": "assistant", "content": "The table shows that the WER for 10^-4 is 98.97, which is higher than the WER for 10^-3, which is 97.07. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2839.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6.1: Crow speech recognition" ], "reasoning": "The table shows that the WER for 10^-4 is 98.97, which is higher than the WER for 10^-3, which is 97.07.", "statement": "The Word Error Rate (WER) is higher for the learning rate of 10^-4 compared to 10^-3.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6.2: Guaraní results using greedy decoding'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The dataset used utterances between 1 and 15 seconds, and all models were trained for 50 epochs with a starting learning rate of 10−4, annealed each epoch.Both the development and training dataset used only utterances between 1 and 15 seconds in length, thus the results shown are not directly comparable to hartmann2016two. Future experiments will be conducted on all the data for more direct comparison. All models were trained for 50 epochs with a starting learning rate of 10−4 and learning rate annealing each epoch." } ], "images": [ "scigen/train/2840.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6.2: Guaraní results using greedy decoding" ], "reasoning": "The dataset used utterances between 1 and 15 seconds, and all models were trained for 50 epochs with a starting learning rate of 10−4, annealed each epoch.", "description": "Both the development and training dataset used only utterances between 1 and 15 seconds in length, thus the results shown are not directly comparable to hartmann2016two. Future experiments will be conducted on all the data for more direct comparison. All models were trained for 50 epochs with a starting learning rate of 10−4 and learning rate annealing each epoch.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 6.2: Guaraní results using greedy decoding', answer the following question. Give detailed solution consisting of each step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the WER for the model with the lowest CER?\n" }, { "role": "assistant", "content": "The table shows that the model with 4 GRU layers has the lowest CER of 44.40. The corresponding WER for this model is 82.08. So, the answer is {\"answer\": \"82.08\"}" } ], "images": [ "scigen/train/2840.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6.2: Guaraní results using greedy decoding" ], "reasoning": "The table shows that the model with 4 GRU layers has the lowest CER of 44.40. The corresponding WER for this model is 82.08.", "question": "What is the WER for the model with the lowest CER?", "answer": "So, the answer is {\"answer\": \"82.08\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The model with 3 GRU layers has a lower CER than the model with 2 GRU layers., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model with 3 GRU layers has a lower CER than the model with 2 GRU layers.\n" }, { "role": "assistant", "content": "The table shows that the model with 3 GRU layers has a CER of 46.96, which is lower than the CER of 47.90 for the model with 2 GRU layers. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2840.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6.2: Guaraní results using greedy decoding" ], "reasoning": "The table shows that the model with 3 GRU layers has a CER of 46.96, which is lower than the CER of 47.90 for the model with 2 GRU layers.", "statement": "The model with 3 GRU layers has a lower CER than the model with 2 GRU layers.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Native Language Identification from Reading results with 10-fold cross-validation for native speakers of Chinese, Japanese, Portuguese and Spanish. In the Shared regime all the participants read the same 78 sentences. In the Individual regime each participant reads a different set of 78 sentences.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The majority baseline selects the native language with the largest number of participants, while the random clusters baseline groups words randomly, matching the number of syntactic categories.We introduce two baselines against which we compare the performance of our feature-sets. The majority baseline selects the native language with the largest number of participants. The random clusters baseline clusters words into groups randomly, with the number of groups set to the number of syntactic categories in our data." } ], "images": [ "scigen/train/2841.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Native Language Identification from Reading results with 10-fold cross-validation for native speakers of Chinese, Japanese, Portuguese and Spanish. In the Shared regime all the participants read the same 78 sentences. In the Individual regime each participant reads a different set of 78 sentences." ], "reasoning": "The majority baseline selects the native language with the largest number of participants, while the random clusters baseline groups words randomly, matching the number of syntactic categories.", "description": "We introduce two baselines against which we compare the performance of our feature-sets. The majority baseline selects the native language with the largest number of participants. The random clusters baseline clusters words into groups randomly, with the number of groups set to the number of syntactic categories in our data.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 2: Native Language Identification from Reading results with 10-fold cross-validation for native speakers of Chinese, Japanese, Portuguese and Spanish. In the Shared regime all the participants read the same 78 sentences. In the Individual regime each participant reads a different set of 78 sentences.\nQuestion: What is the highest score achieved by the Words in Fixed Context (WFC) feature-set in the Shared Sentences Regime?\n" }, { "role": "assistant", "content": "To find the highest score achieved by the WFC feature-set in the Shared Sentences Regime, look at the row labeled 'Words in Fixed Context (WFC)' and identify the highest value in the columns under 'Shared Sentences Regime'. The highest value is 71.03. So, the answer is {\"answer\": \"71.03\"}" } ], "images": [ "scigen/train/2841.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Native Language Identification from Reading results with 10-fold cross-validation for native speakers of Chinese, Japanese, Portuguese and Spanish. In the Shared regime all the participants read the same 78 sentences. In the Individual regime each participant reads a different set of 78 sentences." ], "reasoning": "To find the highest score achieved by the WFC feature-set in the Shared Sentences Regime, look at the row labeled 'Words in Fixed Context (WFC)' and identify the highest value in the columns under 'Shared Sentences Regime'. The highest value is 71.03.", "question": "What is the highest score achieved by the Words in Fixed Context (WFC) feature-set in the Shared Sentences Regime?", "answer": "So, the answer is {\"answer\": \"71.03\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Syntactic Clusters (SC) feature-set achieved a higher score with unigrams than with bigrams in the Individual Sentences Regime. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Syntactic Clusters (SC) feature-set achieved a higher score with unigrams than with bigrams in the Individual Sentences Regime.\n" }, { "role": "assistant", "content": "To verify this statement, compare the scores of the SC feature-set with unigrams and bigrams in the Individual Sentences Regime. The score with unigrams is 48.97, and the score with bigrams is 43.45. Since 48.97 is higher than 43.45, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2841.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Native Language Identification from Reading results with 10-fold cross-validation for native speakers of Chinese, Japanese, Portuguese and Spanish. In the Shared regime all the participants read the same 78 sentences. In the Individual regime each participant reads a different set of 78 sentences." ], "reasoning": "To verify this statement, compare the scores of the SC feature-set with unigrams and bigrams in the Individual Sentences Regime. The score with unigrams is 48.97, and the score with bigrams is 43.45. Since 48.97 is higher than 43.45, the statement is supported.", "statement": "The Syntactic Clusters (SC) feature-set achieved a higher score with unigrams than with bigrams in the Individual Sentences Regime.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Number of participants and mean MET English score by native language group.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The study involved 182 adult participants, including 37 native English speakers and 145 ESL learners from four different native language backgrounds, who were tested for English proficiency using the MET.We recruited 182 adult participants. Of those, 37 are native English speakers and 145 are ESL learners from four native language backgrounds: Chinese, Japanese, Portuguese and Spanish. All the participants in the experiment are native speakers of only one language. The ESL speakers were tested for English proficiency using the grammar and listening sections of the Michigan English test (MET), which consist of 50 multiple choice questions. The English proficiency score was calculated as the number of correctly answered questions on these modules. The majority of the participants scored in the intermediate-advanced proficiency range. Additionally, we collected metadata on gender, age, level of education, duration of English studies and usage, time spent in English speaking countries and proficiency in any additional language spoken." } ], "images": [ "scigen/train/2842.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Number of participants and mean MET English score by native language group." ], "reasoning": "The study involved 182 adult participants, including 37 native English speakers and 145 ESL learners from four different native language backgrounds, who were tested for English proficiency using the MET.", "description": "We recruited 182 adult participants. Of those, 37 are native English speakers and 145 are ESL learners from four native language backgrounds: Chinese, Japanese, Portuguese and Spanish. All the participants in the experiment are native speakers of only one language. The ESL speakers were tested for English proficiency using the grammar and listening sections of the Michigan English test (MET), which consist of 50 multiple choice questions. The English proficiency score was calculated as the number of correctly answered questions on these modules. The majority of the participants scored in the intermediate-advanced proficiency range. Additionally, we collected metadata on gender, age, level of education, duration of English studies and usage, time spent in English speaking countries and proficiency in any additional language spoken.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 1: Number of participants and mean MET English score by native language group.', unfold the steps to solve the question given next. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: What is the average English proficiency score of the ESL learners?\n" }, { "role": "assistant", "content": "To find the average English proficiency score of the ESL learners, sum the English scores of the Chinese, Japanese, Portuguese, and Spanish participants and divide by the number of these groups. So, the answer is {\"answer\": \"41.45\"}." } ], "images": [ "scigen/train/2842.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Number of participants and mean MET English score by native language group." ], "reasoning": "To find the average English proficiency score of the ESL learners, sum the English scores of the Chinese, Japanese, Portuguese, and Spanish participants and divide by the number of these groups.", "question": "What is the average English proficiency score of the ESL learners?", "answer": "So, the answer is {\"answer\": \"41.45\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe majority of the participants scored in the intermediate-advanced proficiency range.\n" }, { "role": "assistant", "content": "The description states that the majority of participants scored in the intermediate-advanced proficiency range, but the table does not provide specific score ranges or distributions to confirm this. Thus, the answer is {\"answer\": \"not enough info\"}." } ], "images": [ "scigen/train/2842.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Number of participants and mean MET English score by native language group." ], "reasoning": "The description states that the majority of participants scored in the intermediate-advanced proficiency range, but the table does not provide specific score ranges or distributions to confirm this.", "statement": "The majority of the participants scored in the intermediate-advanced proficiency range.", "answer": "Thus, the answer is {\"answer\": \"not enough info\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Evaluation results on link prediction with different relation categories.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The STKRL models, especially with attention mechanisms, effectively capture useful textual information, leading to better performance in N-to-N relations.(2) The improvements locate more in N-to-N relations. It indicates that textual information is important when modeling complex relations, and our STKRL models could well capture the useful textual information for better knowledge representations." } ], "images": [ "scigen/train/2843.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Evaluation results on link prediction with different relation categories." ], "reasoning": "The STKRL models, especially with attention mechanisms, effectively capture useful textual information, leading to better performance in N-to-N relations.", "description": "(2) The improvements locate more in N-to-N relations. It indicates that textual information is important when modeling complex relations, and our STKRL models could well capture the useful textual information for better knowledge representations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model has the highest score for Predicting Tail (Hits@10) in N-to-N relations?\nSolve the above question based on the table titled 'Table 2: Evaluation results on link prediction with different relation categories.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the column for Predicting Tail (Hits@10) in N-to-N relations, the highest score is found in the STKRL (LSTM+ATT) model with a score of 83.6. So, the answer is {\"answer\": \"STKRL (LSTM+ATT)\"}." } ], "images": [ "scigen/train/2843.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Evaluation results on link prediction with different relation categories." ], "reasoning": "By examining the column for Predicting Tail (Hits@10) in N-to-N relations, the highest score is found in the STKRL (LSTM+ATT) model with a score of 83.6.", "question": "Which model has the highest score for Predicting Tail (Hits@10) in N-to-N relations?", "answer": "So, the answer is {\"answer\": \"STKRL (LSTM+ATT)\"}." }, { "messages": [ { "role": "user", "content": "The STKRL (RNN+P+ATT) model has the highest score for Predicting Head (Hits@10) in N-to-N relations. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe STKRL (RNN+P+ATT) model has the highest score for Predicting Head (Hits@10) in N-to-N relations.\n" }, { "role": "assistant", "content": "By examining the column for Predicting Head (Hits@10) in N-to-N relations, the highest score is found in the STKRL (LSTM+ATT) model with a score of 53.2, not the STKRL (RNN+P+ATT) model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2843.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Evaluation results on link prediction with different relation categories." ], "reasoning": "By examining the column for Predicting Head (Hits@10) in N-to-N relations, the highest score is found in the STKRL (LSTM+ATT) model with a score of 53.2, not the STKRL (RNN+P+ATT) model.", "statement": "The STKRL (RNN+P+ATT) model has the highest score for Predicting Head (Hits@10) in N-to-N relations.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Evaluation results on triple classification.'.\n" }, { "role": "assistant", "content": "Attention-based models, particularly STKRL (LSTM+ATT), outperform all baselines, demonstrating the importance of attention mechanisms and textual information in knowledge representation.4.3.2 Results From the results, we observe that: (1) Our attention-based models significantly outperform all baselines, which indicates the capability of our methods in modeling knowledge representations. (2) STKRL (RNN w/o ATT) outperforms TransE, which implies the significance of textual information. Moreover, the attention-based STKRL models outperform Wang’s method which also encodes textual information into knowledge representations. It confirms that our sentence encoders can better understand the meaning of sentences, and thus get better performances. (3) Attention is the key component in our model. It can automatically select the more informative reference sentences to represent an entity, alleviating the noises caused by low-quality reference sentences. (4) STKRL (LSTM+ATT) achieves the best performance, successfully capturing the rich information in long-distance dependencies. Besides, STKRL (RNN+P+ATT) outperforms STKRL (RNN+ATT), which also demonstrates the advantages of pooling strategy." } ], "images": [ "scigen/train/2844.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Evaluation results on triple classification." ], "reasoning": "Attention-based models, particularly STKRL (LSTM+ATT), outperform all baselines, demonstrating the importance of attention mechanisms and textual information in knowledge representation.", "description": "4.3.2 Results From the results, we observe that: (1) Our attention-based models significantly outperform all baselines, which indicates the capability of our methods in modeling knowledge representations. (2) STKRL (RNN w/o ATT) outperforms TransE, which implies the significance of textual information. Moreover, the attention-based STKRL models outperform Wang’s method which also encodes textual information into knowledge representations. It confirms that our sentence encoders can better understand the meaning of sentences, and thus get better performances. (3) Attention is the key component in our model. It can automatically select the more informative reference sentences to represent an entity, alleviating the noises caused by low-quality reference sentences. (4) STKRL (LSTM+ATT) achieves the best performance, successfully capturing the rich information in long-distance dependencies. Besides, STKRL (RNN+P+ATT) outperforms STKRL (RNN+ATT), which also demonstrates the advantages of pooling strategy.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 3: Evaluation results on triple classification.\nQuestion: Which method achieves the highest performance on the FB15K dataset?\n" }, { "role": "assistant", "content": "By comparing the performance scores in the table, STKRL (LSTM+ATT) has the highest score of 88.6, indicating it achieves the best performance. So, the answer is {\"answer\": \"STKRL (LSTM+ATT)\"}" } ], "images": [ "scigen/train/2844.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Evaluation results on triple classification." ], "reasoning": "By comparing the performance scores in the table, STKRL (LSTM+ATT) has the highest score of 88.6, indicating it achieves the best performance.", "question": "Which method achieves the highest performance on the FB15K dataset?", "answer": "So, the answer is {\"answer\": \"STKRL (LSTM+ATT)\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim STKRL (RNN w/o ATT) outperforms Wang's method., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nSTKRL (RNN w/o ATT) outperforms Wang's method.\n" }, { "role": "assistant", "content": "The table shows that STKRL (RNN w/o ATT) has a score of 79.2, while Wang's method has a score of 84.2, indicating that Wang's method outperforms STKRL (RNN w/o ATT). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2844.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Evaluation results on triple classification." ], "reasoning": "The table shows that STKRL (RNN w/o ATT) has a score of 79.2, while Wang's method has a score of 84.2, indicating that Wang's method outperforms STKRL (RNN w/o ATT).", "statement": "STKRL (RNN w/o ATT) outperforms Wang's method.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6: Evaluation results on link prediction.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "STKRL (LSTM+ATT) achieves the best performance in both Mean Rank and Hits@10, demonstrating the effectiveness of utilizing better sentence encoders.Our model significantly outperforms all baselines in both Mean Rank and Hits@10, which demonstrates the effectiveness and robustness of our model. (2) STKRL (RNN+P+ATT) and STKRL (LSTM+ATT) significantly outperform Wang ’s method. It is because that we utilize sentence encoders to model textual information with the help of word orders, instead of simply considering separate words used in Wang’s method. Moreover, the attention-based STKRL models could pay more attention to those informative reference sentences when constructing text-based representations. (3) STKRL (LSTM+ATT) achieves the best performances, which indicates the power in utilizing better sentence encoders." } ], "images": [ "scigen/train/2845.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Evaluation results on link prediction." ], "reasoning": "STKRL (LSTM+ATT) achieves the best performance in both Mean Rank and Hits@10, demonstrating the effectiveness of utilizing better sentence encoders.", "description": "Our model significantly outperforms all baselines in both Mean Rank and Hits@10, which demonstrates the effectiveness and robustness of our model. (2) STKRL (RNN+P+ATT) and STKRL (LSTM+ATT) significantly outperform Wang ’s method. It is because that we utilize sentence encoders to model textual information with the help of word orders, instead of simply considering separate words used in Wang’s method. Moreover, the attention-based STKRL models could pay more attention to those informative reference sentences when constructing text-based representations. (3) STKRL (LSTM+ATT) achieves the best performances, which indicates the power in utilizing better sentence encoders.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 6: Evaluation results on link prediction.'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model achieves the best performance in both Mean Rank and Hits@10?\n" }, { "role": "assistant", "content": "By comparing the values in the Mean Rank and Hits@10 columns, STKRL (LSTM+ATT) has the lowest Mean Rank (182 Raw, 51 Filter) and the highest Hits@10 (52.1 Raw, 81.2 Filter), indicating it achieves the best performance. So, the answer is {\"answer\": \"STKRL (LSTM+ATT)\"}" } ], "images": [ "scigen/train/2845.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Evaluation results on link prediction." ], "reasoning": "By comparing the values in the Mean Rank and Hits@10 columns, STKRL (LSTM+ATT) has the lowest Mean Rank (182 Raw, 51 Filter) and the highest Hits@10 (52.1 Raw, 81.2 Filter), indicating it achieves the best performance.", "question": "Which model achieves the best performance in both Mean Rank and Hits@10?", "answer": "So, the answer is {\"answer\": \"STKRL (LSTM+ATT)\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nSTKRL (RNN+P+ATT) outperforms Wang's method in Hits@10.\n" }, { "role": "assistant", "content": "By comparing the Hits@10 values, STKRL (RNN+P+ATT) has Hits@10 values of 52.2 (Raw) and 80.6 (Filter), which are higher than Wang's method with Hits@10 values of 50.9 (Raw) and 78.2 (Filter). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2845.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Evaluation results on link prediction." ], "reasoning": "By comparing the Hits@10 values, STKRL (RNN+P+ATT) has Hits@10 values of 52.2 (Raw) and 80.6 (Filter), which are higher than Wang's method with Hits@10 values of 50.9 (Raw) and 78.2 (Filter).", "statement": "STKRL (RNN+P+ATT) outperforms Wang's method in Hits@10.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Accuracy rates (%) for frame-based phoneme classification on TIMIT for the baseline (NN), the four different algorithms in GBL-SSL [7] and our model, SSSAE'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our system, SSSAE, outperforms all methods except pMP in both 10% and 30% labelled data scenarios.We observe that our system performs better than all the techniques mentioned except the Prior Regularised Measure Propagation (pMP) algorithm." } ], "images": [ "scigen/train/2846.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracy rates (%) for frame-based phoneme classification on TIMIT for the baseline (NN), the four different algorithms in GBL-SSL [7] and our model, SSSAE" ], "reasoning": "Our system, SSSAE, outperforms all methods except pMP in both 10% and 30% labelled data scenarios.", "description": "We observe that our system performs better than all the techniques mentioned except the Prior Regularised Measure Propagation (pMP) algorithm.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: Accuracy rates (%) for frame-based phoneme classification on TIMIT for the baseline (NN), the four different algorithms in GBL-SSL [7] and our model, SSSAE', solve this question and give detail solutions. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich method has the highest test accuracy with 30% labelled data?\n" }, { "role": "assistant", "content": "By comparing the test accuracy values for 30% labelled data, pMP has the highest accuracy at 71.06%. So, the answer is {\"answer\": \"pMP\"}" } ], "images": [ "scigen/train/2846.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracy rates (%) for frame-based phoneme classification on TIMIT for the baseline (NN), the four different algorithms in GBL-SSL [7] and our model, SSSAE" ], "reasoning": "By comparing the test accuracy values for 30% labelled data, pMP has the highest accuracy at 71.06%.", "question": "Which method has the highest test accuracy with 30% labelled data?", "answer": "So, the answer is {\"answer\": \"pMP\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nOur system performs better than the MP algorithm with 10% labelled data.\n" }, { "role": "assistant", "content": "The test accuracy of our system (SSSAE) with 10% labelled data is 67.03%, which is higher than MP's 65.48%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2846.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracy rates (%) for frame-based phoneme classification on TIMIT for the baseline (NN), the four different algorithms in GBL-SSL [7] and our model, SSSAE" ], "reasoning": "The test accuracy of our system (SSSAE) with 10% labelled data is 67.03%, which is higher than MP's 65.48%.", "statement": "Our system performs better than the MP algorithm with 10% labelled data.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Results on frame-based phoneme classification on the validation and test sets on the TIMIT material. Our method (SSSAE) is compared to a neural network trained with supervised backpropagation with the same amount of labelled data. The total number of training frames is 1068818. The value of α is optimised on the validation set as the proportion of labelled examples is varied.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The validation and test accuracy rates for both NN and SSSAE methods are close, indicating good generalization. The optimal value for α depends on the proportion of labeled data, with higher proportions giving more weight to classification error.NN: neural network trained with supervised backpropagation. SSSAE: our method. Both validation and test accuracy rates are shown. Both validation set accuracy and test set accuracy are reported. The hyper-parameters of the neural network such as learning rate tuned using the validation set are also shown in the table. As expected this advantage decreases when the proportion of labelled training examples is increased. The validation and test errors are always very close, indicating that the parameters optimised on the validation set generalise well to the test set. As expected, the optimal value for α is strongly dependent on the proportion of labelled material. The higher the proportion the more weight the algorithm gives to the classification error, compared to the unsupervised reconstruction error." } ], "images": [ "scigen/train/2847.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results on frame-based phoneme classification on the validation and test sets on the TIMIT material. Our method (SSSAE) is compared to a neural network trained with supervised backpropagation with the same amount of labelled data. The total number of training frames is 1068818. The value of α is optimised on the validation set as the proportion of labelled examples is varied." ], "reasoning": "The validation and test accuracy rates for both NN and SSSAE methods are close, indicating good generalization. The optimal value for α depends on the proportion of labeled data, with higher proportions giving more weight to classification error.", "description": "NN: neural network trained with supervised backpropagation. SSSAE: our method. Both validation and test accuracy rates are shown. Both validation set accuracy and test set accuracy are reported. The hyper-parameters of the neural network such as learning rate tuned using the validation set are also shown in the table. As expected this advantage decreases when the proportion of labelled training examples is increased. The validation and test errors are always very close, indicating that the parameters optimised on the validation set generalise well to the test set. As expected, the optimal value for α is strongly dependent on the proportion of labelled material. The higher the proportion the more weight the algorithm gives to the classification error, compared to the unsupervised reconstruction error.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 1: Results on frame-based phoneme classification on the validation and test sets on the TIMIT material. Our method (SSSAE) is compared to a neural network trained with supervised backpropagation with the same amount of labelled data. The total number of training frames is 1068818. The value of α is optimised on the validation set as the proportion of labelled examples is varied.' to sequentially solve the question presented below. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the test accuracy of the SSSAE method when 10% of the TIMIT labeled observations are used?\n" }, { "role": "assistant", "content": "From the table, when 10% of the TIMIT labeled observations are used, the test accuracy for the SSSAE method is shown in the column 'Results on TIMIT SSSAE test acc. (%)'. Thus, the answer is {\"answer\": \"67.03\"}." } ], "images": [ "scigen/train/2847.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results on frame-based phoneme classification on the validation and test sets on the TIMIT material. Our method (SSSAE) is compared to a neural network trained with supervised backpropagation with the same amount of labelled data. The total number of training frames is 1068818. The value of α is optimised on the validation set as the proportion of labelled examples is varied." ], "reasoning": "From the table, when 10% of the TIMIT labeled observations are used, the test accuracy for the SSSAE method is shown in the column 'Results on TIMIT SSSAE test acc. (%)'.", "question": "What is the test accuracy of the SSSAE method when 10% of the TIMIT labeled observations are used?", "answer": "Thus, the answer is {\"answer\": \"67.03\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The neural network method achieves higher validation accuracy than the SSSAE method when 1% of the TIMIT labeled observations are used. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe neural network method achieves higher validation accuracy than the SSSAE method when 1% of the TIMIT labeled observations are used.\n" }, { "role": "assistant", "content": "From the table, when 1% of the TIMIT labeled observations are used, the validation accuracy for the neural network method is 57.46%, and for the SSSAE method, it is 59.65%. Since 57.46% is less than 59.65%, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2847.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results on frame-based phoneme classification on the validation and test sets on the TIMIT material. Our method (SSSAE) is compared to a neural network trained with supervised backpropagation with the same amount of labelled data. The total number of training frames is 1068818. The value of α is optimised on the validation set as the proportion of labelled examples is varied." ], "reasoning": "From the table, when 1% of the TIMIT labeled observations are used, the validation accuracy for the neural network method is 57.46%, and for the SSSAE method, it is 59.65%. Since 57.46% is less than 59.65%, the statement is refuted.", "statement": "The neural network method achieves higher validation accuracy than the SSSAE method when 1% of the TIMIT labeled observations are used.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Performance comparison of our main model variants.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The full model, which incorporates semantic information and dual guiding attention, outperforms the variant model in all evaluation metrics, demonstrating the benefit of these features for iVQA.The baseline is the powerful Show Attend and Tell The model is compared to ours in the context of using dynamic attention to focus on different salient regions at each time step. The proposed model incorporates additional semantic information S based on the object and attribute labels. It uses S to produce enhanced visual features Ev and obtain att0 through dual guiding attention. To investigate whether and how much this semantic information contributes to the major performance gain for the proposed model, we create a variant of the proposed model by dropping the semantic information S and dual guiding attention features att0. It actually outperforms the iVQA model in terms of BLEU-1, BLEU-2, BLEU-3 and METEOR. This validates the visual features extracted at the object-level salient regions more suitable for the task of iVQA than the visual features extracted with conventional deep networks. Nevertheless, the full model still beats the variant in terms of all evaluation metrics. This suggests that the use of semantic information and dual guiding attention is beneficial for iVQA. Enhancing visual features with semantic information that has the same textual modality with the answer and generated partial question is useful for our model to precisely attend to corresponding objects, and consequently improve the overall performance." } ], "images": [ "scigen/train/2848.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance comparison of our main model variants." ], "reasoning": "The full model, which incorporates semantic information and dual guiding attention, outperforms the variant model in all evaluation metrics, demonstrating the benefit of these features for iVQA.", "description": "The baseline is the powerful Show Attend and Tell The model is compared to ours in the context of using dynamic attention to focus on different salient regions at each time step. The proposed model incorporates additional semantic information S based on the object and attribute labels. It uses S to produce enhanced visual features Ev and obtain att0 through dual guiding attention. To investigate whether and how much this semantic information contributes to the major performance gain for the proposed model, we create a variant of the proposed model by dropping the semantic information S and dual guiding attention features att0. It actually outperforms the iVQA model in terms of BLEU-1, BLEU-2, BLEU-3 and METEOR. This validates the visual features extracted at the object-level salient regions more suitable for the task of iVQA than the visual features extracted with conventional deep networks. Nevertheless, the full model still beats the variant in terms of all evaluation metrics. This suggests that the use of semantic information and dual guiding attention is beneficial for iVQA. Enhancing visual features with semantic information that has the same textual modality with the answer and generated partial question is useful for our model to precisely attend to corresponding objects, and consequently improve the overall performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Performance comparison of our main model variants.', answer the question below by showing each progressive step toward the answer. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model achieves a higher BLEU-4 score, the variant without semantic information and dual guiding attention or the full model?\n" }, { "role": "assistant", "content": "Compare the BLEU-4 scores of the variant model and the full model from the table. The variant model has a BLEU-4 score of 0.204, while the full model has a BLEU-4 score of 0.207. So, the answer is {\"answer\": \"full model\"}" } ], "images": [ "scigen/train/2848.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance comparison of our main model variants." ], "reasoning": "Compare the BLEU-4 scores of the variant model and the full model from the table. The variant model has a BLEU-4 score of 0.204, while the full model has a BLEU-4 score of 0.207.", "question": "Which model achieves a higher BLEU-4 score, the variant without semantic information and dual guiding attention or the full model?", "answer": "So, the answer is {\"answer\": \"full model\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe variant model without semantic information and dual guiding attention achieves a higher METEOR score than the full model.\n" }, { "role": "assistant", "content": "Check the METEOR scores for both models in the table. The variant model has a METEOR score of 0.208, while the full model has a METEOR score of 0.209. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2848.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance comparison of our main model variants." ], "reasoning": "Check the METEOR scores for both models in the table. The variant model has a METEOR score of 0.208, while the full model has a METEOR score of 0.209.", "statement": "The variant model without semantic information and dual guiding attention achieves a higher METEOR score than the full model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Results of different models and initialization setups on WikiSplit. Blockwise sorted by SARI score.'.\n" }, { "role": "assistant", "content": "The WikiSplit dataset was used for training models for 300k steps with a global batch size of 256, and bertShare outperformed other setups.The reverse task of sentence fusion is the split-and-rephrase task, which requires rewriting a long sentence into two or more coherent short sentences Narayan et al. We use the WikiSplit dataset Botha et al. Training was done for 300k steps with a global batch size of 256. The input and output are padded to a length of 128, which covers 100% of the training, evaluation and test data. Previous state-of-the-art results by \\newcitewikisplit used a bi-directional LSTM with a copy mechanism Aharoni and Goldberg The shared encoder-decoder setup of bertShare outperforms all other setups. For the larger models with 24 layers, we observed a small over-fitting after 100k steps (~25 epochs), and therefore stop the training early. bertShare and robertaShare perform on par and both outperform their 12 layer counterpart." } ], "images": [ "scigen/train/2849.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of different models and initialization setups on WikiSplit. Blockwise sorted by SARI score." ], "reasoning": "The WikiSplit dataset was used for training models for 300k steps with a global batch size of 256, and bertShare outperformed other setups.", "description": "The reverse task of sentence fusion is the split-and-rephrase task, which requires rewriting a long sentence into two or more coherent short sentences Narayan et al. We use the WikiSplit dataset Botha et al. Training was done for 300k steps with a global batch size of 256. The input and output are padded to a length of 128, which covers 100% of the training, evaluation and test data. Previous state-of-the-art results by \\newcitewikisplit used a bi-directional LSTM with a copy mechanism Aharoni and Goldberg The shared encoder-decoder setup of bertShare outperforms all other setups. For the larger models with 24 layers, we observed a small over-fitting after 100k steps (~25 epochs), and therefore stop the training early. bertShare and robertaShare perform on par and both outperform their 12 layer counterpart.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 3: Results of different models and initialization setups on WikiSplit. Blockwise sorted by SARI score.\nQuestion: Which model achieved the highest BLEU score when initialized with the base checkpoint (12 layers)?\n" }, { "role": "assistant", "content": "To find the model with the highest BLEU score, look at the BLEU column under the 'Initialized with the base checkpoint (12 layers)' section. The highest score is 77.2, achieved by bertShare. So, the answer is {\"answer\": \"bertShare\"}" } ], "images": [ "scigen/train/2849.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of different models and initialization setups on WikiSplit. Blockwise sorted by SARI score." ], "reasoning": "To find the model with the highest BLEU score, look at the BLEU column under the 'Initialized with the base checkpoint (12 layers)' section. The highest score is 77.2, achieved by bertShare.", "question": "Which model achieved the highest BLEU score when initialized with the base checkpoint (12 layers)?", "answer": "So, the answer is {\"answer\": \"bertShare\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nbertShare achieved the highest Exact score when initialized with the large checkpoint (24 layers).\n" }, { "role": "assistant", "content": "To verify this statement, look at the Exact column under the 'Initialized with the large checkpoint (24 layers)' section. The highest Exact score is 16.6, achieved by bertShare. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2849.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of different models and initialization setups on WikiSplit. Blockwise sorted by SARI score." ], "reasoning": "To verify this statement, look at the Exact column under the 'Initialized with the large checkpoint (24 layers)' section. The highest Exact score is 16.6, achieved by bertShare.", "statement": "bertShare achieved the highest Exact score when initialized with the large checkpoint (24 layers).", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Results of different models and initialization techniques on DiscoFuse and subsampled training sets. Blockwise sorted by SARI score on 100% of the training set.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The best performing model with 12 layers is roberta2gpt with a SARI score of 89.9, while the best overall model is robertaShare with 24 layers and a SARI score of 90.3.Training was done for 300k steps with a global batch size of 256. The input and output are padded to a length of 128, which covers 100% of the training, evaluation and test data. We report SARI Xu et al. Previous state-of-the-art results by \\newcitediscofuse used the vanilla transformer model by \\newcitetransformer, with only 7 layers. All models with initialized encoders outperform the baseline by a large margin, with a SARI score of 89.3 compared to 86.9 (bert2rnd vs. rnd2rnd). To measure the effect on smaller training sets, we randomly subsample the training data down to 10% and 1%, i.e. 450k and 45k training examples, respectively. First, we notice, that performance comparable to the baseline is achieved even when training on only 10% of the training data (rnd2rnd vs. robertaShare). Secondly, when using only 1% of the training data setups with fewer randomly initialized parameters (bert2bert vs. bert2rnd) perform better. The best performing 12 layer setup is roberta2gpt with a SARI score of 89.9 only outperformed by 24 layer setup of robertaShare with a SARI score of 90.3." } ], "images": [ "scigen/train/2850.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of different models and initialization techniques on DiscoFuse and subsampled training sets. Blockwise sorted by SARI score on 100% of the training set." ], "reasoning": "The best performing model with 12 layers is roberta2gpt with a SARI score of 89.9, while the best overall model is robertaShare with 24 layers and a SARI score of 90.3.", "description": "Training was done for 300k steps with a global batch size of 256. The input and output are padded to a length of 128, which covers 100% of the training, evaluation and test data. We report SARI Xu et al. Previous state-of-the-art results by \\newcitediscofuse used the vanilla transformer model by \\newcitetransformer, with only 7 layers. All models with initialized encoders outperform the baseline by a large margin, with a SARI score of 89.3 compared to 86.9 (bert2rnd vs. rnd2rnd). To measure the effect on smaller training sets, we randomly subsample the training data down to 10% and 1%, i.e. 450k and 45k training examples, respectively. First, we notice, that performance comparable to the baseline is achieved even when training on only 10% of the training data (rnd2rnd vs. robertaShare). Secondly, when using only 1% of the training data setups with fewer randomly initialized parameters (bert2bert vs. bert2rnd) perform better. The best performing 12 layer setup is roberta2gpt with a SARI score of 89.9 only outperformed by 24 layer setup of robertaShare with a SARI score of 90.3.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Results of different models and initialization techniques on DiscoFuse and subsampled training sets. Blockwise sorted by SARI score on 100% of the training set.', answer the following question. Give detailed solution consisting of each step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model achieved the highest SARI score when initialized with the large checkpoint and trained on 100% of the data?\n" }, { "role": "assistant", "content": "From the table, the model initialized with the large checkpoint and trained on 100% of the data that achieved the highest SARI score is robertaShare with a score of 90.3. So, the answer is {\"answer\": \"robertaShare\"}." } ], "images": [ "scigen/train/2850.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of different models and initialization techniques on DiscoFuse and subsampled training sets. Blockwise sorted by SARI score on 100% of the training set." ], "reasoning": "From the table, the model initialized with the large checkpoint and trained on 100% of the data that achieved the highest SARI score is robertaShare with a score of 90.3.", "question": "Which model achieved the highest SARI score when initialized with the large checkpoint and trained on 100% of the data?", "answer": "So, the answer is {\"answer\": \"robertaShare\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe bert2bert model achieved a higher SARI score than the bert2rnd model when trained on 1% of the data.\n" }, { "role": "assistant", "content": "From the table, the bert2bert model has a SARI score of 81.2, while the bert2rnd model has a SARI score of 81.1 when trained on 1% of the data. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2850.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of different models and initialization techniques on DiscoFuse and subsampled training sets. Blockwise sorted by SARI score on 100% of the training set." ], "reasoning": "From the table, the bert2bert model has a SARI score of 81.2, while the bert2rnd model has a SARI score of 81.1 when trained on 1% of the data. Therefore, the statement is supported.", "statement": "The bert2bert model achieved a higher SARI score than the bert2rnd model when trained on 1% of the data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Uncased BLEU-4 scores on WMT14 English ↔ German newstest2014 and newstest2016 test sets. Models in the middle section use the 110k wordpiece vocabulary that comes with the multilingual BERT checkpoint. In the bottom section, we use the native 32k wordpiece vocabulary extracted from WMT14 train set and a BERT checkpoint pre-trained only on English and German subset of Wikipedia. * leveraging a large number of additional parallel sentence pairs obtained with back-translation; we include this score as a reference to the highest achieved result on newstest2014. The GPT-2 results for En→De (where the GPT-2 initialized decoder is used to decode targets in De) are grayed out as they are apriori penalizing for GPT-2 which was only pretrained on En texts.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Models initialized with BERT checkpoints, especially on the encoder side, show significant performance improvements in translation tasks, with custom BERT checkpoints yielding further gains.In both cases, we use the encoder and decoder with 6 layers and the 32k wordpiece vocabulary extracted from the WMT14 training set. Our implementation obtains slightly higher scores than the original implementation. Note that here all models have encoders and decoders with 12 layers. For BERT models, we use the BERT-Base Multilingual Cased checkpoint to initialize the encoder or the decoder or both, as the task involves one non-English language. This checkpoint has been pre-trained on 108 languages using a multilingual Wikipedia dump with a vocabulary of 110k wordpieces. First, we observe that initializing the model with the BERT checkpoint is most beneficial on the encoder side; our observation is in line with \\newcitexlnet_arxiv19. Furthermore, models initialized with the BERT checkpoint receive a significant boost: bert2rnd compared to the no-initialization rnd2rnd setup scores higher by +4 points on En→De and +3.6 points on De→En on newstest2014. Contrary to the WikiSplit and DiscoFuse task, sharing the encoder and decoder variables did not give an additional boost. This is most likely because a) model capacity is an important factor in MT and b) encoder and decoder have to deal with different grammar and vocabulary. Customized BERT checkpoint. For this experiment we did not include RoBERTa, as the public checkpoint is available for English only. Instead, we train our own checkpoint. We also observe that our implementation of the baseline Transformer, as well as rnd2rnd setup which uses no initialization, perform weaker on newstest2014 compared to the Transformer baselines (with 6 layers and the 32k wordpiece vocabulary) We conjecture that the differences might be due to the larger 110k wordpiece vocabulary trained to handle 104 languages from Wikipedia dump which is suboptimal for WMT14 data and leads to inferior results. To verify this conjecture, we perform the following experiment: we use the 32k wordpiece vocabulary extracted from the WMT14 This provides a further +0.5 (En ↔ De) and +0.8 (De ↔ En) BLEU improvements on newstest2014, and, +1.1 and +0.7 on newstest2016, yielding an overall very strong performance on the challenging WMT14 task. Experiments with the larger models (the last block) show further improvements of up to +1.1 BLEU points." } ], "images": [ "scigen/train/2851.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Uncased BLEU-4 scores on WMT14 English ↔ German newstest2014 and newstest2016 test sets. Models in the middle section use the 110k wordpiece vocabulary that comes with the multilingual BERT checkpoint. In the bottom section, we use the native 32k wordpiece vocabulary extracted from WMT14 train set and a BERT checkpoint pre-trained only on English and German subset of Wikipedia. * leveraging a large number of additional parallel sentence pairs obtained with back-translation; we include this score as a reference to the highest achieved result on newstest2014. The GPT-2 results for En→De (where the GPT-2 initialized decoder is used to decode targets in De) are grayed out as they are apriori penalizing for GPT-2 which was only pretrained on En texts." ], "reasoning": "Models initialized with BERT checkpoints, especially on the encoder side, show significant performance improvements in translation tasks, with custom BERT checkpoints yielding further gains.", "description": "In both cases, we use the encoder and decoder with 6 layers and the 32k wordpiece vocabulary extracted from the WMT14 training set. Our implementation obtains slightly higher scores than the original implementation. Note that here all models have encoders and decoders with 12 layers. For BERT models, we use the BERT-Base Multilingual Cased checkpoint to initialize the encoder or the decoder or both, as the task involves one non-English language. This checkpoint has been pre-trained on 108 languages using a multilingual Wikipedia dump with a vocabulary of 110k wordpieces. First, we observe that initializing the model with the BERT checkpoint is most beneficial on the encoder side; our observation is in line with \\newcitexlnet_arxiv19. Furthermore, models initialized with the BERT checkpoint receive a significant boost: bert2rnd compared to the no-initialization rnd2rnd setup scores higher by +4 points on En→De and +3.6 points on De→En on newstest2014. Contrary to the WikiSplit and DiscoFuse task, sharing the encoder and decoder variables did not give an additional boost. This is most likely because a) model capacity is an important factor in MT and b) encoder and decoder have to deal with different grammar and vocabulary. Customized BERT checkpoint. For this experiment we did not include RoBERTa, as the public checkpoint is available for English only. Instead, we train our own checkpoint. We also observe that our implementation of the baseline Transformer, as well as rnd2rnd setup which uses no initialization, perform weaker on newstest2014 compared to the Transformer baselines (with 6 layers and the 32k wordpiece vocabulary) We conjecture that the differences might be due to the larger 110k wordpiece vocabulary trained to handle 104 languages from Wikipedia dump which is suboptimal for WMT14 data and leads to inferior results. To verify this conjecture, we perform the following experiment: we use the 32k wordpiece vocabulary extracted from the WMT14 This provides a further +0.5 (En ↔ De) and +0.8 (De ↔ En) BLEU improvements on newstest2014, and, +1.1 and +0.7 on newstest2016, yielding an overall very strong performance on the challenging WMT14 task. Experiments with the larger models (the last block) show further improvements of up to +1.1 BLEU points.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 4: Uncased BLEU-4 scores on WMT14 English ↔ German newstest2014 and newstest2016 test sets. Models in the middle section use the 110k wordpiece vocabulary that comes with the multilingual BERT checkpoint. In the bottom section, we use the native 32k wordpiece vocabulary extracted from WMT14 train set and a BERT checkpoint pre-trained only on English and German subset of Wikipedia. * leveraging a large number of additional parallel sentence pairs obtained with back-translation; we include this score as a reference to the highest achieved result on newstest2014. The GPT-2 results for En→De (where the GPT-2 initialized decoder is used to decode targets in De) are grayed out as they are apriori penalizing for GPT-2 which was only pretrained on En texts.', and tackle the question below in a sequential manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model achieved the highest score on newstest2016 De→En when initialized with a custom BERT checkpoint (24 layers) and vocabulary?\n" }, { "role": "assistant", "content": "To find the highest score on newstest2016 De→En, look at the scores under the 'Initialized with a custom BERT checkpoint (24 layers) and vocabulary' section. The highest score is 41.1 achieved by bert2rnd. So, the answer is {\"answer\": \"bert2rnd\"}" } ], "images": [ "scigen/train/2851.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Uncased BLEU-4 scores on WMT14 English ↔ German newstest2014 and newstest2016 test sets. Models in the middle section use the 110k wordpiece vocabulary that comes with the multilingual BERT checkpoint. In the bottom section, we use the native 32k wordpiece vocabulary extracted from WMT14 train set and a BERT checkpoint pre-trained only on English and German subset of Wikipedia. * leveraging a large number of additional parallel sentence pairs obtained with back-translation; we include this score as a reference to the highest achieved result on newstest2014. The GPT-2 results for En→De (where the GPT-2 initialized decoder is used to decode targets in De) are grayed out as they are apriori penalizing for GPT-2 which was only pretrained on En texts." ], "reasoning": "To find the highest score on newstest2016 De→En, look at the scores under the 'Initialized with a custom BERT checkpoint (24 layers) and vocabulary' section. The highest score is 41.1 achieved by bert2rnd.", "question": "Which model achieved the highest score on newstest2016 De→En when initialized with a custom BERT checkpoint (24 layers) and vocabulary?", "answer": "So, the answer is {\"answer\": \"bert2rnd\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe bert2rnd model achieved a score of 30.1 on newstest2014 En→De when initialized with public checkpoints (12 layers) and vocabulary.\n" }, { "role": "assistant", "content": "The table shows that the bert2rnd model achieved a score of 30.1 on newstest2014 En→De when initialized with public checkpoints (12 layers) and vocabulary. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2851.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Uncased BLEU-4 scores on WMT14 English ↔ German newstest2014 and newstest2016 test sets. Models in the middle section use the 110k wordpiece vocabulary that comes with the multilingual BERT checkpoint. In the bottom section, we use the native 32k wordpiece vocabulary extracted from WMT14 train set and a BERT checkpoint pre-trained only on English and German subset of Wikipedia. * leveraging a large number of additional parallel sentence pairs obtained with back-translation; we include this score as a reference to the highest achieved result on newstest2014. The GPT-2 results for En→De (where the GPT-2 initialized decoder is used to decode targets in De) are grayed out as they are apriori penalizing for GPT-2 which was only pretrained on En texts." ], "reasoning": "The table shows that the bert2rnd model achieved a score of 30.1 on newstest2014 En→De when initialized with public checkpoints (12 layers) and vocabulary.", "statement": "The bert2rnd model achieved a score of 30.1 on newstest2014 En→De when initialized with public checkpoints (12 layers) and vocabulary.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: Summarization results of different models and their initialization setups. We compare our setups (the bottom block) against both non-pre-trained (the top block) and pre-trained (the middle block) models on various datasets: the Lead baseline, PtGen See et al. (2017), ConvS2S Gehring et al. (2017), MMN Kim et al. (2019), Bottom-Up Gehrmann et al. (2018), MASS Song et al. (2019), TransLM Khandelwal et al. (2019) and UniLM Dong et al. (2019). The Lead results for the CNN/DailyMail dataset is taken from narayan-etal-2018-ranking, whereas, Lead, PtGen and ConvS2S results on the BBC dataset are taken from narayan-etal-2018-xsum. Our best results are boldfaced and the best results on the datasets are italicized.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The models were trained on CNN/DailyMail, BBC, and Gigaword datasets with different token limits and batch sizes, and evaluated using ROUGE F1 scores.In all three cases, we did not anonymize entities. We worked on the original cased versions of CNN/DailyMail and BBC datasets. For Gigaword we used the lowercased version to match the requirements of the publicly available lowercased test set. During training, the input documents were truncated to 512 tokens for CNN/DailyMail and BBC, and to 128 tokens for Gigaword. Similarly, the length of the summaries was limited to 128 tokens for CNN/DailyMail, 64 for BBC, and 32 for Gigaword. We used a global batch size of 128 document-summary pairs for CNN/DailyMail and BBC, and 256 document-summary pairs for Gigaword. We adapted to different number of training steps depending on the training data sizes. Models were trained for 500k, 300k and 200k steps for the Gigaword, CNN/DailyMail and BBC summarization datasets respectively. In all cases, we used the standard publicly available test sets; these consists of 1951 instances for Gigaword, 11490 for CNN/DailyMail and 11334 for BBC. We report on the ROUGE F1 scores Lin and Hovy Pre-training for sequence generation. Pre-training for seq2seq learning was first done by \\newciteDBLP:journals/corr/RamachandranLL16. They used a language model to pre-train the encoder and decoder of an RNN seq2seq model. Their method improved BLEU scores on newstest2014 by 3 points and ROUGE-L on CNN/Dailymail also by 3 points. However their BLEU score of 24.7 on newstest2014 En→De, compared to 30.6 in this work, and 29.4 ROUGE-L on CNN/Dailymail, compared to 36.33 also show the superiority of the transformer model as well as the masked language model objective of BERT. MASS Song et al. One advantage of this method is that, in contrast to our setups (except for gpt), the encoder-decoder attention mechanism is also pre-trained. The downside of this approach is that the pre-trained model is task-specific and not as general as BERT or GPT-2. UniLM Dong et al. also unifies bidirectional, unidirectional, and sequence to sequence language modeling. At the time of writing, no public checkpoint was available to us. To overcome the issue that the encoder-decoder attention is not pre-trained, \\newciteDBLP:journals/corr/abs-1905-08836 pre-trained a single transformer language model that encodes the source and generates the target. This setup matches our gpt setup. \\newciteDBLP: journals/corr/abs-1901-07291 pre-train their model using casual language modeling (like GPT), masked language modeling (like BERT) and a third new objective called translation language modeling to improve cross-lingual pre-training." } ], "images": [ "scigen/train/2852.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Summarization results of different models and their initialization setups. We compare our setups (the bottom block) against both non-pre-trained (the top block) and pre-trained (the middle block) models on various datasets: the Lead baseline, PtGen See et al. (2017), ConvS2S Gehring et al. (2017), MMN Kim et al. (2019), Bottom-Up Gehrmann et al. (2018), MASS Song et al. (2019), TransLM Khandelwal et al. (2019) and UniLM Dong et al. (2019). The Lead results for the CNN/DailyMail dataset is taken from narayan-etal-2018-ranking, whereas, Lead, PtGen and ConvS2S results on the BBC dataset are taken from narayan-etal-2018-xsum. Our best results are boldfaced and the best results on the datasets are italicized." ], "reasoning": "The models were trained on CNN/DailyMail, BBC, and Gigaword datasets with different token limits and batch sizes, and evaluated using ROUGE F1 scores.", "description": "In all three cases, we did not anonymize entities. We worked on the original cased versions of CNN/DailyMail and BBC datasets. For Gigaword we used the lowercased version to match the requirements of the publicly available lowercased test set. During training, the input documents were truncated to 512 tokens for CNN/DailyMail and BBC, and to 128 tokens for Gigaword. Similarly, the length of the summaries was limited to 128 tokens for CNN/DailyMail, 64 for BBC, and 32 for Gigaword. We used a global batch size of 128 document-summary pairs for CNN/DailyMail and BBC, and 256 document-summary pairs for Gigaword. We adapted to different number of training steps depending on the training data sizes. Models were trained for 500k, 300k and 200k steps for the Gigaword, CNN/DailyMail and BBC summarization datasets respectively. In all cases, we used the standard publicly available test sets; these consists of 1951 instances for Gigaword, 11490 for CNN/DailyMail and 11334 for BBC. We report on the ROUGE F1 scores Lin and Hovy Pre-training for sequence generation. Pre-training for seq2seq learning was first done by \\newciteDBLP:journals/corr/RamachandranLL16. They used a language model to pre-train the encoder and decoder of an RNN seq2seq model. Their method improved BLEU scores on newstest2014 by 3 points and ROUGE-L on CNN/Dailymail also by 3 points. However their BLEU score of 24.7 on newstest2014 En→De, compared to 30.6 in this work, and 29.4 ROUGE-L on CNN/Dailymail, compared to 36.33 also show the superiority of the transformer model as well as the masked language model objective of BERT. MASS Song et al. One advantage of this method is that, in contrast to our setups (except for gpt), the encoder-decoder attention mechanism is also pre-trained. The downside of this approach is that the pre-trained model is task-specific and not as general as BERT or GPT-2. UniLM Dong et al. also unifies bidirectional, unidirectional, and sequence to sequence language modeling. At the time of writing, no public checkpoint was available to us. To overcome the issue that the encoder-decoder attention is not pre-trained, \\newciteDBLP:journals/corr/abs-1905-08836 pre-trained a single transformer language model that encodes the source and generates the target. This setup matches our gpt setup. \\newciteDBLP: journals/corr/abs-1901-07291 pre-train their model using casual language modeling (like GPT), masked language modeling (like BERT) and a third new objective called translation language modeling to improve cross-lingual pre-training.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 5: Summarization results of different models and their initialization setups. We compare our setups (the bottom block) against both non-pre-trained (the top block) and pre-trained (the middle block) models on various datasets: the Lead baseline, PtGen See et al. (2017), ConvS2S Gehring et al. (2017), MMN Kim et al. (2019), Bottom-Up Gehrmann et al. (2018), MASS Song et al. (2019), TransLM Khandelwal et al. (2019) and UniLM Dong et al. (2019). The Lead results for the CNN/DailyMail dataset is taken from narayan-etal-2018-ranking, whereas, Lead, PtGen and ConvS2S results on the BBC dataset are taken from narayan-etal-2018-xsum. Our best results are boldfaced and the best results on the datasets are italicized.' in mind, break down and resolve the question below step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich model achieved the highest ROUGE-1 score on the CNN/DailyMail dataset?\n" }, { "role": "assistant", "content": "By examining the CNN/DailyMail R-1 column, the highest ROUGE-1 score is 43.47, achieved by the UniLM model. So, the answer is {\"answer\": \"UniLM\"}." } ], "images": [ "scigen/train/2852.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Summarization results of different models and their initialization setups. We compare our setups (the bottom block) against both non-pre-trained (the top block) and pre-trained (the middle block) models on various datasets: the Lead baseline, PtGen See et al. (2017), ConvS2S Gehring et al. (2017), MMN Kim et al. (2019), Bottom-Up Gehrmann et al. (2018), MASS Song et al. (2019), TransLM Khandelwal et al. (2019) and UniLM Dong et al. (2019). The Lead results for the CNN/DailyMail dataset is taken from narayan-etal-2018-ranking, whereas, Lead, PtGen and ConvS2S results on the BBC dataset are taken from narayan-etal-2018-xsum. Our best results are boldfaced and the best results on the datasets are italicized." ], "reasoning": "By examining the CNN/DailyMail R-1 column, the highest ROUGE-1 score is 43.47, achieved by the UniLM model.", "question": "Which model achieved the highest ROUGE-1 score on the CNN/DailyMail dataset?", "answer": "So, the answer is {\"answer\": \"UniLM\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe bertShare model achieved the highest ROUGE-L score on the BBC dataset.\n" }, { "role": "assistant", "content": "By examining the BBC XSum R-L column, the highest ROUGE-L score is 33.90, achieved by the robertaShare model, not bertShare. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2852.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Summarization results of different models and their initialization setups. We compare our setups (the bottom block) against both non-pre-trained (the top block) and pre-trained (the middle block) models on various datasets: the Lead baseline, PtGen See et al. (2017), ConvS2S Gehring et al. (2017), MMN Kim et al. (2019), Bottom-Up Gehrmann et al. (2018), MASS Song et al. (2019), TransLM Khandelwal et al. (2019) and UniLM Dong et al. (2019). The Lead results for the CNN/DailyMail dataset is taken from narayan-etal-2018-ranking, whereas, Lead, PtGen and ConvS2S results on the BBC dataset are taken from narayan-etal-2018-xsum. Our best results are boldfaced and the best results on the datasets are italicized." ], "reasoning": "By examining the BBC XSum R-L column, the highest ROUGE-L score is 33.90, achieved by the robertaShare model, not bertShare.", "statement": "The bertShare model achieved the highest ROUGE-L score on the BBC dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: SARI scores on the Discofuse dataset when experimenting with different embedding setups. Each row also includes the setups of all previous rows.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Initializing only the layers and not the word embedding matrix showed that the BERT vocabulary was already optimal for DiscoFuse, while GPT-2 improved with a smaller vocabulary but still lagged behind the fully initialized setup.S5SS0SSS0Px4 Initializing only Layers. Contrary to the previous paragraph, we want to investigate the effect of initializing everything but the word embedding matrix. The embedding matrix accounts for only 10-31% of all learnable parameters and sometimes the vocabulary given from a public checkpoint might not be optimal for a certain task. In these cases, it would be nice to redefine the vocabulary while still leveraging the checkpoint. The latter is probably due to the large vocab of the GPT-2 model which now remains random initialized. We observe almost no change on bertShare, suggesting that the BERT vocabulary was already optimal for DiscoFuse. gpt however, showed a significant improvement using this much smaller vocabulary but is still behind the fully initialized setup. Finally, we experimented with a more sensitive way of training the model, meaning that we fix all warm-started variables for 100k steps. During this pre-training phase, we only train the new word embeddings. After the pre-training, we fine-tune the entire model for another 300k steps. This training scheme resulted in an improvement of 0.5 for the bertShare setup, but overall the number is still way behind the fully initialized setup. For gpt, this training scheme did not result in a satisfying training curve." } ], "images": [ "scigen/train/2853.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: SARI scores on the Discofuse dataset when experimenting with different embedding setups. Each row also includes the setups of all previous rows." ], "reasoning": "Initializing only the layers and not the word embedding matrix showed that the BERT vocabulary was already optimal for DiscoFuse, while GPT-2 improved with a smaller vocabulary but still lagged behind the fully initialized setup.", "description": "S5SS0SSS0Px4 Initializing only Layers. Contrary to the previous paragraph, we want to investigate the effect of initializing everything but the word embedding matrix. The embedding matrix accounts for only 10-31% of all learnable parameters and sometimes the vocabulary given from a public checkpoint might not be optimal for a certain task. In these cases, it would be nice to redefine the vocabulary while still leveraging the checkpoint. The latter is probably due to the large vocab of the GPT-2 model which now remains random initialized. We observe almost no change on bertShare, suggesting that the BERT vocabulary was already optimal for DiscoFuse. gpt however, showed a significant improvement using this much smaller vocabulary but is still behind the fully initialized setup. Finally, we experimented with a more sensitive way of training the model, meaning that we fix all warm-started variables for 100k steps. During this pre-training phase, we only train the new word embeddings. After the pre-training, we fine-tune the entire model for another 300k steps. This training scheme resulted in an improvement of 0.5 for the bertShare setup, but overall the number is still way behind the fully initialized setup. For gpt, this training scheme did not result in a satisfying training curve.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 6: SARI scores on the Discofuse dataset when experimenting with different embedding setups. Each row also includes the setups of all previous rows.', solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat was the effect of pre-training SentencePieces on the gpt model's performance?\n" }, { "role": "assistant", "content": "The table shows that pre-training SentencePieces resulted in a gpt score of 69.7, which is lower than the initial gpt score of 88.0. So, the answer is {\"answer\": \"69.7\"}" } ], "images": [ "scigen/train/2853.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: SARI scores on the Discofuse dataset when experimenting with different embedding setups. Each row also includes the setups of all previous rows." ], "reasoning": "The table shows that pre-training SentencePieces resulted in a gpt score of 69.7, which is lower than the initial gpt score of 88.0.", "question": "What was the effect of pre-training SentencePieces on the gpt model's performance?", "answer": "So, the answer is {\"answer\": \"69.7\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Using task-specific SentencePieces improved the performance of both bertShare and gpt models., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nUsing task-specific SentencePieces improved the performance of both bertShare and gpt models.\n" }, { "role": "assistant", "content": "The table shows that using task-specific SentencePieces resulted in a score of 87.5 for bertShare and 84.2 for gpt, compared to their initial scores of 89.3 and 88.0 respectively, indicating no improvement for bertShare and a slight improvement for gpt. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2853.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: SARI scores on the Discofuse dataset when experimenting with different embedding setups. Each row also includes the setups of all previous rows." ], "reasoning": "The table shows that using task-specific SentencePieces resulted in a score of 87.5 for bertShare and 84.2 for gpt, compared to their initial scores of 89.3 and 88.0 respectively, indicating no improvement for bertShare and a slight improvement for gpt.", "statement": "Using task-specific SentencePieces improved the performance of both bertShare and gpt models.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Inter-annotator agreement per task'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Annotations for defect reports and improvement requests were collected through Figure Eight, with each review labeled by at least 3 annotators, and reliability estimated using Fleiss’ Kappa.We collected annotations for both feedback types through CrowdFlower (recently renamed Figure Eight), an online data enrichment platform. The instructed task is to label every individual review for both defect reports and improvement requests. Every review was annotated by at least 3 annotators, and in some cases even 5 or 7 (when it is recorded a low accuracy of the test questions that inspect the quality of the annotator). In order to estimate the reliability of the aggregated annotations, we computed the inter-annotator agreement (IAA) for each question, using Fleiss’ Kappa measure. We use the majority voting for building our gold standard." } ], "images": [ "scigen/train/2854.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Inter-annotator agreement per task" ], "reasoning": "Annotations for defect reports and improvement requests were collected through Figure Eight, with each review labeled by at least 3 annotators, and reliability estimated using Fleiss’ Kappa.", "description": "We collected annotations for both feedback types through CrowdFlower (recently renamed Figure Eight), an online data enrichment platform. The instructed task is to label every individual review for both defect reports and improvement requests. Every review was annotated by at least 3 annotators, and in some cases even 5 or 7 (when it is recorded a low accuracy of the test questions that inspect the quality of the annotator). In order to estimate the reliability of the aggregated annotations, we computed the inter-annotator agreement (IAA) for each question, using Fleiss’ Kappa measure. We use the majority voting for building our gold standard.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Inter-annotator agreement per task' to sequentially solve the question presented below. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhat is the inter-annotator agreement percentage for reviews containing a defect report?\n" }, { "role": "assistant", "content": "The table shows the agreement percentage for the question 'Does this review contain a defect report?' as 97.1%. So, the answer is {\"answer\": \"97.1\"}." } ], "images": [ "scigen/train/2854.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Inter-annotator agreement per task" ], "reasoning": "The table shows the agreement percentage for the question 'Does this review contain a defect report?' as 97.1%.", "question": "What is the inter-annotator agreement percentage for reviews containing a defect report?", "answer": "So, the answer is {\"answer\": \"97.1\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The inter-annotator agreement for reviews containing an improvement request is higher than for defect reports., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe inter-annotator agreement for reviews containing an improvement request is higher than for defect reports.\n" }, { "role": "assistant", "content": "The table shows that the agreement percentage for improvement requests is 92.75%, which is lower than the 97.1% agreement for defect reports. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2854.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Inter-annotator agreement per task" ], "reasoning": "The table shows that the agreement percentage for improvement requests is 92.75%, which is lower than the 97.1% agreement for defect reports.", "statement": "The inter-annotator agreement for reviews containing an improvement request is higher than for defect reports.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: Results of Human Evaluations on three tabular format datasets in terms of Informativeness, Naturalness and Quality, rating out of 5. For baseline we used gold-standard references in each dataset.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The mean score for informativeness is 5 for all datasets except RoboCup, which has a score of 4.75 due to the high percentage of PASS record types in the training data.Since in our proposed system all meaning labels are forced to exist in the produced dependency tree, the mean score for informativeness factor is 5, except for RoboCup dataset; because in this dataset, each sentence corresponds to one meaning label and 60% of the training sentences are corresponding to PASS record type. So, as a result, at the test time, since test scenarios consist of multiple record types including PASS, a few of the sentences, that have less training sample compare to the other record type (like Offside or BallStopped that have less than 2% training sample in total), are wrongly generated for this record." } ], "images": [ "scigen/train/2855.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results of Human Evaluations on three tabular format datasets in terms of Informativeness, Naturalness and Quality, rating out of 5. For baseline we used gold-standard references in each dataset." ], "reasoning": "The mean score for informativeness is 5 for all datasets except RoboCup, which has a score of 4.75 due to the high percentage of PASS record types in the training data.", "description": "Since in our proposed system all meaning labels are forced to exist in the produced dependency tree, the mean score for informativeness factor is 5, except for RoboCup dataset; because in this dataset, each sentence corresponds to one meaning label and 60% of the training sentences are corresponding to PASS record type. So, as a result, at the test time, since test scenarios consist of multiple record types including PASS, a few of the sentences, that have less training sample compare to the other record type (like Offside or BallStopped that have less than 2% training sample in total), are wrongly generated for this record.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nTable title: Table 5: Results of Human Evaluations on three tabular format datasets in terms of Informativeness, Naturalness and Quality, rating out of 5. For baseline we used gold-standard references in each dataset.\n\nQuestion: What is the difference in the informativeness score between the RoboCup dataset and the other datasets in the proposed system?\n" }, { "role": "assistant", "content": "The informativeness score for the RoboCup dataset is 4.75, while for the Atis and WEATHERGOV datasets, it is 5.00. The difference is 5.00 - 4.75 = 0.25. So, the answer is {\"answer\": \"0.25\"}." } ], "images": [ "scigen/train/2855.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results of Human Evaluations on three tabular format datasets in terms of Informativeness, Naturalness and Quality, rating out of 5. For baseline we used gold-standard references in each dataset." ], "reasoning": "The informativeness score for the RoboCup dataset is 4.75, while for the Atis and WEATHERGOV datasets, it is 5.00. The difference is 5.00 - 4.75 = 0.25.", "question": "What is the difference in the informativeness score between the RoboCup dataset and the other datasets in the proposed system?", "answer": "So, the answer is {\"answer\": \"0.25\"}." }, { "messages": [ { "role": "user", "content": "The RoboCup dataset has the lowest naturalness score in the proposed system. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe RoboCup dataset has the lowest naturalness score in the proposed system.\n" }, { "role": "assistant", "content": "The naturalness scores in the proposed system are 4.09 for Atis, 4.12 for RoboCup, and 4.60 for WEATHERGOV. The RoboCup dataset has a higher naturalness score than Atis, so the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2855.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results of Human Evaluations on three tabular format datasets in terms of Informativeness, Naturalness and Quality, rating out of 5. For baseline we used gold-standard references in each dataset." ], "reasoning": "The naturalness scores in the proposed system are 4.09 for Atis, 4.12 for RoboCup, and 4.60 for WEATHERGOV. The RoboCup dataset has a higher naturalness score than Atis, so the statement is refuted.", "statement": "The RoboCup dataset has the lowest naturalness score in the proposed system.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: In domain results', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The L-SAN+CRF model with BERT achieves the highest scores across all datasets, outperforming the BiLSTM+CRF model with BERT.In-Domain Results We evaluate our model on three news datasets, including CTB, PKU and MSR. Compared with the baseline “BiLSTM+CRF” model, the proposed “L-SAN+CRF” model can achieve similar results, which proves that self-attention network can be a competitive feature extractor for CWS besides recurrent neural network. When replacing word2vec character embedding with BERT, the “BiLSTM+CRF” model gives 41.3%, 30.6% and 31.0% error reduction on CTB6/PKU/MSR, respectively, and the “L-SAN+CRF” model has 41.3%, 32.7% and 41.3% error reductions on three in-domain datasets, respectively. Finally, “L-SAN+CRF” slightly outperforms “BiLSTM+CRF” when using BERT as unigram character representation." } ], "images": [ "scigen/train/2856.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: In domain results" ], "reasoning": "The L-SAN+CRF model with BERT achieves the highest scores across all datasets, outperforming the BiLSTM+CRF model with BERT.", "description": "In-Domain Results We evaluate our model on three news datasets, including CTB, PKU and MSR. Compared with the baseline “BiLSTM+CRF” model, the proposed “L-SAN+CRF” model can achieve similar results, which proves that self-attention network can be a competitive feature extractor for CWS besides recurrent neural network. When replacing word2vec character embedding with BERT, the “BiLSTM+CRF” model gives 41.3%, 30.6% and 31.0% error reduction on CTB6/PKU/MSR, respectively, and the “L-SAN+CRF” model has 41.3%, 32.7% and 41.3% error reductions on three in-domain datasets, respectively. Finally, “L-SAN+CRF” slightly outperforms “BiLSTM+CRF” when using BERT as unigram character representation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model achieves the highest score on the MSR dataset?\nSolve the above question based on the table titled 'Table 4: In domain results'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, the highest score on the MSR dataset is achieved by the L-SAN+CRF+BERT model with a score of 98.3. So, the answer is {\"answer\": \"L-SAN+CRF+BERT\"}" } ], "images": [ "scigen/train/2856.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: In domain results" ], "reasoning": "By examining the table, the highest score on the MSR dataset is achieved by the L-SAN+CRF+BERT model with a score of 98.3.", "question": "Which model achieves the highest score on the MSR dataset?", "answer": "So, the answer is {\"answer\": \"L-SAN+CRF+BERT\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe BiLSTM+CRF model with BERT achieves the highest score on the CTB6 dataset.\n" }, { "role": "assistant", "content": "The table shows that the highest score on the CTB6 dataset is achieved by the L-SAN+CRF+BERT model with a score of 97.4, not the BiLSTM+CRF model with BERT. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2856.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: In domain results" ], "reasoning": "The table shows that the highest score on the CTB6 dataset is achieved by the L-SAN+CRF+BERT model with a score of 97.4, not the BiLSTM+CRF model with BERT.", "statement": "The BiLSTM+CRF model with BERT achieves the highest score on the CTB6 dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Effect of numbers of heads and layers'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The model achieves the best F1-value of 0.956 with 3 layers and 8 heads, and the performance decreases with more layers.Width and Depth Vaswani et al. We investigate the effect of number of layers on CWS, by increasing the number of layers from 1 to 6 while fixing the number of heads to 8. The model achieves the best F1-value 0.956 within 3 layers, after which the performance decreases with the increasing of layers. The F1-value decreases to 0.931 when the number of layers is set to 6. We fix the number of layers to 2 for the remaining experiments. We vary the number of heads in multi-head self-attention to investigate its effect on CWS performance. The number of layers and dimension of head is fix to 2 and 64, respectively. We fix the number of heads to 8 for the remaining experiments." } ], "images": [ "scigen/train/2857.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Effect of numbers of heads and layers" ], "reasoning": "The model achieves the best F1-value of 0.956 with 3 layers and 8 heads, and the performance decreases with more layers.", "description": "Width and Depth Vaswani et al. We investigate the effect of number of layers on CWS, by increasing the number of layers from 1 to 6 while fixing the number of heads to 8. The model achieves the best F1-value 0.956 within 3 layers, after which the performance decreases with the increasing of layers. The F1-value decreases to 0.931 when the number of layers is set to 6. We fix the number of layers to 2 for the remaining experiments. We vary the number of heads in multi-head self-attention to investigate its effect on CWS performance. The number of layers and dimension of head is fix to 2 and 64, respectively. We fix the number of heads to 8 for the remaining experiments.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: Effect of numbers of heads and layers' in mind, break down and resolve the question below step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the F1-value when the number of layers is set to 6 and the number of heads is set to 16?\n" }, { "role": "assistant", "content": "From the table, when the number of layers is 6, the F1-value is 0.931. When the number of heads is 16, the F1-value is 0.955. Since the question asks for the F1-value with 6 layers and 16 heads, we refer to the F1-value for 6 layers, which is 0.931. So, the answer is {\"answer\": \"0.931\"}" } ], "images": [ "scigen/train/2857.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Effect of numbers of heads and layers" ], "reasoning": "From the table, when the number of layers is 6, the F1-value is 0.931. When the number of heads is 16, the F1-value is 0.955. Since the question asks for the F1-value with 6 layers and 16 heads, we refer to the F1-value for 6 layers, which is 0.931.", "question": "What is the F1-value when the number of layers is set to 6 and the number of heads is set to 16?", "answer": "So, the answer is {\"answer\": \"0.931\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model achieves the best F1-value with 4 layers.\n" }, { "role": "assistant", "content": "The table shows that the best F1-value of 0.956 is achieved with 3 layers, not 4 layers. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2857.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Effect of numbers of heads and layers" ], "reasoning": "The table shows that the best F1-value of 0.956 is achieved with 3 layers, not 4 layers.", "statement": "The model achieves the best F1-value with 4 layers.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 5: Cross domain results'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The L-SAN+CRF+BERT+t_b model achieves the highest error reduction across ZX, FR, and DL datasets, indicating its effectiveness in handling out-of-vocabulary words and providing more supervision information.Cross-Domain Results We evaluate our model on the three cross-domain datasets, including ZX, FR and DL. “t” means neural type-supervised method is used to learn POS embedding and domain-specific words is generalized to corresponding tag. In “t_b”, we learn different POS embeddings for different positions in a word. ’s domain adaptation techniques. This may be because ZX, FR and DL are all Chinese novels which contain a large number of noun entities and their wring styles are different from news domain. The result shows that BERT has rarely less effect on cross-domain CWS compared with strong domain adaptation methods. The “L-SAN+CRF+BERT+t” model has 21.15%, 25.96% and 1.54% error reduction on ZX/FR/DL datasets, respectively, which shows that the proposed neural type-supervised method can handle out of vocabulary words more effective. For characters within a word, instead of sharing the same POS embedding of the word, we further distinguish POS embeddings of characters according to their position in a word. The “L-SAN+CRF+BERT+t_b” gives 33.65%, 32.69% and 24.62% on three datasets, respectively. We believe that this is due to more supervision information." } ], "images": [ "scigen/train/2858.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Cross domain results" ], "reasoning": "The L-SAN+CRF+BERT+t_b model achieves the highest error reduction across ZX, FR, and DL datasets, indicating its effectiveness in handling out-of-vocabulary words and providing more supervision information.", "description": "Cross-Domain Results We evaluate our model on the three cross-domain datasets, including ZX, FR and DL. “t” means neural type-supervised method is used to learn POS embedding and domain-specific words is generalized to corresponding tag. In “t_b”, we learn different POS embeddings for different positions in a word. ’s domain adaptation techniques. This may be because ZX, FR and DL are all Chinese novels which contain a large number of noun entities and their wring styles are different from news domain. The result shows that BERT has rarely less effect on cross-domain CWS compared with strong domain adaptation methods. The “L-SAN+CRF+BERT+t” model has 21.15%, 25.96% and 1.54% error reduction on ZX/FR/DL datasets, respectively, which shows that the proposed neural type-supervised method can handle out of vocabulary words more effective. For characters within a word, instead of sharing the same POS embedding of the word, we further distinguish POS embeddings of characters according to their position in a word. The “L-SAN+CRF+BERT+t_b” gives 33.65%, 32.69% and 24.62% on three datasets, respectively. We believe that this is due to more supervision information.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 5: Cross domain results', unfold the steps to solve the question given next. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nQuestion: Which model achieves the highest accuracy on the DL dataset?\n" }, { "role": "assistant", "content": "By comparing the accuracy values in the DL column, the L-SAN+CRF+BERT+t_b model has the highest accuracy of 95.1. So, the answer is {\"answer\": \"L-SAN+CRF+BERT+t_b\"}" } ], "images": [ "scigen/train/2858.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Cross domain results" ], "reasoning": "By comparing the accuracy values in the DL column, the L-SAN+CRF+BERT+t_b model has the highest accuracy of 95.1.", "question": "Which model achieves the highest accuracy on the DL dataset?", "answer": "So, the answer is {\"answer\": \"L-SAN+CRF+BERT+t_b\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe L-SAN+CRF+BERT+t model achieves the highest accuracy on the ZX dataset.\n" }, { "role": "assistant", "content": "By comparing the accuracy values in the ZX column, the L-SAN+CRF+BERT+t_b model has the highest accuracy of 93.1, not the L-SAN+CRF+BERT+t model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2858.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Cross domain results" ], "reasoning": "By comparing the accuracy values in the ZX column, the L-SAN+CRF+BERT+t_b model has the highest accuracy of 93.1, not the L-SAN+CRF+BERT+t model.", "statement": "The L-SAN+CRF+BERT+t model achieves the highest accuracy on the ZX dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 6: Segmentation precision of noun entities with the highest frequency.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The average precision of M1 is 0.55, and using a neural type-supervised domain adaptation method, M2 improves by 0.18 to 0.73.Noun Entity Segmentation Noun entities raise a key problem for cross-domain CWS. M1 and M2 represent “L-SAN+CRF+BERT” and “L-SAN+CRF+BERT+t”, respectively, while M3 represents “L-SAN+CRF+BERT+t_b”. As the table shows, the average precision of MI is 0.55. By using neural type-supervised domain adaptation method, the average precision of M2 has a improvement of 0.18 in absolute value." } ], "images": [ "scigen/train/2859.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Segmentation precision of noun entities with the highest frequency." ], "reasoning": "The average precision of M1 is 0.55, and using a neural type-supervised domain adaptation method, M2 improves by 0.18 to 0.73.", "description": "Noun Entity Segmentation Noun entities raise a key problem for cross-domain CWS. M1 and M2 represent “L-SAN+CRF+BERT” and “L-SAN+CRF+BERT+t”, respectively, while M3 represents “L-SAN+CRF+BERT+t_b”. As the table shows, the average precision of MI is 0.55. By using neural type-supervised domain adaptation method, the average precision of M2 has a improvement of 0.18 in absolute value.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 6: Segmentation precision of noun entities with the highest frequency.', answer the following question. Give detailed solution consisting of each step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the average precision of M3?\n" }, { "role": "assistant", "content": "The table shows the average precision values for M1, M2, and M3. The average precision of M3 is listed as 0.96. So, the answer is {\"answer\": \"0.96\"}" } ], "images": [ "scigen/train/2859.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Segmentation precision of noun entities with the highest frequency." ], "reasoning": "The table shows the average precision values for M1, M2, and M3. The average precision of M3 is listed as 0.96.", "question": "What is the average precision of M3?", "answer": "So, the answer is {\"answer\": \"0.96\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The average precision of M2 is 0.73. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe average precision of M2 is 0.73.\n" }, { "role": "assistant", "content": "The table shows that the average precision of M2 is indeed 0.73. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2859.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Segmentation precision of noun entities with the highest frequency." ], "reasoning": "The table shows that the average precision of M2 is indeed 0.73.", "statement": "The average precision of M2 is 0.73.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: The average dialogue quantity and task profile similarity among tasks of the task clusters grouped by each task’s “best fine-tuning epochs” of BLEU and C Score (where its BLEU and C Score reaches the best in fine-tuning stage) in Persona.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "To determine the impact of data quantity and task profile on fine-tuning, we find the fine-tuning epochs for each task where BLEU and C Score are best, cluster tasks with similar epochs, and calculate the average dialogue quantity and task profile similarity for each cluster.To answer RQ2, we find the fine-tuning epochs for each task in Persona where its BLEU and C Score reaches the best respectively to find the impact of data quantity and the task profile (persona description) on fine-tuning. We cluster the tasks with similar best fine-tuning epoch number and calculate the average dialogue quantity and task profile similarity among tasks for each cluster. We define Jac Score of the persona descriptions to evaluate their task profile similarity as Jac Score=ΣNi=1Jaci/(NJacwhole), where Jaci is the Jaccard similarity of the persona descriptions in cluster i, Jacwhole is the Jaccard similarity of all the persona descriptions, and N is the number of clusters. If Jac Score is large than 1, the persona descriptions are similar to each other in each cluster. Larger Jac Score means better similarity." } ], "images": [ "scigen/train/2860.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The average dialogue quantity and task profile similarity among tasks of the task clusters grouped by each task’s “best fine-tuning epochs” of BLEU and C Score (where its BLEU and C Score reaches the best in fine-tuning stage) in Persona." ], "reasoning": "To determine the impact of data quantity and task profile on fine-tuning, we find the fine-tuning epochs for each task where BLEU and C Score are best, cluster tasks with similar epochs, and calculate the average dialogue quantity and task profile similarity for each cluster.", "description": "To answer RQ2, we find the fine-tuning epochs for each task in Persona where its BLEU and C Score reaches the best respectively to find the impact of data quantity and the task profile (persona description) on fine-tuning. We cluster the tasks with similar best fine-tuning epoch number and calculate the average dialogue quantity and task profile similarity among tasks for each cluster. We define Jac Score of the persona descriptions to evaluate their task profile similarity as Jac Score=ΣNi=1Jaci/(NJacwhole), where Jaci is the Jaccard similarity of the persona descriptions in cluster i, Jacwhole is the Jaccard similarity of all the persona descriptions, and N is the number of clusters. If Jac Score is large than 1, the persona descriptions are similar to each other in each cluster. Larger Jac Score means better similarity.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 1: The average dialogue quantity and task profile similarity among tasks of the task clusters grouped by each task’s “best fine-tuning epochs” of BLEU and C Score (where its BLEU and C Score reaches the best in fine-tuning stage) in Persona.', and sequentially solve the question, writing out each step of your reasoning process. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich cluster has the highest average dialogue quantity for BLEU?\n" }, { "role": "assistant", "content": "To find the cluster with the highest average dialogue quantity for BLEU, we compare the BLEU Dialogue Quantity values across all clusters and identify the highest value. So, the answer is {\"answer\": \"5\"}." } ], "images": [ "scigen/train/2860.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The average dialogue quantity and task profile similarity among tasks of the task clusters grouped by each task’s “best fine-tuning epochs” of BLEU and C Score (where its BLEU and C Score reaches the best in fine-tuning stage) in Persona." ], "reasoning": "To find the cluster with the highest average dialogue quantity for BLEU, we compare the BLEU Dialogue Quantity values across all clusters and identify the highest value.", "question": "Which cluster has the highest average dialogue quantity for BLEU?", "answer": "So, the answer is {\"answer\": \"5\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nCluster 3 has the highest C Score Task Similarity.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the C Score Task Similarity values across all clusters. Cluster 3 has a C Score Task Similarity of 1.09, which is the highest among all clusters. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2860.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The average dialogue quantity and task profile similarity among tasks of the task clusters grouped by each task’s “best fine-tuning epochs” of BLEU and C Score (where its BLEU and C Score reaches the best in fine-tuning stage) in Persona." ], "reasoning": "To verify this statement, we compare the C Score Task Similarity values across all clusters. Cluster 3 has a C Score Task Similarity of 1.09, which is the highest among all clusters.", "statement": "Cluster 3 has the highest C Score Task Similarity.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: The performance on different data quantity and task similarity settings.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "MAML outperforms baselines in low data quantity settings but performs similarly to Transformer-F when tasks are similar.Data Quantity. In Persona, we evaluate Transformer/CNN, Transformer/CNN-F and MAML on 3 data quantity settings: 50/100/120-shot (each task has 50, 100, 120 utterances on average). When the data quantity is small, the advantage of MAML is more significant. In Persona, the C Score and BLEU of MAML outperforms baselines on 50-shot and 100-shot settings, but on 120-shot setting, the BLEU of MAML is lower than Transformer-F. In Weibo, FewRel and Amazon, the percentages that MAML outperforms the baselines by also decrease as the data quantity increasing. This finding is in line with the mechanism of MAML. When there are enough data samples, fine-tuning also performs well, so BLEU of Transformer-F in Persona on 120-shot setting is even better. Task similarity. In Persona and Weibo, each task is a set of dialogues for one user, so tasks are different from each other. We shuffle the samples and randomly divide tasks to construct the setting that tasks are similar to each other. For a fair comparison, each task on this setting also has 120 and 1200 utterances on average in Persona and Weibo respectively. We train and evaluate Transformer-F and MAML on this setting. When tasks are similar to each other, MAML performs comparatively poorly. In Persona and Weibo, the performance of MAML is similar to that of Transformer-F, while MAML performs significantly better than Transformer-F when tasks are different. A possible explanation is that if there is no clear distinction between tasks, the meta-learning setting can be view as a transfer learning setting, which only has a source domain and a target domain, and fine-tuning performs well in transfer learning. So if the tasks are similar to each other, we can simply use Transformer-F rather than MAML." } ], "images": [ "scigen/train/2861.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The performance on different data quantity and task similarity settings." ], "reasoning": "MAML outperforms baselines in low data quantity settings but performs similarly to Transformer-F when tasks are similar.", "description": "Data Quantity. In Persona, we evaluate Transformer/CNN, Transformer/CNN-F and MAML on 3 data quantity settings: 50/100/120-shot (each task has 50, 100, 120 utterances on average). When the data quantity is small, the advantage of MAML is more significant. In Persona, the C Score and BLEU of MAML outperforms baselines on 50-shot and 100-shot settings, but on 120-shot setting, the BLEU of MAML is lower than Transformer-F. In Weibo, FewRel and Amazon, the percentages that MAML outperforms the baselines by also decrease as the data quantity increasing. This finding is in line with the mechanism of MAML. When there are enough data samples, fine-tuning also performs well, so BLEU of Transformer-F in Persona on 120-shot setting is even better. Task similarity. In Persona and Weibo, each task is a set of dialogues for one user, so tasks are different from each other. We shuffle the samples and randomly divide tasks to construct the setting that tasks are similar to each other. For a fair comparison, each task on this setting also has 120 and 1200 utterances on average in Persona and Weibo respectively. We train and evaluate Transformer-F and MAML on this setting. When tasks are similar to each other, MAML performs comparatively poorly. In Persona and Weibo, the performance of MAML is similar to that of Transformer-F, while MAML performs significantly better than Transformer-F when tasks are different. A possible explanation is that if there is no clear distinction between tasks, the meta-learning setting can be view as a transfer learning setting, which only has a source domain and a target domain, and fine-tuning performs well in transfer learning. So if the tasks are similar to each other, we can simply use Transformer-F rather than MAML.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: The performance on different data quantity and task similarity settings.', solve this question and give detail solutions. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhat is the BLEU score of MAML in the 50-shot setting for Persona?\n" }, { "role": "assistant", "content": "To find the BLEU score of MAML in the 50-shot setting for Persona, locate the row corresponding to MAML and the column for Persona BLEU under the 50-shot setting. So, the answer is {\"answer\": \"0.55\"}" } ], "images": [ "scigen/train/2861.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The performance on different data quantity and task similarity settings." ], "reasoning": "To find the BLEU score of MAML in the 50-shot setting for Persona, locate the row corresponding to MAML and the column for Persona BLEU under the 50-shot setting.", "question": "What is the BLEU score of MAML in the 50-shot setting for Persona?", "answer": "So, the answer is {\"answer\": \"0.55\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: MAML performs better than Transformer-F in the 120-shot setting for Persona., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nMAML performs better than Transformer-F in the 120-shot setting for Persona.\n" }, { "role": "assistant", "content": "In the 120-shot setting for Persona, MAML has a BLEU score of 0.89, which is equal to the BLEU score of Transformer-F. Therefore, MAML does not perform better than Transformer-F in this setting. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2861.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The performance on different data quantity and task similarity settings." ], "reasoning": "In the 120-shot setting for Persona, MAML has a BLEU score of 0.89, which is equal to the BLEU score of Transformer-F. Therefore, MAML does not perform better than Transformer-F in this setting.", "statement": "MAML performs better than Transformer-F in the 120-shot setting for Persona.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Alignment distribution entropy for selected de-en models. att stands for attention in Table 1.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The alignment dispersion is consistently lower for free decoding than force decoding, and the Transformer attention interpretation results in higher AER but lower dispersion compared to other architectures.We run German-English alignments under several different SmoothGrad noise deviation σ and report their dispersion as measured by entropy of the (soft) alignment distribution averaged by number of target words. It should also be noted that the alignment dispersion is consistently lower for free decoding than force decoding. This verifies our conjecture that the force decoding setting might introduce more noise in the model behavior, but judging from this result, that gap seems to be minimal. Comparing different architectures, the dispersion of attention weights does not correlate well with the dispersion of word saliency. We also notice that, while the Transformer attention interpretation consistently results in higher AER, its dispersion is lower than the other architectures, indicating that with attention, a lot of the probability mass might be concentrated in the wrong place more often. This corroborates the finding in Raganato and Tiedemann" } ], "images": [ "scigen/train/2862.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Alignment distribution entropy for selected de-en models. att stands for attention in Table 1." ], "reasoning": "The alignment dispersion is consistently lower for free decoding than force decoding, and the Transformer attention interpretation results in higher AER but lower dispersion compared to other architectures.", "description": "We run German-English alignments under several different SmoothGrad noise deviation σ and report their dispersion as measured by entropy of the (soft) alignment distribution averaged by number of target words. It should also be noted that the alignment dispersion is consistently lower for free decoding than force decoding. This verifies our conjecture that the force decoding setting might introduce more noise in the model behavior, but judging from this result, that gap seems to be minimal. Comparing different architectures, the dispersion of attention weights does not correlate well with the dispersion of word saliency. We also notice that, while the Transformer attention interpretation consistently results in higher AER, its dispersion is lower than the other architectures, indicating that with attention, a lot of the probability mass might be concentrated in the wrong place more often. This corroborates the finding in Raganato and Tiedemann", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Alignment distribution entropy for selected de-en models. att stands for attention in Table 1.', answer the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich architecture has the lowest alignment dispersion at σ = 0.15 for free decoding?\n" }, { "role": "assistant", "content": "To find the architecture with the lowest alignment dispersion at σ = 0.15 for free decoding, compare the values in the column σ = 0.15 under the 'free' rows for each architecture. The values are: FConv (1.43), LSTM (1.91), and Transformer (2.62). The lowest value is 1.43 for FConv. So, the answer is {\"answer\": \"FConv\"}." } ], "images": [ "scigen/train/2862.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Alignment distribution entropy for selected de-en models. att stands for attention in Table 1." ], "reasoning": "To find the architecture with the lowest alignment dispersion at σ = 0.15 for free decoding, compare the values in the column σ = 0.15 under the 'free' rows for each architecture. The values are: FConv (1.43), LSTM (1.91), and Transformer (2.62). The lowest value is 1.43 for FConv.", "question": "Which architecture has the lowest alignment dispersion at σ = 0.15 for free decoding?", "answer": "So, the answer is {\"answer\": \"FConv\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe alignment dispersion for LSTM with force decoding is higher than for free decoding at σ = 0.3.\n" }, { "role": "assistant", "content": "To verify this statement, compare the alignment dispersion values for LSTM with force and free decoding at σ = 0.3. The values are: force (2.89) and free (2.88). The dispersion for force decoding is indeed higher than for free decoding. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2862.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Alignment distribution entropy for selected de-en models. att stands for attention in Table 1." ], "reasoning": "To verify this statement, compare the alignment dispersion values for LSTM with force and free decoding at σ = 0.3. The values are: force (2.89) and free (2.88). The dispersion for force decoding is indeed higher than for free decoding.", "statement": "The alignment dispersion for LSTM with force decoding is higher than for free decoding at σ = 0.3.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 7: Model comparison in a setting without opinion term labels. Average results over 5 runs with random initialization are reported. ∗ indicates the proposed method is significantly better than the other baselines (p<0.05) based on one-tailed unpaired t-test.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "IMN and IMN−d outperform other baselines in most cases for aspect term recognition in AE, indicating the benefit of learning opinion term extraction with a well-designed network structure.In this setting, we modify the proposed IMN and IMN−d to recognize aspect terms only in AE. The opinion transmission operation, which sends the opinion term predictions from AE to AS, is omitted as well. Both IMN−d and IMN still significantly outperform other baselines in most cases under this setting. Consistent improvements are not observed in other baseline methods when trained with opinion term labels. These findings suggest that knowledge obtained from learning opinion term extraction is indeed beneficial, however, a carefully-designed network structure is needed to utilize such information. IMN is designed to exploit task correlations by explicitly modeling interactions between tasks, and thus it better integrates knowledge obtained from training different tasks." } ], "images": [ "scigen/train/2863.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Model comparison in a setting without opinion term labels. Average results over 5 runs with random initialization are reported. ∗ indicates the proposed method is significantly better than the other baselines (p<0.05) based on one-tailed unpaired t-test." ], "reasoning": "IMN and IMN−d outperform other baselines in most cases for aspect term recognition in AE, indicating the benefit of learning opinion term extraction with a well-designed network structure.", "description": "In this setting, we modify the proposed IMN and IMN−d to recognize aspect terms only in AE. The opinion transmission operation, which sends the opinion term predictions from AE to AS, is omitted as well. Both IMN−d and IMN still significantly outperform other baselines in most cases under this setting. Consistent improvements are not observed in other baseline methods when trained with opinion term labels. These findings suggest that knowledge obtained from learning opinion term extraction is indeed beneficial, however, a carefully-designed network structure is needed to utilize such information. IMN is designed to exploit task correlations by explicitly modeling interactions between tasks, and thus it better integrates knowledge obtained from training different tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 7: Model comparison in a setting without opinion term labels. Average results over 5 runs with random initialization are reported. ∗ indicates the proposed method is significantly better than the other baselines (p<0.05) based on one-tailed unpaired t-test.', unfold the steps to solve the question given next. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nQuestion: Which method has the highest D1 F1-a score?\n" }, { "role": "assistant", "content": "To find the method with the highest D1 F1-a score, compare the D1 F1-a scores of all methods listed in the table. IMN−d has the highest D1 F1-a score of 83.89. So, the answer is {\"answer\": \"IMN−d\"}" } ], "images": [ "scigen/train/2863.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Model comparison in a setting without opinion term labels. Average results over 5 runs with random initialization are reported. ∗ indicates the proposed method is significantly better than the other baselines (p<0.05) based on one-tailed unpaired t-test." ], "reasoning": "To find the method with the highest D1 F1-a score, compare the D1 F1-a scores of all methods listed in the table. IMN−d has the highest D1 F1-a score of 83.89.", "question": "Which method has the highest D1 F1-a score?", "answer": "So, the answer is {\"answer\": \"IMN−d\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nIMN−d has the highest D2 F1-s score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the D2 F1-s scores of all methods listed in the table. IMN−d has a D2 F1-s score of 57.13, which is not the highest. IMN has the highest D2 F1-s score of 58.04. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2863.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Model comparison in a setting without opinion term labels. Average results over 5 runs with random initialization are reported. ∗ indicates the proposed method is significantly better than the other baselines (p<0.05) based on one-tailed unpaired t-test." ], "reasoning": "To verify this statement, compare the D2 F1-s scores of all methods listed in the table. IMN−d has a D2 F1-s score of 57.13, which is not the highest. IMN has the highest D2 F1-s score of 58.04.", "statement": "IMN−d has the highest D2 F1-s score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Model comparison. Average results over 5 runs with random initialization are reported. ∗ indicates the proposed method is significantly better than the other baselines (p<0.05) based on one-tailed unpaired t-test.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "IMN and IMN−d outperform other methods in both AE and AS tasks, demonstrating the effectiveness of domain-specific knowledge and joint training.Main results. Note that IMN performs co-extraction of aspect and opinion terms in AE, which utilizes additional opinion term labels during training, while the baseline methods except CMLA do not consider this information in their original models. To enable fair comparison, we slightly modify those baselines to perform co-extraction as well, with opinion term labels provided. Specifically, for AE (F1-a and F1-o), IMN−d performs the best in most cases. For AS (acc-s and F1-s), IMN outperforms other methods by large margins. PIPELINE, IMN−d, and the pipeline methods with dTrans also perform reasonably well on this task, outperforming other baselines by moderate margins. All these models utilize knowledge from larger corpora by either joint training of document-level tasks or using domain-specific embeddings. This suggests that domain-specific knowledge is very helpful, and both joint training and domain-specific embeddings are effective ways to transfer such knowledge. Both IMN−d and IMN still significantly outperform other baselines in most cases under this setting. Consistent improvements are not observed in other baseline methods when trained with opinion term labels. These findings suggest that knowledge obtained from learning opinion term extraction is indeed beneficial, however, a carefully-designed network structure is needed to utilize such information. IMN is designed to exploit task correlations by explicitly modeling interactions between tasks, and thus it better integrates knowledge obtained from training different tasks." } ], "images": [ "scigen/train/2864.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Model comparison. Average results over 5 runs with random initialization are reported. ∗ indicates the proposed method is significantly better than the other baselines (p<0.05) based on one-tailed unpaired t-test." ], "reasoning": "IMN and IMN−d outperform other methods in both AE and AS tasks, demonstrating the effectiveness of domain-specific knowledge and joint training.", "description": "Main results. Note that IMN performs co-extraction of aspect and opinion terms in AE, which utilizes additional opinion term labels during training, while the baseline methods except CMLA do not consider this information in their original models. To enable fair comparison, we slightly modify those baselines to perform co-extraction as well, with opinion term labels provided. Specifically, for AE (F1-a and F1-o), IMN−d performs the best in most cases. For AS (acc-s and F1-s), IMN outperforms other methods by large margins. PIPELINE, IMN−d, and the pipeline methods with dTrans also perform reasonably well on this task, outperforming other baselines by moderate margins. All these models utilize knowledge from larger corpora by either joint training of document-level tasks or using domain-specific embeddings. This suggests that domain-specific knowledge is very helpful, and both joint training and domain-specific embeddings are effective ways to transfer such knowledge. Both IMN−d and IMN still significantly outperform other baselines in most cases under this setting. Consistent improvements are not observed in other baseline methods when trained with opinion term labels. These findings suggest that knowledge obtained from learning opinion term extraction is indeed beneficial, however, a carefully-designed network structure is needed to utilize such information. IMN is designed to exploit task correlations by explicitly modeling interactions between tasks, and thus it better integrates knowledge obtained from training different tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Model comparison. Average results over 5 runs with random initialization are reported. ∗ indicates the proposed method is significantly better than the other baselines (p<0.05) based on one-tailed unpaired t-test.\nQuestion: Which method performs the best for D1 F1-a?\nGive a detailed response to the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "For D1 F1-a, the table shows that IMN−d has the highest score of 84.01, indicating it performs the best. So, the answer is {\"answer\": \"IMN−d\"}" } ], "images": [ "scigen/train/2864.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Model comparison. Average results over 5 runs with random initialization are reported. ∗ indicates the proposed method is significantly better than the other baselines (p<0.05) based on one-tailed unpaired t-test." ], "reasoning": "For D1 F1-a, the table shows that IMN−d has the highest score of 84.01, indicating it performs the best.", "question": "Which method performs the best for D1 F1-a?", "answer": "So, the answer is {\"answer\": \"IMN−d\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nIMN−d performs the best for D2 F1-s.\n" }, { "role": "assistant", "content": "For D2 F1-s, the table shows that IMN−d has the highest score of 72.02, indicating it performs the best. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2864.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Model comparison. Average results over 5 runs with random initialization are reported. ∗ indicates the proposed method is significantly better than the other baselines (p<0.05) based on one-tailed unpaired t-test." ], "reasoning": "For D2 F1-s, the table shows that IMN−d has the highest score of 72.02, indicating it performs the best.", "statement": "IMN−d performs the best for D2 F1-s.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: F1-I scores of different model variants. Average results over 5 runs are reported.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Adding message passing components, especially those involving document-level information, significantly improves model performance, while adding document-level tasks with parameter sharing only marginally improves performance.Ablation study. To investigate the impact of different components, we start with a vanilla model which consists of fθs, fθae, and fθas only without any informative message passing, and add other components one at a time. + Message passing-a denotes propagating the outputs from aspect-level tasks only at each message passing iteration. +DS and +DD denote adding DS and DD with parameter sharing only. +Message passing-d denotes involving the document-level information for message passing. We observe that +Message passing-a and +Message passing-d contribute to the performance gains the most, which demonstrates the effectiveness of the proposed message passing mechanism. We also observe that simply adding document-level tasks (+DS/DD) with parameter sharing only marginally improves the performance of IMN−d. This again indicates that domain-specific knowledge has already been captured by domain embeddings, while knowledge obtained from DD and DS via parameter sharing could be redundant in this case. However, +Message passing-d is still helpful with considerable performance gains, showing that aspect-level tasks can benefit from knowing predictions of the relevant document-level tasks." } ], "images": [ "scigen/train/2865.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: F1-I scores of different model variants. Average results over 5 runs are reported." ], "reasoning": "Adding message passing components, especially those involving document-level information, significantly improves model performance, while adding document-level tasks with parameter sharing only marginally improves performance.", "description": "Ablation study. To investigate the impact of different components, we start with a vanilla model which consists of fθs, fθae, and fθas only without any informative message passing, and add other components one at a time. + Message passing-a denotes propagating the outputs from aspect-level tasks only at each message passing iteration. +DS and +DD denote adding DS and DD with parameter sharing only. +Message passing-d denotes involving the document-level information for message passing. We observe that +Message passing-a and +Message passing-d contribute to the performance gains the most, which demonstrates the effectiveness of the proposed message passing mechanism. We also observe that simply adding document-level tasks (+DS/DD) with parameter sharing only marginally improves the performance of IMN−d. This again indicates that domain-specific knowledge has already been captured by domain embeddings, while knowledge obtained from DD and DS via parameter sharing could be redundant in this case. However, +Message passing-d is still helpful with considerable performance gains, showing that aspect-level tasks can benefit from knowing predictions of the relevant document-level tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 4: F1-I scores of different model variants. Average results over 5 runs are reported.', solve the subsequent question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich model variant shows the highest performance on D3?\n" }, { "role": "assistant", "content": "By comparing the D3 column values for all model variants, the highest value is 59.18, which corresponds to the +Message passing-d (IMN) variant. So, the answer is {\"answer\": \"+Message passing-d (IMN)\"}." } ], "images": [ "scigen/train/2865.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: F1-I scores of different model variants. Average results over 5 runs are reported." ], "reasoning": "By comparing the D3 column values for all model variants, the highest value is 59.18, which corresponds to the +Message passing-d (IMN) variant.", "question": "Which model variant shows the highest performance on D3?", "answer": "So, the answer is {\"answer\": \"+Message passing-d (IMN)\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The vanilla model has the lowest performance on D2., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe vanilla model has the lowest performance on D2.\n" }, { "role": "assistant", "content": "By comparing the D2 column values for all model variants, the lowest value is 55.63, which corresponds to the vanilla model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2865.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: F1-I scores of different model variants. Average results over 5 runs are reported." ], "reasoning": "By comparing the D2 column values for all model variants, the lowest value is 55.63, which corresponds to the vanilla model.", "statement": "The vanilla model has the lowest performance on D2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6: F1 scores with different T values using IMN−d. Average results over 5 runs are reported.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The message passing mechanism quickly converges within two or three iterations, with minimal performance improvement beyond that.Impact of T. We have demonstrated the effectiveness of the message passing mechanism. Here, we investigate the impact of the maximum number of iterations We find that convergence is quickly achieved within two or three iterations, and further iterations do not provide considerable performance improvement." } ], "images": [ "scigen/train/2866.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: F1 scores with different T values using IMN−d. Average results over 5 runs are reported." ], "reasoning": "The message passing mechanism quickly converges within two or three iterations, with minimal performance improvement beyond that.", "description": "Impact of T. We have demonstrated the effectiveness of the message passing mechanism. Here, we investigate the impact of the maximum number of iterations We find that convergence is quickly achieved within two or three iterations, and further iterations do not provide considerable performance improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 6: F1 scores with different T values using IMN−d. Average results over 5 runs are reported.', solve the following question step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the highest score achieved in the third iteration?\n" }, { "role": "assistant", "content": "To find the highest score in the third iteration, look at the column labeled '3'. The values are 68.03, 57.82, and 57.66. The highest value is 68.03. So, the answer is {\"answer\": \"68.03\"}." } ], "images": [ "scigen/train/2866.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: F1 scores with different T values using IMN−d. Average results over 5 runs are reported." ], "reasoning": "To find the highest score in the third iteration, look at the column labeled '3'. The values are 68.03, 57.82, and 57.66. The highest value is 68.03.", "question": "What is the highest score achieved in the third iteration?", "answer": "So, the answer is {\"answer\": \"68.03\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe performance improvement is significant after the third iteration.\n" }, { "role": "assistant", "content": "The table shows that the scores do not significantly improve after the third iteration. For example, in D1, the scores are 68.03, 68.11, and 68.26 for iterations 3, 4, and 5 respectively, showing minimal improvement. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2866.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: F1 scores with different T values using IMN−d. Average results over 5 runs are reported." ], "reasoning": "The table shows that the scores do not significantly improve after the third iteration. For example, in D1, the scores are 68.03, 68.11, and 68.26 for iterations 3, 4, and 5 respectively, showing minimal improvement.", "statement": "The performance improvement is significant after the third iteration.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 7.2: Phoneme error rates (%) for end-to-end training from random initialization comparing CTC and segmental models trained with marginal log loss.'.\n" }, { "role": "assistant", "content": "We use 3-layer 256-unit bidirectional LSTMs with CTC, one-state HMM, and two-state HMMs, optimizing marginal log loss with vanilla SGD for 40 epochs, and repeat the experiments with pyramid LSTMs.We use 3-layer 256-unit biderectional LSTMs paired with CTC, one-state HMM, and two-state HMMs. We do not use transition probabilities for two-state HMMs. We optimize marginal log loss with the corresponding weight functions and the corresponding constraint FSTs for CTC, one-state HMM, and two-state HMMs. We run vanilla SGD with step size 0.1 and gradient clipping of norm 5 for 20 epochs. Starting from the best performing model of the first 20 epochs, we run vanilla SGD for another 20 epochs with step size 0.75 decayed by 0.75 after each epoch. The dropout rate is 0.2, and no other explicit regularizer is used except early stopping on the development set. The same experiments are repeated with pyramid LSTMs." } ], "images": [ "scigen/train/2867.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7.2: Phoneme error rates (%) for end-to-end training from random initialization comparing CTC and segmental models trained with marginal log loss." ], "reasoning": "We use 3-layer 256-unit bidirectional LSTMs with CTC, one-state HMM, and two-state HMMs, optimizing marginal log loss with vanilla SGD for 40 epochs, and repeat the experiments with pyramid LSTMs.", "description": "We use 3-layer 256-unit biderectional LSTMs paired with CTC, one-state HMM, and two-state HMMs. We do not use transition probabilities for two-state HMMs. We optimize marginal log loss with the corresponding weight functions and the corresponding constraint FSTs for CTC, one-state HMM, and two-state HMMs. We run vanilla SGD with step size 0.1 and gradient clipping of norm 5 for 20 epochs. Starting from the best performing model of the first 20 epochs, we run vanilla SGD for another 20 epochs with step size 0.75 decayed by 0.75 after each epoch. The dropout rate is 0.2, and no other explicit regularizer is used except early stopping on the development set. The same experiments are repeated with pyramid LSTMs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 7.2: Phoneme error rates (%) for end-to-end training from random initialization comparing CTC and segmental models trained with marginal log loss.', unfold the steps to solve the question given next. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nQuestion: What is the test score for the pyramid LSTM using the CTC method?\n" }, { "role": "assistant", "content": "To find the test score for the pyramid LSTM using the CTC method, locate the 'pyramid LSTM test' column and the 'CTC' row in the table. So, the answer is {\"answer\": \"18.7\"}." } ], "images": [ "scigen/train/2867.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7.2: Phoneme error rates (%) for end-to-end training from random initialization comparing CTC and segmental models trained with marginal log loss." ], "reasoning": "To find the test score for the pyramid LSTM using the CTC method, locate the 'pyramid LSTM test' column and the 'CTC' row in the table.", "question": "What is the test score for the pyramid LSTM using the CTC method?", "answer": "So, the answer is {\"answer\": \"18.7\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe regular LSTM test score for the 1-state HMM is higher than the pyramid LSTM test score for the same method.\n" }, { "role": "assistant", "content": "Compare the 'regular LSMT test' score and the 'pyramid LSTM test' score for the 1-state HMM. The regular LSTM test score is 82.2, which is higher than the pyramid LSTM test score of 17.5. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2867.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7.2: Phoneme error rates (%) for end-to-end training from random initialization comparing CTC and segmental models trained with marginal log loss." ], "reasoning": "Compare the 'regular LSMT test' score and the 'pyramid LSTM test' score for the 1-state HMM. The regular LSTM test score is 82.2, which is higher than the pyramid LSTM test score of 17.5.", "statement": "The regular LSTM test score for the 1-state HMM is higher than the pyramid LSTM test score for the same method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3.1: Letter error rates (%) for a baseline tandem HMM and segmental models trained with various losses and cost functions on the ASL data set.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Segmental models outperform the baseline HMM, with hinge loss and overlap cost being the best performers, while non-convex losses show inconsistent results.The evaluation metric is the letter error rate, which is the percentage of letters that are substituted, inserted, or deleted. We observe three consistent conclusions: Segmental models perform significantly better than the baseline HMM. Across losses, overlap cost is better than MPE cost. Hinge loss with overlap cost is the best performer, but this is only by a small margin, and log loss is very competitive even without using an explicit cost function. Non-convex losses (ramp and EBR) are difficult to optimize and therefore achieve inconsistent results. We suspect a warm start might be able to remedy this." } ], "images": [ "scigen/train/2868.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3.1: Letter error rates (%) for a baseline tandem HMM and segmental models trained with various losses and cost functions on the ASL data set." ], "reasoning": "Segmental models outperform the baseline HMM, with hinge loss and overlap cost being the best performers, while non-convex losses show inconsistent results.", "description": "The evaluation metric is the letter error rate, which is the percentage of letters that are substituted, inserted, or deleted. We observe three consistent conclusions: Segmental models perform significantly better than the baseline HMM. Across losses, overlap cost is better than MPE cost. Hinge loss with overlap cost is the best performer, but this is only by a small margin, and log loss is very competitive even without using an explicit cost function. Non-convex losses (ramp and EBR) are difficult to optimize and therefore achieve inconsistent results. We suspect a warm start might be able to remedy this.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3.1: Letter error rates (%) for a baseline tandem HMM and segmental models trained with various losses and cost functions on the ASL data set.' in mind, break down and resolve the question below step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model and loss combination achieved the lowest average letter error rate?\n" }, { "role": "assistant", "content": "By examining the 'Avg' column, the lowest average letter error rate is 12.3, achieved by the segmental model with tuned ramp loss. So, the answer is {\"answer\": \"tuned ramp\"}." } ], "images": [ "scigen/train/2868.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3.1: Letter error rates (%) for a baseline tandem HMM and segmental models trained with various losses and cost functions on the ASL data set." ], "reasoning": "By examining the 'Avg' column, the lowest average letter error rate is 12.3, achieved by the segmental model with tuned ramp loss.", "question": "Which model and loss combination achieved the lowest average letter error rate?", "answer": "So, the answer is {\"answer\": \"tuned ramp\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The segmental model with log loss achieved the lowest letter error rate for Drucie. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe segmental model with log loss achieved the lowest letter error rate for Drucie.\n" }, { "role": "assistant", "content": "By looking at the 'Drucie' column, the lowest letter error rate is 6.7, achieved by the segmental model with log loss. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2868.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3.1: Letter error rates (%) for a baseline tandem HMM and segmental models trained with various losses and cost functions on the ASL data set." ], "reasoning": "By looking at the 'Drucie' column, the lowest letter error rate is 6.7, achieved by the segmental model with log loss.", "statement": "The segmental model with log loss achieved the lowest letter error rate for Drucie.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4.3: Phoneme error rates (%) with discriminative segmental cascades. The search space for the first pass is denoted H1, and the search space for the second pass H′1↑ is denoted H2, where H1 is pruned to get H′1.'.\n" }, { "role": "assistant", "content": "Adding second-order weights, bigram LM, and NN weights to the second pass system improves performance by 1.8% over the first pass system.We use the sum of the lattice weight, the bigram LM weight, second-order boundary weights, segment NN weights, bi-phone NN bottleneck weights, length indicators, and lexicalized bias as our final weight function for the second level of the cascade. Hinge loss is minimized with AdaGrad for 20 epochs with step size 0.01. Again, no explicit regularizer is used except early stopping based on the PER on the development set. Adding the second-order weights, bigram LM, and the above NN weights gives a 1.8% absolute improvement over our best first-pass system, demonstrating the value of including such expensive features with wider context." } ], "images": [ "scigen/train/2869.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4.3: Phoneme error rates (%) with discriminative segmental cascades. The search space for the first pass is denoted H1, and the search space for the second pass H′1↑ is denoted H2, where H1 is pruned to get H′1." ], "reasoning": "Adding second-order weights, bigram LM, and NN weights to the second pass system improves performance by 1.8% over the first pass system.", "description": "We use the sum of the lattice weight, the bigram LM weight, second-order boundary weights, segment NN weights, bi-phone NN bottleneck weights, length indicators, and lexicalized bias as our final weight function for the second level of the cascade. Hinge loss is minimized with AdaGrad for 20 epochs with step size 0.01. Again, no explicit regularizer is used except early stopping based on the PER on the development set. Adding the second-order weights, bigram LM, and the above NN weights gives a 1.8% absolute improvement over our best first-pass system, demonstrating the value of including such expensive features with wider context.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 4.3: Phoneme error rates (%) with discriminative segmental cascades. The search space for the first pass is denoted H1, and the search space for the second pass H′1↑ is denoted H2, where H1 is pruned to get H′1.', and sequentially solve the question, writing out each step of your reasoning process. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the test performance of the system after adding bi-phone NN bottleneck features?\n" }, { "role": "assistant", "content": "The table shows the test performance of the system after adding various features. The test performance after adding bi-phone NN bottleneck features is 19.9. So, the answer is {\"answer\": \"19.9\"}" } ], "images": [ "scigen/train/2869.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4.3: Phoneme error rates (%) with discriminative segmental cascades. The search space for the first pass is denoted H1, and the search space for the second pass H′1↑ is denoted H2, where H1 is pruned to get H′1." ], "reasoning": "The table shows the test performance of the system after adding various features. The test performance after adding bi-phone NN bottleneck features is 19.9.", "question": "What is the test performance of the system after adding bi-phone NN bottleneck features?", "answer": "So, the answer is {\"answer\": \"19.9\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe second pass system with bigram LM has a dev performance of 19.8.\n" }, { "role": "assistant", "content": "The table indicates that the dev performance of the second pass system with bigram LM is indeed 19.8. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2869.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4.3: Phoneme error rates (%) with discriminative segmental cascades. The search space for the first pass is denoted H1, and the search space for the second pass H′1↑ is denoted H2, where H1 is pruned to get H′1." ], "reasoning": "The table indicates that the dev performance of the second pass system with bigram LM is indeed 19.8.", "statement": "The second pass system with bigram LM has a dev performance of 19.8.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4.4: Phone error rates (%) of proposed and baseline systems.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The proposed system shows a slight improvement over the baseline in the second pass and achieves a final performance within 0.4% of the baseline in the third pass, with significant speedup in training.First, if we compare the one-pass baseline with the proposed two-pass system, our system is close to the baseline. Second, we observe a healthy improvement by just adding the bigram language model score to the second-pass baseline. The improvement for our third-pass system is small but brings our final performance to within 0.4% of the baseline second pass. The speedup mostly comes from the fast convergence of the first pass. In terms of training the segmental models alone, we achieve an 18.0-fold speedup. If the time to train the LSTMs is included, then we obtain a 3.4-fold speedup compared to the baseline." } ], "images": [ "scigen/train/2870.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4.4: Phone error rates (%) of proposed and baseline systems." ], "reasoning": "The proposed system shows a slight improvement over the baseline in the second pass and achieves a final performance within 0.4% of the baseline in the third pass, with significant speedup in training.", "description": "First, if we compare the one-pass baseline with the proposed two-pass system, our system is close to the baseline. Second, we observe a healthy improvement by just adding the bigram language model score to the second-pass baseline. The improvement for our third-pass system is small but brings our final performance to within 0.4% of the baseline second pass. The speedup mostly comes from the fast convergence of the first pass. In terms of training the segmental models alone, we achieve an 18.0-fold speedup. If the time to train the LSTMs is included, then we obtain a 3.4-fold speedup compared to the baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 4.4: Phone error rates (%) of proposed and baseline systems.', solve the following question step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the improvement in the test score from the first pass to the second pass for the proposed system?\n" }, { "role": "assistant", "content": "The test score for the proposed system in the first pass is 23.7, and in the second pass, it is 23.4. The improvement is calculated by subtracting the second pass score from the first pass score. So, the answer is {\"answer\": \"0.3\"}" } ], "images": [ "scigen/train/2870.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4.4: Phone error rates (%) of proposed and baseline systems." ], "reasoning": "The test score for the proposed system in the first pass is 23.7, and in the second pass, it is 23.4. The improvement is calculated by subtracting the second pass score from the first pass score.", "question": "What is the improvement in the test score from the first pass to the second pass for the proposed system?", "answer": "So, the answer is {\"answer\": \"0.3\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe proposed system's development score in the first pass is higher than the baseline's development score in the first pass.\n" }, { "role": "assistant", "content": "The table shows that the proposed system's development score in the first pass is 33.6, while the baseline's development score in the first pass is 21.9. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2870.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4.4: Phone error rates (%) of proposed and baseline systems." ], "reasoning": "The table shows that the proposed system's development score in the first pass is 33.6, while the baseline's development score in the first pass is 21.9.", "statement": "The proposed system's development score in the first pass is higher than the baseline's development score in the first pass.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4.5: Real-time factors for decoding.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The proposed approach achieves a 2.4 times speedup in decoding time and is twice as fast overall compared to the baseline.Next we report on the speedups in training and decoding obtained with our proposed approach. In terms of decoding time alone, we achieve a factor of 2.4 speedup compared to the baseline. If the time of feeding forward LSTMs is included, then our proposed system is two times faster than the baseline." } ], "images": [ "scigen/train/2871.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4.5: Real-time factors for decoding." ], "reasoning": "The proposed approach achieves a 2.4 times speedup in decoding time and is twice as fast overall compared to the baseline.", "description": "Next we report on the speedups in training and decoding obtained with our proposed approach. In terms of decoding time alone, we achieve a factor of 2.4 speedup compared to the baseline. If the time of feeding forward LSTMs is included, then our proposed system is two times faster than the baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 4.5: Real-time factors for decoding.', answer the question below by showing each progressive step toward the answer. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the total overall time for the proposed system compared to the baseline?\n" }, { "role": "assistant", "content": "The total overall time for the proposed system is 0.31, while for the baseline it is 0.67. This shows that the proposed system is faster. So, the answer is {\"answer\": \"0.31\"}" } ], "images": [ "scigen/train/2871.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4.5: Real-time factors for decoding." ], "reasoning": "The total overall time for the proposed system is 0.31, while for the baseline it is 0.67. This shows that the proposed system is faster.", "question": "What is the total overall time for the proposed system compared to the baseline?", "answer": "So, the answer is {\"answer\": \"0.31\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe proposed system is more than twice as fast as the baseline in total overall time.\n" }, { "role": "assistant", "content": "The total overall time for the proposed system is 0.31, and for the baseline it is 0.67. The proposed system is slightly more than twice as fast as the baseline. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2871.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4.5: Real-time factors for decoding." ], "reasoning": "The total overall time for the proposed system is 0.31, and for the baseline it is 0.67. The proposed system is slightly more than twice as fast as the baseline.", "statement": "The proposed system is more than twice as fast as the baseline in total overall time.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4.6: Hours for training the system.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The proposed method achieves an 18.0-fold speedup in training segmental models and a 3.4-fold speedup overall compared to the baseline, primarily due to the fast convergence of the first pass.The speedup mostly comes from the fast convergence of the first pass. In terms of training the segmental models alone, we achieve an 18.0-fold speedup. If the time to train the LSTMs is included, then we obtain a 3.4-fold speedup compared to the baseline." } ], "images": [ "scigen/train/2872.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4.6: Hours for training the system." ], "reasoning": "The proposed method achieves an 18.0-fold speedup in training segmental models and a 3.4-fold speedup overall compared to the baseline, primarily due to the fast convergence of the first pass.", "description": "The speedup mostly comes from the fast convergence of the first pass. In terms of training the segmental models alone, we achieve an 18.0-fold speedup. If the time to train the LSTMs is included, then we obtain a 3.4-fold speedup compared to the baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 4.6: Hours for training the system.', unfold the question presented below, detailing every step of your calculation. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the total time saved in the overall process when using the proposed method compared to the baseline?\n" }, { "role": "assistant", "content": "The total overall time for the baseline is 109.5, and for the proposed method, it is 32.5. The time saved is 109.5 - 32.5. So, the answer is {\"answer\": \"77.0\"}." } ], "images": [ "scigen/train/2872.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4.6: Hours for training the system." ], "reasoning": "The total overall time for the baseline is 109.5, and for the proposed method, it is 32.5. The time saved is 109.5 - 32.5.", "question": "What is the total time saved in the overall process when using the proposed method compared to the baseline?", "answer": "So, the answer is {\"answer\": \"77.0\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe proposed method reduces the total decoding time by more than 45 points compared to the baseline.\n" }, { "role": "assistant", "content": "The total decoding time for the baseline is 50.1, and for the proposed method, it is 2.8. The reduction is 50.1 - 2.8, which is 47.3. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2872.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4.6: Hours for training the system." ], "reasoning": "The total decoding time for the baseline is 50.1, and for the proposed method, it is 2.8. The reduction is 50.1 - 2.8, which is 47.3.", "statement": "The proposed method reduces the total decoding time by more than 45 points compared to the baseline.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5.1: Frame error rates (FER) and phoneme error rates (PER) on the development set comparing the CNN and the LSTM encoder.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The LSTM outperforms the CNN in both FER and PER tasks, likely due to its training on entire utterances compared to the CNN's 15-frame windows.The LSTM performs better than the CNN for both tasks. We suspect that the LSTM’s superior performance is due to the training approaches: the LSTM is trained on entire utterances, while the CNN is trained on batches of 15-frame windows. For the rest of the experiments, we use 3-layer LSTMs as our encoders." } ], "images": [ "scigen/train/2873.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5.1: Frame error rates (FER) and phoneme error rates (PER) on the development set comparing the CNN and the LSTM encoder." ], "reasoning": "The LSTM outperforms the CNN in both FER and PER tasks, likely due to its training on entire utterances compared to the CNN's 15-frame windows.", "description": "The LSTM performs better than the CNN for both tasks. We suspect that the LSTM’s superior performance is due to the training approaches: the LSTM is trained on entire utterances, while the CNN is trained on batches of 15-frame windows. For the rest of the experiments, we use 3-layer LSTMs as our encoders.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 5.1: Frame error rates (FER) and phoneme error rates (PER) on the development set comparing the CNN and the LSTM encoder.' and proceed to solve the following question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the difference in FER performance between the LSTM and CNN?\n" }, { "role": "assistant", "content": "The FER for the CNN is 22.3%, while the FER for the LSTM is 17.8%. The difference is calculated as 22.3% - 17.8%. So, the answer is {\"answer\": \"4.5%\"}" } ], "images": [ "scigen/train/2873.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5.1: Frame error rates (FER) and phoneme error rates (PER) on the development set comparing the CNN and the LSTM encoder." ], "reasoning": "The FER for the CNN is 22.3%, while the FER for the LSTM is 17.8%. The difference is calculated as 22.3% - 17.8%.", "question": "What is the difference in FER performance between the LSTM and CNN?", "answer": "So, the answer is {\"answer\": \"4.5%\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The LSTM has a lower PER than the CNN. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe LSTM has a lower PER than the CNN.\n" }, { "role": "assistant", "content": "The table shows that the PER for the LSTM is 19.7%, while the PER for the CNN is 22.2%. Therefore, the LSTM has a lower PER. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2873.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5.1: Frame error rates (FER) and phoneme error rates (PER) on the development set comparing the CNN and the LSTM encoder." ], "reasoning": "The table shows that the PER for the LSTM is 19.7%, while the PER for the CNN is 22.2%. Therefore, the LSTM has a lower PER.", "statement": "The LSTM has a lower PER than the CNN.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5.2: Phoneme error rates (%) comparing different losses with two-stage training (2s) followed by end-to-end fine-tuning (ft).'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Segmental models trained with different losses show similar results, with marginal log loss performing best after end-to-end fine-tuning.Next, we compare segmental models trained with different losses, including hinge loss, log loss, and marginal log loss. We use the FC weight function and a maximum segment duration of 30 frames. All losses are minimized with RMSProp for 20 epochs with step size 10−4 and decay 0.9. No explicit regularizers are used except early stopping on the development set. All losses achieve similar results with log loss slightly behind. Note that although marginal log loss does not require manual alignments, the frame-wise cross entropy used to train the frame classifier still does. After two-stage training, we use the trained segmental models and frame classifiers as an initialization for end-to-end training. Each loss is optimized with vanilla SGD for 20 epochs with step size 0.1 decayed by 0.75 after each epoch. Gradients are clipped with norm 5. Dropout is added to the LSTMs with rate 0.2, and no other regularizers are used except early stopping on the development set. End-to-end fine-tuning is able to improve the error rates for all losses with marginal log loss being the best performer." } ], "images": [ "scigen/train/2874.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5.2: Phoneme error rates (%) comparing different losses with two-stage training (2s) followed by end-to-end fine-tuning (ft)." ], "reasoning": "Segmental models trained with different losses show similar results, with marginal log loss performing best after end-to-end fine-tuning.", "description": "Next, we compare segmental models trained with different losses, including hinge loss, log loss, and marginal log loss. We use the FC weight function and a maximum segment duration of 30 frames. All losses are minimized with RMSProp for 20 epochs with step size 10−4 and decay 0.9. No explicit regularizers are used except early stopping on the development set. All losses achieve similar results with log loss slightly behind. Note that although marginal log loss does not require manual alignments, the frame-wise cross entropy used to train the frame classifier still does. After two-stage training, we use the trained segmental models and frame classifiers as an initialization for end-to-end training. Each loss is optimized with vanilla SGD for 20 epochs with step size 0.1 decayed by 0.75 after each epoch. Gradients are clipped with norm 5. Dropout is added to the LSTMs with rate 0.2, and no other regularizers are used except early stopping on the development set. End-to-end fine-tuning is able to improve the error rates for all losses with marginal log loss being the best performer.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the 2s test error rate for the log loss before and after fine-tuning?\nSolve the above question based on the table titled 'Table 5.2: Phoneme error rates (%) comparing different losses with two-stage training (2s) followed by end-to-end fine-tuning (ft).'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the 2s test error rate for log loss before fine-tuning as 21.0 and after fine-tuning as 17.3. So, the answer is {\"answer\": \"21.0 and 17.3\"}." } ], "images": [ "scigen/train/2874.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5.2: Phoneme error rates (%) comparing different losses with two-stage training (2s) followed by end-to-end fine-tuning (ft)." ], "reasoning": "The table shows the 2s test error rate for log loss before fine-tuning as 21.0 and after fine-tuning as 17.3.", "question": "What is the 2s test error rate for the log loss before and after fine-tuning?", "answer": "So, the answer is {\"answer\": \"21.0 and 17.3\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe marginal log loss achieves the lowest error rate after fine-tuning on the development set.\n" }, { "role": "assistant", "content": "The table shows that the marginal log loss has an error rate of 16.9 after fine-tuning on the development set, which is the lowest among the listed losses. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2874.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5.2: Phoneme error rates (%) comparing different losses with two-stage training (2s) followed by end-to-end fine-tuning (ft)." ], "reasoning": "The table shows that the marginal log loss has an error rate of 16.9 after fine-tuning on the development set, which is the lowest among the listed losses.", "statement": "The marginal log loss achieves the lowest error rate after fine-tuning on the development set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5.3: Frame error rates (%) of LSTM encoders after end-to-end fine-tuning with different losses .', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Encoders fine-tuned with hinge loss and log loss deviate less from initialization compared to marginal log loss, likely due to the use of ground-truth alignments.After end-to-end fine-tuning, the encoders are not constrained to perform well on frame-wise cross entropy. We evaluate the encoders on the frame classification task to see how much the encoders deviate from the initialization, and whether the intermediate representations trained for frame classification are still preserved. The encoders fine-tuned with hinge loss and log loss deviate less compared to the one fine-tuned with marginal log loss. We suspect this is because hinge loss and log loss use the ground-truth alignments in training while marginal log loss does not." } ], "images": [ "scigen/train/2875.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5.3: Frame error rates (%) of LSTM encoders after end-to-end fine-tuning with different losses ." ], "reasoning": "Encoders fine-tuned with hinge loss and log loss deviate less from initialization compared to marginal log loss, likely due to the use of ground-truth alignments.", "description": "After end-to-end fine-tuning, the encoders are not constrained to perform well on frame-wise cross entropy. We evaluate the encoders on the frame classification task to see how much the encoders deviate from the initialization, and whether the intermediate representations trained for frame classification are still preserved. The encoders fine-tuned with hinge loss and log loss deviate less compared to the one fine-tuned with marginal log loss. We suspect this is because hinge loss and log loss use the ground-truth alignments in training while marginal log loss does not.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 5.3: Frame error rates (%) of LSTM encoders after end-to-end fine-tuning with different losses .\nQuestion: Which fine-tuning method resulted in the highest frame error rate (FER)?\n" }, { "role": "assistant", "content": "The table shows the FER values for different fine-tuning methods. The highest FER value is 26.8, which corresponds to the MLL (marginal log loss) method. So, the answer is {\"answer\": \"MLL\"}." } ], "images": [ "scigen/train/2875.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5.3: Frame error rates (%) of LSTM encoders after end-to-end fine-tuning with different losses ." ], "reasoning": "The table shows the FER values for different fine-tuning methods. The highest FER value is 26.8, which corresponds to the MLL (marginal log loss) method.", "question": "Which fine-tuning method resulted in the highest frame error rate (FER)?", "answer": "So, the answer is {\"answer\": \"MLL\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe log loss fine-tuning method resulted in a frame error rate (FER) of 18.7.\n" }, { "role": "assistant", "content": "The table lists the FER values for different fine-tuning methods. The FER for the log loss method is indeed 18.7. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2875.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5.3: Frame error rates (%) of LSTM encoders after end-to-end fine-tuning with different losses ." ], "reasoning": "The table lists the FER values for different fine-tuning methods. The FER for the log loss method is indeed 18.7.", "statement": "The log loss fine-tuning method resulted in a frame error rate (FER) of 18.7.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5.4: Phoneme error rates (%) on the development set comparing multi-stage training followed by end-to-end fine-tuning (2s+ft) and end-to-end training from random initialization (e2e).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Experiments were conducted to train segmental models end to end from random initialization using the FC weight function, with the best model trained for 40 epochs using vanilla SGD and a dropout rate of 0.2., we conduct experiments to see whether it is possible to train segmental models end to end from random initialization. We use the FC weight function and a maximum duration of 30 frames, the same setting as in the multi-stage experiments. Each loss is optimized with vanilla SGD for 20 epochs with step size 0.1 and gradient clipping with norm 5. The best performing model, chosen in the first 20 epochs, is trained for another 20 epochs with vanilla SGD step size 0.75 decayed by 0.75 after each epoch. Dropout of rate 0.2 is used throughout the training process. No other regularizers are used except early stopping on the development set." } ], "images": [ "scigen/train/2876.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5.4: Phoneme error rates (%) on the development set comparing multi-stage training followed by end-to-end fine-tuning (2s+ft) and end-to-end training from random initialization (e2e)." ], "reasoning": "Experiments were conducted to train segmental models end to end from random initialization using the FC weight function, with the best model trained for 40 epochs using vanilla SGD and a dropout rate of 0.2.", "description": ", we conduct experiments to see whether it is possible to train segmental models end to end from random initialization. We use the FC weight function and a maximum duration of 30 frames, the same setting as in the multi-stage experiments. Each loss is optimized with vanilla SGD for 20 epochs with step size 0.1 and gradient clipping with norm 5. The best performing model, chosen in the first 20 epochs, is trained for another 20 epochs with vanilla SGD step size 0.75 decayed by 0.75 after each epoch. Dropout of rate 0.2 is used throughout the training process. No other regularizers are used except early stopping on the development set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the e2e performance for the log loss function?\nSolve the above question based on the table titled 'Table 5.4: Phoneme error rates (%) on the development set comparing multi-stage training followed by end-to-end fine-tuning (2s+ft) and end-to-end training from random initialization (e2e).'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the performance metrics for different loss functions. For the log loss function, the e2e performance is listed. So, the answer is {\"answer\": \"17.4\"}" } ], "images": [ "scigen/train/2876.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5.4: Phoneme error rates (%) on the development set comparing multi-stage training followed by end-to-end fine-tuning (2s+ft) and end-to-end training from random initialization (e2e)." ], "reasoning": "The table shows the performance metrics for different loss functions. For the log loss function, the e2e performance is listed.", "question": "What is the e2e performance for the log loss function?", "answer": "So, the answer is {\"answer\": \"17.4\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe best performing model for the MLL loss function in the 2s+ft setting achieved a score of 16.9.\n" }, { "role": "assistant", "content": "The table shows the performance metrics for different loss functions. For the MLL loss function in the 2s+ft setting, the score is 16.9. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2876.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5.4: Phoneme error rates (%) on the development set comparing multi-stage training followed by end-to-end fine-tuning (2s+ft) and end-to-end training from random initialization (e2e)." ], "reasoning": "The table shows the performance metrics for different loss functions. For the MLL loss function in the 2s+ft setting, the score is 16.9.", "statement": "The best performing model for the MLL loss function in the 2s+ft setting achieved a score of 16.9.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5.6: Average number of minutes for one epoch of end-to-end training on TIMIT.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Reducing the time resolution with pyramid LSTMs significantly improves the runtime for all losses, and the MLP weight function is about 2.5 times slower than the FC weight function.We compare the time to train segmental models end to end. Times are measured on a 3GHz 4-core CPU. Multithreading is only used for matrix operations; all FST algorithms are implemented single-threaded. Reducing the time resolution with pyramid LSTMs significantly improves the runtime for all losses. The MLP weight function is about 2.5 times slower than the FC weight function." } ], "images": [ "scigen/train/2877.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5.6: Average number of minutes for one epoch of end-to-end training on TIMIT." ], "reasoning": "Reducing the time resolution with pyramid LSTMs significantly improves the runtime for all losses, and the MLP weight function is about 2.5 times slower than the FC weight function.", "description": "We compare the time to train segmental models end to end. Times are measured on a 3GHz 4-core CPU. Multithreading is only used for matrix operations; all FST algorithms are implemented single-threaded. Reducing the time resolution with pyramid LSTMs significantly improves the runtime for all losses. The MLP weight function is about 2.5 times slower than the FC weight function.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 5.6: Average number of minutes for one epoch of end-to-end training on TIMIT.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the runtime improvement for the hinge loss when using pyramid LSTMs compared to regular LSTMs with the FC weight function?\n" }, { "role": "assistant", "content": "The runtime for the hinge loss with regular LSTM and FC weight function is 119, and with pyramid LSTM, it is 38. The improvement is 119 - 38. So, the answer is {\"answer\": \"81\"}." } ], "images": [ "scigen/train/2877.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5.6: Average number of minutes for one epoch of end-to-end training on TIMIT." ], "reasoning": "The runtime for the hinge loss with regular LSTM and FC weight function is 119, and with pyramid LSTM, it is 38. The improvement is 119 - 38.", "question": "What is the runtime improvement for the hinge loss when using pyramid LSTMs compared to regular LSTMs with the FC weight function?", "answer": "So, the answer is {\"answer\": \"81\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The MLP weight function is faster than the FC weight function for the hinge loss. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe MLP weight function is faster than the FC weight function for the hinge loss.\n" }, { "role": "assistant", "content": "The table shows that for the hinge loss, the runtime with the MLP weight function is 83, while with the FC weight function, it is 119. Therefore, the MLP weight function is faster. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2877.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5.6: Average number of minutes for one epoch of end-to-end training on TIMIT." ], "reasoning": "The table shows that for the hinge loss, the runtime with the MLP weight function is 83, while with the FC weight function, it is 119. Therefore, the MLP weight function is faster.", "statement": "The MLP weight function is faster than the FC weight function for the hinge loss.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7.3: Average number of minutes for one epoch of end-to-end training on TIMIT.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Pyramid LSTMs trained with CTC are the best performers and most efficient to train, while frame-based LSTMs decode faster but segmental models are more flexible and may perform better in low-resource settings.Pyramid LSTMs trained with CTC are the best performer while also being the most efficient to train. Due to smaller and simpler search spaces, frame-based LSTMs trained with CTC decode faster than segmental models. While frame-based LSTMs train and decode faster, segmental models are more flexible, so they might be improved with additional feature functions. Explicitly hypothesizing segments can serve as a prior, so segmental models might perform better in low-resource settings." } ], "images": [ "scigen/train/2878.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7.3: Average number of minutes for one epoch of end-to-end training on TIMIT." ], "reasoning": "Pyramid LSTMs trained with CTC are the best performers and most efficient to train, while frame-based LSTMs decode faster but segmental models are more flexible and may perform better in low-resource settings.", "description": "Pyramid LSTMs trained with CTC are the best performer while also being the most efficient to train. Due to smaller and simpler search spaces, frame-based LSTMs trained with CTC decode faster than segmental models. While frame-based LSTMs train and decode faster, segmental models are more flexible, so they might be improved with additional feature functions. Explicitly hypothesizing segments can serve as a prior, so segmental models might perform better in low-resource settings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 7.3: Average number of minutes for one epoch of end-to-end training on TIMIT.', unfold the question presented below, detailing every step of your calculation. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the lowest hinge score in the seg FC category?\n" }, { "role": "assistant", "content": "The table shows the hinge scores for both regular and pyramid LSTMs in the seg FC category. The pyramid LSTM has a hinge score of 38, which is lower than the regular LSTM's score of 119. So, the answer is {\"answer\": \"pyramid LSTM\"}" } ], "images": [ "scigen/train/2878.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7.3: Average number of minutes for one epoch of end-to-end training on TIMIT." ], "reasoning": "The table shows the hinge scores for both regular and pyramid LSTMs in the seg FC category. The pyramid LSTM has a hinge score of 38, which is lower than the regular LSTM's score of 119.", "question": "Which model has the lowest hinge score in the seg FC category?", "answer": "So, the answer is {\"answer\": \"pyramid LSTM\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence Regular LSTMs have a lower log score than pyramid LSTMs in the seg MLP category. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nRegular LSTMs have a lower log score than pyramid LSTMs in the seg MLP category.\n" }, { "role": "assistant", "content": "The table shows that in the seg MLP category, the log score for regular LSTMs is 110, while for pyramid LSTMs it is not listed. Therefore, the statement cannot be verified with the given information. So, the answer is {\"answer\": \"not enough info\"}" } ], "images": [ "scigen/train/2878.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7.3: Average number of minutes for one epoch of end-to-end training on TIMIT." ], "reasoning": "The table shows that in the seg MLP category, the log score for regular LSTMs is 110, while for pyramid LSTMs it is not listed. Therefore, the statement cannot be verified with the given information.", "statement": "Regular LSTMs have a lower log score than pyramid LSTMs in the seg MLP category.", "answer": "So, the answer is {\"answer\": \"not enough info\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 7.4: Real-time factors for decoding comparing CTC and segmental models.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Pyramid LSTMs trained with CTC are the best performer and most efficient to train, while frame-based LSTMs trained with CTC decode faster than segmental models.Pyramid LSTMs trained with CTC are the best performer while also being the most efficient to train. Due to smaller and simpler search spaces, frame-based LSTMs trained with CTC decode faster than segmental models. While frame-based LSTMs train and decode faster, segmental models are more flexible, so they might be improved with additional feature functions. Explicitly hypothesizing segments can serve as a prior, so segmental models might perform better in low-resource settings." } ], "images": [ "scigen/train/2879.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7.4: Real-time factors for decoding comparing CTC and segmental models." ], "reasoning": "Pyramid LSTMs trained with CTC are the best performer and most efficient to train, while frame-based LSTMs trained with CTC decode faster than segmental models.", "description": "Pyramid LSTMs trained with CTC are the best performer while also being the most efficient to train. Due to smaller and simpler search spaces, frame-based LSTMs trained with CTC decode faster than segmental models. While frame-based LSTMs train and decode faster, segmental models are more flexible, so they might be improved with additional feature functions. Explicitly hypothesizing segments can serve as a prior, so segmental models might perform better in low-resource settings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 7.4: Real-time factors for decoding comparing CTC and segmental models.'\nQuestion: Which model and training method combination is the most efficient to train?\n" }, { "role": "assistant", "content": "The description states that Pyramid LSTMs trained with CTC are the most efficient to train. The table shows that Pyramid LSTMs with CTC have a value of 0.07, which is the lowest among all combinations, indicating efficiency. So, the answer is {\"answer\": \"Pyramid LSTMs with CTC\"}." } ], "images": [ "scigen/train/2879.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7.4: Real-time factors for decoding comparing CTC and segmental models." ], "reasoning": "The description states that Pyramid LSTMs trained with CTC are the most efficient to train. The table shows that Pyramid LSTMs with CTC have a value of 0.07, which is the lowest among all combinations, indicating efficiency.", "question": "Which model and training method combination is the most efficient to train?", "answer": "So, the answer is {\"answer\": \"Pyramid LSTMs with CTC\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nSegmental models trained with MLP are more efficient than regular LSTMs trained with CTC.\n" }, { "role": "assistant", "content": "The table shows that segmental models trained with MLP have a value of 0.59, while regular LSTMs trained with CTC have a value of 0.12. Since 0.59 is higher than 0.12, segmental models trained with MLP are less efficient. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2879.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7.4: Real-time factors for decoding comparing CTC and segmental models." ], "reasoning": "The table shows that segmental models trained with MLP have a value of 0.59, while regular LSTMs trained with CTC have a value of 0.12. Since 0.59 is higher than 0.12, segmental models trained with MLP are less efficient.", "statement": "Segmental models trained with MLP are more efficient than regular LSTMs trained with CTC.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 7.5: Letter error rates (%) for signer-dependent models averaged over ten folds.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The frame-based LSTMs trained with CTC perform better than Tandem HMMs but are outperformed by segmental models.For the signer-dependent setting, the data for each signer is split into ten folds, in which eight are for training, one is for development, and one is for testing. We report the letter error rate averaged over the 10 experiments. We train LSTMs with CTC and segmental models with the FCB weight function. Both models are trained with marginal log loss from random initialization. The encoder is a one-layer 128-unit bidirectional LSTM. For CTC, we run vanilla SGD with step sizes in {0.1,0.05,0.025} and gradient clipping with norm 5 for 200 epochs. Dropout is used with a rate of 0.2, and no other explicit regularizer is used except early stopping. Step sizes and early stopping are tuned on the development set for each individual experiment. For segmental models, we run vanilla SGD with step size 0.1 for 75 epochs. The maximum segment length is 75 frames. Other hyperparameters, such as dropout and gradient clipping, are the same as for CTC. The frame-based LSTMs trained with CTC are better than Tandem HMMs, but are behind the segmental models." } ], "images": [ "scigen/train/2880.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7.5: Letter error rates (%) for signer-dependent models averaged over ten folds." ], "reasoning": "The frame-based LSTMs trained with CTC perform better than Tandem HMMs but are outperformed by segmental models.", "description": "For the signer-dependent setting, the data for each signer is split into ten folds, in which eight are for training, one is for development, and one is for testing. We report the letter error rate averaged over the 10 experiments. We train LSTMs with CTC and segmental models with the FCB weight function. Both models are trained with marginal log loss from random initialization. The encoder is a one-layer 128-unit bidirectional LSTM. For CTC, we run vanilla SGD with step sizes in {0.1,0.05,0.025} and gradient clipping with norm 5 for 200 epochs. Dropout is used with a rate of 0.2, and no other explicit regularizer is used except early stopping. Step sizes and early stopping are tuned on the development set for each individual experiment. For segmental models, we run vanilla SGD with step size 0.1 for 75 epochs. The maximum segment length is 75 frames. Other hyperparameters, such as dropout and gradient clipping, are the same as for CTC. The frame-based LSTMs trained with CTC are better than Tandem HMMs, but are behind the segmental models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 7.5: Letter error rates (%) for signer-dependent models averaged over ten folds.' and methodically tackle the question that follows. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the average letter error rate for the CTC model on the test set?\n" }, { "role": "assistant", "content": "To find the average letter error rate for the CTC model on the test set, look at the 'Avg test' column for the CTC row. The value is 11.0. So, the answer is {\"answer\": \"11.0\"}." } ], "images": [ "scigen/train/2880.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7.5: Letter error rates (%) for signer-dependent models averaged over ten folds." ], "reasoning": "To find the average letter error rate for the CTC model on the test set, look at the 'Avg test' column for the CTC row. The value is 11.0.", "question": "What is the average letter error rate for the CTC model on the test set?", "answer": "So, the answer is {\"answer\": \"11.0\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The segmental models with FCB weight function have a lower average letter error rate on the test set compared to the two-stage segmentation FC models., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe segmental models with FCB weight function have a lower average letter error rate on the test set compared to the two-stage segmentation FC models.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Avg test' values for the 'seg FCB' and 'two-stage seg FC' rows. The 'seg FCB' has an average letter error rate of 9.2, while the 'two-stage seg FC' has 8.8. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2880.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7.5: Letter error rates (%) for signer-dependent models averaged over ten folds." ], "reasoning": "To verify this statement, compare the 'Avg test' values for the 'seg FCB' and 'two-stage seg FC' rows. The 'seg FCB' has an average letter error rate of 9.2, while the 'two-stage seg FC' has 8.8. Therefore, the statement is refuted.", "statement": "The segmental models with FCB weight function have a lower average letter error rate on the test set compared to the two-stage segmentation FC models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 7.6: Letter error rates (%) for signer-independent models averaged over eight folds.'.\n" }, { "role": "assistant", "content": "CTC and segmental models trained end to end outperform Tandem HMMs and two-stage segmental models with FC weight function in the signer-independent setting.For the signer-independent setting, we train on three signers and use the last signer for development and testing. Specifically, the data of the last signer is split into ten folds, and we use two folds for development and the rest of the eight folds for testing. Instead of dividing the accumulated error counts by the accumulated label sequence lengths, letter error rates are averaged over the folds. We compare LSTMs trained with CTC and segmental models with FCB weight function. The loss function and training procedure stay the same except that we only run 40 epochs of vanilla SGD for segmental models. In this setting, CTC and segmental models trained end to end perform significantly better than Tandem HMMs and segmental models trained with the FC weight function in two stages." } ], "images": [ "scigen/train/2881.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7.6: Letter error rates (%) for signer-independent models averaged over eight folds." ], "reasoning": "CTC and segmental models trained end to end outperform Tandem HMMs and two-stage segmental models with FC weight function in the signer-independent setting.", "description": "For the signer-independent setting, we train on three signers and use the last signer for development and testing. Specifically, the data of the last signer is split into ten folds, and we use two folds for development and the rest of the eight folds for testing. Instead of dividing the accumulated error counts by the accumulated label sequence lengths, letter error rates are averaged over the folds. We compare LSTMs trained with CTC and segmental models with FCB weight function. The loss function and training procedure stay the same except that we only run 40 epochs of vanilla SGD for segmental models. In this setting, CTC and segmental models trained end to end perform significantly better than Tandem HMMs and segmental models trained with the FC weight function in two stages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 7.6: Letter error rates (%) for signer-independent models averaged over eight folds.', unfold the question presented below, detailing every step of your calculation. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the average test score for the CTC model?\n" }, { "role": "assistant", "content": "The table shows the average test score for the CTC model under the 'Avg test' column. So, the answer is {\"answer\": \"52.0\"}." } ], "images": [ "scigen/train/2881.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7.6: Letter error rates (%) for signer-independent models averaged over eight folds." ], "reasoning": "The table shows the average test score for the CTC model under the 'Avg test' column.", "question": "What is the average test score for the CTC model?", "answer": "So, the answer is {\"answer\": \"52.0\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe two-stage segmental model with FC weight function has a higher average test score than the Tandem HMM.\n" }, { "role": "assistant", "content": "The table shows that the average test score for the two-stage segmental model with FC weight function is 60.6, while the average test score for the Tandem HMM is 57.2. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2881.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7.6: Letter error rates (%) for signer-independent models averaged over eight folds." ], "reasoning": "The table shows that the average test score for the two-stage segmental model with FC weight function is 60.6, while the average test score for the Tandem HMM is 57.2.", "statement": "The two-stage segmental model with FC weight function has a higher average test score than the Tandem HMM.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 7.7: Letter error rates (%) for signer-dependent and signer-independent models averaged over the same eight folds.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Segmental models have lower average letter error rates compared to CTC in both signer-independent and signer-dependent settings.To compare signer-independent and signer-dependent settings, we average the letter error rates over the same eight folds. Segmental models perform significantly better than CTC in both settings." } ], "images": [ "scigen/train/2882.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7.7: Letter error rates (%) for signer-dependent and signer-independent models averaged over the same eight folds." ], "reasoning": "Segmental models have lower average letter error rates compared to CTC in both signer-independent and signer-dependent settings.", "description": "To compare signer-independent and signer-dependent settings, we average the letter error rates over the same eight folds. Segmental models perform significantly better than CTC in both settings.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 7.7: Letter error rates (%) for signer-dependent and signer-independent models averaged over the same eight folds.'. Solve the following question based on the table.\n\nQuestion: What is the average letter error rate for CTC and seg FCB in the signer-dependent setting?\nLet's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the letter error rates for CTC and seg FCB for each individual and the average. For the signer-dependent setting, the average letter error rate for CTC is 52.0 and for seg FCB is 45.0. So, the answer is {\"answer\": \"CTC: 52.0, seg FCB: 45.0\"}" } ], "images": [ "scigen/train/2882.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7.7: Letter error rates (%) for signer-dependent and signer-independent models averaged over the same eight folds." ], "reasoning": "The table shows the letter error rates for CTC and seg FCB for each individual and the average. For the signer-dependent setting, the average letter error rate for CTC is 52.0 and for seg FCB is 45.0.", "question": "What is the average letter error rate for CTC and seg FCB in the signer-dependent setting?", "answer": "So, the answer is {\"answer\": \"CTC: 52.0, seg FCB: 45.0\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nSegmental models have a higher average letter error rate than CTC in the signer-independent setting.\n" }, { "role": "assistant", "content": "The table shows that the average letter error rate for CTC in the signer-independent setting is 10.7, while for seg FCB it is 9.1. Therefore, segmental models have a lower average letter error rate than CTC. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2882.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7.7: Letter error rates (%) for signer-dependent and signer-independent models averaged over the same eight folds." ], "reasoning": "The table shows that the average letter error rate for CTC in the signer-independent setting is 10.7, while for seg FCB it is 9.1. Therefore, segmental models have a lower average letter error rate than CTC.", "statement": "Segmental models have a higher average letter error rate than CTC in the signer-independent setting.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Average runtime per iteration (in ms), with TensorFlow and a GeForce RTX 2080 Ti GPU, on Amazon. Gating runtimes are within brackets.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "CAHAN-SUM and CAHAN-RNN require sequential processing of sentence representations, leading to a 5-22% increase in runtime compared to basic HAN.However, with CAHAN-SUM, the representations of the preceding/following sentences are now required before computing the current sentence representation. With CAHAN-RNN, one even has to wait until the level 2 RNN has processed the preceding/following sentence vectors before being able to encode the current sentence. Therefore, the sentence encoding process, which was parallelizable with basic HAN due to independence, has now become a sequential process. This is why in practice, we observe slightly greater runtime increases, in the range 5-22% (" } ], "images": [ "scigen/train/2883.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Average runtime per iteration (in ms), with TensorFlow and a GeForce RTX 2080 Ti GPU, on Amazon. Gating runtimes are within brackets." ], "reasoning": "CAHAN-SUM and CAHAN-RNN require sequential processing of sentence representations, leading to a 5-22% increase in runtime compared to basic HAN.", "description": "However, with CAHAN-SUM, the representations of the preceding/following sentences are now required before computing the current sentence representation. With CAHAN-RNN, one even has to wait until the level 2 RNN has processed the preceding/following sentence vectors before being able to encode the current sentence. Therefore, the sentence encoding process, which was parallelizable with basic HAN due to independence, has now become a sequential process. This is why in practice, we observe slightly greater runtime increases, in the range 5-22% (", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: Average runtime per iteration (in ms), with TensorFlow and a GeForce RTX 2080 Ti GPU, on Amazon. Gating runtimes are within brackets.'. Solve the following question based on the table.\n\nQuestion: What is the runtime increase percentage range for CAHAN-SUM and CAHAN-RNN compared to basic HAN?\nLet's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The description states that the sentence encoding process for CAHAN-SUM and CAHAN-RNN has become sequential, resulting in a 5-22% increase in runtime compared to the parallelizable basic HAN. So, the answer is {\"answer\": \"5-22%\"}." } ], "images": [ "scigen/train/2883.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Average runtime per iteration (in ms), with TensorFlow and a GeForce RTX 2080 Ti GPU, on Amazon. Gating runtimes are within brackets." ], "reasoning": "The description states that the sentence encoding process for CAHAN-SUM and CAHAN-RNN has become sequential, resulting in a 5-22% increase in runtime compared to the parallelizable basic HAN.", "question": "What is the runtime increase percentage range for CAHAN-SUM and CAHAN-RNN compared to basic HAN?", "answer": "So, the answer is {\"answer\": \"5-22%\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nCAHAN-RNN BI has the highest runtime among the models listed.\n" }, { "role": "assistant", "content": "The table shows that CAHAN-RNN BI has a runtime of 49, which is higher than the runtimes of CAHAN-SUM-Σ LR (37), CAHAN-SUM-Σ BI (43), CAHAN-RNN LR (40), and HAN (35). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2883.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Average runtime per iteration (in ms), with TensorFlow and a GeForce RTX 2080 Ti GPU, on Amazon. Gating runtimes are within brackets." ], "reasoning": "The table shows that CAHAN-RNN BI has a runtime of 49, which is higher than the runtimes of CAHAN-SUM-Σ LR (37), CAHAN-SUM-Σ BI (43), CAHAN-RNN LR (40), and HAN (35).", "statement": "CAHAN-RNN BI has the highest runtime among the models listed.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Ablation experiments on the Stanford Sentiment Treebank test set'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Enabling views to build upon each other in MVN models improves test-set performance, as shown by the highest accuracy of 51.5 when using full MVN.First, we construct a traditional ensemble model. We independently train eight MVN models, each with a single view, to serve as weak learners. We have them vote with equal weight for the final classification, obtaining a test-set accuracy of 50.2. Next, we restrict the views in the MVN to be unaware of each other. This drops performance to 49.0. This drops the test-set performance to 50.5. These experiments suggest that enabling the views to build upon each other is crucial for achieving the best performance." } ], "images": [ "scigen/train/2884.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ablation experiments on the Stanford Sentiment Treebank test set" ], "reasoning": "Enabling views to build upon each other in MVN models improves test-set performance, as shown by the highest accuracy of 51.5 when using full MVN.", "description": "First, we construct a traditional ensemble model. We independently train eight MVN models, each with a single view, to serve as weak learners. We have them vote with equal weight for the final classification, obtaining a test-set accuracy of 50.2. Next, we restrict the views in the MVN to be unaware of each other. This drops performance to 49.0. This drops the test-set performance to 50.5. These experiments suggest that enabling the views to build upon each other is crucial for achieving the best performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: Ablation experiments on the Stanford Sentiment Treebank test set', solve this question and give detail solutions. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the test-set accuracy when MVN models have no horizontal links?\n" }, { "role": "assistant", "content": "The table shows that the test-set accuracy drops to 49.0 when MVN models have no horizontal links. So, the answer is {\"answer\": \"49.0\"}." } ], "images": [ "scigen/train/2884.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ablation experiments on the Stanford Sentiment Treebank test set" ], "reasoning": "The table shows that the test-set accuracy drops to 49.0 when MVN models have no horizontal links.", "question": "What is the test-set accuracy when MVN models have no horizontal links?", "answer": "So, the answer is {\"answer\": \"49.0\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The test-set accuracy of MVN models with length-1 horizontal links is higher than voting by 8 independent, 1-view MVNs., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe test-set accuracy of MVN models with length-1 horizontal links is higher than voting by 8 independent, 1-view MVNs.\n" }, { "role": "assistant", "content": "The table shows that the test-set accuracy of MVN models with length-1 horizontal links is 50.5, which is higher than the 50.2 accuracy obtained by voting by 8 independent, 1-view MVNs. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2884.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ablation experiments on the Stanford Sentiment Treebank test set" ], "reasoning": "The table shows that the test-set accuracy of MVN models with length-1 horizontal links is 50.5, which is higher than the 50.2 accuracy obtained by voting by 8 independent, 1-view MVNs.", "statement": "The test-set accuracy of MVN models with length-1 horizontal links is higher than voting by 8 independent, 1-view MVNs.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Accuracies on the Stanford Sentiment Treebank 5-class classification task; except for the MVN, all results are drawn from (Lei et al., 2015).', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The bag-of-words MVN achieves higher accuracy than most methods but is outperformed by the tree-LSTM.The results indicate that the bag-of-words MVN outperforms most methods, but obtains lower accuracy than the state-of-the-art results achieved by the tree-LSTM" } ], "images": [ "scigen/train/2885.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Accuracies on the Stanford Sentiment Treebank 5-class classification task; except for the MVN, all results are drawn from (Lei et al., 2015)." ], "reasoning": "The bag-of-words MVN achieves higher accuracy than most methods but is outperformed by the tree-LSTM.", "description": "The results indicate that the bag-of-words MVN outperforms most methods, but obtains lower accuracy than the state-of-the-art results achieved by the tree-LSTM", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 1: Accuracies on the Stanford Sentiment Treebank 5-class classification task; except for the MVN, all results are drawn from (Lei et al., 2015).' in mind, break down and resolve the question below step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich method achieves the highest accuracy according to the table?\n" }, { "role": "assistant", "content": "The table shows the accuracy of various methods, with MVN (with convolutional features) achieving the highest accuracy of 51.5. So, the answer is {\"answer\": \"MVN (with convolutional features)\"}" } ], "images": [ "scigen/train/2885.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Accuracies on the Stanford Sentiment Treebank 5-class classification task; except for the MVN, all results are drawn from (Lei et al., 2015)." ], "reasoning": "The table shows the accuracy of various methods, with MVN (with convolutional features) achieving the highest accuracy of 51.5.", "question": "Which method achieves the highest accuracy according to the table?", "answer": "So, the answer is {\"answer\": \"MVN (with convolutional features)\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe tree-LSTM achieves the highest accuracy.\n" }, { "role": "assistant", "content": "The table shows that MVN (with convolutional features) achieves the highest accuracy of 51.5, while tree-LSTM achieves 51.0. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2885.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Accuracies on the Stanford Sentiment Treebank 5-class classification task; except for the MVN, all results are drawn from (Lei et al., 2015)." ], "reasoning": "The table shows that MVN (with convolutional features) achieves the highest accuracy of 51.5, while tree-LSTM achieves 51.0.", "statement": "The tree-LSTM achieves the highest accuracy.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'TABLE II: Performance comparison with different settings and on different test subsets. Overall means the original, SDA and SEA test examples are evaluated together.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our PR method achieves the best overall performance and improves robustness across different models by addressing both over-confidence and over-sensitivity issues simultaneously.First, we investigated whether linguistic constraints can benefit RC models. (a-1) These drops indicate that base RC models cannot handle the over-confidence issue (for SDA examples) or over-sensitivity issue (for SEA examples). (a-2) Compared to other settings, Ori-Training performs best on the original test examples but has the worst overall performance because it overfits the original data and lacks of robustness to the adversarial data. (b-1) Adv-Training obtains better overall performances compared to Ori-Training (62.5→67.8), showing that the model becomes more robust when trained with adversarial examples. (b-2) Compared to Ori-Training , Adv-Training improves the performance on SDA examples (41.9→69.0) but degrades that on SEA examples (67.0→63.6). The inconsistency indicates Adv-Training cannot balance the over-confidence and over-sensitivity issues simultaneously. (c) Compared to Ori-/Adv-Training, our PR method achieves the best overall performance (62.5/67.8 vs. 71.9), verifying the effectiveness of linguistic constraint regularization. Our method can improve the performance on SDA and SEA examples simultaneously, as the constraints account for two types of adversarial examples at the same time. (d) Our PR method improves the robustness of both lightweight models (R-Net and MemReader) and the BERT model, manifesting the linguistic constraint regularization is a versatile method and has a positive effect for all the tested models. Second, we compared the effect of different ways to incorporate linguistic knowledge. For one reason, regularization on the output distribution is more straightforward than feeding traditional feature vectors into the input layer since the symbolic features may vanish after massive nonlinear operations in neural networks. For another reason, PR applies paired features, which are more informative than features assigned to individual words. For example, feature “1790s≠1780s” is more useful than features {“1790s /r/IsA/ DATE”, “1780s /r/IsA/ DATE”}." } ], "images": [ "scigen/train/2886.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Performance comparison with different settings and on different test subsets. Overall means the original, SDA and SEA test examples are evaluated together." ], "reasoning": "Our PR method achieves the best overall performance and improves robustness across different models by addressing both over-confidence and over-sensitivity issues simultaneously.", "description": "First, we investigated whether linguistic constraints can benefit RC models. (a-1) These drops indicate that base RC models cannot handle the over-confidence issue (for SDA examples) or over-sensitivity issue (for SEA examples). (a-2) Compared to other settings, Ori-Training performs best on the original test examples but has the worst overall performance because it overfits the original data and lacks of robustness to the adversarial data. (b-1) Adv-Training obtains better overall performances compared to Ori-Training (62.5→67.8), showing that the model becomes more robust when trained with adversarial examples. (b-2) Compared to Ori-Training , Adv-Training improves the performance on SDA examples (41.9→69.0) but degrades that on SEA examples (67.0→63.6). The inconsistency indicates Adv-Training cannot balance the over-confidence and over-sensitivity issues simultaneously. (c) Compared to Ori-/Adv-Training, our PR method achieves the best overall performance (62.5/67.8 vs. 71.9), verifying the effectiveness of linguistic constraint regularization. Our method can improve the performance on SDA and SEA examples simultaneously, as the constraints account for two types of adversarial examples at the same time. (d) Our PR method improves the robustness of both lightweight models (R-Net and MemReader) and the BERT model, manifesting the linguistic constraint regularization is a versatile method and has a positive effect for all the tested models. Second, we compared the effect of different ways to incorporate linguistic knowledge. For one reason, regularization on the output distribution is more straightforward than feeding traditional feature vectors into the input layer since the symbolic features may vanish after massive nonlinear operations in neural networks. For another reason, PR applies paired features, which are more informative than features assigned to individual words. For example, feature “1790s≠1780s” is more useful than features {“1790s /r/IsA/ DATE”, “1780s /r/IsA/ DATE”}.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'TABLE II: Performance comparison with different settings and on different test subsets. Overall means the original, SDA and SEA test examples are evaluated together.', answer the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich training method achieves the highest overall F1 score for the BERT-base model?\n" }, { "role": "assistant", "content": "The table shows that the BERT-base model trained with the PR (ours) method achieves the highest overall F1 score of 83.3. So, the answer is {\"answer\": \"83.3\"}." } ], "images": [ "scigen/train/2886.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Performance comparison with different settings and on different test subsets. Overall means the original, SDA and SEA test examples are evaluated together." ], "reasoning": "The table shows that the BERT-base model trained with the PR (ours) method achieves the highest overall F1 score of 83.3.", "question": "Which training method achieves the highest overall F1 score for the BERT-base model?", "answer": "So, the answer is {\"answer\": \"83.3\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nAdv-Training improves the performance on SDA examples for the R-Net model.\n" }, { "role": "assistant", "content": "The table shows that for the R-Net model, Adv-Training improves the F1 score on SDA examples from 37.9 (Ori-Training) to 65.6. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2886.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Performance comparison with different settings and on different test subsets. Overall means the original, SDA and SEA test examples are evaluated together." ], "reasoning": "The table shows that for the R-Net model, Adv-Training improves the F1 score on SDA examples from 37.9 (Ori-Training) to 65.6.", "statement": "Adv-Training improves the performance on SDA examples for the R-Net model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'TABLE V: Ablation test to investigate the effect of different constraints.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Models with more constraints perform better, with the full model achieving the highest scores and adversarial training the lowest.We conducted ablation test to investigate the effect of each constraint. MemReader was chosen as the base model and it was evaluated on adversarial examples to discriminate between different constraints. On one hand, adversarial examples violating the former two constraints are observed more frequently than those violating the predicate constraint in our data. On the other hand, determining SEA and SDA examples via entity/lexical constraints is more straightforward and precise than predicate constraint. Second, models with more constraints perform better than those with less constraints: three constraints (Full) > two constraints (−∗) > single constraint ( Only *) > no constraint (Adv-Training)." } ], "images": [ "scigen/train/2887.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Ablation test to investigate the effect of different constraints." ], "reasoning": "Models with more constraints perform better, with the full model achieving the highest scores and adversarial training the lowest.", "description": "We conducted ablation test to investigate the effect of each constraint. MemReader was chosen as the base model and it was evaluated on adversarial examples to discriminate between different constraints. On one hand, adversarial examples violating the former two constraints are observed more frequently than those violating the predicate constraint in our data. On the other hand, determining SEA and SDA examples via entity/lexical constraints is more straightforward and precise than predicate constraint. Second, models with more constraints perform better than those with less constraints: three constraints (Full) > two constraints (−∗) > single constraint ( Only *) > no constraint (Adv-Training).", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'TABLE V: Ablation test to investigate the effect of different constraints.', unfold the question presented below, detailing every step of your calculation. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieved the highest SEA F1 score?\n" }, { "role": "assistant", "content": "The table shows the SEA F1 scores for different models. The highest SEA F1 score is 69.7, achieved by the (Full)PR model. So, the answer is {\"answer\": \"(Full)PR\"}." } ], "images": [ "scigen/train/2887.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Ablation test to investigate the effect of different constraints." ], "reasoning": "The table shows the SEA F1 scores for different models. The highest SEA F1 score is 69.7, achieved by the (Full)PR model.", "question": "Which model achieved the highest SEA F1 score?", "answer": "So, the answer is {\"answer\": \"(Full)PR\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The model with only the predicate constraint has the lowest SDA EM score. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe model with only the predicate constraint has the lowest SDA EM score.\n" }, { "role": "assistant", "content": "The table shows that the model with only the predicate constraint has an SDA EM score of 58.8, which is higher than the SDA EM score of 58.5 for the Adv-Training model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2887.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Ablation test to investigate the effect of different constraints." ], "reasoning": "The table shows that the model with only the predicate constraint has an SDA EM score of 58.8, which is higher than the SDA EM score of 58.5 for the Adv-Training model.", "statement": "The model with only the predicate constraint has the lowest SDA EM score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'TABLE VII: Comparison of the performance in the action detection task when varying the neural network architecture, for GPSR', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The accuracy decreases slightly with more than one layer in neural networks, and bidirectional LSTMs do not significantly improve accuracy.Moreover, there is a small decrease in accuracy when the neural networks possess more than one layer. It can also be observed that the accuracy does not improve significantly when using bidirectional LSTMs. This result was expected, since the output of the command detection task consists in the final output of the sequence." } ], "images": [ "scigen/train/2888.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VII: Comparison of the performance in the action detection task when varying the neural network architecture, for GPSR" ], "reasoning": "The accuracy decreases slightly with more than one layer in neural networks, and bidirectional LSTMs do not significantly improve accuracy.", "description": "Moreover, there is a small decrease in accuracy when the neural networks possess more than one layer. It can also be observed that the accuracy does not improve significantly when using bidirectional LSTMs. This result was expected, since the output of the command detection task consists in the final output of the sequence.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'TABLE VII: Comparison of the performance in the action detection task when varying the neural network architecture, for GPSR'. Solve the following question based on the table.\n\nQuestion: What is the accuracy of a bidirectional LSTM with 2 layers of 500 cells?\nLet's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the accuracy for different configurations of LSTMs. The accuracy for a bidirectional LSTM with 2 layers of 500 cells is listed as 0.908. So, the answer is {\"answer\": \"0.908\"}" } ], "images": [ "scigen/train/2888.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VII: Comparison of the performance in the action detection task when varying the neural network architecture, for GPSR" ], "reasoning": "The table shows the accuracy for different configurations of LSTMs. The accuracy for a bidirectional LSTM with 2 layers of 500 cells is listed as 0.908.", "question": "What is the accuracy of a bidirectional LSTM with 2 layers of 500 cells?", "answer": "So, the answer is {\"answer\": \"0.908\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe accuracy of a single-layer LSTM is higher than that of a double-layer LSTM.\n" }, { "role": "assistant", "content": "The table shows that the accuracy of a single-layer LSTM (0.931) is higher than that of a double-layer LSTM (0.900). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2888.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VII: Comparison of the performance in the action detection task when varying the neural network architecture, for GPSR" ], "reasoning": "The table shows that the accuracy of a single-layer LSTM (0.931) is higher than that of a double-layer LSTM (0.900).", "statement": "The accuracy of a single-layer LSTM is higher than that of a double-layer LSTM.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'TABLE X: Comparison of the performance in the slot filling task when varying the neural network architecture, for GPSR'.\n" }, { "role": "assistant", "content": "Bidirectional LSTMs achieve better results in slot filling tasks due to their ability to consider both previous and following words in a sequence.This happens because the slot filling task corresponds to a more complex task, since it is a sequence tagging, an assignment of a categorical label to each member of the sequence, instead of a single classification. Also, the results obtained are better using bidirectional LSTMs, due to the fact that a classification of a word can depend on the following words instead of only on the previous words." } ], "images": [ "scigen/train/2889.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE X: Comparison of the performance in the slot filling task when varying the neural network architecture, for GPSR" ], "reasoning": "Bidirectional LSTMs achieve better results in slot filling tasks due to their ability to consider both previous and following words in a sequence.", "description": "This happens because the slot filling task corresponds to a more complex task, since it is a sequence tagging, an assignment of a categorical label to each member of the sequence, instead of a single classification. Also, the results obtained are better using bidirectional LSTMs, due to the fact that a classification of a word can depend on the following words instead of only on the previous words.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'TABLE X: Comparison of the performance in the slot filling task when varying the neural network architecture, for GPSR' in mind, break down and resolve the question below step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich model achieved the highest accuracy?\n" }, { "role": "assistant", "content": "The table lists different models and their accuracies. The model with the highest accuracy is DBLSTM - 2 layers of 500 cells with an accuracy of 0.947. So, the answer is {\"answer\": \"DBLSTM - 2 layers of 500 cells\"}." } ], "images": [ "scigen/train/2889.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE X: Comparison of the performance in the slot filling task when varying the neural network architecture, for GPSR" ], "reasoning": "The table lists different models and their accuracies. The model with the highest accuracy is DBLSTM - 2 layers of 500 cells with an accuracy of 0.947.", "question": "Which model achieved the highest accuracy?", "answer": "So, the answer is {\"answer\": \"DBLSTM - 2 layers of 500 cells\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The BLSTM - 1 layer of 500 cells model achieved an accuracy of 0.895. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe BLSTM - 1 layer of 500 cells model achieved an accuracy of 0.895.\n" }, { "role": "assistant", "content": "The table shows that the BLSTM - 1 layer of 500 cells model has an accuracy of 0.895, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2889.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE X: Comparison of the performance in the slot filling task when varying the neural network architecture, for GPSR" ], "reasoning": "The table shows that the BLSTM - 1 layer of 500 cells model has an accuracy of 0.895, which matches the statement.", "statement": "The BLSTM - 1 layer of 500 cells model achieved an accuracy of 0.895.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'TABLE XI: Comparison of the performance in the slot filling task when varying the neural network architecture, for FBM3'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The accuracy for FBM3 is lower due to the higher complexity of arguments compared to the GPSR test, which had simpler arguments.The reasons that justify these results are the same as for the GPSR testset. Comparing the performance in the two tests, it can be observed that the accuracy is quite smaller for FBM3. This is due to the superior arguments complexity, i.e. in the GPSR test the arguments were composed by less words." } ], "images": [ "scigen/train/2890.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE XI: Comparison of the performance in the slot filling task when varying the neural network architecture, for FBM3" ], "reasoning": "The accuracy for FBM3 is lower due to the higher complexity of arguments compared to the GPSR test, which had simpler arguments.", "description": "The reasons that justify these results are the same as for the GPSR testset. Comparing the performance in the two tests, it can be observed that the accuracy is quite smaller for FBM3. This is due to the superior arguments complexity, i.e. in the GPSR test the arguments were composed by less words.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model has the highest accuracy and why is the accuracy lower for FBM3?\nSolve the above question based on the table titled 'TABLE XI: Comparison of the performance in the slot filling task when varying the neural network architecture, for FBM3'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The DBLSTM with 2 layers of 500 cells has the highest accuracy of 0.762. The accuracy for FBM3 is lower due to the superior arguments complexity compared to the GPSR test, where arguments were composed of fewer words. So, the answer is {\"answer\": \"DBLSTM - 2 layers of 500 cells\"}." } ], "images": [ "scigen/train/2890.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE XI: Comparison of the performance in the slot filling task when varying the neural network architecture, for FBM3" ], "reasoning": "The DBLSTM with 2 layers of 500 cells has the highest accuracy of 0.762. The accuracy for FBM3 is lower due to the superior arguments complexity compared to the GPSR test, where arguments were composed of fewer words.", "question": "Which model has the highest accuracy and why is the accuracy lower for FBM3?", "answer": "So, the answer is {\"answer\": \"DBLSTM - 2 layers of 500 cells\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe DBLSTM with 2 layers of 250 cells has the highest accuracy.\n" }, { "role": "assistant", "content": "The table shows that the DBLSTM with 2 layers of 500 cells has the highest accuracy of 0.762, while the DBLSTM with 2 layers of 250 cells has an accuracy of 0.728. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2890.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE XI: Comparison of the performance in the slot filling task when varying the neural network architecture, for FBM3" ], "reasoning": "The table shows that the DBLSTM with 2 layers of 500 cells has the highest accuracy of 0.762, while the DBLSTM with 2 layers of 250 cells has an accuracy of 0.728.", "statement": "The DBLSTM with 2 layers of 250 cells has the highest accuracy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: Performance of our model and the baseline in evidence extraction on the development set in the distractor setting. The correlation is the Kendall tau correlation of the number of predicted evidence sentences and that of gold evidence.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The QFE model shows a larger improvement in precision compared to recall over the baseline.Our model improves both precision and recall, and the improvement in precision is larger. That is, it predicts the number of evidence sentences more accurately than the baseline." } ], "images": [ "scigen/train/2891.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Performance of our model and the baseline in evidence extraction on the development set in the distractor setting. The correlation is the Kendall tau correlation of the number of predicted evidence sentences and that of gold evidence." ], "reasoning": "The QFE model shows a larger improvement in precision compared to recall over the baseline.", "description": "Our model improves both precision and recall, and the improvement in precision is larger. That is, it predicts the number of evidence sentences more accurately than the baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: Performance of our model and the baseline in evidence extraction on the development set in the distractor setting. The correlation is the Kendall tau correlation of the number of predicted evidence sentences and that of gold evidence.'.\n\nQuestion: What is the difference in precision between the QFE model and the baseline?\n" }, { "role": "assistant", "content": "To find the difference in precision, subtract the baseline precision (79.0) from the QFE precision (88.4). So, the answer is {\"answer\": \"9.4\"}." } ], "images": [ "scigen/train/2891.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Performance of our model and the baseline in evidence extraction on the development set in the distractor setting. The correlation is the Kendall tau correlation of the number of predicted evidence sentences and that of gold evidence." ], "reasoning": "To find the difference in precision, subtract the baseline precision (79.0) from the QFE precision (88.4).", "question": "What is the difference in precision between the QFE model and the baseline?", "answer": "So, the answer is {\"answer\": \"9.4\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe QFE model has a higher recall than the baseline.\n" }, { "role": "assistant", "content": "The table shows that the recall for the QFE model is 83.2, which is higher than the baseline recall of 82.4. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2891.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Performance of our model and the baseline in evidence extraction on the development set in the distractor setting. The correlation is the Kendall tau correlation of the number of predicted evidence sentences and that of gold evidence." ], "reasoning": "The table shows that the recall for the QFE model is 83.2, which is higher than the baseline recall of 82.4.", "statement": "The QFE model has a higher recall than the baseline.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 11: Performance of evidence extraction. The top five rows are evaluated on the test set. The comparison of our models is on the development set. The models submitted after the Shared Task have no information about precision or recall.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "QFE ensemble ranked 2nd in precision, 3rd in recall, and 1st in F1 on the test set, while on the development set, it had higher precision than recall.What are the characteristics of our evidence extraction? On the test set, it ranked in 2nd place in precision, 3rd place in recall, and 1st place in F1. As for the results on the development set, QFE extracted with higher precision than recall. This tendency was the same as in the RC evaluation. The single model has a larger difference between precision and recall. The ensemble model improves recall and F1." } ], "images": [ "scigen/train/2892.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Performance of evidence extraction. The top five rows are evaluated on the test set. The comparison of our models is on the development set. The models submitted after the Shared Task have no information about precision or recall." ], "reasoning": "QFE ensemble ranked 2nd in precision, 3rd in recall, and 1st in F1 on the test set, while on the development set, it had higher precision than recall.", "description": "What are the characteristics of our evidence extraction? On the test set, it ranked in 2nd place in precision, 3rd place in recall, and 1st place in F1. As for the results on the development set, QFE extracted with higher precision than recall. This tendency was the same as in the RC evaluation. The single model has a larger difference between precision and recall. The ensemble model improves recall and F1.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 11: Performance of evidence extraction. The top five rows are evaluated on the test set. The comparison of our models is on the development set. The models submitted after the Shared Task have no information about precision or recall.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat was the F1 score of the QFE ensemble model on the test set?\n" }, { "role": "assistant", "content": "The table shows the F1 score of the QFE ensemble model on the test set as 77.7. So, the answer is {\"answer\": \"77.7\"}" } ], "images": [ "scigen/train/2892.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Performance of evidence extraction. The top five rows are evaluated on the test set. The comparison of our models is on the development set. The models submitted after the Shared Task have no information about precision or recall." ], "reasoning": "The table shows the F1 score of the QFE ensemble model on the test set as 77.7.", "question": "What was the F1 score of the QFE ensemble model on the test set?", "answer": "So, the answer is {\"answer\": \"77.7\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The QFE ensemble model had the highest precision on the test set. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe QFE ensemble model had the highest precision on the test set.\n" }, { "role": "assistant", "content": "The table shows that Malon (2018) had the highest precision on the test set with a score of 92.2, not the QFE ensemble model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2892.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Performance of evidence extraction. The top five rows are evaluated on the test set. The comparison of our models is on the development set. The models submitted after the Shared Task have no information about precision or recall." ], "reasoning": "The table shows that Malon (2018) had the highest precision on the test set with a score of 92.2, not the QFE ensemble model.", "statement": "The QFE ensemble model had the highest precision on the test set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Automatic evaluation results for cutting the tail methods and uniform sampling.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "All systems showed similar performance and diversity, indicating that broader exploration does not improve text quality due to the high quality of the expert policy.All the systems obtained comparable results for both performance and diversity based on automatic evaluation metrics suggesting that the benefits of a more broad and cautious exploration do not help the generation of higher quality texts. We conjecture that these results are due to the high quality of the expert policy which is able to provide a reliable and representative training signal for the diversity that the nlg is capable of producing correctly." } ], "images": [ "scigen/train/2893.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Automatic evaluation results for cutting the tail methods and uniform sampling." ], "reasoning": "All systems showed similar performance and diversity, indicating that broader exploration does not improve text quality due to the high quality of the expert policy.", "description": "All the systems obtained comparable results for both performance and diversity based on automatic evaluation metrics suggesting that the benefits of a more broad and cautious exploration do not help the generation of higher quality texts. We conjecture that these results are due to the high quality of the expert policy which is able to provide a reliable and representative training signal for the diversity that the nlg is capable of producing correctly.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nTable title: Table 3: Automatic evaluation results for cutting the tail methods and uniform sampling.\n\nQuestion: Which system achieved the highest BLEU score?\n" }, { "role": "assistant", "content": "To find the system with the highest BLEU score, compare the BLEU scores across all systems. The Nucleus system has the highest BLEU score of 0.488. So, the answer is {\"answer\": \"Nucleus\"}." } ], "images": [ "scigen/train/2893.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Automatic evaluation results for cutting the tail methods and uniform sampling." ], "reasoning": "To find the system with the highest BLEU score, compare the BLEU scores across all systems. The Nucleus system has the highest BLEU score of 0.488.", "question": "Which system achieved the highest BLEU score?", "answer": "So, the answer is {\"answer\": \"Nucleus\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Exact system has the highest Dist-4 score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Dist-4 scores across all systems. The Exact system has a Dist-4 score of 0.429, while the Iols system has a higher Dist-4 score of 0.417. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2893.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Automatic evaluation results for cutting the tail methods and uniform sampling." ], "reasoning": "To verify this statement, compare the Dist-4 scores across all systems. The Exact system has a Dist-4 score of 0.429, while the Iols system has a higher Dist-4 score of 0.417.", "statement": "The Exact system has the highest Dist-4 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Automatic evaluation results for greedy and previous works on variety.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows that while methods like Beam Search and Greedy have higher word overlap metrics (BLEU, METEOR, Mover), they exhibit low diversity (1-SB, Dist-n), whereas methods like SF show higher diversity but lower word overlap.We can see from the low numbers in the diversity metrics (1 - Self-BLEU and distinct-n) that none of these methods produce much output diversity in the task of concept-to-text NLG. Below, we provide some brief analysis on the results. Beam Search: similarly to what has been reported in open-domain nlg research Li et al. For example, for the mr [request(train-leave)] the top 3 beams obtained are “What time would you like to leave?”, “What time would you like to leave by?” and “What time would you like to depart?”. our approaches shows a much higher level of diversity in their output. However, we observe a significant drop in the word overlap metrics (BLEU-4, METEOR and MoverScore). To accurately determine the quality of the output, we will be performing human evaluation." } ], "images": [ "scigen/train/2894.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Automatic evaluation results for greedy and previous works on variety." ], "reasoning": "The table shows that while methods like Beam Search and Greedy have higher word overlap metrics (BLEU, METEOR, Mover), they exhibit low diversity (1-SB, Dist-n), whereas methods like SF show higher diversity but lower word overlap.", "description": "We can see from the low numbers in the diversity metrics (1 - Self-BLEU and distinct-n) that none of these methods produce much output diversity in the task of concept-to-text NLG. Below, we provide some brief analysis on the results. Beam Search: similarly to what has been reported in open-domain nlg research Li et al. For example, for the mr [request(train-leave)] the top 3 beams obtained are “What time would you like to leave?”, “What time would you like to leave by?” and “What time would you like to depart?”. our approaches shows a much higher level of diversity in their output. However, we observe a significant drop in the word overlap metrics (BLEU-4, METEOR and MoverScore). To accurately determine the quality of the output, we will be performing human evaluation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 2: Automatic evaluation results for greedy and previous works on variety.' and proceed to solve the following question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method has the highest Slot Error and what does this indicate about its performance?\n" }, { "role": "assistant", "content": "The SF method has the highest Slot Error at 52.980, indicating it performs poorly in maintaining the correct slots in the generated text. So, the answer is {\"answer\": \"SF\"}." } ], "images": [ "scigen/train/2894.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Automatic evaluation results for greedy and previous works on variety." ], "reasoning": "The SF method has the highest Slot Error at 52.980, indicating it performs poorly in maintaining the correct slots in the generated text.", "question": "Which method has the highest Slot Error and what does this indicate about its performance?", "answer": "So, the answer is {\"answer\": \"SF\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Greedy method has the highest BLEU score among all methods.\n" }, { "role": "assistant", "content": "The table shows that the Greedy method has a BLEU score of 0.654, which is the highest among all listed methods. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2894.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Automatic evaluation results for greedy and previous works on variety." ], "reasoning": "The table shows that the Greedy method has a BLEU score of 0.654, which is the highest among all listed methods.", "statement": "The Greedy method has the highest BLEU score among all methods.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Human Evaluation results.'.\n" }, { "role": "assistant", "content": "We sampled 1500 texts from different mcd-nlg configurations, Nucleus sampling, and greedy inference, and used the Wilcoxon rank sum test to determine statistical significance of observed differences.We sampled 1500 texts from each of the different mcd-nlg configurations, Nucleus sampling and greedy inference. We include the latter to provide a benchmark for the other models. To determine whether the observed differences were statistically significant we used the Wilcoxon rank sum test." } ], "images": [ "scigen/train/2895.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Human Evaluation results." ], "reasoning": "We sampled 1500 texts from different mcd-nlg configurations, Nucleus sampling, and greedy inference, and used the Wilcoxon rank sum test to determine statistical significance of observed differences.", "description": "We sampled 1500 texts from each of the different mcd-nlg configurations, Nucleus sampling and greedy inference. We include the latter to provide a benchmark for the other models. To determine whether the observed differences were statistically significant we used the Wilcoxon rank sum test.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: Human Evaluation results.', solve this question and give detail solutions. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhich configuration had the highest adequacy raw score?\n" }, { "role": "assistant", "content": "By comparing the adequacy raw scores of all configurations, Nucleus had the highest adequacy raw score of 82.581. So, the answer is {\"answer\": \"Nucleus\"}." } ], "images": [ "scigen/train/2895.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Human Evaluation results." ], "reasoning": "By comparing the adequacy raw scores of all configurations, Nucleus had the highest adequacy raw score of 82.581.", "question": "Which configuration had the highest adequacy raw score?", "answer": "So, the answer is {\"answer\": \"Nucleus\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Greedy inference had the highest fluency raw score., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nGreedy inference had the highest fluency raw score.\n" }, { "role": "assistant", "content": "By comparing the fluency raw scores of all configurations, Greedy inference had the highest fluency raw score of 82.555. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2895.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Human Evaluation results." ], "reasoning": "By comparing the fluency raw scores of all configurations, Greedy inference had the highest fluency raw score of 82.555.", "statement": "Greedy inference had the highest fluency raw score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: Human Evaluation results for texts always sampling the last word of the reduced sample pool.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Most configurations show no statistically significant difference, except for LOLS and Nucleus on fluency.Again most configurations show no statistically significant difference between them, with the exception of lols and Nucleus on fluency." } ], "images": [ "scigen/train/2896.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Human Evaluation results for texts always sampling the last word of the reduced sample pool." ], "reasoning": "Most configurations show no statistically significant difference, except for LOLS and Nucleus on fluency.", "description": "Again most configurations show no statistically significant difference between them, with the exception of lols and Nucleus on fluency.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: Human Evaluation results for texts always sampling the last word of the reduced sample pool.', answer the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich configuration has the highest fluency raw score and what is its adequacy raw score?\n" }, { "role": "assistant", "content": "By examining the table, LOLS has the highest fluency raw score of 54.324. The corresponding adequacy raw score for LOLS is 65.509. So, the answer is {\"answer\": \"65.509\"}." } ], "images": [ "scigen/train/2896.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Human Evaluation results for texts always sampling the last word of the reduced sample pool." ], "reasoning": "By examining the table, LOLS has the highest fluency raw score of 54.324. The corresponding adequacy raw score for LOLS is 65.509.", "question": "Which configuration has the highest fluency raw score and what is its adequacy raw score?", "answer": "So, the answer is {\"answer\": \"65.509\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nLOLS has the highest fluency z-score.\n" }, { "role": "assistant", "content": "By examining the table, LOLS has a fluency z-score of 0.104, which is the highest among the configurations listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2896.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Human Evaluation results for texts always sampling the last word of the reduced sample pool." ], "reasoning": "By examining the table, LOLS has a fluency z-score of 0.104, which is the highest among the configurations listed.", "statement": "LOLS has the highest fluency z-score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 10: Comparison of our hybrid Seq2Seq model with related work on Text Normalization. *In contrast with the rest of the presented related work, Monoise leverages additional textual resources.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The hybrid model achieved the highest F1 score among neural models, surpassing previous deep learning approaches.We see that all previous Deep Learning approaches are close to 82% F1 score. Due to the nature of our hybrid model, we were able to achieve the best performance so far among neural models in related work. We compare the incorrect normalizations that our Seq2Seq model and MoNoise - the best performing method - produce." } ], "images": [ "scigen/train/2897.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Comparison of our hybrid Seq2Seq model with related work on Text Normalization. *In contrast with the rest of the presented related work, Monoise leverages additional textual resources." ], "reasoning": "The hybrid model achieved the highest F1 score among neural models, surpassing previous deep learning approaches.", "description": "We see that all previous Deep Learning approaches are close to 82% F1 score. Due to the nature of our hybrid model, we were able to achieve the best performance so far among neural models in related work. We compare the incorrect normalizations that our Seq2Seq model and MoNoise - the best performing method - produce.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 10: Comparison of our hybrid Seq2Seq model with related work on Text Normalization. *In contrast with the rest of the presented related work, Monoise leverages additional textual resources.', and sequentially solve the question, writing out each step of your reasoning process. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model achieved the highest F1 score and what was the score?\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all models in the table, we see that MoNoise achieved the highest F1 score of 86.39. So, the answer is {\"answer\": \"MoNoise, 86.39\"}." } ], "images": [ "scigen/train/2897.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Comparison of our hybrid Seq2Seq model with related work on Text Normalization. *In contrast with the rest of the presented related work, Monoise leverages additional textual resources." ], "reasoning": "By comparing the F1 scores of all models in the table, we see that MoNoise achieved the highest F1 score of 86.39.", "question": "Which model achieved the highest F1 score and what was the score?", "answer": "So, the answer is {\"answer\": \"MoNoise, 86.39\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Hybrid Seq2Seq model achieved the highest precision among all models. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Hybrid Seq2Seq model achieved the highest precision among all models.\n" }, { "role": "assistant", "content": "By comparing the precision values in the table, we see that MoNoise achieved the highest precision of 93.53, not the Hybrid Seq2Seq model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2897.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Comparison of our hybrid Seq2Seq model with related work on Text Normalization. *In contrast with the rest of the presented related work, Monoise leverages additional textual resources." ], "reasoning": "By comparing the precision values in the table, we see that MoNoise achieved the highest precision of 93.53, not the Hybrid Seq2Seq model.", "statement": "The Hybrid Seq2Seq model achieved the highest precision among all models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Best performing hyper-parameter settings of our proposed text normalization models', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Models were tuned using random search on a 10% split of training data, and the best hyper-parameters were used to retrain on the full dataset.Batch size is set to {32,500}. All of our models are bi-directional and use attention. To compare performance, we tune each model separately with random search. To tune the hyper-parameters we used a 10% random split of the training data and performed random search on the hyper-parameter space. Once the best combination was found, we retrained our system using the full training data set." } ], "images": [ "scigen/train/2898.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Best performing hyper-parameter settings of our proposed text normalization models" ], "reasoning": "Models were tuned using random search on a 10% split of training data, and the best hyper-parameters were used to retrain on the full dataset.", "description": "Batch size is set to {32,500}. All of our models are bi-directional and use attention. To compare performance, we tune each model separately with random search. To tune the hyper-parameters we used a 10% random split of the training data and performed random search on the hyper-parameter space. Once the best combination was found, we retrained our system using the full training data set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Best performing hyper-parameter settings of our proposed text normalization models' to sequentially solve the question presented below. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the dropout rate for the model with the highest number of neurons per layer?\n" }, { "role": "assistant", "content": "The model with the highest number of neurons per layer is the secondary Char-level model with 500 neurons per layer. The dropout rate for this model is 0.5. So, the answer is {\"answer\": \"0.5\"}" } ], "images": [ "scigen/train/2898.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Best performing hyper-parameter settings of our proposed text normalization models" ], "reasoning": "The model with the highest number of neurons per layer is the secondary Char-level model with 500 neurons per layer. The dropout rate for this model is 0.5.", "question": "What is the dropout rate for the model with the highest number of neurons per layer?", "answer": "So, the answer is {\"answer\": \"0.5\"}" }, { "messages": [ { "role": "user", "content": "The S2SBPE model has a learning rate of 0.001. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe S2SBPE model has a learning rate of 0.001.\n" }, { "role": "assistant", "content": "The table shows that the S2SBPE model has a learning rate of 0.01, not 0.001. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2898.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Best performing hyper-parameter settings of our proposed text normalization models" ], "reasoning": "The table shows that the S2SBPE model has a learning rate of 0.01, not 0.001.", "statement": "The S2SBPE model has a learning rate of 0.001.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Comparison of our S2S models with word-level baselines.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The S2S model outperforms S2SMulti, indicating that Seq2Seq models can handle both unique and multiple mappings without additional feature engineering.Dict2 shows that handling ambiguous cases inappropriately results in a dramatic drop in precision. It is therefore necessary for a text normalization model to be able to correctly normalize text in the occurrence of multiple mappings. S2SMulti is a baseline method that firstly normalizes terms that have a unique mapping, based on source and target tokens found in the training data, and later on utilizes a word-level Seq2Seq model that is trained to correct one-to-many mappings (an unnormalized token that can be transformed into several standard words, e.g. “ur” → {“you are”, “your”}). We can see that this method has a better performance, which validates our hypothesis that a naive word replacement would not suffice, however the end-to-end Seq2Seq model (S2S) performs better than S2SMulti, i.e. Seq2Seq models can handle both unique and multiple mappings seamlessly without any additional feature engineering effort apart from tokenization. One of our main questions is how crucial contextual information is for text normalization. We test the importance of context by performing two experiments. For our first experiment, we perform the following preprocessing of the training data: we constrain the target-side of each example by replacing each word that remains unchanged (no normalization needed) with a special @self symbol. With this representation all tokens that need normalization are preserved and the model would learn which words remain the same. We call this model S2SSelf. In most cases we observe that our model relies on context to normalize short tokens, while S2SSelf fails to correct such terms." } ], "images": [ "scigen/train/2899.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of our S2S models with word-level baselines." ], "reasoning": "The S2S model outperforms S2SMulti, indicating that Seq2Seq models can handle both unique and multiple mappings without additional feature engineering.", "description": "Dict2 shows that handling ambiguous cases inappropriately results in a dramatic drop in precision. It is therefore necessary for a text normalization model to be able to correctly normalize text in the occurrence of multiple mappings. S2SMulti is a baseline method that firstly normalizes terms that have a unique mapping, based on source and target tokens found in the training data, and later on utilizes a word-level Seq2Seq model that is trained to correct one-to-many mappings (an unnormalized token that can be transformed into several standard words, e.g. “ur” → {“you are”, “your”}). We can see that this method has a better performance, which validates our hypothesis that a naive word replacement would not suffice, however the end-to-end Seq2Seq model (S2S) performs better than S2SMulti, i.e. Seq2Seq models can handle both unique and multiple mappings seamlessly without any additional feature engineering effort apart from tokenization. One of our main questions is how crucial contextual information is for text normalization. We test the importance of context by performing two experiments. For our first experiment, we perform the following preprocessing of the training data: we constrain the target-side of each example by replacing each word that remains unchanged (no normalization needed) with a special @self symbol. With this representation all tokens that need normalization are preserved and the model would learn which words remain the same. We call this model S2SSelf. In most cases we observe that our model relies on context to normalize short tokens, while S2SSelf fails to correct such terms.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: Comparison of our S2S models with word-level baselines.', unfold the steps to solve the question given next. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nQuestion: Which model has the highest precision and what is its method highlight?\n" }, { "role": "assistant", "content": "From the table, Dict1 has the highest precision of 96.00. The method highlight for Dict1 is 'Dictionary (unique mappings)'. So, the answer is {\"answer\": \"Dict1\"}" } ], "images": [ "scigen/train/2899.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of our S2S models with word-level baselines." ], "reasoning": "From the table, Dict1 has the highest precision of 96.00. The method highlight for Dict1 is 'Dictionary (unique mappings)'.", "question": "Which model has the highest precision and what is its method highlight?", "answer": "So, the answer is {\"answer\": \"Dict1\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nS2SSelf has the highest recall among all models.\n" }, { "role": "assistant", "content": "The table shows that S2SSelf has a recall of 65.50, which is not the highest. HS2S has the highest recall of 78.14. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2899.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of our S2S models with word-level baselines." ], "reasoning": "The table shows that S2SSelf has a recall of 65.50, which is not the highest. HS2S has the highest recall of 78.14.", "statement": "S2SSelf has the highest recall among all models.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: Comparison of our HS2S model with additional baselines.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The HS2S model outperforms the S2SChar and S2SBPE models in terms of precision, recall, and F1 score, validating the effectiveness of hybrid word-char Seq2Seq methods.Dict2 shows that handling ambiguous cases inappropriately results in a dramatic drop in precision. It is therefore necessary for a text normalization model to be able to correctly normalize text in the occurrence of multiple mappings. S2SMulti is a baseline method that firstly normalizes terms that have a unique mapping, based on source and target tokens found in the training data, and later on utilizes a word-level Seq2Seq model that is trained to correct one-to-many mappings (an unnormalized token that can be transformed into several standard words, e.g. “ur” → {“you are”, “your”}). We can see that this method has a better performance, which validates our hypothesis that a naive word replacement would not suffice, however the end-to-end Seq2Seq model (S2S) performs better than S2SMulti, i.e. Seq2Seq models can handle both unique and multiple mappings seamlessly without any additional feature engineering effort apart from tokenization." } ], "images": [ "scigen/train/2900.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of our HS2S model with additional baselines." ], "reasoning": "The HS2S model outperforms the S2SChar and S2SBPE models in terms of precision, recall, and F1 score, validating the effectiveness of hybrid word-char Seq2Seq methods.", "description": "Dict2 shows that handling ambiguous cases inappropriately results in a dramatic drop in precision. It is therefore necessary for a text normalization model to be able to correctly normalize text in the occurrence of multiple mappings. S2SMulti is a baseline method that firstly normalizes terms that have a unique mapping, based on source and target tokens found in the training data, and later on utilizes a word-level Seq2Seq model that is trained to correct one-to-many mappings (an unnormalized token that can be transformed into several standard words, e.g. “ur” → {“you are”, “your”}). We can see that this method has a better performance, which validates our hypothesis that a naive word replacement would not suffice, however the end-to-end Seq2Seq model (S2S) performs better than S2SMulti, i.e. Seq2Seq models can handle both unique and multiple mappings seamlessly without any additional feature engineering effort apart from tokenization.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: Comparison of our HS2S model with additional baselines.', solve this question and give detail solutions. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich model has the highest F1 score and what is its precision?\n" }, { "role": "assistant", "content": "The table shows that the HS2S model has the highest F1 score of 83.94. Its precision is 90.66. So, the answer is {\"answer\": \"HS2S with a precision of 90.66\"}" } ], "images": [ "scigen/train/2900.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of our HS2S model with additional baselines." ], "reasoning": "The table shows that the HS2S model has the highest F1 score of 83.94. Its precision is 90.66.", "question": "Which model has the highest F1 score and what is its precision?", "answer": "So, the answer is {\"answer\": \"HS2S with a precision of 90.66\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The S2SBPE model has the highest recall among the listed models., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe S2SBPE model has the highest recall among the listed models.\n" }, { "role": "assistant", "content": "The table shows that the S2SBPE model has a recall of 52.04, which is lower than the recall of both the HS2S model (78.14) and the S2SChar model (70.50). Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2900.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of our HS2S model with additional baselines." ], "reasoning": "The table shows that the S2SBPE model has a recall of 52.04, which is lower than the recall of both the HS2S model (78.14) and the S2SChar model (70.50). Therefore, the statement is refuted.", "statement": "The S2SBPE model has the highest recall among the listed models.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5: Most frequent correct (left table) and incorrect (right table) normalizations of our word-level Seq2Seq model. We present how many times a source tweet was (in)correctly normalized (Count column) as well as how many times that term appears in the source-side of the examples (Source). For cases where a token can be normalized to itself, we include how many times that term appears unchanged (information in parentheses)'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The model struggles with infrequent or ambiguously normalized terms, which can be improved by adding more training data.We perform an extensive error analysis. First, we check the model output, particularly in which cases our model fails. Note that we also keep track of how many times a term remains unchanged, specifically for cases where that term has multiple mappings available We can see that our model can handle multiple mappings when those are adequately represented in the training data. Most of the incorrect normalizations appear less than 50 times in our data (very infrequent) or are ambiguously normalized in some examples and left intact in other examples, e.g. “rt” and “2” remain unchanged a few times, and these are the cases that the model missed. These types of errors can be handled by adding more training data. Furthermore, we should note that our model can also be adjusted to work with distantly supervised data or with ensemble methods." } ], "images": [ "scigen/train/2901.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Most frequent correct (left table) and incorrect (right table) normalizations of our word-level Seq2Seq model. We present how many times a source tweet was (in)correctly normalized (Count column) as well as how many times that term appears in the source-side of the examples (Source). For cases where a token can be normalized to itself, we include how many times that term appears unchanged (information in parentheses)" ], "reasoning": "The model struggles with infrequent or ambiguously normalized terms, which can be improved by adding more training data.", "description": "We perform an extensive error analysis. First, we check the model output, particularly in which cases our model fails. Note that we also keep track of how many times a term remains unchanged, specifically for cases where that term has multiple mappings available We can see that our model can handle multiple mappings when those are adequately represented in the training data. Most of the incorrect normalizations appear less than 50 times in our data (very infrequent) or are ambiguously normalized in some examples and left intact in other examples, e.g. “rt” and “2” remain unchanged a few times, and these are the cases that the model missed. These types of errors can be handled by adding more training data. Furthermore, we should note that our model can also be adjusted to work with distantly supervised data or with ensemble methods.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 5: Most frequent correct (left table) and incorrect (right table) normalizations of our word-level Seq2Seq model. We present how many times a source tweet was (in)correctly normalized (Count column) as well as how many times that term appears in the source-side of the examples (Source). For cases where a token can be normalized to itself, we include how many times that term appears unchanged (information in parentheses)\nQuestion: Which term has the highest count in Source 335 (2) and what is its target normalization?\n" }, { "role": "assistant", "content": "By examining the 'Source 335 (2)' column, the term 'lol' has the highest count of 272. Its target normalization is 'laughing out loud'. So, the answer is {\"answer\": \"laughing out loud\"}." } ], "images": [ "scigen/train/2901.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Most frequent correct (left table) and incorrect (right table) normalizations of our word-level Seq2Seq model. We present how many times a source tweet was (in)correctly normalized (Count column) as well as how many times that term appears in the source-side of the examples (Source). For cases where a token can be normalized to itself, we include how many times that term appears unchanged (information in parentheses)" ], "reasoning": "By examining the 'Source 335 (2)' column, the term 'lol' has the highest count of 272. Its target normalization is 'laughing out loud'.", "question": "Which term has the highest count in Source 335 (2) and what is its target normalization?", "answer": "So, the answer is {\"answer\": \"laughing out loud\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The term 'im' has a higher count in Source 234 than in Source 335 (2). and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe term 'im' has a higher count in Source 234 than in Source 335 (2).\n" }, { "role": "assistant", "content": "The table shows that 'im' has a count of 153 in Source 234 and 182 in Source 335 (2), which means the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2901.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Most frequent correct (left table) and incorrect (right table) normalizations of our word-level Seq2Seq model. We present how many times a source tweet was (in)correctly normalized (Count column) as well as how many times that term appears in the source-side of the examples (Source). For cases where a token can be normalized to itself, we include how many times that term appears unchanged (information in parentheses)" ], "reasoning": "The table shows that 'im' has a count of 153 in Source 234 and 182 in Source 335 (2), which means the statement is incorrect.", "statement": "The term 'im' has a higher count in Source 234 than in Source 335 (2).", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 8: OOV words that our secondary character model has normalized correctly (blue) or incorrectly (red)'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The model correctly normalizes various types of errors, including elongating characters, deleting characters, and typos, achieving a 16.22% success rate on unseen words.Futhermore, we perform error analysis on the secondary character-level model that is trained on synthetic adversarial examples of word pairs. In total, our model correctly normalizes 16.22% of unseen source words. Most frequent errors that are correctly normalized are elongating the last characters, deleting last character [g] in gerund, swapping or replacing characters and typos. There are also several typos that the model was not able to correct, such as missing whitespaces or editing words unnecessarily." } ], "images": [ "scigen/train/2902.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: OOV words that our secondary character model has normalized correctly (blue) or incorrectly (red)" ], "reasoning": "The model correctly normalizes various types of errors, including elongating characters, deleting characters, and typos, achieving a 16.22% success rate on unseen words.", "description": "Futhermore, we perform error analysis on the secondary character-level model that is trained on synthetic adversarial examples of word pairs. In total, our model correctly normalizes 16.22% of unseen source words. Most frequent errors that are correctly normalized are elongating the last characters, deleting last character [g] in gerund, swapping or replacing characters and typos. There are also several typos that the model was not able to correct, such as missing whitespaces or editing words unnecessarily.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 8: OOV words that our secondary character model has normalized correctly (blue) or incorrectly (red)', unfold the question presented below, detailing every step of your calculation. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the confidence percentage for the model's prediction of 'performance' from 'perfomance'?\n" }, { "role": "assistant", "content": "The table shows the source word 'perfomance' and its predicted correction 'performance' with a confidence percentage of 83.90%. So, the answer is {\"answer\": \"83.90%\"}." } ], "images": [ "scigen/train/2902.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: OOV words that our secondary character model has normalized correctly (blue) or incorrectly (red)" ], "reasoning": "The table shows the source word 'perfomance' and its predicted correction 'performance' with a confidence percentage of 83.90%.", "question": "What is the confidence percentage for the model's prediction of 'performance' from 'perfomance'?", "answer": "So, the answer is {\"answer\": \"83.90%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The model has the highest confidence in predicting 'performance' from 'perfomance'. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe model has the highest confidence in predicting 'performance' from 'perfomance'.\n" }, { "role": "assistant", "content": "The table lists 'perfomance' corrected to 'performance' with the highest confidence percentage of 83.90%, which is the highest among all listed predictions. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2902.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: OOV words that our secondary character model has normalized correctly (blue) or incorrectly (red)" ], "reasoning": "The table lists 'perfomance' corrected to 'performance' with the highest confidence percentage of 83.90%, which is the highest among all listed predictions.", "statement": "The model has the highest confidence in predicting 'performance' from 'perfomance'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 8: OOV words that our secondary character model has normalized correctly (blue) or incorrectly (red)', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The model correctly normalizes 16.22% of unseen source words, with frequent errors including elongating last characters, deleting last character [g] in gerund, swapping or replacing characters, and typos.Futhermore, we perform error analysis on the secondary character-level model that is trained on synthetic adversarial examples of word pairs. In total, our model correctly normalizes 16.22% of unseen source words. Most frequent errors that are correctly normalized are elongating the last characters, deleting last character [g] in gerund, swapping or replacing characters and typos. There are also several typos that the model was not able to correct, such as missing whitespaces or editing words unnecessarily." } ], "images": [ "scigen/train/2903.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: OOV words that our secondary character model has normalized correctly (blue) or incorrectly (red)" ], "reasoning": "The model correctly normalizes 16.22% of unseen source words, with frequent errors including elongating last characters, deleting last character [g] in gerund, swapping or replacing characters, and typos.", "description": "Futhermore, we perform error analysis on the secondary character-level model that is trained on synthetic adversarial examples of word pairs. In total, our model correctly normalizes 16.22% of unseen source words. Most frequent errors that are correctly normalized are elongating the last characters, deleting last character [g] in gerund, swapping or replacing characters and typos. There are also several typos that the model was not able to correct, such as missing whitespaces or editing words unnecessarily.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 8: OOV words that our secondary character model has normalized correctly (blue) or incorrectly (red)', answer the following question. Give detailed solution consisting of each step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the confidence percentage for the prediction of 'judgmental'?\n" }, { "role": "assistant", "content": "The table shows the confidence percentage for each source word and its prediction. For 'judgmental', the prediction is 'judgmmental' with a confidence of 79.11%. So, the answer is {\"answer\": \"79.11%\"}" } ], "images": [ "scigen/train/2903.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: OOV words that our secondary character model has normalized correctly (blue) or incorrectly (red)" ], "reasoning": "The table shows the confidence percentage for each source word and its prediction. For 'judgmental', the prediction is 'judgmmental' with a confidence of 79.11%.", "question": "What is the confidence percentage for the prediction of 'judgmental'?", "answer": "So, the answer is {\"answer\": \"79.11%\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model predicted 'happy' with a confidence of over 60%.\n" }, { "role": "assistant", "content": "The table shows that the prediction for 'happppy' is 'happy' with a confidence of 61.67%, which is over 60%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2903.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: OOV words that our secondary character model has normalized correctly (blue) or incorrectly (red)" ], "reasoning": "The table shows that the prediction for 'happppy' is 'happy' with a confidence of 61.67%, which is over 60%.", "statement": "The model predicted 'happy' with a confidence of over 60%.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Sample sentences of different similarity scores from SICK test set data. S is the predicted score and G is the ground truth.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The model's similarity scores are more affected by changes in subject or object words than by changes in other grammatical roles like determiners or adjectives.These results show that the model is insusceptible to change in the voice of sentences. We also examined sentence pairs, which differed only by a single word. The changes in subject or object word affected the predicted similarity scores more than the changes in other grammatical roles like determiner or adjectives. In third example, changing man to woman in the sentence “A man is riding a horse” decreased the similarity score to 3.57, while changing a to some in the sentence “A man is preparing a dish” had little effect on scores. The above examples suggest that the effect of each grammatical relation in the overall meaning composition can be determined only by a detailed investigation of the magnitude of typed dependency embeddings." } ], "images": [ "scigen/train/2904.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Sample sentences of different similarity scores from SICK test set data. S is the predicted score and G is the ground truth." ], "reasoning": "The model's similarity scores are more affected by changes in subject or object words than by changes in other grammatical roles like determiners or adjectives.", "description": "These results show that the model is insusceptible to change in the voice of sentences. We also examined sentence pairs, which differed only by a single word. The changes in subject or object word affected the predicted similarity scores more than the changes in other grammatical roles like determiner or adjectives. In third example, changing man to woman in the sentence “A man is riding a horse” decreased the similarity score to 3.57, while changing a to some in the sentence “A man is preparing a dish” had little effect on scores. The above examples suggest that the effect of each grammatical relation in the overall meaning composition can be determined only by a detailed investigation of the magnitude of typed dependency embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 4: Sample sentences of different similarity scores from SICK test set data. S is the predicted score and G is the ground truth.', and tackle the question below in a sequential manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the similarity score when the subject in the sentence 'A man is riding a horse' is changed to 'A woman is riding a horse'?\n" }, { "role": "assistant", "content": "The table shows that changing 'man' to 'woman' in the sentence 'A man is riding a horse' results in a similarity score of 3.57. So, the answer is {\"answer\": \"3.57\"}." } ], "images": [ "scigen/train/2904.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Sample sentences of different similarity scores from SICK test set data. S is the predicted score and G is the ground truth." ], "reasoning": "The table shows that changing 'man' to 'woman' in the sentence 'A man is riding a horse' results in a similarity score of 3.57.", "question": "What is the similarity score when the subject in the sentence 'A man is riding a horse' is changed to 'A woman is riding a horse'?", "answer": "So, the answer is {\"answer\": \"3.57\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Changing the determiner in the sentence 'A man is preparing a dish' to 'some' significantly affects the similarity score., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nChanging the determiner in the sentence 'A man is preparing a dish' to 'some' significantly affects the similarity score.\n" }, { "role": "assistant", "content": "The table shows that changing 'a' to 'some' in the sentence 'A man is preparing a dish' results in a similarity score of 4.52, which indicates little effect on the score. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2904.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Sample sentences of different similarity scores from SICK test set data. S is the predicted score and G is the ground truth." ], "reasoning": "The table shows that changing 'a' to 'some' in the sentence 'A man is preparing a dish' results in a similarity score of 4.52, which indicates little effect on the score.", "statement": "Changing the determiner in the sentence 'A man is preparing a dish' to 'some' significantly affects the similarity score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Comparison of Typed Dependency Tree-LSTM with other LSTM models for semantic relatedness scoring. The values are taken from previously published results.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The DT-LSTMs and DT-GRUs outperform their sequential counterparts, indicating that hierarchical structures better represent sentence semantics.The DT-LSTMs and DT-GRUs perform better than their sequential counterparts, validating that hierarchical structures are better in representing sentence semantics than sequences." } ], "images": [ "scigen/train/2905.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of Typed Dependency Tree-LSTM with other LSTM models for semantic relatedness scoring. The values are taken from previously published results." ], "reasoning": "The DT-LSTMs and DT-GRUs outperform their sequential counterparts, indicating that hierarchical structures better represent sentence semantics.", "description": "The DT-LSTMs and DT-GRUs perform better than their sequential counterparts, validating that hierarchical structures are better in representing sentence semantics than sequences.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 2: Comparison of Typed Dependency Tree-LSTM with other LSTM models for semantic relatedness scoring. The values are taken from previously published results.', unfold the question presented below, detailing every step of your calculation. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model has the highest Pearson's r value and what does it indicate about hierarchical structures?\n" }, { "role": "assistant", "content": "The Typed DT-LSTM has the highest Pearson's r value of 0.8731, indicating that hierarchical structures are better in representing sentence semantics than sequences. So, the answer is {\"answer\": \"Typed DT-LSTM\"}." } ], "images": [ "scigen/train/2905.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of Typed Dependency Tree-LSTM with other LSTM models for semantic relatedness scoring. The values are taken from previously published results." ], "reasoning": "The Typed DT-LSTM has the highest Pearson's r value of 0.8731, indicating that hierarchical structures are better in representing sentence semantics than sequences.", "question": "Which model has the highest Pearson's r value and what does it indicate about hierarchical structures?", "answer": "So, the answer is {\"answer\": \"Typed DT-LSTM\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The DT-LSTM model has a lower MSE than the Seq. LSTM model., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe DT-LSTM model has a lower MSE than the Seq. LSTM model.\n" }, { "role": "assistant", "content": "The table shows that the DT-LSTM model has an MSE of 0.2532, which is lower than the Seq. LSTM model's MSE of 0.2831. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2905.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of Typed Dependency Tree-LSTM with other LSTM models for semantic relatedness scoring. The values are taken from previously published results." ], "reasoning": "The table shows that the DT-LSTM model has an MSE of 0.2532, which is lower than the Seq. LSTM model's MSE of 0.2831.", "statement": "The DT-LSTM model has a lower MSE than the Seq. LSTM model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Three most similar sentences retrieved by Typed DT-LSTM from the SICK test set for each query sentence. SDT is the score assigned by Dependency Tree-LSTM and STyped−DT is the score by the proposed Typed DT-LSTM.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The DT-LSTM assigns the same score to sentences with different dependency trees, while the Typed DT-LSTM distinguishes them.The DT-LSTM assigns the same score for the sentences, “The turtle is following the fish” and “The fish is following the turtle” because their dependency trees differ only in the edge labels of which the model is unaware. Typed DT-LSTM ’s scores show that the model has learned this difference." } ], "images": [ "scigen/train/2906.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Three most similar sentences retrieved by Typed DT-LSTM from the SICK test set for each query sentence. SDT is the score assigned by Dependency Tree-LSTM and STyped−DT is the score by the proposed Typed DT-LSTM." ], "reasoning": "The DT-LSTM assigns the same score to sentences with different dependency trees, while the Typed DT-LSTM distinguishes them.", "description": "The DT-LSTM assigns the same score for the sentences, “The turtle is following the fish” and “The fish is following the turtle” because their dependency trees differ only in the edge labels of which the model is unaware. Typed DT-LSTM ’s scores show that the model has learned this difference.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nTable title: Table 3: Three most similar sentences retrieved by Typed DT-LSTM from the SICK test set for each query sentence. SDT is the score assigned by Dependency Tree-LSTM and STyped−DT is the score by the proposed Typed DT-LSTM.\n\nQuestion: What is the difference in scores assigned by SDT and STyped-DT for the sentence 'The turtle is following the fish'?\n" }, { "role": "assistant", "content": "The table shows that SDT assigns a score of 4.48 to 'The turtle is following the fish', while STyped-DT assigns a score of 4.81. So, the answer is {\"answer\": \"0.33\"}." } ], "images": [ "scigen/train/2906.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Three most similar sentences retrieved by Typed DT-LSTM from the SICK test set for each query sentence. SDT is the score assigned by Dependency Tree-LSTM and STyped−DT is the score by the proposed Typed DT-LSTM." ], "reasoning": "The table shows that SDT assigns a score of 4.48 to 'The turtle is following the fish', while STyped-DT assigns a score of 4.81.", "question": "What is the difference in scores assigned by SDT and STyped-DT for the sentence 'The turtle is following the fish'?", "answer": "So, the answer is {\"answer\": \"0.33\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe sentence 'A guy is riding a horse' has the highest score in both SDT and STyped-DT.\n" }, { "role": "assistant", "content": "The table shows that 'A guy is riding a horse' has scores of 4.79 in SDT and 4.87 in STyped-DT, which are the highest scores in their respective columns. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2906.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Three most similar sentences retrieved by Typed DT-LSTM from the SICK test set for each query sentence. SDT is the score assigned by Dependency Tree-LSTM and STyped−DT is the score by the proposed Typed DT-LSTM." ], "reasoning": "The table shows that 'A guy is riding a horse' has scores of 4.79 in SDT and 4.87 in STyped-DT, which are the highest scores in their respective columns.", "statement": "The sentence 'A guy is riding a horse' has the highest score in both SDT and STyped-DT.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 6: Comparison of Typed Dependency Tree-LSTM with LSTM models for binary sentiment classification on SST dataset. The values are taken from previously published results.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Typed LSTM outperforms standard LSTM and DT-LSTM in accuracy, is comparable to Bi-LSTM, but is less accurate than CT-LSTM.We found that the proposed model outperformed standard LSTM as well as DT-LSTM in the accuracy of prediction. The accuracy is comparable with that of bidirectional LSTM but lesser than that of the constituency tree-LSTM (CT-LSTM). The results clearly indicate that Typed LSTM is superior to DT-LSTM in recognizing sentiments of text." } ], "images": [ "scigen/train/2907.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Comparison of Typed Dependency Tree-LSTM with LSTM models for binary sentiment classification on SST dataset. The values are taken from previously published results." ], "reasoning": "Typed LSTM outperforms standard LSTM and DT-LSTM in accuracy, is comparable to Bi-LSTM, but is less accurate than CT-LSTM.", "description": "We found that the proposed model outperformed standard LSTM as well as DT-LSTM in the accuracy of prediction. The accuracy is comparable with that of bidirectional LSTM but lesser than that of the constituency tree-LSTM (CT-LSTM). The results clearly indicate that Typed LSTM is superior to DT-LSTM in recognizing sentiments of text.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 6: Comparison of Typed Dependency Tree-LSTM with LSTM models for binary sentiment classification on SST dataset. The values are taken from previously published results.', answer the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model has the highest accuracy in predicting sentiments of text?\n" }, { "role": "assistant", "content": "By comparing the accuracy values of all models listed in the table, CT-LSTM has the highest accuracy at 88.0%. So, the answer is {\"answer\": \"CT-LSTM\"}." } ], "images": [ "scigen/train/2907.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Comparison of Typed Dependency Tree-LSTM with LSTM models for binary sentiment classification on SST dataset. The values are taken from previously published results." ], "reasoning": "By comparing the accuracy values of all models listed in the table, CT-LSTM has the highest accuracy at 88.0%.", "question": "Which model has the highest accuracy in predicting sentiments of text?", "answer": "So, the answer is {\"answer\": \"CT-LSTM\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Typed LSTM has a higher accuracy than Bi-LSTM., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nTyped LSTM has a higher accuracy than Bi-LSTM.\n" }, { "role": "assistant", "content": "The table shows that Typed LSTM has an accuracy of 86.4%, while Bi-LSTM has an accuracy of 87.5%. Therefore, Typed LSTM does not have a higher accuracy than Bi-LSTM. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2907.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Comparison of Typed Dependency Tree-LSTM with LSTM models for binary sentiment classification on SST dataset. The values are taken from previously published results." ], "reasoning": "The table shows that Typed LSTM has an accuracy of 86.4%, while Bi-LSTM has an accuracy of 87.5%. Therefore, Typed LSTM does not have a higher accuracy than Bi-LSTM.", "statement": "Typed LSTM has a higher accuracy than Bi-LSTM.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2. Performance Comparison on TrecQA (clean) dataset. Best result is in boldface and second best is underlined.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "MCAN (FM) is the best performing model, followed by MCAN (NN) and IWAN, with MCAN (SM) remaining competitive.All MCAN variations outperform all existing state-of-the-art models. Notably, MCAN (FM) is currently the best performing model on this extensively studied dataset. MCAN (NN) comes in second which marginally outperforms the highly competitive and recent IWAN model. Finally, MCAN (SM) remains competitive to IWAN, despite naively summing over casted attention features." } ], "images": [ "scigen/train/2908.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2. Performance Comparison on TrecQA (clean) dataset. Best result is in boldface and second best is underlined." ], "reasoning": "MCAN (FM) is the best performing model, followed by MCAN (NN) and IWAN, with MCAN (SM) remaining competitive.", "description": "All MCAN variations outperform all existing state-of-the-art models. Notably, MCAN (FM) is currently the best performing model on this extensively studied dataset. MCAN (NN) comes in second which marginally outperforms the highly competitive and recent IWAN model. Finally, MCAN (SM) remains competitive to IWAN, despite naively summing over casted attention features.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: Table 2. Performance Comparison on TrecQA (clean) dataset. Best result is in boldface and second best is underlined.\nQuestion: Which model has the highest MAP and MRR scores?\n" }, { "role": "assistant", "content": "By examining the table, MCAN (FM) has the highest MAP score of 0.838 and the highest MRR score of 0.904. So, the answer is {\"answer\": \"MCAN (FM)\"}." } ], "images": [ "scigen/train/2908.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2. Performance Comparison on TrecQA (clean) dataset. Best result is in boldface and second best is underlined." ], "reasoning": "By examining the table, MCAN (FM) has the highest MAP score of 0.838 and the highest MRR score of 0.904.", "question": "Which model has the highest MAP and MRR scores?", "answer": "So, the answer is {\"answer\": \"MCAN (FM)\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nMCAN (NN) outperforms IWAN in both MAP and MRR scores.\n" }, { "role": "assistant", "content": "The table shows that MCAN (NN) has a MAP score of 0.827 and an MRR score of 0.890, while IWAN has a MAP score of 0.822 and an MRR score of 0.889. Therefore, MCAN (NN) outperforms IWAN in both metrics. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2908.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2. Performance Comparison on TrecQA (clean) dataset. Best result is in boldface and second best is underlined." ], "reasoning": "The table shows that MCAN (NN) has a MAP score of 0.827 and an MRR score of 0.890, while IWAN has a MAP score of 0.822 and an MRR score of 0.889. Therefore, MCAN (NN) outperforms IWAN in both metrics.", "statement": "MCAN (NN) outperforms IWAN in both MAP and MRR scores.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1. Performance Comparison on Ubuntu Dialogue Corpus. Best result is in boldface and second best is underlined.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "MCAN models significantly outperform existing state-of-the-art models, with MCAN (FM) and MCAN (NN) showing comparable performance and MCAN (SM) slightly lower but still superior to other models.Clearly, we observe that all MCAN models achieve a huge performance gain over existing state-of-the-art models. More specifically, the improvement across all metrics are ≈5%−9% better than KEHNN. The performance improvement over strong baselines such as AP-LSTM and MV-LSTM are even greater, hitting an improvement of 15% in terms of R10@1. This ascertains the effectiveness of the MCAN model. Overall, MCAN (FM) and MCAN (NN) are comparable in terms of performance. MCAN (SM) is marginally lower than both MCAN (FM) and MCAN (NN). However, its performance is still considerably higher than the existing state-of-the-art models." } ], "images": [ "scigen/train/2909.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1. Performance Comparison on Ubuntu Dialogue Corpus. Best result is in boldface and second best is underlined." ], "reasoning": "MCAN models significantly outperform existing state-of-the-art models, with MCAN (FM) and MCAN (NN) showing comparable performance and MCAN (SM) slightly lower but still superior to other models.", "description": "Clearly, we observe that all MCAN models achieve a huge performance gain over existing state-of-the-art models. More specifically, the improvement across all metrics are ≈5%−9% better than KEHNN. The performance improvement over strong baselines such as AP-LSTM and MV-LSTM are even greater, hitting an improvement of 15% in terms of R10@1. This ascertains the effectiveness of the MCAN model. Overall, MCAN (FM) and MCAN (NN) are comparable in terms of performance. MCAN (SM) is marginally lower than both MCAN (FM) and MCAN (NN). However, its performance is still considerably higher than the existing state-of-the-art models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 1. Performance Comparison on Ubuntu Dialogue Corpus. Best result is in boldface and second best is underlined.' to sequentially solve the question presented below. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich MCAN model has the highest R10@1 score and how does it compare to KEHNN?\n" }, { "role": "assistant", "content": "From the table, MCAN (FM) has the highest R10@1 score of 0.551. Comparing this to KEHNN's R10@1 score of 0.460, MCAN (FM) shows a significant improvement. So, the answer is {\"answer\": \"MCAN (FM)\"}." } ], "images": [ "scigen/train/2909.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1. Performance Comparison on Ubuntu Dialogue Corpus. Best result is in boldface and second best is underlined." ], "reasoning": "From the table, MCAN (FM) has the highest R10@1 score of 0.551. Comparing this to KEHNN's R10@1 score of 0.460, MCAN (FM) shows a significant improvement.", "question": "Which MCAN model has the highest R10@1 score and how does it compare to KEHNN?", "answer": "So, the answer is {\"answer\": \"MCAN (FM)\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nMCAN (SM) has a higher R2@1 score than MV-LSTM.\n" }, { "role": "assistant", "content": "From the table, MCAN (SM) has an R2@1 score of 0.831, while MV-LSTM has an R2@1 score of 0.767. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2909.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1. Performance Comparison on Ubuntu Dialogue Corpus. Best result is in boldface and second best is underlined." ], "reasoning": "From the table, MCAN (SM) has an R2@1 score of 0.831, while MV-LSTM has an R2@1 score of 0.767. Therefore, the statement is supported.", "statement": "MCAN (SM) has a higher R2@1 score than MV-LSTM.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3. Performance comparison on QatarLiving dataset for community question answering. Best result is in boldface and second best is underlined.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "MCAN (FM) achieves the highest performance on the dataset, outperforming AI-CNN by 4.1% on P@1 and 1.1% on MAP, and remains competitive with the CTRN model.Our best performing MCAN model achieves state-of-the-art performance on this dataset. Performance improvement over recent, competitive neural network baselines is significant. Notably, the improvement of MCAN (FM) over AI-CNN on the P@1 metric is 4.1% and 1.1% in terms of MAP. MCAN (FM) also achieves competitive results relative to the CTRN model. The performance of MCAN (NN) and MCAN (SM) is lower than MCAN (FM) but still remains competitive on this benchmark." } ], "images": [ "scigen/train/2910.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. Performance comparison on QatarLiving dataset for community question answering. Best result is in boldface and second best is underlined." ], "reasoning": "MCAN (FM) achieves the highest performance on the dataset, outperforming AI-CNN by 4.1% on P@1 and 1.1% on MAP, and remains competitive with the CTRN model.", "description": "Our best performing MCAN model achieves state-of-the-art performance on this dataset. Performance improvement over recent, competitive neural network baselines is significant. Notably, the improvement of MCAN (FM) over AI-CNN on the P@1 metric is 4.1% and 1.1% in terms of MAP. MCAN (FM) also achieves competitive results relative to the CTRN model. The performance of MCAN (NN) and MCAN (SM) is lower than MCAN (FM) but still remains competitive on this benchmark.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3. Performance comparison on QatarLiving dataset for community question answering. Best result is in boldface and second best is underlined.', unfold the question presented below, detailing every step of your calculation. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest P@1 score and how does it compare to AI-CNN in terms of MAP?\n" }, { "role": "assistant", "content": "The table shows that MCAN (FM) has the highest P@1 score of 0.804. Comparing this to AI-CNN, MCAN (FM) also has a higher MAP score of 0.803 compared to AI-CNN's 0.792. So, the answer is {\"answer\": \"MCAN (FM)\"}." } ], "images": [ "scigen/train/2910.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. Performance comparison on QatarLiving dataset for community question answering. Best result is in boldface and second best is underlined." ], "reasoning": "The table shows that MCAN (FM) has the highest P@1 score of 0.804. Comparing this to AI-CNN, MCAN (FM) also has a higher MAP score of 0.803 compared to AI-CNN's 0.792.", "question": "Which model has the highest P@1 score and how does it compare to AI-CNN in terms of MAP?", "answer": "So, the answer is {\"answer\": \"MCAN (FM)\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nMCAN (FM) has a higher MAP score than CTRN.\n" }, { "role": "assistant", "content": "The table shows that MCAN (FM) has a MAP score of 0.803, while CTRN has a MAP score of 0.794. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2910.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. Performance comparison on QatarLiving dataset for community question answering. Best result is in boldface and second best is underlined." ], "reasoning": "The table shows that MCAN (FM) has a MAP score of 0.803, while CTRN has a MAP score of 0.794. Therefore, the statement is supported.", "statement": "MCAN (FM) has a higher MAP score than CTRN.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4. Performance comparison on Reply Prediction on Tweets dataset. Best performance is in boldface and second best is underlined.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "MCAN (FM) achieves the highest performance, outperforming AP-BiLSTM by 3.4% in MRR and 5.3% in P@1, while AI-CNN shows an even greater improvement over AP-BiLSTM.MCAN (FM) achieves the top performance by a significant margin. The performance of MCAN (NN) falls short of MCAN (FM), but is still highly competitive. Our best MCAN model outperforms AP-BiLSTM by 3.4% in terms of MRR and 5.3% in terms of P@1. The performance improvement of AI-CNN is even greater, i.e., 8.4% in terms of MRR and 12.5% in terms of P@1. The strongest baseline is AP-BiLSTM which significantly outperforms AI-CNN and AP-CNN." } ], "images": [ "scigen/train/2911.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4. Performance comparison on Reply Prediction on Tweets dataset. Best performance is in boldface and second best is underlined." ], "reasoning": "MCAN (FM) achieves the highest performance, outperforming AP-BiLSTM by 3.4% in MRR and 5.3% in P@1, while AI-CNN shows an even greater improvement over AP-BiLSTM.", "description": "MCAN (FM) achieves the top performance by a significant margin. The performance of MCAN (NN) falls short of MCAN (FM), but is still highly competitive. Our best MCAN model outperforms AP-BiLSTM by 3.4% in terms of MRR and 5.3% in terms of P@1. The performance improvement of AI-CNN is even greater, i.e., 8.4% in terms of MRR and 12.5% in terms of P@1. The strongest baseline is AP-BiLSTM which significantly outperforms AI-CNN and AP-CNN.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4. Performance comparison on Reply Prediction on Tweets dataset. Best performance is in boldface and second best is underlined.', answer the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nHow much does MCAN (FM) outperform AP-BiLSTM in terms of MRR?\n" }, { "role": "assistant", "content": "MCAN (FM) has an MRR of 0.759, while AP-BiLSTM has an MRR of 0.725. The difference is 0.759 - 0.725 = 0.034, which is 3.4%. So, the answer is {\"answer\": \"3.4%\"}" } ], "images": [ "scigen/train/2911.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4. Performance comparison on Reply Prediction on Tweets dataset. Best performance is in boldface and second best is underlined." ], "reasoning": "MCAN (FM) has an MRR of 0.759, while AP-BiLSTM has an MRR of 0.725. The difference is 0.759 - 0.725 = 0.034, which is 3.4%.", "question": "How much does MCAN (FM) outperform AP-BiLSTM in terms of MRR?", "answer": "So, the answer is {\"answer\": \"3.4%\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nMCAN (FM) outperforms AI-CNN by more than 8% in terms of MRR.\n" }, { "role": "assistant", "content": "MCAN (FM) has an MRR of 0.759, and AI-CNN has an MRR of 0.675. The difference is 0.759 - 0.675 = 0.084, which is 8.4%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2911.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4. Performance comparison on Reply Prediction on Tweets dataset. Best performance is in boldface and second best is underlined." ], "reasoning": "MCAN (FM) has an MRR of 0.759, and AI-CNN has an MRR of 0.675. The difference is 0.759 - 0.675 = 0.084, which is 8.4%.", "statement": "MCAN (FM) outperforms AI-CNN by more than 8% in terms of MRR.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5. Ablation analysis (validation set) on TrecQA dataset.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The table shows the performance impact of removing various components from the MCAN model, with the original configuration achieving the highest MAP and MRR scores.This section aims to demonstrate the relative effectiveness of different components of our proposed MCAN model. We report the scores of seven different configurations. In (1), we replace all highway layers with regular feed-forward neural networks. In (2), we remove the LSTM encoder before the prediction layer. In (3), we remove the entire multi-cast attention mechanism. This is equivalent to removing the twelve attention features. In (4-7), we remove different attention casts, aiming to showcase that removing either one results in some performance drop." } ], "images": [ "scigen/train/2912.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5. Ablation analysis (validation set) on TrecQA dataset." ], "reasoning": "The table shows the performance impact of removing various components from the MCAN model, with the original configuration achieving the highest MAP and MRR scores.", "description": "This section aims to demonstrate the relative effectiveness of different components of our proposed MCAN model. We report the scores of seven different configurations. In (1), we replace all highway layers with regular feed-forward neural networks. In (2), we remove the LSTM encoder before the prediction layer. In (3), we remove the entire multi-cast attention mechanism. This is equivalent to removing the twelve attention features. In (4-7), we remove different attention casts, aiming to showcase that removing either one results in some performance drop.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5. Ablation analysis (validation set) on TrecQA dataset.', answer the following question in a stepwise manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich configuration had the lowest MAP score and what component was removed in that configuration?\n" }, { "role": "assistant", "content": "By examining the table, the configuration with the lowest MAP score is (3) Remove MCA, with a MAP score of 0.670. This configuration removed the multi-cast attention mechanism. So, the answer is {\"answer\": \"Remove MCA\"}." } ], "images": [ "scigen/train/2912.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5. Ablation analysis (validation set) on TrecQA dataset." ], "reasoning": "By examining the table, the configuration with the lowest MAP score is (3) Remove MCA, with a MAP score of 0.670. This configuration removed the multi-cast attention mechanism.", "question": "Which configuration had the lowest MAP score and what component was removed in that configuration?", "answer": "So, the answer is {\"answer\": \"Remove MCA\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Removing the LSTM encoder resulted in the lowest MRR score. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRemoving the LSTM encoder resulted in the lowest MRR score.\n" }, { "role": "assistant", "content": "The table shows that removing the LSTM encoder (2) resulted in an MRR score of 0.809. However, the lowest MRR score is 0.726, which occurred when the alignment component was removed (5). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2912.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5. Ablation analysis (validation set) on TrecQA dataset." ], "reasoning": "The table shows that removing the LSTM encoder (2) resulted in an MRR score of 0.809. However, the lowest MRR score is 0.726, which occurred when the alignment component was removed (5).", "statement": "Removing the LSTM encoder resulted in the lowest MRR score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Discourse connective prediction performance of a Random baseline, the WordPairs model, the decomposable attention model (DA) and human raters.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The DA model, optimized with specific hyper-parameters, outperforms the WordPairs model and human raters in both Macro F1 and Accuracy.In this section, the DA model and the WordPairs model are employed to perform the same task as the human raters, i.e., learning to reconstruct the connective possibly removed from the beginning of the second sentence in each test pair. The DA model is evaluated using the following hyper-parameters optimized on the development set: network size (one hidden layer with 200 neurons), batch size (64), dropout ratios for the F, G, and H networks (0.68, 0.14, and 0.44, respectively), and learning rate (0.0018)." } ], "images": [ "scigen/train/2913.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Discourse connective prediction performance of a Random baseline, the WordPairs model, the decomposable attention model (DA) and human raters." ], "reasoning": "The DA model, optimized with specific hyper-parameters, outperforms the WordPairs model and human raters in both Macro F1 and Accuracy.", "description": "In this section, the DA model and the WordPairs model are employed to perform the same task as the human raters, i.e., learning to reconstruct the connective possibly removed from the beginning of the second sentence in each test pair. The DA model is evaluated using the following hyper-parameters optimized on the development set: network size (one hidden layer with 200 neurons), batch size (64), dropout ratios for the F, G, and H networks (0.68, 0.14, and 0.44, respectively), and learning rate (0.0018).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Discourse connective prediction performance of a Random baseline, the WordPairs model, the decomposable attention model (DA) and human raters.\nQuestion: Which model has the highest Macro F1 score and what is the score?\nSolve the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the Macro F1 scores of all models in the table, the DA model has the highest score of 31.80. So, the answer is {\"answer\": \"DA model with 31.80\"}." } ], "images": [ "scigen/train/2913.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Discourse connective prediction performance of a Random baseline, the WordPairs model, the decomposable attention model (DA) and human raters." ], "reasoning": "By comparing the Macro F1 scores of all models in the table, the DA model has the highest score of 31.80.", "question": "Which model has the highest Macro F1 score and what is the score?", "answer": "So, the answer is {\"answer\": \"DA model with 31.80\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe WordPairs model has a higher Accuracy than the Human Raters.\n" }, { "role": "assistant", "content": "The table shows that the WordPairs model has an Accuracy of 15.60, while the Human Raters have an Accuracy of 23.12, indicating that the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2913.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Discourse connective prediction performance of a Random baseline, the WordPairs model, the decomposable attention model (DA) and human raters." ], "reasoning": "The table shows that the WordPairs model has an Accuracy of 15.60, while the Human Raters have an Accuracy of 23.12, indicating that the statement is incorrect.", "statement": "The WordPairs model has a higher Accuracy than the Human Raters.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Macro-averaged F1 scores for human raters and the decomposable attention model. The three settings are: (A) All test set items; (B) Only the items for which there is a consensus among at least 2 out of 3 raters; (C) Consensus items ignoring those where either ground truth, or the rater assigned, or the model assigned label is [No connective].'.\n" }, { "role": "assistant", "content": "The DA model outperforms human raters in F1 score across different settings, with the smallest difference in less ambiguous cases.The macro-averaged F1 score of human raters is 23.72 which is, quite surprisingly, lower than the F1 score of the DA model, 31.80. The difference is smaller when considering majority votes on the subset of 5 714 tasks for which there is a consensus among at least 2 out of 3 raters, which results in a 30.36 F1 score for the raters. On these less ambiguous cases, the model performance also increases to 32.68. Therefore, we also evaluate the setting in which we exclude the questions for which either the ground-truth label, or the rater-assigned majority label, or the model-assigned label is [No connective]." } ], "images": [ "scigen/train/2914.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Macro-averaged F1 scores for human raters and the decomposable attention model. The three settings are: (A) All test set items; (B) Only the items for which there is a consensus among at least 2 out of 3 raters; (C) Consensus items ignoring those where either ground truth, or the rater assigned, or the model assigned label is [No connective]." ], "reasoning": "The DA model outperforms human raters in F1 score across different settings, with the smallest difference in less ambiguous cases.", "description": "The macro-averaged F1 score of human raters is 23.72 which is, quite surprisingly, lower than the F1 score of the DA model, 31.80. The difference is smaller when considering majority votes on the subset of 5 714 tasks for which there is a consensus among at least 2 out of 3 raters, which results in a 30.36 F1 score for the raters. On these less ambiguous cases, the model performance also increases to 32.68. Therefore, we also evaluate the setting in which we exclude the questions for which either the ground-truth label, or the rater-assigned majority label, or the model-assigned label is [No connective].", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 3: Macro-averaged F1 scores for human raters and the decomposable attention model. The three settings are: (A) All test set items; (B) Only the items for which there is a consensus among at least 2 out of 3 raters; (C) Consensus items ignoring those where either ground truth, or the rater assigned, or the model assigned label is [No connective].'\nQuestion: What is the F1 score of the DA model in setting B?\n" }, { "role": "assistant", "content": "The table shows the F1 scores for both raters and the DA model across different settings. In setting B, the DA model's F1 score is listed. So, the answer is {\"answer\": \"32.68\"}" } ], "images": [ "scigen/train/2914.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Macro-averaged F1 scores for human raters and the decomposable attention model. The three settings are: (A) All test set items; (B) Only the items for which there is a consensus among at least 2 out of 3 raters; (C) Consensus items ignoring those where either ground truth, or the rater assigned, or the model assigned label is [No connective]." ], "reasoning": "The table shows the F1 scores for both raters and the DA model across different settings. In setting B, the DA model's F1 score is listed.", "question": "What is the F1 score of the DA model in setting B?", "answer": "So, the answer is {\"answer\": \"32.68\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe F1 score of human raters is higher than the DA model in setting C.\n" }, { "role": "assistant", "content": "The table shows that in setting C, the F1 score of human raters is 41.97, which is higher than the DA model's F1 score of 36.65. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2914.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Macro-averaged F1 scores for human raters and the decomposable attention model. The three settings are: (A) All test set items; (B) Only the items for which there is a consensus among at least 2 out of 3 raters; (C) Consensus items ignoring those where either ground truth, or the rater assigned, or the model assigned label is [No connective]." ], "reasoning": "The table shows that in setting C, the F1 score of human raters is 41.97, which is higher than the DA model's F1 score of 36.65.", "statement": "The F1 score of human raters is higher than the DA model in setting C.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Performance of the CWS comparison between the schemes', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The future scheme in the model focuses on the right scope of a sentence and performs better than the past scheme in CWS on the NLPCC2016 test set.In generic temporal convolutional network, it focusses on the information at left scope in a sentence (the past scheme). However, in our model, we only focus on the information at the right scope in a sentence (the future scheme). We compare the difference between the schemes for convolutional scope in performance of the CWS on the test set of NLPCC2016. The reason is that starting a dilated convolution from zero paddings by adopting the past scheme will lose the information of characters in front of a sentence. Instead, the future scheme is without the problem that is the loss of the information in front of a sentence." } ], "images": [ "scigen/train/2915.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of the CWS comparison between the schemes" ], "reasoning": "The future scheme in the model focuses on the right scope of a sentence and performs better than the past scheme in CWS on the NLPCC2016 test set.", "description": "In generic temporal convolutional network, it focusses on the information at left scope in a sentence (the past scheme). However, in our model, we only focus on the information at the right scope in a sentence (the future scheme). We compare the difference between the schemes for convolutional scope in performance of the CWS on the test set of NLPCC2016. The reason is that starting a dilated convolution from zero paddings by adopting the past scheme will lose the information of characters in front of a sentence. Instead, the future scheme is without the problem that is the loss of the information in front of a sentence.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Performance of the CWS comparison between the schemes\nQuestion: What is the F-score difference between the past scheme and the future scheme in the CWS performance on the NLPCC2016 test set?\nSolve the above question based on the table information. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The F-score for the past scheme is 92.03, and for the future scheme, it is 92.34. The difference is 92.34 - 92.03 = 0.31. So, the answer is {\"answer\": \"0.31\"}" } ], "images": [ "scigen/train/2915.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of the CWS comparison between the schemes" ], "reasoning": "The F-score for the past scheme is 92.03, and for the future scheme, it is 92.34. The difference is 92.34 - 92.03 = 0.31.", "question": "What is the F-score difference between the past scheme and the future scheme in the CWS performance on the NLPCC2016 test set?", "answer": "So, the answer is {\"answer\": \"0.31\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The future scheme has a higher recall than the past scheme in the CWS performance on the NLPCC2016 test set., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe future scheme has a higher recall than the past scheme in the CWS performance on the NLPCC2016 test set.\n" }, { "role": "assistant", "content": "The table shows that the recall (R) for the past scheme is 92.81, while for the future scheme, it is 92.94. Therefore, the future scheme has a higher recall. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2915.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of the CWS comparison between the schemes" ], "reasoning": "The table shows that the recall (R) for the past scheme is 92.81, while for the future scheme, it is 92.94. Therefore, the future scheme has a higher recall.", "statement": "The future scheme has a higher recall than the past scheme in the CWS performance on the NLPCC2016 test set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Performance comparison of the models. Size means the number of parameters of the models. Speed is the training speed of models and its unit of measurement is sentences/s meaning that how many sentences are processed per second.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The Tcn-no-CRF model has the highest training speed, while the Tcn-with-CRF model has the highest F score.We select the Bi-LSTM-with-CRF and the Tcn-no-CRF as the baseline models, and experiment these models on NLPCC2016 dataset, and show the performance comparison between our model with the models on the test set. The training speed is an important performance to evaluate the power of parallel computing in identical hardware and software environment. Meanwhile, if a product based on the algorithm of deep-learning is trained more quickly, the product is released more early before the other similar product." } ], "images": [ "scigen/train/2916.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance comparison of the models. Size means the number of parameters of the models. Speed is the training speed of models and its unit of measurement is sentences/s meaning that how many sentences are processed per second." ], "reasoning": "The Tcn-no-CRF model has the highest training speed, while the Tcn-with-CRF model has the highest F score.", "description": "We select the Bi-LSTM-with-CRF and the Tcn-no-CRF as the baseline models, and experiment these models on NLPCC2016 dataset, and show the performance comparison between our model with the models on the test set. The training speed is an important performance to evaluate the power of parallel computing in identical hardware and software environment. Meanwhile, if a product based on the algorithm of deep-learning is trained more quickly, the product is released more early before the other similar product.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: Performance comparison of the models. Size means the number of parameters of the models. Speed is the training speed of models and its unit of measurement is sentences/s meaning that how many sentences are processed per second.'. Solve the following question based on the table.\n\nQuestion: Which model has the highest F score and what is its training speed?\nLet's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The Tcn-with-CRF model has the highest F score of 92.34, and its training speed is 263. So, the answer is {\"answer\": \"263\"}" } ], "images": [ "scigen/train/2916.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance comparison of the models. Size means the number of parameters of the models. Speed is the training speed of models and its unit of measurement is sentences/s meaning that how many sentences are processed per second." ], "reasoning": "The Tcn-with-CRF model has the highest F score of 92.34, and its training speed is 263.", "question": "Which model has the highest F score and what is its training speed?", "answer": "So, the answer is {\"answer\": \"263\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The Bi-LSTM-with-CRF model has the lowest training speed., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Bi-LSTM-with-CRF model has the lowest training speed.\n" }, { "role": "assistant", "content": "The Bi-LSTM-with-CRF model has a training speed of 120, which is the lowest among the listed models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2916.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance comparison of the models. Size means the number of parameters of the models. Speed is the training speed of models and its unit of measurement is sentences/s meaning that how many sentences are processed per second." ], "reasoning": "The Bi-LSTM-with-CRF model has a training speed of 120, which is the lowest among the listed models.", "statement": "The Bi-LSTM-with-CRF model has the lowest training speed.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Comparison between our model with other prevalent models of the CWS.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The table compares the performance of different models, including the proposed model, on CITYU and PKU datasets using precision, recall, and F1-score metrics.To better evaluate the performance of the CWS our model with other models premised the Bi-LSTM that other researchers proposed, we contrast the performance of the CWS between our model with prevalent models on the datasets of CITYU and PKU." } ], "images": [ "scigen/train/2917.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison between our model with other prevalent models of the CWS." ], "reasoning": "The table compares the performance of different models, including the proposed model, on CITYU and PKU datasets using precision, recall, and F1-score metrics.", "description": "To better evaluate the performance of the CWS our model with other models premised the Bi-LSTM that other researchers proposed, we contrast the performance of the CWS between our model with prevalent models on the datasets of CITYU and PKU.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Comparison between our model with other prevalent models of the CWS.\nQuestion: Which model achieved the highest F1-score on the CITYU dataset?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest F1-score on the CITYU dataset, look at the CITYU F column and identify the highest value. The highest F1-score is 97.2, achieved by Ma et al. (2018). So, the answer is {\"answer\": \"Ma et al. (2018)\"}." } ], "images": [ "scigen/train/2917.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison between our model with other prevalent models of the CWS." ], "reasoning": "To find the model with the highest F1-score on the CITYU dataset, look at the CITYU F column and identify the highest value. The highest F1-score is 97.2, achieved by Ma et al. (2018).", "question": "Which model achieved the highest F1-score on the CITYU dataset?", "answer": "So, the answer is {\"answer\": \"Ma et al. (2018)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe proposed model achieved the highest precision on the PKU dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the PKU P values for all models. The highest precision on the PKU dataset is 95.8, achieved by Cai and Zhao (2016), not the proposed model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2917.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison between our model with other prevalent models of the CWS." ], "reasoning": "To verify this statement, compare the PKU P values for all models. The highest precision on the PKU dataset is 95.8, achieved by Cai and Zhao (2016), not the proposed model.", "statement": "The proposed model achieved the highest precision on the PKU dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: The clustering results on three datasets. We evaluate both unsupervised and semi-supervised methods.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The CDAC+ method consistently outperforms other baselines across all datasets and evaluation metrics, demonstrating its effectiveness in grouping sentences based on learned intent representations.The proposed CDAC+ method outperforms other baselines by a significant margin in all datasets and evaluation metrics. It shows that our method effectively groups sentences based on the intent representations learned with pairwise classification and constraints, and even can generalize to new intents that we do not know in advance." } ], "images": [ "scigen/train/2918.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The clustering results on three datasets. We evaluate both unsupervised and semi-supervised methods." ], "reasoning": "The CDAC+ method consistently outperforms other baselines across all datasets and evaluation metrics, demonstrating its effectiveness in grouping sentences based on learned intent representations.", "description": "The proposed CDAC+ method outperforms other baselines by a significant margin in all datasets and evaluation metrics. It shows that our method effectively groups sentences based on the intent representations learned with pairwise classification and constraints, and even can generalize to new intents that we do not know in advance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: The clustering results on three datasets. We evaluate both unsupervised and semi-supervised methods.' to sequentially solve the question presented below. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich method has the highest SNIPS NMI score?\n" }, { "role": "assistant", "content": "By examining the SNIPS NMI column, the highest score is found in the row corresponding to the CDAC+ method. So, the answer is {\"answer\": \"CDAC+\"}" } ], "images": [ "scigen/train/2918.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The clustering results on three datasets. We evaluate both unsupervised and semi-supervised methods." ], "reasoning": "By examining the SNIPS NMI column, the highest score is found in the row corresponding to the CDAC+ method.", "question": "Which method has the highest SNIPS NMI score?", "answer": "So, the answer is {\"answer\": \"CDAC+\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe CDAC+ method has the highest StackOverflow ARI score.\n" }, { "role": "assistant", "content": "By checking the StackOverflow ARI column, the highest score is 52.59, which corresponds to the CDAC+ method. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2918.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The clustering results on three datasets. We evaluate both unsupervised and semi-supervised methods." ], "reasoning": "By checking the StackOverflow ARI column, the highest score is 52.59, which corresponds to the CDAC+ method.", "statement": "The CDAC+ method has the highest StackOverflow ARI score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Results for all our methods and the baselines. ‘w/ pre-attn’ and ‘w/ uniform’ are short for ‘with pre-attention’ and ‘with weight of 2 uniformly’. Best scores in each column within each row-wise block are highlighted in bold face. ↑/↓ indicate higher/lower is better. All results are on the test set.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Pre-attention and highway connections in RNNs reduce repetition while maintaining utterance quality, and dynamically weighting tokens helps reduce repetition in transformers.Since the validation repetition (WL2 scores) of models can be very low in the early stage of training when the l-DIMEN score is still very low, we mainly use l-DIMEN to make sure that the results are shown for checkpoints at similar stages. The first block ranging from rows (a)–(c) is for answering RQ1. The second (rows (d)–(g)) and third (rows (h)–(l)) blocks are for answering RQ2 and RQ3, with RNNs and transformer models, respectively. Answer to RQ1: From row (b), we can see that pre-attention is effective in reducing repetition on all three datasets. On top of pre-attention, row (c) shows that highway connections are helpful with repetition reduction on two datasets, namely Dailydialog and Movie Dialogs. BLEU scores show that the quality of the utterances generated by our models (b) and (c) are on a par with those generated by the baseline (a). These results indicate that proper architectural modifications can indeed reduce repetition, and more work needs to be done to find such modifications for transformers. Answer to RQ3: Next, we proceed to our proposed weighting methods. Though TLDR occasionally performs worse than TFL, e.g., row (g) vs row (e) on the Movie Dialogs dataset, we note that this is probably due to sample bias of the test sets. These comparisons indicate that dynamically weighting tokens according to their difficulty is an effective solution to reducing repetition." } ], "images": [ "scigen/train/2919.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results for all our methods and the baselines. ‘w/ pre-attn’ and ‘w/ uniform’ are short for ‘with pre-attention’ and ‘with weight of 2 uniformly’. Best scores in each column within each row-wise block are highlighted in bold face. ↑/↓ indicate higher/lower is better. All results are on the test set." ], "reasoning": "Pre-attention and highway connections in RNNs reduce repetition while maintaining utterance quality, and dynamically weighting tokens helps reduce repetition in transformers.", "description": "Since the validation repetition (WL2 scores) of models can be very low in the early stage of training when the l-DIMEN score is still very low, we mainly use l-DIMEN to make sure that the results are shown for checkpoints at similar stages. The first block ranging from rows (a)–(c) is for answering RQ1. The second (rows (d)–(g)) and third (rows (h)–(l)) blocks are for answering RQ2 and RQ3, with RNNs and transformer models, respectively. Answer to RQ1: From row (b), we can see that pre-attention is effective in reducing repetition on all three datasets. On top of pre-attention, row (c) shows that highway connections are helpful with repetition reduction on two datasets, namely Dailydialog and Movie Dialogs. BLEU scores show that the quality of the utterances generated by our models (b) and (c) are on a par with those generated by the baseline (a). These results indicate that proper architectural modifications can indeed reduce repetition, and more work needs to be done to find such modifications for transformers. Answer to RQ3: Next, we proceed to our proposed weighting methods. Though TLDR occasionally performs worse than TFL, e.g., row (g) vs row (e) on the Movie Dialogs dataset, we note that this is probably due to sample bias of the test sets. These comparisons indicate that dynamically weighting tokens according to their difficulty is an effective solution to reducing repetition.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 1: Results for all our methods and the baselines. ‘w/ pre-attn’ and ‘w/ uniform’ are short for ‘with pre-attention’ and ‘with weight of 2 uniformly’. Best scores in each column within each row-wise block are highlighted in bold face. ↑/↓ indicate higher/lower is better. All results are on the test set.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. \n\nQuestion: How do pre-attention and highway connections affect repetition and utterance quality in RNNs?\n\nYour Solution:\n" }, { "role": "assistant", "content": "From row (b), pre-attention reduces repetition on all datasets, and from row (c), highway connections further reduce repetition on Dailydialog and Movie Dialogs. Both rows (b) and (c) show BLEU scores comparable to the baseline (a), indicating maintained utterance quality. So, the answer is {\"answer\": \"Pre-attention reduces repetition on all datasets, and highway connections further reduce repetition on Dailydialog and Movie Dialogs while maintaining utterance quality.\"}." } ], "images": [ "scigen/train/2919.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results for all our methods and the baselines. ‘w/ pre-attn’ and ‘w/ uniform’ are short for ‘with pre-attention’ and ‘with weight of 2 uniformly’. Best scores in each column within each row-wise block are highlighted in bold face. ↑/↓ indicate higher/lower is better. All results are on the test set." ], "reasoning": "From row (b), pre-attention reduces repetition on all datasets, and from row (c), highway connections further reduce repetition on Dailydialog and Movie Dialogs. Both rows (b) and (c) show BLEU scores comparable to the baseline (a), indicating maintained utterance quality.", "question": "How do pre-attention and highway connections affect repetition and utterance quality in RNNs?", "answer": "So, the answer is {\"answer\": \"Pre-attention reduces repetition on all datasets, and highway connections further reduce repetition on Dailydialog and Movie Dialogs while maintaining utterance quality.\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nHighway connections in RNNs reduce repetition on the Ubuntu Dialogue dataset.\n" }, { "role": "assistant", "content": "Row (c) shows that highway connections in RNNs do not reduce repetition on the Ubuntu Dialogue dataset, as the WL2 score remains the same as the baseline (a). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2919.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results for all our methods and the baselines. ‘w/ pre-attn’ and ‘w/ uniform’ are short for ‘with pre-attention’ and ‘with weight of 2 uniformly’. Best scores in each column within each row-wise block are highlighted in bold face. ↑/↓ indicate higher/lower is better. All results are on the test set." ], "reasoning": "Row (c) shows that highway connections in RNNs do not reduce repetition on the Ubuntu Dialogue dataset, as the WL2 score remains the same as the baseline (a).", "statement": "Highway connections in RNNs reduce repetition on the Ubuntu Dialogue dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: WERs (%) with or without AE layers.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Adding AE layers to the deliberation decoder and LAS beam search models improves performance by around 4%.To help the deliberation decoder better adapt to the shared encoder, we add AE layers for dedicated encoding for the deliberation decoder. The AE consists of a 2-layer LSTM with 2,048 hidden units followed by 640-dimensional projection per layer. Beam search is used for decoding. the model performs around 4% better than without (E4). Similarly, we apply AE to the LAS beam search (B1→B2), and obtain similar improvements." } ], "images": [ "scigen/train/2920.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: WERs (%) with or without AE layers." ], "reasoning": "Adding AE layers to the deliberation decoder and LAS beam search models improves performance by around 4%.", "description": "To help the deliberation decoder better adapt to the shared encoder, we add AE layers for dedicated encoding for the deliberation decoder. The AE consists of a 2-layer LSTM with 2,048 hidden units followed by 640-dimensional projection per layer. Beam search is used for decoding. the model performs around 4% better than without (E4). Similarly, we apply AE to the LAS beam search (B1→B2), and obtain similar improvements.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: WERs (%) with or without AE layers.\nQuestion: What is the WER for the LAS model after applying AE?\nSolve the above question based on the table information. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the WER for the LAS model (B1) as 6.1 and for LAS + AE (B2) as 5.8. The description states that applying AE improves performance. So, the answer is {\"answer\": \"5.8\"}." } ], "images": [ "scigen/train/2920.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: WERs (%) with or without AE layers." ], "reasoning": "The table shows the WER for the LAS model (B1) as 6.1 and for LAS + AE (B2) as 5.8. The description states that applying AE improves performance.", "question": "What is the WER for the LAS model after applying AE?", "answer": "So, the answer is {\"answer\": \"5.8\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe E4 model with AE has a WER of 5.2.\n" }, { "role": "assistant", "content": "The table shows that the E4 + AE model (E7) has a WER of 5.2. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2920.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: WERs (%) with or without AE layers." ], "reasoning": "The table shows that the E4 + AE model (E7) has a WER of 5.2.", "statement": "The E4 model with AE has a WER of 5.2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: WERs (%) of deliberation with different number of RNN-T hypotheses.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "MWER training improves WER from 5.4% to 5.1% when using an 8-hypothesis model, which is used for subsequent experiments.The deliberation decoder may attend to multiple first-pass hypotheses. We encode the hypotheses separately, and then concatenate them as the input to the attention layer. We use a beam size of 8 for RNN-T decoding. Unless stated otherwise, the WER we report is for VS test set. However, after applying MWER training, the WER improves continuously: 5.4% to 5.1%. We suspect that MWER training specifically helps deliberation attend to relevant parts of first-pass hypotheses. Since 8-hypothesis model gives the best performance, we use that for subsequent experiments. MWER training is not used for simplicity." } ], "images": [ "scigen/train/2921.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: WERs (%) of deliberation with different number of RNN-T hypotheses." ], "reasoning": "MWER training improves WER from 5.4% to 5.1% when using an 8-hypothesis model, which is used for subsequent experiments.", "description": "The deliberation decoder may attend to multiple first-pass hypotheses. We encode the hypotheses separately, and then concatenate them as the input to the attention layer. We use a beam size of 8 for RNN-T decoding. Unless stated otherwise, the WER we report is for VS test set. However, after applying MWER training, the WER improves continuously: 5.4% to 5.1%. We suspect that MWER training specifically helps deliberation attend to relevant parts of first-pass hypotheses. Since 8-hypothesis model gives the best performance, we use that for subsequent experiments. MWER training is not used for simplicity.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: WERs (%) of deliberation with different number of RNN-T hypotheses.\nQuestion: What is the WER for the 4-hypothesis model after applying MWER training?\nGive a detailed response to the above question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the WER for different models with and without MWER training. For the 4-hypothesis model, the WER after applying MWER training is 5.2%. So, the answer is {\"answer\": \"5.2%\"}." } ], "images": [ "scigen/train/2921.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: WERs (%) of deliberation with different number of RNN-T hypotheses." ], "reasoning": "The table shows the WER for different models with and without MWER training. For the 4-hypothesis model, the WER after applying MWER training is 5.2%.", "question": "What is the WER for the 4-hypothesis model after applying MWER training?", "answer": "So, the answer is {\"answer\": \"5.2%\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe WER for the 2-hypothesis model improves to 5.3% after applying MWER training.\n" }, { "role": "assistant", "content": "The table shows that the WER for the 2-hypothesis model is 5.4% without MWER training and improves to 5.3% with MWER training. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2921.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: WERs (%) of deliberation with different number of RNN-T hypotheses." ], "reasoning": "The table shows that the WER for the 2-hypothesis model is 5.4% without MWER training and improves to 5.3% with MWER training.", "statement": "The WER for the 2-hypothesis model improves to 5.3% after applying MWER training.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: WERs (%) of baseline RNN-T model and deliberation models with different attention setup.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Using both attentions (E4) results in an 11% relative improvement in WER compared to using either acoustics (E5) or text (E6) alone.We are curious about how different attention (cb vs ce) contribute to deliberation, and thus train separate models where we attend to either acoustics (E5) or text (E6) alone in training and inference. By using both attentions (E4), the model gains another 11% relative improvement. It seems surprising that E6 performs equally to E5. We note this could be because E6 has a bidirectional encoder while E5 does not." } ], "images": [ "scigen/train/2922.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: WERs (%) of baseline RNN-T model and deliberation models with different attention setup." ], "reasoning": "Using both attentions (E4) results in an 11% relative improvement in WER compared to using either acoustics (E5) or text (E6) alone.", "description": "We are curious about how different attention (cb vs ce) contribute to deliberation, and thus train separate models where we attend to either acoustics (E5) or text (E6) alone in training and inference. By using both attentions (E4), the model gains another 11% relative improvement. It seems surprising that E6 performs equally to E5. We note this could be because E6 has a bidirectional encoder while E5 does not.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: WERs (%) of baseline RNN-T model and deliberation models with different attention setup.'. Output the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: What is the relative improvement in WER when using both attentions compared to using acoustics or text alone?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The WER for using both attentions (E4) is 5.4%, while the WER for using either acoustics (E5) or text (E6) alone is 6.1%. The relative improvement is calculated as (6.1 - 5.4) / 6.1 * 100%. So, the answer is {\"answer\": \"11%\"}" } ], "images": [ "scigen/train/2922.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: WERs (%) of baseline RNN-T model and deliberation models with different attention setup." ], "reasoning": "The WER for using both attentions (E4) is 5.4%, while the WER for using either acoustics (E5) or text (E6) alone is 6.1%. The relative improvement is calculated as (6.1 - 5.4) / 6.1 * 100%.", "question": "What is the relative improvement in WER when using both attentions compared to using acoustics or text alone?", "answer": "So, the answer is {\"answer\": \"11%\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nUsing both attentions results in a WER of 5.4%.\n" }, { "role": "assistant", "content": "The table shows that the WER for the model using both attentions (E4) is 5.4%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2922.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: WERs (%) of baseline RNN-T model and deliberation models with different attention setup." ], "reasoning": "The table shows that the WER for the model using both attentions (E4) is 5.4%.", "statement": "Using both attentions results in a WER of 5.4%.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: Comparison of RNN-T, LAS two-pass and deliberation models in WERs (%) and GFLOPS. LAS two-pass and deliberation models are augmented with AE layers. All models are trained with MWER loss except the RNN-T model.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The MWER trained 8-hypothesis deliberation model with AE layers (E9) performs better than LAS rescoring (B4) and beam search (B5), and joint training (E10) brings additional improvements.We include two deliberation models: An MWER trained 8-hypothesis deliberation model with AE layers (E9), and a jointly trained version (E10). We note that all models are MWER trained except the RNN-T model, which we find little improvement. First, we note that two-pass models perform substantially better than RNN-T (B0) in both VS task (15%–25% WERR) and rare word test sets (e.g. up to 30% in E10 for the SxS set). This confirms that second-pass decoding brings additional benefits. Second, the MWER trained 8-hypothesis deliberation model with AE layers (E9) performs significantly better than LAS rescoring (B4) or beam search (B5). When beam search is used for both of the deliberation and LAS models, the WERR is 7% for VS, and 8% for the SxS set. We observe significant improvements for voice command test sets too. Third, joint training (E10) brings an additional 2% relative improvement for VS, 9% for the SxS set, and uniform improvements for voice command test sets. Since the deliberation decoder has a larger size than LAS decoder (67MB vs. 33MB), it requires around 1.8 times GFLOPS as LAS rescoring. The increase mainly comes from the bidirectional encoder for 8 first-pass hypotheses. Latency estimation is complicated, and we will quantify that in future works." } ], "images": [ "scigen/train/2923.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of RNN-T, LAS two-pass and deliberation models in WERs (%) and GFLOPS. LAS two-pass and deliberation models are augmented with AE layers. All models are trained with MWER loss except the RNN-T model." ], "reasoning": "The MWER trained 8-hypothesis deliberation model with AE layers (E9) performs better than LAS rescoring (B4) and beam search (B5), and joint training (E10) brings additional improvements.", "description": "We include two deliberation models: An MWER trained 8-hypothesis deliberation model with AE layers (E9), and a jointly trained version (E10). We note that all models are MWER trained except the RNN-T model, which we find little improvement. First, we note that two-pass models perform substantially better than RNN-T (B0) in both VS task (15%–25% WERR) and rare word test sets (e.g. up to 30% in E10 for the SxS set). This confirms that second-pass decoding brings additional benefits. Second, the MWER trained 8-hypothesis deliberation model with AE layers (E9) performs significantly better than LAS rescoring (B4) or beam search (B5). When beam search is used for both of the deliberation and LAS models, the WERR is 7% for VS, and 8% for the SxS set. We observe significant improvements for voice command test sets too. Third, joint training (E10) brings an additional 2% relative improvement for VS, 9% for the SxS set, and uniform improvements for voice command test sets. Since the deliberation decoder has a larger size than LAS decoder (67MB vs. 33MB), it requires around 1.8 times GFLOPS as LAS rescoring. The increase mainly comes from the bidirectional encoder for 8 first-pass hypotheses. Latency estimation is complicated, and we will quantify that in future works.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: Table 4: Comparison of RNN-T, LAS two-pass and deliberation models in WERs (%) and GFLOPS. LAS two-pass and deliberation models are augmented with AE layers. All models are trained with MWER loss except the RNN-T model.\nQuestion: Which model has the lowest WER (%) for the SxS set and what is its value?\n" }, { "role": "assistant", "content": "By comparing the WER (%) values for the SxS set across all models, we see that the model E10 with joint training has the lowest WER (%) value of 24.3. So, the answer is {\"answer\": \"E10, 24.3\"}" } ], "images": [ "scigen/train/2923.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of RNN-T, LAS two-pass and deliberation models in WERs (%) and GFLOPS. LAS two-pass and deliberation models are augmented with AE layers. All models are trained with MWER loss except the RNN-T model." ], "reasoning": "By comparing the WER (%) values for the SxS set across all models, we see that the model E10 with joint training has the lowest WER (%) value of 24.3.", "question": "Which model has the lowest WER (%) for the SxS set and what is its value?", "answer": "So, the answer is {\"answer\": \"E10, 24.3\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The RNN-T model (B0) has the highest WER (%) for the SxS set. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe RNN-T model (B0) has the highest WER (%) for the SxS set.\n" }, { "role": "assistant", "content": "By examining the WER (%) values for the SxS set, we see that the RNN-T model (B0) has the highest WER (%) value of 35.2. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2923.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of RNN-T, LAS two-pass and deliberation models in WERs (%) and GFLOPS. LAS two-pass and deliberation models are augmented with AE layers. All models are trained with MWER loss except the RNN-T model." ], "reasoning": "By examining the WER (%) values for the SxS set, we see that the RNN-T model (B0) has the highest WER (%) value of 35.2.", "statement": "The RNN-T model (B0) has the highest WER (%) for the SxS set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 1: The number of triplets in different stages of CAIL2019-SCM.'.\n" }, { "role": "assistant", "content": "By calculating the tf-idf similarity and elemental similarity between cases, we can select similar cases to construct our dataset, which legal professionals will then annotate to compare similarities.After annotating these elements, we can assume that cases with similar elements are quite similar. So when we construct the triplets, we calculate the tf-idf similarity and elemental similarity between cases and select those similar cases to construct our dataset. Then, legal professionals will annotate every triplet to see whether sim(A,B)>sim(A,C) or sim(A,B)" } ], "images": [ "scigen/train/2924.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The number of triplets in different stages of CAIL2019-SCM." ], "reasoning": "By calculating the tf-idf similarity and elemental similarity between cases, we can select similar cases to construct our dataset, which legal professionals will then annotate to compare similarities.", "description": "After annotating these elements, we can assume that cases with similar elements are quite similar. So when we construct the triplets, we calculate the tf-idf similarity and elemental similarity between cases and select those similar cases to construct our dataset. Then, legal professionals will annotate every triplet to see whether sim(A,B)>sim(A,C) or sim(A,B)", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: The number of triplets in different stages of CAIL2019-SCM.', solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the total number of cases in the Large Train and Large Test datasets combined?\n" }, { "role": "assistant", "content": "To find the total number of cases in the Large Train and Large Test datasets, add the counts for Large Train (5,102) and Large Test (1,536). So, the answer is {\"answer\": \"6638\"}." } ], "images": [ "scigen/train/2924.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The number of triplets in different stages of CAIL2019-SCM." ], "reasoning": "To find the total number of cases in the Large Train and Large Test datasets, add the counts for Large Train (5,102) and Large Test (1,536).", "question": "What is the total number of cases in the Large Train and Large Test datasets combined?", "answer": "So, the answer is {\"answer\": \"6638\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Small Train dataset has more cases than the Large Valid dataset.\n" }, { "role": "assistant", "content": "The Small Train dataset has 500 cases, while the Large Valid dataset has 1,500 cases. Therefore, the Small Train dataset has fewer cases than the Large Valid dataset. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2924.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The number of triplets in different stages of CAIL2019-SCM." ], "reasoning": "The Small Train dataset has 500 cases, while the Large Valid dataset has 1,500 cases. Therefore, the Small Train dataset has fewer cases than the Large Valid dataset.", "statement": "The Small Train dataset has more cases than the Large Valid dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 7: Fine-tuning results on open-domain datasets. All the scores are the exact match scores on the test set and multiplied by 100.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Conv DrQA performs better than DrQA on CuratedTrec and WikiMovies datasets but slightly worse on WebQuestions.Our Conv DrQA outperforms DrQA on the CuratedTrec and the WikiMovies datasets, while being a little worse on the WebQuestions dataset." } ], "images": [ "scigen/train/2925.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Fine-tuning results on open-domain datasets. All the scores are the exact match scores on the test set and multiplied by 100." ], "reasoning": "Conv DrQA performs better than DrQA on CuratedTrec and WikiMovies datasets but slightly worse on WebQuestions.", "description": "Our Conv DrQA outperforms DrQA on the CuratedTrec and the WikiMovies datasets, while being a little worse on the WebQuestions dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 7: Fine-tuning results on open-domain datasets. All the scores are the exact match scores on the test set and multiplied by 100.'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. \n\nQuestion: Which dataset shows the highest performance improvement of Conv DrQA over DrQA?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the dataset with the highest performance improvement, compare the scores of Conv DrQA and DrQA on each dataset. The improvement is highest on the WikiMovies dataset, where Conv DrQA scores 36.2 compared to DrQA's 35.5. So, the answer is {\"answer\": \"WikiMovies\"}." } ], "images": [ "scigen/train/2925.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Fine-tuning results on open-domain datasets. All the scores are the exact match scores on the test set and multiplied by 100." ], "reasoning": "To find the dataset with the highest performance improvement, compare the scores of Conv DrQA and DrQA on each dataset. The improvement is highest on the WikiMovies dataset, where Conv DrQA scores 36.2 compared to DrQA's 35.5.", "question": "Which dataset shows the highest performance improvement of Conv DrQA over DrQA?", "answer": "So, the answer is {\"answer\": \"WikiMovies\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Conv DrQA performs worse than DrQA on the CuratedTrec dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nConv DrQA performs worse than DrQA on the CuratedTrec dataset.\n" }, { "role": "assistant", "content": "The table shows that Conv DrQA scores 27.2 on CuratedTrec, while DrQA scores 25.9. Therefore, Conv DrQA performs better, not worse. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2925.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Fine-tuning results on open-domain datasets. All the scores are the exact match scores on the test set and multiplied by 100." ], "reasoning": "The table shows that Conv DrQA scores 27.2 on CuratedTrec, while DrQA scores 25.9. Therefore, Conv DrQA performs better, not worse.", "statement": "Conv DrQA performs worse than DrQA on the CuratedTrec dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: BiDAF v.s. Conv BiDAF. EM stands for exact match score.'.\n" }, { "role": "assistant", "content": "Conv-BiDAF achieves significant speed-up in inference time compared to BiDAF while maintaining similar performance in terms of F1-score.as a function of inference GPU time. The figure shows the results across all documents (middle) and for the top 10% shortest and longest documents (left and right plot respectively). Throughout, Conv-BiDAF achieves one to two order of magnitude speed-up at inference time and performs almost as well as the original BiDAF.The plot also shows the F1-score of the R-Net model for which we have no inference timing (therefore shown as a horizontal line)." } ], "images": [ "scigen/train/2926.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: BiDAF v.s. Conv BiDAF. EM stands for exact match score." ], "reasoning": "Conv-BiDAF achieves significant speed-up in inference time compared to BiDAF while maintaining similar performance in terms of F1-score.", "description": "as a function of inference GPU time. The figure shows the results across all documents (middle) and for the top 10% shortest and longest documents (left and right plot respectively). Throughout, Conv-BiDAF achieves one to two order of magnitude speed-up at inference time and performs almost as well as the original BiDAF.The plot also shows the F1-score of the R-Net model for which we have no inference timing (therefore shown as a horizontal line).", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 2: BiDAF v.s. Conv BiDAF. EM stands for exact match score.', unfold the question presented below, detailing every step of your calculation. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the inference GPU time per iteration for Conv-BiDAF with a batch size of 1, and how does it compare to BiDAF?\n" }, { "role": "assistant", "content": "The table shows that the inference GPU time per iteration for Conv-BiDAF with a batch size of 1 is 0.016±0.003 seconds, which is 98 times faster than BiDAF's 1.58±0.06 seconds. So, the answer is {\"answer\": \"0.016±0.003 seconds\"}." } ], "images": [ "scigen/train/2926.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: BiDAF v.s. Conv BiDAF. EM stands for exact match score." ], "reasoning": "The table shows that the inference GPU time per iteration for Conv-BiDAF with a batch size of 1 is 0.016±0.003 seconds, which is 98 times faster than BiDAF's 1.58±0.06 seconds.", "question": "What is the inference GPU time per iteration for Conv-BiDAF with a batch size of 1, and how does it compare to BiDAF?", "answer": "So, the answer is {\"answer\": \"0.016±0.003 seconds\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nConv-BiDAF has a higher Dev F1 score than BiDAF.\n" }, { "role": "assistant", "content": "The table shows that the Dev F1 score for Conv-BiDAF is 77.27±0.41, while for BiDAF it is 77.29±0.38, indicating that Conv-BiDAF does not have a higher Dev F1 score than BiDAF. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2926.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: BiDAF v.s. Conv BiDAF. EM stands for exact match score." ], "reasoning": "The table shows that the Dev F1 score for Conv-BiDAF is 77.27±0.41, while for BiDAF it is 77.29±0.38, indicating that Conv-BiDAF does not have a higher Dev F1 score than BiDAF.", "statement": "Conv-BiDAF has a higher Dev F1 score than BiDAF.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: Comparing variants with different number of layers. EM stands for exact match score. The scores are multiplied by 100. DrQA uses a much larger pre-trained word embedding resulting in more parameters.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Dilation improves answer quality slightly, while the choice of non-linear unit significantly impacts it, with GLU outperforming ReLU. Without residual learning, the model fails to converge.We can see that dilation helps improve answer quality a bit. The choice of non-linear unit has a huge impact on answer quality with GLU performs much better than ReLU. Without residual learning (removing the shortcut connection), the model can never converge to fair point." } ], "images": [ "scigen/train/2927.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparing variants with different number of layers. EM stands for exact match score. The scores are multiplied by 100. DrQA uses a much larger pre-trained word embedding resulting in more parameters." ], "reasoning": "Dilation improves answer quality slightly, while the choice of non-linear unit significantly impacts it, with GLU outperforming ReLU. Without residual learning, the model fails to converge.", "description": "We can see that dilation helps improve answer quality a bit. The choice of non-linear unit has a huge impact on answer quality with GLU performs much better than ReLU. Without residual learning (removing the shortcut connection), the model can never converge to fair point.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 3: Comparing variants with different number of layers. EM stands for exact match score. The scores are multiplied by 100. DrQA uses a much larger pre-trained word embedding resulting in more parameters.', answer the question below by showing each progressive step toward the answer. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model has the highest Dev F1 score and how many parameters does it have?\n" }, { "role": "assistant", "content": "To find the model with the highest Dev F1 score, look at the 'Dev F1' column and identify the highest value, which is 78.96. The corresponding model is DrQA (trained by us) with 33.82M parameters. So, the answer is {\"answer\": \"DrQA (trained by us) with 33.82M parameters\"}" } ], "images": [ "scigen/train/2927.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparing variants with different number of layers. EM stands for exact match score. The scores are multiplied by 100. DrQA uses a much larger pre-trained word embedding resulting in more parameters." ], "reasoning": "To find the model with the highest Dev F1 score, look at the 'Dev F1' column and identify the highest value, which is 78.96. The corresponding model is DrQA (trained by us) with 33.82M parameters.", "question": "Which model has the highest Dev F1 score and how many parameters does it have?", "answer": "So, the answer is {\"answer\": \"DrQA (trained by us) with 33.82M parameters\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence Conv BiDAF (11-51-3 conv layers) has the highest Dev EM score. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nConv BiDAF (11-51-3 conv layers) has the highest Dev EM score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Dev EM scores of all models. The highest Dev EM score is 69.85, achieved by DrQA (trained by us), not Conv BiDAF (11-51-3 conv layers), which has a Dev EM score of 69.49. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2927.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparing variants with different number of layers. EM stands for exact match score. The scores are multiplied by 100. DrQA uses a much larger pre-trained word embedding resulting in more parameters." ], "reasoning": "To verify this statement, compare the Dev EM scores of all models. The highest Dev EM score is 69.85, achieved by DrQA (trained by us), not Conv BiDAF (11-51-3 conv layers), which has a Dev EM score of 69.49.", "statement": "Conv BiDAF (11-51-3 conv layers) has the highest Dev EM score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: TriviaQA Performance. The scores are multiplied by 100. ∗Doc QA (Clark & Gardner, 2017) uses a different training strategy on the whole training set (containing 529K question-answer-evidence tuples), while we follow Joshi et al. (2017) training on only the first 80K question-answer-evidence tuples in the training set.'.\n" }, { "role": "assistant", "content": "Conv DrQA performs slightly worse than DrQA on the Wikipedia split of TriviaQA but is comparable to previous state-of-the-art recurrent networks.On Wikipedia split of TriviaQA, our proposed Conv DrQA is slight worse than our DrQA baseline which beats all previous models, it can still be on a par with the previous state-of-the-art performance of recurrent networks. The numbers are shown in" } ], "images": [ "scigen/train/2928.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: TriviaQA Performance. The scores are multiplied by 100. ∗Doc QA (Clark & Gardner, 2017) uses a different training strategy on the whole training set (containing 529K question-answer-evidence tuples), while we follow Joshi et al. (2017) training on only the first 80K question-answer-evidence tuples in the training set." ], "reasoning": "Conv DrQA performs slightly worse than DrQA on the Wikipedia split of TriviaQA but is comparable to previous state-of-the-art recurrent networks.", "description": "On Wikipedia split of TriviaQA, our proposed Conv DrQA is slight worse than our DrQA baseline which beats all previous models, it can still be on a par with the previous state-of-the-art performance of recurrent networks. The numbers are shown in", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 5: TriviaQA Performance. The scores are multiplied by 100. ∗Doc QA (Clark & Gardner, 2017) uses a different training strategy on the whole training set (containing 529K question-answer-evidence tuples), while we follow Joshi et al. (2017) training on only the first 80K question-answer-evidence tuples in the training set.', unfold the question presented below, detailing every step of your calculation. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model achieved the highest Verified F1 score on the Web dataset?\n" }, { "role": "assistant", "content": "To find the model with the highest Verified F1 score on the Web dataset, look at the Verified F1 column for the Web dataset rows. The highest score is 83.70, achieved by DocQA (Clark & Gardner, 2017). So, the answer is {\"answer\": \"DocQA (Clark & Gardner, 2017)\"}" } ], "images": [ "scigen/train/2928.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: TriviaQA Performance. The scores are multiplied by 100. ∗Doc QA (Clark & Gardner, 2017) uses a different training strategy on the whole training set (containing 529K question-answer-evidence tuples), while we follow Joshi et al. (2017) training on only the first 80K question-answer-evidence tuples in the training set." ], "reasoning": "To find the model with the highest Verified F1 score on the Web dataset, look at the Verified F1 column for the Web dataset rows. The highest score is 83.70, achieved by DocQA (Clark & Gardner, 2017).", "question": "Which model achieved the highest Verified F1 score on the Web dataset?", "answer": "So, the answer is {\"answer\": \"DocQA (Clark & Gardner, 2017)\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Conv DrQA achieved a higher Full EM score than DrQA on the Wikipedia dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nConv DrQA achieved a higher Full EM score than DrQA on the Wikipedia dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Full EM scores of Conv DrQA and DrQA on the Wikipedia dataset. Conv DrQA has a Full EM score of 49.01, while DrQA has a Full EM score of 52.58. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2928.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: TriviaQA Performance. The scores are multiplied by 100. ∗Doc QA (Clark & Gardner, 2017) uses a different training strategy on the whole training set (containing 529K question-answer-evidence tuples), while we follow Joshi et al. (2017) training on only the first 80K question-answer-evidence tuples in the training set." ], "reasoning": "To verify this statement, compare the Full EM scores of Conv DrQA and DrQA on the Wikipedia dataset. Conv DrQA has a Full EM score of 49.01, while DrQA has a Full EM score of 52.58. Therefore, the statement is refuted.", "statement": "Conv DrQA achieved a higher Full EM score than DrQA on the Wikipedia dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5: Evaluation in the semi-supervised training and the transfer learning setups.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The model shows improvements over the baseline BiLSTM in both Semi-supervised Training and Transfer Learning setups, with the highest accuracy achieved in the Transfer Learning setup (M7).So far, we have verified that the effectiveness of the kNN retrieved from the same training set. What if we search the kNN from a dataset of a completely different task? If we consider the text (without the label) information of the kNN from a different task, then this becomes the semi-supervised training setup. If we utilize the label information of the kNN from a different task, where the definition of labels are quite different from the task at hand, then this becomes the transfer learning setup. To reveal the behavior of our model in these two setups, we take the training set of the DBPedia dataset as the corpus to find the kNN for each text in the AG’s News dataset. Here, we should notice that the “attentive-kNN-text” and the “attentive-kNN-label” features are extracted from the kNN from the DBPedia corpus, which is a different classification task. We can see that our model achieves improvements from the baseline (BiLSTM) in both the Semi-supervised Training and the Transfer Learning setups." } ], "images": [ "scigen/train/2929.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Evaluation in the semi-supervised training and the transfer learning setups." ], "reasoning": "The model shows improvements over the baseline BiLSTM in both Semi-supervised Training and Transfer Learning setups, with the highest accuracy achieved in the Transfer Learning setup (M7).", "description": "So far, we have verified that the effectiveness of the kNN retrieved from the same training set. What if we search the kNN from a dataset of a completely different task? If we consider the text (without the label) information of the kNN from a different task, then this becomes the semi-supervised training setup. If we utilize the label information of the kNN from a different task, where the definition of labels are quite different from the task at hand, then this becomes the transfer learning setup. To reveal the behavior of our model in these two setups, we take the training set of the DBPedia dataset as the corpus to find the kNN for each text in the AG’s News dataset. Here, we should notice that the “attentive-kNN-text” and the “attentive-kNN-label” features are extracted from the kNN from the DBPedia corpus, which is a different classification task. We can see that our model achieves improvements from the baseline (BiLSTM) in both the Semi-supervised Training and the Transfer Learning setups.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Evaluation in the semi-supervised training and the transfer learning setups.\nQuestion: Which setup achieved the highest accuracy and what was the accuracy?\nGive a detailed response to the above question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the accuracies in the table, the Transfer Learning setup (M7) achieved the highest accuracy of 93.4. So, the answer is {\"answer\": \"Transfer Learning (M7) with 93.4\"}" } ], "images": [ "scigen/train/2929.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Evaluation in the semi-supervised training and the transfer learning setups." ], "reasoning": "By comparing the accuracies in the table, the Transfer Learning setup (M7) achieved the highest accuracy of 93.4.", "question": "Which setup achieved the highest accuracy and what was the accuracy?", "answer": "So, the answer is {\"answer\": \"Transfer Learning (M7) with 93.4\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The BiLSTM setup achieved the lowest accuracy. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe BiLSTM setup achieved the lowest accuracy.\n" }, { "role": "assistant", "content": "The table shows that the BiLSTM setup has an accuracy of 91.9, which is lower than the accuracies of the other setups. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2929.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Evaluation in the semi-supervised training and the transfer learning setups." ], "reasoning": "The table shows that the BiLSTM setup has an accuracy of 91.9, which is lower than the accuracies of the other setups.", "statement": "The BiLSTM setup achieved the lowest accuracy.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Evaluation on the test sets, and the state-of-the-art models by Zhang, Zhao, and LeCun (2015)a, Xiao and Cho (2016)b, Conneau et al. (2016)c, and Joulin et al. (2016)d.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The BiLSTM with kNN model outperforms the baseline on all datasets and even beats the VDCNN model on AG’s News, DBpedia, and Yahoo! Answers datasets, demonstrating its effectiveness.We construct two models to evaluate on all of the test sets. We find that by utilizing the kNN memory, our BiLSTM with kNN model outperforms the baseline on all datasets. Our model even beats the VDCNN model on the AG’s News, DBpeida and Yahoo! Answers datasets, which shows the effectiveness of our method. Moreover, our kNN memory can be easily adapted into these more complex neural network models." } ], "images": [ "scigen/train/2930.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Evaluation on the test sets, and the state-of-the-art models by Zhang, Zhao, and LeCun (2015)a, Xiao and Cho (2016)b, Conneau et al. (2016)c, and Joulin et al. (2016)d." ], "reasoning": "The BiLSTM with kNN model outperforms the baseline on all datasets and even beats the VDCNN model on AG’s News, DBpedia, and Yahoo! Answers datasets, demonstrating its effectiveness.", "description": "We construct two models to evaluate on all of the test sets. We find that by utilizing the kNN memory, our BiLSTM with kNN model outperforms the baseline on all datasets. Our model even beats the VDCNN model on the AG’s News, DBpeida and Yahoo! Answers datasets, which shows the effectiveness of our method. Moreover, our kNN memory can be easily adapted into these more complex neural network models.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 3: Evaluation on the test sets, and the state-of-the-art models by Zhang, Zhao, and LeCun (2015)a, Xiao and Cho (2016)b, Conneau et al. (2016)c, and Joulin et al. (2016)d.\nQuestion: Which model has the highest accuracy on the DBP dataset?\n" }, { "role": "assistant", "content": "By examining the DBP column, the highest accuracy is 98.7, achieved by the ngrams-TFIDF model. So, the answer is {\"answer\": \"ngrams-TFIDF\"}." } ], "images": [ "scigen/train/2930.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Evaluation on the test sets, and the state-of-the-art models by Zhang, Zhao, and LeCun (2015)a, Xiao and Cho (2016)b, Conneau et al. (2016)c, and Joulin et al. (2016)d." ], "reasoning": "By examining the DBP column, the highest accuracy is 98.7, achieved by the ngrams-TFIDF model.", "question": "Which model has the highest accuracy on the DBP dataset?", "answer": "So, the answer is {\"answer\": \"ngrams-TFIDF\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe BiLSTM with kNN model outperforms the VDCNN model on the AG’s News dataset.\n" }, { "role": "assistant", "content": "The table shows that the BiLSTM with kNN model has an accuracy of 94.2 on the AG’s News dataset, while the VDCNN model has an accuracy of 91.3, indicating that the BiLSTM with kNN model outperforms the VDCNN model on this dataset. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2930.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Evaluation on the test sets, and the state-of-the-art models by Zhang, Zhao, and LeCun (2015)a, Xiao and Cho (2016)b, Conneau et al. (2016)c, and Joulin et al. (2016)d." ], "reasoning": "The table shows that the BiLSTM with kNN model has an accuracy of 94.2 on the AG’s News dataset, while the VDCNN model has an accuracy of 91.3, indicating that the BiLSTM with kNN model outperforms the VDCNN model on this dataset.", "statement": "The BiLSTM with kNN model outperforms the VDCNN model on the AG’s News dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Connectivity metrics for similarity graphs. All examples relate each item to twelve neighbors, with Nw word neighbors and Ne embedding neighbors. For comparison, we show the results using both query replacement (as described in the DualNeighbors algorithm) and with the query expansion method suggested in the papers discussed in Section 2.2. The metrics give the (undirected) spectral gap λ2, the proportion of directed pairs of items that are unconnected across directed edges (u.c.), the average distance (dist) between connected pairs of items, the 90th percentile of the in-degree (din0.9), and the 10th percentile of the number of neighbors within three links (ego(3)0.1).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The DualNeighbors algorithm improves document diversity by replacing documents with their closest word embeddings and allows users to adjust the balance between diversity and relevance by varying the number of neighbors.The DualNeighbors algorithm features two crucial differences compared to other word-embedding based query expansion techniques. Splitting the search explicitly into two types of neighbors allows for a better balance between the connectivity and diversity of the recommended documents. Also, replacing the document with its closest word embeddings, rather than augmenting as other approaches have done, significantly improves the diversity of the recommended documents. Additionally, by varying the number of neighbors displayed by each method, users can manually adjust the balance between diversity and relevance in the results. All of the examples use a total of 12 recommendations for consistency. Generally, we see that adding more edges from the (query expansion) word embedding matrix produces a network with a larger algebraic connectivity, lower average distance between document pairs, and larger third-degree ego scores. The distribution of in-degrees becomes increasingly variable, however, as more edges get mapped to a small set of hubs (documents linked to from a very large number of other documents). These two effects combine so that the most connected network using both corpora have 10 edges from word similarities and 2 edges from the word embedding logic. Generally, including at least one word embedding edges makes the network significantly more connected. The hubness of the network slowly becomes an issue as the proportion of embedding edges grows relative to the total number of edges. To illustrate the importance of using query replacement in the word embedding neighbor function, the table also compares our approach (query replacement) to that of query expansion. Therefore, this modification serves as an important contribution and distinguishing feature of our approach." } ], "images": [ "scigen/train/2931.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Connectivity metrics for similarity graphs. All examples relate each item to twelve neighbors, with Nw word neighbors and Ne embedding neighbors. For comparison, we show the results using both query replacement (as described in the DualNeighbors algorithm) and with the query expansion method suggested in the papers discussed in Section 2.2. The metrics give the (undirected) spectral gap λ2, the proportion of directed pairs of items that are unconnected across directed edges (u.c.), the average distance (dist) between connected pairs of items, the 90th percentile of the in-degree (din0.9), and the 10th percentile of the number of neighbors within three links (ego(3)0.1)." ], "reasoning": "The DualNeighbors algorithm improves document diversity by replacing documents with their closest word embeddings and allows users to adjust the balance between diversity and relevance by varying the number of neighbors.", "description": "The DualNeighbors algorithm features two crucial differences compared to other word-embedding based query expansion techniques. Splitting the search explicitly into two types of neighbors allows for a better balance between the connectivity and diversity of the recommended documents. Also, replacing the document with its closest word embeddings, rather than augmenting as other approaches have done, significantly improves the diversity of the recommended documents. Additionally, by varying the number of neighbors displayed by each method, users can manually adjust the balance between diversity and relevance in the results. All of the examples use a total of 12 recommendations for consistency. Generally, we see that adding more edges from the (query expansion) word embedding matrix produces a network with a larger algebraic connectivity, lower average distance between document pairs, and larger third-degree ego scores. The distribution of in-degrees becomes increasingly variable, however, as more edges get mapped to a small set of hubs (documents linked to from a very large number of other documents). These two effects combine so that the most connected network using both corpora have 10 edges from word similarities and 2 edges from the word embedding logic. Generally, including at least one word embedding edges makes the network significantly more connected. The hubness of the network slowly becomes an issue as the proportion of embedding edges grows relative to the total number of edges. To illustrate the importance of using query replacement in the word embedding neighbor function, the table also compares our approach (query replacement) to that of query expansion. Therefore, this modification serves as an important contribution and distinguishing feature of our approach.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nTable title: Table 3: Connectivity metrics for similarity graphs. All examples relate each item to twelve neighbors, with Nw word neighbors and Ne embedding neighbors. For comparison, we show the results using both query replacement (as described in the DualNeighbors algorithm) and with the query expansion method suggested in the papers discussed in Section 2.2. The metrics give the (undirected) spectral gap λ2, the proportion of directed pairs of items that are unconnected across directed edges (u.c.), the average distance (dist) between connected pairs of items, the 90th percentile of the in-degree (din0.9), and the 10th percentile of the number of neighbors within three links (ego(3)0.1).\n\nQuestion: What is the FSA-OWI λ2 value for the Q. Replacement method with 7 neighbors?\n" }, { "role": "assistant", "content": "To find the FSA-OWI λ2 value for the Q. Replacement method with 7 neighbors, locate the row with 'Q. Replacement' and '7' in the 'Nw' column. The corresponding FSA-OWI λ2 value is 0.056. So, the answer is {\"answer\": \"0.056\"}." } ], "images": [ "scigen/train/2931.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Connectivity metrics for similarity graphs. All examples relate each item to twelve neighbors, with Nw word neighbors and Ne embedding neighbors. For comparison, we show the results using both query replacement (as described in the DualNeighbors algorithm) and with the query expansion method suggested in the papers discussed in Section 2.2. The metrics give the (undirected) spectral gap λ2, the proportion of directed pairs of items that are unconnected across directed edges (u.c.), the average distance (dist) between connected pairs of items, the 90th percentile of the in-degree (din0.9), and the 10th percentile of the number of neighbors within three links (ego(3)0.1)." ], "reasoning": "To find the FSA-OWI λ2 value for the Q. Replacement method with 7 neighbors, locate the row with 'Q. Replacement' and '7' in the 'Nw' column. The corresponding FSA-OWI λ2 value is 0.056.", "question": "What is the FSA-OWI λ2 value for the Q. Replacement method with 7 neighbors?", "answer": "So, the answer is {\"answer\": \"0.056\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The Q. Replacement method with 10 neighbors has a Twitter λ2 value of 0.046., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Q. Replacement method with 10 neighbors has a Twitter λ2 value of 0.046.\n" }, { "role": "assistant", "content": "To verify this statement, locate the row with 'Q. Replacement' and '10' in the 'Nw' column. The corresponding Twitter λ2 value is 0.046, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2931.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Connectivity metrics for similarity graphs. All examples relate each item to twelve neighbors, with Nw word neighbors and Ne embedding neighbors. For comparison, we show the results using both query replacement (as described in the DualNeighbors algorithm) and with the query expansion method suggested in the papers discussed in Section 2.2. The metrics give the (undirected) spectral gap λ2, the proportion of directed pairs of items that are unconnected across directed edges (u.c.), the average distance (dist) between connected pairs of items, the 90th percentile of the in-degree (din0.9), and the 10th percentile of the number of neighbors within three links (ego(3)0.1)." ], "reasoning": "To verify this statement, locate the row with 'Q. Replacement' and '10' in the 'Nw' column. The corresponding Twitter λ2 value is 0.046, which matches the statement.", "statement": "The Q. Replacement method with 10 neighbors has a Twitter λ2 value of 0.046.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Comparison of features used for universal background model training. The i-vectors were extraced only from speech frames and have a dimension of 100. Word error rate is given on the full Hub5’00 dataset. The acoustic model is a BLSTM trained on gammatone filterbank features.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Using Gammatone features with a context of 9 frames reduced to 60 dimensions with LDA gives significantly better performance, achieving the lowest WER of 13.5%.In this paper we use a i-vector adapted neural network acoustic model. On each frame we append a i-vector to the 40-dimensional Gammatone Filterbank (GT) Most prior work report that they use Mel-frequency cepstral coefficients (MFCCs) But we noticed that the i-vector adaptation was not sufficiently effective in adapting to test signals when using MFCCs to estimate the i-vectors. Therefore, we compared MFCC to GT features without further processing as well as with concatenated first and second order derivatives and with temporal context and linear discriminant analysis (LDA) for dimension reduction. Using Gammatone features with a context of 9 frames reduced to 60 dimensions with LDA gives us significantly better performance. We did not check if MFCCs would be better if the acoustic model would also be trained on them." } ], "images": [ "scigen/train/2932.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of features used for universal background model training. The i-vectors were extraced only from speech frames and have a dimension of 100. Word error rate is given on the full Hub5’00 dataset. The acoustic model is a BLSTM trained on gammatone filterbank features." ], "reasoning": "Using Gammatone features with a context of 9 frames reduced to 60 dimensions with LDA gives significantly better performance, achieving the lowest WER of 13.5%.", "description": "In this paper we use a i-vector adapted neural network acoustic model. On each frame we append a i-vector to the 40-dimensional Gammatone Filterbank (GT) Most prior work report that they use Mel-frequency cepstral coefficients (MFCCs) But we noticed that the i-vector adaptation was not sufficiently effective in adapting to test signals when using MFCCs to estimate the i-vectors. Therefore, we compared MFCC to GT features without further processing as well as with concatenated first and second order derivatives and with temporal context and linear discriminant analysis (LDA) for dimension reduction. Using Gammatone features with a context of 9 frames reduced to 60 dimensions with LDA gives us significantly better performance. We did not check if MFCCs would be better if the acoustic model would also be trained on them.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 1: Comparison of features used for universal background model training. The i-vectors were extraced only from speech frames and have a dimension of 100. Word error rate is given on the full Hub5’00 dataset. The acoustic model is a BLSTM trained on gammatone filterbank features.', and sequentially solve the question, writing out each step of your reasoning process. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the WER when using i-vectors with Gammatone features and context+LDA?\n" }, { "role": "assistant", "content": "The table shows that using i-vectors with Gammatone features and context+LDA results in a WER of 13.5%. So, the answer is {\"answer\": \"13.5\"}." } ], "images": [ "scigen/train/2932.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of features used for universal background model training. The i-vectors were extraced only from speech frames and have a dimension of 100. Word error rate is given on the full Hub5’00 dataset. The acoustic model is a BLSTM trained on gammatone filterbank features." ], "reasoning": "The table shows that using i-vectors with Gammatone features and context+LDA results in a WER of 13.5%.", "question": "What is the WER when using i-vectors with Gammatone features and context+LDA?", "answer": "So, the answer is {\"answer\": \"13.5\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Using i-vectors with MFCC features results in a WER of 14.3%. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nUsing i-vectors with MFCC features results in a WER of 14.3%.\n" }, { "role": "assistant", "content": "The table indicates that using i-vectors with MFCC features results in a WER of 14.3%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2932.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of features used for universal background model training. The i-vectors were extraced only from speech frames and have a dimension of 100. Word error rate is given on the full Hub5’00 dataset. The acoustic model is a BLSTM trained on gammatone filterbank features." ], "reasoning": "The table indicates that using i-vectors with MFCC features results in a WER of 14.3%.", "statement": "Using i-vectors with MFCC features results in a WER of 14.3%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: WERs (in %) on Hub5’00 for i-vectors of different dimension and normalization based on GT+context+LDA features.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The adaptation performance, measured by WER, varies significantly with different dimensions and normalization methods.It can also be seen, that the adaptation performance dependents significantly on the dimension and normalization." } ], "images": [ "scigen/train/2933.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: WERs (in %) on Hub5’00 for i-vectors of different dimension and normalization based on GT+context+LDA features." ], "reasoning": "The adaptation performance, measured by WER, varies significantly with different dimensions and normalization methods.", "description": "It can also be seen, that the adaptation performance dependents significantly on the dimension and normalization.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: WERs (in %) on Hub5’00 for i-vectors of different dimension and normalization based on GT+context+LDA features.', answer the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich i-vector normalization method achieves the lowest WER for Dimension 200?\n" }, { "role": "assistant", "content": "To find the lowest WER for Dimension 200, compare the WER values in the column for Dimension 200. The lowest value is 13.3, which corresponds to the Square root normalization method. So, the answer is {\"answer\": \"Square root\"}." } ], "images": [ "scigen/train/2933.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: WERs (in %) on Hub5’00 for i-vectors of different dimension and normalization based on GT+context+LDA features." ], "reasoning": "To find the lowest WER for Dimension 200, compare the WER values in the column for Dimension 200. The lowest value is 13.3, which corresponds to the Square root normalization method.", "question": "Which i-vector normalization method achieves the lowest WER for Dimension 200?", "answer": "So, the answer is {\"answer\": \"Square root\"}." }, { "messages": [ { "role": "user", "content": "The Unity normalization method achieves the lowest WER for Dimension 50. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Unity normalization method achieves the lowest WER for Dimension 50.\n" }, { "role": "assistant", "content": "To verify this statement, compare the WER values for Dimension 50. The lowest value is 13.9, which corresponds to the Unity normalization method. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2933.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: WERs (in %) on Hub5’00 for i-vectors of different dimension and normalization based on GT+context+LDA features." ], "reasoning": "To verify this statement, compare the WER values for Dimension 50. The lowest value is 13.9, which corresponds to the Unity normalization method.", "statement": "The Unity normalization method achieves the lowest WER for Dimension 50.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Detailed WERs (in %) on Hub5’00 comparing the influence of i-vectors, SMBR, and environment adaptation with affine transformation (AT). WERs are given for count based language models and RNNLM.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "SMBR training provides larger relative improvements when i-vectors are used in the baseline, similar to the second pass adaptation.Similar to the second pass adaptation also SMBR training provides larger relative improvements if i-vectors are used in the baseline." } ], "images": [ "scigen/train/2934.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Detailed WERs (in %) on Hub5’00 comparing the influence of i-vectors, SMBR, and environment adaptation with affine transformation (AT). WERs are given for count based language models and RNNLM." ], "reasoning": "SMBR training provides larger relative improvements when i-vectors are used in the baseline, similar to the second pass adaptation.", "description": "Similar to the second pass adaptation also SMBR training provides larger relative improvements if i-vectors are used in the baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Detailed WERs (in %) on Hub5’00 comparing the influence of i-vectors, SMBR, and environment adaptation with affine transformation (AT). WERs are given for count based language models and RNNLM.\nQuestion: What is the Hub5'00 Avg. score for the configuration with AT, i-vectors, and SMBR all set to yes and using LSTM?\nGive a detailed response to the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the Hub5'00 Avg. score for the configuration with AT, i-vectors, and SMBR all set to yes and using LSTM, locate the row where all these conditions are met and read the value under the Hub5'00 Avg. column. So, the answer is {\"answer\": \"10.2\"}." } ], "images": [ "scigen/train/2934.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Detailed WERs (in %) on Hub5’00 comparing the influence of i-vectors, SMBR, and environment adaptation with affine transformation (AT). WERs are given for count based language models and RNNLM." ], "reasoning": "To find the Hub5'00 Avg. score for the configuration with AT, i-vectors, and SMBR all set to yes and using LSTM, locate the row where all these conditions are met and read the value under the Hub5'00 Avg. column.", "question": "What is the Hub5'00 Avg. score for the configuration with AT, i-vectors, and SMBR all set to yes and using LSTM?", "answer": "So, the answer is {\"answer\": \"10.2\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe lowest Hub5'00 Avg. score is achieved with AT, i-vectors, and SMBR all set to yes and using LSTM.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Hub5'00 Avg. scores for all configurations and identify the lowest score. The lowest score is 10.2, which is achieved with AT, i-vectors, and SMBR all set to yes and using LSTM. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2934.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Detailed WERs (in %) on Hub5’00 comparing the influence of i-vectors, SMBR, and environment adaptation with affine transformation (AT). WERs are given for count based language models and RNNLM." ], "reasoning": "To verify this statement, compare the Hub5'00 Avg. scores for all configurations and identify the lowest score. The lowest score is 10.2, which is achieved with AT, i-vectors, and SMBR all set to yes and using LSTM.", "statement": "The lowest Hub5'00 Avg. score is achieved with AT, i-vectors, and SMBR all set to yes and using LSTM.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 9: F-scores of the baseline and the both-retrained models relative to role types on the two data sets. We only list results of the PCFGLA-parser-based system.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The F-scores for both models on A3 and A4 are omitted, and all semantic roles show significant performance improvements.Given that the F-scores for both models are equal to 0 on A3 and A4, we just omit this part. From the figure we can observe that, all the semantic roles achieve significant improvements in performances." } ], "images": [ "scigen/train/2935.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: F-scores of the baseline and the both-retrained models relative to role types on the two data sets. We only list results of the PCFGLA-parser-based system." ], "reasoning": "The F-scores for both models on A3 and A4 are omitted, and all semantic roles show significant performance improvements.", "description": "Given that the F-scores for both models are equal to 0 on A3 and A4, we just omit this part. From the figure we can observe that, all the semantic roles achieve significant improvements in performances.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 9: F-scores of the baseline and the both-retrained models relative to role types on the two data sets. We only list results of the PCFGLA-parser-based system.' to sequentially solve the question presented below. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhat is the F-score improvement for A0 in L1 when both models are retrained compared to the baseline?\n" }, { "role": "assistant", "content": "The F-score for A0 in L1 baseline is 69.49, and when both models are retrained, it is 73.15. The improvement is 73.15 - 69.49. So, the answer is {\"answer\": \"3.66\"}" } ], "images": [ "scigen/train/2935.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: F-scores of the baseline and the both-retrained models relative to role types on the two data sets. We only list results of the PCFGLA-parser-based system." ], "reasoning": "The F-score for A0 in L1 baseline is 69.49, and when both models are retrained, it is 73.15. The improvement is 73.15 - 69.49.", "question": "What is the F-score improvement for A0 in L1 when both models are retrained compared to the baseline?", "answer": "So, the answer is {\"answer\": \"3.66\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe F-score for A1 in L2 is higher when both models are retrained compared to the baseline.\n" }, { "role": "assistant", "content": "The F-score for A1 in L2 baseline is 71.21, and when both models are retrained, it is 74.75, which is higher. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2935.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: F-scores of the baseline and the both-retrained models relative to role types on the two data sets. We only list results of the PCFGLA-parser-based system." ], "reasoning": "The F-score for A1 in L2 baseline is 71.21, and when both models are retrained, it is 74.75, which is higher.", "statement": "The F-score for A1 in L2 is higher when both models are retrained compared to the baseline.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Inter-annotator agreement.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Annotators achieved F-scores above 95 for L1 texts, indicating high qualification, while L2 sentences had slightly lower F-scores above 90, showing good understanding with minor rules needed.For inter-annotator agreement, we evaluate the precision (P), recall (R), and F1-score (F) of the semantic labels given by the two annotators. All L1 texts have F-score above 95, and we take this as a reflection that our annotators are qualified. F-scores on L2 sentences are all above 90, just a little bit lower than those of L1, indicating that L2 sentences can be greatly understood by native speakers. Only modest rules are needed to handle some tricky phenomena:" } ], "images": [ "scigen/train/2936.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Inter-annotator agreement." ], "reasoning": "Annotators achieved F-scores above 95 for L1 texts, indicating high qualification, while L2 sentences had slightly lower F-scores above 90, showing good understanding with minor rules needed.", "description": "For inter-annotator agreement, we evaluate the precision (P), recall (R), and F1-score (F) of the semantic labels given by the two annotators. All L1 texts have F-score above 95, and we take this as a reflection that our annotators are qualified. F-scores on L2 sentences are all above 90, just a little bit lower than those of L1, indicating that L2 sentences can be greatly understood by native speakers. Only modest rules are needed to handle some tricky phenomena:", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 1: Inter-annotator agreement.\nQuestion: What is the F-score for L2 sentences in Japanese (JPN)?\n" }, { "role": "assistant", "content": "To find the F-score for L2 sentences in Japanese, locate the row labeled 'JPN' and 'L2' and check the value under the 'F' column. So, the answer is {\"answer\": \"96.77\"}." } ], "images": [ "scigen/train/2936.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Inter-annotator agreement." ], "reasoning": "To find the F-score for L2 sentences in Japanese, locate the row labeled 'JPN' and 'L2' and check the value under the 'F' column.", "question": "What is the F-score for L2 sentences in Japanese (JPN)?", "answer": "So, the answer is {\"answer\": \"96.77\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe recall for L2 sentences in Russian (RUS) is above 94.\n" }, { "role": "assistant", "content": "To verify this statement, locate the row labeled 'RUS' and 'L2' and check the value under the 'R' column. The recall value is 94.08, which is above 94. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2936.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Inter-annotator agreement." ], "reasoning": "To verify this statement, locate the row labeled 'RUS' and 'L2' and check the value under the 'R' column. The recall value is 94.08, which is above 94.", "statement": "The recall for L2 sentences in Russian (RUS) is above 94.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Performances of the syntax-based and neural syntax-agnostic SRL systems on the L1 and L2 data. “ALL” denotes the overall performance.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Syntax-based systems show smaller F-score drops from L1 to L2 compared to syntax-agnostic systems, indicating better robustness in handling learner texts.For all systems, significant decreases on different mother languages can be consistently observed, highlighting the weakness of applying L1-sentence-trained systems to process learner texts. Comparing the two syntax-based systems with the neural syntax-agnostic system, we find that the overall ΔF, which denotes the F-score drop from L1 to L2, is smaller in the syntax-based framework than in the syntax-agnostic system. On English, Japanese and Russian L2 sentences, the syntax-based system has better performances though it sometimes works worse on the corresponding L1 sentences, indicating the syntax-based systems are more robust when handling learner texts." } ], "images": [ "scigen/train/2937.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performances of the syntax-based and neural syntax-agnostic SRL systems on the L1 and L2 data. “ALL” denotes the overall performance." ], "reasoning": "Syntax-based systems show smaller F-score drops from L1 to L2 compared to syntax-agnostic systems, indicating better robustness in handling learner texts.", "description": "For all systems, significant decreases on different mother languages can be consistently observed, highlighting the weakness of applying L1-sentence-trained systems to process learner texts. Comparing the two syntax-based systems with the neural syntax-agnostic system, we find that the overall ΔF, which denotes the F-score drop from L1 to L2, is smaller in the syntax-based framework than in the syntax-agnostic system. On English, Japanese and Russian L2 sentences, the syntax-based system has better performances though it sometimes works worse on the corresponding L1 sentences, indicating the syntax-based systems are more robust when handling learner texts.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: Performances of the syntax-based and neural syntax-agnostic SRL systems on the L1 and L2 data. “ALL” denotes the overall performance.' and methodically tackle the question that follows. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich system shows a smaller overall ΔF from L1 to L2, syntax-based or syntax-agnostic?\n" }, { "role": "assistant", "content": "By comparing the overall ΔF values in the table, the syntax-based system has a smaller ΔF (-3.59) compared to the syntax-agnostic system (-5.41), indicating it is more robust. So, the answer is {\"answer\": \"syntax-based\"}" } ], "images": [ "scigen/train/2937.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performances of the syntax-based and neural syntax-agnostic SRL systems on the L1 and L2 data. “ALL” denotes the overall performance." ], "reasoning": "By comparing the overall ΔF values in the table, the syntax-based system has a smaller ΔF (-3.59) compared to the syntax-agnostic system (-5.41), indicating it is more robust.", "question": "Which system shows a smaller overall ΔF from L1 to L2, syntax-based or syntax-agnostic?", "answer": "So, the answer is {\"answer\": \"syntax-based\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe syntax-agnostic system has a smaller ΔF on Japanese L2 sentences compared to the syntax-based system.\n" }, { "role": "assistant", "content": "The table shows that the ΔF for the syntax-agnostic system on Japanese L2 sentences is -7.00, while for the syntax-based system it is -3.37. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2937.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performances of the syntax-based and neural syntax-agnostic SRL systems on the L1 and L2 data. “ALL” denotes the overall performance." ], "reasoning": "The table shows that the ΔF for the syntax-agnostic system on Japanese L2 sentences is -7.00, while for the syntax-based system it is -3.37. Therefore, the statement is refuted.", "statement": "The syntax-agnostic system has a smaller ΔF on Japanese L2 sentences compared to the syntax-based system.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 7: Accuracies different PCFGLA-parser-based models on the two test data sets.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Retraining the PCFGLA parser with SRL-consistent sentence pairs improves SRL-oriented syntactic analysis for both L1 and L2 sentences, and combining retrained parser and classifier yields further improvement.Since both the syntactic parser and the SRL classifier can be retrained and thus enhanced, we report the individual impact as well as the combined one. We can clearly see that when the PCFGLA parser is retrained with the SRL-consistent sentence pairs, it is able to provide better SRL-oriented syntactic analysis for the L2 sentences as well as their corrections, which are essentially L1 sentences. The outputs of the L1 sentences that are generated by the deep SRL system are also useful for improving the linear SRL classifier. A non-obvious fact is that such a retrained model yields better analysis for not only L1 but also L2 sentences. Fortunately, combining both results in further improvement." } ], "images": [ "scigen/train/2938.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Accuracies different PCFGLA-parser-based models on the two test data sets." ], "reasoning": "Retraining the PCFGLA parser with SRL-consistent sentence pairs improves SRL-oriented syntactic analysis for both L1 and L2 sentences, and combining retrained parser and classifier yields further improvement.", "description": "Since both the syntactic parser and the SRL classifier can be retrained and thus enhanced, we report the individual impact as well as the combined one. We can clearly see that when the PCFGLA parser is retrained with the SRL-consistent sentence pairs, it is able to provide better SRL-oriented syntactic analysis for the L2 sentences as well as their corrections, which are essentially L1 sentences. The outputs of the L1 sentences that are generated by the deep SRL system are also useful for improving the linear SRL classifier. A non-obvious fact is that such a retrained model yields better analysis for not only L1 but also L2 sentences. Fortunately, combining both results in further improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 7: Accuracies different PCFGLA-parser-based models on the two test data sets.\nQuestion: What is the F1 score for L2 sentences when both the parser and classifier are retrained?\nGive a detailed response to the above question. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the F1 score for L2 sentences when both the parser and classifier are retrained, look at the row labeled 'L2 Both retrained' and find the value under the 'F' column. So, the answer is {\"answer\": \"72.06\"}." } ], "images": [ "scigen/train/2938.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Accuracies different PCFGLA-parser-based models on the two test data sets." ], "reasoning": "To find the F1 score for L2 sentences when both the parser and classifier are retrained, look at the row labeled 'L2 Both retrained' and find the value under the 'F' column.", "question": "What is the F1 score for L2 sentences when both the parser and classifier are retrained?", "answer": "So, the answer is {\"answer\": \"72.06\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nRetraining both the parser and classifier results in the highest F1 score for L1 sentences.\n" }, { "role": "assistant", "content": "To verify this statement, compare the F1 scores for L1 sentences across all conditions. The highest F1 score for L1 sentences is found in the 'Both retrained' row. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2938.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Accuracies different PCFGLA-parser-based models on the two test data sets." ], "reasoning": "To verify this statement, compare the F1 scores for L1 sentences across all conditions. The highest F1 score for L1 sentences is found in the 'Both retrained' row.", "statement": "Retraining both the parser and classifier results in the highest F1 score for L1 sentences.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 8: Accuracies of different neural-parser-based models on the two test data sets.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Retraining the SRL classifier is more effective than using SRL-consistent trees, especially on L2 data, highlighting the strengths of different parsing frameworks.Different from the PCFGLA model, the SRL-consistent trees only yield a slight improvement on the L2 data. On the contrary, retraining the SRL classifier is much more effective. This experiment highlights the different strengths of different frameworks for parsing. Though for standard in-domain test, the neural parser performs better and thus is more and more popular, for some other scenarios, the PCFGLA model is stronger." } ], "images": [ "scigen/train/2939.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Accuracies of different neural-parser-based models on the two test data sets." ], "reasoning": "Retraining the SRL classifier is more effective than using SRL-consistent trees, especially on L2 data, highlighting the strengths of different parsing frameworks.", "description": "Different from the PCFGLA model, the SRL-consistent trees only yield a slight improvement on the L2 data. On the contrary, retraining the SRL classifier is much more effective. This experiment highlights the different strengths of different frameworks for parsing. Though for standard in-domain test, the neural parser performs better and thus is more and more popular, for some other scenarios, the PCFGLA model is stronger.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 8: Accuracies of different neural-parser-based models on the two test data sets.', solve this question and give detail solutions. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the F-score for the L2 data when both the parser and classifier are retrained?\n" }, { "role": "assistant", "content": "The table shows that for L2 data, when both the parser and classifier are retrained, the F-score is 71.36. So, the answer is {\"answer\": \"71.36\"}." } ], "images": [ "scigen/train/2939.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Accuracies of different neural-parser-based models on the two test data sets." ], "reasoning": "The table shows that for L2 data, when both the parser and classifier are retrained, the F-score is 71.36.", "question": "What is the F-score for the L2 data when both the parser and classifier are retrained?", "answer": "So, the answer is {\"answer\": \"71.36\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe baseline F-score for L1 data is higher than the F-score for L2 data when both the parser and classifier are retrained.\n" }, { "role": "assistant", "content": "The table shows that the baseline F-score for L1 data is 73.69, which is higher than the F-score for L2 data when both the parser and classifier are retrained, which is 71.36. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2939.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Accuracies of different neural-parser-based models on the two test data sets." ], "reasoning": "The table shows that the baseline F-score for L1 data is 73.69, which is higher than the F-score for L2 data when both the parser and classifier are retrained, which is 71.36.", "statement": "The baseline F-score for L1 data is higher than the F-score for L2 data when both the parser and classifier are retrained.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Image datasets for the automatic description generation models. We have split the overview into image description datasets (top) and caption datasets (bottom) – see the main text for an explanation of this distinction.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The table lists various datasets used for automatic image description research, detailing the number of images, texts, judgments, and object annotations for each dataset.There is a wide range of datasets for automatic image description research. The images in these datasets are associated with textual descriptions and differ from each other in certain aspects such as in size, the format of the descriptions and in how the descriptions were collected. Here we review common approaches for collecting datasets, the datasets themselves, and evaluation measures for comparing generated descriptions with ground-truth texts. The readers can also refer to the dataset survey by \\citeAFerraro2015 for an analysis similar to ours. It provides a basic comparison of some of the existing language and vision datasets. It is not limited to automatic image description, and it reports some simple statistics and quality metrics such as perplexity, syntactic complexity, and abstract to concrete word ratios." } ], "images": [ "scigen/train/2940.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Image datasets for the automatic description generation models. We have split the overview into image description datasets (top) and caption datasets (bottom) – see the main text for an explanation of this distinction." ], "reasoning": "The table lists various datasets used for automatic image description research, detailing the number of images, texts, judgments, and object annotations for each dataset.", "description": "There is a wide range of datasets for automatic image description research. The images in these datasets are associated with textual descriptions and differ from each other in certain aspects such as in size, the format of the descriptions and in how the descriptions were collected. Here we review common approaches for collecting datasets, the datasets themselves, and evaluation measures for comparing generated descriptions with ground-truth texts. The readers can also refer to the dataset survey by \\citeAFerraro2015 for an analysis similar to ours. It provides a basic comparison of some of the existing language and vision datasets. It is not limited to automatic image description, and it reports some simple statistics and quality metrics such as perplexity, syntactic complexity, and abstract to concrete word ratios.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Image datasets for the automatic description generation models. We have split the overview into image description datasets (top) and caption datasets (bottom) – see the main text for an explanation of this distinction.\nQuestion: Which dataset has the highest number of images and what is the format of its descriptions?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The dataset with the highest number of images is Déjà-Image Captions Chen2015 with 4,000,000 images. The format of its descriptions varies. So, the answer is {\"answer\": \"Déjà-Image Captions Chen2015, Varies\"}." } ], "images": [ "scigen/train/2940.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Image datasets for the automatic description generation models. We have split the overview into image description datasets (top) and caption datasets (bottom) – see the main text for an explanation of this distinction." ], "reasoning": "The dataset with the highest number of images is Déjà-Image Captions Chen2015 with 4,000,000 images. The format of its descriptions varies.", "question": "Which dataset has the highest number of images and what is the format of its descriptions?", "answer": "So, the answer is {\"answer\": \"Déjà-Image Captions Chen2015, Varies\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe MS COCO Lin2014a dataset has complete object annotations.\n" }, { "role": "assistant", "content": "The table shows that the MS COCO Lin2014a dataset has partial object annotations, not complete. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2940.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Image datasets for the automatic description generation models. We have split the overview into image description datasets (top) and caption datasets (bottom) – see the main text for an explanation of this distinction." ], "reasoning": "The table shows that the MS COCO Lin2014a dataset has partial object annotations, not complete.", "statement": "The MS COCO Lin2014a dataset has complete object annotations.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Objective results of the proposed techniques versus the benchmark approach. MCD and BAP are Mel-Cepstral Distortion and Band Aperiodicity distortion, respectively. V/UV error means frame-level voiced/unvoiced prediction error. Root Mean Squared Error (RMSE) of F0 was calculated on a linear frequency scale.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The proposed methods were evaluated using four objective metrics: MCD, BAP, F0 RMSE, and V/UV error rate, with lower values indicating better performance. The G2P Model performed well on Telugu and Hindi, while the Uni-Grapheme model excelled in Tamil.We used following four objective evaluations to assess the performance of the proposed methods in comparison to the benchmark system. MCD: Mel-Cepstral Distortion (MCD) to measure MCC prediction performance. BAP: to measure distortion of BAPs F0 RMSE: Root Mean Squared Error (RMSE) to measure the accuracy of F0 prediction. The error value was calculated on a linear scale instead of log-scale which was used to model the F0 values. V/UV: to measure voiced/unvoiced error. In all these metrics, a lower value gives the better performance. While the objective metrics do not map directly to perceptual quality, they are often useful for system tuning. As expected, the benchmark model performs well on most metrics. While the G2P Model performs well on Telugu and Hindi, the Uni-Grapheme model does well on Tamil. Overall, the proposed approaches compare favourably with the benchmark." } ], "images": [ "scigen/train/2941.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Objective results of the proposed techniques versus the benchmark approach. MCD and BAP are Mel-Cepstral Distortion and Band Aperiodicity distortion, respectively. V/UV error means frame-level voiced/unvoiced prediction error. Root Mean Squared Error (RMSE) of F0 was calculated on a linear frequency scale." ], "reasoning": "The proposed methods were evaluated using four objective metrics: MCD, BAP, F0 RMSE, and V/UV error rate, with lower values indicating better performance. The G2P Model performed well on Telugu and Hindi, while the Uni-Grapheme model excelled in Tamil.", "description": "We used following four objective evaluations to assess the performance of the proposed methods in comparison to the benchmark system. MCD: Mel-Cepstral Distortion (MCD) to measure MCC prediction performance. BAP: to measure distortion of BAPs F0 RMSE: Root Mean Squared Error (RMSE) to measure the accuracy of F0 prediction. The error value was calculated on a linear scale instead of log-scale which was used to model the F0 values. V/UV: to measure voiced/unvoiced error. In all these metrics, a lower value gives the better performance. While the objective metrics do not map directly to perceptual quality, they are often useful for system tuning. As expected, the benchmark model performs well on most metrics. While the G2P Model performs well on Telugu and Hindi, the Uni-Grapheme model does well on Tamil. Overall, the proposed approaches compare favourably with the benchmark.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 3: Objective results of the proposed techniques versus the benchmark approach. MCD and BAP are Mel-Cepstral Distortion and Band Aperiodicity distortion, respectively. V/UV error means frame-level voiced/unvoiced prediction error. Root Mean Squared Error (RMSE) of F0 was calculated on a linear frequency scale.', solve this question and give detail solutions. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model performed best on the MCD metric for Telugu?\n" }, { "role": "assistant", "content": "To determine the best performing model on the MCD metric for Telugu, compare the MCD (dB) values for Telugu across all models. The model with the lowest MCD value is the best performer. So, the answer is {\"answer\": \"G2P Model\"}." } ], "images": [ "scigen/train/2941.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Objective results of the proposed techniques versus the benchmark approach. MCD and BAP are Mel-Cepstral Distortion and Band Aperiodicity distortion, respectively. V/UV error means frame-level voiced/unvoiced prediction error. Root Mean Squared Error (RMSE) of F0 was calculated on a linear frequency scale." ], "reasoning": "To determine the best performing model on the MCD metric for Telugu, compare the MCD (dB) values for Telugu across all models. The model with the lowest MCD value is the best performer.", "question": "Which model performed best on the MCD metric for Telugu?", "answer": "So, the answer is {\"answer\": \"G2P Model\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The Uni-Grapheme model performed best on the V/UV error rate for Hindi., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Uni-Grapheme model performed best on the V/UV error rate for Hindi.\n" }, { "role": "assistant", "content": "To verify this statement, compare the V/UV error rate (%) values for Hindi across all models. The model with the lowest V/UV error rate is the best performer. If the Uni-Grapheme model has the lowest value, the statement is supported. Thus, the answer is {\"answer\": \"not enough info\"}." } ], "images": [ "scigen/train/2941.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Objective results of the proposed techniques versus the benchmark approach. MCD and BAP are Mel-Cepstral Distortion and Band Aperiodicity distortion, respectively. V/UV error means frame-level voiced/unvoiced prediction error. Root Mean Squared Error (RMSE) of F0 was calculated on a linear frequency scale." ], "reasoning": "To verify this statement, compare the V/UV error rate (%) values for Hindi across all models. The model with the lowest V/UV error rate is the best performer. If the Uni-Grapheme model has the lowest value, the statement is supported.", "statement": "The Uni-Grapheme model performed best on the V/UV error rate for Hindi.", "answer": "Thus, the answer is {\"answer\": \"not enough info\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Objective results of the proposed techniques versus the benchmark approach. MCD and BAP are Mel-Cepstral Distortion and Band Aperiodicity distortion, respectively. V/UV error means frame-level voiced/unvoiced prediction error. Root Mean Squared Error (RMSE) of F0 was calculated on a linear frequency scale.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The proposed methods were evaluated using four metrics: MCD, BAP, F0 RMSE, and V/UV, where lower values indicate better performance. The G2P Model performs well on Telugu and Hindi, while the Uni-Grapheme model excels in Tamil, and overall, the proposed methods compare favorably with the benchmark.We used following four objective evaluations to assess the performance of the proposed methods in comparison to the benchmark system. MCD: Mel-Cepstral Distortion (MCD) to measure MCC prediction performance. BAP: to measure distortion of BAPs F0 RMSE: Root Mean Squared Error (RMSE) to measure the accuracy of F0 prediction. The error value was calculated on a linear scale instead of log-scale which was used to model the F0 values. V/UV: to measure voiced/unvoiced error. In all these metrics, a lower value gives the better performance. While the objective metrics do not map directly to perceptual quality, they are often useful for system tuning. As expected, the benchmark model performs well on most metrics. While the G2P Model performs well on Telugu and Hindi, the Uni-Grapheme model does well on Tamil. Overall, the proposed approaches compare favourably with the benchmark." } ], "images": [ "scigen/train/2942.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Objective results of the proposed techniques versus the benchmark approach. MCD and BAP are Mel-Cepstral Distortion and Band Aperiodicity distortion, respectively. V/UV error means frame-level voiced/unvoiced prediction error. Root Mean Squared Error (RMSE) of F0 was calculated on a linear frequency scale." ], "reasoning": "The proposed methods were evaluated using four metrics: MCD, BAP, F0 RMSE, and V/UV, where lower values indicate better performance. The G2P Model performs well on Telugu and Hindi, while the Uni-Grapheme model excels in Tamil, and overall, the proposed methods compare favorably with the benchmark.", "description": "We used following four objective evaluations to assess the performance of the proposed methods in comparison to the benchmark system. MCD: Mel-Cepstral Distortion (MCD) to measure MCC prediction performance. BAP: to measure distortion of BAPs F0 RMSE: Root Mean Squared Error (RMSE) to measure the accuracy of F0 prediction. The error value was calculated on a linear scale instead of log-scale which was used to model the F0 values. V/UV: to measure voiced/unvoiced error. In all these metrics, a lower value gives the better performance. While the objective metrics do not map directly to perceptual quality, they are often useful for system tuning. As expected, the benchmark model performs well on most metrics. While the G2P Model performs well on Telugu and Hindi, the Uni-Grapheme model does well on Tamil. Overall, the proposed approaches compare favourably with the benchmark.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: Objective results of the proposed techniques versus the benchmark approach. MCD and BAP are Mel-Cepstral Distortion and Band Aperiodicity distortion, respectively. V/UV error means frame-level voiced/unvoiced prediction error. Root Mean Squared Error (RMSE) of F0 was calculated on a linear frequency scale.' to sequentially solve the question presented below. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich model has the lowest combined average score for V/UV?\n" }, { "role": "assistant", "content": "To find the model with the lowest combined average score for V/UV, compare the V/UV scores in the 'Combined Average' column for each model. The scores are: Uni-Grapheme-TTS (10.87), Multi-Grapheme-TTS (11.29), G2P-TTS (10.77), and Benchmark (10.86). The lowest score is 10.77, which belongs to the G2P-TTS model. So, the answer is {\"answer\": \"G2P-TTS\"}." } ], "images": [ "scigen/train/2942.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Objective results of the proposed techniques versus the benchmark approach. MCD and BAP are Mel-Cepstral Distortion and Band Aperiodicity distortion, respectively. V/UV error means frame-level voiced/unvoiced prediction error. Root Mean Squared Error (RMSE) of F0 was calculated on a linear frequency scale." ], "reasoning": "To find the model with the lowest combined average score for V/UV, compare the V/UV scores in the 'Combined Average' column for each model. The scores are: Uni-Grapheme-TTS (10.87), Multi-Grapheme-TTS (11.29), G2P-TTS (10.77), and Benchmark (10.86). The lowest score is 10.77, which belongs to the G2P-TTS model.", "question": "Which model has the lowest combined average score for V/UV?", "answer": "So, the answer is {\"answer\": \"G2P-TTS\"}." }, { "messages": [ { "role": "user", "content": "The Benchmark model has the lowest combined average score for MCD. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Benchmark model has the lowest combined average score for MCD.\n" }, { "role": "assistant", "content": "To verify this statement, compare the MCD scores in the 'Combined Average' column for each model. The scores are: Uni-Grapheme-TTS (4.82), Multi-Grapheme-TTS (4.81), G2P-TTS (4.80), and Benchmark (4.52). The lowest score is 4.52, which belongs to the Benchmark model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2942.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Objective results of the proposed techniques versus the benchmark approach. MCD and BAP are Mel-Cepstral Distortion and Band Aperiodicity distortion, respectively. V/UV error means frame-level voiced/unvoiced prediction error. Root Mean Squared Error (RMSE) of F0 was calculated on a linear frequency scale." ], "reasoning": "To verify this statement, compare the MCD scores in the 'Combined Average' column for each model. The scores are: Uni-Grapheme-TTS (4.82), Multi-Grapheme-TTS (4.81), G2P-TTS (4.80), and Benchmark (4.52). The lowest score is 4.52, which belongs to the Benchmark model.", "statement": "The Benchmark model has the lowest combined average score for MCD.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Objective results of the proposed techniques versus the benchmark approach. MCD and BAP are Mel-Cepstral Distortion and Band Aperiodicity distortion, respectively. V/UV error means frame-level voiced/unvoiced prediction error. Root Mean Squared Error (RMSE) of F0 was calculated on a linear frequency scale.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The proposed methods were evaluated using MCD, BAP, F0 RMSE, and V/UV metrics, where lower values indicate better performance. The Uni-Grapheme model performs well on Tamil, while the G2P model excels in Telugu and Hindi.We used following four objective evaluations to assess the performance of the proposed methods in comparison to the benchmark system. MCD: Mel-Cepstral Distortion (MCD) to measure MCC prediction performance. BAP: to measure distortion of BAPs F0 RMSE: Root Mean Squared Error (RMSE) to measure the accuracy of F0 prediction. The error value was calculated on a linear scale instead of log-scale which was used to model the F0 values. V/UV: to measure voiced/unvoiced error. In all these metrics, a lower value gives the better performance. While the objective metrics do not map directly to perceptual quality, they are often useful for system tuning. As expected, the benchmark model performs well on most metrics. While the G2P Model performs well on Telugu and Hindi, the Uni-Grapheme model does well on Tamil. Overall, the proposed approaches compare favourably with the benchmark." } ], "images": [ "scigen/train/2943.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Objective results of the proposed techniques versus the benchmark approach. MCD and BAP are Mel-Cepstral Distortion and Band Aperiodicity distortion, respectively. V/UV error means frame-level voiced/unvoiced prediction error. Root Mean Squared Error (RMSE) of F0 was calculated on a linear frequency scale." ], "reasoning": "The proposed methods were evaluated using MCD, BAP, F0 RMSE, and V/UV metrics, where lower values indicate better performance. The Uni-Grapheme model performs well on Tamil, while the G2P model excels in Telugu and Hindi.", "description": "We used following four objective evaluations to assess the performance of the proposed methods in comparison to the benchmark system. MCD: Mel-Cepstral Distortion (MCD) to measure MCC prediction performance. BAP: to measure distortion of BAPs F0 RMSE: Root Mean Squared Error (RMSE) to measure the accuracy of F0 prediction. The error value was calculated on a linear scale instead of log-scale which was used to model the F0 values. V/UV: to measure voiced/unvoiced error. In all these metrics, a lower value gives the better performance. While the objective metrics do not map directly to perceptual quality, they are often useful for system tuning. As expected, the benchmark model performs well on most metrics. While the G2P Model performs well on Telugu and Hindi, the Uni-Grapheme model does well on Tamil. Overall, the proposed approaches compare favourably with the benchmark.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 3: Objective results of the proposed techniques versus the benchmark approach. MCD and BAP are Mel-Cepstral Distortion and Band Aperiodicity distortion, respectively. V/UV error means frame-level voiced/unvoiced prediction error. Root Mean Squared Error (RMSE) of F0 was calculated on a linear frequency scale.', and tackle the question below in a sequential manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the lowest Mel-Cepstral Distortion (MCD) value?\n" }, { "role": "assistant", "content": "To find the model with the lowest MCD value, compare the MCD values of all models. The Uni-Grapheme-TTS model has the lowest MCD value of 4.87. So, the answer is {\"answer\": \"Uni-Grapheme-TTS\"}." } ], "images": [ "scigen/train/2943.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Objective results of the proposed techniques versus the benchmark approach. MCD and BAP are Mel-Cepstral Distortion and Band Aperiodicity distortion, respectively. V/UV error means frame-level voiced/unvoiced prediction error. Root Mean Squared Error (RMSE) of F0 was calculated on a linear frequency scale." ], "reasoning": "To find the model with the lowest MCD value, compare the MCD values of all models. The Uni-Grapheme-TTS model has the lowest MCD value of 4.87.", "question": "Which model has the lowest Mel-Cepstral Distortion (MCD) value?", "answer": "So, the answer is {\"answer\": \"Uni-Grapheme-TTS\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Benchmark model has the lowest F0 RMSE value.\n" }, { "role": "assistant", "content": "To verify this statement, compare the F0 RMSE values of all models. The Benchmark model has an F0 RMSE value of 4.79, which is the lowest among all models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2943.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Objective results of the proposed techniques versus the benchmark approach. MCD and BAP are Mel-Cepstral Distortion and Band Aperiodicity distortion, respectively. V/UV error means frame-level voiced/unvoiced prediction error. Root Mean Squared Error (RMSE) of F0 was calculated on a linear frequency scale." ], "reasoning": "To verify this statement, compare the F0 RMSE values of all models. The Benchmark model has an F0 RMSE value of 4.79, which is the lowest among all models.", "statement": "The Benchmark model has the lowest F0 RMSE value.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: AP16-OL7 and AP17-OL3 Data Profile', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "AP17-OL3 contains recordings in both quiet and noisy environments for Russian, Korean, and Japanese, while other languages were recorded only in quiet environments.For Mandarin, Cantonese, Vietnamese and Indonesia, the recording was conducted in a quiet environment. As for Russian, Korean and Japanese, there are 2 recording sessions for each speaker: the first session was recorded in a quiet environment and the second was recorded in a noisy environment. The sentences of each language in AP17-OL3 are randomly selected from the original M2ASR corpus. The data volume for each language in AP17-OL3 is about 10 hours of speech signals recorded in reading style. The signals were recorded by mobile phones, with a sampling rate of 16 kHz and a sample size of 16 bits. We selected 1800 utterances for each language as the development set (AP17-OL3-dev), and the rest is used as the training set (AP17-OL3-train). The test set of each language involves 1800 utterances, and is provided separately and denoted by AP17-OL3-test. Compared to AP16-OL7, AP17-OL3 contains much more variations in terms of recording conditions and the number of speakers, which may inevitably increase the difficulty of the challenge task." } ], "images": [ "scigen/train/2944.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: AP16-OL7 and AP17-OL3 Data Profile" ], "reasoning": "AP17-OL3 contains recordings in both quiet and noisy environments for Russian, Korean, and Japanese, while other languages were recorded only in quiet environments.", "description": "For Mandarin, Cantonese, Vietnamese and Indonesia, the recording was conducted in a quiet environment. As for Russian, Korean and Japanese, there are 2 recording sessions for each speaker: the first session was recorded in a quiet environment and the second was recorded in a noisy environment. The sentences of each language in AP17-OL3 are randomly selected from the original M2ASR corpus. The data volume for each language in AP17-OL3 is about 10 hours of speech signals recorded in reading style. The signals were recorded by mobile phones, with a sampling rate of 16 kHz and a sample size of 16 bits. We selected 1800 utterances for each language as the development set (AP17-OL3-dev), and the rest is used as the training set (AP17-OL3-train). The test set of each language involves 1800 utterances, and is provided separately and denoted by AP17-OL3-test. Compared to AP16-OL7, AP17-OL3 contains much more variations in terms of recording conditions and the number of speakers, which may inevitably increase the difficulty of the challenge task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: AP16-OL7 and AP17-OL3 Data Profile'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nHow many utterances are there in total for the AP17-OL3 training set?\n" }, { "role": "assistant", "content": "The description states that the data volume for each language in AP17-OL3 is about 10 hours of speech signals. The table shows the number of utterances per speaker and the number of speakers for each language. Summing up the total utterances for all languages in AP17-OL3 gives the total number of utterances. So, the answer is {\"answer\": \"11100\"}" } ], "images": [ "scigen/train/2944.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: AP16-OL7 and AP17-OL3 Data Profile" ], "reasoning": "The description states that the data volume for each language in AP17-OL3 is about 10 hours of speech signals. The table shows the number of utterances per speaker and the number of speakers for each language. Summing up the total utterances for all languages in AP17-OL3 gives the total number of utterances.", "question": "How many utterances are there in total for the AP17-OL3 training set?", "answer": "So, the answer is {\"answer\": \"11100\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The AP17-OL3 dataset includes recordings of Kazakh in China. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe AP17-OL3 dataset includes recordings of Kazakh in China.\n" }, { "role": "assistant", "content": "The table lists Kazakh in China as part of the AP17-OL3 dataset, confirming the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2944.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: AP16-OL7 and AP17-OL3 Data Profile" ], "reasoning": "The table lists Kazakh in China as part of the AP17-OL3 dataset, confirming the statement.", "statement": "The AP17-OL3 dataset includes recordings of Kazakh in China.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6: METEOR results. In bold, best results. Num stands for Number and Gen, for Gender', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The best results are achieved when classifying number and gender and rescoring number in the large corpus, with a gain of up to +0.7 METEOR.We observe improvement in most cases (when classifying number, gender, both and rescoring), but best results are obtained when classifying number and gender and rescoring number in the large corpus, obtaining a gain up to +0.7 METEOR." } ], "images": [ "scigen/train/2945.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: METEOR results. In bold, best results. Num stands for Number and Gen, for Gender" ], "reasoning": "The best results are achieved when classifying number and gender and rescoring number in the large corpus, with a gain of up to +0.7 METEOR.", "description": "We observe improvement in most cases (when classifying number, gender, both and rescoring), but best results are obtained when classifying number and gender and rescoring number in the large corpus, obtaining a gain up to +0.7 METEOR.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the UN Result for the large set when using Num&Gen and Rescoring?\nSolve the above question based on the table titled 'Table 6: METEOR results. In bold, best results. Num stands for Number and Gen, for Gender'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the UN Result for the large set when using Num&Gen and Rescoring, look at the row corresponding to '+Num&Gen Rescoring' under the 'Large' set. The value in the 'UN Result' column is 57.74. So, the answer is {\"answer\": \"57.74\"}" } ], "images": [ "scigen/train/2945.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: METEOR results. In bold, best results. Num stands for Number and Gen, for Gender" ], "reasoning": "To find the UN Result for the large set when using Num&Gen and Rescoring, look at the row corresponding to '+Num&Gen Rescoring' under the 'Large' set. The value in the 'UN Result' column is 57.74.", "question": "What is the UN Result for the large set when using Num&Gen and Rescoring?", "answer": "So, the answer is {\"answer\": \"57.74\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe UN Result for the small set when using Num&Gen and Rescoring is 55.56.\n" }, { "role": "assistant", "content": "To verify this statement, look at the row corresponding to '+Num&Gen Rescoring(Num)' under the 'Small' set. The value in the 'UN Result' column is 55.56, which matches the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2945.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: METEOR results. In bold, best results. Num stands for Number and Gen, for Gender" ], "reasoning": "To verify this statement, look at the row corresponding to '+Num&Gen Rescoring(Num)' under the 'Small' set. The value in the 'UN Result' column is 55.56, which matches the statement.", "statement": "The UN Result for the small set when using Num&Gen and Rescoring is 55.56.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Values of the different parameters of the classifiers'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The best window size for number and gender classification tasks are 9 and 7 words, respectively, with filter sizes slightly smaller at 7 and 5.Regarding classification parameters, experimentation has shown that number and gender classification tasks have different requirements. The best window size is 9 and 7 words for number and gender, respectively. In both cases increasing this size lowers the accuracy of the system. The vocabulary size is fixed as a trade-off between giving enough information to the system to perform the classification while removing enough words to train the classifier for unknown words. The embedding size of 128 results in stable training, while further increasing this value augmented the training time and hardware cost. The filter size in the convolutional layer reached best results when it was slightly smaller than the window size, being 7 and 5 the best values for number and gender classification, respectively. Finally, increasing LSTM nodes up to 70 improved significantly for both classifiers." } ], "images": [ "scigen/train/2946.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Values of the different parameters of the classifiers" ], "reasoning": "The best window size for number and gender classification tasks are 9 and 7 words, respectively, with filter sizes slightly smaller at 7 and 5.", "description": "Regarding classification parameters, experimentation has shown that number and gender classification tasks have different requirements. The best window size is 9 and 7 words for number and gender, respectively. In both cases increasing this size lowers the accuracy of the system. The vocabulary size is fixed as a trade-off between giving enough information to the system to perform the classification while removing enough words to train the classifier for unknown words. The embedding size of 128 results in stable training, while further increasing this value augmented the training time and hardware cost. The filter size in the convolutional layer reached best results when it was slightly smaller than the window size, being 7 and 5 the best values for number and gender classification, respectively. Finally, increasing LSTM nodes up to 70 improved significantly for both classifiers.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 3: Values of the different parameters of the classifiers\n\nQuestion: What is the best filter size for the small gender classification task?\n" }, { "role": "assistant", "content": "The table shows that the best filter size for the small gender classification task is 5. So, the answer is {\"answer\": \"5\"}" } ], "images": [ "scigen/train/2946.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Values of the different parameters of the classifiers" ], "reasoning": "The table shows that the best filter size for the small gender classification task is 5.", "question": "What is the best filter size for the small gender classification task?", "answer": "So, the answer is {\"answer\": \"5\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The best window size for large number classification is 9 words., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe best window size for large number classification is 9 words.\n" }, { "role": "assistant", "content": "The table confirms that the best window size for large number classification is indeed 9 words. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2946.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Values of the different parameters of the classifiers" ], "reasoning": "The table confirms that the best window size for large number classification is indeed 9 words.", "statement": "The best window size for large number classification is 9 words.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Figure 4: Left: F1 scores on Shared Entity and Shared Relation negatives. The negative examples are for the Query : (Neil Herron, occupation, ?). Right: Macro-avg accuracy on the levy2017zero relation extraction dataset. We split the results based on frequency of the relations in our WikiData training data. DrKIT-all spans refers to a variant of our model which selects from all spans in the corpus, instead of only entity-linked mentions.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "DrKIT outperforms BERT in both shared-entity and shared-relation tasks, particularly excelling in the shared-entity task.We report results on 2 subsets of relations in addition to all relations. The Rare subset comprises of relations with frequencies < 5 in the training data while the ’Frequent’ subset contains the rest. DrKIT on entity-mentions consistently outperforms the other phrase-based models showing the benefit of indexing only entity-mentions in single-hop questions over all spans. Note that DrKit-entities has a high Hits@1 performance on the Rare relations subset, showing that there is generalization to less frequent data due to the natural language representations of entities and relations. In the first experiment, we train and test on shared-entity negatives. Good performance here means the BERT model being probed" } ], "images": [ "scigen/train/2947.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 4: Left: F1 scores on Shared Entity and Shared Relation negatives. The negative examples are for the Query : (Neil Herron, occupation, ?). Right: Macro-avg accuracy on the levy2017zero relation extraction dataset. We split the results based on frequency of the relations in our WikiData training data. DrKIT-all spans refers to a variant of our model which selects from all spans in the corpus, instead of only entity-linked mentions." ], "reasoning": "DrKIT outperforms BERT in both shared-entity and shared-relation tasks, particularly excelling in the shared-entity task.", "description": "We report results on 2 subsets of relations in addition to all relations. The Rare subset comprises of relations with frequencies < 5 in the training data while the ’Frequent’ subset contains the rest. DrKIT on entity-mentions consistently outperforms the other phrase-based models showing the benefit of indexing only entity-mentions in single-hop questions over all spans. Note that DrKit-entities has a high Hits@1 performance on the Rare relations subset, showing that there is generalization to less frequent data due to the natural language representations of entities and relations. In the first experiment, we train and test on shared-entity negatives. Good performance here means the BERT model being probed", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Figure 4: Left: F1 scores on Shared Entity and Shared Relation negatives. The negative examples are for the Query : (Neil Herron, occupation, ?). Right: Macro-avg accuracy on the levy2017zero relation extraction dataset. We split the results based on frequency of the relations in our WikiData training data. DrKIT-all spans refers to a variant of our model which selects from all spans in the corpus, instead of only entity-linked mentions.' and proceed to solve the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model performs better on the shared-entity task, and what is its score?\n" }, { "role": "assistant", "content": "The table shows that DrKIT has a higher score (0.876) compared to BERT (0.850) on the shared-entity task. So, the answer is {\"answer\": \"DrKIT with a score of 0.876\"}" } ], "images": [ "scigen/train/2947.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 4: Left: F1 scores on Shared Entity and Shared Relation negatives. The negative examples are for the Query : (Neil Herron, occupation, ?). Right: Macro-avg accuracy on the levy2017zero relation extraction dataset. We split the results based on frequency of the relations in our WikiData training data. DrKIT-all spans refers to a variant of our model which selects from all spans in the corpus, instead of only entity-linked mentions." ], "reasoning": "The table shows that DrKIT has a higher score (0.876) compared to BERT (0.850) on the shared-entity task.", "question": "Which model performs better on the shared-entity task, and what is its score?", "answer": "So, the answer is {\"answer\": \"DrKIT with a score of 0.876\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim BERT outperforms DrKIT on the shared-relation task., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nBERT outperforms DrKIT on the shared-relation task.\n" }, { "role": "assistant", "content": "The table shows that DrKIT has a higher score (0.846) compared to BERT (0.715) on the shared-relation task. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2947.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 4: Left: F1 scores on Shared Entity and Shared Relation negatives. The negative examples are for the Query : (Neil Herron, occupation, ?). Right: Macro-avg accuracy on the levy2017zero relation extraction dataset. We split the results based on frequency of the relations in our WikiData training data. DrKIT-all spans refers to a variant of our model which selects from all spans in the corpus, instead of only entity-linked mentions." ], "reasoning": "The table shows that DrKIT has a higher score (0.846) compared to BERT (0.715) on the shared-relation task.", "statement": "BERT outperforms DrKIT on the shared-relation task.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: (Left) MetaQA and (Right) WikiData Hits @1 for 1-3 hop sub-tasks. ots: off-the-shelf without re-training. †: obtained from sun2019pullnet. cascade: adapted to multi-hop setting by repeatedly applying Eq. 2. pre: pre-trained on slot-filling. e2e: end-to-end trained on single-hop and multi-hop queries.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "DrKIT demonstrates superior performance in multi-hop question answering without intermediate supervision, outperforming PullNet in 2-hop and 3-hop cases.DrKIT outperforms the prior state-of-the-art by a large margin in the 2-hop and 3-hop cases. The strongest prior method, PullNet (sun2019pullnet; sun2018open), uses a graph neural network model with learned iterative retrieval from the corpus to answer multi-hop questions. It uses the MetaQA KB during training to identify shortest paths between the question entity and answer entity, which are used to supervise the text retrieval and reading modules. DrKIT, on the other hand, has strong performance without such supervision, demonstrating its capability for end-to-end learning. (Adding the same intermediate supervision to DrKIT does not even consistently improve performance—it gives DrKIT a small lift on 1- and 2-hop questions but does not help for 3-hop questions.)" } ], "images": [ "scigen/train/2948.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: (Left) MetaQA and (Right) WikiData Hits @1 for 1-3 hop sub-tasks. ots: off-the-shelf without re-training. †: obtained from sun2019pullnet. cascade: adapted to multi-hop setting by repeatedly applying Eq. 2. pre: pre-trained on slot-filling. e2e: end-to-end trained on single-hop and multi-hop queries." ], "reasoning": "DrKIT demonstrates superior performance in multi-hop question answering without intermediate supervision, outperforming PullNet in 2-hop and 3-hop cases.", "description": "DrKIT outperforms the prior state-of-the-art by a large margin in the 2-hop and 3-hop cases. The strongest prior method, PullNet (sun2019pullnet; sun2018open), uses a graph neural network model with learned iterative retrieval from the corpus to answer multi-hop questions. It uses the MetaQA KB during training to identify shortest paths between the question entity and answer entity, which are used to supervise the text retrieval and reading modules. DrKIT, on the other hand, has strong performance without such supervision, demonstrating its capability for end-to-end learning. (Adding the same intermediate supervision to DrKIT does not even consistently improve performance—it gives DrKIT a small lift on 1- and 2-hop questions but does not help for 3-hop questions.)", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model has the highest performance in the MetaQA 3-hop case?\nSolve the above question based on the table titled 'Table 1: (Left) MetaQA and (Right) WikiData Hits @1 for 1-3 hop sub-tasks. ots: off-the-shelf without re-training. †: obtained from sun2019pullnet. cascade: adapted to multi-hop setting by repeatedly applying Eq. 2. pre: pre-trained on slot-filling. e2e: end-to-end trained on single-hop and multi-hop queries.'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the MetaQA 3-hop column, DrKIT (e2e) has the highest score of 0.876. So, the answer is {\"answer\": \"DrKIT (e2e)\"}" } ], "images": [ "scigen/train/2948.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: (Left) MetaQA and (Right) WikiData Hits @1 for 1-3 hop sub-tasks. ots: off-the-shelf without re-training. †: obtained from sun2019pullnet. cascade: adapted to multi-hop setting by repeatedly applying Eq. 2. pre: pre-trained on slot-filling. e2e: end-to-end trained on single-hop and multi-hop queries." ], "reasoning": "By examining the MetaQA 3-hop column, DrKIT (e2e) has the highest score of 0.876.", "question": "Which model has the highest performance in the MetaQA 3-hop case?", "answer": "So, the answer is {\"answer\": \"DrKIT (e2e)\"}" }, { "messages": [ { "role": "user", "content": "PullNet outperforms DrKIT (e2e) in the MetaQA 2-hop case. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nPullNet outperforms DrKIT (e2e) in the MetaQA 2-hop case.\n" }, { "role": "assistant", "content": "The table shows that DrKIT (e2e) has a score of 0.860 in the MetaQA 2-hop case, while PullNet has a score of 0.810, indicating that DrKIT (e2e) outperforms PullNet. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2948.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: (Left) MetaQA and (Right) WikiData Hits @1 for 1-3 hop sub-tasks. ots: off-the-shelf without re-training. †: obtained from sun2019pullnet. cascade: adapted to multi-hop setting by repeatedly applying Eq. 2. pre: pre-trained on slot-filling. e2e: end-to-end trained on single-hop and multi-hop queries." ], "reasoning": "The table shows that DrKIT (e2e) has a score of 0.860 in the MetaQA 2-hop case, while PullNet has a score of 0.810, indicating that DrKIT (e2e) outperforms PullNet.", "statement": "PullNet outperforms DrKIT (e2e) in the MetaQA 2-hop case.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: (Left) MetaQA and (Right) WikiData Hits @1 for 1-3 hop sub-tasks. ots: off-the-shelf without re-training. †: obtained from sun2019pullnet. cascade: adapted to multi-hop setting by repeatedly applying Eq. 2. pre: pre-trained on slot-filling. e2e: end-to-end trained on single-hop and multi-hop queries.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "DrKIT demonstrates superior performance in multi-hop question answering without intermediate supervision, outperforming prior methods like PullNet.DrKIT outperforms the prior state-of-the-art by a large margin in the 2-hop and 3-hop cases. The strongest prior method, PullNet (sun2019pullnet; sun2018open), uses a graph neural network model with learned iterative retrieval from the corpus to answer multi-hop questions. It uses the MetaQA KB during training to identify shortest paths between the question entity and answer entity, which are used to supervise the text retrieval and reading modules. DrKIT, on the other hand, has strong performance without such supervision, demonstrating its capability for end-to-end learning. (Adding the same intermediate supervision to DrKIT does not even consistently improve performance—it gives DrKIT a small lift on 1- and 2-hop questions but does not help for 3-hop questions.)" } ], "images": [ "scigen/train/2949.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: (Left) MetaQA and (Right) WikiData Hits @1 for 1-3 hop sub-tasks. ots: off-the-shelf without re-training. †: obtained from sun2019pullnet. cascade: adapted to multi-hop setting by repeatedly applying Eq. 2. pre: pre-trained on slot-filling. e2e: end-to-end trained on single-hop and multi-hop queries." ], "reasoning": "DrKIT demonstrates superior performance in multi-hop question answering without intermediate supervision, outperforming prior methods like PullNet.", "description": "DrKIT outperforms the prior state-of-the-art by a large margin in the 2-hop and 3-hop cases. The strongest prior method, PullNet (sun2019pullnet; sun2018open), uses a graph neural network model with learned iterative retrieval from the corpus to answer multi-hop questions. It uses the MetaQA KB during training to identify shortest paths between the question entity and answer entity, which are used to supervise the text retrieval and reading modules. DrKIT, on the other hand, has strong performance without such supervision, demonstrating its capability for end-to-end learning. (Adding the same intermediate supervision to DrKIT does not even consistently improve performance—it gives DrKIT a small lift on 1- and 2-hop questions but does not help for 3-hop questions.)", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: (Left) MetaQA and (Right) WikiData Hits @1 for 1-3 hop sub-tasks. ots: off-the-shelf without re-training. †: obtained from sun2019pullnet. cascade: adapted to multi-hop setting by repeatedly applying Eq. 2. pre: pre-trained on slot-filling. e2e: end-to-end trained on single-hop and multi-hop queries.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest performance in the WikiData 3-hop case?\n" }, { "role": "assistant", "content": "By comparing the values in the WikiData 3-hop column, DrKIT (e2e) has the highest performance with a score of 0.244. So, the answer is {\"answer\": \"DrKIT (e2e)\"}" } ], "images": [ "scigen/train/2949.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: (Left) MetaQA and (Right) WikiData Hits @1 for 1-3 hop sub-tasks. ots: off-the-shelf without re-training. †: obtained from sun2019pullnet. cascade: adapted to multi-hop setting by repeatedly applying Eq. 2. pre: pre-trained on slot-filling. e2e: end-to-end trained on single-hop and multi-hop queries." ], "reasoning": "By comparing the values in the WikiData 3-hop column, DrKIT (e2e) has the highest performance with a score of 0.244.", "question": "Which model has the highest performance in the WikiData 3-hop case?", "answer": "So, the answer is {\"answer\": \"DrKIT (e2e)\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nDrKIT (e2e) outperforms all other models in the WikiData 2-hop case.\n" }, { "role": "assistant", "content": "By examining the WikiData 2-hop column, DrKIT (e2e) has the highest score of 0.469, which is greater than the scores of all other models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2949.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: (Left) MetaQA and (Right) WikiData Hits @1 for 1-3 hop sub-tasks. ots: off-the-shelf without re-training. †: obtained from sun2019pullnet. cascade: adapted to multi-hop setting by repeatedly applying Eq. 2. pre: pre-trained on slot-filling. e2e: end-to-end trained on single-hop and multi-hop queries." ], "reasoning": "By examining the WikiData 2-hop column, DrKIT (e2e) has the highest score of 0.469, which is greater than the scores of all other models listed.", "statement": "DrKIT (e2e) outperforms all other models in the WikiData 2-hop case.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: (Left) Retrieval performance on the HotpotQA benchmark dev set. Q/s denotes the number of queries per second during inference on a single 16-core CPU. Accuracy @k is the fraction where both the correct passages are retrieved in the top k. †: Baselines obtained from das-etal-2019-multi. For DrKIT, we report the performance when the index is pretrained using the WikiData KB alone, the HotpotQA training questions alone, or using both. ∗: Measured on different machines with similar specs. (Right) Overall performance on the HotpotQA task, when passing 10 retrieved passages to a downstream reading comprehension model (yang2018hotpotqa). ‡: From das-etal-2019-multi. ⋄: From qi2019answering. †: Results on the dev set.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "DrKIT shows significant improvements in accuracy and query speed compared to the entity-centric IR baseline, especially at higher retrievals.(Left). We measure the accuracy @k retrievals, which is the fraction of questions for which both the required passages are in the top k retrieved ones. We see an improvement in accuracy across the board, with much higher gains @2 and @5. The main baseline is the entity-centric IR approach which runs a BERT-based re-ranker on 200 pairs of passages for each question. Importantly, DrKIT also improves by over 10x in terms of queries per second during inference. Note that the inference time is measured using a batch size of 1 for both models for fair comparison. DrKIT can be easily run with batch sizes up to 40, but the entity centric IR baseline cannot due to the large number of runs of BERT for each query. When comparing different datasets for pretraining the index, there is not much difference between using the WikiData KB, or the HotpotQA questions. The latter has a better accuracy @2, but overall the best performance is when using a combination of both. While there is a significant improvement over the baseline which uses a TFIDF based retrieval, we see only a small improvement over the passages retrieved by the entity-centric IR baseline, despite the significantly improved accuracy @10 of DrKIT. Among the 33% questions where the top 10 passages do not contain both the correct passages, for around 20% the passage containing the answer is also missing. We conjecture this percentage is lower for the entity-centric IR baseline, and the downstream model is able to answer some of these questions without the other supporting passage." } ], "images": [ "scigen/train/2950.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: (Left) Retrieval performance on the HotpotQA benchmark dev set. Q/s denotes the number of queries per second during inference on a single 16-core CPU. Accuracy @k is the fraction where both the correct passages are retrieved in the top k. †: Baselines obtained from das-etal-2019-multi. For DrKIT, we report the performance when the index is pretrained using the WikiData KB alone, the HotpotQA training questions alone, or using both. ∗: Measured on different machines with similar specs. (Right) Overall performance on the HotpotQA task, when passing 10 retrieved passages to a downstream reading comprehension model (yang2018hotpotqa). ‡: From das-etal-2019-multi. ⋄: From qi2019answering. †: Results on the dev set." ], "reasoning": "DrKIT shows significant improvements in accuracy and query speed compared to the entity-centric IR baseline, especially at higher retrievals.", "description": "(Left). We measure the accuracy @k retrievals, which is the fraction of questions for which both the required passages are in the top k retrieved ones. We see an improvement in accuracy across the board, with much higher gains @2 and @5. The main baseline is the entity-centric IR approach which runs a BERT-based re-ranker on 200 pairs of passages for each question. Importantly, DrKIT also improves by over 10x in terms of queries per second during inference. Note that the inference time is measured using a batch size of 1 for both models for fair comparison. DrKIT can be easily run with batch sizes up to 40, but the entity centric IR baseline cannot due to the large number of runs of BERT for each query. When comparing different datasets for pretraining the index, there is not much difference between using the WikiData KB, or the HotpotQA questions. The latter has a better accuracy @2, but overall the best performance is when using a combination of both. While there is a significant improvement over the baseline which uses a TFIDF based retrieval, we see only a small improvement over the passages retrieved by the entity-centric IR baseline, despite the significantly improved accuracy @10 of DrKIT. Among the 33% questions where the top 10 passages do not contain both the correct passages, for around 20% the passage containing the answer is also missing. We conjecture this percentage is lower for the entity-centric IR baseline, and the downstream model is able to answer some of these questions without the other supporting passage.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: (Left) Retrieval performance on the HotpotQA benchmark dev set. Q/s denotes the number of queries per second during inference on a single 16-core CPU. Accuracy @k is the fraction where both the correct passages are retrieved in the top k. †: Baselines obtained from das-etal-2019-multi. For DrKIT, we report the performance when the index is pretrained using the WikiData KB alone, the HotpotQA training questions alone, or using both. ∗: Measured on different machines with similar specs. (Right) Overall performance on the HotpotQA task, when passing 10 retrieved passages to a downstream reading comprehension model (yang2018hotpotqa). ‡: From das-etal-2019-multi. ⋄: From qi2019answering. †: Results on the dev set.', answer the question below by showing each progressive step toward the answer. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest accuracy at retrieving the top 5 passages?\n" }, { "role": "assistant", "content": "By comparing the 'Accuracy @5' column, DrKIT (Combined) has the highest accuracy at 0.603. So, the answer is {\"answer\": \"DrKIT (Combined)\"}" } ], "images": [ "scigen/train/2950.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: (Left) Retrieval performance on the HotpotQA benchmark dev set. Q/s denotes the number of queries per second during inference on a single 16-core CPU. Accuracy @k is the fraction where both the correct passages are retrieved in the top k. †: Baselines obtained from das-etal-2019-multi. For DrKIT, we report the performance when the index is pretrained using the WikiData KB alone, the HotpotQA training questions alone, or using both. ∗: Measured on different machines with similar specs. (Right) Overall performance on the HotpotQA task, when passing 10 retrieved passages to a downstream reading comprehension model (yang2018hotpotqa). ‡: From das-etal-2019-multi. ⋄: From qi2019answering. †: Results on the dev set." ], "reasoning": "By comparing the 'Accuracy @5' column, DrKIT (Combined) has the highest accuracy at 0.603.", "question": "Which model has the highest accuracy at retrieving the top 5 passages?", "answer": "So, the answer is {\"answer\": \"DrKIT (Combined)\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim DrKIT (WikiData) has the highest accuracy at retrieving the top 20 passages., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nDrKIT (WikiData) has the highest accuracy at retrieving the top 20 passages.\n" }, { "role": "assistant", "content": "The table shows that both DrKIT (WikiData) and DrKIT (Combined) have the same highest accuracy of 0.710 at retrieving the top 20 passages. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2950.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: (Left) Retrieval performance on the HotpotQA benchmark dev set. Q/s denotes the number of queries per second during inference on a single 16-core CPU. Accuracy @k is the fraction where both the correct passages are retrieved in the top k. †: Baselines obtained from das-etal-2019-multi. For DrKIT, we report the performance when the index is pretrained using the WikiData KB alone, the HotpotQA training questions alone, or using both. ∗: Measured on different machines with similar specs. (Right) Overall performance on the HotpotQA task, when passing 10 retrieved passages to a downstream reading comprehension model (yang2018hotpotqa). ‡: From das-etal-2019-multi. ⋄: From qi2019answering. †: Results on the dev set." ], "reasoning": "The table shows that both DrKIT (WikiData) and DrKIT (Combined) have the same highest accuracy of 0.710 at retrieving the top 20 passages.", "statement": "DrKIT (WikiData) has the highest accuracy at retrieving the top 20 passages.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: (Left) Retrieval performance on the HotpotQA benchmark dev set. Q/s denotes the number of queries per second during inference on a single 16-core CPU. Accuracy @k is the fraction where both the correct passages are retrieved in the top k. †: Baselines obtained from das-etal-2019-multi. For DrKIT, we report the performance when the index is pretrained using the WikiData KB alone, the HotpotQA training questions alone, or using both. ∗: Measured on different machines with similar specs. (Right) Overall performance on the HotpotQA task, when passing 10 retrieved passages to a downstream reading comprehension model (yang2018hotpotqa). ‡: From das-etal-2019-multi. ⋄: From qi2019answering. †: Results on the dev set.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "DrKIT shows significant improvement in query speed and accuracy, especially at higher retrievals, compared to the entity-centric IR baseline.(Left). We measure the accuracy @k retrievals, which is the fraction of questions for which both the required passages are in the top k retrieved ones. We see an improvement in accuracy across the board, with much higher gains @2 and @5. The main baseline is the entity-centric IR approach which runs a BERT-based re-ranker on 200 pairs of passages for each question. Importantly, DrKIT also improves by over 10x in terms of queries per second during inference. Note that the inference time is measured using a batch size of 1 for both models for fair comparison. DrKIT can be easily run with batch sizes up to 40, but the entity centric IR baseline cannot due to the large number of runs of BERT for each query. When comparing different datasets for pretraining the index, there is not much difference between using the WikiData KB, or the HotpotQA questions. The latter has a better accuracy @2, but overall the best performance is when using a combination of both. While there is a significant improvement over the baseline which uses a TFIDF based retrieval, we see only a small improvement over the passages retrieved by the entity-centric IR baseline, despite the significantly improved accuracy @10 of DrKIT. Among the 33% questions where the top 10 passages do not contain both the correct passages, for around 20% the passage containing the answer is also missing. We conjecture this percentage is lower for the entity-centric IR baseline, and the downstream model is able to answer some of these questions without the other supporting passage." } ], "images": [ "scigen/train/2951.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: (Left) Retrieval performance on the HotpotQA benchmark dev set. Q/s denotes the number of queries per second during inference on a single 16-core CPU. Accuracy @k is the fraction where both the correct passages are retrieved in the top k. †: Baselines obtained from das-etal-2019-multi. For DrKIT, we report the performance when the index is pretrained using the WikiData KB alone, the HotpotQA training questions alone, or using both. ∗: Measured on different machines with similar specs. (Right) Overall performance on the HotpotQA task, when passing 10 retrieved passages to a downstream reading comprehension model (yang2018hotpotqa). ‡: From das-etal-2019-multi. ⋄: From qi2019answering. †: Results on the dev set." ], "reasoning": "DrKIT shows significant improvement in query speed and accuracy, especially at higher retrievals, compared to the entity-centric IR baseline.", "description": "(Left). We measure the accuracy @k retrievals, which is the fraction of questions for which both the required passages are in the top k retrieved ones. We see an improvement in accuracy across the board, with much higher gains @2 and @5. The main baseline is the entity-centric IR approach which runs a BERT-based re-ranker on 200 pairs of passages for each question. Importantly, DrKIT also improves by over 10x in terms of queries per second during inference. Note that the inference time is measured using a batch size of 1 for both models for fair comparison. DrKIT can be easily run with batch sizes up to 40, but the entity centric IR baseline cannot due to the large number of runs of BERT for each query. When comparing different datasets for pretraining the index, there is not much difference between using the WikiData KB, or the HotpotQA questions. The latter has a better accuracy @2, but overall the best performance is when using a combination of both. While there is a significant improvement over the baseline which uses a TFIDF based retrieval, we see only a small improvement over the passages retrieved by the entity-centric IR baseline, despite the significantly improved accuracy @10 of DrKIT. Among the 33% questions where the top 10 passages do not contain both the correct passages, for around 20% the passage containing the answer is also missing. We conjecture this percentage is lower for the entity-centric IR baseline, and the downstream model is able to answer some of these questions without the other supporting passage.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2: (Left) Retrieval performance on the HotpotQA benchmark dev set. Q/s denotes the number of queries per second during inference on a single 16-core CPU. Accuracy @k is the fraction where both the correct passages are retrieved in the top k. †: Baselines obtained from das-etal-2019-multi. For DrKIT, we report the performance when the index is pretrained using the WikiData KB alone, the HotpotQA training questions alone, or using both. ∗: Measured on different machines with similar specs. (Right) Overall performance on the HotpotQA task, when passing 10 retrieved passages to a downstream reading comprehension model (yang2018hotpotqa). ‡: From das-etal-2019-multi. ⋄: From qi2019answering. †: Results on the dev set.', unfold the steps to solve the question given next. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nQuestion: Which model has the highest F1 score and what is the value?\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all models in the table, the model with the highest F1 score is identified. So, the answer is {\"answer\": \"Golden Ret, 0.486\"}" } ], "images": [ "scigen/train/2951.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: (Left) Retrieval performance on the HotpotQA benchmark dev set. Q/s denotes the number of queries per second during inference on a single 16-core CPU. Accuracy @k is the fraction where both the correct passages are retrieved in the top k. †: Baselines obtained from das-etal-2019-multi. For DrKIT, we report the performance when the index is pretrained using the WikiData KB alone, the HotpotQA training questions alone, or using both. ∗: Measured on different machines with similar specs. (Right) Overall performance on the HotpotQA task, when passing 10 retrieved passages to a downstream reading comprehension model (yang2018hotpotqa). ‡: From das-etal-2019-multi. ⋄: From qi2019answering. †: Results on the dev set." ], "reasoning": "By comparing the F1 scores of all models in the table, the model with the highest F1 score is identified.", "question": "Which model has the highest F1 score and what is the value?", "answer": "So, the answer is {\"answer\": \"Golden Ret, 0.486\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The DrKIT model has the highest EM score. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe DrKIT model has the highest EM score.\n" }, { "role": "assistant", "content": "By comparing the EM scores of all models in the table, it is evident that the Golden Ret model has the highest EM score, not DrKIT. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2951.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: (Left) Retrieval performance on the HotpotQA benchmark dev set. Q/s denotes the number of queries per second during inference on a single 16-core CPU. Accuracy @k is the fraction where both the correct passages are retrieved in the top k. †: Baselines obtained from das-etal-2019-multi. For DrKIT, we report the performance when the index is pretrained using the WikiData KB alone, the HotpotQA training questions alone, or using both. ∗: Measured on different machines with similar specs. (Right) Overall performance on the HotpotQA task, when passing 10 retrieved passages to a downstream reading comprehension model (yang2018hotpotqa). ‡: From das-etal-2019-multi. ⋄: From qi2019answering. †: Results on the dev set." ], "reasoning": "By comparing the EM scores of all models in the table, it is evident that the Golden Ret model has the highest EM score, not DrKIT.", "statement": "The DrKIT model has the highest EM score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Official leaderboard evaluation on the test set of HotpotQA. #Bert refers to the number of calls to BERT (devlin2018bert) in the model. s/Q denotes seconds per query (using batch size 1) for inference on a single 16-core CPU. Answer, Sup Fact and Joint are the official evaluation metrics for HotpotQA. ∗: This is the minimum number of BERT calls based on model and hyperparameter descriptions in the respective papers. †: Computed using code released by authors, using a batch size of 1. ‡: Estimated based on the number of BERT calls, using 0.8s as the time for one call (without considering overhead due to other computation in the model). ⋄: One call to a 5-layer Transformer, and one call to BERT.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "DrKIT+BERT achieves a joint F1 score of 42.88 and is up to 100 times faster than higher-performing models.Lastly, we feed the top 5 passages retrieved by DrKIT to an improved answer span extraction model based on BERT. This model implements a standard architecture for extracting answers from text, and is trained to predict both the answers and the supporting facts. In terms of accuracy, DrKIT+BERT reaches a modest score of 42.88 joint F1, but is considerably faster (up to 100x) than the models which outperform it." } ], "images": [ "scigen/train/2952.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Official leaderboard evaluation on the test set of HotpotQA. #Bert refers to the number of calls to BERT (devlin2018bert) in the model. s/Q denotes seconds per query (using batch size 1) for inference on a single 16-core CPU. Answer, Sup Fact and Joint are the official evaluation metrics for HotpotQA. ∗: This is the minimum number of BERT calls based on model and hyperparameter descriptions in the respective papers. †: Computed using code released by authors, using a batch size of 1. ‡: Estimated based on the number of BERT calls, using 0.8s as the time for one call (without considering overhead due to other computation in the model). ⋄: One call to a 5-layer Transformer, and one call to BERT." ], "reasoning": "DrKIT+BERT achieves a joint F1 score of 42.88 and is up to 100 times faster than higher-performing models.", "description": "Lastly, we feed the top 5 passages retrieved by DrKIT to an improved answer span extraction model based on BERT. This model implements a standard architecture for extracting answers from text, and is trained to predict both the answers and the supporting facts. In terms of accuracy, DrKIT+BERT reaches a modest score of 42.88 joint F1, but is considerably faster (up to 100x) than the models which outperform it.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: Official leaderboard evaluation on the test set of HotpotQA. #Bert refers to the number of calls to BERT (devlin2018bert) in the model. s/Q denotes seconds per query (using batch size 1) for inference on a single 16-core CPU. Answer, Sup Fact and Joint are the official evaluation metrics for HotpotQA. ∗: This is the minimum number of BERT calls based on model and hyperparameter descriptions in the respective papers. †: Computed using code released by authors, using a batch size of 1. ‡: Estimated based on the number of BERT calls, using 0.8s as the time for one call (without considering overhead due to other computation in the model). ⋄: One call to a 5-layer Transformer, and one call to BERT.' to sequentially solve the question presented below. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich system has the highest Answer EM and what is its runtime in seconds per question?\n" }, { "role": "assistant", "content": "The table shows that Rec Ret has the highest Answer EM of 60.04 and a runtime of 133.2 seconds per question. So, the answer is {\"answer\": \"Rec Ret, 133.2\"}" } ], "images": [ "scigen/train/2952.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Official leaderboard evaluation on the test set of HotpotQA. #Bert refers to the number of calls to BERT (devlin2018bert) in the model. s/Q denotes seconds per query (using batch size 1) for inference on a single 16-core CPU. Answer, Sup Fact and Joint are the official evaluation metrics for HotpotQA. ∗: This is the minimum number of BERT calls based on model and hyperparameter descriptions in the respective papers. †: Computed using code released by authors, using a batch size of 1. ‡: Estimated based on the number of BERT calls, using 0.8s as the time for one call (without considering overhead due to other computation in the model). ⋄: One call to a 5-layer Transformer, and one call to BERT." ], "reasoning": "The table shows that Rec Ret has the highest Answer EM of 60.04 and a runtime of 133.2 seconds per question.", "question": "Which system has the highest Answer EM and what is its runtime in seconds per question?", "answer": "So, the answer is {\"answer\": \"Rec Ret, 133.2\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nDrKIT+BERT is the fastest system in terms of runtime per question.\n" }, { "role": "assistant", "content": "The table indicates that DrKIT+BERT has the lowest runtime per question at 1.3 seconds, making it the fastest system. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2952.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Official leaderboard evaluation on the test set of HotpotQA. #Bert refers to the number of calls to BERT (devlin2018bert) in the model. s/Q denotes seconds per query (using batch size 1) for inference on a single 16-core CPU. Answer, Sup Fact and Joint are the official evaluation metrics for HotpotQA. ∗: This is the minimum number of BERT calls based on model and hyperparameter descriptions in the respective papers. †: Computed using code released by authors, using a batch size of 1. ‡: Estimated based on the number of BERT calls, using 0.8s as the time for one call (without considering overhead due to other computation in the model). ⋄: One call to a 5-layer Transformer, and one call to BERT." ], "reasoning": "The table indicates that DrKIT+BERT has the lowest runtime per question at 1.3 seconds, making it the fastest system.", "statement": "DrKIT+BERT is the fastest system in terms of runtime per question.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Labelled attachment score on 13 UD corpora for [1] kulmizev2019deep with BERT pre-training and two of our models.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The BERT G2GTr+H model outperforms the BERT DepTr+CH model in 11 languages, demonstrating the effectiveness of the Graph2Graph Transformer architecture.As the baseline, we use scores of the model proposed by \\newcitekulmizev2019deep, which uses the deep contextualised word representations of BERT and ELMo Peters_2018 as additional input to their parsing models. Our BERT G2GTr+H model performs better on 11 languages, which confirms again the effectiveness of our Graph2Graph Transformer architecture. Since the proposed model of \\newcitekulmizev2019deep also contains a character-level BiLSTM architecture, which builds additional character-level input embeddings, this model performs better on Turkish and Korean, which are morphologically rich languages. We leave to future work the investigation of different mechanisms to input tokens and handle lexical information." } ], "images": [ "scigen/train/2953.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Labelled attachment score on 13 UD corpora for [1] kulmizev2019deep with BERT pre-training and two of our models." ], "reasoning": "The BERT G2GTr+H model outperforms the BERT DepTr+CH model in 11 languages, demonstrating the effectiveness of the Graph2Graph Transformer architecture.", "description": "As the baseline, we use scores of the model proposed by \\newcitekulmizev2019deep, which uses the deep contextualised word representations of BERT and ELMo Peters_2018 as additional input to their parsing models. Our BERT G2GTr+H model performs better on 11 languages, which confirms again the effectiveness of our Graph2Graph Transformer architecture. Since the proposed model of \\newcitekulmizev2019deep also contains a character-level BiLSTM architecture, which builds additional character-level input embeddings, this model performs better on Turkish and Korean, which are morphologically rich languages. We leave to future work the investigation of different mechanisms to input tokens and handle lexical information.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: Labelled attachment score on 13 UD corpora for [1] kulmizev2019deep with BERT pre-training and two of our models.', unfold the steps to solve the question given next. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nQuestion: Which model performs better on Turkish and why?\n" }, { "role": "assistant", "content": "The BERT DepTr+CH model performs better on Turkish with a score of 62.09 compared to the BERT G2GTr+H model's score of 62.20. This is because the BERT DepTr+CH model includes a character-level BiLSTM architecture, which is beneficial for morphologically rich languages like Turkish. So, the answer is {\"answer\": \"BERT DepTr+CH\"}." } ], "images": [ "scigen/train/2953.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Labelled attachment score on 13 UD corpora for [1] kulmizev2019deep with BERT pre-training and two of our models." ], "reasoning": "The BERT DepTr+CH model performs better on Turkish with a score of 62.09 compared to the BERT G2GTr+H model's score of 62.20. This is because the BERT DepTr+CH model includes a character-level BiLSTM architecture, which is beneficial for morphologically rich languages like Turkish.", "question": "Which model performs better on Turkish and why?", "answer": "So, the answer is {\"answer\": \"BERT DepTr+CH\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The BERT G2GTr+H model performs better on all languages compared to the BERT DepTr+CH model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe BERT G2GTr+H model performs better on all languages compared to the BERT DepTr+CH model.\n" }, { "role": "assistant", "content": "The table shows that the BERT G2GTr+H model does not perform better on all languages. For example, the BERT DepTr+CH model performs better on Turkish (62.09 vs. 62.20) and Korean (76.79 vs. 80.17). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2953.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Labelled attachment score on 13 UD corpora for [1] kulmizev2019deep with BERT pre-training and two of our models." ], "reasoning": "The table shows that the BERT G2GTr+H model does not perform better on all languages. For example, the BERT DepTr+CH model performs better on Turkish (62.09 vs. 62.20) and Korean (76.79 vs. 80.17).", "statement": "The BERT G2GTr+H model performs better on all languages compared to the BERT DepTr+CH model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Results on English WSJ Treebank Stanford dependencies. * and ∘ indicate statistically significant improvement (α=0.01) over the model in the row above. Bold indicates not significantly different from the best result in that block.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "BERT initialization significantly improves performance, with up to 32% LAS relative error reduction for G2GTr+H, confirming its effectiveness in enhancing Transformer models with graph input methods.Adding BERT initialisation results in large improvements for all the variants, with up to 32% LAS relative error reduction (RER), for G2GTr+H. This confirms that BERT initialisation helps Transformer with our graph input method, and supports the naturalness of inputting graph relations into the attention mechanism." } ], "images": [ "scigen/train/2954.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results on English WSJ Treebank Stanford dependencies. * and ∘ indicate statistically significant improvement (α=0.01) over the model in the row above. Bold indicates not significantly different from the best result in that block." ], "reasoning": "BERT initialization significantly improves performance, with up to 32% LAS relative error reduction for G2GTr+H, confirming its effectiveness in enhancing Transformer models with graph input methods.", "description": "Adding BERT initialisation results in large improvements for all the variants, with up to 32% LAS relative error reduction (RER), for G2GTr+H. This confirms that BERT initialisation helps Transformer with our graph input method, and supports the naturalness of inputting graph relations into the attention mechanism.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: Results on English WSJ Treebank Stanford dependencies. * and ∘ indicate statistically significant improvement (α=0.01) over the model in the row above. Bold indicates not significantly different from the best result in that block.'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the highest Dev Set LAS achieved with BERT initialization?\n" }, { "role": "assistant", "content": "To find the highest Dev Set LAS with BERT initialization, look at the rows with 'BERT' and identify the maximum value in the 'Dev Set LAS' column. The highest value is 92.55. So, the answer is {\"answer\": \"92.55\"}" } ], "images": [ "scigen/train/2954.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results on English WSJ Treebank Stanford dependencies. * and ∘ indicate statistically significant improvement (α=0.01) over the model in the row above. Bold indicates not significantly different from the best result in that block." ], "reasoning": "To find the highest Dev Set LAS with BERT initialization, look at the rows with 'BERT' and identify the maximum value in the 'Dev Set LAS' column. The highest value is 92.55.", "question": "What is the highest Dev Set LAS achieved with BERT initialization?", "answer": "So, the answer is {\"answer\": \"92.55\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nBERT initialization results in the highest Test Set LAS for G2GTr+H.\n" }, { "role": "assistant", "content": "Check the 'Test Set LAS' column for the row 'BERT G2GTr+H'. The value is 93.66, which is the highest among all rows with BERT initialization. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2954.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results on English WSJ Treebank Stanford dependencies. * and ∘ indicate statistically significant improvement (α=0.01) over the model in the row above. Bold indicates not significantly different from the best result in that block." ], "reasoning": "Check the 'Test Set LAS' column for the row 'BERT G2GTr+H'. The value is 93.66, which is the highest among all rows with BERT initialization.", "statement": "BERT initialization results in the highest Test Set LAS for G2GTr+H.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Comparisons to SoTA on the test set of English WSJ Treebank Stanford dependencies.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The BERT G2GTr+H model significantly outperforms the previous state-of-the-art models in LAS, achieving 93.66%.Both with and without BERT initialisation, the same pattern appears across variants. Adding an embedding of the history sequence of parser actions (DepTr+H) results in a significant improvement (34%/33% LAS RER without/with BERT) compared to the DepTr model. Adding explicit modelling of the composition of sub-phrases (DepTr+CH) results in further improvement with BERT (8.73% LAS RER). The most directly comparable model is the stack-LSTM model of \\newcitedyer-etal-2015-transition, which we used as the basis for our model. The G2GTr model reaches better accuracy and adding BERT greatly improves over the stack-LSTM. Other models include techniques that we could easily add to our model, such as beam search andor-etal-2016-globally; weiss-etal-2015-structured, dynamic oracle ballesteros-carlini-2013-maltdiver; kiperwasser-goldberg-2016-simple, and joint training with a PoS tagger yang2017joint. Nonetheless, compared to the previous state-of-the-art, our BERT G2GTr and BERT G2GTr+H models perform significantly better." } ], "images": [ "scigen/train/2955.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparisons to SoTA on the test set of English WSJ Treebank Stanford dependencies." ], "reasoning": "The BERT G2GTr+H model significantly outperforms the previous state-of-the-art models in LAS, achieving 93.66%.", "description": "Both with and without BERT initialisation, the same pattern appears across variants. Adding an embedding of the history sequence of parser actions (DepTr+H) results in a significant improvement (34%/33% LAS RER without/with BERT) compared to the DepTr model. Adding explicit modelling of the composition of sub-phrases (DepTr+CH) results in further improvement with BERT (8.73% LAS RER). The most directly comparable model is the stack-LSTM model of \\newcitedyer-etal-2015-transition, which we used as the basis for our model. The G2GTr model reaches better accuracy and adding BERT greatly improves over the stack-LSTM. Other models include techniques that we could easily add to our model, such as beam search andor-etal-2016-globally; weiss-etal-2015-structured, dynamic oracle ballesteros-carlini-2013-maltdiver; kiperwasser-goldberg-2016-simple, and joint training with a PoS tagger yang2017joint. Nonetheless, compared to the previous state-of-the-art, our BERT G2GTr and BERT G2GTr+H models perform significantly better.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: Comparisons to SoTA on the test set of English WSJ Treebank Stanford dependencies.', and tackle the question below in a sequential manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model achieves the highest LAS score and what is the score?\n" }, { "role": "assistant", "content": "By examining the table, the BERT G2GTr+H 7-layer+SWAP model achieves the highest LAS score of 93.88%. So, the answer is {\"answer\": \"BERT G2GTr+H 7-layer+SWAP\"}." } ], "images": [ "scigen/train/2955.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparisons to SoTA on the test set of English WSJ Treebank Stanford dependencies." ], "reasoning": "By examining the table, the BERT G2GTr+H 7-layer+SWAP model achieves the highest LAS score of 93.88%.", "question": "Which model achieves the highest LAS score and what is the score?", "answer": "So, the answer is {\"answer\": \"BERT G2GTr+H 7-layer+SWAP\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The BERT G2GTr model achieves a higher LAS score than the DepTr+CH model., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe BERT G2GTr model achieves a higher LAS score than the DepTr+CH model.\n" }, { "role": "assistant", "content": "The table shows that the BERT G2GTr model has an LAS score of 93.63%, while the DepTr+CH model has an LAS score of 89.65%, confirming that the BERT G2GTr model achieves a higher LAS score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2955.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparisons to SoTA on the test set of English WSJ Treebank Stanford dependencies." ], "reasoning": "The table shows that the BERT G2GTr model has an LAS score of 93.63%, while the DepTr+CH model has an LAS score of 89.65%, confirming that the BERT G2GTr model achieves a higher LAS score.", "statement": "The BERT G2GTr model achieves a higher LAS score than the DepTr+CH model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Confusion factors for each viseme map tested'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The division of phonemes across viseme classes varies with each map, and the top four CF mean is 0.41, indicating the better P2V.The division of these phoneme across viseme classes will vary with each different map. (top four CF mean = 0.41) so the better P2V is " } ], "images": [ "scigen/train/2956.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Confusion factors for each viseme map tested" ], "reasoning": "The division of phonemes across viseme classes varies with each map, and the top four CF mean is 0.41, indicating the better P2V.", "description": "The division of these phoneme across viseme classes will vary with each different map. (top four CF mean = 0.41) so the better P2V is ", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich consonant map has the highest mean C value?\nSolve the above question based on the table titled 'Table 3: Confusion factors for each viseme map tested'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the consonant map with the highest mean C value, compare the 'Mean C' values in the 'Consonant Map Woodward' column. The highest value is 32.33, which corresponds to Hazen. So, the answer is {\"answer\": \"Hazen\"}." } ], "images": [ "scigen/train/2956.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Confusion factors for each viseme map tested" ], "reasoning": "To find the consonant map with the highest mean C value, compare the 'Mean C' values in the 'Consonant Map Woodward' column. The highest value is 32.33, which corresponds to Hazen.", "question": "Which consonant map has the highest mean C value?", "answer": "So, the answer is {\"answer\": \"Hazen\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe consonant map with the lowest CF value is Fisher.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'CF' values in the 'Consonant Map Woodward' column. The lowest CF value is 0.16, which corresponds to Disney, Fisher, and Franks. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2956.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Confusion factors for each viseme map tested" ], "reasoning": "To verify this statement, compare the 'CF' values in the 'Consonant Map Woodward' column. The lowest CF value is 0.16, which corresponds to Disney, Fisher, and Franks. Therefore, the statement is supported.", "statement": "The consonant map with the lowest CF value is Fisher.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: Performance of high-performing NLI models on the full, Hard, and Easy NLI test sets.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "NLI models perform better on Easy examples than Hard ones, indicating a reliance on annotation artifacts rather than true textual entailment recognition.While the models correctly classify some Hard examples, the bulk of their success is attributed to the Easy examples. This result implies that the ability of NLI models to recognize textual entailment is lower than previously perceived, and that such models rely heavily on annotation artifacts in the hypothesis to make their predictions." } ], "images": [ "scigen/train/2957.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Performance of high-performing NLI models on the full, Hard, and Easy NLI test sets." ], "reasoning": "NLI models perform better on Easy examples than Hard ones, indicating a reliance on annotation artifacts rather than true textual entailment recognition.", "description": "While the models correctly classify some Hard examples, the bulk of their success is attributed to the Easy examples. This result implies that the ability of NLI models to recognize textual entailment is lower than previously perceived, and that such models rely heavily on annotation artifacts in the hypothesis to make their predictions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 5: Performance of high-performing NLI models on the full, Hard, and Easy NLI test sets.' and methodically tackle the question that follows. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model has the highest accuracy on the MultiNLI Mismatched Easy examples?\n" }, { "role": "assistant", "content": "By comparing the MultiNLI Mismatched Easy column, DIIN has the highest accuracy with a score of 86.8. So, the answer is {\"answer\": \"DIIN\"}." } ], "images": [ "scigen/train/2957.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Performance of high-performing NLI models on the full, Hard, and Easy NLI test sets." ], "reasoning": "By comparing the MultiNLI Mismatched Easy column, DIIN has the highest accuracy with a score of 86.8.", "question": "Which model has the highest accuracy on the MultiNLI Mismatched Easy examples?", "answer": "So, the answer is {\"answer\": \"DIIN\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The ESIM model has the lowest accuracy on the MultiNLI Matched Hard examples. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe ESIM model has the lowest accuracy on the MultiNLI Matched Hard examples.\n" }, { "role": "assistant", "content": "By comparing the MultiNLI Matched Hard column, ESIM has the lowest accuracy with a score of 59.3. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2957.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Performance of high-performing NLI models on the full, Hard, and Easy NLI test sets." ], "reasoning": "By comparing the MultiNLI Matched Hard column, ESIM has the lowest accuracy with a score of 59.3.", "statement": "The ESIM model has the lowest accuracy on the MultiNLI Matched Hard examples.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: The performance of 3-layer R-Transformers (RT-L3) and SDU components on word-level PTB LM.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The tanh-gate SDUs show better performance in terms of eval loss, eval ppl, test loss, and test ppl compared to sigmoid gates and the baseline RT-L3 model.As in Fig. Similar to the word-level R-Transformer, tanh-gate SDUs behave a bit better than the counterpart with sigmoid gates, both showing stable curvatures of convergence." } ], "images": [ "scigen/train/2958.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The performance of 3-layer R-Transformers (RT-L3) and SDU components on word-level PTB LM." ], "reasoning": "The tanh-gate SDUs show better performance in terms of eval loss, eval ppl, test loss, and test ppl compared to sigmoid gates and the baseline RT-L3 model.", "description": "As in Fig. Similar to the word-level R-Transformer, tanh-gate SDUs behave a bit better than the counterpart with sigmoid gates, both showing stable curvatures of convergence.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: The performance of 3-layer R-Transformers (RT-L3) and SDU components on word-level PTB LM.', solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model has the lowest test perplexity?\n" }, { "role": "assistant", "content": "By comparing the test ppl values for all models, the +tanh SDU model has the lowest test perplexity of 84.92. So, the answer is {\"answer\": \"84.92\"}." } ], "images": [ "scigen/train/2958.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The performance of 3-layer R-Transformers (RT-L3) and SDU components on word-level PTB LM." ], "reasoning": "By comparing the test ppl values for all models, the +tanh SDU model has the lowest test perplexity of 84.92.", "question": "Which model has the lowest test perplexity?", "answer": "So, the answer is {\"answer\": \"84.92\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The RT-L3 model has the highest evaluation loss., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe RT-L3 model has the highest evaluation loss.\n" }, { "role": "assistant", "content": "By comparing the eval loss values for all models, the RT-L3 model has the highest evaluation loss of 4.58. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2958.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The performance of 3-layer R-Transformers (RT-L3) and SDU components on word-level PTB LM." ], "reasoning": "By comparing the eval loss values for all models, the RT-L3 model has the highest evaluation loss of 4.58.", "statement": "The RT-L3 model has the highest evaluation loss.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Performance of 3 Layer Transformers and SDU components on char-level PTB LM task. The best performance is marked bold.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "SDUs with tanh gates show a greater improvement in test perplexity compared to those with sigmoid gates.Furthermore, Fig. In which SDUs with tanh gates (8.76% improvement) outperform the counterpart with sigmoid gates (8.2% improvement) in terms of the final perplexity on the test set." } ], "images": [ "scigen/train/2959.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance of 3 Layer Transformers and SDU components on char-level PTB LM task. The best performance is marked bold." ], "reasoning": "SDUs with tanh gates show a greater improvement in test perplexity compared to those with sigmoid gates.", "description": "Furthermore, Fig. In which SDUs with tanh gates (8.76% improvement) outperform the counterpart with sigmoid gates (8.2% improvement) in terms of the final perplexity on the test set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 1: Performance of 3 Layer Transformers and SDU components on char-level PTB LM task. The best performance is marked bold.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. \n\nQuestion: Which model shows the greatest improvement in test perplexity?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows that the model with tanh SDU has a test perplexity of 1.364, which is lower than the test perplexity of the model with sigmoid SDU (1.371). This indicates a greater improvement. So, the answer is {\"answer\": \"+tanh SDU\"}." } ], "images": [ "scigen/train/2959.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance of 3 Layer Transformers and SDU components on char-level PTB LM task. The best performance is marked bold." ], "reasoning": "The table shows that the model with tanh SDU has a test perplexity of 1.364, which is lower than the test perplexity of the model with sigmoid SDU (1.371). This indicates a greater improvement.", "question": "Which model shows the greatest improvement in test perplexity?", "answer": "So, the answer is {\"answer\": \"+tanh SDU\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model with tanh SDU has a lower test loss than the model with sigmoid SDU.\n" }, { "role": "assistant", "content": "The table shows that the test loss for the model with tanh SDU is 0.945, which is lower than the test loss for the model with sigmoid SDU (0.950). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2959.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance of 3 Layer Transformers and SDU components on char-level PTB LM task. The best performance is marked bold." ], "reasoning": "The table shows that the test loss for the model with tanh SDU is 0.945, which is lower than the test loss for the model with sigmoid SDU (0.950).", "statement": "The model with tanh SDU has a lower test loss than the model with sigmoid SDU.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 6: Final results of 12-layer Transformer-XL (XL-L12) and augmented SDUs with different settings.'.\n" }, { "role": "assistant", "content": "SDU components expedite convergence during training, but adding gated units to each Transformer block can cause premature convergence. Lower layers benefit more from gated units, enhancing feature-wise information in shallow layers.The experimental results illustrate that SDU components have contributed to expediting the convergence during training (see Fig. But supplementing gated units on each Transformer block could encounter the premature convergence phenomenon. It is observed from Fig. two layers promote the convergence process and further improve the bpc performance on the dev and test set. Interestingly, the performance does not follow a positive correlation with the increase of gated layer numbers. (see Fig. Therefore, the lower layers benefit more from our proposed gated units than higher layers, again illustrating that SDUs could enhance feature-wise information on shallow layers of deep-stacked Transformer components." } ], "images": [ "scigen/train/2960.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Final results of 12-layer Transformer-XL (XL-L12) and augmented SDUs with different settings." ], "reasoning": "SDU components expedite convergence during training, but adding gated units to each Transformer block can cause premature convergence. Lower layers benefit more from gated units, enhancing feature-wise information in shallow layers.", "description": "The experimental results illustrate that SDU components have contributed to expediting the convergence during training (see Fig. But supplementing gated units on each Transformer block could encounter the premature convergence phenomenon. It is observed from Fig. two layers promote the convergence process and further improve the bpc performance on the dev and test set. Interestingly, the performance does not follow a positive correlation with the increase of gated layer numbers. (see Fig. Therefore, the lower layers benefit more from our proposed gated units than higher layers, again illustrating that SDUs could enhance feature-wise information on shallow layers of deep-stacked Transformer components.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 6: Final results of 12-layer Transformer-XL (XL-L12) and augmented SDUs with different settings.', solve the subsequent question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich model configuration achieved the lowest test bpc?\n" }, { "role": "assistant", "content": "To find the model configuration with the lowest test bpc, we need to compare the 'test bpc' values in the table. The lowest value is 1.06904, achieved by the '+tanh L1-2' configuration. So, the answer is {\"answer\": \"+tanh L1-2\"}." } ], "images": [ "scigen/train/2960.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Final results of 12-layer Transformer-XL (XL-L12) and augmented SDUs with different settings." ], "reasoning": "To find the model configuration with the lowest test bpc, we need to compare the 'test bpc' values in the table. The lowest value is 1.06904, achieved by the '+tanh L1-2' configuration.", "question": "Which model configuration achieved the lowest test bpc?", "answer": "So, the answer is {\"answer\": \"+tanh L1-2\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe model configuration '+tanh L1-2' achieved the lowest eval bpc.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the 'eval bpc' values in the table. The lowest eval bpc value is 1.089, achieved by both '+tanh L1-2' and '+tanh L1-2'. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2960.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Final results of 12-layer Transformer-XL (XL-L12) and augmented SDUs with different settings." ], "reasoning": "To verify this statement, we need to compare the 'eval bpc' values in the table. The lowest eval bpc value is 1.089, achieved by both '+tanh L1-2' and '+tanh L1-2'. Therefore, the statement is supported.", "statement": "The model configuration '+tanh L1-2' achieved the lowest eval bpc.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Summary of training time costs of 6-layer Transformer-XL.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The addition of SDU gates and other modifications results in only a slight increase in time cost compared to the baseline model.Meanwhile, we find that supplementing SDU gates does not increase much of the time cost in comparison with baselines. Instead, the total running time of each experimental setting is quite similar." } ], "images": [ "scigen/train/2961.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Summary of training time costs of 6-layer Transformer-XL." ], "reasoning": "The addition of SDU gates and other modifications results in only a slight increase in time cost compared to the baseline model.", "description": "Meanwhile, we find that supplementing SDU gates does not increase much of the time cost in comparison with baselines. Instead, the total running time of each experimental setting is quite similar.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 7: Summary of training time costs of 6-layer Transformer-XL.'.\n\nQuestion: What is the time cost difference between the baseline model and the model with the highest time cost?\n" }, { "role": "assistant", "content": "The baseline model (xl-L6) has a time cost of 21.16 hours, and the model with the highest time cost (+highway gate) has a time cost of 21.93 hours. The difference is 21.93 - 21.16 = 0.77 hours. So, the answer is {\"answer\": \"0.77 hours\"}" } ], "images": [ "scigen/train/2961.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Summary of training time costs of 6-layer Transformer-XL." ], "reasoning": "The baseline model (xl-L6) has a time cost of 21.16 hours, and the model with the highest time cost (+highway gate) has a time cost of 21.93 hours. The difference is 21.93 - 21.16 = 0.77 hours.", "question": "What is the time cost difference between the baseline model and the model with the highest time cost?", "answer": "So, the answer is {\"answer\": \"0.77 hours\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model with the gated MHDPA has the lowest time cost.\n" }, { "role": "assistant", "content": "The table shows that the model with the gated MHDPA has a time cost of 21.10 hours, which is lower than the baseline model and all other models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2961.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Summary of training time costs of 6-layer Transformer-XL." ], "reasoning": "The table shows that the model with the gated MHDPA has a time cost of 21.10 hours, which is lower than the baseline model and all other models listed.", "statement": "The model with the gated MHDPA has the lowest time cost.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 7: Relation prediction results on FB15K. We use Hits@1 for better comparison because Hits@10 of all the models exceed 95%.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "RPJE outperforms all baseline models in all metrics, demonstrating the effectiveness of using paths and rules for enhancing relation embeddings and prediction.Three typical models representing three types of baselines are implemented. The results illustrate that RPJE outperforms baselines in all metrics. It verifies that paths could provide extra relationships for entity pairs and rules can further create more semantic association for relations to improve relation embeddings and benefit for relation prediction." } ], "images": [ "scigen/train/2962.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Relation prediction results on FB15K. We use Hits@1 for better comparison because Hits@10 of all the models exceed 95%." ], "reasoning": "RPJE outperforms all baseline models in all metrics, demonstrating the effectiveness of using paths and rules for enhancing relation embeddings and prediction.", "description": "Three typical models representing three types of baselines are implemented. The results illustrate that RPJE outperforms baselines in all metrics. It verifies that paths could provide extra relationships for entity pairs and rules can further create more semantic association for relations to improve relation embeddings and benefit for relation prediction.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nTable title: Table 7: Relation prediction results on FB15K. We use Hits@1 for better comparison because Hits@10 of all the models exceed 95%.\n\nQuestion: Which model has the highest Hits@1(%) filtered score?\n" }, { "role": "assistant", "content": "By comparing the Hits@1(%) filtered scores of all models, RPJE has the highest score of 95.3%. So, the answer is {\"answer\": \"RPJE\"}" } ], "images": [ "scigen/train/2962.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Relation prediction results on FB15K. We use Hits@1 for better comparison because Hits@10 of all the models exceed 95%." ], "reasoning": "By comparing the Hits@1(%) filtered scores of all models, RPJE has the highest score of 95.3%.", "question": "Which model has the highest Hits@1(%) filtered score?", "answer": "So, the answer is {\"answer\": \"RPJE\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis RPJE has the lowest MR raw score among all models. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nRPJE has the lowest MR raw score among all models.\n" }, { "role": "assistant", "content": "By comparing the MR raw scores of all models, RPJE has the lowest score of 1.68. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2962.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Relation prediction results on FB15K. We use Hits@1 for better comparison because Hits@10 of all the models exceed 95%." ], "reasoning": "By comparing the MR raw scores of all models, RPJE has the lowest score of 1.68.", "statement": "RPJE has the lowest MR raw score among all models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 5: Entity prediction results on FB15K. The missing values indicate the scores not reported in the original work. The best score is in bold, and the second best in underline.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "RPJE outperforms PTransE, KALE, and RUGE on MRR and Hits@10 (filtered), demonstrating the effectiveness of using logic rules and paths for better embeddings.This demonstrates that RPJE learns more reasonable embeddings for KGs via using logic rules in conjunction with paths. (2) In particular, RPJE outperforms PTransE on each metric, which indicates the superiority of introducing logic rules for providing higher accuracy in paths composition and learning better path embeddings. (3) Compared to the rule-based baselines KALE and RUGE, RPJE obtains the improvements of 56.0%/6.3% on MRR and 18.5%/4.4% on Hits@10 (filtered), which demonstrates the effectiveness of explicitly employing rules for preserving more semantic information and further integrating paths." } ], "images": [ "scigen/train/2963.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Entity prediction results on FB15K. The missing values indicate the scores not reported in the original work. The best score is in bold, and the second best in underline." ], "reasoning": "RPJE outperforms PTransE, KALE, and RUGE on MRR and Hits@10 (filtered), demonstrating the effectiveness of using logic rules and paths for better embeddings.", "description": "This demonstrates that RPJE learns more reasonable embeddings for KGs via using logic rules in conjunction with paths. (2) In particular, RPJE outperforms PTransE on each metric, which indicates the superiority of introducing logic rules for providing higher accuracy in paths composition and learning better path embeddings. (3) Compared to the rule-based baselines KALE and RUGE, RPJE obtains the improvements of 56.0%/6.3% on MRR and 18.5%/4.4% on Hits@10 (filtered), which demonstrates the effectiveness of explicitly employing rules for preserving more semantic information and further integrating paths.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Entity prediction results on FB15K. The missing values indicate the scores not reported in the original work. The best score is in bold, and the second best in underline.\nQuestion: Which model has the highest Hits@10 (filtered) score?\nSolve the above question based on the table information. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the Hits@10 (filtered) scores of all models in the table, RPJE has the highest score of 90.3. So, the answer is {\"answer\": \"RPJE\"}." } ], "images": [ "scigen/train/2963.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Entity prediction results on FB15K. The missing values indicate the scores not reported in the original work. The best score is in bold, and the second best in underline." ], "reasoning": "By comparing the Hits@10 (filtered) scores of all models in the table, RPJE has the highest score of 90.3.", "question": "Which model has the highest Hits@10 (filtered) score?", "answer": "So, the answer is {\"answer\": \"RPJE\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence RPJE has a lower MRR filtered score than KALE. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nRPJE has a lower MRR filtered score than KALE.\n" }, { "role": "assistant", "content": "The table shows that RPJE has an MRR filtered score of 0.816, while KALE has an MRR filtered score of 0.523. Therefore, RPJE has a higher MRR filtered score than KALE. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2963.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Entity prediction results on FB15K. The missing values indicate the scores not reported in the original work. The best score is in bold, and the second best in underline." ], "reasoning": "The table shows that RPJE has an MRR filtered score of 0.816, while KALE has an MRR filtered score of 0.523. Therefore, RPJE has a higher MRR filtered score than KALE.", "statement": "RPJE has a lower MRR filtered score than KALE.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 6: Entity prediction results on FB15K by mapping properties of relations (%) with filtered setting.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "RPJE significantly outperforms all baselines, achieving an average improvement of 4.2% in head entities prediction and 6.5% in tail entities prediction compared to TransG, with the most notable improvements of 12.6% and 13.4% in the toughest tasks.We can observe that: 1) RPJE outperforms all baselines significantly and consistently in regard to all the relation categories. Compared to the best performing baseline TransG, RPJE achieves an average improvement of 4.2% in head entities prediction and 6.5% in tail entities prediction. 2) More interestingly, on the two toughest tasks of predicting head entities of N-1 relation and predicting tail entities of 1-N relation, our approach achieves the best performance improvements approximately 12.6% and 13.4% compared to the best baselines, respectively." } ], "images": [ "scigen/train/2964.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Entity prediction results on FB15K by mapping properties of relations (%) with filtered setting." ], "reasoning": "RPJE significantly outperforms all baselines, achieving an average improvement of 4.2% in head entities prediction and 6.5% in tail entities prediction compared to TransG, with the most notable improvements of 12.6% and 13.4% in the toughest tasks.", "description": "We can observe that: 1) RPJE outperforms all baselines significantly and consistently in regard to all the relation categories. Compared to the best performing baseline TransG, RPJE achieves an average improvement of 4.2% in head entities prediction and 6.5% in tail entities prediction. 2) More interestingly, on the two toughest tasks of predicting head entities of N-1 relation and predicting tail entities of 1-N relation, our approach achieves the best performance improvements approximately 12.6% and 13.4% compared to the best baselines, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 6: Entity prediction results on FB15K by mapping properties of relations (%) with filtered setting.' and proceed to solve the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the average improvement in tail entities prediction achieved by RPJE compared to the best performing baseline?\n" }, { "role": "assistant", "content": "RPJE achieves an average improvement of 6.5% in tail entities prediction compared to the best performing baseline, TransG. So, the answer is {\"answer\": \"6.5%\"}" } ], "images": [ "scigen/train/2964.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Entity prediction results on FB15K by mapping properties of relations (%) with filtered setting." ], "reasoning": "RPJE achieves an average improvement of 6.5% in tail entities prediction compared to the best performing baseline, TransG.", "question": "What is the average improvement in tail entities prediction achieved by RPJE compared to the best performing baseline?", "answer": "So, the answer is {\"answer\": \"6.5%\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis RPJE achieves the best performance improvement of 12.6% in predicting head entities of N-1 relation. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nRPJE achieves the best performance improvement of 12.6% in predicting head entities of N-1 relation.\n" }, { "role": "assistant", "content": "The description states that RPJE achieves the best performance improvement of approximately 12.6% in predicting head entities of N-1 relation compared to the best baselines. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2964.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Entity prediction results on FB15K by mapping properties of relations (%) with filtered setting." ], "reasoning": "The description states that RPJE achieves the best performance improvement of approximately 12.6% in predicting head entities of N-1 relation compared to the best baselines.", "statement": "RPJE achieves the best performance improvement of 12.6% in predicting head entities of N-1 relation.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 8: Entity prediction results on FB15K-237.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "FB15K-237 is a recently constructed dataset with limited existing evaluation results, and Horn rules can be used to enhance semantic associations of relations.Furthermore, we implement the experiments on dataset FB15K-237. Since FB15K-237 is constructed up to date, only a minority of existing works have implemented their experiments and show evaluation results on this dataset, which can be selected as baselines. Although no inverse relation could be observed in FB15K-237, we could employ Horn rules to provide significant supplements for building semantic associations of relations." } ], "images": [ "scigen/train/2965.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Entity prediction results on FB15K-237." ], "reasoning": "FB15K-237 is a recently constructed dataset with limited existing evaluation results, and Horn rules can be used to enhance semantic associations of relations.", "description": "Furthermore, we implement the experiments on dataset FB15K-237. Since FB15K-237 is constructed up to date, only a minority of existing works have implemented their experiments and show evaluation results on this dataset, which can be selected as baselines. Although no inverse relation could be observed in FB15K-237, we could employ Horn rules to provide significant supplements for building semantic associations of relations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nTable title: Table 8: Entity prediction results on FB15K-237.\n\nQuestion: Which model has the highest Hits@10(%) score on the FB15K-237 dataset?\n" }, { "role": "assistant", "content": "By examining the Hits@10(%) column in the table, we see that the highest score is 62.5%, which corresponds to the RPJE model. So, the answer is {\"answer\": \"RPJE\"}." } ], "images": [ "scigen/train/2965.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Entity prediction results on FB15K-237." ], "reasoning": "By examining the Hits@10(%) column in the table, we see that the highest score is 62.5%, which corresponds to the RPJE model.", "question": "Which model has the highest Hits@10(%) score on the FB15K-237 dataset?", "answer": "So, the answer is {\"answer\": \"RPJE\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nRPJE has the lowest Mean Rank (MR) among the models listed.\n" }, { "role": "assistant", "content": "By comparing the MR values in the table, RPJE has an MR of 207, which is the lowest among all the models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2965.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Entity prediction results on FB15K-237." ], "reasoning": "By comparing the MR values in the table, RPJE has an MR of 207, which is the lowest among all the models listed.", "statement": "RPJE has the lowest Mean Rank (MR) among the models listed.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 9: Entity prediction results on WN18 and NELL-995.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "RPJE outperforms other models on both WN18 and NELL-995 datasets, demonstrating its superiority in representation learning on large-scale KGs.We also test the models on datasets WN18 and NELL-995. Three types of typical models are selected as baselines. Few rules can be mined from WN18 due to extremely limited amount of relations. And very parse paths can be extracted from NELL-995 because entities are far more than relations on this dataset. It illustrates the superiority of our approach for representation learning on various large scale KGs. On the other hand, considering the performance gains on FB15K are more than that on WN18, which is because more rules provide more semantic information to RPJE to use. As can be expected, our model RPJE will obtain better performance on datasets which implies more rules and paths." } ], "images": [ "scigen/train/2966.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Entity prediction results on WN18 and NELL-995." ], "reasoning": "RPJE outperforms other models on both WN18 and NELL-995 datasets, demonstrating its superiority in representation learning on large-scale KGs.", "description": "We also test the models on datasets WN18 and NELL-995. Three types of typical models are selected as baselines. Few rules can be mined from WN18 due to extremely limited amount of relations. And very parse paths can be extracted from NELL-995 because entities are far more than relations on this dataset. It illustrates the superiority of our approach for representation learning on various large scale KGs. On the other hand, considering the performance gains on FB15K are more than that on WN18, which is because more rules provide more semantic information to RPJE to use. As can be expected, our model RPJE will obtain better performance on datasets which implies more rules and paths.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 9: Entity prediction results on WN18 and NELL-995.', and tackle the question below in a sequential manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model has the highest Hits@10(%) on the NELL-995 dataset?\n" }, { "role": "assistant", "content": "By comparing the Hits@10(%) values for the NELL-995 dataset in the table, RPJE has the highest value of 50.1. So, the answer is {\"answer\": \"RPJE\"}" } ], "images": [ "scigen/train/2966.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Entity prediction results on WN18 and NELL-995." ], "reasoning": "By comparing the Hits@10(%) values for the NELL-995 dataset in the table, RPJE has the highest value of 50.1.", "question": "Which model has the highest Hits@10(%) on the NELL-995 dataset?", "answer": "So, the answer is {\"answer\": \"RPJE\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: RPJE has the highest MRR on the WN18 dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRPJE has the highest MRR on the WN18 dataset.\n" }, { "role": "assistant", "content": "The table shows that RPJE has an MRR of 0.946 on the WN18 dataset, which is the highest among the listed models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2966.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Entity prediction results on WN18 and NELL-995." ], "reasoning": "The table shows that RPJE has an MRR of 0.946 on the WN18 dataset, which is the highest among the listed models.", "statement": "RPJE has the highest MRR on the WN18 dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: Quantitative results on Flickr30k [51]. “ours” means our own implementation.'.\n" }, { "role": "assistant", "content": "Hal outperforms Max and Sum in most configurations and converges faster, stabilizing after 5 epochs compared to 10 for Max and Sum.Comparing Hal, Sum and Max. On Flickr30k, we experiment three models and Hal achieves significantly better performance than Max and Sum on the first two configurations. Interestingly, while Max fails badly on Flickr30k, it becomes very competitive on MS-COCO. This serves as an evidence of Max easily overfitting to small datasets. In conclusion, Hal maintains its edge over Max and Sum across regardless of data and architecture configurations. Even without global weighting (memory bank), Hal still beats the two triplet losses by a large margin. The equipment of memory bank can usually further boosts rsum by another 3−5. Also, it is worth noticing that Hal converges significantly faster than Max and Sum. Hal stabilizes after approximately 5 epochs while Max and Sum take roughly 10 epochs." } ], "images": [ "scigen/train/2967.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Quantitative results on Flickr30k [51]. “ours” means our own implementation." ], "reasoning": "Hal outperforms Max and Sum in most configurations and converges faster, stabilizing after 5 epochs compared to 10 for Max and Sum.", "description": "Comparing Hal, Sum and Max. On Flickr30k, we experiment three models and Hal achieves significantly better performance than Max and Sum on the first two configurations. Interestingly, while Max fails badly on Flickr30k, it becomes very competitive on MS-COCO. This serves as an evidence of Max easily overfitting to small datasets. In conclusion, Hal maintains its edge over Max and Sum across regardless of data and architecture configurations. Even without global weighting (memory bank), Hal still beats the two triplet losses by a large margin. The equipment of memory bank can usually further boosts rsum by another 3−5. Also, it is worth noticing that Hal converges significantly faster than Max and Sum. Hal stabilizes after approximately 5 epochs while Max and Sum take roughly 10 epochs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 1: Quantitative results on Flickr30k [51]. “ours” means our own implementation.\nQuestion: Which model achieves the highest rsum on SCAN architecture?\n" }, { "role": "assistant", "content": "By examining the rsum values for the SCAN architecture, Hal achieves an rsum of 455.5, which is higher than Max's rsum of 452.2. So, the answer is {\"answer\": \"Hal\"}" } ], "images": [ "scigen/train/2967.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Quantitative results on Flickr30k [51]. “ours” means our own implementation." ], "reasoning": "By examining the rsum values for the SCAN architecture, Hal achieves an rsum of 455.5, which is higher than Max's rsum of 452.2.", "question": "Which model achieves the highest rsum on SCAN architecture?", "answer": "So, the answer is {\"answer\": \"Hal\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Max achieves the highest image-to-text R@1 score on SCAN architecture., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nMax achieves the highest image-to-text R@1 score on SCAN architecture.\n" }, { "role": "assistant", "content": "The table shows that on SCAN architecture, Hal achieves an image-to-text R@1 score of 68.6, which is higher than Max's score of 67.9. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2967.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Quantitative results on Flickr30k [51]. “ours” means our own implementation." ], "reasoning": "The table shows that on SCAN architecture, Hal achieves an image-to-text R@1 score of 68.6, which is higher than Max's score of 67.9.", "statement": "Max achieves the highest image-to-text R@1 score on SCAN architecture.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Quantitative results on MS-COCO [23]. First three blocks (line 2.1-2.12) are using protocol c2 (5k test set); the last two blocks (line 2.13-2.24) is using c1 (1k test set) in convenience of comparing with results reported in previous works. Mb means memory bank.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Hal consistently outperforms Max and Sum across different datasets and configurations, converging faster and achieving higher rsums.Comparing Hal, Sum and Max. On Flickr30k, we experiment three models and Hal achieves significantly better performance than Max and Sum on the first two configurations. Interestingly, while Max fails badly on Flickr30k, it becomes very competitive on MS-COCO. This serves as an evidence of Max easily overfitting to small datasets. In conclusion, Hal maintains its edge over Max and Sum across regardless of data and architecture configurations. Even without global weighting (memory bank), Hal still beats the two triplet losses by a large margin. The equipment of memory bank can usually further boosts rsum by another 3−5. Also, it is worth noticing that Hal converges significantly faster than Max and Sum. Hal stabilizes after approximately 5 epochs while Max and Sum take roughly 10 epochs. Hal vs. State-of-the-art. Notice that we are comparing against works that use frozen image encoder (as we do). With such prior, it is a well-established state-of-the-art on the Text-Image matching task, having much higher rsums than previous works. For SCAN, we pick configurations with the best rsums on both MS-COCO and Flikr30k, switching its learning objective from Max to Hal. On Flikr30k, Max and Hal deliver comparable results. On MS-COCO, Hal is significantly stronger - rsum is further improved to 512.7 with R@1 improved by 7.4 and 3.7 for image→text and text→image respectively. We did not experiment with Mb due to GPU memory limits." } ], "images": [ "scigen/train/2968.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Quantitative results on MS-COCO [23]. First three blocks (line 2.1-2.12) are using protocol c2 (5k test set); the last two blocks (line 2.13-2.24) is using c1 (1k test set) in convenience of comparing with results reported in previous works. Mb means memory bank." ], "reasoning": "Hal consistently outperforms Max and Sum across different datasets and configurations, converging faster and achieving higher rsums.", "description": "Comparing Hal, Sum and Max. On Flickr30k, we experiment three models and Hal achieves significantly better performance than Max and Sum on the first two configurations. Interestingly, while Max fails badly on Flickr30k, it becomes very competitive on MS-COCO. This serves as an evidence of Max easily overfitting to small datasets. In conclusion, Hal maintains its edge over Max and Sum across regardless of data and architecture configurations. Even without global weighting (memory bank), Hal still beats the two triplet losses by a large margin. The equipment of memory bank can usually further boosts rsum by another 3−5. Also, it is worth noticing that Hal converges significantly faster than Max and Sum. Hal stabilizes after approximately 5 epochs while Max and Sum take roughly 10 epochs. Hal vs. State-of-the-art. Notice that we are comparing against works that use frozen image encoder (as we do). With such prior, it is a well-established state-of-the-art on the Text-Image matching task, having much higher rsums than previous works. For SCAN, we pick configurations with the best rsums on both MS-COCO and Flikr30k, switching its learning objective from Max to Hal. On Flikr30k, Max and Hal deliver comparable results. On MS-COCO, Hal is significantly stronger - rsum is further improved to 512.7 with R@1 improved by 7.4 and 3.7 for image→text and text→image respectively. We did not experiment with Mb due to GPU memory limits.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model and configuration achieved the highest rsum on the table?\nSolve the above question based on the table titled 'Table 2: Quantitative results on MS-COCO [23]. First three blocks (line 2.1-2.12) are using protocol c2 (5k test set); the last two blocks (line 2.13-2.24) is using c1 (1k test set) in convenience of comparing with results reported in previous works. Mb means memory bank.'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the 'rsum' column, the highest rsum value is 512.7, achieved by the (t-i AVG) + Hal model. So, the answer is {\"answer\": \"(t-i AVG) + Hal\"}." } ], "images": [ "scigen/train/2968.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Quantitative results on MS-COCO [23]. First three blocks (line 2.1-2.12) are using protocol c2 (5k test set); the last two blocks (line 2.13-2.24) is using c1 (1k test set) in convenience of comparing with results reported in previous works. Mb means memory bank." ], "reasoning": "By examining the 'rsum' column, the highest rsum value is 512.7, achieved by the (t-i AVG) + Hal model.", "question": "Which model and configuration achieved the highest rsum on the table?", "answer": "So, the answer is {\"answer\": \"(t-i AVG) + Hal\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Hal with memory bank achieves an rsum of 466.7. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nHal with memory bank achieves an rsum of 466.7.\n" }, { "role": "assistant", "content": "The table shows that the GRU+ResNet152 architecture with Hal+Mb loss achieves an rsum of 466.7. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2968.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Quantitative results on MS-COCO [23]. First three blocks (line 2.1-2.12) are using protocol c2 (5k test set); the last two blocks (line 2.13-2.24) is using c1 (1k test set) in convenience of comparing with results reported in previous works. Mb means memory bank." ], "reasoning": "The table shows that the GRU+ResNet152 architecture with Hal+Mb loss achieves an rsum of 466.7.", "statement": "Hal with memory bank achieves an rsum of 466.7.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 7: Correlations on the 500-sentence subset.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The Sampled method has the highest correlation on the subset, indicating that adequacy is not necessarily a defining factor for Bleu's performance.We report correlations for the 500-sentence WMT19 de-en subset with human results in Tab. Although a small subset, a few observations can be made. Firstly we see a lot of variance, both across and within methods. Most importantly, the manual paraphrases do not achieve higher results than the other methods, including the baselines. The method with the highest correlation on this subset is again Sampled, which reinforces the previous observations that adequacy is not necessarily a defining factor; even if the paraphrases are of good quality (i.e. produced by humans), these paraphrases will not necessarily help Bleu’s performance. As with the main results tables, here the constraints-based approach tends to help slightly more with segment-level correlations, suggesting that it could be slightly more beneficial to target areas of the translation space for sentenceBleu than to produce as diverse reference as possible." } ], "images": [ "scigen/train/2969.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Correlations on the 500-sentence subset." ], "reasoning": "The Sampled method has the highest correlation on the subset, indicating that adequacy is not necessarily a defining factor for Bleu's performance.", "description": "We report correlations for the 500-sentence WMT19 de-en subset with human results in Tab. Although a small subset, a few observations can be made. Firstly we see a lot of variance, both across and within methods. Most importantly, the manual paraphrases do not achieve higher results than the other methods, including the baselines. The method with the highest correlation on this subset is again Sampled, which reinforces the previous observations that adequacy is not necessarily a defining factor; even if the paraphrases are of good quality (i.e. produced by humans), these paraphrases will not necessarily help Bleu’s performance. As with the main results tables, here the constraints-based approach tends to help slightly more with segment-level correlations, suggesting that it could be slightly more beneficial to target areas of the translation space for sentenceBleu than to produce as diverse reference as possible.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 7: Correlations on the 500-sentence subset.\nQuestion: Which method has the highest correlation in the Correlation System column?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the Correlation System column, the TreeLstm s method has the highest correlation value of 0.941. So, the answer is {\"answer\": \"TreeLstm s\"}" } ], "images": [ "scigen/train/2969.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Correlations on the 500-sentence subset." ], "reasoning": "By examining the Correlation System column, the TreeLstm s method has the highest correlation value of 0.941.", "question": "Which method has the highest correlation in the Correlation System column?", "answer": "So, the answer is {\"answer\": \"TreeLstm s\"}" }, { "messages": [ { "role": "user", "content": "The Human method achieves the highest correlation in the Correlation Segment column. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Human method achieves the highest correlation in the Correlation Segment column.\n" }, { "role": "assistant", "content": "By examining the Correlation Segment column, the highest correlation value is 0.074, achieved by the 3-gram Constraints method, not the Human method. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2969.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Correlations on the 500-sentence subset." ], "reasoning": "By examining the Correlation Segment column, the highest correlation value is 0.074, achieved by the 3-gram Constraints method, not the Human method.", "statement": "The Human method achieves the highest correlation in the Correlation Segment column.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Direct assessment scores for the first hundred sentences of the WMT19 German–English test set'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The human method achieved the highest adequacy score and the highest normalized score, while the sampled method had the lowest scores in both categories.Whilst the task is inherently subjective, the results (Tab. The automatic paraphrase systems are not that far behind, and the scores are further corroborated in that the lowest score was attributed to the sampled output, which we expect to be less faithful to the reference meaning." } ], "images": [ "scigen/train/2970.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Direct assessment scores for the first hundred sentences of the WMT19 German–English test set" ], "reasoning": "The human method achieved the highest adequacy score and the highest normalized score, while the sampled method had the lowest scores in both categories.", "description": "Whilst the task is inherently subjective, the results (Tab. The automatic paraphrase systems are not that far behind, and the scores are further corroborated in that the lowest score was attributed to the sampled output, which we expect to be less faithful to the reference meaning.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Direct assessment scores for the first hundred sentences of the WMT19 German–English test set\nQuestion: Which method had the lowest normalized score and what was its adequacy score?\nSolve the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows that the sampled method had the lowest normalized score of -0.357, and its adequacy score was 85.0. So, the answer is {\"answer\": \"Sampled method with adequacy score of 85.0\"}." } ], "images": [ "scigen/train/2970.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Direct assessment scores for the first hundred sentences of the WMT19 German–English test set" ], "reasoning": "The table shows that the sampled method had the lowest normalized score of -0.357, and its adequacy score was 85.0.", "question": "Which method had the lowest normalized score and what was its adequacy score?", "answer": "So, the answer is {\"answer\": \"Sampled method with adequacy score of 85.0\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Laser method had a higher adequacy score than the TreeLstm method.\n" }, { "role": "assistant", "content": "The table shows that the Laser method had an adequacy score of 90.1, while the TreeLstm method had an adequacy score of 88.0, indicating that the Laser method had a higher adequacy score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2970.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Direct assessment scores for the first hundred sentences of the WMT19 German–English test set" ], "reasoning": "The table shows that the Laser method had an adequacy score of 90.1, while the TreeLstm method had an adequacy score of 88.0, indicating that the Laser method had a higher adequacy score.", "statement": "The Laser method had a higher adequacy score than the TreeLstm method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Diversity scores of paraphrased references averaged over all into-English WMT19 test sets, where n is the number of paraphrases generated. The final row indicates diversity among MT outputs. *Human results are just for the 500-sentence subset.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The table shows that the Human method achieves the highest scores in both DS BOW and DS tree metrics, indicating its effectiveness in encoding relevant information.The results (Tab. ’s method succeeds in encoding relevant information, supported by the fact that random codes (encoding no information), leads to mostly identical paraphrases, differing only in the sentence code selected (removed in post-processing), resulting in almost zero diversity." } ], "images": [ "scigen/train/2971.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Diversity scores of paraphrased references averaged over all into-English WMT19 test sets, where n is the number of paraphrases generated. The final row indicates diversity among MT outputs. *Human results are just for the 500-sentence subset." ], "reasoning": "The table shows that the Human method achieves the highest scores in both DS BOW and DS tree metrics, indicating its effectiveness in encoding relevant information.", "description": "The results (Tab. ’s method succeeds in encoding relevant information, supported by the fact that random codes (encoding no information), leads to mostly identical paraphrases, differing only in the sentence code selected (removed in post-processing), resulting in almost zero diversity.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 4: Diversity scores of paraphrased references averaged over all into-English WMT19 test sets, where n is the number of paraphrases generated. The final row indicates diversity among MT outputs. *Human results are just for the 500-sentence subset.\nQuestion: Which method achieves the highest score in the DS tree metric?\n" }, { "role": "assistant", "content": "By examining the DS tree column, the Human method has the highest score of 0.68. So, the answer is {\"answer\": \"Human\"}." } ], "images": [ "scigen/train/2971.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Diversity scores of paraphrased references averaged over all into-English WMT19 test sets, where n is the number of paraphrases generated. The final row indicates diversity among MT outputs. *Human results are just for the 500-sentence subset." ], "reasoning": "By examining the DS tree column, the Human method has the highest score of 0.68.", "question": "Which method achieves the highest score in the DS tree metric?", "answer": "So, the answer is {\"answer\": \"Human\"}." }, { "messages": [ { "role": "user", "content": "The Random method achieves the lowest score in the DS BOW metric. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Random method achieves the lowest score in the DS BOW metric.\n" }, { "role": "assistant", "content": "By examining the DS BOW column, the Random method has the lowest score of 0.10. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2971.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Diversity scores of paraphrased references averaged over all into-English WMT19 test sets, where n is the number of paraphrases generated. The final row indicates diversity among MT outputs. *Human results are just for the 500-sentence subset." ], "reasoning": "By examining the DS BOW column, the Random method has the lowest score of 0.10.", "statement": "The Random method achieves the lowest score in the DS BOW metric.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 6: (WMT19 segment-level) Kendall’s τ correlations of Bleu with different paraphrase sets (size indicated in brackets) against segment-level human judgements. Results significantly better than sentenceBleu are indicated as follows: p≤0.05 (*), p≤0.01 (**), ≤0.001 (***).'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The Sampled approach achieves some of the greatest gains among the paraphrase approaches, but overall, the type of diversity being achieved is not significantly improving the metric.We again see small improvements over this baseline, although this is inconsistent across languages pairs and not significant. Again, there is little difference in the gains of the diverse methods, even with respect to using random cluster codes, which gave the least diversity. This suggests that the type of diversity being achieved is not successfully improving the metric as might be expected. The Sampled approach achieves amongst the greatest gains, as with system-level scores amongst the paraphrase approaches." } ], "images": [ "scigen/train/2972.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: (WMT19 segment-level) Kendall’s τ correlations of Bleu with different paraphrase sets (size indicated in brackets) against segment-level human judgements. Results significantly better than sentenceBleu are indicated as follows: p≤0.05 (*), p≤0.01 (**), ≤0.001 (***)." ], "reasoning": "The Sampled approach achieves some of the greatest gains among the paraphrase approaches, but overall, the type of diversity being achieved is not significantly improving the metric.", "description": "We again see small improvements over this baseline, although this is inconsistent across languages pairs and not significant. Again, there is little difference in the gains of the diverse methods, even with respect to using random cluster codes, which gave the least diversity. This suggests that the type of diversity being achieved is not successfully improving the metric as might be expected. The Sampled approach achieves amongst the greatest gains, as with system-level scores amongst the paraphrase approaches.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 6: (WMT19 segment-level) Kendall’s τ correlations of Bleu with different paraphrase sets (size indicated in brackets) against segment-level human judgements. Results significantly better than sentenceBleu are indicated as follows: p≤0.05 (*), p≤0.01 (**), ≤0.001 (***).\nQuestion: Which method under the 1.5cmParaphrase baselines (+5) approach achieved the highest average score?\n" }, { "role": "assistant", "content": "Under the 1.5cmParaphrase baselines (+5) approach, the Sampled method achieved the highest average score of 0.232. So, the answer is {\"answer\": \"Sampled\"}" } ], "images": [ "scigen/train/2972.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: (WMT19 segment-level) Kendall’s τ correlations of Bleu with different paraphrase sets (size indicated in brackets) against segment-level human judgements. Results significantly better than sentenceBleu are indicated as follows: p≤0.05 (*), p≤0.01 (**), ≤0.001 (***)." ], "reasoning": "Under the 1.5cmParaphrase baselines (+5) approach, the Sampled method achieved the highest average score of 0.232.", "question": "Which method under the 1.5cmParaphrase baselines (+5) approach achieved the highest average score?", "answer": "So, the answer is {\"answer\": \"Sampled\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe 1.5cmConstraints 4-grams method achieved the highest average score among all methods.\n" }, { "role": "assistant", "content": "The table shows that the 1.5cmConstraints 4-grams method achieved an average score of 0.235, which is not the highest. The highest average score is 0.333 achieved by WMT-19 best. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2972.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: (WMT19 segment-level) Kendall’s τ correlations of Bleu with different paraphrase sets (size indicated in brackets) against segment-level human judgements. Results significantly better than sentenceBleu are indicated as follows: p≤0.05 (*), p≤0.01 (**), ≤0.001 (***)." ], "reasoning": "The table shows that the 1.5cmConstraints 4-grams method achieved an average score of 0.235, which is not the highest. The highest average score is 0.333 achieved by WMT-19 best.", "statement": "The 1.5cmConstraints 4-grams method achieved the highest average score among all methods.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Inter-annotator agreement.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Participants chose the better pronoun text from candidates, with high agreement across language pairs, especially when excluding ties.We asked the participants to choose the text with the better pronoun, their choices being candidate A, candidate B, equivalent translations (tie), “neither is correct”, and “invalid candidates” (the highlighted words are not pronouns or are the wrong pronouns due to misalignment). All examples marked as these last two were excluded from further consideration. Each participant annotated a total of 500 examples per language pair. We also report the proportion of cases where the participants preferred the reference translation over the noisy version (see Avg%Ref). We can see that there is high agreement for all language pairs, ranging from 0.82 to 0.89. The ties seem to be the major source of disagreement: excluding them yields agreements in the range of 0.91–0.97." } ], "images": [ "scigen/train/2973.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Inter-annotator agreement." ], "reasoning": "Participants chose the better pronoun text from candidates, with high agreement across language pairs, especially when excluding ties.", "description": "We asked the participants to choose the text with the better pronoun, their choices being candidate A, candidate B, equivalent translations (tie), “neither is correct”, and “invalid candidates” (the highlighted words are not pronouns or are the wrong pronouns due to misalignment). All examples marked as these last two were excluded from further consideration. Each participant annotated a total of 500 examples per language pair. We also report the proportion of cases where the participants preferred the reference translation over the noisy version (see Avg%Ref). We can see that there is high agreement for all language pairs, ranging from 0.82 to 0.89. The ties seem to be the major source of disagreement: excluding them yields agreements in the range of 0.91–0.97.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 1: Inter-annotator agreement.' and proceed to solve the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich language pair had the highest agreement when excluding ties?\n" }, { "role": "assistant", "content": "By examining the 'AC1 (Excl. Ties)' column, the German to English pair has the highest agreement at 0.97. So, the answer is {\"answer\": \"German to English\"}" } ], "images": [ "scigen/train/2973.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Inter-annotator agreement." ], "reasoning": "By examining the 'AC1 (Excl. Ties)' column, the German to English pair has the highest agreement at 0.97.", "question": "Which language pair had the highest agreement when excluding ties?", "answer": "So, the answer is {\"answer\": \"German to English\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe French to English language pair had the lowest agreement when including ties.\n" }, { "role": "assistant", "content": "By examining the 'AC1 (Incl. Ties)' column, the Russian to English pair had the lowest agreement at 0.82, not the French to English pair. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2973.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Inter-annotator agreement." ], "reasoning": "By examining the 'AC1 (Incl. Ties)' column, the Russian to English pair had the lowest agreement at 0.82, not the French to English pair.", "statement": "The French to English language pair had the lowest agreement when including ties.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: Results on WMT14 with different contexts.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The experiments show that using ELMo improves accuracy significantly over GloVe-BiLSTM, especially in co-reference resolution tasks.The first three experiments in The results on the noisy data (Exp. 1-2) are indicative of how sensitive our model is to pronouns, since the difference between R and R^{\\prime} is in a single pronoun. The results on the reference/system translation pair R and S (Exp. 3) is indicative of performance in a real use-case. In case of the GloVe-BiLSTM model, using attention instead of averaging pronouns does not help. However, using ELMo greatly improves the accuracy of the baseline and also leads to an improvement while using attention. ELMo can model syntactic and semantic information and improve co-reference resolution Peters:2018, which could be a contributing factor." } ], "images": [ "scigen/train/2974.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on WMT14 with different contexts." ], "reasoning": "The experiments show that using ELMo improves accuracy significantly over GloVe-BiLSTM, especially in co-reference resolution tasks.", "description": "The first three experiments in The results on the noisy data (Exp. 1-2) are indicative of how sensitive our model is to pronouns, since the difference between R and R^{\\prime} is in a single pronoun. The results on the reference/system translation pair R and S (Exp. 3) is indicative of performance in a real use-case. In case of the GloVe-BiLSTM model, using attention instead of averaging pronouns does not help. However, using ELMo greatly improves the accuracy of the baseline and also leads to an improvement while using attention. ELMo can model syntactic and semantic information and improve co-reference resolution Peters:2018, which could be a contributing factor.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Results on WMT14 with different contexts.\nQuestion: What is the accuracy improvement when using ELMo instead of GloVe in the NC context setting for the R vs. R' test?\nSolve the above question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "In the NC context setting for the R vs. R' test (Exp. 2), the accuracy with GloVe is 68.97, and with ELMo, it is 88.04. The improvement is 88.04 - 68.97. So, the answer is {\"answer\": \"19.07\"}." } ], "images": [ "scigen/train/2974.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on WMT14 with different contexts." ], "reasoning": "In the NC context setting for the R vs. R' test (Exp. 2), the accuracy with GloVe is 68.97, and with ELMo, it is 88.04. The improvement is 88.04 - 68.97.", "question": "What is the accuracy improvement when using ELMo instead of GloVe in the NC context setting for the R vs. R' test?", "answer": "So, the answer is {\"answer\": \"19.07\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nUsing ELMo improves the accuracy of the baseline in all experiments.\n" }, { "role": "assistant", "content": "The table shows that in all experiments (Exp. 1-8), the accuracy with ELMo is higher than with GloVe, indicating an improvement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2974.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on WMT14 with different contexts." ], "reasoning": "The table shows that in all experiments (Exp. 1-8), the accuracy with ELMo is higher than with GloVe, indicating an improvement.", "statement": "Using ELMo improves the accuracy of the baseline in all experiments.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Accuracy and AC1 Agreement for the ELMo-based model predictions on the study dataset.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The ELMo model's accuracy is lower on the study dataset due to the inclusion of pronoun pairs with low agreement, but the overall agreement with human judgments remains high.Finally, we test our ELMo - common reference context model on the held-out dataset used for the user study. The accuracy of the system is lower on the study dataset. Note that since the training data was filtered based on the pronoun pairs with high agreement from the study, the study dataset contains pronoun pairs with low agreement that were not seen during training. We also calculate the agreement with human judgments excluding ties since our model does not handle ties. The overall agreement with the human judgments remains high. The model scored the noisy version higher than the reference in about 19% of the cases. Of these, 46% were pronoun pairs that were not seen during training." } ], "images": [ "scigen/train/2975.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Accuracy and AC1 Agreement for the ELMo-based model predictions on the study dataset." ], "reasoning": "The ELMo model's accuracy is lower on the study dataset due to the inclusion of pronoun pairs with low agreement, but the overall agreement with human judgments remains high.", "description": "Finally, we test our ELMo - common reference context model on the held-out dataset used for the user study. The accuracy of the system is lower on the study dataset. Note that since the training data was filtered based on the pronoun pairs with high agreement from the study, the study dataset contains pronoun pairs with low agreement that were not seen during training. We also calculate the agreement with human judgments excluding ties since our model does not handle ties. The overall agreement with the human judgments remains high. The model scored the noisy version higher than the reference in about 19% of the cases. Of these, 46% were pronoun pairs that were not seen during training.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Accuracy and AC1 Agreement for the ELMo-based model predictions on the study dataset.\nQuestion: What is the average accuracy of the ELMo model across all languages?\nSolve the above question based on the table information. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the accuracy of the ELMo model for different language pairs. The overall average accuracy is listed at the bottom of the table. So, the answer is {\"answer\": \"81.35\"}" } ], "images": [ "scigen/train/2975.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Accuracy and AC1 Agreement for the ELMo-based model predictions on the study dataset." ], "reasoning": "The table shows the accuracy of the ELMo model for different language pairs. The overall average accuracy is listed at the bottom of the table.", "question": "What is the average accuracy of the ELMo model across all languages?", "answer": "So, the answer is {\"answer\": \"81.35\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The ELMo model has the highest accuracy for Chinese to English translation., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe ELMo model has the highest accuracy for Chinese to English translation.\n" }, { "role": "assistant", "content": "The table lists the accuracy of the ELMo model for various language pairs. The highest accuracy listed is for Chinese to English translation at 82.4. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2975.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Accuracy and AC1 Agreement for the ELMo-based model predictions on the study dataset." ], "reasoning": "The table lists the accuracy of the ELMo model for various language pairs. The highest accuracy listed is for Chinese to English translation at 82.4.", "statement": "The ELMo model has the highest accuracy for Chinese to English translation.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Results of the few-shot experiments with varying numbers of target-language examples k. For each k, we report performance averaged across languages and the difference (Δ) with respect to the zero-shot setting.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Substantial improvements are noted in few-shot learning setups, with lower-level tasks showing massive gains compared to higher-level tasks.We note substantial improvements in few-shot learning setups for all tasks. However, the results also reveal notable differences between different types of tasks. For higher-level language understanding tasks the improvements are less pronounced; the maximum gains after seeing k=1,000 target-language instances and 10 articles, respectively, are between 2.1 and 4.57 points. On the other hand, the gains for the lower-level tasks are massive: between 14.11 and 26 percentage points, choosing the best sampling strategy for each task. Moreover, the gains in all lower-level tasks are substantial even when we add only 10 annotated sentences in the target language (on average, up to 14.45 percentage points on DEP and 13.42 points on POS). What is more, additional experiments (omitted for brevity) show substantial gains for DEP and POS even when we add fewer than 5 annotated sentences. Part-of-Speech Tagging. Hovy et al. measure agreement of crowdsourced POS annotations with expert annotations ; they crowdsource annotations for 1,000 tweets, at a cost of $0.05 for every 10 tokens. With a total of 14,619 tokens in the corpus, this amounts to approximately $73 for 1,000 tweets, which is ≥1,000 sentences. Named Entity Recognition. Bontcheva et al. At a median pay of $11.37/hr, this amounts to around 190 sentences annotated in an hour. In other words, in less than 3 hours, we can collect more than 500 annotated examples." } ], "images": [ "scigen/train/2976.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of the few-shot experiments with varying numbers of target-language examples k. For each k, we report performance averaged across languages and the difference (Δ) with respect to the zero-shot setting." ], "reasoning": "Substantial improvements are noted in few-shot learning setups, with lower-level tasks showing massive gains compared to higher-level tasks.", "description": "We note substantial improvements in few-shot learning setups for all tasks. However, the results also reveal notable differences between different types of tasks. For higher-level language understanding tasks the improvements are less pronounced; the maximum gains after seeing k=1,000 target-language instances and 10 articles, respectively, are between 2.1 and 4.57 points. On the other hand, the gains for the lower-level tasks are massive: between 14.11 and 26 percentage points, choosing the best sampling strategy for each task. Moreover, the gains in all lower-level tasks are substantial even when we add only 10 annotated sentences in the target language (on average, up to 14.45 percentage points on DEP and 13.42 points on POS). What is more, additional experiments (omitted for brevity) show substantial gains for DEP and POS even when we add fewer than 5 annotated sentences. Part-of-Speech Tagging. Hovy et al. measure agreement of crowdsourced POS annotations with expert annotations ; they crowdsource annotations for 1,000 tweets, at a cost of $0.05 for every 10 tokens. With a total of 14,619 tokens in the corpus, this amounts to approximately $73 for 1,000 tweets, which is ≥1,000 sentences. Named Entity Recognition. Bontcheva et al. At a median pay of $11.37/hr, this amounts to around 190 sentences annotated in an hour. In other words, in less than 3 hours, we can collect more than 500 annotated examples.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Results of the few-shot experiments with varying numbers of target-language examples k. For each k, we report performance averaged across languages and the difference (Δ) with respect to the zero-shot setting.\nQuestion: What is the maximum gain in score for the DEP task when k=1000?\nSolve the above question based on the table information. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the maximum gain for the DEP task with different sampling strategies. The highest gain for k=1000 is 26.73 points using the Longest sampling strategy. So, the answer is {\"answer\": \"26.73\"}" } ], "images": [ "scigen/train/2976.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of the few-shot experiments with varying numbers of target-language examples k. For each k, we report performance averaged across languages and the difference (Δ) with respect to the zero-shot setting." ], "reasoning": "The table shows the maximum gain for the DEP task with different sampling strategies. The highest gain for k=1000 is 26.73 points using the Longest sampling strategy.", "question": "What is the maximum gain in score for the DEP task when k=1000?", "answer": "So, the answer is {\"answer\": \"26.73\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The maximum gain for POS tasks when k=1000 is less than 20 points., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe maximum gain for POS tasks when k=1000 is less than 20 points.\n" }, { "role": "assistant", "content": "The table shows the gains for POS tasks with different sampling strategies. The maximum gain for POS tasks when k=1000 is 21.54 points using the Longest sampling strategy. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2976.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of the few-shot experiments with varying numbers of target-language examples k. For each k, we report performance averaged across languages and the difference (Δ) with respect to the zero-shot setting." ], "reasoning": "The table shows the gains for POS tasks with different sampling strategies. The maximum gain for POS tasks when k=1000 is 21.54 points using the Longest sampling strategy.", "statement": "The maximum gain for POS tasks when k=1000 is less than 20 points.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 10: Performance of IOB tagging and entity type prediction.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Despite a slight reduction in performance for entity detection, the overall performance for entity detection and linking remains sufficient for the downstream task, minimally affecting KB-QA performance.And, although ∼ 0.002 performance reduction is observed for entity detection subtask, the performance of entity detection and linking is good enough for the downstream task, which thus poses a very minor effect on the performance of KB-QA." } ], "images": [ "scigen/train/2977.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Performance of IOB tagging and entity type prediction." ], "reasoning": "Despite a slight reduction in performance for entity detection, the overall performance for entity detection and linking remains sufficient for the downstream task, minimally affecting KB-QA performance.", "description": "And, although ∼ 0.002 performance reduction is observed for entity detection subtask, the performance of entity detection and linking is good enough for the downstream task, which thus poses a very minor effect on the performance of KB-QA.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 10: Performance of IOB tagging and entity type prediction.', and tackle the question below in a sequential manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the difference in F1 Score for Entity Type between 'Ours' and 'w/o Multi'?\n" }, { "role": "assistant", "content": "To find the difference in F1 Score for Entity Type between 'Ours' and 'w/o Multi', subtract the F1 Score of 'Ours' (0.9674) from the F1 Score of 'w/o Multi' (0.9717). So, the answer is {\"answer\": \"0.0043\"}." } ], "images": [ "scigen/train/2977.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Performance of IOB tagging and entity type prediction." ], "reasoning": "To find the difference in F1 Score for Entity Type between 'Ours' and 'w/o Multi', subtract the F1 Score of 'Ours' (0.9674) from the F1 Score of 'w/o Multi' (0.9717).", "question": "What is the difference in F1 Score for Entity Type between 'Ours' and 'w/o Multi'?", "answer": "So, the answer is {\"answer\": \"0.0043\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Precision for IOB Tagging is higher in 'Ours' compared to 'w/o Multi'. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Precision for IOB Tagging is higher in 'Ours' compared to 'w/o Multi'.\n" }, { "role": "assistant", "content": "The table shows that the Precision for IOB Tagging is 0.9960 for 'Ours' and 0.9972 for 'w/o Multi'. Since 0.9960 is less than 0.9972, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2977.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Performance of IOB tagging and entity type prediction." ], "reasoning": "The table shows that the Precision for IOB Tagging is 0.9960 for 'Ours' and 0.9972 for 'w/o Multi'. Since 0.9960 is less than 0.9972, the statement is incorrect.", "statement": "The Precision for IOB Tagging is higher in 'Ours' compared to 'w/o Multi'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 9: Sketch accuracy for logical form generation.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Despite a slight reduction in entity detection performance, the overall performance for entity detection and linking remains sufficient for the downstream task, minimally affecting KB-QA performance.And, although ∼ 0.002 performance reduction is observed for entity detection subtask, the performance of entity detection and linking is good enough for the downstream task, which thus poses a very minor effect on the performance of KB-QA." } ], "images": [ "scigen/train/2978.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Sketch accuracy for logical form generation." ], "reasoning": "Despite a slight reduction in entity detection performance, the overall performance for entity detection and linking remains sufficient for the downstream task, minimally affecting KB-QA performance.", "description": "And, although ∼ 0.002 performance reduction is observed for entity detection subtask, the performance of entity detection and linking is good enough for the downstream task, which thus poses a very minor effect on the performance of KB-QA.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 9: Sketch accuracy for logical form generation.', solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the overall performance difference between 'Ours' and 'w/o Multi'?\n" }, { "role": "assistant", "content": "The overall performance for 'Ours' is 0.7940, and for 'w/o Multi' it is 0.7872. The difference is calculated as 0.7940 - 0.7872. So, the answer is {\"answer\": \"0.0068\"}" } ], "images": [ "scigen/train/2978.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Sketch accuracy for logical form generation." ], "reasoning": "The overall performance for 'Ours' is 0.7940, and for 'w/o Multi' it is 0.7872. The difference is calculated as 0.7940 - 0.7872.", "question": "What is the overall performance difference between 'Ours' and 'w/o Multi'?", "answer": "So, the answer is {\"answer\": \"0.0068\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The performance of 'Ours' for Simple Question (Ellipsis) is higher than 0.9., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe performance of 'Ours' for Simple Question (Ellipsis) is higher than 0.9.\n" }, { "role": "assistant", "content": "The table shows that the performance of 'Ours' for Simple Question (Ellipsis) is 0.9073, which is indeed higher than 0.9. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2978.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Sketch accuracy for logical form generation." ], "reasoning": "The table shows that the performance of 'Ours' for Simple Question (Ellipsis) is 0.9073, which is indeed higher than 0.9.", "statement": "The performance of 'Ours' for Simple Question (Ellipsis) is higher than 0.9.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 6: Results on the PubMed validation set with different positional function or different hierarchical information.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The boundary positional function significantly outperforms the lead or no positional functions, and using a non-fully-connected graph with hierarchical information results in better performance than a flat fully-connected graph.We can see that using the wrong positional function hurts the model’s performance when comparing no positional function with lead bias function. Our boundary positional function outperforms the lead or no positional functions significantly. We observe that using a non-fully-connected graph with hierarchical information results in better performance than the flat fully-connected graph." } ], "images": [ "scigen/train/2979.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results on the PubMed validation set with different positional function or different hierarchical information." ], "reasoning": "The boundary positional function significantly outperforms the lead or no positional functions, and using a non-fully-connected graph with hierarchical information results in better performance than a flat fully-connected graph.", "description": "We can see that using the wrong positional function hurts the model’s performance when comparing no positional function with lead bias function. Our boundary positional function outperforms the lead or no positional functions significantly. We observe that using a non-fully-connected graph with hierarchical information results in better performance than the flat fully-connected graph.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 6: Results on the PubMed validation set with different positional function or different hierarchical information.', and tackle the question below in a sequential manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model and positional function combination achieved the highest ROUGE-1 score?\n" }, { "role": "assistant", "content": "To find the highest ROUGE-1 score, we need to compare all the ROUGE-1 scores in the table. The highest ROUGE-1 score is 43.42, achieved by the 'hierarchy-add (ours)' model. So, the answer is {\"answer\": \"hierarchy-add (ours)\"}." } ], "images": [ "scigen/train/2979.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results on the PubMed validation set with different positional function or different hierarchical information." ], "reasoning": "To find the highest ROUGE-1 score, we need to compare all the ROUGE-1 scores in the table. The highest ROUGE-1 score is 43.42, achieved by the 'hierarchy-add (ours)' model.", "question": "Which model and positional function combination achieved the highest ROUGE-1 score?", "answer": "So, the answer is {\"answer\": \"hierarchy-add (ours)\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The lead positional function achieved the lowest ROUGE-2 score. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe lead positional function achieved the lowest ROUGE-2 score.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare all the ROUGE-2 scores in the table. The lowest ROUGE-2 score is 12.13, achieved by the 'lead' positional function. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2979.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results on the PubMed validation set with different positional function or different hierarchical information." ], "reasoning": "To verify this statement, we need to compare all the ROUGE-2 scores in the table. The lowest ROUGE-2 score is 12.13, achieved by the 'lead' positional function.", "statement": "The lead positional function achieved the lowest ROUGE-2 score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Human evaluation results based on 20 sampled reference summaries with 281 system summary sentences. Each reference summary-sentence pair is annotated by two annotators with an average annotator agreement of 73.24%. The results are averaged across 127 sentences from HipoRank and 154 sentences from state-of-the-art unsupervised extractive summarization system PACSUM (Zheng and Lapata, 2019).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "HipoRank outperforms PACSUM in both content coverage and importance, with significant p-values and high inter-annotator agreement.HipoRank is shown to be significantly better than PACSUM in terms of both content coverage (p=0.004) and importance (p=0.014), with high inter-annotator agreements (avg. 73.24%). These results indicate that the use of discourse structure to guide the graph construction with our proposed boundary function and hierarchy information improves summarization quality." } ], "images": [ "scigen/train/2980.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Human evaluation results based on 20 sampled reference summaries with 281 system summary sentences. Each reference summary-sentence pair is annotated by two annotators with an average annotator agreement of 73.24%. The results are averaged across 127 sentences from HipoRank and 154 sentences from state-of-the-art unsupervised extractive summarization system PACSUM (Zheng and Lapata, 2019)." ], "reasoning": "HipoRank outperforms PACSUM in both content coverage and importance, with significant p-values and high inter-annotator agreement.", "description": "HipoRank is shown to be significantly better than PACSUM in terms of both content coverage (p=0.004) and importance (p=0.014), with high inter-annotator agreements (avg. 73.24%). These results indicate that the use of discourse structure to guide the graph construction with our proposed boundary function and hierarchy information improves summarization quality.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 4: Human evaluation results based on 20 sampled reference summaries with 281 system summary sentences. Each reference summary-sentence pair is annotated by two annotators with an average annotator agreement of 73.24%. The results are averaged across 127 sentences from HipoRank and 154 sentences from state-of-the-art unsupervised extractive summarization system PACSUM (Zheng and Lapata, 2019).' to sequentially solve the question presented below. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhich summarization method has a higher percentage of YES for importance?\n" }, { "role": "assistant", "content": "The table shows the percentage of YES for importance for both PACSUM and HipoRank. HipoRank has a higher percentage (71.65%) compared to PACSUM (70.13%). So, the answer is {\"answer\": \"HipoRank\"}." } ], "images": [ "scigen/train/2980.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Human evaluation results based on 20 sampled reference summaries with 281 system summary sentences. Each reference summary-sentence pair is annotated by two annotators with an average annotator agreement of 73.24%. The results are averaged across 127 sentences from HipoRank and 154 sentences from state-of-the-art unsupervised extractive summarization system PACSUM (Zheng and Lapata, 2019)." ], "reasoning": "The table shows the percentage of YES for importance for both PACSUM and HipoRank. HipoRank has a higher percentage (71.65%) compared to PACSUM (70.13%).", "question": "Which summarization method has a higher percentage of YES for importance?", "answer": "So, the answer is {\"answer\": \"HipoRank\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nHipoRank has a higher percentage of YES for content coverage than PACSUM.\n" }, { "role": "assistant", "content": "The table shows that HipoRank has a content coverage percentage of 77.17%, which is higher than PACSUM's 74.03%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2980.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Human evaluation results based on 20 sampled reference summaries with 281 system summary sentences. Each reference summary-sentence pair is annotated by two annotators with an average annotator agreement of 73.24%. The results are averaged across 127 sentences from HipoRank and 154 sentences from state-of-the-art unsupervised extractive summarization system PACSUM (Zheng and Lapata, 2019)." ], "reasoning": "The table shows that HipoRank has a content coverage percentage of 77.17%, which is higher than PACSUM's 74.03%.", "statement": "HipoRank has a higher percentage of YES for content coverage than PACSUM.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Translation accuracy (BLEU) under different settings. The second and third rows list source and target domains respectively. “DAFE w/o Embed” denotes DAFE without embedding learners and “Back-DAFE” denotes back-translation by target-to-source model trained with DAFE. DAFE outperforms other approaches when adapting between domains (row 1-5, column 2-7) and is complementary to back-translation (row 6-8).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Back translation improves the model more than the copy method, while DAFE achieves even better performance. Removing embedding learners degrades performance.The copy method (row 2) and back translation (row 3) both improve the model significantly, with back-translation being a better alternative to copying. DAFE (row 5) achieves superior performance compared to back-translation, with improvements of up to 2 BLEU points. Also, removing the embedding learners leads to degraded performance (row 4), indicating the necessity of their existence." } ], "images": [ "scigen/train/2981.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Translation accuracy (BLEU) under different settings. The second and third rows list source and target domains respectively. “DAFE w/o Embed” denotes DAFE without embedding learners and “Back-DAFE” denotes back-translation by target-to-source model trained with DAFE. DAFE outperforms other approaches when adapting between domains (row 1-5, column 2-7) and is complementary to back-translation (row 6-8)." ], "reasoning": "Back translation improves the model more than the copy method, while DAFE achieves even better performance. Removing embedding learners degrades performance.", "description": "The copy method (row 2) and back translation (row 3) both improve the model significantly, with back-translation being a better alternative to copying. DAFE (row 5) achieves superior performance compared to back-translation, with improvements of up to 2 BLEU points. Also, removing the embedding learners leads to degraded performance (row 4), indicating the necessity of their existence.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Translation accuracy (BLEU) under different settings. The second and third rows list source and target domains respectively. “DAFE w/o Embed” denotes DAFE without embedding learners and “Back-DAFE” denotes back-translation by target-to-source model trained with DAFE. DAFE outperforms other approaches when adapting between domains (row 1-5, column 2-7) and is complementary to back-translation (row 6-8).\nQuestion: Which method achieves the highest performance in German-English MED?\nSolve the above question based on the table information. Let's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the scores in the German-English MED column, the highest score is achieved by the Back-DAFE + DAFE method in row 8 with a score of 14.28. So, the answer is {\"answer\": \"Back-DAFE + DAFE\"}." } ], "images": [ "scigen/train/2981.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Translation accuracy (BLEU) under different settings. The second and third rows list source and target domains respectively. “DAFE w/o Embed” denotes DAFE without embedding learners and “Back-DAFE” denotes back-translation by target-to-source model trained with DAFE. DAFE outperforms other approaches when adapting between domains (row 1-5, column 2-7) and is complementary to back-translation (row 6-8)." ], "reasoning": "By comparing the scores in the German-English MED column, the highest score is achieved by the Back-DAFE + DAFE method in row 8 with a score of 14.28.", "question": "Which method achieves the highest performance in German-English MED?", "answer": "So, the answer is {\"answer\": \"Back-DAFE + DAFE\"}." }, { "messages": [ { "role": "user", "content": "Removing embedding learners leads to improved performance in German-English LAW. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nRemoving embedding learners leads to improved performance in German-English LAW.\n" }, { "role": "assistant", "content": "The description states that removing embedding learners leads to degraded performance. In the table, DAFE w/o Embed (row 4) has a lower score (23.44) compared to DAFE (row 5) with a score of 24.23 in German-English LAW. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2981.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Translation accuracy (BLEU) under different settings. The second and third rows list source and target domains respectively. “DAFE w/o Embed” denotes DAFE without embedding learners and “Back-DAFE” denotes back-translation by target-to-source model trained with DAFE. DAFE outperforms other approaches when adapting between domains (row 1-5, column 2-7) and is complementary to back-translation (row 6-8)." ], "reasoning": "The description states that removing embedding learners leads to degraded performance. In the table, DAFE w/o Embed (row 4) has a lower score (23.44) compared to DAFE (row 5) with a score of 24.23 in German-English LAW.", "statement": "Removing embedding learners leads to improved performance in German-English LAW.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Percentage label and image retrieval accuracy.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The GC method consistently improves performance across both Vision→Language and Language→Vision tasks compared to the NN and NNnrm methods.We consider both the usual image labeling setting (Vision→Language) and the image retrieval setting (Language→Vision). For the Vision→Language task, we use as pivot set the 100K test images (1,000 labels x 100 images/label) and an additional randomly chosen 100K images. The search space is the entire label set of 5,000 words. For Language→Vision, we use as pivot set the entire word list (5,000) and the target set is the entire set of images (500,000). The objects depicted in the images form a set of 5,000 distinct elements, therefore, for the word cat, for example, returning any of the 100 cat images is correct. Chance accuracy in both settings is thus at 1/5,000. We observe that, differently from the translation case, correcting by normalizing the cosine scores of the elements in the target domain (NNnrm) leads to poorer results than no correction. On the other hand, the GC method is consistent across domains, and it improves significantly on the standard NN method in both settings. Note that, while there are differences between the setups, Frome et al." } ], "images": [ "scigen/train/2982.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Percentage label and image retrieval accuracy." ], "reasoning": "The GC method consistently improves performance across both Vision→Language and Language→Vision tasks compared to the NN and NNnrm methods.", "description": "We consider both the usual image labeling setting (Vision→Language) and the image retrieval setting (Language→Vision). For the Vision→Language task, we use as pivot set the 100K test images (1,000 labels x 100 images/label) and an additional randomly chosen 100K images. The search space is the entire label set of 5,000 words. For Language→Vision, we use as pivot set the entire word list (5,000) and the target set is the entire set of images (500,000). The objects depicted in the images form a set of 5,000 distinct elements, therefore, for the word cat, for example, returning any of the 100 cat images is correct. Chance accuracy in both settings is thus at 1/5,000. We observe that, differently from the translation case, correcting by normalizing the cosine scores of the elements in the target domain (NNnrm) leads to poorer results than no correction. On the other hand, the GC method is consistent across domains, and it improves significantly on the standard NN method in both settings. Note that, while there are differences between the setups, Frome et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich method shows the highest improvement in the Language→Vision task?\nSolve the above question based on the table titled 'Table 2: Percentage label and image retrieval accuracy.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that in the Language→Vision task, the GC method has a score of 2.2, which is higher than both the NN and NNnrm methods. So, the answer is {\"answer\": \"GC\"}" } ], "images": [ "scigen/train/2982.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Percentage label and image retrieval accuracy." ], "reasoning": "The table shows that in the Language→Vision task, the GC method has a score of 2.2, which is higher than both the NN and NNnrm methods.", "question": "Which method shows the highest improvement in the Language→Vision task?", "answer": "So, the answer is {\"answer\": \"GC\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe NNnrm method performs better than the NN method in the Vision→Language task.\n" }, { "role": "assistant", "content": "The table shows that in the Vision→Language task, the NNnrm method has a score of 0.8, which is lower than the NN method's score of 1.0. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2982.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Percentage label and image retrieval accuracy." ], "reasoning": "The table shows that in the Vision→Language task, the NNnrm method has a score of 0.8, which is lower than the NN method's score of 1.0.", "statement": "The NNnrm method performs better than the NN method in the Vision→Language task.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Figure 2: Percentage accuracy scores for En→It translation. Left: No regularization and Generalized Cross-validation (GCV) regression varying the training size. Right: Results split by frequency bins, 5K training and no regularization.'.\n" }, { "role": "assistant", "content": "The corrected methods show significant improvements over the standard NN, especially for medium- and low-frequency words, and regularization benefits the corrected measures with small training data but hampers the standard method.We report results without regularization as well as with the regularization parameter λ estimated by generalized cross-validation (GCV) (Hastie Both corrected methods achieve significant improvements over standard NN, ranging from 7% to 14%. For the standard method, the performance decreases as the training data size increases beyond 5K, probably due to the noise added by lower-frequency words. The corrected measures are robust against this effect: adding more training data does not help, but it does not harm them either. Regularization does not improve, and actually hampers the standard method, whereas it benefits the corrected measures when using a small amount of training data (1K), and does not affect performance otherwise. The results by frequency bin show that most of the improvements are brought about for the all-important medium- and low-frequency words. Although not directly comparable, the absolute numbers we obtain are in the range of those reported by Mikolov et al. Furthermore, we observe, similarly to them, that the accuracy scores underestimate the actual performance, as many translations are in fact correct but not present in our gold dictionary." } ], "images": [ "scigen/train/2983.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 2: Percentage accuracy scores for En→It translation. Left: No regularization and Generalized Cross-validation (GCV) regression varying the training size. Right: Results split by frequency bins, 5K training and no regularization." ], "reasoning": "The corrected methods show significant improvements over the standard NN, especially for medium- and low-frequency words, and regularization benefits the corrected measures with small training data but hampers the standard method.", "description": "We report results without regularization as well as with the regularization parameter λ estimated by generalized cross-validation (GCV) (Hastie Both corrected methods achieve significant improvements over standard NN, ranging from 7% to 14%. For the standard method, the performance decreases as the training data size increases beyond 5K, probably due to the noise added by lower-frequency words. The corrected measures are robust against this effect: adding more training data does not help, but it does not harm them either. Regularization does not improve, and actually hampers the standard method, whereas it benefits the corrected measures when using a small amount of training data (1K), and does not affect performance otherwise. The results by frequency bin show that most of the improvements are brought about for the all-important medium- and low-frequency words. Although not directly comparable, the absolute numbers we obtain are in the range of those reported by Mikolov et al. Furthermore, we observe, similarly to them, that the accuracy scores underestimate the actual performance, as many translations are in fact correct but not present in our gold dictionary.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Figure 2: Percentage accuracy scores for En→It translation. Left: No regularization and Generalized Cross-validation (GCV) regression varying the training size. Right: Results split by frequency bins, 5K training and no regularization.', solve the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the performance of the No regularization GC method with a training size of 5K?\n" }, { "role": "assistant", "content": "From the table, locate the row corresponding to a training size of 5K and the column for No regularization GC. The value is 37.7. So, the answer is {\"answer\": \"37.7\"}." } ], "images": [ "scigen/train/2983.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 2: Percentage accuracy scores for En→It translation. Left: No regularization and Generalized Cross-validation (GCV) regression varying the training size. Right: Results split by frequency bins, 5K training and no regularization." ], "reasoning": "From the table, locate the row corresponding to a training size of 5K and the column for No regularization GC. The value is 37.7.", "question": "What is the performance of the No regularization GC method with a training size of 5K?", "answer": "So, the answer is {\"answer\": \"37.7\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The performance of the No regularization NN method decreases as the training data size increases beyond 5K., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe performance of the No regularization NN method decreases as the training data size increases beyond 5K.\n" }, { "role": "assistant", "content": "From the table, the performance of the No regularization NN method is 30.3 at 5K, 30.0 at 10K, and 25.1 at 20K, showing a decrease as the training size increases beyond 5K. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2983.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 2: Percentage accuracy scores for En→It translation. Left: No regularization and Generalized Cross-validation (GCV) regression varying the training size. Right: Results split by frequency bins, 5K training and no regularization." ], "reasoning": "From the table, the performance of the No regularization NN method is 30.3 at 5K, 30.0 at 10K, and 25.1 at 20K, showing a decrease as the training size increases beyond 5K.", "statement": "The performance of the No regularization NN method decreases as the training data size increases beyond 5K.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Figure 2: Percentage accuracy scores for En→It translation. Left: No regularization and Generalized Cross-validation (GCV) regression varying the training size. Right: Results split by frequency bins, 5K training and no regularization.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Corrected methods improve performance significantly over standard NN, especially for medium- and low-frequency words, and regularization benefits corrected measures with small training data.We report results without regularization as well as with the regularization parameter λ estimated by generalized cross-validation (GCV) (Hastie Both corrected methods achieve significant improvements over standard NN, ranging from 7% to 14%. For the standard method, the performance decreases as the training data size increases beyond 5K, probably due to the noise added by lower-frequency words. The corrected measures are robust against this effect: adding more training data does not help, but it does not harm them either. Regularization does not improve, and actually hampers the standard method, whereas it benefits the corrected measures when using a small amount of training data (1K), and does not affect performance otherwise. The results by frequency bin show that most of the improvements are brought about for the all-important medium- and low-frequency words. Although not directly comparable, the absolute numbers we obtain are in the range of those reported by Mikolov et al. Furthermore, we observe, similarly to them, that the accuracy scores underestimate the actual performance, as many translations are in fact correct but not present in our gold dictionary." } ], "images": [ "scigen/train/2984.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 2: Percentage accuracy scores for En→It translation. Left: No regularization and Generalized Cross-validation (GCV) regression varying the training size. Right: Results split by frequency bins, 5K training and no regularization." ], "reasoning": "Corrected methods improve performance significantly over standard NN, especially for medium- and low-frequency words, and regularization benefits corrected measures with small training data.", "description": "We report results without regularization as well as with the regularization parameter λ estimated by generalized cross-validation (GCV) (Hastie Both corrected methods achieve significant improvements over standard NN, ranging from 7% to 14%. For the standard method, the performance decreases as the training data size increases beyond 5K, probably due to the noise added by lower-frequency words. The corrected measures are robust against this effect: adding more training data does not help, but it does not harm them either. Regularization does not improve, and actually hampers the standard method, whereas it benefits the corrected measures when using a small amount of training data (1K), and does not affect performance otherwise. The results by frequency bin show that most of the improvements are brought about for the all-important medium- and low-frequency words. Although not directly comparable, the absolute numbers we obtain are in the range of those reported by Mikolov et al. Furthermore, we observe, similarly to them, that the accuracy scores underestimate the actual performance, as many translations are in fact correct but not present in our gold dictionary.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Figure 2: Percentage accuracy scores for En→It translation. Left: No regularization and Generalized Cross-validation (GCV) regression varying the training size. Right: Results split by frequency bins, 5K training and no regularization.', solve the following question step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the GCV GC score for a training size of 1K?\n" }, { "role": "assistant", "content": "To find the GCV GC score for a training size of 1K, locate the row with Train size 1K and then find the value in the GCV GC column. So, the answer is {\"answer\": \"28.7\"}." } ], "images": [ "scigen/train/2984.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 2: Percentage accuracy scores for En→It translation. Left: No regularization and Generalized Cross-validation (GCV) regression varying the training size. Right: Results split by frequency bins, 5K training and no regularization." ], "reasoning": "To find the GCV GC score for a training size of 1K, locate the row with Train size 1K and then find the value in the GCV GC column.", "question": "What is the GCV GC score for a training size of 1K?", "answer": "So, the answer is {\"answer\": \"28.7\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe GCV NN score decreases as the training size increases beyond 10K.\n" }, { "role": "assistant", "content": "To verify this statement, observe the GCV NN scores for training sizes 10K and 20K. The score decreases from 28.2 to 23.7. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2984.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 2: Percentage accuracy scores for En→It translation. Left: No regularization and Generalized Cross-validation (GCV) regression varying the training size. Right: Results split by frequency bins, 5K training and no regularization." ], "reasoning": "To verify this statement, observe the GCV NN scores for training sizes 10K and 20K. The score decreases from 28.2 to 23.7.", "statement": "The GCV NN score decreases as the training size increases beyond 10K.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Evaluation results on the datasets.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The model achieves the best performance when λ = 3, and the graph matching layer enhances the model's ability to handle entities with different neighbors in two KGs.Following previous works, we used Hits@1 and Hits@10 to evaluate our model, where Hits@k measures the proportion of correctly aligned entities ranked in the top k. Let us first look at the impacts of hop size of GCN2 to our model. In experiments, we find the model achieves the best performance when λ = 3. To better understand on which type of entities that our model could better deal with due to introducing the graph matching layer, we analyze the entities that our model correctly predicts while NodeMatching does not. We find the graph matching layer enhances the ability of our model in handling the entities whose most neighbors in two KGs are different. For such entities, although most local matching information indicate that these two entities are irrelevant, the graph matching layer could alleviate this by propagating the most relevant local matching information throughout the graph." } ], "images": [ "scigen/train/2985.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Evaluation results on the datasets." ], "reasoning": "The model achieves the best performance when λ = 3, and the graph matching layer enhances the model's ability to handle entities with different neighbors in two KGs.", "description": "Following previous works, we used Hits@1 and Hits@10 to evaluate our model, where Hits@k measures the proportion of correctly aligned entities ranked in the top k. Let us first look at the impacts of hop size of GCN2 to our model. In experiments, we find the model achieves the best performance when λ = 3. To better understand on which type of entities that our model could better deal with due to introducing the graph matching layer, we analyze the entities that our model correctly predicts while NodeMatching does not. We find the graph matching layer enhances the ability of our model in handling the entities whose most neighbors in two KGs are different. For such entities, although most local matching information indicate that these two entities are irrelevant, the graph matching layer could alleviate this by propagating the most relevant local matching information throughout the graph.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 1: Evaluation results on the datasets.', and sequentially solve the question, writing out each step of your reasoning process. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the Hits@1 score for the EN-FR language pair when Hop GCN2 is set to 3?\n" }, { "role": "assistant", "content": "To find the Hits@1 score for the EN-FR language pair when Hop GCN2 is set to 3, locate the row 'Hop GCN2 = 3' and the column 'EN-FR @1'. The value is 88.38. So, the answer is {\"answer\": \"88.38\"}" } ], "images": [ "scigen/train/2985.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Evaluation results on the datasets." ], "reasoning": "To find the Hits@1 score for the EN-FR language pair when Hop GCN2 is set to 3, locate the row 'Hop GCN2 = 3' and the column 'EN-FR @1'. The value is 88.38.", "question": "What is the Hits@1 score for the EN-FR language pair when Hop GCN2 is set to 3?", "answer": "So, the answer is {\"answer\": \"88.38\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model achieves the highest Hits@10 score for the ZH-EN language pair when Hop GCN2 is set to 3.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Hits@10 scores for the ZH-EN language pair across different Hop GCN2 settings. The highest score is 78.48, which occurs when Hop GCN2 is set to 3. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2985.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Evaluation results on the datasets." ], "reasoning": "To verify this statement, compare the Hits@10 scores for the ZH-EN language pair across different Hop GCN2 settings. The highest score is 78.48, which occurs when Hop GCN2 is set to 3.", "statement": "The model achieves the highest Hits@10 score for the ZH-EN language pair when Hop GCN2 is set to 3.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Comparison of FST size and WER/Speed.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Despite a 10x filesize difference, the larger language model decodes faster due to reduced perplexity and extra pruning.Three different trigram language models with different pruning thresholds (3e−10, 1e−10, and no pruning, respectively) are used with other parameters held constant. Despite a 10x filesize difference, the decode performs faster using the large language model likely due to reduced perplexity during decoding yielding extra pruning, and subsequently improved speed." } ], "images": [ "scigen/train/2986.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of FST size and WER/Speed." ], "reasoning": "Despite a 10x filesize difference, the larger language model decodes faster due to reduced perplexity and extra pruning.", "description": "Three different trigram language models with different pruning thresholds (3e−10, 1e−10, and no pruning, respectively) are used with other parameters held constant. Despite a 10x filesize difference, the decode performs faster using the large language model likely due to reduced perplexity during decoding yielding extra pruning, and subsequently improved speed.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: 'Table 3: Comparison of FST size and WER/Speed.'\nQuestion: Which language model has the fastest test-clean xRTF?\n" }, { "role": "assistant", "content": "By comparing the test-clean xRTF values, the 3-gram model without pruning has the lowest value of 4.02, indicating the fastest decode time. So, the answer is {\"answer\": \"3-gram\"}." } ], "images": [ "scigen/train/2986.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of FST size and WER/Speed." ], "reasoning": "By comparing the test-clean xRTF values, the 3-gram model without pruning has the lowest value of 4.02, indicating the fastest decode time.", "question": "Which language model has the fastest test-clean xRTF?", "answer": "So, the answer is {\"answer\": \"3-gram\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe 3-gram model with a pruning threshold of 1e-10 has a smaller HCLG size than the 3-gram model with a pruning threshold of 3e-10.\n" }, { "role": "assistant", "content": "The table shows that the HCLG size for the 3-gram model with a pruning threshold of 1e-10 is 467.0 MB, which is larger than the 192.6 MB size for the 3-gram model with a pruning threshold of 3e-10. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2986.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of FST size and WER/Speed." ], "reasoning": "The table shows that the HCLG size for the 3-gram model with a pruning threshold of 1e-10 is 467.0 MB, which is larger than the 192.6 MB size for the 3-gram model with a pruning threshold of 3e-10.", "statement": "The 3-gram model with a pruning threshold of 1e-10 has a smaller HCLG size than the 3-gram model with a pruning threshold of 3e-10.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Validation of lattice quality with LibriSpeech model and test sets.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "We validate that the word error rate (WER) and oracle word error rate (OWER) are within tolerable limits, and measure the lattice density (lat. den.) to confirm the produced lattices are of similar size.We validate that the word error rate (WER) is within tolerable limits, as well as the oracle word error rate (OWER). The Oracle WER is a proxy for determining if all expected alternate paths exist within the lattice. Finally, we measure the lattice density (lat. den.), which is an average measure of outgoing arcs. This confirms the produced lattices are of similar size." } ], "images": [ "scigen/train/2987.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Validation of lattice quality with LibriSpeech model and test sets." ], "reasoning": "We validate that the word error rate (WER) and oracle word error rate (OWER) are within tolerable limits, and measure the lattice density (lat. den.) to confirm the produced lattices are of similar size.", "description": "We validate that the word error rate (WER) is within tolerable limits, as well as the oracle word error rate (OWER). The Oracle WER is a proxy for determining if all expected alternate paths exist within the lattice. Finally, we measure the lattice density (lat. den.), which is an average measure of outgoing arcs. This confirms the produced lattices are of similar size.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Validation of lattice quality with LibriSpeech model and test sets.\nQuestion: What is the difference in test-clean OWER between the Baseline and GPU decoders?\nGive a detailed response to the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the difference in test-clean OWER between the Baseline and GPU decoders, subtract the GPU's test-clean OWER (1.09) from the Baseline's test-clean OWER (1.05). So, the answer is {\"answer\": \"0.04\"}." } ], "images": [ "scigen/train/2987.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Validation of lattice quality with LibriSpeech model and test sets." ], "reasoning": "To find the difference in test-clean OWER between the Baseline and GPU decoders, subtract the GPU's test-clean OWER (1.09) from the Baseline's test-clean OWER (1.05).", "question": "What is the difference in test-clean OWER between the Baseline and GPU decoders?", "answer": "So, the answer is {\"answer\": \"0.04\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe GPU decoder has a higher test-clean WER than the Baseline decoder.\n" }, { "role": "assistant", "content": "The table shows that the test-clean WER for the GPU decoder is 5.51, while for the Baseline decoder it is 5.49. Since 5.51 is greater than 5.49, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2987.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Validation of lattice quality with LibriSpeech model and test sets." ], "reasoning": "The table shows that the test-clean WER for the GPU decoder is 5.51, while for the Baseline decoder it is 5.49. Since 5.51 is greater than 5.49, the statement is supported.", "statement": "The GPU decoder has a higher test-clean WER than the Baseline decoder.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Accuracy comparison with TST.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Our model outperforms TST in sentiment polarity transfer, especially in transferring negative sentences to positive, likely due to better modeling of sentence-outcome associations.We also compare our model with TST for sentiment polarity transfer. We employ the same evaluation metric as used in Shen et al. the sentiment accuracy of the transferred sentences. We define the revised sentences with ratings larger than 3 as positive, smaller than 3 as negative. The results show that our model achieves better accuracy than TST in both transfer directions. One reason is that our method models the associations between each sentence and its outcome, and thus captures the sentiment wordings better. Our model is far better for transferring negative sentences into positive, moreover, both models achieve better performance for this transfer direction. We can probably attribute the reason to the imbalanced training data: 55% positive sentences v.s. 45% negative sentences." } ], "images": [ "scigen/train/2988.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Accuracy comparison with TST." ], "reasoning": "Our model outperforms TST in sentiment polarity transfer, especially in transferring negative sentences to positive, likely due to better modeling of sentence-outcome associations.", "description": "We also compare our model with TST for sentiment polarity transfer. We employ the same evaluation metric as used in Shen et al. the sentiment accuracy of the transferred sentences. We define the revised sentences with ratings larger than 3 as positive, smaller than 3 as negative. The results show that our model achieves better accuracy than TST in both transfer directions. One reason is that our method models the associations between each sentence and its outcome, and thus captures the sentiment wordings better. Our model is far better for transferring negative sentences into positive, moreover, both models achieve better performance for this transfer direction. We can probably attribute the reason to the imbalanced training data: 55% positive sentences v.s. 45% negative sentences.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: Accuracy comparison with TST.', answer the following question. Give detailed solution consisting of each step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the accuracy of our model in transferring positive sentences to negative?\n" }, { "role": "assistant", "content": "The table shows the accuracy of our model in transferring positive sentences to negative as 0.7862. So, the answer is {\"answer\": \"0.7862\"}" } ], "images": [ "scigen/train/2988.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Accuracy comparison with TST." ], "reasoning": "The table shows the accuracy of our model in transferring positive sentences to negative as 0.7862.", "question": "What is the accuracy of our model in transferring positive sentences to negative?", "answer": "So, the answer is {\"answer\": \"0.7862\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nTST has higher accuracy than our model in transferring negative sentences to positive.\n" }, { "role": "assistant", "content": "The table shows that TST has an accuracy of 0.7280 for transferring negative sentences to positive, while our model has an accuracy of 0.8836, which is higher. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2988.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Accuracy comparison with TST." ], "reasoning": "The table shows that TST has an accuracy of 0.7280 for transferring negative sentences to positive, while our model has an accuracy of 0.8836, which is higher.", "statement": "TST has higher accuracy than our model in transferring negative sentences to positive.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 7: Ablation study.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The revised VAE alone does not perform well, but adding more losses gradually improves performance, with dual reconstruction being particularly effective.Recall that our model is a combination of a revised VAE, which disentangles two factors from a sentence to enable the subsequent design, and a coupling component modeling pseudo-parallel sentence pairs. “None” refers to all three losses are removed, and it is basically worse than S2BS, which implies only using the revised VAE does not work well. As more losses added, the performance is gradually improved. Moreover, the dual reconstruction is more effective than the others." } ], "images": [ "scigen/train/2989.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Ablation study." ], "reasoning": "The revised VAE alone does not perform well, but adding more losses gradually improves performance, with dual reconstruction being particularly effective.", "description": "Recall that our model is a combination of a revised VAE, which disentangles two factors from a sentence to enable the subsequent design, and a coupling component modeling pseudo-parallel sentence pairs. “None” refers to all three losses are removed, and it is basically worse than S2BS, which implies only using the revised VAE does not work well. As more losses added, the performance is gradually improved. Moreover, the dual reconstruction is more effective than the others.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 7: Ablation study.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. \n\nQuestion: Which combination of losses shows the best performance at T=5?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By comparing the values in the T=5 column, the combination of all losses (ALL) shows the lowest value, indicating the best performance. So, the answer is {\"answer\": \"ALL\"}" } ], "images": [ "scigen/train/2989.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Ablation study." ], "reasoning": "By comparing the values in the T=5 column, the combination of all losses (ALL) shows the lowest value, indicating the best performance.", "question": "Which combination of losses shows the best performance at T=5?", "answer": "So, the answer is {\"answer\": \"ALL\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The combination of L_sim and L_diff shows better performance than L_diff alone at T=3., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe combination of L_sim and L_diff shows better performance than L_diff alone at T=3.\n" }, { "role": "assistant", "content": "At T=3, the performance of L_sim and L_diff is 0.8218, which is better than the performance of L_diff alone, which is 0.8017. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2989.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Ablation study." ], "reasoning": "At T=3, the performance of L_sim and L_diff is 0.8218, which is better than the performance of L_diff alone, which is 0.8017.", "statement": "The combination of L_sim and L_diff shows better performance than L_diff alone at T=3.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: The classification accuracy of the predicted labels. Partially correct labels have also contributed partially to the scores. † Despite our endeavor to keep the settings comparable to the original model, comparison between our results and theirs would not be fair, since the size of datasets used in our experiments and also the number of classes are different than theirs.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The Binary Logistic Regression model shows lower accuracy scores, especially for Japanese, indicating higher difficulty in classification for larger category sets.The results initially demonstrate that the dataset is not an easy one as the Binary Logistic Regression model is not achieving very high accuracy scores. Besides, the lower scores for Japanese in comparison to the other languages demonstrate the higher difficulty level of classification for a larger category set size for all the models." } ], "images": [ "scigen/train/2990.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The classification accuracy of the predicted labels. Partially correct labels have also contributed partially to the scores. † Despite our endeavor to keep the settings comparable to the original model, comparison between our results and theirs would not be fair, since the size of datasets used in our experiments and also the number of classes are different than theirs." ], "reasoning": "The Binary Logistic Regression model shows lower accuracy scores, especially for Japanese, indicating higher difficulty in classification for larger category sets.", "description": "The results initially demonstrate that the dataset is not an easy one as the Binary Logistic Regression model is not achieving very high accuracy scores. Besides, the lower scores for Japanese in comparison to the other languages demonstrate the higher difficulty level of classification for a larger category set size for all the models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: The classification accuracy of the predicted labels. Partially correct labels have also contributed partially to the scores. † Despite our endeavor to keep the settings comparable to the original model, comparison between our results and theirs would not be fair, since the size of datasets used in our experiments and also the number of classes are different than theirs.' to sequentially solve the question presented below. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich model achieved the highest accuracy score for the French test set?\n" }, { "role": "assistant", "content": "To find the highest accuracy score for the French test set, look at the 'test fr' column and identify the highest value. The highest value is 83.43, achieved by the Joint-NN++ model. So, the answer is {\"answer\": \"Joint-NN++\"}." } ], "images": [ "scigen/train/2990.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The classification accuracy of the predicted labels. Partially correct labels have also contributed partially to the scores. † Despite our endeavor to keep the settings comparable to the original model, comparison between our results and theirs would not be fair, since the size of datasets used in our experiments and also the number of classes are different than theirs." ], "reasoning": "To find the highest accuracy score for the French test set, look at the 'test fr' column and identify the highest value. The highest value is 83.43, achieved by the Joint-NN++ model.", "question": "Which model achieved the highest accuracy score for the French test set?", "answer": "So, the answer is {\"answer\": \"Joint-NN++\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe HMCNR model achieved the lowest accuracy score for the Japanese development set.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'dev ja' column and identify the lowest value. The lowest value is 61.63, achieved by the HMCNR model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2990.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The classification accuracy of the predicted labels. Partially correct labels have also contributed partially to the scores. † Despite our endeavor to keep the settings comparable to the original model, comparison between our results and theirs would not be fair, since the size of datasets used in our experiments and also the number of classes are different than theirs." ], "reasoning": "To verify this statement, look at the 'dev ja' column and identify the lowest value. The lowest value is 61.63, achieved by the HMCNR model.", "statement": "The HMCNR model achieved the lowest accuracy score for the Japanese development set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Selection of texts via Active Learning and accuracy obtained from SVM', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Four steps were performed to select the 100 most complex texts for SVM, resulting in lower accuracy due to the mix between classes, and a final set of 1,456 texts was obtained with a 52% accuracy using 10-fold cross-validation.We performed four steps to select texts for annotation, where each step selected the 100 most complex texts for SVM. The texts that could not be processed due to parsing problems were removed. They show that even when we select the texts in which the classifier has greater indecision in classifying, the SVM has not yet been able to define a boundary between the classes, which led to lower accuracy in classifying data. This shows that there is a mix between classes so that the 108 current features are not able to correctly distinguish the five levels manually annotated. Finally, we conducted a stage of selecting the 100 most easily annotated texts (those with greater distance from SVM separating hyperplanes) in order to contrast with the current distribution of data and the accuracy obtained. We obtained a set of 1,456 texts with the following distribution: 242 texts of level 1, 313 texts of level 2, 338 texts of level 3,287 texts of level 4 and 276 texts of level 5. The accuracy obtained when performing a 10-fold cross-validation experiment with linear kernel SVM and C=1 was 52% (+/- 15)." } ], "images": [ "scigen/train/2991.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Selection of texts via Active Learning and accuracy obtained from SVM" ], "reasoning": "Four steps were performed to select the 100 most complex texts for SVM, resulting in lower accuracy due to the mix between classes, and a final set of 1,456 texts was obtained with a 52% accuracy using 10-fold cross-validation.", "description": "We performed four steps to select texts for annotation, where each step selected the 100 most complex texts for SVM. The texts that could not be processed due to parsing problems were removed. They show that even when we select the texts in which the classifier has greater indecision in classifying, the SVM has not yet been able to define a boundary between the classes, which led to lower accuracy in classifying data. This shows that there is a mix between classes so that the 108 current features are not able to correctly distinguish the five levels manually annotated. Finally, we conducted a stage of selecting the 100 most easily annotated texts (those with greater distance from SVM separating hyperplanes) in order to contrast with the current distribution of data and the accuracy obtained. We obtained a set of 1,456 texts with the following distribution: 242 texts of level 1, 313 texts of level 2, 338 texts of level 3,287 texts of level 4 and 276 texts of level 5. The accuracy obtained when performing a 10-fold cross-validation experiment with linear kernel SVM and C=1 was 52% (+/- 15).", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: Selection of texts via Active Learning and accuracy obtained from SVM' to sequentially solve the question presented below. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat was the accuracy obtained in the first step of text selection?\n" }, { "role": "assistant", "content": "The table shows the accuracy for each step of text selection. The first step had an accuracy of 53% (+/- 11). So, the answer is {\"answer\": \"53% (+/- 11)\"}." } ], "images": [ "scigen/train/2991.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Selection of texts via Active Learning and accuracy obtained from SVM" ], "reasoning": "The table shows the accuracy for each step of text selection. The first step had an accuracy of 53% (+/- 11).", "question": "What was the accuracy obtained in the first step of text selection?", "answer": "So, the answer is {\"answer\": \"53% (+/- 11)\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe accuracy obtained in the third step of text selection was 51% (+/- 13).\n" }, { "role": "assistant", "content": "The table shows the accuracy for each step of text selection. The third step had an accuracy of 51% (+/- 13). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2991.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Selection of texts via Active Learning and accuracy obtained from SVM" ], "reasoning": "The table shows the accuracy for each step of text selection. The third step had an accuracy of 51% (+/- 13).", "statement": "The accuracy obtained in the third step of text selection was 51% (+/- 13).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Model Performance. Models are ranked by an overall performance rather than any single metrics.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Our model, which uses teacher forcing, max pooling, BiGRU, and data augmentation, shows competitive performance in the DSTC7 challenge, but may miss certain temporal dependencies crucial for tasks like coreference resolution.In order to further evaluate our model’s performance, we compare our results with the baseline model; as well as other participants in the DSTC7 challenge on the track of AVSD under two different settings – with and without video related modalities (i3d-flow, i3d-rgb and audio). Our best model fully utilizes the combination of teacher forcing, max pooling, BiGRU and data augmentation techniques. We hypothesize that as we regard the dialogue history as a group of discrete QA pairs, we miss certain inner temporal dependencies, impacting tasks like coreference resolution. While our data demonstrates that the ordering of the dialog history as a whole may not contribute as much information as previously assumed, it is important to consider that phenomena like coreference resolution most likely does depend on temporal location. Fig. Another example is in the case of an ambiguous pronoun. Both situations usually rely on the presence of a previous noun or a certain portion in an image or video. After such information has been deduced, our data suggests that the order of that processed information would likely have little effect on the performance of the system. However, without doing this before shuffling, information that can be obtained through techniques such as coreferencing may be untapped." } ], "images": [ "scigen/train/2992.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Model Performance. Models are ranked by an overall performance rather than any single metrics." ], "reasoning": "Our model, which uses teacher forcing, max pooling, BiGRU, and data augmentation, shows competitive performance in the DSTC7 challenge, but may miss certain temporal dependencies crucial for tasks like coreference resolution.", "description": "In order to further evaluate our model’s performance, we compare our results with the baseline model; as well as other participants in the DSTC7 challenge on the track of AVSD under two different settings – with and without video related modalities (i3d-flow, i3d-rgb and audio). Our best model fully utilizes the combination of teacher forcing, max pooling, BiGRU and data augmentation techniques. We hypothesize that as we regard the dialogue history as a group of discrete QA pairs, we miss certain inner temporal dependencies, impacting tasks like coreference resolution. While our data demonstrates that the ordering of the dialog history as a whole may not contribute as much information as previously assumed, it is important to consider that phenomena like coreference resolution most likely does depend on temporal location. Fig. Another example is in the case of an ambiguous pronoun. Both situations usually rely on the presence of a previous noun or a certain portion in an image or video. After such information has been deduced, our data suggests that the order of that processed information would likely have little effect on the performance of the system. However, without doing this before shuffling, information that can be obtained through techniques such as coreferencing may be untapped.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 1: Model Performance. Models are ranked by an overall performance rather than any single metrics.' in mind, break down and resolve the question below step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhat is the highest CIDEr score achieved by any model in the Video + Text setting?\n" }, { "role": "assistant", "content": "To find the highest CIDEr score in the Video + Text setting, look at the CIDEr column for all rows labeled 'Video + Text'. The highest score is 1.158. So, the answer is {\"answer\": \"1.158\"}." } ], "images": [ "scigen/train/2992.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Model Performance. Models are ranked by an overall performance rather than any single metrics." ], "reasoning": "To find the highest CIDEr score in the Video + Text setting, look at the CIDEr column for all rows labeled 'Video + Text'. The highest score is 1.158.", "question": "What is the highest CIDEr score achieved by any model in the Video + Text setting?", "answer": "So, the answer is {\"answer\": \"1.158\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The baseline model achieved the highest BLEU-1 score in the Text Only setting. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe baseline model achieved the highest BLEU-1 score in the Text Only setting.\n" }, { "role": "assistant", "content": "To verify this statement, compare the BLEU-1 scores in the Text Only setting. The baseline model has a BLEU-1 score of 0.245, which is lower than the highest score of 0.686 achieved by another model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/2992.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Model Performance. Models are ranked by an overall performance rather than any single metrics." ], "reasoning": "To verify this statement, compare the BLEU-1 scores in the Text Only setting. The baseline model has a BLEU-1 score of 0.245, which is lower than the highest score of 0.686 achieved by another model.", "statement": "The baseline model achieved the highest BLEU-1 score in the Text Only setting.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Activity, Entity F1 results reported by previous work (rows 1-4 from serban2017multiresolution), and the All-operations and AutoAugment models.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The input-agnostic AutoAugment model outperforms the All-operations model on both Activity and Entity F1 scores, while the input-aware model is better on Activity F1.Automatic Results: Moreover, our input-agnostic AutoAugment is statistically significantly (p<0.01) better (on Activity and Entity F1) than the strong manual-policy All-operations model, while the input-aware model is stat. signif. (p<0.01) better on Activity F1." } ], "images": [ "scigen/train/2993.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Activity, Entity F1 results reported by previous work (rows 1-4 from serban2017multiresolution), and the All-operations and AutoAugment models." ], "reasoning": "The input-agnostic AutoAugment model outperforms the All-operations model on both Activity and Entity F1 scores, while the input-aware model is better on Activity F1.", "description": "Automatic Results: Moreover, our input-agnostic AutoAugment is statistically significantly (p<0.01) better (on Activity and Entity F1) than the strong manual-policy All-operations model, while the input-aware model is stat. signif. (p<0.01) better on Activity F1.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 1: Activity, Entity F1 results reported by previous work (rows 1-4 from serban2017multiresolution), and the All-operations and AutoAugment models.' in mind, break down and resolve the question below step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model has the highest Entity F1 score?\n" }, { "role": "assistant", "content": "By examining the Entity F1 column, the highest score is 4.00, which corresponds to the Input-agnostic model. So, the answer is {\"answer\": \"Input-agnostic\"}" } ], "images": [ "scigen/train/2993.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Activity, Entity F1 results reported by previous work (rows 1-4 from serban2017multiresolution), and the All-operations and AutoAugment models." ], "reasoning": "By examining the Entity F1 column, the highest score is 4.00, which corresponds to the Input-agnostic model.", "question": "Which model has the highest Entity F1 score?", "answer": "So, the answer is {\"answer\": \"Input-agnostic\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The Input-aware model has a higher Activity F1 score than the All-operations model., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Input-aware model has a higher Activity F1 score than the All-operations model.\n" }, { "role": "assistant", "content": "The Activity F1 score for the Input-aware model is 7.04, which is higher than the All-operations model's score of 6.53. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2993.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Activity, Entity F1 results reported by previous work (rows 1-4 from serban2017multiresolution), and the All-operations and AutoAugment models." ], "reasoning": "The Activity F1 score for the Input-aware model is 7.04, which is higher than the All-operations model's score of 6.53.", "statement": "The Input-aware model has a higher Activity F1 score than the All-operations model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Performance of unigrams versus our similarity-based features using embeddings from Word2Vec'.\n" }, { "role": "assistant", "content": "Using unigrams as features yields the highest F-score of 72.53%, while unweighted and weighted word embedding features alone are less effective.The embedding in this case is Word2Vec. The four rows show baseline sets of features: unigrams, unweighted similarity using word embeddings (S), weighted similarity using word embeddings (WS) and both (i.e., unweighted plus weighted similarities using word embeddings). Using only unigrams as features gives a F-score of 72.53%, while only unweighted and weighted features gives F-score of 69.49% and 58.26% respectively. This validates our intuition that word embedding-based features alone are not sufficient, and should be augmented with other features." } ], "images": [ "scigen/train/2994.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of unigrams versus our similarity-based features using embeddings from Word2Vec" ], "reasoning": "Using unigrams as features yields the highest F-score of 72.53%, while unweighted and weighted word embedding features alone are less effective.", "description": "The embedding in this case is Word2Vec. The four rows show baseline sets of features: unigrams, unweighted similarity using word embeddings (S), weighted similarity using word embeddings (WS) and both (i.e., unweighted plus weighted similarities using word embeddings). Using only unigrams as features gives a F-score of 72.53%, while only unweighted and weighted features gives F-score of 69.49% and 58.26% respectively. This validates our intuition that word embedding-based features alone are not sufficient, and should be augmented with other features.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: Performance of unigrams versus our similarity-based features using embeddings from Word2Vec', solve this question and give detail solutions. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the F-score when using both unweighted and weighted word embedding features?\n" }, { "role": "assistant", "content": "The table shows that using both unweighted and weighted word embedding features results in an F-score of 59.05%. So, the answer is {\"answer\": \"59.05\"}" } ], "images": [ "scigen/train/2994.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of unigrams versus our similarity-based features using embeddings from Word2Vec" ], "reasoning": "The table shows that using both unweighted and weighted word embedding features results in an F-score of 59.05%.", "question": "What is the F-score when using both unweighted and weighted word embedding features?", "answer": "So, the answer is {\"answer\": \"59.05\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Using only weighted similarity features gives the highest F-score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nUsing only weighted similarity features gives the highest F-score.\n" }, { "role": "assistant", "content": "The table shows that using only unigrams as features gives the highest F-score of 72.53%, while weighted similarity features alone give an F-score of 58.26%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2994.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of unigrams versus our similarity-based features using embeddings from Word2Vec" ], "reasoning": "The table shows that using only unigrams as features gives the highest F-score of 72.53%, while weighted similarity features alone give an F-score of 58.26%.", "statement": "Using only weighted similarity features gives the highest F-score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Similarity scores for all pairs of content words in ‘A woman needs a man like a fish needs bicycle’', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Similarity scores between word pairs are computed, and four features are derived: maximum and minimum scores of the most similar and dissimilar word pairs, both unweighted and distance-weighted.The similarity score is the cosine similarity between vectors of two words. To illustrate our features, we use our example ‘A woman needs a man like a fish needs a bicycle’. Using these similarity scores, we compute two sets of features: Unweighted similarity features (S): We first compute similarity scores for all pairs of words (except stop words). We then return four feature values per sentence. Maximum score of most similar word pair Minimum score of most similar word pair Maximum score of most dissimilar word pair Minimum score of most dissimilar word pair For example, in case of the first feature, we consider the most similar word to every word in the sentence, and the corresponding similarity scores. Thus, the first feature in case of our example would have the value 0.766 derived from the man-woman pair and the second feature would take the value 0.078 due to the needs-man pair. The other features are computed in a similar manner. Distance-weighted similarity features (WS): Like in the previous case, we first compute similarity scores for all pairs of words (excluding stop-words). For all similarity scores, we divide them by square of distance between the two words. Thus, the similarity between terms that are close in the sentence is weighted higher than terms which are distant from one another. Thus, for all possible word pairs, we compute four features: Maximum distance-weighted score of most similar word pair Minimum distance-weighted score of most similar word pair Maximum distance-weighted score of most dissimilar word pair Minimum distance-weighted score of most dissimilar word pair These are computed similar to unweighted similarity features." } ], "images": [ "scigen/train/2995.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Similarity scores for all pairs of content words in ‘A woman needs a man like a fish needs bicycle’" ], "reasoning": "Similarity scores between word pairs are computed, and four features are derived: maximum and minimum scores of the most similar and dissimilar word pairs, both unweighted and distance-weighted.", "description": "The similarity score is the cosine similarity between vectors of two words. To illustrate our features, we use our example ‘A woman needs a man like a fish needs a bicycle’. Using these similarity scores, we compute two sets of features: Unweighted similarity features (S): We first compute similarity scores for all pairs of words (except stop words). We then return four feature values per sentence. Maximum score of most similar word pair Minimum score of most similar word pair Maximum score of most dissimilar word pair Minimum score of most dissimilar word pair For example, in case of the first feature, we consider the most similar word to every word in the sentence, and the corresponding similarity scores. Thus, the first feature in case of our example would have the value 0.766 derived from the man-woman pair and the second feature would take the value 0.078 due to the needs-man pair. The other features are computed in a similar manner. Distance-weighted similarity features (WS): Like in the previous case, we first compute similarity scores for all pairs of words (excluding stop-words). For all similarity scores, we divide them by square of distance between the two words. Thus, the similarity between terms that are close in the sentence is weighted higher than terms which are distant from one another. Thus, for all possible word pairs, we compute four features: Maximum distance-weighted score of most similar word pair Minimum distance-weighted score of most similar word pair Maximum distance-weighted score of most dissimilar word pair Minimum distance-weighted score of most dissimilar word pair These are computed similar to unweighted similarity features.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 1: Similarity scores for all pairs of content words in ‘A woman needs a man like a fish needs bicycle’'. Solve the following question based on the table.\n\nQuestion: What is the minimum similarity score of the most similar word pair?\nLet's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the minimum similarity score of the most similar word pair, we look at the similarity scores for all word pairs and identify the lowest score among the highest similarity scores for each word. The highest similarity scores are 0.766 (man-woman), 0.766 (woman-man), 0.151 (fish-man), 0.078 (needs-man), and 0.229 (bicycle-man). The minimum of these scores is 0.078. So, the answer is {\"answer\": \"0.078\"}." } ], "images": [ "scigen/train/2995.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Similarity scores for all pairs of content words in ‘A woman needs a man like a fish needs bicycle’" ], "reasoning": "To find the minimum similarity score of the most similar word pair, we look at the similarity scores for all word pairs and identify the lowest score among the highest similarity scores for each word. The highest similarity scores are 0.766 (man-woman), 0.766 (woman-man), 0.151 (fish-man), 0.078 (needs-man), and 0.229 (bicycle-man). The minimum of these scores is 0.078.", "question": "What is the minimum similarity score of the most similar word pair?", "answer": "So, the answer is {\"answer\": \"0.078\"}." }, { "messages": [ { "role": "user", "content": "The maximum similarity score between any two words is 0.766. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe maximum similarity score between any two words is 0.766.\n" }, { "role": "assistant", "content": "To verify this statement, we need to check the similarity scores between all word pairs. The highest similarity score in the table is 0.766, which occurs between the pairs man-woman and woman-man. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2995.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Similarity scores for all pairs of content words in ‘A woman needs a man like a fish needs bicycle’" ], "reasoning": "To verify this statement, we need to check the similarity scores between all word pairs. The highest similarity score in the table is 0.766, which occurs between the pairs man-woman and woman-man.", "statement": "The maximum similarity score between any two words is 0.766.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Performance obtained on augmenting word embedding features to features from four prior works, for four word embeddings; L: Liebrecht et al. (2013), G: González-Ibánez et al. (2011a), B: Buschmeier et al. (2014) , J: Joshi et al. (2015)'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Word embedding-based features improve F-scores for sarcasm detection across four prior works, with the highest improvement observed for \newciteliebrecht using Dependency Weights.All entries in the tables are higher than the simple unigrams baseline, i.e., F-score for each of the four is higher than unigrams - highlighting that these are better features for sarcasm detection than simple unigrams. Values in bold indicate the best F-score for a given prior work-embedding type combination. In case of \\newciteliebrecht for Word2Vec, the overall improvement in F-score is 4%. Precision increases by 8% while recall remains nearly unchanged. For features given in \\newcitegonzalez, there is a negligible degradation of 0.91% when word embedding-based features based on Word2Vec are used. For \\newcitebuschmeier for Word2Vec, we observe an improvement in F-score from 76.61% to 78.09%. Precision remains nearly unchanged while recall increases. In case of \\newcite22 and Word2Vec, we observe a slight improvement of 0.20% when unweighted (S) features are used. This shows that word embedding-based features are useful, across four past works for Word2Vec. The maximum improvement is observed in case of \\newciteliebrecht. It is around 4% in case of LSA, 5% in case of GloVe, 6% in case of Dependency weight-based and 4% in case of Word2Vec. These improvements are not directly comparable because the four embeddings have different vocabularies (since they are trained on different datasets) and vocabulary sizes, their results cannot be directly compared." } ], "images": [ "scigen/train/2996.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance obtained on augmenting word embedding features to features from four prior works, for four word embeddings; L: Liebrecht et al. (2013), G: González-Ibánez et al. (2011a), B: Buschmeier et al. (2014) , J: Joshi et al. (2015)" ], "reasoning": "Word embedding-based features improve F-scores for sarcasm detection across four prior works, with the highest improvement observed for \newciteliebrecht using Dependency Weights.", "description": "All entries in the tables are higher than the simple unigrams baseline, i.e., F-score for each of the four is higher than unigrams - highlighting that these are better features for sarcasm detection than simple unigrams. Values in bold indicate the best F-score for a given prior work-embedding type combination. In case of \\newciteliebrecht for Word2Vec, the overall improvement in F-score is 4%. Precision increases by 8% while recall remains nearly unchanged. For features given in \\newcitegonzalez, there is a negligible degradation of 0.91% when word embedding-based features based on Word2Vec are used. For \\newcitebuschmeier for Word2Vec, we observe an improvement in F-score from 76.61% to 78.09%. Precision remains nearly unchanged while recall increases. In case of \\newcite22 and Word2Vec, we observe a slight improvement of 0.20% when unweighted (S) features are used. This shows that word embedding-based features are useful, across four past works for Word2Vec. The maximum improvement is observed in case of \\newciteliebrecht. It is around 4% in case of LSA, 5% in case of GloVe, 6% in case of Dependency weight-based and 4% in case of Word2Vec. These improvements are not directly comparable because the four embeddings have different vocabularies (since they are trained on different datasets) and vocabulary sizes, their results cannot be directly compared.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: Performance obtained on augmenting word embedding features to features from four prior works, for four word embeddings; L: Liebrecht et al. (2013), G: González-Ibánez et al. (2011a), B: Buschmeier et al. (2014) , J: Joshi et al. (2015)', answer the following question. Give detailed solution consisting of each step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich prior work shows the highest F-score improvement using Dependency Weights?\n" }, { "role": "assistant", "content": "By comparing the F-scores for Dependency Weights across all prior works, \newciteliebrecht shows the highest improvement with an F-score of 81.19. So, the answer is {\"answer\": \"81.19\"}" } ], "images": [ "scigen/train/2996.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance obtained on augmenting word embedding features to features from four prior works, for four word embeddings; L: Liebrecht et al. (2013), G: González-Ibánez et al. (2011a), B: Buschmeier et al. (2014) , J: Joshi et al. (2015)" ], "reasoning": "By comparing the F-scores for Dependency Weights across all prior works, \newciteliebrecht shows the highest improvement with an F-score of 81.19.", "question": "Which prior work shows the highest F-score improvement using Dependency Weights?", "answer": "So, the answer is {\"answer\": \"81.19\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe F-score for \newcitebuschmeier using Word2Vec is higher than using LSA.\n" }, { "role": "assistant", "content": "The table shows that the F-score for \newcitebuschmeier using Word2Vec is 78.09, while using LSA it is 76.77, indicating that the F-score for Word2Vec is indeed higher. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/2996.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance obtained on augmenting word embedding features to features from four prior works, for four word embeddings; L: Liebrecht et al. (2013), G: González-Ibánez et al. (2011a), B: Buschmeier et al. (2014) , J: Joshi et al. (2015)" ], "reasoning": "The table shows that the F-score for \newcitebuschmeier using Word2Vec is 78.09, while using LSA it is 76.77, indicating that the F-score for Word2Vec is indeed higher.", "statement": "The F-score for \newcitebuschmeier using Word2Vec is higher than using LSA.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Micro F1 scores of the two NER models two test sets.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The hybrid NER model outperformed the i2b2 NER model in both synthesized queries and clinical notes, achieving higher F1 scores.With the above hyperparameter setting, the hybrid NER model achieved a F1 score of 0.995 on synthesized queries and 0.948 on clinical notes while the i2b2 NER model achieved a F1 score of 0.441 on synthesized queries and 0.927 on clinical notes" } ], "images": [ "scigen/train/2997.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Micro F1 scores of the two NER models two test sets." ], "reasoning": "The hybrid NER model outperformed the i2b2 NER model in both synthesized queries and clinical notes, achieving higher F1 scores.", "description": "With the above hyperparameter setting, the hybrid NER model achieved a F1 score of 0.995 on synthesized queries and 0.948 on clinical notes while the i2b2 NER model achieved a F1 score of 0.441 on synthesized queries and 0.927 on clinical notes", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: Micro F1 scores of the two NER models two test sets.', answer the question below by showing each progressive step toward the answer. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model achieved a higher F1 score on clinical notes?\n" }, { "role": "assistant", "content": "Compare the F1 scores of the Hybrid NER model and the i2b2 NER model on clinical notes. The Hybrid NER model achieved 0.948, while the i2b2 NER model achieved 0.927. So, the answer is {\"answer\": \"Hybrid NER\"}" } ], "images": [ "scigen/train/2997.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Micro F1 scores of the two NER models two test sets." ], "reasoning": "Compare the F1 scores of the Hybrid NER model and the i2b2 NER model on clinical notes. The Hybrid NER model achieved 0.948, while the i2b2 NER model achieved 0.927.", "question": "Which model achieved a higher F1 score on clinical notes?", "answer": "So, the answer is {\"answer\": \"Hybrid NER\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The i2b2 NER model achieved a higher F1 score on synthesized queries than on clinical notes., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe i2b2 NER model achieved a higher F1 score on synthesized queries than on clinical notes.\n" }, { "role": "assistant", "content": "The table shows that the i2b2 NER model achieved an F1 score of 0.441 on synthesized queries and 0.927 on clinical notes, indicating that the F1 score on clinical notes is higher. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2997.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Micro F1 scores of the two NER models two test sets." ], "reasoning": "The table shows that the i2b2 NER model achieved an F1 score of 0.441 on synthesized queries and 0.927 on clinical notes, indicating that the F1 score on clinical notes is higher.", "statement": "The i2b2 NER model achieved a higher F1 score on synthesized queries than on clinical notes.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Test results examining the effect of sentence meaning and grammaticality on task performance. Scores are shown for a sample of 100 triples split by whether the generated sentence is grammatical and whether it conveys the correct meaning of the triple.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Sentences with grammatical errors or incorrect meanings negatively impact model performance, as shown by lower F1 scores and quality metrics.In order to measure the impact of sentence generation on our model, we select a sample of 100 sentences and group the results by a) whether the sentence contained a grammatical error, and b) whether the sentence misrepresented the meaning of the triple. For example, the triple (golf, HasProperty, good) yields the sentence “golf is a good”, which is grammatically correct but conveys the wrong meaning. On both Wikipedia mining and CKBC, we find that misrepresenting meaning has an adverse impact on model performance. Future work could therefore focus on designing templates that more reliably encode a relation’s true meaning." } ], "images": [ "scigen/train/2998.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Test results examining the effect of sentence meaning and grammaticality on task performance. Scores are shown for a sample of 100 triples split by whether the generated sentence is grammatical and whether it conveys the correct meaning of the triple." ], "reasoning": "Sentences with grammatical errors or incorrect meanings negatively impact model performance, as shown by lower F1 scores and quality metrics.", "description": "In order to measure the impact of sentence generation on our model, we select a sample of 100 sentences and group the results by a) whether the sentence contained a grammatical error, and b) whether the sentence misrepresented the meaning of the triple. For example, the triple (golf, HasProperty, good) yields the sentence “golf is a good”, which is grammatically correct but conveys the wrong meaning. On both Wikipedia mining and CKBC, we find that misrepresenting meaning has an adverse impact on model performance. Future work could therefore focus on designing templates that more reliably encode a relation’s true meaning.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}. The question is related to the given table titled 'Table 3: Test results examining the effect of sentence meaning and grammaticality on task performance. Scores are shown for a sample of 100 triples split by whether the generated sentence is grammatical and whether it conveys the correct meaning of the triple.'.\n\nQuestion: What is the F1 score for sentences with grammatical errors in Task 1?\n" }, { "role": "assistant", "content": "To find the F1 score for sentences with grammatical errors in Task 1, look at the row labeled 'Ungrammatical' under Task 1 and find the corresponding F1 score. So, the answer is {\"answer\": \"66.7\"}." } ], "images": [ "scigen/train/2998.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Test results examining the effect of sentence meaning and grammaticality on task performance. Scores are shown for a sample of 100 triples split by whether the generated sentence is grammatical and whether it conveys the correct meaning of the triple." ], "reasoning": "To find the F1 score for sentences with grammatical errors in Task 1, look at the row labeled 'Ungrammatical' under Task 1 and find the corresponding F1 score.", "question": "What is the F1 score for sentences with grammatical errors in Task 1?", "answer": "So, the answer is {\"answer\": \"66.7\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nSentences with correct meanings have higher quality scores in Task 2.\n" }, { "role": "assistant", "content": "The table shows that sentences with correct meanings have a quality score of 3.22, which is higher than the quality score for sentences with wrong meanings (1.18) in Task 2. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/2998.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Test results examining the effect of sentence meaning and grammaticality on task performance. Scores are shown for a sample of 100 triples split by whether the generated sentence is grammatical and whether it conveys the correct meaning of the triple." ], "reasoning": "The table shows that sentences with correct meanings have a quality score of 3.22, which is higher than the quality score for sentences with wrong meanings (1.18) in Task 2.", "statement": "Sentences with correct meanings have higher quality scores in Task 2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Main results for Task 1: Commonsense knowledge base completion (test F1 score) and Task 2: Wikipedia mining (quality scores out of 4). Results are included from the sentence generation methods of simple concatenation, hand-crafted templates, templates plus grammatical transformations, and coherency ranking. DNN, Factorized, and Prototypical models are described in jastrzkebski2018.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The Factorized and DNN models significantly outperformed the unsupervised approach in Task 1, achieving F1 scores of 89.2 and 89.0 respectively, compared to 78.8.Our unsupervised approach achieves a test set F1 score of 78.8, comparable to the 79.4 F1 score found by the supervised prototypical approach. The Factorized and DNN models significantly outperformed our approach with F1 scores of 89.2 and 89.0, respectively. Our grid search found an optimal λ value of 1.65 for the Concatenation sentence generation model and 1.55 for the Coherency Ranking model. The Template and Template + Grammar methods found lambda values of 1.20 and 0.95, respectively. Standard errors were calculated using 1000 bootstrap samples of the top 100 triples. The ratings assigned by the two human annotators had a 0.50 Pearson correlation and 0.23 kappa inter-annotator agreement. Rater disagreements occur most frequently when triples are ambiguous or difficult to interpret. Notably, if we bucket the five scores into just two categories of true and false, this disagreement rate drops by 50%. To give a sense of the types of commonsense knowledge our models struggle to capture, we report the top 100 most confident predictions that receive an average score below 3 in the supplementary material. Notably, some of the top 100 triples our model identified were indeed true, but would not be reasonably considered common sense (e.g. (vector bundle, HasProperty, manifold)). This suggests that our approach may be applicable to mining knowledge beyond common sense." } ], "images": [ "scigen/train/2999.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Main results for Task 1: Commonsense knowledge base completion (test F1 score) and Task 2: Wikipedia mining (quality scores out of 4). Results are included from the sentence generation methods of simple concatenation, hand-crafted templates, templates plus grammatical transformations, and coherency ranking. DNN, Factorized, and Prototypical models are described in jastrzkebski2018." ], "reasoning": "The Factorized and DNN models significantly outperformed the unsupervised approach in Task 1, achieving F1 scores of 89.2 and 89.0 respectively, compared to 78.8.", "description": "Our unsupervised approach achieves a test set F1 score of 78.8, comparable to the 79.4 F1 score found by the supervised prototypical approach. The Factorized and DNN models significantly outperformed our approach with F1 scores of 89.2 and 89.0, respectively. Our grid search found an optimal λ value of 1.65 for the Concatenation sentence generation model and 1.55 for the Coherency Ranking model. The Template and Template + Grammar methods found lambda values of 1.20 and 0.95, respectively. Standard errors were calculated using 1000 bootstrap samples of the top 100 triples. The ratings assigned by the two human annotators had a 0.50 Pearson correlation and 0.23 kappa inter-annotator agreement. Rater disagreements occur most frequently when triples are ambiguous or difficult to interpret. Notably, if we bucket the five scores into just two categories of true and false, this disagreement rate drops by 50%. To give a sense of the types of commonsense knowledge our models struggle to capture, we report the top 100 most confident predictions that receive an average score below 3 in the supplementary material. Notably, some of the top 100 triples our model identified were indeed true, but would not be reasonably considered common sense (e.g. (vector bundle, HasProperty, manifold)). This suggests that our approach may be applicable to mining knowledge beyond common sense.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Main results for Task 1: Commonsense knowledge base completion (test F1 score) and Task 2: Wikipedia mining (quality scores out of 4). Results are included from the sentence generation methods of simple concatenation, hand-crafted templates, templates plus grammatical transformations, and coherency ranking. DNN, Factorized, and Prototypical models are described in jastrzkebski2018.', answer the question below by showing each progressive step toward the answer. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model achieved the highest F1 score in Task 1 and what was its score?\n" }, { "role": "assistant", "content": "To find the model with the highest F1 score in Task 1, we compare the F1 scores of all models listed in the table. The DNN model achieved the highest F1 score of 89.2. So, the answer is {\"answer\": \"DNN\"}" } ], "images": [ "scigen/train/2999.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Main results for Task 1: Commonsense knowledge base completion (test F1 score) and Task 2: Wikipedia mining (quality scores out of 4). Results are included from the sentence generation methods of simple concatenation, hand-crafted templates, templates plus grammatical transformations, and coherency ranking. DNN, Factorized, and Prototypical models are described in jastrzkebski2018." ], "reasoning": "To find the model with the highest F1 score in Task 1, we compare the F1 scores of all models listed in the table. The DNN model achieved the highest F1 score of 89.2.", "question": "Which model achieved the highest F1 score in Task 1 and what was its score?", "answer": "So, the answer is {\"answer\": \"DNN\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Template + Grammar model achieved an F1 score higher than the Prototypical model in Task 1.\n" }, { "role": "assistant", "content": "The table shows that the Template + Grammar model achieved an F1 score of 74.4 in Task 1, while the Prototypical model achieved an F1 score of 79.4. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/2999.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Main results for Task 1: Commonsense knowledge base completion (test F1 score) and Task 2: Wikipedia mining (quality scores out of 4). Results are included from the sentence generation methods of simple concatenation, hand-crafted templates, templates plus grammatical transformations, and coherency ranking. DNN, Factorized, and Prototypical models are described in jastrzkebski2018." ], "reasoning": "The table shows that the Template + Grammar model achieved an F1 score of 74.4 in Task 1, while the Prototypical model achieved an F1 score of 79.4. Therefore, the statement is refuted.", "statement": "The Template + Grammar model achieved an F1 score higher than the Prototypical model in Task 1.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 9: Out-of-Vocabulary test set. unique refers to the number of unique values the slot takes in the test set. total is the number of times the slot appears.'.\n" }, { "role": "assistant", "content": "MASS struggles with OOV values and tends to hallucinate, while binmt achieves 91.5% slot realization, showing robustness to unseen values.Unsupervised pre-training through MASS completely fails to generalize to OOV values - none of the slots are realized in the predictions. Looking at the output, we noticed that mass has a strong tendency to hallucinate or simply output slot values seen during training. The poor performance further reinforces the practical popularity of delexicalized models and highlights the need for challenging OOV test sets. binmt on the other hand, remains robust - 91.5% of the slots from the MR are realized in the predictions. The results confirm that NMT can greatly improve the robustness to unseen values. Further reducing the performance gap between seen and unseen values is an important area of future work." } ], "images": [ "scigen/train/3000.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Out-of-Vocabulary test set. unique refers to the number of unique values the slot takes in the test set. total is the number of times the slot appears." ], "reasoning": "MASS struggles with OOV values and tends to hallucinate, while binmt achieves 91.5% slot realization, showing robustness to unseen values.", "description": "Unsupervised pre-training through MASS completely fails to generalize to OOV values - none of the slots are realized in the predictions. Looking at the output, we noticed that mass has a strong tendency to hallucinate or simply output slot values seen during training. The poor performance further reinforces the practical popularity of delexicalized models and highlights the need for challenging OOV test sets. binmt on the other hand, remains robust - 91.5% of the slots from the MR are realized in the predictions. The results confirm that NMT can greatly improve the robustness to unseen values. Further reducing the performance gap between seen and unseen values is an important area of future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 9: Out-of-Vocabulary test set. unique refers to the number of unique values the slot takes in the test set. total is the number of times the slot appears.', answer the following question. Give detailed solution consisting of each step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the accuracy of the slot with the highest number of errors?\n" }, { "role": "assistant", "content": "The slot with the highest number of errors is 'count' with 6 errors. The accuracy for 'count' is 70.0%. So, the answer is {\"answer\": \"70.0%\"}" } ], "images": [ "scigen/train/3000.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Out-of-Vocabulary test set. unique refers to the number of unique values the slot takes in the test set. total is the number of times the slot appears." ], "reasoning": "The slot with the highest number of errors is 'count' with 6 errors. The accuracy for 'count' is 70.0%.", "question": "What is the accuracy of the slot with the highest number of errors?", "answer": "So, the answer is {\"answer\": \"70.0%\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe slot 'food' has the lowest accuracy.\n" }, { "role": "assistant", "content": "The table shows that the 'food' slot has an accuracy of 60.0%, which is the lowest among all the slots listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3000.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Out-of-Vocabulary test set. unique refers to the number of unique values the slot takes in the test set. total is the number of times the slot appears." ], "reasoning": "The table shows that the 'food' slot has an accuracy of 60.0%, which is the lowest among all the slots listed.", "statement": "The slot 'food' has the lowest accuracy.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: Human evaluations for accuracy and fluency'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The binmt model outperforms the mass model in both accuracy and fluency, with a notable 7.5-point lead in accuracy.We conduct accuracy and fluency evaluations for our best model (binmt) and the best lexicalized baseline, mass. In terms of fluency, we note that mass produces quite fluent text, likely due to its strong language model. It would seem that unsupervised learning on unlabeled data is enough to generate fluent text, echoing findings of past work binmt performs slightly better with a score of 4.83. However, when it comes to accuracy, our model gets a high score of 97.5, surpassing mass by 7.5 points. We take this as proof that transfer learning from machine translation helps produce text that is not only fluent, but much better grounded in the structured data." } ], "images": [ "scigen/train/3001.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Human evaluations for accuracy and fluency" ], "reasoning": "The binmt model outperforms the mass model in both accuracy and fluency, with a notable 7.5-point lead in accuracy.", "description": "We conduct accuracy and fluency evaluations for our best model (binmt) and the best lexicalized baseline, mass. In terms of fluency, we note that mass produces quite fluent text, likely due to its strong language model. It would seem that unsupervised learning on unlabeled data is enough to generate fluent text, echoing findings of past work binmt performs slightly better with a score of 4.83. However, when it comes to accuracy, our model gets a high score of 97.5, surpassing mass by 7.5 points. We take this as proof that transfer learning from machine translation helps produce text that is not only fluent, but much better grounded in the structured data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 5: Human evaluations for accuracy and fluency' to sequentially solve the question presented below. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich model has a higher fluency score and by how much?\n" }, { "role": "assistant", "content": "The table shows that binmt has a fluency score of 4.83, while mass has a fluency score of 4.77. Subtracting these values gives a difference of 0.06. So, the answer is {\"answer\": \"binmt by 0.06\"}" } ], "images": [ "scigen/train/3001.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Human evaluations for accuracy and fluency" ], "reasoning": "The table shows that binmt has a fluency score of 4.83, while mass has a fluency score of 4.77. Subtracting these values gives a difference of 0.06.", "question": "Which model has a higher fluency score and by how much?", "answer": "So, the answer is {\"answer\": \"binmt by 0.06\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The mass model has a higher accuracy score than the binmt model., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe mass model has a higher accuracy score than the binmt model.\n" }, { "role": "assistant", "content": "The table shows that the binmt model has an accuracy score of 97.5, while the mass model has an accuracy score of 90. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3001.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Human evaluations for accuracy and fluency" ], "reasoning": "The table shows that the binmt model has an accuracy score of 97.5, while the mass model has an accuracy score of 90. Therefore, the statement is refuted.", "statement": "The mass model has a higher accuracy score than the binmt model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 8: Experiments with low-resource NLG'.\n" }, { "role": "assistant", "content": "NMT pre-training significantly improves performance over MASS, especially with smaller fine-tuning datasets, leading to substantial gains in BLEU and reductions in SER.Next, we fine-tune each of these models on the data-to-text task. Both the high and medium resource models have a comparable SER. Even the low resource model, pre-trained on just 1% of the translation corpora is significantly better than mass, which has been pre-trained on almost 1.6 billion tokens. The results indicate that machine translation based transfer learning can be successfully applied even when the size of parallel corpus is small, and thus holds promise for low-resource languages. In this section we study the effects of transfer learning when the size of the fine-tuning corpus is small. We create two random subsets from the NLG training data of size 100 and 1000. We find that once again, NMT offers substantial gains over MASS. When fine-tuning on 1000 examples, pre-training with NMT is substantially better (20% improvement on SER, +3 on BLEU) than fine-tuning MASS with the full dataset. Remarkably, with just 100 examples, our model outperforms training from scratch on the entire training set by over 3 BLEU, while reducing SER by over 30 points. These results lead us to believe that NMT pre-training can lead to substantial cost savings with respect to training data annotation." } ], "images": [ "scigen/train/3002.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Experiments with low-resource NLG" ], "reasoning": "NMT pre-training significantly improves performance over MASS, especially with smaller fine-tuning datasets, leading to substantial gains in BLEU and reductions in SER.", "description": "Next, we fine-tune each of these models on the data-to-text task. Both the high and medium resource models have a comparable SER. Even the low resource model, pre-trained on just 1% of the translation corpora is significantly better than mass, which has been pre-trained on almost 1.6 billion tokens. The results indicate that machine translation based transfer learning can be successfully applied even when the size of parallel corpus is small, and thus holds promise for low-resource languages. In this section we study the effects of transfer learning when the size of the fine-tuning corpus is small. We create two random subsets from the NLG training data of size 100 and 1000. We find that once again, NMT offers substantial gains over MASS. When fine-tuning on 1000 examples, pre-training with NMT is substantially better (20% improvement on SER, +3 on BLEU) than fine-tuning MASS with the full dataset. Remarkably, with just 100 examples, our model outperforms training from scratch on the entire training set by over 3 BLEU, while reducing SER by over 30 points. These results lead us to believe that NMT pre-training can lead to substantial cost savings with respect to training data annotation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 8: Experiments with low-resource NLG\nQuestion: What is the BLEU score improvement when using binmt over mass for a training size of 1000?\n" }, { "role": "assistant", "content": "For a training size of 1000, the BLEU score for mass is 9.07, while for binmt it is 19.89. The improvement is 19.89 - 9.07. So, the answer is {\"answer\": \"10.82\"}" } ], "images": [ "scigen/train/3002.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Experiments with low-resource NLG" ], "reasoning": "For a training size of 1000, the BLEU score for mass is 9.07, while for binmt it is 19.89. The improvement is 19.89 - 9.07.", "question": "What is the BLEU score improvement when using binmt over mass for a training size of 1000?", "answer": "So, the answer is {\"answer\": \"10.82\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nTraining from scratch with a full dataset results in a higher BLEU score than using mass with a training size of 1000.\n" }, { "role": "assistant", "content": "The BLEU score for training from scratch with a full dataset is 11.19, while for mass with a training size of 1000 it is 9.07. Since 11.19 is greater than 9.07, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3002.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Experiments with low-resource NLG" ], "reasoning": "The BLEU score for training from scratch with a full dataset is 11.19, while for mass with a training size of 1000 it is 9.07. Since 11.19 is greater than 9.07, the statement is supported.", "statement": "Training from scratch with a full dataset results in a higher BLEU score than using mass with a training size of 1000.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 10: Results on delexicalized NLG. † We compute sacrebleu on outputs provided to us by the authors (Dušek and Jurčíček, 2019).'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our model achieves a BLEU score 5 points higher than the best baseline, demonstrating its effectiveness even without a lexicalizer component.Our model outperforms the best baseline by 5 BLEU points, pointing to the applicability of our approach even in the case of delexicalized NLG. We leave the combination of this model with a lexicalizer component for future work." } ], "images": [ "scigen/train/3003.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Results on delexicalized NLG. † We compute sacrebleu on outputs provided to us by the authors (Dušek and Jurčíček, 2019)." ], "reasoning": "Our model achieves a BLEU score 5 points higher than the best baseline, demonstrating its effectiveness even without a lexicalizer component.", "description": "Our model outperforms the best baseline by 5 BLEU points, pointing to the applicability of our approach even in the case of delexicalized NLG. We leave the combination of this model with a lexicalizer component for future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 10: Results on delexicalized NLG. † We compute sacrebleu on outputs provided to us by the authors (Dušek and Jurčíček, 2019).\nQuestion: What is the BLEU score of the model that outperforms the best baseline by 5 points?\nSolve the above question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The description states that the model outperforms the best baseline by 5 BLEU points. The best baseline BLEU score is 25.34, so the model's BLEU score is 30.34. So, the answer is {\"answer\": \"30.34\"}." } ], "images": [ "scigen/train/3003.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Results on delexicalized NLG. † We compute sacrebleu on outputs provided to us by the authors (Dušek and Jurčíček, 2019)." ], "reasoning": "The description states that the model outperforms the best baseline by 5 BLEU points. The best baseline BLEU score is 25.34, so the model's BLEU score is 30.34.", "question": "What is the BLEU score of the model that outperforms the best baseline by 5 points?", "answer": "So, the answer is {\"answer\": \"30.34\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The binmt model has the highest BLEU score among the listed models. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe binmt model has the highest BLEU score among the listed models.\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for three models: baseline-mass (23.48), binmt (30.87), and tgen-delex (25.34). The binmt model has the highest BLEU score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3003.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Results on delexicalized NLG. † We compute sacrebleu on outputs provided to us by the authors (Dušek and Jurčíček, 2019)." ], "reasoning": "The table shows the BLEU scores for three models: baseline-mass (23.48), binmt (30.87), and tgen-delex (25.34). The binmt model has the highest BLEU score.", "statement": "The binmt model has the highest BLEU score among the listed models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: Results of multilingual sentence embedding mapping experiment in terms of accuracy %) and precision@5 (%). The best-performing method is highlighted in bold. (Baseline A: Mikolov2013ExploitingSA Mikolov2013ExploitingSA, Baseline B: dinu2014improving dinu2014improving, Baseline C: Smith2017OfflineBW Smith2017OfflineBW, Baseline D: lample2018word lample2018word, Baseline E: Schwenk2017LearningJM Schwenk2017LearningJM)'.\n" }, { "role": "assistant", "content": "Despite limited training data, the zero-shot Multilingual mapping model outperforms many baselines and achieves competitive retrieval accuracies for German and Spanish without direct pairwise mapping.Though seq-to-seq models can learn full-fledged neural translation models, they do not fare particularly well in resource-constrained scenarios with limited training data. Particularly, even with a parallel sentence pair percentage of just 20%, our model outperforms many baselines that utilize the total amount of training data. Moreover, retrieval accuracies between two source languages German and Spanish obtained by our model are very competitive with baselines receiving supervision for that language pair. Note that in our method, we do not provide any direct pairwise mapping. This proves the effectiveness of our zero-shot Multilingual mapping." } ], "images": [ "scigen/train/3004.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results of multilingual sentence embedding mapping experiment in terms of accuracy %) and precision@5 (%). The best-performing method is highlighted in bold. (Baseline A: Mikolov2013ExploitingSA Mikolov2013ExploitingSA, Baseline B: dinu2014improving dinu2014improving, Baseline C: Smith2017OfflineBW Smith2017OfflineBW, Baseline D: lample2018word lample2018word, Baseline E: Schwenk2017LearningJM Schwenk2017LearningJM)" ], "reasoning": "Despite limited training data, the zero-shot Multilingual mapping model outperforms many baselines and achieves competitive retrieval accuracies for German and Spanish without direct pairwise mapping.", "description": "Though seq-to-seq models can learn full-fledged neural translation models, they do not fare particularly well in resource-constrained scenarios with limited training data. Particularly, even with a parallel sentence pair percentage of just 20%, our model outperforms many baselines that utilize the total amount of training data. Moreover, retrieval accuracies between two source languages German and Spanish obtained by our model are very competitive with baselines receiving supervision for that language pair. Note that in our method, we do not provide any direct pairwise mapping. This proves the effectiveness of our zero-shot Multilingual mapping.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 5: Results of multilingual sentence embedding mapping experiment in terms of accuracy %) and precision@5 (%). The best-performing method is highlighted in bold. (Baseline A: Mikolov2013ExploitingSA Mikolov2013ExploitingSA, Baseline B: dinu2014improving dinu2014improving, Baseline C: Smith2017OfflineBW Smith2017OfflineBW, Baseline D: lample2018word lample2018word, Baseline E: Schwenk2017LearningJM Schwenk2017LearningJM)', solve the following question step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model achieves the highest accuracy for the Tatoeba deu→spa dataset?\n" }, { "role": "assistant", "content": "By comparing the accuracy (Acc) values for the Tatoeba deu→spa dataset across all models, the ABSent model has the highest accuracy with a value of 27.3. So, the answer is {\"answer\": \"ABSent\"}." } ], "images": [ "scigen/train/3004.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results of multilingual sentence embedding mapping experiment in terms of accuracy %) and precision@5 (%). The best-performing method is highlighted in bold. (Baseline A: Mikolov2013ExploitingSA Mikolov2013ExploitingSA, Baseline B: dinu2014improving dinu2014improving, Baseline C: Smith2017OfflineBW Smith2017OfflineBW, Baseline D: lample2018word lample2018word, Baseline E: Schwenk2017LearningJM Schwenk2017LearningJM)" ], "reasoning": "By comparing the accuracy (Acc) values for the Tatoeba deu→spa dataset across all models, the ABSent model has the highest accuracy with a value of 27.3.", "question": "Which model achieves the highest accuracy for the Tatoeba deu→spa dataset?", "answer": "So, the answer is {\"answer\": \"ABSent\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe seq2seq NMT model achieves the highest P@5 score for the Europarl deu→spa dataset.\n" }, { "role": "assistant", "content": "By comparing the P@5 values for the Europarl deu→spa dataset, the seq2seq NMT model has a P@5 score of 5.0, which is not the highest. The highest P@5 score for this dataset is achieved by the ABSent model with a value of 45.3. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3004.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results of multilingual sentence embedding mapping experiment in terms of accuracy %) and precision@5 (%). The best-performing method is highlighted in bold. (Baseline A: Mikolov2013ExploitingSA Mikolov2013ExploitingSA, Baseline B: dinu2014improving dinu2014improving, Baseline C: Smith2017OfflineBW Smith2017OfflineBW, Baseline D: lample2018word lample2018word, Baseline E: Schwenk2017LearningJM Schwenk2017LearningJM)" ], "reasoning": "By comparing the P@5 values for the Europarl deu→spa dataset, the seq2seq NMT model has a P@5 score of 5.0, which is not the highest. The highest P@5 score for this dataset is achieved by the ABSent model with a value of 45.3.", "statement": "The seq2seq NMT model achieves the highest P@5 score for the Europarl deu→spa dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Results of sentence embedding mapping experiment in terms of precision@1 and precision@5. Our proposed ABSent only utilizes 20% of parallel training data and the equal size of unparalleled data for training, while all baselines take 100% training data with parallel labels. The best-performing method is highlighted in bold.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The ABSent approach significantly outperforms other baselines in precision@1 and precision@5 for the deu→eng data from Tatoeba, achieving 46.2% and 65.5% respectively, which are 18.6 and 22.9 percentage points higher than the best baseline.Recall that we only use 20% of parallel sentences (true pairs) to train our model while all the other baselines utilize 100% of parallel sentence pairs for the training. We observe that our ABSent approach still significantly outperforms other baselines by a large margin. Take the deu→eng data from Tatoeba as an example. Our method achieves a precision@1 of 46.2% and a precision@5 of 65.5%, which are 18.6 and 22.9 absolute percentage points higher than the respective results of the best baseline. Similar trends can be observed for other datasets. Note that the results for different languages are not fully comparable due to different sizes of training data." } ], "images": [ "scigen/train/3005.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results of sentence embedding mapping experiment in terms of precision@1 and precision@5. Our proposed ABSent only utilizes 20% of parallel training data and the equal size of unparalleled data for training, while all baselines take 100% training data with parallel labels. The best-performing method is highlighted in bold." ], "reasoning": "The ABSent approach significantly outperforms other baselines in precision@1 and precision@5 for the deu→eng data from Tatoeba, achieving 46.2% and 65.5% respectively, which are 18.6 and 22.9 percentage points higher than the best baseline.", "description": "Recall that we only use 20% of parallel sentences (true pairs) to train our model while all the other baselines utilize 100% of parallel sentence pairs for the training. We observe that our ABSent approach still significantly outperforms other baselines by a large margin. Take the deu→eng data from Tatoeba as an example. Our method achieves a precision@1 of 46.2% and a precision@5 of 65.5%, which are 18.6 and 22.9 absolute percentage points higher than the respective results of the best baseline. Similar trends can be observed for other datasets. Note that the results for different languages are not fully comparable due to different sizes of training data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Results of sentence embedding mapping experiment in terms of precision@1 and precision@5. Our proposed ABSent only utilizes 20% of parallel training data and the equal size of unparalleled data for training, while all baselines take 100% training data with parallel labels. The best-performing method is highlighted in bold.', solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the precision@5 for the ABSent approach on the Tatoeba deu→eng dataset?\n" }, { "role": "assistant", "content": "The table shows the precision@5 for various approaches on different datasets. For the ABSent approach on the Tatoeba deu→eng dataset, the precision@5 is 65.5%. So, the answer is {\"answer\": \"65.5%\"}" } ], "images": [ "scigen/train/3005.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results of sentence embedding mapping experiment in terms of precision@1 and precision@5. Our proposed ABSent only utilizes 20% of parallel training data and the equal size of unparalleled data for training, while all baselines take 100% training data with parallel labels. The best-performing method is highlighted in bold." ], "reasoning": "The table shows the precision@5 for various approaches on different datasets. For the ABSent approach on the Tatoeba deu→eng dataset, the precision@5 is 65.5%.", "question": "What is the precision@5 for the ABSent approach on the Tatoeba deu→eng dataset?", "answer": "So, the answer is {\"answer\": \"65.5%\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The ABSent approach achieves a precision@1 of 46.2% on the Tatoeba deu→eng dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe ABSent approach achieves a precision@1 of 46.2% on the Tatoeba deu→eng dataset.\n" }, { "role": "assistant", "content": "The table shows that the ABSent approach achieves a precision@1 of 46.2% on the Tatoeba deu→eng dataset. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3005.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results of sentence embedding mapping experiment in terms of precision@1 and precision@5. Our proposed ABSent only utilizes 20% of parallel training data and the equal size of unparalleled data for training, while all baselines take 100% training data with parallel labels. The best-performing method is highlighted in bold." ], "reasoning": "The table shows that the ABSent approach achieves a precision@1 of 46.2% on the Tatoeba deu→eng dataset.", "statement": "The ABSent approach achieves a precision@1 of 46.2% on the Tatoeba deu→eng dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Comparison of precision@1 between ABSent (A), uni-Sent (U), and further modified settings. A∖mis, U∖mis are two variants without mismatch loss term. As swaps the weighting strategy (TF-IDF weighting for Tatoeba but not for Europarl). Aw stands for first aligning word-level representations and then generating sentence embeddings.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Introducing mismatch pairs into training improves the effectiveness of both ABSent and uni-Sent models, with appropriate weighting strategies further boosting results.S4SS3SSS0Px3 Influence of Mismatch Loss. This experiment aims to study how this loss function influences the effectiveness of our proposed models. For simplicity, we refer to a model X without the mismatch loss as X∖mis, where X can be either uni-Sent or ABSent. We take 10% of parallel training pairs, leaving other parameters as in the main experiments. Generally, bringing in mismatch pairs into the training improves the effectiveness of both our ABSent model and the uni-Sent version. Choosing an appropriate weighting strategy boosts the experimental results. Since TF-IDF weights words in accordance with their assumed importance, for the Europarl corpus, the 2σ volume of sentence lengths falls between 35 and 60, while for the Tatoeba corpus it is between 8 and 16. In such circumstances, words that appear more frequently in long sentences, especially function words such as ’a’, ’the’, etc. ought to have a lower weight, while infrequent ones ought to have a higher weight. For short sentences, it may make sense to even consider words with higher frequency, so as to not neglect their semantic contribution to the sentence. Thus, simple averaging works better than TF-IDF weighted averaging for the Tatoeba corpus." } ], "images": [ "scigen/train/3006.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of precision@1 between ABSent (A), uni-Sent (U), and further modified settings. A∖mis, U∖mis are two variants without mismatch loss term. As swaps the weighting strategy (TF-IDF weighting for Tatoeba but not for Europarl). Aw stands for first aligning word-level representations and then generating sentence embeddings." ], "reasoning": "Introducing mismatch pairs into training improves the effectiveness of both ABSent and uni-Sent models, with appropriate weighting strategies further boosting results.", "description": "S4SS3SSS0Px3 Influence of Mismatch Loss. This experiment aims to study how this loss function influences the effectiveness of our proposed models. For simplicity, we refer to a model X without the mismatch loss as X∖mis, where X can be either uni-Sent or ABSent. We take 10% of parallel training pairs, leaving other parameters as in the main experiments. Generally, bringing in mismatch pairs into the training improves the effectiveness of both our ABSent model and the uni-Sent version. Choosing an appropriate weighting strategy boosts the experimental results. Since TF-IDF weights words in accordance with their assumed importance, for the Europarl corpus, the 2σ volume of sentence lengths falls between 35 and 60, while for the Tatoeba corpus it is between 8 and 16. In such circumstances, words that appear more frequently in long sentences, especially function words such as ’a’, ’the’, etc. ought to have a lower weight, while infrequent ones ought to have a higher weight. For short sentences, it may make sense to even consider words with higher frequency, so as to not neglect their semantic contribution to the sentence. Thus, simple averaging works better than TF-IDF weighted averaging for the Tatoeba corpus.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: Table 2: Comparison of precision@1 between ABSent (A), uni-Sent (U), and further modified settings. A∖mis, U∖mis are two variants without mismatch loss term. As swaps the weighting strategy (TF-IDF weighting for Tatoeba but not for Europarl). Aw stands for first aligning word-level representations and then generating sentence embeddings.\nQuestion: What is the highest score achieved by the ABSent model (A) in the Europarl corpus?\n" }, { "role": "assistant", "content": "To find the highest score achieved by the ABSent model (A) in the Europarl corpus, look at the row labeled 'A' under the Europarl columns. The highest score is 45.8. So, the answer is {\"answer\": \"45.8\"}." } ], "images": [ "scigen/train/3006.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of precision@1 between ABSent (A), uni-Sent (U), and further modified settings. A∖mis, U∖mis are two variants without mismatch loss term. As swaps the weighting strategy (TF-IDF weighting for Tatoeba but not for Europarl). Aw stands for first aligning word-level representations and then generating sentence embeddings." ], "reasoning": "To find the highest score achieved by the ABSent model (A) in the Europarl corpus, look at the row labeled 'A' under the Europarl columns. The highest score is 45.8.", "question": "What is the highest score achieved by the ABSent model (A) in the Europarl corpus?", "answer": "So, the answer is {\"answer\": \"45.8\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe ABSent model (A) achieved a score of 34.9 in the Tatoeba eng→deu dataset.\n" }, { "role": "assistant", "content": "To verify this statement, look at the row labeled 'A' under the Tatoeba eng→deu column. The score is indeed 34.9. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3006.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of precision@1 between ABSent (A), uni-Sent (U), and further modified settings. A∖mis, U∖mis are two variants without mismatch loss term. As swaps the weighting strategy (TF-IDF weighting for Tatoeba but not for Europarl). Aw stands for first aligning word-level representations and then generating sentence embeddings." ], "reasoning": "To verify this statement, look at the row labeled 'A' under the Tatoeba eng→deu column. The score is indeed 34.9.", "statement": "The ABSent model (A) achieved a score of 34.9 in the Tatoeba eng→deu dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: The result of rewriting quality.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "CRN-RL has the highest BLEU-4 score of 54.2, indicating that reinforcement learning improves the model's performance.Moreover, a 54.2 BLEU-4 score means that the rewritten sentences are very similar to the human references. CRN-RL has a higher score than CRN-Pre-train on BLEU-4, it proves reinforcement learning promotes our model effectively." } ], "images": [ "scigen/train/3007.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The result of rewriting quality." ], "reasoning": "CRN-RL has the highest BLEU-4 score of 54.2, indicating that reinforcement learning improves the model's performance.", "description": "Moreover, a 54.2 BLEU-4 score means that the rewritten sentences are very similar to the human references. CRN-RL has a higher score than CRN-Pre-train on BLEU-4, it proves reinforcement learning promotes our model effectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the BLEU-4 score difference between CRN and CRN-RL?\nSolve the above question based on the table titled 'Table 1: The result of rewriting quality.'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The BLEU-4 score for CRN is 50.9 and for CRN-RL is 54.2. Subtracting 50.9 from 54.2 gives the difference. So, the answer is {\"answer\": \"3.3\"}" } ], "images": [ "scigen/train/3007.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The result of rewriting quality." ], "reasoning": "The BLEU-4 score for CRN is 50.9 and for CRN-RL is 54.2. Subtracting 50.9 from 54.2 gives the difference.", "question": "What is the BLEU-4 score difference between CRN and CRN-RL?", "answer": "So, the answer is {\"answer\": \"3.3\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nCRN-RL has a BLEU-4 score of 54.2.\n" }, { "role": "assistant", "content": "The table shows that CRN-RL has a BLEU-4 score of 54.2. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3007.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The result of rewriting quality." ], "reasoning": "The table shows that CRN-RL has a BLEU-4 score of 54.2.", "statement": "CRN-RL has a BLEU-4 score of 54.2.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: Evaluation results on multi-turn response selection. The numbers of baselines are copied from Zhou et al. (2018)'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Our model achieves comparable performance to the state-of-the-art DAM model by using only the rewritten utterance, indicating its ability to recognize important content and generate self-contained sentences.We can see that our model achieves a comparable performance with state-of-the-art DAM model, but only consuming a rewritten utterance rather than the whole context. This indicates that our model is able to recognize important content in context and generate a self-contained sentence. This argument is also verified by 1 point promotion compared with DAMsingle which only uses the last utterance as an input. Additionally, DAMsingle just underperforms DAM 1 point, meaning that the last utterance is very important for response selection. It supports our assumption that the last utterance is important which is a good prototype for context rewriting." } ], "images": [ "scigen/train/3008.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Evaluation results on multi-turn response selection. The numbers of baselines are copied from Zhou et al. (2018)" ], "reasoning": "Our model achieves comparable performance to the state-of-the-art DAM model by using only the rewritten utterance, indicating its ability to recognize important content and generate self-contained sentences.", "description": "We can see that our model achieves a comparable performance with state-of-the-art DAM model, but only consuming a rewritten utterance rather than the whole context. This indicates that our model is able to recognize important content in context and generate a self-contained sentence. This argument is also verified by 1 point promotion compared with DAMsingle which only uses the last utterance as an input. Additionally, DAMsingle just underperforms DAM 1 point, meaning that the last utterance is very important for response selection. It supports our assumption that the last utterance is important which is a good prototype for context rewriting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 4: Evaluation results on multi-turn response selection. The numbers of baselines are copied from Zhou et al. (2018)' to sequentially solve the question presented below. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the MAP score of the model that uses only the last utterance as input?\n" }, { "role": "assistant", "content": "The table shows the MAP scores for various models. The description mentions that DAMsingle uses only the last utterance as input. By locating DAMsingle in the table, we find its MAP score. So, the answer is {\"answer\": \"0.543\"}" } ], "images": [ "scigen/train/3008.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Evaluation results on multi-turn response selection. The numbers of baselines are copied from Zhou et al. (2018)" ], "reasoning": "The table shows the MAP scores for various models. The description mentions that DAMsingle uses only the last utterance as input. By locating DAMsingle in the table, we find its MAP score.", "question": "What is the MAP score of the model that uses only the last utterance as input?", "answer": "So, the answer is {\"answer\": \"0.543\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The CRN + RL + DAM single model outperforms the DAM model in terms of R10@5. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe CRN + RL + DAM single model outperforms the DAM model in terms of R10@5.\n" }, { "role": "assistant", "content": "The table shows the R10@5 scores for both models. The CRN + RL + DAM single model has an R10@5 score of 0.729, while the DAM model has an R10@5 score of 0.757. Therefore, the statement is incorrect. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3008.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Evaluation results on multi-turn response selection. The numbers of baselines are copied from Zhou et al. (2018)" ], "reasoning": "The table shows the R10@5 scores for both models. The CRN + RL + DAM single model has an R10@5 score of 0.729, while the DAM model has an R10@5 score of 0.757. Therefore, the statement is incorrect.", "statement": "The CRN + RL + DAM single model outperforms the DAM model in terms of R10@5.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: Model performance on the concept prediction task in terms of precision at rank 1, 10, 20, and average rank assigned. We compare BCF (this work), BayesCat, a co-occurrence baseline, Strudel (for English only), and a random baseline. Results are reported for English, German, French, Chinese and Arabic.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "BCF consistently achieves the highest scores, outperforming both the co-occurrence model and BayesCat across all metrics and conditions.Both BCF and the co-occurrence model outperform the random baseline by a large margin, and BCF achieves consistently highest scores. Both Bayesian models (BCF and BayesCat) outperform the co-occurrence model across all metrics and conditions. We assume that plain concept-feature co-occurrence information might be too sparse to provide a strong signal of concept relevance given a set of features. The Bayesian models, on the other hand, learn complex correspondences between features and all concepts in a category. BayesCat and BCF perform comparably given that they exploit local co-occurrence relations in similar ways. BCF learns feature associations which discriminate concepts more accurately, suggesting that the joint learning objective and structured feature information is beneficial." } ], "images": [ "scigen/train/3009.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Model performance on the concept prediction task in terms of precision at rank 1, 10, 20, and average rank assigned. We compare BCF (this work), BayesCat, a co-occurrence baseline, Strudel (for English only), and a random baseline. Results are reported for English, German, French, Chinese and Arabic." ], "reasoning": "BCF consistently achieves the highest scores, outperforming both the co-occurrence model and BayesCat across all metrics and conditions.", "description": "Both BCF and the co-occurrence model outperform the random baseline by a large margin, and BCF achieves consistently highest scores. Both Bayesian models (BCF and BayesCat) outperform the co-occurrence model across all metrics and conditions. We assume that plain concept-feature co-occurrence information might be too sparse to provide a strong signal of concept relevance given a set of features. The Bayesian models, on the other hand, learn complex correspondences between features and all concepts in a category. BayesCat and BCF perform comparably given that they exploit local co-occurrence relations in similar ways. BCF learns feature associations which discriminate concepts more accurately, suggesting that the joint learning objective and structured feature information is beneficial.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Model performance on the concept prediction task in terms of precision at rank 1, 10, 20, and average rank assigned. We compare BCF (this work), BayesCat, a co-occurrence baseline, Strudel (for English only), and a random baseline. Results are reported for English, German, French, Chinese and Arabic.', answer the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model achieves the highest average rank score in Arabic?\n" }, { "role": "assistant", "content": "To find the model with the highest average rank score in Arabic, we compare the avg rank scores of BCF and BayesCat. BCF has an avg rank of 54.9, while BayesCat has an avg rank of 27.5. Therefore, BayesCat achieves the highest average rank score in Arabic. So, the answer is {\"answer\": \"BayesCat\"}." } ], "images": [ "scigen/train/3009.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Model performance on the concept prediction task in terms of precision at rank 1, 10, 20, and average rank assigned. We compare BCF (this work), BayesCat, a co-occurrence baseline, Strudel (for English only), and a random baseline. Results are reported for English, German, French, Chinese and Arabic." ], "reasoning": "To find the model with the highest average rank score in Arabic, we compare the avg rank scores of BCF and BayesCat. BCF has an avg rank of 54.9, while BayesCat has an avg rank of 27.5. Therefore, BayesCat achieves the highest average rank score in Arabic.", "question": "Which model achieves the highest average rank score in Arabic?", "answer": "So, the answer is {\"answer\": \"BayesCat\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The co-occurrence model outperforms the random baseline in all languages., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe co-occurrence model outperforms the random baseline in all languages.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the pr@1, pr@10, and pr@20 scores of the co-occurrence model and the random baseline across all languages. In each case, the co-occurrence model has higher scores than the random baseline. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3009.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Model performance on the concept prediction task in terms of precision at rank 1, 10, 20, and average rank assigned. We compare BCF (this work), BayesCat, a co-occurrence baseline, Strudel (for English only), and a random baseline. Results are reported for English, German, French, Chinese and Arabic." ], "reasoning": "To verify this statement, we compare the pr@1, pr@10, and pr@20 scores of the co-occurrence model and the random baseline across all languages. In each case, the co-occurrence model has higher scores than the random baseline.", "statement": "The co-occurrence model outperforms the random baseline in all languages.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Datasets obtained from language-specific Wikipedia dumps for Arabic (ar), Chinese (ch), English, (en), French (fr), and German (ge). Number of stimuli (concept mentions in linguistic context), concepts, and features (context word types) are shown.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The number of concepts, features, and stimuli varies across languages, with English having the highest number of stimuli and Chinese having the highest number of features.From this data set we derived a set of input stimuli as mentions of a concept from the reference set of concepts in sentence context (cf., In order to obtain balanced data sets, we automatically filtered words of low importance to a concept from contexts, using the term-frequency-inverse-document-frequency (tf-idf) metric. After filtering, we only kept stimuli with 3≤n≤20 context words and at most 1,000 stimuli per target concept. The number of stimuli varies across languages as a function of the number of target concepts, and the size of the respective Wikipedia corpus. BCF, nevertheless, achieves purity scores of 0.4 or higher for all languages, meaning that on average at least 40% of the members of a gold standard category are clustered together by BCF (purity rises to 58% for English). This indicates that meaningful categories emerge throughout. The categories shown are intuitively meaningful; in particular vegetable and clothing Cross-lingual comparisons reveal that, compared to BCF, the performance of the co-occurrence model degrades more severely for languages other than English. The number of concepts (i.e., target items to be ranked) differs across languages so that absolute numbers are not directly comparable." } ], "images": [ "scigen/train/3010.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Datasets obtained from language-specific Wikipedia dumps for Arabic (ar), Chinese (ch), English, (en), French (fr), and German (ge). Number of stimuli (concept mentions in linguistic context), concepts, and features (context word types) are shown." ], "reasoning": "The number of concepts, features, and stimuli varies across languages, with English having the highest number of stimuli and Chinese having the highest number of features.", "description": "From this data set we derived a set of input stimuli as mentions of a concept from the reference set of concepts in sentence context (cf., In order to obtain balanced data sets, we automatically filtered words of low importance to a concept from contexts, using the term-frequency-inverse-document-frequency (tf-idf) metric. After filtering, we only kept stimuli with 3≤n≤20 context words and at most 1,000 stimuli per target concept. The number of stimuli varies across languages as a function of the number of target concepts, and the size of the respective Wikipedia corpus. BCF, nevertheless, achieves purity scores of 0.4 or higher for all languages, meaning that on average at least 40% of the members of a gold standard category are clustered together by BCF (purity rises to 58% for English). This indicates that meaningful categories emerge throughout. The categories shown are intuitively meaningful; in particular vegetable and clothing Cross-lingual comparisons reveal that, compared to BCF, the performance of the co-occurrence model degrades more severely for languages other than English. The number of concepts (i.e., target items to be ranked) differs across languages so that absolute numbers are not directly comparable.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Datasets obtained from language-specific Wikipedia dumps for Arabic (ar), Chinese (ch), English, (en), French (fr), and German (ge). Number of stimuli (concept mentions in linguistic context), concepts, and features (context word types) are shown.', answer the question below by showing each progressive step toward the answer. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich language has the highest number of features and how many are there?\n" }, { "role": "assistant", "content": "By examining the 'features' row in the table, we see that Chinese (ch) has the highest number of features, which is 6,516. So, the answer is {\"answer\": \"6,516\"}." } ], "images": [ "scigen/train/3010.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Datasets obtained from language-specific Wikipedia dumps for Arabic (ar), Chinese (ch), English, (en), French (fr), and German (ge). Number of stimuli (concept mentions in linguistic context), concepts, and features (context word types) are shown." ], "reasoning": "By examining the 'features' row in the table, we see that Chinese (ch) has the highest number of features, which is 6,516.", "question": "Which language has the highest number of features and how many are there?", "answer": "So, the answer is {\"answer\": \"6,516\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis English has the highest number of concepts among the listed languages. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nEnglish has the highest number of concepts among the listed languages.\n" }, { "role": "assistant", "content": "By looking at the 'concepts' row in the table, we see that English (en) has 491 concepts, which is the highest among the listed languages. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3010.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Datasets obtained from language-specific Wikipedia dumps for Arabic (ar), Chinese (ch), English, (en), French (fr), and German (ge). Number of stimuli (concept mentions in linguistic context), concepts, and features (context word types) are shown." ], "reasoning": "By looking at the 'concepts' row in the table, we see that English (en) has 491 concepts, which is the highest among the listed languages.", "statement": "English has the highest number of concepts among the listed languages.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Compare with other models in performance and real-time factor.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The ST-NAT model has the fastest inference speed with an RTF of 0.0056 and achieves a CER of 7.02% on the test set when combined with a transformer language model.From another perspective, the ST-NAT has the fastest inference speed among them, which is only about 1/10 of speech-transformer. The ST-NAT with a transformer language model can achieve the best CER of 7.02% on the test set and an RTF of 0.0292." } ], "images": [ "scigen/train/3011.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Compare with other models in performance and real-time factor." ], "reasoning": "The ST-NAT model has the fastest inference speed with an RTF of 0.0056 and achieves a CER of 7.02% on the test set when combined with a transformer language model.", "description": "From another perspective, the ST-NAT has the fastest inference speed among them, which is only about 1/10 of speech-transformer. The ST-NAT with a transformer language model can achieve the best CER of 7.02% on the test set and an RTF of 0.0292.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Compare with other models in performance and real-time factor.\nQuestion: What is the RTF of the model that achieves the best CER on the test set?\nSolve the above question based on the table information. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The ST-NAT+LM model achieves the best CER of 7.02% on the test set, and its RTF is 0.0292. So, the answer is {\"answer\": \"0.0292\"}" } ], "images": [ "scigen/train/3011.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Compare with other models in performance and real-time factor." ], "reasoning": "The ST-NAT+LM model achieves the best CER of 7.02% on the test set, and its RTF is 0.0292.", "question": "What is the RTF of the model that achieves the best CER on the test set?", "answer": "So, the answer is {\"answer\": \"0.0292\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The ST-NAT model has a faster inference speed than the Speech-Transformer model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe ST-NAT model has a faster inference speed than the Speech-Transformer model.\n" }, { "role": "assistant", "content": "The RTF of the ST-NAT model is 0.0056, which is lower than the RTF of the Speech-Transformer model, which is 0.0504. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3011.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Compare with other models in performance and real-time factor." ], "reasoning": "The RTF of the ST-NAT model is 0.0056, which is lower than the RTF of the Speech-Transformer model, which is 0.0504.", "statement": "The ST-NAT model has a faster inference speed than the Speech-Transformer model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Comparison of the model with different CTC weights α and trigger thresholds β. We evaluate the CER(%) on development and test set, respectively.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The ST-NAT model's performance is influenced by both CTC weights and trigger thresholds, with the best performance achieved at a CTC weight of 0.6 and a trigger threshold of 0.3.We train the ST-NAT model with different CTC weights α and trigger thresholds β from scratch. At the same threshold, the trigger NAT with weight 0.6 can achieve the best performance on development set. The CTC weights and trigger thresholds affect the performance of the model in different aspects. The CTC weights α are used to balance the performance of CTC trigger module and decoder. However, the trigger threshold β are used to determine how many encoder states are triggered. Both weights and thresholds play important roles in the performance of the models." } ], "images": [ "scigen/train/3012.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of the model with different CTC weights α and trigger thresholds β. We evaluate the CER(%) on development and test set, respectively." ], "reasoning": "The ST-NAT model's performance is influenced by both CTC weights and trigger thresholds, with the best performance achieved at a CTC weight of 0.6 and a trigger threshold of 0.3.", "description": "We train the ST-NAT model with different CTC weights α and trigger thresholds β from scratch. At the same threshold, the trigger NAT with weight 0.6 can achieve the best performance on development set. The CTC weights and trigger thresholds affect the performance of the model in different aspects. The CTC weights α are used to balance the performance of CTC trigger module and decoder. However, the trigger threshold β are used to determine how many encoder states are triggered. Both weights and thresholds play important roles in the performance of the models.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 1: Comparison of the model with different CTC weights α and trigger thresholds β. We evaluate the CER(%) on development and test set, respectively.', solve this question and give detail solutions. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the performance of the model with a CTC weight of 0.6 and a trigger threshold of 0.3?\n" }, { "role": "assistant", "content": "To find the performance, locate the row with CTC weight 0.6 and the column with trigger threshold 0.3. The performance values are 6.88 and 7.67. So, the answer is {\"answer\": \"6.88/7.67\"}" } ], "images": [ "scigen/train/3012.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of the model with different CTC weights α and trigger thresholds β. We evaluate the CER(%) on development and test set, respectively." ], "reasoning": "To find the performance, locate the row with CTC weight 0.6 and the column with trigger threshold 0.3. The performance values are 6.88 and 7.67.", "question": "What is the performance of the model with a CTC weight of 0.6 and a trigger threshold of 0.3?", "answer": "So, the answer is {\"answer\": \"6.88/7.67\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The model achieves its best performance with a CTC weight of 0.6 and a trigger threshold of 0.3. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe model achieves its best performance with a CTC weight of 0.6 and a trigger threshold of 0.3.\n" }, { "role": "assistant", "content": "The table shows that the lowest performance values (6.88/7.67) are achieved with a CTC weight of 0.6 and a trigger threshold of 0.3, supporting the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3012.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of the model with different CTC weights α and trigger thresholds β. We evaluate the CER(%) on development and test set, respectively." ], "reasoning": "The table shows that the lowest performance values (6.88/7.67) are achieved with a CTC weight of 0.6 and a trigger threshold of 0.3, supporting the statement.", "statement": "The model achieves its best performance with a CTC weight of 0.6 and a trigger threshold of 0.3.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Comparison of the effects of different trigger threshold on the inference speed. We record the time that the ST-NAT spends on decoding test set and calculate the real-time factor.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Different trigger thresholds have minimal impact on inference speed, with a threshold of 0.7 achieving an RTF of 0.0054 and a latency of nearly 20 milliseconds.Explore the effects of different trigger thresholds on the inference speed. We evaluate our ST-NAT with different trigger thresholds on the inference speed. All the ST-NAT models are trained with a CTC weight of 0.6. When the trigger threshold is 0.7, the model achieves an RTF of 0.0054. It also means the model only has a latency of nearly 20 milliseconds. However, a large threshold does not mean that the model can achieve the best performance. A large trigger threshold might cause the predicted length generated by the CTC trigger to be shorter than the target length, which in turn will hurt the performance of the model. Fortunately, different trigger thresholds have only little effect on the speed of inference, which can even be ignored." } ], "images": [ "scigen/train/3013.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of the effects of different trigger threshold on the inference speed. We record the time that the ST-NAT spends on decoding test set and calculate the real-time factor." ], "reasoning": "Different trigger thresholds have minimal impact on inference speed, with a threshold of 0.7 achieving an RTF of 0.0054 and a latency of nearly 20 milliseconds.", "description": "Explore the effects of different trigger thresholds on the inference speed. We evaluate our ST-NAT with different trigger thresholds on the inference speed. All the ST-NAT models are trained with a CTC weight of 0.6. When the trigger threshold is 0.7, the model achieves an RTF of 0.0054. It also means the model only has a latency of nearly 20 milliseconds. However, a large threshold does not mean that the model can achieve the best performance. A large trigger threshold might cause the predicted length generated by the CTC trigger to be shorter than the target length, which in turn will hurt the performance of the model. Fortunately, different trigger thresholds have only little effect on the speed of inference, which can even be ignored.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Comparison of the effects of different trigger threshold on the inference speed. We record the time that the ST-NAT spends on decoding test set and calculate the real-time factor.', and sequentially solve the question, writing out each step of your reasoning process. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the performance of the model when the trigger threshold is 0.1?\n" }, { "role": "assistant", "content": "The table shows the performance values for different trigger thresholds. For a threshold of 0.1, the performance is listed in the first row under the 'Performance' column. So, the answer is {\"answer\": \"7.88\"}." } ], "images": [ "scigen/train/3013.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of the effects of different trigger threshold on the inference speed. We record the time that the ST-NAT spends on decoding test set and calculate the real-time factor." ], "reasoning": "The table shows the performance values for different trigger thresholds. For a threshold of 0.1, the performance is listed in the first row under the 'Performance' column.", "question": "What is the performance of the model when the trigger threshold is 0.1?", "answer": "So, the answer is {\"answer\": \"7.88\"}." }, { "messages": [ { "role": "user", "content": "The model achieves the lowest RTF at a trigger threshold of 0.7. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model achieves the lowest RTF at a trigger threshold of 0.7.\n" }, { "role": "assistant", "content": "The table lists the RTF values for different trigger thresholds. The RTF for a threshold of 0.7 is 0.0054, which is the lowest among the given thresholds. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3013.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of the effects of different trigger threshold on the inference speed. We record the time that the ST-NAT spends on decoding test set and calculate the real-time factor." ], "reasoning": "The table lists the RTF values for different trigger thresholds. The RTF for a threshold of 0.7 is 0.0054, which is the lowest among the given thresholds.", "statement": "The model achieves the lowest RTF at a trigger threshold of 0.7.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Comparison on the CoNLL 2009 English test set. The first block presents results of other models evaluated for both syntax and semantics on the CoNLL 2009 task. The second block presents our models. The third block presents the best published models, each using its own syntactic preprocessing.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our models, including Syntax-only, Semantics-only, Hybrid, and Joint, outperform previous models such as Gesmundo:09 and Henderson:13, demonstrating the effectiveness of our entire-parser-state representation learner.English All of our models (Syntax-only, Semantics-only, Hybrid and Joint) improve over \\newciteGesmundo:09 and \\newciteHenderson:13, demonstrating the benefit of our entire-parser-state representation learner compared to the more locally scoped model. \\newciteLei:15 use syntactic parses to obtain interaction features between predicates and their arguments and then compress feature representations using a low-rank tensor. \\newciteTackstrom:15 present an exact inference algorithm for SRL based on dynamic programming and their local and structured models make use of many syntactic features from a pipeline; our search procedure is greedy. Their algorithm is adopted by \\newciteFitzgerald:15 for inference in a model that jointly learns representations from a combination of PropBank and FrameNet annotations; we have not experimented with extra annotations." } ], "images": [ "scigen/train/3014.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison on the CoNLL 2009 English test set. The first block presents results of other models evaluated for both syntax and semantics on the CoNLL 2009 task. The second block presents our models. The third block presents the best published models, each using its own syntactic preprocessing." ], "reasoning": "Our models, including Syntax-only, Semantics-only, Hybrid, and Joint, outperform previous models such as Gesmundo:09 and Henderson:13, demonstrating the effectiveness of our entire-parser-state representation learner.", "description": "English All of our models (Syntax-only, Semantics-only, Hybrid and Joint) improve over \\newciteGesmundo:09 and \\newciteHenderson:13, demonstrating the benefit of our entire-parser-state representation learner compared to the more locally scoped model. \\newciteLei:15 use syntactic parses to obtain interaction features between predicates and their arguments and then compress feature representations using a low-rank tensor. \\newciteTackstrom:15 present an exact inference algorithm for SRL based on dynamic programming and their local and structured models make use of many syntactic features from a pipeline; our search procedure is greedy. Their algorithm is adopted by \\newciteFitzgerald:15 for inference in a model that jointly learns representations from a combination of PropBank and FrameNet annotations; we have not experimented with extra annotations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Comparison on the CoNLL 2009 English test set. The first block presents results of other models evaluated for both syntax and semantics on the CoNLL 2009 task. The second block presents our models. The third block presents the best published models, each using its own syntactic preprocessing.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. \n\nQuestion: Which model has the highest LAS score in the table?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table lists LAS scores for various models. The Joint model has the highest LAS score of 89.94. So, the answer is {\"answer\": \"Joint\"}." } ], "images": [ "scigen/train/3014.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison on the CoNLL 2009 English test set. The first block presents results of other models evaluated for both syntax and semantics on the CoNLL 2009 task. The second block presents our models. The third block presents the best published models, each using its own syntactic preprocessing." ], "reasoning": "The table lists LAS scores for various models. The Joint model has the highest LAS score of 89.94.", "question": "Which model has the highest LAS score in the table?", "answer": "So, the answer is {\"answer\": \"Joint\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Hybrid model has a higher Sem. F1 (WSJ) score than the Syntax-only model.\n" }, { "role": "assistant", "content": "The table shows that the Hybrid model has a Sem. F1 (WSJ) score of 84.58, while the Syntax-only model has a score of 84.39. Therefore, the Hybrid model has a higher score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3014.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison on the CoNLL 2009 English test set. The first block presents results of other models evaluated for both syntax and semantics on the CoNLL 2009 task. The second block presents our models. The third block presents the best published models, each using its own syntactic preprocessing." ], "reasoning": "The table shows that the Hybrid model has a Sem. F1 (WSJ) score of 84.58, while the Syntax-only model has a score of 84.39. Therefore, the Hybrid model has a higher score.", "statement": "The Hybrid model has a higher Sem. F1 (WSJ) score than the Syntax-only model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Joint parsers: comparison on the CoNLL 2008 test (WSJ+Brown) set.'.\n" }, { "role": "assistant", "content": "Our joint model outperforms Henderson:08 and is comparable to the best systems on the CoNLL 2008 task, falling behind only Johansson:08.Our joint model significantly outperforms the joint model of \\newciteHenderson:08, from which our set of transitions is derived, showing the benefit of learning a representation for the entire algorithmic state. The best reported systems on the CoNLL 2008 task are due to \\newciteJohansson:08, \\newciteChe:08, \\newciteCiaramita:08 and \\newciteZhao:08, all of which pipeline syntax and semantics; our system’s semantic and overall performance is comparable to these. We fall behind only \\newciteJohansson:08, whose success was attributed to carefully designed global SRL features integrated into a pipeline of classifiers, making them asymptotically slower." } ], "images": [ "scigen/train/3015.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Joint parsers: comparison on the CoNLL 2008 test (WSJ+Brown) set." ], "reasoning": "Our joint model outperforms Henderson:08 and is comparable to the best systems on the CoNLL 2008 task, falling behind only Johansson:08.", "description": "Our joint model significantly outperforms the joint model of \\newciteHenderson:08, from which our set of transitions is derived, showing the benefit of learning a representation for the entire algorithmic state. The best reported systems on the CoNLL 2008 task are due to \\newciteJohansson:08, \\newciteChe:08, \\newciteCiaramita:08 and \\newciteZhao:08, all of which pipeline syntax and semantics; our system’s semantic and overall performance is comparable to these. We fall behind only \\newciteJohansson:08, whose success was attributed to carefully designed global SRL features integrated into a pipeline of classifiers, making them asymptotically slower.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nTable title: Table 2: Joint parsers: comparison on the CoNLL 2008 test (WSJ+Brown) set.\n\nQuestion: Which model has the highest LAS score in the CoNLL 2008 best systems?\n" }, { "role": "assistant", "content": "The table lists the LAS scores for the CoNLL 2008 best systems. The highest LAS score among them is 89.3 by J&N (2008). So, the answer is {\"answer\": \"J&N (2008)\"}." } ], "images": [ "scigen/train/3015.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Joint parsers: comparison on the CoNLL 2008 test (WSJ+Brown) set." ], "reasoning": "The table lists the LAS scores for the CoNLL 2008 best systems. The highest LAS score among them is 89.3 by J&N (2008).", "question": "Which model has the highest LAS score in the CoNLL 2008 best systems?", "answer": "So, the answer is {\"answer\": \"J&N (2008)\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe joint model of this work has the highest Macro F1 score among all models.\n" }, { "role": "assistant", "content": "The table shows the Macro F1 scores for all models. The joint model of this work has a Macro F1 score of 84.9, which is not the highest. J&N (2008) has the highest Macro F1 score of 85.5. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3015.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Joint parsers: comparison on the CoNLL 2008 test (WSJ+Brown) set." ], "reasoning": "The table shows the Macro F1 scores for all models. The joint model of this work has a Macro F1 score of 84.9, which is not the highest. J&N (2008) has the highest Macro F1 score of 85.5.", "statement": "The joint model of this work has the highest Macro F1 score among all models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Comparison of macro F1 scores on the multilingual CoNLL 2009 test set.'.\n" }, { "role": "assistant", "content": "The joint model performs comparably to the top three systems on non-English CoNLL 2009 datasets, with the Chinese parser showing superior performance due to pretrained word embeddings.Multilingual We tested the joint model on the non-English CoNLL 2009 datasets, and the results demonstrate that it adapts easily—it is on par with the top three systems in most cases. We note that our Chinese parser relies on pretrained word embeddings for its superior performance; without them (not shown), it was on par with the others. Japanese is a small-data case (4,393 training examples), illustrating our model’s dependence on reasonably large training datasets." } ], "images": [ "scigen/train/3016.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of macro F1 scores on the multilingual CoNLL 2009 test set." ], "reasoning": "The joint model performs comparably to the top three systems on non-English CoNLL 2009 datasets, with the Chinese parser showing superior performance due to pretrained word embeddings.", "description": "Multilingual We tested the joint model on the non-English CoNLL 2009 datasets, and the results demonstrate that it adapts easily—it is on par with the top three systems in most cases. We note that our Chinese parser relies on pretrained word embeddings for its superior performance; without them (not shown), it was on par with the others. Japanese is a small-data case (4,393 training examples), illustrating our model’s dependence on reasonably large training datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 4: Comparison of macro F1 scores on the multilingual CoNLL 2009 test set.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. \n\nQuestion: Which language has the highest score in the Joint model?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the language with the highest score in the Joint model, we need to compare the scores in the Joint column for all languages. The highest score is 87.45 for English. So, the answer is {\"answer\": \"English\"}" } ], "images": [ "scigen/train/3016.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of macro F1 scores on the multilingual CoNLL 2009 test set." ], "reasoning": "To find the language with the highest score in the Joint model, we need to compare the scores in the Joint column for all languages. The highest score is 87.45 for English.", "question": "Which language has the highest score in the Joint model?", "answer": "So, the answer is {\"answer\": \"English\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Japanese parser has the highest score in the Joint model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Japanese parser has the highest score in the Joint model.\n" }, { "role": "assistant", "content": "The table shows that the highest score in the Joint model is 87.45 for English, not Japanese, which has a score of 80.91. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3016.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of macro F1 scores on the multilingual CoNLL 2009 test set." ], "reasoning": "The table shows that the highest score in the Joint model is 87.45 for English, not Japanese, which has a score of 80.91.", "statement": "The Japanese parser has the highest score in the Joint model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Evaluation results on the test sets of the PERSONA-CHAT data and the CMUDoG data. Numbers in bold mean that improvement over the best baseline is statistically significant (t-test, p-value <0.01).'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "DGMN outperforms all baselines on both data sets across all metrics, with statistically significant improvements. The improvement over Transformer is smaller on CMUDoG than on PERSONA-CHAT due to richer semantics in CMUDoG's wiki documents.We can see that on both data sets, DGMN outperforms all baselines over all metrics, and the improvement is statistically significant (t-test, p-value <0.01). Improvement from DGMN over Transformer on the CMUDoG data is smaller than that on the PERSONA-CHAT data. The reason might be that Transformer can benefit from the wiki documents in CMUDoG that are longer and contain richer semantics than those handcrafted ones in PERSONA-CHAT." } ], "images": [ "scigen/train/3017.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Evaluation results on the test sets of the PERSONA-CHAT data and the CMUDoG data. Numbers in bold mean that improvement over the best baseline is statistically significant (t-test, p-value <0.01)." ], "reasoning": "DGMN outperforms all baselines on both data sets across all metrics, with statistically significant improvements. The improvement over Transformer is smaller on CMUDoG than on PERSONA-CHAT due to richer semantics in CMUDoG's wiki documents.", "description": "We can see that on both data sets, DGMN outperforms all baselines over all metrics, and the improvement is statistically significant (t-test, p-value <0.01). Improvement from DGMN over Transformer on the CMUDoG data is smaller than that on the PERSONA-CHAT data. The reason might be that Transformer can benefit from the wiki documents in CMUDoG that are longer and contain richer semantics than those handcrafted ones in PERSONA-CHAT.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 3: Evaluation results on the test sets of the PERSONA-CHAT data and the CMUDoG data. Numbers in bold mean that improvement over the best baseline is statistically significant (t-test, p-value <0.01).', solve the subsequent question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model has the highest r@5 score on the CMUDoG data set?\n" }, { "role": "assistant", "content": "By examining the r@5 column for the CMUDoG data set, DGMN has the highest score of 91.2. So, the answer is {\"answer\": \"DGMN\"}" } ], "images": [ "scigen/train/3017.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Evaluation results on the test sets of the PERSONA-CHAT data and the CMUDoG data. Numbers in bold mean that improvement over the best baseline is statistically significant (t-test, p-value <0.01)." ], "reasoning": "By examining the r@5 column for the CMUDoG data set, DGMN has the highest score of 91.2.", "question": "Which model has the highest r@5 score on the CMUDoG data set?", "answer": "So, the answer is {\"answer\": \"DGMN\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nDGMN has a higher r@1 score on PERSONA-CHAT Original Persona than on CMUDoG.\n" }, { "role": "assistant", "content": "The r@1 score for DGMN on PERSONA-CHAT Original Persona is 67.6, while on CMUDoG it is 65.6. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3017.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Evaluation results on the test sets of the PERSONA-CHAT data and the CMUDoG data. Numbers in bold mean that improvement over the best baseline is statistically significant (t-test, p-value <0.01)." ], "reasoning": "The r@1 score for DGMN on PERSONA-CHAT Original Persona is 67.6, while on CMUDoG it is 65.6. Therefore, the statement is supported.", "statement": "DGMN has a higher r@1 score on PERSONA-CHAT Original Persona than on CMUDoG.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Performance of DGMN across different length of grounded documents on all data sets.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Short and long profiles in PERSONA-CHAT lead to performance drops due to less useful or more irrelevant information, while longer documents in CMUDoG improve performance due to more relevant information.Finally, we study the relationship between the performance of DGMN and document length by binning text examples in both data into different buckets according to the document length. On the PERSONA-CHAT data, both short profiles and long profiles lead to performance drop, while on the CMUDoG data, the longer the documents are, the better the performance of DGMN is. The reason behind the difference might be that profiles in the PERSONA-CHAT data are handcrafted by crowd workers, and thus semantics among different sentences are relatively independent, while documents in the CMUDoG data come from Wikipedia, and there is rich semantic overlap among sentences. Therefore, short profiles contain less useful information and long profiles contain more irrelevant information, and both will make the matching task more challenging. On the other hand, the longer a wiki document is, the more relevant information it can provide to the matching task." } ], "images": [ "scigen/train/3018.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance of DGMN across different length of grounded documents on all data sets." ], "reasoning": "Short and long profiles in PERSONA-CHAT lead to performance drops due to less useful or more irrelevant information, while longer documents in CMUDoG improve performance due to more relevant information.", "description": "Finally, we study the relationship between the performance of DGMN and document length by binning text examples in both data into different buckets according to the document length. On the PERSONA-CHAT data, both short profiles and long profiles lead to performance drop, while on the CMUDoG data, the longer the documents are, the better the performance of DGMN is. The reason behind the difference might be that profiles in the PERSONA-CHAT data are handcrafted by crowd workers, and thus semantics among different sentences are relatively independent, while documents in the CMUDoG data come from Wikipedia, and there is rich semantic overlap among sentences. Therefore, short profiles contain less useful information and long profiles contain more irrelevant information, and both will make the matching task more challenging. On the other hand, the longer a wiki document is, the more relevant information it can provide to the matching task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Performance of DGMN across different length of grounded documents on all data sets.\nQuestion: What is the r@1 performance for the longest document length in CMUDoG data?\nSolve the above question based on the table information. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The longest document length in CMUDoG data is [350, 515], and the corresponding r@1 performance is 67.4. So, the answer is {\"answer\": \"67.4\"}." } ], "images": [ "scigen/train/3018.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance of DGMN across different length of grounded documents on all data sets." ], "reasoning": "The longest document length in CMUDoG data is [350, 515], and the corresponding r@1 performance is 67.4.", "question": "What is the r@1 performance for the longest document length in CMUDoG data?", "answer": "So, the answer is {\"answer\": \"67.4\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe r@1 performance for the shortest document length in CMUDoG data is higher than the longest document length in PERSONA-CHAT data.\n" }, { "role": "assistant", "content": "The shortest document length in CMUDoG data is [0, 150] with an r@1 performance of 64.0, while the longest document length in PERSONA-CHAT data is [35, 52] with an r@1 performance of 66.7. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3018.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance of DGMN across different length of grounded documents on all data sets." ], "reasoning": "The shortest document length in CMUDoG data is [0, 150] with an r@1 performance of 64.0, while the longest document length in PERSONA-CHAT data is [35, 52] with an r@1 performance of 66.7.", "statement": "The r@1 performance for the shortest document length in CMUDoG data is higher than the longest document length in PERSONA-CHAT data.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: Results of classifying extracted documents with the reference model. Results labelled Proportion indicate selecting up to the indicated percentage of sentences in the review, and results labelled Fixed show the result of selecting a fixed number of sentences from each. ConvNet shows reference model performance on extractions produced by our ConvNet. Word2Vec shows reference performance on extractions produced by the shallow baseline. Rand shows reference performance on randomly created extractions. The final two rows show reference accuracy on the full reviews (Full) and when using the common heuristic of extracting the first and last sentence (First+Last).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Even with reduced sentences, the reference model's accuracy drops by less than 1.5%, and the convolutional model consistently outperforms all baselines.Even keeping only two sentences from each review, the accuracy of the reference model drops by less than 1.5% on the test set. In all cases our convolutional model outperforms all of the baselines we compare against." } ], "images": [ "scigen/train/3019.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results of classifying extracted documents with the reference model. Results labelled Proportion indicate selecting up to the indicated percentage of sentences in the review, and results labelled Fixed show the result of selecting a fixed number of sentences from each. ConvNet shows reference model performance on extractions produced by our ConvNet. Word2Vec shows reference performance on extractions produced by the shallow baseline. Rand shows reference performance on randomly created extractions. The final two rows show reference accuracy on the full reviews (Full) and when using the common heuristic of extracting the first and last sentence (First+Last)." ], "reasoning": "Even with reduced sentences, the reference model's accuracy drops by less than 1.5%, and the convolutional model consistently outperforms all baselines.", "description": "Even keeping only two sentences from each review, the accuracy of the reference model drops by less than 1.5% on the test set. In all cases our convolutional model outperforms all of the baselines we compare against.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: Results of classifying extracted documents with the reference model. Results labelled Proportion indicate selecting up to the indicated percentage of sentences in the review, and results labelled Fixed show the result of selecting a fixed number of sentences from each. ConvNet shows reference model performance on extractions produced by our ConvNet. Word2Vec shows reference performance on extractions produced by the shallow baseline. Rand shows reference performance on randomly created extractions. The final two rows show reference accuracy on the full reviews (Full) and when using the common heuristic of extracting the first and last sentence (First+Last).'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the accuracy of the ConvNet model when using 25% of the data and Fixed Pick 5?\n" }, { "role": "assistant", "content": "To find the accuracy of the ConvNet model with 25% data and Fixed Pick 5, locate the row for 25% and the column for ConvNet under Fixed Pick 5. So, the answer is {\"answer\": \"82.59\"}" } ], "images": [ "scigen/train/3019.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results of classifying extracted documents with the reference model. Results labelled Proportion indicate selecting up to the indicated percentage of sentences in the review, and results labelled Fixed show the result of selecting a fixed number of sentences from each. ConvNet shows reference model performance on extractions produced by our ConvNet. Word2Vec shows reference performance on extractions produced by the shallow baseline. Rand shows reference performance on randomly created extractions. The final two rows show reference accuracy on the full reviews (Full) and when using the common heuristic of extracting the first and last sentence (First+Last)." ], "reasoning": "To find the accuracy of the ConvNet model with 25% data and Fixed Pick 5, locate the row for 25% and the column for ConvNet under Fixed Pick 5.", "question": "What is the accuracy of the ConvNet model when using 25% of the data and Fixed Pick 5?", "answer": "So, the answer is {\"answer\": \"82.59\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The ConvNet model achieves the highest accuracy with 50% data. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe ConvNet model achieves the highest accuracy with 50% data.\n" }, { "role": "assistant", "content": "The table shows that the ConvNet model achieves an accuracy of 82.74% with 50% data, which is higher than the other models' accuracies with the same data proportion. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3019.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results of classifying extracted documents with the reference model. Results labelled Proportion indicate selecting up to the indicated percentage of sentences in the review, and results labelled Fixed show the result of selecting a fixed number of sentences from each. ConvNet shows reference model performance on extractions produced by our ConvNet. Word2Vec shows reference performance on extractions produced by the shallow baseline. Rand shows reference performance on randomly created extractions. The final two rows show reference accuracy on the full reviews (Full) and when using the common heuristic of extracting the first and last sentence (First+Last)." ], "reasoning": "The table shows that the ConvNet model achieves an accuracy of 82.74% with 50% data, which is higher than the other models' accuracies with the same data proportion.", "statement": "The ConvNet model achieves the highest accuracy with 50% data.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: BLEU scores for different models on the WMT data for translation DE↔EN.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "In the translation experiments using EN↔DE WMT-2017 training data, two architectures, RNN and Transformer, were used with BPE preprocessing and beam search during inference.In the translation experiments, we use the standard EN↔DE WMT-2017 training data (bojar-EtAl:2018:WMT1). We perform NMT experiments with two different architectures: RNN (luong:2015:EMNLP) and Transformer (vaswani2017attention). We preprocess the training data with Byte-Pair Encoding (BPE) using 32K merge operations (sennrich-haddow-birch:2016:P16-12). During inference, we use beam search with a beam size of 5." } ], "images": [ "scigen/train/3020.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: BLEU scores for different models on the WMT data for translation DE↔EN." ], "reasoning": "In the translation experiments using EN↔DE WMT-2017 training data, two architectures, RNN and Transformer, were used with BPE preprocessing and beam search during inference.", "description": "In the translation experiments, we use the standard EN↔DE WMT-2017 training data (bojar-EtAl:2018:WMT1). We perform NMT experiments with two different architectures: RNN (luong:2015:EMNLP) and Transformer (vaswani2017attention). We preprocess the training data with Byte-Pair Encoding (BPE) using 32K merge operations (sennrich-haddow-birch:2016:P16-12). During inference, we use beam search with a beam size of 5.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: BLEU scores for different models on the WMT data for translation DE↔EN.', unfold the question presented below, detailing every step of your calculation. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat was the highest score achieved by the Transformer architecture in the EN-DE translation task?\n" }, { "role": "assistant", "content": "To find the highest score achieved by the Transformer architecture in the EN-DE translation task, look at the EN-DE columns for the Transformer row. The highest score is 39.8 in 2018. So, the answer is {\"answer\": \"39.8\"}." } ], "images": [ "scigen/train/3020.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: BLEU scores for different models on the WMT data for translation DE↔EN." ], "reasoning": "To find the highest score achieved by the Transformer architecture in the EN-DE translation task, look at the EN-DE columns for the Transformer row. The highest score is 39.8 in 2018.", "question": "What was the highest score achieved by the Transformer architecture in the EN-DE translation task?", "answer": "So, the answer is {\"answer\": \"39.8\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe RNN architecture achieved a higher score than the Transformer architecture in the DE-EN 2017 translation task.\n" }, { "role": "assistant", "content": "To verify this statement, compare the DE-EN 2017 scores for both architectures. The RNN score is 28.2, while the Transformer score is 32.1. The statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3020.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: BLEU scores for different models on the WMT data for translation DE↔EN." ], "reasoning": "To verify this statement, compare the DE-EN 2017 scores for both architectures. The RNN score is 28.2, while the Transformer score is 32.1. The statement is incorrect.", "statement": "The RNN architecture achieved a higher score than the Transformer architecture in the DE-EN 2017 translation task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: Mean oscillations for SUBNUM variations. In theory the variations should result in zero oscillations for every metric.'.\n" }, { "role": "assistant", "content": "Despite minor sentence variations, both RNN and Transformer models show significant fluctuations in translation quality metrics.While sentence-level metrics are not reliable indicators of translation quality, they do capture fluctuations in translations. With the variations we introduce, in theory there should be no fluctuations in the translations. We observe that even though these sentence variations differ by only one number, there are many cases where an insignificant change in the sentence results in unexpectedly large oscillations. Both RNN and Transformer exhibit this behaviour to a certain extent." } ], "images": [ "scigen/train/3021.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Mean oscillations for SUBNUM variations. In theory the variations should result in zero oscillations for every metric." ], "reasoning": "Despite minor sentence variations, both RNN and Transformer models show significant fluctuations in translation quality metrics.", "description": "While sentence-level metrics are not reliable indicators of translation quality, they do capture fluctuations in translations. With the variations we introduce, in theory there should be no fluctuations in the translations. We observe that even though these sentence variations differ by only one number, there are many cases where an insignificant change in the sentence results in unexpectedly large oscillations. Both RNN and Transformer exhibit this behaviour to a certain extent.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 5: Mean oscillations for SUBNUM variations. In theory the variations should result in zero oscillations for every metric.', and tackle the question below in a sequential manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model has a higher BLEU score and a lower TER score?\n" }, { "role": "assistant", "content": "The BLEU score for RNN is 4.0 and for Transformer is 3.8. The TER score for RNN is 5.2 and for Transformer is 4.2. Therefore, RNN has a higher BLEU score and Transformer has a lower TER score. So, the answer is {\"answer\": \"RNN and Transformer\"}." } ], "images": [ "scigen/train/3021.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Mean oscillations for SUBNUM variations. In theory the variations should result in zero oscillations for every metric." ], "reasoning": "The BLEU score for RNN is 4.0 and for Transformer is 3.8. The TER score for RNN is 5.2 and for Transformer is 4.2. Therefore, RNN has a higher BLEU score and Transformer has a lower TER score.", "question": "Which model has a higher BLEU score and a lower TER score?", "answer": "So, the answer is {\"answer\": \"RNN and Transformer\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe RNN model has a higher LengthRatio than the Transformer model.\n" }, { "role": "assistant", "content": "The LengthRatio for RNN is 5.3 and for Transformer is 3.4, indicating that the RNN model has a higher LengthRatio. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3021.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Mean oscillations for SUBNUM variations. In theory the variations should result in zero oscillations for every metric." ], "reasoning": "The LengthRatio for RNN is 5.3 and for Transformer is 3.4, indicating that the RNN model has a higher LengthRatio.", "statement": "The RNN model has a higher LengthRatio than the Transformer model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Accuracy (MedNLI) and Exact F1 score (i2b2) across various clinical NLP tasks.'.\n" }, { "role": "assistant", "content": "Clinically fine-tuned BioBERT shows improvements over BioBERT or general BERT on MedNLI, i2b2 2010, and i2b2 2012, but not on i2b2 2006 and i2b2 2014.Clinical NLP Tasks On three of the five tasks (MedNLI, i2b2 2010, and i2b2 2012), clinically fine-tuned BioBERT shows improvements over BioBERT or general BERT. However, on our two de-ID tasks, i2b2 2006 and i2b2 2014, clinical BERT offers no improvements over Bio- or general BERT. This is actually not surprising, and is instead, we argue, a direct consequence of the nature of de-ID challenges." } ], "images": [ "scigen/train/3022.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracy (MedNLI) and Exact F1 score (i2b2) across various clinical NLP tasks." ], "reasoning": "Clinically fine-tuned BioBERT shows improvements over BioBERT or general BERT on MedNLI, i2b2 2010, and i2b2 2012, but not on i2b2 2006 and i2b2 2014.", "description": "Clinical NLP Tasks On three of the five tasks (MedNLI, i2b2 2010, and i2b2 2012), clinically fine-tuned BioBERT shows improvements over BioBERT or general BERT. However, on our two de-ID tasks, i2b2 2006 and i2b2 2014, clinical BERT offers no improvements over Bio- or general BERT. This is actually not surprising, and is instead, we argue, a direct consequence of the nature of de-ID challenges.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Accuracy (MedNLI) and Exact F1 score (i2b2) across various clinical NLP tasks.', and sequentially solve the question, writing out each step of your reasoning process. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model shows the highest improvement on the i2b2 2012 task?\n" }, { "role": "assistant", "content": "By comparing the scores for the i2b2 2012 task, Bio+Discharge Summary BERT has the highest score of 87.8. So, the answer is {\"answer\": \"Bio+Discharge Summary BERT\"}." } ], "images": [ "scigen/train/3022.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracy (MedNLI) and Exact F1 score (i2b2) across various clinical NLP tasks." ], "reasoning": "By comparing the scores for the i2b2 2012 task, Bio+Discharge Summary BERT has the highest score of 87.8.", "question": "Which model shows the highest improvement on the i2b2 2012 task?", "answer": "So, the answer is {\"answer\": \"Bio+Discharge Summary BERT\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim BioBERT outperforms Clinical BERT on the i2b2 2006 task., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nBioBERT outperforms Clinical BERT on the i2b2 2006 task.\n" }, { "role": "assistant", "content": "The table shows that BioBERT has a score of 94.8 on the i2b2 2006 task, while Clinical BERT has a score of 91.5. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3022.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracy (MedNLI) and Exact F1 score (i2b2) across various clinical NLP tasks." ], "reasoning": "The table shows that BioBERT has a score of 94.8 on the i2b2 2006 task, while Clinical BERT has a score of 91.5.", "statement": "BioBERT outperforms Clinical BERT on the i2b2 2006 task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4: Effect of the different weighted loss.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "By adjusting the weights λ1, λ2, and λ3, the effect of different weighted losses on IMDB and Yelp datasets is investigated, with specific settings to exclude loss2 or loss3.The λ1, λ2, and λ3 respectively represent the weight of loss1, loss2 and loss3. We investigate the effect of different weighted loss by empirically adjusting their proportion. When λ2 is set to 0, we do not use loss2 to enhance the review representations. Similarly, we set λ3 to 0 to avoid the effect of loss3." } ], "images": [ "scigen/train/3023.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Effect of the different weighted loss." ], "reasoning": "By adjusting the weights λ1, λ2, and λ3, the effect of different weighted losses on IMDB and Yelp datasets is investigated, with specific settings to exclude loss2 or loss3.", "description": "The λ1, λ2, and λ3 respectively represent the weight of loss1, loss2 and loss3. We investigate the effect of different weighted loss by empirically adjusting their proportion. When λ2 is set to 0, we do not use loss2 to enhance the review representations. Similarly, we set λ3 to 0 to avoid the effect of loss3.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 4: Effect of the different weighted loss.'. Solve the following question based on the table.\n\nQuestion: What is the IMDB accuracy when λ1 is 0.4, λ2 is 0.3, and λ3 is 0.3?\nLet's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the IMDB accuracy for the given λ values, locate the row where λ1 is 0.4, λ2 is 0.3, and λ3 is 0.3, and then check the corresponding IMDB accuracy value. So, the answer is {\"answer\": \"0.550\"}." } ], "images": [ "scigen/train/3023.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Effect of the different weighted loss." ], "reasoning": "To find the IMDB accuracy for the given λ values, locate the row where λ1 is 0.4, λ2 is 0.3, and λ3 is 0.3, and then check the corresponding IMDB accuracy value.", "question": "What is the IMDB accuracy when λ1 is 0.4, λ2 is 0.3, and λ3 is 0.3?", "answer": "So, the answer is {\"answer\": \"0.550\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The highest IMDB accuracy is achieved when λ1 is 0.4, λ2 is 0.3, and λ3 is 0.3. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe highest IMDB accuracy is achieved when λ1 is 0.4, λ2 is 0.3, and λ3 is 0.3.\n" }, { "role": "assistant", "content": "To verify this statement, compare the IMDB accuracy values across all rows. The highest IMDB accuracy is 0.550, which occurs when λ1 is 0.4, λ2 is 0.3, and λ3 is 0.3. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3023.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Effect of the different weighted loss." ], "reasoning": "To verify this statement, compare the IMDB accuracy values across all rows. The highest IMDB accuracy is 0.550, which occurs when λ1 is 0.4, λ2 is 0.3, and λ3 is 0.3.", "statement": "The highest IMDB accuracy is achieved when λ1 is 0.4, λ2 is 0.3, and λ3 is 0.3.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Reviews sentiment classification results. Acc.(Accuracy, higher is better) and RMSE(lower is better) are the evaluation metrics. The best performances are in bold. HUAPA outperforms previous best state-of-the-art method significantly(p<0.01).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table compares the performance of various models on IMDB and Yelp datasets, with and without user and product information, by adjusting the weights of different losses.The λ1, λ2, and λ3 respectively represent the weight of loss1, loss2 and loss3. We investigate the effect of different weighted loss by empirically adjusting their proportion. When λ2 is set to 0, we do not use loss2 to enhance the review representations. Similarly, we set λ3 to 0 to avoid the effect of loss3." } ], "images": [ "scigen/train/3024.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Reviews sentiment classification results. Acc.(Accuracy, higher is better) and RMSE(lower is better) are the evaluation metrics. The best performances are in bold. HUAPA outperforms previous best state-of-the-art method significantly(p<0.01)." ], "reasoning": "The table compares the performance of various models on IMDB and Yelp datasets, with and without user and product information, by adjusting the weights of different losses.", "description": "The λ1, λ2, and λ3 respectively represent the weight of loss1, loss2 and loss3. We investigate the effect of different weighted loss by empirically adjusting their proportion. When λ2 is set to 0, we do not use loss2 to enhance the review representations. Similarly, we set λ3 to 0 to avoid the effect of loss3.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Reviews sentiment classification results. Acc.(Accuracy, higher is better) and RMSE(lower is better) are the evaluation metrics. The best performances are in bold. HUAPA outperforms previous best state-of-the-art method significantly(p<0.01).', and sequentially solve the question, writing out each step of your reasoning process. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model has the highest IMDB accuracy when using user and product information?\n" }, { "role": "assistant", "content": "To find the model with the highest IMDB accuracy when using user and product information, look at the 'Models with user and product information' section and identify the highest value in the 'IMDB Acc.' column. So, the answer is {\"answer\": \"HUAPA\"}." } ], "images": [ "scigen/train/3024.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Reviews sentiment classification results. Acc.(Accuracy, higher is better) and RMSE(lower is better) are the evaluation metrics. The best performances are in bold. HUAPA outperforms previous best state-of-the-art method significantly(p<0.01)." ], "reasoning": "To find the model with the highest IMDB accuracy when using user and product information, look at the 'Models with user and product information' section and identify the highest value in the 'IMDB Acc.' column.", "question": "Which model has the highest IMDB accuracy when using user and product information?", "answer": "So, the answer is {\"answer\": \"HUAPA\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe NSC+LA(BiLSTM) model has a higher Yelp 2014 accuracy than the NSC model when using user and product information.\n" }, { "role": "assistant", "content": "Compare the Yelp 2014 accuracy of the NSC+LA(BiLSTM) model and the NSC model in the 'Models with user and product information' section. NSC+LA(BiLSTM) has an accuracy of 0.669, while NSC has an accuracy of 0.637. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3024.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Reviews sentiment classification results. Acc.(Accuracy, higher is better) and RMSE(lower is better) are the evaluation metrics. The best performances are in bold. HUAPA outperforms previous best state-of-the-art method significantly(p<0.01)." ], "reasoning": "Compare the Yelp 2014 accuracy of the NSC+LA(BiLSTM) model and the NSC model in the 'Models with user and product information' section. NSC+LA(BiLSTM) has an accuracy of 0.669, while NSC has an accuracy of 0.637.", "statement": "The NSC+LA(BiLSTM) model has a higher Yelp 2014 accuracy than the NSC model when using user and product information.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Effect of user attention and product attention. HUA only uses user information and local text, and HPA only uses product information and local text.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The hierarchical user attention network (HUA) and hierarchical product attention network (HPA) both improve performance over the NSC+LA(BiLSTM) model, with user attention being more effective than product attention. The combined user and product attention model (HUAPA) achieves the best performance.To investigate the effects of single user attention or product attention, we also implement independent hierarchical user attention network (HUA) and hierarchical product attention network (HPA). From the table, we can observe that: Compared to the model NSC+LA(BiLSTM) only using local semantic attention, HUA and HPA both achieve some improvements, which validates the rationality of incorporating user and product into sentiment classification via attention mechanism. The results also indicate that user attention or product attention can capture more information related to sentiment. The user information is more effective than the product information to enhance review representations. Although some words or sentences in reviews show product’s characteristic, the ratings are finally decided by users. Hence, it is reasonable that the discrimination of user’s preference is more obvious than product’s characteristic. Compared to single user attention or product attention, our model achieves better performance, which indicates that user and product information both contribute to our model. The results demonstrate our user attention and product attention mechanism can catch the specific user’s preference and product’s characteristic." } ], "images": [ "scigen/train/3025.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Effect of user attention and product attention. HUA only uses user information and local text, and HPA only uses product information and local text." ], "reasoning": "The hierarchical user attention network (HUA) and hierarchical product attention network (HPA) both improve performance over the NSC+LA(BiLSTM) model, with user attention being more effective than product attention. The combined user and product attention model (HUAPA) achieves the best performance.", "description": "To investigate the effects of single user attention or product attention, we also implement independent hierarchical user attention network (HUA) and hierarchical product attention network (HPA). From the table, we can observe that: Compared to the model NSC+LA(BiLSTM) only using local semantic attention, HUA and HPA both achieve some improvements, which validates the rationality of incorporating user and product into sentiment classification via attention mechanism. The results also indicate that user attention or product attention can capture more information related to sentiment. The user information is more effective than the product information to enhance review representations. Although some words or sentences in reviews show product’s characteristic, the ratings are finally decided by users. Hence, it is reasonable that the discrimination of user’s preference is more obvious than product’s characteristic. Compared to single user attention or product attention, our model achieves better performance, which indicates that user and product information both contribute to our model. The results demonstrate our user attention and product attention mechanism can catch the specific user’s preference and product’s characteristic.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Effect of user attention and product attention. HUA only uses user information and local text, and HPA only uses product information and local text.\nQuestion: Which model achieves the highest accuracy on the IMDB dataset?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the IMDB Acc. column for all models, we see that the HUAPA model has the highest accuracy with a value of 0.550. So, the answer is {\"answer\": \"HUAPA\"}." } ], "images": [ "scigen/train/3025.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Effect of user attention and product attention. HUA only uses user information and local text, and HPA only uses product information and local text." ], "reasoning": "By comparing the IMDB Acc. column for all models, we see that the HUAPA model has the highest accuracy with a value of 0.550.", "question": "Which model achieves the highest accuracy on the IMDB dataset?", "answer": "So, the answer is {\"answer\": \"HUAPA\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe HPA model achieves a higher accuracy on the Yelp 2013 dataset than the NSC+LA(BiLSTM) model.\n" }, { "role": "assistant", "content": "By comparing the Yelp 2013 Acc. column for the HPA and NSC+LA(BiLSTM) models, we see that the HPA model has an accuracy of 0.641, which is higher than the NSC+LA(BiLSTM) model's accuracy of 0.638. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3025.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Effect of user attention and product attention. HUA only uses user information and local text, and HPA only uses product information and local text." ], "reasoning": "By comparing the Yelp 2013 Acc. column for the HPA and NSC+LA(BiLSTM) models, we see that the HPA model has an accuracy of 0.641, which is higher than the NSC+LA(BiLSTM) model's accuracy of 0.638.", "statement": "The HPA model achieves a higher accuracy on the Yelp 2013 dataset than the NSC+LA(BiLSTM) model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3.6: Impact of feature subsets on SkipNER [12]', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The POS subset is particularly effective for recognizing Location and Person names, while self-trained features and taxonomic relations are more effective for discriminating the class ORG.-Ch2S2SS5 -Chx2T1 0.4pt0.1pt We also study the impact of each subset of features used to train the SkipNER model. This result may indicate that the feature subsets are complementary to each other, thus supporting the proposed method of integrating NER-specific features for training word embeddings. Also, note that the Words only subset corresponds to the skip-gram model. Location, and Person names are particularly well recognized by using the POS subset, while self-trained features and taxonomic relations seem more effective to discriminate the class ORG." } ], "images": [ "scigen/train/3026.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3.6: Impact of feature subsets on SkipNER [12]" ], "reasoning": "The POS subset is particularly effective for recognizing Location and Person names, while self-trained features and taxonomic relations are more effective for discriminating the class ORG.", "description": "-Ch2S2SS5 -Chx2T1 0.4pt0.1pt We also study the impact of each subset of features used to train the SkipNER model. This result may indicate that the feature subsets are complementary to each other, thus supporting the proposed method of integrating NER-specific features for training word embeddings. Also, note that the Words only subset corresponds to the skip-gram model. Location, and Person names are particularly well recognized by using the POS subset, while self-trained features and taxonomic relations seem more effective to discriminate the class ORG.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3.6: Impact of feature subsets on SkipNER [12]'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich SkipNER feature subset is most effective for recognizing Person names?\n" }, { "role": "assistant", "content": "The table shows that the POS subset has the highest score for recognizing Person names with a score of 82.3. So, the answer is {\"answer\": \"POS\"}." } ], "images": [ "scigen/train/3026.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3.6: Impact of feature subsets on SkipNER [12]" ], "reasoning": "The table shows that the POS subset has the highest score for recognizing Person names with a score of 82.3.", "question": "Which SkipNER feature subset is most effective for recognizing Person names?", "answer": "So, the answer is {\"answer\": \"POS\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Words subset is the most effective for recognizing Location names.\n" }, { "role": "assistant", "content": "The table shows that the Words subset has a score of 79.3 for recognizing Location names, while the All subset has a higher score of 80.9. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3026.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3.6: Impact of feature subsets on SkipNER [12]" ], "reasoning": "The table shows that the Words subset has a score of 79.3 for recognizing Location names, while the All subset has a higher score of 80.9.", "statement": "The Words subset is the most effective for recognizing Location names.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2.1: Example BIO tagging for a sample of MUC-7 [11]'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "NER models use the BIO tagging scheme to label sequences, where tags indicate the beginning, inside, or outside of named entities. HMM models, despite their limitations, have been adapted to better capture dependencies between tags and words.-Ch2S2SS5 -Chx2T1 Machine learning approaches typically model the two subtasks of NER as a sequence prediction task using machine learning based approach: given a word sequence W = w0 w1 w2 … wn, the objective of NER is to find the optimal tag sequence T = t0 t1 t2 … tn, where the tag set is pre-defined. One of the most popular tagging schema used by NER approaches is the BIO encoding. The set of BIO tags includes Beginning, Inside, and Outside tags. For example, the BIO scheme for person names includes BPers, IPers and O, which indicate the beginning of a person name, an internal word of a person name and the word belonging to none of the named entity types. Given the tagging schema and word sequence, the sequence labeling model searches for the best tag sequence with the parameters learned from training process. In the following, we review the popular sequence labeling models having been used for NER. Charniak et al. Given the word sequence W = w0 w1 w2 … wn, the best tagging sequence T = t0 t1 t2 … tn is defined as ^T1... n =argmaxT1..nP(T1... n,W1... n) HMM model assumes that the current word wi is generated from a multinomial distribution conditioned on current tag while current tag is only conditioned on previous tags. In a first order HMM, the above joint probability can be expanded to P(T1... n,W1... n)≈n∐i=1P(ti|ti−1)P(wi|ti) However, the assumption made by HMM is too strong for NER as the current tag depends on not only the previous tags but also the other types of characteristics in context. This makes less sense than assuming B-ORG depends on the previous word officer. Classic HMM does not allow such dependency between tags and words. Many variations of HMM model have been proposed for addressing this problem, most of which relax the Markov assumption to allow the dependency between previous tags and observations." } ], "images": [ "scigen/train/3027.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2.1: Example BIO tagging for a sample of MUC-7 [11]" ], "reasoning": "NER models use the BIO tagging scheme to label sequences, where tags indicate the beginning, inside, or outside of named entities. HMM models, despite their limitations, have been adapted to better capture dependencies between tags and words.", "description": "-Ch2S2SS5 -Chx2T1 Machine learning approaches typically model the two subtasks of NER as a sequence prediction task using machine learning based approach: given a word sequence W = w0 w1 w2 … wn, the objective of NER is to find the optimal tag sequence T = t0 t1 t2 … tn, where the tag set is pre-defined. One of the most popular tagging schema used by NER approaches is the BIO encoding. The set of BIO tags includes Beginning, Inside, and Outside tags. For example, the BIO scheme for person names includes BPers, IPers and O, which indicate the beginning of a person name, an internal word of a person name and the word belonging to none of the named entity types. Given the tagging schema and word sequence, the sequence labeling model searches for the best tag sequence with the parameters learned from training process. In the following, we review the popular sequence labeling models having been used for NER. Charniak et al. Given the word sequence W = w0 w1 w2 … wn, the best tagging sequence T = t0 t1 t2 … tn is defined as ^T1... n =argmaxT1..nP(T1... n,W1... n) HMM model assumes that the current word wi is generated from a multinomial distribution conditioned on current tag while current tag is only conditioned on previous tags. In a first order HMM, the above joint probability can be expanded to P(T1... n,W1... n)≈n∐i=1P(ti|ti−1)P(wi|ti) However, the assumption made by HMM is too strong for NER as the current tag depends on not only the previous tags but also the other types of characteristics in context. This makes less sense than assuming B-ORG depends on the previous word officer. Classic HMM does not allow such dependency between tags and words. Many variations of HMM model have been proposed for addressing this problem, most of which relax the Markov assumption to allow the dependency between previous tags and observations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2.1: Example BIO tagging for a sample of MUC-7 [11]' to sequentially solve the question presented below. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the full name of the person who met with Martin Puris?\n" }, { "role": "assistant", "content": "The table shows that Mr. Dooner (B-PER) met with Martin (B-PER) Puris (I-PER). Therefore, the full name of the person who met with Martin Puris is Mr. Dooner. So, the answer is {\"answer\": \"Mr. Dooner\"}" } ], "images": [ "scigen/train/3027.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2.1: Example BIO tagging for a sample of MUC-7 [11]" ], "reasoning": "The table shows that Mr. Dooner (B-PER) met with Martin (B-PER) Puris (I-PER). Therefore, the full name of the person who met with Martin Puris is Mr. Dooner.", "question": "What is the full name of the person who met with Martin Puris?", "answer": "So, the answer is {\"answer\": \"Mr. Dooner\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe meeting discussed the acquisition of Ammirati Puris by McCann.\n" }, { "role": "assistant", "content": "The table indicates that the meeting involved discussions about McCann (B-ORG) acquiring the agency with billings of $400 million. Ammirati (B-ORG) & (I-ORG) Puris (I-ORG) is mentioned in the context of the meeting, supporting the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3027.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2.1: Example BIO tagging for a sample of MUC-7 [11]" ], "reasoning": "The table indicates that the meeting involved discussions about McCann (B-ORG) acquiring the agency with billings of $400 million. Ammirati (B-ORG) & (I-ORG) Puris (I-ORG) is mentioned in the context of the meeting, supporting the statement.", "statement": "The meeting discussed the acquisition of Ammirati Puris by McCann.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3.5: Performance of NER methods in terms of F-score', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our method, Baseline + SkipNER, achieves the highest F-score of 70.19, outperforming all other methods and showing a 2% absolute improvement over the original Skip-gram model.-Ch2S2SS5 -Chx2T1 3.3.2 Evaluations Our method outperforms all the other methods and achieves 2% absolute improvement of F-score in comparison to the original Skip-gram model." } ], "images": [ "scigen/train/3028.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3.5: Performance of NER methods in terms of F-score" ], "reasoning": "Our method, Baseline + SkipNER, achieves the highest F-score of 70.19, outperforming all other methods and showing a 2% absolute improvement over the original Skip-gram model.", "description": "-Ch2S2SS5 -Chx2T1 3.3.2 Evaluations Our method outperforms all the other methods and achieves 2% absolute improvement of F-score in comparison to the original Skip-gram model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3.5: Performance of NER methods in terms of F-score'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich method achieved the highest F-score and by how much did it improve over the original Skip-gram model?\n" }, { "role": "assistant", "content": "The table shows that the Baseline + SkipNER method achieved the highest F-score of 70.19. The original Skip-gram model (Baseline + Skip) has an F-score of 67.89. The improvement is 70.19 - 67.89 = 2.3. So, the answer is {\"answer\": \"Baseline + SkipNER, 2.3\"}." } ], "images": [ "scigen/train/3028.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3.5: Performance of NER methods in terms of F-score" ], "reasoning": "The table shows that the Baseline + SkipNER method achieved the highest F-score of 70.19. The original Skip-gram model (Baseline + Skip) has an F-score of 67.89. The improvement is 70.19 - 67.89 = 2.3.", "question": "Which method achieved the highest F-score and by how much did it improve over the original Skip-gram model?", "answer": "So, the answer is {\"answer\": \"Baseline + SkipNER, 2.3\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The Baseline + Senna method achieved the highest F-score., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Baseline + Senna method achieved the highest F-score.\n" }, { "role": "assistant", "content": "The table shows that the Baseline + SkipNER method achieved the highest F-score of 70.19, while the Baseline + Senna method achieved an F-score of 68.29. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3028.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3.5: Performance of NER methods in terms of F-score" ], "reasoning": "The table shows that the Baseline + SkipNER method achieved the highest F-score of 70.19, while the Baseline + Senna method achieved an F-score of 68.29.", "statement": "The Baseline + Senna method achieved the highest F-score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5.5: Performance of reranking model on TedLium corpus'.\n" }, { "role": "assistant", "content": "The proposed discriminatively trained RBM achieves the greatest WER reduction, particularly with the late fusion of SLP and p-dRBM, validating the effectiveness of using prior knowledge.-Ch2S2SS5 -Chx2T1 0.4pt0.1pt It shows that the proposed discriminatively trained RBM produces greater WER reduction than baseline SLP rerankers. Effectiveness of using prior knowledge is validated by further improving WER over dRBM. The greatest absolute WER reduction (1.3%) is achieved by the late fusion of SLP and p-dRBM, which confirms that our reranker captures information complementary to SLP. In comparison, proposed RBM rerankers, especially p-dRBM, have reduced more errors on chosen keywords without sacrificing overall performance. We further break down the TF-IDF scores into 3 bins. Thanks to hidden variables, our methods are capable of better capturing the discriminative information for most content words. In particular, p-dRBM is shown working significantly better than other methods on words with medium TF-IDF scores (<5), which is a result of injecting named entity words, e.g., washington (TF-IDF=3.87), that mostly have TF-IDF between 3.0 and 5.0." } ], "images": [ "scigen/train/3029.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5.5: Performance of reranking model on TedLium corpus" ], "reasoning": "The proposed discriminatively trained RBM achieves the greatest WER reduction, particularly with the late fusion of SLP and p-dRBM, validating the effectiveness of using prior knowledge.", "description": "-Ch2S2SS5 -Chx2T1 0.4pt0.1pt It shows that the proposed discriminatively trained RBM produces greater WER reduction than baseline SLP rerankers. Effectiveness of using prior knowledge is validated by further improving WER over dRBM. The greatest absolute WER reduction (1.3%) is achieved by the late fusion of SLP and p-dRBM, which confirms that our reranker captures information complementary to SLP. In comparison, proposed RBM rerankers, especially p-dRBM, have reduced more errors on chosen keywords without sacrificing overall performance. We further break down the TF-IDF scores into 3 bins. Thanks to hidden variables, our methods are capable of better capturing the discriminative information for most content words. In particular, p-dRBM is shown working significantly better than other methods on words with medium TF-IDF scores (<5), which is a result of injecting named entity words, e.g., washington (TF-IDF=3.87), that mostly have TF-IDF between 3.0 and 5.0.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 5.5: Performance of reranking model on TedLium corpus' and proceed to solve the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the WER reduction achieved by the late fusion of SLP and p-dRBM?\n" }, { "role": "assistant", "content": "The table shows that the WER for SLP + p-dRBM is 16.91, which is the lowest WER compared to other methods, indicating the greatest reduction. So, the answer is {\"answer\": \"16.91\"}." } ], "images": [ "scigen/train/3029.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5.5: Performance of reranking model on TedLium corpus" ], "reasoning": "The table shows that the WER for SLP + p-dRBM is 16.91, which is the lowest WER compared to other methods, indicating the greatest reduction.", "question": "What is the WER reduction achieved by the late fusion of SLP and p-dRBM?", "answer": "So, the answer is {\"answer\": \"16.91\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe proposed RBM rerankers have reduced more errors on chosen keywords without sacrificing overall performance.\n" }, { "role": "assistant", "content": "The table shows that the WER for p-dRBM is 17.36, and for SLP + p-dRBM is 16.91, both of which are lower than the baseline SLP's WER of 17.76, indicating reduced errors without sacrificing performance. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3029.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5.5: Performance of reranking model on TedLium corpus" ], "reasoning": "The table shows that the WER for p-dRBM is 17.36, and for SLP + p-dRBM is 16.91, both of which are lower than the baseline SLP's WER of 17.76, indicating reduced errors without sacrificing performance.", "statement": "The proposed RBM rerankers have reduced more errors on chosen keywords without sacrificing overall performance.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 6.3: System performance on SentiHood dataset'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The best attention-based model, Sentic LSTM + TA + SA, significantly improves aspect categorization and sentiment classification on SentiHood, but shows smaller improvements on the Semeval-2015 dataset.-Ch2S2SS5 -Chx2T1 In comparison with the non-attention baseline (Bi-LSTM+Avg.), we can find that our best attention-based model significantly improves aspect categorization (by more than 20%) and sentiment classification (approximately 10%) on SentiHood. However, it is notable that, on the Semeval-2015 dataset, the improvement is relatively smaller. We conjecture the reason is that SentiHood has masked the target as a special word “LOCATION”, which resulted less informative than the full name of aspect targets that are used by Semeval-2015." } ], "images": [ "scigen/train/3030.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6.3: System performance on SentiHood dataset" ], "reasoning": "The best attention-based model, Sentic LSTM + TA + SA, significantly improves aspect categorization and sentiment classification on SentiHood, but shows smaller improvements on the Semeval-2015 dataset.", "description": "-Ch2S2SS5 -Chx2T1 In comparison with the non-attention baseline (Bi-LSTM+Avg.), we can find that our best attention-based model significantly improves aspect categorization (by more than 20%) and sentiment classification (approximately 10%) on SentiHood. However, it is notable that, on the Semeval-2015 dataset, the improvement is relatively smaller. We conjecture the reason is that SentiHood has masked the target as a special word “LOCATION”, which resulted less informative than the full name of aspect targets that are used by Semeval-2015.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model achieved the highest sentiment accuracy on the test set?\nSolve the above question based on the table titled 'Table 6.3: System performance on SentiHood dataset'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the sentiment accuracy percentages on the test set, the Sentic LSTM + TA + SA model achieved the highest accuracy with 89.32%. So, the answer is {\"answer\": \"Sentic LSTM + TA + SA\"}." } ], "images": [ "scigen/train/3030.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6.3: System performance on SentiHood dataset" ], "reasoning": "By comparing the sentiment accuracy percentages on the test set, the Sentic LSTM + TA + SA model achieved the highest accuracy with 89.32%.", "question": "Which model achieved the highest sentiment accuracy on the test set?", "answer": "So, the answer is {\"answer\": \"Sentic LSTM + TA + SA\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe LSTM + TA model achieved a sentiment accuracy of 84.29% on the test set.\n" }, { "role": "assistant", "content": "The table shows that the LSTM + TA model achieved a sentiment accuracy of 84.29% on the test set, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3030.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6.3: System performance on SentiHood dataset" ], "reasoning": "The table shows that the LSTM + TA model achieved a sentiment accuracy of 84.29% on the test set, which matches the statement.", "statement": "The LSTM + TA model achieved a sentiment accuracy of 84.29% on the test set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 6.3: System performance on SentiHood dataset'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The best attention-based model significantly improves aspect categorization by more than 20% and sentiment classification by approximately 10% on SentiHood compared to the non-attention baseline.-Ch2S2SS5 -Chx2T1 In comparison with the non-attention baseline (Bi-LSTM+Avg.), we can find that our best attention-based model significantly improves aspect categorization (by more than 20%) and sentiment classification (approximately 10%) on SentiHood. However, it is notable that, on the Semeval-2015 dataset, the improvement is relatively smaller. We conjecture the reason is that SentiHood has masked the target as a special word “LOCATION”, which resulted less informative than the full name of aspect targets that are used by Semeval-2015." } ], "images": [ "scigen/train/3031.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6.3: System performance on SentiHood dataset" ], "reasoning": "The best attention-based model significantly improves aspect categorization by more than 20% and sentiment classification by approximately 10% on SentiHood compared to the non-attention baseline.", "description": "-Ch2S2SS5 -Chx2T1 In comparison with the non-attention baseline (Bi-LSTM+Avg.), we can find that our best attention-based model significantly improves aspect categorization (by more than 20%) and sentiment classification (approximately 10%) on SentiHood. However, it is notable that, on the Semeval-2015 dataset, the improvement is relatively smaller. We conjecture the reason is that SentiHood has masked the target as a special word “LOCATION”, which resulted less informative than the full name of aspect targets that are used by Semeval-2015.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 6.3: System performance on SentiHood dataset', solve the subsequent question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model achieved the highest Aspect Categorization Macro F1 score on the dev set?\n" }, { "role": "assistant", "content": "By examining the Aspect Categorization Macro F1 (%) column for the dev set, the highest score is 80.00 achieved by the model 'LSTM + TA + SA + KB Feat'. So, the answer is {\"answer\": \"LSTM + TA + SA + KB Feat\"}" } ], "images": [ "scigen/train/3031.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6.3: System performance on SentiHood dataset" ], "reasoning": "By examining the Aspect Categorization Macro F1 (%) column for the dev set, the highest score is 80.00 achieved by the model 'LSTM + TA + SA + KB Feat'.", "question": "Which model achieved the highest Aspect Categorization Macro F1 score on the dev set?", "answer": "So, the answer is {\"answer\": \"LSTM + TA + SA + KB Feat\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The model 'LSTM + TA + SA' achieved the highest Aspect Categorization Strict Accuracy on the test set., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe model 'LSTM + TA + SA' achieved the highest Aspect Categorization Strict Accuracy on the test set.\n" }, { "role": "assistant", "content": "By examining the Aspect Categorization Strict Acc. (%) column for the test set, the highest score is 67.43 achieved by the model 'Sentic LSTM + TA + SA', not 'LSTM + TA + SA'. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3031.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6.3: System performance on SentiHood dataset" ], "reasoning": "By examining the Aspect Categorization Strict Acc. (%) column for the test set, the highest score is 67.43 achieved by the model 'Sentic LSTM + TA + SA', not 'LSTM + TA + SA'.", "statement": "The model 'LSTM + TA + SA' achieved the highest Aspect Categorization Strict Accuracy on the test set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 6.4: System performance on Semeval-2015 dataset', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our best attention-based model significantly improves aspect categorization by more than 20% and sentiment classification by approximately 10% on SentiHood, but shows relatively smaller improvement on the Semeval-2015 dataset.-Ch2S2SS5 -Chx2T1 In comparison with the non-attention baseline (Bi-LSTM+Avg.), we can find that our best attention-based model significantly improves aspect categorization (by more than 20%) and sentiment classification (approximately 10%) on SentiHood. However, it is notable that, on the Semeval-2015 dataset, the improvement is relatively smaller. We conjecture the reason is that SentiHood has masked the target as a special word “LOCATION”, which resulted less informative than the full name of aspect targets that are used by Semeval-2015." } ], "images": [ "scigen/train/3032.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6.4: System performance on Semeval-2015 dataset" ], "reasoning": "Our best attention-based model significantly improves aspect categorization by more than 20% and sentiment classification by approximately 10% on SentiHood, but shows relatively smaller improvement on the Semeval-2015 dataset.", "description": "-Ch2S2SS5 -Chx2T1 In comparison with the non-attention baseline (Bi-LSTM+Avg.), we can find that our best attention-based model significantly improves aspect categorization (by more than 20%) and sentiment classification (approximately 10%) on SentiHood. However, it is notable that, on the Semeval-2015 dataset, the improvement is relatively smaller. We conjecture the reason is that SentiHood has masked the target as a special word “LOCATION”, which resulted less informative than the full name of aspect targets that are used by Semeval-2015.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6.4: System performance on Semeval-2015 dataset\nQuestion: Which model has the highest Aspect Categorization Macro F1 score?\nGive a detailed response to the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the Aspect Categorization Macro F1 scores in the table, the highest score is 76.44, achieved by the Sentic LSTM + TA + SA model. So, the answer is {\"answer\": \"Sentic LSTM + TA + SA\"}" } ], "images": [ "scigen/train/3032.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6.4: System performance on Semeval-2015 dataset" ], "reasoning": "By comparing the Aspect Categorization Macro F1 scores in the table, the highest score is 76.44, achieved by the Sentic LSTM + TA + SA model.", "question": "Which model has the highest Aspect Categorization Macro F1 score?", "answer": "So, the answer is {\"answer\": \"Sentic LSTM + TA + SA\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The LSTM+TA+SA+KBA model has the highest Sentiment Acc. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe LSTM+TA+SA+KBA model has the highest Sentiment Acc.\n" }, { "role": "assistant", "content": "The table shows that the Sentiment Acc. for the LSTM+TA+SA+KBA model is 73.10, while the highest Sentiment Acc. is 76.47, achieved by the Sentic LSTM + TA + SA model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3032.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6.4: System performance on Semeval-2015 dataset" ], "reasoning": "The table shows that the Sentiment Acc. for the LSTM+TA+SA+KBA model is 73.10, while the highest Sentiment Acc. is 76.47, achieved by the Sentic LSTM + TA + SA model.", "statement": "The LSTM+TA+SA+KBA model has the highest Sentiment Acc.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled '(b) Hardest (AUCμ,w)'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The table measures the predictive accuracy of word types using ASR and visual features, showing that ASR features generally have greater predictive power.Specifically — for each word type w (e.g., w=beer) we measure how well w is predicted by the classifier based on ASR / spoken tokens AUCt,w (e.g., AUCt,beer = 98) and, conversely, how well w is predicted by the visual classifier AUCv,w (AUCv,beer = 68). For a given word type, we measure its overall difficulty by averaging AUCt,w and AUCv,w; we call this AUCμ,w (AUCμ,beer = 83). Similarly, we measure the difference in difficulty by subtracting AUCt,w and AUCv,w to give AUCΔ,w (AUCΔ,beer = 30) with higher values indicating that a word type is predicted better by the spoken-token features compared to the visual features. We plot AUCt,w versus AUCv,w for 382 words in Fig. Absolute Performance. Points in the upper-right quadrant of Fig. Specific ingredients, e.g., “nori” and “mozzarella,” are often easy to detect, as are actions closely associated with particular objects (e.g., “dough” is almost always the object being “knead”-ed). Conversely, pronouns (e.g., “it”) and conjunctions (e.g., “or”) are universally difficult to predict. Visual vs. ASR-token–based features. In general, ASR-token–based features carry greater predictive power, as evidenced by the skew towards the bottom right in the scatterplot in Fig. One pattern in the cases where speech features perform better Indeed, small, detailed distinctions may be often difficult to make from visual features, e.g., “vegetable oil” and “olive oil” may look identical in most YouTube videos." } ], "images": [ "scigen/train/3033.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) Hardest (AUCμ,w)" ], "reasoning": "The table measures the predictive accuracy of word types using ASR and visual features, showing that ASR features generally have greater predictive power.", "description": "Specifically — for each word type w (e.g., w=beer) we measure how well w is predicted by the classifier based on ASR / spoken tokens AUCt,w (e.g., AUCt,beer = 98) and, conversely, how well w is predicted by the visual classifier AUCv,w (AUCv,beer = 68). For a given word type, we measure its overall difficulty by averaging AUCt,w and AUCv,w; we call this AUCμ,w (AUCμ,beer = 83). Similarly, we measure the difference in difficulty by subtracting AUCt,w and AUCv,w to give AUCΔ,w (AUCΔ,beer = 30) with higher values indicating that a word type is predicted better by the spoken-token features compared to the visual features. We plot AUCt,w versus AUCv,w for 382 words in Fig. Absolute Performance. Points in the upper-right quadrant of Fig. Specific ingredients, e.g., “nori” and “mozzarella,” are often easy to detect, as are actions closely associated with particular objects (e.g., “dough” is almost always the object being “knead”-ed). Conversely, pronouns (e.g., “it”) and conjunctions (e.g., “or”) are universally difficult to predict. Visual vs. ASR-token–based features. In general, ASR-token–based features carry greater predictive power, as evidenced by the skew towards the bottom right in the scatterplot in Fig. One pattern in the cases where speech features perform better Indeed, small, detailed distinctions may be often difficult to make from visual features, e.g., “vegetable oil” and “olive oil” may look identical in most YouTube videos.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the '(b) Hardest (AUCμ,w)', solve the subsequent question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the AUCt,w value for the word 'prepare'?\n" }, { "role": "assistant", "content": "The table lists the AUCt,w values for various words. The value for 'prepare' is given directly in the table. So, the answer is {\"answer\": \"52.3\"}." } ], "images": [ "scigen/train/3033.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) Hardest (AUCμ,w)" ], "reasoning": "The table lists the AUCt,w values for various words. The value for 'prepare' is given directly in the table.", "question": "What is the AUCt,w value for the word 'prepare'?", "answer": "So, the answer is {\"answer\": \"52.3\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The word 'bit' has a higher AUCt,w value than 'about'., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe word 'bit' has a higher AUCt,w value than 'about'.\n" }, { "role": "assistant", "content": "The table shows the AUCt,w values for 'bit' and 'about'. 'Bit' has an AUCt,w value of 51.7, while 'about' has a value of 52.1. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3033.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) Hardest (AUCμ,w)" ], "reasoning": "The table shows the AUCt,w values for 'bit' and 'about'. 'Bit' has an AUCt,w value of 51.7, while 'about' has a value of 52.1.", "statement": "The word 'bit' has a higher AUCt,w value than 'about'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Caption generation performance: AT+Video is a multimodal model that adds visual frame features to AT. A bolded value in a column indicates a statistically-significant improvement, whereas an underline indicates a statistical tie for best (p', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The AT model yields the best unimodal performance, while the AT+Video model outperforms AT and the state-of-the-art video-only model according to ROUGE-L, CIDEr, and METEOR.Generation Experiment Results. For unimodal models, simple baselines like FASC (filtered ASR) and RET (training-caption retrieval) outperform the state-of-the-art video-only model of \\newcitesun2019contrastive, according to the four automatic evaluation metrics. Overall, AT yields the best unimodal performance. Combining ASR and visual signals into a multimodal representation performs even better: the AT+Video model tends to outperform AT (and \\newcitesun2019contrastive), according to ROUGE-L, CIDEr, and METEOR (p " } ], "images": [ "scigen/train/3034.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Caption generation performance: AT+Video is a multimodal model that adds visual frame features to AT. A bolded value in a column indicates a statistically-significant improvement, whereas an underline indicates a statistical tie for best (p" ], "reasoning": "The AT model yields the best unimodal performance, while the AT+Video model outperforms AT and the state-of-the-art video-only model according to ROUGE-L, CIDEr, and METEOR.", "description": "Generation Experiment Results. For unimodal models, simple baselines like FASC (filtered ASR) and RET (training-caption retrieval) outperform the state-of-the-art video-only model of \\newcitesun2019contrastive, according to the four automatic evaluation metrics. Overall, AT yields the best unimodal performance. Combining ASR and visual signals into a multimodal representation performs even better: the AT+Video model tends to outperform AT (and \\newcitesun2019contrastive), according to ROUGE-L, CIDEr, and METEOR (p ", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Caption generation performance: AT+Video is a multimodal model that adds visual frame features to AT. A bolded value in a column indicates a statistically-significant improvement, whereas an underline indicates a statistical tie for best (p', answer the question below by showing each progressive step toward the answer. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model achieves the highest score in the ROUGE-L metric?\n" }, { "role": "assistant", "content": "By comparing the ROUGE-L scores of all models in the table, the AT+Video model has the highest score of 36.65. So, the answer is {\"answer\": \"AT+Video\"}" } ], "images": [ "scigen/train/3034.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Caption generation performance: AT+Video is a multimodal model that adds visual frame features to AT. A bolded value in a column indicates a statistically-significant improvement, whereas an underline indicates a statistical tie for best (p" ], "reasoning": "By comparing the ROUGE-L scores of all models in the table, the AT+Video model has the highest score of 36.65.", "question": "Which model achieves the highest score in the ROUGE-L metric?", "answer": "So, the answer is {\"answer\": \"AT+Video\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The RET model outperforms the sun2019contrastive model in the CIDEr metric. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe RET model outperforms the sun2019contrastive model in the CIDEr metric.\n" }, { "role": "assistant", "content": "The table shows that the RET model has a CIDEr score of 0.80, which is higher than the sun2019contrastive model's CIDEr score of 0.53. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3034.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Caption generation performance: AT+Video is a multimodal model that adds visual frame features to AT. A bolded value in a column indicates a statistically-significant improvement, whereas an underline indicates a statistical tie for best (p" ], "reasoning": "The table shows that the RET model has a CIDEr score of 0.80, which is higher than the sun2019contrastive model's CIDEr score of 0.53.", "statement": "The RET model outperforms the sun2019contrastive model in the CIDEr metric.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled '(a) Easiest (AUCμ,w)'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows the AUCt,w values for various words, indicating how well each word is predicted by the classifier based on ASR/spoken tokens.Specifically — for each word type w (e.g., w=beer) we measure how well w is predicted by the classifier based on ASR / spoken tokens AUCt,w (e.g., AUCt,beer = 98) and, conversely, how well w is predicted by the visual classifier AUCv,w (AUCv,beer = 68). For a given word type, we measure its overall difficulty by averaging AUCt,w and AUCv,w; we call this AUCμ,w (AUCμ,beer = 83). Similarly, we measure the difference in difficulty by subtracting AUCt,w and AUCv,w to give AUCΔ,w (AUCΔ,beer = 30) with higher values indicating that a word type is predicted better by the spoken-token features compared to the visual features. We plot AUCt,w versus AUCv,w for 382 words in Fig. Absolute Performance. Points in the upper-right quadrant of Fig. Specific ingredients, e.g., “nori” and “mozzarella,” are often easy to detect, as are actions closely associated with particular objects (e.g., “dough” is almost always the object being “knead”-ed). Conversely, pronouns (e.g., “it”) and conjunctions (e.g., “or”) are universally difficult to predict. Visual vs. ASR-token–based features. In general, ASR-token–based features carry greater predictive power, as evidenced by the skew towards the bottom right in the scatterplot in Fig. One pattern in the cases where speech features perform better Indeed, small, detailed distinctions may be often difficult to make from visual features, e.g., “vegetable oil” and “olive oil” may look identical in most YouTube videos." } ], "images": [ "scigen/train/3035.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) Easiest (AUCμ,w)" ], "reasoning": "The table shows the AUCt,w values for various words, indicating how well each word is predicted by the classifier based on ASR/spoken tokens.", "description": "Specifically — for each word type w (e.g., w=beer) we measure how well w is predicted by the classifier based on ASR / spoken tokens AUCt,w (e.g., AUCt,beer = 98) and, conversely, how well w is predicted by the visual classifier AUCv,w (AUCv,beer = 68). For a given word type, we measure its overall difficulty by averaging AUCt,w and AUCv,w; we call this AUCμ,w (AUCμ,beer = 83). Similarly, we measure the difference in difficulty by subtracting AUCt,w and AUCv,w to give AUCΔ,w (AUCΔ,beer = 30) with higher values indicating that a word type is predicted better by the spoken-token features compared to the visual features. We plot AUCt,w versus AUCv,w for 382 words in Fig. Absolute Performance. Points in the upper-right quadrant of Fig. Specific ingredients, e.g., “nori” and “mozzarella,” are often easy to detect, as are actions closely associated with particular objects (e.g., “dough” is almost always the object being “knead”-ed). Conversely, pronouns (e.g., “it”) and conjunctions (e.g., “or”) are universally difficult to predict. Visual vs. ASR-token–based features. In general, ASR-token–based features carry greater predictive power, as evidenced by the skew towards the bottom right in the scatterplot in Fig. One pattern in the cases where speech features perform better Indeed, small, detailed distinctions may be often difficult to make from visual features, e.g., “vegetable oil” and “olive oil” may look identical in most YouTube videos.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: (a) Easiest (AUCμ,w)\nQuestion: What is the AUCt,w value for the word 'mozzarella' and how does it compare to the AUCt,w value for 'knead'?\nSolve the above question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table lists the AUCt,w values for different words. The AUCt,w value for 'mozzarella' is 95.8, while the AUCt,w value for 'knead' is 97.8. Comparing these values shows that 'knead' has a higher AUCt,w value than 'mozzarella'. So, the answer is {\"answer\": \"95.8\"}." } ], "images": [ "scigen/train/3035.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) Easiest (AUCμ,w)" ], "reasoning": "The table lists the AUCt,w values for different words. The AUCt,w value for 'mozzarella' is 95.8, while the AUCt,w value for 'knead' is 97.8. Comparing these values shows that 'knead' has a higher AUCt,w value than 'mozzarella'.", "question": "What is the AUCt,w value for the word 'mozzarella' and how does it compare to the AUCt,w value for 'knead'?", "answer": "So, the answer is {\"answer\": \"95.8\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The word 'yeast' has a higher AUCt,w value than 'lettuce'. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe word 'yeast' has a higher AUCt,w value than 'lettuce'.\n" }, { "role": "assistant", "content": "According to the table, the AUCt,w value for 'yeast' is 96.1, while the AUCt,w value for 'lettuce' is 95.3. Therefore, 'yeast' has a higher AUCt,w value than 'lettuce'. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3035.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) Easiest (AUCμ,w)" ], "reasoning": "According to the table, the AUCt,w value for 'yeast' is 96.1, while the AUCt,w value for 'lettuce' is 95.3. Therefore, 'yeast' has a higher AUCt,w value than 'lettuce'.", "statement": "The word 'yeast' has a higher AUCt,w value than 'lettuce'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title '(c) ASR Better (AUCΔ,w)', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The table shows the AUC values for predicting various word types using ASR and visual features, with higher AUC values indicating better prediction accuracy.Specifically — for each word type w (e.g., w=beer) we measure how well w is predicted by the classifier based on ASR / spoken tokens AUCt,w (e.g., AUCt,beer = 98) and, conversely, how well w is predicted by the visual classifier AUCv,w (AUCv,beer = 68). For a given word type, we measure its overall difficulty by averaging AUCt,w and AUCv,w; we call this AUCμ,w (AUCμ,beer = 83). Similarly, we measure the difference in difficulty by subtracting AUCt,w and AUCv,w to give AUCΔ,w (AUCΔ,beer = 30) with higher values indicating that a word type is predicted better by the spoken-token features compared to the visual features. We plot AUCt,w versus AUCv,w for 382 words in Fig. Absolute Performance. Points in the upper-right quadrant of Fig. Specific ingredients, e.g., “nori” and “mozzarella,” are often easy to detect, as are actions closely associated with particular objects (e.g., “dough” is almost always the object being “knead”-ed). Conversely, pronouns (e.g., “it”) and conjunctions (e.g., “or”) are universally difficult to predict. Visual vs. ASR-token–based features. In general, ASR-token–based features carry greater predictive power, as evidenced by the skew towards the bottom right in the scatterplot in Fig. One pattern in the cases where speech features perform better Indeed, small, detailed distinctions may be often difficult to make from visual features, e.g., “vegetable oil” and “olive oil” may look identical in most YouTube videos." } ], "images": [ "scigen/train/3036.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(c) ASR Better (AUCΔ,w)" ], "reasoning": "The table shows the AUC values for predicting various word types using ASR and visual features, with higher AUC values indicating better prediction accuracy.", "description": "Specifically — for each word type w (e.g., w=beer) we measure how well w is predicted by the classifier based on ASR / spoken tokens AUCt,w (e.g., AUCt,beer = 98) and, conversely, how well w is predicted by the visual classifier AUCv,w (AUCv,beer = 68). For a given word type, we measure its overall difficulty by averaging AUCt,w and AUCv,w; we call this AUCμ,w (AUCμ,beer = 83). Similarly, we measure the difference in difficulty by subtracting AUCt,w and AUCv,w to give AUCΔ,w (AUCΔ,beer = 30) with higher values indicating that a word type is predicted better by the spoken-token features compared to the visual features. We plot AUCt,w versus AUCv,w for 382 words in Fig. Absolute Performance. Points in the upper-right quadrant of Fig. Specific ingredients, e.g., “nori” and “mozzarella,” are often easy to detect, as are actions closely associated with particular objects (e.g., “dough” is almost always the object being “knead”-ed). Conversely, pronouns (e.g., “it”) and conjunctions (e.g., “or”) are universally difficult to predict. Visual vs. ASR-token–based features. In general, ASR-token–based features carry greater predictive power, as evidenced by the skew towards the bottom right in the scatterplot in Fig. One pattern in the cases where speech features perform better Indeed, small, detailed distinctions may be often difficult to make from visual features, e.g., “vegetable oil” and “olive oil” may look identical in most YouTube videos.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: (c) ASR Better (AUCΔ,w)\nQuestion: What is the AUC value for predicting the word 'sea' using ASR and visual features?\nSolve the above question based on the table information. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table lists the AUC values for different word types. To find the AUC value for 'sea', locate the row corresponding to 'sea' and read the value in the adjacent column. So, the answer is {\"answer\": \"35.9\"}." } ], "images": [ "scigen/train/3036.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(c) ASR Better (AUCΔ,w)" ], "reasoning": "The table lists the AUC values for different word types. To find the AUC value for 'sea', locate the row corresponding to 'sea' and read the value in the adjacent column.", "question": "What is the AUC value for predicting the word 'sea' using ASR and visual features?", "answer": "So, the answer is {\"answer\": \"35.9\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe word 'beer' has a higher AUC value than 'white'.\n" }, { "role": "assistant", "content": "The table provides the AUC values for different word types. Comparing the values for 'beer' and 'white', 'beer' has an AUC value of 30.3, while 'white' has an AUC value of 31.7. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3036.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(c) ASR Better (AUCΔ,w)" ], "reasoning": "The table provides the AUC values for different word types. Comparing the values for 'beer' and 'white', 'beer' has an AUC value of 30.3, while 'white' has an AUC value of 31.7.", "statement": "The word 'beer' has a higher AUC value than 'white'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Results obtained on QuAC-derived data.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Increasing the beam size from k=1 to k=5 results in significant improvements in BLEU-4 scores but notable drops in BLEU-1 scores, indicating a divergence in results.We use a beam k, and compute the results for k=[1,3,5]. In addition the generated questions with a beam k=5, we also computed the results for k=1 and k=3. While one would expect to see for all the metrics a slight improvement, with increasing beam size, we observe a strong divergence among the results: increasing values for k correspond to a significant improvements in terms of BLEU-4 and notable drops for BLEU-1. A similar phenomena was observed by Ott et al. In our case, one of most frequent generated question is Are there any other interesting aspects about this article ?. Indeed, the frequency of this question in our training set amounts to 4.18% of the questions. On the test set we see that roughly 80% of the generated questions start with the token “are” . Generating this sequence is not very likely with a greedy search (k=1): at any time step during the generation, if any other token has a higher probability, this question will be dismissed. On the other hand, with a higher beam, it is likely to be kept and eventually result as the most probable sequence, among the different remaining beams at the end of the inference." } ], "images": [ "scigen/train/3037.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results obtained on QuAC-derived data." ], "reasoning": "Increasing the beam size from k=1 to k=5 results in significant improvements in BLEU-4 scores but notable drops in BLEU-1 scores, indicating a divergence in results.", "description": "We use a beam k, and compute the results for k=[1,3,5]. In addition the generated questions with a beam k=5, we also computed the results for k=1 and k=3. While one would expect to see for all the metrics a slight improvement, with increasing beam size, we observe a strong divergence among the results: increasing values for k correspond to a significant improvements in terms of BLEU-4 and notable drops for BLEU-1. A similar phenomena was observed by Ott et al. In our case, one of most frequent generated question is Are there any other interesting aspects about this article ?. Indeed, the frequency of this question in our training set amounts to 4.18% of the questions. On the test set we see that roughly 80% of the generated questions start with the token “are” . Generating this sequence is not very likely with a greedy search (k=1): at any time step during the generation, if any other token has a higher probability, this question will be dismissed. On the other hand, with a higher beam, it is likely to be kept and eventually result as the most probable sequence, among the different remaining beams at the end of the inference.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: Results obtained on QuAC-derived data.'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. \n\nQuestion: What is the BLEU-4 score for RL_beam5?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the BLEU-4 score for RL_beam5, locate the BLEU-4 row and then find the corresponding value under the RL_beam5 column. So, the answer is {\"answer\": \"9.85\"}." } ], "images": [ "scigen/train/3037.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results obtained on QuAC-derived data." ], "reasoning": "To find the BLEU-4 score for RL_beam5, locate the BLEU-4 row and then find the corresponding value under the RL_beam5 column.", "question": "What is the BLEU-4 score for RL_beam5?", "answer": "So, the answer is {\"answer\": \"9.85\"}." }, { "messages": [ { "role": "user", "content": "The BLEU-1 score for base_beam1 is higher than for RL_beam1. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe BLEU-1 score for base_beam1 is higher than for RL_beam1.\n" }, { "role": "assistant", "content": "Compare the BLEU-1 scores for base_beam1 and RL_beam1. The BLEU-1 score for base_beam1 is 31.94, while for RL_beam1 it is 30.19. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3037.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results obtained on QuAC-derived data." ], "reasoning": "Compare the BLEU-1 scores for base_beam1 and RL_beam1. The BLEU-1 score for base_beam1 is 31.94, while for RL_beam1 it is 30.19.", "statement": "The BLEU-1 score for base_beam1 is higher than for RL_beam1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 6: The comparison of BLEU scores on the test set of IWSLT14 De-En task among different task windows.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The model trained with a task window of w=2 achieves the best score in the IWSLT14 De-En task, indicating that an appropriate task window can improve model training.S4SS3SSS0Px3 Analysis on Task Window. From the table, we can see that the model trained with w=2 achieves the best score in IWSLT14 De-En task, which proves that an appropriate task window w can help reduce the gap between neighboring stages, and thus help model training." } ], "images": [ "scigen/train/3038.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: The comparison of BLEU scores on the test set of IWSLT14 De-En task among different task windows." ], "reasoning": "The model trained with a task window of w=2 achieves the best score in the IWSLT14 De-En task, indicating that an appropriate task window can improve model training.", "description": "S4SS3SSS0Px3 Analysis on Task Window. From the table, we can see that the model trained with w=2 achieves the best score in IWSLT14 De-En task, which proves that an appropriate task window w can help reduce the gap between neighboring stages, and thus help model training.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: The comparison of BLEU scores on the test set of IWSLT14 De-En task among different task windows.\nQuestion: What is the score achieved by the TCL-NAT model with a task window of w=2?\nSolve the above question based on the table information. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the scores for different task windows. For w=2, the TCL-NAT model achieves a score of 28.16. So, the answer is {\"answer\": \"28.16\"}" } ], "images": [ "scigen/train/3038.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: The comparison of BLEU scores on the test set of IWSLT14 De-En task among different task windows." ], "reasoning": "The table shows the scores for different task windows. For w=2, the TCL-NAT model achieves a score of 28.16.", "question": "What is the score achieved by the TCL-NAT model with a task window of w=2?", "answer": "So, the answer is {\"answer\": \"28.16\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe TCL-NAT model with a task window of w=3 achieves the highest score.\n" }, { "role": "assistant", "content": "The table shows that the TCL-NAT model with a task window of w=2 achieves the highest score of 28.16, while the score for w=3 is 28.00. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3038.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: The comparison of BLEU scores on the test set of IWSLT14 De-En task among different task windows." ], "reasoning": "The table shows that the TCL-NAT model with a task window of w=2 achieves the highest score of 28.16, while the score for w=3 is 28.00.", "statement": "The TCL-NAT model with a task window of w=3 achieves the highest score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: The BLEU scores of our proposed TCL-NAT and the baseline methods on the IWSLT14 De-En, IWSLT16 En-De, WMT14 De-En and WMT14 En-De tasks. NPD 9 indicates results of noisy parallel decoding with 9 candidates, i.e., B = 4, otherwise B = 0.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "TCL-NAT achieves significant improvements over all NAT baselines on different datasets, outperforming ENAT and NAT-Reg with notable margins and achieving better scores and speedup compared to FlowSeq and FCL-NAT.We can see that TCL-NAT achieves significant improvements over all NAT baselines on different datasets. Specifically, we outperform ENAT and NAT-Reg with a notable margin. In addition, compared with NAT-Reg, we do not introduce any auxiliary loss functions in training stage and compared with ENAT, we just copy the source sentence as the decoder input, which does not add the extra workload in inference stage. Compared with FlowSeq, our method (without NPD) achieves better scores on most datasets with a much larger speedup. We also outperform FCL-NAT on most datasets with a less training steps. As for the inference efficiency, we achieve a 16.0 times speedup (NPD 9), which is comparable with state of the art methods (FCL-NAT and ENAT)." } ], "images": [ "scigen/train/3039.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The BLEU scores of our proposed TCL-NAT and the baseline methods on the IWSLT14 De-En, IWSLT16 En-De, WMT14 De-En and WMT14 En-De tasks. NPD 9 indicates results of noisy parallel decoding with 9 candidates, i.e., B = 4, otherwise B = 0." ], "reasoning": "TCL-NAT achieves significant improvements over all NAT baselines on different datasets, outperforming ENAT and NAT-Reg with notable margins and achieving better scores and speedup compared to FlowSeq and FCL-NAT.", "description": "We can see that TCL-NAT achieves significant improvements over all NAT baselines on different datasets. Specifically, we outperform ENAT and NAT-Reg with a notable margin. In addition, compared with NAT-Reg, we do not introduce any auxiliary loss functions in training stage and compared with ENAT, we just copy the source sentence as the decoder input, which does not add the extra workload in inference stage. Compared with FlowSeq, our method (without NPD) achieves better scores on most datasets with a much larger speedup. We also outperform FCL-NAT on most datasets with a less training steps. As for the inference efficiency, we achieve a 16.0 times speedup (NPD 9), which is comparable with state of the art methods (FCL-NAT and ENAT).", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 4: The BLEU scores of our proposed TCL-NAT and the baseline methods on the IWSLT14 De-En, IWSLT16 En-De, WMT14 De-En and WMT14 En-De tasks. NPD 9 indicates results of noisy parallel decoding with 9 candidates, i.e., B = 4, otherwise B = 0.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model achieves the highest score on the IWSLT14 De-En dataset?\n" }, { "role": "assistant", "content": "By examining the IWSLT14 De-En column, we see that TCL-NAT (NPD 9) achieves the highest score of 31.79. So, the answer is {\"answer\": \"TCL-NAT (NPD 9)\"}" } ], "images": [ "scigen/train/3039.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The BLEU scores of our proposed TCL-NAT and the baseline methods on the IWSLT14 De-En, IWSLT16 En-De, WMT14 De-En and WMT14 En-De tasks. NPD 9 indicates results of noisy parallel decoding with 9 candidates, i.e., B = 4, otherwise B = 0." ], "reasoning": "By examining the IWSLT14 De-En column, we see that TCL-NAT (NPD 9) achieves the highest score of 31.79.", "question": "Which model achieves the highest score on the IWSLT14 De-En dataset?", "answer": "So, the answer is {\"answer\": \"TCL-NAT (NPD 9)\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTCL-NAT (NPD 9) achieves a speedup of 16.0 times.\n" }, { "role": "assistant", "content": "The table shows that TCL-NAT (NPD 9) achieves a speedup of 16.0 times, which supports the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3039.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The BLEU scores of our proposed TCL-NAT and the baseline methods on the IWSLT14 De-En, IWSLT16 En-De, WMT14 De-En and WMT14 En-De tasks. NPD 9 indicates results of noisy parallel decoding with 9 candidates, i.e., B = 4, otherwise B = 0." ], "reasoning": "The table shows that TCL-NAT (NPD 9) achieves a speedup of 16.0 times, which supports the statement.", "statement": "TCL-NAT (NPD 9) achieves a speedup of 16.0 times.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: The performance of a trained policy appended with object detectors on instructions containing unseen words (‘red’ and ‘pillar’).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The Dual-Attention model's performance is significantly enhanced by SA and GA1, while GA2 is less critical for SGN.The results indicate that SA and GA1 contribute the most to the performance of the Dual-Attention model. This is expected as GA2 is designed to attend to different objects and attributes based on the question and is used mainly for answer prediction. It is not critical for SGN as the spatial attention map consists of locations of relevant objects, which is sufficient for navigating to the correct object. Dual-Attention models trained only on single tasks work well on SGN, especially with auxiliary tasks. This is because the auxiliary task for single task models includes object detection labels corresponding to the words in the test set. This highlights a key advantage of the proposed model. Due to its modular and interpretable design, the model can be used for transferring the policy to new objects and attributes without fine-tuning as discussed in the following subsection. In contrast, our model can be used for transfer to new words by training an object detector for each new word and appending it to the image representation xI. In order to test this, we train a single-task SGN model using the proposed architecture on the training set for instructions. We use auxiliary tasks but only for words in the vocabulary of the instructions training set. After training the policy, we would like the agent to follow instructions containing test words ‘red’ and ‘pillar’, which the agent has never seen or received any supervision about how this attribute or object looks visually. For transferring the policy, we assume access to two object detectors which would give object detections for ‘red’ and ‘pillar’ separately. We resize the object detections to the size of a feature map in the image representation (H×W) and append them as channels to the image representation. We also append the words ‘red’ and ‘pillar’ to the bag-of-words representations in the same order such that they are aligned with the appended feature maps. We randomly initialize the embeddings of the new words for computing the sentence embedding. This suggests that a trained policy can be scaled to more objects provided the complexity of navigation remains consistent." } ], "images": [ "scigen/train/3040.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The performance of a trained policy appended with object detectors on instructions containing unseen words (‘red’ and ‘pillar’)." ], "reasoning": "The Dual-Attention model's performance is significantly enhanced by SA and GA1, while GA2 is less critical for SGN.", "description": "The results indicate that SA and GA1 contribute the most to the performance of the Dual-Attention model. This is expected as GA2 is designed to attend to different objects and attributes based on the question and is used mainly for answer prediction. It is not critical for SGN as the spatial attention map consists of locations of relevant objects, which is sufficient for navigating to the correct object. Dual-Attention models trained only on single tasks work well on SGN, especially with auxiliary tasks. This is because the auxiliary task for single task models includes object detection labels corresponding to the words in the test set. This highlights a key advantage of the proposed model. Due to its modular and interpretable design, the model can be used for transferring the policy to new objects and attributes without fine-tuning as discussed in the following subsection. In contrast, our model can be used for transfer to new words by training an object detector for each new word and appending it to the image representation xI. In order to test this, we train a single-task SGN model using the proposed architecture on the training set for instructions. We use auxiliary tasks but only for words in the vocabulary of the instructions training set. After training the policy, we would like the agent to follow instructions containing test words ‘red’ and ‘pillar’, which the agent has never seen or received any supervision about how this attribute or object looks visually. For transferring the policy, we assume access to two object detectors which would give object detections for ‘red’ and ‘pillar’ separately. We resize the object detections to the size of a feature map in the image representation (H×W) and append them as channels to the image representation. We also append the words ‘red’ and ‘pillar’ to the bag-of-words representations in the same order such that they are aligned with the appended feature maps. We randomly initialize the embeddings of the new words for computing the sentence embedding. This suggests that a trained policy can be scaled to more objects provided the complexity of navigation remains consistent.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 4: The performance of a trained policy appended with object detectors on instructions containing unseen words (‘red’ and ‘pillar’).', solve the following question step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model configuration has the highest performance on the Aux SGN task?\n" }, { "role": "assistant", "content": "By examining the table, the highest performance on the Aux SGN task is achieved by the 'DA Multi-Task' model with a score of 0.96. So, the answer is {\"answer\": \"DA Multi-Task\"}" } ], "images": [ "scigen/train/3040.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The performance of a trained policy appended with object detectors on instructions containing unseen words (‘red’ and ‘pillar’)." ], "reasoning": "By examining the table, the highest performance on the Aux SGN task is achieved by the 'DA Multi-Task' model with a score of 0.96.", "question": "Which model configuration has the highest performance on the Aux SGN task?", "answer": "So, the answer is {\"answer\": \"DA Multi-Task\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The model without GA2 has the lowest performance on the No Aux SGN task. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model without GA2 has the lowest performance on the No Aux SGN task.\n" }, { "role": "assistant", "content": "The table shows that the model without GA2 has a performance score of 0.80 on the No Aux SGN task, which is not the lowest. The model without GA1 has the lowest performance with a score of 0.14. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3040.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The performance of a trained policy appended with object detectors on instructions containing unseen words (‘red’ and ‘pillar’)." ], "reasoning": "The table shows that the model without GA2 has a performance score of 0.80 on the No Aux SGN task, which is not the lowest. The model without GA1 has the lowest performance with a score of 0.14.", "statement": "The model without GA2 has the lowest performance on the No Aux SGN task.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Accuracy of all models on SGN & EQA test sets for both Easy & Hard difficulties.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The Dual-Attention model outperforms all baselines in both Easy and Hard settings, achieving higher training and test accuracy, indicating better generalization and transfer of knowledge across tasks.We observe that the Dual-Attention model and many baselines achieve nearly 100% accuracy during training in the Easy setting as seen on the left in Figures Even without auxiliary tasks, Dual-Attention model achieves an accuracy of 86% for SGN and 53% for EQA, whereas the best baseline performance is 33% for both SGN and EQA. Performance of all the baselines is worse than ‘Text only’ model on EQA test set, although the training accuracy is close to 100%. This indicates that baselines tend to overfit on the training set and fail to generalize to questions which contain words never seen in training questions. For the Hard setting, the Dual-Attention model achieves a higher training (87% vs. 70%) as well as test performance (82% vs. 39% for SGN, 59% vs. 33% for EQA with Aux) than the baselines (as seen on the right in These results confirm the hypothesis that prior models, which are designed for a single task, lack the ability to align the words in both the tasks and transfer knowledge across tasks. Lower test accuracy on EQA for most models" } ], "images": [ "scigen/train/3041.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracy of all models on SGN & EQA test sets for both Easy & Hard difficulties." ], "reasoning": "The Dual-Attention model outperforms all baselines in both Easy and Hard settings, achieving higher training and test accuracy, indicating better generalization and transfer of knowledge across tasks.", "description": "We observe that the Dual-Attention model and many baselines achieve nearly 100% accuracy during training in the Easy setting as seen on the left in Figures Even without auxiliary tasks, Dual-Attention model achieves an accuracy of 86% for SGN and 53% for EQA, whereas the best baseline performance is 33% for both SGN and EQA. Performance of all the baselines is worse than ‘Text only’ model on EQA test set, although the training accuracy is close to 100%. This indicates that baselines tend to overfit on the training set and fail to generalize to questions which contain words never seen in training questions. For the Hard setting, the Dual-Attention model achieves a higher training (87% vs. 70%) as well as test performance (82% vs. 39% for SGN, 59% vs. 33% for EQA with Aux) than the baselines (as seen on the right in These results confirm the hypothesis that prior models, which are designed for a single task, lack the ability to align the words in both the tasks and transfer knowledge across tasks. Lower test accuracy on EQA for most models", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 2: Accuracy of all models on SGN & EQA test sets for both Easy & Hard difficulties.', unfold the question presented below, detailing every step of your calculation. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the test accuracy of the Dual-Attention model for SGN in the Hard setting without auxiliary tasks?\n" }, { "role": "assistant", "content": "The table shows the test accuracy for the Dual-Attention model in the Hard setting without auxiliary tasks under the column 'Hard No Aux' for SGN, which is 0.82. So, the answer is {\"answer\": \"0.82\"}" } ], "images": [ "scigen/train/3041.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracy of all models on SGN & EQA test sets for both Easy & Hard difficulties." ], "reasoning": "The table shows the test accuracy for the Dual-Attention model in the Hard setting without auxiliary tasks under the column 'Hard No Aux' for SGN, which is 0.82.", "question": "What is the test accuracy of the Dual-Attention model for SGN in the Hard setting without auxiliary tasks?", "answer": "So, the answer is {\"answer\": \"0.82\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe PACMAN model achieves higher test accuracy than the GA model for EQA in the Easy setting with auxiliary tasks.\n" }, { "role": "assistant", "content": "The table shows the test accuracy for the PACMAN model and the GA model for EQA in the Easy setting with auxiliary tasks under the column 'Easy Aux'. PACMAN has an accuracy of 0.33, while GA has an accuracy of 0.24, indicating that PACMAN performs better. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3041.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracy of all models on SGN & EQA test sets for both Easy & Hard difficulties." ], "reasoning": "The table shows the test accuracy for the PACMAN model and the GA model for EQA in the Easy setting with auxiliary tasks under the column 'Easy Aux'. PACMAN has an accuracy of 0.33, while GA has an accuracy of 0.24, indicating that PACMAN performs better.", "statement": "The PACMAN model achieves higher test accuracy than the GA model for EQA in the Easy setting with auxiliary tasks.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: The performance of a trained policy appended with object detectors on instructions containing unseen words (‘red’ and ‘pillar’).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The Dual-Attention model's modular design allows it to transfer policies to new objects and attributes without fine-tuning, leveraging object detectors and auxiliary tasks.The results indicate that SA and GA1 contribute the most to the performance of the Dual-Attention model. This is expected as GA2 is designed to attend to different objects and attributes based on the question and is used mainly for answer prediction. It is not critical for SGN as the spatial attention map consists of locations of relevant objects, which is sufficient for navigating to the correct object. Dual-Attention models trained only on single tasks work well on SGN, especially with auxiliary tasks. This is because the auxiliary task for single task models includes object detection labels corresponding to the words in the test set. This highlights a key advantage of the proposed model. Due to its modular and interpretable design, the model can be used for transferring the policy to new objects and attributes without fine-tuning as discussed in the following subsection. In contrast, our model can be used for transfer to new words by training an object detector for each new word and appending it to the image representation xI. In order to test this, we train a single-task SGN model using the proposed architecture on the training set for instructions. We use auxiliary tasks but only for words in the vocabulary of the instructions training set. After training the policy, we would like the agent to follow instructions containing test words ‘red’ and ‘pillar’, which the agent has never seen or received any supervision about how this attribute or object looks visually. For transferring the policy, we assume access to two object detectors which would give object detections for ‘red’ and ‘pillar’ separately. We resize the object detections to the size of a feature map in the image representation (H×W) and append them as channels to the image representation. We also append the words ‘red’ and ‘pillar’ to the bag-of-words representations in the same order such that they are aligned with the appended feature maps. We randomly initialize the embeddings of the new words for computing the sentence embedding. This suggests that a trained policy can be scaled to more objects provided the complexity of navigation remains consistent." } ], "images": [ "scigen/train/3042.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The performance of a trained policy appended with object detectors on instructions containing unseen words (‘red’ and ‘pillar’)." ], "reasoning": "The Dual-Attention model's modular design allows it to transfer policies to new objects and attributes without fine-tuning, leveraging object detectors and auxiliary tasks.", "description": "The results indicate that SA and GA1 contribute the most to the performance of the Dual-Attention model. This is expected as GA2 is designed to attend to different objects and attributes based on the question and is used mainly for answer prediction. It is not critical for SGN as the spatial attention map consists of locations of relevant objects, which is sufficient for navigating to the correct object. Dual-Attention models trained only on single tasks work well on SGN, especially with auxiliary tasks. This is because the auxiliary task for single task models includes object detection labels corresponding to the words in the test set. This highlights a key advantage of the proposed model. Due to its modular and interpretable design, the model can be used for transferring the policy to new objects and attributes without fine-tuning as discussed in the following subsection. In contrast, our model can be used for transfer to new words by training an object detector for each new word and appending it to the image representation xI. In order to test this, we train a single-task SGN model using the proposed architecture on the training set for instructions. We use auxiliary tasks but only for words in the vocabulary of the instructions training set. After training the policy, we would like the agent to follow instructions containing test words ‘red’ and ‘pillar’, which the agent has never seen or received any supervision about how this attribute or object looks visually. For transferring the policy, we assume access to two object detectors which would give object detections for ‘red’ and ‘pillar’ separately. We resize the object detections to the size of a feature map in the image representation (H×W) and append them as channels to the image representation. We also append the words ‘red’ and ‘pillar’ to the bag-of-words representations in the same order such that they are aligned with the appended feature maps. We randomly initialize the embeddings of the new words for computing the sentence embedding. This suggests that a trained policy can be scaled to more objects provided the complexity of navigation remains consistent.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: 'Table 4: The performance of a trained policy appended with object detectors on instructions containing unseen words (‘red’ and ‘pillar’).'\nQuestion: Which instruction has the highest score in the 'Hard' category?\n" }, { "role": "assistant", "content": "To find the instruction with the highest score in the 'Hard' category, we need to compare the scores in the 'Hard' column. The highest score is 0.93 for the instruction 'Go to the red'. So, the answer is {\"answer\": \"Go to the red\"}." } ], "images": [ "scigen/train/3042.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The performance of a trained policy appended with object detectors on instructions containing unseen words (‘red’ and ‘pillar’)." ], "reasoning": "To find the instruction with the highest score in the 'Hard' category, we need to compare the scores in the 'Hard' column. The highest score is 0.93 for the instruction 'Go to the red'.", "question": "Which instruction has the highest score in the 'Hard' category?", "answer": "So, the answer is {\"answer\": \"Go to the red\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The instruction 'Go to the red object' has a higher score in the 'Easy' category than 'Go to the red pillar'., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe instruction 'Go to the red object' has a higher score in the 'Easy' category than 'Go to the red pillar'.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the 'Easy' scores for 'Go to the red object' (0.99) and 'Go to the red pillar' (0.99). Both instructions have the same score in the 'Easy' category. Thus, the answer is {\"answer\": \"not enough info\"}." } ], "images": [ "scigen/train/3042.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The performance of a trained policy appended with object detectors on instructions containing unseen words (‘red’ and ‘pillar’)." ], "reasoning": "To verify this statement, we compare the 'Easy' scores for 'Go to the red object' (0.99) and 'Go to the red pillar' (0.99). Both instructions have the same score in the 'Easy' category.", "statement": "The instruction 'Go to the red object' has a higher score in the 'Easy' category than 'Go to the red pillar'.", "answer": "Thus, the answer is {\"answer\": \"not enough info\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 6: Accuracy of all the models on the SGN and EQA train and test sets for the House3D Domain.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The Dual-Attention model shows superior performance across both SGN and EQA tasks compared to other models.The results are similar as in Doom: the Dual-Attention model outperforms the baselines by a considerable margin." } ], "images": [ "scigen/train/3043.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Accuracy of all the models on the SGN and EQA train and test sets for the House3D Domain." ], "reasoning": "The Dual-Attention model shows superior performance across both SGN and EQA tasks compared to other models.", "description": "The results are similar as in Doom: the Dual-Attention model outperforms the baselines by a considerable margin.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 6: Accuracy of all the models on the SGN and EQA train and test sets for the House3D Domain.' and proceed to solve the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model has the highest performance on the SGN Test and what is its score?\n" }, { "role": "assistant", "content": "By examining the SGN Test column, the Dual-Attention model has the highest score of 0.47. So, the answer is {\"answer\": \"Dual-Attention\"}." } ], "images": [ "scigen/train/3043.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Accuracy of all the models on the SGN and EQA train and test sets for the House3D Domain." ], "reasoning": "By examining the SGN Test column, the Dual-Attention model has the highest score of 0.47.", "question": "Which model has the highest performance on the SGN Test and what is its score?", "answer": "So, the answer is {\"answer\": \"Dual-Attention\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe GA model has the highest performance on the EQA Train task.\n" }, { "role": "assistant", "content": "By examining the EQA Train column, both GA and FiLM models have the highest score of 0.92. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3043.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Accuracy of all the models on the SGN and EQA train and test sets for the House3D Domain." ], "reasoning": "By examining the EQA Train column, both GA and FiLM models have the highest score of 0.92.", "statement": "The GA model has the highest performance on the EQA Train task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4. Serendipity per attribute. * indicates statistically significant difference with p<0.05.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Manual yields more serendipitous rankings in 'tags' and 'authors' than RecSys, while RecSys is more serendipitous in 'word embeddings'.We find no significant difference in section serendipity, but we do find manual yields more serendipitous rankings in ‘tags’ and ‘authors’ than RecSys. The latter is more serendipitous in ‘word embeddings.’ This is expected, as ‘tags’ and ‘authors’ are two important user-article features, which means recommendations will be steered towards more similar tags and authors (see also our observations with Diversity, above)." } ], "images": [ "scigen/train/3044.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4. Serendipity per attribute. * indicates statistically significant difference with p<0.05." ], "reasoning": "Manual yields more serendipitous rankings in 'tags' and 'authors' than RecSys, while RecSys is more serendipitous in 'word embeddings'.", "description": "We find no significant difference in section serendipity, but we do find manual yields more serendipitous rankings in ‘tags’ and ‘authors’ than RecSys. The latter is more serendipitous in ‘word embeddings.’ This is expected, as ‘tags’ and ‘authors’ are two important user-article features, which means recommendations will be steered towards more similar tags and authors (see also our observations with Diversity, above).", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 4. Serendipity per attribute. * indicates statistically significant difference with p<0.05.' in mind, break down and resolve the question below step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich attribute shows a higher serendipity score for RecSys compared to manual?\n" }, { "role": "assistant", "content": "By comparing the serendipity scores for each attribute, we see that 'word embeddings' has a higher score for RecSys (0.7680) compared to manual (0.7009). So, the answer is {\"answer\": \"Word Embeddings\"}." } ], "images": [ "scigen/train/3044.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4. Serendipity per attribute. * indicates statistically significant difference with p<0.05." ], "reasoning": "By comparing the serendipity scores for each attribute, we see that 'word embeddings' has a higher score for RecSys (0.7680) compared to manual (0.7009).", "question": "Which attribute shows a higher serendipity score for RecSys compared to manual?", "answer": "So, the answer is {\"answer\": \"Word Embeddings\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nManual has a higher serendipity score in 'tags' and 'authors' compared to RecSys.\n" }, { "role": "assistant", "content": "The table shows that the serendipity scores for 'tags' and 'authors' are higher for manual (0.1758 and 0.2187) compared to RecSys (0.2060 and 0.2754). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3044.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4. Serendipity per attribute. * indicates statistically significant difference with p<0.05." ], "reasoning": "The table shows that the serendipity scores for 'tags' and 'authors' are higher for manual (0.1758 and 0.2187) compared to RecSys (0.2060 and 0.2754).", "statement": "Manual has a higher serendipity score in 'tags' and 'authors' compared to RecSys.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1. Offline evaluation performance.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The NDCG score of 0.71 indicates that users' clicked articles ranked relatively high in the recommendations, and recall scores of 0.55 and 0.74 show that over half of the clicked articles rank among the top 10 recommendations.The NDCG score (0.71) tells us that users’ clicked articles ranked relatively high in our recommendations. Recall scores (0.55, 0.74) show us that over half of the clicked articles rank among the top 10 recommendations. Overall, without having a baseline to compare against, we believe the metrics point to an adequate ability of the recommender system to rank read articles highly." } ], "images": [ "scigen/train/3045.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1. Offline evaluation performance." ], "reasoning": "The NDCG score of 0.71 indicates that users' clicked articles ranked relatively high in the recommendations, and recall scores of 0.55 and 0.74 show that over half of the clicked articles rank among the top 10 recommendations.", "description": "The NDCG score (0.71) tells us that users’ clicked articles ranked relatively high in our recommendations. Recall scores (0.55, 0.74) show us that over half of the clicked articles rank among the top 10 recommendations. Overall, without having a baseline to compare against, we believe the metrics point to an adequate ability of the recommender system to rank read articles highly.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1. Offline evaluation performance.', solve the subsequent question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat do the recall scores indicate about the recommender system's performance?\n" }, { "role": "assistant", "content": "The recall scores of 0.55 and 0.74 indicate that over half of the clicked articles rank among the top 10 recommendations, showing the system's adequate ability to rank read articles highly. So, the answer is {\"answer\": \"over half of the clicked articles rank among the top 10 recommendations\"}." } ], "images": [ "scigen/train/3045.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1. Offline evaluation performance." ], "reasoning": "The recall scores of 0.55 and 0.74 indicate that over half of the clicked articles rank among the top 10 recommendations, showing the system's adequate ability to rank read articles highly.", "question": "What do the recall scores indicate about the recommender system's performance?", "answer": "So, the answer is {\"answer\": \"over half of the clicked articles rank among the top 10 recommendations\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe recommender system has a precision score of 0.34 at rank 5.\n" }, { "role": "assistant", "content": "The table shows that the precision score (P@5) for the recommender system is indeed 0.34. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3045.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1. Offline evaluation performance." ], "reasoning": "The table shows that the precision score (P@5) for the recommender system is indeed 0.34.", "statement": "The recommender system has a precision score of 0.34 at rank 5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2. Diversity per article attribute. * indicates statistically significant difference with p<0.05.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The top 5 recommendations are more diverse in 'section' and 'word embeddings,' while highlighted articles show higher diversity in 'authors.'From the table, we see that the top 5 recommendations are more diverse than the highlighted articles in terms of ‘section’ and ‘word embeddings.’ Since there are many hundreds of unique tags, they typically exhibit a low overlap between articles, explaining the relatively high diversity in both sources. In terms of ‘authors,’ the manually curated highlighted articles show higher diversity than recommended articles. This can be explained by our finding that ‘authors’ and ‘tags’ are two of the most important user-article features in our models, which means recommendations will tend to be personalized more strongly towards ‘authors’ and ‘tags’ that are similar to our users’ reading history." } ], "images": [ "scigen/train/3046.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2. Diversity per article attribute. * indicates statistically significant difference with p<0.05." ], "reasoning": "The top 5 recommendations are more diverse in 'section' and 'word embeddings,' while highlighted articles show higher diversity in 'authors.'", "description": "From the table, we see that the top 5 recommendations are more diverse than the highlighted articles in terms of ‘section’ and ‘word embeddings.’ Since there are many hundreds of unique tags, they typically exhibit a low overlap between articles, explaining the relatively high diversity in both sources. In terms of ‘authors,’ the manually curated highlighted articles show higher diversity than recommended articles. This can be explained by our finding that ‘authors’ and ‘tags’ are two of the most important user-article features in our models, which means recommendations will tend to be personalized more strongly towards ‘authors’ and ‘tags’ that are similar to our users’ reading history.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2. Diversity per article attribute. * indicates statistically significant difference with p<0.05.', and sequentially solve the question, writing out each step of your reasoning process. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich attribute shows higher diversity in manually curated highlighted articles compared to recommended articles?\n" }, { "role": "assistant", "content": "By comparing the values in the 'manual' and 'RecSys' columns, we see that 'authors' have a higher diversity score in the 'manual' column (0.9291) compared to the 'RecSys' column (0.8724). So, the answer is {\"answer\": \"authors\"}" } ], "images": [ "scigen/train/3046.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2. Diversity per article attribute. * indicates statistically significant difference with p<0.05." ], "reasoning": "By comparing the values in the 'manual' and 'RecSys' columns, we see that 'authors' have a higher diversity score in the 'manual' column (0.9291) compared to the 'RecSys' column (0.8724).", "question": "Which attribute shows higher diversity in manually curated highlighted articles compared to recommended articles?", "answer": "So, the answer is {\"answer\": \"authors\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Recommended articles show higher diversity in 'tags' compared to manually curated highlighted articles., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRecommended articles show higher diversity in 'tags' compared to manually curated highlighted articles.\n" }, { "role": "assistant", "content": "The table shows that the diversity score for 'tags' in the 'RecSys' column (0.9619) is higher than in the 'manual' column (0.9576). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3046.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2. Diversity per article attribute. * indicates statistically significant difference with p<0.05." ], "reasoning": "The table shows that the diversity score for 'tags' in the 'RecSys' column (0.9619) is higher than in the 'manual' column (0.9576).", "statement": "Recommended articles show higher diversity in 'tags' compared to manually curated highlighted articles.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 6. Reading behavior compared between before (July) and after (August) introduction of the news recommender. * indicates statistically significant difference with p<0.05.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The recommender system increased users' daily article clicks diversity in all attributes except word embeddings.The table shows that when the recommender system is introduced, users’ daily article clicks are more diverse on every attribute except for word embeddings, which suggests our recommender system effectively steers users towards more diverse reading." } ], "images": [ "scigen/train/3047.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6. Reading behavior compared between before (July) and after (August) introduction of the news recommender. * indicates statistically significant difference with p<0.05." ], "reasoning": "The recommender system increased users' daily article clicks diversity in all attributes except word embeddings.", "description": "The table shows that when the recommender system is introduced, users’ daily article clicks are more diverse on every attribute except for word embeddings, which suggests our recommender system effectively steers users towards more diverse reading.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 6. Reading behavior compared between before (July) and after (August) introduction of the news recommender. * indicates statistically significant difference with p<0.05.'. Solve the following question based on the table.\n\nQuestion: Which attribute showed a decrease in diversity from July to August?\nLet's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the values for each attribute between July and August, we see that 'Div WordEmbeddings' is the only attribute with a lower value in August (0.2463) compared to July (0.2565). So, the answer is {\"answer\": \"Div WordEmbeddings\"}." } ], "images": [ "scigen/train/3047.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6. Reading behavior compared between before (July) and after (August) introduction of the news recommender. * indicates statistically significant difference with p<0.05." ], "reasoning": "By comparing the values for each attribute between July and August, we see that 'Div WordEmbeddings' is the only attribute with a lower value in August (0.2463) compared to July (0.2565).", "question": "Which attribute showed a decrease in diversity from July to August?", "answer": "So, the answer is {\"answer\": \"Div WordEmbeddings\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The recommender system increased the diversity of article clicks in the 'Div Tags' attribute., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe recommender system increased the diversity of article clicks in the 'Div Tags' attribute.\n" }, { "role": "assistant", "content": "The table shows that the value for 'Div Tags' increased from 0.6216 in July to 0.6658 in August, indicating an increase in diversity. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3047.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6. Reading behavior compared between before (July) and after (August) introduction of the news recommender. * indicates statistically significant difference with p<0.05." ], "reasoning": "The table shows that the value for 'Div Tags' increased from 0.6216 in July to 0.6658 in August, indicating an increase in diversity.", "statement": "The recommender system increased the diversity of article clicks in the 'Div Tags' attribute.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: UASo of different individual models on test data. The upper and bottom blocks present results obtained by sibling and tri-sibling models respectively. We cannot draw a conclusion whether the complex structure models performs better or not, because the results vary with the structural complexity and the data. This is expected, because structural complexity affects both the empirical risks and the structural risks. As the data (English vs. Chinese) and the structure (Sibling vs. Tri-sibling) are different, the relations between empirical risks and the structural risks are also different. Hence, the varied results.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The complex sibling factorization performs worse than the simple sibling factorization in English but better in Chinese, while the complex tri-sibling factorization performs better than the simple tri-sibling factorization in English but worse in Chinese.We focus on the prediction for overt words only. When we take into account empty categories, more information is available. However, the increased structural complexity affects the algorithms. From the table, we can see that the complex sibling factorization works worse than the simple sibling factorization in English, but works better in Chinese. The results of the tri-sibling factorization are exactly the opposite. The complex tri-sibling factorization works better than the simple tri-sibling factorization in English, but works worse in Chinese. The results can be explained by our theoretical analysis. While the structural complexity is positively correlated with the overfitting risk, it is negatively correlated with the empirical risk. In this task, although the overfitting risk is increased when using the complex structure, the empirical risk is decreased more sometimes. Hence, the results vary both with the structural complexity and the data." } ], "images": [ "scigen/train/3048.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: UASo of different individual models on test data. The upper and bottom blocks present results obtained by sibling and tri-sibling models respectively. We cannot draw a conclusion whether the complex structure models performs better or not, because the results vary with the structural complexity and the data. This is expected, because structural complexity affects both the empirical risks and the structural risks. As the data (English vs. Chinese) and the structure (Sibling vs. Tri-sibling) are different, the relations between empirical risks and the structural risks are also different. Hence, the varied results." ], "reasoning": "The complex sibling factorization performs worse than the simple sibling factorization in English but better in Chinese, while the complex tri-sibling factorization performs better than the simple tri-sibling factorization in English but worse in Chinese.", "description": "We focus on the prediction for overt words only. When we take into account empty categories, more information is available. However, the increased structural complexity affects the algorithms. From the table, we can see that the complex sibling factorization works worse than the simple sibling factorization in English, but works better in Chinese. The results of the tri-sibling factorization are exactly the opposite. The complex tri-sibling factorization works better than the simple tri-sibling factorization in English, but works worse in Chinese. The results can be explained by our theoretical analysis. While the structural complexity is positively correlated with the overfitting risk, it is negatively correlated with the empirical risk. In this task, although the overfitting risk is increased when using the complex structure, the empirical risk is decreased more sometimes. Hence, the results vary both with the structural complexity and the data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 4: UASo of different individual models on test data. The upper and bottom blocks present results obtained by sibling and tri-sibling models respectively. We cannot draw a conclusion whether the complex structure models performs better or not, because the results vary with the structural complexity and the data. This is expected, because structural complexity affects both the empirical risks and the structural risks. As the data (English vs. Chinese) and the structure (Sibling vs. Tri-sibling) are different, the relations between empirical risks and the structural risks are also different. Hence, the varied results.', and tackle the question below in a sequential manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich factorization method performs best in English when considering empty elements?\n" }, { "role": "assistant", "content": "To determine the best performing factorization method in English when considering empty elements, we compare the English scores for all methods with 'Yes' under the Empty Element column. The highest score is 92.41 for Tri-sibling (complex). So, the answer is {\"answer\": \"Tri-sibling (complex)\"}." } ], "images": [ "scigen/train/3048.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: UASo of different individual models on test data. The upper and bottom blocks present results obtained by sibling and tri-sibling models respectively. We cannot draw a conclusion whether the complex structure models performs better or not, because the results vary with the structural complexity and the data. This is expected, because structural complexity affects both the empirical risks and the structural risks. As the data (English vs. Chinese) and the structure (Sibling vs. Tri-sibling) are different, the relations between empirical risks and the structural risks are also different. Hence, the varied results." ], "reasoning": "To determine the best performing factorization method in English when considering empty elements, we compare the English scores for all methods with 'Yes' under the Empty Element column. The highest score is 92.41 for Tri-sibling (complex).", "question": "Which factorization method performs best in English when considering empty elements?", "answer": "So, the answer is {\"answer\": \"Tri-sibling (complex)\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The simple sibling factorization performs better than the complex sibling factorization in Chinese. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe simple sibling factorization performs better than the complex sibling factorization in Chinese.\n" }, { "role": "assistant", "content": "The table shows that the simple sibling factorization has a score of 89.16 in Chinese, while the complex sibling factorization has a score of 89.20. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3048.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: UASo of different individual models on test data. The upper and bottom blocks present results obtained by sibling and tri-sibling models respectively. We cannot draw a conclusion whether the complex structure models performs better or not, because the results vary with the structural complexity and the data. This is expected, because structural complexity affects both the empirical risks and the structural risks. As the data (English vs. Chinese) and the structure (Sibling vs. Tri-sibling) are different, the relations between empirical risks and the structural risks are also different. Hence, the varied results." ], "reasoning": "The table shows that the simple sibling factorization has a score of 89.16 in Chinese, while the complex sibling factorization has a score of 89.20. Therefore, the statement is refuted.", "statement": "The simple sibling factorization performs better than the complex sibling factorization in Chinese.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Comparing SR decoding with the baseline BLSTM models. As we can see, when the order of the model increases, the performance of BLSTM deteriorates most of the time. With SR decoding, the structural complexity is controlled, so that the performance of the higher order model is substantially improved. It is also interesting that the improvement is larger when the order is higher.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Increasing the order of the BLSTM model generally worsens performance, except for the Dutch-NER task where performance improves slightly.First, we apply the proposed scalable multi-order decoding method on BLSTM (BLSTM-SR). As we can see, when the order of the model is increased, the baseline model worsens. The exception is the result of the Dutch-NER task. When the order of the model is increased, the model is slightly improved. It demonstrates that, in practice, although complex structure models have lower empirical risks, the structural risks are more dominant." } ], "images": [ "scigen/train/3049.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparing SR decoding with the baseline BLSTM models. As we can see, when the order of the model increases, the performance of BLSTM deteriorates most of the time. With SR decoding, the structural complexity is controlled, so that the performance of the higher order model is substantially improved. It is also interesting that the improvement is larger when the order is higher." ], "reasoning": "Increasing the order of the BLSTM model generally worsens performance, except for the Dutch-NER task where performance improves slightly.", "description": "First, we apply the proposed scalable multi-order decoding method on BLSTM (BLSTM-SR). As we can see, when the order of the model is increased, the baseline model worsens. The exception is the result of the Dutch-NER task. When the order of the model is increased, the model is slightly improved. It demonstrates that, in practice, although complex structure models have lower empirical risks, the structural risks are more dominant.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 1: Comparing SR decoding with the baseline BLSTM models. As we can see, when the order of the model increases, the performance of BLSTM deteriorates most of the time. With SR decoding, the structural complexity is controlled, so that the performance of the higher order model is substantially improved. It is also interesting that the improvement is larger when the order is higher.', solve this question and give detail solutions. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the test score for the Dutch-NER task when using BLSTM order2 + SR?\n" }, { "role": "assistant", "content": "To find the test score for the Dutch-NER task using BLSTM order2 + SR, locate the row labeled 'BLSTM order2 + SR' and then find the corresponding value under the 'Dutch-NER' column. So, the answer is {\"answer\": \"80.51\"}." } ], "images": [ "scigen/train/3049.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparing SR decoding with the baseline BLSTM models. As we can see, when the order of the model increases, the performance of BLSTM deteriorates most of the time. With SR decoding, the structural complexity is controlled, so that the performance of the higher order model is substantially improved. It is also interesting that the improvement is larger when the order is higher." ], "reasoning": "To find the test score for the Dutch-NER task using BLSTM order2 + SR, locate the row labeled 'BLSTM order2 + SR' and then find the corresponding value under the 'Dutch-NER' column.", "question": "What is the test score for the Dutch-NER task when using BLSTM order2 + SR?", "answer": "So, the answer is {\"answer\": \"80.51\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe BLSTM order3 + SR model has the highest test score for the English-NER task.\n" }, { "role": "assistant", "content": "To verify this statement, compare the test scores for the English-NER task across all rows. The highest score is 90.59, which corresponds to the BLSTM order3 + SR model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3049.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparing SR decoding with the baseline BLSTM models. As we can see, when the order of the model increases, the performance of BLSTM deteriorates most of the time. With SR decoding, the structural complexity is controlled, so that the performance of the higher order model is substantially improved. It is also interesting that the improvement is larger when the order is higher." ], "reasoning": "To verify this statement, compare the test scores for the English-NER task across all rows. The highest score is 90.59, which corresponds to the BLSTM order3 + SR model.", "statement": "The BLSTM order3 + SR model has the highest test score for the English-NER task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Text Chunking: comparing with previous work. Shen and Sarkar (2005) also achieve 95.23%, but their result is based on noun phrase chunking. However, our result is based on all phrase chunking, which has more tags to predict and is more difficult.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The paper achieves state-of-the-art performance in all-phrase chunking, which is more difficult than NP-chunking due to having more tags.We achieve the state-of-the-art in all-phrase chunking. However, they conduct experiments in noun phrase chunking (NP-chunking). All phrase chunking contains much more tags than NP-chunking, which is more difficult." } ], "images": [ "scigen/train/3050.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Text Chunking: comparing with previous work. Shen and Sarkar (2005) also achieve 95.23%, but their result is based on noun phrase chunking. However, our result is based on all phrase chunking, which has more tags to predict and is more difficult." ], "reasoning": "The paper achieves state-of-the-art performance in all-phrase chunking, which is more difficult than NP-chunking due to having more tags.", "description": "We achieve the state-of-the-art in all-phrase chunking. However, they conduct experiments in noun phrase chunking (NP-chunking). All phrase chunking contains much more tags than NP-chunking, which is more difficult.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Text Chunking: comparing with previous work. Shen and Sarkar (2005) also achieve 95.23%, but their result is based on noun phrase chunking. However, our result is based on all phrase chunking, which has more tags to predict and is more difficult.', answer the question below by showing each progressive step toward the answer. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieved the highest F1 score in the experiments?\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all the models listed in the table, the model 'This paper' achieved the highest F1 score of 95.23. So, the answer is {\"answer\": \"This paper\"}." } ], "images": [ "scigen/train/3050.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Text Chunking: comparing with previous work. Shen and Sarkar (2005) also achieve 95.23%, but their result is based on noun phrase chunking. However, our result is based on all phrase chunking, which has more tags to predict and is more difficult." ], "reasoning": "By comparing the F1 scores of all the models listed in the table, the model 'This paper' achieved the highest F1 score of 95.23.", "question": "Which model achieved the highest F1 score in the experiments?", "answer": "So, the answer is {\"answer\": \"This paper\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The model by Sha and Pereira (2003) achieved an F1 score higher than 94.30. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model by Sha and Pereira (2003) achieved an F1 score higher than 94.30.\n" }, { "role": "assistant", "content": "The table shows that the model by Sha and Pereira (2003) achieved an F1 score of exactly 94.30, not higher. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3050.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Text Chunking: comparing with previous work. Shen and Sarkar (2005) also achieve 95.23%, but their result is based on noun phrase chunking. However, our result is based on all phrase chunking, which has more tags to predict and is more difficult." ], "reasoning": "The table shows that the model by Sha and Pereira (2003) achieved an F1 score of exactly 94.30, not higher.", "statement": "The model by Sha and Pereira (2003) achieved an F1 score higher than 94.30.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Numbers of sentences, overt and covert elements in training and test sets.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "We use standard splits for training, validation, and testing, measuring accuracy with UASo for overt words and correctness of empty edges for empty nodes.We use the standard training, validation, and test splits to facilitate comparisons. Accuracy is measured with unlabeled attachment score for all overt words (UASo): the percentage of the overt words with the correct head. We are also concerned with the prediction accuracy for empty elements. To evaluate performance on empty nodes, we consider the correctness of empty edges. We report the percentage of the empty words in the right slot with correct head. The i-th slot in the sentence means that the position immediately after the i-th concrete word. So if we have a sentence with length n, we get n+1 slots." } ], "images": [ "scigen/train/3051.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Numbers of sentences, overt and covert elements in training and test sets." ], "reasoning": "We use standard splits for training, validation, and testing, measuring accuracy with UASo for overt words and correctness of empty edges for empty nodes.", "description": "We use the standard training, validation, and test splits to facilitate comparisons. Accuracy is measured with unlabeled attachment score for all overt words (UASo): the percentage of the overt words with the correct head. We are also concerned with the prediction accuracy for empty elements. To evaluate performance on empty nodes, we consider the correctness of empty edges. We report the percentage of the empty words in the right slot with correct head. The i-th slot in the sentence means that the position immediately after the i-th concrete word. So if we have a sentence with length n, we get n+1 slots.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Numbers of sentences, overt and covert elements in training and test sets.\nQuestion: What is the total number of overt words in the English train and test splits combined?\nSolve the above question based on the table information. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the total number of overt words in the English train and test splits, add the overt words from the English train split (909114) and the English test split (54242). So, the answer is {\"answer\": \"963356\"}" } ], "images": [ "scigen/train/3051.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Numbers of sentences, overt and covert elements in training and test sets." ], "reasoning": "To find the total number of overt words in the English train and test splits, add the overt words from the English train split (909114) and the English test split (54242).", "question": "What is the total number of overt words in the English train and test splits combined?", "answer": "So, the answer is {\"answer\": \"963356\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The Chinese test split has more covert words than the English test split., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Chinese test split has more covert words than the English test split.\n" }, { "role": "assistant", "content": "The table shows that the Chinese test split has 1520 covert words, while the English test split has 3447 covert words. Therefore, the Chinese test split has fewer covert words than the English test split. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3051.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Numbers of sentences, overt and covert elements in training and test sets." ], "reasoning": "The table shows that the Chinese test split has 1520 covert words, while the English test split has 3447 covert words. Therefore, the Chinese test split has fewer covert words than the English test split.", "statement": "The Chinese test split has more covert words than the English test split.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: UASo of different SR decoding models on test data. As we can see, SR decoding consistently improves the complex structure models. All improvements are statistically significant.'.\n" }, { "role": "assistant", "content": "The SR decoding framework effectively reduces the error rate for both English and Chinese, with greater improvements observed in the third-order model.We can see that the SR decoding framework is effective to deal with the structure-based overfitting. This time, the accuracy of analysis for overt words is consistently improved. For the second-order model, SR decoding reduces the error rate by 3.1% for English, and by 3.0% for Chinese. For the third-order model, the error rate reduction of 4.0% for English, and 5.5% for Chinese is achieved by the proposed method. Similar to the sequence labeling tasks, the third-order model is improved more. We suppose the consistent improvements come from the ability of reducing the structural risk of the SR decoding algorithm. Although in this task, the complex structure is sometimes helpful to the accuracy of the parsing, the structural risk still increases. By regularizing the structural complexity, further improvements can be achieved, on top of the decreased empirical risk brought by the complex structure." } ], "images": [ "scigen/train/3052.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: UASo of different SR decoding models on test data. As we can see, SR decoding consistently improves the complex structure models. All improvements are statistically significant." ], "reasoning": "The SR decoding framework effectively reduces the error rate for both English and Chinese, with greater improvements observed in the third-order model.", "description": "We can see that the SR decoding framework is effective to deal with the structure-based overfitting. This time, the accuracy of analysis for overt words is consistently improved. For the second-order model, SR decoding reduces the error rate by 3.1% for English, and by 3.0% for Chinese. For the third-order model, the error rate reduction of 4.0% for English, and 5.5% for Chinese is achieved by the proposed method. Similar to the sequence labeling tasks, the third-order model is improved more. We suppose the consistent improvements come from the ability of reducing the structural risk of the SR decoding algorithm. Although in this task, the complex structure is sometimes helpful to the accuracy of the parsing, the structural risk still increases. By regularizing the structural complexity, further improvements can be achieved, on top of the decreased empirical risk brought by the complex structure.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 5: UASo of different SR decoding models on test data. As we can see, SR decoding consistently improves the complex structure models. All improvements are statistically significant.', and tackle the question below in a sequential manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the error rate reduction for Chinese in the third-order model using SR decoding?\n" }, { "role": "assistant", "content": "The table shows that the Tri-sibling (complex) model for Chinese has an accuracy of 89.82, and with SR decoding, it improves to 90.38, indicating an error rate reduction of 0.56%. So, the answer is {\"answer\": \"0.56%\"}." } ], "images": [ "scigen/train/3052.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: UASo of different SR decoding models on test data. As we can see, SR decoding consistently improves the complex structure models. All improvements are statistically significant." ], "reasoning": "The table shows that the Tri-sibling (complex) model for Chinese has an accuracy of 89.82, and with SR decoding, it improves to 90.38, indicating an error rate reduction of 0.56%.", "question": "What is the error rate reduction for Chinese in the third-order model using SR decoding?", "answer": "So, the answer is {\"answer\": \"0.56%\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe SR decoding framework improves the accuracy of the second-order model for English by 0.26%.\n" }, { "role": "assistant", "content": "The table shows that the Sibling (complex) model for English has an accuracy of 91.70, and with SR decoding, it improves to 91.96, indicating an improvement of 0.26%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3052.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: UASo of different SR decoding models on test data. As we can see, SR decoding consistently improves the complex structure models. All improvements are statistically significant." ], "reasoning": "The table shows that the Sibling (complex) model for English has an accuracy of 91.70, and with SR decoding, it improves to 91.96, indicating an improvement of 0.26%.", "statement": "The SR decoding framework improves the accuracy of the second-order model for English by 0.26%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 6: Timing Results on varying the orders.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The implementation of scalable multi-order decoding shows efficient performance across various NLP tasks, maintaining good scalability.With this implementation, our scalable multi-order decoding performs efficiently on various real-world NLP tasks and keeps good scalability." } ], "images": [ "scigen/train/3053.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Timing Results on varying the orders." ], "reasoning": "The implementation of scalable multi-order decoding shows efficient performance across various NLP tasks, maintaining good scalability.", "description": "With this implementation, our scalable multi-order decoding performs efficiently on various real-world NLP tasks and keeps good scalability.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 6: Timing Results on varying the orders.', unfold the question presented below, detailing every step of your calculation. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the difference in train time for Chunking between BLSTM order1 + SR and BLSTM order3 + SR?\n" }, { "role": "assistant", "content": "To find the difference in train time for Chunking between BLSTM order1 + SR and BLSTM order3 + SR, subtract the train time of BLSTM order1 + SR (443.13) from the train time of BLSTM order3 + SR (459.75). So, the answer is {\"answer\": \"16.62\"}." } ], "images": [ "scigen/train/3053.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Timing Results on varying the orders." ], "reasoning": "To find the difference in train time for Chunking between BLSTM order1 + SR and BLSTM order3 + SR, subtract the train time of BLSTM order1 + SR (443.13) from the train time of BLSTM order3 + SR (459.75).", "question": "What is the difference in train time for Chunking between BLSTM order1 + SR and BLSTM order3 + SR?", "answer": "So, the answer is {\"answer\": \"16.62\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: BLSTM order2 + SR has a higher train time for Dutch-NER than BLSTM order1 + SR., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nBLSTM order2 + SR has a higher train time for Dutch-NER than BLSTM order1 + SR.\n" }, { "role": "assistant", "content": "The table shows that the train time for Dutch-NER is 511.08 for BLSTM order2 + SR and 484.74 for BLSTM order1 + SR, indicating that BLSTM order2 + SR has a higher train time. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3053.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Timing Results on varying the orders." ], "reasoning": "The table shows that the train time for Dutch-NER is 511.08 for BLSTM order2 + SR and 484.74 for BLSTM order1 + SR, indicating that BLSTM order2 + SR has a higher train time.", "statement": "BLSTM order2 + SR has a higher train time for Dutch-NER than BLSTM order1 + SR.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 6: The F1 scores on the adversarial SQuAD test set.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The Mnemonic model, trained with augmented data, shows high robustness with scores of 46.6 on AddSent and 56.0 on AddOneSent.The model in use is exactly the one trained with the original SQuAD data (the one getting 84.6 F1 on test set), but now it is submitted to the adversarial server for evaluation. Again, we only compare the performance of single models. The robustness of our model is probably because it is trained with augmented data. The injected noise in the training data might not only improve the generalization of the model but also make it robust to the adversarial sentences." } ], "images": [ "scigen/train/3054.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: The F1 scores on the adversarial SQuAD test set." ], "reasoning": "The Mnemonic model, trained with augmented data, shows high robustness with scores of 46.6 on AddSent and 56.0 on AddOneSent.", "description": "The model in use is exactly the one trained with the original SQuAD data (the one getting 84.6 F1 on test set), but now it is submitted to the adversarial server for evaluation. Again, we only compare the performance of single models. The robustness of our model is probably because it is trained with augmented data. The injected noise in the training data might not only improve the generalization of the model but also make it robust to the adversarial sentences.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 6: The F1 scores on the adversarial SQuAD test set.', answer the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieved the highest score on the AddOneSent evaluation?\n" }, { "role": "assistant", "content": "By examining the AddOneSent column, the Mnemonic model has the highest score of 56.0. So, the answer is {\"answer\": \"Mnemonic (Hu et al., 2017)\"}" } ], "images": [ "scigen/train/3054.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: The F1 scores on the adversarial SQuAD test set." ], "reasoning": "By examining the AddOneSent column, the Mnemonic model has the highest score of 56.0.", "question": "Which model achieved the highest score on the AddOneSent evaluation?", "answer": "So, the answer is {\"answer\": \"Mnemonic (Hu et al., 2017)\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Mnemonic model achieved the highest score on both AddSent and AddOneSent evaluations. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Mnemonic model achieved the highest score on both AddSent and AddOneSent evaluations.\n" }, { "role": "assistant", "content": "The table shows that the Mnemonic model has the highest scores in both AddSent (46.6) and AddOneSent (56.0) evaluations. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3054.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: The F1 scores on the adversarial SQuAD test set." ], "reasoning": "The table shows that the Mnemonic model has the highest scores in both AddSent (46.6) and AddOneSent (56.0) evaluations.", "statement": "The Mnemonic model achieved the highest score on both AddSent and AddOneSent evaluations.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: Speed comparison between our model and RNN-based models on SQuAD dataset, all with batch size 32. RNN-x-y indicates an RNN with x layers each containing y hidden units. Here, we use bidirectional LSTM as the RNN. The speed is measured by batches/second, so higher is faster.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Our model is significantly faster than RNN-based models, with speedups ranging from 3 to 13 times in training and 4 to 9 times in inference.To measure the speedup of our model against the RNN models, we also test the corresponding model architecture with each encoder block replaced with a stack of bidirectional LSTMs as is used in most existing models. Specifically, each (embedding and model) encoder block is replaced with a 1, 2, or 3 layer Bidirectional LSTMs respectively, as such layer numbers fall into the usual range of the reading comprehension models All of these LSTMs have hidden size 128. We can easily see that our model is significantly faster than all the RNN based models and the speedups range from 3 to 13 times in training and 4 to 9 times in inference." } ], "images": [ "scigen/train/3055.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Speed comparison between our model and RNN-based models on SQuAD dataset, all with batch size 32. RNN-x-y indicates an RNN with x layers each containing y hidden units. Here, we use bidirectional LSTM as the RNN. The speed is measured by batches/second, so higher is faster." ], "reasoning": "Our model is significantly faster than RNN-based models, with speedups ranging from 3 to 13 times in training and 4 to 9 times in inference.", "description": "To measure the speedup of our model against the RNN models, we also test the corresponding model architecture with each encoder block replaced with a stack of bidirectional LSTMs as is used in most existing models. Specifically, each (embedding and model) encoder block is replaced with a 1, 2, or 3 layer Bidirectional LSTMs respectively, as such layer numbers fall into the usual range of the reading comprehension models All of these LSTMs have hidden size 128. We can easily see that our model is significantly faster than all the RNN based models and the speedups range from 3 to 13 times in training and 4 to 9 times in inference.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 3: Speed comparison between our model and RNN-based models on SQuAD dataset, all with batch size 32. RNN-x-y indicates an RNN with x layers each containing y hidden units. Here, we use bidirectional LSTM as the RNN. The speed is measured by batches/second, so higher is faster.', and tackle the question below in a sequential manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the speedup of QANet compared to the RNN-3-128 model during inference?\n" }, { "role": "assistant", "content": "The table shows the inference times for QANet and RNN-3-128 as 8.1 and 0.92 respectively. The speedup is calculated as 8.1 / 0.92, which is approximately 8.8x. So, the answer is {\"answer\": \"8.8x\"}." } ], "images": [ "scigen/train/3055.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Speed comparison between our model and RNN-based models on SQuAD dataset, all with batch size 32. RNN-x-y indicates an RNN with x layers each containing y hidden units. Here, we use bidirectional LSTM as the RNN. The speed is measured by batches/second, so higher is faster." ], "reasoning": "The table shows the inference times for QANet and RNN-3-128 as 8.1 and 0.92 respectively. The speedup is calculated as 8.1 / 0.92, which is approximately 8.8x.", "question": "What is the speedup of QANet compared to the RNN-3-128 model during inference?", "answer": "So, the answer is {\"answer\": \"8.8x\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence QANet is 13.3 times faster than RNN-3-128 during training. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nQANet is 13.3 times faster than RNN-3-128 during training.\n" }, { "role": "assistant", "content": "The table shows that the speedup of QANet compared to RNN-3-128 during training is 13.3x. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3055.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Speed comparison between our model and RNN-based models on SQuAD dataset, all with batch size 32. RNN-x-y indicates an RNN with x layers each containing y hidden units. Here, we use bidirectional LSTM as the RNN. The speed is measured by batches/second, so higher is faster." ], "reasoning": "The table shows that the speedup of QANet compared to RNN-3-128 during training is 13.3x.", "statement": "QANet is 13.3 times faster than RNN-3-128 during training.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 12: Top ranked log-odds ratio of subjective adjectives describing severe threats (CVSS scores ≥ 7.0) versus non-severe threats (CVSS scores < 7.0). Subjective adjectives are identified by using Subjectivity Lexicon (SUB) Wilson et al. (2005).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "We analyze subjective adjectives used to measure threats by extracting tokens marked as JJ, JJP, and JJS, and rank them in the Subjectivity Lexicon.We examine subjective adjectives people use for measuring threats. We run POS tagging to extract all tokens marked as JJ, JJP, and JJS. We then rank subjective adjectives in Subjectivity Lexicon (SUB) Wilson et al. We observe variants people are using for severe threats, e.g. “serious”, “severe”, “malicious”, etc." } ], "images": [ "scigen/train/3056.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 12: Top ranked log-odds ratio of subjective adjectives describing severe threats (CVSS scores ≥ 7.0) versus non-severe threats (CVSS scores < 7.0). Subjective adjectives are identified by using Subjectivity Lexicon (SUB) Wilson et al. (2005)." ], "reasoning": "We analyze subjective adjectives used to measure threats by extracting tokens marked as JJ, JJP, and JJS, and rank them in the Subjectivity Lexicon.", "description": "We examine subjective adjectives people use for measuring threats. We run POS tagging to extract all tokens marked as JJ, JJP, and JJS. We then rank subjective adjectives in Subjectivity Lexicon (SUB) Wilson et al. We observe variants people are using for severe threats, e.g. “serious”, “severe”, “malicious”, etc.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 12: Top ranked log-odds ratio of subjective adjectives describing severe threats (CVSS scores ≥ 7.0) versus non-severe threats (CVSS scores < 7.0). Subjective adjectives are identified by using Subjectivity Lexicon (SUB) Wilson et al. (2005).'.\n\nQuestion: Which adjective has the highest ratio for measuring serious threats?\n" }, { "role": "assistant", "content": "To find the adjective with the highest ratio for measuring serious threats, look at the 'Adj. serious' column and identify the adjective with the highest ratio value. So, the answer is {\"answer\": \"pivotal\"}" } ], "images": [ "scigen/train/3056.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 12: Top ranked log-odds ratio of subjective adjectives describing severe threats (CVSS scores ≥ 7.0) versus non-severe threats (CVSS scores < 7.0). Subjective adjectives are identified by using Subjectivity Lexicon (SUB) Wilson et al. (2005)." ], "reasoning": "To find the adjective with the highest ratio for measuring serious threats, look at the 'Adj. serious' column and identify the adjective with the highest ratio value.", "question": "Which adjective has the highest ratio for measuring serious threats?", "answer": "So, the answer is {\"answer\": \"pivotal\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe adjective 'malicious' is used to measure serious threats.\n" }, { "role": "assistant", "content": "To verify if 'malicious' is used to measure serious threats, check the 'Adj. serious' column. 'Malicious' is listed under 'Adj. fast' with a ratio of 1.20, not under 'Adj. serious'. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3056.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 12: Top ranked log-odds ratio of subjective adjectives describing severe threats (CVSS scores ≥ 7.0) versus non-severe threats (CVSS scores < 7.0). Subjective adjectives are identified by using Subjectivity Lexicon (SUB) Wilson et al. (2005)." ], "reasoning": "To verify if 'malicious' is used to measure serious threats, check the 'Adj. serious' column. 'Malicious' is listed under 'Adj. fast' with a ratio of 1.20, not under 'Adj. serious'.", "statement": "The adjective 'malicious' is used to measure serious threats.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 6: Qualitative severity rankings of vulnerabilities in NVD. (Left) CVSS v2.0 standards and (Right) CVSS v3.0 standards.'.\n" }, { "role": "assistant", "content": "The National Vulnerability Database (NVD) uses the Common Vulnerability Scoring System (CVSS) to measure threat severity, with CVSS v3.0 being the latest version. A threat is considered severe if its CVSS score is ≥ 7.0.A large corpus of users’ opinions We collect all English tweets that explicitly contain the keyword “vulnerability” within this time period, which results in a total number of 976,180 tweets. 377,468 tweets remain after removing tweets without named entities. National Vulnerability Database (NVD) : NVD is the U.S. government database of software vulnerabilities. Started in 2000, NVD covers over 100,000 vulnerabilities, assigning a unique CVE number for each threat. These CVE numbers serve as common identifiers. NVD uses the Common Vulnerability Scoring System (CVSS) to measure the severity of threats. CVSS currently has two versions: CVSS v2.0 and CVSS v3.0 standards. CVSS v3.0 is the latest version released in July 2015. In our first set of experiments, we compare our forecasted threat severity scores against CVSS ratings from the NVD. We define a threat as being severe if its CVSS score is ≥ 7.0. We use the newest v3.0 scoring system, which was developed to improve v2.0." } ], "images": [ "scigen/train/3057.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Qualitative severity rankings of vulnerabilities in NVD. (Left) CVSS v2.0 standards and (Right) CVSS v3.0 standards." ], "reasoning": "The National Vulnerability Database (NVD) uses the Common Vulnerability Scoring System (CVSS) to measure threat severity, with CVSS v3.0 being the latest version. A threat is considered severe if its CVSS score is ≥ 7.0.", "description": "A large corpus of users’ opinions We collect all English tweets that explicitly contain the keyword “vulnerability” within this time period, which results in a total number of 976,180 tweets. 377,468 tweets remain after removing tweets without named entities. National Vulnerability Database (NVD) : NVD is the U.S. government database of software vulnerabilities. Started in 2000, NVD covers over 100,000 vulnerabilities, assigning a unique CVE number for each threat. These CVE numbers serve as common identifiers. NVD uses the Common Vulnerability Scoring System (CVSS) to measure the severity of threats. CVSS currently has two versions: CVSS v2.0 and CVSS v3.0 standards. CVSS v3.0 is the latest version released in July 2015. In our first set of experiments, we compare our forecasted threat severity scores against CVSS ratings from the NVD. We define a threat as being severe if its CVSS score is ≥ 7.0. We use the newest v3.0 scoring system, which was developed to improve v2.0.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 6: Qualitative severity rankings of vulnerabilities in NVD. (Left) CVSS v2.0 standards and (Right) CVSS v3.0 standards.', answer the question below by showing each progressive step toward the answer. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the range of base scores for a threat to be considered critical according to the CVSS v3.0 scoring system?\n" }, { "role": "assistant", "content": "The table shows that a threat is considered critical if its base score is between 9.0 and 10.0. So, the answer is {\"answer\": \"9.0-10.0\"}." } ], "images": [ "scigen/train/3057.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Qualitative severity rankings of vulnerabilities in NVD. (Left) CVSS v2.0 standards and (Right) CVSS v3.0 standards." ], "reasoning": "The table shows that a threat is considered critical if its base score is between 9.0 and 10.0.", "question": "What is the range of base scores for a threat to be considered critical according to the CVSS v3.0 scoring system?", "answer": "So, the answer is {\"answer\": \"9.0-10.0\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis A threat with a base score of 8.5 is considered high severity. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nA threat with a base score of 8.5 is considered high severity.\n" }, { "role": "assistant", "content": "According to the table, a threat with a base score between 7.0 and 8.9 is considered high severity. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3057.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Qualitative severity rankings of vulnerabilities in NVD. (Left) CVSS v2.0 standards and (Right) CVSS v3.0 standards." ], "reasoning": "According to the table, a threat with a base score between 7.0 and 8.9 is considered high severity.", "statement": "A threat with a base score of 8.5 is considered high severity.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 7: Model performance of identifying severe threats (CVSS scores ≥ 7.0) with Precision@k and area under the precision-recall curve (AUC) metrics. For majority random baseline, we average over 10 trails.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our model outperforms both the random baseline and the volume model in predicting high or critical severity threats, achieving 86% accuracy in the top 50 predictions.We evaluate our models’ performance at identifying severe threats five days ahead of the NVD publication date, within their top k predictions. We observe that tweet volume performs better than a random baseline; having a large number of tweets beforehand is a good indicator for high severity, however our approach which analyzes the content of messages discussing software vulnerabilities achieves significantly better performance; 86% of its top 50 forecasts were indeed rated as HIGH or CRITICAL severity in the NVD." } ], "images": [ "scigen/train/3058.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Model performance of identifying severe threats (CVSS scores ≥ 7.0) with Precision@k and area under the precision-recall curve (AUC) metrics. For majority random baseline, we average over 10 trails." ], "reasoning": "Our model outperforms both the random baseline and the volume model in predicting high or critical severity threats, achieving 86% accuracy in the top 50 predictions.", "description": "We evaluate our models’ performance at identifying severe threats five days ahead of the NVD publication date, within their top k predictions. We observe that tweet volume performs better than a random baseline; having a large number of tweets beforehand is a good indicator for high severity, however our approach which analyzes the content of messages discussing software vulnerabilities achieves significantly better performance; 86% of its top 50 forecasts were indeed rated as HIGH or CRITICAL severity in the NVD.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 7: Model performance of identifying severe threats (CVSS scores ≥ 7.0) with Precision@k and area under the precision-recall curve (AUC) metrics. For majority random baseline, we average over 10 trails.' and methodically tackle the question that follows. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the AUC value for the model that analyzes the content of messages discussing software vulnerabilities?\n" }, { "role": "assistant", "content": "The table shows the AUC values for different models. The model that analyzes the content of messages is labeled as 'Our model,' which has an AUC value of 0.658. So, the answer is {\"answer\": \"0.658\"}." } ], "images": [ "scigen/train/3058.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Model performance of identifying severe threats (CVSS scores ≥ 7.0) with Precision@k and area under the precision-recall curve (AUC) metrics. For majority random baseline, we average over 10 trails." ], "reasoning": "The table shows the AUC values for different models. The model that analyzes the content of messages is labeled as 'Our model,' which has an AUC value of 0.658.", "question": "What is the AUC value for the model that analyzes the content of messages discussing software vulnerabilities?", "answer": "So, the answer is {\"answer\": \"0.658\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The volume model has a higher P@50 value than the random baseline. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe volume model has a higher P@50 value than the random baseline.\n" }, { "role": "assistant", "content": "The table shows that the P@50 value for the volume model is 68.0, while the P@50 value for the random baseline is 61.2. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3058.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Model performance of identifying severe threats (CVSS scores ≥ 7.0) with Precision@k and area under the precision-recall curve (AUC) metrics. For majority random baseline, we average over 10 trails." ], "reasoning": "The table shows that the P@50 value for the volume model is 68.0, while the P@50 value for the random baseline is 61.2. Therefore, the statement is supported.", "statement": "The volume model has a higher P@50 value than the random baseline.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 9: Model performance against real-world exploited threats identified by Symantec and Exploit-DB. “True CVSS” refers to ranking CVEs based on actual CVSS scores in NVD. This model is only for reference and can not be used in real practice, as we do not know true CVSS scores when forecasting.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Our model identified 7 out of the top 10 threats as exploited, whereas the actual CVSS v3.0 scores identified only 1 out of the top 10 vulnerabilities as exploited.We evaluate the number of exploited threats identified within our top ranked CVEs. We observe that 7 of top 10 threats from our model were exploited in the wild. We also observe that for the actual CVSS v3.0 scores, only 1 out of the top 10 vulnerabilities was exploited." } ], "images": [ "scigen/train/3059.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Model performance against real-world exploited threats identified by Symantec and Exploit-DB. “True CVSS” refers to ranking CVEs based on actual CVSS scores in NVD. This model is only for reference and can not be used in real practice, as we do not know true CVSS scores when forecasting." ], "reasoning": "Our model identified 7 out of the top 10 threats as exploited, whereas the actual CVSS v3.0 scores identified only 1 out of the top 10 vulnerabilities as exploited.", "description": "We evaluate the number of exploited threats identified within our top ranked CVEs. We observe that 7 of top 10 threats from our model were exploited in the wild. We also observe that for the actual CVSS v3.0 scores, only 1 out of the top 10 vulnerabilities was exploited.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 9: Model performance against real-world exploited threats identified by Symantec and Exploit-DB. “True CVSS” refers to ranking CVEs based on actual CVSS scores in NVD. This model is only for reference and can not be used in real practice, as we do not know true CVSS scores when forecasting.' and methodically tackle the question that follows. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nHow many of the top 10 threats identified by the volume model were exploited in the wild?\n" }, { "role": "assistant", "content": "The volume model identified 60% of the top 10 threats, which means 6 out of the top 10 threats were exploited in the wild. So, the answer is {\"answer\": \"6\"}." } ], "images": [ "scigen/train/3059.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Model performance against real-world exploited threats identified by Symantec and Exploit-DB. “True CVSS” refers to ranking CVEs based on actual CVSS scores in NVD. This model is only for reference and can not be used in real practice, as we do not know true CVSS scores when forecasting." ], "reasoning": "The volume model identified 60% of the top 10 threats, which means 6 out of the top 10 threats were exploited in the wild.", "question": "How many of the top 10 threats identified by the volume model were exploited in the wild?", "answer": "So, the answer is {\"answer\": \"6\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe volume model identified more exploited threats in the top 10 than the true CVSS.\n" }, { "role": "assistant", "content": "The volume model identified 60% of the top 10 threats as exploited, while the true CVSS identified only 10%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3059.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Model performance against real-world exploited threats identified by Symantec and Exploit-DB. “True CVSS” refers to ranking CVEs based on actual CVSS scores in NVD. This model is only for reference and can not be used in real practice, as we do not know true CVSS scores when forecasting." ], "reasoning": "The volume model identified 60% of the top 10 threats as exploited, while the true CVSS identified only 10%.", "statement": "The volume model identified more exploited threats in the top 10 than the true CVSS.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title '(b) Kennard-Stone'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The means of Fedorov and Kennard-Stone results are significantly different from the baseline for most ASAP AES essay sets and training set sizes, except for the anomalous essay set 4.’s models over the baseline. To test for significance, we transform the correlations using the Fisher Z-transform before applying the two-sample t-test at the 0.001 level. The test is an approximation, as the normality assumption of the t-test does not hold, but the correlation coefficients are unimodal and the sample sizes are relatively large, so we assume that the approximation is quite good. For interpretability, the percent change is calculated using the Pearson correlation. The means of Fedorov and Kennard-Stone results are significantly different from the baseline for most ASAP AES essay sets and training set sizes Fedorov and Kennard-Stone, with the exception of the anomalous essay set 4. The k-means results are more often indistinguishable from the baseline." } ], "images": [ "scigen/train/3060.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) Kennard-Stone" ], "reasoning": "The means of Fedorov and Kennard-Stone results are significantly different from the baseline for most ASAP AES essay sets and training set sizes, except for the anomalous essay set 4.", "description": "’s models over the baseline. To test for significance, we transform the correlations using the Fisher Z-transform before applying the two-sample t-test at the 0.001 level. The test is an approximation, as the normality assumption of the t-test does not hold, but the correlation coefficients are unimodal and the sample sizes are relatively large, so we assume that the approximation is quite good. For interpretability, the percent change is calculated using the Pearson correlation. The means of Fedorov and Kennard-Stone results are significantly different from the baseline for most ASAP AES essay sets and training set sizes Fedorov and Kennard-Stone, with the exception of the anomalous essay set 4. The k-means results are more often indistinguishable from the baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. The question is related to the given table titled '(b) Kennard-Stone'.\n\nQuestion: Which essay set shows the most consistent performance across different training set sizes?\n" }, { "role": "assistant", "content": "By examining the table, essay set 2a shows relatively consistent performance across different training set sizes, with values ranging from 15 to 70. So, the answer is {\"answer\": \"2a\"}." } ], "images": [ "scigen/train/3060.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) Kennard-Stone" ], "reasoning": "By examining the table, essay set 2a shows relatively consistent performance across different training set sizes, with values ranging from 15 to 70.", "question": "Which essay set shows the most consistent performance across different training set sizes?", "answer": "So, the answer is {\"answer\": \"2a\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nEssay set 4 has the most negative values across different training set sizes.\n" }, { "role": "assistant", "content": "By examining the table, essay set 4 has several negative values, specifically -3, -2, and -1, which are more frequent compared to other sets. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3060.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) Kennard-Stone" ], "reasoning": "By examining the table, essay set 4 has several negative values, specifically -3, -2, and -1, which are more frequent compared to other sets.", "statement": "Essay set 4 has the most negative values across different training set sizes.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: ASAP AES rubric'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The ASAP AES competition data set includes essay sets with scores ranging from minimum to maximum values, with essay set 7 having scores between 2 and 30.An AWE data set consists of some number of written responses and a set of scores for each response. In our experiments we use the industry track data from the Automated Student Assessment Prize (ASAP) Automated Essay Scoring (AES) competition sponsored by the Hewlett Foundation held in 2012 It contains training and test sets numbered 1 through 8. The target variable of the data sets is the score or sum of scores that an essay was given by one or more human raters. Essay set 2 has two target variables, which we identify as 2a and 2b depending on which target variable the model was trained to predict. For all essay sets the target variable includes all integers between the minimum and maximum scores. The target variable for essay set 7, for example, is the integers 2, 3, …, 30." } ], "images": [ "scigen/train/3061.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: ASAP AES rubric" ], "reasoning": "The ASAP AES competition data set includes essay sets with scores ranging from minimum to maximum values, with essay set 7 having scores between 2 and 30.", "description": "An AWE data set consists of some number of written responses and a set of scores for each response. In our experiments we use the industry track data from the Automated Student Assessment Prize (ASAP) Automated Essay Scoring (AES) competition sponsored by the Hewlett Foundation held in 2012 It contains training and test sets numbered 1 through 8. The target variable of the data sets is the score or sum of scores that an essay was given by one or more human raters. Essay set 2 has two target variables, which we identify as 2a and 2b depending on which target variable the model was trained to predict. For all essay sets the target variable includes all integers between the minimum and maximum scores. The target variable for essay set 7, for example, is the integers 2, 3, …, 30.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}. The question is related to the given table titled 'Table 1: ASAP AES rubric'.\n\nQuestion: What is the maximum score range for any essay set in the ASAP AES competition data?\n" }, { "role": "assistant", "content": "By examining the 'Range' column in the table, the maximum score range is found in essay set 8, which is 0-60. So, the answer is {\"answer\": \"60\"}." } ], "images": [ "scigen/train/3061.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: ASAP AES rubric" ], "reasoning": "By examining the 'Range' column in the table, the maximum score range is found in essay set 8, which is 0-60.", "question": "What is the maximum score range for any essay set in the ASAP AES competition data?", "answer": "So, the answer is {\"answer\": \"60\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Essay set 5 has a maximum score of 4. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nEssay set 5 has a maximum score of 4.\n" }, { "role": "assistant", "content": "The table shows that the range for essay set 5 is 0-4, confirming that the maximum score is 4. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3061.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: ASAP AES rubric" ], "reasoning": "The table shows that the range for essay set 5 is 0-4, confirming that the maximum score is 4.", "statement": "Essay set 5 has a maximum score of 4.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: ASAP AES data summary'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The average number of words in each essay set varies, with response-to-source sets having notably lower average lengths.While relatively small, these test sets are nonetheless sufficient to ensure that the performance we report in the results section approaches the performance one might expect on a much larger sample. The same table shows the rounded average number of words in each essay set. A peculiarity of these sets is that in some cases the average length is quite low. This is true for all of the response-to-source sets, suggesting that students write less when performing a more focused task." } ], "images": [ "scigen/train/3062.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: ASAP AES data summary" ], "reasoning": "The average number of words in each essay set varies, with response-to-source sets having notably lower average lengths.", "description": "While relatively small, these test sets are nonetheless sufficient to ensure that the performance we report in the results section approaches the performance one might expect on a much larger sample. The same table shows the rounded average number of words in each essay set. A peculiarity of these sets is that in some cases the average length is quite low. This is true for all of the response-to-source sets, suggesting that students write less when performing a more focused task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: ASAP AES data summary'.\n\nQuestion: Which set has the highest average number of words and how many words is it?\n" }, { "role": "assistant", "content": "To find the set with the highest average number of words, compare the 'Avg. Words' column for all sets. Set 8 has the highest average number of words at 636. So, the answer is {\"answer\": \"Set 8, 636\"}." } ], "images": [ "scigen/train/3062.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: ASAP AES data summary" ], "reasoning": "To find the set with the highest average number of words, compare the 'Avg. Words' column for all sets. Set 8 has the highest average number of words at 636.", "question": "Which set has the highest average number of words and how many words is it?", "answer": "So, the answer is {\"answer\": \"Set 8, 636\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Set 4 has the lowest average number of words., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nSet 4 has the lowest average number of words.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Avg. Words' column for all sets. Set 4 has the lowest average number of words at 96. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3062.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: ASAP AES data summary" ], "reasoning": "To verify this statement, compare the 'Avg. Words' column for all sets. Set 4 has the lowest average number of words at 96.", "statement": "Set 4 has the lowest average number of words.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title '(a) Fedorov', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The means of Fedorov and Kennard-Stone results are significantly different from the baseline for most ASAP AES essay sets and training set sizes, except for essay set 4.’s models over the baseline. To test for significance, we transform the correlations using the Fisher Z-transform before applying the two-sample t-test at the 0.001 level. The test is an approximation, as the normality assumption of the t-test does not hold, but the correlation coefficients are unimodal and the sample sizes are relatively large, so we assume that the approximation is quite good. For interpretability, the percent change is calculated using the Pearson correlation. The means of Fedorov and Kennard-Stone results are significantly different from the baseline for most ASAP AES essay sets and training set sizes Fedorov and Kennard-Stone, with the exception of the anomalous essay set 4. The k-means results are more often indistinguishable from the baseline." } ], "images": [ "scigen/train/3063.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) Fedorov" ], "reasoning": "The means of Fedorov and Kennard-Stone results are significantly different from the baseline for most ASAP AES essay sets and training set sizes, except for essay set 4.", "description": "’s models over the baseline. To test for significance, we transform the correlations using the Fisher Z-transform before applying the two-sample t-test at the 0.001 level. The test is an approximation, as the normality assumption of the t-test does not hold, but the correlation coefficients are unimodal and the sample sizes are relatively large, so we assume that the approximation is quite good. For interpretability, the percent change is calculated using the Pearson correlation. The means of Fedorov and Kennard-Stone results are significantly different from the baseline for most ASAP AES essay sets and training set sizes Fedorov and Kennard-Stone, with the exception of the anomalous essay set 4. The k-means results are more often indistinguishable from the baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of '(a) Fedorov', answer the following question. Give detailed solution consisting of each step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich essay set has results that are not significantly different from the baseline for most training set sizes?\n" }, { "role": "assistant", "content": "According to the description, essay set 4 is the exception where the means of Fedorov and Kennard-Stone results are not significantly different from the baseline for most training set sizes. So, the answer is {\"answer\": \"4\"}." } ], "images": [ "scigen/train/3063.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) Fedorov" ], "reasoning": "According to the description, essay set 4 is the exception where the means of Fedorov and Kennard-Stone results are not significantly different from the baseline for most training set sizes.", "question": "Which essay set has results that are not significantly different from the baseline for most training set sizes?", "answer": "So, the answer is {\"answer\": \"4\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe k-means results are often significantly different from the baseline.\n" }, { "role": "assistant", "content": "The description states that the k-means results are more often indistinguishable from the baseline, which refutes the statement. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3063.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) Fedorov" ], "reasoning": "The description states that the k-means results are more often indistinguishable from the baseline, which refutes the statement.", "statement": "The k-means results are often significantly different from the baseline.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title '(c) K-means'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The means of Fedorov and Kennard-Stone results are significantly different from the baseline for most ASAP AES essay sets and training set sizes, except for the anomalous essay set 4.’s models over the baseline. To test for significance, we transform the correlations using the Fisher Z-transform before applying the two-sample t-test at the 0.001 level. The test is an approximation, as the normality assumption of the t-test does not hold, but the correlation coefficients are unimodal and the sample sizes are relatively large, so we assume that the approximation is quite good. For interpretability, the percent change is calculated using the Pearson correlation. The means of Fedorov and Kennard-Stone results are significantly different from the baseline for most ASAP AES essay sets and training set sizes Fedorov and Kennard-Stone, with the exception of the anomalous essay set 4. The k-means results are more often indistinguishable from the baseline." } ], "images": [ "scigen/train/3064.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(c) K-means" ], "reasoning": "The means of Fedorov and Kennard-Stone results are significantly different from the baseline for most ASAP AES essay sets and training set sizes, except for the anomalous essay set 4.", "description": "’s models over the baseline. To test for significance, we transform the correlations using the Fisher Z-transform before applying the two-sample t-test at the 0.001 level. The test is an approximation, as the normality assumption of the t-test does not hold, but the correlation coefficients are unimodal and the sample sizes are relatively large, so we assume that the approximation is quite good. For interpretability, the percent change is calculated using the Pearson correlation. The means of Fedorov and Kennard-Stone results are significantly different from the baseline for most ASAP AES essay sets and training set sizes Fedorov and Kennard-Stone, with the exception of the anomalous essay set 4. The k-means results are more often indistinguishable from the baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of '(c) K-means', answer the question below by showing each progressive step toward the answer. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich essay set has the highest score at m=100?\n" }, { "role": "assistant", "content": "To find the essay set with the highest score at m=100, look at the column for m=100 and identify the highest value. The highest value is 25 in Set 8. So, the answer is {\"answer\": \"Set 8\"}." } ], "images": [ "scigen/train/3064.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(c) K-means" ], "reasoning": "To find the essay set with the highest score at m=100, look at the column for m=100 and identify the highest value. The highest value is 25 in Set 8.", "question": "Which essay set has the highest score at m=100?", "answer": "So, the answer is {\"answer\": \"Set 8\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe score for Set 3 at m=50 is higher than the score for Set 4 at m=50.\n" }, { "role": "assistant", "content": "To verify this statement, compare the scores for Set 3 and Set 4 at m=50. Set 3 has a score of 3, while Set 4 has a score of 8. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3064.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(c) K-means" ], "reasoning": "To verify this statement, compare the scores for Set 3 and Set 4 at m=50. Set 3 has a score of 3, while Set 4 has a score of 8. Therefore, the statement is refuted.", "statement": "The score for Set 3 at m=50 is higher than the score for Set 4 at m=50.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title '(a) YouCook2', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "AVLnet significantly improves performance on YouCook2 for both video clip and language retrieval tasks, showing a substantial increase in R@10 over previous models.AVLnet outperforms all prior models on YouCook2 (zero-shot and fine-tune), with a 11.8% and 27.7% absolute increase in performance at R@10 over the previous state-of-the-art for the video clip and language retrieval tasks respectively. Overall, our results indicate that AVLnet has learned powerful language representations despite never seeing text and only using raw audio." } ], "images": [ "scigen/train/3065.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) YouCook2" ], "reasoning": "AVLnet significantly improves performance on YouCook2 for both video clip and language retrieval tasks, showing a substantial increase in R@10 over previous models.", "description": "AVLnet outperforms all prior models on YouCook2 (zero-shot and fine-tune), with a 11.8% and 27.7% absolute increase in performance at R@10 over the previous state-of-the-art for the video clip and language retrieval tasks respectively. Overall, our results indicate that AVLnet has learned powerful language representations despite never seeing text and only using raw audio.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to '(a) YouCook2' to sequentially solve the question presented below. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the R@10 performance of AVLnet on YouCook2 for video clip retrieval when trained with HT100M + YC2?\n" }, { "role": "assistant", "content": "To find the R@10 performance of AVLnet on YouCook2 for video clip retrieval when trained with HT100M + YC2, look at the row 'Ours, AVLnet' and the column 'Video Clip Retrieval - YouCook2 R@10'. The value is 63.0. So, the answer is {\"answer\": \"63.0\"}." } ], "images": [ "scigen/train/3065.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) YouCook2" ], "reasoning": "To find the R@10 performance of AVLnet on YouCook2 for video clip retrieval when trained with HT100M + YC2, look at the row 'Ours, AVLnet' and the column 'Video Clip Retrieval - YouCook2 R@10'. The value is 63.0.", "question": "What is the R@10 performance of AVLnet on YouCook2 for video clip retrieval when trained with HT100M + YC2?", "answer": "So, the answer is {\"answer\": \"63.0\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence AVLnet achieves an R@10 of 72.5 for language retrieval on YouCook2 when trained with HT100M + YC2. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nAVLnet achieves an R@10 of 72.5 for language retrieval on YouCook2 when trained with HT100M + YC2.\n" }, { "role": "assistant", "content": "To verify this statement, look at the row 'Ours, AVLnet' and the column 'Language Retrieval - YouCook2 R@10'. The value is 72.5, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3065.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) YouCook2" ], "reasoning": "To verify this statement, look at the row 'Ours, AVLnet' and the column 'Language Retrieval - YouCook2 R@10'. The value is 72.5, which matches the statement.", "statement": "AVLnet achieves an R@10 of 72.5 for language retrieval on YouCook2 when trained with HT100M + YC2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title '(b) MSR-VTT', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "AVLnet significantly improves performance on YouCook2, achieving a 11.8% and 27.7% increase in R@10 for video clip and language retrieval tasks respectively.AVLnet outperforms all prior models on YouCook2 (zero-shot and fine-tune), with a 11.8% and 27.7% absolute increase in performance at R@10 over the previous state-of-the-art for the video clip and language retrieval tasks respectively. Overall, our results indicate that AVLnet has learned powerful language representations despite never seeing text and only using raw audio." } ], "images": [ "scigen/train/3066.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) MSR-VTT" ], "reasoning": "AVLnet significantly improves performance on YouCook2, achieving a 11.8% and 27.7% increase in R@10 for video clip and language retrieval tasks respectively.", "description": "AVLnet outperforms all prior models on YouCook2 (zero-shot and fine-tune), with a 11.8% and 27.7% absolute increase in performance at R@10 over the previous state-of-the-art for the video clip and language retrieval tasks respectively. Overall, our results indicate that AVLnet has learned powerful language representations despite never seeing text and only using raw audio.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of '(b) MSR-VTT'. Solve the following question based on the table.\n\nQuestion: What is the R@10 performance of AVLnet on the video clip retrieval task using HT100M + MSR-VTT?\nLet's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the R@10 performance of AVLnet on the video clip retrieval task using HT100M + MSR-VTT, locate the row 'Ours, AVLnet' and the column 'Video Clip Retrieval - MSR-VTT R@10'. The value is 51.0. So, the answer is {\"answer\": \"51.0\"}" } ], "images": [ "scigen/train/3066.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) MSR-VTT" ], "reasoning": "To find the R@10 performance of AVLnet on the video clip retrieval task using HT100M + MSR-VTT, locate the row 'Ours, AVLnet' and the column 'Video Clip Retrieval - MSR-VTT R@10'. The value is 51.0.", "question": "What is the R@10 performance of AVLnet on the video clip retrieval task using HT100M + MSR-VTT?", "answer": "So, the answer is {\"answer\": \"51.0\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nAVLnet achieves the highest R@10 performance on the language retrieval task using HT100M + MSR-VTT.\n" }, { "role": "assistant", "content": "To verify if AVLnet achieves the highest R@10 performance on the language retrieval task using HT100M + MSR-VTT, compare the R@10 values in the 'Language Retrieval - MSR-VTT R@10' column. AVLnet has a value of 54.9, which is not the highest as 'Ours, AVLnet-Text' has a higher value of 65.2. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3066.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) MSR-VTT" ], "reasoning": "To verify if AVLnet achieves the highest R@10 performance on the language retrieval task using HT100M + MSR-VTT, compare the R@10 values in the 'Language Retrieval - MSR-VTT R@10' column. AVLnet has a value of 54.9, which is not the highest as 'Ours, AVLnet-Text' has a higher value of 65.2.", "statement": "AVLnet achieves the highest R@10 performance on the language retrieval task using HT100M + MSR-VTT.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title '(c) LSMDC'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "AVLnet significantly outperforms previous models on YouCook2 in both zero-shot and fine-tune settings, showing a substantial increase in R@10 performance for video clip and language retrieval tasks.AVLnet outperforms all prior models on YouCook2 (zero-shot and fine-tune), with a 11.8% and 27.7% absolute increase in performance at R@10 over the previous state-of-the-art for the video clip and language retrieval tasks respectively. Overall, our results indicate that AVLnet has learned powerful language representations despite never seeing text and only using raw audio." } ], "images": [ "scigen/train/3067.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(c) LSMDC" ], "reasoning": "AVLnet significantly outperforms previous models on YouCook2 in both zero-shot and fine-tune settings, showing a substantial increase in R@10 performance for video clip and language retrieval tasks.", "description": "AVLnet outperforms all prior models on YouCook2 (zero-shot and fine-tune), with a 11.8% and 27.7% absolute increase in performance at R@10 over the previous state-of-the-art for the video clip and language retrieval tasks respectively. Overall, our results indicate that AVLnet has learned powerful language representations despite never seeing text and only using raw audio.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled '(c) LSMDC' and methodically tackle the question that follows. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the R@10 performance of AVLnet-Text on the Language Retrieval task using HT100M + LSMDC training set?\n" }, { "role": "assistant", "content": "To find the R@10 performance of AVLnet-Text on the Language Retrieval task, locate the row labeled 'Ours, AVLnet-Text' and the column labeled 'Language Retrieval - LSMDC R@10'. The value is 47.6. So, the answer is {\"answer\": \"47.6\"}" } ], "images": [ "scigen/train/3067.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(c) LSMDC" ], "reasoning": "To find the R@10 performance of AVLnet-Text on the Language Retrieval task, locate the row labeled 'Ours, AVLnet-Text' and the column labeled 'Language Retrieval - LSMDC R@10'. The value is 47.6.", "question": "What is the R@10 performance of AVLnet-Text on the Language Retrieval task using HT100M + LSMDC training set?", "answer": "So, the answer is {\"answer\": \"47.6\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nAVLnet-Text achieves the highest R@10 performance on the Language Retrieval task using HT100M + LSMDC training set.\n" }, { "role": "assistant", "content": "The table shows that AVLnet-Text has an R@10 performance of 47.6 on the Language Retrieval task using HT100M + LSMDC training set, which is the highest value in the column. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3067.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(c) LSMDC" ], "reasoning": "The table shows that AVLnet-Text has an R@10 performance of 47.6 on the Language Retrieval task using HT100M + LSMDC training set, which is the highest value in the column.", "statement": "AVLnet-Text achieves the highest R@10 performance on the Language Retrieval task using HT100M + LSMDC training set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Analysis of AVLnet and AVLnet-Text design choices. Performance of each compared model is reported as video clip retrieval R@10.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table compares the performance of different configurations of AVLnet and AVLnet-Text on video clip retrieval across three datasets, showing the impact of text addition, clip sampling methods, and loss functions.For AVLnet, we investigate the effect of adding text to the audio-visual model during evaluation and fine-tuning, changing the HowTo100M clip sampling method, and varying the loss function. For AVLnet-Text, we investigate the effect of using independent audio and text branches as compared to our language fusion technique. We report each model’s video clip retrieval R@10 on all three evaluative datasets in the zero-shot setting and after fine-tuning. Adding text from the downstream datasets. We evaluate the performance of AVLnet trained on audio and video from HowTo100M and fine-tuned/evaluated on audio, video, and text from YouCook2, MSR-VTT, and LSMDC. This experiment represents the scenario where obtaining text annotations during training is expensive, but text exists or can be obtained for smaller evaluative datasets or real world applications. M is beneficial. Further, this result suggests that AVLnet learns language representations from speech, not just natural sounds or voice characteristics. HowTo100M clip selection. that used video clips segmented at the ASR speech boundaries. Loss functions. We find the MMS loss and the NCE loss to outperform the Max-Margin Ranking loss, prompting us to use the MMS loss in our experiments. Processing text with an independent branch. The MMS loss is applied over each of the modality pairs (audio-video, audio-text, and video-text), and the branches are jointly optimized through the sum of these three losses. During evaluation, we use the sum of the audio and text embedding vectors to retrieve video clips." } ], "images": [ "scigen/train/3068.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Analysis of AVLnet and AVLnet-Text design choices. Performance of each compared model is reported as video clip retrieval R@10." ], "reasoning": "The table compares the performance of different configurations of AVLnet and AVLnet-Text on video clip retrieval across three datasets, showing the impact of text addition, clip sampling methods, and loss functions.", "description": "For AVLnet, we investigate the effect of adding text to the audio-visual model during evaluation and fine-tuning, changing the HowTo100M clip sampling method, and varying the loss function. For AVLnet-Text, we investigate the effect of using independent audio and text branches as compared to our language fusion technique. We report each model’s video clip retrieval R@10 on all three evaluative datasets in the zero-shot setting and after fine-tuning. Adding text from the downstream datasets. We evaluate the performance of AVLnet trained on audio and video from HowTo100M and fine-tuned/evaluated on audio, video, and text from YouCook2, MSR-VTT, and LSMDC. This experiment represents the scenario where obtaining text annotations during training is expensive, but text exists or can be obtained for smaller evaluative datasets or real world applications. M is beneficial. Further, this result suggests that AVLnet learns language representations from speech, not just natural sounds or voice characteristics. HowTo100M clip selection. that used video clips segmented at the ASR speech boundaries. Loss functions. We find the MMS loss and the NCE loss to outperform the Max-Margin Ranking loss, prompting us to use the MMS loss in our experiments. Processing text with an independent branch. The MMS loss is applied over each of the modality pairs (audio-video, audio-text, and video-text), and the branches are jointly optimized through the sum of these three losses. During evaluation, we use the sum of the audio and text embedding vectors to retrieve video clips.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Analysis of AVLnet and AVLnet-Text design choices. Performance of each compared model is reported as video clip retrieval R@10.', and sequentially solve the question, writing out each step of your reasoning process. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich configuration achieved the highest video clip retrieval R@10 score on the YouCook2 Fine-Tune dataset?\n" }, { "role": "assistant", "content": "By examining the table, the highest score on the YouCook2 Fine-Tune dataset is 71.5, achieved by the AVLnet-Text Baseline with Audio & Text Branch Fusion. So, the answer is {\"answer\": \"AVLnet-Text Baseline with Audio & Text Branch Fusion\"}" } ], "images": [ "scigen/train/3068.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Analysis of AVLnet and AVLnet-Text design choices. Performance of each compared model is reported as video clip retrieval R@10." ], "reasoning": "By examining the table, the highest score on the YouCook2 Fine-Tune dataset is 71.5, achieved by the AVLnet-Text Baseline with Audio & Text Branch Fusion.", "question": "Which configuration achieved the highest video clip retrieval R@10 score on the YouCook2 Fine-Tune dataset?", "answer": "So, the answer is {\"answer\": \"AVLnet-Text Baseline with Audio & Text Branch Fusion\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The NCE Loss configuration outperformed the Max-Margin Ranking Loss configuration on the MSR-VTT Zero-Shot dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe NCE Loss configuration outperformed the Max-Margin Ranking Loss configuration on the MSR-VTT Zero-Shot dataset.\n" }, { "role": "assistant", "content": "The table shows that the NCE Loss configuration achieved a score of 40.3 on the MSR-VTT Zero-Shot dataset, while the Max-Margin Ranking Loss configuration achieved a score of 29.8. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3068.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Analysis of AVLnet and AVLnet-Text design choices. Performance of each compared model is reported as video clip retrieval R@10." ], "reasoning": "The table shows that the NCE Loss configuration achieved a score of 40.3 on the MSR-VTT Zero-Shot dataset, while the Max-Margin Ranking Loss configuration achieved a score of 29.8.", "statement": "The NCE Loss configuration outperformed the Max-Margin Ranking Loss configuration on the MSR-VTT Zero-Shot dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE I: Various evaluation matrix comparison of models'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The proposed model outperforms GNMT in BLEU score and TER, while GNMT's perplexity score is not provided.As seen in figures, in most of the cases our model produces comparable result with human translator. Result for BLEU score for our model and Google’s" } ], "images": [ "scigen/train/3069.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Various evaluation matrix comparison of models" ], "reasoning": "The proposed model outperforms GNMT in BLEU score and TER, while GNMT's perplexity score is not provided.", "description": "As seen in figures, in most of the cases our model produces comparable result with human translator. Result for BLEU score for our model and Google’s", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'TABLE I: Various evaluation matrix comparison of models' in mind, break down and resolve the question below step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhat is the difference in BLEU score between the proposed model and GNMT?\n" }, { "role": "assistant", "content": "The BLEU score for the proposed model is 40.33 and for GNMT is 33.66. Subtracting GNMT's score from the proposed model's score gives the difference. So, the answer is {\"answer\": \"6.67\"}." } ], "images": [ "scigen/train/3069.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Various evaluation matrix comparison of models" ], "reasoning": "The BLEU score for the proposed model is 40.33 and for GNMT is 33.66. Subtracting GNMT's score from the proposed model's score gives the difference.", "question": "What is the difference in BLEU score between the proposed model and GNMT?", "answer": "So, the answer is {\"answer\": \"6.67\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe proposed model has a lower TER score than GNMT.\n" }, { "role": "assistant", "content": "The TER score for the proposed model is 0.3913, while for GNMT it is 0.5217. Since 0.3913 is less than 0.5217, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3069.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Various evaluation matrix comparison of models" ], "reasoning": "The TER score for the proposed model is 0.3913, while for GNMT it is 0.5217. Since 0.3913 is less than 0.5217, the statement is supported.", "statement": "The proposed model has a lower TER score than GNMT.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 8: Validation BLEU score (unless stated otherwise) obtained by freezing various parts of the mBART and of adding adapters for Xx → En. ‘ft’ refers to fine-tuning, i.e. unfreezing. We reproduce some rows from Table 3 for reference.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Fine-tuning layer norm parameters and adding adapters both improve performance across all language pairs.Across all language pairs we see improvements from fine-tuning layer norm parameters over not fine-tuning them, and additional improvements from adding adapters, indicating both forms of adding flexibility are useful." } ], "images": [ "scigen/train/3070.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Validation BLEU score (unless stated otherwise) obtained by freezing various parts of the mBART and of adding adapters for Xx → En. ‘ft’ refers to fine-tuning, i.e. unfreezing. We reproduce some rows from Table 3 for reference." ], "reasoning": "Fine-tuning layer norm parameters and adding adapters both improve performance across all language pairs.", "description": "Across all language pairs we see improvements from fine-tuning layer norm parameters over not fine-tuning them, and additional improvements from adding adapters, indicating both forms of adding flexibility are useful.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 8: Validation BLEU score (unless stated otherwise) obtained by freezing various parts of the mBART and of adding adapters for Xx → En. ‘ft’ refers to fine-tuning, i.e. unfreezing. We reproduce some rows from Table 3 for reference.\nQuestion: What is the Si-En score when both the decoder is frozen and layer norm parameters are fine-tuned?\nGive a detailed response to the above question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the Si-En score when both the decoder is frozen and layer norm parameters are fine-tuned, locate the row labeled '(1): Freeze decoder + ft layer norm' and find the corresponding Si-En column value. So, the answer is {\"answer\": \"13.6\"}" } ], "images": [ "scigen/train/3070.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Validation BLEU score (unless stated otherwise) obtained by freezing various parts of the mBART and of adding adapters for Xx → En. ‘ft’ refers to fine-tuning, i.e. unfreezing. We reproduce some rows from Table 3 for reference." ], "reasoning": "To find the Si-En score when both the decoder is frozen and layer norm parameters are fine-tuned, locate the row labeled '(1): Freeze decoder + ft layer norm' and find the corresponding Si-En column value.", "question": "What is the Si-En score when both the decoder is frozen and layer norm parameters are fine-tuned?", "answer": "So, the answer is {\"answer\": \"13.6\"}" }, { "messages": [ { "role": "user", "content": "Adding decoder adapters improves the Vi-En score compared to only fine-tuning layer norm parameters. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nAdding decoder adapters improves the Vi-En score compared to only fine-tuning layer norm parameters.\n" }, { "role": "assistant", "content": "Compare the Vi-En scores for '(1): Freeze decoder + ft layer norm' and '(1) + decoder adapters'. The score improves from 30.0 to 30.3, indicating that adding decoder adapters improves the Vi-En score. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3070.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Validation BLEU score (unless stated otherwise) obtained by freezing various parts of the mBART and of adding adapters for Xx → En. ‘ft’ refers to fine-tuning, i.e. unfreezing. We reproduce some rows from Table 3 for reference." ], "reasoning": "Compare the Vi-En scores for '(1): Freeze decoder + ft layer norm' and '(1) + decoder adapters'. The score improves from 30.0 to 30.3, indicating that adding decoder adapters improves the Vi-En score.", "statement": "Adding decoder adapters improves the Vi-En score compared to only fine-tuning layer norm parameters.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 9: Validation set BLEU (unless stated otherwise) comparing freezing various parts of mBART and En-Ro mBART (pre-trained only on En and Ro data rather than 25 languages), fine-tuned on Ro → En parallel data. ‘ft’ referes to fine-tuning, i.e. unfreezing. ‘Ro-En (200k)’ refers to a random subset of the Ro-En training data of size 200k.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Fine-tuning layer norm parameters and adding adapters both improve performance across all language pairs.Across all language pairs we see improvements from fine-tuning layer norm parameters over not fine-tuning them, and additional improvements from adding adapters, indicating both forms of adding flexibility are useful." } ], "images": [ "scigen/train/3071.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Validation set BLEU (unless stated otherwise) comparing freezing various parts of mBART and En-Ro mBART (pre-trained only on En and Ro data rather than 25 languages), fine-tuned on Ro → En parallel data. ‘ft’ referes to fine-tuning, i.e. unfreezing. ‘Ro-En (200k)’ refers to a random subset of the Ro-En training data of size 200k." ], "reasoning": "Fine-tuning layer norm parameters and adding adapters both improve performance across all language pairs.", "description": "Across all language pairs we see improvements from fine-tuning layer norm parameters over not fine-tuning them, and additional improvements from adding adapters, indicating both forms of adding flexibility are useful.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 9: Validation set BLEU (unless stated otherwise) comparing freezing various parts of mBART and En-Ro mBART (pre-trained only on En and Ro data rather than 25 languages), fine-tuned on Ro → En parallel data. ‘ft’ referes to fine-tuning, i.e. unfreezing. ‘Ro-En (200k)’ refers to a random subset of the Ro-En training data of size 200k.', solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the highest score achieved for the En-Ro mBART Ro-En (608k) language pair?\n" }, { "role": "assistant", "content": "To find the highest score for the En-Ro mBART Ro-En (608k) language pair, we need to look at the column corresponding to this language pair and identify the maximum value. So, the answer is {\"answer\": \"40.6\"}." } ], "images": [ "scigen/train/3071.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Validation set BLEU (unless stated otherwise) comparing freezing various parts of mBART and En-Ro mBART (pre-trained only on En and Ro data rather than 25 languages), fine-tuned on Ro → En parallel data. ‘ft’ referes to fine-tuning, i.e. unfreezing. ‘Ro-En (200k)’ refers to a random subset of the Ro-En training data of size 200k." ], "reasoning": "To find the highest score for the En-Ro mBART Ro-En (608k) language pair, we need to look at the column corresponding to this language pair and identify the maximum value.", "question": "What is the highest score achieved for the En-Ro mBART Ro-En (608k) language pair?", "answer": "So, the answer is {\"answer\": \"40.6\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nFine-tuning the encoder-attention parameters results in the highest score for the Ro-En (200k) language pair.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the scores for the Ro-En (200k) language pair when fine-tuning the encoder-attention parameters with other scores in the same column. The highest score in the Ro-En (200k) column is 40.5, achieved by fine-tuning the encoder-attention parameters. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3071.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Validation set BLEU (unless stated otherwise) comparing freezing various parts of mBART and En-Ro mBART (pre-trained only on En and Ro data rather than 25 languages), fine-tuned on Ro → En parallel data. ‘ft’ referes to fine-tuning, i.e. unfreezing. ‘Ro-En (200k)’ refers to a random subset of the Ro-En training data of size 200k." ], "reasoning": "To verify this statement, we need to compare the scores for the Ro-En (200k) language pair when fine-tuning the encoder-attention parameters with other scores in the same column. The highest score in the Ro-En (200k) column is 40.5, achieved by fine-tuning the encoder-attention parameters.", "statement": "Fine-tuning the encoder-attention parameters results in the highest score for the Ro-En (200k) language pair.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'TABLE II: Generalization errors with 3-middle frames of four algorithms per phoneme : SVM-RBF, Adaboost with SVM, Decision Tree C4.5 and Adaboost with Decision Tree C4.5'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "AdaboostSVM performs similarly or worse than SVM-RBF due to strong SVM classifiers focusing too much on hard samples, leading to correlated errors and performance degradation.We observe that the AdaboostSVM performs similarly or worse than the single SVM-RBF. We think that forcing the strong SVM classifiers (SVM with its best parameters) to concentrate on the very hard samples with too much emphasis is the cause of performance degradation in AdaboostSVM algorithm. Furthermore, for SVM we set a small value of γ and also the most suitable for our data (γ=0.008), which make the SVM classifier stronger. Thus, Adaboost become inefficient because the errors of these component classifiers are highly correlated. Hence, the use of a suitable gamma for SVM component classifier un Adaboost leads to lower the performance of AdaboostSVM for phoneme recognition." } ], "images": [ "scigen/train/3072.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Generalization errors with 3-middle frames of four algorithms per phoneme : SVM-RBF, Adaboost with SVM, Decision Tree C4.5 and Adaboost with Decision Tree C4.5" ], "reasoning": "AdaboostSVM performs similarly or worse than SVM-RBF due to strong SVM classifiers focusing too much on hard samples, leading to correlated errors and performance degradation.", "description": "We observe that the AdaboostSVM performs similarly or worse than the single SVM-RBF. We think that forcing the strong SVM classifiers (SVM with its best parameters) to concentrate on the very hard samples with too much emphasis is the cause of performance degradation in AdaboostSVM algorithm. Furthermore, for SVM we set a small value of γ and also the most suitable for our data (γ=0.008), which make the SVM classifier stronger. Thus, Adaboost become inefficient because the errors of these component classifiers are highly correlated. Hence, the use of a suitable gamma for SVM component classifier un Adaboost leads to lower the performance of AdaboostSVM for phoneme recognition.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: TABLE II: Generalization errors with 3-middle frames of four algorithms per phoneme : SVM-RBF, Adaboost with SVM, Decision Tree C4.5 and Adaboost with Decision Tree C4.5\nQuestion: Which classifier has the highest average performance?\nSolve the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the classifier with the highest average performance, compare the average values for each classifier in the table. C4.5 has the highest average performance with a value of 49.44. So, the answer is {\"answer\": \"C4.5\"}." } ], "images": [ "scigen/train/3072.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Generalization errors with 3-middle frames of four algorithms per phoneme : SVM-RBF, Adaboost with SVM, Decision Tree C4.5 and Adaboost with Decision Tree C4.5" ], "reasoning": "To find the classifier with the highest average performance, compare the average values for each classifier in the table. C4.5 has the highest average performance with a value of 49.44.", "question": "Which classifier has the highest average performance?", "answer": "So, the answer is {\"answer\": \"C4.5\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim AdaboostSVM outperforms SVM in recognizing Nasal phonemes., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nAdaboostSVM outperforms SVM in recognizing Nasal phonemes.\n" }, { "role": "assistant", "content": "The table shows that AdaboostSVM has a performance of 41.57 for Nasal phonemes, while SVM has a performance of 39.46. Therefore, AdaboostSVM outperforms SVM in recognizing Nasal phonemes. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3072.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Generalization errors with 3-middle frames of four algorithms per phoneme : SVM-RBF, Adaboost with SVM, Decision Tree C4.5 and Adaboost with Decision Tree C4.5" ], "reasoning": "The table shows that AdaboostSVM has a performance of 41.57 for Nasal phonemes, while SVM has a performance of 39.46. Therefore, AdaboostSVM outperforms SVM in recognizing Nasal phonemes.", "statement": "AdaboostSVM outperforms SVM in recognizing Nasal phonemes.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Final results.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The model Mix significantly outperforms both Src and Tgt, with average increases of 4.56 points on UAS and 4.59 points on LAS, demonstrating the effectiveness of structural transfer.As shown, the model Tgt gives better averaged performance compared to Src. However, its results on French and Italian are slightly worse, which indicates that noise from translation impacts the quality of the projected treebank. The model Mix gives much better performance compared with Src, demonstrating the effectiveness of structural transfer. Mix also outperforms Tgt significantly, obtaining average increases of 80.93−76.37=4.56 points on UAS and 72.87−68.28=4.59 points on LAS, respectively. The best setting is the model Src + Mix, trained on the combined corpus of the source and code-mixed treebanks, which gives better performance than solely code-mixed treebanks. By comparing with the delexicalized model Delex, we can see that lexicalized features are highly useful for cross-lingual transfer. For the PartProj model, we conduct preliminary experiments on Swedish to tune the ratio of the projected dependencies. The results show that the difference is very small (δ=0.24 for UAS) between 0.9 to 1.0, and the performance degrades significantly as the ratio decreases below 0.9. The observation indicates that this method is probably not effective for filtering low-confidence word alignments. The final results confirm our hypothesis. One possible reason may be the unremoved target words (if the words are removed, the PartProj model with ratio 1.0 will be identical to Tgt), which have been demonstrated noisy previously tiedemann2016synthetic." } ], "images": [ "scigen/train/3073.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Final results." ], "reasoning": "The model Mix significantly outperforms both Src and Tgt, with average increases of 4.56 points on UAS and 4.59 points on LAS, demonstrating the effectiveness of structural transfer.", "description": "As shown, the model Tgt gives better averaged performance compared to Src. However, its results on French and Italian are slightly worse, which indicates that noise from translation impacts the quality of the projected treebank. The model Mix gives much better performance compared with Src, demonstrating the effectiveness of structural transfer. Mix also outperforms Tgt significantly, obtaining average increases of 80.93−76.37=4.56 points on UAS and 72.87−68.28=4.59 points on LAS, respectively. The best setting is the model Src + Mix, trained on the combined corpus of the source and code-mixed treebanks, which gives better performance than solely code-mixed treebanks. By comparing with the delexicalized model Delex, we can see that lexicalized features are highly useful for cross-lingual transfer. For the PartProj model, we conduct preliminary experiments on Swedish to tune the ratio of the projected dependencies. The results show that the difference is very small (δ=0.24 for UAS) between 0.9 to 1.0, and the performance degrades significantly as the ratio decreases below 0.9. The observation indicates that this method is probably not effective for filtering low-confidence word alignments. The final results confirm our hypothesis. One possible reason may be the unremoved target words (if the words are removed, the PartProj model with ratio 1.0 will be identical to Tgt), which have been demonstrated noisy previously tiedemann2016synthetic.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: 'Table 3: Final results.'\nQuestion: What is the average LAS score for the model Mix, and how does it compare to the average LAS score for the model Src?\n" }, { "role": "assistant", "content": "The average LAS score for the model Mix is 72.87, and for the model Src, it is 67.23. The difference is 72.87 - 67.23 = 4.59 points, indicating that Mix outperforms Src by 4.59 points on average. So, the answer is {\"answer\": \"4.59\"}" } ], "images": [ "scigen/train/3073.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Final results." ], "reasoning": "The average LAS score for the model Mix is 72.87, and for the model Src, it is 67.23. The difference is 72.87 - 67.23 = 4.59 points, indicating that Mix outperforms Src by 4.59 points on average.", "question": "What is the average LAS score for the model Mix, and how does it compare to the average LAS score for the model Src?", "answer": "So, the answer is {\"answer\": \"4.59\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The model Src + Mix has the highest UAS score for Italian., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model Src + Mix has the highest UAS score for Italian.\n" }, { "role": "assistant", "content": "The table shows that the UAS score for Italian using the model Src + Mix is 86.13, which is the highest among all models for Italian. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3073.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Final results." ], "reasoning": "The table shows that the UAS score for Italian using the model Src + Mix is 86.13, which is the highest among all models for Italian.", "statement": "The model Src + Mix has the highest UAS score for Italian.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 12: El strong matching results on the Gerbil platform when training on Micropost-train 2016. Micro and Macro F1 scores are shown. The majority of the errors are due to the newer KB version used in this dataset. We highlight in red and blue the best and second best models, respectively.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The joint EL model performs best when sufficient training data with similar characteristics or annotation schemes as the test data is available, as evidenced by the results on different datasets.At this point, our main goal was achieved: if enough training data is available with the same characteristics or annotation schemes as the test data, then our joint EL offers the best model. It is expected that such a NER system designed with a broader generalization scheme in mind would help in this case, which is confirmed by our results on different datasets." } ], "images": [ "scigen/train/3074.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 12: El strong matching results on the Gerbil platform when training on Micropost-train 2016. Micro and Macro F1 scores are shown. The majority of the errors are due to the newer KB version used in this dataset. We highlight in red and blue the best and second best models, respectively." ], "reasoning": "The joint EL model performs best when sufficient training data with similar characteristics or annotation schemes as the test data is available, as evidenced by the results on different datasets.", "description": "At this point, our main goal was achieved: if enough training data is available with the same characteristics or annotation schemes as the test data, then our joint EL offers the best model. It is expected that such a NER system designed with a broader generalization scheme in mind would help in this case, which is confirmed by our results on different datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: Table 12: El strong matching results on the Gerbil platform when training on Micropost-train 2016. Micro and Macro F1 scores are shown. The majority of the errors are due to the newer KB version used in this dataset. We highlight in red and blue the best and second best models, respectively.\nQuestion: Which model achieved the highest MicroTest score?\n" }, { "role": "assistant", "content": "To find the model with the highest MicroTest score, compare the MicroTest scores of all models listed in the table. The highest score is 77.9, achieved by FREME. So, the answer is {\"answer\": \"FREME\"}" } ], "images": [ "scigen/train/3074.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 12: El strong matching results on the Gerbil platform when training on Micropost-train 2016. Micro and Macro F1 scores are shown. The majority of the errors are due to the newer KB version used in this dataset. We highlight in red and blue the best and second best models, respectively." ], "reasoning": "To find the model with the highest MicroTest score, compare the MicroTest scores of all models listed in the table. The highest score is 77.9, achieved by FREME.", "question": "Which model achieved the highest MicroTest score?", "answer": "So, the answer is {\"answer\": \"FREME\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The model on micropost train achieved a higher MicroDev score than the best baseline. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe model on micropost train achieved a higher MicroDev score than the best baseline.\n" }, { "role": "assistant", "content": "The MicroDev score for the model on micropost train is 57.3, while the best baseline's MicroDev score is 37.3. Since 57.3 is higher than 37.3, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3074.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 12: El strong matching results on the Gerbil platform when training on Micropost-train 2016. Micro and Macro F1 scores are shown. The majority of the errors are due to the newer KB version used in this dataset. We highlight in red and blue the best and second best models, respectively." ], "reasoning": "The MicroDev score for the model on micropost train is 57.3, while the best baseline's MicroDev score is 37.3. Since 57.3 is higher than 37.3, the statement is supported.", "statement": "The model on micropost train achieved a higher MicroDev score than the best baseline.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: EL strong matching results on the Gerbil platform. Micro and Macro F1 scores are shown. We highlight the best and second best models, respectively. Training was done on AIDA-train set.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The joint EL model performs best when trained with data that has similar characteristics or annotation schemes as the test data, as confirmed by results on different datasets.At this point, our main goal was achieved: if enough training data is available with the same characteristics or annotation schemes as the test data, then our joint EL offers the best model. It is expected that such a NER system designed with a broader generalization scheme in mind would help in this case, which is confirmed by our results on different datasets." } ], "images": [ "scigen/train/3075.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: EL strong matching results on the Gerbil platform. Micro and Macro F1 scores are shown. We highlight the best and second best models, respectively. Training was done on AIDA-train set." ], "reasoning": "The joint EL model performs best when trained with data that has similar characteristics or annotation schemes as the test data, as confirmed by results on different datasets.", "description": "At this point, our main goal was achieved: if enough training data is available with the same characteristics or annotation schemes as the test data, then our joint EL offers the best model. It is expected that such a NER system designed with a broader generalization scheme in mind would help in this case, which is confirmed by our results on different datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: EL strong matching results on the Gerbil platform. Micro and Macro F1 scores are shown. We highlight the best and second best models, respectively. Training was done on AIDA-train set.'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model achieved the highest F1@MA score on the AIDA A dataset?\n" }, { "role": "assistant", "content": "To find the highest F1@MA score on the AIDA A dataset, look at the AIDA A column and identify the highest value. The highest score is 86.8, achieved by the base model. So, the answer is {\"answer\": \"base model\"}" } ], "images": [ "scigen/train/3075.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: EL strong matching results on the Gerbil platform. Micro and Macro F1 scores are shown. We highlight the best and second best models, respectively. Training was done on AIDA-train set." ], "reasoning": "To find the highest F1@MA score on the AIDA A dataset, look at the AIDA A column and identify the highest value. The highest score is 86.8, achieved by the base model.", "question": "Which model achieved the highest F1@MA score on the AIDA A dataset?", "answer": "So, the answer is {\"answer\": \"base model\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The base model + att + global achieved the highest F1@MA score on the MSNBC dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe base model + att + global achieved the highest F1@MA score on the MSNBC dataset.\n" }, { "role": "assistant", "content": "To verify this statement, look at the MSNBC column and compare the F1@MA scores. The highest score is 65.7, achieved by the base model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3075.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: EL strong matching results on the Gerbil platform. Micro and Macro F1 scores are shown. We highlight the best and second best models, respectively. Training was done on AIDA-train set." ], "reasoning": "To verify this statement, look at the MSNBC column and compare the F1@MA scores. The highest score is 65.7, achieved by the base model.", "statement": "The base model + att + global achieved the highest F1@MA score on the MSNBC dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: AIDA A dataset: Gold mentions are split by the position they appear in the p(e|m) dictionary. In each cell, the upper value is the percentage of the gold mentions that were annotated with the correct entity (recall), whereas the lower value is the percentage of gold mentions for which our system’s highest scored entity is the ground truth entity, but that might not be annotated in the end because its score is below the threshold δ.'.\n" }, { "role": "assistant", "content": "The logp(e|m) prior helps in linking unambiguous mentions but introduces noise for rare entities, making its removal beneficial but impractical.One can see that the logp(e|m) prior is very helpful for correctly linking unambiguous mentions, but is introducing noise when gold entities are not frequent. For this category of rare entities, removing this prior completely will result in a significant improvement, but this is not a practical choice since the gold entity is unknown at test time." } ], "images": [ "scigen/train/3076.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: AIDA A dataset: Gold mentions are split by the position they appear in the p(e|m) dictionary. In each cell, the upper value is the percentage of the gold mentions that were annotated with the correct entity (recall), whereas the lower value is the percentage of gold mentions for which our system’s highest scored entity is the ground truth entity, but that might not be annotated in the end because its score is below the threshold δ." ], "reasoning": "The logp(e|m) prior helps in linking unambiguous mentions but introduces noise for rare entities, making its removal beneficial but impractical.", "description": "One can see that the logp(e|m) prior is very helpful for correctly linking unambiguous mentions, but is introducing noise when gold entities are not frequent. For this category of rare entities, removing this prior completely will result in a significant improvement, but this is not a practical choice since the gold entity is unknown at test time.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: AIDA A dataset: Gold mentions are split by the position they appear in the p(e|m) dictionary. In each cell, the upper value is the percentage of the gold mentions that were annotated with the correct entity (recall), whereas the lower value is the percentage of gold mentions for which our system’s highest scored entity is the ground truth entity, but that might not be annotated in the end because its score is below the threshold δ.', unfold the steps to solve the question given next. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which model has the highest solved% for mentions with a frequency of 1?\n" }, { "role": "assistant", "content": "To find the model with the highest solved% for mentions with a frequency of 1, look at the first row under the 'solved%' column for each model. The ED Global model has the highest solved% at 98.8. So, the answer is {\"answer\": \"ED Global model\"}." } ], "images": [ "scigen/train/3076.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: AIDA A dataset: Gold mentions are split by the position they appear in the p(e|m) dictionary. In each cell, the upper value is the percentage of the gold mentions that were annotated with the correct entity (recall), whereas the lower value is the percentage of gold mentions for which our system’s highest scored entity is the ground truth entity, but that might not be annotated in the end because its score is below the threshold δ." ], "reasoning": "To find the model with the highest solved% for mentions with a frequency of 1, look at the first row under the 'solved%' column for each model. The ED Global model has the highest solved% at 98.8.", "question": "Which model has the highest solved% for mentions with a frequency of 1?", "answer": "So, the answer is {\"answer\": \"ED Global model\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The EL Base model has the lowest solved% for mentions with a frequency of 9+. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe EL Base model has the lowest solved% for mentions with a frequency of 9+.\n" }, { "role": "assistant", "content": "To verify this statement, check the 'solved%' column for mentions with a frequency of 9+ for each model. The EL Base model has a solved% of 54.8, which is not the lowest; the EL Global model has the lowest at 53.9. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3076.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: AIDA A dataset: Gold mentions are split by the position they appear in the p(e|m) dictionary. In each cell, the upper value is the percentage of the gold mentions that were annotated with the correct entity (recall), whereas the lower value is the percentage of gold mentions for which our system’s highest scored entity is the ground truth entity, but that might not be annotated in the end because its score is below the threshold δ." ], "reasoning": "To verify this statement, check the 'solved%' column for mentions with a frequency of 9+ for each model. The EL Base model has a solved% of 54.8, which is not the lowest; the EL Global model has the lowest at 53.9.", "statement": "The EL Base model has the lowest solved% for mentions with a frequency of 9+.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 11: El strong matching results on the Gerbil platform. Micro and Macro F1 scores are shown. Training was done on Gerdaq train dataset. We highlight in red and blue the best and second best models, respectively.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our joint EL model performs best when enough training data with similar characteristics or annotation schemes as the test data is available, as confirmed by results on different datasets.At this point, our main goal was achieved: if enough training data is available with the same characteristics or annotation schemes as the test data, then our joint EL offers the best model. It is expected that such a NER system designed with a broader generalization scheme in mind would help in this case, which is confirmed by our results on different datasets." } ], "images": [ "scigen/train/3077.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: El strong matching results on the Gerbil platform. Micro and Macro F1 scores are shown. Training was done on Gerdaq train dataset. We highlight in red and blue the best and second best models, respectively." ], "reasoning": "Our joint EL model performs best when enough training data with similar characteristics or annotation schemes as the test data is available, as confirmed by results on different datasets.", "description": "At this point, our main goal was achieved: if enough training data is available with the same characteristics or annotation schemes as the test data, then our joint EL offers the best model. It is expected that such a NER system designed with a broader generalization scheme in mind would help in this case, which is confirmed by our results on different datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: 'Table 11: El strong matching results on the Gerbil platform. Micro and Macro F1 scores are shown. Training was done on Gerdaq train dataset. We highlight in red and blue the best and second best models, respectively.'\nQuestion: Which model achieved the highest F1@MA score on the GERDAQ-Dev dataset?\n" }, { "role": "assistant", "content": "To find the highest F1@MA score on the GERDAQ-Dev dataset, compare the scores in the GERDAQ-Dev column. The highest score is 42.7 achieved by the model+att+global on gerdaq train. So, the answer is {\"answer\": \"model+att+global on gerdaq train\"}." } ], "images": [ "scigen/train/3077.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: El strong matching results on the Gerbil platform. Micro and Macro F1 scores are shown. Training was done on Gerdaq train dataset. We highlight in red and blue the best and second best models, respectively." ], "reasoning": "To find the highest F1@MA score on the GERDAQ-Dev dataset, compare the scores in the GERDAQ-Dev column. The highest score is 42.7 achieved by the model+att+global on gerdaq train.", "question": "Which model achieved the highest F1@MA score on the GERDAQ-Dev dataset?", "answer": "So, the answer is {\"answer\": \"model+att+global on gerdaq train\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Kea model achieved the highest F1@MA score on the DBpediaSpotlight dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the F1@MA scores in the DBpediaSpotlight column. The highest score is 34.8 achieved by the model+att+global on gerdaq train, not Kea. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3077.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: El strong matching results on the Gerbil platform. Micro and Macro F1 scores are shown. Training was done on Gerdaq train dataset. We highlight in red and blue the best and second best models, respectively." ], "reasoning": "To verify this statement, compare the F1@MA scores in the DBpediaSpotlight column. The highest score is 34.8 achieved by the model+att+global on gerdaq train, not Kea.", "statement": "The Kea model achieved the highest F1@MA score on the DBpediaSpotlight dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Fine-tuning results for our pretrained baseline (bl) and the policy gradient (pg). The colored results along the left-to-right downward diagonal are in-domain results, dark red being the supervised results and light green the policy gradient fine-tuned results, and each pair of columns compare baseline and system results for each target domain. The Averages rows presents the average out-of-domain transfer scores for each domain. Note that while the PG method has access to more data, this does not invalidate the comparison, seeing that the additional data is relatively easy to obtain in an applied setting.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Policy gradient fine-tuning generally improves performance in domain transfer settings compared to zero-shot transfer baselines, with notable improvements in the attraction domain.We also report the average out-of-domain accuracies for each domain, to illustrate how policy gradient training in general performs compared to the baseline. The table show the performance of transferring from each domain to all other domains. From the results we observe that in almost all domain transfer settings, with the exception of restaurant to attraction, we get a consistent increase in performance when applying policy gradient fine-tuning, compared to the zero-shot transfer baselines. In some instances we also see an increase in performance from further fine-tuning a model after turn-level supervision convergence using only the dialogue-level reward feedback. In the case of attraction, we are even able to increase the accuracy by a large margin using in-domain policy gradient fine-tuning. On average, we see relative improvements of the accuracy, ranging from 0.03 to 0.2, when applying our proposed method of fine-tuning for DST domain transfer." } ], "images": [ "scigen/train/3078.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Fine-tuning results for our pretrained baseline (bl) and the policy gradient (pg). The colored results along the left-to-right downward diagonal are in-domain results, dark red being the supervised results and light green the policy gradient fine-tuned results, and each pair of columns compare baseline and system results for each target domain. The Averages rows presents the average out-of-domain transfer scores for each domain. Note that while the PG method has access to more data, this does not invalidate the comparison, seeing that the additional data is relatively easy to obtain in an applied setting." ], "reasoning": "Policy gradient fine-tuning generally improves performance in domain transfer settings compared to zero-shot transfer baselines, with notable improvements in the attraction domain.", "description": "We also report the average out-of-domain accuracies for each domain, to illustrate how policy gradient training in general performs compared to the baseline. The table show the performance of transferring from each domain to all other domains. From the results we observe that in almost all domain transfer settings, with the exception of restaurant to attraction, we get a consistent increase in performance when applying policy gradient fine-tuning, compared to the zero-shot transfer baselines. In some instances we also see an increase in performance from further fine-tuning a model after turn-level supervision convergence using only the dialogue-level reward feedback. In the case of attraction, we are even able to increase the accuracy by a large margin using in-domain policy gradient fine-tuning. On average, we see relative improvements of the accuracy, ranging from 0.03 to 0.2, when applying our proposed method of fine-tuning for DST domain transfer.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 2: Fine-tuning results for our pretrained baseline (bl) and the policy gradient (pg). The colored results along the left-to-right downward diagonal are in-domain results, dark red being the supervised results and light green the policy gradient fine-tuned results, and each pair of columns compare baseline and system results for each target domain. The Averages rows presents the average out-of-domain transfer scores for each domain. Note that while the PG method has access to more data, this does not invalidate the comparison, seeing that the additional data is relatively easy to obtain in an applied setting.' in mind, break down and resolve the question below step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the average accuracy improvement when applying policy gradient fine-tuning for DST domain transfer?\n" }, { "role": "assistant", "content": "The table shows the average accuracies for each domain. By comparing the baseline (bl) and policy gradient (pg) columns, we can calculate the average improvement. So, the answer is {\"answer\": \"0.03 to 0.2\"}." } ], "images": [ "scigen/train/3078.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Fine-tuning results for our pretrained baseline (bl) and the policy gradient (pg). The colored results along the left-to-right downward diagonal are in-domain results, dark red being the supervised results and light green the policy gradient fine-tuned results, and each pair of columns compare baseline and system results for each target domain. The Averages rows presents the average out-of-domain transfer scores for each domain. Note that while the PG method has access to more data, this does not invalidate the comparison, seeing that the additional data is relatively easy to obtain in an applied setting." ], "reasoning": "The table shows the average accuracies for each domain. By comparing the baseline (bl) and policy gradient (pg) columns, we can calculate the average improvement.", "question": "What is the average accuracy improvement when applying policy gradient fine-tuning for DST domain transfer?", "answer": "So, the answer is {\"answer\": \"0.03 to 0.2\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nPolicy gradient fine-tuning does not improve performance in any domain transfer setting.\n" }, { "role": "assistant", "content": "The table shows that policy gradient fine-tuning improves performance in almost all domain transfer settings, except for restaurant to attraction. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3078.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Fine-tuning results for our pretrained baseline (bl) and the policy gradient (pg). The colored results along the left-to-right downward diagonal are in-domain results, dark red being the supervised results and light green the policy gradient fine-tuned results, and each pair of columns compare baseline and system results for each target domain. The Averages rows presents the average out-of-domain transfer scores for each domain. Note that while the PG method has access to more data, this does not invalidate the comparison, seeing that the additional data is relatively easy to obtain in an applied setting." ], "reasoning": "The table shows that policy gradient fine-tuning improves performance in almost all domain transfer settings, except for restaurant to attraction.", "statement": "Policy gradient fine-tuning does not improve performance in any domain transfer setting.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: Hyperparameters selected by our hyperparameter search when training SqueezeBERT with bells-and-whistles.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Distillation is disabled when α=1.0, and maximum accuracy for QNLI was achieved after just two epochs, not necessarily at the final epoch.We observe two interesting phenomena on this table. The first is regarding the use of distillation. When α=1.0, the teacher logits are ignored, and thus distillation is disabled. The second interesting phenomenon in this table is that maximum accuracy was not necessarily achieved on final epoch. For example, on QNLI, SqueezeBERT converged to its maximum development-set accuracy after just two epochs." } ], "images": [ "scigen/train/3079.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Hyperparameters selected by our hyperparameter search when training SqueezeBERT with bells-and-whistles." ], "reasoning": "Distillation is disabled when α=1.0, and maximum accuracy for QNLI was achieved after just two epochs, not necessarily at the final epoch.", "description": "We observe two interesting phenomena on this table. The first is regarding the use of distillation. When α=1.0, the teacher logits are ignored, and thus distillation is disabled. The second interesting phenomenon in this table is that maximum accuracy was not necessarily achieved on final epoch. For example, on QNLI, SqueezeBERT converged to its maximum development-set accuracy after just two epochs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: Hyperparameters selected by our hyperparameter search when training SqueezeBERT with bells-and-whistles.', answer the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nHow many epochs did it take for SqueezeBERT to achieve maximum development-set accuracy on QNLI?\n" }, { "role": "assistant", "content": "According to the description, SqueezeBERT achieved its maximum development-set accuracy on QNLI after just two epochs. So, the answer is {\"answer\": \"2\"}" } ], "images": [ "scigen/train/3079.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Hyperparameters selected by our hyperparameter search when training SqueezeBERT with bells-and-whistles." ], "reasoning": "According to the description, SqueezeBERT achieved its maximum development-set accuracy on QNLI after just two epochs.", "question": "How many epochs did it take for SqueezeBERT to achieve maximum development-set accuracy on QNLI?", "answer": "So, the answer is {\"answer\": \"2\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Distillation is enabled when α=0.8., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nDistillation is enabled when α=0.8.\n" }, { "role": "assistant", "content": "The description states that distillation is disabled when α=1.0, implying that distillation is enabled when α is less than 1.0. The table shows α=0.8 for several tasks, indicating that distillation is enabled. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3079.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Hyperparameters selected by our hyperparameter search when training SqueezeBERT with bells-and-whistles." ], "reasoning": "The description states that distillation is disabled when α=1.0, implying that distillation is enabled when α is less than 1.0. The table shows α=0.8 for several tasks, indicating that distillation is enabled.", "statement": "Distillation is enabled when α=0.8.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Performance comparison of the Intelligent Voice speaker recognition system with various analysis on the development protocol of NIST SRE 2016.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The fusion of MFCC and PLP resulted in a relative improvement of almost 10% compared to MFCC alone in terms of both Cdet and Cmindet. LDA outperforms NDA in the case of PLP, but NDA resulted in better performance in fusion.In this section we present the results obtained on the protocol provided by NIST on the development set which is supposed to mirror that of evaluation set. The first part of the table indicates the result obtained by the primary system. As can be seen, the fusion of MFCC and PLP (a simple sum of both MFCC and PLP scores) resulted in a relative improvement of almost 10%, as compared to MFCC alone, in terms of both Cdet and Cmindet. In order to quantify the contribution of the different system components we have defined different scenarios. In scenario A, we have analysed the effect of using LDA instead of NDA. As can be seen from the results, LDA outperforms NDA in the case of PLP, however, in fusion we can see that NDA resulted in better performance in terms of the primary metric. Results indicate superior performance using this technique. In scenario C, we investigated the effects of language normalization on the performance of the system. If we replace LN-LDA with simple LDA, we can see performance degradation in MFCC as well as fusion, however, PLP seems not to be adversely affected. For our secondary submission, we incorporated a disjoint portion of the labelled development set (10 out of 20 speakers) in either LN-LDA and in-domain PLDA training. We evaluated the system on almost 6k out of 24k trials from the other portion to avoid any over-fitting, particularly important for the domain adaptation technique. This resulted in a relative improvement of 11% compared to the primary system in terms of the primary metric. However, the results can be misleading, since the recording condition may be the same for all speakers in the development set." } ], "images": [ "scigen/train/3080.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance comparison of the Intelligent Voice speaker recognition system with various analysis on the development protocol of NIST SRE 2016." ], "reasoning": "The fusion of MFCC and PLP resulted in a relative improvement of almost 10% compared to MFCC alone in terms of both Cdet and Cmindet. LDA outperforms NDA in the case of PLP, but NDA resulted in better performance in fusion.", "description": "In this section we present the results obtained on the protocol provided by NIST on the development set which is supposed to mirror that of evaluation set. The first part of the table indicates the result obtained by the primary system. As can be seen, the fusion of MFCC and PLP (a simple sum of both MFCC and PLP scores) resulted in a relative improvement of almost 10%, as compared to MFCC alone, in terms of both Cdet and Cmindet. In order to quantify the contribution of the different system components we have defined different scenarios. In scenario A, we have analysed the effect of using LDA instead of NDA. As can be seen from the results, LDA outperforms NDA in the case of PLP, however, in fusion we can see that NDA resulted in better performance in terms of the primary metric. Results indicate superior performance using this technique. In scenario C, we investigated the effects of language normalization on the performance of the system. If we replace LN-LDA with simple LDA, we can see performance degradation in MFCC as well as fusion, however, PLP seems not to be adversely affected. For our secondary submission, we incorporated a disjoint portion of the labelled development set (10 out of 20 speakers) in either LN-LDA and in-domain PLDA training. We evaluated the system on almost 6k out of 24k trials from the other portion to avoid any over-fitting, particularly important for the domain adaptation technique. This resulted in a relative improvement of 11% compared to the primary system in terms of the primary metric. However, the results can be misleading, since the recording condition may be the same for all speakers in the development set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the equalized Cdet for the primary system using fusion?\nSolve the above question based on the table titled 'Table 2: Performance comparison of the Intelligent Voice speaker recognition system with various analysis on the development protocol of NIST SRE 2016.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the equalized Cdet for the primary system using fusion as 0.6267. So, the answer is {\"answer\": \"0.6267\"}" } ], "images": [ "scigen/train/3080.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance comparison of the Intelligent Voice speaker recognition system with various analysis on the development protocol of NIST SRE 2016." ], "reasoning": "The table shows the equalized Cdet for the primary system using fusion as 0.6267.", "question": "What is the equalized Cdet for the primary system using fusion?", "answer": "So, the answer is {\"answer\": \"0.6267\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Using LDA instead of NDA in Scenario A resulted in better performance for PLP. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nUsing LDA instead of NDA in Scenario A resulted in better performance for PLP.\n" }, { "role": "assistant", "content": "The description states that LDA outperforms NDA in the case of PLP in Scenario A, and the table shows that the equalized Cdet for PLP in Scenario A is 0.7092, which is better than the primary system's PLP equalized Cdet of 0.6982. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3080.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance comparison of the Intelligent Voice speaker recognition system with various analysis on the development protocol of NIST SRE 2016." ], "reasoning": "The description states that LDA outperforms NDA in the case of PLP in Scenario A, and the table shows that the equalized Cdet for PLP in Scenario A is 0.7092, which is better than the primary system's PLP equalized Cdet of 0.6982.", "statement": "Using LDA instead of NDA in Scenario A resulted in better performance for PLP.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Dataset-specific model hyperparameters.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The model performed best with intermediate encoder layer activations as inputs to auxiliary decoders, a dropout rate of 0.3, and decaying auxiliary loss weight during training.Given the small size of the dataset compared to that in Sec. The model was especially sensitive to the auxiliary decoder hyperparameters, with the best performance coming when passing activations from intermediate layers of the encoder stack as inputs to the auxiliary decoders, using slightly more aggressive dropout of 0.3, and decaying the auxiliary loss weight over the course of training in order to encourage the model training to fit the primary S2ST task." } ], "images": [ "scigen/train/3081.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Dataset-specific model hyperparameters." ], "reasoning": "The model performed best with intermediate encoder layer activations as inputs to auxiliary decoders, a dropout rate of 0.3, and decaying auxiliary loss weight during training.", "description": "Given the small size of the dataset compared to that in Sec. The model was especially sensitive to the auxiliary decoder hyperparameters, with the best performance coming when passing activations from intermediate layers of the encoder stack as inputs to the auxiliary decoders, using slightly more aggressive dropout of 0.3, and decaying the auxiliary loss weight over the course of training in order to encourage the model training to fit the primary S2ST task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: Dataset-specific model hyperparameters.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the dropout probability used for the Fisher dataset, and how does it compare to the Conversational dataset?\n" }, { "role": "assistant", "content": "The table shows that the dropout probability for the Fisher dataset is 0.3, while for the Conversational dataset it is 0.2. This indicates that the Fisher dataset uses a slightly more aggressive dropout rate. So, the answer is {\"answer\": \"0.3\"}." } ], "images": [ "scigen/train/3081.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Dataset-specific model hyperparameters." ], "reasoning": "The table shows that the dropout probability for the Fisher dataset is 0.3, while for the Conversational dataset it is 0.2. This indicates that the Fisher dataset uses a slightly more aggressive dropout rate.", "question": "What is the dropout probability used for the Fisher dataset, and how does it compare to the Conversational dataset?", "answer": "So, the answer is {\"answer\": \"0.3\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Fisher dataset uses a dropout probability of 0.3.\n" }, { "role": "assistant", "content": "The table indicates that the dropout probability for the Fisher dataset is indeed 0.3. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3081.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Dataset-specific model hyperparameters." ], "reasoning": "The table indicates that the dropout probability for the Fisher dataset is indeed 0.3.", "statement": "The Fisher dataset uses a dropout probability of 0.3.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: Data Overview', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Event pairs are annotated by their appearance order in text and augmented with flipped-order pairs, while only main-axis events are kept.Note that in all three datasets, event pairs are always annotated by their appearance order in text, i.e. given a labeled event pair (i, j), event i always appears prior to event j in the text. Following Meng et al. , we augment event pairs with flipped-order pairs. That is, if a pair (i, j) exists, pair (j, i) is also added to our dataset with the opposite label. The authors project events on multiple axes and only keep those in the main-axis. Start-point temporal scheme is adopted when out-sourcing the annotation task, which contributes to the performance improvement of machine learning models built on this dataset ." } ], "images": [ "scigen/train/3082.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Data Overview" ], "reasoning": "Event pairs are annotated by their appearance order in text and augmented with flipped-order pairs, while only main-axis events are kept.", "description": "Note that in all three datasets, event pairs are always annotated by their appearance order in text, i.e. given a labeled event pair (i, j), event i always appears prior to event j in the text. Following Meng et al. , we augment event pairs with flipped-order pairs. That is, if a pair (i, j) exists, pair (j, i) is also added to our dataset with the opposite label. The authors project events on multiple axes and only keep those in the main-axis. Start-point temporal scheme is adopted when out-sourcing the annotation task, which contributes to the performance improvement of machine learning models built on this dataset .", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 1: Data Overview', answer the following question. Give detailed solution consisting of each step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nHow many pairs are there in the TCR dataset for the test set?\n" }, { "role": "assistant", "content": "The table shows the number of pairs for the TCR dataset in the test set as 1008. So, the answer is {\"answer\": \"1008\"}." } ], "images": [ "scigen/train/3082.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Data Overview" ], "reasoning": "The table shows the number of pairs for the TCR dataset in the test set as 1008.", "question": "How many pairs are there in the TCR dataset for the test set?", "answer": "So, the answer is {\"answer\": \"1008\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe TCR dataset has no documents in the development set.\n" }, { "role": "assistant", "content": "The table shows that the TCR dataset has 0 documents in the development set. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3082.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Data Overview" ], "reasoning": "The table shows that the TCR dataset has 0 documents in the development set.", "statement": "The TCR dataset has no documents in the development set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Model Performance Breakdown for MATRES. Again, we exclude VAGUE pairs in Micro-average score. * indicates global model outperforms local model with p-value <0.05 per McNemar’s test.'.\n" }, { "role": "assistant", "content": "Both local and structured models outperform the baseline, and the global model improves overall performance by 1.4%, which is statistically significant.Similar to TCR, both our local and structured models outperform this baseline and the global model is able to improve overall model performance by 1.4%; per McNemar’s test, this improvement is statistically significant (with p-value <0.05)." } ], "images": [ "scigen/train/3083.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Model Performance Breakdown for MATRES. Again, we exclude VAGUE pairs in Micro-average score. * indicates global model outperforms local model with p-value <0.05 per McNemar’s test." ], "reasoning": "Both local and structured models outperform the baseline, and the global model improves overall performance by 1.4%, which is statistically significant.", "description": "Similar to TCR, both our local and structured models outperform this baseline and the global model is able to improve overall model performance by 1.4%; per McNemar’s test, this improvement is statistically significant (with p-value <0.05).", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 4: Model Performance Breakdown for MATRES. Again, we exclude VAGUE pairs in Micro-average score. * indicates global model outperforms local model with p-value <0.05 per McNemar’s test.', solve the subsequent question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the micro-average F1 score of the global model, and how does it compare to the local model?\n" }, { "role": "assistant", "content": "The micro-average F1 score of the global model is 81.7, which is 1.4% higher than the local model's score of 80.3. So, the answer is {\"answer\": \"81.7\"}." } ], "images": [ "scigen/train/3083.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Model Performance Breakdown for MATRES. Again, we exclude VAGUE pairs in Micro-average score. * indicates global model outperforms local model with p-value <0.05 per McNemar’s test." ], "reasoning": "The micro-average F1 score of the global model is 81.7, which is 1.4% higher than the local model's score of 80.3.", "question": "What is the micro-average F1 score of the global model, and how does it compare to the local model?", "answer": "So, the answer is {\"answer\": \"81.7\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe global model's micro-average F1 score is higher than the local model's.\n" }, { "role": "assistant", "content": "The table shows that the global model's micro-average F1 score is 81.7, while the local model's is 80.3, confirming that the global model's score is higher. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3083.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Model Performance Breakdown for MATRES. Again, we exclude VAGUE pairs in Micro-average score. * indicates global model outperforms local model with p-value <0.05 per McNemar’s test." ], "reasoning": "The table shows that the global model's micro-average F1 score is 81.7, while the local model's is 80.3, confirming that the global model's score is higher.", "statement": "The global model's micro-average F1 score is higher than the local model's.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 7: Ablation over global constraints: Symmetry and Transitivity. Test is conducted on forward test set and both-way test set, which are denoted as −−−→Test and ←−→Test respectively. The local models trained on forward data and both-way data are denoted as →L and ↔L. Symmetry and Transitivity constraints are denoted as S and T. The results demonstrate that symmetry and transitivity constraints both improve model’s performance.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Data augmentation and explicit symmetry improve model robustness, with the proposed model showing the best performance in both forward and both-way evaluations.Impact of Implicit Symmetry (data augmentation): Examining the contrast between M1 and M3 as well as M2 and M4, we can see significant improvements in both-way evaluation despite slight performance drops in forward evaluation. These comparisons imply that data augmentation can help improve model robustness. Note that \\newcitemeng2018context leveraged this data augmentation trick in their model. Impact of Explicit Symmetry: By comparing the proposed model with M4, the consistent improvements across all datasets demonstrate the benefit of using explicit symmetry property. Model Robustness: Although M1 and M2 show competitive results when evaluated on forward test data, their performance degrade significantly in the both-way evaluation. In contrast, the proposed model achieves strong performances in both test scenarios (best F1 scores except for one), and hence proves the robustness of our proposed method." } ], "images": [ "scigen/train/3084.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Ablation over global constraints: Symmetry and Transitivity. Test is conducted on forward test set and both-way test set, which are denoted as −−−→Test and ←−→Test respectively. The local models trained on forward data and both-way data are denoted as →L and ↔L. Symmetry and Transitivity constraints are denoted as S and T. The results demonstrate that symmetry and transitivity constraints both improve model’s performance." ], "reasoning": "Data augmentation and explicit symmetry improve model robustness, with the proposed model showing the best performance in both forward and both-way evaluations.", "description": "Impact of Implicit Symmetry (data augmentation): Examining the contrast between M1 and M3 as well as M2 and M4, we can see significant improvements in both-way evaluation despite slight performance drops in forward evaluation. These comparisons imply that data augmentation can help improve model robustness. Note that \\newcitemeng2018context leveraged this data augmentation trick in their model. Impact of Explicit Symmetry: By comparing the proposed model with M4, the consistent improvements across all datasets demonstrate the benefit of using explicit symmetry property. Model Robustness: Although M1 and M2 show competitive results when evaluated on forward test data, their performance degrade significantly in the both-way evaluation. In contrast, the proposed model achieves strong performances in both test scenarios (best F1 scores except for one), and hence proves the robustness of our proposed method.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 7: Ablation over global constraints: Symmetry and Transitivity. Test is conducted on forward test set and both-way test set, which are denoted as −−−→Test and ←−→Test respectively. The local models trained on forward data and both-way data are denoted as →L and ↔L. Symmetry and Transitivity constraints are denoted as S and T. The results demonstrate that symmetry and transitivity constraints both improve model’s performance.'.\n\nQuestion: Which model shows the highest F1 score in the TCR both-way evaluation?\n" }, { "role": "assistant", "content": "By examining the TCR both-way evaluation column, the proposed model achieves the highest F1 score of 80.9. So, the answer is {\"answer\": \"80.9\"}." } ], "images": [ "scigen/train/3084.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Ablation over global constraints: Symmetry and Transitivity. Test is conducted on forward test set and both-way test set, which are denoted as −−−→Test and ←−→Test respectively. The local models trained on forward data and both-way data are denoted as →L and ↔L. Symmetry and Transitivity constraints are denoted as S and T. The results demonstrate that symmetry and transitivity constraints both improve model’s performance." ], "reasoning": "By examining the TCR both-way evaluation column, the proposed model achieves the highest F1 score of 80.9.", "question": "Which model shows the highest F1 score in the TCR both-way evaluation?", "answer": "So, the answer is {\"answer\": \"80.9\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim M2 achieves the highest F1 score in the Matres forward evaluation., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nM2 achieves the highest F1 score in the Matres forward evaluation.\n" }, { "role": "assistant", "content": "By looking at the Matres forward evaluation column, M2 has an F1 score of 81.7, which is the highest among all models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3084.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Ablation over global constraints: Symmetry and Transitivity. Test is conducted on forward test set and both-way test set, which are denoted as −−−→Test and ←−→Test respectively. The local models trained on forward data and both-way data are denoted as →L and ↔L. Symmetry and Transitivity constraints are denoted as S and T. The results demonstrate that symmetry and transitivity constraints both improve model’s performance." ], "reasoning": "By looking at the Matres forward evaluation column, M2 has an F1 score of 81.7, which is the highest among all models.", "statement": "M2 achieves the highest F1 score in the Matres forward evaluation.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 8: Ablation study on linguistic feature usage. Additional linguistic features do not lead to significant improvement and even hurt performance in 2 out of 3 datasets. The results show that our proposed framework is semantic-rich and capable of avoiding the usage of additional linguistic feature.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Incorporating linguistic features does not improve model performance across TB-Dense, MATRES, and TCR datasets, as shown by similar or lower scores when features are included.Previous research establish the success of leveraging linguistic features in event relation prediction. One advantage of leveraging contextualized word embedding is to provide rich semantic representation and could potentially avoid the usage of extra linguistic features. Here, we study the impact of incorporating linguistic features to our model by using simple features provided in the original datasets: token distance, tense and polarity of event entities. These additional features likely cause over-fitting and hence do not improve model performance across all three datasets we test. This set of experiments show that linguistic features do not improve the predicting power of our current framework." } ], "images": [ "scigen/train/3085.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Ablation study on linguistic feature usage. Additional linguistic features do not lead to significant improvement and even hurt performance in 2 out of 3 datasets. The results show that our proposed framework is semantic-rich and capable of avoiding the usage of additional linguistic feature." ], "reasoning": "Incorporating linguistic features does not improve model performance across TB-Dense, MATRES, and TCR datasets, as shown by similar or lower scores when features are included.", "description": "Previous research establish the success of leveraging linguistic features in event relation prediction. One advantage of leveraging contextualized word embedding is to provide rich semantic representation and could potentially avoid the usage of extra linguistic features. Here, we study the impact of incorporating linguistic features to our model by using simple features provided in the original datasets: token distance, tense and polarity of event entities. These additional features likely cause over-fitting and hence do not improve model performance across all three datasets we test. This set of experiments show that linguistic features do not improve the predicting power of our current framework.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 8: Ablation study on linguistic feature usage. Additional linguistic features do not lead to significant improvement and even hurt performance in 2 out of 3 datasets. The results show that our proposed framework is semantic-rich and capable of avoiding the usage of additional linguistic feature.', solve this question and give detail solutions. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich dataset shows the highest score when using global without features?\n" }, { "role": "assistant", "content": "By comparing the scores in the 'global-w/o feat.' column, MATRES has the highest score of 81.7. So, the answer is {\"answer\": \"MATRES\"}." } ], "images": [ "scigen/train/3085.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Ablation study on linguistic feature usage. Additional linguistic features do not lead to significant improvement and even hurt performance in 2 out of 3 datasets. The results show that our proposed framework is semantic-rich and capable of avoiding the usage of additional linguistic feature." ], "reasoning": "By comparing the scores in the 'global-w/o feat.' column, MATRES has the highest score of 81.7.", "question": "Which dataset shows the highest score when using global without features?", "answer": "So, the answer is {\"answer\": \"MATRES\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nUsing linguistic features improves the performance of the model on the TB-Dense dataset.\n" }, { "role": "assistant", "content": "The table shows that the score for TB-Dense is higher without linguistic features (63.2) compared to with features (63.0), refuting the statement. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3085.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Ablation study on linguistic feature usage. Additional linguistic features do not lead to significant improvement and even hurt performance in 2 out of 3 datasets. The results show that our proposed framework is semantic-rich and capable of avoiding the usage of additional linguistic feature." ], "reasoning": "The table shows that the score for TB-Dense is higher without linguistic features (63.2) compared to with features (63.0), refuting the statement.", "statement": "Using linguistic features improves the performance of the model on the TB-Dense dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 9: Ablation over word representation: BERT vs GloVe. Although BERT representation largely contributes to the performance boost, our proposed framework remains strong and outperforms current SOTA approaches when GloVe is used.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "BERT representations significantly improve performance across all datasets, and even without BERT, the RNN-based local model and deep structured global model outperform or are comparable to current SOTA.In this section, we explore the impact of contextualized BERT representations under our deep SSVM framework. We replace BERT representations with the GloVe Pennington et al. BERT improves the performance with a significant margin. Besides, even without BERT representations, our RNN-based local model and the deep structured global model still outperform (MATRES and TCR) or are comparable with (TB-Dense) current SOTA. These results confirm the improvements of our method." } ], "images": [ "scigen/train/3086.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Ablation over word representation: BERT vs GloVe. Although BERT representation largely contributes to the performance boost, our proposed framework remains strong and outperforms current SOTA approaches when GloVe is used." ], "reasoning": "BERT representations significantly improve performance across all datasets, and even without BERT, the RNN-based local model and deep structured global model outperform or are comparable to current SOTA.", "description": "In this section, we explore the impact of contextualized BERT representations under our deep SSVM framework. We replace BERT representations with the GloVe Pennington et al. BERT improves the performance with a significant margin. Besides, even without BERT representations, our RNN-based local model and the deep structured global model still outperform (MATRES and TCR) or are comparable with (TB-Dense) current SOTA. These results confirm the improvements of our method.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 9: Ablation over word representation: BERT vs GloVe. Although BERT representation largely contributes to the performance boost, our proposed framework remains strong and outperforms current SOTA approaches when GloVe is used.'.\n\nQuestion: What is the performance of the global-BERT model on the MATRES dataset?\n" }, { "role": "assistant", "content": "The table shows the performance metrics for different models on various datasets. For the MATRES dataset, the global-BERT model achieves a score of 81.7. So, the answer is {\"answer\": \"81.7\"}." } ], "images": [ "scigen/train/3086.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Ablation over word representation: BERT vs GloVe. Although BERT representation largely contributes to the performance boost, our proposed framework remains strong and outperforms current SOTA approaches when GloVe is used." ], "reasoning": "The table shows the performance metrics for different models on various datasets. For the MATRES dataset, the global-BERT model achieves a score of 81.7.", "question": "What is the performance of the global-BERT model on the MATRES dataset?", "answer": "So, the answer is {\"answer\": \"81.7\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The local-Glove model outperforms the previous SOTA on the TB-Dense dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe local-Glove model outperforms the previous SOTA on the TB-Dense dataset.\n" }, { "role": "assistant", "content": "The table shows that the previous SOTA for TB-Dense is 57.0, while the local-Glove model achieves 56.6, which is lower than the previous SOTA. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3086.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Ablation over word representation: BERT vs GloVe. Although BERT representation largely contributes to the performance boost, our proposed framework remains strong and outperforms current SOTA approaches when GloVe is used." ], "reasoning": "The table shows that the previous SOTA for TB-Dense is 57.0, while the local-Glove model achieves 56.6, which is lower than the previous SOTA.", "statement": "The local-Glove model outperforms the previous SOTA on the TB-Dense dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 6: En→X test BLEU on the 103-language corpus'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The one-to-many model outperforms the many-to-many model and baselines across the 10 evaluated language pairs, similar to results observed on the TED talks corpus.Again, both of the massively multilingual models perform better than the baselines when averaged across the 10 evaluated language pairs, while handling up to 102 languages to-and-from English and 204 translation tasks simultaneously. In this case the results are similar to those we observed on the TED talks corpus, where the one-to-many model performs better than the many-to-many model. Again, this advantage may be due to the one-to-many model handling a smaller number of tasks while not being biased towards English in the target side like the many-to-many model." } ], "images": [ "scigen/train/3087.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: En→X test BLEU on the 103-language corpus" ], "reasoning": "The one-to-many model outperforms the many-to-many model and baselines across the 10 evaluated language pairs, similar to results observed on the TED talks corpus.", "description": "Again, both of the massively multilingual models perform better than the baselines when averaged across the 10 evaluated language pairs, while handling up to 102 languages to-and-from English and 204 translation tasks simultaneously. In this case the results are similar to those we observed on the TED talks corpus, where the one-to-many model performs better than the many-to-many model. Again, this advantage may be due to the one-to-many model handling a smaller number of tasks while not being biased towards English in the target side like the many-to-many model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 6: En→X test BLEU on the 103-language corpus\nQuestion: Which model has the highest average score across the 10 evaluated language pairs?\n" }, { "role": "assistant", "content": "By comparing the average scores of the baselines, one-to-many, and many-to-many models, the one-to-many model has the highest average score of 21.68. So, the answer is {\"answer\": \"one-to-many\"}" } ], "images": [ "scigen/train/3087.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: En→X test BLEU on the 103-language corpus" ], "reasoning": "By comparing the average scores of the baselines, one-to-many, and many-to-many models, the one-to-many model has the highest average score of 21.68.", "question": "Which model has the highest average score across the 10 evaluated language pairs?", "answer": "So, the answer is {\"answer\": \"one-to-many\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The many-to-many model performs better than the baselines in the Italian language pair. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe many-to-many model performs better than the baselines in the Italian language pair.\n" }, { "role": "assistant", "content": "The table shows that the many-to-many model has a score of 30.39 for Italian, which is higher than the baselines score of 31.42. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3087.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: En→X test BLEU on the 103-language corpus" ], "reasoning": "The table shows that the many-to-many model has a score of 30.39 for Italian, which is higher than the baselines score of 31.42.", "statement": "The many-to-many model performs better than the baselines in the Italian language pair.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: En→X test BLEU on the TED Talks corpus', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The one-to-many model outperforms the many-to-many model by 2.53 BLEU on average, indicating that increasing the number of source languages causes degradation in the many-to-many model.In this case we see an opposite trend: the many-to-many model performs worse than the one-to-many model by 2.53 BLEU on average. While previous works Wang et al. , this shows that increasing the number of source languages also causes additional degradation in a many-to-many model. This degradation may be due to the English-centric setting: since most of the translation directions the model is trained on are into English, this leaves less capacity for the other target languages (while still performing better than the bilingual baselines on all 8 language pairs). We also note that in this case the results are consistent among the higher and lower resource pairs – the one-to-many model is better than the many-to-many model, which outperforms the bilingual baselines in all cases. This is unlike the difference we saw in the X→En experiments since here we do not have the multi-way-parallel overfitting issue." } ], "images": [ "scigen/train/3088.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: En→X test BLEU on the TED Talks corpus" ], "reasoning": "The one-to-many model outperforms the many-to-many model by 2.53 BLEU on average, indicating that increasing the number of source languages causes degradation in the many-to-many model.", "description": "In this case we see an opposite trend: the many-to-many model performs worse than the one-to-many model by 2.53 BLEU on average. While previous works Wang et al. , this shows that increasing the number of source languages also causes additional degradation in a many-to-many model. This degradation may be due to the English-centric setting: since most of the translation directions the model is trained on are into English, this leaves less capacity for the other target languages (while still performing better than the bilingual baselines on all 8 language pairs). We also note that in this case the results are consistent among the higher and lower resource pairs – the one-to-many model is better than the many-to-many model, which outperforms the bilingual baselines in all cases. This is unlike the difference we saw in the X→En experiments since here we do not have the multi-way-parallel overfitting issue.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 3: En→X test BLEU on the TED Talks corpus\nQuestion: What is the average BLEU score difference between the one-to-many and many-to-many models for the En-Az, En-Be, En-Gl, and En-Sk language pairs?\n" }, { "role": "assistant", "content": "To find the average BLEU score difference, subtract the many-to-many model scores from the one-to-many model scores for each language pair and then calculate the average of these differences. So, the answer is {\"answer\": \"2.53\"}." } ], "images": [ "scigen/train/3088.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: En→X test BLEU on the TED Talks corpus" ], "reasoning": "To find the average BLEU score difference, subtract the many-to-many model scores from the one-to-many model scores for each language pair and then calculate the average of these differences.", "question": "What is the average BLEU score difference between the one-to-many and many-to-many models for the En-Az, En-Be, En-Gl, and En-Sk language pairs?", "answer": "So, the answer is {\"answer\": \"2.53\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe one-to-many model performs better than the many-to-many model for all language pairs.\n" }, { "role": "assistant", "content": "The table shows that the one-to-many model has higher BLEU scores than the many-to-many model for all listed language pairs. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3088.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: En→X test BLEU on the TED Talks corpus" ], "reasoning": "The table shows that the one-to-many model has higher BLEU scores than the many-to-many model for all listed language pairs.", "statement": "The one-to-many model performs better than the many-to-many model for all language pairs.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: X→En test BLEU on the 103-language corpus', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The many-to-one model outperforms the many-to-many model in most cases, indicating that with sufficient training data, the many-to-one model avoids overfitting and performs better.First, we can see that both multilingual models perform better than the baselines in terms of average BLEU. This shows that massively multilingual many-to-many models can work well in realistic settings with millions of training examples, 102 languages and 204 jointly trained directions to-and-from English. Looking more closely, we note several different behaviors in comparison to the low-resource experiments on the TED Talks corpus. First, the many-to-one model here performs better than the many-to-many model. This shows that the previous result was indeed due to the pathologies of the low-resource dataset; when the training data is large enough and not multi-way-parallel there is no overfitting in the many-to-one model, and it outperforms the many-to-many model in most cases while they are trained identically." } ], "images": [ "scigen/train/3089.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: X→En test BLEU on the 103-language corpus" ], "reasoning": "The many-to-one model outperforms the many-to-many model in most cases, indicating that with sufficient training data, the many-to-one model avoids overfitting and performs better.", "description": "First, we can see that both multilingual models perform better than the baselines in terms of average BLEU. This shows that massively multilingual many-to-many models can work well in realistic settings with millions of training examples, 102 languages and 204 jointly trained directions to-and-from English. Looking more closely, we note several different behaviors in comparison to the low-resource experiments on the TED Talks corpus. First, the many-to-one model here performs better than the many-to-many model. This shows that the previous result was indeed due to the pathologies of the low-resource dataset; when the training data is large enough and not multi-way-parallel there is no overfitting in the many-to-one model, and it outperforms the many-to-many model in most cases while they are trained identically.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 5: X→En test BLEU on the 103-language corpus' to sequentially solve the question presented below. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model has the highest average BLEU score?\n" }, { "role": "assistant", "content": "By comparing the average BLEU scores in the table, the many-to-one model has the highest average BLEU score of 31.01. So, the answer is {\"answer\": \"31.01\"}." } ], "images": [ "scigen/train/3089.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: X→En test BLEU on the 103-language corpus" ], "reasoning": "By comparing the average BLEU scores in the table, the many-to-one model has the highest average BLEU score of 31.01.", "question": "Which model has the highest average BLEU score?", "answer": "So, the answer is {\"answer\": \"31.01\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The many-to-many model performs better than the baselines in terms of average BLEU score. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe many-to-many model performs better than the baselines in terms of average BLEU score.\n" }, { "role": "assistant", "content": "The table shows that the many-to-many model has an average BLEU score of 29.97, which is higher than the baselines' average BLEU score of 28.33. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3089.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: X→En test BLEU on the 103-language corpus" ], "reasoning": "The table shows that the many-to-many model has an average BLEU score of 29.97, which is higher than the baselines' average BLEU score of 28.33.", "statement": "The many-to-many model performs better than the baselines in terms of average BLEU score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 7: Supervised performance while varying the number of languages involved'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The 5-to-5 model consistently achieves the highest BLEU scores, indicating a trade-off between the number of languages and translation accuracy with fixed model capacity.We can see that in most cases the best results are obtained using the 5-to-5 model, showing that there is indeed a trade off between the number of languages and translation accuracy when using a fixed model capacity and the same training setup. One may expect that the gaps between the different models should become smaller and even close with more updates, as the models with more languages see less examples per language in each batch, thus requiring more updates to improve in terms of BLEU. However, in our setting these gaps did not close even after the models converged, leaving 2.73 average BLEU difference between the 5-to-5 and the 103-to-103 model." } ], "images": [ "scigen/train/3090.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Supervised performance while varying the number of languages involved" ], "reasoning": "The 5-to-5 model consistently achieves the highest BLEU scores, indicating a trade-off between the number of languages and translation accuracy with fixed model capacity.", "description": "We can see that in most cases the best results are obtained using the 5-to-5 model, showing that there is indeed a trade off between the number of languages and translation accuracy when using a fixed model capacity and the same training setup. One may expect that the gaps between the different models should become smaller and even close with more updates, as the models with more languages see less examples per language in each batch, thus requiring more updates to improve in terms of BLEU. However, in our setting these gaps did not close even after the models converged, leaving 2.73 average BLEU difference between the 5-to-5 and the 103-to-103 model.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 7: Supervised performance while varying the number of languages involved' in mind, break down and resolve the question below step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhat is the average BLEU score difference between the 5-to-5 and the 103-to-103 models?\n" }, { "role": "assistant", "content": "The average BLEU score for the 5-to-5 model is 26.14, and for the 103-to-103 model, it is 23.41. The difference is 26.14 - 23.41. So, the answer is {\"answer\": \"2.73\"}." } ], "images": [ "scigen/train/3090.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Supervised performance while varying the number of languages involved" ], "reasoning": "The average BLEU score for the 5-to-5 model is 26.14, and for the 103-to-103 model, it is 23.41. The difference is 26.14 - 23.41.", "question": "What is the average BLEU score difference between the 5-to-5 and the 103-to-103 models?", "answer": "So, the answer is {\"answer\": \"2.73\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe 5-to-5 model has the highest BLEU score for the En-Fr translation.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for En-Fr translation using the 5-to-5 model is 38.99, which is higher than the scores for the same translation using other models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3090.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Supervised performance while varying the number of languages involved" ], "reasoning": "The table shows that the BLEU score for En-Fr translation using the 5-to-5 model is 38.99, which is higher than the scores for the same translation using other models.", "statement": "The 5-to-5 model has the highest BLEU score for the En-Fr translation.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 8: Zero-Shot performance while varying the number of languages involved'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Increasing the number of languages improves zero-shot translation accuracy, especially for similar languages like Russian and Ukrainian.We then study the effect of the number of languages on zero-shot translation accuracy. Since we find zero-shot accuracy as an interesting measure for model generalization, we hypothesize that by adding more languages, the model is forced to create a more generalized representation to better utilize its capacity, which may improve zero-shot performance. We choose four language pairs for this purpose: Arabic↔French which are distant languages, and Ukrainian↔Russian which are similar. For Arabic↔French the BLEU scores are very low in all cases, with the 50-to-50 and 25-to-25 models being slightly better than rest on Ar-Fr and Fr-Ar respectively. On Russian↔Ukrainian we see clear improvements when increasing the number of languages to more than five." } ], "images": [ "scigen/train/3091.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Zero-Shot performance while varying the number of languages involved" ], "reasoning": "Increasing the number of languages improves zero-shot translation accuracy, especially for similar languages like Russian and Ukrainian.", "description": "We then study the effect of the number of languages on zero-shot translation accuracy. Since we find zero-shot accuracy as an interesting measure for model generalization, we hypothesize that by adding more languages, the model is forced to create a more generalized representation to better utilize its capacity, which may improve zero-shot performance. We choose four language pairs for this purpose: Arabic↔French which are distant languages, and Ukrainian↔Russian which are similar. For Arabic↔French the BLEU scores are very low in all cases, with the 50-to-50 and 25-to-25 models being slightly better than rest on Ar-Fr and Fr-Ar respectively. On Russian↔Ukrainian we see clear improvements when increasing the number of languages to more than five.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 8: Zero-Shot performance while varying the number of languages involved', solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model has the highest average BLEU score?\n" }, { "role": "assistant", "content": "By examining the 'Avg.' column, the model with the highest average BLEU score is the 50-to-50 model with a score of 11.1. So, the answer is {\"answer\": \"50-to-50\"}." } ], "images": [ "scigen/train/3091.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Zero-Shot performance while varying the number of languages involved" ], "reasoning": "By examining the 'Avg.' column, the model with the highest average BLEU score is the 50-to-50 model with a score of 11.1.", "question": "Which model has the highest average BLEU score?", "answer": "So, the answer is {\"answer\": \"50-to-50\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe 25-to-25 model has the highest BLEU score for the Ru-Uk language pair.\n" }, { "role": "assistant", "content": "By examining the 'Ru-Uk' column, the highest BLEU score for the Ru-Uk language pair is 16.67, which is indeed achieved by the 25-to-25 model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3091.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Zero-Shot performance while varying the number of languages involved" ], "reasoning": "By examining the 'Ru-Uk' column, the highest BLEU score for the Ru-Uk language pair is 16.67, which is indeed achieved by the 25-to-25 model.", "statement": "The 25-to-25 model has the highest BLEU score for the Ru-Uk language pair.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Perplexity scores of the LSTM network on word level Language Modeling task (lower is better). Upper and lower parts of the table report results without and with forward dropout respectively. Networks with forward dropout use 0.2 and 0.5 dropout rates in input and output connections respectively. Values in bold show best results for each of the recurrent dropout schemes with and without forward dropout.'.\n" }, { "role": "assistant", "content": "Our approach consistently outperforms alternatives, with per-step mask sampling showing better results than dropping hidden state directly, and forward dropout yielding better results than recurrent dropouts.Results. We make the following observations: (i) our approach shows better results than the alternatives; (ii) per-step mask sampling is better when dropping hidden state directly; (iii) on this task our method using per-step sampling seems to yield results similar to per-sequence sampling; (iv) in this case forward dropout yields better results than any of the three recurrent dropouts; and finally (v)" } ], "images": [ "scigen/train/3092.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Perplexity scores of the LSTM network on word level Language Modeling task (lower is better). Upper and lower parts of the table report results without and with forward dropout respectively. Networks with forward dropout use 0.2 and 0.5 dropout rates in input and output connections respectively. Values in bold show best results for each of the recurrent dropout schemes with and without forward dropout." ], "reasoning": "Our approach consistently outperforms alternatives, with per-step mask sampling showing better results than dropping hidden state directly, and forward dropout yielding better results than recurrent dropouts.", "description": "Results. We make the following observations: (i) our approach shows better results than the alternatives; (ii) per-step mask sampling is better when dropping hidden state directly; (iii) on this task our method using per-step sampling seems to yield results similar to per-sequence sampling; (iv) in this case forward dropout yields better results than any of the three recurrent dropouts; and finally (v)", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: Perplexity scores of the LSTM network on word level Language Modeling task (lower is better). Upper and lower parts of the table report results without and with forward dropout respectively. Networks with forward dropout use 0.2 and 0.5 dropout rates in input and output connections respectively. Values in bold show best results for each of the recurrent dropout schemes with and without forward dropout.'.\n\nQuestion: Which sampling method yields the highest validation score for a dropout rate of 0.5?\n" }, { "role": "assistant", "content": "For a dropout rate of 0.5, the highest validation score is observed under the 'per-sequence' sampling method in the 'moon2015rnndrop' column, with a score of 137.7. So, the answer is {\"answer\": \"per-sequence\"}." } ], "images": [ "scigen/train/3092.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Perplexity scores of the LSTM network on word level Language Modeling task (lower is better). Upper and lower parts of the table report results without and with forward dropout respectively. Networks with forward dropout use 0.2 and 0.5 dropout rates in input and output connections respectively. Values in bold show best results for each of the recurrent dropout schemes with and without forward dropout." ], "reasoning": "For a dropout rate of 0.5, the highest validation score is observed under the 'per-sequence' sampling method in the 'moon2015rnndrop' column, with a score of 137.7.", "question": "Which sampling method yields the highest validation score for a dropout rate of 0.5?", "answer": "So, the answer is {\"answer\": \"per-sequence\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Per-step sampling yields better results than per-sequence sampling for a dropout rate of 0.25., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nPer-step sampling yields better results than per-sequence sampling for a dropout rate of 0.25.\n" }, { "role": "assistant", "content": "For a dropout rate of 0.25, per-step sampling yields scores of 113.0 and 108.7 in the 'moon2015rnndrop' columns, while per-sequence sampling yields scores of 121.0 and 113.0, which are higher. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3092.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Perplexity scores of the LSTM network on word level Language Modeling task (lower is better). Upper and lower parts of the table report results without and with forward dropout respectively. Networks with forward dropout use 0.2 and 0.5 dropout rates in input and output connections respectively. Values in bold show best results for each of the recurrent dropout schemes with and without forward dropout." ], "reasoning": "For a dropout rate of 0.25, per-step sampling yields scores of 113.0 and 108.7 in the 'moon2015rnndrop' columns, while per-sequence sampling yields scores of 121.0 and 113.0, which are higher.", "statement": "Per-step sampling yields better results than per-sequence sampling for a dropout rate of 0.25.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Bit-per-character scores of the LSTM network on character level Language Modelling task (lower is better). Upper and lower parts of the table report results without and with forward dropout respectively. Networks with forward dropout use 0.2 and 0.5 dropout rates in input and output connections respectively. Values in bold show best results for each of the recurrent dropout schemes with and without forward dropout.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Regularizing only the recurrent connections is more beneficial than only the forward ones, and the best result is achieved by a network trained with our dropout at a 0.25 rate and per-step sampling.Results. Note that on this task regularizing only the recurrent connections is more beneficial than only the forward ones. In addition, our approach is the only one that improves over baseline LSTM with forward dropout. The overall best result is achieved by a network trained with our dropout with 0.25 dropout rate and per-step sampling, closely followed by network with \\newcitegal2015dropout dropout." } ], "images": [ "scigen/train/3093.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Bit-per-character scores of the LSTM network on character level Language Modelling task (lower is better). Upper and lower parts of the table report results without and with forward dropout respectively. Networks with forward dropout use 0.2 and 0.5 dropout rates in input and output connections respectively. Values in bold show best results for each of the recurrent dropout schemes with and without forward dropout." ], "reasoning": "Regularizing only the recurrent connections is more beneficial than only the forward ones, and the best result is achieved by a network trained with our dropout at a 0.25 rate and per-step sampling.", "description": "Results. Note that on this task regularizing only the recurrent connections is more beneficial than only the forward ones. In addition, our approach is the only one that improves over baseline LSTM with forward dropout. The overall best result is achieved by a network trained with our dropout with 0.25 dropout rate and per-step sampling, closely followed by network with \\newcitegal2015dropout dropout.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Bit-per-character scores of the LSTM network on character level Language Modelling task (lower is better). Upper and lower parts of the table report results without and with forward dropout respectively. Networks with forward dropout use 0.2 and 0.5 dropout rates in input and output connections respectively. Values in bold show best results for each of the recurrent dropout schemes with and without forward dropout.', answer the following question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the best result achieved by a network trained with our dropout and per-step sampling?\n" }, { "role": "assistant", "content": "The table shows that the best result is achieved by a network trained with our dropout at a 0.25 rate and per-step sampling, with a test score of 1.301. So, the answer is {\"answer\": \"1.301\"}" } ], "images": [ "scigen/train/3093.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Bit-per-character scores of the LSTM network on character level Language Modelling task (lower is better). Upper and lower parts of the table report results without and with forward dropout respectively. Networks with forward dropout use 0.2 and 0.5 dropout rates in input and output connections respectively. Values in bold show best results for each of the recurrent dropout schemes with and without forward dropout." ], "reasoning": "The table shows that the best result is achieved by a network trained with our dropout at a 0.25 rate and per-step sampling, with a test score of 1.301.", "question": "What is the best result achieved by a network trained with our dropout and per-step sampling?", "answer": "So, the answer is {\"answer\": \"1.301\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe best result is achieved by a network trained with a 0.5 dropout rate and per-sequence sampling.\n" }, { "role": "assistant", "content": "The table shows that the best result is achieved by a network trained with our dropout at a 0.25 rate and per-step sampling, not a 0.5 dropout rate and per-sequence sampling. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3093.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Bit-per-character scores of the LSTM network on character level Language Modelling task (lower is better). Upper and lower parts of the table report results without and with forward dropout respectively. Networks with forward dropout use 0.2 and 0.5 dropout rates in input and output connections respectively. Values in bold show best results for each of the recurrent dropout schemes with and without forward dropout." ], "reasoning": "The table shows that the best result is achieved by a network trained with our dropout at a 0.25 rate and per-step sampling, not a 0.5 dropout rate and per-sequence sampling.", "statement": "The best result is achieved by a network trained with a 0.5 dropout rate and per-sequence sampling.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: F1 scores (higher is better) on NER task.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Dropout in recurrent connections improves generalization to longer sequences, with LSTM networks benefiting the most.Results. We note that the gap between networks trained with and without our dropout scheme is larger for networks trained on shorter sequences. It suggests that dropout in recurrent connections might have an impact on how well a network generalizes to sequences that are longer than the ones used during training. The gain from using recurrent dropout is larger for the LSTM network. We have experimented with higher recurrent dropout rates, but found that it led to excessive regularization." } ], "images": [ "scigen/train/3094.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: F1 scores (higher is better) on NER task." ], "reasoning": "Dropout in recurrent connections improves generalization to longer sequences, with LSTM networks benefiting the most.", "description": "Results. We note that the gap between networks trained with and without our dropout scheme is larger for networks trained on shorter sequences. It suggests that dropout in recurrent connections might have an impact on how well a network generalizes to sequences that are longer than the ones used during training. The gain from using recurrent dropout is larger for the LSTM network. We have experimented with higher recurrent dropout rates, but found that it led to excessive regularization.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 4: F1 scores (higher is better) on NER task.' and proceed to solve the following question in a stepwise manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich network shows the largest gain in performance when using a dropout rate of 0.25 for 5 word long sequences?\n" }, { "role": "assistant", "content": "Compare the performance of RNN, LSTM, and GRU networks with a dropout rate of 0.25 for 5 word long sequences. The largest gain is observed in the GRU network, which improves from 86.00 to 86.70. So, the answer is {\"answer\": \"GRU\"}." } ], "images": [ "scigen/train/3094.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: F1 scores (higher is better) on NER task." ], "reasoning": "Compare the performance of RNN, LSTM, and GRU networks with a dropout rate of 0.25 for 5 word long sequences. The largest gain is observed in the GRU network, which improves from 86.00 to 86.70.", "question": "Which network shows the largest gain in performance when using a dropout rate of 0.25 for 5 word long sequences?", "answer": "So, the answer is {\"answer\": \"GRU\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe LSTM network shows the largest gain in performance when using a dropout rate of 0.25 for 15 word long sequences.\n" }, { "role": "assistant", "content": "Compare the performance of RNN, LSTM, and GRU networks with a dropout rate of 0.25 for 15 word long sequences. The LSTM network improves from 86.12 to 86.88, which is not the largest gain. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3094.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: F1 scores (higher is better) on NER task." ], "reasoning": "Compare the performance of RNN, LSTM, and GRU networks with a dropout rate of 0.25 for 15 word long sequences. The LSTM network improves from 86.12 to 86.88, which is not the largest gain.", "statement": "The LSTM network shows the largest gain in performance when using a dropout rate of 0.25 for 15 word long sequences.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: F1 scores (higher is better) on Sentiment Evaluation task', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The GRU network with recurrent dropout achieves the best results over most datasets, except for the Twitter2015 dataset where the LSTM network performs better.Results. Note that in this case our algorithm decreases the performance of the vanilla RNNs while this is not the case for LSTM and GRU networks. This is due to the nature of the problem: differently from LM and NER tasks, a network needs to aggregate information over a long sequence. Vanilla RNNs notoriously have difficulties with this and our dropout scheme impairs their ability to remember even further. The best result over most of the datasets is achieved by the GRU network with recurrent dropout. The only exception is the Twitter2015 dataset, where the LSTM network shows better results." } ], "images": [ "scigen/train/3095.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: F1 scores (higher is better) on Sentiment Evaluation task" ], "reasoning": "The GRU network with recurrent dropout achieves the best results over most datasets, except for the Twitter2015 dataset where the LSTM network performs better.", "description": "Results. Note that in this case our algorithm decreases the performance of the vanilla RNNs while this is not the case for LSTM and GRU networks. This is due to the nature of the problem: differently from LM and NER tasks, a network needs to aggregate information over a long sequence. Vanilla RNNs notoriously have difficulties with this and our dropout scheme impairs their ability to remember even further. The best result over most of the datasets is achieved by the GRU network with recurrent dropout. The only exception is the Twitter2015 dataset, where the LSTM network shows better results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: Table 5: F1 scores (higher is better) on Sentiment Evaluation task\nQuestion: Which network and dropout rate combination achieves the best result on the LiveJournal14 dataset?\n" }, { "role": "assistant", "content": "To find the best result on the LiveJournal14 dataset, compare the scores of all network and dropout rate combinations. The highest score is 72.10, achieved by the GRU network with a 0.25 dropout rate. So, the answer is {\"answer\": \"GRU with 0.25 dropout rate\"}" } ], "images": [ "scigen/train/3095.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: F1 scores (higher is better) on Sentiment Evaluation task" ], "reasoning": "To find the best result on the LiveJournal14 dataset, compare the scores of all network and dropout rate combinations. The highest score is 72.10, achieved by the GRU network with a 0.25 dropout rate.", "question": "Which network and dropout rate combination achieves the best result on the LiveJournal14 dataset?", "answer": "So, the answer is {\"answer\": \"GRU with 0.25 dropout rate\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe LSTM network with a 0.25 dropout rate achieves the best result on the Twitter2015 dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the scores of all network and dropout rate combinations for the Twitter2015 dataset. The highest score is 61.35, achieved by the LSTM network with a 0.25 dropout rate. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3095.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: F1 scores (higher is better) on Sentiment Evaluation task" ], "reasoning": "To verify this statement, compare the scores of all network and dropout rate combinations for the Twitter2015 dataset. The highest score is 61.35, achieved by the LSTM network with a 0.25 dropout rate.", "statement": "The LSTM network with a 0.25 dropout rate achieves the best result on the Twitter2015 dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Difference in Smatch score with and without reification (top is gold, rest are test, bold are differences)'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Reifying relations to nodes increases the number of triples, affecting the Smatch score by requiring more time and memory, and potentially amplifying certain errors.The effect of reification on the Smatch score can be large. By reifying one relation to a node with two relations, the net total of triples increases by two. The result is that Smatch will require more time and memory to compute a score, and the search for the variable mapping may become less stable because there are more nodes to search over. This normalization can affect the Smatch score by amplifying certain kinds of errors and giving partial credit for others. The Collapsed column shows the Smatch score between the gold and test AMRs when the relations are left as-is, and the Reified column shows the score when both gold and test are reified." } ], "images": [ "scigen/train/3096.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Difference in Smatch score with and without reification (top is gold, rest are test, bold are differences)" ], "reasoning": "Reifying relations to nodes increases the number of triples, affecting the Smatch score by requiring more time and memory, and potentially amplifying certain errors.", "description": "The effect of reification on the Smatch score can be large. By reifying one relation to a node with two relations, the net total of triples increases by two. The result is that Smatch will require more time and memory to compute a score, and the search for the variable mapping may become less stable because there are more nodes to search over. This normalization can affect the Smatch score by amplifying certain kinds of errors and giving partial credit for others. The Collapsed column shows the Smatch score between the gold and test AMRs when the relations are left as-is, and the Reified column shows the score when both gold and test are reified.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Difference in Smatch score with and without reification (top is gold, rest are test, bold are differences)\nQuestion: What is the difference in the Smatch score for (a / apple) between the Collapsed and Reified columns?\nSolve the above question based on the table information. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The Smatch score for (a / apple) in the Collapsed column is 0.80, and in the Reified column, it is 0.57. The difference is 0.80 - 0.57. So, the answer is {\"answer\": \"0.23\"}" } ], "images": [ "scigen/train/3096.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Difference in Smatch score with and without reification (top is gold, rest are test, bold are differences)" ], "reasoning": "The Smatch score for (a / apple) in the Collapsed column is 0.80, and in the Reified column, it is 0.57. The difference is 0.80 - 0.57.", "question": "What is the difference in the Smatch score for (a / apple) between the Collapsed and Reified columns?", "answer": "So, the answer is {\"answer\": \"0.23\"}" }, { "messages": [ { "role": "user", "content": "The Smatch score for (a / apple :quant 1) is higher when reified. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Smatch score for (a / apple :quant 1) is higher when reified.\n" }, { "role": "assistant", "content": "The table shows that the Smatch score for (a / apple :quant 1) is 0.67 in the Collapsed column and 0.80 in the Reified column, indicating that the score is indeed higher when reified. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3096.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Difference in Smatch score with and without reification (top is gold, rest are test, bold are differences)" ], "reasoning": "The table shows that the Smatch score for (a / apple :quant 1) is 0.67 in the Collapsed column and 0.80 in the Reified column, indicating that the score is indeed higher when reified.", "statement": "The Smatch score for (a / apple :quant 1) is higher when reified.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Performance of components within the SSS framework.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The SSS model using GloVe embeddings outperforms the ELMo Sem+Seq model and performs slightly better than the BERT Sem+Seq model.The Sem models condition the response generation directly on the word embeddings. We observe that ELMo and BERT perform much better than GloVe embeddings. The Sem+Seq models condition the decoder on the representation obtained after passing the word embeddings through the LSTM layer. These models outperform their respective Sem models. The gain with ELMo is not significant because the underlying architecture already has two BiLSTM layers whcih are anyways being fine-tuned for the task. Hence the addition of one more LSTM layer may not contribute to learning any new sequential word information. Earlier work has suggested that deep contextualized representations capture syntax and co-reference relations Peters et al. We observe that the SSS model using the simpler GloVe embedding outperforms the ELMo Sem+Seq model and performs slightly better than the BERT Sem+Seq model." } ], "images": [ "scigen/train/3097.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of components within the SSS framework." ], "reasoning": "The SSS model using GloVe embeddings outperforms the ELMo Sem+Seq model and performs slightly better than the BERT Sem+Seq model.", "description": "The Sem models condition the response generation directly on the word embeddings. We observe that ELMo and BERT perform much better than GloVe embeddings. The Sem+Seq models condition the decoder on the representation obtained after passing the word embeddings through the LSTM layer. These models outperform their respective Sem models. The gain with ELMo is not significant because the underlying architecture already has two BiLSTM layers whcih are anyways being fine-tuned for the task. Hence the addition of one more LSTM layer may not contribute to learning any new sequential word information. Earlier work has suggested that deep contextualized representations capture syntax and co-reference relations Peters et al. We observe that the SSS model using the simpler GloVe embedding outperforms the ELMo Sem+Seq model and performs slightly better than the BERT Sem+Seq model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nTable title: Table 2: Performance of components within the SSS framework.\n\nQuestion: Which model and embedding combination achieves the highest ROUGE L score?\n" }, { "role": "assistant", "content": "To find the highest ROUGE L score, we need to compare the ROUGE L scores across all model and embedding combinations in the table. The highest ROUGE L score is 35.2, achieved by the BERT SSS model. So, the answer is {\"answer\": \"BERT SSS\"}." } ], "images": [ "scigen/train/3097.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of components within the SSS framework." ], "reasoning": "To find the highest ROUGE L score, we need to compare the ROUGE L scores across all model and embedding combinations in the table. The highest ROUGE L score is 35.2, achieved by the BERT SSS model.", "question": "Which model and embedding combination achieves the highest ROUGE L score?", "answer": "So, the answer is {\"answer\": \"BERT SSS\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe ELMo Sem+Seq model has a higher BLEU score than the GloVe Sem+Seq model.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for the ELMo Sem+Seq model is 14.61, while the BLEU score for the GloVe Sem+Seq model is 14.83. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3097.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of components within the SSS framework." ], "reasoning": "The table shows that the BLEU score for the ELMo Sem+Seq model is 14.61, while the BLEU score for the GloVe Sem+Seq model is 14.83. Therefore, the statement is incorrect.", "statement": "The ELMo Sem+Seq model has a higher BLEU score than the GloVe Sem+Seq model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1: Results of automatic evaluation. Our proposed architecture SSS(BERT) outperforms the baseline methods.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "SSS(BERT) outperforms other models in BLEU, ROUGE-1, ROUGE-2, and ROUGE-L scores, indicating its superior performance in generating conversational responses.SSS(BERT) is our proposed architecture in terms of the SSS framework. We report best results within SSS chosen across 108 configurations comprising of four different graph combinations, three different contextual and structural infusion methods, three M-GCN layers, and, three embeddings. The best model was chosen based on performance of the validation set. We presented 100 randomly sampled outputs to three different annotators. The annotators were asked to pick from four options: A, B, both, and none. The annotators were told these were conversations between friends. Tallying the majority vote, we obtain win/loss/both/none for SSS(BERT) as 29/25/29/17, SSS(GloVe) as 24/17/47/12 and SSS(ELMo) as 22/23/41/14. This suggests qualitative improvement using SSS framework. We also provide some generated examples in the Appendix B1. We found that the SSS framework had less confusion in generating the opening responses than the GTTP baseline. These “conversation starters” have a unique template for every opening scenario and thus have different syntactic structures respectively. We hypothesize that the presence of dependency graphs over these respective sentences helps to alleviate the confusion as seen in Example 1. The second example illustrates why incorporating structural information is important for this task. We also observed that SSS encoder framework does not improve on the aspects of human creativity such as diversity, initiating a context-switch, and common sense reasoning as seen in Example 3." } ], "images": [ "scigen/train/3098.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results of automatic evaluation. Our proposed architecture SSS(BERT) outperforms the baseline methods." ], "reasoning": "SSS(BERT) outperforms other models in BLEU, ROUGE-1, ROUGE-2, and ROUGE-L scores, indicating its superior performance in generating conversational responses.", "description": "SSS(BERT) is our proposed architecture in terms of the SSS framework. We report best results within SSS chosen across 108 configurations comprising of four different graph combinations, three different contextual and structural infusion methods, three M-GCN layers, and, three embeddings. The best model was chosen based on performance of the validation set. We presented 100 randomly sampled outputs to three different annotators. The annotators were asked to pick from four options: A, B, both, and none. The annotators were told these were conversations between friends. Tallying the majority vote, we obtain win/loss/both/none for SSS(BERT) as 29/25/29/17, SSS(GloVe) as 24/17/47/12 and SSS(ELMo) as 22/23/41/14. This suggests qualitative improvement using SSS framework. We also provide some generated examples in the Appendix B1. We found that the SSS framework had less confusion in generating the opening responses than the GTTP baseline. These “conversation starters” have a unique template for every opening scenario and thus have different syntactic structures respectively. We hypothesize that the presence of dependency graphs over these respective sentences helps to alleviate the confusion as seen in Example 1. The second example illustrates why incorporating structural information is important for this task. We also observed that SSS encoder framework does not improve on the aspects of human creativity such as diversity, initiating a context-switch, and common sense reasoning as seen in Example 3.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nTable title: Table 1: Results of automatic evaluation. Our proposed architecture SSS(BERT) outperforms the baseline methods.\n\nQuestion: Which model has the highest ROUGE-1 score and what is the score?\n" }, { "role": "assistant", "content": "By comparing the ROUGE-1 scores of all models, SSS(BERT) has the highest ROUGE-1 score of 40.09. So, the answer is {\"answer\": \"SSS(BERT) with 40.09\"}." } ], "images": [ "scigen/train/3098.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results of automatic evaluation. Our proposed architecture SSS(BERT) outperforms the baseline methods." ], "reasoning": "By comparing the ROUGE-1 scores of all models, SSS(BERT) has the highest ROUGE-1 score of 40.09.", "question": "Which model has the highest ROUGE-1 score and what is the score?", "answer": "So, the answer is {\"answer\": \"SSS(BERT) with 40.09\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim SSS(GloVe) has a higher BLEU score than BiDAF., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nSSS(GloVe) has a higher BLEU score than BiDAF.\n" }, { "role": "assistant", "content": "The BLEU score for SSS(GloVe) is 18.96, while the BLEU score for BiDAF is 16.79, indicating that SSS(GloVe) has a higher BLEU score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3098.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results of automatic evaluation. Our proposed architecture SSS(BERT) outperforms the baseline methods." ], "reasoning": "The BLEU score for SSS(GloVe) is 18.96, while the BLEU score for BiDAF is 16.79, indicating that SSS(GloVe) has a higher BLEU score.", "statement": "SSS(GloVe) has a higher BLEU score than BiDAF.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Performance of different hybrid architectures to combine structural information with sequence information', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The Str-LSTM model captures sequential information better due to its GCN layer, while the Par-GCN-LSTM model performs second best as the LSTM relies only on word embeddings.The response generation task of our dataset is a span based generation task where phrases of text are expected to be copied or generated as they are. The sequential information is thus crucial to reproduce these long phrases from background knowledge. The Str-LSTM model can better capture sequential information with structurally and syntactically rich representations obtained through the initial GCN layer. The Par-GCN-LSTM model performs second best. However, in the parallel model, the LSTM cannot leverage the structural information directly and relies only on the word embeddings. Seq-GCN model performs the worst among all the three as the GCN layer at the top is likely to modify the sequence information from the LSTMs." } ], "images": [ "scigen/train/3099.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of different hybrid architectures to combine structural information with sequence information" ], "reasoning": "The Str-LSTM model captures sequential information better due to its GCN layer, while the Par-GCN-LSTM model performs second best as the LSTM relies only on word embeddings.", "description": "The response generation task of our dataset is a span based generation task where phrases of text are expected to be copied or generated as they are. The sequential information is thus crucial to reproduce these long phrases from background knowledge. The Str-LSTM model can better capture sequential information with structurally and syntactically rich representations obtained through the initial GCN layer. The Par-GCN-LSTM model performs second best. However, in the parallel model, the LSTM cannot leverage the structural information directly and relies only on the word embeddings. Seq-GCN model performs the worst among all the three as the GCN layer at the top is likely to modify the sequence information from the LSTMs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: Performance of different hybrid architectures to combine structural information with sequence information'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model and embedding combination achieved the highest BLEU score?\n" }, { "role": "assistant", "content": "To find the highest BLEU score, compare the BLEU scores across all models and embeddings. The highest BLEU score is 22.78, achieved by the Str-LSTM model with BERT embedding. So, the answer is {\"answer\": \"Str-LSTM with BERT\"}" } ], "images": [ "scigen/train/3099.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of different hybrid architectures to combine structural information with sequence information" ], "reasoning": "To find the highest BLEU score, compare the BLEU scores across all models and embeddings. The highest BLEU score is 22.78, achieved by the Str-LSTM model with BERT embedding.", "question": "Which model and embedding combination achieved the highest BLEU score?", "answer": "So, the answer is {\"answer\": \"Str-LSTM with BERT\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Par-GCN-LSTM model with GloVe embedding achieved a higher BLEU score than the Seq-GCN model with ELMo embedding. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Par-GCN-LSTM model with GloVe embedding achieved a higher BLEU score than the Seq-GCN model with ELMo embedding.\n" }, { "role": "assistant", "content": "The BLEU score for Par-GCN-LSTM with GloVe is 17.1, while the BLEU score for Seq-GCN with ELMo is 18.44. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3099.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of different hybrid architectures to combine structural information with sequence information" ], "reasoning": "The BLEU score for Par-GCN-LSTM with GloVe is 17.1, while the BLEU score for Seq-GCN with ELMo is 18.44. Therefore, the statement is refuted.", "statement": "The Par-GCN-LSTM model with GloVe embedding achieved a higher BLEU score than the Seq-GCN model with ELMo embedding.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Results on the order discrimination and sentence reordering coherence tasks. Our approach trades only a small decrease in performance for improved utility of the sentence embeddings over other approaches, achieving close to or the same as the state-of-the-art.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The PPD-based approach performs slightly worse than ATTOrderNet in order discrimination but achieves similar ranking correlation and slightly lower positional accuracy in sentence reordering.Order discrimination setup. For order discrimination, we use the Accidents and Earthquakes datasets from Barzilay and Lapata The task is to determine which of a permuted ordering of the sentences and the original ordering is the most coherent (in the original order), for twenty such permutations. Performance is measured with the accuracy with which the permuted sentences are identified. Results. For the order discrimination task, we find that on both datasets, our PPD-based approach only slightly underperforms ATTOrderNet Cui et al. Gong et al. ; Logeswaran et al. On the more difficult sentence reordering task, our approach exhibits performance closer to the state-of-the-art, achieving the same ranking correlation and only slightly lower positional accuracy. Given that the publicly available training set for the reordering task is slightly smaller than that used in previous work, it is possible that more data would allow our approach to achieve even better performance. In the next section we will discuss the real-world value offered by our approach that is largely missing from existing approaches." } ], "images": [ "scigen/train/3100.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on the order discrimination and sentence reordering coherence tasks. Our approach trades only a small decrease in performance for improved utility of the sentence embeddings over other approaches, achieving close to or the same as the state-of-the-art." ], "reasoning": "The PPD-based approach performs slightly worse than ATTOrderNet in order discrimination but achieves similar ranking correlation and slightly lower positional accuracy in sentence reordering.", "description": "Order discrimination setup. For order discrimination, we use the Accidents and Earthquakes datasets from Barzilay and Lapata The task is to determine which of a permuted ordering of the sentences and the original ordering is the most coherent (in the original order), for twenty such permutations. Performance is measured with the accuracy with which the permuted sentences are identified. Results. For the order discrimination task, we find that on both datasets, our PPD-based approach only slightly underperforms ATTOrderNet Cui et al. Gong et al. ; Logeswaran et al. On the more difficult sentence reordering task, our approach exhibits performance closer to the state-of-the-art, achieving the same ranking correlation and only slightly lower positional accuracy. Given that the publicly available training set for the reordering task is slightly smaller than that used in previous work, it is possible that more data would allow our approach to achieve even better performance. In the next section we will discuss the real-world value offered by our approach that is largely missing from existing approaches.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 2: Results on the order discrimination and sentence reordering coherence tasks. Our approach trades only a small decrease in performance for improved utility of the sentence embeddings over other approaches, achieving close to or the same as the state-of-the-art.' and proceed to solve the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model achieved the highest accuracy in the Order discrimination Earthquake task?\n" }, { "role": "assistant", "content": "To find the model with the highest accuracy in the Order discrimination Earthquake task, look at the 'Order discrimination Earthquake' column and identify the highest value. The highest value is 99.8, achieved by ATTOrderNet. So, the answer is {\"answer\": \"ATTOrderNet\"}." } ], "images": [ "scigen/train/3100.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on the order discrimination and sentence reordering coherence tasks. Our approach trades only a small decrease in performance for improved utility of the sentence embeddings over other approaches, achieving close to or the same as the state-of-the-art." ], "reasoning": "To find the model with the highest accuracy in the Order discrimination Earthquake task, look at the 'Order discrimination Earthquake' column and identify the highest value. The highest value is 99.8, achieved by ATTOrderNet.", "question": "Which model achieved the highest accuracy in the Order discrimination Earthquake task?", "answer": "So, the answer is {\"answer\": \"ATTOrderNet\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe PPDs model achieved the highest accuracy in the Reordering Acc task.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'Reordering Acc' column and compare the values. The highest value is 56.1, achieved by ATTOrderNet, not PPDs. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3100.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on the order discrimination and sentence reordering coherence tasks. Our approach trades only a small decrease in performance for improved utility of the sentence embeddings over other approaches, achieving close to or the same as the state-of-the-art." ], "reasoning": "To verify this statement, look at the 'Reordering Acc' column and compare the values. The highest value is 56.1, achieved by ATTOrderNet, not PPDs.", "statement": "The PPDs model achieved the highest accuracy in the Reordering Acc task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: The neural sentence position model hyperparameters used in our coherence experiments. The following settings are used across all tasks: batch size of 32, sentence trimming/padding to a length of 25 words, the vocabulary is set to the 1000 most frequent words in the associated training set. The Adamax optimizer is used Kingma and Ba (2014) with default parameters supplied by Keras Chollet et al. (2015).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Hyperparameter tuning for both tasks is done with a random search, selecting the best validation score averaged across 10 folds for order discrimination and three trials for sentence reordering.Training and tuning. Hyperparameter tuning for both tasks is done with a random search, choosing the hyperparameter set with the best validation score averaged across the 10 folds for order discrimination dataset and for three trials for the sentence reordering task." } ], "images": [ "scigen/train/3101.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The neural sentence position model hyperparameters used in our coherence experiments. The following settings are used across all tasks: batch size of 32, sentence trimming/padding to a length of 25 words, the vocabulary is set to the 1000 most frequent words in the associated training set. The Adamax optimizer is used Kingma and Ba (2014) with default parameters supplied by Keras Chollet et al. (2015)." ], "reasoning": "Hyperparameter tuning for both tasks is done with a random search, selecting the best validation score averaged across 10 folds for order discrimination and three trials for sentence reordering.", "description": "Training and tuning. Hyperparameter tuning for both tasks is done with a random search, choosing the hyperparameter set with the best validation score averaged across the 10 folds for order discrimination dataset and for three trials for the sentence reordering task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 1: The neural sentence position model hyperparameters used in our coherence experiments. The following settings are used across all tasks: batch size of 32, sentence trimming/padding to a length of 25 words, the vocabulary is set to the 1000 most frequent words in the associated training set. The Adamax optimizer is used Kingma and Ba (2014) with default parameters supplied by Keras Chollet et al. (2015).', unfold the question presented below, detailing every step of your calculation. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the total number of epochs used for the Order Discrimination task?\n" }, { "role": "assistant", "content": "The Order Discrimination task includes two datasets: Accident and Earthquake. The number of epochs for Accident is 10, and for Earthquake, it is 5. Summing these gives the total number of epochs. So, the answer is {\"answer\": \"15\"}." } ], "images": [ "scigen/train/3101.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The neural sentence position model hyperparameters used in our coherence experiments. The following settings are used across all tasks: batch size of 32, sentence trimming/padding to a length of 25 words, the vocabulary is set to the 1000 most frequent words in the associated training set. The Adamax optimizer is used Kingma and Ba (2014) with default parameters supplied by Keras Chollet et al. (2015)." ], "reasoning": "The Order Discrimination task includes two datasets: Accident and Earthquake. The number of epochs for Accident is 10, and for Earthquake, it is 5. Summing these gives the total number of epochs.", "question": "What is the total number of epochs used for the Order Discrimination task?", "answer": "So, the answer is {\"answer\": \"15\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Reordering task uses a layer dropout of (0.4, 0.2).\n" }, { "role": "assistant", "content": "The table shows that the Reordering task uses a layer dropout of (0.5, 0.25), not (0.4, 0.2). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3101.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The neural sentence position model hyperparameters used in our coherence experiments. The following settings are used across all tasks: batch size of 32, sentence trimming/padding to a length of 25 words, the vocabulary is set to the 1000 most frequent words in the associated training set. The Adamax optimizer is used Kingma and Ba (2014) with default parameters supplied by Keras Chollet et al. (2015)." ], "reasoning": "The table shows that the Reordering task uses a layer dropout of (0.5, 0.25), not (0.4, 0.2).", "statement": "The Reordering task uses a layer dropout of (0.4, 0.2).", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: ROUGE scores on the CNN/DailyMail summarization task. Our PPD-based heuristic outperforms the suite of established heuristic summarizers. However, the higher performance of the deep-learning models demonstrates that training explicitly for summarization is beneficial.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our model outperforms heuristic approaches by approximately 2 points in ROUGE-1 and ROUGE-L, and 1 point in ROUGE-2, but deep-learning models achieve even higher scores.We observe that our model has ROUGE scores superior to the other heuristic approaches by a margin of approximately 2 points for ROUGE-1 and -L and 1 point for ROUGE-2. In contrast, the deep-learning approaches trained explicitly for summarization achieve even higher scores, suggesting that there is more to a good summary than the sentences simply being introductory-like." } ], "images": [ "scigen/train/3102.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: ROUGE scores on the CNN/DailyMail summarization task. Our PPD-based heuristic outperforms the suite of established heuristic summarizers. However, the higher performance of the deep-learning models demonstrates that training explicitly for summarization is beneficial." ], "reasoning": "Our model outperforms heuristic approaches by approximately 2 points in ROUGE-1 and ROUGE-L, and 1 point in ROUGE-2, but deep-learning models achieve even higher scores.", "description": "We observe that our model has ROUGE scores superior to the other heuristic approaches by a margin of approximately 2 points for ROUGE-1 and -L and 1 point for ROUGE-2. In contrast, the deep-learning approaches trained explicitly for summarization achieve even higher scores, suggesting that there is more to a good summary than the sentences simply being introductory-like.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 3: ROUGE scores on the CNN/DailyMail summarization task. Our PPD-based heuristic outperforms the suite of established heuristic summarizers. However, the higher performance of the deep-learning models demonstrates that training explicitly for summarization is beneficial.', and tackle the question below in a sequential manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nHow does the ROUGE-1 score of our model compare to the heuristic approaches?\n" }, { "role": "assistant", "content": "Our model has a ROUGE-1 score of 30.1, which is higher than the heuristic approaches like TextRank (26.2), Luhn (26.4), SumBasic (27.8), and LexRank (28.4). So, the answer is {\"answer\": \"higher\"}." } ], "images": [ "scigen/train/3102.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: ROUGE scores on the CNN/DailyMail summarization task. Our PPD-based heuristic outperforms the suite of established heuristic summarizers. However, the higher performance of the deep-learning models demonstrates that training explicitly for summarization is beneficial." ], "reasoning": "Our model has a ROUGE-1 score of 30.1, which is higher than the heuristic approaches like TextRank (26.2), Luhn (26.4), SumBasic (27.8), and LexRank (28.4).", "question": "How does the ROUGE-1 score of our model compare to the heuristic approaches?", "answer": "So, the answer is {\"answer\": \"higher\"}." }, { "messages": [ { "role": "user", "content": "Our model has a higher ROUGE-2 score than the SummaRuNNer model. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nOur model has a higher ROUGE-2 score than the SummaRuNNer model.\n" }, { "role": "assistant", "content": "The SummaRuNNer model has a ROUGE-2 score of 16.2, while our model has a ROUGE-2 score of 12.6, which is lower. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3102.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: ROUGE scores on the CNN/DailyMail summarization task. Our PPD-based heuristic outperforms the suite of established heuristic summarizers. However, the higher performance of the deep-learning models demonstrates that training explicitly for summarization is beneficial." ], "reasoning": "The SummaRuNNer model has a ROUGE-2 score of 16.2, while our model has a ROUGE-2 score of 12.6, which is lower.", "statement": "Our model has a higher ROUGE-2 score than the SummaRuNNer model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'TABLE III: The results of the models with fixed or continue-trained ECMo representations.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Continue-training with ECMo representations leads to a performance drop in Multi-view and SMN models on both datasets, likely due to over-fitting from additional parameters.Does further optimization under the matching loss help? So far, we fix the pre-trained ECMo representations in matching. We find that “continue-train” makes the performance of Multi-view and SMN drop on both datasets. This phenomenon may stem from the fact that the matching model is prone to over-fitting with the additional parameters coming from HED. On the other hand, fine-tuning with cross-entropy loss may damnify the original contextualized representations." } ], "images": [ "scigen/train/3103.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: The results of the models with fixed or continue-trained ECMo representations." ], "reasoning": "Continue-training with ECMo representations leads to a performance drop in Multi-view and SMN models on both datasets, likely due to over-fitting from additional parameters.", "description": "Does further optimization under the matching loss help? So far, we fix the pre-trained ECMo representations in matching. We find that “continue-train” makes the performance of Multi-view and SMN drop on both datasets. This phenomenon may stem from the fact that the matching model is prone to over-fitting with the additional parameters coming from HED. On the other hand, fine-tuning with cross-entropy loss may damnify the original contextualized representations.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'TABLE III: The results of the models with fixed or continue-trained ECMo representations.', solve this question and give detail solutions. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the impact of continue-training on the performance of Multi-view and SMN models on the Douban Corpus dataset?\n" }, { "role": "assistant", "content": "The table shows that for the Douban Corpus dataset, continue-training with ECMo representations results in lower MAP, MRR, P@1, R10@1, and R10@2 scores for both Multi-view and SMN models compared to their non-continue-trained counterparts. So, the answer is {\"answer\": \"performance drop\"}." } ], "images": [ "scigen/train/3103.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: The results of the models with fixed or continue-trained ECMo representations." ], "reasoning": "The table shows that for the Douban Corpus dataset, continue-training with ECMo representations results in lower MAP, MRR, P@1, R10@1, and R10@2 scores for both Multi-view and SMN models compared to their non-continue-trained counterparts.", "question": "What is the impact of continue-training on the performance of Multi-view and SMN models on the Douban Corpus dataset?", "answer": "So, the answer is {\"answer\": \"performance drop\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Continue-training improves the performance of Multi-view and SMN models on the Ubuntu Corpus dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nContinue-training improves the performance of Multi-view and SMN models on the Ubuntu Corpus dataset.\n" }, { "role": "assistant", "content": "The table shows that for the Ubuntu Corpus dataset, continue-training with ECMo representations results in lower R2@1, R10@1, and R10@5 scores for both Multi-view and SMN models compared to their non-continue-trained counterparts. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3103.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: The results of the models with fixed or continue-trained ECMo representations." ], "reasoning": "The table shows that for the Ubuntu Corpus dataset, continue-training with ECMo representations results in lower R2@1, R10@1, and R10@5 scores for both Multi-view and SMN models compared to their non-continue-trained counterparts.", "statement": "Continue-training improves the performance of Multi-view and SMN models on the Ubuntu Corpus dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'TABLE I: Evaluation results on the two datasets. Numbers in bold mean that improvement to the original models brought by ECMo is statistically significant (t-test, p-value <0.01 ). Numbers marked with ∗ mean that improvement to ELMo (fine-tune) and CoVe is statistically significant (t-test, p-value <0.01 ). We do not include the results of CoVe and ELMo enhanced models on the Douban data because the two models are not available for Chinese data.'.\n" }, { "role": "assistant", "content": "Combining Multi-view and SMN with CoVe, ELMo (fine-tune), and ECMo improves performance on almost all metrics, with ECMo providing the most significant and consistent improvements.We can observe that the performance of Multi-view and SMN improves on almost all metrics after they are combined with CoVe, ELMo (fine-tune), and ECMo, indicating that contextualized vectors are useful to the multi-turn response selection task. Notably, ECMo brings more significant and more consistent improvement to matching models on both datasets, which verifies the effectiveness of the proposed method. With ECMo, a simple Multi-view even performs better than SMN on the Ubuntu data, although SMN is in a more complicated structure, and thus is capable of capturing more semantic information in conversational contexts." } ], "images": [ "scigen/train/3104.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Evaluation results on the two datasets. Numbers in bold mean that improvement to the original models brought by ECMo is statistically significant (t-test, p-value <0.01 ). Numbers marked with ∗ mean that improvement to ELMo (fine-tune) and CoVe is statistically significant (t-test, p-value <0.01 ). We do not include the results of CoVe and ELMo enhanced models on the Douban data because the two models are not available for Chinese data." ], "reasoning": "Combining Multi-view and SMN with CoVe, ELMo (fine-tune), and ECMo improves performance on almost all metrics, with ECMo providing the most significant and consistent improvements.", "description": "We can observe that the performance of Multi-view and SMN improves on almost all metrics after they are combined with CoVe, ELMo (fine-tune), and ECMo, indicating that contextualized vectors are useful to the multi-turn response selection task. Notably, ECMo brings more significant and more consistent improvement to matching models on both datasets, which verifies the effectiveness of the proposed method. With ECMo, a simple Multi-view even performs better than SMN on the Ubuntu data, although SMN is in a more complicated structure, and thus is capable of capturing more semantic information in conversational contexts.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: TABLE I: Evaluation results on the two datasets. Numbers in bold mean that improvement to the original models brought by ECMo is statistically significant (t-test, p-value <0.01 ). Numbers marked with ∗ mean that improvement to ELMo (fine-tune) and CoVe is statistically significant (t-test, p-value <0.01 ). We do not include the results of CoVe and ELMo enhanced models on the Douban data because the two models are not available for Chinese data.\nQuestion: Which combination of models and contextualized vectors achieves the highest R10@1 score on the Ubuntu Corpus?\n" }, { "role": "assistant", "content": "By examining the table, we see that the highest R10@1 score on the Ubuntu Corpus is achieved by SMN + ECMo, with a score of 0.756. So, the answer is {\"answer\": \"SMN + ECMo\"}" } ], "images": [ "scigen/train/3104.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Evaluation results on the two datasets. Numbers in bold mean that improvement to the original models brought by ECMo is statistically significant (t-test, p-value <0.01 ). Numbers marked with ∗ mean that improvement to ELMo (fine-tune) and CoVe is statistically significant (t-test, p-value <0.01 ). We do not include the results of CoVe and ELMo enhanced models on the Douban data because the two models are not available for Chinese data." ], "reasoning": "By examining the table, we see that the highest R10@1 score on the Ubuntu Corpus is achieved by SMN + ECMo, with a score of 0.756.", "question": "Which combination of models and contextualized vectors achieves the highest R10@1 score on the Ubuntu Corpus?", "answer": "So, the answer is {\"answer\": \"SMN + ECMo\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nMulti-view combined with ECMo achieves a higher R10@5 score on the Ubuntu Corpus than SMN combined with ECMo.\n" }, { "role": "assistant", "content": "The table shows that Multi-view + ECMo has an R10@5 score of 0.967 on the Ubuntu Corpus, while SMN + ECMo has an R10@5 score of 0.966, indicating that the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3104.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Evaluation results on the two datasets. Numbers in bold mean that improvement to the original models brought by ECMo is statistically significant (t-test, p-value <0.01 ). Numbers marked with ∗ mean that improvement to ELMo (fine-tune) and CoVe is statistically significant (t-test, p-value <0.01 ). We do not include the results of CoVe and ELMo enhanced models on the Douban data because the two models are not available for Chinese data." ], "reasoning": "The table shows that Multi-view + ECMo has an R10@5 score of 0.967 on the Ubuntu Corpus, while SMN + ECMo has an R10@5 score of 0.966, indicating that the statement is supported.", "statement": "Multi-view combined with ECMo achieves a higher R10@5 score on the Ubuntu Corpus than SMN combined with ECMo.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'TABLE II: Evaluation results of model ablation.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "ECMoG generally improves matching more than ECMoL for Multi-view, while ECMoL is more effective for SMN due to different interaction operations. Fine-tuning is necessary as models trained only on Twitter data perform worse on Ubuntu dialogues.First, we find that both ECMoL and ECMoG are useful to matching. ECMoG generally brings more improvement to matching than ECMoL for Multi-view while the results reverse for SMN. This phenomenon is mainly due to the different interaction operation of the two models. In Multi-view, the response and context are interacted with corresponding global utterance representations, while in SMN, the interaction is performed word-by-word. Second, fine-tuning is necessary as the performance of models drops without it. Since the Ubuntu dialogues have some domain-specific knowledge, a HED model only trained on Twitter cannot fully capture the contextual information in the Ubuntu data, resulting in inaccurate contextualized word representations. We can see that ECMoG and ECMoL shows different robustness to the noise for Multi-view and SMN due to the different interaction operation. Finally, although fine-tuning is important, one cannot only train a HED model on the domain-specific data, because the size of the data is small and a lot of contextual information in common conversation language will be lost. That explains why model+ECMo (Ubuntu-only) is inferior to all other variants." } ], "images": [ "scigen/train/3105.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Evaluation results of model ablation." ], "reasoning": "ECMoG generally improves matching more than ECMoL for Multi-view, while ECMoL is more effective for SMN due to different interaction operations. Fine-tuning is necessary as models trained only on Twitter data perform worse on Ubuntu dialogues.", "description": "First, we find that both ECMoL and ECMoG are useful to matching. ECMoG generally brings more improvement to matching than ECMoL for Multi-view while the results reverse for SMN. This phenomenon is mainly due to the different interaction operation of the two models. In Multi-view, the response and context are interacted with corresponding global utterance representations, while in SMN, the interaction is performed word-by-word. Second, fine-tuning is necessary as the performance of models drops without it. Since the Ubuntu dialogues have some domain-specific knowledge, a HED model only trained on Twitter cannot fully capture the contextual information in the Ubuntu data, resulting in inaccurate contextualized word representations. We can see that ECMoG and ECMoL shows different robustness to the noise for Multi-view and SMN due to the different interaction operation. Finally, although fine-tuning is important, one cannot only train a HED model on the domain-specific data, because the size of the data is small and a lot of contextual information in common conversation language will be lost. That explains why model+ECMo (Ubuntu-only) is inferior to all other variants.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'TABLE II: Evaluation results of model ablation.', and tackle the question below in a sequential manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model configuration shows the highest R10@5 score on the Ubuntu Corpus?\n" }, { "role": "assistant", "content": "To find the highest R10@5 score on the Ubuntu Corpus, we need to compare the R10@5 values across all model configurations. The highest R10@5 score is 0.975, achieved by the 'SMN + ECMo' configuration. So, the answer is {\"answer\": \"SMN + ECMo\"}" } ], "images": [ "scigen/train/3105.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Evaluation results of model ablation." ], "reasoning": "To find the highest R10@5 score on the Ubuntu Corpus, we need to compare the R10@5 values across all model configurations. The highest R10@5 score is 0.975, achieved by the 'SMN + ECMo' configuration.", "question": "Which model configuration shows the highest R10@5 score on the Ubuntu Corpus?", "answer": "So, the answer is {\"answer\": \"SMN + ECMo\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nMulti-view with ECMoG has a higher R10@1 score on the Ubuntu Corpus than SMN with ECMoG.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the R10@1 scores for 'Multi-view + ECMoG' and 'SMN + ECMoG' on the Ubuntu Corpus. 'Multi-view + ECMoG' has an R10@1 score of 0.858, while 'SMN + ECMoG' has an R10@1 score of 0.864. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3105.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Evaluation results of model ablation." ], "reasoning": "To verify this statement, we compare the R10@1 scores for 'Multi-view + ECMoG' and 'SMN + ECMoG' on the Ubuntu Corpus. 'Multi-view + ECMoG' has an R10@1 score of 0.858, while 'SMN + ECMoG' has an R10@1 score of 0.864. Therefore, the statement is refuted.", "statement": "Multi-view with ECMoG has a higher R10@1 score on the Ubuntu Corpus than SMN with ECMoG.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Percentage of reviewers in levels of expertise to the submissions recommended by our model.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "All reviewers had expertise levels no lower than 2, with 87.9% familiar with the topics and 63.6% having a deep understanding.We note that all recommendations made by our system are reasonable considering that all reviewers had expertise levels no lower than 2. The majority (87.9%) of reviewers reported that they were familiar with the topics of the submissions assigned to them, and 63.6% of the reviewers had deep understanding of the submissions." } ], "images": [ "scigen/train/3106.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Percentage of reviewers in levels of expertise to the submissions recommended by our model." ], "reasoning": "All reviewers had expertise levels no lower than 2, with 87.9% familiar with the topics and 63.6% having a deep understanding.", "description": "We note that all recommendations made by our system are reasonable considering that all reviewers had expertise levels no lower than 2. The majority (87.9%) of reviewers reported that they were familiar with the topics of the submissions assigned to them, and 63.6% of the reviewers had deep understanding of the submissions.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: Percentage of reviewers in levels of expertise to the submissions recommended by our model.', solve this question and give detail solutions. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhat percentage of reviewers had an expertise level of at least 4 and were familiar with the topics of the submissions?\n" }, { "role": "assistant", "content": "The table shows that 63.6% of reviewers had an expertise level of at least 4. The description states that 87.9% of reviewers were familiar with the topics. So, the answer is {\"answer\": \"63.6\"}" } ], "images": [ "scigen/train/3106.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Percentage of reviewers in levels of expertise to the submissions recommended by our model." ], "reasoning": "The table shows that 63.6% of reviewers had an expertise level of at least 4. The description states that 87.9% of reviewers were familiar with the topics.", "question": "What percentage of reviewers had an expertise level of at least 4 and were familiar with the topics of the submissions?", "answer": "So, the answer is {\"answer\": \"63.6\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nAll reviewers had an expertise level of at least 3.\n" }, { "role": "assistant", "content": "The table shows that 100% of reviewers had an expertise level of at least 2, but it does not confirm that all had at least level 3. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3106.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Percentage of reviewers in levels of expertise to the submissions recommended by our model." ], "reasoning": "The table shows that 100% of reviewers had an expertise level of at least 2, but it does not confirm that all had at least level 3.", "statement": "All reviewers had an expertise level of at least 3.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: The mean precision of different baselines with optimal hyperparamters on the NIPS dataset. A reviewer is classified as relevant with a TREC score ≥2.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Precision at k (P@k) measures the percentage of relevant reviewers in the top-k recommendations, with k set to 5 and 10 in the experiments. The proposed approach consistently outperforms strong baselines, with Hidden Topic Model and Doc2Vec being competitive.Evaluation Metric. Precision at k (P@k) is a commonly-used ranking metric on NIPS dataset. P@k is defined to be the percentage of relevant reviewers in the top-k recommendations made by the model to a submission. It is likely that the top-k recommendations made by the model contain reviewers whose relevance information is not available in the ground truth. To address this issue, we first discard reviewers that do not have a relevance information prior to calculating P@k. In our experiments, we set k to be 5 and 10. We note that the results for APT 200 and Single Doc were only available for GT1 and we report them as such. Some approaches including Common Topic Model, Hidden Topic Model and LDA required a hyperparameter (number of topics) to be specified. As is shown, our proposed approach consistently outperforms the strong baselines. We also note that Hidden Topic Model and Doc2Vec are competitive approaches in expertise matching compared against probabilistic models." } ], "images": [ "scigen/train/3107.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The mean precision of different baselines with optimal hyperparamters on the NIPS dataset. A reviewer is classified as relevant with a TREC score ≥2." ], "reasoning": "Precision at k (P@k) measures the percentage of relevant reviewers in the top-k recommendations, with k set to 5 and 10 in the experiments. The proposed approach consistently outperforms strong baselines, with Hidden Topic Model and Doc2Vec being competitive.", "description": "Evaluation Metric. Precision at k (P@k) is a commonly-used ranking metric on NIPS dataset. P@k is defined to be the percentage of relevant reviewers in the top-k recommendations made by the model to a submission. It is likely that the top-k recommendations made by the model contain reviewers whose relevance information is not available in the ground truth. To address this issue, we first discard reviewers that do not have a relevance information prior to calculating P@k. In our experiments, we set k to be 5 and 10. We note that the results for APT 200 and Single Doc were only available for GT1 and we report them as such. Some approaches including Common Topic Model, Hidden Topic Model and LDA required a hyperparameter (number of topics) to be specified. As is shown, our proposed approach consistently outperforms the strong baselines. We also note that Hidden Topic Model and Doc2Vec are competitive approaches in expertise matching compared against probabilistic models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 1: The mean precision of different baselines with optimal hyperparamters on the NIPS dataset. A reviewer is classified as relevant with a TREC score ≥2.', answer the following question. Give detailed solution consisting of each step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method and number of topics combination achieved the highest P@5 GT3 score?\n" }, { "role": "assistant", "content": "To find the highest P@5 GT3 score, we need to compare the P@5 GT3 scores across all methods and number of topics. The highest score is 67.2, achieved by the Common Topic Model with 10 topics. So, the answer is {\"answer\": \"Common Topic Model with 10 topics\"}." } ], "images": [ "scigen/train/3107.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The mean precision of different baselines with optimal hyperparamters on the NIPS dataset. A reviewer is classified as relevant with a TREC score ≥2." ], "reasoning": "To find the highest P@5 GT3 score, we need to compare the P@5 GT3 scores across all methods and number of topics. The highest score is 67.2, achieved by the Common Topic Model with 10 topics.", "question": "Which method and number of topics combination achieved the highest P@5 GT3 score?", "answer": "So, the answer is {\"answer\": \"Common Topic Model with 10 topics\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Hidden Topic Model with 20 topics achieved a higher P@10 GT1 score than the Common Topic Model with 20 topics., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Hidden Topic Model with 20 topics achieved a higher P@10 GT1 score than the Common Topic Model with 20 topics.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the P@10 GT1 scores for Hidden Topic Model with 20 topics and Common Topic Model with 20 topics. The Hidden Topic Model with 20 topics has a score of 58.4, while the Common Topic Model with 20 topics has a score of 43.4. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3107.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The mean precision of different baselines with optimal hyperparamters on the NIPS dataset. A reviewer is classified as relevant with a TREC score ≥2." ], "reasoning": "To verify this statement, we compare the P@10 GT1 scores for Hidden Topic Model with 20 topics and Common Topic Model with 20 topics. The Hidden Topic Model with 20 topics has a score of 58.4, while the Common Topic Model with 20 topics has a score of 43.4. Therefore, the statement is refuted.", "statement": "The Hidden Topic Model with 20 topics achieved a higher P@10 GT1 score than the Common Topic Model with 20 topics.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 10: System ranking according to human judgments and QA-based evaluation for the Newsroom-Abs dataset.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "ext-oracle performs best in QA with 41.31% accuracy, while T-ConvS2S outperforms PtGen and ConvS2S despite having lower ROUGE scores.We did not include summaries from the lead, or ConvS2S+Copy as they were significantly inferior to other models. Based on the oracle extracts, participants can answer 41.31% of the questions correctly. Summaries generated by T-ConvS2S, PtGen, and ConvS2S provide answers to 23.28%, 20.98%, and 11.97% of the questions, respectively. ext-oracle performs best on both QA and judgment elicitation evaluations. However, we should point out that ext-oracle has the advantage of selecting the best set of sentences (as determined by ROUGE) without any length constraints. Consequently, summaries generated by ext-oracle tend to be longer with 38.84 words on average Summaries generated by PtGen, ConvS2S, and T-ConvS2S contain 23.72, 19.41 and 20.66 words, respectively, while gold summaries contain 23.26 words. Perhaps unsurprisingly T-ConvS2S, which was slightly lagging behind PtGen on R1 and RL scores, performs better than both PtGen and ConvS2S in terms of correctly answering questions. The QA evaluation shows that T-ConvS2S is able to generate informative summaries with pertinent information embedded in the appropriate context. Pairwise differences between systems are all statistically significant (p<0.01) with the exception of T-ConvS2S and PtGen, and, PtGen and ConvS2S." } ], "images": [ "scigen/train/3108.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: System ranking according to human judgments and QA-based evaluation for the Newsroom-Abs dataset." ], "reasoning": "ext-oracle performs best in QA with 41.31% accuracy, while T-ConvS2S outperforms PtGen and ConvS2S despite having lower ROUGE scores.", "description": "We did not include summaries from the lead, or ConvS2S+Copy as they were significantly inferior to other models. Based on the oracle extracts, participants can answer 41.31% of the questions correctly. Summaries generated by T-ConvS2S, PtGen, and ConvS2S provide answers to 23.28%, 20.98%, and 11.97% of the questions, respectively. ext-oracle performs best on both QA and judgment elicitation evaluations. However, we should point out that ext-oracle has the advantage of selecting the best set of sentences (as determined by ROUGE) without any length constraints. Consequently, summaries generated by ext-oracle tend to be longer with 38.84 words on average Summaries generated by PtGen, ConvS2S, and T-ConvS2S contain 23.72, 19.41 and 20.66 words, respectively, while gold summaries contain 23.26 words. Perhaps unsurprisingly T-ConvS2S, which was slightly lagging behind PtGen on R1 and RL scores, performs better than both PtGen and ConvS2S in terms of correctly answering questions. The QA evaluation shows that T-ConvS2S is able to generate informative summaries with pertinent information embedded in the appropriate context. Pairwise differences between systems are all statistically significant (p<0.01) with the exception of T-ConvS2S and PtGen, and, PtGen and ConvS2S.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 10: System ranking according to human judgments and QA-based evaluation for the Newsroom-Abs dataset.', unfold the steps to solve the question given next. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nQuestion: Which model has the highest QA score and what is that score?\n" }, { "role": "assistant", "content": "The table shows the QA scores for different models. By comparing the QA scores, we see that the gold model has the highest QA score of 90.98. So, the answer is {\"answer\": \"gold, 90.98\"}." } ], "images": [ "scigen/train/3108.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: System ranking according to human judgments and QA-based evaluation for the Newsroom-Abs dataset." ], "reasoning": "The table shows the QA scores for different models. By comparing the QA scores, we see that the gold model has the highest QA score of 90.98.", "question": "Which model has the highest QA score and what is that score?", "answer": "So, the answer is {\"answer\": \"gold, 90.98\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis T-ConvS2S has a higher QA score than PtGen. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nT-ConvS2S has a higher QA score than PtGen.\n" }, { "role": "assistant", "content": "The table shows that T-ConvS2S has a QA score of 23.28, while PtGen has a QA score of 20.98. Therefore, T-ConvS2S has a higher QA score than PtGen. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3108.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: System ranking according to human judgments and QA-based evaluation for the Newsroom-Abs dataset." ], "reasoning": "The table shows that T-ConvS2S has a QA score of 23.28, while PtGen has a QA score of 20.98. Therefore, T-ConvS2S has a higher QA score than PtGen.", "statement": "T-ConvS2S has a higher QA score than PtGen.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: Performance of extractive baselines on CNN, DailyMail, NY Times, Newsroom, and XSum datasets. We report ROUGE scores for the lead baseline and ext-oracle, the extractive oracle system. All results are computed on the test set.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The lead baseline performs well on CNN, DailyMail, NY Times, and Newsroom, indicating extractive summaries, while it performs poorly on XSum, indicating abstractive summaries.The lead baseline performs extremely well on CNN, DailyMail, NY Times and Newsroom confirming that they contain fairly extractive summaries. ext-oracle further shows that improved sentence selection would bring further performance gains to extractive approaches. Interestingly, lead and ext-oracle perform poorly on XSum underlying the fact that it contains genuinely abstractive summaries." } ], "images": [ "scigen/train/3109.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance of extractive baselines on CNN, DailyMail, NY Times, Newsroom, and XSum datasets. We report ROUGE scores for the lead baseline and ext-oracle, the extractive oracle system. All results are computed on the test set." ], "reasoning": "The lead baseline performs well on CNN, DailyMail, NY Times, and Newsroom, indicating extractive summaries, while it performs poorly on XSum, indicating abstractive summaries.", "description": "The lead baseline performs extremely well on CNN, DailyMail, NY Times and Newsroom confirming that they contain fairly extractive summaries. ext-oracle further shows that improved sentence selection would bring further performance gains to extractive approaches. Interestingly, lead and ext-oracle perform poorly on XSum underlying the fact that it contains genuinely abstractive summaries.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 4: Performance of extractive baselines on CNN, DailyMail, NY Times, Newsroom, and XSum datasets. We report ROUGE scores for the lead baseline and ext-oracle, the extractive oracle system. All results are computed on the test set.'.\n\nQuestion: Which dataset shows the highest R1 score for the ext-oracle approach?\n" }, { "role": "assistant", "content": "By comparing the R1 scores for the ext-oracle approach across all datasets, Newsroom-Ext has the highest R1 score of 89.63. So, the answer is {\"answer\": \"Newsroom-Ext\"}" } ], "images": [ "scigen/train/3109.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance of extractive baselines on CNN, DailyMail, NY Times, Newsroom, and XSum datasets. We report ROUGE scores for the lead baseline and ext-oracle, the extractive oracle system. All results are computed on the test set." ], "reasoning": "By comparing the R1 scores for the ext-oracle approach across all datasets, Newsroom-Ext has the highest R1 score of 89.63.", "question": "Which dataset shows the highest R1 score for the ext-oracle approach?", "answer": "So, the answer is {\"answer\": \"Newsroom-Ext\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The lead baseline performs best on the DailyMail dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe lead baseline performs best on the DailyMail dataset.\n" }, { "role": "assistant", "content": "By comparing the lead baseline scores (R1, R2, RL) across all datasets, DailyMail has the highest R1 score of 40.68, confirming the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3109.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance of extractive baselines on CNN, DailyMail, NY Times, Newsroom, and XSum datasets. We report ROUGE scores for the lead baseline and ext-oracle, the extractive oracle system. All results are computed on the test set." ], "reasoning": "By comparing the lead baseline scores (R1, R2, RL) across all datasets, DailyMail has the highest R1 score of 40.68, confirming the statement.", "statement": "The lead baseline performs best on the DailyMail dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Proportion of novel n-grams in summaries generated by various models on the XSum test set.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The table compares the percentage of novel n-grams in generated summaries across different models, highlighting their ability to produce genuinely abstractive summaries.S6SS1SSS0Px2 How Abstractive are the Generated Summaries? We further assessed the extent to which various models are able to perform rewriting by generating genuinely abstractive summaries. We omit extractive systems (lead and ext-oracle) as they are not capable of generating summaries from scratch with novel n-grams." } ], "images": [ "scigen/train/3110.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Proportion of novel n-grams in summaries generated by various models on the XSum test set." ], "reasoning": "The table compares the percentage of novel n-grams in generated summaries across different models, highlighting their ability to produce genuinely abstractive summaries.", "description": "S6SS1SSS0Px2 How Abstractive are the Generated Summaries? We further assessed the extent to which various models are able to perform rewriting by generating genuinely abstractive summaries. We omit extractive systems (lead and ext-oracle) as they are not capable of generating summaries from scratch with novel n-grams.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 7: Proportion of novel n-grams in summaries generated by various models on the XSum test set.', solve this question and give detail solutions. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich model has the highest percentage of novel bigrams in generated summaries?\n" }, { "role": "assistant", "content": "To find the model with the highest percentage of novel bigrams, compare the values in the '% of novel n-grams in generated summaries' column for bigrams. The gold model has the highest percentage at 83.45%. So, the answer is {\"answer\": \"gold\"}." } ], "images": [ "scigen/train/3110.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Proportion of novel n-grams in summaries generated by various models on the XSum test set." ], "reasoning": "To find the model with the highest percentage of novel bigrams, compare the values in the '% of novel n-grams in generated summaries' column for bigrams. The gold model has the highest percentage at 83.45%.", "question": "Which model has the highest percentage of novel bigrams in generated summaries?", "answer": "So, the answer is {\"answer\": \"gold\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Seq2Seq model has the highest percentage of novel unigrams in generated summaries.\n" }, { "role": "assistant", "content": "The table shows that the Seq2Seq model has 36.66% of novel unigrams, while the gold model has 35.76%. Therefore, the statement is correct. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3110.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Proportion of novel n-grams in summaries generated by various models on the XSum test set." ], "reasoning": "The table shows that the Seq2Seq model has 36.66% of novel unigrams, while the gold model has 35.76%. Therefore, the statement is correct.", "statement": "The Seq2Seq model has the highest percentage of novel unigrams in generated summaries.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Figure 5: Summaries ranked from least to most informative from the XSum and Newsroom-Abs test sets. We also show their informativeness scores (higher is better).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "XSum summaries are generally more informative than Newsroom-Abs summaries, with higher informativeness scores and a greater proportion of summaries deemed informative.We measure the informativeness of each dataset as the average score assigned by crowdworkers across summaries; a summary receives a score of 3 if it is deemed informative, 2 if it is partially informative, and 1 if it is uninformative. Therefore, the informativeness score for a dataset as a whole may vary from 1 to 3, with 1 being least informative and 3 being most informative. We also report the proportion of times a dataset was considered informative, partially informative, and uninformative. XSum reference summaries were mostly considered informative (68%), around a quarter of them (26%) were deemed partially informative, and only 6% were found uninformative. In comparison, less than half (48.67%) of Newsroom-Abs summaries were found informative, the remaining being either partially informative or uninformative. XSum achieved an informativeness score of 2.62 compared to 2.30 for the Newsroom dataset. We carried out pairwise comparisons to assess whether informativeness differences are statistically significant. We found that XSum is significantly more informative than Newsroom (using a one-way ANOVA with posthoc Tukey HSD tests; p<0.01). For both datasets, as the size of n-grams increases, their chance of having been seen in the training summaries decreases rapidly. For XSum, this percentage drops to almost 0% for any n-grams larger than size 10. Interestingly, this is not the case for Newsroom-Abs, where more than 4% of n-grams (sizes 10 to 15) in test summaries have been already seen in training summaries. This result suggests that Newsroom-Abs summaries are somewhat formulaic displaying a certain degree of repetition that goes beyond simple phrases. which is rather generic and would apply to any movie, not just the Exorcist. In fact two crowdworkers labeled this summary as uninformative and one as partially informative (informativeness score is 1.33). XSum summaries exhibit more variation for n-grams of size larger than 5 corraborating our claim that Newsroom-Abs summaries are more repetitive." } ], "images": [ "scigen/train/3111.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 5: Summaries ranked from least to most informative from the XSum and Newsroom-Abs test sets. We also show their informativeness scores (higher is better)." ], "reasoning": "XSum summaries are generally more informative than Newsroom-Abs summaries, with higher informativeness scores and a greater proportion of summaries deemed informative.", "description": "We measure the informativeness of each dataset as the average score assigned by crowdworkers across summaries; a summary receives a score of 3 if it is deemed informative, 2 if it is partially informative, and 1 if it is uninformative. Therefore, the informativeness score for a dataset as a whole may vary from 1 to 3, with 1 being least informative and 3 being most informative. We also report the proportion of times a dataset was considered informative, partially informative, and uninformative. XSum reference summaries were mostly considered informative (68%), around a quarter of them (26%) were deemed partially informative, and only 6% were found uninformative. In comparison, less than half (48.67%) of Newsroom-Abs summaries were found informative, the remaining being either partially informative or uninformative. XSum achieved an informativeness score of 2.62 compared to 2.30 for the Newsroom dataset. We carried out pairwise comparisons to assess whether informativeness differences are statistically significant. We found that XSum is significantly more informative than Newsroom (using a one-way ANOVA with posthoc Tukey HSD tests; p<0.01). For both datasets, as the size of n-grams increases, their chance of having been seen in the training summaries decreases rapidly. For XSum, this percentage drops to almost 0% for any n-grams larger than size 10. Interestingly, this is not the case for Newsroom-Abs, where more than 4% of n-grams (sizes 10 to 15) in test summaries have been already seen in training summaries. This result suggests that Newsroom-Abs summaries are somewhat formulaic displaying a certain degree of repetition that goes beyond simple phrases. which is rather generic and would apply to any movie, not just the Exorcist. In fact two crowdworkers labeled this summary as uninformative and one as partially informative (informativeness score is 1.33). XSum summaries exhibit more variation for n-grams of size larger than 5 corraborating our claim that Newsroom-Abs summaries are more repetitive.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the average informativeness score for XSum and Newsroom-Abs summaries?\nSolve the above question based on the table titled 'Figure 5: Summaries ranked from least to most informative from the XSum and Newsroom-Abs test sets. We also show their informativeness scores (higher is better).'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The description states that XSum achieved an informativeness score of 2.62, while Newsroom-Abs achieved a score of 2.30. So, the answer is {\"answer\": \"XSum: 2.62, Newsroom-Abs: 2.30\"}." } ], "images": [ "scigen/train/3111.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 5: Summaries ranked from least to most informative from the XSum and Newsroom-Abs test sets. We also show their informativeness scores (higher is better)." ], "reasoning": "The description states that XSum achieved an informativeness score of 2.62, while Newsroom-Abs achieved a score of 2.30.", "question": "What is the average informativeness score for XSum and Newsroom-Abs summaries?", "answer": "So, the answer is {\"answer\": \"XSum: 2.62, Newsroom-Abs: 2.30\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nXSum summaries are more repetitive than Newsroom-Abs summaries.\n" }, { "role": "assistant", "content": "The description indicates that Newsroom-Abs summaries are more repetitive, with more than 4% of n-grams (sizes 10 to 15) in test summaries already seen in training summaries, while XSum summaries exhibit more variation for n-grams of size larger than 5. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3111.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 5: Summaries ranked from least to most informative from the XSum and Newsroom-Abs test sets. We also show their informativeness scores (higher is better)." ], "reasoning": "The description indicates that Newsroom-Abs summaries are more repetitive, with more than 4% of n-grams (sizes 10 to 15) in test summaries already seen in training summaries, while XSum summaries exhibit more variation for n-grams of size larger than 5.", "statement": "XSum summaries are more repetitive than Newsroom-Abs summaries.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 11: XSum and Newsroom-Abs summaries and their informativeness. The middle column proportionately shows the number times a summary was judged “Informative”, “Partially Informative”, or “Uninformative.” The last column shows the informativeness score for each dataset (higher is better).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "XSum summaries are more informative than Newsroom-Abs summaries, with higher informativeness scores and a greater proportion of summaries deemed informative.We measure the informativeness of each dataset as the average score assigned by crowdworkers across summaries; a summary receives a score of 3 if it is deemed informative, 2 if it is partially informative, and 1 if it is uninformative. Therefore, the informativeness score for a dataset as a whole may vary from 1 to 3, with 1 being least informative and 3 being most informative. We also report the proportion of times a dataset was considered informative, partially informative, and uninformative. XSum reference summaries were mostly considered informative (68%), around a quarter of them (26%) were deemed partially informative, and only 6% were found uninformative. In comparison, less than half (48.67%) of Newsroom-Abs summaries were found informative, the remaining being either partially informative or uninformative. XSum achieved an informativeness score of 2.62 compared to 2.30 for the Newsroom dataset. We carried out pairwise comparisons to assess whether informativeness differences are statistically significant. We found that XSum is significantly more informative than Newsroom (using a one-way ANOVA with posthoc Tukey HSD tests; p<0.01)." } ], "images": [ "scigen/train/3112.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: XSum and Newsroom-Abs summaries and their informativeness. The middle column proportionately shows the number times a summary was judged “Informative”, “Partially Informative”, or “Uninformative.” The last column shows the informativeness score for each dataset (higher is better)." ], "reasoning": "XSum summaries are more informative than Newsroom-Abs summaries, with higher informativeness scores and a greater proportion of summaries deemed informative.", "description": "We measure the informativeness of each dataset as the average score assigned by crowdworkers across summaries; a summary receives a score of 3 if it is deemed informative, 2 if it is partially informative, and 1 if it is uninformative. Therefore, the informativeness score for a dataset as a whole may vary from 1 to 3, with 1 being least informative and 3 being most informative. We also report the proportion of times a dataset was considered informative, partially informative, and uninformative. XSum reference summaries were mostly considered informative (68%), around a quarter of them (26%) were deemed partially informative, and only 6% were found uninformative. In comparison, less than half (48.67%) of Newsroom-Abs summaries were found informative, the remaining being either partially informative or uninformative. XSum achieved an informativeness score of 2.62 compared to 2.30 for the Newsroom dataset. We carried out pairwise comparisons to assess whether informativeness differences are statistically significant. We found that XSum is significantly more informative than Newsroom (using a one-way ANOVA with posthoc Tukey HSD tests; p<0.01).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 11: XSum and Newsroom-Abs summaries and their informativeness. The middle column proportionately shows the number times a summary was judged “Informative”, “Partially Informative”, or “Uninformative.” The last column shows the informativeness score for each dataset (higher is better).', answer the following question. Give detailed solution consisting of each step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich dataset has a higher proportion of summaries considered partially informative?\n" }, { "role": "assistant", "content": "Compare the 'Part. Informative' percentages for XSum and Newsroom-Abs. XSum has 26.00% and Newsroom-Abs has 33.33%. So, the answer is {\"answer\": \"Newsroom-Abs\"}." } ], "images": [ "scigen/train/3112.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: XSum and Newsroom-Abs summaries and their informativeness. The middle column proportionately shows the number times a summary was judged “Informative”, “Partially Informative”, or “Uninformative.” The last column shows the informativeness score for each dataset (higher is better)." ], "reasoning": "Compare the 'Part. Informative' percentages for XSum and Newsroom-Abs. XSum has 26.00% and Newsroom-Abs has 33.33%.", "question": "Which dataset has a higher proportion of summaries considered partially informative?", "answer": "So, the answer is {\"answer\": \"Newsroom-Abs\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence XSum summaries are more often considered uninformative than Newsroom-Abs summaries. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nXSum summaries are more often considered uninformative than Newsroom-Abs summaries.\n" }, { "role": "assistant", "content": "Compare the 'Uninformative' percentages for XSum and Newsroom-Abs. XSum has 6.00% and Newsroom-Abs has 18.00%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3112.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: XSum and Newsroom-Abs summaries and their informativeness. The middle column proportionately shows the number times a summary was judged “Informative”, “Partially Informative”, or “Uninformative.” The last column shows the informativeness score for each dataset (higher is better)." ], "reasoning": "Compare the 'Uninformative' percentages for XSum and Newsroom-Abs. XSum has 6.00% and Newsroom-Abs has 18.00%.", "statement": "XSum summaries are more often considered uninformative than Newsroom-Abs summaries.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 7: LM method does not denoise, but NMT method (proposed) does; and a denoised model has improved general translation accuracy. P1+NMT 20%: fine-tune P1 with top 20% selection by NMT method. P1+NNLM 20%: fine-tune P1 with top 20% selection by NNLM method.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The proposed method (P8) significantly outperforms the baseline (P1) and NNLM method (P9) in BLEU scores across n2014, d2015, and patent datasets, demonstrating its effectiveness on noisy data.The NNLM method does not discern noise and thus the top selection would be as noisy as the baseline data. As a result, fine-tuning the noisy baseline (P1) would not improve. As a matter of fact, the patent BLEU drops over baseline, probably indicating that domain data selection causes data bias. The proposed method, on the other hand, performs clearly better (P8), for example, compared to P9, +2.5 BLEU on n2014, +3.8 BLEU on d2015 and +10.4 BLEU on patent. These prove the effectiveness of the proposed method in producing better systems on noisy data." } ], "images": [ "scigen/train/3113.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: LM method does not denoise, but NMT method (proposed) does; and a denoised model has improved general translation accuracy. P1+NMT 20%: fine-tune P1 with top 20% selection by NMT method. P1+NNLM 20%: fine-tune P1 with top 20% selection by NNLM method." ], "reasoning": "The proposed method (P8) significantly outperforms the baseline (P1) and NNLM method (P9) in BLEU scores across n2014, d2015, and patent datasets, demonstrating its effectiveness on noisy data.", "description": "The NNLM method does not discern noise and thus the top selection would be as noisy as the baseline data. As a result, fine-tuning the noisy baseline (P1) would not improve. As a matter of fact, the patent BLEU drops over baseline, probably indicating that domain data selection causes data bias. The proposed method, on the other hand, performs clearly better (P8), for example, compared to P9, +2.5 BLEU on n2014, +3.8 BLEU on d2015 and +10.4 BLEU on patent. These prove the effectiveness of the proposed method in producing better systems on noisy data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 7: LM method does not denoise, but NMT method (proposed) does; and a denoised model has improved general translation accuracy. P1+NMT 20%: fine-tune P1 with top 20% selection by NMT method. P1+NNLM 20%: fine-tune P1 with top 20% selection by NNLM method.\nQuestion: Which system shows the highest improvement in BLEU score on the patent dataset compared to the baseline?\n" }, { "role": "assistant", "content": "The baseline (P1) has a BLEU score of 37.2 on the patent dataset. The proposed method (P8) has a BLEU score of 45.8, which is an improvement of 8.6 points. The NNLM method (P9) has a BLEU score of 35.4, which is a decrease of 1.8 points. Therefore, P8 shows the highest improvement. So, the answer is {\"answer\": \"P8\"}" } ], "images": [ "scigen/train/3113.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: LM method does not denoise, but NMT method (proposed) does; and a denoised model has improved general translation accuracy. P1+NMT 20%: fine-tune P1 with top 20% selection by NMT method. P1+NNLM 20%: fine-tune P1 with top 20% selection by NNLM method." ], "reasoning": "The baseline (P1) has a BLEU score of 37.2 on the patent dataset. The proposed method (P8) has a BLEU score of 45.8, which is an improvement of 8.6 points. The NNLM method (P9) has a BLEU score of 35.4, which is a decrease of 1.8 points. Therefore, P8 shows the highest improvement.", "question": "Which system shows the highest improvement in BLEU score on the patent dataset compared to the baseline?", "answer": "So, the answer is {\"answer\": \"P8\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe NNLM method (P9) performs better than the baseline (P1) on the n2014 dataset.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for P9 on the n2014 dataset is 31.8, while the BLEU score for the baseline (P1) is 31.6. Since 31.8 is higher than 31.6, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3113.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: LM method does not denoise, but NMT method (proposed) does; and a denoised model has improved general translation accuracy. P1+NMT 20%: fine-tune P1 with top 20% selection by NMT method. P1+NNLM 20%: fine-tune P1 with top 20% selection by NNLM method." ], "reasoning": "The table shows that the BLEU score for P9 on the n2014 dataset is 31.8, while the BLEU score for the baseline (P1) is 31.6. Since 31.8 is higher than 31.6, the statement is supported.", "statement": "The NNLM method (P9) performs better than the baseline (P1) on the n2014 dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6: Nested datasets: Data order is important for denoising. S80%⊃S40%⊃S20% with stricter/smaller set less noisy.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The experiment involves fine-tuning a noisy baseline model on progressively less noisy subsets of data, confirming the importance of data order in denoising.In another experiment, we select 3 data subsets based on the amount of noise in each sentence pair, each subset being noise-reduced to different degree. For example, we select top 80% least noisy sentence pairs (denoted as S80%) out the entire Paracrawl data. Then we select the top half of S80% which is essentially 40% of the Paracrawl data. We denote it as S40%, similarly, S20%, therefore S80%⊃S40%⊃S20%. Then we fine-tune P1 (noisy baseline) on S80% with early stopping on devset, followed by the fine-tuning on S40% and then S20%. This also confirms the importance of the right data order in denoising." } ], "images": [ "scigen/train/3114.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Nested datasets: Data order is important for denoising. S80%⊃S40%⊃S20% with stricter/smaller set less noisy." ], "reasoning": "The experiment involves fine-tuning a noisy baseline model on progressively less noisy subsets of data, confirming the importance of data order in denoising.", "description": "In another experiment, we select 3 data subsets based on the amount of noise in each sentence pair, each subset being noise-reduced to different degree. For example, we select top 80% least noisy sentence pairs (denoted as S80%) out the entire Paracrawl data. Then we select the top half of S80% which is essentially 40% of the Paracrawl data. We denote it as S40%, similarly, S20%, therefore S80%⊃S40%⊃S20%. Then we fine-tune P1 (noisy baseline) on S80% with early stopping on devset, followed by the fine-tuning on S40% and then S20%. This also confirms the importance of the right data order in denoising.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 6: Nested datasets: Data order is important for denoising. S80%⊃S40%⊃S20% with stricter/smaller set less noisy.', answer the following question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the n2014 score for the subset with the least noise?\n" }, { "role": "assistant", "content": "The subset with the least noise is S20%, and the n2014 score for S20% is 34.4. So, the answer is {\"answer\": \"34.4\"}" } ], "images": [ "scigen/train/3114.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Nested datasets: Data order is important for denoising. S80%⊃S40%⊃S20% with stricter/smaller set less noisy." ], "reasoning": "The subset with the least noise is S20%, and the n2014 score for S20% is 34.4.", "question": "What is the n2014 score for the subset with the least noise?", "answer": "So, the answer is {\"answer\": \"34.4\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe patent score improves as the noise in the data subsets decreases.\n" }, { "role": "assistant", "content": "The patent scores for the subsets S80%, S40%, and S20% are 44.3, 45.1, and 45.6 respectively, showing an improvement as the noise decreases. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3114.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Nested datasets: Data order is important for denoising. S80%⊃S40%⊃S20% with stricter/smaller set less noisy." ], "reasoning": "The patent scores for the subsets S80%, S40%, and S20% are 44.3, 45.1, and 45.6 respectively, showing an improvement as the noise decreases.", "statement": "The patent score improves as the noise in the data subsets decreases.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 6: Ablation. Out-of-domain scores for Chunking with POS as auxiliary task, averaged across the 6 source domains.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Learnable α parameters are preferable over constant α parameters, and learned β parameters marginally outperform skip-connections in the hard parameter sharing setting.Different types of sharing may be more important than others. We investigate the impact of i) the α parameters; ii) the β parameters; and iii) the division into subspaces with an orthogonality penalty. We also evaluate whether concatenation of the outputs of each layer is a reasonable alternative to our mixture model. Overall, we find that learnable α parameters are preferable over constant α parameters. Learned β parameters marginally outperform skip-connections in the hard parameter sharing setting, while skip-connections are competitive with learned β values in the learned α setting." } ], "images": [ "scigen/train/3115.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Ablation. Out-of-domain scores for Chunking with POS as auxiliary task, averaged across the 6 source domains." ], "reasoning": "Learnable α parameters are preferable over constant α parameters, and learned β parameters marginally outperform skip-connections in the hard parameter sharing setting.", "description": "Different types of sharing may be more important than others. We investigate the impact of i) the α parameters; ii) the β parameters; and iii) the division into subspaces with an orthogonality penalty. We also evaluate whether concatenation of the outputs of each layer is a reasonable alternative to our mixture model. Overall, we find that learnable α parameters are preferable over constant α parameters. Learned β parameters marginally outperform skip-connections in the hard parameter sharing setting, while skip-connections are competitive with learned β values in the learned α setting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 6: Ablation. Out-of-domain scores for Chunking with POS as auxiliary task, averaged across the 6 source domains.' and proceed to solve the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich layer sharing method achieves the highest average score in the learned α (soft) task sharing setting?\n" }, { "role": "assistant", "content": "To find the highest average score in the learned α (soft) task sharing setting, we compare the average scores of all layer sharing methods under this setting. The highest average score is 86.15, achieved by the 'Mixture + subspaces' method. So, the answer is {\"answer\": \"Mixture + subspaces\"}." } ], "images": [ "scigen/train/3115.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Ablation. Out-of-domain scores for Chunking with POS as auxiliary task, averaged across the 6 source domains." ], "reasoning": "To find the highest average score in the learned α (soft) task sharing setting, we compare the average scores of all layer sharing methods under this setting. The highest average score is 86.15, achieved by the 'Mixture + subspaces' method.", "question": "Which layer sharing method achieves the highest average score in the learned α (soft) task sharing setting?", "answer": "So, the answer is {\"answer\": \"Mixture + subspaces\"}." }, { "messages": [ { "role": "user", "content": "Skip-connections achieve the highest average score in the learned α (soft) task sharing setting. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nSkip-connections achieve the highest average score in the learned α (soft) task sharing setting.\n" }, { "role": "assistant", "content": "The table shows that in the learned α (soft) task sharing setting, the highest average score is 86.15, achieved by the 'Mixture + subspaces' method, not skip-connections. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3115.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Ablation. Out-of-domain scores for Chunking with POS as auxiliary task, averaged across the 6 source domains." ], "reasoning": "The table shows that in the learned α (soft) task sharing setting, the highest average score is 86.15, achieved by the 'Mixture + subspaces' method, not skip-connections.", "statement": "Skip-connections achieve the highest average score in the learned α (soft) task sharing setting.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: Accuracy scores for chunking on in-domain and out-of-domain test sets with POS as auxiliary task. Out-of-domain results for each target domain are averages across the 6 remaining source domains. Error reduction vs. single task: 12.8% (in-domain), 8.9% (out-of-domain); vs. hard parameter sharing: 14.8% (in-domain).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Sluice networks outperform other models in most domains, especially in out-of-domain settings, except for the telephone conversation domain where cross-stitch networks perform better.During training, we uniformly sample from the data for each task. We perform early stopping with patience of 2 based on the main task and hyperparameter optimization on the in-domain development data of the newswire domain. We use the same hyperparameters for all comparison models across all domains. On average, sluice networks significantly outperform all other model architectures on both in-domain and out-of-domain data and perform best for all domains, except for the telephone conversation (tc) domain, where they are outperformed by cross-stitch networks. The performance boost is particularly significant for the out-of-domain setting, where sluice networks add more than 1 point in accuracy compared to hard parameter sharing and almost .5 compared to the strongest baseline on average, demonstrating that sluice networks are particularly useful to help a model generalize better. Overall, this demonstrates that our meta-architecture for learning which parts of multi-task models to share, with a small set of additional parameters to learn, can achieve significant and consistent improvements over strong baseline methods." } ], "images": [ "scigen/train/3116.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracy scores for chunking on in-domain and out-of-domain test sets with POS as auxiliary task. Out-of-domain results for each target domain are averages across the 6 remaining source domains. Error reduction vs. single task: 12.8% (in-domain), 8.9% (out-of-domain); vs. hard parameter sharing: 14.8% (in-domain)." ], "reasoning": "Sluice networks outperform other models in most domains, especially in out-of-domain settings, except for the telephone conversation domain where cross-stitch networks perform better.", "description": "During training, we uniformly sample from the data for each task. We perform early stopping with patience of 2 based on the main task and hyperparameter optimization on the in-domain development data of the newswire domain. We use the same hyperparameters for all comparison models across all domains. On average, sluice networks significantly outperform all other model architectures on both in-domain and out-of-domain data and perform best for all domains, except for the telephone conversation (tc) domain, where they are outperformed by cross-stitch networks. The performance boost is particularly significant for the out-of-domain setting, where sluice networks add more than 1 point in accuracy compared to hard parameter sharing and almost .5 compared to the strongest baseline on average, demonstrating that sluice networks are particularly useful to help a model generalize better. Overall, this demonstrates that our meta-architecture for learning which parts of multi-task models to share, with a small set of additional parameters to learn, can achieve significant and consistent improvements over strong baseline methods.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nTable title: Table 3: Accuracy scores for chunking on in-domain and out-of-domain test sets with POS as auxiliary task. Out-of-domain results for each target domain are averages across the 6 remaining source domains. Error reduction vs. single task: 12.8% (in-domain), 8.9% (out-of-domain); vs. hard parameter sharing: 14.8% (in-domain).\n\nQuestion: Which model performs best in the telephone conversation (tc) domain for in-domain results?\n" }, { "role": "assistant", "content": "The table shows that for in-domain results in the telephone conversation (tc) domain, the cross-stitch networks have the highest score of 91.47. So, the answer is {\"answer\": \"Cross-stitch networks\"}" } ], "images": [ "scigen/train/3116.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracy scores for chunking on in-domain and out-of-domain test sets with POS as auxiliary task. Out-of-domain results for each target domain are averages across the 6 remaining source domains. Error reduction vs. single task: 12.8% (in-domain), 8.9% (out-of-domain); vs. hard parameter sharing: 14.8% (in-domain)." ], "reasoning": "The table shows that for in-domain results in the telephone conversation (tc) domain, the cross-stitch networks have the highest score of 91.47.", "question": "Which model performs best in the telephone conversation (tc) domain for in-domain results?", "answer": "So, the answer is {\"answer\": \"Cross-stitch networks\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Sluice networks outperform all other models in the telephone conversation (tc) domain for out-of-domain results. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nSluice networks outperform all other models in the telephone conversation (tc) domain for out-of-domain results.\n" }, { "role": "assistant", "content": "The table shows that for out-of-domain results in the telephone conversation (tc) domain, cross-stitch networks have a higher score (85.51) compared to sluice networks (81.87). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3116.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracy scores for chunking on in-domain and out-of-domain test sets with POS as auxiliary task. Out-of-domain results for each target domain are averages across the 6 remaining source domains. Error reduction vs. single task: 12.8% (in-domain), 8.9% (out-of-domain); vs. hard parameter sharing: 14.8% (in-domain)." ], "reasoning": "The table shows that for out-of-domain results in the telephone conversation (tc) domain, cross-stitch networks have a higher score (85.51) compared to sluice networks (81.87).", "statement": "Sluice networks outperform all other models in the telephone conversation (tc) domain for out-of-domain results.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: Test accuracy scores for different target domains with nw as source domain for named entity recognition (main task) and simplified semantic role labeling with POS tagging as auxiliary task for baselines and Sluice networks. ID: in-domain. OOD: out-of-domain.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Sluice networks outperform other models in NER and semantic role labeling tasks, achieving the best performance in 5 out of 7 domains for NER and 4 out of 7 domains for semantic role labeling.We now evaluate sluice nets on NER with POS tagging as auxiliary task and simplified semantic role labeling with POS tagging as auxiliary task. Sluice networks outperform the comparison models for both tasks on in-domain test data and successfully generalize to out-of-domain test data on average. They yield the best performance on 5 out of 7 domains and 4 out of 7 domains for NER and semantic role labeling." } ], "images": [ "scigen/train/3117.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Test accuracy scores for different target domains with nw as source domain for named entity recognition (main task) and simplified semantic role labeling with POS tagging as auxiliary task for baselines and Sluice networks. ID: in-domain. OOD: out-of-domain." ], "reasoning": "Sluice networks outperform other models in NER and semantic role labeling tasks, achieving the best performance in 5 out of 7 domains for NER and 4 out of 7 domains for semantic role labeling.", "description": "We now evaluate sluice nets on NER with POS tagging as auxiliary task and simplified semantic role labeling with POS tagging as auxiliary task. Sluice networks outperform the comparison models for both tasks on in-domain test data and successfully generalize to out-of-domain test data on average. They yield the best performance on 5 out of 7 domains and 4 out of 7 domains for NER and semantic role labeling.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 4: Test accuracy scores for different target domains with nw as source domain for named entity recognition (main task) and simplified semantic role labeling with POS tagging as auxiliary task for baselines and Sluice networks. ID: in-domain. OOD: out-of-domain.' and proceed to solve the following question in a stepwise manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nHow many domains did sluice networks achieve the best performance in NER and semantic role labeling tasks?\n" }, { "role": "assistant", "content": "The table shows that sluice networks achieved the best performance in 5 out of 7 domains for NER and 4 out of 7 domains for semantic role labeling. So, the answer is {\"answer\": \"5 for NER and 4 for semantic role labeling\"}." } ], "images": [ "scigen/train/3117.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Test accuracy scores for different target domains with nw as source domain for named entity recognition (main task) and simplified semantic role labeling with POS tagging as auxiliary task for baselines and Sluice networks. ID: in-domain. OOD: out-of-domain." ], "reasoning": "The table shows that sluice networks achieved the best performance in 5 out of 7 domains for NER and 4 out of 7 domains for semantic role labeling.", "question": "How many domains did sluice networks achieve the best performance in NER and semantic role labeling tasks?", "answer": "So, the answer is {\"answer\": \"5 for NER and 4 for semantic role labeling\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nSluice networks achieved the best performance in all domains for NER and semantic role labeling tasks.\n" }, { "role": "assistant", "content": "The table shows that sluice networks achieved the best performance in 5 out of 7 domains for NER and 4 out of 7 domains for semantic role labeling, not all domains. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3117.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Test accuracy scores for different target domains with nw as source domain for named entity recognition (main task) and simplified semantic role labeling with POS tagging as auxiliary task for baselines and Sluice networks. ID: in-domain. OOD: out-of-domain." ], "reasoning": "The table shows that sluice networks achieved the best performance in 5 out of 7 domains for NER and 4 out of 7 domains for semantic role labeling, not all domains.", "statement": "Sluice networks achieved the best performance in all domains for NER and semantic role labeling tasks.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Performance of the 4 different approaches for predicting HTER for English-German (*) indicates a significant improvement (α=0.1) over the baseline', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "MLP4 achieves a higher correlation score than the baseline SVM, but the improvement is significant only for the development set. MLP4 also significantly outperforms the other two methods.MLP4 achieves higher correlation score than the baseline, but the difference is small and it is significant only for the development set. When compared to the other two methods, though, MLP4 achieves a significant improvement. In contrast to the direction German-English, in English-German the MLP with one output performs worse than the baseline. 4×SVM fails to predict HTER as its predictions achieve zero correlation. Since the individual models failed to predict the post-editing operations separately, this may be an indication that among the 4 post-editing operations in English-German there are dependencies which are stronger than the ones in German-English." } ], "images": [ "scigen/train/3118.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of the 4 different approaches for predicting HTER for English-German (*) indicates a significant improvement (α=0.1) over the baseline" ], "reasoning": "MLP4 achieves a higher correlation score than the baseline SVM, but the improvement is significant only for the development set. MLP4 also significantly outperforms the other two methods.", "description": "MLP4 achieves higher correlation score than the baseline, but the difference is small and it is significant only for the development set. When compared to the other two methods, though, MLP4 achieves a significant improvement. In contrast to the direction German-English, in English-German the MLP with one output performs worse than the baseline. 4×SVM fails to predict HTER as its predictions achieve zero correlation. Since the individual models failed to predict the post-editing operations separately, this may be an indication that among the 4 post-editing operations in English-German there are dependencies which are stronger than the ones in German-English.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2: Performance of the 4 different approaches for predicting HTER for English-German (*) indicates a significant improvement (α=0.1) over the baseline', unfold the steps to solve the question given next. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which method achieves the highest correlation score on the development set and how does it compare to the baseline?\n" }, { "role": "assistant", "content": "MLP4 achieves the highest correlation score on the development set with a score of 0.429, which is higher than the baseline SVM score of 0.414. So, the answer is {\"answer\": \"MLP4\"}" } ], "images": [ "scigen/train/3118.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of the 4 different approaches for predicting HTER for English-German (*) indicates a significant improvement (α=0.1) over the baseline" ], "reasoning": "MLP4 achieves the highest correlation score on the development set with a score of 0.429, which is higher than the baseline SVM score of 0.414.", "question": "Which method achieves the highest correlation score on the development set and how does it compare to the baseline?", "answer": "So, the answer is {\"answer\": \"MLP4\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis MLP4 achieves a significant improvement over the baseline SVM on the development set. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nMLP4 achieves a significant improvement over the baseline SVM on the development set.\n" }, { "role": "assistant", "content": "The table shows that MLP4 has a correlation score of 0.429 on the development set, which is higher than the baseline SVM score of 0.414, and the description states that this difference is significant. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3118.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of the 4 different approaches for predicting HTER for English-German (*) indicates a significant improvement (α=0.1) over the baseline" ], "reasoning": "The table shows that MLP4 has a correlation score of 0.429 on the development set, which is higher than the baseline SVM score of 0.414, and the description states that this difference is significant.", "statement": "MLP4 achieves a significant improvement over the baseline SVM on the development set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: Ranked evaluation of the SLT utterances containing ASR errors in tst2012. (Left) Counts of the winner decisions and the percentage of all of the decisions that were influenced by ASR errors. (Right) Mean TER scores across each sentence in the ranked set. The remainder of winner decisions are made on error-free ASR transcripts.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The table shows that for better performing ASR systems like NICT and KIT, NMT translations are slightly better and there are fewer ties in TER scores compared to PBMT.Are there ASR error conditions in which PBMT remains a better solution than NMT, and if so, what are the properties of these utterances that makes them difficult for NMT? We take a closer look at the sentence-level translation scores by ranking the performance of each MT system on the utterances where ASR errors exist, in order to understand how each MT system handles noisy input. For each utterance, we rank the systems based on their the sentence-level TER scores computed on their translation outputs over each ASR hypothesis. We also mark ties, in which both systems yield the same TER score. The neural and mmt scores are tied on over 20% of the utterances. For the better performing ASR systems (e.g. NICT, KIT), we observe a slightly higher proportion of utterances with better NMT translations and a reduced number of ties. we report the average TER scores within each ranking partition of the data. For example, for the utterances that are translated better by mmt, we observe that the average TER scores for neural have an absolute average improvement of 10% in TER over mmt. The converse is also true, suggesting that there is a subset of utterances that mmt translates better than neural." } ], "images": [ "scigen/train/3119.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ranked evaluation of the SLT utterances containing ASR errors in tst2012. (Left) Counts of the winner decisions and the percentage of all of the decisions that were influenced by ASR errors. (Right) Mean TER scores across each sentence in the ranked set. The remainder of winner decisions are made on error-free ASR transcripts." ], "reasoning": "The table shows that for better performing ASR systems like NICT and KIT, NMT translations are slightly better and there are fewer ties in TER scores compared to PBMT.", "description": "Are there ASR error conditions in which PBMT remains a better solution than NMT, and if so, what are the properties of these utterances that makes them difficult for NMT? We take a closer look at the sentence-level translation scores by ranking the performance of each MT system on the utterances where ASR errors exist, in order to understand how each MT system handles noisy input. For each utterance, we rank the systems based on their the sentence-level TER scores computed on their translation outputs over each ASR hypothesis. We also mark ties, in which both systems yield the same TER score. The neural and mmt scores are tied on over 20% of the utterances. For the better performing ASR systems (e.g. NICT, KIT), we observe a slightly higher proportion of utterances with better NMT translations and a reduced number of ties. we report the average TER scores within each ranking partition of the data. For example, for the utterances that are translated better by mmt, we observe that the average TER scores for neural have an absolute average improvement of 10% in TER over mmt. The converse is also true, suggesting that there is a subset of utterances that mmt translates better than neural.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 3: Ranked evaluation of the SLT utterances containing ASR errors in tst2012. (Left) Counts of the winner decisions and the percentage of all of the decisions that were influenced by ASR errors. (Right) Mean TER scores across each sentence in the ranked set. The remainder of winner decisions are made on error-free ASR transcripts.', solve the following question step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich lab has the highest average TER score for neural translations?\n" }, { "role": "assistant", "content": "To find the lab with the highest average TER score for neural translations, we look at the 'TER (avg) neural' column and identify the highest value. So, the answer is {\"answer\": \"fbk\"}." } ], "images": [ "scigen/train/3119.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ranked evaluation of the SLT utterances containing ASR errors in tst2012. (Left) Counts of the winner decisions and the percentage of all of the decisions that were influenced by ASR errors. (Right) Mean TER scores across each sentence in the ranked set. The remainder of winner decisions are made on error-free ASR transcripts." ], "reasoning": "To find the lab with the highest average TER score for neural translations, we look at the 'TER (avg) neural' column and identify the highest value.", "question": "Which lab has the highest average TER score for neural translations?", "answer": "So, the answer is {\"answer\": \"fbk\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe lab 'kit' has a higher average TER score for mmt translations than neural translations.\n" }, { "role": "assistant", "content": "By comparing the 'TER (avg) mmt' and 'TER (avg) neural' columns for the lab 'kit', we see that the average TER score for mmt (46.3) is higher than for neural (41.1). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3119.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ranked evaluation of the SLT utterances containing ASR errors in tst2012. (Left) Counts of the winner decisions and the percentage of all of the decisions that were influenced by ASR errors. (Right) Mean TER scores across each sentence in the ranked set. The remainder of winner decisions are made on error-free ASR transcripts." ], "reasoning": "By comparing the 'TER (avg) mmt' and 'TER (avg) neural' columns for the lab 'kit', we see that the average TER score for mmt (46.3) is higher than for neural (41.1).", "statement": "The lab 'kit' has a higher average TER score for mmt translations than neural translations.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: A comparison of Neural MT versus Phrase-based MT on the SLT evaluation of TED talks (tst2012) from the IWSLT 2013 evaluation campaign [12]. Evaluation results are compared to a gold standard that assumes that no ASR errors occur.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "NMT outperforms PBMT by 4.5 BLEU without ASR errors and by 3 BLEU with ASR errors, while ASR errors decrease BLEU by around 5.5 and increase TER by around 6.NMT outperforms our best PBMT system by 4.5 BLEU in the absence of ASR errors (gold) and by approximately 3 BLEU across all ASR hypothesis inputs. Overall, the introduction of ASR errors results in decreases in BLEU by 5.5(±0.8) and 5.4(±0.8) and TER increases of 6.0(±0.9) and 6.2(±0.9) for mmt and neural, respectively." } ], "images": [ "scigen/train/3120.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: A comparison of Neural MT versus Phrase-based MT on the SLT evaluation of TED talks (tst2012) from the IWSLT 2013 evaluation campaign [12]. Evaluation results are compared to a gold standard that assumes that no ASR errors occur." ], "reasoning": "NMT outperforms PBMT by 4.5 BLEU without ASR errors and by 3 BLEU with ASR errors, while ASR errors decrease BLEU by around 5.5 and increase TER by around 6.", "description": "NMT outperforms our best PBMT system by 4.5 BLEU in the absence of ASR errors (gold) and by approximately 3 BLEU across all ASR hypothesis inputs. Overall, the introduction of ASR errors results in decreases in BLEU by 5.5(±0.8) and 5.4(±0.8) and TER increases of 6.0(±0.9) and 6.2(±0.9) for mmt and neural, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 1: A comparison of Neural MT versus Phrase-based MT on the SLT evaluation of TED talks (tst2012) from the IWSLT 2013 evaluation campaign [12]. Evaluation results are compared to a gold standard that assumes that no ASR errors occur.'.\n\nQuestion: What is the BLEU score difference between the mmt and neural systems for the 'gold' ASR system?\n" }, { "role": "assistant", "content": "The BLEU score for the mmt system with the 'gold' ASR system is 43.4, and for the neural system, it is 47.9. The difference is 47.9 - 43.4. So, the answer is {\"answer\": \"4.5\"}" } ], "images": [ "scigen/train/3120.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: A comparison of Neural MT versus Phrase-based MT on the SLT evaluation of TED talks (tst2012) from the IWSLT 2013 evaluation campaign [12]. Evaluation results are compared to a gold standard that assumes that no ASR errors occur." ], "reasoning": "The BLEU score for the mmt system with the 'gold' ASR system is 43.4, and for the neural system, it is 47.9. The difference is 47.9 - 43.4.", "question": "What is the BLEU score difference between the mmt and neural systems for the 'gold' ASR system?", "answer": "So, the answer is {\"answer\": \"4.5\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe introduction of ASR errors results in a decrease in BLEU by approximately 5.5 for both mmt and neural systems.\n" }, { "role": "assistant", "content": "The description states that ASR errors result in decreases in BLEU by 5.5(±0.8) for mmt and 5.4(±0.8) for neural systems, which is approximately 5.5 for both. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3120.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: A comparison of Neural MT versus Phrase-based MT on the SLT evaluation of TED talks (tst2012) from the IWSLT 2013 evaluation campaign [12]. Evaluation results are compared to a gold standard that assumes that no ASR errors occur." ], "reasoning": "The description states that ASR errors result in decreases in BLEU by 5.5(±0.8) for mmt and 5.4(±0.8) for neural systems, which is approximately 5.5 for both.", "statement": "The introduction of ASR errors results in a decrease in BLEU by approximately 5.5 for both mmt and neural systems.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Average utterance-level translation TER and ΔTER scores for the MMT and Neural MT systems. The average Neural MT TER scores are 3% better than the PBMT counterpart.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Despite higher average TER scores from mmt outputs, the lower ΔTER scores indicate that mmt SLT outputs are closer to the gold standard MT outputs, suggesting NMT's sensitivity to minor ASR errors.Although the average TER scores from the mmt outputs are higher, the ΔTER scores are lower than their neural counterparts, implying that the mmt SLT outputs are closer to their gold standard MT outputs. This may suggest that NMT is more sensitive to local changes to an input caused by minor ASR errors." } ], "images": [ "scigen/train/3121.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Average utterance-level translation TER and ΔTER scores for the MMT and Neural MT systems. The average Neural MT TER scores are 3% better than the PBMT counterpart." ], "reasoning": "Despite higher average TER scores from mmt outputs, the lower ΔTER scores indicate that mmt SLT outputs are closer to the gold standard MT outputs, suggesting NMT's sensitivity to minor ASR errors.", "description": "Although the average TER scores from the mmt outputs are higher, the ΔTER scores are lower than their neural counterparts, implying that the mmt SLT outputs are closer to their gold standard MT outputs. This may suggest that NMT is more sensitive to local changes to an input caused by minor ASR errors.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 2: Average utterance-level translation TER and ΔTER scores for the MMT and Neural MT systems. The average Neural MT TER scores are 3% better than the PBMT counterpart.'. Solve the following question based on the table.\n\nQuestion: Which system has the highest neural TER score and what does this imply about its sensitivity to ASR errors?\nLet's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The system with the highest neural TER score is prke with a score of 46.5. According to the description, higher TER scores imply greater sensitivity to local changes caused by minor ASR errors. So, the answer is {\"answer\": \"prke\"}." } ], "images": [ "scigen/train/3121.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Average utterance-level translation TER and ΔTER scores for the MMT and Neural MT systems. The average Neural MT TER scores are 3% better than the PBMT counterpart." ], "reasoning": "The system with the highest neural TER score is prke with a score of 46.5. According to the description, higher TER scores imply greater sensitivity to local changes caused by minor ASR errors.", "question": "Which system has the highest neural TER score and what does this imply about its sensitivity to ASR errors?", "answer": "So, the answer is {\"answer\": \"prke\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The mmt TER score for the kit system is higher than its neural TER score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe mmt TER score for the kit system is higher than its neural TER score.\n" }, { "role": "assistant", "content": "The table shows that the mmt TER score for the kit system is 45.9, while its neural TER score is 42.7. Since 45.9 is higher than 42.7, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3121.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Average utterance-level translation TER and ΔTER scores for the MMT and Neural MT systems. The average Neural MT TER scores are 3% better than the PBMT counterpart." ], "reasoning": "The table shows that the mmt TER score for the kit system is 45.9, while its neural TER score is 42.7. Since 45.9 is higher than 42.7, the statement is supported.", "statement": "The mmt TER score for the kit system is higher than its neural TER score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: Changes in sentence-level TER rankings as ASR errors are introduced.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The neural model retains a higher rank 7.5% more often than mmt as ASR errors are introduced, and ranking ties remain 55.5% of the time.Across all ASR outputs, 70.2% of the MT evaluation ranking decisions remain the same when ASR errors create noisy input. The neural model retains a higher rank 7.5% more often than mmt as ASR errors are introduced. Ranking ties remain 55.5% of the time. Of the remaining, the neural model outperforms mmt 5.5% more often in the presence of ASR errors (25.0% versus 19.5%). These results confirm that at the corpus level NMT produces higher scoring translations in the presence of ASR errors." } ], "images": [ "scigen/train/3122.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Changes in sentence-level TER rankings as ASR errors are introduced." ], "reasoning": "The neural model retains a higher rank 7.5% more often than mmt as ASR errors are introduced, and ranking ties remain 55.5% of the time.", "description": "Across all ASR outputs, 70.2% of the MT evaluation ranking decisions remain the same when ASR errors create noisy input. The neural model retains a higher rank 7.5% more often than mmt as ASR errors are introduced. Ranking ties remain 55.5% of the time. Of the remaining, the neural model outperforms mmt 5.5% more often in the presence of ASR errors (25.0% versus 19.5%). These results confirm that at the corpus level NMT produces higher scoring translations in the presence of ASR errors.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: Changes in sentence-level TER rankings as ASR errors are introduced.', solve this question and give detail solutions. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat percentage of the time does the neural model outperform mmt in the presence of ASR errors?\n" }, { "role": "assistant", "content": "The table shows that the neural model outperforms mmt 25.0% of the time in the presence of ASR errors. So, the answer is {\"answer\": \"25.0%\"}." } ], "images": [ "scigen/train/3122.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Changes in sentence-level TER rankings as ASR errors are introduced." ], "reasoning": "The table shows that the neural model outperforms mmt 25.0% of the time in the presence of ASR errors.", "question": "What percentage of the time does the neural model outperform mmt in the presence of ASR errors?", "answer": "So, the answer is {\"answer\": \"25.0%\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe neural model retains a higher rank 7.5% more often than mmt as ASR errors are introduced.\n" }, { "role": "assistant", "content": "The description states that the neural model retains a higher rank 7.5% more often than mmt as ASR errors are introduced, which is confirmed by the table. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3122.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Changes in sentence-level TER rankings as ASR errors are introduced." ], "reasoning": "The description states that the neural model retains a higher rank 7.5% more often than mmt as ASR errors are introduced, which is confirmed by the table.", "statement": "The neural model retains a higher rank 7.5% more often than mmt as ASR errors are introduced.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Accuracies computed for different number of Edge Box proposals on the easy and hard tasks of the Visual Madlibs dataset. The NMS threshold 0.75 and mean-pooling is used for all the experiments. Results in %.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Increasing the number of object proposals tends to cover a larger fraction of the input image, but the gain diminishes with larger numbers.The maximal number of object proposals is the second factor of Edge Boxes that we study in this work. A larger number of proposals tend to cover a larger fraction of the input image. Moreover, the higher number together with the higher NMS threshold can assign proposals to both an object, and its parts, effectively forming a multi-scale and multi-parts object representation. The experiments suggest using a larger numbers of proposals, however the gain diminishes with the larger numbers." } ], "images": [ "scigen/train/3123.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracies computed for different number of Edge Box proposals on the easy and hard tasks of the Visual Madlibs dataset. The NMS threshold 0.75 and mean-pooling is used for all the experiments. Results in %." ], "reasoning": "Increasing the number of object proposals tends to cover a larger fraction of the input image, but the gain diminishes with larger numbers.", "description": "The maximal number of object proposals is the second factor of Edge Boxes that we study in this work. A larger number of proposals tend to cover a larger fraction of the input image. Moreover, the higher number together with the higher NMS threshold can assign proposals to both an object, and its parts, effectively forming a multi-scale and multi-parts object representation. The experiments suggest using a larger numbers of proposals, however the gain diminishes with the larger numbers.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2: Accuracies computed for different number of Edge Box proposals on the easy and hard tasks of the Visual Madlibs dataset. The NMS threshold 0.75 and mean-pooling is used for all the experiments. Results in %.', unfold the steps to solve the question given next. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nQuestion: What is the average score for Hard Task 50 and how does it compare to Hard Task 100?\n" }, { "role": "assistant", "content": "The average score for Hard Task 50 is 55.3, and for Hard Task 100 it is 55.8. Comparing these, Hard Task 100 has a slightly higher average score. So, the answer is {\"answer\": \"55.8\"}." } ], "images": [ "scigen/train/3123.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracies computed for different number of Edge Box proposals on the easy and hard tasks of the Visual Madlibs dataset. The NMS threshold 0.75 and mean-pooling is used for all the experiments. Results in %." ], "reasoning": "The average score for Hard Task 50 is 55.3, and for Hard Task 100 it is 55.8. Comparing these, Hard Task 100 has a slightly higher average score.", "question": "What is the average score for Hard Task 50 and how does it compare to Hard Task 100?", "answer": "So, the answer is {\"answer\": \"55.8\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe score for 'Emotion' increases as the number of proposals increases.\n" }, { "role": "assistant", "content": "The scores for 'Emotion' are 37.9, 38.1, 38.8, and 39.4 for Hard Task 10, 25, 50, and 100 respectively, showing a consistent increase. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3123.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracies computed for different number of Edge Box proposals on the easy and hard tasks of the Visual Madlibs dataset. The NMS threshold 0.75 and mean-pooling is used for all the experiments. Results in %." ], "reasoning": "The scores for 'Emotion' are 37.9, 38.1, 38.8, and 39.4 for Hard Task 10, 25, 50, and 100 respectively, showing a consistent increase.", "statement": "The score for 'Emotion' increases as the number of proposals increases.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Accuracies computed for different number of Edge Box proposals on the easy and hard tasks of the Visual Madlibs dataset. The NMS threshold 0.75 and mean-pooling is used for all the experiments. Results in %.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Increasing the number of object proposals tends to cover a larger fraction of the input image, but the gain diminishes with larger numbers.The maximal number of object proposals is the second factor of Edge Boxes that we study in this work. A larger number of proposals tend to cover a larger fraction of the input image. Moreover, the higher number together with the higher NMS threshold can assign proposals to both an object, and its parts, effectively forming a multi-scale and multi-parts object representation. The experiments suggest using a larger numbers of proposals, however the gain diminishes with the larger numbers." } ], "images": [ "scigen/train/3124.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracies computed for different number of Edge Box proposals on the easy and hard tasks of the Visual Madlibs dataset. The NMS threshold 0.75 and mean-pooling is used for all the experiments. Results in %." ], "reasoning": "Increasing the number of object proposals tends to cover a larger fraction of the input image, but the gain diminishes with larger numbers.", "description": "The maximal number of object proposals is the second factor of Edge Boxes that we study in this work. A larger number of proposals tend to cover a larger fraction of the input image. Moreover, the higher number together with the higher NMS threshold can assign proposals to both an object, and its parts, effectively forming a multi-scale and multi-parts object representation. The experiments suggest using a larger numbers of proposals, however the gain diminishes with the larger numbers.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: Accuracies computed for different number of Edge Box proposals on the easy and hard tasks of the Visual Madlibs dataset. The NMS threshold 0.75 and mean-pooling is used for all the experiments. Results in %.', solve this question and give detail solutions. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhat is the highest score for the 'Past' category and with how many proposals is it achieved?\n" }, { "role": "assistant", "content": "The highest score for the 'Past' category is 80.8, which is achieved with 100 proposals. So, the answer is {\"answer\": \"80.8 with 100 proposals\"}." } ], "images": [ "scigen/train/3124.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracies computed for different number of Edge Box proposals on the easy and hard tasks of the Visual Madlibs dataset. The NMS threshold 0.75 and mean-pooling is used for all the experiments. Results in %." ], "reasoning": "The highest score for the 'Past' category is 80.8, which is achieved with 100 proposals.", "question": "What is the highest score for the 'Past' category and with how many proposals is it achieved?", "answer": "So, the answer is {\"answer\": \"80.8 with 100 proposals\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe 'Emotion' category has the lowest score with 10 proposals.\n" }, { "role": "assistant", "content": "The table shows that the 'Emotion' category has a score of 49.9 with 10 proposals, which is indeed the lowest score in the table. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3124.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracies computed for different number of Edge Box proposals on the easy and hard tasks of the Visual Madlibs dataset. The NMS threshold 0.75 and mean-pooling is used for all the experiments. Results in %." ], "reasoning": "The table shows that the 'Emotion' category has a score of 49.9 with 10 proposals, which is indeed the lowest score in the table.", "statement": "The 'Emotion' category has the lowest score with 10 proposals.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Comparison between our Embedded CNN+LSTM approach that computes the similarity between input and candidate answers in the embedding space, and the plain CNN+LSTM original approach from [Yu et al.(2015)Yu, Park, Berg, and Berg]. Since the accuracies of CNN+LSTM [Yu et al.(2015)Yu, Park, Berg, and Berg] are unavailable for two categories, we report average over 10 categories in this case. Results in %.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The 'Embedded CNN+LSTM' method achieves higher accuracy than 'CNN+LSTM' on both easy and hard tasks, supporting the hypothesis that direct comparison in the output embedding space is beneficial.We hypothesize that, likewise to nCCA, in order to choose a completion of the prompt sentence out of four candidates, the comparison between the candidate completions should be directly done in the output embedding space. Note that, here we feed the sentence prompt to LSTM even though it is fixed per category. Our “Embedded CNN+LSTM” outperforms other methods on both tasks confirming our hypothesis. (on the 10 categories that the results for CNN+LSTM are available it achieves 49.8% accuracy on the easy task, which is 2.1 percentage points higher than CNN+LSTM)." } ], "images": [ "scigen/train/3125.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison between our Embedded CNN+LSTM approach that computes the similarity between input and candidate answers in the embedding space, and the plain CNN+LSTM original approach from [Yu et al.(2015)Yu, Park, Berg, and Berg]. Since the accuracies of CNN+LSTM [Yu et al.(2015)Yu, Park, Berg, and Berg] are unavailable for two categories, we report average over 10 categories in this case. Results in %." ], "reasoning": "The 'Embedded CNN+LSTM' method achieves higher accuracy than 'CNN+LSTM' on both easy and hard tasks, supporting the hypothesis that direct comparison in the output embedding space is beneficial.", "description": "We hypothesize that, likewise to nCCA, in order to choose a completion of the prompt sentence out of four candidates, the comparison between the candidate completions should be directly done in the output embedding space. Note that, here we feed the sentence prompt to LSTM even though it is fixed per category. Our “Embedded CNN+LSTM” outperforms other methods on both tasks confirming our hypothesis. (on the 10 categories that the results for CNN+LSTM are available it achieves 49.8% accuracy on the easy task, which is 2.1 percentage points higher than CNN+LSTM).", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 5: Comparison between our Embedded CNN+LSTM approach that computes the similarity between input and candidate answers in the embedding space, and the plain CNN+LSTM original approach from [Yu et al.(2015)Yu, Park, Berg, and Berg]. Since the accuracies of CNN+LSTM [Yu et al.(2015)Yu, Park, Berg, and Berg] are unavailable for two categories, we report average over 10 categories in this case. Results in %.', and sequentially solve the question, writing out each step of your reasoning process. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the accuracy difference between 'CNN+LSTM (ours)' and 'Easy Task Embedded' for the 'Scenes' category on the easy task?\n" }, { "role": "assistant", "content": "The accuracy for 'CNN+LSTM (ours)' in the 'Scenes' category on the easy task is 74.7%, while for 'Easy Task Embedded' it is 70.6%. The difference is 74.7% - 70.6% = 4.1 percentage points. So, the answer is {\"answer\": \"4.1\"}" } ], "images": [ "scigen/train/3125.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison between our Embedded CNN+LSTM approach that computes the similarity between input and candidate answers in the embedding space, and the plain CNN+LSTM original approach from [Yu et al.(2015)Yu, Park, Berg, and Berg]. Since the accuracies of CNN+LSTM [Yu et al.(2015)Yu, Park, Berg, and Berg] are unavailable for two categories, we report average over 10 categories in this case. Results in %." ], "reasoning": "The accuracy for 'CNN+LSTM (ours)' in the 'Scenes' category on the easy task is 74.7%, while for 'Easy Task Embedded' it is 70.6%. The difference is 74.7% - 70.6% = 4.1 percentage points.", "question": "What is the accuracy difference between 'CNN+LSTM (ours)' and 'Easy Task Embedded' for the 'Scenes' category on the easy task?", "answer": "So, the answer is {\"answer\": \"4.1\"}" }, { "messages": [ { "role": "user", "content": "The 'Easy Task Embedded' method achieves higher accuracy than 'CNN+LSTM (ours)' on the 'Emotion' category for the easy task. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe 'Easy Task Embedded' method achieves higher accuracy than 'CNN+LSTM (ours)' on the 'Emotion' category for the easy task.\n" }, { "role": "assistant", "content": "The table shows that for the 'Emotion' category on the easy task, 'CNN+LSTM (ours)' has an accuracy of 36.2%, while 'Easy Task Embedded' has an accuracy of 35.7%. Therefore, 'CNN+LSTM (ours)' has a higher accuracy. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3125.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison between our Embedded CNN+LSTM approach that computes the similarity between input and candidate answers in the embedding space, and the plain CNN+LSTM original approach from [Yu et al.(2015)Yu, Park, Berg, and Berg]. Since the accuracies of CNN+LSTM [Yu et al.(2015)Yu, Park, Berg, and Berg] are unavailable for two categories, we report average over 10 categories in this case. Results in %." ], "reasoning": "The table shows that for the 'Emotion' category on the easy task, 'CNN+LSTM (ours)' has an accuracy of 36.2%, while 'Easy Task Embedded' has an accuracy of 35.7%. Therefore, 'CNN+LSTM (ours)' has a higher accuracy.", "statement": "The 'Easy Task Embedded' method achieves higher accuracy than 'CNN+LSTM (ours)' on the 'Emotion' category for the easy task.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: Human evaluation to estimate consistency and quality of generated videos.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The pixel generation approach produced blurry results with poor composition consistency and visual quality, whereas the Craft model significantly outperformed it in noun, adjective, and verb recall.Modelling Pixels vs Retrieval. We experimented extensively with text conditioned whole video generation using models with and without adversarial losses and obtained poor results. Since generative models tend to work better on images with single entities, we swapped out the target embedding network in the entity retriever by a generator. Given the query embedding at each of the F time steps, the generator produces an appearance image and a segmentation mask. The model is trained using an L1 loss between the masked appearance image and the masked ground truth image, and an L1 loss between the generated and ground truth masks. See supplementary material for more details. This baseline produced blurry results with recognizable colors and shapes for most common characters like Fred, Wilma, Barney, and Betty at best. We also tried GAN and VAE based approaches and got only slightly less blur. Moreover, since the visual quality of the generated previous entities affects the performance of the layout composer, this also translates into poor ratings on the composition consistency metric. Since the semantic fidelity metrics can not be computed for this pixel generation approach, we ran a human evaluation to compare this model to ours. Humans were asked to mark nouns, adjectives and verbs in the sentence missing in the generated video. Craft significantly outperformed the pixel generation approach on noun, adjective, and verb recall (Craft 61.0,54.5,67.8, L1: 37.8,45.9,48.1)." } ], "images": [ "scigen/train/3126.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Human evaluation to estimate consistency and quality of generated videos." ], "reasoning": "The pixel generation approach produced blurry results with poor composition consistency and visual quality, whereas the Craft model significantly outperformed it in noun, adjective, and verb recall.", "description": "Modelling Pixels vs Retrieval. We experimented extensively with text conditioned whole video generation using models with and without adversarial losses and obtained poor results. Since generative models tend to work better on images with single entities, we swapped out the target embedding network in the entity retriever by a generator. Given the query embedding at each of the F time steps, the generator produces an appearance image and a segmentation mask. The model is trained using an L1 loss between the masked appearance image and the masked ground truth image, and an L1 loss between the generated and ground truth masks. See supplementary material for more details. This baseline produced blurry results with recognizable colors and shapes for most common characters like Fred, Wilma, Barney, and Betty at best. We also tried GAN and VAE based approaches and got only slightly less blur. Moreover, since the visual quality of the generated previous entities affects the performance of the layout composer, this also translates into poor ratings on the composition consistency metric. Since the semantic fidelity metrics can not be computed for this pixel generation approach, we ran a human evaluation to compare this model to ours. Humans were asked to mark nouns, adjectives and verbs in the sentence missing in the generated video. Craft significantly outperformed the pixel generation approach on noun, adjective, and verb recall (Craft 61.0,54.5,67.8, L1: 37.8,45.9,48.1).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 5: Human evaluation to estimate consistency and quality of generated videos.', unfold the steps to solve the question given next. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which model had the highest score for Composition Consistency Position and what was the score?\n" }, { "role": "assistant", "content": "The table shows that the 'Ours' model had the highest score for Composition Consistency Position with a score of 1.78. So, the answer is {\"answer\": \"1.78\"}." } ], "images": [ "scigen/train/3126.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Human evaluation to estimate consistency and quality of generated videos." ], "reasoning": "The table shows that the 'Ours' model had the highest score for Composition Consistency Position with a score of 1.78.", "question": "Which model had the highest score for Composition Consistency Position and what was the score?", "answer": "So, the answer is {\"answer\": \"1.78\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The Pixel Generation L1 model had the highest Visual Quality FG score., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Pixel Generation L1 model had the highest Visual Quality FG score.\n" }, { "role": "assistant", "content": "The table shows that the 'Ours' model had the highest Visual Quality FG score of 1.98, not the Pixel Generation L1 model which had a score of 0.96. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3126.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Human evaluation to estimate consistency and quality of generated videos." ], "reasoning": "The table shows that the 'Ours' model had the highest Visual Quality FG score of 1.98, not the Pixel Generation L1 model which had a score of 0.96.", "statement": "The Pixel Generation L1 model had the highest Visual Quality FG score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Classification results of a SVM trained on synthetic samples along with real training samples for different scenarios for cross-corpus experiments'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The study evaluates the use of synthetically generated samples for cross-corpus classification, using MSP-IMPROV as a test set due to its unbalanced class distribution.Having studied the convergence of GAN architectures and evaluating the quality of synthetically generated samples produced by them in a single corpora setting, we now move to performing cross-corpus evaluations. The objective of this experiment is to investigate if synthetically generated samples can be used during classification on an external corpus (as opposed to being applicable for only in-domain tasks). MSP-IMPROV, like IEMOCAP, also has actors participating in dyadic conversations which has then been segmented into utterances and annotated by evaluators. There are 7798 utterances in total spanned across the same four emotion classes. However, the distribution across classes was highly unbalanced with the number of utterances belonging to happy/neutral class more than three times the number of angry/sad samples. This prompted us to use it as a test set rather than training set. We observe that the adversarial errors converge even if the test set is a different corpus than the training set." } ], "images": [ "scigen/train/3127.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Classification results of a SVM trained on synthetic samples along with real training samples for different scenarios for cross-corpus experiments" ], "reasoning": "The study evaluates the use of synthetically generated samples for cross-corpus classification, using MSP-IMPROV as a test set due to its unbalanced class distribution.", "description": "Having studied the convergence of GAN architectures and evaluating the quality of synthetically generated samples produced by them in a single corpora setting, we now move to performing cross-corpus evaluations. The objective of this experiment is to investigate if synthetically generated samples can be used during classification on an external corpus (as opposed to being applicable for only in-domain tasks). MSP-IMPROV, like IEMOCAP, also has actors participating in dyadic conversations which has then been segmented into utterances and annotated by evaluators. There are 7798 utterances in total spanned across the same four emotion classes. However, the distribution across classes was highly unbalanced with the number of utterances belonging to happy/neutral class more than three times the number of angry/sad samples. This prompted us to use it as a test set rather than training set. We observe that the adversarial errors converge even if the test set is a different corpus than the training set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 3: Classification results of a SVM trained on synthetic samples along with real training samples for different scenarios for cross-corpus experiments' and proceed to solve the following question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the UAR (%) for the dataset that combines Real openSMILE and improved-conditional?\n" }, { "role": "assistant", "content": "The table lists various datasets and their corresponding UAR (%). The dataset 'Real openSMILE + improved-conditional' has a UAR of 45.40%. So, the answer is {\"answer\": \"45.40\"}." } ], "images": [ "scigen/train/3127.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Classification results of a SVM trained on synthetic samples along with real training samples for different scenarios for cross-corpus experiments" ], "reasoning": "The table lists various datasets and their corresponding UAR (%). The dataset 'Real openSMILE + improved-conditional' has a UAR of 45.40%.", "question": "What is the UAR (%) for the dataset that combines Real openSMILE and improved-conditional?", "answer": "So, the answer is {\"answer\": \"45.40\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe UAR for the dataset 'Only improved-conditional' is higher than 'Only synthetic 2D'.\n" }, { "role": "assistant", "content": "The table shows that the UAR for 'Only improved-conditional' is 33.96%, while the UAR for 'Only synthetic 2D' is 40.17%. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3127.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Classification results of a SVM trained on synthetic samples along with real training samples for different scenarios for cross-corpus experiments" ], "reasoning": "The table shows that the UAR for 'Only improved-conditional' is 33.96%, while the UAR for 'Only synthetic 2D' is 40.17%. Therefore, the statement is refuted.", "statement": "The UAR for the dataset 'Only improved-conditional' is higher than 'Only synthetic 2D'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: Classification results of a SVM trained using different set-ups involving the synthetically generated 2-D representation of the real 1582-D openSMILE and the synthetically generated higher dimensions samples with and without real data. Feature vectors of same dimensionality were used in the test set'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The SVM model trained with a combination of 2D code vectors and synthetic samples achieved a UAR of 57.58%, while the model trained with real openSMILE and improved-conditional samples achieved the highest UAR of 60.29%.In this experiment, we perform a leave one session out cross-validation experiment on the IEMOCAP dataset. Given that each session contains a unique pair of participants, this evaluation is also speaker independent. We train the vanilla/conditional GAN on four IEMOCAP sessions and generate synthetic samples. We train a Support Vector Machine (SVM) model with radial basis function as kernel, SVMvan, on the 2-dimensional projection space learned by the adversarial auto-encoder and train it under three conditions: (i) using only the synthetic samples generated by the vanilla GAN, (ii) using only the real samples in the four training sessions and, (iii) using a combination of both synthetic and real samples. The trained models are evaluated on the 2-dimensions representations of the test set, as yielded by the adversarial auto-encoder. Similarly, after obtaining samples from the conditional GAN, we train another SVM model, SVMcon, on the 1582-dimensional openSMILE feature space. We again perform the three sets of experiments as mentioned above. SVMcon is evaluated on the test partition in the 1582-dimensional openSMILE feature space. It is clearly evident that by using only the synthetically generated samples for training the SVM we beat the chance accuracy by a big margin. It is worth noting that in case of simple GAN, the SVM’s performance trained with only synthetic data is comparable to that of a SVM trained with actual 2D code vectors. This could probably be because the 2D code vectors follow a specific distribution enforced by the adversarial auto-encoder framework and not just any random distribution. The specific distribution being the mixture of four Gaussian components is not as complex as real world distributions and hence the GAN model could easily learn that distribution. that’s not the case when appending them with synthetic data points generated from improved conditional GAN. An improvement in UAR in both cases shows the potential of using synthetically generated data along with real data for training and classification purposes." } ], "images": [ "scigen/train/3128.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Classification results of a SVM trained using different set-ups involving the synthetically generated 2-D representation of the real 1582-D openSMILE and the synthetically generated higher dimensions samples with and without real data. Feature vectors of same dimensionality were used in the test set" ], "reasoning": "The SVM model trained with a combination of 2D code vectors and synthetic samples achieved a UAR of 57.58%, while the model trained with real openSMILE and improved-conditional samples achieved the highest UAR of 60.29%.", "description": "In this experiment, we perform a leave one session out cross-validation experiment on the IEMOCAP dataset. Given that each session contains a unique pair of participants, this evaluation is also speaker independent. We train the vanilla/conditional GAN on four IEMOCAP sessions and generate synthetic samples. We train a Support Vector Machine (SVM) model with radial basis function as kernel, SVMvan, on the 2-dimensional projection space learned by the adversarial auto-encoder and train it under three conditions: (i) using only the synthetic samples generated by the vanilla GAN, (ii) using only the real samples in the four training sessions and, (iii) using a combination of both synthetic and real samples. The trained models are evaluated on the 2-dimensions representations of the test set, as yielded by the adversarial auto-encoder. Similarly, after obtaining samples from the conditional GAN, we train another SVM model, SVMcon, on the 1582-dimensional openSMILE feature space. We again perform the three sets of experiments as mentioned above. SVMcon is evaluated on the test partition in the 1582-dimensional openSMILE feature space. It is clearly evident that by using only the synthetically generated samples for training the SVM we beat the chance accuracy by a big margin. It is worth noting that in case of simple GAN, the SVM’s performance trained with only synthetic data is comparable to that of a SVM trained with actual 2D code vectors. This could probably be because the 2D code vectors follow a specific distribution enforced by the adversarial auto-encoder framework and not just any random distribution. The specific distribution being the mixture of four Gaussian components is not as complex as real world distributions and hence the GAN model could easily learn that distribution. that’s not the case when appending them with synthetic data points generated from improved conditional GAN. An improvement in UAR in both cases shows the potential of using synthetically generated data along with real data for training and classification purposes.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Classification results of a SVM trained using different set-ups involving the synthetically generated 2-D representation of the real 1582-D openSMILE and the synthetically generated higher dimensions samples with and without real data. Feature vectors of same dimensionality were used in the test set\nQuestion: What is the UAR percentage when using only improved-conditional samples?\nSolve the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows that the UAR percentage for the SVM model trained with only improved-conditional samples is 34.09%. So, the answer is {\"answer\": \"34.09\"}." } ], "images": [ "scigen/train/3128.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Classification results of a SVM trained using different set-ups involving the synthetically generated 2-D representation of the real 1582-D openSMILE and the synthetically generated higher dimensions samples with and without real data. Feature vectors of same dimensionality were used in the test set" ], "reasoning": "The table shows that the UAR percentage for the SVM model trained with only improved-conditional samples is 34.09%.", "question": "What is the UAR percentage when using only improved-conditional samples?", "answer": "So, the answer is {\"answer\": \"34.09\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The SVM model trained with real openSMILE and improved-conditional samples achieved a UAR of 60.29%., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe SVM model trained with real openSMILE and improved-conditional samples achieved a UAR of 60.29%.\n" }, { "role": "assistant", "content": "The table indicates that the UAR percentage for the SVM model trained with real openSMILE and improved-conditional samples is indeed 60.29%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3128.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Classification results of a SVM trained using different set-ups involving the synthetically generated 2-D representation of the real 1582-D openSMILE and the synthetically generated higher dimensions samples with and without real data. Feature vectors of same dimensionality were used in the test set" ], "reasoning": "The table indicates that the UAR percentage for the SVM model trained with real openSMILE and improved-conditional samples is indeed 60.29%.", "statement": "The SVM model trained with real openSMILE and improved-conditional samples achieved a UAR of 60.29%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Classification results of using synthetically generated vectors in the test set'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The experiment evaluates the similarity between real and synthetic data by training an SVM on real data and testing on synthetic data, showing higher accuracy for 2D samples compared to higher dimensional features.In this experiment, in addition to using the real dataset to train the GANs, we also use them to train a SVM for emotion recognition. The synthetic samples were used in the test set. The objective of this experiment is to assess the similarity between real and synthetic data by using a model trained on real data to classify synthetic data. In case of vanilla GAN, the generated 2D representations were used as test set while the compressed 2D representations were used for training the SVM. For conditional GAN the higher dimensional feature vectors were used for training the SVM which was evaluated on the synthetically generated test set. As expected for the higher dimensional features the results shown are similar to what was obtained when the synthetic samples were used for training. For 2D samples the high accuracy suggests its much easier to estimate simpler dimensional distribution than a higher dimensional complex distribution." } ], "images": [ "scigen/train/3129.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Classification results of using synthetically generated vectors in the test set" ], "reasoning": "The experiment evaluates the similarity between real and synthetic data by training an SVM on real data and testing on synthetic data, showing higher accuracy for 2D samples compared to higher dimensional features.", "description": "In this experiment, in addition to using the real dataset to train the GANs, we also use them to train a SVM for emotion recognition. The synthetic samples were used in the test set. The objective of this experiment is to assess the similarity between real and synthetic data by using a model trained on real data to classify synthetic data. In case of vanilla GAN, the generated 2D representations were used as test set while the compressed 2D representations were used for training the SVM. For conditional GAN the higher dimensional feature vectors were used for training the SVM which was evaluated on the synthetically generated test set. As expected for the higher dimensional features the results shown are similar to what was obtained when the synthetic samples were used for training. For 2D samples the high accuracy suggests its much easier to estimate simpler dimensional distribution than a higher dimensional complex distribution.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 2: Classification results of using synthetically generated vectors in the test set\nQuestion: What is the UAR percentage for the 2D code-vectors dataset?\n" }, { "role": "assistant", "content": "The table shows the UAR percentages for different datasets. The UAR percentage for the 2D code-vectors dataset is listed as 97.09%. So, the answer is {\"answer\": \"97.09\"}." } ], "images": [ "scigen/train/3129.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Classification results of using synthetically generated vectors in the test set" ], "reasoning": "The table shows the UAR percentages for different datasets. The UAR percentage for the 2D code-vectors dataset is listed as 97.09%.", "question": "What is the UAR percentage for the 2D code-vectors dataset?", "answer": "So, the answer is {\"answer\": \"97.09\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The UAR percentage for the improved-conditional dataset is higher than that for the 2D code-vectors dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe UAR percentage for the improved-conditional dataset is higher than that for the 2D code-vectors dataset.\n" }, { "role": "assistant", "content": "The table shows that the UAR percentage for the improved-conditional dataset is 35.23%, which is lower than the 97.09% for the 2D code-vectors dataset. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3129.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Classification results of using synthetically generated vectors in the test set" ], "reasoning": "The table shows that the UAR percentage for the improved-conditional dataset is 35.23%, which is lower than the 97.09% for the 2D code-vectors dataset.", "statement": "The UAR percentage for the improved-conditional dataset is higher than that for the 2D code-vectors dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1: Best setting for DNNs, as found on dev.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "We preprocess the data using min-max scaling and train the model for up to 100 epochs with patience of 25, using ReLU activation functions, l2 regularization, and dropout.We preprocess the data using min-max scaling. We use up to 100 epochs with patience of 25, rectified linear units (ReLU) as activation functions, l2 regularization on weights, and dropout Srivastava et al." } ], "images": [ "scigen/train/3130.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Best setting for DNNs, as found on dev." ], "reasoning": "We preprocess the data using min-max scaling and train the model for up to 100 epochs with patience of 25, using ReLU activation functions, l2 regularization, and dropout.", "description": "We preprocess the data using min-max scaling. We use up to 100 epochs with patience of 25, rectified linear units (ReLU) as activation functions, l2 regularization on weights, and dropout Srivastava et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 1: Best setting for DNNs, as found on dev.'. Solve the following question based on the table.\n\nQuestion: Which configuration has the highest number of intermediate layers and what is its batch size?\nLet's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, configuration C has the highest number of intermediate layers (15). The batch size for configuration C is 32. So, the answer is {\"answer\": \"32\"}." } ], "images": [ "scigen/train/3130.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Best setting for DNNs, as found on dev." ], "reasoning": "By examining the table, configuration C has the highest number of intermediate layers (15). The batch size for configuration C is 32.", "question": "Which configuration has the highest number of intermediate layers and what is its batch size?", "answer": "So, the answer is {\"answer\": \"32\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Configuration B uses a dropout rate of 0.3., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nConfiguration B uses a dropout rate of 0.3.\n" }, { "role": "assistant", "content": "The table shows that configuration B uses a dropout rate of 0.2, not 0.3. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3130.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Best setting for DNNs, as found on dev." ], "reasoning": "The table shows that configuration B uses a dropout rate of 0.2, not 0.3.", "statement": "Configuration B uses a dropout rate of 0.3.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2. KnowNERgold: Ablation study by categories on CoNLL2003e-test'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The entity-based component, which relies on entity types from the same KB, is the most time-consuming but contributes significantly to the F1 score.However, the ablation study in Tab. This is somehow expected as the entity specific information is extracted from the same KB and strongly relies on the entity types. The Entity-based component is also the most expensive concerning timing performance. Fig. The Stanford agnostic system was faster than our implementation as it took 158.55 ms per document on average." } ], "images": [ "scigen/train/3131.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2. KnowNERgold: Ablation study by categories on CoNLL2003e-test" ], "reasoning": "The entity-based component, which relies on entity types from the same KB, is the most time-consuming but contributes significantly to the F1 score.", "description": "However, the ablation study in Tab. This is somehow expected as the entity specific information is extracted from the same KB and strongly relies on the entity types. The Entity-based component is also the most expensive concerning timing performance. Fig. The Stanford agnostic system was faster than our implementation as it took 158.55 ms per document on average.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 2. KnowNERgold: Ablation study by categories on CoNLL2003e-test'. Solve the following question based on the table.\n\nQuestion: Which feature category has the highest F1 score?\nLet's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all feature categories, the 'All' category has the highest F1 score of 91.12. So, the answer is {\"answer\": \"91.12\"}" } ], "images": [ "scigen/train/3131.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2. KnowNERgold: Ablation study by categories on CoNLL2003e-test" ], "reasoning": "By comparing the F1 scores of all feature categories, the 'All' category has the highest F1 score of 91.12.", "question": "Which feature category has the highest F1 score?", "answer": "So, the answer is {\"answer\": \"91.12\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe feature category 'A, Name, KB' has the lowest F1 score.\n" }, { "role": "assistant", "content": "By examining the F1 scores in the table, 'A, Name, KB' has the lowest F1 score of 88.73. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3131.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2. KnowNERgold: Ablation study by categories on CoNLL2003e-test" ], "reasoning": "By examining the F1 scores in the table, 'A, Name, KB' has the lowest F1 score of 88.73.", "statement": "The feature category 'A, Name, KB' has the lowest F1 score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5. English: Mention-based performance on CoNLL2003e-test as reported in literature.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "KnowNER performs amongst state-of-the-art NER English systems, achieving an F1 score of 91.12 when using the gold standard for the entity-based step.English. KnowNER performs amongst state-of-the-art NER English systems on both datasets. Tab. The results for KnowNER correspond to a setting using all the knowledge categories when using the gold standard for the entity-based step (as in Radford at al." } ], "images": [ "scigen/train/3132.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5. English: Mention-based performance on CoNLL2003e-test as reported in literature." ], "reasoning": "KnowNER performs amongst state-of-the-art NER English systems, achieving an F1 score of 91.12 when using the gold standard for the entity-based step.", "description": "English. KnowNER performs amongst state-of-the-art NER English systems on both datasets. Tab. The results for KnowNER correspond to a setting using all the knowledge categories when using the gold standard for the entity-based step (as in Radford at al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. The question is related to the given table titled 'Table 5. English: Mention-based performance on CoNLL2003e-test as reported in literature.'.\n\nQuestion: What is the F1 score of the KnowNER system when using the gold standard for the entity-based step?\n" }, { "role": "assistant", "content": "The table lists the F1 scores of various NER systems, including KnowNER. The F1 score for KnowNER when using the gold standard is 91.12. So, the answer is {\"answer\": \"91.12\"}" } ], "images": [ "scigen/train/3132.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5. English: Mention-based performance on CoNLL2003e-test as reported in literature." ], "reasoning": "The table lists the F1 scores of various NER systems, including KnowNER. The F1 score for KnowNER when using the gold standard is 91.12.", "question": "What is the F1 score of the KnowNER system when using the gold standard for the entity-based step?", "answer": "So, the answer is {\"answer\": \"91.12\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nKnowNER gold has a higher F1 score than Luo et al.\n" }, { "role": "assistant", "content": "The table shows that KnowNER gold has an F1 score of 91.12, while Luo et al. has an F1 score of 91.20. Therefore, KnowNER gold does not have a higher F1 score than Luo et al. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3132.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5. English: Mention-based performance on CoNLL2003e-test as reported in literature." ], "reasoning": "The table shows that KnowNER gold has an F1 score of 91.12, while Luo et al. has an F1 score of 91.20. Therefore, KnowNER gold does not have a higher F1 score than Luo et al.", "statement": "KnowNER gold has a higher F1 score than Luo et al.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6. English: F1 Performance for the English language on CoNLL2003e and MUC-7 datasets.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "KnowNER outperforms Stanford CoreNLP (3.6.0) in all categories of the CoNLL2003e test and dev datasets.Tab. (Stanford NER 3.6.0), probably the most widely used NER system to date, which KnowNER outperforms." } ], "images": [ "scigen/train/3133.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6. English: F1 Performance for the English language on CoNLL2003e and MUC-7 datasets." ], "reasoning": "KnowNER outperforms Stanford CoreNLP (3.6.0) in all categories of the CoNLL2003e test and dev datasets.", "description": "Tab. (Stanford NER 3.6.0), probably the most widely used NER system to date, which KnowNER outperforms.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 6. English: F1 Performance for the English language on CoNLL2003e and MUC-7 datasets.\nQuestion: Which system has the highest performance in the PER category for the CoNLL2003e test dataset?\n" }, { "role": "assistant", "content": "By comparing the performance of both systems in the PER category for the CoNLL2003e test dataset, KnowNER aida has a score of 95.99, which is higher than Stanford CoreNLP (3.6.0)'s score of 92.35. So, the answer is {\"answer\": \"KnowNER aida\"}." } ], "images": [ "scigen/train/3133.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6. English: F1 Performance for the English language on CoNLL2003e and MUC-7 datasets." ], "reasoning": "By comparing the performance of both systems in the PER category for the CoNLL2003e test dataset, KnowNER aida has a score of 95.99, which is higher than Stanford CoreNLP (3.6.0)'s score of 92.35.", "question": "Which system has the highest performance in the PER category for the CoNLL2003e test dataset?", "answer": "So, the answer is {\"answer\": \"KnowNER aida\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nKnowNER aida has a higher performance than Stanford CoreNLP (3.6.0) in the MISC category for the CoNLL2003e test dataset.\n" }, { "role": "assistant", "content": "The table shows that KnowNER aida has a score of 81.82 in the MISC category for the CoNLL2003e test dataset, which is higher than Stanford CoreNLP (3.6.0)'s score of 81.51. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3133.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6. English: F1 Performance for the English language on CoNLL2003e and MUC-7 datasets." ], "reasoning": "The table shows that KnowNER aida has a score of 81.82 in the MISC category for the CoNLL2003e test dataset, which is higher than Stanford CoreNLP (3.6.0)'s score of 81.51.", "statement": "KnowNER aida has a higher performance than Stanford CoreNLP (3.6.0) in the MISC category for the CoNLL2003e test dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Dialogue generation performance. All results are averaged over 5 random seeds and reported in percentage (%). Size: Total number of model parameters, P: Precision, R: Recall, L-D: Average dialogue length, L-U: Average utterance length.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "G-DuHA achieves the highest quality dialogue performance, and goal-embedded LMs outperform HREDs, with no significant difference in performance due to model size.For quality measures, G-DuHA significantly outperforms other baselines, implying that it’s able to carry out a higher-quality dialogue. Besides, goal-embedded LMs perform better than HREDs, showing the benefits of our goal embedding module. No significant performance difference is observed with respect to model size variants." } ], "images": [ "scigen/train/3134.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Dialogue generation performance. All results are averaged over 5 random seeds and reported in percentage (%). Size: Total number of model parameters, P: Precision, R: Recall, L-D: Average dialogue length, L-U: Average utterance length." ], "reasoning": "G-DuHA achieves the highest quality dialogue performance, and goal-embedded LMs outperform HREDs, with no significant difference in performance due to model size.", "description": "For quality measures, G-DuHA significantly outperforms other baselines, implying that it’s able to carry out a higher-quality dialogue. Besides, goal-embedded LMs perform better than HREDs, showing the benefits of our goal embedding module. No significant performance difference is observed with respect to model size variants.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1: Dialogue generation performance. All results are averaged over 5 random seeds and reported in percentage (%). Size: Total number of model parameters, P: Precision, R: Recall, L-D: Average dialogue length, L-U: Average utterance length.', solve the subsequent question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhich model has the highest BLEU score and what does this imply about its dialogue quality?\n" }, { "role": "assistant", "content": "The table shows that G-DuHA has the highest BLEU score of 7.48, which implies that it is able to carry out a higher-quality dialogue compared to other models. So, the answer is {\"answer\": \"G-DuHA\"}." } ], "images": [ "scigen/train/3134.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Dialogue generation performance. All results are averaged over 5 random seeds and reported in percentage (%). Size: Total number of model parameters, P: Precision, R: Recall, L-D: Average dialogue length, L-U: Average utterance length." ], "reasoning": "The table shows that G-DuHA has the highest BLEU score of 7.48, which implies that it is able to carry out a higher-quality dialogue compared to other models.", "question": "Which model has the highest BLEU score and what does this imply about its dialogue quality?", "answer": "So, the answer is {\"answer\": \"G-DuHA\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim HRED-XL has a higher BLEU score than LM+G., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nHRED-XL has a higher BLEU score than LM+G.\n" }, { "role": "assistant", "content": "The table shows that HRED-XL has a BLEU score of 5.08, while LM+G has a BLEU score of 6.34. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3134.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Dialogue generation performance. All results are averaged over 5 random seeds and reported in percentage (%). Size: Total number of model parameters, P: Precision, R: Recall, L-D: Average dialogue length, L-U: Average utterance length." ], "reasoning": "The table shows that HRED-XL has a BLEU score of 5.08, while LM+G has a BLEU score of 6.34. Therefore, the statement is refuted.", "statement": "HRED-XL has a higher BLEU score than LM+G.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 7: Overall macro and micro precision and recall for the two new ground truths. Best results are marked in bold.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The UUR metric shows higher macro and micro precision and recall values for both young and elderly populations compared to the baseline.Once again, for both the young and the elderly population based ground truths, the macro and micro precision and recall values for the UUR metric are higher compared to that of the baseline." } ], "images": [ "scigen/train/3135.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Overall macro and micro precision and recall for the two new ground truths. Best results are marked in bold." ], "reasoning": "The UUR metric shows higher macro and micro precision and recall values for both young and elderly populations compared to the baseline.", "description": "Once again, for both the young and the elderly population based ground truths, the macro and micro precision and recall values for the UUR metric are higher compared to that of the baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 7: Overall macro and micro precision and recall for the two new ground truths. Best results are marked in bold.', answer the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the difference in macro precision between the Young-UUR and Young-Baseline?\n" }, { "role": "assistant", "content": "The macro precision for Young-UUR is 0.33 and for Young-Baseline is 0.19. The difference is 0.33 - 0.19. So, the answer is {\"answer\": \"0.14\"}." } ], "images": [ "scigen/train/3135.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Overall macro and micro precision and recall for the two new ground truths. Best results are marked in bold." ], "reasoning": "The macro precision for Young-UUR is 0.33 and for Young-Baseline is 0.19. The difference is 0.33 - 0.19.", "question": "What is the difference in macro precision between the Young-UUR and Young-Baseline?", "answer": "So, the answer is {\"answer\": \"0.14\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The micro precision and recall for Elder-UUR is higher than Elder-Baseline., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe micro precision and recall for Elder-UUR is higher than Elder-Baseline.\n" }, { "role": "assistant", "content": "The micro precision and recall for Elder-UUR is 0.26, while for Elder-Baseline it is 0.23. Since 0.26 is higher than 0.23, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3135.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Overall macro and micro precision and recall for the two new ground truths. Best results are marked in bold." ], "reasoning": "The micro precision and recall for Elder-UUR is 0.26, while for Elder-Baseline it is 0.23. Since 0.26 is higher than 0.23, the statement is supported.", "statement": "The micro precision and recall for Elder-UUR is higher than Elder-Baseline.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 8: Spearman’s correlation between UUR and the ground truth in the different user buckets. Best results are marked in bold.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The Low bucket has the highest ρ value, indicating that users who rarely mix languages show the strongest signals of borrowing.As mentioned earlier, we divide the set of 3577 users into three categories. As we can see, for Low bucket the ρ value is maximum. This points to the fact that the signals of borrowing is strongest from the users who rarely mix languages." } ], "images": [ "scigen/train/3136.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Spearman’s correlation between UUR and the ground truth in the different user buckets. Best results are marked in bold." ], "reasoning": "The Low bucket has the highest ρ value, indicating that users who rarely mix languages show the strongest signals of borrowing.", "description": "As mentioned earlier, we divide the set of 3577 users into three categories. As we can see, for Low bucket the ρ value is maximum. This points to the fact that the signals of borrowing is strongest from the users who rarely mix languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 8: Spearman’s correlation between UUR and the ground truth in the different user buckets. Best results are marked in bold.'.\n\nQuestion: Which bucket has the highest number of users and what is its ρ value?\n" }, { "role": "assistant", "content": "The table shows that the Low bucket has the highest number of users (2531) and its ρ value is 0.65. So, the answer is {\"answer\": \"Low, 0.65\"}." } ], "images": [ "scigen/train/3136.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Spearman’s correlation between UUR and the ground truth in the different user buckets. Best results are marked in bold." ], "reasoning": "The table shows that the Low bucket has the highest number of users (2531) and its ρ value is 0.65.", "question": "Which bucket has the highest number of users and what is its ρ value?", "answer": "So, the answer is {\"answer\": \"Low, 0.65\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Mid bucket has the lowest ρ value.\n" }, { "role": "assistant", "content": "The table shows that the Mid bucket has a ρ value of 0.60, which is higher than the High bucket's ρ value of 0.52 but lower than the Low bucket's ρ value of 0.65. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3136.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Spearman’s correlation between UUR and the ground truth in the different user buckets. Best results are marked in bold." ], "reasoning": "The table shows that the Mid bucket has a ρ value of 0.60, which is higher than the High bucket's ρ value of 0.52 but lower than the Low bucket's ρ value of 0.65.", "statement": "The Mid bucket has the lowest ρ value.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3. [Color online] Accuracy and F1 scores with BERT as embeddings. Here, each target domain represents a 15% held-out data. Blue and red color represent the best and second best values for a given target domain. AWLD, IWLD and YWLD represent training on weakly labeled datasets only. ACAD-AWLD, IMDB-IWLD and YELP-YWLD represents our two-stage training procedure.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The proposed training procedure improves transfer results and performs exceptionally well, even at par with models trained and tested on the same domain. Training only on WLD datasets produced the worst results, but still better than ELMO-based models.BERT performed significantly better than ELMO. Our proposed training procedure performed exceptionally well, in some cases even at par with the models that are trained and tested on the same domain. This training procedure not only improves against standard lift-and-shift models but also leads to higher transfer results. Again, training only on WLD datasets produced the worst results. However, the values are better than the corresponding ELMO-based models. Even though, YELP-YWLD performs marginally poor ( 0.3% lower than YELP), it performs significantly better in other domains such as ADLD (6% higher than YELP) and IMDB (4.7% higher than YELP). Similar transfer improvements are reported for ACAD-AWLD and IMDB-IWLD. This transfer performance improvement reconfirms the usefulness of two-stage training procedure. As expected, domain transfer from SCCD and WEAT is inferior due to high dissimilarity between SCCD and WEAT and other domains." } ], "images": [ "scigen/train/3137.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. [Color online] Accuracy and F1 scores with BERT as embeddings. Here, each target domain represents a 15% held-out data. Blue and red color represent the best and second best values for a given target domain. AWLD, IWLD and YWLD represent training on weakly labeled datasets only. ACAD-AWLD, IMDB-IWLD and YELP-YWLD represents our two-stage training procedure." ], "reasoning": "The proposed training procedure improves transfer results and performs exceptionally well, even at par with models trained and tested on the same domain. Training only on WLD datasets produced the worst results, but still better than ELMO-based models.", "description": "BERT performed significantly better than ELMO. Our proposed training procedure performed exceptionally well, in some cases even at par with the models that are trained and tested on the same domain. This training procedure not only improves against standard lift-and-shift models but also leads to higher transfer results. Again, training only on WLD datasets produced the worst results. However, the values are better than the corresponding ELMO-based models. Even though, YELP-YWLD performs marginally poor ( 0.3% lower than YELP), it performs significantly better in other domains such as ADLD (6% higher than YELP) and IMDB (4.7% higher than YELP). Similar transfer improvements are reported for ACAD-AWLD and IMDB-IWLD. This transfer performance improvement reconfirms the usefulness of two-stage training procedure. As expected, domain transfer from SCCD and WEAT is inferior due to high dissimilarity between SCCD and WEAT and other domains.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 3. [Color online] Accuracy and F1 scores with BERT as embeddings. Here, each target domain represents a 15% held-out data. Blue and red color represent the best and second best values for a given target domain. AWLD, IWLD and YWLD represent training on weakly labeled datasets only. ACAD-AWLD, IMDB-IWLD and YELP-YWLD represents our two-stage training procedure.\nQuestion: Which source domain has the highest F1 score when transferred to the YELP target domain?\n" }, { "role": "assistant", "content": "To find the source domain with the highest F1 score for the YELP target domain, look at the F1 column under the YELP target domain. The highest F1 score is 0.953, achieved by the YELP source domain. So, the answer is {\"answer\": \"YELP\"}." } ], "images": [ "scigen/train/3137.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. [Color online] Accuracy and F1 scores with BERT as embeddings. Here, each target domain represents a 15% held-out data. Blue and red color represent the best and second best values for a given target domain. AWLD, IWLD and YWLD represent training on weakly labeled datasets only. ACAD-AWLD, IMDB-IWLD and YELP-YWLD represents our two-stage training procedure." ], "reasoning": "To find the source domain with the highest F1 score for the YELP target domain, look at the F1 column under the YELP target domain. The highest F1 score is 0.953, achieved by the YELP source domain.", "question": "Which source domain has the highest F1 score when transferred to the YELP target domain?", "answer": "So, the answer is {\"answer\": \"YELP\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The IMDB-IWLD source domain has a higher F1 score when transferred to the ADLD target domain than the YELP-YWLD source domain., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe IMDB-IWLD source domain has a higher F1 score when transferred to the ADLD target domain than the YELP-YWLD source domain.\n" }, { "role": "assistant", "content": "To verify this statement, compare the F1 scores of IMDB-IWLD and YELP-YWLD for the ADLD target domain. IMDB-IWLD has an F1 score of 0.844, while YELP-YWLD has an F1 score of 0.941. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3137.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. [Color online] Accuracy and F1 scores with BERT as embeddings. Here, each target domain represents a 15% held-out data. Blue and red color represent the best and second best values for a given target domain. AWLD, IWLD and YWLD represent training on weakly labeled datasets only. ACAD-AWLD, IMDB-IWLD and YELP-YWLD represents our two-stage training procedure." ], "reasoning": "To verify this statement, compare the F1 scores of IMDB-IWLD and YELP-YWLD for the ADLD target domain. IMDB-IWLD has an F1 score of 0.844, while YELP-YWLD has an F1 score of 0.941. Therefore, the statement is refuted.", "statement": "The IMDB-IWLD source domain has a higher F1 score when transferred to the ADLD target domain than the YELP-YWLD source domain.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2. [Color online] Accuracy and F1 scores with ELMO as embeddings. Here, each target domain represents a 15% held-out data. Blue and red color represent the best and second best values for a given target domain. AWLD, IWLD and YWLD represent training on weakly labeled datasets only. ACAD-AWLD, IMDB-IWLD and YELP-YWLD represents our two-stage training procedure.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The EMLO-based model showed poor performance when trained only on WLD datasets, and marginal performance with the proposed training procedures, indicating limitations in generalizability.We, first of all, discuss the performance score of EMLO-based model. Here, each target domain represents 15% test data. Only training on WLD datasets produced worst results (see the row with source as AWLD). Our proposed training procedure (for example, ACAD-AWLD) produced marginal performance. We witness similar poor and marginal performances for other WLDs and for our other proposed two-stage experiment settings, respectively. We claim that the poorer EMLO’s performance gains in cross-domain classification are primarily due to its current limitations in generalizability as compare to other state-of-the-art embeddings generated from transformer-based language models. Next, we experiment with other competitive models." } ], "images": [ "scigen/train/3138.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2. [Color online] Accuracy and F1 scores with ELMO as embeddings. Here, each target domain represents a 15% held-out data. Blue and red color represent the best and second best values for a given target domain. AWLD, IWLD and YWLD represent training on weakly labeled datasets only. ACAD-AWLD, IMDB-IWLD and YELP-YWLD represents our two-stage training procedure." ], "reasoning": "The EMLO-based model showed poor performance when trained only on WLD datasets, and marginal performance with the proposed training procedures, indicating limitations in generalizability.", "description": "We, first of all, discuss the performance score of EMLO-based model. Here, each target domain represents 15% test data. Only training on WLD datasets produced worst results (see the row with source as AWLD). Our proposed training procedure (for example, ACAD-AWLD) produced marginal performance. We witness similar poor and marginal performances for other WLDs and for our other proposed two-stage experiment settings, respectively. We claim that the poorer EMLO’s performance gains in cross-domain classification are primarily due to its current limitations in generalizability as compare to other state-of-the-art embeddings generated from transformer-based language models. Next, we experiment with other competitive models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2. [Color online] Accuracy and F1 scores with ELMO as embeddings. Here, each target domain represents a 15% held-out data. Blue and red color represent the best and second best values for a given target domain. AWLD, IWLD and YWLD represent training on weakly labeled datasets only. ACAD-AWLD, IMDB-IWLD and YELP-YWLD represents our two-stage training procedure.' and methodically tackle the question that follows. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the F1 score for the ACAD-AWLD source domain when the target domain is IMDB?\n" }, { "role": "assistant", "content": "To find the F1 score for the ACAD-AWLD source domain when the target domain is IMDB, locate the row labeled 'ACAD-AWLD' and the column labeled 'Target domains IMDB'. The F1 score is in the intersection of this row and column. So, the answer is {\"answer\": \"0.764\"}." } ], "images": [ "scigen/train/3138.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2. [Color online] Accuracy and F1 scores with ELMO as embeddings. Here, each target domain represents a 15% held-out data. Blue and red color represent the best and second best values for a given target domain. AWLD, IWLD and YWLD represent training on weakly labeled datasets only. ACAD-AWLD, IMDB-IWLD and YELP-YWLD represents our two-stage training procedure." ], "reasoning": "To find the F1 score for the ACAD-AWLD source domain when the target domain is IMDB, locate the row labeled 'ACAD-AWLD' and the column labeled 'Target domains IMDB'. The F1 score is in the intersection of this row and column.", "question": "What is the F1 score for the ACAD-AWLD source domain when the target domain is IMDB?", "answer": "So, the answer is {\"answer\": \"0.764\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe F1 score for the AWLD source domain when the target domain is SCCD is 0.862.\n" }, { "role": "assistant", "content": "To verify this statement, locate the row labeled 'AWLD' and the column labeled 'Target domains SCCD'. The F1 score at the intersection of this row and column is 0.862, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3138.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2. [Color online] Accuracy and F1 scores with ELMO as embeddings. Here, each target domain represents a 15% held-out data. Blue and red color represent the best and second best values for a given target domain. AWLD, IWLD and YWLD represent training on weakly labeled datasets only. ACAD-AWLD, IMDB-IWLD and YELP-YWLD represents our two-stage training procedure." ], "reasoning": "To verify this statement, locate the row labeled 'AWLD' and the column labeled 'Target domains SCCD'. The F1 score at the intersection of this row and column is 0.862, which matches the statement.", "statement": "The F1 score for the AWLD source domain when the target domain is SCCD is 0.862.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'TABLE VIII: Performance on identifying experienced users.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "We use member levels to categorize users as experienced or inexperienced and evaluate our model's predictions using NDCG.Identifying Experienced Users: Our second task is to find experienced members of this community, who have potential for being citizen journalists. In order to find how good our model predicts the experience level of users, we consider the following as ground-truth for user experience. In NewsTrust, users have Member Levels calculated by the NewsTrust staff based on community engagement, time in the community, other users’ feedback on reviews, profile transparency, and manual validation. We use these member levels to categorize users as experienced or inexperienced. We also computed the Normalized Discounted Cumulative Gain (NDCG) NDCG gives geometrically decreasing weights to predictions at various positions of ranked list: NDCGp=DCGpIDCGp where DCGp=rel1+∑pi=2relilog2i" } ], "images": [ "scigen/train/3139.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VIII: Performance on identifying experienced users." ], "reasoning": "We use member levels to categorize users as experienced or inexperienced and evaluate our model's predictions using NDCG.", "description": "Identifying Experienced Users: Our second task is to find experienced members of this community, who have potential for being citizen journalists. In order to find how good our model predicts the experience level of users, we consider the following as ground-truth for user experience. In NewsTrust, users have Member Levels calculated by the NewsTrust staff based on community engagement, time in the community, other users’ feedback on reviews, profile transparency, and manual validation. We use these member levels to categorize users as experienced or inexperienced. We also computed the Normalized Discounted Cumulative Gain (NDCG) NDCG gives geometrically decreasing weights to predictions at various positions of ranked list: NDCGp=DCGpIDCGp where DCGp=rel1+∑pi=2relilog2i", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'TABLE VIII: Performance on identifying experienced users.', answer the following question. Give detailed solution consisting of each step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has a higher NDCG score?\n" }, { "role": "assistant", "content": "The table shows the NDCG scores for both 'User at learned rate' and 'Our model'. Comparing the two, 'Our model' has a higher NDCG score of 0.97. So, the answer is {\"answer\": \"Our model\"}." } ], "images": [ "scigen/train/3139.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VIII: Performance on identifying experienced users." ], "reasoning": "The table shows the NDCG scores for both 'User at learned rate' and 'Our model'. Comparing the two, 'Our model' has a higher NDCG score of 0.97.", "question": "Which model has a higher NDCG score?", "answer": "So, the answer is {\"answer\": \"Our model\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The F1 score of 'User at learned rate' is higher than 'Our model'., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe F1 score of 'User at learned rate' is higher than 'Our model'.\n" }, { "role": "assistant", "content": "The table shows that the F1 score for 'User at learned rate' is 0.68, while the F1 score for 'Our model' is 0.75. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3139.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VIII: Performance on identifying experienced users." ], "reasoning": "The table shows that the F1 score for 'User at learned rate' is 0.68, while the F1 score for 'Our model' is 0.75. Therefore, the statement is incorrect.", "statement": "The F1 score of 'User at learned rate' is higher than 'Our model'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 6: The robustness analysis on the RCV1 dataset.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The meta-learner demonstrates consistent robustness across various tasks and model structures, as evidenced by the stable performance metrics.Algorithm Robustness Similar to Section The results indicate that the robustness of our meta-learner is consistent within different tasks and model structures, which again shows that the trained meta-learner can generate high-quality and robust policies." } ], "images": [ "scigen/train/3140.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: The robustness analysis on the RCV1 dataset." ], "reasoning": "The meta-learner demonstrates consistent robustness across various tasks and model structures, as evidenced by the stable performance metrics.", "description": "Algorithm Robustness Similar to Section The results indicate that the robustness of our meta-learner is consistent within different tasks and model structures, which again shows that the trained meta-learner can generate high-quality and robust policies.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 6: The robustness analysis on the RCV1 dataset.'.\n\nQuestion: What is the standard deviation of the Macro-F1 score?\n" }, { "role": "assistant", "content": "To find the standard deviation of the Macro-F1 score, look at the row labeled 'Macro-F1' and the column labeled 'STDEV'. The value is 0.0041. So, the answer is {\"answer\": \"0.0041\"}." } ], "images": [ "scigen/train/3140.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: The robustness analysis on the RCV1 dataset." ], "reasoning": "To find the standard deviation of the Macro-F1 score, look at the row labeled 'Macro-F1' and the column labeled 'STDEV'. The value is 0.0041.", "question": "What is the standard deviation of the Macro-F1 score?", "answer": "So, the answer is {\"answer\": \"0.0041\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The worst Micro-F1 score is higher than the best Accuracy score. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe worst Micro-F1 score is higher than the best Accuracy score.\n" }, { "role": "assistant", "content": "The worst Micro-F1 score is 0.855, and the best Accuracy score is 0.652. Since 0.855 is higher than 0.652, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3140.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: The robustness analysis on the RCV1 dataset." ], "reasoning": "The worst Micro-F1 score is 0.855, and the best Accuracy score is 0.652. Since 0.855 is higher than 0.652, the statement is supported.", "statement": "The worst Micro-F1 score is higher than the best Accuracy score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: The experimental results on fine-grained entity typing datasets. Acc.: Accuracy.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Our meta-learning method outperforms all baselines in all three metrics, and the SOTA model with learned training policy surpasses the one with hierarchy-aware training policy.S5SS0SSS0Px3 Results From the results, we observe that: (1) Our meta-learning method can outperform all the baselines in all three metrics, which indicates the capability of our methods in improving the multi-label classification. (2) The SOTA model trained with learned training policy outperforms the one with hierarchy-aware training policy. It is because that our learned training policy can capture not only explicit hierarchical dependencies of labels, but also model other implicit label dependencies. (3) The three different prediction policies can improve the performance of the SOTA classifier. The results are consistent with previous researches that choosing a good prediction policy is an effective way to improve the performance Fan and Lin (4) Compared with OntoNotes and BBN datasets, the FIGER show a relatively less improvement when applying these policies. The reason is that the test set of FIGER is not fine-grained enough (e.g., over 38% of entities are only annotated with /person and no more fine-grained labels) Xin et al." } ], "images": [ "scigen/train/3141.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The experimental results on fine-grained entity typing datasets. Acc.: Accuracy." ], "reasoning": "Our meta-learning method outperforms all baselines in all three metrics, and the SOTA model with learned training policy surpasses the one with hierarchy-aware training policy.", "description": "S5SS0SSS0Px3 Results From the results, we observe that: (1) Our meta-learning method can outperform all the baselines in all three metrics, which indicates the capability of our methods in improving the multi-label classification. (2) The SOTA model trained with learned training policy outperforms the one with hierarchy-aware training policy. It is because that our learned training policy can capture not only explicit hierarchical dependencies of labels, but also model other implicit label dependencies. (3) The three different prediction policies can improve the performance of the SOTA classifier. The results are consistent with previous researches that choosing a good prediction policy is an effective way to improve the performance Fan and Lin (4) Compared with OntoNotes and BBN datasets, the FIGER show a relatively less improvement when applying these policies. The reason is that the test set of FIGER is not fine-grained enough (e.g., over 38% of entities are only annotated with /person and no more fine-grained labels) Xin et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: The experimental results on fine-grained entity typing datasets. Acc.: Accuracy.', answer the following question. Give detailed solution consisting of each step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model shows the highest improvement in FIGER Micro metric?\n" }, { "role": "assistant", "content": "By comparing the FIGER Micro metric values, the SOTA+Meta-Training-Prediction model has the highest value of 0.794. So, the answer is {\"answer\": \"SOTA+Meta-Training-Prediction\"}" } ], "images": [ "scigen/train/3141.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The experimental results on fine-grained entity typing datasets. Acc.: Accuracy." ], "reasoning": "By comparing the FIGER Micro metric values, the SOTA+Meta-Training-Prediction model has the highest value of 0.794.", "question": "Which model shows the highest improvement in FIGER Micro metric?", "answer": "So, the answer is {\"answer\": \"SOTA+Meta-Training-Prediction\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The SOTA model with hierarchy-aware training policy outperforms the one with learned training policy in OntoNotes Macro metric., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe SOTA model with hierarchy-aware training policy outperforms the one with learned training policy in OntoNotes Macro metric.\n" }, { "role": "assistant", "content": "The table shows that the SOTA+Hier-Training model has an OntoNotes Macro metric of 0.690, while the SOTA+Meta-Training-Prediction model has a higher value of 0.719. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3141.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The experimental results on fine-grained entity typing datasets. Acc.: Accuracy." ], "reasoning": "The table shows that the SOTA+Hier-Training model has an OntoNotes Macro metric of 0.690, while the SOTA+Meta-Training-Prediction model has a higher value of 0.719.", "statement": "The SOTA model with hierarchy-aware training policy outperforms the one with learned training policy in OntoNotes Macro metric.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: The robustness analysis on the FIGER dataset.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The experiment shows that the trained meta-learner is robust to different initializations, achieving consistent performance across various metrics.Algorithm Robustness Previous researches Morimoto and Doya Thus, we design an experiment to detect whether the trained meta-leaner is sensitive to the initialization. For each group of policies, we train a classifier with wT and evaluate it with pT using the same metric. The results demonstrate that our trained meta-learner is robust to different initialization, which indicates that the meta-learner in our method can generate high-quality and robust training and prediction policies to improve the multi-label classification." } ], "images": [ "scigen/train/3142.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The robustness analysis on the FIGER dataset." ], "reasoning": "The experiment shows that the trained meta-learner is robust to different initializations, achieving consistent performance across various metrics.", "description": "Algorithm Robustness Previous researches Morimoto and Doya Thus, we design an experiment to detect whether the trained meta-leaner is sensitive to the initialization. For each group of policies, we train a classifier with wT and evaluate it with pT using the same metric. The results demonstrate that our trained meta-learner is robust to different initialization, which indicates that the meta-learner in our method can generate high-quality and robust training and prediction policies to improve the multi-label classification.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: The robustness analysis on the FIGER dataset.\nQuestion: What is the average Macro-F1 score achieved by the trained meta-learner?\nSolve the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table lists the average scores for different metrics. The average Macro-F1 score is found in the row labeled 'Macro-F1' under the 'Average' column. So, the answer is {\"answer\": \"0.827\"}." } ], "images": [ "scigen/train/3142.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The robustness analysis on the FIGER dataset." ], "reasoning": "The table lists the average scores for different metrics. The average Macro-F1 score is found in the row labeled 'Macro-F1' under the 'Average' column.", "question": "What is the average Macro-F1 score achieved by the trained meta-learner?", "answer": "So, the answer is {\"answer\": \"0.827\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The trained meta-learner achieved a best accuracy of 0.689. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe trained meta-learner achieved a best accuracy of 0.689.\n" }, { "role": "assistant", "content": "The table shows the best accuracy score in the 'Best' column of the 'Accuracy' row, which is 0.689. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3142.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The robustness analysis on the FIGER dataset." ], "reasoning": "The table shows the best accuracy score in the 'Best' column of the 'Accuracy' row, which is 0.689.", "statement": "The trained meta-learner achieved a best accuracy of 0.689.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 5: Impact of language similarity on cross-lingual transfer. We simulate a low-resource language scenario by using only 5 hours of Italian CommonVoice data and add 50 hours from another language for pretraining. We fine-tune on 1 hour of Italian supervised data.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Pretraining models on closely-related languages improves performance on low-resource languages, as seen with Italian data.We pretrain models on the 5h as well as 50h of unlabeled data from several other languages: Italian, Spanish, German, English, Russian, Kabyle and Chinese. Finally, we fine-tune each model on 1h of Italian labeled data. Distant languages, e.g., Kabyle or Chinese are less effective. In order to improve performance on a low-resource language, it is best to add unlabeled data from a closely-related language. However one could argue that Pashto and Kurmanji should be closer to each other since they are both Iranian languages. The purpose of this analysis is not to recover full language families but to better understand how our model allocates the latent representations across languages. Future work may investigate whether encouraging shared tokens between similar languages could further help cross-lingual transfer." } ], "images": [ "scigen/train/3143.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Impact of language similarity on cross-lingual transfer. We simulate a low-resource language scenario by using only 5 hours of Italian CommonVoice data and add 50 hours from another language for pretraining. We fine-tune on 1 hour of Italian supervised data." ], "reasoning": "Pretraining models on closely-related languages improves performance on low-resource languages, as seen with Italian data.", "description": "We pretrain models on the 5h as well as 50h of unlabeled data from several other languages: Italian, Spanish, German, English, Russian, Kabyle and Chinese. Finally, we fine-tune each model on 1h of Italian labeled data. Distant languages, e.g., Kabyle or Chinese are less effective. In order to improve performance on a low-resource language, it is best to add unlabeled data from a closely-related language. However one could argue that Pashto and Kurmanji should be closer to each other since they are both Iranian languages. The purpose of this analysis is not to recover full language families but to better understand how our model allocates the latent representations across languages. Future work may investigate whether encouraging shared tokens between similar languages could further help cross-lingual transfer.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 5: Impact of language similarity on cross-lingual transfer. We simulate a low-resource language scenario by using only 5 hours of Italian CommonVoice data and add 50 hours from another language for pretraining. We fine-tune on 1 hour of Italian supervised data.' and proceed to solve the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich language had the highest performance when pretrained on 5h of Italian data and 50h of its own data?\n" }, { "role": "assistant", "content": "By comparing the performance scores of each language when pretrained on 5h of Italian data and 50h of its own data, we see that Russian (ru) had the highest score of 28.1. So, the answer is {\"answer\": \"ru\"}." } ], "images": [ "scigen/train/3143.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Impact of language similarity on cross-lingual transfer. We simulate a low-resource language scenario by using only 5 hours of Italian CommonVoice data and add 50 hours from another language for pretraining. We fine-tune on 1 hour of Italian supervised data." ], "reasoning": "By comparing the performance scores of each language when pretrained on 5h of Italian data and 50h of its own data, we see that Russian (ru) had the highest score of 28.1.", "question": "Which language had the highest performance when pretrained on 5h of Italian data and 50h of its own data?", "answer": "So, the answer is {\"answer\": \"ru\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nPretraining on 5h of Italian data and 50h of Spanish data resulted in a performance score higher than 20.\n" }, { "role": "assistant", "content": "The table shows that pretraining on 5h of Italian data and 50h of Spanish data resulted in a performance score of 16.8, which is less than 20. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3143.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Impact of language similarity on cross-lingual transfer. We simulate a low-resource language scenario by using only 5 hours of Italian CommonVoice data and add 50 hours from another language for pretraining. We fine-tune on 1 hour of Italian supervised data." ], "reasoning": "The table shows that pretraining on 5h of Italian data and 50h of Spanish data resulted in a performance score of 16.8, which is less than 20.", "statement": "Pretraining on 5h of Italian data and 50h of Spanish data resulted in a performance score higher than 20.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: An ablation study of ReSAN.'.\n" }, { "role": "assistant", "content": "Removing the soft self-attention module decreases test accuracy the most, while removing unselected heads has the least impact on accuracy.One by one, each component is removed and the changes in test accuracy are recorded. the hard attention module, 2) soft self-attention module and 3) both hard attention and soft self-attention modules. In terms of prediction quality, the results show that 1) the unselected head tokens do contribute to the prediction, bringing 0.2% improvement; 2) using separate RSS modules to select the head and dependent tokens improves accuracy by 0.5%; and 3) hard attention and soft self-attention modules improve the accuracy by 0.3% and 2.9% respectively. In terms of inference time, it shows that 1) the two variants are more time-efficient but have poorer performance; and 2) applying the RSS modules to self-attention or attention improves not only performance but also time efficiency." } ], "images": [ "scigen/train/3144.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: An ablation study of ReSAN." ], "reasoning": "Removing the soft self-attention module decreases test accuracy the most, while removing unselected heads has the least impact on accuracy.", "description": "One by one, each component is removed and the changes in test accuracy are recorded. the hard attention module, 2) soft self-attention module and 3) both hard attention and soft self-attention modules. In terms of prediction quality, the results show that 1) the unselected head tokens do contribute to the prediction, bringing 0.2% improvement; 2) using separate RSS modules to select the head and dependent tokens improves accuracy by 0.5%; and 3) hard attention and soft self-attention modules improve the accuracy by 0.3% and 2.9% respectively. In terms of inference time, it shows that 1) the two variants are more time-efficient but have poorer performance; and 2) applying the RSS modules to self-attention or attention improves not only performance but also time efficiency.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 2: An ablation study of ReSAN.', unfold the question presented below, detailing every step of your calculation. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich component's removal results in the highest decrease in test accuracy?\n" }, { "role": "assistant", "content": "By comparing the test accuracy values, removing the soft self-attention module results in the highest decrease in test accuracy, dropping it to 83.4. So, the answer is {\"answer\": \"soft self-attention module\"}." } ], "images": [ "scigen/train/3144.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: An ablation study of ReSAN." ], "reasoning": "By comparing the test accuracy values, removing the soft self-attention module results in the highest decrease in test accuracy, dropping it to 83.4.", "question": "Which component's removal results in the highest decrease in test accuracy?", "answer": "So, the answer is {\"answer\": \"soft self-attention module\"}." }, { "messages": [ { "role": "user", "content": "Removing the hard attention module results in a test accuracy of 86.0. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nRemoving the hard attention module results in a test accuracy of 86.0.\n" }, { "role": "assistant", "content": "The table shows that removing the hard attention module results in a test accuracy of 86.0, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3144.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: An ablation study of ReSAN." ], "reasoning": "The table shows that removing the hard attention module results in a test accuracy of 86.0, which matches the statement.", "statement": "Removing the hard attention module results in a test accuracy of 86.0.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: Experimental results for different methods on SNLI. |θ|: the number of parameters (excluding word embedding part). T(s)/epoch: average training time (second) per epoch. Inference T(s): average inference time (second) for all dev data on SNLI with a batch size of 100.'.\n" }, { "role": "assistant", "content": "ReSAN outperforms all sentence encoding-based methods and achieves the best test accuracy with fewer parameters compared to other models.Compared to the methods from official leaderboard, ReSAN outperforms all the sentence encoding based methods and achieves the best test accuracy. Specifically, compared to the last best models, i.e., 600D Gumbel TreeLSTM encoders and 600D Residual stacked encoders, ReSAN uses far fewer parameters with better performance. Moreover, in contrast to the RNN/CNN based models with attention or memory module, ReSAN uses attention-only modules with equal or fewer parameters but outperforms them by a large margin, e.g., 600D Bi-LSTM + intra-attention (+3.0%), 300D NSE encoders (+1.7%) and 600D Deep Gated Attn (+0.8%). Furthermore, ReSAN even outperforms the 300D SPINN-PI encoders by 3.1%. , which is a recursive model and uses the result of an external semantic parsing tree as an extra input." } ], "images": [ "scigen/train/3145.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Experimental results for different methods on SNLI. |θ|: the number of parameters (excluding word embedding part). T(s)/epoch: average training time (second) per epoch. Inference T(s): average inference time (second) for all dev data on SNLI with a batch size of 100." ], "reasoning": "ReSAN outperforms all sentence encoding-based methods and achieves the best test accuracy with fewer parameters compared to other models.", "description": "Compared to the methods from official leaderboard, ReSAN outperforms all the sentence encoding based methods and achieves the best test accuracy. Specifically, compared to the last best models, i.e., 600D Gumbel TreeLSTM encoders and 600D Residual stacked encoders, ReSAN uses far fewer parameters with better performance. Moreover, in contrast to the RNN/CNN based models with attention or memory module, ReSAN uses attention-only modules with equal or fewer parameters but outperforms them by a large margin, e.g., 600D Bi-LSTM + intra-attention (+3.0%), 300D NSE encoders (+1.7%) and 600D Deep Gated Attn (+0.8%). Furthermore, ReSAN even outperforms the 300D SPINN-PI encoders by 3.1%. , which is a recursive model and uses the result of an external semantic parsing tree as an extra input.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Experimental results for different methods on SNLI. |θ|: the number of parameters (excluding word embedding part). T(s)/epoch: average training time (second) per epoch. Inference T(s): average inference time (second) for all dev data on SNLI with a batch size of 100.', solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieves the highest test accuracy and how many parameters does it use?\n" }, { "role": "assistant", "content": "The table shows that the 300D ReSAN model achieves the highest test accuracy of 86.3% and uses 3.1 million parameters. So, the answer is {\"answer\": \"300D ReSAN, 3.1m\"}." } ], "images": [ "scigen/train/3145.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Experimental results for different methods on SNLI. |θ|: the number of parameters (excluding word embedding part). T(s)/epoch: average training time (second) per epoch. Inference T(s): average inference time (second) for all dev data on SNLI with a batch size of 100." ], "reasoning": "The table shows that the 300D ReSAN model achieves the highest test accuracy of 86.3% and uses 3.1 million parameters.", "question": "Which model achieves the highest test accuracy and how many parameters does it use?", "answer": "So, the answer is {\"answer\": \"300D ReSAN, 3.1m\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nReSAN achieves the highest test accuracy among all models listed in the table.\n" }, { "role": "assistant", "content": "The table shows that the 300D ReSAN model has the highest test accuracy of 86.3%, which is higher than any other model listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3145.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Experimental results for different methods on SNLI. |θ|: the number of parameters (excluding word embedding part). T(s)/epoch: average training time (second) per epoch. Inference T(s): average inference time (second) for all dev data on SNLI with a batch size of 100." ], "reasoning": "The table shows that the 300D ReSAN model has the highest test accuracy of 86.3%, which is higher than any other model listed.", "statement": "ReSAN achieves the highest test accuracy among all models listed in the table.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Test performance of LEN trained on different trajectories. \"−>\" denotes to training order. The first row demonstrates two datasets (i.e., 1 and 2) without distracting features while the second row illustrates datasets (i.e., 3 and 4) with distracting features. FRAR demonstrates our algorithm which optimizes learning trajectory to prevent distracting features from affecting the training of learning algorithms.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The FRAR model optimizes training trajectory to prevent distracting features from affecting training, achieving a significant accuracy boost compared to training on a single dataset.To tackle the problem of abstract reasoning with distraction, we take inspirations from human learning in which knowledge is taught progressively according to a specific order as our reasoning abilities build up. As we see from the results, when no distracting features are present (first row), changing the order of the training has little impacts on the actual results. When distracting features are present (second row), however, the trajectory of training data significantly affects the training outcome. The FRAR model that we propose to optimize training trajectory in order to prevent distracting features from affecting the training achieves a significant boost of 15.1% compares to training on a single dataset. This suggests that we are able to achieve better training performance by changing the order that the learning algorithm receives the training data. We set two groups of experiments to examine training trajectory generated by the teacher model. D1 denotes that we instantiate the “and” relation on the type of “shape” as reasoning attributes and does not set the distracting attribute. D2 denotes that the reasoning attribution is based on the “size shape” and do not set the distracting attribute. D3 is similar to D1, but “size” is set a random value as the distracting attribute. D4 is similar to D2, but “type” is set a random value as the distracting attribute. In summary, there not exist distracting attributes in D1 and D2. For D3 and D4, “size” and “type” are distracting attributes respectively. We conduct experiments as follows. Without distracting attributes, D1 and D2 can promote each other to encode the reasoning attributes, thus improving the accuracy of the model. Adjusting the training trajectory in the dataset without distracting attributes only provides a small increase in the performance. It demonstrates that a model without the influence of distracting attributes is able to encode all the attributes into satisfactory embedding and perform abstract reasoning. However, joint training in the dataset D3 and D4 with distracting attributes do not promote each other. Then, our proposed model is able to find a more proper training trajectory and achieve an obvious improvement." } ], "images": [ "scigen/train/3146.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Test performance of LEN trained on different trajectories. \"−>\" denotes to training order. The first row demonstrates two datasets (i.e., 1 and 2) without distracting features while the second row illustrates datasets (i.e., 3 and 4) with distracting features. FRAR demonstrates our algorithm which optimizes learning trajectory to prevent distracting features from affecting the training of learning algorithms." ], "reasoning": "The FRAR model optimizes training trajectory to prevent distracting features from affecting training, achieving a significant accuracy boost compared to training on a single dataset.", "description": "To tackle the problem of abstract reasoning with distraction, we take inspirations from human learning in which knowledge is taught progressively according to a specific order as our reasoning abilities build up. As we see from the results, when no distracting features are present (first row), changing the order of the training has little impacts on the actual results. When distracting features are present (second row), however, the trajectory of training data significantly affects the training outcome. The FRAR model that we propose to optimize training trajectory in order to prevent distracting features from affecting the training achieves a significant boost of 15.1% compares to training on a single dataset. This suggests that we are able to achieve better training performance by changing the order that the learning algorithm receives the training data. We set two groups of experiments to examine training trajectory generated by the teacher model. D1 denotes that we instantiate the “and” relation on the type of “shape” as reasoning attributes and does not set the distracting attribute. D2 denotes that the reasoning attribution is based on the “size shape” and do not set the distracting attribute. D3 is similar to D1, but “size” is set a random value as the distracting attribute. D4 is similar to D2, but “type” is set a random value as the distracting attribute. In summary, there not exist distracting attributes in D1 and D2. For D3 and D4, “size” and “type” are distracting attributes respectively. We conduct experiments as follows. Without distracting attributes, D1 and D2 can promote each other to encode the reasoning attributes, thus improving the accuracy of the model. Adjusting the training trajectory in the dataset without distracting attributes only provides a small increase in the performance. It demonstrates that a model without the influence of distracting attributes is able to encode all the attributes into satisfactory embedding and perform abstract reasoning. However, joint training in the dataset D3 and D4 with distracting attributes do not promote each other. Then, our proposed model is able to find a more proper training trajectory and achieve an obvious improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: Test performance of LEN trained on different trajectories. \"−>\" denotes to training order. The first row demonstrates two datasets (i.e., 1 and 2) without distracting features while the second row illustrates datasets (i.e., 3 and 4) with distracting features. FRAR demonstrates our algorithm which optimizes learning trajectory to prevent distracting features from affecting the training of learning algorithms.', answer the question below by showing each progressive step toward the answer. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the accuracy improvement achieved by the FRAR model on dataset 1 compared to the 1+2 training trajectory?\n" }, { "role": "assistant", "content": "The accuracy for the 1+2 training trajectory on dataset 1 is 81.0%, while the FRAR model achieves 82.1%, resulting in an improvement of 1.1%. So, the answer is {\"answer\": \"1.1%\"}" } ], "images": [ "scigen/train/3146.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Test performance of LEN trained on different trajectories. \"−>\" denotes to training order. The first row demonstrates two datasets (i.e., 1 and 2) without distracting features while the second row illustrates datasets (i.e., 3 and 4) with distracting features. FRAR demonstrates our algorithm which optimizes learning trajectory to prevent distracting features from affecting the training of learning algorithms." ], "reasoning": "The accuracy for the 1+2 training trajectory on dataset 1 is 81.0%, while the FRAR model achieves 82.1%, resulting in an improvement of 1.1%.", "question": "What is the accuracy improvement achieved by the FRAR model on dataset 1 compared to the 1+2 training trajectory?", "answer": "So, the answer is {\"answer\": \"1.1%\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe FRAR model achieves the highest accuracy on dataset 3.\n" }, { "role": "assistant", "content": "The table shows that the highest accuracy on dataset 3 is 67.6%, achieved by the FRAR model. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3146.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Test performance of LEN trained on different trajectories. \"−>\" denotes to training order. The first row demonstrates two datasets (i.e., 1 and 2) without distracting features while the second row illustrates datasets (i.e., 3 and 4) with distracting features. FRAR demonstrates our algorithm which optimizes learning trajectory to prevent distracting features from affecting the training of learning algorithms." ], "reasoning": "The table shows that the highest accuracy on dataset 3 is 67.6%, achieved by the FRAR model.", "statement": "The FRAR model achieves the highest accuracy on dataset 3.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Test performance of all models trained on the neutral split of the PGM dataset. Teacher Model denotes that using the teacher model to determine the appropriate training trajectory. Type loss denotes that adding category label of questions into loss functions.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The Logic Embedding Network (LEN) and its variant T-LEN outperform baseline models in accuracy, with T-LEN + Teacher Model + type loss achieving the highest performance.Baseline Models We compare a comprehensive list of baseline models. The WReN model Barrett et al. Our proposed Logic Embedding Network (LEN) and its variant with two-stream (i.e.g, T-LEN) achieve a much better performance when comparing to baseline algorithms." } ], "images": [ "scigen/train/3147.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Test performance of all models trained on the neutral split of the PGM dataset. Teacher Model denotes that using the teacher model to determine the appropriate training trajectory. Type loss denotes that adding category label of questions into loss functions." ], "reasoning": "The Logic Embedding Network (LEN) and its variant T-LEN outperform baseline models in accuracy, with T-LEN + Teacher Model + type loss achieving the highest performance.", "description": "Baseline Models We compare a comprehensive list of baseline models. The WReN model Barrett et al. Our proposed Logic Embedding Network (LEN) and its variant with two-stream (i.e.g, T-LEN) achieve a much better performance when comparing to baseline algorithms.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Test performance of all models trained on the neutral split of the PGM dataset. Teacher Model denotes that using the teacher model to determine the appropriate training trajectory. Type loss denotes that adding category label of questions into loss functions.', and sequentially solve the question, writing out each step of your reasoning process. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieves the highest accuracy and what is its percentage?\n" }, { "role": "assistant", "content": "By examining the table, the model T-LEN + Teacher Model + type loss has the highest accuracy at 88.9%. So, the answer is {\"answer\": \"T-LEN + Teacher Model + type loss, 88.9%\"}." } ], "images": [ "scigen/train/3147.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Test performance of all models trained on the neutral split of the PGM dataset. Teacher Model denotes that using the teacher model to determine the appropriate training trajectory. Type loss denotes that adding category label of questions into loss functions." ], "reasoning": "By examining the table, the model T-LEN + Teacher Model + type loss has the highest accuracy at 88.9%.", "question": "Which model achieves the highest accuracy and what is its percentage?", "answer": "So, the answer is {\"answer\": \"T-LEN + Teacher Model + type loss, 88.9%\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The LEN model achieves an accuracy of 68.1%., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe LEN model achieves an accuracy of 68.1%.\n" }, { "role": "assistant", "content": "The table shows that the LEN model has an accuracy of 68.1%, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3147.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Test performance of all models trained on the neutral split of the PGM dataset. Teacher Model denotes that using the teacher model to determine the appropriate training trajectory. Type loss denotes that adding category label of questions into loss functions." ], "reasoning": "The table shows that the LEN model has an accuracy of 68.1%, which matches the statement.", "statement": "The LEN model achieves an accuracy of 68.1%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: POS tagging results.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Ling et al. achieved the best result to date with the JMTAB model, which outperformed other methods in F1 score.The best result to date has been achieved by Ling et al. Incorporating the character-based encoders into our JMT model would be an interesting direction, but we have shown that the simple pre-trained character n-gram embeddings lead to the promising result." } ], "images": [ "scigen/train/3148.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: POS tagging results." ], "reasoning": "Ling et al. achieved the best result to date with the JMTAB model, which outperformed other methods in F1 score.", "description": "The best result to date has been achieved by Ling et al. Incorporating the character-based encoders into our JMT model would be an interesting direction, but we have shown that the simple pre-trained character n-gram embeddings lead to the promising result.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: POS tagging results.'.\n\nQuestion: Which method achieved the highest F1 score according to the table?\n" }, { "role": "assistant", "content": "The table lists various methods and their corresponding F1 scores. The highest F1 score is 95.77, achieved by the JMTAB method. So, the answer is {\"answer\": \"JMTAB\"}" } ], "images": [ "scigen/train/3148.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: POS tagging results." ], "reasoning": "The table lists various methods and their corresponding F1 scores. The highest F1 score is 95.77, achieved by the JMTAB method.", "question": "Which method achieved the highest F1 score according to the table?", "answer": "So, the answer is {\"answer\": \"JMTAB\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe method by Søgaard and Goldberg (2016) achieved an F1 score higher than 95.15.\n" }, { "role": "assistant", "content": "According to the table, the method by Søgaard and Goldberg (2016) achieved an F1 score of 95.56, which is higher than 95.15. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3148.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: POS tagging results." ], "reasoning": "According to the table, the method by Søgaard and Goldberg (2016) achieved an F1 score of 95.56, which is higher than 95.15.", "statement": "The method by Søgaard and Goldberg (2016) achieved an F1 score higher than 95.15.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: POS tagging results.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Ling et al. achieved the best result with an accuracy of 97.78%, suggesting that incorporating character-based encoders into the JMT model could be beneficial.The best result to date has been achieved by Ling et al. Incorporating the character-based encoders into our JMT model would be an interesting direction, but we have shown that the simple pre-trained character n-gram embeddings lead to the promising result." } ], "images": [ "scigen/train/3149.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: POS tagging results." ], "reasoning": "Ling et al. achieved the best result with an accuracy of 97.78%, suggesting that incorporating character-based encoders into the JMT model could be beneficial.", "description": "The best result to date has been achieved by Ling et al. Incorporating the character-based encoders into our JMT model would be an interesting direction, but we have shown that the simple pre-trained character n-gram embeddings lead to the promising result.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: POS tagging results.', answer the following question. Give detailed solution consisting of each step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the accuracy difference between the best result and the JMTall method?\n" }, { "role": "assistant", "content": "The best result is from Ling et al. with an accuracy of 97.78%, and the JMTall method has an accuracy of 97.55%. The difference is 97.78% - 97.55% = 0.23%. So, the answer is {\"answer\": \"0.23%\"}." } ], "images": [ "scigen/train/3149.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: POS tagging results." ], "reasoning": "The best result is from Ling et al. with an accuracy of 97.78%, and the JMTall method has an accuracy of 97.55%. The difference is 97.78% - 97.55% = 0.23%.", "question": "What is the accuracy difference between the best result and the JMTall method?", "answer": "So, the answer is {\"answer\": \"0.23%\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe accuracy of the method by Kumar et al. (2016) is higher than that of the method by Søgaard (2011).\n" }, { "role": "assistant", "content": "The table shows that Kumar et al. (2016) has an accuracy of 97.56%, while Søgaard (2011) has an accuracy of 97.50%. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3149.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: POS tagging results." ], "reasoning": "The table shows that Kumar et al. (2016) has an accuracy of 97.56%, while Søgaard (2011) has an accuracy of 97.50%. Therefore, the statement is supported.", "statement": "The accuracy of the method by Kumar et al. (2016) is higher than that of the method by Søgaard (2011).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: POS tagging results.'.\n" }, { "role": "assistant", "content": "Incorporating character-based encoders into the JMT model could be beneficial, as shown by the promising results achieved with pre-trained character n-gram embeddings.The best result to date has been achieved by Ling et al. Incorporating the character-based encoders into our JMT model would be an interesting direction, but we have shown that the simple pre-trained character n-gram embeddings lead to the promising result." } ], "images": [ "scigen/train/3150.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: POS tagging results." ], "reasoning": "Incorporating character-based encoders into the JMT model could be beneficial, as shown by the promising results achieved with pre-trained character n-gram embeddings.", "description": "The best result to date has been achieved by Ling et al. Incorporating the character-based encoders into our JMT model would be an interesting direction, but we have shown that the simple pre-trained character n-gram embeddings lead to the promising result.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: POS tagging results.', solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method achieved the highest UAS score, and what was the score?\n" }, { "role": "assistant", "content": "By examining the table, the method with the highest UAS score is 'Dozat and Manning (2017)' with a score of 95.74. So, the answer is {\"answer\": \"Dozat and Manning (2017) with 95.74\"}" } ], "images": [ "scigen/train/3150.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: POS tagging results." ], "reasoning": "By examining the table, the method with the highest UAS score is 'Dozat and Manning (2017)' with a score of 95.74.", "question": "Which method achieved the highest UAS score, and what was the score?", "answer": "So, the answer is {\"answer\": \"Dozat and Manning (2017) with 95.74\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The method by Dozat and Manning (2017) achieved the highest LAS score. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe method by Dozat and Manning (2017) achieved the highest LAS score.\n" }, { "role": "assistant", "content": "By examining the table, the method by Dozat and Manning (2017) has the highest LAS score of 94.08. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3150.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: POS tagging results." ], "reasoning": "By examining the table, the method by Dozat and Manning (2017) has the highest LAS score of 94.08.", "statement": "The method by Dozat and Manning (2017) achieved the highest LAS score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: Semantic relatedness results.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The JMTDE method outperforms previous state-of-the-art methods by Zhou et al. and Tai et al., despite using a simple max-pooling strategy.The result of “JMTDE” is already better than the previous state-of-the-art results. Both of Zhou et al. and Tai et al. However, our method uses the simple max-pooling strategy, which suggests that it is worth investigating such simple methods before developing complex methods for simple tasks. Currently, our JMT model does not explicitly use the learned dependency structures, and thus the explicit use of the output from the dependency layer should be an interesting direction of future work. The previous state-of-the-art result in Yin et al. Again, our simple max-pooling strategy achieves the state-of-the-art result boosted by the joint training. These results show the importance of jointly handling related tasks." } ], "images": [ "scigen/train/3151.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Semantic relatedness results." ], "reasoning": "The JMTDE method outperforms previous state-of-the-art methods by Zhou et al. and Tai et al., despite using a simple max-pooling strategy.", "description": "The result of “JMTDE” is already better than the previous state-of-the-art results. Both of Zhou et al. and Tai et al. However, our method uses the simple max-pooling strategy, which suggests that it is worth investigating such simple methods before developing complex methods for simple tasks. Currently, our JMT model does not explicitly use the learned dependency structures, and thus the explicit use of the output from the dependency layer should be an interesting direction of future work. The previous state-of-the-art result in Yin et al. Again, our simple max-pooling strategy achieves the state-of-the-art result boosted by the joint training. These results show the importance of jointly handling related tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 5: Semantic relatedness results.'. Solve the following question based on the table.\n\nQuestion: Which method achieved the lowest MSE and what is its value?\nLet's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the MSE values in the table, the method JMTall achieved the lowest MSE with a value of 0.233. So, the answer is {\"answer\": \"JMTall, 0.233\"}." } ], "images": [ "scigen/train/3151.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Semantic relatedness results." ], "reasoning": "By comparing the MSE values in the table, the method JMTall achieved the lowest MSE with a value of 0.233.", "question": "Which method achieved the lowest MSE and what is its value?", "answer": "So, the answer is {\"answer\": \"JMTall, 0.233\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe JMTDE method achieved a lower MSE than the method by Tai et al. (2015).\n" }, { "role": "assistant", "content": "The table shows that the JMTDE method has an MSE of 0.238, which is lower than the MSE of 0.253 achieved by Tai et al. (2015). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3151.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Semantic relatedness results." ], "reasoning": "The table shows that the JMTDE method has an MSE of 0.238, which is lower than the MSE of 0.253 achieved by Tai et al. (2015).", "statement": "The JMTDE method achieved a lower MSE than the method by Tai et al. (2015).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5: Semantic relatedness results.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The JMTDE method outperforms previous state-of-the-art results, including those by Zhou et al. and Tai et al., using a simple max-pooling strategy.The result of “JMTDE” is already better than the previous state-of-the-art results. Both of Zhou et al. and Tai et al. However, our method uses the simple max-pooling strategy, which suggests that it is worth investigating such simple methods before developing complex methods for simple tasks. Currently, our JMT model does not explicitly use the learned dependency structures, and thus the explicit use of the output from the dependency layer should be an interesting direction of future work. The previous state-of-the-art result in Yin et al. Again, our simple max-pooling strategy achieves the state-of-the-art result boosted by the joint training. These results show the importance of jointly handling related tasks." } ], "images": [ "scigen/train/3152.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Semantic relatedness results." ], "reasoning": "The JMTDE method outperforms previous state-of-the-art results, including those by Zhou et al. and Tai et al., using a simple max-pooling strategy.", "description": "The result of “JMTDE” is already better than the previous state-of-the-art results. Both of Zhou et al. and Tai et al. However, our method uses the simple max-pooling strategy, which suggests that it is worth investigating such simple methods before developing complex methods for simple tasks. Currently, our JMT model does not explicitly use the learned dependency structures, and thus the explicit use of the output from the dependency layer should be an interesting direction of future work. The previous state-of-the-art result in Yin et al. Again, our simple max-pooling strategy achieves the state-of-the-art result boosted by the joint training. These results show the importance of jointly handling related tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 5: Semantic relatedness results.' and proceed to solve the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the accuracy of the JMTDE method compared to Yin et al. (2016)?\n" }, { "role": "assistant", "content": "The table shows that the accuracy of the JMTDE method is 86.8%, while the accuracy of Yin et al. (2016) is 86.2%. So, the answer is {\"answer\": \"86.8%\"}." } ], "images": [ "scigen/train/3152.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Semantic relatedness results." ], "reasoning": "The table shows that the accuracy of the JMTDE method is 86.8%, while the accuracy of Yin et al. (2016) is 86.2%.", "question": "What is the accuracy of the JMTDE method compared to Yin et al. (2016)?", "answer": "So, the answer is {\"answer\": \"86.8%\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe JMTDE method has a higher accuracy than the method by Lai and Hockenmaier (2014).\n" }, { "role": "assistant", "content": "The table shows that the accuracy of the JMTDE method is 86.8%, while the accuracy of Lai and Hockenmaier (2014) is 84.6%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3152.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Semantic relatedness results." ], "reasoning": "The table shows that the accuracy of the JMTDE method is 86.8%, while the accuracy of Lai and Hockenmaier (2014) is 84.6%.", "statement": "The JMTDE method has a higher accuracy than the method by Lai and Hockenmaier (2014).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 9: Effectiveness of the Successive Regularization (SR) and the Vertical Connections (VC).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Successive regularization improves chunking accuracy significantly, while other tasks remain relatively unaffected, indicating its effectiveness for imbalanced dataset sizes.We can see that the accuracy of chunking is improved by the successive regularization, while other results are not affected so much. The chunking dataset used here is relatively small compared with other low-level tasks, POS tagging and dependency parsing. Thus, these results suggest that the successive regularization is effective when dataset sizes are imbalanced." } ], "images": [ "scigen/train/3153.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Effectiveness of the Successive Regularization (SR) and the Vertical Connections (VC)." ], "reasoning": "Successive regularization improves chunking accuracy significantly, while other tasks remain relatively unaffected, indicating its effectiveness for imbalanced dataset sizes.", "description": "We can see that the accuracy of chunking is improved by the successive regularization, while other results are not affected so much. The chunking dataset used here is relatively small compared with other low-level tasks, POS tagging and dependency parsing. Thus, these results suggest that the successive regularization is effective when dataset sizes are imbalanced.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 9: Effectiveness of the Successive Regularization (SR) and the Vertical Connections (VC).', solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich task shows the most improvement in accuracy with successive regularization?\n" }, { "role": "assistant", "content": "By comparing the accuracy values in the 'JMTall' and 'w/o SR' columns, chunking shows the most improvement from 97.13 to 97.59. So, the answer is {\"answer\": \"Chunking\"}" } ], "images": [ "scigen/train/3153.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Effectiveness of the Successive Regularization (SR) and the Vertical Connections (VC)." ], "reasoning": "By comparing the accuracy values in the 'JMTall' and 'w/o SR' columns, chunking shows the most improvement from 97.13 to 97.59.", "question": "Which task shows the most improvement in accuracy with successive regularization?", "answer": "So, the answer is {\"answer\": \"Chunking\"}" }, { "messages": [ { "role": "user", "content": "Successive regularization has the least impact on the accuracy of dependency parsing LAS. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nSuccessive regularization has the least impact on the accuracy of dependency parsing LAS.\n" }, { "role": "assistant", "content": "The accuracy for dependency parsing LAS changes minimally from 92.57 to 92.60 with successive regularization, indicating the least impact. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3153.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Effectiveness of the Successive Regularization (SR) and the Vertical Connections (VC)." ], "reasoning": "The accuracy for dependency parsing LAS changes minimally from 92.57 to 92.60 with successive regularization, indicating the least impact.", "statement": "Successive regularization has the least impact on the accuracy of dependency parsing LAS.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 10: Effects of the order of training.'.\n" }, { "role": "assistant", "content": "Starting from lower-level tasks and gradually moving to higher-level tasks improves model effectiveness, as shown by the drop in semantic task scores with the random strategy.S6SS2SSS0Px6 Order of training Our hypothesis is that it is important to start from the lower-level tasks and gradually move to the higher-level tasks. We see that the scores of the semantic tasks drop by the random strategy. In our preliminary experiments, we have found that constructing the mini-batch samples from different tasks also hampers the effectiveness of our model, which also supports our hypothesis." } ], "images": [ "scigen/train/3154.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Effects of the order of training." ], "reasoning": "Starting from lower-level tasks and gradually moving to higher-level tasks improves model effectiveness, as shown by the drop in semantic task scores with the random strategy.", "description": "S6SS2SSS0Px6 Order of training Our hypothesis is that it is important to start from the lower-level tasks and gradually move to the higher-level tasks. We see that the scores of the semantic tasks drop by the random strategy. In our preliminary experiments, we have found that constructing the mini-batch samples from different tasks also hampers the effectiveness of our model, which also supports our hypothesis.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 10: Effects of the order of training.'.\n\nQuestion: Which strategy resulted in a higher score for the Entailment task?\n" }, { "role": "assistant", "content": "The table shows the scores for the Entailment task under both the JMTall and Random strategies. The JMTall strategy has a score of 84.6, while the Random strategy has a score of 83.2. So, the answer is {\"answer\": \"JMTall\"}." } ], "images": [ "scigen/train/3154.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Effects of the order of training." ], "reasoning": "The table shows the scores for the Entailment task under both the JMTall and Random strategies. The JMTall strategy has a score of 84.6, while the Random strategy has a score of 83.2.", "question": "Which strategy resulted in a higher score for the Entailment task?", "answer": "So, the answer is {\"answer\": \"JMTall\"}." }, { "messages": [ { "role": "user", "content": "The JMTall strategy resulted in a higher score for the Dependency UAS task compared to the Random strategy. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe JMTall strategy resulted in a higher score for the Dependency UAS task compared to the Random strategy.\n" }, { "role": "assistant", "content": "The table shows that the score for the Dependency UAS task is 94.51 for the JMTall strategy and 94.66 for the Random strategy. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3154.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Effects of the order of training." ], "reasoning": "The table shows that the score for the Dependency UAS task is 94.51 for the JMTall strategy and 94.66 for the Random strategy. Therefore, the statement is incorrect.", "statement": "The JMTall strategy resulted in a higher score for the Dependency UAS task compared to the Random strategy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 12: Effects of the character embeddings.'.\n" }, { "role": "assistant", "content": "Using both word and character n-gram embeddings improves the accuracy of POS, chunking, and dependency parsing tasks compared to using only word embeddings.Character n-gram embeddings The column of “W&C” corresponds to using both of the word and character n-gram embeddings, and that of “Only W” corresponds to using only the word embeddings. These results clearly show that jointly using the pre-trained word and character n-gram embeddings is helpful in improving the results. The pre-training of the character n-gram embeddings is also effective; for example, without the pre-training, the POS accuracy drops from 97.52% to 97.38% and the chunking accuracy drops from 95.65% to 95.14%." } ], "images": [ "scigen/train/3155.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 12: Effects of the character embeddings." ], "reasoning": "Using both word and character n-gram embeddings improves the accuracy of POS, chunking, and dependency parsing tasks compared to using only word embeddings.", "description": "Character n-gram embeddings The column of “W&C” corresponds to using both of the word and character n-gram embeddings, and that of “Only W” corresponds to using only the word embeddings. These results clearly show that jointly using the pre-trained word and character n-gram embeddings is helpful in improving the results. The pre-training of the character n-gram embeddings is also effective; for example, without the pre-training, the POS accuracy drops from 97.52% to 97.38% and the chunking accuracy drops from 95.65% to 95.14%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 12: Effects of the character embeddings.' and proceed to solve the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the difference in POS accuracy between using both word and character n-gram embeddings and using only word embeddings?\n" }, { "role": "assistant", "content": "The POS accuracy with both word and character n-gram embeddings is 97.52%, while with only word embeddings it is 96.26%. The difference is 97.52% - 96.26% = 1.26%. So, the answer is {\"answer\": \"1.26%\"}" } ], "images": [ "scigen/train/3155.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 12: Effects of the character embeddings." ], "reasoning": "The POS accuracy with both word and character n-gram embeddings is 97.52%, while with only word embeddings it is 96.26%. The difference is 97.52% - 96.26% = 1.26%.", "question": "What is the difference in POS accuracy between using both word and character n-gram embeddings and using only word embeddings?", "answer": "So, the answer is {\"answer\": \"1.26%\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nUsing only word embeddings results in higher chunking accuracy than using both word and character n-gram embeddings.\n" }, { "role": "assistant", "content": "The chunking accuracy with both word and character n-gram embeddings is 95.65%, while with only word embeddings it is 94.92%. Therefore, using only word embeddings does not result in higher chunking accuracy. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3155.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 12: Effects of the character embeddings." ], "reasoning": "The chunking accuracy with both word and character n-gram embeddings is 95.65%, while with only word embeddings it is 94.92%. Therefore, using only word embeddings does not result in higher chunking accuracy.", "statement": "Using only word embeddings results in higher chunking accuracy than using both word and character n-gram embeddings.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 14: Dependency parsing scores on the development set with and without the character n-gram embeddings, focusing on UAS and LAS for unknown words. The overall scores are taken from Table 12. There are 976 unknown words in the sentences of the development set.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Using character-level information improves the LAS score significantly, suggesting the benefit of incorporating character n-gram embeddings along with word embeddings.Again, we can see that using the character-level information is effective, and in particular, the improvement of the LAS score is large. These results suggest that it is better to use not only the word embeddings, but also the character n-gram embeddings by default." } ], "images": [ "scigen/train/3156.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 14: Dependency parsing scores on the development set with and without the character n-gram embeddings, focusing on UAS and LAS for unknown words. The overall scores are taken from Table 12. There are 976 unknown words in the sentences of the development set." ], "reasoning": "Using character-level information improves the LAS score significantly, suggesting the benefit of incorporating character n-gram embeddings along with word embeddings.", "description": "Again, we can see that using the character-level information is effective, and in particular, the improvement of the LAS score is large. These results suggest that it is better to use not only the word embeddings, but also the character n-gram embeddings by default.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 14: Dependency parsing scores on the development set with and without the character n-gram embeddings, focusing on UAS and LAS for unknown words. The overall scores are taken from Table 12. There are 976 unknown words in the sentences of the development set.' and methodically tackle the question that follows. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the difference in the LAS scores for unknown words between using W&C and Only W?\n" }, { "role": "assistant", "content": "The LAS score for unknown words using W&C is 87.81, while using Only W is 81.05. The difference is 87.81 - 81.05 = 6.76. So, the answer is {\"answer\": \"6.76\"}." } ], "images": [ "scigen/train/3156.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 14: Dependency parsing scores on the development set with and without the character n-gram embeddings, focusing on UAS and LAS for unknown words. The overall scores are taken from Table 12. There are 976 unknown words in the sentences of the development set." ], "reasoning": "The LAS score for unknown words using W&C is 87.81, while using Only W is 81.05. The difference is 87.81 - 81.05 = 6.76.", "question": "What is the difference in the LAS scores for unknown words between using W&C and Only W?", "answer": "So, the answer is {\"answer\": \"6.76\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Using character-level information results in a higher UAS score for unknown words., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nUsing character-level information results in a higher UAS score for unknown words.\n" }, { "role": "assistant", "content": "The UAS score for unknown words using W&C is 92.21, while using Only W is 91.39. Since 92.21 is higher than 91.39, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3156.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 14: Dependency parsing scores on the development set with and without the character n-gram embeddings, focusing on UAS and LAS for unknown words. The overall scores are taken from Table 12. There are 976 unknown words in the sentences of the development set." ], "reasoning": "The UAS score for unknown words using W&C is 92.21, while using Only W is 91.39. Since 92.21 is higher than 91.39, the statement is supported.", "statement": "Using character-level information results in a higher UAS score for unknown words.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Ablation Study'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The table shows that each modification strategy incrementally improves the performance of direct parsing.The first line displays the performance of direct parsing without any modifications. The next three lines shows the performance with the modification strategies added in turn. As demonstrated in the table, these subtle modifications all play a useful role in improving performance." } ], "images": [ "scigen/train/3157.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation Study" ], "reasoning": "The table shows that each modification strategy incrementally improves the performance of direct parsing.", "description": "The first line displays the performance of direct parsing without any modifications. The next three lines shows the performance with the modification strategies added in turn. As demonstrated in the table, these subtle modifications all play a useful role in improving performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: Ablation Study'. Solve the following question based on the table.\n\nQuestion: What is the improvement in UAS when Punctuation Modification is added to Direct Parsing?\nLet's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The UAS for Direct Parsing is 0.500, and the UAS for Direct Parsing with Punctuation Modification is 0.607. The improvement is 0.607 - 0.500. So, the answer is {\"answer\": \"0.107\"}" } ], "images": [ "scigen/train/3157.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation Study" ], "reasoning": "The UAS for Direct Parsing is 0.500, and the UAS for Direct Parsing with Punctuation Modification is 0.607. The improvement is 0.607 - 0.500.", "question": "What is the improvement in UAS when Punctuation Modification is added to Direct Parsing?", "answer": "So, the answer is {\"answer\": \"0.107\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nTwo-part Parsing results in the highest LAS score.\n" }, { "role": "assistant", "content": "The table shows that the LAS score for Two-part Parsing is 0.384, which is higher than the LAS scores for all other methods listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3157.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation Study" ], "reasoning": "The table shows that the LAS score for Two-part Parsing is 0.384, which is higher than the LAS scores for all other methods listed.", "statement": "Two-part Parsing results in the highest LAS score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Performance Comparison with Other Parsing Models', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Zero-shot method significantly outperforms the Random parser and Supervised(Chinese) in both UAS and LAS metrics, indicating effective leveraging of parallel English and Chinese texts.The top four rows are performance tested on our Chinese corpus and the bottom two on SciDTB. From Human(English) and Human(Chinese), we can see that discourse labeling is a difficult task for both languages. Our Zero-shot method significantly outperforms the Random parser, meaning that parallel English and Chinese texts have similar discourse structures, and that our method effectively leverages such information. Zero-shot also performs about 12% and 11% higher than the Supervised(Chinese) with respect to the UAS and LAS metrics, because our corpus is too small to support supervision well enough. Compared with Supervised(English), the performance of Zero-shot is acceptable in terms of identifying the head EDU, but barely satisfactory in labeling the relations, which might be explained by different statistical distributions of relations types in Chinese and English.." } ], "images": [ "scigen/train/3158.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance Comparison with Other Parsing Models" ], "reasoning": "Zero-shot method significantly outperforms the Random parser and Supervised(Chinese) in both UAS and LAS metrics, indicating effective leveraging of parallel English and Chinese texts.", "description": "The top four rows are performance tested on our Chinese corpus and the bottom two on SciDTB. From Human(English) and Human(Chinese), we can see that discourse labeling is a difficult task for both languages. Our Zero-shot method significantly outperforms the Random parser, meaning that parallel English and Chinese texts have similar discourse structures, and that our method effectively leverages such information. Zero-shot also performs about 12% and 11% higher than the Supervised(Chinese) with respect to the UAS and LAS metrics, because our corpus is too small to support supervision well enough. Compared with Supervised(English), the performance of Zero-shot is acceptable in terms of identifying the head EDU, but barely satisfactory in labeling the relations, which might be explained by different statistical distributions of relations types in Chinese and English..", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: Performance Comparison with Other Parsing Models', solve this question and give detail solutions. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich method has the highest LAS score among those tested on the Chinese corpus?\n" }, { "role": "assistant", "content": "The table shows LAS scores for different methods. Among those tested on the Chinese corpus (top four rows), Human(Chinese) has the highest LAS score of 0.673. So, the answer is {\"answer\": \"Human(Chinese)\"}." } ], "images": [ "scigen/train/3158.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance Comparison with Other Parsing Models" ], "reasoning": "The table shows LAS scores for different methods. Among those tested on the Chinese corpus (top four rows), Human(Chinese) has the highest LAS score of 0.673.", "question": "Which method has the highest LAS score among those tested on the Chinese corpus?", "answer": "So, the answer is {\"answer\": \"Human(Chinese)\"}." }, { "messages": [ { "role": "user", "content": "Zero-shot method performs better than Supervised(Chinese) in UAS and LAS metrics. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nZero-shot method performs better than Supervised(Chinese) in UAS and LAS metrics.\n" }, { "role": "assistant", "content": "The table shows that Zero-shot has a UAS of 0.643 and LAS of 0.384, while Supervised(Chinese) has a UAS of 0.525 and LAS of 0.276. Therefore, Zero-shot performs better in both metrics. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3158.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance Comparison with Other Parsing Models" ], "reasoning": "The table shows that Zero-shot has a UAS of 0.643 and LAS of 0.384, while Supervised(Chinese) has a UAS of 0.525 and LAS of 0.276. Therefore, Zero-shot performs better in both metrics.", "statement": "Zero-shot method performs better than Supervised(Chinese) in UAS and LAS metrics.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The proposed model consistently outperforms almost all other methods across various datasets, horizons, and metrics, demonstrating its superiority in MTS forecasting.On typical MTS datasets, we chose the best model on the validation set using RAE/RSE/CORR as the metric for the testing set. Both tables show that the proposed model outperforms almost all other methods on all datasets, horizons, and metrics. Also, our models are able to deal with a wide range of dataset size, from the smallest 534 KB Exchange Rate dataset to the largest 172 MB Solar Energy dataset. In these results, the proposed model consistently demonstrates its superiority for MTS forecasting." } ], "images": [ "scigen/train/3159.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018)." ], "reasoning": "The proposed model consistently outperforms almost all other methods across various datasets, horizons, and metrics, demonstrating its superiority in MTS forecasting.", "description": "On typical MTS datasets, we chose the best model on the validation set using RAE/RSE/CORR as the metric for the testing set. Both tables show that the proposed model outperforms almost all other methods on all datasets, horizons, and metrics. Also, our models are able to deal with a wide range of dataset size, from the smallest 534 KB Exchange Rate dataset to the largest 172 MB Solar Energy dataset. In these results, the proposed model consistently demonstrates its superiority for MTS forecasting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018).'.\n\nQuestion: Which model has the highest CORR for the Exchange Rate 3 horizon?\n" }, { "role": "assistant", "content": "To find the model with the highest CORR for the Exchange Rate 3 horizon, look at the column labeled 'Exchange Rate 3' and identify the highest value. The highest CORR value is 0.9790, which belongs to 'Our model'. So, the answer is {\"answer\": \"Our model\"}." } ], "images": [ "scigen/train/3159.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018)." ], "reasoning": "To find the model with the highest CORR for the Exchange Rate 3 horizon, look at the column labeled 'Exchange Rate 3' and identify the highest value. The highest CORR value is 0.9790, which belongs to 'Our model'.", "question": "Which model has the highest CORR for the Exchange Rate 3 horizon?", "answer": "So, the answer is {\"answer\": \"Our model\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The SETAR model has the highest CORR for the Electricity 24 horizon. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe SETAR model has the highest CORR for the Electricity 24 horizon.\n" }, { "role": "assistant", "content": "To verify this statement, look at the column labeled 'Electricity 24' and compare the CORR values for all models. The highest CORR value in this column is 0.9171, which belongs to the SETAR model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3159.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018)." ], "reasoning": "To verify this statement, look at the column labeled 'Electricity 24' and compare the CORR values for all models. The highest CORR value in this column is 0.9171, which belongs to the SETAR model.", "statement": "The SETAR model has the highest CORR for the Electricity 24 horizon.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The proposed model outperforms almost all other methods across various datasets, horizons, and metrics, demonstrating its superiority in MTS forecasting.On typical MTS datasets, we chose the best model on the validation set using RAE/RSE/CORR as the metric for the testing set. Both tables show that the proposed model outperforms almost all other methods on all datasets, horizons, and metrics. Also, our models are able to deal with a wide range of dataset size, from the smallest 534 KB Exchange Rate dataset to the largest 172 MB Solar Energy dataset. In these results, the proposed model consistently demonstrates its superiority for MTS forecasting." } ], "images": [ "scigen/train/3160.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018)." ], "reasoning": "The proposed model outperforms almost all other methods across various datasets, horizons, and metrics, demonstrating its superiority in MTS forecasting.", "description": "On typical MTS datasets, we chose the best model on the validation set using RAE/RSE/CORR as the metric for the testing set. Both tables show that the proposed model outperforms almost all other methods on all datasets, horizons, and metrics. Also, our models are able to deal with a wide range of dataset size, from the smallest 534 KB Exchange Rate dataset to the largest 172 MB Solar Energy dataset. In these results, the proposed model consistently demonstrates its superiority for MTS forecasting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018).', unfold the question presented below, detailing every step of your calculation. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the lowest RAE for the Solar Energy 3 horizon?\n" }, { "role": "assistant", "content": "By examining the RAE values for the Solar Energy 3 horizon, the LSTNet-Attn model has the lowest RAE value of 0.0900. So, the answer is {\"answer\": \"LSTNet-Attn\"}." } ], "images": [ "scigen/train/3160.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018)." ], "reasoning": "By examining the RAE values for the Solar Energy 3 horizon, the LSTNet-Attn model has the lowest RAE value of 0.0900.", "question": "Which model has the lowest RAE for the Solar Energy 3 horizon?", "answer": "So, the answer is {\"answer\": \"LSTNet-Attn\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe proposed model has the lowest RAE for the Traffic 3 horizon.\n" }, { "role": "assistant", "content": "By examining the RAE values for the Traffic 3 horizon, the proposed model has an RAE value of 0.2991, which is the lowest among all models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3160.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018)." ], "reasoning": "By examining the RAE values for the Traffic 3 horizon, the proposed model has an RAE value of 0.2991, which is the lowest among all models.", "statement": "The proposed model has the lowest RAE for the Traffic 3 horizon.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The proposed model outperforms almost all other methods across various datasets, horizons, and metrics, demonstrating its superiority in MTS forecasting.On typical MTS datasets, we chose the best model on the validation set using RAE/RSE/CORR as the metric for the testing set. Both tables show that the proposed model outperforms almost all other methods on all datasets, horizons, and metrics. Also, our models are able to deal with a wide range of dataset size, from the smallest 534 KB Exchange Rate dataset to the largest 172 MB Solar Energy dataset. In these results, the proposed model consistently demonstrates its superiority for MTS forecasting." } ], "images": [ "scigen/train/3161.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018)." ], "reasoning": "The proposed model outperforms almost all other methods across various datasets, horizons, and metrics, demonstrating its superiority in MTS forecasting.", "description": "On typical MTS datasets, we chose the best model on the validation set using RAE/RSE/CORR as the metric for the testing set. Both tables show that the proposed model outperforms almost all other methods on all datasets, horizons, and metrics. Also, our models are able to deal with a wide range of dataset size, from the smallest 534 KB Exchange Rate dataset to the largest 172 MB Solar Energy dataset. In these results, the proposed model consistently demonstrates its superiority for MTS forecasting.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018).' in mind, break down and resolve the question below step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich model has the lowest RAE for the Exchange Rate 3 horizon?\n" }, { "role": "assistant", "content": "By examining the table, the lowest RAE for the Exchange Rate 3 horizon is 0.0136, achieved by the SETAR model. So, the answer is {\"answer\": \"SETAR\"}." } ], "images": [ "scigen/train/3161.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018)." ], "reasoning": "By examining the table, the lowest RAE for the Exchange Rate 3 horizon is 0.0136, achieved by the SETAR model.", "question": "Which model has the lowest RAE for the Exchange Rate 3 horizon?", "answer": "So, the answer is {\"answer\": \"SETAR\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe proposed model has the lowest RAE for the Electricity 3 horizon.\n" }, { "role": "assistant", "content": "The table shows that the proposed model has an RAE of 0.0463 for the Electricity 3 horizon, which is lower than all other models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3161.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018)." ], "reasoning": "The table shows that the proposed model has an RAE of 0.0463 for the Electricity 3 horizon, which is lower than all other models listed.", "statement": "The proposed model has the lowest RAE for the Electricity 3 horizon.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018).'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The proposed model outperforms almost all other methods across various datasets, horizons, and metrics, demonstrating its superiority in MTS forecasting.On typical MTS datasets, we chose the best model on the validation set using RAE/RSE/CORR as the metric for the testing set. Both tables show that the proposed model outperforms almost all other methods on all datasets, horizons, and metrics. Also, our models are able to deal with a wide range of dataset size, from the smallest 534 KB Exchange Rate dataset to the largest 172 MB Solar Energy dataset. In these results, the proposed model consistently demonstrates its superiority for MTS forecasting." } ], "images": [ "scigen/train/3162.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018)." ], "reasoning": "The proposed model outperforms almost all other methods across various datasets, horizons, and metrics, demonstrating its superiority in MTS forecasting.", "description": "On typical MTS datasets, we chose the best model on the validation set using RAE/RSE/CORR as the metric for the testing set. Both tables show that the proposed model outperforms almost all other methods on all datasets, horizons, and metrics. Also, our models are able to deal with a wide range of dataset size, from the smallest 534 KB Exchange Rate dataset to the largest 172 MB Solar Energy dataset. In these results, the proposed model consistently demonstrates its superiority for MTS forecasting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018).\nQuestion: What is the RSE value for the proposed model on the Solar Energy dataset with a horizon of 24?\n" }, { "role": "assistant", "content": "To find the RSE value for the proposed model on the Solar Energy dataset with a horizon of 24, locate the 'Our model' row and the 'Solar Energy 24' column. The value is 0.4389. So, the answer is {\"answer\": \"0.4389\"}." } ], "images": [ "scigen/train/3162.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018)." ], "reasoning": "To find the RSE value for the proposed model on the Solar Energy dataset with a horizon of 24, locate the 'Our model' row and the 'Solar Energy 24' column. The value is 0.4389.", "question": "What is the RSE value for the proposed model on the Solar Energy dataset with a horizon of 24?", "answer": "So, the answer is {\"answer\": \"0.4389\"}." }, { "messages": [ { "role": "user", "content": "The proposed model has the lowest RSE value for the Traffic 12 dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe proposed model has the lowest RSE value for the Traffic 12 dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the RSE values for the Traffic 12 dataset across all models. The proposed model has an RSE value of 0.4641, which is indeed the lowest. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3162.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018)." ], "reasoning": "To verify this statement, compare the RSE values for the Traffic 12 dataset across all models. The proposed model has an RSE value of 0.4641, which is indeed the lowest.", "statement": "The proposed model has the lowest RSE value for the Traffic 12 dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The proposed model outperforms almost all other methods on all datasets, horizons, and metrics, demonstrating its superiority for MTS forecasting.On typical MTS datasets, we chose the best model on the validation set using RAE/RSE/CORR as the metric for the testing set. Both tables show that the proposed model outperforms almost all other methods on all datasets, horizons, and metrics. Also, our models are able to deal with a wide range of dataset size, from the smallest 534 KB Exchange Rate dataset to the largest 172 MB Solar Energy dataset. In these results, the proposed model consistently demonstrates its superiority for MTS forecasting." } ], "images": [ "scigen/train/3163.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018)." ], "reasoning": "The proposed model outperforms almost all other methods on all datasets, horizons, and metrics, demonstrating its superiority for MTS forecasting.", "description": "On typical MTS datasets, we chose the best model on the validation set using RAE/RSE/CORR as the metric for the testing set. Both tables show that the proposed model outperforms almost all other methods on all datasets, horizons, and metrics. Also, our models are able to deal with a wide range of dataset size, from the smallest 534 KB Exchange Rate dataset to the largest 172 MB Solar Energy dataset. In these results, the proposed model consistently demonstrates its superiority for MTS forecasting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018).'.\n\nQuestion: Which model has the lowest RSE for the Exchange Rate 3 horizon?\n" }, { "role": "assistant", "content": "By comparing the RSE values for the Exchange Rate 3 horizon, the proposed model has the lowest RSE value of 0.0174. So, the answer is {\"answer\": \"Our model\"}." } ], "images": [ "scigen/train/3163.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018)." ], "reasoning": "By comparing the RSE values for the Exchange Rate 3 horizon, the proposed model has the lowest RSE value of 0.0174.", "question": "Which model has the lowest RSE for the Exchange Rate 3 horizon?", "answer": "So, the answer is {\"answer\": \"Our model\"}." }, { "messages": [ { "role": "user", "content": "The proposed model has the highest RSE for the Electricity 24 horizon. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe proposed model has the highest RSE for the Electricity 24 horizon.\n" }, { "role": "assistant", "content": "The table shows that the proposed model has an RSE of 0.1006 for the Electricity 24 horizon, which is not the highest. Other models like AR and LSTNet-Attn have higher RSE values. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3163.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018)." ], "reasoning": "The table shows that the proposed model has an RSE of 0.1006 for the Electricity 24 horizon, which is not the highest. Other models like AR and LSTNet-Attn have higher RSE values.", "statement": "The proposed model has the highest RSE for the Electricity 24 horizon.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The proposed model outperforms almost all other methods on all datasets, horizons, and metrics, demonstrating its superiority for MTS forecasting.On typical MTS datasets, we chose the best model on the validation set using RAE/RSE/CORR as the metric for the testing set. Both tables show that the proposed model outperforms almost all other methods on all datasets, horizons, and metrics. Also, our models are able to deal with a wide range of dataset size, from the smallest 534 KB Exchange Rate dataset to the largest 172 MB Solar Energy dataset. In these results, the proposed model consistently demonstrates its superiority for MTS forecasting." } ], "images": [ "scigen/train/3164.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018)." ], "reasoning": "The proposed model outperforms almost all other methods on all datasets, horizons, and metrics, demonstrating its superiority for MTS forecasting.", "description": "On typical MTS datasets, we chose the best model on the validation set using RAE/RSE/CORR as the metric for the testing set. Both tables show that the proposed model outperforms almost all other methods on all datasets, horizons, and metrics. Also, our models are able to deal with a wide range of dataset size, from the smallest 534 KB Exchange Rate dataset to the largest 172 MB Solar Energy dataset. In these results, the proposed model consistently demonstrates its superiority for MTS forecasting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018).'.\n\nQuestion: Which model has the highest CORR value for the Solar Energy 24 horizon?\n" }, { "role": "assistant", "content": "To find the model with the highest CORR value for the Solar Energy 24 horizon, look at the column labeled 'Solar Energy 24' and identify the highest value. The highest value is 0.9081, which corresponds to 'Our model'. So, the answer is {\"answer\": \"Our model\"}." } ], "images": [ "scigen/train/3164.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018)." ], "reasoning": "To find the model with the highest CORR value for the Solar Energy 24 horizon, look at the column labeled 'Solar Energy 24' and identify the highest value. The highest value is 0.9081, which corresponds to 'Our model'.", "question": "Which model has the highest CORR value for the Solar Energy 24 horizon?", "answer": "So, the answer is {\"answer\": \"Our model\"}." }, { "messages": [ { "role": "user", "content": "The proposed model has the highest CORR value for the Traffic 3 horizon. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe proposed model has the highest CORR value for the Traffic 3 horizon.\n" }, { "role": "assistant", "content": "To verify this statement, look at the column labeled 'Traffic 3' and compare the CORR values of all models. The highest value is 0.8812, which corresponds to 'Our model'. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3164.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018)." ], "reasoning": "To verify this statement, look at the column labeled 'Traffic 3' and compare the CORR values of all models. The highest value is 0.8812, which corresponds to 'Our model'.", "statement": "The proposed model has the highest CORR value for the Traffic 3 horizon.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Model ablation on QA4IE-SEQ-S. The first line is our original model and each of the following lines is the original model with a component ablated.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The Self Matching Layer improves performance but increases computational cost by 40%, while the LSTMBlockFusedCell accelerates training by 6 times without affecting performance.We do not involve Attention Flow Layer and Pointer Network Decoder as they cannot be replaced by other architectures with the model still working. We can observe that the first three components can effectively improve the performance but Self Matching Layer makes the training more computationally expensive by 40%. Besides, the LSTMBlockFusedCell works effectively and accelerates the training process by 6 times without influencing the performance." } ], "images": [ "scigen/train/3165.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Model ablation on QA4IE-SEQ-S. The first line is our original model and each of the following lines is the original model with a component ablated." ], "reasoning": "The Self Matching Layer improves performance but increases computational cost by 40%, while the LSTMBlockFusedCell accelerates training by 6 times without affecting performance.", "description": "We do not involve Attention Flow Layer and Pointer Network Decoder as they cannot be replaced by other architectures with the model still working. We can observe that the first three components can effectively improve the performance but Self Matching Layer makes the training more computationally expensive by 40%. Besides, the LSTMBlockFusedCell works effectively and accelerates the training process by 6 times without influencing the performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: Model ablation on QA4IE-SEQ-S. The first line is our original model and each of the following lines is the original model with a component ablated.', solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich component increases computational cost and by how much?\n" }, { "role": "assistant", "content": "The description states that the Self Matching Layer makes the training more computationally expensive by 40%. So, the answer is {\"answer\": \"Self Matching Layer, 40%\"}" } ], "images": [ "scigen/train/3165.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Model ablation on QA4IE-SEQ-S. The first line is our original model and each of the following lines is the original model with a component ablated." ], "reasoning": "The description states that the Self Matching Layer makes the training more computationally expensive by 40%.", "question": "Which component increases computational cost and by how much?", "answer": "So, the answer is {\"answer\": \"Self Matching Layer, 40%\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe LSTMBlockFusedCell accelerates the training process by 6 times without influencing the performance.\n" }, { "role": "assistant", "content": "The description explicitly states that the LSTMBlockFusedCell works effectively and accelerates the training process by 6 times without influencing the performance. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3165.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Model ablation on QA4IE-SEQ-S. The first line is our original model and each of the following lines is the original model with a component ablated." ], "reasoning": "The description explicitly states that the LSTMBlockFusedCell works effectively and accelerates the training process by 6 times without influencing the performance.", "statement": "The LSTMBlockFusedCell accelerates the training process by 6 times without influencing the performance.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 6: Results of three Open IE baselines on the dev set of QA4IE-SPAN-S.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Despite filtering, the precision and recall of extracted triples remain very low, around 1%, indicating poor performance in identifying ground truth answers.We can observe that most of the extracted triples are not related to ground truths and the precision and recall are all very low (around 1%) although we have already helped the baseline systems locate the sentences containing ground truth answers." } ], "images": [ "scigen/train/3166.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results of three Open IE baselines on the dev set of QA4IE-SPAN-S." ], "reasoning": "Despite filtering, the precision and recall of extracted triples remain very low, around 1%, indicating poor performance in identifying ground truth answers.", "description": "We can observe that most of the extracted triples are not related to ground truths and the precision and recall are all very low (around 1%) although we have already helped the baseline systems locate the sentences containing ground truth answers.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 6: Results of three Open IE baselines on the dev set of QA4IE-SPAN-S.', solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich system had the highest number of true positives after filtering?\n" }, { "role": "assistant", "content": "To determine which system had the highest number of true positives after filtering, compare the '#True Positive' values for Open IE 4, Stanford IE, and ClausIE. Open IE 4 has 403 true positives, Stanford IE has 301, and ClausIE has 133. Therefore, Open IE 4 had the highest number of true positives after filtering. So, the answer is {\"answer\": \"Open IE 4\"}." } ], "images": [ "scigen/train/3166.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results of three Open IE baselines on the dev set of QA4IE-SPAN-S." ], "reasoning": "To determine which system had the highest number of true positives after filtering, compare the '#True Positive' values for Open IE 4, Stanford IE, and ClausIE. Open IE 4 has 403 true positives, Stanford IE has 301, and ClausIE has 133. Therefore, Open IE 4 had the highest number of true positives after filtering.", "question": "Which system had the highest number of true positives after filtering?", "answer": "So, the answer is {\"answer\": \"Open IE 4\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Stanford IE extracted the most triples before filtering., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nStanford IE extracted the most triples before filtering.\n" }, { "role": "assistant", "content": "To verify this statement, compare the '#Extracted Triples' values for Open IE 4, Stanford IE, and ClausIE. Stanford IE extracted 120,147 triples, which is the highest compared to Open IE 4's 32,309 and ClausIE's 75,078. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3166.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results of three Open IE baselines on the dev set of QA4IE-SPAN-S." ], "reasoning": "To verify this statement, compare the '#Extracted Triples' values for Open IE 4, Stanford IE, and ClausIE. Stanford IE extracted 120,147 triples, which is the highest compared to Open IE 4's 32,309 and ClausIE's 75,078.", "statement": "Stanford IE extracted the most triples before filtering.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Percentage of summary sentences that are faithful, grammatical, etc. according to human evaluation of several state-of-the-art summarization systems (see §2 for details).'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Despite high ROUGE scores, systems like DCA and Bottom-Up have low faithfulness, highlighting the need for summarization models to improve faithfulness.Our findings suggest that system summary sentences formed by fusion have low faithfulness (61.7% on average) as compared to the reference summaries. This demonstrates the need for current summarization models to put more emphasis on improving the faithfulness of generated summaries. Surprisingly, the highest performing systems, DCA and Bottom-Up, according to ROUGE result in the lowest scores for being faithful to the article. While we cannot attribute the drop in faithfulness to an over-emphasis on optimizing automatic metrics, we can state that higher ROUGE scores does not necessarily lead to more faithful summaries, as other works have shown Falke et al. Bottom-Up, interestingly, is 20 points lower than PG, which it is closely based on. It uses an external content selector to choose what words to copy from the article. While identifying summary-worthy content improved ROUGE, we believe that Bottom-Up stitches together sections of content that do not necessarily belong together. Thus, it is important to identify not just summary-worthy content, but also mergeable content." } ], "images": [ "scigen/train/3167.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Percentage of summary sentences that are faithful, grammatical, etc. according to human evaluation of several state-of-the-art summarization systems (see §2 for details)." ], "reasoning": "Despite high ROUGE scores, systems like DCA and Bottom-Up have low faithfulness, highlighting the need for summarization models to improve faithfulness.", "description": "Our findings suggest that system summary sentences formed by fusion have low faithfulness (61.7% on average) as compared to the reference summaries. This demonstrates the need for current summarization models to put more emphasis on improving the faithfulness of generated summaries. Surprisingly, the highest performing systems, DCA and Bottom-Up, according to ROUGE result in the lowest scores for being faithful to the article. While we cannot attribute the drop in faithfulness to an over-emphasis on optimizing automatic metrics, we can state that higher ROUGE scores does not necessarily lead to more faithful summaries, as other works have shown Falke et al. Bottom-Up, interestingly, is 20 points lower than PG, which it is closely based on. It uses an external content selector to choose what words to copy from the article. While identifying summary-worthy content improved ROUGE, we believe that Bottom-Up stitches together sections of content that do not necessarily belong together. Thus, it is important to identify not just summary-worthy content, but also mergeable content.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Percentage of summary sentences that are faithful, grammatical, etc. according to human evaluation of several state-of-the-art summarization systems (see §2 for details).\nQuestion: Which system has the highest faithfulness score?\nGive a detailed response to the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the 'Faithful' column in the table, the system with the highest faithfulness score is identified. So, the answer is {\"answer\": \"Reference\"}" } ], "images": [ "scigen/train/3167.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Percentage of summary sentences that are faithful, grammatical, etc. according to human evaluation of several state-of-the-art summarization systems (see §2 for details)." ], "reasoning": "By examining the 'Faithful' column in the table, the system with the highest faithfulness score is identified.", "question": "Which system has the highest faithfulness score?", "answer": "So, the answer is {\"answer\": \"Reference\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Bottom-Up has a higher faithfulness score than Novel. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nBottom-Up has a higher faithfulness score than Novel.\n" }, { "role": "assistant", "content": "The table shows that Bottom-Up has a faithfulness score of 56.9, while Novel has a score of 58.5, indicating that the statement is incorrect. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3167.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Percentage of summary sentences that are faithful, grammatical, etc. according to human evaluation of several state-of-the-art summarization systems (see §2 for details)." ], "reasoning": "The table shows that Bottom-Up has a faithfulness score of 56.9, while Novel has a score of 58.5, indicating that the statement is incorrect.", "statement": "Bottom-Up has a higher faithfulness score than Novel.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Results for each merging method. Concatenation has high faithfulness, grammaticality, and coverage, while Replacement and Other have much lower scores.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Grammaticality is high for all merging categories, while coverage and faithfulness are higher for concatenation methods compared to other complex merging paradigms.Does the way sentences are fused affect their faithfulness and grammaticality? Grammaticality is relatively high for all merging categories. Coverage is also high for balanced/imbalanced concatenation and replacement, meaning the heuristic works succesfully for these forms of sentence merging. It does not perform as well on the Other category. This is understandable, since sentences formed in a more complex manner will be harder to identify using simple word overlap. Faithfulness has a similar trend, with summaries generated using concatenation being more likely to be faithful to the original article. This may explain why PG is the most faithful of the systems, while being the simplest—it uses concatenation more than any of the other systems. We believe more effort can be directed towards improving the more complex merging paradigms, such as entity replacement." } ], "images": [ "scigen/train/3168.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results for each merging method. Concatenation has high faithfulness, grammaticality, and coverage, while Replacement and Other have much lower scores." ], "reasoning": "Grammaticality is high for all merging categories, while coverage and faithfulness are higher for concatenation methods compared to other complex merging paradigms.", "description": "Does the way sentences are fused affect their faithfulness and grammaticality? Grammaticality is relatively high for all merging categories. Coverage is also high for balanced/imbalanced concatenation and replacement, meaning the heuristic works succesfully for these forms of sentence merging. It does not perform as well on the Other category. This is understandable, since sentences formed in a more complex manner will be harder to identify using simple word overlap. Faithfulness has a similar trend, with summaries generated using concatenation being more likely to be faithful to the original article. This may explain why PG is the most faithful of the systems, while being the simplest—it uses concatenation more than any of the other systems. We believe more effort can be directed towards improving the more complex merging paradigms, such as entity replacement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: Results for each merging method. Concatenation has high faithfulness, grammaticality, and coverage, while Replacement and Other have much lower scores.', and sequentially solve the question, writing out each step of your reasoning process. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich merging category has the highest coverage?\n" }, { "role": "assistant", "content": "By comparing the coverage values in the table, the highest coverage is found in the 'Bal Concat' category with a value of 94.43. So, the answer is {\"answer\": \"Bal Concat\"}" } ], "images": [ "scigen/train/3168.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results for each merging method. Concatenation has high faithfulness, grammaticality, and coverage, while Replacement and Other have much lower scores." ], "reasoning": "By comparing the coverage values in the table, the highest coverage is found in the 'Bal Concat' category with a value of 94.43.", "question": "Which merging category has the highest coverage?", "answer": "So, the answer is {\"answer\": \"Bal Concat\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The 'Replacement' category has the lowest grammaticality score. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe 'Replacement' category has the lowest grammaticality score.\n" }, { "role": "assistant", "content": "By examining the grammaticality scores in the table, the 'Other' category has the lowest grammaticality score of 68.23, not the 'Replacement' category. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3168.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results for each merging method. Concatenation has high faithfulness, grammaticality, and coverage, while Replacement and Other have much lower scores." ], "reasoning": "By examining the grammaticality scores in the table, the 'Other' category has the lowest grammaticality score of 68.23, not the 'Replacement' category.", "statement": "The 'Replacement' category has the lowest grammaticality score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 9: Selected shallow features one may hypothesize impact evaluation scores negatively (SI).'.\n" }, { "role": "assistant", "content": "The GEval tool's performance is affected by punctuation and negation, with features like commas and the word 'not' leading to misclassifications.Further investigation of problematic cases in a paradigm of no-box debugging with GEval tool [gralinski-etal-2019-geval] revealed the most worsening features, that is features It seems that our system tend to return ranges without adjacent punctuation. This is the case of sentences such as The new CIA Director Haspel, who ‘tortured some folks,’ probably can’t travel to the EU, where only the quoted text was returned whereas annotation assumes it should be returned with apostrophes and comma. This remark can be used to slightly improve overall results with simple post-processing. Returned and conjunction refers to the cases where it connects two propaganda spans. The system frequently returns them as single span, contrary to what is expected in the gold standard. One of the frequent predictors of low accuracy is a comma character present within the span to be classified. It can be probably attributed to the fact that its presence is a good indicator of span linguistic complexity. Another determinant of inefficiency turned out to be a negation—around a half of the sentences containing word not were misclassified by the system. Suggested features of a quotation mark before the span and the digram according to after the span are related to reported or indirect speech. Explanation of worsening effect of other features is not as evident as in the case of mentioned above. Moreover, it seems there is no obvious way of improving the final results with our findings and a more detailed analysis might be required." } ], "images": [ "scigen/train/3169.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Selected shallow features one may hypothesize impact evaluation scores negatively (SI)." ], "reasoning": "The GEval tool's performance is affected by punctuation and negation, with features like commas and the word 'not' leading to misclassifications.", "description": "Further investigation of problematic cases in a paradigm of no-box debugging with GEval tool [gralinski-etal-2019-geval] revealed the most worsening features, that is features It seems that our system tend to return ranges without adjacent punctuation. This is the case of sentences such as The new CIA Director Haspel, who ‘tortured some folks,’ probably can’t travel to the EU, where only the quoted text was returned whereas annotation assumes it should be returned with apostrophes and comma. This remark can be used to slightly improve overall results with simple post-processing. Returned and conjunction refers to the cases where it connects two propaganda spans. The system frequently returns them as single span, contrary to what is expected in the gold standard. One of the frequent predictors of low accuracy is a comma character present within the span to be classified. It can be probably attributed to the fact that its presence is a good indicator of span linguistic complexity. Another determinant of inefficiency turned out to be a negation—around a half of the sentences containing word not were misclassified by the system. Suggested features of a quotation mark before the span and the digram according to after the span are related to reported or indirect speech. Explanation of worsening effect of other features is not as evident as in the case of mentioned above. Moreover, it seems there is no obvious way of improving the final results with our findings and a more detailed analysis might be required.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich feature has the highest count and what is its P-value?\nSolve the above question based on the table titled 'Table 9: Selected shallow features one may hypothesize impact evaluation scores negatively (SI).'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the counts and P-values for various features. The feature with the highest count is 'quotation' with a count of 58 and a P-value of 0.050. So, the answer is {\"answer\": \"quotation with P-value 0.050\"}." } ], "images": [ "scigen/train/3169.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Selected shallow features one may hypothesize impact evaluation scores negatively (SI)." ], "reasoning": "The table shows the counts and P-values for various features. The feature with the highest count is 'quotation' with a count of 58 and a P-value of 0.050.", "question": "Which feature has the highest count and what is its P-value?", "answer": "So, the answer is {\"answer\": \"quotation with P-value 0.050\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe feature 'dot' has a lower P-value than 'question'.\n" }, { "role": "assistant", "content": "The table lists the P-values for 'dot' as 0.037 and 'question' as 0.036. Since 0.037 is higher than 0.036, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3169.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Selected shallow features one may hypothesize impact evaluation scores negatively (SI)." ], "reasoning": "The table lists the P-values for 'dot' as 0.037 and 'question' as 0.036. Since 0.037 is higher than 0.036, the statement is incorrect.", "statement": "The feature 'dot' has a lower P-value than 'question'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: Best scores on the dev set achieved with RoBERTa large model on SI task. Mean, standard deviation and maximum across 10 runs with different random seeds. Numbers in brackets indicate how many self-training iterations were used.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Models trained with different random seeds for 60K steps showed that smaller batch size and dropout are beneficial initially but negatively impact the self-training phase.Models with different random seeds were trained for 60K steps with an evaluation performed every 2K steps. This is equivalent to approximately 30 epochs and per-epoch validation in a scenario without data generated during the self-training procedure. Whereas use of smaller batch size and dropout is beneficial for the initial training without noisy data, it impacts self-training phase negatively. Obviously, the largest negative impact is observed when disabling dropout during training on the small amount of manually annotated data." } ], "images": [ "scigen/train/3170.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Best scores on the dev set achieved with RoBERTa large model on SI task. Mean, standard deviation and maximum across 10 runs with different random seeds. Numbers in brackets indicate how many self-training iterations were used." ], "reasoning": "Models trained with different random seeds for 60K steps showed that smaller batch size and dropout are beneficial initially but negatively impact the self-training phase.", "description": "Models with different random seeds were trained for 60K steps with an evaluation performed every 2K steps. This is equivalent to approximately 30 epochs and per-epoch validation in a scenario without data generated during the self-training procedure. Whereas use of smaller batch size and dropout is beneficial for the initial training without noisy data, it impacts self-training phase negatively. Obviously, the largest negative impact is observed when disabling dropout during training on the small amount of manually annotated data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: 'Table 3: Best scores on the dev set achieved with RoBERTa large model on SI task. Mean, standard deviation and maximum across 10 runs with different random seeds. Numbers in brackets indicate how many self-training iterations were used.'\nQuestion: What is the highest FLC-F1 max score achieved with self-training and CRF enabled?\n" }, { "role": "assistant", "content": "To find the highest FLC-F1 max score with self-training and CRF enabled, look for rows where both CRF and self-training are enabled and identify the highest max score. So, the answer is {\"answer\": \"51.8\"}." } ], "images": [ "scigen/train/3170.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Best scores on the dev set achieved with RoBERTa large model on SI task. Mean, standard deviation and maximum across 10 runs with different random seeds. Numbers in brackets indicate how many self-training iterations were used." ], "reasoning": "To find the highest FLC-F1 max score with self-training and CRF enabled, look for rows where both CRF and self-training are enabled and identify the highest max score.", "question": "What is the highest FLC-F1 max score achieved with self-training and CRF enabled?", "answer": "So, the answer is {\"answer\": \"51.8\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The highest FLC-F1 max score without self-training is 50.2. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe highest FLC-F1 max score without self-training is 50.2.\n" }, { "role": "assistant", "content": "To verify this statement, check the rows where self-training is not enabled and identify the highest max score. The highest max score without self-training is 50.2. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3170.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Best scores on the dev set achieved with RoBERTa large model on SI task. Mean, standard deviation and maximum across 10 runs with different random seeds. Numbers in brackets indicate how many self-training iterations were used." ], "reasoning": "To verify this statement, check the rows where self-training is not enabled and identify the highest max score. The highest max score without self-training is 50.2.", "statement": "The highest FLC-F1 max score without self-training is 50.2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 9: Selected shallow features one may hypothesize impact evaluation scores negatively (SI).', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The GEval tool identified features such as commas, negations, and quotation marks as predictors of low accuracy in no-box debugging, suggesting that these features complicate span classification.Further investigation of problematic cases in a paradigm of no-box debugging with GEval tool [gralinski-etal-2019-geval] revealed the most worsening features, that is features It seems that our system tend to return ranges without adjacent punctuation. This is the case of sentences such as The new CIA Director Haspel, who ‘tortured some folks,’ probably can’t travel to the EU, where only the quoted text was returned whereas annotation assumes it should be returned with apostrophes and comma. This remark can be used to slightly improve overall results with simple post-processing. Returned and conjunction refers to the cases where it connects two propaganda spans. The system frequently returns them as single span, contrary to what is expected in the gold standard. One of the frequent predictors of low accuracy is a comma character present within the span to be classified. It can be probably attributed to the fact that its presence is a good indicator of span linguistic complexity. Another determinant of inefficiency turned out to be a negation—around a half of the sentences containing word not were misclassified by the system. Suggested features of a quotation mark before the span and the digram according to after the span are related to reported or indirect speech. Explanation of worsening effect of other features is not as evident as in the case of mentioned above. Moreover, it seems there is no obvious way of improving the final results with our findings and a more detailed analysis might be required." } ], "images": [ "scigen/train/3171.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Selected shallow features one may hypothesize impact evaluation scores negatively (SI)." ], "reasoning": "The GEval tool identified features such as commas, negations, and quotation marks as predictors of low accuracy in no-box debugging, suggesting that these features complicate span classification.", "description": "Further investigation of problematic cases in a paradigm of no-box debugging with GEval tool [gralinski-etal-2019-geval] revealed the most worsening features, that is features It seems that our system tend to return ranges without adjacent punctuation. This is the case of sentences such as The new CIA Director Haspel, who ‘tortured some folks,’ probably can’t travel to the EU, where only the quoted text was returned whereas annotation assumes it should be returned with apostrophes and comma. This remark can be used to slightly improve overall results with simple post-processing. Returned and conjunction refers to the cases where it connects two propaganda spans. The system frequently returns them as single span, contrary to what is expected in the gold standard. One of the frequent predictors of low accuracy is a comma character present within the span to be classified. It can be probably attributed to the fact that its presence is a good indicator of span linguistic complexity. Another determinant of inefficiency turned out to be a negation—around a half of the sentences containing word not were misclassified by the system. Suggested features of a quotation mark before the span and the digram according to after the span are related to reported or indirect speech. Explanation of worsening effect of other features is not as evident as in the case of mentioned above. Moreover, it seems there is no obvious way of improving the final results with our findings and a more detailed analysis might be required.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 9: Selected shallow features one may hypothesize impact evaluation scores negatively (SI).'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. \n\nQuestion: Which feature has the highest count and what is its P-value?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table lists various features along with their counts and P-values. The feature with the highest count is 'comma' with a count of 119 and a P-value of <0.001. So, the answer is {\"answer\": \"comma with P-value <0.001\"}." } ], "images": [ "scigen/train/3171.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Selected shallow features one may hypothesize impact evaluation scores negatively (SI)." ], "reasoning": "The table lists various features along with their counts and P-values. The feature with the highest count is 'comma' with a count of 119 and a P-value of <0.001.", "question": "Which feature has the highest count and what is its P-value?", "answer": "So, the answer is {\"answer\": \"comma with P-value <0.001\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe feature 'not' has a count of 62 and a P-value of 0.013.\n" }, { "role": "assistant", "content": "The table shows that the feature 'not' has a count of 62 and a P-value of 0.013, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3171.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Selected shallow features one may hypothesize impact evaluation scores negatively (SI)." ], "reasoning": "The table shows that the feature 'not' has a count of 62 and a P-value of 0.013, which matches the statement.", "statement": "The feature 'not' has a count of 62 and a P-value of 0.013.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: Experiments on summaries. Higher is better for all metrics. Sections 4.1 and 4.2 explain the IR and neural models, respectively.'.\n" }, { "role": "assistant", "content": "The span prediction model performs best among neural benchmarks but struggles due to low lexical and grammatical overlap between summaries and stories.Each chunk contains 200 tokens. Higher is better for all metrics. Reading comprehension of summaries is similar to a number of previous reading comprehension tasks where questions were constructed based on the context document. However, plot summaries tend to contain more intricate event time lines and a larger number of characters, and in this sense, are more complex to follow than news articles or paragraphs from Wikipedia. Given the retrieved chunks, we experimented with several neural models using them as context. While is does slightly better on the other metrics, it clearly fails to make use of the retrieved context to gain a distinctive margin over the no-context Seq2Seq model. Increasing the number of retrieved chunks, and thereby recall of possibly relevant parts of the story, had only a minor positive effect. The span prediction model—which here also uses selected chunks for context—does especially poorly in this setup. While this model provided the best neural results on the summaries task, we suspect that its performance was particularly badly hurt by the fact that there is so little lexical and grammatical overlap between the source of the questions (summaries) and the context provided (stories). As with the AS Reader, we observed no significant differences for varying number of chunks." } ], "images": [ "scigen/train/3172.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Experiments on summaries. Higher is better for all metrics. Sections 4.1 and 4.2 explain the IR and neural models, respectively." ], "reasoning": "The span prediction model performs best among neural benchmarks but struggles due to low lexical and grammatical overlap between summaries and stories.", "description": "Each chunk contains 200 tokens. Higher is better for all metrics. Reading comprehension of summaries is similar to a number of previous reading comprehension tasks where questions were constructed based on the context document. However, plot summaries tend to contain more intricate event time lines and a larger number of characters, and in this sense, are more complex to follow than news articles or paragraphs from Wikipedia. Given the retrieved chunks, we experimented with several neural models using them as context. While is does slightly better on the other metrics, it clearly fails to make use of the retrieved context to gain a distinctive margin over the no-context Seq2Seq model. Increasing the number of retrieved chunks, and thereby recall of possibly relevant parts of the story, had only a minor positive effect. The span prediction model—which here also uses selected chunks for context—does especially poorly in this setup. While this model provided the best neural results on the summaries task, we suspect that its performance was particularly badly hurt by the fact that there is so little lexical and grammatical overlap between the source of the questions (summaries) and the context provided (stories). As with the AS Reader, we observed no significant differences for varying number of chunks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Experiments on summaries. Higher is better for all metrics. Sections 4.1 and 4.2 explain the IR and neural models, respectively.\nQuestion: Which neural benchmark model has the highest Validation/Test Bleu-1 score?\nSolve the above question based on the table information. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest Validation/Test Bleu-1 score among neural benchmark models, compare the scores of Seq2Seq, Attention Sum Reader, and Span Prediction. Span Prediction has the highest score with 33.45/33.72. So, the answer is {\"answer\": \"Span Prediction\"}." } ], "images": [ "scigen/train/3172.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Experiments on summaries. Higher is better for all metrics. Sections 4.1 and 4.2 explain the IR and neural models, respectively." ], "reasoning": "To find the highest Validation/Test Bleu-1 score among neural benchmark models, compare the scores of Seq2Seq, Attention Sum Reader, and Span Prediction. Span Prediction has the highest score with 33.45/33.72.", "question": "Which neural benchmark model has the highest Validation/Test Bleu-1 score?", "answer": "So, the answer is {\"answer\": \"Span Prediction\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The Attention Sum Reader model has the highest Validation/Test Rouge-L score among neural benchmarks., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Attention Sum Reader model has the highest Validation/Test Rouge-L score among neural benchmarks.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Validation/Test Rouge-L scores of the neural benchmark models. Span Prediction has the highest score with 36.74/36.30, not Attention Sum Reader. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3172.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Experiments on summaries. Higher is better for all metrics. Sections 4.1 and 4.2 explain the IR and neural models, respectively." ], "reasoning": "To verify this statement, compare the Validation/Test Rouge-L scores of the neural benchmark models. Span Prediction has the highest score with 36.74/36.30, not Attention Sum Reader.", "statement": "The Attention Sum Reader model has the highest Validation/Test Rouge-L score among neural benchmarks.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6: Experiments on full stories. Each chunk contains 200 tokens. Higher is better for all metrics. Sections 4.1 and 4.2 explain the IR and neural models, respectively. Note that the human scores are based on answering questions given summaries, same as in Table 5.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The span-selection oracle IR model performs worse on full stories compared to summaries, especially for longer answers, confirming the task's design motivation.As expected (and desired), we observe a decline in performance of the span-selection oracle IR model, compared with the results on summaries. This is unsurprising as the questions were constructed on summaries and confirms the initial motivation for designing this task. As previously, we considered all spans of a given length across the entire story for this model. For short answers of one or two words—typically main characters in a story—the candidate, i.e. the closest span to the reference answer, is easily found due to being mentioned throughout the text. For longer answers it becomes much less likely, compared to the summaries, that a high-scoring span can be found in the story. Note that this distinguishes NarrativeQA from many of the reviewed datasets." } ], "images": [ "scigen/train/3173.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Experiments on full stories. Each chunk contains 200 tokens. Higher is better for all metrics. Sections 4.1 and 4.2 explain the IR and neural models, respectively. Note that the human scores are based on answering questions given summaries, same as in Table 5." ], "reasoning": "The span-selection oracle IR model performs worse on full stories compared to summaries, especially for longer answers, confirming the task's design motivation.", "description": "As expected (and desired), we observe a decline in performance of the span-selection oracle IR model, compared with the results on summaries. This is unsurprising as the questions were constructed on summaries and confirms the initial motivation for designing this task. As previously, we considered all spans of a given length across the entire story for this model. For short answers of one or two words—typically main characters in a story—the candidate, i.e. the closest span to the reference answer, is easily found due to being mentioned throughout the text. For longer answers it becomes much less likely, compared to the summaries, that a high-scoring span can be found in the story. Note that this distinguishes NarrativeQA from many of the reviewed datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 6: Experiments on full stories. Each chunk contains 200 tokens. Higher is better for all metrics. Sections 4.1 and 4.2 explain the IR and neural models, respectively. Note that the human scores are based on answering questions given summaries, same as in Table 5.', solve the subsequent question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model has the highest Validation/Test Rouge-L score among the Neural Benchmarks?\n" }, { "role": "assistant", "content": "To find the highest Validation/Test Rouge-L score among the Neural Benchmarks, we look at the 'Validation/Test Rouge-L' column under the 'Neural Benchmarks' section. The highest score is 14.86/14.02 for the 'Attention Sum Reader given 20 chunks' model. So, the answer is {\"answer\": \"Attention Sum Reader given 20 chunks\"}." } ], "images": [ "scigen/train/3173.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Experiments on full stories. Each chunk contains 200 tokens. Higher is better for all metrics. Sections 4.1 and 4.2 explain the IR and neural models, respectively. Note that the human scores are based on answering questions given summaries, same as in Table 5." ], "reasoning": "To find the highest Validation/Test Rouge-L score among the Neural Benchmarks, we look at the 'Validation/Test Rouge-L' column under the 'Neural Benchmarks' section. The highest score is 14.86/14.02 for the 'Attention Sum Reader given 20 chunks' model.", "question": "Which model has the highest Validation/Test Rouge-L score among the Neural Benchmarks?", "answer": "So, the answer is {\"answer\": \"Attention Sum Reader given 20 chunks\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Bleu-1 score for the Oracle IR Model given answer (ans. length) is higher than the Bleu-1 score for the Neural Benchmark Attention Sum Reader given 10 chunks.\n" }, { "role": "assistant", "content": "The Bleu-1 score for the Oracle IR Model given answer (ans. length) is 41.81/42.37, while the Bleu-1 score for the Neural Benchmark Attention Sum Reader given 10 chunks is 20.0/19.09. Since 41.81/42.37 is higher than 20.0/19.09, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3173.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Experiments on full stories. Each chunk contains 200 tokens. Higher is better for all metrics. Sections 4.1 and 4.2 explain the IR and neural models, respectively. Note that the human scores are based on answering questions given summaries, same as in Table 5." ], "reasoning": "The Bleu-1 score for the Oracle IR Model given answer (ans. length) is 41.81/42.37, while the Bleu-1 score for the Neural Benchmark Attention Sum Reader given 10 chunks is 20.0/19.09. Since 41.81/42.37 is higher than 20.0/19.09, the statement is supported.", "statement": "The Bleu-1 score for the Oracle IR Model given answer (ans. length) is higher than the Bleu-1 score for the Neural Benchmark Attention Sum Reader given 10 chunks.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: Unsupervised cross-lingual word embeddings Cosine similarity and L2 distance between source words and their translations. Pearson correlation on SemEval’17 cross-lingual word similarity task of Camacho-Collados et al. (2017).', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "XLM outperforms MUSE and Concat in cross-lingual word similarity, achieving higher cosine similarity and lower L2 distance.Unsupervised cross-lingual word embeddings The MUSE, Concat and XLM (MLM) methods provide unsupervised cross-lingual word embedding spaces that have different properties. We also evaluate the quality of the cosine similarity measure via the SemEval’17 cross-lingual word similarity task of Camacho-Collados et al. We observe that XLM outperforms both MUSE and Concat on cross-lingual word similarity, reaching a Pearson correlation of 0.69. Interestingly, word translation pairs are also far closer in the XLM cross-lingual word embedding space than for MUSE or Concat. Specifically, MUSE obtains 0.38 and 5.13 for cosine similarity and L2 distance while XLM gives 0.55 and 2.64 for the same metrics. Note that XLM embeddings have the particularity of being trained together with a sentence encoder which may enforce this closeness, while MUSE and Concat are based on fastText word embeddings." } ], "images": [ "scigen/train/3174.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Unsupervised cross-lingual word embeddings Cosine similarity and L2 distance between source words and their translations. Pearson correlation on SemEval’17 cross-lingual word similarity task of Camacho-Collados et al. (2017)." ], "reasoning": "XLM outperforms MUSE and Concat in cross-lingual word similarity, achieving higher cosine similarity and lower L2 distance.", "description": "Unsupervised cross-lingual word embeddings The MUSE, Concat and XLM (MLM) methods provide unsupervised cross-lingual word embedding spaces that have different properties. We also evaluate the quality of the cosine similarity measure via the SemEval’17 cross-lingual word similarity task of Camacho-Collados et al. We observe that XLM outperforms both MUSE and Concat on cross-lingual word similarity, reaching a Pearson correlation of 0.69. Interestingly, word translation pairs are also far closer in the XLM cross-lingual word embedding space than for MUSE or Concat. Specifically, MUSE obtains 0.38 and 5.13 for cosine similarity and L2 distance while XLM gives 0.55 and 2.64 for the same metrics. Note that XLM embeddings have the particularity of being trained together with a sentence encoder which may enforce this closeness, while MUSE and Concat are based on fastText word embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 5: Unsupervised cross-lingual word embeddings Cosine similarity and L2 distance between source words and their translations. Pearson correlation on SemEval’17 cross-lingual word similarity task of Camacho-Collados et al. (2017).' to sequentially solve the question presented below. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhich method achieves the highest Pearson correlation in the SemEval'17 cross-lingual word similarity task?\n" }, { "role": "assistant", "content": "The table shows the Pearson correlation values for MUSE, Concat, and XLM in the SemEval'17 task. XLM has the highest value of 0.69. So, the answer is {\"answer\": \"XLM\"}" } ], "images": [ "scigen/train/3174.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Unsupervised cross-lingual word embeddings Cosine similarity and L2 distance between source words and their translations. Pearson correlation on SemEval’17 cross-lingual word similarity task of Camacho-Collados et al. (2017)." ], "reasoning": "The table shows the Pearson correlation values for MUSE, Concat, and XLM in the SemEval'17 task. XLM has the highest value of 0.69.", "question": "Which method achieves the highest Pearson correlation in the SemEval'17 cross-lingual word similarity task?", "answer": "So, the answer is {\"answer\": \"XLM\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nXLM has a higher cosine similarity than MUSE and Concat.\n" }, { "role": "assistant", "content": "The table shows that XLM has a cosine similarity of 0.55, which is higher than MUSE's 0.38 and Concat's 0.36. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3174.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Unsupervised cross-lingual word embeddings Cosine similarity and L2 distance between source words and their translations. Pearson correlation on SemEval’17 cross-lingual word similarity task of Camacho-Collados et al. (2017)." ], "reasoning": "The table shows that XLM has a cosine similarity of 0.55, which is higher than MUSE's 0.38 and Concat's 0.36.", "statement": "XLM has a higher cosine similarity than MUSE and Concat.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Results on cross-lingual classification accuracy. Test accuracy on the 15 XNLI languages. We report results for machine translation baselines and zero-shot classification approaches based on cross-lingual sentence encoders. XLM (MLM) corresponds to our unsupervised approach trained only on monolingual corpora, and XLM (MLM+TLM) corresponds to our supervised method that leverages both monolingual and parallel data through the TLM objective. Δ corresponds to the average accuracy.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The XLM models are evaluated using the XNLI dataset by fine-tuning on English NLI data and testing on 15 languages, comparing unsupervised and supervised cross-lingual language models.Our pretrained XLM models provide general-purpose cross-lingual text representations. Similar to monolingual language model fine-tuning Radford et al. We use the cross-lingual natural language inference (XNLI) dataset to evaluate our approach. Precisely, we add a linear classifier on top of the first hidden state of the pretrained Transformer, and fine-tune all parameters on the English NLI training dataset. We then evaluate the capacity of our model to make correct NLI predictions in the 15 XNLI languages. Following Conneau et al. , we evaluate two types of pretrained cross-lingual encoders: an unsupervised cross-lingual language model that uses the MLM objective on monolingual corpora only; and a supervised cross-lingual language model that combines both the MLM and the TLM loss using additional parallel data. Following Conneau et al. We report the XNLI baselines of Conneau et al." } ], "images": [ "scigen/train/3175.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results on cross-lingual classification accuracy. Test accuracy on the 15 XNLI languages. We report results for machine translation baselines and zero-shot classification approaches based on cross-lingual sentence encoders. XLM (MLM) corresponds to our unsupervised approach trained only on monolingual corpora, and XLM (MLM+TLM) corresponds to our supervised method that leverages both monolingual and parallel data through the TLM objective. Δ corresponds to the average accuracy." ], "reasoning": "The XLM models are evaluated using the XNLI dataset by fine-tuning on English NLI data and testing on 15 languages, comparing unsupervised and supervised cross-lingual language models.", "description": "Our pretrained XLM models provide general-purpose cross-lingual text representations. Similar to monolingual language model fine-tuning Radford et al. We use the cross-lingual natural language inference (XNLI) dataset to evaluate our approach. Precisely, we add a linear classifier on top of the first hidden state of the pretrained Transformer, and fine-tune all parameters on the English NLI training dataset. We then evaluate the capacity of our model to make correct NLI predictions in the 15 XNLI languages. Following Conneau et al. , we evaluate two types of pretrained cross-lingual encoders: an unsupervised cross-lingual language model that uses the MLM objective on monolingual corpora only; and a supervised cross-lingual language model that combines both the MLM and the TLM loss using additional parallel data. Following Conneau et al. We report the XNLI baselines of Conneau et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Results on cross-lingual classification accuracy. Test accuracy on the 15 XNLI languages. We report results for machine translation baselines and zero-shot classification approaches based on cross-lingual sentence encoders. XLM (MLM) corresponds to our unsupervised approach trained only on monolingual corpora, and XLM (MLM+TLM) corresponds to our supervised method that leverages both monolingual and parallel data through the TLM objective. Δ corresponds to the average accuracy.\nQuestion: What is the highest score achieved by the XLM (MLM+TLM) model in the evaluation of cross-lingual sentence encoders?\nSolve the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the highest score achieved by the XLM (MLM+TLM) model, look at the row labeled 'XLM (MLM+TLM)' under the 'Evaluation of cross-lingual sentence encoders' section and identify the highest value across all languages. So, the answer is {\"answer\": \"85.0\"}" } ], "images": [ "scigen/train/3175.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results on cross-lingual classification accuracy. Test accuracy on the 15 XNLI languages. We report results for machine translation baselines and zero-shot classification approaches based on cross-lingual sentence encoders. XLM (MLM) corresponds to our unsupervised approach trained only on monolingual corpora, and XLM (MLM+TLM) corresponds to our supervised method that leverages both monolingual and parallel data through the TLM objective. Δ corresponds to the average accuracy." ], "reasoning": "To find the highest score achieved by the XLM (MLM+TLM) model, look at the row labeled 'XLM (MLM+TLM)' under the 'Evaluation of cross-lingual sentence encoders' section and identify the highest value across all languages.", "question": "What is the highest score achieved by the XLM (MLM+TLM) model in the evaluation of cross-lingual sentence encoders?", "answer": "So, the answer is {\"answer\": \"85.0\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe XLM (MLM+TLM) model achieved a score of 85.0 in the English language evaluation of cross-lingual sentence encoders.\n" }, { "role": "assistant", "content": "The table shows that the XLM (MLM+TLM) model achieved a score of 85.0 in the 'en' column under the 'Evaluation of cross-lingual sentence encoders' section. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3175.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results on cross-lingual classification accuracy. Test accuracy on the 15 XNLI languages. We report results for machine translation baselines and zero-shot classification approaches based on cross-lingual sentence encoders. XLM (MLM) corresponds to our unsupervised approach trained only on monolingual corpora, and XLM (MLM+TLM) corresponds to our supervised method that leverages both monolingual and parallel data through the TLM objective. Δ corresponds to the average accuracy." ], "reasoning": "The table shows that the XLM (MLM+TLM) model achieved a score of 85.0 in the 'en' column under the 'Evaluation of cross-lingual sentence encoders' section.", "statement": "The XLM (MLM+TLM) model achieved a score of 85.0 in the English language evaluation of cross-lingual sentence encoders.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Results on unsupervised MT. BLEU scores on WMT’14 English-French, WMT’16 German-English and WMT’16 Romanian-English. For our results, the first two columns indicate the model used to pretrain the encoder and the decoder. “ - ” means the model was randomly initialized. EMB corresponds to pretraining the lookup table with cross-lingual embeddings, CLM and MLM correspond to pretraining with models trained on the CLM or MLM objectives.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Pretraining both the encoder and decoder with MLM leads to significant improvements in BLEU scores across multiple language pairs, outperforming CLM pretraining and random initialization.Pretraining is a key ingredient of unsupervised neural machine translation (UNMT) Lample et al. Lample et al. We propose to take this idea one step further by pretraining the entire encoder and decoder with a cross-lingual language model to bootstrap the iterative process of UNMT. We explore various initialization schemes and evaluate their impact on several standard machine translation benchmarks, including WMT’14 English-French, WMT’16 English-German and WMT’16 English-Romanian. For both the encoder and the decoder, we consider different possible initializations: CLM pretraining, MLM pretraining, or random initialization, which results in 9 different settings. We then follow Lample et al. and train the model with a denoising auto-encoding loss along with an online back-translation loss. We compare our approach with the ones of Lample et al. For each language pair, we observe significant improvements over the previous state of the art. We re-implemented the NMT approach of Lample et al. (EMB), and obtained better results than reported in their paper. We expect that this is due to our multi-GPU implementation which uses significantly larger batches. In German-English, our best model outperforms the previous unsupervised approach by more than 9.1 BLEU, and 13.3 BLEU if we only consider neural unsupervised approaches. Compared to pretraining only the lookup table (EMB), pretraining both the encoder and decoder with MLM leads to consistent significant improvements of up to 7 BLEU on German-English. We also observe that the MLM objective pretraining consistently outperforms the CLM one, going from 30.4 to 33.4 BLEU on English-French, and from 28.0 to 31.8 on Romanian-English. These results are consistent with the ones of Devlin et al. We also observe that the encoder is the most important element to pretrain: when compared to pretraining both the encoder and the decoder, pretraining only the decoder leads to a significant drop in performance, while pretraining only the encoder only has a small impact on the final BLEU score." } ], "images": [ "scigen/train/3176.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on unsupervised MT. BLEU scores on WMT’14 English-French, WMT’16 German-English and WMT’16 Romanian-English. For our results, the first two columns indicate the model used to pretrain the encoder and the decoder. “ - ” means the model was randomly initialized. EMB corresponds to pretraining the lookup table with cross-lingual embeddings, CLM and MLM correspond to pretraining with models trained on the CLM or MLM objectives." ], "reasoning": "Pretraining both the encoder and decoder with MLM leads to significant improvements in BLEU scores across multiple language pairs, outperforming CLM pretraining and random initialization.", "description": "Pretraining is a key ingredient of unsupervised neural machine translation (UNMT) Lample et al. Lample et al. We propose to take this idea one step further by pretraining the entire encoder and decoder with a cross-lingual language model to bootstrap the iterative process of UNMT. We explore various initialization schemes and evaluate their impact on several standard machine translation benchmarks, including WMT’14 English-French, WMT’16 English-German and WMT’16 English-Romanian. For both the encoder and the decoder, we consider different possible initializations: CLM pretraining, MLM pretraining, or random initialization, which results in 9 different settings. We then follow Lample et al. and train the model with a denoising auto-encoding loss along with an online back-translation loss. We compare our approach with the ones of Lample et al. For each language pair, we observe significant improvements over the previous state of the art. We re-implemented the NMT approach of Lample et al. (EMB), and obtained better results than reported in their paper. We expect that this is due to our multi-GPU implementation which uses significantly larger batches. In German-English, our best model outperforms the previous unsupervised approach by more than 9.1 BLEU, and 13.3 BLEU if we only consider neural unsupervised approaches. Compared to pretraining only the lookup table (EMB), pretraining both the encoder and decoder with MLM leads to consistent significant improvements of up to 7 BLEU on German-English. We also observe that the MLM objective pretraining consistently outperforms the CLM one, going from 30.4 to 33.4 BLEU on English-French, and from 28.0 to 31.8 on Romanian-English. These results are consistent with the ones of Devlin et al. We also observe that the encoder is the most important element to pretrain: when compared to pretraining both the encoder and the decoder, pretraining only the decoder leads to a significant drop in performance, while pretraining only the encoder only has a small impact on the final BLEU score.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Results on unsupervised MT. BLEU scores on WMT’14 English-French, WMT’16 German-English and WMT’16 Romanian-English. For our results, the first two columns indicate the model used to pretrain the encoder and the decoder. “ - ” means the model was randomly initialized. EMB corresponds to pretraining the lookup table with cross-lingual embeddings, CLM and MLM correspond to pretraining with models trained on the CLM or MLM objectives.', answer the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the BLEU score for the best model on the en-de language pair when both the encoder and decoder are pretrained with MLM?\n" }, { "role": "assistant", "content": "To find the BLEU score for the best model on the en-de language pair with both encoder and decoder pretrained with MLM, look at the en-de column under 'Our results for different encoder and decoder initializations' and find the row labeled 'MLM MLM'. The BLEU score is 26.44. So, the answer is {\"answer\": \"26.44\"}" } ], "images": [ "scigen/train/3176.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on unsupervised MT. BLEU scores on WMT’14 English-French, WMT’16 German-English and WMT’16 Romanian-English. For our results, the first two columns indicate the model used to pretrain the encoder and the decoder. “ - ” means the model was randomly initialized. EMB corresponds to pretraining the lookup table with cross-lingual embeddings, CLM and MLM correspond to pretraining with models trained on the CLM or MLM objectives." ], "reasoning": "To find the BLEU score for the best model on the en-de language pair with both encoder and decoder pretrained with MLM, look at the en-de column under 'Our results for different encoder and decoder initializations' and find the row labeled 'MLM MLM'. The BLEU score is 26.44.", "question": "What is the BLEU score for the best model on the en-de language pair when both the encoder and decoder are pretrained with MLM?", "answer": "So, the answer is {\"answer\": \"26.44\"}" }, { "messages": [ { "role": "user", "content": "Pretraining only the decoder leads to a significant drop in performance compared to pretraining both the encoder and decoder. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nPretraining only the decoder leads to a significant drop in performance compared to pretraining both the encoder and decoder.\n" }, { "role": "assistant", "content": "The description states that pretraining only the decoder leads to a significant drop in performance compared to pretraining both the encoder and decoder. The table shows that for the en-de language pair, pretraining both with MLM results in a BLEU score of 26.44, while pretraining only the decoder with MLM results in a BLEU score of 21.63, confirming a significant drop. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3176.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on unsupervised MT. BLEU scores on WMT’14 English-French, WMT’16 German-English and WMT’16 Romanian-English. For our results, the first two columns indicate the model used to pretrain the encoder and the decoder. “ - ” means the model was randomly initialized. EMB corresponds to pretraining the lookup table with cross-lingual embeddings, CLM and MLM correspond to pretraining with models trained on the CLM or MLM objectives." ], "reasoning": "The description states that pretraining only the decoder leads to a significant drop in performance compared to pretraining both the encoder and decoder. The table shows that for the en-de language pair, pretraining both with MLM results in a BLEU score of 26.44, while pretraining only the decoder with MLM results in a BLEU score of 21.63, confirming a significant drop.", "statement": "Pretraining only the decoder leads to a significant drop in performance compared to pretraining both the encoder and decoder.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Results on supervised MT. BLEU scores on WMT’16 Romanian-English. The previous state-of-the-art of Sennrich et al. (2016) uses both back-translation and an ensemble model. ro ↔ en corresponds to models trained on both directions.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Pretraining with the MLM objective provides the best performance in BLEU score, and models with back-translation achieve the highest BLEU score of 38.5.We also investigate the impact of cross-lingual language modeling pretraining for supervised machine translation, and extend the approach of Ramachandran et al. Models with back-translation are trained with the same monolingual data as language models used for pretraining. As in the unsupervised setting, we observe that pretraining provides a significant boost in BLEU score for each configuration, and that pretraining with the MLM objective leads to the best performance. Also, while models with back-translation have access to the same amount of monolingual data as the pretrained models, they are not able to generalize as well on the evaluation sets. Our bidirectional model trained with back-translation obtains the best performance and reaches 38.5 BLEU, outperforming the previous SOTA of Sennrich et al. (based on back-translation and ensemble models) by more than 4 BLEU." } ], "images": [ "scigen/train/3177.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on supervised MT. BLEU scores on WMT’16 Romanian-English. The previous state-of-the-art of Sennrich et al. (2016) uses both back-translation and an ensemble model. ro ↔ en corresponds to models trained on both directions." ], "reasoning": "Pretraining with the MLM objective provides the best performance in BLEU score, and models with back-translation achieve the highest BLEU score of 38.5.", "description": "We also investigate the impact of cross-lingual language modeling pretraining for supervised machine translation, and extend the approach of Ramachandran et al. Models with back-translation are trained with the same monolingual data as language models used for pretraining. As in the unsupervised setting, we observe that pretraining provides a significant boost in BLEU score for each configuration, and that pretraining with the MLM objective leads to the best performance. Also, while models with back-translation have access to the same amount of monolingual data as the pretrained models, they are not able to generalize as well on the evaluation sets. Our bidirectional model trained with back-translation obtains the best performance and reaches 38.5 BLEU, outperforming the previous SOTA of Sennrich et al. (based on back-translation and ensemble models) by more than 4 BLEU.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Results on supervised MT. BLEU scores on WMT’16 Romanian-English. The previous state-of-the-art of Sennrich et al. (2016) uses both back-translation and an ensemble model. ro ↔ en corresponds to models trained on both directions.', solve the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the BLEU score for the bidirectional model trained with back-translation and MLM pretraining?\n" }, { "role": "assistant", "content": "The table shows that the bidirectional model (ro ↔ en) trained with back-translation (BT) and MLM pretraining achieves a BLEU score of 38.54. So, the answer is {\"answer\": \"38.54\"}" } ], "images": [ "scigen/train/3177.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on supervised MT. BLEU scores on WMT’16 Romanian-English. The previous state-of-the-art of Sennrich et al. (2016) uses both back-translation and an ensemble model. ro ↔ en corresponds to models trained on both directions." ], "reasoning": "The table shows that the bidirectional model (ro ↔ en) trained with back-translation (BT) and MLM pretraining achieves a BLEU score of 38.54.", "question": "What is the BLEU score for the bidirectional model trained with back-translation and MLM pretraining?", "answer": "So, the answer is {\"answer\": \"38.54\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The bidirectional model trained with back-translation and MLM pretraining achieves a BLEU score of 38.54. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe bidirectional model trained with back-translation and MLM pretraining achieves a BLEU score of 38.54.\n" }, { "role": "assistant", "content": "The table indicates that the bidirectional model (ro ↔ en) trained with back-translation (BT) and MLM pretraining achieves a BLEU score of 38.54. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3177.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on supervised MT. BLEU scores on WMT’16 Romanian-English. The previous state-of-the-art of Sennrich et al. (2016) uses both back-translation and an ensemble model. ro ↔ en corresponds to models trained on both directions." ], "reasoning": "The table indicates that the bidirectional model (ro ↔ en) trained with back-translation (BT) and MLM pretraining achieves a BLEU score of 38.54.", "statement": "The bidirectional model trained with back-translation and MLM pretraining achieves a BLEU score of 38.54.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Results on language modeling. Nepali perplexity when using additional data from a similar language (Hindi) or a distant one (English).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Using additional data from Hindi significantly reduces the perplexity of a Nepali language model, and combining data from both English and Hindi yields the lowest perplexity.For low-resource languages, it is often beneficial to leverage data in similar but higher-resource languages, especially when they share a significant fraction of their vocabularies. For instance, there are about 100k sentences written in Nepali on Wikipedia, and about 6 times more in Hindi. These two languages also have more than 80% of their tokens in common in a shared BPE vocabulary of 100k subword units. , we investigate the impact of cross-lingual language modeling for improving the perplexity of a Nepali language model. To do so, we train a Nepali language model on Wikipedia, together with additional data from either English or Hindi. While Nepali and English are distant languages, Nepali and Hindi are similar as they share the same Devanagari script and have a common Sanskrit ancestor. When using English data, we reduce the perplexity on the Nepali language model by 17.1 points, going from 157.2 for Nepali-only language modeling to 140.1 when using English. Using additional data from Hindi, we get a much larger perplexity reduction of 41.6. Finally, by leveraging data from both English and Hindi, we reduce the perplexity even more to 109.3 on Nepali. The gains in perplexity from cross-lingual language modeling can be partly explained by the n-grams anchor points that are shared across languages, for instance in Wikipedia articles. The cross-lingual language model can thus transfer the additional context provided by the Hindi or English monolingual corpora through these anchor points to improve the Nepali language model." } ], "images": [ "scigen/train/3178.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on language modeling. Nepali perplexity when using additional data from a similar language (Hindi) or a distant one (English)." ], "reasoning": "Using additional data from Hindi significantly reduces the perplexity of a Nepali language model, and combining data from both English and Hindi yields the lowest perplexity.", "description": "For low-resource languages, it is often beneficial to leverage data in similar but higher-resource languages, especially when they share a significant fraction of their vocabularies. For instance, there are about 100k sentences written in Nepali on Wikipedia, and about 6 times more in Hindi. These two languages also have more than 80% of their tokens in common in a shared BPE vocabulary of 100k subword units. , we investigate the impact of cross-lingual language modeling for improving the perplexity of a Nepali language model. To do so, we train a Nepali language model on Wikipedia, together with additional data from either English or Hindi. While Nepali and English are distant languages, Nepali and Hindi are similar as they share the same Devanagari script and have a common Sanskrit ancestor. When using English data, we reduce the perplexity on the Nepali language model by 17.1 points, going from 157.2 for Nepali-only language modeling to 140.1 when using English. Using additional data from Hindi, we get a much larger perplexity reduction of 41.6. Finally, by leveraging data from both English and Hindi, we reduce the perplexity even more to 109.3 on Nepali. The gains in perplexity from cross-lingual language modeling can be partly explained by the n-grams anchor points that are shared across languages, for instance in Wikipedia articles. The cross-lingual language model can thus transfer the additional context provided by the Hindi or English monolingual corpora through these anchor points to improve the Nepali language model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 4: Results on language modeling. Nepali perplexity when using additional data from a similar language (Hindi) or a distant one (English).\nQuestion: What is the reduction in perplexity when training a Nepali language model with both English and Hindi data compared to using only Nepali data?\n" }, { "role": "assistant", "content": "The perplexity for Nepali-only language modeling is 157.2, and it reduces to 109.3 when using both English and Hindi data. The reduction is 157.2 - 109.3. So, the answer is {\"answer\": \"47.9\"}" } ], "images": [ "scigen/train/3178.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on language modeling. Nepali perplexity when using additional data from a similar language (Hindi) or a distant one (English)." ], "reasoning": "The perplexity for Nepali-only language modeling is 157.2, and it reduces to 109.3 when using both English and Hindi data. The reduction is 157.2 - 109.3.", "question": "What is the reduction in perplexity when training a Nepali language model with both English and Hindi data compared to using only Nepali data?", "answer": "So, the answer is {\"answer\": \"47.9\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nUsing additional data from Hindi reduces the perplexity of a Nepali language model by more than 40 points.\n" }, { "role": "assistant", "content": "The table shows that using Nepali + Hindi reduces the perplexity from 157.2 to 115.6, which is a reduction of 41.6 points. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3178.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on language modeling. Nepali perplexity when using additional data from a similar language (Hindi) or a distant one (English)." ], "reasoning": "The table shows that using Nepali + Hindi reduces the perplexity from 157.2 to 115.6, which is a reduction of 41.6 points.", "statement": "Using additional data from Hindi reduces the perplexity of a Nepali language model by more than 40 points.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Character-wise accuracy of Structured Prediction Cascades (Weiss et al., 2012) on OCR dataset.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The model's performance improves significantly with higher-order context, achieving an accuracy of 98.54% with 6-grams.The structured prediction cascades of Weiss et al. The excellent performance of these large-clique models is consequence of the fact that the data contains only 55 unique words, written by 150 different people. Once the model has access to enough higher-order context, the problem becomes much easier to solve." } ], "images": [ "scigen/train/3179.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Character-wise accuracy of Structured Prediction Cascades (Weiss et al., 2012) on OCR dataset." ], "reasoning": "The model's performance improves significantly with higher-order context, achieving an accuracy of 98.54% with 6-grams.", "description": "The structured prediction cascades of Weiss et al. The excellent performance of these large-clique models is consequence of the fact that the data contains only 55 unique words, written by 150 different people. Once the model has access to enough higher-order context, the problem becomes much easier to solve.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 2: Character-wise accuracy of Structured Prediction Cascades (Weiss et al., 2012) on OCR dataset.', unfold the question presented below, detailing every step of your calculation. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the accuracy of the model when using 4-grams?\n" }, { "role": "assistant", "content": "The table shows the accuracy for different n-grams. For 4-grams, the accuracy is listed as 97.21%. So, the answer is {\"answer\": \"97.21%\"}" } ], "images": [ "scigen/train/3179.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Character-wise accuracy of Structured Prediction Cascades (Weiss et al., 2012) on OCR dataset." ], "reasoning": "The table shows the accuracy for different n-grams. For 4-grams, the accuracy is listed as 97.21%.", "question": "What is the accuracy of the model when using 4-grams?", "answer": "So, the answer is {\"answer\": \"97.21%\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model achieves over 95% accuracy with 3-grams.\n" }, { "role": "assistant", "content": "The table shows that the accuracy for 3-grams is 96.20%, which is over 95%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3179.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Character-wise accuracy of Structured Prediction Cascades (Weiss et al., 2012) on OCR dataset." ], "reasoning": "The table shows that the accuracy for 3-grams is 96.20%, which is over 95%.", "statement": "The model achieves over 95% accuracy with 3-grams.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: Comparison of F1 scores on Citation Extraction dataset. We compare MAP inference F1 scores of our non-local energy model and the specialized dual decomposition model of Anzaroot et al. (2014). Both variants learn global regularities that significantly improve performance.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our augmented model (Non-Local Energies) slightly outperforms the Soft-DD procedure by Anzaroot et al., despite their use of a specialized solver and learning algorithm.We can see that our baseline chain has slightly higher accuracy than the baseline approach of Anzaroot et al. Our augmented model (Non-Local Energies) matches and very slightly beats their soft dual decomposition (Soft-DD) procedure. This is especially impressive because they employ a specialized linear-programming solver and learning algorithm adapted to the task of MAP inference under hinge-loss soft constraints, whereas we simply plug in our general learning and inference algorithms for non-local structured prediction – applicable to any set of energy functions." } ], "images": [ "scigen/train/3180.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of F1 scores on Citation Extraction dataset. We compare MAP inference F1 scores of our non-local energy model and the specialized dual decomposition model of Anzaroot et al. (2014). Both variants learn global regularities that significantly improve performance." ], "reasoning": "Our augmented model (Non-Local Energies) slightly outperforms the Soft-DD procedure by Anzaroot et al., despite their use of a specialized solver and learning algorithm.", "description": "We can see that our baseline chain has slightly higher accuracy than the baseline approach of Anzaroot et al. Our augmented model (Non-Local Energies) matches and very slightly beats their soft dual decomposition (Soft-DD) procedure. This is especially impressive because they employ a specialized linear-programming solver and learning algorithm adapted to the task of MAP inference under hinge-loss soft constraints, whereas we simply plug in our general learning and inference algorithms for non-local structured prediction – applicable to any set of energy functions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 1: Comparison of F1 scores on Citation Extraction dataset. We compare MAP inference F1 scores of our non-local energy model and the specialized dual decomposition model of Anzaroot et al. (2014). Both variants learn global regularities that significantly improve performance.'\nQuestion: Which model has the highest F1 score?\n" }, { "role": "assistant", "content": "The table shows the F1 scores for different models. The Non-local Energies model has the highest F1 score of 95.47. So, the answer is {\"answer\": \"Non-local Energies\"}." } ], "images": [ "scigen/train/3180.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of F1 scores on Citation Extraction dataset. We compare MAP inference F1 scores of our non-local energy model and the specialized dual decomposition model of Anzaroot et al. (2014). Both variants learn global regularities that significantly improve performance." ], "reasoning": "The table shows the F1 scores for different models. The Non-local Energies model has the highest F1 score of 95.47.", "question": "Which model has the highest F1 score?", "answer": "So, the answer is {\"answer\": \"Non-local Energies\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Soft-DD procedure by Anzaroot et al. has a higher F1 score than our baseline., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Soft-DD procedure by Anzaroot et al. has a higher F1 score than our baseline.\n" }, { "role": "assistant", "content": "The table shows that the F1 score for Soft-DD by Anzaroot et al. is 95.39, which is higher than our baseline's F1 score of 94.47. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3180.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of F1 scores on Citation Extraction dataset. We compare MAP inference F1 scores of our non-local energy model and the specialized dual decomposition model of Anzaroot et al. (2014). Both variants learn global regularities that significantly improve performance." ], "reasoning": "The table shows that the F1 score for Soft-DD by Anzaroot et al. is 95.39, which is higher than our baseline's F1 score of 94.47.", "statement": "The Soft-DD procedure by Anzaroot et al. has a higher F1 score than our baseline.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Character-wise accuracy of our baselines, and models using learned non-local energies on Handwriting Recognition dataset. Note that word classifier baseline is also given in character-wise accuracy for comparison.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The non-local energy model significantly outperforms the baseline bigram chain model, and the 55-class logistic regression classifier, despite tuning, still performs below the best structured models.Adding the non-local energy brings our performance well above the baseline bigram chain model, and our training procedure is able to give substantially better performance when ψ depends on the above input features. Of course, since the dataset has only 55 actual labels, and some of those are not valid for different input sequences due to length mismatches, this is arguably a classification problem as much as a structured prediction problem. To address this, we create another baseline, which is a constrained 55-class logistic regression classifier (constrained to only allow choosing output classes with appropriate lengths given the input). We also tune the number of random Fourier features as a hyperparameter to give the classifier as much expressive power as possible. As we can see, the performance is still significantly below the best structured models, indicating that the interplay between local and global structure is important." } ], "images": [ "scigen/train/3181.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Character-wise accuracy of our baselines, and models using learned non-local energies on Handwriting Recognition dataset. Note that word classifier baseline is also given in character-wise accuracy for comparison." ], "reasoning": "The non-local energy model significantly outperforms the baseline bigram chain model, and the 55-class logistic regression classifier, despite tuning, still performs below the best structured models.", "description": "Adding the non-local energy brings our performance well above the baseline bigram chain model, and our training procedure is able to give substantially better performance when ψ depends on the above input features. Of course, since the dataset has only 55 actual labels, and some of those are not valid for different input sequences due to length mismatches, this is arguably a classification problem as much as a structured prediction problem. To address this, we create another baseline, which is a constrained 55-class logistic regression classifier (constrained to only allow choosing output classes with appropriate lengths given the input). We also tune the number of random Fourier features as a hyperparameter to give the classifier as much expressive power as possible. As we can see, the performance is still significantly below the best structured models, indicating that the interplay between local and global structure is important.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 4: Character-wise accuracy of our baselines, and models using learned non-local energies on Handwriting Recognition dataset. Note that word classifier baseline is also given in character-wise accuracy for comparison.' and proceed to solve the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model achieved the highest accuracy and how does it compare to the baseline bigram chain model?\n" }, { "role": "assistant", "content": "The table shows that the 'Lw ψ (MM)' model achieved the highest accuracy of 98.83%, which is significantly higher than the baseline bigram chain model's accuracy of 84.93%. So, the answer is {\"answer\": \"Lw ψ (MM)\"}." } ], "images": [ "scigen/train/3181.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Character-wise accuracy of our baselines, and models using learned non-local energies on Handwriting Recognition dataset. Note that word classifier baseline is also given in character-wise accuracy for comparison." ], "reasoning": "The table shows that the 'Lw ψ (MM)' model achieved the highest accuracy of 98.83%, which is significantly higher than the baseline bigram chain model's accuracy of 84.93%.", "question": "Which model achieved the highest accuracy and how does it compare to the baseline bigram chain model?", "answer": "So, the answer is {\"answer\": \"Lw ψ (MM)\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe 55-Class Classifier (MM) model outperformed the 2-gram (base model).\n" }, { "role": "assistant", "content": "The table shows that the 55-Class Classifier (MM) model has an accuracy of 86.06%, which is higher than the 2-gram (base model) accuracy of 84.93%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3181.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Character-wise accuracy of our baselines, and models using learned non-local energies on Handwriting Recognition dataset. Note that word classifier baseline is also given in character-wise accuracy for comparison." ], "reasoning": "The table shows that the 55-Class Classifier (MM) model has an accuracy of 86.06%, which is higher than the 2-gram (base model) accuracy of 84.93%.", "statement": "The 55-Class Classifier (MM) model outperformed the 2-gram (base model).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Discourse marker prediction accuracy percentages on Wiki20 and Discovery datasets. Human Raters and Decomposable Attention are from [15]. Bi-LSTM is from [22] and the last two are ours.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The BERT model finetuned on Discovery achieves a high accuracy of 32.9, outperforming the Bi-LSTM model's 22.2 on the same dataset.The accuracy of BERT on the Discovery test data is quite high given the large number of classes (174, perfectly balanced) and sometimes their low semantic distinguishability. This accuracy is significantly higher than the score of the Bi-LSTM model in the setup of \\newcitesileo2019discovery. The BERT model finetuned on Discovery outperforms human performance reported on Wiki20 with no other adaptation than discarding markers not in Wiki20 during inference. These results suggest that the BERT+Discovery model captures a significant part of the use of discourse markers; in the following section, we will apply it to the prediction of discourse markers for individual categories." } ], "images": [ "scigen/train/3182.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Discourse marker prediction accuracy percentages on Wiki20 and Discovery datasets. Human Raters and Decomposable Attention are from [15]. Bi-LSTM is from [22] and the last two are ours." ], "reasoning": "The BERT model finetuned on Discovery achieves a high accuracy of 32.9, outperforming the Bi-LSTM model's 22.2 on the same dataset.", "description": "The accuracy of BERT on the Discovery test data is quite high given the large number of classes (174, perfectly balanced) and sometimes their low semantic distinguishability. This accuracy is significantly higher than the score of the Bi-LSTM model in the setup of \\newcitesileo2019discovery. The BERT model finetuned on Discovery outperforms human performance reported on Wiki20 with no other adaptation than discarding markers not in Wiki20 during inference. These results suggest that the BERT+Discovery model captures a significant part of the use of discourse markers; in the following section, we will apply it to the prediction of discourse markers for individual categories.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Discourse marker prediction accuracy percentages on Wiki20 and Discovery datasets. Human Raters and Decomposable Attention are from [15]. Bi-LSTM is from [22] and the last two are ours.', answer the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the accuracy of the BERT+Discovery model on the Discovery test data, and how does it compare to the Bi-LSTM model?\n" }, { "role": "assistant", "content": "The table shows that the BERT+Discovery model has an accuracy of 32.9 on the Discovery test data, which is higher than the Bi-LSTM model's accuracy of 22.2. So, the answer is {\"answer\": \"32.9\"}." } ], "images": [ "scigen/train/3182.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Discourse marker prediction accuracy percentages on Wiki20 and Discovery datasets. Human Raters and Decomposable Attention are from [15]. Bi-LSTM is from [22] and the last two are ours." ], "reasoning": "The table shows that the BERT+Discovery model has an accuracy of 32.9 on the Discovery test data, which is higher than the Bi-LSTM model's accuracy of 22.2.", "question": "What is the accuracy of the BERT+Discovery model on the Discovery test data, and how does it compare to the Bi-LSTM model?", "answer": "So, the answer is {\"answer\": \"32.9\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe BERT+Discovery model outperforms the Bi-LSTM model on the Discovery test data.\n" }, { "role": "assistant", "content": "The table indicates that the BERT+Discovery model has an accuracy of 32.9 on the Discovery test data, while the Bi-LSTM model has an accuracy of 22.2, showing that BERT+Discovery outperforms Bi-LSTM. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3182.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Discourse marker prediction accuracy percentages on Wiki20 and Discovery datasets. Human Raters and Decomposable Attention are from [15]. Bi-LSTM is from [22] and the last two are ours." ], "reasoning": "The table indicates that the BERT+Discovery model has an accuracy of 32.9 on the Discovery test data, while the Bi-LSTM model has an accuracy of 22.2, showing that BERT+Discovery outperforms Bi-LSTM.", "statement": "The BERT+Discovery model outperforms the Bi-LSTM model on the Discovery test data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Ranking modifications by their impact on the target. We compare the true ranking of candidate triples with a number of approximations using ranking correlation coefficients. We compare our method with influence function (IF) with and without Hessian, and ranking the candidates based on their score, on two KGs (d=10, averaged over 10 random targets). For the sake of brevity, we represent the Spearman’s ρ and Kendall’s τ rank correlation coefficients simply as ρ and τ.'.\n" }, { "role": "assistant", "content": "CRIAGE is more scalable than the Influence Function, which is constrained by memory limitations due to its use of the complete Hessian matrix.CRIAGE performs comparably to the influence function, confirming that our approximation is accurate. Influence function is slightly more accurate because they use the complete Hessian matrix over all the parameters, while we only approximate the change by calculating the Hessian over eo. The effect of this difference on scalability is dramatic, constraining IF to very small graphs and small embedding dimensionality (d≤10) before we run out of memory. As it shows, CRIAGE is mostly unaffected by the number of entities while IF increases quadratically. Considering that real-world KGs have tens of thousands of times more entities, making IF unfeasible for them." } ], "images": [ "scigen/train/3183.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ranking modifications by their impact on the target. We compare the true ranking of candidate triples with a number of approximations using ranking correlation coefficients. We compare our method with influence function (IF) with and without Hessian, and ranking the candidates based on their score, on two KGs (d=10, averaged over 10 random targets). For the sake of brevity, we represent the Spearman’s ρ and Kendall’s τ rank correlation coefficients simply as ρ and τ." ], "reasoning": "CRIAGE is more scalable than the Influence Function, which is constrained by memory limitations due to its use of the complete Hessian matrix.", "description": "CRIAGE performs comparably to the influence function, confirming that our approximation is accurate. Influence function is slightly more accurate because they use the complete Hessian matrix over all the parameters, while we only approximate the change by calculating the Hessian over eo. The effect of this difference on scalability is dramatic, constraining IF to very small graphs and small embedding dimensionality (d≤10) before we run out of memory. As it shows, CRIAGE is mostly unaffected by the number of entities while IF increases quadratically. Considering that real-world KGs have tens of thousands of times more entities, making IF unfeasible for them.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Ranking modifications by their impact on the target. We compare the true ranking of candidate triples with a number of approximations using ranking correlation coefficients. We compare our method with influence function (IF) with and without Hessian, and ranking the candidates based on their score, on two KGs (d=10, averaged over 10 random targets). For the sake of brevity, we represent the Spearman’s ρ and Kendall’s τ rank correlation coefficients simply as ρ and τ.\nQuestion: Which method shows the highest accuracy for Kinship Adding in terms of ρ?\nSolve the above question based on the table information. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest accuracy for Kinship Adding in terms of ρ, compare the ρ values under the Kinship Adding column. Influence Function has the highest value of 0.99. So, the answer is {\"answer\": \"Influence Function\"}." } ], "images": [ "scigen/train/3183.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ranking modifications by their impact on the target. We compare the true ranking of candidate triples with a number of approximations using ranking correlation coefficients. We compare our method with influence function (IF) with and without Hessian, and ranking the candidates based on their score, on two KGs (d=10, averaged over 10 random targets). For the sake of brevity, we represent the Spearman’s ρ and Kendall’s τ rank correlation coefficients simply as ρ and τ." ], "reasoning": "To find the highest accuracy for Kinship Adding in terms of ρ, compare the ρ values under the Kinship Adding column. Influence Function has the highest value of 0.99.", "question": "Which method shows the highest accuracy for Kinship Adding in terms of ρ?", "answer": "So, the answer is {\"answer\": \"Influence Function\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nCRIAGE performs better than the Influence Function without Hessian for Nations Removing in terms of τ.\n" }, { "role": "assistant", "content": "For Nations Removing in terms of τ, CRIAGE has a value of 0.85, while the Influence Function without Hessian has a value of 0.1. Therefore, CRIAGE performs better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3183.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ranking modifications by their impact on the target. We compare the true ranking of candidate triples with a number of approximations using ranking correlation coefficients. We compare our method with influence function (IF) with and without Hessian, and ranking the candidates based on their score, on two KGs (d=10, averaged over 10 random targets). For the sake of brevity, we represent the Spearman’s ρ and Kendall’s τ rank correlation coefficients simply as ρ and τ." ], "reasoning": "For Nations Removing in terms of τ, CRIAGE has a value of 0.85, while the Influence Function without Hessian has a value of 0.1. Therefore, CRIAGE performs better.", "statement": "CRIAGE performs better than the Influence Function without Hessian for Nations Removing in terms of τ.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: Inverter Functions Accuracy, we calculate the accuracy of our inverter networks in correctly recovering the pairs of subject and relation from the test set of our benchmarks.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The inverter networks achieve high accuracy in recovering subject and relation pairs from the test set, demonstrating their effectiveness.We evaluate the performance of our inverter networks (one for each model/dataset) on correctly recovering the pairs of subject and relation from the test set of our benchmarks, given the zs,r. The accuracy of recovered pairs ( As shown, our networks achieve a very high accuracy, demonstrating their ability to invert vectors zs,r to {s,r} pairs." } ], "images": [ "scigen/train/3184.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Inverter Functions Accuracy, we calculate the accuracy of our inverter networks in correctly recovering the pairs of subject and relation from the test set of our benchmarks." ], "reasoning": "The inverter networks achieve high accuracy in recovering subject and relation pairs from the test set, demonstrating their effectiveness.", "description": "We evaluate the performance of our inverter networks (one for each model/dataset) on correctly recovering the pairs of subject and relation from the test set of our benchmarks, given the zs,r. The accuracy of recovered pairs ( As shown, our networks achieve a very high accuracy, demonstrating their ability to invert vectors zs,r to {s,r} pairs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 1: Inverter Functions Accuracy, we calculate the accuracy of our inverter networks in correctly recovering the pairs of subject and relation from the test set of our benchmarks.', unfold the question presented below, detailing every step of your calculation. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model/dataset combination has the highest accuracy for recovering {s, r} pairs?\n" }, { "role": "assistant", "content": "To find the highest accuracy for recovering {s, r} pairs, compare the values in the 'Recover {s, r}' row across all columns. The highest value is 98.0 in the YAGO ConvE column. So, the answer is {\"answer\": \"YAGO ConvE\"}." } ], "images": [ "scigen/train/3184.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Inverter Functions Accuracy, we calculate the accuracy of our inverter networks in correctly recovering the pairs of subject and relation from the test set of our benchmarks." ], "reasoning": "To find the highest accuracy for recovering {s, r} pairs, compare the values in the 'Recover {s, r}' row across all columns. The highest value is 98.0 in the YAGO ConvE column.", "question": "Which model/dataset combination has the highest accuracy for recovering {s, r} pairs?", "answer": "So, the answer is {\"answer\": \"YAGO ConvE\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe WordNet ConvE model has a higher accuracy for recovering r than the YAGO DistMult model.\n" }, { "role": "assistant", "content": "The table shows that the accuracy for recovering r is 95.3 for WordNet ConvE and 99.0 for YAGO DistMult. Since 95.3 is less than 99.0, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3184.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Inverter Functions Accuracy, we calculate the accuracy of our inverter networks in correctly recovering the pairs of subject and relation from the test set of our benchmarks." ], "reasoning": "The table shows that the accuracy for recovering r is 95.3 for WordNet ConvE and 99.0 for YAGO DistMult. Since 95.3 is less than 99.0, the statement is refuted.", "statement": "The WordNet ConvE model has a higher accuracy for recovering r than the YAGO DistMult model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Robustness of Representation Models, the effect of adversarial attack on link prediction task. We consider two scenario for the target triples, 1) choosing the whole test dataset as the targets (All-Test) and 2) choosing a subset of test data that models are uncertain about them (Uncertain-Test).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "CRIAGE-Add outperforms baselines in attacking KG representations, while ConvE is more robust against designed attacks and YAGO3-10 models are more robust than WN18 models.All-Test CRIAGE-Add outperforms the baselines, demonstrating its ability to effectively attack the KG representations. It seems DistMult is more robust against random attacks, while ConvE is more robust against designed attacks. CRIAGE-FT is more effective than CRIAGE-Add since changing the score of a fake fact is easier than of actual facts; there is no existing evidence to support fake facts. We also see that YAGO3-10 models are more robust than those for WN18. Uncertain-Test To better understand the effect of attacks, we consider a subset of test triples that 1) the model predicts correctly, 2) difference between their scores and the negative sample with the highest score is minimum. The attacks are much more effective in this scenario, causing a considerable drop in the metrics. Further, in addition to CRIAGE significantly outperforming other baselines, they indicate that ConvE’s confidence is much more robust." } ], "images": [ "scigen/train/3185.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Robustness of Representation Models, the effect of adversarial attack on link prediction task. We consider two scenario for the target triples, 1) choosing the whole test dataset as the targets (All-Test) and 2) choosing a subset of test data that models are uncertain about them (Uncertain-Test)." ], "reasoning": "CRIAGE-Add outperforms baselines in attacking KG representations, while ConvE is more robust against designed attacks and YAGO3-10 models are more robust than WN18 models.", "description": "All-Test CRIAGE-Add outperforms the baselines, demonstrating its ability to effectively attack the KG representations. It seems DistMult is more robust against random attacks, while ConvE is more robust against designed attacks. CRIAGE-FT is more effective than CRIAGE-Add since changing the score of a fake fact is easier than of actual facts; there is no existing evidence to support fake facts. We also see that YAGO3-10 models are more robust than those for WN18. Uncertain-Test To better understand the effect of attacks, we consider a subset of test triples that 1) the model predicts correctly, 2) difference between their scores and the negative sample with the highest score is minimum. The attacks are much more effective in this scenario, causing a considerable drop in the metrics. Further, in addition to CRIAGE significantly outperforming other baselines, they indicate that ConvE’s confidence is much more robust.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Robustness of Representation Models, the effect of adversarial attack on link prediction task. We consider two scenario for the target triples, 1) choosing the whole test dataset as the targets (All-Test) and 2) choosing a subset of test data that models are uncertain about them (Uncertain-Test).\nQuestion: Which model and attack combination has the highest Hits@1 score for the YAGO3-10 All-Test?\nGive a detailed response to the above question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest Hits@1 score for the YAGO3-10 All-Test, we need to compare the Hits@1 scores for all model and attack combinations listed under the YAGO3-10 All-Test column. So, the answer is {\"answer\": \"ConvE\"}." } ], "images": [ "scigen/train/3185.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Robustness of Representation Models, the effect of adversarial attack on link prediction task. We consider two scenario for the target triples, 1) choosing the whole test dataset as the targets (All-Test) and 2) choosing a subset of test data that models are uncertain about them (Uncertain-Test)." ], "reasoning": "To find the highest Hits@1 score for the YAGO3-10 All-Test, we need to compare the Hits@1 scores for all model and attack combinations listed under the YAGO3-10 All-Test column.", "question": "Which model and attack combination has the highest Hits@1 score for the YAGO3-10 All-Test?", "answer": "So, the answer is {\"answer\": \"ConvE\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis DistMult with CRIAGE-Add has a higher MRR for WN18 All-Test than ConvE with CRIAGE-Add. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nDistMult with CRIAGE-Add has a higher MRR for WN18 All-Test than ConvE with CRIAGE-Add.\n" }, { "role": "assistant", "content": "The table shows that DistMult with CRIAGE-Add has an MRR of 0.86 for WN18 All-Test, while ConvE with CRIAGE-Add has an MRR of 0.897 for WN18 All-Test. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3185.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Robustness of Representation Models, the effect of adversarial attack on link prediction task. We consider two scenario for the target triples, 1) choosing the whole test dataset as the targets (All-Test) and 2) choosing a subset of test data that models are uncertain about them (Uncertain-Test)." ], "reasoning": "The table shows that DistMult with CRIAGE-Add has an MRR of 0.86 for WN18 All-Test, while ConvE with CRIAGE-Add has an MRR of 0.897 for WN18 All-Test. Therefore, the statement is refuted.", "statement": "DistMult with CRIAGE-Add has a higher MRR for WN18 All-Test than ConvE with CRIAGE-Add.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 6: Error Detection Accuracy in the neighborhood of 100 chosen samples. We choose the neighbor with the least value of Δ(s′,r′)(s,r,o) as the incorrect fact. This experiment assumes we know each target fact has exactly one error.'.\n" }, { "role": "assistant", "content": "CRIAGE outperforms both random and lowest methods in detecting errors in noisy training data, achieving higher accuracy in both Hits@1 and Hits@2 metrics.To evaluate this application, we inject random triples into the graph, and measure the ability of CRIAGE to detect the errors using our optimization. We consider two types of incorrect triples: 1) incorrect triples in the form of ⟨s′,r,o⟩ where s′ is chosen randomly from all of the entities, and 2) incorrect triples in the form of ⟨s′,r′,o⟩ where s′ and r′ are chosen randomly. We choose 100 random triples from the observed graph, and for each of them, add an incorrect triple (in each of the two scenarios) to its neighborhood. Then, after retraining DistMult on this noisy training data, we identify error triples through a search over the neighbors of the 100 facts. When compared with baselines that randomly choose one of the neighbors, or assume that the fact with the lowest score is incorrect, we see that CRIAGE outperforms both of these with a considerable gap, obtaining an accuracy of 42% and 55% in detecting errors." } ], "images": [ "scigen/train/3186.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Error Detection Accuracy in the neighborhood of 100 chosen samples. We choose the neighbor with the least value of Δ(s′,r′)(s,r,o) as the incorrect fact. This experiment assumes we know each target fact has exactly one error." ], "reasoning": "CRIAGE outperforms both random and lowest methods in detecting errors in noisy training data, achieving higher accuracy in both Hits@1 and Hits@2 metrics.", "description": "To evaluate this application, we inject random triples into the graph, and measure the ability of CRIAGE to detect the errors using our optimization. We consider two types of incorrect triples: 1) incorrect triples in the form of ⟨s′,r,o⟩ where s′ is chosen randomly from all of the entities, and 2) incorrect triples in the form of ⟨s′,r′,o⟩ where s′ and r′ are chosen randomly. We choose 100 random triples from the observed graph, and for each of them, add an incorrect triple (in each of the two scenarios) to its neighborhood. Then, after retraining DistMult on this noisy training data, we identify error triples through a search over the neighbors of the 100 facts. When compared with baselines that randomly choose one of the neighbors, or assume that the fact with the lowest score is incorrect, we see that CRIAGE outperforms both of these with a considerable gap, obtaining an accuracy of 42% and 55% in detecting errors.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 6: Error Detection Accuracy in the neighborhood of 100 chosen samples. We choose the neighbor with the least value of Δ(s′,r′)(s,r,o) as the incorrect fact. This experiment assumes we know each target fact has exactly one error.', solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the Hits@2 accuracy for CRIAGE when detecting errors in the form of ⟨s′,r,o⟩?\n" }, { "role": "assistant", "content": "To find the Hits@2 accuracy for CRIAGE when detecting errors in the form of ⟨s′,r,o⟩, look at the corresponding cell in the table under the column ⟨s′,r,o⟩ Noise Hits@2 for the CRIAGE row. So, the answer is {\"answer\": \"62\"}." } ], "images": [ "scigen/train/3186.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Error Detection Accuracy in the neighborhood of 100 chosen samples. We choose the neighbor with the least value of Δ(s′,r′)(s,r,o) as the incorrect fact. This experiment assumes we know each target fact has exactly one error." ], "reasoning": "To find the Hits@2 accuracy for CRIAGE when detecting errors in the form of ⟨s′,r,o⟩, look at the corresponding cell in the table under the column ⟨s′,r,o⟩ Noise Hits@2 for the CRIAGE row.", "question": "What is the Hits@2 accuracy for CRIAGE when detecting errors in the form of ⟨s′,r,o⟩?", "answer": "So, the answer is {\"answer\": \"62\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nCRIAGE has a higher Hits@1 accuracy than the random method for detecting errors in the form of ⟨s′,r′,o⟩.\n" }, { "role": "assistant", "content": "The table shows that CRIAGE has a Hits@1 accuracy of 55 for ⟨s′,r′,o⟩, while the random method has a Hits@1 accuracy of 19.7. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3186.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Error Detection Accuracy in the neighborhood of 100 chosen samples. We choose the neighbor with the least value of Δ(s′,r′)(s,r,o) as the incorrect fact. This experiment assumes we know each target fact has exactly one error." ], "reasoning": "The table shows that CRIAGE has a Hits@1 accuracy of 55 for ⟨s′,r′,o⟩, while the random method has a Hits@1 accuracy of 19.7.", "statement": "CRIAGE has a higher Hits@1 accuracy than the random method for detecting errors in the form of ⟨s′,r′,o⟩.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Classification results on different tasks using the BoW features, the SDAE representations computed from the BoW (SDAE-BoW), the doc2vec representations, the concatenated SDAE-BoW and doc2vec representations ([doc2vec, SDAE-BoW]) with Cohen’s κ score, the BoCUI features, and the SDAE vectors computed from the BoCUI (SDAE-BoCUI). AUC-ROC values are reported for the mortality tasks, and weighted F-score for the others.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The table shows that combining doc2vec and SDAE-BoW models results in higher agreement scores, indicating model complementarity.the SDAE-BoCUI (the initial SDAE input is BoCUI), and the doc2vec models as input features for different tasks, as opposed to using the BoW and the BoCUI sparse features. We find that the agreement scores are not high, which may indicate that the models learn complimentary information. We then concatenate the two dense representations (model ensemble) to analyze model complementarity." } ], "images": [ "scigen/train/3187.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Classification results on different tasks using the BoW features, the SDAE representations computed from the BoW (SDAE-BoW), the doc2vec representations, the concatenated SDAE-BoW and doc2vec representations ([doc2vec, SDAE-BoW]) with Cohen’s κ score, the BoCUI features, and the SDAE vectors computed from the BoCUI (SDAE-BoCUI). AUC-ROC values are reported for the mortality tasks, and weighted F-score for the others." ], "reasoning": "The table shows that combining doc2vec and SDAE-BoW models results in higher agreement scores, indicating model complementarity.", "description": "the SDAE-BoCUI (the initial SDAE input is BoCUI), and the doc2vec models as input features for different tasks, as opposed to using the BoW and the BoCUI sparse features. We find that the agreement scores are not high, which may indicate that the models learn complimentary information. We then concatenate the two dense representations (model ensemble) to analyze model complementarity.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 1: Classification results on different tasks using the BoW features, the SDAE representations computed from the BoW (SDAE-BoW), the doc2vec representations, the concatenated SDAE-BoW and doc2vec representations ([doc2vec, SDAE-BoW]) with Cohen’s κ score, the BoCUI features, and the SDAE vectors computed from the BoCUI (SDAE-BoCUI). AUC-ROC values are reported for the mortality tasks, and weighted F-score for the others.', unfold the question presented below, detailing every step of your calculation. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich approach has the highest Mortality In_hosp score?\n" }, { "role": "assistant", "content": "To find the highest Mortality In_hosp score, compare the values in the Mortality In_hosp column. The highest value is 0.9457, which corresponds to the BoW approach. So, the answer is {\"answer\": \"BoW\"}" } ], "images": [ "scigen/train/3187.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Classification results on different tasks using the BoW features, the SDAE representations computed from the BoW (SDAE-BoW), the doc2vec representations, the concatenated SDAE-BoW and doc2vec representations ([doc2vec, SDAE-BoW]) with Cohen’s κ score, the BoCUI features, and the SDAE vectors computed from the BoCUI (SDAE-BoCUI). AUC-ROC values are reported for the mortality tasks, and weighted F-score for the others." ], "reasoning": "To find the highest Mortality In_hosp score, compare the values in the Mortality In_hosp column. The highest value is 0.9457, which corresponds to the BoW approach.", "question": "Which approach has the highest Mortality In_hosp score?", "answer": "So, the answer is {\"answer\": \"BoW\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The SDAE-BoW approach has a higher Mortality 30_days score than the doc2vec approach. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe SDAE-BoW approach has a higher Mortality 30_days score than the doc2vec approach.\n" }, { "role": "assistant", "content": "The table shows that the Mortality 30_days score for SDAE-BoW is 0.7965, while for doc2vec it is 0.7680. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3187.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Classification results on different tasks using the BoW features, the SDAE representations computed from the BoW (SDAE-BoW), the doc2vec representations, the concatenated SDAE-BoW and doc2vec representations ([doc2vec, SDAE-BoW]) with Cohen’s κ score, the BoCUI features, and the SDAE vectors computed from the BoCUI (SDAE-BoCUI). AUC-ROC values are reported for the mortality tasks, and weighted F-score for the others." ], "reasoning": "The table shows that the Mortality 30_days score for SDAE-BoW is 0.7965, while for doc2vec it is 0.7680. Therefore, the statement is supported.", "statement": "The SDAE-BoW approach has a higher Mortality 30_days score than the doc2vec approach.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: Hyperparameters for stacked denoising autoencoder to learn dense patient representations, obtained after a randomized search. The default learning rate of 0.001 is used.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "We use sigmoid activation for encoding and linear activation for decoding, and optimize classifiers with categorical cross-entropy error during finetuning, determining hyperparameters through randomized search.We use the sigmoid activation function for the encoding layers, and the linear activation function to decode real values. During the finetuning phase, we train the classifiers to minimize the categorical cross-entropy error using the same optimizer. We determine the number of layers, the dimensionality, and the dropout proportion also using a randomized hyperparameter search." } ], "images": [ "scigen/train/3188.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Hyperparameters for stacked denoising autoencoder to learn dense patient representations, obtained after a randomized search. The default learning rate of 0.001 is used." ], "reasoning": "We use sigmoid activation for encoding and linear activation for decoding, and optimize classifiers with categorical cross-entropy error during finetuning, determining hyperparameters through randomized search.", "description": "We use the sigmoid activation function for the encoding layers, and the linear activation function to decode real values. During the finetuning phase, we train the classifiers to minimize the categorical cross-entropy error using the same optimizer. We determine the number of layers, the dimensionality, and the dropout proportion also using a randomized hyperparameter search.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 1: Hyperparameters for stacked denoising autoencoder to learn dense patient representations, obtained after a randomized search. The default learning rate of 0.001 is used.', unfold the steps to solve the question given next. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nQuestion: What is the hidden dimension for the Bag-of-concepts feature set?\n" }, { "role": "assistant", "content": "The table lists the hidden dimensions for each feature set. For the Bag-of-concepts feature set, the hidden dimension is 300. So, the answer is {\"answer\": \"300\"}." } ], "images": [ "scigen/train/3188.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Hyperparameters for stacked denoising autoencoder to learn dense patient representations, obtained after a randomized search. The default learning rate of 0.001 is used." ], "reasoning": "The table lists the hidden dimensions for each feature set. For the Bag-of-concepts feature set, the hidden dimension is 300.", "question": "What is the hidden dimension for the Bag-of-concepts feature set?", "answer": "So, the answer is {\"answer\": \"300\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Bag-of-words feature set has a dropout proportion of 0.05.\n" }, { "role": "assistant", "content": "The table shows the dropout proportion for each feature set. The Bag-of-words feature set has a dropout proportion of 0.05. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3188.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Hyperparameters for stacked denoising autoencoder to learn dense patient representations, obtained after a randomized search. The default learning rate of 0.001 is used." ], "reasoning": "The table shows the dropout proportion for each feature set. The Bag-of-words feature set has a dropout proportion of 0.05.", "statement": "The Bag-of-words feature set has a dropout proportion of 0.05.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Hyperparameters for feedforward neural network classifiers for different tasks and feature sets, obtained after a randomized search. The default learning rate of 0.001 is used.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Dense patient representations are used as input features for training neural network classifiers on various tasks, and performance is evaluated to understand the task-independent nature of the representations.We use the dense patient representations as input features to train feedforward neural network classifiers on multiple independent tasks. We evaluate the performance on a range of tasks to gain insight into the task independent nature of the representations, and the information encoded within the vectors. We disregard the instances that do not have a task label." } ], "images": [ "scigen/train/3189.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Hyperparameters for feedforward neural network classifiers for different tasks and feature sets, obtained after a randomized search. The default learning rate of 0.001 is used." ], "reasoning": "Dense patient representations are used as input features for training neural network classifiers on various tasks, and performance is evaluated to understand the task-independent nature of the representations.", "description": "We use the dense patient representations as input features to train feedforward neural network classifiers on multiple independent tasks. We evaluate the performance on a range of tasks to gain insight into the task independent nature of the representations, and the information encoded within the vectors. We disregard the instances that do not have a task label.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: Hyperparameters for feedforward neural network classifiers for different tasks and feature sets, obtained after a randomized search. The default learning rate of 0.001 is used.' and methodically tackle the question that follows. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich task uses the highest number of layers in its neural network classifier?\n" }, { "role": "assistant", "content": "By examining the 'Number of layers' column, the task 'In_hosp' with the 'doc2vec' feature set uses 10 layers, which is the highest number of layers. So, the answer is {\"answer\": \"In_hosp\"}." } ], "images": [ "scigen/train/3189.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Hyperparameters for feedforward neural network classifiers for different tasks and feature sets, obtained after a randomized search. The default learning rate of 0.001 is used." ], "reasoning": "By examining the 'Number of layers' column, the task 'In_hosp' with the 'doc2vec' feature set uses 10 layers, which is the highest number of layers.", "question": "Which task uses the highest number of layers in its neural network classifier?", "answer": "So, the answer is {\"answer\": \"In_hosp\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe 'Gender' task uses the 'BoW' feature set with 1 layer and a sigmoid activation function.\n" }, { "role": "assistant", "content": "By checking the 'Gender' task row, it shows that the 'BoW' feature set indeed uses 1 layer and a sigmoid activation function. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3189.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Hyperparameters for feedforward neural network classifiers for different tasks and feature sets, obtained after a randomized search. The default learning rate of 0.001 is used." ], "reasoning": "By checking the 'Gender' task row, it shows that the 'BoW' feature set indeed uses 1 layer and a sigmoid activation function.", "statement": "The 'Gender' task uses the 'BoW' feature set with 1 layer and a sigmoid activation function.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Reuters-21578 Subset (3,003 instances, 93 Classes): Google News Word Vector.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The SVM classifier has the highest recall at the baseline level, and using a Word Vector model improves recall for both MNB and SVM classifiers with statistical significance.These results show that at the baseline level the SVM classifier produces the highest recall. The MNB results are the lowest across all four classification models, this is likely a result of the skew in the data having a large influence on the prior probability component of the MNB model and therefore it is more likely to make classifications into the largest classes. These results show that using a Word Vector model to enrich the bag-of-words representation improves both micro and macro recall results of the MNB and SVM classifiers. For both of these linear classifiers the Wilcoxon Signed-Ranked test shows statistical significance of p<0.000005 in improving recall. In the above evaluation we trained a domain specific Word Vector model using the full Reuters-21578 dataset. The benefit of the domain specific Word Vector model is that it should be expected that words which have close domain specific similarities are modelled better than a general model trained over a much larger corpus of text. To evaluate this hypothesis we performed a second evaluation of the Reuters dataset which used Mikolov’s Google News pre-trained Word Vector model for enrichment. This pre-trained model was trained over 100 billion English words containing three million unique words and phrases. These results show that the large general Google News model can also be used with our algorithm to enrich Bag-of-words vectors and improve the recall of these classifiers. Although these results are statistically significant (p<0.05), the error reduction is much smaller than the previous results, which is likely a result of the additional words having less relevance to the financial news domain." } ], "images": [ "scigen/train/3190.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Reuters-21578 Subset (3,003 instances, 93 Classes): Google News Word Vector." ], "reasoning": "The SVM classifier has the highest recall at the baseline level, and using a Word Vector model improves recall for both MNB and SVM classifiers with statistical significance.", "description": "These results show that at the baseline level the SVM classifier produces the highest recall. The MNB results are the lowest across all four classification models, this is likely a result of the skew in the data having a large influence on the prior probability component of the MNB model and therefore it is more likely to make classifications into the largest classes. These results show that using a Word Vector model to enrich the bag-of-words representation improves both micro and macro recall results of the MNB and SVM classifiers. For both of these linear classifiers the Wilcoxon Signed-Ranked test shows statistical significance of p<0.000005 in improving recall. In the above evaluation we trained a domain specific Word Vector model using the full Reuters-21578 dataset. The benefit of the domain specific Word Vector model is that it should be expected that words which have close domain specific similarities are modelled better than a general model trained over a much larger corpus of text. To evaluate this hypothesis we performed a second evaluation of the Reuters dataset which used Mikolov’s Google News pre-trained Word Vector model for enrichment. This pre-trained model was trained over 100 billion English words containing three million unique words and phrases. These results show that the large general Google News model can also be used with our algorithm to enrich Bag-of-words vectors and improve the recall of these classifiers. Although these results are statistically significant (p<0.05), the error reduction is much smaller than the previous results, which is likely a result of the additional words having less relevance to the financial news domain.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: Reuters-21578 Subset (3,003 instances, 93 Classes): Google News Word Vector.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich classifier showed the highest improvement in macro recall when using Word Vector enrichment?\n" }, { "role": "assistant", "content": "To find the classifier with the highest improvement in macro recall, compare the 'Macro Recall WV Enrichment' values for each classifier. The SVM classifier has the highest value of 0.475. So, the answer is {\"answer\": \"SVM\"}" } ], "images": [ "scigen/train/3190.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Reuters-21578 Subset (3,003 instances, 93 Classes): Google News Word Vector." ], "reasoning": "To find the classifier with the highest improvement in macro recall, compare the 'Macro Recall WV Enrichment' values for each classifier. The SVM classifier has the highest value of 0.475.", "question": "Which classifier showed the highest improvement in macro recall when using Word Vector enrichment?", "answer": "So, the answer is {\"answer\": \"SVM\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The CNN classifier showed an improvement in micro recall with Word Vector enrichment. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe CNN classifier showed an improvement in micro recall with Word Vector enrichment.\n" }, { "role": "assistant", "content": "The table shows that the 'Micro Recall WV Enrichment' value for CNN is 0.759, which is lower than the baseline value of 0.775, indicating a decrease rather than an improvement. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3190.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Reuters-21578 Subset (3,003 instances, 93 Classes): Google News Word Vector." ], "reasoning": "The table shows that the 'Micro Recall WV Enrichment' value for CNN is 0.759, which is lower than the baseline value of 0.775, indicating a decrease rather than an improvement.", "statement": "The CNN classifier showed an improvement in micro recall with Word Vector enrichment.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Reuters-21578 Subset (3,003 instances, 93 Classes): Google News Word Vector.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The SVM classifier has the highest recall at the baseline level, and using a Word Vector model improves recall for both MNB and SVM classifiers, with statistical significance.These results show that at the baseline level the SVM classifier produces the highest recall. The MNB results are the lowest across all four classification models, this is likely a result of the skew in the data having a large influence on the prior probability component of the MNB model and therefore it is more likely to make classifications into the largest classes. These results show that using a Word Vector model to enrich the bag-of-words representation improves both micro and macro recall results of the MNB and SVM classifiers. For both of these linear classifiers the Wilcoxon Signed-Ranked test shows statistical significance of p<0.000005 in improving recall. In the above evaluation we trained a domain specific Word Vector model using the full Reuters-21578 dataset. The benefit of the domain specific Word Vector model is that it should be expected that words which have close domain specific similarities are modelled better than a general model trained over a much larger corpus of text. To evaluate this hypothesis we performed a second evaluation of the Reuters dataset which used Mikolov’s Google News pre-trained Word Vector model for enrichment. This pre-trained model was trained over 100 billion English words containing three million unique words and phrases. These results show that the large general Google News model can also be used with our algorithm to enrich Bag-of-words vectors and improve the recall of these classifiers. Although these results are statistically significant (p<0.05), the error reduction is much smaller than the previous results, which is likely a result of the additional words having less relevance to the financial news domain." } ], "images": [ "scigen/train/3191.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Reuters-21578 Subset (3,003 instances, 93 Classes): Google News Word Vector." ], "reasoning": "The SVM classifier has the highest recall at the baseline level, and using a Word Vector model improves recall for both MNB and SVM classifiers, with statistical significance.", "description": "These results show that at the baseline level the SVM classifier produces the highest recall. The MNB results are the lowest across all four classification models, this is likely a result of the skew in the data having a large influence on the prior probability component of the MNB model and therefore it is more likely to make classifications into the largest classes. These results show that using a Word Vector model to enrich the bag-of-words representation improves both micro and macro recall results of the MNB and SVM classifiers. For both of these linear classifiers the Wilcoxon Signed-Ranked test shows statistical significance of p<0.000005 in improving recall. In the above evaluation we trained a domain specific Word Vector model using the full Reuters-21578 dataset. The benefit of the domain specific Word Vector model is that it should be expected that words which have close domain specific similarities are modelled better than a general model trained over a much larger corpus of text. To evaluate this hypothesis we performed a second evaluation of the Reuters dataset which used Mikolov’s Google News pre-trained Word Vector model for enrichment. This pre-trained model was trained over 100 billion English words containing three million unique words and phrases. These results show that the large general Google News model can also be used with our algorithm to enrich Bag-of-words vectors and improve the recall of these classifiers. Although these results are statistically significant (p<0.05), the error reduction is much smaller than the previous results, which is likely a result of the additional words having less relevance to the financial news domain.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Reuters-21578 Subset (3,003 instances, 93 Classes): Google News Word Vector.', answer the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the macro recall error reduction for the SVM classifier when using Word Vector enrichment?\n" }, { "role": "assistant", "content": "The table shows the macro recall error reduction for the SVM classifier as 1.08% when using Word Vector enrichment. So, the answer is {\"answer\": \"1.08%\"}" } ], "images": [ "scigen/train/3191.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Reuters-21578 Subset (3,003 instances, 93 Classes): Google News Word Vector." ], "reasoning": "The table shows the macro recall error reduction for the SVM classifier as 1.08% when using Word Vector enrichment.", "question": "What is the macro recall error reduction for the SVM classifier when using Word Vector enrichment?", "answer": "So, the answer is {\"answer\": \"1.08%\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe MNB classifier has a higher micro recall baseline than the SVM classifier.\n" }, { "role": "assistant", "content": "The table shows that the micro recall baseline for MNB is 0.765, which is lower than the SVM's micro recall baseline of 0.842. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3191.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Reuters-21578 Subset (3,003 instances, 93 Classes): Google News Word Vector." ], "reasoning": "The table shows that the micro recall baseline for MNB is 0.765, which is lower than the SVM's micro recall baseline of 0.842.", "statement": "The MNB classifier has a higher micro recall baseline than the SVM classifier.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: DRC Dataset (2,159 instances, 64 Classes): Domain Specific Word Vector.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The SVM classifier shows higher baseline performance compared to the MNB classifier. Enriching the bag-of-words model improves recall for both classifiers, with statistically significant results.The baseline results for the SVM classifier again shows much higher performance than the baseline MNB result. When we enrich the bag-of-words model, we get a gain in both micro and macro recall for the MNB classifier. For the SVM classifier we get a much smaller gain in micro recall, however, the results are statistically significant (p<0.0005). All of these results are statistically significant (p<0.00005). However, despite the improvement with the enrichment process, the linear classifiers still perform very poorly on this dataset. An alternative treatment of this dataset is to configure the classification algorithms to produce a ranked top-3 predictions of the class label which a human “coder” can then select from (cf." } ], "images": [ "scigen/train/3192.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: DRC Dataset (2,159 instances, 64 Classes): Domain Specific Word Vector." ], "reasoning": "The SVM classifier shows higher baseline performance compared to the MNB classifier. Enriching the bag-of-words model improves recall for both classifiers, with statistically significant results.", "description": "The baseline results for the SVM classifier again shows much higher performance than the baseline MNB result. When we enrich the bag-of-words model, we get a gain in both micro and macro recall for the MNB classifier. For the SVM classifier we get a much smaller gain in micro recall, however, the results are statistically significant (p<0.0005). All of these results are statistically significant (p<0.00005). However, despite the improvement with the enrichment process, the linear classifiers still perform very poorly on this dataset. An alternative treatment of this dataset is to configure the classification algorithms to produce a ranked top-3 predictions of the class label which a human “coder” can then select from (cf.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: DRC Dataset (2,159 instances, 64 Classes): Domain Specific Word Vector.\nQuestion: What is the percentage gain in Macro Recall Error Reduction for the MNB classifier after WV Enrichment?\nSolve the above question based on the table information. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the Macro Recall Error Reduction for the MNB classifier after WV Enrichment is 4.23%. So, the answer is {\"answer\": \"4.23%\"}" } ], "images": [ "scigen/train/3192.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: DRC Dataset (2,159 instances, 64 Classes): Domain Specific Word Vector." ], "reasoning": "The table shows that the Macro Recall Error Reduction for the MNB classifier after WV Enrichment is 4.23%.", "question": "What is the percentage gain in Macro Recall Error Reduction for the MNB classifier after WV Enrichment?", "answer": "So, the answer is {\"answer\": \"4.23%\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe SVM classifier has a higher Micro Recall Baseline than the MNB classifier.\n" }, { "role": "assistant", "content": "The table shows that the Micro Recall Baseline for the SVM classifier is 0.678, which is higher than the MNB classifier's Micro Recall Baseline of 0.625. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3192.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: DRC Dataset (2,159 instances, 64 Classes): Domain Specific Word Vector." ], "reasoning": "The table shows that the Micro Recall Baseline for the SVM classifier is 0.678, which is higher than the MNB classifier's Micro Recall Baseline of 0.625.", "statement": "The SVM classifier has a higher Micro Recall Baseline than the MNB classifier.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: DRC Dataset (2,159 instances, 64 Classes): Domain Specific Word Vector.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The SVM classifier shows higher baseline performance than the MNB classifier, but both classifiers show improvement in recall with enrichment, with statistically significant results.The baseline results for the SVM classifier again shows much higher performance than the baseline MNB result. When we enrich the bag-of-words model, we get a gain in both micro and macro recall for the MNB classifier. For the SVM classifier we get a much smaller gain in micro recall, however, the results are statistically significant (p<0.0005). All of these results are statistically significant (p<0.00005). However, despite the improvement with the enrichment process, the linear classifiers still perform very poorly on this dataset. An alternative treatment of this dataset is to configure the classification algorithms to produce a ranked top-3 predictions of the class label which a human “coder” can then select from (cf." } ], "images": [ "scigen/train/3193.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: DRC Dataset (2,159 instances, 64 Classes): Domain Specific Word Vector." ], "reasoning": "The SVM classifier shows higher baseline performance than the MNB classifier, but both classifiers show improvement in recall with enrichment, with statistically significant results.", "description": "The baseline results for the SVM classifier again shows much higher performance than the baseline MNB result. When we enrich the bag-of-words model, we get a gain in both micro and macro recall for the MNB classifier. For the SVM classifier we get a much smaller gain in micro recall, however, the results are statistically significant (p<0.0005). All of these results are statistically significant (p<0.00005). However, despite the improvement with the enrichment process, the linear classifiers still perform very poorly on this dataset. An alternative treatment of this dataset is to configure the classification algorithms to produce a ranked top-3 predictions of the class label which a human “coder” can then select from (cf.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: DRC Dataset (2,159 instances, 64 Classes): Domain Specific Word Vector.\nQuestion: What is the percentage gain in Micro Recall for the MNB classifier after enrichment?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The Micro Recall for the MNB classifier increases from 0.300 to 0.337 after enrichment, resulting in a gain of 5.36%. So, the answer is {\"answer\": \"5.36%\"}" } ], "images": [ "scigen/train/3193.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: DRC Dataset (2,159 instances, 64 Classes): Domain Specific Word Vector." ], "reasoning": "The Micro Recall for the MNB classifier increases from 0.300 to 0.337 after enrichment, resulting in a gain of 5.36%.", "question": "What is the percentage gain in Micro Recall for the MNB classifier after enrichment?", "answer": "So, the answer is {\"answer\": \"5.36%\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe SVM classifier has a higher baseline Macro Recall than the MNB classifier.\n" }, { "role": "assistant", "content": "The table shows that the baseline Macro Recall for the SVM classifier is 0.215, while for the MNB classifier it is 0.115, indicating that the SVM classifier has a higher baseline Macro Recall. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3193.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: DRC Dataset (2,159 instances, 64 Classes): Domain Specific Word Vector." ], "reasoning": "The table shows that the baseline Macro Recall for the SVM classifier is 0.215, while for the MNB classifier it is 0.115, indicating that the SVM classifier has a higher baseline Macro Recall.", "statement": "The SVM classifier has a higher baseline Macro Recall than the MNB classifier.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: DRC Dataset (2,159 instances, 64 Classes): Domain Specific Word Vector.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The SVM classifier shows higher baseline performance than the MNB classifier. Enriching the bag-of-words model improves both micro and macro recall for MNB, while SVM shows a smaller gain in micro recall.The baseline results for the SVM classifier again shows much higher performance than the baseline MNB result. When we enrich the bag-of-words model, we get a gain in both micro and macro recall for the MNB classifier. For the SVM classifier we get a much smaller gain in micro recall, however, the results are statistically significant (p<0.0005). All of these results are statistically significant (p<0.00005). However, despite the improvement with the enrichment process, the linear classifiers still perform very poorly on this dataset. An alternative treatment of this dataset is to configure the classification algorithms to produce a ranked top-3 predictions of the class label which a human “coder” can then select from (cf." } ], "images": [ "scigen/train/3194.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: DRC Dataset (2,159 instances, 64 Classes): Domain Specific Word Vector." ], "reasoning": "The SVM classifier shows higher baseline performance than the MNB classifier. Enriching the bag-of-words model improves both micro and macro recall for MNB, while SVM shows a smaller gain in micro recall.", "description": "The baseline results for the SVM classifier again shows much higher performance than the baseline MNB result. When we enrich the bag-of-words model, we get a gain in both micro and macro recall for the MNB classifier. For the SVM classifier we get a much smaller gain in micro recall, however, the results are statistically significant (p<0.0005). All of these results are statistically significant (p<0.00005). However, despite the improvement with the enrichment process, the linear classifiers still perform very poorly on this dataset. An alternative treatment of this dataset is to configure the classification algorithms to produce a ranked top-3 predictions of the class label which a human “coder” can then select from (cf.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 4: DRC Dataset (2,159 instances, 64 Classes): Domain Specific Word Vector.' and methodically tackle the question that follows. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the percentage error reduction in macro recall for the MNB classifier after enrichment?\n" }, { "role": "assistant", "content": "The table shows the macro recall error reduction for the MNB classifier as 5.59%. So, the answer is {\"answer\": \"5.59%\"}" } ], "images": [ "scigen/train/3194.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: DRC Dataset (2,159 instances, 64 Classes): Domain Specific Word Vector." ], "reasoning": "The table shows the macro recall error reduction for the MNB classifier as 5.59%.", "question": "What is the percentage error reduction in macro recall for the MNB classifier after enrichment?", "answer": "So, the answer is {\"answer\": \"5.59%\"}" }, { "messages": [ { "role": "user", "content": "The SVM classifier has a higher baseline micro recall than the MNB classifier. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe SVM classifier has a higher baseline micro recall than the MNB classifier.\n" }, { "role": "assistant", "content": "The table shows that the baseline micro recall for the SVM classifier is 0.590, which is higher than the MNB classifier's baseline micro recall of 0.494. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3194.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: DRC Dataset (2,159 instances, 64 Classes): Domain Specific Word Vector." ], "reasoning": "The table shows that the baseline micro recall for the SVM classifier is 0.590, which is higher than the MNB classifier's baseline micro recall of 0.494.", "statement": "The SVM classifier has a higher baseline micro recall than the MNB classifier.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Performance comparison with other studies on the CHiME2 test set.'.\n" }, { "role": "assistant", "content": "The proposed approach has a higher WER compared to Wang et al. and Weninger et al., but performs better than Narayanan-Wang and Chen et al.the performance of our system relative to other published results in the field. (e.g., PNCC, MRCG). We use an off-the-shelf Kaldi recipe using DNN-HMM to do speech recognition, as well as a simple 2 layer feed-forward network to do spectral mapping. Again, mimic loss can theoretically be used to improve the results of any front-end system." } ], "images": [ "scigen/train/3195.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance comparison with other studies on the CHiME2 test set." ], "reasoning": "The proposed approach has a higher WER compared to Wang et al. and Weninger et al., but performs better than Narayanan-Wang and Chen et al.", "description": "the performance of our system relative to other published results in the field. (e.g., PNCC, MRCG). We use an off-the-shelf Kaldi recipe using DNN-HMM to do speech recognition, as well as a simple 2 layer feed-forward network to do spectral mapping. Again, mimic loss can theoretically be used to improve the results of any front-end system.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Performance comparison with other studies on the CHiME2 test set.', solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich study has the lowest WER and how does it compare to the proposed approach?\n" }, { "role": "assistant", "content": "The table shows that Wang et al. has the lowest WER at 10.6, which is lower than the proposed approach's WER of 14.7. So, the answer is {\"answer\": \"Wang et al.\"}." } ], "images": [ "scigen/train/3195.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance comparison with other studies on the CHiME2 test set." ], "reasoning": "The table shows that Wang et al. has the lowest WER at 10.6, which is lower than the proposed approach's WER of 14.7.", "question": "Which study has the lowest WER and how does it compare to the proposed approach?", "answer": "So, the answer is {\"answer\": \"Wang et al.\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe proposed approach has the highest WER among the listed studies.\n" }, { "role": "assistant", "content": "The table shows that the proposed approach has a WER of 14.7, which is not the highest. Chen et al. has the highest WER at 16.0. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3195.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance comparison with other studies on the CHiME2 test set." ], "reasoning": "The table shows that the proposed approach has a WER of 14.7, which is not the highest. Chen et al. has the highest WER at 16.0.", "statement": "The proposed approach has the highest WER among the listed studies.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Experimental results on the CHiME2 test set; CE WER is the word error rate of a DNN-HMM hybrid system trained using a cross-entropy criterion. sMBR WER is the error rate after sequential minimum Bayes risk training.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The difference in performance between pre- and post-softmax targets may be due to a mismatch between target domain and loss criterion, with pre-softmax targets performing better.The difference in performance between pre- and post-softmax targets may be due to a mismatch between target domain and loss criterion; ongoing work suggests that a cross-entropy mimic loss on post-softmax targets performs similarly to MSE on pre-softmax. Furthermore, the information loss in the softmax normalization may “broaden” the allowable spectral mappings, harming generalization. This suggests that it is helpful for the noisy recognizer not only to have the same targets as the clean recognizer, but also to learn to behave in the same way as the clean recognizer." } ], "images": [ "scigen/train/3196.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Experimental results on the CHiME2 test set; CE WER is the word error rate of a DNN-HMM hybrid system trained using a cross-entropy criterion. sMBR WER is the error rate after sequential minimum Bayes risk training." ], "reasoning": "The difference in performance between pre- and post-softmax targets may be due to a mismatch between target domain and loss criterion, with pre-softmax targets performing better.", "description": "The difference in performance between pre- and post-softmax targets may be due to a mismatch between target domain and loss criterion; ongoing work suggests that a cross-entropy mimic loss on post-softmax targets performs similarly to MSE on pre-softmax. Furthermore, the information loss in the softmax normalization may “broaden” the allowable spectral mappings, harming generalization. This suggests that it is helpful for the noisy recognizer not only to have the same targets as the clean recognizer, but also to learn to behave in the same way as the clean recognizer.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 1: Experimental results on the CHiME2 test set; CE WER is the word error rate of a DNN-HMM hybrid system trained using a cross-entropy criterion. sMBR WER is the error rate after sequential minimum Bayes risk training.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. \n\nQuestion: What is the CE WER for the spectral input to Kaldi with pre-softmax mimic loss?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows the CE WER for different spectral inputs to Kaldi. The row for 'w/ pre-softmax mimic loss' shows a CE WER of 15.7. So, the answer is {\"answer\": \"15.7\"}." } ], "images": [ "scigen/train/3196.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Experimental results on the CHiME2 test set; CE WER is the word error rate of a DNN-HMM hybrid system trained using a cross-entropy criterion. sMBR WER is the error rate after sequential minimum Bayes risk training." ], "reasoning": "The table shows the CE WER for different spectral inputs to Kaldi. The row for 'w/ pre-softmax mimic loss' shows a CE WER of 15.7.", "question": "What is the CE WER for the spectral input to Kaldi with pre-softmax mimic loss?", "answer": "So, the answer is {\"answer\": \"15.7\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The spectral input to Kaldi with pre-softmax mimic loss has the lowest sMBR WER., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe spectral input to Kaldi with pre-softmax mimic loss has the lowest sMBR WER.\n" }, { "role": "assistant", "content": "The table shows the sMBR WER for different spectral inputs to Kaldi. The row for 'w/ pre-softmax mimic loss' shows the lowest sMBR WER of 14.7. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3196.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Experimental results on the CHiME2 test set; CE WER is the word error rate of a DNN-HMM hybrid system trained using a cross-entropy criterion. sMBR WER is the error rate after sequential minimum Bayes risk training." ], "reasoning": "The table shows the sMBR WER for different spectral inputs to Kaldi. The row for 'w/ pre-softmax mimic loss' shows the lowest sMBR WER of 14.7.", "statement": "The spectral input to Kaldi with pre-softmax mimic loss has the lowest sMBR WER.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Results for our new evaluation set and our algorithm on the evaluation data provided by Christopher Potts (p.c.). The columns correspond to the level of agreement between participants. I.e. how many participants (out of 10) agreed on the first adjective being either stronger or weaker than the second. Making this requirement more strict reduces the amount of test items that we could use, but increases the precision of our evaluation set and our algorithm.'.\n" }, { "role": "assistant", "content": "The combination of the MPQA and WKB data provides a reasonable estimate of the correct ordering of adjectival scales, but the algorithm performs better on the elicited data than on the proposed evaluation set.After we finished our data-analysis, Christopher Potts (p.c.) shared the results of an online experiment carried out on Amazon’s Mechanical Turk. Participants were shown a set of adjective pairs, and asked for each pair to judge whether the first adjective is stronger, weaker or as strong as the second adjective. This is exactly the kind of data we need to evaluate the order of automatically identified scales. We observe that the combination of the MPQA and WKB data provides a reasonable estimate of the correct ordering of adjectival scales, but our algorithm does much better on the elicited data than on our proposed evaluation set." } ], "images": [ "scigen/train/3197.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results for our new evaluation set and our algorithm on the evaluation data provided by Christopher Potts (p.c.). The columns correspond to the level of agreement between participants. I.e. how many participants (out of 10) agreed on the first adjective being either stronger or weaker than the second. Making this requirement more strict reduces the amount of test items that we could use, but increases the precision of our evaluation set and our algorithm." ], "reasoning": "The combination of the MPQA and WKB data provides a reasonable estimate of the correct ordering of adjectival scales, but the algorithm performs better on the elicited data than on the proposed evaluation set.", "description": "After we finished our data-analysis, Christopher Potts (p.c.) shared the results of an online experiment carried out on Amazon’s Mechanical Turk. Participants were shown a set of adjective pairs, and asked for each pair to judge whether the first adjective is stronger, weaker or as strong as the second adjective. This is exactly the kind of data we need to evaluate the order of automatically identified scales. We observe that the combination of the MPQA and WKB data provides a reasonable estimate of the correct ordering of adjectival scales, but our algorithm does much better on the elicited data than on our proposed evaluation set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 4: Results for our new evaluation set and our algorithm on the evaluation data provided by Christopher Potts (p.c.). The columns correspond to the level of agreement between participants. I.e. how many participants (out of 10) agreed on the first adjective being either stronger or weaker than the second. Making this requirement more strict reduces the amount of test items that we could use, but increases the precision of our evaluation set and our algorithm.\nQuestion: What is the accuracy of the proposed data set 9?\n" }, { "role": "assistant", "content": "To find the accuracy of the proposed data set 9, look at the column labeled 'Our proposed data set 9' and find the value in the 'Accuracy' row. So, the answer is {\"answer\": \"93\"}." } ], "images": [ "scigen/train/3197.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results for our new evaluation set and our algorithm on the evaluation data provided by Christopher Potts (p.c.). The columns correspond to the level of agreement between participants. I.e. how many participants (out of 10) agreed on the first adjective being either stronger or weaker than the second. Making this requirement more strict reduces the amount of test items that we could use, but increases the precision of our evaluation set and our algorithm." ], "reasoning": "To find the accuracy of the proposed data set 9, look at the column labeled 'Our proposed data set 9' and find the value in the 'Accuracy' row.", "question": "What is the accuracy of the proposed data set 9?", "answer": "So, the answer is {\"answer\": \"93\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe proposed data set 10 has the highest accuracy.\n" }, { "role": "assistant", "content": "To verify this statement, compare the accuracy values of all proposed data sets. The highest accuracy value is 100, which belongs to the proposed data set 10. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3197.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results for our new evaluation set and our algorithm on the evaluation data provided by Christopher Potts (p.c.). The columns correspond to the level of agreement between participants. I.e. how many participants (out of 10) agreed on the first adjective being either stronger or weaker than the second. Making this requirement more strict reduces the amount of test items that we could use, but increases the precision of our evaluation set and our algorithm." ], "reasoning": "To verify this statement, compare the accuracy values of all proposed data sets. The highest accuracy value is 100, which belongs to the proposed data set 10.", "statement": "The proposed data set 10 has the highest accuracy.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Pair counts for each similarity measure, along with MPQA evaluation scores (percentage correct) for each similarity measure–filtering method combination.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table shows significant differences in the number of results between various similarity measures, with the pattern-based method having the highest recall and the LSA-based method producing nearly ten times as many pairs as Roget’s thesaurus.The table shows big differences in the amount of results between the different similarity measures. Whereas we get 1533 results using Roget’s thesaurus, our LSA-based method produces nearly ten times as many pairs of scalemates. The pattern-based method turns out to have a very high recall, with 32,470 different pairs of adjectives. Out of all these pairs, 2,611 scalemates have contrasting subjectivity measures in the MPQA database. 1,590 (60.9%) of these pairs are correctly predicted to be in ⟨weak, strong⟩ order. A two-tailed Fisher’s exact test reveals that the difference between our results and Potts’ \\shortcitepotts2011developing data is not statistically significant (p=0.1547)." } ], "images": [ "scigen/train/3198.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Pair counts for each similarity measure, along with MPQA evaluation scores (percentage correct) for each similarity measure–filtering method combination." ], "reasoning": "The table shows significant differences in the number of results between various similarity measures, with the pattern-based method having the highest recall and the LSA-based method producing nearly ten times as many pairs as Roget’s thesaurus.", "description": "The table shows big differences in the amount of results between the different similarity measures. Whereas we get 1533 results using Roget’s thesaurus, our LSA-based method produces nearly ten times as many pairs of scalemates. The pattern-based method turns out to have a very high recall, with 32,470 different pairs of adjectives. Out of all these pairs, 2,611 scalemates have contrasting subjectivity measures in the MPQA database. 1,590 (60.9%) of these pairs are correctly predicted to be in ⟨weak, strong⟩ order. A two-tailed Fisher’s exact test reveals that the difference between our results and Potts’ \\shortcitepotts2011developing data is not statistically significant (p=0.1547).", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Pair counts for each similarity measure, along with MPQA evaluation scores (percentage correct) for each similarity measure–filtering method combination.', answer the question below by showing each progressive step toward the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method has the highest score for the 'None' filter?\n" }, { "role": "assistant", "content": "To find the method with the highest score for the 'None' filter, look at the 'Score' column for rows where the 'Filter' is 'None'. The highest score in this category is 70.92 for WordNet. So, the answer is {\"answer\": \"WordNet\"}." } ], "images": [ "scigen/train/3198.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Pair counts for each similarity measure, along with MPQA evaluation scores (percentage correct) for each similarity measure–filtering method combination." ], "reasoning": "To find the method with the highest score for the 'None' filter, look at the 'Score' column for rows where the 'Filter' is 'None'. The highest score in this category is 70.92 for WordNet.", "question": "Which method has the highest score for the 'None' filter?", "answer": "So, the answer is {\"answer\": \"WordNet\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The LSA-based method produces nearly ten times as many pairs as Roget’s thesaurus. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe LSA-based method produces nearly ten times as many pairs as Roget’s thesaurus.\n" }, { "role": "assistant", "content": "The table shows that the LSA-based method produces 15,233 pairs, while Roget’s thesaurus produces 1,533 pairs. 15,233 is nearly ten times 1,533. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3198.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Pair counts for each similarity measure, along with MPQA evaluation scores (percentage correct) for each similarity measure–filtering method combination." ], "reasoning": "The table shows that the LSA-based method produces 15,233 pairs, while Roget’s thesaurus produces 1,533 pairs. 15,233 is nearly ten times 1,533.", "statement": "The LSA-based method produces nearly ten times as many pairs as Roget’s thesaurus.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Results for the evaluation using only pairs for which the MPQA and WKB data agreed on the ordering. Scores are given for the unfiltered data. Filtering generally had a negative effect on the score of about one percent.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The WKB evaluation scores are slightly lower than those from the MPQA dataset, and they agree on only 62% of the orderings, raising concerns about their reliability as individual metrics.The WKB evaluation yields slightly lower scores than those obtained with the MPQA dataset (56–60%). But how reliable are those scores? To find out, we took the raw scalemates and compared the orderings predicted by the MPQA and WKB datasets. It turns out that they agree on only 62% of the orderings. This is a surprisingly low number, which casts doubt on the value of these data sets as an individual evaluation metric for adjectival scales. We made the evaluation more robust by combining the two evaluation sets, using only those pairs for which both sets agree on the order." } ], "images": [ "scigen/train/3199.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results for the evaluation using only pairs for which the MPQA and WKB data agreed on the ordering. Scores are given for the unfiltered data. Filtering generally had a negative effect on the score of about one percent." ], "reasoning": "The WKB evaluation scores are slightly lower than those from the MPQA dataset, and they agree on only 62% of the orderings, raising concerns about their reliability as individual metrics.", "description": "The WKB evaluation yields slightly lower scores than those obtained with the MPQA dataset (56–60%). But how reliable are those scores? To find out, we took the raw scalemates and compared the orderings predicted by the MPQA and WKB datasets. It turns out that they agree on only 62% of the orderings. This is a surprisingly low number, which casts doubt on the value of these data sets as an individual evaluation metric for adjectival scales. We made the evaluation more robust by combining the two evaluation sets, using only those pairs for which both sets agree on the order.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 3: Results for the evaluation using only pairs for which the MPQA and WKB data agreed on the ordering. Scores are given for the unfiltered data. Filtering generally had a negative effect on the score of about one percent.', and tackle the question below in a sequential manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich method has the highest score and how many items does it have?\n" }, { "role": "assistant", "content": "To find the method with the highest score, we look at the 'Score' column and identify the highest value, which is 77.27 for the WordNet method. Then, we check the '# Items' column for WordNet, which is 66. So, the answer is {\"answer\": \"WordNet with 66 items\"}." } ], "images": [ "scigen/train/3199.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results for the evaluation using only pairs for which the MPQA and WKB data agreed on the ordering. Scores are given for the unfiltered data. Filtering generally had a negative effect on the score of about one percent." ], "reasoning": "To find the method with the highest score, we look at the 'Score' column and identify the highest value, which is 77.27 for the WordNet method. Then, we check the '# Items' column for WordNet, which is 66.", "question": "Which method has the highest score and how many items does it have?", "answer": "So, the answer is {\"answer\": \"WordNet with 66 items\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe method with the highest score has fewer than 100 items.\n" }, { "role": "assistant", "content": "The table shows that the method with the highest score is WordNet with a score of 77.27. The '# Items' column for WordNet shows 66 items, which is fewer than 100. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3199.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results for the evaluation using only pairs for which the MPQA and WKB data agreed on the ordering. Scores are given for the unfiltered data. Filtering generally had a negative effect on the score of about one percent." ], "reasoning": "The table shows that the method with the highest score is WordNet with a score of 77.27. The '# Items' column for WordNet shows 66 items, which is fewer than 100.", "statement": "The method with the highest score has fewer than 100 items.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Performances on answer coverage, where “Anscov” denotes the metric of answer coverage. “λ” is the weight of the answer-aware loss in Equation 16.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our model with answer-aware loss improves answer coverage without degrading BLEU4 scores, indicating better question generation.It can be seen that our model, incorporating answer-aware loss, has a significant improvement on answer coverage while there is no performance degradation on BLEU4 compared with λ=0, which indicates that answer-aware loss contributes to generating better questions. Especially, the generated questions are more precise because they refer to more definitive answers with high Anscov." } ], "images": [ "scigen/train/3200.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performances on answer coverage, where “Anscov” denotes the metric of answer coverage. “λ” is the weight of the answer-aware loss in Equation 16." ], "reasoning": "Our model with answer-aware loss improves answer coverage without degrading BLEU4 scores, indicating better question generation.", "description": "It can be seen that our model, incorporating answer-aware loss, has a significant improvement on answer coverage while there is no performance degradation on BLEU4 compared with λ=0, which indicates that answer-aware loss contributes to generating better questions. Especially, the generated questions are more precise because they refer to more definitive answers with high Anscov.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Performances on answer coverage, where “Anscov” denotes the metric of answer coverage. “λ” is the weight of the answer-aware loss in Equation 16.', unfold the question presented below, detailing every step of your calculation. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model configuration achieves the highest Anscov score?\n" }, { "role": "assistant", "content": "By examining the Anscov column, the highest score is 65.50, achieved by 'Our Modelans_loss' with λ=0.5. So, the answer is {\"answer\": \"Our Modelans_loss with λ=0.5\"}" } ], "images": [ "scigen/train/3200.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performances on answer coverage, where “Anscov” denotes the metric of answer coverage. “λ” is the weight of the answer-aware loss in Equation 16." ], "reasoning": "By examining the Anscov column, the highest score is 65.50, achieved by 'Our Modelans_loss' with λ=0.5.", "question": "Which model configuration achieves the highest Anscov score?", "answer": "So, the answer is {\"answer\": \"Our Modelans_loss with λ=0.5\"}" }, { "messages": [ { "role": "user", "content": "Our Modelans_loss with λ=0.2 has the highest BLEU4 score. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nOur Modelans_loss with λ=0.2 has the highest BLEU4 score.\n" }, { "role": "assistant", "content": "The table shows that 'Our Modelans_loss' with λ=0.2 has a BLEU4 score of 41.72, which is the highest among all configurations. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3200.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performances on answer coverage, where “Anscov” denotes the metric of answer coverage. “λ” is the weight of the answer-aware loss in Equation 16." ], "reasoning": "The table shows that 'Our Modelans_loss' with λ=0.2 has a BLEU4 score of 41.72, which is the highest among all configurations.", "statement": "Our Modelans_loss with λ=0.2 has the highest BLEU4 score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Overall comparisons on the test data, where “ans_loss” represents answer-aware loss.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Our model outperforms all baselines in BLEU4, ROUGE L, and METEOR metrics, with a significant BLEU4 score increase of 4.53 compared to the strongest baseline by Elsahar et al.It is evident that our model is remarkably better than baselines on all metrics, where the BLEU4 score increases 4.53 compared with the strongest baseline Elsahar et al." } ], "images": [ "scigen/train/3201.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Overall comparisons on the test data, where “ans_loss” represents answer-aware loss." ], "reasoning": "Our model outperforms all baselines in BLEU4, ROUGE L, and METEOR metrics, with a significant BLEU4 score increase of 4.53 compared to the strongest baseline by Elsahar et al.", "description": "It is evident that our model is remarkably better than baselines on all metrics, where the BLEU4 score increases 4.53 compared with the strongest baseline Elsahar et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Overall comparisons on the test data, where “ans_loss” represents answer-aware loss.\nQuestion: What is the BLEU4 score difference between the strongest baseline and Our Modelans_loss?\nSolve the above question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The BLEU4 score for the strongest baseline (N18-1020) is 36.56, and for Our Modelans_loss, it is 41.72. The difference is 41.72 - 36.56. So, the answer is {\"answer\": \"5.16\"}" } ], "images": [ "scigen/train/3201.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Overall comparisons on the test data, where “ans_loss” represents answer-aware loss." ], "reasoning": "The BLEU4 score for the strongest baseline (N18-1020) is 36.56, and for Our Modelans_loss, it is 41.72. The difference is 41.72 - 36.56.", "question": "What is the BLEU4 score difference between the strongest baseline and Our Modelans_loss?", "answer": "So, the answer is {\"answer\": \"5.16\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nOur Modelans_loss has the highest METEOR score.\n" }, { "role": "assistant", "content": "The METEOR score for Our Modelans_loss is 48.13, which is higher than the METEOR scores of all other models listed in the table. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3201.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Overall comparisons on the test data, where “ans_loss” represents answer-aware loss." ], "reasoning": "The METEOR score for Our Modelans_loss is 48.13, which is higher than the METEOR scores of all other models listed in the table.", "statement": "Our Modelans_loss has the highest METEOR score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Performances on predicate identification.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The model's ability to identify predicates was evaluated using 100 generated questions, with a Kappa score of 0.611 and a p-value less than 0.005.To evaluate the ability of our model on predicate identification, we sample 100 generated questions from each model, and then two annotators are employed to judge whether the generated question expresses the given predicate. The Kappa for inter-annotator statistics is 0.611, and p-value for all scores is less than 0.005." } ], "images": [ "scigen/train/3202.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performances on predicate identification." ], "reasoning": "The model's ability to identify predicates was evaluated using 100 generated questions, with a Kappa score of 0.611 and a p-value less than 0.005.", "description": "To evaluate the ability of our model on predicate identification, we sample 100 generated questions from each model, and then two annotators are employed to judge whether the generated question expresses the given predicate. The Kappa for inter-annotator statistics is 0.611, and p-value for all scores is less than 0.005.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: Performances on predicate identification.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model had the highest predicate identification score?\n" }, { "role": "assistant", "content": "By comparing the predicate identification scores of the models listed in the table, we see that 'Our Modelans_loss' has the highest score of 75.5. So, the answer is {\"answer\": \"Our Modelans_loss\"}" } ], "images": [ "scigen/train/3202.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performances on predicate identification." ], "reasoning": "By comparing the predicate identification scores of the models listed in the table, we see that 'Our Modelans_loss' has the highest score of 75.5.", "question": "Which model had the highest predicate identification score?", "answer": "So, the answer is {\"answer\": \"Our Modelans_loss\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe model N18-1020 had a predicate identification score higher than 70.\n" }, { "role": "assistant", "content": "The table shows that the predicate identification score for the model N18-1020 is 71.5, which is higher than 70. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3202.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performances on predicate identification." ], "reasoning": "The table shows that the predicate identification score for the model N18-1020 is 71.5, which is higher than 70.", "statement": "The model N18-1020 had a predicate identification score higher than 70.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Performances on naturalness.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Human evaluation of generated questions shows that the model 'Our Modelans_loss' has the highest naturalness score of 3.56, compared to P16-1056 and N18-1020.Human evaluation is important for generated questions. Following \\newciteN18-1020, we sample 100 questions from each system, and then two annotators measure the naturalness by a score of 0-5. The Kappa coefficient for inter-annotator is 0.629, and p-value for all scores is less than 0.005." } ], "images": [ "scigen/train/3203.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Performances on naturalness." ], "reasoning": "Human evaluation of generated questions shows that the model 'Our Modelans_loss' has the highest naturalness score of 3.56, compared to P16-1056 and N18-1020.", "description": "Human evaluation is important for generated questions. Following \\newciteN18-1020, we sample 100 questions from each system, and then two annotators measure the naturalness by a score of 0-5. The Kappa coefficient for inter-annotator is 0.629, and p-value for all scores is less than 0.005.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 5: Performances on naturalness.' to sequentially solve the question presented below. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhich model has the highest naturalness score?\n" }, { "role": "assistant", "content": "By comparing the naturalness scores of the models listed in the table, 'Our Modelans_loss' has the highest score of 3.56. So, the answer is {\"answer\": \"Our Modelans_loss\"}" } ], "images": [ "scigen/train/3203.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Performances on naturalness." ], "reasoning": "By comparing the naturalness scores of the models listed in the table, 'Our Modelans_loss' has the highest score of 3.56.", "question": "Which model has the highest naturalness score?", "answer": "So, the answer is {\"answer\": \"Our Modelans_loss\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model N18-1020 has a higher naturalness score than P16-1056.\n" }, { "role": "assistant", "content": "The table shows that the naturalness score of N18-1020 is 2.23, which is lower than the score of P16-1056, which is 2.96. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3203.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Performances on naturalness." ], "reasoning": "The table shows that the naturalness score of N18-1020 is 2.23, which is lower than the score of P16-1056, which is 2.96.", "statement": "The model N18-1020 has a higher naturalness score than P16-1056.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 6: Performances of whether using the pre-trained KB embedding by transE.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Pre-trained KB embeddings improve the performance of KBQG models, but the context-augmented fact encoder helps mitigate performance degradation when embeddings are not used.Pre-trained KB embeddings may provide rich structured relational information among entities. However, it heavily relies on large-scale triplets, which is time and resource-intensive. To investigate the effectiveness of pre-trained KB embedding for KBQG, we report the performance of KBQG whether using pre-trained KB embeddings by simply applying In comparison, \\newciteN18-1020 obtain obvious degradation on all metrics while there is only a slight decline in our model. We believe that it may owe to the context-augmented fact encoder since our model drops to 40.87 on the BLEU4 score without context-augmented fact encoder and transE embeddings." } ], "images": [ "scigen/train/3204.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Performances of whether using the pre-trained KB embedding by transE." ], "reasoning": "Pre-trained KB embeddings improve the performance of KBQG models, but the context-augmented fact encoder helps mitigate performance degradation when embeddings are not used.", "description": "Pre-trained KB embeddings may provide rich structured relational information among entities. However, it heavily relies on large-scale triplets, which is time and resource-intensive. To investigate the effectiveness of pre-trained KB embedding for KBQG, we report the performance of KBQG whether using pre-trained KB embeddings by simply applying In comparison, \\newciteN18-1020 obtain obvious degradation on all metrics while there is only a slight decline in our model. We believe that it may owe to the context-augmented fact encoder since our model drops to 40.87 on the BLEU4 score without context-augmented fact encoder and transE embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: Table 6: Performances of whether using the pre-trained KB embedding by transE.\nQuestion: What is the BLEU4 score of the N18-1020 model without using TransE embeddings?\n" }, { "role": "assistant", "content": "The table shows the BLEU4 scores for different models with and without TransE embeddings. For the N18-1020 model without TransE embeddings, the BLEU4 score is listed. So, the answer is {\"answer\": \"33.67\"}" } ], "images": [ "scigen/train/3204.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Performances of whether using the pre-trained KB embedding by transE." ], "reasoning": "The table shows the BLEU4 scores for different models with and without TransE embeddings. For the N18-1020 model without TransE embeddings, the BLEU4 score is listed.", "question": "What is the BLEU4 score of the N18-1020 model without using TransE embeddings?", "answer": "So, the answer is {\"answer\": \"33.67\"}" }, { "messages": [ { "role": "user", "content": "The Our Modelans_loss model has a higher BLEU4 score with TransE embeddings than without. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Our Modelans_loss model has a higher BLEU4 score with TransE embeddings than without.\n" }, { "role": "assistant", "content": "The table shows the BLEU4 scores for the Our Modelans_loss model with and without TransE embeddings. The score with TransE embeddings is 41.72, which is higher than the score without TransE embeddings, which is 41.55. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3204.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Performances of whether using the pre-trained KB embedding by transE." ], "reasoning": "The table shows the BLEU4 scores for the Our Modelans_loss model with and without TransE embeddings. The score with TransE embeddings is 41.72, which is higher than the score without TransE embeddings, which is 41.55.", "statement": "The Our Modelans_loss model has a higher BLEU4 score with TransE embeddings than without.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 7: Performances of generated questions for QA.'.\n" }, { "role": "assistant", "content": "Combining human-labeled data with model-generated data improves QA system performance, with our model achieving the highest accuracy.Previous experiments demonstrate that our model can deliver more precise questions. To further prove the effectiveness of our model, we will see how useful the generated questions are for training a question answering system over knowledge bases. Specifically, we combine human-labeled data with the same amount of model-generated data to a typical QA system (Mohammed et al., 2018). We can observe that adding generative questions may weaken the performance of QA Our generated questions achieve the best performance on the QA system. It indicates that our model generates more precise question and has improved QA performances greatly." } ], "images": [ "scigen/train/3205.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Performances of generated questions for QA." ], "reasoning": "Combining human-labeled data with model-generated data improves QA system performance, with our model achieving the highest accuracy.", "description": "Previous experiments demonstrate that our model can deliver more precise questions. To further prove the effectiveness of our model, we will see how useful the generated questions are for training a question answering system over knowledge bases. Specifically, we combine human-labeled data with the same amount of model-generated data to a typical QA system (Mohammed et al., 2018). We can observe that adding generative questions may weaken the performance of QA Our generated questions achieve the best performance on the QA system. It indicates that our model generates more precise question and has improved QA performances greatly.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 7: Performances of generated questions for QA.', solve the following question step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich combination of data achieved the highest accuracy in the QA system?\n" }, { "role": "assistant", "content": "The table shows different combinations of data types and their corresponding accuracies. The highest accuracy is 69.57, achieved by combining human-labeled data with our model-generated data. So, the answer is {\"answer\": \"69.57\"}" } ], "images": [ "scigen/train/3205.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Performances of generated questions for QA." ], "reasoning": "The table shows different combinations of data types and their corresponding accuracies. The highest accuracy is 69.57, achieved by combining human-labeled data with our model-generated data.", "question": "Which combination of data achieved the highest accuracy in the QA system?", "answer": "So, the answer is {\"answer\": \"69.57\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nCombining human-labeled data with Serban et al.'s generated data achieved the lowest accuracy.\n" }, { "role": "assistant", "content": "The table shows that combining human-labeled data with Serban et al.'s generated data resulted in an accuracy of 68.53, which is the lowest among the listed combinations. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3205.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Performances of generated questions for QA." ], "reasoning": "The table shows that combining human-labeled data with Serban et al.'s generated data resulted in an accuracy of 68.53, which is the lowest among the listed combinations.", "statement": "Combining human-labeled data with Serban et al.'s generated data achieved the lowest accuracy.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1: Accuracies of Methods Before Ensemble for IMDB dataset', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The WARV model, which uses weighted word vectors, achieves the highest performance with 94.95% accuracy, indicating the importance of positive and negative word strengths in sentiment analysis.The method that employs weighted word vectors achieves the highest performance. This shows that the positive and negative strengths of words obtained from the data affect on determining the correct sentiment. Since the ARV and WARV models having separate word2vec and glove vectors, we use only these 600-dimensional versions in the experiments that follow. Our WARV model has 94.95 % accuracy. It means that it misclassified 1263 reviews out of 25000 reviews." } ], "images": [ "scigen/train/3206.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Accuracies of Methods Before Ensemble for IMDB dataset" ], "reasoning": "The WARV model, which uses weighted word vectors, achieves the highest performance with 94.95% accuracy, indicating the importance of positive and negative word strengths in sentiment analysis.", "description": "The method that employs weighted word vectors achieves the highest performance. This shows that the positive and negative strengths of words obtained from the data affect on determining the correct sentiment. Since the ARV and WARV models having separate word2vec and glove vectors, we use only these 600-dimensional versions in the experiments that follow. Our WARV model has 94.95 % accuracy. It means that it misclassified 1263 reviews out of 25000 reviews.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Accuracies of Methods Before Ensemble for IMDB dataset\nQuestion: How many reviews did the WARV model misclassify out of 25000 reviews?\nGive a detailed response to the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The description states that the WARV model has an accuracy of 94.95%, which means it misclassified 1263 reviews out of 25000 reviews. So, the answer is {\"answer\": \"1263\"}." } ], "images": [ "scigen/train/3206.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Accuracies of Methods Before Ensemble for IMDB dataset" ], "reasoning": "The description states that the WARV model has an accuracy of 94.95%, which means it misclassified 1263 reviews out of 25000 reviews.", "question": "How many reviews did the WARV model misclassify out of 25000 reviews?", "answer": "So, the answer is {\"answer\": \"1263\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The WARV model has the highest accuracy among all the methods listed. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe WARV model has the highest accuracy among all the methods listed.\n" }, { "role": "assistant", "content": "The table shows that the WARV model has an accuracy of 94.95%, which is the highest among all the methods listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3206.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Accuracies of Methods Before Ensemble for IMDB dataset" ], "reasoning": "The table shows that the WARV model has an accuracy of 94.95%, which is the highest among all the methods listed.", "statement": "The WARV model has the highest accuracy among all the methods listed.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Accuracies of Methods After Ensemble for IMDB dataset'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The WARV model is included in all the ensembles with the highest success rates, and combining WARV with NBSVM achieves the best result of 95.6%.We see that the WARV model is included in all the ensembles with the highest success rates. The NBSVM and CNN methods also contribute to the performance in the models formed of more than two classifiers. We observe that the success rates we obtain outperform those in Mesnil by about three percent. Especially when our WARV and Wang’s NBSVM methods are ensembled, we obtain the state of art result (95.6%)." } ], "images": [ "scigen/train/3207.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracies of Methods After Ensemble for IMDB dataset" ], "reasoning": "The WARV model is included in all the ensembles with the highest success rates, and combining WARV with NBSVM achieves the best result of 95.6%.", "description": "We see that the WARV model is included in all the ensembles with the highest success rates. The NBSVM and CNN methods also contribute to the performance in the models formed of more than two classifiers. We observe that the success rates we obtain outperform those in Mesnil by about three percent. Especially when our WARV and Wang’s NBSVM methods are ensembled, we obtain the state of art result (95.6%).", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Accuracies of Methods After Ensemble for IMDB dataset' to sequentially solve the question presented below. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhat is the highest success rate achieved by combining WARV and NBSVM methods?\n" }, { "role": "assistant", "content": "The table shows that the highest success rate achieved by combining WARV and NBSVM methods is 95.56% in Ensemble-1. So, the answer is {\"answer\": \"95.56%\"}." } ], "images": [ "scigen/train/3207.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracies of Methods After Ensemble for IMDB dataset" ], "reasoning": "The table shows that the highest success rate achieved by combining WARV and NBSVM methods is 95.56% in Ensemble-1.", "question": "What is the highest success rate achieved by combining WARV and NBSVM methods?", "answer": "So, the answer is {\"answer\": \"95.56%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Combining WARV and NBSVM methods achieves a success rate of 95.56%. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nCombining WARV and NBSVM methods achieves a success rate of 95.56%.\n" }, { "role": "assistant", "content": "The table shows that the combination of WARV and NBSVM methods achieves a success rate of 95.56% in Ensemble-1. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3207.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracies of Methods After Ensemble for IMDB dataset" ], "reasoning": "The table shows that the combination of WARV and NBSVM methods achieves a success rate of 95.56% in Ensemble-1.", "statement": "Combining WARV and NBSVM methods achieves a success rate of 95.56%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: Validation perplexity (lower is better) for different features and architectures of the gating model. Each experiment is an average over 10 runs.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "While extra features decrease perplexity, the LSTM architecture has the most significant impact on reducing perplexity.While extra features decrease the perplexity, the effect of the model architecture is much more impactful. On the following, we only consider the LSTM architecture for the gating model." } ], "images": [ "scigen/train/3208.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Validation perplexity (lower is better) for different features and architectures of the gating model. Each experiment is an average over 10 runs." ], "reasoning": "While extra features decrease perplexity, the LSTM architecture has the most significant impact on reducing perplexity.", "description": "While extra features decrease the perplexity, the effect of the model architecture is much more impactful. On the following, we only consider the LSTM architecture for the gating model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 4: Validation perplexity (lower is better) for different features and architectures of the gating model. Each experiment is an average over 10 runs.' and methodically tackle the question that follows. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the perplexity of the LSTM model with the FULL feature set?\n" }, { "role": "assistant", "content": "The table shows the perplexity values for different models and feature sets. For the LSTM model with the FULL feature set, the perplexity is 29.85. So, the answer is {\"answer\": \"29.85\"}." } ], "images": [ "scigen/train/3208.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Validation perplexity (lower is better) for different features and architectures of the gating model. Each experiment is an average over 10 runs." ], "reasoning": "The table shows the perplexity values for different models and feature sets. For the LSTM model with the FULL feature set, the perplexity is 29.85.", "question": "What is the perplexity of the LSTM model with the FULL feature set?", "answer": "So, the answer is {\"answer\": \"29.85\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe MLP model with the SIMPLE feature set has a lower perplexity than the LIN model with the FULL feature set.\n" }, { "role": "assistant", "content": "The table shows that the MLP model with the SIMPLE feature set has a perplexity of 30.35, while the LIN model with the FULL feature set has a perplexity of 30.75. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3208.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Validation perplexity (lower is better) for different features and architectures of the gating model. Each experiment is an average over 10 runs." ], "reasoning": "The table shows that the MLP model with the SIMPLE feature set has a perplexity of 30.35, while the LIN model with the FULL feature set has a perplexity of 30.75. Therefore, the statement is supported.", "statement": "The MLP model with the SIMPLE feature set has a lower perplexity than the LIN model with the FULL feature set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Average compactness scores.'.\n" }, { "role": "assistant", "content": "HLTA-c consistently achieved the highest compactness scores across all datasets and the highest coherence scores except for News-1k, where it was outperformed by another method.We see that HLTA-c achieved the highest compactness score in all cases. It also achieved the highest coherence score in all cases except for News-1k. The differences between the scores for HLTA-c and those for the LDA-based methods are often large. On the AI dataset, for instance, HLTA-c achieved a coherence score of -16.18 and a compactness score of 0.299. The corresponding scores for nHDP are -18.66 and 0.144 respectively." } ], "images": [ "scigen/train/3209.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Average compactness scores." ], "reasoning": "HLTA-c consistently achieved the highest compactness scores across all datasets and the highest coherence scores except for News-1k, where it was outperformed by another method.", "description": "We see that HLTA-c achieved the highest compactness score in all cases. It also achieved the highest coherence score in all cases except for News-1k. The differences between the scores for HLTA-c and those for the LDA-based methods are often large. On the AI dataset, for instance, HLTA-c achieved a coherence score of -16.18 and a compactness score of 0.299. The corresponding scores for nHDP are -18.66 and 0.144 respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Average compactness scores.', unfold the question presented below, detailing every step of your calculation. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich method achieved the highest compactness score on the AI dataset?\n" }, { "role": "assistant", "content": "The table shows the compactness scores for different methods across various datasets. For the AI dataset, HLTA-c achieved the highest compactness score of 0.229. So, the answer is {\"answer\": \"HLTA-c\"}" } ], "images": [ "scigen/train/3209.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Average compactness scores." ], "reasoning": "The table shows the compactness scores for different methods across various datasets. For the AI dataset, HLTA-c achieved the highest compactness score of 0.229.", "question": "Which method achieved the highest compactness score on the AI dataset?", "answer": "So, the answer is {\"answer\": \"HLTA-c\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim HLTA-c achieved the highest coherence score on the AI dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nHLTA-c achieved the highest coherence score on the AI dataset.\n" }, { "role": "assistant", "content": "The description states that HLTA-c achieved a coherence score of -16.18 on the AI dataset, which is higher than the coherence score of -18.66 achieved by nHDP. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3209.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Average compactness scores." ], "reasoning": "The description states that HLTA-c achieved a coherence score of -16.18 on the AI dataset, which is higher than the coherence score of -18.66 achieved by nHDP.", "statement": "HLTA-c achieved the highest coherence score on the AI dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Main results of our neural sequence chunking models on slot filling task.'.\n" }, { "role": "assistant", "content": "Model III has the best F1 score for both ATIS and LARGE datasets compared to the baseline and other models, indicating its superior performance.(∼99% vs. ∼80%). This is because the ATIS data is much easier for segmentation than LARGE data. Also, compared to the small ATIS vocabulary (572 words), it is harder to learn a good segmentation model with a more complicated vocabulary (about 16k words) in LARGE data. , we observe that Model III has the best F1 score as compared to the baseline and other neural chunking models. Another observation is that Model I and II get better improvements over baseline even though they are poor at segmentation in slot filling task." } ], "images": [ "scigen/train/3210.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Main results of our neural sequence chunking models on slot filling task." ], "reasoning": "Model III has the best F1 score for both ATIS and LARGE datasets compared to the baseline and other models, indicating its superior performance.", "description": "(∼99% vs. ∼80%). This is because the ATIS data is much easier for segmentation than LARGE data. Also, compared to the small ATIS vocabulary (572 words), it is harder to learn a good segmentation model with a more complicated vocabulary (about 16k words) in LARGE data. , we observe that Model III has the best F1 score as compared to the baseline and other neural chunking models. Another observation is that Model I and II get better improvements over baseline even though they are poor at segmentation in slot filling task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Main results of our neural sequence chunking models on slot filling task.\nQuestion: Which model has the highest LARGE Segment-F1 score?\nGive a detailed response to the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the LARGE Segment-F1 scores in the table, Model III has the highest score of 82.44. So, the answer is {\"answer\": \"Model III\"}." } ], "images": [ "scigen/train/3210.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Main results of our neural sequence chunking models on slot filling task." ], "reasoning": "By comparing the LARGE Segment-F1 scores in the table, Model III has the highest score of 82.44.", "question": "Which model has the highest LARGE Segment-F1 score?", "answer": "So, the answer is {\"answer\": \"Model III\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nModel I has a higher ATIS F1 score than the baseline.\n" }, { "role": "assistant", "content": "The table shows that Model I has an ATIS F1 score of 95.25, which is higher than the baseline score of 95.23. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3210.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Main results of our neural sequence chunking models on slot filling task." ], "reasoning": "The table shows that Model I has an ATIS F1 score of 95.25, which is higher than the baseline score of 95.23.", "statement": "Model I has a higher ATIS F1 score than the baseline.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Text chunking results of our neural sequence chunking models.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Model III outperforms the baseline on both the final F1 score and the segmentation F1-score, while Model I and Model II have comparable results to the baseline.“F1” is the final evaluation metric, and “segment-F1” refers to the segmentation F1-score. From the table, we can see that Model I and Model II only have comparable results with the baseline on both evaluation metrics - segment-F1 and final F1 score. Hence, we infer that using IOB labels to do segmentation independently might not be a good choice. However, Model III outperforms the baseline on both segmentation and labeling." } ], "images": [ "scigen/train/3211.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Text chunking results of our neural sequence chunking models." ], "reasoning": "Model III outperforms the baseline on both the final F1 score and the segmentation F1-score, while Model I and Model II have comparable results to the baseline.", "description": "“F1” is the final evaluation metric, and “segment-F1” refers to the segmentation F1-score. From the table, we can see that Model I and Model II only have comparable results with the baseline on both evaluation metrics - segment-F1 and final F1 score. Hence, we infer that using IOB labels to do segmentation independently might not be a good choice. However, Model III outperforms the baseline on both segmentation and labeling.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: 'Table 1: Text chunking results of our neural sequence chunking models.'\nQuestion: Which model has the highest segment-F1 score?\n" }, { "role": "assistant", "content": "By comparing the segment-F1 scores of all models, Model III has the highest segment-F1 score of 95.75. So, the answer is {\"answer\": \"Model III\"}." } ], "images": [ "scigen/train/3211.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Text chunking results of our neural sequence chunking models." ], "reasoning": "By comparing the segment-F1 scores of all models, Model III has the highest segment-F1 score of 95.75.", "question": "Which model has the highest segment-F1 score?", "answer": "So, the answer is {\"answer\": \"Model III\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nModel I has a higher final F1 score than the baseline.\n" }, { "role": "assistant", "content": "The table shows that Model I has a final F1 score of 94.01, which is lower than the baseline's final F1 score of 94.13. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3211.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Text chunking results of our neural sequence chunking models." ], "reasoning": "The table shows that Model I has a final F1 score of 94.01, which is lower than the baseline's final F1 score of 94.13.", "statement": "Model I has a higher final F1 score than the baseline.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Comparison with published results on the CoNLL chunking dataset.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The BiGRU-CRF model achieved an F1-score of 94.66, while the Model III (Ours) achieved a state-of-the-art performance of 94.72 without using CRF.\\citeauthorhuang2015bidirectional used a BiLSTM-CRF framework together with a lot of handcraft features. \\citeauthoryang2016multi extend this framework and employ a GRU to incorporate the character information of words, rather than using handcrafted features. To our best knowledge, they got the current best results 94.66 on the text chunking task. without using CRF, we get a state-of-the-art performance of 94.72." } ], "images": [ "scigen/train/3212.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison with published results on the CoNLL chunking dataset." ], "reasoning": "The BiGRU-CRF model achieved an F1-score of 94.66, while the Model III (Ours) achieved a state-of-the-art performance of 94.72 without using CRF.", "description": "\\citeauthorhuang2015bidirectional used a BiLSTM-CRF framework together with a lot of handcraft features. \\citeauthoryang2016multi extend this framework and employ a GRU to incorporate the character information of words, rather than using handcrafted features. To our best knowledge, they got the current best results 94.66 on the text chunking task. without using CRF, we get a state-of-the-art performance of 94.72.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 2: Comparison with published results on the CoNLL chunking dataset.', solve the following question step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich method achieved the highest F1-score without using CRF?\n" }, { "role": "assistant", "content": "The table lists various methods and their F1-scores. The description mentions that Model III (Ours) achieved a state-of-the-art performance of 94.72 without using CRF. So, the answer is {\"answer\": \"Model III (Ours)\"}" } ], "images": [ "scigen/train/3212.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison with published results on the CoNLL chunking dataset." ], "reasoning": "The table lists various methods and their F1-scores. The description mentions that Model III (Ours) achieved a state-of-the-art performance of 94.72 without using CRF.", "question": "Which method achieved the highest F1-score without using CRF?", "answer": "So, the answer is {\"answer\": \"Model III (Ours)\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The BiLSTM-CRF model achieved the highest F1-score. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe BiLSTM-CRF model achieved the highest F1-score.\n" }, { "role": "assistant", "content": "The table shows that the BiLSTM-CRF model achieved an F1-score of 94.46, while Model III (Ours) achieved the highest F1-score of 94.72. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3212.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison with published results on the CoNLL chunking dataset." ], "reasoning": "The table shows that the BiLSTM-CRF model achieved an F1-score of 94.46, while Model III (Ours) achieved the highest F1-score of 94.72.", "statement": "The BiLSTM-CRF model achieved the highest F1-score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5: Segment-F1 on different chunk-lengths.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Model III improves the segmentation F1-score on long chunks in LARGE data by 2% over the baseline, achieving better performance on all chunk lengths.We further investigate the segmentation process and show the segmentation F1-score The results demonstrate that the poor performance on LARGE data is mainly due to the bad performance on identifying long chunks (around 55%). Our Model III improves this score by 2% over baseline (54.88% vs. 56.59%). As the absolute performance on this subset is still low, future research efforts should focus on improving this performance. In addition, Model I and II get comparable segmentation results with the baseline model on one-words chunks, while being worse on longer chunks, further supporting this analysis. Finally, our Model III could achieve better F1 score on all chunk lengths." } ], "images": [ "scigen/train/3213.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Segment-F1 on different chunk-lengths." ], "reasoning": "Model III improves the segmentation F1-score on long chunks in LARGE data by 2% over the baseline, achieving better performance on all chunk lengths.", "description": "We further investigate the segmentation process and show the segmentation F1-score The results demonstrate that the poor performance on LARGE data is mainly due to the bad performance on identifying long chunks (around 55%). Our Model III improves this score by 2% over baseline (54.88% vs. 56.59%). As the absolute performance on this subset is still low, future research efforts should focus on improving this performance. In addition, Model I and II get comparable segmentation results with the baseline model on one-words chunks, while being worse on longer chunks, further supporting this analysis. Finally, our Model III could achieve better F1 score on all chunk lengths.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 5: Segment-F1 on different chunk-lengths.' to sequentially solve the question presented below. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhat is the F1-score of Model III on LARGE data for chunks of length 1?\n" }, { "role": "assistant", "content": "The table shows the F1-scores for different models on various chunk lengths. For Model III on LARGE data for chunks of length 1, the score is 89.01. So, the answer is {\"answer\": \"89.01\"}" } ], "images": [ "scigen/train/3213.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Segment-F1 on different chunk-lengths." ], "reasoning": "The table shows the F1-scores for different models on various chunk lengths. For Model III on LARGE data for chunks of length 1, the score is 89.01.", "question": "What is the F1-score of Model III on LARGE data for chunks of length 1?", "answer": "So, the answer is {\"answer\": \"89.01\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nModel I performs better than the baseline on LARGE data for chunks of length 2.\n" }, { "role": "assistant", "content": "The table shows that Model I has an F1-score of 87.27 on LARGE data for chunks of length 2, while the baseline has a score of 88.48. Therefore, Model I does not perform better than the baseline. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3213.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Segment-F1 on different chunk-lengths." ], "reasoning": "The table shows that Model I has an F1-score of 87.27 on LARGE data for chunks of length 2, while the baseline has a score of 88.48. Therefore, Model I does not perform better than the baseline.", "statement": "Model I performs better than the baseline on LARGE data for chunks of length 2.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 7: Comparison with published results on the ATIS data'.\n" }, { "role": "assistant", "content": "Our Model III achieves the highest F1-score of 95.86%, surpassing all previous methods listed in the table.As shown in the table, many researchers have done a lot of work which uses deep neural networks for slot filling. Compared with previous results, our Model III gets the state-of-the-art performance 95.86%." } ], "images": [ "scigen/train/3214.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Comparison with published results on the ATIS data" ], "reasoning": "Our Model III achieves the highest F1-score of 95.86%, surpassing all previous methods listed in the table.", "description": "As shown in the table, many researchers have done a lot of work which uses deep neural networks for slot filling. Compared with previous results, our Model III gets the state-of-the-art performance 95.86%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 7: Comparison with published results on the ATIS data', solve the subsequent question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhich method achieves the highest F1-score in the table?\n" }, { "role": "assistant", "content": "By comparing the F1-scores of all methods listed in the table, Model III achieves the highest F1-score of 95.86%. So, the answer is {\"answer\": \"Model III\"}." } ], "images": [ "scigen/train/3214.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Comparison with published results on the ATIS data" ], "reasoning": "By comparing the F1-scores of all methods listed in the table, Model III achieves the highest F1-score of 95.86%.", "question": "Which method achieves the highest F1-score in the table?", "answer": "So, the answer is {\"answer\": \"Model III\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Bi-RNN method achieves an F1-score higher than 95.00%.\n" }, { "role": "assistant", "content": "The table shows that the Bi-RNN method has an F1-score of 94.73%, which is lower than 95.00%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3214.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Comparison with published results on the ATIS data" ], "reasoning": "The table shows that the Bi-RNN method has an F1-score of 94.73%, which is lower than 95.00%.", "statement": "The Bi-RNN method achieves an F1-score higher than 95.00%.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Macro F1 average on the W-TOX and W-ATT datasets.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The BERT model with dropout 0.2 achieved the highest macro F1 scores on both W-ATT and W-TOX datasets, surpassing other models including those by Mishra et al.State-of-the-art performance on W-TOX and W-ATT with BERT finetuning: Mishra et al. From their work it is unclear if the model is tuned on validation, and same data was used along with test to report numbers. Hence, we only use their number as reference. We have also observed better numbers with their approach. We have achieved state of the art macro F1 score on W-ATT and W-TOX datasets with BERT finetuning. We have also added performance of BERT Wordpiece tokenized text with word based models for comparison, with their numbers running really close to those of BERT." } ], "images": [ "scigen/train/3215.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Macro F1 average on the W-TOX and W-ATT datasets." ], "reasoning": "The BERT model with dropout 0.2 achieved the highest macro F1 scores on both W-ATT and W-TOX datasets, surpassing other models including those by Mishra et al.", "description": "State-of-the-art performance on W-TOX and W-ATT with BERT finetuning: Mishra et al. From their work it is unclear if the model is tuned on validation, and same data was used along with test to report numbers. Hence, we only use their number as reference. We have also observed better numbers with their approach. We have achieved state of the art macro F1 score on W-ATT and W-TOX datasets with BERT finetuning. We have also added performance of BERT Wordpiece tokenized text with word based models for comparison, with their numbers running really close to those of BERT.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Macro F1 average on the W-TOX and W-ATT datasets.', answer the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model achieved the highest macro F1 score on the W-TOX dataset?\n" }, { "role": "assistant", "content": "By examining the table, the BERT model with dropout 0.2 has the highest score of 90.6 on the W-TOX dataset. So, the answer is {\"answer\": \"BERT (dropout 0.2)\"}." } ], "images": [ "scigen/train/3215.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Macro F1 average on the W-TOX and W-ATT datasets." ], "reasoning": "By examining the table, the BERT model with dropout 0.2 has the highest score of 90.6 on the W-TOX dataset.", "question": "Which model achieved the highest macro F1 score on the W-TOX dataset?", "answer": "So, the answer is {\"answer\": \"BERT (dropout 0.2)\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe BERT model with dropout 0.2 achieved the highest macro F1 score on the W-ATT dataset.\n" }, { "role": "assistant", "content": "The table shows that the BERT model with dropout 0.2 has the highest score of 89.5 on the W-ATT dataset, which is higher than any other model listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3215.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Macro F1 average on the W-TOX and W-ATT datasets." ], "reasoning": "The table shows that the BERT model with dropout 0.2 has the highest score of 89.5 on the W-ATT dataset, which is higher than any other model listed.", "statement": "The BERT model with dropout 0.2 achieved the highest macro F1 score on the W-ATT dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Comparison of different methods on the MOVIES dataset. All results in this table, except for the last row, are taken from Kim (2014).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table compares various sentiment analysis methods on the MOVIES dataset, highlighting their test accuracy and references.Our methodology is assessed through the application of several classifiers on the MOVIES dataset that has been used extensively in literature for the evaluation of the various methodologies proposed for sentiment analysis. the authors report a series of experimental results using Convolutional Neural Networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. In Socher et al. a novel machine learning framework based on Recursive Autoencoders (RAE) for sentence-level prediction of sentiment label distributions, is introduced. The method supports learning vector space representations for multi-word phrases, without using any pre-defined sentiment lexica or polarity shifting rules. Also, in Socher et al. This model leverages the CCG combinatory operators to guide a nonlinear transformation of meaning within a sentence. They use this model to learn high dimensional embeddings for sentences and evaluate them in a range of tasks, demonstrating that the incorporation of syntax allows a concise model to learn representations that are both effective and general. In Wang and Manning the dropout training model Hinton et al. (zeroing) hidden units and input features during training of neural networks. This process speeds up training without losing classification performance. Nakagawa et al. Dong et al. Unlike previous works that employ syntactic parsing results for sentiment analysis, they develop a statistical parser to directly analyze the sentiment structure of a sentence." } ], "images": [ "scigen/train/3216.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of different methods on the MOVIES dataset. All results in this table, except for the last row, are taken from Kim (2014)." ], "reasoning": "The table compares various sentiment analysis methods on the MOVIES dataset, highlighting their test accuracy and references.", "description": "Our methodology is assessed through the application of several classifiers on the MOVIES dataset that has been used extensively in literature for the evaluation of the various methodologies proposed for sentiment analysis. the authors report a series of experimental results using Convolutional Neural Networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. In Socher et al. a novel machine learning framework based on Recursive Autoencoders (RAE) for sentence-level prediction of sentiment label distributions, is introduced. The method supports learning vector space representations for multi-word phrases, without using any pre-defined sentiment lexica or polarity shifting rules. Also, in Socher et al. This model leverages the CCG combinatory operators to guide a nonlinear transformation of meaning within a sentence. They use this model to learn high dimensional embeddings for sentences and evaluate them in a range of tasks, demonstrating that the incorporation of syntax allows a concise model to learn representations that are both effective and general. In Wang and Manning the dropout training model Hinton et al. (zeroing) hidden units and input features during training of neural networks. This process speeds up training without losing classification performance. Nakagawa et al. Dong et al. Unlike previous works that employ syntactic parsing results for sentiment analysis, they develop a statistical parser to directly analyze the sentiment structure of a sentence.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Comparison of different methods on the MOVIES dataset. All results in this table, except for the last row, are taken from Kim (2014).\nQuestion: Which method has the highest test accuracy and who is the author?\nGive a detailed response to the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the 'Test Accuracy' column, the highest value is 0.815 for the 'CNN-non-static' method. The author is Kim (2014). So, the answer is {\"answer\": \"CNN-non-static by Kim (2014)\"}" } ], "images": [ "scigen/train/3216.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of different methods on the MOVIES dataset. All results in this table, except for the last row, are taken from Kim (2014)." ], "reasoning": "By examining the 'Test Accuracy' column, the highest value is 0.815 for the 'CNN-non-static' method. The author is Kim (2014).", "question": "Which method has the highest test accuracy and who is the author?", "answer": "So, the answer is {\"answer\": \"CNN-non-static by Kim (2014)\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe MV-RNN method has a test accuracy of 0.790.\n" }, { "role": "assistant", "content": "The table shows that the MV-RNN method has a test accuracy of 0.790, confirming the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3216.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of different methods on the MOVIES dataset. All results in this table, except for the last row, are taken from Kim (2014)." ], "reasoning": "The table shows that the MV-RNN method has a test accuracy of 0.790, confirming the statement.", "statement": "The MV-RNN method has a test accuracy of 0.790.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Figure 4: Matching n-grams in EMNLP2017.'.\n" }, { "role": "assistant", "content": "The table compares the world level perplexity of three models: Random, ScratchGAN, and MLE, with ScratchGAN showing a significantly lower perplexity than Random but higher than MLE.A common criticism of GAN models is that they produce realistic samples by overfitting to the training set, e.g. by copying text snippets. For a selection of ScratchGAN samples we find and present the nearest neighbors present in the training set. We consider two similarity measures, a 3-gram cosine similarity — to capture copied word sequences, and a cosine similarity from embeddings produced by the Universal Sentence Encoder —to capture semantically similar sentences. Additionally we perform a quantitive analysis over the full set of samples; we also compare the longest matching n-grams between text from the training set and (a) ScratchGAN samples, (b) MLE samples, and (c) text from the validation set. We conclude the generator is producing genuinely novel sentences, although they are not always grammatically or thematically consistent." } ], "images": [ "scigen/train/3217.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 4: Matching n-grams in EMNLP2017." ], "reasoning": "The table compares the world level perplexity of three models: Random, ScratchGAN, and MLE, with ScratchGAN showing a significantly lower perplexity than Random but higher than MLE.", "description": "A common criticism of GAN models is that they produce realistic samples by overfitting to the training set, e.g. by copying text snippets. For a selection of ScratchGAN samples we find and present the nearest neighbors present in the training set. We consider two similarity measures, a 3-gram cosine similarity — to capture copied word sequences, and a cosine similarity from embeddings produced by the Universal Sentence Encoder —to capture semantically similar sentences. Additionally we perform a quantitive analysis over the full set of samples; we also compare the longest matching n-grams between text from the training set and (a) ScratchGAN samples, (b) MLE samples, and (c) text from the validation set. We conclude the generator is producing genuinely novel sentences, although they are not always grammatically or thematically consistent.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Figure 4: Matching n-grams in EMNLP2017.' and proceed to solve the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model has the lowest world level perplexity?\n" }, { "role": "assistant", "content": "The table lists the world level perplexity for three models: Random (5725), ScratchGAN (154), and MLE (42). The lowest value is 42, which corresponds to the MLE model. So, the answer is {\"answer\": \"MLE\"}." } ], "images": [ "scigen/train/3217.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 4: Matching n-grams in EMNLP2017." ], "reasoning": "The table lists the world level perplexity for three models: Random (5725), ScratchGAN (154), and MLE (42). The lowest value is 42, which corresponds to the MLE model.", "question": "Which model has the lowest world level perplexity?", "answer": "So, the answer is {\"answer\": \"MLE\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: ScratchGAN has a higher world level perplexity than MLE., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nScratchGAN has a higher world level perplexity than MLE.\n" }, { "role": "assistant", "content": "The table shows that ScratchGAN has a world level perplexity of 154, while MLE has a world level perplexity of 42. Since 154 is higher than 42, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3217.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 4: Matching n-grams in EMNLP2017." ], "reasoning": "The table shows that ScratchGAN has a world level perplexity of 154, while MLE has a world level perplexity of 42. Since 154 is higher than 42, the statement is supported.", "statement": "ScratchGAN has a higher world level perplexity than MLE.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: FED sensitivity on EMNLP2017 News.'.\n" }, { "role": "assistant", "content": "ScratchGAN shows low variance and stability in training, achieving a consistent FED score across multiple runs and hyperparameters.Despite the high variance of REINFORCE gradients and the often unstable GAN training dynamics, our training procedure is very stable, due to the use of large batch sizes and chosen reward structure. The low variance across hyperparameters shows that ScratchGAN is not sensitive to changes in learning rate, REINFORCE discount factor, regularization or LSTM feature sizes, as long as these are kept in a reasonable range. When we fixed hyperparameters and repeated an experiment across 50 seeds, we obtained very similar FED score; no divergence or mode collapse occurred in any of the 50 runs. For WikiText-103, the results are similar (0.055 ± 0.003). Here we clarify the definition of the subset of hyper-parameter space that we used to show the stability of our training procedure." } ], "images": [ "scigen/train/3218.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: FED sensitivity on EMNLP2017 News." ], "reasoning": "ScratchGAN shows low variance and stability in training, achieving a consistent FED score across multiple runs and hyperparameters.", "description": "Despite the high variance of REINFORCE gradients and the often unstable GAN training dynamics, our training procedure is very stable, due to the use of large batch sizes and chosen reward structure. The low variance across hyperparameters shows that ScratchGAN is not sensitive to changes in learning rate, REINFORCE discount factor, regularization or LSTM feature sizes, as long as these are kept in a reasonable range. When we fixed hyperparameters and repeated an experiment across 50 seeds, we obtained very similar FED score; no divergence or mode collapse occurred in any of the 50 runs. For WikiText-103, the results are similar (0.055 ± 0.003). Here we clarify the definition of the subset of hyper-parameter space that we used to show the stability of our training procedure.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: FED sensitivity on EMNLP2017 News.', answer the following question. Give detailed solution consisting of each step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the FED score for ScratchGAN and how does it demonstrate stability?\n" }, { "role": "assistant", "content": "The FED score for ScratchGAN is 0.015, and the description states that ScratchGAN's training procedure is stable with low variance across hyperparameters and consistent results across 50 runs. So, the answer is {\"answer\": \"0.015\"}." } ], "images": [ "scigen/train/3218.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: FED sensitivity on EMNLP2017 News." ], "reasoning": "The FED score for ScratchGAN is 0.015, and the description states that ScratchGAN's training procedure is stable with low variance across hyperparameters and consistent results across 50 runs.", "question": "What is the FED score for ScratchGAN and how does it demonstrate stability?", "answer": "So, the answer is {\"answer\": \"0.015\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nScratchGAN has a higher FED score than SeqGAN-step (no pretraining).\n" }, { "role": "assistant", "content": "The table shows that ScratchGAN has a FED score of 0.015, which is lower than SeqGAN-step (no pretraining) with a FED score of 0.084. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3218.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: FED sensitivity on EMNLP2017 News." ], "reasoning": "The table shows that ScratchGAN has a FED score of 0.015, which is lower than SeqGAN-step (no pretraining) with a FED score of 0.084.", "statement": "ScratchGAN has a higher FED score than SeqGAN-step (no pretraining).", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Single model perplexity on validation and test sets for the Penn Treebank language modeling task. For our models and the models of Zaremba et al. (2014) and Gal (2015), medium and large refer to a 650 and 1500 units two layer LSTM respectively. The medium pointer sentinel-LSTM model achieves lower perplexity than the large LSTM model of Gal (2015) while using a third of the parameters and without using the computationally expensive Monte Carlo (MC) dropout averaging at test time. Parameter numbers with ‡ are estimates based upon our understanding of the model and with reference to Kim et al. (2016).'.\n" }, { "role": "assistant", "content": "The medium pointer sentinel-LSTM model achieves lower perplexity than large LSTM models with fewer parameters, specifically less than a third of the parameters used in large variational LSTM models.The medium pointer sentinel-LSTM model also achieves lower perplexity than the large LSTM models. Note that the best performing large variational LSTM model uses computationally intensive Monte Carlo (MC) dropout averaging. Monte Carlo dropout averaging is a general improvement for any sequence model that uses dropout but comes at a greatly increased test time cost. The pointer sentinel-LSTM is able to achieve these results with far fewer parameters than other models with comparable performance, specifically with less than a third the parameters used in the large variational LSTM models." } ], "images": [ "scigen/train/3219.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Single model perplexity on validation and test sets for the Penn Treebank language modeling task. For our models and the models of Zaremba et al. (2014) and Gal (2015), medium and large refer to a 650 and 1500 units two layer LSTM respectively. The medium pointer sentinel-LSTM model achieves lower perplexity than the large LSTM model of Gal (2015) while using a third of the parameters and without using the computationally expensive Monte Carlo (MC) dropout averaging at test time. Parameter numbers with ‡ are estimates based upon our understanding of the model and with reference to Kim et al. (2016)." ], "reasoning": "The medium pointer sentinel-LSTM model achieves lower perplexity than large LSTM models with fewer parameters, specifically less than a third of the parameters used in large variational LSTM models.", "description": "The medium pointer sentinel-LSTM model also achieves lower perplexity than the large LSTM models. Note that the best performing large variational LSTM model uses computationally intensive Monte Carlo (MC) dropout averaging. Monte Carlo dropout averaging is a general improvement for any sequence model that uses dropout but comes at a greatly increased test time cost. The pointer sentinel-LSTM is able to achieve these results with far fewer parameters than other models with comparable performance, specifically with less than a third the parameters used in the large variational LSTM models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model achieves the lowest test perplexity with fewer parameters than the large variational LSTM models?\nSolve the above question based on the table titled 'Table 2: Single model perplexity on validation and test sets for the Penn Treebank language modeling task. For our models and the models of Zaremba et al. (2014) and Gal (2015), medium and large refer to a 650 and 1500 units two layer LSTM respectively. The medium pointer sentinel-LSTM model achieves lower perplexity than the large LSTM model of Gal (2015) while using a third of the parameters and without using the computationally expensive Monte Carlo (MC) dropout averaging at test time. Parameter numbers with ‡ are estimates based upon our understanding of the model and with reference to Kim et al. (2016).'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the Pointer Sentinel-LSTM (medium) model has a test perplexity of 70.9, which is lower than the large variational LSTM models, and it uses only 21M parameters, which is less than a third of the parameters used by the large variational LSTM models (66M). So, the answer is {\"answer\": \"Pointer Sentinel-LSTM (medium)\"}." } ], "images": [ "scigen/train/3219.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Single model perplexity on validation and test sets for the Penn Treebank language modeling task. For our models and the models of Zaremba et al. (2014) and Gal (2015), medium and large refer to a 650 and 1500 units two layer LSTM respectively. The medium pointer sentinel-LSTM model achieves lower perplexity than the large LSTM model of Gal (2015) while using a third of the parameters and without using the computationally expensive Monte Carlo (MC) dropout averaging at test time. Parameter numbers with ‡ are estimates based upon our understanding of the model and with reference to Kim et al. (2016)." ], "reasoning": "The table shows that the Pointer Sentinel-LSTM (medium) model has a test perplexity of 70.9, which is lower than the large variational LSTM models, and it uses only 21M parameters, which is less than a third of the parameters used by the large variational LSTM models (66M).", "question": "Which model achieves the lowest test perplexity with fewer parameters than the large variational LSTM models?", "answer": "So, the answer is {\"answer\": \"Pointer Sentinel-LSTM (medium)\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe medium pointer sentinel-LSTM model achieves lower test perplexity than the large variational LSTM models.\n" }, { "role": "assistant", "content": "The table shows that the Pointer Sentinel-LSTM (medium) model has a test perplexity of 70.9, which is lower than the test perplexity of the large variational LSTM models (75.2 and 73.4). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3219.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Single model perplexity on validation and test sets for the Penn Treebank language modeling task. For our models and the models of Zaremba et al. (2014) and Gal (2015), medium and large refer to a 650 and 1500 units two layer LSTM respectively. The medium pointer sentinel-LSTM model achieves lower perplexity than the large LSTM model of Gal (2015) while using a third of the parameters and without using the computationally expensive Monte Carlo (MC) dropout averaging at test time. Parameter numbers with ‡ are estimates based upon our understanding of the model and with reference to Kim et al. (2016)." ], "reasoning": "The table shows that the Pointer Sentinel-LSTM (medium) model has a test perplexity of 70.9, which is lower than the test perplexity of the large variational LSTM models (75.2 and 73.4).", "statement": "The medium pointer sentinel-LSTM model achieves lower test perplexity than the large variational LSTM models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Results of ablation study on MT (Seq2Seq).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The ablation study shows that removing the kernel function or using a single-sense model leads to a performance decline, confirming the necessity of multi-modal distribution and dynamic allocation in KerBS.We perform ablation study of three variants of KerBS on the MT task. KerBS w/o kernel removes the kernel function from KerBS, so that distribution variances are no longer explicitly controlled. KerBS with single sense replaces the multi-sense model with single-sense one, which also leads to performance decline. This further confirms our assumption that the distribution of context vectors is multi-modal. In such cases, the output layer should also be multi-modal. In KerBS w/o dynamic allocation, each word is allocated with a fixed number of senses. Though it still performs better than single sense models, it is slightly worse than full KerBS model, which shows the necessity of dynamic allocation." } ], "images": [ "scigen/train/3220.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results of ablation study on MT (Seq2Seq)." ], "reasoning": "The ablation study shows that removing the kernel function or using a single-sense model leads to a performance decline, confirming the necessity of multi-modal distribution and dynamic allocation in KerBS.", "description": "We perform ablation study of three variants of KerBS on the MT task. KerBS w/o kernel removes the kernel function from KerBS, so that distribution variances are no longer explicitly controlled. KerBS with single sense replaces the multi-sense model with single-sense one, which also leads to performance decline. This further confirms our assumption that the distribution of context vectors is multi-modal. In such cases, the output layer should also be multi-modal. In KerBS w/o dynamic allocation, each word is allocated with a fixed number of senses. Though it still performs better than single sense models, it is slightly worse than full KerBS model, which shows the necessity of dynamic allocation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 5: Results of ablation study on MT (Seq2Seq).\nQuestion: Which variant of KerBS has the lowest BLEU-4 score?\n" }, { "role": "assistant", "content": "By comparing the BLEU-4 scores of the different KerBS variants, the 'w/o kernel' variant has the lowest score of 26.79. So, the answer is {\"answer\": \"w/o kernel\"}." } ], "images": [ "scigen/train/3220.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results of ablation study on MT (Seq2Seq)." ], "reasoning": "By comparing the BLEU-4 scores of the different KerBS variants, the 'w/o kernel' variant has the lowest score of 26.79.", "question": "Which variant of KerBS has the lowest BLEU-4 score?", "answer": "So, the answer is {\"answer\": \"w/o kernel\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The KerBS model without dynamic allocation performs better than the one without the kernel function. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe KerBS model without dynamic allocation performs better than the one without the kernel function.\n" }, { "role": "assistant", "content": "The BLEU-4 score for 'w/o dynamic allocation' is 27.00, which is higher than the score for 'w/o kernel' at 26.79, indicating better performance. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3220.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results of ablation study on MT (Seq2Seq)." ], "reasoning": "The BLEU-4 score for 'w/o dynamic allocation' is 27.00, which is higher than the score for 'w/o kernel' at 26.79, indicating better performance.", "statement": "The KerBS model without dynamic allocation performs better than the one without the kernel function.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: WER (%) comparison of PIT and SOT. Test data were generated by mixing “test_clean”. Evaluation results with unmatched training/testing conditions were shown with parenthesis. Note that we trained the PIT model with up to 2 speakers since 3-output PIT required impractical training time.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "SOT outperformed PIT in recognizing 1-, 2-, and 3-speaker evaluation data with fewer parameters and faster training speed.Note that we applied neither an additional language model nor feature-domain data augmentation In this experiment, we used 1024-dim model for both PIT and SOT. As shown in the table, PIT showed severe WER degradation for the 1-speaker case even when the training data contained many 1-speaker examples. SOT achieved significantly better results than the PIT model trained on 1- and 2-speaker mixtures for both 1- and 2-speaker evaluation data while recognizing 3-speaker evaluation data, with fewer parameters. For reference, the training speed for the 3-speaker SOT was roughly 30% faster (including I/O) than the 2-output PIT. The difference of the training speed would become much larger if we use PIT with more output branches." } ], "images": [ "scigen/train/3221.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: WER (%) comparison of PIT and SOT. Test data were generated by mixing “test_clean”. Evaluation results with unmatched training/testing conditions were shown with parenthesis. Note that we trained the PIT model with up to 2 speakers since 3-output PIT required impractical training time." ], "reasoning": "SOT outperformed PIT in recognizing 1-, 2-, and 3-speaker evaluation data with fewer parameters and faster training speed.", "description": "Note that we applied neither an additional language model nor feature-domain data augmentation In this experiment, we used 1024-dim model for both PIT and SOT. As shown in the table, PIT showed severe WER degradation for the 1-speaker case even when the training data contained many 1-speaker examples. SOT achieved significantly better results than the PIT model trained on 1- and 2-speaker mixtures for both 1- and 2-speaker evaluation data while recognizing 3-speaker evaluation data, with fewer parameters. For reference, the training speed for the 3-speaker SOT was roughly 30% faster (including I/O) than the 2-output PIT. The difference of the training speed would become much larger if we use PIT with more output branches.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 4: WER (%) comparison of PIT and SOT. Test data were generated by mixing “test_clean”. Evaluation results with unmatched training/testing conditions were shown with parenthesis. Note that we trained the PIT model with up to 2 speakers since 3-output PIT required impractical training time.', solve the subsequent question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model had the fewest parameters and how many were there?\n" }, { "role": "assistant", "content": "The table shows that the SOT model had 135.6M parameters, which is fewer than the 160.7M parameters of both 2-output PIT models. So, the answer is {\"answer\": \"SOT with 135.6M parameters\"}." } ], "images": [ "scigen/train/3221.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: WER (%) comparison of PIT and SOT. Test data were generated by mixing “test_clean”. Evaluation results with unmatched training/testing conditions were shown with parenthesis. Note that we trained the PIT model with up to 2 speakers since 3-output PIT required impractical training time." ], "reasoning": "The table shows that the SOT model had 135.6M parameters, which is fewer than the 160.7M parameters of both 2-output PIT models.", "question": "Which model had the fewest parameters and how many were there?", "answer": "So, the answer is {\"answer\": \"SOT with 135.6M parameters\"}." }, { "messages": [ { "role": "user", "content": "SOT had a faster training speed than the 2-output PIT model. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nSOT had a faster training speed than the 2-output PIT model.\n" }, { "role": "assistant", "content": "The description states that the training speed for the 3-speaker SOT was roughly 30% faster than the 2-output PIT. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3221.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: WER (%) comparison of PIT and SOT. Test data were generated by mixing “test_clean”. Evaluation results with unmatched training/testing conditions were shown with parenthesis. Note that we trained the PIT model with up to 2 speakers since 3-output PIT required impractical training time." ], "reasoning": "The description states that the training speed for the 3-speaker SOT was roughly 30% faster than the 2-output PIT.", "statement": "SOT had a faster training speed than the 2-output PIT model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: WER(%) of 512-dim models for 2-speaker-mixed speech. Note that the WERs for single speaker speech by the single-speaker ASR were 5.4% and 5.7% for dev_clean and test_clean, respectively.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The 'first-in, first-out' training achieved a better WER than the 'minimum loss speaker order' training, despite having a lower computational cost.Surprisingly, the “first-in, first-out” training achieved a better WER than the “minimum loss speaker order” training although the former approach can be executed with a computational cost of O(1) with respect to the number of speakers. Based on this finding, we used the “first-in, first-out” training for the rest of the experiments." } ], "images": [ "scigen/train/3222.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: WER(%) of 512-dim models for 2-speaker-mixed speech. Note that the WERs for single speaker speech by the single-speaker ASR were 5.4% and 5.7% for dev_clean and test_clean, respectively." ], "reasoning": "The 'first-in, first-out' training achieved a better WER than the 'minimum loss speaker order' training, despite having a lower computational cost.", "description": "Surprisingly, the “first-in, first-out” training achieved a better WER than the “minimum loss speaker order” training although the former approach can be executed with a computational cost of O(1) with respect to the number of speakers. Based on this finding, we used the “first-in, first-out” training for the rest of the experiments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: WER(%) of 512-dim models for 2-speaker-mixed speech. Note that the WERs for single speaker speech by the single-speaker ASR were 5.4% and 5.7% for dev_clean and test_clean, respectively.\nQuestion: Which training method achieved the lowest WER on the test_clean dataset?\nGive a detailed response to the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the 'first-in, first-out' training method achieved a WER of 16.5% on the test_clean dataset, which is lower than the WERs of the other methods. So, the answer is {\"answer\": \"SOT (first-in, first-out)\"}." } ], "images": [ "scigen/train/3222.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: WER(%) of 512-dim models for 2-speaker-mixed speech. Note that the WERs for single speaker speech by the single-speaker ASR were 5.4% and 5.7% for dev_clean and test_clean, respectively." ], "reasoning": "The table shows that the 'first-in, first-out' training method achieved a WER of 16.5% on the test_clean dataset, which is lower than the WERs of the other methods.", "question": "Which training method achieved the lowest WER on the test_clean dataset?", "answer": "So, the answer is {\"answer\": \"SOT (first-in, first-out)\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe 'minimum loss speaker order' training achieved a better WER on the dev_clean dataset than the 'first-in, first-out' training.\n" }, { "role": "assistant", "content": "The table shows that the 'minimum loss speaker order' training achieved a WER of 17.4% on the dev_clean dataset, which is higher than the WER of 17.0% achieved by the 'first-in, first-out' training. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3222.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: WER(%) of 512-dim models for 2-speaker-mixed speech. Note that the WERs for single speaker speech by the single-speaker ASR were 5.4% and 5.7% for dev_clean and test_clean, respectively." ], "reasoning": "The table shows that the 'minimum loss speaker order' training achieved a WER of 17.4% on the dev_clean dataset, which is higher than the WER of 17.0% achieved by the 'first-in, first-out' training.", "statement": "The 'minimum loss speaker order' training achieved a better WER on the dev_clean dataset than the 'first-in, first-out' training.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: WER (%) of 512-dim models for various mixtures of training and test data. Test data were generated by mixing “test_clean”. Evaluation results with unmatched training/testing conditions were shown with parenthesis.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The SOT model showed no degradation and sometimes outperformed the single-speaker ASR model, likely due to data augmentation from mixing training utterances.We then evaluated the same 512-dim model with a training set consisting of various numbers of speakers. It should be emphasized that the SOT model did not show any degradation for the single-speaker case, sometimes even outperformed the single-speaker ASR model. This might be because of the data augmentation effect resulting from mixing training utterances." } ], "images": [ "scigen/train/3223.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: WER (%) of 512-dim models for various mixtures of training and test data. Test data were generated by mixing “test_clean”. Evaluation results with unmatched training/testing conditions were shown with parenthesis." ], "reasoning": "The SOT model showed no degradation and sometimes outperformed the single-speaker ASR model, likely due to data augmentation from mixing training utterances.", "description": "We then evaluated the same 512-dim model with a training set consisting of various numbers of speakers. It should be emphasized that the SOT model did not show any degradation for the single-speaker case, sometimes even outperformed the single-speaker ASR model. This might be because of the data augmentation effect resulting from mixing training utterances.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: WER (%) of 512-dim models for various mixtures of training and test data. Test data were generated by mixing “test_clean”. Evaluation results with unmatched training/testing conditions were shown with parenthesis.' to sequentially solve the question presented below. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nHow did the SOT model with 1, 2, and 3 speakers in training data perform in Test Data 3 compared to the Single-spk ASR model?\n" }, { "role": "assistant", "content": "The SOT model with 1, 2, and 3 speakers in training data had a performance of 34.3 in Test Data 3, while the Single-spk ASR model had a performance of 92.7 in Test Data 3. So, the answer is {\"answer\": \"34.3\"}." } ], "images": [ "scigen/train/3223.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: WER (%) of 512-dim models for various mixtures of training and test data. Test data were generated by mixing “test_clean”. Evaluation results with unmatched training/testing conditions were shown with parenthesis." ], "reasoning": "The SOT model with 1, 2, and 3 speakers in training data had a performance of 34.3 in Test Data 3, while the Single-spk ASR model had a performance of 92.7 in Test Data 3.", "question": "How did the SOT model with 1, 2, and 3 speakers in training data perform in Test Data 3 compared to the Single-spk ASR model?", "answer": "So, the answer is {\"answer\": \"34.3\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The SOT model with 2 speakers in training data performed better in Test Data 2 than the Single-spk ASR model., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe SOT model with 2 speakers in training data performed better in Test Data 2 than the Single-spk ASR model.\n" }, { "role": "assistant", "content": "The SOT model with 2 speakers in training data had a performance of 16.5 in Test Data 2, while the Single-spk ASR model had a performance of 68.5 in Test Data 2. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3223.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: WER (%) of 512-dim models for various mixtures of training and test data. Test data were generated by mixing “test_clean”. Evaluation results with unmatched training/testing conditions were shown with parenthesis." ], "reasoning": "The SOT model with 2 speakers in training data had a performance of 16.5 in Test Data 2, while the Single-spk ASR model had a performance of 68.5 in Test Data 2.", "statement": "The SOT model with 2 speakers in training data performed better in Test Data 2 than the Single-spk ASR model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: WER (%) for different numbers of parameters and architectures for SOT model trained with the mixture of 1, 2, and 3 speakers. Test data were generated by mixing “test_clean.”'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The SOT model with SAA has a smaller size than the naive SOT model because one 1024-dim bidirectional LSTM layer was removed from the encoder.We found that the large model size was essential for SOT to achieve good results. Note that the SOT model size of with SAA is smaller than the naive SOT because we removed one 1024-dim bidirectional LSTM layer from the encoder instead of simply inserting one 1024-dim unidirectional LSTM after the attention module." } ], "images": [ "scigen/train/3224.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: WER (%) for different numbers of parameters and architectures for SOT model trained with the mixture of 1, 2, and 3 speakers. Test data were generated by mixing “test_clean.”" ], "reasoning": "The SOT model with SAA has a smaller size than the naive SOT model because one 1024-dim bidirectional LSTM layer was removed from the encoder.", "description": "We found that the large model size was essential for SOT to achieve good results. Note that the SOT model size of with SAA is smaller than the naive SOT because we removed one 1024-dim bidirectional LSTM layer from the encoder instead of simply inserting one 1024-dim unidirectional LSTM after the attention module.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: WER (%) for different numbers of parameters and architectures for SOT model trained with the mixture of 1, 2, and 3 speakers. Test data were generated by mixing “test_clean.”\nQuestion: What is the total number of speakers in the test data for the SOT model with SAA?\nSolve the above question based on the table information. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the SOT model with SAA (1024 Model Dim with SAA) has a total of 16.5 speakers in the test data. So, the answer is {\"answer\": \"16.5\"}." } ], "images": [ "scigen/train/3224.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: WER (%) for different numbers of parameters and architectures for SOT model trained with the mixture of 1, 2, and 3 speakers. Test data were generated by mixing “test_clean.”" ], "reasoning": "The table shows that the SOT model with SAA (1024 Model Dim with SAA) has a total of 16.5 speakers in the test data.", "question": "What is the total number of speakers in the test data for the SOT model with SAA?", "answer": "So, the answer is {\"answer\": \"16.5\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe SOT model with SAA has fewer parameters than the naive SOT model with 1024 Model Dim.\n" }, { "role": "assistant", "content": "The table shows that the SOT model with SAA has 135.6M parameters, while the naive SOT model with 1024 Model Dim has 143.9M parameters. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3224.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: WER (%) for different numbers of parameters and architectures for SOT model trained with the mixture of 1, 2, and 3 speakers. Test data were generated by mixing “test_clean.”" ], "reasoning": "The table shows that the SOT model with SAA has 135.6M parameters, while the naive SOT model with 1024 Model Dim has 143.9M parameters.", "statement": "The SOT model with SAA has fewer parameters than the naive SOT model with 1024 Model Dim.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: Results on image-to-sentence retrieval task with text-domain adversarial attacks. For each caption, we generate 5 adversarial fake captions which do not match the images. Thus, the models need to retrieve 5 positive captions from 30,000 candidate captions.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Unified VSE demonstrates the highest robustness across all types of adversarial attacks, outperforming all baselines, while VSE++ performs the worst.VSE++ performs worst as it is only optimized for the retrieval performance on the dataset. Its sentence encoder is insensitive to a small perturbation in the text. VSE-C explicitly generates the adversarial captions based on human-designed rules as hard negative examples during training, which makes it relatively robust to those adversarial attacks. Unified VSE shows strong robustness across all types of adversarial attacks and outperforms all baselines. Ablation study: semantic components. We now delve into the effectiveness of different semantic components by choosing different combinations of components for the caption embedding. For example, in UniVSEobj, only object nouns are selected and aggregated as ucomp." } ], "images": [ "scigen/train/3225.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results on image-to-sentence retrieval task with text-domain adversarial attacks. For each caption, we generate 5 adversarial fake captions which do not match the images. Thus, the models need to retrieve 5 positive captions from 30,000 candidate captions." ], "reasoning": "Unified VSE demonstrates the highest robustness across all types of adversarial attacks, outperforming all baselines, while VSE++ performs the worst.", "description": "VSE++ performs worst as it is only optimized for the retrieval performance on the dataset. Its sentence encoder is insensitive to a small perturbation in the text. VSE-C explicitly generates the adversarial captions based on human-designed rules as hard negative examples during training, which makes it relatively robust to those adversarial attacks. Unified VSE shows strong robustness across all types of adversarial attacks and outperforms all baselines. Ablation study: semantic components. We now delve into the effectiveness of different semantic components by choosing different combinations of components for the caption embedding. For example, in UniVSEobj, only object nouns are selected and aggregated as ucomp.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Results on image-to-sentence retrieval task with text-domain adversarial attacks. For each caption, we generate 5 adversarial fake captions which do not match the images. Thus, the models need to retrieve 5 positive captions from 30,000 candidate captions.', solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model shows the highest total sum in the table?\n" }, { "role": "assistant", "content": "By examining the 'total sum' column, we see that 'UniVSE (u sent+u comp)' has the highest total sum of 603.0. So, the answer is {\"answer\": \"UniVSE (u sent+u comp)\"}" } ], "images": [ "scigen/train/3225.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results on image-to-sentence retrieval task with text-domain adversarial attacks. For each caption, we generate 5 adversarial fake captions which do not match the images. Thus, the models need to retrieve 5 positive captions from 30,000 candidate captions." ], "reasoning": "By examining the 'total sum' column, we see that 'UniVSE (u sent+u comp)' has the highest total sum of 603.0.", "question": "Which model shows the highest total sum in the table?", "answer": "So, the answer is {\"answer\": \"UniVSE (u sent+u comp)\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: VSE-C outperforms VSE++ in all metrics., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nVSE-C outperforms VSE++ in all metrics.\n" }, { "role": "assistant", "content": "By comparing the values of VSE-C and VSE++ across all metrics, it is evident that VSE-C has higher values in every metric. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3225.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results on image-to-sentence retrieval task with text-domain adversarial attacks. For each caption, we generate 5 adversarial fake captions which do not match the images. Thus, the models need to retrieve 5 positive captions from 30,000 candidate captions." ], "reasoning": "By comparing the values of VSE-C and VSE++ across all metrics, it is evident that VSE-C has higher values in every metric.", "statement": "VSE-C outperforms VSE++ in all metrics.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: The mAP performance on the unified text-to-image retrieval task. Please refer to the text for details.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "UniVSE trained with all kinds of contrastive samples performs best overall, showing a significant improvement in retrieval performance compared to other baselines.We extend the word-to-scene retrieval used by Shi et al. In this task, models receive queries of different semantic levels, including single words (e.g., “Clock.”), noun phrases (e.g., “White clock.”), relational phrases (e.g., “Clocks on wall”) and full sentences. For all baselines, the texts of different types as treated as full sentences. Ablation study: contrastive learning of components. We evaluate the effectiveness of using contrastive samples for different semantic components. The same notation applies to other models. The UniVSE trained with a certain type of contrastive examples (e.g., UniVSEobj with contrastive nouns) consistently improves the retrieval performance of the same type of queries (e.g., retrieving images from a single noun). UniVSE trained with all kinds of contrastive samples performs best in overall and shows a significant gap w.r.t. other baselines." } ], "images": [ "scigen/train/3226.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The mAP performance on the unified text-to-image retrieval task. Please refer to the text for details." ], "reasoning": "UniVSE trained with all kinds of contrastive samples performs best overall, showing a significant improvement in retrieval performance compared to other baselines.", "description": "We extend the word-to-scene retrieval used by Shi et al. In this task, models receive queries of different semantic levels, including single words (e.g., “Clock.”), noun phrases (e.g., “White clock.”), relational phrases (e.g., “Clocks on wall”) and full sentences. For all baselines, the texts of different types as treated as full sentences. Ablation study: contrastive learning of components. We evaluate the effectiveness of using contrastive samples for different semantic components. The same notation applies to other models. The UniVSE trained with a certain type of contrastive examples (e.g., UniVSEobj with contrastive nouns) consistently improves the retrieval performance of the same type of queries (e.g., retrieving images from a single noun). UniVSE trained with all kinds of contrastive samples performs best in overall and shows a significant gap w.r.t. other baselines.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: The mAP performance on the unified text-to-image retrieval task. Please refer to the text for details.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest retrieval performance for relational phrases?\n" }, { "role": "assistant", "content": "To find the model with the highest retrieval performance for relational phrases, we look at the 'rel' column. UniVSE rel has the highest score of 39.57. So, the answer is {\"answer\": \"UniVSE rel\"}" } ], "images": [ "scigen/train/3226.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The mAP performance on the unified text-to-image retrieval task. Please refer to the text for details." ], "reasoning": "To find the model with the highest retrieval performance for relational phrases, we look at the 'rel' column. UniVSE rel has the highest score of 39.57.", "question": "Which model has the highest retrieval performance for relational phrases?", "answer": "So, the answer is {\"answer\": \"UniVSE rel\"}" }, { "messages": [ { "role": "user", "content": "UniVSE obj has the highest retrieval performance for single nouns. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nUniVSE obj has the highest retrieval performance for single nouns.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the 'obj' column. UniVSE obj has a score of 39.71, which is the highest among all models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3226.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The mAP performance on the unified text-to-image retrieval task. Please refer to the text for details." ], "reasoning": "To verify this statement, we look at the 'obj' column. UniVSE obj has a score of 39.71, which is the highest among all models.", "statement": "UniVSE obj has the highest retrieval performance for single nouns.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Model performance on WikiQA. +cnt: word matching features introduced in Yang et al. (2015). The base QA model is BiLSTM. Best results in each group are shown in bold.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Para4QA outperforms related baselines in MAP and MMR, and performance drops most when NMT paraphrases are removed.We report MAP and MMR which evaluate the relative ranks of correct answers among the candidate sentences for a question. Again, we observe that Para4QA outperforms related baselines (see BiLSTM, DataAugment, AvgPara, and SepPara). Ablation experiments show that performance drops most when NMT paraphrases are removed. When word matching features are used (see +cnt in the third block), Para4QA reaches state of the art performance." } ], "images": [ "scigen/train/3227.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Model performance on WikiQA. +cnt: word matching features introduced in Yang et al. (2015). The base QA model is BiLSTM. Best results in each group are shown in bold." ], "reasoning": "Para4QA outperforms related baselines in MAP and MMR, and performance drops most when NMT paraphrases are removed.", "description": "We report MAP and MMR which evaluate the relative ranks of correct answers among the candidate sentences for a question. Again, we observe that Para4QA outperforms related baselines (see BiLSTM, DataAugment, AvgPara, and SepPara). Ablation experiments show that performance drops most when NMT paraphrases are removed. When word matching features are used (see +cnt in the third block), Para4QA reaches state of the art performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 5: Model performance on WikiQA. +cnt: word matching features introduced in Yang et al. (2015). The base QA model is BiLSTM. Best results in each group are shown in bold.', solve the following question step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the MAP score when NMT paraphrases are removed from Para4QA?\n" }, { "role": "assistant", "content": "The table shows that the MAP score for Para4QA is 0.6759, and when NMT paraphrases are removed, the MAP score is 0.6528. So, the answer is {\"answer\": \"0.6528\"}." } ], "images": [ "scigen/train/3227.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Model performance on WikiQA. +cnt: word matching features introduced in Yang et al. (2015). The base QA model is BiLSTM. Best results in each group are shown in bold." ], "reasoning": "The table shows that the MAP score for Para4QA is 0.6759, and when NMT paraphrases are removed, the MAP score is 0.6528.", "question": "What is the MAP score when NMT paraphrases are removed from Para4QA?", "answer": "So, the answer is {\"answer\": \"0.6528\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Para4QA achieves the highest MRR score among all methods. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nPara4QA achieves the highest MRR score among all methods.\n" }, { "role": "assistant", "content": "The table shows that KvMemNet+cnt has the highest MRR score of 0.7265, while Para4QA has an MRR score of 0.6918. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3227.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Model performance on WikiQA. +cnt: word matching features introduced in Yang et al. (2015). The base QA model is BiLSTM. Best results in each group are shown in bold." ], "reasoning": "The table shows that KvMemNet+cnt has the highest MRR score of 0.7265, while Para4QA has an MRR score of 0.6918.", "statement": "Para4QA achieves the highest MRR score among all methods.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Questions and their top-five paraphrases with probabilities learned by the model. The Freebase relations used to query the correct answers are shown in brackets. The original question is underlined. Questions with incorrect predictions are in myredred.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The paraphrase model improves question-answering accuracy by generating paraphrases that increase the overlap between question and predicate words, thereby yielding higher probabilities for appropriate answers.Examples of paraphrases and their probabilities pθ(q′|q) The two examples are taken from the development set of GraphQuestions and WebQuestions, respectively. We also show the Freebase relations used to query the correct answers. In the first example, the original question cannot yield the correct answer because of the mismatch between the question and the knowledge base. The paraphrase contains “role” in place of “sort of part”, increasing the chance of overlap between the question and the predicate words. The second question contains an informal expression “play 4”, which confuses the QA model. The paraphrase model generates “play for” and predicts a high paraphrase score for it. More generally, we observe that the model tends to give higher probabilities pθ(q′|q) to paraphrases biased towards delivering appropriate answers." } ], "images": [ "scigen/train/3228.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Questions and their top-five paraphrases with probabilities learned by the model. The Freebase relations used to query the correct answers are shown in brackets. The original question is underlined. Questions with incorrect predictions are in myredred." ], "reasoning": "The paraphrase model improves question-answering accuracy by generating paraphrases that increase the overlap between question and predicate words, thereby yielding higher probabilities for appropriate answers.", "description": "Examples of paraphrases and their probabilities pθ(q′|q) The two examples are taken from the development set of GraphQuestions and WebQuestions, respectively. We also show the Freebase relations used to query the correct answers. In the first example, the original question cannot yield the correct answer because of the mismatch between the question and the knowledge base. The paraphrase contains “role” in place of “sort of part”, increasing the chance of overlap between the question and the predicate words. The second question contains an informal expression “play 4”, which confuses the QA model. The paraphrase model generates “play for” and predicts a high paraphrase score for it. More generally, we observe that the model tends to give higher probabilities pθ(q′|q) to paraphrases biased towards delivering appropriate answers.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: Questions and their top-five paraphrases with probabilities learned by the model. The Freebase relations used to query the correct answers are shown in brackets. The original question is underlined. Questions with incorrect predictions are in myredred.\nQuestion: Which paraphrase has the highest probability for the question about Shaq's team?\nSolve the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the paraphrase with the highest probability for the question about Shaq's team, look at the second set of examples related to sports.sports_team_roster.team and identify the paraphrase with the highest pθ(q′|q) value. So, the answer is {\"answer\": \"what team do shaq play for\"}." } ], "images": [ "scigen/train/3228.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Questions and their top-five paraphrases with probabilities learned by the model. The Freebase relations used to query the correct answers are shown in brackets. The original question is underlined. Questions with incorrect predictions are in myredred." ], "reasoning": "To find the paraphrase with the highest probability for the question about Shaq's team, look at the second set of examples related to sports.sports_team_roster.team and identify the paraphrase with the highest pθ(q′|q) value.", "question": "Which paraphrase has the highest probability for the question about Shaq's team?", "answer": "So, the answer is {\"answer\": \"what team do shaq play for\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe paraphrase 'what role do queen play in concert' has the highest probability for the question about Queen's role in a concert.\n" }, { "role": "assistant", "content": "To verify this statement, look at the first set of examples related to music.concert_performance.performance_role and identify the paraphrase with the highest pθ(q′|q) value. The highest probability is 0.0847 for 'what role do queen play in concert'. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3228.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Questions and their top-five paraphrases with probabilities learned by the model. The Freebase relations used to query the correct answers are shown in brackets. The original question is underlined. Questions with incorrect predictions are in myredred." ], "reasoning": "To verify this statement, look at the first set of examples related to music.concert_performance.performance_role and identify the paraphrase with the highest pθ(q′|q) value. The highest probability is 0.0847 for 'what role do queen play in concert'.", "statement": "The paraphrase 'what role do queen play in concert' has the highest probability for the question about Queen's role in a concert.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 7: We group GraphQuestions into simple and complex questions and report model performance in each split. Best results in each group are shown in bold. The values in brackets are absolute improvements of average F1 scores.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Our method shows improvements in both simple and complex questions, with a more pronounced impact on simple questions.Finally, we examined how our method fares on simple versus complex questions. We performed this analysis on GraphQuestions as it contains a larger proportion of complex questions. We consider questions that contain a single relation as simple. Complex questions have multiple relations or require aggregation. We observe improvements for both types of questions, with the impact on simple questions being more pronounced. This is not entirely surprising as it is easier to generate paraphrases and predict the paraphrase scores for simpler questions." } ], "images": [ "scigen/train/3229.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: We group GraphQuestions into simple and complex questions and report model performance in each split. Best results in each group are shown in bold. The values in brackets are absolute improvements of average F1 scores." ], "reasoning": "Our method shows improvements in both simple and complex questions, with a more pronounced impact on simple questions.", "description": "Finally, we examined how our method fares on simple versus complex questions. We performed this analysis on GraphQuestions as it contains a larger proportion of complex questions. We consider questions that contain a single relation as simple. Complex questions have multiple relations or require aggregation. We observe improvements for both types of questions, with the impact on simple questions being more pronounced. This is not entirely surprising as it is easier to generate paraphrases and predict the paraphrase scores for simpler questions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 7: We group GraphQuestions into simple and complex questions and report model performance in each split. Best results in each group are shown in bold. The values in brackets are absolute improvements of average F1 scores.', solve the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the improvement in Average F1 (%) for simple questions using Para4QA compared to SimpleGraph?\n" }, { "role": "assistant", "content": "To find the improvement, subtract the Average F1 (%) for SimpleGraph from the Average F1 (%) for Para4QA for simple questions. So, the answer is {\"answer\": \"6.5\"}." } ], "images": [ "scigen/train/3229.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: We group GraphQuestions into simple and complex questions and report model performance in each split. Best results in each group are shown in bold. The values in brackets are absolute improvements of average F1 scores." ], "reasoning": "To find the improvement, subtract the Average F1 (%) for SimpleGraph from the Average F1 (%) for Para4QA for simple questions.", "question": "What is the improvement in Average F1 (%) for simple questions using Para4QA compared to SimpleGraph?", "answer": "So, the answer is {\"answer\": \"6.5\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nPara4QA has a higher Average F1 (%) for complex questions compared to SimpleGraph.\n" }, { "role": "assistant", "content": "The table shows that Para4QA has an Average F1 (%) of 16.0 for complex questions, while SimpleGraph has an Average F1 (%) of 12.2. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3229.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: We group GraphQuestions into simple and complex questions and report model performance in each split. Best results in each group are shown in bold. The values in brackets are absolute improvements of average F1 scores." ], "reasoning": "The table shows that Para4QA has an Average F1 (%) of 16.0 for complex questions, while SimpleGraph has an Average F1 (%) of 12.2.", "statement": "Para4QA has a higher Average F1 (%) for complex questions compared to SimpleGraph.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Average accuracy [0,1] and average rank [60,1] of different strategies in 81 corpus, the last row indicates the number of times the best representation was predicted. (1) Nearest corpus, (2) classification, (3) performance regression, (4) rank prediction.'.\n" }, { "role": "assistant", "content": "The table compares the performance of four meta-learning strategies and a random method against the best solution in terms of average accuracy, average rank, and the number of times the best representation was selected.We evaluated the 4 meta-learning strategies with unseen tasks following a leave-one-out setting, using the results from 60 representations in the rest of the tasks as knowledge to decide which representation to recommend. The objective for the strategies, then, is to select what in exhaustive search was found to be the best representation. We compared the average performance achieved by our strategies in 5 runs against the best solution found and the average performance of all of the considered representations." } ], "images": [ "scigen/train/3230.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Average accuracy [0,1] and average rank [60,1] of different strategies in 81 corpus, the last row indicates the number of times the best representation was predicted. (1) Nearest corpus, (2) classification, (3) performance regression, (4) rank prediction." ], "reasoning": "The table compares the performance of four meta-learning strategies and a random method against the best solution in terms of average accuracy, average rank, and the number of times the best representation was selected.", "description": "We evaluated the 4 meta-learning strategies with unseen tasks following a leave-one-out setting, using the results from 60 representations in the rest of the tasks as knowledge to decide which representation to recommend. The objective for the strategies, then, is to select what in exhaustive search was found to be the best representation. We compared the average performance achieved by our strategies in 5 runs against the best solution found and the average performance of all of the considered representations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: Average accuracy [0,1] and average rank [60,1] of different strategies in 81 corpus, the last row indicates the number of times the best representation was predicted. (1) Nearest corpus, (2) classification, (3) performance regression, (4) rank prediction.', and tackle the question below in a sequential manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich method achieved the highest average accuracy among the meta-learning strategies?\n" }, { "role": "assistant", "content": "To find the method with the highest average accuracy among the meta-learning strategies, compare the 'Avg Accu' values for methods (1), (2), (3), and (4). Method (2) has the highest average accuracy of 75.25±0.12. So, the answer is {\"answer\": \"Method (2)\"}" } ], "images": [ "scigen/train/3230.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Average accuracy [0,1] and average rank [60,1] of different strategies in 81 corpus, the last row indicates the number of times the best representation was predicted. (1) Nearest corpus, (2) classification, (3) performance regression, (4) rank prediction." ], "reasoning": "To find the method with the highest average accuracy among the meta-learning strategies, compare the 'Avg Accu' values for methods (1), (2), (3), and (4). Method (2) has the highest average accuracy of 75.25±0.12.", "question": "Which method achieved the highest average accuracy among the meta-learning strategies?", "answer": "So, the answer is {\"answer\": \"Method (2)\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Method (4) has the lowest average rank among the meta-learning strategies., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nMethod (4) has the lowest average rank among the meta-learning strategies.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Avg Rank' values for methods (1), (2), (3), and (4). Method (4) has an average rank of 8.51±0.34, which is lower than the ranks of methods (1), (2), and (3). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3230.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Average accuracy [0,1] and average rank [60,1] of different strategies in 81 corpus, the last row indicates the number of times the best representation was predicted. (1) Nearest corpus, (2) classification, (3) performance regression, (4) rank prediction." ], "reasoning": "To verify this statement, compare the 'Avg Rank' values for methods (1), (2), (3), and (4). Method (4) has an average rank of 8.51±0.34, which is lower than the ranks of methods (1), (2), and (3).", "statement": "Method (4) has the lowest average rank among the meta-learning strategies.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 7: Performance comparison on end-to-end relation extraction (at the highest F1 point) on the three datasets.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "CoType outperforms all other methods on F1 score across all three datasets, demonstrating its effectiveness in relation extraction.3. Performance on Relation Extraction. To test the domain independence of CoType framework, we conduct evaluations on the end-to-end relation extraction. As only MultiR and DS-Joint are able to detection entity and relation mentions in their own framework, we apply our detection results to other compared methods. In particular, results at each method’s highest F1 score point are reported, after tuning the threshold for each method for determining whether a test mention is None or some target type. Overall, CoType outperforms all other methods on F1 score on all three datasets. We observe that DS-Joint and MultiR suffer from low recall, since their entity detection modules do not work well on DL (where many tokens have false negative tags). This demonstrates the effectiveness of the proposed domain-agnostic text segmentation algorithm (see Sec. We found that the incremental diagram of learning embedding (i.e., CoType-TwoStep) brings only marginal improvement. In Fig. BioInfer datasets further show that CoType can still achieve descent precision with good recall preserved. 4. Scalability. CoType demonstrates a linear runtime trend (which validates our time complexity in Sec." } ], "images": [ "scigen/train/3231.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Performance comparison on end-to-end relation extraction (at the highest F1 point) on the three datasets." ], "reasoning": "CoType outperforms all other methods on F1 score across all three datasets, demonstrating its effectiveness in relation extraction.", "description": "3. Performance on Relation Extraction. To test the domain independence of CoType framework, we conduct evaluations on the end-to-end relation extraction. As only MultiR and DS-Joint are able to detection entity and relation mentions in their own framework, we apply our detection results to other compared methods. In particular, results at each method’s highest F1 score point are reported, after tuning the threshold for each method for determining whether a test mention is None or some target type. Overall, CoType outperforms all other methods on F1 score on all three datasets. We observe that DS-Joint and MultiR suffer from low recall, since their entity detection modules do not work well on DL (where many tokens have false negative tags). This demonstrates the effectiveness of the proposed domain-agnostic text segmentation algorithm (see Sec. We found that the incremental diagram of learning embedding (i.e., CoType-TwoStep) brings only marginal improvement. In Fig. BioInfer datasets further show that CoType can still achieve descent precision with good recall preserved. 4. Scalability. CoType demonstrates a linear runtime trend (which validates our time complexity in Sec.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 7: Performance comparison on end-to-end relation extraction (at the highest F1 point) on the three datasets.', unfold the steps to solve the question given next. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nQuestion: Which method has the highest F1 score on the BioInfer dataset?\n" }, { "role": "assistant", "content": "To find the method with the highest F1 score on the BioInfer dataset, we look at the BioInfer F1 column and identify the highest value, which is 0.474 for CoType. So, the answer is {\"answer\": \"CoType\"}" } ], "images": [ "scigen/train/3231.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Performance comparison on end-to-end relation extraction (at the highest F1 point) on the three datasets." ], "reasoning": "To find the method with the highest F1 score on the BioInfer dataset, we look at the BioInfer F1 column and identify the highest value, which is 0.474 for CoType.", "question": "Which method has the highest F1 score on the BioInfer dataset?", "answer": "So, the answer is {\"answer\": \"CoType\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nDS-Joint has the highest precision on the NYT dataset.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the NYT Prec column and compare the precision values. The highest precision value is 0.574, which belongs to DS-Joint. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3231.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Performance comparison on end-to-end relation extraction (at the highest F1 point) on the three datasets." ], "reasoning": "To verify this statement, we look at the NYT Prec column and compare the precision values. The highest precision value is 0.574, which belongs to DS-Joint.", "statement": "DS-Joint has the highest precision on the NYT dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: A study of type label noise. (1): %entity mentions with multiple sibling entity types (e.g., actor, singer) in the given entity type hierarchy; (2): %relation mentions with multiple relation types, for the three experiment datasets.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The BioInfer dataset has the highest number of entity types and the highest percentage of noisy entity mentions compared to NYT and Wiki-KBP.In Fig. (e.g., born_in, president_of) are assigned to the relation mention in sentence S1 (while only born_in is correct within the context). Similarly, all KB types for Barack Obama (e.g., politician, artist) are assigned to the mention “Obama\" in S1 (while only person is true). Label noise becomes an impediment to learn effective type classifiers." } ], "images": [ "scigen/train/3232.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: A study of type label noise. (1): %entity mentions with multiple sibling entity types (e.g., actor, singer) in the given entity type hierarchy; (2): %relation mentions with multiple relation types, for the three experiment datasets." ], "reasoning": "The BioInfer dataset has the highest number of entity types and the highest percentage of noisy entity mentions compared to NYT and Wiki-KBP.", "description": "In Fig. (e.g., born_in, president_of) are assigned to the relation mention in sentence S1 (while only born_in is correct within the context). Similarly, all KB types for Barack Obama (e.g., politician, artist) are assigned to the mention “Obama\" in S1 (while only person is true). Label noise becomes an impediment to learn effective type classifiers.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: A study of type label noise. (1): %entity mentions with multiple sibling entity types (e.g., actor, singer) in the given entity type hierarchy; (2): %relation mentions with multiple relation types, for the three experiment datasets.', answer the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich dataset has the highest percentage of noisy relation mentions?\n" }, { "role": "assistant", "content": "By comparing the percentage of noisy relation mentions across the datasets, BioInfer has the highest percentage at 41.12%. So, the answer is {\"answer\": \"BioInfer\"}." } ], "images": [ "scigen/train/3232.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: A study of type label noise. (1): %entity mentions with multiple sibling entity types (e.g., actor, singer) in the given entity type hierarchy; (2): %relation mentions with multiple relation types, for the three experiment datasets." ], "reasoning": "By comparing the percentage of noisy relation mentions across the datasets, BioInfer has the highest percentage at 41.12%.", "question": "Which dataset has the highest percentage of noisy relation mentions?", "answer": "So, the answer is {\"answer\": \"BioInfer\"}." }, { "messages": [ { "role": "user", "content": "The NYT dataset has more entity types than the Wiki-KBP dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe NYT dataset has more entity types than the Wiki-KBP dataset.\n" }, { "role": "assistant", "content": "The table shows that NYT has 47 entity types while Wiki-KBP has 126 entity types, so the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3232.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: A study of type label noise. (1): %entity mentions with multiple sibling entity types (e.g., actor, singer) in the given entity type hierarchy; (2): %relation mentions with multiple relation types, for the three experiment datasets." ], "reasoning": "The table shows that NYT has 47 entity types while Wiki-KBP has 126 entity types, so the statement is incorrect.", "statement": "The NYT dataset has more entity types than the Wiki-KBP dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 5: Performance comparison of entity recognition and typing (using strict, micro and macro metrics [26]) on the three datasets.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "CoType outperforms other methods on all metrics across three datasets, achieving an 8% improvement on Micro-F1 over the next best method on the NYT dataset.1. Performance on Entity Recognition and Typing. We apply our detection results (i.e., M) as input for other methods. the comparison results on the three datasets. Overall, CoType outperforms others on all metrics on all three datasets (e.g., it obtains a 8% improvement on Micro-F1 over the next best method on NYT dataset)." } ], "images": [ "scigen/train/3233.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Performance comparison of entity recognition and typing (using strict, micro and macro metrics [26]) on the three datasets." ], "reasoning": "CoType outperforms other methods on all metrics across three datasets, achieving an 8% improvement on Micro-F1 over the next best method on the NYT dataset.", "description": "1. Performance on Entity Recognition and Typing. We apply our detection results (i.e., M) as input for other methods. the comparison results on the three datasets. Overall, CoType outperforms others on all metrics on all three datasets (e.g., it obtains a 8% improvement on Micro-F1 over the next best method on NYT dataset).", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: Performance comparison of entity recognition and typing (using strict, micro and macro metrics [26]) on the three datasets.'.\n\nQuestion: Which method has the highest Micro-F1 score on the NYT dataset?\n" }, { "role": "assistant", "content": "To find the method with the highest Micro-F1 score on the NYT dataset, compare the NYT Mi-F1 scores of all methods. CoType has the highest score of 0.66. So, the answer is {\"answer\": \"CoType\"}." } ], "images": [ "scigen/train/3233.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Performance comparison of entity recognition and typing (using strict, micro and macro metrics [26]) on the three datasets." ], "reasoning": "To find the method with the highest Micro-F1 score on the NYT dataset, compare the NYT Mi-F1 scores of all methods. CoType has the highest score of 0.66.", "question": "Which method has the highest Micro-F1 score on the NYT dataset?", "answer": "So, the answer is {\"answer\": \"CoType\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: CoType has the highest S-F1 score on the BioInfer dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nCoType has the highest S-F1 score on the BioInfer dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the BioInfer S-F1 scores of all methods. CoType has the highest score of 0.74. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3233.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Performance comparison of entity recognition and typing (using strict, micro and macro metrics [26]) on the three datasets." ], "reasoning": "To verify this statement, compare the BioInfer S-F1 scores of all methods. CoType has the highest score of 0.74.", "statement": "CoType has the highest S-F1 score on the BioInfer dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 6: Performance comparison on relation classification accuracy over ground-truth relation mentions on the three datasets.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "CoType achieves the highest accuracy across all datasets, demonstrating the effectiveness of its translation-based embedding objective in capturing entity-relation cross-constraints.2. Performance on Relation Classification. To test the effectiveness of the learned embeddings in representing type semantics of relation mentions, we compare with other methods on classifying the ground-truth relation mention in the evaluation set by target types CoType achieves superior accuracy compared to all other methods and variants (e.g., obtains over 10% enhancement on both the NYT and BioInfer datasets over the next best method). All compared methods (except for MultiR) simply treat DL as “perfectly labeled” when training models. Comparing CoType-RM with MultiR, superior performance of CoType-RM demonstrates the effectiveness of partial-label loss over multi-instance learning. Finally, CoType outperforms CoType-RM and CoType-TwoStep validates that the propose translation-based embedding objective is effective in capturing entity-relation cross-constraints." } ], "images": [ "scigen/train/3234.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Performance comparison on relation classification accuracy over ground-truth relation mentions on the three datasets." ], "reasoning": "CoType achieves the highest accuracy across all datasets, demonstrating the effectiveness of its translation-based embedding objective in capturing entity-relation cross-constraints.", "description": "2. Performance on Relation Classification. To test the effectiveness of the learned embeddings in representing type semantics of relation mentions, we compare with other methods on classifying the ground-truth relation mention in the evaluation set by target types CoType achieves superior accuracy compared to all other methods and variants (e.g., obtains over 10% enhancement on both the NYT and BioInfer datasets over the next best method). All compared methods (except for MultiR) simply treat DL as “perfectly labeled” when training models. Comparing CoType-RM with MultiR, superior performance of CoType-RM demonstrates the effectiveness of partial-label loss over multi-instance learning. Finally, CoType outperforms CoType-RM and CoType-TwoStep validates that the propose translation-based embedding objective is effective in capturing entity-relation cross-constraints.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 6: Performance comparison on relation classification accuracy over ground-truth relation mentions on the three datasets.' and proceed to solve the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich method achieves the highest accuracy on the BioInfer dataset?\n" }, { "role": "assistant", "content": "By comparing the accuracy values for each method on the BioInfer dataset, CoType has the highest accuracy with a value of 0.617. So, the answer is {\"answer\": \"CoType\"}." } ], "images": [ "scigen/train/3234.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Performance comparison on relation classification accuracy over ground-truth relation mentions on the three datasets." ], "reasoning": "By comparing the accuracy values for each method on the BioInfer dataset, CoType has the highest accuracy with a value of 0.617.", "question": "Which method achieves the highest accuracy on the BioInfer dataset?", "answer": "So, the answer is {\"answer\": \"CoType\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nCoType-RM outperforms CoType-TwoStep on the NYT dataset.\n" }, { "role": "assistant", "content": "By comparing the accuracy values for CoType-RM and CoType-TwoStep on the NYT dataset, CoType-RM has an accuracy of 0.812, while CoType-TwoStep has an accuracy of 0.829. Therefore, CoType-RM does not outperform CoType-TwoStep on the NYT dataset. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3234.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Performance comparison on relation classification accuracy over ground-truth relation mentions on the three datasets." ], "reasoning": "By comparing the accuracy values for CoType-RM and CoType-TwoStep on the NYT dataset, CoType-RM has an accuracy of 0.812, while CoType-TwoStep has an accuracy of 0.829. Therefore, CoType-RM does not outperform CoType-TwoStep on the NYT dataset.", "statement": "CoType-RM outperforms CoType-TwoStep on the NYT dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Fine-grained WSD F1.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "KnowBert-W+W matches the performance of KnowBert-WordNet and provides competitive performance compared to other baselines like ELMo and BERT.By linking to nodes in our WordNet graph and restricting to gold lemmas at test time we can recast the WSD task under our general entity linking framework. The ELMo and BERT baselines use a nearest neighbor approach trained on the SemCor dataset, similar to the evaluation in Melamud et al. Raganato et al. As can be seen, KnowBert provides competitive performance, and KnowBert-W+W is able to match the performance of KnowBert-WordNet despite incorporating both Wikipedia and WordNet." } ], "images": [ "scigen/train/3235.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Fine-grained WSD F1." ], "reasoning": "KnowBert-W+W matches the performance of KnowBert-WordNet and provides competitive performance compared to other baselines like ELMo and BERT.", "description": "By linking to nodes in our WordNet graph and restricting to gold lemmas at test time we can recast the WSD task under our general entity linking framework. The ELMo and BERT baselines use a nearest neighbor approach trained on the SemCor dataset, similar to the evaluation in Melamud et al. Raganato et al. As can be seen, KnowBert provides competitive performance, and KnowBert-W+W is able to match the performance of KnowBert-WordNet despite incorporating both Wikipedia and WordNet.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: Fine-grained WSD F1.' and methodically tackle the question that follows. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich system has the highest F1 score?\n" }, { "role": "assistant", "content": "By examining the F1 scores in the table, KnowBert-W+W has the highest F1 score of 75.1. So, the answer is {\"answer\": \"KnowBert-W+W\"}" } ], "images": [ "scigen/train/3235.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Fine-grained WSD F1." ], "reasoning": "By examining the F1 scores in the table, KnowBert-W+W has the highest F1 score of 75.1.", "question": "Which system has the highest F1 score?", "answer": "So, the answer is {\"answer\": \"KnowBert-W+W\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nKnowBert-WordNet has a higher F1 score than BERTLARGE.\n" }, { "role": "assistant", "content": "The table shows that KnowBert-WordNet has an F1 score of 74.9, while BERTLARGE has an F1 score of 73.9, indicating that KnowBert-WordNet has a higher F1 score. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3235.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Fine-grained WSD F1." ], "reasoning": "The table shows that KnowBert-WordNet has an F1 score of 74.9, while BERTLARGE has an F1 score of 73.9, indicating that KnowBert-WordNet has a higher F1 score.", "statement": "KnowBert-WordNet has a higher F1 score than BERTLARGE.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: Comparison of masked LM perplexity, Wikidata probing MRR, and number of parameters (in millions) in the masked LM (word piece embeddings, transformer layers, and output layers), KAR, and entity embeddings for BERT and KnowBert. The table also includes the total time to run one forward and backward pass (in seconds) on a TITAN Xp GPU (12 GB RAM) for a batch of 32 sentence pairs with total length 80 word pieces. Due to memory constraints, the BERTLARGE batch is accumulated over two smaller batches.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "KnowBert models, derived from BERTBASE, outperform BERTLARGE in masked LM perplexity.To rule out minor differences due to our data preparation, the BERT models are fine-tuned on our training data before being evaluated. Overall, KnowBert improves the masked LM perplexity, with all KnowBert models outperforming BERTLARGE, despite being derived from BERTBASE." } ], "images": [ "scigen/train/3236.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of masked LM perplexity, Wikidata probing MRR, and number of parameters (in millions) in the masked LM (word piece embeddings, transformer layers, and output layers), KAR, and entity embeddings for BERT and KnowBert. The table also includes the total time to run one forward and backward pass (in seconds) on a TITAN Xp GPU (12 GB RAM) for a batch of 32 sentence pairs with total length 80 word pieces. Due to memory constraints, the BERTLARGE batch is accumulated over two smaller batches." ], "reasoning": "KnowBert models, derived from BERTBASE, outperform BERTLARGE in masked LM perplexity.", "description": "To rule out minor differences due to our data preparation, the BERT models are fine-tuned on our training data before being evaluated. Overall, KnowBert improves the masked LM perplexity, with all KnowBert models outperforming BERTLARGE, despite being derived from BERTBASE.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 1: Comparison of masked LM perplexity, Wikidata probing MRR, and number of parameters (in millions) in the masked LM (word piece embeddings, transformer layers, and output layers), KAR, and entity embeddings for BERT and KnowBert. The table also includes the total time to run one forward and backward pass (in seconds) on a TITAN Xp GPU (12 GB RAM) for a batch of 32 sentence pairs with total length 80 word pieces. Due to memory constraints, the BERTLARGE batch is accumulated over two smaller batches.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. \n\nQuestion: Which model has the lowest perplexity and what is its Wikidata MRR?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows that KnowBert-W+W has the lowest perplexity (3.5) and its Wikidata MRR is 0.31. So, the answer is {\"answer\": \"KnowBert-W+W with Wikidata MRR of 0.31\"}" } ], "images": [ "scigen/train/3236.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of masked LM perplexity, Wikidata probing MRR, and number of parameters (in millions) in the masked LM (word piece embeddings, transformer layers, and output layers), KAR, and entity embeddings for BERT and KnowBert. The table also includes the total time to run one forward and backward pass (in seconds) on a TITAN Xp GPU (12 GB RAM) for a batch of 32 sentence pairs with total length 80 word pieces. Due to memory constraints, the BERTLARGE batch is accumulated over two smaller batches." ], "reasoning": "The table shows that KnowBert-W+W has the lowest perplexity (3.5) and its Wikidata MRR is 0.31.", "question": "Which model has the lowest perplexity and what is its Wikidata MRR?", "answer": "So, the answer is {\"answer\": \"KnowBert-W+W with Wikidata MRR of 0.31\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence KnowBert-Wiki has a higher Wikidata MRR than BERTLARGE. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nKnowBert-Wiki has a higher Wikidata MRR than BERTLARGE.\n" }, { "role": "assistant", "content": "The table shows that KnowBert-Wiki has a Wikidata MRR of 0.26, while BERTLARGE has a Wikidata MRR of 0.11. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3236.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of masked LM perplexity, Wikidata probing MRR, and number of parameters (in millions) in the masked LM (word piece embeddings, transformer layers, and output layers), KAR, and entity embeddings for BERT and KnowBert. The table also includes the total time to run one forward and backward pass (in seconds) on a TITAN Xp GPU (12 GB RAM) for a batch of 32 sentence pairs with total length 80 word pieces. Due to memory constraints, the BERTLARGE batch is accumulated over two smaller batches." ], "reasoning": "The table shows that KnowBert-Wiki has a Wikidata MRR of 0.26, while BERTLARGE has a Wikidata MRR of 0.11.", "statement": "KnowBert-Wiki has a higher Wikidata MRR than BERTLARGE.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: End-to-end entity linking strong match, micro averaged F1.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "KnowBert's performance is lower than Kolitsas et al. due to the selective annotation in the AIDA data, which only annotates named entities, while KnowBert's candidate selector generates mentions for all entities including common nouns.Here, KnowBert’s performance lags behind the current state-of-the-art model from Kolitsas et al. Daiber et al. We believe this is due to the selective annotation in the AIDA data that only annotates named entities. The CrossWikis-based candidate selector used in KnowBert generates candidate mentions for all entities including common nouns from which KnowBert may be learning to extract information, at the detriment of specializing to maximize linking performance for AIDA." } ], "images": [ "scigen/train/3237.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: End-to-end entity linking strong match, micro averaged F1." ], "reasoning": "KnowBert's performance is lower than Kolitsas et al. due to the selective annotation in the AIDA data, which only annotates named entities, while KnowBert's candidate selector generates mentions for all entities including common nouns.", "description": "Here, KnowBert’s performance lags behind the current state-of-the-art model from Kolitsas et al. Daiber et al. We believe this is due to the selective annotation in the AIDA data that only annotates named entities. The CrossWikis-based candidate selector used in KnowBert generates candidate mentions for all entities including common nouns from which KnowBert may be learning to extract information, at the detriment of specializing to maximize linking performance for AIDA.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: Table 3: End-to-end entity linking strong match, micro averaged F1.\nQuestion: Which system has the highest performance on AIDA-A and what is the score?\n" }, { "role": "assistant", "content": "By comparing the scores in the AIDA-A column, Kolitsas et al. (2018) has the highest performance with a score of 86.6. So, the answer is {\"answer\": \"Kolitsas et al. (2018) with 86.6\"}." } ], "images": [ "scigen/train/3237.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: End-to-end entity linking strong match, micro averaged F1." ], "reasoning": "By comparing the scores in the AIDA-A column, Kolitsas et al. (2018) has the highest performance with a score of 86.6.", "question": "Which system has the highest performance on AIDA-A and what is the score?", "answer": "So, the answer is {\"answer\": \"Kolitsas et al. (2018) with 86.6\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nKnowBert-Wiki outperforms Hoffart et al. (2011) on AIDA-B.\n" }, { "role": "assistant", "content": "By comparing the scores in the AIDA-B column, Hoffart et al. (2011) has a score of 71.9, while KnowBert-Wiki has a score of 74.4, indicating that KnowBert-Wiki outperforms Hoffart et al. (2011) on AIDA-B. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3237.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: End-to-end entity linking strong match, micro averaged F1." ], "reasoning": "By comparing the scores in the AIDA-B column, Hoffart et al. (2011) has a score of 71.9, while KnowBert-Wiki has a score of 74.4, indicating that KnowBert-Wiki outperforms Hoffart et al. (2011) on AIDA-B.", "statement": "KnowBert-Wiki outperforms Hoffart et al. (2011) on AIDA-B.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 7: Results (WERs) using mono and multi-lingual acoustic models; the off-diagonals numbers represent WERs obtained on non-native speech.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The multi-lingual system shows more robustness against non-native speech, with significantly lower WERs for young students speaking a foreign language when the target language is English compared to German.Nevertheless, the multi-lingual system shows more robustness against non-native speech: the off-diagonals WERs, that represent the performance for young students speaking a foreign language, are significantly lower when the target language is English (39.8% against 53.2 and 31.7 against 40.4) whilst it similar in case of German (i.e. 18.5% against 17.4%)." } ], "images": [ "scigen/train/3238.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Results (WERs) using mono and multi-lingual acoustic models; the off-diagonals numbers represent WERs obtained on non-native speech." ], "reasoning": "The multi-lingual system shows more robustness against non-native speech, with significantly lower WERs for young students speaking a foreign language when the target language is English compared to German.", "description": "Nevertheless, the multi-lingual system shows more robustness against non-native speech: the off-diagonals WERs, that represent the performance for young students speaking a foreign language, are significantly lower when the target language is English (39.8% against 53.2 and 31.7 against 40.4) whilst it similar in case of German (i.e. 18.5% against 17.4%).", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 7: Results (WERs) using mono and multi-lingual acoustic models; the off-diagonals numbers represent WERs obtained on non-native speech.' and methodically tackle the question that follows. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the WER for German speakers using multi-lingual AMs when the target language is English?\n" }, { "role": "assistant", "content": "To find the WER for German speakers using multi-lingual AMs when the target language is English, locate the intersection of the 'German' row and 'English' column under 'multi-lingual AM'. So, the answer is {\"answer\": \"31.7\"}." } ], "images": [ "scigen/train/3238.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Results (WERs) using mono and multi-lingual acoustic models; the off-diagonals numbers represent WERs obtained on non-native speech." ], "reasoning": "To find the WER for German speakers using multi-lingual AMs when the target language is English, locate the intersection of the 'German' row and 'English' column under 'multi-lingual AM'.", "question": "What is the WER for German speakers using multi-lingual AMs when the target language is English?", "answer": "So, the answer is {\"answer\": \"31.7\"}." }, { "messages": [ { "role": "user", "content": "The WER for Italian speakers using mono-lingual AMs when the target language is German is 17.4%. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe WER for Italian speakers using mono-lingual AMs when the target language is German is 17.4%.\n" }, { "role": "assistant", "content": "To verify this statement, locate the intersection of the 'Italian' row and 'German' column under 'mono-lingual AMs'. The value is 17.4%, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3238.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Results (WERs) using mono and multi-lingual acoustic models; the off-diagonals numbers represent WERs obtained on non-native speech." ], "reasoning": "To verify this statement, locate the intersection of the 'Italian' row and 'German' column under 'mono-lingual AMs'. The value is 17.4%, which matches the statement.", "statement": "The WER for Italian speakers using mono-lingual AMs when the target language is German is 17.4%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 8: WERs obtained with mono- and multi-lingual acoustic models adapted to non-native speech using the three modalities m1,m2,m3.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The multi-lingual system improves WER for Italian students speaking German and English, and for Germans speaking English.Indeed, also in this case, the multi-lingual system exhibits a better behavior, allowing to produce more effective models for non-native speech: the WERs related to Italian students speaking in both German and English decrease from 11.1% to 9.6% and from 16.0% to 15.4%, respectively; larger gain is observed in case of Germans speaking in English (WER from 18.3% to 15.2%)." } ], "images": [ "scigen/train/3239.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: WERs obtained with mono- and multi-lingual acoustic models adapted to non-native speech using the three modalities m1,m2,m3." ], "reasoning": "The multi-lingual system improves WER for Italian students speaking German and English, and for Germans speaking English.", "description": "Indeed, also in this case, the multi-lingual system exhibits a better behavior, allowing to produce more effective models for non-native speech: the WERs related to Italian students speaking in both German and English decrease from 11.1% to 9.6% and from 16.0% to 15.4%, respectively; larger gain is observed in case of Germans speaking in English (WER from 18.3% to 15.2%).", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 8: WERs obtained with mono- and multi-lingual acoustic models adapted to non-native speech using the three modalities m1,m2,m3.', and tackle the question below in a sequential manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the WER improvement for Germans speaking English when using adapted multi-lingual AM (m2) compared to adapted mono-lingual AMs (m1)?\n" }, { "role": "assistant", "content": "The WER for Germans speaking English using adapted mono-lingual AMs (m1) is 18.3%, and using adapted multi-lingual AM (m2) is 15.2%. The improvement is calculated as 18.3% - 15.2%. So, the answer is {\"answer\": \"3.1%\"}" } ], "images": [ "scigen/train/3239.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: WERs obtained with mono- and multi-lingual acoustic models adapted to non-native speech using the three modalities m1,m2,m3." ], "reasoning": "The WER for Germans speaking English using adapted mono-lingual AMs (m1) is 18.3%, and using adapted multi-lingual AM (m2) is 15.2%. The improvement is calculated as 18.3% - 15.2%.", "question": "What is the WER improvement for Germans speaking English when using adapted multi-lingual AM (m2) compared to adapted mono-lingual AMs (m1)?", "answer": "So, the answer is {\"answer\": \"3.1%\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe WER for Italian students speaking German improves when using adapted multi-lingual AM (m2) compared to adapted mono-lingual AMs (m1).\n" }, { "role": "assistant", "content": "The WER for Italian students speaking German using adapted mono-lingual AMs (m1) is 11.1%, and using adapted multi-lingual AM (m2) is 9.6%. The WER decreases, indicating an improvement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3239.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: WERs obtained with mono- and multi-lingual acoustic models adapted to non-native speech using the three modalities m1,m2,m3." ], "reasoning": "The WER for Italian students speaking German using adapted mono-lingual AMs (m1) is 11.1%, and using adapted multi-lingual AM (m2) is 9.6%. The WER decreases, indicating an improvement.", "statement": "The WER for Italian students speaking German improves when using adapted multi-lingual AM (m2) compared to adapted mono-lingual AMs (m1).", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title '(b) Our model'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "GraLap with only FF shows a 41% improvement in ρ over SVR+O, and the inclusion of PF further improves the model marginally.We observe that GraLap with only FF outperforms SVR+O with 41% improvement of ρ. As expected, the inclusion of PF into the model improves the model marginally. However, the overall performance of GraLap is significantly higher than any of the baselines (p<0.01)." } ], "images": [ "scigen/train/3240.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) Our model" ], "reasoning": "GraLap with only FF shows a 41% improvement in ρ over SVR+O, and the inclusion of PF further improves the model marginally.", "description": "We observe that GraLap with only FF outperforms SVR+O with 41% improvement of ρ. As expected, the inclusion of PF into the model improves the model marginally. However, the overall performance of GraLap is significantly higher than any of the baselines (p<0.01).", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the '(b) Our model', solve the subsequent question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhat is the ρ value for the model that includes PF?\n" }, { "role": "assistant", "content": "The table shows that the model with PF (No. 6) has a ρ value of 0.90. So, the answer is {\"answer\": \"0.90\"}." } ], "images": [ "scigen/train/3240.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) Our model" ], "reasoning": "The table shows that the model with PF (No. 6) has a ρ value of 0.90.", "question": "What is the ρ value for the model that includes PF?", "answer": "So, the answer is {\"answer\": \"0.90\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The model with SF has a higher ρ value than the model with MF. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model with SF has a higher ρ value than the model with MF.\n" }, { "role": "assistant", "content": "The table shows that the model with SF (No. 5) has a ρ value of 0.90, while the model with MF (No. 4) has a ρ value of 0.89. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3240.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) Our model" ], "reasoning": "The table shows that the model with SF (No. 5) has a ρ value of 0.90, while the model with MF (No. 4) has a ρ value of 0.89.", "statement": "The model with SF has a higher ρ value than the model with MF.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title '(a) Baselines'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "GraLap with only FF shows a 41% improvement in ρ over SVR+O, and the inclusion of PF improves the model marginally, making GraLap significantly better than any baselines (p<0.01).We observe that GraLap with only FF outperforms SVR+O with 41% improvement of ρ. As expected, the inclusion of PF into the model improves the model marginally. However, the overall performance of GraLap is significantly higher than any of the baselines (p<0.01)." } ], "images": [ "scigen/train/3241.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) Baselines" ], "reasoning": "GraLap with only FF shows a 41% improvement in ρ over SVR+O, and the inclusion of PF improves the model marginally, making GraLap significantly better than any baselines (p<0.01).", "description": "We observe that GraLap with only FF outperforms SVR+O with 41% improvement of ρ. As expected, the inclusion of PF into the model improves the model marginally. However, the overall performance of GraLap is significantly higher than any of the baselines (p<0.01).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title '(a) Baselines', unfold the steps to solve the question given next. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nQuestion: Which model has the highest ρ value and how does it compare to the RMSE of the model with the lowest ρ value?\n" }, { "role": "assistant", "content": "The model with the highest ρ value is [HTML]96FFFB SVR+O with ρ = 0.56. The model with the lowest ρ value is Uniform with ρ = -0.05. Comparing their RMSE values, [HTML]96FFFB SVR+O has an RMSE of 1.92, while Uniform has an RMSE of 2.09. So, the answer is {\"answer\": \"[HTML]96FFFB SVR+O has the highest ρ value of 0.56 and Uniform has the lowest ρ value of -0.05 with RMSE values of 1.92 and 2.09 respectively.\"}." } ], "images": [ "scigen/train/3241.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) Baselines" ], "reasoning": "The model with the highest ρ value is [HTML]96FFFB SVR+O with ρ = 0.56. The model with the lowest ρ value is Uniform with ρ = -0.05. Comparing their RMSE values, [HTML]96FFFB SVR+O has an RMSE of 1.92, while Uniform has an RMSE of 2.09.", "question": "Which model has the highest ρ value and how does it compare to the RMSE of the model with the lowest ρ value?", "answer": "So, the answer is {\"answer\": \"[HTML]96FFFB SVR+O has the highest ρ value of 0.56 and Uniform has the lowest ρ value of -0.05 with RMSE values of 1.92 and 2.09 respectively.\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The model with the highest R2 value is C4.5SSL., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model with the highest R2 value is C4.5SSL.\n" }, { "role": "assistant", "content": "The table shows that C4.5SSL has an R2 value of 2.46, which is the highest among the listed models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3241.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) Baselines" ], "reasoning": "The table shows that C4.5SSL has an R2 value of 2.46, which is the highest among the listed models.", "statement": "The model with the highest R2 value is C4.5SSL.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 7: Average and 95th percentile of context length of datasets in wordpieces. WH: WikiHop, TQA: TriviaQA, HQA: HotpotQA, ON: OntoNotes, HY: Hyperpartisan news'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The Longformer model is evaluated on multiple long document tasks to determine if its attention mechanism can replace the standard self-attention in BERT models and simplify task-specific models.We apply Longformer to multiple long document tasks, including QA, coreference resolution and classification. Tab. Our primary goal is to evaluate whether our attention mechanism can act as a replacement for the standard self-attention mechanism in BERT style models, and to perform controlled trials against a strong baseline. We are also interested in evaluating whether we can replace complicated task specific models necessitated by BERT’s limited context with simpler models that just concatenate all available context into a single sequence." } ], "images": [ "scigen/train/3242.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Average and 95th percentile of context length of datasets in wordpieces. WH: WikiHop, TQA: TriviaQA, HQA: HotpotQA, ON: OntoNotes, HY: Hyperpartisan news" ], "reasoning": "The Longformer model is evaluated on multiple long document tasks to determine if its attention mechanism can replace the standard self-attention in BERT models and simplify task-specific models.", "description": "We apply Longformer to multiple long document tasks, including QA, coreference resolution and classification. Tab. Our primary goal is to evaluate whether our attention mechanism can act as a replacement for the standard self-attention mechanism in BERT style models, and to perform controlled trials against a strong baseline. We are also interested in evaluating whether we can replace complicated task specific models necessitated by BERT’s limited context with simpler models that just concatenate all available context into a single sequence.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 7: Average and 95th percentile of context length of datasets in wordpieces. WH: WikiHop, TQA: TriviaQA, HQA: HotpotQA, ON: OntoNotes, HY: Hyperpartisan news', unfold the steps to solve the question given next. Output the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: What is the average number of wordpieces for the TQA task?\n" }, { "role": "assistant", "content": "The table lists the average number of wordpieces for various tasks, including TQA, which is 6,589. So, the answer is {\"answer\": \"6,589\"}" } ], "images": [ "scigen/train/3242.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Average and 95th percentile of context length of datasets in wordpieces. WH: WikiHop, TQA: TriviaQA, HQA: HotpotQA, ON: OntoNotes, HY: Hyperpartisan news" ], "reasoning": "The table lists the average number of wordpieces for various tasks, including TQA, which is 6,589.", "question": "What is the average number of wordpieces for the TQA task?", "answer": "So, the answer is {\"answer\": \"6,589\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe 95th percentile of wordpieces for the HQA task is higher than that for the HY task.\n" }, { "role": "assistant", "content": "The table shows that the 95th percentile of wordpieces for the HQA task is 1,889, while for the HY task it is 1,975, indicating that the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3242.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Average and 95th percentile of context length of datasets in wordpieces. WH: WikiHop, TQA: TriviaQA, HQA: HotpotQA, ON: OntoNotes, HY: Hyperpartisan news" ], "reasoning": "The table shows that the 95th percentile of wordpieces for the HQA task is 1,889, while for the HY task it is 1,975, indicating that the statement is refuted.", "statement": "The 95th percentile of wordpieces for the HQA task is higher than that for the HY task.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Top: changing window size across layers. Bottom: with/without dilation (@ 150K steps on phase1)', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Increasing the window size from the bottom to the top layer leads to the best performance, while adding dilation to two heads further improves it.The top of Tab. We observe that increasing the window size from the bottom to the top layer leads to the best performance, arranging them in the reverse way leads to worse performance, and using a fixed window size (the average of window sizes of the other configuration) leads to a performance that it is in between. The bottom of Tab. Adding some dilation to two heads leads to some improvement compared with no dilation at all. We ran the small model experiments on 4 RTX8000 GPUs for 16 days. For the large model, we ran experiments on 8 RTX8000 GPUs for 13 days. Most of our hyperparameter search is similar to the ablation in Tab. We experimented with absolute position embeddings and learned position embeddings, dropout values of [0.1, 0.2] (small model) and [0.1, 0.4] (large model), pre-layernorm and post-layernorm Xiong et al. Number of gradient updates/phase reported in Tab." } ], "images": [ "scigen/train/3243.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Top: changing window size across layers. Bottom: with/without dilation (@ 150K steps on phase1)" ], "reasoning": "Increasing the window size from the bottom to the top layer leads to the best performance, while adding dilation to two heads further improves it.", "description": "The top of Tab. We observe that increasing the window size from the bottom to the top layer leads to the best performance, arranging them in the reverse way leads to worse performance, and using a fixed window size (the average of window sizes of the other configuration) leads to a performance that it is in between. The bottom of Tab. Adding some dilation to two heads leads to some improvement compared with no dilation at all. We ran the small model experiments on 4 RTX8000 GPUs for 16 days. For the large model, we ran experiments on 8 RTX8000 GPUs for 13 days. Most of our hyperparameter search is similar to the ablation in Tab. We experimented with absolute position embeddings and learned position embeddings, dropout values of [0.1, 0.2] (small model) and [0.1, 0.4] (large model), pre-layernorm and post-layernorm Xiong et al. Number of gradient updates/phase reported in Tab.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: Table 4: Top: changing window size across layers. Bottom: with/without dilation (@ 150K steps on phase1)\nQuestion: What is the Dev BPC for the model with the best performance?\n" }, { "role": "assistant", "content": "The table shows that the model with 'Dilation on 2 heads' has the lowest Dev BPC of 1.20, indicating the best performance. So, the answer is {\"answer\": \"1.20\"}." } ], "images": [ "scigen/train/3243.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Top: changing window size across layers. Bottom: with/without dilation (@ 150K steps on phase1)" ], "reasoning": "The table shows that the model with 'Dilation on 2 heads' has the lowest Dev BPC of 1.20, indicating the best performance.", "question": "What is the Dev BPC for the model with the best performance?", "answer": "So, the answer is {\"answer\": \"1.20\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model with a fixed window size has the worst performance.\n" }, { "role": "assistant", "content": "The table shows that the model with 'Decreasing w (from 512 to 32)' has the highest Dev BPC of 1.24, indicating the worst performance, not the fixed window size model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3243.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Top: changing window size across layers. Bottom: with/without dilation (@ 150K steps on phase1)" ], "reasoning": "The table shows that the model with 'Decreasing w (from 512 to 32)' has the highest Dev BPC of 1.24, indicating the worst performance, not the fixed window size model.", "statement": "The model with a fixed window size has the worst performance.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 8: Summary of finetuning results on QA, coreference resolution, and document classification. Results are on the development sets comparing our Longformer-base with RoBERTa-base. TriviaQA, Hyperpartisan metrics are F1, WikiHop and IMDB use accuracy, HotpotQa is joint F1, OntoNotes is average F1.'.\n" }, { "role": "assistant", "content": "Longformer shows significant improvements over RoBERTa in tasks requiring long context, such as WikiHop and Hyperpartisan, while the gains are smaller for tasks with shorter context requirements like IMDB and OntoNotes.We observe that Longformer consistently outperforms the RoBERTa baseline. Its performance gain is especially obvious for tasks that require long context such as WikiHop and Hyperpartisan. For TriviaQA, the improvement is more modest as the local context is often sufficient to answer the question. In the case of HotpotQA, the supporting fact auxiliary supervision allows models to easily find relevant contexts and then focus on local context, leading to smaller gains. This is contrasted with WikiHop that only includes distant supervision of intermediate reasoning chains, where our approach excels by reasoning over the entire context. On the IMDB and OntoNotes datasets the performance gains are smaller. For IMDB, the majority of the dataset consists of short documents and thus it is expected to see smaller improvements. For OntoNotes, we found that the distance between any two mentions is typically quite small so that a baseline that processes smaller chunks separately is able to stitch together mentions into coreference chains without considering cross chunk interactions." } ], "images": [ "scigen/train/3244.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Summary of finetuning results on QA, coreference resolution, and document classification. Results are on the development sets comparing our Longformer-base with RoBERTa-base. TriviaQA, Hyperpartisan metrics are F1, WikiHop and IMDB use accuracy, HotpotQa is joint F1, OntoNotes is average F1." ], "reasoning": "Longformer shows significant improvements over RoBERTa in tasks requiring long context, such as WikiHop and Hyperpartisan, while the gains are smaller for tasks with shorter context requirements like IMDB and OntoNotes.", "description": "We observe that Longformer consistently outperforms the RoBERTa baseline. Its performance gain is especially obvious for tasks that require long context such as WikiHop and Hyperpartisan. For TriviaQA, the improvement is more modest as the local context is often sufficient to answer the question. In the case of HotpotQA, the supporting fact auxiliary supervision allows models to easily find relevant contexts and then focus on local context, leading to smaller gains. This is contrasted with WikiHop that only includes distant supervision of intermediate reasoning chains, where our approach excels by reasoning over the entire context. On the IMDB and OntoNotes datasets the performance gains are smaller. For IMDB, the majority of the dataset consists of short documents and thus it is expected to see smaller improvements. For OntoNotes, we found that the distance between any two mentions is typically quite small so that a baseline that processes smaller chunks separately is able to stitch together mentions into coreference chains without considering cross chunk interactions.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 8: Summary of finetuning results on QA, coreference resolution, and document classification. Results are on the development sets comparing our Longformer-base with RoBERTa-base. TriviaQA, Hyperpartisan metrics are F1, WikiHop and IMDB use accuracy, HotpotQa is joint F1, OntoNotes is average F1.' in mind, break down and resolve the question below step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhich model performs better on the QA WikiHop task and by how much?\n" }, { "role": "assistant", "content": "To determine which model performs better on the QA WikiHop task, compare the scores of RoBERTa-base and Longformer-base. Longformer-base scores 75.0, while RoBERTa-base scores 72.4. The difference is 75.0 - 72.4 = 2.6. So, the answer is {\"answer\": \"Longformer by 2.6\"}" } ], "images": [ "scigen/train/3244.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Summary of finetuning results on QA, coreference resolution, and document classification. Results are on the development sets comparing our Longformer-base with RoBERTa-base. TriviaQA, Hyperpartisan metrics are F1, WikiHop and IMDB use accuracy, HotpotQa is joint F1, OntoNotes is average F1." ], "reasoning": "To determine which model performs better on the QA WikiHop task, compare the scores of RoBERTa-base and Longformer-base. Longformer-base scores 75.0, while RoBERTa-base scores 72.4. The difference is 75.0 - 72.4 = 2.6.", "question": "Which model performs better on the QA WikiHop task and by how much?", "answer": "So, the answer is {\"answer\": \"Longformer by 2.6\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Longformer outperforms RoBERTa on the QA HotpotQA task. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nLongformer outperforms RoBERTa on the QA HotpotQA task.\n" }, { "role": "assistant", "content": "The table shows that Longformer-base has a score of 64.4 on QA HotpotQA, while RoBERTa-base has a score of 63.5, indicating that Longformer outperforms RoBERTa on this task. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3244.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Summary of finetuning results on QA, coreference resolution, and document classification. Results are on the development sets comparing our Longformer-base with RoBERTa-base. TriviaQA, Hyperpartisan metrics are F1, WikiHop and IMDB use accuracy, HotpotQa is joint F1, OntoNotes is average F1." ], "reasoning": "The table shows that Longformer-base has a score of 64.4 on QA HotpotQA, while RoBERTa-base has a score of 63.5, indicating that Longformer outperforms RoBERTa on this task.", "statement": "Longformer outperforms RoBERTa on the QA HotpotQA task.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 13: Hyperparameters of the QA models. All models use a similar scheduler with linear warmup and decay.'.\n" }, { "role": "assistant", "content": "The best configuration for training models includes different learning rates and warmup steps for WikiHop, TriviaQA, and HotpotQA, with all other hyperparameters similar to RoBERTa’s.Hyperparameters of the best configuration are listed in Tab. All other hyperparameters are similar to RoBERTa ’s. For hyperparameter search, we only tuned LR for the RoBERTa baseline and tried rates [3e-5, 5e-5, 1e-4], then used the best, which is 3e-5, for all subsequent experiments with no further tuning. We trained the Longformer-large with the best configuration once and submitted its output to the leaderboard. We ran our experiments on 32GB V100 GPUs. Small model takes 1 day to train on 4 GPUs, while large model takes 1 day on 8 GPUs." } ], "images": [ "scigen/train/3245.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 13: Hyperparameters of the QA models. All models use a similar scheduler with linear warmup and decay." ], "reasoning": "The best configuration for training models includes different learning rates and warmup steps for WikiHop, TriviaQA, and HotpotQA, with all other hyperparameters similar to RoBERTa’s.", "description": "Hyperparameters of the best configuration are listed in Tab. All other hyperparameters are similar to RoBERTa ’s. For hyperparameter search, we only tuned LR for the RoBERTa baseline and tried rates [3e-5, 5e-5, 1e-4], then used the best, which is 3e-5, for all subsequent experiments with no further tuning. We trained the Longformer-large with the best configuration once and submitted its output to the leaderboard. We ran our experiments on 32GB V100 GPUs. Small model takes 1 day to train on 4 GPUs, while large model takes 1 day on 8 GPUs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 13: Hyperparameters of the QA models. All models use a similar scheduler with linear warmup and decay.' and methodically tackle the question that follows. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the learning rate used for the HotpotQA configuration?\n" }, { "role": "assistant", "content": "The table lists the learning rates for each configuration. For HotpotQA, the learning rate is specified as 5e-5. So, the answer is {\"answer\": \"5e-5\"}" } ], "images": [ "scigen/train/3245.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 13: Hyperparameters of the QA models. All models use a similar scheduler with linear warmup and decay." ], "reasoning": "The table lists the learning rates for each configuration. For HotpotQA, the learning rate is specified as 5e-5.", "question": "What is the learning rate used for the HotpotQA configuration?", "answer": "So, the answer is {\"answer\": \"5e-5\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The WikiHop configuration uses 1000 warmup steps., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe WikiHop configuration uses 1000 warmup steps.\n" }, { "role": "assistant", "content": "The table shows that the WikiHop configuration uses 200 warmup steps, not 1000. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3245.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 13: Hyperparameters of the QA models. All models use a similar scheduler with linear warmup and decay." ], "reasoning": "The table shows that the WikiHop configuration uses 200 warmup steps, not 1000.", "statement": "The WikiHop configuration uses 1000 warmup steps.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 8: Ablation study for Training Signal Annealing (TSA) on Yelp-5 and CIFAR-10. The shown numbers are error rates.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The exp-schedule achieves the best performance with the lowest score of 41.35 on Yelp-5.More specifically, the best performance is achieved when we choose to postpone releasing the supervised training signal to the end of the training, i.e, exp-schedule leads to the best performance." } ], "images": [ "scigen/train/3246.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Ablation study for Training Signal Annealing (TSA) on Yelp-5 and CIFAR-10. The shown numbers are error rates." ], "reasoning": "The exp-schedule achieves the best performance with the lowest score of 41.35 on Yelp-5.", "description": "More specifically, the best performance is achieved when we choose to postpone releasing the supervised training signal to the end of the training, i.e, exp-schedule leads to the best performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 8: Ablation study for Training Signal Annealing (TSA) on Yelp-5 and CIFAR-10. The shown numbers are error rates.\nQuestion: Which TSA schedule achieves the best performance on Yelp-5?\n" }, { "role": "assistant", "content": "The table shows the performance scores for different TSA schedules on Yelp-5. The exp-schedule has the lowest score of 41.35, indicating the best performance. So, the answer is {\"answer\": \"exp-schedule\"}." } ], "images": [ "scigen/train/3246.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Ablation study for Training Signal Annealing (TSA) on Yelp-5 and CIFAR-10. The shown numbers are error rates." ], "reasoning": "The table shows the performance scores for different TSA schedules on Yelp-5. The exp-schedule has the lowest score of 41.35, indicating the best performance.", "question": "Which TSA schedule achieves the best performance on Yelp-5?", "answer": "So, the answer is {\"answer\": \"exp-schedule\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe linear-schedule achieves the best performance on Yelp-5.\n" }, { "role": "assistant", "content": "The table shows that the exp-schedule has the lowest score of 41.35, which is better than the linear-schedule's score of 45.41. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3246.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Ablation study for Training Signal Annealing (TSA) on Yelp-5 and CIFAR-10. The shown numbers are error rates." ], "reasoning": "The table shows that the exp-schedule has the lowest score of 41.35, which is better than the linear-schedule's score of 45.41. Therefore, the statement is refuted.", "statement": "The linear-schedule achieves the best performance on Yelp-5.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Performance of CINEX on recognizing counting quantifier mentions, with different architectures and in comparison with the baseline. Highest F1-score per relation in boldface.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Feature-based CRF models are the most robust for sequence labeling, but neural models achieve higher F1-scores for containsAdmin. Adding a CRF layer to bidirectional LSTM models improves performance but still falls short of feature-based CRF models.the performance results of different architectures wrt. precision, recall and F1-score. As one can see, feature-based CRF models are the most robust sequence labeling approach across relations for this task, although the neural models achieve higher F1-score with 3.3 percentage point difference for containsAdmin. Adding a CRF layer on top of bidirectional LSTM models improves performance across relations, although this architecture still fails to beat the feature-based CRF models in most cases. We conjecture that this is due to neural models being much more prone to overfitting to noisy distantly supervised training data. Still, both feature-based and neural models consistently outperform the baseline by a large margin, in particular wrt. precision." } ], "images": [ "scigen/train/3247.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of CINEX on recognizing counting quantifier mentions, with different architectures and in comparison with the baseline. Highest F1-score per relation in boldface." ], "reasoning": "Feature-based CRF models are the most robust for sequence labeling, but neural models achieve higher F1-scores for containsAdmin. Adding a CRF layer to bidirectional LSTM models improves performance but still falls short of feature-based CRF models.", "description": "the performance results of different architectures wrt. precision, recall and F1-score. As one can see, feature-based CRF models are the most robust sequence labeling approach across relations for this task, although the neural models achieve higher F1-score with 3.3 percentage point difference for containsAdmin. Adding a CRF layer on top of bidirectional LSTM models improves performance across relations, although this architecture still fails to beat the feature-based CRF models in most cases. We conjecture that this is due to neural models being much more prone to overfitting to noisy distantly supervised training data. Still, both feature-based and neural models consistently outperform the baseline by a large margin, in particular wrt. precision.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 2: Performance of CINEX on recognizing counting quantifier mentions, with different architectures and in comparison with the baseline. Highest F1-score per relation in boldface.' and proceed to solve the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model achieves the highest F1-score for the containsAdmin relation?\n" }, { "role": "assistant", "content": "The table shows that the CINEX biLSTM-CRF model achieves the highest F1-score for the containsAdmin relation with a score of 81.4. So, the answer is {\"answer\": \"CINEX biLSTM-CRF\"}" } ], "images": [ "scigen/train/3247.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of CINEX on recognizing counting quantifier mentions, with different architectures and in comparison with the baseline. Highest F1-score per relation in boldface." ], "reasoning": "The table shows that the CINEX biLSTM-CRF model achieves the highest F1-score for the containsAdmin relation with a score of 81.4.", "question": "Which model achieves the highest F1-score for the containsAdmin relation?", "answer": "So, the answer is {\"answer\": \"CINEX biLSTM-CRF\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The CINEX CRF model achieves the highest F1-score for the containsAdmin relation. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe CINEX CRF model achieves the highest F1-score for the containsAdmin relation.\n" }, { "role": "assistant", "content": "The table shows that the CINEX biLSTM-CRF model, not the CINEX CRF model, achieves the highest F1-score for the containsAdmin relation with a score of 81.4. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3247.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of CINEX on recognizing counting quantifier mentions, with different architectures and in comparison with the baseline. Highest F1-score per relation in boldface." ], "reasoning": "The table shows that the CINEX biLSTM-CRF model, not the CINEX CRF model, achieves the highest F1-score for the containsAdmin relation with a score of 81.4.", "statement": "The CINEX CRF model achieves the highest F1-score for the containsAdmin relation.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Performance of CINEX-CRF on recognizing counting quantifier mentions, per mention type. Numt. stands for number-related terms, Art. for indefinite articles. Baseline comparison is only for cardinals (highest F1-score per relation in boldface).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "CINEX-CRF achieves higher precision and F1-scores compared to the baseline, especially in the hasSpouse relation.This provides a more fair comparison with the baseline that only considers cardinal numbers. Still, CINEX-CRF achieves a higher precision on all relations, and a higher F1-score on 4 out of 5. We also see variety within the mention types and relations, ordinals for instance being well picked up for hasSpouse, but badly for hasChild." } ], "images": [ "scigen/train/3248.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of CINEX-CRF on recognizing counting quantifier mentions, per mention type. Numt. stands for number-related terms, Art. for indefinite articles. Baseline comparison is only for cardinals (highest F1-score per relation in boldface)." ], "reasoning": "CINEX-CRF achieves higher precision and F1-scores compared to the baseline, especially in the hasSpouse relation.", "description": "This provides a more fair comparison with the baseline that only considers cardinal numbers. Still, CINEX-CRF achieves a higher precision on all relations, and a higher F1-score on 4 out of 5. We also see variety within the mention types and relations, ordinals for instance being well picked up for hasSpouse, but badly for hasChild.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: Table 3: Performance of CINEX-CRF on recognizing counting quantifier mentions, per mention type. Numt. stands for number-related terms, Art. for indefinite articles. Baseline comparison is only for cardinals (highest F1-score per relation in boldface).\nQuestion: Which relation has the highest F1-score using CINEX-CRF (per type) Cardinals?\n" }, { "role": "assistant", "content": "By examining the F1-scores under the CINEX-CRF (per type) Cardinals column, the highest F1-score is for the containsAdmin relation with a score of 83.5. So, the answer is {\"answer\": \"containsAdmin\"}" } ], "images": [ "scigen/train/3248.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of CINEX-CRF on recognizing counting quantifier mentions, per mention type. Numt. stands for number-related terms, Art. for indefinite articles. Baseline comparison is only for cardinals (highest F1-score per relation in boldface)." ], "reasoning": "By examining the F1-scores under the CINEX-CRF (per type) Cardinals column, the highest F1-score is for the containsAdmin relation with a score of 83.5.", "question": "Which relation has the highest F1-score using CINEX-CRF (per type) Cardinals?", "answer": "So, the answer is {\"answer\": \"containsAdmin\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe hasChild relation has the lowest precision using CINEX-CRF (per type) Ordinals.\n" }, { "role": "assistant", "content": "By comparing the precision values under the CINEX-CRF (per type) Ordinals column, the hasChild relation has a precision of 7.7, which is the lowest among the listed relations. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3248.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of CINEX-CRF on recognizing counting quantifier mentions, per mention type. Numt. stands for number-related terms, Art. for indefinite articles. Baseline comparison is only for cardinals (highest F1-score per relation in boldface)." ], "reasoning": "By comparing the precision values under the CINEX-CRF (per type) Ordinals column, the hasChild relation has a precision of 7.7, which is the lowest among the listed relations.", "statement": "The hasChild relation has the lowest precision using CINEX-CRF (per type) Ordinals.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Performance of CINEX-CRF in consolidating counting quantifier mentions wrt. precision (P), coverage (Cov) and MAE. Numt. stands for number-related terms, Art. for articles. Results per type show contribution (Contr) to overall output and precision of individual types.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Number-related terms and articles improve coverage for containsWork and hasMember but reduce precision for hasChild. Overall, considering compositionality and mention types other than cardinals enhances both accuracy and coverage, with a maximum MAE of 2.6 across relations.We report the results broken down per mention type, as well as in overall. Number-related terms and articles are beneficial in improving coverage particularly for containsWork and hasMember, yet produce low precision results for hasChild, possibly due to spurious indefinite articles frequently identified as counting quantifiers. Overall, taking compositionality as well as mention types other than cardinals into account improve both accuracy and coverage of the system, with MAE of not more than 2.6 across relations. We obtain a high accuracy of 92.3% for hasChild and 71.9% for hasSpouse. A main reason is that ordinals only reliably express lower bounds (see e.g. fourth incorrect example). If one considers ordinals as correct whenever they are not higher than the true count, the reported precision scores increase from 14.3-63.2% to 85.7-89.5%." } ], "images": [ "scigen/train/3249.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance of CINEX-CRF in consolidating counting quantifier mentions wrt. precision (P), coverage (Cov) and MAE. Numt. stands for number-related terms, Art. for articles. Results per type show contribution (Contr) to overall output and precision of individual types." ], "reasoning": "Number-related terms and articles improve coverage for containsWork and hasMember but reduce precision for hasChild. Overall, considering compositionality and mention types other than cardinals enhances both accuracy and coverage, with a maximum MAE of 2.6 across relations.", "description": "We report the results broken down per mention type, as well as in overall. Number-related terms and articles are beneficial in improving coverage particularly for containsWork and hasMember, yet produce low precision results for hasChild, possibly due to spurious indefinite articles frequently identified as counting quantifiers. Overall, taking compositionality as well as mention types other than cardinals into account improve both accuracy and coverage of the system, with MAE of not more than 2.6 across relations. We obtain a high accuracy of 92.3% for hasChild and 71.9% for hasSpouse. A main reason is that ordinals only reliably express lower bounds (see e.g. fourth incorrect example). If one considers ordinals as correct whenever they are not higher than the true count, the reported precision scores increase from 14.3-63.2% to 85.7-89.5%.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 4: Performance of CINEX-CRF in consolidating counting quantifier mentions wrt. precision (P), coverage (Cov) and MAE. Numt. stands for number-related terms, Art. for articles. Results per type show contribution (Contr) to overall output and precision of individual types.\nQuestion: What is the precision score for hasSpouse using CINEX-CRF (per type) Ordinals?\n" }, { "role": "assistant", "content": "The table shows the precision score for hasSpouse using CINEX-CRF (per type) Ordinals as 63.2%. So, the answer is {\"answer\": \"63.2%\"}." } ], "images": [ "scigen/train/3249.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance of CINEX-CRF in consolidating counting quantifier mentions wrt. precision (P), coverage (Cov) and MAE. Numt. stands for number-related terms, Art. for articles. Results per type show contribution (Contr) to overall output and precision of individual types." ], "reasoning": "The table shows the precision score for hasSpouse using CINEX-CRF (per type) Ordinals as 63.2%.", "question": "What is the precision score for hasSpouse using CINEX-CRF (per type) Ordinals?", "answer": "So, the answer is {\"answer\": \"63.2%\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe MAE for containsAdmin using CINEX-CRF is 1.7.\n" }, { "role": "assistant", "content": "The table indicates that the MAE for containsAdmin using CINEX-CRF is indeed 1.7. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3249.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance of CINEX-CRF in consolidating counting quantifier mentions wrt. precision (P), coverage (Cov) and MAE. Numt. stands for number-related terms, Art. for articles. Results per type show contribution (Contr) to overall output and precision of individual types." ], "reasoning": "The table indicates that the MAE for containsAdmin using CINEX-CRF is indeed 1.7.", "statement": "The MAE for containsAdmin using CINEX-CRF is 1.7.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: RACE and DREAM test accuracy of various judge models using the full passage. Our agents use these models to find evidence. The models cover a spectrum of QA ability. (*) reports ceiling accuracy from original dataset papers.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "BERT models outperform word-based baselines, with BERTLARGE achieving the highest accuracy, but no model reaches human performance on RACE or DREAM.Our setup is not directly comparable to standard QA setups, as we aim to evaluate evidence rather than raw QA accuracy. However, each judge model’s accuracy is useful to know for analysis purposes. BERT models significantly outperform word-based baselines (TFIDF and fastText), and BERTLARGE achieves the best overall accuracy. No model achieves the estimated human ceiling for either RACE lai2017race or DREAM sun2018dream." } ], "images": [ "scigen/train/3250.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: RACE and DREAM test accuracy of various judge models using the full passage. Our agents use these models to find evidence. The models cover a spectrum of QA ability. (*) reports ceiling accuracy from original dataset papers." ], "reasoning": "BERT models outperform word-based baselines, with BERTLARGE achieving the highest accuracy, but no model reaches human performance on RACE or DREAM.", "description": "Our setup is not directly comparable to standard QA setups, as we aim to evaluate evidence rather than raw QA accuracy. However, each judge model’s accuracy is useful to know for analysis purposes. BERT models significantly outperform word-based baselines (TFIDF and fastText), and BERTLARGE achieves the best overall accuracy. No model achieves the estimated human ceiling for either RACE lai2017race or DREAM sun2018dream.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nTable title: Table 2: RACE and DREAM test accuracy of various judge models using the full passage. Our agents use these models to find evidence. The models cover a spectrum of QA ability. (*) reports ceiling accuracy from original dataset papers.\n\nQuestion: Which model achieves the highest accuracy on the DREAM dataset?\n" }, { "role": "assistant", "content": "By comparing the accuracy values for each model on the DREAM dataset, BERTLARGE has the highest accuracy at 64.9. So, the answer is {\"answer\": \"BERTLARGE\"}" } ], "images": [ "scigen/train/3250.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: RACE and DREAM test accuracy of various judge models using the full passage. Our agents use these models to find evidence. The models cover a spectrum of QA ability. (*) reports ceiling accuracy from original dataset papers." ], "reasoning": "By comparing the accuracy values for each model on the DREAM dataset, BERTLARGE has the highest accuracy at 64.9.", "question": "Which model achieves the highest accuracy on the DREAM dataset?", "answer": "So, the answer is {\"answer\": \"BERTLARGE\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim TFIDF (S, A) achieves higher accuracy than fastText (S, A) on the RACE dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nTFIDF (S, A) achieves higher accuracy than fastText (S, A) on the RACE dataset.\n" }, { "role": "assistant", "content": "The table shows that TFIDF (S, A) has an accuracy of 31.6 on RACE, while fastText (S, A) has an accuracy of 30.4, indicating that TFIDF (S, A) performs better. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3250.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: RACE and DREAM test accuracy of various judge models using the full passage. Our agents use these models to find evidence. The models cover a spectrum of QA ability. (*) reports ceiling accuracy from original dataset papers." ], "reasoning": "The table shows that TFIDF (S, A) has an accuracy of 31.6 on RACE, while fastText (S, A) has an accuracy of 30.4, indicating that TFIDF (S, A) performs better.", "statement": "TFIDF (S, A) achieves higher accuracy than fastText (S, A) on the RACE dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4: We train a judge on short RACE passages and test its generalization to long passages. The judge is more accurate on long passages when it answers based on only sentences chosen by competing agents (last 5 rows) instead of the full passage. BERT-based agents aid generalization even under test-time domain shift (from RACE to DREAM).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The judge model outperforms the answer-only BERT baseline by 4% using the full passage and by 10% using agent-chosen sentences, demonstrating improved generalization with the aid of search and learned agents.Using the full passage, the judge outperforms an answer-only BERT baseline by 4% (44.1% vs. 40.2%). When answering using the smaller set of agent-chosen sentences, the judge outperforms the baseline by 10% (50.2% vs. 40.2%), more than doubling its relative use of the passage. Both search and learned agents aid the judge model in generalizing to longer passages. The improved generalization is not simply a result of the judge using a shorter passage, as shown by the random sentence selection baseline (44.7%)." } ], "images": [ "scigen/train/3251.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: We train a judge on short RACE passages and test its generalization to long passages. The judge is more accurate on long passages when it answers based on only sentences chosen by competing agents (last 5 rows) instead of the full passage. BERT-based agents aid generalization even under test-time domain shift (from RACE to DREAM)." ], "reasoning": "The judge model outperforms the answer-only BERT baseline by 4% using the full passage and by 10% using agent-chosen sentences, demonstrating improved generalization with the aid of search and learned agents.", "description": "Using the full passage, the judge outperforms an answer-only BERT baseline by 4% (44.1% vs. 40.2%). When answering using the smaller set of agent-chosen sentences, the judge outperforms the baseline by 10% (50.2% vs. 40.2%), more than doubling its relative use of the passage. Both search and learned agents aid the judge model in generalizing to longer passages. The improved generalization is not simply a result of the judge using a shorter passage, as shown by the random sentence selection baseline (44.7%).", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 4: We train a judge on short RACE passages and test its generalization to long passages. The judge is more accurate on long passages when it answers based on only sentences chosen by competing agents (last 5 rows) instead of the full passage. BERT-based agents aid generalization even under test-time domain shift (from RACE to DREAM).'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nHow much does the judge model outperform the answer-only BERT baseline when using the full passage?\n" }, { "role": "assistant", "content": "The judge model outperforms the answer-only BERT baseline by 4% when using the full passage, as indicated by the scores 44.1% vs. 40.2%. So, the answer is {\"answer\": \"4%\"}" } ], "images": [ "scigen/train/3251.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: We train a judge on short RACE passages and test its generalization to long passages. The judge is more accurate on long passages when it answers based on only sentences chosen by competing agents (last 5 rows) instead of the full passage. BERT-based agents aid generalization even under test-time domain shift (from RACE to DREAM)." ], "reasoning": "The judge model outperforms the answer-only BERT baseline by 4% when using the full passage, as indicated by the scores 44.1% vs. 40.2%.", "question": "How much does the judge model outperform the answer-only BERT baseline when using the full passage?", "answer": "So, the answer is {\"answer\": \"4%\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe judge model outperforms the answer-only BERT baseline by 10% when using the full passage.\n" }, { "role": "assistant", "content": "The table shows that the judge model outperforms the answer-only BERT baseline by 4% (44.1% vs. 40.2%) when using the full passage, not 10%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3251.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: We train a judge on short RACE passages and test its generalization to long passages. The judge is more accurate on long passages when it answers based on only sentences chosen by competing agents (last 5 rows) instead of the full passage. BERT-based agents aid generalization even under test-time domain shift (from RACE to DREAM)." ], "reasoning": "The table shows that the judge model outperforms the answer-only BERT baseline by 4% (44.1% vs. 40.2%) when using the full passage, not 10%.", "statement": "The judge model outperforms the answer-only BERT baseline by 10% when using the full passage.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: Generalizing to harder questions: We train a judge to answer questions with RACE’s Middle School exam questions only. We test its generalization to High School exam questions. The judge is more accurate when using evidence agent sentences (last 5 rows) rather than the full passage.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Simplifying passages by removing extraneous sentences can improve generalization but may reduce accuracy on easier questions.Simplifying the passage by providing key sentences may aid generalization by e.g. removing extraneous or distracting sentences from passages with more uncommon words or complex sentence structure. Such improvements come at the cost of accuracy on easier, word-matching questions, where it may be simpler to answer with the full passage as seen in training." } ], "images": [ "scigen/train/3252.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Generalizing to harder questions: We train a judge to answer questions with RACE’s Middle School exam questions only. We test its generalization to High School exam questions. The judge is more accurate when using evidence agent sentences (last 5 rows) rather than the full passage." ], "reasoning": "Simplifying passages by removing extraneous sentences can improve generalization but may reduce accuracy on easier questions.", "description": "Simplifying the passage by providing key sentences may aid generalization by e.g. removing extraneous or distracting sentences from passages with more uncommon words or complex sentence structure. Such improvements come at the cost of accuracy on easier, word-matching questions, where it may be simpler to answer with the full passage as seen in training.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: Generalizing to harder questions: We train a judge to answer questions with RACE’s Middle School exam questions only. We test its generalization to High School exam questions. The judge is more accurate when using evidence agent sentences (last 5 rows) rather than the full passage.'.\n\nQuestion: Which sentence selection method achieved the highest score for School Level Middle?\n" }, { "role": "assistant", "content": "To find the highest score for School Level Middle, compare all the scores in the School Level Middle column. The highest score is 70.8, achieved by the 'Full Passage' method. So, the answer is {\"answer\": \"Full Passage\"}." } ], "images": [ "scigen/train/3252.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Generalizing to harder questions: We train a judge to answer questions with RACE’s Middle School exam questions only. We test its generalization to High School exam questions. The judge is more accurate when using evidence agent sentences (last 5 rows) rather than the full passage." ], "reasoning": "To find the highest score for School Level Middle, compare all the scores in the School Level Middle column. The highest score is 70.8, achieved by the 'Full Passage' method.", "question": "Which sentence selection method achieved the highest score for School Level Middle?", "answer": "So, the answer is {\"answer\": \"Full Passage\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The 'Search over BERTBASE' method achieved the highest score for School Level High. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe 'Search over BERTBASE' method achieved the highest score for School Level High.\n" }, { "role": "assistant", "content": "To verify this statement, compare the scores in the School Level High column. The highest score is 63.2, achieved by the 'Full Passage' method, not 'Search over BERTBASE' which scored 53.0. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3252.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Generalizing to harder questions: We train a judge to answer questions with RACE’s Middle School exam questions only. We test its generalization to High School exam questions. The judge is more accurate when using evidence agent sentences (last 5 rows) rather than the full passage." ], "reasoning": "To verify this statement, compare the scores in the School Level High column. The highest score is 63.2, achieved by the 'Full Passage' method, not 'Search over BERTBASE' which scored 53.0.", "statement": "The 'Search over BERTBASE' method achieved the highest score for School Level High.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 11: Human evaluations: Search Agents select evidence by querying the specified judge model, and Learned Agents predict the strongest evidence w.r.t. a judge model (BERTBASE); humans then answer the question using the selected evidence sentence (without the full passage).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Neural methods generally achieve larger gains over word-based methods on multi-sentence reasoning questions on RACE.Models that perform better generally do so across all categories. However, methods incorporating neural methods generally achieve larger gains over word-based methods on multi-sentence reasoning questions on RACE." } ], "images": [ "scigen/train/3253.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Human evaluations: Search Agents select evidence by querying the specified judge model, and Learned Agents predict the strongest evidence w.r.t. a judge model (BERTBASE); humans then answer the question using the selected evidence sentence (without the full passage)." ], "reasoning": "Neural methods generally achieve larger gains over word-based methods on multi-sentence reasoning questions on RACE.", "description": "Models that perform better generally do so across all categories. However, methods incorporating neural methods generally achieve larger gains over word-based methods on multi-sentence reasoning questions on RACE.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich method achieves the highest percentage of human selection for multi-sentence reasoning questions on RACE?\nSolve the above question based on the table titled 'Table 11: Human evaluations: Search Agents select evidence by querying the specified judge model, and Learned Agents predict the strongest evidence w.r.t. a judge model (BERTBASE); humans then answer the question using the selected evidence sentence (without the full passage).'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the percentages in the 'Multi-Sent. Reasoning' column for RACE, the highest value is 42.3% achieved by the method 'p(i)'. So, the answer is {\"answer\": \"p(i)\"}." } ], "images": [ "scigen/train/3253.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Human evaluations: Search Agents select evidence by querying the specified judge model, and Learned Agents predict the strongest evidence w.r.t. a judge model (BERTBASE); humans then answer the question using the selected evidence sentence (without the full passage)." ], "reasoning": "By comparing the percentages in the 'Multi-Sent. Reasoning' column for RACE, the highest value is 42.3% achieved by the method 'p(i)'.", "question": "Which method achieves the highest percentage of human selection for multi-sentence reasoning questions on RACE?", "answer": "So, the answer is {\"answer\": \"p(i)\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe method 'Human Selection' achieves the highest percentage of human selection for overall questions on RACE.\n" }, { "role": "assistant", "content": "By comparing the percentages in the 'Overall' column for RACE, the highest value is 42.0% achieved by the method 'p(i)', not 'Human Selection' which has 38.1%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3253.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Human evaluations: Search Agents select evidence by querying the specified judge model, and Learned Agents predict the strongest evidence w.r.t. a judge model (BERTBASE); humans then answer the question using the selected evidence sentence (without the full passage)." ], "reasoning": "By comparing the percentages in the 'Overall' column for RACE, the highest value is 42.0% achieved by the method 'p(i)', not 'Human Selection' which has 38.1%.", "statement": "The method 'Human Selection' achieves the highest percentage of human selection for overall questions on RACE.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Variable fine-tuning in BERT. All results are evaluated using mean reciprocal rank (MRR).'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The models achieve the best performance when fine-tuning the top 4 layers for Ubuntu and the top 6 layers for Advising.Houlsby et al. They suggested that small data sets may be sub-optimal, when fine-tuning the whole BERT layers. Inspired by this assumption, we conduct an experiment The models achieve the best performance when T=4 and T=6, for Ubuntu and Advising, respectively. We demonstrate that utilizing this application is effective on not only small sets but also domain-specific sets. We also experiment how effective data augmentation is for both data sets. Formerly, both training sets contain 1:1 ratio of positive and negative responses. We change the ratio of the samples to 1:4 by increasing negative samples. The process of how we select negative samples is heuristic, and the best performance is obtained at ration of 1:4. It actually affects the performance improvement of Ubuntu, but not for Advising. We hypothesize that the Advising Corpus, unlike Ubuntu, is created with a number of sub-dialogs extracted from the original one. We obtain 0.3% higher R10@1 compared with the model trained on original one. We set same experiment conditions with the BERT-VFT that shows the best performance." } ], "images": [ "scigen/train/3254.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Variable fine-tuning in BERT. All results are evaluated using mean reciprocal rank (MRR)." ], "reasoning": "The models achieve the best performance when fine-tuning the top 4 layers for Ubuntu and the top 6 layers for Advising.", "description": "Houlsby et al. They suggested that small data sets may be sub-optimal, when fine-tuning the whole BERT layers. Inspired by this assumption, we conduct an experiment The models achieve the best performance when T=4 and T=6, for Ubuntu and Advising, respectively. We demonstrate that utilizing this application is effective on not only small sets but also domain-specific sets. We also experiment how effective data augmentation is for both data sets. Formerly, both training sets contain 1:1 ratio of positive and negative responses. We change the ratio of the samples to 1:4 by increasing negative samples. The process of how we select negative samples is heuristic, and the best performance is obtained at ration of 1:4. It actually affects the performance improvement of Ubuntu, but not for Advising. We hypothesize that the Advising Corpus, unlike Ubuntu, is created with a number of sub-dialogs extracted from the original one. We obtain 0.3% higher R10@1 compared with the model trained on original one. We set same experiment conditions with the BERT-VFT that shows the best performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Variable fine-tuning in BERT. All results are evaluated using mean reciprocal rank (MRR).\nQuestion: What is the performance of the model when fine-tuning the top 4 layers for Ubuntu and the top 6 layers for Advising?\nSolve the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows that the performance of the model when fine-tuning the top 4 layers for Ubuntu is 0.910 and for the top 6 layers for Advising is 0.400. So, the answer is {\"answer\": \"0.910 for Ubuntu and 0.400 for Advising\"}" } ], "images": [ "scigen/train/3254.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Variable fine-tuning in BERT. All results are evaluated using mean reciprocal rank (MRR)." ], "reasoning": "The table shows that the performance of the model when fine-tuning the top 4 layers for Ubuntu is 0.910 and for the top 6 layers for Advising is 0.400.", "question": "What is the performance of the model when fine-tuning the top 4 layers for Ubuntu and the top 6 layers for Advising?", "answer": "So, the answer is {\"answer\": \"0.910 for Ubuntu and 0.400 for Advising\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence Fine-tuning the top 4 layers achieves the best performance for both Ubuntu and Advising. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nFine-tuning the top 4 layers achieves the best performance for both Ubuntu and Advising.\n" }, { "role": "assistant", "content": "The table shows that fine-tuning the top 4 layers achieves the best performance for Ubuntu (0.910), but the best performance for Advising is achieved by fine-tuning the top 6 layers (0.400). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3254.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Variable fine-tuning in BERT. All results are evaluated using mean reciprocal rank (MRR)." ], "reasoning": "The table shows that fine-tuning the top 4 layers achieves the best performance for Ubuntu (0.910), but the best performance for Advising is achieved by fine-tuning the top 6 layers (0.400).", "statement": "Fine-tuning the top 4 layers achieves the best performance for both Ubuntu and Advising.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Model comparison on Ubuntu Corpus V1.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "BERT-VFT achieves new state-of-the-art performance with improvements of 5.9%, 3.4%, and 0.9% on R10@1, R10@2, and R10@5 respectively, compared to the previous state-of-the-art method.We conduct experiments on two data sets, Ubuntu Corpus V1 and Advising Corpus. It is observed that BERT-VFT achieves new state-of-the-art performance and it obtains performance improvement by 5.9%, 3.4%, 0.9% on R10@k, where k={1,2,5}, respectively, compared to the previous state-of-the-art method. Focusing on R10@1 metric, the performance of a vanilla BERTbase is 0.817 and comparing our main approach, which is BERT-DPT, shows much better performance (improvement by 3.4%). In addition, we especially point out comparing BERT-VFT with deep attention matching network (DAM) Zhou et al. Domain-specific optimized BERT-VPT model outperforms with performance improvement by 8.8% compared to the general transformer based model." } ], "images": [ "scigen/train/3255.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Model comparison on Ubuntu Corpus V1." ], "reasoning": "BERT-VFT achieves new state-of-the-art performance with improvements of 5.9%, 3.4%, and 0.9% on R10@1, R10@2, and R10@5 respectively, compared to the previous state-of-the-art method.", "description": "We conduct experiments on two data sets, Ubuntu Corpus V1 and Advising Corpus. It is observed that BERT-VFT achieves new state-of-the-art performance and it obtains performance improvement by 5.9%, 3.4%, 0.9% on R10@k, where k={1,2,5}, respectively, compared to the previous state-of-the-art method. Focusing on R10@1 metric, the performance of a vanilla BERTbase is 0.817 and comparing our main approach, which is BERT-DPT, shows much better performance (improvement by 3.4%). In addition, we especially point out comparing BERT-VFT with deep attention matching network (DAM) Zhou et al. Domain-specific optimized BERT-VPT model outperforms with performance improvement by 8.8% compared to the general transformer based model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the performance improvement of BERT-VFT over ESIM on the R10@1 metric?\nSolve the above question based on the table titled 'Table 1: Model comparison on Ubuntu Corpus V1.'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The performance of ESIM on R10@1 is 0.796, and the performance of BERT-VFT on R10@1 is 0.855. The improvement is calculated as 0.855 - 0.796 = 0.059, which is a 5.9% improvement. So, the answer is {\"answer\": \"5.9%\"}" } ], "images": [ "scigen/train/3255.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Model comparison on Ubuntu Corpus V1." ], "reasoning": "The performance of ESIM on R10@1 is 0.796, and the performance of BERT-VFT on R10@1 is 0.855. The improvement is calculated as 0.855 - 0.796 = 0.059, which is a 5.9% improvement.", "question": "What is the performance improvement of BERT-VFT over ESIM on the R10@1 metric?", "answer": "So, the answer is {\"answer\": \"5.9%\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nBERT-DPT shows a 3.4% improvement over BERT base on the R10@1 metric.\n" }, { "role": "assistant", "content": "The performance of BERT base on R10@1 is 0.817, and the performance of BERT-DPT on R10@1 is 0.851. The improvement is calculated as (0.851 - 0.817) / 0.817 * 100 = 4.16%, which is more than 3.4%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3255.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Model comparison on Ubuntu Corpus V1." ], "reasoning": "The performance of BERT base on R10@1 is 0.817, and the performance of BERT-DPT on R10@1 is 0.851. The improvement is calculated as (0.851 - 0.817) / 0.817 * 100 = 4.16%, which is more than 3.4%.", "statement": "BERT-DPT shows a 3.4% improvement over BERT base on the R10@1 metric.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Evaluation results on the Advising Corpus.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The BERT-VFT model achieves the highest R100@1 and MRR scores compared to other models, indicating superior performance.The former baseline evaluate BERTbase on the data set, but there is substantial performance difference from what we obtain." } ], "images": [ "scigen/train/3256.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Evaluation results on the Advising Corpus." ], "reasoning": "The BERT-VFT model achieves the highest R100@1 and MRR scores compared to other models, indicating superior performance.", "description": "The former baseline evaluate BERTbase on the data set, but there is substantial performance difference from what we obtain.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 2: Evaluation results on the Advising Corpus.\nQuestion: Which model has the highest R100@1 score and what is the value?\n" }, { "role": "assistant", "content": "By examining the R100@1 column, the BERT-VFT model has the highest score of 0.274. So, the answer is {\"answer\": \"BERT-VFT\"}." } ], "images": [ "scigen/train/3256.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Evaluation results on the Advising Corpus." ], "reasoning": "By examining the R100@1 column, the BERT-VFT model has the highest score of 0.274.", "question": "Which model has the highest R100@1 score and what is the value?", "answer": "So, the answer is {\"answer\": \"BERT-VFT\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nBERTbase outperforms BERT-DPT in terms of R100@10.\n" }, { "role": "assistant", "content": "The table shows that BERTbase has an R100@10 score of 0.656, while BERT-DPT has a score of 0.668. Therefore, BERTbase does not outperform BERT-DPT in this metric. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3256.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Evaluation results on the Advising Corpus." ], "reasoning": "The table shows that BERTbase has an R100@10 score of 0.656, while BERT-DPT has a score of 0.668. Therefore, BERTbase does not outperform BERT-DPT in this metric.", "statement": "BERTbase outperforms BERT-DPT in terms of R100@10.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3.4: Letter error rates (%) with iterated forced-alignment (FA) adaptation.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The adaptation process involves using force-aligned frame labels from a signer-independent recognizer, fine-tuning DNNs, re-aligning with the adapted recognizer, and fine-tuning again, without changing the recognizer.We next attempt to improve the performance of adaptation in the absence of ground-truth (manually annotated) peak labels. Using only the letter label sequence for the adaptation data, we use the signer-independent tandem recognizer to get force-aligned frame labels. We then adapt (fine-tune) the DNNs using the force-aligned adaptation data (as before in the FA case). We then re-align the test signer’s adaptation data with the adapted recognizer. Finally, we adapt the DNNs again with the re-aligned data. Throughout this experiment, we do not change the recognizer but only update the DNNs. We use first-pass SCRF with letter classifiers for these experiments." } ], "images": [ "scigen/train/3257.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3.4: Letter error rates (%) with iterated forced-alignment (FA) adaptation." ], "reasoning": "The adaptation process involves using force-aligned frame labels from a signer-independent recognizer, fine-tuning DNNs, re-aligning with the adapted recognizer, and fine-tuning again, without changing the recognizer.", "description": "We next attempt to improve the performance of adaptation in the absence of ground-truth (manually annotated) peak labels. Using only the letter label sequence for the adaptation data, we use the signer-independent tandem recognizer to get force-aligned frame labels. We then adapt (fine-tune) the DNNs using the force-aligned adaptation data (as before in the FA case). We then re-align the test signer’s adaptation data with the adapted recognizer. Finally, we adapt the DNNs again with the re-aligned data. Throughout this experiment, we do not change the recognizer but only update the DNNs. We use first-pass SCRF with letter classifiers for these experiments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3.4: Letter error rates (%) with iterated forced-alignment (FA) adaptation.', answer the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the mean score for fine-tuning with FA + realignment?\n" }, { "role": "assistant", "content": "To find the mean score for fine-tuning with FA + realignment, look at the column labeled 'Fine-tuning with FA + realignment Mean' in the table. So, the answer is {\"answer\": \"27.9\"}" } ], "images": [ "scigen/train/3257.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3.4: Letter error rates (%) with iterated forced-alignment (FA) adaptation." ], "reasoning": "To find the mean score for fine-tuning with FA + realignment, look at the column labeled 'Fine-tuning with FA + realignment Mean' in the table.", "question": "What is the mean score for fine-tuning with FA + realignment?", "answer": "So, the answer is {\"answer\": \"27.9\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Fine-tuning with FA + realignment for Signer 3 resulted in a score of 30.5., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nFine-tuning with FA + realignment for Signer 3 resulted in a score of 30.5.\n" }, { "role": "assistant", "content": "The table shows the score for 'Fine-tuning with FA + realignment Signer 3' as 30.5, which matches the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3257.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3.4: Letter error rates (%) with iterated forced-alignment (FA) adaptation." ], "reasoning": "The table shows the score for 'Fine-tuning with FA + realignment Signer 3' as 30.5, which matches the statement.", "statement": "Fine-tuning with FA + realignment for Signer 3 resulted in a score of 30.5.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3.1: Letter error rate (%) on four test signers.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "SCRF rescoring improves over the tandem HMM, and the first-pass SCRF outperforms the others, indicating better performance despite the small number of training signers.SCRF rescoring improves over the tandem HMM, and the first-pass SCRF outperforms the others. The poor performance is perhaps to be expected with such a small number of training signers." } ], "images": [ "scigen/train/3258.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3.1: Letter error rate (%) on four test signers." ], "reasoning": "SCRF rescoring improves over the tandem HMM, and the first-pass SCRF outperforms the others, indicating better performance despite the small number of training signers.", "description": "SCRF rescoring improves over the tandem HMM, and the first-pass SCRF outperforms the others. The poor performance is perhaps to be expected with such a small number of training signers.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: 'Table 3.1: Letter error rate (%) on four test signers.'\nQuestion: What is the mean score for the first-pass SCRF for the signer-independent category?\n" }, { "role": "assistant", "content": "To find the mean score for the first-pass SCRF for the signer-independent category, look at the '1st-pass SCRF Mean' column for the 'Signer-independent' row. So, the answer is {\"answer\": \"60.6\"}" } ], "images": [ "scigen/train/3258.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3.1: Letter error rate (%) on four test signers." ], "reasoning": "To find the mean score for the first-pass SCRF for the signer-independent category, look at the '1st-pass SCRF Mean' column for the 'Signer-independent' row.", "question": "What is the mean score for the first-pass SCRF for the signer-independent category?", "answer": "So, the answer is {\"answer\": \"60.6\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe mean score for the tandem HMM for the forced alignment category is higher than the mean score for the rescoring SCRF for the same category.\n" }, { "role": "assistant", "content": "The mean score for the tandem HMM for the forced alignment category is 33.6, while the mean score for the rescoring SCRF for the same category is 32.0. Since 33.6 is higher than 32.0, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3258.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3.1: Letter error rate (%) on four test signers." ], "reasoning": "The mean score for the tandem HMM for the forced alignment category is 33.6, while the mean score for the rescoring SCRF for the same category is 32.0. Since 33.6 is higher than 32.0, the statement is supported.", "statement": "The mean score for the tandem HMM for the forced alignment category is higher than the mean score for the rescoring SCRF for the same category.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3.2: Letter error rates (%) for different settings of SCRF and DNN training in the signer-adapted case. Details are given in Section 3.5.1.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Training the DNNs from scratch on the adaptation data while using the SCRF trained on the training signers improves performance, but the best results are achieved with signer-independent SCRFs and DNNs fine-tuned on the adaptation data.In this section we consider alternatives to the adaptation setting we have chosen – adapting the DNNs while using sequence models (HMMs/SCRFs) trained only on signer-independent data. We fix the model to a first-pass SCRF and the adaptation data to 20% of the test signer’s data annotated with ground-truth peak labels. In this setting, we consider two alternative ways of using the adaptation data: (1) using the adaptation data from the test signer to train both the DNNs and sequence model from scratch, ignoring the signer-independent training set; and (2) training the DNNs from scratch on the adaptation data, but using the SCRF trained on the training signers. We compare these options with our best results using the signer-independent SCRF and DNNs fine-tuned on the adaptation data. Frame error rates of DNNs trained from scratch using 20% of the test signer’s However, training the SCRF on the training signers but DNNs from scratch on the adaptation data (option (2) above) improves performance further. However, neither of these outperforms our previous best approach of signer-independent SCRFs plus DNNs fine-tuned on the adaptation data. It may be due to fact that small improvement of DNNs can effect letter recognition performance more." } ], "images": [ "scigen/train/3259.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3.2: Letter error rates (%) for different settings of SCRF and DNN training in the signer-adapted case. Details are given in Section 3.5.1." ], "reasoning": "Training the DNNs from scratch on the adaptation data while using the SCRF trained on the training signers improves performance, but the best results are achieved with signer-independent SCRFs and DNNs fine-tuned on the adaptation data.", "description": "In this section we consider alternatives to the adaptation setting we have chosen – adapting the DNNs while using sequence models (HMMs/SCRFs) trained only on signer-independent data. We fix the model to a first-pass SCRF and the adaptation data to 20% of the test signer’s data annotated with ground-truth peak labels. In this setting, we consider two alternative ways of using the adaptation data: (1) using the adaptation data from the test signer to train both the DNNs and sequence model from scratch, ignoring the signer-independent training set; and (2) training the DNNs from scratch on the adaptation data, but using the SCRF trained on the training signers. We compare these options with our best results using the signer-independent SCRF and DNNs fine-tuned on the adaptation data. Frame error rates of DNNs trained from scratch using 20% of the test signer’s However, training the SCRF on the training signers but DNNs from scratch on the adaptation data (option (2) above) improves performance further. However, neither of these outperforms our previous best approach of signer-independent SCRFs plus DNNs fine-tuned on the adaptation data. It may be due to fact that small improvement of DNNs can effect letter recognition performance more.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3.2: Letter error rates (%) for different settings of SCRF and DNN training in the signer-adapted case. Details are given in Section 3.5.1.' and methodically tackle the question that follows. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the mean error rate for the signer-independent SCRF and DNNs trained from scratch?\n" }, { "role": "assistant", "content": "To find the mean error rate for the signer-independent SCRF and DNNs trained from scratch, look at the column labeled 'Sig.-indep. SCRF, DNNs trained from scratch Mean'. The value is 19.7. So, the answer is {\"answer\": \"19.7\"}." } ], "images": [ "scigen/train/3259.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3.2: Letter error rates (%) for different settings of SCRF and DNN training in the signer-adapted case. Details are given in Section 3.5.1." ], "reasoning": "To find the mean error rate for the signer-independent SCRF and DNNs trained from scratch, look at the column labeled 'Sig.-indep. SCRF, DNNs trained from scratch Mean'. The value is 19.7.", "question": "What is the mean error rate for the signer-independent SCRF and DNNs trained from scratch?", "answer": "So, the answer is {\"answer\": \"19.7\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe lowest error rate for any signer is achieved with the signer-independent model with fine-tuned DNN.\n" }, { "role": "assistant", "content": "To verify this statement, compare the lowest error rates for each signer across all methods. The lowest error rate is 10.6, achieved by the signer-independent model with fine-tuned DNN for signer 2. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3259.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3.2: Letter error rates (%) for different settings of SCRF and DNN training in the signer-adapted case. Details are given in Section 3.5.1." ], "reasoning": "To verify this statement, compare the lowest error rates for each signer across all methods. The lowest error rate is 10.6, achieved by the signer-independent model with fine-tuned DNN for signer 2.", "statement": "The lowest error rate for any signer is achieved with the signer-independent model with fine-tuned DNN.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3.5: Letter error rates (%) with second pass cascade.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The approach shows a statistically significant improvement at the p=0.05 level for signer-independent experiments, but does not improve recognition in the second pass for signer-dependent settings.This improvement, while small, is statistically significant at the p=0.05 level. These results combine the most successful ideas from our experiments and form our final best result for signer-independent experiment. But for signer-dependent setting, this approach achieves the comparable performance and does not improve the recognition in the second pass." } ], "images": [ "scigen/train/3260.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3.5: Letter error rates (%) with second pass cascade." ], "reasoning": "The approach shows a statistically significant improvement at the p=0.05 level for signer-independent experiments, but does not improve recognition in the second pass for signer-dependent settings.", "description": "This improvement, while small, is statistically significant at the p=0.05 level. These results combine the most successful ideas from our experiments and form our final best result for signer-independent experiment. But for signer-dependent setting, this approach achieves the comparable performance and does not improve the recognition in the second pass.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3.5: Letter error rates (%) with second pass cascade.', answer the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the mean score for signer-adaptation in the 2nd pass?\n" }, { "role": "assistant", "content": "To find the mean score for signer-adaptation in the 2nd pass, look at the 'Signer-adaptation Mean' column in the 2nd pass row. So, the answer is {\"answer\": \"16.2\"}." } ], "images": [ "scigen/train/3260.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3.5: Letter error rates (%) with second pass cascade." ], "reasoning": "To find the mean score for signer-adaptation in the 2nd pass, look at the 'Signer-adaptation Mean' column in the 2nd pass row.", "question": "What is the mean score for signer-adaptation in the 2nd pass?", "answer": "So, the answer is {\"answer\": \"16.2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe approach improves the recognition in the second pass for signer-dependent settings.\n" }, { "role": "assistant", "content": "The table shows that the mean score for signer-dependent settings remains the same (7.6) from the 1st pass to the 2nd pass, indicating no improvement. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3260.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3.5: Letter error rates (%) with second pass cascade." ], "reasoning": "The table shows that the mean score for signer-dependent settings remains the same (7.6) from the 1st pass to the 2nd pass, indicating no improvement.", "statement": "The approach improves the recognition in the second pass for signer-dependent settings.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Comparison of 10 fold average test intent accuracy and slot tagging F1 score percentages of the sequence-to-sequence model on ATIS corpus with increasing size of training set, using RASA (RASA, 2018) as the base parser.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The sequence-to-sequence paraphrase model with RASA achieves higher intent accuracy but does not improve slot tagging performance.Next, we compare the performance of the sequence-to-sequence paraphrase model, when used with RASA as the base parser. RASA uses a kernel SVM classifier along with feature selection. Hence, in general it has a worse performance than attention BiRNN parser. However, it has the advantage of fast training time. Once again, we observe that the paraphrase model is able to achieve high gains in intent accuracy over the baseline RASA parser. The slot tagging performance do not improve with this model as previously observed with attention BiRNN parser. As mentioned before, due to the lack of slot tagging confidence scores in RASA, we are unable to use our RNN based paraphrase model with RASA." } ], "images": [ "scigen/train/3261.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of 10 fold average test intent accuracy and slot tagging F1 score percentages of the sequence-to-sequence model on ATIS corpus with increasing size of training set, using RASA (RASA, 2018) as the base parser." ], "reasoning": "The sequence-to-sequence paraphrase model with RASA achieves higher intent accuracy but does not improve slot tagging performance.", "description": "Next, we compare the performance of the sequence-to-sequence paraphrase model, when used with RASA as the base parser. RASA uses a kernel SVM classifier along with feature selection. Hence, in general it has a worse performance than attention BiRNN parser. However, it has the advantage of fast training time. Once again, we observe that the paraphrase model is able to achieve high gains in intent accuracy over the baseline RASA parser. The slot tagging performance do not improve with this model as previously observed with attention BiRNN parser. As mentioned before, due to the lack of slot tagging confidence scores in RASA, we are unable to use our RNN based paraphrase model with RASA.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 4: Comparison of 10 fold average test intent accuracy and slot tagging F1 score percentages of the sequence-to-sequence model on ATIS corpus with increasing size of training set, using RASA (RASA, 2018) as the base parser.', solve the subsequent question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhat is the accuracy of the Seq-to-seq paraphrase + RASA model with a training dataset size of 3500?\n" }, { "role": "assistant", "content": "The table shows the accuracy of the Seq-to-seq paraphrase + RASA model for different training dataset sizes. For a dataset size of 3500, the accuracy is 91.62. So, the answer is {\"answer\": \"91.62\"}" } ], "images": [ "scigen/train/3261.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of 10 fold average test intent accuracy and slot tagging F1 score percentages of the sequence-to-sequence model on ATIS corpus with increasing size of training set, using RASA (RASA, 2018) as the base parser." ], "reasoning": "The table shows the accuracy of the Seq-to-seq paraphrase + RASA model for different training dataset sizes. For a dataset size of 3500, the accuracy is 91.62.", "question": "What is the accuracy of the Seq-to-seq paraphrase + RASA model with a training dataset size of 3500?", "answer": "So, the answer is {\"answer\": \"91.62\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Seq-to-seq paraphrase + RASA model has an F1 score of 84.71 with a training dataset size of 3500.\n" }, { "role": "assistant", "content": "The table shows the F1 scores for different models and dataset sizes. The F1 score for the Seq-to-seq paraphrase + RASA model with a dataset size of 3500 is 84.55, not 84.71. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3261.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of 10 fold average test intent accuracy and slot tagging F1 score percentages of the sequence-to-sequence model on ATIS corpus with increasing size of training set, using RASA (RASA, 2018) as the base parser." ], "reasoning": "The table shows the F1 scores for different models and dataset sizes. The F1 score for the Seq-to-seq paraphrase + RASA model with a dataset size of 3500 is 84.55, not 84.71.", "statement": "The Seq-to-seq paraphrase + RASA model has an F1 score of 84.71 with a training dataset size of 3500.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5: Comparison of 10 fold average test intent accuracy percentage of all our models on simulated ATIS log corpus with increasing size of training set, using both attention BiRNN (Liu and Lane, 2016) and RASA (RASA, 2018) as the base parser. The models are trained on original ATIS dataset but tested on ATIS log corpus.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Despite a distribution mismatch with the ATIS training data, paraphrase models show improved intent detection accuracy across different base parsers.Finally, to validate the robustness of our models in an adversarial post deployment scenario, we test the performance of our models on the simulated ATIS log corpus. Due to a distribution mismatch with the ATIS training data, all models perform worse in this dataset, as expected. However, we still observe that, irrespective of the base parser used, our paraphrase models achieve an improved intent detection accuracy." } ], "images": [ "scigen/train/3262.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison of 10 fold average test intent accuracy percentage of all our models on simulated ATIS log corpus with increasing size of training set, using both attention BiRNN (Liu and Lane, 2016) and RASA (RASA, 2018) as the base parser. The models are trained on original ATIS dataset but tested on ATIS log corpus." ], "reasoning": "Despite a distribution mismatch with the ATIS training data, paraphrase models show improved intent detection accuracy across different base parsers.", "description": "Finally, to validate the robustness of our models in an adversarial post deployment scenario, we test the performance of our models on the simulated ATIS log corpus. Due to a distribution mismatch with the ATIS training data, all models perform worse in this dataset, as expected. However, we still observe that, irrespective of the base parser used, our paraphrase models achieve an improved intent detection accuracy.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich parser achieved the highest intent detection accuracy with a training dataset size of 2500?\nSolve the above question based on the table titled 'Table 5: Comparison of 10 fold average test intent accuracy percentage of all our models on simulated ATIS log corpus with increasing size of training set, using both attention BiRNN (Liu and Lane, 2016) and RASA (RASA, 2018) as the base parser. The models are trained on original ATIS dataset but tested on ATIS log corpus.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the parser with the highest intent detection accuracy for a training dataset size of 2500, compare the accuracy values in the column 'Training dataset size 2500'. The highest value is 86.08, achieved by the 'Seq-to-seq paraphrase + RASA' parser. So, the answer is {\"answer\": \"Seq-to-seq paraphrase + RASA\"}." } ], "images": [ "scigen/train/3262.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison of 10 fold average test intent accuracy percentage of all our models on simulated ATIS log corpus with increasing size of training set, using both attention BiRNN (Liu and Lane, 2016) and RASA (RASA, 2018) as the base parser. The models are trained on original ATIS dataset but tested on ATIS log corpus." ], "reasoning": "To find the parser with the highest intent detection accuracy for a training dataset size of 2500, compare the accuracy values in the column 'Training dataset size 2500'. The highest value is 86.08, achieved by the 'Seq-to-seq paraphrase + RASA' parser.", "question": "Which parser achieved the highest intent detection accuracy with a training dataset size of 2500?", "answer": "So, the answer is {\"answer\": \"Seq-to-seq paraphrase + RASA\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe RNN paraphrase + BiRNN parser achieved the highest accuracy with a training dataset size of 1500.\n" }, { "role": "assistant", "content": "To verify this statement, compare the accuracy values in the column 'Training dataset size 1500'. The highest value is 85.14, achieved by the 'Seq-to-seq paraphrase + RASA' parser, not the 'RNN paraphrase + BiRNN' parser. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3262.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison of 10 fold average test intent accuracy percentage of all our models on simulated ATIS log corpus with increasing size of training set, using both attention BiRNN (Liu and Lane, 2016) and RASA (RASA, 2018) as the base parser. The models are trained on original ATIS dataset but tested on ATIS log corpus." ], "reasoning": "To verify this statement, compare the accuracy values in the column 'Training dataset size 1500'. The highest value is 85.14, achieved by the 'Seq-to-seq paraphrase + RASA' parser, not the 'RNN paraphrase + BiRNN' parser.", "statement": "The RNN paraphrase + BiRNN parser achieved the highest accuracy with a training dataset size of 1500.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Results of the binary discrimination task on the WSJ. * indicates significance (p<0.01) over all the other models based on the randomization test. Egrid models are significantly worse than MTLSOX and Concatgrs on the PRA metric and significantly worse than all models on TPRA.999Joty et al. (2018) reported 0.885 PRA for their Egrid CNNlex, which we were unable to replicate using their code; however, this is still lower compared to our results.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The MTL model, which incorporates GR types, achieves the highest PRA and TPRA scores, outperforming other models and demonstrating the effectiveness of using a supervision signal at the bottom layers.Binary Classification. The results demonstrate the effectiveness of our MTL approach using a supervision signal at the bottom layers based on the words’ GR types, which significantly outperforms all other approaches and achieves state-of-the-art results on the WSJ (0.932 PRA and 0.941 TPRA). The performance of the Egrid neural models shows that despite their ability to rank a document higher than its incoherent counterparts (0.876 and 0.846 PRA), they do not generalize when documents are compared against counterparts from the whole test set (0.656 and 0.566 TPRA). This could be partly attributed to the pairwise training strategy adopted by these models and their inability to compare entity-transition patterns across different topics. The table also shows that models that utilize compositions over textual units to form document representations (the last four models) are significantly more effective than those explicitly utilizing only the local transitions between sentences (LC model). Furthermore, we observe that incorporating GR types (MTL, MTLSOX and Concatgrs) gives significantly better results compared to the STL model that is GR-agnostic. The superiority of the MTL model over Concatgrs and MTLSOX demonstrates that learning the GR types, within an MTL framework, allows the model to learn richer contextual representations (but also to be more efficient at testing time compared to e.g., Concatgrs since it does not require external linguistic tools)." } ], "images": [ "scigen/train/3263.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of the binary discrimination task on the WSJ. * indicates significance (p<0.01) over all the other models based on the randomization test. Egrid models are significantly worse than MTLSOX and Concatgrs on the PRA metric and significantly worse than all models on TPRA.999Joty et al. (2018) reported 0.885 PRA for their Egrid CNNlex, which we were unable to replicate using their code; however, this is still lower compared to our results." ], "reasoning": "The MTL model, which incorporates GR types, achieves the highest PRA and TPRA scores, outperforming other models and demonstrating the effectiveness of using a supervision signal at the bottom layers.", "description": "Binary Classification. The results demonstrate the effectiveness of our MTL approach using a supervision signal at the bottom layers based on the words’ GR types, which significantly outperforms all other approaches and achieves state-of-the-art results on the WSJ (0.932 PRA and 0.941 TPRA). The performance of the Egrid neural models shows that despite their ability to rank a document higher than its incoherent counterparts (0.876 and 0.846 PRA), they do not generalize when documents are compared against counterparts from the whole test set (0.656 and 0.566 TPRA). This could be partly attributed to the pairwise training strategy adopted by these models and their inability to compare entity-transition patterns across different topics. The table also shows that models that utilize compositions over textual units to form document representations (the last four models) are significantly more effective than those explicitly utilizing only the local transitions between sentences (LC model). Furthermore, we observe that incorporating GR types (MTL, MTLSOX and Concatgrs) gives significantly better results compared to the STL model that is GR-agnostic. The superiority of the MTL model over Concatgrs and MTLSOX demonstrates that learning the GR types, within an MTL framework, allows the model to learn richer contextual representations (but also to be more efficient at testing time compared to e.g., Concatgrs since it does not require external linguistic tools).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Results of the binary discrimination task on the WSJ. * indicates significance (p<0.01) over all the other models based on the randomization test. Egrid models are significantly worse than MTLSOX and Concatgrs on the PRA metric and significantly worse than all models on TPRA.999Joty et al. (2018) reported 0.885 PRA for their Egrid CNNlex, which we were unable to replicate using their code; however, this is still lower compared to our results.\nQuestion: Which model achieves the highest PRA and TPRA scores, and what are those scores?\nSolve the above question based on the table information. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the MTL model has the highest PRA and TPRA scores, which are 0.932 and 0.941 respectively. So, the answer is {\"answer\": \"MTL with PRA 0.932 and TPRA 0.941\"}" } ], "images": [ "scigen/train/3263.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of the binary discrimination task on the WSJ. * indicates significance (p<0.01) over all the other models based on the randomization test. Egrid models are significantly worse than MTLSOX and Concatgrs on the PRA metric and significantly worse than all models on TPRA.999Joty et al. (2018) reported 0.885 PRA for their Egrid CNNlex, which we were unable to replicate using their code; however, this is still lower compared to our results." ], "reasoning": "The table shows that the MTL model has the highest PRA and TPRA scores, which are 0.932 and 0.941 respectively.", "question": "Which model achieves the highest PRA and TPRA scores, and what are those scores?", "answer": "So, the answer is {\"answer\": \"MTL with PRA 0.932 and TPRA 0.941\"}" }, { "messages": [ { "role": "user", "content": "The LC model achieves a higher PRA score than the STL model. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe LC model achieves a higher PRA score than the STL model.\n" }, { "role": "assistant", "content": "The table shows that the LC model has a PRA score of 0.741, while the STL model has a PRA score of 0.877, indicating that the LC model does not achieve a higher PRA score than the STL model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3263.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of the binary discrimination task on the WSJ. * indicates significance (p<0.01) over all the other models based on the randomization test. Egrid models are significantly worse than MTLSOX and Concatgrs on the PRA metric and significantly worse than all models on TPRA.999Joty et al. (2018) reported 0.885 PRA for their Egrid CNNlex, which we were unable to replicate using their code; however, this is still lower compared to our results." ], "reasoning": "The table shows that the LC model has a PRA score of 0.741, while the STL model has a PRA score of 0.877, indicating that the LC model does not achieve a higher PRA score than the STL model.", "statement": "The LC model achieves a higher PRA score than the STL model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 10: Derived Boolean utility matrix enriched by auxiliary information'.\n" }, { "role": "assistant", "content": "The user profile vectors contain many zero components, leading to a loss of similarity information.⎛⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜⎝100100011010001100100001011010001100001010000100100011101010000100010010000010100100000000⎞⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟⎠∘⎛⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜⎝100100000000110011001000001110001101100011000000110000000100100010011001000001011000000010110001000⎞⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟⎠ □ However, in this case in the obtained user profiles vectors most of the components are getting zeros, and thus we lose similarity information." } ], "images": [ "scigen/train/3264.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Derived Boolean utility matrix enriched by auxiliary information" ], "reasoning": "The user profile vectors contain many zero components, leading to a loss of similarity information.", "description": "⎛⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜⎝100100011010001100100001011010001100001010000100100011101010000100010010000010100100000000⎞⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟⎠∘⎛⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜⎝100100000000110011001000001110001101100011000000110000000100100010011001000001011000000010110001000⎞⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟⎠ □ However, in this case in the obtained user profiles vectors most of the components are getting zeros, and thus we lose similarity information.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 10: Derived Boolean utility matrix enriched by auxiliary information' in mind, break down and resolve the question below step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich user has the highest similarity to user u1 based on the table?\n" }, { "role": "assistant", "content": "To find the user with the highest similarity to u1, compare the vectors of all users with u1. The user with the most matching components (1s in the same positions) will have the highest similarity. So, the answer is {\"answer\": \"u4\"}." } ], "images": [ "scigen/train/3264.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Derived Boolean utility matrix enriched by auxiliary information" ], "reasoning": "To find the user with the highest similarity to u1, compare the vectors of all users with u1. The user with the most matching components (1s in the same positions) will have the highest similarity.", "question": "Which user has the highest similarity to user u1 based on the table?", "answer": "So, the answer is {\"answer\": \"u4\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nUser u3 has more similarities with user u2 than with user u5.\n" }, { "role": "assistant", "content": "Compare the vectors of u3 with u2 and u5. Count the number of matching components (1s in the same positions) for each comparison. u3 has 7 matches with u2 and 5 matches with u5. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3264.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Derived Boolean utility matrix enriched by auxiliary information" ], "reasoning": "Compare the vectors of u3 with u2 and u5. Count the number of matching components (1s in the same positions) for each comparison. u3 has 7 matches with u2 and 5 matches with u5.", "statement": "User u3 has more similarities with user u2 than with user u5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Results for the Spearman rank correlation (ρ×100).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "LDMI outperforms other approaches on the maxSimC metric across all vector dimensions and shows significant improvement on the GOBW dataset, while performing comparably on the avgSimC metric.For all the vector dimensions, LDMI performs better than the competing approaches on the maxSimC metric. For the GOBW dataset, LDMI shows an average improvement of about 13% over the NP-MSSG and 10% over the MSSG on the maxSimC metric. The average is taken over all vector dimensions. This shows the advantage of LDMI over the competing approaches. For the avgSimC metric, LDMI performs at par with the competing approaches. The other approaches are not as effective in identifying the correct number of senses, leading to noisy clusters and hence, poor performance on the maxSimC metric. LDMI also performs better than LDMI-SK on both maxSimC and avgSimC, demonstrating the effectiveness of the clustering approach employed by LDMI over spherical k-means." } ], "images": [ "scigen/train/3265.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results for the Spearman rank correlation (ρ×100)." ], "reasoning": "LDMI outperforms other approaches on the maxSimC metric across all vector dimensions and shows significant improvement on the GOBW dataset, while performing comparably on the avgSimC metric.", "description": "For all the vector dimensions, LDMI performs better than the competing approaches on the maxSimC metric. For the GOBW dataset, LDMI shows an average improvement of about 13% over the NP-MSSG and 10% over the MSSG on the maxSimC metric. The average is taken over all vector dimensions. This shows the advantage of LDMI over the competing approaches. For the avgSimC metric, LDMI performs at par with the competing approaches. The other approaches are not as effective in identifying the correct number of senses, leading to noisy clusters and hence, poor performance on the maxSimC metric. LDMI also performs better than LDMI-SK on both maxSimC and avgSimC, demonstrating the effectiveness of the clustering approach employed by LDMI over spherical k-means.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Results for the Spearman rank correlation (ρ×100).\nQuestion: What is the maxSimC score for LDMI on the GOBW dataset with a vector dimension of 200?\nGive a detailed response to the above question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the maxSimC score for LDMI on the GOBW dataset with a vector dimension of 200, locate the row where the dataset is GOBW, the model is LDMI, and the vector dimension (d) is 200. The maxSimC score in this row is 63.9. So, the answer is {\"answer\": \"63.9\"}." } ], "images": [ "scigen/train/3265.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results for the Spearman rank correlation (ρ×100)." ], "reasoning": "To find the maxSimC score for LDMI on the GOBW dataset with a vector dimension of 200, locate the row where the dataset is GOBW, the model is LDMI, and the vector dimension (d) is 200. The maxSimC score in this row is 63.9.", "question": "What is the maxSimC score for LDMI on the GOBW dataset with a vector dimension of 200?", "answer": "So, the answer is {\"answer\": \"63.9\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: LDMI achieves a maxSimC score of 60.6 on the GOBW dataset with a vector dimension of 50., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nLDMI achieves a maxSimC score of 60.6 on the GOBW dataset with a vector dimension of 50.\n" }, { "role": "assistant", "content": "To verify this statement, locate the row where the dataset is GOBW, the model is LDMI, and the vector dimension (d) is 50. The maxSimC score in this row is 60.6, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3265.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results for the Spearman rank correlation (ρ×100)." ], "reasoning": "To verify this statement, locate the row where the dataset is GOBW, the model is LDMI, and the vector dimension (d) is 50. The maxSimC score in this row is 60.6, which matches the statement.", "statement": "LDMI achieves a maxSimC score of 60.6 on the GOBW dataset with a vector dimension of 50.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 7. Question ranking feature ablation study.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The feature ablation study shows that all feature groups positively contribute to the model, with interaction features providing only marginal improvement.Ranking Feature Ablation Study. In this experiment, we study which feature group contributes the most to the question ranking model. In Tab. The results show that all feature groups positively contribute to the model and should be retained. Note that adding interaction features only improve the performance marginally. This is because tree-based model can capture some feature correlations without explicit signal." } ], "images": [ "scigen/train/3266.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7. Question ranking feature ablation study." ], "reasoning": "The feature ablation study shows that all feature groups positively contribute to the model, with interaction features providing only marginal improvement.", "description": "Ranking Feature Ablation Study. In this experiment, we study which feature group contributes the most to the question ranking model. In Tab. The results show that all feature groups positively contribute to the model and should be retained. Note that adding interaction features only improve the performance marginally. This is because tree-based model can capture some feature correlations without explicit signal.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 7. Question ranking feature ablation study.', solve this question and give detail solutions. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich feature group, when removed, resulted in the highest AUROC score?\n" }, { "role": "assistant", "content": "By comparing the AUROC scores for each feature group removal, the highest AUROC score is observed when the job feature is removed. So, the answer is {\"answer\": \"job feature\"}." } ], "images": [ "scigen/train/3266.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7. Question ranking feature ablation study." ], "reasoning": "By comparing the AUROC scores for each feature group removal, the highest AUROC score is observed when the job feature is removed.", "question": "Which feature group, when removed, resulted in the highest AUROC score?", "answer": "So, the answer is {\"answer\": \"job feature\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Removing the question feature resulted in the lowest Precision@1 score. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nRemoving the question feature resulted in the lowest Precision@1 score.\n" }, { "role": "assistant", "content": "By comparing the Precision@1 scores for each feature group removal, the lowest Precision@1 score is observed when the question feature is removed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3266.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7. Question ranking feature ablation study." ], "reasoning": "By comparing the Precision@1 scores for each feature group removal, the lowest Precision@1 score is observed when the question feature is removed.", "statement": "Removing the question feature resulted in the lowest Precision@1 score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 6. Question ranking offline evaluation.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The J2Q-QR-XGB-pairwise model shows the highest performance in NDCG metrics, indicating its effectiveness in predicting job poster actions on screening questions.Question Ranking. We evaluated four question ranking models listed in Sec. As shown in Tab. the proposed J2Q-QR-XGB-pairwise model outperforms other baselines with up to 24.03% improvement in NDCG. This significant improvement indicates the effectiveness of the proposed model on predicting job poster actions on screening questions. Based on the observation, we chose the pairwise J2Q model as our online question ranking model due to its great ranking performance." } ], "images": [ "scigen/train/3267.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6. Question ranking offline evaluation." ], "reasoning": "The J2Q-QR-XGB-pairwise model shows the highest performance in NDCG metrics, indicating its effectiveness in predicting job poster actions on screening questions.", "description": "Question Ranking. We evaluated four question ranking models listed in Sec. As shown in Tab. the proposed J2Q-QR-XGB-pairwise model outperforms other baselines with up to 24.03% improvement in NDCG. This significant improvement indicates the effectiveness of the proposed model on predicting job poster actions on screening questions. Based on the observation, we chose the pairwise J2Q model as our online question ranking model due to its great ranking performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 6. Question ranking offline evaluation.' and methodically tackle the question that follows. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest Precision@1 and what is its value?\n" }, { "role": "assistant", "content": "By examining the table, the J2Q-QR-XGB-Pairwise model has the highest Precision@1 value of 0.8428. So, the answer is {\"answer\": \"J2Q-QR-XGB-Pairwise with 0.8428\"}." } ], "images": [ "scigen/train/3267.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6. Question ranking offline evaluation." ], "reasoning": "By examining the table, the J2Q-QR-XGB-Pairwise model has the highest Precision@1 value of 0.8428.", "question": "Which model has the highest Precision@1 and what is its value?", "answer": "So, the answer is {\"answer\": \"J2Q-QR-XGB-Pairwise with 0.8428\"}." }, { "messages": [ { "role": "user", "content": "The Rule-based model has the lowest AUROC value. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Rule-based model has the lowest AUROC value.\n" }, { "role": "assistant", "content": "By comparing the AUROC values in the table, the Rule-based model has the lowest AUROC value of 0.6408. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3267.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6. Question ranking offline evaluation." ], "reasoning": "By comparing the AUROC values in the table, the Rule-based model has the lowest AUROC value of 0.6408.", "statement": "The Rule-based model has the lowest AUROC value.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Classification accuracy of the different trained models on their respective test sets'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The study evaluates different attention network architectures (HAN and FLAN) with variations in encoder contextualization (conv and noenc) on multiple datasets, noting that model accuracy does not affect the analysis.Specifically, we use the additive formulation originally defined in Bahdanau et al. Given attention layer ℓ’s learned parameters, element i of a sequence, and its encoded representation hi, the attention weight αi is computed using ℓ’s learned context vector cℓ as follows: ui= tanh(Wℓhi+bℓ) αi= expu⊤icℓ∑iexpu⊤icℓ We evaluate on the original HAN architecture, but we also vary that architecture in two key ways: Number of attention layers: besides exploring models with a final layer of attention over sentence representations (which we denote with a “HAN” prefix), we also train “flat” attention networks with only one layer of attention over all contextualized word tokens (which we denote with a “FLAN” prefix). In either case, though, we only run tests on models’ final layer of attention. Reach of encoder contextualization: The original HAN uses recurrent encoders to contextualize input tokens prior to an attention layer (specifically, bidirectional GRUs running over the full sequence). We denote this variant of an architecture with a “conv” suffix. Finally, we also test models that are trained with no contextualizing encoder at all; we denote these with a “noenc” suffix. We note that our IMDB data and Yelp data are different sets of reviews from those used by Yang et al. We were unable to reach a comparable performance for the Amazon dataset (and Yelp dataset, although different) to that in Yang et al. We suspect that this is due to not pretraining the word2vec embeddings used by the model for long enough, combined with memory limitations on our hardware that necessitated decreasing our batch size in many cases. However, as noted in section 3, the analysis that we perform does not depend on model accuracy. It’s also worth noting that for the datasets for which we are able to get results that either pass or come close to the accuracies listed in the original HAN paper, the patterns we see in the results for the tests that we run are the same as the patterns that we see for the others." } ], "images": [ "scigen/train/3268.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Classification accuracy of the different trained models on their respective test sets" ], "reasoning": "The study evaluates different attention network architectures (HAN and FLAN) with variations in encoder contextualization (conv and noenc) on multiple datasets, noting that model accuracy does not affect the analysis.", "description": "Specifically, we use the additive formulation originally defined in Bahdanau et al. Given attention layer ℓ’s learned parameters, element i of a sequence, and its encoded representation hi, the attention weight αi is computed using ℓ’s learned context vector cℓ as follows: ui= tanh(Wℓhi+bℓ) αi= expu⊤icℓ∑iexpu⊤icℓ We evaluate on the original HAN architecture, but we also vary that architecture in two key ways: Number of attention layers: besides exploring models with a final layer of attention over sentence representations (which we denote with a “HAN” prefix), we also train “flat” attention networks with only one layer of attention over all contextualized word tokens (which we denote with a “FLAN” prefix). In either case, though, we only run tests on models’ final layer of attention. Reach of encoder contextualization: The original HAN uses recurrent encoders to contextualize input tokens prior to an attention layer (specifically, bidirectional GRUs running over the full sequence). We denote this variant of an architecture with a “conv” suffix. Finally, we also test models that are trained with no contextualizing encoder at all; we denote these with a “noenc” suffix. We note that our IMDB data and Yelp data are different sets of reviews from those used by Yang et al. We were unable to reach a comparable performance for the Amazon dataset (and Yelp dataset, although different) to that in Yang et al. We suspect that this is due to not pretraining the word2vec embeddings used by the model for long enough, combined with memory limitations on our hardware that necessitated decreasing our batch size in many cases. However, as noted in section 3, the analysis that we perform does not depend on model accuracy. It’s also worth noting that for the datasets for which we are able to get results that either pass or come close to the accuracies listed in the original HAN paper, the patterns we see in the results for the tests that we run are the same as the patterns that we see for the others.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Classification accuracy of the different trained models on their respective test sets\nQuestion: Which model architecture achieved the highest accuracy on the Yahoo Answers dataset?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the highest accuracy on the Yahoo Answers dataset, compare the accuracy values across all model architectures listed in the table for Yahoo Answers. So, the answer is {\"answer\": \"FLANrnn\"}" } ], "images": [ "scigen/train/3268.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Classification accuracy of the different trained models on their respective test sets" ], "reasoning": "To find the highest accuracy on the Yahoo Answers dataset, compare the accuracy values across all model architectures listed in the table for Yahoo Answers.", "question": "Which model architecture achieved the highest accuracy on the Yahoo Answers dataset?", "answer": "So, the answer is {\"answer\": \"FLANrnn\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The HANrnn model achieved the highest accuracy on the IMDB dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe HANrnn model achieved the highest accuracy on the IMDB dataset.\n" }, { "role": "assistant", "content": "The table shows the accuracy values for the IMDB dataset across different models. The highest accuracy for IMDB is 50.3, achieved by the HANrnn model. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3268.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Classification accuracy of the different trained models on their respective test sets" ], "reasoning": "The table shows the accuracy values for the IMDB dataset across different models. The highest accuracy for IMDB is 50.3, achieved by the HANrnn model.", "statement": "The HANrnn model achieved the highest accuracy on the IMDB dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Comparison between the standard Transformer model and our latent variable model.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our model uses a phrase lattice and empty phrases to improve flexibility and slightly outperforms the standard model.Our model is different from the standard model in two aspects. First, our model uses a phrase lattice to represent the search space. Second, empty phrases are introduced to make the search space more flexible than that of conventional SMT. We find that our model slightly improves over the standard model, suggesting that we can use the phrase-based search space to replace the standard search space for lexically and structurally constrained decoding." } ], "images": [ "scigen/train/3269.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison between the standard Transformer model and our latent variable model." ], "reasoning": "Our model uses a phrase lattice and empty phrases to improve flexibility and slightly outperforms the standard model.", "description": "Our model is different from the standard model in two aspects. First, our model uses a phrase lattice to represent the search space. Second, empty phrases are introduced to make the search space more flexible than that of conventional SMT. We find that our model slightly improves over the standard model, suggesting that we can use the phrase-based search space to replace the standard search space for lexically and structurally constrained decoding.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nHow does the performance of 'this work' compare to the Transformer model on MT06 and MT08?\nSolve the above question based on the table titled 'Table 2: Comparison between the standard Transformer model and our latent variable model.'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that 'this work' has scores of 47.77 on MT06 and 38.14 on MT08, while the Transformer model has scores of 47.44 on MT06 and 37.49 on MT08. 'This work' outperforms the Transformer model on both MT06 and MT08. So, the answer is {\"answer\": \"this work outperforms the Transformer model on both MT06 and MT08\"}." } ], "images": [ "scigen/train/3269.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison between the standard Transformer model and our latent variable model." ], "reasoning": "The table shows that 'this work' has scores of 47.77 on MT06 and 38.14 on MT08, while the Transformer model has scores of 47.44 on MT06 and 37.49 on MT08. 'This work' outperforms the Transformer model on both MT06 and MT08.", "question": "How does the performance of 'this work' compare to the Transformer model on MT06 and MT08?", "answer": "So, the answer is {\"answer\": \"this work outperforms the Transformer model on both MT06 and MT08\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Transformer model performs better than 'this work' on MT08., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Transformer model performs better than 'this work' on MT08.\n" }, { "role": "assistant", "content": "The table shows that the Transformer model has a score of 37.49 on MT08, while 'this work' has a score of 38.14 on MT08. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3269.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison between the standard Transformer model and our latent variable model." ], "reasoning": "The table shows that the Transformer model has a score of 37.49 on MT08, while 'this work' has a score of 38.14 on MT08. Therefore, the statement is refuted.", "statement": "The Transformer model performs better than 'this work' on MT08.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: Results on the webpage translation task. “w/o tag” denotes the BLEU score without considering HTML tags, “w/ tag” denotes the BLEU score considering HTML tags, and “in tag” denotes the BLEU score considering only the text enclosed by tags. Note that Remove does not have the “in tag” BLEU score because all tags are removed.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Our approach achieves the best performance in all evaluation metrics by fully preserving structural constraints without losing translation quality.As Remove ignores all HTML tags, it is not capable of imposing structural constraints. Split ensures that the structural constraints can be imposed correctly because the sentence segments between HTML tags are translated independently, but the translation quality drops dramatically. Match and Align take the full advantage of standard NMT to translate the textual parts but often fail to recover HTML tags correctly after decoding. According to the translations, it seems that Google uses a strategy similar to Split but achieves much higher BLEU scores because it used much larger training data than all other methods. Our approach achieves the best performance in terms of all evaluation metrics by fully preserving the structural constraints without losing translation quality." } ], "images": [ "scigen/train/3270.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on the webpage translation task. “w/o tag” denotes the BLEU score without considering HTML tags, “w/ tag” denotes the BLEU score considering HTML tags, and “in tag” denotes the BLEU score considering only the text enclosed by tags. Note that Remove does not have the “in tag” BLEU score because all tags are removed." ], "reasoning": "Our approach achieves the best performance in all evaluation metrics by fully preserving structural constraints without losing translation quality.", "description": "As Remove ignores all HTML tags, it is not capable of imposing structural constraints. Split ensures that the structural constraints can be imposed correctly because the sentence segments between HTML tags are translated independently, but the translation quality drops dramatically. Match and Align take the full advantage of standard NMT to translate the textual parts but often fail to recover HTML tags correctly after decoding. According to the translations, it seems that Google uses a strategy similar to Split but achieves much higher BLEU scores because it used much larger training data than all other methods. Our approach achieves the best performance in terms of all evaluation metrics by fully preserving the structural constraints without losing translation quality.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 4: Results on the webpage translation task. “w/o tag” denotes the BLEU score without considering HTML tags, “w/ tag” denotes the BLEU score considering HTML tags, and “in tag” denotes the BLEU score considering only the text enclosed by tags. Note that Remove does not have the “in tag” BLEU score because all tags are removed.' and methodically tackle the question that follows. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method achieves the highest BLEU score for translations with tags?\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for different methods. The highest score for translations with tags (w/ tag) is achieved by 'Ours' with a score of 37.52. So, the answer is {\"answer\": \"Ours\"}." } ], "images": [ "scigen/train/3270.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on the webpage translation task. “w/o tag” denotes the BLEU score without considering HTML tags, “w/ tag” denotes the BLEU score considering HTML tags, and “in tag” denotes the BLEU score considering only the text enclosed by tags. Note that Remove does not have the “in tag” BLEU score because all tags are removed." ], "reasoning": "The table shows the BLEU scores for different methods. The highest score for translations with tags (w/ tag) is achieved by 'Ours' with a score of 37.52.", "question": "Which method achieves the highest BLEU score for translations with tags?", "answer": "So, the answer is {\"answer\": \"Ours\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Split method achieves the highest BLEU score for translations without tags.\n" }, { "role": "assistant", "content": "The table shows that the highest BLEU score for translations without tags (w/o tag) is achieved by 'Ours' with a score of 33.42, not by Split. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3270.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on the webpage translation task. “w/o tag” denotes the BLEU score without considering HTML tags, “w/ tag” denotes the BLEU score considering HTML tags, and “in tag” denotes the BLEU score considering only the text enclosed by tags. Note that Remove does not have the “in tag” BLEU score because all tags are removed." ], "reasoning": "The table shows that the highest BLEU score for translations without tags (w/o tag) is achieved by 'Ours' with a score of 33.42, not by Split.", "statement": "The Split method achieves the highest BLEU score for translations without tags.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 1: Sentences in the selection process and final corpus size. #arg-sent: argumentative sentences. #arg-unit: argumentative units. increase in %: increase of #arg-unit compared to #arg-sent.'.\n" }, { "role": "assistant", "content": "Sentences are ranked by aggregated scores from document, argument, and stance ranks, and selected probabilistically to ensure diversity until 500 sentences per stance and topic are reached.Since we want to know, whether knowledge transfer to unknown topics is possible with few annotated topics, we limited the selection to \\citeauthorStab2018b (\\citeyearStab2018b) ’s eight topics (cf. This also increases comparability with related work. The topics are general enough to have good coverage in Common Crawl. They are also controversial and hence a good choice for argument mining with an expected diverse set of supporting and opposing arguments. We pre-classified the selected sentences with a sentence-level argument mining model following \\citeauthorstab2018argumentext (\\citeyearstab2018argumentext) and available via the ArgumenText Classify API. The API returns for each sentence (i) an argument confidence score arg_score in [0.0,1.0) (we discard sentences with arg_score<0.5), (ii) the stance on the sentence level (PRO or CON) and (iii) the stance confidence score stance_score. This information was used together with the doc_score to rank sentences for a selection in the following crowd annotation process. We convert scores for documents, arguments and stance into ranks di, ai and si. We then sum the three ranks to get an aggregated score for each sentence: aggi =di+ai+si. Sentences are divided by topic and pre-classified stance and ranked according to aggi, for each combination separately. We then go down the ranked list selecting each sentence with probability 0.5 until the target size of n=500 per stance and topic is reached; otherwise we do additional passes through the list. We adopted this probabilistic selection to ensure diversity – otherwise a long document with high relevance score at the top of the list might dominate the dataset with its sentences." } ], "images": [ "scigen/train/3271.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Sentences in the selection process and final corpus size. #arg-sent: argumentative sentences. #arg-unit: argumentative units. increase in %: increase of #arg-unit compared to #arg-sent." ], "reasoning": "Sentences are ranked by aggregated scores from document, argument, and stance ranks, and selected probabilistically to ensure diversity until 500 sentences per stance and topic are reached.", "description": "Since we want to know, whether knowledge transfer to unknown topics is possible with few annotated topics, we limited the selection to \\citeauthorStab2018b (\\citeyearStab2018b) ’s eight topics (cf. This also increases comparability with related work. The topics are general enough to have good coverage in Common Crawl. They are also controversial and hence a good choice for argument mining with an expected diverse set of supporting and opposing arguments. We pre-classified the selected sentences with a sentence-level argument mining model following \\citeauthorstab2018argumentext (\\citeyearstab2018argumentext) and available via the ArgumenText Classify API. The API returns for each sentence (i) an argument confidence score arg_score in [0.0,1.0) (we discard sentences with arg_score<0.5), (ii) the stance on the sentence level (PRO or CON) and (iii) the stance confidence score stance_score. This information was used together with the doc_score to rank sentences for a selection in the following crowd annotation process. We convert scores for documents, arguments and stance into ranks di, ai and si. We then sum the three ranks to get an aggregated score for each sentence: aggi =di+ai+si. Sentences are divided by topic and pre-classified stance and ranked according to aggi, for each combination separately. We then go down the ranked list selecting each sentence with probability 0.5 until the target size of n=500 per stance and topic is reached; otherwise we do additional passes through the list. We adopted this probabilistic selection to ensure diversity – otherwise a long document with high relevance score at the top of the list might dominate the dataset with its sentences.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Sentences in the selection process and final corpus size. #arg-sent: argumentative sentences. #arg-unit: argumentative units. increase in %: increase of #arg-unit compared to #arg-sent.\nQuestion: Which topic had the highest percentage increase in argument units?\nSolve the above question based on the table information. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the 'increase in %' column, the topic with the highest percentage increase is identified as 'school uniforms' with a +15.15% increase. So, the answer is {\"answer\": \"school uniforms\"}." } ], "images": [ "scigen/train/3271.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Sentences in the selection process and final corpus size. #arg-sent: argumentative sentences. #arg-unit: argumentative units. increase in %: increase of #arg-unit compared to #arg-sent." ], "reasoning": "By examining the 'increase in %' column, the topic with the highest percentage increase is identified as 'school uniforms' with a +15.15% increase.", "question": "Which topic had the highest percentage increase in argument units?", "answer": "So, the answer is {\"answer\": \"school uniforms\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The topic of cloning had more argument units than the topic of gun control., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe topic of cloning had more argument units than the topic of gun control.\n" }, { "role": "assistant", "content": "By comparing the '#arg-unit' column for cloning (380) and gun control (587), it is clear that gun control had more argument units than cloning. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3271.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Sentences in the selection process and final corpus size. #arg-sent: argumentative sentences. #arg-unit: argumentative units. increase in %: increase of #arg-unit compared to #arg-sent." ], "reasoning": "By comparing the '#arg-unit' column for cloning (380) and gun control (587), it is clear that gun control had more argument units than cloning.", "statement": "The topic of cloning had more argument units than the topic of gun control.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 7: Classification performance for deepgeo with the addition of noise and loss term l.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The addition of noise and loss helps push elements to the extrema, with noise being slightly more effective. Increasing R from 100 to 400 slightly increases middle values, indicating more unused hidden units, but performance remains within a 1% gap.We see that the addition of noise and l helps in pushing the elements to the extrema. The noise term appears to work a little better than l, as the frequency for the −1.0 and +1.0 bins is higher. We also observe that there is a small increase in middle/zero values as R increases from 100 to 400, suggesting that there are more unused hidden units when number of parameters increases. The performance drops a little, but generally stays within a gap of 1%. This suggests that both noise and l works well in binarising r without trading off classification accuracy significantly." } ], "images": [ "scigen/train/3272.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Classification performance for deepgeo with the addition of noise and loss term l." ], "reasoning": "The addition of noise and loss helps push elements to the extrema, with noise being slightly more effective. Increasing R from 100 to 400 slightly increases middle values, indicating more unused hidden units, but performance remains within a 1% gap.", "description": "We see that the addition of noise and l helps in pushing the elements to the extrema. The noise term appears to work a little better than l, as the frequency for the −1.0 and +1.0 bins is higher. We also observe that there is a small increase in middle/zero values as R increases from 100 to 400, suggesting that there are more unused hidden units when number of parameters increases. The performance drops a little, but generally stays within a gap of 1%. This suggests that both noise and l works well in binarising r without trading off classification accuracy significantly.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. The question is related to the given table titled 'Table 7: Classification performance for deepgeo with the addition of noise and loss term l.'.\n\nQuestion: How does the performance of deepgeo + noise compare to deepgeo + loss as R increases from 100 to 400?\n" }, { "role": "assistant", "content": "As R increases from 100 to 400, the performance of deepgeo + noise increases from 0.396 to 0.419, while the performance of deepgeo + loss increases from 0.410 to 0.418. Both methods show an increase in performance, with deepgeo + noise starting lower but ending slightly higher. So, the answer is {\"answer\": \"deepgeo + noise increases from 0.396 to 0.419, while deepgeo + loss increases from 0.410 to 0.418\"}." } ], "images": [ "scigen/train/3272.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Classification performance for deepgeo with the addition of noise and loss term l." ], "reasoning": "As R increases from 100 to 400, the performance of deepgeo + noise increases from 0.396 to 0.419, while the performance of deepgeo + loss increases from 0.410 to 0.418. Both methods show an increase in performance, with deepgeo + noise starting lower but ending slightly higher.", "question": "How does the performance of deepgeo + noise compare to deepgeo + loss as R increases from 100 to 400?", "answer": "So, the answer is {\"answer\": \"deepgeo + noise increases from 0.396 to 0.419, while deepgeo + loss increases from 0.410 to 0.418\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe performance of deepgeo + noise is consistently higher than deepgeo + loss.\n" }, { "role": "assistant", "content": "The table shows that deepgeo + noise starts at 0.396 and ends at 0.419, while deepgeo + loss starts at 0.410 and ends at 0.418. Therefore, deepgeo + noise is not consistently higher than deepgeo + loss. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3272.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Classification performance for deepgeo with the addition of noise and loss term l." ], "reasoning": "The table shows that deepgeo + noise starts at 0.396 and ends at 0.419, while deepgeo + loss starts at 0.410 and ends at 0.418. Therefore, deepgeo + noise is not consistently higher than deepgeo + loss.", "statement": "The performance of deepgeo + noise is consistently higher than deepgeo + loss.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Feature ablation results.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "User location is crucial for task accuracy, as its exclusion significantly reduces accuracy, while other features have minimal impact.We see that the two most important features are the user location and tweet message. These observations reveal that self-declared user location appears to be a reliable source of location, as task accuracy drops by almost half when this feature is excluded. For the other features, they generally have a small or negligible impact." } ], "images": [ "scigen/train/3273.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Feature ablation results." ], "reasoning": "User location is crucial for task accuracy, as its exclusion significantly reduces accuracy, while other features have minimal impact.", "description": "We see that the two most important features are the user location and tweet message. These observations reveal that self-declared user location appears to be a reliable source of location, as task accuracy drops by almost half when this feature is excluded. For the other features, they generally have a small or negligible impact.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: Table 5: Feature ablation results.\nQuestion: Which feature's exclusion results in the highest drop in accuracy?\n" }, { "role": "assistant", "content": "By comparing the accuracy drops when each feature is excluded, the exclusion of the 'Location' feature results in the highest drop in accuracy, from 0.428 to 0.228, a decrease of 0.200. So, the answer is {\"answer\": \"Location\"}." } ], "images": [ "scigen/train/3273.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Feature ablation results." ], "reasoning": "By comparing the accuracy drops when each feature is excluded, the exclusion of the 'Location' feature results in the highest drop in accuracy, from 0.428 to 0.228, a decrease of 0.200.", "question": "Which feature's exclusion results in the highest drop in accuracy?", "answer": "So, the answer is {\"answer\": \"Location\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nExcluding the 'Text' feature results in the highest drop in accuracy.\n" }, { "role": "assistant", "content": "The table shows that excluding the 'Location' feature results in the highest drop in accuracy (0.200), not the 'Text' feature, which results in a drop of 0.086. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3273.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Feature ablation results." ], "reasoning": "The table shows that excluding the 'Location' feature results in the highest drop in accuracy (0.200), not the 'Text' feature, which results in a drop of 0.086.", "statement": "Excluding the 'Text' feature results in the highest drop in accuracy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 6: Retrieval MAP performance.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Adding noise and loss to deepgeo improves performance, especially at higher bit sizes, while changing input from word2vec to deepgeo significantly enhances retrieval for lsh.Looking at deepgeo systems (column 2–4), we see that adding noise and l helps, although the impact is greater when the bit size is large (300/400 bits). For lsh, which uses no label information, word2vec input produces poor binary code for retrieval. Changing the input to deepgeo improves retrieval considerably, implying that the representation produced by deepgeo captures geolocation information." } ], "images": [ "scigen/train/3274.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Retrieval MAP performance." ], "reasoning": "Adding noise and loss to deepgeo improves performance, especially at higher bit sizes, while changing input from word2vec to deepgeo significantly enhances retrieval for lsh.", "description": "Looking at deepgeo systems (column 2–4), we see that adding noise and l helps, although the impact is greater when the bit size is large (300/400 bits). For lsh, which uses no label information, word2vec input produces poor binary code for retrieval. Changing the input to deepgeo improves retrieval considerably, implying that the representation produced by deepgeo captures geolocation information.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the impact of adding noise to deepgeo at 400 bits?\nSolve the above question based on the table titled 'Table 6: Retrieval MAP performance.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To determine the impact of adding noise to deepgeo at 400 bits, compare the deepgeo score (0.132) with the deepgeo + noise score (0.135). The score increases from 0.132 to 0.135, indicating an improvement. So, the answer is {\"answer\": \"0.135\"}." } ], "images": [ "scigen/train/3274.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Retrieval MAP performance." ], "reasoning": "To determine the impact of adding noise to deepgeo at 400 bits, compare the deepgeo score (0.132) with the deepgeo + noise score (0.135). The score increases from 0.132 to 0.135, indicating an improvement.", "question": "What is the impact of adding noise to deepgeo at 400 bits?", "answer": "So, the answer is {\"answer\": \"0.135\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nChanging the input to deepgeo improves retrieval for lsh at 300 bits.\n" }, { "role": "assistant", "content": "To verify this statement, compare the lsh word2vec score (0.021) with the lsh deepgeo score (0.082) at 300 bits. The score improves from 0.021 to 0.082, supporting the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3274.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Retrieval MAP performance." ], "reasoning": "To verify this statement, compare the lsh word2vec score (0.021) with the lsh deepgeo score (0.082) at 300 bits. The score improves from 0.021 to 0.082, supporting the statement.", "statement": "Changing the input to deepgeo improves retrieval for lsh at 300 bits.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 6: The comparisons of choosing 1-best and N-best hypotheses of each subsystem for system combination. The performance of N-best hypotheses is no better than 1-best regardless which voting schemes are used.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Using N-best hypotheses does not significantly improve results over 1-best in maximal confidence score voting, and frequency-based voting for N-best is slightly worse on the Tencet Chat data set.Choice of 1-best vs. N-best in system combination. Here N-best (N=10) distinct hypotheses of each subsystem are prepared for combination. Firstly, if we use the voting scheme in Section 4.4, i.e., maximal confidence score voting, choosing N-best does not offer any further benefits. Although N-best hypotheses make the WTN contain more branchings and words choices, maximal confidence score voting almost gets the same result as with 1-best hypothesis. Moreover, we conduct another experiment using frequency of occurrence as voting score for N-best subsystems combination. We find that the results are close to 1-best on WSJ data set but slightly worse on Tencet Chat data set. This is because that one subsystem’s error may repeat many times in N-best hypotheses, which distorts the following frequency-based voting. Furthermore, considering the computational cost of N-best hypotheses, 1-best from each subsystem with maximal confidence score may be preferred." } ], "images": [ "scigen/train/3275.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: The comparisons of choosing 1-best and N-best hypotheses of each subsystem for system combination. The performance of N-best hypotheses is no better than 1-best regardless which voting schemes are used." ], "reasoning": "Using N-best hypotheses does not significantly improve results over 1-best in maximal confidence score voting, and frequency-based voting for N-best is slightly worse on the Tencet Chat data set.", "description": "Choice of 1-best vs. N-best in system combination. Here N-best (N=10) distinct hypotheses of each subsystem are prepared for combination. Firstly, if we use the voting scheme in Section 4.4, i.e., maximal confidence score voting, choosing N-best does not offer any further benefits. Although N-best hypotheses make the WTN contain more branchings and words choices, maximal confidence score voting almost gets the same result as with 1-best hypothesis. Moreover, we conduct another experiment using frequency of occurrence as voting score for N-best subsystems combination. We find that the results are close to 1-best on WSJ data set but slightly worse on Tencet Chat data set. This is because that one subsystem’s error may repeat many times in N-best hypotheses, which distorts the following frequency-based voting. Furthermore, considering the computational cost of N-best hypotheses, 1-best from each subsystem with maximal confidence score may be preferred.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. The question is related to the given table titled 'Table 6: The comparisons of choosing 1-best and N-best hypotheses of each subsystem for system combination. The performance of N-best hypotheses is no better than 1-best regardless which voting schemes are used.'.\n\nQuestion: What is the WER for N-best (Frequency) on the WSJ data set?\n" }, { "role": "assistant", "content": "The table shows the WER for different methods on the WSJ data set. For N-best (Frequency), the WER is listed as 4.32/7.76%. So, the answer is {\"answer\": \"4.32/7.76%\"}." } ], "images": [ "scigen/train/3275.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: The comparisons of choosing 1-best and N-best hypotheses of each subsystem for system combination. The performance of N-best hypotheses is no better than 1-best regardless which voting schemes are used." ], "reasoning": "The table shows the WER for different methods on the WSJ data set. For N-best (Frequency), the WER is listed as 4.32/7.76%.", "question": "What is the WER for N-best (Frequency) on the WSJ data set?", "answer": "So, the answer is {\"answer\": \"4.32/7.76%\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: N-best (Ours) has a higher WER on the WSJ data set compared to 1-best (Ours)., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nN-best (Ours) has a higher WER on the WSJ data set compared to 1-best (Ours).\n" }, { "role": "assistant", "content": "The table shows that the WER for N-best (Ours) on the WSJ data set is 4.30/7.65%, while for 1-best (Ours) it is 4.29/7.65%. Since 4.30 is higher than 4.29, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3275.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: The comparisons of choosing 1-best and N-best hypotheses of each subsystem for system combination. The performance of N-best hypotheses is no better than 1-best regardless which voting schemes are used." ], "reasoning": "The table shows that the WER for N-best (Ours) on the WSJ data set is 4.30/7.65%, while for 1-best (Ours) it is 4.29/7.65%. Since 4.30 is higher than 4.29, the statement is supported.", "statement": "N-best (Ours) has a higher WER on the WSJ data set compared to 1-best (Ours).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Kendall’s tau (\\tau) on the WMT12 dataset for alternative architectures using 4metrics+syntax25+Wiki-GW25 as input.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Switching from a multi-layer to a single-layer architecture results in a 0.6 drop in Kendall's tau average, indicating the deeper architecture's value.We can see that switching from our multi-layer architecture to a single-layer one yields an absolute drop of 0.6 \\tau. This suggests that there is value in using the deeper, pairwise layer architecture." } ], "images": [ "scigen/train/3276.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Kendall’s tau (\\tau) on the WMT12 dataset for alternative architectures using 4metrics+syntax25+Wiki-GW25 as input." ], "reasoning": "Switching from a multi-layer to a single-layer architecture results in a 0.6 drop in Kendall's tau average, indicating the deeper architecture's value.", "description": "We can see that switching from our multi-layer architecture to a single-layer one yields an absolute drop of 0.6 \\tau. This suggests that there is value in using the deeper, pairwise layer architecture.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nTable title: Table 4: Kendall’s tau (\\tau) on the WMT12 dataset for alternative architectures using 4metrics+syntax25+Wiki-GW25 as input.\n\nQuestion: What is the difference in Kendall's tau for the German dataset between the single-layer and multi-layer architectures?\n" }, { "role": "assistant", "content": "To find the difference in Kendall's tau for the German dataset, subtract the single-layer value (32.06) from the multi-layer value (33.19). So, the answer is {\"answer\": \"1.13\"}." } ], "images": [ "scigen/train/3276.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Kendall’s tau (\\tau) on the WMT12 dataset for alternative architectures using 4metrics+syntax25+Wiki-GW25 as input." ], "reasoning": "To find the difference in Kendall's tau for the German dataset, subtract the single-layer value (32.06) from the multi-layer value (33.19).", "question": "What is the difference in Kendall's tau for the German dataset between the single-layer and multi-layer architectures?", "answer": "So, the answer is {\"answer\": \"1.13\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The multi-layer architecture performs better than the single-layer architecture for the Spanish dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe multi-layer architecture performs better than the single-layer architecture for the Spanish dataset.\n" }, { "role": "assistant", "content": "The table shows that the Kendall's tau for the Spanish dataset is 30.38 for the multi-layer architecture and 30.03 for the single-layer architecture, indicating better performance for the multi-layer architecture. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3276.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Kendall’s tau (\\tau) on the WMT12 dataset for alternative architectures using 4metrics+syntax25+Wiki-GW25 as input." ], "reasoning": "The table shows that the Kendall's tau for the Spanish dataset is 30.38 for the multi-layer architecture and 30.03 for the single-layer architecture, indicating better performance for the multi-layer architecture.", "statement": "The multi-layer architecture performs better than the single-layer architecture for the Spanish dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Kendall’s tau (\\tau) on the WMT12 dataset for various metrics. Notes: (i) the version of Meteor that took part in the WMT12 competition (marked with {}^{*} in section IV of the table) is different from the one used in our experiments (section I of the table), (ii) values marked as na were not reported by the authors.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Combining the four metrics in a flat neural net yields a τ of 27.06, which is better than the best of the four metrics by 3.5 points absolute, and adding embedding vectors further improves the τ to nearly 30.We will refer to the set of these four metrics as 4metrics. These metrics are not tuned and achieve Kendall’s \\tau between 18.5 and 23.5. and Wiki-GW25. These networks achieve modest \\tau values around 10, which should not be surprising: they use very general vector representations and have no access to word or n-gram overlap or to length information, which are very important features to compute similarity against the reference. However, as will be discussed below, their contribution is complementary to the four previous evaluation metrics and will lead to significant improvements in combination with them. We can see that just combining the four metrics in a flat neural net (i.e., no hidden layer), which is equivalent to a logistic regression, yields a \\tau of 27.06, which is better than the best of the four metrics by 3.5 points absolute, and also better by over 1.5 points absolute than the best metric that participated at the WMT12 metrics task competition (spede07pP with \\tau=25.4). Indeed, 4metrics is a strong mix that involves not only simple lexical overlap but also approximate matching, paraphrases, edit distance, lengths, etc. Yet, adding to 4metrics the embedding vectors yields sizeable further improvements: +1.5 and +2.0 points absolute when adding syntax25 and Wiki-GW25, respectively. Finally, adding both yields even further improvements close to \\tau of 30 (+2.64 \\tau points), showing that lexical semantics and syntactic representations are complementary." } ], "images": [ "scigen/train/3277.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Kendall’s tau (\\tau) on the WMT12 dataset for various metrics. Notes: (i) the version of Meteor that took part in the WMT12 competition (marked with {}^{*} in section IV of the table) is different from the one used in our experiments (section I of the table), (ii) values marked as na were not reported by the authors." ], "reasoning": "Combining the four metrics in a flat neural net yields a τ of 27.06, which is better than the best of the four metrics by 3.5 points absolute, and adding embedding vectors further improves the τ to nearly 30.", "description": "We will refer to the set of these four metrics as 4metrics. These metrics are not tuned and achieve Kendall’s \\tau between 18.5 and 23.5. and Wiki-GW25. These networks achieve modest \\tau values around 10, which should not be surprising: they use very general vector representations and have no access to word or n-gram overlap or to length information, which are very important features to compute similarity against the reference. However, as will be discussed below, their contribution is complementary to the four previous evaluation metrics and will lead to significant improvements in combination with them. We can see that just combining the four metrics in a flat neural net (i.e., no hidden layer), which is equivalent to a logistic regression, yields a \\tau of 27.06, which is better than the best of the four metrics by 3.5 points absolute, and also better by over 1.5 points absolute than the best metric that participated at the WMT12 metrics task competition (spede07pP with \\tau=25.4). Indeed, 4metrics is a strong mix that involves not only simple lexical overlap but also approximate matching, paraphrases, edit distance, lengths, etc. Yet, adding to 4metrics the embedding vectors yields sizeable further improvements: +1.5 and +2.0 points absolute when adding syntax25 and Wiki-GW25, respectively. Finally, adding both yields even further improvements close to \\tau of 30 (+2.64 \\tau points), showing that lexical semantics and syntactic representations are complementary.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Kendall’s tau (\\tau) on the WMT12 dataset for various metrics. Notes: (i) the version of Meteor that took part in the WMT12 competition (marked with {}^{*} in section IV of the table) is different from the one used in our experiments (section I of the table), (ii) values marked as na were not reported by the authors.\nQuestion: What is the Kendall's τ average for the 4metrics+syntax25+Wiki-GW25 combination?\nSolve the above question based on the table information. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the Kendall's τ average for the 4metrics+syntax25+Wiki-GW25 combination is 29.70. So, the answer is {\"answer\": \"29.70\"}." } ], "images": [ "scigen/train/3277.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Kendall’s tau (\\tau) on the WMT12 dataset for various metrics. Notes: (i) the version of Meteor that took part in the WMT12 competition (marked with {}^{*} in section IV of the table) is different from the one used in our experiments (section I of the table), (ii) values marked as na were not reported by the authors." ], "reasoning": "The table shows that the Kendall's τ average for the 4metrics+syntax25+Wiki-GW25 combination is 29.70.", "question": "What is the Kendall's τ average for the 4metrics+syntax25+Wiki-GW25 combination?", "answer": "So, the answer is {\"answer\": \"29.70\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Kendall's τ average for the 4metrics combination is higher than that of the best metric from the WMT12 competition.\n" }, { "role": "assistant", "content": "The table shows that the Kendall's τ average for the 4metrics combination is 27.06, which is higher than the best metric from the WMT12 competition (spede07pP) with a τ of 25.4. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3277.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Kendall’s tau (\\tau) on the WMT12 dataset for various metrics. Notes: (i) the version of Meteor that took part in the WMT12 competition (marked with {}^{*} in section IV of the table) is different from the one used in our experiments (section I of the table), (ii) values marked as na were not reported by the authors." ], "reasoning": "The table shows that the Kendall's τ average for the 4metrics combination is 27.06, which is higher than the best metric from the WMT12 competition (spede07pP) with a τ of 25.4.", "statement": "The Kendall's τ average for the 4metrics combination is higher than that of the best metric from the WMT12 competition.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Kendall’s \\tau on WMT12 for neural networks using BLEUcomp, a decomposed version of BLEU. For comparison, the last line shows a combination using BLEU instead of BLEUcomp.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The single-layer neural network (logistic regression) achieves a Kendall's τau AVG of 19.75, which is more than 1 point higher than BLEU's 18.46.We see that using a single-layer neural network, which is equivalent to logistic regression, outperforms BLEU by more than +1 \\tau points absolute." } ], "images": [ "scigen/train/3278.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Kendall’s \\tau on WMT12 for neural networks using BLEUcomp, a decomposed version of BLEU. For comparison, the last line shows a combination using BLEU instead of BLEUcomp." ], "reasoning": "The single-layer neural network (logistic regression) achieves a Kendall's τau AVG of 19.75, which is more than 1 point higher than BLEU's 18.46.", "description": "We see that using a single-layer neural network, which is equivalent to logistic regression, outperforms BLEU by more than +1 \\tau points absolute.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: Kendall’s \\tau on WMT12 for neural networks using BLEUcomp, a decomposed version of BLEU. For comparison, the last line shows a combination using BLEU instead of BLEUcomp.'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich system achieves the highest Kendall's τau AVG score?\n" }, { "role": "assistant", "content": "By comparing the Kendall's τau AVG scores of all systems, the system 'BLEUcomp+syntax25+Wiki-GW25' has the highest score of 26.15. So, the answer is {\"answer\": \"BLEUcomp+syntax25+Wiki-GW25\"}" } ], "images": [ "scigen/train/3278.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Kendall’s \\tau on WMT12 for neural networks using BLEUcomp, a decomposed version of BLEU. For comparison, the last line shows a combination using BLEU instead of BLEUcomp." ], "reasoning": "By comparing the Kendall's τau AVG scores of all systems, the system 'BLEUcomp+syntax25+Wiki-GW25' has the highest score of 26.15.", "question": "Which system achieves the highest Kendall's τau AVG score?", "answer": "So, the answer is {\"answer\": \"BLEUcomp+syntax25+Wiki-GW25\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The BLEUcomp+Wiki-GW25 system has a higher Kendall's τau AVG score than BLEUcomp+syntax25., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe BLEUcomp+Wiki-GW25 system has a higher Kendall's τau AVG score than BLEUcomp+syntax25.\n" }, { "role": "assistant", "content": "The BLEUcomp+Wiki-GW25 system has a Kendall's τau AVG score of 24.92, while the BLEUcomp+syntax25 system has a score of 23.70. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3278.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Kendall’s \\tau on WMT12 for neural networks using BLEUcomp, a decomposed version of BLEU. For comparison, the last line shows a combination using BLEU instead of BLEUcomp." ], "reasoning": "The BLEUcomp+Wiki-GW25 system has a Kendall's τau AVG score of 24.92, while the BLEUcomp+syntax25 system has a score of 23.70. Therefore, the statement is supported.", "statement": "The BLEUcomp+Wiki-GW25 system has a higher Kendall's τau AVG score than BLEUcomp+syntax25.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Average Kendall’s \\tau on WMT12 for semantic vectors trained on different text collections. Shown are results (i) when using the semantic vectors alone, and (ii) when combining them with 4metrics and syntax25. The improvements over Wiki-GW25 are marked in bold.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Higher dimensional vectors (300-400) generally perform better by 1-2 τ points when used alone compared to 50-dimensional vectors, but show minimal improvement when combined with 4metrics+syntax25.One interesting aspect to explore is the effect of the dimensionality of the input embeddings. Here, we studied the impact of using semantic vectors of bigger sizes, trained on different and larger text collections. We can see that, compared to the 50-dimensional Wiki-GW25, 300-400 dimensional vectors are generally better by 1-2 \\tau points absolute when used in isolation; however, when used in combination with 4metrics+syntax25, they do not offer much gain (up to +0.2), and in some cases, we observe a slight drop in performance. We suspect that the variability across the different collections is due to a domain mismatch. Yet, we defer this question for future work." } ], "images": [ "scigen/train/3279.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Average Kendall’s \\tau on WMT12 for semantic vectors trained on different text collections. Shown are results (i) when using the semantic vectors alone, and (ii) when combining them with 4metrics and syntax25. The improvements over Wiki-GW25 are marked in bold." ], "reasoning": "Higher dimensional vectors (300-400) generally perform better by 1-2 τ points when used alone compared to 50-dimensional vectors, but show minimal improvement when combined with 4metrics+syntax25.", "description": "One interesting aspect to explore is the effect of the dimensionality of the input embeddings. Here, we studied the impact of using semantic vectors of bigger sizes, trained on different and larger text collections. We can see that, compared to the 50-dimensional Wiki-GW25, 300-400 dimensional vectors are generally better by 1-2 \\tau points absolute when used in isolation; however, when used in combination with 4metrics+syntax25, they do not offer much gain (up to +0.2), and in some cases, we observe a slight drop in performance. We suspect that the variability across the different collections is due to a domain mismatch. Yet, we defer this question for future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Average Kendall’s \\tau on WMT12 for semantic vectors trained on different text collections. Shown are results (i) when using the semantic vectors alone, and (ii) when combining them with 4metrics and syntax25. The improvements over Wiki-GW25 are marked in bold.\nQuestion: Which source has the highest performance when used alone?\nSolve the above question based on the table information. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the source with the highest performance when used alone, compare the 'Alone' column values. The highest value is 12.35 from Composes400. So, the answer is {\"answer\": \"Composes400\"}." } ], "images": [ "scigen/train/3279.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Average Kendall’s \\tau on WMT12 for semantic vectors trained on different text collections. Shown are results (i) when using the semantic vectors alone, and (ii) when combining them with 4metrics and syntax25. The improvements over Wiki-GW25 are marked in bold." ], "reasoning": "To find the source with the highest performance when used alone, compare the 'Alone' column values. The highest value is 12.35 from Composes400.", "question": "Which source has the highest performance when used alone?", "answer": "So, the answer is {\"answer\": \"Composes400\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe combination of Wiki-GW300 with 4metrics+syntax25 offers the highest performance.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Comb.' column values. The highest value is 29.90 from Wiki-GW300. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3279.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Average Kendall’s \\tau on WMT12 for semantic vectors trained on different text collections. Shown are results (i) when using the semantic vectors alone, and (ii) when combining them with 4metrics and syntax25. The improvements over Wiki-GW25 are marked in bold." ], "reasoning": "To verify this statement, compare the 'Comb.' column values. The highest value is 29.90 from Wiki-GW300.", "statement": "The combination of Wiki-GW300 with 4metrics+syntax25 offers the highest performance.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: Kendall’s tau (\\tau) on WMT12 for alternative cost functions using 4metrics+syntax25+Wiki-GW25.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Using the Kendall cost enables effective learning but is eventually outperformed by the logistic cost due to slower convergence and poor initialization.our Kendall cost. We can see that using the Kendall cost enables effective learning, although it is eventually outperformed by the logistic cost. Our investigation revealed that this was due to a combination of slower convergence and poor initialization. Therefore, we further experimented with a setup where we first used the logistic cost to pre-train the neural network, and then we switched to the Kendall cost in order to perform some finer tuning." } ], "images": [ "scigen/train/3280.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Kendall’s tau (\\tau) on WMT12 for alternative cost functions using 4metrics+syntax25+Wiki-GW25." ], "reasoning": "Using the Kendall cost enables effective learning but is eventually outperformed by the logistic cost due to slower convergence and poor initialization.", "description": "our Kendall cost. We can see that using the Kendall cost enables effective learning, although it is eventually outperformed by the logistic cost. Our investigation revealed that this was due to a combination of slower convergence and poor initialization. Therefore, we further experimented with a setup where we first used the logistic cost to pre-train the neural network, and then we switched to the Kendall cost in order to perform some finer tuning.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 5: Kendall’s tau (\\tau) on WMT12 for alternative cost functions using 4metrics+syntax25+Wiki-GW25.', unfold the question presented below, detailing every step of your calculation. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the average Kendall's tau score when using the logistic cost for pre-training followed by Kendall cost for fine-tuning?\n" }, { "role": "assistant", "content": "The table shows the average Kendall's tau score for different methods. The average score for the method using logistic cost for pre-training followed by Kendall cost for fine-tuning is 29.92. So, the answer is {\"answer\": \"29.92\"}." } ], "images": [ "scigen/train/3280.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Kendall’s tau (\\tau) on WMT12 for alternative cost functions using 4metrics+syntax25+Wiki-GW25." ], "reasoning": "The table shows the average Kendall's tau score for different methods. The average score for the method using logistic cost for pre-training followed by Kendall cost for fine-tuning is 29.92.", "question": "What is the average Kendall's tau score when using the logistic cost for pre-training followed by Kendall cost for fine-tuning?", "answer": "So, the answer is {\"answer\": \"29.92\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Kendall cost alone has a higher average Kendall's tau score than the logistic cost.\n" }, { "role": "assistant", "content": "The table shows that the average Kendall's tau score for Kendall cost alone is 29.53, while for logistic cost it is 29.70. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3280.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Kendall’s tau (\\tau) on WMT12 for alternative cost functions using 4metrics+syntax25+Wiki-GW25." ], "reasoning": "The table shows that the average Kendall's tau score for Kendall cost alone is 29.53, while for logistic cost it is 29.70. Therefore, the statement is refuted.", "statement": "The Kendall cost alone has a higher average Kendall's tau score than the logistic cost.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Results of mh-PQA'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our ROP models achieve new state-of-the-art results, with significant improvements in H@10 on both BaseKGP and EnhancedKGP datasets, demonstrating the effectiveness of recurrent neural networks and intermediate entity encoding.Results. top baselines on BaseKGP (block 1), ROP results on BaseKGP (block 2) and ROP results on EnhancedKGP (block 3). Overall, our ROP models all get new state-of-the-art by large margins (2–4% H@10 on BaseKGP, 4–6% H@10 on EnhancedKGP). The improvements of the second block over the first are evidence that recurrent neural networks are an appropriate paradigm in this task. The third block shows that encoding intermediate entities in an appropriate way, like ROP does, can give a further boost." } ], "images": [ "scigen/train/3281.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of mh-PQA" ], "reasoning": "Our ROP models achieve new state-of-the-art results, with significant improvements in H@10 on both BaseKGP and EnhancedKGP datasets, demonstrating the effectiveness of recurrent neural networks and intermediate entity encoding.", "description": "Results. top baselines on BaseKGP (block 1), ROP results on BaseKGP (block 2) and ROP results on EnhancedKGP (block 3). Overall, our ROP models all get new state-of-the-art by large margins (2–4% H@10 on BaseKGP, 4–6% H@10 on EnhancedKGP). The improvements of the second block over the first are evidence that recurrent neural networks are an appropriate paradigm in this task. The third block shows that encoding intermediate entities in an appropriate way, like ROP does, can give a further boost.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 3: Results of mh-PQA', and tackle the question below in a sequential manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the H@10 improvement of ROP_ARC2 (GRU) over Comp-Bilinear on BaseKGP?\n" }, { "role": "assistant", "content": "To find the H@10 improvement, compare the H@10 values of ROP_ARC2 (GRU) and Comp-Bilinear on BaseKGP. ROP_ARC2 (GRU) has an H@10 of 54.9, while Comp-Bilinear has an H@10 of 42.1. The improvement is 54.9 - 42.1. So, the answer is {\"answer\": \"12.8\"}" } ], "images": [ "scigen/train/3281.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of mh-PQA" ], "reasoning": "To find the H@10 improvement, compare the H@10 values of ROP_ARC2 (GRU) and Comp-Bilinear on BaseKGP. ROP_ARC2 (GRU) has an H@10 of 54.9, while Comp-Bilinear has an H@10 of 42.1. The improvement is 54.9 - 42.1.", "question": "What is the H@10 improvement of ROP_ARC2 (GRU) over Comp-Bilinear on BaseKGP?", "answer": "So, the answer is {\"answer\": \"12.8\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: ROP_ARC3 (eGRU) achieves the highest MQ score on EnhancedKGP., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nROP_ARC3 (eGRU) achieves the highest MQ score on EnhancedKGP.\n" }, { "role": "assistant", "content": "The table shows that ROP_ARC3 (eGRU) has an MQ score of 90.7, but it is listed under the 2-4 section, not EnhancedKGP. The highest MQ score on EnhancedKGP is 89.4 by ROP_ARC1. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3281.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of mh-PQA" ], "reasoning": "The table shows that ROP_ARC3 (eGRU) has an MQ score of 90.7, but it is listed under the 2-4 section, not EnhancedKGP. The highest MQ score on EnhancedKGP is 89.4 by ROP_ARC1.", "statement": "ROP_ARC3 (eGRU) achieves the highest MQ score on EnhancedKGP.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Results of mh-KBC task'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "RNN-path-type improves results by using entity types, while ROP_ARC3 achieves the highest MAP score.Results. It ignores all information about within-path entities and trains separate models per relation. further improves the result by representing entities with the sum of their type embeddings. Thus, RNN-path-type uses extra information, the entity types. Apart from these baselines, it is also feasible to compare with the baselines based on composition of one-hop triple-based embedding models." } ], "images": [ "scigen/train/3282.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results of mh-KBC task" ], "reasoning": "RNN-path-type improves results by using entity types, while ROP_ARC3 achieves the highest MAP score.", "description": "Results. It ignores all information about within-path entities and trains separate models per relation. further improves the result by representing entities with the sum of their type embeddings. Thus, RNN-path-type uses extra information, the entity types. Apart from these baselines, it is also feasible to compare with the baselines based on composition of one-hop triple-based embedding models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Results of mh-KBC task', answer the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model achieves the highest MAP score and what is the score?\n" }, { "role": "assistant", "content": "By comparing the MAP scores of all models, ROP_ARC3 has the highest score of 76.16. So, the answer is {\"answer\": \"ROP_ARC3 with 76.16\"}" } ], "images": [ "scigen/train/3282.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results of mh-KBC task" ], "reasoning": "By comparing the MAP scores of all models, ROP_ARC3 has the highest score of 76.16.", "question": "Which model achieves the highest MAP score and what is the score?", "answer": "So, the answer is {\"answer\": \"ROP_ARC3 with 76.16\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: ROP_ARC1 has a higher MAP score than RNN-path-types., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nROP_ARC1 has a higher MAP score than RNN-path-types.\n" }, { "role": "assistant", "content": "ROP_ARC1 has a MAP score of 74.23, which is higher than RNN-path-types' score of 73.26. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3282.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results of mh-KBC task" ], "reasoning": "ROP_ARC1 has a MAP score of 74.23, which is higher than RNN-path-types' score of 73.26.", "statement": "ROP_ARC1 has a higher MAP score than RNN-path-types.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Performance on the linguistic probing tasks of evaluating linguistics embedded in the encoder outputs. “Base” denotes the representations from Tranformer-Based encoder. “Ssr” denotes shallow sentence representation. “Dsr” denotes deep sentence representation. “Avg” denotes the average accuracy of each category.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Lower layers in the baseline model embed more surface information, while higher layers encode more semantics. Integrating shallow sentence representation improves semantic tasks but not surface or syntactic tasks, while deep sentence representation brings more overall improvements.From the tale, we can see that For different encoder layers in the baseline model (see “L4 in Base”, “L5 in Base” and “L6 in Base”), lower layers embed more about surface information while higher layers encode more semantics, which are consistent with previous findings in Raganato and Tiedemann Integrating the shallow sentence representation (“+ Ssr”) obtains improvement over the baseline on semantic tasks (75.33 vs. 74.61), while fails to improve on the surface (77.32 vs. 77.60) and syntactic tasks (64.88 vs. 65.00). This may indicate that the shallow representations that exploits only the top encoder layer (“L6 in Base”) encodes more semantic information. Introducing deep sentence representation (“+ Dsr”) brings more improvements. The reason is that our deep sentence representation is induced from the sentence representations of all the encoder layers, and lower layers that contain abound surface and syntactic information are exploited." } ], "images": [ "scigen/train/3283.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance on the linguistic probing tasks of evaluating linguistics embedded in the encoder outputs. “Base” denotes the representations from Tranformer-Based encoder. “Ssr” denotes shallow sentence representation. “Dsr” denotes deep sentence representation. “Avg” denotes the average accuracy of each category." ], "reasoning": "Lower layers in the baseline model embed more surface information, while higher layers encode more semantics. Integrating shallow sentence representation improves semantic tasks but not surface or syntactic tasks, while deep sentence representation brings more overall improvements.", "description": "From the tale, we can see that For different encoder layers in the baseline model (see “L4 in Base”, “L5 in Base” and “L6 in Base”), lower layers embed more about surface information while higher layers encode more semantics, which are consistent with previous findings in Raganato and Tiedemann Integrating the shallow sentence representation (“+ Ssr”) obtains improvement over the baseline on semantic tasks (75.33 vs. 74.61), while fails to improve on the surface (77.32 vs. 77.60) and syntactic tasks (64.88 vs. 65.00). This may indicate that the shallow representations that exploits only the top encoder layer (“L6 in Base”) encodes more semantic information. Introducing deep sentence representation (“+ Dsr”) brings more improvements. The reason is that our deep sentence representation is induced from the sentence representations of all the encoder layers, and lower layers that contain abound surface and syntactic information are exploited.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Performance on the linguistic probing tasks of evaluating linguistics embedded in the encoder outputs. “Base” denotes the representations from Tranformer-Based encoder. “Ssr” denotes shallow sentence representation. “Dsr” denotes deep sentence representation. “Avg” denotes the average accuracy of each category.\nQuestion: Which model configuration shows the highest improvement in semantic tasks?\nSolve the above question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the 'Semantic Avg' scores, the '+ Dsr' model configuration shows the highest improvement with a score of 75.32. So, the answer is {\"answer\": \"75.32\"}" } ], "images": [ "scigen/train/3283.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance on the linguistic probing tasks of evaluating linguistics embedded in the encoder outputs. “Base” denotes the representations from Tranformer-Based encoder. “Ssr” denotes shallow sentence representation. “Dsr” denotes deep sentence representation. “Avg” denotes the average accuracy of each category." ], "reasoning": "By comparing the 'Semantic Avg' scores, the '+ Dsr' model configuration shows the highest improvement with a score of 75.32.", "question": "Which model configuration shows the highest improvement in semantic tasks?", "answer": "So, the answer is {\"answer\": \"75.32\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The '+ Ssr' model configuration improves the performance on surface tasks compared to the baseline. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe '+ Ssr' model configuration improves the performance on surface tasks compared to the baseline.\n" }, { "role": "assistant", "content": "The 'Surface Avg' score for '+ Ssr' is 77.32, which is lower than the 'Surface Avg' score for 'L6 in Base' at 77.60, indicating that '+ Ssr' does not improve performance on surface tasks compared to the baseline. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3283.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance on the linguistic probing tasks of evaluating linguistics embedded in the encoder outputs. “Base” denotes the representations from Tranformer-Based encoder. “Ssr” denotes shallow sentence representation. “Dsr” denotes deep sentence representation. “Avg” denotes the average accuracy of each category." ], "reasoning": "The 'Surface Avg' score for '+ Ssr' is 77.32, which is lower than the 'Surface Avg' score for 'L6 in Base' at 77.60, indicating that '+ Ssr' does not improve performance on surface tasks compared to the baseline.", "statement": "The '+ Ssr' model configuration improves the performance on surface tasks compared to the baseline.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Case-sensitive BLEU scores on WMT14 En⇒De and En⇒Fr test sets. “↑/⇑”: significant over Transformer counterpart (p<0.05/0.01), tested by bootstrap resampling.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Deep sentential context representation improves translation performance, with Deep (Tam) consistently outperforming Deep (Rnn) in the Transformer-Big configuration.As seen, exploiting deep sentential context representation consistently improves translation performance across language pairs and model architectures, demonstrating the necessity and effectiveness of modeling sentential context for NMT. Among them, Transformer-Base with deep sentential context achieves comparable performance with the vanilla Transformer-Big, with only less than half of the parameters (114.4M vs. 264.1M, not shown in the table). Furthermore, Deep (Tam) consistently outperforms Deep (RNN) in the Transformer-Big configuration. One possible reason is that the big models benefit more from the improved gradient flow with the transparent attention Bapna et al." } ], "images": [ "scigen/train/3284.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Case-sensitive BLEU scores on WMT14 En⇒De and En⇒Fr test sets. “↑/⇑”: significant over Transformer counterpart (p<0.05/0.01), tested by bootstrap resampling." ], "reasoning": "Deep sentential context representation improves translation performance, with Deep (Tam) consistently outperforming Deep (Rnn) in the Transformer-Big configuration.", "description": "As seen, exploiting deep sentential context representation consistently improves translation performance across language pairs and model architectures, demonstrating the necessity and effectiveness of modeling sentential context for NMT. Among them, Transformer-Base with deep sentential context achieves comparable performance with the vanilla Transformer-Big, with only less than half of the parameters (114.4M vs. 264.1M, not shown in the table). Furthermore, Deep (Tam) consistently outperforms Deep (RNN) in the Transformer-Big configuration. One possible reason is that the big models benefit more from the improved gradient flow with the transparent attention Bapna et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: Case-sensitive BLEU scores on WMT14 En⇒De and En⇒Fr test sets. “↑/⇑”: significant over Transformer counterpart (p<0.05/0.01), tested by bootstrap resampling.'.\n\nQuestion: Which model configuration achieves the highest En⇒Fr translation performance?\n" }, { "role": "assistant", "content": "By examining the table, the highest En⇒Fr translation performance is achieved by the Transformer-Big with Deep (Tam) configuration, which has a score of 42.04. So, the answer is {\"answer\": \"Transformer-Big with Deep (Tam)\"}" } ], "images": [ "scigen/train/3284.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Case-sensitive BLEU scores on WMT14 En⇒De and En⇒Fr test sets. “↑/⇑”: significant over Transformer counterpart (p<0.05/0.01), tested by bootstrap resampling." ], "reasoning": "By examining the table, the highest En⇒Fr translation performance is achieved by the Transformer-Big with Deep (Tam) configuration, which has a score of 42.04.", "question": "Which model configuration achieves the highest En⇒Fr translation performance?", "answer": "So, the answer is {\"answer\": \"Transformer-Big with Deep (Tam)\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Transformer-Base with Deep (Tam) achieves higher En⇒De performance than Transformer-Big with Deep (Rnn)., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nTransformer-Base with Deep (Tam) achieves higher En⇒De performance than Transformer-Big with Deep (Rnn).\n" }, { "role": "assistant", "content": "According to the table, Transformer-Base with Deep (Tam) has an En⇒De score of 28.33, while Transformer-Big with Deep (Rnn) has a higher score of 29.04. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3284.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Case-sensitive BLEU scores on WMT14 En⇒De and En⇒Fr test sets. “↑/⇑”: significant over Transformer counterpart (p<0.05/0.01), tested by bootstrap resampling." ], "reasoning": "According to the table, Transformer-Base with Deep (Tam) has an En⇒De score of 28.33, while Transformer-Big with Deep (Rnn) has a higher score of 29.04. Therefore, the statement is refuted.", "statement": "Transformer-Base with Deep (Tam) achieves higher En⇒De performance than Transformer-Big with Deep (Rnn).", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'TABLE III: EER results on semi text-independent task'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The d-vector system outperforms the i-vector system with cosine distance, but i-vector performance improves significantly with LDA and PLDA, surpassing d-vectors. Adding a new hidden layer for NLDR further enhances d-vector performance.It can be observed that with the simple cosine distance, the d-vector system outperforms the i-vector system in a significant way. This demonstrates that the discriminatively learned d-vectors are more discriminative for speakers when compared with the generatively learned i-vectors. However, when the discriminative normalization methods (LDA and PLDA) are employed, the performance of the i-vector system is significantly improved and better than that of the d-vector system. The discriminative methods contribute very little to the d-vector system. This is not supervising, as the d-vectors have been discriminative already. Nevertheless, the slight improvement with LDA suggests that there is some redundancy in d-vectors. Motivated by this idea, a new hidden layer with a small number of units is inserted into the DNN structure, as shown in Fig. The dimension of the new layer is set to 100, which is the best choice in our test. Compared to LDA, this approach can be regarded as a non-linear dimension reduction (NLDR). In this experiment, the phone posteriors are included in the input of the DNN structure, as shown in Fig. The phone posteriors are produced by a DNN model that was trained for ASR with a Chinese database consisting of 6000 hours of speech data. The phone set consists of 66 toneless initial and finals in Chinese, plus the silence phone. It can be seen that the phone-dependent training leads to marginal but consistent performance improvement for the d-vector system. The NLDR approach is also applied, and an additional gain is obtained." } ], "images": [ "scigen/train/3285.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: EER results on semi text-independent task" ], "reasoning": "The d-vector system outperforms the i-vector system with cosine distance, but i-vector performance improves significantly with LDA and PLDA, surpassing d-vectors. Adding a new hidden layer for NLDR further enhances d-vector performance.", "description": "It can be observed that with the simple cosine distance, the d-vector system outperforms the i-vector system in a significant way. This demonstrates that the discriminatively learned d-vectors are more discriminative for speakers when compared with the generatively learned i-vectors. However, when the discriminative normalization methods (LDA and PLDA) are employed, the performance of the i-vector system is significantly improved and better than that of the d-vector system. The discriminative methods contribute very little to the d-vector system. This is not supervising, as the d-vectors have been discriminative already. Nevertheless, the slight improvement with LDA suggests that there is some redundancy in d-vectors. Motivated by this idea, a new hidden layer with a small number of units is inserted into the DNN structure, as shown in Fig. The dimension of the new layer is set to 100, which is the best choice in our test. Compared to LDA, this approach can be regarded as a non-linear dimension reduction (NLDR). In this experiment, the phone posteriors are included in the input of the DNN structure, as shown in Fig. The phone posteriors are produced by a DNN model that was trained for ASR with a Chinese database consisting of 6000 hours of speech data. The phone set consists of 66 toneless initial and finals in Chinese, plus the silence phone. It can be seen that the phone-dependent training leads to marginal but consistent performance improvement for the d-vector system. The NLDR approach is also applied, and an additional gain is obtained.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'TABLE III: EER results on semi text-independent task', answer the question below by showing each progressive step toward the answer. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the cosine distance performance of the d-vector system without PDTR?\n" }, { "role": "assistant", "content": "The table shows that the cosine distance performance of the d-vector system without PDTR is 13.58. So, the answer is {\"answer\": \"13.58\"}." } ], "images": [ "scigen/train/3285.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: EER results on semi text-independent task" ], "reasoning": "The table shows that the cosine distance performance of the d-vector system without PDTR is 13.58.", "question": "What is the cosine distance performance of the d-vector system without PDTR?", "answer": "So, the answer is {\"answer\": \"13.58\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The i-vector system performs better than the d-vector system with PLDA. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe i-vector system performs better than the d-vector system with PLDA.\n" }, { "role": "assistant", "content": "The table shows that the i-vector system has a PLDA performance of 8.70, while the d-vector system has a PLDA performance of 15.45 and 15.48. Therefore, the i-vector system performs better than the d-vector system with PLDA. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3285.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: EER results on semi text-independent task" ], "reasoning": "The table shows that the i-vector system has a PLDA performance of 8.70, while the d-vector system has a PLDA performance of 15.45 and 15.48. Therefore, the i-vector system performs better than the d-vector system with PLDA.", "statement": "The i-vector system performs better than the d-vector system with PLDA.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'TABLE I: EER results on text-dependent task'.\n" }, { "role": "assistant", "content": "The i-vector system consistently outperforms the d-vector system across all scoring methods and phrases.The tests are based on three scoring methods: the basic cosine distance, the cosine distance after reducing the dimension to 80 by LDA, and the score provided by PLDA. It can be seen that the d-vector system obtains reasonable performance, however, the results are much worse than those with the i-vector system." } ], "images": [ "scigen/train/3286.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: EER results on text-dependent task" ], "reasoning": "The i-vector system consistently outperforms the d-vector system across all scoring methods and phrases.", "description": "The tests are based on three scoring methods: the basic cosine distance, the cosine distance after reducing the dimension to 80 by LDA, and the score provided by PLDA. It can be seen that the d-vector system obtains reasonable performance, however, the results are much worse than those with the i-vector system.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'TABLE I: EER results on text-dependent task', solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the EER% for the i-vector system using PLDA for phrase P2?\n" }, { "role": "assistant", "content": "To find the EER% for the i-vector system using PLDA for phrase P2, locate the row for i-vector and P2, then find the value under the EER% PLDA column. So, the answer is {\"answer\": \"2.76\"}." } ], "images": [ "scigen/train/3286.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: EER results on text-dependent task" ], "reasoning": "To find the EER% for the i-vector system using PLDA for phrase P2, locate the row for i-vector and P2, then find the value under the EER% PLDA column.", "question": "What is the EER% for the i-vector system using PLDA for phrase P2?", "answer": "So, the answer is {\"answer\": \"2.76\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe d-vector system has a higher EER% than the i-vector system for phrase P1 using LDA.\n" }, { "role": "assistant", "content": "Compare the EER% values for the d-vector and i-vector systems for phrase P1 using LDA. The d-vector system has an EER% of 9.52, while the i-vector system has an EER% of 4.62. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3286.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: EER results on text-dependent task" ], "reasoning": "Compare the EER% values for the d-vector and i-vector systems for phrase P1 using LDA. The d-vector system has an EER% of 9.52, while the i-vector system has an EER% of 4.62.", "statement": "The d-vector system has a higher EER% than the i-vector system for phrase P1 using LDA.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'TABLE II: EER results with additional data'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "With more training data, d-vector systems generally improve, but the PLDA model becomes less effective.It can be observed that with more training data, the performance of d-vector systems is generally improved, despite that the extra data are recordings of other phrases. Another observation is that with more training data, the PLDA model tends to be less effective. This can be possibly explained by the fact that d-vectors are derived from activations of neural network units and so probably do not fit the linear Gaussian model that PLDA assumes." } ], "images": [ "scigen/train/3287.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: EER results with additional data" ], "reasoning": "With more training data, d-vector systems generally improve, but the PLDA model becomes less effective.", "description": "It can be observed that with more training data, the performance of d-vector systems is generally improved, despite that the extra data are recordings of other phrases. Another observation is that with more training data, the PLDA model tends to be less effective. This can be possibly explained by the fact that d-vectors are derived from activations of neural network units and so probably do not fit the linear Gaussian model that PLDA assumes.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the EER% for P2 using Cosine when trained with P1, P2, ..., P15?\nSolve the above question based on the table titled 'TABLE II: EER results with additional data'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the EER% for P2 using Cosine when trained with P1, P2, ..., P15, locate the row for P2 with the training data P1, P2, ..., P15 and check the EER% Cosine column. So, the answer is {\"answer\": \"8.33\"}" } ], "images": [ "scigen/train/3287.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: EER results with additional data" ], "reasoning": "To find the EER% for P2 using Cosine when trained with P1, P2, ..., P15, locate the row for P2 with the training data P1, P2, ..., P15 and check the EER% Cosine column.", "question": "What is the EER% for P2 using Cosine when trained with P1, P2, ..., P15?", "answer": "So, the answer is {\"answer\": \"8.33\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe EER% for P1 using LDA improves with more training data.\n" }, { "role": "assistant", "content": "Compare the EER% LDA for P1 with different amounts of training data. The EER% LDA decreases from 9.52 to 8.29 to 8.14 as more training data is used. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3287.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: EER results with additional data" ], "reasoning": "Compare the EER% LDA for P1 with different amounts of training data. The EER% LDA decreases from 9.52 to 8.29 to 8.14 as more training data is used.", "statement": "The EER% for P1 using LDA improves with more training data.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 8: Relative effective context length (RECL) comparison. See text for the definition of RECL and r. The first three models and the last four models are compared as two model groups when we calculate RECL (RECL is computed on a model group rather than a single model). Each group has the same parameter budget.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "RECL is a new metric for fair comparison among models, measuring the relative improvement over the best short context model, with Transformer-XL showing significantly longer RECL than recurrent networks and Transformer.Khandelwal et al. ECL is the longest length to which increasing the context span would lead to a gain more than a threshold. However, ECL ignores the fact that it is harder to get improvement when a model already achieves a lower perplexity using only a shorter context, and thus it is not suitable for fair comparison among multiple models. We instead propose a new metric called Relative Effective Context Length (RECL). RECL is defined on a model group instead of a single model, and the gain of a long context is measure by the relative improvement over the best short context model. As such, the model group shares the same baseline to enable fair comparison. RECL also has a parameter r, which means constraining the comparison on top-r hard examples. The RECL of Transformer-XL is 80% and 450% longer than recurrent networks and Transformer respectively. Both the recurrence mechanism and our positional encodings contribute to a longer RECL. This further substantiates our argument that Transformer-XL is able to model longer-term dependency." } ], "images": [ "scigen/train/3288.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Relative effective context length (RECL) comparison. See text for the definition of RECL and r. The first three models and the last four models are compared as two model groups when we calculate RECL (RECL is computed on a model group rather than a single model). Each group has the same parameter budget." ], "reasoning": "RECL is a new metric for fair comparison among models, measuring the relative improvement over the best short context model, with Transformer-XL showing significantly longer RECL than recurrent networks and Transformer.", "description": "Khandelwal et al. ECL is the longest length to which increasing the context span would lead to a gain more than a threshold. However, ECL ignores the fact that it is harder to get improvement when a model already achieves a lower perplexity using only a shorter context, and thus it is not suitable for fair comparison among multiple models. We instead propose a new metric called Relative Effective Context Length (RECL). RECL is defined on a model group instead of a single model, and the gain of a long context is measure by the relative improvement over the best short context model. As such, the model group shares the same baseline to enable fair comparison. RECL also has a parameter r, which means constraining the comparison on top-r hard examples. The RECL of Transformer-XL is 80% and 450% longer than recurrent networks and Transformer respectively. Both the recurrence mechanism and our positional encodings contribute to a longer RECL. This further substantiates our argument that Transformer-XL is able to model longer-term dependency.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 8: Relative effective context length (RECL) comparison. See text for the definition of RECL and r. The first three models and the last four models are compared as two model groups when we calculate RECL (RECL is computed on a model group rather than a single model). Each group has the same parameter budget.', answer the question below by showing each progressive step toward the answer. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the RECL of Transformer-XL 151M at r=0.5 and how does it compare to QRNN at the same r value?\n" }, { "role": "assistant", "content": "The RECL of Transformer-XL 151M at r=0.5 is 800, while the RECL of QRNN at r=0.5 is 400. Transformer-XL 151M has a higher RECL compared to QRNN at r=0.5. So, the answer is {\"answer\": \"800\"}." } ], "images": [ "scigen/train/3288.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Relative effective context length (RECL) comparison. See text for the definition of RECL and r. The first three models and the last four models are compared as two model groups when we calculate RECL (RECL is computed on a model group rather than a single model). Each group has the same parameter budget." ], "reasoning": "The RECL of Transformer-XL 151M at r=0.5 is 800, while the RECL of QRNN at r=0.5 is 400. Transformer-XL 151M has a higher RECL compared to QRNN at r=0.5.", "question": "What is the RECL of Transformer-XL 151M at r=0.5 and how does it compare to QRNN at the same r value?", "answer": "So, the answer is {\"answer\": \"800\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe RECL of Transformer-XL 128M at r=1.0 is higher than that of LSTM at r=0.5.\n" }, { "role": "assistant", "content": "The table shows that the RECL of Transformer-XL 128M at r=1.0 is 500, while the RECL of LSTM at r=0.5 is 300. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3288.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Relative effective context length (RECL) comparison. See text for the definition of RECL and r. The first three models and the last four models are compared as two model groups when we calculate RECL (RECL is computed on a model group rather than a single model). Each group has the same parameter budget." ], "reasoning": "The table shows that the RECL of Transformer-XL 128M at r=1.0 is 500, while the RECL of LSTM at r=0.5 is 300. Therefore, the statement is supported.", "statement": "The RECL of Transformer-XL 128M at r=1.0 is higher than that of LSTM at r=0.5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Comparison with state-of-the-art results on enwik8.'.\n" }, { "role": "assistant", "content": "The 12-layer Transformer-XL achieves a new state-of-the-art result with a bpc of 1.06, outperforming the 12-layer vanilla Transformer from Al-Rfou et al., and matches the performance of their 64-layer network. Increasing the model size to 18 and 24 layers further improves the performance, breaking through the 1.0 bpc barrier.The dataset enwik8 contains 100M bytes of unprocessed Wikipedia text. Under the model size constraint, the 12-layer Transformer-XL achieves a new SoTA result, outperforming the 12-layer vanilla Transformer from Al-Rfou et al. Notably, our 12-layer architecture achieves the same result as the 64-layer network from Al-Rfou et al. In order to see whether better performances can be obtained by increasing the model size, we train 18-layer and 24-layer Transformer-XLs with increased model sizes. With the attention length 784 during training and 3,800 during evaluation, we obtained a new SoTA result and our method is the first to break through 1.0 on widely-studied character-level benchmarks. Different from Al-Rfou et al." } ], "images": [ "scigen/train/3289.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison with state-of-the-art results on enwik8." ], "reasoning": "The 12-layer Transformer-XL achieves a new state-of-the-art result with a bpc of 1.06, outperforming the 12-layer vanilla Transformer from Al-Rfou et al., and matches the performance of their 64-layer network. Increasing the model size to 18 and 24 layers further improves the performance, breaking through the 1.0 bpc barrier.", "description": "The dataset enwik8 contains 100M bytes of unprocessed Wikipedia text. Under the model size constraint, the 12-layer Transformer-XL achieves a new SoTA result, outperforming the 12-layer vanilla Transformer from Al-Rfou et al. Notably, our 12-layer architecture achieves the same result as the 64-layer network from Al-Rfou et al. In order to see whether better performances can be obtained by increasing the model size, we train 18-layer and 24-layer Transformer-XLs with increased model sizes. With the attention length 784 during training and 3,800 during evaluation, we obtained a new SoTA result and our method is the first to break through 1.0 on widely-studied character-level benchmarks. Different from Al-Rfou et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Comparison with state-of-the-art results on enwik8.' to sequentially solve the question presented below. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the bpc of the 24-layer Transformer-XL model?\n" }, { "role": "assistant", "content": "The table shows the bpc values for different models. The row for 'Ours - 24L Transformer-XL' indicates a bpc of 0.99. So, the answer is {\"answer\": \"0.99\"}" } ], "images": [ "scigen/train/3289.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison with state-of-the-art results on enwik8." ], "reasoning": "The table shows the bpc values for different models. The row for 'Ours - 24L Transformer-XL' indicates a bpc of 0.99.", "question": "What is the bpc of the 24-layer Transformer-XL model?", "answer": "So, the answer is {\"answer\": \"0.99\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe 12-layer Transformer-XL model has fewer parameters than the 12-layer vanilla Transformer from Al-Rfou et al.\n" }, { "role": "assistant", "content": "The table shows that the 12-layer Transformer-XL model has 41M parameters, while the 12-layer vanilla Transformer from Al-Rfou et al. has 44M parameters. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3289.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison with state-of-the-art results on enwik8." ], "reasoning": "The table shows that the 12-layer Transformer-XL model has 41M parameters, while the 12-layer vanilla Transformer from Al-Rfou et al. has 44M parameters.", "statement": "The 12-layer Transformer-XL model has fewer parameters than the 12-layer vanilla Transformer from Al-Rfou et al.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Comparison with state-of-the-art results on text8.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Transformer-XL achieves the new state-of-the-art result on text8 with a bpc of 1.08, outperforming previous models.Similar to but different from enwik8, text8 contains 100M processed Wikipedia characters created by lowering case the text and removing any character other than the 26 letters a through z, and space. Due to the similarity, we simply adapt the best model and the same hyper-parameters on enwik8 to text8 without further tuning. Again, Transformer-XL achieves the new SoTA result with a clear margin." } ], "images": [ "scigen/train/3290.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison with state-of-the-art results on text8." ], "reasoning": "Transformer-XL achieves the new state-of-the-art result on text8 with a bpc of 1.08, outperforming previous models.", "description": "Similar to but different from enwik8, text8 contains 100M processed Wikipedia characters created by lowering case the text and removing any character other than the 26 letters a through z, and space. Due to the similarity, we simply adapt the best model and the same hyper-parameters on enwik8 to text8 without further tuning. Again, Transformer-XL achieves the new SoTA result with a clear margin.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 3: Comparison with state-of-the-art results on text8.', solve this question and give detail solutions. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model has the lowest bpc and how many parameters does it have?\n" }, { "role": "assistant", "content": "The table shows that the 'Ours - 24L Transformer-XL' model has the lowest bpc of 1.08 and it has 277M parameters. So, the answer is {\"answer\": \"Ours - 24L Transformer-XL with 277M parameters\"}." } ], "images": [ "scigen/train/3290.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison with state-of-the-art results on text8." ], "reasoning": "The table shows that the 'Ours - 24L Transformer-XL' model has the lowest bpc of 1.08 and it has 277M parameters.", "question": "Which model has the lowest bpc and how many parameters does it have?", "answer": "So, the answer is {\"answer\": \"Ours - 24L Transformer-XL with 277M parameters\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The model with the highest number of parameters has a bpc of 1.13., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe model with the highest number of parameters has a bpc of 1.13.\n" }, { "role": "assistant", "content": "The table shows that the model 'Al-Rfou et al. (2018) - 64L Transformer' has the highest number of parameters (235M) and a bpc of 1.13. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3290.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison with state-of-the-art results on text8." ], "reasoning": "The table shows that the model 'Al-Rfou et al. (2018) - 64L Transformer' has the highest number of parameters (235M) and a bpc of 1.13.", "statement": "The model with the highest number of parameters has a bpc of 1.13.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Comparison with state-of-the-art results on One Billion Word. ⋄ indicates contemporary work.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Despite being designed for long-term dependencies, Transformer-XL significantly improves short-term dependency modeling, reducing the single-model SoTA from 23.7 to 21.8.One Billion Word does not preserve any long-term dependency because sentences have been shuffled. Consequently, this dataset mainly tests the ability of modeling only short-term dependency. Although Transformer-XL is mainly designed to better capture longer-term dependency, it dramatically improves the single-model SoTA from 23.7 to 21.8. Specifically, Transformer-XL significantly outperforms a contemporary method using vanilla Transformers Baevski and Auli suggesting the advantage of Transformer-XL is generalizable to modeling short sequences." } ], "images": [ "scigen/train/3291.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison with state-of-the-art results on One Billion Word. ⋄ indicates contemporary work." ], "reasoning": "Despite being designed for long-term dependencies, Transformer-XL significantly improves short-term dependency modeling, reducing the single-model SoTA from 23.7 to 21.8.", "description": "One Billion Word does not preserve any long-term dependency because sentences have been shuffled. Consequently, this dataset mainly tests the ability of modeling only short-term dependency. Although Transformer-XL is mainly designed to better capture longer-term dependency, it dramatically improves the single-model SoTA from 23.7 to 21.8. Specifically, Transformer-XL significantly outperforms a contemporary method using vanilla Transformers Baevski and Auli suggesting the advantage of Transformer-XL is generalizable to modeling short sequences.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}. The question is related to the given table titled 'Table 4: Comparison with state-of-the-art results on One Billion Word. ⋄ indicates contemporary work.'.\n\nQuestion: What is the perplexity (PPL) of the Transformer-XL Large model and how does it compare to the previous single-model SoTA?\n" }, { "role": "assistant", "content": "The table shows that the Transformer-XL Large model has a perplexity of 21.8, which is an improvement over the previous single-model SoTA of 23.7. So, the answer is {\"answer\": \"21.8\"}." } ], "images": [ "scigen/train/3291.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison with state-of-the-art results on One Billion Word. ⋄ indicates contemporary work." ], "reasoning": "The table shows that the Transformer-XL Large model has a perplexity of 21.8, which is an improvement over the previous single-model SoTA of 23.7.", "question": "What is the perplexity (PPL) of the Transformer-XL Large model and how does it compare to the previous single-model SoTA?", "answer": "So, the answer is {\"answer\": \"21.8\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Transformer-XL Large model has the lowest perplexity among all models listed in the table. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Transformer-XL Large model has the lowest perplexity among all models listed in the table.\n" }, { "role": "assistant", "content": "The table shows that the Transformer-XL Large model has a perplexity of 21.8, which is the lowest among all the models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3291.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison with state-of-the-art results on One Billion Word. ⋄ indicates contemporary work." ], "reasoning": "The table shows that the Transformer-XL Large model has a perplexity of 21.8, which is the lowest among all the models listed.", "statement": "The Transformer-XL Large model has the lowest perplexity among all models listed in the table.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 6: Ablation study on WikiText-103. For the first two blocks, we use a slightly smaller model (128M parameters). † indicates that the corresponding row is reduced to the same setting as the Transformer network in Al-Rfou et al. (2018), except that two auxiliary losses are not implemented in our experiments. “PPL init” refers to using the same length as training. “PPL best” indicates the perplexity obtained by using the optimal length. “Attn Len” is the shortest possible attention length during evaluation to achieve the corresponding result (PPL best). Increasing the attention length during evaluation improves performance only when our positional encoding is used. The “Transformer-XL (151M)” setting uses a standard parameter budget as previous work Merity et al. (2018), where we observe a similar effect when increasing the attention length during evaluation.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Absolute encodings work well with half losses because they exclude positions with short attention lengths, and increasing attention length at test time improves perplexity.The first study is performed on WikiText-103, which requires modeling long-term dependency. Among the compared encoding schemes, Shaw et al. “Full” and “half” losses refer to applying a cross entropy loss to all or the recent half positions in the segment. We found that absolute encodings only work well with half losses because half losses exclude positions with very short attention lengths during training for better generalization. Although the backpropagation length during training is only 128, with the two techniques the attention length can be increased to 640 at test time. In the standard setting with 151M parameters, the perplexity decreases as the attention length increases." } ], "images": [ "scigen/train/3292.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Ablation study on WikiText-103. For the first two blocks, we use a slightly smaller model (128M parameters). † indicates that the corresponding row is reduced to the same setting as the Transformer network in Al-Rfou et al. (2018), except that two auxiliary losses are not implemented in our experiments. “PPL init” refers to using the same length as training. “PPL best” indicates the perplexity obtained by using the optimal length. “Attn Len” is the shortest possible attention length during evaluation to achieve the corresponding result (PPL best). Increasing the attention length during evaluation improves performance only when our positional encoding is used. The “Transformer-XL (151M)” setting uses a standard parameter budget as previous work Merity et al. (2018), where we observe a similar effect when increasing the attention length during evaluation." ], "reasoning": "Absolute encodings work well with half losses because they exclude positions with short attention lengths, and increasing attention length at test time improves perplexity.", "description": "The first study is performed on WikiText-103, which requires modeling long-term dependency. Among the compared encoding schemes, Shaw et al. “Full” and “half” losses refer to applying a cross entropy loss to all or the recent half positions in the segment. We found that absolute encodings only work well with half losses because half losses exclude positions with very short attention lengths during training for better generalization. Although the backpropagation length during training is only 128, with the two techniques the attention length can be increased to 640 at test time. In the standard setting with 151M parameters, the perplexity decreases as the attention length increases.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 6: Ablation study on WikiText-103. For the first two blocks, we use a slightly smaller model (128M parameters). † indicates that the corresponding row is reduced to the same setting as the Transformer network in Al-Rfou et al. (2018), except that two auxiliary losses are not implemented in our experiments. “PPL init” refers to using the same length as training. “PPL best” indicates the perplexity obtained by using the optimal length. “Attn Len” is the shortest possible attention length during evaluation to achieve the corresponding result (PPL best). Increasing the attention length during evaluation improves performance only when our positional encoding is used. The “Transformer-XL (151M)” setting uses a standard parameter budget as previous work Merity et al. (2018), where we observe a similar effect when increasing the attention length during evaluation.' and methodically tackle the question that follows. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the perplexity best score for the Transformer-XL (151M) model with full loss and 640 attention length?\n" }, { "role": "assistant", "content": "The table shows that the Transformer-XL (151M) model with full loss and 640 attention length has a PPL best score of 23.09. So, the answer is {\"answer\": \"23.09\"}." } ], "images": [ "scigen/train/3292.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Ablation study on WikiText-103. For the first two blocks, we use a slightly smaller model (128M parameters). † indicates that the corresponding row is reduced to the same setting as the Transformer network in Al-Rfou et al. (2018), except that two auxiliary losses are not implemented in our experiments. “PPL init” refers to using the same length as training. “PPL best” indicates the perplexity obtained by using the optimal length. “Attn Len” is the shortest possible attention length during evaluation to achieve the corresponding result (PPL best). Increasing the attention length during evaluation improves performance only when our positional encoding is used. The “Transformer-XL (151M)” setting uses a standard parameter budget as previous work Merity et al. (2018), where we observe a similar effect when increasing the attention length during evaluation." ], "reasoning": "The table shows that the Transformer-XL (151M) model with full loss and 640 attention length has a PPL best score of 23.09.", "question": "What is the perplexity best score for the Transformer-XL (151M) model with full loss and 640 attention length?", "answer": "So, the answer is {\"answer\": \"23.09\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Transformer-XL (128M) model with full loss and 500 attention length has the lowest perplexity best score.\n" }, { "role": "assistant", "content": "The table shows that the Transformer-XL (128M) model with full loss and 500 attention length has a PPL best score of 26.77, which is not the lowest. The lowest PPL best score is 23.09 for the Transformer-XL (151M) model with full loss and 640 attention length. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3292.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Ablation study on WikiText-103. For the first two blocks, we use a slightly smaller model (128M parameters). † indicates that the corresponding row is reduced to the same setting as the Transformer network in Al-Rfou et al. (2018), except that two auxiliary losses are not implemented in our experiments. “PPL init” refers to using the same length as training. “PPL best” indicates the perplexity obtained by using the optimal length. “Attn Len” is the shortest possible attention length during evaluation to achieve the corresponding result (PPL best). Increasing the attention length during evaluation improves performance only when our positional encoding is used. The “Transformer-XL (151M)” setting uses a standard parameter budget as previous work Merity et al. (2018), where we observe a similar effect when increasing the attention length during evaluation." ], "reasoning": "The table shows that the Transformer-XL (128M) model with full loss and 500 attention length has a PPL best score of 26.77, which is not the lowest. The lowest PPL best score is 23.09 for the Transformer-XL (151M) model with full loss and 640 attention length.", "statement": "The Transformer-XL (128M) model with full loss and 500 attention length has the lowest perplexity best score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 7: Ablation study on One Billion Word, a dataset without long-term dependency.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The study isolates the effects of resolving context fragmentation by using a dataset that does not require long-term dependency and trains a 20-layer Transformer-XL for 400K steps, showing superior performance with their relative positional encodings.The second study targets at isolating the effects of resolving the context fragmentation problem from the benefit of capturing longer context length. In order to achieve this goal, we deliberately choose a dataset that does not require long-term dependency, so that any improvement from establishing the recurrence can be attributed to solving the context fragmentation. Specifically, we perform this controlled experiment on the One Billion Word dataset, which can only benefit from removing the context fragmentation. We train a 20-layer Transformer-XL with ∼0.3B parameters for 400K steps. Moreover, our relative positional encodings is also superior to Shaw et al." } ], "images": [ "scigen/train/3293.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Ablation study on One Billion Word, a dataset without long-term dependency." ], "reasoning": "The study isolates the effects of resolving context fragmentation by using a dataset that does not require long-term dependency and trains a 20-layer Transformer-XL for 400K steps, showing superior performance with their relative positional encodings.", "description": "The second study targets at isolating the effects of resolving the context fragmentation problem from the benefit of capturing longer context length. In order to achieve this goal, we deliberately choose a dataset that does not require long-term dependency, so that any improvement from establishing the recurrence can be attributed to solving the context fragmentation. Specifically, we perform this controlled experiment on the One Billion Word dataset, which can only benefit from removing the context fragmentation. We train a 20-layer Transformer-XL with ∼0.3B parameters for 400K steps. Moreover, our relative positional encodings is also superior to Shaw et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nTable title: Table 7: Ablation study on One Billion Word, a dataset without long-term dependency.\n\nQuestion: What is the perplexity (PPL) of the model trained without recurrence?\n" }, { "role": "assistant", "content": "The table lists the perplexity (PPL) values for different methods. The row labeled 'Without recurrence' shows a PPL of 27.1. So, the answer is {\"answer\": \"27.1\"}" } ], "images": [ "scigen/train/3293.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Ablation study on One Billion Word, a dataset without long-term dependency." ], "reasoning": "The table lists the perplexity (PPL) values for different methods. The row labeled 'Without recurrence' shows a PPL of 27.1.", "question": "What is the perplexity (PPL) of the model trained without recurrence?", "answer": "So, the answer is {\"answer\": \"27.1\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model using Shaw et al. (2018) encodings has a lower perplexity than the model without recurrence.\n" }, { "role": "assistant", "content": "The table shows that the model with Shaw et al. (2018) encodings has a PPL of 25.7, which is lower than the PPL of 27.1 for the model without recurrence. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3293.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Ablation study on One Billion Word, a dataset without long-term dependency." ], "reasoning": "The table shows that the model with Shaw et al. (2018) encodings has a PPL of 25.7, which is lower than the PPL of 27.1 for the model without recurrence.", "statement": "The model using Shaw et al. (2018) encodings has a lower perplexity than the model without recurrence.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 6: F1 scores on 4 subsets of TriviaQA dev, grouped by the size of their answer string sets {\\cal{A}} and corresponding set of possible mentions {\\cal{I}}. \\Delta indicates the improvement from H2-P to H3-D.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Noisier subsets with larger sets of consistent spans show more significant improvements, highlighting the importance of document-level modeling for handling label noise.We categorize examples based on the size of their answer string set, |\\mathcal{A}|, and the size of their corresponding set of {\\cal{A}}-consistent spans, |\\mathcal{I}|. In general, we expect \\mathcal{Q}^{sl} and \\mathcal{Q}^{ll} to be noisier due to the larger \\mathcal{I}, where \\mathcal{Q}^{sl} potentially includes many irrelevant mentions while \\mathcal{Q}^{ll} likely contains more incorrect answer strings (false aliases). We can observe that the improvement is more significant for these noisier subsets, suggesting document-level modeling is crucial for handling both types of label noise." } ], "images": [ "scigen/train/3294.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: F1 scores on 4 subsets of TriviaQA dev, grouped by the size of their answer string sets {\\cal{A}} and corresponding set of possible mentions {\\cal{I}}. \\Delta indicates the improvement from H2-P to H3-D." ], "reasoning": "Noisier subsets with larger sets of consistent spans show more significant improvements, highlighting the importance of document-level modeling for handling label noise.", "description": "We categorize examples based on the size of their answer string set, |\\mathcal{A}|, and the size of their corresponding set of {\\cal{A}}-consistent spans, |\\mathcal{I}|. In general, we expect \\mathcal{Q}^{sl} and \\mathcal{Q}^{ll} to be noisier due to the larger \\mathcal{I}, where \\mathcal{Q}^{sl} potentially includes many irrelevant mentions while \\mathcal{Q}^{ll} likely contains more incorrect answer strings (false aliases). We can observe that the improvement is more significant for these noisier subsets, suggesting document-level modeling is crucial for handling both types of label noise.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: F1 scores on 4 subsets of TriviaQA dev, grouped by the size of their answer string sets {\\cal{A}} and corresponding set of possible mentions {\\cal{I}}. \\Delta indicates the improvement from H2-P to H3-D.\nQuestion: Which subset has the highest H2-P value and what is its corresponding Delta?\nGive a detailed response to the above question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the subset with the highest H2-P value, compare the H2-P values across all subsets. The subset with the highest H2-P value is \\mathcal{Q}^{l} with an H2-P value of 86.3. The corresponding Delta for this subset is 2.1. So, the answer is {\"answer\": \"2.1\"}" } ], "images": [ "scigen/train/3294.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: F1 scores on 4 subsets of TriviaQA dev, grouped by the size of their answer string sets {\\cal{A}} and corresponding set of possible mentions {\\cal{I}}. \\Delta indicates the improvement from H2-P to H3-D." ], "reasoning": "To find the subset with the highest H2-P value, compare the H2-P values across all subsets. The subset with the highest H2-P value is \\mathcal{Q}^{l} with an H2-P value of 86.3. The corresponding Delta for this subset is 2.1.", "question": "Which subset has the highest H2-P value and what is its corresponding Delta?", "answer": "So, the answer is {\"answer\": \"2.1\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe subset \\mathcal{Q}^{ss} has a Delta value greater than 4.\n" }, { "role": "assistant", "content": "The table shows that the Delta value for the subset \\mathcal{Q}^{ss} is 4.0, which is not greater than 4. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3294.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: F1 scores on 4 subsets of TriviaQA dev, grouped by the size of their answer string sets {\\cal{A}} and corresponding set of possible mentions {\\cal{I}}. \\Delta indicates the improvement from H2-P to H3-D." ], "reasoning": "The table shows that the Delta value for the subset \\mathcal{Q}^{ss} is 4.0, which is not greater than 4.", "statement": "The subset \\mathcal{Q}^{ss} has a Delta value greater than 4.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Comparison of distant supervision hypotheses using MML-Pos objectives on TriviaQA and NarrativeQA dev sets.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The table compares the performance of models using Max and Sum inference under probability space and distant supervision assumptions, reporting F1 and EM scores for TriviaQA and Rouge-L scores for NarrativeQA.In this subsection, we compare probability space and distant supervision assumptions. The performance of models with both Max and Sum inference is shown. We report F1 and Exact Match (EM) scores for TriviaQA, and Rouge-L scores for NarrativeQA." } ], "images": [ "scigen/train/3295.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of distant supervision hypotheses using MML-Pos objectives on TriviaQA and NarrativeQA dev sets." ], "reasoning": "The table compares the performance of models using Max and Sum inference under probability space and distant supervision assumptions, reporting F1 and EM scores for TriviaQA and Rouge-L scores for NarrativeQA.", "description": "In this subsection, we compare probability space and distant supervision assumptions. The performance of models with both Max and Sum inference is shown. We report F1 and Exact Match (EM) scores for TriviaQA, and Rouge-L scores for NarrativeQA.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nTable title: Table 3: Comparison of distant supervision hypotheses using MML-Pos objectives on TriviaQA and NarrativeQA dev sets.\n\nQuestion: Which model and inference combination achieved the highest F1 score for TriviaQA?\n" }, { "role": "assistant", "content": "To find the highest F1 score for TriviaQA, we need to look at the 'TriviaQA F1' column and identify the highest value, which is 75.3, achieved by the H3-D model with Sum inference. So, the answer is {\"answer\": \"H3-D Sum\"}." } ], "images": [ "scigen/train/3295.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of distant supervision hypotheses using MML-Pos objectives on TriviaQA and NarrativeQA dev sets." ], "reasoning": "To find the highest F1 score for TriviaQA, we need to look at the 'TriviaQA F1' column and identify the highest value, which is 75.3, achieved by the H3-D model with Sum inference.", "question": "Which model and inference combination achieved the highest F1 score for TriviaQA?", "answer": "So, the answer is {\"answer\": \"H3-D Sum\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The H1-P model with Max inference achieved a higher F1 score for TriviaQA than the H1-D model with Sum inference., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe H1-P model with Max inference achieved a higher F1 score for TriviaQA than the H1-D model with Sum inference.\n" }, { "role": "assistant", "content": "The F1 score for the H1-P model with Max inference is 67.9, while the F1 score for the H1-D model with Sum inference is 65.2. Since 67.9 is higher than 65.2, the statement is supported by the table. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3295.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of distant supervision hypotheses using MML-Pos objectives on TriviaQA and NarrativeQA dev sets." ], "reasoning": "The F1 score for the H1-P model with Max inference is 67.9, while the F1 score for the H1-D model with Sum inference is 65.2. Since 67.9 is higher than 65.2, the statement is supported by the table.", "statement": "The H1-P model with Max inference achieved a higher F1 score for TriviaQA than the H1-D model with Sum inference.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: An overview of word similarity evaluation benchmarks. All is the current best reported score on each data set across all models (including the models that exploit curated knowledge bases and hand-crafted lexical resources, see supplementary material). Text denotes the best reported score for a model that learns solely on the basis of distributional information. All scores are Spearman’s ρ correlations.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "SimVerb-3500 achieves higher inter-annotator agreement (IAA-1 and IAA-2) compared to other verb similarity evaluation resources, indicating better consistency among human raters.There is another fundamental limitation of all current verb similarity evaluation resources: automatic approaches have reached or surpassed the inter-annotator agreement ceiling. For instance, while the average pairwise correlation between annotators on SL-222 is Spearman’s ρ correlation of 0.717, the best performing automatic system reaches ρ=0.727 SimVerb-3500 does not inherit this anomaly (see Tab. A complementary measure would smooth individual annotator effects. For this aim, our IAA-2 (mean) measure compares the average correlation of a human rater with the average of all the other raters. SimVerb-3500 obtains ρ = 0.84 (IAA-1) and ρ = 0.86 (IAA-2), a very good agreement compared to other benchmarks (see Tab." } ], "images": [ "scigen/train/3296.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: An overview of word similarity evaluation benchmarks. All is the current best reported score on each data set across all models (including the models that exploit curated knowledge bases and hand-crafted lexical resources, see supplementary material). Text denotes the best reported score for a model that learns solely on the basis of distributional information. All scores are Spearman’s ρ correlations." ], "reasoning": "SimVerb-3500 achieves higher inter-annotator agreement (IAA-1 and IAA-2) compared to other verb similarity evaluation resources, indicating better consistency among human raters.", "description": "There is another fundamental limitation of all current verb similarity evaluation resources: automatic approaches have reached or surpassed the inter-annotator agreement ceiling. For instance, while the average pairwise correlation between annotators on SL-222 is Spearman’s ρ correlation of 0.717, the best performing automatic system reaches ρ=0.727 SimVerb-3500 does not inherit this anomaly (see Tab. A complementary measure would smooth individual annotator effects. For this aim, our IAA-2 (mean) measure compares the average correlation of a human rater with the average of all the other raters. SimVerb-3500 obtains ρ = 0.84 (IAA-1) and ρ = 0.86 (IAA-2), a very good agreement compared to other benchmarks (see Tab.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 2: An overview of word similarity evaluation benchmarks. All is the current best reported score on each data set across all models (including the models that exploit curated knowledge bases and hand-crafted lexical resources, see supplementary material). Text denotes the best reported score for a model that learns solely on the basis of distributional information. All scores are Spearman’s ρ correlations.\nQuestion: Which evaluation set has the highest IAA-2 score and what is its corresponding 'All' score?\n" }, { "role": "assistant", "content": "SimVerb-3500 has the highest IAA-2 score of 0.86. The corresponding 'All' score for SimVerb-3500 is 0.63. So, the answer is {\"answer\": \"SimVerb-3500, 0.63\"}." } ], "images": [ "scigen/train/3296.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: An overview of word similarity evaluation benchmarks. All is the current best reported score on each data set across all models (including the models that exploit curated knowledge bases and hand-crafted lexical resources, see supplementary material). Text denotes the best reported score for a model that learns solely on the basis of distributional information. All scores are Spearman’s ρ correlations." ], "reasoning": "SimVerb-3500 has the highest IAA-2 score of 0.86. The corresponding 'All' score for SimVerb-3500 is 0.63.", "question": "Which evaluation set has the highest IAA-2 score and what is its corresponding 'All' score?", "answer": "So, the answer is {\"answer\": \"SimVerb-3500, 0.63\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nSimLex has a higher IAA-1 score than WSim.\n" }, { "role": "assistant", "content": "The table shows that SimLex has an IAA-1 score of 0.67, while WSim also has an IAA-1 score of 0.67. Therefore, SimLex does not have a higher IAA-1 score than WSim. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3296.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: An overview of word similarity evaluation benchmarks. All is the current best reported score on each data set across all models (including the models that exploit curated knowledge bases and hand-crafted lexical resources, see supplementary material). Text denotes the best reported score for a model that learns solely on the basis of distributional information. All scores are Spearman’s ρ correlations." ], "reasoning": "The table shows that SimLex has an IAA-1 score of 0.67, while WSim also has an IAA-1 score of 0.67. Therefore, SimLex does not have a higher IAA-1 score than WSim.", "statement": "SimLex has a higher IAA-1 score than WSim.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: Example verb pairs from SimVerb-3500.'.\n" }, { "role": "assistant", "content": "The SimVerb-3500 evaluation set contains similarity ratings for 3,500 verb pairs, with ratings from 0 to 10, where higher ratings indicate higher similarity.In order to satisfy C1-C3, the new SimVerb-3500 evaluation set contains similarity ratings for 3,500 verb pairs, containing 827 verb types in total and 3 member verbs for each top-level VerbNet class. The rating scale goes from 0 (not similar at all) to 10 (synonymous). We employed the SimLex-999 annotation guidelines. In particular, we instructed annotators to give low ratings to antonyms, and to distinguish between similarity and relatedness. Pairs that are related but not similar (e.g., to snore / to snooze, to walk / to crawl) thus have a fairly low rating. Several example pairs are provided in Tab." } ], "images": [ "scigen/train/3297.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Example verb pairs from SimVerb-3500." ], "reasoning": "The SimVerb-3500 evaluation set contains similarity ratings for 3,500 verb pairs, with ratings from 0 to 10, where higher ratings indicate higher similarity.", "description": "In order to satisfy C1-C3, the new SimVerb-3500 evaluation set contains similarity ratings for 3,500 verb pairs, containing 827 verb types in total and 3 member verbs for each top-level VerbNet class. The rating scale goes from 0 (not similar at all) to 10 (synonymous). We employed the SimLex-999 annotation guidelines. In particular, we instructed annotators to give low ratings to antonyms, and to distinguish between similarity and relatedness. Pairs that are related but not similar (e.g., to snore / to snooze, to walk / to crawl) thus have a fairly low rating. Several example pairs are provided in Tab.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: Table 1: Example verb pairs from SimVerb-3500.\nQuestion: What is the similarity rating for the verb pair 'to cook / to bake'?\n" }, { "role": "assistant", "content": "The table lists the similarity ratings for various verb pairs. To find the rating for 'to cook / to bake', locate the pair in the table and read the corresponding rating. So, the answer is {\"answer\": \"7.80\"}." } ], "images": [ "scigen/train/3297.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Example verb pairs from SimVerb-3500." ], "reasoning": "The table lists the similarity ratings for various verb pairs. To find the rating for 'to cook / to bake', locate the pair in the table and read the corresponding rating.", "question": "What is the similarity rating for the verb pair 'to cook / to bake'?", "answer": "So, the answer is {\"answer\": \"7.80\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe verb pair 'to stay / to leave' has a higher similarity rating than 'to walk / to crawl'.\n" }, { "role": "assistant", "content": "The table shows the similarity ratings for 'to stay / to leave' as 0.17 and 'to walk / to crawl' as 2.32. Since 0.17 is less than 2.32, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3297.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Example verb pairs from SimVerb-3500." ], "reasoning": "The table shows the similarity ratings for 'to stay / to leave' as 0.17 and 'to walk / to crawl' as 2.32. Since 0.17 is less than 2.32, the statement is incorrect.", "statement": "The verb pair 'to stay / to leave' has a higher similarity rating than 'to walk / to crawl'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Evaluation of state-of-the representation learning models on the full SimVerb-3500 set (SV-3500), the Simlex-999 verb subset containing 222 pairs (SL-222), cross-validated subsets of 222 pairs from SV-3500 (CV-222), and the SimVerb-3500 development (DEV-500) and test set (TEST-3000).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The reported results for CV-222 are averages of ten 10-fold cross-validation runs, and the ranking of models is consistent on the test and full SV-3500 sets.Tab. Since the number of evaluation pairs may influence the results, we ideally want to compare sets of equal size for a fair comparison. Picking one random subset of 222 pairs would bias the results towards the selected pairs, and even using 10-fold cross-validation we found variations up to 0.05 depending on which subsets were used. Therefore, we employ a 2-level 10-fold cross-validation where new random subsets are picked in each iteration of each model. The numbers reported as CV-222 are averages of these ten 10-fold cross-validation runs. The reported results come very close to the correlation on the full data set for all models. Our development set contains 500 pairs, selected to ensure a broad coverage in terms of similarity ranges (i.e., non-similar and highly similar pairs, as well as pairs of medium similarity are represented) and top-level VN classes (each class is represented by at least 1 member verb). The test set includes the remaining 3,000 verb pairs. The performances of representation learning architectures on the dev and test sets are reported in Tab. The ranking of models is identical on the test and the full SV-3500 set, with slight differences in ranking on the development set." } ], "images": [ "scigen/train/3298.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Evaluation of state-of-the representation learning models on the full SimVerb-3500 set (SV-3500), the Simlex-999 verb subset containing 222 pairs (SL-222), cross-validated subsets of 222 pairs from SV-3500 (CV-222), and the SimVerb-3500 development (DEV-500) and test set (TEST-3000)." ], "reasoning": "The reported results for CV-222 are averages of ten 10-fold cross-validation runs, and the ranking of models is consistent on the test and full SV-3500 sets.", "description": "Tab. Since the number of evaluation pairs may influence the results, we ideally want to compare sets of equal size for a fair comparison. Picking one random subset of 222 pairs would bias the results towards the selected pairs, and even using 10-fold cross-validation we found variations up to 0.05 depending on which subsets were used. Therefore, we employ a 2-level 10-fold cross-validation where new random subsets are picked in each iteration of each model. The numbers reported as CV-222 are averages of these ten 10-fold cross-validation runs. The reported results come very close to the correlation on the full data set for all models. Our development set contains 500 pairs, selected to ensure a broad coverage in terms of similarity ranges (i.e., non-similar and highly similar pairs, as well as pairs of medium similarity are represented) and top-level VN classes (each class is represented by at least 1 member verb). The test set includes the remaining 3,000 verb pairs. The performances of representation learning architectures on the dev and test sets are reported in Tab. The ranking of models is identical on the test and the full SV-3500 set, with slight differences in ranking on the development set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Evaluation of state-of-the representation learning models on the full SimVerb-3500 set (SV-3500), the Simlex-999 verb subset containing 222 pairs (SL-222), cross-validated subsets of 222 pairs from SV-3500 (CV-222), and the SimVerb-3500 development (DEV-500) and test set (TEST-3000).\nQuestion: Which model has the highest performance on the TEST-3000 set?\nSolve the above question based on the table information. Let's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest performance on the TEST-3000 set, we need to compare the values in the TEST-3000 column and identify the highest value. So, the answer is {\"answer\": \"Paragram+CF (d=300)\"}." } ], "images": [ "scigen/train/3298.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Evaluation of state-of-the representation learning models on the full SimVerb-3500 set (SV-3500), the Simlex-999 verb subset containing 222 pairs (SL-222), cross-validated subsets of 222 pairs from SV-3500 (CV-222), and the SimVerb-3500 development (DEV-500) and test set (TEST-3000)." ], "reasoning": "To find the model with the highest performance on the TEST-3000 set, we need to compare the values in the TEST-3000 column and identify the highest value.", "question": "Which model has the highest performance on the TEST-3000 set?", "answer": "So, the answer is {\"answer\": \"Paragram+CF (d=300)\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The SGNS-BOW-PW (d=300) model has a higher performance on the DEV-500 set than the SGNS-DEPS-PW (d=300) model., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe SGNS-BOW-PW (d=300) model has a higher performance on the DEV-500 set than the SGNS-DEPS-PW (d=300) model.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the performance values of SGNS-BOW-PW (d=300) and SGNS-DEPS-PW (d=300) models on the DEV-500 set. SGNS-BOW-PW (d=300) has a value of 0.333, while SGNS-DEPS-PW (d=300) has a value of 0.401. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3298.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Evaluation of state-of-the representation learning models on the full SimVerb-3500 set (SV-3500), the Simlex-999 verb subset containing 222 pairs (SL-222), cross-validated subsets of 222 pairs from SV-3500 (CV-222), and the SimVerb-3500 development (DEV-500) and test set (TEST-3000)." ], "reasoning": "To verify this statement, we compare the performance values of SGNS-BOW-PW (d=300) and SGNS-DEPS-PW (d=300) models on the DEV-500 set. SGNS-BOW-PW (d=300) has a value of 0.333, while SGNS-DEPS-PW (d=300) has a value of 0.401.", "statement": "The SGNS-BOW-PW (d=300) model has a higher performance on the DEV-500 set than the SGNS-DEPS-PW (d=300) model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: Spearman’s ρ correlation between human judgments and model’s cosine similarity by VerbNet Class. We chose classes #13 Verbs of Change of Possession, #31 Verbs of Psychological State, #37 Verbs of Communication, #45 Verbs of Change of State, and #51 Verbs of Motion as examples. All are large classes with more than 100 pairs each, and the frequencies of member verbs are distributed in a similar way.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The analysis of SimVerb-3500 shows that verbs in the same top-level VerbNet class often do not have high similarity scores, with 603 out of 1378 pairs scoring below 5.S6SS0SSS0Px3 VerbNet Classes Another analysis enabled by SimVerb-3500 is investigating the connection between VerbNet classes and human similarity judgments. We find that verbs in the same top-level VerbNet class are often not assigned high similarity score. Out of 1378 pairs where verbs share the top-level VerbNet class, 603 have a score lower than 5. Tab. When a verb belongs to multiple classes, we count it for each class (see Footnote 2). We run the analysis on the five largest VN classes, each with more than 100 pairs with paired verbs belonging to the same class." } ], "images": [ "scigen/train/3299.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Spearman’s ρ correlation between human judgments and model’s cosine similarity by VerbNet Class. We chose classes #13 Verbs of Change of Possession, #31 Verbs of Psychological State, #37 Verbs of Communication, #45 Verbs of Change of State, and #51 Verbs of Motion as examples. All are large classes with more than 100 pairs each, and the frequencies of member verbs are distributed in a similar way." ], "reasoning": "The analysis of SimVerb-3500 shows that verbs in the same top-level VerbNet class often do not have high similarity scores, with 603 out of 1378 pairs scoring below 5.", "description": "S6SS0SSS0Px3 VerbNet Classes Another analysis enabled by SimVerb-3500 is investigating the connection between VerbNet classes and human similarity judgments. We find that verbs in the same top-level VerbNet class are often not assigned high similarity score. Out of 1378 pairs where verbs share the top-level VerbNet class, 603 have a score lower than 5. Tab. When a verb belongs to multiple classes, we count it for each class (see Footnote 2). We run the analysis on the five largest VN classes, each with more than 100 pairs with paired verbs belonging to the same class.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model has the highest similarity score for verb pair #51?\nSolve the above question based on the table titled 'Table 4: Spearman’s ρ correlation between human judgments and model’s cosine similarity by VerbNet Class. We chose classes #13 Verbs of Change of Possession, #31 Verbs of Psychological State, #37 Verbs of Communication, #45 Verbs of Change of State, and #51 Verbs of Motion as examples. All are large classes with more than 100 pairs each, and the frequencies of member verbs are distributed in a similar way.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest similarity score for verb pair #51, compare the values in the #51 column. The highest value is 0.614, which corresponds to the Paragram+CF model. So, the answer is {\"answer\": \"Paragram+CF\"}" } ], "images": [ "scigen/train/3299.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Spearman’s ρ correlation between human judgments and model’s cosine similarity by VerbNet Class. We chose classes #13 Verbs of Change of Possession, #31 Verbs of Psychological State, #37 Verbs of Communication, #45 Verbs of Change of State, and #51 Verbs of Motion as examples. All are large classes with more than 100 pairs each, and the frequencies of member verbs are distributed in a similar way." ], "reasoning": "To find the model with the highest similarity score for verb pair #51, compare the values in the #51 column. The highest value is 0.614, which corresponds to the Paragram+CF model.", "question": "Which model has the highest similarity score for verb pair #51?", "answer": "So, the answer is {\"answer\": \"Paragram+CF\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe SGNS-BOW-8B model has the lowest similarity score for verb pair #13.\n" }, { "role": "assistant", "content": "To verify this statement, compare the values in the #13 column. The lowest value is 0.171, which corresponds to the SymPat-8B model, not the SGNS-BOW-8B model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3299.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Spearman’s ρ correlation between human judgments and model’s cosine similarity by VerbNet Class. We chose classes #13 Verbs of Change of Possession, #31 Verbs of Psychological State, #37 Verbs of Communication, #45 Verbs of Change of State, and #51 Verbs of Motion as examples. All are large classes with more than 100 pairs each, and the frequencies of member verbs are distributed in a similar way." ], "reasoning": "To verify this statement, compare the values in the #13 column. The lowest value is 0.171, which corresponds to the SymPat-8B model, not the SGNS-BOW-8B model.", "statement": "The SGNS-BOW-8B model has the lowest similarity score for verb pair #13.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Spearman’s ρ correlation between human judgments and model’s cosine similarity based on pair relation type. Relations are based on WordNet, and included in the dataset. The classes are of different size, 373 pairs with no relation (NR), 306 synonym (SYN) pairs, and 800 hyper/hyponym (HYP) pairs. Frequencies of member verbs are distributed in a similar way.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Some models perform well across different lexical relations, while others excel specifically in synonyms and hyper-/hyponyms.Lexical Relations SimVerb-3500 contains relation annotations (e.g., antonyms, synonyms, hyper-/hyponyms, no relation) for all pairs extracted automatically from WordNet. Evaluating per-relation subsets, we observe that some models draw their strength from good performance across different relations. Others have low performance on these pairs, but do very well on synonyms and hyper-/hyponyms. Selected results of this analysis are in Tab. ’s ρ may be problematic with certain categories, e.g., with antonyms. It evaluates pairs according to their ranking; for antonyms the ranking is arbitrary - every antonym pair should have a very low rating, hence they are not included in Tab. A similar effect occurs with highly ranked synonyms, but to a much lesser degree than with antonyms." } ], "images": [ "scigen/train/3300.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Spearman’s ρ correlation between human judgments and model’s cosine similarity based on pair relation type. Relations are based on WordNet, and included in the dataset. The classes are of different size, 373 pairs with no relation (NR), 306 synonym (SYN) pairs, and 800 hyper/hyponym (HYP) pairs. Frequencies of member verbs are distributed in a similar way." ], "reasoning": "Some models perform well across different lexical relations, while others excel specifically in synonyms and hyper-/hyponyms.", "description": "Lexical Relations SimVerb-3500 contains relation annotations (e.g., antonyms, synonyms, hyper-/hyponyms, no relation) for all pairs extracted automatically from WordNet. Evaluating per-relation subsets, we observe that some models draw their strength from good performance across different relations. Others have low performance on these pairs, but do very well on synonyms and hyper-/hyponyms. Selected results of this analysis are in Tab. ’s ρ may be problematic with certain categories, e.g., with antonyms. It evaluates pairs according to their ranking; for antonyms the ranking is arbitrary - every antonym pair should have a very low rating, hence they are not included in Tab. A similar effect occurs with highly ranked synonyms, but to a much lesser degree than with antonyms.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 5: Spearman’s ρ correlation between human judgments and model’s cosine similarity based on pair relation type. Relations are based on WordNet, and included in the dataset. The classes are of different size, 373 pairs with no relation (NR), 306 synonym (SYN) pairs, and 800 hyper/hyponym (HYP) pairs. Frequencies of member verbs are distributed in a similar way.' and proceed to solve the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest performance on synonyms?\n" }, { "role": "assistant", "content": "By examining the SYN column, the model with the highest value is Paragram (d=300) with a score of 0.356. So, the answer is {\"answer\": \"Paragram (d=300)\"}" } ], "images": [ "scigen/train/3300.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Spearman’s ρ correlation between human judgments and model’s cosine similarity based on pair relation type. Relations are based on WordNet, and included in the dataset. The classes are of different size, 373 pairs with no relation (NR), 306 synonym (SYN) pairs, and 800 hyper/hyponym (HYP) pairs. Frequencies of member verbs are distributed in a similar way." ], "reasoning": "By examining the SYN column, the model with the highest value is Paragram (d=300) with a score of 0.356.", "question": "Which model has the highest performance on synonyms?", "answer": "So, the answer is {\"answer\": \"Paragram (d=300)\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSGNS-BOW-8B (d=500) has a higher performance on hyper-/hyponyms than SGNS-DEPS-8B (d=500).\n" }, { "role": "assistant", "content": "By comparing the HYP column values, SGNS-BOW-8B (d=500) has a score of 0.338, which is lower than SGNS-DEPS-8B (d=500) with a score of 0.378. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3300.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Spearman’s ρ correlation between human judgments and model’s cosine similarity based on pair relation type. Relations are based on WordNet, and included in the dataset. The classes are of different size, 373 pairs with no relation (NR), 306 synonym (SYN) pairs, and 800 hyper/hyponym (HYP) pairs. Frequencies of member verbs are distributed in a similar way." ], "reasoning": "By comparing the HYP column values, SGNS-BOW-8B (d=500) has a score of 0.338, which is lower than SGNS-DEPS-8B (d=500) with a score of 0.378.", "statement": "SGNS-BOW-8B (d=500) has a higher performance on hyper-/hyponyms than SGNS-DEPS-8B (d=500).", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'TABLE IV: The basic information of the corpus'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The table compares nine models across six datasets, highlighting differences in parameters such as K, N, Len, and V.To show the effects and differences of the above nine models, we select the following six datasets to verify the models." } ], "images": [ "scigen/train/3301.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: The basic information of the corpus" ], "reasoning": "The table compares nine models across six datasets, highlighting differences in parameters such as K, N, Len, and V.", "description": "To show the effects and differences of the above nine models, we select the following six datasets to verify the models.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'TABLE IV: The basic information of the corpus' in mind, break down and resolve the question below step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich dataset has the highest value for K and what is its corresponding value for V?\n" }, { "role": "assistant", "content": "To find the dataset with the highest value for K, we look at the K column and identify the highest value, which is 152 for GoogleNews. Then, we find the corresponding value for V in the same row, which is 8,110. So, the answer is {\"answer\": \"GoogleNews, 8,110\"}." } ], "images": [ "scigen/train/3301.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: The basic information of the corpus" ], "reasoning": "To find the dataset with the highest value for K, we look at the K column and identify the highest value, which is 152 for GoogleNews. Then, we find the corresponding value for V in the same row, which is 8,110.", "question": "Which dataset has the highest value for K and what is its corresponding value for V?", "answer": "So, the answer is {\"answer\": \"GoogleNews, 8,110\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe dataset with the highest number of entries (N) is Biomedicine.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the N column and compare the values. The highest value in the N column is 19,448, which corresponds to the Biomedicine dataset. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3301.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: The basic information of the corpus" ], "reasoning": "To verify this statement, we look at the N column and compare the values. The highest value in the N column is 19,448, which corresponds to the Biomedicine dataset.", "statement": "The dataset with the highest number of entries (N) is Biomedicine.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'TABLE V: Purity and NMI value of all models on six datasets.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "All models outperform LDA except SATM, and performance varies by dataset. WNTM excels on several datasets but not PascalFlickr, while GPU-PDMM performs well on all except SearchSnippets.We can see that all models outperform long text topic modeling (LDA), except SATM. Here, similar to the conclusions in classification, we can observe that the performance of approaches is highly data set dependent. WNTM achieves the best performance on several datasets but performs poorly on PascalFlickr. GPU-PDMM performs very well on all datasets except SearchSnippets." } ], "images": [ "scigen/train/3302.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Purity and NMI value of all models on six datasets." ], "reasoning": "All models outperform LDA except SATM, and performance varies by dataset. WNTM excels on several datasets but not PascalFlickr, while GPU-PDMM performs well on all except SearchSnippets.", "description": "We can see that all models outperform long text topic modeling (LDA), except SATM. Here, similar to the conclusions in classification, we can observe that the performance of approaches is highly data set dependent. WNTM achieves the best performance on several datasets but performs poorly on PascalFlickr. GPU-PDMM performs very well on all datasets except SearchSnippets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'TABLE V: Purity and NMI value of all models on six datasets.'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: Which model has the highest mean value performance?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the model with the highest mean value performance, we need to compare the mean values of all models. GPU-PDMM (Purity) has the highest mean value of 0.636±0.015. So, the answer is {\"answer\": \"GPU-PDMM (Purity)\"}." } ], "images": [ "scigen/train/3302.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Purity and NMI value of all models on six datasets." ], "reasoning": "To find the model with the highest mean value performance, we need to compare the mean values of all models. GPU-PDMM (Purity) has the highest mean value of 0.636±0.015.", "question": "Which model has the highest mean value performance?", "answer": "So, the answer is {\"answer\": \"GPU-PDMM (Purity)\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis WNTM performs poorly on PascalFlickr. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWNTM performs poorly on PascalFlickr.\n" }, { "role": "assistant", "content": "The table shows that WNTM has a performance value of 0.295±0.013 on PascalFlickr, which is lower compared to its performance on other datasets, supporting the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3302.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Purity and NMI value of all models on six datasets." ], "reasoning": "The table shows that WNTM has a performance value of 0.295±0.013 on PascalFlickr, which is lower compared to its performance on other datasets, supporting the statement.", "statement": "WNTM performs poorly on PascalFlickr.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE VII: The average runtime of initiation and per iteration of each model on Biomedicine (in milliseconds).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "LDA and DMM are the most efficient methods, while GPU-PDMM is the slowest due to higher computational costs.Among all models evaluated, LDA and DMM are the most efficient methods as expected. GPU-DMM is slightly slower than DMM and LDA, due to a similar Gibbs sampling process with GSDMM. LF-DMM and GPU-PDMM take much more time than GPU-DMM, because GPU-PDMM spends more time for the computational costs involved in sampling Zd and LF-DMM need much time for optimizing the topic vectors. We can see that GPU-PDMM is the slowest modeling compared with other models." } ], "images": [ "scigen/train/3303.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VII: The average runtime of initiation and per iteration of each model on Biomedicine (in milliseconds)." ], "reasoning": "LDA and DMM are the most efficient methods, while GPU-PDMM is the slowest due to higher computational costs.", "description": "Among all models evaluated, LDA and DMM are the most efficient methods as expected. GPU-DMM is slightly slower than DMM and LDA, due to a similar Gibbs sampling process with GSDMM. LF-DMM and GPU-PDMM take much more time than GPU-DMM, because GPU-PDMM spends more time for the computational costs involved in sampling Zd and LF-DMM need much time for optimizing the topic vectors. We can see that GPU-PDMM is the slowest modeling compared with other models.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'TABLE VII: The average runtime of initiation and per iteration of each model on Biomedicine (in milliseconds).' in mind, break down and resolve the question below step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich model has the highest per iteration time and why?\n" }, { "role": "assistant", "content": "GPU-PDMM has the highest per iteration time of 9685.44 because it spends more time on the computational costs involved in sampling Zd. So, the answer is {\"answer\": \"GPU-PDMM\"}" } ], "images": [ "scigen/train/3303.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VII: The average runtime of initiation and per iteration of each model on Biomedicine (in milliseconds)." ], "reasoning": "GPU-PDMM has the highest per iteration time of 9685.44 because it spends more time on the computational costs involved in sampling Zd.", "question": "Which model has the highest per iteration time and why?", "answer": "So, the answer is {\"answer\": \"GPU-PDMM\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nLF-DMM has a higher per iteration time than GPU-DMM.\n" }, { "role": "assistant", "content": "LF-DMM has a per iteration time of 2243.03, which is higher than GPU-DMM's per iteration time of 53.83. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3303.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VII: The average runtime of initiation and per iteration of each model on Biomedicine (in milliseconds)." ], "reasoning": "LF-DMM has a per iteration time of 2243.03, which is higher than GPU-DMM's per iteration time of 53.83.", "statement": "LF-DMM has a higher per iteration time than GPU-DMM.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 1: Automatic evaluation on the ROCStories and VIST datasets. We use “B-4”, “MET”, “R-L”, and “PPL” to denote BLEU-4, METEOR, ROUGE-L, and perplexity scores, respectively.'.\n" }, { "role": "assistant", "content": "Direct outperforms other baselines in BLEU, METEOR, and perplexity despite not using high-level plots, possibly due to the short length of stories in the datasets.Skeleton generally performs worse than other baselines except that it obtains lower perplexities than Separate on both datasets. Surprisingly, although Direct does not use any high-level plot, it outperforms other baselines in terms of BLEU, METEOR, and perplexity. One possible reason is that the stories in both datasets are relatively short. The superiority of hierarchical models over direct mapping might increase when generating very long stories." } ], "images": [ "scigen/train/3304.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Automatic evaluation on the ROCStories and VIST datasets. We use “B-4”, “MET”, “R-L”, and “PPL” to denote BLEU-4, METEOR, ROUGE-L, and perplexity scores, respectively." ], "reasoning": "Direct outperforms other baselines in BLEU, METEOR, and perplexity despite not using high-level plots, possibly due to the short length of stories in the datasets.", "description": "Skeleton generally performs worse than other baselines except that it obtains lower perplexities than Separate on both datasets. Surprisingly, although Direct does not use any high-level plot, it outperforms other baselines in terms of BLEU, METEOR, and perplexity. One possible reason is that the stories in both datasets are relatively short. The superiority of hierarchical models over direct mapping might increase when generating very long stories.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 1: Automatic evaluation on the ROCStories and VIST datasets. We use “B-4”, “MET”, “R-L”, and “PPL” to denote BLEU-4, METEOR, ROUGE-L, and perplexity scores, respectively.', and sequentially solve the question, writing out each step of your reasoning process. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model has the lowest perplexity on the ROCStories dataset?\n" }, { "role": "assistant", "content": "The table shows the perplexity (PPL) scores for different models on the ROCStories dataset. The model with the lowest perplexity score is Latent with a score of 12.33. So, the answer is {\"answer\": \"Latent\"}." } ], "images": [ "scigen/train/3304.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Automatic evaluation on the ROCStories and VIST datasets. We use “B-4”, “MET”, “R-L”, and “PPL” to denote BLEU-4, METEOR, ROUGE-L, and perplexity scores, respectively." ], "reasoning": "The table shows the perplexity (PPL) scores for different models on the ROCStories dataset. The model with the lowest perplexity score is Latent with a score of 12.33.", "question": "Which model has the lowest perplexity on the ROCStories dataset?", "answer": "So, the answer is {\"answer\": \"Latent\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Skeleton model has the highest BLEU score on the VIST dataset.\n" }, { "role": "assistant", "content": "The table shows the BLEU scores (B-4) for different models on the VIST dataset. The Skeleton model has a BLEU score of 0.96, which is not the highest. The Latent model has the highest BLEU score of 3.35. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3304.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Automatic evaluation on the ROCStories and VIST datasets. We use “B-4”, “MET”, “R-L”, and “PPL” to denote BLEU-4, METEOR, ROUGE-L, and perplexity scores, respectively." ], "reasoning": "The table shows the BLEU scores (B-4) for different models on the VIST dataset. The Skeleton model has a BLEU score of 0.96, which is not the highest. The Latent model has the highest BLEU score of 3.35.", "statement": "The Skeleton model has the highest BLEU score on the VIST dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Human evaluation on the ROCStories and VIST datasets. We use “F” to denote fluency, “R” to denote relevance, “C” to denote coherence, and “M” to denote the geometric mean of fluency, relevance, and coherence.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Our approach achieves the highest scores on the VIST dataset across all metrics and outperforms all baselines on the ROCStories dataset in fluency, relevance, and G-Mean, except for coherence where Separate scores slightly higher.While all the six methods achieve close fluency scores because of the use of neural networks, the gaps on relevance and coherence are much larger. On the ROCStories dataset, our approach achieves significantly better results than all baselines in terms of fluency, relevance, and G-Mean except that Separate obtains a slightly higher coherence score than our approach. On the VIST dataset, our method achieves the highest scores on all the metrics by large margins. These differences are statistically significant (p<0.01)." } ], "images": [ "scigen/train/3305.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Human evaluation on the ROCStories and VIST datasets. We use “F” to denote fluency, “R” to denote relevance, “C” to denote coherence, and “M” to denote the geometric mean of fluency, relevance, and coherence." ], "reasoning": "Our approach achieves the highest scores on the VIST dataset across all metrics and outperforms all baselines on the ROCStories dataset in fluency, relevance, and G-Mean, except for coherence where Separate scores slightly higher.", "description": "While all the six methods achieve close fluency scores because of the use of neural networks, the gaps on relevance and coherence are much larger. On the ROCStories dataset, our approach achieves significantly better results than all baselines in terms of fluency, relevance, and G-Mean except that Separate obtains a slightly higher coherence score than our approach. On the VIST dataset, our method achieves the highest scores on all the metrics by large margins. These differences are statistically significant (p<0.01).", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Human evaluation on the ROCStories and VIST datasets. We use “F” to denote fluency, “R” to denote relevance, “C” to denote coherence, and “M” to denote the geometric mean of fluency, relevance, and coherence.' to sequentially solve the question presented below. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich model achieves the highest coherence score on the ROCStories dataset?\n" }, { "role": "assistant", "content": "The table shows that the Separate model achieves the highest coherence score (6.15) on the ROCStories dataset. So, the answer is {\"answer\": \"Separate\"}." } ], "images": [ "scigen/train/3305.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Human evaluation on the ROCStories and VIST datasets. We use “F” to denote fluency, “R” to denote relevance, “C” to denote coherence, and “M” to denote the geometric mean of fluency, relevance, and coherence." ], "reasoning": "The table shows that the Separate model achieves the highest coherence score (6.15) on the ROCStories dataset.", "question": "Which model achieves the highest coherence score on the ROCStories dataset?", "answer": "So, the answer is {\"answer\": \"Separate\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Latent model achieves the highest fluency score on the VIST dataset.\n" }, { "role": "assistant", "content": "The table shows that the Latent model has the highest fluency score (6.43) on the VIST dataset. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3305.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Human evaluation on the ROCStories and VIST datasets. We use “F” to denote fluency, “R” to denote relevance, “C” to denote coherence, and “M” to denote the geometric mean of fluency, relevance, and coherence." ], "reasoning": "The table shows that the Latent model has the highest fluency score (6.43) on the VIST dataset.", "statement": "The Latent model achieves the highest fluency score on the VIST dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: The results of ablation study. We use “PM” to denote the planning model, “SM” to denote the summarization model, and “PT” to denote pre-training.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Removing the summarization model leads to the worst results, indicating its critical role in improving performance.Our approach reduces to Direct by removing the planning model. Note that the summarization model is also disabled in this case. Only discarding the summarization model leads to the worst results. This suggests that the summarization model is critical for improving the performance of our approach: it is difficult to learn a good planning model without the guidance of the summarization model. Besides, the performance of our method also drops without pre-training, which indicates the effectiveness of pre-training in our approach." } ], "images": [ "scigen/train/3306.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The results of ablation study. We use “PM” to denote the planning model, “SM” to denote the summarization model, and “PT” to denote pre-training." ], "reasoning": "Removing the summarization model leads to the worst results, indicating its critical role in improving performance.", "description": "Our approach reduces to Direct by removing the planning model. Note that the summarization model is also disabled in this case. Only discarding the summarization model leads to the worst results. This suggests that the summarization model is critical for improving the performance of our approach: it is difficult to learn a good planning model without the guidance of the summarization model. Besides, the performance of our method also drops without pre-training, which indicates the effectiveness of pre-training in our approach.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: The results of ablation study. We use “PM” to denote the planning model, “SM” to denote the summarization model, and “PT” to denote pre-training.\nQuestion: Which model configuration leads to the worst BLEU score?\nSolve the above question based on the table information. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the model without the summarization model (SM) has the lowest BLEU score of 2.90. So, the answer is {\"answer\": \"- SM\"}." } ], "images": [ "scigen/train/3306.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The results of ablation study. We use “PM” to denote the planning model, “SM” to denote the summarization model, and “PT” to denote pre-training." ], "reasoning": "The table shows that the model without the summarization model (SM) has the lowest BLEU score of 2.90.", "question": "Which model configuration leads to the worst BLEU score?", "answer": "So, the answer is {\"answer\": \"- SM\"}." }, { "messages": [ { "role": "user", "content": "The model without pre-training has the highest METEOR score. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe model without pre-training has the highest METEOR score.\n" }, { "role": "assistant", "content": "The table shows that the model without pre-training (PT) has a METEOR score of 10.32, which is not the highest. The highest METEOR score is 10.61 for the Latent model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3306.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The results of ablation study. We use “PM” to denote the planning model, “SM” to denote the summarization model, and “PT” to denote pre-training." ], "reasoning": "The table shows that the model without pre-training (PT) has a METEOR score of 10.32, which is not the highest. The highest METEOR score is 10.61 for the Latent model.", "statement": "The model without pre-training has the highest METEOR score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: Comparison with variational inference on the ROCStories dataset. We use “VI” to denote variational inference (see Section 3.2).'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows that training with the latent model yields better BLEU, METEOR, and ROUGE-L scores compared to variational inference, indicating improved performance.Note that Eq. The expectation in Eq. Therefore, we are interested in the expectation with respect to the planning model. We find that training by variation inference deteriorates the performance of our model. One possible reason is that in variational inference the generation model is not exposed to the outlines generated by the planning model during training. During inference, however, the generation model takes the outline output by the planning model as one input. This mismatch between training and inference seems to be harmful for the NSG task. In contrast, the training objective in Eq." } ], "images": [ "scigen/train/3307.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison with variational inference on the ROCStories dataset. We use “VI” to denote variational inference (see Section 3.2)." ], "reasoning": "The table shows that training with the latent model yields better BLEU, METEOR, and ROUGE-L scores compared to variational inference, indicating improved performance.", "description": "Note that Eq. The expectation in Eq. Therefore, we are interested in the expectation with respect to the planning model. We find that training by variation inference deteriorates the performance of our model. One possible reason is that in variational inference the generation model is not exposed to the outlines generated by the planning model during training. During inference, however, the generation model takes the outline output by the planning model as one input. This mismatch between training and inference seems to be harmful for the NSG task. In contrast, the training objective in Eq.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 4: Comparison with variational inference on the ROCStories dataset. We use “VI” to denote variational inference (see Section 3.2).'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: Which training method yields the highest METEOR score and what is the score?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows that the latent training method yields the highest METEOR score of 10.61. So, the answer is {\"answer\": \"Latent, 10.61\"}." } ], "images": [ "scigen/train/3307.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison with variational inference on the ROCStories dataset. We use “VI” to denote variational inference (see Section 3.2)." ], "reasoning": "The table shows that the latent training method yields the highest METEOR score of 10.61.", "question": "Which training method yields the highest METEOR score and what is the score?", "answer": "So, the answer is {\"answer\": \"Latent, 10.61\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nVariational inference training yields a higher BLEU score than latent training.\n" }, { "role": "assistant", "content": "The table shows that variational inference training yields a BLEU score of 3.41, which is lower than the latent training BLEU score of 3.70. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3307.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison with variational inference on the ROCStories dataset. We use “VI” to denote variational inference (see Section 3.2)." ], "reasoning": "The table shows that variational inference training yields a BLEU score of 3.41, which is lower than the latent training BLEU score of 3.70.", "statement": "Variational inference training yields a higher BLEU score than latent training.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Precision, recall, and F-score on the MSRPC paraphrase recognition dataset, using the concatenation and subtraction combination methods for sentence embeddings. Highest performing settings are marked in bold.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Precision decreases by 1% with second-order embeddings alone in concatenation, while recall increases.set on the MSRPC test set, under the concatenation and subtraction sentence combination schema. With concatenated feature vectors, precision varies slightly from the first-order baseline in the various concatenated settings, but decreases by 1% absolute when using second-order embeddings alone. Interestingly, recall behaves inversely: it decreases significantly in the concatenated settings, but increases when using second-order embeddings alone. The impact of second-order information in the subtractive combination scheme is less clear; as with concatenation, precision drops with second-order embeddings alone, but recall is most strongly affected by using multiple samples in the graph induction step." } ], "images": [ "scigen/train/3308.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Precision, recall, and F-score on the MSRPC paraphrase recognition dataset, using the concatenation and subtraction combination methods for sentence embeddings. Highest performing settings are marked in bold." ], "reasoning": "Precision decreases by 1% with second-order embeddings alone in concatenation, while recall increases.", "description": "set on the MSRPC test set, under the concatenation and subtraction sentence combination schema. With concatenated feature vectors, precision varies slightly from the first-order baseline in the various concatenated settings, but decreases by 1% absolute when using second-order embeddings alone. Interestingly, recall behaves inversely: it decreases significantly in the concatenated settings, but increases when using second-order embeddings alone. The impact of second-order information in the subtractive combination scheme is less clear; as with concatenation, precision drops with second-order embeddings alone, but recall is most strongly affected by using multiple samples in the graph induction step.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 5: Precision, recall, and F-score on the MSRPC paraphrase recognition dataset, using the concatenation and subtraction combination methods for sentence embeddings. Highest performing settings are marked in bold.', and tackle the question below in a sequential manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the precision for the 'node2vec NN' setting in the concatenation schema?\n" }, { "role": "assistant", "content": "The table shows the precision for 'node2vec NN' under the 'Concat Pr' column. So, the answer is {\"answer\": \"68.7\"}." } ], "images": [ "scigen/train/3308.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Precision, recall, and F-score on the MSRPC paraphrase recognition dataset, using the concatenation and subtraction combination methods for sentence embeddings. Highest performing settings are marked in bold." ], "reasoning": "The table shows the precision for 'node2vec NN' under the 'Concat Pr' column.", "question": "What is the precision for the 'node2vec NN' setting in the concatenation schema?", "answer": "So, the answer is {\"answer\": \"68.7\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The recall for the 'word2vec' setting is higher in the concatenation schema than in the subtraction schema., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe recall for the 'word2vec' setting is higher in the concatenation schema than in the subtraction schema.\n" }, { "role": "assistant", "content": "The table shows that the recall for 'word2vec' is 93.2 in concatenation and 50.6 in subtraction. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3308.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Precision, recall, and F-score on the MSRPC paraphrase recognition dataset, using the concatenation and subtraction combination methods for sentence embeddings. Highest performing settings are marked in bold." ], "reasoning": "The table shows that the recall for 'word2vec' is 93.2 in concatenation and 50.6 in subtraction.", "statement": "The recall for the 'word2vec' setting is higher in the concatenation schema than in the subtraction schema.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Overall results on the CoNLL-03 NER task; highest performing features are marked in bold. node2vecNN refers to second-order embeddings with a single sample; NN3 denotes the 3-sample case. Concatenated refers to concatenating first-order word2vec embeddings with various second-order embeddings. Pr = Precision, Rec = Recall.'.\n" }, { "role": "assistant", "content": "Second-order embeddings perform nearly as well as word2vec, with a slight decrease in precision and recall. Using multiple embedding samples increases precision but decreases recall.The second-order embeddings alone perform nearly as well as word2vec embeddings, achieving 1.5% absolute lower precision, and only 0.5% absolute lower recall. Interestingly, using multiple embedding samples to generate the graph increases precision to nearly 87% at the expense of recall (a drop of over 1% absolute compared to the single sample). Concatenating skip-gram and second-order embeddings gives overall performance similar to skip-gram alone, with slightly lower precision and identical recall (when using a single sample in graph induction) or decreased (with multiple samples or smaller neighborhoods). Increasing the neighborhood size to k=25 appears to introduce additional noise in the neighborhood graph, and decreases performance by a point across the board." } ], "images": [ "scigen/train/3309.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Overall results on the CoNLL-03 NER task; highest performing features are marked in bold. node2vecNN refers to second-order embeddings with a single sample; NN3 denotes the 3-sample case. Concatenated refers to concatenating first-order word2vec embeddings with various second-order embeddings. Pr = Precision, Rec = Recall." ], "reasoning": "Second-order embeddings perform nearly as well as word2vec, with a slight decrease in precision and recall. Using multiple embedding samples increases precision but decreases recall.", "description": "The second-order embeddings alone perform nearly as well as word2vec embeddings, achieving 1.5% absolute lower precision, and only 0.5% absolute lower recall. Interestingly, using multiple embedding samples to generate the graph increases precision to nearly 87% at the expense of recall (a drop of over 1% absolute compared to the single sample). Concatenating skip-gram and second-order embeddings gives overall performance similar to skip-gram alone, with slightly lower precision and identical recall (when using a single sample in graph induction) or decreased (with multiple samples or smaller neighborhoods). Increasing the neighborhood size to k=25 appears to introduce additional noise in the neighborhood graph, and decreases performance by a point across the board.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nTable title: Table 2: Overall results on the CoNLL-03 NER task; highest performing features are marked in bold. node2vecNN refers to second-order embeddings with a single sample; NN3 denotes the 3-sample case. Concatenated refers to concatenating first-order word2vec embeddings with various second-order embeddings. Pr = Precision, Rec = Recall.\n\nQuestion: What is the precision and recall for the concatenated embeddings with k=5?\n" }, { "role": "assistant", "content": "The table shows the precision (Pr) and recall (Rec) for different settings. For the concatenated embeddings with k=5, the precision is 87.30 and the recall is 86.75. So, the answer is {\"answer\": \"Precision: 87.30, Recall: 86.75\"}" } ], "images": [ "scigen/train/3309.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Overall results on the CoNLL-03 NER task; highest performing features are marked in bold. node2vecNN refers to second-order embeddings with a single sample; NN3 denotes the 3-sample case. Concatenated refers to concatenating first-order word2vec embeddings with various second-order embeddings. Pr = Precision, Rec = Recall." ], "reasoning": "The table shows the precision (Pr) and recall (Rec) for different settings. For the concatenated embeddings with k=5, the precision is 87.30 and the recall is 86.75.", "question": "What is the precision and recall for the concatenated embeddings with k=5?", "answer": "So, the answer is {\"answer\": \"Precision: 87.30, Recall: 86.75\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Using multiple embedding samples increases precision to nearly 87% but decreases recall by over 1%., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nUsing multiple embedding samples increases precision to nearly 87% but decreases recall by over 1%.\n" }, { "role": "assistant", "content": "The description states that using multiple embedding samples increases precision to nearly 87% and decreases recall by over 1%. The table shows that the precision for concatenated (NN3) is 87.53 and the recall is 86.75, which supports the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3309.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Overall results on the CoNLL-03 NER task; highest performing features are marked in bold. node2vecNN refers to second-order embeddings with a single sample; NN3 denotes the 3-sample case. Concatenated refers to concatenating first-order word2vec embeddings with various second-order embeddings. Pr = Precision, Rec = Recall." ], "reasoning": "The description states that using multiple embedding samples increases precision to nearly 87% and decreases recall by over 1%. The table shows that the precision for concatenated (NN3) is 87.53 and the recall is 86.75, which supports the statement.", "statement": "Using multiple embedding samples increases precision to nearly 87% but decreases recall by over 1%.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: Results on CoNLL-03 NER task by named entity type. n is the number of entities of each type in the gold dataset. Highest performance in each metric on each entity type is marked in bold.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Nearest neighbor information significantly improves MISC precision by 8.4%, while PER precision drops with second-order information but recall increases slightly.Most strikingly, nearest neighbor information is critical for recognizing MISC entities, leading to 8.4% absolute increase in precision when using multi-sample second order embeddings alone, and similar increases with concatenation. Recall on MISC falls slightly when incorporating second-order information, though the noisy k=25 graph does increase recall by 0.2% absolute. The converse pattern emerges for PER entities, where precision falls by over a point when including any second-order information, but recall increases slightly with single-sample nearest neighbor information. ORG and LOC are less clear, with small variations in precision and recall." } ], "images": [ "scigen/train/3310.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on CoNLL-03 NER task by named entity type. n is the number of entities of each type in the gold dataset. Highest performance in each metric on each entity type is marked in bold." ], "reasoning": "Nearest neighbor information significantly improves MISC precision by 8.4%, while PER precision drops with second-order information but recall increases slightly.", "description": "Most strikingly, nearest neighbor information is critical for recognizing MISC entities, leading to 8.4% absolute increase in precision when using multi-sample second order embeddings alone, and similar increases with concatenation. Recall on MISC falls slightly when incorporating second-order information, though the noisy k=25 graph does increase recall by 0.2% absolute. The converse pattern emerges for PER entities, where precision falls by over a point when including any second-order information, but recall increases slightly with single-sample nearest neighbor information. ORG and LOC are less clear, with small variations in precision and recall.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Results on CoNLL-03 NER task by named entity type. n is the number of entities of each type in the gold dataset. Highest performance in each metric on each entity type is marked in bold.\nQuestion: What is the highest precision for MISC entities and under which setting is it achieved?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest precision for MISC entities, look at the 'Misc (n=702) Pr' column and identify the highest value. The highest precision is 79.2, achieved under the 'Concat (k=25)' setting. So, the answer is {\"answer\": \"Concat (k=25)\"}." } ], "images": [ "scigen/train/3310.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on CoNLL-03 NER task by named entity type. n is the number of entities of each type in the gold dataset. Highest performance in each metric on each entity type is marked in bold." ], "reasoning": "To find the highest precision for MISC entities, look at the 'Misc (n=702) Pr' column and identify the highest value. The highest precision is 79.2, achieved under the 'Concat (k=25)' setting.", "question": "What is the highest precision for MISC entities and under which setting is it achieved?", "answer": "So, the answer is {\"answer\": \"Concat (k=25)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe highest recall for LOC entities is achieved with the 'Concat (NN3)' setting.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'Loc (n=1668) Rec' column and identify the highest value. The highest recall is 92.0, achieved under the 'Concat (k=25)' setting, not 'Concat (NN3)'. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3310.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on CoNLL-03 NER task by named entity type. n is the number of entities of each type in the gold dataset. Highest performance in each metric on each entity type is marked in bold." ], "reasoning": "To verify this statement, look at the 'Loc (n=1668) Rec' column and identify the highest value. The highest recall is 92.0, achieved under the 'Concat (k=25)' setting, not 'Concat (NN3)'.", "statement": "The highest recall for LOC entities is achieved with the 'Concat (NN3)' setting.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: Accuracy on SNLI development and test sets.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Augmenting skip-gram embeddings with second-order information increases overall performance, with multiple samples or different settings for k showing equivalent or superior performance.In contrast to the NER results, here we see an increase in overall performance when augmenting skip-gram embeddings with second-order information. The second-order embeddings alone give only slightly worse results than the skip-gram embeddings, with around a 1% drop in test accuracy. Interestingly, concatenating skip-gram with a single k=10 second-order embedding decreases performance, but using multiple samples or different settings for k show equivalent or superior performance." } ], "images": [ "scigen/train/3311.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Accuracy on SNLI development and test sets." ], "reasoning": "Augmenting skip-gram embeddings with second-order information increases overall performance, with multiple samples or different settings for k showing equivalent or superior performance.", "description": "In contrast to the NER results, here we see an increase in overall performance when augmenting skip-gram embeddings with second-order information. The second-order embeddings alone give only slightly worse results than the skip-gram embeddings, with around a 1% drop in test accuracy. Interestingly, concatenating skip-gram with a single k=10 second-order embedding decreases performance, but using multiple samples or different settings for k show equivalent or superior performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: Accuracy on SNLI development and test sets.', solve this question and give detail solutions. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich setting achieved the highest test accuracy?\n" }, { "role": "assistant", "content": "By comparing the 'Acc %(Test)' column, the highest test accuracy is found in the 'Concatenated (NN3)' setting with 82.70%. So, the answer is {\"answer\": \"Concatenated (NN3)\"}." } ], "images": [ "scigen/train/3311.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Accuracy on SNLI development and test sets." ], "reasoning": "By comparing the 'Acc %(Test)' column, the highest test accuracy is found in the 'Concatenated (NN3)' setting with 82.70%.", "question": "Which setting achieved the highest test accuracy?", "answer": "So, the answer is {\"answer\": \"Concatenated (NN3)\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The concatenated (k=5) setting achieved the highest test accuracy., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe concatenated (k=5) setting achieved the highest test accuracy.\n" }, { "role": "assistant", "content": "The table shows that the 'Concatenated (NN3)' setting achieved the highest test accuracy with 82.70%, while the 'Concatenated (k=5)' setting achieved 82.64%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3311.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Accuracy on SNLI development and test sets." ], "reasoning": "The table shows that the 'Concatenated (NN3)' setting achieved the highest test accuracy with 82.70%, while the 'Concatenated (k=5)' setting achieved 82.64%.", "statement": "The concatenated (k=5) setting achieved the highest test accuracy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Results for Padó, McRae and Ferretti, Instruments and Locations, with WO computed on PPMI matrix, varying the number of fillers (i.e. 10, 30 and 50) and the types of dependency contexts (i.e. ALL, SO and PREP). The best results of our system are in bold. A baseline reimplementing Baroni and Lenci (2010) – with 10, 30 and 50 fillers – and state of the art results from previous literature are reported for comparison.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The table presents the scores of different systems based on varying dependency contexts and the number of fillers, comparing them with baseline and state-of-the-art models.Settings. To prove our hypotheses and verify the consistency of the system, we tested a large range of settings, varying: the number of fillers used to build the prototype, with the most typical values in the literature ranging between 10 and 50. We report the results for 10, 30 and 50 fillers the types of the dependency relations used for calculating the overlap: we report results for the SO, PREP and ALL settings; the value of N, that is the number of top contexts that we take into account when computing the weighted overlap. Baseline and State of the Art. In the first three rows, the table shows the scores obtained by our system varying the types of dependency contexts (i.e. ALL, SO, PREP) and the number of fillers considered for the prototype (i.e. 10, 30 and 50). The other rows respectively show i) the scores obtained by calculating the vector cosine between the role prototype vector (i.e. the vector obtained by summing the most typical fillers, with no salience ranking of the dimensions) and the candidate filler vector and ii) the scores reported in the literature for the best unsupervised and supervised models. At the best of our knowledge, the result for the instruments is the best reported until now in the literature. This is particularly interesting because – as pointed out by Sayeed and Demberg I ate a pizza with Mark). Unlike all the other datasets, instrument prototypes built with more fillers tend to be more noisy and therefore to pull down both the vector cosine and WO performance Systems based on semantic role labeling have an advantage in this sense, as they do not have to deal with prepositional ambiguity. As briefly mentioned above, in our experiments, we tested both PPMI and PLMI as weighting measures." } ], "images": [ "scigen/train/3312.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results for Padó, McRae and Ferretti, Instruments and Locations, with WO computed on PPMI matrix, varying the number of fillers (i.e. 10, 30 and 50) and the types of dependency contexts (i.e. ALL, SO and PREP). The best results of our system are in bold. A baseline reimplementing Baroni and Lenci (2010) – with 10, 30 and 50 fillers – and state of the art results from previous literature are reported for comparison." ], "reasoning": "The table presents the scores of different systems based on varying dependency contexts and the number of fillers, comparing them with baseline and state-of-the-art models.", "description": "Settings. To prove our hypotheses and verify the consistency of the system, we tested a large range of settings, varying: the number of fillers used to build the prototype, with the most typical values in the literature ranging between 10 and 50. We report the results for 10, 30 and 50 fillers the types of the dependency relations used for calculating the overlap: we report results for the SO, PREP and ALL settings; the value of N, that is the number of top contexts that we take into account when computing the weighted overlap. Baseline and State of the Art. In the first three rows, the table shows the scores obtained by our system varying the types of dependency contexts (i.e. ALL, SO, PREP) and the number of fillers considered for the prototype (i.e. 10, 30 and 50). The other rows respectively show i) the scores obtained by calculating the vector cosine between the role prototype vector (i.e. the vector obtained by summing the most typical fillers, with no salience ranking of the dimensions) and the candidate filler vector and ii) the scores reported in the literature for the best unsupervised and supervised models. At the best of our knowledge, the result for the instruments is the best reported until now in the literature. This is particularly interesting because – as pointed out by Sayeed and Demberg I ate a pizza with Mark). Unlike all the other datasets, instrument prototypes built with more fillers tend to be more noisy and therefore to pull down both the vector cosine and WO performance Systems based on semantic role labeling have an advantage in this sense, as they do not have to deal with prepositional ambiguity. As briefly mentioned above, in our experiments, we tested both PPMI and PLMI as weighting measures.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: Results for Padó, McRae and Ferretti, Instruments and Locations, with WO computed on PPMI matrix, varying the number of fillers (i.e. 10, 30 and 50) and the types of dependency contexts (i.e. ALL, SO and PREP). The best results of our system are in bold. A baseline reimplementing Baroni and Lenci (2010) – with 10, 30 and 50 fillers – and state of the art results from previous literature are reported for comparison.' in mind, break down and resolve the question below step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhat is the highest score obtained for the Ferretti - Instruments PREP setting?\n" }, { "role": "assistant", "content": "To find the highest score for the Ferretti - Instruments PREP setting, we need to look at the column labeled 'Ferretti - Instruments PREP' and identify the highest value. The highest score in this column is 0.50, achieved by the PPMI system with 30 fillers. So, the answer is {\"answer\": \"0.50\"}." } ], "images": [ "scigen/train/3312.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results for Padó, McRae and Ferretti, Instruments and Locations, with WO computed on PPMI matrix, varying the number of fillers (i.e. 10, 30 and 50) and the types of dependency contexts (i.e. ALL, SO and PREP). The best results of our system are in bold. A baseline reimplementing Baroni and Lenci (2010) – with 10, 30 and 50 fillers – and state of the art results from previous literature are reported for comparison." ], "reasoning": "To find the highest score for the Ferretti - Instruments PREP setting, we need to look at the column labeled 'Ferretti - Instruments PREP' and identify the highest value. The highest score in this column is 0.50, achieved by the PPMI system with 30 fillers.", "question": "What is the highest score obtained for the Ferretti - Instruments PREP setting?", "answer": "So, the answer is {\"answer\": \"0.50\"}." }, { "messages": [ { "role": "user", "content": "The PPMI system with 50 fillers achieved the highest score for the Mcrae SO setting. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe PPMI system with 50 fillers achieved the highest score for the Mcrae SO setting.\n" }, { "role": "assistant", "content": "To verify this statement, we need to look at the 'Mcrae SO' column and identify the highest score. The highest score in this column is 0.33, achieved by Baroni and Lenci (2010), not the PPMI system with 50 fillers. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3312.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results for Padó, McRae and Ferretti, Instruments and Locations, with WO computed on PPMI matrix, varying the number of fillers (i.e. 10, 30 and 50) and the types of dependency contexts (i.e. ALL, SO and PREP). The best results of our system are in bold. A baseline reimplementing Baroni and Lenci (2010) – with 10, 30 and 50 fillers – and state of the art results from previous literature are reported for comparison." ], "reasoning": "To verify this statement, we need to look at the 'Mcrae SO' column and identify the highest score. The highest score in this column is 0.33, achieved by Baroni and Lenci (2010), not the PPMI system with 50 fillers.", "statement": "The PPMI system with 50 fillers achieved the highest score for the Mcrae SO setting.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: IBM-1 model entropy and perplexity (Ppl) on the training and development set for the controlled scenario using different synthetic generation methods.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Training set perplexity varies with generation methods due to differing hypothesis quality, with restricted search methods showing higher entropy and lower perplexity.These are the perplexities (Ppl) of the model on the training and development data and the entropy of a target-to-source IBM-1 model Brown et al. The training set Ppl varies strongly with each generation method since each produces hypotheses of differing quality. All methods with a restricted search space have a larger translation entropy and smaller training Ppl than natural data. This is due to the sentences being less noisy and also the translation options being less varied. Unrestricted sampling seems to overshoot the statistics of natural data, attaining higher entropy values." } ], "images": [ "scigen/train/3313.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: IBM-1 model entropy and perplexity (Ppl) on the training and development set for the controlled scenario using different synthetic generation methods." ], "reasoning": "Training set perplexity varies with generation methods due to differing hypothesis quality, with restricted search methods showing higher entropy and lower perplexity.", "description": "These are the perplexities (Ppl) of the model on the training and development data and the entropy of a target-to-source IBM-1 model Brown et al. The training set Ppl varies strongly with each generation method since each produces hypotheses of differing quality. All methods with a restricted search space have a larger translation entropy and smaller training Ppl than natural data. This is due to the sentences being less noisy and also the translation options being less varied. Unrestricted sampling seems to overshoot the statistics of natural data, attaining higher entropy values.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 2: IBM-1 model entropy and perplexity (Ppl) on the training and development set for the controlled scenario using different synthetic generation methods.' and proceed to solve the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich generation method has the highest entropy and what is its corresponding training perplexity?\n" }, { "role": "assistant", "content": "The table shows that the 'sampling' method has the highest entropy value of 3.13. The corresponding training perplexity (Ppl Train) for this method is 9.07. So, the answer is {\"answer\": \"sampling, 9.07\"}." } ], "images": [ "scigen/train/3313.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: IBM-1 model entropy and perplexity (Ppl) on the training and development set for the controlled scenario using different synthetic generation methods." ], "reasoning": "The table shows that the 'sampling' method has the highest entropy value of 3.13. The corresponding training perplexity (Ppl Train) for this method is 9.07.", "question": "Which generation method has the highest entropy and what is its corresponding training perplexity?", "answer": "So, the answer is {\"answer\": \"sampling, 9.07\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe beam search method has a lower training perplexity than the reference method.\n" }, { "role": "assistant", "content": "The table shows that the training perplexity (Ppl Train) for the beam search method is 2.74, while for the reference method it is 5.18. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3313.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: IBM-1 model entropy and perplexity (Ppl) on the training and development set for the controlled scenario using different synthetic generation methods." ], "reasoning": "The table shows that the training perplexity (Ppl Train) for the beam search method is 2.74, while for the reference method it is 5.18. Therefore, the statement is supported.", "statement": "The beam search method has a lower training perplexity than the reference method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Evaluation results on automatic metrics. Numbers in bold indicate the best performing model on the corresponding metrics.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our model outperforms all baseline methods on most metrics across all datasets, has a smaller parameter size even during training compared to HRED, and enjoys a fast decoding process.Our model outperforms all baseline methods on most of the metrics on all the three datasets. The last two columns of the tables compare different models in terms of parameter size and decoding speed. Note that in training, the auxiliary tasks contain parameters outside the generation model. Therefore, in the column of parameter size, we report two numbers for our model with the one before “/” parameter size in training and the one after “/” parameter size of the generation model. It is remarkable that the parameter size of our model, even in training, is smaller than HRED. In spite of this, the model still outperforms ReCoSa with only 19.5%/18.2%/14.0% parameters on the DailyDialog/PERSONA-CHAT/Ubuntu data. This is because (1) the auxiliary tasks can effectively aid the learning of the generation model in our method; and (2) ReCoSa, although in a deep structure, is still inadequate in terms of context modeling due to the RNN-based encoder and the only utterance-level attention. Besides the superior performance on response quality, our model also enjoys a fast decoding process, thanks to the small model size. In terms of decoding speed, our model is comparable with HRED, and 2x faster than ReCoSa. The generation model of SNN is just a simple RNN sequence-to-sequence with one layer encoder and one layer decoder. Therefore, our model is comparable with SSN in terms of complexity and speed. However, SSN is worse than our model on response quality due to (1) the RNN-based seq2seq model in SSN is worse than a transformer-based structure on the benchmarks used in this work, which has been indicated by Sankar et al. ; (2) SSN only considers utterance order, while we also leverage word order, word content, and utterance content in learning. In fact, we find that the proposed auxiliary tasks can improve a 2-layer (one for encoder and one for decoder) RNN-based seq2seq model as well, as reported in Supplementary Material. On most metrics, RNN with full auxiliary tasks is better than SSN but worse than the proposed model." } ], "images": [ "scigen/train/3314.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Evaluation results on automatic metrics. Numbers in bold indicate the best performing model on the corresponding metrics." ], "reasoning": "Our model outperforms all baseline methods on most metrics across all datasets, has a smaller parameter size even during training compared to HRED, and enjoys a fast decoding process.", "description": "Our model outperforms all baseline methods on most of the metrics on all the three datasets. The last two columns of the tables compare different models in terms of parameter size and decoding speed. Note that in training, the auxiliary tasks contain parameters outside the generation model. Therefore, in the column of parameter size, we report two numbers for our model with the one before “/” parameter size in training and the one after “/” parameter size of the generation model. It is remarkable that the parameter size of our model, even in training, is smaller than HRED. In spite of this, the model still outperforms ReCoSa with only 19.5%/18.2%/14.0% parameters on the DailyDialog/PERSONA-CHAT/Ubuntu data. This is because (1) the auxiliary tasks can effectively aid the learning of the generation model in our method; and (2) ReCoSa, although in a deep structure, is still inadequate in terms of context modeling due to the RNN-based encoder and the only utterance-level attention. Besides the superior performance on response quality, our model also enjoys a fast decoding process, thanks to the small model size. In terms of decoding speed, our model is comparable with HRED, and 2x faster than ReCoSa. The generation model of SNN is just a simple RNN sequence-to-sequence with one layer encoder and one layer decoder. Therefore, our model is comparable with SSN in terms of complexity and speed. However, SSN is worse than our model on response quality due to (1) the RNN-based seq2seq model in SSN is worse than a transformer-based structure on the benchmarks used in this work, which has been indicated by Sankar et al. ; (2) SSN only considers utterance order, while we also leverage word order, word content, and utterance content in learning. In fact, we find that the proposed auxiliary tasks can improve a 2-layer (one for encoder and one for decoder) RNN-based seq2seq model as well, as reported in Supplementary Material. On most metrics, RNN with full auxiliary tasks is better than SSN but worse than the proposed model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Evaluation results on automatic metrics. Numbers in bold indicate the best performing model on the corresponding metrics.', answer the following question in a stepwise manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the BLEU score of our model on the DailyDialog dataset?\n" }, { "role": "assistant", "content": "To find the BLEU score of our model on the DailyDialog dataset, locate the row corresponding to 'DailyDialog' and 'Our Model' and check the BLEU column. So, the answer is {\"answer\": \"1.658\"}" } ], "images": [ "scigen/train/3314.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Evaluation results on automatic metrics. Numbers in bold indicate the best performing model on the corresponding metrics." ], "reasoning": "To find the BLEU score of our model on the DailyDialog dataset, locate the row corresponding to 'DailyDialog' and 'Our Model' and check the BLEU column.", "question": "What is the BLEU score of our model on the DailyDialog dataset?", "answer": "So, the answer is {\"answer\": \"1.658\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nOur model has a smaller parameter size than HRED on the DailyDialog dataset.\n" }, { "role": "assistant", "content": "The table shows that the parameter size of our model on the DailyDialog dataset is 20.3M/14.4M, which is smaller than HRED's parameter size of 34.5M. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3314.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Evaluation results on automatic metrics. Numbers in bold indicate the best performing model on the corresponding metrics." ], "reasoning": "The table shows that the parameter size of our model on the DailyDialog dataset is 20.3M/14.4M, which is smaller than HRED's parameter size of 34.5M.", "statement": "Our model has a smaller parameter size than HRED on the DailyDialog dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Human evaluation results. The ratios are calculated by combining annotations from three judges together.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our model outperforms all baseline models in terms of win percentage and has kappa values mostly exceeding 0.6, indicating substantial agreement among annotators.We can see that our model outperforms all baseline models, and most of the kappa values exceed 0.6 indicating substantial agreement among the annotators. Based on the annotation results, we find that our model tends to generate diverse and context consistent responses, indicating the effect of the auxiliary tasks." } ], "images": [ "scigen/train/3315.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Human evaluation results. The ratios are calculated by combining annotations from three judges together." ], "reasoning": "Our model outperforms all baseline models in terms of win percentage and has kappa values mostly exceeding 0.6, indicating substantial agreement among annotators.", "description": "We can see that our model outperforms all baseline models, and most of the kappa values exceed 0.6 indicating substantial agreement among the annotators. Based on the annotation results, we find that our model tends to generate diverse and context consistent responses, indicating the effect of the auxiliary tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: Human evaluation results. The ratios are calculated by combining annotations from three judges together.' to sequentially solve the question presented below. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich model comparison has the highest kappa value in the PERSONA-CHAT dataset?\n" }, { "role": "assistant", "content": "By examining the PERSONA-CHAT kappa values, the highest kappa value is 0.867 for 'Our Model v.s. HRED'. So, the answer is {\"answer\": \"Our Model v.s. HRED\"}." } ], "images": [ "scigen/train/3315.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Human evaluation results. The ratios are calculated by combining annotations from three judges together." ], "reasoning": "By examining the PERSONA-CHAT kappa values, the highest kappa value is 0.867 for 'Our Model v.s. HRED'.", "question": "Which model comparison has the highest kappa value in the PERSONA-CHAT dataset?", "answer": "So, the answer is {\"answer\": \"Our Model v.s. HRED\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nOur model has the highest win percentage in the DailyDialog dataset.\n" }, { "role": "assistant", "content": "By examining the DailyDialog win percentages, the highest win percentage is 42.57% for 'Our Model v.s. HRED'. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3315.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Human evaluation results. The ratios are calculated by combining annotations from three judges together." ], "reasoning": "By examining the DailyDialog win percentages, the highest win percentage is 42.57% for 'Our Model v.s. HRED'.", "statement": "Our model has the highest win percentage in the DailyDialog dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Results of ablation study.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Removing any auxiliary tasks results in a performance drop, with order recovery tasks being more crucial on DailyDialog and UDC, and word-level recovery tasks being more important on PERSONA-CHAT.First of all, all auxiliary tasks are useful as removing any of them will cause a performance drop. When all auxiliary tasks are removed, the approach degenerates to learning a 2-layer transformer architecture through MLE. Without any optimization on context understanding, the simple structure is worse than ReCoSa. Secondly, on DailyDialog and UDC, order recovery tasks are more crucial than content recovery tasks due to the order insensitive nature of self-attention. Finally, on PERSONA-CHAT, word-level recovery tasks matter more than utterance-level recovery tasks. This might stem from the fact that in PERSONA-CHAT, dialogues highly depend on the profiles used as contexts. In many cases, utterances are just formed by copying a proportion of words from the profiles. Thus, recognizing the semantic connections and the relationship among words in contexts is more critical for the data." } ], "images": [ "scigen/train/3316.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of ablation study." ], "reasoning": "Removing any auxiliary tasks results in a performance drop, with order recovery tasks being more crucial on DailyDialog and UDC, and word-level recovery tasks being more important on PERSONA-CHAT.", "description": "First of all, all auxiliary tasks are useful as removing any of them will cause a performance drop. When all auxiliary tasks are removed, the approach degenerates to learning a 2-layer transformer architecture through MLE. Without any optimization on context understanding, the simple structure is worse than ReCoSa. Secondly, on DailyDialog and UDC, order recovery tasks are more crucial than content recovery tasks due to the order insensitive nature of self-attention. Finally, on PERSONA-CHAT, word-level recovery tasks matter more than utterance-level recovery tasks. This might stem from the fact that in PERSONA-CHAT, dialogues highly depend on the profiles used as contexts. In many cases, utterances are just formed by copying a proportion of words from the profiles. Thus, recognizing the semantic connections and the relationship among words in contexts is more critical for the data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Results of ablation study.\nQuestion: Which model variant has the highest DailyDialog BLEU score?\nSolve the above question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows that the 'full tasks' model variant has the highest DailyDialog BLEU score of 1.658. So, the answer is {\"answer\": \"full tasks\"}" } ], "images": [ "scigen/train/3316.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of ablation study." ], "reasoning": "The table shows that the 'full tasks' model variant has the highest DailyDialog BLEU score of 1.658.", "question": "Which model variant has the highest DailyDialog BLEU score?", "answer": "So, the answer is {\"answer\": \"full tasks\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Masked word recovery has the highest DailyDialog distinct-1 score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nMasked word recovery has the highest DailyDialog distinct-1 score.\n" }, { "role": "assistant", "content": "The table shows that the 'full tasks' model variant has the highest DailyDialog distinct-1 score of 3.457, not the 'masked word recovery' variant. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3316.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of ablation study." ], "reasoning": "The table shows that the 'full tasks' model variant has the highest DailyDialog distinct-1 score of 3.457, not the 'masked word recovery' variant.", "statement": "Masked word recovery has the highest DailyDialog distinct-1 score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Results (accuracy in %) for within-corpus and cross-corpus pitch accent detection using the baseline acoustic model, with added unigram GloVe word embeddings and 10 bottleneck features, and using only the embeddings as features (shown in italics). The results are averaged across 10 cross-validation splits and 5 repetitions of each experiment.'.\n" }, { "role": "assistant", "content": "Performance drops in cross-corpus settings compared to within-corpus settings, with BURNC being the easiest and BDC the most difficult corpus for pitch accent detection.As expected, we observe that the performance in the cross-corpus settings drops when compared to the within-corpus setting. BURNC appears to be by far the easiest corpus for pitch accent detection, resulting in accuracy levels obtained while training on BDC or LeaP that are higher than those obtained on the respective within-corpus test sets. The ALL experiments show a slight rise in accuracy that does not quite reach the baseline within-corpus performance, except on LeaP. The BDC datasets appears to be the most difficult to model using this method. Overall, the acoustic model can be said to generalize reasonably well across corpora and genres and" } ], "images": [ "scigen/train/3317.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results (accuracy in %) for within-corpus and cross-corpus pitch accent detection using the baseline acoustic model, with added unigram GloVe word embeddings and 10 bottleneck features, and using only the embeddings as features (shown in italics). The results are averaged across 10 cross-validation splits and 5 repetitions of each experiment." ], "reasoning": "Performance drops in cross-corpus settings compared to within-corpus settings, with BURNC being the easiest and BDC the most difficult corpus for pitch accent detection.", "description": "As expected, we observe that the performance in the cross-corpus settings drops when compared to the within-corpus setting. BURNC appears to be by far the easiest corpus for pitch accent detection, resulting in accuracy levels obtained while training on BDC or LeaP that are higher than those obtained on the respective within-corpus test sets. The ALL experiments show a slight rise in accuracy that does not quite reach the baseline within-corpus performance, except on LeaP. The BDC datasets appears to be the most difficult to model using this method. Overall, the acoustic model can be said to generalize reasonably well across corpora and genres and", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 3: Results (accuracy in %) for within-corpus and cross-corpus pitch accent detection using the baseline acoustic model, with added unigram GloVe word embeddings and 10 bottleneck features, and using only the embeddings as features (shown in italics). The results are averaged across 10 cross-validation splits and 5 repetitions of each experiment.\nQuestion: Which corpus had the highest accuracy when trained on ALL using the acoustic+embs method?\n" }, { "role": "assistant", "content": "To find the highest accuracy when trained on ALL using the acoustic+embs method, look at the row labeled 'ALL' and the column for 'acoustic+embs'. The highest value is 83.4 in the LeaP column. So, the answer is {\"answer\": \"LeaP\"}." } ], "images": [ "scigen/train/3317.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results (accuracy in %) for within-corpus and cross-corpus pitch accent detection using the baseline acoustic model, with added unigram GloVe word embeddings and 10 bottleneck features, and using only the embeddings as features (shown in italics). The results are averaged across 10 cross-validation splits and 5 repetitions of each experiment." ], "reasoning": "To find the highest accuracy when trained on ALL using the acoustic+embs method, look at the row labeled 'ALL' and the column for 'acoustic+embs'. The highest value is 83.4 in the LeaP column.", "question": "Which corpus had the highest accuracy when trained on ALL using the acoustic+embs method?", "answer": "So, the answer is {\"answer\": \"LeaP\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The BDC corpus had the lowest accuracy when trained on BURNC using the embs-only method., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe BDC corpus had the lowest accuracy when trained on BURNC using the embs-only method.\n" }, { "role": "assistant", "content": "To verify this, look at the row labeled 'BURNC' and the column for 'embs-only'. The BDC column shows an accuracy of 71.6, which is the lowest among the values in that row. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3317.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results (accuracy in %) for within-corpus and cross-corpus pitch accent detection using the baseline acoustic model, with added unigram GloVe word embeddings and 10 bottleneck features, and using only the embeddings as features (shown in italics). The results are averaged across 10 cross-validation splits and 5 repetitions of each experiment." ], "reasoning": "To verify this, look at the row labeled 'BURNC' and the column for 'embs-only'. The BDC column shows an accuracy of 71.6, which is the lowest among the values in that row.", "statement": "The BDC corpus had the lowest accuracy when trained on BURNC using the embs-only method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 6: Stopword accuracy (%) using unigram embeddings and 10 bottleneck features.'.\n" }, { "role": "assistant", "content": "Stopwords are more likely to be labeled correctly when there is some information about the word identity available, regardless of how they are represented as vectors.In word2vec, stopwords are represented as OOV with a zero vector, wheras in GloVe, they are part of the vocabulary. On the LeaP and BDC datasets, we observe better performance on these words with both types of embeddings. It appears that stopwords are more likely to be labelled correctly (which is usually not accented) when there is some information about the word identity available, regardless of how they are represented as vectors. The results on BURNC do not show a clear pattern. Overall, these numbers appear to be reflective of the overall increase in performance obtained by adding the lexical information." } ], "images": [ "scigen/train/3318.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Stopword accuracy (%) using unigram embeddings and 10 bottleneck features." ], "reasoning": "Stopwords are more likely to be labeled correctly when there is some information about the word identity available, regardless of how they are represented as vectors.", "description": "In word2vec, stopwords are represented as OOV with a zero vector, wheras in GloVe, they are part of the vocabulary. On the LeaP and BDC datasets, we observe better performance on these words with both types of embeddings. It appears that stopwords are more likely to be labelled correctly (which is usually not accented) when there is some information about the word identity available, regardless of how they are represented as vectors. The results on BURNC do not show a clear pattern. Overall, these numbers appear to be reflective of the overall increase in performance obtained by adding the lexical information.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 6: Stopword accuracy (%) using unigram embeddings and 10 bottleneck features.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich embedding method shows the highest performance on the LeaP dataset?\n" }, { "role": "assistant", "content": "By comparing the performance scores on the LeaP dataset, GloVe shows a performance of 94.2, and w2v shows a performance of 94.3. Therefore, w2v has the highest performance on the LeaP dataset. So, the answer is {\"answer\": \"w2v\"}." } ], "images": [ "scigen/train/3318.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Stopword accuracy (%) using unigram embeddings and 10 bottleneck features." ], "reasoning": "By comparing the performance scores on the LeaP dataset, GloVe shows a performance of 94.2, and w2v shows a performance of 94.3. Therefore, w2v has the highest performance on the LeaP dataset.", "question": "Which embedding method shows the highest performance on the LeaP dataset?", "answer": "So, the answer is {\"answer\": \"w2v\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe GloVe embedding method performs better than the baseline on the BDC dataset.\n" }, { "role": "assistant", "content": "The table shows that the GloVe embedding method has a performance score of 92.7 on the BDC dataset, while the baseline has a score of 88.9. Therefore, the GloVe embedding method performs better than the baseline on the BDC dataset. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3318.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Stopword accuracy (%) using unigram embeddings and 10 bottleneck features." ], "reasoning": "The table shows that the GloVe embedding method has a performance score of 92.7 on the BDC dataset, while the baseline has a score of 88.9. Therefore, the GloVe embedding method performs better than the baseline on the BDC dataset.", "statement": "The GloVe embedding method performs better than the baseline on the BDC dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Unsupervised Word Discovery results with 4,643 sentences.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The reverse model with alignment smoothing significantly improves both token and type retrieval, confirming that reversing the alignment direction enhances segmentation.Moreover, we show that the proposed reverse model considerably improves type and token retrieval. This seems to confirm the hypothesis that reversing the alignment direction results in a better segmentation (because the attention model has to align each Mboshi symbol to French words with a total probability of 1)." } ], "images": [ "scigen/train/3319.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Unsupervised Word Discovery results with 4,643 sentences." ], "reasoning": "The reverse model with alignment smoothing significantly improves both token and type retrieval, confirming that reversing the alignment direction enhances segmentation.", "description": "Moreover, we show that the proposed reverse model considerably improves type and token retrieval. This seems to confirm the hypothesis that reversing the alignment direction results in a better segmentation (because the attention model has to align each Mboshi symbol to French words with a total probability of 1).", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 2: Unsupervised Word Discovery results with 4,643 sentences.'\nQuestion: Which model has the highest TYPES Recall score?\n" }, { "role": "assistant", "content": "To find the model with the highest TYPES Recall score, compare the TYPES Recall values for all models. The Reverse Model (Fr-Mb) with Alignment Smoothing has the highest TYPES Recall score of 27.23. So, the answer is {\"answer\": \"Reverse Model (Fr-Mb) with Alignment Smoothing\"}." } ], "images": [ "scigen/train/3319.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Unsupervised Word Discovery results with 4,643 sentences." ], "reasoning": "To find the model with the highest TYPES Recall score, compare the TYPES Recall values for all models. The Reverse Model (Fr-Mb) with Alignment Smoothing has the highest TYPES Recall score of 27.23.", "question": "Which model has the highest TYPES Recall score?", "answer": "So, the answer is {\"answer\": \"Reverse Model (Fr-Mb) with Alignment Smoothing\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Base Model (Mb-Fr) with Alignment Smoothing has a higher TOKENS Precision score than the Reverse Model (Fr-Mb).\n" }, { "role": "assistant", "content": "The TOKENS Precision score for the Base Model (Mb-Fr) with Alignment Smoothing is 5.85, while the TOKENS Precision score for the Reverse Model (Fr-Mb) is 10.02. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3319.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Unsupervised Word Discovery results with 4,643 sentences." ], "reasoning": "The TOKENS Precision score for the Base Model (Mb-Fr) with Alignment Smoothing is 5.85, while the TOKENS Precision score for the Reverse Model (Fr-Mb) is 10.02. Therefore, the statement is refuted.", "statement": "The Base Model (Mb-Fr) with Alignment Smoothing has a higher TOKENS Precision score than the Reverse Model (Fr-Mb).", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Comparison between the NB model (dpseg) and the reverse model with alignment smoothing (AS) for unsupervised word discovery. The scores were obtained by averaging over three instances of each model.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The Reverse Model retrieves more from the vocabulary with higher recall, while dpseg has higher precision and a slightly better F-score.Although the former is able to retrieve more from the vocabulary, the latter has higher precision, and both are close in terms of F-score. Additionally, ours has the advantage of providing clues for translation." } ], "images": [ "scigen/train/3320.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison between the NB model (dpseg) and the reverse model with alignment smoothing (AS) for unsupervised word discovery. The scores were obtained by averaging over three instances of each model." ], "reasoning": "The Reverse Model retrieves more from the vocabulary with higher recall, while dpseg has higher precision and a slightly better F-score.", "description": "Although the former is able to retrieve more from the vocabulary, the latter has higher precision, and both are close in terms of F-score. Additionally, ours has the advantage of providing clues for translation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 4: Comparison between the NB model (dpseg) and the reverse model with alignment smoothing (AS) for unsupervised word discovery. The scores were obtained by averaging over three instances of each model.'.\n\nQuestion: Which model has the highest F-score and what is its value?\n" }, { "role": "assistant", "content": "To determine the model with the highest F-score, compare the F-score values of both models. dpseg has an F-score of 20.45, which is higher than the Reverse Model's F-score of 19.36. So, the answer is {\"answer\": \"dpseg with 20.45\"}." } ], "images": [ "scigen/train/3320.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison between the NB model (dpseg) and the reverse model with alignment smoothing (AS) for unsupervised word discovery. The scores were obtained by averaging over three instances of each model." ], "reasoning": "To determine the model with the highest F-score, compare the F-score values of both models. dpseg has an F-score of 20.45, which is higher than the Reverse Model's F-score of 19.36.", "question": "Which model has the highest F-score and what is its value?", "answer": "So, the answer is {\"answer\": \"dpseg with 20.45\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Reverse Model has a higher precision than dpseg. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Reverse Model has a higher precision than dpseg.\n" }, { "role": "assistant", "content": "The table shows that the Reverse Model has a precision of 15.02, while dpseg has a precision of 38.32. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3320.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison between the NB model (dpseg) and the reverse model with alignment smoothing (AS) for unsupervised word discovery. The scores were obtained by averaging over three instances of each model." ], "reasoning": "The table shows that the Reverse Model has a precision of 15.02, while dpseg has a precision of 38.32. Therefore, the statement is incorrect.", "statement": "The Reverse Model has a higher precision than dpseg.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Means and standard deviations of sentiment scores of combinations of positive emojis and plain verb messages of three valences.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The table shows the consistency of workers' scales across surveys and the effectiveness of control conditions, highlighting changes in sentiment scores due to emojis.This indicates the consistency of workers’ scales cross surveys, as well as the effectiveness of the control conditions. We discuss increments or decrements of sentiment scores brought by emojis in this section." } ], "images": [ "scigen/train/3321.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Means and standard deviations of sentiment scores of combinations of positive emojis and plain verb messages of three valences." ], "reasoning": "The table shows the consistency of workers' scales across surveys and the effectiveness of control conditions, highlighting changes in sentiment scores due to emojis.", "description": "This indicates the consistency of workers’ scales cross surveys, as well as the effectiveness of the control conditions. We discuss increments or decrements of sentiment scores brought by emojis in this section.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 6: Means and standard deviations of sentiment scores of combinations of positive emojis and plain verb messages of three valences.', answer the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the highest Positive M score recorded in the table?\n" }, { "role": "assistant", "content": "To find the highest Positive M score, we need to compare all the Positive M values in the table. The highest Positive M score is 6.78. So, the answer is {\"answer\": \"6.78\"}" } ], "images": [ "scigen/train/3321.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Means and standard deviations of sentiment scores of combinations of positive emojis and plain verb messages of three valences." ], "reasoning": "To find the highest Positive M score, we need to compare all the Positive M values in the table. The highest Positive M score is 6.78.", "question": "What is the highest Positive M score recorded in the table?", "answer": "So, the answer is {\"answer\": \"6.78\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe control condition has the lowest Neutral M score.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the Neutral M scores in the table. The control condition has a Neutral M score of 4.25, which is indeed the lowest. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3321.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Means and standard deviations of sentiment scores of combinations of positive emojis and plain verb messages of three valences." ], "reasoning": "To verify this statement, we need to compare the Neutral M scores in the table. The control condition has a Neutral M score of 4.25, which is indeed the lowest.", "statement": "The control condition has the lowest Neutral M score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: Means and standard deviations of willingness scores of each emoji for each intention. Seven intentions from left to right are: expressing sentiment, strengthening expression, adjusting tone, describing content, expressing humor, expressing intimacy, and expressing irony.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "A one-way repeated measure ANOVA shows significant differences in emoji scores for each intention, leading to pairwise linear contrast tests to determine the most proper emoji type for each intention.We also study the differences between different types of emojis (positive, neutral, negative, and non-facial). A one-way repeated measure ANOVA is first performed for each intention, to test the equivalence between the mean values of the scores of all the emojis. The tests on all intentions report significant differences between these mean values, indicating that the emojis are used with divergent willingness levels for the intentions. Therefore, we construct pairwise linear contract tests between emoji types for each intention. A contrast is computed as the average of the scores of the five emojis of a type. For an intention, if a contrast test indicates that the mean score of a type of emoji is significantly higher than that of another type, then the former type is considered more proper. If no significant difference is detected, then the two types are considered equally proper/improper for the intention. Please note that, for the conciseness of the narration in this paper, we do not list the p-values of all the statistical tests. The p-values of the significant differences reported in the paper are all lower than 0.05." } ], "images": [ "scigen/train/3322.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Means and standard deviations of willingness scores of each emoji for each intention. Seven intentions from left to right are: expressing sentiment, strengthening expression, adjusting tone, describing content, expressing humor, expressing intimacy, and expressing irony." ], "reasoning": "A one-way repeated measure ANOVA shows significant differences in emoji scores for each intention, leading to pairwise linear contrast tests to determine the most proper emoji type for each intention.", "description": "We also study the differences between different types of emojis (positive, neutral, negative, and non-facial). A one-way repeated measure ANOVA is first performed for each intention, to test the equivalence between the mean values of the scores of all the emojis. The tests on all intentions report significant differences between these mean values, indicating that the emojis are used with divergent willingness levels for the intentions. Therefore, we construct pairwise linear contract tests between emoji types for each intention. A contrast is computed as the average of the scores of the five emojis of a type. For an intention, if a contrast test indicates that the mean score of a type of emoji is significantly higher than that of another type, then the former type is considered more proper. If no significant difference is detected, then the two types are considered equally proper/improper for the intention. Please note that, for the conciseness of the narration in this paper, we do not list the p-values of all the statistical tests. The p-values of the significant differences reported in the paper are all lower than 0.05.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 5: Means and standard deviations of willingness scores of each emoji for each intention. Seven intentions from left to right are: expressing sentiment, strengthening expression, adjusting tone, describing content, expressing humor, expressing intimacy, and expressing irony.' in mind, break down and resolve the question below step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich type of emoji has the highest mean score for Intention 1?\n" }, { "role": "assistant", "content": "To find the highest mean score for Intention 1, compare the mean scores (M) of all emoji types listed under Intention 1. The highest mean score is 6.78, which belongs to the 'Neg' (negative) emoji type. So, the answer is {\"answer\": \"Neg\"}." } ], "images": [ "scigen/train/3322.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Means and standard deviations of willingness scores of each emoji for each intention. Seven intentions from left to right are: expressing sentiment, strengthening expression, adjusting tone, describing content, expressing humor, expressing intimacy, and expressing irony." ], "reasoning": "To find the highest mean score for Intention 1, compare the mean scores (M) of all emoji types listed under Intention 1. The highest mean score is 6.78, which belongs to the 'Neg' (negative) emoji type.", "question": "Which type of emoji has the highest mean score for Intention 1?", "answer": "So, the answer is {\"answer\": \"Neg\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nPositive emojis have the highest mean score for Intention 3.\n" }, { "role": "assistant", "content": "To verify this statement, compare the mean scores (M) of positive emojis with other types for Intention 3. The highest mean score for Intention 3 is 5.51, which belongs to the 'Pos' (positive) emoji type. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3322.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Means and standard deviations of willingness scores of each emoji for each intention. Seven intentions from left to right are: expressing sentiment, strengthening expression, adjusting tone, describing content, expressing humor, expressing intimacy, and expressing irony." ], "reasoning": "To verify this statement, compare the mean scores (M) of positive emojis with other types for Intention 3. The highest mean score for Intention 3 is 5.51, which belongs to the 'Pos' (positive) emoji type.", "statement": "Positive emojis have the highest mean score for Intention 3.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 9: Means and standard deviations of sentiment scores of combinations of non-facial emojis and plain verb messages of three valences.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows the consistency of workers' scales across surveys and the impact of control conditions on sentiment scores, including changes due to emojis.This indicates the consistency of workers’ scales cross surveys, as well as the effectiveness of the control conditions. We discuss increments or decrements of sentiment scores brought by emojis in this section." } ], "images": [ "scigen/train/3323.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Means and standard deviations of sentiment scores of combinations of non-facial emojis and plain verb messages of three valences." ], "reasoning": "The table shows the consistency of workers' scales across surveys and the impact of control conditions on sentiment scores, including changes due to emojis.", "description": "This indicates the consistency of workers’ scales cross surveys, as well as the effectiveness of the control conditions. We discuss increments or decrements of sentiment scores brought by emojis in this section.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 9: Means and standard deviations of sentiment scores of combinations of non-facial emojis and plain verb messages of three valences.'.\n\nQuestion: What is the highest positive sentiment mean score recorded in the table?\n" }, { "role": "assistant", "content": "To find the highest positive sentiment mean score, we need to look at the 'Positive M' column and identify the highest value. So, the answer is {\"answer\": \"6.72\"}." } ], "images": [ "scigen/train/3323.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Means and standard deviations of sentiment scores of combinations of non-facial emojis and plain verb messages of three valences." ], "reasoning": "To find the highest positive sentiment mean score, we need to look at the 'Positive M' column and identify the highest value.", "question": "What is the highest positive sentiment mean score recorded in the table?", "answer": "So, the answer is {\"answer\": \"6.72\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The control condition has the lowest neutral sentiment mean score., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe control condition has the lowest neutral sentiment mean score.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the 'Neutral M' scores across all conditions and see if the control condition has the lowest value. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3323.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Means and standard deviations of sentiment scores of combinations of non-facial emojis and plain verb messages of three valences." ], "reasoning": "To verify this statement, we need to compare the 'Neutral M' scores across all conditions and see if the control condition has the lowest value.", "statement": "The control condition has the lowest neutral sentiment mean score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 7: Accuracy of beginning ranking and logic ranking. Larger scores are better.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The table shows the performance of different models on BR and LR tasks, with 'Ours' model achieving the highest accuracy in both tasks.For BR, we first sampled 9 negative beginnings (first sentence) for a true story, and then calculated the perplexity of the 10 stories. If the true story has the lowest perplexity by our model, it is regarded as a correct prediction. As for LR, since each story in ROCStories consists of five sentences, we produced four shuffled versions by switching each pair of adjacent sentences. We then used our model to score the five stories with perplexity. A prediction is regarded as correct if the true story has the lowest score. We randomly sampled 1,000 human-written stories from the test set in our evaluation." } ], "images": [ "scigen/train/3324.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Accuracy of beginning ranking and logic ranking. Larger scores are better." ], "reasoning": "The table shows the performance of different models on BR and LR tasks, with 'Ours' model achieving the highest accuracy in both tasks.", "description": "For BR, we first sampled 9 negative beginnings (first sentence) for a true story, and then calculated the perplexity of the 10 stories. If the true story has the lowest perplexity by our model, it is regarded as a correct prediction. As for LR, since each story in ROCStories consists of five sentences, we produced four shuffled versions by switching each pair of adjacent sentences. We then used our model to score the five stories with perplexity. A prediction is regarded as correct if the true story has the lowest score. We randomly sampled 1,000 human-written stories from the test set in our evaluation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 7: Accuracy of beginning ranking and logic ranking. Larger scores are better.\nQuestion: Which model has the highest accuracy in the LR task?\nGive a detailed response to the above question. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the LR (%) column, the 'Ours' model has the highest accuracy with 72.7%. So, the answer is {\"answer\": \"Ours\"}" } ], "images": [ "scigen/train/3324.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Accuracy of beginning ranking and logic ranking. Larger scores are better." ], "reasoning": "By comparing the LR (%) column, the 'Ours' model has the highest accuracy with 72.7%.", "question": "Which model has the highest accuracy in the LR task?", "answer": "So, the answer is {\"answer\": \"Ours\"}" }, { "messages": [ { "role": "user", "content": "The GPT-2 (Pretrain) model has the lowest accuracy in the BR task. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe GPT-2 (Pretrain) model has the lowest accuracy in the BR task.\n" }, { "role": "assistant", "content": "By comparing the BR (%) column, the GPT-2 (Pretrain) model has the lowest accuracy with 59.3%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3324.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Accuracy of beginning ranking and logic ranking. Larger scores are better." ], "reasoning": "By comparing the BR (%) column, the GPT-2 (Pretrain) model has the lowest accuracy with 59.3%.", "statement": "The GPT-2 (Pretrain) model has the lowest accuracy in the BR task.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Automatic evaluation results. The best performance is highlighted in bold. And the results of golden story are in italic. The perplexity scores marked with N/A are not comparable with ours because the corresponding models tokenize stories by words rather than by byte pair encodings used in GPT-2.'.\n" }, { "role": "assistant", "content": "Our model outperforms GPT-2 variants in perplexity and BLEU scores, indicating better fluency and overlap with reference stories, and also shows higher knowledge coverage and distinct-4 scores, demonstrating more diverse story generation.Results Note that the perplexity scores of some baselines are not comparable with ours because they tokenize stories by words rather than by byte pair encodings as used in GPT-2. Thus, we did not provide these scores. Our model outperforms the variants of GPT-2 in terms of perplexity, and has higher BLEU scores than all the baselines, indicating better fluency and more overlaps with the reference stories. Our model also has higher knowledge coverage and distinct-4 scores, showing that our model can generate more diverse stories with more abundant knowledge. However, we observed that pretraining might lead to more severe repetition by comparing three variants of GPT-2. Our model effectively improves the situation but still performs worse than the baselines with task-specific architectures, for instance, the planning-based models (e.g., DSRL). Besides, compared with training from scratch, fine-tuned GPT-2 performs much better in fluency (lower perplexity scores) but suffers from worse repetition, and only improve slightly in coverage and diversity. Furthermore, pretrained GPT-2 has the lowest coverage and distinct-4, which further verifies our hypothesis that GPT-2 lacks the necessary knowledge to expand a story plot." } ], "images": [ "scigen/train/3325.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Automatic evaluation results. The best performance is highlighted in bold. And the results of golden story are in italic. The perplexity scores marked with N/A are not comparable with ours because the corresponding models tokenize stories by words rather than by byte pair encodings used in GPT-2." ], "reasoning": "Our model outperforms GPT-2 variants in perplexity and BLEU scores, indicating better fluency and overlap with reference stories, and also shows higher knowledge coverage and distinct-4 scores, demonstrating more diverse story generation.", "description": "Results Note that the perplexity scores of some baselines are not comparable with ours because they tokenize stories by words rather than by byte pair encodings as used in GPT-2. Thus, we did not provide these scores. Our model outperforms the variants of GPT-2 in terms of perplexity, and has higher BLEU scores than all the baselines, indicating better fluency and more overlaps with the reference stories. Our model also has higher knowledge coverage and distinct-4 scores, showing that our model can generate more diverse stories with more abundant knowledge. However, we observed that pretraining might lead to more severe repetition by comparing three variants of GPT-2. Our model effectively improves the situation but still performs worse than the baselines with task-specific architectures, for instance, the planning-based models (e.g., DSRL). Besides, compared with training from scratch, fine-tuned GPT-2 performs much better in fluency (lower perplexity scores) but suffers from worse repetition, and only improve slightly in coverage and diversity. Furthermore, pretrained GPT-2 has the lowest coverage and distinct-4, which further verifies our hypothesis that GPT-2 lacks the necessary knowledge to expand a story plot.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 4: Automatic evaluation results. The best performance is highlighted in bold. And the results of golden story are in italic. The perplexity scores marked with N/A are not comparable with ours because the corresponding models tokenize stories by words rather than by byte pair encodings used in GPT-2.' and methodically tackle the question that follows. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest distinct-4 score?\n" }, { "role": "assistant", "content": "By examining the distinct-4 scores in the table, the Golden Story model has the highest distinct-4 score of 89.51. So, the answer is {\"answer\": \"Golden Story\"}" } ], "images": [ "scigen/train/3325.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Automatic evaluation results. The best performance is highlighted in bold. And the results of golden story are in italic. The perplexity scores marked with N/A are not comparable with ours because the corresponding models tokenize stories by words rather than by byte pair encodings used in GPT-2." ], "reasoning": "By examining the distinct-4 scores in the table, the Golden Story model has the highest distinct-4 score of 89.51.", "question": "Which model has the highest distinct-4 score?", "answer": "So, the answer is {\"answer\": \"Golden Story\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe DSRL model has the lowest perplexity score.\n" }, { "role": "assistant", "content": "The table shows that the DSRL model has a perplexity score of 0.293, which is indeed the lowest among all listed models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3325.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Automatic evaluation results. The best performance is highlighted in bold. And the results of golden story are in italic. The perplexity scores marked with N/A are not comparable with ours because the corresponding models tokenize stories by words rather than by byte pair encodings used in GPT-2." ], "reasoning": "The table shows that the DSRL model has a perplexity score of 0.293, which is indeed the lowest among all listed models.", "statement": "The DSRL model has the lowest perplexity score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: Manual evaluation results. The scores indicate the percentages of Win, Lose or Tie when our model is compared with a baseline. κ denotes Fleiss’ kappa (all are fair agreement or moderate agreement). The scores marked with * mean p-value< 0.05 and ** indicates p-value< 0.01 in sign test.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Post-training on knowledge bases and multi-task learning significantly improve grammar and logic in story generation.all the results show fair agreement (0.2≤κ≤0.4) or moderate agreement (0.4≤κ≤0.6). We also conducted sign test to check the significance of the differences. The results indicate that our model performs significantly better than other baselines in both metrics. More specifically, post-training on knowledge bases leads to significant improvements in grammar and logic by offering more knowledge for expanding the story plots. And multi-task learning further enhances the performance in logic and does not affect fluency of generated stories." } ], "images": [ "scigen/train/3326.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Manual evaluation results. The scores indicate the percentages of Win, Lose or Tie when our model is compared with a baseline. κ denotes Fleiss’ kappa (all are fair agreement or moderate agreement). The scores marked with * mean p-value< 0.05 and ** indicates p-value< 0.01 in sign test." ], "reasoning": "Post-training on knowledge bases and multi-task learning significantly improve grammar and logic in story generation.", "description": "all the results show fair agreement (0.2≤κ≤0.4) or moderate agreement (0.4≤κ≤0.6). We also conducted sign test to check the significance of the differences. The results indicate that our model performs significantly better than other baselines in both metrics. More specifically, post-training on knowledge bases leads to significant improvements in grammar and logic by offering more knowledge for expanding the story plots. And multi-task learning further enhances the performance in logic and does not affect fluency of generated stories.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 5: Manual evaluation results. The scores indicate the percentages of Win, Lose or Tie when our model is compared with a baseline. κ denotes Fleiss’ kappa (all are fair agreement or moderate agreement). The scores marked with * mean p-value< 0.05 and ** indicates p-value< 0.01 in sign test.', and sequentially solve the question, writing out each step of your reasoning process. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model comparison shows the highest percentage of grammaticality win?\n" }, { "role": "assistant", "content": "By examining the 'Grammaticality Win (%)' column, the comparison 'Ours vs. DSRL' shows the highest percentage at 58.0%. So, the answer is {\"answer\": \"Ours vs. DSRL\"}." } ], "images": [ "scigen/train/3326.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Manual evaluation results. The scores indicate the percentages of Win, Lose or Tie when our model is compared with a baseline. κ denotes Fleiss’ kappa (all are fair agreement or moderate agreement). The scores marked with * mean p-value< 0.05 and ** indicates p-value< 0.01 in sign test." ], "reasoning": "By examining the 'Grammaticality Win (%)' column, the comparison 'Ours vs. DSRL' shows the highest percentage at 58.0%.", "question": "Which model comparison shows the highest percentage of grammaticality win?", "answer": "So, the answer is {\"answer\": \"Ours vs. DSRL\"}." }, { "messages": [ { "role": "user", "content": "The model without pretraining has the lowest logicality win percentage. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model without pretraining has the lowest logicality win percentage.\n" }, { "role": "assistant", "content": "By examining the 'Logicality Win (%)' column, the model comparison 'Ours vs. Ours w/o Knowledge' has the lowest logicality win percentage at 48.0%, not the model without pretraining. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3326.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Manual evaluation results. The scores indicate the percentages of Win, Lose or Tie when our model is compared with a baseline. κ denotes Fleiss’ kappa (all are fair agreement or moderate agreement). The scores marked with * mean p-value< 0.05 and ** indicates p-value< 0.01 in sign test." ], "reasoning": "By examining the 'Logicality Win (%)' column, the model comparison 'Ours vs. Ours w/o Knowledge' has the lowest logicality win percentage at 48.0%, not the model without pretraining.", "statement": "The model without pretraining has the lowest logicality win percentage.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Final prediction F1 score of our model on the auxiliary story classification task in terms of the four types of story sets respectively, and the proportional distribution of the predicted story types of the generated stories by different models.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The table shows that the model with external knowledge and auxiliary tasks generates more reasonable stories, as indicated by higher F1 scores and lower proportions of fake stories compared to baselines.Both metrics are computed on 1,000 samples from the test set. We can observe that it is relatively easier to detect fake stories with repeated plots (D4) than those with disordered logic (D2) and unrelated topics (D3). When using the auxiliary story classifier to classify the generated stories, pretrained GPT-2 is considered to generate more fake stories, with only 15.83% stories of type D1, which agrees with the previous automatic evaluation especially in terms of repetition. Besides, our model performs better than baselines, indicating that the external knowledge and the auxiliary task can encourage our model to generate more reasonable stories." } ], "images": [ "scigen/train/3327.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Final prediction F1 score of our model on the auxiliary story classification task in terms of the four types of story sets respectively, and the proportional distribution of the predicted story types of the generated stories by different models." ], "reasoning": "The table shows that the model with external knowledge and auxiliary tasks generates more reasonable stories, as indicated by higher F1 scores and lower proportions of fake stories compared to baselines.", "description": "Both metrics are computed on 1,000 samples from the test set. We can observe that it is relatively easier to detect fake stories with repeated plots (D4) than those with disordered logic (D2) and unrelated topics (D3). When using the auxiliary story classifier to classify the generated stories, pretrained GPT-2 is considered to generate more fake stories, with only 15.83% stories of type D1, which agrees with the previous automatic evaluation especially in terms of repetition. Besides, our model performs better than baselines, indicating that the external knowledge and the auxiliary task can encourage our model to generate more reasonable stories.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: Final prediction F1 score of our model on the auxiliary story classification task in terms of the four types of story sets respectively, and the proportional distribution of the predicted story types of the generated stories by different models.\nQuestion: Which model has the highest proportional distribution of D1 type stories?\nGive a detailed response to the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, we see that the 'Ours' model has the highest proportional distribution of D1 type stories at 90.12%. So, the answer is {\"answer\": \"Ours\"}" } ], "images": [ "scigen/train/3327.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Final prediction F1 score of our model on the auxiliary story classification task in terms of the four types of story sets respectively, and the proportional distribution of the predicted story types of the generated stories by different models." ], "reasoning": "By examining the table, we see that the 'Ours' model has the highest proportional distribution of D1 type stories at 90.12%.", "question": "Which model has the highest proportional distribution of D1 type stories?", "answer": "So, the answer is {\"answer\": \"Ours\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The GPT-2 (Pretrain) model generates the highest proportion of D2 type stories. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe GPT-2 (Pretrain) model generates the highest proportion of D2 type stories.\n" }, { "role": "assistant", "content": "The table shows that the GPT-2 (Pretrain) model has a proportional distribution of 40.8% for D2 type stories, which is the highest among all models for this story type. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3327.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Final prediction F1 score of our model on the auxiliary story classification task in terms of the four types of story sets respectively, and the proportional distribution of the predicted story types of the generated stories by different models." ], "reasoning": "The table shows that the GPT-2 (Pretrain) model has a proportional distribution of 40.8% for D2 type stories, which is the highest among all models for this story type.", "statement": "The GPT-2 (Pretrain) model generates the highest proportion of D2 type stories.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 8: Accuracy (Acc, %) of relation ranking and winning rates (%) of pair-wise comparisons which require selecting a more reasonable sentence from two candidates, each from Correct (C), Wrong (W), or Training (T) templates.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our model outperforms others in accuracy and has a higher winning rate in distinguishing correct templates from wrong ones.(Random chance scores 33.3%). Furthermore, our model prefers the correct template compared with the wrong one (winning rate of 71.91%), and has a close preference between the training and correct templates (winning rate of 55.76%). By contrast, GPT-2 without any external knowledge relies more on frequency to score relations, and thus can hardly tell the differences among different types of sentences. The results indicate that our model can capture not only simple relevance between head and tail entities/events, but also the specific causal relations." } ], "images": [ "scigen/train/3328.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Accuracy (Acc, %) of relation ranking and winning rates (%) of pair-wise comparisons which require selecting a more reasonable sentence from two candidates, each from Correct (C), Wrong (W), or Training (T) templates." ], "reasoning": "Our model outperforms others in accuracy and has a higher winning rate in distinguishing correct templates from wrong ones.", "description": "(Random chance scores 33.3%). Furthermore, our model prefers the correct template compared with the wrong one (winning rate of 71.91%), and has a close preference between the training and correct templates (winning rate of 55.76%). By contrast, GPT-2 without any external knowledge relies more on frequency to score relations, and thus can hardly tell the differences among different types of sentences. The results indicate that our model can capture not only simple relevance between head and tail entities/events, but also the specific causal relations.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 8: Accuracy (Acc, %) of relation ranking and winning rates (%) of pair-wise comparisons which require selecting a more reasonable sentence from two candidates, each from Correct (C), Wrong (W), or Training (T) templates.', solve this question and give detail solutions. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model has the highest accuracy?\n" }, { "role": "assistant", "content": "By comparing the accuracy values in the table, 'Ours' model has the highest accuracy of 67.07%. So, the answer is {\"answer\": \"Ours\"}" } ], "images": [ "scigen/train/3328.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Accuracy (Acc, %) of relation ranking and winning rates (%) of pair-wise comparisons which require selecting a more reasonable sentence from two candidates, each from Correct (C), Wrong (W), or Training (T) templates." ], "reasoning": "By comparing the accuracy values in the table, 'Ours' model has the highest accuracy of 67.07%.", "question": "Which model has the highest accuracy?", "answer": "So, the answer is {\"answer\": \"Ours\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: GPT-2 (Pretrain) has a higher winning rate in Comparison Pairs T vs. W than GPT-2 (Fine-tune)., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nGPT-2 (Pretrain) has a higher winning rate in Comparison Pairs T vs. W than GPT-2 (Fine-tune).\n" }, { "role": "assistant", "content": "The table shows that GPT-2 (Pretrain) has a winning rate of 49.87% in Comparison Pairs T vs. W, while GPT-2 (Fine-tune) has a winning rate of 56.01%. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3328.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Accuracy (Acc, %) of relation ranking and winning rates (%) of pair-wise comparisons which require selecting a more reasonable sentence from two candidates, each from Correct (C), Wrong (W), or Training (T) templates." ], "reasoning": "The table shows that GPT-2 (Pretrain) has a winning rate of 49.87% in Comparison Pairs T vs. W, while GPT-2 (Fine-tune) has a winning rate of 56.01%. Therefore, the statement is refuted.", "statement": "GPT-2 (Pretrain) has a higher winning rate in Comparison Pairs T vs. W than GPT-2 (Fine-tune).", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 10: Distribution of error types for different models.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Our model reduces chaotic logic and repetition errors more effectively than Fusion and GPT-2 (Fine-tune), but generating coherent stories remains challenging.We manually annotated four types of error from the lost stories: repetition (repeating the same scenes), unrelated entities or events (with some wrong keywords but a reasonable main plot), conflicting logic (wrong causal relation or temporal order), and chaotic scenes (difficult to understand). We can observe that unrelated entities/events and conflicting orders make up most of the errors for all the models. Compared with Fusion, GPT-2 (Fine-tune) reduces chaotic scenes effectively but still suffers from severe repetition. Equipped with external knowledge and multi-task learning, our model can further reduce chaotic logic and meanwhile avoid repetition. However, the analysis result illustrates that generating a coherent and reasonable story is challenging." } ], "images": [ "scigen/train/3329.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Distribution of error types for different models." ], "reasoning": "Our model reduces chaotic logic and repetition errors more effectively than Fusion and GPT-2 (Fine-tune), but generating coherent stories remains challenging.", "description": "We manually annotated four types of error from the lost stories: repetition (repeating the same scenes), unrelated entities or events (with some wrong keywords but a reasonable main plot), conflicting logic (wrong causal relation or temporal order), and chaotic scenes (difficult to understand). We can observe that unrelated entities/events and conflicting orders make up most of the errors for all the models. Compared with Fusion, GPT-2 (Fine-tune) reduces chaotic scenes effectively but still suffers from severe repetition. Equipped with external knowledge and multi-task learning, our model can further reduce chaotic logic and meanwhile avoid repetition. However, the analysis result illustrates that generating a coherent and reasonable story is challenging.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 10: Distribution of error types for different models.', answer the question below by showing each progressive step toward the answer. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model has the highest percentage of conflicting logic errors?\n" }, { "role": "assistant", "content": "By comparing the conflicting logic error percentages, GPT-2 (Fine-tune) has the highest percentage at 24.50%. So, the answer is {\"answer\": \"GPT-2 (Fine-tune)\"}." } ], "images": [ "scigen/train/3329.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Distribution of error types for different models." ], "reasoning": "By comparing the conflicting logic error percentages, GPT-2 (Fine-tune) has the highest percentage at 24.50%.", "question": "Which model has the highest percentage of conflicting logic errors?", "answer": "So, the answer is {\"answer\": \"GPT-2 (Fine-tune)\"}." }, { "messages": [ { "role": "user", "content": "Fusion has the lowest percentage of chaotic errors. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nFusion has the lowest percentage of chaotic errors.\n" }, { "role": "assistant", "content": "The table shows that 'Ours' has the lowest percentage of chaotic errors at 1.00%, while Fusion has 13.50%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3329.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Distribution of error types for different models." ], "reasoning": "The table shows that 'Ours' has the lowest percentage of chaotic errors at 1.00%, while Fusion has 13.50%.", "statement": "Fusion has the lowest percentage of chaotic errors.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Overall performance of different methods on the three datasets.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The R-GAT model improves the performance of GAT and outperforms baseline models like ASGCN and CDT. Incorporating R-GAT with BERT achieves a new state of the art, demonstrating its effectiveness in sentiment analysis.5.3.1 Overall Performance First, the R-GAT model outperforms most of the baseline models. Second, the performance of GAT can be significantly improved when incorporated with relational heads in our aspect-oriented dependency tree structure. It also outperforms the baseline models of ASGCN, and CDT, which also involve syntactic information in different ways. This proves that our R-GAT is better at encoding the syntactic information. Third, the basic BERT can already outperform all the existing ABSA models by significant margins, demonstrating the power of this large pre-trained model in this task. Nevertheless, after incorporating our R-GAT (R-GAT+BERT), this strong model sees further improvement and has achieved a new state of the art. These results have demonstrated the effectiveness of our R-GAT in capturing important syntactic structures for sentiment analysis." } ], "images": [ "scigen/train/3330.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Overall performance of different methods on the three datasets." ], "reasoning": "The R-GAT model improves the performance of GAT and outperforms baseline models like ASGCN and CDT. Incorporating R-GAT with BERT achieves a new state of the art, demonstrating its effectiveness in sentiment analysis.", "description": "5.3.1 Overall Performance First, the R-GAT model outperforms most of the baseline models. Second, the performance of GAT can be significantly improved when incorporated with relational heads in our aspect-oriented dependency tree structure. It also outperforms the baseline models of ASGCN, and CDT, which also involve syntactic information in different ways. This proves that our R-GAT is better at encoding the syntactic information. Third, the basic BERT can already outperform all the existing ABSA models by significant margins, demonstrating the power of this large pre-trained model in this task. Nevertheless, after incorporating our R-GAT (R-GAT+BERT), this strong model sees further improvement and has achieved a new state of the art. These results have demonstrated the effectiveness of our R-GAT in capturing important syntactic structures for sentiment analysis.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Overall performance of different methods on the three datasets.', answer the question below by showing each progressive step toward the answer. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model achieved the highest Restaurant Accuracy and what is its value?\n" }, { "role": "assistant", "content": "To find the model with the highest Restaurant Accuracy, look at the 'Restaurant Accuracy' column and identify the highest value. The highest value is 86.60, achieved by the R-GAT+BERT model. So, the answer is {\"answer\": \"R-GAT+BERT with 86.60\"}." } ], "images": [ "scigen/train/3330.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Overall performance of different methods on the three datasets." ], "reasoning": "To find the model with the highest Restaurant Accuracy, look at the 'Restaurant Accuracy' column and identify the highest value. The highest value is 86.60, achieved by the R-GAT+BERT model.", "question": "Which model achieved the highest Restaurant Accuracy and what is its value?", "answer": "So, the answer is {\"answer\": \"R-GAT+BERT with 86.60\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The R-GAT model outperforms the GAT model in Restaurant Macro-F1. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe R-GAT model outperforms the GAT model in Restaurant Macro-F1.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Restaurant Macro-F1 values of the R-GAT and GAT models. The R-GAT model has a Restaurant Macro-F1 of 76.08, while the GAT model has a Restaurant Macro-F1 of 67.17. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3330.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Overall performance of different methods on the three datasets." ], "reasoning": "To verify this statement, compare the Restaurant Macro-F1 values of the R-GAT and GAT models. The R-GAT model has a Restaurant Macro-F1 of 76.08, while the GAT model has a Restaurant Macro-F1 of 67.17. Therefore, the statement is supported.", "statement": "The R-GAT model outperforms the GAT model in Restaurant Macro-F1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Results of R-GAT based on two different parsers, where UAS and LAS are metrics to evaluate the parsers and higher scores mean better performance.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The Biaffine parser achieves higher sentiment classification accuracies across all datasets compared to the Stanford parser, indicating its superior performance.. From the table, we can find that the better Biaffine parser results in higher sentiment classification accuracies. Moreover, it further implies that while existing parsers can capture most of the syntactic structures correctly, our method has the potential to be further improved with the advances of parsing techniques." } ], "images": [ "scigen/train/3331.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of R-GAT based on two different parsers, where UAS and LAS are metrics to evaluate the parsers and higher scores mean better performance." ], "reasoning": "The Biaffine parser achieves higher sentiment classification accuracies across all datasets compared to the Stanford parser, indicating its superior performance.", "description": ". From the table, we can find that the better Biaffine parser results in higher sentiment classification accuracies. Moreover, it further implies that while existing parsers can capture most of the syntactic structures correctly, our method has the potential to be further improved with the advances of parsing techniques.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: Results of R-GAT based on two different parsers, where UAS and LAS are metrics to evaluate the parsers and higher scores mean better performance.' and methodically tackle the question that follows. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich parser has the highest performance in the Laptop dataset?\n" }, { "role": "assistant", "content": "The table shows the performance of two parsers, Stanford and Biaffine, across different datasets. By comparing the values in the 'Dataset Laptop' column, we see that the Biaffine parser has a higher score (0.7742) than the Stanford parser (0.7539). So, the answer is {\"answer\": \"Biaffine\"}" } ], "images": [ "scigen/train/3331.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of R-GAT based on two different parsers, where UAS and LAS are metrics to evaluate the parsers and higher scores mean better performance." ], "reasoning": "The table shows the performance of two parsers, Stanford and Biaffine, across different datasets. By comparing the values in the 'Dataset Laptop' column, we see that the Biaffine parser has a higher score (0.7742) than the Stanford parser (0.7539).", "question": "Which parser has the highest performance in the Laptop dataset?", "answer": "So, the answer is {\"answer\": \"Biaffine\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Stanford parser has a higher performance in the Restaurant dataset compared to the Biaffine parser., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Stanford parser has a higher performance in the Restaurant dataset compared to the Biaffine parser.\n" }, { "role": "assistant", "content": "The table shows the performance of the Stanford and Biaffine parsers in the Restaurant dataset. The Stanford parser has a score of 0.8133, while the Biaffine parser has a higher score of 0.8330. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3331.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of R-GAT based on two different parsers, where UAS and LAS are metrics to evaluate the parsers and higher scores mean better performance." ], "reasoning": "The table shows the performance of the Stanford and Biaffine parsers in the Restaurant dataset. The Stanford parser has a score of 0.8133, while the Biaffine parser has a higher score of 0.8330.", "statement": "The Stanford parser has a higher performance in the Restaurant dataset compared to the Biaffine parser.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: Results of ablation study, where “Ordinary” means using ordinary dependency trees, “Reshaped” denotes using the aspect-oriented trees, and “*-n:con” denote the aspect-oriented tree without using n:con.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The ablation study shows that the R-GAT method with the reshaped tree structure performs best, and removing the virtual relation n:con significantly reduces performance.Ablation Study We further conduct an ablation study to evaluate the influence of the aspect-oriented dependency tree structure and the relational heads. We present the results on ordinary dependency trees for comparison. Furthermore, after removing the virtual relation n:con, the performance of R-GAT drops considerably. We manually examined the misclassified samples and found that most of them can be attributed to poor parsing results where aspects and their opinion words are incorrectly connected. This study validates that adding the n:con relation can effectively alleviate the parsing problem and allows our model to be robust. In this paper, the maximal number of n is set to 4 according to empirical tests. Other values of n are also explored but the results are not any better. This may suggest that words with too long dependency distances from the target aspect are unlikely to be useful for this task." } ], "images": [ "scigen/train/3332.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of ablation study, where “Ordinary” means using ordinary dependency trees, “Reshaped” denotes using the aspect-oriented trees, and “*-n:con” denote the aspect-oriented tree without using n:con." ], "reasoning": "The ablation study shows that the R-GAT method with the reshaped tree structure performs best, and removing the virtual relation n:con significantly reduces performance.", "description": "Ablation Study We further conduct an ablation study to evaluate the influence of the aspect-oriented dependency tree structure and the relational heads. We present the results on ordinary dependency trees for comparison. Furthermore, after removing the virtual relation n:con, the performance of R-GAT drops considerably. We manually examined the misclassified samples and found that most of them can be attributed to poor parsing results where aspects and their opinion words are incorrectly connected. This study validates that adding the n:con relation can effectively alleviate the parsing problem and allows our model to be robust. In this paper, the maximal number of n is set to 4 according to empirical tests. Other values of n are also explored but the results are not any better. This may suggest that words with too long dependency distances from the target aspect are unlikely to be useful for this task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 4: Results of ablation study, where “Ordinary” means using ordinary dependency trees, “Reshaped” denotes using the aspect-oriented trees, and “*-n:con” denote the aspect-oriented tree without using n:con.', and sequentially solve the question, writing out each step of your reasoning process. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the performance of R-GAT on the Restaurant dataset when using the reshaped tree structure?\n" }, { "role": "assistant", "content": "The table shows the performance of different methods on various datasets. For the Restaurant dataset with the reshaped tree structure, the R-GAT method achieves a score of 83.30. So, the answer is {\"answer\": \"83.30\"}." } ], "images": [ "scigen/train/3332.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of ablation study, where “Ordinary” means using ordinary dependency trees, “Reshaped” denotes using the aspect-oriented trees, and “*-n:con” denote the aspect-oriented tree without using n:con." ], "reasoning": "The table shows the performance of different methods on various datasets. For the Restaurant dataset with the reshaped tree structure, the R-GAT method achieves a score of 83.30.", "question": "What is the performance of R-GAT on the Restaurant dataset when using the reshaped tree structure?", "answer": "So, the answer is {\"answer\": \"83.30\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nRemoving the virtual relation n:con improves the performance of R-GAT on the Twitter dataset.\n" }, { "role": "assistant", "content": "The table shows that removing the virtual relation n:con results in a lower performance (70.95) compared to the R-GAT method with the reshaped tree structure (75.57) on the Twitter dataset. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3332.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of ablation study, where “Ordinary” means using ordinary dependency trees, “Reshaped” denotes using the aspect-oriented trees, and “*-n:con” denote the aspect-oriented tree without using n:con." ], "reasoning": "The table shows that removing the virtual relation n:con results in a lower performance (70.95) compared to the R-GAT method with the reshaped tree structure (75.57) on the Twitter dataset.", "statement": "Removing the virtual relation n:con improves the performance of R-GAT on the Twitter dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: Results of error analysis of R-GAT and R-GAT+BERT on 100 misclassified examples from the Restaurant dataset. The reasons are classified into four categories, for which a sample is given. The upper table corresponds to the results of R-GAT and the lower one corresponds to R-GAT+BERT.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The error analysis of misclassified examples from two models reveals that comprehension difficulties, advice without sentiment clues, and double negation expressions are significant challenges., we randomly select 100 misclassified examples by two models (R-GAT and R-GAT+BERT) from the Restaurant dataset. After looking into these bad cases, we find the reasons behind can be classified into four categories. The second category is due to the difficulty in comprehension, which may demand deep language understanding techniques such as natural language inference. The third category is caused by the advice which only recommend or disrecommend people to try, with no obvious clues in the sentences indicating the sentiments. The fourth category is caused by double negation expression, which is also difficult for current models. Through the error analysis, we can note that although current models have achieved appealing progress, there are still some complicated sentences beyond their capabilities. There ought to be more advanced natural language processing techniques and learning algorithms developed to further address them." } ], "images": [ "scigen/train/3333.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results of error analysis of R-GAT and R-GAT+BERT on 100 misclassified examples from the Restaurant dataset. The reasons are classified into four categories, for which a sample is given. The upper table corresponds to the results of R-GAT and the lower one corresponds to R-GAT+BERT." ], "reasoning": "The error analysis of misclassified examples from two models reveals that comprehension difficulties, advice without sentiment clues, and double negation expressions are significant challenges.", "description": ", we randomly select 100 misclassified examples by two models (R-GAT and R-GAT+BERT) from the Restaurant dataset. After looking into these bad cases, we find the reasons behind can be classified into four categories. The second category is due to the difficulty in comprehension, which may demand deep language understanding techniques such as natural language inference. The third category is caused by the advice which only recommend or disrecommend people to try, with no obvious clues in the sentences indicating the sentiments. The fourth category is caused by double negation expression, which is also difficult for current models. Through the error analysis, we can note that although current models have achieved appealing progress, there are still some complicated sentences beyond their capabilities. There ought to be more advanced natural language processing techniques and learning algorithms developed to further address them.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 5: Results of error analysis of R-GAT and R-GAT+BERT on 100 misclassified examples from the Restaurant dataset. The reasons are classified into four categories, for which a sample is given. The upper table corresponds to the results of R-GAT and the lower one corresponds to R-GAT+BERT.' and proceed to solve the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat percentage of misclassified examples are due to comprehension difficulties and advice without sentiment clues combined?\n" }, { "role": "assistant", "content": "The table shows that 32% of misclassified examples are due to comprehension difficulties and 6% are due to advice without sentiment clues. Adding these percentages gives 32% + 6% = 38%. So, the answer is {\"answer\": \"38\"}" } ], "images": [ "scigen/train/3333.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results of error analysis of R-GAT and R-GAT+BERT on 100 misclassified examples from the Restaurant dataset. The reasons are classified into four categories, for which a sample is given. The upper table corresponds to the results of R-GAT and the lower one corresponds to R-GAT+BERT." ], "reasoning": "The table shows that 32% of misclassified examples are due to comprehension difficulties and 6% are due to advice without sentiment clues. Adding these percentages gives 32% + 6% = 38%.", "question": "What percentage of misclassified examples are due to comprehension difficulties and advice without sentiment clues combined?", "answer": "So, the answer is {\"answer\": \"38\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe majority of misclassified examples are due to comprehension difficulties.\n" }, { "role": "assistant", "content": "The table shows that 32% of misclassified examples are due to comprehension difficulties, while 46% are due to neutral examples. Since 46% is greater than 32%, the majority of misclassified examples are not due to comprehension difficulties. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3333.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results of error analysis of R-GAT and R-GAT+BERT on 100 misclassified examples from the Restaurant dataset. The reasons are classified into four categories, for which a sample is given. The upper table corresponds to the results of R-GAT and the lower one corresponds to R-GAT+BERT." ], "reasoning": "The table shows that 32% of misclassified examples are due to comprehension difficulties, while 46% are due to neutral examples. Since 46% is greater than 32%, the majority of misclassified examples are not due to comprehension difficulties.", "statement": "The majority of misclassified examples are due to comprehension difficulties.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: Results of error analysis of R-GAT and R-GAT+BERT on 100 misclassified examples from the Restaurant dataset. The reasons are classified into four categories, for which a sample is given. The upper table corresponds to the results of R-GAT and the lower one corresponds to R-GAT+BERT.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The error analysis of 100 misclassified examples by two models from the Restaurant dataset reveals that comprehension difficulties, advice without clear sentiment clues, and double negation expressions are significant challenges for current models., we randomly select 100 misclassified examples by two models (R-GAT and R-GAT+BERT) from the Restaurant dataset. After looking into these bad cases, we find the reasons behind can be classified into four categories. The second category is due to the difficulty in comprehension, which may demand deep language understanding techniques such as natural language inference. The third category is caused by the advice which only recommend or disrecommend people to try, with no obvious clues in the sentences indicating the sentiments. The fourth category is caused by double negation expression, which is also difficult for current models. Through the error analysis, we can note that although current models have achieved appealing progress, there are still some complicated sentences beyond their capabilities. There ought to be more advanced natural language processing techniques and learning algorithms developed to further address them." } ], "images": [ "scigen/train/3334.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results of error analysis of R-GAT and R-GAT+BERT on 100 misclassified examples from the Restaurant dataset. The reasons are classified into four categories, for which a sample is given. The upper table corresponds to the results of R-GAT and the lower one corresponds to R-GAT+BERT." ], "reasoning": "The error analysis of 100 misclassified examples by two models from the Restaurant dataset reveals that comprehension difficulties, advice without clear sentiment clues, and double negation expressions are significant challenges for current models.", "description": ", we randomly select 100 misclassified examples by two models (R-GAT and R-GAT+BERT) from the Restaurant dataset. After looking into these bad cases, we find the reasons behind can be classified into four categories. The second category is due to the difficulty in comprehension, which may demand deep language understanding techniques such as natural language inference. The third category is caused by the advice which only recommend or disrecommend people to try, with no obvious clues in the sentences indicating the sentiments. The fourth category is caused by double negation expression, which is also difficult for current models. Through the error analysis, we can note that although current models have achieved appealing progress, there are still some complicated sentences beyond their capabilities. There ought to be more advanced natural language processing techniques and learning algorithms developed to further address them.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Results of error analysis of R-GAT and R-GAT+BERT on 100 misclassified examples from the Restaurant dataset. The reasons are classified into four categories, for which a sample is given. The upper table corresponds to the results of R-GAT and the lower one corresponds to R-GAT+BERT.\nQuestion: What is the total number of misclassified examples caused by comprehension and advice?\nSolve the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the total number of misclassified examples caused by comprehension and advice, add the number of examples in the 'Comprehension' category (31) to the number in the 'Advice' category (5). So, the answer is {\"answer\": \"36\"}" } ], "images": [ "scigen/train/3334.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results of error analysis of R-GAT and R-GAT+BERT on 100 misclassified examples from the Restaurant dataset. The reasons are classified into four categories, for which a sample is given. The upper table corresponds to the results of R-GAT and the lower one corresponds to R-GAT+BERT." ], "reasoning": "To find the total number of misclassified examples caused by comprehension and advice, add the number of examples in the 'Comprehension' category (31) to the number in the 'Advice' category (5).", "question": "What is the total number of misclassified examples caused by comprehension and advice?", "answer": "So, the answer is {\"answer\": \"36\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The majority of misclassified examples are due to comprehension difficulties., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe majority of misclassified examples are due to comprehension difficulties.\n" }, { "role": "assistant", "content": "The table shows that 31 out of 100 misclassified examples are due to comprehension difficulties, which is the highest number among the categories listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3334.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results of error analysis of R-GAT and R-GAT+BERT on 100 misclassified examples from the Restaurant dataset. The reasons are classified into four categories, for which a sample is given. The upper table corresponds to the results of R-GAT and the lower one corresponds to R-GAT+BERT." ], "reasoning": "The table shows that 31 out of 100 misclassified examples are due to comprehension difficulties, which is the highest number among the categories listed.", "statement": "The majority of misclassified examples are due to comprehension difficulties.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: PESQ scores, number of parameters and inference times. Inference time is reported with 330 test utterances with a batch size of 10.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "DEF-BERT consistently outperforms WPE and achieves comparable performance with CL-BLSTM with fewer parameters.We first compare the performance of WPE with BLSTM based methods and a basic BERT (DEF-BERT) based method. It is clear that DEF-BERT outperforms WPE consistently and achieves comparable performance with CL-BLSTM method with less parameters (16.9M vs 18.2M). The third experiment compares BLSTM-based sequence models with BERT-based sequence models using the same pre-sequence layers CNN2d, CL and CNN1d. BERT benefits from a stack of CNN2d and LSTM more than BLSTM, probably due to BERT favoring positional information." } ], "images": [ "scigen/train/3335.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: PESQ scores, number of parameters and inference times. Inference time is reported with 330 test utterances with a batch size of 10." ], "reasoning": "DEF-BERT consistently outperforms WPE and achieves comparable performance with CL-BLSTM with fewer parameters.", "description": "We first compare the performance of WPE with BLSTM based methods and a basic BERT (DEF-BERT) based method. It is clear that DEF-BERT outperforms WPE consistently and achieves comparable performance with CL-BLSTM method with less parameters (16.9M vs 18.2M). The third experiment compares BLSTM-based sequence models with BERT-based sequence models using the same pre-sequence layers CNN2d, CL and CNN1d. BERT benefits from a stack of CNN2d and LSTM more than BLSTM, probably due to BERT favoring positional information.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 2: PESQ scores, number of parameters and inference times. Inference time is reported with 330 test utterances with a batch size of 10.\nQuestion: Which method achieves the highest performance with the least parameters?\n" }, { "role": "assistant", "content": "DEF-BERT achieves comparable performance with CL-BLSTM but with fewer parameters (16.9M vs 18.2M). So, the answer is {\"answer\": \"DEF-BERT\"}" } ], "images": [ "scigen/train/3335.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: PESQ scores, number of parameters and inference times. Inference time is reported with 330 test utterances with a batch size of 10." ], "reasoning": "DEF-BERT achieves comparable performance with CL-BLSTM but with fewer parameters (16.9M vs 18.2M).", "question": "Which method achieves the highest performance with the least parameters?", "answer": "So, the answer is {\"answer\": \"DEF-BERT\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nBERT benefits more from a stack of CNN2d and LSTM than BLSTM.\n" }, { "role": "assistant", "content": "The description states that BERT benefits more from a stack of CNN2d and LSTM than BLSTM, and the table shows that BERT has higher performance metrics in these configurations. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3335.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: PESQ scores, number of parameters and inference times. Inference time is reported with 330 test utterances with a batch size of 10." ], "reasoning": "The description states that BERT benefits more from a stack of CNN2d and LSTM than BLSTM, and the table shows that BERT has higher performance metrics in these configurations.", "statement": "BERT benefits more from a stack of CNN2d and LSTM than BLSTM.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: PESQ of CNN1d-BERT with Griffin-Lim 32 iterations (GL) and Neural vocoder (NV)', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Parallel WaveGan on GPU is about 10 times faster than Griffin-Lim with comparable performance, but neural vocoders are rarely used for speech dereverberation and denoising.Griffin-Lim with 32 iterations obtains an real time factor (RTF) of 0.319, whereas Parallel WaveGan on GPU obtains an RTF of 0.027. Decoding with Parallel WaveGan is about 10 times faster than Griffin-Lim with comparable performance. However, decoding using a neural vocoder is rarely considered in the context of speech dereverberation and denoising." } ], "images": [ "scigen/train/3336.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: PESQ of CNN1d-BERT with Griffin-Lim 32 iterations (GL) and Neural vocoder (NV)" ], "reasoning": "Parallel WaveGan on GPU is about 10 times faster than Griffin-Lim with comparable performance, but neural vocoders are rarely used for speech dereverberation and denoising.", "description": "Griffin-Lim with 32 iterations obtains an real time factor (RTF) of 0.319, whereas Parallel WaveGan on GPU obtains an RTF of 0.027. Decoding with Parallel WaveGan is about 10 times faster than Griffin-Lim with comparable performance. However, decoding using a neural vocoder is rarely considered in the context of speech dereverberation and denoising.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 3: PESQ of CNN1d-BERT with Griffin-Lim 32 iterations (GL) and Neural vocoder (NV)', solve the following question step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the real time factor (RTF) for Griffin-Lim at T60 of 0.6?\n" }, { "role": "assistant", "content": "The table shows the RTF values for Griffin-Lim (GL) and Neural Vocoder (NV) at different T60 values. For T60 of 0.6, the RTF for Griffin-Lim is 1.752. So, the answer is {\"answer\": \"1.752\"}." } ], "images": [ "scigen/train/3336.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: PESQ of CNN1d-BERT with Griffin-Lim 32 iterations (GL) and Neural vocoder (NV)" ], "reasoning": "The table shows the RTF values for Griffin-Lim (GL) and Neural Vocoder (NV) at different T60 values. For T60 of 0.6, the RTF for Griffin-Lim is 1.752.", "question": "What is the real time factor (RTF) for Griffin-Lim at T60 of 0.6?", "answer": "So, the answer is {\"answer\": \"1.752\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nNeural vocoder has a lower RTF than Griffin-Lim at T60 of 0.3.\n" }, { "role": "assistant", "content": "The table shows the RTF values for Griffin-Lim (GL) and Neural Vocoder (NV) at different T60 values. For T60 of 0.3, the RTF for Neural Vocoder is 1.903, which is higher than Griffin-Lim's 1.941. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3336.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: PESQ of CNN1d-BERT with Griffin-Lim 32 iterations (GL) and Neural vocoder (NV)" ], "reasoning": "The table shows the RTF values for Griffin-Lim (GL) and Neural Vocoder (NV) at different T60 values. For T60 of 0.3, the RTF for Neural Vocoder is 1.903, which is higher than Griffin-Lim's 1.941.", "statement": "Neural vocoder has a lower RTF than Griffin-Lim at T60 of 0.3.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Impact of constraints on BiDAF. Each score represents the average span F1 on our test set (i.e. official dev set) among 3 random runs. Constrained models and ELMo models are built on top of BiDAF. We set ρ=2 for both R1 and R2 across all percentages.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Our framework improves model performance using rules R1 and R2, especially with smaller training sets, and the improvement diminishes with larger datasets.Can our framework use rules over named neurons to improve model performance? The answer is yes. We experiment with rules R1 and R2 on incrementally larger training data. We see that our framework can indeed use logic to inform model learning and prediction without any extra trainable parameters needed. The improvement is particularly strong with small training sets. With more data, neural models are less reliant on external information. As a result, the improvement with larger datasets is smaller." } ], "images": [ "scigen/train/3337.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Impact of constraints on BiDAF. Each score represents the average span F1 on our test set (i.e. official dev set) among 3 random runs. Constrained models and ELMo models are built on top of BiDAF. We set ρ=2 for both R1 and R2 across all percentages." ], "reasoning": "Our framework improves model performance using rules R1 and R2, especially with smaller training sets, and the improvement diminishes with larger datasets.", "description": "Can our framework use rules over named neurons to improve model performance? The answer is yes. We experiment with rules R1 and R2 on incrementally larger training data. We see that our framework can indeed use logic to inform model learning and prediction without any extra trainable parameters needed. The improvement is particularly strong with small training sets. With more data, neural models are less reliant on external information. As a result, the improvement with larger datasets is smaller.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 2: Impact of constraints on BiDAF. Each score represents the average span F1 on our test set (i.e. official dev set) among 3 random runs. Constrained models and ELMo models are built on top of BiDAF. We set ρ=2 for both R1 and R2 across all percentages.\nQuestion: What is the performance improvement of the model with 10% training data when using both ELMo and R1 compared to BiDAF alone?\n" }, { "role": "assistant", "content": "The performance with 10% training data using BiDAF alone is 57.5. When using both ELMo and R1, the performance is 73.0. The improvement is 73.0 - 57.5. So, the answer is {\"answer\": \"15.5\"}" } ], "images": [ "scigen/train/3337.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Impact of constraints on BiDAF. Each score represents the average span F1 on our test set (i.e. official dev set) among 3 random runs. Constrained models and ELMo models are built on top of BiDAF. We set ρ=2 for both R1 and R2 across all percentages." ], "reasoning": "The performance with 10% training data using BiDAF alone is 57.5. When using both ELMo and R1, the performance is 73.0. The improvement is 73.0 - 57.5.", "question": "What is the performance improvement of the model with 10% training data when using both ELMo and R1 compared to BiDAF alone?", "answer": "So, the answer is {\"answer\": \"15.5\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model's performance with 40% training data using R1 is higher than using R2.\n" }, { "role": "assistant", "content": "With 40% training data, the performance using R1 is 72.6, while using R2 is 71.9. Therefore, the performance with R1 is indeed higher than with R2. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3337.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Impact of constraints on BiDAF. Each score represents the average span F1 on our test set (i.e. official dev set) among 3 random runs. Constrained models and ELMo models are built on top of BiDAF. We set ρ=2 for both R1 and R2 across all percentages." ], "reasoning": "With 40% training data, the performance using R1 is 72.6, while using R2 is 71.9. Therefore, the performance with R1 is indeed higher than with R2.", "statement": "The model's performance with 40% training data using R1 is higher than using R2.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Impact of constraints on L-DAtt network. Each score represents the average accuracy on SNLI test set among 3 random runs. For both N1 and N2, we set ρ=(8,8,8,8,4) for the five different percentages. For the noisy constraint N3, ρ=(2,2,1,1,1).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "With smaller datasets, constraints like N1 and N2 provide useful inductive bias, but with larger datasets, the models perform on par with the baseline.S4SS2SSS0Px3 How does our framework perform with large training data? The SNLI dataset is a large dataset with over half-million examples. The straightforward constraint N1 performs strongly with ≤2% data while its conservative alternative N2 works better with a larger set. However, with full dataset, our augmented models perform only on par with baseline even with lowered scaling factor ρ. These observations suggest that if a large dataset is available, it may be better to believe the data, but with smaller datasets, constraints can provide useful inductive bias for the models." } ], "images": [ "scigen/train/3338.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Impact of constraints on L-DAtt network. Each score represents the average accuracy on SNLI test set among 3 random runs. For both N1 and N2, we set ρ=(8,8,8,8,4) for the five different percentages. For the noisy constraint N3, ρ=(2,2,1,1,1)." ], "reasoning": "With smaller datasets, constraints like N1 and N2 provide useful inductive bias, but with larger datasets, the models perform on par with the baseline.", "description": "S4SS2SSS0Px3 How does our framework perform with large training data? The SNLI dataset is a large dataset with over half-million examples. The straightforward constraint N1 performs strongly with ≤2% data while its conservative alternative N2 works better with a larger set. However, with full dataset, our augmented models perform only on par with baseline even with lowered scaling factor ρ. These observations suggest that if a large dataset is available, it may be better to believe the data, but with smaller datasets, constraints can provide useful inductive bias for the models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Impact of constraints on L-DAtt network. Each score represents the average accuracy on SNLI test set among 3 random runs. For both N1 and N2, we set ρ=(8,8,8,8,4) for the five different percentages. For the noisy constraint N3, ρ=(2,2,1,1,1).', unfold the question presented below, detailing every step of your calculation. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the performance of the model with the N1 constraint when trained with 2% of the data?\n" }, { "role": "assistant", "content": "To find the performance of the model with the N1 constraint when trained with 2% of the data, look at the intersection of the 2% row and the +N1 column. So, the answer is {\"answer\": \"70.5\"}." } ], "images": [ "scigen/train/3338.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Impact of constraints on L-DAtt network. Each score represents the average accuracy on SNLI test set among 3 random runs. For both N1 and N2, we set ρ=(8,8,8,8,4) for the five different percentages. For the noisy constraint N3, ρ=(2,2,1,1,1)." ], "reasoning": "To find the performance of the model with the N1 constraint when trained with 2% of the data, look at the intersection of the 2% row and the +N1 column.", "question": "What is the performance of the model with the N1 constraint when trained with 2% of the data?", "answer": "So, the answer is {\"answer\": \"70.5\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model with the N2 constraint performs better than the baseline when trained with 5% of the data.\n" }, { "role": "assistant", "content": "To verify this statement, compare the performance values in the 5% row for the L-DAtt (baseline) and +N2 columns. The baseline is 73.4, and the N2 constraint is 76.6, which is higher. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3338.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Impact of constraints on L-DAtt network. Each score represents the average accuracy on SNLI test set among 3 random runs. For both N1 and N2, we set ρ=(8,8,8,8,4) for the five different percentages. For the noisy constraint N3, ρ=(2,2,1,1,1)." ], "reasoning": "To verify this statement, compare the performance values in the 5% row for the L-DAtt (baseline) and +N2 columns. The baseline is 73.4, and the N2 constraint is 76.6, which is higher.", "statement": "The model with the N2 constraint performs better than the baseline when trained with 5% of the data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Impact of constraints on BiLSTM tagger. Each score represents the average accuracy on test set of 3 random runs. The columns of +CRF, +C1:5, and +CRF,C1:5 are on top of the BiLSTM baseline. For C1:4, ρ=4 for all percentages. For C5, ρ=16.'.\n" }, { "role": "assistant", "content": "Local augmentation combined with global inference outperforms other models, especially with full data, while CRF alone performs worse with less than 40% data.S4SS3SSS0Px3 How does local augmentation compare with global inference? While a first-order Markov model (\\eg, the BiLSTM-CRF) can learn pairwise constraints such as C1:4, we see that our framework can better inform the model. Interestingly, the CRF model performed even worse than the baseline with ≤40% data. This suggests that global inference relies on more training examples to learn its scoring function. In contrast, our constrained models performed strongly even with small training sets. And by combining these two orthogonal methods, our locally augmented CRF performed the best with full data." } ], "images": [ "scigen/train/3339.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Impact of constraints on BiLSTM tagger. Each score represents the average accuracy on test set of 3 random runs. The columns of +CRF, +C1:5, and +CRF,C1:5 are on top of the BiLSTM baseline. For C1:4, ρ=4 for all percentages. For C5, ρ=16." ], "reasoning": "Local augmentation combined with global inference outperforms other models, especially with full data, while CRF alone performs worse with less than 40% data.", "description": "S4SS3SSS0Px3 How does local augmentation compare with global inference? While a first-order Markov model (\\eg, the BiLSTM-CRF) can learn pairwise constraints such as C1:4, we see that our framework can better inform the model. Interestingly, the CRF model performed even worse than the baseline with ≤40% data. This suggests that global inference relies on more training examples to learn its scoring function. In contrast, our constrained models performed strongly even with small training sets. And by combining these two orthogonal methods, our locally augmented CRF performed the best with full data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 4: Impact of constraints on BiLSTM tagger. Each score represents the average accuracy on test set of 3 random runs. The columns of +CRF, +C1:5, and +CRF,C1:5 are on top of the BiLSTM baseline. For C1:4, ρ=4 for all percentages. For C5, ρ=16.' to sequentially solve the question presented below. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich model performed the best with 40% training data?\n" }, { "role": "assistant", "content": "From the table, with 40% training data, the model '+CRF, C1:5' has the highest score of 93.5. So, the answer is {\"answer\": \"+CRF, C1:5\"}." } ], "images": [ "scigen/train/3339.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Impact of constraints on BiLSTM tagger. Each score represents the average accuracy on test set of 3 random runs. The columns of +CRF, +C1:5, and +CRF,C1:5 are on top of the BiLSTM baseline. For C1:4, ρ=4 for all percentages. For C5, ρ=16." ], "reasoning": "From the table, with 40% training data, the model '+CRF, C1:5' has the highest score of 93.5.", "question": "Which model performed the best with 40% training data?", "answer": "So, the answer is {\"answer\": \"+CRF, C1:5\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The BiLSTM model performed better than the CRF model with 20% training data., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe BiLSTM model performed better than the CRF model with 20% training data.\n" }, { "role": "assistant", "content": "From the table, with 20% training data, the BiLSTM model scored 90.9, while the CRF model scored 90.8. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3339.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Impact of constraints on BiLSTM tagger. Each score represents the average accuracy on test set of 3 random runs. The columns of +CRF, +C1:5, and +CRF,C1:5 are on top of the BiLSTM baseline. For C1:4, ρ=4 for all percentages. For C5, ρ=16." ], "reasoning": "From the table, with 20% training data, the BiLSTM model scored 90.9, while the CRF model scored 90.8.", "statement": "The BiLSTM model performed better than the CRF model with 20% training data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: F1-score when excluding some features from all feature set'.\n" }, { "role": "assistant", "content": "Excluding the Jaccard feature significantly drops the F1-score to around 0.5, indicating its crucial contribution to SVM-Rank performance.: The contribution of each feature was investigated by using leave-one-out test. However when excluding Jaccard, F1-score drops to around 0.5. In contrast, when other features are excluded individually from the feature set, the result remains stable or goes up slightly. From this result, we conclude that Jaccard feature significantly contributes to SVM-Rank performance." } ], "images": [ "scigen/train/3340.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: F1-score when excluding some features from all feature set" ], "reasoning": "Excluding the Jaccard feature significantly drops the F1-score to around 0.5, indicating its crucial contribution to SVM-Rank performance.", "description": ": The contribution of each feature was investigated by using leave-one-out test. However when excluding Jaccard, F1-score drops to around 0.5. In contrast, when other features are excluded individually from the feature set, the result remains stable or goes up slightly. From this result, we conclude that Jaccard feature significantly contributes to SVM-Rank performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 4: F1-score when excluding some features from all feature set'. Solve the following question based on the table.\n\nQuestion: What happens to the F1-score when the Jaccard feature is excluded?\nLet's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that excluding the Jaccard feature results in an F1-score of 0.5069, which is a significant drop compared to the other features. So, the answer is {\"answer\": \"0.5069\"}." } ], "images": [ "scigen/train/3340.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: F1-score when excluding some features from all feature set" ], "reasoning": "The table shows that excluding the Jaccard feature results in an F1-score of 0.5069, which is a significant drop compared to the other features.", "question": "What happens to the F1-score when the Jaccard feature is excluded?", "answer": "So, the answer is {\"answer\": \"0.5069\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Excluding the Jaccard feature results in the lowest F1-score among all features., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nExcluding the Jaccard feature results in the lowest F1-score among all features.\n" }, { "role": "assistant", "content": "The table indicates that excluding the Jaccard feature results in an F1-score of 0.5069, which is the lowest compared to excluding other features. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3340.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: F1-score when excluding some features from all feature set" ], "reasoning": "The table indicates that excluding the Jaccard feature results in an F1-score of 0.5069, which is the lowest compared to excluding other features.", "statement": "Excluding the Jaccard feature results in the lowest F1-score among all features.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: IR results with various methods in COLIEE 2016.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Ignoring references and focusing on individual articles improved the system's performance.In other words, an article could refer to the whole other articles or to their paragraphs. In our experiment, however, we found that this approach makes the system confused to rank articles and leads to worse performance. Because of that, we ignored the reference and only took into account individual articles themselves." } ], "images": [ "scigen/train/3341.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: IR results with various methods in COLIEE 2016." ], "reasoning": "Ignoring references and focusing on individual articles improved the system's performance.", "description": "In other words, an article could refer to the whole other articles or to their paragraphs. In our experiment, however, we found that this approach makes the system confused to rank articles and leads to worse performance. Because of that, we ignored the reference and only took into account individual articles themselves.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 5: IR results with various methods in COLIEE 2016.\nQuestion: Which method achieved the highest F1-score?\n" }, { "role": "assistant", "content": "By comparing the F1-scores of all methods, the 'With splitting + No references' method has the highest F1-score of 0.6087. So, the answer is {\"answer\": \"With splitting + No references\"}" } ], "images": [ "scigen/train/3341.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: IR results with various methods in COLIEE 2016." ], "reasoning": "By comparing the F1-scores of all methods, the 'With splitting + No references' method has the highest F1-score of 0.6087.", "question": "Which method achieved the highest F1-score?", "answer": "So, the answer is {\"answer\": \"With splitting + No references\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The method 'Non-splitting + With references' achieved the lowest F1-score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe method 'Non-splitting + With references' achieved the lowest F1-score.\n" }, { "role": "assistant", "content": "By comparing the F1-scores of all methods, the 'Non-splitting + With references' method has the lowest F1-score of 0.5326. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3341.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: IR results with various methods in COLIEE 2016." ], "reasoning": "By comparing the F1-scores of all methods, the 'Non-splitting + With references' method has the lowest F1-score of 0.5326.", "statement": "The method 'Non-splitting + With references' achieved the lowest F1-score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 7: Phase 2 results with different models'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "CNN models are sensitive to initial parameter values, and CNN with LSI performs better than CNN with TF-IDF due to the sparsity of TF-IDF vectors.In CNN models, we found that these models are sensitive to the initial value of parameters. Different values lead to large difference in results (± 5%). Therefore, each model was run n times (n=10) and we chose the best-optimized parameters against the validation set. Also, CNN with LSI produces a better result as opposed to CNN with TF-IDF. We suspect that this is because TF-IDF vector is large but quite sparse (most values are zero), therefore it increases the number of parameters in CNN and consequently makes the model to be overfitted easily." } ], "images": [ "scigen/train/3342.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Phase 2 results with different models" ], "reasoning": "CNN models are sensitive to initial parameter values, and CNN with LSI performs better than CNN with TF-IDF due to the sparsity of TF-IDF vectors.", "description": "In CNN models, we found that these models are sensitive to the initial value of parameters. Different values lead to large difference in results (± 5%). Therefore, each model was run n times (n=10) and we chose the best-optimized parameters against the validation set. Also, CNN with LSI produces a better result as opposed to CNN with TF-IDF. We suspect that this is because TF-IDF vector is large but quite sparse (most values are zero), therefore it increases the number of parameters in CNN and consequently makes the model to be overfitted easily.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nTable title: Table 7: Phase 2 results with different models\n\nQuestion: Which method achieved the highest accuracy percentage?\n" }, { "role": "assistant", "content": "By comparing the accuracy percentages of all methods listed in the table, 'Our model' achieved the highest accuracy percentage of 57.6%. So, the answer is {\"answer\": \"57.6\"}." } ], "images": [ "scigen/train/3342.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Phase 2 results with different models" ], "reasoning": "By comparing the accuracy percentages of all methods listed in the table, 'Our model' achieved the highest accuracy percentage of 57.6%.", "question": "Which method achieved the highest accuracy percentage?", "answer": "So, the answer is {\"answer\": \"57.6\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nCNN with LSI has a higher accuracy percentage than CNN with TF-IDF.\n" }, { "role": "assistant", "content": "The table shows that the accuracy percentage for CNN with LSI is 54.5%, while the accuracy percentage for CNN with TF-IDF is 53.0%. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3342.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Phase 2 results with different models" ], "reasoning": "The table shows that the accuracy percentage for CNN with LSI is 54.5%, while the accuracy percentage for CNN with TF-IDF is 53.0%. Therefore, the statement is supported.", "statement": "CNN with LSI has a higher accuracy percentage than CNN with TF-IDF.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 9: Task 3 results with various scenarios', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The accuracy of the No voting approach is lower than both Voting without ratio and Voting with ratio due to the influence of the IR task's performance. The large disparity between the first and second relevant article scores negatively impacts the Voting with ratio approach.The result of No voting approach is influenced by IR task’s performance, so the accuracy is not as high as using voting. The relevant score disparity between the first and second relevant article is large, which causes a worse result of Voting with ratio compared to Voting without ratio." } ], "images": [ "scigen/train/3343.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Task 3 results with various scenarios" ], "reasoning": "The accuracy of the No voting approach is lower than both Voting without ratio and Voting with ratio due to the influence of the IR task's performance. The large disparity between the first and second relevant article scores negatively impacts the Voting with ratio approach.", "description": "The result of No voting approach is influenced by IR task’s performance, so the accuracy is not as high as using voting. The relevant score disparity between the first and second relevant article is large, which causes a worse result of Voting with ratio compared to Voting without ratio.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: 'Table 9: Task 3 results with various scenarios'\nQuestion: Which voting approach has the highest accuracy percentage?\n" }, { "role": "assistant", "content": "To determine the voting approach with the highest accuracy, compare the accuracy percentages of No voting, Voting without ratio, and Voting with ratio. Voting without ratio has the highest accuracy at 49.4%. So, the answer is {\"answer\": \"Voting without ratio\"}." } ], "images": [ "scigen/train/3343.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Task 3 results with various scenarios" ], "reasoning": "To determine the voting approach with the highest accuracy, compare the accuracy percentages of No voting, Voting without ratio, and Voting with ratio. Voting without ratio has the highest accuracy at 49.4%.", "question": "Which voting approach has the highest accuracy percentage?", "answer": "So, the answer is {\"answer\": \"Voting without ratio\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe No voting approach has the highest accuracy percentage.\n" }, { "role": "assistant", "content": "The table shows that the No voting approach has an accuracy of 45.6%, which is lower than both Voting without ratio (49.4%) and Voting with ratio (48.1%). Therefore, the statement is refuted by the table. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3343.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Task 3 results with various scenarios" ], "reasoning": "The table shows that the No voting approach has an accuracy of 45.6%, which is lower than both Voting without ratio (49.4%) and Voting with ratio (48.1%). Therefore, the statement is refuted by the table.", "statement": "The No voting approach has the highest accuracy percentage.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 10: Formal run in phase 2 & 3, COLIEE 2016.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "In phase 2, iList7 and KIS-1 are the best systems, while in phase 3, UofA achieves the best score.In phase 2, two best systems are iList7 and KIS-1. iList7 applies major voting of decision tree, SVM and CNN with various features; KIS-1 just uses simple rules of subjective cases and an end-of-sentence expression. In phase 3, UofA achives the best score. It extracts the article segment which related to the query. This system also performs paraphrasing and detects condition-conclusion-exceptions for the query/article. From the experimental results, deep learning models do not show their advantages in case of a small dataset. On the other hand, providing handcraft features and rules are shown to be useful in this case." } ], "images": [ "scigen/train/3344.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Formal run in phase 2 & 3, COLIEE 2016." ], "reasoning": "In phase 2, iList7 and KIS-1 are the best systems, while in phase 3, UofA achieves the best score.", "description": "In phase 2, two best systems are iList7 and KIS-1. iList7 applies major voting of decision tree, SVM and CNN with various features; KIS-1 just uses simple rules of subjective cases and an end-of-sentence expression. In phase 3, UofA achives the best score. It extracts the article segment which related to the query. This system also performs paraphrasing and detects condition-conclusion-exceptions for the query/article. From the experimental results, deep learning models do not show their advantages in case of a small dataset. On the other hand, providing handcraft features and rules are shown to be useful in this case.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 10: Formal run in phase 2 & 3, COLIEE 2016.', unfold the question presented below, detailing every step of your calculation. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich method achieved the highest score in Phase 3?\n" }, { "role": "assistant", "content": "From the table, UofA achieved the highest score in Phase 3 with a score of 0.5579. So, the answer is {\"answer\": \"UofA\"}" } ], "images": [ "scigen/train/3344.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Formal run in phase 2 & 3, COLIEE 2016." ], "reasoning": "From the table, UofA achieved the highest score in Phase 3 with a score of 0.5579.", "question": "Which method achieved the highest score in Phase 3?", "answer": "So, the answer is {\"answer\": \"UofA\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Deep learning models show their advantages in case of a small dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nDeep learning models show their advantages in case of a small dataset.\n" }, { "role": "assistant", "content": "The description states that deep learning models do not show their advantages in case of a small dataset, which refutes the statement. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3344.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Formal run in phase 2 & 3, COLIEE 2016." ], "reasoning": "The description states that deep learning models do not show their advantages in case of a small dataset, which refutes the statement.", "statement": "Deep learning models show their advantages in case of a small dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: The impact of removal of emotion word hashtags on the emotion intensities of tweets.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The removal of emotion word hashtags generally causes a drop in emotion intensity scores, but there are instances where it causes a rise or has no impact.We analyzed the scores in our dataset to create scatter plots where each point corresponds to a HQT–NQT tweet pair, the x-axis is the emotion intensity score of the HQT tweet, and the y-axis is the score of the NQT tweet. We observe that in a majority of the cases, the points are on the lower-right side of the diagonal, indicating that the removal of the emotion word hashtag causes the emotion intensity of the tweet to drop. However, we do see a number of points on the upper-left side of the diagonal (indicating a rise), and some exactly on the diagonal (indicating no impact), due to the removal of a hashtag. Also observe that the removal of a hashtag can result in a drop in emotion scores for some tweets, but a rise for others (e.g., see the three labeled points for #nervous in the plot). We observe a similar pattern for other emotions as well (plots not shown here)." } ], "images": [ "scigen/train/3345.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The impact of removal of emotion word hashtags on the emotion intensities of tweets." ], "reasoning": "The removal of emotion word hashtags generally causes a drop in emotion intensity scores, but there are instances where it causes a rise or has no impact.", "description": "We analyzed the scores in our dataset to create scatter plots where each point corresponds to a HQT–NQT tweet pair, the x-axis is the emotion intensity score of the HQT tweet, and the y-axis is the score of the NQT tweet. We observe that in a majority of the cases, the points are on the lower-right side of the diagonal, indicating that the removal of the emotion word hashtag causes the emotion intensity of the tweet to drop. However, we do see a number of points on the upper-left side of the diagonal (indicating a rise), and some exactly on the diagonal (indicating no impact), due to the removal of a hashtag. Also observe that the removal of a hashtag can result in a drop in emotion scores for some tweets, but a rise for others (e.g., see the three labeled points for #nervous in the plot). We observe a similar pattern for other emotions as well (plots not shown here).", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: Table 3: The impact of removal of emotion word hashtags on the emotion intensities of tweets.\nQuestion: Which emotion has the highest percentage of tweet pairs that show a rise in emotion intensity after the removal of the hashtag?\n" }, { "role": "assistant", "content": "By comparing the '% Tweets Pairs Rise' column for all emotions, we see that 'joy' has the highest percentage at 26.5%. So, the answer is {\"answer\": \"joy\"}." } ], "images": [ "scigen/train/3345.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The impact of removal of emotion word hashtags on the emotion intensities of tweets." ], "reasoning": "By comparing the '% Tweets Pairs Rise' column for all emotions, we see that 'joy' has the highest percentage at 26.5%.", "question": "Which emotion has the highest percentage of tweet pairs that show a rise in emotion intensity after the removal of the hashtag?", "answer": "So, the answer is {\"answer\": \"joy\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe average emotion intensity score drop for 'fear' is higher than that for 'anger'.\n" }, { "role": "assistant", "content": "By comparing the 'Average Emotion Intensity Score Drop' for 'fear' (0.18) and 'anger' (0.15), we see that the statement is correct. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3345.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The impact of removal of emotion word hashtags on the emotion intensities of tweets." ], "reasoning": "By comparing the 'Average Emotion Intensity Score Drop' for 'fear' (0.18) and 'anger' (0.15), we see that the statement is correct.", "statement": "The average emotion intensity score drop for 'fear' is higher than that for 'anger'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Split-half reliabilities (as measured by Pearson correlation and Spearman rank correlation) for the anger, fear, joy, and sadness tweets in the Tweet Emotion Intensity Dataset.'.\n" }, { "role": "assistant", "content": "The correlations for anger are slightly lower, indicating it is more difficult to ascertain the degrees of anger from tweets compared to other emotions.Since each tuple in this dataset was annotated by three annotators (odd number), we calculate SHR by randomly placing one or two annotations per tuple in one bin and the remaining (two or one) annotations for the tuple in another bin. Then two sets of intensity scores (and rankings) are calculated from the annotations in each of the two bins. The process is repeated 100 times and the correlations across the two sets of rankings and intensity scores are averaged. However, the correlations are slightly lower for anger indicating that it is relative more difficult to ascertain the degrees of anger of speakers from their tweets. Note that SHR indicates the quality of annotations obtained when using only half the number of annotations. The correlations obtained when repeating the experiment with three annotations for each 4-tuple is expected to be even higher." } ], "images": [ "scigen/train/3346.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Split-half reliabilities (as measured by Pearson correlation and Spearman rank correlation) for the anger, fear, joy, and sadness tweets in the Tweet Emotion Intensity Dataset." ], "reasoning": "The correlations for anger are slightly lower, indicating it is more difficult to ascertain the degrees of anger from tweets compared to other emotions.", "description": "Since each tuple in this dataset was annotated by three annotators (odd number), we calculate SHR by randomly placing one or two annotations per tuple in one bin and the remaining (two or one) annotations for the tuple in another bin. Then two sets of intensity scores (and rankings) are calculated from the annotations in each of the two bins. The process is repeated 100 times and the correlations across the two sets of rankings and intensity scores are averaged. However, the correlations are slightly lower for anger indicating that it is relative more difficult to ascertain the degrees of anger of speakers from their tweets. Note that SHR indicates the quality of annotations obtained when using only half the number of annotations. The correlations obtained when repeating the experiment with three annotations for each 4-tuple is expected to be even higher.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Split-half reliabilities (as measured by Pearson correlation and Spearman rank correlation) for the anger, fear, joy, and sadness tweets in the Tweet Emotion Intensity Dataset.\nQuestion: Which emotion has the highest correlation in both Spearman and Pearson metrics?\nGive a detailed response to the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the Spearman and Pearson values for all emotions, joy has the highest correlation in both metrics with values of 0.881 and 0.882 respectively. So, the answer is {\"answer\": \"joy\"}" } ], "images": [ "scigen/train/3346.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Split-half reliabilities (as measured by Pearson correlation and Spearman rank correlation) for the anger, fear, joy, and sadness tweets in the Tweet Emotion Intensity Dataset." ], "reasoning": "By comparing the Spearman and Pearson values for all emotions, joy has the highest correlation in both metrics with values of 0.881 and 0.882 respectively.", "question": "Which emotion has the highest correlation in both Spearman and Pearson metrics?", "answer": "So, the answer is {\"answer\": \"joy\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe emotion with the lowest Spearman correlation is anger.\n" }, { "role": "assistant", "content": "By examining the Spearman correlation values, anger has the lowest value of 0.779 compared to other emotions. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3346.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Split-half reliabilities (as measured by Pearson correlation and Spearman rank correlation) for the anger, fear, joy, and sadness tweets in the Tweet Emotion Intensity Dataset." ], "reasoning": "By examining the Spearman correlation values, anger has the lowest value of 0.779 compared to other emotions.", "statement": "The emotion with the lowest Spearman correlation is anger.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: Pearson correlations (r) of emotion intensity predictions with gold scores. Best results for each column are shown in bold: highest score by a feature set, highest score using a single lexicon, and highest score using feature set combinations.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "NRC-Hash-Emo is the most predictive single lexicon, and NRC-Aff-Int has the highest correlation for sadness (0.71).We developed our system by training on the official training sets and applying the learned models to the development sets. Once system parameters were frozen, the system trained on the combined training and development corpora. These models were applied to the official test sets. The last column ‘avg.’ shows the macro-average of the correlations for all of the emotions. Among the lexicons, NRC-Hash-Emo is the most predictive single lexicon. Lexicons that include Twitter-specific entries, lexicons that include intensity scores, and lexicons that label emotions and not just sentiment, tend to be more predictive on this task–dataset combination. NRC-Aff-Int has real-valued fine-grained word–emotion association scores for all the words in NRC-EmoLex that were marked as being associated with anger, fear, joy, and sadness. The correlations for anger, fear, and joy are similar (around 0.65), but the correlation for sadness is markedly higher (0.71). SentiStrength focuses solely on positive and negative classes, but provides numeric scores for each." } ], "images": [ "scigen/train/3347.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Pearson correlations (r) of emotion intensity predictions with gold scores. Best results for each column are shown in bold: highest score by a feature set, highest score using a single lexicon, and highest score using feature set combinations." ], "reasoning": "NRC-Hash-Emo is the most predictive single lexicon, and NRC-Aff-Int has the highest correlation for sadness (0.71).", "description": "We developed our system by training on the official training sets and applying the learned models to the development sets. Once system parameters were frozen, the system trained on the combined training and development corpora. These models were applied to the official test sets. The last column ‘avg.’ shows the macro-average of the correlations for all of the emotions. Among the lexicons, NRC-Hash-Emo is the most predictive single lexicon. Lexicons that include Twitter-specific entries, lexicons that include intensity scores, and lexicons that label emotions and not just sentiment, tend to be more predictive on this task–dataset combination. NRC-Aff-Int has real-valued fine-grained word–emotion association scores for all the words in NRC-EmoLex that were marked as being associated with anger, fear, joy, and sadness. The correlations for anger, fear, and joy are similar (around 0.65), but the correlation for sadness is markedly higher (0.71). SentiStrength focuses solely on positive and negative classes, but provides numeric scores for each.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 5: Pearson correlations (r) of emotion intensity predictions with gold scores. Best results for each column are shown in bold: highest score by a feature set, highest score using a single lexicon, and highest score using feature set combinations.', answer the following question. Give detailed solution consisting of each step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich individual lexicon has the highest average correlation?\n" }, { "role": "assistant", "content": "By examining the 'avg.' column for individual lexicons, NRC-Hash-Emo has the highest average correlation of 0.53. So, the answer is {\"answer\": \"NRC-Hash-Emo\"}" } ], "images": [ "scigen/train/3347.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Pearson correlations (r) of emotion intensity predictions with gold scores. Best results for each column are shown in bold: highest score by a feature set, highest score using a single lexicon, and highest score using feature set combinations." ], "reasoning": "By examining the 'avg.' column for individual lexicons, NRC-Hash-Emo has the highest average correlation of 0.53.", "question": "Which individual lexicon has the highest average correlation?", "answer": "So, the answer is {\"answer\": \"NRC-Hash-Emo\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence NRC-Aff-Int has the highest correlation for sadness among all lexicons. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nNRC-Aff-Int has the highest correlation for sadness among all lexicons.\n" }, { "role": "assistant", "content": "The table shows that NRC-Aff-Int has a correlation of 0.71 for sadness, which is the highest among all lexicons. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3347.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Pearson correlations (r) of emotion intensity predictions with gold scores. Best results for each column are shown in bold: highest score by a feature set, highest score using a single lexicon, and highest score using feature set combinations." ], "reasoning": "The table shows that NRC-Aff-Int has a correlation of 0.71 for sadness, which is the highest among all lexicons.", "statement": "NRC-Aff-Int has the highest correlation for sadness among all lexicons.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 6: Pearson correlations on a subset of the test set where gold scores ≥0.5.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Correlation scores are generally lower in the 0.5 to 1 range of intensity scores, making the task harder. Lexicons remain crucial, but embeddings and word ngrams are close behind, while SentiStrength is less useful.Firstly, the correlation scores are in general lower here in the 0.5 to 1 range of intensity scores than in the experiments over the full intensity range. This is simply because this is a harder task as now the systems do not benefit by making coarse distinctions over whether a tweet is in the lower range or in the higher range. Nonetheless, we observe that many of the broad patterns of results stay the same, with some differences. Lexicons still play a crucial role, however, now embeddings and word ngrams are not far behind. SentiStrength seems to be less useful in this range, suggesting that its main benefit was separating low- and high-intensity sadness words. NRC-Hash-Emo is still the source of the most predictive lexicon features." } ], "images": [ "scigen/train/3348.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Pearson correlations on a subset of the test set where gold scores ≥0.5." ], "reasoning": "Correlation scores are generally lower in the 0.5 to 1 range of intensity scores, making the task harder. Lexicons remain crucial, but embeddings and word ngrams are close behind, while SentiStrength is less useful.", "description": "Firstly, the correlation scores are in general lower here in the 0.5 to 1 range of intensity scores than in the experiments over the full intensity range. This is simply because this is a harder task as now the systems do not benefit by making coarse distinctions over whether a tweet is in the lower range or in the higher range. Nonetheless, we observe that many of the broad patterns of results stay the same, with some differences. Lexicons still play a crucial role, however, now embeddings and word ngrams are not far behind. SentiStrength seems to be less useful in this range, suggesting that its main benefit was separating low- and high-intensity sadness words. NRC-Hash-Emo is still the source of the most predictive lexicon features.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 6: Pearson correlations on a subset of the test set where gold scores ≥0.5.', answer the following question. Give detailed solution consisting of each step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich individual feature set has the highest average correlation score?\n" }, { "role": "assistant", "content": "To find the individual feature set with the highest average correlation score, look at the 'avg.' column under 'Individual feature sets'. The highest value is 0.44 for 'all lexicons (L)'. So, the answer is {\"answer\": \"all lexicons (L)\"}" } ], "images": [ "scigen/train/3348.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Pearson correlations on a subset of the test set where gold scores ≥0.5." ], "reasoning": "To find the individual feature set with the highest average correlation score, look at the 'avg.' column under 'Individual feature sets'. The highest value is 0.44 for 'all lexicons (L)'.", "question": "Which individual feature set has the highest average correlation score?", "answer": "So, the answer is {\"answer\": \"all lexicons (L)\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nNRC-Hash-Emo has the highest correlation score for anger.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'anger' column under 'Individual Lexicons'. The highest value is 0.43 for 'NRC-Hash-Emo'. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3348.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Pearson correlations on a subset of the test set where gold scores ≥0.5." ], "reasoning": "To verify this statement, look at the 'anger' column under 'Individual Lexicons'. The highest value is 0.43 for 'NRC-Hash-Emo'.", "statement": "NRC-Hash-Emo has the highest correlation score for anger.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 7: Emotion intensity transfer Pearson correlation on all target tweets.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Negative emotions are positively correlated with each other and negatively correlated with joy, with correlations ranging from r=0.23 to r=0.65, indicating varying predictive power among emotions.The numbers in the diagonal are results obtained using training and test data pertaining to the same emotion. These results are upperbound benchmarks for the non-diagonal results, which are expected to be lower. We observe that negative emotions are positively correlated with each other and negatively correlated with the only positive emotion (joy). The absolute values of these correlations go from r=0.23 to r=0.65. This shows that all of the emotion pairs are correlated at least to some extent, but that in some cases, for example, when learning from fear data and predicting sadness scores, one can obtain results (r=0.63) close to the upperbound benchmark (r=0.65). This means that even though one emotion may be strongly predictive of another, the predictive power need not be similar in the other direction. We also found that training on a simple combination of both the fear and sadness data and using the model to predict sadness obtained a correlation of 0.67 (exceeding the score obtained with just the sadness training set)." } ], "images": [ "scigen/train/3349.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Emotion intensity transfer Pearson correlation on all target tweets." ], "reasoning": "Negative emotions are positively correlated with each other and negatively correlated with joy, with correlations ranging from r=0.23 to r=0.65, indicating varying predictive power among emotions.", "description": "The numbers in the diagonal are results obtained using training and test data pertaining to the same emotion. These results are upperbound benchmarks for the non-diagonal results, which are expected to be lower. We observe that negative emotions are positively correlated with each other and negatively correlated with the only positive emotion (joy). The absolute values of these correlations go from r=0.23 to r=0.65. This shows that all of the emotion pairs are correlated at least to some extent, but that in some cases, for example, when learning from fear data and predicting sadness scores, one can obtain results (r=0.63) close to the upperbound benchmark (r=0.65). This means that even though one emotion may be strongly predictive of another, the predictive power need not be similar in the other direction. We also found that training on a simple combination of both the fear and sadness data and using the model to predict sadness obtained a correlation of 0.67 (exceeding the score obtained with just the sadness training set).", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 7: Emotion intensity transfer Pearson correlation on all target tweets.', and tackle the question below in a sequential manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the correlation when training on fear data and predicting sadness scores, and how does it compare to the upperbound benchmark?\n" }, { "role": "assistant", "content": "The correlation when training on fear data and predicting sadness scores is 0.63. The upperbound benchmark for predicting sadness scores is 0.65. Therefore, the correlation of 0.63 is close to the upperbound benchmark of 0.65. So, the answer is {\"answer\": \"0.63\"}." } ], "images": [ "scigen/train/3349.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Emotion intensity transfer Pearson correlation on all target tweets." ], "reasoning": "The correlation when training on fear data and predicting sadness scores is 0.63. The upperbound benchmark for predicting sadness scores is 0.65. Therefore, the correlation of 0.63 is close to the upperbound benchmark of 0.65.", "question": "What is the correlation when training on fear data and predicting sadness scores, and how does it compare to the upperbound benchmark?", "answer": "So, the answer is {\"answer\": \"0.63\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTraining on a combination of fear and sadness data to predict sadness scores results in a correlation of 0.67.\n" }, { "role": "assistant", "content": "The description states that training on a combination of fear and sadness data to predict sadness scores results in a correlation of 0.67, which exceeds the score obtained with just the sadness training set. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3349.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Emotion intensity transfer Pearson correlation on all target tweets." ], "reasoning": "The description states that training on a combination of fear and sadness data to predict sadness scores results in a correlation of 0.67, which exceeds the score obtained with just the sadness training set.", "statement": "Training on a combination of fear and sadness data to predict sadness scores results in a correlation of 0.67.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: The validation and test accuracy (%) of the rsDNC/BrsDNC and others on CNN dataset.'.\n" }, { "role": "assistant", "content": "The bidirectional model BrsDNC outperforms the unidirectional rsDNC on the test set, and the memory savings of the CBMU result in significantly reduced training time.S6SS2SSS0Px3 Results Both models achieve passable results compared to task-specific state-of-the-art models. But when focusing on the fact that our model is trained without any adaption to the task like sentence representations or word-windows, without hyperparameter tuning or any other optimization it is a satisfactory result. The bidirectional model outperforms the unidirectional rsDNC, but only on the test set. The training time of our implementation is 14/16 hours per epoch on the rsDNC/BrsDNC. A DNC/BDNC requires up to 35/41h per epoch. The overall training time is 7 epochs on average. The memory savings of the CBMU result in 4 training days instead of 12. This shows the model’s adaptability to perform other tasks without any task-specific adjustment and its scalability due to a drastic reduction of training time." } ], "images": [ "scigen/train/3350.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The validation and test accuracy (%) of the rsDNC/BrsDNC and others on CNN dataset." ], "reasoning": "The bidirectional model BrsDNC outperforms the unidirectional rsDNC on the test set, and the memory savings of the CBMU result in significantly reduced training time.", "description": "S6SS2SSS0Px3 Results Both models achieve passable results compared to task-specific state-of-the-art models. But when focusing on the fact that our model is trained without any adaption to the task like sentence representations or word-windows, without hyperparameter tuning or any other optimization it is a satisfactory result. The bidirectional model outperforms the unidirectional rsDNC, but only on the test set. The training time of our implementation is 14/16 hours per epoch on the rsDNC/BrsDNC. A DNC/BDNC requires up to 35/41h per epoch. The overall training time is 7 epochs on average. The memory savings of the CBMU result in 4 training days instead of 12. This shows the model’s adaptability to perform other tasks without any task-specific adjustment and its scalability due to a drastic reduction of training time.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: The validation and test accuracy (%) of the rsDNC/BrsDNC and others on CNN dataset.'. The final result should be presented in the JSON format of {\"answer\": \"\"}. \n\nQuestion: Which model has the highest test score and how does its training time compare to the rsDNC?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The GA Reader has the highest test score of 77.9. The training time for rsDNC is 14 hours per epoch, while a DNC/BDNC requires up to 35/41 hours per epoch, indicating that GA Reader, being a DNC/BDNC model, has a longer training time. So, the answer is {\"answer\": \"GA Reader\"}" } ], "images": [ "scigen/train/3350.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The validation and test accuracy (%) of the rsDNC/BrsDNC and others on CNN dataset." ], "reasoning": "The GA Reader has the highest test score of 77.9. The training time for rsDNC is 14 hours per epoch, while a DNC/BDNC requires up to 35/41 hours per epoch, indicating that GA Reader, being a DNC/BDNC model, has a longer training time.", "question": "Which model has the highest test score and how does its training time compare to the rsDNC?", "answer": "So, the answer is {\"answer\": \"GA Reader\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe BrsDNC model has a higher test score than the rsDNC model.\n" }, { "role": "assistant", "content": "The table shows that the BrsDNC model has a test score of 69.8, while the rsDNC model has a test score of 69.0, confirming that BrsDNC has a higher test score. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3350.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The validation and test accuracy (%) of the rsDNC/BrsDNC and others on CNN dataset." ], "reasoning": "The table shows that the BrsDNC model has a test score of 69.8, while the rsDNC model has a test score of 69.0, confirming that BrsDNC has a higher test score.", "statement": "The BrsDNC model has a higher test score than the rsDNC model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: Event detection performance on the CG task 2013 test dataset.'.\n" }, { "role": "assistant", "content": "The SBNN model achieves comparable performance to the TEES model in event detection without using syntactic and hand-engineered features, as evidenced by similar F1 scores.Our model achieves performance comparable to the state-of-the-art TEES event detection module without the use of any syntactic and hand-engineered features, suggesting it can be applied to other domains with no need for feature engineering. We validated it to have no significant statistical difference with the TEES model (" } ], "images": [ "scigen/train/3351.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Event detection performance on the CG task 2013 test dataset." ], "reasoning": "The SBNN model achieves comparable performance to the TEES model in event detection without using syntactic and hand-engineered features, as evidenced by similar F1 scores.", "description": "Our model achieves performance comparable to the state-of-the-art TEES event detection module without the use of any syntactic and hand-engineered features, suggesting it can be applied to other domains with no need for feature engineering. We validated it to have no significant statistical difference with the TEES model (", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 1: Event detection performance on the CG task 2013 test dataset.', solve this question and give detail solutions. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model has a higher precision (P) score?\n" }, { "role": "assistant", "content": "The table shows the precision (P) scores for both models. The SBNN model has a precision score of 63.67, while the TEES model has a precision score of 61.42. So, the answer is {\"answer\": \"SBNN\"}." } ], "images": [ "scigen/train/3351.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Event detection performance on the CG task 2013 test dataset." ], "reasoning": "The table shows the precision (P) scores for both models. The SBNN model has a precision score of 63.67, while the TEES model has a precision score of 61.42.", "question": "Which model has a higher precision (P) score?", "answer": "So, the answer is {\"answer\": \"SBNN\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe SBNN model has a higher recall (R) score than the TEES model.\n" }, { "role": "assistant", "content": "The table shows the recall (R) scores for both models. The SBNN model has a recall score of 51.43, while the TEES model has a recall score of 52.93. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3351.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Event detection performance on the CG task 2013 test dataset." ], "reasoning": "The table shows the recall (R) scores for both models. The SBNN model has a recall score of 51.43, while the TEES model has a recall score of 52.93.", "statement": "The SBNN model has a higher recall (R) score than the TEES model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Event detection performance on CG task 2013 development set for SBNN with the top three performing k-values and when k=1.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The best value for k was found to be 8, achieving an F1-score of 54.36%, which is 2.2 percentage points higher than TEES.To gain a deeper insight about the model, we performed analyses on the development set. We tested 2i values for i=1,2,3,…,11 and found that the best value was 8 with F1-score of 54.36%, which is 2.2 percentage points (pp) higher than TEES." } ], "images": [ "scigen/train/3352.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Event detection performance on CG task 2013 development set for SBNN with the top three performing k-values and when k=1." ], "reasoning": "The best value for k was found to be 8, achieving an F1-score of 54.36%, which is 2.2 percentage points higher than TEES.", "description": "To gain a deeper insight about the model, we performed analyses on the development set. We tested 2i values for i=1,2,3,…,11 and found that the best value was 8 with F1-score of 54.36%, which is 2.2 percentage points (pp) higher than TEES.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Event detection performance on CG task 2013 development set for SBNN with the top three performing k-values and when k=1.', answer the following question. Give detailed solution consisting of each step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the precision (P) value for the model with the highest F1-score?\n" }, { "role": "assistant", "content": "The model with the highest F1-score is SBNN with k=8, which has an F1-score of 54.36%. The precision (P) value for this model is 63.60. So, the answer is {\"answer\": \"63.60\"}." } ], "images": [ "scigen/train/3352.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Event detection performance on CG task 2013 development set for SBNN with the top three performing k-values and when k=1." ], "reasoning": "The model with the highest F1-score is SBNN with k=8, which has an F1-score of 54.36%. The precision (P) value for this model is 63.60.", "question": "What is the precision (P) value for the model with the highest F1-score?", "answer": "So, the answer is {\"answer\": \"63.60\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe recall (R) value for SBNN with k=64 is higher than that for TEES.\n" }, { "role": "assistant", "content": "The recall (R) value for SBNN with k=64 is 49.16, while the recall (R) value for TEES is 48.21. Since 49.16 is higher than 48.21, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3352.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Event detection performance on CG task 2013 development set for SBNN with the top three performing k-values and when k=1." ], "reasoning": "The recall (R) value for SBNN with k=64 is 49.16, while the recall (R) value for TEES is 48.21. Since 49.16 is higher than 48.21, the statement is supported.", "statement": "The recall (R) value for SBNN with k=64 is higher than that for TEES.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: Comparison on computational efficiency on the CG task 2013 development dataset.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "SBNN performs fewer classifications and takes less time than TEES, indicating higher computational efficiency.SBNN performs fewer classifications and in less time than TEES, implying it is more computationally efficient." } ], "images": [ "scigen/train/3353.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison on computational efficiency on the CG task 2013 development dataset." ], "reasoning": "SBNN performs fewer classifications and takes less time than TEES, indicating higher computational efficiency.", "description": "SBNN performs fewer classifications and in less time than TEES, implying it is more computationally efficient.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}. The question is related to the given table titled 'Table 3: Comparison on computational efficiency on the CG task 2013 development dataset.'.\n\nQuestion: Which model is more computationally efficient based on the number of classifications and running time?\n" }, { "role": "assistant", "content": "SBNN performs 4,093 classifications in 131 seconds, while TEES performs 6,141 classifications in 155 seconds. Since SBNN performs fewer classifications in less time, it is more computationally efficient. So, the answer is {\"answer\": \"SBNN\"}." } ], "images": [ "scigen/train/3353.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison on computational efficiency on the CG task 2013 development dataset." ], "reasoning": "SBNN performs 4,093 classifications in 131 seconds, while TEES performs 6,141 classifications in 155 seconds. Since SBNN performs fewer classifications in less time, it is more computationally efficient.", "question": "Which model is more computationally efficient based on the number of classifications and running time?", "answer": "So, the answer is {\"answer\": \"SBNN\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim TEES is more computationally efficient than SBNN., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nTEES is more computationally efficient than SBNN.\n" }, { "role": "assistant", "content": "The table shows that TEES performs more classifications (6,141) and takes more time (155 seconds) compared to SBNN, which performs fewer classifications (4,093) and takes less time (131 seconds). Therefore, TEES is not more computationally efficient than SBNN. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3353.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison on computational efficiency on the CG task 2013 development dataset." ], "reasoning": "The table shows that TEES performs more classifications (6,141) and takes more time (155 seconds) compared to SBNN, which performs fewer classifications (4,093) and takes less time (131 seconds). Therefore, TEES is not more computationally efficient than SBNN.", "statement": "TEES is more computationally efficient than SBNN.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Nested and overlapping event detection F1 (%) score performance on the CG task 2013 development set.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Our model, SBNN k=8, achieves higher F1-scores than TEES due to its ability to maintain multiple beams and detect events from all these beams during search.Our model yields higher F1-scores than TEES which can be attributed to its ability to maintain multiple beams and to detect events from all these beams during search." } ], "images": [ "scigen/train/3354.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Nested and overlapping event detection F1 (%) score performance on the CG task 2013 development set." ], "reasoning": "Our model, SBNN k=8, achieves higher F1-scores than TEES due to its ability to maintain multiple beams and detect events from all these beams during search.", "description": "Our model yields higher F1-scores than TEES which can be attributed to its ability to maintain multiple beams and to detect events from all these beams during search.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Nested and overlapping event detection F1 (%) score performance on the CG task 2013 development set.', solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model has the highest F1-score for Nested events?\n" }, { "role": "assistant", "content": "By comparing the F1-scores for Nested events in the table, SBNN k=8 has a higher score (45.24) compared to TEES (42.70). So, the answer is {\"answer\": \"SBNN k=8\"}." } ], "images": [ "scigen/train/3354.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Nested and overlapping event detection F1 (%) score performance on the CG task 2013 development set." ], "reasoning": "By comparing the F1-scores for Nested events in the table, SBNN k=8 has a higher score (45.24) compared to TEES (42.70).", "question": "Which model has the highest F1-score for Nested events?", "answer": "So, the answer is {\"answer\": \"SBNN k=8\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTEES has a higher F1-score for Flat events than SBNN k=8.\n" }, { "role": "assistant", "content": "The table shows that the F1-score for Flat events is 56.81 for TEES and 60.5 for SBNN k=8, indicating that SBNN k=8 has a higher score. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3354.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Nested and overlapping event detection F1 (%) score performance on the CG task 2013 development set." ], "reasoning": "The table shows that the F1-score for Flat events is 56.81 for TEES and 60.5 for SBNN k=8, indicating that SBNN k=8 has a higher score.", "statement": "TEES has a higher F1-score for Flat events than SBNN k=8.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Automatic evaluation results for unimodal and multimodal models'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The T+I-Merge model excels in CIDEr and METEOR scores, while the Unimodal model performs slightly better in BLEU and has lower perplexity.The T+I-Merge architecture outperforms all other models on CIDEr and METEOR, while the unimodal model, relying only on textual premises, is marginally better on BLEU and has slightly lower perplexity." } ], "images": [ "scigen/train/3355.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Automatic evaluation results for unimodal and multimodal models" ], "reasoning": "The T+I-Merge model excels in CIDEr and METEOR scores, while the Unimodal model performs slightly better in BLEU and has lower perplexity.", "description": "The T+I-Merge architecture outperforms all other models on CIDEr and METEOR, while the unimodal model, relying only on textual premises, is marginally better on BLEU and has slightly lower perplexity.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Automatic evaluation results for unimodal and multimodal models\nQuestion: Which model has the highest CIDEr score and what is its perplexity?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows that the T+I-Merge model has the highest CIDEr score of 0.955. The perplexity for the T+I-Merge model is 7.26. So, the answer is {\"answer\": \"7.26\"}." } ], "images": [ "scigen/train/3355.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Automatic evaluation results for unimodal and multimodal models" ], "reasoning": "The table shows that the T+I-Merge model has the highest CIDEr score of 0.955. The perplexity for the T+I-Merge model is 7.26.", "question": "Which model has the highest CIDEr score and what is its perplexity?", "answer": "So, the answer is {\"answer\": \"7.26\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The T+I-Merge model has the lowest perplexity among all models. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe T+I-Merge model has the lowest perplexity among all models.\n" }, { "role": "assistant", "content": "The table shows that the T+I-Merge model has a perplexity of 7.26, which is lower than the perplexity of the other models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3355.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Automatic evaluation results for unimodal and multimodal models" ], "reasoning": "The table shows that the T+I-Merge model has a perplexity of 7.26, which is lower than the perplexity of the other models listed.", "statement": "The T+I-Merge model has the lowest perplexity among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 8: “Translation” precision @10, when seeking to retrieve the true translation of an English sentence from a bag of 200k Italian sentences, or vice versa, averaged over 5k samples. We first obtain bilingual word vectors, using either the word dictionary provided by Dinu, or by constructing a phrase dictionary from Europarl. We set ns= 12800 in the inverted softmax.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Dinu’s method shows rapid improvement from precision @1 to @5 to @10, excelling with the word dictionary @1 and the phrase dictionary @5 and @10.Once again, the inverted softmax performs well when retrieving the Italian translations of English sentences, but is less effective translating Italian sentences into English. However, the performance of Dinu’s method appears to rise more rapidly than other methods as we transition from precision @1 to @5 to @10. Additionally, while Dinu’s method performs better when using the word dictionary @1, it prefers the phrase dictionary @5 and @10." } ], "images": [ "scigen/train/3356.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: “Translation” precision @10, when seeking to retrieve the true translation of an English sentence from a bag of 200k Italian sentences, or vice versa, averaged over 5k samples. We first obtain bilingual word vectors, using either the word dictionary provided by Dinu, or by constructing a phrase dictionary from Europarl. We set ns= 12800 in the inverted softmax." ], "reasoning": "Dinu’s method shows rapid improvement from precision @1 to @5 to @10, excelling with the word dictionary @1 and the phrase dictionary @5 and @10.", "description": "Once again, the inverted softmax performs well when retrieving the Italian translations of English sentences, but is less effective translating Italian sentences into English. However, the performance of Dinu’s method appears to rise more rapidly than other methods as we transition from precision @1 to @5 to @10. Additionally, while Dinu’s method performs better when using the word dictionary @1, it prefers the phrase dictionary @5 and @10.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 8: “Translation” precision @10, when seeking to retrieve the true translation of an English sentence from a bag of 200k Italian sentences, or vice versa, averaged over 5k samples. We first obtain bilingual word vectors, using either the word dictionary provided by Dinu, or by constructing a phrase dictionary from Europarl. We set ns= 12800 in the inverted softmax.', solve the subsequent question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich method performs best for translating Italian sentences into English using the phrase dictionary?\n" }, { "role": "assistant", "content": "To determine the best method for translating Italian sentences into English using the phrase dictionary, we need to compare the values in the 'Italian to English: Phrase dictionary' column. The highest value in this column is 0.849, which corresponds to Dinu et al. So, the answer is {\"answer\": \"Dinu et al.\"}." } ], "images": [ "scigen/train/3356.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: “Translation” precision @10, when seeking to retrieve the true translation of an English sentence from a bag of 200k Italian sentences, or vice versa, averaged over 5k samples. We first obtain bilingual word vectors, using either the word dictionary provided by Dinu, or by constructing a phrase dictionary from Europarl. We set ns= 12800 in the inverted softmax." ], "reasoning": "To determine the best method for translating Italian sentences into English using the phrase dictionary, we need to compare the values in the 'Italian to English: Phrase dictionary' column. The highest value in this column is 0.849, which corresponds to Dinu et al.", "question": "Which method performs best for translating Italian sentences into English using the phrase dictionary?", "answer": "So, the answer is {\"answer\": \"Dinu et al.\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nSVD performs better than Mikolov et al. for translating English sentences to Italian using the word dictionary.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the values in the 'English to Italian: Word dictionary' column for SVD and Mikolov et al. SVD has a value of 0.441, while Mikolov et al. has a value of 0.228. Since 0.441 is greater than 0.228, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3356.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: “Translation” precision @10, when seeking to retrieve the true translation of an English sentence from a bag of 200k Italian sentences, or vice versa, averaged over 5k samples. We first obtain bilingual word vectors, using either the word dictionary provided by Dinu, or by constructing a phrase dictionary from Europarl. We set ns= 12800 in the inverted softmax." ], "reasoning": "To verify this statement, we compare the values in the 'English to Italian: Word dictionary' column for SVD and Mikolov et al. SVD has a value of 0.441, while Mikolov et al. has a value of 0.228. Since 0.441 is greater than 0.228, the statement is supported.", "statement": "SVD performs better than Mikolov et al. for translating English sentences to Italian using the word dictionary.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Translation performance using the pseudo dictionary of identical character strings.'.\n" }, { "role": "assistant", "content": "The pseudo dictionary method achieves a mean translation performance @1 of 40% from English to Italian, outperforming Mikolov and Dinu's methods which achieve 1% and 6% respectively.Even when using this pseudo dictionary prepared with no expert bilingual knowledge, we still achieve a mean translation performance @1 of 40% from English to Italian on our test set. By contrast, Mikolov and Dinu ’s methods achieve precisions of just 1% and 6% respectively. CCA also performs well, although it became significantly more computationally expensive when the vocabulary size increased." } ], "images": [ "scigen/train/3357.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Translation performance using the pseudo dictionary of identical character strings." ], "reasoning": "The pseudo dictionary method achieves a mean translation performance @1 of 40% from English to Italian, outperforming Mikolov and Dinu's methods which achieve 1% and 6% respectively.", "description": "Even when using this pseudo dictionary prepared with no expert bilingual knowledge, we still achieve a mean translation performance @1 of 40% from English to Italian on our test set. By contrast, Mikolov and Dinu ’s methods achieve precisions of just 1% and 6% respectively. CCA also performs well, although it became significantly more computationally expensive when the vocabulary size increased.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Translation performance using the pseudo dictionary of identical character strings.', solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the precision @1 for the method 'This work' when translating from English to Italian?\n" }, { "role": "assistant", "content": "The table shows the precision @1 for various methods. For 'This work' translating from English to Italian, the precision @1 is 0.399. So, the answer is {\"answer\": \"0.399\"}" } ], "images": [ "scigen/train/3357.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Translation performance using the pseudo dictionary of identical character strings." ], "reasoning": "The table shows the precision @1 for various methods. For 'This work' translating from English to Italian, the precision @1 is 0.399.", "question": "What is the precision @1 for the method 'This work' when translating from English to Italian?", "answer": "So, the answer is {\"answer\": \"0.399\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe CCA method achieves a precision @10 of 0.530 when translating from English to Italian.\n" }, { "role": "assistant", "content": "The table shows the precision @10 for the CCA method when translating from English to Italian as 0.530. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3357.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Translation performance using the pseudo dictionary of identical character strings." ], "reasoning": "The table shows the precision @10 for the CCA method when translating from English to Italian as 0.530.", "statement": "The CCA method achieves a precision @10 of 0.530 when translating from English to Italian.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Translation performance, using the Europarl corpus as a phrase dictionary.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "By using the Europarl corpus to form bilingual word vectors, the method achieves 42.8% precision @1 for English to Italian translations and 37.5% precision for Italian to English translations, which is competitive with leading methods.As outlined earlier, we can form simple sentence vectors in the word vector space by summing and normalising over the words contained in a sentence. These sentence vectors can be used in two different tasks. First, we can use the Europarl corpus as a training dictionary, whereby the matrices XD and YD are formed from the sentence vectors of translation pairs. By applying the SVD to the first 500k sentences in this “phrase dictionary”, we obtain a set of bilingual word vectors from which we can retrieve translations of individual words. We achieve 42.8% precision @1 when translating from English into Italian and 37.5% precision when translating from Italian into English, comparable to the accuracy achieved using the expert word dictionary on the same test set. It is difficult to compare the two approaches, since they require different training data. However our performance appears competitive with Bilbowa, a leading method for learning bilingual vectors online from monolingual corpora and aligned text We do not include results for CCA due to the computational complexity on a dictionary of this size." } ], "images": [ "scigen/train/3358.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Translation performance, using the Europarl corpus as a phrase dictionary." ], "reasoning": "By using the Europarl corpus to form bilingual word vectors, the method achieves 42.8% precision @1 for English to Italian translations and 37.5% precision for Italian to English translations, which is competitive with leading methods.", "description": "As outlined earlier, we can form simple sentence vectors in the word vector space by summing and normalising over the words contained in a sentence. These sentence vectors can be used in two different tasks. First, we can use the Europarl corpus as a training dictionary, whereby the matrices XD and YD are formed from the sentence vectors of translation pairs. By applying the SVD to the first 500k sentences in this “phrase dictionary”, we obtain a set of bilingual word vectors from which we can retrieve translations of individual words. We achieve 42.8% precision @1 when translating from English into Italian and 37.5% precision when translating from Italian into English, comparable to the accuracy achieved using the expert word dictionary on the same test set. It is difficult to compare the two approaches, since they require different training data. However our performance appears competitive with Bilbowa, a leading method for learning bilingual vectors online from monolingual corpora and aligned text We do not include results for CCA due to the computational complexity on a dictionary of this size.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 5: Translation performance, using the Europarl corpus as a phrase dictionary.', solve this question and give detail solutions. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhat is the precision @5 for English to Italian translations using the method described in this work?\n" }, { "role": "assistant", "content": "The table shows the precision values for different translation methods. For English to Italian translations using 'This work', the precision @5 is listed. So, the answer is {\"answer\": \"0.589\"}." } ], "images": [ "scigen/train/3358.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Translation performance, using the Europarl corpus as a phrase dictionary." ], "reasoning": "The table shows the precision values for different translation methods. For English to Italian translations using 'This work', the precision @5 is listed.", "question": "What is the precision @5 for English to Italian translations using the method described in this work?", "answer": "So, the answer is {\"answer\": \"0.589\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The precision @10 for Italian to English translations using Mikolov et al.'s method is higher than 0.5., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe precision @10 for Italian to English translations using Mikolov et al.'s method is higher than 0.5.\n" }, { "role": "assistant", "content": "The table lists the precision @10 for Italian to English translations using Mikolov et al.'s method as 0.39, which is lower than 0.5. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3358.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Translation performance, using the Europarl corpus as a phrase dictionary." ], "reasoning": "The table lists the precision @10 for Italian to English translations using Mikolov et al.'s method as 0.39, which is lower than 0.5.", "statement": "The precision @10 for Italian to English translations using Mikolov et al.'s method is higher than 0.5.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 6: “Translation” precision @1, when seeking to retrieve the true translation of an English sentence from a bag of 200k Italian sentences, or vice versa, averaged over 5k samples. We first obtain bilingual word vectors, using either the word dictionary provided by Dinu, or by constructing a phrase dictionary from Europarl. We set ns= 12800 in the inverted softmax.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The inverted softmax method achieves the highest precision for English to Italian translations using the phrase dictionary, while SVD achieves the highest precision for Italian to English translations using the phrase dictionary.Second, we can apply our orthogonal transformation to retrieve the Italian translation of an English sentence, or vice versa. To achieve this, we hold back the final 200k English and Italian sentences from our 500k sample of Europarl, and attempt to retrieve the true translation of a given sentence in this test set. We obtain the orthogonal transformation by performing the SVD on either the expert word dictionary provided by Dinu, or on a phrase dictionary formed from the first 300k sentences from Europarl. For simplicity, we do not apply dimensionality reduction here. For precision @1, most approaches favour the phrase dictionary, while Dinu’s method favours the word dictionary. We show in the appendix that all methods favour the phrase dictionary for precision @5 and @10. Remarkably, given no information except the sentence vectors, we are able to retrieve the correct translation of an English sentence with 67.8% precision. This is particularly surprising, since we are using the simplest possible sentence vectors, which have no information about word order or sentence length. When training the inverted softmax, the inverse temperature β diverged, and the “translation” performance from English to Italian significantly exceeded the performance from Italian to English. This improved the performance from Italian to English from 48.6% to 65.6%, which suggests that the optimal retrieval approach would be able to tune continuously between the conventional softmax and the inverted softmax. Once again, the inverted softmax performs well when retrieving the Italian translations of English sentences, but is less effective translating Italian sentences into English. However, the performance of Dinu’s method appears to rise more rapidly than other methods as we transition from precision @1 to @5 to @10. Additionally, while Dinu’s method performs better when using the word dictionary @1, it prefers the phrase dictionary @5 and @10." } ], "images": [ "scigen/train/3359.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: “Translation” precision @1, when seeking to retrieve the true translation of an English sentence from a bag of 200k Italian sentences, or vice versa, averaged over 5k samples. We first obtain bilingual word vectors, using either the word dictionary provided by Dinu, or by constructing a phrase dictionary from Europarl. We set ns= 12800 in the inverted softmax." ], "reasoning": "The inverted softmax method achieves the highest precision for English to Italian translations using the phrase dictionary, while SVD achieves the highest precision for Italian to English translations using the phrase dictionary.", "description": "Second, we can apply our orthogonal transformation to retrieve the Italian translation of an English sentence, or vice versa. To achieve this, we hold back the final 200k English and Italian sentences from our 500k sample of Europarl, and attempt to retrieve the true translation of a given sentence in this test set. We obtain the orthogonal transformation by performing the SVD on either the expert word dictionary provided by Dinu, or on a phrase dictionary formed from the first 300k sentences from Europarl. For simplicity, we do not apply dimensionality reduction here. For precision @1, most approaches favour the phrase dictionary, while Dinu’s method favours the word dictionary. We show in the appendix that all methods favour the phrase dictionary for precision @5 and @10. Remarkably, given no information except the sentence vectors, we are able to retrieve the correct translation of an English sentence with 67.8% precision. This is particularly surprising, since we are using the simplest possible sentence vectors, which have no information about word order or sentence length. When training the inverted softmax, the inverse temperature β diverged, and the “translation” performance from English to Italian significantly exceeded the performance from Italian to English. This improved the performance from Italian to English from 48.6% to 65.6%, which suggests that the optimal retrieval approach would be able to tune continuously between the conventional softmax and the inverted softmax. Once again, the inverted softmax performs well when retrieving the Italian translations of English sentences, but is less effective translating Italian sentences into English. However, the performance of Dinu’s method appears to rise more rapidly than other methods as we transition from precision @1 to @5 to @10. Additionally, while Dinu’s method performs better when using the word dictionary @1, it prefers the phrase dictionary @5 and @10.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich method achieves the highest precision for translating Italian to English using the phrase dictionary?\nSolve the above question based on the table titled 'Table 6: “Translation” precision @1, when seeking to retrieve the true translation of an English sentence from a bag of 200k Italian sentences, or vice versa, averaged over 5k samples. We first obtain bilingual word vectors, using either the word dictionary provided by Dinu, or by constructing a phrase dictionary from Europarl. We set ns= 12800 in the inverted softmax.'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the method with the highest precision for translating Italian to English using the phrase dictionary, we look at the column 'Italian to English: Phrase dictionary' and identify the highest value, which is 0.656 achieved by SVD. So, the answer is {\"answer\": \"SVD\"}." } ], "images": [ "scigen/train/3359.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: “Translation” precision @1, when seeking to retrieve the true translation of an English sentence from a bag of 200k Italian sentences, or vice versa, averaged over 5k samples. We first obtain bilingual word vectors, using either the word dictionary provided by Dinu, or by constructing a phrase dictionary from Europarl. We set ns= 12800 in the inverted softmax." ], "reasoning": "To find the method with the highest precision for translating Italian to English using the phrase dictionary, we look at the column 'Italian to English: Phrase dictionary' and identify the highest value, which is 0.656 achieved by SVD.", "question": "Which method achieves the highest precision for translating Italian to English using the phrase dictionary?", "answer": "So, the answer is {\"answer\": \"SVD\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe inverted softmax method achieves the highest precision for English to Italian translations using the word dictionary.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the column 'English to Italian: Word dictionary' and compare the precision values. The highest value is 0.546, which is achieved by the inverted softmax method. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3359.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: “Translation” precision @1, when seeking to retrieve the true translation of an English sentence from a bag of 200k Italian sentences, or vice versa, averaged over 5k samples. We first obtain bilingual word vectors, using either the word dictionary provided by Dinu, or by constructing a phrase dictionary from Europarl. We set ns= 12800 in the inverted softmax." ], "reasoning": "To verify this statement, we look at the column 'English to Italian: Word dictionary' and compare the precision values. The highest value is 0.546, which is achieved by the inverted softmax method.", "statement": "The inverted softmax method achieves the highest precision for English to Italian translations using the word dictionary.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 7: “Translation” precision @5, when seeking to retrieve the true translation of an English sentence from a bag of 200k Italian sentences, or vice versa, averaged over 5k samples. We first obtain bilingual word vectors, using either the word dictionary provided by Dinu, or by constructing a phrase dictionary from Europarl. We set ns= 12800 in the inverted softmax.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Dinu's method performs better with the word dictionary at precision @1 but prefers the phrase dictionary at precision @5 and @10.Once again, the inverted softmax performs well when retrieving the Italian translations of English sentences, but is less effective translating Italian sentences into English. However, the performance of Dinu’s method appears to rise more rapidly than other methods as we transition from precision @1 to @5 to @10. Additionally, while Dinu’s method performs better when using the word dictionary @1, it prefers the phrase dictionary @5 and @10." } ], "images": [ "scigen/train/3360.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: “Translation” precision @5, when seeking to retrieve the true translation of an English sentence from a bag of 200k Italian sentences, or vice versa, averaged over 5k samples. We first obtain bilingual word vectors, using either the word dictionary provided by Dinu, or by constructing a phrase dictionary from Europarl. We set ns= 12800 in the inverted softmax." ], "reasoning": "Dinu's method performs better with the word dictionary at precision @1 but prefers the phrase dictionary at precision @5 and @10.", "description": "Once again, the inverted softmax performs well when retrieving the Italian translations of English sentences, but is less effective translating Italian sentences into English. However, the performance of Dinu’s method appears to rise more rapidly than other methods as we transition from precision @1 to @5 to @10. Additionally, while Dinu’s method performs better when using the word dictionary @1, it prefers the phrase dictionary @5 and @10.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 7: “Translation” precision @5, when seeking to retrieve the true translation of an English sentence from a bag of 200k Italian sentences, or vice versa, averaged over 5k samples. We first obtain bilingual word vectors, using either the word dictionary provided by Dinu, or by constructing a phrase dictionary from Europarl. We set ns= 12800 in the inverted softmax.\nQuestion: Which method has the highest performance for translating English to Italian using the phrase dictionary?\nSolve the above question based on the table information. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the values in the 'English to Italian: Phrase dictionary' column, the highest value is 0.825, which corresponds to the 'inverted softmax' method. So, the answer is {\"answer\": \"inverted softmax\"}." } ], "images": [ "scigen/train/3360.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: “Translation” precision @5, when seeking to retrieve the true translation of an English sentence from a bag of 200k Italian sentences, or vice versa, averaged over 5k samples. We first obtain bilingual word vectors, using either the word dictionary provided by Dinu, or by constructing a phrase dictionary from Europarl. We set ns= 12800 in the inverted softmax." ], "reasoning": "By comparing the values in the 'English to Italian: Phrase dictionary' column, the highest value is 0.825, which corresponds to the 'inverted softmax' method.", "question": "Which method has the highest performance for translating English to Italian using the phrase dictionary?", "answer": "So, the answer is {\"answer\": \"inverted softmax\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nDinu's method performs better than SVD for translating Italian to English using the word dictionary.\n" }, { "role": "assistant", "content": "By comparing the values in the 'Italian to English: Word dictionary' column, Dinu's method has a value of 0.713, while SVD has a value of 0.619. Therefore, Dinu's method performs better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3360.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: “Translation” precision @5, when seeking to retrieve the true translation of an English sentence from a bag of 200k Italian sentences, or vice versa, averaged over 5k samples. We first obtain bilingual word vectors, using either the word dictionary provided by Dinu, or by constructing a phrase dictionary from Europarl. We set ns= 12800 in the inverted softmax." ], "reasoning": "By comparing the values in the 'Italian to English: Word dictionary' column, Dinu's method has a value of 0.713, while SVD has a value of 0.619. Therefore, Dinu's method performs better.", "statement": "Dinu's method performs better than SVD for translating Italian to English using the word dictionary.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Performance comparison on NIST datasets. ∗ is used to indicate the improvement over RNNsearch⋆ is statistically significant [Collins et al.2005] (p<0.01).'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Rnmt outperforms both RNNsearch and VRNMT in all test datasets, achieving the highest average BLEU score.We compared our system Rnmt with the two baseline systems RNNsearch and RNNsearch⋆ both on the NIST Zh-En and the WMT17 En-De translation tasks. As Rnmt was implemented on the basis of RNNsearch⋆, in the strict sense, RNNsearch⋆ is the baseline. Besides, comparison between our model to VRNMT shows that proposed simple model stably produces better performance on all test datasets and outperforms VRNMT 1.04 BLEU score on average." } ], "images": [ "scigen/train/3361.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance comparison on NIST datasets. ∗ is used to indicate the improvement over RNNsearch⋆ is statistically significant [Collins et al.2005] (p<0.01)." ], "reasoning": "Rnmt outperforms both RNNsearch and VRNMT in all test datasets, achieving the highest average BLEU score.", "description": "We compared our system Rnmt with the two baseline systems RNNsearch and RNNsearch⋆ both on the NIST Zh-En and the WMT17 En-De translation tasks. As Rnmt was implemented on the basis of RNNsearch⋆, in the strict sense, RNNsearch⋆ is the baseline. Besides, comparison between our model to VRNMT shows that proposed simple model stably produces better performance on all test datasets and outperforms VRNMT 1.04 BLEU score on average.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Performance comparison on NIST datasets. ∗ is used to indicate the improvement over RNNsearch⋆ is statistically significant [Collins et al.2005] (p<0.01).', solve the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich system achieved the highest BLEU score on the MT04 dataset?\n" }, { "role": "assistant", "content": "By comparing the BLEU scores of all systems on the MT04 dataset, Rnmt achieved the highest score of 42.01. So, the answer is {\"answer\": \"Rnmt\"}." } ], "images": [ "scigen/train/3361.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance comparison on NIST datasets. ∗ is used to indicate the improvement over RNNsearch⋆ is statistically significant [Collins et al.2005] (p<0.01)." ], "reasoning": "By comparing the BLEU scores of all systems on the MT04 dataset, Rnmt achieved the highest score of 42.01.", "question": "Which system achieved the highest BLEU score on the MT04 dataset?", "answer": "So, the answer is {\"answer\": \"Rnmt\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRnmt has the highest average BLEU score among all systems.\n" }, { "role": "assistant", "content": "The table shows that Rnmt has an average BLEU score of 39.21, which is higher than the average scores of RNNsearch (33.59), RNNsearch⋆ (37.73), and VRNMT (38.17). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3361.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance comparison on NIST datasets. ∗ is used to indicate the improvement over RNNsearch⋆ is statistically significant [Collins et al.2005] (p<0.01)." ], "reasoning": "The table shows that Rnmt has an average BLEU score of 39.21, which is higher than the average scores of RNNsearch (33.59), RNNsearch⋆ (37.73), and VRNMT (38.17).", "statement": "Rnmt has the highest average BLEU score among all systems.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Performance comparison on WMT17 En-De datasets.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The relationships produced by RNs improve translation performance and are more suitable for NMT compared to external syntactic knowledge.Given the above results, we can conclude that RN can indeed learn the relationships between the source words and these relationships are useful and bring improvement on the translation performance. We also compared our method with the work of Bastings et al. \\shortcitebastings2017graph which requires the injection of external syntactic knowledge, to see whether the relationships produced by RNs can lead to better translation than the syntax from supervised learning. We believe that the relationships of the source words derived from RNs do not necessarily conform to human cognition, but it can be simultaneously tailored with the other parts of the translation system. In this way, RNs can generate the relationships more suitable for the NMT." } ], "images": [ "scigen/train/3362.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance comparison on WMT17 En-De datasets." ], "reasoning": "The relationships produced by RNs improve translation performance and are more suitable for NMT compared to external syntactic knowledge.", "description": "Given the above results, we can conclude that RN can indeed learn the relationships between the source words and these relationships are useful and bring improvement on the translation performance. We also compared our method with the work of Bastings et al. \\shortcitebastings2017graph which requires the injection of external syntactic knowledge, to see whether the relationships produced by RNs can lead to better translation than the syntax from supervised learning. We believe that the relationships of the source words derived from RNs do not necessarily conform to human cognition, but it can be simultaneously tailored with the other parts of the translation system. In this way, RNs can generate the relationships more suitable for the NMT.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 2: Performance comparison on WMT17 En-De datasets.', unfold the question presented below, detailing every step of your calculation. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich system has the highest average score across all tests?\n" }, { "role": "assistant", "content": "By comparing the average scores of all systems, Rnmt has the highest average score of 24.1. So, the answer is {\"answer\": \"Rnmt\"}." } ], "images": [ "scigen/train/3362.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance comparison on WMT17 En-De datasets." ], "reasoning": "By comparing the average scores of all systems, Rnmt has the highest average score of 24.1.", "question": "Which system has the highest average score across all tests?", "answer": "So, the answer is {\"answer\": \"Rnmt\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nRNNsearch* outperforms RNNsearch in test16.\n" }, { "role": "assistant", "content": "The table shows that RNNsearch* has a score of 25.6 in test16, which is higher than RNNsearch's score of 20.9. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3362.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance comparison on WMT17 En-De datasets." ], "reasoning": "The table shows that RNNsearch* has a score of 25.6 in test16, which is higher than RNNsearch's score of 20.9.", "statement": "RNNsearch* outperforms RNNsearch in test16.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Performance comparison on WMT17 En-De datasets.'.\n" }, { "role": "assistant", "content": "The RNmt system outperforms the BiRNN+GCN system in translation performance, indicating that RNs can effectively learn and utilize relationships between source words.Given the above results, we can conclude that RN can indeed learn the relationships between the source words and these relationships are useful and bring improvement on the translation performance. We also compared our method with the work of Bastings et al. \\shortcitebastings2017graph which requires the injection of external syntactic knowledge, to see whether the relationships produced by RNs can lead to better translation than the syntax from supervised learning. We believe that the relationships of the source words derived from RNs do not necessarily conform to human cognition, but it can be simultaneously tailored with the other parts of the translation system. In this way, RNs can generate the relationships more suitable for the NMT." } ], "images": [ "scigen/train/3363.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance comparison on WMT17 En-De datasets." ], "reasoning": "The RNmt system outperforms the BiRNN+GCN system in translation performance, indicating that RNs can effectively learn and utilize relationships between source words.", "description": "Given the above results, we can conclude that RN can indeed learn the relationships between the source words and these relationships are useful and bring improvement on the translation performance. We also compared our method with the work of Bastings et al. \\shortcitebastings2017graph which requires the injection of external syntactic knowledge, to see whether the relationships produced by RNs can lead to better translation than the syntax from supervised learning. We believe that the relationships of the source words derived from RNs do not necessarily conform to human cognition, but it can be simultaneously tailored with the other parts of the translation system. In this way, RNs can generate the relationships more suitable for the NMT.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 2: Performance comparison on WMT17 En-De datasets.', solve the following question step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich system achieved a higher score on test16, and what does this imply about the effectiveness of RNs?\n" }, { "role": "assistant", "content": "The RNmt system achieved a higher score of 25.4 on test16 compared to the BiRNN+GCN system's score of 23.9. This implies that RNs can effectively learn and utilize relationships between source words, leading to better translation performance. So, the answer is {\"answer\": \"RNmt\"}." } ], "images": [ "scigen/train/3363.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance comparison on WMT17 En-De datasets." ], "reasoning": "The RNmt system achieved a higher score of 25.4 on test16 compared to the BiRNN+GCN system's score of 23.9. This implies that RNs can effectively learn and utilize relationships between source words, leading to better translation performance.", "question": "Which system achieved a higher score on test16, and what does this imply about the effectiveness of RNs?", "answer": "So, the answer is {\"answer\": \"RNmt\"}." }, { "messages": [ { "role": "user", "content": "The BiRNN+GCN system achieved a higher score on test16 than the RNmt system. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe BiRNN+GCN system achieved a higher score on test16 than the RNmt system.\n" }, { "role": "assistant", "content": "The table shows that the BiRNN+GCN system scored 23.9 on test16, while the RNmt system scored 25.4. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3363.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance comparison on WMT17 En-De datasets." ], "reasoning": "The table shows that the BiRNN+GCN system scored 23.9 on test16, while the RNmt system scored 25.4. Therefore, the statement is incorrect.", "statement": "The BiRNN+GCN system achieved a higher score on test16 than the RNmt system.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: Comparison of alignment quality on NIST Zh-En translation task.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The Rnmt system outperforms the RNNsearch⋆ baseline by 1.72 BLEU points and has a 1.1 points lower AER score, indicating better translation and alignment quality.It illustrates that our system Rnmt can produce better translations than the baseline RNNsearch⋆, a difference of 1.72 BLEU points. Besides, the AER score is 1.1 points lower than the baseline model. Note that the smaller the AER score, the better the alignment quality." } ], "images": [ "scigen/train/3364.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of alignment quality on NIST Zh-En translation task." ], "reasoning": "The Rnmt system outperforms the RNNsearch⋆ baseline by 1.72 BLEU points and has a 1.1 points lower AER score, indicating better translation and alignment quality.", "description": "It illustrates that our system Rnmt can produce better translations than the baseline RNNsearch⋆, a difference of 1.72 BLEU points. Besides, the AER score is 1.1 points lower than the baseline model. Note that the smaller the AER score, the better the alignment quality.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 4: Comparison of alignment quality on NIST Zh-En translation task.' to sequentially solve the question presented below. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhat is the difference in BLEU points between the Rnmt system and the RNNsearch⋆ baseline?\n" }, { "role": "assistant", "content": "The BLEU score for RNNsearch⋆ is 22.40 and for Rnmt is 24.12. The difference is calculated as 24.12 - 22.40. So, the answer is {\"answer\": \"1.72\"}." } ], "images": [ "scigen/train/3364.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of alignment quality on NIST Zh-En translation task." ], "reasoning": "The BLEU score for RNNsearch⋆ is 22.40 and for Rnmt is 24.12. The difference is calculated as 24.12 - 22.40.", "question": "What is the difference in BLEU points between the Rnmt system and the RNNsearch⋆ baseline?", "answer": "So, the answer is {\"answer\": \"1.72\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Rnmt system has a higher AER score than the RNNsearch⋆ baseline., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Rnmt system has a higher AER score than the RNNsearch⋆ baseline.\n" }, { "role": "assistant", "content": "The table shows that the AER score for Rnmt is 45.66, which is lower than the AER score for RNNsearch⋆, which is 46.76. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3364.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of alignment quality on NIST Zh-En translation task." ], "reasoning": "The table shows that the AER score for Rnmt is 45.66, which is lower than the AER score for RNNsearch⋆, which is 46.76.", "statement": "The Rnmt system has a higher AER score than the RNNsearch⋆ baseline.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3. Results of different similarity functions in the matching pattern-based module on CQADupStack.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The dot function achieved the best performance in all evaluation metrics for the Match2 model, indicating its effectiveness in capturing detailed interactions for pattern similarity.4.4.1. Pattern Similarity Function Here, we study the performance of five candidate functions. As we can see, the choice of pattern similarity function does affect the performance of the Match2 model. Specifically, the dot function has achieved the best performance in terms of all the evaluation metrics. The reason might be that the dot function could capture the detailed interactions of each dimension, which are useful in identifying the similarities between matching patterns. In the following experiments, we will use the dot as the pattern similarity function." } ], "images": [ "scigen/train/3365.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. Results of different similarity functions in the matching pattern-based module on CQADupStack." ], "reasoning": "The dot function achieved the best performance in all evaluation metrics for the Match2 model, indicating its effectiveness in capturing detailed interactions for pattern similarity.", "description": "4.4.1. Pattern Similarity Function Here, we study the performance of five candidate functions. As we can see, the choice of pattern similarity function does affect the performance of the Match2 model. Specifically, the dot function has achieved the best performance in terms of all the evaluation metrics. The reason might be that the dot function could capture the detailed interactions of each dimension, which are useful in identifying the similarities between matching patterns. In the following experiments, we will use the dot as the pattern similarity function.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3. Results of different similarity functions in the matching pattern-based module on CQADupStack.' and methodically tackle the question that follows. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich pattern similarity function achieved the highest F1 score?\n" }, { "role": "assistant", "content": "By examining the table, we see that the dot function has the highest F1 score of 55.72, which is the best among all the functions listed. So, the answer is {\"answer\": \"dot\"}." } ], "images": [ "scigen/train/3365.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. Results of different similarity functions in the matching pattern-based module on CQADupStack." ], "reasoning": "By examining the table, we see that the dot function has the highest F1 score of 55.72, which is the best among all the functions listed.", "question": "Which pattern similarity function achieved the highest F1 score?", "answer": "So, the answer is {\"answer\": \"dot\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The l1 function achieved the highest precision. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe l1 function achieved the highest precision.\n" }, { "role": "assistant", "content": "The table shows that the dot function has the highest precision of 56.34, while the l1 function has a precision of 55.90. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3365.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. Results of different similarity functions in the matching pattern-based module on CQADupStack." ], "reasoning": "The table shows that the dot function has the highest precision of 56.34, while the l1 function has a precision of 55.90. Therefore, the statement is refuted.", "statement": "The l1 function achieved the highest precision.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 82: Splits of “Multi30K-MMT” dataset for English, German, French and Czech.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The dataset contains images and captions for training, validation, and test splits, with 29,000 images and captions for training, 1,014 for validation, and 1,000 for testing.Along with English, it contains human translated German, French and Czech sentences. \\shortcitewangvatex:2019 dataset is created for English and Chinese to perform machine translation with video and also multilingual video description generation." } ], "images": [ "scigen/train/3366.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 82: Splits of “Multi30K-MMT” dataset for English, German, French and Czech." ], "reasoning": "The dataset contains images and captions for training, validation, and test splits, with 29,000 images and captions for training, 1,014 for validation, and 1,000 for testing.", "description": "Along with English, it contains human translated German, French and Czech sentences. \\shortcitewangvatex:2019 dataset is created for English and Chinese to perform machine translation with video and also multilingual video description generation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 82: Splits of “Multi30K-MMT” dataset for English, German, French and Czech.', solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nHow many images and captions are there in the validation split?\n" }, { "role": "assistant", "content": "The table shows that the validation split contains 1,014 images and 1,014 captions. So, the answer is {\"answer\": \"1,014\"}" } ], "images": [ "scigen/train/3366.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 82: Splits of “Multi30K-MMT” dataset for English, German, French and Czech." ], "reasoning": "The table shows that the validation split contains 1,014 images and 1,014 captions.", "question": "How many images and captions are there in the validation split?", "answer": "So, the answer is {\"answer\": \"1,014\"}" }, { "messages": [ { "role": "user", "content": "The training split contains 30,000 images. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe training split contains 30,000 images.\n" }, { "role": "assistant", "content": "The table shows that the training split contains 29,000 images, not 30,000. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3366.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 82: Splits of “Multi30K-MMT” dataset for English, German, French and Czech." ], "reasoning": "The table shows that the training split contains 29,000 images, not 30,000.", "statement": "The training split contains 30,000 images.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 8: Robustness in BLEU[%]. Train: IWSLT’17 EN-FR adding noisy augmented data; Test: IWSLT’17 test by varying edit distance.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The system was tested with clean, noisy, and phonetic encoded data, performing up to 5 edit distance operations.We test the system robustness with noise augmented data on IWSLT’17 EN-FR. In training, we randomly sample 20% of words and substitute each of them with another word, which we randomly select according to its word2vec similarity to the substituted word. We concatenate the clean and noisy data and their phonetic encoded data. For each test sentence, we perform 0, 1, 2, 3, 4, or 5 times of the edit distance operation. We uniform randomly sample the word(s), the type of edit (from deletion, substitution, and insertion), and the substitution word. We trained models on below data: 1. Clean data (C); 2. Noisy data (N); 3. Noisy data concatenated with its Soundex encoding (N+S); 4. Combined clean and noisy data (C+N) as in [cheng2018towards]." } ], "images": [ "scigen/train/3367.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Robustness in BLEU[%]. Train: IWSLT’17 EN-FR adding noisy augmented data; Test: IWSLT’17 test by varying edit distance." ], "reasoning": "The system was tested with clean, noisy, and phonetic encoded data, performing up to 5 edit distance operations.", "description": "We test the system robustness with noise augmented data on IWSLT’17 EN-FR. In training, we randomly sample 20% of words and substitute each of them with another word, which we randomly select according to its word2vec similarity to the substituted word. We concatenate the clean and noisy data and their phonetic encoded data. For each test sentence, we perform 0, 1, 2, 3, 4, or 5 times of the edit distance operation. We uniform randomly sample the word(s), the type of edit (from deletion, substitution, and insertion), and the substitution word. We trained models on below data: 1. Clean data (C); 2. Noisy data (N); 3. Noisy data concatenated with its Soundex encoding (N+S); 4. Combined clean and noisy data (C+N) as in [cheng2018towards].", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 8: Robustness in BLEU[%]. Train: IWSLT’17 EN-FR adding noisy augmented data; Test: IWSLT’17 test by varying edit distance.', unfold the steps to solve the question given next. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nQuestion: Which source data had the highest score for 3 edit distance operations?\n" }, { "role": "assistant", "content": "To find the highest score for 3 edit distance operations, compare the values in the column 'No. of Operations 3' for all source data. The highest value is 27.14 for C+N+Soundex. So, the answer is {\"answer\": \"C+N+Soundex\"}" } ], "images": [ "scigen/train/3367.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Robustness in BLEU[%]. Train: IWSLT’17 EN-FR adding noisy augmented data; Test: IWSLT’17 test by varying edit distance." ], "reasoning": "To find the highest score for 3 edit distance operations, compare the values in the column 'No. of Operations 3' for all source data. The highest value is 27.14 for C+N+Soundex.", "question": "Which source data had the highest score for 3 edit distance operations?", "answer": "So, the answer is {\"answer\": \"C+N+Soundex\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The clean data had the lowest score for 5 edit distance operations., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe clean data had the lowest score for 5 edit distance operations.\n" }, { "role": "assistant", "content": "To verify this, compare the values in the column 'No. of Operations 5' for all source data. The clean data score is 21.11, which is the lowest among all the scores. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3367.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Robustness in BLEU[%]. Train: IWSLT’17 EN-FR adding noisy augmented data; Test: IWSLT’17 test by varying edit distance." ], "reasoning": "To verify this, compare the values in the column 'No. of Operations 5' for all source data. The clean data score is 21.11, which is the lowest among all the scores.", "statement": "The clean data had the lowest score for 5 edit distance operations.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Density result (Converge threshold: 0.001; 1, 2, 3 nearest neighbour). Smaller values, more distributed words in each cluster.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The description explains how the density of point-sets is measured using the distance to the nearest neighbors and how different methods like Pinyin, Random, and K-Means are compared based on this metric.For each point x in the smoothed convex hull of all words/characters, let Di(x) be the distance between x and the i-th nearest neighbor of x in the space X. We then look at either the maximum of Di(x) over all x, or the average. Choosing a larger i captures the ‘density’ of the point-set at larger scales, which is a parameter that can be tuned to be more robust against noise. We numerically integrate the convex hull surface by randomly sampling the points, which are a linear combination of the convex hull corner weighted uniformly at random. Pinyin is the most well-distributed, then random clustering, followed by K-Means." } ], "images": [ "scigen/train/3368.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Density result (Converge threshold: 0.001; 1, 2, 3 nearest neighbour). Smaller values, more distributed words in each cluster." ], "reasoning": "The description explains how the density of point-sets is measured using the distance to the nearest neighbors and how different methods like Pinyin, Random, and K-Means are compared based on this metric.", "description": "For each point x in the smoothed convex hull of all words/characters, let Di(x) be the distance between x and the i-th nearest neighbor of x in the space X. We then look at either the maximum of Di(x) over all x, or the average. Choosing a larger i captures the ‘density’ of the point-set at larger scales, which is a parameter that can be tuned to be more robust against noise. We numerically integrate the convex hull surface by randomly sampling the points, which are a linear combination of the convex hull corner weighted uniformly at random. Pinyin is the most well-distributed, then random clustering, followed by K-Means.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Density result (Converge threshold: 0.001; 1, 2, 3 nearest neighbour). Smaller values, more distributed words in each cluster.', answer the question below by showing each progressive step toward the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method has the lowest maximum distance for the first nearest neighbor?\n" }, { "role": "assistant", "content": "To find the method with the lowest maximum distance for the first nearest neighbor, we look at the 'Max 1' column and compare the values for K-Means, Random, and Pinyin. The lowest value is 0.08 for Pinyin. So, the answer is {\"answer\": \"Pinyin\"}." } ], "images": [ "scigen/train/3368.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Density result (Converge threshold: 0.001; 1, 2, 3 nearest neighbour). Smaller values, more distributed words in each cluster." ], "reasoning": "To find the method with the lowest maximum distance for the first nearest neighbor, we look at the 'Max 1' column and compare the values for K-Means, Random, and Pinyin. The lowest value is 0.08 for Pinyin.", "question": "Which method has the lowest maximum distance for the first nearest neighbor?", "answer": "So, the answer is {\"answer\": \"Pinyin\"}." }, { "messages": [ { "role": "user", "content": "The Pinyin method has the highest sum for the first nearest neighbor. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Pinyin method has the highest sum for the first nearest neighbor.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the 'Sum 1' values for K-Means, Random, and Pinyin. The values are 19.3, 15.5, and 7.19 respectively. Pinyin has the lowest sum, not the highest. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3368.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Density result (Converge threshold: 0.001; 1, 2, 3 nearest neighbour). Smaller values, more distributed words in each cluster." ], "reasoning": "To verify this statement, we compare the 'Sum 1' values for K-Means, Random, and Pinyin. The values are 19.3, 15.5, and 7.19 respectively. Pinyin has the lowest sum, not the highest.", "statement": "The Pinyin method has the highest sum for the first nearest neighbor.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: Translation results in BLEU[%] on large task WMT’18 Bio; Dev: Khresmoi; Test: EDP’17 test. BPE: 89k.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The WMT’18 Biomedical task dataset was used to achieve improvements in BLEU scores for English-French and French-English translations, with the highest improvements seen using W+Metaphone coding.Additionally, we experimented on English-French and French-English with a large dataset: the WMT’18 Biomedical task that contains more than 2 million sentences. We achieved a significant improvement of 2.21 BLEU points for English-French and 4.27 BLEU points for French-English, respectively." } ], "images": [ "scigen/train/3369.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Translation results in BLEU[%] on large task WMT’18 Bio; Dev: Khresmoi; Test: EDP’17 test. BPE: 89k." ], "reasoning": "The WMT’18 Biomedical task dataset was used to achieve improvements in BLEU scores for English-French and French-English translations, with the highest improvements seen using W+Metaphone coding.", "description": "Additionally, we experimented on English-French and French-English with a large dataset: the WMT’18 Biomedical task that contains more than 2 million sentences. We achieved a significant improvement of 2.21 BLEU points for English-French and 4.27 BLEU points for French-English, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: 'Table 5: Translation results in BLEU[%] on large task WMT’18 Bio; Dev: Khresmoi; Test: EDP’17 test. BPE: 89k.'\nQuestion: What coding method achieved the highest BLEU score improvement for French-English translation?\n" }, { "role": "assistant", "content": "The table shows the BLEU score improvements for different coding methods. The highest improvement for French-English translation is 4.27, achieved by W+NYSIIS. So, the answer is {\"answer\": \"W+NYSIIS\"}" } ], "images": [ "scigen/train/3369.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Translation results in BLEU[%] on large task WMT’18 Bio; Dev: Khresmoi; Test: EDP’17 test. BPE: 89k." ], "reasoning": "The table shows the BLEU score improvements for different coding methods. The highest improvement for French-English translation is 4.27, achieved by W+NYSIIS.", "question": "What coding method achieved the highest BLEU score improvement for French-English translation?", "answer": "So, the answer is {\"answer\": \"W+NYSIIS\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The W+Soundex coding method achieved a BLEU score improvement for French-English translation., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe W+Soundex coding method achieved a BLEU score improvement for French-English translation.\n" }, { "role": "assistant", "content": "The table does not provide any BLEU score improvement for the W+Soundex coding method for French-English translation. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3369.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Translation results in BLEU[%] on large task WMT’18 Bio; Dev: Khresmoi; Test: EDP’17 test. BPE: 89k." ], "reasoning": "The table does not provide any BLEU score improvement for the W+Soundex coding method for French-English translation.", "statement": "The W+Soundex coding method achieved a BLEU score improvement for French-English translation.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 7: Translation results in BLEU[%] for ZH-EN.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Adding auxiliary information such as Pinyin, Pinyin in letters, and Wubi encoding significantly improves BLEU scores over the baseline, with the highest improvement seen with W+random clustering characters.We apply Pinyin [pinyinwubipackage], and Pinyin segmented into letters, and Wubi encoding [pinyinwubipackage]. We achieve significant improvement over the baseline by adding auxiliary information: 0.87 BLEU points with Pinyin, 1.68 BLEU points with Pinyin in letters, and 1.11 BLEU points with Wubi, respectively. Randomly clustering on Chinese characters and words both improve the baseline with 1.49 and 1.47 BLEU points, respectively, a more significant improvement than that of the K-Means clustering." } ], "images": [ "scigen/train/3370.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Translation results in BLEU[%] for ZH-EN." ], "reasoning": "Adding auxiliary information such as Pinyin, Pinyin in letters, and Wubi encoding significantly improves BLEU scores over the baseline, with the highest improvement seen with W+random clustering characters.", "description": "We apply Pinyin [pinyinwubipackage], and Pinyin segmented into letters, and Wubi encoding [pinyinwubipackage]. We achieve significant improvement over the baseline by adding auxiliary information: 0.87 BLEU points with Pinyin, 1.68 BLEU points with Pinyin in letters, and 1.11 BLEU points with Wubi, respectively. Randomly clustering on Chinese characters and words both improve the baseline with 1.49 and 1.47 BLEU points, respectively, a more significant improvement than that of the K-Means clustering.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 7: Translation results in BLEU[%] for ZH-EN.', answer the question below by showing each progressive step toward the answer. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich coding method achieved the highest BLEU score improvement over the baseline?\n" }, { "role": "assistant", "content": "By comparing the BLEU scores in the table, the highest score is achieved by W+random clustering characters with a score of 18.49. So, the answer is {\"answer\": \"W+random clustering characters\"}" } ], "images": [ "scigen/train/3370.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Translation results in BLEU[%] for ZH-EN." ], "reasoning": "By comparing the BLEU scores in the table, the highest score is achieved by W+random clustering characters with a score of 18.49.", "question": "Which coding method achieved the highest BLEU score improvement over the baseline?", "answer": "So, the answer is {\"answer\": \"W+random clustering characters\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Wubi encoding achieved a BLEU score higher than the baseline., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWubi encoding achieved a BLEU score higher than the baseline.\n" }, { "role": "assistant", "content": "The table shows that Wubi encoding has a BLEU score of 14.43, which is lower than the baseline score of 17.00. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3370.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Translation results in BLEU[%] for ZH-EN." ], "reasoning": "The table shows that Wubi encoding has a BLEU score of 14.43, which is lower than the baseline score of 17.00.", "statement": "Wubi encoding achieved a BLEU score higher than the baseline.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Fusion results approaches for two OOV KWS approaches in terms of MTWV metric'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Combining all systems for both approaches results in an overall accuracy improvement of 7.4% relative MTWV over the best fusion result.The list-level combination of all the systems for both approaches provides an additional improvement in overall accuracy (MTWV=0.795), which corresponds to 7.4% of relative MTWV improvement over the best fusion result." } ], "images": [ "scigen/train/3371.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Fusion results approaches for two OOV KWS approaches in terms of MTWV metric" ], "reasoning": "Combining all systems for both approaches results in an overall accuracy improvement of 7.4% relative MTWV over the best fusion result.", "description": "The list-level combination of all the systems for both approaches provides an additional improvement in overall accuracy (MTWV=0.795), which corresponds to 7.4% of relative MTWV improvement over the best fusion result.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 2: Fusion results approaches for two OOV KWS approaches in terms of MTWV metric\nQuestion: What is the MTWV improvement percentage when combining all systems for both approaches?\n" }, { "role": "assistant", "content": "The description states that the combination of all systems for both approaches provides a 7.4% relative MTWV improvement over the best fusion result. So, the answer is {\"answer\": \"7.4%\"}" } ], "images": [ "scigen/train/3371.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Fusion results approaches for two OOV KWS approaches in terms of MTWV metric" ], "reasoning": "The description states that the combination of all systems for both approaches provides a 7.4% relative MTWV improvement over the best fusion result.", "question": "What is the MTWV improvement percentage when combining all systems for both approaches?", "answer": "So, the answer is {\"answer\": \"7.4%\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe MTWV for the 'Lists' approach using the OOV decoder is higher than using Proxies.\n" }, { "role": "assistant", "content": "The table shows that the MTWV for the 'Lists' approach using the OOV decoder is 0.740, while using Proxies it is 0.682, indicating that the MTWV for the OOV decoder is indeed higher. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3371.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Fusion results approaches for two OOV KWS approaches in terms of MTWV metric" ], "reasoning": "The table shows that the MTWV for the 'Lists' approach using the OOV decoder is 0.740, while using Proxies it is 0.682, indicating that the MTWV for the OOV decoder is indeed higher.", "statement": "The MTWV for the 'Lists' approach using the OOV decoder is higher than using Proxies.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Comparison of two approaches for OOV KWS in terms of MTWV metric and speed for different AMs'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The proposed OOV decoder outperforms the proxy-based search in terms of MTWV metric and speed, providing a fast search method with low RTF.Experimental results show that the proposed OOV decoder significantly outperforms (in terms of MTWV metric and speed) The real time factor (RTF) was calculated per word. The comparison of the two OOV KWS approaches on the lattice-level fusion results of the nine different ASR systems fusion of the OOV decoder and the proxy-based search (in the point of low RTF) can provide a very fast search method that significantly outperforms the both approaches in MTWV." } ], "images": [ "scigen/train/3372.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of two approaches for OOV KWS in terms of MTWV metric and speed for different AMs" ], "reasoning": "The proposed OOV decoder outperforms the proxy-based search in terms of MTWV metric and speed, providing a fast search method with low RTF.", "description": "Experimental results show that the proposed OOV decoder significantly outperforms (in terms of MTWV metric and speed) The real time factor (RTF) was calculated per word. The comparison of the two OOV KWS approaches on the lattice-level fusion results of the nine different ASR systems fusion of the OOV decoder and the proxy-based search (in the point of low RTF) can provide a very fast search method that significantly outperforms the both approaches in MTWV.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 1: Comparison of two approaches for OOV KWS in terms of MTWV metric and speed for different AMs', answer the following question. Give detailed solution consisting of each step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich ASR system has the highest MTWV for the OOV decoder?\n" }, { "role": "assistant", "content": "To find the ASR system with the highest MTWV for the OOV decoder, look at the 'OOV decoder MTWV' column and identify the highest value, which is 0.630 for DMN9. So, the answer is {\"answer\": \"DMN9\"}." } ], "images": [ "scigen/train/3372.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of two approaches for OOV KWS in terms of MTWV metric and speed for different AMs" ], "reasoning": "To find the ASR system with the highest MTWV for the OOV decoder, look at the 'OOV decoder MTWV' column and identify the highest value, which is 0.630 for DMN9.", "question": "Which ASR system has the highest MTWV for the OOV decoder?", "answer": "So, the answer is {\"answer\": \"DMN9\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The OOV decoder has a lower RTF than the proxies for all ASR systems. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe OOV decoder has a lower RTF than the proxies for all ASR systems.\n" }, { "role": "assistant", "content": "By comparing the 'OOV decoder RTF' and 'Proxies RTF' columns for all ASR systems, it is evident that the OOV decoder RTF values are consistently higher than the Proxies RTF values. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3372.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of two approaches for OOV KWS in terms of MTWV metric and speed for different AMs" ], "reasoning": "By comparing the 'OOV decoder RTF' and 'Proxies RTF' columns for all ASR systems, it is evident that the OOV decoder RTF values are consistently higher than the Proxies RTF values.", "statement": "The OOV decoder has a lower RTF than the proxies for all ASR systems.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 7: Effect of beam width', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Greedy search is used as an approximation method for full inference over decision variables, as shown by varying beam widths and their corresponding BLEU scores.We perform greedy search as an approximation to full inference over the set of decision variables" } ], "images": [ "scigen/train/3373.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Effect of beam width" ], "reasoning": "Greedy search is used as an approximation method for full inference over decision variables, as shown by varying beam widths and their corresponding BLEU scores.", "description": "We perform greedy search as an approximation to full inference over the set of decision variables", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 7: Effect of beam width\nQuestion: What is the difference in dev sBLEU score between beam width 1 and beam width 10?\nSolve the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the difference in dev sBLEU score between beam width 1 and beam width 10, subtract the score at beam width 10 from the score at beam width 1: 65.58 - 47.07. So, the answer is {\"answer\": \"18.51\"}" } ], "images": [ "scigen/train/3373.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Effect of beam width" ], "reasoning": "To find the difference in dev sBLEU score between beam width 1 and beam width 10, subtract the score at beam width 10 from the score at beam width 1: 65.58 - 47.07.", "question": "What is the difference in dev sBLEU score between beam width 1 and beam width 10?", "answer": "So, the answer is {\"answer\": \"18.51\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The test cBLEU score decreases as the beam width increases. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe test cBLEU score decreases as the beam width increases.\n" }, { "role": "assistant", "content": "By examining the test cBLEU scores for beam widths 1, 2, 5, and 10, we see that the scores decrease from 70.39 to 51.01 as the beam width increases. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3373.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Effect of beam width" ], "reasoning": "By examining the test cBLEU scores for beam widths 1, 2, 5, and 10, we see that the scores decrease from 70.39 to 51.01 as the beam width increases.", "statement": "The test cBLEU score decreases as the beam width increases.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Primary WeatherGov results'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our method outperforms previous state-of-the-art methods in F-1, sBLEU, and cBLEU scores with relative improvements of 11.94%, 58.88%, and 36.68%, respectively.We report the performance of content selection and surface realization using F-1 and two BLEU scores (standard sBLEU and the customized cBLEU of \\newciteangeli-10), respectively (Sec. Our method achieves the best results reported to-date on all three metrics, with relative improvements of 11.94% (F-1), 58.88% (sBLEU), and 36.68% (cBLEU) over the previous state-of-the-art." } ], "images": [ "scigen/train/3374.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Primary WeatherGov results" ], "reasoning": "Our method outperforms previous state-of-the-art methods in F-1, sBLEU, and cBLEU scores with relative improvements of 11.94%, 58.88%, and 36.68%, respectively.", "description": "We report the performance of content selection and surface realization using F-1 and two BLEU scores (standard sBLEU and the customized cBLEU of \\newciteangeli-10), respectively (Sec. Our method achieves the best results reported to-date on all three metrics, with relative improvements of 11.94% (F-1), 58.88% (sBLEU), and 36.68% (cBLEU) over the previous state-of-the-art.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat are the F-1, sBLEU, and cBLEU scores achieved by 'Our model'?\nSolve the above question based on the table titled 'Table 1: Primary WeatherGov results'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that 'Our model' achieved an F-1 score of 73.21, an sBLEU score of 61.01, and a cBLEU score of 70.39. So, the answer is {\"answer\": \"F-1: 73.21, sBLEU: 61.01, cBLEU: 70.39\"}." } ], "images": [ "scigen/train/3374.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Primary WeatherGov results" ], "reasoning": "The table shows that 'Our model' achieved an F-1 score of 73.21, an sBLEU score of 61.01, and a cBLEU score of 70.39.", "question": "What are the F-1, sBLEU, and cBLEU scores achieved by 'Our model'?", "answer": "So, the answer is {\"answer\": \"F-1: 73.21, sBLEU: 61.01, cBLEU: 70.39\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The method 'citecite.angeli-10ALK10' achieved an sBLEU score higher than 38.00. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe method 'citecite.angeli-10ALK10' achieved an sBLEU score higher than 38.00.\n" }, { "role": "assistant", "content": "The table shows that 'citecite.angeli-10ALK10' achieved an sBLEU score of 38.40, which is higher than 38.00. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3374.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Primary WeatherGov results" ], "reasoning": "The table shows that 'citecite.angeli-10ALK10' achieved an sBLEU score of 38.40, which is higher than 38.00.", "statement": "The method 'citecite.angeli-10ALK10' achieved an sBLEU score higher than 38.00.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: Encoder ablation (dev set)'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The encoder significantly improves performance metrics, indicating its ability to capture essential relationships among records.Encoder Ablation Next, we consider the effectiveness of the encoder. We attribute this improvement to the LSTM-RNN’s ability to capture the relationships that exist among the records, which is known to be essential to selective generation" } ], "images": [ "scigen/train/3375.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Encoder ablation (dev set)" ], "reasoning": "The encoder significantly improves performance metrics, indicating its ability to capture essential relationships among records.", "description": "Encoder Ablation Next, we consider the effectiveness of the encoder. We attribute this improvement to the LSTM-RNN’s ability to capture the relationships that exist among the records, which is known to be essential to selective generation", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the difference in F-1 score when using the encoder compared to not using it?\nSolve the above question based on the table titled 'Table 4: Encoder ablation (dev set)'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The F-1 score with the encoder is 76.28, and without the encoder, it is 57.45. The difference is 76.28 - 57.45. So, the answer is {\"answer\": \"18.83\"}." } ], "images": [ "scigen/train/3375.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Encoder ablation (dev set)" ], "reasoning": "The F-1 score with the encoder is 76.28, and without the encoder, it is 57.45. The difference is 76.28 - 57.45.", "question": "What is the difference in F-1 score when using the encoder compared to not using it?", "answer": "So, the answer is {\"answer\": \"18.83\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe encoder improves the sBLEU score by more than 10 points.\n" }, { "role": "assistant", "content": "The sBLEU score with the encoder is 65.58, and without the encoder, it is 56.47. The difference is 65.58 - 56.47, which is 9.11 points. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3375.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Encoder ablation (dev set)" ], "reasoning": "The sBLEU score with the encoder is 65.58, and without the encoder, it is 56.47. The difference is 65.58 - 56.47, which is 9.11 points.", "statement": "The encoder improves the sBLEU score by more than 10 points.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: RoboCup results'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our model achieved the highest F-1 score of 81.58 and a competitive sBLEUG score of 29.40 on the RoboCup dataset.Following previous work, we perform two experiments on the RoboCup dataset Additionally, we achieve an selection F-1 score of 81.58, which is also the best result reported to-date. In the case of assumed (known) ground-truth content selection, our model attains an sBLEUG score of 29.40, which is competitive with the state-of-the-art." } ], "images": [ "scigen/train/3376.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: RoboCup results" ], "reasoning": "Our model achieved the highest F-1 score of 81.58 and a competitive sBLEUG score of 29.40 on the RoboCup dataset.", "description": "Following previous work, we perform two experiments on the RoboCup dataset Additionally, we achieve an selection F-1 score of 81.58, which is also the best result reported to-date. In the case of assumed (known) ground-truth content selection, our model attains an sBLEUG score of 29.40, which is competitive with the state-of-the-art.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 6: RoboCup results\n\nQuestion: What is the sBLEUG score of the model with the highest F-1 score?\n" }, { "role": "assistant", "content": "The model with the highest F-1 score is 'Our model' with an F-1 score of 81.58. The corresponding sBLEUG score for this model is 29.40. So, the answer is {\"answer\": \"29.40\"}" } ], "images": [ "scigen/train/3376.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: RoboCup results" ], "reasoning": "The model with the highest F-1 score is 'Our model' with an F-1 score of 81.58. The corresponding sBLEUG score for this model is 29.40.", "question": "What is the sBLEUG score of the model with the highest F-1 score?", "answer": "So, the answer is {\"answer\": \"29.40\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe model with the highest sBLEUG score also has the highest F-1 score.\n" }, { "role": "assistant", "content": "The model with the highest sBLEUG score is 'citecite.konstas-12KL12' with a score of 30.90. The model with the highest F-1 score is 'Our model' with a score of 81.58. These are different models. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3376.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: RoboCup results" ], "reasoning": "The model with the highest sBLEUG score is 'citecite.konstas-12KL12' with a score of 30.90. The model with the highest F-1 score is 'Our model' with a score of 81.58. These are different models.", "statement": "The model with the highest sBLEUG score also has the highest F-1 score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Accuracy percentage for all train-test pairs. Domains on rows are source domains and columns are target domains. Domain labels are D1: Amazon Instant Video, D2: Automotive, D3: Baby, D4: Beauty, D5: Books, D6: Clothing Accessories, D7: Electronics, D8: Health, D9: Home, D10: Kitchen, D11: Movies TV, D12: Music, D13: Office Products, D14: Patio, D15: Pet Supplies, D15: Shoes, D16: Software, D17: Sports Outdoors, D18: Tools Home Improvement, D19: Toys Games, D20: Video Games.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The dataset is normalized by removing numerical values, punctuations, stop words, and changing all words to lower case. An LSTM-based sentiment classifier with pre-trained GloVe embeddings is trained for 20 epochs with a dropout factor of 0.2 and sigmoid activation, achieving a 5-fold classification accuracy with an 8000-2000 train-test split.We normalize the dataset by removing numerical values, punctuations, stop words, and changing all words to the lower case. To train the sentiment classifier, we use an LSTM-based sentiment classifier. It consists of an embedding layer initialized with pre-trained GloVe word embeddings of 100 dimensions. We train this model for 20 epochs with a dropout factor of 0.2 and use sigmoid as the activation function. For In-domain sentiment analysis, we report a 5-fold classification accuracy with a train-test split of 8000 and 2000 reviews." } ], "images": [ "scigen/train/3377.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Accuracy percentage for all train-test pairs. Domains on rows are source domains and columns are target domains. Domain labels are D1: Amazon Instant Video, D2: Automotive, D3: Baby, D4: Beauty, D5: Books, D6: Clothing Accessories, D7: Electronics, D8: Health, D9: Home, D10: Kitchen, D11: Movies TV, D12: Music, D13: Office Products, D14: Patio, D15: Pet Supplies, D15: Shoes, D16: Software, D17: Sports Outdoors, D18: Tools Home Improvement, D19: Toys Games, D20: Video Games." ], "reasoning": "The dataset is normalized by removing numerical values, punctuations, stop words, and changing all words to lower case. An LSTM-based sentiment classifier with pre-trained GloVe embeddings is trained for 20 epochs with a dropout factor of 0.2 and sigmoid activation, achieving a 5-fold classification accuracy with an 8000-2000 train-test split.", "description": "We normalize the dataset by removing numerical values, punctuations, stop words, and changing all words to the lower case. To train the sentiment classifier, we use an LSTM-based sentiment classifier. It consists of an embedding layer initialized with pre-trained GloVe word embeddings of 100 dimensions. We train this model for 20 epochs with a dropout factor of 0.2 and use sigmoid as the activation function. For In-domain sentiment analysis, we report a 5-fold classification accuracy with a train-test split of 8000 and 2000 reviews.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 1: Accuracy percentage for all train-test pairs. Domains on rows are source domains and columns are target domains. Domain labels are D1: Amazon Instant Video, D2: Automotive, D3: Baby, D4: Beauty, D5: Books, D6: Clothing Accessories, D7: Electronics, D8: Health, D9: Home, D10: Kitchen, D11: Movies TV, D12: Music, D13: Office Products, D14: Patio, D15: Pet Supplies, D15: Shoes, D16: Software, D17: Sports Outdoors, D18: Tools Home Improvement, D19: Toys Games, D20: Video Games.', answer the following question. Give detailed solution consisting of each step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the highest value in the table and which column and row does it belong to?\n" }, { "role": "assistant", "content": "To find the highest value in the table, we need to scan through all the cells and identify the maximum value. The highest value is 86.77, which is located in column D19 and row D5. So, the answer is {\"answer\": \"86.77 in D19 and D5\"}" } ], "images": [ "scigen/train/3377.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Accuracy percentage for all train-test pairs. Domains on rows are source domains and columns are target domains. Domain labels are D1: Amazon Instant Video, D2: Automotive, D3: Baby, D4: Beauty, D5: Books, D6: Clothing Accessories, D7: Electronics, D8: Health, D9: Home, D10: Kitchen, D11: Movies TV, D12: Music, D13: Office Products, D14: Patio, D15: Pet Supplies, D15: Shoes, D16: Software, D17: Sports Outdoors, D18: Tools Home Improvement, D19: Toys Games, D20: Video Games." ], "reasoning": "To find the highest value in the table, we need to scan through all the cells and identify the maximum value. The highest value is 86.77, which is located in column D19 and row D5.", "question": "What is the highest value in the table and which column and row does it belong to?", "answer": "So, the answer is {\"answer\": \"86.77 in D19 and D5\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe highest value in the table is in column D19 and row D5.\n" }, { "role": "assistant", "content": "By scanning through the table, we find that the highest value is indeed 86.77, which is located in column D19 and row D5. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3377.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Accuracy percentage for all train-test pairs. Domains on rows are source domains and columns are target domains. Domain labels are D1: Amazon Instant Video, D2: Automotive, D3: Baby, D4: Beauty, D5: Books, D6: Clothing Accessories, D7: Electronics, D8: Health, D9: Home, D10: Kitchen, D11: Movies TV, D12: Music, D13: Office Products, D14: Patio, D15: Pet Supplies, D15: Shoes, D16: Software, D17: Sports Outdoors, D18: Tools Home Improvement, D19: Toys Games, D20: Video Games." ], "reasoning": "By scanning through the table, we find that the highest value is indeed 86.77, which is located in column D19 and row D5.", "statement": "The highest value in the table is in column D19 and row D5.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Our reccomendation chart based on CDSA results: In Domain Accuracy(when source and target domains are same), Average CDSA Accuracy Degradation(average cross-domain testing accuracy loss over all target domains), Best Source Domain, Best Target Domain for each domain.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "D15 has the highest average accuracy degradation, while D18 has the least, appearing four times as the best source domain. D9 is the most frequent best target domain.We also show in-domain sentiment analysis accuracy, the best source domain (on which CDSA classifier is trained), and the best target domain (on which CDSA classifier is tested) in the table. D15 suffers from the maximum average accuracy degradation, and D18 performs the best with least average accuracy degradation, which is also supported by its number of appearances i.e., 4, as the best source domain in the table. As for the best target domain, D9 appears the maximum number of times. The degradation suffered is as high as 23.18%. This highlights the motivation of these experiments: the choice of a source domain is critical. We also observe that the automative domain (D2) is the best source domain for clothing (D6), both being unrelated domains in terms of the products they discuss. This holds for many other domain pairs, implying that mere intuition is not enough for source domain selection." } ], "images": [ "scigen/train/3378.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Our reccomendation chart based on CDSA results: In Domain Accuracy(when source and target domains are same), Average CDSA Accuracy Degradation(average cross-domain testing accuracy loss over all target domains), Best Source Domain, Best Target Domain for each domain." ], "reasoning": "D15 has the highest average accuracy degradation, while D18 has the least, appearing four times as the best source domain. D9 is the most frequent best target domain.", "description": "We also show in-domain sentiment analysis accuracy, the best source domain (on which CDSA classifier is trained), and the best target domain (on which CDSA classifier is tested) in the table. D15 suffers from the maximum average accuracy degradation, and D18 performs the best with least average accuracy degradation, which is also supported by its number of appearances i.e., 4, as the best source domain in the table. As for the best target domain, D9 appears the maximum number of times. The degradation suffered is as high as 23.18%. This highlights the motivation of these experiments: the choice of a source domain is critical. We also observe that the automative domain (D2) is the best source domain for clothing (D6), both being unrelated domains in terms of the products they discuss. This holds for many other domain pairs, implying that mere intuition is not enough for source domain selection.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: Our reccomendation chart based on CDSA results: In Domain Accuracy(when source and target domains are same), Average CDSA Accuracy Degradation(average cross-domain testing accuracy loss over all target domains), Best Source Domain, Best Target Domain for each domain.' to sequentially solve the question presented below. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich domain has the highest average CDSA accuracy degradation and what is its value?\n" }, { "role": "assistant", "content": "By examining the 'Average CDSA Accuracy Degradation' column, D15 has the highest value of 23.18%. So, the answer is {\"answer\": \"D15, 23.18%\"}" } ], "images": [ "scigen/train/3378.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Our reccomendation chart based on CDSA results: In Domain Accuracy(when source and target domains are same), Average CDSA Accuracy Degradation(average cross-domain testing accuracy loss over all target domains), Best Source Domain, Best Target Domain for each domain." ], "reasoning": "By examining the 'Average CDSA Accuracy Degradation' column, D15 has the highest value of 23.18%.", "question": "Which domain has the highest average CDSA accuracy degradation and what is its value?", "answer": "So, the answer is {\"answer\": \"D15, 23.18%\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nD18 appears the most times as the best target domain.\n" }, { "role": "assistant", "content": "The table shows that D18 appears only once as the best target domain, while D9 appears the most times. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3378.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Our reccomendation chart based on CDSA results: In Domain Accuracy(when source and target domains are same), Average CDSA Accuracy Degradation(average cross-domain testing accuracy loss over all target domains), Best Source Domain, Best Target Domain for each domain." ], "reasoning": "The table shows that D18 appears only once as the best target domain, while D9 appears the most times.", "statement": "D18 appears the most times as the best target domain.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title '(b) Goal navigation with RL'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Our model achieves higher accuracy and outperforms baselines in MSE and policy quality in all settings, and is competitive under MD.Our model obtains a slightly higher accuracy of 72% on classification compared to 71% by t-VGG, and outperforms the baselines in MSE and policy quality (PQ) in all settings. Under MD, our model is competitive with the baselines and achieves significantly higher scores in some cases." } ], "images": [ "scigen/train/3379.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) Goal navigation with RL" ], "reasoning": "Our model achieves higher accuracy and outperforms baselines in MSE and policy quality in all settings, and is competitive under MD.", "description": "Our model obtains a slightly higher accuracy of 72% on classification compared to 71% by t-VGG, and outperforms the baselines in MSE and policy quality (PQ) in all settings. Under MD, our model is competitive with the baselines and achieves significantly higher scores in some cases.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about '(b) Goal navigation with RL', solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model achieves the highest PW global PQ score?\n" }, { "role": "assistant", "content": "To find the model with the highest PW global PQ score, compare the values in the PW global PQ column. The highest value is 0.93, achieved by t-RNetAttn (ours). So, the answer is {\"answer\": \"t-RNetAttn (ours)\"}." } ], "images": [ "scigen/train/3379.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) Goal navigation with RL" ], "reasoning": "To find the model with the highest PW global PQ score, compare the values in the PW global PQ column. The highest value is 0.93, achieved by t-RNetAttn (ours).", "question": "Which model achieves the highest PW global PQ score?", "answer": "So, the answer is {\"answer\": \"t-RNetAttn (ours)\"}." }, { "messages": [ { "role": "user", "content": "t-RNetAttn (ours) achieves the highest ISI PQ score. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nt-RNetAttn (ours) achieves the highest ISI PQ score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the ISI PQ scores of all models. The highest ISI PQ score is 0.84, achieved by t-RNetAttn (ours). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3379.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) Goal navigation with RL" ], "reasoning": "To verify this statement, compare the ISI PQ scores of all models. The highest ISI PQ score is 0.84, achieved by t-RNetAttn (ours).", "statement": "t-RNetAttn (ours) achieves the highest ISI PQ score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled '(c) Value map regression'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Our model achieves higher accuracy and outperforms baselines in MSE and policy quality in all settings.Our model obtains a slightly higher accuracy of 72% on classification compared to 71% by t-VGG, and outperforms the baselines in MSE and policy quality (PQ) in all settings. Under MD, our model is competitive with the baselines and achieves significantly higher scores in some cases." } ], "images": [ "scigen/train/3380.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(c) Value map regression" ], "reasoning": "Our model achieves higher accuracy and outperforms baselines in MSE and policy quality in all settings.", "description": "Our model obtains a slightly higher accuracy of 72% on classification compared to 71% by t-VGG, and outperforms the baselines in MSE and policy quality (PQ) in all settings. Under MD, our model is competitive with the baselines and achieves significantly higher scores in some cases.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: (c) Value map regression\n\nQuestion: What is the PW local MSE score for t-RNetAttn compared to t-CNN?\n" }, { "role": "assistant", "content": "The PW local MSE score for t-RNetAttn is 0.22, while for t-CNN it is 0.25. Therefore, t-RNetAttn has a lower PW local MSE score. So, the answer is {\"answer\": \"0.22\"}" } ], "images": [ "scigen/train/3380.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(c) Value map regression" ], "reasoning": "The PW local MSE score for t-RNetAttn is 0.22, while for t-CNN it is 0.25. Therefore, t-RNetAttn has a lower PW local MSE score.", "question": "What is the PW local MSE score for t-RNetAttn compared to t-CNN?", "answer": "So, the answer is {\"answer\": \"0.22\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nt-RNetAttn has the lowest PW local MSE score.\n" }, { "role": "assistant", "content": "The table shows that t-RNetAttn has a PW local MSE score of 0.22, which is lower than the scores of t-CNN (0.25) and t-UVFA (3.23). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3380.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(c) Value map regression" ], "reasoning": "The table shows that t-RNetAttn has a PW local MSE score of 0.22, which is lower than the scores of t-CNN (0.25) and t-UVFA (3.23).", "statement": "t-RNetAttn has the lowest PW local MSE score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Comparison to the state-of-the-art methods with single model on the VQA dataset. † indicates concurrent works.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "VL-BERT surpasses BUTD by over 5% in accuracy and achieves better performance than other concurrent works except for LXMERT.Pre-training VL-BERT improves the performance by 1.6%, which validates the importance of pre-training. VL-BERT shares the same input (i.e., question, image, and RoIs), output and experimental protocol with BUTD, a prevalent model specifically designed for the task. Still, VL-BERT surpasses BUTD by over 5% in accuracy. Except for LXMERT, our VL-BERT achieves better performance than the other concurrent works. This is because LXMERT is pre-trained on massive visual question answering data (aggregating almost all the VQA datasets based on COCO and Visual Genome). While our model is only pre-trained on captioning and text- only dataset, where there is still gap with the VQA task." } ], "images": [ "scigen/train/3381.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison to the state-of-the-art methods with single model on the VQA dataset. † indicates concurrent works." ], "reasoning": "VL-BERT surpasses BUTD by over 5% in accuracy and achieves better performance than other concurrent works except for LXMERT.", "description": "Pre-training VL-BERT improves the performance by 1.6%, which validates the importance of pre-training. VL-BERT shares the same input (i.e., question, image, and RoIs), output and experimental protocol with BUTD, a prevalent model specifically designed for the task. Still, VL-BERT surpasses BUTD by over 5% in accuracy. Except for LXMERT, our VL-BERT achieves better performance than the other concurrent works. This is because LXMERT is pre-trained on massive visual question answering data (aggregating almost all the VQA datasets based on COCO and Visual Genome). While our model is only pre-trained on captioning and text- only dataset, where there is still gap with the VQA task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Comparison to the state-of-the-art methods with single model on the VQA dataset. † indicates concurrent works.\nQuestion: Which model achieves the highest test-std accuracy?\nSolve the above question based on the table information. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the test-std accuracies of all models in the table, LXMERT has the highest test-std accuracy of 72.54. So, the answer is {\"answer\": \"LXMERT\"}" } ], "images": [ "scigen/train/3381.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison to the state-of-the-art methods with single model on the VQA dataset. † indicates concurrent works." ], "reasoning": "By comparing the test-std accuracies of all models in the table, LXMERT has the highest test-std accuracy of 72.54.", "question": "Which model achieves the highest test-std accuracy?", "answer": "So, the answer is {\"answer\": \"LXMERT\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nVL-BERTBASE without pre-training performs better than BUTD.\n" }, { "role": "assistant", "content": "The table shows that VL-BERTBASE without pre-training has a test-dev accuracy of 69.58, which is higher than BUTD's test-dev accuracy of 65.32. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3381.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison to the state-of-the-art methods with single model on the VQA dataset. † indicates concurrent works." ], "reasoning": "The table shows that VL-BERTBASE without pre-training has a test-dev accuracy of 69.58, which is higher than BUTD's test-dev accuracy of 65.32.", "statement": "VL-BERTBASE without pre-training performs better than BUTD.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Comparison to the state-of-the-art methods with single model on the VCR dataset. † indicates concurrent works.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "VL-BERT outperforms R2C and other concurrent works, achieving state-of-the-art performance without using ad-hoc task-specific modules.Pre-training VL-BERT improves the performance by 1.0% in the final Q → AR task, which validates the effectiveness of pre-training. Compared with R2C, we do not use ad-hoc task-specific modules. Instead, we simply adopt the generic representation of VL-BERT and jointly train the whole model end-to-end. Despite the same input, output and experimental protocol as R2C, VL-BERT outperforms R2C by large margins, indicating the power of our simple cross-modal architecture. Compared with other concurrent works, i.e., ViLBERT, VisualBERT and B2T2, our VL-BERT achieves the state-of-the-art performance." } ], "images": [ "scigen/train/3382.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison to the state-of-the-art methods with single model on the VCR dataset. † indicates concurrent works." ], "reasoning": "VL-BERT outperforms R2C and other concurrent works, achieving state-of-the-art performance without using ad-hoc task-specific modules.", "description": "Pre-training VL-BERT improves the performance by 1.0% in the final Q → AR task, which validates the effectiveness of pre-training. Compared with R2C, we do not use ad-hoc task-specific modules. Instead, we simply adopt the generic representation of VL-BERT and jointly train the whole model end-to-end. Despite the same input, output and experimental protocol as R2C, VL-BERT outperforms R2C by large margins, indicating the power of our simple cross-modal architecture. Compared with other concurrent works, i.e., ViLBERT, VisualBERT and B2T2, our VL-BERT achieves the state-of-the-art performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the test performance of VL-BERTBASE in the Q → AR task?\nSolve the above question based on the table titled 'Table 1: Comparison to the state-of-the-art methods with single model on the VCR dataset. † indicates concurrent works.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the test performance of VL-BERTBASE in the Q → AR task, locate the row for VL-BERTBASE and the column for Q → AR test. The value is 55.2. So, the answer is {\"answer\": \"55.2\"}." } ], "images": [ "scigen/train/3382.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison to the state-of-the-art methods with single model on the VCR dataset. † indicates concurrent works." ], "reasoning": "To find the test performance of VL-BERTBASE in the Q → AR task, locate the row for VL-BERTBASE and the column for Q → AR test. The value is 55.2.", "question": "What is the test performance of VL-BERTBASE in the Q → AR task?", "answer": "So, the answer is {\"answer\": \"55.2\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nVL-BERTBASE outperforms VisualBERT in the Q → AR test task.\n" }, { "role": "assistant", "content": "Compare the test performance of VL-BERTBASE and VisualBERT in the Q → AR task. VL-BERTBASE has a score of 55.2, while VisualBERT has a score of 52.4. Therefore, VL-BERTBASE outperforms VisualBERT. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3382.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison to the state-of-the-art methods with single model on the VCR dataset. † indicates concurrent works." ], "reasoning": "Compare the test performance of VL-BERTBASE and VisualBERT in the Q → AR task. VL-BERTBASE has a score of 55.2, while VisualBERT has a score of 52.4. Therefore, VL-BERTBASE outperforms VisualBERT.", "statement": "VL-BERTBASE outperforms VisualBERT in the Q → AR test task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Comparison to the state-of-the-art methods with single model on the RefCOCO+ dataset. † indicates concurrent work.'.\n" }, { "role": "assistant", "content": "VL-BERT significantly improves performance over MAttNet and achieves comparable results to ViLBERT without task-specific designs.Pre-trained VL-BERT significantly improves the performance. Compared with MAttNet, VL-BERT is much simpler without task-specific architecture designs, yet much better. VL-BERT achieves comparable performance with the concurrent work of ViLBERT." } ], "images": [ "scigen/train/3383.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison to the state-of-the-art methods with single model on the RefCOCO+ dataset. † indicates concurrent work." ], "reasoning": "VL-BERT significantly improves performance over MAttNet and achieves comparable results to ViLBERT without task-specific designs.", "description": "Pre-trained VL-BERT significantly improves the performance. Compared with MAttNet, VL-BERT is much simpler without task-specific architecture designs, yet much better. VL-BERT achieves comparable performance with the concurrent work of ViLBERT.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 3: Comparison to the state-of-the-art methods with single model on the RefCOCO+ dataset. † indicates concurrent work.\nQuestion: Which model achieves the highest performance on the testA set for Ground-truth Regions?\n" }, { "role": "assistant", "content": "By comparing the values in the 'Ground-truth Regions' column for the testA set, VL-BERTLARGE achieves the highest performance with a score of 83.62. So, the answer is {\"answer\": \"VL-BERTLARGE\"}." } ], "images": [ "scigen/train/3383.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison to the state-of-the-art methods with single model on the RefCOCO+ dataset. † indicates concurrent work." ], "reasoning": "By comparing the values in the 'Ground-truth Regions' column for the testA set, VL-BERTLARGE achieves the highest performance with a score of 83.62.", "question": "Which model achieves the highest performance on the testA set for Ground-truth Regions?", "answer": "So, the answer is {\"answer\": \"VL-BERTLARGE\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nVL-BERTBASE outperforms MAttNet on the val set for Detected Regions.\n" }, { "role": "assistant", "content": "By comparing the values in the 'Detected Regions' column for the val set, VL-BERTBASE has a score of 71.60, which is higher than MAttNet's score of 65.33. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3383.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison to the state-of-the-art methods with single model on the RefCOCO+ dataset. † indicates concurrent work." ], "reasoning": "By comparing the values in the 'Detected Regions' column for the val set, VL-BERTBASE has a score of 71.60, which is higher than MAttNet's score of 65.33.", "statement": "VL-BERTBASE outperforms MAttNet on the val set for Detected Regions.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Figure 4: Analysis and visualization of the best model’s result with different reading text levels.'.\n" }, { "role": "assistant", "content": "The system performed best on 2nd grade questions with over 78% accuracy, while it struggled more with 3rd to 5th grade questions, achieving less than 68% accuracy.We can observe that the difficulty of the reading comprehension task increases together with the level of reading text. The system could answer questions of the 2nd grade well, over 78% in accuracy. It was more challenging to predict correct answers for questions of the 3rd to 5th grades (less than 68%). The performance on 1st grade questions is not as high as that on the 2nd grade questions because the amount of questions of the 1st grade is much fewer than those of other grades." } ], "images": [ "scigen/train/3384.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 4: Analysis and visualization of the best model’s result with different reading text levels." ], "reasoning": "The system performed best on 2nd grade questions with over 78% accuracy, while it struggled more with 3rd to 5th grade questions, achieving less than 68% accuracy.", "description": "We can observe that the difficulty of the reading comprehension task increases together with the level of reading text. The system could answer questions of the 2nd grade well, over 78% in accuracy. It was more challenging to predict correct answers for questions of the 3rd to 5th grades (less than 68%). The performance on 1st grade questions is not as high as that on the 2nd grade questions because the amount of questions of the 1st grade is much fewer than those of other grades.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Figure 4: Analysis and visualization of the best model’s result with different reading text levels.' and methodically tackle the question that follows. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the accuracy percentage for 3rd grade questions and how does it compare to the accuracy percentage for 2nd grade questions?\n" }, { "role": "assistant", "content": "The accuracy for 3rd grade questions is 67.86%, which is lower than the accuracy for 2nd grade questions, which is 78.85%. So, the answer is {\"answer\": \"67.86% is lower than 78.85%\"}" } ], "images": [ "scigen/train/3384.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 4: Analysis and visualization of the best model’s result with different reading text levels." ], "reasoning": "The accuracy for 3rd grade questions is 67.86%, which is lower than the accuracy for 2nd grade questions, which is 78.85%.", "question": "What is the accuracy percentage for 3rd grade questions and how does it compare to the accuracy percentage for 2nd grade questions?", "answer": "So, the answer is {\"answer\": \"67.86% is lower than 78.85%\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe system answered more questions correctly for the 3rd grade than for the 4th grade.\n" }, { "role": "assistant", "content": "The table shows that the system answered 57 questions correctly for the 3rd grade and 47 questions correctly for the 4th grade. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3384.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 4: Analysis and visualization of the best model’s result with different reading text levels." ], "reasoning": "The table shows that the system answered 57 questions correctly for the 3rd grade and 47 questions correctly for the 4th grade.", "statement": "The system answered more questions correctly for the 3rd grade than for the 4th grade.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Comparison between our corpus and the MCTest corpus.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our corpus has fewer reading texts but more questions and higher average words per text, question, and answer compared to the MCTest corpus.In this section, we compare our corpus with the MCTest corpus. The size of the MCTest corpus is approximately the same as our corpus. As can be seen from the table, although the number of reading texts in our corpus is less than that of the MCTest corpus, the number of questions of our corpus is greater. Besides, the average numbers of words per reading text, per question and per answer in our corpus are also higher than those of the MCTest corpus." } ], "images": [ "scigen/train/3385.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison between our corpus and the MCTest corpus." ], "reasoning": "Our corpus has fewer reading texts but more questions and higher average words per text, question, and answer compared to the MCTest corpus.", "description": "In this section, we compare our corpus with the MCTest corpus. The size of the MCTest corpus is approximately the same as our corpus. As can be seen from the table, although the number of reading texts in our corpus is less than that of the MCTest corpus, the number of questions of our corpus is greater. Besides, the average numbers of words per reading text, per question and per answer in our corpus are also higher than those of the MCTest corpus.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: Comparison between our corpus and the MCTest corpus.' to sequentially solve the question presented below. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nHow does the average number of words per answer in our corpus compare to that in the MCTest (560) corpus?\n" }, { "role": "assistant", "content": "The table shows that the average number of words per answer in our corpus is 7.5, while in the MCTest (560) corpus, it is 3.4. Therefore, our corpus has a higher average number of words per answer. So, the answer is {\"answer\": \"7.5\"}." } ], "images": [ "scigen/train/3385.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison between our corpus and the MCTest corpus." ], "reasoning": "The table shows that the average number of words per answer in our corpus is 7.5, while in the MCTest (560) corpus, it is 3.4. Therefore, our corpus has a higher average number of words per answer.", "question": "How does the average number of words per answer in our corpus compare to that in the MCTest (560) corpus?", "answer": "So, the answer is {\"answer\": \"7.5\"}." }, { "messages": [ { "role": "user", "content": "The number of questions in our corpus is less than that in the MCTest (500) corpus. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe number of questions in our corpus is less than that in the MCTest (500) corpus.\n" }, { "role": "assistant", "content": "The table shows that the number of questions in our corpus is 2,783, while in the MCTest (500) corpus, it is 2,000. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3385.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison between our corpus and the MCTest corpus." ], "reasoning": "The table shows that the number of questions in our corpus is 2,783, while in the MCTest (500) corpus, it is 2,000. Therefore, the statement is refuted.", "statement": "The number of questions in our corpus is less than that in the MCTest (500) corpus.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: Experimental results of different models with various pre-trained word embeddings on our corpus ViMMRC.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The proposed method with ELMO word embedding achieves the highest accuracy on both the test and development sets, outperforming other methods by a significant margin.Sliding Window and Distance-based Sliding Window achieve different performances, 58.50% and 60.55%, on the development set but they have the same accuracy of 56.30% on the test set. Our proposed method achieves the accuracies over 60% on the test set and over 61% on the development set. Specifically, this method with the ELMO word embedding achieves the highest results on both of the test and development sets, 65.99% and 61.81%, respectively. This proves that our proposed method is more effective than the other methods for the Vietnamese MMRC task at present. It can be seen that the results are influenced by the methods when combined with these word embeddings. In particular, both of the lexical-based method and the neural-based method have better results when using word embeddings, approximately 5% higher. The experimental results showed that Elmo is the best among the other word embeddings." } ], "images": [ "scigen/train/3386.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Experimental results of different models with various pre-trained word embeddings on our corpus ViMMRC." ], "reasoning": "The proposed method with ELMO word embedding achieves the highest accuracy on both the test and development sets, outperforming other methods by a significant margin.", "description": "Sliding Window and Distance-based Sliding Window achieve different performances, 58.50% and 60.55%, on the development set but they have the same accuracy of 56.30% on the test set. Our proposed method achieves the accuracies over 60% on the test set and over 61% on the development set. Specifically, this method with the ELMO word embedding achieves the highest results on both of the test and development sets, 65.99% and 61.81%, respectively. This proves that our proposed method is more effective than the other methods for the Vietnamese MMRC task at present. It can be seen that the results are influenced by the methods when combined with these word embeddings. In particular, both of the lexical-based method and the neural-based method have better results when using word embeddings, approximately 5% higher. The experimental results showed that Elmo is the best among the other word embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 4: Experimental results of different models with various pre-trained word embeddings on our corpus ViMMRC.', and sequentially solve the question, writing out each step of your reasoning process. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich method achieves the highest accuracy on the development set?\n" }, { "role": "assistant", "content": "The table shows that the method 'DSW + Elmo' achieves the highest accuracy on the development set with 65.99%. So, the answer is {\"answer\": \"DSW + Elmo\"}" } ], "images": [ "scigen/train/3386.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Experimental results of different models with various pre-trained word embeddings on our corpus ViMMRC." ], "reasoning": "The table shows that the method 'DSW + Elmo' achieves the highest accuracy on the development set with 65.99%.", "question": "Which method achieves the highest accuracy on the development set?", "answer": "So, the answer is {\"answer\": \"DSW + Elmo\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Co-match + Elmo method achieves over 50% accuracy on the test set., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Co-match + Elmo method achieves over 50% accuracy on the test set.\n" }, { "role": "assistant", "content": "The table shows that the Co-match + Elmo method achieves 44.94% accuracy on the test set, which is below 50%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3386.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Experimental results of different models with various pre-trained word embeddings on our corpus ViMMRC." ], "reasoning": "The table shows that the Co-match + Elmo method achieves 44.94% accuracy on the test set, which is below 50%.", "statement": "The Co-match + Elmo method achieves over 50% accuracy on the test set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: Experimental results of our proposed model on the test set of MCTest.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The proposed method with Glove word embeddings significantly outperforms the baseline model on the MC160 test by 9.17%, demonstrating its effectiveness.In order to prove our proposed method works well in other languages, we also implement this method on the English corpus called MCTest (richardson2013). In this work, we conduct experiments with the baseline model (Distance Sliding Window) and our proposed method with the Glove word embeddings. As a result, the performances of our proposed model are higher than the baseline model. In particular, the gap between the baseline model and our model are significant with 9.17% on the MC160 test, while the accuracy of the baseline is substantially lower than our model, approximately 3%. These experiments illustrate our proposed method is also effective on other languages." } ], "images": [ "scigen/train/3387.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Experimental results of our proposed model on the test set of MCTest." ], "reasoning": "The proposed method with Glove word embeddings significantly outperforms the baseline model on the MC160 test by 9.17%, demonstrating its effectiveness.", "description": "In order to prove our proposed method works well in other languages, we also implement this method on the English corpus called MCTest (richardson2013). In this work, we conduct experiments with the baseline model (Distance Sliding Window) and our proposed method with the Glove word embeddings. As a result, the performances of our proposed model are higher than the baseline model. In particular, the gap between the baseline model and our model are significant with 9.17% on the MC160 test, while the accuracy of the baseline is substantially lower than our model, approximately 3%. These experiments illustrate our proposed method is also effective on other languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 5: Experimental results of our proposed model on the test set of MCTest.\nQuestion: What is the accuracy difference between the baseline model and the proposed method with Glove.6B.50d on the MC160 test?\n" }, { "role": "assistant", "content": "The baseline model has an accuracy of 50.00% on the MC160 test, while the proposed method with Glove.6B.50d has an accuracy of 59.17%. The difference is 59.17% - 50.00% = 9.17%. So, the answer is {\"answer\": \"9.17%\"}" } ], "images": [ "scigen/train/3387.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Experimental results of our proposed model on the test set of MCTest." ], "reasoning": "The baseline model has an accuracy of 50.00% on the MC160 test, while the proposed method with Glove.6B.50d has an accuracy of 59.17%. The difference is 59.17% - 50.00% = 9.17%.", "question": "What is the accuracy difference between the baseline model and the proposed method with Glove.6B.50d on the MC160 test?", "answer": "So, the answer is {\"answer\": \"9.17%\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe proposed method with Glove.6B.200d has the highest accuracy on the MC500 test.\n" }, { "role": "assistant", "content": "The table shows that the proposed method with Glove.6B.300d has the highest accuracy on the MC500 test with 50.83%, not the Glove.6B.200d which has 51.00%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3387.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Experimental results of our proposed model on the test set of MCTest." ], "reasoning": "The table shows that the proposed method with Glove.6B.300d has the highest accuracy on the MC500 test with 50.83%, not the Glove.6B.200d which has 51.00%.", "statement": "The proposed method with Glove.6B.200d has the highest accuracy on the MC500 test.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Figure 3: Analysis and visualization of the best model’s result with different groups of the question length.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The best model performed better on questions with 16-20 words, achieving an accuracy of 71.43%, while shorter questions resulted in lower accuracy.To verify whether the length of question was a reason for the poor performance of our best model, we measured the performances of the best model according to the question length. In particular, we divided the development set into five groups corresponding to the following question lengths: ≤10, 10−15, 16−20, 21−25, and ≥26 words. As can be seen from the figure, questions of the 16−20 word length resulted in better performance than questions of other lengths. For short questions, our method predicts less effective. This may be because short questions contain less information beneficial to search for the correct answer. In particular, the performances on shorter questions (64.15% for the ≤10-word questions and 65.18% for 10−15 word questions) are lower than the performances on longer questions which are over 66% in accuracy." } ], "images": [ "scigen/train/3388.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 3: Analysis and visualization of the best model’s result with different groups of the question length." ], "reasoning": "The best model performed better on questions with 16-20 words, achieving an accuracy of 71.43%, while shorter questions resulted in lower accuracy.", "description": "To verify whether the length of question was a reason for the poor performance of our best model, we measured the performances of the best model according to the question length. In particular, we divided the development set into five groups corresponding to the following question lengths: ≤10, 10−15, 16−20, 21−25, and ≥26 words. As can be seen from the figure, questions of the 16−20 word length resulted in better performance than questions of other lengths. For short questions, our method predicts less effective. This may be because short questions contain less information beneficial to search for the correct answer. In particular, the performances on shorter questions (64.15% for the ≤10-word questions and 65.18% for 10−15 word questions) are lower than the performances on longer questions which are over 66% in accuracy.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Figure 3: Analysis and visualization of the best model’s result with different groups of the question length.\nQuestion: What is the accuracy percentage for questions with lengths between 21 and 25 words?\n" }, { "role": "assistant", "content": "From the table, the accuracy for questions with lengths between 21 and 25 words is given in the 'Acc. (%)' column corresponding to the '21-25' row. So, the answer is {\"answer\": \"66.67\"}." } ], "images": [ "scigen/train/3388.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 3: Analysis and visualization of the best model’s result with different groups of the question length." ], "reasoning": "From the table, the accuracy for questions with lengths between 21 and 25 words is given in the 'Acc. (%)' column corresponding to the '21-25' row.", "question": "What is the accuracy percentage for questions with lengths between 21 and 25 words?", "answer": "So, the answer is {\"answer\": \"66.67\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe model performed best on questions with lengths between 11 and 15 words.\n" }, { "role": "assistant", "content": "The table shows that the highest accuracy percentage is for questions with lengths between 16 and 20 words (71.43%), not 11 and 15 words (65.18%). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3388.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 3: Analysis and visualization of the best model’s result with different groups of the question length." ], "reasoning": "The table shows that the highest accuracy percentage is for questions with lengths between 16 and 20 words (71.43%), not 11 and 15 words (65.18%).", "statement": "The model performed best on questions with lengths between 11 and 15 words.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Figure 5: Analysis and visualization of the best model’s result with different types of reasoning.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The system performs best on word matching and paraphrasing reasoning types, with accuracies of 92.11% and 82.93%, respectively.We also performed analysis to see how the reasoning types influence the best MMRC model. We found that the system determines answers more efficiently for the of the word matching and the paraphrasing reasoning types (WM and PP), 92.11% and 82.93% in accuracy, respectively. In contrast, complex forms of reasoning resulted in lower performances. They include single-sentence reasoning, multi-sentence reasoning, and ambiguous-or-insufficient." } ], "images": [ "scigen/train/3389.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 5: Analysis and visualization of the best model’s result with different types of reasoning." ], "reasoning": "The system performs best on word matching and paraphrasing reasoning types, with accuracies of 92.11% and 82.93%, respectively.", "description": "We also performed analysis to see how the reasoning types influence the best MMRC model. We found that the system determines answers more efficiently for the of the word matching and the paraphrasing reasoning types (WM and PP), 92.11% and 82.93% in accuracy, respectively. In contrast, complex forms of reasoning resulted in lower performances. They include single-sentence reasoning, multi-sentence reasoning, and ambiguous-or-insufficient.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Figure 5: Analysis and visualization of the best model’s result with different types of reasoning.\nQuestion: What is the accuracy percentage for single-sentence reasoning (SSR) and how does it compare to multi-sentence reasoning (MSR)?\nSolve the above question based on the table information. Let's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The accuracy for SSR is 52.94%, while for MSR it is 50.00%. Comparing these, SSR has a higher accuracy than MSR. So, the answer is {\"answer\": \"SSR has a higher accuracy than MSR.\"}" } ], "images": [ "scigen/train/3389.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 5: Analysis and visualization of the best model’s result with different types of reasoning." ], "reasoning": "The accuracy for SSR is 52.94%, while for MSR it is 50.00%. Comparing these, SSR has a higher accuracy than MSR.", "question": "What is the accuracy percentage for single-sentence reasoning (SSR) and how does it compare to multi-sentence reasoning (MSR)?", "answer": "So, the answer is {\"answer\": \"SSR has a higher accuracy than MSR.\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The accuracy for ambiguous-or-insufficient reasoning is higher than for multi-sentence reasoning. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe accuracy for ambiguous-or-insufficient reasoning is higher than for multi-sentence reasoning.\n" }, { "role": "assistant", "content": "The table shows that both ambiguous-or-insufficient reasoning (Aol) and multi-sentence reasoning (MSR) have the same accuracy of 50.00%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3389.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 5: Analysis and visualization of the best model’s result with different types of reasoning." ], "reasoning": "The table shows that both ambiguous-or-insufficient reasoning (Aol) and multi-sentence reasoning (MSR) have the same accuracy of 50.00%.", "statement": "The accuracy for ambiguous-or-insufficient reasoning is higher than for multi-sentence reasoning.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 7: Performance of compositional training of KBC and Path Query dataset.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Jointly training for KBC and Path queries improves path query performance significantly, especially with a 4:1 KBC to Path dataset ratio, though it slightly reduces KBC performance.We found that jointly training for KBC queries and Path queries allowed the embeddings of entities and relations to be trained for both the tasks. We also observed that combining multiple copies of KBC dataset with Path query dataset helped in path training as well as in stabilizing the performance metrics of original KBC task. The KBC to Path dataset ratio of 4:1 gave the best performance on path queries for both FB15k-Path and Wiki100-Path. Overall, it resulted in a massive increase in performance in path queries with a marginal reduction in KBC performance. Our empirical evaluation reveals a few interesting insights. First of all, it is worth noting that Wiki100, even with much lower performance on KBC task as compared to FB15k, admits a stronger baseline under regex queries. The performance gains of MLP and FP models observed on FB15k dataset – e.g., about 33% improvement in HITS@10, do not seem to carry over to Wiki100 setting, which shows just 6% improvement over the baseline (HITS@10 with UNIT-AVG). This shows that Wiki100 is a challenging KB to model regex queries." } ], "images": [ "scigen/train/3390.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Performance of compositional training of KBC and Path Query dataset." ], "reasoning": "Jointly training for KBC and Path queries improves path query performance significantly, especially with a 4:1 KBC to Path dataset ratio, though it slightly reduces KBC performance.", "description": "We found that jointly training for KBC queries and Path queries allowed the embeddings of entities and relations to be trained for both the tasks. We also observed that combining multiple copies of KBC dataset with Path query dataset helped in path training as well as in stabilizing the performance metrics of original KBC task. The KBC to Path dataset ratio of 4:1 gave the best performance on path queries for both FB15k-Path and Wiki100-Path. Overall, it resulted in a massive increase in performance in path queries with a marginal reduction in KBC performance. Our empirical evaluation reveals a few interesting insights. First of all, it is worth noting that Wiki100, even with much lower performance on KBC task as compared to FB15k, admits a stronger baseline under regex queries. The performance gains of MLP and FP models observed on FB15k dataset – e.g., about 33% improvement in HITS@10, do not seem to carry over to Wiki100 setting, which shows just 6% improvement over the baseline (HITS@10 with UNIT-AVG). This shows that Wiki100 is a challenging KB to model regex queries.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 7: Performance of compositional training of KBC and Path Query dataset.' to sequentially solve the question presented below. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhat is the improvement in Path Queries HITS@10 for the FB15k dataset when trained on KBC + Path compared to just KBC?\n" }, { "role": "assistant", "content": "The Path Queries HITS@10 for FB15k when trained on KBC is 4.20, and when trained on KBC + Path, it is 39.60. The improvement is 39.60 - 4.20. So, the answer is {\"answer\": \"35.40\"}." } ], "images": [ "scigen/train/3390.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Performance of compositional training of KBC and Path Query dataset." ], "reasoning": "The Path Queries HITS@10 for FB15k when trained on KBC is 4.20, and when trained on KBC + Path, it is 39.60. The improvement is 39.60 - 4.20.", "question": "What is the improvement in Path Queries HITS@10 for the FB15k dataset when trained on KBC + Path compared to just KBC?", "answer": "So, the answer is {\"answer\": \"35.40\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nTraining on KBC + Path improves Path Queries HITS@1 for Wiki100 dataset.\n" }, { "role": "assistant", "content": "The Path Queries HITS@1 for Wiki100 when trained on KBC is 7.64, and when trained on KBC + Path, it is 13.5. This shows an improvement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3390.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Performance of compositional training of KBC and Path Query dataset." ], "reasoning": "The Path Queries HITS@1 for Wiki100 when trained on KBC is 7.64, and when trained on KBC + Path, it is 13.5. This shows an improvement.", "statement": "Training on KBC + Path improves Path Queries HITS@1 for Wiki100 dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: KBC Performance on FB15k and Wiki100'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Using full softmax instead of a fixed number of negative samples resulted in a marginal improvement of 0.83% in Hits@10 and 1.1% in MRR for FB15k compared to Trouillon et al.Given a scoring function ϕ, for (e1,r,e2) ∈T, the log-likelihood loss minimizes: −2∗ϕ(e1,r,e2)+log(∑e′2∈Eϕ(e1,r,e′2))+log(∑e′1∈Eϕ(e′1,r,e2)) (16) For FB15k, we got a marginal improvement of 0.83% points in Hits@10 and 1.1% points in MRR compared to Trouillon et al. We attribute this to using full softmax, instead of a fixed number of negative samples. For Wiki100, we establish the baseline of 46.27% Hits@10 and 31.51% MRR." } ], "images": [ "scigen/train/3391.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: KBC Performance on FB15k and Wiki100" ], "reasoning": "Using full softmax instead of a fixed number of negative samples resulted in a marginal improvement of 0.83% in Hits@10 and 1.1% in MRR for FB15k compared to Trouillon et al.", "description": "Given a scoring function ϕ, for (e1,r,e2) ∈T, the log-likelihood loss minimizes: −2∗ϕ(e1,r,e2)+log(∑e′2∈Eϕ(e1,r,e′2))+log(∑e′1∈Eϕ(e′1,r,e2)) (16) For FB15k, we got a marginal improvement of 0.83% points in Hits@10 and 1.1% points in MRR compared to Trouillon et al. We attribute this to using full softmax, instead of a fixed number of negative samples. For Wiki100, we establish the baseline of 46.27% Hits@10 and 31.51% MRR.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 6: KBC Performance on FB15k and Wiki100', solve the following question step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the difference in MRR between FB15k and Wiki100?\n" }, { "role": "assistant", "content": "The MRR for FB15k is 66.51 and for Wiki100 is 31.51. The difference is 66.51 - 31.51. So, the answer is {\"answer\": \"35\"}" } ], "images": [ "scigen/train/3391.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: KBC Performance on FB15k and Wiki100" ], "reasoning": "The MRR for FB15k is 66.51 and for Wiki100 is 31.51. The difference is 66.51 - 31.51.", "question": "What is the difference in MRR between FB15k and Wiki100?", "answer": "So, the answer is {\"answer\": \"35\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Hits@1 score for Wiki100 is higher than the Hits@10 score for FB15k. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Hits@1 score for Wiki100 is higher than the Hits@10 score for FB15k.\n" }, { "role": "assistant", "content": "The Hits@1 score for Wiki100 is 23.84, while the Hits@10 score for FB15k is 83.23. Since 23.84 is not higher than 83.23, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3391.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: KBC Performance on FB15k and Wiki100" ], "reasoning": "The Hits@1 score for Wiki100 is 23.84, while the Hits@10 score for FB15k is 83.23. Since 23.84 is not higher than 83.23, the statement is refuted.", "statement": "The Hits@1 score for Wiki100 is higher than the Hits@10 score for FB15k.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 8: Performance of Operator Training over Regex Queries.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Unit Transformation for Fkplus and Sum for Fdisjunct provide the strongest baseline, while operator functions with training parameters outperform this baseline.Within operator functions that don’t involve any training, Unit Transformation for Fkplus and Sum for Fdisjunct provide the strongest baseline. Operator functions which involve training parameters outperform this baseline." } ], "images": [ "scigen/train/3392.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Performance of Operator Training over Regex Queries." ], "reasoning": "Unit Transformation for Fkplus and Sum for Fdisjunct provide the strongest baseline, while operator functions with training parameters outperform this baseline.", "description": "Within operator functions that don’t involve any training, Unit Transformation for Fkplus and Sum for Fdisjunct provide the strongest baseline. Operator functions which involve training parameters outperform this baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 8: Performance of Operator Training over Regex Queries.\nQuestion: Which combination of Fkplus and Fdisjunct models achieves the highest HITS@1 score on the Wiki100-Regex dataset?\n" }, { "role": "assistant", "content": "To find the highest HITS@1 score on the Wiki100-Regex dataset, look at the HITS@1 column under the Wiki100-Regex dataset. The highest value is 19.42, achieved by the combination of FP for Fkplus and MLP for Fdisjunct. So, the answer is {\"answer\": \"FP and MLP\"}" } ], "images": [ "scigen/train/3392.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Performance of Operator Training over Regex Queries." ], "reasoning": "To find the highest HITS@1 score on the Wiki100-Regex dataset, look at the HITS@1 column under the Wiki100-Regex dataset. The highest value is 19.42, achieved by the combination of FP for Fkplus and MLP for Fdisjunct.", "question": "Which combination of Fkplus and Fdisjunct models achieves the highest HITS@1 score on the Wiki100-Regex dataset?", "answer": "So, the answer is {\"answer\": \"FP and MLP\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe combination of UNIT for Fkplus and AVG for Fdisjunct outperforms the baseline in MRR on the FB15k-Regex dataset.\n" }, { "role": "assistant", "content": "The baseline MRR on the FB15k-Regex dataset is 6.24. The combination of UNIT for Fkplus and AVG for Fdisjunct has an MRR of 7.47, which is higher than the baseline. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3392.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Performance of Operator Training over Regex Queries." ], "reasoning": "The baseline MRR on the FB15k-Regex dataset is 6.24. The combination of UNIT for Fkplus and AVG for Fdisjunct has an MRR of 7.47, which is higher than the baseline.", "statement": "The combination of UNIT for Fkplus and AVG for Fdisjunct outperforms the baseline in MRR on the FB15k-Regex dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 12:'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The metrics for both training and non-training models decrease significantly as the complexity of the query increases, indicating a need for improved approaches.We note that for every model (training or non-training), the metrics decrease drastically, sometimes by orders of magnitude, as the complexity of query increases. This was witnessed in evaluation of r+1→r1/r+2→r+1/r+2. Similar trend is exhibited for (r1|r2)→(r1|r2)+. These observations further reinforce the need to further investigate approaches to improve the performance of these queries." } ], "images": [ "scigen/train/3393.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 12:" ], "reasoning": "The metrics for both training and non-training models decrease significantly as the complexity of the query increases, indicating a need for improved approaches.", "description": "We note that for every model (training or non-training), the metrics decrease drastically, sometimes by orders of magnitude, as the complexity of query increases. This was witnessed in evaluation of r+1→r1/r+2→r+1/r+2. Similar trend is exhibited for (r1|r2)→(r1|r2)+. These observations further reinforce the need to further investigate approaches to improve the performance of these queries.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 12:', unfold the steps to solve the question given next. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: What is the MRR for the (r1|r2)→(r1|r2)+ query using the FP + MLP model?\n" }, { "role": "assistant", "content": "To find the MRR for the (r1|r2)→(r1|r2)+ query using the FP + MLP model, locate the row corresponding to (r1|r2)→(r1|r2)+ and find the MRR value under the FP + MLP column. So, the answer is {\"answer\": \"25.79\"}." } ], "images": [ "scigen/train/3393.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 12:" ], "reasoning": "To find the MRR for the (r1|r2)→(r1|r2)+ query using the FP + MLP model, locate the row corresponding to (r1|r2)→(r1|r2)+ and find the MRR value under the FP + MLP column.", "question": "What is the MRR for the (r1|r2)→(r1|r2)+ query using the FP + MLP model?", "answer": "So, the answer is {\"answer\": \"25.79\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The HITS@10 for the r+1→r1/r+2→r+1/r+2 query using the UNIT + AVG model is higher than 5., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe HITS@10 for the r+1→r1/r+2→r+1/r+2 query using the UNIT + AVG model is higher than 5.\n" }, { "role": "assistant", "content": "To verify this statement, locate the HITS@10 value for the r+1→r1/r+2→r+1/r+2 query using the UNIT + AVG model. The value is 5.44, which is higher than 5. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3393.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 12:" ], "reasoning": "To verify this statement, locate the HITS@10 value for the r+1→r1/r+2→r+1/r+2 query using the UNIT + AVG model. The value is 5.44, which is higher than 5.", "statement": "The HITS@10 for the r+1→r1/r+2→r+1/r+2 query using the UNIT + AVG model is higher than 5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Krippendorff’s α inter-annotator agreement by language and topic.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table shows the distribution of annotations by topic (migrants and LGBT) and language (Slovene and English) across different variable types (nominal and ordinal).In this subsection we perform an analysis of the distribution of all the annotations by topic and language." } ], "images": [ "scigen/train/3394.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Krippendorff’s α inter-annotator agreement by language and topic." ], "reasoning": "The table shows the distribution of annotations by topic (migrants and LGBT) and language (Slovene and English) across different variable types (nominal and ordinal).", "description": "In this subsection we perform an analysis of the distribution of all the annotations by topic and language.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Krippendorff’s α inter-annotator agreement by language and topic.', answer the following question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich group has the highest nominal type annotation value?\n" }, { "role": "assistant", "content": "To find the group with the highest nominal type annotation value, compare the values in the 'nominal' rows for all groups. The highest value is 0.528 for Slovene migrants. So, the answer is {\"answer\": \"Slovene migrants\"}" } ], "images": [ "scigen/train/3394.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Krippendorff’s α inter-annotator agreement by language and topic." ], "reasoning": "To find the group with the highest nominal type annotation value, compare the values in the 'nominal' rows for all groups. The highest value is 0.528 for Slovene migrants.", "question": "Which group has the highest nominal type annotation value?", "answer": "So, the answer is {\"answer\": \"Slovene migrants\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nEnglish migrants have a higher ordinal type annotation value than Slovene LGBT.\n" }, { "role": "assistant", "content": "The table shows that the ordinal type annotation value for English migrants is 0.516, while for Slovene LGBT it is 0.590. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3394.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Krippendorff’s α inter-annotator agreement by language and topic." ], "reasoning": "The table shows that the ordinal type annotation value for English migrants is 0.516, while for Slovene LGBT it is 0.590. Therefore, the statement is refuted.", "statement": "English migrants have a higher ordinal type annotation value than Slovene LGBT.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: Size of the datasets by language and topic.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The average discussion thread length is similar across topics and languages, except for the LGBT topic in Slovene, which is significantly shorter.The results show that the length of discussion threads is similar across topics and languages, with the LGBT topic in Slovene being the only strong deviation, where the average discussion thread length is four to five times shorter than the rest of the topics and languages." } ], "images": [ "scigen/train/3395.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Size of the datasets by language and topic." ], "reasoning": "The average discussion thread length is similar across topics and languages, except for the LGBT topic in Slovene, which is significantly shorter.", "description": "The results show that the length of discussion threads is similar across topics and languages, with the LGBT topic in Slovene being the only strong deviation, where the average discussion thread length is four to five times shorter than the rest of the topics and languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 1: Size of the datasets by language and topic.'. Solve the following question based on the table.\n\nQuestion: Which topic and language combination has the shortest average discussion thread length?\nLet's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the average thread lengths across all topics and languages, the Slovene LGBT topic has the shortest average thread length of 49. So, the answer is {\"answer\": \"Slovene LGBT\"}." } ], "images": [ "scigen/train/3395.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Size of the datasets by language and topic." ], "reasoning": "By comparing the average thread lengths across all topics and languages, the Slovene LGBT topic has the shortest average thread length of 49.", "question": "Which topic and language combination has the shortest average discussion thread length?", "answer": "So, the answer is {\"answer\": \"Slovene LGBT\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The average thread length for English migrants is longer than for Slovene migrants. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe average thread length for English migrants is longer than for Slovene migrants.\n" }, { "role": "assistant", "content": "The table shows that the average thread length for English migrants is 366, while for Slovene migrants it is 218, indicating that the statement is true. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3395.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Size of the datasets by language and topic." ], "reasoning": "The table shows that the average thread length for English migrants is 366, while for Slovene migrants it is 218, indicating that the statement is true.", "statement": "The average thread length for English migrants is longer than for Slovene migrants.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: Bleu scores for NMT systems built on the PMIndia corpus, for English to/from languages of India, setting aside 1000 sentences for dev and 1000 for test.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Translation into Dravidian languages shows low BLEU scores due to their agglutinative nature, while translation into English generally scores higher.We evaluated on tokenized text, using the multi-bleu.perl script from Moses, since sacrebleu post1:2018:WMT does not currently support South Asian languages. we observer very low bleu scores for translation into languages of India, especially for the Dravidian languages (Kannada, Malayalam, Tamil and Telugu). The Dravidian languages are all agglutinative, making inward translation challenging, but also making evaluation of translation using word-based metrics like bleu less reliable. For the languages with very small (less than 10k) data set sizes , the translation from/to Assamese shows poor results, as expected, but translation into and out of Manipuri and Urdu scores much higher, despite the data set size. It’s possible that the translation sample for these languages is more domain-specific than for other languages. For translation into English, the scores are nearly always higher, and sometimes much higher. This observation suggests that the low scores for translation into languages of India are mainly caused by the difficulties posed by the languages themselves, and not by data set problems." } ], "images": [ "scigen/train/3396.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Bleu scores for NMT systems built on the PMIndia corpus, for English to/from languages of India, setting aside 1000 sentences for dev and 1000 for test." ], "reasoning": "Translation into Dravidian languages shows low BLEU scores due to their agglutinative nature, while translation into English generally scores higher.", "description": "We evaluated on tokenized text, using the multi-bleu.perl script from Moses, since sacrebleu post1:2018:WMT does not currently support South Asian languages. we observer very low bleu scores for translation into languages of India, especially for the Dravidian languages (Kannada, Malayalam, Tamil and Telugu). The Dravidian languages are all agglutinative, making inward translation challenging, but also making evaluation of translation using word-based metrics like bleu less reliable. For the languages with very small (less than 10k) data set sizes , the translation from/to Assamese shows poor results, as expected, but translation into and out of Manipuri and Urdu scores much higher, despite the data set size. It’s possible that the translation sample for these languages is more domain-specific than for other languages. For translation into English, the scores are nearly always higher, and sometimes much higher. This observation suggests that the low scores for translation into languages of India are mainly caused by the difficulties posed by the languages themselves, and not by data set problems.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 5: Bleu scores for NMT systems built on the PMIndia corpus, for English to/from languages of India, setting aside 1000 sentences for dev and 1000 for test.' and methodically tackle the question that follows. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich language has the highest BLEU score for translation from English?\n" }, { "role": "assistant", "content": "By examining the 'en-X' column, the highest score is for 'hi' with a score of 23.4. So, the answer is {\"answer\": \"hi\"}" } ], "images": [ "scigen/train/3396.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Bleu scores for NMT systems built on the PMIndia corpus, for English to/from languages of India, setting aside 1000 sentences for dev and 1000 for test." ], "reasoning": "By examining the 'en-X' column, the highest score is for 'hi' with a score of 23.4.", "question": "Which language has the highest BLEU score for translation from English?", "answer": "So, the answer is {\"answer\": \"hi\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence Translation into Assamese has the lowest BLEU score. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nTranslation into Assamese has the lowest BLEU score.\n" }, { "role": "assistant", "content": "By examining the 'en-X' column, the lowest score is for 'ml' with a score of 1.8, not 'as'. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3396.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Bleu scores for NMT systems built on the PMIndia corpus, for English to/from languages of India, setting aside 1000 sentences for dev and 1000 for test." ], "reasoning": "By examining the 'en-X' column, the lowest score is for 'ml' with a score of 1.8, not 'as'.", "statement": "Translation into Assamese has the lowest BLEU score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 10: The classification results for major classes without family history on 15 types of selected CUIs'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "By combining rule-based and machine learning approaches, we can achieve comparable classification performance with the rule-based approach, making the method portable between different EHR systems.Due to the limitation of machine learning methods on small samples, in this section, we perform the classification only on the major classes that have enough samples to train a machine learning model. For intuitive judgments, we only use the ‘Present’ and ‘Absent’ records in the training data to train the classification model. For textual judgments, we only consider the ‘Present’ and ‘Unmentioned’ records. In addition, by combining the rule-based approach and machine learning based approaches, we can achieve a comparable classification performance with the total rule-based approach, and more importantly, this method can be portable between different HER systems.by combining the rule-based approach and machine learning based approaches, we can achieve a comparable classification performance with the total rule-based approach, and more importantly, this method can be portable between different EHR systems. This is as expected due to the limitation of machine learning methods on small samples. Thus, in our portable phenotyping system, we can use the rule-based method for the minor class classification and use machine learning methods for the major class classification." } ], "images": [ "scigen/train/3397.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: The classification results for major classes without family history on 15 types of selected CUIs" ], "reasoning": "By combining rule-based and machine learning approaches, we can achieve comparable classification performance with the rule-based approach, making the method portable between different EHR systems.", "description": "Due to the limitation of machine learning methods on small samples, in this section, we perform the classification only on the major classes that have enough samples to train a machine learning model. For intuitive judgments, we only use the ‘Present’ and ‘Absent’ records in the training data to train the classification model. For textual judgments, we only consider the ‘Present’ and ‘Unmentioned’ records. In addition, by combining the rule-based approach and machine learning based approaches, we can achieve a comparable classification performance with the total rule-based approach, and more importantly, this method can be portable between different HER systems.by combining the rule-based approach and machine learning based approaches, we can achieve a comparable classification performance with the total rule-based approach, and more importantly, this method can be portable between different EHR systems. This is as expected due to the limitation of machine learning methods on small samples. Thus, in our portable phenotyping system, we can use the rule-based method for the minor class classification and use machine learning methods for the major class classification.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: 'Table 10: The classification results for major classes without family history on 15 types of selected CUIs'\nQuestion: Which method has the highest Intuitive F-Macro score for textual judgments?\n" }, { "role": "assistant", "content": "To find the highest Intuitive F-Macro score for textual judgments, look at the 'Intuitive F-Macro' column under the 'Textual' section. The highest score is 0.7849, achieved by the DT method. So, the answer is {\"answer\": \"DT\"}" } ], "images": [ "scigen/train/3397.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: The classification results for major classes without family history on 15 types of selected CUIs" ], "reasoning": "To find the highest Intuitive F-Macro score for textual judgments, look at the 'Intuitive F-Macro' column under the 'Textual' section. The highest score is 0.7849, achieved by the DT method.", "question": "Which method has the highest Intuitive F-Macro score for textual judgments?", "answer": "So, the answer is {\"answer\": \"DT\"}" }, { "messages": [ { "role": "user", "content": "The DT method has the highest Intuitive P-Macro score for intuitive judgments. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe DT method has the highest Intuitive P-Macro score for intuitive judgments.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'Intuitive P-Macro' column under the 'Intuitive' section. The highest score is 0.6783, achieved by the DT method. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3397.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: The classification results for major classes without family history on 15 types of selected CUIs" ], "reasoning": "To verify this statement, look at the 'Intuitive P-Macro' column under the 'Intuitive' section. The highest score is 0.6783, achieved by the DT method.", "statement": "The DT method has the highest Intuitive P-Macro score for intuitive judgments.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: The classification results on all CUIs corresponding to the original records'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Filtering out family history related CUIs and performing classifications shows how this section affects classification results.To disclose how a section (e.g. Family History) in the records can affect the classification results, we filter out the family history related CUIs and perform the classifications." } ], "images": [ "scigen/train/3398.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The classification results on all CUIs corresponding to the original records" ], "reasoning": "Filtering out family history related CUIs and performing classifications shows how this section affects classification results.", "description": "To disclose how a section (e.g. Family History) in the records can affect the classification results, we filter out the family history related CUIs and perform the classifications.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: The classification results on all CUIs corresponding to the original records', solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich classifier has the highest intuitive P-Micro score after filtering out family history related CUIs?\n" }, { "role": "assistant", "content": "To find the classifier with the highest intuitive P-Micro score after filtering out family history related CUIs, we look at the second section of the table. The highest score in the intuitive P-Micro column is 0.9436, achieved by DT. So, the answer is {\"answer\": \"DT\"}." } ], "images": [ "scigen/train/3398.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The classification results on all CUIs corresponding to the original records" ], "reasoning": "To find the classifier with the highest intuitive P-Micro score after filtering out family history related CUIs, we look at the second section of the table. The highest score in the intuitive P-Micro column is 0.9436, achieved by DT.", "question": "Which classifier has the highest intuitive P-Micro score after filtering out family history related CUIs?", "answer": "So, the answer is {\"answer\": \"DT\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nDT has the highest intuitive R-Micro score both before and after filtering out family history related CUIs.\n" }, { "role": "assistant", "content": "To verify this statement, we check the intuitive R-Micro scores for DT in both sections of the table. DT has the highest intuitive R-Micro score of 0.9281 before filtering and 0.9436 after filtering, which are indeed the highest in their respective sections. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3398.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The classification results on all CUIs corresponding to the original records" ], "reasoning": "To verify this statement, we check the intuitive R-Micro scores for DT in both sections of the table. DT has the highest intuitive R-Micro score of 0.9281 before filtering and 0.9436 after filtering, which are indeed the highest in their respective sections.", "statement": "DT has the highest intuitive R-Micro score both before and after filtering out family history related CUIs.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: The classification results without family history related CUIs', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Filtering out family history related CUIs and performing classifications can show how these CUIs affect classification results, indicating that the 15 clinically relevant semantic types of CUIs are informative for classification.To disclose how a section (e.g. Family History) in the records can affect the classification results, we filter out the family history related CUIs and perform the classifications. We also conduct experiments on a list of selected CUIs without family history. This may indicate that the 15 clinically relevant semantic types of CUIs are quite informative for classification." } ], "images": [ "scigen/train/3399.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The classification results without family history related CUIs" ], "reasoning": "Filtering out family history related CUIs and performing classifications can show how these CUIs affect classification results, indicating that the 15 clinically relevant semantic types of CUIs are informative for classification.", "description": "To disclose how a section (e.g. Family History) in the records can affect the classification results, we filter out the family history related CUIs and perform the classifications. We also conduct experiments on a list of selected CUIs without family history. This may indicate that the 15 clinically relevant semantic types of CUIs are quite informative for classification.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: The classification results without family history related CUIs', answer the following question. Give detailed solution consisting of each step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich classifier has the highest Intuitive P-Micro score?\n" }, { "role": "assistant", "content": "To find the classifier with the highest Intuitive P-Micro score, we look at the Intuitive P-Micro column and identify the highest value, which is 0.9475, achieved by the DT classifier. So, the answer is {\"answer\": \"DT\"}" } ], "images": [ "scigen/train/3399.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The classification results without family history related CUIs" ], "reasoning": "To find the classifier with the highest Intuitive P-Micro score, we look at the Intuitive P-Micro column and identify the highest value, which is 0.9475, achieved by the DT classifier.", "question": "Which classifier has the highest Intuitive P-Micro score?", "answer": "So, the answer is {\"answer\": \"DT\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe DT classifier has the highest Intuitive R-Micro score.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the Intuitive R-Micro column and identify the highest value, which is 0.9475, achieved by the DT classifier. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3399.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The classification results without family history related CUIs" ], "reasoning": "To verify this statement, we look at the Intuitive R-Micro column and identify the highest value, which is 0.9475, achieved by the DT classifier.", "statement": "The DT classifier has the highest Intuitive R-Micro score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 8: The classification results for major classes on all CUIs corresponding to the original records'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "By combining rule-based and machine learning approaches, we achieve comparable classification performance, making the method portable between different EHR systems.Due to the limitation of machine learning methods on small samples, in this section, we perform the classification only on the major classes that have enough samples to train a machine learning model. For intuitive judgments, we only use the ‘Present’ and ‘Absent’ records in the training data to train the classification model. For textual judgments, we only consider the ‘Present’ and ‘Unmentioned’ records. In addition, by combining the rule-based approach and machine learning based approaches, we can achieve a comparable classification performance with the total rule-based approach, and more importantly, this method can be portable between different HER systems.by combining the rule-based approach and machine learning based approaches, we can achieve a comparable classification performance with the total rule-based approach, and more importantly, this method can be portable between different EHR systems. This is as expected due to the limitation of machine learning methods on small samples. Thus, in our portable phenotyping system, we can use the rule-based method for the minor class classification and use machine learning methods for the major class classification." } ], "images": [ "scigen/train/3400.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: The classification results for major classes on all CUIs corresponding to the original records" ], "reasoning": "By combining rule-based and machine learning approaches, we achieve comparable classification performance, making the method portable between different EHR systems.", "description": "Due to the limitation of machine learning methods on small samples, in this section, we perform the classification only on the major classes that have enough samples to train a machine learning model. For intuitive judgments, we only use the ‘Present’ and ‘Absent’ records in the training data to train the classification model. For textual judgments, we only consider the ‘Present’ and ‘Unmentioned’ records. In addition, by combining the rule-based approach and machine learning based approaches, we can achieve a comparable classification performance with the total rule-based approach, and more importantly, this method can be portable between different HER systems.by combining the rule-based approach and machine learning based approaches, we can achieve a comparable classification performance with the total rule-based approach, and more importantly, this method can be portable between different EHR systems. This is as expected due to the limitation of machine learning methods on small samples. Thus, in our portable phenotyping system, we can use the rule-based method for the minor class classification and use machine learning methods for the major class classification.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 8: The classification results for major classes on all CUIs corresponding to the original records\nQuestion: Which method has the highest Intuitive F-Micro score for textual judgments?\nSolve the above question based on the table information. Let's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest Intuitive F-Micro score for textual judgments, look at the 'Intuitive F-Micro' column under the 'Textual' section. The highest score is 0.9545 achieved by DT. So, the answer is {\"answer\": \"DT\"}" } ], "images": [ "scigen/train/3400.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: The classification results for major classes on all CUIs corresponding to the original records" ], "reasoning": "To find the highest Intuitive F-Micro score for textual judgments, look at the 'Intuitive F-Micro' column under the 'Textual' section. The highest score is 0.9545 achieved by DT.", "question": "Which method has the highest Intuitive F-Micro score for textual judgments?", "answer": "So, the answer is {\"answer\": \"DT\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence DT has the highest Intuitive P-Micro score for intuitive judgments. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nDT has the highest Intuitive P-Micro score for intuitive judgments.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'Intuitive P-Micro' column under the 'Intuitive' section. The highest score is 0.9311 achieved by DT. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3400.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: The classification results for major classes on all CUIs corresponding to the original records" ], "reasoning": "To verify this statement, look at the 'Intuitive P-Micro' column under the 'Intuitive' section. The highest score is 0.9311 achieved by DT.", "statement": "DT has the highest Intuitive P-Micro score for intuitive judgments.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 9: The classification results for major classes without family history related CUIs', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "By combining rule-based and machine learning approaches, we can achieve comparable classification performance, making the method portable between different EHR systems.Due to the limitation of machine learning methods on small samples, in this section, we perform the classification only on the major classes that have enough samples to train a machine learning model. For intuitive judgments, we only use the ‘Present’ and ‘Absent’ records in the training data to train the classification model. For textual judgments, we only consider the ‘Present’ and ‘Unmentioned’ records. In addition, by combining the rule-based approach and machine learning based approaches, we can achieve a comparable classification performance with the total rule-based approach, and more importantly, this method can be portable between different HER systems.by combining the rule-based approach and machine learning based approaches, we can achieve a comparable classification performance with the total rule-based approach, and more importantly, this method can be portable between different EHR systems. This is as expected due to the limitation of machine learning methods on small samples. Thus, in our portable phenotyping system, we can use the rule-based method for the minor class classification and use machine learning methods for the major class classification." } ], "images": [ "scigen/train/3401.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: The classification results for major classes without family history related CUIs" ], "reasoning": "By combining rule-based and machine learning approaches, we can achieve comparable classification performance, making the method portable between different EHR systems.", "description": "Due to the limitation of machine learning methods on small samples, in this section, we perform the classification only on the major classes that have enough samples to train a machine learning model. For intuitive judgments, we only use the ‘Present’ and ‘Absent’ records in the training data to train the classification model. For textual judgments, we only consider the ‘Present’ and ‘Unmentioned’ records. In addition, by combining the rule-based approach and machine learning based approaches, we can achieve a comparable classification performance with the total rule-based approach, and more importantly, this method can be portable between different HER systems.by combining the rule-based approach and machine learning based approaches, we can achieve a comparable classification performance with the total rule-based approach, and more importantly, this method can be portable between different EHR systems. This is as expected due to the limitation of machine learning methods on small samples. Thus, in our portable phenotyping system, we can use the rule-based method for the minor class classification and use machine learning methods for the major class classification.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 9: The classification results for major classes without family history related CUIs'.\n\nQuestion: Which method achieves the highest Intuitive F-Micro score for textual judgments?\n" }, { "role": "assistant", "content": "For textual judgments, the highest Intuitive F-Micro score is achieved by DT with a score of 0.9546. So, the answer is {\"answer\": \"DT\"}" } ], "images": [ "scigen/train/3401.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: The classification results for major classes without family history related CUIs" ], "reasoning": "For textual judgments, the highest Intuitive F-Micro score is achieved by DT with a score of 0.9546.", "question": "Which method achieves the highest Intuitive F-Micro score for textual judgments?", "answer": "So, the answer is {\"answer\": \"DT\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nDT achieves the highest Intuitive P-Macro score for intuitive judgments.\n" }, { "role": "assistant", "content": "For intuitive judgments, DT achieves the highest Intuitive P-Macro score with a value of 0.6829. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3401.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: The classification results for major classes without family history related CUIs" ], "reasoning": "For intuitive judgments, DT achieves the highest Intuitive P-Macro score with a value of 0.6829.", "statement": "DT achieves the highest Intuitive P-Macro score for intuitive judgments.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled '(a)', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The MFM method achieves the highest accuracy in classification tasks, outperforming both unimodal and multimodal methods.SVHN and MNIST are images with different styles but the same labels (digits 0∼9). We randomly pair 100,000 SVHN and MNIST images that have the same label, creating a multimodal dataset which we call SVHN+MNIST. 80,000 pairs are used for training and the rest for testing. Prediction: We perform experiments on both unimodal and multimodal classification tasks. We achieve better classification performance from unimodal to multimodal which is not surprising since more information is given. More importantly, MFM outperforms MM, which suggests that MFM learns improved factorized representations for discriminative tasks. Generation: We fix one variable out of Z=[Za1, Za2, and Zy] and randomly sample the other two variables from prior PZ. This suggests that MFM is able to factorize multimodal representations into multimodal discriminative factors (labels) and modality-specific generative factors (styles)." } ], "images": [ "scigen/train/3402.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a)" ], "reasoning": "The MFM method achieves the highest accuracy in classification tasks, outperforming both unimodal and multimodal methods.", "description": "SVHN and MNIST are images with different styles but the same labels (digits 0∼9). We randomly pair 100,000 SVHN and MNIST images that have the same label, creating a multimodal dataset which we call SVHN+MNIST. 80,000 pairs are used for training and the rest for testing. Prediction: We perform experiments on both unimodal and multimodal classification tasks. We achieve better classification performance from unimodal to multimodal which is not surprising since more information is given. More importantly, MFM outperforms MM, which suggests that MFM learns improved factorized representations for discriminative tasks. Generation: We fix one variable out of Z=[Za1, Za2, and Zy] and randomly sample the other two variables from prior PZ. This suggests that MFM is able to factorize multimodal representations into multimodal discriminative factors (labels) and modality-specific generative factors (styles).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: (a)\nQuestion: Which method achieves the highest accuracy in classification tasks?\nSolve the above question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows the accuracy of different methods, with MFM having the highest accuracy of 99.36. So, the answer is {\"answer\": \"MFM\"}." } ], "images": [ "scigen/train/3402.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a)" ], "reasoning": "The table shows the accuracy of different methods, with MFM having the highest accuracy of 99.36.", "question": "Which method achieves the highest accuracy in classification tasks?", "answer": "So, the answer is {\"answer\": \"MFM\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe MM method achieves higher accuracy than the UM(MNIST) method.\n" }, { "role": "assistant", "content": "The table shows that the MM method has an accuracy of 99.20, while the UM(MNIST) method has an accuracy of 99.01, indicating that MM has higher accuracy. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3402.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a)" ], "reasoning": "The table shows that the MM method has an accuracy of 99.20, while the UM(MNIST) method has an accuracy of 99.01, indicating that MM has higher accuracy.", "statement": "The MM method achieves higher accuracy than the UM(MNIST) method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Figure 3: Models used in the ablation studies of MFM. Each model removes a design component from our model. Modality reconstruction and sentiment prediction results are reported on CMU-MOSI with best results in bold. Factorizing multimodal representations into multimodal discriminative factors and modality-specific generative factors are crucial for improved performance.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Factorizing multimodal representations into multimodal discriminative factors and modality-specific generative factors improves performance in both sentiment prediction and modality reconstruction.Ablation Study: These models are designed to analyze the effects of using a multimodal discriminative factor, a hybrid generative-discriminative objective, factorized generative-discriminative factors and modality-specific generative factors towards both modality reconstruction and label prediction. MC extends MA by optimizing a hybrid generative-discriminative objective over modality-specific factors. ME factorizes the representation into separate generative and discriminative factors. Finally, MFM is obtained from ME by using modality-specific generative factors instead of a joint multimodal generative factor. For sentiment prediction, using 1) a multimodal discriminative factor outperforms modality-specific discriminative factors (MD>MC, MB>MA), and 2) adding generative capabilities to the model improves performance (MC>MA, ME>MB). For both sentiment prediction and modality reconstruction, 3) factorizing into separate generative and discriminative factors improves performance (ME>MD), and 4) using modality-specific generative factors outperforms multimodal generative factors (MFM >ME). These observations support our design decisions of factorizing multimodal representations into multimodal discriminative factors and modality-specific generative factors." } ], "images": [ "scigen/train/3403.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 3: Models used in the ablation studies of MFM. Each model removes a design component from our model. Modality reconstruction and sentiment prediction results are reported on CMU-MOSI with best results in bold. Factorizing multimodal representations into multimodal discriminative factors and modality-specific generative factors are crucial for improved performance." ], "reasoning": "Factorizing multimodal representations into multimodal discriminative factors and modality-specific generative factors improves performance in both sentiment prediction and modality reconstruction.", "description": "Ablation Study: These models are designed to analyze the effects of using a multimodal discriminative factor, a hybrid generative-discriminative objective, factorized generative-discriminative factors and modality-specific generative factors towards both modality reconstruction and label prediction. MC extends MA by optimizing a hybrid generative-discriminative objective over modality-specific factors. ME factorizes the representation into separate generative and discriminative factors. Finally, MFM is obtained from ME by using modality-specific generative factors instead of a joint multimodal generative factor. For sentiment prediction, using 1) a multimodal discriminative factor outperforms modality-specific discriminative factors (MD>MC, MB>MA), and 2) adding generative capabilities to the model improves performance (MC>MA, ME>MB). For both sentiment prediction and modality reconstruction, 3) factorizing into separate generative and discriminative factors improves performance (ME>MD), and 4) using modality-specific generative factors outperforms multimodal generative factors (MFM >ME). These observations support our design decisions of factorizing multimodal representations into multimodal discriminative factors and modality-specific generative factors.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Figure 3: Models used in the ablation studies of MFM. Each model removes a design component from our model. Modality reconstruction and sentiment prediction results are reported on CMU-MOSI with best results in bold. Factorizing multimodal representations into multimodal discriminative factors and modality-specific generative factors are crucial for improved performance.', answer the following question. Give detailed solution consisting of each step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model has the lowest MSE for CMU-MOSI X Reconstruction (v)?\n" }, { "role": "assistant", "content": "To find the model with the lowest MSE for CMU-MOSI X Reconstruction (v), compare the MSE (v) values for all models. The model with the lowest value is MFM with an MSE of 0.0183. So, the answer is {\"answer\": \"MFM\"}." } ], "images": [ "scigen/train/3403.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 3: Models used in the ablation studies of MFM. Each model removes a design component from our model. Modality reconstruction and sentiment prediction results are reported on CMU-MOSI with best results in bold. Factorizing multimodal representations into multimodal discriminative factors and modality-specific generative factors are crucial for improved performance." ], "reasoning": "To find the model with the lowest MSE for CMU-MOSI X Reconstruction (v), compare the MSE (v) values for all models. The model with the lowest value is MFM with an MSE of 0.0183.", "question": "Which model has the lowest MSE for CMU-MOSI X Reconstruction (v)?", "answer": "So, the answer is {\"answer\": \"MFM\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The model ME has a higher Acc_7 score than the model MC. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model ME has a higher Acc_7 score than the model MC.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Acc_7 scores of models ME and MC. ME has an Acc_7 score of 35.9, while MC has an Acc_7 score of 34.8. Therefore, the statement is true. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3403.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 3: Models used in the ablation studies of MFM. Each model removes a design component from our model. Modality reconstruction and sentiment prediction results are reported on CMU-MOSI with best results in bold. Factorizing multimodal representations into multimodal discriminative factors and modality-specific generative factors are crucial for improved performance." ], "reasoning": "To verify this statement, compare the Acc_7 scores of models ME and MC. ME has an Acc_7 score of 35.9, while MC has an Acc_7 score of 34.8. Therefore, the statement is true.", "statement": "The model ME has a higher Acc_7 score than the model MC.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: The effect of missing modalities on multimodal data reconstruction and sentiment prediction on CMU-MOSI. MFM with surrogate inference is able to better handle missing modalities during test time as compared to the purely generative (Seq2Seq) or purely discriminative baselines.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "MFM is robust to missing modalities, with language being the most critical for discriminative performance, while sentiment prediction is less affected by missing acoustic and visual features.Additionally, MFM with missing modalities performs close to MFM with all modalities observed. This fact indicates that MFM can learn representations that are relatively robust to missing modalities. In addition, discriminative performance is most affected when the language modality is missing, which is consistent with prior work which indicates that language is most informative in human multimodal language ( On the other hand, sentiment prediction is more robust to missing acoustic and visual features. Finally, we observe that reconstructing the low-level acoustic and visual features is easier as compared to the high-dimensional language features that contain high-level semantic meaning." } ], "images": [ "scigen/train/3404.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The effect of missing modalities on multimodal data reconstruction and sentiment prediction on CMU-MOSI. MFM with surrogate inference is able to better handle missing modalities during test time as compared to the purely generative (Seq2Seq) or purely discriminative baselines." ], "reasoning": "MFM is robust to missing modalities, with language being the most critical for discriminative performance, while sentiment prediction is less affected by missing acoustic and visual features.", "description": "Additionally, MFM with missing modalities performs close to MFM with all modalities observed. This fact indicates that MFM can learn representations that are relatively robust to missing modalities. In addition, discriminative performance is most affected when the language modality is missing, which is consistent with prior work which indicates that language is most informative in human multimodal language ( On the other hand, sentiment prediction is more robust to missing acoustic and visual features. Finally, we observe that reconstructing the low-level acoustic and visual features is easier as compared to the high-dimensional language features that contain high-level semantic meaning.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 2: The effect of missing modalities on multimodal data reconstruction and sentiment prediction on CMU-MOSI. MFM with surrogate inference is able to better handle missing modalities during test time as compared to the purely generative (Seq2Seq) or purely discriminative baselines.' in mind, break down and resolve the question below step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich modality's absence most affects discriminative performance according to the table and description?\n" }, { "role": "assistant", "content": "The description states that discriminative performance is most affected when the language modality is missing. The table shows that the prediction accuracy and F1 score are lowest when the language modality is missing. So, the answer is {\"answer\": \"language\"}." } ], "images": [ "scigen/train/3404.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The effect of missing modalities on multimodal data reconstruction and sentiment prediction on CMU-MOSI. MFM with surrogate inference is able to better handle missing modalities during test time as compared to the purely generative (Seq2Seq) or purely discriminative baselines." ], "reasoning": "The description states that discriminative performance is most affected when the language modality is missing. The table shows that the prediction accuracy and F1 score are lowest when the language modality is missing.", "question": "Which modality's absence most affects discriminative performance according to the table and description?", "answer": "So, the answer is {\"answer\": \"language\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nMFM performs better in reconstructing visual features compared to acoustic features.\n" }, { "role": "assistant", "content": "The table shows that the reconstruction MSE for visual features (0.0215) is lower than for acoustic features (0.0468), indicating better performance in reconstructing visual features. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3404.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The effect of missing modalities on multimodal data reconstruction and sentiment prediction on CMU-MOSI. MFM with surrogate inference is able to better handle missing modalities during test time as compared to the purely generative (Seq2Seq) or purely discriminative baselines." ], "reasoning": "The table shows that the reconstruction MSE for visual features (0.0215) is lower than for acoustic features (0.0468), indicating better performance in reconstructing visual features.", "statement": "MFM performs better in reconstructing visual features compared to acoustic features.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Figure 4: Analyzing the multimodal representations learnt in MFM via information-based (entire dataset) and gradient-based interpretation methods (single video) on CMU-MOSI.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The language modality is the most informative towards sentiment prediction on CMU-MOSI, followed by the acoustic modality.Firstly, an information-based interpretation method is chosen to summarize the contribution of each modality towards the multimodal representations. Since Fy is a common cause of ^X1:M, we can compare MI(Fy,^X1), ⋯,MI(Fy,^XM), where MI(⋅,⋅) denotes the mutual information measure between Fy and generated modality ^Xi. Higher MI(Fy,^Xi) indicates greater contribution from Fy to ^Xi. We observe that on CMU-MOSI, the language modality is most informative towards sentiment prediction, followed by the acoustic modality. Secondly, a gradient-based interpretation method to used analyze the contribution of each modality for every time step in multimodal time series data. We measure the gradient of the generated modality with respect to the target factors (e.g., Fy). Let {x1,x2,⋯,xM} denote multimodal time series data where xi represents modality i, and ^xi=[^x1i,⋯,^xti,⋯,^xTi] denote generated modality i across time steps t∈[1,T]. The gradient ∇fy(^xi) measures the extent to which changes in factor fy∼P(Fy|X1:M=x1: M) influences the generation of sequence ^xi. We observe that multimodal communicative behaviors that are indicative of speaker sentiment such as positive words (e.g. “very profound and deep”) and informative acoustic features (e.g. hesitant and emphasized tone of voice) indeed correspond to increases in ∇fy(^xi)." } ], "images": [ "scigen/train/3405.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 4: Analyzing the multimodal representations learnt in MFM via information-based (entire dataset) and gradient-based interpretation methods (single video) on CMU-MOSI." ], "reasoning": "The language modality is the most informative towards sentiment prediction on CMU-MOSI, followed by the acoustic modality.", "description": "Firstly, an information-based interpretation method is chosen to summarize the contribution of each modality towards the multimodal representations. Since Fy is a common cause of ^X1:M, we can compare MI(Fy,^X1), ⋯,MI(Fy,^XM), where MI(⋅,⋅) denotes the mutual information measure between Fy and generated modality ^Xi. Higher MI(Fy,^Xi) indicates greater contribution from Fy to ^Xi. We observe that on CMU-MOSI, the language modality is most informative towards sentiment prediction, followed by the acoustic modality. Secondly, a gradient-based interpretation method to used analyze the contribution of each modality for every time step in multimodal time series data. We measure the gradient of the generated modality with respect to the target factors (e.g., Fy). Let {x1,x2,⋯,xM} denote multimodal time series data where xi represents modality i, and ^xi=[^x1i,⋯,^xti,⋯,^xTi] denote generated modality i across time steps t∈[1,T]. The gradient ∇fy(^xi) measures the extent to which changes in factor fy∼P(Fy|X1:M=x1: M) influences the generation of sequence ^xi. We observe that multimodal communicative behaviors that are indicative of speaker sentiment such as positive words (e.g. “very profound and deep”) and informative acoustic features (e.g. hesitant and emphasized tone of voice) indeed correspond to increases in ∇fy(^xi).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Figure 4: Analyzing the multimodal representations learnt in MFM via information-based (entire dataset) and gradient-based interpretation methods (single video) on CMU-MOSI.\nQuestion: Which modality has the highest mutual information with Fy on CMU-MOSI?\nSolve the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows the mutual information values for different modalities on CMU-MOSI. The highest value is for the language modality (rℓ) with 0.307. So, the answer is {\"answer\": \"language modality\"}." } ], "images": [ "scigen/train/3405.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 4: Analyzing the multimodal representations learnt in MFM via information-based (entire dataset) and gradient-based interpretation methods (single video) on CMU-MOSI." ], "reasoning": "The table shows the mutual information values for different modalities on CMU-MOSI. The highest value is for the language modality (rℓ) with 0.307.", "question": "Which modality has the highest mutual information with Fy on CMU-MOSI?", "answer": "So, the answer is {\"answer\": \"language modality\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe visual modality is the least informative towards sentiment prediction on CMU-MOSI.\n" }, { "role": "assistant", "content": "The table shows that the visual modality (rv) has the lowest mutual information value of 0.030, making it the least informative. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3405.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 4: Analyzing the multimodal representations learnt in MFM via information-based (entire dataset) and gradient-based interpretation methods (single video) on CMU-MOSI." ], "reasoning": "The table shows that the visual modality (rv) has the lowest mutual information value of 0.030, making it the least informative.", "statement": "The visual modality is the least informative towards sentiment prediction on CMU-MOSI.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 6: Information-Based interpretation results showing ratios ri=MI(Fy,^Xi)MI(Fai,^Xi), i∈{(ℓ)anguage,(v)isual,(a)coustic} for the POM dataset for personality traits prediction.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The language modality is the most informative, while the visual and acoustic modalities are almost equally informative.We observe that the language modality is also the most informative while the visual and acoustic modalities are almost equally informative. For example, the same sentence “this movie was great” can convey significantly different messages on speaker confidence depending on whether it was said in a loud and exciting voice, with eye contact, or powerful gesticulation." } ], "images": [ "scigen/train/3406.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Information-Based interpretation results showing ratios ri=MI(Fy,^Xi)MI(Fai,^Xi), i∈{(ℓ)anguage,(v)isual,(a)coustic} for the POM dataset for personality traits prediction." ], "reasoning": "The language modality is the most informative, while the visual and acoustic modalities are almost equally informative.", "description": "We observe that the language modality is also the most informative while the visual and acoustic modalities are almost equally informative. For example, the same sentence “this movie was great” can convey significantly different messages on speaker confidence depending on whether it was said in a loud and exciting voice, with eye contact, or powerful gesticulation.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 6: Information-Based interpretation results showing ratios ri=MI(Fy,^Xi)MI(Fai,^Xi), i∈{(ℓ)anguage,(v)isual,(a)coustic} for the POM dataset for personality traits prediction.'. Solve the following question based on the table.\n\nQuestion: Which modality has the highest ratio according to the table?\nLet's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the ratios for language, visual, and acoustic modalities. The highest ratio is for the language modality with a value of 1.090. So, the answer is {\"answer\": \"language\"}." } ], "images": [ "scigen/train/3406.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Information-Based interpretation results showing ratios ri=MI(Fy,^Xi)MI(Fai,^Xi), i∈{(ℓ)anguage,(v)isual,(a)coustic} for the POM dataset for personality traits prediction." ], "reasoning": "The table shows the ratios for language, visual, and acoustic modalities. The highest ratio is for the language modality with a value of 1.090.", "question": "Which modality has the highest ratio according to the table?", "answer": "So, the answer is {\"answer\": \"language\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe visual modality is more informative than the acoustic modality.\n" }, { "role": "assistant", "content": "The table shows that the ratio for the visual modality (0.996) is higher than the ratio for the acoustic modality (0.898), indicating that the visual modality is more informative. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3406.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Information-Based interpretation results showing ratios ri=MI(Fy,^Xi)MI(Fai,^Xi), i∈{(ℓ)anguage,(v)isual,(a)coustic} for the POM dataset for personality traits prediction." ], "reasoning": "The table shows that the ratio for the visual modality (0.996) is higher than the ratio for the acoustic modality (0.898), indicating that the visual modality is more informative.", "statement": "The visual modality is more informative than the acoustic modality.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Figure 1: Model pre-training characteristics for various combinations of convolutional layers dimensions. Time and the resulting model size are reported in minutes and megabytes (MB) respectively.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The pre-training task on the MIMIC-III dataset for 350 epochs used various configurations of convolutional layers, each trained on a GTX 2080 Ti GPU, with training losses plotted logarithmically.The pre-training task was performed on the entire MIMIC-III data set for 350 epochs using a number of configurations of the width and depth of the convolutional layers. Each configuration was trained on a single GTX 2080 Ti GPU. The corresponding training losses, logarithmically scaled, are plotted in Fig." } ], "images": [ "scigen/train/3407.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 1: Model pre-training characteristics for various combinations of convolutional layers dimensions. Time and the resulting model size are reported in minutes and megabytes (MB) respectively." ], "reasoning": "The pre-training task on the MIMIC-III dataset for 350 epochs used various configurations of convolutional layers, each trained on a GTX 2080 Ti GPU, with training losses plotted logarithmically.", "description": "The pre-training task was performed on the entire MIMIC-III data set for 350 epochs using a number of configurations of the width and depth of the convolutional layers. Each configuration was trained on a single GTX 2080 Ti GPU. The corresponding training losses, logarithmically scaled, are plotted in Fig.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Figure 1: Model pre-training characteristics for various combinations of convolutional layers dimensions. Time and the resulting model size are reported in minutes and megabytes (MB) respectively.\nQuestion: What is the size of the model with the highest depth?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the size of the model with the highest depth, look for the configuration with the highest depth value in the 'Depth' column, which is 16, and then find the corresponding size in the 'Size' column. So, the answer is {\"answer\": \"66.1\"}" } ], "images": [ "scigen/train/3407.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 1: Model pre-training characteristics for various combinations of convolutional layers dimensions. Time and the resulting model size are reported in minutes and megabytes (MB) respectively." ], "reasoning": "To find the size of the model with the highest depth, look for the configuration with the highest depth value in the 'Depth' column, which is 16, and then find the corresponding size in the 'Size' column.", "question": "What is the size of the model with the highest depth?", "answer": "So, the answer is {\"answer\": \"66.1\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The configuration with a width of 128 has a training time of 90. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe configuration with a width of 128 has a training time of 90.\n" }, { "role": "assistant", "content": "Check the row where the width is 128 and verify if the training time in the 'Time' column is 90. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3407.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 1: Model pre-training characteristics for various combinations of convolutional layers dimensions. Time and the resulting model size are reported in minutes and megabytes (MB) respectively." ], "reasoning": "Check the row where the width is 128 and verify if the training time in the 'Time' column is 90.", "statement": "The configuration with a width of 128 has a training time of 90.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Change in accuracy with more training data. Delta denotes a relative improvement.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Training the NER model on increasing fractions of the dataset improves accuracy, with the highest accuracy achieved at 100% of the training set.Generally, collecting more training data will improve the model accuracy and lead to better generalisation. We simulated, using the Prodigy library and ’train-curve’ recipe, an acquisition of more data by training of NER model on fractions (25%, 50%, 75% and 100%) of the training set and evaluating on the test set." } ], "images": [ "scigen/train/3408.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Change in accuracy with more training data. Delta denotes a relative improvement." ], "reasoning": "Training the NER model on increasing fractions of the dataset improves accuracy, with the highest accuracy achieved at 100% of the training set.", "description": "Generally, collecting more training data will improve the model accuracy and lead to better generalisation. We simulated, using the Prodigy library and ’train-curve’ recipe, an acquisition of more data by training of NER model on fractions (25%, 50%, 75% and 100%) of the training set and evaluating on the test set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: Change in accuracy with more training data. Delta denotes a relative improvement.', and tackle the question below in a sequential manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the accuracy improvement when increasing the training data from 25% to 75%?\n" }, { "role": "assistant", "content": "The accuracy at 25% of the training data is 90.66, and at 75% it is 92.42. The improvement is calculated as 92.42 - 90.66. So, the answer is {\"answer\": \"1.76\"}." } ], "images": [ "scigen/train/3408.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Change in accuracy with more training data. Delta denotes a relative improvement." ], "reasoning": "The accuracy at 25% of the training data is 90.66, and at 75% it is 92.42. The improvement is calculated as 92.42 - 90.66.", "question": "What is the accuracy improvement when increasing the training data from 25% to 75%?", "answer": "So, the answer is {\"answer\": \"1.76\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The accuracy improvement from 50% to 100% of the training data is greater than from 25% to 50%. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe accuracy improvement from 50% to 100% of the training data is greater than from 25% to 50%.\n" }, { "role": "assistant", "content": "The accuracy improvement from 50% to 100% is 92.63 - 91.93 = 0.70, and from 25% to 50% is 91.93 - 90.66 = 1.27. Since 0.70 is less than 1.27, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3408.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Change in accuracy with more training data. Delta denotes a relative improvement." ], "reasoning": "The accuracy improvement from 50% to 100% is 92.63 - 91.93 = 0.70, and from 25% to 50% is 91.93 - 90.66 = 1.27. Since 0.70 is less than 1.27, the statement is refuted.", "statement": "The accuracy improvement from 50% to 100% of the training data is greater than from 25% to 50%.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4: Classification accuracy (%) on adversarial examples by human evaluation.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "IGA achieves a slightly better human evaluation than GA by choosing synonyms within a small embedding distance and avoiding local minimum, despite not using a syntax check module.This is counter-intuitive as we do not adopt the syntax check module as GA does, however, as IGA chooses synonyms within a small embedding distance and tries to avoid local minimum to achieve a lower word substitution rate, leading IGA a slightly better human evaluation." } ], "images": [ "scigen/train/3409.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Classification accuracy (%) on adversarial examples by human evaluation." ], "reasoning": "IGA achieves a slightly better human evaluation than GA by choosing synonyms within a small embedding distance and avoiding local minimum, despite not using a syntax check module.", "description": "This is counter-intuitive as we do not adopt the syntax check module as GA does, however, as IGA chooses synonyms within a small embedding distance and tries to avoid local minimum to achieve a lower word substitution rate, leading IGA a slightly better human evaluation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 4: Classification accuracy (%) on adversarial examples by human evaluation.', solve the following question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model has the highest score for the LSTM architecture?\n" }, { "role": "assistant", "content": "To find the highest score for the LSTM architecture, compare the scores of GA and IGA under the LSTM column. IGA has a score of 89.0, which is higher than GA's score of 88.0. So, the answer is {\"answer\": \"IGA\"}" } ], "images": [ "scigen/train/3409.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Classification accuracy (%) on adversarial examples by human evaluation." ], "reasoning": "To find the highest score for the LSTM architecture, compare the scores of GA and IGA under the LSTM column. IGA has a score of 89.0, which is higher than GA's score of 88.0.", "question": "Which model has the highest score for the LSTM architecture?", "answer": "So, the answer is {\"answer\": \"IGA\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence GA has a higher score than IGA for the Word-CNN architecture. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nGA has a higher score than IGA for the Word-CNN architecture.\n" }, { "role": "assistant", "content": "To verify this statement, compare the scores of GA and IGA under the Word-CNN column. GA has a score of 88.9, while IGA has a score of 89.3, indicating that IGA has a higher score. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3409.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Classification accuracy (%) on adversarial examples by human evaluation." ], "reasoning": "To verify this statement, compare the scores of GA and IGA under the Word-CNN column. GA has a score of 88.9, while IGA has a score of 89.3, indicating that IGA has a higher score.", "statement": "GA has a higher score than IGA for the Word-CNN architecture.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: The classification accuracy (%) of various models for adversarial examples generated on other models on Yahoo! Answers for evaluating the transferability. * indicates that the adversarial examples are generated based on this model.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "SEM yields the highest classification accuracy on almost all models with adversarial examples generated by other models.To evaluate the ability of preventing the transferability of adversarial examples, we generate adversarial examples on each model under normal training, and test them on other models with or without defense on Yahoo! Answers. Almost on all models with adversarial examples generated by other models, SEM could yield the highest classification accuracy. Transferability. For instance, if we generate adversarial examples on Word-CNN (column 2, NT), GA can achieve better transferability on LSTM with NT (column 5) while IGA can achieve better transferability on LSTM with AT and SEM (column 6, 7)." } ], "images": [ "scigen/train/3410.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The classification accuracy (%) of various models for adversarial examples generated on other models on Yahoo! Answers for evaluating the transferability. * indicates that the adversarial examples are generated based on this model." ], "reasoning": "SEM yields the highest classification accuracy on almost all models with adversarial examples generated by other models.", "description": "To evaluate the ability of preventing the transferability of adversarial examples, we generate adversarial examples on each model under normal training, and test them on other models with or without defense on Yahoo! Answers. Almost on all models with adversarial examples generated by other models, SEM could yield the highest classification accuracy. Transferability. For instance, if we generate adversarial examples on Word-CNN (column 2, NT), GA can achieve better transferability on LSTM with NT (column 5) while IGA can achieve better transferability on LSTM with AT and SEM (column 6, 7).", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: Table 2: The classification accuracy (%) of various models for adversarial examples generated on other models on Yahoo! Answers for evaluating the transferability. * indicates that the adversarial examples are generated based on this model.\nQuestion: Which model and defense combination achieves the highest classification accuracy when adversarial examples are generated by GA on Word-CNN NT?\n" }, { "role": "assistant", "content": "Adversarial examples generated by GA on Word-CNN NT (column 2) achieve the highest classification accuracy on LSTM NT (column 5) with 66.0. So, the answer is {\"answer\": \"LSTM NT\"}" } ], "images": [ "scigen/train/3410.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The classification accuracy (%) of various models for adversarial examples generated on other models on Yahoo! Answers for evaluating the transferability. * indicates that the adversarial examples are generated based on this model." ], "reasoning": "Adversarial examples generated by GA on Word-CNN NT (column 2) achieve the highest classification accuracy on LSTM NT (column 5) with 66.0.", "question": "Which model and defense combination achieves the highest classification accuracy when adversarial examples are generated by GA on Word-CNN NT?", "answer": "So, the answer is {\"answer\": \"LSTM NT\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nSEM achieves the highest classification accuracy on LSTM when adversarial examples are generated by IGA.\n" }, { "role": "assistant", "content": "When adversarial examples are generated by IGA, SEM achieves the highest classification accuracy on LSTM (column 7) with 61.1. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3410.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The classification accuracy (%) of various models for adversarial examples generated on other models on Yahoo! Answers for evaluating the transferability. * indicates that the adversarial examples are generated based on this model." ], "reasoning": "When adversarial examples are generated by IGA, SEM achieves the highest classification accuracy on LSTM (column 7) with 61.1.", "statement": "SEM achieves the highest classification accuracy on LSTM when adversarial examples are generated by IGA.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: Accuracy rates (in percentage) in identifying the authors in the three collections, using several machine learning algorithms. Results are shown with the input comprising the whole distance matrices (without MDS) and applying MDS on the matrices. For the three last algorithms MDS improved accuracy in all cases.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Using MDS significantly improved classification accuracy across all algorithms and collections, with the highest accuracy of 98.75% achieved by KNN for the third collection.The approach based on distance matrices as input for the classification algorithms was applied for the three collections, for which the success score for classification by chance is 1/8=12.5%. It seems therefore that reducing the dimensionality actually amounted to an efficient feature selection, probably eliminating data that brought noise to the analysis. With MDS, typical accuracy rates were above 90% and the maximum value was 98.75% obtained with KNN for the third collection which corresponds to only one text (out of 80) not correctly classified. These scores demonstrate the added value of using the network structure over relying only on the frequency of appearance of features. Significantly, the higher scores for the approach introduced here are maintained when changing the classification algorithm (KNN, NB, and RBFN), which indicates the robustness of the proposed metrics. Also worth noting is that the present approach outperforms a previous one where the topology of networks was taken without considering the labels of the nodes (words) Akimushkin et al. In addition, the approach presented is less demanding, both computationally and conceptually, than the previous one." } ], "images": [ "scigen/train/3411.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Accuracy rates (in percentage) in identifying the authors in the three collections, using several machine learning algorithms. Results are shown with the input comprising the whole distance matrices (without MDS) and applying MDS on the matrices. For the three last algorithms MDS improved accuracy in all cases." ], "reasoning": "Using MDS significantly improved classification accuracy across all algorithms and collections, with the highest accuracy of 98.75% achieved by KNN for the third collection.", "description": "The approach based on distance matrices as input for the classification algorithms was applied for the three collections, for which the success score for classification by chance is 1/8=12.5%. It seems therefore that reducing the dimensionality actually amounted to an efficient feature selection, probably eliminating data that brought noise to the analysis. With MDS, typical accuracy rates were above 90% and the maximum value was 98.75% obtained with KNN for the third collection which corresponds to only one text (out of 80) not correctly classified. These scores demonstrate the added value of using the network structure over relying only on the frequency of appearance of features. Significantly, the higher scores for the approach introduced here are maintained when changing the classification algorithm (KNN, NB, and RBFN), which indicates the robustness of the proposed metrics. Also worth noting is that the present approach outperforms a previous one where the topology of networks was taken without considering the labels of the nodes (words) Akimushkin et al. In addition, the approach presented is less demanding, both computationally and conceptually, than the previous one.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 1: Accuracy rates (in percentage) in identifying the authors in the three collections, using several machine learning algorithms. Results are shown with the input comprising the whole distance matrices (without MDS) and applying MDS on the matrices. For the three last algorithms MDS improved accuracy in all cases.', and sequentially solve the question, writing out each step of your reasoning process. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat was the highest accuracy rate achieved using MDS and which algorithm and collection was it associated with?\n" }, { "role": "assistant", "content": "The table shows that the highest accuracy rate using MDS was 98.75%, achieved by the KNN algorithm for Collection 3. So, the answer is {\"answer\": \"98.75% with KNN for Collection 3\"}." } ], "images": [ "scigen/train/3411.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Accuracy rates (in percentage) in identifying the authors in the three collections, using several machine learning algorithms. Results are shown with the input comprising the whole distance matrices (without MDS) and applying MDS on the matrices. For the three last algorithms MDS improved accuracy in all cases." ], "reasoning": "The table shows that the highest accuracy rate using MDS was 98.75%, achieved by the KNN algorithm for Collection 3.", "question": "What was the highest accuracy rate achieved using MDS and which algorithm and collection was it associated with?", "answer": "So, the answer is {\"answer\": \"98.75% with KNN for Collection 3\"}." }, { "messages": [ { "role": "user", "content": "Using MDS, the KNN algorithm achieved the highest accuracy rate for Collection 3. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nUsing MDS, the KNN algorithm achieved the highest accuracy rate for Collection 3.\n" }, { "role": "assistant", "content": "The table indicates that with MDS, the KNN algorithm achieved the highest accuracy rate of 98.75% for Collection 3. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3411.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Accuracy rates (in percentage) in identifying the authors in the three collections, using several machine learning algorithms. Results are shown with the input comprising the whole distance matrices (without MDS) and applying MDS on the matrices. For the three last algorithms MDS improved accuracy in all cases." ], "reasoning": "The table indicates that with MDS, the KNN algorithm achieved the highest accuracy rate of 98.75% for Collection 3.", "statement": "Using MDS, the KNN algorithm achieved the highest accuracy rate for Collection 3.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Evaluation results of baselines and the proposed model.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The PG-Net+TA model achieves the highest performance across all metrics compared to the other models.5.2.1 Evaluation I: Between the two baselines, the pointer-generator model obtains higher performance than attentive seq2seq model. Moreover, with topic-aware attentive modeling, both baseline models obtain significant improvement, and the proposed PG-Net+TA achieves the best performance." } ], "images": [ "scigen/train/3412.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Evaluation results of baselines and the proposed model." ], "reasoning": "The PG-Net+TA model achieves the highest performance across all metrics compared to the other models.", "description": "5.2.1 Evaluation I: Between the two baselines, the pointer-generator model obtains higher performance than attentive seq2seq model. Moreover, with topic-aware attentive modeling, both baseline models obtain significant improvement, and the proposed PG-Net+TA achieves the best performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: Evaluation results of baselines and the proposed model.', answer the question below by showing each progressive step toward the answer. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model achieves the highest ROUGE-2 F1 score?\n" }, { "role": "assistant", "content": "By comparing the ROUGE-2 F1 scores of all models, the PG-Net+TA model has the highest score of 0.4906. So, the answer is {\"answer\": \"PG-Net+TA\"}." } ], "images": [ "scigen/train/3412.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Evaluation results of baselines and the proposed model." ], "reasoning": "By comparing the ROUGE-2 F1 scores of all models, the PG-Net+TA model has the highest score of 0.4906.", "question": "Which model achieves the highest ROUGE-2 F1 score?", "answer": "So, the answer is {\"answer\": \"PG-Net+TA\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Attn Seq2Seq model has the highest ROUGE-1 Precision score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Attn Seq2Seq model has the highest ROUGE-1 Precision score.\n" }, { "role": "assistant", "content": "The table shows that the Attn Seq2Seq model has a ROUGE-1 Precision score of 0.3495, which is lower than the scores of the other models. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3412.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Evaluation results of baselines and the proposed model." ], "reasoning": "The table shows that the Attn Seq2Seq model has a ROUGE-1 Precision score of 0.3495, which is lower than the scores of the other models.", "statement": "The Attn Seq2Seq model has the highest ROUGE-1 Precision score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Figure 4: Word and character-level sequence labeling of the sentence Anh rời EU hôm_qua. (UK left EU yesterday.)'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The dataset is converted from word-level to character-level sequences, with each character of a word labeled with the same tag.The training data for NER is in CoNLL-2003 format, where both input and output sequence are annotated at word-level. For this reason, it is necessary to convert the dataset from word-level sequences to character-level sequences. We use a simple method in which all characters of a word are labeled with the same tag. For example, the label of all characters of a person named entity is P. Similarly, all characters of location, organization, and miscellaneous tokens are labelled with letters L, G, and M respectively. Anh rời EU hôm qua (UK left EU yesterday) and" } ], "images": [ "scigen/train/3413.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 4: Word and character-level sequence labeling of the sentence Anh rời EU hôm_qua. (UK left EU yesterday.)" ], "reasoning": "The dataset is converted from word-level to character-level sequences, with each character of a word labeled with the same tag.", "description": "The training data for NER is in CoNLL-2003 format, where both input and output sequence are annotated at word-level. For this reason, it is necessary to convert the dataset from word-level sequences to character-level sequences. We use a simple method in which all characters of a word are labeled with the same tag. For example, the label of all characters of a person named entity is P. Similarly, all characters of location, organization, and miscellaneous tokens are labelled with letters L, G, and M respectively. Anh rời EU hôm qua (UK left EU yesterday) and", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Figure 4: Word and character-level sequence labeling of the sentence Anh rời EU hôm_qua. (UK left EU yesterday.)', answer the following question. Give detailed solution consisting of each step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat label is assigned to the characters of the word 'Anh'?\n" }, { "role": "assistant", "content": "The table shows that the word 'Anh' is labeled with 'G' for each character. So, the answer is {\"answer\": \"G\"}" } ], "images": [ "scigen/train/3413.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 4: Word and character-level sequence labeling of the sentence Anh rời EU hôm_qua. (UK left EU yesterday.)" ], "reasoning": "The table shows that the word 'Anh' is labeled with 'G' for each character.", "question": "What label is assigned to the characters of the word 'Anh'?", "answer": "So, the answer is {\"answer\": \"G\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The word 'EU' is labeled with 'L'. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe word 'EU' is labeled with 'L'.\n" }, { "role": "assistant", "content": "The table shows that the word 'EU' is labeled with 'G' for each character, not 'L'. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3413.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 4: Word and character-level sequence labeling of the sentence Anh rời EU hôm_qua. (UK left EU yesterday.)" ], "reasoning": "The table shows that the word 'EU' is labeled with 'G' for each character, not 'L'.", "statement": "The word 'EU' is labeled with 'L'.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 7: Comparison to participating NER systems at VLSP 2016'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The VLSP 2016 workshop evaluated Vietnamese NER systems using LOC, ORG, and PER entity types, with most systems relying on CRF and ME models, while the new system uses an end-to-end model.4.3.4 Comparison with Previous Systems In VLSP 2016 workshop, several different systems have been proposed for Vietnamese NER. In this campaign, they have evaluated over three entities types LOC, ORG, PER. In all fairness, we also evaluate our performances over these tags on the same training and test set. That system, however, used many hand-crafted features to improve the performance of maximum entropy classifier (ME) while our system is truly end-to-end model that takes only word sequences as inputs. Most approaches in VLSP 2016 used the CRF and ME models, whose performance is heavily dependent on feature engineering." } ], "images": [ "scigen/train/3414.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Comparison to participating NER systems at VLSP 2016" ], "reasoning": "The VLSP 2016 workshop evaluated Vietnamese NER systems using LOC, ORG, and PER entity types, with most systems relying on CRF and ME models, while the new system uses an end-to-end model.", "description": "4.3.4 Comparison with Previous Systems In VLSP 2016 workshop, several different systems have been proposed for Vietnamese NER. In this campaign, they have evaluated over three entities types LOC, ORG, PER. In all fairness, we also evaluate our performances over these tags on the same training and test set. That system, however, used many hand-crafted features to improve the performance of maximum entropy classifier (ME) while our system is truly end-to-end model that takes only word sequences as inputs. Most approaches in VLSP 2016 used the CRF and ME models, whose performance is heavily dependent on feature engineering.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 7: Comparison to participating NER systems at VLSP 2016' in mind, break down and resolve the question below step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhich model had the highest performance in the VLSP 2016 workshop?\n" }, { "role": "assistant", "content": "The table shows the performance of different models, with the ME model having the highest performance at 88.78. So, the answer is {\"answer\": \"ME\"}." } ], "images": [ "scigen/train/3414.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Comparison to participating NER systems at VLSP 2016" ], "reasoning": "The table shows the performance of different models, with the ME model having the highest performance at 88.78.", "question": "Which model had the highest performance in the VLSP 2016 workshop?", "answer": "So, the answer is {\"answer\": \"ME\"}." }, { "messages": [ { "role": "user", "content": "The Bi-LSTM-CNN-CRF model had the lowest performance in the VLSP 2016 workshop. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Bi-LSTM-CNN-CRF model had the lowest performance in the VLSP 2016 workshop.\n" }, { "role": "assistant", "content": "The table shows that the CRF model had the lowest performance at 78.40, not the Bi-LSTM-CNN-CRF model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3414.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Comparison to participating NER systems at VLSP 2016" ], "reasoning": "The table shows that the CRF model had the lowest performance at 78.40, not the Bi-LSTM-CNN-CRF model.", "statement": "The Bi-LSTM-CNN-CRF model had the lowest performance in the VLSP 2016 workshop.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'TABLE II: Correlation between Metrics with Semantic Score'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The correlation coefficients for each model increase with the abstraction levels of code from text, syntax, to semantic representations.These results follow a similar trend: for each model, the correlation coefficients increase accordingly to the abstraction levels of code from text, syntax, to semantic representations in three metrics. For example, for mppSMT, the correlation coefficient between STS and semantic score is 0.549, whereas that for TRS is much greater at 0.820. The highest value is the correlation coefficient between GRS and semantic score at 0.910. A reason for this phenomenon is that for a pair of source code, if they are textually identical, they have the same AST representation, and the exact-match in AST leads to the equivalence between the corresponding PDGs. Meanwhile, the equivalence in a higher abstraction level does not necessarily mean the equivalence in a lower abstraction. For example, Fig." } ], "images": [ "scigen/train/3415.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Correlation between Metrics with Semantic Score" ], "reasoning": "The correlation coefficients for each model increase with the abstraction levels of code from text, syntax, to semantic representations.", "description": "These results follow a similar trend: for each model, the correlation coefficients increase accordingly to the abstraction levels of code from text, syntax, to semantic representations in three metrics. For example, for mppSMT, the correlation coefficient between STS and semantic score is 0.549, whereas that for TRS is much greater at 0.820. The highest value is the correlation coefficient between GRS and semantic score at 0.910. A reason for this phenomenon is that for a pair of source code, if they are textually identical, they have the same AST representation, and the exact-match in AST leads to the equivalence between the corresponding PDGs. Meanwhile, the equivalence in a higher abstraction level does not necessarily mean the equivalence in a lower abstraction. For example, Fig.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'TABLE II: Correlation between Metrics with Semantic Score', unfold the steps to solve the question given next. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nQuestion: What is the correlation coefficient between STS and semantic score for the GNMT model?\n" }, { "role": "assistant", "content": "The table shows the correlation coefficients for different models and metrics. For the GNMT model, the correlation coefficient between STS and semantic score is listed under the STS column. So, the answer is {\"answer\": \"0.692\"}." } ], "images": [ "scigen/train/3415.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Correlation between Metrics with Semantic Score" ], "reasoning": "The table shows the correlation coefficients for different models and metrics. For the GNMT model, the correlation coefficient between STS and semantic score is listed under the STS column.", "question": "What is the correlation coefficient between STS and semantic score for the GNMT model?", "answer": "So, the answer is {\"answer\": \"0.692\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The correlation coefficient between TRS and semantic score for lpSMT is 0.786., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe correlation coefficient between TRS and semantic score for lpSMT is 0.786.\n" }, { "role": "assistant", "content": "The table lists the correlation coefficients for different models and metrics. For the lpSMT model, the correlation coefficient between TRS and semantic score is 0.786. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3415.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Correlation between Metrics with Semantic Score" ], "reasoning": "The table lists the correlation coefficients for different models and metrics. For the lpSMT model, the correlation coefficient between TRS and semantic score is 0.786.", "statement": "The correlation coefficient between TRS and semantic score for lpSMT is 0.786.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: English↔Romanian translation results (Bleu) on dev (newsdev2016), and test (newstest2016). Submitted system in bold.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The EN→RO language pair has the smallest amount of parallel training data, and dropout improved its Bleu score by 4–5 points.This language pair has the smallest amount of parallel training data, and we found dropout to be very effective, yielding improvements of 4–5 Bleu." } ], "images": [ "scigen/train/3416.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: English↔Romanian translation results (Bleu) on dev (newsdev2016), and test (newstest2016). Submitted system in bold." ], "reasoning": "The EN→RO language pair has the smallest amount of parallel training data, and dropout improved its Bleu score by 4–5 points.", "description": "This language pair has the smallest amount of parallel training data, and we found dropout to be very effective, yielding improvements of 4–5 Bleu.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: English↔Romanian translation results (Bleu) on dev (newsdev2016), and test (newstest2016). Submitted system in bold.'.\n\nQuestion: Which system achieved the highest Bleu score for the RO→EN test set?\n" }, { "role": "assistant", "content": "To find the highest Bleu score for the RO→EN test set, look at the RO→EN test column and identify the highest value, which is 35.3 achieved by the +ensemble system. So, the answer is {\"answer\": \"+ensemble\"}." } ], "images": [ "scigen/train/3416.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: English↔Romanian translation results (Bleu) on dev (newsdev2016), and test (newstest2016). Submitted system in bold." ], "reasoning": "To find the highest Bleu score for the RO→EN test set, look at the RO→EN test column and identify the highest value, which is 35.3 achieved by the +ensemble system.", "question": "Which system achieved the highest Bleu score for the RO→EN test set?", "answer": "So, the answer is {\"answer\": \"+ensemble\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The +synthetic system achieved the highest Bleu score for the EN→RO test set., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe +synthetic system achieved the highest Bleu score for the EN→RO test set.\n" }, { "role": "assistant", "content": "To verify this statement, check the EN→RO test column. The highest value is 28.2, achieved by the +ensemble system, not the +synthetic system. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3416.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: English↔Romanian translation results (Bleu) on dev (newsdev2016), and test (newstest2016). Submitted system in bold." ], "reasoning": "To verify this statement, check the EN→RO test column. The highest value is 28.2, achieved by the +ensemble system, not the +synthetic system.", "statement": "The +synthetic system achieved the highest Bleu score for the EN→RO test set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: English↔German translation results (Bleu) on dev (newstest2015) and test (newstest2016). Submitted system in bold.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Training with a mix of parallel and synthetic data improves Bleu scores by 3.4–5.7, and using an ensemble of the last 4 checkpoints adds 1.3–1.7 Bleu. Reranking with a right-to-left model yields an additional 0.6–1.1 Bleu improvement.We observe improvements of 3.4–5.7 Bleu from training with a mix of parallel and synthetic data, compared to the baseline that is only trained on parallel data. Using an ensemble of the last 4 checkpoints gives further improvements (1.3–1.7 Bleu). Our submitted system includes reranking of the 50-best output of the left-to-right model with a right-to-left model – again an ensemble of the last 4 checkpoints – with uniform weights. This yields an improvements of 0.6–1.1 Bleu." } ], "images": [ "scigen/train/3417.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: English↔German translation results (Bleu) on dev (newstest2015) and test (newstest2016). Submitted system in bold." ], "reasoning": "Training with a mix of parallel and synthetic data improves Bleu scores by 3.4–5.7, and using an ensemble of the last 4 checkpoints adds 1.3–1.7 Bleu. Reranking with a right-to-left model yields an additional 0.6–1.1 Bleu improvement.", "description": "We observe improvements of 3.4–5.7 Bleu from training with a mix of parallel and synthetic data, compared to the baseline that is only trained on parallel data. Using an ensemble of the last 4 checkpoints gives further improvements (1.3–1.7 Bleu). Our submitted system includes reranking of the 50-best output of the left-to-right model with a right-to-left model – again an ensemble of the last 4 checkpoints – with uniform weights. This yields an improvements of 0.6–1.1 Bleu.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: English↔German translation results (Bleu) on dev (newstest2015) and test (newstest2016). Submitted system in bold.', answer the question below by showing each progressive step toward the answer. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the Bleu score improvement from the baseline to the +r2l reranking system for the EN→DE test?\n" }, { "role": "assistant", "content": "The baseline Bleu score for EN→DE test is 26.8. The +r2l reranking system's Bleu score for EN→DE test is 34.2. The improvement is 34.2 - 26.8. So, the answer is {\"answer\": \"7.4\"}" } ], "images": [ "scigen/train/3417.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: English↔German translation results (Bleu) on dev (newstest2015) and test (newstest2016). Submitted system in bold." ], "reasoning": "The baseline Bleu score for EN→DE test is 26.8. The +r2l reranking system's Bleu score for EN→DE test is 34.2. The improvement is 34.2 - 26.8.", "question": "What is the Bleu score improvement from the baseline to the +r2l reranking system for the EN→DE test?", "answer": "So, the answer is {\"answer\": \"7.4\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe +synthetic system improves the DE→EN test Bleu score by more than 7 points compared to the baseline.\n" }, { "role": "assistant", "content": "The baseline DE→EN test Bleu score is 28.5. The +synthetic system's DE→EN test Bleu score is 36.2. The improvement is 36.2 - 28.5, which is 7.7 points. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3417.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: English↔German translation results (Bleu) on dev (newstest2015) and test (newstest2016). Submitted system in bold." ], "reasoning": "The baseline DE→EN test Bleu score is 28.5. The +synthetic system's DE→EN test Bleu score is 36.2. The improvement is 36.2 - 28.5, which is 7.7 points.", "statement": "The +synthetic system improves the DE→EN test Bleu score by more than 7 points compared to the baseline.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: English↔Czech translation results (Bleu) on dev (newstest2015) and test (newstest2016). Submitted system in bold.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The Czech→English model was trained using synthetic data in increasing amounts, and the final model was an ensemble of 8 systems.The right-left model was trained using a similar process, but with the target side of the parallel corpus reversed prior to training. The resulting model had a slightly lower Bleu score on the dev data than the standard left-right model. For Czech→English the training process was similar to the above, except that we created the synthetic training data (back-translated from samples of news2015 monolingual English) in batches of 2.5M, and so were able to observe the effect of increasing the amount of synthetic data. After training a baseline model on all the WMT16 parallel set, we continued training with a parallel corpus consisting of 2 copies of the 2.5M sentences of back-translated data, 5 copies of news-commentary v11, and a matching quantity of data sampled from Czeng 1.6pre. After training this to convergence, we restarted training from the baseline model using 5M sentences of back-translated data, 5 copies of news-commentary v11, and a matching quantity of data sampled from Czeng 1.6pre. We repeated this with 7.5M sentences from news2015 monolingual, and then with 10M sentences of news2015. The back-translations were, as for English→Czech, created with an earlier NMT model trained on WMT15 data. Our final Czech→English was an ensemble of 8 systems – the last 4 save-points of the 10M synthetic data run, and the last 4 save-points of the 7.5M run." } ], "images": [ "scigen/train/3418.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: English↔Czech translation results (Bleu) on dev (newstest2015) and test (newstest2016). Submitted system in bold." ], "reasoning": "The Czech→English model was trained using synthetic data in increasing amounts, and the final model was an ensemble of 8 systems.", "description": "The right-left model was trained using a similar process, but with the target side of the parallel corpus reversed prior to training. The resulting model had a slightly lower Bleu score on the dev data than the standard left-right model. For Czech→English the training process was similar to the above, except that we created the synthetic training data (back-translated from samples of news2015 monolingual English) in batches of 2.5M, and so were able to observe the effect of increasing the amount of synthetic data. After training a baseline model on all the WMT16 parallel set, we continued training with a parallel corpus consisting of 2 copies of the 2.5M sentences of back-translated data, 5 copies of news-commentary v11, and a matching quantity of data sampled from Czeng 1.6pre. After training this to convergence, we restarted training from the baseline model using 5M sentences of back-translated data, 5 copies of news-commentary v11, and a matching quantity of data sampled from Czeng 1.6pre. We repeated this with 7.5M sentences from news2015 monolingual, and then with 10M sentences of news2015. The back-translations were, as for English→Czech, created with an earlier NMT model trained on WMT15 data. Our final Czech→English was an ensemble of 8 systems – the last 4 save-points of the 10M synthetic data run, and the last 4 save-points of the 7.5M run.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: English↔Czech translation results (Bleu) on dev (newstest2015) and test (newstest2016). Submitted system in bold.\nQuestion: What was the Bleu score for the +ensemble8 system on the CS→EN test data?\nSolve the above question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows the Bleu scores for different systems. For the +ensemble8 system, the CS→EN test score is highlighted as 31.4. So, the answer is {\"answer\": \"31.4\"}" } ], "images": [ "scigen/train/3418.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: English↔Czech translation results (Bleu) on dev (newstest2015) and test (newstest2016). Submitted system in bold." ], "reasoning": "The table shows the Bleu scores for different systems. For the +ensemble8 system, the CS→EN test score is highlighted as 31.4.", "question": "What was the Bleu score for the +ensemble8 system on the CS→EN test data?", "answer": "So, the answer is {\"answer\": \"31.4\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe +r2l reranking system had the highest Bleu score on the EN→CS test data.\n" }, { "role": "assistant", "content": "The table shows that the +r2l reranking system had a Bleu score of 25.8 on the EN→CS test data, which is higher than the other systems listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3418.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: English↔Czech translation results (Bleu) on dev (newstest2015) and test (newstest2016). Submitted system in bold." ], "reasoning": "The table shows that the +r2l reranking system had a Bleu score of 25.8 on the EN→CS test data, which is higher than the other systems listed.", "statement": "The +r2l reranking system had the highest Bleu score on the EN→CS test data.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Czech→English translation results (Bleu) on dev (newstest2015) and test (newstest2016), after continued training with increasing amounts of back-translated synthetic data. For each row, training was continued from the baseline model until convergence.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The initial batch of back-translated data provides the most significant performance gain, but further increases in back-translated data continue to improve performance gradually.We also show in We see that most of the gain from back-translation comes with the first batch, but increasing the amount of back-translated data does gradually improve performance." } ], "images": [ "scigen/train/3419.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Czech→English translation results (Bleu) on dev (newstest2015) and test (newstest2016), after continued training with increasing amounts of back-translated synthetic data. For each row, training was continued from the baseline model until convergence." ], "reasoning": "The initial batch of back-translated data provides the most significant performance gain, but further increases in back-translated data continue to improve performance gradually.", "description": "We also show in We see that most of the gain from back-translation comes with the first batch, but increasing the amount of back-translated data does gradually improve performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 4: Czech→English translation results (Bleu) on dev (newstest2015) and test (newstest2016), after continued training with increasing amounts of back-translated synthetic data. For each row, training was continued from the baseline model until convergence.', unfold the steps to solve the question given next. Output the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: What is the best single test score for the system with +10M synthetic data?\n" }, { "role": "assistant", "content": "To find the best single test score for the system with +10M synthetic data, look at the row labeled '+10M synthetic' and the column labeled 'best single test'. The value is 30.1. So, the answer is {\"answer\": \"30.1\"}." } ], "images": [ "scigen/train/3419.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Czech→English translation results (Bleu) on dev (newstest2015) and test (newstest2016), after continued training with increasing amounts of back-translated synthetic data. For each row, training was continued from the baseline model until convergence." ], "reasoning": "To find the best single test score for the system with +10M synthetic data, look at the row labeled '+10M synthetic' and the column labeled 'best single test'. The value is 30.1.", "question": "What is the best single test score for the system with +10M synthetic data?", "answer": "So, the answer is {\"answer\": \"30.1\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The ensemble4 test score for the baseline system is higher than the best single test score for the +2.5M synthetic system., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe ensemble4 test score for the baseline system is higher than the best single test score for the +2.5M synthetic system.\n" }, { "role": "assistant", "content": "The ensemble4 test score for the baseline system is 26.8, while the best single test score for the +2.5M synthetic system is 29.4. Since 26.8 is not higher than 29.4, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3419.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Czech→English translation results (Bleu) on dev (newstest2015) and test (newstest2016), after continued training with increasing amounts of back-translated synthetic data. For each row, training was continued from the baseline model until convergence." ], "reasoning": "The ensemble4 test score for the baseline system is 26.8, while the best single test score for the +2.5M synthetic system is 29.4. Since 26.8 is not higher than 29.4, the statement is refuted.", "statement": "The ensemble4 test score for the baseline system is higher than the best single test score for the +2.5M synthetic system.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 6: English↔Russian translation results (Bleu) on dev (newstest2015) and test (newstest2016). Submitted system in bold.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The use of synthetic training data significantly improves BLEU scores for all language pairs, with further improvements achieved through ensemble methods.As for the other language pairs, we observe strong improvements from synthetic training data (4–4.4 Bleu). Ensembles yield another 1.1–1.7 Bleu." } ], "images": [ "scigen/train/3420.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: English↔Russian translation results (Bleu) on dev (newstest2015) and test (newstest2016). Submitted system in bold." ], "reasoning": "The use of synthetic training data significantly improves BLEU scores for all language pairs, with further improvements achieved through ensemble methods.", "description": "As for the other language pairs, we observe strong improvements from synthetic training data (4–4.4 Bleu). Ensembles yield another 1.1–1.7 Bleu.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 6: English↔Russian translation results (Bleu) on dev (newstest2015) and test (newstest2016). Submitted system in bold.', answer the question below by showing each progressive step toward the answer. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the BLEU score improvement from baseline to ensemble for the EN→RU test set?\n" }, { "role": "assistant", "content": "The BLEU score for the EN→RU test set improves from 20.3 in the baseline to 26.0 with the ensemble method. The improvement is calculated as 26.0 - 20.3. So, the answer is {\"answer\": \"5.7\"}" } ], "images": [ "scigen/train/3420.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: English↔Russian translation results (Bleu) on dev (newstest2015) and test (newstest2016). Submitted system in bold." ], "reasoning": "The BLEU score for the EN→RU test set improves from 20.3 in the baseline to 26.0 with the ensemble method. The improvement is calculated as 26.0 - 20.3.", "question": "What is the BLEU score improvement from baseline to ensemble for the EN→RU test set?", "answer": "So, the answer is {\"answer\": \"5.7\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The BLEU score for the RU→EN dev set with synthetic data is 27.1. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe BLEU score for the RU→EN dev set with synthetic data is 27.1.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for the RU→EN dev set with synthetic data is indeed 27.1. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3420.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: English↔Russian translation results (Bleu) on dev (newstest2015) and test (newstest2016). Submitted system in bold." ], "reasoning": "The table shows that the BLEU score for the RU→EN dev set with synthetic data is indeed 27.1.", "statement": "The BLEU score for the RU→EN dev set with synthetic data is 27.1.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: POS tagging accuracies on the test set with gold tokenization. [⋆] denotes a result with a pre-trained POS tagger. We do not provide accuracy results of the pre-trained POS taggers on CRAFT because CRAFT uses an extended PTB POS tag set (i.e. there are POS tags in CRAFT that are not defined in the original PTB POS tag set). Corpus-level accuracy differences of at least 0.17% in GENIA and 0.26% in CRAFT between two POS tagging models are significant at p≤0.05. Here, we compute sentence-level accuracies, then use paired t-test to measure the significance level.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The model trained on newswire data shows lower accuracy compared to retrained tagging models, as evidenced by the GENIA and CRAFT scores.(about 38K sentences) of English PTB WSJ text; given the use of newswire training data, it is unsurprising that this model produces lower accuracy than the retrained tagging models. our 85% training set). It does not support a (re)-training process." } ], "images": [ "scigen/train/3421.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: POS tagging accuracies on the test set with gold tokenization. [⋆] denotes a result with a pre-trained POS tagger. We do not provide accuracy results of the pre-trained POS taggers on CRAFT because CRAFT uses an extended PTB POS tag set (i.e. there are POS tags in CRAFT that are not defined in the original PTB POS tag set). Corpus-level accuracy differences of at least 0.17% in GENIA and 0.26% in CRAFT between two POS tagging models are significant at p≤0.05. Here, we compute sentence-level accuracies, then use paired t-test to measure the significance level." ], "reasoning": "The model trained on newswire data shows lower accuracy compared to retrained tagging models, as evidenced by the GENIA and CRAFT scores.", "description": "(about 38K sentences) of English PTB WSJ text; given the use of newswire training data, it is unsurprising that this model produces lower accuracy than the retrained tagging models. our 85% training set). It does not support a (re)-training process.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: 'Table 3: POS tagging accuracies on the test set with gold tokenization. [⋆] denotes a result with a pre-trained POS tagger. We do not provide accuracy results of the pre-trained POS taggers on CRAFT because CRAFT uses an extended PTB POS tag set (i.e. there are POS tags in CRAFT that are not defined in the original PTB POS tag set). Corpus-level accuracy differences of at least 0.17% in GENIA and 0.26% in CRAFT between two POS tagging models are significant at p≤0.05. Here, we compute sentence-level accuracies, then use paired t-test to measure the significance level.'\nQuestion: Which model has the highest accuracy on the GENIA dataset?\n" }, { "role": "assistant", "content": "To find the model with the highest accuracy on the GENIA dataset, compare the GENIA scores of all models listed in the table. The model with the highest score is jPTDP-v1 with an accuracy of 98.66. So, the answer is {\"answer\": \"jPTDP-v1\"}." } ], "images": [ "scigen/train/3421.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: POS tagging accuracies on the test set with gold tokenization. [⋆] denotes a result with a pre-trained POS tagger. We do not provide accuracy results of the pre-trained POS taggers on CRAFT because CRAFT uses an extended PTB POS tag set (i.e. there are POS tags in CRAFT that are not defined in the original PTB POS tag set). Corpus-level accuracy differences of at least 0.17% in GENIA and 0.26% in CRAFT between two POS tagging models are significant at p≤0.05. Here, we compute sentence-level accuracies, then use paired t-test to measure the significance level." ], "reasoning": "To find the model with the highest accuracy on the GENIA dataset, compare the GENIA scores of all models listed in the table. The model with the highest score is jPTDP-v1 with an accuracy of 98.66.", "question": "Which model has the highest accuracy on the GENIA dataset?", "answer": "So, the answer is {\"answer\": \"jPTDP-v1\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The + CNN-char model has the highest accuracy on the CRAFT dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe + CNN-char model has the highest accuracy on the CRAFT dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the CRAFT scores of all models listed in the table. The + CNN-char model has a score of 97.51, but the + LSTM-char model has a higher score of 97.56. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3421.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: POS tagging accuracies on the test set with gold tokenization. [⋆] denotes a result with a pre-trained POS tagger. We do not provide accuracy results of the pre-trained POS taggers on CRAFT because CRAFT uses an extended PTB POS tag set (i.e. there are POS tags in CRAFT that are not defined in the original PTB POS tag set). Corpus-level accuracy differences of at least 0.17% in GENIA and 0.26% in CRAFT between two POS tagging models are significant at p≤0.05. Here, we compute sentence-level accuracies, then use paired t-test to measure the significance level." ], "reasoning": "To verify this statement, compare the CRAFT scores of all models listed in the table. The + CNN-char model has a score of 97.51, but the + LSTM-char model has a higher score of 97.56.", "statement": "The + CNN-char model has the highest accuracy on the CRAFT dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: Parsing results on the test set with predicted POS tags and gold tokenization (except [G] which denotes results when employing gold POS tags in both training and testing phases). “Without punctuation” refers to results excluding punctuation and other symbols from evaluation. “Exact match” denotes the percentage of sentences whose predicted trees are entirely correct [25]. [∙] denotes the use of the pre-trained Stanford tagger for predicting POS tags on test set, instead of using the retrained NLP4J-POS model. Score differences between the “retrained” parsers on both corpora are significant at p≤0.001 using McNemar’s test (except UAS scores obtained by Stanford-Biaffine-v2 for gold and predicted POS tags on GENIA, i.e. 92.51 vs. 92.31 and 92.84 vs. 92.64, where p≤0.05).'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Stanford-Biaffine achieves the highest parsing results on both GENIA and CRAFT datasets, while Stanford-NNdep obtains the lowest scores.In this section, we present our empirical approach to evaluate different POS tagging and dependency parsing models on benchmark biomedical corpora. The first five rows show parsing results on the GENIA test set of “pre-trained” parsers. Both pre-trained NNdep and Biaffine models were trained on a dependency treebank of 40K sentences, which was converted from the English PTB sections 2–21. The remaining rows show results of our retrained dependency parsing models. Regarding the retrained parsing models, on both GENIA and CRAFT, Stanford-Biaffine achieves the highest parsing results with LAS at 91.23% and UAS at 92.64% on GENIA, and LAS at 90.77% and UAS at 92.67% on CRAFT, computed without punctuations. Stanford-NNdep obtains the lowest scores; about 3.5% and 5% absolute lower than Stanford-Biaffine on GENIA and CRAFT, respectively. jPTDP is ranked second, obtaining about 1% and 2% lower scores than Stanford-Biaffine and 1.5% and 1% higher scores (without punctuation) than NLP4J-dep on GENIA and CRAFT, respectively. the intrinsic UAS and LAS (F1) scores on the pre-processed segmented BioNLP 2009 development sentences (i.e. scores with respect to predicted segmentation), for which these sentences contain event interactions. Although gold event annotations in the BioNLP 2009 test set are not available to public, it is likely that we would obtain the similar intrinsic UAS and LAS scores on the pre-processed segmented test sentences containing event interactions. This is logical because the BioNLP 2009 shared task dataset was a subset of the GENIA corpus. Among the four dependency parsers trained on GENIA, Stanford-Biaffine, jPTDP and NLP4J-dep produce similar event extraction scores on the development set, while on the the test set jPTDP and NLP4J-dep obtain the lowest and highest scores, respectively." } ], "images": [ "scigen/train/3422.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Parsing results on the test set with predicted POS tags and gold tokenization (except [G] which denotes results when employing gold POS tags in both training and testing phases). “Without punctuation” refers to results excluding punctuation and other symbols from evaluation. “Exact match” denotes the percentage of sentences whose predicted trees are entirely correct [25]. [∙] denotes the use of the pre-trained Stanford tagger for predicting POS tags on test set, instead of using the retrained NLP4J-POS model. Score differences between the “retrained” parsers on both corpora are significant at p≤0.001 using McNemar’s test (except UAS scores obtained by Stanford-Biaffine-v2 for gold and predicted POS tags on GENIA, i.e. 92.51 vs. 92.31 and 92.84 vs. 92.64, where p≤0.05)." ], "reasoning": "Stanford-Biaffine achieves the highest parsing results on both GENIA and CRAFT datasets, while Stanford-NNdep obtains the lowest scores.", "description": "In this section, we present our empirical approach to evaluate different POS tagging and dependency parsing models on benchmark biomedical corpora. The first five rows show parsing results on the GENIA test set of “pre-trained” parsers. Both pre-trained NNdep and Biaffine models were trained on a dependency treebank of 40K sentences, which was converted from the English PTB sections 2–21. The remaining rows show results of our retrained dependency parsing models. Regarding the retrained parsing models, on both GENIA and CRAFT, Stanford-Biaffine achieves the highest parsing results with LAS at 91.23% and UAS at 92.64% on GENIA, and LAS at 90.77% and UAS at 92.67% on CRAFT, computed without punctuations. Stanford-NNdep obtains the lowest scores; about 3.5% and 5% absolute lower than Stanford-Biaffine on GENIA and CRAFT, respectively. jPTDP is ranked second, obtaining about 1% and 2% lower scores than Stanford-Biaffine and 1.5% and 1% higher scores (without punctuation) than NLP4J-dep on GENIA and CRAFT, respectively. the intrinsic UAS and LAS (F1) scores on the pre-processed segmented BioNLP 2009 development sentences (i.e. scores with respect to predicted segmentation), for which these sentences contain event interactions. Although gold event annotations in the BioNLP 2009 test set are not available to public, it is likely that we would obtain the similar intrinsic UAS and LAS scores on the pre-processed segmented test sentences containing event interactions. This is logical because the BioNLP 2009 shared task dataset was a subset of the GENIA corpus. Among the four dependency parsers trained on GENIA, Stanford-Biaffine, jPTDP and NLP4J-dep produce similar event extraction scores on the development set, while on the the test set jPTDP and NLP4J-dep obtain the lowest and highest scores, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 4: Parsing results on the test set with predicted POS tags and gold tokenization (except [G] which denotes results when employing gold POS tags in both training and testing phases). “Without punctuation” refers to results excluding punctuation and other symbols from evaluation. “Exact match” denotes the percentage of sentences whose predicted trees are entirely correct [25]. [∙] denotes the use of the pre-trained Stanford tagger for predicting POS tags on test set, instead of using the retrained NLP4J-POS model. Score differences between the “retrained” parsers on both corpora are significant at p≤0.001 using McNemar’s test (except UAS scores obtained by Stanford-Biaffine-v2 for gold and predicted POS tags on GENIA, i.e. 92.51 vs. 92.31 and 92.84 vs. 92.64, where p≤0.05).'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich retrained model achieves the highest LAS score on the GENIA dataset without punctuation?\n" }, { "role": "assistant", "content": "From the table, the retrained Stanford-Biaffine-v2 model achieves the highest LAS score of 91.23% on the GENIA dataset without punctuation. So, the answer is {\"answer\": \"Stanford-Biaffine-v2\"}." } ], "images": [ "scigen/train/3422.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Parsing results on the test set with predicted POS tags and gold tokenization (except [G] which denotes results when employing gold POS tags in both training and testing phases). “Without punctuation” refers to results excluding punctuation and other symbols from evaluation. “Exact match” denotes the percentage of sentences whose predicted trees are entirely correct [25]. [∙] denotes the use of the pre-trained Stanford tagger for predicting POS tags on test set, instead of using the retrained NLP4J-POS model. Score differences between the “retrained” parsers on both corpora are significant at p≤0.001 using McNemar’s test (except UAS scores obtained by Stanford-Biaffine-v2 for gold and predicted POS tags on GENIA, i.e. 92.51 vs. 92.31 and 92.84 vs. 92.64, where p≤0.05)." ], "reasoning": "From the table, the retrained Stanford-Biaffine-v2 model achieves the highest LAS score of 91.23% on the GENIA dataset without punctuation.", "question": "Which retrained model achieves the highest LAS score on the GENIA dataset without punctuation?", "answer": "So, the answer is {\"answer\": \"Stanford-Biaffine-v2\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Stanford-NNdep achieves the highest UAS score on the CRAFT dataset without punctuation. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nStanford-NNdep achieves the highest UAS score on the CRAFT dataset without punctuation.\n" }, { "role": "assistant", "content": "From the table, the highest UAS score on the CRAFT dataset without punctuation is achieved by Stanford-Biaffine-v2 with a score of 92.67%, not Stanford-NNdep. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3422.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Parsing results on the test set with predicted POS tags and gold tokenization (except [G] which denotes results when employing gold POS tags in both training and testing phases). “Without punctuation” refers to results excluding punctuation and other symbols from evaluation. “Exact match” denotes the percentage of sentences whose predicted trees are entirely correct [25]. [∙] denotes the use of the pre-trained Stanford tagger for predicting POS tags on test set, instead of using the retrained NLP4J-POS model. Score differences between the “retrained” parsers on both corpora are significant at p≤0.001 using McNemar’s test (except UAS scores obtained by Stanford-Biaffine-v2 for gold and predicted POS tags on GENIA, i.e. 92.51 vs. 92.31 and 92.84 vs. 92.64, where p≤0.05)." ], "reasoning": "From the table, the highest UAS score on the CRAFT dataset without punctuation is achieved by Stanford-Biaffine-v2 with a score of 92.67%, not Stanford-NNdep.", "statement": "Stanford-NNdep achieves the highest UAS score on the CRAFT dataset without punctuation.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: LAS (F1) scores of Stanford-Biaffine on GENIA, by frequent dependency labels in the left dependencies. “Prop.” denotes the occurrence proportion in each distance bin.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "On the GENIA corpus, Stanford-Biaffine and jPTDP achieve higher scores for longer distances in bins -4, -5, and < -5, likely due to the structural characteristics of sentences and the frequency of difficult-to-predict dependency relations like amod and nn.On both corpora, higher scores are also associated with shorter distances. There is one surprising exception: on GENIA, in distance bins of −4, −5 and < −5, Stanford-Biaffine and jPTDP obtain higher scores for longer distances. This may result from the structural characteristics of sentences in the GENIA corpus. We find amod and nn are the two most difficult to predict dependency relations (the same finding applied to jPTDP). They appear much more frequently in the bins −4 and −5 than in bin < −5, explaining the higher overall score for bin < −5." } ], "images": [ "scigen/train/3423.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: LAS (F1) scores of Stanford-Biaffine on GENIA, by frequent dependency labels in the left dependencies. “Prop.” denotes the occurrence proportion in each distance bin." ], "reasoning": "On the GENIA corpus, Stanford-Biaffine and jPTDP achieve higher scores for longer distances in bins -4, -5, and < -5, likely due to the structural characteristics of sentences and the frequency of difficult-to-predict dependency relations like amod and nn.", "description": "On both corpora, higher scores are also associated with shorter distances. There is one surprising exception: on GENIA, in distance bins of −4, −5 and < −5, Stanford-Biaffine and jPTDP obtain higher scores for longer distances. This may result from the structural characteristics of sentences in the GENIA corpus. We find amod and nn are the two most difficult to predict dependency relations (the same finding applied to jPTDP). They appear much more frequently in the bins −4 and −5 than in bin < −5, explaining the higher overall score for bin < −5.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 5: LAS (F1) scores of Stanford-Biaffine on GENIA, by frequent dependency labels in the left dependencies. “Prop.” denotes the occurrence proportion in each distance bin.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. \n\nQuestion: Which dependency relation has the highest LAS score in the bin < -5?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the dependency relation with the highest LAS score in the bin < -5, look at the LAS column under the < -5 bin. The highest LAS score is 98.49, which corresponds to the 'mark' dependency relation. So, the answer is {\"answer\": \"mark\"}." } ], "images": [ "scigen/train/3423.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: LAS (F1) scores of Stanford-Biaffine on GENIA, by frequent dependency labels in the left dependencies. “Prop.” denotes the occurrence proportion in each distance bin." ], "reasoning": "To find the dependency relation with the highest LAS score in the bin < -5, look at the LAS column under the < -5 bin. The highest LAS score is 98.49, which corresponds to the 'mark' dependency relation.", "question": "Which dependency relation has the highest LAS score in the bin < -5?", "answer": "So, the answer is {\"answer\": \"mark\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The nn dependency relation has a higher LAS score in the bin < -5 than in the bin -5. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe nn dependency relation has a higher LAS score in the bin < -5 than in the bin -5.\n" }, { "role": "assistant", "content": "To verify this statement, compare the LAS scores of the nn dependency relation in the < -5 and -5 bins. The LAS score for nn in the < -5 bin is 74.38, and in the -5 bin, it is 77.42. Since 74.38 is less than 77.42, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3423.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: LAS (F1) scores of Stanford-Biaffine on GENIA, by frequent dependency labels in the left dependencies. “Prop.” denotes the occurrence proportion in each distance bin." ], "reasoning": "To verify this statement, compare the LAS scores of the nn dependency relation in the < -5 and -5 bins. The LAS score for nn in the < -5 bin is 74.38, and in the -5 bin, it is 77.42. Since 74.38 is less than 77.42, the statement is refuted.", "statement": "The nn dependency relation has a higher LAS score in the bin < -5 than in the bin -5.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: LAS by the basic Stanford dependency labels on GENIA. “Avg.” denotes the averaged score of the four dependency parsers.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Stanford-Biaffine generally achieves the highest scores for most relation types on both GENIA and CRAFT corpora, except for specific tags where other models like jPTDP, NLP4J-dep, and NNdep perform better.In most cases, Stanford-Biaffine obtains the highest score for each relation type on both corpora with the following exceptions: on GENIA, jPTDP gets the highest results to aux, dep and nn (as well as nsubjpass), while NLP4J-dep and NNdep obtain the highest scores for auxpass and num, respectively. On GENIA the labels associated with the highest average LAS scores (generally >90%) are amod, aux, auxpass, det, dobj, mark, nsubj, nsubjpass, pobj and root whereas on CRAFT they are NMOD, OBJ, PMOD, PRD, ROOT, SBJ, SUB and VC. These labels either correspond to short dependencies (e.g. aux, auxpass and VC), have strong lexical indications (e.g. det, pobj and PMOD), or occur very often (e.g. amod, subj, NMOD and SBJ). Stanford-Biaffine achieves the highest scores on all these tags except TO where the traditional feature-based model NLP4J-dep obtains the highest score Among listed tags VBG is the least and second least frequent one in GENIA and CRAFT, respectively, and generally associates to longer dependency distances. So, it is reasonable that the lowest scores we obtain on both corpora are accounted for by VBG. The coordinating conjunction tag CC also often corresponds to long dependencies, thus resulting in biggest ranges across parsers on both GENIA and CRAFT. On the remaining POS tags, we generally find similar patterns across parsers and corpora, except for IN and VB where parsers produce 8+% higher scores for IN on GENIA than on CRAFT, and vice versa producing 9+% lower scores for VB on GENIA. Regarding VB, on CRAFT it usually associates to a short dependency distance of 1 word (i.e. head and dependent words are next to each other) with a rate at 80%, and to a distance of 2 words at 15%, while on GENIA it associates with longer dependency distances with a rate at 17% for the distance of 1 word, 31% for the distance of 2 words and 34% for a distance of >5 words. So, parsers obtain much higher scores for VB on CRAFT than on GENIA." } ], "images": [ "scigen/train/3424.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: LAS by the basic Stanford dependency labels on GENIA. “Avg.” denotes the averaged score of the four dependency parsers." ], "reasoning": "Stanford-Biaffine generally achieves the highest scores for most relation types on both GENIA and CRAFT corpora, except for specific tags where other models like jPTDP, NLP4J-dep, and NNdep perform better.", "description": "In most cases, Stanford-Biaffine obtains the highest score for each relation type on both corpora with the following exceptions: on GENIA, jPTDP gets the highest results to aux, dep and nn (as well as nsubjpass), while NLP4J-dep and NNdep obtain the highest scores for auxpass and num, respectively. On GENIA the labels associated with the highest average LAS scores (generally >90%) are amod, aux, auxpass, det, dobj, mark, nsubj, nsubjpass, pobj and root whereas on CRAFT they are NMOD, OBJ, PMOD, PRD, ROOT, SBJ, SUB and VC. These labels either correspond to short dependencies (e.g. aux, auxpass and VC), have strong lexical indications (e.g. det, pobj and PMOD), or occur very often (e.g. amod, subj, NMOD and SBJ). Stanford-Biaffine achieves the highest scores on all these tags except TO where the traditional feature-based model NLP4J-dep obtains the highest score Among listed tags VBG is the least and second least frequent one in GENIA and CRAFT, respectively, and generally associates to longer dependency distances. So, it is reasonable that the lowest scores we obtain on both corpora are accounted for by VBG. The coordinating conjunction tag CC also often corresponds to long dependencies, thus resulting in biggest ranges across parsers on both GENIA and CRAFT. On the remaining POS tags, we generally find similar patterns across parsers and corpora, except for IN and VB where parsers produce 8+% higher scores for IN on GENIA than on CRAFT, and vice versa producing 9+% lower scores for VB on GENIA. Regarding VB, on CRAFT it usually associates to a short dependency distance of 1 word (i.e. head and dependent words are next to each other) with a rate at 80%, and to a distance of 2 words at 15%, while on GENIA it associates with longer dependency distances with a rate at 17% for the distance of 1 word, 31% for the distance of 2 words and 34% for a distance of >5 words. So, parsers obtain much higher scores for VB on CRAFT than on GENIA.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 6: LAS by the basic Stanford dependency labels on GENIA. “Avg.” denotes the averaged score of the four dependency parsers.', answer the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model achieves the highest score for the 'auxpass' relation type?\n" }, { "role": "assistant", "content": "By examining the table, we see that for the 'auxpass' relation type, the highest score is 99.49, which is achieved by the NLP4J model. So, the answer is {\"answer\": \"NLP4J\"}." } ], "images": [ "scigen/train/3424.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: LAS by the basic Stanford dependency labels on GENIA. “Avg.” denotes the averaged score of the four dependency parsers." ], "reasoning": "By examining the table, we see that for the 'auxpass' relation type, the highest score is 99.49, which is achieved by the NLP4J model.", "question": "Which model achieves the highest score for the 'auxpass' relation type?", "answer": "So, the answer is {\"answer\": \"NLP4J\"}." }, { "messages": [ { "role": "user", "content": "Stanford-Biaffine achieves the highest score for the 'num' relation type. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nStanford-Biaffine achieves the highest score for the 'num' relation type.\n" }, { "role": "assistant", "content": "The table shows that for the 'num' relation type, the highest score is 90.27, which is achieved by the NNdep model, not Stanford-Biaffine. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3424.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: LAS by the basic Stanford dependency labels on GENIA. “Avg.” denotes the averaged score of the four dependency parsers." ], "reasoning": "The table shows that for the 'num' relation type, the highest score is 90.27, which is achieved by the NNdep model, not Stanford-Biaffine.", "statement": "Stanford-Biaffine achieves the highest score for the 'num' relation type.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 7: LAS by the CoNLL 2008 dependency labels on CRAFT.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Stanford-Biaffine generally achieves the highest scores across most relation types, with exceptions on specific tags where other models like jPTDP, NLP4J-dep, and NNdep perform better.In most cases, Stanford-Biaffine obtains the highest score for each relation type on both corpora with the following exceptions: on GENIA, jPTDP gets the highest results to aux, dep and nn (as well as nsubjpass), while NLP4J-dep and NNdep obtain the highest scores for auxpass and num, respectively. On GENIA the labels associated with the highest average LAS scores (generally >90%) are amod, aux, auxpass, det, dobj, mark, nsubj, nsubjpass, pobj and root whereas on CRAFT they are NMOD, OBJ, PMOD, PRD, ROOT, SBJ, SUB and VC. These labels either correspond to short dependencies (e.g. aux, auxpass and VC), have strong lexical indications (e.g. det, pobj and PMOD), or occur very often (e.g. amod, subj, NMOD and SBJ). Stanford-Biaffine achieves the highest scores on all these tags except TO where the traditional feature-based model NLP4J-dep obtains the highest score Among listed tags VBG is the least and second least frequent one in GENIA and CRAFT, respectively, and generally associates to longer dependency distances. So, it is reasonable that the lowest scores we obtain on both corpora are accounted for by VBG. The coordinating conjunction tag CC also often corresponds to long dependencies, thus resulting in biggest ranges across parsers on both GENIA and CRAFT." } ], "images": [ "scigen/train/3425.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: LAS by the CoNLL 2008 dependency labels on CRAFT." ], "reasoning": "Stanford-Biaffine generally achieves the highest scores across most relation types, with exceptions on specific tags where other models like jPTDP, NLP4J-dep, and NNdep perform better.", "description": "In most cases, Stanford-Biaffine obtains the highest score for each relation type on both corpora with the following exceptions: on GENIA, jPTDP gets the highest results to aux, dep and nn (as well as nsubjpass), while NLP4J-dep and NNdep obtain the highest scores for auxpass and num, respectively. On GENIA the labels associated with the highest average LAS scores (generally >90%) are amod, aux, auxpass, det, dobj, mark, nsubj, nsubjpass, pobj and root whereas on CRAFT they are NMOD, OBJ, PMOD, PRD, ROOT, SBJ, SUB and VC. These labels either correspond to short dependencies (e.g. aux, auxpass and VC), have strong lexical indications (e.g. det, pobj and PMOD), or occur very often (e.g. amod, subj, NMOD and SBJ). Stanford-Biaffine achieves the highest scores on all these tags except TO where the traditional feature-based model NLP4J-dep obtains the highest score Among listed tags VBG is the least and second least frequent one in GENIA and CRAFT, respectively, and generally associates to longer dependency distances. So, it is reasonable that the lowest scores we obtain on both corpora are accounted for by VBG. The coordinating conjunction tag CC also often corresponds to long dependencies, thus resulting in biggest ranges across parsers on both GENIA and CRAFT.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 7: LAS by the CoNLL 2008 dependency labels on CRAFT.', solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model achieves the highest score for the 'OBJ' relation type?\n" }, { "role": "assistant", "content": "By examining the table, we see that for the 'OBJ' relation type, the highest score is achieved by Stanford-Biaffine with a score of 96.51. So, the answer is {\"answer\": \"Stanford-Biaffine\"}." } ], "images": [ "scigen/train/3425.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: LAS by the CoNLL 2008 dependency labels on CRAFT." ], "reasoning": "By examining the table, we see that for the 'OBJ' relation type, the highest score is achieved by Stanford-Biaffine with a score of 96.51.", "question": "Which model achieves the highest score for the 'OBJ' relation type?", "answer": "So, the answer is {\"answer\": \"Stanford-Biaffine\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nStanford-Biaffine achieves the highest score for the 'LOC' relation type.\n" }, { "role": "assistant", "content": "By examining the table, we see that for the 'LOC' relation type, the highest score is achieved by NNdep with a score of 68.31, not Stanford-Biaffine. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3425.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: LAS by the CoNLL 2008 dependency labels on CRAFT." ], "reasoning": "By examining the table, we see that for the 'LOC' relation type, the highest score is achieved by NNdep with a score of 68.31, not Stanford-Biaffine.", "statement": "Stanford-Biaffine achieves the highest score for the 'LOC' relation type.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 8: LAS by POS tag of the dependent.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Stanford-Biaffine achieves the highest scores on all tags except TO, where NLP4J-dep scores highest. VBG is the least frequent tag in GENIA and second least in CRAFT, leading to the lowest scores on both corpora.Stanford-Biaffine achieves the highest scores on all these tags except TO where the traditional feature-based model NLP4J-dep obtains the highest score Among listed tags VBG is the least and second least frequent one in GENIA and CRAFT, respectively, and generally associates to longer dependency distances. So, it is reasonable that the lowest scores we obtain on both corpora are accounted for by VBG. The coordinating conjunction tag CC also often corresponds to long dependencies, thus resulting in biggest ranges across parsers on both GENIA and CRAFT. On the remaining POS tags, we generally find similar patterns across parsers and corpora, except for IN and VB where parsers produce 8+% higher scores for IN on GENIA than on CRAFT, and vice versa producing 9+% lower scores for VB on GENIA. Regarding VB, on CRAFT it usually associates to a short dependency distance of 1 word (i.e. head and dependent words are next to each other) with a rate at 80%, and to a distance of 2 words at 15%, while on GENIA it associates with longer dependency distances with a rate at 17% for the distance of 1 word, 31% for the distance of 2 words and 34% for a distance of >5 words. So, parsers obtain much higher scores for VB on CRAFT than on GENIA." } ], "images": [ "scigen/train/3426.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: LAS by POS tag of the dependent." ], "reasoning": "Stanford-Biaffine achieves the highest scores on all tags except TO, where NLP4J-dep scores highest. VBG is the least frequent tag in GENIA and second least in CRAFT, leading to the lowest scores on both corpora.", "description": "Stanford-Biaffine achieves the highest scores on all these tags except TO where the traditional feature-based model NLP4J-dep obtains the highest score Among listed tags VBG is the least and second least frequent one in GENIA and CRAFT, respectively, and generally associates to longer dependency distances. So, it is reasonable that the lowest scores we obtain on both corpora are accounted for by VBG. The coordinating conjunction tag CC also often corresponds to long dependencies, thus resulting in biggest ranges across parsers on both GENIA and CRAFT. On the remaining POS tags, we generally find similar patterns across parsers and corpora, except for IN and VB where parsers produce 8+% higher scores for IN on GENIA than on CRAFT, and vice versa producing 9+% lower scores for VB on GENIA. Regarding VB, on CRAFT it usually associates to a short dependency distance of 1 word (i.e. head and dependent words are next to each other) with a rate at 80%, and to a distance of 2 words at 15%, while on GENIA it associates with longer dependency distances with a rate at 17% for the distance of 1 word, 31% for the distance of 2 words and 34% for a distance of >5 words. So, parsers obtain much higher scores for VB on CRAFT than on GENIA.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 8: LAS by POS tag of the dependent.\nQuestion: Which tag has the highest score on the GENIA corpus using the NLP4J model?\nSolve the above question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By examining the table, we see that the highest score for the GENIA corpus using the NLP4J model is for the IN tag with a score of 97.39. So, the answer is {\"answer\": \"IN\"}." } ], "images": [ "scigen/train/3426.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: LAS by POS tag of the dependent." ], "reasoning": "By examining the table, we see that the highest score for the GENIA corpus using the NLP4J model is for the IN tag with a score of 97.39.", "question": "Which tag has the highest score on the GENIA corpus using the NLP4J model?", "answer": "So, the answer is {\"answer\": \"IN\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The tag VB has the highest score on the CRAFT corpus using the Biaffine model., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe tag VB has the highest score on the CRAFT corpus using the Biaffine model.\n" }, { "role": "assistant", "content": "By examining the table, we see that the highest score for the CRAFT corpus using the Biaffine model is for the VBZ tag with a score of 93.42, not VB. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3426.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: LAS by POS tag of the dependent." ], "reasoning": "By examining the table, we see that the highest score for the CRAFT corpus using the Biaffine model is for the VBZ tag with a score of 93.42, not VB.", "statement": "The tag VB has the highest score on the CRAFT corpus using the Biaffine model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 10: UAS and LAS (F1) scores of re-trained models on the pre-segmented BioNLP-2009 development sentences which contain event interactions. Scores are computed on all tokens using the evaluation script from the CoNLL 2017 shared task [31].', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Stanford-Biaffine, jPTDP, and NLP4J-dep produce similar event extraction scores on the development set, while on the test set, jPTDP and NLP4J-dep obtain the lowest and highest scores, respectively.the intrinsic UAS and LAS (F1) scores on the pre-processed segmented BioNLP 2009 development sentences (i.e. scores with respect to predicted segmentation), for which these sentences contain event interactions. Although gold event annotations in the BioNLP 2009 test set are not available to public, it is likely that we would obtain the similar intrinsic UAS and LAS scores on the pre-processed segmented test sentences containing event interactions. This is logical because the BioNLP 2009 shared task dataset was a subset of the GENIA corpus. Among the four dependency parsers trained on GENIA, Stanford-Biaffine, jPTDP and NLP4J-dep produce similar event extraction scores on the development set, while on the the test set jPTDP and NLP4J-dep obtain the lowest and highest scores, respectively." } ], "images": [ "scigen/train/3427.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: UAS and LAS (F1) scores of re-trained models on the pre-segmented BioNLP-2009 development sentences which contain event interactions. Scores are computed on all tokens using the evaluation script from the CoNLL 2017 shared task [31]." ], "reasoning": "Stanford-Biaffine, jPTDP, and NLP4J-dep produce similar event extraction scores on the development set, while on the test set, jPTDP and NLP4J-dep obtain the lowest and highest scores, respectively.", "description": "the intrinsic UAS and LAS (F1) scores on the pre-processed segmented BioNLP 2009 development sentences (i.e. scores with respect to predicted segmentation), for which these sentences contain event interactions. Although gold event annotations in the BioNLP 2009 test set are not available to public, it is likely that we would obtain the similar intrinsic UAS and LAS scores on the pre-processed segmented test sentences containing event interactions. This is logical because the BioNLP 2009 shared task dataset was a subset of the GENIA corpus. Among the four dependency parsers trained on GENIA, Stanford-Biaffine, jPTDP and NLP4J-dep produce similar event extraction scores on the development set, while on the the test set jPTDP and NLP4J-dep obtain the lowest and highest scores, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 10: UAS and LAS (F1) scores of re-trained models on the pre-segmented BioNLP-2009 development sentences which contain event interactions. Scores are computed on all tokens using the evaluation script from the CoNLL 2017 shared task [31].', solve the subsequent question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich parser has the highest LAS score on the pre-processed segmented BioNLP 2009 development sentences?\n" }, { "role": "assistant", "content": "The table shows the LAS scores for different parsers. By comparing the LAS scores, we see that Biaffine has the highest LAS score of 94.82. So, the answer is {\"answer\": \"Biaffine\"}." } ], "images": [ "scigen/train/3427.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: UAS and LAS (F1) scores of re-trained models on the pre-segmented BioNLP-2009 development sentences which contain event interactions. Scores are computed on all tokens using the evaluation script from the CoNLL 2017 shared task [31]." ], "reasoning": "The table shows the LAS scores for different parsers. By comparing the LAS scores, we see that Biaffine has the highest LAS score of 94.82.", "question": "Which parser has the highest LAS score on the pre-processed segmented BioNLP 2009 development sentences?", "answer": "So, the answer is {\"answer\": \"Biaffine\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nNLP4J-dep has the lowest UAS score among the four parsers.\n" }, { "role": "assistant", "content": "The table shows the UAS scores for different parsers. By comparing the UAS scores, we see that NNdep has the lowest UAS score of 91.02, not NLP4J-dep. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3427.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: UAS and LAS (F1) scores of re-trained models on the pre-segmented BioNLP-2009 development sentences which contain event interactions. Scores are computed on all tokens using the evaluation script from the CoNLL 2017 shared task [31]." ], "reasoning": "The table shows the UAS scores for different parsers. By comparing the UAS scores, we see that NNdep has the lowest UAS score of 91.02, not NLP4J-dep.", "statement": "NLP4J-dep has the lowest UAS score among the four parsers.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 11: Biomedical event extraction results. The subscripts denote results for which TEES is trained without the dependency labels.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The Stanford-Biaffine parser (v2) trained on GENIA achieved the highest F1 score on the development set, while NLP4J-dep trained on GENIA achieved the highest F1 score on the test set.In this section, we present our empirical approach to evaluate different POS tagging and dependency parsing models on benchmark biomedical corpora. Their system used the Stanford-Biaffine parser (v2) trained on a dataset combining PTB, Brown corpus, and GENIA treebank data. The second row presents our score for the pre-trained BLLIP+Bio model; remaining rows show scores using re-trained parsing models. This is logical because the BioNLP 2009 shared task dataset was a subset of the GENIA corpus. Among the four dependency parsers trained on GENIA, Stanford-Biaffine, jPTDP and NLP4J-dep produce similar event extraction scores on the development set, while on the the test set jPTDP and NLP4J-dep obtain the lowest and highest scores, respectively. In most cases, compared to using dependency labels, event extraction scores drop on the development set (except NLP4J-dep trained on CRAFT), while they increase on the test set (except NLP4J-dep trained on GENIA and Stanford-NNdep trained on CRAFT). Without dependency labels, better event extraction scores on the development set corresponds to better scores on the test set. In addition, the differences in these event extraction scores without dependency labels are more consistent with the parsing performance differences than the scores with dependency labels." } ], "images": [ "scigen/train/3428.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Biomedical event extraction results. The subscripts denote results for which TEES is trained without the dependency labels." ], "reasoning": "The Stanford-Biaffine parser (v2) trained on GENIA achieved the highest F1 score on the development set, while NLP4J-dep trained on GENIA achieved the highest F1 score on the test set.", "description": "In this section, we present our empirical approach to evaluate different POS tagging and dependency parsing models on benchmark biomedical corpora. Their system used the Stanford-Biaffine parser (v2) trained on a dataset combining PTB, Brown corpus, and GENIA treebank data. The second row presents our score for the pre-trained BLLIP+Bio model; remaining rows show scores using re-trained parsing models. This is logical because the BioNLP 2009 shared task dataset was a subset of the GENIA corpus. Among the four dependency parsers trained on GENIA, Stanford-Biaffine, jPTDP and NLP4J-dep produce similar event extraction scores on the development set, while on the the test set jPTDP and NLP4J-dep obtain the lowest and highest scores, respectively. In most cases, compared to using dependency labels, event extraction scores drop on the development set (except NLP4J-dep trained on CRAFT), while they increase on the test set (except NLP4J-dep trained on GENIA and Stanford-NNdep trained on CRAFT). Without dependency labels, better event extraction scores on the development set corresponds to better scores on the test set. In addition, the differences in these event extraction scores without dependency labels are more consistent with the parsing performance differences than the scores with dependency labels.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 11: Biomedical event extraction results. The subscripts denote results for which TEES is trained without the dependency labels.', solve the subsequent question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich system achieved the highest F1 score on the test set when trained on the GENIA corpus?\n" }, { "role": "assistant", "content": "From the table, we see that among the systems trained on the GENIA corpus, NLP4J-dep achieved the highest F1 score on the test set with a score of 50.25. So, the answer is {\"answer\": \"NLP4J-dep\"}" } ], "images": [ "scigen/train/3428.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Biomedical event extraction results. The subscripts denote results for which TEES is trained without the dependency labels." ], "reasoning": "From the table, we see that among the systems trained on the GENIA corpus, NLP4J-dep achieved the highest F1 score on the test set with a score of 50.25.", "question": "Which system achieved the highest F1 score on the test set when trained on the GENIA corpus?", "answer": "So, the answer is {\"answer\": \"NLP4J-dep\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The Stanford-Biaffine parser (v2) trained on the GENIA corpus achieved the highest F1 score on the development set., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Stanford-Biaffine parser (v2) trained on the GENIA corpus achieved the highest F1 score on the development set.\n" }, { "role": "assistant", "content": "The table shows that the Stanford-Biaffine parser (v2) trained on the GENIA corpus achieved an F1 score of 53.34 on the development set, which is the highest among the systems listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3428.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Biomedical event extraction results. The subscripts denote results for which TEES is trained without the dependency labels." ], "reasoning": "The table shows that the Stanford-Biaffine parser (v2) trained on the GENIA corpus achieved an F1 score of 53.34 on the development set, which is the highest among the systems listed.", "statement": "The Stanford-Biaffine parser (v2) trained on the GENIA corpus achieved the highest F1 score on the development set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'TABLE V: Sentiment Analysis for the Presidential Debates: f1 stands for 1-gram, f2 stands for 2-gram, f3 stands for (1+2)-gram, f4 stands for (1+2)-gram+MISC, f5 stands for DOC, f6 stands for DOC+TOPIC.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The table compares the Hamming Loss of different methods across various features, indicating that lower values are more desirable.We use 3 different datasets for the problem of sentiment analysis, as already mentioned. We use this metric because our problem is in the multi-class classification domain. However, the single-label classifiers like SVM, Naive Bayes, Elman RNN cannot be evaluated against this metric directly. To do this, we split the predicted labels into a format that is consistent with that of multi-label classifiers like RaKel. In the table, f1, f2, f3, f4, f5 and f6 denote the features 1-gram, 2-gram, (1+2)-gram, (1+2)-gram+MISC, DOC and DOC+TOPIC respectively. Note that lower values of hamming losses are more desirable." } ], "images": [ "scigen/train/3429.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Sentiment Analysis for the Presidential Debates: f1 stands for 1-gram, f2 stands for 2-gram, f3 stands for (1+2)-gram, f4 stands for (1+2)-gram+MISC, f5 stands for DOC, f6 stands for DOC+TOPIC." ], "reasoning": "The table compares the Hamming Loss of different methods across various features, indicating that lower values are more desirable.", "description": "We use 3 different datasets for the problem of sentiment analysis, as already mentioned. We use this metric because our problem is in the multi-class classification domain. However, the single-label classifiers like SVM, Naive Bayes, Elman RNN cannot be evaluated against this metric directly. To do this, we split the predicted labels into a format that is consistent with that of multi-label classifiers like RaKel. In the table, f1, f2, f3, f4, f5 and f6 denote the features 1-gram, 2-gram, (1+2)-gram, (1+2)-gram+MISC, DOC and DOC+TOPIC respectively. Note that lower values of hamming losses are more desirable.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: TABLE V: Sentiment Analysis for the Presidential Debates: f1 stands for 1-gram, f2 stands for 2-gram, f3 stands for (1+2)-gram, f4 stands for (1+2)-gram+MISC, f5 stands for DOC, f6 stands for DOC+TOPIC.\nQuestion: Which method has the lowest Hamming Loss for feature f5?\n" }, { "role": "assistant", "content": "To find the method with the lowest Hamming Loss for feature f5, compare the values in the Hamming Loss f5 column. The lowest value is 0.385, which corresponds to the RaKel method. So, the answer is {\"answer\": \"RaKel\"}." } ], "images": [ "scigen/train/3429.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Sentiment Analysis for the Presidential Debates: f1 stands for 1-gram, f2 stands for 2-gram, f3 stands for (1+2)-gram, f4 stands for (1+2)-gram+MISC, f5 stands for DOC, f6 stands for DOC+TOPIC." ], "reasoning": "To find the method with the lowest Hamming Loss for feature f5, compare the values in the Hamming Loss f5 column. The lowest value is 0.385, which corresponds to the RaKel method.", "question": "Which method has the lowest Hamming Loss for feature f5?", "answer": "So, the answer is {\"answer\": \"RaKel\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nSVM has a higher Hamming Loss for feature f1 compared to Naive Bayes.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Hamming Loss values for feature f1 between SVM and Naive Bayes. SVM has a Hamming Loss of 0.467, which is lower than Naive Bayes' Hamming Loss of 0.504. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3429.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Sentiment Analysis for the Presidential Debates: f1 stands for 1-gram, f2 stands for 2-gram, f3 stands for (1+2)-gram, f4 stands for (1+2)-gram+MISC, f5 stands for DOC, f6 stands for DOC+TOPIC." ], "reasoning": "To verify this statement, compare the Hamming Loss values for feature f1 between SVM and Naive Bayes. SVM has a Hamming Loss of 0.467, which is lower than Naive Bayes' Hamming Loss of 0.504.", "statement": "SVM has a higher Hamming Loss for feature f1 compared to Naive Bayes.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'TABLE V: Sentiment Analysis for the Presidential Debates: f1 stands for 1-gram, f2 stands for 2-gram, f3 stands for (1+2)-gram, f4 stands for (1+2)-gram+MISC, f5 stands for DOC, f6 stands for DOC+TOPIC.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The table compares the Hamming Loss values for different methods and features, showing that lower values are more desirable.We use 3 different datasets for the problem of sentiment analysis, as already mentioned. We use this metric because our problem is in the multi-class classification domain. However, the single-label classifiers like SVM, Naive Bayes, Elman RNN cannot be evaluated against this metric directly. To do this, we split the predicted labels into a format that is consistent with that of multi-label classifiers like RaKel. In the table, f1, f2, f3, f4, f5 and f6 denote the features 1-gram, 2-gram, (1+2)-gram, (1+2)-gram+MISC, DOC and DOC+TOPIC respectively. Note that lower values of hamming losses are more desirable." } ], "images": [ "scigen/train/3430.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Sentiment Analysis for the Presidential Debates: f1 stands for 1-gram, f2 stands for 2-gram, f3 stands for (1+2)-gram, f4 stands for (1+2)-gram+MISC, f5 stands for DOC, f6 stands for DOC+TOPIC." ], "reasoning": "The table compares the Hamming Loss values for different methods and features, showing that lower values are more desirable.", "description": "We use 3 different datasets for the problem of sentiment analysis, as already mentioned. We use this metric because our problem is in the multi-class classification domain. However, the single-label classifiers like SVM, Naive Bayes, Elman RNN cannot be evaluated against this metric directly. To do this, we split the predicted labels into a format that is consistent with that of multi-label classifiers like RaKel. In the table, f1, f2, f3, f4, f5 and f6 denote the features 1-gram, 2-gram, (1+2)-gram, (1+2)-gram+MISC, DOC and DOC+TOPIC respectively. Note that lower values of hamming losses are more desirable.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: TABLE V: Sentiment Analysis for the Presidential Debates: f1 stands for 1-gram, f2 stands for 2-gram, f3 stands for (1+2)-gram, f4 stands for (1+2)-gram+MISC, f5 stands for DOC, f6 stands for DOC+TOPIC.\nQuestion: Which method has the lowest Hamming Loss for feature f5?\nGive a detailed response to the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the method with the lowest Hamming Loss for feature f5, compare the values in the Hamming Loss f5 column. The lowest value is 0.394, which corresponds to the RaKel method. So, the answer is {\"answer\": \"RaKel\"}." } ], "images": [ "scigen/train/3430.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Sentiment Analysis for the Presidential Debates: f1 stands for 1-gram, f2 stands for 2-gram, f3 stands for (1+2)-gram, f4 stands for (1+2)-gram+MISC, f5 stands for DOC, f6 stands for DOC+TOPIC." ], "reasoning": "To find the method with the lowest Hamming Loss for feature f5, compare the values in the Hamming Loss f5 column. The lowest value is 0.394, which corresponds to the RaKel method.", "question": "Which method has the lowest Hamming Loss for feature f5?", "answer": "So, the answer is {\"answer\": \"RaKel\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nNaive Bayes has the highest Hamming Loss for feature f1.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Hamming Loss values for feature f1 across all methods. Naive Bayes has a Hamming Loss of 0.521 for feature f1, which is indeed the highest value in that column. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3430.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Sentiment Analysis for the Presidential Debates: f1 stands for 1-gram, f2 stands for 2-gram, f3 stands for (1+2)-gram, f4 stands for (1+2)-gram+MISC, f5 stands for DOC, f6 stands for DOC+TOPIC." ], "reasoning": "To verify this statement, compare the Hamming Loss values for feature f1 across all methods. Naive Bayes has a Hamming Loss of 0.521 for feature f1, which is indeed the highest value in that column.", "statement": "Naive Bayes has the highest Hamming Loss for feature f1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'TABLE V: Sentiment Analysis for the Presidential Debates: f1 stands for 1-gram, f2 stands for 2-gram, f3 stands for (1+2)-gram, f4 stands for (1+2)-gram+MISC, f5 stands for DOC, f6 stands for DOC+TOPIC.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table compares the Hamming Loss values for different methods across various features, showing that RaKel generally has the lowest Hamming Loss values, making it more desirable for multi-label classification.We use 3 different datasets for the problem of sentiment analysis, as already mentioned. We use this metric because our problem is in the multi-class classification domain. However, the single-label classifiers like SVM, Naive Bayes, Elman RNN cannot be evaluated against this metric directly. To do this, we split the predicted labels into a format that is consistent with that of multi-label classifiers like RaKel. In the table, f1, f2, f3, f4, f5 and f6 denote the features 1-gram, 2-gram, (1+2)-gram, (1+2)-gram+MISC, DOC and DOC+TOPIC respectively. Note that lower values of hamming losses are more desirable." } ], "images": [ "scigen/train/3431.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Sentiment Analysis for the Presidential Debates: f1 stands for 1-gram, f2 stands for 2-gram, f3 stands for (1+2)-gram, f4 stands for (1+2)-gram+MISC, f5 stands for DOC, f6 stands for DOC+TOPIC." ], "reasoning": "The table compares the Hamming Loss values for different methods across various features, showing that RaKel generally has the lowest Hamming Loss values, making it more desirable for multi-label classification.", "description": "We use 3 different datasets for the problem of sentiment analysis, as already mentioned. We use this metric because our problem is in the multi-class classification domain. However, the single-label classifiers like SVM, Naive Bayes, Elman RNN cannot be evaluated against this metric directly. To do this, we split the predicted labels into a format that is consistent with that of multi-label classifiers like RaKel. In the table, f1, f2, f3, f4, f5 and f6 denote the features 1-gram, 2-gram, (1+2)-gram, (1+2)-gram+MISC, DOC and DOC+TOPIC respectively. Note that lower values of hamming losses are more desirable.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'TABLE V: Sentiment Analysis for the Presidential Debates: f1 stands for 1-gram, f2 stands for 2-gram, f3 stands for (1+2)-gram, f4 stands for (1+2)-gram+MISC, f5 stands for DOC, f6 stands for DOC+TOPIC.', solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich method has the lowest Hamming Loss for feature f5?\n" }, { "role": "assistant", "content": "To find the method with the lowest Hamming Loss for feature f5, compare the values in the 'Hamming Loss f5' column. RaKel has the lowest value of 0.398. So, the answer is {\"answer\": \"RaKel\"}." } ], "images": [ "scigen/train/3431.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Sentiment Analysis for the Presidential Debates: f1 stands for 1-gram, f2 stands for 2-gram, f3 stands for (1+2)-gram, f4 stands for (1+2)-gram+MISC, f5 stands for DOC, f6 stands for DOC+TOPIC." ], "reasoning": "To find the method with the lowest Hamming Loss for feature f5, compare the values in the 'Hamming Loss f5' column. RaKel has the lowest value of 0.398.", "question": "Which method has the lowest Hamming Loss for feature f5?", "answer": "So, the answer is {\"answer\": \"RaKel\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nSVM has a lower Hamming Loss for feature f3 compared to Naive Bayes.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Hamming Loss values for feature f3 between SVM and Naive Bayes. SVM has a Hamming Loss of 0.481, while Naive Bayes has a Hamming Loss of 0.545, confirming that SVM has a lower value. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3431.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Sentiment Analysis for the Presidential Debates: f1 stands for 1-gram, f2 stands for 2-gram, f3 stands for (1+2)-gram, f4 stands for (1+2)-gram+MISC, f5 stands for DOC, f6 stands for DOC+TOPIC." ], "reasoning": "To verify this statement, compare the Hamming Loss values for feature f3 between SVM and Naive Bayes. SVM has a Hamming Loss of 0.481, while Naive Bayes has a Hamming Loss of 0.545, confirming that SVM has a lower value.", "statement": "SVM has a lower Hamming Loss for feature f3 compared to Naive Bayes.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Results of LightPAFF on GPT-2 tasks in terms of perplexity (ppl). “w/ FT” and “w/o FT” mean the pre-trained model is evaluated on downstream tasks with and without fine-tuning. “GPT-2” represents the model released by Radford et al. (2019) and is taken as the teacher model, “Transformer small” and “GPT-2 small” share the same model structure and number of parameters, with the only difference that “Transformer small” has no GPT-2 pre-training, “LightPAFF” is our method applied on “GPT-2 small”.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "LightPAFF achieves better performance than Transformer small and GPT-2 small models with the same parameter size, and approaches the performance of the GPT-2 teacher model with significantly fewer parameters.Accuracy of It can be seen that lightPAFF achieves large improvements than the models under the same size of parameters without pre-training (Transformer small) and with pre-training (GPT-2 small), while approaches the perplexity of the GPT-2 teacher model in the downstream tasks but with only 1/5× number of parameters." } ], "images": [ "scigen/train/3432.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results of LightPAFF on GPT-2 tasks in terms of perplexity (ppl). “w/ FT” and “w/o FT” mean the pre-trained model is evaluated on downstream tasks with and without fine-tuning. “GPT-2” represents the model released by Radford et al. (2019) and is taken as the teacher model, “Transformer small” and “GPT-2 small” share the same model structure and number of parameters, with the only difference that “Transformer small” has no GPT-2 pre-training, “LightPAFF” is our method applied on “GPT-2 small”." ], "reasoning": "LightPAFF achieves better performance than Transformer small and GPT-2 small models with the same parameter size, and approaches the performance of the GPT-2 teacher model with significantly fewer parameters.", "description": "Accuracy of It can be seen that lightPAFF achieves large improvements than the models under the same size of parameters without pre-training (Transformer small) and with pre-training (GPT-2 small), while approaches the perplexity of the GPT-2 teacher model in the downstream tasks but with only 1/5× number of parameters.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Results of LightPAFF on GPT-2 tasks in terms of perplexity (ppl). “w/ FT” and “w/o FT” mean the pre-trained model is evaluated on downstream tasks with and without fine-tuning. “GPT-2” represents the model released by Radford et al. (2019) and is taken as the teacher model, “Transformer small” and “GPT-2 small” share the same model structure and number of parameters, with the only difference that “Transformer small” has no GPT-2 pre-training, “LightPAFF” is our method applied on “GPT-2 small”.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. \n\nQuestion: Which model achieves the lowest perplexity on the WikiText103 w/ FT task?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the model with the lowest perplexity on the WikiText103 w/ FT task, compare the values in the WikiText103 w/ FT column. LightPAFF has a perplexity of 16.4, which is the lowest among all models. So, the answer is {\"answer\": \"LightPAFF\"}." } ], "images": [ "scigen/train/3432.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results of LightPAFF on GPT-2 tasks in terms of perplexity (ppl). “w/ FT” and “w/o FT” mean the pre-trained model is evaluated on downstream tasks with and without fine-tuning. “GPT-2” represents the model released by Radford et al. (2019) and is taken as the teacher model, “Transformer small” and “GPT-2 small” share the same model structure and number of parameters, with the only difference that “Transformer small” has no GPT-2 pre-training, “LightPAFF” is our method applied on “GPT-2 small”." ], "reasoning": "To find the model with the lowest perplexity on the WikiText103 w/ FT task, compare the values in the WikiText103 w/ FT column. LightPAFF has a perplexity of 16.4, which is the lowest among all models.", "question": "Which model achieves the lowest perplexity on the WikiText103 w/ FT task?", "answer": "So, the answer is {\"answer\": \"LightPAFF\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis LightPAFF has a lower perplexity on the PTB w/ FT task compared to GPT-2 small. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nLightPAFF has a lower perplexity on the PTB w/ FT task compared to GPT-2 small.\n" }, { "role": "assistant", "content": "To verify this statement, compare the perplexity values of LightPAFF and GPT-2 small on the PTB w/ FT task. LightPAFF has a perplexity of 22.8, while GPT-2 small has a perplexity of 31.2, indicating that LightPAFF indeed has a lower perplexity. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3432.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results of LightPAFF on GPT-2 tasks in terms of perplexity (ppl). “w/ FT” and “w/o FT” mean the pre-trained model is evaluated on downstream tasks with and without fine-tuning. “GPT-2” represents the model released by Radford et al. (2019) and is taken as the teacher model, “Transformer small” and “GPT-2 small” share the same model structure and number of parameters, with the only difference that “Transformer small” has no GPT-2 pre-training, “LightPAFF” is our method applied on “GPT-2 small”." ], "reasoning": "To verify this statement, compare the perplexity values of LightPAFF and GPT-2 small on the PTB w/ FT task. LightPAFF has a perplexity of 22.8, while GPT-2 small has a perplexity of 31.2, indicating that LightPAFF indeed has a lower perplexity.", "statement": "LightPAFF has a lower perplexity on the PTB w/ FT task compared to GPT-2 small.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 8: The comparison of accuracy, and average number of input tokens in the pre-training task between BERT, GPT-2 and MASS.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "BERT achieves the highest accuracy at 0.73, while GPT-2 has the lowest at 0.42, suggesting that BERT's pre-training task is easier.We test the three models on a held-out dataset. In particular, we compute the accuracy of token predictions. It can be seen that BERT achieves the highest accuracy while GPT-2 the lowest. Since the three models are of similar complexity (e.g., with similar architecture and similar number of parameters), our hypothesis is that the pre-training task of BERT is easiest and that of GPT-2 is the most difficult, which is verified intuitively as below." } ], "images": [ "scigen/train/3433.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: The comparison of accuracy, and average number of input tokens in the pre-training task between BERT, GPT-2 and MASS." ], "reasoning": "BERT achieves the highest accuracy at 0.73, while GPT-2 has the lowest at 0.42, suggesting that BERT's pre-training task is easier.", "description": "We test the three models on a held-out dataset. In particular, we compute the accuracy of token predictions. It can be seen that BERT achieves the highest accuracy while GPT-2 the lowest. Since the three models are of similar complexity (e.g., with similar architecture and similar number of parameters), our hypothesis is that the pre-training task of BERT is easiest and that of GPT-2 is the most difficult, which is verified intuitively as below.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 8: The comparison of accuracy, and average number of input tokens in the pre-training task between BERT, GPT-2 and MASS.', and sequentially solve the question, writing out each step of your reasoning process. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model has the highest percentage of input tokens and what is its accuracy?\n" }, { "role": "assistant", "content": "By examining the table, BERT has the highest percentage of input tokens at 85% and its accuracy is 0.73. So, the answer is {\"answer\": \"BERT, 0.73\"}" } ], "images": [ "scigen/train/3433.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: The comparison of accuracy, and average number of input tokens in the pre-training task between BERT, GPT-2 and MASS." ], "reasoning": "By examining the table, BERT has the highest percentage of input tokens at 85% and its accuracy is 0.73.", "question": "Which model has the highest percentage of input tokens and what is its accuracy?", "answer": "So, the answer is {\"answer\": \"BERT, 0.73\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nMASS has a higher accuracy than GPT-2.\n" }, { "role": "assistant", "content": "The table shows that MASS has an accuracy of 0.62, which is higher than GPT-2's accuracy of 0.42. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3433.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: The comparison of accuracy, and average number of input tokens in the pre-training task between BERT, GPT-2 and MASS." ], "reasoning": "The table shows that MASS has an accuracy of 0.62, which is higher than GPT-2's accuracy of 0.42.", "statement": "MASS has a higher accuracy than GPT-2.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Results for our ensembles of meta-classifiers applied to Toefl11. Best result per column in bold, best result per row grouping is underlined.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Tree-based methods, particularly random forests, significantly improve performance compared to a single decision tree, but still fall short of the LDA meta-classifier.As expected, we observe that the tree-based methods receive a substantial performance increase compared to the single decision tree meta-classifier from the previous model. Random forests provide the biggest boost, improving performance by almost 10%. However, this is still lower than our LDA meta-classifier." } ], "images": [ "scigen/train/3434.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Results for our ensembles of meta-classifiers applied to Toefl11. Best result per column in bold, best result per row grouping is underlined." ], "reasoning": "Tree-based methods, particularly random forests, significantly improve performance compared to a single decision tree, but still fall short of the LDA meta-classifier.", "description": "As expected, we observe that the tree-based methods receive a substantial performance increase compared to the single decision tree meta-classifier from the previous model. Random forests provide the biggest boost, improving performance by almost 10%. However, this is still lower than our LDA meta-classifier.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 7: Results for our ensembles of meta-classifiers applied to Toefl11. Best result per column in bold, best result per row grouping is underlined.'. Solve the following question based on the table.\n\nQuestion: Which method provides the highest test accuracy, and how does it compare to the LDA meta-classifier?\nLet's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that LDA has the highest test accuracy at 87.1%, which is slightly higher than the LDA meta-classifier's test accuracy of 86.8%. So, the answer is {\"answer\": \"LDA\"}." } ], "images": [ "scigen/train/3434.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Results for our ensembles of meta-classifiers applied to Toefl11. Best result per column in bold, best result per row grouping is underlined." ], "reasoning": "The table shows that LDA has the highest test accuracy at 87.1%, which is slightly higher than the LDA meta-classifier's test accuracy of 86.8%.", "question": "Which method provides the highest test accuracy, and how does it compare to the LDA meta-classifier?", "answer": "So, the answer is {\"answer\": \"LDA\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Random forests have a higher test accuracy than the LDA meta-classifier., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nRandom forests have a higher test accuracy than the LDA meta-classifier.\n" }, { "role": "assistant", "content": "The table shows that the test accuracy for Random Forest is 84.6%, which is lower than the LDA meta-classifier's test accuracy of 86.8%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3434.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Results for our ensembles of meta-classifiers applied to Toefl11. Best result per column in bold, best result per row grouping is underlined." ], "reasoning": "The table shows that the test accuracy for Random Forest is 84.6%, which is lower than the LDA meta-classifier's test accuracy of 86.8%.", "statement": "Random forests have a higher test accuracy than the LDA meta-classifier.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 8: Per-class performance breakdown of our top system’s results on the Toefl11-Test set. Best result per column in bold. Best and worst performances per column as also highlighted in green/yellow.'.\n" }, { "role": "assistant", "content": "Removing Hindi and Telugu from the confusion matrix improves the average F1-score from 0.87 to 0.89.As shown in the confusion matrix, Hindi and Telugu have the worst performance. Recalculating the values without those two classes, the average F1-score improves to 0.89." } ], "images": [ "scigen/train/3435.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Per-class performance breakdown of our top system’s results on the Toefl11-Test set. Best result per column in bold. Best and worst performances per column as also highlighted in green/yellow." ], "reasoning": "Removing Hindi and Telugu from the confusion matrix improves the average F1-score from 0.87 to 0.89.", "description": "As shown in the confusion matrix, Hindi and Telugu have the worst performance. Recalculating the values without those two classes, the average F1-score improves to 0.89.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 8: Per-class performance breakdown of our top system’s results on the Toefl11-Test set. Best result per column in bold. Best and worst performances per column as also highlighted in green/yellow.' and proceed to solve the following question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the average F1-score after removing Hindi and Telugu?\n" }, { "role": "assistant", "content": "The description states that recalculating the values without Hindi and Telugu improves the average F1-score to 0.89. So, the answer is {\"answer\": \"0.89\"}." } ], "images": [ "scigen/train/3435.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Per-class performance breakdown of our top system’s results on the Toefl11-Test set. Best result per column in bold. Best and worst performances per column as also highlighted in green/yellow." ], "reasoning": "The description states that recalculating the values without Hindi and Telugu improves the average F1-score to 0.89.", "question": "What is the average F1-score after removing Hindi and Telugu?", "answer": "So, the answer is {\"answer\": \"0.89\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe average F1-score is 0.87 when including all classes.\n" }, { "role": "assistant", "content": "The table shows that the average F1-score is 0.87 when all classes are included. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3435.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Per-class performance breakdown of our top system’s results on the Toefl11-Test set. Best result per column in bold. Best and worst performances per column as also highlighted in green/yellow." ], "reasoning": "The table shows that the average F1-score is 0.87 when all classes are included.", "statement": "The average F1-score is 0.87 when including all classes.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2. Code Summarization Results of CO3 and Baselines', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "CO3, built on Seq2Seq, improves performance by over 15% in BLEU4 and METEOR due to code generation, dual learning, and multi-task learning.To evaluate the performance of code summarization task, we use CODE-NN and a strong Seq2Seq model equipped with attention mechanism as baselines. CO3 is built upon the Seq2Seq model, and we find the introduction of code generation task and the adoption of dual learning and multi-task learning improve the performance by more than 15% in terms of BLEU4 and METEOR, which verifies the effectiveness of our approach. How each of these design choices contributes to the improvement will analysed in Sections 5.4 through 5.6." } ], "images": [ "scigen/train/3436.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2. Code Summarization Results of CO3 and Baselines" ], "reasoning": "CO3, built on Seq2Seq, improves performance by over 15% in BLEU4 and METEOR due to code generation, dual learning, and multi-task learning.", "description": "To evaluate the performance of code summarization task, we use CODE-NN and a strong Seq2Seq model equipped with attention mechanism as baselines. CO3 is built upon the Seq2Seq model, and we find the introduction of code generation task and the adoption of dual learning and multi-task learning improve the performance by more than 15% in terms of BLEU4 and METEOR, which verifies the effectiveness of our approach. How each of these design choices contributes to the improvement will analysed in Sections 5.4 through 5.6.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2. Code Summarization Results of CO3 and Baselines' and methodically tackle the question that follows. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the BLEU4 score for the model that shows the highest performance in Python?\n" }, { "role": "assistant", "content": "The table shows the BLEU4 scores for different models in Python. The highest BLEU4 score for Python is 0.124, which is achieved by 'Our Model'. So, the answer is {\"answer\": \"0.124\"}" } ], "images": [ "scigen/train/3436.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2. Code Summarization Results of CO3 and Baselines" ], "reasoning": "The table shows the BLEU4 scores for different models in Python. The highest BLEU4 score for Python is 0.124, which is achieved by 'Our Model'.", "question": "What is the BLEU4 score for the model that shows the highest performance in Python?", "answer": "So, the answer is {\"answer\": \"0.124\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The CoaCor model has the lowest SQL METEOR score. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe CoaCor model has the lowest SQL METEOR score.\n" }, { "role": "assistant", "content": "The table lists the SQL METEOR scores for different models. The CoaCor model has a SQL METEOR score of 0.029, which is the lowest among the listed models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3436.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2. Code Summarization Results of CO3 and Baselines" ], "reasoning": "The table lists the SQL METEOR scores for different models. The CoaCor model has a SQL METEOR score of 0.029, which is the lowest among the listed models.", "statement": "The CoaCor model has the lowest SQL METEOR score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1. Code Retrieval Results of CO3 and Baselines. We appiyed T-test on the results between Our Model and baselines, and all p-values are smaller than 0.05, which means that the improvement is stable and statistically significant.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "CoaCor outperforms DCS significantly in retrieval metrics, but CO3 surpasses CoaCor in both SQL and Python performance.is a state-of-the-art model proposed recently. Since CoaCor uses ranking metrics for retrieval as reward to producing retrieval-friendly code summary and further assembles two diverse retrieval models, CoaCor outperforms DCS by a large margin. Despite the simplicity of CO3, it outperforms CoaCor by nearly 0.01 for SQL in terms of both MRR and NDGG. The performance margin between CoaCor and CO3 is even larger for Python, which speaks to the superiority of CO3." } ], "images": [ "scigen/train/3437.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1. Code Retrieval Results of CO3 and Baselines. We appiyed T-test on the results between Our Model and baselines, and all p-values are smaller than 0.05, which means that the improvement is stable and statistically significant." ], "reasoning": "CoaCor outperforms DCS significantly in retrieval metrics, but CO3 surpasses CoaCor in both SQL and Python performance.", "description": "is a state-of-the-art model proposed recently. Since CoaCor uses ranking metrics for retrieval as reward to producing retrieval-friendly code summary and further assembles two diverse retrieval models, CoaCor outperforms DCS by a large margin. Despite the simplicity of CO3, it outperforms CoaCor by nearly 0.01 for SQL in terms of both MRR and NDGG. The performance margin between CoaCor and CO3 is even larger for Python, which speaks to the superiority of CO3.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1. Code Retrieval Results of CO3 and Baselines. We appiyed T-test on the results between Our Model and baselines, and all p-values are smaller than 0.05, which means that the improvement is stable and statistically significant.', answer the question below by showing each progressive step toward the answer. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model has the highest performance in Python MRR?\n" }, { "role": "assistant", "content": "By comparing the Python MRR values in the table, we see that 'Our Model' has the highest Python MRR of 0.682. So, the answer is {\"answer\": \"Our Model\"}" } ], "images": [ "scigen/train/3437.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1. Code Retrieval Results of CO3 and Baselines. We appiyed T-test on the results between Our Model and baselines, and all p-values are smaller than 0.05, which means that the improvement is stable and statistically significant." ], "reasoning": "By comparing the Python MRR values in the table, we see that 'Our Model' has the highest Python MRR of 0.682.", "question": "Which model has the highest performance in Python MRR?", "answer": "So, the answer is {\"answer\": \"Our Model\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis CoaCor has a higher SQL NDCG than DCS. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nCoaCor has a higher SQL NDCG than DCS.\n" }, { "role": "assistant", "content": "The table shows that CoaCor has an SQL NDCG of 0.670, while DCS has an SQL NDCG of 0.629, indicating that CoaCor's SQL NDCG is indeed higher. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3437.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1. Code Retrieval Results of CO3 and Baselines. We appiyed T-test on the results between Our Model and baselines, and all p-values are smaller than 0.05, which means that the improvement is stable and statistically significant." ], "reasoning": "The table shows that CoaCor has an SQL NDCG of 0.670, while DCS has an SQL NDCG of 0.629, indicating that CoaCor's SQL NDCG is indeed higher.", "statement": "CoaCor has a higher SQL NDCG than DCS.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5. Rank of Code Retrieval for Examples.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "CO3 outperforms other models in both examples due to its ability to capture intrinsic connections among tasks through dual and multi-task learning.The examples show that our model not only achieves the best rank in code retrieval task, but also generates a human-readable summary. This indicates that CO3 can capture the inner association between human-provided text and code more accurately, which is consistent with previous quantitative analysis. More details are explained below. In the second example, due to the more complex logic of the query, none of the code summarization models were able to achieve a good result. Compared with the human-written summary, all the generated summaries lost the key information about “compare“ and “discarding.“ However, even in this complex scenario, CO3 did successfully find the corresponding code snippet. The main reason behind this is that CO3 can capture the intrinsic connection among tasks of code summarization, code generation and code retrieval better through an end-to-end model, thanks to dual learning and multi-task learning. More specifically, the three tasks collaborate to generate more accurate and richer semantic representations of both natural language and source code. These representations are vectors in a continuous semantic space, and contain most of the key information. The discrete words generated by sampling from continuous space may lose some key information, but those continuous representations can still maintain such information and thus assist down-stream tasks such as code retrieval better." } ], "images": [ "scigen/train/3438.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5. Rank of Code Retrieval for Examples." ], "reasoning": "CO3 outperforms other models in both examples due to its ability to capture intrinsic connections among tasks through dual and multi-task learning.", "description": "The examples show that our model not only achieves the best rank in code retrieval task, but also generates a human-readable summary. This indicates that CO3 can capture the inner association between human-provided text and code more accurately, which is consistent with previous quantitative analysis. More details are explained below. In the second example, due to the more complex logic of the query, none of the code summarization models were able to achieve a good result. Compared with the human-written summary, all the generated summaries lost the key information about “compare“ and “discarding.“ However, even in this complex scenario, CO3 did successfully find the corresponding code snippet. The main reason behind this is that CO3 can capture the intrinsic connection among tasks of code summarization, code generation and code retrieval better through an end-to-end model, thanks to dual learning and multi-task learning. More specifically, the three tasks collaborate to generate more accurate and richer semantic representations of both natural language and source code. These representations are vectors in a continuous semantic space, and contain most of the key information. The discrete words generated by sampling from continuous space may lose some key information, but those continuous representations can still maintain such information and thus assist down-stream tasks such as code retrieval better.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nTable title: Table 5. Rank of Code Retrieval for Examples.\n\nQuestion: Which model achieved the best rank in both examples?\n" }, { "role": "assistant", "content": "The table shows the ranks of different models in two examples. CO3 has the lowest rank (1) in both Example(a) and Example(b), indicating it achieved the best rank. So, the answer is {\"answer\": \"CO3\"}." } ], "images": [ "scigen/train/3438.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5. Rank of Code Retrieval for Examples." ], "reasoning": "The table shows the ranks of different models in two examples. CO3 has the lowest rank (1) in both Example(a) and Example(b), indicating it achieved the best rank.", "question": "Which model achieved the best rank in both examples?", "answer": "So, the answer is {\"answer\": \"CO3\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nCO3 achieved the worst rank in Example(b).\n" }, { "role": "assistant", "content": "The table shows that CO3 achieved a rank of 1 in Example(b), which is the best rank, not the worst. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3438.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5. Rank of Code Retrieval for Examples." ], "reasoning": "The table shows that CO3 achieved a rank of 1 in Example(b), which is the best rank, not the worst.", "statement": "CO3 achieved the worst rank in Example(b).", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Evaluation results on the datasets.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Combining the Easy-to-Hard decoding method with an enhanced alignment model improves overall performance and boosts existing baselines.When these two methods are combined, the overall performance is further improved, outperforming previous works. We also investigate whether our proposed reasoning methods could also boost existing baselines. Recall that, the Easy-to-Hard decoding method requires an enhanced alignment model that could integrate the easy alignment information. Since designing enhanced versions for these baselines is beyond our goal, here we only enforce that that the aligned entities found in the easy alignments have the same surface form. We find that this simplified strategy could still improve these baselines, which also suggests that our proposed decoding strategy is generally helpful to the alignment models." } ], "images": [ "scigen/train/3439.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Evaluation results on the datasets." ], "reasoning": "Combining the Easy-to-Hard decoding method with an enhanced alignment model improves overall performance and boosts existing baselines.", "description": "When these two methods are combined, the overall performance is further improved, outperforming previous works. We also investigate whether our proposed reasoning methods could also boost existing baselines. Recall that, the Easy-to-Hard decoding method requires an enhanced alignment model that could integrate the easy alignment information. Since designing enhanced versions for these baselines is beyond our goal, here we only enforce that that the aligned entities found in the easy alignments have the same surface form. We find that this simplified strategy could still improve these baselines, which also suggests that our proposed decoding strategy is generally helpful to the alignment models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: 'Table 2: Evaluation results on the datasets.'\nQuestion: Which method achieved the highest performance in the FR-EN alignment?\n" }, { "role": "assistant", "content": "By examining the table, the method GM-EHD-JEA achieved the highest performance in the FR-EN alignment with a score of 92.43. So, the answer is {\"answer\": \"GM-EHD-JEA\"}." } ], "images": [ "scigen/train/3439.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Evaluation results on the datasets." ], "reasoning": "By examining the table, the method GM-EHD-JEA achieved the highest performance in the FR-EN alignment with a score of 92.43.", "question": "Which method achieved the highest performance in the FR-EN alignment?", "answer": "So, the answer is {\"answer\": \"GM-EHD-JEA\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The GM method outperforms the RDGCN method in the ZH-EN alignment., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe GM method outperforms the RDGCN method in the ZH-EN alignment.\n" }, { "role": "assistant", "content": "The table shows that the GM method has a score of 67.93 in the ZH-EN alignment, while the RDGCN method has a score of 70.75, indicating that the GM method does not outperform the RDGCN method. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3439.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Evaluation results on the datasets." ], "reasoning": "The table shows that the GM method has a score of 67.93 in the ZH-EN alignment, while the RDGCN method has a score of 70.75, indicating that the GM method does not outperform the RDGCN method.", "statement": "The GM method outperforms the RDGCN method in the ZH-EN alignment.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: Dataset summary.'.\n" }, { "role": "assistant", "content": "The datasets include Chinese, English, Japanese, and French versions of DBpedia, each containing 15,000 inter-language links, with a training/testing split of 30% and 70%, respectively.These datasets are built upon Chinese, English, Japanese and French versions of DBpedia Each dataset contains 15,000 inter-language links connecting equivalent entities in two KGs of different languages. We use the same training/testing split as previous works, 30% for training, and 70% for testing. Evaluation Metrics. Like previous works, we use Hits@1 to evaluate our model, where a Hits@1 score (higher is better) is computed by measuring the proportion of correctly aligned entities ranked in the top one. Comparison Models. MTransE Model Variants. To evaluate different reasoning methods, we provide three implementation variants of our model for ablation studies, including (1) X-EHD: the baseline model X that only uses our proposed Easy-to-Hard Decoding strategy; (2) X-JEA: the baseline model X that only uses our proposed Joint Entity Alignment method; (3) X-EHD-JEA: the baseline model X that uses both of these two reasoning methods. Implementation details. For the configurations of the alignment model, we use the same settings as Xu et al. The learning rate is set to 0.001. The hop size of two GCN layers is set to 2 and 3, respectively. Following Wu et al. For all datasets, we first use the baseline model to retrieve the top 10 alignments, normalize their scores as probabilities and then perform the proposed coordinated reasoning methods over them. For the Easy-to-Hard decoding method, α is set to 0.75, and K is set to 20. For the joint entity alignment, τ is set to 0.10. For training the enhanced alignment model, for each topic graph pair, we randomly choose at most two gold alignments from the ground truth as the simulated easy alignments." } ], "images": [ "scigen/train/3440.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Dataset summary." ], "reasoning": "The datasets include Chinese, English, Japanese, and French versions of DBpedia, each containing 15,000 inter-language links, with a training/testing split of 30% and 70%, respectively.", "description": "These datasets are built upon Chinese, English, Japanese and French versions of DBpedia Each dataset contains 15,000 inter-language links connecting equivalent entities in two KGs of different languages. We use the same training/testing split as previous works, 30% for training, and 70% for testing. Evaluation Metrics. Like previous works, we use Hits@1 to evaluate our model, where a Hits@1 score (higher is better) is computed by measuring the proportion of correctly aligned entities ranked in the top one. Comparison Models. MTransE Model Variants. To evaluate different reasoning methods, we provide three implementation variants of our model for ablation studies, including (1) X-EHD: the baseline model X that only uses our proposed Easy-to-Hard Decoding strategy; (2) X-JEA: the baseline model X that only uses our proposed Joint Entity Alignment method; (3) X-EHD-JEA: the baseline model X that uses both of these two reasoning methods. Implementation details. For the configurations of the alignment model, we use the same settings as Xu et al. The learning rate is set to 0.001. The hop size of two GCN layers is set to 2 and 3, respectively. Following Wu et al. For all datasets, we first use the baseline model to retrieve the top 10 alignments, normalize their scores as probabilities and then perform the proposed coordinated reasoning methods over them. For the Easy-to-Hard decoding method, α is set to 0.75, and K is set to 20. For the joint entity alignment, τ is set to 0.10. For training the enhanced alignment model, for each topic graph pair, we randomly choose at most two gold alignments from the ground truth as the simulated easy alignments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1: Dataset summary.', solve the subsequent question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich dataset has the highest number of triples?\n" }, { "role": "assistant", "content": "To find the dataset with the highest number of triples, compare the 'Triples' column for each dataset. The dataset DBP15K FR-EN has the highest number of triples with 278,590. So, the answer is {\"answer\": \"DBP15K FR-EN\"}" } ], "images": [ "scigen/train/3440.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Dataset summary." ], "reasoning": "To find the dataset with the highest number of triples, compare the 'Triples' column for each dataset. The dataset DBP15K FR-EN has the highest number of triples with 278,590.", "question": "Which dataset has the highest number of triples?", "answer": "So, the answer is {\"answer\": \"DBP15K FR-EN\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe DBP15K ZH-EN dataset has more entities than the DBP15K JA-EN dataset.\n" }, { "role": "assistant", "content": "The DBP15K ZH-EN dataset has 98,125 entities, while the DBP15K JA-EN dataset has 95,680 entities. Therefore, the statement is correct. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3440.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Dataset summary." ], "reasoning": "The DBP15K ZH-EN dataset has 98,125 entities, while the DBP15K JA-EN dataset has 95,680 entities. Therefore, the statement is correct.", "statement": "The DBP15K ZH-EN dataset has more entities than the DBP15K JA-EN dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: Performance and computation time for different τ values, where Max sub-space shows the number of source nodes in the largest sub-space. Baseline accuracy is 89.38.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Lowering the alignment-dropping threshold τ increases accuracy but significantly increases computation time, with τ=0.05 being infeasible due to excessive running time.Let us first look at the impacts of alignment-dropping threshold τ to both the performance and running time for our joint entity alignment algorithm. For example, when τ is changed from 0.15 to 0.10, the accuracy could increase by 0.12% but the computation time dramatically increases from 39s to almost 25 minutes. Moreover, if τ is set to 0.05, we cannot even get the results. We find that the size of the maximal sub-space under τ=0.05 is 3 times more than the size under τ=0.10, thus the running time under τ=0.05 is expected to be roughly 32 hours, which is 81 (34) times than the time under τ=0.10. The running time does not significantly change when increasing τ from 0.15 to 0.20, because the Hungarian algorithm does not take much time for this situation, and the most time consumption is data processing." } ], "images": [ "scigen/train/3441.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance and computation time for different τ values, where Max sub-space shows the number of source nodes in the largest sub-space. Baseline accuracy is 89.38." ], "reasoning": "Lowering the alignment-dropping threshold τ increases accuracy but significantly increases computation time, with τ=0.05 being infeasible due to excessive running time.", "description": "Let us first look at the impacts of alignment-dropping threshold τ to both the performance and running time for our joint entity alignment algorithm. For example, when τ is changed from 0.15 to 0.10, the accuracy could increase by 0.12% but the computation time dramatically increases from 39s to almost 25 minutes. Moreover, if τ is set to 0.05, we cannot even get the results. We find that the size of the maximal sub-space under τ=0.05 is 3 times more than the size under τ=0.10, thus the running time under τ=0.05 is expected to be roughly 32 hours, which is 81 (34) times than the time under τ=0.10. The running time does not significantly change when increasing τ from 0.15 to 0.20, because the Hungarian algorithm does not take much time for this situation, and the most time consumption is data processing.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Performance and computation time for different τ values, where Max sub-space shows the number of source nodes in the largest sub-space. Baseline accuracy is 89.38.\nQuestion: What is the accuracy (FR-EN(hit@1)) when τ is set to 0.10?\nGive a detailed response to the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the accuracy (FR-EN(hit@1)) for different values of τ. For τ=0.10, the accuracy is listed as 91.02. So, the answer is {\"answer\": \"91.02\"}" } ], "images": [ "scigen/train/3441.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance and computation time for different τ values, where Max sub-space shows the number of source nodes in the largest sub-space. Baseline accuracy is 89.38." ], "reasoning": "The table shows the accuracy (FR-EN(hit@1)) for different values of τ. For τ=0.10, the accuracy is listed as 91.02.", "question": "What is the accuracy (FR-EN(hit@1)) when τ is set to 0.10?", "answer": "So, the answer is {\"answer\": \"91.02\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The computation time for τ=0.05 is 24 minutes and 34 seconds., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe computation time for τ=0.05 is 24 minutes and 34 seconds.\n" }, { "role": "assistant", "content": "The table indicates that the computation time for τ=0.05 is not available, as it is marked with a dash. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3441.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance and computation time for different τ values, where Max sub-space shows the number of source nodes in the largest sub-space. Baseline accuracy is 89.38." ], "reasoning": "The table indicates that the computation time for τ=0.05 is not available, as it is marked with a dash.", "statement": "The computation time for τ=0.05 is 24 minutes and 34 seconds.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Hit@1 accuracies and decoding rounds on the development set for different α values. The first row lists the accuracies of the GM baseline.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Decreasing the probability threshold α improves performance until 0.75, but further reduction introduces incorrect alignments, requiring more decoding rounds.We also investigated the impact of the probability threshold α on the performance for our Easy-to-Hard decoding method. hit@1 accuracies on these datasets. We can see that our model could benefit from decreasing α until it reaches 0.75. It is expected to find that lower α may hurt the performance since it incorporates some incorrect predictions as easy (gold) alignments into the model. Recall that in our decoding algorithm, we continuously perform the inference until less than K new easy alignments are found in the previous round. To better understand why more converge rounds are required, we analyzed the intermediate established alignments during the inference. We find this is due to those incorrect alignments introduced by reducing α produce a chain reaction, which offers the model more confidence about some uncertain but incorrect alignments, resulting in more decoding rounds." } ], "images": [ "scigen/train/3442.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Hit@1 accuracies and decoding rounds on the development set for different α values. The first row lists the accuracies of the GM baseline." ], "reasoning": "Decreasing the probability threshold α improves performance until 0.75, but further reduction introduces incorrect alignments, requiring more decoding rounds.", "description": "We also investigated the impact of the probability threshold α on the performance for our Easy-to-Hard decoding method. hit@1 accuracies on these datasets. We can see that our model could benefit from decreasing α until it reaches 0.75. It is expected to find that lower α may hurt the performance since it incorporates some incorrect predictions as easy (gold) alignments into the model. Recall that in our decoding algorithm, we continuously perform the inference until less than K new easy alignments are found in the previous round. To better understand why more converge rounds are required, we analyzed the intermediate established alignments during the inference. We find this is due to those incorrect alignments introduced by reducing α produce a chain reaction, which offers the model more confidence about some uncertain but incorrect alignments, resulting in more decoding rounds.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 4: Hit@1 accuracies and decoding rounds on the development set for different α values. The first row lists the accuracies of the GM baseline.' and methodically tackle the question that follows. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the hit@1 accuracy for JA-EN when α is 0.75?\n" }, { "role": "assistant", "content": "To find the hit@1 accuracy for JA-EN when α is 0.75, locate the row where α is 0.75 and then find the corresponding value under the JA-EN column. So, the answer is {\"answer\": \"76.62\"}." } ], "images": [ "scigen/train/3442.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Hit@1 accuracies and decoding rounds on the development set for different α values. The first row lists the accuracies of the GM baseline." ], "reasoning": "To find the hit@1 accuracy for JA-EN when α is 0.75, locate the row where α is 0.75 and then find the corresponding value under the JA-EN column.", "question": "What is the hit@1 accuracy for JA-EN when α is 0.75?", "answer": "So, the answer is {\"answer\": \"76.62\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The hit@1 accuracy for ZH-EN is highest when α is 0.75., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe hit@1 accuracy for ZH-EN is highest when α is 0.75.\n" }, { "role": "assistant", "content": "To verify this statement, compare the hit@1 accuracy values for ZH-EN across different α values. The highest value for ZH-EN is 72.09, which occurs when α is 0.75. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3442.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Hit@1 accuracies and decoding rounds on the development set for different α values. The first row lists the accuracies of the GM baseline." ], "reasoning": "To verify this statement, compare the hit@1 accuracy values for ZH-EN across different α values. The highest value for ZH-EN is 72.09, which occurs when α is 0.75.", "statement": "The hit@1 accuracy for ZH-EN is highest when α is 0.75.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title '(a) MTL setting (i.e. training on all 175 tasks).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "RobustTC-MTL achieves significantly higher accuracy compared to other MTL methods due to task diversity and task-clustering reducing conflicts.Note that the holistic MTL methods achieve much better results compared to single-task CNNs. This is because the tasks usually have smaller training and development sets, and both the model parameters learned on training set and the hyper-parameters selected on development set can easily lead to over-fitting. RobustTC-MTL achieves large improvement (5.5%) over the best MTL baseline, because the tasks here are more diverse than the sentiment classification tasks and task-clustering greatly reduces conflicts from irrelevant tasks." } ], "images": [ "scigen/train/3443.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) MTL setting (i.e. training on all 175 tasks)." ], "reasoning": "RobustTC-MTL achieves significantly higher accuracy compared to other MTL methods due to task diversity and task-clustering reducing conflicts.", "description": "Note that the holistic MTL methods achieve much better results compared to single-task CNNs. This is because the tasks usually have smaller training and development sets, and both the model parameters learned on training set and the hyper-parameters selected on development set can easily lead to over-fitting. RobustTC-MTL achieves large improvement (5.5%) over the best MTL baseline, because the tasks here are more diverse than the sentiment classification tasks and task-clustering greatly reduces conflicts from irrelevant tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nTable title: (a) MTL setting (i.e. training on all 175 tasks).\n\nQuestion: What is the average accuracy improvement of RobustTC-MTL with clus=20 over the best holistic MTL-CNN model?\n" }, { "role": "assistant", "content": "The best holistic MTL-CNN model has an average accuracy of 62.45. RobustTC-MTL with clus=20 has an average accuracy of 68.11. The improvement is 68.11 - 62.45. So, the answer is {\"answer\": \"5.66\"}" } ], "images": [ "scigen/train/3443.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) MTL setting (i.e. training on all 175 tasks)." ], "reasoning": "The best holistic MTL-CNN model has an average accuracy of 62.45. RobustTC-MTL with clus=20 has an average accuracy of 68.11. The improvement is 68.11 - 62.45.", "question": "What is the average accuracy improvement of RobustTC-MTL with clus=20 over the best holistic MTL-CNN model?", "answer": "So, the answer is {\"answer\": \"5.66\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: RobustTC-MTL with clus=30 achieves the highest average accuracy., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nRobustTC-MTL with clus=30 achieves the highest average accuracy.\n" }, { "role": "assistant", "content": "The table shows that RobustTC-MTL with clus=20 achieves the highest average accuracy of 68.11, which is higher than the 66.74 accuracy of RobustTC-MTL with clus=30. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3443.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) MTL setting (i.e. training on all 175 tasks)." ], "reasoning": "The table shows that RobustTC-MTL with clus=20 achieves the highest average accuracy of 68.11, which is higher than the 66.74 accuracy of RobustTC-MTL with clus=30.", "statement": "RobustTC-MTL with clus=30 achieves the highest average accuracy.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled '(a) MTL setting (i.e. training on all 69 tasks).'.\n" }, { "role": "assistant", "content": "The RobustTC-MTL method based on task clustering significantly outperforms all baselines, especially in scenarios with limited training resources.Since most of the tasks have a significant amount of training data, the single-task baselines achieve good results. Because the conflicts among some tasks (e.g. the 2-star bucket tasks and 5-star bucket tasks require opposite labels on 4-star examples), the holistic MTL-CNN does not show accuracy improvements compared to the single-task methods. It also lags behind the holistic MTL-CNN model trained only on 12 target domains, which indicates that the holistic MTL-CNN cannot leverage large number of background tasks. Our RobustTC-MTL method based on task clustering achieves a significant improvement over all the baselines. Adaptation to New Tasks ( FSL Setting) Due to the limited training resources, all the baselines perform poorly. Our RobustTC-FSL gives far better results compared to all baselines (>6%). It is also significantly better than applying Eq. without clustering (78.85%), i.e. using single-task model from each task instead of cluster-models for P(y|x;⋅). Such result further emphasizes the importance of task clustering for deep models, when better performance could be achieved with such models." } ], "images": [ "scigen/train/3444.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) MTL setting (i.e. training on all 69 tasks)." ], "reasoning": "The RobustTC-MTL method based on task clustering significantly outperforms all baselines, especially in scenarios with limited training resources.", "description": "Since most of the tasks have a significant amount of training data, the single-task baselines achieve good results. Because the conflicts among some tasks (e.g. the 2-star bucket tasks and 5-star bucket tasks require opposite labels on 4-star examples), the holistic MTL-CNN does not show accuracy improvements compared to the single-task methods. It also lags behind the holistic MTL-CNN model trained only on 12 target domains, which indicates that the holistic MTL-CNN cannot leverage large number of background tasks. Our RobustTC-MTL method based on task clustering achieves a significant improvement over all the baselines. Adaptation to New Tasks ( FSL Setting) Due to the limited training resources, all the baselines perform poorly. Our RobustTC-FSL gives far better results compared to all baselines (>6%). It is also significantly better than applying Eq. without clustering (78.85%), i.e. using single-task model from each task instead of cluster-models for P(y|x;⋅). Such result further emphasizes the importance of task clustering for deep models, when better performance could be achieved with such models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: (a) MTL setting (i.e. training on all 69 tasks).\nQuestion: Which model achieves the highest average accuracy according to the table?\nGive a detailed response to the above question. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, the RobustTC-MTL model with clus=10 achieves the highest average accuracy of 86.73. So, the answer is {\"answer\": \"RobustTC-MTL with clus=10\"}." } ], "images": [ "scigen/train/3444.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) MTL setting (i.e. training on all 69 tasks)." ], "reasoning": "By examining the table, the RobustTC-MTL model with clus=10 achieves the highest average accuracy of 86.73.", "question": "Which model achieves the highest average accuracy according to the table?", "answer": "So, the answer is {\"answer\": \"RobustTC-MTL with clus=10\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Holistic MTL-CNN model trained only on 12 target domains achieves an average accuracy of 85.71. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Holistic MTL-CNN model trained only on 12 target domains achieves an average accuracy of 85.71.\n" }, { "role": "assistant", "content": "The table shows that the Holistic MTL-CNN model trained only on target domains has an average accuracy of 85.71, which supports the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3444.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) MTL setting (i.e. training on all 69 tasks)." ], "reasoning": "The table shows that the Holistic MTL-CNN model trained only on target domains has an average accuracy of 85.71, which supports the statement.", "statement": "The Holistic MTL-CNN model trained only on 12 target domains achieves an average accuracy of 85.71.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title '(b) Few-shot learning setting (five-shot).'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "RobustTC-MTL and RobustTC-FSL methods based on task clustering achieve significant improvements over all baselines, especially in limited training resource scenarios.Since most of the tasks have a significant amount of training data, the single-task baselines achieve good results. Because the conflicts among some tasks (e.g. the 2-star bucket tasks and 5-star bucket tasks require opposite labels on 4-star examples), the holistic MTL-CNN does not show accuracy improvements compared to the single-task methods. It also lags behind the holistic MTL-CNN model trained only on 12 target domains, which indicates that the holistic MTL-CNN cannot leverage large number of background tasks. Our RobustTC-MTL method based on task clustering achieves a significant improvement over all the baselines. Adaptation to New Tasks ( FSL Setting) Due to the limited training resources, all the baselines perform poorly. Our RobustTC-FSL gives far better results compared to all baselines (>6%). It is also significantly better than applying Eq. without clustering (78.85%), i.e. using single-task model from each task instead of cluster-models for P(y|x;⋅). Such result further emphasizes the importance of task clustering for deep models, when better performance could be achieved with such models." } ], "images": [ "scigen/train/3445.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) Few-shot learning setting (five-shot)." ], "reasoning": "RobustTC-MTL and RobustTC-FSL methods based on task clustering achieve significant improvements over all baselines, especially in limited training resource scenarios.", "description": "Since most of the tasks have a significant amount of training data, the single-task baselines achieve good results. Because the conflicts among some tasks (e.g. the 2-star bucket tasks and 5-star bucket tasks require opposite labels on 4-star examples), the holistic MTL-CNN does not show accuracy improvements compared to the single-task methods. It also lags behind the holistic MTL-CNN model trained only on 12 target domains, which indicates that the holistic MTL-CNN cannot leverage large number of background tasks. Our RobustTC-MTL method based on task clustering achieves a significant improvement over all the baselines. Adaptation to New Tasks ( FSL Setting) Due to the limited training resources, all the baselines perform poorly. Our RobustTC-FSL gives far better results compared to all baselines (>6%). It is also significantly better than applying Eq. without clustering (78.85%), i.e. using single-task model from each task instead of cluster-models for P(y|x;⋅). Such result further emphasizes the importance of task clustering for deep models, when better performance could be achieved with such models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of '(b) Few-shot learning setting (five-shot).' and proceed to solve the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model achieved the highest average accuracy in the RobustTC-FSL setting with 5 clusters?\n" }, { "role": "assistant", "content": "The table shows the average accuracies of different models. In the RobustTC-FSL setting with 5 clusters, the model achieved an average accuracy of 83.12, which is the highest among all models listed. So, the answer is {\"answer\": \"83.12\"}" } ], "images": [ "scigen/train/3445.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) Few-shot learning setting (five-shot)." ], "reasoning": "The table shows the average accuracies of different models. In the RobustTC-FSL setting with 5 clusters, the model achieved an average accuracy of 83.12, which is the highest among all models listed.", "question": "Which model achieved the highest average accuracy in the RobustTC-FSL setting with 5 clusters?", "answer": "So, the answer is {\"answer\": \"83.12\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Single-task FastText w/ pre-trained emb model achieved an average accuracy higher than 65.73.\n" }, { "role": "assistant", "content": "The table shows that the Single-task FastText w/ pre-trained emb model achieved an average accuracy of 63.05, which is lower than 65.73. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3445.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) Few-shot learning setting (five-shot)." ], "reasoning": "The table shows that the Single-task FastText w/ pre-trained emb model achieved an average accuracy of 63.05, which is lower than 65.73.", "statement": "The Single-task FastText w/ pre-trained emb model achieved an average accuracy higher than 65.73.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled '(b) FSL setting (one-shot + 20 examples).'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Holistic MTL methods outperform single-task CNNs due to smaller training and development sets, and RobustTC-MTL achieves significant improvement by reducing conflicts from irrelevant tasks.Note that the holistic MTL methods achieve much better results compared to single-task CNNs. This is because the tasks usually have smaller training and development sets, and both the model parameters learned on training set and the hyper-parameters selected on development set can easily lead to over-fitting. RobustTC-MTL achieves large improvement (5.5%) over the best MTL baseline, because the tasks here are more diverse than the sentiment classification tasks and task-clustering greatly reduces conflicts from irrelevant tasks." } ], "images": [ "scigen/train/3446.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) FSL setting (one-shot + 20 examples)." ], "reasoning": "Holistic MTL methods outperform single-task CNNs due to smaller training and development sets, and RobustTC-MTL achieves significant improvement by reducing conflicts from irrelevant tasks.", "description": "Note that the holistic MTL methods achieve much better results compared to single-task CNNs. This is because the tasks usually have smaller training and development sets, and both the model parameters learned on training set and the hyper-parameters selected on development set can easily lead to over-fitting. RobustTC-MTL achieves large improvement (5.5%) over the best MTL baseline, because the tasks here are more diverse than the sentiment classification tasks and task-clustering greatly reduces conflicts from irrelevant tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: '(b) FSL setting (one-shot + 20 examples).'\nQuestion: What is the average accuracy improvement of Adaptive RobustTC-FSL (clus=20) over the best MTL baseline?\n" }, { "role": "assistant", "content": "The best MTL baseline is 'Fine-tuned holistic MTL-CNN' with an average accuracy of 30.36. Adaptive RobustTC-FSL (clus=20) has an average accuracy of 42.97. The improvement is calculated as 42.97 - 30.36. So, the answer is {\"answer\": \"12.61\"}" } ], "images": [ "scigen/train/3446.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) FSL setting (one-shot + 20 examples)." ], "reasoning": "The best MTL baseline is 'Fine-tuned holistic MTL-CNN' with an average accuracy of 30.36. Adaptive RobustTC-FSL (clus=20) has an average accuracy of 42.97. The improvement is calculated as 42.97 - 30.36.", "question": "What is the average accuracy improvement of Adaptive RobustTC-FSL (clus=20) over the best MTL baseline?", "answer": "So, the answer is {\"answer\": \"12.61\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Adaptive RobustTC-FSL (clus=20) achieves the highest average accuracy among all models., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nAdaptive RobustTC-FSL (clus=20) achieves the highest average accuracy among all models.\n" }, { "role": "assistant", "content": "The table shows that Adaptive RobustTC-FSL (clus=20) has an average accuracy of 42.97, which is higher than any other model listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3446.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) FSL setting (one-shot + 20 examples)." ], "reasoning": "The table shows that Adaptive RobustTC-FSL (clus=20) has an average accuracy of 42.97, which is higher than any other model listed.", "statement": "Adaptive RobustTC-FSL (clus=20) achieves the highest average accuracy among all models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: Accuracy of Multi-Task Learning on the 1,491 dialog intent classification tasks.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The holistic MTL-CNN shows a 6% improvement over single-task CNNs, achieving around 66% accuracy on development sets, similar to the single-task model.Compared to the results on the 175 tasks, the holistic MTL-CNN achieves larger improvement (6%) over the single-task CNNs, which is a stronger baseline. Similar as the observation on the 175 tasks, here the main reason for its improvement is the consistent development and test performance due to holistic multi-task training approach: both the single-task and holistic multi-task model achieve around 66% average accuracy on development sets. Instead, we only use the information of ∼4.5% of all the task-pairs, and our algorithm still achieves a significant improvement over the baselines." } ], "images": [ "scigen/train/3447.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracy of Multi-Task Learning on the 1,491 dialog intent classification tasks." ], "reasoning": "The holistic MTL-CNN shows a 6% improvement over single-task CNNs, achieving around 66% accuracy on development sets, similar to the single-task model.", "description": "Compared to the results on the 175 tasks, the holistic MTL-CNN achieves larger improvement (6%) over the single-task CNNs, which is a stronger baseline. Similar as the observation on the 175 tasks, here the main reason for its improvement is the consistent development and test performance due to holistic multi-task training approach: both the single-task and holistic multi-task model achieve around 66% average accuracy on development sets. Instead, we only use the information of ∼4.5% of all the task-pairs, and our algorithm still achieves a significant improvement over the baselines.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Accuracy of Multi-Task Learning on the 1,491 dialog intent classification tasks.\nQuestion: What is the average accuracy improvement of the holistic MTL-CNN over the single-task CNN?\nSolve the above question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The single-task CNN has an average accuracy of 60.49%, while the holistic MTL-CNN has an average accuracy of 66.42%. The improvement is calculated as 66.42% - 60.49% = 5.93%, which is approximately 6%. So, the answer is {\"answer\": \"6%\"}" } ], "images": [ "scigen/train/3447.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracy of Multi-Task Learning on the 1,491 dialog intent classification tasks." ], "reasoning": "The single-task CNN has an average accuracy of 60.49%, while the holistic MTL-CNN has an average accuracy of 66.42%. The improvement is calculated as 66.42% - 60.49% = 5.93%, which is approximately 6%.", "question": "What is the average accuracy improvement of the holistic MTL-CNN over the single-task CNN?", "answer": "So, the answer is {\"answer\": \"6%\"}" }, { "messages": [ { "role": "user", "content": "The holistic MTL-CNN achieves around 66% average accuracy on development sets. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe holistic MTL-CNN achieves around 66% average accuracy on development sets.\n" }, { "role": "assistant", "content": "The table shows that the holistic MTL-CNN has an average accuracy of 66.42%, which supports the statement that it achieves around 66% average accuracy on development sets. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3447.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracy of Multi-Task Learning on the 1,491 dialog intent classification tasks." ], "reasoning": "The table shows that the holistic MTL-CNN has an average accuracy of 66.42%, which supports the statement that it achieves around 66% average accuracy on development sets.", "statement": "The holistic MTL-CNN achieves around 66% average accuracy on development sets.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1: Test set BLEU scores on pure translation for models trained with varying amounts of paired sentences.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "GNMT-Multi achieves higher BLEU scores than GNMT and VNMT with 40K and 400K paired sentences due to parameter sharing, and adding monolingual data further increases BLEU scores.When trained with 40K and 400K paired sentences, GNMT has a small advantage over VNMT in terms of BLEU scores across all language pairs. However, both models tend to overfit on these relatively small amounts of paired data. As a result, GNMT-Multi achieves much higher BLEU scores with both 40K and 400K paired sentences, due to the parameter sharing between languages. Adding monolingual data produces yet another significant increase in BLEU scores. In fact, GNMT-Multi-SSL trained with only 400K paired sentences achieves performance comparable with GNMT trained with 4M paired sentences. Even with 4M paired sentences, adding monolingual data is helpful, with GNMT-Multi-SSL outperforming the other models." } ], "images": [ "scigen/train/3448.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Test set BLEU scores on pure translation for models trained with varying amounts of paired sentences." ], "reasoning": "GNMT-Multi achieves higher BLEU scores than GNMT and VNMT with 40K and 400K paired sentences due to parameter sharing, and adding monolingual data further increases BLEU scores.", "description": "When trained with 40K and 400K paired sentences, GNMT has a small advantage over VNMT in terms of BLEU scores across all language pairs. However, both models tend to overfit on these relatively small amounts of paired data. As a result, GNMT-Multi achieves much higher BLEU scores with both 40K and 400K paired sentences, due to the parameter sharing between languages. Adding monolingual data produces yet another significant increase in BLEU scores. In fact, GNMT-Multi-SSL trained with only 400K paired sentences achieves performance comparable with GNMT trained with 4M paired sentences. Even with 4M paired sentences, adding monolingual data is helpful, with GNMT-Multi-SSL outperforming the other models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Test set BLEU scores on pure translation for models trained with varying amounts of paired sentences.', answer the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model achieves the highest BLEU score for the EN→FR language pair with 400K paired sentences?\n" }, { "role": "assistant", "content": "To find the highest BLEU score for the EN→FR language pair with 400K paired sentences, compare the BLEU scores of VNMT, GNMT, GNMT-Multi, and GNMT-Multi-SSL in the 400K paired data row under the EN→FR column. GNMT-Multi-SSL has the highest score of 36.55. So, the answer is {\"answer\": \"GNMT-Multi-SSL\"}" } ], "images": [ "scigen/train/3448.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Test set BLEU scores on pure translation for models trained with varying amounts of paired sentences." ], "reasoning": "To find the highest BLEU score for the EN→FR language pair with 400K paired sentences, compare the BLEU scores of VNMT, GNMT, GNMT-Multi, and GNMT-Multi-SSL in the 400K paired data row under the EN→FR column. GNMT-Multi-SSL has the highest score of 36.55.", "question": "Which model achieves the highest BLEU score for the EN→FR language pair with 400K paired sentences?", "answer": "So, the answer is {\"answer\": \"GNMT-Multi-SSL\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: GNMT-Multi-SSL achieves the highest BLEU score for the ES→EN language pair with 4M paired sentences., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nGNMT-Multi-SSL achieves the highest BLEU score for the ES→EN language pair with 4M paired sentences.\n" }, { "role": "assistant", "content": "To verify this statement, compare the BLEU scores for the ES→EN language pair with 4M paired sentences. GNMT-Multi-SSL has the highest score of 45.08. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3448.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Test set BLEU scores on pure translation for models trained with varying amounts of paired sentences." ], "reasoning": "To verify this statement, compare the BLEU scores for the ES→EN language pair with 4M paired sentences. GNMT-Multi-SSL has the highest score of 45.08.", "statement": "GNMT-Multi-SSL achieves the highest BLEU score for the ES→EN language pair with 4M paired sentences.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Test set KL divergence values () for the model trained with 4M paired sentences, averaged across languages.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The values for GNMT, GNMT-Multi, and GNMT-Multi-SSL are higher than VNMT, indicating these models rely more on the latent variable.The higher values for GNMT, GNMT-Multi and GNMT-Multi-SSL clearly indicate that these models are placing higher reliance on the latent variable than is VNMT." } ], "images": [ "scigen/train/3449.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Test set KL divergence values () for the model trained with 4M paired sentences, averaged across languages." ], "reasoning": "The values for GNMT, GNMT-Multi, and GNMT-Multi-SSL are higher than VNMT, indicating these models rely more on the latent variable.", "description": "The higher values for GNMT, GNMT-Multi and GNMT-Multi-SSL clearly indicate that these models are placing higher reliance on the latent variable than is VNMT.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: Test set KL divergence values () for the model trained with 4M paired sentences, averaged across languages.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model places the highest reliance on the latent variable?\n" }, { "role": "assistant", "content": "By comparing the values in the table, GNMT-Multi-SSL has the highest value, indicating the highest reliance on the latent variable. So, the answer is {\"answer\": \"GNMT-Multi-SSL\"}." } ], "images": [ "scigen/train/3449.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Test set KL divergence values () for the model trained with 4M paired sentences, averaged across languages." ], "reasoning": "By comparing the values in the table, GNMT-Multi-SSL has the highest value, indicating the highest reliance on the latent variable.", "question": "Which model places the highest reliance on the latent variable?", "answer": "So, the answer is {\"answer\": \"GNMT-Multi-SSL\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim GNMT-Multi places higher reliance on the latent variable than GNMT., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nGNMT-Multi places higher reliance on the latent variable than GNMT.\n" }, { "role": "assistant", "content": "The table shows that GNMT-Multi has a higher value (9.661) compared to GNMT (5.581), supporting the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3449.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Test set KL divergence values () for the model trained with 4M paired sentences, averaged across languages." ], "reasoning": "The table shows that GNMT-Multi has a higher value (9.661) compared to GNMT (5.581), supporting the statement.", "statement": "GNMT-Multi places higher reliance on the latent variable than GNMT.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: Test set BLEU scores for missing word translation. We use the model parameters trained with 4M paired sentences.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "GNMT performs better than VNMT in translating sentences with missing words by relying on its learned representation to infer and produce accurate translations.4.3.2 Missing word translation In order to demonstrate that GNMT does indeed learn a useful representation of the sentence’s semantic meaning, we perform missing word translation (i.e. where the source sentence has missing words). The model is forced to rely on its learned representation in order to infer what the missing words could be, and then to produce a good translation accordingly." } ], "images": [ "scigen/train/3450.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Test set BLEU scores for missing word translation. We use the model parameters trained with 4M paired sentences." ], "reasoning": "GNMT performs better than VNMT in translating sentences with missing words by relying on its learned representation to infer and produce accurate translations.", "description": "4.3.2 Missing word translation In order to demonstrate that GNMT does indeed learn a useful representation of the sentence’s semantic meaning, we perform missing word translation (i.e. where the source sentence has missing words). The model is forced to rely on its learned representation in order to infer what the missing words could be, and then to produce a good translation accordingly.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 4: Test set BLEU scores for missing word translation. We use the model parameters trained with 4M paired sentences.'. Solve the following question based on the table.\n\nQuestion: Which system has the highest score for translating from French to English?\nLet's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest score for translating from French to English, compare the scores in the FR → EN column for both VNMT and GNMT. GNMT has a score of 28.27, which is higher than VNMT's score of 23.51. So, the answer is {\"answer\": \"GNMT\"}." } ], "images": [ "scigen/train/3450.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Test set BLEU scores for missing word translation. We use the model parameters trained with 4M paired sentences." ], "reasoning": "To find the highest score for translating from French to English, compare the scores in the FR → EN column for both VNMT and GNMT. GNMT has a score of 28.27, which is higher than VNMT's score of 23.51.", "question": "Which system has the highest score for translating from French to English?", "answer": "So, the answer is {\"answer\": \"GNMT\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis GNMT outperforms VNMT in translating from English to Spanish. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nGNMT outperforms VNMT in translating from English to Spanish.\n" }, { "role": "assistant", "content": "The table shows that GNMT has a score of 33.23 for translating from English to Spanish, while VNMT has a score of 26.99. Therefore, GNMT outperforms VNMT in this translation task. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3450.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Test set BLEU scores for missing word translation. We use the model parameters trained with 4M paired sentences." ], "reasoning": "The table shows that GNMT has a score of 33.23 for translating from English to Spanish, while VNMT has a score of 26.99. Therefore, GNMT outperforms VNMT in this translation task.", "statement": "GNMT outperforms VNMT in translating from English to Spanish.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6: Test set BLEU scores for unseen pair translation. We use the VNMT and GNMT parameters trained with 4M paired sentences. For GNMT-Multi and GNMT-Multi-SSL, we train new models with 4M paired sentences, but with the respective language pairs excluded during training.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "GNMT-Multi-SSL outperforms all other models, while GNMT-Multi slightly outperforms VNMT and GNMT.In this context, VNMT and GNMT perform similarly in terms of BLEU scores. However, both models are consistently outperformed by GNMT-Multi (albeit only by a small amount). Using monolingual data is very effective in this context, with GNMT-Multi-SSL outperforming all other models." } ], "images": [ "scigen/train/3451.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Test set BLEU scores for unseen pair translation. We use the VNMT and GNMT parameters trained with 4M paired sentences. For GNMT-Multi and GNMT-Multi-SSL, we train new models with 4M paired sentences, but with the respective language pairs excluded during training." ], "reasoning": "GNMT-Multi-SSL outperforms all other models, while GNMT-Multi slightly outperforms VNMT and GNMT.", "description": "In this context, VNMT and GNMT perform similarly in terms of BLEU scores. However, both models are consistently outperformed by GNMT-Multi (albeit only by a small amount). Using monolingual data is very effective in this context, with GNMT-Multi-SSL outperforming all other models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: Test set BLEU scores for unseen pair translation. We use the VNMT and GNMT parameters trained with 4M paired sentences. For GNMT-Multi and GNMT-Multi-SSL, we train new models with 4M paired sentences, but with the respective language pairs excluded during training.\nQuestion: Which model has the highest BLEU score for the unseen ES → EN translation?\nSolve the above question based on the table information. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest BLEU score for the unseen ES → EN translation, compare the scores in the ES → EN column. GNMT-Multi-SSL has the highest score of 37.43. So, the answer is {\"answer\": \"GNMT-Multi-SSL\"}." } ], "images": [ "scigen/train/3451.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Test set BLEU scores for unseen pair translation. We use the VNMT and GNMT parameters trained with 4M paired sentences. For GNMT-Multi and GNMT-Multi-SSL, we train new models with 4M paired sentences, but with the respective language pairs excluded during training." ], "reasoning": "To find the highest BLEU score for the unseen ES → EN translation, compare the scores in the ES → EN column. GNMT-Multi-SSL has the highest score of 37.43.", "question": "Which model has the highest BLEU score for the unseen ES → EN translation?", "answer": "So, the answer is {\"answer\": \"GNMT-Multi-SSL\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nGNMT-Multi has the highest BLEU score for the unseen FR → EN translation.\n" }, { "role": "assistant", "content": "To verify this statement, compare the BLEU scores for the unseen FR → EN translation. GNMT-Multi-SSL has the highest score of 33.57, not GNMT-Multi. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3451.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Test set BLEU scores for unseen pair translation. We use the VNMT and GNMT parameters trained with 4M paired sentences. For GNMT-Multi and GNMT-Multi-SSL, we train new models with 4M paired sentences, but with the respective language pairs excluded during training." ], "reasoning": "To verify this statement, compare the BLEU scores for the unseen FR → EN translation. GNMT-Multi-SSL has the highest score of 33.57, not GNMT-Multi.", "statement": "GNMT-Multi has the highest BLEU score for the unseen FR → EN translation.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Detailed evaluation results for various multilingual modeling approaches based on GPT-C. Model performance metrics are reported on multilingual test sample.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The MultiGPT-C model with double heads achieved the best performance across various metrics, including PPL, ROUGE-L Precision, ROUGE-L Recall, and Edit similarity, compared to other training methods.To prepare a multilingual version of the IntelliCode Compose, we have extracted a shared sub-token vocabulary for Python, C#, JavaScript and TypeScript programming languages using the BPE tokenizer. We explored and compared four following ways of training multilingual GPT-C models: Language-agnostic baseline completely disregards the language type information, effectively attempting to train the model as monolingual. We have found this approach to underperform significantly as compared to the monolingual versions for each language. Language-type embedding. Nlang denotes the number of programming languages in the training dataset. Language specific control codes, or prefixes, is an approach introduced in [T5, CTRL] that facilitates constrained language modeling. In what follows, for each programming language we insert a sequence of tokens in the beginning of each training sample according to: \"lang ∗ remaining token sequence\", where lang∈{Python,C#,JavaScript,TypeScript}. In expectation, control codes would signal the neural network that a given sequence belongs to a particular programming language. As shown in Tab. this approach works rather well, yielding results comparable with monolingual counterparts. Finally, we add a programming language classification task during model pretraining, an approach inspired by natural language inference (NLI) RocStories and SWAG tasks [mostafazadeh-etal-2016-corpus, zellers2018swag]. As such, the model is trained using two optimization objectives: language modeling and multiple choice classification to detect programming language." } ], "images": [ "scigen/train/3452.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Detailed evaluation results for various multilingual modeling approaches based on GPT-C. Model performance metrics are reported on multilingual test sample." ], "reasoning": "The MultiGPT-C model with double heads achieved the best performance across various metrics, including PPL, ROUGE-L Precision, ROUGE-L Recall, and Edit similarity, compared to other training methods.", "description": "To prepare a multilingual version of the IntelliCode Compose, we have extracted a shared sub-token vocabulary for Python, C#, JavaScript and TypeScript programming languages using the BPE tokenizer. We explored and compared four following ways of training multilingual GPT-C models: Language-agnostic baseline completely disregards the language type information, effectively attempting to train the model as monolingual. We have found this approach to underperform significantly as compared to the monolingual versions for each language. Language-type embedding. Nlang denotes the number of programming languages in the training dataset. Language specific control codes, or prefixes, is an approach introduced in [T5, CTRL] that facilitates constrained language modeling. In what follows, for each programming language we insert a sequence of tokens in the beginning of each training sample according to: \"lang ∗ remaining token sequence\", where lang∈{Python,C#,JavaScript,TypeScript}. In expectation, control codes would signal the neural network that a given sequence belongs to a particular programming language. As shown in Tab. this approach works rather well, yielding results comparable with monolingual counterparts. Finally, we add a programming language classification task during model pretraining, an approach inspired by natural language inference (NLI) RocStories and SWAG tasks [mostafazadeh-etal-2016-corpus, zellers2018swag]. As such, the model is trained using two optimization objectives: language modeling and multiple choice classification to detect programming language.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 5: Detailed evaluation results for various multilingual modeling approaches based on GPT-C. Model performance metrics are reported on multilingual test sample.', solve this question and give detail solutions. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhich model achieved the highest ROUGE-L Recall and what was its value?\n" }, { "role": "assistant", "content": "By comparing the ROUGE-L Recall values in the table, the MultiGPT-C (double heads) model achieved the highest ROUGE-L Recall with a value of 0.76. So, the answer is {\"answer\": \"0.76\"}." } ], "images": [ "scigen/train/3452.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Detailed evaluation results for various multilingual modeling approaches based on GPT-C. Model performance metrics are reported on multilingual test sample." ], "reasoning": "By comparing the ROUGE-L Recall values in the table, the MultiGPT-C (double heads) model achieved the highest ROUGE-L Recall with a value of 0.76.", "question": "Which model achieved the highest ROUGE-L Recall and what was its value?", "answer": "So, the answer is {\"answer\": \"0.76\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Baseline model has the smallest model size. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Baseline model has the smallest model size.\n" }, { "role": "assistant", "content": "The table shows that the Baseline model has a model size of 374M, which is the same as the Control codes and MultiGPT-C (double heads) models, but smaller than the Language Embedding model. Thus, the answer is {\"answer\": \"not enough info\"}." } ], "images": [ "scigen/train/3452.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Detailed evaluation results for various multilingual modeling approaches based on GPT-C. Model performance metrics are reported on multilingual test sample." ], "reasoning": "The table shows that the Baseline model has a model size of 374M, which is the same as the Control codes and MultiGPT-C (double heads) models, but smaller than the Language Embedding model.", "statement": "The Baseline model has the smallest model size.", "answer": "Thus, the answer is {\"answer\": \"not enough info\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: Inference speed comparison for scenarios with different beam widths and sequence lengths, with different beam search setup. The inference speed is measured using the small model as described in Tab. 3.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Naive beam search requires L×k inference calls, increasing time, while batched inference reduces calls to L, improving efficiency.A naive beam search implementation would iterate over the top k candidates at every step to produce the output vector. However, for a sequence of length L, this would require L×k inference calls to the model, significantly increasing the inference time and degrading the overall real-time user experience. Instead, we aggregate top k candidates and perform batched inference calls at every decoding step, which reduces the number of inference calls to L. Tab." } ], "images": [ "scigen/train/3453.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Inference speed comparison for scenarios with different beam widths and sequence lengths, with different beam search setup. The inference speed is measured using the small model as described in Tab. 3." ], "reasoning": "Naive beam search requires L×k inference calls, increasing time, while batched inference reduces calls to L, improving efficiency.", "description": "A naive beam search implementation would iterate over the top k candidates at every step to produce the output vector. However, for a sequence of length L, this would require L×k inference calls to the model, significantly increasing the inference time and degrading the overall real-time user experience. Instead, we aggregate top k candidates and perform batched inference calls at every decoding step, which reduces the number of inference calls to L. Tab.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: Inference speed comparison for scenarios with different beam widths and sequence lengths, with different beam search setup. The inference speed is measured using the small model as described in Tab. 3.', answer the following question. Give detailed solution consisting of each step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the inference time for a sequence of length 25 with k=15 using Parallel Search?\n" }, { "role": "assistant", "content": "For L=25 and k=15, the table shows the inference time for Parallel Search as 3000 ms. So, the answer is {\"answer\": \"3000 ms\"}" } ], "images": [ "scigen/train/3453.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Inference speed comparison for scenarios with different beam widths and sequence lengths, with different beam search setup. The inference speed is measured using the small model as described in Tab. 3." ], "reasoning": "For L=25 and k=15, the table shows the inference time for Parallel Search as 3000 ms.", "question": "What is the inference time for a sequence of length 25 with k=15 using Parallel Search?", "answer": "So, the answer is {\"answer\": \"3000 ms\"}" }, { "messages": [ { "role": "user", "content": "Cached Search is always faster than Sequential Search. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nCached Search is always faster than Sequential Search.\n" }, { "role": "assistant", "content": "The table shows that for L=10 and k=1, Cached Search (220 ms) is faster than Sequential Search (250 ms), and for L=10 and k=10, Cached Search (820 ms) is faster than Sequential Search (1700 ms). For L=25 and k=15, Cached Search (2700 ms) is faster than Sequential Search (7500 ms). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3453.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Inference speed comparison for scenarios with different beam widths and sequence lengths, with different beam search setup. The inference speed is measured using the small model as described in Tab. 3." ], "reasoning": "The table shows that for L=10 and k=1, Cached Search (220 ms) is faster than Sequential Search (250 ms), and for L=10 and k=10, Cached Search (820 ms) is faster than Sequential Search (1700 ms). For L=25 and k=15, Cached Search (2700 ms) is faster than Sequential Search (7500 ms).", "statement": "Cached Search is always faster than Sequential Search.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 6: Thread reconstruction results; † indicates significant difference from COS-sim (p'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The coherence model outperforms the baselines, improving thread-level accuracy by 2.7% and edge-level accuracy by 2.5% over the best baseline (COS-sim).The low thread-level accuracy across all the systems prove that reconstructing an entire tree is a difficult task. Models are reasonably accurate at the edge level. Our coherence model shows promising results, yielding substantial improvements over the baselines. It delivers 2.7% improvements in thread-level and 2.5% in edge-level accuracy over the best baseline (COS-sim)." } ], "images": [ "scigen/train/3454.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Thread reconstruction results; † indicates significant difference from COS-sim (p" ], "reasoning": "The coherence model outperforms the baselines, improving thread-level accuracy by 2.7% and edge-level accuracy by 2.5% over the best baseline (COS-sim).", "description": "The low thread-level accuracy across all the systems prove that reconstructing an entire tree is a difficult task. Models are reasonably accurate at the edge level. Our coherence model shows promising results, yielding substantial improvements over the baselines. It delivers 2.7% improvements in thread-level and 2.5% in edge-level accuracy over the best baseline (COS-sim).", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 6: Thread reconstruction results; † indicates significant difference from COS-sim (p', and tackle the question below in a sequential manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model shows the highest thread-level accuracy and by how much does it improve over the best baseline?\n" }, { "role": "assistant", "content": "The table shows that the Conv. Entity Grid model has the highest thread-level accuracy at 30.33%. The description states that this model improves thread-level accuracy by 2.7% over the best baseline, which is COS-sim with 27.66%. So, the answer is {\"answer\": \"Conv. Entity Grid with 30.33%\"}." } ], "images": [ "scigen/train/3454.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Thread reconstruction results; † indicates significant difference from COS-sim (p" ], "reasoning": "The table shows that the Conv. Entity Grid model has the highest thread-level accuracy at 30.33%. The description states that this model improves thread-level accuracy by 2.7% over the best baseline, which is COS-sim with 27.66%.", "question": "Which model shows the highest thread-level accuracy and by how much does it improve over the best baseline?", "answer": "So, the answer is {\"answer\": \"Conv. Entity Grid with 30.33%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Conv. Entity Grid model has the highest edge-level F1 score. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Conv. Entity Grid model has the highest edge-level F1 score.\n" }, { "role": "assistant", "content": "The table shows that the Conv. Entity Grid model has an edge-level F1 score of 53.59, which is higher than the other models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3454.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Thread reconstruction results; † indicates significant difference from COS-sim (p" ], "reasoning": "The table shows that the Conv. Entity Grid model has an edge-level F1 score of 53.59, which is higher than the other models listed.", "statement": "The Conv. Entity Grid model has the highest edge-level F1 score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 9: WE is Word Embedding layer, UL is Utterance Layer, CL is Conversation Layer, LR is Logistic regression and CRF is Conditional Random Field.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Adding utterance and conversation layers improves accuracy, and replacing LR with CRF further enhances performance.From the table, we observe that the models WE, WE+UL, and WE+UL+CL with LR layer at the top produce an accuracy of 71.4%, 72.2%, and 74.1%, respectively. In the final layer, if LR is replaced with CRF then the accuracy of WE, WE+UL, and WE+UL+CL (Hierarchical Bi-LSTM-CRF) is 72.2%, 72.7%, and 79.2%, respectively. From these results it is clear that adding additional layers, viz. utterance layer and conversation layer, improve the results by a few notches. Also, replacing LR with CRF further improves the results. Note that the accuracy of WE+UL with LR and WE with CRF is same. We understand that the output of utterance layer at each time step is a vector representing the context of the utterance till that word. The word vector at the last time step is the final representation of the utterance. This means, adding an utterance layer generates a compositional vector of all words in an utterance, and thus serves as a good representation of all words in the utterance. Adding the utterance layer and replacing the LR with CRF in the existing model produces more or less the same result. Addition of conversation layer results in major improvement in the accuracy, approximately 2% absolute points with LR in the final layer, and 6% absolute points with CRF . This is because the output of conversation layer for an utterance is a representational vector capturing the context of itself and utterances preceding it." } ], "images": [ "scigen/train/3455.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: WE is Word Embedding layer, UL is Utterance Layer, CL is Conversation Layer, LR is Logistic regression and CRF is Conditional Random Field." ], "reasoning": "Adding utterance and conversation layers improves accuracy, and replacing LR with CRF further enhances performance.", "description": "From the table, we observe that the models WE, WE+UL, and WE+UL+CL with LR layer at the top produce an accuracy of 71.4%, 72.2%, and 74.1%, respectively. In the final layer, if LR is replaced with CRF then the accuracy of WE, WE+UL, and WE+UL+CL (Hierarchical Bi-LSTM-CRF) is 72.2%, 72.7%, and 79.2%, respectively. From these results it is clear that adding additional layers, viz. utterance layer and conversation layer, improve the results by a few notches. Also, replacing LR with CRF further improves the results. Note that the accuracy of WE+UL with LR and WE with CRF is same. We understand that the output of utterance layer at each time step is a vector representing the context of the utterance till that word. The word vector at the last time step is the final representation of the utterance. This means, adding an utterance layer generates a compositional vector of all words in an utterance, and thus serves as a good representation of all words in the utterance. Adding the utterance layer and replacing the LR with CRF in the existing model produces more or less the same result. Addition of conversation layer results in major improvement in the accuracy, approximately 2% absolute points with LR in the final layer, and 6% absolute points with CRF . This is because the output of conversation layer for an utterance is a representational vector capturing the context of itself and utterances preceding it.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 9: WE is Word Embedding layer, UL is Utterance Layer, CL is Conversation Layer, LR is Logistic regression and CRF is Conditional Random Field.', solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the accuracy improvement when replacing LR with CRF for the WE+UL+CL model?\n" }, { "role": "assistant", "content": "The accuracy of the WE+UL+CL model with LR is 74.1%, and with CRF it is 79.2%. The improvement is 79.2% - 74.1% = 5.1%. So, the answer is {\"answer\": \"5.1%\"}" } ], "images": [ "scigen/train/3455.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: WE is Word Embedding layer, UL is Utterance Layer, CL is Conversation Layer, LR is Logistic regression and CRF is Conditional Random Field." ], "reasoning": "The accuracy of the WE+UL+CL model with LR is 74.1%, and with CRF it is 79.2%. The improvement is 79.2% - 74.1% = 5.1%.", "question": "What is the accuracy improvement when replacing LR with CRF for the WE+UL+CL model?", "answer": "So, the answer is {\"answer\": \"5.1%\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Replacing LR with CRF in the WE model improves accuracy by 1%., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nReplacing LR with CRF in the WE model improves accuracy by 1%.\n" }, { "role": "assistant", "content": "The accuracy of the WE model with LR is 71.4%, and with CRF it is 72.2%. The improvement is 72.2% - 71.4% = 0.8%, which is not 1%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3455.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: WE is Word Embedding layer, UL is Utterance Layer, CL is Conversation Layer, LR is Logistic regression and CRF is Conditional Random Field." ], "reasoning": "The accuracy of the WE model with LR is 71.4%, and with CRF it is 72.2%. The improvement is 72.2% - 71.4% = 0.8%, which is not 1%.", "statement": "Replacing LR with CRF in the WE model improves accuracy by 1%.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Hyperparameter tuning – the 2nd column lists the various values tried, while the 3rd column lists the final value chosen for the corresponding hyperparameter.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The model was trained with a learning rate of 1.0, reduced by 0.5 every 5 epochs, using 300D Glove embeddings, a dropout of 0.2, and one stacked LSTM layer.Conversations with the same number of utterances were grouped together into mini-batches, and each utterance in a mini-batch was padded to the maximum length for that batch. The maximum batch-size allowed was 64. We used L2 regularization of 1e−4 in the form of weight decay and the Adadelta optimizer. All other hyper-parameters were selected by tuning one hyper-parameter at a time while keeping the others fixed. The hyper-parameters were tuned using the SwDA validation set. The final set of hyper-parameters were then used to train two different models, one each on SwDA and MRDA training datasets. Dropout was applied to the embeddings obtained from the output of each encoder. The learning rate was initialized to 1.0 and reduced by a factor of 0.5 every 5 epochs. Early stopping is also used on the validation set with a patience of 5 epochs. Increasing the number of stacked LSTM layers reduced the accuracy of the model, so we settled with only one layer." } ], "images": [ "scigen/train/3456.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Hyperparameter tuning – the 2nd column lists the various values tried, while the 3rd column lists the final value chosen for the corresponding hyperparameter." ], "reasoning": "The model was trained with a learning rate of 1.0, reduced by 0.5 every 5 epochs, using 300D Glove embeddings, a dropout of 0.2, and one stacked LSTM layer.", "description": "Conversations with the same number of utterances were grouped together into mini-batches, and each utterance in a mini-batch was padded to the maximum length for that batch. The maximum batch-size allowed was 64. We used L2 regularization of 1e−4 in the form of weight decay and the Adadelta optimizer. All other hyper-parameters were selected by tuning one hyper-parameter at a time while keeping the others fixed. The hyper-parameters were tuned using the SwDA validation set. The final set of hyper-parameters were then used to train two different models, one each on SwDA and MRDA training datasets. Dropout was applied to the embeddings obtained from the output of each encoder. The learning rate was initialized to 1.0 and reduced by a factor of 0.5 every 5 epochs. Early stopping is also used on the validation set with a patience of 5 epochs. Increasing the number of stacked LSTM layers reduced the accuracy of the model, so we settled with only one layer.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: Hyperparameter tuning – the 2nd column lists the various values tried, while the 3rd column lists the final value chosen for the corresponding hyperparameter.', unfold the steps to solve the question given next. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nQuestion: What was the final dropout rate used in the model training?\n" }, { "role": "assistant", "content": "The table shows that the final dropout rate used was 0.2. So, the answer is {\"answer\": \"0.2\"}." } ], "images": [ "scigen/train/3456.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Hyperparameter tuning – the 2nd column lists the various values tried, while the 3rd column lists the final value chosen for the corresponding hyperparameter." ], "reasoning": "The table shows that the final dropout rate used was 0.2.", "question": "What was the final dropout rate used in the model training?", "answer": "So, the answer is {\"answer\": \"0.2\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model used Word2Vec embeddings.\n" }, { "role": "assistant", "content": "The table indicates that the final word embedding used was 300D Glove, not Word2Vec. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3456.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Hyperparameter tuning – the 2nd column lists the various values tried, while the 3rd column lists the final value chosen for the corresponding hyperparameter." ], "reasoning": "The table indicates that the final word embedding used was 300D Glove, not Word2Vec.", "statement": "The model used Word2Vec embeddings.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: Comparing accuracy of our method (Hierarchical Bi-LSTM-CRF) with other methods in the literature on SwDA dataset.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The Hierarchical Bi-LSTM-CRF model achieved the highest accuracy of 79.2%, outperforming the DRLM-Conditional model by 2.2% absolute points.The results show that our Hierarchical Bi-LSTM-CRF model outperforms the state-of-the-art. Our model improved the DA labeling accuracy over DRLM-Conditional model by 2.2% absolute points. In order to further analyze the results, we looked into the confusion matrix to know which labels are incorrectly/correctly assigned to utterances. Among them the most confused pairs are (sd,sv) and (aa,b) which represent (statement-non-opinion, statement-opinion) and (agree-accept, acknowledge) respectively. The total number of utterances with DA ’sd’, ’sv’, ’aa’, and ’b’ are 1317, 717, 208, and 762, respectively. 103 utterances (7.8%) with true label non-opinion were predicted incorrectly as opinion, whereas, 1155 utterances (87.7%) with true label non-opinion were predicted correctly. Similarly, 200 utterances (27.9%) with true label opinion were predicted incorrectly as non-opinion whereas 473 utterances (66%) with true label opinion were predicted correctly. On further analysis of the cause of this confusion between these two class pairs, we identified that there are utterances which were classified correctly by the model, however, they were marked incorrectly classified because of bias in the ground truth. For some of the utterances, classes were not distinguishable even by humans because of the subjectivity." } ], "images": [ "scigen/train/3457.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparing accuracy of our method (Hierarchical Bi-LSTM-CRF) with other methods in the literature on SwDA dataset." ], "reasoning": "The Hierarchical Bi-LSTM-CRF model achieved the highest accuracy of 79.2%, outperforming the DRLM-Conditional model by 2.2% absolute points.", "description": "The results show that our Hierarchical Bi-LSTM-CRF model outperforms the state-of-the-art. Our model improved the DA labeling accuracy over DRLM-Conditional model by 2.2% absolute points. In order to further analyze the results, we looked into the confusion matrix to know which labels are incorrectly/correctly assigned to utterances. Among them the most confused pairs are (sd,sv) and (aa,b) which represent (statement-non-opinion, statement-opinion) and (agree-accept, acknowledge) respectively. The total number of utterances with DA ’sd’, ’sv’, ’aa’, and ’b’ are 1317, 717, 208, and 762, respectively. 103 utterances (7.8%) with true label non-opinion were predicted incorrectly as opinion, whereas, 1155 utterances (87.7%) with true label non-opinion were predicted correctly. Similarly, 200 utterances (27.9%) with true label opinion were predicted incorrectly as non-opinion whereas 473 utterances (66%) with true label opinion were predicted correctly. On further analysis of the cause of this confusion between these two class pairs, we identified that there are utterances which were classified correctly by the model, however, they were marked incorrectly classified because of bias in the ground truth. For some of the utterances, classes were not distinguishable even by humans because of the subjectivity.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Comparing accuracy of our method (Hierarchical Bi-LSTM-CRF) with other methods in the literature on SwDA dataset.', answer the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the accuracy difference between the Hierarchical Bi-LSTM-CRF model and the LSTM-Softmax model?\n" }, { "role": "assistant", "content": "The Hierarchical Bi-LSTM-CRF model has an accuracy of 79.2%, while the LSTM-Softmax model has an accuracy of 75.8%. The difference is calculated as 79.2% - 75.8%. So, the answer is {\"answer\": \"3.4%\"}" } ], "images": [ "scigen/train/3457.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparing accuracy of our method (Hierarchical Bi-LSTM-CRF) with other methods in the literature on SwDA dataset." ], "reasoning": "The Hierarchical Bi-LSTM-CRF model has an accuracy of 79.2%, while the LSTM-Softmax model has an accuracy of 75.8%. The difference is calculated as 79.2% - 75.8%.", "question": "What is the accuracy difference between the Hierarchical Bi-LSTM-CRF model and the LSTM-Softmax model?", "answer": "So, the answer is {\"answer\": \"3.4%\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The Hierarchical Bi-LSTM-CRF model has an accuracy of 79.2%, which is 2.2% higher than the DRLM-Conditional model., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Hierarchical Bi-LSTM-CRF model has an accuracy of 79.2%, which is 2.2% higher than the DRLM-Conditional model.\n" }, { "role": "assistant", "content": "The table shows that the Hierarchical Bi-LSTM-CRF model has an accuracy of 79.2%, and the DRLM-Conditional model has an accuracy of 77.0%. The difference is 79.2% - 77.0% = 2.2%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3457.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparing accuracy of our method (Hierarchical Bi-LSTM-CRF) with other methods in the literature on SwDA dataset." ], "reasoning": "The table shows that the Hierarchical Bi-LSTM-CRF model has an accuracy of 79.2%, and the DRLM-Conditional model has an accuracy of 77.0%. The difference is 79.2% - 77.0% = 2.2%.", "statement": "The Hierarchical Bi-LSTM-CRF model has an accuracy of 79.2%, which is 2.2% higher than the DRLM-Conditional model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 7: Comparing Accuracy of our method (Bi-LSTM-CRF) with other methods in the literature on the MRDA dataset.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Our method, Hierarchical Bi-LSTM-CRF, outperforms the state-of-the-art by 4.1%, achieving an accuracy of 90.9%.From this table, it is clear that our method outperforms the state-of-the-art by a significant margin i.e. by 4.1%. Except for the class label ’B’, all other DA class labels are predicted accurately. Approximately 21% of DA class label ’B’ are incorrectly predicted as ’S’. One of the reasons for this behavior is that the MRDA dataset is highly imbalanced, with more than 50% of the utterances labeled as class ’S’." } ], "images": [ "scigen/train/3458.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Comparing Accuracy of our method (Bi-LSTM-CRF) with other methods in the literature on the MRDA dataset." ], "reasoning": "Our method, Hierarchical Bi-LSTM-CRF, outperforms the state-of-the-art by 4.1%, achieving an accuracy of 90.9%.", "description": "From this table, it is clear that our method outperforms the state-of-the-art by a significant margin i.e. by 4.1%. Except for the class label ’B’, all other DA class labels are predicted accurately. Approximately 21% of DA class label ’B’ are incorrectly predicted as ’S’. One of the reasons for this behavior is that the MRDA dataset is highly imbalanced, with more than 50% of the utterances labeled as class ’S’.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 7: Comparing Accuracy of our method (Bi-LSTM-CRF) with other methods in the literature on the MRDA dataset.' and methodically tackle the question that follows. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the accuracy difference between the Hierarchical Bi-LSTM-CRF model and the CNN model?\n" }, { "role": "assistant", "content": "The accuracy of the Hierarchical Bi-LSTM-CRF model is 90.9%, and the accuracy of the CNN model is 84.6%. The difference is calculated as 90.9% - 84.6%. So, the answer is {\"answer\": \"6.3%\"}" } ], "images": [ "scigen/train/3458.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Comparing Accuracy of our method (Bi-LSTM-CRF) with other methods in the literature on the MRDA dataset." ], "reasoning": "The accuracy of the Hierarchical Bi-LSTM-CRF model is 90.9%, and the accuracy of the CNN model is 84.6%. The difference is calculated as 90.9% - 84.6%.", "question": "What is the accuracy difference between the Hierarchical Bi-LSTM-CRF model and the CNN model?", "answer": "So, the answer is {\"answer\": \"6.3%\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Hierarchical Bi-LSTM-CRF model has an accuracy of 90.9%. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Hierarchical Bi-LSTM-CRF model has an accuracy of 90.9%.\n" }, { "role": "assistant", "content": "The table shows that the Hierarchical Bi-LSTM-CRF model has an accuracy of 90.9%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3458.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Comparing Accuracy of our method (Bi-LSTM-CRF) with other methods in the literature on the MRDA dataset." ], "reasoning": "The table shows that the Hierarchical Bi-LSTM-CRF model has an accuracy of 90.9%.", "statement": "The Hierarchical Bi-LSTM-CRF model has an accuracy of 90.9%.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 10: Accuracy obtained using two extensions to the Hierarchical Bi-LSTM-CRF model.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The model computes attention weights for each utterance vector based on previous utterances, and these weights are used to create a context vector that is concatenated with the current utterance vector for classification.In our model, after obtaining utterance vectors from the conversation layer, a normalized attention weight vector is computed for each utterance vector, by computing its similarity from previous utterance vectors. These attention weights are then used to compute the context vector by taking a weighted sum of the previous K utterance vectors. The new context vector is concatenated to the utterance vector produced by the conversation layer to obtain new utterance vector, which is input to the classification layer. We experimented with this attention by varying the length of the context (number of previous utterances) i.e. K∈(10,5,3). In a conversation, an utterance at time step t is mostly dependent upon the previous two or three utterances." } ], "images": [ "scigen/train/3459.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Accuracy obtained using two extensions to the Hierarchical Bi-LSTM-CRF model." ], "reasoning": "The model computes attention weights for each utterance vector based on previous utterances, and these weights are used to create a context vector that is concatenated with the current utterance vector for classification.", "description": "In our model, after obtaining utterance vectors from the conversation layer, a normalized attention weight vector is computed for each utterance vector, by computing its similarity from previous utterance vectors. These attention weights are then used to compute the context vector by taking a weighted sum of the previous K utterance vectors. The new context vector is concatenated to the utterance vector produced by the conversation layer to obtain new utterance vector, which is input to the classification layer. We experimented with this attention by varying the length of the context (number of previous utterances) i.e. K∈(10,5,3). In a conversation, an utterance at time step t is mostly dependent upon the previous two or three utterances.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 10: Accuracy obtained using two extensions to the Hierarchical Bi-LSTM-CRF model.', solve the subsequent question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the accuracy when the context length is 5?\n" }, { "role": "assistant", "content": "The table shows the accuracy for different context lengths. For context length 5, the accuracy is 78.3%. So, the answer is {\"answer\": \"78.3\"}." } ], "images": [ "scigen/train/3459.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Accuracy obtained using two extensions to the Hierarchical Bi-LSTM-CRF model." ], "reasoning": "The table shows the accuracy for different context lengths. For context length 5, the accuracy is 78.3%.", "question": "What is the accuracy when the context length is 5?", "answer": "So, the answer is {\"answer\": \"78.3\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The highest accuracy is achieved with a context length of 5., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe highest accuracy is achieved with a context length of 5.\n" }, { "role": "assistant", "content": "The table shows that the accuracy for context length 5 is 78.3%, which is higher than the accuracies for context lengths 10 and 3. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3459.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Accuracy obtained using two extensions to the Hierarchical Bi-LSTM-CRF model." ], "reasoning": "The table shows that the accuracy for context length 5 is 78.3%, which is higher than the accuracies for context lengths 10 and 3.", "statement": "The highest accuracy is achieved with a context length of 5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Main results on the two datasets. R-1, R-2, R-L represent the ROUGE scores. PG Baseline represents our baseline augmented pointer-generator; RLR, RLC and RLR+C represent RL training with the ROUGE reward alone, with the factual correctness reward alone and with both. All the ROUGE scores have a 95% confidence interval of at most ±0.6. F1 scores for extractive models were not evaluated for the reason discussed in Section 5.3.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The PG baseline model outperforms both BanditSum and LexRank on ROUGE scores, indicating that abstractive summarization is necessary for generating high-quality summaries.We first notice that while the neural extractive model, BanditSum, outperforms the non-neural extractive method on ROUGE scores, our PG baseline model substantially outperforms both of them, suggesting that on both datasets abstractive summarization is necessary to generate summaries comparable to human-written ones." } ], "images": [ "scigen/train/3460.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Main results on the two datasets. R-1, R-2, R-L represent the ROUGE scores. PG Baseline represents our baseline augmented pointer-generator; RLR, RLC and RLR+C represent RL training with the ROUGE reward alone, with the factual correctness reward alone and with both. All the ROUGE scores have a 95% confidence interval of at most ±0.6. F1 scores for extractive models were not evaluated for the reason discussed in Section 5.3." ], "reasoning": "The PG baseline model outperforms both BanditSum and LexRank on ROUGE scores, indicating that abstractive summarization is necessary for generating high-quality summaries.", "description": "We first notice that while the neural extractive model, BanditSum, outperforms the non-neural extractive method on ROUGE scores, our PG baseline model substantially outperforms both of them, suggesting that on both datasets abstractive summarization is necessary to generate summaries comparable to human-written ones.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Main results on the two datasets. R-1, R-2, R-L represent the ROUGE scores. PG Baseline represents our baseline augmented pointer-generator; RLR, RLC and RLR+C represent RL training with the ROUGE reward alone, with the factual correctness reward alone and with both. All the ROUGE scores have a 95% confidence interval of at most ±0.6. F1 scores for extractive models were not evaluated for the reason discussed in Section 5.3.\nQuestion: Which system has the highest Stanford Factual F1 score?\nSolve the above question based on the table information. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the Stanford Factual F1 column, we see that the highest score is 65.9, achieved by the PG + RLC system. So, the answer is {\"answer\": \"PG + RLC\"}." } ], "images": [ "scigen/train/3460.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Main results on the two datasets. R-1, R-2, R-L represent the ROUGE scores. PG Baseline represents our baseline augmented pointer-generator; RLR, RLC and RLR+C represent RL training with the ROUGE reward alone, with the factual correctness reward alone and with both. All the ROUGE scores have a 95% confidence interval of at most ±0.6. F1 scores for extractive models were not evaluated for the reason discussed in Section 5.3." ], "reasoning": "By examining the Stanford Factual F1 column, we see that the highest score is 65.9, achieved by the PG + RLC system.", "question": "Which system has the highest Stanford Factual F1 score?", "answer": "So, the answer is {\"answer\": \"PG + RLC\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nBanditSum has a higher Stanford R-1 score than LexRank.\n" }, { "role": "assistant", "content": "The table shows that BanditSum has a Stanford R-1 score of 32.7, while LexRank has a score of 26.8, confirming that BanditSum's score is higher. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3460.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Main results on the two datasets. R-1, R-2, R-L represent the ROUGE scores. PG Baseline represents our baseline augmented pointer-generator; RLR, RLC and RLR+C represent RL training with the ROUGE reward alone, with the factual correctness reward alone and with both. All the ROUGE scores have a 95% confidence interval of at most ±0.6. F1 scores for extractive models were not evaluated for the reason discussed in Section 5.3." ], "reasoning": "The table shows that BanditSum has a Stanford R-1 score of 32.7, while LexRank has a score of 26.8, confirming that BanditSum's score is higher.", "statement": "BanditSum has a higher Stanford R-1 score than LexRank.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Test set factual F1 scores for each variable on the Stanford dataset. ∗ marks statistically significant improvements with p', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Improvements in RLR+C are observed across all variables tested, with varying distributions of improvement depending on initialization.We find that on both datasets, improvements in RLR+C can be observed on all variables tested. We further find that, as we change the initialization across different training runs, while the overall improvement on factual F1 stays approximately unchanged, the distribution of the improvement on different variables can vary substantially. Developing a training strategy for fine-grained control over different variables is an interesting direction for future work." } ], "images": [ "scigen/train/3461.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Test set factual F1 scores for each variable on the Stanford dataset. ∗ marks statistically significant improvements with p" ], "reasoning": "Improvements in RLR+C are observed across all variables tested, with varying distributions of improvement depending on initialization.", "description": "We find that on both datasets, improvements in RLR+C can be observed on all variables tested. We further find that, as we change the initialization across different training runs, while the overall improvement on factual F1 stays approximately unchanged, the distribution of the improvement on different variables can vary substantially. Developing a training strategy for fine-grained control over different variables is an interesting direction for future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: Test set factual F1 scores for each variable on the Stanford dataset. ∗ marks statistically significant improvements with p'. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich variable showed the highest improvement in RLR+C compared to PG Baseline?\n" }, { "role": "assistant", "content": "By comparing the Δ values for each variable, we find that Pneumothorax has the highest improvement with a Δ of +13.4. So, the answer is {\"answer\": \"Pneumothorax\"}." } ], "images": [ "scigen/train/3461.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Test set factual F1 scores for each variable on the Stanford dataset. ∗ marks statistically significant improvements with p" ], "reasoning": "By comparing the Δ values for each variable, we find that Pneumothorax has the highest improvement with a Δ of +13.4.", "question": "Which variable showed the highest improvement in RLR+C compared to PG Baseline?", "answer": "So, the answer is {\"answer\": \"Pneumothorax\"}." }, { "messages": [ { "role": "user", "content": "The improvement in RLR+C for Edema is greater than that for Airspace Opacity. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe improvement in RLR+C for Edema is greater than that for Airspace Opacity.\n" }, { "role": "assistant", "content": "The table shows that the Δ for Edema is +12.5, while the Δ for Airspace Opacity is +10.3, indicating that the improvement for Edema is indeed greater. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3461.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Test set factual F1 scores for each variable on the Stanford dataset. ∗ marks statistically significant improvements with p" ], "reasoning": "The table shows that the Δ for Edema is +12.5, while the Δ for Airspace Opacity is +10.3, indicating that the improvement for Edema is indeed greater.", "statement": "The improvement in RLR+C for Edema is greater than that for Airspace Opacity.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: Perplexity scores as evaluated by the trained radiology impression LM on the test set human references and model predictions.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Extractive models have higher perplexity scores than humans because radiologists use more compressed language in summaries than in findings.We find that while extractive models can achieve non-trivial overlap with references, their perplexity scores tend to be much higher than humans. We conjecture that this is because radiologists are trained to write the summaries with more compressed language than when they are writing the findings, therefore sentences directly extracted from the findings tend to be more verbose than needed." } ], "images": [ "scigen/train/3462.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Perplexity scores as evaluated by the trained radiology impression LM on the test set human references and model predictions." ], "reasoning": "Extractive models have higher perplexity scores than humans because radiologists use more compressed language in summaries than in findings.", "description": "We find that while extractive models can achieve non-trivial overlap with references, their perplexity scores tend to be much higher than humans. We conjecture that this is because radiologists are trained to write the summaries with more compressed language than when they are writing the findings, therefore sentences directly extracted from the findings tend to be more verbose than needed.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 5: Perplexity scores as evaluated by the trained radiology impression LM on the test set human references and model predictions.', solve the subsequent question in a stepwise manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich system has the highest perplexity score for RIH?\n" }, { "role": "assistant", "content": "To find the system with the highest perplexity score for RIH, compare the RIH perplexity scores of all systems listed in the table. BanditSum has the highest RIH perplexity score of 40.9. So, the answer is {\"answer\": \"BanditSum\"}." } ], "images": [ "scigen/train/3462.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Perplexity scores as evaluated by the trained radiology impression LM on the test set human references and model predictions." ], "reasoning": "To find the system with the highest perplexity score for RIH, compare the RIH perplexity scores of all systems listed in the table. BanditSum has the highest RIH perplexity score of 40.9.", "question": "Which system has the highest perplexity score for RIH?", "answer": "So, the answer is {\"answer\": \"BanditSum\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe PG Baseline system has a lower perplexity score than humans for both Stanford and RIH.\n" }, { "role": "assistant", "content": "The table shows that the PG Baseline system has a Stanford perplexity score of 4.8 and an RIH perplexity score of 3.8, both of which are lower than the human scores of 6.7 and 5.5, respectively. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3462.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Perplexity scores as evaluated by the trained radiology impression LM on the test set human references and model predictions." ], "reasoning": "The table shows that the PG Baseline system has a Stanford perplexity score of 4.8 and an RIH perplexity score of 3.8, both of which are lower than the human scores of 6.7 and 5.5, respectively.", "statement": "The PG Baseline system has a lower perplexity score than humans for both Stanford and RIH.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled '(d) fluency, document, N=50'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Lower inter-rater agreement in three out of four conditions is attributed to the quality of machine translations and lack of guidelines on error severity.In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency" } ], "images": [ "scigen/train/3463.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(d) fluency, document, N=50" ], "reasoning": "Lower inter-rater agreement in three out of four conditions is attributed to the quality of machine translations and lack of guidelines on error severity.", "description": "In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about '(d) fluency, document, N=50', solve the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the sum of the values in the row labeled 't' and column labeled 'b'?\n" }, { "role": "assistant", "content": "To find the sum of the values in the row labeled 't' and column labeled 'b', add the values 4, 5, and 9. So, the answer is {\"answer\": \"18\"}" } ], "images": [ "scigen/train/3463.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(d) fluency, document, N=50" ], "reasoning": "To find the sum of the values in the row labeled 't' and column labeled 'b', add the values 4, 5, and 9.", "question": "What is the sum of the values in the row labeled 't' and column labeled 'b'?", "answer": "So, the answer is {\"answer\": \"18\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe value in the intersection of row 'a' and column 't' is 1.\n" }, { "role": "assistant", "content": "The table shows that the value at the intersection of row 'a' and column 't' is indeed 1. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3463.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(d) fluency, document, N=50" ], "reasoning": "The table shows that the value at the intersection of row 'a' and column 't' is indeed 1.", "statement": "The value in the intersection of row 'a' and column 't' is 1.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Ratings by rater and condition. Greyed-out fields indicate that raters had access to full documents for which we elicited sentence-level judgements; these are not considered for total results.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Raters showed lower inter-rater agreement in three out of four conditions due to the quality of machine translations and lack of guidelines on error severity.Raters choose between three labels for each item: MT is better than Human (a), Human is better than MT (b), or tie (t). Besides percent agreement (same label), we calculate Cohen’s kappa coefficient In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency" } ], "images": [ "scigen/train/3464.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Ratings by rater and condition. Greyed-out fields indicate that raters had access to full documents for which we elicited sentence-level judgements; these are not considered for total results." ], "reasoning": "Raters showed lower inter-rater agreement in three out of four conditions due to the quality of machine translations and lack of guidelines on error severity.", "description": "Raters choose between three labels for each item: MT is better than Human (a), Human is better than MT (b), or tie (t). Besides percent agreement (same label), we calculate Cohen’s kappa coefficient In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 1: Ratings by rater and condition. Greyed-out fields indicate that raters had access to full documents for which we elicited sentence-level judgements; these are not considered for total results.\n\nQuestion: What is the total number of times raters chose 'Sentence Human' for both Fluency and Adequacy?\n" }, { "role": "assistant", "content": "To find the total number of 'Sentence Human' choices, sum the values in the 'Sentence Human' column for both Fluency and Adequacy sections. So, the answer is {\"answer\": \"192\"}." } ], "images": [ "scigen/train/3464.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Ratings by rater and condition. Greyed-out fields indicate that raters had access to full documents for which we elicited sentence-level judgements; these are not considered for total results." ], "reasoning": "To find the total number of 'Sentence Human' choices, sum the values in the 'Sentence Human' column for both Fluency and Adequacy sections.", "question": "What is the total number of times raters chose 'Sentence Human' for both Fluency and Adequacy?", "answer": "So, the answer is {\"answer\": \"192\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nRaters chose 'Document Human' more frequently than 'Document MT' for Fluency.\n" }, { "role": "assistant", "content": "Compare the total values for 'Document Human' and 'Document MT' in the Fluency section. 'Document Human' has a total of 99, while 'Document MT' has a total of 44. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3464.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Ratings by rater and condition. Greyed-out fields indicate that raters had access to full documents for which we elicited sentence-level judgements; these are not considered for total results." ], "reasoning": "Compare the total values for 'Document Human' and 'Document MT' in the Fluency section. 'Document Human' has a total of 99, while 'Document MT' has a total of 44.", "statement": "Raters chose 'Document Human' more frequently than 'Document MT' for Fluency.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled '(a) fluency, document, N=50', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Inter-rater agreement is lower in three out of four conditions due to the quality of translations and lack of guidelines on error severity.In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency" } ], "images": [ "scigen/train/3465.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) fluency, document, N=50" ], "reasoning": "Inter-rater agreement is lower in three out of four conditions due to the quality of translations and lack of guidelines on error severity.", "description": "In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the '(a) fluency, document, N=50', solve the subsequent question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhat is the sum of the values in the row labeled 'b'?\n" }, { "role": "assistant", "content": "To find the sum of the values in the row labeled 'b', add the values in the cells of that row: 2 + 9 + 18. So, the answer is {\"answer\": \"29\"}." } ], "images": [ "scigen/train/3465.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) fluency, document, N=50" ], "reasoning": "To find the sum of the values in the row labeled 'b', add the values in the cells of that row: 2 + 9 + 18.", "question": "What is the sum of the values in the row labeled 'b'?", "answer": "So, the answer is {\"answer\": \"29\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe highest value in the table is in the row labeled 'b' and the column labeled 'B2 b'.\n" }, { "role": "assistant", "content": "The highest value in the table is 18, which is located in the row labeled 'b' and the column labeled 'B2 b'. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3465.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) fluency, document, N=50" ], "reasoning": "The highest value in the table is 18, which is located in the row labeled 'b' and the column labeled 'B2 b'.", "statement": "The highest value in the table is in the row labeled 'b' and the column labeled 'B2 b'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title '(b) fluency, document, N=50'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Inter-rater agreement is lower in three out of four conditions due to the quality of translations and lack of guidelines on error severity.In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency" } ], "images": [ "scigen/train/3466.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) fluency, document, N=50" ], "reasoning": "Inter-rater agreement is lower in three out of four conditions due to the quality of translations and lack of guidelines on error severity.", "description": "In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled '(b) fluency, document, N=50' in mind, break down and resolve the question below step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the total score for the 'b' row?\n" }, { "role": "assistant", "content": "To find the total score for the 'b' row, sum the values in the 'b' row: 3 + 8 + 18. So, the answer is {\"answer\": \"29\"}." } ], "images": [ "scigen/train/3466.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) fluency, document, N=50" ], "reasoning": "To find the total score for the 'b' row, sum the values in the 'b' row: 3 + 8 + 18.", "question": "What is the total score for the 'b' row?", "answer": "So, the answer is {\"answer\": \"29\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe highest score in the table is in the 'C b' column.\n" }, { "role": "assistant", "content": "The highest score in the table is 18, which is in the 'C b' column. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3466.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) fluency, document, N=50" ], "reasoning": "The highest score in the table is 18, which is in the 'C b' column.", "statement": "The highest score in the table is in the 'C b' column.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled '(c) fluency, document, N=50', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Inter-rater agreement is lower in three out of four conditions due to the quality of translations and lack of guidelines on error severity.In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency" } ], "images": [ "scigen/train/3467.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(c) fluency, document, N=50" ], "reasoning": "Inter-rater agreement is lower in three out of four conditions due to the quality of translations and lack of guidelines on error severity.", "description": "In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about '(c) fluency, document, N=50', solve the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the total number of assessments for fluency in condition 'b'?\n" }, { "role": "assistant", "content": "To find the total number of assessments for fluency in condition 'b', sum the values in the 'D b' column. So, the answer is {\"answer\": \"22\"}." } ], "images": [ "scigen/train/3467.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(c) fluency, document, N=50" ], "reasoning": "To find the total number of assessments for fluency in condition 'b', sum the values in the 'D b' column.", "question": "What is the total number of assessments for fluency in condition 'b'?", "answer": "So, the answer is {\"answer\": \"22\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe highest number of assessments for adequacy is in condition 'b'.\n" }, { "role": "assistant", "content": "The table shows that the highest number of assessments for adequacy (At) is 8 in condition 'b'. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3467.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(c) fluency, document, N=50" ], "reasoning": "The table shows that the highest number of assessments for adequacy (At) is 8 in condition 'b'.", "statement": "The highest number of assessments for adequacy is in condition 'b'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled '(e) fluency, document, N=50'.\n" }, { "role": "assistant", "content": "Lower inter-rater agreement is observed in three out of four conditions due to the quality of translations and lack of guidelines on error severity.In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency" } ], "images": [ "scigen/train/3468.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(e) fluency, document, N=50" ], "reasoning": "Lower inter-rater agreement is observed in three out of four conditions due to the quality of translations and lack of guidelines on error severity.", "description": "In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nTable title: (e) fluency, document, N=50\n\nQuestion: What is the total score for condition 'b' in column 'D b'?\n" }, { "role": "assistant", "content": "To find the total score for condition 'b' in column 'D b', sum the values in the 'D b' column for row 'b'. The values are 1, 8, and 13. Adding these gives 1 + 8 + 13 = 22. So, the answer is {\"answer\": \"22\"}." } ], "images": [ "scigen/train/3468.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(e) fluency, document, N=50" ], "reasoning": "To find the total score for condition 'b' in column 'D b', sum the values in the 'D b' column for row 'b'. The values are 1, 8, and 13. Adding these gives 1 + 8 + 13 = 22.", "question": "What is the total score for condition 'b' in column 'D b'?", "answer": "So, the answer is {\"answer\": \"22\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe highest score in column 'D t' is 9.\n" }, { "role": "assistant", "content": "To verify this statement, look at the values in column 'D t'. The values are 1, 7, and 9. The highest value among these is 9. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3468.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(e) fluency, document, N=50" ], "reasoning": "To verify this statement, look at the values in column 'D t'. The values are 1, 7, and 9. The highest value among these is 9.", "statement": "The highest score in column 'D t' is 9.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title '(f) fluency, document, N=50'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Inter-rater agreement is lower in three out of four conditions due to the quality of translations and lack of guidelines on error severity.In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency" } ], "images": [ "scigen/train/3469.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(f) fluency, document, N=50" ], "reasoning": "Inter-rater agreement is lower in three out of four conditions due to the quality of translations and lack of guidelines on error severity.", "description": "In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of '(f) fluency, document, N=50', unfold the question presented below, detailing every step of your calculation. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the total score for condition 'Ct' across all document types?\n" }, { "role": "assistant", "content": "To find the total score for condition 'Ct', sum the scores across all document types: 1 (Da) + 7 (Dt) + 6 (Db). So, the answer is {\"answer\": \"14\"}." } ], "images": [ "scigen/train/3469.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(f) fluency, document, N=50" ], "reasoning": "To find the total score for condition 'Ct', sum the scores across all document types: 1 (Da) + 7 (Dt) + 6 (Db).", "question": "What is the total score for condition 'Ct' across all document types?", "answer": "So, the answer is {\"answer\": \"14\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe score for document type 'Db' is highest for condition 'b'.\n" }, { "role": "assistant", "content": "The table shows that the score for document type 'Db' under condition 'b' is 14, which is higher than the scores for 'Db' under other conditions. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3469.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(f) fluency, document, N=50" ], "reasoning": "The table shows that the score for document type 'Db' under condition 'b' is 14, which is higher than the scores for 'Db' under other conditions.", "statement": "The score for document type 'Db' is highest for condition 'b'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled '(g) adequacy, document, N=50'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Inter-rater agreement is generally low due to the quality of translations and lack of guidelines, with more uniformity in fluency assessments.In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency" } ], "images": [ "scigen/train/3470.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(g) adequacy, document, N=50" ], "reasoning": "Inter-rater agreement is generally low due to the quality of translations and lack of guidelines, with more uniformity in fluency assessments.", "description": "In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: (g) adequacy, document, N=50\nQuestion: What is the total score for 'a' across all conditions?\n" }, { "role": "assistant", "content": "To find the total score for 'a', sum the values in the row corresponding to 'a': 4 + 9 + 13. So, the answer is {\"answer\": \"26\"}." } ], "images": [ "scigen/train/3470.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(g) adequacy, document, N=50" ], "reasoning": "To find the total score for 'a', sum the values in the row corresponding to 'a': 4 + 9 + 13.", "question": "What is the total score for 'a' across all conditions?", "answer": "So, the answer is {\"answer\": \"26\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe assignment of ties is more uniform in documents assessed for fluency.\n" }, { "role": "assistant", "content": "The description states that the assignment of ties is more uniform in documents assessed for fluency, which is supported by the table showing consistent scores for 't' in the fluency condition. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3470.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(g) adequacy, document, N=50" ], "reasoning": "The description states that the assignment of ties is more uniform in documents assessed for fluency, which is supported by the table showing consistent scores for 't' in the fluency condition.", "statement": "The assignment of ties is more uniform in documents assessed for fluency.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title '(h) adequacy, document, N=50'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Lower inter-rater agreement is observed in three out of four conditions due to the quality of translations and lack of error severity guidelines.In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency" } ], "images": [ "scigen/train/3471.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(h) adequacy, document, N=50" ], "reasoning": "Lower inter-rater agreement is observed in three out of four conditions due to the quality of translations and lack of error severity guidelines.", "description": "In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of '(h) adequacy, document, N=50' and proceed to solve the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the total score for condition 'a' across all columns?\n" }, { "role": "assistant", "content": "To find the total score for condition 'a', sum the values in the row corresponding to 'a' across all columns: 9 + 4 + 13. So, the answer is {\"answer\": \"26\"}." } ], "images": [ "scigen/train/3471.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(h) adequacy, document, N=50" ], "reasoning": "To find the total score for condition 'a', sum the values in the row corresponding to 'a' across all columns: 9 + 4 + 13.", "question": "What is the total score for condition 'a' across all columns?", "answer": "So, the answer is {\"answer\": \"26\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe assignment of ties is more uniform in documents assessed for fluency.\n" }, { "role": "assistant", "content": "The description states that the assignment of ties is more uniform in documents assessed for fluency, but the table does not provide information about fluency assessments. Thus, the answer is {\"answer\": \"not enough info\"}." } ], "images": [ "scigen/train/3471.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(h) adequacy, document, N=50" ], "reasoning": "The description states that the assignment of ties is more uniform in documents assessed for fluency, but the table does not provide information about fluency assessments.", "statement": "The assignment of ties is more uniform in documents assessed for fluency.", "answer": "Thus, the answer is {\"answer\": \"not enough info\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title '(i) adequacy, document, N=50'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Inter-rater agreement is lower in three out of four conditions due to the quality of translations and lack of guidelines on error severity.In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency" } ], "images": [ "scigen/train/3472.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(i) adequacy, document, N=50" ], "reasoning": "Inter-rater agreement is lower in three out of four conditions due to the quality of translations and lack of guidelines on error severity.", "description": "In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled '(i) adequacy, document, N=50' and methodically tackle the question that follows. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the total number of pairwise rankings for Ht?\n" }, { "role": "assistant", "content": "To find the total number of pairwise rankings for Ht, sum the values in the Ht column: 1 (a) + 0 (Et) + 2 (b). So, the answer is {\"answer\": \"3\"}." } ], "images": [ "scigen/train/3472.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(i) adequacy, document, N=50" ], "reasoning": "To find the total number of pairwise rankings for Ht, sum the values in the Ht column: 1 (a) + 0 (Et) + 2 (b).", "question": "What is the total number of pairwise rankings for Ht?", "answer": "So, the answer is {\"answer\": \"3\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe assignment of ties is more uniform in documents assessed for fluency.\n" }, { "role": "assistant", "content": "The description states that the assignment of ties is more uniform in documents assessed for fluency, but the table does not provide information on fluency assessment. Thus, the answer is {\"answer\": \"not enough info\"}." } ], "images": [ "scigen/train/3472.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(i) adequacy, document, N=50" ], "reasoning": "The description states that the assignment of ties is more uniform in documents assessed for fluency, but the table does not provide information on fluency assessment.", "statement": "The assignment of ties is more uniform in documents assessed for fluency.", "answer": "Thus, the answer is {\"answer\": \"not enough info\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title '(j) adequacy, document, N=50'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Inter-rater agreement is lower in three out of four conditions due to the quality of translations and lack of guidelines on error severity.In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency" } ], "images": [ "scigen/train/3473.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(j) adequacy, document, N=50" ], "reasoning": "Inter-rater agreement is lower in three out of four conditions due to the quality of translations and lack of guidelines on error severity.", "description": "In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}. The question is related to the given table titled '(j) adequacy, document, N=50'.\n\nQuestion: What is the total score for condition 'b' in the table?\n" }, { "role": "assistant", "content": "To find the total score for condition 'b', sum the values in the row corresponding to 'b': 4 + 2 + 19. So, the answer is {\"answer\": \"25\"}." } ], "images": [ "scigen/train/3473.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(j) adequacy, document, N=50" ], "reasoning": "To find the total score for condition 'b', sum the values in the row corresponding to 'b': 4 + 2 + 19.", "question": "What is the total score for condition 'b' in the table?", "answer": "So, the answer is {\"answer\": \"25\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The inter-rater agreement for condition 'a' is higher than for condition 't'. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe inter-rater agreement for condition 'a' is higher than for condition 't'.\n" }, { "role": "assistant", "content": "The table shows that the scores for condition 'a' are 7, 1, and 2, while the scores for condition 't' are 7, 1, and 7. Since the scores for condition 't' are higher overall, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3473.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(j) adequacy, document, N=50" ], "reasoning": "The table shows that the scores for condition 'a' are 7, 1, and 2, while the scores for condition 't' are 7, 1, and 7. Since the scores for condition 't' are higher overall, the statement is refuted.", "statement": "The inter-rater agreement for condition 'a' is higher than for condition 't'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title '(k) adequacy, document, N=50'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Inter-rater agreement is lower in three out of four conditions due to the quality of translations and lack of guidelines on error severity.In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency" } ], "images": [ "scigen/train/3474.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(k) adequacy, document, N=50" ], "reasoning": "Inter-rater agreement is lower in three out of four conditions due to the quality of translations and lack of guidelines on error severity.", "description": "In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the total number of assessments for fluency (Ft) in all conditions?\nSolve the above question based on the table titled '(k) adequacy, document, N=50'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the total number of assessments for fluency (Ft), sum the values in the row labeled 'Ft': 8 (H a) + 0 (H t) + 7 (H b). So, the answer is {\"answer\": \"15\"}." } ], "images": [ "scigen/train/3474.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(k) adequacy, document, N=50" ], "reasoning": "To find the total number of assessments for fluency (Ft), sum the values in the row labeled 'Ft': 8 (H a) + 0 (H t) + 7 (H b).", "question": "What is the total number of assessments for fluency (Ft) in all conditions?", "answer": "So, the answer is {\"answer\": \"15\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe highest number of assessments for fluency (Ft) is in condition H a.\n" }, { "role": "assistant", "content": "Check the values in the row labeled 'Ft' and compare them: 8 (H a), 0 (H t), and 7 (H b). The highest value is 8 in condition H a. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3474.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(k) adequacy, document, N=50" ], "reasoning": "Check the values in the row labeled 'Ft' and compare them: 8 (H a), 0 (H t), and 7 (H b). The highest value is 8 in condition H a.", "statement": "The highest number of assessments for fluency (Ft) is in condition H a.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title '(l) adequacy, document, N=50'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Inter-rater agreement is lower in three out of four conditions due to the quality of translations and lack of guidelines on error severity.In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency" } ], "images": [ "scigen/train/3475.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(l) adequacy, document, N=50" ], "reasoning": "Inter-rater agreement is lower in three out of four conditions due to the quality of translations and lack of guidelines on error severity.", "description": "In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: (l) adequacy, document, N=50\nQuestion: What is the total number of pairwise rankings for condition 'H b'?\nSolve the above question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the total number of pairwise rankings for condition 'H b', sum the values in the 'H b' column: 5 (a, b) + 2 (Gt, b) + 20 (b, b). So, the answer is {\"answer\": \"27\"}." } ], "images": [ "scigen/train/3475.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(l) adequacy, document, N=50" ], "reasoning": "To find the total number of pairwise rankings for condition 'H b', sum the values in the 'H b' column: 5 (a, b) + 2 (Gt, b) + 20 (b, b).", "question": "What is the total number of pairwise rankings for condition 'H b'?", "answer": "So, the answer is {\"answer\": \"27\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe inter-rater agreement for condition 'H a' is higher than for condition 'H t'.\n" }, { "role": "assistant", "content": "To compare inter-rater agreement, look at the values in the 'H a' and 'H t' columns. The total for 'H a' is 11 (a, a) + 1 (Gt, a) + 8 (b, a) = 20. The total for 'H t' is 2 (a, t) + 1 (Gt, t) + 0 (b, t) = 3. Since 20 is higher than 3, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3475.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(l) adequacy, document, N=50" ], "reasoning": "To compare inter-rater agreement, look at the values in the 'H a' and 'H t' columns. The total for 'H a' is 11 (a, a) + 1 (Gt, a) + 8 (b, a) = 20. The total for 'H t' is 2 (a, t) + 1 (Gt, t) + 0 (b, t) = 3. Since 20 is higher than 3, the statement is supported.", "statement": "The inter-rater agreement for condition 'H a' is higher than for condition 'H t'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title '(m) fluency, sentence, N=104'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Inter-rater agreement is lower in three out of four conditions due to the quality of translations and lack of guidelines on error severity.In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency" } ], "images": [ "scigen/train/3476.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(m) fluency, sentence, N=104" ], "reasoning": "Inter-rater agreement is lower in three out of four conditions due to the quality of translations and lack of guidelines on error severity.", "description": "In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the total number of pairwise rankings for condition 'a' in B1?\nSolve the above question based on the table titled '(m) fluency, sentence, N=104'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the total number of pairwise rankings for condition 'a' in B1, sum the values in the 'a' row: 16 + 1 + 13. So, the answer is {\"answer\": \"30\"}" } ], "images": [ "scigen/train/3476.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(m) fluency, sentence, N=104" ], "reasoning": "To find the total number of pairwise rankings for condition 'a' in B1, sum the values in the 'a' row: 16 + 1 + 13.", "question": "What is the total number of pairwise rankings for condition 'a' in B1?", "answer": "So, the answer is {\"answer\": \"30\"}" }, { "messages": [ { "role": "user", "content": "The assignment of ties is more uniform in documents assessed for fluency. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe assignment of ties is more uniform in documents assessed for fluency.\n" }, { "role": "assistant", "content": "The description states that the assignment of ties is more uniform in documents assessed for fluency, but the table does not provide information about fluency assessment. So, the answer is {\"answer\": \"not enough info\"}" } ], "images": [ "scigen/train/3476.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(m) fluency, sentence, N=104" ], "reasoning": "The description states that the assignment of ties is more uniform in documents assessed for fluency, but the table does not provide information about fluency assessment.", "statement": "The assignment of ties is more uniform in documents assessed for fluency.", "answer": "So, the answer is {\"answer\": \"not enough info\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named '(n) adequacy, sentence, N=104'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Inter-rater agreement is lower in three out of four conditions due to the quality of translations and lack of guidelines on error severity.In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency" } ], "images": [ "scigen/train/3477.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(n) adequacy, sentence, N=104" ], "reasoning": "Inter-rater agreement is lower in three out of four conditions due to the quality of translations and lack of guidelines on error severity.", "description": "In pairwise rankings of machine translation outputs, κ coefficients typically centre around 0.3 We observe lower inter-rater agreement in three out of four conditions, and attribute this to two reasons. Firstly, the quality of the machine translations produced by Hassan et al. Secondly, we do not provide guidelines detailing error severity and thus assume that raters have differing interpretations of what constitutes a “better” or “worse” translation. The assignment of ties is more uniform in documents assessed for fluency", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: '(n) adequacy, sentence, N=104'\nQuestion: What is the total number of pairwise rankings for condition 'a' across all fluency assessments?\n" }, { "role": "assistant", "content": "To find the total number of pairwise rankings for condition 'a', sum the values in the row corresponding to 'a' across all columns: 31 + 6 + 22. So, the answer is {\"answer\": \"59\"}." } ], "images": [ "scigen/train/3477.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(n) adequacy, sentence, N=104" ], "reasoning": "To find the total number of pairwise rankings for condition 'a', sum the values in the row corresponding to 'a' across all columns: 31 + 6 + 22.", "question": "What is the total number of pairwise rankings for condition 'a' across all fluency assessments?", "answer": "So, the answer is {\"answer\": \"59\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe assignment of ties is more uniform in documents assessed for fluency.\n" }, { "role": "assistant", "content": "The table does not provide information about the uniformity of tie assignments in documents assessed for fluency. Thus, the answer is {\"answer\": \"not enough info\"}." } ], "images": [ "scigen/train/3477.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(n) adequacy, sentence, N=104" ], "reasoning": "The table does not provide information about the uniformity of tie assignments in documents assessed for fluency.", "statement": "The assignment of ties is more uniform in documents assessed for fluency.", "answer": "Thus, the answer is {\"answer\": \"not enough info\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Results of NMT models of different capacities.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The Transformer model variants Small, Base, and Big have increasing layer dimensionality, but the extracted phrase table size only marginally increases.We vary the layer dimensionality of Transformer, and obtain three model variants: Small (256), Base (512), and Big (1024). However, the extracted phrase table is only marginally increased." } ], "images": [ "scigen/train/3478.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of NMT models of different capacities." ], "reasoning": "The Transformer model variants Small, Base, and Big have increasing layer dimensionality, but the extracted phrase table size only marginally increases.", "description": "We vary the layer dimensionality of Transformer, and obtain three model variants: Small (256), Base (512), and Big (1024). However, the extracted phrase table is only marginally increased.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 2: Results of NMT models of different capacities.', solve the following question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the NMT BLEU score for the model variant with the largest phrase table size?\n" }, { "role": "assistant", "content": "The model variant with the largest phrase table size is Small with 7.7M. The NMT BLEU score for Small is 25.45. So, the answer is {\"answer\": \"25.45\"}." } ], "images": [ "scigen/train/3478.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of NMT models of different capacities." ], "reasoning": "The model variant with the largest phrase table size is Small with 7.7M. The NMT BLEU score for Small is 25.45.", "question": "What is the NMT BLEU score for the model variant with the largest phrase table size?", "answer": "So, the answer is {\"answer\": \"25.45\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Big model variant has the highest NMT BLEU score.\n" }, { "role": "assistant", "content": "The table shows that the Big model variant has an NMT BLEU score of 28.40, which is higher than the scores for the Small and Base variants. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3478.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of NMT models of different capacities." ], "reasoning": "The table shows that the Big model variant has an NMT BLEU score of 28.40, which is higher than the scores for the Small and Base variants.", "statement": "The Big model variant has the highest NMT BLEU score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: Comparison of the phrase table extracted from the full training data (“Full”) and NMT models (“NMT”). “All” denotes the whole phrase table, “Shared” denotes the intersection of two tables, and “Non-shared” denotes the complement. Note that the probabilities of “Shared” phrases are different for the two tables.'.\n" }, { "role": "assistant", "content": "NMT models provide better probability estimation for distilled phrases, as evidenced by higher BLEU scores in the Shared table compared to the Full table, confirming that NMT discards low-quality phrase pairs.(17.90 vs. 17.91), but with only a half of phrases (9.0M vs. 17.5M). In addition, NMT provides a better probability estimation for the distilled phrases (“Shared”, 17.90 vs. 17.32). In the “Non-Shared” table, 78.2% of the phrase pairs share the same source phrase with the “Shared” table, of which 83.2% have a lower translation probability. The results empirically confirm our hypothesis that NMT models distill the bilingual knowledge by discarding those low-quality phrase pairs." } ], "images": [ "scigen/train/3479.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of the phrase table extracted from the full training data (“Full”) and NMT models (“NMT”). “All” denotes the whole phrase table, “Shared” denotes the intersection of two tables, and “Non-shared” denotes the complement. Note that the probabilities of “Shared” phrases are different for the two tables." ], "reasoning": "NMT models provide better probability estimation for distilled phrases, as evidenced by higher BLEU scores in the Shared table compared to the Full table, confirming that NMT discards low-quality phrase pairs.", "description": "(17.90 vs. 17.91), but with only a half of phrases (9.0M vs. 17.5M). In addition, NMT provides a better probability estimation for the distilled phrases (“Shared”, 17.90 vs. 17.32). In the “Non-Shared” table, 78.2% of the phrase pairs share the same source phrase with the “Shared” table, of which 83.2% have a lower translation probability. The results empirically confirm our hypothesis that NMT models distill the bilingual knowledge by discarding those low-quality phrase pairs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 1: Comparison of the phrase table extracted from the full training data (“Full”) and NMT models (“NMT”). “All” denotes the whole phrase table, “Shared” denotes the intersection of two tables, and “Non-shared” denotes the complement. Note that the probabilities of “Shared” phrases are different for the two tables.', solve the following question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the BLEU score difference between the Shared and Non-Shared tables for the Full phrase table?\n" }, { "role": "assistant", "content": "The BLEU score for the Shared table in the Full phrase table is 17.32, and for the Non-Shared table, it is 4.50. The difference is calculated as 17.32 - 4.50. So, the answer is {\"answer\": \"12.82\"}" } ], "images": [ "scigen/train/3479.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of the phrase table extracted from the full training data (“Full”) and NMT models (“NMT”). “All” denotes the whole phrase table, “Shared” denotes the intersection of two tables, and “Non-shared” denotes the complement. Note that the probabilities of “Shared” phrases are different for the two tables." ], "reasoning": "The BLEU score for the Shared table in the Full phrase table is 17.32, and for the Non-Shared table, it is 4.50. The difference is calculated as 17.32 - 4.50.", "question": "What is the BLEU score difference between the Shared and Non-Shared tables for the Full phrase table?", "answer": "So, the answer is {\"answer\": \"12.82\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The NMT model has a higher BLEU score for the Shared table compared to the Full phrase table. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe NMT model has a higher BLEU score for the Shared table compared to the Full phrase table.\n" }, { "role": "assistant", "content": "The BLEU score for the Shared table in the NMT model is 17.90, while for the Full phrase table, it is 17.32. Since 17.90 is higher than 17.32, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3479.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of the phrase table extracted from the full training data (“Full”) and NMT models (“NMT”). “All” denotes the whole phrase table, “Shared” denotes the intersection of two tables, and “Non-shared” denotes the complement. Note that the probabilities of “Shared” phrases are different for the two tables." ], "reasoning": "The BLEU score for the Shared table in the NMT model is 17.90, while for the Full phrase table, it is 17.32. Since 17.90 is higher than 17.32, the statement is supported.", "statement": "The NMT model has a higher BLEU score for the Shared table compared to the Full phrase table.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Comparison of phrases from three capacities.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Enlarging model capacity improves NMT performance by better exploiting complex patterns beyond bilingual lexicons, with shared phrases adding most value.The phrases shared by all models take the overwhelming majority, which add most value to the translation performance. We conjecture that enlarging capacity improves NMT performance by better exploiting complex patterns beyond bilingual lexicons. This also confirms our intuition that bilingual lexicons can be a crucial early step in assessing the knowledge in NMT models." } ], "images": [ "scigen/train/3480.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of phrases from three capacities." ], "reasoning": "Enlarging model capacity improves NMT performance by better exploiting complex patterns beyond bilingual lexicons, with shared phrases adding most value.", "description": "The phrases shared by all models take the overwhelming majority, which add most value to the translation performance. We conjecture that enlarging capacity improves NMT performance by better exploiting complex patterns beyond bilingual lexicons. This also confirms our intuition that bilingual lexicons can be a crucial early step in assessing the knowledge in NMT models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 3: Comparison of phrases from three capacities.'.\n\nQuestion: Which model has the highest Non-Shared BLEU score and what does this imply about its performance?\n" }, { "role": "assistant", "content": "The table shows that the Base model has the highest Non-Shared BLEU score of 3.57, implying it performs better in translating non-shared phrases compared to the Small and Big models. So, the answer is {\"answer\": \"Base\"}." } ], "images": [ "scigen/train/3480.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of phrases from three capacities." ], "reasoning": "The table shows that the Base model has the highest Non-Shared BLEU score of 3.57, implying it performs better in translating non-shared phrases compared to the Small and Big models.", "question": "Which model has the highest Non-Shared BLEU score and what does this imply about its performance?", "answer": "So, the answer is {\"answer\": \"Base\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Big model has the highest Shared BLEU score.\n" }, { "role": "assistant", "content": "The table shows that the Small model has the highest Shared BLEU score of 17.53, not the Big model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3480.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of phrases from three capacities." ], "reasoning": "The table shows that the Small model has the highest Shared BLEU score of 17.53, not the Big model.", "statement": "The Big model has the highest Shared BLEU score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: Tokens with the -fund- root in the Isolezwe articles of August 26 and 27, 2015 (part of the NIC).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The lexical diversity is high due to the simplicity of the measure, which counts different forms of the same root as separate words, leading to a higher type-to-token ratio.The lexical diversity is very high for the OOspell file, which is largely due to having combined different word lists, rather than texts, and as such should be disregarded for comparison. Overall, the lexical diversity is high, compared to, say, English. An important reason for this large difference is due to the simplicity of the measure. For instance, amaphoyisa (‘police’, plural) and namaphoyisa (‘and the police’, plural) in the NIC are counted as two different words, but semantically refer to one concept; amaphoyisa occurs only 27 times, yet its root -phoyis- occurs 47 times in just the first news item set (2657 tokens) of the NIC. There are many more such cases in the corpora, such as -mali ‘money’, also in the NIC: imali, kwemali, yimali, onemali, osozimali, kwezimali, ngezimali, which are, respectively of -, and -, that/which/ who has -, of - (pl.), about/by/with/per - (pl.) money. In the simple type-to-token measure, they are all counted as different types. This does not explain the substantial difference in lexical diversity between the UDHR and SAC, however, which are of the same genre. The UDHR has a high lexical diversity due to it being a small document. For instance, the subset of the NIC with Isoleszwe news articles of only August 7, 2015, has a similar lexical diversity of 0.55 on its 2667 tokens. So, this is not unusual. abafundi ‘students’ is the base noun, and bafundi, nabafundi, kubafundi, kwabafundi, wabafundi, and yabafundi are the modified nouns, thus standing in a ratio of 1:6 as types and 10:9 as tokens, and in the singular as umfundi with kunomfundi and mfundi as 1:2 and 3:1, respectively. This could be done likewise for all words, and taking the median over it to obtain λθ and λt, respectively. If there were only these two, then λθ would be 0.50 and λt 3. Filling this into the equation results in a calibrated lexical diversity of TTcal=0.50∗21050.67∗3774=0.42 (5) compared to the original 21053774=0.56." } ], "images": [ "scigen/train/3481.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Tokens with the -fund- root in the Isolezwe articles of August 26 and 27, 2015 (part of the NIC)." ], "reasoning": "The lexical diversity is high due to the simplicity of the measure, which counts different forms of the same root as separate words, leading to a higher type-to-token ratio.", "description": "The lexical diversity is very high for the OOspell file, which is largely due to having combined different word lists, rather than texts, and as such should be disregarded for comparison. Overall, the lexical diversity is high, compared to, say, English. An important reason for this large difference is due to the simplicity of the measure. For instance, amaphoyisa (‘police’, plural) and namaphoyisa (‘and the police’, plural) in the NIC are counted as two different words, but semantically refer to one concept; amaphoyisa occurs only 27 times, yet its root -phoyis- occurs 47 times in just the first news item set (2657 tokens) of the NIC. There are many more such cases in the corpora, such as -mali ‘money’, also in the NIC: imali, kwemali, yimali, onemali, osozimali, kwezimali, ngezimali, which are, respectively of -, and -, that/which/ who has -, of - (pl.), about/by/with/per - (pl.) money. In the simple type-to-token measure, they are all counted as different types. This does not explain the substantial difference in lexical diversity between the UDHR and SAC, however, which are of the same genre. The UDHR has a high lexical diversity due to it being a small document. For instance, the subset of the NIC with Isoleszwe news articles of only August 7, 2015, has a similar lexical diversity of 0.55 on its 2667 tokens. So, this is not unusual. abafundi ‘students’ is the base noun, and bafundi, nabafundi, kubafundi, kwabafundi, wabafundi, and yabafundi are the modified nouns, thus standing in a ratio of 1:6 as types and 10:9 as tokens, and in the singular as umfundi with kunomfundi and mfundi as 1:2 and 3:1, respectively. This could be done likewise for all words, and taking the median over it to obtain λθ and λt, respectively. If there were only these two, then λθ would be 0.50 and λt 3. Filling this into the equation results in a calibrated lexical diversity of TTcal=0.50∗21050.67∗3774=0.42 (5) compared to the original 21053774=0.56.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Tokens with the -fund- root in the Isolezwe articles of August 26 and 27, 2015 (part of the NIC).\nQuestion: What is the total count of tokens related to 'students' in the table?\nSolve the above question based on the table information. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the total count of tokens related to 'students', sum the counts of all tokens that translate to 'students' or its variations: abafundi (20), bafundi (6), umfundi (6), nabafundi (5), kubafundi (3), kwabafundi (2), abafundela (1), abafundisa (1), abazifundisayo (1), bayazifundisa (1), efunda (1), ezifundela (1), kunomfundi (1), mfundi (1), wabafundi (1), yabafundi (1). So, the answer is {\"answer\": \"52\"}." } ], "images": [ "scigen/train/3481.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Tokens with the -fund- root in the Isolezwe articles of August 26 and 27, 2015 (part of the NIC)." ], "reasoning": "To find the total count of tokens related to 'students', sum the counts of all tokens that translate to 'students' or its variations: abafundi (20), bafundi (6), umfundi (6), nabafundi (5), kubafundi (3), kwabafundi (2), abafundela (1), abafundisa (1), abazifundisayo (1), bayazifundisa (1), efunda (1), ezifundela (1), kunomfundi (1), mfundi (1), wabafundi (1), yabafundi (1).", "question": "What is the total count of tokens related to 'students' in the table?", "answer": "So, the answer is {\"answer\": \"52\"}." }, { "messages": [ { "role": "user", "content": "The token 'abafundi' has the highest count in the table. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe token 'abafundi' has the highest count in the table.\n" }, { "role": "assistant", "content": "The table shows that 'abafundi' has a count of 20, which is the highest count among all tokens listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3481.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Tokens with the -fund- root in the Isolezwe articles of August 26 and 27, 2015 (part of the NIC)." ], "reasoning": "The table shows that 'abafundi' has a count of 20, which is the highest count among all tokens listed.", "statement": "The token 'abafundi' has the highest count in the table.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Other orthographic peculiarities: percentage of tokens that have a vowel as final character, incidence of consecutive vowels, and the number of r’s in the document.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Statistical tests show that vowel-ending distributions for isiZulu, isiXhosa, and isiNdebele do not differ significantly, while Sepedi, Sesotho, and Setswana also do not differ significantly.Three other typical features we know that generally hold for isiZulu orthography As visually it looks like there are ‘clusters’ of languages regarding the final vowel , we subject the data to statistical tests (χ2). First, H0 and Ha are as follows, for the two categorical variables under consideration: Vowel-ending distribution for set of languages does not differ significantly. Vowel-ending distribution for a set of languages do differ significantly. It has a χ2=40, so with the degrees of freedom (df) of 16, we obtain p <0.001, i.e., they are statistically significantly different. Visually, especially Shona looks like an outlier and Runyankore somewhat similar, but these remaining four still results in significance with an α of 0.05 (χ2=23.41, df = 12, p = 0.024). IsiZulu, isiXhosa, and isiNdebele do not differ significantly (χ2=4.6, df = 8, p = 0.7993). (χ2=16.9, df = 8, p = 0.0308), whereas Sepedi, Sesotho, and Setswana are not statistically significantly different from each other (χ2=2.49, df = 8, p = 0.9622)." } ], "images": [ "scigen/train/3482.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Other orthographic peculiarities: percentage of tokens that have a vowel as final character, incidence of consecutive vowels, and the number of r’s in the document." ], "reasoning": "Statistical tests show that vowel-ending distributions for isiZulu, isiXhosa, and isiNdebele do not differ significantly, while Sepedi, Sesotho, and Setswana also do not differ significantly.", "description": "Three other typical features we know that generally hold for isiZulu orthography As visually it looks like there are ‘clusters’ of languages regarding the final vowel , we subject the data to statistical tests (χ2). First, H0 and Ha are as follows, for the two categorical variables under consideration: Vowel-ending distribution for set of languages does not differ significantly. Vowel-ending distribution for a set of languages do differ significantly. It has a χ2=40, so with the degrees of freedom (df) of 16, we obtain p <0.001, i.e., they are statistically significantly different. Visually, especially Shona looks like an outlier and Runyankore somewhat similar, but these remaining four still results in significance with an α of 0.05 (χ2=23.41, df = 12, p = 0.024). IsiZulu, isiXhosa, and isiNdebele do not differ significantly (χ2=4.6, df = 8, p = 0.7993). (χ2=16.9, df = 8, p = 0.0308), whereas Sepedi, Sesotho, and Setswana are not statistically significantly different from each other (χ2=2.49, df = 8, p = 0.9622).", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 2: Other orthographic peculiarities: percentage of tokens that have a vowel as final character, incidence of consecutive vowels, and the number of r’s in the document.' and proceed to solve the following question in a stepwise manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich language has the highest percentage of final vowels (% FV) and how does it compare to the language with the lowest percentage?\n" }, { "role": "assistant", "content": "Zulu has the highest percentage of final vowels at 99.90%, while English has the lowest at 28.13%. So, the answer is {\"answer\": \"Zulu has the highest % FV at 99.90% and English has the lowest % FV at 28.13%\"}." } ], "images": [ "scigen/train/3482.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Other orthographic peculiarities: percentage of tokens that have a vowel as final character, incidence of consecutive vowels, and the number of r’s in the document." ], "reasoning": "Zulu has the highest percentage of final vowels at 99.90%, while English has the lowest at 28.13%.", "question": "Which language has the highest percentage of final vowels (% FV) and how does it compare to the language with the lowest percentage?", "answer": "So, the answer is {\"answer\": \"Zulu has the highest % FV at 99.90% and English has the lowest % FV at 28.13%\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nZulu, Xhosa, and Ndebele have similar vowel-ending distributions.\n" }, { "role": "assistant", "content": "The description states that isiZulu, isiXhosa, and isiNdebele do not differ significantly in their vowel-ending distributions (χ2=4.6, df=8, p=0.7993). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3482.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Other orthographic peculiarities: percentage of tokens that have a vowel as final character, incidence of consecutive vowels, and the number of r’s in the document." ], "reasoning": "The description states that isiZulu, isiXhosa, and isiNdebele do not differ significantly in their vowel-ending distributions (χ2=4.6, df=8, p=0.7993).", "statement": "Zulu, Xhosa, and Ndebele have similar vowel-ending distributions.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Final vowel characteristics with examples; % c. = percent consonant-ending tokens.'.\n" }, { "role": "assistant", "content": "The NIC and SAC corpora have the highest percentage of consonant-ending tokens, while OOspell has the lowest, indicating that NIC contains many named entities and SAC has multiple foreign words.The final vowel issue deserved closer inspection, for isiZulu words typically end with a vowel. The single consonant-ending word in the isiZulu UDHR is a loanword, kuCharter. Besides the general distribution across vowels, such as 7 times as many ending with an -a than an -u, it is worth noting the relative outliers with more a-endings in OOspell, o-endings in the UDHR, and near-absence of u-ending tokens in the UC. The curated and quality texts of OOspell and UDHR have an extremely low percentage of consonant-ending tokens, such that one relatively safely could design grammars as intended and obtain good performance. The UC and SAC less so, but for different reasons: the SAC has multiple foreign words, whereas the UC has many errors where the words should have a vowel, as noted above with sowehlul. Finally, there are relatively many consonant-ending tokens in the NIC, because there are many (valid) named entities." } ], "images": [ "scigen/train/3483.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Final vowel characteristics with examples; % c. = percent consonant-ending tokens." ], "reasoning": "The NIC and SAC corpora have the highest percentage of consonant-ending tokens, while OOspell has the lowest, indicating that NIC contains many named entities and SAC has multiple foreign words.", "description": "The final vowel issue deserved closer inspection, for isiZulu words typically end with a vowel. The single consonant-ending word in the isiZulu UDHR is a loanword, kuCharter. Besides the general distribution across vowels, such as 7 times as many ending with an -a than an -u, it is worth noting the relative outliers with more a-endings in OOspell, o-endings in the UDHR, and near-absence of u-ending tokens in the UC. The curated and quality texts of OOspell and UDHR have an extremely low percentage of consonant-ending tokens, such that one relatively safely could design grammars as intended and obtain good performance. The UC and SAC less so, but for different reasons: the SAC has multiple foreign words, whereas the UC has many errors where the words should have a vowel, as noted above with sowehlul. Finally, there are relatively many consonant-ending tokens in the NIC, because there are many (valid) named entities.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 4: Final vowel characteristics with examples; % c. = percent consonant-ending tokens.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: Which corpus has the highest percentage of consonant-ending tokens and why?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows that SAC has the highest percentage of consonant-ending tokens at 9.57%. The description explains that this is due to the presence of multiple foreign words in the SAC corpus. So, the answer is {\"answer\": \"SAC\"}." } ], "images": [ "scigen/train/3483.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Final vowel characteristics with examples; % c. = percent consonant-ending tokens." ], "reasoning": "The table shows that SAC has the highest percentage of consonant-ending tokens at 9.57%. The description explains that this is due to the presence of multiple foreign words in the SAC corpus.", "question": "Which corpus has the highest percentage of consonant-ending tokens and why?", "answer": "So, the answer is {\"answer\": \"SAC\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The UC corpus has the lowest percentage of consonant-ending tokens., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe UC corpus has the lowest percentage of consonant-ending tokens.\n" }, { "role": "assistant", "content": "The table shows that OOspell has the lowest percentage of consonant-ending tokens at 0.16%, which is lower than UC's 1.75%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3483.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Final vowel characteristics with examples; % c. = percent consonant-ending tokens." ], "reasoning": "The table shows that OOspell has the lowest percentage of consonant-ending tokens at 0.16%, which is lower than UC's 1.75%.", "statement": "The UC corpus has the lowest percentage of consonant-ending tokens.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Tabelle 2: SacreBLEU for English-Russian models trained with data back-translated using a single model vs. an ensemble of two models'.\n" }, { "role": "assistant", "content": "The study found that a 1:1 ratio of synthetic to bitext data performed best for back-translation, and models trained on this data showed improved performance.In this work we used back-translations obtained by sampling Edunov et al. We found that models trained on data Previous work also found that upsampling the bitext data can improve back-translation We adopt this method to tune the amount of bitext and synthetic data the model is trained on. We find a ratio of 1:1 synthetic to bitext data to perform the best." } ], "images": [ "scigen/train/3484.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Tabelle 2: SacreBLEU for English-Russian models trained with data back-translated using a single model vs. an ensemble of two models" ], "reasoning": "The study found that a 1:1 ratio of synthetic to bitext data performed best for back-translation, and models trained on this data showed improved performance.", "description": "In this work we used back-translations obtained by sampling Edunov et al. We found that models trained on data Previous work also found that upsampling the bitext data can improve back-translation We adopt this method to tune the amount of bitext and synthetic data the model is trained on. We find a ratio of 1:1 synthetic to bitext data to perform the best.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. The question is related to the given table titled 'Tabelle 2: SacreBLEU for English-Russian models trained with data back-translated using a single model vs. an ensemble of two models'.\n\nQuestion: What was the performance difference between the En→Ru Single Model and En→Ru Ensemble for newstest17?\n" }, { "role": "assistant", "content": "To find the performance difference, subtract the score of the En→Ru Single Model from the score of the En→Ru Ensemble for newstest17. The scores are 36.77 and 36.57 respectively, so the difference is 36.77 - 36.57 = 0.20. So, the answer is {\"answer\": \"0.20\"}." } ], "images": [ "scigen/train/3484.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Tabelle 2: SacreBLEU for English-Russian models trained with data back-translated using a single model vs. an ensemble of two models" ], "reasoning": "To find the performance difference, subtract the score of the En→Ru Single Model from the score of the En→Ru Ensemble for newstest17. The scores are 36.77 and 36.57 respectively, so the difference is 36.77 - 36.57 = 0.20.", "question": "What was the performance difference between the En→Ru Single Model and En→Ru Ensemble for newstest17?", "answer": "So, the answer is {\"answer\": \"0.20\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The En→Ru Ensemble model performed better than the En→Ru Single Model for all test sets., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe En→Ru Ensemble model performed better than the En→Ru Single Model for all test sets.\n" }, { "role": "assistant", "content": "By comparing the scores for each test set (newstest15, newstest16, newstest17, and newstest18), we see that the En→Ru Ensemble model has higher or equal scores compared to the En→Ru Single Model for all test sets. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3484.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Tabelle 2: SacreBLEU for English-Russian models trained with data back-translated using a single model vs. an ensemble of two models" ], "reasoning": "By comparing the scores for each test set (newstest15, newstest16, newstest17, and newstest18), we see that the En→Ru Ensemble model has higher or equal scores compared to the En→Ru Single Model for all test sets.", "statement": "The En→Ru Ensemble model performed better than the En→Ru Single Model for all test sets.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Tabelle 4: Perplexity scores for language models on bolded target languages in all translation directions'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Our forward models are ensembles of fine-tuned and back-translated models, and we use the reverse direction model as the channel model. The Russian language model performs worse than the German and English models due to less monolingual data.For all translation directions, our forward models are ensembles of fine-tuned and back-translated models. Since we compete in both directions for both language pairs, for any given translation direction we can use the forward model for the reverse direction as the channel model. Our language models for each of the target languages English, German, and Russian, are big Transformer decoder models with FFN 8192. We train the language models on the monolingual Newscrawl dataset, and use document level context for the English and German models. With a smaller amount of monolingual Russian data available, we observe that our Russian language model performs worse than the German and English language models." } ], "images": [ "scigen/train/3485.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Tabelle 4: Perplexity scores for language models on bolded target languages in all translation directions" ], "reasoning": "Our forward models are ensembles of fine-tuned and back-translated models, and we use the reverse direction model as the channel model. The Russian language model performs worse than the German and English models due to less monolingual data.", "description": "For all translation directions, our forward models are ensembles of fine-tuned and back-translated models. Since we compete in both directions for both language pairs, for any given translation direction we can use the forward model for the reverse direction as the channel model. Our language models for each of the target languages English, German, and Russian, are big Transformer decoder models with FFN 8192. We train the language models on the monolingual Newscrawl dataset, and use document level context for the English and German models. With a smaller amount of monolingual Russian data available, we observe that our Russian language model performs worse than the German and English language models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Tabelle 4: Perplexity scores for language models on bolded target languages in all translation directions\nQuestion: Which newstest has the highest score for the En-Ru translation direction?\nSolve the above question based on the table information. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest score for the En-Ru translation direction, look at the En-Ru column and identify the highest value. The highest score is 42.7 in newstest13. So, the answer is {\"answer\": \"newstest13\"}." } ], "images": [ "scigen/train/3485.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Tabelle 4: Perplexity scores for language models on bolded target languages in all translation directions" ], "reasoning": "To find the highest score for the En-Ru translation direction, look at the En-Ru column and identify the highest value. The highest score is 42.7 in newstest13.", "question": "Which newstest has the highest score for the En-Ru translation direction?", "answer": "So, the answer is {\"answer\": \"newstest13\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe En-De score for newstest14 is higher than the De-En score for newstest16.\n" }, { "role": "assistant", "content": "The En-De score for newstest14 is 21.4, and the De-En score for newstest16 is 22.0. Since 21.4 is not higher than 22.0, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3485.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Tabelle 4: Perplexity scores for language models on bolded target languages in all translation directions" ], "reasoning": "The En-De score for newstest14 is 21.4, and the De-En score for newstest16 is 22.0. Since 21.4 is not higher than 22.0, the statement is refuted.", "statement": "The En-De score for newstest14 is higher than the De-En score for newstest16.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Model ablations on the full Wikipedia development set. For row labeled **, explanation provided in Section §4.3.'.\n" }, { "role": "assistant", "content": "The 3-Level Cascade, Multi-loss model achieves the best performance with an EM score of 52.18, while the 3-Level Cascade, Combined Level 1 model, although high, is less consistent with an average score of 49.30.Our final approach (3-Level Cascade, Multi-loss) achieves the best performance. Training with only a single loss in level 3 (3-Level Cascade, Single Loss) leads to a considerable decrease in performance, signifying the effect of using a multi-loss architecture. It is unclear if combining the two submodels in level 1 into a single feed-forward network that is a function of the question, span and short context (3-Level Cascade, Combined Level 1) is significantly better than our final model. Although we found it could obtain high results, it was less consistent across different runs and gave lower scores on average (49.30) compared to our approach averaged over 4 runs (51.03). Finally, the last three rows show the results of using only smaller components of our model instead of the entire architecture. In particular, our model without the aggregation submodel (Level 1 + Level 2 Only) performs considerably worse, showing the value of aggregating multiple mentions of the same span across the document. As expected, the level 1 only models are the weakest, showing that attending to the document is a powerful method for reading comprehension. Figure 3 (left) shows the behavior of the k-best predictions of these smaller components. While the difference between the level 1 models becomes more enhanced when considering the top-k candidates, the difference between the model without the aggregation submodel (Level 1 + Level 2 Only) and our full model is no longer significant, indicating that the former might not be able to distinguish between the best answer candidates as well as the latter." } ], "images": [ "scigen/train/3486.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Model ablations on the full Wikipedia development set. For row labeled **, explanation provided in Section §4.3." ], "reasoning": "The 3-Level Cascade, Multi-loss model achieves the best performance with an EM score of 52.18, while the 3-Level Cascade, Combined Level 1 model, although high, is less consistent with an average score of 49.30.", "description": "Our final approach (3-Level Cascade, Multi-loss) achieves the best performance. Training with only a single loss in level 3 (3-Level Cascade, Single Loss) leads to a considerable decrease in performance, signifying the effect of using a multi-loss architecture. It is unclear if combining the two submodels in level 1 into a single feed-forward network that is a function of the question, span and short context (3-Level Cascade, Combined Level 1) is significantly better than our final model. Although we found it could obtain high results, it was less consistent across different runs and gave lower scores on average (49.30) compared to our approach averaged over 4 runs (51.03). Finally, the last three rows show the results of using only smaller components of our model instead of the entire architecture. In particular, our model without the aggregation submodel (Level 1 + Level 2 Only) performs considerably worse, showing the value of aggregating multiple mentions of the same span across the document. As expected, the level 1 only models are the weakest, showing that attending to the document is a powerful method for reading comprehension. Figure 3 (left) shows the behavior of the k-best predictions of these smaller components. While the difference between the level 1 models becomes more enhanced when considering the top-k candidates, the difference between the model without the aggregation submodel (Level 1 + Level 2 Only) and our full model is no longer significant, indicating that the former might not be able to distinguish between the best answer candidates as well as the latter.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Model ablations on the full Wikipedia development set. For row labeled **, explanation provided in Section §4.3.', answer the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the EM score of the 3-Level Cascade, Single Loss model?\n" }, { "role": "assistant", "content": "The table shows the EM scores for different models. The 3-Level Cascade, Single Loss model has an EM score of 43.48. So, the answer is {\"answer\": \"43.48\"}" } ], "images": [ "scigen/train/3486.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Model ablations on the full Wikipedia development set. For row labeled **, explanation provided in Section §4.3." ], "reasoning": "The table shows the EM scores for different models. The 3-Level Cascade, Single Loss model has an EM score of 43.48.", "question": "What is the EM score of the 3-Level Cascade, Single Loss model?", "answer": "So, the answer is {\"answer\": \"43.48\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The 3-Level Cascade, Combined Level 1 model has a higher EM score than the 3-Level Cascade, Multi-loss model., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe 3-Level Cascade, Combined Level 1 model has a higher EM score than the 3-Level Cascade, Multi-loss model.\n" }, { "role": "assistant", "content": "The table shows that the 3-Level Cascade, Combined Level 1 model has an EM score of 52.25, which is higher than the 3-Level Cascade, Multi-loss model's EM score of 52.18. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3486.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Model ablations on the full Wikipedia development set. For row labeled **, explanation provided in Section §4.3." ], "reasoning": "The table shows that the 3-Level Cascade, Combined Level 1 model has an EM score of 52.25, which is higher than the 3-Level Cascade, Multi-loss model's EM score of 52.18.", "statement": "The 3-Level Cascade, Combined Level 1 model has a higher EM score than the 3-Level Cascade, Multi-loss model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Accuracy on herb prediction'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "PLLM achieves the highest accuracy, indicating the importance of considering the whole prescription for predicting the next herb.The accuracy of N-gram model is even higher than RNNLM model, which shows that directly transfer the language modeling method from NLP may not be a good idea when predicting the next herb. CBOW model is slightly different from the original one, we average all the herb embeddings of the prescription, based on which we predict the blanked one. We assume the reason that it gives a rather good result is that it makes use of a wider range of context. In this experiment, our PLLM gets the best result, which is much higher than other models. We assume that it is necessary to consider the whole prescription when predicting the next herb." } ], "images": [ "scigen/train/3487.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Accuracy on herb prediction" ], "reasoning": "PLLM achieves the highest accuracy, indicating the importance of considering the whole prescription for predicting the next herb.", "description": "The accuracy of N-gram model is even higher than RNNLM model, which shows that directly transfer the language modeling method from NLP may not be a good idea when predicting the next herb. CBOW model is slightly different from the original one, we average all the herb embeddings of the prescription, based on which we predict the blanked one. We assume the reason that it gives a rather good result is that it makes use of a wider range of context. In this experiment, our PLLM gets the best result, which is much higher than other models. We assume that it is necessary to consider the whole prescription when predicting the next herb.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: Accuracy on herb prediction', answer the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model has the highest accuracy and why?\n" }, { "role": "assistant", "content": "The table shows that PLLM has the highest accuracy at 32.04. The description explains that this is because PLLM considers the whole prescription when predicting the next herb. So, the answer is {\"answer\": \"PLLM\"}." } ], "images": [ "scigen/train/3487.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Accuracy on herb prediction" ], "reasoning": "The table shows that PLLM has the highest accuracy at 32.04. The description explains that this is because PLLM considers the whole prescription when predicting the next herb.", "question": "Which model has the highest accuracy and why?", "answer": "So, the answer is {\"answer\": \"PLLM\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The RNNLM model has the lowest accuracy among the models. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe RNNLM model has the lowest accuracy among the models.\n" }, { "role": "assistant", "content": "The table shows that the RNNLM model has an accuracy of 16.50, which is lower than the accuracies of the N-gram, CBOW, and PLLM models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3487.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Accuracy on herb prediction" ], "reasoning": "The table shows that the RNNLM model has an accuracy of 16.50, which is lower than the accuracies of the N-gram, CBOW, and PLLM models.", "statement": "The RNNLM model has the lowest accuracy among the models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Correlation with Professionals’ judgment. Student means the correlation score with the Junior student.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The PLLM model achieves the highest Spearman's R score, surpassing the student model by over 10%, indicating its ability to learn useful knowledge from the dataset with unsupervised learning.From the table we can see that PLLM model gives the best result, which surpasses the result of the student by over 10%. This phenomenon shows that our PLLM model can learn some useful knowledge out of the prescriptions in the dataset with unsupervised learning. An overall description of the prescription can indeed help predicting the herb. The simple model CBOW also gives a rather good result of 49.33%. The traditional LSA model doesn’t perform well in this experiment, maybe because it omits the local information of the herbs, which plays a more important role in TCM prescriptions. GloVe suffers the same problem that the objective of global part influences the representation of the local context." } ], "images": [ "scigen/train/3488.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Correlation with Professionals’ judgment. Student means the correlation score with the Junior student." ], "reasoning": "The PLLM model achieves the highest Spearman's R score, surpassing the student model by over 10%, indicating its ability to learn useful knowledge from the dataset with unsupervised learning.", "description": "From the table we can see that PLLM model gives the best result, which surpasses the result of the student by over 10%. This phenomenon shows that our PLLM model can learn some useful knowledge out of the prescriptions in the dataset with unsupervised learning. An overall description of the prescription can indeed help predicting the herb. The simple model CBOW also gives a rather good result of 49.33%. The traditional LSA model doesn’t perform well in this experiment, maybe because it omits the local information of the herbs, which plays a more important role in TCM prescriptions. GloVe suffers the same problem that the objective of global part influences the representation of the local context.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 3: Correlation with Professionals’ judgment. Student means the correlation score with the Junior student.'.\n\nQuestion: Which model has the highest Spearman's R score and by how much does it surpass the student model?\n" }, { "role": "assistant", "content": "The PLLM model has the highest Spearman's R score of 0.5535. The student model has a score of 0.4506. The difference is 0.5535 - 0.4506 = 0.1029, which is over 10%. So, the answer is {\"answer\": \"PLLM, 0.1029\"}" } ], "images": [ "scigen/train/3488.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Correlation with Professionals’ judgment. Student means the correlation score with the Junior student." ], "reasoning": "The PLLM model has the highest Spearman's R score of 0.5535. The student model has a score of 0.4506. The difference is 0.5535 - 0.4506 = 0.1029, which is over 10%.", "question": "Which model has the highest Spearman's R score and by how much does it surpass the student model?", "answer": "So, the answer is {\"answer\": \"PLLM, 0.1029\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The CBOW model performs better than the RNLM model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe CBOW model performs better than the RNLM model.\n" }, { "role": "assistant", "content": "The CBOW model has a Spearman's R score of 0.4933, while the RNLM model has a score of 0.5158. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3488.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Correlation with Professionals’ judgment. Student means the correlation score with the Junior student." ], "reasoning": "The CBOW model has a Spearman's R score of 0.4933, while the RNLM model has a score of 0.5158. Therefore, the statement is refuted.", "statement": "The CBOW model performs better than the RNLM model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Micro-F1 scores of our method tested on multiple datasets, compared with other four baseline methods. The first two methods are GNN-based while the last two are PLM-based. Their performances come from their original papers, as quoted above.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our model outperformed the baseline models across different test settings, with a margin ranging from 1% to 8%.Given different test settings, we find that our model generally outperformed the baseline models, with a margin over the optimal baseline ranging from 1% to 8%." } ], "images": [ "scigen/train/3489.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Micro-F1 scores of our method tested on multiple datasets, compared with other four baseline methods. The first two methods are GNN-based while the last two are PLM-based. Their performances come from their original papers, as quoted above." ], "reasoning": "Our model outperformed the baseline models across different test settings, with a margin ranging from 1% to 8%.", "description": "Given different test settings, we find that our model generally outperformed the baseline models, with a margin over the optimal baseline ranging from 1% to 8%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Micro-F1 scores of our method tested on multiple datasets, compared with other four baseline methods. The first two methods are GNN-based while the last two are PLM-based. Their performances come from their original papers, as quoted above.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. \n\nQuestion: What is the highest score achieved by the baseline models across all datasets?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the highest score achieved by the baseline models, we need to look at the scores of C-AGGCN, GraphRel2p, BERTEM-MTB, and HBT across all datasets. The highest score among these is 89.5 by BERTEM-MTB on SemEval. So, the answer is {\"answer\": \"89.5\"}." } ], "images": [ "scigen/train/3489.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Micro-F1 scores of our method tested on multiple datasets, compared with other four baseline methods. The first two methods are GNN-based while the last two are PLM-based. Their performances come from their original papers, as quoted above." ], "reasoning": "To find the highest score achieved by the baseline models, we need to look at the scores of C-AGGCN, GraphRel2p, BERTEM-MTB, and HBT across all datasets. The highest score among these is 89.5 by BERTEM-MTB on SemEval.", "question": "What is the highest score achieved by the baseline models across all datasets?", "answer": "So, the answer is {\"answer\": \"89.5\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Our model achieved the highest score on the WebNLG dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nOur model achieved the highest score on the WebNLG dataset.\n" }, { "role": "assistant", "content": "The table shows that our model scored 96.3 on the WebNLG dataset, which is higher than the scores of the baseline models (42.9 by GraphRel2p and 88.8 by HBT). Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3489.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Micro-F1 scores of our method tested on multiple datasets, compared with other four baseline methods. The first two methods are GNN-based while the last two are PLM-based. Their performances come from their original papers, as quoted above." ], "reasoning": "The table shows that our model scored 96.3 on the WebNLG dataset, which is higher than the scores of the baseline models (42.9 by GraphRel2p and 88.8 by HBT). Therefore, the statement is supported.", "statement": "Our model achieved the highest score on the WebNLG dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Bleu-4 Evaluation and Reconstruction Loss for the Sentence Auto-Encoder on the Karpathy test split of MSCOCO 2014 caption dataset [21].', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The Vanilla SAE model outperforms the Denoising SAE model in both Bleu-4 and Lossrecons metrics, indicating its effectiveness with non-corrupted inputs.Given the inputs words are not corrupted, the vanilla model outperforms the denoising one in both metrics. This is expected as the denoising model is only trained with corrupted input sequences. The loss for both the Vanilla and Denoising SAE start from a relatively high value of approximately 0.8 and 0.4 respectively, and converge to a significantly low error of 0.1 and 0.2. For a better analysis, we also compute the Bleu-4 metrics on our decoded caption against the 5 ground-truth captions. This indicates that an entire caption can be compressed in a low dimensional vector (1024) and can be successfuly reconstructed. In the next discussions, we show that they are effective to be used as a regularizer in our image captioning model. On the other hand, the Denoising model being robust to noisy summary vectors provide enough training signal to improve the image captioning decoder when initialized with hlast." } ], "images": [ "scigen/train/3490.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Bleu-4 Evaluation and Reconstruction Loss for the Sentence Auto-Encoder on the Karpathy test split of MSCOCO 2014 caption dataset [21]." ], "reasoning": "The Vanilla SAE model outperforms the Denoising SAE model in both Bleu-4 and Lossrecons metrics, indicating its effectiveness with non-corrupted inputs.", "description": "Given the inputs words are not corrupted, the vanilla model outperforms the denoising one in both metrics. This is expected as the denoising model is only trained with corrupted input sequences. The loss for both the Vanilla and Denoising SAE start from a relatively high value of approximately 0.8 and 0.4 respectively, and converge to a significantly low error of 0.1 and 0.2. For a better analysis, we also compute the Bleu-4 metrics on our decoded caption against the 5 ground-truth captions. This indicates that an entire caption can be compressed in a low dimensional vector (1024) and can be successfuly reconstructed. In the next discussions, we show that they are effective to be used as a regularizer in our image captioning model. On the other hand, the Denoising model being robust to noisy summary vectors provide enough training signal to improve the image captioning decoder when initialized with hlast.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: Bleu-4 Evaluation and Reconstruction Loss for the Sentence Auto-Encoder on the Karpathy test split of MSCOCO 2014 caption dataset [21].', and tackle the question below in a sequential manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the difference in Bleu-4 scores between the Vanilla SAE and Denoising SAE models?\n" }, { "role": "assistant", "content": "The table shows the Bleu-4 scores for the Vanilla SAE as 96.33 and for the Denoising SAE as 89.79. Subtracting these values gives the difference. So, the answer is {\"answer\": \"6.54\"}" } ], "images": [ "scigen/train/3490.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Bleu-4 Evaluation and Reconstruction Loss for the Sentence Auto-Encoder on the Karpathy test split of MSCOCO 2014 caption dataset [21]." ], "reasoning": "The table shows the Bleu-4 scores for the Vanilla SAE as 96.33 and for the Denoising SAE as 89.79. Subtracting these values gives the difference.", "question": "What is the difference in Bleu-4 scores between the Vanilla SAE and Denoising SAE models?", "answer": "So, the answer is {\"answer\": \"6.54\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Denoising SAE model has a lower Lossrecons value than the Vanilla SAE model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Denoising SAE model has a lower Lossrecons value than the Vanilla SAE model.\n" }, { "role": "assistant", "content": "The table shows that the Lossrecons value for the Denoising SAE model is 0.23, which is higher than the Vanilla SAE model's value of 0.12. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3490.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Bleu-4 Evaluation and Reconstruction Loss for the Sentence Auto-Encoder on the Karpathy test split of MSCOCO 2014 caption dataset [21]." ], "reasoning": "The table shows that the Lossrecons value for the Denoising SAE model is 0.23, which is higher than the Vanilla SAE model's value of 0.12.", "statement": "The Denoising SAE model has a lower Lossrecons value than the Vanilla SAE model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Evaluation scores for our image captioning model in Figure 3 (a) with the soft-attention module and, ablations of our two-stream attention module.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The models with 128 and 256 latent categories significantly outperform the baseline, with 128 achieving the highest CIDEr score.We replace this attention mechanism with our proposed one and evaluate its performance for different number of latent categories (C). The models with latent categories 64, 128 and 256 outperform the baseline, setting C to either of 128 and 256 result in significant improvements. The higher CIDEr score for 128 shows the model’s capability to generate more descriptive human-like sentences. We also observe that the model with 256 categories successfully localizes more subtle semantics such as “holding”." } ], "images": [ "scigen/train/3491.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Evaluation scores for our image captioning model in Figure 3 (a) with the soft-attention module and, ablations of our two-stream attention module." ], "reasoning": "The models with 128 and 256 latent categories significantly outperform the baseline, with 128 achieving the highest CIDEr score.", "description": "We replace this attention mechanism with our proposed one and evaluate its performance for different number of latent categories (C). The models with latent categories 64, 128 and 256 outperform the baseline, setting C to either of 128 and 256 result in significant improvements. The higher CIDEr score for 128 shows the model’s capability to generate more descriptive human-like sentences. We also observe that the model with 256 categories successfully localizes more subtle semantics such as “holding”.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Evaluation scores for our image captioning model in Figure 3 (a) with the soft-attention module and, ablations of our two-stream attention module.\nQuestion: Which model configuration achieves the highest CIDEr score and what is the score?\nSolve the above question based on the table information. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the Two-Stream Attention model with 128 latent categories achieves the highest CIDEr score of 117.33. So, the answer is {\"answer\": \"117.33\"}." } ], "images": [ "scigen/train/3491.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Evaluation scores for our image captioning model in Figure 3 (a) with the soft-attention module and, ablations of our two-stream attention module." ], "reasoning": "The table shows that the Two-Stream Attention model with 128 latent categories achieves the highest CIDEr score of 117.33.", "question": "Which model configuration achieves the highest CIDEr score and what is the score?", "answer": "So, the answer is {\"answer\": \"117.33\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Two-Stream Attention model with 256 latent categories has the highest B-1 score.\n" }, { "role": "assistant", "content": "The table shows that the Two-Stream Attention model with 256 latent categories has the highest B-1 score of 79.12. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3491.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Evaluation scores for our image captioning model in Figure 3 (a) with the soft-attention module and, ablations of our two-stream attention module." ], "reasoning": "The table shows that the Two-Stream Attention model with 256 latent categories has the highest B-1 score of 79.12.", "statement": "The Two-Stream Attention model with 256 latent categories has the highest B-1 score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 8: Evaluation results of dataless document classification of coarse-grained classes measured in micro-averaged F1 along with # of dimensions (concepts) at which corresponding performance is achieved.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The CRX model embeddings perform the best on both tasks, with the CCX model giving close F1 scores at fewer dimensions, indicating its competitive advantage when computational cost is a decisive criterion.Classification at the top level is easier than the fine-grained level. Nevertheless, as with fine-grained classification, ESA still peaks with a few hundred dimensions of the sparse BoC vector. Both the CRX & CCX models achieve equal performance to ESA at very few dimensions (≤6). Densification using the CRX model embeddings still performs the best on both tasks. Interestingly, the CCX model gives very close F1 scores to the CRX model at less dimensions (@4 with Sport vs. Politics, and @60 with Sport vs. Religion) indicating its competitive advantage when training computational cost is a decisive criteria. The CCX model, still performs better than ESA, WEmax, and WEhung on both tasks. Bootstrapping did not improve the results on this task significantly (if any)." } ], "images": [ "scigen/train/3492.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Evaluation results of dataless document classification of coarse-grained classes measured in micro-averaged F1 along with # of dimensions (concepts) at which corresponding performance is achieved." ], "reasoning": "The CRX model embeddings perform the best on both tasks, with the CCX model giving close F1 scores at fewer dimensions, indicating its competitive advantage when computational cost is a decisive criterion.", "description": "Classification at the top level is easier than the fine-grained level. Nevertheless, as with fine-grained classification, ESA still peaks with a few hundred dimensions of the sparse BoC vector. Both the CRX & CCX models achieve equal performance to ESA at very few dimensions (≤6). Densification using the CRX model embeddings still performs the best on both tasks. Interestingly, the CCX model gives very close F1 scores to the CRX model at less dimensions (@4 with Sport vs. Politics, and @60 with Sport vs. Religion) indicating its competitive advantage when training computational cost is a decisive criteria. The CCX model, still performs better than ESA, WEmax, and WEhung on both tasks. Bootstrapping did not improve the results on this task significantly (if any).", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 8: Evaluation results of dataless document classification of coarse-grained classes measured in micro-averaged F1 along with # of dimensions (concepts) at which corresponding performance is achieved.', answer the question below by showing each progressive step toward the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieved the highest F1 score in the Sport vs. Religion task?\n" }, { "role": "assistant", "content": "By examining the table, the highest F1 score in the Sport vs. Religion task is 95.91, achieved by the CRX model. So, the answer is {\"answer\": \"CRX\"}." } ], "images": [ "scigen/train/3492.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Evaluation results of dataless document classification of coarse-grained classes measured in micro-averaged F1 along with # of dimensions (concepts) at which corresponding performance is achieved." ], "reasoning": "By examining the table, the highest F1 score in the Sport vs. Religion task is 95.91, achieved by the CRX model.", "question": "Which model achieved the highest F1 score in the Sport vs. Religion task?", "answer": "So, the answer is {\"answer\": \"CRX\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The CCX model achieved the highest F1 score in the Sport vs. Politics task. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe CCX model achieved the highest F1 score in the Sport vs. Politics task.\n" }, { "role": "assistant", "content": "The table shows that the highest F1 score in the Sport vs. Politics task is 93.20, achieved by the CRX model with bootstrapping, not the CCX model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3492.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Evaluation results of dataless document classification of coarse-grained classes measured in micro-averaged F1 along with # of dimensions (concepts) at which corresponding performance is achieved." ], "reasoning": "The table shows that the highest F1 score in the Sport vs. Politics task is 93.20, achieved by the CRX model with bootstrapping, not the CCX model.", "statement": "The CCX model achieved the highest F1 score in the Sport vs. Politics task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Evaluation of concept embeddings for measuring entity semantic relatedness using Spearman rank-order correlation (ρ). Overall, the CCX model gives the best results outperforming all other models. It comes 1st on 3 categories (bold), and 2nd on the other two (underlined).'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The CCX model outperforms all other models across five domains, surpassing KORE by 1.6%, while the CRX model performs the worst.As we can notice the CCX model achieves the best overall performance on the five domains combined exceeding its successor KORE by 1.6%. The CRX model on the other hand came last on this task." } ], "images": [ "scigen/train/3493.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Evaluation of concept embeddings for measuring entity semantic relatedness using Spearman rank-order correlation (ρ). Overall, the CCX model gives the best results outperforming all other models. It comes 1st on 3 categories (bold), and 2nd on the other two (underlined)." ], "reasoning": "The CCX model outperforms all other models across five domains, surpassing KORE by 1.6%, while the CRX model performs the worst.", "description": "As we can notice the CCX model achieves the best overall performance on the five domains combined exceeding its successor KORE by 1.6%. The CRX model on the other hand came last on this task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 3: Evaluation of concept embeddings for measuring entity semantic relatedness using Spearman rank-order correlation (ρ). Overall, the CCX model gives the best results outperforming all other models. It comes 1st on 3 categories (bold), and 2nd on the other two (underlined).', unfold the steps to solve the question given next. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nQuestion: Which model has the highest overall performance and by what percentage does it exceed its successor?\n" }, { "role": "assistant", "content": "The table shows that the CCX model has the highest overall performance with a score of 0.714, which is 1.6% higher than the KORE model's score of 0.698. So, the answer is {\"answer\": \"CCX, 1.6%\"}." } ], "images": [ "scigen/train/3493.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Evaluation of concept embeddings for measuring entity semantic relatedness using Spearman rank-order correlation (ρ). Overall, the CCX model gives the best results outperforming all other models. It comes 1st on 3 categories (bold), and 2nd on the other two (underlined)." ], "reasoning": "The table shows that the CCX model has the highest overall performance with a score of 0.714, which is 1.6% higher than the KORE model's score of 0.698.", "question": "Which model has the highest overall performance and by what percentage does it exceed its successor?", "answer": "So, the answer is {\"answer\": \"CCX, 1.6%\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe CRX model has the lowest performance across all domains combined.\n" }, { "role": "assistant", "content": "The table indicates that the CRX model has the lowest overall performance score of 0.586, which is lower than all other models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3493.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Evaluation of concept embeddings for measuring entity semantic relatedness using Spearman rank-order correlation (ρ). Overall, the CCX model gives the best results outperforming all other models. It comes 1st on 3 categories (bold), and 2nd on the other two (underlined)." ], "reasoning": "The table indicates that the CRX model has the lowest overall performance score of 0.586, which is lower than all other models listed.", "statement": "The CRX model has the lowest performance across all domains combined.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Accuracy of concept categorization. The CRX model with bootstrapping gives the best results outperforming all other models.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The CRX model with bootstrapping outperforms all other models significantly, demonstrating the efficacy of learning concept embeddings from concept mentions compared to more complex models.Accuracy is calculated by dividing the number of correctly classified concepts by the total number of concepts in the given dataset. Scores of all other methods are obtained from \\shortciteAli2016joint. While the CCX model performance is much less than CRX. With bootstrapping, the CCX model performance improves on both datasets. CRX with bootstrapping outperforms all other models by significant percentages. These results show that learning concept embeddings from concept mentions is actually different from training the skip-gram model on phrases or multiword expressions. This is clear from the significant performance gains we get from the CRX and CCX models compared to WEMikolov which was trained using skip-gram on phrases. Additionally, the results demonstrate the efficacy of our models which simply learn concept embeddings from concept mentions in free-text corpus compared to the more complex models which require category or relational information such as TransE, CE, and HCE." } ], "images": [ "scigen/train/3494.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Accuracy of concept categorization. The CRX model with bootstrapping gives the best results outperforming all other models." ], "reasoning": "The CRX model with bootstrapping outperforms all other models significantly, demonstrating the efficacy of learning concept embeddings from concept mentions compared to more complex models.", "description": "Accuracy is calculated by dividing the number of correctly classified concepts by the total number of concepts in the given dataset. Scores of all other methods are obtained from \\shortciteAli2016joint. While the CCX model performance is much less than CRX. With bootstrapping, the CCX model performance improves on both datasets. CRX with bootstrapping outperforms all other models by significant percentages. These results show that learning concept embeddings from concept mentions is actually different from training the skip-gram model on phrases or multiword expressions. This is clear from the significant performance gains we get from the CRX and CCX models compared to WEMikolov which was trained using skip-gram on phrases. Additionally, the results demonstrate the efficacy of our models which simply learn concept embeddings from concept mentions in free-text corpus compared to the more complex models which require category or relational information such as TransE, CE, and HCE.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 5: Accuracy of concept categorization. The CRX model with bootstrapping gives the best results outperforming all other models.', unfold the question presented below, detailing every step of your calculation. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model achieves the highest accuracy on the DOTA-single dataset?\n" }, { "role": "assistant", "content": "By examining the table, the highest accuracy on the DOTA-single dataset is achieved by the CRX model with bootstrapping, with a score of 0.98. So, the answer is {\"answer\": \"CRX +bootstrap\"}" } ], "images": [ "scigen/train/3494.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Accuracy of concept categorization. The CRX model with bootstrapping gives the best results outperforming all other models." ], "reasoning": "By examining the table, the highest accuracy on the DOTA-single dataset is achieved by the CRX model with bootstrapping, with a score of 0.98.", "question": "Which model achieves the highest accuracy on the DOTA-single dataset?", "answer": "So, the answer is {\"answer\": \"CRX +bootstrap\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe CCX model with bootstrapping achieves an accuracy of 0.85 on the DOTA-mult dataset.\n" }, { "role": "assistant", "content": "The table shows that the CCX model with bootstrapping achieves an accuracy of 0.85 on the DOTA-mult dataset, which supports the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3494.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Accuracy of concept categorization. The CRX model with bootstrapping gives the best results outperforming all other models." ], "reasoning": "The table shows that the CCX model with bootstrapping achieves an accuracy of 0.85 on the DOTA-mult dataset, which supports the statement.", "statement": "The CCX model with bootstrapping achieves an accuracy of 0.85 on the DOTA-mult dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 7: Evaluation results of dataless document classification of fine-grained classes measured in micro-averaged F1 along with # of dimensions (concepts) in the BoC at which corresponding performance is achieved.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The CRX model achieves the best F1 scores on all three tasks, significantly improving performance with fewer concepts compared to ESA.As we can notice, ESA achieves its peak performance with a few hundred dimensions of the sparse BoC vector. Using our densification mechanism, both the CRX & CCX models achieve equal performance to ESA at many fewer dimensions. Densification using the CRX model embeddings gives the best F1 scores on the three tasks. Interestingly, the CRX model improves the F1 score by ∼7% using only 14 concepts on Autos vs. Motorcycles, and by ∼3% using 70 concepts on Guns vs. Mideast vs. Misc. The CCX model, still performs better than ESA on 2 out of the 3 tasks. Both WEmax and WEhung improve the performance over ESA but not as our CRX model." } ], "images": [ "scigen/train/3495.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Evaluation results of dataless document classification of fine-grained classes measured in micro-averaged F1 along with # of dimensions (concepts) in the BoC at which corresponding performance is achieved." ], "reasoning": "The CRX model achieves the best F1 scores on all three tasks, significantly improving performance with fewer concepts compared to ESA.", "description": "As we can notice, ESA achieves its peak performance with a few hundred dimensions of the sparse BoC vector. Using our densification mechanism, both the CRX & CCX models achieve equal performance to ESA at many fewer dimensions. Densification using the CRX model embeddings gives the best F1 scores on the three tasks. Interestingly, the CRX model improves the F1 score by ∼7% using only 14 concepts on Autos vs. Motorcycles, and by ∼3% using 70 concepts on Guns vs. Mideast vs. Misc. The CCX model, still performs better than ESA on 2 out of the 3 tasks. Both WEmax and WEhung improve the performance over ESA but not as our CRX model.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 7: Evaluation results of dataless document classification of fine-grained classes measured in micro-averaged F1 along with # of dimensions (concepts) in the BoC at which corresponding performance is achieved.', solve this question and give detail solutions. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhich model achieves the highest F1 score on the Guns vs. Mideast vs. Misc task and at how many concepts?\n" }, { "role": "assistant", "content": "From the table, the highest F1 score on the Guns vs. Mideast vs. Misc task is 77.43, achieved by the CRX (best) model using 5 concepts. So, the answer is {\"answer\": \"CRX (best) at 5 concepts\"}." } ], "images": [ "scigen/train/3495.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Evaluation results of dataless document classification of fine-grained classes measured in micro-averaged F1 along with # of dimensions (concepts) in the BoC at which corresponding performance is achieved." ], "reasoning": "From the table, the highest F1 score on the Guns vs. Mideast vs. Misc task is 77.43, achieved by the CRX (best) model using 5 concepts.", "question": "Which model achieves the highest F1 score on the Guns vs. Mideast vs. Misc task and at how many concepts?", "answer": "So, the answer is {\"answer\": \"CRX (best) at 5 concepts\"}." }, { "messages": [ { "role": "user", "content": "The WE hung model achieves a higher F1 score than the CCX model on the Autos vs. Motorcycles task. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe WE hung model achieves a higher F1 score than the CCX model on the Autos vs. Motorcycles task.\n" }, { "role": "assistant", "content": "From the table, the WE hung model achieves an F1 score of 73.75 on the Autos vs. Motorcycles task, while the CCX (best) model achieves 69.70. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3495.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Evaluation results of dataless document classification of fine-grained classes measured in micro-averaged F1 along with # of dimensions (concepts) in the BoC at which corresponding performance is achieved." ], "reasoning": "From the table, the WE hung model achieves an F1 score of 73.75 on the Autos vs. Motorcycles task, while the CCX (best) model achieves 69.70. Therefore, the statement is supported.", "statement": "The WE hung model achieves a higher F1 score than the CCX model on the Autos vs. Motorcycles task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: The websites included in our study.'.\n" }, { "role": "assistant", "content": "In Sweden, digital immigration-critical milieus have unmoderated comment sections where users express hate, and comments were collected from 2014/09/01 to 2017/10/01.In Sweden, as well as in several other European countries, there has been a recent surge in activity and formation of movements that are critical of immigration. These immigration-critical groups show a high interactivity on social media and on websites. In Sweden, there are several digital immigration-critical milieus with a similar structure: articles published by editorial staff and user-generated comments. The commentary fields are not moderated, which makes the comments an important scene to express hate toward journalists, politicians, artists, and other public figures. The comment section allows readers to respond to an editorial article instantly. The editorial articles generally focuses on topics such as crimes, migration, politics and societal issues. For each website, we have downloaded all comments between 2014/09/01 to 2017/10/01. Note that the embeddings used for term suggestions are also trained on this data." } ], "images": [ "scigen/train/3496.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The websites included in our study." ], "reasoning": "In Sweden, digital immigration-critical milieus have unmoderated comment sections where users express hate, and comments were collected from 2014/09/01 to 2017/10/01.", "description": "In Sweden, as well as in several other European countries, there has been a recent surge in activity and formation of movements that are critical of immigration. These immigration-critical groups show a high interactivity on social media and on websites. In Sweden, there are several digital immigration-critical milieus with a similar structure: articles published by editorial staff and user-generated comments. The commentary fields are not moderated, which makes the comments an important scene to express hate toward journalists, politicians, artists, and other public figures. The comment section allows readers to respond to an editorial article instantly. The editorial articles generally focuses on topics such as crimes, migration, politics and societal issues. For each website, we have downloaded all comments between 2014/09/01 to 2017/10/01. Note that the embeddings used for term suggestions are also trained on this data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: The websites included in our study.', and sequentially solve the question, writing out each step of your reasoning process. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich website has the highest number of comments?\n" }, { "role": "assistant", "content": "To find the website with the highest number of comments, compare the '# comments' column for each website. The website with the highest value is avpixlat.info with 2,904,933 comments. So, the answer is {\"answer\": \"avpixlat.info\"}." } ], "images": [ "scigen/train/3496.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The websites included in our study." ], "reasoning": "To find the website with the highest number of comments, compare the '# comments' column for each website. The website with the highest value is avpixlat.info with 2,904,933 comments.", "question": "Which website has the highest number of comments?", "answer": "So, the answer is {\"answer\": \"avpixlat.info\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nnordiskungdom.com has the fewest number of comments.\n" }, { "role": "assistant", "content": "To verify this statement, compare the '# comments' column for each website. The website with the fewest number of comments is nordiskungdom.com with 117 comments. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3496.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The websites included in our study." ], "reasoning": "To verify this statement, compare the '# comments' column for each website. The website with the fewest number of comments is nordiskungdom.com with 117 comments.", "statement": "nordiskungdom.com has the fewest number of comments.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Number of times each Swedish minister is mentioned in the comments during the time period.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Stefan Löfven is the most frequently mentioned politician with over 10,000 mentions, followed by Morgan Johansson and Margot Wallström. Expressions of hate correlate with frequency of mention.Obviously, the Prime Minister Stefan Löfven is the most frequently mentioned politician, with more than 10,000 mentions during the analyzed period. The second most mentioned politician in the studies data is Morgan Johansson, the Swedish Minister of Justice and Home Affairs, and the third most mentioned minister is Margot Wallström, Minister for Foreign Affairs. As we noted in the last section, expressions of hate seem to correlate with frequency of mention (at least in the data we have studied). On the other hand, counting proportions, as we do in the right part of the figure, risks overestimating the significance of very rare events. A perhaps more useful measure might be to calculate deviations from the expected amount of hateful comments for each minister. As an example, Morgan Johansson is mentioned 3 142 by his full name in our data. Looking at the actual frequency counts, we see that 3 mentions contain swearwords, 8 contain anger, 14 contain naughtiness, and 5 contain general threat. For the last three categories, the actual counts are much higher than would be expected, indicating that these are significant measurements." } ], "images": [ "scigen/train/3497.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Number of times each Swedish minister is mentioned in the comments during the time period." ], "reasoning": "Stefan Löfven is the most frequently mentioned politician with over 10,000 mentions, followed by Morgan Johansson and Margot Wallström. Expressions of hate correlate with frequency of mention.", "description": "Obviously, the Prime Minister Stefan Löfven is the most frequently mentioned politician, with more than 10,000 mentions during the analyzed period. The second most mentioned politician in the studies data is Morgan Johansson, the Swedish Minister of Justice and Home Affairs, and the third most mentioned minister is Margot Wallström, Minister for Foreign Affairs. As we noted in the last section, expressions of hate seem to correlate with frequency of mention (at least in the data we have studied). On the other hand, counting proportions, as we do in the right part of the figure, risks overestimating the significance of very rare events. A perhaps more useful measure might be to calculate deviations from the expected amount of hateful comments for each minister. As an example, Morgan Johansson is mentioned 3 142 by his full name in our data. Looking at the actual frequency counts, we see that 3 mentions contain swearwords, 8 contain anger, 14 contain naughtiness, and 5 contain general threat. For the last three categories, the actual counts are much higher than would be expected, indicating that these are significant measurements.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: Number of times each Swedish minister is mentioned in the comments during the time period.' and methodically tackle the question that follows. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWho is the third most mentioned politician and how many times are they mentioned?\n" }, { "role": "assistant", "content": "The table lists politicians and their mention counts. The third most mentioned politician is Margot Wallström with 2,681 mentions. So, the answer is {\"answer\": \"Margot Wallström\"}." } ], "images": [ "scigen/train/3497.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Number of times each Swedish minister is mentioned in the comments during the time period." ], "reasoning": "The table lists politicians and their mention counts. The third most mentioned politician is Margot Wallström with 2,681 mentions.", "question": "Who is the third most mentioned politician and how many times are they mentioned?", "answer": "So, the answer is {\"answer\": \"Margot Wallström\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Morgan Johansson is mentioned more than 3,000 times in the data., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nMorgan Johansson is mentioned more than 3,000 times in the data.\n" }, { "role": "assistant", "content": "The table shows that Morgan Johansson is mentioned 3,142 times, which is more than 3,000. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3497.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Number of times each Swedish minister is mentioned in the comments during the time period." ], "reasoning": "The table shows that Morgan Johansson is mentioned 3,142 times, which is more than 3,000.", "statement": "Morgan Johansson is mentioned more than 3,000 times in the data.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Comparison of accuracy with the baselines on the SimpleQuestions and WebQuestions'.\n" }, { "role": "assistant", "content": "Our method achieves the highest performance on the SimpleQuestions dataset and outperforms the best baseline methods on the WebQuestions dataset by 2.79 points.Our method comes close to the performance of the state-of-the-art method on SimpleQuestions dataset. On the WebQuestions dataset, our method outperforms the best baseline methods by 2.79 points. Questions and relations in the WebQuestion dataset are longer and the information loss problem is more serious, thus ABWIM outperforms the encoding-comparing baseline methods. The experimental results demonstrate the effectiveness of ABWIM, which learns the word-level interactions and aggregates the comparison results with multi-kernel CNN networks." } ], "images": [ "scigen/train/3498.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of accuracy with the baselines on the SimpleQuestions and WebQuestions" ], "reasoning": "Our method achieves the highest performance on the SimpleQuestions dataset and outperforms the best baseline methods on the WebQuestions dataset by 2.79 points.", "description": "Our method comes close to the performance of the state-of-the-art method on SimpleQuestions dataset. On the WebQuestions dataset, our method outperforms the best baseline methods by 2.79 points. Questions and relations in the WebQuestion dataset are longer and the information loss problem is more serious, thus ABWIM outperforms the encoding-comparing baseline methods. The experimental results demonstrate the effectiveness of ABWIM, which learns the word-level interactions and aggregates the comparison results with multi-kernel CNN networks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 2: Comparison of accuracy with the baselines on the SimpleQuestions and WebQuestions', unfold the question presented below, detailing every step of your calculation. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the performance difference between our method and HR-Bi-LSTM on the WebQuestions dataset?\n" }, { "role": "assistant", "content": "Our method scores 85.32 on the WebQuestions dataset, while HR-Bi-LSTM scores 82.53. The difference is 85.32 - 82.53 = 2.79 points. So, the answer is {\"answer\": \"2.79\"}" } ], "images": [ "scigen/train/3498.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of accuracy with the baselines on the SimpleQuestions and WebQuestions" ], "reasoning": "Our method scores 85.32 on the WebQuestions dataset, while HR-Bi-LSTM scores 82.53. The difference is 85.32 - 82.53 = 2.79 points.", "question": "What is the performance difference between our method and HR-Bi-LSTM on the WebQuestions dataset?", "answer": "So, the answer is {\"answer\": \"2.79\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nOur method outperforms Bi-LSTM on the SimpleQuestions dataset.\n" }, { "role": "assistant", "content": "The table shows that our method scores 93.5 on the SimpleQuestions dataset, while Bi-LSTM scores 91.2. Therefore, our method outperforms Bi-LSTM. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3498.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of accuracy with the baselines on the SimpleQuestions and WebQuestions" ], "reasoning": "The table shows that our method scores 93.5 on the SimpleQuestions dataset, while Bi-LSTM scores 91.2. Therefore, our method outperforms Bi-LSTM.", "statement": "Our method outperforms Bi-LSTM on the SimpleQuestions dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: Ablation experiment results'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "ABWIM combines bi-directional attention and word-level interaction mechanisms, achieving state-of-the-art results on both SimpleQA and WebQA tasks.First, both tasks benefit from both the bi-directional attention mechanism, especially for WebQA dataset (84.44% vs 78.41%). Attention mechanism enables the model to focus on important parts of the sequence and get a better representation. Second, comparing the sequences using word-level representations also outperforms the traditional encoding-comparing model, because multiple comparison operations are performed before information in the question is merged into a single vector. Finally, ABWIM, which combines the bi-directional attention and word-level interaction mechanisms, achieves state-of-the-art results on both tasks with accuracy 93.5% and 85.32% respectively. First, ABWIM outperforms several strong baselines, though there is no preprocessing operation. It indicates the effectiveness of learning attention-based word alignments between question and relation. Second, a context aware representation of words can further improve the result and the preprocessing layer is also important for the overall performance. The LSTM layer, which is capable of learning long range dependency, outperforms the CNN and gated liner transformation. Finally, CNN and gated linear transformation do not rely on the computations of previous timestep, so they can fully utilize the computational capability of GPU and are more faster to be trained and perform inference." } ], "images": [ "scigen/train/3499.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation experiment results" ], "reasoning": "ABWIM combines bi-directional attention and word-level interaction mechanisms, achieving state-of-the-art results on both SimpleQA and WebQA tasks.", "description": "First, both tasks benefit from both the bi-directional attention mechanism, especially for WebQA dataset (84.44% vs 78.41%). Attention mechanism enables the model to focus on important parts of the sequence and get a better representation. Second, comparing the sequences using word-level representations also outperforms the traditional encoding-comparing model, because multiple comparison operations are performed before information in the question is merged into a single vector. Finally, ABWIM, which combines the bi-directional attention and word-level interaction mechanisms, achieves state-of-the-art results on both tasks with accuracy 93.5% and 85.32% respectively. First, ABWIM outperforms several strong baselines, though there is no preprocessing operation. It indicates the effectiveness of learning attention-based word alignments between question and relation. Second, a context aware representation of words can further improve the result and the preprocessing layer is also important for the overall performance. The LSTM layer, which is capable of learning long range dependency, outperforms the CNN and gated liner transformation. Finally, CNN and gated linear transformation do not rely on the computations of previous timestep, so they can fully utilize the computational capability of GPU and are more faster to be trained and perform inference.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Ablation experiment results'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. \n\nQuestion: Which model achieved the highest accuracy on the WebQA dataset?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows the accuracy of different models on the WebQA dataset. ABWIM achieved the highest accuracy of 85.32%. So, the answer is {\"answer\": \"ABWIM\"}" } ], "images": [ "scigen/train/3499.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation experiment results" ], "reasoning": "The table shows the accuracy of different models on the WebQA dataset. ABWIM achieved the highest accuracy of 85.32%.", "question": "Which model achieved the highest accuracy on the WebQA dataset?", "answer": "So, the answer is {\"answer\": \"ABWIM\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The encoding-comparing model with bi-directional attention achieved higher accuracy on the SimpleQA dataset than the word-level interaction model without attention. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe encoding-comparing model with bi-directional attention achieved higher accuracy on the SimpleQA dataset than the word-level interaction model without attention.\n" }, { "role": "assistant", "content": "The table shows that the encoding-comparing model with bi-directional attention achieved 93.2% accuracy on SimpleQA, while the word-level interaction model without attention achieved 93.0%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3499.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation experiment results" ], "reasoning": "The table shows that the encoding-comparing model with bi-directional attention achieved 93.2% accuracy on SimpleQA, while the word-level interaction model without attention achieved 93.0%.", "statement": "The encoding-comparing model with bi-directional attention achieved higher accuracy on the SimpleQA dataset than the word-level interaction model without attention.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Annotation quality of annotations collected from fluent speaker (FS) and non-speaker (NS) annotators against the gold data.'.\n" }, { "role": "assistant", "content": "FS data has higher F1 scores compared to NS data, with Indonesian having the highest NS F1 score and Hindi the lowest.Annotation Quality refers to the results of participant annotation compared to the existing gold annotations on the same documents. In this evaluation, no model is trained, and we simply calculate the F1 scores by treating NS annotations as predictions themselves. Unsurprisingly, the F1 scores of FS data vastly exceed scores of NS data. There is also an expected decrease in NS scores correlated with perceived language difficulty, with Indonesian having the highest quality, and Hindi having the lowest." } ], "images": [ "scigen/train/3500.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Annotation quality of annotations collected from fluent speaker (FS) and non-speaker (NS) annotators against the gold data." ], "reasoning": "FS data has higher F1 scores compared to NS data, with Indonesian having the highest NS F1 score and Hindi the lowest.", "description": "Annotation Quality refers to the results of participant annotation compared to the existing gold annotations on the same documents. In this evaluation, no model is trained, and we simply calculate the F1 scores by treating NS annotations as predictions themselves. Unsurprisingly, the F1 scores of FS data vastly exceed scores of NS data. There is also an expected decrease in NS scores correlated with perceived language difficulty, with Indonesian having the highest quality, and Hindi having the lowest.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Annotation quality of annotations collected from fluent speaker (FS) and non-speaker (NS) annotators against the gold data.', unfold the question presented below, detailing every step of your calculation. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich language has the highest FS F1 score and what is it?\n" }, { "role": "assistant", "content": "To find the language with the highest FS F1 score, compare the FS F1 scores for Indonesian, Russian, and Hindi. Hindi has the highest FS F1 score of 82.9. So, the answer is {\"answer\": \"Hindi\"}." } ], "images": [ "scigen/train/3500.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Annotation quality of annotations collected from fluent speaker (FS) and non-speaker (NS) annotators against the gold data." ], "reasoning": "To find the language with the highest FS F1 score, compare the FS F1 scores for Indonesian, Russian, and Hindi. Hindi has the highest FS F1 score of 82.9.", "question": "Which language has the highest FS F1 score and what is it?", "answer": "So, the answer is {\"answer\": \"Hindi\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Indonesian has the highest NS F1 score among the languages., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nIndonesian has the highest NS F1 score among the languages.\n" }, { "role": "assistant", "content": "The table shows the NS F1 scores for Indonesian, Russian, and Hindi. Indonesian has an NS F1 score of 57.7, which is higher than Russian's 50.9 and Hindi's 42.8. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3500.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Annotation quality of annotations collected from fluent speaker (FS) and non-speaker (NS) annotators against the gold data." ], "reasoning": "The table shows the NS F1 scores for Indonesian, Russian, and Hindi. Indonesian has an NS F1 score of 57.7, which is higher than Russian's 50.9 and Hindi's 42.8.", "statement": "Indonesian has the highest NS F1 score among the languages.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 7: Automatic stylistic evaluation metrics (higher is better). Paired t-test base vs. +style all p<0.05.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table measures various metrics such as vocab size, average sentence length, number of adjectives, and text entropy for different models, with higher scores indicating more variability.For vocab, we find the number of unique words in all outputs for each model. We find the average sentence length (SentLen) by counting the number of words, and find the total number of times an adjective is used (Row 3) and average number of adjectives per reference for each model (Row 4). We compute Shannon text entropy (E) as: E=−∑x∈Vft∗log2(ft), where V is the vocab size in all outputs generated by the model, f is the frequency of a term (in this case, a trigram), and t counts the number of terms in all outputs. Finally, we count the instances of contrast (e.g. “but” and “although”), and aggregation (e.g. “both” and “also”). For all metrics, higher scores indicate more variability (indicated by ↑)." } ], "images": [ "scigen/train/3501.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Automatic stylistic evaluation metrics (higher is better). Paired t-test base vs. +style all p<0.05." ], "reasoning": "The table measures various metrics such as vocab size, average sentence length, number of adjectives, and text entropy for different models, with higher scores indicating more variability.", "description": "For vocab, we find the number of unique words in all outputs for each model. We find the average sentence length (SentLen) by counting the number of words, and find the total number of times an adjective is used (Row 3) and average number of adjectives per reference for each model (Row 4). We compute Shannon text entropy (E) as: E=−∑x∈Vft∗log2(ft), where V is the vocab size in all outputs generated by the model, f is the frequency of a term (in this case, a trigram), and t counts the number of terms in all outputs. Finally, we count the instances of contrast (e.g. “but” and “although”), and aggregation (e.g. “both” and “also”). For all metrics, higher scores indicate more variability (indicated by ↑).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 7: Automatic stylistic evaluation metrics (higher is better). Paired t-test base vs. +style all p<0.05.\nQuestion: Which model has the highest number of adjectives used?\nGive a detailed response to the above question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest number of adjectives used, look at the '# Adjs' row and identify the highest value. So, the answer is {\"answer\": \"37k\"}." } ], "images": [ "scigen/train/3501.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Automatic stylistic evaluation metrics (higher is better). Paired t-test base vs. +style all p<0.05." ], "reasoning": "To find the model with the highest number of adjectives used, look at the '# Adjs' row and identify the highest value.", "question": "Which model has the highest number of adjectives used?", "answer": "So, the answer is {\"answer\": \"37k\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe +Style model has the highest average sentence length.\n" }, { "role": "assistant", "content": "To verify this statement, compare the values in the 'SentLen' row for all models and check if the +Style model has the highest value. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3501.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Automatic stylistic evaluation metrics (higher is better). Paired t-test base vs. +style all p<0.05." ], "reasoning": "To verify this statement, compare the values in the 'SentLen' row for all models and check if the +Style model has the highest value.", "statement": "The +Style model has the highest average sentence length.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 6: Automatic semantic evaluation (higher is better for all but SER).', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The table shows that as more information is added, the BLEU, METEOR, CIDEr, and NIST scores increase, indicating better performance, while the Avg SER also increases but remains below 10%.Machine Translation Metrics. We begin with an automatic evaluation using standard metrics frequently used for machine translation. (weighted n-gram precision), where higher numbers indicate better overlap (shown with the ↑). We note that while these measures are common for machine translation, they are not well-suited to this task, since they are based on n-gram overlap which is not a constraint within the model; we include them for comparative purposes. This means that the base outputs errors are computed over only value slots (since they don’t contain adjectives), and the rest of the errors are computed over both value and adjective slots. Naturally, base, with no access to style information, has the best (lowest) SER. But we note that there is not a large increase in SER as more information is added – even for the most difficult setting, +style, the models make an error on less than 10% of the slots in a given mr, on average." } ], "images": [ "scigen/train/3502.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Automatic semantic evaluation (higher is better for all but SER)." ], "reasoning": "The table shows that as more information is added, the BLEU, METEOR, CIDEr, and NIST scores increase, indicating better performance, while the Avg SER also increases but remains below 10%.", "description": "Machine Translation Metrics. We begin with an automatic evaluation using standard metrics frequently used for machine translation. (weighted n-gram precision), where higher numbers indicate better overlap (shown with the ↑). We note that while these measures are common for machine translation, they are not well-suited to this task, since they are based on n-gram overlap which is not a constraint within the model; we include them for comparative purposes. This means that the base outputs errors are computed over only value slots (since they don’t contain adjectives), and the rest of the errors are computed over both value and adjective slots. Naturally, base, with no access to style information, has the best (lowest) SER. But we note that there is not a large increase in SER as more information is added – even for the most difficult setting, +style, the models make an error on less than 10% of the slots in a given mr, on average.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: Table 6: Automatic semantic evaluation (higher is better for all but SER).\nQuestion: What is the highest CIDEr score achieved and under which setting?\n" }, { "role": "assistant", "content": "The highest CIDEr score is found in the +Style column, which has a value of 1.838. So, the answer is {\"answer\": \"1.838\"}." } ], "images": [ "scigen/train/3502.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Automatic semantic evaluation (higher is better for all but SER)." ], "reasoning": "The highest CIDEr score is found in the +Style column, which has a value of 1.838.", "question": "What is the highest CIDEr score achieved and under which setting?", "answer": "So, the answer is {\"answer\": \"1.838\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Base setting has the lowest Avg SER.\n" }, { "role": "assistant", "content": "The table shows that the Base setting has an Avg SER of 0.053, which is the lowest among all settings. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3502.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Automatic semantic evaluation (higher is better for all but SER)." ], "reasoning": "The table shows that the Base setting has an Avg SER of 0.053, which is the lowest among all settings.", "statement": "The Base setting has the lowest Avg SER.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Results on BioASQ questions.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "DIEBOLDS outperforms Freebase in MRR, MAP, and Recall, with Recall being about 80% higher.We compare the triples of Freebase and DIEBOLDS pipeline. (Output of DIEBOLDS only comes from the target corpora.) The evaluation metrics are Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR), commonly used for question answering, as well as Recall. It shows the higher scored triples from DIEBOLDS have reasonably good accuracy. On the other hand, Freebase does not have The recall value of DIEBOLDS is about 80% higher than that of Freebase. It shows that DIEBOLDS returns richer knowledge." } ], "images": [ "scigen/train/3503.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on BioASQ questions." ], "reasoning": "DIEBOLDS outperforms Freebase in MRR, MAP, and Recall, with Recall being about 80% higher.", "description": "We compare the triples of Freebase and DIEBOLDS pipeline. (Output of DIEBOLDS only comes from the target corpora.) The evaluation metrics are Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR), commonly used for question answering, as well as Recall. It shows the higher scored triples from DIEBOLDS have reasonably good accuracy. On the other hand, Freebase does not have The recall value of DIEBOLDS is about 80% higher than that of Freebase. It shows that DIEBOLDS returns richer knowledge.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 3: Results on BioASQ questions.', and tackle the question below in a sequential manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the difference in Recall between DIEBOLDS and Freebase?\n" }, { "role": "assistant", "content": "The Recall for DIEBOLDS is 0.195 and for Freebase is 0.109. The difference is 0.195 - 0.109 = 0.086. So, the answer is {\"answer\": \"0.086\"}" } ], "images": [ "scigen/train/3503.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on BioASQ questions." ], "reasoning": "The Recall for DIEBOLDS is 0.195 and for Freebase is 0.109. The difference is 0.195 - 0.109 = 0.086.", "question": "What is the difference in Recall between DIEBOLDS and Freebase?", "answer": "So, the answer is {\"answer\": \"0.086\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: DIEBOLDS has a higher MAP score than Freebase., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nDIEBOLDS has a higher MAP score than Freebase.\n" }, { "role": "assistant", "content": "The table shows that DIEBOLDS has a MAP score of 0.092, while Freebase has a MAP score of 0.025, indicating that DIEBOLDS has a higher MAP score. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3503.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on BioASQ questions." ], "reasoning": "The table shows that DIEBOLDS has a MAP score of 0.092, while Freebase has a MAP score of 0.025, indicating that DIEBOLDS has a higher MAP score.", "statement": "DIEBOLDS has a higher MAP score than Freebase.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Comparison between baselines and DIEBOLDS on extraction results of the labeled pages.'.\n" }, { "role": "assistant", "content": "DIEBOLDS and its variants outperform all baselines in all metrics, with DIEBOLDS showing significant improvement over DS1 in F1 scores for both disease and drug domains.DIEBOLDS and its variants outperform the baselines in all metrics. DIEBOLDS is the most effective pipeline in both domains, and its improvement over DS1, pure distant supervision, on F1 is about 20% in disease domain, and more than 100% in drug domain." } ], "images": [ "scigen/train/3504.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison between baselines and DIEBOLDS on extraction results of the labeled pages." ], "reasoning": "DIEBOLDS and its variants outperform all baselines in all metrics, with DIEBOLDS showing significant improvement over DS1 in F1 scores for both disease and drug domains.", "description": "DIEBOLDS and its variants outperform the baselines in all metrics. DIEBOLDS is the most effective pipeline in both domains, and its improvement over DS1, pure distant supervision, on F1 is about 20% in disease domain, and more than 100% in drug domain.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: 'Table 2: Comparison between baselines and DIEBOLDS on extraction results of the labeled pages.'\nQuestion: What is the F1 score improvement of DIEBOLDS over DS1 in the drug domain?\n" }, { "role": "assistant", "content": "To find the F1 score improvement of DIEBOLDS over DS1 in the drug domain, subtract the F1 score of DS1 (0.037) from the F1 score of DIEBOLDS (0.090). So, the answer is {\"answer\": \"0.053\"}" } ], "images": [ "scigen/train/3504.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison between baselines and DIEBOLDS on extraction results of the labeled pages." ], "reasoning": "To find the F1 score improvement of DIEBOLDS over DS1 in the drug domain, subtract the F1 score of DS1 (0.037) from the F1 score of DIEBOLDS (0.090).", "question": "What is the F1 score improvement of DIEBOLDS over DS1 in the drug domain?", "answer": "So, the answer is {\"answer\": \"0.053\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: DIEBOLDS has the highest F1 score in the disease domain., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nDIEBOLDS has the highest F1 score in the disease domain.\n" }, { "role": "assistant", "content": "To verify this statement, compare the F1 scores in the disease domain for all methods. DIEBOLDS has an F1 score of 0.209, which is higher than any other method listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3504.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison between baselines and DIEBOLDS on extraction results of the labeled pages." ], "reasoning": "To verify this statement, compare the F1 scores in the disease domain for all methods. DIEBOLDS has an F1 score of 0.209, which is higher than any other method listed.", "statement": "DIEBOLDS has the highest F1 score in the disease domain.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Analysis of word embedding strategies and similarity heuristics. Bold numbers are the highest value in each row; underlined ones are the highest in each column.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The virtual-sentence strategy consistently outperforms the other two strategies across all three matching heuristics, indicating its effectiveness in learning dialogue-specific word embeddings.We observe that our virtual-sentence strategy consistently outperforms the other two in all three matching heuristics. The results suggest that combining a query and a reply does provide more information in learning dialogue-specific word embeddings." } ], "images": [ "scigen/train/3505.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Analysis of word embedding strategies and similarity heuristics. Bold numbers are the highest value in each row; underlined ones are the highest in each column." ], "reasoning": "The virtual-sentence strategy consistently outperforms the other two strategies across all three matching heuristics, indicating its effectiveness in learning dialogue-specific word embeddings.", "description": "We observe that our virtual-sentence strategy consistently outperforms the other two in all three matching heuristics. The results suggest that combining a query and a reply does provide more information in learning dialogue-specific word embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2: Analysis of word embedding strategies and similarity heuristics. Bold numbers are the highest value in each row; underlined ones are the highest in each column.', unfold the steps to solve the question given next. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nQuestion: Which embedding strategy has the highest heuristic-max F score?\n" }, { "role": "assistant", "content": "By comparing the heuristic-max F scores of all embedding strategies, we see that the virtual-sentence strategy has the highest score of 74.7. So, the answer is {\"answer\": \"Virtual-sentence\"}." } ], "images": [ "scigen/train/3505.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Analysis of word embedding strategies and similarity heuristics. Bold numbers are the highest value in each row; underlined ones are the highest in each column." ], "reasoning": "By comparing the heuristic-max F scores of all embedding strategies, we see that the virtual-sentence strategy has the highest score of 74.7.", "question": "Which embedding strategy has the highest heuristic-max F score?", "answer": "So, the answer is {\"answer\": \"Virtual-sentence\"}." }, { "messages": [ { "role": "user", "content": "The within-sentence strategy has the highest sum pooling F score. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe within-sentence strategy has the highest sum pooling F score.\n" }, { "role": "assistant", "content": "The table shows that the virtual-sentence strategy has the highest sum pooling F score of 74.8, while the within-sentence strategy has a sum pooling F score of 69.4. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3505.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Analysis of word embedding strategies and similarity heuristics. Bold numbers are the highest value in each row; underlined ones are the highest in each column." ], "reasoning": "The table shows that the virtual-sentence strategy has the highest sum pooling F score of 74.8, while the within-sentence strategy has a sum pooling F score of 69.4.", "statement": "The within-sentence strategy has the highest sum pooling F score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Dialogue session segmentation performance in terms of precision (P), recall (R) and F-measure (F). Results are in percentage.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Our approach outperforms baselines in precision and recall, achieving an F-score over 20% higher than baseline methods.As seen, our approach significantly outperforms baselines by a large margin in terms of both precision and recall. Besides, we can see that MMD obtains low performance, which is mainly because the approach cannot be easily adapted to other datasets like short sentences of conversation utterances. In summary, we achieve an F-score higher than baseline methods by more than 20%, showing the effectiveness of enhancing TextTiling with modern word embeddings." } ], "images": [ "scigen/train/3506.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Dialogue session segmentation performance in terms of precision (P), recall (R) and F-measure (F). Results are in percentage." ], "reasoning": "Our approach outperforms baselines in precision and recall, achieving an F-score over 20% higher than baseline methods.", "description": "As seen, our approach significantly outperforms baselines by a large margin in terms of both precision and recall. Besides, we can see that MMD obtains low performance, which is mainly because the approach cannot be easily adapted to other datasets like short sentences of conversation utterances. In summary, we achieve an F-score higher than baseline methods by more than 20%, showing the effectiveness of enhancing TextTiling with modern word embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Dialogue session segmentation performance in terms of precision (P), recall (R) and F-measure (F). Results are in percentage.\nQuestion: What is the F-score of the method that uses TextTiling with tf-idf and embeddings trained by virtual sentences?\nSolve the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows that the method using TextTiling with tf-idf and embeddings trained by virtual sentences has an F-score of 74.7. So, the answer is {\"answer\": \"74.7\"}." } ], "images": [ "scigen/train/3506.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Dialogue session segmentation performance in terms of precision (P), recall (R) and F-measure (F). Results are in percentage." ], "reasoning": "The table shows that the method using TextTiling with tf-idf and embeddings trained by virtual sentences has an F-score of 74.7.", "question": "What is the F-score of the method that uses TextTiling with tf-idf and embeddings trained by virtual sentences?", "answer": "So, the answer is {\"answer\": \"74.7\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe MMD method has the lowest precision among all methods.\n" }, { "role": "assistant", "content": "The table shows that the MMD method has a precision of 27.0, which is lower than the precision of all other methods listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3506.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Dialogue session segmentation performance in terms of precision (P), recall (R) and F-measure (F). Results are in percentage." ], "reasoning": "The table shows that the MMD method has a precision of 27.0, which is lower than the precision of all other methods listed.", "statement": "The MMD method has the lowest precision among all methods.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: A retrieval dialogue system with fixed context (2 previous utterances) and the proposed sentence segmentation (virtual sentences with heuristic-max).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The session segmentation method outperforms the fixed-context method in both p@1 and nDCG metrics.As demonstrated, our method outperforms the simple fixed-context approach in terms of both metrics. We computed the inner-annotator agreement: std = 0.309;" } ], "images": [ "scigen/train/3507.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: A retrieval dialogue system with fixed context (2 previous utterances) and the proposed sentence segmentation (virtual sentences with heuristic-max)." ], "reasoning": "The session segmentation method outperforms the fixed-context method in both p@1 and nDCG metrics.", "description": "As demonstrated, our method outperforms the simple fixed-context approach in terms of both metrics. We computed the inner-annotator agreement: std = 0.309;", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: A retrieval dialogue system with fixed context (2 previous utterances) and the proposed sentence segmentation (virtual sentences with heuristic-max).'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. \n\nQuestion: Which method has a higher p@1 score?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows that the session segmentation method has a p@1 score of 0.521, which is higher than the fixed-context method's p@1 score of 0.484. So, the answer is {\"answer\": \"Session segmentation\"}" } ], "images": [ "scigen/train/3507.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: A retrieval dialogue system with fixed context (2 previous utterances) and the proposed sentence segmentation (virtual sentences with heuristic-max)." ], "reasoning": "The table shows that the session segmentation method has a p@1 score of 0.521, which is higher than the fixed-context method's p@1 score of 0.484.", "question": "Which method has a higher p@1 score?", "answer": "So, the answer is {\"answer\": \"Session segmentation\"}" }, { "messages": [ { "role": "user", "content": "The fixed-context method has a higher nDCG score than the session segmentation method. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe fixed-context method has a higher nDCG score than the session segmentation method.\n" }, { "role": "assistant", "content": "The table shows that the fixed-context method has an nDCG score of 0.699, while the session segmentation method has a higher nDCG score of 0.737. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3507.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: A retrieval dialogue system with fixed context (2 previous utterances) and the proposed sentence segmentation (virtual sentences with heuristic-max)." ], "reasoning": "The table shows that the fixed-context method has an nDCG score of 0.699, while the session segmentation method has a higher nDCG score of 0.737.", "statement": "The fixed-context method has a higher nDCG score than the session segmentation method.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: Results on the trial data for subtask C.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The character n-gram model with linguistic and emoji features achieved the highest macro F1 score and accuracy, but both metrics dropped when entity information was added.Since there are only 31 instances in the trial set and it is rather imbalanced, we can see that the highest accuracy is reached by classifying all examples as IND, i.e., the all IND baseline. Even though none of the classifiers outperformed the baseline in terms of accuracy, all the classifiers achieved significantly higher macro F1 scores, which shows that they are better at identifying the other two classes. After adding linguistic and emoji features, the character n-gram model showed a slight improvement in macro F1 score and achieved the highest accuracy along with the simple character n-gram model. But both macro F1 and accuracy dropped when entity information was added." } ], "images": [ "scigen/train/3508.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on the trial data for subtask C." ], "reasoning": "The character n-gram model with linguistic and emoji features achieved the highest macro F1 score and accuracy, but both metrics dropped when entity information was added.", "description": "Since there are only 31 instances in the trial set and it is rather imbalanced, we can see that the highest accuracy is reached by classifying all examples as IND, i.e., the all IND baseline. Even though none of the classifiers outperformed the baseline in terms of accuracy, all the classifiers achieved significantly higher macro F1 scores, which shows that they are better at identifying the other two classes. After adding linguistic and emoji features, the character n-gram model showed a slight improvement in macro F1 score and achieved the highest accuracy along with the simple character n-gram model. But both macro F1 and accuracy dropped when entity information was added.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: Results on the trial data for subtask C.', solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model achieved the highest macro F1 score?\n" }, { "role": "assistant", "content": "By comparing the F1 macro scores in the table, the SVMchar+ling+emoji model has the highest score of 0.3971. So, the answer is {\"answer\": \"SVMchar+ling+emoji\"}." } ], "images": [ "scigen/train/3508.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on the trial data for subtask C." ], "reasoning": "By comparing the F1 macro scores in the table, the SVMchar+ling+emoji model has the highest score of 0.3971.", "question": "Which model achieved the highest macro F1 score?", "answer": "So, the answer is {\"answer\": \"SVMchar+ling+emoji\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe SVMword-ngram model achieved the highest accuracy.\n" }, { "role": "assistant", "content": "The table shows that the SVMword-ngram model has an accuracy of 0.6774, which is lower than the accuracies of the All IND baseline (0.8387), SVMcharacter-ngram (0.8065), and SVMchar+ling+emoji (0.8065). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3508.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on the trial data for subtask C." ], "reasoning": "The table shows that the SVMword-ngram model has an accuracy of 0.6774, which is lower than the accuracies of the All IND baseline (0.8387), SVMcharacter-ngram (0.8065), and SVMchar+ling+emoji (0.8065).", "statement": "The SVMword-ngram model achieved the highest accuracy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: The official UM-IU@LING result for subtask A, in comparison to the baselines.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The BERT classifier achieved a macro F1 score of 0.8136, which is significantly higher than the baseline of 0.4189, indicating its effectiveness in identifying offensive content in tweets.The BERT classifier achieved a macro F1 score of 0.8136, clearly exceeding the baseline of 0.4189 and ranking the system 3rd out of 103 submissions. This demonstrates that our model can effectively identify whether a given tweet contains offensive content or not. One explanation of the results may be the classifier’s preference for the majority class. But it is possible that our classifier may not capture some of the subtle nuances in meaning and contexts. However, the results also show that the macro F1 score is only about 4.5 percent points lower than the accuracy (0.8136 vs. 0.8570). This is a clear indication that the classifier is successful in modeling the minority class of offensive tweets." } ], "images": [ "scigen/train/3509.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The official UM-IU@LING result for subtask A, in comparison to the baselines." ], "reasoning": "The BERT classifier achieved a macro F1 score of 0.8136, which is significantly higher than the baseline of 0.4189, indicating its effectiveness in identifying offensive content in tweets.", "description": "The BERT classifier achieved a macro F1 score of 0.8136, clearly exceeding the baseline of 0.4189 and ranking the system 3rd out of 103 submissions. This demonstrates that our model can effectively identify whether a given tweet contains offensive content or not. One explanation of the results may be the classifier’s preference for the majority class. But it is possible that our classifier may not capture some of the subtle nuances in meaning and contexts. However, the results also show that the macro F1 score is only about 4.5 percent points lower than the accuracy (0.8136 vs. 0.8570). This is a clear indication that the classifier is successful in modeling the minority class of offensive tweets.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 1: The official UM-IU@LING result for subtask A, in comparison to the baselines.', solve this question and give detail solutions. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nHow much higher is the BERT classifier's macro F1 score compared to the All NOT baseline?\n" }, { "role": "assistant", "content": "The BERT classifier's macro F1 score is 0.8136, and the All NOT baseline's macro F1 score is 0.4189. Subtracting the baseline from the BERT score gives 0.8136 - 0.4189 = 0.3947. So, the answer is {\"answer\": \"0.3947\"}" } ], "images": [ "scigen/train/3509.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The official UM-IU@LING result for subtask A, in comparison to the baselines." ], "reasoning": "The BERT classifier's macro F1 score is 0.8136, and the All NOT baseline's macro F1 score is 0.4189. Subtracting the baseline from the BERT score gives 0.8136 - 0.4189 = 0.3947.", "question": "How much higher is the BERT classifier's macro F1 score compared to the All NOT baseline?", "answer": "So, the answer is {\"answer\": \"0.3947\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe BERT classifier's accuracy is more than double the All OFF baseline accuracy.\n" }, { "role": "assistant", "content": "The BERT classifier's accuracy is 0.8570, and the All OFF baseline accuracy is 0.2790. Doubling the All OFF baseline accuracy gives 0.2790 * 2 = 0.5580, which is less than 0.8570. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3509.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The official UM-IU@LING result for subtask A, in comparison to the baselines." ], "reasoning": "The BERT classifier's accuracy is 0.8570, and the All OFF baseline accuracy is 0.2790. Doubling the All OFF baseline accuracy gives 0.2790 * 2 = 0.5580, which is less than 0.8570.", "statement": "The BERT classifier's accuracy is more than double the All OFF baseline accuracy.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results on the trial data for subtask A.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "BERTbase-uncased achieved the best performance on the trial data, while SVM classifier had higher accuracy but lower macro F1 compared to BERT.Generally, these variants of BERTbase tend to give similar performance but BERTbase-uncased achieved the best performance on the trial data. It is unclear why BERTbase-multilingual-uncased did not learn to perform the task beyond the baseline. Additional hyperparameter tuning might be necessary in this case. Overall, these results demonstrate that though BERT can give superior performance in detecting hate speech, it is somewhat sensitive to the change of hyperparameters. We also find that the SVM classifier achieved a higher accuracy on the trial data, but there is a significant drop in macro F1 when compared with the BERT model. This shows that the BERT model performs better on the minority class." } ], "images": [ "scigen/train/3510.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on the trial data for subtask A." ], "reasoning": "BERTbase-uncased achieved the best performance on the trial data, while SVM classifier had higher accuracy but lower macro F1 compared to BERT.", "description": "Generally, these variants of BERTbase tend to give similar performance but BERTbase-uncased achieved the best performance on the trial data. It is unclear why BERTbase-multilingual-uncased did not learn to perform the task beyond the baseline. Additional hyperparameter tuning might be necessary in this case. Overall, these results demonstrate that though BERT can give superior performance in detecting hate speech, it is somewhat sensitive to the change of hyperparameters. We also find that the SVM classifier achieved a higher accuracy on the trial data, but there is a significant drop in macro F1 when compared with the BERT model. This shows that the BERT model performs better on the minority class.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: Table 2: Results on the trial data for subtask A.\nQuestion: Which system achieved the highest F1 macro score and what was its accuracy?\n" }, { "role": "assistant", "content": "The table shows that BERTbase-uncased achieved the highest F1 macro score of 0.8388 and its accuracy was 0.8722. So, the answer is {\"answer\": \"BERTbase-uncased with an accuracy of 0.8722\"}" } ], "images": [ "scigen/train/3510.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on the trial data for subtask A." ], "reasoning": "The table shows that BERTbase-uncased achieved the highest F1 macro score of 0.8388 and its accuracy was 0.8722.", "question": "Which system achieved the highest F1 macro score and what was its accuracy?", "answer": "So, the answer is {\"answer\": \"BERTbase-uncased with an accuracy of 0.8722\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence BERTbase-multilingual-uncased achieved the lowest F1 macro score. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nBERTbase-multilingual-uncased achieved the lowest F1 macro score.\n" }, { "role": "assistant", "content": "The table shows that BERTbase-multilingual-uncased has an F1 macro score of 0.4300, which is higher than the All OFF baseline score of 0.1934. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3510.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on the trial data for subtask A." ], "reasoning": "The table shows that BERTbase-multilingual-uncased has an F1 macro score of 0.4300, which is higher than the All OFF baseline score of 0.1934.", "statement": "BERTbase-multilingual-uncased achieved the lowest F1 macro score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: The official UM-IU@LING results (SVM) for subtask C.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The SVM classifier's macro F1 score of 0.5243 is significantly higher than the baseline scores, indicating that the sparsity of training data for the minority class OTH negatively impacts performance.The macro F1 score of the SVM classifier is 0.5243, which is considerably higher than the baseline and ranks the system 27th out of 65 submissions. This clearly shows that the sparsity of training data for the minority class OTH affects the performance of our classifier negatively." } ], "images": [ "scigen/train/3511.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The official UM-IU@LING results (SVM) for subtask C." ], "reasoning": "The SVM classifier's macro F1 score of 0.5243 is significantly higher than the baseline scores, indicating that the sparsity of training data for the minority class OTH negatively impacts performance.", "description": "The macro F1 score of the SVM classifier is 0.5243, which is considerably higher than the baseline and ranks the system 27th out of 65 submissions. This clearly shows that the sparsity of training data for the minority class OTH affects the performance of our classifier negatively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: The official UM-IU@LING results (SVM) for subtask C.', solve the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the accuracy of the SVM classifier, and how does it compare to the All OTH baseline?\n" }, { "role": "assistant", "content": "The accuracy of the SVM classifier is 0.6854, which is significantly higher than the All OTH baseline accuracy of 0.1643. So, the answer is {\"answer\": \"0.6854\"}." } ], "images": [ "scigen/train/3511.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The official UM-IU@LING results (SVM) for subtask C." ], "reasoning": "The accuracy of the SVM classifier is 0.6854, which is significantly higher than the All OTH baseline accuracy of 0.1643.", "question": "What is the accuracy of the SVM classifier, and how does it compare to the All OTH baseline?", "answer": "So, the answer is {\"answer\": \"0.6854\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe SVM classifier has a higher F1 macro score than all the baselines.\n" }, { "role": "assistant", "content": "The table shows that the SVM classifier has an F1 macro score of 0.5243, which is higher than the F1 macro scores of all the baselines (All GRP: 0.1787, All IND: 0.2130, All OTH: 0.0941). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3511.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The official UM-IU@LING results (SVM) for subtask C." ], "reasoning": "The table shows that the SVM classifier has an F1 macro score of 0.5243, which is higher than the F1 macro scores of all the baselines (All GRP: 0.1787, All IND: 0.2130, All OTH: 0.0941).", "statement": "The SVM classifier has a higher F1 macro score than all the baselines.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: Accuracy on single-sentence binary classification tasks from SentEval, where max-pooling is done over padded values instead of over the length of the sentence. Experiments are split between Sorted where sentences are sorted in order of length prior to batching and Unsorted where they are not.'.\n" }, { "role": "assistant", "content": "The performance drops significantly when batches are unsorted, especially for MR and CR, due to max pooling over padded values changing negative values to zero.The first half of the table shows results when max pooling with padding is used and the batches are sorted. The second half of the table shows performance when the batches are unsorted. As can be seen by the table, the performance has a significant drop-off when the batches are unsorted, especially for MR and CR. Max pooling over padded values changes negative values in the features of longer sentences to zero. This is because if the largest value in the hidden representations over the length of the sentence is negative, the padded zeros will be greater. Thus, longer sentences, when grouped with shorter ones, will have more sparse representations. We tried to reproduce this effect by using a ReLU, but it didn’t increase performance. In fact it is 0.0 for CR, one of the tasks most affected by this phenomenon." } ], "images": [ "scigen/train/3512.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Accuracy on single-sentence binary classification tasks from SentEval, where max-pooling is done over padded values instead of over the length of the sentence. Experiments are split between Sorted where sentences are sorted in order of length prior to batching and Unsorted where they are not." ], "reasoning": "The performance drops significantly when batches are unsorted, especially for MR and CR, due to max pooling over padded values changing negative values to zero.", "description": "The first half of the table shows results when max pooling with padding is used and the batches are sorted. The second half of the table shows performance when the batches are unsorted. As can be seen by the table, the performance has a significant drop-off when the batches are unsorted, especially for MR and CR. Max pooling over padded values changes negative values in the features of longer sentences to zero. This is because if the largest value in the hidden representations over the length of the sentence is negative, the padded zeros will be greater. Thus, longer sentences, when grouped with shorter ones, will have more sparse representations. We tried to reproduce this effect by using a ReLU, but it didn’t increase performance. In fact it is 0.0 for CR, one of the tasks most affected by this phenomenon.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Accuracy on single-sentence binary classification tasks from SentEval, where max-pooling is done over padded values instead of over the length of the sentence. Experiments are split between Sorted where sentences are sorted in order of length prior to batching and Unsorted where they are not.\nQuestion: Which model shows the highest performance for the MR task when batches are sorted?\nGive a detailed response to the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest performance for the MR task when batches are sorted, look at the 'Sorted' rows under the 'MR' column. The highest value is 82.7, which corresponds to the GenSen model. So, the answer is {\"answer\": \"GenSen\"}." } ], "images": [ "scigen/train/3512.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Accuracy on single-sentence binary classification tasks from SentEval, where max-pooling is done over padded values instead of over the length of the sentence. Experiments are split between Sorted where sentences are sorted in order of length prior to batching and Unsorted where they are not." ], "reasoning": "To find the highest performance for the MR task when batches are sorted, look at the 'Sorted' rows under the 'MR' column. The highest value is 82.7, which corresponds to the GenSen model.", "question": "Which model shows the highest performance for the MR task when batches are sorted?", "answer": "So, the answer is {\"answer\": \"GenSen\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe performance for the CR task is higher when batches are sorted compared to when they are unsorted.\n" }, { "role": "assistant", "content": "To verify this statement, compare the CR values for sorted and unsorted batches. For all models (RandLSTM, InferSent, and GenSen), the CR values are higher when batches are sorted. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3512.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Accuracy on single-sentence binary classification tasks from SentEval, where max-pooling is done over padded values instead of over the length of the sentence. Experiments are split between Sorted where sentences are sorted in order of length prior to batching and Unsorted where they are not." ], "reasoning": "To verify this statement, compare the CR values for sorted and unsorted batches. For all models (RandLSTM, InferSent, and GenSen), the CR values are higher when batches are sorted.", "statement": "The performance for the CR task is higher when batches are sorted compared to when they are unsorted.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Performance (accuracy for all tasks except SICK-R and STSB, for which we report Pearson’s r) on all ten downstream tasks where all models have 4096 dimensions with the exception of BOE (300) and ST-LN (4800). Standard deviations are show in parentheses. InferSent-1 is the paper version with GloVe (G) embeddings, InferSent-2 has fixed padding and uses FastText (F) embeddings, and InferSent-3 has fixed padding and uses GloVe embeddings. We also show the difference between the best random architecture (BestRand) and InferSent-3 and ST-LN, respectively. The average performance difference between the best random architecture and InferSent-3 and ST-LN is 1.7 and -0.4 respectively.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "By using better pre-trained word embeddings and projecting to higher-dimensional representations, we can improve SkipThought's performance and make fair comparisons with InferSent.We hypothesize that SkipThought might do a lot better if it had been trained with better pre-trained word embeddings. If we take Cover’s theorem to the limit, we can project to an even higher-dimensional representation as long as we can still easily fit things onto a modern GPU: In order to make for a fair comparison, we can also randomly project InferSent and SkipThought representations to the same dimensionality and examine performance." } ], "images": [ "scigen/train/3513.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Performance (accuracy for all tasks except SICK-R and STSB, for which we report Pearson’s r) on all ten downstream tasks where all models have 4096 dimensions with the exception of BOE (300) and ST-LN (4800). Standard deviations are show in parentheses. InferSent-1 is the paper version with GloVe (G) embeddings, InferSent-2 has fixed padding and uses FastText (F) embeddings, and InferSent-3 has fixed padding and uses GloVe embeddings. We also show the difference between the best random architecture (BestRand) and InferSent-3 and ST-LN, respectively. The average performance difference between the best random architecture and InferSent-3 and ST-LN is 1.7 and -0.4 respectively." ], "reasoning": "By using better pre-trained word embeddings and projecting to higher-dimensional representations, we can improve SkipThought's performance and make fair comparisons with InferSent.", "description": "We hypothesize that SkipThought might do a lot better if it had been trained with better pre-trained word embeddings. If we take Cover’s theorem to the limit, we can project to an even higher-dimensional representation as long as we can still easily fit things onto a modern GPU: In order to make for a fair comparison, we can also randomly project InferSent and SkipThought representations to the same dimensionality and examine performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}. The question is related to the given table titled 'Table 1: Performance (accuracy for all tasks except SICK-R and STSB, for which we report Pearson’s r) on all ten downstream tasks where all models have 4096 dimensions with the exception of BOE (300) and ST-LN (4800). Standard deviations are show in parentheses. InferSent-1 is the paper version with GloVe (G) embeddings, InferSent-2 has fixed padding and uses FastText (F) embeddings, and InferSent-3 has fixed padding and uses GloVe embeddings. We also show the difference between the best random architecture (BestRand) and InferSent-3 and ST-LN, respectively. The average performance difference between the best random architecture and InferSent-3 and ST-LN is 1.7 and -0.4 respectively.'.\n\nQuestion: Which model has the highest performance on the TREC dataset?\n" }, { "role": "assistant", "content": "To determine the highest performance on the TREC dataset, we need to compare the TREC scores of all models listed in the table. The highest score is 88.8, achieved by the BOREP model. So, the answer is {\"answer\": \"BOREP\"}" } ], "images": [ "scigen/train/3513.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Performance (accuracy for all tasks except SICK-R and STSB, for which we report Pearson’s r) on all ten downstream tasks where all models have 4096 dimensions with the exception of BOE (300) and ST-LN (4800). Standard deviations are show in parentheses. InferSent-1 is the paper version with GloVe (G) embeddings, InferSent-2 has fixed padding and uses FastText (F) embeddings, and InferSent-3 has fixed padding and uses GloVe embeddings. We also show the difference between the best random architecture (BestRand) and InferSent-3 and ST-LN, respectively. The average performance difference between the best random architecture and InferSent-3 and ST-LN is 1.7 and -0.4 respectively." ], "reasoning": "To determine the highest performance on the TREC dataset, we need to compare the TREC scores of all models listed in the table. The highest score is 88.8, achieved by the BOREP model.", "question": "Which model has the highest performance on the TREC dataset?", "answer": "So, the answer is {\"answer\": \"BOREP\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nInferSent-1 has the highest performance on the MR dataset.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the MR scores of all models listed in the table. InferSent-1 has a score of 81.1, which is the highest among all models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3513.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Performance (accuracy for all tasks except SICK-R and STSB, for which we report Pearson’s r) on all ten downstream tasks where all models have 4096 dimensions with the exception of BOE (300) and ST-LN (4800). Standard deviations are show in parentheses. InferSent-1 is the paper version with GloVe (G) embeddings, InferSent-2 has fixed padding and uses FastText (F) embeddings, and InferSent-3 has fixed padding and uses GloVe embeddings. We also show the difference between the best random architecture (BestRand) and InferSent-3 and ST-LN, respectively. The average performance difference between the best random architecture and InferSent-3 and ST-LN is 1.7 and -0.4 respectively." ], "reasoning": "To verify this statement, we need to compare the MR scores of all models listed in the table. InferSent-1 has a score of 81.1, which is the highest among all models.", "statement": "InferSent-1 has the highest performance on the MR dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Performance on a set of probing tasks defined in (Conneau et al., 2018). All random architecture models are 4096 dimensions and were selected by tuning over validation performance on the classification tasks.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "ESNs and RandLSTMs generally outperform BOE and BOREP on tasks requiring word order knowledge, while ESNs and InferSent perform similarly on many tasks, with Skipthought-LN often outperforming both.From the table, we see that ESNs and RandLSTMs outperform BOE and BOREP on most of the tasks, especially those that require knowledge of the order of the words. This indicates that these models, even though initialized randomly, are capturing order, as one would expect. We also see that ESNs and InferSent are fairly close on many of the tasks, with Skipthought-LN generally outperforming both." } ], "images": [ "scigen/train/3514.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance on a set of probing tasks defined in (Conneau et al., 2018). All random architecture models are 4096 dimensions and were selected by tuning over validation performance on the classification tasks." ], "reasoning": "ESNs and RandLSTMs generally outperform BOE and BOREP on tasks requiring word order knowledge, while ESNs and InferSent perform similarly on many tasks, with Skipthought-LN often outperforming both.", "description": "From the table, we see that ESNs and RandLSTMs outperform BOE and BOREP on most of the tasks, especially those that require knowledge of the order of the words. This indicates that these models, even though initialized randomly, are capturing order, as one would expect. We also see that ESNs and InferSent are fairly close on many of the tasks, with Skipthought-LN generally outperforming both.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}. The question is related to the given table titled 'Table 3: Performance on a set of probing tasks defined in (Conneau et al., 2018). All random architecture models are 4096 dimensions and were selected by tuning over validation performance on the classification tasks.'.\n\nQuestion: Which model has the highest score in the SentLen task and how does it compare to the model with the highest score in the WC task?\n" }, { "role": "assistant", "content": "The highest score in the SentLen task is 80.6 by InferSent-3. The highest score in the WC task is 97.1 by BOREP. Comparing these, BOREP has a higher score in WC than InferSent-3 in SentLen. So, the answer is {\"answer\": \"BOREP has a higher score in WC than InferSent-3 in SentLen\"}." } ], "images": [ "scigen/train/3514.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance on a set of probing tasks defined in (Conneau et al., 2018). All random architecture models are 4096 dimensions and were selected by tuning over validation performance on the classification tasks." ], "reasoning": "The highest score in the SentLen task is 80.6 by InferSent-3. The highest score in the WC task is 97.1 by BOREP. Comparing these, BOREP has a higher score in WC than InferSent-3 in SentLen.", "question": "Which model has the highest score in the SentLen task and how does it compare to the model with the highest score in the WC task?", "answer": "So, the answer is {\"answer\": \"BOREP has a higher score in WC than InferSent-3 in SentLen\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRandLSTM has a higher score in the Tense task than ESN.\n" }, { "role": "assistant", "content": "The score for RandLSTM in the Tense task is 86.6, while the score for ESN in the Tense task is also 86.6. Therefore, RandLSTM does not have a higher score than ESN in the Tense task. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3514.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance on a set of probing tasks defined in (Conneau et al., 2018). All random architecture models are 4096 dimensions and were selected by tuning over validation performance on the classification tasks." ], "reasoning": "The score for RandLSTM in the Tense task is 86.6, while the score for ESN in the Tense task is also 86.6. Therefore, RandLSTM does not have a higher score than ESN in the Tense task.", "statement": "RandLSTM has a higher score in the Tense task than ESN.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Number of descriptions whose prompting label (column Emotion) agrees with the emotion labeled by all Phase-2 annotators (=5), by at least four (≥4), at least three (≥3), at least two (≥2), at least one (≥1).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The agreement on emotions between Phase 1 and Phase 2 is similar for both German and English, with half of the descriptions showing perfect intersubjective validity.Agreement on emotions. We next analyze to what extent the emotions labelled in Phase 2 agree with the prompting emotion presented in Phase 1. Agreement is similar between deISEAR and enISEAR. This indicates that the German items, although short, are sufficiently informative. In both languages, the agreement drops across the columns, yet half of the descriptions show perfect intersubjective validity (=5): 505 for German, 499 for English. We interpret this as a sign of quality. The off-diagonal cells indicate disagreements. In both languages, annotators perceive Shame descriptions as expressing Guilt, and vice versa (35% and 15% for English, 17% and 19% for German)." } ], "images": [ "scigen/train/3515.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Number of descriptions whose prompting label (column Emotion) agrees with the emotion labeled by all Phase-2 annotators (=5), by at least four (≥4), at least three (≥3), at least two (≥2), at least one (≥1)." ], "reasoning": "The agreement on emotions between Phase 1 and Phase 2 is similar for both German and English, with half of the descriptions showing perfect intersubjective validity.", "description": "Agreement on emotions. We next analyze to what extent the emotions labelled in Phase 2 agree with the prompting emotion presented in Phase 1. Agreement is similar between deISEAR and enISEAR. This indicates that the German items, although short, are sufficiently informative. In both languages, the agreement drops across the columns, yet half of the descriptions show perfect intersubjective validity (=5): 505 for German, 499 for English. We interpret this as a sign of quality. The off-diagonal cells indicate disagreements. In both languages, annotators perceive Shame descriptions as expressing Guilt, and vice versa (35% and 15% for English, 17% and 19% for German).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Number of descriptions whose prompting label (column Emotion) agrees with the emotion labeled by all Phase-2 annotators (=5), by at least four (≥4), at least three (≥3), at least two (≥2), at least one (≥1).\nQuestion: What is the total number of descriptions that show perfect intersubjective validity for both German and English?\nSolve the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the total number of descriptions that show perfect intersubjective validity, sum the values in the 'German =5' and 'English =5' columns. So, the answer is {\"answer\": \"1004\"}." } ], "images": [ "scigen/train/3515.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Number of descriptions whose prompting label (column Emotion) agrees with the emotion labeled by all Phase-2 annotators (=5), by at least four (≥4), at least three (≥3), at least two (≥2), at least one (≥1)." ], "reasoning": "To find the total number of descriptions that show perfect intersubjective validity, sum the values in the 'German =5' and 'English =5' columns.", "question": "What is the total number of descriptions that show perfect intersubjective validity for both German and English?", "answer": "So, the answer is {\"answer\": \"1004\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The number of descriptions with perfect intersubjective validity is higher for English than for German., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe number of descriptions with perfect intersubjective validity is higher for English than for German.\n" }, { "role": "assistant", "content": "The table shows that the number of descriptions with perfect intersubjective validity is 505 for German and 499 for English, indicating that the statement is false. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3515.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Number of descriptions whose prompting label (column Emotion) agrees with the emotion labeled by all Phase-2 annotators (=5), by at least four (≥4), at least three (≥3), at least two (≥2), at least one (≥1)." ], "reasoning": "The table shows that the number of descriptions with perfect intersubjective validity is 505 for German and 499 for English, indicating that the statement is false.", "statement": "The number of descriptions with perfect intersubjective validity is higher for English than for German.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Event type analysis: Cells are counts of post-annotation out of 55 descriptions for each emotion.'.\n" }, { "role": "assistant", "content": "Fear more often targets the future than other emotions, and most event descriptions involve other participants, especially in English.In both English and German, only a few units depict general and future events, in line with the annotation guidelines. Fear more often targets the future than other emotions. Most event descriptions involve other participants, especially in English. In general, events seem to affect authors themselves more than other people, particularly in the case of Joy and Fear. Exceptions are Guilt and Sadness, for which there is a predominance of events whose effects bear down on others. Regarding the aspect of situational control, Shame and Guilt dominate. Guilt is particularly more frequent in descriptions in which the author is presumably responsible. These observations echo the findings by \\newcitetracy2006appraisal." } ], "images": [ "scigen/train/3516.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Event type analysis: Cells are counts of post-annotation out of 55 descriptions for each emotion." ], "reasoning": "Fear more often targets the future than other emotions, and most event descriptions involve other participants, especially in English.", "description": "In both English and German, only a few units depict general and future events, in line with the annotation guidelines. Fear more often targets the future than other emotions. Most event descriptions involve other participants, especially in English. In general, events seem to affect authors themselves more than other people, particularly in the case of Joy and Fear. Exceptions are Guilt and Sadness, for which there is a predominance of events whose effects bear down on others. Regarding the aspect of situational control, Shame and Guilt dominate. Guilt is particularly more frequent in descriptions in which the author is presumably responsible. These observations echo the findings by \\newcitetracy2006appraisal.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 3: Event type analysis: Cells are counts of post-annotation out of 55 descriptions for each emotion.', solve this question and give detail solutions. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich emotion in German has the highest frequency of events where the author is presumably responsible?\n" }, { "role": "assistant", "content": "To find the emotion in German with the highest frequency of events where the author is presumably responsible, look at the 'Responsible' row under the 'German' section. The highest value is 51, which corresponds to Guilt. So, the answer is {\"answer\": \"Guilt\"}." } ], "images": [ "scigen/train/3516.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Event type analysis: Cells are counts of post-annotation out of 55 descriptions for each emotion." ], "reasoning": "To find the emotion in German with the highest frequency of events where the author is presumably responsible, look at the 'Responsible' row under the 'German' section. The highest value is 51, which corresponds to Guilt.", "question": "Which emotion in German has the highest frequency of events where the author is presumably responsible?", "answer": "So, the answer is {\"answer\": \"Guilt\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis In English, Joy is more frequently associated with social events than Fear. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nIn English, Joy is more frequently associated with social events than Fear.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Social' row values for Joy and Fear under the 'English' section. Joy has a value of 41, while Fear has a value of 30. Therefore, Joy is more frequently associated with social events than Fear. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3516.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Event type analysis: Cells are counts of post-annotation out of 55 descriptions for each emotion." ], "reasoning": "To verify this statement, compare the 'Social' row values for Joy and Fear under the 'English' section. Joy has a value of 41, while Fear has a value of 30. Therefore, Joy is more frequently associated with social events than Fear.", "statement": "In English, Joy is more frequently associated with social events than Fear.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Results (in %) on the test set. [♠] denotes results reported on a 950/50 training/development split rather than our 900/100 split. As indicated, Att-BiLSTM-CRF used LSTM-char word embeddings.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "All models with the CRF layer outperformed their respective baseline BiLSTM models in F1 scores for all entity categories, and chemical NER consistently outperformed disease NER.The first three rows show the performance of baseline models without the CRF layer, the next three rows show the performance of BiLSTM-CRF models without additional features, and then the next three rows show the results for BiLSTM-CRF models with additional gazetteer features. In particular, on chemical NER, both BiLSTM-CRF-based models with character-level word embeddings obtained the same F1 score (91.94%), while on disease NER the model with CNN-char obtained slightly higher performance (83.01%) than the model with LSTM-char (82.83%). All models with the CRF layer outperformed their respective baseline BiLSTM models in F1 scores for all entity categories. The pattern of chemical NER outperforming disease NER is consistent across all tools. our 900/100 split). Dnorm Leaman et al. The tmChem Leaman et al." } ], "images": [ "scigen/train/3517.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results (in %) on the test set. [♠] denotes results reported on a 950/50 training/development split rather than our 900/100 split. As indicated, Att-BiLSTM-CRF used LSTM-char word embeddings." ], "reasoning": "All models with the CRF layer outperformed their respective baseline BiLSTM models in F1 scores for all entity categories, and chemical NER consistently outperformed disease NER.", "description": "The first three rows show the performance of baseline models without the CRF layer, the next three rows show the performance of BiLSTM-CRF models without additional features, and then the next three rows show the results for BiLSTM-CRF models with additional gazetteer features. In particular, on chemical NER, both BiLSTM-CRF-based models with character-level word embeddings obtained the same F1 score (91.94%), while on disease NER the model with CNN-char obtained slightly higher performance (83.01%) than the model with LSTM-char (82.83%). All models with the CRF layer outperformed their respective baseline BiLSTM models in F1 scores for all entity categories. The pattern of chemical NER outperforming disease NER is consistent across all tools. our 900/100 split). Dnorm Leaman et al. The tmChem Leaman et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 3: Results (in %) on the test set. [♠] denotes results reported on a 950/50 training/development split rather than our 900/100 split. As indicated, Att-BiLSTM-CRF used LSTM-char word embeddings.', solve this question and give detail solutions. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich model achieved the highest F1 score for chemical NER and what was the score?\n" }, { "role": "assistant", "content": "From the table, the highest F1 score for chemical NER is achieved by the Att-BiLSTM-CRFPOS+Chunking+Gazetteer (LSTM-char) model with a score of 92.57%. So, the answer is {\"answer\": \"Att-BiLSTM-CRFPOS+Chunking+Gazetteer (LSTM-char) with 92.57%\"}" } ], "images": [ "scigen/train/3517.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results (in %) on the test set. [♠] denotes results reported on a 950/50 training/development split rather than our 900/100 split. As indicated, Att-BiLSTM-CRF used LSTM-char word embeddings." ], "reasoning": "From the table, the highest F1 score for chemical NER is achieved by the Att-BiLSTM-CRFPOS+Chunking+Gazetteer (LSTM-char) model with a score of 92.57%.", "question": "Which model achieved the highest F1 score for chemical NER and what was the score?", "answer": "So, the answer is {\"answer\": \"Att-BiLSTM-CRFPOS+Chunking+Gazetteer (LSTM-char) with 92.57%\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe BiLSTM-CRF+Gazetteer+CNN-char model achieved the highest F1 score for disease NER.\n" }, { "role": "assistant", "content": "From the table, the highest F1 score for disease NER is achieved by the BiLSTM-CRF+Gazetteer+CNN-char model with a score of 82.94%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3517.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results (in %) on the test set. [♠] denotes results reported on a 950/50 training/development split rather than our 900/100 split. As indicated, Att-BiLSTM-CRF used LSTM-char word embeddings." ], "reasoning": "From the table, the highest F1 score for disease NER is achieved by the BiLSTM-CRF+Gazetteer+CNN-char model with a score of 82.94%.", "statement": "The BiLSTM-CRF+Gazetteer+CNN-char model achieved the highest F1 score for disease NER.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: The perplexity of real paired knowledge on four datasets.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The knowledge generation models RealLM and Decoupling effectively capture conditional information from dialogue history, as evidenced by their performance across different datasets.The performance of knowledge generation models. To evaluate RealLM and Decoupling, we first feed the dialogue history (x), and then compute the Pσ(^z|x). The results indicates that the knowledge generation models trained by RealLM and Decoupling can catch the conditional information of knowledge given dialogue history, although they share parameters with the response generation parts." } ], "images": [ "scigen/train/3518.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The perplexity of real paired knowledge on four datasets." ], "reasoning": "The knowledge generation models RealLM and Decoupling effectively capture conditional information from dialogue history, as evidenced by their performance across different datasets.", "description": "The performance of knowledge generation models. To evaluate RealLM and Decoupling, we first feed the dialogue history (x), and then compute the Pσ(^z|x). The results indicates that the knowledge generation models trained by RealLM and Decoupling can catch the conditional information of knowledge given dialogue history, although they share parameters with the response generation parts.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 4: The perplexity of real paired knowledge on four datasets.', unfold the steps to solve the question given next. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which model has the highest score on the WOW-unseen dataset?\n" }, { "role": "assistant", "content": "To find the model with the highest score on the WOW-unseen dataset, compare the scores of LM, RealLM, and Decoupling. The scores are 46.51, 46.47, and 43.98 respectively. The highest score is 46.51 by LM. So, the answer is {\"answer\": \"LM\"}." } ], "images": [ "scigen/train/3518.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The perplexity of real paired knowledge on four datasets." ], "reasoning": "To find the model with the highest score on the WOW-unseen dataset, compare the scores of LM, RealLM, and Decoupling. The scores are 46.51, 46.47, and 43.98 respectively. The highest score is 46.51 by LM.", "question": "Which model has the highest score on the WOW-unseen dataset?", "answer": "So, the answer is {\"answer\": \"LM\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRealLM outperforms Decoupling on the LIGHT dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the scores of RealLM and Decoupling on the LIGHT dataset. RealLM has a score of 4.22, while Decoupling has a score of 23.61. Since 4.22 is less than 23.61, RealLM does not outperform Decoupling on the LIGHT dataset. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3518.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The perplexity of real paired knowledge on four datasets." ], "reasoning": "To verify this statement, compare the scores of RealLM and Decoupling on the LIGHT dataset. RealLM has a score of 4.22, while Decoupling has a score of 23.61. Since 4.22 is less than 23.61, RealLM does not outperform Decoupling on the LIGHT dataset.", "statement": "RealLM outperforms Decoupling on the LIGHT dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Comparison of various DFSMN models in objective measures', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The DFSMN models are evaluated based on various objective measures, including RMSE, U/V prediction errors, MCD, BAPD, and total MSE, with different configurations of layers and orders.All DFSMN models are a composition of DFSMN layers and fully-connected layers. One DFSMN layer has 2048 hidden units and 512 projection units, meanwhile each fully-connected layer has 2048 hidden units. Besides, column 4 is in form of N1,N2,s1,s2, indicating that each DFSMN layer looks back N1 frames with stride s1 and looks ahead N2 frames with stride s2. Here we let DFSMN look back and ahead same frames for simplicity. Since the class of FSMN models has not been applied to TTS tasks ever before, we start exploration from a DFSMN model which is shallow and short-sighted. 3 DFSMN layers and the memory order and stride are both 1, which makes it close to a DNN. We then increase the order of DFSMN models successively (1⇒2⇒5⇒10) by fixing the number of DFSMN layers. From D to F, we fix the order and stride as 10,10,2,2 and increase the network depth (3⇒6⇒10). Again, from F, we continue to increase the order of DFSMN models (10⇒20⇒40⇒80), finally reaching a context window of 3200 frames, approaching max length of utterances in the corpus. These objective measures include root mean squared error (RMSE) of F0 in Hz, unvoiced/voiced (U/V) prediction errors, mel-cepstral distortion (MCD) in dB, BAP Distortion (BAPD) and the total MSE on normalized acoustic features. The model size and the FLOPS (floating-point operations to generate per second speech) are also recorded. All objective measures are calculated on the 3 hour validation data." } ], "images": [ "scigen/train/3519.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of various DFSMN models in objective measures" ], "reasoning": "The DFSMN models are evaluated based on various objective measures, including RMSE, U/V prediction errors, MCD, BAPD, and total MSE, with different configurations of layers and orders.", "description": "All DFSMN models are a composition of DFSMN layers and fully-connected layers. One DFSMN layer has 2048 hidden units and 512 projection units, meanwhile each fully-connected layer has 2048 hidden units. Besides, column 4 is in form of N1,N2,s1,s2, indicating that each DFSMN layer looks back N1 frames with stride s1 and looks ahead N2 frames with stride s2. Here we let DFSMN look back and ahead same frames for simplicity. Since the class of FSMN models has not been applied to TTS tasks ever before, we start exploration from a DFSMN model which is shallow and short-sighted. 3 DFSMN layers and the memory order and stride are both 1, which makes it close to a DNN. We then increase the order of DFSMN models successively (1⇒2⇒5⇒10) by fixing the number of DFSMN layers. From D to F, we fix the order and stride as 10,10,2,2 and increase the network depth (3⇒6⇒10). Again, from F, we continue to increase the order of DFSMN models (10⇒20⇒40⇒80), finally reaching a context window of 3200 frames, approaching max length of utterances in the corpus. These objective measures include root mean squared error (RMSE) of F0 in Hz, unvoiced/voiced (U/V) prediction errors, mel-cepstral distortion (MCD) in dB, BAP Distortion (BAPD) and the total MSE on normalized acoustic features. The model size and the FLOPS (floating-point operations to generate per second speech) are also recorded. All objective measures are calculated on the 3 hour validation data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: Comparison of various DFSMN models in objective measures'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich DFSMN model has the lowest Objective Measures MSE and what is its network depth?\n" }, { "role": "assistant", "content": "By examining the 'Objective Measures MSE' column, the lowest value is 0.0269, which corresponds to model I. The '#Layers' column shows that model I has a network depth of 10+2. So, the answer is {\"answer\": \"10+2\"}." } ], "images": [ "scigen/train/3519.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of various DFSMN models in objective measures" ], "reasoning": "By examining the 'Objective Measures MSE' column, the lowest value is 0.0269, which corresponds to model I. The '#Layers' column shows that model I has a network depth of 10+2.", "question": "Which DFSMN model has the lowest Objective Measures MSE and what is its network depth?", "answer": "So, the answer is {\"answer\": \"10+2\"}." }, { "messages": [ { "role": "user", "content": "Model D has the highest Objective Measures F0 RMSE among all DFSMN models. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nModel D has the highest Objective Measures F0 RMSE among all DFSMN models.\n" }, { "role": "assistant", "content": "By comparing the 'Objective Measures F0 RMSE' values for all DFSMN models, model A has the highest value of 33.41, not model D. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3519.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of various DFSMN models in objective measures" ], "reasoning": "By comparing the 'Objective Measures F0 RMSE' values for all DFSMN models, model A has the highest value of 33.41, not model D.", "statement": "Model D has the highest Objective Measures F0 RMSE among all DFSMN models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Ablation study and comparison with Transformer-based alternatives. All results are reported after the REINFORCE optimization stage.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Using three encoding and decoding layers in the Transformer improves CIDEr score significantly, and further enhancements are achieved with meshed connectivity and the M2 Transformer model.Firstly, we investigate the impact of the number of encoding and decoding layers on captioning performance. Varying the number of layers, we observe a significant increase in performance when using three encoding and three decoding layers, which leads to 123.6 CIDEr. We hypothesize that this is due to the reduced training set size and to the lower semantic complexities of sentences in captioning with respect to those of language understanding tasks. Following this finding, all subsequent experiments will use three layers. Attention on Attention baseline. We also evaluate a recent proposal that can be straightforwardly applied to the Transformer as an alternative to standard dot-product attention. Specifically, we evaluate the addition of the “Attention on Attention” (AoA) approach [huang2019attention] to the attentive layers, both in the encoder and in the decoder. Noticeably, in [huang2019attention] this has been done with a Recurrent language model with attention, but the approach is sufficiently general to be applied to any attention stage. In this case, the result of dot-product attention is concatenated with the initial query and fed to two fully connected layers to obtain an information vector and a sigmoidal attention gate, then the two vectors are multiplied together. The final result is used as an alternative to the standard dot-product attention. Meshed Connectivity. We then evaluate the role of the meshed connections between encoder and decoder layers. Using this 1-to-1 connectivity schema already brings an improvement with respect to using the output of the last encoder layer as in the standard Transformer (123.6 CIDEr vs 129.2 CIDEr), thus confirming that exploiting a multi-level encoding of image regions is beneficial. When we instead use our meshed connectivity schema, that exploits relationships encoded at all levels and weights them with a sigmoid gating, we observe a further performance improvement, from 129.2 CIDEr to 131.2 CIDEr. This amounts to a total improvement of 7.6 points with respect to the standard Transformer. Also, the result of our full model is superior to that obtained using the AoA. Role of persistent memory. We evaluate the role of memory vectors in both the 1-to-1 configuration and in the final configuration with meshed connections. Further experiments on the number of memory vectors can be found in the supplementary material." } ], "images": [ "scigen/train/3520.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Ablation study and comparison with Transformer-based alternatives. All results are reported after the REINFORCE optimization stage." ], "reasoning": "Using three encoding and decoding layers in the Transformer improves CIDEr score significantly, and further enhancements are achieved with meshed connectivity and the M2 Transformer model.", "description": "Firstly, we investigate the impact of the number of encoding and decoding layers on captioning performance. Varying the number of layers, we observe a significant increase in performance when using three encoding and three decoding layers, which leads to 123.6 CIDEr. We hypothesize that this is due to the reduced training set size and to the lower semantic complexities of sentences in captioning with respect to those of language understanding tasks. Following this finding, all subsequent experiments will use three layers. Attention on Attention baseline. We also evaluate a recent proposal that can be straightforwardly applied to the Transformer as an alternative to standard dot-product attention. Specifically, we evaluate the addition of the “Attention on Attention” (AoA) approach [huang2019attention] to the attentive layers, both in the encoder and in the decoder. Noticeably, in [huang2019attention] this has been done with a Recurrent language model with attention, but the approach is sufficiently general to be applied to any attention stage. In this case, the result of dot-product attention is concatenated with the initial query and fed to two fully connected layers to obtain an information vector and a sigmoidal attention gate, then the two vectors are multiplied together. The final result is used as an alternative to the standard dot-product attention. Meshed Connectivity. We then evaluate the role of the meshed connections between encoder and decoder layers. Using this 1-to-1 connectivity schema already brings an improvement with respect to using the output of the last encoder layer as in the standard Transformer (123.6 CIDEr vs 129.2 CIDEr), thus confirming that exploiting a multi-level encoding of image regions is beneficial. When we instead use our meshed connectivity schema, that exploits relationships encoded at all levels and weights them with a sigmoid gating, we observe a further performance improvement, from 129.2 CIDEr to 131.2 CIDEr. This amounts to a total improvement of 7.6 points with respect to the standard Transformer. Also, the result of our full model is superior to that obtained using the AoA. Role of persistent memory. We evaluate the role of memory vectors in both the 1-to-1 configuration and in the final configuration with meshed connections. Further experiments on the number of memory vectors can be found in the supplementary material.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 1: Ablation study and comparison with Transformer-based alternatives. All results are reported after the REINFORCE optimization stage.'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. \n\nQuestion: What is the CIDEr score improvement when using the M2 Transformer compared to the standard Transformer with 6 layers?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The standard Transformer with 6 layers has a CIDEr score of 121.8, while the M2 Transformer has a CIDEr score of 131.2. The improvement is calculated as 131.2 - 121.8. So, the answer is {\"answer\": \"9.4\"}" } ], "images": [ "scigen/train/3520.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Ablation study and comparison with Transformer-based alternatives. All results are reported after the REINFORCE optimization stage." ], "reasoning": "The standard Transformer with 6 layers has a CIDEr score of 121.8, while the M2 Transformer has a CIDEr score of 131.2. The improvement is calculated as 131.2 - 121.8.", "question": "What is the CIDEr score improvement when using the M2 Transformer compared to the standard Transformer with 6 layers?", "answer": "So, the answer is {\"answer\": \"9.4\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe M2 Transformer with softmax has a higher CIDEr score than the Transformer with AoA.\n" }, { "role": "assistant", "content": "The table shows that the M2 Transformer with softmax has a CIDEr score of 130.3, while the Transformer with AoA has a CIDEr score of 129.1. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3520.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Ablation study and comparison with Transformer-based alternatives. All results are reported after the REINFORCE optimization stage." ], "reasoning": "The table shows that the M2 Transformer with softmax has a CIDEr score of 130.3, while the Transformer with AoA has a CIDEr score of 129.1.", "statement": "The M2 Transformer with softmax has a higher CIDEr score than the Transformer with AoA.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Comparison with the state of the art on the “Karpathy” test split, in single-model setting.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Our method, M2 Transformer, surpasses all other approaches in BLEU-4, METEOR, and CIDEr, while being competitive in BLEU-1 and SPICE, and slightly worse in ROUGE compared to AoANet.Single model. As it can be observed, our method surpasses all other approaches in terms of BLEU-4, METEOR and CIDEr, while being competitive on BLEU-1 and SPICE with the best performer, and slightly worse on ROUGE with respect to AoANet [huang2019attention]. In particular, it advances the current state of the art on CIDEr by 1.4 points." } ], "images": [ "scigen/train/3521.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison with the state of the art on the “Karpathy” test split, in single-model setting." ], "reasoning": "Our method, M2 Transformer, surpasses all other approaches in BLEU-4, METEOR, and CIDEr, while being competitive in BLEU-1 and SPICE, and slightly worse in ROUGE compared to AoANet.", "description": "Single model. As it can be observed, our method surpasses all other approaches in terms of BLEU-4, METEOR and CIDEr, while being competitive on BLEU-1 and SPICE with the best performer, and slightly worse on ROUGE with respect to AoANet [huang2019attention]. In particular, it advances the current state of the art on CIDEr by 1.4 points.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Comparison with the state of the art on the “Karpathy” test split, in single-model setting.', and sequentially solve the question, writing out each step of your reasoning process. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich method achieves the highest score in CIDEr?\n" }, { "role": "assistant", "content": "By comparing the CIDEr (C) scores of all methods, M2 Transformer has the highest score of 131.2. So, the answer is {\"answer\": \"M2 Transformer\"}" } ], "images": [ "scigen/train/3521.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison with the state of the art on the “Karpathy” test split, in single-model setting." ], "reasoning": "By comparing the CIDEr (C) scores of all methods, M2 Transformer has the highest score of 131.2.", "question": "Which method achieves the highest score in CIDEr?", "answer": "So, the answer is {\"answer\": \"M2 Transformer\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nM2 Transformer has the highest METEOR score among all methods.\n" }, { "role": "assistant", "content": "By comparing the METEOR (M) scores of all methods, M2 Transformer has the highest score of 29.2. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3521.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison with the state of the art on the “Karpathy” test split, in single-model setting." ], "reasoning": "By comparing the METEOR (M) scores of all methods, M2 Transformer has the highest score of 29.2.", "statement": "M2 Transformer has the highest METEOR score among all methods.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 3: Comparison with the state of the art on the “Karpathy” test split, using an ensemble of models.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The ensemble of four models achieves the best performance across all metrics, with a notable increase of 2.5 CIDEr points compared to the current state of the art.Ensemble model. Following the common practice [rennie2017self, huang2019attention] of building an ensemble of models, we also report the performances of our approach when averaging the output probability distributions of multiple and independently trained instances of our model. Noticeably, when using four models our approach achieves the best performance according to all metrics, with an increase of 2.5 CIDEr points with respect to the current state of the art [huang2019attention]." } ], "images": [ "scigen/train/3522.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison with the state of the art on the “Karpathy” test split, using an ensemble of models." ], "reasoning": "The ensemble of four models achieves the best performance across all metrics, with a notable increase of 2.5 CIDEr points compared to the current state of the art.", "description": "Ensemble model. Following the common practice [rennie2017self, huang2019attention] of building an ensemble of models, we also report the performances of our approach when averaging the output probability distributions of multiple and independently trained instances of our model. Noticeably, when using four models our approach achieves the best performance according to all metrics, with an increase of 2.5 CIDEr points with respect to the current state of the art [huang2019attention].", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 3: Comparison with the state of the art on the “Karpathy” test split, using an ensemble of models.\nQuestion: Which model achieved the highest CIDEr score when using an ensemble of four models?\n" }, { "role": "assistant", "content": "By examining the table, the M2 Transformer model achieved the highest CIDEr score of 134.5 when using an ensemble of four models. So, the answer is {\"answer\": \"M2 Transformer\"}" } ], "images": [ "scigen/train/3522.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison with the state of the art on the “Karpathy” test split, using an ensemble of models." ], "reasoning": "By examining the table, the M2 Transformer model achieved the highest CIDEr score of 134.5 when using an ensemble of four models.", "question": "Which model achieved the highest CIDEr score when using an ensemble of four models?", "answer": "So, the answer is {\"answer\": \"M2 Transformer\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe SCST model achieved the highest CIDEr score when using an ensemble of four models.\n" }, { "role": "assistant", "content": "The table shows that the M2 Transformer model, not the SCST model, achieved the highest CIDEr score of 134.5 when using an ensemble of four models. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3522.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison with the state of the art on the “Karpathy” test split, using an ensemble of models." ], "reasoning": "The table shows that the M2 Transformer model, not the SCST model, achieved the highest CIDEr score of 134.5 when using an ensemble of four models.", "statement": "The SCST model achieved the highest CIDEr score when using an ensemble of four models.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Leaderboard of various methods on the online MS-COCO test server.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The M2 Transformer model surpasses the current state of the art on all metrics, achieving an advancement of 1.4 CIDEr points with respect to the best performer.Online Evaluation. In this case, we use the ensemble of four models previously described, trained on the “Karpathy” training split. The evaluation is done on the COCO test split, for which ground-truth annotations are not publicly available. For fairness of comparison, they also used an ensemble configuration. As it can be seen, our method surpasses the current state of the art on all metrics, achieving an advancement of 1.4 CIDEr points with respect to the best performer." } ], "images": [ "scigen/train/3523.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Leaderboard of various methods on the online MS-COCO test server." ], "reasoning": "The M2 Transformer model surpasses the current state of the art on all metrics, achieving an advancement of 1.4 CIDEr points with respect to the best performer.", "description": "Online Evaluation. In this case, we use the ensemble of four models previously described, trained on the “Karpathy” training split. The evaluation is done on the COCO test split, for which ground-truth annotations are not publicly available. For fairness of comparison, they also used an ensemble configuration. As it can be seen, our method surpasses the current state of the art on all metrics, achieving an advancement of 1.4 CIDEr points with respect to the best performer.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Leaderboard of various methods on the online MS-COCO test server.\nQuestion: Which model achieved the highest CIDEr score on the COCO test split?\nSolve the above question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the CIDEr scores of all models in the table, the M2 Transformer model has the highest CIDEr score of 132.1. So, the answer is {\"answer\": \"M2 Transformer\"}" } ], "images": [ "scigen/train/3523.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Leaderboard of various methods on the online MS-COCO test server." ], "reasoning": "By comparing the CIDEr scores of all models in the table, the M2 Transformer model has the highest CIDEr score of 132.1.", "question": "Which model achieved the highest CIDEr score on the COCO test split?", "answer": "So, the answer is {\"answer\": \"M2 Transformer\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe GCN-LSTM model achieved the highest BLEU-4 c40 score.\n" }, { "role": "assistant", "content": "The table shows that the M2 Transformer model achieved the highest BLEU-4 c40 score of 72.8, not the GCN-LSTM model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3523.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Leaderboard of various methods on the online MS-COCO test server." ], "reasoning": "The table shows that the M2 Transformer model achieved the highest BLEU-4 c40 score of 72.8, not the GCN-LSTM model.", "statement": "The GCN-LSTM model achieved the highest BLEU-4 c40 score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 5: Performances on nocaps validation set, for in-domain and out-of-domain captioning.'.\n" }, { "role": "assistant", "content": "The use of Constrained Beam Search (CBS) significantly enhances the performance of models, especially in out-of-domain captioning, as shown by the higher scores in the table.We also assess the performance of our approach when dealing with images containing object categories that are not seen in the training set. We compare with Up-Down [anderson2018bottom] and Neural Baby Talk [lu2018neural], when using GloVe word embeddings and Constrained Beam Search (CBS) [anderson2016guided] to address the generation of out-of-vocabulary words and constrain the presence of categories detected by an object detector. To compare with our model, we use a simplified implementation of the procedure described in [agrawal2019nocaps] to extract constraints, without using word phrases. As expected, the use of CBS significantly enhances the performances, in particular on out-of-domain captioning." } ], "images": [ "scigen/train/3524.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Performances on nocaps validation set, for in-domain and out-of-domain captioning." ], "reasoning": "The use of Constrained Beam Search (CBS) significantly enhances the performance of models, especially in out-of-domain captioning, as shown by the higher scores in the table.", "description": "We also assess the performance of our approach when dealing with images containing object categories that are not seen in the training set. We compare with Up-Down [anderson2018bottom] and Neural Baby Talk [lu2018neural], when using GloVe word embeddings and Constrained Beam Search (CBS) [anderson2016guided] to address the generation of out-of-vocabulary words and constrain the presence of categories detected by an object detector. To compare with our model, we use a simplified implementation of the procedure described in [agrawal2019nocaps] to extract constraints, without using word phrases. As expected, the use of CBS significantly enhances the performances, in particular on out-of-domain captioning.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: Performances on nocaps validation set, for in-domain and out-of-domain captioning.'.\n\nQuestion: Which model achieved the highest overall CIDEr score?\n" }, { "role": "assistant", "content": "To find the model with the highest overall CIDEr score, we look at the 'Overall CIDEr' column and identify the model with the highest value. The M2 Transformer + CBS model has the highest overall CIDEr score of 75.0. So, the answer is {\"answer\": \"M2 Transformer + CBS\"}" } ], "images": [ "scigen/train/3524.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Performances on nocaps validation set, for in-domain and out-of-domain captioning." ], "reasoning": "To find the model with the highest overall CIDEr score, we look at the 'Overall CIDEr' column and identify the model with the highest value. The M2 Transformer + CBS model has the highest overall CIDEr score of 75.0.", "question": "Which model achieved the highest overall CIDEr score?", "answer": "So, the answer is {\"answer\": \"M2 Transformer + CBS\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Transformer model without CBS has a higher out-of-domain CIDEr score than the NBT + CBS model.\n" }, { "role": "assistant", "content": "The out-of-domain CIDEr score for the Transformer model without CBS is 29.7, while the NBT + CBS model has an out-of-domain CIDEr score of 62.4. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3524.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Performances on nocaps validation set, for in-domain and out-of-domain captioning." ], "reasoning": "The out-of-domain CIDEr score for the Transformer model without CBS is 29.7, while the NBT + CBS model has an out-of-domain CIDEr score of 62.4. Therefore, the statement is refuted.", "statement": "The Transformer model without CBS has a higher out-of-domain CIDEr score than the NBT + CBS model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 7: Captioning results of M2 Transformer using different numbers of encoder and decoder layers.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The M2 Transformer shows no significant performance improvement when varying the number of encoding and decoding layers, even with non-symmetric architectures.Encoder and decoder layers. To complement the analysis presented in Sec. 4.3, we also investigate the performance of the M2 Transformer when changing the number of encoding and decoding layers. As our model can deal with a different number of encoding and decoding layers, we also experimented with non symmetric encoding-decoding architectures, without however noticing significant improvements in performance." } ], "images": [ "scigen/train/3525.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Captioning results of M2 Transformer using different numbers of encoder and decoder layers." ], "reasoning": "The M2 Transformer shows no significant performance improvement when varying the number of encoding and decoding layers, even with non-symmetric architectures.", "description": "Encoder and decoder layers. To complement the analysis presented in Sec. 4.3, we also investigate the performance of the M2 Transformer when changing the number of encoding and decoding layers. As our model can deal with a different number of encoding and decoding layers, we also experimented with non symmetric encoding-decoding architectures, without however noticing significant improvements in performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 7: Captioning results of M2 Transformer using different numbers of encoder and decoder layers.' to sequentially solve the question presented below. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhat is the highest score for the C metric when using different layers in the M2 Transformer?\n" }, { "role": "assistant", "content": "By examining the table, the highest score for the C metric is found in the row with 3 layers, which is 131.2. So, the answer is {\"answer\": \"131.2\"}." } ], "images": [ "scigen/train/3525.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Captioning results of M2 Transformer using different numbers of encoder and decoder layers." ], "reasoning": "By examining the table, the highest score for the C metric is found in the row with 3 layers, which is 131.2.", "question": "What is the highest score for the C metric when using different layers in the M2 Transformer?", "answer": "So, the answer is {\"answer\": \"131.2\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe M2 Transformer achieves the highest B-4 score with 3 layers.\n" }, { "role": "assistant", "content": "The table shows that the B-4 score for 3 layers is 39.1, which is the highest compared to 38.6 for both 2 and 4 layers. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3525.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Captioning results of M2 Transformer using different numbers of encoder and decoder layers." ], "reasoning": "The table shows that the B-4 score for 3 layers is 39.1, which is the highest compared to 38.6 for both 2 and 4 layers.", "statement": "The M2 Transformer achieves the highest B-4 score with 3 layers.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 8: Breakdown of SPICE F-scores over various subcategories.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The M2 Transformer model outperforms the Up-Down and Transformer models in SPICE, object identification, attributes, relationships, color, count, and size.SPICE F-scores. As it can be seen, our model significantly improves on identifying objects, attributes and relationships between objects." } ], "images": [ "scigen/train/3526.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Breakdown of SPICE F-scores over various subcategories." ], "reasoning": "The M2 Transformer model outperforms the Up-Down and Transformer models in SPICE, object identification, attributes, relationships, color, count, and size.", "description": "SPICE F-scores. As it can be seen, our model significantly improves on identifying objects, attributes and relationships between objects.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 8: Breakdown of SPICE F-scores over various subcategories.' and proceed to solve the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model has the highest score in identifying relationships between objects?\n" }, { "role": "assistant", "content": "By comparing the 'Rel.' column for all models, the M2 Transformer has the highest score of 11.6. So, the answer is {\"answer\": \"M2 Transformer\"}." } ], "images": [ "scigen/train/3526.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Breakdown of SPICE F-scores over various subcategories." ], "reasoning": "By comparing the 'Rel.' column for all models, the M2 Transformer has the highest score of 11.6.", "question": "Which model has the highest score in identifying relationships between objects?", "answer": "So, the answer is {\"answer\": \"M2 Transformer\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Transformer model has the lowest score in identifying attributes.\n" }, { "role": "assistant", "content": "By comparing the 'Attr.' column for all models, the Transformer model has a score of 9.6, which is the lowest among the three models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3526.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Breakdown of SPICE F-scores over various subcategories." ], "reasoning": "By comparing the 'Attr.' column for all models, the Transformer model has a score of 9.6, which is the lowest among the three models.", "statement": "The Transformer model has the lowest score in identifying attributes.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Sequence Masking: Greedy WER, LibriSpeech for Jasper 10x4 after 50 epochs'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Masking layer norm and batch norm calculations improved performance, but masking both convolutions and batch norm resulted in worse performance.During batching, all sequences are padded to match the longest sequence. These padded values caused issues when using layer norm. We applied a sequence mask to exclude padding values from the mean and variance calculation. Further, we computed mean and variance over both the time dimension and channels similar to the sequence-wise normalization proposed by Laurent et al. In addition to masking layer norm, we additionally applied masking prior to the convolution operation, and masking the mean and variance calculations in batch norm. Interestingly, we found that while masking before convolution gives a lower WER, using masks for both convolutions and batch norm results in worse performance." } ], "images": [ "scigen/train/3527.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Sequence Masking: Greedy WER, LibriSpeech for Jasper 10x4 after 50 epochs" ], "reasoning": "Masking layer norm and batch norm calculations improved performance, but masking both convolutions and batch norm resulted in worse performance.", "description": "During batching, all sequences are padded to match the longest sequence. These padded values caused issues when using layer norm. We applied a sequence mask to exclude padding values from the mean and variance calculation. Further, we computed mean and variance over both the time dimension and channels similar to the sequence-wise normalization proposed by Laurent et al. In addition to masking layer norm, we additionally applied masking prior to the convolution operation, and masking the mean and variance calculations in batch norm. Interestingly, we found that while masking before convolution gives a lower WER, using masks for both convolutions and batch norm results in worse performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Sequence Masking: Greedy WER, LibriSpeech for Jasper 10x4 after 50 epochs\nQuestion: Which masking technique resulted in the lowest WER on the Dev Clean dataset?\nGive a detailed response to the above question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the 'Dev Clean' column values for each masking technique, we see that 'Conv Mask' has the lowest WER of 5.66. So, the answer is {\"answer\": \"Conv Mask\"}." } ], "images": [ "scigen/train/3527.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Sequence Masking: Greedy WER, LibriSpeech for Jasper 10x4 after 50 epochs" ], "reasoning": "By comparing the 'Dev Clean' column values for each masking technique, we see that 'Conv Mask' has the lowest WER of 5.66.", "question": "Which masking technique resulted in the lowest WER on the Dev Clean dataset?", "answer": "So, the answer is {\"answer\": \"Conv Mask\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nMasking both convolutions and batch norm resulted in the lowest WER on the Dev Other dataset.\n" }, { "role": "assistant", "content": "The table shows that 'Conv+BN Mask' has a WER of 16.97 on the Dev Other dataset, which is not the lowest. 'Conv Mask' has the lowest WER of 16.77. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3527.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Sequence Masking: Greedy WER, LibriSpeech for Jasper 10x4 after 50 epochs" ], "reasoning": "The table shows that 'Conv+BN Mask' has a WER of 16.97 on the Dev Other dataset, which is not the lowest. 'Conv Mask' has the lowest WER of 16.77.", "statement": "Masking both convolutions and batch norm resulted in the lowest WER on the Dev Other dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Normalization and Activation: Greedy WER, LibriSpeech after 50 epochs'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Layer norm with GAU performed best on the smaller model, while batch norm with ReLU outperformed other choices on the larger model.ReLU, clipped ReLU (cReLU), and leaky ReLU (lReLU) 2 types of gated units: gated linear units (GLU) We found that layer norm with GAU performed the best on the smaller model. Layer norm with ReLU and batch norm with ReLU came second and third in our tests. Using these 3, we conducted further experiments on a larger Jasper10x4. For larger models, we noticed that batch norm with ReLU outperformed other choices. Thus, leading us to decide on batch normalization and ReLU for our architecture." } ], "images": [ "scigen/train/3528.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Normalization and Activation: Greedy WER, LibriSpeech after 50 epochs" ], "reasoning": "Layer norm with GAU performed best on the smaller model, while batch norm with ReLU outperformed other choices on the larger model.", "description": "ReLU, clipped ReLU (cReLU), and leaky ReLU (lReLU) 2 types of gated units: gated linear units (GLU) We found that layer norm with GAU performed the best on the smaller model. Layer norm with ReLU and batch norm with ReLU came second and third in our tests. Using these 3, we conducted further experiments on a larger Jasper10x4. For larger models, we noticed that batch norm with ReLU outperformed other choices. Thus, leading us to decide on batch normalization and ReLU for our architecture.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: Table 2: Normalization and Activation: Greedy WER, LibriSpeech after 50 epochs\nQuestion: Which normalization and activation combination had the lowest 'Dev Clean' score for the Jasper 10x4 model?\n" }, { "role": "assistant", "content": "To find the lowest 'Dev Clean' score for the Jasper 10x4 model, look at the 'Dev Clean' column for the Jasper 10x4 rows. The lowest score is 6.15, which corresponds to the combination of Batch Norm and ReLU. So, the answer is {\"answer\": \"Batch Norm and ReLU\"}" } ], "images": [ "scigen/train/3528.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Normalization and Activation: Greedy WER, LibriSpeech after 50 epochs" ], "reasoning": "To find the lowest 'Dev Clean' score for the Jasper 10x4 model, look at the 'Dev Clean' column for the Jasper 10x4 rows. The lowest score is 6.15, which corresponds to the combination of Batch Norm and ReLU.", "question": "Which normalization and activation combination had the lowest 'Dev Clean' score for the Jasper 10x4 model?", "answer": "So, the answer is {\"answer\": \"Batch Norm and ReLU\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nLayer norm with GAU had the lowest 'Dev Clean' score for the Jasper 5x3 model.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'Dev Clean' scores for the Jasper 5x3 model. The lowest score is 8.35, which corresponds to the combination of Layer Norm and GAU. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3528.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Normalization and Activation: Greedy WER, LibriSpeech after 50 epochs" ], "reasoning": "To verify this statement, look at the 'Dev Clean' scores for the Jasper 5x3 model. The lowest score is 8.35, which corresponds to the combination of Layer Norm and GAU.", "statement": "Layer norm with GAU had the lowest 'Dev Clean' score for the Jasper 5x3 model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: Ablation results w.r.t the contextual encoder modules on IEMOCAP dataset.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The speaker-level encoder is crucial for mitigating long-distance dependency issues, and removing both encoders results in a significant drop in F1 score to 36.7%, highlighting the importance of contextual modeling.We remove them one at a time and found that the speaker-level encoder is slightly more important in overall performance. This is due to speaker-level encoder mitigating long distance dependency issue of sequential encoder and DialogueRNN. Removing both of them results in a very poor F1 score of 36.7 %, which demonstrates the importance of contextual modelling in conversational emotion recognition." } ], "images": [ "scigen/train/3529.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ablation results w.r.t the contextual encoder modules on IEMOCAP dataset." ], "reasoning": "The speaker-level encoder is crucial for mitigating long-distance dependency issues, and removing both encoders results in a significant drop in F1 score to 36.7%, highlighting the importance of contextual modeling.", "description": "We remove them one at a time and found that the speaker-level encoder is slightly more important in overall performance. This is due to speaker-level encoder mitigating long distance dependency issue of sequential encoder and DialogueRNN. Removing both of them results in a very poor F1 score of 36.7 %, which demonstrates the importance of contextual modelling in conversational emotion recognition.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: Ablation results w.r.t the contextual encoder modules on IEMOCAP dataset.', solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the F1 score when both the sequential encoder and speaker-level encoder are removed?\n" }, { "role": "assistant", "content": "The table shows the F1 scores for different combinations of the sequential encoder and speaker-level encoder. When both are removed, the F1 score is 36.75%. So, the answer is {\"answer\": \"36.75\"}." } ], "images": [ "scigen/train/3529.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ablation results w.r.t the contextual encoder modules on IEMOCAP dataset." ], "reasoning": "The table shows the F1 scores for different combinations of the sequential encoder and speaker-level encoder. When both are removed, the F1 score is 36.75%.", "question": "What is the F1 score when both the sequential encoder and speaker-level encoder are removed?", "answer": "So, the answer is {\"answer\": \"36.75\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe F1 score is higher when only the speaker-level encoder is used compared to when only the sequential encoder is used.\n" }, { "role": "assistant", "content": "The table shows that the F1 score is 56.71% when only the speaker-level encoder is used, and 55.30% when only the sequential encoder is used. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3529.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ablation results w.r.t the contextual encoder modules on IEMOCAP dataset." ], "reasoning": "The table shows that the F1 score is 56.71% when only the speaker-level encoder is used, and 55.30% when only the sequential encoder is used.", "statement": "The F1 score is higher when only the speaker-level encoder is used compared to when only the sequential encoder is used.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'TABLE III: Results of the CNN-based image-input model and the bi-directional LSTM text-input model on Twemoji-Images, as well as the fusion of the two.'.\n" }, { "role": "assistant", "content": "The fusion of image and text modalities significantly improves performance across most metrics, indicating complementary information between the two.As described previously, we train a model to predict emoji based on CNN representations of images. We also present results for testing the text-trained model on this subset. We see that the image modality is competitive to the text modality for the prediction of emoji. This suggests that the emoji may often be as related to the images as they are to the text content. Overall, the performance of the models are broadly similar to those on the full Twemoji dataset, which is encouraging. It suggests that the relationship between the input data and the annotation is not too dissimilar to the whole set in this subset. , we provide scores for a fusion of both the image and text modalities. We see a significant improvement across most metrics through the fusion of both modalities, which tells us that they have complementary information. Though this could be an artifact of the representations used in either modality, it is reasonable to assume that the semantics of the emoji are not strictly tied to either modality, which is evidence that emoji should be considered as a modality in their own right. parameter α. We see that the curve hits its peak near the center, with a skew toward the text input. This suggests a slightly stronger correlation between the emoji modality and text than between emoji and images." } ], "images": [ "scigen/train/3530.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Results of the CNN-based image-input model and the bi-directional LSTM text-input model on Twemoji-Images, as well as the fusion of the two." ], "reasoning": "The fusion of image and text modalities significantly improves performance across most metrics, indicating complementary information between the two.", "description": "As described previously, we train a model to predict emoji based on CNN representations of images. We also present results for testing the text-trained model on this subset. We see that the image modality is competitive to the text modality for the prediction of emoji. This suggests that the emoji may often be as related to the images as they are to the text content. Overall, the performance of the models are broadly similar to those on the full Twemoji dataset, which is encouraging. It suggests that the relationship between the input data and the annotation is not too dissimilar to the whole set in this subset. , we provide scores for a fusion of both the image and text modalities. We see a significant improvement across most metrics through the fusion of both modalities, which tells us that they have complementary information. Though this could be an artifact of the representations used in either modality, it is reasonable to assume that the semantics of the emoji are not strictly tied to either modality, which is evidence that emoji should be considered as a modality in their own right. parameter α. We see that the curve hits its peak near the center, with a skew toward the text input. This suggests a slightly stronger correlation between the emoji modality and text than between emoji and images.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'TABLE III: Results of the CNN-based image-input model and the bi-directional LSTM text-input model on Twemoji-Images, as well as the fusion of the two.', solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the msAP score for the fusion of image and text modalities?\n" }, { "role": "assistant", "content": "The table shows the msAP scores for different models. The msAP score for the fusion of image and text modalities is 27.0. So, the answer is {\"answer\": \"27.0\"}" } ], "images": [ "scigen/train/3530.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Results of the CNN-based image-input model and the bi-directional LSTM text-input model on Twemoji-Images, as well as the fusion of the two." ], "reasoning": "The table shows the msAP scores for different models. The msAP score for the fusion of image and text modalities is 27.0.", "question": "What is the msAP score for the fusion of image and text modalities?", "answer": "So, the answer is {\"answer\": \"27.0\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe fusion model has a higher Top-1 score than both single modality models.\n" }, { "role": "assistant", "content": "The table shows that the Top-1 score for the fusion model is 20.6, which is higher than the Top-1 scores for both the image-only model (14.7) and the text-only model (17.7). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3530.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Results of the CNN-based image-input model and the bi-directional LSTM text-input model on Twemoji-Images, as well as the fusion of the two." ], "reasoning": "The table shows that the Top-1 score for the fusion model is 20.6, which is higher than the Top-1 scores for both the image-only model (14.7) and the text-only model (17.7).", "statement": "The fusion model has a higher Top-1 score than both single modality models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE V: Emoji Anticipation results, reported on Twemoji-Images. Emoji are predicted without any direct supervision data, analogous to what must be done when new emoji are released. We see improvement across all metrics when a fusion of the input modalities is used.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The fusion model significantly outperforms random guesses, achieving a top-1 accuracy of 1.5% compared to 0.08% for random guesses.We observe that, as would be expected, the overall scores are much lower than the supervised approaches in the previous section. Though the results are small, they are significantly above random. The top-1 accuracy of random guesses on the Twemoji-Images test set is on the order of 0.08% compared with 1.5% for the fusion of the zero-shot results." } ], "images": [ "scigen/train/3531.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Emoji Anticipation results, reported on Twemoji-Images. Emoji are predicted without any direct supervision data, analogous to what must be done when new emoji are released. We see improvement across all metrics when a fusion of the input modalities is used." ], "reasoning": "The fusion model significantly outperforms random guesses, achieving a top-1 accuracy of 1.5% compared to 0.08% for random guesses.", "description": "We observe that, as would be expected, the overall scores are much lower than the supervised approaches in the previous section. Though the results are small, they are significantly above random. The top-1 accuracy of random guesses on the Twemoji-Images test set is on the order of 0.08% compared with 1.5% for the fusion of the zero-shot results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'TABLE V: Emoji Anticipation results, reported on Twemoji-Images. Emoji are predicted without any direct supervision data, analogous to what must be done when new emoji are released. We see improvement across all metrics when a fusion of the input modalities is used.', and sequentially solve the question, writing out each step of your reasoning process. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the msAP score for the Fusion model?\n" }, { "role": "assistant", "content": "The msAP score for the Fusion model is found in the last column of the table under the Fusion row. So, the answer is {\"answer\": \"2.5\"}." } ], "images": [ "scigen/train/3531.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Emoji Anticipation results, reported on Twemoji-Images. Emoji are predicted without any direct supervision data, analogous to what must be done when new emoji are released. We see improvement across all metrics when a fusion of the input modalities is used." ], "reasoning": "The msAP score for the Fusion model is found in the last column of the table under the Fusion row.", "question": "What is the msAP score for the Fusion model?", "answer": "So, the answer is {\"answer\": \"2.5\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Zero-shot Text model has a higher Top-10 accuracy than the Zero-shot Images model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Zero-shot Text model has a higher Top-10 accuracy than the Zero-shot Images model.\n" }, { "role": "assistant", "content": "The Top-10 accuracy for the Zero-shot Text model is 3.9%, while for the Zero-shot Images model it is 4.3%, indicating that the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3531.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Emoji Anticipation results, reported on Twemoji-Images. Emoji are predicted without any direct supervision data, analogous to what must be done when new emoji are released. We see improvement across all metrics when a fusion of the input modalities is used." ], "reasoning": "The Top-10 accuracy for the Zero-shot Text model is 3.9%, while for the Zero-shot Images model it is 4.3%, indicating that the statement is incorrect.", "statement": "The Zero-shot Text model has a higher Top-10 accuracy than the Zero-shot Images model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'TABLE VI: Query-by-Emoji results for both supervised and zero-shot baselines. Results are reported in percentage mAP. In the supervised setting, we find the images to slightly outperform the text, but in the zero-shot setting the performance is reversed.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Image-based methods slightly outperform text-based and fusion methods in retrieving tweets, unlike the emoji prediction task where text was superior.Surprisingly, we see that retrieving tweets using only the supervised image understanding slightly outperforms both text-only and the fusion of the two. This result is markedly different from the emoji prediction task where text outperformed images. This could possibly be the result of a very strong correlation within high probability image-emoji pairs." } ], "images": [ "scigen/train/3532.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VI: Query-by-Emoji results for both supervised and zero-shot baselines. Results are reported in percentage mAP. In the supervised setting, we find the images to slightly outperform the text, but in the zero-shot setting the performance is reversed." ], "reasoning": "Image-based methods slightly outperform text-based and fusion methods in retrieving tweets, unlike the emoji prediction task where text was superior.", "description": "Surprisingly, we see that retrieving tweets using only the supervised image understanding slightly outperforms both text-only and the fusion of the two. This result is markedly different from the emoji prediction task where text outperformed images. This could possibly be the result of a very strong correlation within high probability image-emoji pairs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: TABLE VI: Query-by-Emoji results for both supervised and zero-shot baselines. Results are reported in percentage mAP. In the supervised setting, we find the images to slightly outperform the text, but in the zero-shot setting the performance is reversed.\nQuestion: Which method performed best on the Twemoji (Images) dataset?\nGive a detailed response to the above question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the values in the Twemoji (Images) column, the CNN (Image) method has the highest score of 22.0. So, the answer is {\"answer\": \"CNN (Image)\"}." } ], "images": [ "scigen/train/3532.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VI: Query-by-Emoji results for both supervised and zero-shot baselines. Results are reported in percentage mAP. In the supervised setting, we find the images to slightly outperform the text, but in the zero-shot setting the performance is reversed." ], "reasoning": "By comparing the values in the Twemoji (Images) column, the CNN (Image) method has the highest score of 22.0.", "question": "Which method performed best on the Twemoji (Images) dataset?", "answer": "So, the answer is {\"answer\": \"CNN (Image)\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The Fusion method outperformed the LSTM (Text) method on the Twemoji (Balanced) dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Fusion method outperformed the LSTM (Text) method on the Twemoji (Balanced) dataset.\n" }, { "role": "assistant", "content": "The table shows that the Fusion method scored 21.2 on the Twemoji (Balanced) dataset, while the LSTM (Text) method scored 35.5, indicating that the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3532.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VI: Query-by-Emoji results for both supervised and zero-shot baselines. Results are reported in percentage mAP. In the supervised setting, we find the images to slightly outperform the text, but in the zero-shot setting the performance is reversed." ], "reasoning": "The table shows that the Fusion method scored 21.2 on the Twemoji (Balanced) dataset, while the LSTM (Text) method scored 35.5, indicating that the statement is incorrect.", "statement": "The Fusion method outperformed the LSTM (Text) method on the Twemoji (Balanced) dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Replace in Sequence', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The LSTM Encoder-Decoder can replace words in a sequence by keeping the top 20 percent of words in the vocabulary, which simplifies the task of removing redundant words.We next show that the LSTM Encoder-Decoder can replace words in a sequence. For the sequence pairs (X,Y), where X = (x1,x2,... ,x25) Y = (y1,y2, ...,y25) yi = ximodn xi,yi∈V We let n=2,20,200 when |V|=10,100,1000. We only keep the top 20 percent of words in the vocabulary, and use these words to replace all matching words in the output sequence. Lexical simplification is similar, in which we can regard the top 20 percent of words in the vocabulary as simple and common words and all the other words are complex words. Similarly, we can also think of the top 20 percent of words as meaningful and important words, other words are redundant. Therefore it also suits the task of removing redundant words. Compared with the sorting operation, the replacement operation is easier to train when the vocabulary is large, or the size of training data is not large." } ], "images": [ "scigen/train/3533.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Replace in Sequence" ], "reasoning": "The LSTM Encoder-Decoder can replace words in a sequence by keeping the top 20 percent of words in the vocabulary, which simplifies the task of removing redundant words.", "description": "We next show that the LSTM Encoder-Decoder can replace words in a sequence. For the sequence pairs (X,Y), where X = (x1,x2,... ,x25) Y = (y1,y2, ...,y25) yi = ximodn xi,yi∈V We let n=2,20,200 when |V|=10,100,1000. We only keep the top 20 percent of words in the vocabulary, and use these words to replace all matching words in the output sequence. Lexical simplification is similar, in which we can regard the top 20 percent of words in the vocabulary as simple and common words and all the other words are complex words. Similarly, we can also think of the top 20 percent of words as meaningful and important words, other words are redundant. Therefore it also suits the task of removing redundant words. Compared with the sorting operation, the replacement operation is easier to train when the vocabulary is large, or the size of training data is not large.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Replace in Sequence\nQuestion: What is the validation accuracy when |V|=100 and H=256?\nSolve the above question based on the table information. Let's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "From the table, when |V|=100 and H=256, the validation accuracy is found in the 'Train,Val,Test' column. Thus, the answer is {\"answer\": \"0.9997\"}." } ], "images": [ "scigen/train/3533.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Replace in Sequence" ], "reasoning": "From the table, when |V|=100 and H=256, the validation accuracy is found in the 'Train,Val,Test' column.", "question": "What is the validation accuracy when |V|=100 and H=256?", "answer": "Thus, the answer is {\"answer\": \"0.9997\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe highest test accuracy is achieved when |V|=1000.\n" }, { "role": "assistant", "content": "From the table, the highest test accuracy is 0.9975, which is achieved when |V|=100 and H=256. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3533.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Replace in Sequence" ], "reasoning": "From the table, the highest test accuracy is 0.9975, which is achieved when |V|=100 and H=256.", "statement": "The highest test accuracy is achieved when |V|=1000.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: Reverse Sequence'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Increasing the number of hidden neurons in the LSTM layers from 128 to 256 improves model performance, allowing it to achieve higher accuracy with fewer epochs.We first conduct experiments to show that the LSTM Encoder-Decoder can reverse a sequence after training on a large set of sequence pairs (X,Y), where X = (x1,x2,... ,x25) Y = (x25,x24,...,x1) xi∈V V,H,E represent the vocabulary size, the number of hidden neurons in the LSTM layer, and the training epoch, respectively. The size of our training data is extremely important for the model. The size of the vocabulary also influences the performance. A larger vocabulary requires more training data and more hidden neurons in the LSTM layers. By increasing the number of neurons in the LSTM layers from 128 to 256, we produce a higher capacity model with more neurons that can be trained with fewer epochs and achieve a higher accuracy." } ], "images": [ "scigen/train/3534.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Reverse Sequence" ], "reasoning": "Increasing the number of hidden neurons in the LSTM layers from 128 to 256 improves model performance, allowing it to achieve higher accuracy with fewer epochs.", "description": "We first conduct experiments to show that the LSTM Encoder-Decoder can reverse a sequence after training on a large set of sequence pairs (X,Y), where X = (x1,x2,... ,x25) Y = (x25,x24,...,x1) xi∈V V,H,E represent the vocabulary size, the number of hidden neurons in the LSTM layer, and the training epoch, respectively. The size of our training data is extremely important for the model. The size of the vocabulary also influences the performance. A larger vocabulary requires more training data and more hidden neurons in the LSTM layers. By increasing the number of neurons in the LSTM layers from 128 to 256, we produce a higher capacity model with more neurons that can be trained with fewer epochs and achieve a higher accuracy.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: Reverse Sequence'. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the test accuracy when the vocabulary size is 100 and the number of hidden neurons is 256?\n" }, { "role": "assistant", "content": "From the table, when V=100 and H=256, the test accuracy is given in the 'Train,Val,Test' column as the third value in the sequence. So, the answer is {\"answer\": \"0.9787\"}." } ], "images": [ "scigen/train/3534.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Reverse Sequence" ], "reasoning": "From the table, when V=100 and H=256, the test accuracy is given in the 'Train,Val,Test' column as the third value in the sequence.", "question": "What is the test accuracy when the vocabulary size is 100 and the number of hidden neurons is 256?", "answer": "So, the answer is {\"answer\": \"0.9787\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model achieves a validation accuracy of over 0.95 when the vocabulary size is 1000 and the number of hidden neurons is 256.\n" }, { "role": "assistant", "content": "From the table, when V=1000 and H=256, the validation accuracy is given in the 'Train,Val,Test' column as the second value in the sequence, which is 0.9151. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3534.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Reverse Sequence" ], "reasoning": "From the table, when V=1000 and H=256, the validation accuracy is given in the 'Train,Val,Test' column as the second value in the sequence, which is 0.9151.", "statement": "The model achieves a validation accuracy of over 0.95 when the vocabulary size is 1000 and the number of hidden neurons is 256.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Sort Sequence', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Increasing the vocabulary size to 1000 makes training harder, but with enough training data, the model can still achieve high accuracy in sorting sequences.The datasets consist of sequence pairs (X,Y), where X = (x1,x2,... ,x25) Y =sorted(x1,x2, ... ,x25) xi∈V Similarly, the size of the vocabulary, training data, and neurons influence the performance. It is harder to train if we increase the vocabulary to 1000, but the model can still learn the sorting rule with a high accuracy if provided enough training data." } ], "images": [ "scigen/train/3535.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Sort Sequence" ], "reasoning": "Increasing the vocabulary size to 1000 makes training harder, but with enough training data, the model can still achieve high accuracy in sorting sequences.", "description": "The datasets consist of sequence pairs (X,Y), where X = (x1,x2,... ,x25) Y =sorted(x1,x2, ... ,x25) xi∈V Similarly, the size of the vocabulary, training data, and neurons influence the performance. It is harder to train if we increase the vocabulary to 1000, but the model can still learn the sorting rule with a high accuracy if provided enough training data.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 2: Sort Sequence\nQuestion: What is the validation accuracy when the vocabulary size is 1000 and the training data is 135k?\n" }, { "role": "assistant", "content": "From the table, when the vocabulary size (V) is 1000 and the training data is 135k, the validation accuracy is 0.7958. So, the answer is {\"answer\": \"0.7958\"}." } ], "images": [ "scigen/train/3535.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Sort Sequence" ], "reasoning": "From the table, when the vocabulary size (V) is 1000 and the training data is 135k, the validation accuracy is 0.7958.", "question": "What is the validation accuracy when the vocabulary size is 1000 and the training data is 135k?", "answer": "So, the answer is {\"answer\": \"0.7958\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model achieves a validation accuracy of 0.9907 with a vocabulary size of 100.\n" }, { "role": "assistant", "content": "The table shows that with a vocabulary size (V) of 100 and training data of 135k, the validation accuracy is 0.9907. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3535.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Sort Sequence" ], "reasoning": "The table shows that with a vocabulary size (V) of 100 and training data of 135k, the validation accuracy is 0.9907.", "statement": "The model achieves a validation accuracy of 0.9907 with a vocabulary size of 100.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: F1 score of KMCNN and other methods'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "KMCNN consistently outperformed other models across all datasets in terms of F1 score, precision, and recall.Generally, KMCNN achieved the best performance on all of the four datasets in terms of F1 score, precision and recall." } ], "images": [ "scigen/train/3536.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: F1 score of KMCNN and other methods" ], "reasoning": "KMCNN consistently outperformed other models across all datasets in terms of F1 score, precision, and recall.", "description": "Generally, KMCNN achieved the best performance on all of the four datasets in terms of F1 score, precision and recall.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nTable title: Table 3: F1 score of KMCNN and other methods\n\nQuestion: Which model achieved the highest score on the NHGRI-EBI GWAS Catalog dataset?\n" }, { "role": "assistant", "content": "By comparing the scores on the NHGRI-EBI GWAS Catalog dataset, KMCNN achieved the highest score of 99.401. So, the answer is {\"answer\": \"KMCNN\"}." } ], "images": [ "scigen/train/3536.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: F1 score of KMCNN and other methods" ], "reasoning": "By comparing the scores on the NHGRI-EBI GWAS Catalog dataset, KMCNN achieved the highest score of 99.401.", "question": "Which model achieved the highest score on the NHGRI-EBI GWAS Catalog dataset?", "answer": "So, the answer is {\"answer\": \"KMCNN\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nMCNN achieved the highest score on the MycoSet dataset.\n" }, { "role": "assistant", "content": "By comparing the scores on the MycoSet dataset, KMCNN achieved the highest score of 63.302, not MCNN. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3536.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: F1 score of KMCNN and other methods" ], "reasoning": "By comparing the scores on the MycoSet dataset, KMCNN achieved the highest score of 63.302, not MCNN.", "statement": "MCNN achieved the highest score on the MycoSet dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Experiment settings'.\n" }, { "role": "assistant", "content": "The model uses filter sizes of 1, 2, and 3 for 1-gram, 2-gram, and 3-gram language models, with a drop rate of 0.5 to prevent overfitting.The filter sizes are set as 1, 2 and 3 respectively, which corresponds to 1-gram, 2-gram, and 3-gram language models. The drop rate is set to 0.5 in order to avoid over-fitting." } ], "images": [ "scigen/train/3537.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Experiment settings" ], "reasoning": "The model uses filter sizes of 1, 2, and 3 for 1-gram, 2-gram, and 3-gram language models, with a drop rate of 0.5 to prevent overfitting.", "description": "The filter sizes are set as 1, 2 and 3 respectively, which corresponds to 1-gram, 2-gram, and 3-gram language models. The drop rate is set to 0.5 in order to avoid over-fitting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Experiment settings', solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the embedding dimension and how does the drop rate help the model?\n" }, { "role": "assistant", "content": "The embedding dimension is 200, and the drop rate of 0.5 helps to avoid overfitting by randomly dropping units during training. So, the answer is {\"answer\": \"200\"}." } ], "images": [ "scigen/train/3537.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Experiment settings" ], "reasoning": "The embedding dimension is 200, and the drop rate of 0.5 helps to avoid overfitting by randomly dropping units during training.", "question": "What is the embedding dimension and how does the drop rate help the model?", "answer": "So, the answer is {\"answer\": \"200\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The model uses a drop rate of 0.5 to avoid overfitting. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe model uses a drop rate of 0.5 to avoid overfitting.\n" }, { "role": "assistant", "content": "The table shows that the drop rate is set to 0.5, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3537.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Experiment settings" ], "reasoning": "The table shows that the drop rate is set to 0.5, which matches the statement.", "statement": "The model uses a drop rate of 0.5 to avoid overfitting.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Precision of KMCNN and other methods'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "KMCNN consistently outperformed other models across all datasets in terms of F1 score, precision, and recall.Generally, KMCNN achieved the best performance on all of the four datasets in terms of F1 score, precision and recall." } ], "images": [ "scigen/train/3538.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Precision of KMCNN and other methods" ], "reasoning": "KMCNN consistently outperformed other models across all datasets in terms of F1 score, precision, and recall.", "description": "Generally, KMCNN achieved the best performance on all of the four datasets in terms of F1 score, precision and recall.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: Precision of KMCNN and other methods', solve this question and give detail solutions. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich model achieved the highest F1 score on the NHGRI-EBI GWAS Catalog dataset?\n" }, { "role": "assistant", "content": "To determine the model with the highest F1 score on the NHGRI-EBI GWAS Catalog dataset, compare the F1 scores of MCNN, KCNN, and KMCNN. KMCNN has the highest score of 99.203. So, the answer is {\"answer\": \"KMCNN\"}." } ], "images": [ "scigen/train/3538.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Precision of KMCNN and other methods" ], "reasoning": "To determine the model with the highest F1 score on the NHGRI-EBI GWAS Catalog dataset, compare the F1 scores of MCNN, KCNN, and KMCNN. KMCNN has the highest score of 99.203.", "question": "Which model achieved the highest F1 score on the NHGRI-EBI GWAS Catalog dataset?", "answer": "So, the answer is {\"answer\": \"KMCNN\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nKMCNN achieved the highest F1 score on the MycoSet dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the F1 scores of MCNN, KCNN, and KMCNN on the MycoSet dataset. KMCNN has a score of 61.236, which is higher than MCNN's 60.260 but lower than KCNN's 62.078. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3538.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Precision of KMCNN and other methods" ], "reasoning": "To verify this statement, compare the F1 scores of MCNN, KCNN, and KMCNN on the MycoSet dataset. KMCNN has a score of 61.236, which is higher than MCNN's 60.260 but lower than KCNN's 62.078.", "statement": "KMCNN achieved the highest F1 score on the MycoSet dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5: Recall of KMCNN and other methods', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "KMCNN achieved the highest F1 score, precision, and recall across all four datasets.Generally, KMCNN achieved the best performance on all of the four datasets in terms of F1 score, precision and recall." } ], "images": [ "scigen/train/3539.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Recall of KMCNN and other methods" ], "reasoning": "KMCNN achieved the highest F1 score, precision, and recall across all four datasets.", "description": "Generally, KMCNN achieved the best performance on all of the four datasets in terms of F1 score, precision and recall.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 5: Recall of KMCNN and other methods', and sequentially solve the question, writing out each step of your reasoning process. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model achieved the highest score on the MycoSet dataset?\n" }, { "role": "assistant", "content": "By comparing the scores on the MycoSet dataset, KMCNN achieved the highest score of 65.596. So, the answer is {\"answer\": \"KMCNN\"}" } ], "images": [ "scigen/train/3539.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Recall of KMCNN and other methods" ], "reasoning": "By comparing the scores on the MycoSet dataset, KMCNN achieved the highest score of 65.596.", "question": "Which model achieved the highest score on the MycoSet dataset?", "answer": "So, the answer is {\"answer\": \"KMCNN\"}" }, { "messages": [ { "role": "user", "content": "MCNN achieved the highest score on the NHGRI-EBI GWAS Catalog dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nMCNN achieved the highest score on the NHGRI-EBI GWAS Catalog dataset.\n" }, { "role": "assistant", "content": "The table shows that KMCNN achieved the highest score of 99.600 on the NHGRI-EBI GWAS Catalog dataset, not MCNN. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3539.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Recall of KMCNN and other methods" ], "reasoning": "The table shows that KMCNN achieved the highest score of 99.600 on the NHGRI-EBI GWAS Catalog dataset, not MCNN.", "statement": "MCNN achieved the highest score on the NHGRI-EBI GWAS Catalog dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Accuracy for each error type in the LingEval97 contrastive set. Hard-coding self-attention results in slightly lower accuracy for most error types, while more significant degradation is observed when hard-coding self and cross attention. We refer readers to Sennrich (2017) for descriptions of each error type.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "hc-all is less accurate than base at detecting substitutions but better at detecting inserted negations.This dataset measures targeted errors on thirteen different linguistic phenomena such as agreement and adequacy. hc- all is 11% less accurate (absolute) at detecting these substitutions than base (94% vs 83%). On the other hand, both hc-sa and hc-all are actually better than base at detecting inserted negations, with hc-all achieving a robust 98.7% accuracy. We leave further exploration of this phenomenon to future work. This result confirms that self-attention is important for modeling some long-distance phenomena, and that cross attention may be even more crucial." } ], "images": [ "scigen/train/3540.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Accuracy for each error type in the LingEval97 contrastive set. Hard-coding self-attention results in slightly lower accuracy for most error types, while more significant degradation is observed when hard-coding self and cross attention. We refer readers to Sennrich (2017) for descriptions of each error type." ], "reasoning": "hc-all is less accurate than base at detecting substitutions but better at detecting inserted negations.", "description": "This dataset measures targeted errors on thirteen different linguistic phenomena such as agreement and adequacy. hc- all is 11% less accurate (absolute) at detecting these substitutions than base (94% vs 83%). On the other hand, both hc-sa and hc-all are actually better than base at detecting inserted negations, with hc-all achieving a robust 98.7% accuracy. We leave further exploration of this phenomenon to future work. This result confirms that self-attention is important for modeling some long-distance phenomena, and that cross attention may be even more crucial.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model has the highest accuracy for detecting polarity-particle-kein-ins errors?\nSolve the above question based on the table titled 'Table 4: Accuracy for each error type in the LingEval97 contrastive set. Hard-coding self-attention results in slightly lower accuracy for most error types, while more significant degradation is observed when hard-coding self and cross attention. We refer readers to Sennrich (2017) for descriptions of each error type.'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the accuracy for detecting polarity-particle-kein-ins errors, with hc-all achieving the highest accuracy at 98.7%. So, the answer is {\"answer\": \"hc-all\"}." } ], "images": [ "scigen/train/3540.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Accuracy for each error type in the LingEval97 contrastive set. Hard-coding self-attention results in slightly lower accuracy for most error types, while more significant degradation is observed when hard-coding self and cross attention. We refer readers to Sennrich (2017) for descriptions of each error type." ], "reasoning": "The table shows the accuracy for detecting polarity-particle-kein-ins errors, with hc-all achieving the highest accuracy at 98.7%.", "question": "Which model has the highest accuracy for detecting polarity-particle-kein-ins errors?", "answer": "So, the answer is {\"answer\": \"hc-all\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nhc-all is better than base at detecting polarity-particle-nicht-ins errors.\n" }, { "role": "assistant", "content": "The table shows that hc-all has an accuracy of 89.8% for detecting polarity-particle-nicht-ins errors, which is higher than base's accuracy of 92.6%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3540.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Accuracy for each error type in the LingEval97 contrastive set. Hard-coding self-attention results in slightly lower accuracy for most error types, while more significant degradation is observed when hard-coding self and cross attention. We refer readers to Sennrich (2017) for descriptions of each error type." ], "reasoning": "The table shows that hc-all has an accuracy of 89.8% for detecting polarity-particle-nicht-ins errors, which is higher than base's accuracy of 92.6%.", "statement": "hc-all is better than base at detecting polarity-particle-nicht-ins errors.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 2: Ablation study on the SQuAD split-2 test set.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Turning off DAN significantly reduces the BLEU-4 score, demonstrating its effectiveness, while fine-tuning with REINFORCE and using BERT further enhance performance.By turning off DAN, the BLEU-4 score of G2Ssta (similarly for G2Sdyn) dramatically drops from 16.96% to 12.62%, which shows the effectiveness of DAN. We can see the advantages of Graph2Seq learning over Seq2Seq learning by comparing the performance between G2Ssta and Seq2Seq. Fine-tuning the model using REINFORCE can further improve the model performance, which shows the benefits of directly optimizing evaluation metrics. We also find that BERT has a considerable impact on the performance. Lastly, we find that static graph construction slightly outperforms dynamic graph construction." } ], "images": [ "scigen/train/3541.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ablation study on the SQuAD split-2 test set." ], "reasoning": "Turning off DAN significantly reduces the BLEU-4 score, demonstrating its effectiveness, while fine-tuning with REINFORCE and using BERT further enhance performance.", "description": "By turning off DAN, the BLEU-4 score of G2Ssta (similarly for G2Sdyn) dramatically drops from 16.96% to 12.62%, which shows the effectiveness of DAN. We can see the advantages of Graph2Seq learning over Seq2Seq learning by comparing the performance between G2Ssta and Seq2Seq. Fine-tuning the model using REINFORCE can further improve the model performance, which shows the benefits of directly optimizing evaluation metrics. We also find that BERT has a considerable impact on the performance. Lastly, we find that static graph construction slightly outperforms dynamic graph construction.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: Ablation study on the SQuAD split-2 test set.' and methodically tackle the question that follows. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the BLEU-4 score of G2S dyn without DAN and how does it compare to G2S sta without DAN?\n" }, { "role": "assistant", "content": "The BLEU-4 score of G2S dyn without DAN is 12.58, which is lower than the BLEU-4 score of G2S sta without DAN, which is 12.62. So, the answer is {\"answer\": \"G2S dyn without DAN: 12.58, G2S sta without DAN: 12.62\"}" } ], "images": [ "scigen/train/3541.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ablation study on the SQuAD split-2 test set." ], "reasoning": "The BLEU-4 score of G2S dyn without DAN is 12.58, which is lower than the BLEU-4 score of G2S sta without DAN, which is 12.62.", "question": "What is the BLEU-4 score of G2S dyn without DAN and how does it compare to G2S sta without DAN?", "answer": "So, the answer is {\"answer\": \"G2S dyn without DAN: 12.58, G2S sta without DAN: 12.62\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nG2S sta with BERT has a higher BLEU-4 score than G2S dyn with BERT.\n" }, { "role": "assistant", "content": "The BLEU-4 score of G2S sta with BERT is 18.02, which is higher than the BLEU-4 score of G2S dyn with BERT, which is 17.56. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3541.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ablation study on the SQuAD split-2 test set." ], "reasoning": "The BLEU-4 score of G2S sta with BERT is 18.02, which is higher than the BLEU-4 score of G2S dyn with BERT, which is 17.56.", "statement": "G2S sta with BERT has a higher BLEU-4 score than G2S dyn with BERT.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Automatic evaluation results on the SQuAD test set.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Our full model G2Ssta+BERT+RL achieves the highest scores across multiple metrics, significantly outperforming previous state-of-the-art methods like CGC-QG.Experimental results and analysis. First of all, our full model G2Ssta+BERT+RL outperforms previous state-of-the-art methods by a great margin. Compared to previous methods like CGC-QG" } ], "images": [ "scigen/train/3542.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Automatic evaluation results on the SQuAD test set." ], "reasoning": "Our full model G2Ssta+BERT+RL achieves the highest scores across multiple metrics, significantly outperforming previous state-of-the-art methods like CGC-QG.", "description": "Experimental results and analysis. First of all, our full model G2Ssta+BERT+RL outperforms previous state-of-the-art methods by a great margin. Compared to previous methods like CGC-QG", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Automatic evaluation results on the SQuAD test set.\nQuestion: Which method achieved the highest ROUGE-L score?\nSolve the above question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the ROUGE-L scores of all methods listed in the table, G2Ssta+BERT+RL has the highest score of 46.02. So, the answer is {\"answer\": \"G2Ssta+BERT+RL\"}" } ], "images": [ "scigen/train/3542.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Automatic evaluation results on the SQuAD test set." ], "reasoning": "By comparing the ROUGE-L scores of all methods listed in the table, G2Ssta+BERT+RL has the highest score of 46.02.", "question": "Which method achieved the highest ROUGE-L score?", "answer": "So, the answer is {\"answer\": \"G2Ssta+BERT+RL\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe method G2Ssta+BERT+RL achieved the highest BLEU-4 score.\n" }, { "role": "assistant", "content": "By comparing the BLEU-4 scores of all methods listed in the table, G2Ssta+BERT+RL has the highest score of 17.94. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3542.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Automatic evaluation results on the SQuAD test set." ], "reasoning": "By comparing the BLEU-4 scores of all methods listed in the table, G2Ssta+BERT+RL has the highest score of 17.94.", "statement": "The method G2Ssta+BERT+RL achieved the highest BLEU-4 score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Human evaluation results on the SQuAD split-2 test set.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our best performing model, G2S sta+BERT+RL, outperforms the strong baseline method MPQG+R in all metrics and achieves results close to the ground-truth.We can see that our best performing model achieves good results even compared to the ground-truth, and outperforms the strong baseline method MPQG+R. Our error analysis shows that main syntactic error occurs in repeated/unknown words in generated questions. Further, the slightly lower quality on semantics also impacts the relevance." } ], "images": [ "scigen/train/3543.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Human evaluation results on the SQuAD split-2 test set." ], "reasoning": "Our best performing model, G2S sta+BERT+RL, outperforms the strong baseline method MPQG+R in all metrics and achieves results close to the ground-truth.", "description": "We can see that our best performing model achieves good results even compared to the ground-truth, and outperforms the strong baseline method MPQG+R. Our error analysis shows that main syntactic error occurs in repeated/unknown words in generated questions. Further, the slightly lower quality on semantics also impacts the relevance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich method has the highest relevance percentage?\nSolve the above question based on the table titled 'Table 3: Human evaluation results on the SQuAD split-2 test set.'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the relevance percentages of all methods, the Ground-truth method has the highest relevance percentage at 4.25%. So, the answer is {\"answer\": \"Ground-truth\"}." } ], "images": [ "scigen/train/3543.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Human evaluation results on the SQuAD split-2 test set." ], "reasoning": "By comparing the relevance percentages of all methods, the Ground-truth method has the highest relevance percentage at 4.25%.", "question": "Which method has the highest relevance percentage?", "answer": "So, the answer is {\"answer\": \"Ground-truth\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe G2S sta+BERT+RL method has a higher syntactically correct percentage than the MPQG+R method.\n" }, { "role": "assistant", "content": "The table shows that the G2S sta+BERT+RL method has a syntactically correct percentage of 4.41%, which is higher than the MPQG+R method's 4.34%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3543.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Human evaluation results on the SQuAD split-2 test set." ], "reasoning": "The table shows that the G2S sta+BERT+RL method has a syntactically correct percentage of 4.41%, which is higher than the MPQG+R method's 4.34%.", "statement": "The G2S sta+BERT+RL method has a higher syntactically correct percentage than the MPQG+R method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: Entity extraction results on the ACE2005 test set with gold-standard mention boundaries.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The KBLSTM-CRF model outperforms other models in entity extraction, demonstrating the effectiveness of leveraging KB information.We first report results on entity extraction with gold-standard boundaries for multi-word mentions. This allows us to focus on the performance of entity type prediction without considering mention boundary errors and the noise they introduce in retrieving candidate KB concepts. We find that the CRF objective generally outperforms the softmax objective. Our KBLSTM-CRF model significantly improves over its counterpart BiLSTM-Fea-CRF. This demonstrates the effectiveness of KBLSTM architecture in leveraging KB information." } ], "images": [ "scigen/train/3544.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Entity extraction results on the ACE2005 test set with gold-standard mention boundaries." ], "reasoning": "The KBLSTM-CRF model outperforms other models in entity extraction, demonstrating the effectiveness of leveraging KB information.", "description": "We first report results on entity extraction with gold-standard boundaries for multi-word mentions. This allows us to focus on the performance of entity type prediction without considering mention boundary errors and the noise they introduce in retrieving candidate KB concepts. We find that the CRF objective generally outperforms the softmax objective. Our KBLSTM-CRF model significantly improves over its counterpart BiLSTM-Fea-CRF. This demonstrates the effectiveness of KBLSTM architecture in leveraging KB information.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 1: Entity extraction results on the ACE2005 test set with gold-standard mention boundaries.' in mind, break down and resolve the question below step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model has the highest F1 score?\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all models in the table, the KBLSTM-CRF model has the highest F1 score of 88.0. So, the answer is {\"answer\": \"KBLSTM-CRF\"}." } ], "images": [ "scigen/train/3544.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Entity extraction results on the ACE2005 test set with gold-standard mention boundaries." ], "reasoning": "By comparing the F1 scores of all models in the table, the KBLSTM-CRF model has the highest F1 score of 88.0.", "question": "Which model has the highest F1 score?", "answer": "So, the answer is {\"answer\": \"KBLSTM-CRF\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The BiLSTM-Fea model has a higher precision than the BiLSTM-CRF model. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe BiLSTM-Fea model has a higher precision than the BiLSTM-CRF model.\n" }, { "role": "assistant", "content": "The table shows that the BiLSTM-Fea model has a precision of 86.1, while the BiLSTM-CRF model has a precision of 87.3. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3544.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Entity extraction results on the ACE2005 test set with gold-standard mention boundaries." ], "reasoning": "The table shows that the BiLSTM-Fea model has a precision of 86.1, while the BiLSTM-CRF model has a precision of 87.3. Therefore, the statement is incorrect.", "statement": "The BiLSTM-Fea model has a higher precision than the BiLSTM-CRF model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Ablation results with different KBs.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The KBLSTM-CRF model achieves the highest performance when incorporating both KBs, outperforming the BiLSTM-Fea-CRF model in all settings.We find that the KBLSTM-CRF outperforms the BiLSTM-Fea-CRF in all the settings and that incorporating both KBs leads to the best performance." } ], "images": [ "scigen/train/3545.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ablation results with different KBs." ], "reasoning": "The KBLSTM-CRF model achieves the highest performance when incorporating both KBs, outperforming the BiLSTM-Fea-CRF model in all settings.", "description": "We find that the KBLSTM-CRF outperforms the BiLSTM-Fea-CRF in all the settings and that incorporating both KBs leads to the best performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Ablation results with different KBs.', solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model and KB combination achieved the highest F1 score?\n" }, { "role": "assistant", "content": "By comparing the F1 scores across all model and KB combinations, the KBLSTM-CRF model with both KBs has the highest F1 score of 88.0. So, the answer is {\"answer\": \"KBLSTM-CRF with both KBs\"}" } ], "images": [ "scigen/train/3545.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ablation results with different KBs." ], "reasoning": "By comparing the F1 scores across all model and KB combinations, the KBLSTM-CRF model with both KBs has the highest F1 score of 88.0.", "question": "Which model and KB combination achieved the highest F1 score?", "answer": "So, the answer is {\"answer\": \"KBLSTM-CRF with both KBs\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The BiLSTM-Fea-CRF model with NELL KB has a higher F1 score than the KBLSTM-CRF model with WordNet KB. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe BiLSTM-Fea-CRF model with NELL KB has a higher F1 score than the KBLSTM-CRF model with WordNet KB.\n" }, { "role": "assistant", "content": "The F1 score for BiLSTM-Fea-CRF with NELL is 86.6, while the F1 score for KBLSTM-CRF with WordNet is 87.3. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3545.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ablation results with different KBs." ], "reasoning": "The F1 score for BiLSTM-Fea-CRF with NELL is 86.6, while the F1 score for KBLSTM-CRF with WordNet is 87.3. Therefore, the statement is refuted.", "statement": "The BiLSTM-Fea-CRF model with NELL KB has a higher F1 score than the KBLSTM-CRF model with WordNet KB.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Entity extraction results on the ACE2005 test set.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The KBLSTM-CRF model achieves the highest F1 score, demonstrating the best performance among all models on the ACE2005 dataset.Next, we evaluate our models on entity extraction with predicted mention boundaries. We then treat the predicted chunks as units for entity type labeling. Interestingly, we find that using BiLSTM-CRF without any KB information already gives strong performance compared to previous work. The KBLSTM-CRF model demonstrates the best performance among all the models and achieves the new state-of-the-art performance on the ACE2005 dataset." } ], "images": [ "scigen/train/3546.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Entity extraction results on the ACE2005 test set." ], "reasoning": "The KBLSTM-CRF model achieves the highest F1 score, demonstrating the best performance among all models on the ACE2005 dataset.", "description": "Next, we evaluate our models on entity extraction with predicted mention boundaries. We then treat the predicted chunks as units for entity type labeling. Interestingly, we find that using BiLSTM-CRF without any KB information already gives strong performance compared to previous work. The KBLSTM-CRF model demonstrates the best performance among all the models and achieves the new state-of-the-art performance on the ACE2005 dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 3: Entity extraction results on the ACE2005 test set.\nQuestion: Which model achieves the highest F1 score?\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all models in the table, the KBLSTM-CRF model has the highest F1 score of 85.7. So, the answer is {\"answer\": \"KBLSTM-CRF\"}" } ], "images": [ "scigen/train/3546.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Entity extraction results on the ACE2005 test set." ], "reasoning": "By comparing the F1 scores of all models in the table, the KBLSTM-CRF model has the highest F1 score of 85.7.", "question": "Which model achieves the highest F1 score?", "answer": "So, the answer is {\"answer\": \"KBLSTM-CRF\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe BiLSTM-CRF model achieves an F1 score higher than 83.6.\n" }, { "role": "assistant", "content": "The table shows that the BiLSTM-CRF model has an F1 score of 83.6, which is not higher than 83.6. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3546.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Entity extraction results on the ACE2005 test set." ], "reasoning": "The table shows that the BiLSTM-CRF model has an F1 score of 83.6, which is not higher than 83.6.", "statement": "The BiLSTM-CRF model achieves an F1 score higher than 83.6.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Entity extraction results on the OntoNotes 5.0 test set.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The KBLSTM-CRF model shows competitive results compared to feature-based models and significant improvements over BiLSTM and BiLSTM-Fea models, with smaller benefits on OntoNotes due to lower NELL coverage and larger training data.We compare our models with the existing feature-based models \\newciteratinov2009design and \\newcitedurrett2014joint, which both employ heavy feature engineering to bring in external knowledge. Our KBLSTM-CRF model shows competitive results compared to their results. It also presents significant improvements compared to the BiLSTM and BiLSTM-Fea models. Note that the benefit of adding KB information is smaller on OntoNotes compared to ACE2005. We believe that there are two main reasons. One is that NELL has a lower coverage of entity mentions in OntoNotes than in ACE2005 (57% vs. 65%). Second, OntoNotes has a significantly larger amount of training data, which allows for more accurate models without much help from external resources." } ], "images": [ "scigen/train/3547.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Entity extraction results on the OntoNotes 5.0 test set." ], "reasoning": "The KBLSTM-CRF model shows competitive results compared to feature-based models and significant improvements over BiLSTM and BiLSTM-Fea models, with smaller benefits on OntoNotes due to lower NELL coverage and larger training data.", "description": "We compare our models with the existing feature-based models \\newciteratinov2009design and \\newcitedurrett2014joint, which both employ heavy feature engineering to bring in external knowledge. Our KBLSTM-CRF model shows competitive results compared to their results. It also presents significant improvements compared to the BiLSTM and BiLSTM-Fea models. Note that the benefit of adding KB information is smaller on OntoNotes compared to ACE2005. We believe that there are two main reasons. One is that NELL has a lower coverage of entity mentions in OntoNotes than in ACE2005 (57% vs. 65%). Second, OntoNotes has a significantly larger amount of training data, which allows for more accurate models without much help from external resources.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 4: Entity extraction results on the OntoNotes 5.0 test set.\n\nQuestion: Which model has the highest F1 score?\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all models in the table, the KBLSTM-CRF model has the highest F1 score of 86.4. So, the answer is {\"answer\": \"KBLSTM-CRF\"}." } ], "images": [ "scigen/train/3547.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Entity extraction results on the OntoNotes 5.0 test set." ], "reasoning": "By comparing the F1 scores of all models in the table, the KBLSTM-CRF model has the highest F1 score of 86.4.", "question": "Which model has the highest F1 score?", "answer": "So, the answer is {\"answer\": \"KBLSTM-CRF\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe BiLSTM-Fea-CRF model has a higher F1 score than the BiLSTM-CRF model.\n" }, { "role": "assistant", "content": "The table shows that the BiLSTM-Fea-CRF model has an F1 score of 86.0, while the BiLSTM-CRF model has an F1 score of 85.9. Therefore, the statement is correct. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3547.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Entity extraction results on the OntoNotes 5.0 test set." ], "reasoning": "The table shows that the BiLSTM-Fea-CRF model has an F1 score of 86.0, while the BiLSTM-CRF model has an F1 score of 85.9. Therefore, the statement is correct.", "statement": "The BiLSTM-Fea-CRF model has a higher F1 score than the BiLSTM-CRF model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: event extraction results on the ACE2005 test set.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The KBLSTM-CRF model achieves the best performance with an F1 score of 69.7, outperforming previous state-of-the-art methods without using gold-standard entities.4.2.1 Results We compare our models with the prior state-of-the-art approaches for event extraction, including neural and non-neural ones: JointBeam refers to the joint beam search approach with local and global features Li et al. The second block shows the previously reported results for the neural models. Note that they both make use of gold-standard entity annotations. The third block shows the results of our models. We can see that our KBLSTM models significantly outperform the BiLSTM and BiLSTM-Fea models, which again confirms their effectiveness in leveraging KB information. The KBLSTM-CRF model achieves the best performance and outperforms the previous state-of-the-art methods without having access to any gold-standard entities." } ], "images": [ "scigen/train/3548.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: event extraction results on the ACE2005 test set." ], "reasoning": "The KBLSTM-CRF model achieves the best performance with an F1 score of 69.7, outperforming previous state-of-the-art methods without using gold-standard entities.", "description": "4.2.1 Results We compare our models with the prior state-of-the-art approaches for event extraction, including neural and non-neural ones: JointBeam refers to the joint beam search approach with local and global features Li et al. The second block shows the previously reported results for the neural models. Note that they both make use of gold-standard entity annotations. The third block shows the results of our models. We can see that our KBLSTM models significantly outperform the BiLSTM and BiLSTM-Fea models, which again confirms their effectiveness in leveraging KB information. The KBLSTM-CRF model achieves the best performance and outperforms the previous state-of-the-art methods without having access to any gold-standard entities.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 5: event extraction results on the ACE2005 test set.', unfold the steps to solve the question given next. Output the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which model achieves the highest F1 score without using gold-standard entities?\n" }, { "role": "assistant", "content": "The description states that the KBLSTM-CRF model achieves the best performance and outperforms previous state-of-the-art methods without using gold-standard entities. The table shows that the KBLSTM-CRF model has the highest F1 score of 69.7. So, the answer is {\"answer\": \"KBLSTM-CRF\"}." } ], "images": [ "scigen/train/3548.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: event extraction results on the ACE2005 test set." ], "reasoning": "The description states that the KBLSTM-CRF model achieves the best performance and outperforms previous state-of-the-art methods without using gold-standard entities. The table shows that the KBLSTM-CRF model has the highest F1 score of 69.7.", "question": "Which model achieves the highest F1 score without using gold-standard entities?", "answer": "So, the answer is {\"answer\": \"KBLSTM-CRF\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe KBLSTM model outperforms the BiLSTM-Fea model in terms of F1 score.\n" }, { "role": "assistant", "content": "The table shows that the KBLSTM model has an F1 score of 68.7, while the BiLSTM-Fea model has an F1 score of 65.5. Therefore, the KBLSTM model outperforms the BiLSTM-Fea model in terms of F1 score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3548.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: event extraction results on the ACE2005 test set." ], "reasoning": "The table shows that the KBLSTM model has an F1 score of 68.7, while the BiLSTM-Fea model has an F1 score of 65.5. Therefore, the KBLSTM model outperforms the BiLSTM-Fea model in terms of F1 score.", "statement": "The KBLSTM model outperforms the BiLSTM-Fea model in terms of F1 score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Comparison of our method to other methods at varying difficulty levels: from short paths consisting of one landmark to long paths consisting of four landmarks. SPL↑ is used as the metric.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Our method significantly outperforms prior works in SPL (%) across varying navigation difficulties, improving from 72.21 to 74.28 for 1 landmark, 37.71 to 43.08 for 2 landmarks, and 23.16 to 27.96 for 3 landmarks compared to ma2019self.In addition to the evaluation for the long-range (i.e. 4-landmark) navigation, we also study the performance of all these trained methods when evaluated at varying difficulty levels: from short navigation paths consisting of one landmark to long ones consisting of four landmarks. We evaluate under SPL and compare our method with the prior works as before. We observe that our method outperforms all the other prior works by a large margin. For instance, our method improves SPL (%) from 72.21 to 74.28 for short routes having 1 landmark, from 37.71 to 43.08 for routes with 2 landmarks, and from 23.16 to 27.96 for routes with 3 landmarks, when compared to ma2019self." } ], "images": [ "scigen/train/3549.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of our method to other methods at varying difficulty levels: from short paths consisting of one landmark to long paths consisting of four landmarks. SPL↑ is used as the metric." ], "reasoning": "Our method significantly outperforms prior works in SPL (%) across varying navigation difficulties, improving from 72.21 to 74.28 for 1 landmark, 37.71 to 43.08 for 2 landmarks, and 23.16 to 27.96 for 3 landmarks compared to ma2019self.", "description": "In addition to the evaluation for the long-range (i.e. 4-landmark) navigation, we also study the performance of all these trained methods when evaluated at varying difficulty levels: from short navigation paths consisting of one landmark to long ones consisting of four landmarks. We evaluate under SPL and compare our method with the prior works as before. We observe that our method outperforms all the other prior works by a large margin. For instance, our method improves SPL (%) from 72.21 to 74.28 for short routes having 1 landmark, from 37.71 to 43.08 for routes with 2 landmarks, and from 23.16 to 27.96 for routes with 3 landmarks, when compared to ma2019self.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Comparison of our method to other methods at varying difficulty levels: from short paths consisting of one landmark to long paths consisting of four landmarks. SPL↑ is used as the metric.\nQuestion: What is the SPL (%) improvement of our method over ma2019self for routes with 3 landmarks?\nSolve the above question based on the table information. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the SPL (%) improvement for routes with 3 landmarks, compare the SPL of our method (27.96) with that of ma2019self (23.16). The improvement is 27.96 - 23.16. So, the answer is {\"answer\": \"4.8\"}." } ], "images": [ "scigen/train/3549.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of our method to other methods at varying difficulty levels: from short paths consisting of one landmark to long paths consisting of four landmarks. SPL↑ is used as the metric." ], "reasoning": "To find the SPL (%) improvement for routes with 3 landmarks, compare the SPL of our method (27.96) with that of ma2019self (23.16). The improvement is 27.96 - 23.16.", "question": "What is the SPL (%) improvement of our method over ma2019self for routes with 3 landmarks?", "answer": "So, the answer is {\"answer\": \"4.8\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nOur method achieves an SPL (%) of 43.08 for routes with 2 landmarks.\n" }, { "role": "assistant", "content": "The table shows that for routes with 2 landmarks, our method achieves an SPL (%) of 43.08. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3549.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of our method to other methods at varying difficulty levels: from short paths consisting of one landmark to long paths consisting of four landmarks. SPL↑ is used as the metric." ], "reasoning": "The table shows that for routes with 2 landmarks, our method achieves an SPL (%) of 43.08.", "statement": "Our method achieves an SPL (%) of 43.08 for routes with 2 landmarks.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Comparison with prior s-o-t-a methods. The numbers of RConcat is taken from the paper chen2018touchdown.'.\n" }, { "role": "assistant", "content": "Our method, trained on the Talk2Nav dataset, outperforms other top-performing VLN methods on the TouchDown dataset in terms of SPL, Navigation Error, and Total Steps.We also evaluate our method on the TouchDown dataset chen2018touchdown. The Touchdown dataset has navigational instructions quite similar to the TalkNav dataset in terms of text length and the navigation environment. However, the Touchdown dataset has no correspondence between sub-descriptions and visual landmarks, and between sub-descriptions and sub-routes. Hence, we cannot train our method on the Touchdown dataset. We train our model on the Talk2Nav dataset and then evaluate it on the Touchdown dataset. We keep the model setting and the approach the same as used for our Talk2Nav dataset. We compare the performance of our method with Student-Forcing anderson2018vision, RPA look:before:leap:eccv18 and RConcat chen2018touchdown. We observe that our approach also outperforms other top-performing VLN methods on the TouchDown dataset chen2018touchdown." } ], "images": [ "scigen/train/3550.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison with prior s-o-t-a methods. The numbers of RConcat is taken from the paper chen2018touchdown." ], "reasoning": "Our method, trained on the Talk2Nav dataset, outperforms other top-performing VLN methods on the TouchDown dataset in terms of SPL, Navigation Error, and Total Steps.", "description": "We also evaluate our method on the TouchDown dataset chen2018touchdown. The Touchdown dataset has navigational instructions quite similar to the TalkNav dataset in terms of text length and the navigation environment. However, the Touchdown dataset has no correspondence between sub-descriptions and visual landmarks, and between sub-descriptions and sub-routes. Hence, we cannot train our method on the Touchdown dataset. We train our model on the Talk2Nav dataset and then evaluate it on the Touchdown dataset. We keep the model setting and the approach the same as used for our Talk2Nav dataset. We compare the performance of our method with Student-Forcing anderson2018vision, RPA look:before:leap:eccv18 and RConcat chen2018touchdown. We observe that our approach also outperforms other top-performing VLN methods on the TouchDown dataset chen2018touchdown.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 3: Comparison with prior s-o-t-a methods. The numbers of RConcat is taken from the paper chen2018touchdown.', solve the subsequent question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich method has the lowest Navigation Error and how does it compare to the highest Navigation Error?\n" }, { "role": "assistant", "content": "Our method has the lowest Navigation Error at 102.41, while RConcat chen2018touchdown has the highest Navigation Error at 234.57. The difference between the lowest and highest Navigation Error is 132.16. So, the answer is {\"answer\": \"102.41\"}" } ], "images": [ "scigen/train/3550.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison with prior s-o-t-a methods. The numbers of RConcat is taken from the paper chen2018touchdown." ], "reasoning": "Our method has the lowest Navigation Error at 102.41, while RConcat chen2018touchdown has the highest Navigation Error at 234.57. The difference between the lowest and highest Navigation Error is 132.16.", "question": "Which method has the lowest Navigation Error and how does it compare to the highest Navigation Error?", "answer": "So, the answer is {\"answer\": \"102.41\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The RPA look:before:leap:eccv18 method has the highest SPL., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe RPA look:before:leap:eccv18 method has the highest SPL.\n" }, { "role": "assistant", "content": "The table shows that the RPA look:before:leap:eccv18 method has an SPL of 13.27, which is lower than the SPL of our method at 20.45. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3550.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison with prior s-o-t-a methods. The numbers of RConcat is taken from the paper chen2018touchdown." ], "reasoning": "The table shows that the RPA look:before:leap:eccv18 method has an SPL of 13.27, which is lower than the SPL of our method at 20.45. Therefore, the statement is refuted.", "statement": "The RPA look:before:leap:eccv18 method has the highest SPL.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: Bridging languages ranks for Hausa and Uzbek translated 20-newsgroups data.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Arabic is the best bridging language for both Hausa and Uzbek, and factors like language family, region, and Wikipedia size influence the ranking.Given the fact that for both Hausa and Uzbek, Arabic outperforms English for bridged CLDDC, and the fact that there are more local languages out of 7,000 languages in the world that cannot be translated to English but may be able to be translated to local popular language, we want to evaluate which language can be the best language as a bridge for the SWLs. All the translation of words are performed by Google Translate. The results show that Arabic is the best bridge for both languages. For all the 39 SWLs, we also checked the bridged results based on all the 49 LWLs, and we selected the best bridging languages and report the classification results. Some of the top bridging LWLs are in the same family with the target languages. For example, Arabic, Hebrew, and Hausa are in the Afro-Asiatic family. Moreover, region of the native speakers is also reflected. Persian speakers in Iran live relatively near to Uzbek speakers in Uzbekistan. However, writing system, language family, and region are not the only factors that affects the ranking. For example, Korean and Japanese are also ranked in the top ten, but none of the above factors appears. Besides other linguistic factors, we also presume that either the size of Wikipedia or the less ambiguity of translation may help them result in relatively good accuracy. Then the remaining question is how to automatically select a good bridging LWL for the SWL document classification." } ], "images": [ "scigen/train/3551.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Bridging languages ranks for Hausa and Uzbek translated 20-newsgroups data." ], "reasoning": "Arabic is the best bridging language for both Hausa and Uzbek, and factors like language family, region, and Wikipedia size influence the ranking.", "description": "Given the fact that for both Hausa and Uzbek, Arabic outperforms English for bridged CLDDC, and the fact that there are more local languages out of 7,000 languages in the world that cannot be translated to English but may be able to be translated to local popular language, we want to evaluate which language can be the best language as a bridge for the SWLs. All the translation of words are performed by Google Translate. The results show that Arabic is the best bridge for both languages. For all the 39 SWLs, we also checked the bridged results based on all the 49 LWLs, and we selected the best bridging languages and report the classification results. Some of the top bridging LWLs are in the same family with the target languages. For example, Arabic, Hebrew, and Hausa are in the Afro-Asiatic family. Moreover, region of the native speakers is also reflected. Persian speakers in Iran live relatively near to Uzbek speakers in Uzbekistan. However, writing system, language family, and region are not the only factors that affects the ranking. For example, Korean and Japanese are also ranked in the top ten, but none of the above factors appears. Besides other linguistic factors, we also presume that either the size of Wikipedia or the less ambiguity of translation may help them result in relatively good accuracy. Then the remaining question is how to automatically select a good bridging LWL for the SWL document classification.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 3: Bridging languages ranks for Hausa and Uzbek translated 20-newsgroups data.', answer the question below by showing each progressive step toward the answer. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich language has the highest accuracy as a bridging language for both Hausa and Uzbek?\n" }, { "role": "assistant", "content": "By comparing the accuracy values for both Hausa and Uzbek, Arabic has the highest accuracy for both languages. So, the answer is {\"answer\": \"Arabic\"}." } ], "images": [ "scigen/train/3551.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Bridging languages ranks for Hausa and Uzbek translated 20-newsgroups data." ], "reasoning": "By comparing the accuracy values for both Hausa and Uzbek, Arabic has the highest accuracy for both languages.", "question": "Which language has the highest accuracy as a bridging language for both Hausa and Uzbek?", "answer": "So, the answer is {\"answer\": \"Arabic\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Korean is the second-best bridging language for both Hausa and Uzbek. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nKorean is the second-best bridging language for both Hausa and Uzbek.\n" }, { "role": "assistant", "content": "The table shows that Korean is ranked third for Hausa and second for Uzbek, not second for both. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3551.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Bridging languages ranks for Hausa and Uzbek translated 20-newsgroups data." ], "reasoning": "The table shows that Korean is ranked third for Hausa and second for Uzbek, not second for both.", "statement": "Korean is the second-best bridging language for both Hausa and Uzbek.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: F1 scores on the SemEval 2010 dataset.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The distributed representation of NoComp improved performance from 77.3 to 79.9, and GAC achieved the highest performance of 82.0, which was further improved to 83.7 by incorporating external resources.The first group of the table provides basic features and enhancements with the distributed representations. We can observe a significant improvement even from the distributed representation of NoComp (77.3 to 79.9). Moreover, the distributed representation that exhibited the high performance on the pattern similarity task was also successful on this task; GAC, which yielded the highest performance on the pattern similarity task, also achieved the best performance (82.0) of all encoders on this task. We could further improve the performance of SVM + GAC by incorporating external resources in the similar manner as the previous studies did. Concretely, SVM + GAC achieved 83.7 F1 score by adding features for WordNet, named entities (NE), and dependency paths explained in \\newcitehashimoto-EtAl:2015:CoNLL." } ], "images": [ "scigen/train/3552.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: F1 scores on the SemEval 2010 dataset." ], "reasoning": "The distributed representation of NoComp improved performance from 77.3 to 79.9, and GAC achieved the highest performance of 82.0, which was further improved to 83.7 by incorporating external resources.", "description": "The first group of the table provides basic features and enhancements with the distributed representations. We can observe a significant improvement even from the distributed representation of NoComp (77.3 to 79.9). Moreover, the distributed representation that exhibited the high performance on the pattern similarity task was also successful on this task; GAC, which yielded the highest performance on the pattern similarity task, also achieved the best performance (82.0) of all encoders on this task. We could further improve the performance of SVM + GAC by incorporating external resources in the similar manner as the previous studies did. Concretely, SVM + GAC achieved 83.7 F1 score by adding features for WordNet, named entities (NE), and dependency paths explained in \\newcitehashimoto-EtAl:2015:CoNLL.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: F1 scores on the SemEval 2010 dataset.'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the F1 score achieved by SVM + GAC with additional features for WordNet, named entities (NE), and dependency paths?\n" }, { "role": "assistant", "content": "The table shows that SVM + GAC achieved an F1 score of 82.0, and the description states that this score was improved to 83.7 by adding features for WordNet, named entities (NE), and dependency paths. So, the answer is {\"answer\": \"83.7\"}" } ], "images": [ "scigen/train/3552.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: F1 scores on the SemEval 2010 dataset." ], "reasoning": "The table shows that SVM + GAC achieved an F1 score of 82.0, and the description states that this score was improved to 83.7 by adding features for WordNet, named entities (NE), and dependency paths.", "question": "What is the F1 score achieved by SVM + GAC with additional features for WordNet, named entities (NE), and dependency paths?", "answer": "So, the answer is {\"answer\": \"83.7\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe highest F1 score achieved by any method is 85.6.\n" }, { "role": "assistant", "content": "The table shows that the highest F1 score achieved is 85.6 by depLCNN + NS with embeddings, dependency, and WordNet. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3552.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: F1 scores on the SemEval 2010 dataset." ], "reasoning": "The table shows that the highest F1 score achieved is 85.6 by depLCNN + NS with embeddings, dependency, and WordNet.", "statement": "The highest F1 score achieved by any method is 85.6.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: Spearman’s rank correlations on different pattern lengths (number of dimensions d=500).'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Additive composition performs well for shorter relational patterns (lengths of 2 and 3) but poorly for longer ones (lengths of 4 and 5+), while RNN and its variants excel at longer patterns.Here, the length of a relational-pattern pair is defined by the maximum of the lengths of two patterns in the pair. In length of 1, all methods achieve the same correlation score because they use the same word vector xt. The table shows that additive composition (Add) performs well for shorter relational patterns (lengths of 2 and 3) but poorly for longer ones (lengths of 4 and 5+). GAC also exhibits the similar tendency to Add, but it outperforms Add for shorter patterns (lengths of 2 and 3) probably because of the adaptive control of input and forget gates. In contrast, RNN and its variants (RNN, GRU, and LSTM) enjoy the advantage on longer patterns (lengths of 4 and 5+)." } ], "images": [ "scigen/train/3553.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Spearman’s rank correlations on different pattern lengths (number of dimensions d=500)." ], "reasoning": "Additive composition performs well for shorter relational patterns (lengths of 2 and 3) but poorly for longer ones (lengths of 4 and 5+), while RNN and its variants excel at longer patterns.", "description": "Here, the length of a relational-pattern pair is defined by the maximum of the lengths of two patterns in the pair. In length of 1, all methods achieve the same correlation score because they use the same word vector xt. The table shows that additive composition (Add) performs well for shorter relational patterns (lengths of 2 and 3) but poorly for longer ones (lengths of 4 and 5+). GAC also exhibits the similar tendency to Add, but it outperforms Add for shorter patterns (lengths of 2 and 3) probably because of the adaptive control of input and forget gates. In contrast, RNN and its variants (RNN, GRU, and LSTM) enjoy the advantage on longer patterns (lengths of 4 and 5+).", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 1: Spearman’s rank correlations on different pattern lengths (number of dimensions d=500).', and tackle the question below in a sequential manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich method achieves the highest correlation score for relational patterns of length 3?\n" }, { "role": "assistant", "content": "To find the method with the highest correlation score for length 3, look at the row corresponding to length 3 and identify the highest value in that row. So, the answer is {\"answer\": \"GAC\"}." } ], "images": [ "scigen/train/3553.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Spearman’s rank correlations on different pattern lengths (number of dimensions d=500)." ], "reasoning": "To find the method with the highest correlation score for length 3, look at the row corresponding to length 3 and identify the highest value in that row.", "question": "Which method achieves the highest correlation score for relational patterns of length 3?", "answer": "So, the answer is {\"answer\": \"GAC\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Additive composition performs better than LSTM for relational patterns of length 2. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nAdditive composition performs better than LSTM for relational patterns of length 2.\n" }, { "role": "assistant", "content": "Compare the correlation scores of Add and LSTM for length 2. Add has a score of 0.319, while LSTM has a score of 0.257, indicating that Add performs better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3553.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Spearman’s rank correlations on different pattern lengths (number of dimensions d=500)." ], "reasoning": "Compare the correlation scores of Add and LSTM for length 2. Add has a score of 0.319, while LSTM has a score of 0.257, indicating that Add performs better.", "statement": "Additive composition performs better than LSTM for relational patterns of length 2.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: Specific entropy of the Sample texts based on Chinese words, where Ns is the number of words in the Sample text, σw is the entropy of the text, and σw/10k is the entropy of the text normalized to 10000 word length.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The total specific entropy for Sample texts I-IV are 508.5, 229.4, 8.399, and 7.106 respectively, multiplied by 10^-3.Then we add up the entropy contribution of all observed sentences of all lengths in the Sample texts. This provides the total specific entropy σw=(508.5, 229.4, 8.399, 7.106) ⋅10−3, (8) for Sample texts I−IV respectively." } ], "images": [ "scigen/train/3554.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Specific entropy of the Sample texts based on Chinese words, where Ns is the number of words in the Sample text, σw is the entropy of the text, and σw/10k is the entropy of the text normalized to 10000 word length." ], "reasoning": "The total specific entropy for Sample texts I-IV are 508.5, 229.4, 8.399, and 7.106 respectively, multiplied by 10^-3.", "description": "Then we add up the entropy contribution of all observed sentences of all lengths in the Sample texts. This provides the total specific entropy σw=(508.5, 229.4, 8.399, 7.106) ⋅10−3, (8) for Sample texts I−IV respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 4: Specific entropy of the Sample texts based on Chinese words, where Ns is the number of words in the Sample text, σw is the entropy of the text, and σw/10k is the entropy of the text normalized to 10000 word length.'.\n\nQuestion: Which Sample text has the highest total specific entropy?\n" }, { "role": "assistant", "content": "By comparing the total specific entropy values (σw) for Sample texts I-IV, Sample text I has the highest value of 508.5 ⋅ 10^-3. So, the answer is {\"answer\": \"I\"}" } ], "images": [ "scigen/train/3554.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Specific entropy of the Sample texts based on Chinese words, where Ns is the number of words in the Sample text, σw is the entropy of the text, and σw/10k is the entropy of the text normalized to 10000 word length." ], "reasoning": "By comparing the total specific entropy values (σw) for Sample texts I-IV, Sample text I has the highest value of 508.5 ⋅ 10^-3.", "question": "Which Sample text has the highest total specific entropy?", "answer": "So, the answer is {\"answer\": \"I\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSample text III has a higher total specific entropy than Sample text IV.\n" }, { "role": "assistant", "content": "The total specific entropy (σw) for Sample text III is 8.399 ⋅ 10^-3, while for Sample text IV it is 7.106 ⋅ 10^-3. Therefore, Sample text III has a higher total specific entropy than Sample text IV. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3554.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Specific entropy of the Sample texts based on Chinese words, where Ns is the number of words in the Sample text, σw is the entropy of the text, and σw/10k is the entropy of the text normalized to 10000 word length." ], "reasoning": "The total specific entropy (σw) for Sample text III is 8.399 ⋅ 10^-3, while for Sample text IV it is 7.106 ⋅ 10^-3. Therefore, Sample text III has a higher total specific entropy than Sample text IV.", "statement": "Sample text III has a higher total specific entropy than Sample text IV.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 6: Specific entropy of the Sample texts based on English words, where Ns is the number of words in the Sample text, σw is the entropy of the text, and σw/10k is the entropy of the text normalized to 10000 word length.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The table shows the total specific entropy for all sentences for four English sample texts, calculated by adding the entropy contribution of all observed sentences of all lengths.Then we add up the entropy contribution of all observed sentences of all lengths. This provides the total specific entropy for all sentences for the four English Sample texts." } ], "images": [ "scigen/train/3555.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Specific entropy of the Sample texts based on English words, where Ns is the number of words in the Sample text, σw is the entropy of the text, and σw/10k is the entropy of the text normalized to 10000 word length." ], "reasoning": "The table shows the total specific entropy for all sentences for four English sample texts, calculated by adding the entropy contribution of all observed sentences of all lengths.", "description": "Then we add up the entropy contribution of all observed sentences of all lengths. This provides the total specific entropy for all sentences for the four English Sample texts.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 6: Specific entropy of the Sample texts based on English words, where Ns is the number of words in the Sample text, σw is the entropy of the text, and σw/10k is the entropy of the text normalized to 10000 word length.', solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich sample text has the highest total specific entropy contribution per 10,000 words?\n" }, { "role": "assistant", "content": "To find the sample text with the highest total specific entropy contribution per 10,000 words, we compare the values in the column 'σw/10 k'. The highest value is 1.163·10^-1, which corresponds to Sample IV. So, the answer is {\"answer\": \"IV\"}." } ], "images": [ "scigen/train/3555.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Specific entropy of the Sample texts based on English words, where Ns is the number of words in the Sample text, σw is the entropy of the text, and σw/10k is the entropy of the text normalized to 10000 word length." ], "reasoning": "To find the sample text with the highest total specific entropy contribution per 10,000 words, we compare the values in the column 'σw/10 k'. The highest value is 1.163·10^-1, which corresponds to Sample IV.", "question": "Which sample text has the highest total specific entropy contribution per 10,000 words?", "answer": "So, the answer is {\"answer\": \"IV\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Sample II has a higher total specific entropy contribution per 10,000 words than Sample III. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nSample II has a higher total specific entropy contribution per 10,000 words than Sample III.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the values in the column 'σw/10 k' for Sample II and Sample III. Sample II has a value of 7.078·10^-3, while Sample III has a value of 1.700·10^-2. Since 7.078·10^-3 is less than 1.700·10^-2, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3555.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Specific entropy of the Sample texts based on English words, where Ns is the number of words in the Sample text, σw is the entropy of the text, and σw/10k is the entropy of the text normalized to 10000 word length." ], "reasoning": "To verify this statement, we compare the values in the column 'σw/10 k' for Sample II and Sample III. Sample II has a value of 7.078·10^-3, while Sample III has a value of 1.700·10^-2. Since 7.078·10^-3 is less than 1.700·10^-2, the statement is refuted.", "statement": "Sample II has a higher total specific entropy contribution per 10,000 words than Sample III.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Results on 1% of OBW and full OBW training set. We report 2 variants of perplexities on the test set. The experiments where coverage is restricted are indicated by (R). See Section 4.3 for explanations.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Using external information to compute embeddings of unknown words improves performance, with GloVe embeddings resulting in the best perplexity.The training was early-stopped using a development set. Similarly to our other experiments, using external information to compute embeddings of unknown words helps in all cases. We observe a significant gain even for dict1, which is remarkable as this model has the same architecture and parameters as the baseline. We note that lemma+lowercase performs worse than any model with the dictionary, which suggests that dictionary definitions are used in a non-trivial way. Adding spelling consistently helps more than adding dictionary definitions. Using both dictionary and spelling is consistently slightly better than using just spelling, and the improvement is more pronounced in the restricted setting. Using GloVe embeddings results in the best perplexity. Switching to the full training set shrank all the gaps." } ], "images": [ "scigen/train/3556.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on 1% of OBW and full OBW training set. We report 2 variants of perplexities on the test set. The experiments where coverage is restricted are indicated by (R). See Section 4.3 for explanations." ], "reasoning": "Using external information to compute embeddings of unknown words improves performance, with GloVe embeddings resulting in the best perplexity.", "description": "The training was early-stopped using a development set. Similarly to our other experiments, using external information to compute embeddings of unknown words helps in all cases. We observe a significant gain even for dict1, which is remarkable as this model has the same architecture and parameters as the baseline. We note that lemma+lowercase performs worse than any model with the dictionary, which suggests that dictionary definitions are used in a non-trivial way. Adding spelling consistently helps more than adding dictionary definitions. Using both dictionary and spelling is consistently slightly better than using just spelling, and the improvement is more pronounced in the restricted setting. Using GloVe embeddings results in the best perplexity. Switching to the full training set shrank all the gaps.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Results on 1% of OBW and full OBW training set. We report 2 variants of perplexities on the test set. The experiments where coverage is restricted are indicated by (R). See Section 4.3 for explanations.', unfold the question presented below, detailing every step of your calculation. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieves the best perplexity on the 100% PPL after OOV metric?\n" }, { "role": "assistant", "content": "By examining the 100% PPL after OOV column, we see that the glove model has the lowest perplexity value of 14.20. So, the answer is {\"answer\": \"glove\"}" } ], "images": [ "scigen/train/3556.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on 1% of OBW and full OBW training set. We report 2 variants of perplexities on the test set. The experiments where coverage is restricted are indicated by (R). See Section 4.3 for explanations." ], "reasoning": "By examining the 100% PPL after OOV column, we see that the glove model has the lowest perplexity value of 14.20.", "question": "Which model achieves the best perplexity on the 100% PPL after OOV metric?", "answer": "So, the answer is {\"answer\": \"glove\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe dict1 model performs better than the baseline model in the 1% PPL metric.\n" }, { "role": "assistant", "content": "The table shows that the dict1 model has a 1% PPL of 69.14, which is lower (better) than the baseline model's 1% PPL of 73.07. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3556.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on 1% of OBW and full OBW training set. We report 2 variants of perplexities on the test set. The experiments where coverage is restricted are indicated by (R). See Section 4.3 for explanations." ], "reasoning": "The table shows that the dict1 model has a 1% PPL of 69.14, which is lower (better) than the baseline model's 1% PPL of 73.07.", "statement": "The dict1 model performs better than the baseline model in the 1% PPL metric.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Exact match (EM) ratio for different models on SQuAD development and tests set. “dict” stands for dictionary, “MP” stands for mean pooling.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Adding external information significantly improves the baseline model's performance, with the spelling+dict model achieving the highest EM dev score of 63.06.We report the exact match ratio as computed by the evaluation tools provided with the dataset, which is basically the accuracy of selecting the right answer. We report the average over multiple runs on the development set. Looking at the results one can see that adding any external information results in a significant improvement over the baseline model (B) (3.7 - 10.5 points). When the dictionary alone is used, mean pooling (D3) performs similarly to LSTM (D4). For the model with mean pooling, we verified that the matrix W in computing ec(w) is necessary for best results (see model D2), and back-propagating through the process of reading definition is helpful (see model D1). We thereby establish that our method is preferable to the one by Long et al." } ], "images": [ "scigen/train/3557.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Exact match (EM) ratio for different models on SQuAD development and tests set. “dict” stands for dictionary, “MP” stands for mean pooling." ], "reasoning": "Adding external information significantly improves the baseline model's performance, with the spelling+dict model achieving the highest EM dev score of 63.06.", "description": "We report the exact match ratio as computed by the evaluation tools provided with the dataset, which is basically the accuracy of selecting the right answer. We report the average over multiple runs on the development set. Looking at the results one can see that adding any external information results in a significant improvement over the baseline model (B) (3.7 - 10.5 points). When the dictionary alone is used, mean pooling (D3) performs similarly to LSTM (D4). For the model with mean pooling, we verified that the matrix W in computing ec(w) is necessary for best results (see model D2), and back-propagating through the process of reading definition is helpful (see model D1). We thereby establish that our method is preferable to the one by Long et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Exact match (EM) ratio for different models on SQuAD development and tests set. “dict” stands for dictionary, “MP” stands for mean pooling.\nQuestion: Which model achieved the highest EM dev score?\nSolve the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the EM dev scores of all models, the spelling+dict (SD) model has the highest score of 63.06. So, the answer is {\"answer\": \"spelling+dict (SD)\"}" } ], "images": [ "scigen/train/3557.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Exact match (EM) ratio for different models on SQuAD development and tests set. “dict” stands for dictionary, “MP” stands for mean pooling." ], "reasoning": "By comparing the EM dev scores of all models, the spelling+dict (SD) model has the highest score of 63.06.", "question": "Which model achieved the highest EM dev score?", "answer": "So, the answer is {\"answer\": \"spelling+dict (SD)\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The baseline model achieved an EM dev score higher than 55. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe baseline model achieved an EM dev score higher than 55.\n" }, { "role": "assistant", "content": "The baseline model (B) has an EM dev score of 52.58, which is lower than 55. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3557.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Exact match (EM) ratio for different models on SQuAD development and tests set. “dict” stands for dictionary, “MP” stands for mean pooling." ], "reasoning": "The baseline model (B) has an EM dev score of 52.58, which is lower than 55.", "statement": "The baseline model achieved an EM dev score higher than 55.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Results on SNLI and MultiNLI. X/Y means X percent accuracy on the development set and Y percent accuracy on the test set. “dict” stands for “dictionary”.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Using dictionary definitions significantly improves performance across SNLI and MultiNLI datasets compared to baseline and spelling, but not as much as GloVe embeddings.Using dictionary definitions allows us to bridge ≈40% of the gap between training from scratch and using embeddings pretrained on 840 billion words, and this improvement is consistent on both datasets. Compared to the SQuAD results, an important difference is that spelling was not as useful on SNLI and MultiNLI." } ], "images": [ "scigen/train/3558.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on SNLI and MultiNLI. X/Y means X percent accuracy on the development set and Y percent accuracy on the test set. “dict” stands for “dictionary”." ], "reasoning": "Using dictionary definitions significantly improves performance across SNLI and MultiNLI datasets compared to baseline and spelling, but not as much as GloVe embeddings.", "description": "Using dictionary definitions allows us to bridge ≈40% of the gap between training from scratch and using embeddings pretrained on 840 billion words, and this improvement is consistent on both datasets. Compared to the SQuAD results, an important difference is that spelling was not as useful on SNLI and MultiNLI.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: Results on SNLI and MultiNLI. X/Y means X percent accuracy on the development set and Y percent accuracy on the test set. “dict” stands for “dictionary”.' and methodically tackle the question that follows. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the accuracy of the dictionary method on the MultiNLI matched dataset?\n" }, { "role": "assistant", "content": "The dictionary method shows an accuracy of 71.39% on the MultiNLI matched dataset, which is higher than both the baseline and spelling methods. So, the answer is {\"answer\": \"71.39\"}" } ], "images": [ "scigen/train/3558.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on SNLI and MultiNLI. X/Y means X percent accuracy on the development set and Y percent accuracy on the test set. “dict” stands for “dictionary”." ], "reasoning": "The dictionary method shows an accuracy of 71.39% on the MultiNLI matched dataset, which is higher than both the baseline and spelling methods.", "question": "What is the accuracy of the dictionary method on the MultiNLI matched dataset?", "answer": "So, the answer is {\"answer\": \"71.39\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The GloVe method achieves the highest accuracy on the SNLI dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe GloVe method achieves the highest accuracy on the SNLI dataset.\n" }, { "role": "assistant", "content": "The table shows that the GloVe method has the highest accuracy on the SNLI dataset with values of 87.20% and 86.39%, which are higher than the other methods listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3558.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on SNLI and MultiNLI. X/Y means X percent accuracy on the development set and Y percent accuracy on the test set. “dict” stands for “dictionary”." ], "reasoning": "The table shows that the GloVe method has the highest accuracy on the SNLI dataset with values of 87.20% and 86.39%, which are higher than the other methods listed.", "statement": "The GloVe method achieves the highest accuracy on the SNLI dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Results on the En-De, En-Vi and De-En test sets. Compared with the baseline models, “impl.” denotes our own implementation.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Explicit Sparse Transformer achieves the highest BLEU scores for En-De, En-Vi, and De-En translations, outperforming other models.For En-De, Transformer-based models outperform the previous methods. Compared with the result of Transformer (transformer), Explicit Sparse Transformer reaches 29.4 in BLEU score evaluation, outperforming vanilla Transformer by 0.3 BLEU score. Our model, Explicit Sparse Transformer, achieves a much better performance, 31.1, by a margin of 0.5 over vanilla Transformer. For De-En, we demonstrate that Transformer-based models outperform the other baselines. Compared with Transformer, our Explicit Sparse Transformer reaches a better performance, 35.6. Its advantage is +0.3. To the best of our knowledge, Explicit Sparse Transformer reaches a top line performance on the dataset." } ], "images": [ "scigen/train/3559.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results on the En-De, En-Vi and De-En test sets. Compared with the baseline models, “impl.” denotes our own implementation." ], "reasoning": "Explicit Sparse Transformer achieves the highest BLEU scores for En-De, En-Vi, and De-En translations, outperforming other models.", "description": "For En-De, Transformer-based models outperform the previous methods. Compared with the result of Transformer (transformer), Explicit Sparse Transformer reaches 29.4 in BLEU score evaluation, outperforming vanilla Transformer by 0.3 BLEU score. Our model, Explicit Sparse Transformer, achieves a much better performance, 31.1, by a margin of 0.5 over vanilla Transformer. For De-En, we demonstrate that Transformer-based models outperform the other baselines. Compared with Transformer, our Explicit Sparse Transformer reaches a better performance, 35.6. Its advantage is +0.3. To the best of our knowledge, Explicit Sparse Transformer reaches a top line performance on the dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 1: Results on the En-De, En-Vi and De-En test sets. Compared with the baseline models, “impl.” denotes our own implementation.', unfold the steps to solve the question given next. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: What is the BLEU score difference between Explicit Sparse Transformer and Transformer (ott2018scaling) for En-De translation?\n" }, { "role": "assistant", "content": "The BLEU score for Explicit Sparse Transformer in En-De is 29.4, and for Transformer (ott2018scaling) it is 29.1. The difference is 29.4 - 29.1 = 0.3. So, the answer is {\"answer\": \"0.3\"}" } ], "images": [ "scigen/train/3559.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results on the En-De, En-Vi and De-En test sets. Compared with the baseline models, “impl.” denotes our own implementation." ], "reasoning": "The BLEU score for Explicit Sparse Transformer in En-De is 29.4, and for Transformer (ott2018scaling) it is 29.1. The difference is 29.4 - 29.1 = 0.3.", "question": "What is the BLEU score difference between Explicit Sparse Transformer and Transformer (ott2018scaling) for En-De translation?", "answer": "So, the answer is {\"answer\": \"0.3\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nExplicit Sparse Transformer achieves the highest BLEU score for De-En translation.\n" }, { "role": "assistant", "content": "The table shows that Explicit Sparse Transformer has a BLEU score of 35.6 for De-En, which is the highest among all listed models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3559.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results on the En-De, En-Vi and De-En test sets. Compared with the baseline models, “impl.” denotes our own implementation." ], "reasoning": "The table shows that Explicit Sparse Transformer has a BLEU score of 35.6 for De-En, which is the highest among all listed models.", "statement": "Explicit Sparse Transformer achieves the highest BLEU score for De-En translation.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Results on the MSCOCO Karpathy test split.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The Explicit Sparse Transformer outperforms the Transformer by +0.4 in BLEU-4, +0.3 in METEOR, and +0.7 in CIDEr, demonstrating its effectiveness in Image Captioning.Transformer outperforms the mentioned baseline models. Explicit Sparse Transformer outperforms the implemented Transformer by +0.4 in terms of BLEU-4, +0.3 in terms of METEOR, +0.7 in terms of CIDEr. , which consistently proves its effectiveness in Image Captioning." } ], "images": [ "scigen/train/3560.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on the MSCOCO Karpathy test split." ], "reasoning": "The Explicit Sparse Transformer outperforms the Transformer by +0.4 in BLEU-4, +0.3 in METEOR, and +0.7 in CIDEr, demonstrating its effectiveness in Image Captioning.", "description": "Transformer outperforms the mentioned baseline models. Explicit Sparse Transformer outperforms the implemented Transformer by +0.4 in terms of BLEU-4, +0.3 in terms of METEOR, +0.7 in terms of CIDEr. , which consistently proves its effectiveness in Image Captioning.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Results on the MSCOCO Karpathy test split.', answer the following question. Give detailed solution consisting of each step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest BLEU-4 score and how does it compare to the Transformer in terms of BLEU-4?\n" }, { "role": "assistant", "content": "The UpDown model has the highest BLEU-4 score of 36.2. The Transformer has a BLEU-4 score of 35.3. Therefore, the UpDown model outperforms the Transformer by 0.9 in terms of BLEU-4. So, the answer is {\"answer\": \"UpDown\"}." } ], "images": [ "scigen/train/3560.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on the MSCOCO Karpathy test split." ], "reasoning": "The UpDown model has the highest BLEU-4 score of 36.2. The Transformer has a BLEU-4 score of 35.3. Therefore, the UpDown model outperforms the Transformer by 0.9 in terms of BLEU-4.", "question": "Which model has the highest BLEU-4 score and how does it compare to the Transformer in terms of BLEU-4?", "answer": "So, the answer is {\"answer\": \"UpDown\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe Explicit Sparse Transformer has the highest METEOR score among all models.\n" }, { "role": "assistant", "content": "The table shows that the Explicit Sparse Transformer has a METEOR score of 28.0, which is the highest among all the models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3560.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on the MSCOCO Karpathy test split." ], "reasoning": "The table shows that the Explicit Sparse Transformer has a METEOR score of 28.0, which is the highest among all the models listed.", "statement": "The Explicit Sparse Transformer has the highest METEOR score among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Comparison with state-of-the-art results on enwiki8. Explicit Sparse Transformer-XL refers to the Transformer with our sparsification method.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Transformer-XL performs better than other strong baselines, and Explicit Sparse Transformer outperforms Transformer-XL.Compared with the other strong baselines, Transformer-XL can reach a better performance, and Explicit Sparse Transformer outperforms Transformer-XL with an advantage." } ], "images": [ "scigen/train/3561.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison with state-of-the-art results on enwiki8. Explicit Sparse Transformer-XL refers to the Transformer with our sparsification method." ], "reasoning": "Transformer-XL performs better than other strong baselines, and Explicit Sparse Transformer outperforms Transformer-XL.", "description": "Compared with the other strong baselines, Transformer-XL can reach a better performance, and Explicit Sparse Transformer outperforms Transformer-XL with an advantage.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Comparison with state-of-the-art results on enwiki8. Explicit Sparse Transformer-XL refers to the Transformer with our sparsification method.', answer the following question in a stepwise manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model has the lowest BPC and how does it compare to Transformer-XL?\n" }, { "role": "assistant", "content": "The model with the lowest BPC is Explicit Sparse Transformer-XL with a BPC of 1.05, which is better than Transformer-XL's BPC of 1.06. So, the answer is {\"answer\": \"Explicit Sparse Transformer-XL\"}" } ], "images": [ "scigen/train/3561.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison with state-of-the-art results on enwiki8. Explicit Sparse Transformer-XL refers to the Transformer with our sparsification method." ], "reasoning": "The model with the lowest BPC is Explicit Sparse Transformer-XL with a BPC of 1.05, which is better than Transformer-XL's BPC of 1.06.", "question": "Which model has the lowest BPC and how does it compare to Transformer-XL?", "answer": "So, the answer is {\"answer\": \"Explicit Sparse Transformer-XL\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Transformer-XL has a better BPC than Adaptive-span., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nTransformer-XL has a better BPC than Adaptive-span.\n" }, { "role": "assistant", "content": "The BPC of Transformer-XL is 1.06, which is higher than the BPC of Adaptive-span, which is 1.02. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3561.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison with state-of-the-art results on enwiki8. Explicit Sparse Transformer-XL refers to the Transformer with our sparsification method." ], "reasoning": "The BPC of Transformer-XL is 1.06, which is higher than the BPC of Adaptive-span, which is 1.02.", "statement": "Transformer-XL has a better BPC than Adaptive-span.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: Results of the ablation study of the sparsification at different phases on the En-Vi test set. “Base” denotes vanilla Transformer. “T” denotes only adding the sparsification in the training phase, and “T&P” denotes adding it at both phases as the implementation of Explicit Sparse Transformer does.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Adding sparsification during training improves performance by 0.3 BLEU scores, indicating that the vanilla Transformer may be overparameterized.We are surprised to find that only adding the sparsification in the training phase can also bring an improvement in the performance. The improvement of 0.3 BLEU scores shows that vanilla Transformer may be overparameterized and the sparsification encourages the simplification of the model." } ], "images": [ "scigen/train/3562.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results of the ablation study of the sparsification at different phases on the En-Vi test set. “Base” denotes vanilla Transformer. “T” denotes only adding the sparsification in the training phase, and “T&P” denotes adding it at both phases as the implementation of Explicit Sparse Transformer does." ], "reasoning": "Adding sparsification during training improves performance by 0.3 BLEU scores, indicating that the vanilla Transformer may be overparameterized.", "description": "We are surprised to find that only adding the sparsification in the training phase can also bring an improvement in the performance. The improvement of 0.3 BLEU scores shows that vanilla Transformer may be overparameterized and the sparsification encourages the simplification of the model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the BLEU score improvement when adding sparsification during training for the En-Vi task?\nSolve the above question based on the table titled 'Table 5: Results of the ablation study of the sparsification at different phases on the En-Vi test set. “Base” denotes vanilla Transformer. “T” denotes only adding the sparsification in the training phase, and “T&P” denotes adding it at both phases as the implementation of Explicit Sparse Transformer does.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for the En-Vi task under different conditions. The base score is 27.4, and with sparsification during training (T), the score is 27.7. The improvement is 27.7 - 27.4. So, the answer is {\"answer\": \"0.3\"}." } ], "images": [ "scigen/train/3562.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results of the ablation study of the sparsification at different phases on the En-Vi test set. “Base” denotes vanilla Transformer. “T” denotes only adding the sparsification in the training phase, and “T&P” denotes adding it at both phases as the implementation of Explicit Sparse Transformer does." ], "reasoning": "The table shows the BLEU scores for the En-Vi task under different conditions. The base score is 27.4, and with sparsification during training (T), the score is 27.7. The improvement is 27.7 - 27.4.", "question": "What is the BLEU score improvement when adding sparsification during training for the En-Vi task?", "answer": "So, the answer is {\"answer\": \"0.3\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe BLEU score for the En-Vi task with both training and pruning is 27.8.\n" }, { "role": "assistant", "content": "The table shows the BLEU scores for the En-Vi task under different conditions. The score with both training and pruning (T&P) is 27.8. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3562.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results of the ablation study of the sparsification at different phases on the En-Vi test set. “Base” denotes vanilla Transformer. “T” denotes only adding the sparsification in the training phase, and “T&P” denotes adding it at both phases as the implementation of Explicit Sparse Transformer does." ], "reasoning": "The table shows the BLEU scores for the En-Vi task under different conditions. The score with both training and pruning (T&P) is 27.8.", "statement": "The BLEU score for the En-Vi task with both training and pruning is 27.8.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: Comparing variants of data augmentation.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Augmenting the 'Other' class degrades performance by 1.1%, and deep CNN architectures are less effective than RNNs due to sentence boundary padding.For data augmentation, it is natural that the inversed Other relation is also in the Other class itself. We deem the Other class contains mainly noise, and is inimical to our model. Then we conducted another experiment where we only augmented the Other class. The result verifies our conjecture as we obtained an even larger degradation of 1.1% in this setting. We observe that a single layer of CNN is also effective, yielding an F1-score slightly worse than our RNN. But the deep architecture hurts the performance of CNNs in this task. One plausible explanation is that, when convolution is performed, the beginning and end of a sentence are typically padded with a special symbol or simply zero. However, the shortest dependency path between two entities is usually not very long (∼4 on average). Hence, sentence boundaries may play a large role in convolution, which makes CNNs vulnerable." } ], "images": [ "scigen/train/3563.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparing variants of data augmentation." ], "reasoning": "Augmenting the 'Other' class degrades performance by 1.1%, and deep CNN architectures are less effective than RNNs due to sentence boundary padding.", "description": "For data augmentation, it is natural that the inversed Other relation is also in the Other class itself. We deem the Other class contains mainly noise, and is inimical to our model. Then we conducted another experiment where we only augmented the Other class. The result verifies our conjecture as we obtained an even larger degradation of 1.1% in this setting. We observe that a single layer of CNN is also effective, yielding an F1-score slightly worse than our RNN. But the deep architecture hurts the performance of CNNs in this task. One plausible explanation is that, when convolution is performed, the beginning and end of a sentence are typically padded with a special symbol or simply zero. However, the shortest dependency path between two entities is usually not very long (∼4 on average). Hence, sentence boundaries may play a large role in convolution, which makes CNNs vulnerable.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 1: Comparing variants of data augmentation.', unfold the question presented below, detailing every step of your calculation. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the F1-score when only the 'Other' class is augmented, and how does it compare to no augmentation?\n" }, { "role": "assistant", "content": "The F1-score when only the 'Other' class is augmented is 83.01. Comparing this to the F1-score with no augmentation, which is 84.16, shows a degradation of 1.15%. So, the answer is {\"answer\": \"83.01\"}." } ], "images": [ "scigen/train/3563.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparing variants of data augmentation." ], "reasoning": "The F1-score when only the 'Other' class is augmented is 83.01. Comparing this to the F1-score with no augmentation, which is 84.16, shows a degradation of 1.15%.", "question": "What is the F1-score when only the 'Other' class is augmented, and how does it compare to no augmentation?", "answer": "So, the answer is {\"answer\": \"83.01\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nAugmenting directed relations only results in the highest F1-score.\n" }, { "role": "assistant", "content": "The table shows that augmenting directed relations only yields an F1-score of 86.10, which is the highest among the listed variants. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3563.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparing variants of data augmentation." ], "reasoning": "The table shows that augmenting directed relations only yields an F1-score of 86.10, which is the highest among the listed variants.", "statement": "Augmenting directed relations only results in the highest F1-score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Comparing variants of data augmentation.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Augmenting the Other class degrades performance by 1.1%, and while a single layer CNN is effective, deeper CNN architectures hurt performance due to sentence boundary padding.For data augmentation, it is natural that the inversed Other relation is also in the Other class itself. We deem the Other class contains mainly noise, and is inimical to our model. Then we conducted another experiment where we only augmented the Other class. The result verifies our conjecture as we obtained an even larger degradation of 1.1% in this setting. We observe that a single layer of CNN is also effective, yielding an F1-score slightly worse than our RNN. But the deep architecture hurts the performance of CNNs in this task. One plausible explanation is that, when convolution is performed, the beginning and end of a sentence are typically padded with a special symbol or simply zero. However, the shortest dependency path between two entities is usually not very long (∼4 on average). Hence, sentence boundaries may play a large role in convolution, which makes CNNs vulnerable." } ], "images": [ "scigen/train/3564.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparing variants of data augmentation." ], "reasoning": "Augmenting the Other class degrades performance by 1.1%, and while a single layer CNN is effective, deeper CNN architectures hurt performance due to sentence boundary padding.", "description": "For data augmentation, it is natural that the inversed Other relation is also in the Other class itself. We deem the Other class contains mainly noise, and is inimical to our model. Then we conducted another experiment where we only augmented the Other class. The result verifies our conjecture as we obtained an even larger degradation of 1.1% in this setting. We observe that a single layer of CNN is also effective, yielding an F1-score slightly worse than our RNN. But the deep architecture hurts the performance of CNNs in this task. One plausible explanation is that, when convolution is performed, the beginning and end of a sentence are typically padded with a special symbol or simply zero. However, the shortest dependency path between two entities is usually not very long (∼4 on average). Hence, sentence boundaries may play a large role in convolution, which makes CNNs vulnerable.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 1: Comparing variants of data augmentation.', unfold the steps to solve the question given next. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nQuestion: What is the F1-score of a single layer RNN compared to a single layer CNN?\n" }, { "role": "assistant", "content": "The table shows that the F1-score for a single layer (Depth 1) RNN is 84.43, while for a single layer (Depth 1) CNN it is 84.01. So, the answer is {\"answer\": \"84.43\"}." } ], "images": [ "scigen/train/3564.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparing variants of data augmentation." ], "reasoning": "The table shows that the F1-score for a single layer (Depth 1) RNN is 84.43, while for a single layer (Depth 1) CNN it is 84.01.", "question": "What is the F1-score of a single layer RNN compared to a single layer CNN?", "answer": "So, the answer is {\"answer\": \"84.43\"}." }, { "messages": [ { "role": "user", "content": "A single layer RNN performs better than a single layer CNN. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nA single layer RNN performs better than a single layer CNN.\n" }, { "role": "assistant", "content": "The table shows that the F1-score for a single layer (Depth 1) RNN is 84.43, which is higher than the F1-score for a single layer (Depth 1) CNN, which is 84.01. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3564.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparing variants of data augmentation." ], "reasoning": "The table shows that the F1-score for a single layer (Depth 1) RNN is 84.43, which is higher than the F1-score for a single layer (Depth 1) CNN, which is 84.01.", "statement": "A single layer RNN performs better than a single layer CNN.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Comparison of previous relation classification systems.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Traditional feature-based methods like SVM achieved an F1-score of 82.2% by using various handcrafted features.The first entry in the table presents the highest performance achieved by traditional feature-based methods. \\newcite2010SVM feed a variety of handcrafted features to the SVM classifier and achieve an F1-score of 82.2%." } ], "images": [ "scigen/train/3565.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of previous relation classification systems." ], "reasoning": "Traditional feature-based methods like SVM achieved an F1-score of 82.2% by using various handcrafted features.", "description": "The first entry in the table presents the highest performance achieved by traditional feature-based methods. \\newcite2010SVM feed a variety of handcrafted features to the SVM classifier and achieve an F1-score of 82.2%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Comparison of previous relation classification systems.', solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieved the highest F1-score and what features did it use?\n" }, { "role": "assistant", "content": "By examining the table, the highest F1-score is 86.1%, achieved by the DRNNs model using data augmentation. So, the answer is {\"answer\": \"DRNNs with data augmentation\"}" } ], "images": [ "scigen/train/3565.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of previous relation classification systems." ], "reasoning": "By examining the table, the highest F1-score is 86.1%, achieved by the DRNNs model using data augmentation.", "question": "Which model achieved the highest F1-score and what features did it use?", "answer": "So, the answer is {\"answer\": \"DRNNs with data augmentation\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The CR-CNN model achieved an F1-score higher than 84%. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe CR-CNN model achieved an F1-score higher than 84%.\n" }, { "role": "assistant", "content": "The table shows that the CR-CNN model achieved an F1-score of 84.1% with position embeddings, which is higher than 84%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3565.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of previous relation classification systems." ], "reasoning": "The table shows that the CR-CNN model achieved an F1-score of 84.1% with position embeddings, which is higher than 84%.", "statement": "The CR-CNN model achieved an F1-score higher than 84%.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Final decoding accuracy results for WMT English-German and English-Romanian on various test sets (newstest 2010 – 2016, except 2013 our validation set). We report the average vocabulary size per sentence, coverage of the reference and decoding time in milliseconds for newstest2015 and newstest2016. The parameter column indicates the maximum number of selected candidates per source word or phrase.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Reducing the output vocabulary significantly speeds up translation while maintaining comparable accuracy to the full vocabulary setting.What is the exact speed and accuracy trade-off when reducing the output vocabulary? For our best methods (word alignments, phrase alignments and SVMs) we pick points such that vocabularies are kept small while maintaining good accuracy compared to the full vocabulary setting. For co-occurrence counts and bilingual PCA we choose settings with comparable speed. On English-German translation we achieve more than a 10-fold speed-up over the full vocabulary setting. Accuracy for the word alignment-based selection matches the full vocabulary setting on most test sets or decreases only slightly. On English-Romanian, we achieve a speed-up of over 5 times with word alignments at 28.1 BLEU versus 27.9 BLEU for the full vocabulary baseline. The smaller speed-up on English-Romanian is due to the smaller vocab of the baseline in this setting which is 50k compared to 100k for English-German. This is because training scores the vocabulary exactly once per target position, while beam search has to score multiple hypotheses at each generation step." } ], "images": [ "scigen/train/3566.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Final decoding accuracy results for WMT English-German and English-Romanian on various test sets (newstest 2010 – 2016, except 2013 our validation set). We report the average vocabulary size per sentence, coverage of the reference and decoding time in milliseconds for newstest2015 and newstest2016. The parameter column indicates the maximum number of selected candidates per source word or phrase." ], "reasoning": "Reducing the output vocabulary significantly speeds up translation while maintaining comparable accuracy to the full vocabulary setting.", "description": "What is the exact speed and accuracy trade-off when reducing the output vocabulary? For our best methods (word alignments, phrase alignments and SVMs) we pick points such that vocabularies are kept small while maintaining good accuracy compared to the full vocabulary setting. For co-occurrence counts and bilingual PCA we choose settings with comparable speed. On English-German translation we achieve more than a 10-fold speed-up over the full vocabulary setting. Accuracy for the word alignment-based selection matches the full vocabulary setting on most test sets or decreases only slightly. On English-Romanian, we achieve a speed-up of over 5 times with word alignments at 28.1 BLEU versus 27.9 BLEU for the full vocabulary baseline. The smaller speed-up on English-Romanian is due to the smaller vocab of the baseline in this setting which is 50k compared to 100k for English-German. This is because training scores the vocabulary exactly once per target position, while beam search has to score multiple hypotheses at each generation step.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 1: Final decoding accuracy results for WMT English-German and English-Romanian on various test sets (newstest 2010 – 2016, except 2013 our validation set). We report the average vocabulary size per sentence, coverage of the reference and decoding time in milliseconds for newstest2015 and newstest2016. The parameter column indicates the maximum number of selected candidates per source word or phrase.', solve this question and give detail solutions. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the speed-up achieved on English-Romanian translation using word alignments compared to the full vocabulary setting?\n" }, { "role": "assistant", "content": "The table shows that the full vocabulary setting for English-Romanian has a time of 966, while the word alignment setting has a time of 186. The speed-up is calculated by dividing the full vocabulary time by the word alignment time. So, the answer is {\"answer\": \"over 5 times\"}" } ], "images": [ "scigen/train/3566.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Final decoding accuracy results for WMT English-German and English-Romanian on various test sets (newstest 2010 – 2016, except 2013 our validation set). We report the average vocabulary size per sentence, coverage of the reference and decoding time in milliseconds for newstest2015 and newstest2016. The parameter column indicates the maximum number of selected candidates per source word or phrase." ], "reasoning": "The table shows that the full vocabulary setting for English-Romanian has a time of 966, while the word alignment setting has a time of 186. The speed-up is calculated by dividing the full vocabulary time by the word alignment time.", "question": "What is the speed-up achieved on English-Romanian translation using word alignments compared to the full vocabulary setting?", "answer": "So, the answer is {\"answer\": \"over 5 times\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe word alignment method achieves a higher BLEU score than the full vocabulary setting on English-Romanian translation.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for word alignment on English-Romanian is 28.1, while the BLEU score for the full vocabulary setting is 27.9. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3566.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Final decoding accuracy results for WMT English-German and English-Romanian on various test sets (newstest 2010 – 2016, except 2013 our validation set). We report the average vocabulary size per sentence, coverage of the reference and decoding time in milliseconds for newstest2015 and newstest2016. The parameter column indicates the maximum number of selected candidates per source word or phrase." ], "reasoning": "The table shows that the BLEU score for word alignment on English-Romanian is 28.1, while the BLEU score for the full vocabulary setting is 27.9.", "statement": "The word alignment method achieves a higher BLEU score than the full vocabulary setting on English-Romanian translation.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: BLEU scores obtained by a baseline SMT (without and with an CSLM) and a project-adapted SMT with baseline (unadapted) CSLM and adapted CSLM. The first value in every cell is the BLEU score obtained with respect to the reference translation of the human translator; the second one is calculated with respect to all the 3 references created by the professional translators (i.e. obtained by post-edition) and an independent reference.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The CSLM adaptation improves the BLEU score of the baseline SMT system by 2.3 to 3.4 points, and further adapting the SMT system to new data significantly enhances translation quality.In order to evaluate the impact of the CSLM adaptation on the SMT system, we performed various translation experiments. The BLEU scores of the various systems using the baseline and the adapted CSLMs are presented. We run tests with three different human translators - for the sake of clarity, we provide detailed results for one translator only. The observed tendencies are similar for the two other translators. First of all, one can see that the CSLM improves the BLEU score of the baseline systems between 2.3 to 3.4 BLEU points, e.g. for Day 2 from 44.07 to 46.61. Adapting the whole SMT system to the new data improves significantly the translation quality, e.g. from 46.61 to 52.01 for Day 2, without changing the CSLM. The proposed adaptation scheme of the CSLM achieves additional important improvements, in average 2.6 BLEU points. This gain is relatively constant for all days." } ], "images": [ "scigen/train/3567.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: BLEU scores obtained by a baseline SMT (without and with an CSLM) and a project-adapted SMT with baseline (unadapted) CSLM and adapted CSLM. The first value in every cell is the BLEU score obtained with respect to the reference translation of the human translator; the second one is calculated with respect to all the 3 references created by the professional translators (i.e. obtained by post-edition) and an independent reference." ], "reasoning": "The CSLM adaptation improves the BLEU score of the baseline SMT system by 2.3 to 3.4 points, and further adapting the SMT system to new data significantly enhances translation quality.", "description": "In order to evaluate the impact of the CSLM adaptation on the SMT system, we performed various translation experiments. The BLEU scores of the various systems using the baseline and the adapted CSLMs are presented. We run tests with three different human translators - for the sake of clarity, we provide detailed results for one translator only. The observed tendencies are similar for the two other translators. First of all, one can see that the CSLM improves the BLEU score of the baseline systems between 2.3 to 3.4 BLEU points, e.g. for Day 2 from 44.07 to 46.61. Adapting the whole SMT system to the new data improves significantly the translation quality, e.g. from 46.61 to 52.01 for Day 2, without changing the CSLM. The proposed adaptation scheme of the CSLM achieves additional important improvements, in average 2.6 BLEU points. This gain is relatively constant for all days.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 5: BLEU scores obtained by a baseline SMT (without and with an CSLM) and a project-adapted SMT with baseline (unadapted) CSLM and adapted CSLM. The first value in every cell is the BLEU score obtained with respect to the reference translation of the human translator; the second one is calculated with respect to all the 3 references created by the professional translators (i.e. obtained by post-edition) and an independent reference.' in mind, break down and resolve the question below step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the BLEU score improvement for Day 3 when using the adapted CSLM compared to the baseline CSLM?\n" }, { "role": "assistant", "content": "The BLEU score for Day 3 with the baseline CSLM is 57.35, and with the adapted CSLM, it is 60.23. The improvement is calculated as 60.23 - 57.35. So, the answer is {\"answer\": \"2.88\"}" } ], "images": [ "scigen/train/3567.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: BLEU scores obtained by a baseline SMT (without and with an CSLM) and a project-adapted SMT with baseline (unadapted) CSLM and adapted CSLM. The first value in every cell is the BLEU score obtained with respect to the reference translation of the human translator; the second one is calculated with respect to all the 3 references created by the professional translators (i.e. obtained by post-edition) and an independent reference." ], "reasoning": "The BLEU score for Day 3 with the baseline CSLM is 57.35, and with the adapted CSLM, it is 60.23. The improvement is calculated as 60.23 - 57.35.", "question": "What is the BLEU score improvement for Day 3 when using the adapted CSLM compared to the baseline CSLM?", "answer": "So, the answer is {\"answer\": \"2.88\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe BLEU score for Day 4 with the adapted CSLM is higher than 72.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for Day 4 with the adapted CSLM is 72.05, which is higher than 72. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3567.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: BLEU scores obtained by a baseline SMT (without and with an CSLM) and a project-adapted SMT with baseline (unadapted) CSLM and adapted CSLM. The first value in every cell is the BLEU score obtained with respect to the reference translation of the human translator; the second one is calculated with respect to all the 3 references created by the professional translators (i.e. obtained by post-edition) and an independent reference." ], "reasoning": "The table shows that the BLEU score for Day 4 with the adapted CSLM is 72.05, which is higher than 72.", "statement": "The BLEU score for Day 4 with the adapted CSLM is higher than 72.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Comparative BLEU scores for the English/German systems. Italic values in parenthesis are for information only. They are biased since the reference translations are used in training.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The CSLM with additional layers achieved the highest BLEU score of 21.26 on Day 2, showing a significant improvement over the baseline back-off LM.The 4-gram back-off LM built on the selected data has a perplexity of 151.1 on the domain-specific development data. Given the fact that an CSLM can be very efficiently trained on long context windows, we used a 28-gram in all experiments. By these means we hope to capture the long range dependencies of German. The projection layer of the CSLM was of dimension 320, followed by three tanh hidden layers of size 1024 and a softmax output layer of 32k neurons (short-list). This short-list accounts for around 92 % of the tokens used in the corpus. The initial learning rate was set to 0.06 and exponentially decreased over the iterations. The network converged after 7 epochs with a perplexity of 96.6, i.e. a 36% relative reduction. The total training time is less than 7 hours on a Nvidia K20x GPU. When no CSLM is used, the BLEU score on Day 2 increases from 19.31 to 20.14 (+0.83). This is achieved by adapting the translation and back-off LM Both CSLM adaptation schemes obtained quite similar BLEU scores: 21.12 and 21.26 respectively, the insertion of one additional tanh layer having a slight advantage. Overall, the adapted CSLM yields an improvement of 1.12 BLEU (20.14 to 21.26) while it was about 1 point BLEU for the domain-adapted system (19.31 to 20.28). This nicely shows the effectiveness of our adaptation scheme, which can be applied in a couple of minutes. A second set of experiments was performed to confirm the effectiveness of our adaptation procedure on a different language pair: English/French. In the MT community it is well known that the translation into German is a very hard task which is reflected in the low BLEU scores around 20 On the other hand, our baseline SMT system for the English/French language pair has a BLEU score well above 40. One may argue that it is more complicated to further improve such a system." } ], "images": [ "scigen/train/3568.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparative BLEU scores for the English/German systems. Italic values in parenthesis are for information only. They are biased since the reference translations are used in training." ], "reasoning": "The CSLM with additional layers achieved the highest BLEU score of 21.26 on Day 2, showing a significant improvement over the baseline back-off LM.", "description": "The 4-gram back-off LM built on the selected data has a perplexity of 151.1 on the domain-specific development data. Given the fact that an CSLM can be very efficiently trained on long context windows, we used a 28-gram in all experiments. By these means we hope to capture the long range dependencies of German. The projection layer of the CSLM was of dimension 320, followed by three tanh hidden layers of size 1024 and a softmax output layer of 32k neurons (short-list). This short-list accounts for around 92 % of the tokens used in the corpus. The initial learning rate was set to 0.06 and exponentially decreased over the iterations. The network converged after 7 epochs with a perplexity of 96.6, i.e. a 36% relative reduction. The total training time is less than 7 hours on a Nvidia K20x GPU. When no CSLM is used, the BLEU score on Day 2 increases from 19.31 to 20.14 (+0.83). This is achieved by adapting the translation and back-off LM Both CSLM adaptation schemes obtained quite similar BLEU scores: 21.12 and 21.26 respectively, the insertion of one additional tanh layer having a slight advantage. Overall, the adapted CSLM yields an improvement of 1.12 BLEU (20.14 to 21.26) while it was about 1 point BLEU for the domain-adapted system (19.31 to 20.28). This nicely shows the effectiveness of our adaptation scheme, which can be applied in a couple of minutes. A second set of experiments was performed to confirm the effectiveness of our adaptation procedure on a different language pair: English/French. In the MT community it is well known that the translation into German is a very hard task which is reflected in the low BLEU scores around 20 On the other hand, our baseline SMT system for the English/French language pair has a BLEU score well above 40. One may argue that it is more complicated to further improve such a system.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 2: Comparative BLEU scores for the English/German systems. Italic values in parenthesis are for information only. They are biased since the reference translations are used in training.'\nQuestion: What is the BLEU score on Day 2 for the CSLM with continued training?\n" }, { "role": "assistant", "content": "The table shows the BLEU score for the CSLM with continued training under the 'BLEU score Day 2' column, which is 21.12. So, the answer is {\"answer\": \"21.12\"}." } ], "images": [ "scigen/train/3568.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparative BLEU scores for the English/German systems. Italic values in parenthesis are for information only. They are biased since the reference translations are used in training." ], "reasoning": "The table shows the BLEU score for the CSLM with continued training under the 'BLEU score Day 2' column, which is 21.12.", "question": "What is the BLEU score on Day 2 for the CSLM with continued training?", "answer": "So, the answer is {\"answer\": \"21.12\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe BLEU score on Day 2 for the back-off LM with project adapted data selection is higher than 20.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score on Day 2 for the back-off LM with project adapted data selection is 20.14, which is higher than 20. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3568.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparative BLEU scores for the English/German systems. Italic values in parenthesis are for information only. They are biased since the reference translations are used in training." ], "reasoning": "The table shows that the BLEU score on Day 2 for the back-off LM with project adapted data selection is 20.14, which is higher than 20.", "statement": "The BLEU score on Day 2 for the back-off LM with project adapted data selection is higher than 20.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Percentage of POS tags in extracted words for different kernel sizes.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "As the kernel size increases, the model becomes more sensitive to nouns and proper nouns, while the proportion of function words decreases.The most frequent POS tag among the extracted word is adjective. With increasing network capacity, the model becomes sensitive to nouns and proper nouns. The proportion of function words decreases with the increasing kernel size which suggests that it is unlikely that the filters of large kernel size would capture more complex phrases." } ], "images": [ "scigen/train/3569.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Percentage of POS tags in extracted words for different kernel sizes." ], "reasoning": "As the kernel size increases, the model becomes more sensitive to nouns and proper nouns, while the proportion of function words decreases.", "description": "The most frequent POS tag among the extracted word is adjective. With increasing network capacity, the model becomes sensitive to nouns and proper nouns. The proportion of function words decreases with the increasing kernel size which suggests that it is unlikely that the filters of large kernel size would capture more complex phrases.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: 'Table 3: Percentage of POS tags in extracted words for different kernel sizes.'\nQuestion: What happens to the proportion of adjectives and nouns as the kernel size increases?\n" }, { "role": "assistant", "content": "The proportion of adjectives decreases from 46.1% to 30.7%, while the proportion of nouns increases from 14.6% to 24.6% as the kernel size increases. So, the answer is {\"answer\": \"Adjectives decrease, nouns increase\"}." } ], "images": [ "scigen/train/3569.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Percentage of POS tags in extracted words for different kernel sizes." ], "reasoning": "The proportion of adjectives decreases from 46.1% to 30.7%, while the proportion of nouns increases from 14.6% to 24.6% as the kernel size increases.", "question": "What happens to the proportion of adjectives and nouns as the kernel size increases?", "answer": "So, the answer is {\"answer\": \"Adjectives decrease, nouns increase\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe proportion of verbs increases with increasing kernel size.\n" }, { "role": "assistant", "content": "The table shows that the proportion of verbs increases from 11.2% to 20.1% as the kernel size increases from 1 to 4, but then slightly decreases to 19.0% at kernel size 5. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3569.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Percentage of POS tags in extracted words for different kernel sizes." ], "reasoning": "The table shows that the proportion of verbs increases from 11.2% to 20.1% as the kernel size increases from 1 to 4, but then slightly decreases to 19.0% at kernel size 5.", "statement": "The proportion of verbs increases with increasing kernel size.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Percentage of contrasting word pairs belonging to various sub-types. The sub-type “reversives\" applies only to verbs. The sub-type “gradable\" applies only to adjectives and adverbs.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Complementaries are the most common among adverbs, nouns, and verbs, while antipodals are most common among adjectives.Observe that rows for all categories other than the disjoints have percentages greater than 60%. This means that a number of contrasting word pairs can be classified into more than one kind. Complementaries are the most common kind in case of adverbs, nouns, and verbs, whereas antipodals are most common among adjectives. A majority of the adjective and adverb contrasting pairs are gradable, but more than 30% of the pairs are not. Most of the verb pairs are reversives (91.6%). Disjoint pairs are much less common than all the other categories considered, and they are most prominent among adjectives (28%), and least among verb pairs (1.7%)." } ], "images": [ "scigen/train/3570.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Percentage of contrasting word pairs belonging to various sub-types. The sub-type “reversives\" applies only to verbs. The sub-type “gradable\" applies only to adjectives and adverbs." ], "reasoning": "Complementaries are the most common among adverbs, nouns, and verbs, while antipodals are most common among adjectives.", "description": "Observe that rows for all categories other than the disjoints have percentages greater than 60%. This means that a number of contrasting word pairs can be classified into more than one kind. Complementaries are the most common kind in case of adverbs, nouns, and verbs, whereas antipodals are most common among adjectives. A majority of the adjective and adverb contrasting pairs are gradable, but more than 30% of the pairs are not. Most of the verb pairs are reversives (91.6%). Disjoint pairs are much less common than all the other categories considered, and they are most prominent among adjectives (28%), and least among verb pairs (1.7%).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Percentage of contrasting word pairs belonging to various sub-types. The sub-type “reversives\" applies only to verbs. The sub-type “gradable\" applies only to adjectives and adverbs.\nQuestion: Which sub-type is most common among verbs?\nSolve the above question based on the table information. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that Complementaries have the highest percentage (98.3%) among verbs. So, the answer is {\"answer\": \"Complementaries\"}" } ], "images": [ "scigen/train/3570.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Percentage of contrasting word pairs belonging to various sub-types. The sub-type “reversives\" applies only to verbs. The sub-type “gradable\" applies only to adjectives and adverbs." ], "reasoning": "The table shows that Complementaries have the highest percentage (98.3%) among verbs.", "question": "Which sub-type is most common among verbs?", "answer": "So, the answer is {\"answer\": \"Complementaries\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nDisjoint pairs are most prominent among adverbs.\n" }, { "role": "assistant", "content": "The table shows that Disjoint pairs are most prominent among adjectives (28%), not adverbs (14.4%). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3570.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Percentage of contrasting word pairs belonging to various sub-types. The sub-type “reversives\" applies only to verbs. The sub-type “gradable\" applies only to adjectives and adverbs." ], "reasoning": "The table shows that Disjoint pairs are most prominent among adjectives (28%), not adverbs (14.4%).", "statement": "Disjoint pairs are most prominent among adverbs.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 7: Pointwise mutual information (PMI) of word pairs. High positive values imply a tendency to co-occur in text more often than random chance.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The high-contrast pairs have a significantly higher average PMI and standard deviation compared to the random pairs and synonyms sets, indicating a higher tendency to co-occur.We calculated the pointwise mutual information (PMI) If two words occurred within a window of five adjacent words in a sentence, they were marked as co-occurring (same window as Church and Hanks \\shortciteChurchH89 used in their seminal work on word–word associations). Observe that the high-contrast pairs have a much higher tendency to co-occur than the random pairs control set, and also the synonyms set. However, the high-contrast set has a large standard deviation. A two-sample t-test revealed that the high-contrast set is significantly different from the random set (p<0.05), and also that the high-contrast set is significantly different from the synonyms set (p<0.05)." } ], "images": [ "scigen/train/3571.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Pointwise mutual information (PMI) of word pairs. High positive values imply a tendency to co-occur in text more often than random chance." ], "reasoning": "The high-contrast pairs have a significantly higher average PMI and standard deviation compared to the random pairs and synonyms sets, indicating a higher tendency to co-occur.", "description": "We calculated the pointwise mutual information (PMI) If two words occurred within a window of five adjacent words in a sentence, they were marked as co-occurring (same window as Church and Hanks \\shortciteChurchH89 used in their seminal work on word–word associations). Observe that the high-contrast pairs have a much higher tendency to co-occur than the random pairs control set, and also the synonyms set. However, the high-contrast set has a large standard deviation. A two-sample t-test revealed that the high-contrast set is significantly different from the random set (p<0.05), and also that the high-contrast set is significantly different from the synonyms set (p<0.05).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 7: Pointwise mutual information (PMI) of word pairs. High positive values imply a tendency to co-occur in text more often than random chance.', unfold the steps to solve the question given next. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nQuestion: Which set has the highest standard deviation in PMI?\n" }, { "role": "assistant", "content": "By comparing the standard deviation values in the table, the high-contrast set has the highest standard deviation of 2.255. So, the answer is {\"answer\": \"high-contrast set\"}" } ], "images": [ "scigen/train/3571.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Pointwise mutual information (PMI) of word pairs. High positive values imply a tendency to co-occur in text more often than random chance." ], "reasoning": "By comparing the standard deviation values in the table, the high-contrast set has the highest standard deviation of 2.255.", "question": "Which set has the highest standard deviation in PMI?", "answer": "So, the answer is {\"answer\": \"high-contrast set\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The synonyms set has a higher average PMI than the random pairs set. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe synonyms set has a higher average PMI than the random pairs set.\n" }, { "role": "assistant", "content": "The table shows that the average PMI for the synonyms set is 0.412, which is higher than the average PMI for the random pairs set, which is 0.032. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3571.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Pointwise mutual information (PMI) of word pairs. High positive values imply a tendency to co-occur in text more often than random chance." ], "reasoning": "The table shows that the average PMI for the synonyms set is 0.412, which is higher than the average PMI for the random pairs set, which is 0.032.", "statement": "The synonyms set has a higher average PMI than the random pairs set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 8: Distributional similarity of word pairs. The measure proposed in Lin (1998) was used.'.\n" }, { "role": "assistant", "content": "High-contrast pairs have a higher average distributional similarity than both random pairs and synonyms, but with a large standard deviation, indicating that additional cues are needed to distinguish between contrasting and synonymous pairs.We used the same sets of high-contrast pairs, synonyms, and random pairs described in the previous sub-section to gather empirical proof of the distributional hypothesis. We calculated the distributional similarity between each pair in the three sets using Lin’s \\shortciteLin98B measure. Observe that the high-contrast set has a much higher average distributional similarity than the random pairs control set, and interestingly it is also higher than the synonyms set. Once again, the high-contrast set has a large standard deviation. A two-sample t-test revealed that the high-contrast set is significantly different from both the random set and the synonyms set with a confidence interval of 0.05. This demonstrates that relative to other word pairs, high-contrast pairs tend to occur in similar contexts. We also find that the synonyms set has a significantly higher distributional similarity than the random pairs set (p<0.05). This shows that near-synonymous word pairs also occur in similar contexts (the distributional hypothesis of similarity). Further, a consequence of the large standard deviations in the cases of both high-contrast pairs and synonyms means that distributional similarity alone is not sufficient to determine whether two words are contrasting or synonymous. An automatic method for recognizing contrast will require additional cues. Our method uses PMI and other sources of information described in the next section." } ], "images": [ "scigen/train/3572.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Distributional similarity of word pairs. The measure proposed in Lin (1998) was used." ], "reasoning": "High-contrast pairs have a higher average distributional similarity than both random pairs and synonyms, but with a large standard deviation, indicating that additional cues are needed to distinguish between contrasting and synonymous pairs.", "description": "We used the same sets of high-contrast pairs, synonyms, and random pairs described in the previous sub-section to gather empirical proof of the distributional hypothesis. We calculated the distributional similarity between each pair in the three sets using Lin’s \\shortciteLin98B measure. Observe that the high-contrast set has a much higher average distributional similarity than the random pairs control set, and interestingly it is also higher than the synonyms set. Once again, the high-contrast set has a large standard deviation. A two-sample t-test revealed that the high-contrast set is significantly different from both the random set and the synonyms set with a confidence interval of 0.05. This demonstrates that relative to other word pairs, high-contrast pairs tend to occur in similar contexts. We also find that the synonyms set has a significantly higher distributional similarity than the random pairs set (p<0.05). This shows that near-synonymous word pairs also occur in similar contexts (the distributional hypothesis of similarity). Further, a consequence of the large standard deviations in the cases of both high-contrast pairs and synonyms means that distributional similarity alone is not sufficient to determine whether two words are contrasting or synonymous. An automatic method for recognizing contrast will require additional cues. Our method uses PMI and other sources of information described in the next section.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 8: Distributional similarity of word pairs. The measure proposed in Lin (1998) was used.' and proceed to solve the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich set has the highest average distributional similarity?\n" }, { "role": "assistant", "content": "By comparing the average distributional similarity values in the table, the opposites set has the highest value of 0.064. So, the answer is {\"answer\": \"opposites set\"}." } ], "images": [ "scigen/train/3572.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Distributional similarity of word pairs. The measure proposed in Lin (1998) was used." ], "reasoning": "By comparing the average distributional similarity values in the table, the opposites set has the highest value of 0.064.", "question": "Which set has the highest average distributional similarity?", "answer": "So, the answer is {\"answer\": \"opposites set\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The synonyms set has a higher average distributional similarity than the random pairs set., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe synonyms set has a higher average distributional similarity than the random pairs set.\n" }, { "role": "assistant", "content": "The table shows that the synonyms set has an average distributional similarity of 0.056, which is higher than the random pairs set's 0.036. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3572.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Distributional similarity of word pairs. The measure proposed in Lin (1998) was used." ], "reasoning": "The table shows that the synonyms set has an average distributional similarity of 0.056, which is higher than the random pairs set's 0.036.", "statement": "The synonyms set has a higher average distributional similarity than the random pairs set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 11: Percentage of contrast questions correctly answered by the automatic method, where different questions sets correspond to target–answer pairs of different kinds. The automatic method did not use WordNet seeds for this task. The results shown for ‘ALL’ are micro-averages, that is, they are the results for the master set of 1269 contrast questions.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The automatic method achieves the highest F-scores on gradable adjectives, antipodals, and complementaries, with precisions and recalls significantly higher than those of disjoint opposites.The column ‘# instances’ shows the number of questions in each of the datasets. The performance of our method on the complete dataset is shown in the last row ALL. Observe that the F-score of 0.85 is markedly higher than the score obtained on the GRE-preparatory questions. This is expected because the GRE questions involved vocabulary from a higher reading level, and included carefully chosen distractors to confuse the examinee. The automatic method obtains highest F-score on the datasets of gradable adjectives (0.90), antipodals (0.89), and complementaries (0.89). The precisions and recalls for these opposites are significantly higher than those of disjoint opposites. The recall for reversives is also significantly lower than that the gradable adjectives, antipodals, and complementaries, but precision on reversives is quite good (0.93)." } ], "images": [ "scigen/train/3573.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Percentage of contrast questions correctly answered by the automatic method, where different questions sets correspond to target–answer pairs of different kinds. The automatic method did not use WordNet seeds for this task. The results shown for ‘ALL’ are micro-averages, that is, they are the results for the master set of 1269 contrast questions." ], "reasoning": "The automatic method achieves the highest F-scores on gradable adjectives, antipodals, and complementaries, with precisions and recalls significantly higher than those of disjoint opposites.", "description": "The column ‘# instances’ shows the number of questions in each of the datasets. The performance of our method on the complete dataset is shown in the last row ALL. Observe that the F-score of 0.85 is markedly higher than the score obtained on the GRE-preparatory questions. This is expected because the GRE questions involved vocabulary from a higher reading level, and included carefully chosen distractors to confuse the examinee. The automatic method obtains highest F-score on the datasets of gradable adjectives (0.90), antipodals (0.89), and complementaries (0.89). The precisions and recalls for these opposites are significantly higher than those of disjoint opposites. The recall for reversives is also significantly lower than that the gradable adjectives, antipodals, and complementaries, but precision on reversives is quite good (0.93).", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. The question is related to the given table titled 'Table 11: Percentage of contrast questions correctly answered by the automatic method, where different questions sets correspond to target–answer pairs of different kinds. The automatic method did not use WordNet seeds for this task. The results shown for ‘ALL’ are micro-averages, that is, they are the results for the master set of 1269 contrast questions.'.\n\nQuestion: Which dataset has the highest F-score and what is its value?\n" }, { "role": "assistant", "content": "By examining the F-scores in the table, the highest F-score is found in the gradable adjectives dataset with a value of 0.90. So, the answer is {\"answer\": \"Gradable adjectives, 0.90\"}" } ], "images": [ "scigen/train/3573.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Percentage of contrast questions correctly answered by the automatic method, where different questions sets correspond to target–answer pairs of different kinds. The automatic method did not use WordNet seeds for this task. The results shown for ‘ALL’ are micro-averages, that is, they are the results for the master set of 1269 contrast questions." ], "reasoning": "By examining the F-scores in the table, the highest F-score is found in the gradable adjectives dataset with a value of 0.90.", "question": "Which dataset has the highest F-score and what is its value?", "answer": "So, the answer is {\"answer\": \"Gradable adjectives, 0.90\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe recall for reversives is higher than that for antipodals.\n" }, { "role": "assistant", "content": "The table shows that the recall for reversives is 0.74, while the recall for antipodals is 0.84, indicating that the recall for reversives is actually lower. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3573.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Percentage of contrast questions correctly answered by the automatic method, where different questions sets correspond to target–answer pairs of different kinds. The automatic method did not use WordNet seeds for this task. The results shown for ‘ALL’ are micro-averages, that is, they are the results for the master set of 1269 contrast questions." ], "reasoning": "The table shows that the recall for reversives is 0.74, while the recall for antipodals is 0.84, indicating that the recall for reversives is actually lower.", "statement": "The recall for reversives is higher than that for antipodals.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 12: Percentage of contrast questions correctly answered by the automatic method, where different questions sets correspond to different parts-of-speech.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The method performs best on adverbs with an F-score of 0.89 and worst on verbs with an F-score of 0.80. Recall on adverbs is significantly higher than on adjectives, and recall on adjectives is significantly higher than on verbs.Observe that performances on all parts of speech are fairly high. The method deals with adverb pairs best (F-score of 0.89), and the lowest performance is for verbs (F-score of 0.80). The precision values obtained between on the data from any two parts of speech are not significantly different. However, the recall obtained on the adverbs is significantly higher than that obtained on adjectives, and the recall on adjectives is significantly higher than that obtained on verbs. The difference between the recalls on adverbs and nouns is not significant. We used the Fisher Exact Test and a confidence interval of 95% for all significance testing reported in this section." } ], "images": [ "scigen/train/3574.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 12: Percentage of contrast questions correctly answered by the automatic method, where different questions sets correspond to different parts-of-speech." ], "reasoning": "The method performs best on adverbs with an F-score of 0.89 and worst on verbs with an F-score of 0.80. Recall on adverbs is significantly higher than on adjectives, and recall on adjectives is significantly higher than on verbs.", "description": "Observe that performances on all parts of speech are fairly high. The method deals with adverb pairs best (F-score of 0.89), and the lowest performance is for verbs (F-score of 0.80). The precision values obtained between on the data from any two parts of speech are not significantly different. However, the recall obtained on the adverbs is significantly higher than that obtained on adjectives, and the recall on adjectives is significantly higher than that obtained on verbs. The difference between the recalls on adverbs and nouns is not significant. We used the Fisher Exact Test and a confidence interval of 95% for all significance testing reported in this section.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 12: Percentage of contrast questions correctly answered by the automatic method, where different questions sets correspond to different parts-of-speech.' and proceed to solve the following question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich part of speech has the highest recall and what is its value?\n" }, { "role": "assistant", "content": "By comparing the recall (R) values in the table, adverbs have the highest recall value of 0.84. So, the answer is {\"answer\": \"Adverbs with a recall of 0.84\"}" } ], "images": [ "scigen/train/3574.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 12: Percentage of contrast questions correctly answered by the automatic method, where different questions sets correspond to different parts-of-speech." ], "reasoning": "By comparing the recall (R) values in the table, adverbs have the highest recall value of 0.84.", "question": "Which part of speech has the highest recall and what is its value?", "answer": "So, the answer is {\"answer\": \"Adverbs with a recall of 0.84\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe precision values for all parts of speech are significantly different.\n" }, { "role": "assistant", "content": "The description states that the precision values obtained between any two parts of speech are not significantly different. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3574.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 12: Percentage of contrast questions correctly answered by the automatic method, where different questions sets correspond to different parts-of-speech." ], "reasoning": "The description states that the precision values obtained between any two parts of speech are not significantly different.", "statement": "The precision values for all parts of speech are significantly different.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: Results on test set of Quora datasets. “RR”: remove repeats.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Removing repeated tokens has a minimal impact on the PNAT model's BLEU score, but significantly affects the NAT-base model's BLEU score.NAT is less effective to distinguish adjacent decoder hidden states, which is copied from the adjacent source representation. To further study this problem, we proposed to evaluate the gains of simply remove the repeated tokens. Removing repeated tokens has little impact for PNAT model, with only 0.05 BLEU differences. However for the NAT-base model, the gap comes with almost 1 BLEU (0.89). The results clearly demonstrate that the explicitly position model essentially learns the sequential information for sequence generation." } ], "images": [ "scigen/train/3575.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on test set of Quora datasets. “RR”: remove repeats." ], "reasoning": "Removing repeated tokens has a minimal impact on the PNAT model's BLEU score, but significantly affects the NAT-base model's BLEU score.", "description": "NAT is less effective to distinguish adjacent decoder hidden states, which is copied from the adjacent source representation. To further study this problem, we proposed to evaluate the gains of simply remove the repeated tokens. Removing repeated tokens has little impact for PNAT model, with only 0.05 BLEU differences. However for the NAT-base model, the gap comes with almost 1 BLEU (0.89). The results clearly demonstrate that the explicitly position model essentially learns the sequential information for sequence generation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 4: Results on test set of Quora datasets. “RR”: remove repeats.'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the BLEU score difference for the NAT-base model when repeated tokens are removed?\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for the NAT-base model with repeated tokens removed is 19.45, compared to 20.34 with repeated tokens, resulting in a difference of 0.89. So, the answer is {\"answer\": \"0.89\"}." } ], "images": [ "scigen/train/3575.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on test set of Quora datasets. “RR”: remove repeats." ], "reasoning": "The table shows that the BLEU score for the NAT-base model with repeated tokens removed is 19.45, compared to 20.34 with repeated tokens, resulting in a difference of 0.89.", "question": "What is the BLEU score difference for the NAT-base model when repeated tokens are removed?", "answer": "So, the answer is {\"answer\": \"0.89\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRemoving repeated tokens has a significant impact on the PNAT model's BLEU score.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score difference for the PNAT model when repeated tokens are removed is only 0.05, indicating a minimal impact. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3575.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on test set of Quora datasets. “RR”: remove repeats." ], "reasoning": "The table shows that the BLEU score difference for the PNAT model when repeated tokens are removed is only 0.05, indicating a minimal impact.", "statement": "Removing repeated tokens has a significant impact on the PNAT model's BLEU score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Results on validation set and test set of Quora.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "PNAT achieves the highest BLEU scores in both validation and test sets, outperforming the Transformer model by around 3.5 BLEU points.In consist with our intuition, PNAT achieves the best result on this task and even surpass Transformer around 3.5 BLEU. The NAT model is not powerful enough to capture the latent position relationship. The comparison between NAT-base and PNAT shows that explicit position modeling in PNAT plays a crucial role in generating sentences." } ], "images": [ "scigen/train/3576.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on validation set and test set of Quora." ], "reasoning": "PNAT achieves the highest BLEU scores in both validation and test sets, outperforming the Transformer model by around 3.5 BLEU points.", "description": "In consist with our intuition, PNAT achieves the best result on this task and even surpass Transformer around 3.5 BLEU. The NAT model is not powerful enough to capture the latent position relationship. The comparison between NAT-base and PNAT shows that explicit position modeling in PNAT plays a crucial role in generating sentences.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: Results on validation set and test set of Quora.' and methodically tackle the question that follows. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieves the highest BLEU score on the test set, and by how much does it surpass the Transformer model?\n" }, { "role": "assistant", "content": "The table shows that PNAT achieves the highest BLEU score on the test set with a score of 29.00. The Transformer model has a BLEU score of 25.46 on the test set. The difference is 29.00 - 25.46 = 3.54. So, the answer is {\"answer\": \"PNAT, 3.54\"}." } ], "images": [ "scigen/train/3576.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on validation set and test set of Quora." ], "reasoning": "The table shows that PNAT achieves the highest BLEU score on the test set with a score of 29.00. The Transformer model has a BLEU score of 25.46 on the test set. The difference is 29.00 - 25.46 = 3.54.", "question": "Which model achieves the highest BLEU score on the test set, and by how much does it surpass the Transformer model?", "answer": "So, the answer is {\"answer\": \"PNAT, 3.54\"}." }, { "messages": [ { "role": "user", "content": "The NAT-base model achieves a BLEU score higher than 20 on the validation set. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe NAT-base model achieves a BLEU score higher than 20 on the validation set.\n" }, { "role": "assistant", "content": "The table shows that the NAT-base model has a BLEU score of 19.80 on the validation set, which is less than 20. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3576.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on validation set and test set of Quora." ], "reasoning": "The table shows that the NAT-base model has a BLEU score of 19.80 on the validation set, which is less than 20.", "statement": "The NAT-base model achieves a BLEU score higher than 20 on the validation set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: Results on validation set of WMT14 DE-EN with different position strategy. “HSP” means the reference position sequence derived from the heuristic position searching.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The PNAT with HSP achieves the highest position accuracy but has a lower BLEU score compared to the Transformer model, indicating a trade-off between accuracy and translation quality.First, we analyze whether the position derived from the heuristic search is suitable for use as supervision to the position predictor. We evaluate the effectiveness of the searched position by training a PNAT as before and testing with the heuristic searched position instead of the predicted position. The non-autoregressive position model is less effective than the current autoregressive position model, both in the accuracy of the permutation and the relative position. Even though the current PNAT with a simple AR-Predictor has surpassed the previous NAT model, the position accuracy is still less desirable (say, less than 30%) and has a great exploration space. There is also a trade-off between the effectiveness and efficiency, the choice of the non-autoregressive means the efficiency and the choice of autoregressive means the effectiveness." } ], "images": [ "scigen/train/3577.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on validation set of WMT14 DE-EN with different position strategy. “HSP” means the reference position sequence derived from the heuristic position searching." ], "reasoning": "The PNAT with HSP achieves the highest position accuracy but has a lower BLEU score compared to the Transformer model, indicating a trade-off between accuracy and translation quality.", "description": "First, we analyze whether the position derived from the heuristic search is suitable for use as supervision to the position predictor. We evaluate the effectiveness of the searched position by training a PNAT as before and testing with the heuristic searched position instead of the predicted position. The non-autoregressive position model is less effective than the current autoregressive position model, both in the accuracy of the permutation and the relative position. Even though the current PNAT with a simple AR-Predictor has surpassed the previous NAT model, the position accuracy is still less desirable (say, less than 30%) and has a great exploration space. There is also a trade-off between the effectiveness and efficiency, the choice of the non-autoregressive means the efficiency and the choice of autoregressive means the effectiveness.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Results on validation set of WMT14 DE-EN with different position strategy. “HSP” means the reference position sequence derived from the heuristic position searching.\nQuestion: Which model has the highest WMT14 DE-EN BLEU score and what is its speed up?\nSolve the above question based on the table information. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the Transformer (beam=4) model has the highest WMT14 DE-EN BLEU score of 30.68 and a speed up of 1.0x. So, the answer is {\"answer\": \"Transformer (beam=4), 1.0x\"}." } ], "images": [ "scigen/train/3577.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on validation set of WMT14 DE-EN with different position strategy. “HSP” means the reference position sequence derived from the heuristic position searching." ], "reasoning": "The table shows that the Transformer (beam=4) model has the highest WMT14 DE-EN BLEU score of 30.68 and a speed up of 1.0x.", "question": "Which model has the highest WMT14 DE-EN BLEU score and what is its speed up?", "answer": "So, the answer is {\"answer\": \"Transformer (beam=4), 1.0x\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe PNAT with AR-Predictor has a higher position accuracy than the PNAT with NAR-Predictor.\n" }, { "role": "assistant", "content": "The table shows that the PNAT with AR-Predictor has a permutation accuracy of 25.30% and a relative accuracy of 59.27%, while the PNAT with NAR-Predictor has a permutation accuracy of 23.11% and a relative accuracy of 55.57%. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3577.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on validation set of WMT14 DE-EN with different position strategy. “HSP” means the reference position sequence derived from the heuristic position searching." ], "reasoning": "The table shows that the PNAT with AR-Predictor has a permutation accuracy of 25.30% and a relative accuracy of 59.27%, while the PNAT with NAR-Predictor has a permutation accuracy of 23.11% and a relative accuracy of 55.57%. Therefore, the statement is supported.", "statement": "The PNAT with AR-Predictor has a higher position accuracy than the PNAT with NAR-Predictor.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'TABLE II: Speaker identification errors and speaker verification equal error rates (EER) are shown for all subsystems and score fusion methods. Results with duration-based score fusion are also reported where applicable. Systems with best performance for identification and verification tests are shown as bold. For identification and verification experiments we have 2625×81 and 2625×81×152 tests respectively. Distance between upper 95% confidence interval and error mean for identification and verification tests are 0.1% and 0.02% respectively.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Two sets of experiments were conducted on test data, with the first combining all data and the second training models for each duration condition separately, resulting in high error rates due to short test durations.Two sets of experiments were done on the test data. In the first set of experiments, all 81 sets of data were combined and accuracy of each subsystem as well as the results for score fusion using mean, LR, and NN were calculated. Score fusion models were trained using the development data. In the second experiment, LR and NN score fusion models were trained on each enrollment and development duration condition separately, creating 81 model for each system. Accordingly, the tests were done using the models trained with the corresponding enrollment and test duration, and accuracies were reported on the whole data. It is important to note that the high error rates in this table are caused by the very short tests durations (0.1, 0.2, 0.5, and 1 seconds). In fact, experiments with very short durations on at least one side of enrollment or test cover 69% of the total experiments. One might expect that training a score fusion model for each duration condition will reduce the performance of the fused scores by limiting the amount of training data." } ], "images": [ "scigen/train/3578.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Speaker identification errors and speaker verification equal error rates (EER) are shown for all subsystems and score fusion methods. Results with duration-based score fusion are also reported where applicable. Systems with best performance for identification and verification tests are shown as bold. For identification and verification experiments we have 2625×81 and 2625×81×152 tests respectively. Distance between upper 95% confidence interval and error mean for identification and verification tests are 0.1% and 0.02% respectively." ], "reasoning": "Two sets of experiments were conducted on test data, with the first combining all data and the second training models for each duration condition separately, resulting in high error rates due to short test durations.", "description": "Two sets of experiments were done on the test data. In the first set of experiments, all 81 sets of data were combined and accuracy of each subsystem as well as the results for score fusion using mean, LR, and NN were calculated. Score fusion models were trained using the development data. In the second experiment, LR and NN score fusion models were trained on each enrollment and development duration condition separately, creating 81 model for each system. Accordingly, the tests were done using the models trained with the corresponding enrollment and test duration, and accuracies were reported on the whole data. It is important to note that the high error rates in this table are caused by the very short tests durations (0.1, 0.2, 0.5, and 1 seconds). In fact, experiments with very short durations on at least one side of enrollment or test cover 69% of the total experiments. One might expect that training a score fusion model for each duration condition will reduce the performance of the fused scores by limiting the amount of training data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: TABLE II: Speaker identification errors and speaker verification equal error rates (EER) are shown for all subsystems and score fusion methods. Results with duration-based score fusion are also reported where applicable. Systems with best performance for identification and verification tests are shown as bold. For identification and verification experiments we have 2625×81 and 2625×81×152 tests respectively. Distance between upper 95% confidence interval and error mean for identification and verification tests are 0.1% and 0.02% respectively.\n\nQuestion: What is the overall identification error rate for the score fusion using LR?\n" }, { "role": "assistant", "content": "The table shows the overall identification error rates for different subsystems and score fusion methods. The overall identification error rate for score fusion using LR is 44.25. So, the answer is {\"answer\": \"44.25\"}" } ], "images": [ "scigen/train/3578.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Speaker identification errors and speaker verification equal error rates (EER) are shown for all subsystems and score fusion methods. Results with duration-based score fusion are also reported where applicable. Systems with best performance for identification and verification tests are shown as bold. For identification and verification experiments we have 2625×81 and 2625×81×152 tests respectively. Distance between upper 95% confidence interval and error mean for identification and verification tests are 0.1% and 0.02% respectively." ], "reasoning": "The table shows the overall identification error rates for different subsystems and score fusion methods. The overall identification error rate for score fusion using LR is 44.25.", "question": "What is the overall identification error rate for the score fusion using LR?", "answer": "So, the answer is {\"answer\": \"44.25\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe duration-based verification EER for score fusion using NN is higher than that for score fusion using LR.\n" }, { "role": "assistant", "content": "The table shows that the duration-based verification EER for score fusion using NN is 13.46, which is higher than the 13.39 for score fusion using LR. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3578.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Speaker identification errors and speaker verification equal error rates (EER) are shown for all subsystems and score fusion methods. Results with duration-based score fusion are also reported where applicable. Systems with best performance for identification and verification tests are shown as bold. For identification and verification experiments we have 2625×81 and 2625×81×152 tests respectively. Distance between upper 95% confidence interval and error mean for identification and verification tests are 0.1% and 0.02% respectively." ], "reasoning": "The table shows that the duration-based verification EER for score fusion using NN is 13.46, which is higher than the 13.39 for score fusion using LR.", "statement": "The duration-based verification EER for score fusion using NN is higher than that for score fusion using LR.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 9: Average compactness scores.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "HLTA methods consistently outperform other methods across all datasets and versions, with significantly higher scores.For LDA-based methods, we reported their scores on both datasets of binary and bags-of-words versions. There is no distinct advantage of choosing either version. We see that the scores for the topics produced by HLTA are significantly higher than the those obtained by other methods in all cases." } ], "images": [ "scigen/train/3579.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Average compactness scores." ], "reasoning": "HLTA methods consistently outperform other methods across all datasets and versions, with significantly higher scores.", "description": "For LDA-based methods, we reported their scores on both datasets of binary and bags-of-words versions. There is no distinct advantage of choosing either version. We see that the scores for the topics produced by HLTA are significantly higher than the those obtained by other methods in all cases.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 9: Average compactness scores.\n\nQuestion: Which method achieved the highest score on the NYT dataset?\n" }, { "role": "assistant", "content": "By examining the table, we see that the highest score on the NYT dataset is 0.338±0.002, achieved by HLTA-step. So, the answer is {\"answer\": \"HLTA-step\"}." } ], "images": [ "scigen/train/3579.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Average compactness scores." ], "reasoning": "By examining the table, we see that the highest score on the NYT dataset is 0.338±0.002, achieved by HLTA-step.", "question": "Which method achieved the highest score on the NYT dataset?", "answer": "So, the answer is {\"answer\": \"HLTA-step\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nHLTA-batch achieved the highest score on the NIPS-5k dataset.\n" }, { "role": "assistant", "content": "The table shows that HLTA-batch scored 0.279±0.008 on the NIPS-5k dataset, which is higher than any other method listed for that dataset. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3579.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Average compactness scores." ], "reasoning": "The table shows that HLTA-batch scored 0.279±0.008 on the NIPS-5k dataset, which is higher than any other method listed for that dataset.", "statement": "HLTA-batch achieved the highest score on the NIPS-5k dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Document partition given by latent variable Z21.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The table shows the probabilities of words occurring in two clusters, Z21=s0 and Z21=s1, with Z21=s1 having higher probabilities for words like space, nasa, and orbit, indicating a topic related to NASA.There are totally 14 latent variables in the toy example. Each latent variable has two states and hence partitions the document collection into two soft clusters. To figure out what the partition and the two clusters are about, we need to consider the relationship between the latent variable and the observed variables in its subtree. Take Z21 as an example. Denote the two document clusters it gives as Z21= s0 and Z21=s1. We see that the cluster Z21=s1 consists of 5% of the documents. In this cluster, the words such as space, nasa and orbit occur with relatively high probabilities. It is clearly a meaningful and is interpreted as a topic. One might label the topic “NASA”. The other cluster Z21= s0 consists of 95% of the documents. In this cluster, the words occur with low probabilities. We interpret it as a background topic. There are three subtle issues concerning The first issue is how the word variables are ordered. To answer the question, we need the mutual information (MI) I(X;Y) The words placed at the top of the table have the highest MI with Z21. They are the best ones to characterize the difference between the two clusters because their occurrence probabilities in the two clusters differ the most. They occur with high probabilities in the clusters Z21=s1 and with low probabilities in Z21 =s0. If one is to choose only the top, say 5, words to characterize the topic Z21=s1, then the best words to pick are space, nasa, orbit, earth and shuttle. The computation takes linear time because the model is tree-structured." } ], "images": [ "scigen/train/3580.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Document partition given by latent variable Z21." ], "reasoning": "The table shows the probabilities of words occurring in two clusters, Z21=s0 and Z21=s1, with Z21=s1 having higher probabilities for words like space, nasa, and orbit, indicating a topic related to NASA.", "description": "There are totally 14 latent variables in the toy example. Each latent variable has two states and hence partitions the document collection into two soft clusters. To figure out what the partition and the two clusters are about, we need to consider the relationship between the latent variable and the observed variables in its subtree. Take Z21 as an example. Denote the two document clusters it gives as Z21= s0 and Z21=s1. We see that the cluster Z21=s1 consists of 5% of the documents. In this cluster, the words such as space, nasa and orbit occur with relatively high probabilities. It is clearly a meaningful and is interpreted as a topic. One might label the topic “NASA”. The other cluster Z21= s0 consists of 95% of the documents. In this cluster, the words occur with low probabilities. We interpret it as a background topic. There are three subtle issues concerning The first issue is how the word variables are ordered. To answer the question, we need the mutual information (MI) I(X;Y) The words placed at the top of the table have the highest MI with Z21. They are the best ones to characterize the difference between the two clusters because their occurrence probabilities in the two clusters differ the most. They occur with high probabilities in the clusters Z21=s1 and with low probabilities in Z21 =s0. If one is to choose only the top, say 5, words to characterize the topic Z21=s1, then the best words to pick are space, nasa, orbit, earth and shuttle. The computation takes linear time because the model is tree-structured.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 1: Document partition given by latent variable Z21.', answer the following question. Give detailed solution consisting of each step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat are the top 3 words with the highest probabilities in the Z21=s1 cluster?\n" }, { "role": "assistant", "content": "To find the top 3 words with the highest probabilities in the Z21=s1 cluster, we look at the s1 column and identify the words with the highest values. The top 3 words are space (0.58), nasa (0.43), and orbit (0.33). So, the answer is {\"answer\": \"space, nasa, orbit\"}" } ], "images": [ "scigen/train/3580.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Document partition given by latent variable Z21." ], "reasoning": "To find the top 3 words with the highest probabilities in the Z21=s1 cluster, we look at the s1 column and identify the words with the highest values. The top 3 words are space (0.58), nasa (0.43), and orbit (0.33).", "question": "What are the top 3 words with the highest probabilities in the Z21=s1 cluster?", "answer": "So, the answer is {\"answer\": \"space, nasa, orbit\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe word 'earth' has a higher probability in the Z21=s1 cluster than in the Z21=s0 cluster.\n" }, { "role": "assistant", "content": "The table shows that the probability of the word 'earth' in the Z21=s1 cluster is 0.33, while in the Z21=s0 cluster it is 0.01. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3580.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Document partition given by latent variable Z21." ], "reasoning": "The table shows that the probability of the word 'earth' in the Z21=s1 cluster is 0.33, while in the Z21=s0 cluster it is 0.01. Therefore, the statement is supported.", "statement": "The word 'earth' has a higher probability in the Z21=s1 cluster than in the Z21=s0 cluster.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 6: Per-document held-out loglikelihood on binary data: Best scores are marked in bold. The sign “-” indicates non-termination after 72 hours, and “NR” stands for “not run’.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "HLTA-step is more efficient than HLTA-batch on large datasets, with no significant decrease in model quality.In our experiments, each dataset was randomly partitioned into a training set with 80% of the data, and a test set with 20% of the data. Models were learned from the training set, and per-document loglikelihood was calculated on the held-out test set. For comparability, only the results on binary data are included. We see that HLTA-step is significantly more efficient than HLTA-batch on large datasets, while there is virtually no decrease in model quality. Note that all algorithms have parameters that control computational complexity. Thus, running time comparison is only meaningful when it is done with reference to model quality. that HLTA achieved much better model quality than the alternative algorithms using comparable or less time." } ], "images": [ "scigen/train/3581.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Per-document held-out loglikelihood on binary data: Best scores are marked in bold. The sign “-” indicates non-termination after 72 hours, and “NR” stands for “not run’." ], "reasoning": "HLTA-step is more efficient than HLTA-batch on large datasets, with no significant decrease in model quality.", "description": "In our experiments, each dataset was randomly partitioned into a training set with 80% of the data, and a test set with 20% of the data. Models were learned from the training set, and per-document loglikelihood was calculated on the held-out test set. For comparability, only the results on binary data are included. We see that HLTA-step is significantly more efficient than HLTA-batch on large datasets, while there is virtually no decrease in model quality. Note that all algorithms have parameters that control computational complexity. Thus, running time comparison is only meaningful when it is done with reference to model quality. that HLTA achieved much better model quality than the alternative algorithms using comparable or less time.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 6: Per-document held-out loglikelihood on binary data: Best scores are marked in bold. The sign “-” indicates non-termination after 72 hours, and “NR” stands for “not run’.', and sequentially solve the question, writing out each step of your reasoning process. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich algorithm has the lowest running time on the NIPS-1k dataset?\n" }, { "role": "assistant", "content": "By comparing the running times for the NIPS-1k dataset, HLTA-batch has the lowest running time of 5.6±0.1 minutes. So, the answer is {\"answer\": \"HLTA-batch\"}" } ], "images": [ "scigen/train/3581.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Per-document held-out loglikelihood on binary data: Best scores are marked in bold. The sign “-” indicates non-termination after 72 hours, and “NR” stands for “not run’." ], "reasoning": "By comparing the running times for the NIPS-1k dataset, HLTA-batch has the lowest running time of 5.6±0.1 minutes.", "question": "Which algorithm has the lowest running time on the NIPS-1k dataset?", "answer": "So, the answer is {\"answer\": \"HLTA-batch\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence HLTA-step has a lower running time than HLTA-batch on the News-5k dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nHLTA-step has a lower running time than HLTA-batch on the News-5k dataset.\n" }, { "role": "assistant", "content": "The table shows that HLTA-step has a running time of 133±5 minutes, while HLTA-batch has a running time of 432±39 minutes on the News-5k dataset. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3581.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Per-document held-out loglikelihood on binary data: Best scores are marked in bold. The sign “-” indicates non-termination after 72 hours, and “NR” stands for “not run’." ], "reasoning": "The table shows that HLTA-step has a running time of 133±5 minutes, while HLTA-batch has a running time of 432±39 minutes on the News-5k dataset.", "statement": "HLTA-step has a lower running time than HLTA-batch on the News-5k dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Comparison with state-of-the-art models on the LCSTS test set. R-1, R-2, and R-L denote ROUGE-1, ROUGE-2, and ROUGE-L, respectively. The models with a suffix of ‘W’ in the table are word-based, while the rest of models are character-based.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Our superAE model significantly outperforms the Seq2Seq baseline and recent summarization systems, demonstrating its efficiency and the contribution of adversarial learning.We first compare our model with Seq2Seq baseline. It shows that our superAE model has a large improvement over the Seq2Seq baseline by 7.1 ROUGE-1, 6.1 ROUGE-2, and 7.0 ROUGE-L, which demonstrates the efficiency of our model. Moreover, we compare our model with the recent summarization systems, which have been evaluated on the same training set and the test sets as ours. Their results are directly reported in the referred articles. It shows that our superAE outperforms all of these models, with a relative gain of 2.2 ROUGE-1, 1.8 ROUGE-2, and 2.0 ROUGE-L. We also perform ablation study by removing the adversarial learning component, in order to show its contribution. It shows that the adversarial learning improves the performance of 1.5 ROUGE-1, 0.7 ROUGE-2, and 1.0 ROUGE-L." } ], "images": [ "scigen/train/3582.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison with state-of-the-art models on the LCSTS test set. R-1, R-2, and R-L denote ROUGE-1, ROUGE-2, and ROUGE-L, respectively. The models with a suffix of ‘W’ in the table are word-based, while the rest of models are character-based." ], "reasoning": "Our superAE model significantly outperforms the Seq2Seq baseline and recent summarization systems, demonstrating its efficiency and the contribution of adversarial learning.", "description": "We first compare our model with Seq2Seq baseline. It shows that our superAE model has a large improvement over the Seq2Seq baseline by 7.1 ROUGE-1, 6.1 ROUGE-2, and 7.0 ROUGE-L, which demonstrates the efficiency of our model. Moreover, we compare our model with the recent summarization systems, which have been evaluated on the same training set and the test sets as ours. Their results are directly reported in the referred articles. It shows that our superAE outperforms all of these models, with a relative gain of 2.2 ROUGE-1, 1.8 ROUGE-2, and 2.0 ROUGE-L. We also perform ablation study by removing the adversarial learning component, in order to show its contribution. It shows that the adversarial learning improves the performance of 1.5 ROUGE-1, 0.7 ROUGE-2, and 1.0 ROUGE-L.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Comparison with state-of-the-art models on the LCSTS test set. R-1, R-2, and R-L denote ROUGE-1, ROUGE-2, and ROUGE-L, respectively. The models with a suffix of ‘W’ in the table are word-based, while the rest of models are character-based.\nQuestion: How much does the superAE model improve over the Seq2Seq baseline in terms of ROUGE-1?\nSolve the above question based on the table information. Let's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The description states that the superAE model improves over the Seq2Seq baseline by 7.1 ROUGE-1. The table shows that the Seq2Seq baseline has a ROUGE-1 score of 32.1, and the superAE model has a ROUGE-1 score of 39.2, confirming the 7.1 improvement. So, the answer is {\"answer\": \"7.1\"}" } ], "images": [ "scigen/train/3582.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison with state-of-the-art models on the LCSTS test set. R-1, R-2, and R-L denote ROUGE-1, ROUGE-2, and ROUGE-L, respectively. The models with a suffix of ‘W’ in the table are word-based, while the rest of models are character-based." ], "reasoning": "The description states that the superAE model improves over the Seq2Seq baseline by 7.1 ROUGE-1. The table shows that the Seq2Seq baseline has a ROUGE-1 score of 32.1, and the superAE model has a ROUGE-1 score of 39.2, confirming the 7.1 improvement.", "question": "How much does the superAE model improve over the Seq2Seq baseline in terms of ROUGE-1?", "answer": "So, the answer is {\"answer\": \"7.1\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The superAE model outperforms all recent summarization systems in terms of ROUGE-2., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe superAE model outperforms all recent summarization systems in terms of ROUGE-2.\n" }, { "role": "assistant", "content": "The description states that the superAE model outperforms all recent summarization systems with a relative gain of 1.8 ROUGE-2. The table shows that the highest ROUGE-2 score among recent summarization systems is 22.6 by RNN-distChen et al. (2016), while the superAE model has a ROUGE-2 score of 26.0, confirming the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3582.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison with state-of-the-art models on the LCSTS test set. R-1, R-2, and R-L denote ROUGE-1, ROUGE-2, and ROUGE-L, respectively. The models with a suffix of ‘W’ in the table are word-based, while the rest of models are character-based." ], "reasoning": "The description states that the superAE model outperforms all recent summarization systems with a relative gain of 1.8 ROUGE-2. The table shows that the highest ROUGE-2 score among recent summarization systems is 22.6 by RNN-distChen et al. (2016), while the superAE model has a ROUGE-2 score of 26.0, confirming the statement.", "statement": "The superAE model outperforms all recent summarization systems in terms of ROUGE-2.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: Results on TREC-COVID Round 1. For ‘Manual’ submission, we manually chose better results from dmis-rnd1-run1 and dmis-rnd1-run2 for each query.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "covidAsk can perform IR but is less effective compared to systems dedicated to IR due to the fundamental differences between QA and IR tasks.The results of our submissions (KoreaUniversity_DMIS) show that covidAsk is capable of performing IR, although it is not very effective compared to other systems that are fully dedicated to IR rather than QA. This is due to the fact that QA and IR are fundamentally different tasks, as it is also possible for documents unrelated to the question itself to contain correct answers. The discrepancy between evaluation metrics used for QA and IR also imply that the two tasks put emphasis on different aspects. For more details regarding TREC-COVID Round 1 submissions, please refer to the TREC-COVID Round 1 Archive: https://ir.nist.gov/covidSubmit/archive/archive-round1.html." } ], "images": [ "scigen/train/3583.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on TREC-COVID Round 1. For ‘Manual’ submission, we manually chose better results from dmis-rnd1-run1 and dmis-rnd1-run2 for each query." ], "reasoning": "covidAsk can perform IR but is less effective compared to systems dedicated to IR due to the fundamental differences between QA and IR tasks.", "description": "The results of our submissions (KoreaUniversity_DMIS) show that covidAsk is capable of performing IR, although it is not very effective compared to other systems that are fully dedicated to IR rather than QA. This is due to the fact that QA and IR are fundamentally different tasks, as it is also possible for documents unrelated to the question itself to contain correct answers. The discrepancy between evaluation metrics used for QA and IR also imply that the two tasks put emphasis on different aspects. For more details regarding TREC-COVID Round 1 submissions, please refer to the TREC-COVID Round 1 Archive: https://ir.nist.gov/covidSubmit/archive/archive-round1.html.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nTable title: Table 5: Results on TREC-COVID Round 1. For ‘Manual’ submission, we manually chose better results from dmis-rnd1-run1 and dmis-rnd1-run2 for each query.\n\nQuestion: Which run by KoreaUniversity_DMIS had the highest NDCG@10 score?\n" }, { "role": "assistant", "content": "To find the run by KoreaUniversity_DMIS with the highest NDCG@10 score, compare the NDCG@10 scores of all three runs listed under KoreaUniversity_DMIS. The highest NDCG@10 score among them is 0.4649 for dmis-rnd1-run3. So, the answer is {\"answer\": \"dmis-rnd1-run3\"}" } ], "images": [ "scigen/train/3583.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on TREC-COVID Round 1. For ‘Manual’ submission, we manually chose better results from dmis-rnd1-run1 and dmis-rnd1-run2 for each query." ], "reasoning": "To find the run by KoreaUniversity_DMIS with the highest NDCG@10 score, compare the NDCG@10 scores of all three runs listed under KoreaUniversity_DMIS. The highest NDCG@10 score among them is 0.4649 for dmis-rnd1-run3.", "question": "Which run by KoreaUniversity_DMIS had the highest NDCG@10 score?", "answer": "So, the answer is {\"answer\": \"dmis-rnd1-run3\"}" }, { "messages": [ { "role": "user", "content": "The run by KoreaUniversity_DMIS that used SQuAD + NQ for training had the lowest MAP score. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe run by KoreaUniversity_DMIS that used SQuAD + NQ for training had the lowest MAP score.\n" }, { "role": "assistant", "content": "To verify this statement, check the MAP scores of all runs by KoreaUniversity_DMIS. The run that used SQuAD + NQ for training is dmis-rnd1-run2, which has a MAP score of 0.0676. This is indeed the lowest MAP score among the three runs. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3583.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on TREC-COVID Round 1. For ‘Manual’ submission, we manually chose better results from dmis-rnd1-run1 and dmis-rnd1-run2 for each query." ], "reasoning": "To verify this statement, check the MAP scores of all runs by KoreaUniversity_DMIS. The run that used SQuAD + NQ for training is dmis-rnd1-run2, which has a MAP score of 0.0676. This is indeed the lowest MAP score among the three runs.", "statement": "The run by KoreaUniversity_DMIS that used SQuAD + NQ for training had the lowest MAP score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Results on COVID-19 Questions'.\n" }, { "role": "assistant", "content": "DenSPI + Sparc trained on SQuAD performs best on interrogative questions, while DenSPI (unpublished) trained on NQ excels on keyword questions.When DenSPI + Sparc is trained on SQuAD, its performance on interrogative questions is generally superior to other models. Although the performance on keyword questions when trained on both SQuAD and NQ improves with 2020-04-10 articles, it does not perform any better with 2020-04-10-recent articles. Lastly, we include our recent ongoing effort to improve the performance of DenSPI (DenSPI (unpublished)) which is purely trained on Natural Questions. Its performance on keyword questions largely outperforms other models. As shown in the last row, we continue to update covidAsk with updated versions of CORD-19 (2020-06-14-recent). Answerability of questions for a large amount of unstructured text: Although we tried to set a threshold for the scores of answers, the distribution of scores were very inconsistent across different questions (e.g., a top 1 answer with a low score is often correct). Leveraging domain adapted datasets: We tested whether using the BioASQ dataset" } ], "images": [ "scigen/train/3584.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on COVID-19 Questions" ], "reasoning": "DenSPI + Sparc trained on SQuAD performs best on interrogative questions, while DenSPI (unpublished) trained on NQ excels on keyword questions.", "description": "When DenSPI + Sparc is trained on SQuAD, its performance on interrogative questions is generally superior to other models. Although the performance on keyword questions when trained on both SQuAD and NQ improves with 2020-04-10 articles, it does not perform any better with 2020-04-10-recent articles. Lastly, we include our recent ongoing effort to improve the performance of DenSPI (DenSPI (unpublished)) which is purely trained on Natural Questions. Its performance on keyword questions largely outperforms other models. As shown in the last row, we continue to update covidAsk with updated versions of CORD-19 (2020-06-14-recent). Answerability of questions for a large amount of unstructured text: Although we tried to set a threshold for the scores of answers, the distribution of scores were very inconsistent across different questions (e.g., a top 1 answer with a low score is often correct). Leveraging domain adapted datasets: We tested whether using the BioASQ dataset", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 4: Results on COVID-19 Questions'. The final result should be presented in the JSON format of {\"answer\": \"\"}. \n\nQuestion: Which model has the highest performance on keyword questions at EMsent@1?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By examining the 'Keyword EMsent@1' column, DenSPI (unpublished) trained on NQ with 2020-04-10-recent articles has the highest value of 0.1897. So, the answer is {\"answer\": \"DenSPI (unpublished)\"}." } ], "images": [ "scigen/train/3584.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on COVID-19 Questions" ], "reasoning": "By examining the 'Keyword EMsent@1' column, DenSPI (unpublished) trained on NQ with 2020-04-10-recent articles has the highest value of 0.1897.", "question": "Which model has the highest performance on keyword questions at EMsent@1?", "answer": "So, the answer is {\"answer\": \"DenSPI (unpublished)\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nDenSPI + Sparc trained on SQuAD + NQ performs better on keyword questions than DenSPI (unpublished) trained on NQ.\n" }, { "role": "assistant", "content": "Comparing the 'Keyword EMsent@1' values, DenSPI + Sparc trained on SQuAD + NQ has a maximum value of 0.1552, while DenSPI (unpublished) trained on NQ has a higher value of 0.1897. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3584.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on COVID-19 Questions" ], "reasoning": "Comparing the 'Keyword EMsent@1' values, DenSPI + Sparc trained on SQuAD + NQ has a maximum value of 0.1552, while DenSPI (unpublished) trained on NQ has a higher value of 0.1897.", "statement": "DenSPI + Sparc trained on SQuAD + NQ performs better on keyword questions than DenSPI (unpublished) trained on NQ.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 8: Unsupervised rejection using variation ratio uncertainty estimates for each event–fold in the PHEME dataset. EE – Ebola-Essien; FE – Ferguson unrest; GU – Gurlitt; OT – Ottawa shooting; PT – Prince-Toronto; PM – Putin missing; SS – Sydney Siege; CH – Charlie Hebdo; GW – Germanwings crash.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "During cross-validation, each fold is used as a testing set once, and predictions are aggregated for evaluation and rejection on the complete dataset.As we have explained in the experimental setup section of the main paper, during the cross-validation iterations each fold becomes a testing set once. We first aggregate predictions from each testing fold, and then perform evaluation and unsupervised rejection on the complete dataset. Alternatively, we could first perform the rejection procedure on each fold and then either aggregate the instances together for the evaluation (see This also shows in the difference between the corresponding tables of the two set ups discussed in this section, which is more notable for PHEME" } ], "images": [ "scigen/train/3585.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Unsupervised rejection using variation ratio uncertainty estimates for each event–fold in the PHEME dataset. EE – Ebola-Essien; FE – Ferguson unrest; GU – Gurlitt; OT – Ottawa shooting; PT – Prince-Toronto; PM – Putin missing; SS – Sydney Siege; CH – Charlie Hebdo; GW – Germanwings crash." ], "reasoning": "During cross-validation, each fold is used as a testing set once, and predictions are aggregated for evaluation and rejection on the complete dataset.", "description": "As we have explained in the experimental setup section of the main paper, during the cross-validation iterations each fold becomes a testing set once. We first aggregate predictions from each testing fold, and then perform evaluation and unsupervised rejection on the complete dataset. Alternatively, we could first perform the rejection procedure on each fold and then either aggregate the instances together for the evaluation (see This also shows in the difference between the corresponding tables of the two set ups discussed in this section, which is more notable for PHEME", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 8: Unsupervised rejection using variation ratio uncertainty estimates for each event–fold in the PHEME dataset. EE – Ebola-Essien; FE – Ferguson unrest; GU – Gurlitt; OT – Ottawa shooting; PT – Prince-Toronto; PM – Putin missing; SS – Sydney Siege; CH – Charlie Hebdo; GW – Germanwings crash.' in mind, break down and resolve the question below step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the highest value for the PT column and at what percentage does it occur?\n" }, { "role": "assistant", "content": "To find the highest value in the PT column, we compare all the values in that column. The highest value is 0.655, which occurs at 50%. So, the answer is {\"answer\": \"0.655 at 50%\"}" } ], "images": [ "scigen/train/3585.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Unsupervised rejection using variation ratio uncertainty estimates for each event–fold in the PHEME dataset. EE – Ebola-Essien; FE – Ferguson unrest; GU – Gurlitt; OT – Ottawa shooting; PT – Prince-Toronto; PM – Putin missing; SS – Sydney Siege; CH – Charlie Hebdo; GW – Germanwings crash." ], "reasoning": "To find the highest value in the PT column, we compare all the values in that column. The highest value is 0.655, which occurs at 50%.", "question": "What is the highest value for the PT column and at what percentage does it occur?", "answer": "So, the answer is {\"answer\": \"0.655 at 50%\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe lowest value in the FE column is 0.014.\n" }, { "role": "assistant", "content": "To verify this statement, we compare all the values in the FE column. The lowest value is indeed 0.014, which occurs at 50%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3585.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Unsupervised rejection using variation ratio uncertainty estimates for each event–fold in the PHEME dataset. EE – Ebola-Essien; FE – Ferguson unrest; GU – Gurlitt; OT – Ottawa shooting; PT – Prince-Toronto; PM – Putin missing; SS – Sydney Siege; CH – Charlie Hebdo; GW – Germanwings crash." ], "reasoning": "To verify this statement, we compare all the values in the FE column. The lowest value is indeed 0.014, which occurs at 50%.", "statement": "The lowest value in the FE column is 0.014.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: How rejecting instances using supervised and unsupervised methods affects model performance across datasets, in terms of both accuracy and macro F-score. Performance values were obtained in a separate set of experiments, by removing one of the folds from the training set, as supervised models needed an extra development set to be trained on.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Training a meta-classifier on a subset of data not used for the rumour verification model harmed performance on PHEME and Twitter 16 due to less training data and class imbalance.To perform supervised rejection we need to train a meta-classifier on a subset of data that was not used for training the rumour verification model. Therefore in a separate set of experiments we exclude one of the folds (development set) from training of the verification model. We run cross-validation with one less fold and at each step obtain predictions and uncertainty estimates for both the test fold and the development set. We then use the predictions and uncertainty values predicted for the instances in the development set as training instances in our rejection meta-models, which we then evaluate on each of the corresponding test folds, thus obtaining the combined predictions for all of the folds in the dataset except for the development. Having less training data harmed performance on PHEME and Twitter 16. We believe that the reason the same effect on macro-F score is not observed in PHEME is the class imbalance in this dataset." } ], "images": [ "scigen/train/3586.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: How rejecting instances using supervised and unsupervised methods affects model performance across datasets, in terms of both accuracy and macro F-score. Performance values were obtained in a separate set of experiments, by removing one of the folds from the training set, as supervised models needed an extra development set to be trained on." ], "reasoning": "Training a meta-classifier on a subset of data not used for the rumour verification model harmed performance on PHEME and Twitter 16 due to less training data and class imbalance.", "description": "To perform supervised rejection we need to train a meta-classifier on a subset of data that was not used for training the rumour verification model. Therefore in a separate set of experiments we exclude one of the folds (development set) from training of the verification model. We run cross-validation with one less fold and at each step obtain predictions and uncertainty estimates for both the test fold and the development set. We then use the predictions and uncertainty values predicted for the instances in the development set as training instances in our rejection meta-models, which we then evaluate on each of the corresponding test folds, thus obtaining the combined predictions for all of the folds in the dataset except for the development. Having less training data harmed performance on PHEME and Twitter 16. We believe that the reason the same effect on macro-F score is not observed in PHEME is the class imbalance in this dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the accuracy of the supervised rejection model using the SVM classifier on the PHEME dataset?\nSolve the above question based on the table titled 'Table 2: How rejecting instances using supervised and unsupervised methods affects model performance across datasets, in terms of both accuracy and macro F-score. Performance values were obtained in a separate set of experiments, by removing one of the folds from the training set, as supervised models needed an extra development set to be trained on.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the accuracy of the supervised rejection model using the SVM classifier on the PHEME dataset as 0.399. So, the answer is {\"answer\": \"0.399\"}" } ], "images": [ "scigen/train/3586.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: How rejecting instances using supervised and unsupervised methods affects model performance across datasets, in terms of both accuracy and macro F-score. Performance values were obtained in a separate set of experiments, by removing one of the folds from the training set, as supervised models needed an extra development set to be trained on." ], "reasoning": "The table shows the accuracy of the supervised rejection model using the SVM classifier on the PHEME dataset as 0.399.", "question": "What is the accuracy of the supervised rejection model using the SVM classifier on the PHEME dataset?", "answer": "So, the answer is {\"answer\": \"0.399\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe macro-F score for the supervised rejection model using the RF classifier on the Twitter 15 dataset is higher than 0.8.\n" }, { "role": "assistant", "content": "The table shows that the macro-F score for the supervised rejection model using the RF classifier on the Twitter 15 dataset is 0.829, which is higher than 0.8. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3586.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: How rejecting instances using supervised and unsupervised methods affects model performance across datasets, in terms of both accuracy and macro F-score. Performance values were obtained in a separate set of experiments, by removing one of the folds from the training set, as supervised models needed an extra development set to be trained on." ], "reasoning": "The table shows that the macro-F score for the supervised rejection model using the RF classifier on the Twitter 15 dataset is 0.829, which is higher than 0.8.", "statement": "The macro-F score for the supervised rejection model using the RF classifier on the Twitter 15 dataset is higher than 0.8.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Per-class f1-scores of branch-LSTM model on each of the datasets.'.\n" }, { "role": "assistant", "content": "In all datasets, the True class has significantly lower levels of uncertainty, while the Non-Rumour class in Twitter 15 and 16 has the highest uncertainty.Is higher uncertainty associated with a particular class label? In all datasets the True class has significantly lower levels of uncertainty (using Kruskal and Wallis The difference between False and Unverified is not statistically significant in any cases. Aleatoric uncertainty shows a similar pattern for the class labels. In Twitter 15,16 the Non-Rumour class has the highest uncertainty (and relatively lower f1 score)." } ], "images": [ "scigen/train/3587.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Per-class f1-scores of branch-LSTM model on each of the datasets." ], "reasoning": "In all datasets, the True class has significantly lower levels of uncertainty, while the Non-Rumour class in Twitter 15 and 16 has the highest uncertainty.", "description": "Is higher uncertainty associated with a particular class label? In all datasets the True class has significantly lower levels of uncertainty (using Kruskal and Wallis The difference between False and Unverified is not statistically significant in any cases. Aleatoric uncertainty shows a similar pattern for the class labels. In Twitter 15,16 the Non-Rumour class has the highest uncertainty (and relatively lower f1 score).", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nTable title: Table 3: Per-class f1-scores of branch-LSTM model on each of the datasets.\n\nQuestion: Which class has the highest uncertainty in the Twitter 15 dataset?\n" }, { "role": "assistant", "content": "The table shows the uncertainty levels for different classes across datasets. In the Twitter 15 dataset, the Non-Rumour class has the highest uncertainty with a value of 0.503. So, the answer is {\"answer\": \"Non-Rumour\"}." } ], "images": [ "scigen/train/3587.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Per-class f1-scores of branch-LSTM model on each of the datasets." ], "reasoning": "The table shows the uncertainty levels for different classes across datasets. In the Twitter 15 dataset, the Non-Rumour class has the highest uncertainty with a value of 0.503.", "question": "Which class has the highest uncertainty in the Twitter 15 dataset?", "answer": "So, the answer is {\"answer\": \"Non-Rumour\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe True class has the highest uncertainty in the PHEME dataset.\n" }, { "role": "assistant", "content": "The table shows that in the PHEME dataset, the True class has an uncertainty level of 0.569, which is higher than the False and Unverified classes. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3587.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Per-class f1-scores of branch-LSTM model on each of the datasets." ], "reasoning": "The table shows that in the PHEME dataset, the True class has an uncertainty level of 0.569, which is higher than the False and Unverified classes.", "statement": "The True class has the highest uncertainty in the PHEME dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Expected Calibration Error before and after applying calibration over uncertainty estimates. S – softmax (LCS), A – aleatoric uncertainty, VR – variation ratio.'.\n" }, { "role": "assistant", "content": "Histogram Binning significantly improves calibration for all types of uncertainty across all datasets, with VR showing the best initial calibration.We measure and compare the ECE for all types of uncertainty. We apply Histogram Binning, a simple yet effective approach to improve the calibration for each type of uncertainty. We use the experiment setup with one of the folds reserved as development set to train the calibration method. We convert uncertainty estimates u into confidence scores as 1−u, and for aleatoric uncertainty we normalise it to be in [0,1]. -Softmax (S), Aleatoric (A), Variation Ratio (VR)- where a lower value indicates better calibration (calibration curves can be found in appendix E). Initial ECE for PHEME is higher than for Twitter 15 and 16 datasets. VR has the best initial calibration, however Histogram Binning notably improves calibration across all datasets and uncertainty types." } ], "images": [ "scigen/train/3588.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Expected Calibration Error before and after applying calibration over uncertainty estimates. S – softmax (LCS), A – aleatoric uncertainty, VR – variation ratio." ], "reasoning": "Histogram Binning significantly improves calibration for all types of uncertainty across all datasets, with VR showing the best initial calibration.", "description": "We measure and compare the ECE for all types of uncertainty. We apply Histogram Binning, a simple yet effective approach to improve the calibration for each type of uncertainty. We use the experiment setup with one of the folds reserved as development set to train the calibration method. We convert uncertainty estimates u into confidence scores as 1−u, and for aleatoric uncertainty we normalise it to be in [0,1]. -Softmax (S), Aleatoric (A), Variation Ratio (VR)- where a lower value indicates better calibration (calibration curves can be found in appendix E). Initial ECE for PHEME is higher than for Twitter 15 and 16 datasets. VR has the best initial calibration, however Histogram Binning notably improves calibration across all datasets and uncertainty types.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Expected Calibration Error before and after applying calibration over uncertainty estimates. S – softmax (LCS), A – aleatoric uncertainty, VR – variation ratio.\nQuestion: Which dataset and uncertainty type combination had the highest initial ECE?\nGive a detailed response to the above question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the 'No calibration' columns for each dataset, we see that PHEME with Aleatoric uncertainty (A) has the highest initial ECE value of 0.683. So, the answer is {\"answer\": \"PHEME Aleatoric\"}" } ], "images": [ "scigen/train/3588.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Expected Calibration Error before and after applying calibration over uncertainty estimates. S – softmax (LCS), A – aleatoric uncertainty, VR – variation ratio." ], "reasoning": "By comparing the 'No calibration' columns for each dataset, we see that PHEME with Aleatoric uncertainty (A) has the highest initial ECE value of 0.683.", "question": "Which dataset and uncertainty type combination had the highest initial ECE?", "answer": "So, the answer is {\"answer\": \"PHEME Aleatoric\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nHistogram Binning improved the calibration for Twitter 15 dataset more than for Twitter 16 dataset.\n" }, { "role": "assistant", "content": "By comparing the 'No calibration' and 'Histogram Binning' values for both Twitter 15 and Twitter 16 datasets, we see that the improvement for Twitter 15 (S: 0.265 to 0.056, A: 0.333 to 0.039, VR: 0.216 to 0.062) is greater than for Twitter 16 (S: 0.191 to 0.164, A: 0.196 to 0.079, VR: 0.121 to 0.044). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3588.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Expected Calibration Error before and after applying calibration over uncertainty estimates. S – softmax (LCS), A – aleatoric uncertainty, VR – variation ratio." ], "reasoning": "By comparing the 'No calibration' and 'Histogram Binning' values for both Twitter 15 and Twitter 16 datasets, we see that the improvement for Twitter 15 (S: 0.265 to 0.056, A: 0.333 to 0.039, VR: 0.216 to 0.062) is greater than for Twitter 16 (S: 0.191 to 0.164, A: 0.196 to 0.079, VR: 0.121 to 0.044).", "statement": "Histogram Binning improved the calibration for Twitter 15 dataset more than for Twitter 16 dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 9: Performance (accuracy) after per-fold unsupervised rejection on PHEME dataset for all types of uncertainty. LCS – Least Confidence Sampling; MC – Margin of Confidence, RC – Ratio of Confidence and E – Entropy based on a single output of a softmax layer (as opposed to Entropy, Variance and Variation ratio that are based on multiple softmax samples).'.\n" }, { "role": "assistant", "content": "During cross-validation, predictions from each testing fold are aggregated for evaluation and rejection, with notable differences in results for PHEME.As we have explained in the experimental setup section of the main paper, during the cross-validation iterations each fold becomes a testing set once. We first aggregate predictions from each testing fold, and then perform evaluation and unsupervised rejection on the complete dataset. Alternatively, we could first perform the rejection procedure on each fold and then either aggregate the instances together for the evaluation (see This also shows in the difference between the corresponding tables of the two set ups discussed in this section, which is more notable for PHEME" } ], "images": [ "scigen/train/3589.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Performance (accuracy) after per-fold unsupervised rejection on PHEME dataset for all types of uncertainty. LCS – Least Confidence Sampling; MC – Margin of Confidence, RC – Ratio of Confidence and E – Entropy based on a single output of a softmax layer (as opposed to Entropy, Variance and Variation ratio that are based on multiple softmax samples)." ], "reasoning": "During cross-validation, predictions from each testing fold are aggregated for evaluation and rejection, with notable differences in results for PHEME.", "description": "As we have explained in the experimental setup section of the main paper, during the cross-validation iterations each fold becomes a testing set once. We first aggregate predictions from each testing fold, and then perform evaluation and unsupervised rejection on the complete dataset. Alternatively, we could first perform the rejection procedure on each fold and then either aggregate the instances together for the evaluation (see This also shows in the difference between the corresponding tables of the two set ups discussed in this section, which is more notable for PHEME", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 9: Performance (accuracy) after per-fold unsupervised rejection on PHEME dataset for all types of uncertainty. LCS – Least Confidence Sampling; MC – Margin of Confidence, RC – Ratio of Confidence and E – Entropy based on a single output of a softmax layer (as opposed to Entropy, Variance and Variation ratio that are based on multiple softmax samples).' to sequentially solve the question presented below. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhat is the highest value for Entropy and at what percentage does it occur?\n" }, { "role": "assistant", "content": "By examining the Entropy column, the highest value is 0.413, which occurs at 50%. So, the answer is {\"answer\": \"0.413 at 50%\"}" } ], "images": [ "scigen/train/3589.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Performance (accuracy) after per-fold unsupervised rejection on PHEME dataset for all types of uncertainty. LCS – Least Confidence Sampling; MC – Margin of Confidence, RC – Ratio of Confidence and E – Entropy based on a single output of a softmax layer (as opposed to Entropy, Variance and Variation ratio that are based on multiple softmax samples)." ], "reasoning": "By examining the Entropy column, the highest value is 0.413, which occurs at 50%.", "question": "What is the highest value for Entropy and at what percentage does it occur?", "answer": "So, the answer is {\"answer\": \"0.413 at 50%\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The highest value for Variance occurs at 50%., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe highest value for Variance occurs at 50%.\n" }, { "role": "assistant", "content": "By examining the Variance column, the highest value is 0.413, which occurs at 50%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3589.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Performance (accuracy) after per-fold unsupervised rejection on PHEME dataset for all types of uncertainty. LCS – Least Confidence Sampling; MC – Margin of Confidence, RC – Ratio of Confidence and E – Entropy based on a single output of a softmax layer (as opposed to Entropy, Variance and Variation ratio that are based on multiple softmax samples)." ], "reasoning": "By examining the Variance column, the highest value is 0.413, which occurs at 50%.", "statement": "The highest value for Variance occurs at 50%.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 10: Performance (accuracy) after per-fold unsupervised rejection on Twitter 15 dataset for all types of uncertainty. LCS – Least Confidence Sampling; MC – Margin of Confidence, RC – Ratio of Confidence and E – Entropy based on a single output of a softmax layer (as opposed to Entropy, Variance and Variation ratio that are based on multiple softmax samples).'.\n" }, { "role": "assistant", "content": "The evaluation and unsupervised rejection can be performed either after aggregating predictions from each testing fold or on each fold before aggregation, with notable differences in results for PHEME.As we have explained in the experimental setup section of the main paper, during the cross-validation iterations each fold becomes a testing set once. We first aggregate predictions from each testing fold, and then perform evaluation and unsupervised rejection on the complete dataset. Alternatively, we could first perform the rejection procedure on each fold and then either aggregate the instances together for the evaluation (see This also shows in the difference between the corresponding tables of the two set ups discussed in this section, which is more notable for PHEME" } ], "images": [ "scigen/train/3590.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Performance (accuracy) after per-fold unsupervised rejection on Twitter 15 dataset for all types of uncertainty. LCS – Least Confidence Sampling; MC – Margin of Confidence, RC – Ratio of Confidence and E – Entropy based on a single output of a softmax layer (as opposed to Entropy, Variance and Variation ratio that are based on multiple softmax samples)." ], "reasoning": "The evaluation and unsupervised rejection can be performed either after aggregating predictions from each testing fold or on each fold before aggregation, with notable differences in results for PHEME.", "description": "As we have explained in the experimental setup section of the main paper, during the cross-validation iterations each fold becomes a testing set once. We first aggregate predictions from each testing fold, and then perform evaluation and unsupervised rejection on the complete dataset. Alternatively, we could first perform the rejection procedure on each fold and then either aggregate the instances together for the evaluation (see This also shows in the difference between the corresponding tables of the two set ups discussed in this section, which is more notable for PHEME", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 10: Performance (accuracy) after per-fold unsupervised rejection on Twitter 15 dataset for all types of uncertainty. LCS – Least Confidence Sampling; MC – Margin of Confidence, RC – Ratio of Confidence and E – Entropy based on a single output of a softmax layer (as opposed to Entropy, Variance and Variation ratio that are based on multiple softmax samples).'. Solve the following question based on the table.\n\nQuestion: What is the highest variance value in the table?\nLet's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest variance value, we need to look at the 'Variance' column and identify the maximum value. The highest value in the 'Variance' column is 0.756. So, the answer is {\"answer\": \"0.756\"}" } ], "images": [ "scigen/train/3590.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Performance (accuracy) after per-fold unsupervised rejection on Twitter 15 dataset for all types of uncertainty. LCS – Least Confidence Sampling; MC – Margin of Confidence, RC – Ratio of Confidence and E – Entropy based on a single output of a softmax layer (as opposed to Entropy, Variance and Variation ratio that are based on multiple softmax samples)." ], "reasoning": "To find the highest variance value, we need to look at the 'Variance' column and identify the maximum value. The highest value in the 'Variance' column is 0.756.", "question": "What is the highest variance value in the table?", "answer": "So, the answer is {\"answer\": \"0.756\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The variance value for 50% is higher than for 60%. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe variance value for 50% is higher than for 60%.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the variance values for 50% and 60%. The variance value for 50% is 0.756, and for 60% it is 0.724. Since 0.756 is higher than 0.724, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3590.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Performance (accuracy) after per-fold unsupervised rejection on Twitter 15 dataset for all types of uncertainty. LCS – Least Confidence Sampling; MC – Margin of Confidence, RC – Ratio of Confidence and E – Entropy based on a single output of a softmax layer (as opposed to Entropy, Variance and Variation ratio that are based on multiple softmax samples)." ], "reasoning": "To verify this statement, we compare the variance values for 50% and 60%. The variance value for 50% is 0.756, and for 60% it is 0.724. Since 0.756 is higher than 0.724, the statement is supported.", "statement": "The variance value for 50% is higher than for 60%.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 11: Performance (accuracy) after per-fold unsupervised rejection on Twitter 15 dataset for all types of uncertainty. LCS – Least Confidence Sampling; MC – Margin of Confidence, RC – Ratio of Confidence and E – Entropy based on a single output of a softmax layer (as opposed to Entropy, Variance and Variation ratio that are based on multiple softmax samples).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The evaluation and rejection procedures can be performed either before or after aggregating predictions from each testing fold, with notable differences in results for PHEME.As we have explained in the experimental setup section of the main paper, during the cross-validation iterations each fold becomes a testing set once. We first aggregate predictions from each testing fold, and then perform evaluation and unsupervised rejection on the complete dataset. Alternatively, we could first perform the rejection procedure on each fold and then either aggregate the instances together for the evaluation (see This also shows in the difference between the corresponding tables of the two set ups discussed in this section, which is more notable for PHEME" } ], "images": [ "scigen/train/3591.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Performance (accuracy) after per-fold unsupervised rejection on Twitter 15 dataset for all types of uncertainty. LCS – Least Confidence Sampling; MC – Margin of Confidence, RC – Ratio of Confidence and E – Entropy based on a single output of a softmax layer (as opposed to Entropy, Variance and Variation ratio that are based on multiple softmax samples)." ], "reasoning": "The evaluation and rejection procedures can be performed either before or after aggregating predictions from each testing fold, with notable differences in results for PHEME.", "description": "As we have explained in the experimental setup section of the main paper, during the cross-validation iterations each fold becomes a testing set once. We first aggregate predictions from each testing fold, and then perform evaluation and unsupervised rejection on the complete dataset. Alternatively, we could first perform the rejection procedure on each fold and then either aggregate the instances together for the evaluation (see This also shows in the difference between the corresponding tables of the two set ups discussed in this section, which is more notable for PHEME", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 11: Performance (accuracy) after per-fold unsupervised rejection on Twitter 15 dataset for all types of uncertainty. LCS – Least Confidence Sampling; MC – Margin of Confidence, RC – Ratio of Confidence and E – Entropy based on a single output of a softmax layer (as opposed to Entropy, Variance and Variation ratio that are based on multiple softmax samples).\nQuestion: At which percentage does the Entropy value reach its maximum?\nSolve the above question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By examining the Entropy column, the highest value is found at the 50% row. So, the answer is {\"answer\": \"50%\"}" } ], "images": [ "scigen/train/3591.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Performance (accuracy) after per-fold unsupervised rejection on Twitter 15 dataset for all types of uncertainty. LCS – Least Confidence Sampling; MC – Margin of Confidence, RC – Ratio of Confidence and E – Entropy based on a single output of a softmax layer (as opposed to Entropy, Variance and Variation ratio that are based on multiple softmax samples)." ], "reasoning": "By examining the Entropy column, the highest value is found at the 50% row.", "question": "At which percentage does the Entropy value reach its maximum?", "answer": "So, the answer is {\"answer\": \"50%\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Variation ratio at 60% is higher than at 70%. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Variation ratio at 60% is higher than at 70%.\n" }, { "role": "assistant", "content": "By comparing the Variation ratio values, 60% has a value of 0.928, which is lower than the 70% value of 0.898. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3591.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Performance (accuracy) after per-fold unsupervised rejection on Twitter 15 dataset for all types of uncertainty. LCS – Least Confidence Sampling; MC – Margin of Confidence, RC – Ratio of Confidence and E – Entropy based on a single output of a softmax layer (as opposed to Entropy, Variance and Variation ratio that are based on multiple softmax samples)." ], "reasoning": "By comparing the Variation ratio values, 60% has a value of 0.928, which is lower than the 70% value of 0.898.", "statement": "The Variation ratio at 60% is higher than at 70%.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: WERs for Wall Street Journal. The ReLU DNN requires no pretraining and matches the WER of the Kaldi s5 recipe which uses DBN pretraining.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The ReLU DNN, which does not require pretraining, achieved a WER of 3.79, matching the pretrained Sigmoid DNN, suggesting that pretraining may not be necessary with sufficient supervised data.We also built a ReLU DNN which requires no pretraining. The ReLU DNN consisted of 4 layers of 2048 ReLU neurons followed by softmax and trained with geometrically decayed SGD. We also experimented with deeper and wider networks, however we found this 5 layer architecture to be the best. Our ReLU DNN is much easier to train (e.g., no expensive pretraining) and achieves a WER of 3.79 matching the WER of the pretrained Sigmoid DNN. The ReLU DNN results suggest that pretraining may not be necessary given sufficient supervised data and is competitive for the acoustic modelling task." } ], "images": [ "scigen/train/3592.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: WERs for Wall Street Journal. The ReLU DNN requires no pretraining and matches the WER of the Kaldi s5 recipe which uses DBN pretraining." ], "reasoning": "The ReLU DNN, which does not require pretraining, achieved a WER of 3.79, matching the pretrained Sigmoid DNN, suggesting that pretraining may not be necessary with sufficient supervised data.", "description": "We also built a ReLU DNN which requires no pretraining. The ReLU DNN consisted of 4 layers of 2048 ReLU neurons followed by softmax and trained with geometrically decayed SGD. We also experimented with deeper and wider networks, however we found this 5 layer architecture to be the best. Our ReLU DNN is much easier to train (e.g., no expensive pretraining) and achieves a WER of 3.79 matching the WER of the pretrained Sigmoid DNN. The ReLU DNN results suggest that pretraining may not be necessary given sufficient supervised data and is competitive for the acoustic modelling task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: WERs for Wall Street Journal. The ReLU DNN requires no pretraining and matches the WER of the Kaldi s5 recipe which uses DBN pretraining.', answer the question below by showing each progressive step toward the answer. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model achieved the lowest WER on the eval92 dataset?\n" }, { "role": "assistant", "content": "By comparing the WER values for the eval92 dataset in the table, the DNN ReLU model achieved a WER of 3.79, which is the lowest among the listed models. So, the answer is {\"answer\": \"DNN ReLU\"}." } ], "images": [ "scigen/train/3592.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: WERs for Wall Street Journal. The ReLU DNN requires no pretraining and matches the WER of the Kaldi s5 recipe which uses DBN pretraining." ], "reasoning": "By comparing the WER values for the eval92 dataset in the table, the DNN ReLU model achieved a WER of 3.79, which is the lowest among the listed models.", "question": "Which model achieved the lowest WER on the eval92 dataset?", "answer": "So, the answer is {\"answer\": \"DNN ReLU\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe GMM Kaldi tri4b model achieved a lower WER on the dev93 dataset compared to the DNN Kaldi s5 model.\n" }, { "role": "assistant", "content": "The table shows that the GMM Kaldi tri4b model has a WER of 9.39 on the dev93 dataset, while the DNN Kaldi s5 model has a WER of 6.68. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3592.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: WERs for Wall Street Journal. The ReLU DNN requires no pretraining and matches the WER of the Kaldi s5 recipe which uses DBN pretraining." ], "reasoning": "The table shows that the GMM Kaldi tri4b model has a WER of 9.39 on the dev93 dataset, while the DNN Kaldi s5 model has a WER of 6.68. Therefore, the statement is incorrect.", "statement": "The GMM Kaldi tri4b model achieved a lower WER on the dev93 dataset compared to the DNN Kaldi s5 model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: BLSTM WERs for Wall Street Journal. Larger recurrent models tend to perform better without overfitting. The deep BLSTM models do not yield any substantial gains over their single layer counterparts.'.\n" }, { "role": "assistant", "content": "The deep BLSTM model with two layers and 512 bidirectional cells per layer achieved a WER of 4.25, while the single layer BLSTM model with 1024 bidirectional cells achieved a WER of 4.16.Our deep BLSTM models contain two layers, the cell size reported is per direction per layer (e.g., total cells are quadrupled). Our deep BLSTM experiments give mixed results. For the same number of cells per layer, the deep model performs slightly better. However, if we fixed the number of parameters, the single layer BLSTM model performs slightly better, the single layer of 1024 bidirectional cells achieved a WER of 4.16 while the deep two layer BLSTM model with 512 bidirectional cells per layer achieved a WER 4.25." } ], "images": [ "scigen/train/3593.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: BLSTM WERs for Wall Street Journal. Larger recurrent models tend to perform better without overfitting. The deep BLSTM models do not yield any substantial gains over their single layer counterparts." ], "reasoning": "The deep BLSTM model with two layers and 512 bidirectional cells per layer achieved a WER of 4.25, while the single layer BLSTM model with 1024 bidirectional cells achieved a WER of 4.16.", "description": "Our deep BLSTM models contain two layers, the cell size reported is per direction per layer (e.g., total cells are quadrupled). Our deep BLSTM experiments give mixed results. For the same number of cells per layer, the deep model performs slightly better. However, if we fixed the number of parameters, the single layer BLSTM model performs slightly better, the single layer of 1024 bidirectional cells achieved a WER of 4.16 while the deep two layer BLSTM model with 512 bidirectional cells per layer achieved a WER 4.25.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 2: BLSTM WERs for Wall Street Journal. Larger recurrent models tend to perform better without overfitting. The deep BLSTM models do not yield any substantial gains over their single layer counterparts.\nQuestion: What is the WER for the single layer BLSTM model with 768 bidirectional cells on eval92?\n" }, { "role": "assistant", "content": "The table shows the WER for different cell sizes and layers. For a single layer BLSTM model with 768 bidirectional cells, the WER on eval92 is 4.16. So, the answer is {\"answer\": \"4.16\"}." } ], "images": [ "scigen/train/3593.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: BLSTM WERs for Wall Street Journal. Larger recurrent models tend to perform better without overfitting. The deep BLSTM models do not yield any substantial gains over their single layer counterparts." ], "reasoning": "The table shows the WER for different cell sizes and layers. For a single layer BLSTM model with 768 bidirectional cells, the WER on eval92 is 4.16.", "question": "What is the WER for the single layer BLSTM model with 768 bidirectional cells on eval92?", "answer": "So, the answer is {\"answer\": \"4.16\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The single layer BLSTM model with 512 bidirectional cells achieved a WER of 4.36 on eval92., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe single layer BLSTM model with 512 bidirectional cells achieved a WER of 4.36 on eval92.\n" }, { "role": "assistant", "content": "The table shows that the single layer BLSTM model with 512 bidirectional cells achieved a WER of 4.36 on eval92. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3593.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: BLSTM WERs for Wall Street Journal. Larger recurrent models tend to perform better without overfitting. The deep BLSTM models do not yield any substantial gains over their single layer counterparts." ], "reasoning": "The table shows that the single layer BLSTM model with 512 bidirectional cells achieved a WER of 4.36 on eval92.", "statement": "The single layer BLSTM model with 512 bidirectional cells achieved a WER of 4.36 on eval92.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Ablation effects of our TC-DNN-BLSTM-DNN model. The DNNs and Time Convolution are used for signal and context projections. We show that all components are critical to obtain the best performing model.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The TC-DNN-BLSTM-DNN model improves WER from 3.76 to 3.47 by adding time convolution, achieving a 0.32 absolute WER reduction or 8% relative improvement.Finally, our TC-DNN-BLSTM-DNN model combines the DNN-BLSTM-DNN with input time convolution. Our model further improves from 3.76 WER without time convolution to 3.47 WER with time convolution. Compared to the DNN models, we achieve 0.32 absolute WER reduction or 8% relatively. We hypothesize the time convolution gives a richer signal representation to the DNN signal processor and consequently the BLSTM model to consume. The time convolution also relieves the LSTM computation power to learning long term dependencies, rather than short term dependencies." } ], "images": [ "scigen/train/3594.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Ablation effects of our TC-DNN-BLSTM-DNN model. The DNNs and Time Convolution are used for signal and context projections. We show that all components are critical to obtain the best performing model." ], "reasoning": "The TC-DNN-BLSTM-DNN model improves WER from 3.76 to 3.47 by adding time convolution, achieving a 0.32 absolute WER reduction or 8% relative improvement.", "description": "Finally, our TC-DNN-BLSTM-DNN model combines the DNN-BLSTM-DNN with input time convolution. Our model further improves from 3.76 WER without time convolution to 3.47 WER with time convolution. Compared to the DNN models, we achieve 0.32 absolute WER reduction or 8% relatively. We hypothesize the time convolution gives a richer signal representation to the DNN signal processor and consequently the BLSTM model to consume. The time convolution also relieves the LSTM computation power to learning long term dependencies, rather than short term dependencies.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: Ablation effects of our TC-DNN-BLSTM-DNN model. The DNNs and Time Convolution are used for signal and context projections. We show that all components are critical to obtain the best performing model.', and sequentially solve the question, writing out each step of your reasoning process. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the WER improvement on eval92 when time convolution is added to the DNN-BLSTM-DNN model?\n" }, { "role": "assistant", "content": "The WER for the DNN-BLSTM-DNN model on eval92 is 3.76, and the WER for the TC-DNN-BLSTM-DNN model on eval92 is 3.47. The improvement is 3.76 - 3.47. So, the answer is {\"answer\": \"0.29\"}" } ], "images": [ "scigen/train/3594.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Ablation effects of our TC-DNN-BLSTM-DNN model. The DNNs and Time Convolution are used for signal and context projections. We show that all components are critical to obtain the best performing model." ], "reasoning": "The WER for the DNN-BLSTM-DNN model on eval92 is 3.76, and the WER for the TC-DNN-BLSTM-DNN model on eval92 is 3.47. The improvement is 3.76 - 3.47.", "question": "What is the WER improvement on eval92 when time convolution is added to the DNN-BLSTM-DNN model?", "answer": "So, the answer is {\"answer\": \"0.29\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe TC-DNN-BLSTM-DNN model has the lowest WER on eval92.\n" }, { "role": "assistant", "content": "The table shows that the TC-DNN-BLSTM-DNN model has a WER of 3.47 on eval92, which is lower than the WERs of the other models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3594.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Ablation effects of our TC-DNN-BLSTM-DNN model. The DNNs and Time Convolution are used for signal and context projections. We show that all components are critical to obtain the best performing model." ], "reasoning": "The table shows that the TC-DNN-BLSTM-DNN model has a WER of 3.47 on eval92, which is lower than the WERs of the other models listed.", "statement": "The TC-DNN-BLSTM-DNN model has the lowest WER on eval92.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: Effects of distributed optimization for our TC-DNN-BLSTM-DNN model. The ASGD experiments uses 3 independent SGD shards.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The ASGD model converges faster than the SGD model, taking fewer epochs and less time, but results in a slightly worse test WER.Our baseline TC-DNN-BLSTM-DNN SGD system took 17 epochs or 51 wall clock hours to converge to a dev and test WER of 6.58 and 3.47. Our distributed implementation converges in 14 epochs and 16.8 wall clock hours, achieves a dev and test WER of 6.57 and 3.72. The distributed optimization is able to match the dev WER, however the test WER is significantly worse. It is unclear whether this WER differential is due to the asynchronicity characteristic of the optimizer or due to the small datasets, we suspect with larger datasets the gap between the ASGD and SGD will shrink. The conclusion we draw is that ASGD can converge much quicker and faster, however there may be a impact to final WER performance." } ], "images": [ "scigen/train/3595.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Effects of distributed optimization for our TC-DNN-BLSTM-DNN model. The ASGD experiments uses 3 independent SGD shards." ], "reasoning": "The ASGD model converges faster than the SGD model, taking fewer epochs and less time, but results in a slightly worse test WER.", "description": "Our baseline TC-DNN-BLSTM-DNN SGD system took 17 epochs or 51 wall clock hours to converge to a dev and test WER of 6.58 and 3.47. Our distributed implementation converges in 14 epochs and 16.8 wall clock hours, achieves a dev and test WER of 6.57 and 3.72. The distributed optimization is able to match the dev WER, however the test WER is significantly worse. It is unclear whether this WER differential is due to the asynchronicity characteristic of the optimizer or due to the small datasets, we suspect with larger datasets the gap between the ASGD and SGD will shrink. The conclusion we draw is that ASGD can converge much quicker and faster, however there may be a impact to final WER performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Effects of distributed optimization for our TC-DNN-BLSTM-DNN model. The ASGD experiments uses 3 independent SGD shards.', solve the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nHow much faster did the ASGD model converge compared to the SGD model in terms of wall clock hours?\n" }, { "role": "assistant", "content": "The SGD model took 51.5 hours to converge, while the ASGD model took 16.8 hours. The difference in convergence time is 51.5 - 16.8. So, the answer is {\"answer\": \"34.7\"}" } ], "images": [ "scigen/train/3595.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Effects of distributed optimization for our TC-DNN-BLSTM-DNN model. The ASGD experiments uses 3 independent SGD shards." ], "reasoning": "The SGD model took 51.5 hours to converge, while the ASGD model took 16.8 hours. The difference in convergence time is 51.5 - 16.8.", "question": "How much faster did the ASGD model converge compared to the SGD model in terms of wall clock hours?", "answer": "So, the answer is {\"answer\": \"34.7\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The ASGD model achieved a better dev WER than the SGD model., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe ASGD model achieved a better dev WER than the SGD model.\n" }, { "role": "assistant", "content": "The table shows that the dev WER for the ASGD model is 6.57, while for the SGD model it is 6.58. Since 6.57 is lower than 6.58, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3595.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Effects of distributed optimization for our TC-DNN-BLSTM-DNN model. The ASGD experiments uses 3 independent SGD shards." ], "reasoning": "The table shows that the dev WER for the ASGD model is 6.57, while for the SGD model it is 6.58. Since 6.57 is lower than 6.58, the statement is supported.", "statement": "The ASGD model achieved a better dev WER than the SGD model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: Effect of decoding hyperparameters (bi-directional).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Base2 achieved a lower CER than Base1, and Prop further improved over Base2, obtaining the best CER regardless of the decoder type.Base2 obtained a lower CER than Base1. This indicates the effectiveness of the data-driven VAD method. Prop achieved further improvement over Base2. CTC is a mapping from acoustic features to characters whereas the standard data-driven VAD is a mapping from acoustic features to a simple binary symbol indicating speech or non-speech. The performance gain may be because the proposed method was able to detect speech considering linguistic information provided by CTC. Prop obtained the best CER regardless of the decoder type. In the following experiments, the joint decoding using the CTC prefix score was used." } ], "images": [ "scigen/train/3596.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Effect of decoding hyperparameters (bi-directional)." ], "reasoning": "Base2 achieved a lower CER than Base1, and Prop further improved over Base2, obtaining the best CER regardless of the decoder type.", "description": "Base2 obtained a lower CER than Base1. This indicates the effectiveness of the data-driven VAD method. Prop achieved further improvement over Base2. CTC is a mapping from acoustic features to characters whereas the standard data-driven VAD is a mapping from acoustic features to a simple binary symbol indicating speech or non-speech. The performance gain may be because the proposed method was able to detect speech considering linguistic information provided by CTC. Prop obtained the best CER regardless of the decoder type. In the following experiments, the joint decoding using the CTC prefix score was used.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: Effect of decoding hyperparameters (bi-directional).'.\n\nQuestion: What is the CER for the Prop method on the test set?\n" }, { "role": "assistant", "content": "The description states that Prop achieved the best CER regardless of the decoder type, and the table shows the lowest test CER value is 15.4%. So, the answer is {\"answer\": \"15.4%\"}" } ], "images": [ "scigen/train/3596.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Effect of decoding hyperparameters (bi-directional)." ], "reasoning": "The description states that Prop achieved the best CER regardless of the decoder type, and the table shows the lowest test CER value is 15.4%.", "question": "What is the CER for the Prop method on the test set?", "answer": "So, the answer is {\"answer\": \"15.4%\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Base2 achieved a CER of 16.6% on the test set., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nBase2 achieved a CER of 16.6% on the test set.\n" }, { "role": "assistant", "content": "The table shows that the CER for the second row, which corresponds to Base2, is 16.6% on the test set. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3596.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Effect of decoding hyperparameters (bi-directional)." ], "reasoning": "The table shows that the CER for the second row, which corresponds to Base2, is 16.6% on the test set.", "statement": "Base2 achieved a CER of 16.6% on the test set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Effect of value of offset margin.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The onset and offset margins are crucial for correct recognition, and a medium threshold value like V=16 provides reasonable recognition performance.The initial experiment only used the CTC decoding instead of the joint CTC/attention decoding. It was varied from 0 to 3 while minimum blank duration threshold V=16 and offset margin me=2. It can be said from the table that the onset margin is important to achieve correct recognition results. The value of me was also varied from 0 to 3 while V=16 and ms=2. The performance difference between with and without the offset margin, me>0 and me=0, was significantly large. These two margins seem to buffer the time-lag of the CTC softmax output. To investigate the sensitivity to the change of the minimum blank duration threshold, the value of V was varied with the fixed onset and offset margins (ms=2 and me=2). From the table, a low CER can be obtained by selecting a large threshold value, as expected. However, reasonable recognition performance could be achieved by a medium threshold value, e.g., V=16. This means that 640 (=16×4×10) ms was used as a threshold value. The duration seems to be reasonable for detecting short pause, indicating that the proposed method can intuitively tune VAD parameters in contrast to the conventional VAD methods requiring non-intuitive parameters such as an energy threshold." } ], "images": [ "scigen/train/3597.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Effect of value of offset margin." ], "reasoning": "The onset and offset margins are crucial for correct recognition, and a medium threshold value like V=16 provides reasonable recognition performance.", "description": "The initial experiment only used the CTC decoding instead of the joint CTC/attention decoding. It was varied from 0 to 3 while minimum blank duration threshold V=16 and offset margin me=2. It can be said from the table that the onset margin is important to achieve correct recognition results. The value of me was also varied from 0 to 3 while V=16 and ms=2. The performance difference between with and without the offset margin, me>0 and me=0, was significantly large. These two margins seem to buffer the time-lag of the CTC softmax output. To investigate the sensitivity to the change of the minimum blank duration threshold, the value of V was varied with the fixed onset and offset margins (ms=2 and me=2). From the table, a low CER can be obtained by selecting a large threshold value, as expected. However, reasonable recognition performance could be achieved by a medium threshold value, e.g., V=16. This means that 640 (=16×4×10) ms was used as a threshold value. The duration seems to be reasonable for detecting short pause, indicating that the proposed method can intuitively tune VAD parameters in contrast to the conventional VAD methods requiring non-intuitive parameters such as an energy threshold.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Effect of value of offset margin.', solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the average CER when ms=2?\n" }, { "role": "assistant", "content": "From the table, when ms=2, the average CER is 8.4%. So, the answer is {\"answer\": \"8.4\"}." } ], "images": [ "scigen/train/3597.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Effect of value of offset margin." ], "reasoning": "From the table, when ms=2, the average CER is 8.4%.", "question": "What is the average CER when ms=2?", "answer": "So, the answer is {\"answer\": \"8.4\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The average CER is lowest when ms=2., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe average CER is lowest when ms=2.\n" }, { "role": "assistant", "content": "From the table, the average CER is 8.7% for ms=0, 8.4% for ms=1, 8.4% for ms=2, and 8.4% for ms=3. The lowest average CER is 8.4%, which occurs at ms=1, ms=2, and ms=3. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3597.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Effect of value of offset margin." ], "reasoning": "From the table, the average CER is 8.7% for ms=0, 8.4% for ms=1, 8.4% for ms=2, and 8.4% for ms=3. The lowest average CER is 8.4%, which occurs at ms=1, ms=2, and ms=3.", "statement": "The average CER is lowest when ms=2.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Effect of value of offset margin.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The onset and offset margins significantly impact recognition performance, with a medium threshold value like V=16 providing reasonable results.The initial experiment only used the CTC decoding instead of the joint CTC/attention decoding. It was varied from 0 to 3 while minimum blank duration threshold V=16 and offset margin me=2. It can be said from the table that the onset margin is important to achieve correct recognition results. The value of me was also varied from 0 to 3 while V=16 and ms=2. The performance difference between with and without the offset margin, me>0 and me=0, was significantly large. These two margins seem to buffer the time-lag of the CTC softmax output. To investigate the sensitivity to the change of the minimum blank duration threshold, the value of V was varied with the fixed onset and offset margins (ms=2 and me=2). From the table, a low CER can be obtained by selecting a large threshold value, as expected. However, reasonable recognition performance could be achieved by a medium threshold value, e.g., V=16. This means that 640 (=16×4×10) ms was used as a threshold value. The duration seems to be reasonable for detecting short pause, indicating that the proposed method can intuitively tune VAD parameters in contrast to the conventional VAD methods requiring non-intuitive parameters such as an energy threshold." } ], "images": [ "scigen/train/3598.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Effect of value of offset margin." ], "reasoning": "The onset and offset margins significantly impact recognition performance, with a medium threshold value like V=16 providing reasonable results.", "description": "The initial experiment only used the CTC decoding instead of the joint CTC/attention decoding. It was varied from 0 to 3 while minimum blank duration threshold V=16 and offset margin me=2. It can be said from the table that the onset margin is important to achieve correct recognition results. The value of me was also varied from 0 to 3 while V=16 and ms=2. The performance difference between with and without the offset margin, me>0 and me=0, was significantly large. These two margins seem to buffer the time-lag of the CTC softmax output. To investigate the sensitivity to the change of the minimum blank duration threshold, the value of V was varied with the fixed onset and offset margins (ms=2 and me=2). From the table, a low CER can be obtained by selecting a large threshold value, as expected. However, reasonable recognition performance could be achieved by a medium threshold value, e.g., V=16. This means that 640 (=16×4×10) ms was used as a threshold value. The duration seems to be reasonable for detecting short pause, indicating that the proposed method can intuitively tune VAD parameters in contrast to the conventional VAD methods requiring non-intuitive parameters such as an energy threshold.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Effect of value of offset margin.', solve the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the average recognition performance when the offset margin me is 3?\n" }, { "role": "assistant", "content": "The table shows the average recognition performance for different values of the offset margin me. When me is 3, the average performance is 8.3%. So, the answer is {\"answer\": \"8.3\"}." } ], "images": [ "scigen/train/3598.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Effect of value of offset margin." ], "reasoning": "The table shows the average recognition performance for different values of the offset margin me. When me is 3, the average performance is 8.3%.", "question": "What is the average recognition performance when the offset margin me is 3?", "answer": "So, the answer is {\"answer\": \"8.3\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The best average recognition performance is achieved when the offset margin me is 3., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe best average recognition performance is achieved when the offset margin me is 3.\n" }, { "role": "assistant", "content": "The table shows the average recognition performance for different values of the offset margin me. The lowest average percentage, indicating the best performance, is 8.3% when me is 3. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3598.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Effect of value of offset margin." ], "reasoning": "The table shows the average recognition performance for different values of the offset margin me. The lowest average percentage, indicating the best performance, is 8.3% when me is 3.", "statement": "The best average recognition performance is achieved when the offset margin me is 3.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Effect of value of offset margin.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The table shows that a low CER can be achieved with a large threshold value, with the best performance at V=24.The initial experiment only used the CTC decoding instead of the joint CTC/attention decoding. It was varied from 0 to 3 while minimum blank duration threshold V=16 and offset margin me=2. It can be said from the table that the onset margin is important to achieve correct recognition results. The value of me was also varied from 0 to 3 while V=16 and ms=2. The performance difference between with and without the offset margin, me>0 and me=0, was significantly large. These two margins seem to buffer the time-lag of the CTC softmax output. To investigate the sensitivity to the change of the minimum blank duration threshold, the value of V was varied with the fixed onset and offset margins (ms=2 and me=2). From the table, a low CER can be obtained by selecting a large threshold value, as expected. However, reasonable recognition performance could be achieved by a medium threshold value, e.g., V=16. This means that 640 (=16×4×10) ms was used as a threshold value. The duration seems to be reasonable for detecting short pause, indicating that the proposed method can intuitively tune VAD parameters in contrast to the conventional VAD methods requiring non-intuitive parameters such as an energy threshold." } ], "images": [ "scigen/train/3599.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Effect of value of offset margin." ], "reasoning": "The table shows that a low CER can be achieved with a large threshold value, with the best performance at V=24.", "description": "The initial experiment only used the CTC decoding instead of the joint CTC/attention decoding. It was varied from 0 to 3 while minimum blank duration threshold V=16 and offset margin me=2. It can be said from the table that the onset margin is important to achieve correct recognition results. The value of me was also varied from 0 to 3 while V=16 and ms=2. The performance difference between with and without the offset margin, me>0 and me=0, was significantly large. These two margins seem to buffer the time-lag of the CTC softmax output. To investigate the sensitivity to the change of the minimum blank duration threshold, the value of V was varied with the fixed onset and offset margins (ms=2 and me=2). From the table, a low CER can be obtained by selecting a large threshold value, as expected. However, reasonable recognition performance could be achieved by a medium threshold value, e.g., V=16. This means that 640 (=16×4×10) ms was used as a threshold value. The duration seems to be reasonable for detecting short pause, indicating that the proposed method can intuitively tune VAD parameters in contrast to the conventional VAD methods requiring non-intuitive parameters such as an energy threshold.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: Table 2: Effect of value of offset margin.\nQuestion: What is the average CER when the minimum blank duration threshold V is set to 16?\n" }, { "role": "assistant", "content": "From the table, when V is set to 16, the average CER is 8.4%. So, the answer is {\"answer\": \"8.4\"}." } ], "images": [ "scigen/train/3599.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Effect of value of offset margin." ], "reasoning": "From the table, when V is set to 16, the average CER is 8.4%.", "question": "What is the average CER when the minimum blank duration threshold V is set to 16?", "answer": "So, the answer is {\"answer\": \"8.4\"}." }, { "messages": [ { "role": "user", "content": "The lowest average CER is achieved when the minimum blank duration threshold V is 24. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe lowest average CER is achieved when the minimum blank duration threshold V is 24.\n" }, { "role": "assistant", "content": "The table shows that the lowest average CER is 7.8%, which occurs when V is 24. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3599.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Effect of value of offset margin." ], "reasoning": "The table shows that the lowest average CER is 7.8%, which occurs when V is 24.", "statement": "The lowest average CER is achieved when the minimum blank duration threshold V is 24.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Effect of decoding hyperparameters (bi-directional).'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Base2 achieved a lower CER than Base1, indicating the effectiveness of the data-driven VAD method, while Prop achieved further improvement over Base2, obtaining the best CER regardless of the decoder type.Base2 obtained a lower CER than Base1. This indicates the effectiveness of the data-driven VAD method. Prop achieved further improvement over Base2. CTC is a mapping from acoustic features to characters whereas the standard data-driven VAD is a mapping from acoustic features to a simple binary symbol indicating speech or non-speech. The performance gain may be because the proposed method was able to detect speech considering linguistic information provided by CTC. Prop obtained the best CER regardless of the decoder type. In the following experiments, the joint decoding using the CTC prefix score was used." } ], "images": [ "scigen/train/3600.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Effect of decoding hyperparameters (bi-directional)." ], "reasoning": "Base2 achieved a lower CER than Base1, indicating the effectiveness of the data-driven VAD method, while Prop achieved further improvement over Base2, obtaining the best CER regardless of the decoder type.", "description": "Base2 obtained a lower CER than Base1. This indicates the effectiveness of the data-driven VAD method. Prop achieved further improvement over Base2. CTC is a mapping from acoustic features to characters whereas the standard data-driven VAD is a mapping from acoustic features to a simple binary symbol indicating speech or non-speech. The performance gain may be because the proposed method was able to detect speech considering linguistic information provided by CTC. Prop obtained the best CER regardless of the decoder type. In the following experiments, the joint decoding using the CTC prefix score was used.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich method obtained the best CER in the Joint decoder type?\nSolve the above question based on the table titled 'Table 5: Effect of decoding hyperparameters (bi-directional).'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the CER values for different methods and decoder types. The Joint decoder type has the lowest CER value for Oracle at 6.1%, followed by Prop at 7.6%. So, the answer is {\"answer\": \"Oracle\"}" } ], "images": [ "scigen/train/3600.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Effect of decoding hyperparameters (bi-directional)." ], "reasoning": "The table shows the CER values for different methods and decoder types. The Joint decoder type has the lowest CER value for Oracle at 6.1%, followed by Prop at 7.6%.", "question": "Which method obtained the best CER in the Joint decoder type?", "answer": "So, the answer is {\"answer\": \"Oracle\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nProp achieved the best CER in the Attention decoder type.\n" }, { "role": "assistant", "content": "The table shows that in the Attention decoder type, Oracle has the lowest CER at 7.7%, while Prop has a CER of 8.3%, which is higher than Oracle. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3600.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Effect of decoding hyperparameters (bi-directional)." ], "reasoning": "The table shows that in the Attention decoder type, Oracle has the lowest CER at 7.7%, while Prop has a CER of 8.3%, which is higher than Oracle.", "statement": "Prop achieved the best CER in the Attention decoder type.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5: Effect of decoding hyperparameters (bi-directional).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Base2 achieved a lower CER than Base1, and Prop further improved over Base2, obtaining the best CER regardless of the decoder type.Base2 obtained a lower CER than Base1. This indicates the effectiveness of the data-driven VAD method. Prop achieved further improvement over Base2. CTC is a mapping from acoustic features to characters whereas the standard data-driven VAD is a mapping from acoustic features to a simple binary symbol indicating speech or non-speech. The performance gain may be because the proposed method was able to detect speech considering linguistic information provided by CTC. Prop obtained the best CER regardless of the decoder type. In the following experiments, the joint decoding using the CTC prefix score was used." } ], "images": [ "scigen/train/3601.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Effect of decoding hyperparameters (bi-directional)." ], "reasoning": "Base2 achieved a lower CER than Base1, and Prop further improved over Base2, obtaining the best CER regardless of the decoder type.", "description": "Base2 obtained a lower CER than Base1. This indicates the effectiveness of the data-driven VAD method. Prop achieved further improvement over Base2. CTC is a mapping from acoustic features to characters whereas the standard data-driven VAD is a mapping from acoustic features to a simple binary symbol indicating speech or non-speech. The performance gain may be because the proposed method was able to detect speech considering linguistic information provided by CTC. Prop obtained the best CER regardless of the decoder type. In the following experiments, the joint decoding using the CTC prefix score was used.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 5: Effect of decoding hyperparameters (bi-directional).', solve the following question step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the CER for the Prop method on the test set?\n" }, { "role": "assistant", "content": "The description states that Prop achieved the best CER regardless of the decoder type, and the table shows the lowest test CER value. So, the answer is {\"answer\": \"18.9\"}." } ], "images": [ "scigen/train/3601.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Effect of decoding hyperparameters (bi-directional)." ], "reasoning": "The description states that Prop achieved the best CER regardless of the decoder type, and the table shows the lowest test CER value.", "question": "What is the CER for the Prop method on the test set?", "answer": "So, the answer is {\"answer\": \"18.9\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe CER for Base2 on the dev set is 16.0%.\n" }, { "role": "assistant", "content": "The description indicates that Base2 achieved a lower CER than Base1, and the table shows a CER of 16.0% for one of the methods. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3601.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Effect of decoding hyperparameters (bi-directional)." ], "reasoning": "The description indicates that Base2 achieved a lower CER than Base1, and the table shows a CER of 16.0% for one of the methods.", "statement": "The CER for Base2 on the dev set is 16.0%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 8: Performance comparison on TED-LIUM corpus (uni-directional).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The proposed method achieved the lowest RTF of 0.18 while maintaining competitive dev and test percentages.The proposed method again outperformed the baseline methods for both the cases, indicating the effectiveness of the proposed method." } ], "images": [ "scigen/train/3602.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Performance comparison on TED-LIUM corpus (uni-directional)." ], "reasoning": "The proposed method achieved the lowest RTF of 0.18 while maintaining competitive dev and test percentages.", "description": "The proposed method again outperformed the baseline methods for both the cases, indicating the effectiveness of the proposed method.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 8: Performance comparison on TED-LIUM corpus (uni-directional).'. Solve the following question based on the table.\n\nQuestion: What is the RTF value for the proposed method with both vectorization and GPU enabled?\nLet's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the proposed method with both vectorization and GPU enabled has an RTF value of 0.18. So, the answer is {\"answer\": \"0.18\"}." } ], "images": [ "scigen/train/3602.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Performance comparison on TED-LIUM corpus (uni-directional)." ], "reasoning": "The table shows that the proposed method with both vectorization and GPU enabled has an RTF value of 0.18.", "question": "What is the RTF value for the proposed method with both vectorization and GPU enabled?", "answer": "So, the answer is {\"answer\": \"0.18\"}." }, { "messages": [ { "role": "user", "content": "The proposed method with both vectorization and GPU enabled has the highest test percentage. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe proposed method with both vectorization and GPU enabled has the highest test percentage.\n" }, { "role": "assistant", "content": "The table shows that the test percentage for the proposed method with both vectorization and GPU enabled is 19.0%, which is the highest among all methods. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3602.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Performance comparison on TED-LIUM corpus (uni-directional)." ], "reasoning": "The table shows that the test percentage for the proposed method with both vectorization and GPU enabled is 19.0%, which is the highest among all methods.", "statement": "The proposed method with both vectorization and GPU enabled has the highest test percentage.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 8: F1-score with different implementations of the sequence modeling layer. ExSoftword is the shorthand of Char-based+bichar+ExSoftword.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The LSTM-based architecture outperformed the CNN- and transformer-based architectures, and our methods consistently outperformed their corresponding ExSoftword baselines.From the table, we can first see that the LSTM-based architecture performed better than the CNN- and transformer- based architectures. In addition, our methods with different sequence modeling layers consistently outperformed their corresponding ExSoftword baselines. This shows that our method is applicable to different neural sequence modeling architectures for exploiting lexicon information." } ], "images": [ "scigen/train/3603.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: F1-score with different implementations of the sequence modeling layer. ExSoftword is the shorthand of Char-based+bichar+ExSoftword." ], "reasoning": "The LSTM-based architecture outperformed the CNN- and transformer-based architectures, and our methods consistently outperformed their corresponding ExSoftword baselines.", "description": "From the table, we can first see that the LSTM-based architecture performed better than the CNN- and transformer- based architectures. In addition, our methods with different sequence modeling layers consistently outperformed their corresponding ExSoftword baselines. This shows that our method is applicable to different neural sequence modeling architectures for exploiting lexicon information.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 8: F1-score with different implementations of the sequence modeling layer. ExSoftword is the shorthand of Char-based+bichar+ExSoftword.\n\nQuestion: Which model had the highest performance on the Resume dataset?\n" }, { "role": "assistant", "content": "By comparing the scores in the Resume column, the proposed (CNN) model had the highest score of 95.02. So, the answer is {\"answer\": \"proposed (CNN)\"}." } ], "images": [ "scigen/train/3603.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: F1-score with different implementations of the sequence modeling layer. ExSoftword is the shorthand of Char-based+bichar+ExSoftword." ], "reasoning": "By comparing the scores in the Resume column, the proposed (CNN) model had the highest score of 95.02.", "question": "Which model had the highest performance on the Resume dataset?", "answer": "So, the answer is {\"answer\": \"proposed (CNN)\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe proposed (Transformer) model outperformed the ExSoftword (CNN) model on the MSRA dataset.\n" }, { "role": "assistant", "content": "The proposed (Transformer) model scored 90.48 on the MSRA dataset, while the ExSoftword (CNN) model scored 90.02. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3603.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: F1-score with different implementations of the sequence modeling layer. ExSoftword is the shorthand of Char-based+bichar+ExSoftword." ], "reasoning": "The proposed (Transformer) model scored 90.48 on the MSRA dataset, while the ExSoftword (CNN) model scored 90.02. Therefore, the statement is supported.", "statement": "The proposed (Transformer) model outperformed the ExSoftword (CNN) model on the MSRA dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: F1-score of our method with different implementations of vs. MP denotes the mean-pooling algorithm depicted in Eq. (13), WP denotes the frequency weighted pooling algorithm depicted in Eq. (14), and SWP denotes the smoothed weighted pooling algorithm depicted in Eq. (15).', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The weighted pooling algorithm generally performs better than mean-pooling and smoothed weighted pooling algorithms across different datasets.This also means that we should map each word set into a fixed-dimensional vector. To achieve this purpose, we first tried the mean-pooling algorithm to get the vector representation of a word set S: vs(S)=1|S|∑w∈Sew(w). (13) Here, ew denotes the word embedding lookup table. Through the comparison with Lattice-LSTM, we find out that in Lattice-LSTM, it applies a dynamic attention algorithm to weigh each matched word related to a single character. Motivated by this practice, we propose to weighing the representation of each word in the word set to get the pooling representation of the word set. However, considering the computational efficiency, we do not want to apply a dynamical weighing algorithm, like attention, to get the weight of each word. With this in mind, we propose to using the frequency of the word as an indication of its weight. The basic idea beneath this algorithm is that the more times a character sequence occurs in the data, the more likely it is a word. Note that, the frequency of a word is a static value and can be obtained offline. This can greatly accelerate the calculation of the weight of each word (e.g., using a lookup table). From the table, we can see that the weighted pooling algorithm performs generally better than the other two implementations. Of course, we may obtain better results with the smoothed weighted pooling algorithm by reducing the value of c (when c=0, it is equivalent to the weighted pooling algorithm). We did not do so for two reasons. The first one is to guarantee the generality of our system for unexplored tasks. The second one is that the performance of the weighted pooling algorithm is good enough compared with other state-of-the-art baselines. Therefore, in the following experiments, we in default applied the weighted pooling algorithm to implement vs." } ], "images": [ "scigen/train/3604.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: F1-score of our method with different implementations of vs. MP denotes the mean-pooling algorithm depicted in Eq. (13), WP denotes the frequency weighted pooling algorithm depicted in Eq. (14), and SWP denotes the smoothed weighted pooling algorithm depicted in Eq. (15)." ], "reasoning": "The weighted pooling algorithm generally performs better than mean-pooling and smoothed weighted pooling algorithms across different datasets.", "description": "This also means that we should map each word set into a fixed-dimensional vector. To achieve this purpose, we first tried the mean-pooling algorithm to get the vector representation of a word set S: vs(S)=1|S|∑w∈Sew(w). (13) Here, ew denotes the word embedding lookup table. Through the comparison with Lattice-LSTM, we find out that in Lattice-LSTM, it applies a dynamic attention algorithm to weigh each matched word related to a single character. Motivated by this practice, we propose to weighing the representation of each word in the word set to get the pooling representation of the word set. However, considering the computational efficiency, we do not want to apply a dynamical weighing algorithm, like attention, to get the weight of each word. With this in mind, we propose to using the frequency of the word as an indication of its weight. The basic idea beneath this algorithm is that the more times a character sequence occurs in the data, the more likely it is a word. Note that, the frequency of a word is a static value and can be obtained offline. This can greatly accelerate the calculation of the weight of each word (e.g., using a lookup table). From the table, we can see that the weighted pooling algorithm performs generally better than the other two implementations. Of course, we may obtain better results with the smoothed weighted pooling algorithm by reducing the value of c (when c=0, it is equivalent to the weighted pooling algorithm). We did not do so for two reasons. The first one is to guarantee the generality of our system for unexplored tasks. The second one is that the performance of the weighted pooling algorithm is good enough compared with other state-of-the-art baselines. Therefore, in the following experiments, we in default applied the weighted pooling algorithm to implement vs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: F1-score of our method with different implementations of vs. MP denotes the mean-pooling algorithm depicted in Eq. (13), WP denotes the frequency weighted pooling algorithm depicted in Eq. (14), and SWP denotes the smoothed weighted pooling algorithm depicted in Eq. (15).'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich algorithm has the highest average performance across all datasets?\n" }, { "role": "assistant", "content": "By comparing the average performance values of MP, WP, and SWP, we see that WP has the highest average performance with a value of 0.8131. So, the answer is {\"answer\": \"WP\"}." } ], "images": [ "scigen/train/3604.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: F1-score of our method with different implementations of vs. MP denotes the mean-pooling algorithm depicted in Eq. (13), WP denotes the frequency weighted pooling algorithm depicted in Eq. (14), and SWP denotes the smoothed weighted pooling algorithm depicted in Eq. (15)." ], "reasoning": "By comparing the average performance values of MP, WP, and SWP, we see that WP has the highest average performance with a value of 0.8131.", "question": "Which algorithm has the highest average performance across all datasets?", "answer": "So, the answer is {\"answer\": \"WP\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe weighted pooling algorithm outperforms the mean-pooling algorithm on the Resume dataset.\n" }, { "role": "assistant", "content": "The table shows that the weighted pooling algorithm (WP) has a performance of 0.9559 on the Resume dataset, while the mean-pooling algorithm (MP) has a performance of 0.9533. Therefore, WP outperforms MP on the Resume dataset. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3604.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: F1-score of our method with different implementations of vs. MP denotes the mean-pooling algorithm depicted in Eq. (13), WP denotes the frequency weighted pooling algorithm depicted in Eq. (14), and SWP denotes the smoothed weighted pooling algorithm depicted in Eq. (15)." ], "reasoning": "The table shows that the weighted pooling algorithm (WP) has a performance of 0.9559 on the Resume dataset, while the mean-pooling algorithm (MP) has a performance of 0.9533. Therefore, WP outperforms MP on the Resume dataset.", "statement": "The weighted pooling algorithm outperforms the mean-pooling algorithm on the Resume dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Inference speed (average sentences per second, the larger the better) of our method with different implementations of the sequence modeling layer compared with Lattice-LSTM and LR-CNN.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our method with the CNN-based sequence modeling layer has the fastest inference speed compared to Lattice-LSTM, LR-CNN, and other sequence modeling layers.The speed was evaluated by average sentences per second using a GPU (NVIDIA TITAN X). For a fair comparison with Lattice-LSTM and LR-CNN, we set the batch size of our method to 1 at inference time. From the table, we can see that our method has a much faster inference speed than Lattice-LSTM when using the LSTM-based sequence modeling layer, and it was also much faster than LR-CNN, which used an CNN architecture to implement the sequence modeling layer. And as expected, our method with the CNN-based sequence modeling layer showed some advantage in inference speed than those with the LSTM-based and Transformer-based sequence model layer." } ], "images": [ "scigen/train/3605.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Inference speed (average sentences per second, the larger the better) of our method with different implementations of the sequence modeling layer compared with Lattice-LSTM and LR-CNN." ], "reasoning": "Our method with the CNN-based sequence modeling layer has the fastest inference speed compared to Lattice-LSTM, LR-CNN, and other sequence modeling layers.", "description": "The speed was evaluated by average sentences per second using a GPU (NVIDIA TITAN X). For a fair comparison with Lattice-LSTM and LR-CNN, we set the batch size of our method to 1 at inference time. From the table, we can see that our method has a much faster inference speed than Lattice-LSTM when using the LSTM-based sequence modeling layer, and it was also much faster than LR-CNN, which used an CNN architecture to implement the sequence modeling layer. And as expected, our method with the CNN-based sequence modeling layer showed some advantage in inference speed than those with the LSTM-based and Transformer-based sequence model layer.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: Inference speed (average sentences per second, the larger the better) of our method with different implementations of the sequence modeling layer compared with Lattice-LSTM and LR-CNN.', answer the following question. Give detailed solution consisting of each step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model has the highest average inference speed across all datasets?\n" }, { "role": "assistant", "content": "To find the model with the highest average inference speed, calculate the average speed for each model across all datasets. The proposed (CNN) model has the highest average speed. So, the answer is {\"answer\": \"proposed (CNN)\"}" } ], "images": [ "scigen/train/3605.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Inference speed (average sentences per second, the larger the better) of our method with different implementations of the sequence modeling layer compared with Lattice-LSTM and LR-CNN." ], "reasoning": "To find the model with the highest average inference speed, calculate the average speed for each model across all datasets. The proposed (CNN) model has the highest average speed.", "question": "Which model has the highest average inference speed across all datasets?", "answer": "So, the answer is {\"answer\": \"proposed (CNN)\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe proposed (Transformer) model has a faster inference speed than LR-CNN on the MSRA dataset.\n" }, { "role": "assistant", "content": "The table shows that the proposed (Transformer) model has an inference speed of 58.18 on the MSRA dataset, while LR-CNN has an inference speed of 23.20. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3605.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Inference speed (average sentences per second, the larger the better) of our method with different implementations of the sequence modeling layer compared with Lattice-LSTM and LR-CNN." ], "reasoning": "The table shows that the proposed (Transformer) model has an inference speed of 58.18 on the MSRA dataset, while LR-CNN has an inference speed of 23.20. Therefore, the statement is refuted.", "statement": "The proposed (Transformer) model has a faster inference speed than LR-CNN on the MSRA dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 5: Performance on MSRA.'.\n" }, { "role": "assistant", "content": "Our method significantly outperformed Lattice-LSTM and other baselines in recall and F1-score, demonstrating its effectiveness on the MSRA dataset.The word-based methods were built on the automatic segmentation results generated by the segmenter trained on training data of MSRA. Compared methods included the best statistical models on this data set, which leveraged rich handcrafted features Chen et al. ; Zhang et al. et al. Dong et al. From the table, we observe that our method obtained a statistically significant improvement over Lattice-LSTM and other comparative baselines on the recall and F1-score, verifying the effectiveness of our method on this data set." } ], "images": [ "scigen/train/3606.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Performance on MSRA." ], "reasoning": "Our method significantly outperformed Lattice-LSTM and other baselines in recall and F1-score, demonstrating its effectiveness on the MSRA dataset.", "description": "The word-based methods were built on the automatic segmentation results generated by the segmenter trained on training data of MSRA. Compared methods included the best statistical models on this data set, which leveraged rich handcrafted features Chen et al. ; Zhang et al. et al. Dong et al. From the table, we observe that our method obtained a statistically significant improvement over Lattice-LSTM and other comparative baselines on the recall and F1-score, verifying the effectiveness of our method on this data set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 5: Performance on MSRA.', and sequentially solve the question, writing out each step of your reasoning process. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieved the highest F1-score?\n" }, { "role": "assistant", "content": "By examining the F1-score column, we see that the LR-CNN Gui et al. proposed (LSTM) model has the highest F1-score of 93.71. So, the answer is {\"answer\": \"LR-CNN Gui et al. proposed (LSTM)\"}" } ], "images": [ "scigen/train/3606.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Performance on MSRA." ], "reasoning": "By examining the F1-score column, we see that the LR-CNN Gui et al. proposed (LSTM) model has the highest F1-score of 93.71.", "question": "Which model achieved the highest F1-score?", "answer": "So, the answer is {\"answer\": \"LR-CNN Gui et al. proposed (LSTM)\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Word-based (LSTM) model achieved a recall higher than 85.\n" }, { "role": "assistant", "content": "The recall (R) for the Word-based (LSTM) model is 83.06, which is less than 85. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3606.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Performance on MSRA." ], "reasoning": "The recall (R) for the Word-based (LSTM) model is 83.06, which is less than 85.", "statement": "The Word-based (LSTM) model achieved a recall higher than 85.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Perplexity and classification accuracy on the test set for the four word-based datasets.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The FactorCell model achieves the best perplexity across all datasets and the highest classification accuracy in three out of four datasets, tying with SoftmaxBias on AgNews.In each of the four datasets, the FactorCell model gives the best perplexity. For classification accuracy, there is a bigger difference between the models, and the FactorCell model is the most accurate on three out of four datasets and tied with the SoftmaxBias model on AgNews. For DBPedia and TripAdvisor, most of the improvement in perplexity relative to the unadapted case is achieved by the SoftmaxBias model with smaller relative improvements coming from the increased power of the ConcatCell and FactorCell models. For Yelp, the perplexity improvements are small; the FactorCell model is just 1.3% better than the unadapted model. The figure compares perplexity on the x-axis with accuracy on the y-axis with both metrics computed on the development set. Each point in this figure represents a different instance of the model trained with random hyperparameter settings and the best results are in the upper right corner of each plot. The color/shape differences of the points correspond to the three classes of models: FactorCell, ConcatCell, and SoftmaxBias." } ], "images": [ "scigen/train/3607.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Perplexity and classification accuracy on the test set for the four word-based datasets." ], "reasoning": "The FactorCell model achieves the best perplexity across all datasets and the highest classification accuracy in three out of four datasets, tying with SoftmaxBias on AgNews.", "description": "In each of the four datasets, the FactorCell model gives the best perplexity. For classification accuracy, there is a bigger difference between the models, and the FactorCell model is the most accurate on three out of four datasets and tied with the SoftmaxBias model on AgNews. For DBPedia and TripAdvisor, most of the improvement in perplexity relative to the unadapted case is achieved by the SoftmaxBias model with smaller relative improvements coming from the increased power of the ConcatCell and FactorCell models. For Yelp, the perplexity improvements are small; the FactorCell model is just 1.3% better than the unadapted model. The figure compares perplexity on the x-axis with accuracy on the y-axis with both metrics computed on the development set. Each point in this figure represents a different instance of the model trained with random hyperparameter settings and the best results are in the upper right corner of each plot. The color/shape differences of the points correspond to the three classes of models: FactorCell, ConcatCell, and SoftmaxBias.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model has the highest classification accuracy on the TripAdvisor dataset?\nSolve the above question based on the table titled 'Table 3: Perplexity and classification accuracy on the test set for the four word-based datasets.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest classification accuracy on the TripAdvisor dataset, look at the TripAdvisor ACC column. The FactorCell model has the highest accuracy with a value of 58.2. So, the answer is {\"answer\": \"FactorCell\"}" } ], "images": [ "scigen/train/3607.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Perplexity and classification accuracy on the test set for the four word-based datasets." ], "reasoning": "To find the model with the highest classification accuracy on the TripAdvisor dataset, look at the TripAdvisor ACC column. The FactorCell model has the highest accuracy with a value of 58.2.", "question": "Which model has the highest classification accuracy on the TripAdvisor dataset?", "answer": "So, the answer is {\"answer\": \"FactorCell\"}" }, { "messages": [ { "role": "user", "content": "The ConcatCell model has the best perplexity on the DBPedia dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe ConcatCell model has the best perplexity on the DBPedia dataset.\n" }, { "role": "assistant", "content": "To verify this statement, check the DBPedia PPL column. The FactorCell model has the best perplexity with a value of 37.7, not the ConcatCell model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3607.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Perplexity and classification accuracy on the test set for the four word-based datasets." ], "reasoning": "To verify this statement, check the DBPedia PPL column. The FactorCell model has the best perplexity with a value of 37.7, not the ConcatCell model.", "statement": "The ConcatCell model has the best perplexity on the DBPedia dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Perplexity and classification accuracies for the EuroTwitter and GeoTwitter datasets.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The FactorCell model has the lowest perplexity and highest accuracy for both datasets, outperforming the ConcatCell model in classification accuracy.The FactorCell model has the lowest perplexity and the highest accuracy for both datasets. Again, the FactorCell model clearly improves on the ConcatCell as measured by classification accuracy. Consistent with our hypothesis, adapting the softmax bias is not effective for these small vocabulary character-based tasks. The SoftmaxBias model has small perplexity improvements (<1%) and low classification accuracies." } ], "images": [ "scigen/train/3608.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Perplexity and classification accuracies for the EuroTwitter and GeoTwitter datasets." ], "reasoning": "The FactorCell model has the lowest perplexity and highest accuracy for both datasets, outperforming the ConcatCell model in classification accuracy.", "description": "The FactorCell model has the lowest perplexity and the highest accuracy for both datasets. Again, the FactorCell model clearly improves on the ConcatCell as measured by classification accuracy. Consistent with our hypothesis, adapting the softmax bias is not effective for these small vocabulary character-based tasks. The SoftmaxBias model has small perplexity improvements (<1%) and low classification accuracies.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 4: Perplexity and classification accuracies for the EuroTwitter and GeoTwitter datasets.'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. \n\nQuestion: Which model has the highest classification accuracy for the GeoTwitter dataset?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows the classification accuracies for different models on the GeoTwitter dataset. The FactorCell model has the highest accuracy at 63.5. So, the answer is {\"answer\": \"FactorCell\"}." } ], "images": [ "scigen/train/3608.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Perplexity and classification accuracies for the EuroTwitter and GeoTwitter datasets." ], "reasoning": "The table shows the classification accuracies for different models on the GeoTwitter dataset. The FactorCell model has the highest accuracy at 63.5.", "question": "Which model has the highest classification accuracy for the GeoTwitter dataset?", "answer": "So, the answer is {\"answer\": \"FactorCell\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe SoftmaxBias model has the lowest perplexity for the EuroTwitter dataset.\n" }, { "role": "assistant", "content": "The table shows the perplexity values for different models on the EuroTwitter dataset. The FactorCell model has the lowest perplexity at 6.07, not the SoftmaxBias model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3608.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Perplexity and classification accuracies for the EuroTwitter and GeoTwitter datasets." ], "reasoning": "The table shows the perplexity values for different models on the EuroTwitter dataset. The FactorCell model has the lowest perplexity at 6.07, not the SoftmaxBias model.", "statement": "The SoftmaxBias model has the lowest perplexity for the EuroTwitter dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: The top boosted words in the Softmax bias layer for different context settings in a FactorCell model.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The context embeddings primarily boost words related to geography, such as neighborhoods and local attractions, which dominate the output layer and influence classification performance.Another way of understanding what the context embeddings represent is to compute the softmax bias projection Qc and examine the words that experience the biggest increase in probability. In each case, the top words are strongly related to geography and include names of neighborhoods, local attractions, and other hotels in the same city. The top boosted words are relatively unaffected by changing the rating. (Recall that the hotel identifier and the user rating are the only two inputs used to create the context embedding.) This table combined with the other visualizations indicates that location effects tend to dominate in the output layer, which may explain why the two models adapting the recurrent network seem to have a bigger impact on classification performance." } ], "images": [ "scigen/train/3609.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The top boosted words in the Softmax bias layer for different context settings in a FactorCell model." ], "reasoning": "The context embeddings primarily boost words related to geography, such as neighborhoods and local attractions, which dominate the output layer and influence classification performance.", "description": "Another way of understanding what the context embeddings represent is to compute the softmax bias projection Qc and examine the words that experience the biggest increase in probability. In each case, the top words are strongly related to geography and include names of neighborhoods, local attractions, and other hotels in the same city. The top boosted words are relatively unaffected by changing the rating. (Recall that the hotel identifier and the user rating are the only two inputs used to create the context embedding.) This table combined with the other visualizations indicates that location effects tend to dominate in the output layer, which may explain why the two models adapting the recurrent network seem to have a bigger impact on classification performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nTable title: Table 5: The top boosted words in the Softmax bias layer for different context settings in a FactorCell model.\n\nQuestion: Which hotel has the highest rating and what are its top boosted words?\n" }, { "role": "assistant", "content": "The table shows that the Amalfi hotel has the highest rating of 5. The top boosted words for Amalfi are 'amalfi, chicago, allegro, burnham, sable, michigan, acme, conrad, talbott, wrigley'. So, the answer is {\"answer\": \"amalfi, chicago, allegro, burnham, sable, michigan, acme, conrad, talbott, wrigley\"}." } ], "images": [ "scigen/train/3609.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The top boosted words in the Softmax bias layer for different context settings in a FactorCell model." ], "reasoning": "The table shows that the Amalfi hotel has the highest rating of 5. The top boosted words for Amalfi are 'amalfi, chicago, allegro, burnham, sable, michigan, acme, conrad, talbott, wrigley'.", "question": "Which hotel has the highest rating and what are its top boosted words?", "answer": "So, the answer is {\"answer\": \"amalfi, chicago, allegro, burnham, sable, michigan, acme, conrad, talbott, wrigley\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe BLVD Hotel Suites has a higher class than the Four Points Sheraton.\n" }, { "role": "assistant", "content": "The table shows that the BLVD Hotel Suites has a class of 2.5, while the Four Points Sheraton has a class of 3.0. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3609.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The top boosted words in the Softmax bias layer for different context settings in a FactorCell model." ], "reasoning": "The table shows that the BLVD Hotel Suites has a class of 2.5, while the Four Points Sheraton has a class of 3.0. Therefore, the statement is refuted.", "statement": "The BLVD Hotel Suites has a higher class than the Four Points Sheraton.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'TABLE VI: Evaluation results of our proposed U2U matching framework on the test set of the Ubuntu Dialogue Corpus V2. The RNN suffix of the U2U model denotes replacing the attention aggregation with the RNN aggregation in the aggregation module.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The U2U-IMN model with the default attention strategy for response aggregation achieved slightly better performance than with RNN aggregation, indicating that chronological relationships among utterances in short sequences may not be essential.One key characteristic of the U2U matching framework is the response aggregation step that generates a single embedding vector based on the embedding vectors of response utterances. We can see than the U2U-IMN model with the default attention strategy for response aggregation achieved slightly better performance than that with RNN aggregation, which supported our assumption that the chronological relationships among utterances in short sequences may not be essential in the aggregation module. Some further analysis on these two aggregation strategies are given in the following." } ], "images": [ "scigen/train/3610.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VI: Evaluation results of our proposed U2U matching framework on the test set of the Ubuntu Dialogue Corpus V2. The RNN suffix of the U2U model denotes replacing the attention aggregation with the RNN aggregation in the aggregation module." ], "reasoning": "The U2U-IMN model with the default attention strategy for response aggregation achieved slightly better performance than with RNN aggregation, indicating that chronological relationships among utterances in short sequences may not be essential.", "description": "One key characteristic of the U2U matching framework is the response aggregation step that generates a single embedding vector based on the embedding vectors of response utterances. We can see than the U2U-IMN model with the default attention strategy for response aggregation achieved slightly better performance than that with RNN aggregation, which supported our assumption that the chronological relationships among utterances in short sequences may not be essential in the aggregation module. Some further analysis on these two aggregation strategies are given in the following.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model achieved better performance in R2@1, and what does this suggest about the importance of chronological relationships in short sequences?\nSolve the above question based on the table titled 'TABLE VI: Evaluation results of our proposed U2U matching framework on the test set of the Ubuntu Dialogue Corpus V2. The RNN suffix of the U2U model denotes replacing the attention aggregation with the RNN aggregation in the aggregation module.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The U2U-IMN model achieved a higher R2@1 score (0.943) compared to the U2U-IMN with RNN aggregation (0.942), suggesting that chronological relationships among utterances in short sequences may not be essential. So, the answer is {\"answer\": \"U2U-IMN\"}" } ], "images": [ "scigen/train/3610.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VI: Evaluation results of our proposed U2U matching framework on the test set of the Ubuntu Dialogue Corpus V2. The RNN suffix of the U2U model denotes replacing the attention aggregation with the RNN aggregation in the aggregation module." ], "reasoning": "The U2U-IMN model achieved a higher R2@1 score (0.943) compared to the U2U-IMN with RNN aggregation (0.942), suggesting that chronological relationships among utterances in short sequences may not be essential.", "question": "Which model achieved better performance in R2@1, and what does this suggest about the importance of chronological relationships in short sequences?", "answer": "So, the answer is {\"answer\": \"U2U-IMN\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe U2U-IMN model with the default attention strategy for response aggregation achieved better performance in R10@1 than the U2U-IMN with RNN aggregation.\n" }, { "role": "assistant", "content": "The table shows that the U2U-IMN model achieved an R10@1 score of 0.762, while the U2U-IMN with RNN aggregation achieved a score of 0.758, supporting the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3610.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VI: Evaluation results of our proposed U2U matching framework on the test set of the Ubuntu Dialogue Corpus V2. The RNN suffix of the U2U model denotes replacing the attention aggregation with the RNN aggregation in the aggregation module." ], "reasoning": "The table shows that the U2U-IMN model achieved an R10@1 score of 0.762, while the U2U-IMN with RNN aggregation achieved a score of 0.758, supporting the statement.", "statement": "The U2U-IMN model with the default attention strategy for response aggregation achieved better performance in R10@1 than the U2U-IMN with RNN aggregation.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'TABLE V: Comparisons between U2R-IMN and U2U-IMN models on several subsets of the test set of the Ubuntu Dialogue Corpus V2. U2R-IMN denotes the model with concatenation of the utterances in a response. In each subset, the correct responses are composed of 1, 2, or 3 utterances.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The U2U-IMN model shows improved performance over the U2R-IMN model, especially as the number of utterances in the correct responses increases.To further verify the effectiveness of our proposed U2U matching framework, we split the test set of the Ubuntu Dialogue Corpus V2 dataset according to the number of utterances in their correct responses. Then, the performances on these subsets of the U2U-IMN model were compared with those of the model (denoted U2R-IMN) As demonstrated, the U2U framework can help improve the performance by exploiting the relationships among the utterances in a response. We can see that the advantage of the U2U-IMN model over the U2R-IMN model became larger when the correct responses were composed of more utterances. This was consistent with the motivation of the U2U matching framework. Considering that only 30% of responses in the Ubuntu Dialogue Corpus V2 dataset consisted of multiple utterances, a larger overall improvement may be achieved when applying our proposed U2U models to datasets containing more responses with multiple utterances." } ], "images": [ "scigen/train/3611.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE V: Comparisons between U2R-IMN and U2U-IMN models on several subsets of the test set of the Ubuntu Dialogue Corpus V2. U2R-IMN denotes the model with concatenation of the utterances in a response. In each subset, the correct responses are composed of 1, 2, or 3 utterances." ], "reasoning": "The U2U-IMN model shows improved performance over the U2R-IMN model, especially as the number of utterances in the correct responses increases.", "description": "To further verify the effectiveness of our proposed U2U matching framework, we split the test set of the Ubuntu Dialogue Corpus V2 dataset according to the number of utterances in their correct responses. Then, the performances on these subsets of the U2U-IMN model were compared with those of the model (denoted U2R-IMN) As demonstrated, the U2U framework can help improve the performance by exploiting the relationships among the utterances in a response. We can see that the advantage of the U2U-IMN model over the U2R-IMN model became larger when the correct responses were composed of more utterances. This was consistent with the motivation of the U2U matching framework. Considering that only 30% of responses in the Ubuntu Dialogue Corpus V2 dataset consisted of multiple utterances, a larger overall improvement may be achieved when applying our proposed U2U models to datasets containing more responses with multiple utterances.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: 'TABLE V: Comparisons between U2R-IMN and U2U-IMN models on several subsets of the test set of the Ubuntu Dialogue Corpus V2. U2R-IMN denotes the model with concatenation of the utterances in a response. In each subset, the correct responses are composed of 1, 2, or 3 utterances.'\nQuestion: What is the R10@1 performance of the U2U-IMN model for responses with 3 utterances?\n" }, { "role": "assistant", "content": "To find the R10@1 performance of the U2U-IMN model for responses with 3 utterances, locate the row for U2U-IMN with 3 utterances and check the value under the R10@1 column. So, the answer is {\"answer\": \"0.904\"}" } ], "images": [ "scigen/train/3611.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE V: Comparisons between U2R-IMN and U2U-IMN models on several subsets of the test set of the Ubuntu Dialogue Corpus V2. U2R-IMN denotes the model with concatenation of the utterances in a response. In each subset, the correct responses are composed of 1, 2, or 3 utterances." ], "reasoning": "To find the R10@1 performance of the U2U-IMN model for responses with 3 utterances, locate the row for U2U-IMN with 3 utterances and check the value under the R10@1 column.", "question": "What is the R10@1 performance of the U2U-IMN model for responses with 3 utterances?", "answer": "So, the answer is {\"answer\": \"0.904\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe U2U-IMN model has a higher R2@1 performance than the U2R-IMN model for responses with 2 utterances.\n" }, { "role": "assistant", "content": "Compare the R2@1 performance values for the U2U-IMN and U2R-IMN models for responses with 2 utterances. The U2U-IMN model has an R2@1 value of 0.956, while the U2R-IMN model has an R2@1 value of 0.952. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3611.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE V: Comparisons between U2R-IMN and U2U-IMN models on several subsets of the test set of the Ubuntu Dialogue Corpus V2. U2R-IMN denotes the model with concatenation of the utterances in a response. In each subset, the correct responses are composed of 1, 2, or 3 utterances." ], "reasoning": "Compare the R2@1 performance values for the U2U-IMN and U2R-IMN models for responses with 2 utterances. The U2U-IMN model has an R2@1 value of 0.956, while the U2R-IMN model has an R2@1 value of 0.952.", "statement": "The U2U-IMN model has a higher R2@1 performance than the U2R-IMN model for responses with 2 utterances.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'TABLE VIII: Ablation tests of the context-to-response (C2R) and response-to-context (R2C) representations in the U2U-IMN model on the four datasets.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Ablating either context-to-response or response-to-context representations in the U2U-IMN model results in performance degradation, highlighting the importance of bidirectional matching.The bidirectional context-to-response and response-to-context representations in the U2U-IMN model were ablated. Specifically, when the context-to-response representation was ablated, the context representation given by the sentence encoding module {˜Ucm}ncm=1 was sent to the aggregation module directly, and only the response representation {˜Urn}nrn=1 was enhanced by the interactive matching module to obtain {Ur,matn}nrn=1 before aggregation. Similar operations were conducted to ablate the response-to-context representation. We can see that ablation of either the context-to-response or response-to-context representations resulted in a performance degradation, which indicates the effectiveness of the bidirectional matching between contexts and responses in the interactive matching module. A serious performance degradation can be observed when ablating the matching representations of both directions." } ], "images": [ "scigen/train/3612.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VIII: Ablation tests of the context-to-response (C2R) and response-to-context (R2C) representations in the U2U-IMN model on the four datasets." ], "reasoning": "Ablating either context-to-response or response-to-context representations in the U2U-IMN model results in performance degradation, highlighting the importance of bidirectional matching.", "description": "The bidirectional context-to-response and response-to-context representations in the U2U-IMN model were ablated. Specifically, when the context-to-response representation was ablated, the context representation given by the sentence encoding module {˜Ucm}ncm=1 was sent to the aggregation module directly, and only the response representation {˜Urn}nrn=1 was enhanced by the interactive matching module to obtain {Ur,matn}nrn=1 before aggregation. Similar operations were conducted to ablate the response-to-context representation. We can see that ablation of either the context-to-response or response-to-context representations resulted in a performance degradation, which indicates the effectiveness of the bidirectional matching between contexts and responses in the interactive matching module. A serious performance degradation can be observed when ablating the matching representations of both directions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'TABLE VIII: Ablation tests of the context-to-response (C2R) and response-to-context (R2C) representations in the U2U-IMN model on the four datasets.', unfold the steps to solve the question given next. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nQuestion: What is the R10@1 performance of the U2U-IMN model on the Douban dataset?\n" }, { "role": "assistant", "content": "To find the R10@1 performance of the U2U-IMN model on the Douban dataset, locate the row corresponding to the Douban dataset and the U2U-IMN model, then read the value under the R10@1 column. So, the answer is {\"answer\": \"0.259\"}." } ], "images": [ "scigen/train/3612.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VIII: Ablation tests of the context-to-response (C2R) and response-to-context (R2C) representations in the U2U-IMN model on the four datasets." ], "reasoning": "To find the R10@1 performance of the U2U-IMN model on the Douban dataset, locate the row corresponding to the Douban dataset and the U2U-IMN model, then read the value under the R10@1 column.", "question": "What is the R10@1 performance of the U2U-IMN model on the Douban dataset?", "answer": "So, the answer is {\"answer\": \"0.259\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Ablating both context-to-response and response-to-context representations in the U2U-IMN model on the Ubuntu V1 dataset results in an R10@5 performance of 0.954., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nAblating both context-to-response and response-to-context representations in the U2U-IMN model on the Ubuntu V1 dataset results in an R10@5 performance of 0.954.\n" }, { "role": "assistant", "content": "To verify this statement, locate the row corresponding to the Ubuntu V1 dataset and the model with both context-to-response and response-to-context representations ablated (-C2R&R2C), then check the value under the R10@5 column. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3612.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VIII: Ablation tests of the context-to-response (C2R) and response-to-context (R2C) representations in the U2U-IMN model on the four datasets." ], "reasoning": "To verify this statement, locate the row corresponding to the Ubuntu V1 dataset and the model with both context-to-response and response-to-context representations ablated (-C2R&R2C), then check the value under the R10@5 column.", "statement": "Ablating both context-to-response and response-to-context representations in the U2U-IMN model on the Ubuntu V1 dataset results in an R10@5 performance of 0.954.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'TABLE X: Ablation tests of the distance-based prior for interactive matching on the four datasets.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The distance-based prior improved performance more on Chinese datasets (Douban, E-commerce) than on English datasets (Ubuntu V1, Ubuntu V2).The exponential prior based on sentence-level distances in Eq. We can see that the performance decreased on most metrics. Meanwhile, we can see that this distance-based prior provided larger improvements on the two Chinese datasets than on the two English datasets. The estimated prior function Φ(D)=e−0.536D−0.00001 in Eq. We can see that larger weights were assigned to the utterances closer to the response." } ], "images": [ "scigen/train/3613.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE X: Ablation tests of the distance-based prior for interactive matching on the four datasets." ], "reasoning": "The distance-based prior improved performance more on Chinese datasets (Douban, E-commerce) than on English datasets (Ubuntu V1, Ubuntu V2).", "description": "The exponential prior based on sentence-level distances in Eq. We can see that the performance decreased on most metrics. Meanwhile, we can see that this distance-based prior provided larger improvements on the two Chinese datasets than on the two English datasets. The estimated prior function Φ(D)=e−0.536D−0.00001 in Eq. We can see that larger weights were assigned to the utterances closer to the response.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'TABLE X: Ablation tests of the distance-based prior for interactive matching on the four datasets.' and proceed to solve the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich dataset showed the largest improvement in R10@5 when using the prior model?\n" }, { "role": "assistant", "content": "By comparing the R10@5 values for each dataset with and without the prior model, we see that the Douban dataset showed an improvement from 0.791 to 0.782, which is a decrease. The E-commerce dataset showed an improvement from 0.966 to 0.968, which is an increase of 0.002. So, the answer is {\"answer\": \"E-commerce\"}" } ], "images": [ "scigen/train/3613.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE X: Ablation tests of the distance-based prior for interactive matching on the four datasets." ], "reasoning": "By comparing the R10@5 values for each dataset with and without the prior model, we see that the Douban dataset showed an improvement from 0.791 to 0.782, which is a decrease. The E-commerce dataset showed an improvement from 0.966 to 0.968, which is an increase of 0.002.", "question": "Which dataset showed the largest improvement in R10@5 when using the prior model?", "answer": "So, the answer is {\"answer\": \"E-commerce\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe prior model improved the R10@1 score for the Ubuntu V1 dataset.\n" }, { "role": "assistant", "content": "By comparing the R10@1 values for the Ubuntu V1 dataset with and without the prior model, we see that the score decreased from 0.790 to 0.787. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3613.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE X: Ablation tests of the distance-based prior for interactive matching on the four datasets." ], "reasoning": "By comparing the R10@1 values for the Ubuntu V1 dataset with and without the prior model, we see that the score decreased from 0.790 to 0.787.", "statement": "The prior model improved the R10@1 score for the Ubuntu V1 dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Comparison of different strategies. GE: genre embedding. TT: Topic Trace mechanism. WM0 is the model without GE or TT.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Incorporating genre embedding and Topic Trace mechanism into the model significantly improves performance, reducing uncertainty and outperforming baselines.It is worth mentioning that the improvement partially results from the genre embedding. By incorporating structural and phonological control into the model, WM greatly reduces the uncertainty of generation. As we can see, even without genre embedding, our model still outperforms baselines prominently. Besides, Topic Trace mechanism further improves performance." } ], "images": [ "scigen/train/3614.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison of different strategies. GE: genre embedding. TT: Topic Trace mechanism. WM0 is the model without GE or TT." ], "reasoning": "Incorporating genre embedding and Topic Trace mechanism into the model significantly improves performance, reducing uncertainty and outperforming baselines.", "description": "It is worth mentioning that the improvement partially results from the genre embedding. By incorporating structural and phonological control into the model, WM greatly reduces the uncertainty of generation. As we can see, even without genre embedding, our model still outperforms baselines prominently. Besides, Topic Trace mechanism further improves performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: Comparison of different strategies. GE: genre embedding. TT: Topic Trace mechanism. WM0 is the model without GE or TT.', answer the following question. Give detailed solution consisting of each step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhat is the BLEU score for Quatrains when both genre embedding and Topic Trace mechanism are incorporated?\n" }, { "role": "assistant", "content": "To find the BLEU score for Quatrains with both genre embedding and Topic Trace mechanism, look at the row labeled 'WM0+GE+TT' under the column 'Quatrains BLEU'. So, the answer is {\"answer\": \"1.315\"}." } ], "images": [ "scigen/train/3614.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison of different strategies. GE: genre embedding. TT: Topic Trace mechanism. WM0 is the model without GE or TT." ], "reasoning": "To find the BLEU score for Quatrains with both genre embedding and Topic Trace mechanism, look at the row labeled 'WM0+GE+TT' under the column 'Quatrains BLEU'.", "question": "What is the BLEU score for Quatrains when both genre embedding and Topic Trace mechanism are incorporated?", "answer": "So, the answer is {\"answer\": \"1.315\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe model with genre embedding and Topic Trace mechanism has the lowest PP score for Iambics.\n" }, { "role": "assistant", "content": "To verify this, compare the PP scores for Iambics across all strategies. The row 'WM0+GE+TT' under the column 'Iambics PP' has the lowest score of 72. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3614.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison of different strategies. GE: genre embedding. TT: Topic Trace mechanism. WM0 is the model without GE or TT." ], "reasoning": "To verify this, compare the PP scores for Iambics across all strategies. The row 'WM0+GE+TT' under the column 'Iambics PP' has the lowest score of 72.", "statement": "The model with genre embedding and Topic Trace mechanism has the lowest PP score for Iambics.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 4: Human evaluation results. Diacritic ** (p<0.01) indicates WM significantly outperforms baselines; ++ (p<0.01) indicates Human is significantly better than all models. The Intraclass Correlation Coefficient of the four groups of scores is 0.5, which indicates an acceptable inter-annotator agreement.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "WM achieves better results than other models on quatrains, getting close to Human on Coherence and Relevance, while Planning gets the worst results on Fluency and Meaning.WM achieves better results than other models. On quatrains, WM gets close to Human on Coherence and Relevance. Planning gets the worst results on Fluency and Meaning. This is mainly because planning mechanism can’t guarantee the quality of planned sub-keywords and the fixed keywords order loses some freedom of topic expression, hurting fluency and meaning. iPoet gets the lowest score on Relevance, since it packs all topic words into one vector, resulting in a low topic expression ratio. By contrast, WM maintains keywords in the topic memory independently and the expression order is flexibly decided by the model in terms of the history. Benefiting from TT, an unexpressed word still has the chance to be generated in later lines. Thus WM gets a comparable score with Human on Relevance. FCPG performs worst on Coherence. As discussed in Section 2, FCPG generates the whole poem as a long sequence and the history is saved in RNN state implicitly, which therefore can’t be utilized effectively. On iambics and lyrics, WM gets better results, but there is still a distinct gap with Human. Iambic is a quite complex form and the longest iambic in our testing set consists of more than 150 characters (25 lines). It’s much harder for the model to generate a high-quality iambic. For lyrics, due to the limited small data, the results are not as good as we expected. We put the requirements of structure and phonology into Fluency criterion. As a result, WM gets a much higher Fluency score than baselines, benefiting from the genre embedding." } ], "images": [ "scigen/train/3615.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Human evaluation results. Diacritic ** (p<0.01) indicates WM significantly outperforms baselines; ++ (p<0.01) indicates Human is significantly better than all models. The Intraclass Correlation Coefficient of the four groups of scores is 0.5, which indicates an acceptable inter-annotator agreement." ], "reasoning": "WM achieves better results than other models on quatrains, getting close to Human on Coherence and Relevance, while Planning gets the worst results on Fluency and Meaning.", "description": "WM achieves better results than other models. On quatrains, WM gets close to Human on Coherence and Relevance. Planning gets the worst results on Fluency and Meaning. This is mainly because planning mechanism can’t guarantee the quality of planned sub-keywords and the fixed keywords order loses some freedom of topic expression, hurting fluency and meaning. iPoet gets the lowest score on Relevance, since it packs all topic words into one vector, resulting in a low topic expression ratio. By contrast, WM maintains keywords in the topic memory independently and the expression order is flexibly decided by the model in terms of the history. Benefiting from TT, an unexpressed word still has the chance to be generated in later lines. Thus WM gets a comparable score with Human on Relevance. FCPG performs worst on Coherence. As discussed in Section 2, FCPG generates the whole poem as a long sequence and the history is saved in RNN state implicitly, which therefore can’t be utilized effectively. On iambics and lyrics, WM gets better results, but there is still a distinct gap with Human. Iambic is a quite complex form and the longest iambic in our testing set consists of more than 150 characters (25 lines). It’s much harder for the model to generate a high-quality iambic. For lyrics, due to the limited small data, the results are not as good as we expected. We put the requirements of structure and phonology into Fluency criterion. As a result, WM gets a much higher Fluency score than baselines, benefiting from the genre embedding.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 4: Human evaluation results. Diacritic ** (p<0.01) indicates WM significantly outperforms baselines; ++ (p<0.01) indicates Human is significantly better than all models. The Intraclass Correlation Coefficient of the four groups of scores is 0.5, which indicates an acceptable inter-annotator agreement.', answer the question below by showing each progressive step toward the answer. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model performs the worst on Fluency and Meaning for quatrains?\n" }, { "role": "assistant", "content": "The description states that Planning gets the worst results on Fluency and Meaning. Looking at the table, Planning has the lowest scores for Fluency (2.28) and Meaning (2.13) among the models for quatrains. So, the answer is {\"answer\": \"Planning\"}" } ], "images": [ "scigen/train/3615.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Human evaluation results. Diacritic ** (p<0.01) indicates WM significantly outperforms baselines; ++ (p<0.01) indicates Human is significantly better than all models. The Intraclass Correlation Coefficient of the four groups of scores is 0.5, which indicates an acceptable inter-annotator agreement." ], "reasoning": "The description states that Planning gets the worst results on Fluency and Meaning. Looking at the table, Planning has the lowest scores for Fluency (2.28) and Meaning (2.13) among the models for quatrains.", "question": "Which model performs the worst on Fluency and Meaning for quatrains?", "answer": "So, the answer is {\"answer\": \"Planning\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nWM gets the highest score on Relevance for quatrains.\n" }, { "role": "assistant", "content": "The table shows that WM has a Relevance score of 3.77 for quatrains, which is higher than the scores of other models for quatrains. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3615.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Human evaluation results. Diacritic ** (p<0.01) indicates WM significantly outperforms baselines; ++ (p<0.01) indicates Human is significantly better than all models. The Intraclass Correlation Coefficient of the four groups of scores is 0.5, which indicates an acceptable inter-annotator agreement." ], "reasoning": "The table shows that WM has a Relevance score of 3.77 for quatrains, which is higher than the scores of other models for quatrains.", "statement": "WM gets the highest score on Relevance for quatrains.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: Small training samples (Spearman ρ). OOV represents Out of Vocabulary words encountered while testing.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The method improves accuracy across all languages, with morphologically richer languages showing more significant increases.The method seems to perform well on small datasets with an increase in accuracy on all datasets across all languages - English, German, French, Spanish, Persian and Hindi. Morphologically richer languages show more increase with respect to others." } ], "images": [ "scigen/train/3616.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Small training samples (Spearman ρ). OOV represents Out of Vocabulary words encountered while testing." ], "reasoning": "The method improves accuracy across all languages, with morphologically richer languages showing more significant increases.", "description": "The method seems to perform well on small datasets with an increase in accuracy on all datasets across all languages - English, German, French, Spanish, Persian and Hindi. Morphologically richer languages show more increase with respect to others.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Small training samples (Spearman ρ). OOV represents Out of Vocabulary words encountered while testing.\nQuestion: Which language had the highest accuracy increase from SG to SG+EXP?\nGive a detailed response to the above question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the language with the highest accuracy increase, subtract the SG accuracy from the SG+EXP accuracy for each language. The highest increase is for Spanish, with an increase from 61 to 73.1. So, the answer is {\"answer\": \"Spanish\"}" } ], "images": [ "scigen/train/3616.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Small training samples (Spearman ρ). OOV represents Out of Vocabulary words encountered while testing." ], "reasoning": "To find the language with the highest accuracy increase, subtract the SG accuracy from the SG+EXP accuracy for each language. The highest increase is for Spanish, with an increase from 61 to 73.1.", "question": "Which language had the highest accuracy increase from SG to SG+EXP?", "answer": "So, the answer is {\"answer\": \"Spanish\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe method showed an increase in accuracy for Persian from SG to SG+EXP.\n" }, { "role": "assistant", "content": "The table shows that the accuracy for Persian increased from 20.5 in SG to 22.7 in SG+EXP. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3616.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Small training samples (Spearman ρ). OOV represents Out of Vocabulary words encountered while testing." ], "reasoning": "The table shows that the accuracy for Persian increased from 20.5 in SG to 22.7 in SG+EXP.", "statement": "The method showed an increase in accuracy for Persian from SG to SG+EXP.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 8: Comparison between SG and SG + Exp regarding word pair ', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Training new embeddings on the expanded corpus increases the similarity score for 'censorships' and brings its morphological variants closer.We see that the words “censorship” and its rarer morphological variant “censorships” have very low similarity score and the similar words for “censorships” are also not very informative. By training new embeddings on the expanded corpus we find that not only does the similarity score increase but the other moprhological variants also come closer to the word." } ], "images": [ "scigen/train/3617.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Comparison between SG and SG + Exp regarding word pair " ], "reasoning": "Training new embeddings on the expanded corpus increases the similarity score for 'censorships' and brings its morphological variants closer.", "description": "We see that the words “censorship” and its rarer morphological variant “censorships” have very low similarity score and the similar words for “censorships” are also not very informative. By training new embeddings on the expanded corpus we find that not only does the similarity score increase but the other moprhological variants also come closer to the word.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 8: Comparison between SG and SG + Exp regarding word pair ', solve this question and give detail solutions. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the similarity score for 'censorships' using the SG model?\n" }, { "role": "assistant", "content": "The table shows the similarity score for 'censorships' using the SG model as 0.43. So, the answer is {\"answer\": \"0.43\"}." } ], "images": [ "scigen/train/3617.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Comparison between SG and SG + Exp regarding word pair " ], "reasoning": "The table shows the similarity score for 'censorships' using the SG model as 0.43.", "question": "What is the similarity score for 'censorships' using the SG model?", "answer": "So, the answer is {\"answer\": \"0.43\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe similarity score for 'censorships' increased after training on the expanded corpus.\n" }, { "role": "assistant", "content": "The table shows that the similarity score for 'censorships' increased from 0.43 to 0.87 after training on the expanded corpus. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3617.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Comparison between SG and SG + Exp regarding word pair " ], "reasoning": "The table shows that the similarity score for 'censorships' increased from 0.43 to 0.87 after training on the expanded corpus.", "statement": "The similarity score for 'censorships' increased after training on the expanded corpus.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 9: Tranined on 1B tokens(English) - Comparison between different systems after Morph step (Spearman ρ). Note that the scores on WS and RG are unchanged because the frequencies of all the words are above the threshold'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The accuracy of SG + Exp + Morph is higher than SG + 2-Exp + Morph due to a decrease in analogical regularities with more artificial sentences.We observe that the accuracy of SG + Exp + Morph > SG + 2-Exp + This happens because of decrease in analogical regularities as more artificial sentences are introduced." } ], "images": [ "scigen/train/3618.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Tranined on 1B tokens(English) - Comparison between different systems after Morph step (Spearman ρ). Note that the scores on WS and RG are unchanged because the frequencies of all the words are above the threshold" ], "reasoning": "The accuracy of SG + Exp + Morph is higher than SG + 2-Exp + Morph due to a decrease in analogical regularities with more artificial sentences.", "description": "We observe that the accuracy of SG + Exp + Morph > SG + 2-Exp + This happens because of decrease in analogical regularities as more artificial sentences are introduced.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: 'Table 9: Tranined on 1B tokens(English) - Comparison between different systems after Morph step (Spearman ρ). Note that the scores on WS and RG are unchanged because the frequencies of all the words are above the threshold'\nQuestion: Which system has the highest RG score and why?\n" }, { "role": "assistant", "content": "The system SG + Exp + Morph has the highest RG score of 80.4, which is higher than SG + 2-Exp + Morph's 80.3, due to fewer artificial sentences leading to better analogical regularities. So, the answer is {\"answer\": \"SG + Exp + Morph\"}." } ], "images": [ "scigen/train/3618.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Tranined on 1B tokens(English) - Comparison between different systems after Morph step (Spearman ρ). Note that the scores on WS and RG are unchanged because the frequencies of all the words are above the threshold" ], "reasoning": "The system SG + Exp + Morph has the highest RG score of 80.4, which is higher than SG + 2-Exp + Morph's 80.3, due to fewer artificial sentences leading to better analogical regularities.", "question": "Which system has the highest RG score and why?", "answer": "So, the answer is {\"answer\": \"SG + Exp + Morph\"}." }, { "messages": [ { "role": "user", "content": "SG + 3-Exp + Morph has a higher WS score than SG + Exp + Morph. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nSG + 3-Exp + Morph has a higher WS score than SG + Exp + Morph.\n" }, { "role": "assistant", "content": "The table shows that SG + 3-Exp + Morph has a WS score of 71.3, which is lower than SG + Exp + Morph's WS score of 73.8. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3618.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Tranined on 1B tokens(English) - Comparison between different systems after Morph step (Spearman ρ). Note that the scores on WS and RG are unchanged because the frequencies of all the words are above the threshold" ], "reasoning": "The table shows that SG + 3-Exp + Morph has a WS score of 71.3, which is lower than SG + Exp + Morph's WS score of 73.8.", "statement": "SG + 3-Exp + Morph has a higher WS score than SG + Exp + Morph.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: Context sentences and corresponding attention weights for the made-up word “petfel”'.\n" }, { "role": "assistant", "content": "Sentences (2) and (4) are most attended to by the model due to their informative and frequent words like 'instrument', 'chimes', and 'music'.The model attends most to sentences (2) and (4); consistently, the embeddings obtained from those sentences are very similar. Furthermore, of all four sentences, these two are the ones best suited for a simple averaging model as they contain informative, frequent words like “instrument”, “chimes” and “music”." } ], "images": [ "scigen/train/3619.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Context sentences and corresponding attention weights for the made-up word “petfel”" ], "reasoning": "Sentences (2) and (4) are most attended to by the model due to their informative and frequent words like 'instrument', 'chimes', and 'music'.", "description": "The model attends most to sentences (2) and (4); consistently, the embeddings obtained from those sentences are very similar. Furthermore, of all four sentences, these two are the ones best suited for a simple averaging model as they contain informative, frequent words like “instrument”, “chimes” and “music”.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: Context sentences and corresponding attention weights for the made-up word “petfel”'.\n\nQuestion: Which sentences contain the most informative and frequent words according to the model?\n" }, { "role": "assistant", "content": "The description states that sentences (2) and (4) contain informative and frequent words like 'instrument', 'chimes', and 'music', making them best suited for a simple averaging model. So, the answer is {\"answer\": \"sentences (2) and (4)\"}." } ], "images": [ "scigen/train/3619.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Context sentences and corresponding attention weights for the made-up word “petfel”" ], "reasoning": "The description states that sentences (2) and (4) contain informative and frequent words like 'instrument', 'chimes', and 'music', making them best suited for a simple averaging model.", "question": "Which sentences contain the most informative and frequent words according to the model?", "answer": "So, the answer is {\"answer\": \"sentences (2) and (4)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model attends most to sentences (1) and (3).\n" }, { "role": "assistant", "content": "The description clearly states that the model attends most to sentences (2) and (4), not (1) and (3). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3619.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Context sentences and corresponding attention weights for the made-up word “petfel”" ], "reasoning": "The description clearly states that the model attends most to sentences (2) and (4), not (1) and (3).", "statement": "The model attends most to sentences (1) and (3).", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: Average cosine similarities for the VecMap evaluation, scaled by a factor of 100. †: Downsampled words were included in the training set.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Including downsampled words during training helps models outperform skipgram for 32 or more occurrences but performs worse for less frequent words.As baselines, we train skipgram and fastText on the downsampled corpus. We then train Mimick Pinter et al. We also try a variant where the downsampled words are included in the training set (i.e., the mimicking models explicitly learn to reproduce their skipgram embeddings). This allows the model to learn representations of those words not completely from scratch, but to also make use of their original embeddings. Accordingly, we expect this variant to only be helpful if a word is not too rare, i.e. its original embedding is already of decent quality. The comparably poor performance of Mimick is consistent with the observation of Pinter et al. Given four or more contexts, AM leads to consistent improvements over FCM. The variants that include downsampled words during training (†) still outperform skipgram for 32 and more observations, but perform worse than the default models for less frequent words." } ], "images": [ "scigen/train/3620.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Average cosine similarities for the VecMap evaluation, scaled by a factor of 100. †: Downsampled words were included in the training set." ], "reasoning": "Including downsampled words during training helps models outperform skipgram for 32 or more occurrences but performs worse for less frequent words.", "description": "As baselines, we train skipgram and fastText on the downsampled corpus. We then train Mimick Pinter et al. We also try a variant where the downsampled words are included in the training set (i.e., the mimicking models explicitly learn to reproduce their skipgram embeddings). This allows the model to learn representations of those words not completely from scratch, but to also make use of their original embeddings. Accordingly, we expect this variant to only be helpful if a word is not too rare, i.e. its original embedding is already of decent quality. The comparably poor performance of Mimick is consistent with the observation of Pinter et al. Given four or more contexts, AM leads to consistent improvements over FCM. The variants that include downsampled words during training (†) still outperform skipgram for 32 and more observations, but perform worse than the default models for less frequent words.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Average cosine similarities for the VecMap evaluation, scaled by a factor of 100. †: Downsampled words were included in the training set.\nQuestion: Which model performs best for 32 occurrences?\nSolve the above question based on the table information. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, we see that AM† has the highest value (60.5) for 32 occurrences. So, the answer is {\"answer\": \"AM†\"}" } ], "images": [ "scigen/train/3620.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Average cosine similarities for the VecMap evaluation, scaled by a factor of 100. †: Downsampled words were included in the training set." ], "reasoning": "By examining the table, we see that AM† has the highest value (60.5) for 32 occurrences.", "question": "Which model performs best for 32 occurrences?", "answer": "So, the answer is {\"answer\": \"AM†\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Mimick performs better than skipgram for 4 occurrences., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nMimick performs better than skipgram for 4 occurrences.\n" }, { "role": "assistant", "content": "The table shows that Mimick has a value of 12.1 for 4 occurrences, while skipgram has a value of 30.9, indicating that Mimick performs worse. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3620.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Average cosine similarities for the VecMap evaluation, scaled by a factor of 100. †: Downsampled words were included in the training set." ], "reasoning": "The table shows that Mimick has a value of 12.1 for 4 occurrences, while skipgram has a value of 30.9, indicating that Mimick performs worse.", "statement": "Mimick performs better than skipgram for 4 occurrences.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Results on the Name Typing dataset for various word frequencies f. The model that uses a linear combination of AM embeddings with skipgram is denoted AM+skip.'.\n" }, { "role": "assistant", "content": "AM significantly outperforms other models up to 16 occurrences, and the combination of skipgram and AM yields the best overall results.In accordance with results from previous experiments, AM performs drastically better than the baselines for up to 16 occurrences. Notably, the linear combination of skipgram and AM achieves by far the best overall results." } ], "images": [ "scigen/train/3621.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on the Name Typing dataset for various word frequencies f. The model that uses a linear combination of AM embeddings with skipgram is denoted AM+skip." ], "reasoning": "AM significantly outperforms other models up to 16 occurrences, and the combination of skipgram and AM yields the best overall results.", "description": "In accordance with results from previous experiments, AM performs drastically better than the baselines for up to 16 occurrences. Notably, the linear combination of skipgram and AM achieves by far the best overall results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 3: Results on the Name Typing dataset for various word frequencies f. The model that uses a linear combination of AM embeddings with skipgram is denoted AM+skip.', answer the question below by showing each progressive step toward the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieves the highest F1 score for f∈{1,100}?\n" }, { "role": "assistant", "content": "By examining the F1 scores for f∈{1,100}, AM+skip achieves the highest F1 score of 60.5. So, the answer is {\"answer\": \"AM+skip\"}" } ], "images": [ "scigen/train/3621.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on the Name Typing dataset for various word frequencies f. The model that uses a linear combination of AM embeddings with skipgram is denoted AM+skip." ], "reasoning": "By examining the F1 scores for f∈{1,100}, AM+skip achieves the highest F1 score of 60.5.", "question": "Which model achieves the highest F1 score for f∈{1,100}?", "answer": "So, the answer is {\"answer\": \"AM+skip\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nAM achieves the highest accuracy for f=1.\n" }, { "role": "assistant", "content": "The table shows that both AM and AM+skip achieve the highest accuracy of 87.8 for f=1. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3621.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on the Name Typing dataset for various word frequencies f. The model that uses a linear combination of AM embeddings with skipgram is denoted AM+skip." ], "reasoning": "The table shows that both AM and AM+skip achieve the highest accuracy of 87.8 for f=1.", "statement": "AM achieves the highest accuracy for f=1.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: Spearman’s ρ for the Chimeras task given 2, 4 and 6 context sentences for the made-up word', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "AM-ctx consistently outperforms FCM-ctx, and context attention improves results, especially for 6 sentences.The Chimeras (CHIMERA) dataset Lazaridou et al. CHIMERA provides only six contexts for each made-up word, so it is not ideal for evaluating our model. Nonetheless, we can still use it to analyze the difference of FCM (no attention) and AM (using attention). As the surface-form of the made-up words was constructed randomly and thus carries no meaning at all, we restrict ourselves to the context parts of FCM and AM (referred to as FCM-ctx and AM-ctx). We do not report results for Mimick as its representations for novel words are entirely based on their surface form. While AM performs worse than previous methods for 2–4 sentences, it drastically improves over the best result currently published for 6 sentences. Again, context attention consistently improves results: AM-ctx performs better than FCM-ctx, regardless of the number of contexts. Since A La Carte Khodak et al. , the method performing best for 2–4 contexts, is conceptually similar to FCM, it most likely would similarly benefit from context attention." } ], "images": [ "scigen/train/3622.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Spearman’s ρ for the Chimeras task given 2, 4 and 6 context sentences for the made-up word" ], "reasoning": "AM-ctx consistently outperforms FCM-ctx, and context attention improves results, especially for 6 sentences.", "description": "The Chimeras (CHIMERA) dataset Lazaridou et al. CHIMERA provides only six contexts for each made-up word, so it is not ideal for evaluating our model. Nonetheless, we can still use it to analyze the difference of FCM (no attention) and AM (using attention). As the surface-form of the made-up words was constructed randomly and thus carries no meaning at all, we restrict ourselves to the context parts of FCM and AM (referred to as FCM-ctx and AM-ctx). We do not report results for Mimick as its representations for novel words are entirely based on their surface form. While AM performs worse than previous methods for 2–4 sentences, it drastically improves over the best result currently published for 6 sentences. Again, context attention consistently improves results: AM-ctx performs better than FCM-ctx, regardless of the number of contexts. Since A La Carte Khodak et al. , the method performing best for 2–4 contexts, is conceptually similar to FCM, it most likely would similarly benefit from context attention.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 4: Spearman’s ρ for the Chimeras task given 2, 4 and 6 context sentences for the made-up word' and proceed to solve the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model shows the highest performance for 6 sentences?\n" }, { "role": "assistant", "content": "By comparing the values in the 6 sentences column, AM-ctx has the highest value of 0.436. So, the answer is {\"answer\": \"AM-ctx\"}." } ], "images": [ "scigen/train/3622.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Spearman’s ρ for the Chimeras task given 2, 4 and 6 context sentences for the made-up word" ], "reasoning": "By comparing the values in the 6 sentences column, AM-ctx has the highest value of 0.436.", "question": "Which model shows the highest performance for 6 sentences?", "answer": "So, the answer is {\"answer\": \"AM-ctx\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nA La Carte performs better than Nonce2Vec for 4 sentences.\n" }, { "role": "assistant", "content": "By comparing the values in the 4 sentences column, A La Carte has a value of 0.384, which is higher than Nonce2Vec's value of 0.367. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3622.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Spearman’s ρ for the Chimeras task given 2, 4 and 6 context sentences for the made-up word" ], "reasoning": "By comparing the values in the 4 sentences column, A La Carte has a value of 0.384, which is higher than Nonce2Vec's value of 0.367.", "statement": "A La Carte performs better than Nonce2Vec for 4 sentences.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6: Significance results for the VecMap evaluation. Each cell lists the numbers of word occurrences for which the model of the row performs significantly better than the model of the column (p<0.05). For example, FCM is significantly better than skipgram for 1, 2, 4, 8 and 16 contexts.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Both FCM and AM outperform skipgram embeddings for up to 16 contexts, but AM† is significantly better than FCM† for more than one context.For VecMap, given two models m1 and m2, we count the number of times that the embedding assigned to a word w by m1 is closer to the gold embedding of w than the embedding assigned by m2; we do so for each number of occurrences separately. As can be seen, both FCM and AM perform significantly better than the original skipgram embeddings for up to 16 contexts, but the difference between FCM and AM is only significant given one or four contexts. However, for the variants that include downsampled words during training, AM† (using attention) is significantly better than FCM† (without attention) given more than one context." } ], "images": [ "scigen/train/3623.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Significance results for the VecMap evaluation. Each cell lists the numbers of word occurrences for which the model of the row performs significantly better than the model of the column (p<0.05). For example, FCM is significantly better than skipgram for 1, 2, 4, 8 and 16 contexts." ], "reasoning": "Both FCM and AM outperform skipgram embeddings for up to 16 contexts, but AM† is significantly better than FCM† for more than one context.", "description": "For VecMap, given two models m1 and m2, we count the number of times that the embedding assigned to a word w by m1 is closer to the gold embedding of w than the embedding assigned by m2; we do so for each number of occurrences separately. As can be seen, both FCM and AM perform significantly better than the original skipgram embeddings for up to 16 contexts, but the difference between FCM and AM is only significant given one or four contexts. However, for the variants that include downsampled words during training, AM† (using attention) is significantly better than FCM† (without attention) given more than one context.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 6: Significance results for the VecMap evaluation. Each cell lists the numbers of word occurrences for which the model of the row performs significantly better than the model of the column (p<0.05). For example, FCM is significantly better than skipgram for 1, 2, 4, 8 and 16 contexts.'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model performs significantly better than FCM† for more than one context?\n" }, { "role": "assistant", "content": "According to the description, AM† (using attention) is significantly better than FCM† (without attention) given more than one context. So, the answer is {\"answer\": \"AM†\"}" } ], "images": [ "scigen/train/3623.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Significance results for the VecMap evaluation. Each cell lists the numbers of word occurrences for which the model of the row performs significantly better than the model of the column (p<0.05). For example, FCM is significantly better than skipgram for 1, 2, 4, 8 and 16 contexts." ], "reasoning": "According to the description, AM† (using attention) is significantly better than FCM† (without attention) given more than one context.", "question": "Which model performs significantly better than FCM† for more than one context?", "answer": "So, the answer is {\"answer\": \"AM†\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nAM performs significantly better than FCM for up to 16 contexts.\n" }, { "role": "assistant", "content": "The description states that both FCM and AM perform significantly better than the original skipgram embeddings for up to 16 contexts, but does not specify that AM performs significantly better than FCM for up to 16 contexts. So, the answer is {\"answer\": \"not enough info\"}" } ], "images": [ "scigen/train/3623.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Significance results for the VecMap evaluation. Each cell lists the numbers of word occurrences for which the model of the row performs significantly better than the model of the column (p<0.05). For example, FCM is significantly better than skipgram for 1, 2, 4, 8 and 16 contexts." ], "reasoning": "The description states that both FCM and AM perform significantly better than the original skipgram embeddings for up to 16 contexts, but does not specify that AM performs significantly better than FCM for up to 16 contexts.", "statement": "AM performs significantly better than FCM for up to 16 contexts.", "answer": "So, the answer is {\"answer\": \"not enough info\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 6: Match percentages (M%) showing fraction of stories for which generated continuations contain the desired frame. Additional results are in the Appendix.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Most frames have high match accuracies, but frames like 'Food' and 'Observable_body_parts' have lower accuracy due to their concrete nature.In order to check how frequently the generated output matches the desired frames, we generate continuations for the top 100 frames (one frame for each continuation) for all stories in the development set. We check whether the frame appears in the specific continuation using SEMAFOR. Most frames have very high match accuracies, but there are a few frames with much lower accuracy, such as “Food” and “Observable_body_parts”. These are more concrete frames that may be difficult to reasonably incorporate in certain story contexts." } ], "images": [ "scigen/train/3624.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Match percentages (M%) showing fraction of stories for which generated continuations contain the desired frame. Additional results are in the Appendix." ], "reasoning": "Most frames have high match accuracies, but frames like 'Food' and 'Observable_body_parts' have lower accuracy due to their concrete nature.", "description": "In order to check how frequently the generated output matches the desired frames, we generate continuations for the top 100 frames (one frame for each continuation) for all stories in the development set. We check whether the frame appears in the specific continuation using SEMAFOR. Most frames have very high match accuracies, but there are a few frames with much lower accuracy, such as “Food” and “Observable_body_parts”. These are more concrete frames that may be difficult to reasonably incorporate in certain story contexts.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 6: Match percentages (M%) showing fraction of stories for which generated continuations contain the desired frame. Additional results are in the Appendix.', solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich frame has the lowest match accuracy in the Locative_relation category?\n" }, { "role": "assistant", "content": "By examining the match accuracies in the Locative_relation category, 'Observable_body_parts' has the lowest accuracy at 74.2%. So, the answer is {\"answer\": \"Observable_body_parts\"}" } ], "images": [ "scigen/train/3624.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Match percentages (M%) showing fraction of stories for which generated continuations contain the desired frame. Additional results are in the Appendix." ], "reasoning": "By examining the match accuracies in the Locative_relation category, 'Observable_body_parts' has the lowest accuracy at 74.2%.", "question": "Which frame has the lowest match accuracy in the Locative_relation category?", "answer": "So, the answer is {\"answer\": \"Observable_body_parts\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe frame 'Self_motion' has a match accuracy higher than 90%.\n" }, { "role": "assistant", "content": "The table shows that the match accuracy for 'Self_motion' is 86.4%, which is lower than 90%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3624.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Match percentages (M%) showing fraction of stories for which generated continuations contain the desired frame. Additional results are in the Appendix." ], "reasoning": "The table shows that the match accuracy for 'Self_motion' is 86.4%, which is lower than 90%.", "statement": "The frame 'Self_motion' has a match accuracy higher than 90%.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 8: Automatic metrics for baseline system and when using oracle values for control attributes. For the gold standard continuation, we report only the story scorer results because they do not require a gold standard (unlike the other metrics).'.\n" }, { "role": "assistant", "content": "Using oracle attributes, the perplexity decreases and the ROUGE and BLEU scores increase compared to the seq2seq baseline. Frame semantics are particularly useful as control attributes.Unsurprisingly, compared with the seq2seq baseline, the perplexity decreases and the ROUGE and BLEU scores increase with oracle attributes. We also find that the scores from the story scorer, which does not use the gold standard while scoring, also show improvements over the baseline. We note that frame semantics form one of the most useful control attributes, aside from those that use words directly." } ], "images": [ "scigen/train/3625.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Automatic metrics for baseline system and when using oracle values for control attributes. For the gold standard continuation, we report only the story scorer results because they do not require a gold standard (unlike the other metrics)." ], "reasoning": "Using oracle attributes, the perplexity decreases and the ROUGE and BLEU scores increase compared to the seq2seq baseline. Frame semantics are particularly useful as control attributes.", "description": "Unsurprisingly, compared with the seq2seq baseline, the perplexity decreases and the ROUGE and BLEU scores increase with oracle attributes. We also find that the scores from the story scorer, which does not use the gold standard while scoring, also show improvements over the baseline. We note that frame semantics form one of the most useful control attributes, aside from those that use words directly.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 8: Automatic metrics for baseline system and when using oracle values for control attributes. For the gold standard continuation, we report only the story scorer results because they do not require a gold standard (unlike the other metrics).', solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich attribute shows the highest ROUGE-1 score and how does it compare to the seq2seq baseline?\n" }, { "role": "assistant", "content": "The table shows that the BOW attribute has the highest ROUGE-1 score of 54.5, which is significantly higher than the seq2seq baseline score of 27.0. So, the answer is {\"answer\": \"BOW\"}" } ], "images": [ "scigen/train/3625.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Automatic metrics for baseline system and when using oracle values for control attributes. For the gold standard continuation, we report only the story scorer results because they do not require a gold standard (unlike the other metrics)." ], "reasoning": "The table shows that the BOW attribute has the highest ROUGE-1 score of 54.5, which is significantly higher than the seq2seq baseline score of 27.0.", "question": "Which attribute shows the highest ROUGE-1 score and how does it compare to the seq2seq baseline?", "answer": "So, the answer is {\"answer\": \"BOW\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe frames attribute has the lowest perplexity score.\n" }, { "role": "assistant", "content": "The table indicates that the frames attribute has a perplexity score of 15.0, which is the lowest among all listed attributes. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3625.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Automatic metrics for baseline system and when using oracle values for control attributes. For the gold standard continuation, we report only the story scorer results because they do not require a gold standard (unlike the other metrics)." ], "reasoning": "The table indicates that the frames attribute has a perplexity score of 15.0, which is the lowest among all listed attributes.", "statement": "The frames attribute has the lowest perplexity score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 10: Human preferences when given three continuations from each pair of systems.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Workers prefer BS over TS and reranking over BS, despite TS and predicted systems yielding higher diversity.We observe that workers prefer the BS baseline over TS, although TS yields higher diversity. This could be because the continuations from BS are shorter, simpler, and more fluent. In addition, we observe that workers prefer the outputs from the reranking system over BS more often than not. Although the predicted frame system yields more diverse outputs, workers still prefer BS, likely due to the difficulty in predicting frames. The reranking and predicted frame systems are both preferred to TS, though the gap is smaller with the predicted system. We also see that generating helpful suggestions is a difficult task: in many cases workers thought neither system was helpful, especially when given the outputs from BS/TS or TS/predicted." } ], "images": [ "scigen/train/3626.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Human preferences when given three continuations from each pair of systems." ], "reasoning": "Workers prefer BS over TS and reranking over BS, despite TS and predicted systems yielding higher diversity.", "description": "We observe that workers prefer the BS baseline over TS, although TS yields higher diversity. This could be because the continuations from BS are shorter, simpler, and more fluent. In addition, we observe that workers prefer the outputs from the reranking system over BS more often than not. Although the predicted frame system yields more diverse outputs, workers still prefer BS, likely due to the difficulty in predicting frames. The reranking and predicted frame systems are both preferred to TS, though the gap is smaller with the predicted system. We also see that generating helpful suggestions is a difficult task: in many cases workers thought neither system was helpful, especially when given the outputs from BS/TS or TS/predicted.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 10: Human preferences when given three continuations from each pair of systems.\nQuestion: Which system pair had the highest number of 'Human Preference 1' votes?\nGive a detailed response to the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the 'Human Preference 1' column, the highest value is 43, which corresponds to the BS and TS system pair. So, the answer is {\"answer\": \"BS and TS\"}." } ], "images": [ "scigen/train/3626.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Human preferences when given three continuations from each pair of systems." ], "reasoning": "By examining the 'Human Preference 1' column, the highest value is 43, which corresponds to the BS and TS system pair.", "question": "Which system pair had the highest number of 'Human Preference 1' votes?", "answer": "So, the answer is {\"answer\": \"BS and TS\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nWorkers prefer the reranking system over the predicted system.\n" }, { "role": "assistant", "content": "The table shows that in the reranking vs predicted comparison, reranking received 27 votes for 'Human Preference 1' and 24 votes for 'Human Preference 2', indicating a preference for reranking. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3626.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Human preferences when given three continuations from each pair of systems." ], "reasoning": "The table shows that in the reranking vs predicted comparison, reranking received 27 votes for 'Human Preference 1' and 24 votes for 'Human Preference 2', indicating a preference for reranking.", "statement": "Workers prefer the reranking system over the predicted system.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Model ablation study on the Flickr30k Entities val set.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Forcing attended regions to be similar to localized regions does not significantly improve captioning performance or grounding accuracy, likely due to noisy localized regions during initial training.Should we explicitly make attended image regions to be similar to localized image regions? One possible way to regularize the attention mechanism of the decoder is to explicitly optimize ^rt↦^rlt via KL divergence over two soft-attention weights αt and βt. We use a single run unsupervised baseline with a fix random seed as baseline model for ablation study. We can see that when explicitly forcing the attended regions to be similar to the localized regions, both the captioning performance and the grounding accuracy remain similar to the baseline (unsup.). We conjecture that this is due to the noisy localized regions at the initial training stage. When forcing the attended regions to be similar to noisy localized regions, the Language LSTM will eventually learn to not rely on the attended region at each step for generating sequence of words. To verify, we increase the weight for attention consistency loss and observed that it has lower grounding accuracy (F1all = 3.2), but the captioning will reach similar performance while taking 1.5x longer to reach convergence. Is using only the generated word for localization necessarily? This forces the localizer to rely only on the word embedding without biasing it with the memorized information from the Attention LSTM. We also measured inter-rater agreement between each pair of human subjects: 72.7% of ratings are the same, 4.9% of ratings are the opposite, and 22.4% of ratings could be ambiguous (e.g., one chose A is better, the other chose they are about the same)." } ], "images": [ "scigen/train/3627.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Model ablation study on the Flickr30k Entities val set." ], "reasoning": "Forcing attended regions to be similar to localized regions does not significantly improve captioning performance or grounding accuracy, likely due to noisy localized regions during initial training.", "description": "Should we explicitly make attended image regions to be similar to localized image regions? One possible way to regularize the attention mechanism of the decoder is to explicitly optimize ^rt↦^rlt via KL divergence over two soft-attention weights αt and βt. We use a single run unsupervised baseline with a fix random seed as baseline model for ablation study. We can see that when explicitly forcing the attended regions to be similar to the localized regions, both the captioning performance and the grounding accuracy remain similar to the baseline (unsup.). We conjecture that this is due to the noisy localized regions at the initial training stage. When forcing the attended regions to be similar to noisy localized regions, the Language LSTM will eventually learn to not rely on the attended region at each step for generating sequence of words. To verify, we increase the weight for attention consistency loss and observed that it has lower grounding accuracy (F1all = 3.2), but the captioning will reach similar performance while taking 1.5x longer to reach convergence. Is using only the generated word for localization necessarily? This forces the localizer to rely only on the word embedding without biasing it with the memorized information from the Attention LSTM. We also measured inter-rater agreement between each pair of human subjects: 72.7% of ratings are the same, 4.9% of ratings are the opposite, and 22.4% of ratings could be ambiguous (e.g., one chose A is better, the other chose they are about the same).", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 3: Model ablation study on the Flickr30k Entities val set.\nQuestion: What is the grounding accuracy (F1all) for the Cyclical model?\n" }, { "role": "assistant", "content": "The table shows the grounding accuracy (F1all) for different models. The Cyclical model has a grounding accuracy (F1all) of 5.63. So, the answer is {\"answer\": \"5.63\"}" } ], "images": [ "scigen/train/3627.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Model ablation study on the Flickr30k Entities val set." ], "reasoning": "The table shows the grounding accuracy (F1all) for different models. The Cyclical model has a grounding accuracy (F1all) of 5.63.", "question": "What is the grounding accuracy (F1all) for the Cyclical model?", "answer": "So, the answer is {\"answer\": \"5.63\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The Localizer using hA model has the highest captioning evaluation score in terms of C., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Localizer using hA model has the highest captioning evaluation score in terms of C.\n" }, { "role": "assistant", "content": "The table shows the captioning evaluation scores for different models. The Cyclical model has the highest C score of 62.2, while the Localizer using hA model has a C score of 61.8. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3627.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Model ablation study on the Flickr30k Entities val set." ], "reasoning": "The table shows the captioning evaluation scores for different models. The Cyclical model has the highest C score of 62.2, while the Localizer using hA model has a C score of 61.8.", "statement": "The Localizer using hA model has the highest captioning evaluation score in terms of C.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Performance comparison on the ActivityNet-Entities val set. *: our results are averaged across five runs. Only numbers reported by multiple runs are considered to be bolded.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The proposed method significantly improved grounding accuracy by 25% to 30% for F1all and F1loc, and around 40% for F1all per sent and F1loc per sent, compared to the baseline.ActivityNet-Entities. We can see that our baseline again achieved comparable performance to the state of the arts. The proposed method then significantly improved the grounding accuracy around 25% to 30% relative grounding accuracy improvements for both F1\\textsubscriptall and F1\\textsubscriptloc and around 40% for F1\\textsubscriptall@underlineper@underlinesent and F1\\textsubscriptloc@underlineper@underlinesent. Interestingly, we observed that baseline with grounding supervision does not improve the captioning accuracy, different from the observation in previous work Grounding performance when using a better object detector. These experimental settings follow the widely used procedure for visual captioning systems: extract regional proposal features and generate visual captions by attending to those extracted visual features. One might ask, what if we have a better object detector that can extract robust visual representation that are better aligned with the word embeddings? Will visual grounding still an issue for captioning?" } ], "images": [ "scigen/train/3628.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance comparison on the ActivityNet-Entities val set. *: our results are averaged across five runs. Only numbers reported by multiple runs are considered to be bolded." ], "reasoning": "The proposed method significantly improved grounding accuracy by 25% to 30% for F1all and F1loc, and around 40% for F1all per sent and F1loc per sent, compared to the baseline.", "description": "ActivityNet-Entities. We can see that our baseline again achieved comparable performance to the state of the arts. The proposed method then significantly improved the grounding accuracy around 25% to 30% relative grounding accuracy improvements for both F1\\textsubscriptall and F1\\textsubscriptloc and around 40% for F1\\textsubscriptall@underlineper@underlinesent and F1\\textsubscriptloc@underlineper@underlinesent. Interestingly, we observed that baseline with grounding supervision does not improve the captioning accuracy, different from the observation in previous work Grounding performance when using a better object detector. These experimental settings follow the widely used procedure for visual captioning systems: extract regional proposal features and generate visual captions by attending to those extracted visual features. One might ask, what if we have a better object detector that can extract robust visual representation that are better aligned with the word embeddings? Will visual grounding still an issue for captioning?", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Performance comparison on the ActivityNet-Entities val set. *: our results are averaged across five runs. Only numbers reported by multiple runs are considered to be bolded.', answer the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the F1loc per sent improvement percentage for the Cyclical* method compared to the Baseline* method?\n" }, { "role": "assistant", "content": "The table shows that the F1loc per sent for the Baseline* method is 31.68 (+0%) and for the Cyclical* method is 41.56 (+43%). The improvement percentage is 43%. So, the answer is {\"answer\": \"43%\"}" } ], "images": [ "scigen/train/3628.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance comparison on the ActivityNet-Entities val set. *: our results are averaged across five runs. Only numbers reported by multiple runs are considered to be bolded." ], "reasoning": "The table shows that the F1loc per sent for the Baseline* method is 31.68 (+0%) and for the Cyclical* method is 41.56 (+43%). The improvement percentage is 43%.", "question": "What is the F1loc per sent improvement percentage for the Cyclical* method compared to the Baseline* method?", "answer": "So, the answer is {\"answer\": \"43%\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Cyclical* method achieved the highest F1all score.\n" }, { "role": "assistant", "content": "The table shows that the highest F1all score is 7.30 (+100%) achieved by the Baseline* method with grounding supervision, not the Cyclical* method which has an F1all score of 4.71 (+26%). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3628.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance comparison on the ActivityNet-Entities val set. *: our results are averaged across five runs. Only numbers reported by multiple runs are considered to be bolded." ], "reasoning": "The table shows that the highest F1all score is 7.30 (+100%) achieved by the Baseline* method with grounding supervision, not the Cyclical* method which has an F1all score of 4.71 (+26%).", "statement": "The Cyclical* method achieved the highest F1all score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: Model ablation study on the Flickr30k Entities val set.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Explicitly forcing attended regions to be similar to localized regions does not significantly improve captioning performance or grounding accuracy due to noisy localized regions during initial training.Should we explicitly make attended image regions to be similar to localized image regions? One possible way to regularize the attention mechanism of the decoder is to explicitly optimize ^rt↦^rlt via KL divergence over two soft-attention weights αt and βt. We use a single run unsupervised baseline with a fix random seed as baseline model for ablation study. We can see that when explicitly forcing the attended regions to be similar to the localized regions, both the captioning performance and the grounding accuracy remain similar to the baseline (unsup.). We conjecture that this is due to the noisy localized regions at the initial training stage. When forcing the attended regions to be similar to noisy localized regions, the Language LSTM will eventually learn to not rely on the attended region at each step for generating sequence of words. To verify, we increase the weight for attention consistency loss and observed that it has lower grounding accuracy (F1all = 3.2), but the captioning will reach similar performance while taking 1.5x longer to reach convergence. Is using only the generated word for localization necessarily? This forces the localizer to rely only on the word embedding without biasing it with the memorized information from the Attention LSTM. We also measured inter-rater agreement between each pair of human subjects: 72.7% of ratings are the same, 4.9% of ratings are the opposite, and 22.4% of ratings could be ambiguous (e.g., one chose A is better, the other chose they are about the same)." } ], "images": [ "scigen/train/3629.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Model ablation study on the Flickr30k Entities val set." ], "reasoning": "Explicitly forcing attended regions to be similar to localized regions does not significantly improve captioning performance or grounding accuracy due to noisy localized regions during initial training.", "description": "Should we explicitly make attended image regions to be similar to localized image regions? One possible way to regularize the attention mechanism of the decoder is to explicitly optimize ^rt↦^rlt via KL divergence over two soft-attention weights αt and βt. We use a single run unsupervised baseline with a fix random seed as baseline model for ablation study. We can see that when explicitly forcing the attended regions to be similar to the localized regions, both the captioning performance and the grounding accuracy remain similar to the baseline (unsup.). We conjecture that this is due to the noisy localized regions at the initial training stage. When forcing the attended regions to be similar to noisy localized regions, the Language LSTM will eventually learn to not rely on the attended region at each step for generating sequence of words. To verify, we increase the weight for attention consistency loss and observed that it has lower grounding accuracy (F1all = 3.2), but the captioning will reach similar performance while taking 1.5x longer to reach convergence. Is using only the generated word for localization necessarily? This forces the localizer to rely only on the word embedding without biasing it with the memorized information from the Attention LSTM. We also measured inter-rater agreement between each pair of human subjects: 72.7% of ratings are the same, 4.9% of ratings are the opposite, and 22.4% of ratings could be ambiguous (e.g., one chose A is better, the other chose they are about the same).", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 3: Model ablation study on the Flickr30k Entities val set.' and proceed to solve the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat percentage of human evaluations found the cyclical method to be better?\n" }, { "role": "assistant", "content": "The table shows that 28.1% of human evaluations found the cyclical method to be better. So, the answer is {\"answer\": \"28.1\"}" } ], "images": [ "scigen/train/3629.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Model ablation study on the Flickr30k Entities val set." ], "reasoning": "The table shows that 28.1% of human evaluations found the cyclical method to be better.", "question": "What percentage of human evaluations found the cyclical method to be better?", "answer": "So, the answer is {\"answer\": \"28.1\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The majority of human evaluations found the baseline method to be better. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe majority of human evaluations found the baseline method to be better.\n" }, { "role": "assistant", "content": "The table shows that only 24.8% of human evaluations found the baseline method to be better, which is not the majority. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3629.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Model ablation study on the Flickr30k Entities val set." ], "reasoning": "The table shows that only 24.8% of human evaluations found the baseline method to be better, which is not the majority.", "statement": "The majority of human evaluations found the baseline method to be better.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 6: Performance comparison on the ActivityNet-Entities test set. Grounding evaluation metrics on per generated sentences are not available on the test server.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The proposed method significantly improved grounding accuracy by 25% to 30% for both F1all and F1loc, and around 40% for F1all@sent and F1loc@sent, compared to the baseline.ActivityNet-Entities. We can see that our baseline again achieved comparable performance to the state of the arts. The proposed method then significantly improved the grounding accuracy around 25% to 30% relative grounding accuracy improvements for both F1\\textsubscriptall and F1\\textsubscriptloc and around 40% for F1\\textsubscriptall@underlineper@underlinesent and F1\\textsubscriptloc@underlineper@underlinesent. Interestingly, we observed that baseline with grounding supervision does not improve the captioning accuracy, different from the observation in previous work" } ], "images": [ "scigen/train/3630.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Performance comparison on the ActivityNet-Entities test set. Grounding evaluation metrics on per generated sentences are not available on the test server." ], "reasoning": "The proposed method significantly improved grounding accuracy by 25% to 30% for both F1all and F1loc, and around 40% for F1all@sent and F1loc@sent, compared to the baseline.", "description": "ActivityNet-Entities. We can see that our baseline again achieved comparable performance to the state of the arts. The proposed method then significantly improved the grounding accuracy around 25% to 30% relative grounding accuracy improvements for both F1\\textsubscriptall and F1\\textsubscriptloc and around 40% for F1\\textsubscriptall@underlineper@underlinesent and F1\\textsubscriptloc@underlineper@underlinesent. Interestingly, we observed that baseline with grounding supervision does not improve the captioning accuracy, different from the observation in previous work", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: Performance comparison on the ActivityNet-Entities test set. Grounding evaluation metrics on per generated sentences are not available on the test server.\nQuestion: What is the F1loc improvement percentage for the Cyclical method compared to the baseline without grounding supervision?\nSolve the above question based on the table information. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The F1loc for the baseline without grounding supervision is 12.0, and for the Cyclical method, it is 15.6. The improvement percentage is calculated as ((15.6 - 12.0) / 12.0) * 100%. So, the answer is {\"answer\": \"30%\"}" } ], "images": [ "scigen/train/3630.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Performance comparison on the ActivityNet-Entities test set. Grounding evaluation metrics on per generated sentences are not available on the test server." ], "reasoning": "The F1loc for the baseline without grounding supervision is 12.0, and for the Cyclical method, it is 15.6. The improvement percentage is calculated as ((15.6 - 12.0) / 12.0) * 100%.", "question": "What is the F1loc improvement percentage for the Cyclical method compared to the baseline without grounding supervision?", "answer": "So, the answer is {\"answer\": \"30%\"}" }, { "messages": [ { "role": "user", "content": "The GVD method with grounding supervision achieved the highest F1all score. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe GVD method with grounding supervision achieved the highest F1all score.\n" }, { "role": "assistant", "content": "The table shows that the GVD method with grounding supervision has an F1all score of 7.59, which is the highest among all methods listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3630.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Performance comparison on the ActivityNet-Entities test set. Grounding evaluation metrics on per generated sentences are not available on the test server." ], "reasoning": "The table shows that the GVD method with grounding supervision has an F1all score of 7.59, which is the highest among all methods listed.", "statement": "The GVD method with grounding supervision achieved the highest F1all score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 7: Performance comparison on the Flickr30k Entities test set. All results are averaged across five runs.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Cyclical (zero-representation) improves all captioning scores over baseline with a CIDEr improvement of 2.4 and also enhances grounding accuracy per sentence.For the first variant, Cyclical (zero-loss), we observed that the captioning performance stays the same while grounding accuracy has a small improvement. On the other hand, for the second variant, Cyclical (zero-representation), we can see that all captioning scores are improved over baseline with CIDEr improved 2.4 (see . We can also see that grounding accuracy on per sentence basis further improved as well. We then conducted further experiments on both ActivityNet-Entities and Flickr30k Entities with unrealistically perfect object detector In summary: on the Flickr30k Entities test set, we observed that CIDEr is better and grounding per sentence better, on the ActivityNet-Entities val set, the captioning performances are about the same but grounding accuracy became worse, and on the Flickr30k Entities test set with unrealistically perfect object detector, captioning performances are slightly worse but grounding accuracy improved. We thus keep the most general variant ”Cyclical” which treats all words equally." } ], "images": [ "scigen/train/3631.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Performance comparison on the Flickr30k Entities test set. All results are averaged across five runs." ], "reasoning": "Cyclical (zero-representation) improves all captioning scores over baseline with a CIDEr improvement of 2.4 and also enhances grounding accuracy per sentence.", "description": "For the first variant, Cyclical (zero-loss), we observed that the captioning performance stays the same while grounding accuracy has a small improvement. On the other hand, for the second variant, Cyclical (zero-representation), we can see that all captioning scores are improved over baseline with CIDEr improved 2.4 (see . We can also see that grounding accuracy on per sentence basis further improved as well. We then conducted further experiments on both ActivityNet-Entities and Flickr30k Entities with unrealistically perfect object detector In summary: on the Flickr30k Entities test set, we observed that CIDEr is better and grounding per sentence better, on the ActivityNet-Entities val set, the captioning performances are about the same but grounding accuracy became worse, and on the Flickr30k Entities test set with unrealistically perfect object detector, captioning performances are slightly worse but grounding accuracy improved. We thus keep the most general variant ”Cyclical” which treats all words equally.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 7: Performance comparison on the Flickr30k Entities test set. All results are averaged across five runs.' to sequentially solve the question presented below. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich method shows the highest improvement in grounding accuracy per sentence?\n" }, { "role": "assistant", "content": "By comparing the 'F1all per sent' column, Cyclical (zero-representation) has the highest value of 16.30, indicating the highest improvement in grounding accuracy per sentence. So, the answer is {\"answer\": \"Cyclical (zero-representation)\"}" } ], "images": [ "scigen/train/3631.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Performance comparison on the Flickr30k Entities test set. All results are averaged across five runs." ], "reasoning": "By comparing the 'F1all per sent' column, Cyclical (zero-representation) has the highest value of 16.30, indicating the highest improvement in grounding accuracy per sentence.", "question": "Which method shows the highest improvement in grounding accuracy per sentence?", "answer": "So, the answer is {\"answer\": \"Cyclical (zero-representation)\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nCyclical (zero-loss) has the highest CIDEr score.\n" }, { "role": "assistant", "content": "The table shows that Cyclical (zero-representation) has the highest CIDEr score of 62.0, not Cyclical (zero-loss) which has a CIDEr score of 60.1. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3631.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Performance comparison on the Flickr30k Entities test set. All results are averaged across five runs." ], "reasoning": "The table shows that Cyclical (zero-representation) has the highest CIDEr score of 62.0, not Cyclical (zero-loss) which has a CIDEr score of 60.1.", "statement": "Cyclical (zero-loss) has the highest CIDEr score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 8: Performance comparison on the ActivityNet-Entities val set. All results are averaged across five runs.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Cyclical (zero-representation) improves all captioning scores over baseline, with CIDEr improved by 2.4, and grounding accuracy per sentence also improved.For the first variant, Cyclical (zero-loss), we observed that the captioning performance stays the same while grounding accuracy has a small improvement. On the other hand, for the second variant, Cyclical (zero-representation), we can see that all captioning scores are improved over baseline with CIDEr improved 2.4 (see . We can also see that grounding accuracy on per sentence basis further improved as well. We then conducted further experiments on both ActivityNet-Entities and Flickr30k Entities with unrealistically perfect object detector In summary: on the Flickr30k Entities test set, we observed that CIDEr is better and grounding per sentence better, on the ActivityNet-Entities val set, the captioning performances are about the same but grounding accuracy became worse, and on the Flickr30k Entities test set with unrealistically perfect object detector, captioning performances are slightly worse but grounding accuracy improved. We thus keep the most general variant ”Cyclical” which treats all words equally." } ], "images": [ "scigen/train/3632.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Performance comparison on the ActivityNet-Entities val set. All results are averaged across five runs." ], "reasoning": "Cyclical (zero-representation) improves all captioning scores over baseline, with CIDEr improved by 2.4, and grounding accuracy per sentence also improved.", "description": "For the first variant, Cyclical (zero-loss), we observed that the captioning performance stays the same while grounding accuracy has a small improvement. On the other hand, for the second variant, Cyclical (zero-representation), we can see that all captioning scores are improved over baseline with CIDEr improved 2.4 (see . We can also see that grounding accuracy on per sentence basis further improved as well. We then conducted further experiments on both ActivityNet-Entities and Flickr30k Entities with unrealistically perfect object detector In summary: on the Flickr30k Entities test set, we observed that CIDEr is better and grounding per sentence better, on the ActivityNet-Entities val set, the captioning performances are about the same but grounding accuracy became worse, and on the Flickr30k Entities test set with unrealistically perfect object detector, captioning performances are slightly worse but grounding accuracy improved. We thus keep the most general variant ”Cyclical” which treats all words equally.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 8: Performance comparison on the ActivityNet-Entities val set. All results are averaged across five runs.', unfold the question presented below, detailing every step of your calculation. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method shows the highest improvement in CIDEr score compared to the baseline?\n" }, { "role": "assistant", "content": "The table shows that the CIDEr score for the baseline is 45.9, for Cyclical it is 46.4, and for Cyclical (zero-representation) it is 46.6. The highest improvement is seen in Cyclical (zero-representation) with an increase of 0.7 over Cyclical and 2.4 over the baseline. So, the answer is {\"answer\": \"Cyclical (zero-representation)\"}" } ], "images": [ "scigen/train/3632.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Performance comparison on the ActivityNet-Entities val set. All results are averaged across five runs." ], "reasoning": "The table shows that the CIDEr score for the baseline is 45.9, for Cyclical it is 46.4, and for Cyclical (zero-representation) it is 46.6. The highest improvement is seen in Cyclical (zero-representation) with an increase of 0.7 over Cyclical and 2.4 over the baseline.", "question": "Which method shows the highest improvement in CIDEr score compared to the baseline?", "answer": "So, the answer is {\"answer\": \"Cyclical (zero-representation)\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Cyclical (zero-representation) has the highest F1all grounding accuracy., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nCyclical (zero-representation) has the highest F1all grounding accuracy.\n" }, { "role": "assistant", "content": "The table shows that the F1all grounding accuracy for Cyclical (zero-representation) is 4.48, which is lower than Cyclical's 4.68. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3632.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Performance comparison on the ActivityNet-Entities val set. All results are averaged across five runs." ], "reasoning": "The table shows that the F1all grounding accuracy for Cyclical (zero-representation) is 4.48, which is lower than Cyclical's 4.68.", "statement": "Cyclical (zero-representation) has the highest F1all grounding accuracy.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 9: Grounding performance when using better object detector on the Flickr30k Entities test set (results are averaged three runs).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Cyclical (zero-representation) improves all captioning scores over baseline and further improves grounding accuracy per sentence, while Cyclical (zero-loss) shows small improvement in grounding accuracy with unchanged captioning performance.For the first variant, Cyclical (zero-loss), we observed that the captioning performance stays the same while grounding accuracy has a small improvement. On the other hand, for the second variant, Cyclical (zero-representation), we can see that all captioning scores are improved over baseline with CIDEr improved 2.4 (see . We can also see that grounding accuracy on per sentence basis further improved as well. We then conducted further experiments on both ActivityNet-Entities and Flickr30k Entities with unrealistically perfect object detector In summary: on the Flickr30k Entities test set, we observed that CIDEr is better and grounding per sentence better, on the ActivityNet-Entities val set, the captioning performances are about the same but grounding accuracy became worse, and on the Flickr30k Entities test set with unrealistically perfect object detector, captioning performances are slightly worse but grounding accuracy improved. We thus keep the most general variant ”Cyclical” which treats all words equally." } ], "images": [ "scigen/train/3633.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Grounding performance when using better object detector on the Flickr30k Entities test set (results are averaged three runs)." ], "reasoning": "Cyclical (zero-representation) improves all captioning scores over baseline and further improves grounding accuracy per sentence, while Cyclical (zero-loss) shows small improvement in grounding accuracy with unchanged captioning performance.", "description": "For the first variant, Cyclical (zero-loss), we observed that the captioning performance stays the same while grounding accuracy has a small improvement. On the other hand, for the second variant, Cyclical (zero-representation), we can see that all captioning scores are improved over baseline with CIDEr improved 2.4 (see . We can also see that grounding accuracy on per sentence basis further improved as well. We then conducted further experiments on both ActivityNet-Entities and Flickr30k Entities with unrealistically perfect object detector In summary: on the Flickr30k Entities test set, we observed that CIDEr is better and grounding per sentence better, on the ActivityNet-Entities val set, the captioning performances are about the same but grounding accuracy became worse, and on the Flickr30k Entities test set with unrealistically perfect object detector, captioning performances are slightly worse but grounding accuracy improved. We thus keep the most general variant ”Cyclical” which treats all words equally.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 9: Grounding performance when using better object detector on the Flickr30k Entities test set (results are averaged three runs).', answer the following question. Give detailed solution consisting of each step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich method shows the highest grounding accuracy per sentence?\n" }, { "role": "assistant", "content": "By comparing the 'Grounding Evaluation' column for 'F1all per sent', the highest value is 48.99 for Cyclical (zero-representation). So, the answer is {\"answer\": \"Cyclical (zero-representation)\"}" } ], "images": [ "scigen/train/3633.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Grounding performance when using better object detector on the Flickr30k Entities test set (results are averaged three runs)." ], "reasoning": "By comparing the 'Grounding Evaluation' column for 'F1all per sent', the highest value is 48.99 for Cyclical (zero-representation).", "question": "Which method shows the highest grounding accuracy per sentence?", "answer": "So, the answer is {\"answer\": \"Cyclical (zero-representation)\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nCyclical (zero-representation) has the highest CIDEr score among all methods.\n" }, { "role": "assistant", "content": "By comparing the 'Captioning Evaluation' column for 'C', the highest CIDEr score is 80.2 for Cyclical, not Cyclical (zero-representation). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3633.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Grounding performance when using better object detector on the Flickr30k Entities test set (results are averaged three runs)." ], "reasoning": "By comparing the 'Captioning Evaluation' column for 'C', the highest CIDEr score is 80.2 for Cyclical, not Cyclical (zero-representation).", "statement": "Cyclical (zero-representation) has the highest CIDEr score among all methods.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 11: Performance comparison on the Flickr30k Entities val set with different weightings on decoding and reconstruction losses. All results are averaged across five runs.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The grid search on the validation set for loss weighting consistently improved both captioning and grounding accuracy compared to the baseline.The weighting between the two losses was chosen with a grid search on the val set. We can see that when comparing to the baseline, all different loss weightings consistently improved both captioning and grounding accuracy." } ], "images": [ "scigen/train/3634.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Performance comparison on the Flickr30k Entities val set with different weightings on decoding and reconstruction losses. All results are averaged across five runs." ], "reasoning": "The grid search on the validation set for loss weighting consistently improved both captioning and grounding accuracy compared to the baseline.", "description": "The weighting between the two losses was chosen with a grid search on the val set. We can see that when comparing to the baseline, all different loss weightings consistently improved both captioning and grounding accuracy.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 11: Performance comparison on the Flickr30k Entities val set with different weightings on decoding and reconstruction losses. All results are averaged across five runs.', solve this question and give detail solutions. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich loss weighting resulted in the highest F1loc per sent score?\n" }, { "role": "assistant", "content": "By comparing the F1loc per sent scores for all loss weightings, the highest score is 15.41, which corresponds to the (0.2, 0.8) weighting. So, the answer is {\"answer\": \"(0.2, 0.8)\"}." } ], "images": [ "scigen/train/3634.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Performance comparison on the Flickr30k Entities val set with different weightings on decoding and reconstruction losses. All results are averaged across five runs." ], "reasoning": "By comparing the F1loc per sent scores for all loss weightings, the highest score is 15.41, which corresponds to the (0.2, 0.8) weighting.", "question": "Which loss weighting resulted in the highest F1loc per sent score?", "answer": "So, the answer is {\"answer\": \"(0.2, 0.8)\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe (0.6, 0.4) loss weighting improved the B@1 score compared to the baseline.\n" }, { "role": "assistant", "content": "The B@1 score for the baseline is 69.7, while the B@1 score for the (0.6, 0.4) loss weighting is 70.4, which is an improvement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3634.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Performance comparison on the Flickr30k Entities val set with different weightings on decoding and reconstruction losses. All results are averaged across five runs." ], "reasoning": "The B@1 score for the baseline is 69.7, while the B@1 score for the (0.6, 0.4) loss weighting is 70.4, which is an improvement.", "statement": "The (0.6, 0.4) loss weighting improved the B@1 score compared to the baseline.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 6: Results on the 2-variable thematic-fit evaluation. Spearman’s ρ correlation.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The synchronous training regime, particularly the sync+shared variant, leads to peak performance across all evaluation sets, outperforming asynchronous training.Thematic-Fit Evaluation. (G15), and 2) current state-of-the-art tensor-based neural model by Tilk et al. (TK16). We find that vectors taken from the model trained in the joint 3-group SVO setup perform on a par with state-of-the-art models also in the 2-group evaluation on SV and VO subsets. Vectors trained explicitly in the 2-group setup using three times more data lead to substantial improvements on PADO414. As a general finding, our function-specific approach leads to peak performance on both data sets. The results are similar with 25-dim SVO vectors. We reach the peak scores on all evaluation sets using the sync+shared variant. We suspect that asynchronous training deteriorates performance because each sub-network overwrites the updates of other sub-networks as their training is not tied through a joint loss function. On the other hand, the synchronous training regime guides the model towards making updates that can benefit all sub-networks." } ], "images": [ "scigen/train/3635.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results on the 2-variable thematic-fit evaluation. Spearman’s ρ correlation." ], "reasoning": "The synchronous training regime, particularly the sync+shared variant, leads to peak performance across all evaluation sets, outperforming asynchronous training.", "description": "Thematic-Fit Evaluation. (G15), and 2) current state-of-the-art tensor-based neural model by Tilk et al. (TK16). We find that vectors taken from the model trained in the joint 3-group SVO setup perform on a par with state-of-the-art models also in the 2-group evaluation on SV and VO subsets. Vectors trained explicitly in the 2-group setup using three times more data lead to substantial improvements on PADO414. As a general finding, our function-specific approach leads to peak performance on both data sets. The results are similar with 25-dim SVO vectors. We reach the peak scores on all evaluation sets using the sync+shared variant. We suspect that asynchronous training deteriorates performance because each sub-network overwrites the updates of other sub-networks as their training is not tied through a joint loss function. On the other hand, the synchronous training regime guides the model towards making updates that can benefit all sub-networks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 6: Results on the 2-variable thematic-fit evaluation. Spearman’s ρ correlation.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: Which training regime achieves the highest score on the KS108 Addition evaluation set?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows that the sync+shared variant achieves the highest score of 0.78 on the KS108 Addition evaluation set. So, the answer is {\"answer\": \"0.78\"}." } ], "images": [ "scigen/train/3635.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results on the 2-variable thematic-fit evaluation. Spearman’s ρ correlation." ], "reasoning": "The table shows that the sync+shared variant achieves the highest score of 0.78 on the KS108 Addition evaluation set.", "question": "Which training regime achieves the highest score on the KS108 Addition evaluation set?", "answer": "So, the answer is {\"answer\": \"0.78\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The async shared training regime achieves the highest score on the GS199 Network evaluation set. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe async shared training regime achieves the highest score on the GS199 Network evaluation set.\n" }, { "role": "assistant", "content": "The table shows that the sync shared training regime achieves the highest score of 0.52 on the GS199 Network evaluation set, not the async shared regime. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3635.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results on the 2-variable thematic-fit evaluation. Spearman’s ρ correlation." ], "reasoning": "The table shows that the sync shared training regime achieves the highest score of 0.52 on the GS199 Network evaluation set, not the async shared regime.", "statement": "The async shared training regime achieves the highest score on the GS199 Network evaluation set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 6: Results on the 2-variable thematic-fit evaluation. Spearman’s ρ correlation.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The synchronous training regime using the sync+shared variant leads to peak performance on all evaluation sets, outperforming state-of-the-art models.Thematic-Fit Evaluation. (G15), and 2) current state-of-the-art tensor-based neural model by Tilk et al. (TK16). We find that vectors taken from the model trained in the joint 3-group SVO setup perform on a par with state-of-the-art models also in the 2-group evaluation on SV and VO subsets. Vectors trained explicitly in the 2-group setup using three times more data lead to substantial improvements on PADO414. As a general finding, our function-specific approach leads to peak performance on both data sets. The results are similar with 25-dim SVO vectors. We reach the peak scores on all evaluation sets using the sync+shared variant. We suspect that asynchronous training deteriorates performance because each sub-network overwrites the updates of other sub-networks as their training is not tied through a joint loss function. On the other hand, the synchronous training regime guides the model towards making updates that can benefit all sub-networks." } ], "images": [ "scigen/train/3636.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results on the 2-variable thematic-fit evaluation. Spearman’s ρ correlation." ], "reasoning": "The synchronous training regime using the sync+shared variant leads to peak performance on all evaluation sets, outperforming state-of-the-art models.", "description": "Thematic-Fit Evaluation. (G15), and 2) current state-of-the-art tensor-based neural model by Tilk et al. (TK16). We find that vectors taken from the model trained in the joint 3-group SVO setup perform on a par with state-of-the-art models also in the 2-group evaluation on SV and VO subsets. Vectors trained explicitly in the 2-group setup using three times more data lead to substantial improvements on PADO414. As a general finding, our function-specific approach leads to peak performance on both data sets. The results are similar with 25-dim SVO vectors. We reach the peak scores on all evaluation sets using the sync+shared variant. We suspect that asynchronous training deteriorates performance because each sub-network overwrites the updates of other sub-networks as their training is not tied through a joint loss function. On the other hand, the synchronous training regime guides the model towards making updates that can benefit all sub-networks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 6: Results on the 2-variable thematic-fit evaluation. Spearman’s ρ correlation.' to sequentially solve the question presented below. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich setup achieved the highest score on the PADO414 VO evaluation?\n" }, { "role": "assistant", "content": "The table shows that the 'Ours SV-VO (d=25)' setup achieved the highest score of 0.61 on the PADO414 VO evaluation. So, the answer is {\"answer\": \"0.61\"}." } ], "images": [ "scigen/train/3636.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results on the 2-variable thematic-fit evaluation. Spearman’s ρ correlation." ], "reasoning": "The table shows that the 'Ours SV-VO (d=25)' setup achieved the highest score of 0.61 on the PADO414 VO evaluation.", "question": "Which setup achieved the highest score on the PADO414 VO evaluation?", "answer": "So, the answer is {\"answer\": \"0.61\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The G15 baseline outperformed the TK16 baseline on the MST1444 full evaluation., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe G15 baseline outperformed the TK16 baseline on the MST1444 full evaluation.\n" }, { "role": "assistant", "content": "The table shows that the G15 baseline scored 0.33, while the TK16 baseline scored 0.38 on the MST1444 full evaluation, indicating that the TK16 baseline outperformed the G15 baseline. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3636.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results on the 2-variable thematic-fit evaluation. Spearman’s ρ correlation." ], "reasoning": "The table shows that the G15 baseline scored 0.33, while the TK16 baseline scored 0.38 on the MST1444 full evaluation, indicating that the TK16 baseline outperformed the G15 baseline.", "statement": "The G15 baseline outperformed the TK16 baseline on the MST1444 full evaluation.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: Situation Entity Type Classification Results on the Test Set of MASC+Wiki. We report accuracy (Acc) and macro-average F1 (Macro).'.\n" }, { "role": "assistant", "content": "The paragraph-level model with a CRF layer achieves the highest performance, improving macro-average F1-score and accuracy by 0.4% and 0.7% respectively over the basic paragraph-level model.The second section shows the result of our implemented clause-level Bi-LSTM baseline, which already outperforms the previous best model. This result proves the effectiveness of the Bi-LSTM + max pooling approach in clause representation learning Conneau et al. The third section reports the performance of the paragraph-level models that uses paragraph-wide contexts as input. Compared with the baseline clause-level Bi-LSTM model, the basic paragraph-level model achieves 3.5% and 3.3% of performance gains in macro-average F1-score and accuracy respectively. Building on top of the basic paragraph-level model, the CRF layer further improves the SE type prediction performance slightly by 0.4% and 0.7% in macro-average F1-score and accuracy respectively. Therefore, our full model with the CRF layer achieves the state-of-the-art performance on the MASC+Wiki corpus." } ], "images": [ "scigen/train/3637.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Situation Entity Type Classification Results on the Test Set of MASC+Wiki. We report accuracy (Acc) and macro-average F1 (Macro)." ], "reasoning": "The paragraph-level model with a CRF layer achieves the highest performance, improving macro-average F1-score and accuracy by 0.4% and 0.7% respectively over the basic paragraph-level model.", "description": "The second section shows the result of our implemented clause-level Bi-LSTM baseline, which already outperforms the previous best model. This result proves the effectiveness of the Bi-LSTM + max pooling approach in clause representation learning Conneau et al. The third section reports the performance of the paragraph-level models that uses paragraph-wide contexts as input. Compared with the baseline clause-level Bi-LSTM model, the basic paragraph-level model achieves 3.5% and 3.3% of performance gains in macro-average F1-score and accuracy respectively. Building on top of the basic paragraph-level model, the CRF layer further improves the SE type prediction performance slightly by 0.4% and 0.7% in macro-average F1-score and accuracy respectively. Therefore, our full model with the CRF layer achieves the state-of-the-art performance on the MASC+Wiki corpus.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nTable title: Table 3: Situation Entity Type Classification Results on the Test Set of MASC+Wiki. We report accuracy (Acc) and macro-average F1 (Macro).\n\nQuestion: What is the improvement in accuracy when adding a CRF layer to the basic paragraph-level model?\n" }, { "role": "assistant", "content": "The basic paragraph-level model has an accuracy of 80.0%, and the paragraph-level model with a CRF layer has an accuracy of 80.7%. The improvement is 0.7%. So, the answer is {\"answer\": \"0.7%\"}." } ], "images": [ "scigen/train/3637.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Situation Entity Type Classification Results on the Test Set of MASC+Wiki. We report accuracy (Acc) and macro-average F1 (Macro)." ], "reasoning": "The basic paragraph-level model has an accuracy of 80.0%, and the paragraph-level model with a CRF layer has an accuracy of 80.7%. The improvement is 0.7%.", "question": "What is the improvement in accuracy when adding a CRF layer to the basic paragraph-level model?", "answer": "So, the answer is {\"answer\": \"0.7%\"}." }, { "messages": [ { "role": "user", "content": "The clause-level Bi-LSTM model outperforms the GRU model by more than 5% in macro-average F1-score. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe clause-level Bi-LSTM model outperforms the GRU model by more than 5% in macro-average F1-score.\n" }, { "role": "assistant", "content": "The clause-level Bi-LSTM model has a macro-average F1-score of 73.5%, while the GRU model has a macro-average F1-score of 68.0%. The difference is 5.5%, which is more than 5%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3637.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Situation Entity Type Classification Results on the Test Set of MASC+Wiki. We report accuracy (Acc) and macro-average F1 (Macro)." ], "reasoning": "The clause-level Bi-LSTM model has a macro-average F1-score of 73.5%, while the GRU model has a macro-average F1-score of 68.0%. The difference is 5.5%, which is more than 5%.", "statement": "The clause-level Bi-LSTM model outperforms the GRU model by more than 5% in macro-average F1-score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 7: Confusion Matrix of the Paragraph-level Model + CRF on the Training Set of MASC+Wiki with 10-Fold Cross-Validation.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Improving situation entity type classification requires accurately detecting events within a clause and identifying the genericity of main referents.We can see that the four situation entity types, including two eventualities (STATE and EVENT) and two general statives (GENERIC and GENERALIZING), are often mutually confused with each other. To further improve the performance of situation entity type classification, it is important to accurately detect events within a clause (for fixing STATE/EVENT errors) and identify the genericity of main referents (for fixing STATE/GENERIC and GENERIC/GENERALIZING errors), which can be potentially achieved by incorporating linguistic features into neural net models." } ], "images": [ "scigen/train/3638.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Confusion Matrix of the Paragraph-level Model + CRF on the Training Set of MASC+Wiki with 10-Fold Cross-Validation." ], "reasoning": "Improving situation entity type classification requires accurately detecting events within a clause and identifying the genericity of main referents.", "description": "We can see that the four situation entity types, including two eventualities (STATE and EVENT) and two general statives (GENERIC and GENERALIZING), are often mutually confused with each other. To further improve the performance of situation entity type classification, it is important to accurately detect events within a clause (for fixing STATE/EVENT errors) and identify the genericity of main referents (for fixing STATE/GENERIC and GENERIC/GENERALIZING errors), which can be potentially achieved by incorporating linguistic features into neural net models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 7: Confusion Matrix of the Paragraph-level Model + CRF on the Training Set of MASC+Wiki with 10-Fold Cross-Validation.' to sequentially solve the question presented below. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nHow many times were GENERIC types incorrectly predicted as STATE?\n" }, { "role": "assistant", "content": "To find the number of times GENERIC types were incorrectly predicted as STATE, look at the row for GENERIC (GENI) and the column for STATE (STA). The value is 1157. So, the answer is {\"answer\": \"1157\"}" } ], "images": [ "scigen/train/3638.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Confusion Matrix of the Paragraph-level Model + CRF on the Training Set of MASC+Wiki with 10-Fold Cross-Validation." ], "reasoning": "To find the number of times GENERIC types were incorrectly predicted as STATE, look at the row for GENERIC (GENI) and the column for STATE (STA). The value is 1157.", "question": "How many times were GENERIC types incorrectly predicted as STATE?", "answer": "So, the answer is {\"answer\": \"1157\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis EVENT types were more frequently misclassified as STATE than as GENERIC. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nEVENT types were more frequently misclassified as STATE than as GENERIC.\n" }, { "role": "assistant", "content": "To verify this, compare the number of EVENT types misclassified as STATE (819) with the number misclassified as GENERIC (242). Since 819 is greater than 242, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3638.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Confusion Matrix of the Paragraph-level Model + CRF on the Training Set of MASC+Wiki with 10-Fold Cross-Validation." ], "reasoning": "To verify this, compare the number of EVENT types misclassified as STATE (819) with the number misclassified as GENERIC (242). Since 819 is greater than 242, the statement is supported.", "statement": "EVENT types were more frequently misclassified as STATE than as GENERIC.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5. Results on insuranceQA. The results of models marked with ⋆ are reported from (Tran and Niedereée, 2018).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "iGGSA with multihop-seq-att achieves the highest scores on both Test1 and Test2, outperforming other models.Results on insuranceQA We compare GGSA and iGGSA with baselines on insuranceQA dataset. Note that the result of our implementation for Multihop-Sequential-LSTM (our impl.) is better than the result from the original paper. This is due to some details, such as pretrained word embedding and hyperparameter tuning, are different. Besides max-pooling and attention, we also adopt multihop sequential attention as composition module for iGGSA, which is named iGGSA with multihop-seq-att. We can see that it achieves the state-of-art result on this dataset, outperforming the counterpart with LSTM as an encoder. The results verify the effectiveness of our GGSA and iGGSA." } ], "images": [ "scigen/train/3639.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5. Results on insuranceQA. The results of models marked with ⋆ are reported from (Tran and Niedereée, 2018)." ], "reasoning": "iGGSA with multihop-seq-att achieves the highest scores on both Test1 and Test2, outperforming other models.", "description": "Results on insuranceQA We compare GGSA and iGGSA with baselines on insuranceQA dataset. Note that the result of our implementation for Multihop-Sequential-LSTM (our impl.) is better than the result from the original paper. This is due to some details, such as pretrained word embedding and hyperparameter tuning, are different. Besides max-pooling and attention, we also adopt multihop sequential attention as composition module for iGGSA, which is named iGGSA with multihop-seq-att. We can see that it achieves the state-of-art result on this dataset, outperforming the counterpart with LSTM as an encoder. The results verify the effectiveness of our GGSA and iGGSA.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 5. Results on insuranceQA. The results of models marked with ⋆ are reported from (Tran and Niedereée, 2018).', solve this question and give detail solutions. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich model achieved the highest score on Test2 and what was the score?\n" }, { "role": "assistant", "content": "By examining the table, we see that 'iGGSA with multihop-seq-att' achieved the highest score on Test2 with a score of 71.40. So, the answer is {\"answer\": \"iGGSA with multihop-seq-att, 71.40\"}" } ], "images": [ "scigen/train/3639.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5. Results on insuranceQA. The results of models marked with ⋆ are reported from (Tran and Niedereée, 2018)." ], "reasoning": "By examining the table, we see that 'iGGSA with multihop-seq-att' achieved the highest score on Test2 with a score of 71.40.", "question": "Which model achieved the highest score on Test2 and what was the score?", "answer": "So, the answer is {\"answer\": \"iGGSA with multihop-seq-att, 71.40\"}" }, { "messages": [ { "role": "user", "content": "Multihop-Sequential-LSTM (our impl.) achieved a higher score on Test1 than GGSA with attention. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nMultihop-Sequential-LSTM (our impl.) achieved a higher score on Test1 than GGSA with attention.\n" }, { "role": "assistant", "content": "The table shows that Multihop-Sequential-LSTM (our impl.) scored 71.09 on Test1, while GGSA with attention scored 71.29, which is lower. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3639.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5. Results on insuranceQA. The results of models marked with ⋆ are reported from (Tran and Niedereée, 2018)." ], "reasoning": "The table shows that Multihop-Sequential-LSTM (our impl.) scored 71.09 on Test1, while GGSA with attention scored 71.29, which is lower.", "statement": "Multihop-Sequential-LSTM (our impl.) achieved a higher score on Test1 than GGSA with attention.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2. Comparison with LSTM and global self-attention on insuranceQA when using max-pooling or attention as composition module.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The GGSA model outperforms both QA-LSTM and global self-attention in both max-pooling and attention composition modules, demonstrating its effectiveness.Effectiveness of GGSA Block Firstly, we aim to prove that our proposed model GGSA as an encoder block can outperform LSTM and global self-attention. The comparison is performed in the cases of using max-pooling and attention as composition module. Because QA-LSTM and global self-attention have no interaction between questions and answers before upper structure, we don’t compare them with iGGSA. We can find that even GGSA can outperform these two baselines, which proves the effectiveness of GGSA." } ], "images": [ "scigen/train/3640.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2. Comparison with LSTM and global self-attention on insuranceQA when using max-pooling or attention as composition module." ], "reasoning": "The GGSA model outperforms both QA-LSTM and global self-attention in both max-pooling and attention composition modules, demonstrating its effectiveness.", "description": "Effectiveness of GGSA Block Firstly, we aim to prove that our proposed model GGSA as an encoder block can outperform LSTM and global self-attention. The comparison is performed in the cases of using max-pooling and attention as composition module. Because QA-LSTM and global self-attention have no interaction between questions and answers before upper structure, we don’t compare them with iGGSA. We can find that even GGSA can outperform these two baselines, which proves the effectiveness of GGSA.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 2. Comparison with LSTM and global self-attention on insuranceQA when using max-pooling or attention as composition module.\n\nQuestion: Which model achieved the highest score in Test2?\n" }, { "role": "assistant", "content": "By comparing the Test2 scores of all models, we find that GGSA with attention achieved the highest score. So, the answer is {\"answer\": \"GGSA with attention\"}." } ], "images": [ "scigen/train/3640.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2. Comparison with LSTM and global self-attention on insuranceQA when using max-pooling or attention as composition module." ], "reasoning": "By comparing the Test2 scores of all models, we find that GGSA with attention achieved the highest score.", "question": "Which model achieved the highest score in Test2?", "answer": "So, the answer is {\"answer\": \"GGSA with attention\"}." }, { "messages": [ { "role": "user", "content": "GGSA with max-pooling outperformed QA-LSTM with attention in Test1. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nGGSA with max-pooling outperformed QA-LSTM with attention in Test1.\n" }, { "role": "assistant", "content": "By comparing the Test1 scores, GGSA with max-pooling scored 68.10, while QA-LSTM with attention scored 71.10, indicating that the statement is false. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3640.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2. Comparison with LSTM and global self-attention on insuranceQA when using max-pooling or attention as composition module." ], "reasoning": "By comparing the Test1 scores, GGSA with max-pooling scored 68.10, while QA-LSTM with attention scored 71.10, indicating that the statement is false.", "statement": "GGSA with max-pooling outperformed QA-LSTM with attention in Test1.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3. Comparison of group self-attention and local self-attention. All models use max-pooling for composition.'.\n" }, { "role": "assistant", "content": "GGSA outperforms local self-attention in terms of accuracy and efficiency, while local self-attention (+gate) achieves comparable accuracy to GGSA but is slower.We have introduced that both group self-attention and local self-attention can be adopted in our GGSA model. Here, we compare these two self-attention methods in details. Local self-attention represents the model that a word in the sequence only attends to the words in the sliding window. The result of local self-attention is much lower than GGSA due to the lack of global information, which also proves the effectiveness of GGSA. We also incorporate the global information gate into local self-attention, which named local self-attention (+gate). The result of local self-attention (+gate) is comparable to GGSA, but with a much slower running speed. For efficiency consideration, we choose group self-attention as the key building block to design GGSA/iGGSA." } ], "images": [ "scigen/train/3641.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. Comparison of group self-attention and local self-attention. All models use max-pooling for composition." ], "reasoning": "GGSA outperforms local self-attention in terms of accuracy and efficiency, while local self-attention (+gate) achieves comparable accuracy to GGSA but is slower.", "description": "We have introduced that both group self-attention and local self-attention can be adopted in our GGSA model. Here, we compare these two self-attention methods in details. Local self-attention represents the model that a word in the sequence only attends to the words in the sliding window. The result of local self-attention is much lower than GGSA due to the lack of global information, which also proves the effectiveness of GGSA. We also incorporate the global information gate into local self-attention, which named local self-attention (+gate). The result of local self-attention (+gate) is comparable to GGSA, but with a much slower running speed. For efficiency consideration, we choose group self-attention as the key building block to design GGSA/iGGSA.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3. Comparison of group self-attention and local self-attention. All models use max-pooling for composition.'. Solve the following question based on the table.\n\nQuestion: Which model has the highest accuracy on Test1 and what is its running speed?\nLet's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that GGSA has the highest accuracy on Test1 with a score of 68.10, and its running speed is 0.32 seconds per batch. So, the answer is {\"answer\": \"0.32\"}" } ], "images": [ "scigen/train/3641.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. Comparison of group self-attention and local self-attention. All models use max-pooling for composition." ], "reasoning": "The table shows that GGSA has the highest accuracy on Test1 with a score of 68.10, and its running speed is 0.32 seconds per batch.", "question": "Which model has the highest accuracy on Test1 and what is its running speed?", "answer": "So, the answer is {\"answer\": \"0.32\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence Local self-attention (+gate) is faster than GGSA. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nLocal self-attention (+gate) is faster than GGSA.\n" }, { "role": "assistant", "content": "The table indicates that local self-attention (+gate) has a running speed of 1.30 seconds per batch, while GGSA has a running speed of 0.32 seconds per batch, making GGSA faster. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3641.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. Comparison of group self-attention and local self-attention. All models use max-pooling for composition." ], "reasoning": "The table indicates that local self-attention (+gate) has a running speed of 1.30 seconds per batch, while GGSA has a running speed of 0.32 seconds per batch, making GGSA faster.", "statement": "Local self-attention (+gate) is faster than GGSA.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4. Effectiveness of offset, global information gate and interaction mechanism. All models use max-pooling for composition.'.\n" }, { "role": "assistant", "content": "The addition of offset strategy and global information gate improves the accuracy of group self-attention, and the interaction mechanism further enhances the performance, making iGGSA the most effective model.Effectiveness of Offset Strategy, Global Information Gate and Interaction Mechanism We also prove the effectiveness of offset strategy, global information gate and interaction mechanism. Group self-attention is the model in which the global multi-head attention is simply replaced by group multi-head attention without offset and global information gate. We can see that the accuracy of group self-attention is lower than that of global self-attention. This is reasonable because group self-attention itself does not have the ability to model global information. By adding offset strategy to group self-attention, the accuracy is improved. This improvement is due to the expansion of its receptive field, which proves the effectiveness of offset strategy. The accuracy can be further improved by adding global information gate, which constitutes our proposed model GGSA finally. GGSA outperforms global self-attention, which indicates that it is effective to distinguish local information from global information. This result also indicates that the global information gate can incorporate the global information into the representation of words, which proves that the motivation of global information gate is right. Furthermore, iGGSA gains an improvement of 3% compared to GGSA, which proves the effectiveness of interaction mechanism proposed in this paper." } ], "images": [ "scigen/train/3642.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4. Effectiveness of offset, global information gate and interaction mechanism. All models use max-pooling for composition." ], "reasoning": "The addition of offset strategy and global information gate improves the accuracy of group self-attention, and the interaction mechanism further enhances the performance, making iGGSA the most effective model.", "description": "Effectiveness of Offset Strategy, Global Information Gate and Interaction Mechanism We also prove the effectiveness of offset strategy, global information gate and interaction mechanism. Group self-attention is the model in which the global multi-head attention is simply replaced by group multi-head attention without offset and global information gate. We can see that the accuracy of group self-attention is lower than that of global self-attention. This is reasonable because group self-attention itself does not have the ability to model global information. By adding offset strategy to group self-attention, the accuracy is improved. This improvement is due to the expansion of its receptive field, which proves the effectiveness of offset strategy. The accuracy can be further improved by adding global information gate, which constitutes our proposed model GGSA finally. GGSA outperforms global self-attention, which indicates that it is effective to distinguish local information from global information. This result also indicates that the global information gate can incorporate the global information into the representation of words, which proves that the motivation of global information gate is right. Furthermore, iGGSA gains an improvement of 3% compared to GGSA, which proves the effectiveness of interaction mechanism proposed in this paper.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 4. Effectiveness of offset, global information gate and interaction mechanism. All models use max-pooling for composition.' and proceed to solve the following question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieved the highest accuracy in Test1?\n" }, { "role": "assistant", "content": "By comparing the accuracy scores in Test1 for all models, iGGSA has the highest score of 71.33. So, the answer is {\"answer\": \"iGGSA\"}." } ], "images": [ "scigen/train/3642.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4. Effectiveness of offset, global information gate and interaction mechanism. All models use max-pooling for composition." ], "reasoning": "By comparing the accuracy scores in Test1 for all models, iGGSA has the highest score of 71.33.", "question": "Which model achieved the highest accuracy in Test1?", "answer": "So, the answer is {\"answer\": \"iGGSA\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe group self-attention model with offset strategy has a higher accuracy in Test2 than the global self-attention model.\n" }, { "role": "assistant", "content": "The table shows that the group self-attention model with offset strategy has an accuracy of 63.25 in Test2, while the global self-attention model has an accuracy of 64.03 in Test2. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3642.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4. Effectiveness of offset, global information gate and interaction mechanism. All models use max-pooling for composition." ], "reasoning": "The table shows that the group self-attention model with offset strategy has an accuracy of 63.25 in Test2, while the global self-attention model has an accuracy of 64.03 in Test2.", "statement": "The group self-attention model with offset strategy has a higher accuracy in Test2 than the global self-attention model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6. Results on yahooQA. All models are optimized by pointwise loss. The results of models marked with ⋆ are reported from (Tay et al., 2018a).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The GGSA and iGGSA models outperform all baselines, including the state-of-the-art CTRN, on the yahooQA dataset.Results on yahooQA We also evaluate our models and baselines on yahooQA dataset. We can see that our proposed models GGSA and iGGSA outperform the baselines, including the state-of-the-art baseline CTRN, by a large margin. Once again, the results on yahooQA verify the effectiveness of our GGSA and iGGSA." } ], "images": [ "scigen/train/3643.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6. Results on yahooQA. All models are optimized by pointwise loss. The results of models marked with ⋆ are reported from (Tay et al., 2018a)." ], "reasoning": "The GGSA and iGGSA models outperform all baselines, including the state-of-the-art CTRN, on the yahooQA dataset.", "description": "Results on yahooQA We also evaluate our models and baselines on yahooQA dataset. We can see that our proposed models GGSA and iGGSA outperform the baselines, including the state-of-the-art baseline CTRN, by a large margin. Once again, the results on yahooQA verify the effectiveness of our GGSA and iGGSA.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 6. Results on yahooQA. All models are optimized by pointwise loss. The results of models marked with ⋆ are reported from (Tay et al., 2018a).', solve the subsequent question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhich model achieved the highest P@1 score on the yahooQA dataset?\n" }, { "role": "assistant", "content": "By examining the table, we can see that the model 'iGGSA with attention' achieved the highest P@1 score of 74.26. So, the answer is {\"answer\": \"iGGSA with attention\"}." } ], "images": [ "scigen/train/3643.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6. Results on yahooQA. All models are optimized by pointwise loss. The results of models marked with ⋆ are reported from (Tay et al., 2018a)." ], "reasoning": "By examining the table, we can see that the model 'iGGSA with attention' achieved the highest P@1 score of 74.26.", "question": "Which model achieved the highest P@1 score on the yahooQA dataset?", "answer": "So, the answer is {\"answer\": \"iGGSA with attention\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe GGSA with max-pooling model outperformed the CTRN model in terms of MRR.\n" }, { "role": "assistant", "content": "The table shows that the GGSA with max-pooling model has an MRR of 78.15, while the CTRN model has an MRR of 77.26, indicating that GGSA with max-pooling outperformed CTRN. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3643.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6. Results on yahooQA. All models are optimized by pointwise loss. The results of models marked with ⋆ are reported from (Tay et al., 2018a)." ], "reasoning": "The table shows that the GGSA with max-pooling model has an MRR of 78.15, while the CTRN model has an MRR of 77.26, indicating that GGSA with max-pooling outperformed CTRN.", "statement": "The GGSA with max-pooling model outperformed the CTRN model in terms of MRR.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 7. The comparison of pairwise loss and pointwise loss on yahooQA.'.\n" }, { "role": "assistant", "content": "Pairwise loss improves P@1 metric, while pointwise loss enhances MRR metric for GGSA on yahooQA.The performance of GGSA is also sensitive to loss functions. We have introduced two loss functions before, which named pairwise loss and pointwise loss respectively. Here, we design a comparison of these two loss functions on yahooQA. The outputs of models play different roles by using different loss functions. For pairwise loss, the models generate the matching score of question-answer pairs for ranking. While for pointwise loss, the models generate the probability of current answer corresponding to the question. We can find that pairwise loss is better than pointwise loss for P@1 metric, while pointwise loss is superior than pairwise loss for MRR metric. Our experiments are mainly designed for comparing GGSA with other encoders like LSTM, rather than comparing upper structures. So the loss function adopted in each dataset is selected for fair comparison. With this comparison of these two loss functions, the state-of-the-art P@1 result on yahooQA can be further improved by using pairwise loss." } ], "images": [ "scigen/train/3644.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7. The comparison of pairwise loss and pointwise loss on yahooQA." ], "reasoning": "Pairwise loss improves P@1 metric, while pointwise loss enhances MRR metric for GGSA on yahooQA.", "description": "The performance of GGSA is also sensitive to loss functions. We have introduced two loss functions before, which named pairwise loss and pointwise loss respectively. Here, we design a comparison of these two loss functions on yahooQA. The outputs of models play different roles by using different loss functions. For pairwise loss, the models generate the matching score of question-answer pairs for ranking. While for pointwise loss, the models generate the probability of current answer corresponding to the question. We can find that pairwise loss is better than pointwise loss for P@1 metric, while pointwise loss is superior than pairwise loss for MRR metric. Our experiments are mainly designed for comparing GGSA with other encoders like LSTM, rather than comparing upper structures. So the loss function adopted in each dataset is selected for fair comparison. With this comparison of these two loss functions, the state-of-the-art P@1 result on yahooQA can be further improved by using pairwise loss.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 7. The comparison of pairwise loss and pointwise loss on yahooQA.', answer the question below by showing each progressive step toward the answer. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model and loss function combination achieves the highest P@1 score?\n" }, { "role": "assistant", "content": "By examining the table, the highest P@1 score is achieved by iGGSA with attention using pairwise loss, with a score of 78.45. So, the answer is {\"answer\": \"iGGSA with attention (pairwise loss)\"}" } ], "images": [ "scigen/train/3644.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7. The comparison of pairwise loss and pointwise loss on yahooQA." ], "reasoning": "By examining the table, the highest P@1 score is achieved by iGGSA with attention using pairwise loss, with a score of 78.45.", "question": "Which model and loss function combination achieves the highest P@1 score?", "answer": "So, the answer is {\"answer\": \"iGGSA with attention (pairwise loss)\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nGGSA with max-pooling using pairwise loss has a higher MRR than GGSA with max-pooling using pointwise loss.\n" }, { "role": "assistant", "content": "The table shows that GGSA with max-pooling using pairwise loss has an MRR of 76.40, while GGSA with max-pooling using pointwise loss has an MRR of 78.15, indicating that the statement is incorrect. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3644.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7. The comparison of pairwise loss and pointwise loss on yahooQA." ], "reasoning": "The table shows that GGSA with max-pooling using pairwise loss has an MRR of 76.40, while GGSA with max-pooling using pointwise loss has an MRR of 78.15, indicating that the statement is incorrect.", "statement": "GGSA with max-pooling using pairwise loss has a higher MRR than GGSA with max-pooling using pointwise loss.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 8: Word-overlap metrics scores (see Section 7.1) for all primary systems, plus the average of all metrics’ values normalised into the 0-1 range. The list is sorted by the normalised average; any values higher than the corresponding baseline are marked in bold. System architectures are coded with colours and symbols: ♡seq2seq, ♢other data-driven, ♣rule-based, ♠template-based.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The TGen baseline is very strong in word-overlap metrics, with no primary system beating it in all metrics. Slug comes close, and some systems beat TGen in one metric but not others.It is apparent that the TGen baseline is very strong in terms of word-overlap metrics: No primary system is able to beat it in terms of all metrics, or in terms of the normalised metrics’ mean – only Slug comes very close. Several other systems manage to beat TGen in one of the metrics but not in others. All five metrics showed a very similar pattern. For comparison, we also measure similarity of system outputs against the reference texts in the test set, as well as a subset of the test set with a single, randomly sampled reference text per MR." } ], "images": [ "scigen/train/3645.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Word-overlap metrics scores (see Section 7.1) for all primary systems, plus the average of all metrics’ values normalised into the 0-1 range. The list is sorted by the normalised average; any values higher than the corresponding baseline are marked in bold. System architectures are coded with colours and symbols: ♡seq2seq, ♢other data-driven, ♣rule-based, ♠template-based." ], "reasoning": "The TGen baseline is very strong in word-overlap metrics, with no primary system beating it in all metrics. Slug comes close, and some systems beat TGen in one metric but not others.", "description": "It is apparent that the TGen baseline is very strong in terms of word-overlap metrics: No primary system is able to beat it in terms of all metrics, or in terms of the normalised metrics’ mean – only Slug comes very close. Several other systems manage to beat TGen in one of the metrics but not in others. All five metrics showed a very similar pattern. For comparison, we also measure similarity of system outputs against the reference texts in the test set, as well as a subset of the test set with a single, randomly sampled reference text per MR.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: 'Table 8: Word-overlap metrics scores (see Section 7.1) for all primary systems, plus the average of all metrics’ values normalised into the 0-1 range. The list is sorted by the normalised average; any values higher than the corresponding baseline are marked in bold. System architectures are coded with colours and symbols: ♡seq2seq, ♢other data-driven, ♣rule-based, ♠template-based.'\nQuestion: Which system has the highest NIST score?\n" }, { "role": "assistant", "content": "To find the system with the highest NIST score, look at the NIST column and identify the highest value. The highest NIST score is 8.6130, achieved by the system [0.5pt/2pt]. So, the answer is {\"answer\": \"[0.5pt/2pt]\"}." } ], "images": [ "scigen/train/3645.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Word-overlap metrics scores (see Section 7.1) for all primary systems, plus the average of all metrics’ values normalised into the 0-1 range. The list is sorted by the normalised average; any values higher than the corresponding baseline are marked in bold. System architectures are coded with colours and symbols: ♡seq2seq, ♢other data-driven, ♣rule-based, ♠template-based." ], "reasoning": "To find the system with the highest NIST score, look at the NIST column and identify the highest value. The highest NIST score is 8.6130, achieved by the system [0.5pt/2pt].", "question": "Which system has the highest NIST score?", "answer": "So, the answer is {\"answer\": \"[0.5pt/2pt]\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe system Harv has the highest ROUGE-L score.\n" }, { "role": "assistant", "content": "To verify this statement, look at the ROUGE-L column and compare the scores. The highest ROUGE-L score is 0.7083, achieved by the system Zhang, not Harv. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3645.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Word-overlap metrics scores (see Section 7.1) for all primary systems, plus the average of all metrics’ values normalised into the 0-1 range. The list is sorted by the normalised average; any values higher than the corresponding baseline are marked in bold. System architectures are coded with colours and symbols: ♡seq2seq, ♢other data-driven, ♣rule-based, ♠template-based." ], "reasoning": "To verify this statement, look at the ROUGE-L column and compare the scores. The highest ROUGE-L score is 0.7083, achieved by the system Zhang, not Harv.", "statement": "The system Harv has the highest ROUGE-L score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Figure 5: Similarity of the systems’ outputs as measured by automatic metrics (mean of normalised BLEU, NIST, METEOR, ROUGE-L and CIDEr where one system output is used as reference). Systems are sorted by their architecture. For comparison, we also include metrics values against the full test set with multiple references (test set all) and against a single-reference randomly sampled subset of the test set (test set rand). The table on the right shows mean values of similarity of each system against all other systems (average over columns on the left, excluding the 1st line). System architectures are coded with colours and symbols: ♡seq2seq, ♢other data-driven, ♣rule-based, ♠template-based.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "All five metrics showed similar patterns, with systems aiming at output diversity exhibiting lower similarity, while rule-based and template-based systems showed less similarity compared to data-driven systems.All five metrics showed a very similar pattern. For comparison, we also measure similarity of system outputs against the reference texts in the test set, as well as a subset of the test set with a single, randomly sampled reference text per MR. The exception to this rule in case of the Chen and Sheff2 systems can be explained by the brevity of their outputs (cf. Systems that aim at output diversity (ZHAW1, ZHAW2, Slug-alt and mainly Adapt) also exhibit lowered similarity of their outputs to those of other systems, which might indicate that their outputs are indeed more original. The outputs of rule-based and template-based systems are markedly less similar to other outputs than that of the data-driven systems." } ], "images": [ "scigen/train/3646.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 5: Similarity of the systems’ outputs as measured by automatic metrics (mean of normalised BLEU, NIST, METEOR, ROUGE-L and CIDEr where one system output is used as reference). Systems are sorted by their architecture. For comparison, we also include metrics values against the full test set with multiple references (test set all) and against a single-reference randomly sampled subset of the test set (test set rand). The table on the right shows mean values of similarity of each system against all other systems (average over columns on the left, excluding the 1st line). System architectures are coded with colours and symbols: ♡seq2seq, ♢other data-driven, ♣rule-based, ♠template-based." ], "reasoning": "All five metrics showed similar patterns, with systems aiming at output diversity exhibiting lower similarity, while rule-based and template-based systems showed less similarity compared to data-driven systems.", "description": "All five metrics showed a very similar pattern. For comparison, we also measure similarity of system outputs against the reference texts in the test set, as well as a subset of the test set with a single, randomly sampled reference text per MR. The exception to this rule in case of the Chen and Sheff2 systems can be explained by the brevity of their outputs (cf. Systems that aim at output diversity (ZHAW1, ZHAW2, Slug-alt and mainly Adapt) also exhibit lowered similarity of their outputs to those of other systems, which might indicate that their outputs are indeed more original. The outputs of rule-based and template-based systems are markedly less similar to other outputs than that of the data-driven systems.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Figure 5: Similarity of the systems’ outputs as measured by automatic metrics (mean of normalised BLEU, NIST, METEOR, ROUGE-L and CIDEr where one system output is used as reference). Systems are sorted by their architecture. For comparison, we also include metrics values against the full test set with multiple references (test set all) and against a single-reference randomly sampled subset of the test set (test set rand). The table on the right shows mean values of similarity of each system against all other systems (average over columns on the left, excluding the 1st line). System architectures are coded with colours and symbols: ♡seq2seq, ♢other data-driven, ♣rule-based, ♠template-based.', unfold the steps to solve the question given next. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which system had the highest mean similarity score?\n" }, { "role": "assistant", "content": "To find the system with the highest mean similarity score, look at the 'Mean' column and identify the highest value, which is 0.48, corresponding to the TGen system. So, the answer is {\"answer\": \"TGen\"}." } ], "images": [ "scigen/train/3646.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 5: Similarity of the systems’ outputs as measured by automatic metrics (mean of normalised BLEU, NIST, METEOR, ROUGE-L and CIDEr where one system output is used as reference). Systems are sorted by their architecture. For comparison, we also include metrics values against the full test set with multiple references (test set all) and against a single-reference randomly sampled subset of the test set (test set rand). The table on the right shows mean values of similarity of each system against all other systems (average over columns on the left, excluding the 1st line). System architectures are coded with colours and symbols: ♡seq2seq, ♢other data-driven, ♣rule-based, ♠template-based." ], "reasoning": "To find the system with the highest mean similarity score, look at the 'Mean' column and identify the highest value, which is 0.48, corresponding to the TGen system.", "question": "Which system had the highest mean similarity score?", "answer": "So, the answer is {\"answer\": \"TGen\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The system with the lowest mean similarity score is FORGe1., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe system with the lowest mean similarity score is FORGe1.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'Mean' column and identify the lowest value, which is 0.31, corresponding to the FORGe1 system. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3646.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 5: Similarity of the systems’ outputs as measured by automatic metrics (mean of normalised BLEU, NIST, METEOR, ROUGE-L and CIDEr where one system output is used as reference). Systems are sorted by their architecture. For comparison, we also include metrics values against the full test set with multiple references (test set all) and against a single-reference randomly sampled subset of the test set (test set rand). The table on the right shows mean values of similarity of each system against all other systems (average over columns on the left, excluding the 1st line). System architectures are coded with colours and symbols: ♡seq2seq, ♢other data-driven, ♣rule-based, ♠template-based." ], "reasoning": "To verify this statement, look at the 'Mean' column and identify the lowest value, which is 0.31, corresponding to the FORGe1 system.", "statement": "The system with the lowest mean similarity score is FORGe1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Perplexity Results on Linked WikiText-2. Results for models marked with * are obtained using importance sampling.'.\n" }, { "role": "assistant", "content": "KGLM achieves significantly lower perplexity and unknown penalized perplexity compared to other models, highlighting the importance of knowledge graphs in language modeling.We observe that the KGLM attains substantially lower perplexity than the other entity-based language models (44.1 vs. 76.1/85.4), providing strong evidence that leveraging knowledge graphs is crucial for accurate language modeling. Furthermore, KGLM significantly outperforms all models in unknown penalized perplexity, demonstrating its ability to generate rare tokens." } ], "images": [ "scigen/train/3647.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Perplexity Results on Linked WikiText-2. Results for models marked with * are obtained using importance sampling." ], "reasoning": "KGLM achieves significantly lower perplexity and unknown penalized perplexity compared to other models, highlighting the importance of knowledge graphs in language modeling.", "description": "We observe that the KGLM attains substantially lower perplexity than the other entity-based language models (44.1 vs. 76.1/85.4), providing strong evidence that leveraging knowledge graphs is crucial for accurate language modeling. Furthermore, KGLM significantly outperforms all models in unknown penalized perplexity, demonstrating its ability to generate rare tokens.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 3: Perplexity Results on Linked WikiText-2. Results for models marked with * are obtained using importance sampling.' and proceed to solve the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model has the lowest unknown penalized perplexity (UPP) and what is its value?\n" }, { "role": "assistant", "content": "By comparing the UPP values in the table, KGLM has the lowest UPP value of 88.5. So, the answer is {\"answer\": \"KGLM, 88.5\"}" } ], "images": [ "scigen/train/3647.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Perplexity Results on Linked WikiText-2. Results for models marked with * are obtained using importance sampling." ], "reasoning": "By comparing the UPP values in the table, KGLM has the lowest UPP value of 88.5.", "question": "Which model has the lowest unknown penalized perplexity (UPP) and what is its value?", "answer": "So, the answer is {\"answer\": \"KGLM, 88.5\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nKGLM has a perplexity (PPL) value higher than EntityCopyNet.\n" }, { "role": "assistant", "content": "The table shows that KGLM has a PPL of 44.1, which is lower than EntityCopyNet's PPL of 76.1. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3647.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Perplexity Results on Linked WikiText-2. Results for models marked with * are obtained using importance sampling." ], "reasoning": "The table shows that KGLM has a PPL of 44.1, which is lower than EntityCopyNet's PPL of 76.1.", "statement": "KGLM has a perplexity (PPL) value higher than EntityCopyNet.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: The overall alignment performance for all models on the DBP15K datasets. Numbers in bold indicate the best performance.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "BootEA achieves the highest Hits@10 score on DBP15KZH−EN, outperforming GCN and other translation-based models, while GCN captures more structural characteristics of knowledge graphs.By using a bootstrapping process to iteratively explore many unlabeled data, BootEA gives the best Hits@10 score on DBP15KZH−EN and clearly outperforms GCN and other translation-based models. It is not surprising that GCN outperforms most translation-based models, i.e., JE, MTransE, JAPE and IPTransE. By performing graph convolution over an entity’s neighbors, GCN is able to capture more structural characteristics of knowledge graphs, especially when using more GCN layers, while the translation assumption in translation-based models focuses more on the relationship among heads, tails and relations." } ], "images": [ "scigen/train/3648.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The overall alignment performance for all models on the DBP15K datasets. Numbers in bold indicate the best performance." ], "reasoning": "BootEA achieves the highest Hits@10 score on DBP15KZH−EN, outperforming GCN and other translation-based models, while GCN captures more structural characteristics of knowledge graphs.", "description": "By using a bootstrapping process to iteratively explore many unlabeled data, BootEA gives the best Hits@10 score on DBP15KZH−EN and clearly outperforms GCN and other translation-based models. It is not surprising that GCN outperforms most translation-based models, i.e., JE, MTransE, JAPE and IPTransE. By performing graph convolution over an entity’s neighbors, GCN is able to capture more structural characteristics of knowledge graphs, especially when using more GCN layers, while the translation assumption in translation-based models focuses more on the relationship among heads, tails and relations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: The overall alignment performance for all models on the DBP15K datasets. Numbers in bold indicate the best performance.', and sequentially solve the question, writing out each step of your reasoning process. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest Hits@10 score for FR-EN?\n" }, { "role": "assistant", "content": "By examining the Hits@10 scores for FR-EN in the table, RDGCN has the highest score of 95.72. So, the answer is {\"answer\": \"RDGCN\"}" } ], "images": [ "scigen/train/3648.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The overall alignment performance for all models on the DBP15K datasets. Numbers in bold indicate the best performance." ], "reasoning": "By examining the Hits@10 scores for FR-EN in the table, RDGCN has the highest score of 95.72.", "question": "Which model has the highest Hits@10 score for FR-EN?", "answer": "So, the answer is {\"answer\": \"RDGCN\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nBootEA has the highest Hits@10 score for ZH-EN.\n" }, { "role": "assistant", "content": "The table shows that BootEA has a Hits@10 score of 84.75 for ZH-EN, which is the highest among all models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3648.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The overall alignment performance for all models on the DBP15K datasets. Numbers in bold indicate the best performance." ], "reasoning": "The table shows that BootEA has a Hits@10 score of 84.75 for ZH-EN, which is the highest among all models listed.", "statement": "BootEA has the highest Hits@10 score for ZH-EN.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled '(a) De-Gur350', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The model uses character n-grams of varying lengths to capture both short suffixes and longer roots, and the experiment checks the influence of these n-grams on performance.The proposed model relies on the use of character n-grams to represent words as vectors. As mentioned in Sec. This choice was arbitrary, motivated by the fact that n-grams of these lengths will cover a wide range of information. They would include short suffixes (corresponding to conjugations and declensions for instance) as well as longer roots. In this experiment, we empirically check for the influence of the range of n-grams that we use on performance." } ], "images": [ "scigen/train/3649.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) De-Gur350" ], "reasoning": "The model uses character n-grams of varying lengths to capture both short suffixes and longer roots, and the experiment checks the influence of these n-grams on performance.", "description": "The proposed model relies on the use of character n-grams to represent words as vectors. As mentioned in Sec. This choice was arbitrary, motivated by the fact that n-grams of these lengths will cover a wide range of information. They would include short suffixes (corresponding to conjugations and declensions for instance) as well as longer roots. In this experiment, we empirically check for the influence of the range of n-grams that we use on performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title '(a) De-Gur350', unfold the steps to solve the question given next. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nQuestion: What is the highest performance score achieved using 4-grams?\n" }, { "role": "assistant", "content": "To find the highest performance score using 4-grams, look at the column labeled '4' and identify the maximum value. So, the answer is {\"answer\": \"71\"}." } ], "images": [ "scigen/train/3649.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) De-Gur350" ], "reasoning": "To find the highest performance score using 4-grams, look at the column labeled '4' and identify the maximum value.", "question": "What is the highest performance score achieved using 4-grams?", "answer": "So, the answer is {\"answer\": \"71\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe highest performance score using 5-grams is higher than the highest score using 3-grams.\n" }, { "role": "assistant", "content": "The highest score using 5-grams is 71, while the highest score using 3-grams is 70. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3649.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) De-Gur350" ], "reasoning": "The highest score using 5-grams is 71, while the highest score using 3-grams is 70. Therefore, the statement is supported.", "statement": "The highest performance score using 5-grams is higher than the highest score using 3-grams.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: Correlation between human judgement and similarity scores on word similarity datasets. We train both our model and the word2vec baseline on normalized Wikipedia dumps. Evaluation datasets contain words that are not part of the training set, so we represent them using null vectors (sisg-). With our model, we also compute vectors for unseen words by summing the n-gram vectors (sisg).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Using subword information to compute vectors for out-of-vocabulary words (sisg) is always at least as good as using null vectors (sisg-), demonstrating the advantage of character n-grams.Some words from these datasets do not appear in our training data, and thus, we cannot obtain word representation for these words using the cbow and skipgram baselines. In order to provide comparable results, we propose by default to use null vectors for these words. Since our model exploits subword information, we can also compute valid representations for out-of-vocabulary words. We do so by taking the sum of its n-gram vectors. When OOV words are represented using null vectors we refer to our method as sisg- and sisg otherwise (Subword Information Skip Gram). Moreover, computing vectors for out-of-vocabulary words (sisg) is always at least as good as not doing so (sisg-). This proves the advantage of using subword information in the form of character n-grams." } ], "images": [ "scigen/train/3650.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Correlation between human judgement and similarity scores on word similarity datasets. We train both our model and the word2vec baseline on normalized Wikipedia dumps. Evaluation datasets contain words that are not part of the training set, so we represent them using null vectors (sisg-). With our model, we also compute vectors for unseen words by summing the n-gram vectors (sisg)." ], "reasoning": "Using subword information to compute vectors for out-of-vocabulary words (sisg) is always at least as good as using null vectors (sisg-), demonstrating the advantage of character n-grams.", "description": "Some words from these datasets do not appear in our training data, and thus, we cannot obtain word representation for these words using the cbow and skipgram baselines. In order to provide comparable results, we propose by default to use null vectors for these words. Since our model exploits subword information, we can also compute valid representations for out-of-vocabulary words. We do so by taking the sum of its n-gram vectors. When OOV words are represented using null vectors we refer to our method as sisg- and sisg otherwise (Subword Information Skip Gram). Moreover, computing vectors for out-of-vocabulary words (sisg) is always at least as good as not doing so (sisg-). This proves the advantage of using subword information in the form of character n-grams.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 1: Correlation between human judgement and similarity scores on word similarity datasets. We train both our model and the word2vec baseline on normalized Wikipedia dumps. Evaluation datasets contain words that are not part of the training set, so we represent them using null vectors (sisg-). With our model, we also compute vectors for unseen words by summing the n-gram vectors (sisg).\n\nQuestion: What is the difference in performance between sisg and sisg- for the dataset 'Gur350' in German (De)?\n" }, { "role": "assistant", "content": "For the dataset 'Gur350' in German (De), the performance of sisg is 70, while the performance of sisg- is 64. The difference is 70 - 64 = 6. So, the answer is {\"answer\": \"6\"}" } ], "images": [ "scigen/train/3650.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Correlation between human judgement and similarity scores on word similarity datasets. We train both our model and the word2vec baseline on normalized Wikipedia dumps. Evaluation datasets contain words that are not part of the training set, so we represent them using null vectors (sisg-). With our model, we also compute vectors for unseen words by summing the n-gram vectors (sisg)." ], "reasoning": "For the dataset 'Gur350' in German (De), the performance of sisg is 70, while the performance of sisg- is 64. The difference is 70 - 64 = 6.", "question": "What is the difference in performance between sisg and sisg- for the dataset 'Gur350' in German (De)?", "answer": "So, the answer is {\"answer\": \"6\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe performance of sisg is always better than cbow for all datasets.\n" }, { "role": "assistant", "content": "By comparing the sisg and cbow columns for all datasets, we see that sisg is not always better than cbow. For example, in the 'WS353' dataset in English (En), cbow has a score of 73, while sisg has a score of 71. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3650.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Correlation between human judgement and similarity scores on word similarity datasets. We train both our model and the word2vec baseline on normalized Wikipedia dumps. Evaluation datasets contain words that are not part of the training set, so we represent them using null vectors (sisg-). With our model, we also compute vectors for unseen words by summing the n-gram vectors (sisg)." ], "reasoning": "By comparing the sisg and cbow columns for all datasets, we see that sisg is not always better than cbow. For example, in the 'WS353' dataset in English (En), cbow has a score of 73, while sisg has a score of 71.", "statement": "The performance of sisg is always better than cbow for all datasets.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Accuracy of our model and baselines on word analogy tasks for Czech, German, English and Italian. We report results for semantic and syntactic analogies separately.'.\n" }, { "role": "assistant", "content": "Morphological information improves syntactic tasks, especially for morphologically rich languages like Czech and German, but degrades performance for semantic tasks in German and Italian.We observe that morphological information significantly improves the syntactic tasks; our approach outperforms the baselines. In contrast, it does not help for semantic questions, and even degrades the performance for German and Italian. Note that this is tightly related to the choice of the length of character n-grams that we consider. We show in Sec. Another interesting observation is that, as expected, the improvement over the baselines is more important for morphologically rich languages, such as Czech and German." } ], "images": [ "scigen/train/3651.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracy of our model and baselines on word analogy tasks for Czech, German, English and Italian. We report results for semantic and syntactic analogies separately." ], "reasoning": "Morphological information improves syntactic tasks, especially for morphologically rich languages like Czech and German, but degrades performance for semantic tasks in German and Italian.", "description": "We observe that morphological information significantly improves the syntactic tasks; our approach outperforms the baselines. In contrast, it does not help for semantic questions, and even degrades the performance for German and Italian. Note that this is tightly related to the choice of the length of character n-grams that we consider. We show in Sec. Another interesting observation is that, as expected, the improvement over the baselines is more important for morphologically rich languages, such as Czech and German.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 2: Accuracy of our model and baselines on word analogy tasks for Czech, German, English and Italian. We report results for semantic and syntactic analogies separately.\nQuestion: Which language shows the highest performance in semantic tasks using the 'sg' method?\n" }, { "role": "assistant", "content": "To find the highest performance in semantic tasks using the 'sg' method, we look at the 'sg' column for rows labeled 'Semantic'. The highest value is 78.5 for English. So, the answer is {\"answer\": \"English\"}" } ], "images": [ "scigen/train/3651.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracy of our model and baselines on word analogy tasks for Czech, German, English and Italian. We report results for semantic and syntactic analogies separately." ], "reasoning": "To find the highest performance in semantic tasks using the 'sg' method, we look at the 'sg' column for rows labeled 'Semantic'. The highest value is 78.5 for English.", "question": "Which language shows the highest performance in semantic tasks using the 'sg' method?", "answer": "So, the answer is {\"answer\": \"English\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nMorphological information improves syntactic tasks for Italian.\n" }, { "role": "assistant", "content": "The table shows that for Italian, the syntactic task performance is higher than the semantic task performance, indicating that morphological information improves syntactic tasks. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3651.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracy of our model and baselines on word analogy tasks for Czech, German, English and Italian. We report results for semantic and syntactic analogies separately." ], "reasoning": "The table shows that for Italian, the syntactic task performance is higher than the semantic task performance, indicating that morphological information improves syntactic tasks.", "statement": "Morphological information improves syntactic tasks for Italian.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 3: Spearman’s rank correlation coefficient between human judgement and model scores for different methods using morphology to learn word representations. We keep all the word pairs of the evaluation set and obtain representations for out-of-vocabulary words with our model by summing the vectors of character n-grams. Our model was trained on the same datasets as the methods we are comparing to (hence the two lines of results for our approach).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Our model, trained on the same datasets as previous methods, outperforms other subword information techniques, especially in German due to its handling of noun compounding.We also compare our approach to previous work on word vectors incorporating subword information on word similarity tasks. The methods used are: the recursive neural network of \\newciteluong2013better, the morpheme cbow of \\newciteqiu2014colearning and the morphological transformations of \\newcitesoricut2015unsupervised. In order to make the results comparable, we trained our model on the same datasets as the methods we are comparing to: the English Wikipedia data released by \\newciteshaoul2010westbury, and the news crawl data from the 2013 WMT shared task for German, Spanish and French. We also compare our approach to the log-bilinear language model introduced by \\newcitebotha2014compositional, which was trained on the Europarl and news commentary corpora. Again, we trained our model on the same data to make the results comparable. Using our model, we obtain representations of out-of-vocabulary words by summing the representations of character n-grams. We observe that our simple approach performs well relative to techniques based on subword information obtained from morphological segmentors. We also observe that our approach outperforms the \\newcitesoricut2015unsupervised method, which is based on prefix and suffix analysis. The large improvement for German is due to the fact that their approach does not model noun compounding, contrary to ours." } ], "images": [ "scigen/train/3652.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Spearman’s rank correlation coefficient between human judgement and model scores for different methods using morphology to learn word representations. We keep all the word pairs of the evaluation set and obtain representations for out-of-vocabulary words with our model by summing the vectors of character n-grams. Our model was trained on the same datasets as the methods we are comparing to (hence the two lines of results for our approach)." ], "reasoning": "Our model, trained on the same datasets as previous methods, outperforms other subword information techniques, especially in German due to its handling of noun compounding.", "description": "We also compare our approach to previous work on word vectors incorporating subword information on word similarity tasks. The methods used are: the recursive neural network of \\newciteluong2013better, the morpheme cbow of \\newciteqiu2014colearning and the morphological transformations of \\newcitesoricut2015unsupervised. In order to make the results comparable, we trained our model on the same datasets as the methods we are comparing to: the English Wikipedia data released by \\newciteshaoul2010westbury, and the news crawl data from the 2013 WMT shared task for German, Spanish and French. We also compare our approach to the log-bilinear language model introduced by \\newcitebotha2014compositional, which was trained on the Europarl and news commentary corpora. Again, we trained our model on the same data to make the results comparable. Using our model, we obtain representations of out-of-vocabulary words by summing the representations of character n-grams. We observe that our simple approach performs well relative to techniques based on subword information obtained from morphological segmentors. We also observe that our approach outperforms the \\newcitesoricut2015unsupervised method, which is based on prefix and suffix analysis. The large improvement for German is due to the fact that their approach does not model noun compounding, contrary to ours.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 3: Spearman’s rank correlation coefficient between human judgement and model scores for different methods using morphology to learn word representations. We keep all the word pairs of the evaluation set and obtain representations for out-of-vocabulary words with our model by summing the vectors of character n-grams. Our model was trained on the same datasets as the methods we are comparing to (hence the two lines of results for our approach).' and proceed to solve the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method performed best on the De Gur350 dataset?\n" }, { "role": "assistant", "content": "By comparing the scores in the De Gur350 column, the highest score is 73, achieved by the 'sisg' method. So, the answer is {\"answer\": \"sisg\"}" } ], "images": [ "scigen/train/3652.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Spearman’s rank correlation coefficient between human judgement and model scores for different methods using morphology to learn word representations. We keep all the word pairs of the evaluation set and obtain representations for out-of-vocabulary words with our model by summing the vectors of character n-grams. Our model was trained on the same datasets as the methods we are comparing to (hence the two lines of results for our approach)." ], "reasoning": "By comparing the scores in the De Gur350 column, the highest score is 73, achieved by the 'sisg' method.", "question": "Which method performed best on the De Gur350 dataset?", "answer": "So, the answer is {\"answer\": \"sisg\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe 'sisg' method outperformed 'soricut2015unsupervised' on the En WS353 dataset.\n" }, { "role": "assistant", "content": "The table shows that 'sisg' scored 54 on the En WS353 dataset, while 'soricut2015unsupervised' scored 71. Therefore, 'sisg' did not outperform 'soricut2015unsupervised' on this dataset. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3652.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Spearman’s rank correlation coefficient between human judgement and model scores for different methods using morphology to learn word representations. We keep all the word pairs of the evaluation set and obtain representations for out-of-vocabulary words with our model by summing the vectors of character n-grams. Our model was trained on the same datasets as the methods we are comparing to (hence the two lines of results for our approach)." ], "reasoning": "The table shows that 'sisg' scored 54 on the En WS353 dataset, while 'soricut2015unsupervised' scored 71. Therefore, 'sisg' did not outperform 'soricut2015unsupervised' on this dataset.", "statement": "The 'sisg' method outperformed 'soricut2015unsupervised' on the En WS353 dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption '(b) De Semantic'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The model uses character n-grams to represent words as vectors, covering a range of information from short suffixes to longer roots, and checks the influence of n-gram ranges on performance.The proposed model relies on the use of character n-grams to represent words as vectors. As mentioned in Sec. This choice was arbitrary, motivated by the fact that n-grams of these lengths will cover a wide range of information. They would include short suffixes (corresponding to conjugations and declensions for instance) as well as longer roots. In this experiment, we empirically check for the influence of the range of n-grams that we use on performance." } ], "images": [ "scigen/train/3653.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) De Semantic" ], "reasoning": "The model uses character n-grams to represent words as vectors, covering a range of information from short suffixes to longer roots, and checks the influence of n-gram ranges on performance.", "description": "The proposed model relies on the use of character n-grams to represent words as vectors. As mentioned in Sec. This choice was arbitrary, motivated by the fact that n-grams of these lengths will cover a wide range of information. They would include short suffixes (corresponding to conjugations and declensions for instance) as well as longer roots. In this experiment, we empirically check for the influence of the range of n-grams that we use on performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about '(b) De Semantic'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. \n\nQuestion: What is the highest score achieved when the n-gram length is 6?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the highest score for n-gram length 6, look at the column under 6 and identify the highest value, which is 65. So, the answer is {\"answer\": \"65\"}." } ], "images": [ "scigen/train/3653.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) De Semantic" ], "reasoning": "To find the highest score for n-gram length 6, look at the column under 6 and identify the highest value, which is 65.", "question": "What is the highest score achieved when the n-gram length is 6?", "answer": "So, the answer is {\"answer\": \"65\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The highest score for an n-gram length of 4 is 63., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe highest score for an n-gram length of 4 is 63.\n" }, { "role": "assistant", "content": "To verify this, check the column under 4 for the highest value, which is indeed 63. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3653.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) De Semantic" ], "reasoning": "To verify this, check the column under 4 for the highest value, which is indeed 63.", "statement": "The highest score for an n-gram length of 4 is 63.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named '(c) De Syntactic'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The model uses character n-grams of varying lengths to capture a wide range of linguistic information, including short suffixes and longer roots, to empirically check their influence on performance.The proposed model relies on the use of character n-grams to represent words as vectors. As mentioned in Sec. This choice was arbitrary, motivated by the fact that n-grams of these lengths will cover a wide range of information. They would include short suffixes (corresponding to conjugations and declensions for instance) as well as longer roots. In this experiment, we empirically check for the influence of the range of n-grams that we use on performance." } ], "images": [ "scigen/train/3654.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(c) De Syntactic" ], "reasoning": "The model uses character n-grams of varying lengths to capture a wide range of linguistic information, including short suffixes and longer roots, to empirically check their influence on performance.", "description": "The proposed model relies on the use of character n-grams to represent words as vectors. As mentioned in Sec. This choice was arbitrary, motivated by the fact that n-grams of these lengths will cover a wide range of information. They would include short suffixes (corresponding to conjugations and declensions for instance) as well as longer roots. In this experiment, we empirically check for the influence of the range of n-grams that we use on performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about '(c) De Syntactic'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. \n\nQuestion: What is the highest score achieved for n-grams of length 5?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the highest score for n-grams of length 5, look at the column labeled '5' and identify the highest value, which is 56. So, the answer is {\"answer\": \"56\"}" } ], "images": [ "scigen/train/3654.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(c) De Syntactic" ], "reasoning": "To find the highest score for n-grams of length 5, look at the column labeled '5' and identify the highest value, which is 56.", "question": "What is the highest score achieved for n-grams of length 5?", "answer": "So, the answer is {\"answer\": \"56\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe highest score for n-grams of length 4 is 56.\n" }, { "role": "assistant", "content": "To verify this statement, look at the column labeled '4' and identify the highest value, which is indeed 56. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3654.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(c) De Syntactic" ], "reasoning": "To verify this statement, look at the column labeled '4' and identify the highest value, which is indeed 56.", "statement": "The highest score for n-grams of length 4 is 56.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled '(e) En Semantic'.\n" }, { "role": "assistant", "content": "The model uses character n-grams of varying lengths to capture both short suffixes and longer roots, and the experiment checks the influence of n-gram ranges on performance.The proposed model relies on the use of character n-grams to represent words as vectors. As mentioned in Sec. This choice was arbitrary, motivated by the fact that n-grams of these lengths will cover a wide range of information. They would include short suffixes (corresponding to conjugations and declensions for instance) as well as longer roots. In this experiment, we empirically check for the influence of the range of n-grams that we use on performance." } ], "images": [ "scigen/train/3655.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(e) En Semantic" ], "reasoning": "The model uses character n-grams of varying lengths to capture both short suffixes and longer roots, and the experiment checks the influence of n-gram ranges on performance.", "description": "The proposed model relies on the use of character n-grams to represent words as vectors. As mentioned in Sec. This choice was arbitrary, motivated by the fact that n-grams of these lengths will cover a wide range of information. They would include short suffixes (corresponding to conjugations and declensions for instance) as well as longer roots. In this experiment, we empirically check for the influence of the range of n-grams that we use on performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled '(e) En Semantic', solve this question and give detail solutions. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the highest score achieved for an n-gram length of 5?\n" }, { "role": "assistant", "content": "To find the highest score for an n-gram length of 5, look at the column labeled '5' and identify the highest value, which is 80. So, the answer is {\"answer\": \"80\"}." } ], "images": [ "scigen/train/3655.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(e) En Semantic" ], "reasoning": "To find the highest score for an n-gram length of 5, look at the column labeled '5' and identify the highest value, which is 80.", "question": "What is the highest score achieved for an n-gram length of 5?", "answer": "So, the answer is {\"answer\": \"80\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe highest score for an n-gram length of 6 is 80.\n" }, { "role": "assistant", "content": "To verify this statement, check the column labeled '6' and identify the highest value, which is indeed 80. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3655.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(e) En Semantic" ], "reasoning": "To verify this statement, check the column labeled '6' and identify the highest value, which is indeed 80.", "statement": "The highest score for an n-gram length of 6 is 80.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title '(f) En Syntactic'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The model uses character n-grams of varying lengths to capture both short suffixes and longer roots, and the experiment checks the influence of these n-gram ranges on performance.The proposed model relies on the use of character n-grams to represent words as vectors. As mentioned in Sec. This choice was arbitrary, motivated by the fact that n-grams of these lengths will cover a wide range of information. They would include short suffixes (corresponding to conjugations and declensions for instance) as well as longer roots. In this experiment, we empirically check for the influence of the range of n-grams that we use on performance." } ], "images": [ "scigen/train/3656.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(f) En Syntactic" ], "reasoning": "The model uses character n-grams of varying lengths to capture both short suffixes and longer roots, and the experiment checks the influence of these n-gram ranges on performance.", "description": "The proposed model relies on the use of character n-grams to represent words as vectors. As mentioned in Sec. This choice was arbitrary, motivated by the fact that n-grams of these lengths will cover a wide range of information. They would include short suffixes (corresponding to conjugations and declensions for instance) as well as longer roots. In this experiment, we empirically check for the influence of the range of n-grams that we use on performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of '(f) En Syntactic'. Solve the following question based on the table.\n\nQuestion: What is the highest score achieved in the table and which n-gram range does it correspond to?\nLet's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The highest score in the table is 75, which appears in the cells corresponding to the n-gram ranges (3, 5), (4, 5), and (4, 6). So, the answer is {\"answer\": \"75\"}." } ], "images": [ "scigen/train/3656.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(f) En Syntactic" ], "reasoning": "The highest score in the table is 75, which appears in the cells corresponding to the n-gram ranges (3, 5), (4, 5), and (4, 6).", "question": "What is the highest score achieved in the table and which n-gram range does it correspond to?", "answer": "So, the answer is {\"answer\": \"75\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe highest score in the table is achieved with an n-gram range of 4 to 6.\n" }, { "role": "assistant", "content": "The highest score in the table is 75, and it appears in the cells corresponding to the n-gram ranges (4, 5) and (4, 6), which includes the range 4 to 6. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3656.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(f) En Syntactic" ], "reasoning": "The highest score in the table is 75, and it appears in the cells corresponding to the n-gram ranges (4, 5) and (4, 6), which includes the range 4 to 6.", "statement": "The highest score in the table is achieved with an n-gram range of 4 to 6.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Test perplexity on the language modeling task, for 5 different languages. We compare to two state of the art approaches: CLBL refers to the work of botha2014compositional and CANLM refers to the work of kim2016character.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Our model, a recurrent neural network with 650 LSTM units, uses Adagrad for learning and is compared against two baselines, reporting test perplexity for different configurations.Our model is a recurrent neural network with 650 LSTM units, regularized with dropout (with probability of 0.5) and weight decay (regularization parameter of 10−5). We learn the parameters using the Adagrad algorithm with a learning rate of 0.1, clipping the gradients which have a norm larger than 1.0. We initialize the weight of the network in the range [−0.05,0.05], and use a batch size of 20. Two baselines are considered: we compare our approach to the log-bilinear language model of \\newcitebotha2014compositional and the character aware language model of \\newcitekim2016character. We trained word vectors with character n-grams on the training set of the language modeling task and use them to initialize the lookup table of our language model. We report the test perplexity of our model without using pre-trained word vectors (LSTM), with word vectors pre-trained without subword information (sg) and with our vectors (sisg)." } ], "images": [ "scigen/train/3657.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Test perplexity on the language modeling task, for 5 different languages. We compare to two state of the art approaches: CLBL refers to the work of botha2014compositional and CANLM refers to the work of kim2016character." ], "reasoning": "Our model, a recurrent neural network with 650 LSTM units, uses Adagrad for learning and is compared against two baselines, reporting test perplexity for different configurations.", "description": "Our model is a recurrent neural network with 650 LSTM units, regularized with dropout (with probability of 0.5) and weight decay (regularization parameter of 10−5). We learn the parameters using the Adagrad algorithm with a learning rate of 0.1, clipping the gradients which have a norm larger than 1.0. We initialize the weight of the network in the range [−0.05,0.05], and use a batch size of 20. Two baselines are considered: we compare our approach to the log-bilinear language model of \\newcitebotha2014compositional and the character aware language model of \\newcitekim2016character. We trained word vectors with character n-grams on the training set of the language modeling task and use them to initialize the lookup table of our language model. We report the test perplexity of our model without using pre-trained word vectors (LSTM), with word vectors pre-trained without subword information (sg) and with our vectors (sisg).", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 5: Test perplexity on the language modeling task, for 5 different languages. We compare to two state of the art approaches: CLBL refers to the work of botha2014compositional and CANLM refers to the work of kim2016character.\nQuestion: What is the test perplexity of the LSTM model for the French language?\n" }, { "role": "assistant", "content": "To find the test perplexity of the LSTM model for the French language, locate the 'Fr' column and the 'LSTM' row in the table. The value at their intersection is 173. Thus, the answer is {\"answer\": \"173\"}." } ], "images": [ "scigen/train/3657.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Test perplexity on the language modeling task, for 5 different languages. We compare to two state of the art approaches: CLBL refers to the work of botha2014compositional and CANLM refers to the work of kim2016character." ], "reasoning": "To find the test perplexity of the LSTM model for the French language, locate the 'Fr' column and the 'LSTM' row in the table. The value at their intersection is 173.", "question": "What is the test perplexity of the LSTM model for the French language?", "answer": "Thus, the answer is {\"answer\": \"173\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The test perplexity of the sisg model for the German language is 206. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe test perplexity of the sisg model for the German language is 206.\n" }, { "role": "assistant", "content": "To verify this statement, locate the 'De' column and the 'sisg' row in the table. The value at their intersection is 206, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3657.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Test perplexity on the language modeling task, for 5 different languages. We compare to two state of the art approaches: CLBL refers to the work of botha2014compositional and CANLM refers to the work of kim2016character." ], "reasoning": "To verify this statement, locate the 'De' column and the 'sisg' row in the table. The value at their intersection is 206, which matches the statement.", "statement": "The test perplexity of the sisg model for the German language is 206.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: Model ablation results', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The ablation study shows that removing the reading network or prediction layer results in a performance drop, with the 'No Reading' variant performing the worst.Ablation study : We compare the full model of DeepCom with the following variants: (1) No Reading: the entire reading network is replaced by a TF-IDF based keyword extractor, and top 40 keywords (tuned on validation sets) are fed to the generation network; (2) No Prediction: the prediction layer of the reading network is removed, and thus the entire V is used in the generation network; and We can see that all variants suffer from performance drop and No Reading is the worst among the three variants. Thus, we can conclude that (1) span prediction cannot be simply replaced by TF-IDF based keyword extraction, as the former is based on a deep comprehension of news articles and calibrated in the end-to-end learning process; (2) even with sophisticated representations, one cannot directly feed the entire article to the generation network, as comment generation is vulnerable to the noise in the article; and (3) pre-training is useful, but optimizing the lower bound of the true objective is still beneficial." } ], "images": [ "scigen/train/3658.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Model ablation results" ], "reasoning": "The ablation study shows that removing the reading network or prediction layer results in a performance drop, with the 'No Reading' variant performing the worst.", "description": "Ablation study : We compare the full model of DeepCom with the following variants: (1) No Reading: the entire reading network is replaced by a TF-IDF based keyword extractor, and top 40 keywords (tuned on validation sets) are fed to the generation network; (2) No Prediction: the prediction layer of the reading network is removed, and thus the entire V is used in the generation network; and We can see that all variants suffer from performance drop and No Reading is the worst among the three variants. Thus, we can conclude that (1) span prediction cannot be simply replaced by TF-IDF based keyword extraction, as the former is based on a deep comprehension of news articles and calibrated in the end-to-end learning process; (2) even with sophisticated representations, one cannot directly feed the entire article to the generation network, as comment generation is vulnerable to the noise in the article; and (3) pre-training is useful, but optimizing the lower bound of the true objective is still beneficial.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 5: Model ablation results\nQuestion: Which variant has the lowest METEOR score on the Tencent dataset?\n" }, { "role": "assistant", "content": "By examining the METEOR scores for the Tencent dataset, the 'No Reading' variant has the lowest score of 0.096. So, the answer is {\"answer\": \"No Reading\"}." } ], "images": [ "scigen/train/3658.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Model ablation results" ], "reasoning": "By examining the METEOR scores for the Tencent dataset, the 'No Reading' variant has the lowest score of 0.096.", "question": "Which variant has the lowest METEOR score on the Tencent dataset?", "answer": "So, the answer is {\"answer\": \"No Reading\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe 'No Prediction' variant has a higher BLEU-1 score than the 'No Sampling' variant on the Yahoo dataset.\n" }, { "role": "assistant", "content": "By comparing the BLEU-1 scores for the Yahoo dataset, the 'No Prediction' variant has a score of 0.531, which is lower than the 'No Sampling' variant's score of 0.609. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3658.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Model ablation results" ], "reasoning": "By comparing the BLEU-1 scores for the Yahoo dataset, the 'No Prediction' variant has a score of 0.531, which is lower than the 'No Sampling' variant's score of 0.609.", "statement": "The 'No Prediction' variant has a higher BLEU-1 score than the 'No Sampling' variant on the Yahoo dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: Evaluation results on automatic metrics and human judgment. Human evaluation results are calculated by combining labels from the three judges. “Kappa” means Fleiss’ kappa. Numbers in bold mean that improvement over the best baseline is statistically significant.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "DeepCom significantly outperforms other baseline methods on most automatic metrics, especially on BLEU-1 and W-BLEU-1, and achieves substantial agreement in human evaluation with a Fleiss' kappa over 0.6.On most automatic metrics, DeepCom outperforms baseline methods, and the improvement is statistically significant (t-test with p-value <0.01). The improvement on BLEU-1 and W-BLEU-1 is much bigger than that on other metrics. This is because BLEU-1 only measures the proportion of matched unigrams out of the total number of unigrams in the generated comments. In human evaluation, although the absolute numbers are different from those reported in Qin et al. In human evaluation, the values of Fleiss’ kappa over all models are more than 0.6, indicating substantial agreement among the annotators. Although built in a complicated structure, GANN does not bring much improvement over other baseline methods, which demonstrates that only using news titles is not enough in comment generation. IR-TC and Att-TC represent the best retrieval model and the best generation model among the baselines on both datasets, implying that news bodies, even used in a simple way, can provide useful information to comment generation." } ], "images": [ "scigen/train/3659.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Evaluation results on automatic metrics and human judgment. Human evaluation results are calculated by combining labels from the three judges. “Kappa” means Fleiss’ kappa. Numbers in bold mean that improvement over the best baseline is statistically significant." ], "reasoning": "DeepCom significantly outperforms other baseline methods on most automatic metrics, especially on BLEU-1 and W-BLEU-1, and achieves substantial agreement in human evaluation with a Fleiss' kappa over 0.6.", "description": "On most automatic metrics, DeepCom outperforms baseline methods, and the improvement is statistically significant (t-test with p-value <0.01). The improvement on BLEU-1 and W-BLEU-1 is much bigger than that on other metrics. This is because BLEU-1 only measures the proportion of matched unigrams out of the total number of unigrams in the generated comments. In human evaluation, although the absolute numbers are different from those reported in Qin et al. In human evaluation, the values of Fleiss’ kappa over all models are more than 0.6, indicating substantial agreement among the annotators. Although built in a complicated structure, GANN does not bring much improvement over other baseline methods, which demonstrates that only using news titles is not enough in comment generation. IR-TC and Att-TC represent the best retrieval model and the best generation model among the baselines on both datasets, implying that news bodies, even used in a simple way, can provide useful information to comment generation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Evaluation results on automatic metrics and human judgment. Human evaluation results are calculated by combining labels from the three judges. “Kappa” means Fleiss’ kappa. Numbers in bold mean that improvement over the best baseline is statistically significant.\nQuestion: Which model has the highest BLEU-1 score on the Tencent dataset?\nSolve the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By examining the BLEU-1 column for the Tencent dataset, DeepCom has the highest BLEU-1 score of 0.721. So, the answer is {\"answer\": \"DeepCom\"}" } ], "images": [ "scigen/train/3659.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Evaluation results on automatic metrics and human judgment. Human evaluation results are calculated by combining labels from the three judges. “Kappa” means Fleiss’ kappa. Numbers in bold mean that improvement over the best baseline is statistically significant." ], "reasoning": "By examining the BLEU-1 column for the Tencent dataset, DeepCom has the highest BLEU-1 score of 0.721.", "question": "Which model has the highest BLEU-1 score on the Tencent dataset?", "answer": "So, the answer is {\"answer\": \"DeepCom\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim DeepCom has the highest W-BLEU-1 score on the Yahoo dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nDeepCom has the highest W-BLEU-1 score on the Yahoo dataset.\n" }, { "role": "assistant", "content": "By checking the W-BLEU-1 column for the Yahoo dataset, DeepCom has the highest score of 0.665. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3659.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Evaluation results on automatic metrics and human judgment. Human evaluation results are calculated by combining labels from the three judges. “Kappa” means Fleiss’ kappa. Numbers in bold mean that improvement over the best baseline is statistically significant." ], "reasoning": "By checking the W-BLEU-1 column for the Yahoo dataset, DeepCom has the highest score of 0.665.", "statement": "DeepCom has the highest W-BLEU-1 score on the Yahoo dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1: Number of instances with associated label and language.'.\n" }, { "role": "assistant", "content": "Turkish has the highest number of examples at 31,277, while Danish has the fewest at 2,960. Offensive sentences make up 23.21% of the total data, with Danish having the lowest percentage of offensive sentences at 12.97%.Turkish has the maximum examples with 31,277 examples, whereas Danish has 2,960 examples, comprising 48.70% and 4.60% of total data, respectively. Further analysis shows that positive instances, signifying offensive languages, are significantly lesser than non-offensive sentences. Offensive sentences comprise only 23.21% of total data. This percentage, however, varies from language to language, ranging from 12.97% for Danish to 33.23% for English." } ], "images": [ "scigen/train/3660.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Number of instances with associated label and language." ], "reasoning": "Turkish has the highest number of examples at 31,277, while Danish has the fewest at 2,960. Offensive sentences make up 23.21% of the total data, with Danish having the lowest percentage of offensive sentences at 12.97%.", "description": "Turkish has the maximum examples with 31,277 examples, whereas Danish has 2,960 examples, comprising 48.70% and 4.60% of total data, respectively. Further analysis shows that positive instances, signifying offensive languages, are significantly lesser than non-offensive sentences. Offensive sentences comprise only 23.21% of total data. This percentage, however, varies from language to language, ranging from 12.97% for Danish to 33.23% for English.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich language has the highest percentage of offensive sentences?\nSolve the above question based on the table titled 'Table 1: Number of instances with associated label and language.'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the language with the highest percentage of offensive sentences, we need to calculate the percentage of positive instances for each language. Turkish: (6046/31277)*100 ≈ 19.33%, English: (4400/13240)*100 ≈ 33.23%, Greek: (2486/8743)*100 ≈ 28.44%, Arabic: (1589/8000)*100 ≈ 19.86%, Danish: (384/2960)*100 ≈ 12.97%. English has the highest percentage of offensive sentences at 33.23%. So, the answer is {\"answer\": \"English\"}." } ], "images": [ "scigen/train/3660.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Number of instances with associated label and language." ], "reasoning": "To find the language with the highest percentage of offensive sentences, we need to calculate the percentage of positive instances for each language. Turkish: (6046/31277)*100 ≈ 19.33%, English: (4400/13240)*100 ≈ 33.23%, Greek: (2486/8743)*100 ≈ 28.44%, Arabic: (1589/8000)*100 ≈ 19.86%, Danish: (384/2960)*100 ≈ 12.97%. English has the highest percentage of offensive sentences at 33.23%.", "question": "Which language has the highest percentage of offensive sentences?", "answer": "So, the answer is {\"answer\": \"English\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Danish has the highest percentage of offensive sentences. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nDanish has the highest percentage of offensive sentences.\n" }, { "role": "assistant", "content": "The table shows that Danish has 384 positive instances out of 2,960 total instances, which is approximately 12.97%. This is the lowest percentage of offensive sentences among the languages listed. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3660.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Number of instances with associated label and language." ], "reasoning": "The table shows that Danish has 384 positive instances out of 2,960 total instances, which is approximately 12.97%. This is the lowest percentage of offensive sentences among the languages listed.", "statement": "Danish has the highest percentage of offensive sentences.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Experimental Results for Validation dataset for our final model.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The performance metrics vary significantly with languages, with F1 scores ranging from 0.676 in Danish to 0.729 in Arabic, and Accuracy ranging from 0.808 in English to 0.915 in Danish.We observe that the performance metrics obtained vary significantly with the languages, with F1 varying from 0.676 in Danish to 0.729 in Arabic, and Accuracy varying from 0.808 in English to 0.915 in Danish." } ], "images": [ "scigen/train/3661.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Experimental Results for Validation dataset for our final model." ], "reasoning": "The performance metrics vary significantly with languages, with F1 scores ranging from 0.676 in Danish to 0.729 in Arabic, and Accuracy ranging from 0.808 in English to 0.915 in Danish.", "description": "We observe that the performance metrics obtained vary significantly with the languages, with F1 varying from 0.676 in Danish to 0.729 in Arabic, and Accuracy varying from 0.808 in English to 0.915 in Danish.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 2: Experimental Results for Validation dataset for our final model.', solve the following question step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich language has the highest accuracy and what is its F1 score?\n" }, { "role": "assistant", "content": "To find the language with the highest accuracy, we look at the Accuracy column and identify Danish with an accuracy of 0.915. Then, we check the F1 score for Danish, which is 0.676. So, the answer is {\"answer\": \"0.676\"}." } ], "images": [ "scigen/train/3661.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Experimental Results for Validation dataset for our final model." ], "reasoning": "To find the language with the highest accuracy, we look at the Accuracy column and identify Danish with an accuracy of 0.915. Then, we check the F1 score for Danish, which is 0.676.", "question": "Which language has the highest accuracy and what is its F1 score?", "answer": "So, the answer is {\"answer\": \"0.676\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The language with the highest F1 score also has the highest accuracy. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe language with the highest F1 score also has the highest accuracy.\n" }, { "role": "assistant", "content": "The highest F1 score is 0.729 in Arabic, and the highest accuracy is 0.915 in Danish. Since these are different languages, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3661.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Experimental Results for Validation dataset for our final model." ], "reasoning": "The highest F1 score is 0.729 in Arabic, and the highest accuracy is 0.915 in Danish. Since these are different languages, the statement is refuted.", "statement": "The language with the highest F1 score also has the highest accuracy.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Experiments performed for German Offensive Language Detection demonstrating zero-shot learning.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our model, despite not using supervised data in German, achieves competitive performance with a 1.3% lower Accuracy and a 4.06% higher Precision compared to ParaschivCercel2019.Our experiments, illustrated in Thus our model, despite not using supervised data in German, achieves competitive performance with a difference of 1.3% in Accuracy, further showing an increase in the metric of Precision by a margin of 4.06%." } ], "images": [ "scigen/train/3662.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Experiments performed for German Offensive Language Detection demonstrating zero-shot learning." ], "reasoning": "Our model, despite not using supervised data in German, achieves competitive performance with a 1.3% lower Accuracy and a 4.06% higher Precision compared to ParaschivCercel2019.", "description": "Our experiments, illustrated in Thus our model, despite not using supervised data in German, achieves competitive performance with a difference of 1.3% in Accuracy, further showing an increase in the metric of Precision by a margin of 4.06%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: Experiments performed for German Offensive Language Detection demonstrating zero-shot learning.', answer the following question. Give detailed solution consisting of each step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the difference in Recall between ParaschivCercel2019 and Our Model (Zero-Shot Learning)?\n" }, { "role": "assistant", "content": "To find the difference in Recall, subtract the Recall of Our Model (0.682) from the Recall of ParaschivCercel2019 (0.776). So, the answer is {\"answer\": \"0.094\"}." } ], "images": [ "scigen/train/3662.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Experiments performed for German Offensive Language Detection demonstrating zero-shot learning." ], "reasoning": "To find the difference in Recall, subtract the Recall of Our Model (0.682) from the Recall of ParaschivCercel2019 (0.776).", "question": "What is the difference in Recall between ParaschivCercel2019 and Our Model (Zero-Shot Learning)?", "answer": "So, the answer is {\"answer\": \"0.094\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Our Model (Zero-Shot Learning) has a higher F1 score than ParaschivCercel2019. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nOur Model (Zero-Shot Learning) has a higher F1 score than ParaschivCercel2019.\n" }, { "role": "assistant", "content": "The table shows that the F1 score of Our Model (0.700) is lower than that of ParaschivCercel2019 (0.770). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3662.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Experiments performed for German Offensive Language Detection demonstrating zero-shot learning." ], "reasoning": "The table shows that the F1 score of Our Model (0.700) is lower than that of ParaschivCercel2019 (0.770).", "statement": "Our Model (Zero-Shot Learning) has a higher F1 score than ParaschivCercel2019.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Zero-annotation transfer learning F1 scores on 2012 Spanish TAC KBP slot-filling task. Adding a translation dictionary improves all encoder-based models. Ensembling LSTM and USchema models performs the best.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Tying word embeddings between Spanish and English significantly improves the LSTM model, and ensembling this with USchema yields the highest F1 score.In addition to verifying our models on the 2013 and 2014 English slot-filling task, we evaluate our Spanish models on the 2012 TAC Spanish slot-filling evaluation. Because this TAC track was never officially run, the coverage of facts in the available annotation is very small, resulting in many correct predictions being marked incorrectly as precision errors. Tying word embeddings between the two languages results in substantial improvements for the LSTM. We see that ensembling the non-dictionary LSTM with USchema gives a slight boost over USchema alone, but ensembling the dictionary-tied LSTM with USchema provides a significant increase of nearly 4 F1 points over the highest-scoring single model, USchema. Clearly, grounding the Spanish data using a translation dictionary provides much better Spanish word representations. These improvements are complementary to the baseline USchema model, and yield impressive results when ensembled." } ], "images": [ "scigen/train/3663.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Zero-annotation transfer learning F1 scores on 2012 Spanish TAC KBP slot-filling task. Adding a translation dictionary improves all encoder-based models. Ensembling LSTM and USchema models performs the best." ], "reasoning": "Tying word embeddings between Spanish and English significantly improves the LSTM model, and ensembling this with USchema yields the highest F1 score.", "description": "In addition to verifying our models on the 2013 and 2014 English slot-filling task, we evaluate our Spanish models on the 2012 TAC Spanish slot-filling evaluation. Because this TAC track was never officially run, the coverage of facts in the available annotation is very small, resulting in many correct predictions being marked incorrectly as precision errors. Tying word embeddings between the two languages results in substantial improvements for the LSTM. We see that ensembling the non-dictionary LSTM with USchema gives a slight boost over USchema alone, but ensembling the dictionary-tied LSTM with USchema provides a significant increase of nearly 4 F1 points over the highest-scoring single model, USchema. Clearly, grounding the Spanish data using a translation dictionary provides much better Spanish word representations. These improvements are complementary to the baseline USchema model, and yield impressive results when ensembled.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model achieved the highest F1 score?\nSolve the above question based on the table titled 'Table 4: Zero-annotation transfer learning F1 scores on 2012 Spanish TAC KBP slot-filling task. Adding a translation dictionary improves all encoder-based models. Ensembling LSTM and USchema models performs the best.'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the F1 scores of all models in the table, the USchema+LSTM+Dict model has the highest F1 score of 20.0. So, the answer is {\"answer\": \"USchema+LSTM+Dict\"}" } ], "images": [ "scigen/train/3663.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Zero-annotation transfer learning F1 scores on 2012 Spanish TAC KBP slot-filling task. Adding a translation dictionary improves all encoder-based models. Ensembling LSTM and USchema models performs the best." ], "reasoning": "By comparing the F1 scores of all models in the table, the USchema+LSTM+Dict model has the highest F1 score of 20.0.", "question": "Which model achieved the highest F1 score?", "answer": "So, the answer is {\"answer\": \"USchema+LSTM+Dict\"}" }, { "messages": [ { "role": "user", "content": "The USchema model alone has a higher F1 score than the LSTM+Dict model. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe USchema model alone has a higher F1 score than the LSTM+Dict model.\n" }, { "role": "assistant", "content": "The table shows that the USchema model has an F1 score of 16.3, while the LSTM+Dict model has an F1 score of 15.2. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3663.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Zero-annotation transfer learning F1 scores on 2012 Spanish TAC KBP slot-filling task. Adding a translation dictionary improves all encoder-based models. Ensembling LSTM and USchema models performs the best." ], "reasoning": "The table shows that the USchema model has an F1 score of 16.3, while the LSTM+Dict model has an F1 score of 15.2.", "statement": "The USchema model alone has a higher F1 score than the LSTM+Dict model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Results on the out-of-domain test set.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Our model is robust to syntax mistakes but does not outperform the syntax-agnostic model of DBLP: journals/corr/MarcheggianiFT17.For English, in the CoNLL shared task, systems are also evaluated on the out-of-domain dataset. Statistical models are typically less accurate when they are applied to out-of-domain data. Consequently, the predicted syntax for the out-of-domain test set is of lower quality, which negatively affects the quality of GCN embeddings. This suggests that our model is fairly robust to mistakes in syntax. As expected though, our model does not outperform the syntax-agnostic model of DBLP: journals/corr/MarcheggianiFT17." } ], "images": [ "scigen/train/3664.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on the out-of-domain test set." ], "reasoning": "Our model is robust to syntax mistakes but does not outperform the syntax-agnostic model of DBLP: journals/corr/MarcheggianiFT17.", "description": "For English, in the CoNLL shared task, systems are also evaluated on the out-of-domain dataset. Statistical models are typically less accurate when they are applied to out-of-domain data. Consequently, the predicted syntax for the out-of-domain test set is of lower quality, which negatively affects the quality of GCN embeddings. This suggests that our model is fairly robust to mistakes in syntax. As expected though, our model does not outperform the syntax-agnostic model of DBLP: journals/corr/MarcheggianiFT17.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Results on the out-of-domain test set.\nQuestion: Which system has the highest F1 score in the table?\nGive a detailed response to the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the system with the highest F1 score, look at the F1 column and identify the highest value. The highest F1 score is 78.9, achieved by 'Ours (ensemble 3x)'. So, the answer is {\"answer\": \"Ours (ensemble 3x)\"}." } ], "images": [ "scigen/train/3664.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on the out-of-domain test set." ], "reasoning": "To find the system with the highest F1 score, look at the F1 column and identify the highest value. The highest F1 score is 78.9, achieved by 'Ours (ensemble 3x)'.", "question": "Which system has the highest F1 score in the table?", "answer": "So, the answer is {\"answer\": \"Ours (ensemble 3x)\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The system 'Ours (local)' has a higher F1 score than 'DBLP: journals/corr/MarcheggianiFT17 (local)'. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe system 'Ours (local)' has a higher F1 score than 'DBLP: journals/corr/MarcheggianiFT17 (local)'.\n" }, { "role": "assistant", "content": "Compare the F1 scores of 'Ours (local)' and 'DBLP: journals/corr/MarcheggianiFT17 (local)'. 'Ours (local)' has an F1 score of 77.2, while 'DBLP: journals/corr/MarcheggianiFT17 (local)' has an F1 score of 77.3. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3664.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on the out-of-domain test set." ], "reasoning": "Compare the F1 scores of 'Ours (local)' and 'DBLP: journals/corr/MarcheggianiFT17 (local)'. 'Ours (local)' has an F1 score of 77.2, while 'DBLP: journals/corr/MarcheggianiFT17 (local)' has an F1 score of 77.3. Therefore, the statement is refuted.", "statement": "The system 'Ours (local)' has a higher F1 score than 'DBLP: journals/corr/MarcheggianiFT17 (local)'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 3: Results on the test set for English.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Our local model outperforms the best global and ensemble models, achieving 88.0% F1, and further improves to 89.1% F1 when using an ensemble of 3 models.Our model is local, as all the argument detection and labeling decisions are conditionally independent: their interaction is captured solely by the LSTM+GCN encoder. This makes our model fast and simple, though, as shown in previous work, global modeling of the structured output is beneficial. We leave this extension for future work. Interestingly, we outperform even the best global model and the best ensemble of global models, without using global modeling or ensembles. When we create an ensemble of 3 models with the product-of-expert combination rule, we improve by 1.2% over the best previous result, achieving 89.1% F1." } ], "images": [ "scigen/train/3665.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on the test set for English." ], "reasoning": "Our local model outperforms the best global and ensemble models, achieving 88.0% F1, and further improves to 89.1% F1 when using an ensemble of 3 models.", "description": "Our model is local, as all the argument detection and labeling decisions are conditionally independent: their interaction is captured solely by the LSTM+GCN encoder. This makes our model fast and simple, though, as shown in previous work, global modeling of the structured output is beneficial. We leave this extension for future work. Interestingly, we outperform even the best global model and the best ensemble of global models, without using global modeling or ensembles. When we create an ensemble of 3 models with the product-of-expert combination rule, we improve by 1.2% over the best previous result, achieving 89.1% F1.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Results on the test set for English.\nQuestion: What is the F1 score of the best global model and how does it compare to our local model?\nGive a detailed response to the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The best global model, roth-lapata:2016:P16-1 (global), has an F1 score of 87.7%. Our local model has an F1 score of 88.0%, which is higher than the best global model. So, the answer is {\"answer\": \"88.0%\"}" } ], "images": [ "scigen/train/3665.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on the test set for English." ], "reasoning": "The best global model, roth-lapata:2016:P16-1 (global), has an F1 score of 87.7%. Our local model has an F1 score of 88.0%, which is higher than the best global model.", "question": "What is the F1 score of the best global model and how does it compare to our local model?", "answer": "So, the answer is {\"answer\": \"88.0%\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nOur local model achieves a higher F1 score than the best global model.\n" }, { "role": "assistant", "content": "The table shows that our local model has an F1 score of 88.0%, while the best global model, roth-lapata:2016:P16-1 (global), has an F1 score of 87.7%. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3665.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on the test set for English." ], "reasoning": "The table shows that our local model has an F1 score of 88.0%, while the best global model, roth-lapata:2016:P16-1 (global), has an F1 score of 87.7%. Therefore, the statement is supported.", "statement": "Our local model achieves a higher F1 score than the best global model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Effect of noise in terms of F1 on the development set.'.\n" }, { "role": "assistant", "content": "Adding noise to either side reduces F1 scores, with agreement-based learning showing more robustness compared to independent training.In row 1, “0+0” denotes there is no noise, which can be seen as an upper bound. Adding noise, either on the Chinese side or on the English side, deteriorates the F1 scores for all methods. Adding noise on the English side makes predicting phrase alignment in the C → E direction more challenging due to the enlarged search space. The situation is similar in the reverse direction. It is clear that agreement-based learning is more robust to noise: while independent training suffers from a reduction of 40% in terms of F1 for the “20K + 20K” setting, agreement-based learning still achieves F1 scores over 70%." } ], "images": [ "scigen/train/3666.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Effect of noise in terms of F1 on the development set." ], "reasoning": "Adding noise to either side reduces F1 scores, with agreement-based learning showing more robustness compared to independent training.", "description": "In row 1, “0+0” denotes there is no noise, which can be seen as an upper bound. Adding noise, either on the Chinese side or on the English side, deteriorates the F1 scores for all methods. Adding noise on the English side makes predicting phrase alignment in the C → E direction more challenging due to the enlarged search space. The situation is similar in the reverse direction. It is clear that agreement-based learning is more robust to noise: while independent training suffers from a reduction of 40% in terms of F1 for the “20K + 20K” setting, agreement-based learning still achieves F1 scores over 70%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Effect of noise in terms of F1 on the development set.', and sequentially solve the question, writing out each step of your reasoning process. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the F1 score for the C → E direction when there is no noise on either side?\n" }, { "role": "assistant", "content": "The table shows that when there is no noise on either side (0 noise C and 0 noise E), the F1 score for the C → E direction is 58.5. So, the answer is {\"answer\": \"58.5\"}." } ], "images": [ "scigen/train/3666.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Effect of noise in terms of F1 on the development set." ], "reasoning": "The table shows that when there is no noise on either side (0 noise C and 0 noise E), the F1 score for the C → E direction is 58.5.", "question": "What is the F1 score for the C → E direction when there is no noise on either side?", "answer": "So, the answer is {\"answer\": \"58.5\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nAdding noise on the English side makes predicting phrase alignment in the C → E direction more challenging.\n" }, { "role": "assistant", "content": "The description states that adding noise on the English side enlarges the search space, making prediction more challenging. The table shows a decrease in F1 scores for the C → E direction when noise is added to the English side. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3666.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Effect of noise in terms of F1 on the development set." ], "reasoning": "The description states that adding noise on the English side enlarges the search space, making prediction more challenging. The table shows a decrease in F1 scores for the C → E direction when noise is added to the English side.", "statement": "Adding noise on the English side makes predicting phrase alignment in the C → E direction more challenging.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Results on domain adaptation for machine translation.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Agreement-based learning achieves higher BLEU scores and smaller parallel corpora compared to independent learning, as it rules out unlikely phrase pairs by encouraging consensus between models.At iteration 0, only the out-domain corpus is used and the BLEU score is 5.61. All methods iteratively extract parallel phrases from non-parallel corpora and enlarge the extracted parallel corpus. We find that agreement-based learning achieves much higher BLEU scores while obtains a smaller parallel corpus as compared with independent learning. One possible reason is that the agreement-based learning rules out most unlikely phrase pairs by encouraging consensus between two models." } ], "images": [ "scigen/train/3667.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on domain adaptation for machine translation." ], "reasoning": "Agreement-based learning achieves higher BLEU scores and smaller parallel corpora compared to independent learning, as it rules out unlikely phrase pairs by encouraging consensus between models.", "description": "At iteration 0, only the out-domain corpus is used and the BLEU score is 5.61. All methods iteratively extract parallel phrases from non-parallel corpora and enlarge the extracted parallel corpus. We find that agreement-based learning achieves much higher BLEU scores while obtains a smaller parallel corpus as compared with independent learning. One possible reason is that the agreement-based learning rules out most unlikely phrase pairs by encouraging consensus between two models.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 4: Results on domain adaptation for machine translation.'. Solve the following question based on the table.\n\nQuestion: What is the BLEU score for C→E at iteration 5, and how does it compare to the BLEU score for E→C at the same iteration?\nLet's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "At iteration 5, the BLEU score for C→E is 9.40, while the BLEU score for E→C is 8.87. The BLEU score for C→E is higher than that for E→C. So, the answer is {\"answer\": \"9.40\"}" } ], "images": [ "scigen/train/3667.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on domain adaptation for machine translation." ], "reasoning": "At iteration 5, the BLEU score for C→E is 9.40, while the BLEU score for E→C is 8.87. The BLEU score for C→E is higher than that for E→C.", "question": "What is the BLEU score for C→E at iteration 5, and how does it compare to the BLEU score for E→C at the same iteration?", "answer": "So, the answer is {\"answer\": \"9.40\"}" }, { "messages": [ { "role": "user", "content": "The BLEU score for Inner at iteration 10 is higher than the BLEU score for Outer at iteration 10. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe BLEU score for Inner at iteration 10 is higher than the BLEU score for Outer at iteration 10.\n" }, { "role": "assistant", "content": "At iteration 10, the BLEU score for Inner is 19.78, while the BLEU score for Outer is 19.06. The BLEU score for Inner is indeed higher than the BLEU score for Outer. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3667.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on domain adaptation for machine translation." ], "reasoning": "At iteration 10, the BLEU score for Inner is 19.78, while the BLEU score for Outer is 19.06. The BLEU score for Inner is indeed higher than the BLEU score for Outer.", "statement": "The BLEU score for Inner at iteration 10 is higher than the BLEU score for Outer at iteration 10.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Human evaluation of generation models.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Our method outperforms three baselines in all metrics, but there is still a gap between generated and expert-written punchlines, indicating humor generation remains challenging.Human evaluation results are shown in Tab. Our method performs better than three baselines in all metrics. These results demonstrate the effectiveness of our model with knowledge enhanced. Nevertheless, there is still a gap between generated punchlines and expert-written punchlines across all aspects, indicating that humor generation remains an open challenge. Interestingly, the funniness score of human-written jokes is not very high, due to different people’s sensitivity to humor. It is consistent with Petrović and Matthews" } ], "images": [ "scigen/train/3668.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Human evaluation of generation models." ], "reasoning": "Our method outperforms three baselines in all metrics, but there is still a gap between generated and expert-written punchlines, indicating humor generation remains challenging.", "description": "Human evaluation results are shown in Tab. Our method performs better than three baselines in all metrics. These results demonstrate the effectiveness of our model with knowledge enhanced. Nevertheless, there is still a gap between generated punchlines and expert-written punchlines across all aspects, indicating that humor generation remains an open challenge. Interestingly, the funniness score of human-written jokes is not very high, due to different people’s sensitivity to humor. It is consistent with Petrović and Matthews", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 3: Human evaluation of generation models.', solve the following question step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method has the highest fluency score?\n" }, { "role": "assistant", "content": "By comparing the fluency scores of all methods in the table, the human-written method has the highest fluency score of 4.06. So, the answer is {\"answer\": \"Human-written\"}" } ], "images": [ "scigen/train/3668.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Human evaluation of generation models." ], "reasoning": "By comparing the fluency scores of all methods in the table, the human-written method has the highest fluency score of 4.06.", "question": "Which method has the highest fluency score?", "answer": "So, the answer is {\"answer\": \"Human-written\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nOur model has the highest funniness score among all methods.\n" }, { "role": "assistant", "content": "The table shows that the human-written method has the highest funniness score of 3.30, while our model has a funniness score of 2.60. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3668.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Human evaluation of generation models." ], "reasoning": "The table shows that the human-written method has the highest funniness score of 3.30, while our model has a funniness score of 2.60.", "statement": "Our model has the highest funniness score among all methods.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Automatic evaluations of generation models.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Transformer-based methods outperform other baselines, and incorporating background knowledge further enhances performance.Tab. We find that: (1) As expected, Transformer based methods perform better than other baselines. (2) Background knowledge can promote punchline generation, by comparing our method with origin Transformer." } ], "images": [ "scigen/train/3669.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Automatic evaluations of generation models." ], "reasoning": "Transformer-based methods outperform other baselines, and incorporating background knowledge further enhances performance.", "description": "Tab. We find that: (1) As expected, Transformer based methods perform better than other baselines. (2) Background knowledge can promote punchline generation, by comparing our method with origin Transformer.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Automatic evaluations of generation models.', answer the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich method achieves the highest ROUGE-2 score?\n" }, { "role": "assistant", "content": "By comparing the ROUGE-2 scores of all methods, 'Our model' achieves the highest score of 9.47. So, the answer is {\"answer\": \"Our model\"}" } ], "images": [ "scigen/train/3669.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Automatic evaluations of generation models." ], "reasoning": "By comparing the ROUGE-2 scores of all methods, 'Our model' achieves the highest score of 9.47.", "question": "Which method achieves the highest ROUGE-2 score?", "answer": "So, the answer is {\"answer\": \"Our model\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe CopyNet method has a higher ROUGE-1 score than the S2S-GRU method.\n" }, { "role": "assistant", "content": "The table shows that the ROUGE-1 score for CopyNet is 22.31, which is lower than the ROUGE-1 score for S2S-GRU, which is 22.79. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3669.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Automatic evaluations of generation models." ], "reasoning": "The table shows that the ROUGE-1 score for CopyNet is 22.31, which is lower than the ROUGE-1 score for S2S-GRU, which is 22.79.", "statement": "The CopyNet method has a higher ROUGE-1 score than the S2S-GRU method.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Comparison between the performance of MLP, single LSTM, single BLSTM and Additive-BLSTM. As mentioned in section 2.2, Cross-Delta refers to cross syllable delta feature, In-Delta refers to syllable internal delta feature. True durations are used.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The Additive-BLSTM model with In-Delta performs the best on both Mandarin and Cantonese speech datasets, and adding residual f0 contours to base f0 contours improves the naturalness of f0 contours.For MLP, single LSTM and single BLSTM, features of all the levels are concatenated together as input. And the Additive-BLSTM model with In-Delta performs the best on both Mandarin and Cantonese speech datasets. In the figure, adding the residual f0 contours on the base f0 contours can make the f0 contours rise and fall in a more natural way and also more similar to the natural contours." } ], "images": [ "scigen/train/3670.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison between the performance of MLP, single LSTM, single BLSTM and Additive-BLSTM. As mentioned in section 2.2, Cross-Delta refers to cross syllable delta feature, In-Delta refers to syllable internal delta feature. True durations are used." ], "reasoning": "The Additive-BLSTM model with In-Delta performs the best on both Mandarin and Cantonese speech datasets, and adding residual f0 contours to base f0 contours improves the naturalness of f0 contours.", "description": "For MLP, single LSTM and single BLSTM, features of all the levels are concatenated together as input. And the Additive-BLSTM model with In-Delta performs the best on both Mandarin and Cantonese speech datasets. In the figure, adding the residual f0 contours on the base f0 contours can make the f0 contours rise and fall in a more natural way and also more similar to the natural contours.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: Comparison between the performance of MLP, single LSTM, single BLSTM and Additive-BLSTM. As mentioned in section 2.2, Cross-Delta refers to cross syllable delta feature, In-Delta refers to syllable internal delta feature. True durations are used.', solve this question and give detail solutions. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich model has the lowest rmse for Cantonese Syl Level?\n" }, { "role": "assistant", "content": "To find the model with the lowest rmse for Cantonese Syl Level, compare the rmse values in the Cantonese Syl Level column. The Additive-BLSTM In-Delta model has the lowest rmse value of 18.750. So, the answer is {\"answer\": \"Additive-BLSTM In-Delta\"}." } ], "images": [ "scigen/train/3670.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison between the performance of MLP, single LSTM, single BLSTM and Additive-BLSTM. As mentioned in section 2.2, Cross-Delta refers to cross syllable delta feature, In-Delta refers to syllable internal delta feature. True durations are used." ], "reasoning": "To find the model with the lowest rmse for Cantonese Syl Level, compare the rmse values in the Cantonese Syl Level column. The Additive-BLSTM In-Delta model has the lowest rmse value of 18.750.", "question": "Which model has the lowest rmse for Cantonese Syl Level?", "answer": "So, the answer is {\"answer\": \"Additive-BLSTM In-Delta\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Additive-BLSTM model with In-Delta has the highest correlation for Mandarin Syl Level.\n" }, { "role": "assistant", "content": "To verify this statement, compare the correlation values for Mandarin Syl Level. The Additive-BLSTM In-Delta model has a correlation of 0.828, which is the highest among all models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3670.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison between the performance of MLP, single LSTM, single BLSTM and Additive-BLSTM. As mentioned in section 2.2, Cross-Delta refers to cross syllable delta feature, In-Delta refers to syllable internal delta feature. True durations are used." ], "reasoning": "To verify this statement, compare the correlation values for Mandarin Syl Level. The Additive-BLSTM In-Delta model has a correlation of 0.828, which is the highest among all models.", "statement": "The Additive-BLSTM model with In-Delta has the highest correlation for Mandarin Syl Level.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Performance of the vector space models (BM25, TFIDFchar), and neural ranking models (BERTen, BioBERT, BERTmulti) over Wikipedia page retrieval given a UMLS concept. The normalised recall (N. recall@k) is calculated by excluding queries for which the candidate generator (BM25) didn’t retrieve the relevant document.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "BM25 outperforms TFIDFchar in all top k positions, despite TFIDFchar's previously reported strong performance in entity linking.The performance of the candidate generation methods for word- In all top k positions, BM25 outperforms TFIDFchar, which is surprising given that TFIDFchar was reported to achieve strong performance for entity linking in murty-etal-2018-hierarchical." } ], "images": [ "scigen/train/3671.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Performance of the vector space models (BM25, TFIDFchar), and neural ranking models (BERTen, BioBERT, BERTmulti) over Wikipedia page retrieval given a UMLS concept. The normalised recall (N. recall@k) is calculated by excluding queries for which the candidate generator (BM25) didn’t retrieve the relevant document." ], "reasoning": "BM25 outperforms TFIDFchar in all top k positions, despite TFIDFchar's previously reported strong performance in entity linking.", "description": "The performance of the candidate generation methods for word- In all top k positions, BM25 outperforms TFIDFchar, which is surprising given that TFIDFchar was reported to achieve strong performance for entity linking in murty-etal-2018-hierarchical.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Performance of the vector space models (BM25, TFIDFchar), and neural ranking models (BERTen, BioBERT, BERTmulti) over Wikipedia page retrieval given a UMLS concept. The normalised recall (N. recall@k) is calculated by excluding queries for which the candidate generator (BM25) didn’t retrieve the relevant document.\nQuestion: Which method has the highest recall@1?\nSolve the above question based on the table information. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the recall@1 values for all methods, BERTmulti has the highest recall@1 with a value of 72. So, the answer is {\"answer\": \"BERTmulti\"}." } ], "images": [ "scigen/train/3671.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Performance of the vector space models (BM25, TFIDFchar), and neural ranking models (BERTen, BioBERT, BERTmulti) over Wikipedia page retrieval given a UMLS concept. The normalised recall (N. recall@k) is calculated by excluding queries for which the candidate generator (BM25) didn’t retrieve the relevant document." ], "reasoning": "By comparing the recall@1 values for all methods, BERTmulti has the highest recall@1 with a value of 72.", "question": "Which method has the highest recall@1?", "answer": "So, the answer is {\"answer\": \"BERTmulti\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim BM25 has the highest recall@4., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nBM25 has the highest recall@4.\n" }, { "role": "assistant", "content": "The table shows that BERTmulti has the highest recall@4 with a value of 81, not BM25. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3671.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Performance of the vector space models (BM25, TFIDFchar), and neural ranking models (BERTen, BioBERT, BERTmulti) over Wikipedia page retrieval given a UMLS concept. The normalised recall (N. recall@k) is calculated by excluding queries for which the candidate generator (BM25) didn’t retrieve the relevant document." ], "reasoning": "The table shows that BERTmulti has the highest recall@4 with a value of 81, not BM25.", "statement": "BM25 has the highest recall@4.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: The total number, average number of alias names, and supported languages per concept in UMLS, Wikipedia and WikiUMLS.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "To improve multilingual alias encoding, a model like BERTmulti is needed to embed tokens from different languages into the same space, and the WikiUMLS dataset provides a large number of aliases and languages for this purpose.The major shortcoming of BERT and BioBERT is that they don’t encode multilingual aliases effectively, particularly if the scripts are different. This multilingual data can be utilised for triangulation of the matching pair. For example, GERD is a term used in UMLS for Gastroesophageal reflux disease, and is also a given name in many Germanic languages. If it is used for retrieval, many Wikipedia pages related to people with that name will also rank high. However, if alias names of GERD in other languages (e.g. Japanese) are used in the query and documents, the disambiguation becomes easier. To encode multilingual alias concept names we require a model that embeds tokens of different languages into the same embedding space. To this end, we also experiment with multilingual BERT (BERTmulti). We split the matching pairs into roughly 10k, 2k, and 5.8k for training, validation, and test set, respectively. There are about 3 million remaining UMLS concepts from various vocabularies that are not aligned to Wikipedia, which we hope to align in this work. Statistics of UMLS, Wikipedia, and the WikiUMLS" } ], "images": [ "scigen/train/3672.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The total number, average number of alias names, and supported languages per concept in UMLS, Wikipedia and WikiUMLS." ], "reasoning": "To improve multilingual alias encoding, a model like BERTmulti is needed to embed tokens from different languages into the same space, and the WikiUMLS dataset provides a large number of aliases and languages for this purpose.", "description": "The major shortcoming of BERT and BioBERT is that they don’t encode multilingual aliases effectively, particularly if the scripts are different. This multilingual data can be utilised for triangulation of the matching pair. For example, GERD is a term used in UMLS for Gastroesophageal reflux disease, and is also a given name in many Germanic languages. If it is used for retrieval, many Wikipedia pages related to people with that name will also rank high. However, if alias names of GERD in other languages (e.g. Japanese) are used in the query and documents, the disambiguation becomes easier. To encode multilingual alias concept names we require a model that embeds tokens of different languages into the same embedding space. To this end, we also experiment with multilingual BERT (BERTmulti). We split the matching pairs into roughly 10k, 2k, and 5.8k for training, validation, and test set, respectively. There are about 3 million remaining UMLS concepts from various vocabularies that are not aligned to Wikipedia, which we hope to align in this work. Statistics of UMLS, Wikipedia, and the WikiUMLS", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: The total number, average number of alias names, and supported languages per concept in UMLS, Wikipedia and WikiUMLS.', solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich dataset has the highest number of aliases?\n" }, { "role": "assistant", "content": "From the table, compare the '#aliases' column for UMLS, Wikipedia, and WikiUMLS. Wikipedia has the highest number of aliases with 13.0 million. So, the answer is {\"answer\": \"Wikipedia\"}" } ], "images": [ "scigen/train/3672.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The total number, average number of alias names, and supported languages per concept in UMLS, Wikipedia and WikiUMLS." ], "reasoning": "From the table, compare the '#aliases' column for UMLS, Wikipedia, and WikiUMLS. Wikipedia has the highest number of aliases with 13.0 million.", "question": "Which dataset has the highest number of aliases?", "answer": "So, the answer is {\"answer\": \"Wikipedia\"}" }, { "messages": [ { "role": "user", "content": "WikiUMLS has more concepts than UMLS. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nWikiUMLS has more concepts than UMLS.\n" }, { "role": "assistant", "content": "The table shows that UMLS has 3.7 million concepts, while WikiUMLS has 17.8 thousand concepts. Therefore, UMLS has more concepts than WikiUMLS. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3672.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The total number, average number of alias names, and supported languages per concept in UMLS, Wikipedia and WikiUMLS." ], "reasoning": "The table shows that UMLS has 3.7 million concepts, while WikiUMLS has 17.8 thousand concepts. Therefore, UMLS has more concepts than WikiUMLS.", "statement": "WikiUMLS has more concepts than UMLS.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 6: Term 2 for DQIc3, with SIML=0.4'.\n" }, { "role": "assistant", "content": "The second term uses the threshold from the first term and a lower bound on the number of sentences meeting this threshold, aiming to minimize the count of sentences failing to meet it.The second term utilizes two hyperparameters, the threshold from the first term and the lower bound on the number of sentences that should meet this threshold. The summation term should therefore be low, as it counts the number of sentences that fail to meet the threshold." } ], "images": [ "scigen/train/3673.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Term 2 for DQIc3, with SIML=0.4" ], "reasoning": "The second term uses the threshold from the first term and a lower bound on the number of sentences meeting this threshold, aiming to minimize the count of sentences failing to meet it.", "description": "The second term utilizes two hyperparameters, the threshold from the first term and the lower bound on the number of sentences that should meet this threshold. The summation term should therefore be low, as it counts the number of sentences that fail to meet the threshold.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 6: Term 2 for DQIc3, with SIML=0.4'. Solve the following question based on the table.\n\nQuestion: What is the difference between the 'Good' and 'Bad' splits for e=0.25?\nLet's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the difference, subtract the 'Bad' value from the 'Good' value for e=0.25. The values are 0.0468 for 'Good' and 0.0404 for 'Bad'. The difference is 0.0468 - 0.0404 = 0.0064. So, the answer is {\"answer\": \"0.0064\"}" } ], "images": [ "scigen/train/3673.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Term 2 for DQIc3, with SIML=0.4" ], "reasoning": "To find the difference, subtract the 'Bad' value from the 'Good' value for e=0.25. The values are 0.0468 for 'Good' and 0.0404 for 'Bad'. The difference is 0.0468 - 0.0404 = 0.0064.", "question": "What is the difference between the 'Good' and 'Bad' splits for e=0.25?", "answer": "So, the answer is {\"answer\": \"0.0064\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe 'Good' split for e=0.33 is higher than the 'Bad' split for e=0.33.\n" }, { "role": "assistant", "content": "The table shows that the 'Good' split for e=0.33 is 0.0244, while the 'Bad' split for e=0.33 is 0.0216. Since 0.0244 is greater than 0.0216, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3673.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Term 2 for DQIc3, with SIML=0.4" ], "reasoning": "The table shows that the 'Good' split for e=0.33 is 0.0244, while the 'Bad' split for e=0.33 is 0.0216. Since 0.0244 is greater than 0.0216, the statement is supported.", "statement": "The 'Good' split for e=0.33 is higher than the 'Bad' split for e=0.33.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Term-wise and Overall Values for DQIc1', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The Good category has a higher DQI than the Bad category, primarily due to the significant difference in the first term (T1).Which category has higher DQI? Of the three terms in this component, the first term showed the most significant difference. Even though the second and third term are higher for the good category, the difference is less than expected." } ], "images": [ "scigen/train/3674.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Term-wise and Overall Values for DQIc1" ], "reasoning": "The Good category has a higher DQI than the Bad category, primarily due to the significant difference in the first term (T1).", "description": "Which category has higher DQI? Of the three terms in this component, the first term showed the most significant difference. Even though the second and third term are higher for the good category, the difference is less than expected.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Term-wise and Overall Values for DQIc1\nQuestion: Which term showed the most significant difference between the Good and Bad categories?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the values of T1, T2, and T3 for both categories, T1 shows the most significant difference (1.8996 for Good and 0.6416 for Bad). So, the answer is {\"answer\": \"T1\"}." } ], "images": [ "scigen/train/3674.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Term-wise and Overall Values for DQIc1" ], "reasoning": "By comparing the values of T1, T2, and T3 for both categories, T1 shows the most significant difference (1.8996 for Good and 0.6416 for Bad).", "question": "Which term showed the most significant difference between the Good and Bad categories?", "answer": "So, the answer is {\"answer\": \"T1\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Good category has a higher DQI than the Bad category.\n" }, { "role": "assistant", "content": "The DQI for the Good category is 7.6578, which is higher than the DQI for the Bad category, which is 6.1609. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3674.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Term-wise and Overall Values for DQIc1" ], "reasoning": "The DQI for the Good category is 7.6578, which is higher than the DQI for the Bad category, which is 6.1609.", "statement": "The Good category has a higher DQI than the Bad category.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: Term 1 for DQIc3', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "AFLite's hyperparameter affects sentence similarity thresholds, impacting class imbalance and standard deviation.The first term has a hyperparameter that dictates the minimum similarity threshold. Over the dataset, given each sentence in turn, all other sentences are checked against it and those which don’t meet the threshold are counted. The standard deviation of this series should be low, and is inversely proportional to the term’s value. The accountability of this term is similar to class imbalance. This might indicate that, AFLite is not considering imbalance due to sentence similarity." } ], "images": [ "scigen/train/3675.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Term 1 for DQIc3" ], "reasoning": "AFLite's hyperparameter affects sentence similarity thresholds, impacting class imbalance and standard deviation.", "description": "The first term has a hyperparameter that dictates the minimum similarity threshold. Over the dataset, given each sentence in turn, all other sentences are checked against it and those which don’t meet the threshold are counted. The standard deviation of this series should be low, and is inversely proportional to the term’s value. The accountability of this term is similar to class imbalance. This might indicate that, AFLite is not considering imbalance due to sentence similarity.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nTable title: Table 5: Term 1 for DQIc3\nQuestion: What is the difference in the SIML=0.4 scores between the Good and Bad splits?\n" }, { "role": "assistant", "content": "To find the difference, subtract the Good split score from the Bad split score for SIML=0.4. So, the answer is {\"answer\": \"2.3123\"}." } ], "images": [ "scigen/train/3675.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Term 1 for DQIc3" ], "reasoning": "To find the difference, subtract the Good split score from the Bad split score for SIML=0.4.", "question": "What is the difference in the SIML=0.4 scores between the Good and Bad splits?", "answer": "So, the answer is {\"answer\": \"2.3123\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The SIML=0.35 score for the Good split is higher than the SIML=0.3 score for the Bad split., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe SIML=0.35 score for the Good split is higher than the SIML=0.3 score for the Bad split.\n" }, { "role": "assistant", "content": "The table shows that the SIML=0.35 score for the Good split is 11.3955, which is higher than the SIML=0.3 score for the Bad split, which is 10.3842. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3675.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Term 1 for DQIc3" ], "reasoning": "The table shows that the SIML=0.35 score for the Good split is 11.3955, which is higher than the SIML=0.3 score for the Bad split, which is 10.3842.", "statement": "The SIML=0.35 score for the Good split is higher than the SIML=0.3 score for the Bad split.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 9: Term 1 for DQIc5'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The term should be high if the similarity between premise hypothesis pairs is far from the hyperparameter, indicating potential spurious bias.The first term computes if the sentence similarity across a given sample meets a threshold, which is a hyperparameter. This sum should be low and so the term should be high, because if the similarity between premise hypothesis pairs is far from the hyperparameter, the sample might give rise to spurious bias." } ], "images": [ "scigen/train/3676.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Term 1 for DQIc5" ], "reasoning": "The term should be high if the similarity between premise hypothesis pairs is far from the hyperparameter, indicating potential spurious bias.", "description": "The first term computes if the sentence similarity across a given sample meets a threshold, which is a hyperparameter. This sum should be low and so the term should be high, because if the similarity between premise hypothesis pairs is far from the hyperparameter, the sample might give rise to spurious bias.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 9: Term 1 for DQIc5\nQuestion: What is the difference in the ISIM value between Good and Bad splits at ISIM=0.5?\nSolve the above question based on the table information. Let's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the difference, subtract the ISIM value for the Bad split from the ISIM value for the Good split at ISIM=0.5. So, the answer is {\"answer\": \"0.0331\"}" } ], "images": [ "scigen/train/3676.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Term 1 for DQIc5" ], "reasoning": "To find the difference, subtract the ISIM value for the Bad split from the ISIM value for the Good split at ISIM=0.5.", "question": "What is the difference in the ISIM value between Good and Bad splits at ISIM=0.5?", "answer": "So, the answer is {\"answer\": \"0.0331\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The ISIM value for the Good split is higher than the Bad split at ISIM=0.4., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe ISIM value for the Good split is higher than the Bad split at ISIM=0.4.\n" }, { "role": "assistant", "content": "Comparing the ISIM values at ISIM=0.4, the Good split has a value of 2.8763, which is higher than the Bad split value of 2.8558. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3676.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Term 1 for DQIc5" ], "reasoning": "Comparing the ISIM values at ISIM=0.4, the Good split has a value of 2.8763, which is higher than the Bad split value of 2.8558.", "statement": "The ISIM value for the Good split is higher than the Bad split at ISIM=0.4.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 10: Terms 2,3,4,5,6 for DQIc5', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The second term measures the mean length variation between good and bad categories, which should be minimal to avoid bias.Second Term The second term measures the length variation in the good and bad categories, between the premise and hypothesis. This variation is computed as a mean of differences. The mean should be less so that the model does not get a chance to use hypothesis length as an artifact. Even though the term has a higher value for good category, it appears to be almost the same for both categories." } ], "images": [ "scigen/train/3677.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Terms 2,3,4,5,6 for DQIc5" ], "reasoning": "The second term measures the mean length variation between good and bad categories, which should be minimal to avoid bias.", "description": "Second Term The second term measures the length variation in the good and bad categories, between the premise and hypothesis. This variation is computed as a mean of differences. The mean should be less so that the model does not get a chance to use hypothesis length as an artifact. Even though the term has a higher value for good category, it appears to be almost the same for both categories.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the difference in the T5 values between the good and bad categories?\nSolve the above question based on the table titled 'Table 10: Terms 2,3,4,5,6 for DQIc5'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the difference in T5 values, subtract the T5 value for the bad category from the T5 value for the good category: 20.3518 - 19.9288. So, the answer is {\"answer\": \"0.423\"}" } ], "images": [ "scigen/train/3677.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Terms 2,3,4,5,6 for DQIc5" ], "reasoning": "To find the difference in T5 values, subtract the T5 value for the bad category from the T5 value for the good category: 20.3518 - 19.9288.", "question": "What is the difference in the T5 values between the good and bad categories?", "answer": "So, the answer is {\"answer\": \"0.423\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe T2 value is higher for the good category compared to the bad category.\n" }, { "role": "assistant", "content": "The T2 value for the good category is 0.1439, while for the bad category it is 0.1430. Since 0.1439 is greater than 0.1430, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3677.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Terms 2,3,4,5,6 for DQIc5" ], "reasoning": "The T2 value for the good category is 0.1439, while for the bad category it is 0.1430. Since 0.1439 is greater than 0.1430, the statement is supported.", "statement": "The T2 value is higher for the good category compared to the bad category.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 12: Sample counts for Splits across Labels'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Contradiction samples have a higher ratio in both good and bad splits compared to entailment and neutral labels.S6SS6SSS0Px2 Contradiction samples are seen to be more prone to spurious bias In order to compute the terms, the good and bad splits of data were further divided into three subsets each, corresponding to the gold labels of samples. On creating these subsets, we note that the ratio of contradiction samples in the good and bad categories is much higher than that seen in the case of entailment and neutral labels." } ], "images": [ "scigen/train/3678.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 12: Sample counts for Splits across Labels" ], "reasoning": "Contradiction samples have a higher ratio in both good and bad splits compared to entailment and neutral labels.", "description": "S6SS6SSS0Px2 Contradiction samples are seen to be more prone to spurious bias In order to compute the terms, the good and bad splits of data were further divided into three subsets each, corresponding to the gold labels of samples. On creating these subsets, we note that the ratio of contradiction samples in the good and bad categories is much higher than that seen in the case of entailment and neutral labels.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 12: Sample counts for Splits across Labels' and proceed to solve the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the total number of contradiction samples in both good and bad splits?\n" }, { "role": "assistant", "content": "To find the total number of contradiction samples, add the contradiction samples from the good split (708) and the bad split (6118). So, the answer is {\"answer\": \"6826\"}." } ], "images": [ "scigen/train/3678.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 12: Sample counts for Splits across Labels" ], "reasoning": "To find the total number of contradiction samples, add the contradiction samples from the good split (708) and the bad split (6118).", "question": "What is the total number of contradiction samples in both good and bad splits?", "answer": "So, the answer is {\"answer\": \"6826\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The number of neutral samples in the bad split is higher than the number of entailment samples in the good split. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe number of neutral samples in the bad split is higher than the number of entailment samples in the good split.\n" }, { "role": "assistant", "content": "The table shows that there are 5008 neutral samples in the bad split and 1110 entailment samples in the good split, confirming the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3678.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 12: Sample counts for Splits across Labels" ], "reasoning": "The table shows that there are 5008 neutral samples in the bad split and 1110 entailment samples in the good split, confirming the statement.", "statement": "The number of neutral samples in the bad split is higher than the number of entailment samples in the good split.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 23: T4 for DQIc6'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The standard deviation of sentence length difference between premise and hypothesis is higher for entailment and neutral labels, but lower for contradiction labels.The fourth term calculates the standard deviation of sentence length difference between premise and hypothesis across labels. The standard deviation needs to be higher to ensure that there exist samples of varying difference betweeen premise and hypothesis length, and the model is not overfitted towards a fixed length difference. It passes for entailment and neutral label. It fails for contradiction label though both the terms are very close in that case." } ], "images": [ "scigen/train/3679.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 23: T4 for DQIc6" ], "reasoning": "The standard deviation of sentence length difference between premise and hypothesis is higher for entailment and neutral labels, but lower for contradiction labels.", "description": "The fourth term calculates the standard deviation of sentence length difference between premise and hypothesis across labels. The standard deviation needs to be higher to ensure that there exist samples of varying difference betweeen premise and hypothesis length, and the model is not overfitted towards a fixed length difference. It passes for entailment and neutral label. It fails for contradiction label though both the terms are very close in that case.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 23: T4 for DQIc6'. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. \n\nQuestion: Which label has the highest standard deviation of sentence length difference between premise and hypothesis?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By comparing the values in the T4 column, the highest standard deviation is found under the Good-Contradiction label with a value of 0.0197. So, the answer is {\"answer\": \"Good-Contradiction\"}." } ], "images": [ "scigen/train/3679.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 23: T4 for DQIc6" ], "reasoning": "By comparing the values in the T4 column, the highest standard deviation is found under the Good-Contradiction label with a value of 0.0197.", "question": "Which label has the highest standard deviation of sentence length difference between premise and hypothesis?", "answer": "So, the answer is {\"answer\": \"Good-Contradiction\"}." }, { "messages": [ { "role": "user", "content": "The standard deviation of sentence length difference for Bad-Entailment is higher than for Bad-Contradiction. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe standard deviation of sentence length difference for Bad-Entailment is higher than for Bad-Contradiction.\n" }, { "role": "assistant", "content": "The table shows that the standard deviation for Bad-Entailment is 0.0021, while for Bad-Contradiction it is 0.0020, which means the statement is correct. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3679.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 23: T4 for DQIc6" ], "reasoning": "The table shows that the standard deviation for Bad-Entailment is 0.0021, while for Bad-Contradiction it is 0.0020, which means the statement is correct.", "statement": "The standard deviation of sentence length difference for Bad-Entailment is higher than for Bad-Contradiction.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 24: T5 for DQIc6', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The term calculates the sum of standard deviations of frequency vectors for repeated units in a granularity, normalized by the size of the set of units, with lower values indicating a better split.The fifth term first computes the frequency of each unit in a granularity, to form vectors of length three for each unit. The standard deviation of this vector is calculated for each unit, if the unit is repeated. If the unit is not repeated, then it is not considered in our calculation. The sum of these standard deviations is calculated across the given granularity. This sum is normalized by division by the size of the set of units for that granularity, across all labels. The expectation is that the good split will show lower values of this term compared to the bad split, as lesser variance within labels is desirable. So, the term has been reversed to have direct proportionality with DQI." } ], "images": [ "scigen/train/3680.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 24: T5 for DQIc6" ], "reasoning": "The term calculates the sum of standard deviations of frequency vectors for repeated units in a granularity, normalized by the size of the set of units, with lower values indicating a better split.", "description": "The fifth term first computes the frequency of each unit in a granularity, to form vectors of length three for each unit. The standard deviation of this vector is calculated for each unit, if the unit is repeated. If the unit is not repeated, then it is not considered in our calculation. The sum of these standard deviations is calculated across the given granularity. This sum is normalized by division by the size of the set of units for that granularity, across all labels. The expectation is that the good split will show lower values of this term compared to the bad split, as lesser variance within labels is desirable. So, the term has been reversed to have direct proportionality with DQI.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 24: T5 for DQIc6', answer the following question. Give detailed solution consisting of each step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich granularity shows the highest value for the 'Good' split?\n" }, { "role": "assistant", "content": "To find the highest value for the 'Good' split, compare the values in the 'Good' column. The highest value is 15.3475 for 'Sentences'. So, the answer is {\"answer\": \"Sentences\"}." } ], "images": [ "scigen/train/3680.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 24: T5 for DQIc6" ], "reasoning": "To find the highest value for the 'Good' split, compare the values in the 'Good' column. The highest value is 15.3475 for 'Sentences'.", "question": "Which granularity shows the highest value for the 'Good' split?", "answer": "So, the answer is {\"answer\": \"Sentences\"}." }, { "messages": [ { "role": "user", "content": "The 'Bad' split has a higher value for 'Bigrams' than the 'Good' split. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe 'Bad' split has a higher value for 'Bigrams' than the 'Good' split.\n" }, { "role": "assistant", "content": "Comparing the values for 'Bigrams' in the 'Good' and 'Bad' columns, the 'Bad' split has a value of 0.4893, which is higher than the 'Good' split value of 0.3646. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3680.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 24: T5 for DQIc6" ], "reasoning": "Comparing the values for 'Bigrams' in the 'Good' and 'Bad' columns, the 'Bad' split has a value of 0.4893, which is higher than the 'Good' split value of 0.3646.", "statement": "The 'Bad' split has a higher value for 'Bigrams' than the 'Good' split.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 26: DQIc7'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The 'Good' category has slightly higher DQI values compared to the 'Bad' category across all SSMIL values.Which category has higher DQI? However, both the values are very similar. This might indicate that, AFLite is not properly incorporating this lead while filtering." } ], "images": [ "scigen/train/3681.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 26: DQIc7" ], "reasoning": "The 'Good' category has slightly higher DQI values compared to the 'Bad' category across all SSMIL values.", "description": "Which category has higher DQI? However, both the values are very similar. This might indicate that, AFLite is not properly incorporating this lead while filtering.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 26: DQIc7'. The final result should be presented in the JSON format of {\"answer\": \"\"}. \n\nQuestion: Which category has higher DQI values across all SSMIL values?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By comparing the DQI values for 'Good' and 'Bad' categories across SSMIL=0.2, SSMIL=0.3, and SSMIL=0.4, we see that 'Good' has higher values in each case. So, the answer is {\"answer\": \"Good\"}" } ], "images": [ "scigen/train/3681.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 26: DQIc7" ], "reasoning": "By comparing the DQI values for 'Good' and 'Bad' categories across SSMIL=0.2, SSMIL=0.3, and SSMIL=0.4, we see that 'Good' has higher values in each case.", "question": "Which category has higher DQI values across all SSMIL values?", "answer": "So, the answer is {\"answer\": \"Good\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe 'Bad' category has higher DQI values than the 'Good' category for SSMIL=0.3.\n" }, { "role": "assistant", "content": "The table shows that for SSMIL=0.3, the 'Good' category has a DQI value of 0.0042, which is higher than the 'Bad' category's DQI value of 0.0040. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3681.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 26: DQIc7" ], "reasoning": "The table shows that for SSMIL=0.3, the 'Good' category has a DQI value of 0.0042, which is higher than the 'Bad' category's DQI value of 0.0040.", "statement": "The 'Bad' category has higher DQI values than the 'Good' category for SSMIL=0.3.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 14: Blackbox Attack Success Rate after inserting the whitebox generated adversarial sentence to different positions for BERT-QA.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Appending adversarial sentences at the beginning of the paragraph generally achieves the best attack performance, while appending them at the end is slightly less effective.To further explore how the location of adversarial sentences affects the attack success rate, we conduct the ablation experiments by varying the position of appended adversarial sentence. We generate the adversarial sentences from the whitebox BERT classification and QA models. Then we inject those adversaries into different positions of the original paragraph and test in another blackbox BERT with the same architecture but different parameters. We see in most time appending the adversarial sentence at the beginning of the paragraph achieves the best attack performance. Also the performance of appending the adversarial sentence at the end of the paragraph is usually slightly weaker than front. This observation suggests that the BERT model might pay more attention to the both ends of the paragraphs and tend to overlook the content in the middle." } ], "images": [ "scigen/train/3682.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 14: Blackbox Attack Success Rate after inserting the whitebox generated adversarial sentence to different positions for BERT-QA." ], "reasoning": "Appending adversarial sentences at the beginning of the paragraph generally achieves the best attack performance, while appending them at the end is slightly less effective.", "description": "To further explore how the location of adversarial sentences affects the attack success rate, we conduct the ablation experiments by varying the position of appended adversarial sentence. We generate the adversarial sentences from the whitebox BERT classification and QA models. Then we inject those adversaries into different positions of the original paragraph and test in another blackbox BERT with the same architecture but different parameters. We see in most time appending the adversarial sentence at the beginning of the paragraph achieves the best attack performance. Also the performance of appending the adversarial sentence at the end of the paragraph is usually slightly weaker than front. This observation suggests that the BERT model might pay more attention to the both ends of the paragraphs and tend to overlook the content in the middle.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 14: Blackbox Attack Success Rate after inserting the whitebox generated adversarial sentence to different positions for BERT-QA.' and methodically tackle the question that follows. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the EM score for appending adversarial sentences at the middle of the paragraph?\n" }, { "role": "assistant", "content": "The table shows the EM scores for different positions of adversarial sentences. For the middle position, the EM score is 51.3. So, the answer is {\"answer\": \"51.3\"}." } ], "images": [ "scigen/train/3682.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 14: Blackbox Attack Success Rate after inserting the whitebox generated adversarial sentence to different positions for BERT-QA." ], "reasoning": "The table shows the EM scores for different positions of adversarial sentences. For the middle position, the EM score is 51.3.", "question": "What is the EM score for appending adversarial sentences at the middle of the paragraph?", "answer": "So, the answer is {\"answer\": \"51.3\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Appending adversarial sentences at the front of the paragraph achieves the highest F1 score., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nAppending adversarial sentences at the front of the paragraph achieves the highest F1 score.\n" }, { "role": "assistant", "content": "The table shows that appending adversarial sentences at the front of the paragraph does not achieve the highest F1 score; the highest F1 score is achieved when appending them in the middle. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3682.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 14: Blackbox Attack Success Rate after inserting the whitebox generated adversarial sentence to different positions for BERT-QA." ], "reasoning": "The table shows that appending adversarial sentences at the front of the paragraph does not achieve the highest F1 score; the highest F1 score is achieved when appending them in the middle.", "statement": "Appending adversarial sentences at the front of the paragraph achieves the highest F1 score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: Blackbox attack success rates on sentiment analysis. The transferability-based blackbox attack uses adversarial text generated from whitebox BERT model to attack blackbox SAM, and vice versa. TF is short for TextFooler.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The table compares the success rates of blackbox attacks using different models and attack methods, highlighting the highest success rates for each method.Blackbox Attack. We perform transferability based blackbox attacks. We compare our blackbox attack success rate with the blackbox baseline TextFooler and blackbox Seq2Sick based on transferability." } ], "images": [ "scigen/train/3683.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Blackbox attack success rates on sentiment analysis. The transferability-based blackbox attack uses adversarial text generated from whitebox BERT model to attack blackbox SAM, and vice versa. TF is short for TextFooler." ], "reasoning": "The table compares the success rates of blackbox attacks using different models and attack methods, highlighting the highest success rates for each method.", "description": "Blackbox Attack. We perform transferability based blackbox attacks. We compare our blackbox attack success rate with the blackbox baseline TextFooler and blackbox Seq2Sick based on transferability.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Blackbox attack success rates on sentiment analysis. The transferability-based blackbox attack uses adversarial text generated from whitebox BERT model to attack blackbox SAM, and vice versa. TF is short for TextFooler.\nQuestion: Which model and attack method combination achieved the highest success rate for Scatter Attack Adv(Word)?\nGive a detailed response to the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest success rate for Scatter Attack Adv(Word), we look at the corresponding column and identify the highest value. The highest value in this column is 0.679, achieved by the SAM model with the untargeted attack. So, the answer is {\"answer\": \"SAM untargeted\"}." } ], "images": [ "scigen/train/3683.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Blackbox attack success rates on sentiment analysis. The transferability-based blackbox attack uses adversarial text generated from whitebox BERT model to attack blackbox SAM, and vice versa. TF is short for TextFooler." ], "reasoning": "To find the highest success rate for Scatter Attack Adv(Word), we look at the corresponding column and identify the highest value. The highest value in this column is 0.679, achieved by the SAM model with the untargeted attack.", "question": "Which model and attack method combination achieved the highest success rate for Scatter Attack Adv(Word)?", "answer": "So, the answer is {\"answer\": \"SAM untargeted\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The BERT model with the untargeted Concat Attack Adv(Word) achieved a higher success rate than the SAM model with the targeted Concat Attack Adv(Word). and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe BERT model with the untargeted Concat Attack Adv(Word) achieved a higher success rate than the SAM model with the targeted Concat Attack Adv(Word).\n" }, { "role": "assistant", "content": "To verify this statement, we compare the success rates of the BERT model with the untargeted Concat Attack Adv(Word) and the SAM model with the targeted Concat Attack Adv(Word). The success rate for BERT untargeted is 0.686, while for SAM targeted it is 0.516. Since 0.686 is greater than 0.516, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3683.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Blackbox attack success rates on sentiment analysis. The transferability-based blackbox attack uses adversarial text generated from whitebox BERT model to attack blackbox SAM, and vice versa. TF is short for TextFooler." ], "reasoning": "To verify this statement, we compare the success rates of the BERT model with the untargeted Concat Attack Adv(Word) and the SAM model with the targeted Concat Attack Adv(Word). The success rate for BERT untargeted is 0.686, while for SAM targeted it is 0.516. Since 0.686 is greater than 0.516, the statement is supported.", "statement": "The BERT model with the untargeted Concat Attack Adv(Word) achieved a higher success rate than the SAM model with the targeted Concat Attack Adv(Word).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Whitebox attack results on QA in terms of exact match rates and F1 scores by the official evaluation script. The lower EM and F1 scores mean the better attack success rate.'.\n" }, { "role": "assistant", "content": "BERT shows a larger performance drop under adversarial evaluation compared to BiDAF, indicating its insecurity.Adversarial Evaluation. We perform the whitebox attack with different attack methods on our testing models. It is worth noting although BERT has better performances than BiDAF, the performance drop for BERT ΔF1BERT is 55.4 larger than the performance drop for BiDAF ΔF1BiDAF=53.0, which again proves the BERT is insecure under the adversarial evaluation. We also find the position targeted attack is slightly stronger than the answer targeted attack. We assume it is because the answer targeted attack has fixed targeted answer and limited freedom to alter the appended sentence, but the position targeted attack has more freedom to alter the fake answer from the targeted position spans. We also tried the scatter attack on QA though the performances are not good. It turns out QA systems highly rely on the relationship between questions and contextual clues, which is hard to break when setting an arbitrary token to a target answer. We discussed in Appendix A.3 the untargeted scatter attack can work well and outperform the baseline methods." } ], "images": [ "scigen/train/3684.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Whitebox attack results on QA in terms of exact match rates and F1 scores by the official evaluation script. The lower EM and F1 scores mean the better attack success rate." ], "reasoning": "BERT shows a larger performance drop under adversarial evaluation compared to BiDAF, indicating its insecurity.", "description": "Adversarial Evaluation. We perform the whitebox attack with different attack methods on our testing models. It is worth noting although BERT has better performances than BiDAF, the performance drop for BERT ΔF1BERT is 55.4 larger than the performance drop for BiDAF ΔF1BiDAF=53.0, which again proves the BERT is insecure under the adversarial evaluation. We also find the position targeted attack is slightly stronger than the answer targeted attack. We assume it is because the answer targeted attack has fixed targeted answer and limited freedom to alter the appended sentence, but the position targeted attack has more freedom to alter the fake answer from the targeted position spans. We also tried the scatter attack on QA though the performances are not good. It turns out QA systems highly rely on the relationship between questions and contextual clues, which is hard to break when setting an arbitrary token to a target answer. We discussed in Appendix A.3 the untargeted scatter attack can work well and outperform the baseline methods.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 4: Whitebox attack results on QA in terms of exact match rates and F1 scores by the official evaluation script. The lower EM and F1 scores mean the better attack success rate.' in mind, break down and resolve the question below step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich model has a higher F1 score under the Position Targeted Attack Adv(Sent) method?\n" }, { "role": "assistant", "content": "Compare the F1 scores of BERT and BiDAF under the Position Targeted Attack Adv(Sent) method. BERT has an F1 score of 53.8, while BiDAF has an F1 score of 34.0. So, the answer is {\"answer\": \"BERT\"}" } ], "images": [ "scigen/train/3684.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Whitebox attack results on QA in terms of exact match rates and F1 scores by the official evaluation script. The lower EM and F1 scores mean the better attack success rate." ], "reasoning": "Compare the F1 scores of BERT and BiDAF under the Position Targeted Attack Adv(Sent) method. BERT has an F1 score of 53.8, while BiDAF has an F1 score of 34.0.", "question": "Which model has a higher F1 score under the Position Targeted Attack Adv(Sent) method?", "answer": "So, the answer is {\"answer\": \"BERT\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Position Targeted Attack Adv(Word) method is stronger than the Answer Targeted Attack Adv(Word) method for both BERT and BiDAF.\n" }, { "role": "assistant", "content": "Compare the scores under the Position Targeted Attack Adv(Word) and Answer Targeted Attack Adv(Word) methods for both BERT and BiDAF. For BERT, 33.2 (Position) is higher than 47.3 (Answer). For BiDAF, 17.6 (Position) is higher than 23.6 (Answer). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3684.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Whitebox attack results on QA in terms of exact match rates and F1 scores by the official evaluation script. The lower EM and F1 scores mean the better attack success rate." ], "reasoning": "Compare the scores under the Position Targeted Attack Adv(Word) and Answer Targeted Attack Adv(Word) methods for both BERT and BiDAF. For BERT, 33.2 (Position) is higher than 47.3 (Answer). For BiDAF, 17.6 (Position) is higher than 23.6 (Answer).", "statement": "The Position Targeted Attack Adv(Word) method is stronger than the Answer Targeted Attack Adv(Word) method for both BERT and BiDAF.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 5: Targeted Attack Results of whitebox attack on QA. Here, the targeted exact match rates and targeted F1 Score measures how many model outputs match the targeted fake answers. Higher targeted EM and F1 mean higher targeted attack success rate. UT is short for Universal Trigger baseline.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "AdvCodec(Word) has the highest targeted attack ability on QA models, outperforming the baseline Universal Triggers.Then we test the targeted results of whitebox attack methods on QA models. It shows that AdvCodec(Word) has the best targeted attack ability on QA. And all our attack methods outperform the baseline(Universal Triggers) when it comes to the targeted results." } ], "images": [ "scigen/train/3685.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Targeted Attack Results of whitebox attack on QA. Here, the targeted exact match rates and targeted F1 Score measures how many model outputs match the targeted fake answers. Higher targeted EM and F1 mean higher targeted attack success rate. UT is short for Universal Trigger baseline." ], "reasoning": "AdvCodec(Word) has the highest targeted attack ability on QA models, outperforming the baseline Universal Triggers.", "description": "Then we test the targeted results of whitebox attack methods on QA models. It shows that AdvCodec(Word) has the best targeted attack ability on QA. And all our attack methods outperform the baseline(Universal Triggers) when it comes to the targeted results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 5: Targeted Attack Results of whitebox attack on QA. Here, the targeted exact match rates and targeted F1 Score measures how many model outputs match the targeted fake answers. Higher targeted EM and F1 mean higher targeted attack success rate. UT is short for Universal Trigger baseline.', answer the following question in a stepwise manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich attack method has the highest target F1 score on the BiDAF model?\n" }, { "role": "assistant", "content": "The table shows that the Adv(Word) method has the highest target F1 score of 75.6 on the BiDAF model. So, the answer is {\"answer\": \"Adv(Word)\"}" } ], "images": [ "scigen/train/3685.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Targeted Attack Results of whitebox attack on QA. Here, the targeted exact match rates and targeted F1 Score measures how many model outputs match the targeted fake answers. Higher targeted EM and F1 mean higher targeted attack success rate. UT is short for Universal Trigger baseline." ], "reasoning": "The table shows that the Adv(Word) method has the highest target F1 score of 75.6 on the BiDAF model.", "question": "Which attack method has the highest target F1 score on the BiDAF model?", "answer": "So, the answer is {\"answer\": \"Adv(Word)\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The BERT model has a higher target EM score with Adv(Word) than with Adv(Sent)., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe BERT model has a higher target EM score with Adv(Word) than with Adv(Sent).\n" }, { "role": "assistant", "content": "The table shows that the BERT model has a target EM score of 46.5 with Adv(Word) and 32.4 with Adv(Sent), indicating that the statement is true. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3685.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Targeted Attack Results of whitebox attack on QA. Here, the targeted exact match rates and targeted F1 Score measures how many model outputs match the targeted fake answers. Higher targeted EM and F1 mean higher targeted attack success rate. UT is short for Universal Trigger baseline." ], "reasoning": "The table shows that the BERT model has a target EM score of 46.5 with Adv(Word) and 32.4 with Adv(Sent), indicating that the statement is true.", "statement": "The BERT model has a higher target EM score with Adv(Word) than with Adv(Sent).", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 6: BlackBox attack results on QA in terms of exact match rates and F1 scores. The transferability-based blackbox attack uses adversarial text generated from whitebox models (annotated as (w)) to attack different blakcbox models (annotated as (b)).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The proposed methods outperform the baseline method (AddSent) when transferring adversarial texts among models with the same architectures.Blackbox Attack. We also transfer adversarial texts generated from whitebox attacks to perform blackbox attacks. All our proposed methods outperform the baseline method(AddSent) when transferring the adversaries among models with same architectures." } ], "images": [ "scigen/train/3686.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: BlackBox attack results on QA in terms of exact match rates and F1 scores. The transferability-based blackbox attack uses adversarial text generated from whitebox models (annotated as (w)) to attack different blakcbox models (annotated as (b))." ], "reasoning": "The proposed methods outperform the baseline method (AddSent) when transferring adversarial texts among models with the same architectures.", "description": "Blackbox Attack. We also transfer adversarial texts generated from whitebox attacks to perform blackbox attacks. All our proposed methods outperform the baseline method(AddSent) when transferring the adversaries among models with same architectures.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: BlackBox attack results on QA in terms of exact match rates and F1 scores. The transferability-based blackbox attack uses adversarial text generated from whitebox models (annotated as (w)) to attack different blakcbox models (annotated as (b)).\nQuestion: Which attack method has the highest F1 score for Position Targeted Attack Adv(Sent) when transferring from BiDAF(w) to BERT(b)?\nGive a detailed response to the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest F1 score for Position Targeted Attack Adv(Sent) when transferring from BiDAF(w) to BERT(b), we look at the F1 scores in the corresponding row and column. The highest F1 score is 62.9. So, the answer is {\"answer\": \"62.9\"}." } ], "images": [ "scigen/train/3686.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: BlackBox attack results on QA in terms of exact match rates and F1 scores. The transferability-based blackbox attack uses adversarial text generated from whitebox models (annotated as (w)) to attack different blakcbox models (annotated as (b))." ], "reasoning": "To find the highest F1 score for Position Targeted Attack Adv(Sent) when transferring from BiDAF(w) to BERT(b), we look at the F1 scores in the corresponding row and column. The highest F1 score is 62.9.", "question": "Which attack method has the highest F1 score for Position Targeted Attack Adv(Sent) when transferring from BiDAF(w) to BERT(b)?", "answer": "So, the answer is {\"answer\": \"62.9\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe EM score for Answer Targeted Attack Adv(Word) when transferring from BERT(w) to BiDAF(b) is higher than the baseline method.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the EM score for Answer Targeted Attack Adv(Word) when transferring from BERT(w) to BiDAF(b) with the baseline method. The EM score is 28.9, which is higher than the baseline score of 22.3. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3686.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: BlackBox attack results on QA in terms of exact match rates and F1 scores. The transferability-based blackbox attack uses adversarial text generated from whitebox models (annotated as (w)) to attack different blakcbox models (annotated as (b))." ], "reasoning": "To verify this statement, we compare the EM score for Answer Targeted Attack Adv(Word) when transferring from BERT(w) to BiDAF(b) with the baseline method. The EM score is 28.9, which is higher than the baseline score of 22.3.", "statement": "The EM score for Answer Targeted Attack Adv(Word) when transferring from BERT(w) to BiDAF(b) is higher than the baseline method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 9: Human performance on QA', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Human participants showed a drop in accuracy from 0.95 on the original dataset to 0.82 on both AdvCodec(Sent) and AdvCodec(Word) adversarial datasets.To ensure that our generated adversarial text are compatible with the original paragraph, we ask human participants to perform the sentiment classification and question answering task both on the original dataset and adversarial dataset. Adversarial dataset on sentiment classification consists of AdvCodec(Sent) concatenative adversarial examples and AdvCodec(Word) scatter attack exmaples. Adversarial dataset on QA consists of concatenative adversarial examples genereated by both AdvCodec(Sent) and AdvCodec(Word). More specifically, we respectively prepare 100 benign and adversarial data pairs for both QA and sentiment classification, and hand out them to 505 Amazon Turks. Each turk is requested to answer at least 5 question and at most 15 questions for the QA task and judge the sentiment for at least 10 paragraphs and at most 20 paragraphs for the sentiment classification task. We also perform a majority vote over Turk’s answers for the same question. While we can spot a drop from the benign to adversarial datasets, we conduct an error analysis in QA and find the error examples are noisy and not necessarily caused by our adversarial text. For adversarial data in the sentiment classification task, we notice that the generated tokens or appended sentences have opposite sentiment from the benign one. However, our evaluation results show human readers can naturally ignore abnormal tokens and make correct judgement according to the context." } ], "images": [ "scigen/train/3687.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Human performance on QA" ], "reasoning": "Human participants showed a drop in accuracy from 0.95 on the original dataset to 0.82 on both AdvCodec(Sent) and AdvCodec(Word) adversarial datasets.", "description": "To ensure that our generated adversarial text are compatible with the original paragraph, we ask human participants to perform the sentiment classification and question answering task both on the original dataset and adversarial dataset. Adversarial dataset on sentiment classification consists of AdvCodec(Sent) concatenative adversarial examples and AdvCodec(Word) scatter attack exmaples. Adversarial dataset on QA consists of concatenative adversarial examples genereated by both AdvCodec(Sent) and AdvCodec(Word). More specifically, we respectively prepare 100 benign and adversarial data pairs for both QA and sentiment classification, and hand out them to 505 Amazon Turks. Each turk is requested to answer at least 5 question and at most 15 questions for the QA task and judge the sentiment for at least 10 paragraphs and at most 20 paragraphs for the sentiment classification task. We also perform a majority vote over Turk’s answers for the same question. While we can spot a drop from the benign to adversarial datasets, we conduct an error analysis in QA and find the error examples are noisy and not necessarily caused by our adversarial text. For adversarial data in the sentiment classification task, we notice that the generated tokens or appended sentences have opposite sentiment from the benign one. However, our evaluation results show human readers can naturally ignore abnormal tokens and make correct judgement according to the context.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the accuracy drop from the original dataset to the adversarial datasets?\nSolve the above question based on the table titled 'Table 9: Human performance on QA'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The original dataset has an accuracy of 0.95, while both AdvCodec(Sent) and AdvCodec(Word) adversarial datasets have an accuracy of 0.82. The drop in accuracy is calculated by subtracting 0.82 from 0.95. So, the answer is {\"answer\": \"0.13\"}." } ], "images": [ "scigen/train/3687.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Human performance on QA" ], "reasoning": "The original dataset has an accuracy of 0.95, while both AdvCodec(Sent) and AdvCodec(Word) adversarial datasets have an accuracy of 0.82. The drop in accuracy is calculated by subtracting 0.82 from 0.95.", "question": "What is the accuracy drop from the original dataset to the adversarial datasets?", "answer": "So, the answer is {\"answer\": \"0.13\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The accuracy of the AdvCodec(Word) adversarial dataset is higher than the AdvCodec(Sent) adversarial dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe accuracy of the AdvCodec(Word) adversarial dataset is higher than the AdvCodec(Sent) adversarial dataset.\n" }, { "role": "assistant", "content": "The table shows that the accuracy for both AdvCodec(Word) and AdvCodec(Sent) adversarial datasets is 0.82, indicating that their accuracies are equal. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3687.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Human performance on QA" ], "reasoning": "The table shows that the accuracy for both AdvCodec(Word) and AdvCodec(Sent) adversarial datasets is 0.82, indicating that their accuracies are equal.", "statement": "The accuracy of the AdvCodec(Word) adversarial dataset is higher than the AdvCodec(Sent) adversarial dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 9: Human performance on QA'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Human readers can naturally ignore abnormal tokens and make correct judgments according to the context, despite a drop in performance from benign to adversarial datasets.To ensure that our generated adversarial text are compatible with the original paragraph, we ask human participants to perform the sentiment classification and question answering task both on the original dataset and adversarial dataset. Adversarial dataset on sentiment classification consists of AdvCodec(Sent) concatenative adversarial examples and AdvCodec(Word) scatter attack exmaples. Adversarial dataset on QA consists of concatenative adversarial examples genereated by both AdvCodec(Sent) and AdvCodec(Word). More specifically, we respectively prepare 100 benign and adversarial data pairs for both QA and sentiment classification, and hand out them to 505 Amazon Turks. Each turk is requested to answer at least 5 question and at most 15 questions for the QA task and judge the sentiment for at least 10 paragraphs and at most 20 paragraphs for the sentiment classification task. We also perform a majority vote over Turk’s answers for the same question. While we can spot a drop from the benign to adversarial datasets, we conduct an error analysis in QA and find the error examples are noisy and not necessarily caused by our adversarial text. For adversarial data in the sentiment classification task, we notice that the generated tokens or appended sentences have opposite sentiment from the benign one. However, our evaluation results show human readers can naturally ignore abnormal tokens and make correct judgement according to the context." } ], "images": [ "scigen/train/3688.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Human performance on QA" ], "reasoning": "Human readers can naturally ignore abnormal tokens and make correct judgments according to the context, despite a drop in performance from benign to adversarial datasets.", "description": "To ensure that our generated adversarial text are compatible with the original paragraph, we ask human participants to perform the sentiment classification and question answering task both on the original dataset and adversarial dataset. Adversarial dataset on sentiment classification consists of AdvCodec(Sent) concatenative adversarial examples and AdvCodec(Word) scatter attack exmaples. Adversarial dataset on QA consists of concatenative adversarial examples genereated by both AdvCodec(Sent) and AdvCodec(Word). More specifically, we respectively prepare 100 benign and adversarial data pairs for both QA and sentiment classification, and hand out them to 505 Amazon Turks. Each turk is requested to answer at least 5 question and at most 15 questions for the QA task and judge the sentiment for at least 10 paragraphs and at most 20 paragraphs for the sentiment classification task. We also perform a majority vote over Turk’s answers for the same question. While we can spot a drop from the benign to adversarial datasets, we conduct an error analysis in QA and find the error examples are noisy and not necessarily caused by our adversarial text. For adversarial data in the sentiment classification task, we notice that the generated tokens or appended sentences have opposite sentiment from the benign one. However, our evaluation results show human readers can naturally ignore abnormal tokens and make correct judgement according to the context.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 9: Human performance on QA', unfold the steps to solve the question given next. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nQuestion: What is the difference in Majority F1 score between the Origin method and the AdvCodec(Sent) method?\n" }, { "role": "assistant", "content": "The Majority F1 score for the Origin method is 90.987, and for the AdvCodec(Sent) method, it is 81.784. The difference is calculated as 90.987 - 81.784. So, the answer is {\"answer\": \"9.203\"}" } ], "images": [ "scigen/train/3688.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Human performance on QA" ], "reasoning": "The Majority F1 score for the Origin method is 90.987, and for the AdvCodec(Sent) method, it is 81.784. The difference is calculated as 90.987 - 81.784.", "question": "What is the difference in Majority F1 score between the Origin method and the AdvCodec(Sent) method?", "answer": "So, the answer is {\"answer\": \"9.203\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Majority F1 score for AdvCodec(Word) is higher than for AdvCodec(Sent).\n" }, { "role": "assistant", "content": "The table shows that the Majority F1 score for AdvCodec(Word) is 82.897, which is higher than the score for AdvCodec(Sent) at 81.784. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3688.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Human performance on QA" ], "reasoning": "The table shows that the Majority F1 score for AdvCodec(Word) is 82.897, which is higher than the score for AdvCodec(Sent) at 81.784.", "statement": "The Majority F1 score for AdvCodec(Word) is higher than for AdvCodec(Sent).", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 11: Whitebox attack results on BERT-QA in terms of exact match rates and F1 scores by the official evaluation script. The lower EM and F1 scores mean the better attack success rate.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The untargeted scatter attack on QA achieved a higher success rate than Jia and Liang's method.We also performed the untargeted scatter attack on QA. We insert 30 random tokens ( but no more than 1/3 the total words of the paragraph) over the paragraph, optimize and find the adversarial tokens that can cause model output the wrong answers in the untargeted manner. We can see the untargeted scatter attack can also achieve a higher untargeted attack success rate than Jia and Liang" } ], "images": [ "scigen/train/3689.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Whitebox attack results on BERT-QA in terms of exact match rates and F1 scores by the official evaluation script. The lower EM and F1 scores mean the better attack success rate." ], "reasoning": "The untargeted scatter attack on QA achieved a higher success rate than Jia and Liang's method.", "description": "We also performed the untargeted scatter attack on QA. We insert 30 random tokens ( but no more than 1/3 the total words of the paragraph) over the paragraph, optimize and find the adversarial tokens that can cause model output the wrong answers in the untargeted manner. We can see the untargeted scatter attack can also achieve a higher untargeted attack success rate than Jia and Liang", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 11: Whitebox attack results on BERT-QA in terms of exact match rates and F1 scores by the official evaluation script. The lower EM and F1 scores mean the better attack success rate.\nQuestion: What is the success rate of the untargeted scatter attack for the BERT model in terms of F1 score?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the success rate of the untargeted scatter attack for the BERT model in terms of F1 score as 49.7. So, the answer is {\"answer\": \"49.7\"}." } ], "images": [ "scigen/train/3689.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Whitebox attack results on BERT-QA in terms of exact match rates and F1 scores by the official evaluation script. The lower EM and F1 scores mean the better attack success rate." ], "reasoning": "The table shows the success rate of the untargeted scatter attack for the BERT model in terms of F1 score as 49.7.", "question": "What is the success rate of the untargeted scatter attack for the BERT model in terms of F1 score?", "answer": "So, the answer is {\"answer\": \"49.7\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe untargeted scatter attack on QA achieved a success rate of 34.3 for the BERT model in terms of EM score.\n" }, { "role": "assistant", "content": "The table shows that the success rate of the untargeted scatter attack for the BERT model in terms of EM score is 34.3. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3689.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Whitebox attack results on BERT-QA in terms of exact match rates and F1 scores by the official evaluation script. The lower EM and F1 scores mean the better attack success rate." ], "reasoning": "The table shows that the success rate of the untargeted scatter attack for the BERT model in terms of EM score is 34.3.", "statement": "The untargeted scatter attack on QA achieved a success rate of 34.3 for the BERT model in terms of EM score.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 12: Whitebox attack results on BERT-QA in terms of exact match rates and F1 scores by the official evaluation script. The lower EM and F1 scores mean the better attack success rate.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Different initial sentence selection methods significantly impact attack success rates, highlighting their importance in reducing iteration steps and achieving faster convergence.Experimental Results. We tried the above initial sentence selection methods on AdvCodec(Word) and perform position targeted attack on BERT-QA given the same maximum optimization steps. From the table, we find using different initialization methods will greatly affect the attack success rates. Therefore, the initial sentence selection methods are indeed important to help reduce the number of iteration steps and fastly converge to the optimal z∗ that can attack the model." } ], "images": [ "scigen/train/3690.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 12: Whitebox attack results on BERT-QA in terms of exact match rates and F1 scores by the official evaluation script. The lower EM and F1 scores mean the better attack success rate." ], "reasoning": "Different initial sentence selection methods significantly impact attack success rates, highlighting their importance in reducing iteration steps and achieving faster convergence.", "description": "Experimental Results. We tried the above initial sentence selection methods on AdvCodec(Word) and perform position targeted attack on BERT-QA given the same maximum optimization steps. From the table, we find using different initialization methods will greatly affect the attack success rates. Therefore, the initial sentence selection methods are indeed important to help reduce the number of iteration steps and fastly converge to the optimal z∗ that can attack the model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 12: Whitebox attack results on BERT-QA in terms of exact match rates and F1 scores by the official evaluation script. The lower EM and F1 scores mean the better attack success rate.' and proceed to solve the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich initial sentence selection method resulted in the lowest attack success rate for BERT?\n" }, { "role": "assistant", "content": "By comparing the attack success rates for different initial sentence selection methods, we find that the Position Targeted Attack Question-based method has the lowest success rate. So, the answer is {\"answer\": \"Position Targeted Attack Question-based\"}" } ], "images": [ "scigen/train/3690.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 12: Whitebox attack results on BERT-QA in terms of exact match rates and F1 scores by the official evaluation script. The lower EM and F1 scores mean the better attack success rate." ], "reasoning": "By comparing the attack success rates for different initial sentence selection methods, we find that the Position Targeted Attack Question-based method has the lowest success rate.", "question": "Which initial sentence selection method resulted in the lowest attack success rate for BERT?", "answer": "So, the answer is {\"answer\": \"Position Targeted Attack Question-based\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Position Targeted Attack Answer-based method has a higher attack success rate than the Baseline AddSent method. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Position Targeted Attack Answer-based method has a higher attack success rate than the Baseline AddSent method.\n" }, { "role": "assistant", "content": "The table shows that the Position Targeted Attack Answer-based method has a success rate of 50.6 for EM and 55.2 for F1, both of which are higher than the Baseline AddSent method's success rates of 46.8 for EM and 52.6 for F1. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3690.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 12: Whitebox attack results on BERT-QA in terms of exact match rates and F1 scores by the official evaluation script. The lower EM and F1 scores mean the better attack success rate." ], "reasoning": "The table shows that the Position Targeted Attack Answer-based method has a success rate of 50.6 for EM and 55.2 for F1, both of which are higher than the Baseline AddSent method's success rates of 46.8 for EM and 52.6 for F1.", "statement": "The Position Targeted Attack Answer-based method has a higher attack success rate than the Baseline AddSent method.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 14: Blackbox Attack Success Rate after inserting the whitebox generated adversarial sentence to different positions for BERT-QA.'.\n" }, { "role": "assistant", "content": "Appending adversarial sentences at the beginning of the paragraph generally achieves the highest attack success rate, while appending at the end is slightly less effective.To further explore how the location of adversarial sentences affects the attack success rate, we conduct the ablation experiments by varying the position of appended adversarial sentence. We generate the adversarial sentences from the whitebox BERT classification and QA models. Then we inject those adversaries into different positions of the original paragraph and test in another blackbox BERT with the same architecture but different parameters. We see in most time appending the adversarial sentence at the beginning of the paragraph achieves the best attack performance. Also the performance of appending the adversarial sentence at the end of the paragraph is usually slightly weaker than front. This observation suggests that the BERT model might pay more attention to the both ends of the paragraphs and tend to overlook the content in the middle." } ], "images": [ "scigen/train/3691.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 14: Blackbox Attack Success Rate after inserting the whitebox generated adversarial sentence to different positions for BERT-QA." ], "reasoning": "Appending adversarial sentences at the beginning of the paragraph generally achieves the highest attack success rate, while appending at the end is slightly less effective.", "description": "To further explore how the location of adversarial sentences affects the attack success rate, we conduct the ablation experiments by varying the position of appended adversarial sentence. We generate the adversarial sentences from the whitebox BERT classification and QA models. Then we inject those adversaries into different positions of the original paragraph and test in another blackbox BERT with the same architecture but different parameters. We see in most time appending the adversarial sentence at the beginning of the paragraph achieves the best attack performance. Also the performance of appending the adversarial sentence at the end of the paragraph is usually slightly weaker than front. This observation suggests that the BERT model might pay more attention to the both ends of the paragraphs and tend to overlook the content in the middle.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 14: Blackbox Attack Success Rate after inserting the whitebox generated adversarial sentence to different positions for BERT-QA.' and methodically tackle the question that follows. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the attack success rate when appending untargeted adversarial words at the front of the paragraph?\n" }, { "role": "assistant", "content": "The table shows the attack success rates for different methods and positions. For untargeted adversarial words at the front, the success rate is 0.878. So, the answer is {\"answer\": \"0.878\"}" } ], "images": [ "scigen/train/3691.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 14: Blackbox Attack Success Rate after inserting the whitebox generated adversarial sentence to different positions for BERT-QA." ], "reasoning": "The table shows the attack success rates for different methods and positions. For untargeted adversarial words at the front, the success rate is 0.878.", "question": "What is the attack success rate when appending untargeted adversarial words at the front of the paragraph?", "answer": "So, the answer is {\"answer\": \"0.878\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence Appending targeted adversarial sentences at the back of the paragraph achieves a higher attack success rate than at the front. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nAppending targeted adversarial sentences at the back of the paragraph achieves a higher attack success rate than at the front.\n" }, { "role": "assistant", "content": "The table shows that the attack success rate for targeted adversarial sentences is 0.220 at the back and 0.217 at the front, indicating that the back has a slightly higher success rate. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3691.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 14: Blackbox Attack Success Rate after inserting the whitebox generated adversarial sentence to different positions for BERT-QA." ], "reasoning": "The table shows that the attack success rate for targeted adversarial sentences is 0.220 at the back and 0.217 at the front, indicating that the back has a slightly higher success rate.", "statement": "Appending targeted adversarial sentences at the back of the paragraph achieves a higher attack success rate than at the front.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: Label counts in the dataset.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "After excluding ambiguous categories and merging extreme biases, the dataset was reduced to 864 news media, categorized into left, center, and right biases.After manual inspection, we noticed that the left-center and right-center labels are ill-defined, ambiguous transitionary categories. Therefore, we decided to exclude news media with these labels. Also, to reduce the impact of potentially subjective decisions made by the annotators, we merged the extreme-left and extreme-right media with the left and right categories, respectively. As a result, we model political bias on a 3-point scale (left, center, and right), and the dataset got reduced to 864 news media." } ], "images": [ "scigen/train/3692.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Label counts in the dataset." ], "reasoning": "After excluding ambiguous categories and merging extreme biases, the dataset was reduced to 864 news media, categorized into left, center, and right biases.", "description": "After manual inspection, we noticed that the left-center and right-center labels are ill-defined, ambiguous transitionary categories. Therefore, we decided to exclude news media with these labels. Also, to reduce the impact of potentially subjective decisions made by the annotators, we merged the extreme-left and extreme-right media with the left and right categories, respectively. As a result, we model political bias on a 3-point scale (left, center, and right), and the dataset got reduced to 864 news media.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 1: Label counts in the dataset.' and methodically tackle the question that follows. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nHow many news media are categorized as having a high factuality?\n" }, { "role": "assistant", "content": "The table shows that 453 news media are categorized as having high factuality. So, the answer is {\"answer\": \"453\"}" } ], "images": [ "scigen/train/3692.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Label counts in the dataset." ], "reasoning": "The table shows that 453 news media are categorized as having high factuality.", "question": "How many news media are categorized as having a high factuality?", "answer": "So, the answer is {\"answer\": \"453\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis There are more news media with a center political bias than with a right political bias. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThere are more news media with a center political bias than with a right political bias.\n" }, { "role": "assistant", "content": "The table shows that there are 272 news media with a center political bias and 349 with a right political bias, so the statement is incorrect. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3692.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Label counts in the dataset." ], "reasoning": "The table shows that there are 272 news media with a center political bias and 349 with a right political bias, so the statement is incorrect.", "statement": "There are more news media with a center political bias than with a right political bias.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Scores over variant source languages (6k sentences for Ro & Lv, and 10k for Ko). “Multi\" means the Multi-lingual NMT baseline.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Using a 6k sentences corpus for Ro and Lv and 10k for Ko, applying both universal tokens and mixture of experts modules improves translation quality for all language pairs.To have a fair comparison, we use only 6k sentences corpus for both Ro and Lv with all the settings and 10k for Ko. It is obvious that applying both the universal tokens and mixture of experts modules improve the overall translation quality for all the language pairs and the improvements are additive." } ], "images": [ "scigen/train/3693.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Scores over variant source languages (6k sentences for Ro & Lv, and 10k for Ko). “Multi\" means the Multi-lingual NMT baseline." ], "reasoning": "Using a 6k sentences corpus for Ro and Lv and 10k for Ko, applying both universal tokens and mixture of experts modules improves translation quality for all language pairs.", "description": "To have a fair comparison, we use only 6k sentences corpus for both Ro and Lv with all the settings and 10k for Ko. It is obvious that applying both the universal tokens and mixture of experts modules improve the overall translation quality for all the language pairs and the improvements are additive.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: Scores over variant source languages (6k sentences for Ro & Lv, and 10k for Ko). “Multi\" means the Multi-lingual NMT baseline.'.\n\nQuestion: What is the highest translation quality score achieved for the Ro source language?\n" }, { "role": "assistant", "content": "To find the highest translation quality score for Ro, look at the '+MoLE' column for all rows with 'Ro' as the source language. The highest score is 20.51. So, the answer is {\"answer\": \"20.51\"}." } ], "images": [ "scigen/train/3693.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Scores over variant source languages (6k sentences for Ro & Lv, and 10k for Ko). “Multi\" means the Multi-lingual NMT baseline." ], "reasoning": "To find the highest translation quality score for Ro, look at the '+MoLE' column for all rows with 'Ro' as the source language. The highest score is 20.51.", "question": "What is the highest translation quality score achieved for the Ro source language?", "answer": "So, the answer is {\"answer\": \"20.51\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe translation quality score for Ko with the +ULR setting is higher than with the Multi setting.\n" }, { "role": "assistant", "content": "For Ko, the Multi setting score is 2.45 and the +ULR setting score is 5.49, which is higher. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3693.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Scores over variant source languages (6k sentences for Ro & Lv, and 10k for Ko). “Multi\" means the Multi-lingual NMT baseline." ], "reasoning": "For Ko, the Multi setting score is 2.45 and the +ULR setting score is 5.49, which is higher.", "statement": "The translation quality score for Ko with the +ULR setting is higher than with the Multi setting.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: BLEU scores evaluated on test set (6k), compared with ULR and MoLE. “vanilla\" is the standard NMT system trained only on Ro-En training set', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The proposed ULR method significantly improves Multi-NMT performance, especially when combined with MoLE and BT, achieving a BLEU score of 22.92.Using Multi-NMT with the assistance of 7.8M auxiliary language sentence pairs, Ro-En translation performance gets a substantial improvement which, however, is still limited to be usable. By contrast, the proposed ULR boosts the Multi-NMT significantly with +5.07 BLEU, which is further boosted to +7.98 BLEU when incorporating sentence-level information using both MoLE and BT. Furthermore, it is also shown that ULR works better when a trainable transformation matrix A is used (4th vs 5th row in the table). Note that, although still 5∼6 BLEU scores lower than the full data (×100 large) model." } ], "images": [ "scigen/train/3694.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: BLEU scores evaluated on test set (6k), compared with ULR and MoLE. “vanilla\" is the standard NMT system trained only on Ro-En training set" ], "reasoning": "The proposed ULR method significantly improves Multi-NMT performance, especially when combined with MoLE and BT, achieving a BLEU score of 22.92.", "description": "Using Multi-NMT with the assistance of 7.8M auxiliary language sentence pairs, Ro-En translation performance gets a substantial improvement which, however, is still limited to be usable. By contrast, the proposed ULR boosts the Multi-NMT significantly with +5.07 BLEU, which is further boosted to +7.98 BLEU when incorporating sentence-level information using both MoLE and BT. Furthermore, it is also shown that ULR works better when a trainable transformation matrix A is used (4th vs 5th row in the table). Note that, although still 5∼6 BLEU scores lower than the full data (×100 large) model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: BLEU scores evaluated on test set (6k), compared with ULR and MoLE. “vanilla\" is the standard NMT system trained only on Ro-En training set' and methodically tackle the question that follows. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the BLEU score improvement when Multi-NMT is combined with ULR, MoLE, and BT compared to Multi-NMT alone?\n" }, { "role": "assistant", "content": "The BLEU score for Multi-NMT alone is 14.94. When combined with ULR, MoLE, and BT, the BLEU score is 22.92. The improvement is 22.92 - 14.94. So, the answer is {\"answer\": \"7.98\"}" } ], "images": [ "scigen/train/3694.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: BLEU scores evaluated on test set (6k), compared with ULR and MoLE. “vanilla\" is the standard NMT system trained only on Ro-En training set" ], "reasoning": "The BLEU score for Multi-NMT alone is 14.94. When combined with ULR, MoLE, and BT, the BLEU score is 22.92. The improvement is 22.92 - 14.94.", "question": "What is the BLEU score improvement when Multi-NMT is combined with ULR, MoLE, and BT compared to Multi-NMT alone?", "answer": "So, the answer is {\"answer\": \"7.98\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe BLEU score for Multi-NMT combined with ULR and BT is higher than when combined with ULR and MoLE.\n" }, { "role": "assistant", "content": "The BLEU score for Multi-NMT combined with ULR and BT is 22.35, while the score for Multi-NMT combined with ULR and MoLE is 20.51. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3694.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: BLEU scores evaluated on test set (6k), compared with ULR and MoLE. “vanilla\" is the standard NMT system trained only on Ro-En training set" ], "reasoning": "The BLEU score for Multi-NMT combined with ULR and BT is 22.35, while the score for Multi-NMT combined with ULR and MoLE is 20.51.", "statement": "The BLEU score for Multi-NMT combined with ULR and BT is higher than when combined with ULR and MoLE.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: Cross-validation results for ablation tests.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows that the score of g'in is 68.2, which is lower than gin's score of 70.2, indicating the effectiveness of the averaged n-gram embeddings.We also provide a score using a simplified version of gin, where the feature vector g′in is computed by averaging the word embeddings [W(wini);~W(wini)] of the words between the noun pairs. This feature vector g′in then serves as a bag-of-words feature. The difference between the scores of gin and g′in shows the effectiveness of our averaged n-gram embeddings." } ], "images": [ "scigen/train/3695.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Cross-validation results for ablation tests." ], "reasoning": "The table shows that the score of g'in is 68.2, which is lower than gin's score of 70.2, indicating the effectiveness of the averaged n-gram embeddings.", "description": "We also provide a score using a simplified version of gin, where the feature vector g′in is computed by averaging the word embeddings [W(wini);~W(wini)] of the words between the noun pairs. This feature vector g′in then serves as a bag-of-words feature. The difference between the scores of gin and g′in shows the effectiveness of our averaged n-gram embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 4: Cross-validation results for ablation tests.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. \n\nQuestion: What is the difference between the scores of gin and g'in?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The score of gin is 70.2 and the score of g'in is 68.2. The difference is calculated as 70.2 - 68.2. So, the answer is {\"answer\": \"2.0\"}" } ], "images": [ "scigen/train/3695.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Cross-validation results for ablation tests." ], "reasoning": "The score of gin is 70.2 and the score of g'in is 68.2. The difference is calculated as 70.2 - 68.2.", "question": "What is the difference between the scores of gin and g'in?", "answer": "So, the answer is {\"answer\": \"2.0\"}" }, { "messages": [ { "role": "user", "content": "The score of gin is higher than the score of g'in. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe score of gin is higher than the score of g'in.\n" }, { "role": "assistant", "content": "The table shows that the score of gin is 70.2 and the score of g'in is 68.2, confirming that gin's score is higher. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3695.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Cross-validation results for ablation tests." ], "reasoning": "The table shows that the score of gin is 70.2 and the score of g'in is 68.2, confirming that gin's score is higher.", "statement": "The score of gin is higher than the score of g'in.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'TABLE XIV: Accuracies on the SelQA evaluation set for answer triggering w.r.t. question types.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Models perform better on longer questions and sections, suggesting an ensemble model could be beneficial.Interestingly, each model shows different strength on different types, which may suggest a possibility of an ensemble model. All the models tend to perform better as questions become longer. Similarly as in the answer sentence selection task, since longer questions are more informative, it is understandable. Interestingly, once the section becomes longer, the accuracy increases. We hypothesize that such a behavior might be caused by the fact that it is easier for the models to decide whether the context of the section is the same as the context of the question when there is more information (sentences) in the section. Thus, this phenomenon is related to the task of answer triggering, where the model not only choose the sentence with the answer, but must decide if the context matches first." } ], "images": [ "scigen/train/3696.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE XIV: Accuracies on the SelQA evaluation set for answer triggering w.r.t. question types." ], "reasoning": "Models perform better on longer questions and sections, suggesting an ensemble model could be beneficial.", "description": "Interestingly, each model shows different strength on different types, which may suggest a possibility of an ensemble model. All the models tend to perform better as questions become longer. Similarly as in the answer sentence selection task, since longer questions are more informative, it is understandable. Interestingly, once the section becomes longer, the accuracy increases. We hypothesize that such a behavior might be caused by the fact that it is easier for the models to decide whether the context of the section is the same as the context of the question when there is more information (sentences) in the section. Thus, this phenomenon is related to the task of answer triggering, where the model not only choose the sentence with the answer, but must decide if the context matches first.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'TABLE XIV: Accuracies on the SelQA evaluation set for answer triggering w.r.t. question types.', solve the following question step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest accuracy for 'Why' type questions?\n" }, { "role": "assistant", "content": "To find the model with the highest accuracy for 'Why' type questions, compare the accuracy values for 'Why' across CNN0, CNN2, and RNN1. The highest value is 57.89 for RNN1. So, the answer is {\"answer\": \"RNN1\"}" } ], "images": [ "scigen/train/3696.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE XIV: Accuracies on the SelQA evaluation set for answer triggering w.r.t. question types." ], "reasoning": "To find the model with the highest accuracy for 'Why' type questions, compare the accuracy values for 'Why' across CNN0, CNN2, and RNN1. The highest value is 57.89 for RNN1.", "question": "Which model has the highest accuracy for 'Why' type questions?", "answer": "So, the answer is {\"answer\": \"RNN1\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nCNN2 performs best on 'Where' type questions.\n" }, { "role": "assistant", "content": "To verify this statement, compare the accuracy values for 'Where' type questions across CNN0, CNN2, and RNN1. CNN2 has the highest value of 51.85. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3696.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE XIV: Accuracies on the SelQA evaluation set for answer triggering w.r.t. question types." ], "reasoning": "To verify this statement, compare the accuracy values for 'Where' type questions across CNN0, CNN2, and RNN1. CNN2 has the highest value of 51.85.", "statement": "CNN2 performs best on 'Where' type questions.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE VII: MRR scores on the SelQA evaluation set for answer sentence selection with respect to topics.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Models perform well on deterministic topics like 'Country' and 'Historical Events', but struggle with 'TV', 'Arts', and 'Music' due to fewer overlapping words and segmented documents.All models show strength on topics such as ‘Country’ and ‘Historical Events’, which is comprehensible since questions in these topics tend to be deterministic. On the other hand, most models show weakness on topics such as ‘TV’, ‘Arts’, or ‘Music’. This may be due to the fact that not many overlapping words are found between question and answer pairs in these documents, which also consist of many segments caused by bullet points." } ], "images": [ "scigen/train/3697.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE VII: MRR scores on the SelQA evaluation set for answer sentence selection with respect to topics." ], "reasoning": "Models perform well on deterministic topics like 'Country' and 'Historical Events', but struggle with 'TV', 'Arts', and 'Music' due to fewer overlapping words and segmented documents.", "description": "All models show strength on topics such as ‘Country’ and ‘Historical Events’, which is comprehensible since questions in these topics tend to be deterministic. On the other hand, most models show weakness on topics such as ‘TV’, ‘Arts’, or ‘Music’. This may be due to the fact that not many overlapping words are found between question and answer pairs in these documents, which also consist of many segments caused by bullet points.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'TABLE VII: MRR scores on the SelQA evaluation set for answer sentence selection with respect to topics.', solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich topic has the highest score for the RNN1 model?\n" }, { "role": "assistant", "content": "To find the highest score for the RNN1 model, compare the RNN1 scores across all topics. The highest score is 93.87 for the 'Country' topic. So, the answer is {\"answer\": \"Country\"}" } ], "images": [ "scigen/train/3697.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE VII: MRR scores on the SelQA evaluation set for answer sentence selection with respect to topics." ], "reasoning": "To find the highest score for the RNN1 model, compare the RNN1 scores across all topics. The highest score is 93.87 for the 'Country' topic.", "question": "Which topic has the highest score for the RNN1 model?", "answer": "So, the answer is {\"answer\": \"Country\"}" }, { "messages": [ { "role": "user", "content": "The CNN2 model performs best on the 'Historical Events' topic. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe CNN2 model performs best on the 'Historical Events' topic.\n" }, { "role": "assistant", "content": "To verify this statement, compare the CNN2 scores across all topics. The highest score for CNN2 is 92.61, which is for the 'Historical Events' topic. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3697.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE VII: MRR scores on the SelQA evaluation set for answer sentence selection with respect to topics." ], "reasoning": "To verify this statement, compare the CNN2 scores across all topics. The highest score for CNN2 is 92.61, which is for the 'Historical Events' topic.", "statement": "The CNN2 model performs best on the 'Historical Events' topic.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'TABLE IX: MRR scores on the SelQA evaluation set for answer sentence selection w.r.t. question types.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "CNN models excel in 'who' questions, while RNN models excel in 'when' questions, and all models except RNN0 perform better with longer questions.The CNN models show strength on the ‘who’ type, whereas the RNN models show strength on the ‘when’ type. Each model varies on showing their weakness, which we will explore in the future. All models except for RNN0 tend to perform better as questions become longer. This makes sense since longer questions are usually more informative. On the other hand, models generally perform worse as sections become longer, which also makes sense because the models have to select the answer contexts from larger pools." } ], "images": [ "scigen/train/3698.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IX: MRR scores on the SelQA evaluation set for answer sentence selection w.r.t. question types." ], "reasoning": "CNN models excel in 'who' questions, while RNN models excel in 'when' questions, and all models except RNN0 perform better with longer questions.", "description": "The CNN models show strength on the ‘who’ type, whereas the RNN models show strength on the ‘when’ type. Each model varies on showing their weakness, which we will explore in the future. All models except for RNN0 tend to perform better as questions become longer. This makes sense since longer questions are usually more informative. On the other hand, models generally perform worse as sections become longer, which also makes sense because the models have to select the answer contexts from larger pools.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'TABLE IX: MRR scores on the SelQA evaluation set for answer sentence selection w.r.t. question types.', and sequentially solve the question, writing out each step of your reasoning process. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model has the highest accuracy for 'when' type questions?\n" }, { "role": "assistant", "content": "The table shows the accuracy of different models for various question types. For 'when' type questions, RNN1 has the highest accuracy at 90.35. So, the answer is {\"answer\": \"RNN1\"}" } ], "images": [ "scigen/train/3698.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IX: MRR scores on the SelQA evaluation set for answer sentence selection w.r.t. question types." ], "reasoning": "The table shows the accuracy of different models for various question types. For 'when' type questions, RNN1 has the highest accuracy at 90.35.", "question": "Which model has the highest accuracy for 'when' type questions?", "answer": "So, the answer is {\"answer\": \"RNN1\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nRNN0 performs better than CNN0 for 'who' type questions.\n" }, { "role": "assistant", "content": "The table shows that for 'who' type questions, CNN0 has an accuracy of 85.46, while RNN0 has an accuracy of 80.36. Therefore, CNN0 performs better than RNN0 for 'who' type questions. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3698.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IX: MRR scores on the SelQA evaluation set for answer sentence selection w.r.t. question types." ], "reasoning": "The table shows that for 'who' type questions, CNN0 has an accuracy of 85.46, while RNN0 has an accuracy of 80.36. Therefore, CNN0 performs better than RNN0 for 'who' type questions.", "statement": "RNN0 performs better than CNN0 for 'who' type questions.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'TABLE XII: Accuracies on the SelQA evaluation set for answer triggering with respect to topics.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "CNN2 performs best on 'Country' and 'Historical Events', while RNN1 has a smaller standard deviation but lower overall performance.The accuracy is measured on the subset of questions that contain at least one answer among candidates; the top ranked sentence is taken and checked for the correct answer. Similar to answer sentence selection, CNN2 stills shows strength on topics such as ‘Country’ and ‘Historical Events’, but the trend is not as clear for the other models. The worst performing topics are ‘TV’, ‘Music’ and ‘Art’. Such a noticeable difference might be caused by the unusual semantic sentence constructions of the text. Sections in these categories often contain listings, bullet-pointed texts etc., which is problematic for the models to properly take care of. How to correctly understand and solve question from such context will be a challenge to the future systems. Also, interestingly, the standard deviation is much smaller for RNN1 (3.9%) compared to the CNN models (10-12%) although RNN1’s overall performance is lower." } ], "images": [ "scigen/train/3699.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE XII: Accuracies on the SelQA evaluation set for answer triggering with respect to topics." ], "reasoning": "CNN2 performs best on 'Country' and 'Historical Events', while RNN1 has a smaller standard deviation but lower overall performance.", "description": "The accuracy is measured on the subset of questions that contain at least one answer among candidates; the top ranked sentence is taken and checked for the correct answer. Similar to answer sentence selection, CNN2 stills shows strength on topics such as ‘Country’ and ‘Historical Events’, but the trend is not as clear for the other models. The worst performing topics are ‘TV’, ‘Music’ and ‘Art’. Such a noticeable difference might be caused by the unusual semantic sentence constructions of the text. Sections in these categories often contain listings, bullet-pointed texts etc., which is problematic for the models to properly take care of. How to correctly understand and solve question from such context will be a challenge to the future systems. Also, interestingly, the standard deviation is much smaller for RNN1 (3.9%) compared to the CNN models (10-12%) although RNN1’s overall performance is lower.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: TABLE XII: Accuracies on the SelQA evaluation set for answer triggering with respect to topics.\nQuestion: Which model has the highest accuracy for the 'Country' topic?\nGive a detailed response to the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the accuracy for each model on different topics. For the 'Country' topic, CNN2 has the highest accuracy at 61.54. So, the answer is {\"answer\": \"CNN2\"}" } ], "images": [ "scigen/train/3699.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE XII: Accuracies on the SelQA evaluation set for answer triggering with respect to topics." ], "reasoning": "The table shows the accuracy for each model on different topics. For the 'Country' topic, CNN2 has the highest accuracy at 61.54.", "question": "Which model has the highest accuracy for the 'Country' topic?", "answer": "So, the answer is {\"answer\": \"CNN2\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRNN1 has the highest accuracy for the 'Travel' topic.\n" }, { "role": "assistant", "content": "The table shows the accuracy for each model on different topics. For the 'Travel' topic, RNN1 has an accuracy of 48.78, which is higher than CNN0's 42.68 but lower than CNN2's 50.00. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3699.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE XII: Accuracies on the SelQA evaluation set for answer triggering with respect to topics." ], "reasoning": "The table shows the accuracy for each model on different topics. For the 'Travel' topic, RNN1 has an accuracy of 48.78, which is higher than CNN0's 42.68 but lower than CNN2's 50.00.", "statement": "RNN1 has the highest accuracy for the 'Travel' topic.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 3: Cross domain performance ( models trained on company A’s data, tested on company B’s data).'.\n" }, { "role": "assistant", "content": "The text-based model shows a significant drop in performance when applied to a different domain, with an F1-score of 0.11, indicating that textual features are closely tied to the training domain.the model’s performance when it was trained and tested on the same domain), with a degradation of only 9% in F1-score In addition, the results also show that the text-based model performs poorly when applied to a different domain (F1-score of 0.11). This may occur since textual features are closely tied to the training domain." } ], "images": [ "scigen/train/3700.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Cross domain performance ( models trained on company A’s data, tested on company B’s data)." ], "reasoning": "The text-based model shows a significant drop in performance when applied to a different domain, with an F1-score of 0.11, indicating that textual features are closely tied to the training domain.", "description": "the model’s performance when it was trained and tested on the same domain), with a degradation of only 9% in F1-score In addition, the results also show that the text-based model performs poorly when applied to a different domain (F1-score of 0.11). This may occur since textual features are closely tied to the training domain.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 3: Cross domain performance ( models trained on company A’s data, tested on company B’s data).\nQuestion: What is the F1-score of the text-based model for Egregious and Non-Egregious categories?\n" }, { "role": "assistant", "content": "The table shows the F1-scores for the text-based model in both Egregious and Non-Egregious categories. For Egregious, the F1-score is 0.11, and for Non-Egregious, it is 0.96. So, the answer is {\"answer\": \"0.11 and 0.96\"}." } ], "images": [ "scigen/train/3700.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Cross domain performance ( models trained on company A’s data, tested on company B’s data)." ], "reasoning": "The table shows the F1-scores for the text-based model in both Egregious and Non-Egregious categories. For Egregious, the F1-score is 0.11, and for Non-Egregious, it is 0.96.", "question": "What is the F1-score of the text-based model for Egregious and Non-Egregious categories?", "answer": "So, the answer is {\"answer\": \"0.11 and 0.96\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe EGR model has the highest recall for Egregious cases.\n" }, { "role": "assistant", "content": "The table shows that the EGR model has a recall of 0.81 for Egregious cases, which is higher than the recall values for the Rule-based and Text-based models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3700.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Cross domain performance ( models trained on company A’s data, tested on company B’s data)." ], "reasoning": "The table shows that the EGR model has a recall of 0.81 for Egregious cases, which is higher than the recall values for the Rule-based and Text-based models.", "statement": "The EGR model has the highest recall for Egregious cases.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Cross-validation results for the baselines and EGR models.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The EGR model improved the recall and F1-score for the egregious class by 41% and 18% respectively compared to the text-based model, while their precision was similar.Specifically, for the egregious class, the precision obtained by the text-based and EGR models were similar. This indicates that the text analyzed by both models encodes some information about egregiousness. On the other hand, for the recall and hence the F1-score, the EGR model relatively improved the text-based model by 41% and 18%, respectively. We will further analyze the models below." } ], "images": [ "scigen/train/3701.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Cross-validation results for the baselines and EGR models." ], "reasoning": "The EGR model improved the recall and F1-score for the egregious class by 41% and 18% respectively compared to the text-based model, while their precision was similar.", "description": "Specifically, for the egregious class, the precision obtained by the text-based and EGR models were similar. This indicates that the text analyzed by both models encodes some information about egregiousness. On the other hand, for the recall and hence the F1-score, the EGR model relatively improved the text-based model by 41% and 18%, respectively. We will further analyze the models below.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Cross-validation results for the baselines and EGR models.' to sequentially solve the question presented below. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the percentage improvement in recall for the egregious class when using the EGR model compared to the text-based model?\n" }, { "role": "assistant", "content": "The recall for the egregious class using the text-based model is 0.56, and using the EGR model is 0.79. The percentage improvement is calculated as ((0.79 - 0.56) / 0.56) * 100. So, the answer is {\"answer\": \"41%\"}." } ], "images": [ "scigen/train/3701.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Cross-validation results for the baselines and EGR models." ], "reasoning": "The recall for the egregious class using the text-based model is 0.56, and using the EGR model is 0.79. The percentage improvement is calculated as ((0.79 - 0.56) / 0.56) * 100.", "question": "What is the percentage improvement in recall for the egregious class when using the EGR model compared to the text-based model?", "answer": "So, the answer is {\"answer\": \"41%\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe EGR model has a higher precision for the egregious class compared to the text-based model.\n" }, { "role": "assistant", "content": "The precision for the egregious class using the EGR model is 0.47, while using the text-based model is 0.46. Since 0.47 is higher than 0.46, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3701.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Cross-validation results for the baselines and EGR models." ], "reasoning": "The precision for the egregious class using the EGR model is 0.47, while using the text-based model is 0.46. Since 0.47 is higher than 0.46, the statement is supported.", "statement": "The EGR model has a higher precision for the egregious class compared to the text-based model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Data splits for the IAPR-TC12 and Multi30k datasets.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The datasets IAPR-TC12 and Multi30k are split into training, validation, and testing sets, with training and validation sets further divided into image-English and image-German pairs.We utilize two benchmark image-pivot translation datasets IAPR-TC12 and Multi30k. We randomly select 18K images for training, 1K for validation and 1K for testing. Since in the realistic image-pivot setting, images in different languages are mostly non-overlapped, we randomly split the training and validation set into two parts of equal size. One part is constructed only with image-English pairs and the other only with image-German pairs. We utilize the former task annotation, where each image is annotated with one English description and its German translation. We also apply the similar non-overlapping split operation as in IAPR-TC12 to simulate the non-overlap setting." } ], "images": [ "scigen/train/3702.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Data splits for the IAPR-TC12 and Multi30k datasets." ], "reasoning": "The datasets IAPR-TC12 and Multi30k are split into training, validation, and testing sets, with training and validation sets further divided into image-English and image-German pairs.", "description": "We utilize two benchmark image-pivot translation datasets IAPR-TC12 and Multi30k. We randomly select 18K images for training, 1K for validation and 1K for testing. Since in the realistic image-pivot setting, images in different languages are mostly non-overlapped, we randomly split the training and validation set into two parts of equal size. One part is constructed only with image-English pairs and the other only with image-German pairs. We utilize the former task annotation, where each image is annotated with one English description and its German translation. We also apply the similar non-overlapping split operation as in IAPR-TC12 to simulate the non-overlap setting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1: Data splits for the IAPR-TC12 and Multi30k datasets.', solve the subsequent question in a stepwise manner. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nHow many image-English pairs are used for training in the Multi30k dataset?\n" }, { "role": "assistant", "content": "The table shows that the Multi30k dataset has 14.5k image-English pairs used for training. So, the answer is {\"answer\": \"14.5k\"}" } ], "images": [ "scigen/train/3702.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Data splits for the IAPR-TC12 and Multi30k datasets." ], "reasoning": "The table shows that the Multi30k dataset has 14.5k image-English pairs used for training.", "question": "How many image-English pairs are used for training in the Multi30k dataset?", "answer": "So, the answer is {\"answer\": \"14.5k\"}" }, { "messages": [ { "role": "user", "content": "The IAPR-TC12 dataset has more image-German pairs for validation than the Multi30k dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe IAPR-TC12 dataset has more image-German pairs for validation than the Multi30k dataset.\n" }, { "role": "assistant", "content": "The table shows that both the IAPR-TC12 and Multi30k datasets have 500 and 507 image-German pairs for validation, respectively. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3702.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Data splits for the IAPR-TC12 and Multi30k datasets." ], "reasoning": "The table shows that both the IAPR-TC12 and Multi30k datasets have 500 and 507 image-German pairs for validation, respectively. Therefore, the statement is refuted.", "statement": "The IAPR-TC12 dataset has more image-German pairs for validation than the Multi30k dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: The BLEU4 performance of different methods for image-pivoted zero-resource machine translation.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The combination of Lpivot and Lae achieves the best translation performance on both datasets, demonstrating the effectiveness of re-weighting and auto-encoding losses.Since our approach utilizes word translation learned in the first progressive step to benefit sentence translation from two losses, we compare each of the loss and their combination with previous methods. The Lpivot achieves superior performance to previous image-pivoted methods on both datasets and translation directions. It demonstrates the effectiveness of the re-weighting approach to suppress noises in image-pivoted sentence pairs, while such noises are ignored in previous methods. The auto-encoding loss Lae brings more significant performance gains, which shows that re-using word embedding matrices from the image-pivoted word translation is effective for the NMT model to encode language-agnostic sentence representation. The two losses are also complementary with each other and the combination of them achieves the best translation performance on the two datasets." } ], "images": [ "scigen/train/3703.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The BLEU4 performance of different methods for image-pivoted zero-resource machine translation." ], "reasoning": "The combination of Lpivot and Lae achieves the best translation performance on both datasets, demonstrating the effectiveness of re-weighting and auto-encoding losses.", "description": "Since our approach utilizes word translation learned in the first progressive step to benefit sentence translation from two losses, we compare each of the loss and their combination with previous methods. The Lpivot achieves superior performance to previous image-pivoted methods on both datasets and translation directions. It demonstrates the effectiveness of the re-weighting approach to suppress noises in image-pivoted sentence pairs, while such noises are ignored in previous methods. The auto-encoding loss Lae brings more significant performance gains, which shows that re-using word embedding matrices from the image-pivoted word translation is effective for the NMT model to encode language-agnostic sentence representation. The two losses are also complementary with each other and the combination of them achieves the best translation performance on the two datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: The BLEU4 performance of different methods for image-pivoted zero-resource machine translation.' and methodically tackle the question that follows. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the performance of the Lpivot+Lae method on the IAPR-TC12 De-En dataset?\n" }, { "role": "assistant", "content": "The table shows the performance of various methods on different datasets. For the IAPR-TC12 De-En dataset, the Lpivot+Lae method achieves a score of 61.3. So, the answer is {\"answer\": \"61.3\"}" } ], "images": [ "scigen/train/3703.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The BLEU4 performance of different methods for image-pivoted zero-resource machine translation." ], "reasoning": "The table shows the performance of various methods on different datasets. For the IAPR-TC12 De-En dataset, the Lpivot+Lae method achieves a score of 61.3.", "question": "What is the performance of the Lpivot+Lae method on the IAPR-TC12 De-En dataset?", "answer": "So, the answer is {\"answer\": \"61.3\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe S-txt-img method outperforms the Multi-agents method on the Multi30k En-De dataset.\n" }, { "role": "assistant", "content": "The table shows that the S-txt-img method has a score of 7.7 on the Multi30k En-De dataset, while the Multi-agents method does not have a recorded score for this dataset. So, the answer is {\"answer\": \"not enough info\"}" } ], "images": [ "scigen/train/3703.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The BLEU4 performance of different methods for image-pivoted zero-resource machine translation." ], "reasoning": "The table shows that the S-txt-img method has a score of 7.7 on the Multi30k En-De dataset, while the Multi-agents method does not have a recorded score for this dataset.", "statement": "The S-txt-img method outperforms the Multi-agents method on the Multi30k En-De dataset.", "answer": "So, the answer is {\"answer\": \"not enough info\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: The BLEU4 scores of image caption model and image-pivoted translation model on IAPR-TC12 and Multi30k datasets.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Translation performance is higher than image captioning performance due to the constrained nature of translation output, and improving image captioning can enhance zero-resource machine translation.Firstly, the translation performance is much higher than the image captioning performance. This is mainly because the translation output is more constrained by the input while the caption output is more diverse. So the image caption model directly used in previous image-pivot translation methods might be a main bottleneck. Secondly, the translation performance is proportional to the image captioning performance, which indicates that better image captioning can further improve our zero-resource machine translation." } ], "images": [ "scigen/train/3704.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: The BLEU4 scores of image caption model and image-pivoted translation model on IAPR-TC12 and Multi30k datasets." ], "reasoning": "Translation performance is higher than image captioning performance due to the constrained nature of translation output, and improving image captioning can enhance zero-resource machine translation.", "description": "Firstly, the translation performance is much higher than the image captioning performance. This is mainly because the translation output is more constrained by the input while the caption output is more diverse. So the image caption model directly used in previous image-pivot translation methods might be a main bottleneck. Secondly, the translation performance is proportional to the image captioning performance, which indicates that better image captioning can further improve our zero-resource machine translation.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 6: The BLEU4 scores of image caption model and image-pivoted translation model on IAPR-TC12 and Multi30k datasets.' in mind, break down and resolve the question below step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the translation performance for IAPR-TC12 I/De-En and how does it compare to the caption performance for the same dataset?\n" }, { "role": "assistant", "content": "The translation performance for IAPR-TC12 I/De-En is 61.3, which is significantly higher than the caption performance of 23.0 for the same dataset. So, the answer is {\"answer\": \"61.3\"}" } ], "images": [ "scigen/train/3704.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: The BLEU4 scores of image caption model and image-pivoted translation model on IAPR-TC12 and Multi30k datasets." ], "reasoning": "The translation performance for IAPR-TC12 I/De-En is 61.3, which is significantly higher than the caption performance of 23.0 for the same dataset.", "question": "What is the translation performance for IAPR-TC12 I/De-En and how does it compare to the caption performance for the same dataset?", "answer": "So, the answer is {\"answer\": \"61.3\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe translation performance for Multi30k I/De-En is higher than the caption performance for IAPR-TC12 I/En-De.\n" }, { "role": "assistant", "content": "The translation performance for Multi30k I/De-En is 23.0, which is higher than the caption performance for IAPR-TC12 I/En-De, which is 18.9. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3704.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: The BLEU4 scores of image caption model and image-pivoted translation model on IAPR-TC12 and Multi30k datasets." ], "reasoning": "The translation performance for Multi30k I/De-En is 23.0, which is higher than the caption performance for IAPR-TC12 I/En-De, which is 18.9.", "statement": "The translation performance for Multi30k I/De-En is higher than the caption performance for IAPR-TC12 I/En-De.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Perplexity on the test set of Penn Treebank. We show performance for the ranking v.s. binary loss algorithms, with different values for K, and with/without regularization.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "As the number of negative examples increases, the performance of NCE-ranking and NCE-binary improves and eventually surpasses MLE.As shown in the table, the performance of NCE-ranking and NCE-binary improves as the number of negative examples increases, and finally outperforms the MLE." } ], "images": [ "scigen/train/3705.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Perplexity on the test set of Penn Treebank. We show performance for the ranking v.s. binary loss algorithms, with different values for K, and with/without regularization." ], "reasoning": "As the number of negative examples increases, the performance of NCE-ranking and NCE-binary improves and eventually surpasses MLE.", "description": "As shown in the table, the performance of NCE-ranking and NCE-binary improves as the number of negative examples increases, and finally outperforms the MLE.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 1: Perplexity on the test set of Penn Treebank. We show performance for the ranking v.s. binary loss algorithms, with different values for K, and with/without regularization.' in mind, break down and resolve the question below step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhat is the performance of NCE-ranking with K=1600 for the Medium dataset?\n" }, { "role": "assistant", "content": "To find the performance of NCE-ranking with K=1600 for the Medium dataset, look at the intersection of the 'Medium 82.7' column and the 'K=1600' row under the 'Ranking' sub-column. So, the answer is {\"answer\": \"81.7\"}." } ], "images": [ "scigen/train/3705.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Perplexity on the test set of Penn Treebank. We show performance for the ranking v.s. binary loss algorithms, with different values for K, and with/without regularization." ], "reasoning": "To find the performance of NCE-ranking with K=1600 for the Medium dataset, look at the intersection of the 'Medium 82.7' column and the 'K=1600' row under the 'Ranking' sub-column.", "question": "What is the performance of NCE-ranking with K=1600 for the Medium dataset?", "answer": "So, the answer is {\"answer\": \"81.7\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe performance of reg-Binary (K=1600) for the Large dataset is better than the performance of NCE-binary with K=200 for the same dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the performance values of reg-Binary (K=1600) and NCE-binary with K=200 for the Large dataset. The performance of reg-Binary (K=1600) is 75.7, while the performance of NCE-binary with K=200 is 76.0. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3705.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Perplexity on the test set of Penn Treebank. We show performance for the ranking v.s. binary loss algorithms, with different values for K, and with/without regularization." ], "reasoning": "To verify this statement, compare the performance values of reg-Binary (K=1600) and NCE-binary with K=200 for the Large dataset. The performance of reg-Binary (K=1600) is 75.7, while the performance of NCE-binary with K=200 is 76.0.", "statement": "The performance of reg-Binary (K=1600) for the Large dataset is better than the performance of NCE-binary with K=200 for the same dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Automatic evaluations of the proposed method and baselines. ACC evaluates sentiment transformation. BLEU evaluates content preservation. G-score is the geometric mean of ACC and BLEU.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "CAAE and MDAL have low BLEU scores on both Yelp and Amazon datasets, indicating poor content preservation despite achieving desired sentiment.ACC evaluates sentiment transformation. BLEU evaluates semantic content preservation. G-score represents the geometric mean of ACC and BLEU. CAAE and MDAL achieve much lower BLEU scores, 1.17 and 1.64 on the Yelp dataset, 0.56 and 0.27 on the Amazon dataset. The low BLEU scores indicate the worrying content preservation performance to some extent. Even with the desired sentiment, the irrelevant generated text leads to worse overall performance. In general, these two systems work more like sentiment-aware language models that generate text only based on the target sentiment and neglect the source input. The main reason is that these two systems attempt to separate emotional information from non-emotional content in a hidden layer, where all information is complicatedly mixed together. It is difficult to only modify emotional information without any loss of non-emotional semantic content." } ], "images": [ "scigen/train/3706.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Automatic evaluations of the proposed method and baselines. ACC evaluates sentiment transformation. BLEU evaluates content preservation. G-score is the geometric mean of ACC and BLEU." ], "reasoning": "CAAE and MDAL have low BLEU scores on both Yelp and Amazon datasets, indicating poor content preservation despite achieving desired sentiment.", "description": "ACC evaluates sentiment transformation. BLEU evaluates semantic content preservation. G-score represents the geometric mean of ACC and BLEU. CAAE and MDAL achieve much lower BLEU scores, 1.17 and 1.64 on the Yelp dataset, 0.56 and 0.27 on the Amazon dataset. The low BLEU scores indicate the worrying content preservation performance to some extent. Even with the desired sentiment, the irrelevant generated text leads to worse overall performance. In general, these two systems work more like sentiment-aware language models that generate text only based on the target sentiment and neglect the source input. The main reason is that these two systems attempt to separate emotional information from non-emotional content in a hidden layer, where all information is complicatedly mixed together. It is difficult to only modify emotional information without any loss of non-emotional semantic content.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 1: Automatic evaluations of the proposed method and baselines. ACC evaluates sentiment transformation. BLEU evaluates content preservation. G-score is the geometric mean of ACC and BLEU.', answer the following question. Give detailed solution consisting of each step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich method achieved the highest G-score on the Yelp dataset?\n" }, { "role": "assistant", "content": "The table shows the G-scores for CAAE, MDAL, and the Proposed Method on the Yelp dataset. The Proposed Method has the highest G-score of 42.38. So, the answer is {\"answer\": \"Proposed Method\"}." } ], "images": [ "scigen/train/3706.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Automatic evaluations of the proposed method and baselines. ACC evaluates sentiment transformation. BLEU evaluates content preservation. G-score is the geometric mean of ACC and BLEU." ], "reasoning": "The table shows the G-scores for CAAE, MDAL, and the Proposed Method on the Yelp dataset. The Proposed Method has the highest G-score of 42.38.", "question": "Which method achieved the highest G-score on the Yelp dataset?", "answer": "So, the answer is {\"answer\": \"Proposed Method\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Proposed Method achieved the highest ACC score on the Amazon dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Proposed Method achieved the highest ACC score on the Amazon dataset.\n" }, { "role": "assistant", "content": "The table shows the ACC scores for CAAE, MDAL, and the Proposed Method on the Amazon dataset. CAAE has the highest ACC score of 84.19, not the Proposed Method. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3706.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Automatic evaluations of the proposed method and baselines. ACC evaluates sentiment transformation. BLEU evaluates content preservation. G-score is the geometric mean of ACC and BLEU." ], "reasoning": "The table shows the ACC scores for CAAE, MDAL, and the Proposed Method on the Amazon dataset. CAAE has the highest ACC score of 84.19, not the Proposed Method.", "statement": "The Proposed Method achieved the highest ACC score on the Amazon dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Human evaluations of the proposed method and baselines. Sentiment evaluates sentiment transformation. Semantic evaluates content preservation.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The proposed method significantly improves semantic preservation on both Yelp and Amazon datasets, achieving the highest G-score despite lower sentiment accuracy compared to CAAE.It can be clearly seen that the proposed method obviously improves semantic preservation. The semantic score is increased from 3.87 to 5.08 on the Yelp dataset, and from 3.22 to 4.67 on the Amazon dataset. In general, our proposed model achieves the best overall performance. Furthermore, it also needs to be noticed that with the large improvement in content preservation, the sentiment accuracy of the proposed method is lower than that of CAAE on the two datasets. It shows that simultaneously promoting sentiment transformation and content preservation remains to be studied further." } ], "images": [ "scigen/train/3707.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Human evaluations of the proposed method and baselines. Sentiment evaluates sentiment transformation. Semantic evaluates content preservation." ], "reasoning": "The proposed method significantly improves semantic preservation on both Yelp and Amazon datasets, achieving the highest G-score despite lower sentiment accuracy compared to CAAE.", "description": "It can be clearly seen that the proposed method obviously improves semantic preservation. The semantic score is increased from 3.87 to 5.08 on the Yelp dataset, and from 3.22 to 4.67 on the Amazon dataset. In general, our proposed model achieves the best overall performance. Furthermore, it also needs to be noticed that with the large improvement in content preservation, the sentiment accuracy of the proposed method is lower than that of CAAE on the two datasets. It shows that simultaneously promoting sentiment transformation and content preservation remains to be studied further.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 2: Human evaluations of the proposed method and baselines. Sentiment evaluates sentiment transformation. Semantic evaluates content preservation.', unfold the steps to solve the question given next. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nQuestion: What is the G-score of the proposed method on the Amazon dataset?\n" }, { "role": "assistant", "content": "The table shows the G-score for the proposed method on the Amazon dataset as 6.08, which is the highest among the methods listed. So, the answer is {\"answer\": \"6.08\"}" } ], "images": [ "scigen/train/3707.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Human evaluations of the proposed method and baselines. Sentiment evaluates sentiment transformation. Semantic evaluates content preservation." ], "reasoning": "The table shows the G-score for the proposed method on the Amazon dataset as 6.08, which is the highest among the methods listed.", "question": "What is the G-score of the proposed method on the Amazon dataset?", "answer": "So, the answer is {\"answer\": \"6.08\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe proposed method has the highest sentiment score on the Yelp dataset.\n" }, { "role": "assistant", "content": "The table shows that the proposed method has a sentiment score of 6.99 on the Yelp dataset, which is lower than both CAAE (7.67) and MDAL (7.12). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3707.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Human evaluations of the proposed method and baselines. Sentiment evaluates sentiment transformation. Semantic evaluates content preservation." ], "reasoning": "The table shows that the proposed method has a sentiment score of 6.99 on the Yelp dataset, which is lower than both CAAE (7.67) and MDAL (7.12).", "statement": "The proposed method has the highest sentiment score on the Yelp dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Accuracies (in %) on the SwDA test set, baseline with no context (NC) and Utt-Att-BiRNN model with context (WC)', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Despite having lower dimensionality, word-embeddings mean representations from ConceptNet perform comparably to higher-dimensional character LM representations.Character LM and word-embeddings mean utterance representations perform quite well for this task. Surprisingly, the word-embeddings mean representations of the utterances used from the ConceptNet seem to show good results given the fact of the low dimensionality of the embeddings (300) compared to character LM (4096) size." } ], "images": [ "scigen/train/3708.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Accuracies (in %) on the SwDA test set, baseline with no context (NC) and Utt-Att-BiRNN model with context (WC)" ], "reasoning": "Despite having lower dimensionality, word-embeddings mean representations from ConceptNet perform comparably to higher-dimensional character LM representations.", "description": "Character LM and word-embeddings mean utterance representations perform quite well for this task. Surprisingly, the word-embeddings mean representations of the utterances used from the ConceptNet seem to show good results given the fact of the low dimensionality of the embeddings (300) compared to character LM (4096) size.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nTable title: Table 1: Accuracies (in %) on the SwDA test set, baseline with no context (NC) and Utt-Att-BiRNN model with context (WC)\n\nQuestion: Which model from 'Our work' has the highest WC score?\n" }, { "role": "assistant", "content": "To find the model with the highest WC score from 'Our work', compare the WC scores of all models listed under 'Our work'. The highest WC score is 77.42, achieved by the 'Average char-word-level concatenated rep. predictions' model. So, the answer is {\"answer\": \"Average char-word-level concatenated rep. predictions\"}." } ], "images": [ "scigen/train/3708.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Accuracies (in %) on the SwDA test set, baseline with no context (NC) and Utt-Att-BiRNN model with context (WC)" ], "reasoning": "To find the model with the highest WC score from 'Our work', compare the WC scores of all models listed under 'Our work'. The highest WC score is 77.42, achieved by the 'Average char-word-level concatenated rep. predictions' model.", "question": "Which model from 'Our work' has the highest WC score?", "answer": "So, the answer is {\"answer\": \"Average char-word-level concatenated rep. predictions\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe word-embeddings mean representations from 'Our work' have a higher NC score than the character LM representations.\n" }, { "role": "assistant", "content": "The NC score for word-embeddings mean representations is 71.73, while the NC score for character LM representations is 71.84. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3708.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Accuracies (in %) on the SwDA test set, baseline with no context (NC) and Utt-Att-BiRNN model with context (WC)" ], "reasoning": "The NC score for word-embeddings mean representations is 71.73, while the NC score for character LM representations is 71.84. Therefore, the statement is refuted.", "statement": "The word-embeddings mean representations from 'Our work' have a higher NC score than the character LM representations.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Median of the maximum increase in loss in the vicinity of the dev set samples for RoBERTa-Large model finetuned with different methods. Vanilla models are naturally trained RoBERTa’s. M-Inc: Max Inc, M-Inc (R): Max Inc (R). Nat Loss (N-Loss) is the loss value on clean samples. Notice we require all clean samples here to be correctly classified by all models, which results in 227, 850 and 355 samples for RTE, CoLA and MRPC, respectively. We also give the variance in the Appendix.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "PGD reliably finds ΔLmax for RoBERTa models, and to ensure comparability, only samples correctly classified by all evaluated models are considered.In practice, we use PGD steps as in Eq. We found that when using a step size of 5⋅10−3 and ϵ=0.01∥X∥F, the PGD iterations converge to almost the same value, starting from 100 different random initializations of δ for the RoBERTa models, trained with or without FreeLB. This indicates that PGD reliably finds ΔLmax for these models. Therefore, we compute ΔLmax(X,ϵ) for each X via a 2000-step PGD. Samples with small margins exist even for models with perfect accuracy, which could give a false sense of vulnerability of the model. To rule out the outlier effect and make ΔLmax(X,ϵ) comparable across different samples, we only consider samples that all the evaluated models can correctly classify, and search for an ϵ for each sample such that the reference model can correctly classify all samples within the ϵ ball. However, such choice of per-sample ϵ favors the reference model by design." } ], "images": [ "scigen/train/3709.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Median of the maximum increase in loss in the vicinity of the dev set samples for RoBERTa-Large model finetuned with different methods. Vanilla models are naturally trained RoBERTa’s. M-Inc: Max Inc, M-Inc (R): Max Inc (R). Nat Loss (N-Loss) is the loss value on clean samples. Notice we require all clean samples here to be correctly classified by all models, which results in 227, 850 and 355 samples for RTE, CoLA and MRPC, respectively. We also give the variance in the Appendix." ], "reasoning": "PGD reliably finds ΔLmax for RoBERTa models, and to ensure comparability, only samples correctly classified by all evaluated models are considered.", "description": "In practice, we use PGD steps as in Eq. We found that when using a step size of 5⋅10−3 and ϵ=0.01∥X∥F, the PGD iterations converge to almost the same value, starting from 100 different random initializations of δ for the RoBERTa models, trained with or without FreeLB. This indicates that PGD reliably finds ΔLmax for these models. Therefore, we compute ΔLmax(X,ϵ) for each X via a 2000-step PGD. Samples with small margins exist even for models with perfect accuracy, which could give a false sense of vulnerability of the model. To rule out the outlier effect and make ΔLmax(X,ϵ) comparable across different samples, we only consider samples that all the evaluated models can correctly classify, and search for an ϵ for each sample such that the reference model can correctly classify all samples within the ϵ ball. However, such choice of per-sample ϵ favors the reference model by design.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 5: Median of the maximum increase in loss in the vicinity of the dev set samples for RoBERTa-Large model finetuned with different methods. Vanilla models are naturally trained RoBERTa’s. M-Inc: Max Inc, M-Inc (R): Max Inc (R). Nat Loss (N-Loss) is the loss value on clean samples. Notice we require all clean samples here to be correctly classified by all models, which results in 227, 850 and 355 samples for RTE, CoLA and MRPC, respectively. We also give the variance in the Appendix.', answer the question below by showing each progressive step toward the answer. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the CoLA N-Loss value for the PGD method?\n" }, { "role": "assistant", "content": "To find the CoLA N-Loss value for the PGD method, locate the row labeled 'PGD' and then find the value under the 'CoLA N-Loss' column. Thus, the answer is {\"answer\": \"436.1\"}." } ], "images": [ "scigen/train/3709.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Median of the maximum increase in loss in the vicinity of the dev set samples for RoBERTa-Large model finetuned with different methods. Vanilla models are naturally trained RoBERTa’s. M-Inc: Max Inc, M-Inc (R): Max Inc (R). Nat Loss (N-Loss) is the loss value on clean samples. Notice we require all clean samples here to be correctly classified by all models, which results in 227, 850 and 355 samples for RTE, CoLA and MRPC, respectively. We also give the variance in the Appendix." ], "reasoning": "To find the CoLA N-Loss value for the PGD method, locate the row labeled 'PGD' and then find the value under the 'CoLA N-Loss' column.", "question": "What is the CoLA N-Loss value for the PGD method?", "answer": "Thus, the answer is {\"answer\": \"436.1\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The FreeLB method has the lowest RTE M-Inc (R) value. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe FreeLB method has the lowest RTE M-Inc (R) value.\n" }, { "role": "assistant", "content": "To verify this statement, compare the RTE M-Inc (R) values for all methods. The FreeLB method has a value of 2.6, which is lower than the values for Vanilla (5.3) and PGD (4.9). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3709.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Median of the maximum increase in loss in the vicinity of the dev set samples for RoBERTa-Large model finetuned with different methods. Vanilla models are naturally trained RoBERTa’s. M-Inc: Max Inc, M-Inc (R): Max Inc (R). Nat Loss (N-Loss) is the loss value on clean samples. Notice we require all clean samples here to be correctly classified by all models, which results in 227, 850 and 355 samples for RTE, CoLA and MRPC, respectively. We also give the variance in the Appendix." ], "reasoning": "To verify this statement, compare the RTE M-Inc (R) values for all methods. The FreeLB method has a value of 2.6, which is lower than the values for Vanilla (5.3) and PGD (4.9).", "statement": "The FreeLB method has the lowest RTE M-Inc (R) value.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'None'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our models outperform the baseline but are not competitive with neural network-based approaches like RAE and ABCNN-3 due to the lack of additional features and the unsuitability of MLP for this task.The previous approaches are: (1) Baseline, cosine similarity with tf-idf weighting; (2) RAE, recursive autoencoder with dynamic pooling; (3) combine-skip+feats, skip-thought vectors with features; (4) ABCNN-3, attention-based convolutional nets; and (5) TF-KLD, matrix factorization with supervised reweighting. First, all our models are able to outperform the baseline. We only compare our models with the neural networks-based approaches, including RAE and ABCNN-3 for a fair comparison. We find that our models do not prove to be very competitive. After a careful analysis, we conclude that the reasons are (1) our models are pure neural networks-based, we don’t add any features to identify paraphrases while the other methods have used additional features; (2) The MLP is not very suitable in this task. We attempt to replace the MLP with the cosine distance and euclidean distance in our future work. Although our models have not yet matched the SOTA performance, we obtain an improvement of +2.3 accuracy by Attentive Tree-LSTMs when we incorporate the attention into the standard Tree-LSTM. The loss function is the same as the paraphrase identification since this task is also a binary classification task. Since this dataset is a fresh and uncompleted dataset, we only compare our models with Baudis et al. \\shortcitebaudis2016joint who have evaluated several models on it. It is observed that tree-structured models are more competitive than the sequential counterparts. As we expected, the attentive models can outperform all non-attentional counterparts." } ], "images": [ "scigen/train/3710.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "None" ], "reasoning": "Our models outperform the baseline but are not competitive with neural network-based approaches like RAE and ABCNN-3 due to the lack of additional features and the unsuitability of MLP for this task.", "description": "The previous approaches are: (1) Baseline, cosine similarity with tf-idf weighting; (2) RAE, recursive autoencoder with dynamic pooling; (3) combine-skip+feats, skip-thought vectors with features; (4) ABCNN-3, attention-based convolutional nets; and (5) TF-KLD, matrix factorization with supervised reweighting. First, all our models are able to outperform the baseline. We only compare our models with the neural networks-based approaches, including RAE and ABCNN-3 for a fair comparison. We find that our models do not prove to be very competitive. After a careful analysis, we conclude that the reasons are (1) our models are pure neural networks-based, we don’t add any features to identify paraphrases while the other methods have used additional features; (2) The MLP is not very suitable in this task. We attempt to replace the MLP with the cosine distance and euclidean distance in our future work. Although our models have not yet matched the SOTA performance, we obtain an improvement of +2.3 accuracy by Attentive Tree-LSTMs when we incorporate the attention into the standard Tree-LSTM. The loss function is the same as the paraphrase identification since this task is also a binary classification task. Since this dataset is a fresh and uncompleted dataset, we only compare our models with Baudis et al. \\shortcitebaudis2016joint who have evaluated several models on it. It is observed that tree-structured models are more competitive than the sequential counterparts. As we expected, the attentive models can outperform all non-attentional counterparts.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. The question is related to the given table titled 'None'.\n\nQuestion: Which method achieved the highest F1 score?\n" }, { "role": "assistant", "content": "To find the method with the highest F1 score, we look at the F1(%) column and identify the highest value, which is 85.9% achieved by TF-KLD. So, the answer is {\"answer\": \"TF-KLD\"}" } ], "images": [ "scigen/train/3710.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "None" ], "reasoning": "To find the method with the highest F1 score, we look at the F1(%) column and identify the highest value, which is 85.9% achieved by TF-KLD.", "question": "Which method achieved the highest F1 score?", "answer": "So, the answer is {\"answer\": \"TF-KLD\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Attentive Tree-LSTMs model achieved an accuracy of 75.8%.\n" }, { "role": "assistant", "content": "The table shows that the Attentive Tree-LSTMs model has an accuracy of 75.8%, which matches the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3710.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "None" ], "reasoning": "The table shows that the Attentive Tree-LSTMs model has an accuracy of 75.8%, which matches the statement.", "statement": "The Attentive Tree-LSTMs model achieved an accuracy of 75.8%.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Test set results on the SICK dataset. The first group is previous results, and remaining is ours.'.\n" }, { "role": "assistant", "content": "Our Attentive Tree-LSTMs achieve state-of-the-art performance with a Pearson's r of 0.8730, outperforming previous methods such as ConvNet and combine-skip+COCO.We first compare our results against the previous results. Kiros et al. \\shortcitekiros2015skip presents an unsupervised approach to learn the universal sentence vectors without depending on a specific task. Their Combine–skip+COCO model improve the Pearson’s r to 0.8655, but a weakness is that their sentence vectors are high-dimensional vectors (2400D). Training the skip-thoughts vectors needs a lot of time and space. He et al. \\shortcitehe2015multi show the effectiveness of convolutional nets with the similarity measurement layer for modelling sentence similarity. Their ConvNet outperforms ECNU with +0.027 Pearson ’s r. We can observe that dependency Tree-LSTM, combine-skip+COCO and ConvNet almost achieve the same performance and our Attentive Tree-LSTMs outperforms these three methods around +0.005 points. Comparison to ECNU, our Attentive Tree-LSTMs gains an improvement of +0.032 and achieves the state-of-the-art performance. Comparison to the non-attentional baselines (such as Tree-LSTMs), the attention mechanism (such as Attentive Tree-LSTMs) gives us a boost of around +0.007. All results highlight that our attentive Tree-RNNs are well suited for the semantic similarity task." } ], "images": [ "scigen/train/3711.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Test set results on the SICK dataset. The first group is previous results, and remaining is ours." ], "reasoning": "Our Attentive Tree-LSTMs achieve state-of-the-art performance with a Pearson's r of 0.8730, outperforming previous methods such as ConvNet and combine-skip+COCO.", "description": "We first compare our results against the previous results. Kiros et al. \\shortcitekiros2015skip presents an unsupervised approach to learn the universal sentence vectors without depending on a specific task. Their Combine–skip+COCO model improve the Pearson’s r to 0.8655, but a weakness is that their sentence vectors are high-dimensional vectors (2400D). Training the skip-thoughts vectors needs a lot of time and space. He et al. \\shortcitehe2015multi show the effectiveness of convolutional nets with the similarity measurement layer for modelling sentence similarity. Their ConvNet outperforms ECNU with +0.027 Pearson ’s r. We can observe that dependency Tree-LSTM, combine-skip+COCO and ConvNet almost achieve the same performance and our Attentive Tree-LSTMs outperforms these three methods around +0.005 points. Comparison to ECNU, our Attentive Tree-LSTMs gains an improvement of +0.032 and achieves the state-of-the-art performance. Comparison to the non-attentional baselines (such as Tree-LSTMs), the attention mechanism (such as Attentive Tree-LSTMs) gives us a boost of around +0.007. All results highlight that our attentive Tree-RNNs are well suited for the semantic similarity task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: Test set results on the SICK dataset. The first group is previous results, and remaining is ours.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}. \n\nQuestion: What is the Pearson's r value for the Attentive (Dependency) Tree-LSTMs model?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows the Pearson's r value for various models. The Attentive (Dependency) Tree-LSTMs model has a Pearson's r value of 0.8730. So, the answer is {\"answer\": \"0.8730\"}" } ], "images": [ "scigen/train/3711.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Test set results on the SICK dataset. The first group is previous results, and remaining is ours." ], "reasoning": "The table shows the Pearson's r value for various models. The Attentive (Dependency) Tree-LSTMs model has a Pearson's r value of 0.8730.", "question": "What is the Pearson's r value for the Attentive (Dependency) Tree-LSTMs model?", "answer": "So, the answer is {\"answer\": \"0.8730\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The ConvNet model has a higher Pearson's r value than the combine-skip+COCO model., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe ConvNet model has a higher Pearson's r value than the combine-skip+COCO model.\n" }, { "role": "assistant", "content": "The table shows that the ConvNet model has a Pearson's r value of 0.8686, while the combine-skip+COCO model has a Pearson's r value of 0.8655. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3711.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Test set results on the SICK dataset. The first group is previous results, and remaining is ours." ], "reasoning": "The table shows that the ConvNet model has a Pearson's r value of 0.8686, while the combine-skip+COCO model has a Pearson's r value of 0.8655.", "statement": "The ConvNet model has a higher Pearson's r value than the combine-skip+COCO model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'None'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Our models outperform the baseline but are not very competitive compared to neural network-based approaches like RAE and ABCNN-3 due to the lack of additional features and the unsuitability of MLP for this task.The previous approaches are: (1) Baseline, cosine similarity with tf-idf weighting; (2) RAE, recursive autoencoder with dynamic pooling; (3) combine-skip+feats, skip-thought vectors with features; (4) ABCNN-3, attention-based convolutional nets; and (5) TF-KLD, matrix factorization with supervised reweighting. First, all our models are able to outperform the baseline. We only compare our models with the neural networks-based approaches, including RAE and ABCNN-3 for a fair comparison. We find that our models do not prove to be very competitive. After a careful analysis, we conclude that the reasons are (1) our models are pure neural networks-based, we don’t add any features to identify paraphrases while the other methods have used additional features; (2) The MLP is not very suitable in this task. We attempt to replace the MLP with the cosine distance and euclidean distance in our future work. Although our models have not yet matched the SOTA performance, we obtain an improvement of +2.3 accuracy by Attentive Tree-LSTMs when we incorporate the attention into the standard Tree-LSTM. The loss function is the same as the paraphrase identification since this task is also a binary classification task. Since this dataset is a fresh and uncompleted dataset, we only compare our models with Baudis et al. \\shortcitebaudis2016joint who have evaluated several models on it. It is observed that tree-structured models are more competitive than the sequential counterparts. As we expected, the attentive models can outperform all non-attentional counterparts." } ], "images": [ "scigen/train/3712.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "None" ], "reasoning": "Our models outperform the baseline but are not very competitive compared to neural network-based approaches like RAE and ABCNN-3 due to the lack of additional features and the unsuitability of MLP for this task.", "description": "The previous approaches are: (1) Baseline, cosine similarity with tf-idf weighting; (2) RAE, recursive autoencoder with dynamic pooling; (3) combine-skip+feats, skip-thought vectors with features; (4) ABCNN-3, attention-based convolutional nets; and (5) TF-KLD, matrix factorization with supervised reweighting. First, all our models are able to outperform the baseline. We only compare our models with the neural networks-based approaches, including RAE and ABCNN-3 for a fair comparison. We find that our models do not prove to be very competitive. After a careful analysis, we conclude that the reasons are (1) our models are pure neural networks-based, we don’t add any features to identify paraphrases while the other methods have used additional features; (2) The MLP is not very suitable in this task. We attempt to replace the MLP with the cosine distance and euclidean distance in our future work. Although our models have not yet matched the SOTA performance, we obtain an improvement of +2.3 accuracy by Attentive Tree-LSTMs when we incorporate the attention into the standard Tree-LSTM. The loss function is the same as the paraphrase identification since this task is also a binary classification task. Since this dataset is a fresh and uncompleted dataset, we only compare our models with Baudis et al. \\shortcitebaudis2016joint who have evaluated several models on it. It is observed that tree-structured models are more competitive than the sequential counterparts. As we expected, the attentive models can outperform all non-attentional counterparts.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'None', solve this question and give detail solutions. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich method achieved the highest test accuracy when attention was incorporated?\n" }, { "role": "assistant", "content": "The table shows that the Attentive Tree-LSTMs method achieved the highest test accuracy of 72.5% when attention was incorporated. So, the answer is {\"answer\": \"Attentive Tree-LSTMs\"}" } ], "images": [ "scigen/train/3712.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "None" ], "reasoning": "The table shows that the Attentive Tree-LSTMs method achieved the highest test accuracy of 72.5% when attention was incorporated.", "question": "Which method achieved the highest test accuracy when attention was incorporated?", "answer": "So, the answer is {\"answer\": \"Attentive Tree-LSTMs\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Tree-GRUs method achieved a higher test accuracy than the Seq-LSTMs method.\n" }, { "role": "assistant", "content": "The table shows that the Tree-GRUs method achieved a test accuracy of 70.6%, which is higher than the Seq-LSTMs method's test accuracy of 63.3%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3712.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "None" ], "reasoning": "The table shows that the Tree-GRUs method achieved a test accuracy of 70.6%, which is higher than the Seq-LSTMs method's test accuracy of 63.3%.", "statement": "The Tree-GRUs method achieved a higher test accuracy than the Seq-LSTMs method.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Example predictions from the test set. GT: ground truth, Pred: predicted score.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The model performs well on the SICK dataset due to simple sentence structures and limited vocabulary, but struggles with numerical recognition on the MSRP dataset and identifying true answers on the AI2-8grade dataset.The first group shows that our model is able to predict semantic similarity score nearly perfectly on the SICK dataset. We argue the reason is that the sentences of SICK dataset are image and video descriptions whose sentence structure is relatively simple and there are less uncommon words and named entities in the vocabulary. The second group gives us three examples on the MSRP test set. We find that our model can identify whether two fact statements are paraphrases, but fails to recognize the numbers (in group 2, line 3). We presents the examples on AI2-8grade dataset in the last group. We can observe that our model is efficient to select the false questions, while our model is difficult to select the true answers, unless the evidence of question is very strong." } ], "images": [ "scigen/train/3713.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Example predictions from the test set. GT: ground truth, Pred: predicted score." ], "reasoning": "The model performs well on the SICK dataset due to simple sentence structures and limited vocabulary, but struggles with numerical recognition on the MSRP dataset and identifying true answers on the AI2-8grade dataset.", "description": "The first group shows that our model is able to predict semantic similarity score nearly perfectly on the SICK dataset. We argue the reason is that the sentences of SICK dataset are image and video descriptions whose sentence structure is relatively simple and there are less uncommon words and named entities in the vocabulary. The second group gives us three examples on the MSRP test set. We find that our model can identify whether two fact statements are paraphrases, but fails to recognize the numbers (in group 2, line 3). We presents the examples on AI2-8grade dataset in the last group. We can observe that our model is efficient to select the false questions, while our model is difficult to select the true answers, unless the evidence of question is very strong.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Example predictions from the test set. GT: ground truth, Pred: predicted score.\nQuestion: Why does the model perform nearly perfectly on the SICK dataset?\nGive a detailed response to the above question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The model performs nearly perfectly on the SICK dataset because the sentences are image and video descriptions with simple structures and fewer uncommon words and named entities. So, the answer is {\"answer\": \"simple structures and fewer uncommon words\"}" } ], "images": [ "scigen/train/3713.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Example predictions from the test set. GT: ground truth, Pred: predicted score." ], "reasoning": "The model performs nearly perfectly on the SICK dataset because the sentences are image and video descriptions with simple structures and fewer uncommon words and named entities.", "question": "Why does the model perform nearly perfectly on the SICK dataset?", "answer": "So, the answer is {\"answer\": \"simple structures and fewer uncommon words\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The model can identify paraphrases on the MSRP test set but fails to recognize numbers., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe model can identify paraphrases on the MSRP test set but fails to recognize numbers.\n" }, { "role": "assistant", "content": "The table shows that the model correctly identifies paraphrases in the first two examples of the MSRP test set but fails in the third example due to numerical differences. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3713.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Example predictions from the test set. GT: ground truth, Pred: predicted score." ], "reasoning": "The table shows that the model correctly identifies paraphrases in the first two examples of the MSRP test set but fails in the third example due to numerical differences.", "statement": "The model can identify paraphrases on the MSRP test set but fails to recognize numbers.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: Perplexity and Bleu scores on validation data. S2S stands for the baseline seq2seq model, Cornell stands for the training setup presented in Section 4.3.2, Cornell S stands for Section 4.3.3, OpenSubtitles stands for Section 4.3.4 and OpenSubtitles F stands for Section 4.3.5.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The Cornell model scores were computed after 20K training steps, while the Cornell S model scores were computed after 16K steps. The OpenSubtitles model did not overfit but had stagnated loss due to possible inadequate learning rate or small model size.This means that for the Cornell model the scores were computed after 20K training steps and for the Cornell S model, scores were computed after 16K steps. For the OpenSubtitles training the model didn’t seem to overfit, however after a quick drop the loss stagnated for the remainder of the training, which could be because the learning rate was not adequate or the model was too small to be able to learn more." } ], "images": [ "scigen/train/3714.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Perplexity and Bleu scores on validation data. S2S stands for the baseline seq2seq model, Cornell stands for the training setup presented in Section 4.3.2, Cornell S stands for Section 4.3.3, OpenSubtitles stands for Section 4.3.4 and OpenSubtitles F stands for Section 4.3.5." ], "reasoning": "The Cornell model scores were computed after 20K training steps, while the Cornell S model scores were computed after 16K steps. The OpenSubtitles model did not overfit but had stagnated loss due to possible inadequate learning rate or small model size.", "description": "This means that for the Cornell model the scores were computed after 20K training steps and for the Cornell S model, scores were computed after 16K steps. For the OpenSubtitles training the model didn’t seem to overfit, however after a quick drop the loss stagnated for the remainder of the training, which could be because the learning rate was not adequate or the model was too small to be able to learn more.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1: Perplexity and Bleu scores on validation data. S2S stands for the baseline seq2seq model, Cornell stands for the training setup presented in Section 4.3.2, Cornell S stands for Section 4.3.3, OpenSubtitles stands for Section 4.3.4 and OpenSubtitles F stands for Section 4.3.5.', solve the subsequent question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the perplexity score for the OpenSubtitles model?\n" }, { "role": "assistant", "content": "The table shows the perplexity scores for different models. The perplexity score for the OpenSubtitles model is listed under the 'OpenSubtitles' column. So, the answer is {\"answer\": \"11.7\"}" } ], "images": [ "scigen/train/3714.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Perplexity and Bleu scores on validation data. S2S stands for the baseline seq2seq model, Cornell stands for the training setup presented in Section 4.3.2, Cornell S stands for Section 4.3.3, OpenSubtitles stands for Section 4.3.4 and OpenSubtitles F stands for Section 4.3.5." ], "reasoning": "The table shows the perplexity scores for different models. The perplexity score for the OpenSubtitles model is listed under the 'OpenSubtitles' column.", "question": "What is the perplexity score for the OpenSubtitles model?", "answer": "So, the answer is {\"answer\": \"11.7\"}" }, { "messages": [ { "role": "user", "content": "The Bleu score for the Cornell S model is higher than the Bleu score for the OpenSubtitles model. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Bleu score for the Cornell S model is higher than the Bleu score for the OpenSubtitles model.\n" }, { "role": "assistant", "content": "The table shows the Bleu scores for different models. The Bleu score for the Cornell S model is 4.2, while the Bleu score for the OpenSubtitles model is 6.8, which is higher. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3714.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Perplexity and Bleu scores on validation data. S2S stands for the baseline seq2seq model, Cornell stands for the training setup presented in Section 4.3.2, Cornell S stands for Section 4.3.3, OpenSubtitles stands for Section 4.3.4 and OpenSubtitles F stands for Section 4.3.5." ], "reasoning": "The table shows the Bleu scores for different models. The Bleu score for the Cornell S model is 4.2, while the Bleu score for the OpenSubtitles model is 6.8, which is higher.", "statement": "The Bleu score for the Cornell S model is higher than the Bleu score for the OpenSubtitles model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 5: Subjects’ preference for nearest visual neighbour of words in Kiela et al. (2014) vs. random pictures. Figure of merit is percentage proportion of significant results in favor of nearest neighbour across words. Results are reported for the whole set, as well as for words above (concrete) and below (abstract) the concreteness rating median. The unseen column reports results when words exposed to direct visual evidence during training are discarded. The words columns report set cardinality.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "MMSkip-gram-A representation is more meaningfully related to concrete words than abstract words, and exposure to direct visual evidence during training significantly impacts this relationship.We quantify performance in terms of proportion of words for which the number of votes for the nearest neighbour picture is significantly above chance according to a two-tailed binomial test. We set significance at p<0.05 after adjusting all p-values with the Holm correction for running 198 statistical tests. MMSkip-gram-A representation is meaningfully related to the word. As expected, this is more often the case for concrete than abstract words. Still, we also observe a significant preference for the model-predicted nearest picture for about one fourth of the abstract terms. Whether a word was exposed to direct visual evidence during training is of course making a big difference, and this factor interacts with concreteness, as only two abstract words were matched with images during training. When we limit evaluation to word representations that were not exposed to pictures during training, the difference between concrete and abstract terms, while still large, becomes less dramatic than if all words are considered." } ], "images": [ "scigen/train/3715.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Subjects’ preference for nearest visual neighbour of words in Kiela et al. (2014) vs. random pictures. Figure of merit is percentage proportion of significant results in favor of nearest neighbour across words. Results are reported for the whole set, as well as for words above (concrete) and below (abstract) the concreteness rating median. The unseen column reports results when words exposed to direct visual evidence during training are discarded. The words columns report set cardinality." ], "reasoning": "MMSkip-gram-A representation is more meaningfully related to concrete words than abstract words, and exposure to direct visual evidence during training significantly impacts this relationship.", "description": "We quantify performance in terms of proportion of words for which the number of votes for the nearest neighbour picture is significantly above chance according to a two-tailed binomial test. We set significance at p<0.05 after adjusting all p-values with the Holm correction for running 198 statistical tests. MMSkip-gram-A representation is meaningfully related to the word. As expected, this is more often the case for concrete than abstract words. Still, we also observe a significant preference for the model-predicted nearest picture for about one fourth of the abstract terms. Whether a word was exposed to direct visual evidence during training is of course making a big difference, and this factor interacts with concreteness, as only two abstract words were matched with images during training. When we limit evaluation to word representations that were not exposed to pictures during training, the difference between concrete and abstract terms, while still large, becomes less dramatic than if all words are considered.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 5: Subjects’ preference for nearest visual neighbour of words in Kiela et al. (2014) vs. random pictures. Figure of merit is percentage proportion of significant results in favor of nearest neighbour across words. Results are reported for the whole set, as well as for words above (concrete) and below (abstract) the concreteness rating median. The unseen column reports results when words exposed to direct visual evidence during training are discarded. The words columns report set cardinality.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}. \n\nQuestion: What is the global proportion of words for which the MMSkip-gram-A representation is meaningfully related to the word?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The table shows the global proportion of words for which the MMSkip-gram-A representation is meaningfully related to the word as 48%. So, the answer is {\"answer\": \"48%\"}." } ], "images": [ "scigen/train/3715.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Subjects’ preference for nearest visual neighbour of words in Kiela et al. (2014) vs. random pictures. Figure of merit is percentage proportion of significant results in favor of nearest neighbour across words. Results are reported for the whole set, as well as for words above (concrete) and below (abstract) the concreteness rating median. The unseen column reports results when words exposed to direct visual evidence during training are discarded. The words columns report set cardinality." ], "reasoning": "The table shows the global proportion of words for which the MMSkip-gram-A representation is meaningfully related to the word as 48%.", "question": "What is the global proportion of words for which the MMSkip-gram-A representation is meaningfully related to the word?", "answer": "So, the answer is {\"answer\": \"48%\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe MMSkip-gram-A representation is meaningfully related to 73% of abstract words.\n" }, { "role": "assistant", "content": "The table shows that the MMSkip-gram-A representation is meaningfully related to 23% of abstract words, not 73%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3715.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Subjects’ preference for nearest visual neighbour of words in Kiela et al. (2014) vs. random pictures. Figure of merit is percentage proportion of significant results in favor of nearest neighbour across words. Results are reported for the whole set, as well as for words above (concrete) and below (abstract) the concreteness rating median. The unseen column reports results when words exposed to direct visual evidence during training are discarded. The words columns report set cardinality." ], "reasoning": "The table shows that the MMSkip-gram-A representation is meaningfully related to 23% of abstract words, not 73%.", "statement": "The MMSkip-gram-A representation is meaningfully related to 73% of abstract words.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title '(b)'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The entropy of multimodal vectors correlates with the degree of abstractness of words, as shown by high correlation values in models like MMSkip-gram-A and MMSkip-gram-B*.A natural corollary of this idea is that visually-grounded representations of abstract concepts should be more diverse: If you think of dogs, very similar images of specific dogs will come to mind. You can also imagine the abstract notion of freedom, but the nature of the related imagery will be much more varied. Recently, \\newciteKiela:etal:2014 have proposed to measure abstractness by exploiting this very same intuition. However, they rely on manual annotation of pictures via Google Images and define an ad-hoc measure of image dispersion. We conjecture that the representations naturally induced by our models display a similar property. In particular, the entropy of our multimodal vectors, being an expression of how varied the information they encode is, should correlate with the degree of abstractness of the corresponding words. et al. set. To test the hypothesis quantitatively, we measure the correlation of entropy and concreteness on the 200 words in the \\newciteKiela:etal:2014 set. (MMSkip-gram-B*) achieve very high correlation (but, interestingly, not MMSkip-gram-B). This is further evidence that multimodal learning is grounding the representations of both concrete and abstract words in meaningful ways." } ], "images": [ "scigen/train/3716.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b)" ], "reasoning": "The entropy of multimodal vectors correlates with the degree of abstractness of words, as shown by high correlation values in models like MMSkip-gram-A and MMSkip-gram-B*.", "description": "A natural corollary of this idea is that visually-grounded representations of abstract concepts should be more diverse: If you think of dogs, very similar images of specific dogs will come to mind. You can also imagine the abstract notion of freedom, but the nature of the related imagery will be much more varied. Recently, \\newciteKiela:etal:2014 have proposed to measure abstractness by exploiting this very same intuition. However, they rely on manual annotation of pictures via Google Images and define an ad-hoc measure of image dispersion. We conjecture that the representations naturally induced by our models display a similar property. In particular, the entropy of our multimodal vectors, being an expression of how varied the information they encode is, should correlate with the degree of abstractness of the corresponding words. et al. set. To test the hypothesis quantitatively, we measure the correlation of entropy and concreteness on the 200 words in the \\newciteKiela:etal:2014 set. (MMSkip-gram-B*) achieve very high correlation (but, interestingly, not MMSkip-gram-B). This is further evidence that multimodal learning is grounding the representations of both concrete and abstract words in meaningful ways.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about '(b)', and tackle the question below in a sequential manner. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model has the highest correlation with concreteness on the 200 words in the Kiela et al. set?\n" }, { "role": "assistant", "content": "The table shows the correlation values (ρ) for different models. MMSkip-gram-A has the highest correlation value of 0.75. So, the answer is {\"answer\": \"MMSkip-gram-A\"}." } ], "images": [ "scigen/train/3716.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b)" ], "reasoning": "The table shows the correlation values (ρ) for different models. MMSkip-gram-A has the highest correlation value of 0.75.", "question": "Which model has the highest correlation with concreteness on the 200 words in the Kiela et al. set?", "answer": "So, the answer is {\"answer\": \"MMSkip-gram-A\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nMMSkip-gram-B* has a higher correlation than MMSkip-gram-A.\n" }, { "role": "assistant", "content": "The table shows that MMSkip-gram-B* has a correlation value of 0.71, while MMSkip-gram-A has a higher correlation value of 0.75. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3716.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b)" ], "reasoning": "The table shows that MMSkip-gram-B* has a correlation value of 0.71, while MMSkip-gram-A has a higher correlation value of 0.75.", "statement": "MMSkip-gram-B* has a higher correlation than MMSkip-gram-A.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: Accuracy on the Spider development and test sets, by difficulty as defined by Yu et al. (2018b).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Performance drops with increasing difficulty, and the generalization gap is reduced when RAT-SQL is augmented with BERT.As expected, performance drops with increasing difficulty. The overall generalization gap between development and test of RAT-SQL was strongly affected by the significant drop in accuracy (9%) on the extra hard questions. When RAT-SQL is augmented with BERT, the generalization gaps of most difficulties are reduced." } ], "images": [ "scigen/train/3717.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracy on the Spider development and test sets, by difficulty as defined by Yu et al. (2018b)." ], "reasoning": "Performance drops with increasing difficulty, and the generalization gap is reduced when RAT-SQL is augmented with BERT.", "description": "As expected, performance drops with increasing difficulty. The overall generalization gap between development and test of RAT-SQL was strongly affected by the significant drop in accuracy (9%) on the extra hard questions. When RAT-SQL is augmented with BERT, the generalization gaps of most difficulties are reduced.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Accuracy on the Spider development and test sets, by difficulty as defined by Yu et al. (2018b).', unfold the question presented below, detailing every step of your calculation. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the overall accuracy for RAT-SQL on the test set?\n" }, { "role": "assistant", "content": "To find the overall accuracy for RAT-SQL on the test set, look at the 'All' column under the 'Test' row for RAT-SQL. So, the answer is {\"answer\": \"57.2\"}." } ], "images": [ "scigen/train/3717.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracy on the Spider development and test sets, by difficulty as defined by Yu et al. (2018b)." ], "reasoning": "To find the overall accuracy for RAT-SQL on the test set, look at the 'All' column under the 'Test' row for RAT-SQL.", "question": "What is the overall accuracy for RAT-SQL on the test set?", "answer": "So, the answer is {\"answer\": \"57.2\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The accuracy of RAT-SQL + BERT on medium difficulty questions is higher on the test set than on the development set. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe accuracy of RAT-SQL + BERT on medium difficulty questions is higher on the test set than on the development set.\n" }, { "role": "assistant", "content": "The table shows that the accuracy of RAT-SQL + BERT on medium difficulty questions is 71.3 on the test set and 73.6 on the development set, which means the statement is false. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3717.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracy on the Spider development and test sets, by difficulty as defined by Yu et al. (2018b)." ], "reasoning": "The table shows that the accuracy of RAT-SQL + BERT on medium difficulty questions is 71.3 on the test set and 73.6 on the development set, which means the statement is false.", "statement": "The accuracy of RAT-SQL + BERT on medium difficulty questions is higher on the test set than on the development set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: RAT-SQL accuracy on WikiSQL, trained without BERT augmentation or execution-guided decoding (EG). Compared to other approaches without EG. “LF Acc” = Logical Form Accuracy; “Ex. Acc” = Execution Accuracy.'.\n" }, { "role": "assistant", "content": "The simplified value decoding in RAT-SQL leads to lower accuracy compared to state-of-the-art models, and improving this mechanism is planned for future work.Most of the gap between its accuracy and state of the art is due to the simplified implementation of value decoding, which is required for WikiSQL evaluation but not in Spider. Our value decoding for these experiments is a simple token-based pointer mechanism, which often fails to retrieve multi-token value constants accurately. A robust value decoding mechanism in RAT-SQL is an important extension that we plan to address outside the scope of this work." } ], "images": [ "scigen/train/3718.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: RAT-SQL accuracy on WikiSQL, trained without BERT augmentation or execution-guided decoding (EG). Compared to other approaches without EG. “LF Acc” = Logical Form Accuracy; “Ex. Acc” = Execution Accuracy." ], "reasoning": "The simplified value decoding in RAT-SQL leads to lower accuracy compared to state-of-the-art models, and improving this mechanism is planned for future work.", "description": "Most of the gap between its accuracy and state of the art is due to the simplified implementation of value decoding, which is required for WikiSQL evaluation but not in Spider. Our value decoding for these experiments is a simple token-based pointer mechanism, which often fails to retrieve multi-token value constants accurately. A robust value decoding mechanism in RAT-SQL is an important extension that we plan to address outside the scope of this work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 5: RAT-SQL accuracy on WikiSQL, trained without BERT augmentation or execution-guided decoding (EG). Compared to other approaches without EG. “LF Acc” = Logical Form Accuracy; “Ex. Acc” = Execution Accuracy.', answer the following question. Give detailed solution consisting of each step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest Test Ex. Acc% and what is its value?\n" }, { "role": "assistant", "content": "To find the model with the highest Test Ex. Acc%, compare the Test Ex. Acc% values for all models. IncSQL has the highest Test Ex. Acc% of 83.7%. So, the answer is {\"answer\": \"IncSQL with 83.7%\"}." } ], "images": [ "scigen/train/3718.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: RAT-SQL accuracy on WikiSQL, trained without BERT augmentation or execution-guided decoding (EG). Compared to other approaches without EG. “LF Acc” = Logical Form Accuracy; “Ex. Acc” = Execution Accuracy." ], "reasoning": "To find the model with the highest Test Ex. Acc%, compare the Test Ex. Acc% values for all models. IncSQL has the highest Test Ex. Acc% of 83.7%.", "question": "Which model has the highest Test Ex. Acc% and what is its value?", "answer": "So, the answer is {\"answer\": \"IncSQL with 83.7%\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRAT-SQL has the highest Dev LF Acc% among all models.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Dev LF Acc% values for all models. RAT-SQL has a Dev LF Acc% of 73.6%, which is not the highest. MQAN has the highest Dev LF Acc% of 76.1%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3718.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: RAT-SQL accuracy on WikiSQL, trained without BERT augmentation or execution-guided decoding (EG). Compared to other approaches without EG. “LF Acc” = Logical Form Accuracy; “Ex. Acc” = Execution Accuracy." ], "reasoning": "To verify this statement, compare the Dev LF Acc% values for all models. RAT-SQL has a Dev LF Acc% of 73.6%, which is not the highest. MQAN has the highest Dev LF Acc% of 76.1%.", "statement": "RAT-SQL has the highest Dev LF Acc% among all models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6: Accuracy (exact match %) on the development set given an oracle providing correct columns and tables (“Oracle columns”) and/or the AST sketch structure (“Oracle sketch”).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The table shows the accuracy improvements of the RAT-SQL model with different oracle enhancements, highlighting the highest accuracy with both oracle sketch and oracle columns.S5SS4SSS0Px2 The Need for Schema Linking One natural question is how often does the decoder fail to select the correct column, even with the schema encoding and linking improvements we have made. For “oracle sketch”, at every grammar nonterminal the decoder is forced to choose the correct production so the final SQL sketch exactly matches that of the ground truth. The rest of the decoding proceeds conditioned on that choice. Likewise, “oracle columns” forces the decoder to emit the correct column/table at terminal nodes." } ], "images": [ "scigen/train/3719.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Accuracy (exact match %) on the development set given an oracle providing correct columns and tables (“Oracle columns”) and/or the AST sketch structure (“Oracle sketch”)." ], "reasoning": "The table shows the accuracy improvements of the RAT-SQL model with different oracle enhancements, highlighting the highest accuracy with both oracle sketch and oracle columns.", "description": "S5SS4SSS0Px2 The Need for Schema Linking One natural question is how often does the decoder fail to select the correct column, even with the schema encoding and linking improvements we have made. For “oracle sketch”, at every grammar nonterminal the decoder is forced to choose the correct production so the final SQL sketch exactly matches that of the ground truth. The rest of the decoding proceeds conditioned on that choice. Likewise, “oracle columns” forces the decoder to emit the correct column/table at terminal nodes.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nTable title: Table 6: Accuracy (exact match %) on the development set given an oracle providing correct columns and tables (“Oracle columns”) and/or the AST sketch structure (“Oracle sketch”).\n\nQuestion: What is the accuracy improvement when both oracle sketch and oracle columns are used compared to the base RAT-SQL model?\n" }, { "role": "assistant", "content": "The base RAT-SQL model has an accuracy of 62.7, while the RAT-SQL model with both oracle sketch and oracle columns has an accuracy of 99.4. The improvement is 99.4 - 62.7. So, the answer is {\"answer\": \"36.7\"}" } ], "images": [ "scigen/train/3719.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Accuracy (exact match %) on the development set given an oracle providing correct columns and tables (“Oracle columns”) and/or the AST sketch structure (“Oracle sketch”)." ], "reasoning": "The base RAT-SQL model has an accuracy of 62.7, while the RAT-SQL model with both oracle sketch and oracle columns has an accuracy of 99.4. The improvement is 99.4 - 62.7.", "question": "What is the accuracy improvement when both oracle sketch and oracle columns are used compared to the base RAT-SQL model?", "answer": "So, the answer is {\"answer\": \"36.7\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The RAT-SQL model with oracle columns has an accuracy of 69.8. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe RAT-SQL model with oracle columns has an accuracy of 69.8.\n" }, { "role": "assistant", "content": "The table shows that the RAT-SQL model with oracle columns indeed has an accuracy of 69.8. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3719.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Accuracy (exact match %) on the development set given an oracle providing correct columns and tables (“Oracle columns”) and/or the AST sketch structure (“Oracle sketch”)." ], "reasoning": "The table shows that the RAT-SQL model with oracle columns indeed has an accuracy of 69.8.", "statement": "The RAT-SQL model with oracle columns has an accuracy of 69.8.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 4: Relevance scores for the word “minister” in three different test sentences from CoNLL dataset.'.\n" }, { "role": "assistant", "content": "The importance of the word 'minister' for the entity type PER varies based on its proximity to the entity word, with higher scores when 'minister' is closer to the entity.In this section, we analyze how the position of context words affects their scores obtained by M_LRC method. We do this analysis for real sentences present in the test sets as well as on artificial sentences. We achieve this by applying the proposed techniques at an individual sentence level. M_WF method indicates that “minister” has high importance for entity type PER matching with our intuition. In the first sentence, there is no entity word for PER, hence, the score for “minister”, corresponding to entity PER is zero. In the second sentence, the score is higher, though not too high as the word is relatively far from the relevant entity word. However, the score is much higher in the third sentence where “minister” is right before the entity words “Margaret Thatcher”. Relative scores obtained by using different neural models also match with the general notion that RNN tends to forget long range context (second sentence) compared to LSTM and GRU, and is quite good for short distance context (third sentence)." } ], "images": [ "scigen/train/3720.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Relevance scores for the word “minister” in three different test sentences from CoNLL dataset." ], "reasoning": "The importance of the word 'minister' for the entity type PER varies based on its proximity to the entity word, with higher scores when 'minister' is closer to the entity.", "description": "In this section, we analyze how the position of context words affects their scores obtained by M_LRC method. We do this analysis for real sentences present in the test sets as well as on artificial sentences. We achieve this by applying the proposed techniques at an individual sentence level. M_WF method indicates that “minister” has high importance for entity type PER matching with our intuition. In the first sentence, there is no entity word for PER, hence, the score for “minister”, corresponding to entity PER is zero. In the second sentence, the score is higher, though not too high as the word is relatively far from the relevant entity word. However, the score is much higher in the third sentence where “minister” is right before the entity words “Margaret Thatcher”. Relative scores obtained by using different neural models also match with the general notion that RNN tends to forget long range context (second sentence) compared to LSTM and GRU, and is quite good for short distance context (third sentence).", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 4: Relevance scores for the word “minister” in three different test sentences from CoNLL dataset.', answer the question below by showing each progressive step toward the answer. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich neural model shows the highest score for the word 'minister' when it is right before the entity words 'Margaret Thatcher'?\n" }, { "role": "assistant", "content": "The table shows that in the third sentence, where 'minister' is right before 'Margaret Thatcher', the RNN model has the highest score of 239.793 compared to LSTM and GRU. So, the answer is {\"answer\": \"RNN\"}." } ], "images": [ "scigen/train/3720.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Relevance scores for the word “minister” in three different test sentences from CoNLL dataset." ], "reasoning": "The table shows that in the third sentence, where 'minister' is right before 'Margaret Thatcher', the RNN model has the highest score of 239.793 compared to LSTM and GRU.", "question": "Which neural model shows the highest score for the word 'minister' when it is right before the entity words 'Margaret Thatcher'?", "answer": "So, the answer is {\"answer\": \"RNN\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe LSTM model tends to forget long-range context more than the RNN model.\n" }, { "role": "assistant", "content": "The description states that RNN tends to forget long-range context compared to LSTM and GRU. The table shows that in the second sentence, the LSTM model has a higher score (2.576) than the RNN model (0.163), indicating that LSTM retains long-range context better. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3720.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Relevance scores for the word “minister” in three different test sentences from CoNLL dataset." ], "reasoning": "The description states that RNN tends to forget long-range context compared to LSTM and GRU. The table shows that in the second sentence, the LSTM model has a higher score (2.576) than the RNN model (0.163), indicating that LSTM retains long-range context better.", "statement": "The LSTM model tends to forget long-range context more than the RNN model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title '(a)'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The LSTM model assigns high scores to words like 'vice-chairman', 'ruling', 'congress', and 'minister', which align with their intuitive importance in the context of the sentence.At a sentence level, we only consider our best performing model, LSTM. It uses a sentence from CoNLL dataset - “Saturday ’s national congress of the ruling Czech (I-ORG) Civic (I-ORG) Democratic (I-ORG) Party ( I-ORG) ODS (I-ORG)) will discuss making the party more efficient and transparent , Foreign Minister and ODS (I-ORG) vice-chairman Josef (I-PER) Zieleniec (I-PER), said on Friday .”. The tags for all entity words are mentioned alongside each word. Notice the high scores for “vice-chairman”, “ruling”, “congress”, “minister” meets the intuitive understanding of these words. Interestingly, round brackets get the maximum scores for M_SLL method, which may be attributed to their frequent use with ORG entity words. Similarly, sentence taken from JNLPBA dataset is: “the number of glucocorticoid (B-protein) receptor (I-protein) sites in lymphocytes (B-cell_type) and plasma cortisol concentrations were measured in dgdg patients who had recovered from major depressive disorder and dgdg healthy control subjects .”. Again, higher scores for “sites” and “plasma” for cell_type are in agreement with overall scores given to them." } ], "images": [ "scigen/train/3721.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a)" ], "reasoning": "The LSTM model assigns high scores to words like 'vice-chairman', 'ruling', 'congress', and 'minister', which align with their intuitive importance in the context of the sentence.", "description": "At a sentence level, we only consider our best performing model, LSTM. It uses a sentence from CoNLL dataset - “Saturday ’s national congress of the ruling Czech (I-ORG) Civic (I-ORG) Democratic (I-ORG) Party ( I-ORG) ODS (I-ORG)) will discuss making the party more efficient and transparent , Foreign Minister and ODS (I-ORG) vice-chairman Josef (I-PER) Zieleniec (I-PER), said on Friday .”. The tags for all entity words are mentioned alongside each word. Notice the high scores for “vice-chairman”, “ruling”, “congress”, “minister” meets the intuitive understanding of these words. Interestingly, round brackets get the maximum scores for M_SLL method, which may be attributed to their frequent use with ORG entity words. Similarly, sentence taken from JNLPBA dataset is: “the number of glucocorticoid (B-protein) receptor (I-protein) sites in lymphocytes (B-cell_type) and plasma cortisol concentrations were measured in dgdg patients who had recovered from major depressive disorder and dgdg healthy control subjects .”. Again, higher scores for “sites” and “plasma” for cell_type are in agreement with overall scores given to them.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about '(a)', answer the following question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich word has the highest score according to the LSTM model?\n" }, { "role": "assistant", "content": "By examining the table, the word with the highest score is identified by comparing the scores of all words. So, the answer is {\"answer\": \"(\"}" } ], "images": [ "scigen/train/3721.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a)" ], "reasoning": "By examining the table, the word with the highest score is identified by comparing the scores of all words.", "question": "Which word has the highest score according to the LSTM model?", "answer": "So, the answer is {\"answer\": \"(\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe word 'minister' has a higher score than 'congress'.\n" }, { "role": "assistant", "content": "By comparing the scores of 'minister' (0.403) and 'congress' (0.728), it is evident that 'congress' has a higher score. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3721.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a)" ], "reasoning": "By comparing the scores of 'minister' (0.403) and 'congress' (0.728), it is evident that 'congress' has a higher score.", "statement": "The word 'minister' has a higher score than 'congress'.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title '(b)'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The LSTM model assigns high scores to words like 'vice-chairman', 'ruling', 'congress', and 'minister' in the CoNLL dataset, and 'sites' and 'plasma' in the JNLPBA dataset, aligning with their contextual importance.At a sentence level, we only consider our best performing model, LSTM. It uses a sentence from CoNLL dataset - “Saturday ’s national congress of the ruling Czech (I-ORG) Civic (I-ORG) Democratic (I-ORG) Party ( I-ORG) ODS (I-ORG)) will discuss making the party more efficient and transparent , Foreign Minister and ODS (I-ORG) vice-chairman Josef (I-PER) Zieleniec (I-PER), said on Friday .”. The tags for all entity words are mentioned alongside each word. Notice the high scores for “vice-chairman”, “ruling”, “congress”, “minister” meets the intuitive understanding of these words. Interestingly, round brackets get the maximum scores for M_SLL method, which may be attributed to their frequent use with ORG entity words. Similarly, sentence taken from JNLPBA dataset is: “the number of glucocorticoid (B-protein) receptor (I-protein) sites in lymphocytes (B-cell_type) and plasma cortisol concentrations were measured in dgdg patients who had recovered from major depressive disorder and dgdg healthy control subjects .”. Again, higher scores for “sites” and “plasma” for cell_type are in agreement with overall scores given to them." } ], "images": [ "scigen/train/3722.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b)" ], "reasoning": "The LSTM model assigns high scores to words like 'vice-chairman', 'ruling', 'congress', and 'minister' in the CoNLL dataset, and 'sites' and 'plasma' in the JNLPBA dataset, aligning with their contextual importance.", "description": "At a sentence level, we only consider our best performing model, LSTM. It uses a sentence from CoNLL dataset - “Saturday ’s national congress of the ruling Czech (I-ORG) Civic (I-ORG) Democratic (I-ORG) Party ( I-ORG) ODS (I-ORG)) will discuss making the party more efficient and transparent , Foreign Minister and ODS (I-ORG) vice-chairman Josef (I-PER) Zieleniec (I-PER), said on Friday .”. The tags for all entity words are mentioned alongside each word. Notice the high scores for “vice-chairman”, “ruling”, “congress”, “minister” meets the intuitive understanding of these words. Interestingly, round brackets get the maximum scores for M_SLL method, which may be attributed to their frequent use with ORG entity words. Similarly, sentence taken from JNLPBA dataset is: “the number of glucocorticoid (B-protein) receptor (I-protein) sites in lymphocytes (B-cell_type) and plasma cortisol concentrations were measured in dgdg patients who had recovered from major depressive disorder and dgdg healthy control subjects .”. Again, higher scores for “sites” and “plasma” for cell_type are in agreement with overall scores given to them.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about '(b)', and tackle the question below in a sequential manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich word has the highest Score (CT) in the table?\n" }, { "role": "assistant", "content": "To find the word with the highest Score (CT), look at the 'Score (CT)' column and identify the maximum value. The word 'in' has the highest Score (CT) of 80.745. So, the answer is {\"answer\": \"in\"}." } ], "images": [ "scigen/train/3722.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b)" ], "reasoning": "To find the word with the highest Score (CT), look at the 'Score (CT)' column and identify the maximum value. The word 'in' has the highest Score (CT) of 80.745.", "question": "Which word has the highest Score (CT) in the table?", "answer": "So, the answer is {\"answer\": \"in\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe word 'number' has the highest Score (Pr) in the table.\n" }, { "role": "assistant", "content": "To verify this statement, check the 'Score (Pr)' column for the highest value. The word 'number' has a Score (Pr) of 10.148, which is the highest in the table. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3722.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b)" ], "reasoning": "To verify this statement, check the 'Score (Pr)' column for the highest value. The word 'number' has a Score (Pr) of 10.148, which is the highest in the table.", "statement": "The word 'number' has the highest Score (Pr) in the table.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 1. The average absolute correlation of sentence embedding’s dimensions (Equation (1)) on the Wikipedia-1mio dataset. The compressed embeddings’ correlation is about 3-4 lower than the original embeddings’.'.\n" }, { "role": "assistant", "content": "The table shows a significant reduction in average correlation percentages between original and compressed embeddings, demonstrating the decorrelating effect of the compressor.To test this hypothesis, we inspect the scale of correlations between dimensions in the embeddings: Let e1,... ,en denote d-dimensional embeddings, and let ρ(i,j) (for i,j=1,... ,d) denote the correlation between two dimensions in these embeddings. We measure the overall correlation within the embeddings as (1) 1d2d∑i=1d∑j=1|ρ(i,j)|. We observe a strong reduction of correlation between the embeddings by a factor of about 3-4, which illustrates the decorrelating effect of our compressor." } ], "images": [ "scigen/train/3723.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1. The average absolute correlation of sentence embedding’s dimensions (Equation (1)) on the Wikipedia-1mio dataset. The compressed embeddings’ correlation is about 3-4 lower than the original embeddings’." ], "reasoning": "The table shows a significant reduction in average correlation percentages between original and compressed embeddings, demonstrating the decorrelating effect of the compressor.", "description": "To test this hypothesis, we inspect the scale of correlations between dimensions in the embeddings: Let e1,... ,en denote d-dimensional embeddings, and let ρ(i,j) (for i,j=1,... ,d) denote the correlation between two dimensions in these embeddings. We measure the overall correlation within the embeddings as (1) 1d2d∑i=1d∑j=1|ρ(i,j)|. We observe a strong reduction of correlation between the embeddings by a factor of about 3-4, which illustrates the decorrelating effect of our compressor.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 1. The average absolute correlation of sentence embedding’s dimensions (Equation (1)) on the Wikipedia-1mio dataset. The compressed embeddings’ correlation is about 3-4 lower than the original embeddings’.', solve this question and give detail solutions. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhat is the average correlation percentage reduction for the 'infersent' embedding type?\n" }, { "role": "assistant", "content": "To find the reduction, subtract the compressed correlation percentage from the original correlation percentage for 'infersent'. The original is 10.17% and the compressed is 2.26%, so the reduction is 10.17% - 2.26%. So, the answer is {\"answer\": \"7.91%\"}" } ], "images": [ "scigen/train/3723.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1. The average absolute correlation of sentence embedding’s dimensions (Equation (1)) on the Wikipedia-1mio dataset. The compressed embeddings’ correlation is about 3-4 lower than the original embeddings’." ], "reasoning": "To find the reduction, subtract the compressed correlation percentage from the original correlation percentage for 'infersent'. The original is 10.17% and the compressed is 2.26%, so the reduction is 10.17% - 2.26%.", "question": "What is the average correlation percentage reduction for the 'infersent' embedding type?", "answer": "So, the answer is {\"answer\": \"7.91%\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The 'mbow' embedding type shows the smallest reduction in average correlation percentage. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe 'mbow' embedding type shows the smallest reduction in average correlation percentage.\n" }, { "role": "assistant", "content": "To verify this, compare the reduction in average correlation percentages for all embedding types. The reductions are: infersent (10.17% - 2.26% = 7.91%), mbow (9.31% - 2.73% = 6.58%), and sent2vec (5.37% - 1.83% = 3.54%). The smallest reduction is for 'sent2vec', not 'mbow'. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3723.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1. The average absolute correlation of sentence embedding’s dimensions (Equation (1)) on the Wikipedia-1mio dataset. The compressed embeddings’ correlation is about 3-4 lower than the original embeddings’." ], "reasoning": "To verify this, compare the reduction in average correlation percentages for all embedding types. The reductions are: infersent (10.17% - 2.26% = 7.91%), mbow (9.31% - 2.73% = 6.58%), and sent2vec (5.37% - 1.83% = 3.54%). The smallest reduction is for 'sent2vec', not 'mbow'.", "statement": "The 'mbow' embedding type shows the smallest reduction in average correlation percentage.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Tweet Matching.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Arc-II outperforms other models significantly in this easier task with more training instances and random negatives.This task is slightly easier than Experiment I , with more training instances and purely random negatives. It requires less about the grammatical rigor but more on detailed modeling of loose and local matching patterns (e.g., work-overtime⇔ rest). Again Arc-II beats other models with large margins, while two convolutional sentence models Arc-I and SENNA+MLP come next." } ], "images": [ "scigen/train/3724.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Tweet Matching." ], "reasoning": "Arc-II outperforms other models significantly in this easier task with more training instances and random negatives.", "description": "This task is slightly easier than Experiment I , with more training instances and purely random negatives. It requires less about the grammatical rigor but more on detailed modeling of loose and local matching patterns (e.g., work-overtime⇔ rest). Again Arc-II beats other models with large margins, while two convolutional sentence models Arc-I and SENNA+MLP come next.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Tweet Matching.\nQuestion: Which model achieved the highest P@1(%) in the easier task with more training instances and random negatives?\nSolve the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows the P@1(%) for various models, and the description mentions that Arc-II beats other models with large margins. So, the answer is {\"answer\": \"Arc-II\"}" } ], "images": [ "scigen/train/3724.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Tweet Matching." ], "reasoning": "The table shows the P@1(%) for various models, and the description mentions that Arc-II beats other models with large margins.", "question": "Which model achieved the highest P@1(%) in the easier task with more training instances and random negatives?", "answer": "So, the answer is {\"answer\": \"Arc-II\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nArc-I achieved a P@1(%) of 59.18 in the easier task with more training instances and random negatives.\n" }, { "role": "assistant", "content": "The table lists the P@1(%) for Arc-I as 59.18, which matches the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3724.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Tweet Matching." ], "reasoning": "The table lists the P@1(%) for Arc-I as 59.18, which matches the statement.", "statement": "Arc-I achieved a P@1(%) of 59.18 in the easier task with more training instances and random negatives.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: Sentence Completion.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Arc-II significantly outperforms Arc-I, demonstrating the effectiveness of joint modeling of matching and sentence meaning, while SENNA+MLP performs well but is limited by its shallow architecture.The two proposed models get nearly half of the cases Arc-II outperforms Arc-I significantly, showing the power of joint modeling of matching and sentence meaning. As another convolutional model, SENNA+MLP performs fairly well on this task, although still running behind the proposed convolutional architectures since it is too shallow to adequately model the sentence. It is a bit surprising that uRAE comes last on this task, which might be caused by the facts that 1) the representation model (including word-embedding) is not trained on Reuters, and 2) the split-sentence setting hurts the parsing, which is vital to the quality of learned sentence representation." } ], "images": [ "scigen/train/3725.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Sentence Completion." ], "reasoning": "Arc-II significantly outperforms Arc-I, demonstrating the effectiveness of joint modeling of matching and sentence meaning, while SENNA+MLP performs well but is limited by its shallow architecture.", "description": "The two proposed models get nearly half of the cases Arc-II outperforms Arc-I significantly, showing the power of joint modeling of matching and sentence meaning. As another convolutional model, SENNA+MLP performs fairly well on this task, although still running behind the proposed convolutional architectures since it is too shallow to adequately model the sentence. It is a bit surprising that uRAE comes last on this task, which might be caused by the facts that 1) the representation model (including word-embedding) is not trained on Reuters, and 2) the split-sentence setting hurts the parsing, which is vital to the quality of learned sentence representation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 1: Sentence Completion.', solve the following question step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest P@1(%) and what does this indicate about its performance?\n" }, { "role": "assistant", "content": "Arc-II has the highest P@1(%) at 49.62%, indicating that it significantly outperforms other models, showcasing the power of joint modeling of matching and sentence meaning. So, the answer is {\"answer\": \"Arc-II\"}" } ], "images": [ "scigen/train/3725.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Sentence Completion." ], "reasoning": "Arc-II has the highest P@1(%) at 49.62%, indicating that it significantly outperforms other models, showcasing the power of joint modeling of matching and sentence meaning.", "question": "Which model has the highest P@1(%) and what does this indicate about its performance?", "answer": "So, the answer is {\"answer\": \"Arc-II\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: uRAE+MLP has the lowest P@1(%) among the models listed., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nuRAE+MLP has the lowest P@1(%) among the models listed.\n" }, { "role": "assistant", "content": "The table shows that uRAE+MLP has a P@1(%) of 25.76%, which is the lowest among the models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3725.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Sentence Completion." ], "reasoning": "The table shows that uRAE+MLP has a P@1(%) of 25.76%, which is the lowest among the models listed.", "statement": "uRAE+MLP has the lowest P@1(%) among the models listed.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: Accuracy on the analogy task with mean and standard deviation computed using three random seeds when initializing the word2vec model. The best results for each category and corpus are in bold.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Context encoders outperform word2vec in tasks with single-sense words, but clustering context vectors could improve performance in tasks with multi-sense words.To capture some of the semantic relations between words (e.g. the first four task categories) it can be advantageous to use context encoders instead of word2vec. One reason for the ConEcs’ superior performance on some of the task categories, but not others, might be that the city and country names compared in the first four task categories only have a single sense (referring to the respective location), while the words asked for in other task categories can have multiple meanings. For example, “run” can be used as both a noun or a verb, additionally, in some contexts it refers to the sport activity while other times it is used in a more abstract sense, e.g. in the context of someone running for president. Therefore, the results in the other task categories might improve if the words’ context vectors are first clustered and then the ConEc embedding is generated by multiplying the word2vec embeddings with the average of only those context vectors corresponding to the word’s sense most appropriate for the task category." } ], "images": [ "scigen/train/3726.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Accuracy on the analogy task with mean and standard deviation computed using three random seeds when initializing the word2vec model. The best results for each category and corpus are in bold." ], "reasoning": "Context encoders outperform word2vec in tasks with single-sense words, but clustering context vectors could improve performance in tasks with multi-sense words.", "description": "To capture some of the semantic relations between words (e.g. the first four task categories) it can be advantageous to use context encoders instead of word2vec. One reason for the ConEcs’ superior performance on some of the task categories, but not others, might be that the city and country names compared in the first four task categories only have a single sense (referring to the respective location), while the words asked for in other task categories can have multiple meanings. For example, “run” can be used as both a noun or a verb, additionally, in some contexts it refers to the sport activity while other times it is used in a more abstract sense, e.g. in the context of someone running for president. Therefore, the results in the other task categories might improve if the words’ context vectors are first clustered and then the ConEc embedding is generated by multiplying the word2vec embeddings with the average of only those context vectors corresponding to the word’s sense most appropriate for the task category.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1: Accuracy on the analogy task with mean and standard deviation computed using three random seeds when initializing the word2vec model. The best results for each category and corpus are in bold.', solve the subsequent question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhich method has the highest score for the 'capital-common-countries' task category?\n" }, { "role": "assistant", "content": "The 'capital-common-countries' task category has the highest score of 83.1± for the 1-billion Context Encoder method. So, the answer is {\"answer\": \"1-billion Context Encoder\"}." } ], "images": [ "scigen/train/3726.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Accuracy on the analogy task with mean and standard deviation computed using three random seeds when initializing the word2vec model. The best results for each category and corpus are in bold." ], "reasoning": "The 'capital-common-countries' task category has the highest score of 83.1± for the 1-billion Context Encoder method.", "question": "Which method has the highest score for the 'capital-common-countries' task category?", "answer": "So, the answer is {\"answer\": \"1-billion Context Encoder\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe text8 (10 iter) Context Encoder outperforms the 1-billion word2vec in the 'family' task category.\n" }, { "role": "assistant", "content": "The table shows that the text8 (10 iter) Context Encoder has a score of 77.2±, while the 1-billion word2vec has a score of 78.7± in the 'family' task category. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3726.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Accuracy on the analogy task with mean and standard deviation computed using three random seeds when initializing the word2vec model. The best results for each category and corpus are in bold." ], "reasoning": "The table shows that the text8 (10 iter) Context Encoder has a score of 77.2±, while the 1-billion word2vec has a score of 78.7± in the 'family' task category.", "statement": "The text8 (10 iter) Context Encoder outperforms the 1-billion word2vec in the 'family' task category.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 14: Macro average by ambiguous word type (Term (T), Abbreviation (A), Term-Abbreviation (TA)) for the MSH WSD set.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "SVM with word embeddings and unigrams performs the best across all categories, while LSTM is better for terms when unigrams and word embeddings are used separately.As defined in Jimeno-Yepes et al. Terms (T) are the most difficult to disambiguate, while abbreviations seem to be the easiest set. SVM with word embeddings and unigrams performs the best on all the categories. LSTM seems to be better for terms compared to SVM when unigrams and word embeddings are used separately. This different seems to be less clear for abbreviations and ATs. SVM with word embeddings seems to perform less well for the AT group in comparison to the performance in other categories with other methods." } ], "images": [ "scigen/train/3727.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 14: Macro average by ambiguous word type (Term (T), Abbreviation (A), Term-Abbreviation (TA)) for the MSH WSD set." ], "reasoning": "SVM with word embeddings and unigrams performs the best across all categories, while LSTM is better for terms when unigrams and word embeddings are used separately.", "description": "As defined in Jimeno-Yepes et al. Terms (T) are the most difficult to disambiguate, while abbreviations seem to be the easiest set. SVM with word embeddings and unigrams performs the best on all the categories. LSTM seems to be better for terms compared to SVM when unigrams and word embeddings are used separately. This different seems to be less clear for abbreviations and ATs. SVM with word embeddings seems to perform less well for the AT group in comparison to the performance in other categories with other methods.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 14: Macro average by ambiguous word type (Term (T), Abbreviation (A), Term-Abbreviation (TA)) for the MSH WSD set.', solve the subsequent question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich method performs the best for the AT category?\n" }, { "role": "assistant", "content": "By comparing the scores for the AT category, SVM with word embeddings, S500, W50, and unigrams has the highest score of 96.50. So, the answer is {\"answer\": \"SVM WE S500 W50+Unigrams\"}" } ], "images": [ "scigen/train/3727.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 14: Macro average by ambiguous word type (Term (T), Abbreviation (A), Term-Abbreviation (TA)) for the MSH WSD set." ], "reasoning": "By comparing the scores for the AT category, SVM with word embeddings, S500, W50, and unigrams has the highest score of 96.50.", "question": "Which method performs the best for the AT category?", "answer": "So, the answer is {\"answer\": \"SVM WE S500 W50+Unigrams\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nLSTM with word embeddings performs better than SVM with unigrams for the T category.\n" }, { "role": "assistant", "content": "The table shows that LSTM with word embeddings has a score of 92.04 for the T category, while SVM with unigrams has a score of 90.23. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3727.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 14: Macro average by ambiguous word type (Term (T), Abbreviation (A), Term-Abbreviation (TA)) for the MSH WSD set." ], "reasoning": "The table shows that LSTM with word embeddings has a score of 92.04 for the T category, while SVM with unigrams has a score of 90.23.", "statement": "LSTM with word embeddings performs better than SVM with unigrams for the T category.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Macro and micro average disambiguation results NLM WSD'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Unigrams are a strong feature representation, and combining different feature sets does not improve performance over unigrams. NB performs significantly better than SVM, and all results are better compared to the maximum frequency sense baseline.Unigrams is as well a strong feature representation, on the other hand combining different feature sets do not improve the performance over unigrams. NB is significantly better than SVM, which is another difference compared to the MSH WSD data set. All the results are better compared to the maximum frequency sense baseline (macro average: 82.63 and micro average: 82.31)." } ], "images": [ "scigen/train/3728.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Macro and micro average disambiguation results NLM WSD" ], "reasoning": "Unigrams are a strong feature representation, and combining different feature sets does not improve performance over unigrams. NB performs significantly better than SVM, and all results are better compared to the maximum frequency sense baseline.", "description": "Unigrams is as well a strong feature representation, on the other hand combining different feature sets do not improve the performance over unigrams. NB is significantly better than SVM, which is another difference compared to the MSH WSD data set. All the results are better compared to the maximum frequency sense baseline (macro average: 82.63 and micro average: 82.31).", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Macro and micro average disambiguation results NLM WSD', unfold the question presented below, detailing every step of your calculation. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich feature set and machine learning model combination has the highest performance?\n" }, { "role": "assistant", "content": "By comparing the values in the table, the highest performance is achieved with the Unigrams feature set using the NB model, with scores of 88.61 and 88.73. So, the answer is {\"answer\": \"Unigrams with NB\"}." } ], "images": [ "scigen/train/3728.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Macro and micro average disambiguation results NLM WSD" ], "reasoning": "By comparing the values in the table, the highest performance is achieved with the Unigrams feature set using the NB model, with scores of 88.61 and 88.73.", "question": "Which feature set and machine learning model combination has the highest performance?", "answer": "So, the answer is {\"answer\": \"Unigrams with NB\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Combining different feature sets improves performance over unigrams. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nCombining different feature sets improves performance over unigrams.\n" }, { "role": "assistant", "content": "The description states that combining different feature sets does not improve performance over unigrams, and the table shows that the highest scores are achieved with unigrams alone. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3728.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Macro and micro average disambiguation results NLM WSD" ], "reasoning": "The description states that combining different feature sets does not improve performance over unigrams, and the table shows that the highest scores are achieved with unigrams alone.", "statement": "Combining different feature sets improves performance over unigrams.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 9: Macro and micro average LSTM results compared to SVM unigram and bigrams and word embeddings NLM WSD set.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Using word embeddings improves performance over unigrams, and LSTM shows slight improvement in macro averaging compared to SVM and word embeddings.Using word embeddings significantly improve over using unigrams. LSTM shows some improvement in macro averaging. which is not significant compared to SVM and word embeddings." } ], "images": [ "scigen/train/3729.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Macro and micro average LSTM results compared to SVM unigram and bigrams and word embeddings NLM WSD set." ], "reasoning": "Using word embeddings improves performance over unigrams, and LSTM shows slight improvement in macro averaging compared to SVM and word embeddings.", "description": "Using word embeddings significantly improve over using unigrams. LSTM shows some improvement in macro averaging. which is not significant compared to SVM and word embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 9: Macro and micro average LSTM results compared to SVM unigram and bigrams and word embeddings NLM WSD set.' in mind, break down and resolve the question below step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhich configuration has the highest macro accuracy?\n" }, { "role": "assistant", "content": "By comparing the macro accuracy values in the table, the configuration with the highest macro accuracy is LSTM S500 W50 with a value of 90.64. So, the answer is {\"answer\": \"LSTM S500 W50\"}" } ], "images": [ "scigen/train/3729.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Macro and micro average LSTM results compared to SVM unigram and bigrams and word embeddings NLM WSD set." ], "reasoning": "By comparing the macro accuracy values in the table, the configuration with the highest macro accuracy is LSTM S500 W50 with a value of 90.64.", "question": "Which configuration has the highest macro accuracy?", "answer": "So, the answer is {\"answer\": \"LSTM S500 W50\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nSVM with word embeddings and S150 W50 configuration has higher macro accuracy than NB unigrams.\n" }, { "role": "assistant", "content": "The table shows that SVM with word embeddings and S150 W50 configuration has a macro accuracy of 90.58, which is higher than the macro accuracy of NB unigrams, which is 88.61. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3729.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Macro and micro average LSTM results compared to SVM unigram and bigrams and word embeddings NLM WSD set." ], "reasoning": "The table shows that SVM with word embeddings and S150 W50 configuration has a macro accuracy of 90.58, which is higher than the macro accuracy of NB unigrams, which is 88.61.", "statement": "SVM with word embeddings and S150 W50 configuration has higher macro accuracy than NB unigrams.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 12: Macro average results for ambiguous words grouped by number of senses for the MSH WSD set.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Words with 2 senses are easier to disambiguate than words with 3 senses, and methods using word embeddings show a larger performance drop between 2 and 3 senses.We have grouped ambiguous terms according to several criteria. Results are shown for words with 2 senses (189 words) and 3 senses (12 words). Ambiguous words with 4 and 5 senses appear only one in the data set. Macro average shows that words with 2 senses are easier to disambiguate and that words with 3 senses are slightly more complicated. Methods relying solely on word embeddings as features seem to have a larger drop in performance between 2 word senses and 3 word senses." } ], "images": [ "scigen/train/3730.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 12: Macro average results for ambiguous words grouped by number of senses for the MSH WSD set." ], "reasoning": "Words with 2 senses are easier to disambiguate than words with 3 senses, and methods using word embeddings show a larger performance drop between 2 and 3 senses.", "description": "We have grouped ambiguous terms according to several criteria. Results are shown for words with 2 senses (189 words) and 3 senses (12 words). Ambiguous words with 4 and 5 senses appear only one in the data set. Macro average shows that words with 2 senses are easier to disambiguate and that words with 3 senses are slightly more complicated. Methods relying solely on word embeddings as features seem to have a larger drop in performance between 2 word senses and 3 word senses.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 12: Macro average results for ambiguous words grouped by number of senses for the MSH WSD set.', solve the subsequent question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nWhich method has the highest performance for words with 2 senses?\n" }, { "role": "assistant", "content": "To find the method with the highest performance for words with 2 senses, compare the values in the '2 senses' column. The highest value is 96.03. So, the answer is {\"answer\": \"SVM WE S500 W50+Unigrams\"}." } ], "images": [ "scigen/train/3730.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 12: Macro average results for ambiguous words grouped by number of senses for the MSH WSD set." ], "reasoning": "To find the method with the highest performance for words with 2 senses, compare the values in the '2 senses' column. The highest value is 96.03.", "question": "Which method has the highest performance for words with 2 senses?", "answer": "So, the answer is {\"answer\": \"SVM WE S500 W50+Unigrams\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe SVM WE S500 W50 method has a higher performance for words with 2 senses than the LSTM WE S500 W50 method.\n" }, { "role": "assistant", "content": "Compare the performance values for the SVM WE S500 W50 and LSTM WE S500 W50 methods in the '2 senses' column. SVM WE S500 W50 has a performance of 94.75, while LSTM WE S500 W50 has a performance of 95.00. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3730.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 12: Macro average results for ambiguous words grouped by number of senses for the MSH WSD set." ], "reasoning": "Compare the performance values for the SVM WE S500 W50 and LSTM WE S500 W50 methods in the '2 senses' column. SVM WE S500 W50 has a performance of 94.75, while LSTM WE S500 W50 has a performance of 95.00.", "statement": "The SVM WE S500 W50 method has a higher performance for words with 2 senses than the LSTM WE S500 W50 method.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 13: Macro average results for ambiguous words grouped by number of senses for the NLM WSD set.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Words with 2 senses are disambiguated with higher accuracy compared to words with 3 senses, as shown by the higher accuracy percentages across all methods.The NLM WSD data set has 34 ambiguous words annotated with 2 senses and 6 with 3 senses. The word cold is the only word with 4 senses annotated. We find that words with 2 senses are typically disambiguated with higher accuracy, while 3 senses seem to be disambiguated with lower accuracy. There is a top of 100 examples for each ambiguous word, so if 3 senses appear, there is less training data per ambiguous word sense." } ], "images": [ "scigen/train/3731.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 13: Macro average results for ambiguous words grouped by number of senses for the NLM WSD set." ], "reasoning": "Words with 2 senses are disambiguated with higher accuracy compared to words with 3 senses, as shown by the higher accuracy percentages across all methods.", "description": "The NLM WSD data set has 34 ambiguous words annotated with 2 senses and 6 with 3 senses. The word cold is the only word with 4 senses annotated. We find that words with 2 senses are typically disambiguated with higher accuracy, while 3 senses seem to be disambiguated with lower accuracy. There is a top of 100 examples for each ambiguous word, so if 3 senses appear, there is less training data per ambiguous word sense.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 13: Macro average results for ambiguous words grouped by number of senses for the NLM WSD set.\nQuestion: Which method has the highest accuracy for disambiguating words with 2 senses?\nSolve the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the accuracy percentages for words with 2 senses across all methods, the highest accuracy is 93.40 achieved by LSTM WE S150 W50. So, the answer is {\"answer\": \"LSTM WE S150 W50\"}" } ], "images": [ "scigen/train/3731.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 13: Macro average results for ambiguous words grouped by number of senses for the NLM WSD set." ], "reasoning": "By comparing the accuracy percentages for words with 2 senses across all methods, the highest accuracy is 93.40 achieved by LSTM WE S150 W50.", "question": "Which method has the highest accuracy for disambiguating words with 2 senses?", "answer": "So, the answer is {\"answer\": \"LSTM WE S150 W50\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe SVM WE S150 W50 method has the lowest accuracy for disambiguating words with 3 senses.\n" }, { "role": "assistant", "content": "By comparing the accuracy percentages for words with 3 senses across all methods, the lowest accuracy is 73.68 achieved by SVM WE S150 W50. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3731.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 13: Macro average results for ambiguous words grouped by number of senses for the NLM WSD set." ], "reasoning": "By comparing the accuracy percentages for words with 3 senses across all methods, the lowest accuracy is 73.68 achieved by SVM WE S150 W50.", "statement": "The SVM WE S150 W50 method has the lowest accuracy for disambiguating words with 3 senses.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Experimental results on the test set of Quasar-T. R3 is a RL-based method. Results of GA, BiDAF and R3 are copied from (Lin et al., 2018). DSQA+STM outperforms the best baseline (DSQA+DS) significantly (t-test, p-value <0.05, DS=distant supervision).'.\n" }, { "role": "assistant", "content": "DSQA+STM outperforms DSQA and DSQA+DS in both EM and F1 metrics, demonstrating the benefits of Self-Training.Compared with the base models DSQA and DSQA+DS, DSQA+STM achieved better performance in both metrics, which verifies that DSQA can also benefit from Self-Training. Our method is general and can improve both lightweight and heavyweight models, like LSTM-based and BERT-based models, in different tasks." } ], "images": [ "scigen/train/3732.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Experimental results on the test set of Quasar-T. R3 is a RL-based method. Results of GA, BiDAF and R3 are copied from (Lin et al., 2018). DSQA+STM outperforms the best baseline (DSQA+DS) significantly (t-test, p-value <0.05, DS=distant supervision)." ], "reasoning": "DSQA+STM outperforms DSQA and DSQA+DS in both EM and F1 metrics, demonstrating the benefits of Self-Training.", "description": "Compared with the base models DSQA and DSQA+DS, DSQA+STM achieved better performance in both metrics, which verifies that DSQA can also benefit from Self-Training. Our method is general and can improve both lightweight and heavyweight models, like LSTM-based and BERT-based models, in different tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 4: Experimental results on the test set of Quasar-T. R3 is a RL-based method. Results of GA, BiDAF and R3 are copied from (Lin et al., 2018). DSQA+STM outperforms the best baseline (DSQA+DS) significantly (t-test, p-value <0.05, DS=distant supervision).' to sequentially solve the question presented below. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhich model achieved the highest F1 score and what does this indicate about the effectiveness of Self-Training?\n" }, { "role": "assistant", "content": "The table shows that the model DSQA+STM achieved the highest F1 score of 49.2, indicating that Self-Training improves performance. So, the answer is {\"answer\": \"DSQA+STM\"}." } ], "images": [ "scigen/train/3732.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Experimental results on the test set of Quasar-T. R3 is a RL-based method. Results of GA, BiDAF and R3 are copied from (Lin et al., 2018). DSQA+STM outperforms the best baseline (DSQA+DS) significantly (t-test, p-value <0.05, DS=distant supervision)." ], "reasoning": "The table shows that the model DSQA+STM achieved the highest F1 score of 49.2, indicating that Self-Training improves performance.", "question": "Which model achieved the highest F1 score and what does this indicate about the effectiveness of Self-Training?", "answer": "So, the answer is {\"answer\": \"DSQA+STM\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nDSQA+STM achieved the highest EM score among all models.\n" }, { "role": "assistant", "content": "The table shows that DSQA+STM has the highest EM score of 41.8, which is higher than all other models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3732.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Experimental results on the test set of Quasar-T. R3 is a RL-based method. Results of GA, BiDAF and R3 are copied from (Lin et al., 2018). DSQA+STM outperforms the best baseline (DSQA+DS) significantly (t-test, p-value <0.05, DS=distant supervision)." ], "reasoning": "The table shows that DSQA+STM has the highest EM score of 41.8, which is higher than all other models listed.", "statement": "DSQA+STM achieved the highest EM score among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Results on three multiple-choice reading comprehension datasets. (F1a: F1 score on all answer-options; F1m: macro-average F1 score of all questions; EM0: exact match.) Note that there is no golden evidence label on RACE and DREAM. The results for DPL (deep programming logic) are copied from (Wang et al., 2019). Significance tests were conducted between BERT-HA+STM and the best baseline of each column (t-test). ‡ means p-value <0.01, and † means p-value <0.05.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "STM consistently improved BERT-HA on RACE-H, MultiRC, and DREAM across all metrics, but the improvement on RACE-M was limited due to its simplicity.We adopt the metrics from the referred papers. STM improved BERT-HA consistently on RACE-H, MultiRC and DREAM in terms of all the metrics. However, the improvement on RACE-M is limited (1.0 gain on the test sets). The reason may be that RACE-M is much simpler than RACE-H, and thus, it is not challenging for the evidence extractor of BERT-HA to find the correct evidence on RACE-M." } ], "images": [ "scigen/train/3733.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on three multiple-choice reading comprehension datasets. (F1a: F1 score on all answer-options; F1m: macro-average F1 score of all questions; EM0: exact match.) Note that there is no golden evidence label on RACE and DREAM. The results for DPL (deep programming logic) are copied from (Wang et al., 2019). Significance tests were conducted between BERT-HA+STM and the best baseline of each column (t-test). ‡ means p-value <0.01, and † means p-value <0.05." ], "reasoning": "STM consistently improved BERT-HA on RACE-H, MultiRC, and DREAM across all metrics, but the improvement on RACE-M was limited due to its simplicity.", "description": "We adopt the metrics from the referred papers. STM improved BERT-HA consistently on RACE-H, MultiRC and DREAM in terms of all the metrics. However, the improvement on RACE-M is limited (1.0 gain on the test sets). The reason may be that RACE-M is much simpler than RACE-H, and thus, it is not challenging for the evidence extractor of BERT-HA to find the correct evidence on RACE-M.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nTable title: Table 3: Results on three multiple-choice reading comprehension datasets. (F1a: F1 score on all answer-options; F1m: macro-average F1 score of all questions; EM0: exact match.) Note that there is no golden evidence label on RACE and DREAM. The results for DPL (deep programming logic) are copied from (Wang et al., 2019). Significance tests were conducted between BERT-HA+STM and the best baseline of each column (t-test). ‡ means p-value <0.01, and † means p-value <0.05.\n\nQuestion: What is the accuracy improvement of BERT-HA+STM over BERT-HA on the RACE-M Test set?\n" }, { "role": "assistant", "content": "BERT-HA has an accuracy of 68.2 on the RACE-M Test set, while BERT-HA+STM has an accuracy of 69.2. The improvement is 1.0. So, the answer is {\"answer\": \"1.0\"}" } ], "images": [ "scigen/train/3733.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on three multiple-choice reading comprehension datasets. (F1a: F1 score on all answer-options; F1m: macro-average F1 score of all questions; EM0: exact match.) Note that there is no golden evidence label on RACE and DREAM. The results for DPL (deep programming logic) are copied from (Wang et al., 2019). Significance tests were conducted between BERT-HA+STM and the best baseline of each column (t-test). ‡ means p-value <0.01, and † means p-value <0.05." ], "reasoning": "BERT-HA has an accuracy of 68.2 on the RACE-M Test set, while BERT-HA+STM has an accuracy of 69.2. The improvement is 1.0.", "question": "What is the accuracy improvement of BERT-HA+STM over BERT-HA on the RACE-M Test set?", "answer": "So, the answer is {\"answer\": \"1.0\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nBERT-HA+STM achieved the highest accuracy on the DREAM Test set.\n" }, { "role": "assistant", "content": "The table shows that BERT-HA+STM has an accuracy of 65.8 on the DREAM Test set, which is higher than any other model listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3733.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on three multiple-choice reading comprehension datasets. (F1a: F1 score on all answer-options; F1m: macro-average F1 score of all questions; EM0: exact match.) Note that there is no golden evidence label on RACE and DREAM. The results for DPL (deep programming logic) are copied from (Wang et al., 2019). Significance tests were conducted between BERT-HA+STM and the best baseline of each column (t-test). ‡ means p-value <0.01, and † means p-value <0.05." ], "reasoning": "The table shows that BERT-HA+STM has an accuracy of 65.8 on the DREAM Test set, which is higher than any other model listed.", "statement": "BERT-HA+STM achieved the highest accuracy on the DREAM Test set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: Evidence extraction evaluation on the development sets of CoQA and MultiRC. P@k / R@k represent precision / recall of the generated evidence labels, respectively for top k predicted evidence sentences.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "BERT-HA+STM significantly outperformed BERT-HA across all metrics, indicating that the STM augmentation provided more evidential information for the answer predictor.It can be seen that BERT-HA+STM outperformed the base model BERT-HA by a large margin in terms of all the metrics. As a result, the evidence extractor augmented with STM provided more evidential information for the answer predictor, which may explain the improvements of BERT-HA+STM on the two datasets." } ], "images": [ "scigen/train/3734.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Evidence extraction evaluation on the development sets of CoQA and MultiRC. P@k / R@k represent precision / recall of the generated evidence labels, respectively for top k predicted evidence sentences." ], "reasoning": "BERT-HA+STM significantly outperformed BERT-HA across all metrics, indicating that the STM augmentation provided more evidential information for the answer predictor.", "description": "It can be seen that BERT-HA+STM outperformed the base model BERT-HA by a large margin in terms of all the metrics. As a result, the evidence extractor augmented with STM provided more evidential information for the answer predictor, which may explain the improvements of BERT-HA+STM on the two datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich model achieved the highest CoQA P@1 score?\nSolve the above question based on the table titled 'Table 5: Evidence extraction evaluation on the development sets of CoQA and MultiRC. P@k / R@k represent precision / recall of the generated evidence labels, respectively for top k predicted evidence sentences.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the CoQA P@1 column, the highest score is 53.6, achieved by the BERT-HA+Gold model. So, the answer is {\"answer\": \"BERT-HA+Gold\"}" } ], "images": [ "scigen/train/3734.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Evidence extraction evaluation on the development sets of CoQA and MultiRC. P@k / R@k represent precision / recall of the generated evidence labels, respectively for top k predicted evidence sentences." ], "reasoning": "By examining the CoQA P@1 column, the highest score is 53.6, achieved by the BERT-HA+Gold model.", "question": "Which model achieved the highest CoQA P@1 score?", "answer": "So, the answer is {\"answer\": \"BERT-HA+Gold\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nBERT-HA+STM (iter 3) outperformed BERT-HA in MultiRC R@2.\n" }, { "role": "assistant", "content": "The MultiRC R@2 score for BERT-HA is 49.8, while for BERT-HA+STM (iter 3) it is 55.3, indicating that BERT-HA+STM (iter 3) did outperform BERT-HA in this metric. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3734.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Evidence extraction evaluation on the development sets of CoQA and MultiRC. P@k / R@k represent precision / recall of the generated evidence labels, respectively for top k predicted evidence sentences." ], "reasoning": "The MultiRC R@2 score for BERT-HA is 49.8, while for BERT-HA+STM (iter 3) it is 55.3, indicating that BERT-HA+STM (iter 3) did outperform BERT-HA in this metric.", "statement": "BERT-HA+STM (iter 3) outperformed BERT-HA in MultiRC R@2.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 6: Answer prediction accuracy (Ans. Acc) and evidence extraction accuracy (Evi. Acc) on the development set of CoQA.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "RoBERTa-HA+STM significantly improved evidence extraction accuracy compared to RoBERTa-HA, but the improvement in answer prediction accuracy was marginal.To evaluate the improvement of STM over stronger pretrained models, we employed RoBERTa-large STM significantly improved the evidence extraction (Evi. Acc) of the base model. However, the improvement on answer prediction (Ans. Acc) is marginal. One reason is that RoBERTa-HA achieved so high performance that there was limited room to improve. Another possible explanation is that evidence information is not important for such stronger models to generate answers. In other words, they may be more adept at exploiting data bias to make answer prediction. In comparison, weaker pretrained models, such as BERT-base, can benefit from evidence information due to their weaker ability to exploit data bias." } ], "images": [ "scigen/train/3735.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Answer prediction accuracy (Ans. Acc) and evidence extraction accuracy (Evi. Acc) on the development set of CoQA." ], "reasoning": "RoBERTa-HA+STM significantly improved evidence extraction accuracy compared to RoBERTa-HA, but the improvement in answer prediction accuracy was marginal.", "description": "To evaluate the improvement of STM over stronger pretrained models, we employed RoBERTa-large STM significantly improved the evidence extraction (Evi. Acc) of the base model. However, the improvement on answer prediction (Ans. Acc) is marginal. One reason is that RoBERTa-HA achieved so high performance that there was limited room to improve. Another possible explanation is that evidence information is not important for such stronger models to generate answers. In other words, they may be more adept at exploiting data bias to make answer prediction. In comparison, weaker pretrained models, such as BERT-base, can benefit from evidence information due to their weaker ability to exploit data bias.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 6: Answer prediction accuracy (Ans. Acc) and evidence extraction accuracy (Evi. Acc) on the development set of CoQA.'. Solve the following question based on the table.\n\nQuestion: What is the percentage improvement in evidence extraction accuracy when using RoBERTa-HA+STM compared to RoBERTa-HA?\nLet's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that RoBERTa-HA has an evidence extraction accuracy of 13.8, while RoBERTa-HA+STM has an evidence extraction accuracy of 19.3, which is a 40% improvement. So, the answer is {\"answer\": \"40%\"}" } ], "images": [ "scigen/train/3735.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Answer prediction accuracy (Ans. Acc) and evidence extraction accuracy (Evi. Acc) on the development set of CoQA." ], "reasoning": "The table shows that RoBERTa-HA has an evidence extraction accuracy of 13.8, while RoBERTa-HA+STM has an evidence extraction accuracy of 19.3, which is a 40% improvement.", "question": "What is the percentage improvement in evidence extraction accuracy when using RoBERTa-HA+STM compared to RoBERTa-HA?", "answer": "So, the answer is {\"answer\": \"40%\"}" }, { "messages": [ { "role": "user", "content": "RoBERTa-HA+STM has a higher answer prediction accuracy than RoBERTa-HA. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nRoBERTa-HA+STM has a higher answer prediction accuracy than RoBERTa-HA.\n" }, { "role": "assistant", "content": "The table shows that RoBERTa-HA has an answer prediction accuracy of 92.6, while RoBERTa-HA+STM has an answer prediction accuracy of 92.7, indicating a higher accuracy. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3735.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Answer prediction accuracy (Ans. Acc) and evidence extraction accuracy (Evi. Acc) on the development set of CoQA." ], "reasoning": "The table shows that RoBERTa-HA has an answer prediction accuracy of 92.6, while RoBERTa-HA+STM has an answer prediction accuracy of 92.7, indicating a higher accuracy.", "statement": "RoBERTa-HA+STM has a higher answer prediction accuracy than RoBERTa-HA.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: Quantitative Performance Evaluation on three datasets.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "SelfORE outperforms all baseline models in B3 F1, V-measure F1, and ARI, achieving a balanced performance with the highest Precision and Homogeneity.UIE-PCNN is considered as the previous state-of-the-art result. We enhance this baseline by replacing PCNN and GolVe embedding with the proposed BERT-based encoder and classifier. The enhanced state-of-the-art model, namely UIE-BERT, achieves the best performance among baselines. The proposed SelfORE model outperforms all baseline models consistently on B3 F1/Precision, V-measure F1/Homogeneity and ARI. SelfORE on average achieves 7.0% higher in B3 F1, 3.4% higher in V-measure F1 and 7.7% higher in ARI across three dataset when comparing with previous state-of-the-art. Unlike baseline methods which achieve high B3 Recall but low Precision, or high V-measure Completeness but low Homogeneity, our model obtains a more balanced performance while achieving the highest Precision and Homogeneity, although the B3 Recall and V-measure Completeness are less satisfactory. Having high precision and homogeneity scores can be a quite appealing property for precision-oriented applications in the real-world. More specifically, without self-supervised signals for relational feature learning, SelfORE w/o Classification gives us 14.4% less performance averaged over all metrics on all datasets. Similarly, Adaptive Clustering gives 6.2% performance boost in average over all metrics when comparing with the hard-assignment alternative (SelfORE w/o Adaptive Clustering)." } ], "images": [ "scigen/train/3736.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Quantitative Performance Evaluation on three datasets." ], "reasoning": "SelfORE outperforms all baseline models in B3 F1, V-measure F1, and ARI, achieving a balanced performance with the highest Precision and Homogeneity.", "description": "UIE-PCNN is considered as the previous state-of-the-art result. We enhance this baseline by replacing PCNN and GolVe embedding with the proposed BERT-based encoder and classifier. The enhanced state-of-the-art model, namely UIE-BERT, achieves the best performance among baselines. The proposed SelfORE model outperforms all baseline models consistently on B3 F1/Precision, V-measure F1/Homogeneity and ARI. SelfORE on average achieves 7.0% higher in B3 F1, 3.4% higher in V-measure F1 and 7.7% higher in ARI across three dataset when comparing with previous state-of-the-art. Unlike baseline methods which achieve high B3 Recall but low Precision, or high V-measure Completeness but low Homogeneity, our model obtains a more balanced performance while achieving the highest Precision and Homogeneity, although the B3 Recall and V-measure Completeness are less satisfactory. Having high precision and homogeneity scores can be a quite appealing property for precision-oriented applications in the real-world. More specifically, without self-supervised signals for relational feature learning, SelfORE w/o Classification gives us 14.4% less performance averaged over all metrics on all datasets. Similarly, Adaptive Clustering gives 6.2% performance boost in average over all metrics when comparing with the hard-assignment alternative (SelfORE w/o Adaptive Clustering).", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 1: Quantitative Performance Evaluation on three datasets.'. Solve the following question based on the table.\n\nQuestion: What is the B3 F1 score for the SelfORE model on the NYT+FB dataset?\nLet's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the B3 F1 score for the SelfORE model on the NYT+FB dataset, locate the row for SelfORE under the NYT+FB dataset and check the B3 F1 column. So, the answer is {\"answer\": \"49.1\"}." } ], "images": [ "scigen/train/3736.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Quantitative Performance Evaluation on three datasets." ], "reasoning": "To find the B3 F1 score for the SelfORE model on the NYT+FB dataset, locate the row for SelfORE under the NYT+FB dataset and check the B3 F1 column.", "question": "What is the B3 F1 score for the SelfORE model on the NYT+FB dataset?", "answer": "So, the answer is {\"answer\": \"49.1\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nSelfORE achieves the highest V-measure Homogeneity score on the T-REX SPO dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the V-measure Homogeneity scores for all models on the T-REX SPO dataset. SelfORE has a V-measure Homogeneity score of 42.5, which is the highest among all models listed for this dataset. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3736.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Quantitative Performance Evaluation on three datasets." ], "reasoning": "To verify this statement, compare the V-measure Homogeneity scores for all models on the T-REX SPO dataset. SelfORE has a V-measure Homogeneity score of 42.5, which is the highest among all models listed for this dataset.", "statement": "SelfORE achieves the highest V-measure Homogeneity score on the T-REX SPO dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Inter-Annotator Kappa-agreement for the relation validation task on TACRED Dev and Test splits (H1,H2 = human re-annotators, H = revised labels, C = original TACRED crowd-generated labels).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Annotation quality is high with κTest=0.87 and κDev=0.80, but agreement with crowdsourced labels is lower, especially for Challenging examples.It is very high at κTest=0.87 and κDev=0.80, indicating a high annotation quality. For both Test and Dev, it is higher for the easier Control groups than for the Challenging groups. In contrast, the average agreement between our annotators and the crowdsourced labels is much lower at κTest=0.55,κDev=0.53, and lowest for Challenging examples (e.g., κTest=0.44)." } ], "images": [ "scigen/train/3737.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Inter-Annotator Kappa-agreement for the relation validation task on TACRED Dev and Test splits (H1,H2 = human re-annotators, H = revised labels, C = original TACRED crowd-generated labels)." ], "reasoning": "Annotation quality is high with κTest=0.87 and κDev=0.80, but agreement with crowdsourced labels is lower, especially for Challenging examples.", "description": "It is very high at κTest=0.87 and κDev=0.80, indicating a high annotation quality. For both Test and Dev, it is higher for the easier Control groups than for the Challenging groups. In contrast, the average agreement between our annotators and the crowdsourced labels is much lower at κTest=0.55,κDev=0.53, and lowest for Challenging examples (e.g., κTest=0.44).", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: Inter-Annotator Kappa-agreement for the relation validation task on TACRED Dev and Test splits (H1,H2 = human re-annotators, H = revised labels, C = original TACRED crowd-generated labels).' to sequentially solve the question presented below. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhat is the κ value for the Control group in the Test set for H1,H2?\n" }, { "role": "assistant", "content": "The table shows the κ values for different groups and sets. For the Control group in the Test set for H1,H2, the value is 0.94. So, the answer is {\"answer\": \"0.94\"}" } ], "images": [ "scigen/train/3737.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Inter-Annotator Kappa-agreement for the relation validation task on TACRED Dev and Test splits (H1,H2 = human re-annotators, H = revised labels, C = original TACRED crowd-generated labels)." ], "reasoning": "The table shows the κ values for different groups and sets. For the Control group in the Test set for H1,H2, the value is 0.94.", "question": "What is the κ value for the Control group in the Test set for H1,H2?", "answer": "So, the answer is {\"answer\": \"0.94\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe κ value for the Challenging group in the Dev set for H,C is higher than for the Control group in the same set.\n" }, { "role": "assistant", "content": "The table shows that the κ value for the Challenging group in the Dev set for H,C is 0.43, while for the Control group it is 0.95. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3737.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Inter-Annotator Kappa-agreement for the relation validation task on TACRED Dev and Test splits (H1,H2 = human re-annotators, H = revised labels, C = original TACRED crowd-generated labels)." ], "reasoning": "The table shows that the κ value for the Challenging group in the Dev set for H,C is 0.43, while for the Control group it is 0.95. Therefore, the statement is refuted.", "statement": "The κ value for the Challenging group in the Dev set for H,C is higher than for the Control group in the same set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 5: Test set F1 score on TACRED, our revised version, and weighted by difficulty (on revised). The weight per instance is determined by the number of incorrect predictions in our set of 49 RE models. The result suggests that SpanBERT better generalizes to more challenging examples, e.g. complex sentential context.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "SpanBERT and KnowBERT show similar performance on the full test set, but SpanBERT's span-level pre-training and entity masking allow it to generalize better to challenging examples.To test how much the two models balance context and argument information, we apply rewriting to alter the instances belonging to a group and observe the impact on performance. We use two strategies: (1) we remove all tokens outside the span between head and tail argument (outside), and (2) we remove all tokens between the two arguments (between). We find that SpanBERT’s performance on per:loc drops from 62.1 F1 to 57.7 (outside) and 43.3 (between), whereas KnowBERT ’s score decreases from 63.7 F1 to 60.9 and 50.1, respectively. On same_nertag&positive, we observe a drop from 89.2 F1 to 58.2 (outside) and 47.7 (between) for SpanBERT. KnowBERT achieves a score of 89.4, which drops to 83.8 and 49.0. The larger drop in performance on same_nertag&positive suggests that SpanBERT, which uses entity masking, focuses more on the context, whereas KnowBERT focuses on the entity content because the model has access to the arguments. Surprisingly, both models show similar performance on the full test set This suggests that combining both approaches may further improve RE performance. To test this hypothesis, we change the evaluation setup and assign a weight to each instance based on the number of correct predictions. An example that is correctly classified by all 49 baseline models would receive a weight of zero – and thus effectively be ignored – whereas an instance misclassified by all models receives a weight of one. KnowBERT has the second highest score of 58.7, 3% less than SpanBERT. The performance of TRE and CNN is much worse at 48.8 and 34.8 F1, respectively. The result suggests that SpanBERT’s span-level pre-training and entity masking are beneficial for RE and allow the model to generalize better to challenging examples. Given this observation, we propose to consider an instance’s difficulty during evaluation." } ], "images": [ "scigen/train/3738.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Test set F1 score on TACRED, our revised version, and weighted by difficulty (on revised). The weight per instance is determined by the number of incorrect predictions in our set of 49 RE models. The result suggests that SpanBERT better generalizes to more challenging examples, e.g. complex sentential context." ], "reasoning": "SpanBERT and KnowBERT show similar performance on the full test set, but SpanBERT's span-level pre-training and entity masking allow it to generalize better to challenging examples.", "description": "To test how much the two models balance context and argument information, we apply rewriting to alter the instances belonging to a group and observe the impact on performance. We use two strategies: (1) we remove all tokens outside the span between head and tail argument (outside), and (2) we remove all tokens between the two arguments (between). We find that SpanBERT’s performance on per:loc drops from 62.1 F1 to 57.7 (outside) and 43.3 (between), whereas KnowBERT ’s score decreases from 63.7 F1 to 60.9 and 50.1, respectively. On same_nertag&positive, we observe a drop from 89.2 F1 to 58.2 (outside) and 47.7 (between) for SpanBERT. KnowBERT achieves a score of 89.4, which drops to 83.8 and 49.0. The larger drop in performance on same_nertag&positive suggests that SpanBERT, which uses entity masking, focuses more on the context, whereas KnowBERT focuses on the entity content because the model has access to the arguments. Surprisingly, both models show similar performance on the full test set This suggests that combining both approaches may further improve RE performance. To test this hypothesis, we change the evaluation setup and assign a weight to each instance based on the number of correct predictions. An example that is correctly classified by all 49 baseline models would receive a weight of zero – and thus effectively be ignored – whereas an instance misclassified by all models receives a weight of one. KnowBERT has the second highest score of 58.7, 3% less than SpanBERT. The performance of TRE and CNN is much worse at 48.8 and 34.8 F1, respectively. The result suggests that SpanBERT’s span-level pre-training and entity masking are beneficial for RE and allow the model to generalize better to challenging examples. Given this observation, we propose to consider an instance’s difficulty during evaluation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nTable title: Table 5: Test set F1 score on TACRED, our revised version, and weighted by difficulty (on revised). The weight per instance is determined by the number of incorrect predictions in our set of 49 RE models. The result suggests that SpanBERT better generalizes to more challenging examples, e.g. complex sentential context.\nQuestion: Which model has the highest score in the Revised evaluation setup?\n" }, { "role": "assistant", "content": "By comparing the scores in the Revised column, we see that KnowBERT has the highest score of 79.3. So, the answer is {\"answer\": \"KnowBERT\"}" } ], "images": [ "scigen/train/3738.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Test set F1 score on TACRED, our revised version, and weighted by difficulty (on revised). The weight per instance is determined by the number of incorrect predictions in our set of 49 RE models. The result suggests that SpanBERT better generalizes to more challenging examples, e.g. complex sentential context." ], "reasoning": "By comparing the scores in the Revised column, we see that KnowBERT has the highest score of 79.3.", "question": "Which model has the highest score in the Revised evaluation setup?", "answer": "So, the answer is {\"answer\": \"KnowBERT\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence SpanBERT has the highest score in the Weighted evaluation setup. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSpanBERT has the highest score in the Weighted evaluation setup.\n" }, { "role": "assistant", "content": "By comparing the scores in the Weighted column, we see that SpanBERT has the highest score of 61.9. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3738.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Test set F1 score on TACRED, our revised version, and weighted by difficulty (on revised). The weight per instance is determined by the number of incorrect predictions in our set of 49 RE models. The result suggests that SpanBERT better generalizes to more challenging examples, e.g. complex sentential context." ], "reasoning": "By comparing the scores in the Weighted column, we see that SpanBERT has the highest score of 61.9.", "statement": "SpanBERT has the highest score in the Weighted evaluation setup.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: The language pairs improved in the first two iterations of our method. The results demonstrate that our method achieves better accuracy than the +BT and +Forward baseline on the low-resource translation edges in the graph. T indicates iteration step. Av. indicates averaged BLEU scores. Note that, we just demonstrate the preliminary results to verify the effectiveness of our language graph distillation method.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The table shows BLEU scores for different language pairs using various training methods, with the highest scores achieved using language graph distillation.In this section, we show the preliminary experimental results of the proposed language graph distillation. The third column is the BLEU score obtained by the basic multilingual model (Initial). The forth, five and sixth columns are the results trained with only one-hop back-translation (+BT), only forward distillation (+Forward) and our language graph distillation (+Graph: both forward and backward distillation) respectively. The +BT baseline is obtained by training the selected language pairs with one-hop back-translation data, while the +Forward baseline is obtained by training the selected language pairs with all the forward distillation data." } ], "images": [ "scigen/train/3739.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The language pairs improved in the first two iterations of our method. The results demonstrate that our method achieves better accuracy than the +BT and +Forward baseline on the low-resource translation edges in the graph. T indicates iteration step. Av. indicates averaged BLEU scores. Note that, we just demonstrate the preliminary results to verify the effectiveness of our language graph distillation method." ], "reasoning": "The table shows BLEU scores for different language pairs using various training methods, with the highest scores achieved using language graph distillation.", "description": "In this section, we show the preliminary experimental results of the proposed language graph distillation. The third column is the BLEU score obtained by the basic multilingual model (Initial). The forth, five and sixth columns are the results trained with only one-hop back-translation (+BT), only forward distillation (+Forward) and our language graph distillation (+Graph: both forward and backward distillation) respectively. The +BT baseline is obtained by training the selected language pairs with one-hop back-translation data, while the +Forward baseline is obtained by training the selected language pairs with all the forward distillation data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 2: The language pairs improved in the first two iterations of our method. The results demonstrate that our method achieves better accuracy than the +BT and +Forward baseline on the low-resource translation edges in the graph. T indicates iteration step. Av. indicates averaged BLEU scores. Note that, we just demonstrate the preliminary results to verify the effectiveness of our language graph distillation method.', solve the following question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the average BLEU score improvement for the +Graph method compared to the Initial method?\n" }, { "role": "assistant", "content": "To find the average BLEU score improvement for the +Graph method compared to the Initial method, subtract the Initial average score from the +Graph average score for both T=0 and T=1. For T=0, the improvement is 1.57, and for T=1, the improvement is 3.13. So, the answer is {\"answer\": \"3.13\"}" } ], "images": [ "scigen/train/3739.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The language pairs improved in the first two iterations of our method. The results demonstrate that our method achieves better accuracy than the +BT and +Forward baseline on the low-resource translation edges in the graph. T indicates iteration step. Av. indicates averaged BLEU scores. Note that, we just demonstrate the preliminary results to verify the effectiveness of our language graph distillation method." ], "reasoning": "To find the average BLEU score improvement for the +Graph method compared to the Initial method, subtract the Initial average score from the +Graph average score for both T=0 and T=1. For T=0, the improvement is 1.57, and for T=1, the improvement is 3.13.", "question": "What is the average BLEU score improvement for the +Graph method compared to the Initial method?", "answer": "So, the answer is {\"answer\": \"3.13\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The +BT method achieved the highest BLEU score for the He→Nb language pair. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe +BT method achieved the highest BLEU score for the He→Nb language pair.\n" }, { "role": "assistant", "content": "The table shows that for the He→Nb language pair, the +BT method achieved a BLEU score of 18.78, which is higher than the scores for the Initial, +Forward, and +Graph methods. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3739.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The language pairs improved in the first two iterations of our method. The results demonstrate that our method achieves better accuracy than the +BT and +Forward baseline on the low-resource translation edges in the graph. T indicates iteration step. Av. indicates averaged BLEU scores. Note that, we just demonstrate the preliminary results to verify the effectiveness of our language graph distillation method." ], "reasoning": "The table shows that for the He→Nb language pair, the +BT method achieved a BLEU score of 18.78, which is higher than the scores for the Initial, +Forward, and +Graph methods.", "statement": "The +BT method achieved the highest BLEU score for the He→Nb language pair.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Distribution of IEST data.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The dataset was preprocessed to remove tweets with multiple emotion words and duplicates, then stratified sampling was used to create a balanced training set of 153,600 instances, a trial set of 9,600 instances, and a test set of 28,800 instances.The initially retrieved large dataset has a distribution of 25 % surprise, 23 % sadness, 18 % joy, 16 % fear, 10 % anger, 8 % disgust. We discard tweets with more than one emotion word, as well as exact duplicates, and mask usernames and URLs. From this set, we randomly sample 80 % of the tweets to form the training set (153,600 instances), 5 % as trial set (9,600 instances), and 15 % as test set (28,800 instances). We perform stratified sampling to obtain a balanced dataset. While the shared task took place, two errors in the data preprocessing were discovered by participants (the use of the word unhappy as synonym for sadness, which lead to inconsistent preprocessing in the context of negated expressions, and the occurrence of instances without emotion words)." } ], "images": [ "scigen/train/3740.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Distribution of IEST data." ], "reasoning": "The dataset was preprocessed to remove tweets with multiple emotion words and duplicates, then stratified sampling was used to create a balanced training set of 153,600 instances, a trial set of 9,600 instances, and a test set of 28,800 instances.", "description": "The initially retrieved large dataset has a distribution of 25 % surprise, 23 % sadness, 18 % joy, 16 % fear, 10 % anger, 8 % disgust. We discard tweets with more than one emotion word, as well as exact duplicates, and mask usernames and URLs. From this set, we randomly sample 80 % of the tweets to form the training set (153,600 instances), 5 % as trial set (9,600 instances), and 15 % as test set (28,800 instances). We perform stratified sampling to obtain a balanced dataset. While the shared task took place, two errors in the data preprocessing were discovered by participants (the use of the word unhappy as synonym for sadness, which lead to inconsistent preprocessing in the context of negated expressions, and the occurrence of instances without emotion words).", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Distribution of IEST data.\nQuestion: What is the total number of instances in the training set for the emotion 'Joy'?\nGive a detailed response to the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "From the table, the number of training instances for 'Joy' is listed as 27,958. So, the answer is {\"answer\": \"27958\"}" } ], "images": [ "scigen/train/3740.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Distribution of IEST data." ], "reasoning": "From the table, the number of training instances for 'Joy' is listed as 27,958.", "question": "What is the total number of instances in the training set for the emotion 'Joy'?", "answer": "So, the answer is {\"answer\": \"27958\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe training set for 'Sadness' contains more instances than the training set for 'Disgust'.\n" }, { "role": "assistant", "content": "The table shows that the training set for 'Sadness' has 23,165 instances, while the training set for 'Disgust' has 25,558 instances, which means 'Disgust' has more instances. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3740.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Distribution of IEST data." ], "reasoning": "The table shows that the training set for 'Sadness' has 23,165 instances, while the training set for 'Disgust' has 25,558 instances, which means 'Disgust' has more instances.", "statement": "The training set for 'Sadness' contains more instances than the training set for 'Disgust'.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 5: Official results of IEST 2018. Participants who did not report on the system details did not get assigned a rank and are reported in gray. Column B provides the first row in the results table to which the respective row is significantly different (confidence level 0.99), tested with bootstrap resampling.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Twenty-six teams, consisting of 56 people, responded to a post-competition survey, and their results are reported in the table.We received submissions through CodaLab from thirty participants. Twenty-six teams responded to a post-competition survey providing additional information regarding team members (56 people in total) and the systems that were developed. For the remaining analyses and the ranking, we only report on these twenty-six teams. \\captionof table Results by emotion class. Note that this table is limited to the six emotion labels of interest in the data set. However, other labels predicted than these six were taken into account for calculation of the final macro F1 score." } ], "images": [ "scigen/train/3741.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Official results of IEST 2018. Participants who did not report on the system details did not get assigned a rank and are reported in gray. Column B provides the first row in the results table to which the respective row is significantly different (confidence level 0.99), tested with bootstrap resampling." ], "reasoning": "Twenty-six teams, consisting of 56 people, responded to a post-competition survey, and their results are reported in the table.", "description": "We received submissions through CodaLab from thirty participants. Twenty-six teams responded to a post-competition survey providing additional information regarding team members (56 people in total) and the systems that were developed. For the remaining analyses and the ranking, we only report on these twenty-six teams. \\captionof table Results by emotion class. Note that this table is limited to the six emotion labels of interest in the data set. However, other labels predicted than these six were taken into account for calculation of the final macro F1 score.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 5: Official results of IEST 2018. Participants who did not report on the system details did not get assigned a rank and are reported in gray. Column B provides the first row in the results table to which the respective row is significantly different (confidence level 0.99), tested with bootstrap resampling.' in mind, break down and resolve the question below step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich team had the highest F1 score and what was their rank?\n" }, { "role": "assistant", "content": "To find the team with the highest F1 score, look for the highest value in the F1 column. The team with the highest F1 score is Amobee with an F1 score of 71.45, and their rank is 1. So, the answer is {\"answer\": \"Amobee, Rank 1\"}." } ], "images": [ "scigen/train/3741.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Official results of IEST 2018. Participants who did not report on the system details did not get assigned a rank and are reported in gray. Column B provides the first row in the results table to which the respective row is significantly different (confidence level 0.99), tested with bootstrap resampling." ], "reasoning": "To find the team with the highest F1 score, look for the highest value in the F1 column. The team with the highest F1 score is Amobee with an F1 score of 71.45, and their rank is 1.", "question": "Which team had the highest F1 score and what was their rank?", "answer": "So, the answer is {\"answer\": \"Amobee, Rank 1\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The team with the lowest F1 score is NLP_LDW. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe team with the lowest F1 score is NLP_LDW.\n" }, { "role": "assistant", "content": "To verify this statement, look for the lowest value in the F1 column. The lowest F1 score is 21.03, which belongs to the team NLP_LDW. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3741.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Official results of IEST 2018. Participants who did not report on the system details did not get assigned a rank and are reported in gray. Column B provides the first row in the results table to which the respective row is significantly different (confidence level 0.99), tested with bootstrap resampling." ], "reasoning": "To verify this statement, look for the lowest value in the F1 column. The lowest F1 score is 21.03, which belongs to the team NLP_LDW.", "statement": "The team with the lowest F1 score is NLP_LDW.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 9: Confusion Matrix Sample Annotated by Humans in Crowdsourcing', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Disgust is often confused with anger, and fear is often confused with sadness. Surprise is often confused with anger and joy.There are differences in the difficulty of the task for different emotions: The accuracy (F1) by emotion is 57 % (46 %) for anger, 15 % (21 %) for disgust, 42 % (51 %) for fear, 77 % (58 %) for joy, 59 % (52 %) for sadness and 34 % (39 %) for surprise. Disgust is often confused with anger, followed by fear being confused with sadness. Surprise is often confused with anger and joy." } ], "images": [ "scigen/train/3742.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9: Confusion Matrix Sample Annotated by Humans in Crowdsourcing" ], "reasoning": "Disgust is often confused with anger, and fear is often confused with sadness. Surprise is often confused with anger and joy.", "description": "There are differences in the difficulty of the task for different emotions: The accuracy (F1) by emotion is 57 % (46 %) for anger, 15 % (21 %) for disgust, 42 % (51 %) for fear, 77 % (58 %) for joy, 59 % (52 %) for sadness and 34 % (39 %) for surprise. Disgust is often confused with anger, followed by fear being confused with sadness. Surprise is often confused with anger and joy.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 9: Confusion Matrix Sample Annotated by Humans in Crowdsourcing' in mind, break down and resolve the question below step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich emotion is most frequently confused with joy?\n" }, { "role": "assistant", "content": "From the table, the highest number of predicted labels for joy (J) is 398, which corresponds to gold label J (joy). The next highest number is 132, which corresponds to gold label Su (surprise). Therefore, surprise is most frequently confused with joy. So, the answer is {\"answer\": \"surprise\"}." } ], "images": [ "scigen/train/3742.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9: Confusion Matrix Sample Annotated by Humans in Crowdsourcing" ], "reasoning": "From the table, the highest number of predicted labels for joy (J) is 398, which corresponds to gold label J (joy). The next highest number is 132, which corresponds to gold label Su (surprise). Therefore, surprise is most frequently confused with joy.", "question": "Which emotion is most frequently confused with joy?", "answer": "So, the answer is {\"answer\": \"surprise\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Fear is most frequently confused with sadness., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nFear is most frequently confused with sadness.\n" }, { "role": "assistant", "content": "From the table, the highest number of predicted labels for sadness (Sa) is 401, which corresponds to gold label Sa (sadness). The next highest number is 120, which corresponds to gold label F (fear). Therefore, fear is most frequently confused with sadness. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3742.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9: Confusion Matrix Sample Annotated by Humans in Crowdsourcing" ], "reasoning": "From the table, the highest number of predicted labels for sadness (Sa) is 401, which corresponds to gold label Sa (sadness). The next highest number is 120, which corresponds to gold label F (fear). Therefore, fear is most frequently confused with sadness.", "statement": "Fear is most frequently confused with sadness.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Word (% WER) and phoneme error rate (% PER) for each of the test languages, on the Babel conversational development test sets.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Experiments were conducted on four languages closest to Kurmanji using a 6-layer bidirectional LSTM network, and performance improved by 6% when additional training data was used.To align with project goals, we chose to perform experiments on a set of four languages which are the closest/ have maximum phone overlap with Kurmanji – Kazakh, Turkish, Mongolian and Haitian. We used a 6-layer bidirectional LSTM network with 360 cells in each direction, which performed best on average across the majority of Babel languages in a systematic search experiment. For consistency, we used absolutely identical settings across all languages, and did not perform any language-specific tuning, other than choosing the lowest perplexity language model between 3-gram and 4-gram models for WFST-based decoding. Techniques such as blank scaling and applying a softmax temperature can often improve results significantly, but we did not apply any of them here for consistency. Again, this setup performed best on average on a larger set of languages. If we fine-tune the entire model towards each language specifically, performance improves further, by 4.4% on average over the baseline. If we roughly double the amount of training data by adding the Switchboard 300h training set to the “MLing” training data, performance improves yet again, for both the universal (MLing+SBWD) and language-specific (MLing+SWBD & FT) case. Overall, WER and PER improve by about 6% absolute (" } ], "images": [ "scigen/train/3743.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Word (% WER) and phoneme error rate (% PER) for each of the test languages, on the Babel conversational development test sets." ], "reasoning": "Experiments were conducted on four languages closest to Kurmanji using a 6-layer bidirectional LSTM network, and performance improved by 6% when additional training data was used.", "description": "To align with project goals, we chose to perform experiments on a set of four languages which are the closest/ have maximum phone overlap with Kurmanji – Kazakh, Turkish, Mongolian and Haitian. We used a 6-layer bidirectional LSTM network with 360 cells in each direction, which performed best on average across the majority of Babel languages in a systematic search experiment. For consistency, we used absolutely identical settings across all languages, and did not perform any language-specific tuning, other than choosing the lowest perplexity language model between 3-gram and 4-gram models for WFST-based decoding. Techniques such as blank scaling and applying a softmax temperature can often improve results significantly, but we did not apply any of them here for consistency. Again, this setup performed best on average on a larger set of languages. If we fine-tune the entire model towards each language specifically, performance improves further, by 4.4% on average over the baseline. If we roughly double the amount of training data by adding the Switchboard 300h training set to the “MLing” training data, performance improves yet again, for both the universal (MLing+SBWD) and language-specific (MLing+SWBD & FT) case. Overall, WER and PER improve by about 6% absolute (", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Word (% WER) and phoneme error rate (% PER) for each of the test languages, on the Babel conversational development test sets.', answer the question below by showing each progressive step toward the answer. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhat is the lowest WER achieved for Kazakh and under which model?\n" }, { "role": "assistant", "content": "To find the lowest WER for Kazakh, we look at the Kazakh WER column and identify the lowest value, which is 48.2, achieved under the 'MLing + SWBD & FT' model. So, the answer is {\"answer\": \"48.2\"}" } ], "images": [ "scigen/train/3743.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Word (% WER) and phoneme error rate (% PER) for each of the test languages, on the Babel conversational development test sets." ], "reasoning": "To find the lowest WER for Kazakh, we look at the Kazakh WER column and identify the lowest value, which is 48.2, achieved under the 'MLing + SWBD & FT' model.", "question": "What is the lowest WER achieved for Kazakh and under which model?", "answer": "So, the answer is {\"answer\": \"48.2\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe lowest PER for Turkish was achieved using the MLing & FineTuning (FT) model.\n" }, { "role": "assistant", "content": "To verify this, we look at the Turkish PER column and identify the lowest value, which is 31.9, achieved under the 'MLing + SWBD & FT' model, not the 'MLing & FineTuning (FT)' model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3743.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Word (% WER) and phoneme error rate (% PER) for each of the test languages, on the Babel conversational development test sets." ], "reasoning": "To verify this, we look at the Turkish PER column and identify the lowest value, which is 31.9, achieved under the 'MLing + SWBD & FT' model, not the 'MLing & FineTuning (FT)' model.", "statement": "The lowest PER for Turkish was achieved using the MLing & FineTuning (FT) model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: Word error rate (% WER) on the test languages when switching the SWBD data with 300 hrs equivalent of Babel.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Adding Switchboard data outperforms adding more unrelated Babel languages in improving the model's performance on low resource languages.Given that adding a seemingly unrelated, but high resource language improved the performance of the model on four low resource languages, we further studied the impact of varying the source(s) of the extra data. Specifically, we replaced the 300 h Switchboard corpus with four more unrelated Babel languages, “BAB300” composed of Tamil, Amharic, Pashto, and Tagalog. We can see that adding Switchboard data outperforms adding more unrelated Babel languages." } ], "images": [ "scigen/train/3744.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Word error rate (% WER) on the test languages when switching the SWBD data with 300 hrs equivalent of Babel." ], "reasoning": "Adding Switchboard data outperforms adding more unrelated Babel languages in improving the model's performance on low resource languages.", "description": "Given that adding a seemingly unrelated, but high resource language improved the performance of the model on four low resource languages, we further studied the impact of varying the source(s) of the extra data. Specifically, we replaced the 300 h Switchboard corpus with four more unrelated Babel languages, “BAB300” composed of Tamil, Amharic, Pashto, and Tagalog. We can see that adding Switchboard data outperforms adding more unrelated Babel languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 3: Word error rate (% WER) on the test languages when switching the SWBD data with 300 hrs equivalent of Babel.\nQuestion: Which model performed better on Kazakh?\n" }, { "role": "assistant", "content": "Compare the scores for Kazakh under both models. MLing + BAB300 scored 57.5, while MLing + SWBD scored 52.3. So, the answer is {\"answer\": \"MLing + BAB300\"}." } ], "images": [ "scigen/train/3744.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Word error rate (% WER) on the test languages when switching the SWBD data with 300 hrs equivalent of Babel." ], "reasoning": "Compare the scores for Kazakh under both models. MLing + BAB300 scored 57.5, while MLing + SWBD scored 52.3.", "question": "Which model performed better on Kazakh?", "answer": "So, the answer is {\"answer\": \"MLing + BAB300\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The model with Switchboard data performed better on Turkish than the model with Babel languages., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model with Switchboard data performed better on Turkish than the model with Babel languages.\n" }, { "role": "assistant", "content": "Compare the scores for Turkish under both models. MLing + SWBD scored 51.3, while MLing + BAB300 scored 52.0. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3744.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Word error rate (% WER) on the test languages when switching the SWBD data with 300 hrs equivalent of Babel." ], "reasoning": "Compare the scores for Turkish under both models. MLing + SWBD scored 51.3, while MLing + BAB300 scored 52.0.", "statement": "The model with Switchboard data performed better on Turkish than the model with Babel languages.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Comparison of frame vs phoneme input on Spanish-English SLT, with average BLEU improvement and average reduction in training time.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Using phoneme-like embeddings, we see a 13% relative performance improvement across all data sizes, with up to 5.2 BLEU on the full dataset, and an 80% reduction in source lengths improves training time.On our target task, we compare different subsets of the data to see how our method compares under different data conditions, using the full 160 hours as well as 40 and 20 hour subsets. When we use our phoneme-like embeddings, we see relative performance improvements of 13% on all data sizes, or up to 5.2 BLEU on the full dataset. Further, in reducing source lengths by ∼80%, training time is improved." } ], "images": [ "scigen/train/3745.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of frame vs phoneme input on Spanish-English SLT, with average BLEU improvement and average reduction in training time." ], "reasoning": "Using phoneme-like embeddings, we see a 13% relative performance improvement across all data sizes, with up to 5.2 BLEU on the full dataset, and an 80% reduction in source lengths improves training time.", "description": "On our target task, we compare different subsets of the data to see how our method compares under different data conditions, using the full 160 hours as well as 40 and 20 hour subsets. When we use our phoneme-like embeddings, we see relative performance improvements of 13% on all data sizes, or up to 5.2 BLEU on the full dataset. Further, in reducing source lengths by ∼80%, training time is improved.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 2: Comparison of frame vs phoneme input on Spanish-English SLT, with average BLEU improvement and average reduction in training time.'. Solve the following question based on the table.\n\nQuestion: What is the BLEU improvement when using the full dataset?\nLet's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the BLEU improvement (BLEU Δ) for the full dataset is +5.2. So, the answer is {\"answer\": \"+5.2\"}" } ], "images": [ "scigen/train/3745.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of frame vs phoneme input on Spanish-English SLT, with average BLEU improvement and average reduction in training time." ], "reasoning": "The table shows that the BLEU improvement (BLEU Δ) for the full dataset is +5.2.", "question": "What is the BLEU improvement when using the full dataset?", "answer": "So, the answer is {\"answer\": \"+5.2\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe training time improvement for the 40-hour subset is 52%.\n" }, { "role": "assistant", "content": "The table indicates that the training time improvement (Time Δ) for the 40-hour subset is indeed 52%. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3745.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of frame vs phoneme input on Spanish-English SLT, with average BLEU improvement and average reduction in training time." ], "reasoning": "The table indicates that the training time improvement (Time Δ) for the 40-hour subset is indeed 52%.", "statement": "The training time improvement for the 40-hour subset is 52%.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Comparing effects of target preprocessing with different sources on BLEU, Spanish-English 40hr'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The 1k BPE model, with a target length of 14, performs best due to its source sequence length being closest to the final encoder state length of 22.Previous work on sequence-to-sequence speech translation has used encoder downsampling of 4×, while 8× is more common among sequence-to-sequence ASR systems Zhang et al. We use encoder downsampling of 4×, concatenating adjacent states after each layer. By averaging frames per local phoneme label in addition to encoder downsampling, source sequence lengths are further reduced on average by 79%, yielding final encoder state lengths of 22, closest in length to 1k BPE targets (14) rather than characters (50). Given that the 1k BPE model performs best, it does appear that more similar source and target lengths boost performance." } ], "images": [ "scigen/train/3746.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparing effects of target preprocessing with different sources on BLEU, Spanish-English 40hr" ], "reasoning": "The 1k BPE model, with a target length of 14, performs best due to its source sequence length being closest to the final encoder state length of 22.", "description": "Previous work on sequence-to-sequence speech translation has used encoder downsampling of 4×, while 8× is more common among sequence-to-sequence ASR systems Zhang et al. We use encoder downsampling of 4×, concatenating adjacent states after each layer. By averaging frames per local phoneme label in addition to encoder downsampling, source sequence lengths are further reduced on average by 79%, yielding final encoder state lengths of 22, closest in length to 1k BPE targets (14) rather than characters (50). Given that the 1k BPE model performs best, it does appear that more similar source and target lengths boost performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 3: Comparing effects of target preprocessing with different sources on BLEU, Spanish-English 40hr\nQuestion: Which target preprocessing method has the highest Frames dev value?\n" }, { "role": "assistant", "content": "By examining the Frames dev column, the highest value is 19.5, which corresponds to the 1k BPE target preprocessing method. So, the answer is {\"answer\": \"1k bpe\"}." } ], "images": [ "scigen/train/3746.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparing effects of target preprocessing with different sources on BLEU, Spanish-English 40hr" ], "reasoning": "By examining the Frames dev column, the highest value is 19.5, which corresponds to the 1k BPE target preprocessing method.", "question": "Which target preprocessing method has the highest Frames dev value?", "answer": "So, the answer is {\"answer\": \"1k bpe\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe chars target preprocessing method has the longest target length.\n" }, { "role": "assistant", "content": "By looking at the Target Length column, the chars method has a length of 50.2, which is the longest among all methods listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3746.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparing effects of target preprocessing with different sources on BLEU, Spanish-English 40hr" ], "reasoning": "By looking at the Target Length column, the chars method has a length of 50.2, which is the longest among all methods listed.", "statement": "The chars target preprocessing method has the longest target length.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Comparison of frame vs phoneme input on Spanish ASR, with average reduction in WER and average reduction in training time.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The method was tested on ASR to determine its general effectiveness, showing monotonic alignments.To test whether the approach is a generally more effective input representation or only an aid in the particularly complex learning task of speech translation where it helps to reduce the distance between inputs and outputs, we apply our method to ASR, where are alignments are monotonic. We note that Weiss et al." } ], "images": [ "scigen/train/3747.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of frame vs phoneme input on Spanish ASR, with average reduction in WER and average reduction in training time." ], "reasoning": "The method was tested on ASR to determine its general effectiveness, showing monotonic alignments.", "description": "To test whether the approach is a generally more effective input representation or only an aid in the particularly complex learning task of speech translation where it helps to reduce the distance between inputs and outputs, we apply our method to ASR, where are alignments are monotonic. We note that Weiss et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 4: Comparison of frame vs phoneme input on Spanish ASR, with average reduction in WER and average reduction in training time.\nQuestion: What is the WER Δ for the 40hr data set?\n" }, { "role": "assistant", "content": "To find the WER Δ for the 40hr data set, locate the row labeled '40hr' and find the value under the 'WER Δ' column. So, the answer is {\"answer\": \"-9.2\"}." } ], "images": [ "scigen/train/3747.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of frame vs phoneme input on Spanish ASR, with average reduction in WER and average reduction in training time." ], "reasoning": "To find the WER Δ for the 40hr data set, locate the row labeled '40hr' and find the value under the 'WER Δ' column.", "question": "What is the WER Δ for the 40hr data set?", "answer": "So, the answer is {\"answer\": \"-9.2\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Full data set has the lowest Frames dev value. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Full data set has the lowest Frames dev value.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'Frames dev' values for all data sets. The Full data set has a Frames dev value of 33.4, which is lower than the 40hr and 20hr data sets. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3747.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of frame vs phoneme input on Spanish ASR, with average reduction in WER and average reduction in training time." ], "reasoning": "To verify this statement, compare the 'Frames dev' values for all data sets. The Full data set has a Frames dev value of 33.4, which is lower than the 40hr and 20hr data sets.", "statement": "The Full data set has the lowest Frames dev value.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: Adjusted hyperparameter ranges for the AV method Unmasking. Note that the most important modifications affect the hyperparameters U_{1} and U_{4}.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The hyperparameters for ProfAV, Unmasking, and IM were tuned on the respective training corpora, with adjustments made to accommodate shorter documents compared to the original settings.Regarding ProfAV, IM, Unmasking and GenUnmasking, we used our own implementations, where for the first two we employed the same hyperparameter ranges described in the original papers. In the original definition of this method, Koppel and Schler [KoppelAVOneClassClassification:2004, KoppelUnmasking:2007] considered entire books to train and evaluate Unmasking, which differ in lengths from the documents used in our experiments. Therefore, instead of using the original fixed hyperparameter settings (which would make Unmasking inapplicable in our evaluation setting), we decided to consider individual hyperparameter ranges with values that are more appropriate for shorter documents as available in our corpora. Hyperparameters of ProfAV, Unmasking and IM tuned on the respective training corpora with the following notation: L_{u} = Profile size of the unknown document; L_{k} = Profile size of the known document; n = The n-order of character n-grams; d = Dissimilarity function (cf. [StamatatosProfileCNG:2014, Section 3.1]); M = Number of most similar documents that serve as potential impostors; N = Number of actual impostors from among the potential impostors; k = Number of iterations." } ], "images": [ "scigen/train/3748.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Adjusted hyperparameter ranges for the AV method Unmasking. Note that the most important modifications affect the hyperparameters U_{1} and U_{4}." ], "reasoning": "The hyperparameters for ProfAV, Unmasking, and IM were tuned on the respective training corpora, with adjustments made to accommodate shorter documents compared to the original settings.", "description": "Regarding ProfAV, IM, Unmasking and GenUnmasking, we used our own implementations, where for the first two we employed the same hyperparameter ranges described in the original papers. In the original definition of this method, Koppel and Schler [KoppelAVOneClassClassification:2004, KoppelUnmasking:2007] considered entire books to train and evaluate Unmasking, which differ in lengths from the documents used in our experiments. Therefore, instead of using the original fixed hyperparameter settings (which would make Unmasking inapplicable in our evaluation setting), we decided to consider individual hyperparameter ranges with values that are more appropriate for shorter documents as available in our corpora. Hyperparameters of ProfAV, Unmasking and IM tuned on the respective training corpora with the following notation: L_{u} = Profile size of the unknown document; L_{k} = Profile size of the known document; n = The n-order of character n-grams; d = Dissimilarity function (cf. [StamatatosProfileCNG:2014, Section 3.1]); M = Number of most similar documents that serve as potential impostors; N = Number of actual impostors from among the potential impostors; k = Number of iterations.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 4: Adjusted hyperparameter ranges for the AV method Unmasking. Note that the most important modifications affect the hyperparameters U_{1} and U_{4}.\nQuestion: What is the original setting for the chunk sizes in words (U_{4})?\n" }, { "role": "assistant", "content": "The table lists the original setting for each hyperparameter. For U_{4}, the chunk sizes in words, the original setting is 500. So, the answer is {\"answer\": \"500\"}." } ], "images": [ "scigen/train/3748.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Adjusted hyperparameter ranges for the AV method Unmasking. Note that the most important modifications affect the hyperparameters U_{1} and U_{4}." ], "reasoning": "The table lists the original setting for each hyperparameter. For U_{4}, the chunk sizes in words, the original setting is 500.", "question": "What is the original setting for the chunk sizes in words (U_{4})?", "answer": "So, the answer is {\"answer\": \"500\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe original setting for the number of iterations (U_{3}) is 5.\n" }, { "role": "assistant", "content": "The table shows that the original setting for the number of iterations (U_{3}) is 10, not 5. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3748.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Adjusted hyperparameter ranges for the AV method Unmasking. Note that the most important modifications affect the hyperparameters U_{1} and U_{4}." ], "reasoning": "The table shows that the original setting for the number of iterations (U_{3}) is 10, not 5.", "statement": "The original setting for the number of iterations (U_{3}) is 5.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6: Evaluation results of TAVeer and the ten selected baseline methods. Bold and underlined values represent the best and second best results.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "TAVeer outperforms all baselines on the Stack and Amazon corpora, ranks second on Reddit, and is on par with the strongest baseline on Yelp.After we have trained TAVeer and the ten selected baselines on the four training corpora, we evaluated all AV methods on the respective test corpora. As can be seen in this table, TAVeer outperforms all ten baselines on the demanding corpus \\mathcal{C}_{\\mathrm{Stack}}, which demonstrates its robustness under cross-topic conditions. In addition, TAVeer surpasses all baseline methods on \\mathcal{C}_{\\mathrm{Amazon}} and is ranked second on \\mathcal{C}_{\\mathrm{Reddit}}, indicating that the method is also suitable for mixed-topic corpora. On the corpus \\mathcal{C}_{\\mathrm{Yelp}}, TAVeer is on par with the strongest baseline IM, which shows that the method is also applicable in scenarios where the documents consist of a few sentences. It should be emphasized, that half of the baseline methods (\\textsf{IM}\\mbox{},\\textsf{ProfAV}\\mbox{},\\textsf{OCCAV}\\mbox{} and NNCD) make use of character n-grams which, according to the literature, represent the strongest features in the field of AV. In this regard, the results demonstrate that punctuation- and TA-based features alone, are similarly effective and in some cases even better. To gain an insight into what led to these results, we therefore conduct a more detailed analysis regarding our approach. For this purpose, we first take a closer look at the models TAVeer learned on the basis of the training corpora, in order to understand their behavior on the test corpora. Using the proposed interpretation technique (cf. we then perform a more fine-grained analysis in which we investigate which specific features contributed to TAVeer’s predictions." } ], "images": [ "scigen/train/3749.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Evaluation results of TAVeer and the ten selected baseline methods. Bold and underlined values represent the best and second best results." ], "reasoning": "TAVeer outperforms all baselines on the Stack and Amazon corpora, ranks second on Reddit, and is on par with the strongest baseline on Yelp.", "description": "After we have trained TAVeer and the ten selected baselines on the four training corpora, we evaluated all AV methods on the respective test corpora. As can be seen in this table, TAVeer outperforms all ten baselines on the demanding corpus \\mathcal{C}_{\\mathrm{Stack}}, which demonstrates its robustness under cross-topic conditions. In addition, TAVeer surpasses all baseline methods on \\mathcal{C}_{\\mathrm{Amazon}} and is ranked second on \\mathcal{C}_{\\mathrm{Reddit}}, indicating that the method is also suitable for mixed-topic corpora. On the corpus \\mathcal{C}_{\\mathrm{Yelp}}, TAVeer is on par with the strongest baseline IM, which shows that the method is also applicable in scenarios where the documents consist of a few sentences. It should be emphasized, that half of the baseline methods (\\textsf{IM}\\mbox{},\\textsf{ProfAV}\\mbox{},\\textsf{OCCAV}\\mbox{} and NNCD) make use of character n-grams which, according to the literature, represent the strongest features in the field of AV. In this regard, the results demonstrate that punctuation- and TA-based features alone, are similarly effective and in some cases even better. To gain an insight into what led to these results, we therefore conduct a more detailed analysis regarding our approach. For this purpose, we first take a closer look at the models TAVeer learned on the basis of the training corpora, in order to understand their behavior on the test corpora. Using the proposed interpretation technique (cf. we then perform a more fine-grained analysis in which we investigate which specific features contributed to TAVeer’s predictions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 6: Evaluation results of TAVeer and the ten selected baseline methods. Bold and underlined values represent the best and second best results.', and tackle the question below in a sequential manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich method has the highest accuracy on the Yelp corpus?\n" }, { "role": "assistant", "content": "By examining the table, we see that COAV has the highest accuracy on the Yelp corpus with an accuracy of 0.836. So, the answer is {\"answer\": \"COAV\"}" } ], "images": [ "scigen/train/3749.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Evaluation results of TAVeer and the ten selected baseline methods. Bold and underlined values represent the best and second best results." ], "reasoning": "By examining the table, we see that COAV has the highest accuracy on the Yelp corpus with an accuracy of 0.836.", "question": "Which method has the highest accuracy on the Yelp corpus?", "answer": "So, the answer is {\"answer\": \"COAV\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nTAVeer has the highest AUC on the Amazon corpus.\n" }, { "role": "assistant", "content": "The table shows that TAVeer has an AUC of 0.842 on the Amazon corpus, which is the highest among all methods listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3749.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Evaluation results of TAVeer and the ten selected baseline methods. Bold and underlined values represent the best and second best results." ], "reasoning": "The table shows that TAVeer has an AUC of 0.842 on the Amazon corpus, which is the highest among all methods listed.", "statement": "TAVeer has the highest AUC on the Amazon corpus.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'TABLE II: Results (percentage) on multilingual task for the four emotions analyzed (anger, sadness, happiness, anxiety). P stands for precision, R for recall, and F1 for F-score (or F1 measure).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The multilingual raw waveform CNN achieved an average F-score of 67.7%, which improved to 68.5% after fine-tuning, outperforming other models.They are represented in terms of precision, recall and F-score over each emotion and language. We obtained an average F-score of 67.7% (68.5% after fine-tuning the last layer) across all the languages using our raw waveform CNN trained on multiple languages. We obtained an average of 55.2% with the same model trained on a single language, 58.2% from the multilingual spectrogram baseline and 60% from the same baseline trained on single languages. Overall, this yields a relative improvement of 12.8% of the multilingual raw waveform CNN over the second best model, the spectrogram CNN trained on individual languages." } ], "images": [ "scigen/train/3750.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Results (percentage) on multilingual task for the four emotions analyzed (anger, sadness, happiness, anxiety). P stands for precision, R for recall, and F1 for F-score (or F1 measure)." ], "reasoning": "The multilingual raw waveform CNN achieved an average F-score of 67.7%, which improved to 68.5% after fine-tuning, outperforming other models.", "description": "They are represented in terms of precision, recall and F-score over each emotion and language. We obtained an average F-score of 67.7% (68.5% after fine-tuning the last layer) across all the languages using our raw waveform CNN trained on multiple languages. We obtained an average of 55.2% with the same model trained on a single language, 58.2% from the multilingual spectrogram baseline and 60% from the same baseline trained on single languages. Overall, this yields a relative improvement of 12.8% of the multilingual raw waveform CNN over the second best model, the spectrogram CNN trained on individual languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'TABLE II: Results (percentage) on multilingual task for the four emotions analyzed (anger, sadness, happiness, anxiety). P stands for precision, R for recall, and F1 for F-score (or F1 measure).', answer the question below by showing each progressive step toward the answer. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the average F-score of the multilingual raw waveform CNN after fine-tuning?\n" }, { "role": "assistant", "content": "The table shows the average F-score for different models. The multilingual raw waveform CNN has an average F-score of 67.7%, which improves to 68.5% after fine-tuning. So, the answer is {\"answer\": \"68.5%\"}" } ], "images": [ "scigen/train/3750.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Results (percentage) on multilingual task for the four emotions analyzed (anger, sadness, happiness, anxiety). P stands for precision, R for recall, and F1 for F-score (or F1 measure)." ], "reasoning": "The table shows the average F-score for different models. The multilingual raw waveform CNN has an average F-score of 67.7%, which improves to 68.5% after fine-tuning.", "question": "What is the average F-score of the multilingual raw waveform CNN after fine-tuning?", "answer": "So, the answer is {\"answer\": \"68.5%\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The multilingual raw waveform CNN achieved an average F-score of 55.2%., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe multilingual raw waveform CNN achieved an average F-score of 55.2%.\n" }, { "role": "assistant", "content": "The table shows that the multilingual raw waveform CNN achieved an average F-score of 67.7%, not 55.2%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3750.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Results (percentage) on multilingual task for the four emotions analyzed (anger, sadness, happiness, anxiety). P stands for precision, R for recall, and F1 for F-score (or F1 measure)." ], "reasoning": "The table shows that the multilingual raw waveform CNN achieved an average F-score of 67.7%, not 55.2%.", "statement": "The multilingual raw waveform CNN achieved an average F-score of 55.2%.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'TABLE III: Results (percentage) on multilingual task for Big Five personality traits analyzed. Mean Absolute Error (MAE), Accuracy (A), Precision (P), Recall (R), and F-score (F1) are shown.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The fine-tuned multilingual model using raw waveforms achieves the highest average F-score of 62.4% across three languages, showing improvements over both the single language approach and the spectrogram approach.The fine-tuned multilingual model performs best on the test sets in terms of F-score. For the multilingual model using raw waveforms, we obtained an average F-score over the three languages of 62.4%. Training this same model on each language individually resulted in an average F-score over the languages of 56.7%. Using a spectrogram instead of raw waveforms gives an average F-score of 58.8%. Thus, our multilingual efforts show a relative improvement of 6.3% over the spectrogram approach and 10.1% over the single language approach." } ], "images": [ "scigen/train/3751.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Results (percentage) on multilingual task for Big Five personality traits analyzed. Mean Absolute Error (MAE), Accuracy (A), Precision (P), Recall (R), and F-score (F1) are shown." ], "reasoning": "The fine-tuned multilingual model using raw waveforms achieves the highest average F-score of 62.4% across three languages, showing improvements over both the single language approach and the spectrogram approach.", "description": "The fine-tuned multilingual model performs best on the test sets in terms of F-score. For the multilingual model using raw waveforms, we obtained an average F-score over the three languages of 62.4%. Training this same model on each language individually resulted in an average F-score over the languages of 56.7%. Using a spectrogram instead of raw waveforms gives an average F-score of 58.8%. Thus, our multilingual efforts show a relative improvement of 6.3% over the spectrogram approach and 10.1% over the single language approach.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'TABLE III: Results (percentage) on multilingual task for Big Five personality traits analyzed. Mean Absolute Error (MAE), Accuracy (A), Precision (P), Recall (R), and F-score (F1) are shown.', solve this question and give detail solutions. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhat is the average F-score for the fine-tuned multilingual model using raw waveforms across the three languages?\n" }, { "role": "assistant", "content": "The table shows the average F-score for the fine-tuned multilingual model using raw waveforms is 62.4%, which is higher than the single language approach (56.7%) and the spectrogram approach (58.8%). So, the answer is {\"answer\": \"62.4%\"}" } ], "images": [ "scigen/train/3751.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Results (percentage) on multilingual task for Big Five personality traits analyzed. Mean Absolute Error (MAE), Accuracy (A), Precision (P), Recall (R), and F-score (F1) are shown." ], "reasoning": "The table shows the average F-score for the fine-tuned multilingual model using raw waveforms is 62.4%, which is higher than the single language approach (56.7%) and the spectrogram approach (58.8%).", "question": "What is the average F-score for the fine-tuned multilingual model using raw waveforms across the three languages?", "answer": "So, the answer is {\"answer\": \"62.4%\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe fine-tuned multilingual model using raw waveforms has an average F-score of 62.4% across three languages.\n" }, { "role": "assistant", "content": "The table confirms that the fine-tuned multilingual model using raw waveforms achieves an average F-score of 62.4% across the three languages. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3751.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Results (percentage) on multilingual task for Big Five personality traits analyzed. Mean Absolute Error (MAE), Accuracy (A), Precision (P), Recall (R), and F-score (F1) are shown." ], "reasoning": "The table confirms that the fine-tuned multilingual model using raw waveforms achieves an average F-score of 62.4% across the three languages.", "statement": "The fine-tuned multilingual model using raw waveforms has an average F-score of 62.4% across three languages.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 4: Accuracy of pie chart prediction in Simulated (Simul), FigureQA (FigQA), annotated (Annot) and Excel pie chart data sets'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The Annotated data set has the lowest overall accuracy due to the difficulty in accurately predicting the percentage of slices, despite achieving a high accuracy of 77.8% for percentage prediction with less than 5% errors.Different from bar charts, we define a 2-tuple to represent the prediction of each slice in a pie chart, (legend, percentage). The error for pie charts is defined as: Error=|Percentagepred−PercentageGT|PercentageGT. (9) The results show a great success on the FigureQA data set since their pies have less variance and are included in our training data set. The whole accuracy for the Annotated data set is much worse than the other two data sets because the accurate prediction of percentage of slices is hard, although it gets a high accuracy of 77.8% for percentage prediction in the Annotated data set with less than 5% errors." } ], "images": [ "scigen/train/3752.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Accuracy of pie chart prediction in Simulated (Simul), FigureQA (FigQA), annotated (Annot) and Excel pie chart data sets" ], "reasoning": "The Annotated data set has the lowest overall accuracy due to the difficulty in accurately predicting the percentage of slices, despite achieving a high accuracy of 77.8% for percentage prediction with less than 5% errors.", "description": "Different from bar charts, we define a 2-tuple to represent the prediction of each slice in a pie chart, (legend, percentage). The error for pie charts is defined as: Error=|Percentagepred−PercentageGT|PercentageGT. (9) The results show a great success on the FigureQA data set since their pies have less variance and are included in our training data set. The whole accuracy for the Annotated data set is much worse than the other two data sets because the accurate prediction of percentage of slices is hard, although it gets a high accuracy of 77.8% for percentage prediction in the Annotated data set with less than 5% errors.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}. The question is related to the given table titled 'Table 4: Accuracy of pie chart prediction in Simulated (Simul), FigureQA (FigQA), annotated (Annot) and Excel pie chart data sets'.\n\nQuestion: What is the accuracy for predicting the percentage of slices with less than 5% errors in the Annotated data set?\n" }, { "role": "assistant", "content": "The table shows the accuracy for predicting the percentage of slices with less than 5% errors in the Annotated data set as 77.8%. So, the answer is {\"answer\": \"77.8%\"}." } ], "images": [ "scigen/train/3752.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Accuracy of pie chart prediction in Simulated (Simul), FigureQA (FigQA), annotated (Annot) and Excel pie chart data sets" ], "reasoning": "The table shows the accuracy for predicting the percentage of slices with less than 5% errors in the Annotated data set as 77.8%.", "question": "What is the accuracy for predicting the percentage of slices with less than 5% errors in the Annotated data set?", "answer": "So, the answer is {\"answer\": \"77.8%\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Annotated data set has the highest accuracy for predicting the percentage of slices with less than 5% errors.\n" }, { "role": "assistant", "content": "The table shows that the Annotated data set has an accuracy of 77.8% for predicting the percentage of slices with less than 5% errors, which is lower than the accuracies for the Simul, FigQA, and Excel data sets. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3752.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Accuracy of pie chart prediction in Simulated (Simul), FigureQA (FigQA), annotated (Annot) and Excel pie chart data sets" ], "reasoning": "The table shows that the Annotated data set has an accuracy of 77.8% for predicting the percentage of slices with less than 5% errors, which is lower than the accuracies for the Simul, FigQA, and Excel data sets.", "statement": "The Annotated data set has the highest accuracy for predicting the percentage of slices with less than 5% errors.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Accuracy of bar chart prediction in Simulated (Simul), FigureQA (FigQA), annotated (Annot) and Excel bar chart data sets', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The evaluation metric uses a 4-tuple to represent each bar's prediction in a bar chart, considering x-tick label, value, lower y-tick label, and upper y-tick label, and defines true positive charts as those with correct title, x-, y-axis labels, and all 4-tuples with less than 1% error in value.To further evaluate the performance of the bar chart data extraction model, we propose the following evaluation metric. We use a 4-tuple to represent the prediction of each bar in a bar chart: (x-tick label, value, lower y-tick label, upper y-tick label). In the 4-tuple, x-tick label is matched to the bar by the object matching branch (x-tick label can be either below the bar or in the legend), value is predicted using the top boundary location of the bar. The introduction of the lower and upper y-tick labels makes the evaluation more reliable since the predicted value can slightly differ from the ground truth. The lower y-tick label is the y-tick label immediately below the actual value of the bar (and similarly defined upper y-tick label). Besides the 4-tuple, each bar chart has its prediction of title, and x- and y-axis labels. , y-axis labels in the first 4 rows represent the percentage of correctly predicted chart instances in each test data set. In all elements consisting of text we count true positive only if all words match correctly. We define true positive charts (”ALL”) as predicted with correct title, x-, y-axis labels and all 4-tuples with less than 1% error in value. The following 11 rows are based on each tuple prediction. For example, the true positive instance for “tuple 10% err” means the tuple is predicted with a less than 10% error in value and the remaining 3 textual elements are completely correct. The error is defined as: Error=|Valuepred−ValueGT||ValueGT| (8) where ValueGT is the ground truth value of each bar." } ], "images": [ "scigen/train/3753.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Accuracy of bar chart prediction in Simulated (Simul), FigureQA (FigQA), annotated (Annot) and Excel bar chart data sets" ], "reasoning": "The evaluation metric uses a 4-tuple to represent each bar's prediction in a bar chart, considering x-tick label, value, lower y-tick label, and upper y-tick label, and defines true positive charts as those with correct title, x-, y-axis labels, and all 4-tuples with less than 1% error in value.", "description": "To further evaluate the performance of the bar chart data extraction model, we propose the following evaluation metric. We use a 4-tuple to represent the prediction of each bar in a bar chart: (x-tick label, value, lower y-tick label, upper y-tick label). In the 4-tuple, x-tick label is matched to the bar by the object matching branch (x-tick label can be either below the bar or in the legend), value is predicted using the top boundary location of the bar. The introduction of the lower and upper y-tick labels makes the evaluation more reliable since the predicted value can slightly differ from the ground truth. The lower y-tick label is the y-tick label immediately below the actual value of the bar (and similarly defined upper y-tick label). Besides the 4-tuple, each bar chart has its prediction of title, and x- and y-axis labels. , y-axis labels in the first 4 rows represent the percentage of correctly predicted chart instances in each test data set. In all elements consisting of text we count true positive only if all words match correctly. We define true positive charts (”ALL”) as predicted with correct title, x-, y-axis labels and all 4-tuples with less than 1% error in value. The following 11 rows are based on each tuple prediction. For example, the true positive instance for “tuple 10% err” means the tuple is predicted with a less than 10% error in value and the remaining 3 textual elements are completely correct. The error is defined as: Error=|Valuepred−ValueGT||ValueGT| (8) where ValueGT is the ground truth value of each bar.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: Accuracy of bar chart prediction in Simulated (Simul), FigureQA (FigQA), annotated (Annot) and Excel bar chart data sets'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: Which dataset has the highest percentage of correctly predicted chart instances with a title, x-axis label, y-axis label, and all 4-tuples with less than 1% error in value?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the dataset with the highest percentage of correctly predicted chart instances, look at the 'ALL' row and compare the values for Simul, FigQA, Annot, and Excel. The highest value indicates the dataset with the best performance. So, the answer is {\"answer\": \"Simul\"}." } ], "images": [ "scigen/train/3753.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Accuracy of bar chart prediction in Simulated (Simul), FigureQA (FigQA), annotated (Annot) and Excel bar chart data sets" ], "reasoning": "To find the dataset with the highest percentage of correctly predicted chart instances, look at the 'ALL' row and compare the values for Simul, FigQA, Annot, and Excel. The highest value indicates the dataset with the best performance.", "question": "Which dataset has the highest percentage of correctly predicted chart instances with a title, x-axis label, y-axis label, and all 4-tuples with less than 1% error in value?", "answer": "So, the answer is {\"answer\": \"Simul\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Annot dataset has the lowest percentage of correctly predicted chart instances with a title, x-axis label, y-axis label, and all 4-tuples with less than 1% error in value. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Annot dataset has the lowest percentage of correctly predicted chart instances with a title, x-axis label, y-axis label, and all 4-tuples with less than 1% error in value.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'ALL' row and compare the values for Simul, FigQA, Annot, and Excel. The lowest value indicates the dataset with the worst performance. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3753.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Accuracy of bar chart prediction in Simulated (Simul), FigureQA (FigQA), annotated (Annot) and Excel bar chart data sets" ], "reasoning": "To verify this statement, look at the 'ALL' row and compare the values for Simul, FigQA, Annot, and Excel. The lowest value indicates the dataset with the worst performance.", "statement": "The Annot dataset has the lowest percentage of correctly predicted chart instances with a title, x-axis label, y-axis label, and all 4-tuples with less than 1% error in value.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: The developement performance with cross-domain concatenation for languages which has multiple treebanks. single means training the parser on it own treebank without concatenation. # train shows the number of training sentences in the treebank measured in thousand.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "For treebanks with smaller training sets, concatenation improves performance, while for larger training sets, it does not.Numbers of sentences in the training set are also shown in this table. The general trend is that for the treebank with small training set, cross-domain concatenation achieves better performance. While for those with large training set, concatenation does not improve the performance or even worsen the results." } ], "images": [ "scigen/train/3754.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The developement performance with cross-domain concatenation for languages which has multiple treebanks. single means training the parser on it own treebank without concatenation. # train shows the number of training sentences in the treebank measured in thousand." ], "reasoning": "For treebanks with smaller training sets, concatenation improves performance, while for larger training sets, it does not.", "description": "Numbers of sentences in the training set are also shown in this table. The general trend is that for the treebank with small training set, cross-domain concatenation achieves better performance. While for those with large training set, concatenation does not improve the performance or even worsen the results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: 'Table 2: The developement performance with cross-domain concatenation for languages which has multiple treebanks. single means training the parser on it own treebank without concatenation. # train shows the number of training sentences in the treebank measured in thousand.'\nQuestion: Which treebank has the highest performance improvement with concatenation?\n" }, { "role": "assistant", "content": "To find the treebank with the highest performance improvement, compare the difference between the 'concat' and 'single' scores for each treebank. The largest difference indicates the highest improvement. So, the answer is {\"answer\": \"apino\"}" } ], "images": [ "scigen/train/3754.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The developement performance with cross-domain concatenation for languages which has multiple treebanks. single means training the parser on it own treebank without concatenation. # train shows the number of training sentences in the treebank measured in thousand." ], "reasoning": "To find the treebank with the highest performance improvement, compare the difference between the 'concat' and 'single' scores for each treebank. The largest difference indicates the highest improvement.", "question": "Which treebank has the highest performance improvement with concatenation?", "answer": "So, the answer is {\"answer\": \"apino\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nConcatenation improves performance for the 'lines' treebank.\n" }, { "role": "assistant", "content": "The table shows that the 'lines' treebank has a higher score for 'concat' (85.76) compared to 'single' (84.64), indicating an improvement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3754.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The developement performance with cross-domain concatenation for languages which has multiple treebanks. single means training the parser on it own treebank without concatenation. # train shows the number of training sentences in the treebank measured in thousand." ], "reasoning": "The table shows that the 'lines' treebank has a higher score for 'concat' (85.76) compared to 'single' (84.64), indicating an improvement.", "statement": "Concatenation improves performance for the 'lines' treebank.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: The developement performance with cross-domain concatenation for languages which has multiple treebanks. single means training the parser on it own treebank without concatenation. # train shows the number of training sentences in the treebank measured in thousand.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "For treebanks with smaller training sets, cross-domain concatenation improves performance, while for larger training sets, it does not improve or worsens performance.Numbers of sentences in the training set are also shown in this table. The general trend is that for the treebank with small training set, cross-domain concatenation achieves better performance. While for those with large training set, concatenation does not improve the performance or even worsen the results." } ], "images": [ "scigen/train/3755.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The developement performance with cross-domain concatenation for languages which has multiple treebanks. single means training the parser on it own treebank without concatenation. # train shows the number of training sentences in the treebank measured in thousand." ], "reasoning": "For treebanks with smaller training sets, cross-domain concatenation improves performance, while for larger training sets, it does not improve or worsens performance.", "description": "Numbers of sentences in the training set are also shown in this table. The general trend is that for the treebank with small training set, cross-domain concatenation achieves better performance. While for those with large training set, concatenation does not improve the performance or even worsen the results.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: The developement performance with cross-domain concatenation for languages which has multiple treebanks. single means training the parser on it own treebank without concatenation. # train shows the number of training sentences in the treebank measured in thousand.', answer the question below by showing each progressive step toward the answer. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich treebank has the highest performance improvement with concatenation?\n" }, { "role": "assistant", "content": "Compare the performance of single and concatenated models for each treebank. The treebank with the highest positive difference indicates the highest performance improvement. So, the answer is {\"answer\": \"spoken\"}" } ], "images": [ "scigen/train/3755.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The developement performance with cross-domain concatenation for languages which has multiple treebanks. single means training the parser on it own treebank without concatenation. # train shows the number of training sentences in the treebank measured in thousand." ], "reasoning": "Compare the performance of single and concatenated models for each treebank. The treebank with the highest positive difference indicates the highest performance improvement.", "question": "Which treebank has the highest performance improvement with concatenation?", "answer": "So, the answer is {\"answer\": \"spoken\"}" }, { "messages": [ { "role": "user", "content": "The concatenation method improves the performance for the 'lines' treebank. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe concatenation method improves the performance for the 'lines' treebank.\n" }, { "role": "assistant", "content": "The performance for 'lines' with single is 83.86, and with concatenation, it is 85.30, indicating an improvement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3755.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The developement performance with cross-domain concatenation for languages which has multiple treebanks. single means training the parser on it own treebank without concatenation. # train shows the number of training sentences in the treebank measured in thousand." ], "reasoning": "The performance for 'lines' with single is 83.86, and with concatenation, it is 85.30, indicating an improvement.", "statement": "The concatenation method improves the performance for the 'lines' treebank.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 4: Cross-lingual concatenation results. The results for ug_udt and uk_iu are obtained on the development set. The results for ga_idt and sme_giella are obtained with udpipe by 5-fold cross validation.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Concatenating treebanks from different languages improves performance on uk_iu, achieving the highest score of 90.74.Unfortunately, concatenating treebanks from different languages only achieves improved performance on uk_iu. This results also indicate that in cross lingual parsing, sophisticated methods like word embeddings transfer Guo et al. Guo et al." } ], "images": [ "scigen/train/3756.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Cross-lingual concatenation results. The results for ug_udt and uk_iu are obtained on the development set. The results for ga_idt and sme_giella are obtained with udpipe by 5-fold cross validation." ], "reasoning": "Concatenating treebanks from different languages improves performance on uk_iu, achieving the highest score of 90.74.", "description": "Unfortunately, concatenating treebanks from different languages only achieves improved performance on uk_iu. This results also indicate that in cross lingual parsing, sophisticated methods like word embeddings transfer Guo et al. Guo et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 4: Cross-lingual concatenation results. The results for ug_udt and uk_iu are obtained on the development set. The results for ga_idt and sme_giella are obtained with udpipe by 5-fold cross validation.', unfold the steps to solve the question given next. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nQuestion: Which language pair achieved the highest performance improvement when concatenating treebanks?\n" }, { "role": "assistant", "content": "The table shows that concatenating treebanks for uk_iu with +ru_syntagus achieved the highest performance improvement with a score of 90.74. So, the answer is {\"answer\": \"uk_iu\"}." } ], "images": [ "scigen/train/3756.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Cross-lingual concatenation results. The results for ug_udt and uk_iu are obtained on the development set. The results for ga_idt and sme_giella are obtained with udpipe by 5-fold cross validation." ], "reasoning": "The table shows that concatenating treebanks for uk_iu with +ru_syntagus achieved the highest performance improvement with a score of 90.74.", "question": "Which language pair achieved the highest performance improvement when concatenating treebanks?", "answer": "So, the answer is {\"answer\": \"uk_iu\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Concatenating treebanks from different languages improves performance on ga_idt. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nConcatenating treebanks from different languages improves performance on ga_idt.\n" }, { "role": "assistant", "content": "The table shows that concatenating treebanks for ga_idt with +en_ewt resulted in a score of 51.00, which is lower than the original ga_idt score of 62.84. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3756.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Cross-lingual concatenation results. The results for ug_udt and uk_iu are obtained on the development set. The results for ga_idt and sme_giella are obtained with udpipe by 5-fold cross validation." ], "reasoning": "The table shows that concatenating treebanks for ga_idt with +en_ewt resulted in a score of 51.00, which is lower than the original ga_idt score of 62.84. Therefore, the statement is refuted.", "statement": "Concatenating treebanks from different languages improves performance on ga_idt.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Figure 12: Self-Chat ACUTE-Eval (engagingness) shows a small win (not significant) for using persona contexts after fine-tuning on the BST tasks.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The Generative BST 2.7B model with Persona context scored higher than the same model without context.This context can both improve the bot’s consistency as well as add potential talking points that it can work into the conversation. To tease apart the impact of adding context vs. fine-tuning on BST but not using contexts at conversation time, we compared them against each other." } ], "images": [ "scigen/train/3757.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 12: Self-Chat ACUTE-Eval (engagingness) shows a small win (not significant) for using persona contexts after fine-tuning on the BST tasks." ], "reasoning": "The Generative BST 2.7B model with Persona context scored higher than the same model without context.", "description": "This context can both improve the bot’s consistency as well as add potential talking points that it can work into the conversation. To tease apart the impact of adding context vs. fine-tuning on BST but not using contexts at conversation time, we compared them against each other.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Figure 12: Self-Chat ACUTE-Eval (engagingness) shows a small win (not significant) for using persona contexts after fine-tuning on the BST tasks.' in mind, break down and resolve the question below step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the difference in scores between the Generative BST 2.7B model with Persona context and without context?\n" }, { "role": "assistant", "content": "The score for the Generative BST 2.7B model with Persona context is 53, and the score without context is 47. The difference is 53 - 47. So, the answer is {\"answer\": \"6\"}." } ], "images": [ "scigen/train/3757.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 12: Self-Chat ACUTE-Eval (engagingness) shows a small win (not significant) for using persona contexts after fine-tuning on the BST tasks." ], "reasoning": "The score for the Generative BST 2.7B model with Persona context is 53, and the score without context is 47. The difference is 53 - 47.", "question": "What is the difference in scores between the Generative BST 2.7B model with Persona context and without context?", "answer": "So, the answer is {\"answer\": \"6\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The Generative BST 2.7B model with Persona context scored 6 points higher than the model without context., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Generative BST 2.7B model with Persona context scored 6 points higher than the model without context.\n" }, { "role": "assistant", "content": "The table shows that the Generative BST 2.7B model with Persona context scored 53, and the model without context scored 47. The difference is 6 points. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3757.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 12: Self-Chat ACUTE-Eval (engagingness) shows a small win (not significant) for using persona contexts after fine-tuning on the BST tasks." ], "reasoning": "The table shows that the Generative BST 2.7B model with Persona context scored 53, and the model without context scored 47. The difference is 6 points.", "statement": "The Generative BST 2.7B model with Persona context scored 6 points higher than the model without context.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Perplexity of the pre-trained and fine-tuned models on the validation set for BST datasets. Note that perplexity is not directly comparable between the 90M models and the larger models as 90M models use a different dictionary. Fine-tuning gives gains for each skill (task) compared to pre-training on pushshift.io Reddit alone.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Fine-tuning models on datasets like ConvAI2, Wizard of Wikipedia, Empathetic Dialogues, and Blended Skill Talk significantly improves their perplexity, indicating enhanced performance in open-domain dialogue tasks.We also report perplexity both before and after fine-tuning each of these models on the ConvAI2, Wizard of Wikipedia, Empathetic Dialogues, and Blended Skill Talk datasets. They show that fine-tuning gives relatively large improvements in perplexity on these tasks, which could hence translate into improved ability at these skills when conducting open-domain dialogue." } ], "images": [ "scigen/train/3758.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Perplexity of the pre-trained and fine-tuned models on the validation set for BST datasets. Note that perplexity is not directly comparable between the 90M models and the larger models as 90M models use a different dictionary. Fine-tuning gives gains for each skill (task) compared to pre-training on pushshift.io Reddit alone." ], "reasoning": "Fine-tuning models on datasets like ConvAI2, Wizard of Wikipedia, Empathetic Dialogues, and Blended Skill Talk significantly improves their perplexity, indicating enhanced performance in open-domain dialogue tasks.", "description": "We also report perplexity both before and after fine-tuning each of these models on the ConvAI2, Wizard of Wikipedia, Empathetic Dialogues, and Blended Skill Talk datasets. They show that fine-tuning gives relatively large improvements in perplexity on these tasks, which could hence translate into improved ability at these skills when conducting open-domain dialogue.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 3: Perplexity of the pre-trained and fine-tuned models on the validation set for BST datasets. Note that perplexity is not directly comparable between the 90M models and the larger models as 90M models use a different dictionary. Fine-tuning gives gains for each skill (task) compared to pre-training on pushshift.io Reddit alone.', and tackle the question below in a sequential manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model has the lowest average perplexity after fine-tuning?\n" }, { "role": "assistant", "content": "To find the model with the lowest average perplexity, we need to compare the 'Avg.' column values for all models. The model with the lowest value in this column is the one with the lowest average perplexity. So, the answer is {\"answer\": \"BST Generative (2.7B)\"}." } ], "images": [ "scigen/train/3758.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Perplexity of the pre-trained and fine-tuned models on the validation set for BST datasets. Note that perplexity is not directly comparable between the 90M models and the larger models as 90M models use a different dictionary. Fine-tuning gives gains for each skill (task) compared to pre-training on pushshift.io Reddit alone." ], "reasoning": "To find the model with the lowest average perplexity, we need to compare the 'Avg.' column values for all models. The model with the lowest value in this column is the one with the lowest average perplexity.", "question": "Which model has the lowest average perplexity after fine-tuning?", "answer": "So, the answer is {\"answer\": \"BST Generative (2.7B)\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The BST Generative model with 2.7B parameters has a lower average perplexity than the pushshift.io Reddit Generative model with 9.4B parameters., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe BST Generative model with 2.7B parameters has a lower average perplexity than the pushshift.io Reddit Generative model with 9.4B parameters.\n" }, { "role": "assistant", "content": "By comparing the 'Avg.' column values for the BST Generative model with 2.7B parameters (8.98) and the pushshift.io Reddit Generative model with 9.4B parameters (12.95), we can see that the BST Generative model has a lower average perplexity. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3758.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Perplexity of the pre-trained and fine-tuned models on the validation set for BST datasets. Note that perplexity is not directly comparable between the 90M models and the larger models as 90M models use a different dictionary. Fine-tuning gives gains for each skill (task) compared to pre-training on pushshift.io Reddit alone." ], "reasoning": "By comparing the 'Avg.' column values for the BST Generative model with 2.7B parameters (8.98) and the pushshift.io Reddit Generative model with 9.4B parameters (12.95), we can see that the BST Generative model has a lower average perplexity.", "statement": "The BST Generative model with 2.7B parameters has a lower average perplexity than the pushshift.io Reddit Generative model with 9.4B parameters.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Figure 8: Self-Chat ACUTE-Eval (engagingness): comparing beam-blocking variants. Blocking both context and response 3-grams during generation gives highest scores, however, none of these results are significant.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Blocking increases performance, as seen in the higher scores for 3-gram Context + Response Blocks, although the results were not significant.Blocking tends to increase performance, in line with other works, although the results were not significant. We employ full blocking in the remainder of our experiments." } ], "images": [ "scigen/train/3759.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 8: Self-Chat ACUTE-Eval (engagingness): comparing beam-blocking variants. Blocking both context and response 3-grams during generation gives highest scores, however, none of these results are significant." ], "reasoning": "Blocking increases performance, as seen in the higher scores for 3-gram Context + Response Blocks, although the results were not significant.", "description": "Blocking tends to increase performance, in line with other works, although the results were not significant. We employ full blocking in the remainder of our experiments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Figure 8: Self-Chat ACUTE-Eval (engagingness): comparing beam-blocking variants. Blocking both context and response 3-grams during generation gives highest scores, however, none of these results are significant.\nQuestion: What is the performance score for 3-gram Response Blocks with Beam Blocking Block?\nSolve the above question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows that the score for 3-gram Response Blocks with Beam Blocking Block is 54. So, the answer is {\"answer\": \"54\"}" } ], "images": [ "scigen/train/3759.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 8: Self-Chat ACUTE-Eval (engagingness): comparing beam-blocking variants. Blocking both context and response 3-grams during generation gives highest scores, however, none of these results are significant." ], "reasoning": "The table shows that the score for 3-gram Response Blocks with Beam Blocking Block is 54.", "question": "What is the performance score for 3-gram Response Blocks with Beam Blocking Block?", "answer": "So, the answer is {\"answer\": \"54\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe performance score for 3-gram Context + Response Blocks with Beam Blocking None is higher than with Beam Blocking Block.\n" }, { "role": "assistant", "content": "The table shows that the score for 3-gram Context + Response Blocks with Beam Blocking None is 41, which is lower than the score of 59 with Beam Blocking Block. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3759.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 8: Self-Chat ACUTE-Eval (engagingness): comparing beam-blocking variants. Blocking both context and response 3-grams during generation gives highest scores, however, none of these results are significant." ], "reasoning": "The table shows that the score for 3-gram Context + Response Blocks with Beam Blocking None is 41, which is lower than the score of 59 with Beam Blocking Block.", "statement": "The performance score for 3-gram Context + Response Blocks with Beam Blocking None is higher than with Beam Blocking Block.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Figure 22: Counts of most common 3-grams from the BST Generative 2.7B model (likelihood) from the conversation logs when talking to crowdworkers, compared to those of the same model trained with unlikelihood, and to human logs (for the same number of utterances).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Generative models using beam search tend to overuse common phrases and underuse rare ones, making conversations less engaging, and current evaluation methods fail to capture this issue.It has been observed that generative models employing beam search decoding (or other methods that approximately choose the most likely utterance) tend to generate common words too frequently, and rare words too infrequently, as compared to the human distribution Holtzman et al. In dialogue, humans can interpret this as technically correct, but unengaging, in the extreme this is the so-called “I don’t know” problem, where models tend to output such non-committal utterances. Using sampling to select lower likelihood generations can help, but at the risk of saying something which makes less sense. It appears that even our best models using beam search are still exhibiting such behavior. We have found that encouraging the length of the generations to be longer helps, in that the model is forced to generate something more detailed, but the problem still remains. Given that there are only 100 conversations, the expressions “do you like”, “lot of fun”, “have any hobbies” etc. are clearly over-expressed compared to human-human conversations. We note that the current evaluation does not seem to expose this as boring because the conversations are short and are evaluated separately. Unfortunately, this made a very small or negative impact in our ACUTE-Evals of engagingness, see For engagingness, as explained, we believe this is because the current evaluation technique employing short conversations cannot measure this phenomenon well." } ], "images": [ "scigen/train/3760.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 22: Counts of most common 3-grams from the BST Generative 2.7B model (likelihood) from the conversation logs when talking to crowdworkers, compared to those of the same model trained with unlikelihood, and to human logs (for the same number of utterances)." ], "reasoning": "Generative models using beam search tend to overuse common phrases and underuse rare ones, making conversations less engaging, and current evaluation methods fail to capture this issue.", "description": "It has been observed that generative models employing beam search decoding (or other methods that approximately choose the most likely utterance) tend to generate common words too frequently, and rare words too infrequently, as compared to the human distribution Holtzman et al. In dialogue, humans can interpret this as technically correct, but unengaging, in the extreme this is the so-called “I don’t know” problem, where models tend to output such non-committal utterances. Using sampling to select lower likelihood generations can help, but at the risk of saying something which makes less sense. It appears that even our best models using beam search are still exhibiting such behavior. We have found that encouraging the length of the generations to be longer helps, in that the model is forced to generate something more detailed, but the problem still remains. Given that there are only 100 conversations, the expressions “do you like”, “lot of fun”, “have any hobbies” etc. are clearly over-expressed compared to human-human conversations. We note that the current evaluation does not seem to expose this as boring because the conversations are short and are evaluated separately. Unfortunately, this made a very small or negative impact in our ACUTE-Evals of engagingness, see For engagingness, as explained, we believe this is because the current evaluation technique employing short conversations cannot measure this phenomenon well.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich n-gram has the highest frequency in the MLE column and how does its frequency compare to the Human column?\nSolve the above question based on the table titled 'Figure 22: Counts of most common 3-grams from the BST Generative 2.7B model (likelihood) from the conversation logs when talking to crowdworkers, compared to those of the same model trained with unlikelihood, and to human logs (for the same number of utterances).'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The n-gram 'Do you have' has the highest frequency in the MLE column with a count of 110, while in the Human column, it has a frequency of 6. So, the answer is {\"answer\": \"Do you have\"}." } ], "images": [ "scigen/train/3760.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 22: Counts of most common 3-grams from the BST Generative 2.7B model (likelihood) from the conversation logs when talking to crowdworkers, compared to those of the same model trained with unlikelihood, and to human logs (for the same number of utterances)." ], "reasoning": "The n-gram 'Do you have' has the highest frequency in the MLE column with a count of 110, while in the Human column, it has a frequency of 6.", "question": "Which n-gram has the highest frequency in the MLE column and how does its frequency compare to the Human column?", "answer": "So, the answer is {\"answer\": \"Do you have\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe n-gram 'you have any' is more frequent in the Human column than in the Unlikelihood column.\n" }, { "role": "assistant", "content": "The table shows that 'you have any' has a frequency of 2 in the Human column and 46 in the Unlikelihood column, indicating it is less frequent in the Human column. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3760.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 22: Counts of most common 3-grams from the BST Generative 2.7B model (likelihood) from the conversation logs when talking to crowdworkers, compared to those of the same model trained with unlikelihood, and to human logs (for the same number of utterances)." ], "reasoning": "The table shows that 'you have any' has a frequency of 2 in the Human column and 46 in the Unlikelihood column, indicating it is less frequent in the Human column.", "statement": "The n-gram 'you have any' is more frequent in the Human column than in the Unlikelihood column.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Figure 10: Target population label and the interaction/relation context.'.\n" }, { "role": "assistant", "content": "Two types of experiments were conducted for each target population: restricted, involving only direct relations, and extended, involving co-occurring populations and their relations.One achieved two experiments with each target population each one corresponding to two different initialization sets of relations. The first set is called ”restricted” and is related to only relations of a target population with all populations with which it is linked. The other kind of experiment, called ”extented”, gathers all populations co-occuring with the target population, and populations linked to these co-occuring populations; the relations settled as init parameters are relations between these all populations." } ], "images": [ "scigen/train/3761.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Figure 10: Target population label and the interaction/relation context." ], "reasoning": "Two types of experiments were conducted for each target population: restricted, involving only direct relations, and extended, involving co-occurring populations and their relations.", "description": "One achieved two experiments with each target population each one corresponding to two different initialization sets of relations. The first set is called ”restricted” and is related to only relations of a target population with all populations with which it is linked. The other kind of experiment, called ”extented”, gathers all populations co-occuring with the target population, and populations linked to these co-occuring populations; the relations settled as init parameters are relations between these all populations.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Figure 10: Target population label and the interaction/relation context.', solve this question and give detail solutions. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich target agent label has the highest number of relations in the extended experiment?\n" }, { "role": "assistant", "content": "To find the target agent label with the highest number of relations in the extended experiment, compare the '#relations' values for the rows labeled as '(extended)'. So, the answer is {\"answer\": \"adsorption (extended)\"}." } ], "images": [ "scigen/train/3761.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Figure 10: Target population label and the interaction/relation context." ], "reasoning": "To find the target agent label with the highest number of relations in the extended experiment, compare the '#relations' values for the rows labeled as '(extended)'.", "question": "Which target agent label has the highest number of relations in the extended experiment?", "answer": "So, the answer is {\"answer\": \"adsorption (extended)\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The target agent label 'tor' has more populations in the extended experiment than 'actb'., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe target agent label 'tor' has more populations in the extended experiment than 'actb'.\n" }, { "role": "assistant", "content": "Compare the '#populations' values for 'tor (extended)' and 'actb (extended)'. 'tor (extended)' has 416 populations, while 'actb (extended)' has 474 populations. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3761.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Figure 10: Target population label and the interaction/relation context." ], "reasoning": "Compare the '#populations' values for 'tor (extended)' and 'actb (extended)'. 'tor (extended)' has 416 populations, while 'actb (extended)' has 474 populations.", "statement": "The target agent label 'tor' has more populations in the extended experiment than 'actb'.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Ablation study of the algorithm proposed by Artetxe et al., (2018b) on the same four languages. We performed seven ablations and report the original and reproduced best accuracy (best), average accuracy (avg), success rate (s) and average runtime in minutes (t). Bold values represent significant differences between the original and reproduced results. *We did not reproduce this ablation due to time constraints as more than three days per run would have been required to reach convergence. †The authors reported a bug on the code they used for generating the CSLS values which made their code only yield zero accuracies.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The accuracy results are within the 95% confidence interval except for unsupervised initialization ablation and runtimes, and some configurations require additional RAM and CPU resources.Amongst other things, we note that the accuracy results we obtained are all within the 95 % confidence interval given by our 25 runs, with the only exceptions being the unsupervised initialization ablation and the runtimes. Our results also showed great differences in the algorithm ’s runtimes, even though we used the same graphics card as the original paper. We also point out that not all ablation study configurations can be run with the same compute power, i.e. when removing the vocabulary cutoff parameter, the matrices no longer fit inside a GPU’s memory and we have to prepare additional RAM space and CPU resources to run the script. However, when we attempted to reproduce the frequency-based vocabulary cutoff ablation where n is set to 100k, we were unable to obtain a single run to complete even after 3 days of computations." } ], "images": [ "scigen/train/3762.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ablation study of the algorithm proposed by Artetxe et al., (2018b) on the same four languages. We performed seven ablations and report the original and reproduced best accuracy (best), average accuracy (avg), success rate (s) and average runtime in minutes (t). Bold values represent significant differences between the original and reproduced results. *We did not reproduce this ablation due to time constraints as more than three days per run would have been required to reach convergence. †The authors reported a bug on the code they used for generating the CSLS values which made their code only yield zero accuracies." ], "reasoning": "The accuracy results are within the 95% confidence interval except for unsupervised initialization ablation and runtimes, and some configurations require additional RAM and CPU resources.", "description": "Amongst other things, we note that the accuracy results we obtained are all within the 95 % confidence interval given by our 25 runs, with the only exceptions being the unsupervised initialization ablation and the runtimes. Our results also showed great differences in the algorithm ’s runtimes, even though we used the same graphics card as the original paper. We also point out that not all ablation study configurations can be run with the same compute power, i.e. when removing the vocabulary cutoff parameter, the matrices no longer fit inside a GPU’s memory and we have to prepare additional RAM space and CPU resources to run the script. However, when we attempted to reproduce the frequency-based vocabulary cutoff ablation where n is set to 100k, we were unable to obtain a single run to complete even after 3 days of computations.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 2: Ablation study of the algorithm proposed by Artetxe et al., (2018b) on the same four languages. We performed seven ablations and report the original and reproduced best accuracy (best), average accuracy (avg), success rate (s) and average runtime in minutes (t). Bold values represent significant differences between the original and reproduced results. *We did not reproduce this ablation due to time constraints as more than three days per run would have been required to reach convergence. †The authors reported a bug on the code they used for generating the CSLS values which made their code only yield zero accuracies.', unfold the question presented below, detailing every step of your calculation. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the EN-IT best score for the reproduced bidirectional configuration?\n" }, { "role": "assistant", "content": "To find the EN-IT best score for the reproduced bidirectional configuration, locate the 'Reproduced' row under the 'Bidirectional' section and find the value in the 'EN-IT best' column. So, the answer is {\"answer\": \"49.1\"}" } ], "images": [ "scigen/train/3762.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ablation study of the algorithm proposed by Artetxe et al., (2018b) on the same four languages. We performed seven ablations and report the original and reproduced best accuracy (best), average accuracy (avg), success rate (s) and average runtime in minutes (t). Bold values represent significant differences between the original and reproduced results. *We did not reproduce this ablation due to time constraints as more than three days per run would have been required to reach convergence. †The authors reported a bug on the code they used for generating the CSLS values which made their code only yield zero accuracies." ], "reasoning": "To find the EN-IT best score for the reproduced bidirectional configuration, locate the 'Reproduced' row under the 'Bidirectional' section and find the value in the 'EN-IT best' column.", "question": "What is the EN-IT best score for the reproduced bidirectional configuration?", "answer": "So, the answer is {\"answer\": \"49.1\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The reproduced cutoff (n=100k) configuration has the highest EN-FI t score. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe reproduced cutoff (n=100k) configuration has the highest EN-FI t score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the EN-FI t scores across all configurations. The reproduced cutoff (n=100k) configuration has an EN-FI t score of 114.5, which is the highest among all configurations. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3762.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ablation study of the algorithm proposed by Artetxe et al., (2018b) on the same four languages. We performed seven ablations and report the original and reproduced best accuracy (best), average accuracy (avg), success rate (s) and average runtime in minutes (t). Bold values represent significant differences between the original and reproduced results. *We did not reproduce this ablation due to time constraints as more than three days per run would have been required to reach convergence. †The authors reported a bug on the code they used for generating the CSLS values which made their code only yield zero accuracies." ], "reasoning": "To verify this statement, compare the EN-FI t scores across all configurations. The reproduced cutoff (n=100k) configuration has an EN-FI t score of 114.5, which is the highest among all configurations.", "statement": "The reproduced cutoff (n=100k) configuration has the highest EN-FI t score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Best accuracy (best), average accuracy (avg), success rate (s) and average runtime in minutes (t) of the full system on four other languages, Estonian (ET), Persian (FA), Latvian (LV) and Vietnamese (VI). These languages have been carefully selected to illustrate the robustness of the algorithm. They have very different roots then the English language. We also conducted an ablation study to illustrate the stability of the algorithm on such languages.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The algorithm performs poorly on Latvian and Vietnamese languages, and struggles to converge without unsupervised initialization and the stochastic procedure.We carefully selected four new languages that are characterized by very different roots than the one used in the original paper. We used Estonian (ET) which is a language that gets its root from Finno-Ugric, the same as Finnish. We also selected Persian (FA), Latvian (LV) and Vietnamese (VI). We observed similar behavior for the Persian language. Interestingly, even with the full system, the algorithm poorly performs on Latvian and Vietnamese languages. We conducted the same ablation study as with the original languages. We can see that the algorithm does not converge without an unsupervised initialization and without the stochastic procedure. It also struggles to converge on three languages out of four when the CSLS component is turned off. These results clearly show that the proposed method may become brittle when the target language shares fewer commonalities with the source language." } ], "images": [ "scigen/train/3763.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Best accuracy (best), average accuracy (avg), success rate (s) and average runtime in minutes (t) of the full system on four other languages, Estonian (ET), Persian (FA), Latvian (LV) and Vietnamese (VI). These languages have been carefully selected to illustrate the robustness of the algorithm. They have very different roots then the English language. We also conducted an ablation study to illustrate the stability of the algorithm on such languages." ], "reasoning": "The algorithm performs poorly on Latvian and Vietnamese languages, and struggles to converge without unsupervised initialization and the stochastic procedure.", "description": "We carefully selected four new languages that are characterized by very different roots than the one used in the original paper. We used Estonian (ET) which is a language that gets its root from Finno-Ugric, the same as Finnish. We also selected Persian (FA), Latvian (LV) and Vietnamese (VI). We observed similar behavior for the Persian language. Interestingly, even with the full system, the algorithm poorly performs on Latvian and Vietnamese languages. We conducted the same ablation study as with the original languages. We can see that the algorithm does not converge without an unsupervised initialization and without the stochastic procedure. It also struggles to converge on three languages out of four when the CSLS component is turned off. These results clearly show that the proposed method may become brittle when the target language shares fewer commonalities with the source language.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 3: Best accuracy (best), average accuracy (avg), success rate (s) and average runtime in minutes (t) of the full system on four other languages, Estonian (ET), Persian (FA), Latvian (LV) and Vietnamese (VI). These languages have been carefully selected to illustrate the robustness of the algorithm. They have very different roots then the English language. We also conducted an ablation study to illustrate the stability of the algorithm on such languages.\nQuestion: Which language showed the highest score for the 'Vecmap' method?\n" }, { "role": "assistant", "content": "To find the highest score for the 'Vecmap' method, we need to look at the 'Vecmap' row and compare the scores across all languages. The highest score is 41.5 for the EN-VI best column. So, the answer is {\"answer\": \"Vietnamese\"}" } ], "images": [ "scigen/train/3763.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Best accuracy (best), average accuracy (avg), success rate (s) and average runtime in minutes (t) of the full system on four other languages, Estonian (ET), Persian (FA), Latvian (LV) and Vietnamese (VI). These languages have been carefully selected to illustrate the robustness of the algorithm. They have very different roots then the English language. We also conducted an ablation study to illustrate the stability of the algorithm on such languages." ], "reasoning": "To find the highest score for the 'Vecmap' method, we need to look at the 'Vecmap' row and compare the scores across all languages. The highest score is 41.5 for the EN-VI best column.", "question": "Which language showed the highest score for the 'Vecmap' method?", "answer": "So, the answer is {\"answer\": \"Vietnamese\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe algorithm performs well on the Latvian language with the 'Vecmap' method.\n" }, { "role": "assistant", "content": "The table shows that the 'Vecmap' method has a score of 23.4 for EN-LV best, which is relatively low compared to other languages, indicating poor performance. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3763.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Best accuracy (best), average accuracy (avg), success rate (s) and average runtime in minutes (t) of the full system on four other languages, Estonian (ET), Persian (FA), Latvian (LV) and Vietnamese (VI). These languages have been carefully selected to illustrate the robustness of the algorithm. They have very different roots then the English language. We also conducted an ablation study to illustrate the stability of the algorithm on such languages." ], "reasoning": "The table shows that the 'Vecmap' method has a score of 23.4 for EN-LV best, which is relatively low compared to other languages, indicating poor performance.", "statement": "The algorithm performs well on the Latvian language with the 'Vecmap' method.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Sememe prediction MAPs for in-vocabulary (IV) and out-of-vocabulary (OOV) words of different models. +TW-WS signifies removing word splitting in the +TW operation.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The SCorP(+TW) model significantly outperforms the baseline methods, especially for OOV words, demonstrating its superior generalization capability. Removing word splitting in the SCorP(+TW,-WS) model results in a performance drop, highlighting the importance of word splitting.From the table, we can observe that our plain SCorP model and its variant SCorP(+TW) dramatically outperform the two baseline methods, especially for the OOV words. This manifests our model’s extraordinary generalization capability to tackle the OOV problem. We also confirm the effectiveness of word splitting workaround by a lesion experiment. After removing word splitting for target words in +TW (denoted by + TW-WS), SCorP(+TW,-WS) model performs considerably worse in sememe prediction for the OOV words, even close to the plain SCorP model making no use of target word embeddings." } ], "images": [ "scigen/train/3764.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Sememe prediction MAPs for in-vocabulary (IV) and out-of-vocabulary (OOV) words of different models. +TW-WS signifies removing word splitting in the +TW operation." ], "reasoning": "The SCorP(+TW) model significantly outperforms the baseline methods, especially for OOV words, demonstrating its superior generalization capability. Removing word splitting in the SCorP(+TW,-WS) model results in a performance drop, highlighting the importance of word splitting.", "description": "From the table, we can observe that our plain SCorP model and its variant SCorP(+TW) dramatically outperform the two baseline methods, especially for the OOV words. This manifests our model’s extraordinary generalization capability to tackle the OOV problem. We also confirm the effectiveness of word splitting workaround by a lesion experiment. After removing word splitting for target words in +TW (denoted by + TW-WS), SCorP(+TW,-WS) model performs considerably worse in sememe prediction for the OOV words, even close to the plain SCorP model making no use of target word embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 3: Sememe prediction MAPs for in-vocabulary (IV) and out-of-vocabulary (OOV) words of different models. +TW-WS signifies removing word splitting in the +TW operation.\nQuestion: Which model shows the highest performance for OOV words and what does this indicate about its generalization capability?\n" }, { "role": "assistant", "content": "The table shows that the SCorP(+TW) model has the highest performance for OOV words with a score of 54.30. This indicates that the SCorP(+TW) model has superior generalization capability compared to other models. So, the answer is {\"answer\": \"SCorP(+TW)\"}" } ], "images": [ "scigen/train/3764.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Sememe prediction MAPs for in-vocabulary (IV) and out-of-vocabulary (OOV) words of different models. +TW-WS signifies removing word splitting in the +TW operation." ], "reasoning": "The table shows that the SCorP(+TW) model has the highest performance for OOV words with a score of 54.30. This indicates that the SCorP(+TW) model has superior generalization capability compared to other models.", "question": "Which model shows the highest performance for OOV words and what does this indicate about its generalization capability?", "answer": "So, the answer is {\"answer\": \"SCorP(+TW)\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The SCorP(+TW,-WS) model performs better than the plain SCorP model for OOV words., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe SCorP(+TW,-WS) model performs better than the plain SCorP model for OOV words.\n" }, { "role": "assistant", "content": "The table shows that the SCorP(+TW,-WS) model has a score of 51.20 for OOV words, while the plain SCorP model has a score of 51.05. Therefore, the SCorP(+TW,-WS) model performs slightly better than the plain SCorP model for OOV words. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3764.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Sememe prediction MAPs for in-vocabulary (IV) and out-of-vocabulary (OOV) words of different models. +TW-WS signifies removing word splitting in the +TW operation." ], "reasoning": "The table shows that the SCorP(+TW,-WS) model has a score of 51.20 for OOV words, while the plain SCorP model has a score of 51.05. Therefore, the SCorP(+TW,-WS) model performs slightly better than the plain SCorP model for OOV words.", "statement": "The SCorP(+TW,-WS) model performs better than the plain SCorP model for OOV words.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: Sememe prediction MAPs for words with different frequency ranges. The numbers of words in the four ranges are 31481, 10714, 4528 and 1660 respectively.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "SCorP(+SE,TW) consistently outperforms other models across all word frequency ranges, demonstrating its robustness, while CSP's performance drops significantly with low-frequency words.SCorP(+SE,TW) produces the best performance in all the word frequency ranges, which manifests its remarkable robustness. CSP, which relies heavily on word embeddings, suffers a sharp performance drop when faced with low-frequency words (word frequency less than 50), although they perform acceptably on high-frequency words. LD+Seq2Seq is less affected by word frequencies, but it badly lags behind other models on whichever word frequency range." } ], "images": [ "scigen/train/3765.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Sememe prediction MAPs for words with different frequency ranges. The numbers of words in the four ranges are 31481, 10714, 4528 and 1660 respectively." ], "reasoning": "SCorP(+SE,TW) consistently outperforms other models across all word frequency ranges, demonstrating its robustness, while CSP's performance drops significantly with low-frequency words.", "description": "SCorP(+SE,TW) produces the best performance in all the word frequency ranges, which manifests its remarkable robustness. CSP, which relies heavily on word embeddings, suffers a sharp performance drop when faced with low-frequency words (word frequency less than 50), although they perform acceptably on high-frequency words. LD+Seq2Seq is less affected by word frequencies, but it badly lags behind other models on whichever word frequency range.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: 'Table 4: Sememe prediction MAPs for words with different frequency ranges. The numbers of words in the four ranges are 31481, 10714, 4528 and 1660 respectively.'\nQuestion: Which model shows the highest performance in the 50-500 word frequency range?\n" }, { "role": "assistant", "content": "By comparing the performance of all models in the 50-500 word frequency range, SCorP(+TW,SE) has the highest score of 62.94. So, the answer is {\"answer\": \"SCorP(+TW,SE)\"}" } ], "images": [ "scigen/train/3765.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Sememe prediction MAPs for words with different frequency ranges. The numbers of words in the four ranges are 31481, 10714, 4528 and 1660 respectively." ], "reasoning": "By comparing the performance of all models in the 50-500 word frequency range, SCorP(+TW,SE) has the highest score of 62.94.", "question": "Which model shows the highest performance in the 50-500 word frequency range?", "answer": "So, the answer is {\"answer\": \"SCorP(+TW,SE)\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence LD+Seq2Seq performs better than CSP in the word frequency range of 50-500. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nLD+Seq2Seq performs better than CSP in the word frequency range of 50-500.\n" }, { "role": "assistant", "content": "In the 50-500 word frequency range, LD+Seq2Seq has a score of 33.61, while CSP has a score of 46.60, indicating that CSP performs better. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3765.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Sememe prediction MAPs for words with different frequency ranges. The numbers of words in the four ranges are 31481, 10714, 4528 and 1660 respectively." ], "reasoning": "In the 50-500 word frequency range, LD+Seq2Seq has a score of 33.61, while CSP has a score of 46.60, indicating that CSP performs better.", "statement": "LD+Seq2Seq performs better than CSP in the word frequency range of 50-500.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Main Results on ACE 2005.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Our model architecture achieves better results compared to previous state-of-the-art methods, even without domain adaptation, and shows a smaller performance gap between multi-relation and single-relation settings.The first observation is that our model architecture achieves much better results compared to the previous state-of-the-art methods. Note that our method was not designed for domain adaptation, it still outperforms those methods with domain adaptation. This result further demonstrates its effectiveness. Therefore, we compare the same methods when applied to the multi-relation and single-relation settings. For BERTSP with entity indicators on inputs, it is expected to perform slightly better in the single-relation setting, because of the mixture of information from multiple pairs. A 2% gap is observed as expected. By comparison, our full model has a much smaller performance gap between two different settings (and no consistent performance drop over different domains)." } ], "images": [ "scigen/train/3766.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Main Results on ACE 2005." ], "reasoning": "Our model architecture achieves better results compared to previous state-of-the-art methods, even without domain adaptation, and shows a smaller performance gap between multi-relation and single-relation settings.", "description": "The first observation is that our model architecture achieves much better results compared to the previous state-of-the-art methods. Note that our method was not designed for domain adaptation, it still outperforms those methods with domain adaptation. This result further demonstrates its effectiveness. Therefore, we compare the same methods when applied to the multi-relation and single-relation settings. For BERTSP with entity indicators on inputs, it is expected to perform slightly better in the single-relation setting, because of the mixture of information from multiple pairs. A 2% gap is observed as expected. By comparison, our full model has a much smaller performance gap between two different settings (and no consistent performance drop over different domains).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Main Results on ACE 2005.', answer the following question in a stepwise manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich method has the highest average score in the single-relation per pass setting?\n" }, { "role": "assistant", "content": "To find the method with the highest average score in the single-relation per pass setting, compare the 'avg' column values for all methods listed under 'Single-Relation per Pass'. The highest value is 63.14 for 'Entity-Aware BERTSP (our full model)'. So, the answer is {\"answer\": \"Entity-Aware BERTSP (our full model)\"}" } ], "images": [ "scigen/train/3766.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Main Results on ACE 2005." ], "reasoning": "To find the method with the highest average score in the single-relation per pass setting, compare the 'avg' column values for all methods listed under 'Single-Relation per Pass'. The highest value is 63.14 for 'Entity-Aware BERTSP (our full model)'.", "question": "Which method has the highest average score in the single-relation per pass setting?", "answer": "So, the answer is {\"answer\": \"Entity-Aware BERTSP (our full model)\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nBERTSP with entity-indicator on input-layer performs better in the single-relation setting compared to the multi-relation setting.\n" }, { "role": "assistant", "content": "To verify this statement, compare the average scores of 'BERTSP with entity-indicator on input-layer' in both settings. The single-relation setting has an average score of 61.36, while the multi-relation setting has an average score of 59.36, confirming the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3766.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Main Results on ACE 2005." ], "reasoning": "To verify this statement, compare the average scores of 'BERTSP with entity-indicator on input-layer' in both settings. The single-relation setting has an average score of 61.36, while the multi-relation setting has an average score of 59.36, confirming the statement.", "statement": "BERTSP with entity-indicator on input-layer performs better in the single-relation setting compared to the multi-relation setting.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Our model with different prediction modules.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The linear predictor performs better than MLP and Biaff, as shown by the higher average score, supporting the idea that pre-trained encoders are powerful enough without additional complex layers.Results show that the usage of the linear predictor gives better results. This is consistent with the motivation of the pre-trained encoders: by unsupervised pre-training the encoders are expected to be sufficiently powerful thus adding more complex layers on top does not improve the capacity but leads to more free parameters and higher risk of over-fitting." } ], "images": [ "scigen/train/3767.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Our model with different prediction modules." ], "reasoning": "The linear predictor performs better than MLP and Biaff, as shown by the higher average score, supporting the idea that pre-trained encoders are powerful enough without additional complex layers.", "description": "Results show that the usage of the linear predictor gives better results. This is consistent with the motivation of the pre-trained encoders: by unsupervised pre-training the encoders are expected to be sufficiently powerful thus adding more complex layers on top does not improve the capacity but leads to more free parameters and higher risk of over-fitting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 2: Our model with different prediction modules.', and tackle the question below in a sequential manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich method has the highest average score?\n" }, { "role": "assistant", "content": "By comparing the average scores of the methods, the Linear method has the highest average score of 63.14. So, the answer is {\"answer\": \"Linear\"}" } ], "images": [ "scigen/train/3767.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Our model with different prediction modules." ], "reasoning": "By comparing the average scores of the methods, the Linear method has the highest average score of 63.14.", "question": "Which method has the highest average score?", "answer": "So, the answer is {\"answer\": \"Linear\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Biaff method has the lowest score in the 'wl' category.\n" }, { "role": "assistant", "content": "By examining the 'wl' column, the Biaff method has a score of 55.60, which is higher than the MLP method's score of 54.72, making the statement incorrect. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3767.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Our model with different prediction modules." ], "reasoning": "By examining the 'wl' column, the Biaff method has a score of 55.60, which is higher than the MLP method's score of 54.72, making the statement incorrect.", "statement": "The Biaff method has the lowest score in the 'wl' category.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Results on SemEval 2018 Task 7, Sub-Task 1.1.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our Entity-Aware BERTSP shows a slight drop in Macro-F1 but an improvement in Micro-F1 when predicting multiple relations in one-pass compared to the top-ranked system by Rotsztejn et al.Our Entity-Aware BERTSP gives comparable results to the top-ranked system Rotsztejn et al. When predicting multiple relations in one-pass, we have 0.9% drop on Macro-F1, but a further 0.8% improvement on Micro-F1. Note that the system Rotsztejn et al." } ], "images": [ "scigen/train/3768.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on SemEval 2018 Task 7, Sub-Task 1.1." ], "reasoning": "Our Entity-Aware BERTSP shows a slight drop in Macro-F1 but an improvement in Micro-F1 when predicting multiple relations in one-pass compared to the top-ranked system by Rotsztejn et al.", "description": "Our Entity-Aware BERTSP gives comparable results to the top-ranked system Rotsztejn et al. When predicting multiple relations in one-pass, we have 0.9% drop on Macro-F1, but a further 0.8% improvement on Micro-F1. Note that the system Rotsztejn et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. The question is related to the given table titled 'Table 3: Results on SemEval 2018 Task 7, Sub-Task 1.1.'.\n\nQuestion: What is the difference in Averaged F1 Micro between our single-per-pass and multiple-per-pass methods?\n" }, { "role": "assistant", "content": "The table shows that the Averaged F1 Micro for our single-per-pass method is 83.1, while for our multiple-per-pass method it is 83.9. The difference is 83.9 - 83.1 = 0.8. So, the answer is {\"answer\": \"0.8\"}." } ], "images": [ "scigen/train/3768.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on SemEval 2018 Task 7, Sub-Task 1.1." ], "reasoning": "The table shows that the Averaged F1 Micro for our single-per-pass method is 83.1, while for our multiple-per-pass method it is 83.9. The difference is 83.9 - 83.1 = 0.8.", "question": "What is the difference in Averaged F1 Micro between our single-per-pass and multiple-per-pass methods?", "answer": "So, the answer is {\"answer\": \"0.8\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence Our multiple-per-pass method has a higher Averaged F1 Macro than the method by Luan et al. (2018). and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nOur multiple-per-pass method has a higher Averaged F1 Macro than the method by Luan et al. (2018).\n" }, { "role": "assistant", "content": "The table shows that the Averaged F1 Macro for our multiple-per-pass method is 80.5, while for the method by Luan et al. (2018) it is 78.9. Since 80.5 is greater than 78.9, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3768.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on SemEval 2018 Task 7, Sub-Task 1.1." ], "reasoning": "The table shows that the Averaged F1 Macro for our multiple-per-pass method is 80.5, while for the method by Luan et al. (2018) it is 78.9. Since 80.5 is greater than 78.9, the statement is supported.", "statement": "Our multiple-per-pass method has a higher Averaged F1 Macro than the method by Luan et al. (2018).", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Таблица 3: English BERT (base) and multilingual BERT accuracies on the English stimuli from marvinlinzen18. Monolingual results are taken from bertgoldberg19.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Multilingual BERT outperformed English BERT in Sentential Complements, Short VP Coordination, and Across a Prepositional Phrase, but underperformed in Within an Object Relative Clause, Across an Object Relative Clause (no relative pronoun), and Reflexive Anaphora Across a Relative Clause.Multilingual BERT performed better than English BERT on Sentential Complements, Short VP Coordination, and Across a Prepositional Phrase, but worse on Within an Object Relative Clause, Across an Object Relative Clause (no relative pronoun), and in Reflexive Anaphora Across a Relative Clause. The omission of the relative pronoun that caused a sharp drop in performance in mBERT, and a milder drop in English BERT. Otherwise, both models had similar accuracies on other stimuli. These results reinforce the finding in LSTMs that multilingual models generally underperform monolingual models of the same architecture, though there are specific contexts in which they can perform slightly better." } ], "images": [ "scigen/train/3769.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Таблица 3: English BERT (base) and multilingual BERT accuracies on the English stimuli from marvinlinzen18. Monolingual results are taken from bertgoldberg19." ], "reasoning": "Multilingual BERT outperformed English BERT in Sentential Complements, Short VP Coordination, and Across a Prepositional Phrase, but underperformed in Within an Object Relative Clause, Across an Object Relative Clause (no relative pronoun), and Reflexive Anaphora Across a Relative Clause.", "description": "Multilingual BERT performed better than English BERT on Sentential Complements, Short VP Coordination, and Across a Prepositional Phrase, but worse on Within an Object Relative Clause, Across an Object Relative Clause (no relative pronoun), and in Reflexive Anaphora Across a Relative Clause. The omission of the relative pronoun that caused a sharp drop in performance in mBERT, and a milder drop in English BERT. Otherwise, both models had similar accuracies on other stimuli. These results reinforce the finding in LSTMs that multilingual models generally underperform monolingual models of the same architecture, though there are specific contexts in which they can perform slightly better.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Таблица 3: English BERT (base) and multilingual BERT accuracies on the English stimuli from marvinlinzen18. Monolingual results are taken from bertgoldberg19.', answer the following question. Give detailed solution consisting of each step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model had a higher accuracy in the 'Within object rel. clause (no that)' category?\n" }, { "role": "assistant", "content": "To determine which model had a higher accuracy in the 'Within object rel. clause (no that)' category, compare the values in the table. Mono has 0.79 and Multi has 0.61. So, the answer is {\"answer\": \"Mono\"}." } ], "images": [ "scigen/train/3769.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Таблица 3: English BERT (base) and multilingual BERT accuracies on the English stimuli from marvinlinzen18. Monolingual results are taken from bertgoldberg19." ], "reasoning": "To determine which model had a higher accuracy in the 'Within object rel. clause (no that)' category, compare the values in the table. Mono has 0.79 and Multi has 0.61.", "question": "Which model had a higher accuracy in the 'Within object rel. clause (no that)' category?", "answer": "So, the answer is {\"answer\": \"Mono\"}." }, { "messages": [ { "role": "user", "content": "Multilingual BERT had a higher average accuracy than English BERT. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nMultilingual BERT had a higher average accuracy than English BERT.\n" }, { "role": "assistant", "content": "The table shows that the average accuracy for Mono is 0.89 and for Multi is 0.87, indicating that English BERT had a higher average accuracy. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3769.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Таблица 3: English BERT (base) and multilingual BERT accuracies on the English stimuli from marvinlinzen18. Monolingual results are taken from bertgoldberg19." ], "reasoning": "The table shows that the average accuracy for Mono is 0.89 and for Multi is 0.87, indicating that English BERT had a higher average accuracy.", "statement": "Multilingual BERT had a higher average accuracy than English BERT.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Таблица 1: LSTM LM test perplexities and accuracies on CLAMS across languages for the language-specific monolingual models and for our multilingual model. Results are averaged across five random initializations. Chance accuracy is 0.5. Boldfaced numbers indicate the model that achieved the highest performance on a given construction across languages.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Monolingual models performed almost perfectly on short dependencies without attractors, while performance was only slightly above chance in the Across an Object Relative Clause condition for all languages except German.Accuracy on short dependencies without attractors—Simple Agreement and Short VP Coordination—was close to perfect in all languages. This suggests that all monolingual models learned the basic facts of agreement, and were able to apply them to the vocabulary items in our materials. At the other end of the spectrum, performance was only slightly higher than chance in the Across an Object Relative Clause condition for all languages except German, suggesting that LSTMs tend to struggle with center embedding—that is, when a subject-verb dependency is nested within another dependency of the same kind marvinlinzen18; noji2020analysis." } ], "images": [ "scigen/train/3770.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Таблица 1: LSTM LM test perplexities and accuracies on CLAMS across languages for the language-specific monolingual models and for our multilingual model. Results are averaged across five random initializations. Chance accuracy is 0.5. Boldfaced numbers indicate the model that achieved the highest performance on a given construction across languages." ], "reasoning": "Monolingual models performed almost perfectly on short dependencies without attractors, while performance was only slightly above chance in the Across an Object Relative Clause condition for all languages except German.", "description": "Accuracy on short dependencies without attractors—Simple Agreement and Short VP Coordination—was close to perfect in all languages. This suggests that all monolingual models learned the basic facts of agreement, and were able to apply them to the vocabulary items in our materials. At the other end of the spectrum, performance was only slightly higher than chance in the Across an Object Relative Clause condition for all languages except German, suggesting that LSTMs tend to struggle with center embedding—that is, when a subject-verb dependency is nested within another dependency of the same kind marvinlinzen18; noji2020analysis.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Таблица 1: LSTM LM test perplexities and accuracies on CLAMS across languages for the language-specific monolingual models and for our multilingual model. Results are averaged across five random initializations. Chance accuracy is 0.5. Boldfaced numbers indicate the model that achieved the highest performance on a given construction across languages.\nQuestion: Which language had the highest accuracy in the Across object relative clause condition?\n" }, { "role": "assistant", "content": "To find the language with the highest accuracy in the Across object relative clause condition, look at the row labeled 'Across object rel. clause' and identify the highest value. The highest value is 0.81 for German Mono. So, the answer is {\"answer\": \"German Mono\"}." } ], "images": [ "scigen/train/3770.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Таблица 1: LSTM LM test perplexities and accuracies on CLAMS across languages for the language-specific monolingual models and for our multilingual model. Results are averaged across five random initializations. Chance accuracy is 0.5. Boldfaced numbers indicate the model that achieved the highest performance on a given construction across languages." ], "reasoning": "To find the language with the highest accuracy in the Across object relative clause condition, look at the row labeled 'Across object rel. clause' and identify the highest value. The highest value is 0.81 for German Mono.", "question": "Which language had the highest accuracy in the Across object relative clause condition?", "answer": "So, the answer is {\"answer\": \"German Mono\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: All monolingual models had perfect accuracy in Simple agreement., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nAll monolingual models had perfect accuracy in Simple agreement.\n" }, { "role": "assistant", "content": "To verify this statement, check the 'Simple agreement' row for all monolingual models. The values are 1.00 for English Mono, French Mono, and German Mono, but Hebrew Mono and Russian Mono have values less than 1.00. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3770.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Таблица 1: LSTM LM test perplexities and accuracies on CLAMS across languages for the language-specific monolingual models and for our multilingual model. Results are averaged across five random initializations. Chance accuracy is 0.5. Boldfaced numbers indicate the model that achieved the highest performance on a given construction across languages." ], "reasoning": "To verify this statement, check the 'Simple agreement' row for all monolingual models. The values are 1.00 for English Mono, French Mono, and German Mono, but Hebrew Mono and Russian Mono have values less than 1.00.", "statement": "All monolingual models had perfect accuracy in Simple agreement.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Таблица 2: Multilingual BERT accuracies on CLAMS. If a hyphen is present, this means that all focus verbs for that particular language and construction were out-of-vocabulary. Chance accuracy is 0.5.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "mBERT's accuracy is high in languages with Latin scripts, particularly English, French, and German, but varies with morphological complexity and script type.mBERT’s accuracies on all stimuli. Performance on CLAMS was fairly high in the languages that are written in Latin script (English, French and German). On English in particular, accuracy was high across conditions, ranging between 0.83 and 0.88 for sentences with relative clauses, and between 0.92 and 1.00 for the remaining conditions. Accuracy in German was also high: above 0.90 on all constructions except Across Subject Relative Clause, where it was 0.73. French accuracy was more variable: high for most conditions, but low for Across Subject Relative Clause and Across Prepositional Phrase. We see the opposite trend for mBERT In other words, accuracy is likely to decrease with increasing morphological complexity. This unexpected inverse correlation may be an artifact of mBERT’s limited vocabulary, especially in non-Latin scripts. Morphologically complex languages have more unique word types. In some languages, this issue can be mitigated to some extent by splitting the word into subword units, as BERT does; however, the effectiveness of such a strategy would be limited at best in a language with non-concatenative morphology such as Hebrew. Finally, we stress that the exclusion of certain stimulus types and the differing amount of training data per-language act as confounding variables, rendering a comparison between mBERT and LSTMs difficult." } ], "images": [ "scigen/train/3771.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Таблица 2: Multilingual BERT accuracies on CLAMS. If a hyphen is present, this means that all focus verbs for that particular language and construction were out-of-vocabulary. Chance accuracy is 0.5." ], "reasoning": "mBERT's accuracy is high in languages with Latin scripts, particularly English, French, and German, but varies with morphological complexity and script type.", "description": "mBERT’s accuracies on all stimuli. Performance on CLAMS was fairly high in the languages that are written in Latin script (English, French and German). On English in particular, accuracy was high across conditions, ranging between 0.83 and 0.88 for sentences with relative clauses, and between 0.92 and 1.00 for the remaining conditions. Accuracy in German was also high: above 0.90 on all constructions except Across Subject Relative Clause, where it was 0.73. French accuracy was more variable: high for most conditions, but low for Across Subject Relative Clause and Across Prepositional Phrase. We see the opposite trend for mBERT In other words, accuracy is likely to decrease with increasing morphological complexity. This unexpected inverse correlation may be an artifact of mBERT’s limited vocabulary, especially in non-Latin scripts. Morphologically complex languages have more unique word types. In some languages, this issue can be mitigated to some extent by splitting the word into subword units, as BERT does; however, the effectiveness of such a strategy would be limited at best in a language with non-concatenative morphology such as Hebrew. Finally, we stress that the exclusion of certain stimulus types and the differing amount of training data per-language act as confounding variables, rendering a comparison between mBERT and LSTMs difficult.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Таблица 2: Multilingual BERT accuracies on CLAMS. If a hyphen is present, this means that all focus verbs for that particular language and construction were out-of-vocabulary. Chance accuracy is 0.5.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the accuracy of mBERT on VP coordination (long) in German?\n" }, { "role": "assistant", "content": "To find the accuracy of mBERT on VP coordination (long) in German, locate the row labeled 'VP coordination (long)' and the column labeled 'German'. So, the answer is {\"answer\": \"1.00\"}" } ], "images": [ "scigen/train/3771.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Таблица 2: Multilingual BERT accuracies on CLAMS. If a hyphen is present, this means that all focus verbs for that particular language and construction were out-of-vocabulary. Chance accuracy is 0.5." ], "reasoning": "To find the accuracy of mBERT on VP coordination (long) in German, locate the row labeled 'VP coordination (long)' and the column labeled 'German'.", "question": "What is the accuracy of mBERT on VP coordination (long) in German?", "answer": "So, the answer is {\"answer\": \"1.00\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis mBERT's accuracy on Across subject relative clause in French is higher than in Hebrew. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nmBERT's accuracy on Across subject relative clause in French is higher than in Hebrew.\n" }, { "role": "assistant", "content": "To verify this statement, compare the accuracy values for 'Across subject relative clause' in the French and Hebrew columns. The accuracy in French is 0.57, and in Hebrew, it is 0.61. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3771.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Таблица 2: Multilingual BERT accuracies on CLAMS. If a hyphen is present, this means that all focus verbs for that particular language and construction were out-of-vocabulary. Chance accuracy is 0.5." ], "reasoning": "To verify this statement, compare the accuracy values for 'Across subject relative clause' in the French and Hebrew columns. The accuracy in French is 0.57, and in Hebrew, it is 0.61.", "statement": "mBERT's accuracy on Across subject relative clause in French is higher than in Hebrew.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Таблица 6: Number of minimal sets for all languages and stimulus types using animate nouns.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The LSTM model is evaluated on various linguistic structures, excluding inanimate-noun cases due to their complexity and the impact of grammatical gender on model performance.These are also the number of examples that the LSTM is evaluated on. We do not include inanimate-noun cases in our evaluations for now, since these are much more difficult to replicate cross-linguistically. Indeed, grammatical gender is a confounding variable which—according to preliminary experiments—does have an effect on model performance. Additionally, Hebrew has differing inflections depending on the combination of the subject and object noun genders, which means that we rarely have all needed inflections in the vocabulary." } ], "images": [ "scigen/train/3772.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Таблица 6: Number of minimal sets for all languages and stimulus types using animate nouns." ], "reasoning": "The LSTM model is evaluated on various linguistic structures, excluding inanimate-noun cases due to their complexity and the impact of grammatical gender on model performance.", "description": "These are also the number of examples that the LSTM is evaluated on. We do not include inanimate-noun cases in our evaluations for now, since these are much more difficult to replicate cross-linguistically. Indeed, grammatical gender is a confounding variable which—according to preliminary experiments—does have an effect on model performance. Additionally, Hebrew has differing inflections depending on the combination of the subject and object noun genders, which means that we rarely have all needed inflections in the vocabulary.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Таблица 6: Number of minimal sets for all languages and stimulus types using animate nouns.', solve the subsequent question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the total number of examples evaluated for Hebrew in the 'Across prepositional phrase' and 'Simple agreement' categories?\n" }, { "role": "assistant", "content": "To find the total number of examples for Hebrew in the specified categories, sum the values in the 'Across prepositional phrase' and 'Simple agreement' rows for Hebrew: 5600 + 140. So, the answer is {\"answer\": \"5740\"}." } ], "images": [ "scigen/train/3772.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Таблица 6: Number of minimal sets for all languages and stimulus types using animate nouns." ], "reasoning": "To find the total number of examples for Hebrew in the specified categories, sum the values in the 'Across prepositional phrase' and 'Simple agreement' rows for Hebrew: 5600 + 140.", "question": "What is the total number of examples evaluated for Hebrew in the 'Across prepositional phrase' and 'Simple agreement' categories?", "answer": "So, the answer is {\"answer\": \"5740\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The number of examples evaluated for German in the 'VP coordination (short)' category is higher than that for English. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe number of examples evaluated for German in the 'VP coordination (short)' category is higher than that for English.\n" }, { "role": "assistant", "content": "The table shows that the number of examples for both German and English in the 'VP coordination (short)' category is 980, which means the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3772.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Таблица 6: Number of minimal sets for all languages and stimulus types using animate nouns." ], "reasoning": "The table shows that the number of examples for both German and English in the 'VP coordination (short)' category is 980, which means the statement is incorrect.", "statement": "The number of examples evaluated for German in the 'VP coordination (short)' category is higher than that for English.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Таблица 7: Number of minimal sets used by BERT (English monolingual only) and mBERT for evaluation. The number of monolingual English examples is the same as in bertgoldberg19. Hyphens indicate non-replicable stimulus types, and 0 indicates that all focus verbs for a given stimulus type were out-of-vocabulary.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The table compares subject-verb agreement and reflexive anaphora across multiple languages, showing more examples for English due to the inclusion of both animate and inanimate nouns.Important to note is that for these evaluations, we use stimulus types containing both animate and inanimate nouns to better match bertgoldberg19’s experimental setup; this is why we have more examples for English in this table than for the LSTM evaluations. Including or excluding inanimate nouns was found to make no significant difference in the final scores (for BERT or mBERT) regardless, since the performance of the model never diverges by more than 0.02 for animate vs. inanimate stimulus types." } ], "images": [ "scigen/train/3773.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Таблица 7: Number of minimal sets used by BERT (English monolingual only) and mBERT for evaluation. The number of monolingual English examples is the same as in bertgoldberg19. Hyphens indicate non-replicable stimulus types, and 0 indicates that all focus verbs for a given stimulus type were out-of-vocabulary." ], "reasoning": "The table compares subject-verb agreement and reflexive anaphora across multiple languages, showing more examples for English due to the inclusion of both animate and inanimate nouns.", "description": "Important to note is that for these evaluations, we use stimulus types containing both animate and inanimate nouns to better match bertgoldberg19’s experimental setup; this is why we have more examples for English in this table than for the LSTM evaluations. Including or excluding inanimate nouns was found to make no significant difference in the final scores (for BERT or mBERT) regardless, since the performance of the model never diverges by more than 0.02 for animate vs. inanimate stimulus types.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Таблица 7: Number of minimal sets used by BERT (English monolingual only) and mBERT for evaluation. The number of monolingual English examples is the same as in bertgoldberg19. Hyphens indicate non-replicable stimulus types, and 0 indicates that all focus verbs for a given stimulus type were out-of-vocabulary.' in mind, break down and resolve the question below step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the difference in subject-verb agreement scores between English Mono and German for VP coordination (short)?\n" }, { "role": "assistant", "content": "The table shows that the subject-verb agreement score for English Mono in VP coordination (short) is 720, while for German it is 700. The difference is 720 - 700 = 20. So, the answer is {\"answer\": \"20\"}." } ], "images": [ "scigen/train/3773.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Таблица 7: Number of minimal sets used by BERT (English monolingual only) and mBERT for evaluation. The number of monolingual English examples is the same as in bertgoldberg19. Hyphens indicate non-replicable stimulus types, and 0 indicates that all focus verbs for a given stimulus type were out-of-vocabulary." ], "reasoning": "The table shows that the subject-verb agreement score for English Mono in VP coordination (short) is 720, while for German it is 700. The difference is 720 - 700 = 20.", "question": "What is the difference in subject-verb agreement scores between English Mono and German for VP coordination (short)?", "answer": "So, the answer is {\"answer\": \"20\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe subject-verb agreement score for French in a sentential complement is 1440.\n" }, { "role": "assistant", "content": "The table shows that there is no score listed for French in a sentential complement, indicating that the statement is not supported by the table. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3773.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Таблица 7: Number of minimal sets used by BERT (English monolingual only) and mBERT for evaluation. The number of monolingual English examples is the same as in bertgoldberg19. Hyphens indicate non-replicable stimulus types, and 0 indicates that all focus verbs for a given stimulus type were out-of-vocabulary." ], "reasoning": "The table shows that there is no score listed for French in a sentential complement, indicating that the statement is not supported by the table.", "statement": "The subject-verb agreement score for French in a sentential complement is 1440.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 2: Results on TriviaQA web using our pipelined method. We significantly improve upon the baseline by combining the preprocessing procedures, TF-IDF paragraph selection, the sum objective, and our model design.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The implementation of the baseline from Joshi et al. achieved an EM of 41.08 and an F1 of 47.40, while the refined model with TF-IDF and sum objective achieved state-of-the-art results with an EM of 61.10 and an F1 of 66.04.First, we do an ablation study on TriviaQA web to show the effects of our proposed methods for our pipeline model. We start with an implementation of the baseline from Joshi et al. Their system selects paragraphs by taking the first 400 tokens of each document, uses BiDAF Seo et al. ’s cross entropy loss function during training. Paragraphs of size 800 are used at test time. significantly, likely because we are not subsampling the data. We find both TF-IDF ranking and the sum objective to be effective; even without changing the model we achieve state-of-the-art results. Using our refined model increases the gain by another 4 points." } ], "images": [ "scigen/train/3774.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on TriviaQA web using our pipelined method. We significantly improve upon the baseline by combining the preprocessing procedures, TF-IDF paragraph selection, the sum objective, and our model design." ], "reasoning": "The implementation of the baseline from Joshi et al. achieved an EM of 41.08 and an F1 of 47.40, while the refined model with TF-IDF and sum objective achieved state-of-the-art results with an EM of 61.10 and an F1 of 66.04.", "description": "First, we do an ablation study on TriviaQA web to show the effects of our proposed methods for our pipeline model. We start with an implementation of the baseline from Joshi et al. Their system selects paragraphs by taking the first 400 tokens of each document, uses BiDAF Seo et al. ’s cross entropy loss function during training. Paragraphs of size 800 are used at test time. significantly, likely because we are not subsampling the data. We find both TF-IDF ranking and the sum objective to be effective; even without changing the model we achieve state-of-the-art results. Using our refined model increases the gain by another 4 points.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 2: Results on TriviaQA web using our pipelined method. We significantly improve upon the baseline by combining the preprocessing procedures, TF-IDF paragraph selection, the sum objective, and our model design.\nQuestion: What is the F1 score of the model that uses BiDAF with TF-IDF and sum objective?\n" }, { "role": "assistant", "content": "The table shows the F1 scores for different models. The model that uses BiDAF with TF-IDF and sum objective has an F1 score of 62.44. So, the answer is {\"answer\": \"62.44\"}" } ], "images": [ "scigen/train/3774.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on TriviaQA web using our pipelined method. We significantly improve upon the baseline by combining the preprocessing procedures, TF-IDF paragraph selection, the sum objective, and our model design." ], "reasoning": "The table shows the F1 scores for different models. The model that uses BiDAF with TF-IDF and sum objective has an F1 score of 62.44.", "question": "What is the F1 score of the model that uses BiDAF with TF-IDF and sum objective?", "answer": "So, the answer is {\"answer\": \"62.44\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The baseline model from Joshi et al. achieved an EM score higher than 50. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe baseline model from Joshi et al. achieved an EM score higher than 50.\n" }, { "role": "assistant", "content": "The table shows that the baseline model from Joshi et al. achieved an EM score of 41.08, which is lower than 50. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3774.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on TriviaQA web using our pipelined method. We significantly improve upon the baseline by combining the preprocessing procedures, TF-IDF paragraph selection, the sum objective, and our model design." ], "reasoning": "The table shows that the baseline model from Joshi et al. achieved an EM score of 41.08, which is lower than 50.", "statement": "The baseline model from Joshi et al. achieved an EM score higher than 50.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Published TriviaQA results. We advance the state of the art by about 15 points both test sets.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The S-Norm model achieved the highest scores on both the dev and verified sets, indicating its superior performance.We use the shared-norm approach for evaluation on the TriviaQA test set. We found that increasing the paragraph size to 800 at test time, and re-training the model on paragraphs of size 600, was slightly beneficial, allowing our model to reach 66.04 EM and 70.98 F1 on the dev set. Note that human annotators have estimated that only 75.4% of the question-document pairs contain sufficient evidence to answer the question Joshi et al. However, the score of 83.7 F1 on the verified set suggests that there is still room for improvement." } ], "images": [ "scigen/train/3775.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Published TriviaQA results. We advance the state of the art by about 15 points both test sets." ], "reasoning": "The S-Norm model achieved the highest scores on both the dev and verified sets, indicating its superior performance.", "description": "We use the shared-norm approach for evaluation on the TriviaQA test set. We found that increasing the paragraph size to 800 at test time, and re-training the model on paragraphs of size 600, was slightly beneficial, allowing our model to reach 66.04 EM and 70.98 F1 on the dev set. Note that human annotators have estimated that only 75.4% of the question-document pairs contain sufficient evidence to answer the question Joshi et al. However, the score of 83.7 F1 on the verified set suggests that there is still room for improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Published TriviaQA results. We advance the state of the art by about 15 points both test sets.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}. \n\nQuestion: Which model achieved the highest F1 score on the verified set?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By comparing the Verified F1 scores of all models, the S-Norm model has the highest score of 83.70. So, the answer is {\"answer\": \"S-Norm\"}" } ], "images": [ "scigen/train/3775.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Published TriviaQA results. We advance the state of the art by about 15 points both test sets." ], "reasoning": "By comparing the Verified F1 scores of all models, the S-Norm model has the highest score of 83.70.", "question": "Which model achieved the highest F1 score on the verified set?", "answer": "So, the answer is {\"answer\": \"S-Norm\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The Mnemonic Reader model achieved a higher EM score on the verified set than the MEMEN model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Mnemonic Reader model achieved a higher EM score on the verified set than the MEMEN model.\n" }, { "role": "assistant", "content": "The Mnemonic Reader model has a Verified EM score of 54.45, while the MEMEN model has a Verified EM score of 49.28. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3775.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Published TriviaQA results. We advance the state of the art by about 15 points both test sets." ], "reasoning": "The Mnemonic Reader model has a Verified EM score of 54.45, while the MEMEN model has a Verified EM score of 49.28.", "statement": "The Mnemonic Reader model achieved a higher EM score on the verified set than the MEMEN model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Distribution of frequency of unique utterances. The utterances with high frequency correspond to the common pass-phrases. For the utterances with lower frequency, each of these are spoken by 3 different speakers, and 4-fold augmentation gives a total of 12.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The data set includes 2484 utterances with 460 unique transcriptions, increasing the total number of utterances to 9936.Our primary reason for using this data is that it contains both common and unique utterances across a variety of speakers. We use the enrollment utterances corresponding to the common, unique, and free-choice pass-phrase portions of the data set. This gives us a total of 2484 utterances, comprising 460 unique transcriptions. This increases the total number of utterances to 9936." } ], "images": [ "scigen/train/3776.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Distribution of frequency of unique utterances. The utterances with high frequency correspond to the common pass-phrases. For the utterances with lower frequency, each of these are spoken by 3 different speakers, and 4-fold augmentation gives a total of 12." ], "reasoning": "The data set includes 2484 utterances with 460 unique transcriptions, increasing the total number of utterances to 9936.", "description": "Our primary reason for using this data is that it contains both common and unique utterances across a variety of speakers. We use the enrollment utterances corresponding to the common, unique, and free-choice pass-phrase portions of the data set. This gives us a total of 2484 utterances, comprising 460 unique transcriptions. This increases the total number of utterances to 9936.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: 'Table 2: Distribution of frequency of unique utterances. The utterances with high frequency correspond to the common pass-phrases. For the utterances with lower frequency, each of these are spoken by 3 different speakers, and 4-fold augmentation gives a total of 12.'\nQuestion: What is the total number of utterances for the unique utterances with a frequency of 456?\n" }, { "role": "assistant", "content": "The table shows that the unique utterances with a frequency of 456 have a total of 3648 utterances. So, the answer is {\"answer\": \"3648\"}" } ], "images": [ "scigen/train/3776.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Distribution of frequency of unique utterances. The utterances with high frequency correspond to the common pass-phrases. For the utterances with lower frequency, each of these are spoken by 3 different speakers, and 4-fold augmentation gives a total of 12." ], "reasoning": "The table shows that the unique utterances with a frequency of 456 have a total of 3648 utterances.", "question": "What is the total number of utterances for the unique utterances with a frequency of 456?", "answer": "So, the answer is {\"answer\": \"3648\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The total number of utterances for 450 unique utterances is 5400. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe total number of utterances for 450 unique utterances is 5400.\n" }, { "role": "assistant", "content": "The table shows that for 450 unique utterances, the total number of utterances is indeed 5400. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3776.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Distribution of frequency of unique utterances. The utterances with high frequency correspond to the common pass-phrases. For the utterances with lower frequency, each of these are spoken by 3 different speakers, and 4-fold augmentation gives a total of 12." ], "reasoning": "The table shows that for 450 unique utterances, the total number of utterances is indeed 5400.", "statement": "The total number of utterances for 450 unique utterances is 5400.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: Performance of the embeddings in predicting the 10 common phrase utterances. P, R, and F denote precision, recall, and F-score, respectively.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "x-vectors, although trained to predict speaker labels, retain some lexical content information, performing well on specific words but poorly on common words, similar to i-vectors but with more pronounced differences.Since several of these utterances are spoken by multiple speakers, it seems very plausible that to achieve a high recognition performance, the embedding must incidentally capture some transcription information in addition to the speaker information. Although the x-vector DNN is trained on a large amount of unconstrained speech to predict only the speaker label, we find the x-vectors (with or without augmentation) are still not wholly invariant to lexical content, and they do retain information about what was spoken. It may be observed that x-vectors are good at recognizing words such as google, lawyers, password, and so on, but fare poorly on more common words such as and, he, and in. While i-vectors show a similar trend, the difference is less pronounced." } ], "images": [ "scigen/train/3777.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of the embeddings in predicting the 10 common phrase utterances. P, R, and F denote precision, recall, and F-score, respectively." ], "reasoning": "x-vectors, although trained to predict speaker labels, retain some lexical content information, performing well on specific words but poorly on common words, similar to i-vectors but with more pronounced differences.", "description": "Since several of these utterances are spoken by multiple speakers, it seems very plausible that to achieve a high recognition performance, the embedding must incidentally capture some transcription information in addition to the speaker information. Although the x-vector DNN is trained on a large amount of unconstrained speech to predict only the speaker label, we find the x-vectors (with or without augmentation) are still not wholly invariant to lexical content, and they do retain information about what was spoken. It may be observed that x-vectors are good at recognizing words such as google, lawyers, password, and so on, but fare poorly on more common words such as and, he, and in. While i-vectors show a similar trend, the difference is less pronounced.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 3: Performance of the embeddings in predicting the 10 common phrase utterances. P, R, and F denote precision, recall, and F-score, respectively.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich vector type and dimension combination has the highest recall?\n" }, { "role": "assistant", "content": "To find the highest recall, compare the recall values for both i-vectors and x-vectors across all dimensions. The highest recall value is 0.86 for x-vectors at dimension 768. So, the answer is {\"answer\": \"x-vector at dimension 768\"}" } ], "images": [ "scigen/train/3777.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of the embeddings in predicting the 10 common phrase utterances. P, R, and F denote precision, recall, and F-score, respectively." ], "reasoning": "To find the highest recall, compare the recall values for both i-vectors and x-vectors across all dimensions. The highest recall value is 0.86 for x-vectors at dimension 768.", "question": "Which vector type and dimension combination has the highest recall?", "answer": "So, the answer is {\"answer\": \"x-vector at dimension 768\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nx-vectors perform better than i-vectors in terms of precision at dimension 512.\n" }, { "role": "assistant", "content": "Comparing the precision values at dimension 512, x-vectors have a precision of 0.77 while i-vectors have a precision of 0.64, indicating that x-vectors perform better. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3777.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of the embeddings in predicting the 10 common phrase utterances. P, R, and F denote precision, recall, and F-score, respectively." ], "reasoning": "Comparing the precision values at dimension 512, x-vectors have a precision of 0.77 while i-vectors have a precision of 0.64, indicating that x-vectors perform better.", "statement": "x-vectors perform better than i-vectors in terms of precision at dimension 512.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: Which tasks help RTE? Pairwise differences are statistically significant (p'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Training on the RTE task with the MNLI dataset alone slightly outperforms training on all tasks, indicating that the large MNLI dataset sufficiently regularizes the model.Comparing Combinations of Tasks. A related benefit of multi-task learning is the transfer of learned “knowledge” between closely related tasks. We investigate these two benefits by comparing several models on the RTE task, including one trained with a very closely related task (MNLI, a much large textual entailment dataset) and one trained with fairly unrelated tasks (QQP, CoLA, and SST). We use Single→Multi distillation (Single→Single in the case of the RTE-only model). We find both sets of auxiliary tasks improve RTE performance, suggesting that both benefits are playing a role in improving multi-task models. Interestingly, RTE + MNLI alone slightly outperforms the model performing all tasks, perhaps because training on MNLI, which has a very large dataset, is already enough to sufficiently regularize the model." } ], "images": [ "scigen/train/3778.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Which tasks help RTE? Pairwise differences are statistically significant (p" ], "reasoning": "Training on the RTE task with the MNLI dataset alone slightly outperforms training on all tasks, indicating that the large MNLI dataset sufficiently regularizes the model.", "description": "Comparing Combinations of Tasks. A related benefit of multi-task learning is the transfer of learned “knowledge” between closely related tasks. We investigate these two benefits by comparing several models on the RTE task, including one trained with a very closely related task (MNLI, a much large textual entailment dataset) and one trained with fairly unrelated tasks (QQP, CoLA, and SST). We use Single→Multi distillation (Single→Single in the case of the RTE-only model). We find both sets of auxiliary tasks improve RTE performance, suggesting that both benefits are playing a role in improving multi-task models. Interestingly, RTE + MNLI alone slightly outperforms the model performing all tasks, perhaps because training on MNLI, which has a very large dataset, is already enough to sufficiently regularize the model.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 5: Which tasks help RTE? Pairwise differences are statistically significant (p' to sequentially solve the question presented below. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich combination of tasks resulted in the highest RTE score?\n" }, { "role": "assistant", "content": "By comparing the RTE scores for each combination of tasks in the table, the highest score is found in the RTE + MNLI combination with a score of 83.4. So, the answer is {\"answer\": \"RTE + MNLI\"}." } ], "images": [ "scigen/train/3778.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Which tasks help RTE? Pairwise differences are statistically significant (p" ], "reasoning": "By comparing the RTE scores for each combination of tasks in the table, the highest score is found in the RTE + MNLI combination with a score of 83.4.", "question": "Which combination of tasks resulted in the highest RTE score?", "answer": "So, the answer is {\"answer\": \"RTE + MNLI\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTraining on RTE + QQP + CoLA + SST resulted in the highest RTE score.\n" }, { "role": "assistant", "content": "The table shows that the RTE + QQP + CoLA + SST combination has an RTE score of 75.1, which is not the highest. The highest score is 83.4 for the RTE + MNLI combination. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3778.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Which tasks help RTE? Pairwise differences are statistically significant (p" ], "reasoning": "The table shows that the RTE + QQP + CoLA + SST combination has an RTE score of 75.1, which is not the highest. The highest score is 83.4 for the RTE + MNLI combination.", "statement": "Training on RTE + QQP + CoLA + SST resulted in the highest RTE score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 1: Comparison of methods on the GLUE dev set. ∗, ∗∗, and ∗∗∗ indicate statistically significant (p', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Single→Multi knowledge distillation improves or matches performance on all tasks except STS, where it does not work well due to the lack of class distribution aiding learning.Main Results. While standard multi-task training improves over single-task training for RTE (likely because it is closely related to MNLI), there is no improvement on the other tasks. In contrast, Single→Multi knowledge distillation improves or matches the performance of the other methods on all tasks except STS, the only regression task in GLUE. We believe distillation does not work well for regression tasks because there is no distribution over classes passed on by the teacher to aid learning." } ], "images": [ "scigen/train/3779.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison of methods on the GLUE dev set. ∗, ∗∗, and ∗∗∗ indicate statistically significant (p" ], "reasoning": "Single→Multi knowledge distillation improves or matches performance on all tasks except STS, where it does not work well due to the lack of class distribution aiding learning.", "description": "Main Results. While standard multi-task training improves over single-task training for RTE (likely because it is closely related to MNLI), there is no improvement on the other tasks. In contrast, Single→Multi knowledge distillation improves or matches the performance of the other methods on all tasks except STS, the only regression task in GLUE. We believe distillation does not work well for regression tasks because there is no distribution over classes passed on by the teacher to aid learning.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Comparison of methods on the GLUE dev set. ∗, ∗∗, and ∗∗∗ indicate statistically significant (p', answer the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model shows the highest improvement in the RTEh task?\n" }, { "role": "assistant", "content": "By comparing the RTEh scores across all models, Single→Multi shows the highest score of 82.8, indicating the highest improvement. So, the answer is {\"answer\": \"Single→Multi\"}" } ], "images": [ "scigen/train/3779.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison of methods on the GLUE dev set. ∗, ∗∗, and ∗∗∗ indicate statistically significant (p" ], "reasoning": "By comparing the RTEh scores across all models, Single→Multi shows the highest score of 82.8, indicating the highest improvement.", "question": "Which model shows the highest improvement in the RTEh task?", "answer": "So, the answer is {\"answer\": \"Single→Multi\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nSingle→Single model performs better than Multi model on the CoLAa task.\n" }, { "role": "assistant", "content": "The table shows that the Single→Single model has a CoLAa score of 61.7, which is higher than the Multi model score of 60.3. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3779.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison of methods on the GLUE dev set. ∗, ∗∗, and ∗∗∗ indicate statistically significant (p" ], "reasoning": "The table shows that the Single→Single model has a CoLAa score of 61.7, which is higher than the Multi model score of 60.3.", "statement": "Single→Single model performs better than Multi model on the CoLAa task.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Comparison of test set results. *MT-DNNKD is distilled from a diverse ensemble of models.'.\n" }, { "role": "assistant", "content": "The table compares various models' GLUE scores, highlighting that BERT-Large + BAM (ours) achieved a score of 82.3, which is competitive with other top-performing models.Test Set Results. We compare against recent work by submitting to the GLUE leaderboard. We use Single→Multi distillation. Following the procedure used by BERT, we train multiple models and submit the one with the highest average dev set score to the test set. BERT trained 10 models for each task (80 total); we trained 20 multi-task models. Single-Task Fine-Tuning. That is, we do not further train the model on individual tasks after the multi-task training finishes. While single-task fine-tuning improves results, we think to some extent it defeats the purpose of multi-task learning: the result of training is one model for each task instead of a model that can perform all of the tasks. Compared to having many single-task models, a multi-task model is simpler to deploy, faster to run, and arguably more scientifically interesting from the perspective of building general language-processing systems." } ], "images": [ "scigen/train/3780.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of test set results. *MT-DNNKD is distilled from a diverse ensemble of models." ], "reasoning": "The table compares various models' GLUE scores, highlighting that BERT-Large + BAM (ours) achieved a score of 82.3, which is competitive with other top-performing models.", "description": "Test Set Results. We compare against recent work by submitting to the GLUE leaderboard. We use Single→Multi distillation. Following the procedure used by BERT, we train multiple models and submit the one with the highest average dev set score to the test set. BERT trained 10 models for each task (80 total); we trained 20 multi-task models. Single-Task Fine-Tuning. That is, we do not further train the model on individual tasks after the multi-task training finishes. While single-task fine-tuning improves results, we think to some extent it defeats the purpose of multi-task learning: the result of training is one model for each task instead of a model that can perform all of the tasks. Compared to having many single-task models, a multi-task model is simpler to deploy, faster to run, and arguably more scientifically interesting from the perspective of building general language-processing systems.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 2: Comparison of test set results. *MT-DNNKD is distilled from a diverse ensemble of models.' and proceed to solve the following question in a stepwise manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich model achieved the highest GLUE score according to the table?\n" }, { "role": "assistant", "content": "To find the highest GLUE score, we need to compare the scores of all models listed in the table. The highest score is 83.7, achieved by MT-DNN KD* Liu et al. (2019a). So, the answer is {\"answer\": \"MT-DNN KD* Liu et al. (2019a)\"}" } ], "images": [ "scigen/train/3780.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of test set results. *MT-DNNKD is distilled from a diverse ensemble of models." ], "reasoning": "To find the highest GLUE score, we need to compare the scores of all models listed in the table. The highest score is 83.7, achieved by MT-DNN KD* Liu et al. (2019a).", "question": "Which model achieved the highest GLUE score according to the table?", "answer": "So, the answer is {\"answer\": \"MT-DNN KD* Liu et al. (2019a)\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe BERT-Large + BAM model achieved a GLUE score higher than 83.0.\n" }, { "role": "assistant", "content": "The table shows that the BERT-Large + BAM model achieved a GLUE score of 82.3, which is not higher than 83.0. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3780.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of test set results. *MT-DNNKD is distilled from a diverse ensemble of models." ], "reasoning": "The table shows that the BERT-Large + BAM model achieved a GLUE score of 82.3, which is not higher than 83.0.", "statement": "The BERT-Large + BAM model achieved a GLUE score higher than 83.0.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Combining multi-task training with single-task fine-tuning. Improvements are statistically significant (p'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Single-task fine-tuning improves results slightly over Single→Multi distillation, indicating that distillation provides many benefits of single-task training while producing a unified model.Single-task fine-tuning initializes models with multi-task-learned weights and then performs single-task training. Hyperparameters are the same as for our single-task models except we use a smaller learning rate of 1e-5. While single-task fine-tuning unsurprisingly improves results, the gain on top of Single→Multi distillation is small, reinforcing the claim that distillation provides many of the benefits of single-task training while producing a single unified model instead of many task-specific models." } ], "images": [ "scigen/train/3781.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Combining multi-task training with single-task fine-tuning. Improvements are statistically significant (p" ], "reasoning": "Single-task fine-tuning improves results slightly over Single→Multi distillation, indicating that distillation provides many benefits of single-task training while producing a unified model.", "description": "Single-task fine-tuning initializes models with multi-task-learned weights and then performs single-task training. Hyperparameters are the same as for our single-task models except we use a smaller learning rate of 1e-5. While single-task fine-tuning unsurprisingly improves results, the gain on top of Single→Multi distillation is small, reinforcing the claim that distillation provides many of the benefits of single-task training while producing a single unified model instead of many task-specific models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 3: Combining multi-task training with single-task fine-tuning. Improvements are statistically significant (p', answer the following question. Give detailed solution consisting of each step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the average score of the Single→Multi model after single-task fine-tuning?\n" }, { "role": "assistant", "content": "The Single→Multi model has an average score of 86.0, and single-task fine-tuning adds 0.1 to this score. So, the answer is {\"answer\": \"86.1\"}" } ], "images": [ "scigen/train/3781.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Combining multi-task training with single-task fine-tuning. Improvements are statistically significant (p" ], "reasoning": "The Single→Multi model has an average score of 86.0, and single-task fine-tuning adds 0.1 to this score.", "question": "What is the average score of the Single→Multi model after single-task fine-tuning?", "answer": "So, the answer is {\"answer\": \"86.1\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nSingle-task fine-tuning improves the Multi model's average score by 0.3.\n" }, { "role": "assistant", "content": "The table shows that the Multi model's average score increases by 0.3 after single-task fine-tuning. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3781.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Combining multi-task training with single-task fine-tuning. Improvements are statistically significant (p" ], "reasoning": "The table shows that the Multi model's average score increases by 0.3 after single-task fine-tuning.", "statement": "Single-task fine-tuning improves the Multi model's average score by 0.3.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 1: CIDEr scores for image captioning using bag of objects variants as visual representations. We compare the results of using ground truth annotations (GT) and the output of a detector (Detect). As comparison we also provide, in the first row, the results of using a ResNet-152 POOL5 CNN image embedding with our implementation of an end-to-end IC system.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Object-based representations generally show higher or comparable CIDEr scores compared to pure CNN embeddings, with frequency counts being essential and the combination of all three features producing the best results.Compared to a pure CNN embedding (ResNet-152 POOL5), our object-based representations show higher (for ground truth annotations) or comparable CIDEr scores (for detectors). Our first observation is that frequency counts are essential to IC. Using normalized counts as a representation gives poorer results, which intuitively makes sense: An image with 20 cars and 10 people is significantly different from an image with two cars and one person. Using binarized counts (presence or absence) brings the score further down. This is to be expected: An image with one person is very different from one with 10 people. The combination of all three features produces the best results. These results are interesting, as adding spatial information of even just one object per category can produce a better score. This has, to our knowledge, not been previously demonstrated. The performance of using an explicit detector rather than ground truth annotations is poorer, as expected from noisy detections. However, the overall trend generally remains similar, except for the combination of all three features which gave poorer scores." } ], "images": [ "scigen/train/3782.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: CIDEr scores for image captioning using bag of objects variants as visual representations. We compare the results of using ground truth annotations (GT) and the output of a detector (Detect). As comparison we also provide, in the first row, the results of using a ResNet-152 POOL5 CNN image embedding with our implementation of an end-to-end IC system." ], "reasoning": "Object-based representations generally show higher or comparable CIDEr scores compared to pure CNN embeddings, with frequency counts being essential and the combination of all three features producing the best results.", "description": "Compared to a pure CNN embedding (ResNet-152 POOL5), our object-based representations show higher (for ground truth annotations) or comparable CIDEr scores (for detectors). Our first observation is that frequency counts are essential to IC. Using normalized counts as a representation gives poorer results, which intuitively makes sense: An image with 20 cars and 10 people is significantly different from an image with two cars and one person. Using binarized counts (presence or absence) brings the score further down. This is to be expected: An image with one person is very different from one with 10 people. The combination of all three features produces the best results. These results are interesting, as adding spatial information of even just one object per category can produce a better score. This has, to our knowledge, not been previously demonstrated. The performance of using an explicit detector rather than ground truth annotations is poorer, as expected from noisy detections. However, the overall trend generally remains similar, except for the combination of all three features which gave poorer scores.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 1: CIDEr scores for image captioning using bag of objects variants as visual representations. We compare the results of using ground truth annotations (GT) and the output of a detector (Detect). As comparison we also provide, in the first row, the results of using a ResNet-152 POOL5 CNN image embedding with our implementation of an end-to-end IC system.', answer the following question. Give detailed solution consisting of each step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat representation combination produces the highest CIDEr score for ground truth annotations?\n" }, { "role": "assistant", "content": "The table shows various representations and their CIDEr scores for ground truth annotations (GT). The highest score for GT is 0.849, which corresponds to the 'All three features' representation. So, the answer is {\"answer\": \"All three features\"}." } ], "images": [ "scigen/train/3782.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: CIDEr scores for image captioning using bag of objects variants as visual representations. We compare the results of using ground truth annotations (GT) and the output of a detector (Detect). As comparison we also provide, in the first row, the results of using a ResNet-152 POOL5 CNN image embedding with our implementation of an end-to-end IC system." ], "reasoning": "The table shows various representations and their CIDEr scores for ground truth annotations (GT). The highest score for GT is 0.849, which corresponds to the 'All three features' representation.", "question": "What representation combination produces the highest CIDEr score for ground truth annotations?", "answer": "So, the answer is {\"answer\": \"All three features\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nUsing binarized counts produces the lowest CIDEr score for ground truth annotations.\n" }, { "role": "assistant", "content": "The table shows that the CIDEr score for binarized counts under ground truth annotations (GT) is 0.751. However, the normalized counts representation has a lower score of 0.762, refuting the statement. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3782.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: CIDEr scores for image captioning using bag of objects variants as visual representations. We compare the results of using ground truth annotations (GT) and the output of a detector (Detect). As comparison we also provide, in the first row, the results of using a ResNet-152 POOL5 CNN image embedding with our implementation of an end-to-end IC system." ], "reasoning": "The table shows that the CIDEr score for binarized counts under ground truth annotations (GT) is 0.751. However, the normalized counts representation has a lower score of 0.762, refuting the statement.", "statement": "Using binarized counts produces the lowest CIDEr score for ground truth annotations.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: CIDEr scores for captioning comparing the use of min, max or average pooling of either object size or distance features, using ground truth annotations.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Smaller object instances are less important for IC, and using the object furthest from the image centre gave slightly better results than the one closest.We also experimented with different methods for aggregating multiple instances of the same category, in addition to choosing the biggest instance and the instance closest to the image centre. For example, choosing the smallest instance (min pooling) or the instance furthest away from the image centre (max pooling), or just averaging them (mean pooling). For object size, the findings are as expected: Smaller object instances are less important for IC, although averaging them works comparably well. Surprisingly, in the case of distance, using the object furthest from the image centre actually gave slightly better results than the one closest. Further inspection revealed that aggregating instances is not effective in some cases. We found that the positional information (and interaction with other objects) captured by the object further away may sometimes represent the semantics of the image better than the object in the centre of the image. Representing the person as the one furthest from the image will result in some inference (from training data) that there could be more than one person in the image sitting around the kitchen counter rather than a single person standing at the kitchen counter." } ], "images": [ "scigen/train/3783.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: CIDEr scores for captioning comparing the use of min, max or average pooling of either object size or distance features, using ground truth annotations." ], "reasoning": "Smaller object instances are less important for IC, and using the object furthest from the image centre gave slightly better results than the one closest.", "description": "We also experimented with different methods for aggregating multiple instances of the same category, in addition to choosing the biggest instance and the instance closest to the image centre. For example, choosing the smallest instance (min pooling) or the instance furthest away from the image centre (max pooling), or just averaging them (mean pooling). For object size, the findings are as expected: Smaller object instances are less important for IC, although averaging them works comparably well. Surprisingly, in the case of distance, using the object furthest from the image centre actually gave slightly better results than the one closest. Further inspection revealed that aggregating instances is not effective in some cases. We found that the positional information (and interaction with other objects) captured by the object further away may sometimes represent the semantics of the image better than the object in the centre of the image. Representing the person as the one furthest from the image will result in some inference (from training data) that there could be more than one person in the image sitting around the kitchen counter rather than a single person standing at the kitchen counter.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 2: CIDEr scores for captioning comparing the use of min, max or average pooling of either object size or distance features, using ground truth annotations.'.\n\nQuestion: Which pooling method gave the best result for object size?\n" }, { "role": "assistant", "content": "By comparing the values for object size across min, max, and mean pooling methods, the highest value is 0.793 for max pooling. So, the answer is {\"answer\": \"Max\"}" } ], "images": [ "scigen/train/3783.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: CIDEr scores for captioning comparing the use of min, max or average pooling of either object size or distance features, using ground truth annotations." ], "reasoning": "By comparing the values for object size across min, max, and mean pooling methods, the highest value is 0.793 for max pooling.", "question": "Which pooling method gave the best result for object size?", "answer": "So, the answer is {\"answer\": \"Max\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nMean pooling gave the best result for object distance.\n" }, { "role": "assistant", "content": "By comparing the values for object distance across min, max, and mean pooling methods, the highest value is 0.768 for max pooling, not mean pooling. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3783.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: CIDEr scores for captioning comparing the use of min, max or average pooling of either object size or distance features, using ground truth annotations." ], "reasoning": "By comparing the values for object distance across min, max, and mean pooling methods, the highest value is 0.768 for max pooling, not mean pooling.", "statement": "Mean pooling gave the best result for object distance.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: k-Nearest Neighbour (k=5) trial on the ground truth bag of objects (Freq.) and the projected bag of objects (Proj.) representations. The references are captions of 5-nearest images in each space. Exact represents a subset of 2301 samples where all the 5 neighbours have 0 distance (replicas) and ¬ represents nearest neighbours that are not replicas of the test image.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The analysis shows that exact image matches have perfect scores across all metrics, while projected image representations perform better than frequency-based methods for non-exact matches.Further analysis showed that 2301 out of 5000 captions had nearest images at a zero distance, i.e., the same exact representation was seen at least 5 times in training (note that CIDEr gives a score of 10 only if the test caption and all references are the same). We found that among the non-exact image matches, the projected image representation better captures candidates in the training set than bag of objects. Note that the nearest neighbours are an approximation since we do not know the exact distance metric derived from the LSTM. We observe that the captions for unseen representations seem to be interpolated from multiple neighbouring points in the projection space, but further work is needed to analyze the hidden representations of the LSTM to understand the language model and to give firmer conclusions." } ], "images": [ "scigen/train/3784.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: k-Nearest Neighbour (k=5) trial on the ground truth bag of objects (Freq.) and the projected bag of objects (Proj.) representations. The references are captions of 5-nearest images in each space. Exact represents a subset of 2301 samples where all the 5 neighbours have 0 distance (replicas) and ¬ represents nearest neighbours that are not replicas of the test image." ], "reasoning": "The analysis shows that exact image matches have perfect scores across all metrics, while projected image representations perform better than frequency-based methods for non-exact matches.", "description": "Further analysis showed that 2301 out of 5000 captions had nearest images at a zero distance, i.e., the same exact representation was seen at least 5 times in training (note that CIDEr gives a score of 10 only if the test caption and all references are the same). We found that among the non-exact image matches, the projected image representation better captures candidates in the training set than bag of objects. Note that the nearest neighbours are an approximation since we do not know the exact distance metric derived from the LSTM. We observe that the captions for unseen representations seem to be interpolated from multiple neighbouring points in the projection space, but further work is needed to analyze the hidden representations of the LSTM to understand the language model and to give firmer conclusions.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 3: k-Nearest Neighbour (k=5) trial on the ground truth bag of objects (Freq.) and the projected bag of objects (Proj.) representations. The references are captions of 5-nearest images in each space. Exact represents a subset of 2301 samples where all the 5 neighbours have 0 distance (replicas) and ¬ represents nearest neighbours that are not replicas of the test image.', solve this question and give detail solutions. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the CIDEr score for the projected image representation among non-exact matches?\n" }, { "role": "assistant", "content": "The table shows the CIDEr score for different types of image representations. For projected image representation among non-exact matches, the CIDEr score is listed. So, the answer is {\"answer\": \"5.638\"}" } ], "images": [ "scigen/train/3784.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: k-Nearest Neighbour (k=5) trial on the ground truth bag of objects (Freq.) and the projected bag of objects (Proj.) representations. The references are captions of 5-nearest images in each space. Exact represents a subset of 2301 samples where all the 5 neighbours have 0 distance (replicas) and ¬ represents nearest neighbours that are not replicas of the test image." ], "reasoning": "The table shows the CIDEr score for different types of image representations. For projected image representation among non-exact matches, the CIDEr score is listed.", "question": "What is the CIDEr score for the projected image representation among non-exact matches?", "answer": "So, the answer is {\"answer\": \"5.638\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The BLEU score for frequent image representations is higher than for projected image representations among non-exact matches., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe BLEU score for frequent image representations is higher than for projected image representations among non-exact matches.\n" }, { "role": "assistant", "content": "The table shows that the BLEU score for frequent image representations is 0.757, while for projected image representations among non-exact matches, it is 0.837. Therefore, the statement is incorrect. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3784.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: k-Nearest Neighbour (k=5) trial on the ground truth bag of objects (Freq.) and the projected bag of objects (Proj.) representations. The references are captions of 5-nearest images in each space. Exact represents a subset of 2301 samples where all the 5 neighbours have 0 distance (replicas) and ¬ represents nearest neighbours that are not replicas of the test image." ], "reasoning": "The table shows that the BLEU score for frequent image representations is 0.757, while for projected image representations among non-exact matches, it is 0.837. Therefore, the statement is incorrect.", "statement": "The BLEU score for frequent image representations is higher than for projected image representations among non-exact matches.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: CIDEr scores for image captioning using representations encoding spatial information of instances derived from ground truth annotations, with either fixed hyperparameters (Section 3.1) or with hyperparameter tuning. † Results taken from Yin and Ordonez (2017).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Encoding spatial information improves performance, with width and height being the most informative features, while positional information is less informative.All experiments in this subsection use ground truth annotations – we expect the results of using an object detector to be slightly worse but in most cases follow a similar trend, as shown in the previous section. Encoding spatial information led to substantially better performance over bag of objects alone. Consistent with our previous observation, w and h (bounding box width and height) seems to be the most informative feature combination – it performs well even without positional information. Area (a) is less informative than the combination of w and h, possibly because it compresses width-height ratio information despite discarding noise from background regions. Positional information (x, y) does not seem to be as informative, consistent with observations from previous work We note that the results with our simpler image representation are comparable to the ones reported in \\newciteYinOrdonez:2017, which use more complex models to encode similar image information. Interestingly, we observe that positional information (x, y) work better than before tuning in this case." } ], "images": [ "scigen/train/3785.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: CIDEr scores for image captioning using representations encoding spatial information of instances derived from ground truth annotations, with either fixed hyperparameters (Section 3.1) or with hyperparameter tuning. † Results taken from Yin and Ordonez (2017)." ], "reasoning": "Encoding spatial information improves performance, with width and height being the most informative features, while positional information is less informative.", "description": "All experiments in this subsection use ground truth annotations – we expect the results of using an object detector to be slightly worse but in most cases follow a similar trend, as shown in the previous section. Encoding spatial information led to substantially better performance over bag of objects alone. Consistent with our previous observation, w and h (bounding box width and height) seems to be the most informative feature combination – it performs well even without positional information. Area (a) is less informative than the combination of w and h, possibly because it compresses width-height ratio information despite discarding noise from background regions. Positional information (x, y) does not seem to be as informative, consistent with observations from previous work We note that the results with our simpler image representation are comparable to the ones reported in \\newciteYinOrdonez:2017, which use more complex models to encode similar image information. Interestingly, we observe that positional information (x, y) work better than before tuning in this case.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: CIDEr scores for image captioning using representations encoding spatial information of instances derived from ground truth annotations, with either fixed hyperparameters (Section 3.1) or with hyperparameter tuning. † Results taken from Yin and Ordonez (2017).', solve this question and give detail solutions. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich feature set showed the highest performance after tuning?\n" }, { "role": "assistant", "content": "To find the highest performance after tuning, look at the 'Tuned' column and identify the highest value. The highest value is 0.922, which corresponds to the LSTM feature set. So, the answer is {\"answer\": \"LSTM\"}." } ], "images": [ "scigen/train/3785.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: CIDEr scores for image captioning using representations encoding spatial information of instances derived from ground truth annotations, with either fixed hyperparameters (Section 3.1) or with hyperparameter tuning. † Results taken from Yin and Ordonez (2017)." ], "reasoning": "To find the highest performance after tuning, look at the 'Tuned' column and identify the highest value. The highest value is 0.922, which corresponds to the LSTM feature set.", "question": "Which feature set showed the highest performance after tuning?", "answer": "So, the answer is {\"answer\": \"LSTM\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe combination of width and height (w, h) performs better than area (a) after tuning.\n" }, { "role": "assistant", "content": "Compare the performance of (w, h) and (a) in the 'Tuned' column. The performance of (w, h) is 0.920, while the performance of (a) is 0.857. Therefore, (w, h) performs better than (a) after tuning. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3785.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: CIDEr scores for image captioning using representations encoding spatial information of instances derived from ground truth annotations, with either fixed hyperparameters (Section 3.1) or with hyperparameter tuning. † Results taken from Yin and Ordonez (2017)." ], "reasoning": "Compare the performance of (w, h) and (a) in the 'Tuned' column. The performance of (w, h) is 0.920, while the performance of (a) is 0.857. Therefore, (w, h) performs better than (a) after tuning.", "statement": "The combination of width and height (w, h) performs better than area (a) after tuning.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 5: Results of untuned sentence representing models on QQP dataset.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "BiLSTMSRA outperforms BiLSTMKD on both accuracy and F1 metrics, and achieves scores close to BERTBASE.It should be noted that, in this table, ELMO, BERTBASE (CLS) and BERTBASE (averaged) are introduced as the comparison basis, since they can give the SOTA untuned sentence representations for the similarity measurement. The comparison mainly focuses on the performances of our proposed BiLSTMSRA and BiLSTMKD. For a thorough comparison, we define the training objective of BiLSTMKD as fitting the cosine similarity score of the sentence pair directly given by the pre-trained BERT, which means both the teacher BERT and KD do not utilize the labels of QQP dataset. It can be seen that, even though the training goal of BiLSTMKD is more direct than BiLSTMSRA, our BiLSTMSRA outperforms the former on both the metrics, and meanwhile achieves scores closed to those of BERTBASE. Besides, we can also observe that, for sentence similarity quantification, averaging the context word embeddings as the sentence representation (ELMO and BERTBASE (averaged)) works better than taking the final hidden state corresponding to the [CLS] token (BERTBASE (CLS))." } ], "images": [ "scigen/train/3786.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results of untuned sentence representing models on QQP dataset." ], "reasoning": "BiLSTMSRA outperforms BiLSTMKD on both accuracy and F1 metrics, and achieves scores close to BERTBASE.", "description": "It should be noted that, in this table, ELMO, BERTBASE (CLS) and BERTBASE (averaged) are introduced as the comparison basis, since they can give the SOTA untuned sentence representations for the similarity measurement. The comparison mainly focuses on the performances of our proposed BiLSTMSRA and BiLSTMKD. For a thorough comparison, we define the training objective of BiLSTMKD as fitting the cosine similarity score of the sentence pair directly given by the pre-trained BERT, which means both the teacher BERT and KD do not utilize the labels of QQP dataset. It can be seen that, even though the training goal of BiLSTMKD is more direct than BiLSTMSRA, our BiLSTMSRA outperforms the former on both the metrics, and meanwhile achieves scores closed to those of BERTBASE. Besides, we can also observe that, for sentence similarity quantification, averaging the context word embeddings as the sentence representation (ELMO and BERTBASE (averaged)) works better than taking the final hidden state corresponding to the [CLS] token (BERTBASE (CLS)).", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 5: Results of untuned sentence representing models on QQP dataset.' and methodically tackle the question that follows. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model achieves the highest accuracy and how does BiLSTMSRA compare to it?\n" }, { "role": "assistant", "content": "BERTBASE (averaged) achieves the highest accuracy at 66.4. BiLSTMSRA has an accuracy of 62.9, which is close to BERTBASE (averaged). So, the answer is {\"answer\": \"BERTBASE (averaged)\"}." } ], "images": [ "scigen/train/3786.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results of untuned sentence representing models on QQP dataset." ], "reasoning": "BERTBASE (averaged) achieves the highest accuracy at 66.4. BiLSTMSRA has an accuracy of 62.9, which is close to BERTBASE (averaged).", "question": "Which model achieves the highest accuracy and how does BiLSTMSRA compare to it?", "answer": "So, the answer is {\"answer\": \"BERTBASE (averaged)\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nBiLSTMSRA has a higher F1 score than BERTBASE (CLS).\n" }, { "role": "assistant", "content": "The table shows that BiLSTMSRA has an F1 score of 61.0, while BERTBASE (CLS) also has an F1 score of 61.0. Therefore, the statement is not supported. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3786.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results of untuned sentence representing models on QQP dataset." ], "reasoning": "The table shows that BiLSTMSRA has an F1 score of 61.0, while BERTBASE (CLS) also has an F1 score of 61.0. Therefore, the statement is not supported.", "statement": "BiLSTMSRA has a higher F1 score than BERTBASE (CLS).", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: The accuracy scores evaluated on the SST-2 validation set. The models are trained with different proportions of the training data.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "BERTLARGE achieves the highest accuracy across all training data percentages, while BiLSTMSRA shows faster convergence and less dependency on training data.BERTLARGE trained on the corresponding data stands for the teacher model of each BiLSTMKD. With no doubt, all the models can achieve better results using more training data, while BERT performs the best. BERT even successfully predicts 91.9% of validation samples under only 20% training data. Comparing with the pure BiLSTM models, the BiLSTMKD models slightly improve the performances by 1%∼2%, whereas BiLSTMSRA outperforms the best BiLSTM model as well as the BiLSTMKD trained with 20% and 30% percent data respectively. Besides, similar to BERT, the difference of accuracy between BiLSTMSRA trained with 20% and the one using 100% corpus is relatively small. This phenomenon indicates that our model converges faster and is less dependent on the amount of training data for downstream tasks." } ], "images": [ "scigen/train/3787.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The accuracy scores evaluated on the SST-2 validation set. The models are trained with different proportions of the training data." ], "reasoning": "BERTLARGE achieves the highest accuracy across all training data percentages, while BiLSTMSRA shows faster convergence and less dependency on training data.", "description": "BERTLARGE trained on the corresponding data stands for the teacher model of each BiLSTMKD. With no doubt, all the models can achieve better results using more training data, while BERT performs the best. BERT even successfully predicts 91.9% of validation samples under only 20% training data. Comparing with the pure BiLSTM models, the BiLSTMKD models slightly improve the performances by 1%∼2%, whereas BiLSTMSRA outperforms the best BiLSTM model as well as the BiLSTMKD trained with 20% and 30% percent data respectively. Besides, similar to BERT, the difference of accuracy between BiLSTMSRA trained with 20% and the one using 100% corpus is relatively small. This phenomenon indicates that our model converges faster and is less dependent on the amount of training data for downstream tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: The accuracy scores evaluated on the SST-2 validation set. The models are trained with different proportions of the training data.' in mind, break down and resolve the question below step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhich model achieves the highest accuracy with 20% training data?\n" }, { "role": "assistant", "content": "By comparing the accuracy values for each model at 20% training data, BERTLARGE has the highest accuracy at 91.9%. So, the answer is {\"answer\": \"BERTLARGE\"}" } ], "images": [ "scigen/train/3787.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The accuracy scores evaluated on the SST-2 validation set. The models are trained with different proportions of the training data." ], "reasoning": "By comparing the accuracy values for each model at 20% training data, BERTLARGE has the highest accuracy at 91.9%.", "question": "Which model achieves the highest accuracy with 20% training data?", "answer": "So, the answer is {\"answer\": \"BERTLARGE\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis BiLSTMKD outperforms BiLSTMSRA with 30% training data. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nBiLSTMKD outperforms BiLSTMSRA with 30% training data.\n" }, { "role": "assistant", "content": "The table shows that BiLSTMSRA has an accuracy of 87.3% with 30% training data, while BiLSTMKD has an accuracy of 83.2%, indicating that BiLSTMSRA outperforms BiLSTMKD. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3787.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The accuracy scores evaluated on the SST-2 validation set. The models are trained with different proportions of the training data." ], "reasoning": "The table shows that BiLSTMSRA has an accuracy of 87.3% with 30% training data, while BiLSTMKD has an accuracy of 83.2%, indicating that BiLSTMSRA outperforms BiLSTMKD.", "statement": "BiLSTMKD outperforms BiLSTMSRA with 30% training data.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 6: F-measures for the eight methods.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The methods performed better on informal datasets like MySpace and Twitter, with the highest F-measure on MySpace being 0.958 by PANAS-t, while formal datasets like BBC had lower performance, with the highest F-measure being 0.532 by SentiStrength.Overall, we note that the eight methods yielded wide variation in their results across the different datasets. We observe better performance on datasets that contain more expressed sentiment, such as social network messages (e.g., Twitter and MySpace) and lower performance on formal datasets (e.g., BBC and Digg). For instance, on BBC posts (i.e., formal content), the highest F-measure was 0.532, from SentiStrength On the other hand, for the MySpace dataset (i.e., informal content), the highest F-measure was obtained by PANAS-t (0.958) and the average F-measure for all 8 methods was 72%. This might indicate that each method complements the others in different ways." } ], "images": [ "scigen/train/3788.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: F-measures for the eight methods." ], "reasoning": "The methods performed better on informal datasets like MySpace and Twitter, with the highest F-measure on MySpace being 0.958 by PANAS-t, while formal datasets like BBC had lower performance, with the highest F-measure being 0.532 by SentiStrength.", "description": "Overall, we note that the eight methods yielded wide variation in their results across the different datasets. We observe better performance on datasets that contain more expressed sentiment, such as social network messages (e.g., Twitter and MySpace) and lower performance on formal datasets (e.g., BBC and Digg). For instance, on BBC posts (i.e., formal content), the highest F-measure was 0.532, from SentiStrength On the other hand, for the MySpace dataset (i.e., informal content), the highest F-measure was obtained by PANAS-t (0.958) and the average F-measure for all 8 methods was 72%. This might indicate that each method complements the others in different ways.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 6: F-measures for the eight methods.'. Solve the following question based on the table.\n\nQuestion: Which method had the highest F-measure on the BBC dataset and what was the value?\nLet's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The description states that the highest F-measure on the BBC dataset was 0.532 by SentiStrength. By checking the table, we confirm that SentiStrength has an F-measure of 0.532 on the BBC dataset. So, the answer is {\"answer\": \"SentiStrength, 0.532\"}" } ], "images": [ "scigen/train/3788.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: F-measures for the eight methods." ], "reasoning": "The description states that the highest F-measure on the BBC dataset was 0.532 by SentiStrength. By checking the table, we confirm that SentiStrength has an F-measure of 0.532 on the BBC dataset.", "question": "Which method had the highest F-measure on the BBC dataset and what was the value?", "answer": "So, the answer is {\"answer\": \"SentiStrength, 0.532\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The average F-measure for all methods on the MySpace dataset was 72%., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe average F-measure for all methods on the MySpace dataset was 72%.\n" }, { "role": "assistant", "content": "The description states that the average F-measure for all 8 methods on the MySpace dataset was 72%. By calculating the average of the MySpace column in the table, we get (0.958 + 0.952 + 0.710 + 0.884 + 0.837 + 0.915 + 0.925 + 0.862) / 8 = 0.880375, which is approximately 88%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3788.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: F-measures for the eight methods." ], "reasoning": "The description states that the average F-measure for all 8 methods on the MySpace dataset was 72%. By calculating the average of the MySpace column in the table, we get (0.958 + 0.952 + 0.710 + 0.884 + 0.837 + 0.915 + 0.925 + 0.862) / 8 = 0.880375, which is approximately 88%.", "statement": "The average F-measure for all methods on the MySpace dataset was 72%.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Summary information of the six major topics events studied'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table lists events with their periods and keywords, which were used to extract relevant tweets for sentiment analysis. SentiWordNet and SenticNet have the highest coverage of relevant tweets, while Emoticons and PANAS-t have the lowest.To extract tweets relevant to these events, we first identified the sets of keywords describing the topics by consulting news websites, blogs, Wikipedia, and informed individuals. Given our selected list of keywords, we identified the topics by searching for keywords in the tweet dataset. This process is very similar to the way in which mining and monitoring tools to crawl data about specific topics. We limited the duration of each event because popular keywords are typically hijacked by spammers after a certain amount of time. The first column contains a short name for the event, which we use to refer to them in the rest of the paper. While the table does not show the ground truth sentiment of the six events, we can utilize these events to compare the predicted sentiments across different methods. As shown in the figure, SentiWordNet and SenticNet have the highest coverage with 90% and 91%, respectively, followed by SentiStrength with 69%. Emoticons and PANAS-t can interpret less than 10% of all relevant tweets. In the other events SentiStrength, LIWC, and SASA had ranked in third and fourth positions." } ], "images": [ "scigen/train/3789.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Summary information of the six major topics events studied" ], "reasoning": "The table lists events with their periods and keywords, which were used to extract relevant tweets for sentiment analysis. SentiWordNet and SenticNet have the highest coverage of relevant tweets, while Emoticons and PANAS-t have the lowest.", "description": "To extract tweets relevant to these events, we first identified the sets of keywords describing the topics by consulting news websites, blogs, Wikipedia, and informed individuals. Given our selected list of keywords, we identified the topics by searching for keywords in the tweet dataset. This process is very similar to the way in which mining and monitoring tools to crawl data about specific topics. We limited the duration of each event because popular keywords are typically hijacked by spammers after a certain amount of time. The first column contains a short name for the event, which we use to refer to them in the rest of the paper. While the table does not show the ground truth sentiment of the six events, we can utilize these events to compare the predicted sentiments across different methods. As shown in the figure, SentiWordNet and SenticNet have the highest coverage with 90% and 91%, respectively, followed by SentiStrength with 69%. Emoticons and PANAS-t can interpret less than 10% of all relevant tweets. In the other events SentiStrength, LIWC, and SASA had ranked in third and fourth positions.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Summary information of the six major topics events studied', answer the question below by showing each progressive step toward the answer. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich event had keywords related to a virus outbreak and what was the period?\n" }, { "role": "assistant", "content": "The table shows that the event 'H1N1' had keywords related to a virus outbreak, such as 'outbreak', 'virus', 'influenza', 'pandemi*', 'h1n1', 'swine', and 'world health organization'. The period for this event is 06.09–26.2009. So, the answer is {\"answer\": \"H1N1, 06.09–26.2009\"}." } ], "images": [ "scigen/train/3789.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Summary information of the six major topics events studied" ], "reasoning": "The table shows that the event 'H1N1' had keywords related to a virus outbreak, such as 'outbreak', 'virus', 'influenza', 'pandemi*', 'h1n1', 'swine', and 'world health organization'. The period for this event is 06.09–26.2009.", "question": "Which event had keywords related to a virus outbreak and what was the period?", "answer": "So, the answer is {\"answer\": \"H1N1, 06.09–26.2009\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe 2008US-Elect event had keywords related to voting and candidates.\n" }, { "role": "assistant", "content": "The table shows that the 2008US-Elect event had keywords such as 'voting', 'vote', 'candidate', 'campaign', 'mccain', 'democrat*', 'republican*', 'obama', and 'bush', which are related to voting and candidates. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3789.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Summary information of the six major topics events studied" ], "reasoning": "The table shows that the 2008US-Elect event had keywords such as 'voting', 'vote', 'candidate', 'campaign', 'mccain', 'democrat*', 'republican*', 'obama', and 'bush', which are related to voting and candidates.", "statement": "The 2008US-Elect event had keywords related to voting and candidates.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: Average prediction performance for all labeled dataset.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Emoticons had the best F-measure score of 0.846 but the lowest coverage, while SentiStrength had the second-best F-measure score with much higher coverage.For the F-measure, a score of 1 is ideal and 0 is the worst possible. The method with the best F-measure was Emoticons (0.846), which had the lowest coverage. The second best method in terms of F-measure is SentiStrength, which obtained a much higher coverage than Emoticons. It is important to note that the SentiStrength version we are using is already trained, probably with this entire dataset. Thus, running experiments with SentiStrength using this dataset would be potentially biased, as it would be training and testing with the same dataset. Instead, we compute the prediction performance metrics for SentiStrengh based on the numbers they reported in their experiments" } ], "images": [ "scigen/train/3790.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Average prediction performance for all labeled dataset." ], "reasoning": "Emoticons had the best F-measure score of 0.846 but the lowest coverage, while SentiStrength had the second-best F-measure score with much higher coverage.", "description": "For the F-measure, a score of 1 is ideal and 0 is the worst possible. The method with the best F-measure was Emoticons (0.846), which had the lowest coverage. The second best method in terms of F-measure is SentiStrength, which obtained a much higher coverage than Emoticons. It is important to note that the SentiStrength version we are using is already trained, probably with this entire dataset. Thus, running experiments with SentiStrength using this dataset would be potentially biased, as it would be training and testing with the same dataset. Instead, we compute the prediction performance metrics for SentiStrengh based on the numbers they reported in their experiments", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 5: Average prediction performance for all labeled dataset.', solve the subsequent question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich method had the highest precision and what was its F-measure?\n" }, { "role": "assistant", "content": "The method with the highest precision is Happiness Index with a precision of 0.945. Its F-measure is 0.665. So, the answer is {\"answer\": \"Happiness Index, 0.665\"}" } ], "images": [ "scigen/train/3790.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Average prediction performance for all labeled dataset." ], "reasoning": "The method with the highest precision is Happiness Index with a precision of 0.945. Its F-measure is 0.665.", "question": "Which method had the highest precision and what was its F-measure?", "answer": "So, the answer is {\"answer\": \"Happiness Index, 0.665\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis SentiStrength had the highest recall among all methods. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nSentiStrength had the highest recall among all methods.\n" }, { "role": "assistant", "content": "The table shows that Emoticons had the highest recall of 0.856, while SentiStrength had a recall of 0.767. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3790.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Average prediction performance for all labeled dataset." ], "reasoning": "The table shows that Emoticons had the highest recall of 0.856, while SentiStrength had a recall of 0.767.", "statement": "SentiStrength had the highest recall among all methods.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 2: Results of proposed models on test sets of eight CWS datasets. There are three blocks. The first block consists of two baseline models: Bi-LSTM and stacked Bi-LSTM. The second block consists of our proposed three models without adversarial training. The third block consists of our proposed three models with adversarial training. Here, P, R, F, OOV indicate the precision, recall, F value and OOV recall rate respectively. The maximum F values in each block are highlighted for each dataset.'.\n" }, { "role": "assistant", "content": "Adversarial training improves performance across datasets, with Model-I slightly outperforming Model-II and Model-III.(3) In the third block, we introduce adversarial training. By introducing adversarial training, the performances are further boosted, and Model -I is slightly better than Model-II and Model-III. The adversarial training tries to make shared layer keep criteria-invariant features. The reason may be that the shared parameters bias to other segmentation criteria and introduce noisy features into shared parameters. When we additionally incorporate the adversarial strategy, we observe that the performance on MSRA is improved and outperforms the baseline results. We could also observe the improvements on other datasets. However, the boost from the adversarial strategy is not significant. The main reason might be that the proposed three sharing models implicitly attempt to keep invariant features by shared parameters and learn discrepancies by the task layer. We further investigate the benefits of the proposed models by comparing the error distributions between the single-criterion learning (baseline model Bi-LSTM) and multi-criteria learning ( Model- I and Model" } ], "images": [ "scigen/train/3791.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results of proposed models on test sets of eight CWS datasets. There are three blocks. The first block consists of two baseline models: Bi-LSTM and stacked Bi-LSTM. The second block consists of our proposed three models without adversarial training. The third block consists of our proposed three models with adversarial training. Here, P, R, F, OOV indicate the precision, recall, F value and OOV recall rate respectively. The maximum F values in each block are highlighted for each dataset." ], "reasoning": "Adversarial training improves performance across datasets, with Model-I slightly outperforming Model-II and Model-III.", "description": "(3) In the third block, we introduce adversarial training. By introducing adversarial training, the performances are further boosted, and Model -I is slightly better than Model-II and Model-III. The adversarial training tries to make shared layer keep criteria-invariant features. The reason may be that the shared parameters bias to other segmentation criteria and introduce noisy features into shared parameters. When we additionally incorporate the adversarial strategy, we observe that the performance on MSRA is improved and outperforms the baseline results. We could also observe the improvements on other datasets. However, the boost from the adversarial strategy is not significant. The main reason might be that the proposed three sharing models implicitly attempt to keep invariant features by shared parameters and learn discrepancies by the task layer. We further investigate the benefits of the proposed models by comparing the error distributions between the single-criterion learning (baseline model Bi-LSTM) and multi-criteria learning ( Model- I and Model", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 2: Results of proposed models on test sets of eight CWS datasets. There are three blocks. The first block consists of two baseline models: Bi-LSTM and stacked Bi-LSTM. The second block consists of our proposed three models without adversarial training. The third block consists of our proposed three models with adversarial training. Here, P, R, F, OOV indicate the precision, recall, F value and OOV recall rate respectively. The maximum F values in each block are highlighted for each dataset.', solve the subsequent question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model and setting achieved the highest average score with adversarial training?\n" }, { "role": "assistant", "content": "By examining the table, we see that Model-I+ADV with the F setting achieved the highest average score of 94.98. So, the answer is {\"answer\": \"Model-I+ADV with F setting\"}." } ], "images": [ "scigen/train/3791.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results of proposed models on test sets of eight CWS datasets. There are three blocks. The first block consists of two baseline models: Bi-LSTM and stacked Bi-LSTM. The second block consists of our proposed three models without adversarial training. The third block consists of our proposed three models with adversarial training. Here, P, R, F, OOV indicate the precision, recall, F value and OOV recall rate respectively. The maximum F values in each block are highlighted for each dataset." ], "reasoning": "By examining the table, we see that Model-I+ADV with the F setting achieved the highest average score of 94.98.", "question": "Which model and setting achieved the highest average score with adversarial training?", "answer": "So, the answer is {\"answer\": \"Model-I+ADV with F setting\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nModel-II+ADV with the P setting achieved the highest score on the MSRA dataset.\n" }, { "role": "assistant", "content": "The table shows that Model-II+ADV with the P setting scored 96.02 on the MSRA dataset, which is not the highest score. The highest score on the MSRA dataset is 96.14 by Model-I+ADV with the R setting. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3791.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results of proposed models on test sets of eight CWS datasets. There are three blocks. The first block consists of two baseline models: Bi-LSTM and stacked Bi-LSTM. The second block consists of our proposed three models without adversarial training. The third block consists of our proposed three models with adversarial training. Here, P, R, F, OOV indicate the precision, recall, F value and OOV recall rate respectively. The maximum F values in each block are highlighted for each dataset." ], "reasoning": "The table shows that Model-II+ADV with the P setting scored 96.02 on the MSRA dataset, which is not the highest score. The highest score on the MSRA dataset is 96.14 by Model-I+ADV with the R setting.", "statement": "Model-II+ADV with the P setting achieved the highest score on the MSRA dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Performance on 3 traditional Chinese datasets. Model-I∗ means that the shared parameters are trained on 5 simplified Chinese datasets and are fixed for traditional Chinese datasets. Here, we conduct Model-I without incorporating adversarial training strategy.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The model was first trained on simplified Chinese datasets and then on traditional Chinese datasets independently, resulting in improved performance across multiple datasets.Traditional Chinese and simplified Chinese are two similar languages with slightly difference on character forms (e.g. multiple traditional characters might map to one simplified character). We investigate that if datasets in traditional Chinese and simplified Chinese could help each other. I on 3 traditional Chinese datasets under the help of 5 simplified Chinese datasets. Specifically, we firstly train the model on simplified Chinese datasets, then we train traditional Chinese datasets independently with shared parameters fixed." } ], "images": [ "scigen/train/3792.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance on 3 traditional Chinese datasets. Model-I∗ means that the shared parameters are trained on 5 simplified Chinese datasets and are fixed for traditional Chinese datasets. Here, we conduct Model-I without incorporating adversarial training strategy." ], "reasoning": "The model was first trained on simplified Chinese datasets and then on traditional Chinese datasets independently, resulting in improved performance across multiple datasets.", "description": "Traditional Chinese and simplified Chinese are two similar languages with slightly difference on character forms (e.g. multiple traditional characters might map to one simplified character). We investigate that if datasets in traditional Chinese and simplified Chinese could help each other. I on 3 traditional Chinese datasets under the help of 5 simplified Chinese datasets. Specifically, we firstly train the model on simplified Chinese datasets, then we train traditional Chinese datasets independently with shared parameters fixed.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the average score improvement of Model-I* over the Baseline (Bi-LSTM) model?\nSolve the above question based on the table titled 'Table 3: Performance on 3 traditional Chinese datasets. Model-I∗ means that the shared parameters are trained on 5 simplified Chinese datasets and are fixed for traditional Chinese datasets. Here, we conduct Model-I without incorporating adversarial training strategy.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the average score improvement, subtract the average score of the Baseline (Bi-LSTM) model (93.78) from the average score of Model-I* (94.19). So, the answer is {\"answer\": \"0.41\"}." } ], "images": [ "scigen/train/3792.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance on 3 traditional Chinese datasets. Model-I∗ means that the shared parameters are trained on 5 simplified Chinese datasets and are fixed for traditional Chinese datasets. Here, we conduct Model-I without incorporating adversarial training strategy." ], "reasoning": "To find the average score improvement, subtract the average score of the Baseline (Bi-LSTM) model (93.78) from the average score of Model-I* (94.19).", "question": "What is the average score improvement of Model-I* over the Baseline (Bi-LSTM) model?", "answer": "So, the answer is {\"answer\": \"0.41\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis Model-I* performed better than the Baseline (Bi-LSTM) model on the CITYU dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nModel-I* performed better than the Baseline (Bi-LSTM) model on the CITYU dataset.\n" }, { "role": "assistant", "content": "The table shows that Model-I* scored 95.20 on the CITYU dataset, while the Baseline (Bi-LSTM) model scored 94.07, indicating that Model-I* performed better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3792.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance on 3 traditional Chinese datasets. Model-I∗ means that the shared parameters are trained on 5 simplified Chinese datasets and are fixed for traditional Chinese datasets. Here, we conduct Model-I without incorporating adversarial training strategy." ], "reasoning": "The table shows that Model-I* scored 95.20 on the CITYU dataset, while the Baseline (Bi-LSTM) model scored 94.07, indicating that Model-I* performed better.", "statement": "Model-I* performed better than the Baseline (Bi-LSTM) model on the CITYU dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: ASR results (WER) on Isolated-Sentences augmenting either Isolated-Sentences or the union of Isolated-Sentences and LibriSpeech with TTS utterances trained on LibriSpeech or Isolated-Sentences'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The synthesis quality of the TTS model trained on Isolated-Sentences is lower due to noisy source data, and ASR training is explored using both Isolated-Sentences and LibriSpeech corpora.We train a TTS model based on the Isolated-Sentences material. Since the source data is noisy, we find the synthesis quality to be lower. We then synthesize the Isolated-Sentences training data with this TTS model, as well as the LibriSpeech TTS model. Since LibriSpeech (960hrs) is much larger than Isolated-Sentences (76hrs), we also explore ASR training using both corpora." } ], "images": [ "scigen/train/3793.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: ASR results (WER) on Isolated-Sentences augmenting either Isolated-Sentences or the union of Isolated-Sentences and LibriSpeech with TTS utterances trained on LibriSpeech or Isolated-Sentences" ], "reasoning": "The synthesis quality of the TTS model trained on Isolated-Sentences is lower due to noisy source data, and ASR training is explored using both Isolated-Sentences and LibriSpeech corpora.", "description": "We train a TTS model based on the Isolated-Sentences material. Since the source data is noisy, we find the synthesis quality to be lower. We then synthesize the Isolated-Sentences training data with this TTS model, as well as the LibriSpeech TTS model. Since LibriSpeech (960hrs) is much larger than Isolated-Sentences (76hrs), we also explore ASR training using both corpora.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: 'Table 5: ASR results (WER) on Isolated-Sentences augmenting either Isolated-Sentences or the union of Isolated-Sentences and LibriSpeech with TTS utterances trained on LibriSpeech or Isolated-Sentences'\nQuestion: What is the WER for the TTS model trained on LibriSpeech using the Train Set IS?\n" }, { "role": "assistant", "content": "The table shows the WER for different TTS models and training sets. For the TTS model trained on LibriSpeech using the Train Set IS, the WER is 34.2. So, the answer is {\"answer\": \"34.2\"}" } ], "images": [ "scigen/train/3793.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: ASR results (WER) on Isolated-Sentences augmenting either Isolated-Sentences or the union of Isolated-Sentences and LibriSpeech with TTS utterances trained on LibriSpeech or Isolated-Sentences" ], "reasoning": "The table shows the WER for different TTS models and training sets. For the TTS model trained on LibriSpeech using the Train Set IS, the WER is 34.2.", "question": "What is the WER for the TTS model trained on LibriSpeech using the Train Set IS?", "answer": "So, the answer is {\"answer\": \"34.2\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The WER for the TTS model trained on Isolated-Sentences using the Train Set IS+LS is higher than the WER for the TTS model trained on LibriSpeech using the same training set., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe WER for the TTS model trained on Isolated-Sentences using the Train Set IS+LS is higher than the WER for the TTS model trained on LibriSpeech using the same training set.\n" }, { "role": "assistant", "content": "The table shows that the WER for the TTS model trained on Isolated-Sentences using the Train Set IS+LS is 29.8, while the WER for the TTS model trained on LibriSpeech using the same training set is 29.6. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3793.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: ASR results (WER) on Isolated-Sentences augmenting either Isolated-Sentences or the union of Isolated-Sentences and LibriSpeech with TTS utterances trained on LibriSpeech or Isolated-Sentences" ], "reasoning": "The table shows that the WER for the TTS model trained on Isolated-Sentences using the Train Set IS+LS is 29.8, while the WER for the TTS model trained on LibriSpeech using the same training set is 29.6. Therefore, the statement is refuted.", "statement": "The WER for the TTS model trained on Isolated-Sentences using the Train Set IS+LS is higher than the WER for the TTS model trained on LibriSpeech using the same training set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: LibriSpeech Word Error Rate (WER) by augmentation with TTS utterances using different d-vector approaches.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Multi-speaker TTS is necessary for effective data augmentation for ASR, as it improves performance by capturing acoustic diversity.Based on the three speaker representation approaches, we generate synthesized utterances for data augmentation. Each of these experiments include a complete synthesized copy of the human speech training data. The LibriSpeech corpus contains two test (and development) partitions, one that is relatively clean (test-clean and dev-clean) and one that is not (test-other and dev-other). While this improvement is modest, the fact that performance is improved at all speaks to the coherence between the multi-speaker TTS utterances and human speech, as well as the ability for d-vector speaker representation to reasonably capture some of the acoustic diversity expressed by different speakers. For comparison, we repeated this experiment with augmentation material from high-quality single-speaker TTS using a traditional TTS frontend and WaveNet vocoder. Thus, TTS quality is a necessary but not sufficient criteria for effective data augmentation. Multi-speaker TTS is necessary for effective data augmentation for ASR." } ], "images": [ "scigen/train/3794.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: LibriSpeech Word Error Rate (WER) by augmentation with TTS utterances using different d-vector approaches." ], "reasoning": "Multi-speaker TTS is necessary for effective data augmentation for ASR, as it improves performance by capturing acoustic diversity.", "description": "Based on the three speaker representation approaches, we generate synthesized utterances for data augmentation. Each of these experiments include a complete synthesized copy of the human speech training data. The LibriSpeech corpus contains two test (and development) partitions, one that is relatively clean (test-clean and dev-clean) and one that is not (test-other and dev-other). While this improvement is modest, the fact that performance is improved at all speaks to the coherence between the multi-speaker TTS utterances and human speech, as well as the ability for d-vector speaker representation to reasonably capture some of the acoustic diversity expressed by different speakers. For comparison, we repeated this experiment with augmentation material from high-quality single-speaker TTS using a traditional TTS frontend and WaveNet vocoder. Thus, TTS quality is a necessary but not sufficient criteria for effective data augmentation. Multi-speaker TTS is necessary for effective data augmentation for ASR.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 1: LibriSpeech Word Error Rate (WER) by augmentation with TTS utterances using different d-vector approaches.' in mind, break down and resolve the question below step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich augmentation method resulted in the lowest error rate for the test-other partition?\n" }, { "role": "assistant", "content": "By comparing the error rates for the test-other partition across different augmentation methods, we see that the 'Sampled' method has the lowest error rate of 13.78. So, the answer is {\"answer\": \"Sampled\"}" } ], "images": [ "scigen/train/3794.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: LibriSpeech Word Error Rate (WER) by augmentation with TTS utterances using different d-vector approaches." ], "reasoning": "By comparing the error rates for the test-other partition across different augmentation methods, we see that the 'Sampled' method has the lowest error rate of 13.78.", "question": "Which augmentation method resulted in the lowest error rate for the test-other partition?", "answer": "So, the answer is {\"answer\": \"Sampled\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe 'Original' augmentation method resulted in the highest error rate for the test-clean partition.\n" }, { "role": "assistant", "content": "By examining the error rates for the test-clean partition, the 'Random' method has the highest error rate of 4.92, not the 'Original' method. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3794.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: LibriSpeech Word Error Rate (WER) by augmentation with TTS utterances using different d-vector approaches." ], "reasoning": "By examining the error rates for the test-clean partition, the 'Random' method has the highest error rate of 4.92, not the 'Original' method.", "statement": "The 'Original' augmentation method resulted in the highest error rate for the test-clean partition.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Evaluation results on BioScope and PK using NegEx and NegBio. Performance is measured by precision (P), recall (R), and F1-score (F) on negations.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "NegBio outperformed NegEx on BioScope with a 25.5% increase in precision and a 13.6% increase in F1-score, and also achieved slightly higher precision (2.7%) and F1-scores (0.2%) on PK.On BioScope, we observed that, the overall performance of NegBio was higher than that of NegEx with a substantial 25.5% increase in precision and 13.6% increase in F1-score. On PK, NegBio also achieved slightly higher precision (2.7%) and F1-scores (0.2%)." } ], "images": [ "scigen/train/3795.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Evaluation results on BioScope and PK using NegEx and NegBio. Performance is measured by precision (P), recall (R), and F1-score (F) on negations." ], "reasoning": "NegBio outperformed NegEx on BioScope with a 25.5% increase in precision and a 13.6% increase in F1-score, and also achieved slightly higher precision (2.7%) and F1-scores (0.2%) on PK.", "description": "On BioScope, we observed that, the overall performance of NegBio was higher than that of NegEx with a substantial 25.5% increase in precision and 13.6% increase in F1-score. On PK, NegBio also achieved slightly higher precision (2.7%) and F1-scores (0.2%).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 4: Evaluation results on BioScope and PK using NegEx and NegBio. Performance is measured by precision (P), recall (R), and F1-score (F) on negations.', unfold the steps to solve the question given next. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nQuestion: What is the difference in precision between NegBio and NegEx on BioScope?\n" }, { "role": "assistant", "content": "NegBio has a precision of 96.1 on BioScope, while NegEx has a precision of 70.6. The difference is 96.1 - 70.6. So, the answer is {\"answer\": \"25.5\"}" } ], "images": [ "scigen/train/3795.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Evaluation results on BioScope and PK using NegEx and NegBio. Performance is measured by precision (P), recall (R), and F1-score (F) on negations." ], "reasoning": "NegBio has a precision of 96.1 on BioScope, while NegEx has a precision of 70.6. The difference is 96.1 - 70.6.", "question": "What is the difference in precision between NegBio and NegEx on BioScope?", "answer": "So, the answer is {\"answer\": \"25.5\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence NegBio has a higher recall than NegEx on PK. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nNegBio has a higher recall than NegEx on PK.\n" }, { "role": "assistant", "content": "The recall for NegBio on PK is 88.6, while the recall for NegEx on PK is 91.2. Therefore, NegBio does not have a higher recall than NegEx on PK. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3795.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Evaluation results on BioScope and PK using NegEx and NegBio. Performance is measured by precision (P), recall (R), and F1-score (F) on negations." ], "reasoning": "The recall for NegBio on PK is 88.6, while the recall for NegEx on PK is 91.2. Therefore, NegBio does not have a higher recall than NegEx on PK.", "statement": "NegBio has a higher recall than NegEx on PK.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 3: Evaluation results on OpenI, ChestX-ray using (1) MetaMap, (2) MetaMap and NegEx, and (3) MetaMap and NegBio. Performance is measured by precision (P), recall (R), and F1-score (F) on positive findings.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Negation detection significantly improves precision and F1-score, with NegBio achieving the highest overall performance.The \\nth1 and \\nth2 rows demonstrate that negation/uncertainty detection dramatically improves the precision (from 13.8% to 77.2%), even with a baseline approach. As a result, the F1-score also increases significantly (from 23.8% to 80.7%). This observation proves the usefulness of negation detection in the task of information extraction. The 2nd and 3rd rows compare NegEx with our method NegBio. Overall, NegBio achieved a higher precision of 89.8%, recall of 85.0%, and F1-score of 87.3% on OpenI." } ], "images": [ "scigen/train/3796.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Evaluation results on OpenI, ChestX-ray using (1) MetaMap, (2) MetaMap and NegEx, and (3) MetaMap and NegBio. Performance is measured by precision (P), recall (R), and F1-score (F) on positive findings." ], "reasoning": "Negation detection significantly improves precision and F1-score, with NegBio achieving the highest overall performance.", "description": "The \\nth1 and \\nth2 rows demonstrate that negation/uncertainty detection dramatically improves the precision (from 13.8% to 77.2%), even with a baseline approach. As a result, the F1-score also increases significantly (from 23.8% to 80.7%). This observation proves the usefulness of negation detection in the task of information extraction. The 2nd and 3rd rows compare NegEx with our method NegBio. Overall, NegBio achieved a higher precision of 89.8%, recall of 85.0%, and F1-score of 87.3% on OpenI.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 3: Evaluation results on OpenI, ChestX-ray using (1) MetaMap, (2) MetaMap and NegEx, and (3) MetaMap and NegBio. Performance is measured by precision (P), recall (R), and F1-score (F) on positive findings.' in mind, break down and resolve the question below step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhat is the precision of MetaMap+NegBio on OpenI?\n" }, { "role": "assistant", "content": "The table shows the precision of MetaMap+NegBio on OpenI as 89.8%. So, the answer is {\"answer\": \"89.8%\"}." } ], "images": [ "scigen/train/3796.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Evaluation results on OpenI, ChestX-ray using (1) MetaMap, (2) MetaMap and NegEx, and (3) MetaMap and NegBio. Performance is measured by precision (P), recall (R), and F1-score (F) on positive findings." ], "reasoning": "The table shows the precision of MetaMap+NegBio on OpenI as 89.8%.", "question": "What is the precision of MetaMap+NegBio on OpenI?", "answer": "So, the answer is {\"answer\": \"89.8%\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis NegBio achieved a higher recall than MetaMap+NegEx on OpenI. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nNegBio achieved a higher recall than MetaMap+NegEx on OpenI.\n" }, { "role": "assistant", "content": "The table shows that NegBio has a recall of 85.0% on OpenI, while MetaMap+NegEx has a recall of 84.6%, indicating that NegBio achieved a higher recall. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3796.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Evaluation results on OpenI, ChestX-ray using (1) MetaMap, (2) MetaMap and NegEx, and (3) MetaMap and NegBio. Performance is measured by precision (P), recall (R), and F1-score (F) on positive findings." ], "reasoning": "The table shows that NegBio has a recall of 85.0% on OpenI, while MetaMap+NegEx has a recall of 84.6%, indicating that NegBio achieved a higher recall.", "statement": "NegBio achieved a higher recall than MetaMap+NegEx on OpenI.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Effect of original vs retrieved sentences for generic cloze-style question generation.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Using retrieved sentences instead of original ones in question generation improves performance, as shown by higher EM and F1 scores.Effect of retrieved sentences: We test the effect of retrieved vs original sentences as input to question generation when using generic cloze questions. The retrieval process may return sentences which do not match the original context. On a random sample, 15/18 retrieved sentences were judged as entirely relevant to the original sentence. This retrieval is already quite good, as we use a high quality ElasticSearch retrieval and use the original context sentence as the query, not just the answer word. While we do not explicitly ensure that the retrieved sentence has the same meaning, we find that the search results with entity matching gives largely semantically matching sentences. Additionally, we believe the sentences which have loosely related meaning may act as a regularization factor which prevent the downstream QA model from learning only string matching patterns. Along these lines, Lewis et al. We believe that loosely related context sentences can act as a more intuitive noise function, and investigating the role of the semantic match of the retrieved sentences is an important direction for future work. For the sections which follow, we only show results of retrieved sentences, as the trend of improved performance held across all experiments." } ], "images": [ "scigen/train/3797.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Effect of original vs retrieved sentences for generic cloze-style question generation." ], "reasoning": "Using retrieved sentences instead of original ones in question generation improves performance, as shown by higher EM and F1 scores.", "description": "Effect of retrieved sentences: We test the effect of retrieved vs original sentences as input to question generation when using generic cloze questions. The retrieval process may return sentences which do not match the original context. On a random sample, 15/18 retrieved sentences were judged as entirely relevant to the original sentence. This retrieval is already quite good, as we use a high quality ElasticSearch retrieval and use the original context sentence as the query, not just the answer word. While we do not explicitly ensure that the retrieved sentence has the same meaning, we find that the search results with entity matching gives largely semantically matching sentences. Additionally, we believe the sentences which have loosely related meaning may act as a regularization factor which prevent the downstream QA model from learning only string matching patterns. Along these lines, Lewis et al. We believe that loosely related context sentences can act as a more intuitive noise function, and investigating the role of the semantic match of the retrieved sentences is an important direction for future work. For the sections which follow, we only show results of retrieved sentences, as the trend of improved performance held across all experiments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Effect of original vs retrieved sentences for generic cloze-style question generation.\nQuestion: What is the difference in EM scores between Cloze-style original and Cloze-style retrieved training procedures?\nGive a detailed response to the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The EM score for Cloze-style original is 17.36, and for Cloze-style retrieved, it is 30.53. The difference is 30.53 - 17.36. So, the answer is {\"answer\": \"13.17\"}" } ], "images": [ "scigen/train/3797.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Effect of original vs retrieved sentences for generic cloze-style question generation." ], "reasoning": "The EM score for Cloze-style original is 17.36, and for Cloze-style retrieved, it is 30.53. The difference is 30.53 - 17.36.", "question": "What is the difference in EM scores between Cloze-style original and Cloze-style retrieved training procedures?", "answer": "So, the answer is {\"answer\": \"13.17\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe F1 score for Cloze-style retrieved is higher than that for Cloze-style original.\n" }, { "role": "assistant", "content": "The table shows that the F1 score for Cloze-style retrieved is 39.61, while for Cloze-style original, it is 25.90. Since 39.61 is greater than 25.90, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3797.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Effect of original vs retrieved sentences for generic cloze-style question generation." ], "reasoning": "The table shows that the F1 score for Cloze-style retrieved is 39.61, while for Cloze-style original, it is 25.90. Since 39.61 is greater than 25.90, the statement is supported.", "statement": "The F1 score for Cloze-style retrieved is higher than that for Cloze-style original.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 2: Effect of order of template, wh word and question mark on downstream QA performance.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The 'Wh + B + A + ?' template performs best among the template-based methods, indicating the importance of the wh-component and question mark in improving QA performance.Effect of template components: We evaluate the effect of individual template components on downstream QA performance. Wh template methods improve largely over the simple cloze templates. “Wh + B + A + ?” performs best among the template-based methods, as having the Wh word at the beginning most resembles the target SQuAD domain and switching the order of Fragment B and Fragment A may force the model to learn more complex relationships from the question. We additionally test the effect of the wh-component and the question mark added at the end of the sentence. Using the same data as “Wh + B + A + ?” but removing the wh-component results in a large decrease in performance. We believe that this is because the wh-component signals the type of possible answer entities, which helps narrow down the space of possible answers. Removing the question mark at the end of the template also results in decreased performance, but not as large as removing the wh-component. This may be a result of BERT pretraining which expects certain punctuation based on sentence structure. We note that these questions may not be grammatical, which may have an impact on performance. Improving the question quality makes a difference in performance as seen from the jump from cloze-style questions to template questions. The ablation studies suggest that a combination of question relevance, though matching entities, and question formulation, as described above, determine downstream performance. Balancing those two components is an interesting problem and we leave improving grammaticality and fluency through means such as language model generation for future experiments. Using the most-common wh word by grouping named entities into 5 categories according to Lewis et al. These results suggest that information about named entity type signaled by the wh-word does provide important information to the model but further information beyond wh-simple does not improve results significantly." } ], "images": [ "scigen/train/3798.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Effect of order of template, wh word and question mark on downstream QA performance." ], "reasoning": "The 'Wh + B + A + ?' template performs best among the template-based methods, indicating the importance of the wh-component and question mark in improving QA performance.", "description": "Effect of template components: We evaluate the effect of individual template components on downstream QA performance. Wh template methods improve largely over the simple cloze templates. “Wh + B + A + ?” performs best among the template-based methods, as having the Wh word at the beginning most resembles the target SQuAD domain and switching the order of Fragment B and Fragment A may force the model to learn more complex relationships from the question. We additionally test the effect of the wh-component and the question mark added at the end of the sentence. Using the same data as “Wh + B + A + ?” but removing the wh-component results in a large decrease in performance. We believe that this is because the wh-component signals the type of possible answer entities, which helps narrow down the space of possible answers. Removing the question mark at the end of the template also results in decreased performance, but not as large as removing the wh-component. This may be a result of BERT pretraining which expects certain punctuation based on sentence structure. We note that these questions may not be grammatical, which may have an impact on performance. Improving the question quality makes a difference in performance as seen from the jump from cloze-style questions to template questions. The ablation studies suggest that a combination of question relevance, though matching entities, and question formulation, as described above, determine downstream performance. Balancing those two components is an interesting problem and we leave improving grammaticality and fluency through means such as language model generation for future experiments. Using the most-common wh word by grouping named entities into 5 categories according to Lewis et al. These results suggest that information about named entity type signaled by the wh-word does provide important information to the model but further information beyond wh-simple does not improve results significantly.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: Effect of order of template, wh word and question mark on downstream QA performance.' and methodically tackle the question that follows. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich template method has the highest F1 score?\n" }, { "role": "assistant", "content": "By examining the F1 scores in the table, the 'Wh + B + A + ?' template has the highest F1 score of 56.82. So, the answer is {\"answer\": \"Wh + B + A + ?\"}" } ], "images": [ "scigen/train/3798.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Effect of order of template, wh word and question mark on downstream QA performance." ], "reasoning": "By examining the F1 scores in the table, the 'Wh + B + A + ?' template has the highest F1 score of 56.82.", "question": "Which template method has the highest F1 score?", "answer": "So, the answer is {\"answer\": \"Wh + B + A + ?\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe 'Wh + B + A + ?' template has the highest EM score.\n" }, { "role": "assistant", "content": "The table shows that the 'Wh + B + A + ?' template has the highest EM score of 46.09, which supports the statement. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3798.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Effect of order of template, wh word and question mark on downstream QA performance." ], "reasoning": "The table shows that the 'Wh + B + A + ?' template has the highest EM score of 46.09, which supports the statement.", "statement": "The 'Wh + B + A + ?' template has the highest EM score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Effect of query and context matching for retrieved input to question generation module on downstream QA performance.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Auxiliary matching improves performance, with the best results achieved using both query and context matching.Effect of filtering by entity matching: Besides ensuring that the retrieved sentence and query sentence share the answer entity, we require that at least one additional matching entity appears in both query sentence and entire context. Auxillary matching leads to improvements over no matching when using template-based data, with best results using matching with both query and context. Matching may filter some sentences whose topic are too far from the original context. We leave further investigation of the effect of retrieved sentence relevance to future work." } ], "images": [ "scigen/train/3799.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Effect of query and context matching for retrieved input to question generation module on downstream QA performance." ], "reasoning": "Auxiliary matching improves performance, with the best results achieved using both query and context matching.", "description": "Effect of filtering by entity matching: Besides ensuring that the retrieved sentence and query sentence share the answer entity, we require that at least one additional matching entity appears in both query sentence and entire context. Auxillary matching leads to improvements over no matching when using template-based data, with best results using matching with both query and context. Matching may filter some sentences whose topic are too far from the original context. We leave further investigation of the effect of retrieved sentence relevance to future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: Effect of query and context matching for retrieved input to question generation module on downstream QA performance.'. Solve the following question based on the table.\n\nQuestion: What is the EM score when using both query and context matching?\nLet's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the EM score for different matching procedures. The highest EM score is achieved with query and context matching. So, the answer is {\"answer\": \"46.09\"}" } ], "images": [ "scigen/train/3799.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Effect of query and context matching for retrieved input to question generation module on downstream QA performance." ], "reasoning": "The table shows the EM score for different matching procedures. The highest EM score is achieved with query and context matching.", "question": "What is the EM score when using both query and context matching?", "answer": "So, the answer is {\"answer\": \"46.09\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nQuery matching alone results in a higher F1 score than context matching alone.\n" }, { "role": "assistant", "content": "The table shows that the F1 score for query matching is 54.87, while the F1 score for context matching is 55.35. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3799.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Effect of query and context matching for retrieved input to question generation module on downstream QA performance." ], "reasoning": "The table shows that the F1 score for query matching is 54.87, while the F1 score for context matching is 55.35.", "statement": "Query matching alone results in a higher F1 score than context matching alone.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: A comparison of top results using the BERT-large model.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The BERT-large model used in this study achieved higher F1 scores on both the SQuAD test set and the SQuAD-NER subset compared to the model reported by Lewis et al., 2019.SQuAD F1 results reflect results on the hidden SQuAD test set. We report single-model numbers; \\newcitelewis-etal-2019-unsupervised report an ensemble method achieving 56.40 F1 and a best single model achieving 54.7 F1. We make use of the whole-word-masking version of BERT-large, although using the original BERT-large gives similar performance of 62.69 on the SQuAD dev set. We report numbers on the sample of SQuAD questions which are named entities, which we refer to as SQuAD-NER. The subset corresponding to the SQuAD development dataset has 4,338 samples, and may differ slightly from Lewis et al. We also trained a fully-supervised model on the SQuAD training dataset with varying amounts of data and found our unsupervised performance equals the supervised performance trained on about 3,000 labeled examples." } ], "images": [ "scigen/train/3800.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: A comparison of top results using the BERT-large model." ], "reasoning": "The BERT-large model used in this study achieved higher F1 scores on both the SQuAD test set and the SQuAD-NER subset compared to the model reported by Lewis et al., 2019.", "description": "SQuAD F1 results reflect results on the hidden SQuAD test set. We report single-model numbers; \\newcitelewis-etal-2019-unsupervised report an ensemble method achieving 56.40 F1 and a best single model achieving 54.7 F1. We make use of the whole-word-masking version of BERT-large, although using the original BERT-large gives similar performance of 62.69 on the SQuAD dev set. We report numbers on the sample of SQuAD questions which are named entities, which we refer to as SQuAD-NER. The subset corresponding to the SQuAD development dataset has 4,338 samples, and may differ slightly from Lewis et al. We also trained a fully-supervised model on the SQuAD training dataset with varying amounts of data and found our unsupervised performance equals the supervised performance trained on about 3,000 labeled examples.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: A comparison of top results using the BERT-large model.\nQuestion: What is the difference in SQuAD Test F1 scores between the BERT-large model used in this study and the one reported by Lewis et al., 2019?\nSolve the above question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The BERT-large model used in this study achieved an F1 score of 64.04 on the SQuAD test set, while the model reported by Lewis et al., 2019 achieved an F1 score of 56.40. The difference is calculated by subtracting 56.40 from 64.04. So, the answer is {\"answer\": \"7.64\"}" } ], "images": [ "scigen/train/3800.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: A comparison of top results using the BERT-large model." ], "reasoning": "The BERT-large model used in this study achieved an F1 score of 64.04 on the SQuAD test set, while the model reported by Lewis et al., 2019 achieved an F1 score of 56.40. The difference is calculated by subtracting 56.40 from 64.04.", "question": "What is the difference in SQuAD Test F1 scores between the BERT-large model used in this study and the one reported by Lewis et al., 2019?", "answer": "So, the answer is {\"answer\": \"7.64\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe BERT-large model used in this study achieved a higher SQuAD-NER F1 score than the model reported by Lewis et al., 2019.\n" }, { "role": "assistant", "content": "The table shows that the BERT-large model used in this study achieved an SQuAD-NER F1 score of 77.55, while the model reported by Lewis et al., 2019 achieved an F1 score of 64.50. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3800.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: A comparison of top results using the BERT-large model." ], "reasoning": "The table shows that the BERT-large model used in this study achieved an SQuAD-NER F1 score of 77.55, while the model reported by Lewis et al., 2019 achieved an F1 score of 64.50. Therefore, the statement is supported.", "statement": "The BERT-large model used in this study achieved a higher SQuAD-NER F1 score than the model reported by Lewis et al., 2019.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Final NER F1 scores for the CoNLL 2003 shared task. On the top are the systems presented in this paper, and on the bottom we have baseline systems. The best results within each area are highlighted in bold. Lin and Wu 2009 use massive private industrial query-log data in training.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Lex-0.01 achieves a state-of-the-art F1 score of 90.90 on the test set, matching the highest F1 score previously reported by Lin and Wu (2009) but without using massive private data.Note that using traditional unsupervised Skip-gram embeddings is worse than Brown clusters. In contrast, our lexicon-infused phrase embeddings Lex-0.01 achieves 90.90—a state-of-the-art F1 score for the test set. This result matches the highest F1 previously reported, in Lin and Wu \\shortcitelin2009phrase, but is the first system to do so without using massive private data. Our result is signficantly better than the previous best using public data." } ], "images": [ "scigen/train/3801.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Final NER F1 scores for the CoNLL 2003 shared task. On the top are the systems presented in this paper, and on the bottom we have baseline systems. The best results within each area are highlighted in bold. Lin and Wu 2009 use massive private industrial query-log data in training." ], "reasoning": "Lex-0.01 achieves a state-of-the-art F1 score of 90.90 on the test set, matching the highest F1 score previously reported by Lin and Wu (2009) but without using massive private data.", "description": "Note that using traditional unsupervised Skip-gram embeddings is worse than Brown clusters. In contrast, our lexicon-infused phrase embeddings Lex-0.01 achieves 90.90—a state-of-the-art F1 score for the test set. This result matches the highest F1 previously reported, in Lin and Wu \\shortcitelin2009phrase, but is the first system to do so without using massive private data. Our result is signficantly better than the previous best using public data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Final NER F1 scores for the CoNLL 2003 shared task. On the top are the systems presented in this paper, and on the bottom we have baseline systems. The best results within each area are highlighted in bold. Lin and Wu 2009 use massive private industrial query-log data in training.' to sequentially solve the question presented below. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich system achieved the highest F1 score on the test set without using massive private data?\n" }, { "role": "assistant", "content": "The description states that Lex-0.01 achieved a state-of-the-art F1 score of 90.90 on the test set, matching the highest F1 score previously reported by Lin and Wu (2009) but without using massive private data. The table shows that 'Baseline + Gaz + LexEmb' achieved a test score of 90.90. So, the answer is {\"answer\": \"Baseline + Gaz + LexEmb\"}" } ], "images": [ "scigen/train/3801.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Final NER F1 scores for the CoNLL 2003 shared task. On the top are the systems presented in this paper, and on the bottom we have baseline systems. The best results within each area are highlighted in bold. Lin and Wu 2009 use massive private industrial query-log data in training." ], "reasoning": "The description states that Lex-0.01 achieved a state-of-the-art F1 score of 90.90 on the test set, matching the highest F1 score previously reported by Lin and Wu (2009) but without using massive private data. The table shows that 'Baseline + Gaz + LexEmb' achieved a test score of 90.90.", "question": "Which system achieved the highest F1 score on the test set without using massive private data?", "answer": "So, the answer is {\"answer\": \"Baseline + Gaz + LexEmb\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The system 'Baseline + Gaz + LexEmb' achieved a state-of-the-art F1 score of 90.90 on the test set. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe system 'Baseline + Gaz + LexEmb' achieved a state-of-the-art F1 score of 90.90 on the test set.\n" }, { "role": "assistant", "content": "The table shows that 'Baseline + Gaz + LexEmb' achieved a test score of 90.90, which matches the highest F1 score previously reported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3801.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Final NER F1 scores for the CoNLL 2003 shared task. On the top are the systems presented in this paper, and on the bottom we have baseline systems. The best results within each area are highlighted in bold. Lin and Wu 2009 use massive private industrial query-log data in training." ], "reasoning": "The table shows that 'Baseline + Gaz + LexEmb' achieved a test score of 90.90, which matches the highest F1 score previously reported.", "statement": "The system 'Baseline + Gaz + LexEmb' achieved a state-of-the-art F1 score of 90.90 on the test set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1: Accuracy for Semantic-Syntactic task, when restricted to Top 30K words. Lex-0.01 refers to a model trained with lexicons, where 0.01% of negative examples were used for training.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Lexicon-infused embeddings, particularly lex-0.01, outperform Skip-gram in accuracy without increasing computation cost.We find that lexicon-infused embeddings perform better than Skip-gram. Further, lex-0.01 performs the best, and we use this model for further NER experiments. There was no perceptible difference in computation cost from learning lexicon-infused embeddings versus learning standard Skip-gram embeddings." } ], "images": [ "scigen/train/3802.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Accuracy for Semantic-Syntactic task, when restricted to Top 30K words. Lex-0.01 refers to a model trained with lexicons, where 0.01% of negative examples were used for training." ], "reasoning": "Lexicon-infused embeddings, particularly lex-0.01, outperform Skip-gram in accuracy without increasing computation cost.", "description": "We find that lexicon-infused embeddings perform better than Skip-gram. Further, lex-0.01 performs the best, and we use this model for further NER experiments. There was no perceptible difference in computation cost from learning lexicon-infused embeddings versus learning standard Skip-gram embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 1: Accuracy for Semantic-Syntactic task, when restricted to Top 30K words. Lex-0.01 refers to a model trained with lexicons, where 0.01% of negative examples were used for training.', answer the following question. Give detailed solution consisting of each step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model has the highest accuracy according to the table?\n" }, { "role": "assistant", "content": "The table shows the accuracy of different models, with Lex-0.01 having the highest accuracy at 30.72. So, the answer is {\"answer\": \"Lex-0.01\"}" } ], "images": [ "scigen/train/3802.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Accuracy for Semantic-Syntactic task, when restricted to Top 30K words. Lex-0.01 refers to a model trained with lexicons, where 0.01% of negative examples were used for training." ], "reasoning": "The table shows the accuracy of different models, with Lex-0.01 having the highest accuracy at 30.72.", "question": "Which model has the highest accuracy according to the table?", "answer": "So, the answer is {\"answer\": \"Lex-0.01\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Lex-0.05 has a higher accuracy than Skip-gram., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nLex-0.05 has a higher accuracy than Skip-gram.\n" }, { "role": "assistant", "content": "The table shows that Lex-0.05 has an accuracy of 30.37, which is higher than Skip-gram's accuracy of 29.89. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3802.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Accuracy for Semantic-Syntactic task, when restricted to Top 30K words. Lex-0.01 refers to a model trained with lexicons, where 0.01% of negative examples were used for training." ], "reasoning": "The table shows that Lex-0.05 has an accuracy of 30.37, which is higher than Skip-gram's accuracy of 29.89.", "statement": "Lex-0.05 has a higher accuracy than Skip-gram.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Final NER F1 scores for Ontonotes 5.0 dataset. The results in bold face are the best on each evaluation set.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Skip-gram embeddings provide marginally better results than Lexicon infused embeddings, and both outperform Brown Clusters on the Ontonotes NER task.We found that both Skip-gram and Lexicon infused embeddings give better results than using Brown Clusters as features. However, in this case Skip-gram embeddings give marginally better results. (So as not to jeopardize our ability to fairly do further research on this task, we did not analyze the test set errors that may explain this.) These are, to the best of our knowledge, the first published performance numbers on the Ontonotes NER task." } ], "images": [ "scigen/train/3803.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Final NER F1 scores for Ontonotes 5.0 dataset. The results in bold face are the best on each evaluation set." ], "reasoning": "Skip-gram embeddings provide marginally better results than Lexicon infused embeddings, and both outperform Brown Clusters on the Ontonotes NER task.", "description": "We found that both Skip-gram and Lexicon infused embeddings give better results than using Brown Clusters as features. However, in this case Skip-gram embeddings give marginally better results. (So as not to jeopardize our ability to fairly do further research on this task, we did not analyze the test set errors that may explain this.) These are, to the best of our knowledge, the first published performance numbers on the Ontonotes NER task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: Final NER F1 scores for Ontonotes 5.0 dataset. The results in bold face are the best on each evaluation set.' to sequentially solve the question presented below. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich system achieved the highest test score?\n" }, { "role": "assistant", "content": "To find the highest test score, we need to look at the 'Test' column and identify the maximum value. The highest test score is 82.30, achieved by the 'Baseline + Gaz + Skip-gram' system. So, the answer is {\"answer\": \"Baseline + Gaz + Skip-gram\"}" } ], "images": [ "scigen/train/3803.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Final NER F1 scores for Ontonotes 5.0 dataset. The results in bold face are the best on each evaluation set." ], "reasoning": "To find the highest test score, we need to look at the 'Test' column and identify the maximum value. The highest test score is 82.30, achieved by the 'Baseline + Gaz + Skip-gram' system.", "question": "Which system achieved the highest test score?", "answer": "So, the answer is {\"answer\": \"Baseline + Gaz + Skip-gram\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Baseline + Skip-gram achieved the highest development score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nBaseline + Skip-gram achieved the highest development score.\n" }, { "role": "assistant", "content": "To verify this statement, we need to look at the 'Dev' column and identify the maximum value. The highest development score is 80.81, achieved by the 'Baseline + Gaz + LexEmb' system, not 'Baseline + Skip-gram'. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3803.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Final NER F1 scores for Ontonotes 5.0 dataset. The results in bold face are the best on each evaluation set." ], "reasoning": "To verify this statement, we need to look at the 'Dev' column and identify the maximum value. The highest development score is 80.81, achieved by the 'Baseline + Gaz + LexEmb' system, not 'Baseline + Skip-gram'.", "statement": "Baseline + Skip-gram achieved the highest development score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: Results on the generated fixed data. The average and ensemble scores were obtained by using 10 models for each architecture.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The grid-based model outperforms both the B.O.F and L.O baselines in both development and test sets, indicating its superior performance.We split this data into train (70%), dev (15%) and test (15%) sets while preserving the proportion of each subtask. The grid based model is significantly better than the B.O.F baseline. The L.O baseline reaches an accuracy higher than the proportion of the language only data. One reason might be that since the model uses the previous action as the input of the decoder, it captures some action sequence patterns." } ], "images": [ "scigen/train/3804.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Results on the generated fixed data. The average and ensemble scores were obtained by using 10 models for each architecture." ], "reasoning": "The grid-based model outperforms both the B.O.F and L.O baselines in both development and test sets, indicating its superior performance.", "description": "We split this data into train (70%), dev (15%) and test (15%) sets while preserving the proportion of each subtask. The grid based model is significantly better than the B.O.F baseline. The L.O baseline reaches an accuracy higher than the proportion of the language only data. One reason might be that since the model uses the previous action as the input of the decoder, it captures some action sequence patterns.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Results on the generated fixed data. The average and ensemble scores were obtained by using 10 models for each architecture.\nQuestion: What is the average accuracy of the B.O.F baseline on the test set?\nSolve the above question based on the table information. Let's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the average accuracy of the B.O.F baseline on the test set, look at the 'Test Avg.' column for the B.O.F row, which shows 84.61. So, the answer is {\"answer\": \"84.61\"}" } ], "images": [ "scigen/train/3804.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Results on the generated fixed data. The average and ensemble scores were obtained by using 10 models for each architecture." ], "reasoning": "To find the average accuracy of the B.O.F baseline on the test set, look at the 'Test Avg.' column for the B.O.F row, which shows 84.61.", "question": "What is the average accuracy of the B.O.F baseline on the test set?", "answer": "So, the answer is {\"answer\": \"84.61\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe L.O baseline has a higher average accuracy on the test set than the B.O.F baseline.\n" }, { "role": "assistant", "content": "The table shows that the L.O baseline has an average accuracy of 55.03 on the test set, while the B.O.F baseline has an average accuracy of 84.61. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3804.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Results on the generated fixed data. The average and ensemble scores were obtained by using 10 models for each architecture." ], "reasoning": "The table shows that the L.O baseline has an average accuracy of 55.03 on the test set, while the B.O.F baseline has an average accuracy of 84.61. Therefore, the statement is refuted.", "statement": "The L.O baseline has a higher average accuracy on the test set than the B.O.F baseline.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3: Overall results. Results of the previous works were obtained using the “vTest” procedure if not explicitly stated otherwise.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our final model achieved the highest score on Single-Sentence without tuning for each map or using the test set for tuning.Our final model achieved the state-of-the-art result on Single-Sentence even though we do not tune the model for each map and use the test set for tuning." } ], "images": [ "scigen/train/3805.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Overall results. Results of the previous works were obtained using the “vTest” procedure if not explicitly stated otherwise." ], "reasoning": "Our final model achieved the highest score on Single-Sentence without tuning for each map or using the test set for tuning.", "description": "Our final model achieved the state-of-the-art result on Single-Sentence even though we do not tune the model for each map and use the test set for tuning.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Overall results. Results of the previous works were obtained using the “vTest” procedure if not explicitly stated otherwise.'. The final result should be presented in the JSON format of {\"answer\": \"\"}. \n\nQuestion: What is the highest score achieved on the Single-Sentence task, and which model achieved it?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By examining the Single-Sentence column, the highest score is 72.82, achieved by 'Our Model (ens=10)'. So, the answer is {\"answer\": \"72.82\"}" } ], "images": [ "scigen/train/3805.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Overall results. Results of the previous works were obtained using the “vTest” procedure if not explicitly stated otherwise." ], "reasoning": "By examining the Single-Sentence column, the highest score is 72.82, achieved by 'Our Model (ens=10)'.", "question": "What is the highest score achieved on the Single-Sentence task, and which model achieved it?", "answer": "So, the answer is {\"answer\": \"72.82\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence Our final model achieved the highest score on the Single-Sentence task. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nOur final model achieved the highest score on the Single-Sentence task.\n" }, { "role": "assistant", "content": "The table shows that 'Our Model (ens=10)' achieved the highest score of 72.82 on the Single-Sentence task. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3805.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Overall results. Results of the previous works were obtained using the “vTest” procedure if not explicitly stated otherwise." ], "reasoning": "The table shows that 'Our Model (ens=10)' achieved the highest score of 72.82 on the Single-Sentence task.", "statement": "Our final model achieved the highest score on the Single-Sentence task.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: The performance (test accuracy) comparison of baselines and the proposed model. Results were obtained using ensemble of three models for each architecture.'.\n" }, { "role": "assistant", "content": "The Language Only baseline performs well on visual tasks due to non-perceptual clues in instructions, such as directional commands.The Language Only baseline achieves surprisingly good results on the visual tasks without receiving perceptual information. One reason for this is that some instructions for the visual tasks contain non-perceptual clues to solve the task, e.g. ”turn right to the chair”, ”move forward two steps to the easel”. For example, most of the action sequences are (RIGHT, STOP) in the Orient task." } ], "images": [ "scigen/train/3806.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The performance (test accuracy) comparison of baselines and the proposed model. Results were obtained using ensemble of three models for each architecture." ], "reasoning": "The Language Only baseline performs well on visual tasks due to non-perceptual clues in instructions, such as directional commands.", "description": "The Language Only baseline achieves surprisingly good results on the visual tasks without receiving perceptual information. One reason for this is that some instructions for the visual tasks contain non-perceptual clues to solve the task, e.g. ”turn right to the chair”, ”move forward two steps to the easel”. For example, most of the action sequences are (RIGHT, STOP) in the Orient task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 4: The performance (test accuracy) comparison of baselines and the proposed model. Results were obtained using ensemble of three models for each architecture.' and proceed to solve the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich task has the highest score for the 'Ours' method?\n" }, { "role": "assistant", "content": "By comparing the scores for each task under the 'Ours' method, the highest score is found in the 'Description' task with a score of 89.42. So, the answer is {\"answer\": \"Description\"}." } ], "images": [ "scigen/train/3806.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The performance (test accuracy) comparison of baselines and the proposed model. Results were obtained using ensemble of three models for each architecture." ], "reasoning": "By comparing the scores for each task under the 'Ours' method, the highest score is found in the 'Description' task with a score of 89.42.", "question": "Which task has the highest score for the 'Ours' method?", "answer": "So, the answer is {\"answer\": \"Description\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe 'Move Until' task has the lowest score for the 'L.O.' method.\n" }, { "role": "assistant", "content": "By examining the scores for the 'L.O.' method, the 'Move Until' task has the lowest score of 38.3. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3806.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The performance (test accuracy) comparison of baselines and the proposed model. Results were obtained using ensemble of three models for each architecture." ], "reasoning": "By examining the scores for the 'L.O.' method, the 'Move Until' task has the lowest score of 38.3.", "statement": "The 'Move Until' task has the lowest score for the 'L.O.' method.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: Mean accuracy by sector for the majority class baseline, three benchmarks, and OmniGraph with two graph kernels. The cases where the sector mean is significantly better than the baseline are marked by *. OmniGraph is significantly better than all three benchmarks in all cases.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "OmniGraphWL and OmniGraphNEW outperform the baseline with statistical significance in six and seven sectors respectively, while none of the benchmarks outperform the baseline.Both versions of OmniGraph significantly outperform the three benchmarks. The cells with asterisks represent a difference from the baseline that is statistically significant. OmniGraphWL beats the baseline with statistical significance in six sectors, and OmniGraphNEW in seven. Note that none of the benchmarks outperforms the baseline." } ], "images": [ "scigen/train/3807.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Mean accuracy by sector for the majority class baseline, three benchmarks, and OmniGraph with two graph kernels. The cases where the sector mean is significantly better than the baseline are marked by *. OmniGraph is significantly better than all three benchmarks in all cases." ], "reasoning": "OmniGraphWL and OmniGraphNEW outperform the baseline with statistical significance in six and seven sectors respectively, while none of the benchmarks outperform the baseline.", "description": "Both versions of OmniGraph significantly outperform the three benchmarks. The cells with asterisks represent a difference from the baseline that is statistically significant. OmniGraphWL beats the baseline with statistical significance in six sectors, and OmniGraphNEW in seven. Note that none of the benchmarks outperforms the baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nIn how many sectors does OmniGraphNEW outperform the baseline with statistical significance?\nSolve the above question based on the table titled 'Table 1: Mean accuracy by sector for the majority class baseline, three benchmarks, and OmniGraph with two graph kernels. The cases where the sector mean is significantly better than the baseline are marked by *. OmniGraph is significantly better than all three benchmarks in all cases.'. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "According to the description, OmniGraphNEW beats the baseline with statistical significance in seven sectors. So, the answer is {\"answer\": \"7\"}" } ], "images": [ "scigen/train/3807.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Mean accuracy by sector for the majority class baseline, three benchmarks, and OmniGraph with two graph kernels. The cases where the sector mean is significantly better than the baseline are marked by *. OmniGraph is significantly better than all three benchmarks in all cases." ], "reasoning": "According to the description, OmniGraphNEW beats the baseline with statistical significance in seven sectors.", "question": "In how many sectors does OmniGraphNEW outperform the baseline with statistical significance?", "answer": "So, the answer is {\"answer\": \"7\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nOmniGraphWL outperforms the baseline with statistical significance in seven sectors.\n" }, { "role": "assistant", "content": "The description states that OmniGraphWL beats the baseline with statistical significance in six sectors, not seven. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3807.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Mean accuracy by sector for the majority class baseline, three benchmarks, and OmniGraph with two graph kernels. The cases where the sector mean is significantly better than the baseline are marked by *. OmniGraph is significantly better than all three benchmarks in all cases." ], "reasoning": "The description states that OmniGraphWL beats the baseline with statistical significance in six sectors, not seven.", "statement": "OmniGraphWL outperforms the baseline with statistical significance in seven sectors.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Mean accuracy for gfbf data experiments, where OmniGraph (in boldface) significantly outperforms the baseline and three benchmarks.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "OmniGraph with graph kernel learning (WL or NEW kernels) performs much better than other methods, with both versions showing superior performance over BOW, dependency trees, and semantic trees.We use the percentage of the majority class as the baseline, and compare the same five methods as in our previous experiment. On the benefactive/malefactive task, BOW obtains a 10% improvement over the baseline. Structured representations significantly improve over BOW. SemTreeFWD, which incorporates the semantic frame features and a sentiment lexicon, improves the performance by another 5%. The dependency tree performance is similar to SemTreeFWD. OmniGraph with graph kernel learning (WL or NEW kernels) performs much better. Dependency trees and semantic trees do not improve over BOW. Both versions of OmniGraph, however, have superior performance." } ], "images": [ "scigen/train/3808.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Mean accuracy for gfbf data experiments, where OmniGraph (in boldface) significantly outperforms the baseline and three benchmarks." ], "reasoning": "OmniGraph with graph kernel learning (WL or NEW kernels) performs much better than other methods, with both versions showing superior performance over BOW, dependency trees, and semantic trees.", "description": "We use the percentage of the majority class as the baseline, and compare the same five methods as in our previous experiment. On the benefactive/malefactive task, BOW obtains a 10% improvement over the baseline. Structured representations significantly improve over BOW. SemTreeFWD, which incorporates the semantic frame features and a sentiment lexicon, improves the performance by another 5%. The dependency tree performance is similar to SemTreeFWD. OmniGraph with graph kernel learning (WL or NEW kernels) performs much better. Dependency trees and semantic trees do not improve over BOW. Both versions of OmniGraph, however, have superior performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Mean accuracy for gfbf data experiments, where OmniGraph (in boldface) significantly outperforms the baseline and three benchmarks.', answer the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich method shows the highest performance improvement over the baseline in the Benefactive/Malefactive task?\n" }, { "role": "assistant", "content": "The table shows that OmniGraph WL has the highest performance in the Benefactive/Malefactive task with a score of 83.17, which is significantly higher than the baseline of 56.65. So, the answer is {\"answer\": \"OmniGraph WL\"}." } ], "images": [ "scigen/train/3808.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Mean accuracy for gfbf data experiments, where OmniGraph (in boldface) significantly outperforms the baseline and three benchmarks." ], "reasoning": "The table shows that OmniGraph WL has the highest performance in the Benefactive/Malefactive task with a score of 83.17, which is significantly higher than the baseline of 56.65.", "question": "Which method shows the highest performance improvement over the baseline in the Benefactive/Malefactive task?", "answer": "So, the answer is {\"answer\": \"OmniGraph WL\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nDependency trees improve over BOW in the WriterAttitude task.\n" }, { "role": "assistant", "content": "The table shows that the performance of DepTree in the WriterAttitude task is 66.16, which is slightly higher than BOW's performance of 66.61. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3808.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Mean accuracy for gfbf data experiments, where OmniGraph (in boldface) significantly outperforms the baseline and three benchmarks." ], "reasoning": "The table shows that the performance of DepTree in the WriterAttitude task is 66.16, which is slightly higher than BOW's performance of 66.61. Therefore, the statement is refuted.", "statement": "Dependency trees improve over BOW in the WriterAttitude task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 6: Precision, Recall and F1-scores for UnENT comparing scores using BERT and fastText multilingual embeddings.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "BERT consistently underperforms compared to fastText across all languages and finetuning settings.BERT performs poorly compared to fastText in every language and almost for each of the finetuning settings." } ], "images": [ "scigen/train/3809.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Precision, Recall and F1-scores for UnENT comparing scores using BERT and fastText multilingual embeddings." ], "reasoning": "BERT consistently underperforms compared to fastText across all languages and finetuning settings.", "description": "BERT performs poorly compared to fastText in every language and almost for each of the finetuning settings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 6: Precision, Recall and F1-scores for UnENT comparing scores using BERT and fastText multilingual embeddings.'.\n\nQuestion: What is the highest F1 score achieved by BERT across all settings and languages?\n" }, { "role": "assistant", "content": "To find the highest F1 score achieved by BERT, we need to look at the 'BERT F1' column and identify the maximum value. The highest F1 score for BERT is 83.39. So, the answer is {\"answer\": \"83.39\"}" } ], "images": [ "scigen/train/3809.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Precision, Recall and F1-scores for UnENT comparing scores using BERT and fastText multilingual embeddings." ], "reasoning": "To find the highest F1 score achieved by BERT, we need to look at the 'BERT F1' column and identify the maximum value. The highest F1 score for BERT is 83.39.", "question": "What is the highest F1 score achieved by BERT across all settings and languages?", "answer": "So, the answer is {\"answer\": \"83.39\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nBERT achieves a higher F1 score than fastText in the EN-IT 0 setting.\n" }, { "role": "assistant", "content": "In the EN-IT 0 setting, BERT has an F1 score of 10.70, while fastText has an F1 score of 4.44. Therefore, BERT achieves a higher F1 score than fastText in this setting. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3809.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Precision, Recall and F1-scores for UnENT comparing scores using BERT and fastText multilingual embeddings." ], "reasoning": "In the EN-IT 0 setting, BERT has an F1 score of 10.70, while fastText has an F1 score of 4.44. Therefore, BERT achieves a higher F1 score than fastText in this setting.", "statement": "BERT achieves a higher F1 score than fastText in the EN-IT 0 setting.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 5: Category-wise improvement (ΔF1) of NER systems over the baseline model.'.\n" }, { "role": "assistant", "content": "The table shows the category-wise improvement of NER systems when trained using word clusters from different languages compared to the baseline model.In order to verify our hypothesis that certain NEs, specially the names of people and locations might not go orthographic changes while transferring to similar languages we look at the category-wise improvement of the NER systems when trained using word clusters of a language (other than self) that gives the best results against the baseline model. For example, for Spanish NER we compare the baseline model with the model trained using French word clusters." } ], "images": [ "scigen/train/3810.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Category-wise improvement (ΔF1) of NER systems over the baseline model." ], "reasoning": "The table shows the category-wise improvement of NER systems when trained using word clusters from different languages compared to the baseline model.", "description": "In order to verify our hypothesis that certain NEs, specially the names of people and locations might not go orthographic changes while transferring to similar languages we look at the category-wise improvement of the NER systems when trained using word clusters of a language (other than self) that gives the best results against the baseline model. For example, for Spanish NER we compare the baseline model with the model trained using French word clusters.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 5: Category-wise improvement (ΔF1) of NER systems over the baseline model.' in mind, break down and resolve the question below step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich language's word clusters resulted in the highest improvement for the PER category?\n" }, { "role": "assistant", "content": "To find the language with the highest improvement for the PER category, look at the values in the PER row and identify the highest value. So, the answer is {\"answer\": \"German\"}." } ], "images": [ "scigen/train/3810.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Category-wise improvement (ΔF1) of NER systems over the baseline model." ], "reasoning": "To find the language with the highest improvement for the PER category, look at the values in the PER row and identify the highest value.", "question": "Which language's word clusters resulted in the highest improvement for the PER category?", "answer": "So, the answer is {\"answer\": \"German\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe average improvement for the LOC category is higher than that for the ORG category.\n" }, { "role": "assistant", "content": "Compare the average improvement values for the LOC and ORG categories. The average for LOC is 2.25, and for ORG, it is 1.50. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3810.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Category-wise improvement (ΔF1) of NER systems over the baseline model." ], "reasoning": "Compare the average improvement values for the LOC and ORG categories. The average for LOC is 2.25, and for ORG, it is 1.50.", "statement": "The average improvement for the LOC category is higher than that for the ORG category.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: F1 scores of NER systems trained using noisy twitter data.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The use of Twitter clusters for NER in English, German, Dutch, and Spanish shows significant improvements over the baseline, with the highest improvement seen in German.The labeled NE training data for all of the languages we have used comes from the newswire domain. Thus the news-commentary data (cf. Sec. Since we cannot always expect to obtain in-domain data, we use word clusters 850 million tokens clustered into 1000 classes for generalization For English, German and French we again obtain a highly significant improvement; however the improvements obtained using the out-of-domain data are lesser than that obtained using in-domain data." } ], "images": [ "scigen/train/3811.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: F1 scores of NER systems trained using noisy twitter data." ], "reasoning": "The use of Twitter clusters for NER in English, German, Dutch, and Spanish shows significant improvements over the baseline, with the highest improvement seen in German.", "description": "The labeled NE training data for all of the languages we have used comes from the newswire domain. Thus the news-commentary data (cf. Sec. Since we cannot always expect to obtain in-domain data, we use word clusters 850 million tokens clustered into 1000 classes for generalization For English, German and French we again obtain a highly significant improvement; however the improvements obtained using the out-of-domain data are lesser than that obtained using in-domain data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: F1 scores of NER systems trained using noisy twitter data.', answer the following question. Give detailed solution consisting of each step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich language showed the highest improvement in NER performance when using Twitter clusters compared to the baseline?\n" }, { "role": "assistant", "content": "To find the language with the highest improvement, subtract the baseline score from the Twitter clusters score for each language. The highest difference indicates the highest improvement. So, the answer is {\"answer\": \"German\"}." } ], "images": [ "scigen/train/3811.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: F1 scores of NER systems trained using noisy twitter data." ], "reasoning": "To find the language with the highest improvement, subtract the baseline score from the Twitter clusters score for each language. The highest difference indicates the highest improvement.", "question": "Which language showed the highest improvement in NER performance when using Twitter clusters compared to the baseline?", "answer": "So, the answer is {\"answer\": \"German\"}." }, { "messages": [ { "role": "user", "content": "The use of Twitter clusters improved NER performance for all languages listed in the table. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe use of Twitter clusters improved NER performance for all languages listed in the table.\n" }, { "role": "assistant", "content": "The table shows that the Twitter clusters scores are higher than the baseline scores for English, German, Dutch, and Spanish, indicating an improvement for all languages. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3811.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: F1 scores of NER systems trained using noisy twitter data." ], "reasoning": "The table shows that the Twitter clusters scores are higher than the baseline scores for English, German, Dutch, and Spanish, indicating an improvement for all languages.", "statement": "The use of Twitter clusters improved NER performance for all languages listed in the table.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 4: Comparison of the draft and reference sentences in Smith. FRE and PPL scores were calculated once in each sentence and then averaged over all the sentences in the development set of Smith.'.\n" }, { "role": "assistant", "content": "The draft sentences have higher PPL scores compared to the reference sentences, indicating they are less predictable.We computed some sentence-level linguistic measures over the dataset sentences: Finally, we analyzed the draft and the reference sentences using PPL calculated by a 5-gram language model trained on ACL Anthology papers. The higher PPL scores in the draft sentences" } ], "images": [ "scigen/train/3812.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of the draft and reference sentences in Smith. FRE and PPL scores were calculated once in each sentence and then averaged over all the sentences in the development set of Smith." ], "reasoning": "The draft sentences have higher PPL scores compared to the reference sentences, indicating they are less predictable.", "description": "We computed some sentence-level linguistic measures over the dataset sentences: Finally, we analyzed the draft and the reference sentences using PPL calculated by a 5-gram language model trained on ACL Anthology papers. The higher PPL scores in the draft sentences", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 4: Comparison of the draft and reference sentences in Smith. FRE and PPL scores were calculated once in each sentence and then averaged over all the sentences in the development set of Smith.', and tackle the question below in a sequential manner. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich dataset has a higher percentage of passive voice usage?\n" }, { "role": "assistant", "content": "By comparing the passive voice percentages, Draft X has 34.0% while Reference Y has 29.6%. So, the answer is {\"answer\": \"Draft X\"}." } ], "images": [ "scigen/train/3812.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of the draft and reference sentences in Smith. FRE and PPL scores were calculated once in each sentence and then averaged over all the sentences in the development set of Smith." ], "reasoning": "By comparing the passive voice percentages, Draft X has 34.0% while Reference Y has 29.6%.", "question": "Which dataset has a higher percentage of passive voice usage?", "answer": "So, the answer is {\"answer\": \"Draft X\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe reference sentences have a higher word repetition percentage than the draft sentences.\n" }, { "role": "assistant", "content": "The table shows that Draft X has a word repetition percentage of 33.0%, while Reference Y has 28.6%. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3812.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of the draft and reference sentences in Smith. FRE and PPL scores were calculated once in each sentence and then averaged over all the sentences in the development set of Smith." ], "reasoning": "The table shows that Draft X has a word repetition percentage of 33.0%, while Reference Y has 28.6%. Therefore, the statement is refuted.", "statement": "The reference sentences have a higher word repetition percentage than the draft sentences.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 6: Results of quantitative evaluation. Gramm. denotes the grammaticality score.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The ED-ND model achieves the highest scores in BLEU, ROUGE-L, BERT-P, BERT-R, BERT-F, and PPL metrics, indicating its superior performance in generating noise closer to real-world drafts.We observed that the ED-ND model outperforms the other models in nearly all evaluation metrics. This finding suggests that the Enc-Dec noising methods induced noise closer to real-world drafts compared with the heuristic methods." } ], "images": [ "scigen/train/3813.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results of quantitative evaluation. Gramm. denotes the grammaticality score." ], "reasoning": "The ED-ND model achieves the highest scores in BLEU, ROUGE-L, BERT-P, BERT-R, BERT-F, and PPL metrics, indicating its superior performance in generating noise closer to real-world drafts.", "description": "We observed that the ED-ND model outperforms the other models in nearly all evaluation metrics. This finding suggests that the Enc-Dec noising methods induced noise closer to real-world drafts compared with the heuristic methods.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 6: Results of quantitative evaluation. Gramm. denotes the grammaticality score.\n\nQuestion: Which model has the lowest perplexity (PPL) score, and what does this imply about its performance?\n" }, { "role": "assistant", "content": "The table shows that the ED-ND model has the lowest PPL score of 236. Lower PPL scores indicate that the model's generated text is more similar to real-world drafts, suggesting that the ED-ND model performs better in this aspect. So, the answer is {\"answer\": \"ED-ND\"}." } ], "images": [ "scigen/train/3813.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results of quantitative evaluation. Gramm. denotes the grammaticality score." ], "reasoning": "The table shows that the ED-ND model has the lowest PPL score of 236. Lower PPL scores indicate that the model's generated text is more similar to real-world drafts, suggesting that the ED-ND model performs better in this aspect.", "question": "Which model has the lowest perplexity (PPL) score, and what does this imply about its performance?", "answer": "So, the answer is {\"answer\": \"ED-ND\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The ED-ND model has the highest F0.5 score among all models. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe ED-ND model has the highest F0.5 score among all models.\n" }, { "role": "assistant", "content": "The table shows that the ED-ND model has an F0.5 score of 19.2, which is lower than the GEC model's F0.5 score of 22.6. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3813.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results of quantitative evaluation. Gramm. denotes the grammaticality score." ], "reasoning": "The table shows that the ED-ND model has an F0.5 score of 19.2, which is lower than the GEC model's F0.5 score of 22.6. Therefore, the statement is refuted.", "statement": "The ED-ND model has the highest F0.5 score among all models.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: First A/B testing experiment topical specificity and coherence by policy. The columns are average number of noun phrases per system utterance (System NPs), average number of overlapping words between the user’s utterance and the system’s response (This Turn), and average number of overlapping words between the user’s utterance and the system’s response in the next turn (Next Turn). Stop words are excluded. 95% confidence intervals are also shown.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The Bottleneck Simulator has the highest scores in System NPs, Word Overlap This Turn, and Word Overlap Next Turn, indicating its superior performance and risk-tolerant strategy.This indicates that the Bottleneck Simulator has the most coherent and engaging dialogues out of all the evaluated policies. This is in agreement with its excellent performance w.r.t. real-world user scores and w.r.t. the preliminary evaluation. A further analysis of the selected responses indicates that the Bottleneck Simulator has learned a more risk tolerant strategy." } ], "images": [ "scigen/train/3814.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: First A/B testing experiment topical specificity and coherence by policy. The columns are average number of noun phrases per system utterance (System NPs), average number of overlapping words between the user’s utterance and the system’s response (This Turn), and average number of overlapping words between the user’s utterance and the system’s response in the next turn (Next Turn). Stop words are excluded. 95% confidence intervals are also shown." ], "reasoning": "The Bottleneck Simulator has the highest scores in System NPs, Word Overlap This Turn, and Word Overlap Next Turn, indicating its superior performance and risk-tolerant strategy.", "description": "This indicates that the Bottleneck Simulator has the most coherent and engaging dialogues out of all the evaluated policies. This is in agreement with its excellent performance w.r.t. real-world user scores and w.r.t. the preliminary evaluation. A further analysis of the selected responses indicates that the Bottleneck Simulator has learned a more risk tolerant strategy.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: First A/B testing experiment topical specificity and coherence by policy. The columns are average number of noun phrases per system utterance (System NPs), average number of overlapping words between the user’s utterance and the system’s response (This Turn), and average number of overlapping words between the user’s utterance and the system’s response in the next turn (Next Turn). Stop words are excluded. 95% confidence intervals are also shown.', answer the following question. Give detailed solution consisting of each step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich policy has the highest Word Overlap This Turn score?\n" }, { "role": "assistant", "content": "By comparing the Word Overlap This Turn scores for all policies, the Bottleneck Simulator has the highest score of 11.28±0.30. So, the answer is {\"answer\": \"Bottleneck Simulator\"}." } ], "images": [ "scigen/train/3814.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: First A/B testing experiment topical specificity and coherence by policy. The columns are average number of noun phrases per system utterance (System NPs), average number of overlapping words between the user’s utterance and the system’s response (This Turn), and average number of overlapping words between the user’s utterance and the system’s response in the next turn (Next Turn). Stop words are excluded. 95% confidence intervals are also shown." ], "reasoning": "By comparing the Word Overlap This Turn scores for all policies, the Bottleneck Simulator has the highest score of 11.28±0.30.", "question": "Which policy has the highest Word Overlap This Turn score?", "answer": "So, the answer is {\"answer\": \"Bottleneck Simulator\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe Bottleneck Simulator has the lowest System NPs score.\n" }, { "role": "assistant", "content": "The table shows that the Bottleneck Simulator has a System NPs score of 1.98±0.08, which is the highest among all policies, not the lowest. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3814.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: First A/B testing experiment topical specificity and coherence by policy. The columns are average number of noun phrases per system utterance (System NPs), average number of overlapping words between the user’s utterance and the system’s response (This Turn), and average number of overlapping words between the user’s utterance and the system’s response in the next turn (Next Turn). Stop words are excluded. 95% confidence intervals are also shown." ], "reasoning": "The table shows that the Bottleneck Simulator has a System NPs score of 1.98±0.08, which is the highest among all policies, not the lowest.", "statement": "The Bottleneck Simulator has the lowest System NPs score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Average percentage of completed games for Home World (± 95% confidence intervals). Q-learning baseline policy was trained once until reaching 15% game completion on average.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The Bottleneck Simulator policy achieved the highest performance at 36.8% with a cluster size of 24, outperforming the State Abstraction policy at 26.5% and the baseline policy at 15%.We evaluate the policies based on the percentage of games completed. It is important to note that since our goal is to evaluate sample efficiency, our policies have been trained on far fewer episodes compared to Narasimhan et al. Further, we have retained the baseline policy’s hyper-parameters for the Bottleneck Simulator policies while reporting the results. We observe peak performance at 36.8% for the Bottleneck Simulator policy with a cluster size of 24, which is significantly higher than the State Abstraction policy at 26.5% and the baseline policy at only 15%. This shows empirically that the Bottleneck Simulator policy is the most sample efficient algorithm, since all policies have been trained on the same number of examples. Finally, it should be noted that the State Abstraction and Bottleneck Simulator policies are complementary and could potentially be combined (e.g. by training a State Abstraction policy from samples generated by the Bottleneck Simulator environment model)." } ], "images": [ "scigen/train/3815.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Average percentage of completed games for Home World (± 95% confidence intervals). Q-learning baseline policy was trained once until reaching 15% game completion on average." ], "reasoning": "The Bottleneck Simulator policy achieved the highest performance at 36.8% with a cluster size of 24, outperforming the State Abstraction policy at 26.5% and the baseline policy at 15%.", "description": "We evaluate the policies based on the percentage of games completed. It is important to note that since our goal is to evaluate sample efficiency, our policies have been trained on far fewer episodes compared to Narasimhan et al. Further, we have retained the baseline policy’s hyper-parameters for the Bottleneck Simulator policies while reporting the results. We observe peak performance at 36.8% for the Bottleneck Simulator policy with a cluster size of 24, which is significantly higher than the State Abstraction policy at 26.5% and the baseline policy at only 15%. This shows empirically that the Bottleneck Simulator policy is the most sample efficient algorithm, since all policies have been trained on the same number of examples. Finally, it should be noted that the State Abstraction and Bottleneck Simulator policies are complementary and could potentially be combined (e.g. by training a State Abstraction policy from samples generated by the Bottleneck Simulator environment model).", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 1: Average percentage of completed games for Home World (± 95% confidence intervals). Q-learning baseline policy was trained once until reaching 15% game completion on average.\nQuestion: What is the performance of the Bottleneck Simulator policy with a cluster size of 24?\n" }, { "role": "assistant", "content": "The table shows the performance of different policies with various cluster sizes. For the Bottleneck Simulator policy with a cluster size of 24, the performance is 36.8%. So, the answer is {\"answer\": \"36.8%\"}." } ], "images": [ "scigen/train/3815.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Average percentage of completed games for Home World (± 95% confidence intervals). Q-learning baseline policy was trained once until reaching 15% game completion on average." ], "reasoning": "The table shows the performance of different policies with various cluster sizes. For the Bottleneck Simulator policy with a cluster size of 24, the performance is 36.8%.", "question": "What is the performance of the Bottleneck Simulator policy with a cluster size of 24?", "answer": "So, the answer is {\"answer\": \"36.8%\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe State Abstraction policy performs better than the Bottleneck Simulator policy with a cluster size of 24.\n" }, { "role": "assistant", "content": "The table shows that the Bottleneck Simulator policy with a cluster size of 24 has a performance of 36.8%, which is higher than the State Abstraction policy's performance of 26.5% with the same cluster size. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3815.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Average percentage of completed games for Home World (± 95% confidence intervals). Q-learning baseline policy was trained once until reaching 15% game completion on average." ], "reasoning": "The table shows that the Bottleneck Simulator policy with a cluster size of 24 has a performance of 36.8%, which is higher than the State Abstraction policy's performance of 26.5% with the same cluster size.", "statement": "The State Abstraction policy performs better than the Bottleneck Simulator policy with a cluster size of 24.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Precision and recall scores for each class on Essay-Content. underlined: best result; bold: improves against SVM withstanding standard deviation.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The ArgRanker models show a significant improvement in attack precision compared to SVM, with the highest improvement being 31.4 percentage points by ArgRanker vote, while maintaining recall levels.The very rare attack-class is detected with a much greater precision compared with the SVM. The difference can range from an improvement of 5.6 pp. (ArgRankerY/N, relative improvement: 28%) up to a maximum improvement of 31.4 pp. (ArgRankervote, relative improvement: 157%). With such a large increase in precision, one might expect a drop in recall – however, this is only the case to a very small extent." } ], "images": [ "scigen/train/3816.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Precision and recall scores for each class on Essay-Content. underlined: best result; bold: improves against SVM withstanding standard deviation." ], "reasoning": "The ArgRanker models show a significant improvement in attack precision compared to SVM, with the highest improvement being 31.4 percentage points by ArgRanker vote, while maintaining recall levels.", "description": "The very rare attack-class is detected with a much greater precision compared with the SVM. The difference can range from an improvement of 5.6 pp. (ArgRankerY/N, relative improvement: 28%) up to a maximum improvement of 31.4 pp. (ArgRankervote, relative improvement: 157%). With such a large increase in precision, one might expect a drop in recall – however, this is only the case to a very small extent.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Precision and recall scores for each class on Essay-Content. underlined: best result; bold: improves against SVM withstanding standard deviation.\nQuestion: Which ArgRanker model shows the highest improvement in attack precision compared to SVM with features?\nSolve the above question based on the table information. Let's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the attack precision values, ArgRanker vote has the highest precision at 51.4%, which is 31.4 percentage points higher than SVM with features at 20%. So, the answer is {\"answer\": \"ArgRanker vote\"}" } ], "images": [ "scigen/train/3816.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Precision and recall scores for each class on Essay-Content. underlined: best result; bold: improves against SVM withstanding standard deviation." ], "reasoning": "By comparing the attack precision values, ArgRanker vote has the highest precision at 51.4%, which is 31.4 percentage points higher than SVM with features at 20%.", "question": "Which ArgRanker model shows the highest improvement in attack precision compared to SVM with features?", "answer": "So, the answer is {\"answer\": \"ArgRanker vote\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis ArgRanker A/ A has the highest support recall among all models. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nArgRanker A/ A has the highest support recall among all models.\n" }, { "role": "assistant", "content": "The table shows that ArgRanker vote has the highest support recall at 98.4%, not ArgRanker A/ A which has 96.2%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3816.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Precision and recall scores for each class on Essay-Content. underlined: best result; bold: improves against SVM withstanding standard deviation." ], "reasoning": "The table shows that ArgRanker vote has the highest support recall at 98.4%, not ArgRanker A/ A which has 96.2%.", "statement": "ArgRanker A/ A has the highest support recall among all models.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: The results of the publicly available model from [12] and the proposed ResNet34-MFB80-AM-TrainData-IV on the short utterances protocol (generated from VoxCeleb1).', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The proposed model showed significantly better quality for various short durations (1 sec, 2 sec, and 5 sec) compared to the Thin ResNet34 model.A comparison protocol was generated by randomly sampling 100 target and 100 imposter pairs for each speaker from a total of 1,251 speakers in VoxCeleb1, resulting in 250,048 unique comparisons. The observed differences can be attributed to the differences in test protocols. Nevertheless, synchronous testing showed significantly better quality of the proposed model for various short durations (1 sec, 2 sec and 5 sec)." } ], "images": [ "scigen/train/3817.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The results of the publicly available model from [12] and the proposed ResNet34-MFB80-AM-TrainData-IV on the short utterances protocol (generated from VoxCeleb1)." ], "reasoning": "The proposed model showed significantly better quality for various short durations (1 sec, 2 sec, and 5 sec) compared to the Thin ResNet34 model.", "description": "A comparison protocol was generated by randomly sampling 100 target and 100 imposter pairs for each speaker from a total of 1,251 speakers in VoxCeleb1, resulting in 250,048 unique comparisons. The observed differences can be attributed to the differences in test protocols. Nevertheless, synchronous testing showed significantly better quality of the proposed model for various short durations (1 sec, 2 sec and 5 sec).", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nTable title: Table 3: The results of the publicly available model from [12] and the proposed ResNet34-MFB80-AM-TrainData-IV on the short utterances protocol (generated from VoxCeleb1).\nQuestion: Which embedding extractor has the lowest EER for 5-second durations?\n" }, { "role": "assistant", "content": "By comparing the EER values for 5-second durations in the table, the ResNet34-MFB80-AM-TrainData-IV (1s) has the lowest EER of 2.26%. So, the answer is {\"answer\": \"ResNet34-MFB80-AM-TrainData-IV (1s)\"}." } ], "images": [ "scigen/train/3817.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The results of the publicly available model from [12] and the proposed ResNet34-MFB80-AM-TrainData-IV on the short utterances protocol (generated from VoxCeleb1)." ], "reasoning": "By comparing the EER values for 5-second durations in the table, the ResNet34-MFB80-AM-TrainData-IV (1s) has the lowest EER of 2.26%.", "question": "Which embedding extractor has the lowest EER for 5-second durations?", "answer": "So, the answer is {\"answer\": \"ResNet34-MFB80-AM-TrainData-IV (1s)\"}." }, { "messages": [ { "role": "user", "content": "The Thin ResNet34 model has a lower EER for 2-second durations compared to the ResNet34-MFB80-AM-TrainData-IV (1s) model. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Thin ResNet34 model has a lower EER for 2-second durations compared to the ResNet34-MFB80-AM-TrainData-IV (1s) model.\n" }, { "role": "assistant", "content": "The table shows that the EER for 2-second durations is 6.59% for Thin ResNet34 and 4.48% for ResNet34-MFB80-AM-TrainData-IV (1s), indicating that the statement is false. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3817.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The results of the publicly available model from [12] and the proposed ResNet34-MFB80-AM-TrainData-IV on the short utterances protocol (generated from VoxCeleb1)." ], "reasoning": "The table shows that the EER for 2-second durations is 6.59% for Thin ResNet34 and 4.48% for ResNet34-MFB80-AM-TrainData-IV (1s), indicating that the statement is false.", "statement": "The Thin ResNet34 model has a lower EER for 2-second durations compared to the ResNet34-MFB80-AM-TrainData-IV (1s) model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: Results of the investigated systems for VOiCES (eval set), VOiCES (dev set) and VoxCeleb1-O (cleaned) protocols in relation to different length of test waveform.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The experiments did not use normalization and U-net based VAD, as shown by the results of the ResNet34-MFB80-AM-TrainData-I model, which was trained on 1-second and 2-second segments.In order to simplify the experiments and improve the reproducibility of our results, we did not use normalization and U-net based VAD for these tests. This is confirmed by the results of ResNet34-MFB80-AM-TrainData-I model, trained on 1-second and 2-second segments. Thus, it is possible to slightly improve the quality of the systems for short durations in this way." } ], "images": [ "scigen/train/3818.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of the investigated systems for VOiCES (eval set), VOiCES (dev set) and VoxCeleb1-O (cleaned) protocols in relation to different length of test waveform." ], "reasoning": "The experiments did not use normalization and U-net based VAD, as shown by the results of the ResNet34-MFB80-AM-TrainData-I model, which was trained on 1-second and 2-second segments.", "description": "In order to simplify the experiments and improve the reproducibility of our results, we did not use normalization and U-net based VAD for these tests. This is confirmed by the results of ResNet34-MFB80-AM-TrainData-I model, trained on 1-second and 2-second segments. Thus, it is possible to slightly improve the quality of the systems for short durations in this way.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. The question is related to the given table titled 'Table 4: Results of the investigated systems for VOiCES (eval set), VOiCES (dev set) and VoxCeleb1-O (cleaned) protocols in relation to different length of test waveform.'.\n\nQuestion: What is the EER for the ResNet34-MFB80-AM-TrainData-I model on the VOiCES (eval set) for 2-second segments?\n" }, { "role": "assistant", "content": "The table shows the EER for different models and segment durations. For the ResNet34-MFB80-AM-TrainData-I model on the VOiCES (eval set) for 2-second segments, the EER is 9.46%. So, the answer is {\"answer\": \"9.46%\"}" } ], "images": [ "scigen/train/3818.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of the investigated systems for VOiCES (eval set), VOiCES (dev set) and VoxCeleb1-O (cleaned) protocols in relation to different length of test waveform." ], "reasoning": "The table shows the EER for different models and segment durations. For the ResNet34-MFB80-AM-TrainData-I model on the VOiCES (eval set) for 2-second segments, the EER is 9.46%.", "question": "What is the EER for the ResNet34-MFB80-AM-TrainData-I model on the VOiCES (eval set) for 2-second segments?", "answer": "So, the answer is {\"answer\": \"9.46%\"}" }, { "messages": [ { "role": "user", "content": "The ResNet34-MFB80-AM-TrainData-I model has the lowest EER on the VOiCES (eval set) for 2-second segments. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe ResNet34-MFB80-AM-TrainData-I model has the lowest EER on the VOiCES (eval set) for 2-second segments.\n" }, { "role": "assistant", "content": "The table shows the EER for different models and segment durations. The ResNet34-MFB80-AM-TrainData-I model has an EER of 9.46% on the VOiCES (eval set) for 2-second segments, which is the lowest among all models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3818.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of the investigated systems for VOiCES (eval set), VOiCES (dev set) and VoxCeleb1-O (cleaned) protocols in relation to different length of test waveform." ], "reasoning": "The table shows the EER for different models and segment durations. The ResNet34-MFB80-AM-TrainData-I model has an EER of 9.46% on the VOiCES (eval set) for 2-second segments, which is the lowest among all models listed.", "statement": "The ResNet34-MFB80-AM-TrainData-I model has the lowest EER on the VOiCES (eval set) for 2-second segments.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 4: Comparison of different loss functions and weight ratios for guided alignment.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Different cost ratios and heuristics were experimented with to balance decoder and attention weight costs, showing improvements in alignment for the IWSLT task but not for the e-commerce task.To balance decoder cost and the attention weight cost, we experimented with different weights for these costs. Besides fixing the cost ratios during training, we also apply a heuristic to adjust the ratio as the training is progressing. One approach is to set a high value for the alignment cost in the beginning, then decay the weight to 90% after every epoch, finally eliminating the influence of the alignment after some time. This approach helps for the IWSLT task, but not on the e-commerce task. We assume that the alignment for the TED talk sentences seems to be easier for NMT to learn on its own than the alignment between product titles and their translations. Since the cross-entropy function has a consistent form as decoder cost, we decided to use it in further experiments. We extracted the NMT attention weights and marked the connection with the highest score as hard alignment for each word. It can be seen from the graph that the guided alignment training truly improves the alignment correspondence." } ], "images": [ "scigen/train/3819.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Comparison of different loss functions and weight ratios for guided alignment." ], "reasoning": "Different cost ratios and heuristics were experimented with to balance decoder and attention weight costs, showing improvements in alignment for the IWSLT task but not for the e-commerce task.", "description": "To balance decoder cost and the attention weight cost, we experimented with different weights for these costs. Besides fixing the cost ratios during training, we also apply a heuristic to adjust the ratio as the training is progressing. One approach is to set a high value for the alignment cost in the beginning, then decay the weight to 90% after every epoch, finally eliminating the influence of the alignment after some time. This approach helps for the IWSLT task, but not on the e-commerce task. We assume that the alignment for the TED talk sentences seems to be easier for NMT to learn on its own than the alignment between product titles and their translations. Since the cross-entropy function has a consistent form as decoder cost, we decided to use it in further experiments. We extracted the NMT attention weights and marked the connection with the highest score as hard alignment for each word. It can be seen from the graph that the guided alignment training truly improves the alignment correspondence.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 4: Comparison of different loss functions and weight ratios for guided alignment.' in mind, break down and resolve the question below step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich approach resulted in the lowest TER % for the E-commerce En→Fr task?\n" }, { "role": "assistant", "content": "By examining the table, the approach '+squared error (1:1)' resulted in the lowest TER % of 64.5 for the E-commerce En→Fr task. Thus, the answer is {\"answer\": \"64.5\"}." } ], "images": [ "scigen/train/3819.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Comparison of different loss functions and weight ratios for guided alignment." ], "reasoning": "By examining the table, the approach '+squared error (1:1)' resulted in the lowest TER % of 64.5 for the E-commerce En→Fr task.", "question": "Which approach resulted in the lowest TER % for the E-commerce En→Fr task?", "answer": "Thus, the answer is {\"answer\": \"64.5\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The baseline NMT approach had the highest BLEU % score. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe baseline NMT approach had the highest BLEU % score.\n" }, { "role": "assistant", "content": "The table shows that the baseline NMT approach had a BLEU % score of 18.6, which is lower than all other approaches listed. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3819.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Comparison of different loss functions and weight ratios for guided alignment." ], "reasoning": "The table shows that the baseline NMT approach had a BLEU % score of 18.6, which is lower than all other approaches listed.", "statement": "The baseline NMT approach had the highest BLEU % score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Comparison of different approaches for topic-aware NMT.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The system with prefixed human-labeled categories has a BLEU score of 18.3%, which is below the baseline of 18.6%, while the system with readout human-labeled categories significantly outperforms the baseline with a BLEU score of 19.7%.The BLEU score of such system (18.3%) is even below the baseline (18.6%). In contrast, the human-labeled categories are more reliable and are able to positively influence word selection in the NMT decoder, significantly (19.7% BLEU) outperforming the baseline." } ], "images": [ "scigen/train/3820.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison of different approaches for topic-aware NMT." ], "reasoning": "The system with prefixed human-labeled categories has a BLEU score of 18.3%, which is below the baseline of 18.6%, while the system with readout human-labeled categories significantly outperforms the baseline with a BLEU score of 19.7%.", "description": "The BLEU score of such system (18.3%) is even below the baseline (18.6%). In contrast, the human-labeled categories are more reliable and are able to positively influence word selection in the NMT decoder, significantly (19.7% BLEU) outperforming the baseline.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Comparison of different approaches for topic-aware NMT.\nQuestion: Which system has the highest BLEU score and how does it compare to the baseline?\nSolve the above question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The system with readout human-labeled categories has the highest BLEU score of 19.7%, which significantly outperforms the baseline BLEU score of 18.6%. So, the answer is {\"answer\": \"readout human-labeled categories\"}." } ], "images": [ "scigen/train/3820.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison of different approaches for topic-aware NMT." ], "reasoning": "The system with readout human-labeled categories has the highest BLEU score of 19.7%, which significantly outperforms the baseline BLEU score of 18.6%.", "question": "Which system has the highest BLEU score and how does it compare to the baseline?", "answer": "So, the answer is {\"answer\": \"readout human-labeled categories\"}." }, { "messages": [ { "role": "user", "content": "The system with readout LDA topics has a BLEU score higher than the baseline. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe system with readout LDA topics has a BLEU score higher than the baseline.\n" }, { "role": "assistant", "content": "The table shows that the system with readout LDA topics has a BLEU score of 14.5%, which is lower than the baseline BLEU score of 18.6%. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3820.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison of different approaches for topic-aware NMT." ], "reasoning": "The table shows that the system with readout LDA topics has a BLEU score of 14.5%, which is lower than the baseline BLEU score of 18.6%.", "statement": "The system with readout LDA topics has a BLEU score higher than the baseline.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1: Prevalence in absolute counts of translation divergences as defined in dorr1994machine. Row headings are explained in § 6.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Hand-crafted translation divergences, despite their insights, are less frequent empirically, as shown in the En-Zh data in DX17.It is evident that these types only account for a small portion of the encountered divergences, the point already made for En-Zh in DX17. It seems then that “hand-crafted” translation divergences, however insightful they may be, receive attention disproportionate to their empirical frequency." } ], "images": [ "scigen/train/3821.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Prevalence in absolute counts of translation divergences as defined in dorr1994machine. Row headings are explained in § 6." ], "reasoning": "Hand-crafted translation divergences, despite their insights, are less frequent empirically, as shown in the En-Zh data in DX17.", "description": "It is evident that these types only account for a small portion of the encountered divergences, the point already made for En-Zh in DX17. It seems then that “hand-crafted” translation divergences, however insightful they may be, receive attention disproportionate to their empirical frequency.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 1: Prevalence in absolute counts of translation divergences as defined in dorr1994machine. Row headings are explained in § 6.', unfold the steps to solve the question given next. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which language has the highest number of 'Structural' divergences?\n" }, { "role": "assistant", "content": "To find the language with the highest number of 'Structural' divergences, look at the 'Structural' row and compare the values across all languages. The highest value is 83 in the 'ru' column. So, the answer is {\"answer\": \"ru\"}." } ], "images": [ "scigen/train/3821.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Prevalence in absolute counts of translation divergences as defined in dorr1994machine. Row headings are explained in § 6." ], "reasoning": "To find the language with the highest number of 'Structural' divergences, look at the 'Structural' row and compare the values across all languages. The highest value is 83 in the 'ru' column.", "question": "Which language has the highest number of 'Structural' divergences?", "answer": "So, the answer is {\"answer\": \"ru\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The 'Promotional' divergence type is not present in any language., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe 'Promotional' divergence type is not present in any language.\n" }, { "role": "assistant", "content": "The table shows that the 'Promotional' row has 0 values for all languages, indicating that this type of divergence is not present in any language. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3821.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Prevalence in absolute counts of translation divergences as defined in dorr1994machine. Row headings are explained in § 6." ], "reasoning": "The table shows that the 'Promotional' row has 0 values for all languages, indicating that this type of divergence is not present in any language.", "statement": "The 'Promotional' divergence type is not present in any language.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 3: BLEU score comparison. For simplicity, we compare neural S2S models using greedy search.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "All neural S2S models outperform the original questions (Raw) on CANARD Dev and Test, except for LSTM on CAsT, indicating PLMs' generalization capability on the CQR task.In general, all neural S2S models perform better than the original questions (Raw), except for LSTM on CAsT. This indicates that the PLMs (GPT2, BERT, UniLM, and T5) have obtained at least some generalization capability on the CQR task." } ], "images": [ "scigen/train/3822.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: BLEU score comparison. For simplicity, we compare neural S2S models using greedy search." ], "reasoning": "All neural S2S models outperform the original questions (Raw) on CANARD Dev and Test, except for LSTM on CAsT, indicating PLMs' generalization capability on the CQR task.", "description": "In general, all neural S2S models perform better than the original questions (Raw), except for LSTM on CAsT. This indicates that the PLMs (GPT2, BERT, UniLM, and T5) have obtained at least some generalization capability on the CQR task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 3: BLEU score comparison. For simplicity, we compare neural S2S models using greedy search.' to sequentially solve the question presented below. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich model performed the best on the CAsT dataset?\n" }, { "role": "assistant", "content": "By comparing the scores of all models on the CAsT dataset, T5-base has the highest score of 75.07. So, the answer is {\"answer\": \"T5-base\"}." } ], "images": [ "scigen/train/3822.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: BLEU score comparison. For simplicity, we compare neural S2S models using greedy search." ], "reasoning": "By comparing the scores of all models on the CAsT dataset, T5-base has the highest score of 75.07.", "question": "Which model performed the best on the CAsT dataset?", "answer": "So, the answer is {\"answer\": \"T5-base\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nLSTM performed better than Raw on the CANARD Dev dataset.\n" }, { "role": "assistant", "content": "The table shows that LSTM scored 43.68 on CANARD Dev, while Raw scored 33.84, indicating LSTM performed better. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3822.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: BLEU score comparison. For simplicity, we compare neural S2S models using greedy search." ], "reasoning": "The table shows that LSTM scored 43.68 on CANARD Dev, while Raw scored 33.84, indicating LSTM performed better.", "statement": "LSTM performed better than Raw on the CANARD Dev dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Model settings.'.\n" }, { "role": "assistant", "content": "We train models on the CANARD training set, select the best checkpoint based on development set performance, and evaluate on the CANARD test set and CAsT dataset.We train each model on the CANARD training set and select the checkpoint with the best performance on development set. In addition to comparing model performance on the CANARD test set, we directly use the model trained on CANARD to perform CQR on the CAsT dataset." } ], "images": [ "scigen/train/3823.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Model settings." ], "reasoning": "We train models on the CANARD training set, select the best checkpoint based on development set performance, and evaluate on the CANARD test set and CAsT dataset.", "description": "We train each model on the CANARD training set and select the checkpoint with the best performance on development set. In addition to comparing model performance on the CANARD test set, we directly use the model trained on CANARD to perform CQR on the CAsT dataset.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 2: Model settings.', solve the following question step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model has the smallest batch size and what is its learning rate?\n" }, { "role": "assistant", "content": "By examining the table, we see that the LSTM model has the smallest batch size of 16. Its corresponding learning rate is 0.15. So, the answer is {\"answer\": \"0.15\"}" } ], "images": [ "scigen/train/3823.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Model settings." ], "reasoning": "By examining the table, we see that the LSTM model has the smallest batch size of 16. Its corresponding learning rate is 0.15.", "question": "Which model has the smallest batch size and what is its learning rate?", "answer": "So, the answer is {\"answer\": \"0.15\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe T5-base model has the largest number of parameters.\n" }, { "role": "assistant", "content": "The table shows that the T5-base model has 220M parameters, which is not the largest. GPT-2-medium has the largest number of parameters at 345M. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3823.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Model settings." ], "reasoning": "The table shows that the T5-base model has 220M parameters, which is not the largest. GPT-2-medium has the largest number of parameters at 345M.", "statement": "The T5-base model has the largest number of parameters.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title '(c) COCO'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows that uni and tfidf have the highest retrievability scores across all metrics, indicating a strong positive correlation between concreteness and retrievability.Absolute retrieval quality. We show results for R@1%, but R@5% and R@10% are similar. I3-OpenImages image features underperform relative to RN-ImageNet and are omitted for space reasons, though the results are similar. Retrievability vs. Concreteness. In all cases, there is a strong positive correlation between concreteness and retrievability, which provides evidence that more concrete concepts are easier to retrieve." } ], "images": [ "scigen/train/3824.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(c) COCO" ], "reasoning": "The table shows that uni and tfidf have the highest retrievability scores across all metrics, indicating a strong positive correlation between concreteness and retrievability.", "description": "Absolute retrieval quality. We show results for R@1%, but R@5% and R@10% are similar. I3-OpenImages image features underperform relative to RN-ImageNet and are omitted for space reasons, though the results are similar. Retrievability vs. Concreteness. In all cases, there is a strong positive correlation between concreteness and retrievability, which provides evidence that more concrete concepts are easier to retrieve.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich method has the highest retrievability score in the DCCA metric?\nSolve the above question based on the table titled '(c) COCO'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the DCCA column, we see that the highest score is 75.0, which corresponds to the uni method. So, the answer is {\"answer\": \"uni\"}." } ], "images": [ "scigen/train/3824.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(c) COCO" ], "reasoning": "By examining the DCCA column, we see that the highest score is 75.0, which corresponds to the uni method.", "question": "Which method has the highest retrievability score in the DCCA metric?", "answer": "So, the answer is {\"answer\": \"uni\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe PV method has the lowest retrievability score in the NP metric.\n" }, { "role": "assistant", "content": "By looking at the NP column, the lowest score is 14.1, which corresponds to the PV method. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3824.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(c) COCO" ], "reasoning": "By looking at the NP column, the lowest score is 14.1, which corresponds to the PV method.", "statement": "The PV method has the lowest retrievability score in the NP metric.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title '(a) Wikipedia'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows that tfidf has the highest DCCA score, indicating it has the best retrieval quality among the methods compared.Absolute retrieval quality. We show results for R@1%, but R@5% and R@10% are similar. I3-OpenImages image features underperform relative to RN-ImageNet and are omitted for space reasons, though the results are similar. Retrievability vs. Concreteness. In all cases, there is a strong positive correlation between concreteness and retrievability, which provides evidence that more concrete concepts are easier to retrieve." } ], "images": [ "scigen/train/3825.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) Wikipedia" ], "reasoning": "The table shows that tfidf has the highest DCCA score, indicating it has the best retrieval quality among the methods compared.", "description": "Absolute retrieval quality. We show results for R@1%, but R@5% and R@10% are similar. I3-OpenImages image features underperform relative to RN-ImageNet and are omitted for space reasons, though the results are similar. Retrievability vs. Concreteness. In all cases, there is a strong positive correlation between concreteness and retrievability, which provides evidence that more concrete concepts are easier to retrieve.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about '(a) Wikipedia', solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method has the highest DCCA score?\n" }, { "role": "assistant", "content": "By examining the DCCA column, we see that tfidf has the highest score of 38.7. So, the answer is {\"answer\": \"tfidf\"}." } ], "images": [ "scigen/train/3825.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) Wikipedia" ], "reasoning": "By examining the DCCA column, we see that tfidf has the highest score of 38.7.", "question": "Which method has the highest DCCA score?", "answer": "So, the answer is {\"answer\": \"tfidf\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis PV has a higher LS score than LDA. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nPV has a higher LS score than LDA.\n" }, { "role": "assistant", "content": "By comparing the LS scores, PV has a score of 30.8 while LDA has a score of 36.2, so the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3825.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) Wikipedia" ], "reasoning": "By comparing the LS scores, PV has a score of 30.8 while LDA has a score of 36.2, so the statement is incorrect.", "statement": "PV has a higher LS score than LDA.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title '(b) British Library'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The table shows that LDA has the highest LS score, while PV has the highest DCCA score.Absolute retrieval quality. We show results for R@1%, but R@5% and R@10% are similar. I3-OpenImages image features underperform relative to RN-ImageNet and are omitted for space reasons, though the results are similar. Retrievability vs. Concreteness. In all cases, there is a strong positive correlation between concreteness and retrievability, which provides evidence that more concrete concepts are easier to retrieve." } ], "images": [ "scigen/train/3826.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) British Library" ], "reasoning": "The table shows that LDA has the highest LS score, while PV has the highest DCCA score.", "description": "Absolute retrieval quality. We show results for R@1%, but R@5% and R@10% are similar. I3-OpenImages image features underperform relative to RN-ImageNet and are omitted for space reasons, though the results are similar. Retrievability vs. Concreteness. In all cases, there is a strong positive correlation between concreteness and retrievability, which provides evidence that more concrete concepts are easier to retrieve.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: (b) British Library\n\nQuestion: Which model has the highest score in the DCCA column?\n" }, { "role": "assistant", "content": "By examining the DCCA column, we see that the highest score is 17.8, which corresponds to the PV model. So, the answer is {\"answer\": \"PV\"}." } ], "images": [ "scigen/train/3826.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) British Library" ], "reasoning": "By examining the DCCA column, we see that the highest score is 17.8, which corresponds to the PV model.", "question": "Which model has the highest score in the DCCA column?", "answer": "So, the answer is {\"answer\": \"PV\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The tfidf model has the highest score in the LS column., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe tfidf model has the highest score in the LS column.\n" }, { "role": "assistant", "content": "The table shows that the highest score in the LS column is 17.1, which corresponds to the LDA model, not the tfidf model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3826.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) British Library" ], "reasoning": "The table shows that the highest score in the LS column is 17.1, which corresponds to the LDA model, not the tfidf model.", "statement": "The tfidf model has the highest score in the LS column.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title '(d) Flickr'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The table shows that tfidf has the highest retrievability scores across NP, LS, and NS, indicating a strong positive correlation between concreteness and retrievability.Absolute retrieval quality. We show results for R@1%, but R@5% and R@10% are similar. I3-OpenImages image features underperform relative to RN-ImageNet and are omitted for space reasons, though the results are similar. Retrievability vs. Concreteness. In all cases, there is a strong positive correlation between concreteness and retrievability, which provides evidence that more concrete concepts are easier to retrieve." } ], "images": [ "scigen/train/3827.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(d) Flickr" ], "reasoning": "The table shows that tfidf has the highest retrievability scores across NP, LS, and NS, indicating a strong positive correlation between concreteness and retrievability.", "description": "Absolute retrieval quality. We show results for R@1%, but R@5% and R@10% are similar. I3-OpenImages image features underperform relative to RN-ImageNet and are omitted for space reasons, though the results are similar. Retrievability vs. Concreteness. In all cases, there is a strong positive correlation between concreteness and retrievability, which provides evidence that more concrete concepts are easier to retrieve.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nTable title: (d) Flickr\n\nQuestion: Which method has the highest retrievability score in the NS column?\n" }, { "role": "assistant", "content": "By examining the NS column, we see that the highest score is 63.9, which corresponds to the tfidf method. So, the answer is {\"answer\": \"tfidf\"}." } ], "images": [ "scigen/train/3827.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(d) Flickr" ], "reasoning": "By examining the NS column, we see that the highest score is 63.9, which corresponds to the tfidf method.", "question": "Which method has the highest retrievability score in the NS column?", "answer": "So, the answer is {\"answer\": \"tfidf\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe uni method has the highest retrievability score in the LS column.\n" }, { "role": "assistant", "content": "By examining the LS column, we see that the highest score is 62.5, which corresponds to the uni method. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3827.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(d) Flickr" ], "reasoning": "By examining the LS column, we see that the highest score is 62.5, which corresponds to the uni method.", "statement": "The uni method has the highest retrievability score in the LS column.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'TABLE I: Results on emotion classification task (best results in bold)'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The model with the best misclassification rate on the validation set was selected, and training stopped after 12 epochs without improvement. UA Recall is used as the benchmark measure.The selected model used on the test set is the one with best misclassification rate on the validation set, and training was stopped after 12 epochs of training without improvement of the misclassification rate of the validation set. Since the number of utterances from each class may vary, we report the Unweighted Average (UA) Recall, which is the official benchmark measure on this task. The UA Recall is defined to be 1nn∑i=1ri, where n is the number of classes, and the recall ri is the number of correctly classified examples from class i (true positives) divided by the total number of examples from" } ], "images": [ "scigen/train/3828.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Results on emotion classification task (best results in bold)" ], "reasoning": "The model with the best misclassification rate on the validation set was selected, and training stopped after 12 epochs without improvement. UA Recall is used as the benchmark measure.", "description": "The selected model used on the test set is the one with best misclassification rate on the validation set, and training was stopped after 12 epochs of training without improvement of the misclassification rate of the validation set. Since the number of utterances from each class may vary, we report the Unweighted Average (UA) Recall, which is the official benchmark measure on this task. The UA Recall is defined to be 1nn∑i=1ri, where n is the number of classes, and the recall ri is the number of correctly classified examples from class i (true positives) divided by the total number of examples from", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'TABLE I: Results on emotion classification task (best results in bold)', and sequentially solve the question, writing out each step of your reasoning process. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model achieved the highest UA Recall on the test set for 2 labels?\n" }, { "role": "assistant", "content": "To find the model with the highest UA Recall for 2 labels, compare the UA Recall values in the 'Test set UA Recall [%] 2 labels' column. The CLSTM model has the highest value of 70.59 ± 0.58. So, the answer is {\"answer\": \"CLSTM\"}" } ], "images": [ "scigen/train/3828.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Results on emotion classification task (best results in bold)" ], "reasoning": "To find the model with the highest UA Recall for 2 labels, compare the UA Recall values in the 'Test set UA Recall [%] 2 labels' column. The CLSTM model has the highest value of 70.59 ± 0.58.", "question": "Which model achieved the highest UA Recall on the test set for 2 labels?", "answer": "So, the answer is {\"answer\": \"CLSTM\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Extended CLSTM model achieved the highest UA Recall on the test set for 5 labels.\n" }, { "role": "assistant", "content": "To verify this statement, compare the UA Recall values in the 'Test set UA Recall [%] 5 labels' column. The Extended CLSTM model has a UA Recall of 39.72 ± 0.2, which is higher than the other models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3828.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Results on emotion classification task (best results in bold)" ], "reasoning": "To verify this statement, compare the UA Recall values in the 'Test set UA Recall [%] 5 labels' column. The Extended CLSTM model has a UA Recall of 39.72 ± 0.2, which is higher than the other models.", "statement": "The Extended CLSTM model achieved the highest UA Recall on the test set for 5 labels.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'TABLE IV: Different Implementations of CLSTM (best results in bold). Details in the text.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The evaluation of six CLSTM model implementations showed that using cell states generally outperformed hidden states, but no single implementation was consistently superior.We empirically evaluated six different implementations of the CLSTM model, by using the two sequences (h1,…,hn) and (c1,…,cn) of hidden and cell states, each with the three operations: max, mean and using the last. We compared the different implementations of the CLSTM model both on the emotion classification task and the age and gender classification task. For each task we used the same network architecture and training procedure as was used in the above experiments, with the only difference being the implementation of the CLSTM layers (as in the experiments above, the emotion classification model contains two CLSTM layers, and the age and gender classification model contains one CLSTM layer). We observed that in all four tasks, the average results of implementations using the cell states is better than the average results of implementations using the hidden states, but still in one task of the four the implementation yielding the best result used the hidden states. The results regarding which one of the six different implementations is preferable are ambiguous, and we cannot point out one implementation which is better than the others. In addition, comparing the usage of max, mean and the last vector of a sequence also gave ambiguous results, with a slight preference towards using the max operator." } ], "images": [ "scigen/train/3829.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE IV: Different Implementations of CLSTM (best results in bold). Details in the text." ], "reasoning": "The evaluation of six CLSTM model implementations showed that using cell states generally outperformed hidden states, but no single implementation was consistently superior.", "description": "We empirically evaluated six different implementations of the CLSTM model, by using the two sequences (h1,…,hn) and (c1,…,cn) of hidden and cell states, each with the three operations: max, mean and using the last. We compared the different implementations of the CLSTM model both on the emotion classification task and the age and gender classification task. For each task we used the same network architecture and training procedure as was used in the above experiments, with the only difference being the implementation of the CLSTM layers (as in the experiments above, the emotion classification model contains two CLSTM layers, and the age and gender classification model contains one CLSTM layer). We observed that in all four tasks, the average results of implementations using the cell states is better than the average results of implementations using the hidden states, but still in one task of the four the implementation yielding the best result used the hidden states. The results regarding which one of the six different implementations is preferable are ambiguous, and we cannot point out one implementation which is better than the others. In addition, comparing the usage of max, mean and the last vector of a sequence also gave ambiguous results, with a slight preference towards using the max operator.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'TABLE IV: Different Implementations of CLSTM (best results in bold). Details in the text.', solve the following question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich implementation yielded the highest Test set UA Recall [%] for the Emotion 2 classes task?\n" }, { "role": "assistant", "content": "To find the highest Test set UA Recall [%] for the Emotion 2 classes task, we need to look at the first column of the table. The highest value in this column is 70.59 ± 0.58, which corresponds to the 'cell states, last' implementation. So, the answer is {\"answer\": \"cell states, last\"}" } ], "images": [ "scigen/train/3829.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE IV: Different Implementations of CLSTM (best results in bold). Details in the text." ], "reasoning": "To find the highest Test set UA Recall [%] for the Emotion 2 classes task, we need to look at the first column of the table. The highest value in this column is 70.59 ± 0.58, which corresponds to the 'cell states, last' implementation.", "question": "Which implementation yielded the highest Test set UA Recall [%] for the Emotion 2 classes task?", "answer": "So, the answer is {\"answer\": \"cell states, last\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe implementation using cell states and the max operator yielded the highest average Test set UA Recall [%].\n" }, { "role": "assistant", "content": "To verify this statement, we need to look at the 'Test set UA Recall [%] Average' column. The highest value in this column is 57.97, which corresponds to the 'cell states, mean' implementation, not the 'cell states, max' implementation. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3829.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE IV: Different Implementations of CLSTM (best results in bold). Details in the text." ], "reasoning": "To verify this statement, we need to look at the 'Test set UA Recall [%] Average' column. The highest value in this column is 57.97, which corresponds to the 'cell states, mean' implementation, not the 'cell states, max' implementation.", "statement": "The implementation using cell states and the max operator yielded the highest average Test set UA Recall [%].", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'TABLE II: Behavior Identification Accuracy (%) using Multitask Sentence Embeddings'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The multitask objective improved classification accuracy for unsupervised sentence embeddings in all behaviors except Positivity and for supervised learning in all behaviors except Humor, achieving the highest mean accuracy overall.The addition of the multitask objective improved the classification accuracy of unsupervised sentence embeddings from the conversation model across all behaviors except Positivity in unsupervised classification with k-Means. Under supervised learning using k-NN, our multitask embeddings improved accuracy on all behaviors except Humor. In terms of mean accuracy, our multitask embeddings performed better than other sentence embeddings with an absolute improvement over no multitasking of 1.07% and 3.24% for unsupervised and supervised methods respectively. Our multitask embeddings also achieved the highest mean accuracy over all the sentence embeddings tested." } ], "images": [ "scigen/train/3830.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE II: Behavior Identification Accuracy (%) using Multitask Sentence Embeddings" ], "reasoning": "The multitask objective improved classification accuracy for unsupervised sentence embeddings in all behaviors except Positivity and for supervised learning in all behaviors except Humor, achieving the highest mean accuracy overall.", "description": "The addition of the multitask objective improved the classification accuracy of unsupervised sentence embeddings from the conversation model across all behaviors except Positivity in unsupervised classification with k-Means. Under supervised learning using k-NN, our multitask embeddings improved accuracy on all behaviors except Humor. In terms of mean accuracy, our multitask embeddings performed better than other sentence embeddings with an absolute improvement over no multitasking of 1.07% and 3.24% for unsupervised and supervised methods respectively. Our multitask embeddings also achieved the highest mean accuracy over all the sentence embeddings tested.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'TABLE II: Behavior Identification Accuracy (%) using Multitask Sentence Embeddings', solve this question and give detail solutions. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhich embedding model achieved the highest mean accuracy using k-NN?\n" }, { "role": "assistant", "content": "By examining the 'Mean Accuracy' column under the k-NN method, the highest value is 78.77, which corresponds to the '+ Online MTL (proposed)' embedding model. So, the answer is {\"answer\": \"78.77\"}." } ], "images": [ "scigen/train/3830.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE II: Behavior Identification Accuracy (%) using Multitask Sentence Embeddings" ], "reasoning": "By examining the 'Mean Accuracy' column under the k-NN method, the highest value is 78.77, which corresponds to the '+ Online MTL (proposed)' embedding model.", "question": "Which embedding model achieved the highest mean accuracy using k-NN?", "answer": "So, the answer is {\"answer\": \"78.77\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe multitask embeddings improved accuracy on all behaviors except Humor in supervised learning using k-NN.\n" }, { "role": "assistant", "content": "The description states that multitask embeddings improved accuracy on all behaviors except Humor in supervised learning using k-NN, and the table confirms that the Humor accuracy for '+ Online MTL (proposed)' is 60.0, which is not the highest. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3830.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE II: Behavior Identification Accuracy (%) using Multitask Sentence Embeddings" ], "reasoning": "The description states that multitask embeddings improved accuracy on all behaviors except Humor in supervised learning using k-NN, and the table confirms that the Humor accuracy for '+ Online MTL (proposed)' is 60.0, which is not the highest.", "statement": "The multitask embeddings improved accuracy on all behaviors except Humor in supervised learning using k-NN.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'TABLE III: Weighted Accuracy of Emotion Recognition on IEMOCAP'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Online MTL improved the weighted accuracy of unsupervised embeddings by 8.02%, with the highest accuracy obtained using the Universal Sentence Encoder.It should be noted that there is no consensus on data split and evaluation conditions in IEMOCAP, and while we made every effort to be consistent with other works the results may not be directly comparable. However, when comparing among our implementation using sentence embeddings we observed that online MTL improved the weighted accuracy (WA) of unsupervised embeddings by an absolute value of 8.02% which is more than 14% relative improvement. The highest accuracy was obtained using embeddings from the Universal Sentence Encoder, however our implementation was a close second by less than one percent." } ], "images": [ "scigen/train/3831.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE III: Weighted Accuracy of Emotion Recognition on IEMOCAP" ], "reasoning": "Online MTL improved the weighted accuracy of unsupervised embeddings by 8.02%, with the highest accuracy obtained using the Universal Sentence Encoder.", "description": "It should be noted that there is no consensus on data split and evaluation conditions in IEMOCAP, and while we made every effort to be consistent with other works the results may not be directly comparable. However, when comparing among our implementation using sentence embeddings we observed that online MTL improved the weighted accuracy (WA) of unsupervised embeddings by an absolute value of 8.02% which is more than 14% relative improvement. The highest accuracy was obtained using embeddings from the Universal Sentence Encoder, however our implementation was a close second by less than one percent.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'TABLE III: Weighted Accuracy of Emotion Recognition on IEMOCAP', solve the following question step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the weighted accuracy improvement of the proposed Online MTL method compared to Lex-eVector?\n" }, { "role": "assistant", "content": "The weighted accuracy of Lex-eVector is 57.40%, and the weighted accuracy of the proposed Online MTL method is 63.84%. The improvement is calculated as 63.84% - 57.40% = 6.44%. So, the answer is {\"answer\": \"6.44%\"}" } ], "images": [ "scigen/train/3831.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE III: Weighted Accuracy of Emotion Recognition on IEMOCAP" ], "reasoning": "The weighted accuracy of Lex-eVector is 57.40%, and the weighted accuracy of the proposed Online MTL method is 63.84%. The improvement is calculated as 63.84% - 57.40% = 6.44%.", "question": "What is the weighted accuracy improvement of the proposed Online MTL method compared to Lex-eVector?", "answer": "So, the answer is {\"answer\": \"6.44%\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The Universal Sentence Encoder achieved the highest weighted accuracy among all methods., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Universal Sentence Encoder achieved the highest weighted accuracy among all methods.\n" }, { "role": "assistant", "content": "The table shows that the Universal Sentence Encoder + DNN has the highest weighted accuracy (WA) of 64.83%, which is higher than all other methods listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3831.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE III: Weighted Accuracy of Emotion Recognition on IEMOCAP" ], "reasoning": "The table shows that the Universal Sentence Encoder + DNN has the highest weighted accuracy (WA) of 64.83%, which is higher than all other methods listed.", "statement": "The Universal Sentence Encoder achieved the highest weighted accuracy among all methods.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'TABLE I: Grounding accuracy of UMD Refexp and INGRESS on the RefCOCO dataset, with human-annotated ground-truth (HGT) object proposals and automatically generated MCG object proposals.'.\n" }, { "role": "assistant", "content": "The two-stage grounding process in INGRESS does not offer significant benefits due to the small number of object proposals per image, averaging 10.2 ground-truth and 7.4 MCG proposals.S5SS1SSS0Px2 Results The correctness of a grounding result is based on the overlap between the output and the ground-truth image regions. The grounding is deemed correct if the intersection-over-union (IoU) measure between the two region is greater than 0.5. INGRESS adopts a two-stage grounding process in order to reduce the number of relevant object proposals processed in complex scenes. On average, the validation and test sets contain 10.2 ground-truth object proposals and 7.4 MCG object proposals per image. As the number of object proposals per image is small, the two-stage grounding process does not offer significant benefits." } ], "images": [ "scigen/train/3832.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "TABLE I: Grounding accuracy of UMD Refexp and INGRESS on the RefCOCO dataset, with human-annotated ground-truth (HGT) object proposals and automatically generated MCG object proposals." ], "reasoning": "The two-stage grounding process in INGRESS does not offer significant benefits due to the small number of object proposals per image, averaging 10.2 ground-truth and 7.4 MCG proposals.", "description": "S5SS1SSS0Px2 Results The correctness of a grounding result is based on the overlap between the output and the ground-truth image regions. The grounding is deemed correct if the intersection-over-union (IoU) measure between the two region is greater than 0.5. INGRESS adopts a two-stage grounding process in order to reduce the number of relevant object proposals processed in complex scenes. On average, the validation and test sets contain 10.2 ground-truth object proposals and 7.4 MCG object proposals per image. As the number of object proposals per image is small, the two-stage grounding process does not offer significant benefits.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'TABLE I: Grounding accuracy of UMD Refexp and INGRESS on the RefCOCO dataset, with human-annotated ground-truth (HGT) object proposals and automatically generated MCG object proposals.', solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the highest MCG (%) INGRESS score among the datasets?\n" }, { "role": "assistant", "content": "To find the highest MCG (%) INGRESS score, compare the MCG (%) INGRESS values for Val, TestA, and TestB datasets. The scores are 58.3, 60.3, and 55.0 respectively. The highest score is 60.3. So, the answer is {\"answer\": \"60.3\"}." } ], "images": [ "scigen/train/3832.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "TABLE I: Grounding accuracy of UMD Refexp and INGRESS on the RefCOCO dataset, with human-annotated ground-truth (HGT) object proposals and automatically generated MCG object proposals." ], "reasoning": "To find the highest MCG (%) INGRESS score, compare the MCG (%) INGRESS values for Val, TestA, and TestB datasets. The scores are 58.3, 60.3, and 55.0 respectively. The highest score is 60.3.", "question": "What is the highest MCG (%) INGRESS score among the datasets?", "answer": "So, the answer is {\"answer\": \"60.3\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The HGT (%) UMD Refexp score for TestA is higher than that for Val., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe HGT (%) UMD Refexp score for TestA is higher than that for Val.\n" }, { "role": "assistant", "content": "Compare the HGT (%) UMD Refexp scores for TestA and Val. TestA has a score of 74.1, while Val has a score of 75.5. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3832.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "TABLE I: Grounding accuracy of UMD Refexp and INGRESS on the RefCOCO dataset, with human-annotated ground-truth (HGT) object proposals and automatically generated MCG object proposals." ], "reasoning": "Compare the HGT (%) UMD Refexp scores for TestA and Val. TestA has a score of 74.1, while Val has a score of 75.5. Therefore, the statement is incorrect.", "statement": "The HGT (%) UMD Refexp score for TestA is higher than that for Val.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: Perplexities of word-based backoff n-gram, RNN n-gram and backoff approximated RNN language models as function of n-gram order'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "RNN-based language models show a consistent improvement in perplexity with increasing context, unlike BNLMs which saturate around 5-gram.What can be clearly seen at the first glance is the superiority of RNN-based language models over BNLMs. Perplexity of RNN n-gram models improves step by step as we increase the modeling context, while results of BNLMs saturates at around 5-gram. This can be explained by the fact that recurrent models can provide a more accurate probability estimate for unobserved n-grams with the help of distributed modeling of input tokens. This implies that this model takes (theoretically) all previous words into account to estimate probability." } ], "images": [ "scigen/train/3833.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Perplexities of word-based backoff n-gram, RNN n-gram and backoff approximated RNN language models as function of n-gram order" ], "reasoning": "RNN-based language models show a consistent improvement in perplexity with increasing context, unlike BNLMs which saturate around 5-gram.", "description": "What can be clearly seen at the first glance is the superiority of RNN-based language models over BNLMs. Perplexity of RNN n-gram models improves step by step as we increase the modeling context, while results of BNLMs saturates at around 5-gram. This can be explained by the fact that recurrent models can provide a more accurate probability estimate for unobserved n-grams with the help of distributed modeling of input tokens. This implies that this model takes (theoretically) all previous words into account to estimate probability.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 2: Perplexities of word-based backoff n-gram, RNN n-gram and backoff approximated RNN language models as function of n-gram order' and methodically tackle the question that follows. Output the final answer as JSON in the format {\"answer\": \"\"}.\nAt what order does the perplexity of RNN n-gram models first drop below 50?\n" }, { "role": "assistant", "content": "By examining the RNN n-gram column, we see that the perplexity first drops below 50 at order 10 with a value of 49.5. So, the answer is {\"answer\": \"10\"}." } ], "images": [ "scigen/train/3833.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Perplexities of word-based backoff n-gram, RNN n-gram and backoff approximated RNN language models as function of n-gram order" ], "reasoning": "By examining the RNN n-gram column, we see that the perplexity first drops below 50 at order 10 with a value of 49.5.", "question": "At what order does the perplexity of RNN n-gram models first drop below 50?", "answer": "So, the answer is {\"answer\": \"10\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe perplexity of BNLM models improves consistently with increasing order.\n" }, { "role": "assistant", "content": "The table shows that the perplexity of BNLM models does not improve consistently; it saturates around order 5. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3833.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Perplexities of word-based backoff n-gram, RNN n-gram and backoff approximated RNN language models as function of n-gram order" ], "reasoning": "The table shows that the perplexity of BNLM models does not improve consistently; it saturates around order 5.", "statement": "The perplexity of BNLM models improves consistently with increasing order.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Perplexities of morph-based backoff n-gram, RNN n-gram and backoff approximated RNN language models as function of n-gram order'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Morph-based perplexities are lower than word-based ones due to different vocabulary sizes, and interpolated models reduce perplexity by 10-15%. Generating larger corpora with morph-based RNNLMs further decreases perplexity.Although morph-based perplexities are lower than word-based ones, note that the two perplexities can not be directly compared, since the vocabulary size of the two model types differs (50k vs. 30k). The morph-based results related to RNN-BNLM 100M model are also very similar to the word-based ones. The approximated model itself is a bit worse than the original BNLM; however, the interpolated model reduces perplexity with around 10-15%. The question naturally arises: what if a much larger corpus is generated with the morph-based RNNLM. In order to answer this question we generated a ten times bigger corpus containing 1 billion morphs. Moreover, the interpolated model (BNLM + RNN-BNLM 1B) further decreases perplexity, which suggests that in the future it may be useful to generate even larger corpora." } ], "images": [ "scigen/train/3834.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Perplexities of morph-based backoff n-gram, RNN n-gram and backoff approximated RNN language models as function of n-gram order" ], "reasoning": "Morph-based perplexities are lower than word-based ones due to different vocabulary sizes, and interpolated models reduce perplexity by 10-15%. Generating larger corpora with morph-based RNNLMs further decreases perplexity.", "description": "Although morph-based perplexities are lower than word-based ones, note that the two perplexities can not be directly compared, since the vocabulary size of the two model types differs (50k vs. 30k). The morph-based results related to RNN-BNLM 100M model are also very similar to the word-based ones. The approximated model itself is a bit worse than the original BNLM; however, the interpolated model reduces perplexity with around 10-15%. The question naturally arises: what if a much larger corpus is generated with the morph-based RNNLM. In order to answer this question we generated a ten times bigger corpus containing 1 billion morphs. Moreover, the interpolated model (BNLM + RNN-BNLM 1B) further decreases perplexity, which suggests that in the future it may be useful to generate even larger corpora.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nTable title: Table 3: Perplexities of morph-based backoff n-gram, RNN n-gram and backoff approximated RNN language models as function of n-gram order\nQuestion: What is the perplexity of the BNLM + RNN-BNLM 1B model for order 5?\n" }, { "role": "assistant", "content": "To find the perplexity of the BNLM + RNN-BNLM 1B model for order 5, locate the row corresponding to order 5 and the column for BNLM + RNN-BNLM 1B. The value is 60.8. So, the answer is {\"answer\": \"60.8\"}." } ], "images": [ "scigen/train/3834.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Perplexities of morph-based backoff n-gram, RNN n-gram and backoff approximated RNN language models as function of n-gram order" ], "reasoning": "To find the perplexity of the BNLM + RNN-BNLM 1B model for order 5, locate the row corresponding to order 5 and the column for BNLM + RNN-BNLM 1B. The value is 60.8.", "question": "What is the perplexity of the BNLM + RNN-BNLM 1B model for order 5?", "answer": "So, the answer is {\"answer\": \"60.8\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The perplexity of the RNN-BNLM 100M model for order 4 is higher than that of the BNLM model for the same order., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe perplexity of the RNN-BNLM 100M model for order 4 is higher than that of the BNLM model for the same order.\n" }, { "role": "assistant", "content": "To verify this statement, compare the perplexity values for order 4 in the RNN-BNLM 100M and BNLM columns. The RNN-BNLM 100M model has a perplexity of 80.9, which is higher than the BNLM model's perplexity of 76.2. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3834.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Perplexities of morph-based backoff n-gram, RNN n-gram and backoff approximated RNN language models as function of n-gram order" ], "reasoning": "To verify this statement, compare the perplexity values for order 4 in the RNN-BNLM 100M and BNLM columns. The RNN-BNLM 100M model has a perplexity of 80.9, which is higher than the BNLM model's perplexity of 76.2.", "statement": "The perplexity of the RNN-BNLM 100M model for order 4 is higher than that of the BNLM model for the same order.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: Arguments of was arrested with their pret’s.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Greater values of k correspond to the k+1’th best result at a node, and the k-best results may include various variants, with the edge score never removed from the total score.Greater values of k correspond to k+1’th best result at node The k-best results at that node may include any of the following variants (the list is not complete) : Notice the similarity between Eq. . The difference is that when we explore candidates of kj’s greater than zero, we replace the contribution of mj’th child: the kj’th best score is replaced with kj+1’th best score. However, the edge score (C0mj) is never taken out of the total score of Ckn. This is motivated by the first of the two conditions above. The information that the temporal modifier (node 17) is an argument with a very low score should not disappear from the subsequent scorings of node 12’s candidates. Otherwise a subsequent result may get a higher score than the best one, violating the decreasing property of the scoring function, as the final line below shows:" } ], "images": [ "scigen/train/3835.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Arguments of was arrested with their pret’s." ], "reasoning": "Greater values of k correspond to the k+1’th best result at a node, and the k-best results may include various variants, with the edge score never removed from the total score.", "description": "Greater values of k correspond to k+1’th best result at node The k-best results at that node may include any of the following variants (the list is not complete) : Notice the similarity between Eq. . The difference is that when we explore candidates of kj’s greater than zero, we replace the contribution of mj’th child: the kj’th best score is replaced with kj+1’th best score. However, the edge score (C0mj) is never taken out of the total score of Ckn. This is motivated by the first of the two conditions above. The information that the temporal modifier (node 17) is an argument with a very low score should not disappear from the subsequent scorings of node 12’s candidates. Otherwise a subsequent result may get a higher score than the best one, violating the decreasing property of the scoring function, as the final line below shows:", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 1: Arguments of was arrested with their pret’s.\nQuestion: What is the score of the temporal modifier node?\n" }, { "role": "assistant", "content": "The temporal modifier is node 17, and its score is given in the table as 0.05. So, the answer is {\"answer\": \"0.05\"}" } ], "images": [ "scigen/train/3835.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Arguments of was arrested with their pret’s." ], "reasoning": "The temporal modifier is node 17, and its score is given in the table as 0.05.", "question": "What is the score of the temporal modifier node?", "answer": "So, the answer is {\"answer\": \"0.05\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe score of node 12 is higher than the score of node 15.\n" }, { "role": "assistant", "content": "The table shows that the score of node 12 (arrested12) is not directly given, but the score of node 15 (home15) is 0.22. Without the score of node 12, we cannot compare the two. So, the answer is {\"answer\": \"not enough info\"}" } ], "images": [ "scigen/train/3835.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Arguments of was arrested with their pret’s." ], "reasoning": "The table shows that the score of node 12 (arrested12) is not directly given, but the score of node 15 (home15) is 0.22. Without the score of node 12, we cannot compare the two.", "statement": "The score of node 12 is higher than the score of node 15.", "answer": "So, the answer is {\"answer\": \"not enough info\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Results for the baseline and our two algorithms.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The Top-down + NSS approach has a slightly better F1-score than the original Top-down approach, but the difference is not statistically significant.(Top-down + NSS is the extended variant described in Sec. The NSS version, which incorporates a prediction on the number of children to keep for each node, is slightly better than the original Top-down approach, but the results are not statistically significant." } ], "images": [ "scigen/train/3836.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results for the baseline and our two algorithms." ], "reasoning": "The Top-down + NSS approach has a slightly better F1-score than the original Top-down approach, but the difference is not statistically significant.", "description": "(Top-down + NSS is the extended variant described in Sec. The NSS version, which incorporates a prediction on the number of children to keep for each node, is slightly better than the original Top-down approach, but the results are not statistically significant.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}. The question is related to the given table titled 'Table 3: Results for the baseline and our two algorithms.'.\n\nQuestion: Which approach has the highest F1-score and what is its compression rate?\n" }, { "role": "assistant", "content": "The table shows that the Top-down + NSS approach has the highest F1-score of 77.2 and a compression rate of 38.1%. So, the answer is {\"answer\": \"77.2 and 38.1%\"}." } ], "images": [ "scigen/train/3836.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results for the baseline and our two algorithms." ], "reasoning": "The table shows that the Top-down + NSS approach has the highest F1-score of 77.2 and a compression rate of 38.1%.", "question": "Which approach has the highest F1-score and what is its compression rate?", "answer": "So, the answer is {\"answer\": \"77.2 and 38.1%\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe ILP approach has the lowest compression rate.\n" }, { "role": "assistant", "content": "The table shows that the ILP approach has a compression rate of 46.5%, which is higher than both the Top-down and Top-down + NSS approaches. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3836.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results for the baseline and our two algorithms." ], "reasoning": "The table shows that the ILP approach has a compression rate of 46.5%, which is higher than both the Top-down and Top-down + NSS approaches.", "statement": "The ILP approach has the lowest compression rate.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 7: The percentage of words with high/low valence, arousal, and dominance scores in the SOLO corpus. ‘general’ stands for ‘General Tweets Corpus’. The highest numbers in each row are in bold. Within each row, all the differences are statistically significant (Chi-squared test, p<0.0001).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The table shows the percentage of words with high and low scores in valence, arousal, and dominance for different categories, with significant differences within each row.We consider words that appear in the VAD lexicon, and count the percentage of words that have high/low valence, arousal, and dominance scores. (The SOLO query words are excluded from the analysis.) For all three dimensions, we consider scores greater than or equal to 0.75 as high scores, and scores lower than or equal to 0.25 as low scores. Within each row, all the differences are statistically significant (Chi-squared test, p<0.0001)." } ], "images": [ "scigen/train/3837.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: The percentage of words with high/low valence, arousal, and dominance scores in the SOLO corpus. ‘general’ stands for ‘General Tweets Corpus’. The highest numbers in each row are in bold. Within each row, all the differences are statistically significant (Chi-squared test, p<0.0001)." ], "reasoning": "The table shows the percentage of words with high and low scores in valence, arousal, and dominance for different categories, with significant differences within each row.", "description": "We consider words that appear in the VAD lexicon, and count the percentage of words that have high/low valence, arousal, and dominance scores. (The SOLO query words are excluded from the analysis.) For all three dimensions, we consider scores greater than or equal to 0.75 as high scores, and scores lower than or equal to 0.25 as low scores. Within each row, all the differences are statistically significant (Chi-squared test, p<0.0001).", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. The question is related to the given table titled 'Table 7: The percentage of words with high/low valence, arousal, and dominance scores in the SOLO corpus. ‘general’ stands for ‘General Tweets Corpus’. The highest numbers in each row are in bold. Within each row, all the differences are statistically significant (Chi-squared test, p<0.0001).'.\n\nQuestion: Which category has the highest percentage of words with high valence scores?\n" }, { "role": "assistant", "content": "To find the category with the highest percentage of words with high valence scores, compare the 'high' valence scores across all categories. So, the answer is {\"answer\": \"solitude\"}." } ], "images": [ "scigen/train/3837.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: The percentage of words with high/low valence, arousal, and dominance scores in the SOLO corpus. ‘general’ stands for ‘General Tweets Corpus’. The highest numbers in each row are in bold. Within each row, all the differences are statistically significant (Chi-squared test, p<0.0001)." ], "reasoning": "To find the category with the highest percentage of words with high valence scores, compare the 'high' valence scores across all categories.", "question": "Which category has the highest percentage of words with high valence scores?", "answer": "So, the answer is {\"answer\": \"solitude\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe 'loneliness' category has the highest percentage of words with low arousal scores.\n" }, { "role": "assistant", "content": "To verify this statement, compare the 'low' arousal scores across all categories and check if 'loneliness' has the highest percentage. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3837.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: The percentage of words with high/low valence, arousal, and dominance scores in the SOLO corpus. ‘general’ stands for ‘General Tweets Corpus’. The highest numbers in each row are in bold. Within each row, all the differences are statistically significant (Chi-squared test, p<0.0001)." ], "reasoning": "To verify this statement, compare the 'low' arousal scores across all categories and check if 'loneliness' has the highest percentage.", "statement": "The 'loneliness' category has the highest percentage of words with low arousal scores.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 10: Percentage of words associated with different age groups. Within each age group (column), all the differences are statistically significant (Chi-squared test, p<0.0001).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The lonely sub-corpus has higher percentages of words associated with younger age groups and lower percentages with older age groups compared to general tweets, while the solitude sub-corpus shows the opposite pattern.Using the Word–Age Association Lexicon, we calculate the percentage of words associated with each age group in each sub-corpus (loneliness, lonely, solitude, and general tweets). For this, we divide the number of occurrences of words associated with a particular age group by the total number of occurrences of all the words in the lexicon. Within each age group, all the differences between the numbers for each sub-corpus (loneliness, lonely, and solitude) and the general tweets are statistically significant (Chi-squared test, p<0.0001). Observe that the lonely sub-corpus has higher percentages of words associated with the two younger groups (as compared with the general tweets) and lower percentage of words associated with the two older groups. The differences for the teenage group and the older adults (30+ years old) are particularly large (21% increase for the teenage group, 13% decrease for the 30 and over group). The solitude sub-corpus shows the opposite pattern with lower percentage of words associated with the two younger groups and higher percentage of words associated with the two older groups. The differences between the numbers for the loneliness corpus and the general tweets are relatively small for all four age groups. These results suggest that there are more younger people (especially teenagers) among the tweeters that use the word lonely when talking about being alone and, therefore, have more negative experiences when alone, than among the tweeters that use the word solitude and have more positive attitudes to the state of being alone. This finding does not support the psychology literature that proposes that adolescence may be a time when being alone is adaptive and enjoyable It is possible, however, that adolescents may use Twitter to vent or share feelings about loneliness more often than other age groups." } ], "images": [ "scigen/train/3838.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: Percentage of words associated with different age groups. Within each age group (column), all the differences are statistically significant (Chi-squared test, p<0.0001)." ], "reasoning": "The lonely sub-corpus has higher percentages of words associated with younger age groups and lower percentages with older age groups compared to general tweets, while the solitude sub-corpus shows the opposite pattern.", "description": "Using the Word–Age Association Lexicon, we calculate the percentage of words associated with each age group in each sub-corpus (loneliness, lonely, solitude, and general tweets). For this, we divide the number of occurrences of words associated with a particular age group by the total number of occurrences of all the words in the lexicon. Within each age group, all the differences between the numbers for each sub-corpus (loneliness, lonely, and solitude) and the general tweets are statistically significant (Chi-squared test, p<0.0001). Observe that the lonely sub-corpus has higher percentages of words associated with the two younger groups (as compared with the general tweets) and lower percentage of words associated with the two older groups. The differences for the teenage group and the older adults (30+ years old) are particularly large (21% increase for the teenage group, 13% decrease for the 30 and over group). The solitude sub-corpus shows the opposite pattern with lower percentage of words associated with the two younger groups and higher percentage of words associated with the two older groups. The differences between the numbers for the loneliness corpus and the general tweets are relatively small for all four age groups. These results suggest that there are more younger people (especially teenagers) among the tweeters that use the word lonely when talking about being alone and, therefore, have more negative experiences when alone, than among the tweeters that use the word solitude and have more positive attitudes to the state of being alone. This finding does not support the psychology literature that proposes that adolescence may be a time when being alone is adaptive and enjoyable It is possible, however, that adolescents may use Twitter to vent or share feelings about loneliness more often than other age groups.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nTable title: 'Table 10: Percentage of words associated with different age groups. Within each age group (column), all the differences are statistically significant (Chi-squared test, p<0.0001).'\nQuestion: What is the percentage difference of words associated with the 30+ age group between the lonely and solitude sub-corpora?\n" }, { "role": "assistant", "content": "The percentage of words associated with the 30+ age group in the lonely sub-corpus is 48.2%, and in the solitude sub-corpus, it is 57.2%. The difference is 57.2% - 48.2% = 9%. So, the answer is {\"answer\": \"9%\"}." } ], "images": [ "scigen/train/3838.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: Percentage of words associated with different age groups. Within each age group (column), all the differences are statistically significant (Chi-squared test, p<0.0001)." ], "reasoning": "The percentage of words associated with the 30+ age group in the lonely sub-corpus is 48.2%, and in the solitude sub-corpus, it is 57.2%. The difference is 57.2% - 48.2% = 9%.", "question": "What is the percentage difference of words associated with the 30+ age group between the lonely and solitude sub-corpora?", "answer": "So, the answer is {\"answer\": \"9%\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe percentage of words associated with the 13 to 18 age group is higher in the loneliness sub-corpus than in the general tweets.\n" }, { "role": "assistant", "content": "The table shows that the percentage of words associated with the 13 to 18 age group in the loneliness sub-corpus is 29.8%, while in the general tweets, it is 31%. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3838.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: Percentage of words associated with different age groups. Within each age group (column), all the differences are statistically significant (Chi-squared test, p<0.0001)." ], "reasoning": "The table shows that the percentage of words associated with the 13 to 18 age group in the loneliness sub-corpus is 29.8%, while in the general tweets, it is 31%. Therefore, the statement is refuted.", "statement": "The percentage of words associated with the 13 to 18 age group is higher in the loneliness sub-corpus than in the general tweets.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5. The outline of the dataset after the first stage of annotation.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Most samples were annotated unequivocally, with 86% considered non-harmful and only 2% considered harmful by all annotators.I Stage of Annotation After the analysis of annotator performance, we looked at the data from the point of view of the annotated samples. Therefore, we focused on agreements calculated per sample, and not per annotator. Most of the samples (almost 90%) were annotated unequivocally, which was a positive result suggesting good quality of the dataset and supporting the good performance of annotators. Grand majority (86%) of annotations was considered as not harmful. Only about 2% was considered harmful without a doubt by all annotators in the first stage of annotations. There was also a small number (28) of samples for which none of the annotators had any idea how to classify them (uncertain / I don’t know), and also a small number of samples (17) which were not annotated, probably by accident. The 28 samples which all three annotators were not able to annotate as well as the missing annotations were forwarded to a cyberbullying expert for final decision. The remaining 1452 samples (11.37%) annotated equivocally with some level of disagreement were used in the second stage of annotation." } ], "images": [ "scigen/train/3839.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5. The outline of the dataset after the first stage of annotation." ], "reasoning": "Most samples were annotated unequivocally, with 86% considered non-harmful and only 2% considered harmful by all annotators.", "description": "I Stage of Annotation After the analysis of annotator performance, we looked at the data from the point of view of the annotated samples. Therefore, we focused on agreements calculated per sample, and not per annotator. Most of the samples (almost 90%) were annotated unequivocally, which was a positive result suggesting good quality of the dataset and supporting the good performance of annotators. Grand majority (86%) of annotations was considered as not harmful. Only about 2% was considered harmful without a doubt by all annotators in the first stage of annotations. There was also a small number (28) of samples for which none of the annotators had any idea how to classify them (uncertain / I don’t know), and also a small number of samples (17) which were not annotated, probably by accident. The 28 samples which all three annotators were not able to annotate as well as the missing annotations were forwarded to a cyberbullying expert for final decision. The remaining 1452 samples (11.37%) annotated equivocally with some level of disagreement were used in the second stage of annotation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nHow many samples were annotated as harmful without a doubt by all annotators in the first stage?\nSolve the above question based on the table titled 'Table 5. The outline of the dataset after the first stage of annotation.'. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that 285 samples were considered harmful, which is 2.23% of the total 12772 samples. So, the answer is {\"answer\": \"285\"}." } ], "images": [ "scigen/train/3839.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5. The outline of the dataset after the first stage of annotation." ], "reasoning": "The table shows that 285 samples were considered harmful, which is 2.23% of the total 12772 samples.", "question": "How many samples were annotated as harmful without a doubt by all annotators in the first stage?", "answer": "So, the answer is {\"answer\": \"285\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe majority of samples were considered harmful by all annotators.\n" }, { "role": "assistant", "content": "The table shows that only 285 samples (2.23%) were considered harmful, while 11007 samples (86.18%) were considered non-harmful. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3839.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5. The outline of the dataset after the first stage of annotation." ], "reasoning": "The table shows that only 285 samples (2.23%) were considered harmful, while 11007 samples (86.18%) were considered non-harmful.", "statement": "The majority of samples were considered harmful by all annotators.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1. The number of correctly annotated samples (out of 30) for the people that performed the annotation.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Sixteen candidates were tested with 30 samples, and the top eight were retained based on their performance.Sixteen (16) initial candidates responded to our call. The candidates were given an initial test to eliminate possible outliers and low performance annotators. In the initial test the candidates were given 30 random samples to annotate with already prepared gold standard answers. The top eight candidates were retained, while the low performance half was rejected." } ], "images": [ "scigen/train/3840.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1. The number of correctly annotated samples (out of 30) for the people that performed the annotation." ], "reasoning": "Sixteen candidates were tested with 30 samples, and the top eight were retained based on their performance.", "description": "Sixteen (16) initial candidates responded to our call. The candidates were given an initial test to eliminate possible outliers and low performance annotators. In the initial test the candidates were given 30 random samples to annotate with already prepared gold standard answers. The top eight candidates were retained, while the low performance half was rejected.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the average percentage score of the top eight candidates?\nSolve the above question based on the table titled 'Table 1. The number of correctly annotated samples (out of 30) for the people that performed the annotation.'. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the average percentage score, sum the percentages of all eight candidates and divide by 8. The percentages are 80.0%, 80.0%, 76.7%, 73.3%, 73.3%, 70.0%, 70.0%, and 66.7%. So, the answer is {\"answer\": \"73.75%\"}." } ], "images": [ "scigen/train/3840.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1. The number of correctly annotated samples (out of 30) for the people that performed the annotation." ], "reasoning": "To find the average percentage score, sum the percentages of all eight candidates and divide by 8. The percentages are 80.0%, 80.0%, 76.7%, 73.3%, 73.3%, 70.0%, 70.0%, and 66.7%.", "question": "What is the average percentage score of the top eight candidates?", "answer": "So, the answer is {\"answer\": \"73.75%\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe lowest percentage score among the top eight candidates is 66.7%.\n" }, { "role": "assistant", "content": "The table shows that the lowest percentage score among the top eight candidates is indeed 66.7%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3840.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1. The number of correctly annotated samples (out of 30) for the people that performed the annotation." ], "reasoning": "The table shows that the lowest percentage score among the top eight candidates is indeed 66.7%.", "statement": "The lowest percentage score among the top eight candidates is 66.7%.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 6. The outline of the ambiguous results and the disambiguation scheme for each type of ambiguity.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Ambiguities were resolved by considering remaining annotators' answers, applying weighting schemes, and expert verification.There were only four possible situations of ambiguity. Firstly, if one of the annotators selected I don’t know (later IDK or uncertain), or there was no annotation, the remaining two annotators were taken into consideration. Here, if both selected the same answer, we considered the case as solved (however, we still checked those samples in the second stage of annotations for final confirmation). If the results were scrambled, we applied appropriate weighting scheme to propose an initial proposed estimated value (either 1 for harmful or 0 for non-harmful), and asked an additional cyberbullying expert to verify the choice. Weighting was based on each annotator’s average agreement score (the higher the better). Also, when none of the annotators selected IDK but the results were still scrambled, we also applied an appropriate weighting scheme with expert’s verification. Finally, when there were two IDK answers, we considered the remaining non ambiguous answer as a potentially correct, but with a strong voice from the second stage of annotations and the additional expert." } ], "images": [ "scigen/train/3841.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6. The outline of the ambiguous results and the disambiguation scheme for each type of ambiguity." ], "reasoning": "Ambiguities were resolved by considering remaining annotators' answers, applying weighting schemes, and expert verification.", "description": "There were only four possible situations of ambiguity. Firstly, if one of the annotators selected I don’t know (later IDK or uncertain), or there was no annotation, the remaining two annotators were taken into consideration. Here, if both selected the same answer, we considered the case as solved (however, we still checked those samples in the second stage of annotations for final confirmation). If the results were scrambled, we applied appropriate weighting scheme to propose an initial proposed estimated value (either 1 for harmful or 0 for non-harmful), and asked an additional cyberbullying expert to verify the choice. Weighting was based on each annotator’s average agreement score (the higher the better). Also, when none of the annotators selected IDK but the results were still scrambled, we also applied an appropriate weighting scheme with expert’s verification. Finally, when there were two IDK answers, we considered the remaining non ambiguous answer as a potentially correct, but with a strong voice from the second stage of annotations and the additional expert.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 6. The outline of the ambiguous results and the disambiguation scheme for each type of ambiguity.', unfold the question presented below, detailing every step of your calculation. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nHow many cases required both weighting and expert verification for disambiguation?\n" }, { "role": "assistant", "content": "To find the number of cases requiring both weighting and expert verification, sum the cases listed under '1 IDK unequivocal', '2 IDK', and 'scrambled'. So, the answer is {\"answer\": \"1283\"}." } ], "images": [ "scigen/train/3841.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6. The outline of the ambiguous results and the disambiguation scheme for each type of ambiguity." ], "reasoning": "To find the number of cases requiring both weighting and expert verification, sum the cases listed under '1 IDK unequivocal', '2 IDK', and 'scrambled'.", "question": "How many cases required both weighting and expert verification for disambiguation?", "answer": "So, the answer is {\"answer\": \"1283\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nAll cases with one IDK were resolved without expert verification.\n" }, { "role": "assistant", "content": "The table shows that cases with '1 IDK unequivocal' and '1 IDK scrambled' required expert verification. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3841.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6. The outline of the ambiguous results and the disambiguation scheme for each type of ambiguity." ], "reasoning": "The table shows that cases with '1 IDK unequivocal' and '1 IDK scrambled' required expert verification.", "statement": "All cases with one IDK were resolved without expert verification.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 6.5: Flickr30K experiments without Word2Vec initialization. R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good).'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Word2Vec initialization improves system accuracy slightly, with R@10:avg_txt for image annotation increasing by 3.3% and for image search by 3.86%.A quick comparison between the performance metrics in these two tables shows that although word2vec initialization does help in improving the accuracy of the system, but the effects are not extremely drastic. For example R@10:avg_txt for image annotation increases by 3.3% while for image search increases by 3.86%." } ], "images": [ "scigen/train/3842.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6.5: Flickr30K experiments without Word2Vec initialization. R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good)." ], "reasoning": "Word2Vec initialization improves system accuracy slightly, with R@10:avg_txt for image annotation increasing by 3.3% and for image search by 3.86%.", "description": "A quick comparison between the performance metrics in these two tables shows that although word2vec initialization does help in improving the accuracy of the system, but the effects are not extremely drastic. For example R@10:avg_txt for image annotation increases by 3.3% while for image search increases by 3.86%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 6.5: Flickr30K experiments without Word2Vec initialization. R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good).' and proceed to solve the following question in a stepwise manner. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the increase in R@10:avg_txt for image annotation when using Word2Vec initialization?\n" }, { "role": "assistant", "content": "The R@10:avg_txt for image annotation without Word2Vec is 29.5, and with Word2Vec is 40.48. The increase is calculated as 40.48 - 29.5. So, the answer is {\"answer\": \"10.98\"}" } ], "images": [ "scigen/train/3842.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6.5: Flickr30K experiments without Word2Vec initialization. R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good)." ], "reasoning": "The R@10:avg_txt for image annotation without Word2Vec is 29.5, and with Word2Vec is 40.48. The increase is calculated as 40.48 - 29.5.", "question": "What is the increase in R@10:avg_txt for image annotation when using Word2Vec initialization?", "answer": "So, the answer is {\"answer\": \"10.98\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nWord2Vec initialization increases the R@10:avg_txt for image search by 3.86%.\n" }, { "role": "assistant", "content": "The R@10:avg_txt for image search without Word2Vec is 29.04, and with Word2Vec is 40.34. The increase is calculated as (40.34 - 29.04) / 29.04 * 100, which is approximately 38.86%. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3842.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6.5: Flickr30K experiments without Word2Vec initialization. R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good)." ], "reasoning": "The R@10:avg_txt for image search without Word2Vec is 29.04, and with Word2Vec is 40.34. The increase is calculated as (40.34 - 29.04) / 29.04 * 100, which is approximately 38.86%.", "statement": "Word2Vec initialization increases the R@10:avg_txt for image search by 3.86%.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 6.2: Comparison between two different training methodologies: I2T and T2I. BoW means unsing unigram word features and 2g means using bigram. All results are given in Recall@10: avg_txt. nhu is the hidden layer in the textual model, while nemb is the size of embedding dimension.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The performance of the system varies based on whether it is specialized for image annotation (I2T) or image search (T2I), with consistent differences observed across various model architectures.I2T and T2I. We notice that this choice actually does impact the performance of our system in a peculiar way; it specializes it to a particular task. We compare the performance across several models architectures and consistently notice the same phenomenon. Hence for all future results, whenever we refer to image annotation we report I2T results, and for image search we report T2I results." } ], "images": [ "scigen/train/3843.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6.2: Comparison between two different training methodologies: I2T and T2I. BoW means unsing unigram word features and 2g means using bigram. All results are given in Recall@10: avg_txt. nhu is the hidden layer in the textual model, while nemb is the size of embedding dimension." ], "reasoning": "The performance of the system varies based on whether it is specialized for image annotation (I2T) or image search (T2I), with consistent differences observed across various model architectures.", "description": "I2T and T2I. We notice that this choice actually does impact the performance of our system in a peculiar way; it specializes it to a particular task. We compare the performance across several models architectures and consistently notice the same phenomenon. Hence for all future results, whenever we refer to image annotation we report I2T results, and for image search we report T2I results.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 6.2: Comparison between two different training methodologies: I2T and T2I. BoW means unsing unigram word features and 2g means using bigram. All results are given in Recall@10: avg_txt. nhu is the hidden layer in the textual model, while nemb is the size of embedding dimension.' in mind, break down and resolve the question below step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model architecture has the highest performance for I2T image annotation?\n" }, { "role": "assistant", "content": "To find the highest performance for I2T image annotation, compare the I2T scores in the 'Training methodology: I2T vs T2I image annotation' column. The highest score is 45.4. So, the answer is {\"answer\": \"45.4\"}." } ], "images": [ "scigen/train/3843.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6.2: Comparison between two different training methodologies: I2T and T2I. BoW means unsing unigram word features and 2g means using bigram. All results are given in Recall@10: avg_txt. nhu is the hidden layer in the textual model, while nemb is the size of embedding dimension." ], "reasoning": "To find the highest performance for I2T image annotation, compare the I2T scores in the 'Training methodology: I2T vs T2I image annotation' column. The highest score is 45.4.", "question": "Which model architecture has the highest performance for I2T image annotation?", "answer": "So, the answer is {\"answer\": \"45.4\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model with nhu=0, nemb=1000, 2g has the highest performance for T2I image search.\n" }, { "role": "assistant", "content": "To verify this statement, compare the T2I scores in the 'Training methodology: I2T vs T2I image search' column. The highest score is 43.74, which corresponds to the model with nhu=0, nemb=1000, 2g. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3843.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6.2: Comparison between two different training methodologies: I2T and T2I. BoW means unsing unigram word features and 2g means using bigram. All results are given in Recall@10: avg_txt. nhu is the hidden layer in the textual model, while nemb is the size of embedding dimension." ], "reasoning": "To verify this statement, compare the T2I scores in the 'Training methodology: I2T vs T2I image search' column. The highest score is 43.74, which corresponds to the model with nhu=0, nemb=1000, 2g.", "statement": "The model with nhu=0, nemb=1000, 2g has the highest performance for T2I image search.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 6.4: Flickr30K experiments with unigram BoW model and Word2Vec initialization. R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good).'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The word2vec initialization improves system accuracy, with R@10:avg_txt for image annotation increasing by 3.3% and for image search by 3.86%.The hidden layer of textual model is fixed to 300 units, since the word2vec embeddings have dimensionality of 300. We fixed the hidden layer of the trainable visual model to 1,000 units, and the size of multimodal embedding vector is also fixed to 1,000. A quick comparison between the performance metrics in these two tables shows that although word2vec initialization does help in improving the accuracy of the system, but the effects are not extremely drastic. For example R@10:avg_txt for image annotation increases by 3.3% while for image search increases by 3.86%." } ], "images": [ "scigen/train/3844.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6.4: Flickr30K experiments with unigram BoW model and Word2Vec initialization. R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good)." ], "reasoning": "The word2vec initialization improves system accuracy, with R@10:avg_txt for image annotation increasing by 3.3% and for image search by 3.86%.", "description": "The hidden layer of textual model is fixed to 300 units, since the word2vec embeddings have dimensionality of 300. We fixed the hidden layer of the trainable visual model to 1,000 units, and the size of multimodal embedding vector is also fixed to 1,000. A quick comparison between the performance metrics in these two tables shows that although word2vec initialization does help in improving the accuracy of the system, but the effects are not extremely drastic. For example R@10:avg_txt for image annotation increases by 3.3% while for image search increases by 3.86%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 6.4: Flickr30K experiments with unigram BoW model and Word2Vec initialization. R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good).', and sequentially solve the question, writing out each step of your reasoning process. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the R@K: avg_txt value for BoW Image Search when k=10?\n" }, { "role": "assistant", "content": "To find the R@K: avg_txt value for BoW Image Search when k=10, look at the column labeled 'our model: BoW Image Search' and the row labeled 'R@K: avg_txt' under k=10. So, the answer is {\"answer\": \"41.08\"}." } ], "images": [ "scigen/train/3844.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6.4: Flickr30K experiments with unigram BoW model and Word2Vec initialization. R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good)." ], "reasoning": "To find the R@K: avg_txt value for BoW Image Search when k=10, look at the column labeled 'our model: BoW Image Search' and the row labeled 'R@K: avg_txt' under k=10.", "question": "What is the R@K: avg_txt value for BoW Image Search when k=10?", "answer": "So, the answer is {\"answer\": \"41.08\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe R@K: rnd_txt value for BoW Image Annotation is higher than for BoW Image Search when k=5.\n" }, { "role": "assistant", "content": "To verify this statement, compare the R@K: rnd_txt values for BoW Image Annotation and BoW Image Search under k=5. BoW Image Annotation has a value of 29.2, while BoW Image Search has a value of 28.9. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3844.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6.4: Flickr30K experiments with unigram BoW model and Word2Vec initialization. R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good)." ], "reasoning": "To verify this statement, compare the R@K: rnd_txt values for BoW Image Annotation and BoW Image Search under k=5. BoW Image Annotation has a value of 29.2, while BoW Image Search has a value of 28.9.", "statement": "The R@K: rnd_txt value for BoW Image Annotation is higher than for BoW Image Search when k=5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 6.6: Comparison between different n-gram models, on Flickr30K data. 2g is bi-gram, 3g is trigram and tk3 is combination of tri-gram and skip-gram features. In all case size of input vocabulary is 50,000 and all rest model parameters are similar. The bold figures show best results in the specific row. nhu is the hidden layer in the textual model, while nemb is the size of embedding dimension.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The n-gram model was used instead of the BoW model, with a fixed vocabulary size of 50,000, and experiments were conducted using bigrams, trigrams, and trigrams with skip grams.We replaced the BoW textual model with n-gram model. N-gram models conserve some sentence-order information and so theoretically they act as better features. In our experiments we tried bigrams (2g), trigrams (3g) and trigrams + skip grams (tk3). In all these cases the vocabulary size is fixed to 50,000 which is the size of input layer of textual model MLP." } ], "images": [ "scigen/train/3845.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6.6: Comparison between different n-gram models, on Flickr30K data. 2g is bi-gram, 3g is trigram and tk3 is combination of tri-gram and skip-gram features. In all case size of input vocabulary is 50,000 and all rest model parameters are similar. The bold figures show best results in the specific row. nhu is the hidden layer in the textual model, while nemb is the size of embedding dimension." ], "reasoning": "The n-gram model was used instead of the BoW model, with a fixed vocabulary size of 50,000, and experiments were conducted using bigrams, trigrams, and trigrams with skip grams.", "description": "We replaced the BoW textual model with n-gram model. N-gram models conserve some sentence-order information and so theoretically they act as better features. In our experiments we tried bigrams (2g), trigrams (3g) and trigrams + skip grams (tk3). In all these cases the vocabulary size is fixed to 50,000 which is the size of input layer of textual model MLP.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich n-gram model and configuration achieved the highest rPrecision(5) score?\nSolve the above question based on the table titled 'Table 6.6: Comparison between different n-gram models, on Flickr30K data. 2g is bi-gram, 3g is trigram and tk3 is combination of tri-gram and skip-gram features. In all case size of input vocabulary is 50,000 and all rest model parameters are similar. The bold figures show best results in the specific row. nhu is the hidden layer in the textual model, while nemb is the size of embedding dimension.'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest rPrecision(5) score, we need to compare the rPrecision(5) values for all n-gram models (2g, 3g, tk3) across all configurations. The highest value is 12.2 for the 2g model with nhu=0 and nemb=300. So, the answer is {\"answer\": \"2g with nhu=0 and nemb=300\"}." } ], "images": [ "scigen/train/3845.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6.6: Comparison between different n-gram models, on Flickr30K data. 2g is bi-gram, 3g is trigram and tk3 is combination of tri-gram and skip-gram features. In all case size of input vocabulary is 50,000 and all rest model parameters are similar. The bold figures show best results in the specific row. nhu is the hidden layer in the textual model, while nemb is the size of embedding dimension." ], "reasoning": "To find the highest rPrecision(5) score, we need to compare the rPrecision(5) values for all n-gram models (2g, 3g, tk3) across all configurations. The highest value is 12.2 for the 2g model with nhu=0 and nemb=300.", "question": "Which n-gram model and configuration achieved the highest rPrecision(5) score?", "answer": "So, the answer is {\"answer\": \"2g with nhu=0 and nemb=300\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The highest R@10: avg_t x t score was achieved by the 3g model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe highest R@10: avg_t x t score was achieved by the 3g model.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the R@10: avg_t x t scores for all n-gram models (2g, 3g, tk3) across all configurations. The highest score is 42.7, achieved by the 3g model with nhu=300 and nemb=1000. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3845.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6.6: Comparison between different n-gram models, on Flickr30K data. 2g is bi-gram, 3g is trigram and tk3 is combination of tri-gram and skip-gram features. In all case size of input vocabulary is 50,000 and all rest model parameters are similar. The bold figures show best results in the specific row. nhu is the hidden layer in the textual model, while nemb is the size of embedding dimension." ], "reasoning": "To verify this statement, we need to compare the R@10: avg_t x t scores for all n-gram models (2g, 3g, tk3) across all configurations. The highest score is 42.7, achieved by the 3g model with nhu=300 and nemb=1000.", "statement": "The highest R@10: avg_t x t score was achieved by the 3g model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 6.7: Comparison between different input word features: binary and tf−idf, on Flickr30K data. In all case size of input vocabulary is 50,000 and the inputs are either 2g, 3g, or tk3, and all rest model parameters are similar. The bold figures show best results in the specific row. nhu is the hidden layer in the textual model, while nemb is the size of mbedding dimension.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Despite intuition suggesting that tf-idf features should perform better, empirical evidence shows that binary features outperform tf-idf in most models.Following this we also experimented on using either binary or tf-idf valued features. Intuition suggests that tf-idf encode more information and so should perform better than binary features. However, empirical evidence suggests otherwise as binary features performed better in most of our models. Hence for future models we stick with binary features." } ], "images": [ "scigen/train/3846.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6.7: Comparison between different input word features: binary and tf−idf, on Flickr30K data. In all case size of input vocabulary is 50,000 and the inputs are either 2g, 3g, or tk3, and all rest model parameters are similar. The bold figures show best results in the specific row. nhu is the hidden layer in the textual model, while nemb is the size of mbedding dimension." ], "reasoning": "Despite intuition suggesting that tf-idf features should perform better, empirical evidence shows that binary features outperform tf-idf in most models.", "description": "Following this we also experimented on using either binary or tf-idf valued features. Intuition suggests that tf-idf encode more information and so should perform better than binary features. However, empirical evidence suggests otherwise as binary features performed better in most of our models. Hence for future models we stick with binary features.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 6.7: Comparison between different input word features: binary and tf−idf, on Flickr30K data. In all case size of input vocabulary is 50,000 and the inputs are either 2g, 3g, or tk3, and all rest model parameters are similar. The bold figures show best results in the specific row. nhu is the hidden layer in the textual model, while nemb is the size of mbedding dimension.'. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model had the highest rPrecision(5) score using binary features?\n" }, { "role": "assistant", "content": "To find the model with the highest rPrecision(5) score using binary features, we look at the 'rPrecision(5)' column under 'binary' and identify the highest value, which is 12.2 for the model 'nhu=0, nemb=300, 2g'. So, the answer is {\"answer\": \"nhu=0, nemb=300, 2g\"}" } ], "images": [ "scigen/train/3846.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6.7: Comparison between different input word features: binary and tf−idf, on Flickr30K data. In all case size of input vocabulary is 50,000 and the inputs are either 2g, 3g, or tk3, and all rest model parameters are similar. The bold figures show best results in the specific row. nhu is the hidden layer in the textual model, while nemb is the size of mbedding dimension." ], "reasoning": "To find the model with the highest rPrecision(5) score using binary features, we look at the 'rPrecision(5)' column under 'binary' and identify the highest value, which is 12.2 for the model 'nhu=0, nemb=300, 2g'.", "question": "Which model had the highest rPrecision(5) score using binary features?", "answer": "So, the answer is {\"answer\": \"nhu=0, nemb=300, 2g\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model 'nhu=1000, nemb=1000, 2g' had the highest R@K: avg txt score using binary features.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the 'R@K: avg txt' column under 'binary' and identify the highest value, which is 43.36 for the model 'nhu=1000, nemb=1000, 2g'. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3846.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6.7: Comparison between different input word features: binary and tf−idf, on Flickr30K data. In all case size of input vocabulary is 50,000 and the inputs are either 2g, 3g, or tk3, and all rest model parameters are similar. The bold figures show best results in the specific row. nhu is the hidden layer in the textual model, while nemb is the size of mbedding dimension." ], "reasoning": "To verify this statement, we look at the 'R@K: avg txt' column under 'binary' and identify the highest value, which is 43.36 for the model 'nhu=1000, nemb=1000, 2g'.", "statement": "The model 'nhu=1000, nemb=1000, 2g' had the highest R@K: avg txt score using binary features.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 6.8: Flickr30K experiments with bigram model and binary inputs. R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good).', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The best performing model has 1,000 unit hidden layers in both textual and trainable visual models, and a 1,000 unit multimodal embedding vector, achieving optimal performance with a 300 dimensional embedding.Finally we select the best performing model as having 1,000 unit hidden layers in both textual and trainable visual models, and also 1,000 unit multimodal embedding vector. We reached the best performance using a 300 dimensional embedding (linearly connected with 5,000 input units). The small trainable visual network also does not contain any hidden layer as well, and the 4,096 dimensional OverFeat feature vectors are linearly transformed into the 300 embedding dimension." } ], "images": [ "scigen/train/3847.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6.8: Flickr30K experiments with bigram model and binary inputs. R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good)." ], "reasoning": "The best performing model has 1,000 unit hidden layers in both textual and trainable visual models, and a 1,000 unit multimodal embedding vector, achieving optimal performance with a 300 dimensional embedding.", "description": "Finally we select the best performing model as having 1,000 unit hidden layers in both textual and trainable visual models, and also 1,000 unit multimodal embedding vector. We reached the best performance using a 300 dimensional embedding (linearly connected with 5,000 input units). The small trainable visual network also does not contain any hidden layer as well, and the 4,096 dimensional OverFeat feature vectors are linearly transformed into the 300 embedding dimension.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6.8: Flickr30K experiments with bigram model and binary inputs. R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good).\nQuestion: What is the rPrecision(5) value for the n-gram Image Annotation model?\nSolve the above question based on the table information. Let's think step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The rPrecision(5) value for the n-gram Image Annotation model is listed in the first row of the table under the 'our model: n-gram Image Annotation' columns. So, the answer is {\"answer\": \"11.68\"}" } ], "images": [ "scigen/train/3847.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6.8: Flickr30K experiments with bigram model and binary inputs. R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good)." ], "reasoning": "The rPrecision(5) value for the n-gram Image Annotation model is listed in the first row of the table under the 'our model: n-gram Image Annotation' columns.", "question": "What is the rPrecision(5) value for the n-gram Image Annotation model?", "answer": "So, the answer is {\"answer\": \"11.68\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe n-gram Image Search model with k=5 has an R@K: avg_txt value of 31.24.\n" }, { "role": "assistant", "content": "The table shows that the R@K: avg_txt value for the n-gram Image Search model with k=5 is 31.24. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3847.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6.8: Flickr30K experiments with bigram model and binary inputs. R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good)." ], "reasoning": "The table shows that the R@K: avg_txt value for the n-gram Image Search model with k=5 is 31.24.", "statement": "The n-gram Image Search model with k=5 has an R@K: avg_txt value of 31.24.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 6.9: Flickr30K experiments with bigram model and binary inputs. R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good).', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The best performance was achieved using a 300-dimensional embedding with 5,000 input units, and the OverFeat feature vectors were linearly transformed into the 300 embedding dimension without any hidden layer.We reached the best performance using a 300 dimensional embedding (linearly connected with 5,000 input units). The small trainable visual network also does not contain any hidden layer as well, and the 4,096 dimensional OverFeat feature vectors are linearly transformed into the 300 embedding dimension." } ], "images": [ "scigen/train/3848.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6.9: Flickr30K experiments with bigram model and binary inputs. R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good)." ], "reasoning": "The best performance was achieved using a 300-dimensional embedding with 5,000 input units, and the OverFeat feature vectors were linearly transformed into the 300 embedding dimension without any hidden layer.", "description": "We reached the best performance using a 300 dimensional embedding (linearly connected with 5,000 input units). The small trainable visual network also does not contain any hidden layer as well, and the 4,096 dimensional OverFeat feature vectors are linearly transformed into the 300 embedding dimension.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 6.9: Flickr30K experiments with bigram model and binary inputs. R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good).' in mind, break down and resolve the question below step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhat is the R@K: avg_txt value for k=5 in the shallow Image Annotation model?\n" }, { "role": "assistant", "content": "To find the R@K: avg_txt value for k=5 in the shallow Image Annotation model, locate the column where k=5 and find the corresponding value in the R@K: avg_txt row. So, the answer is {\"answer\": \"30.94\"}" } ], "images": [ "scigen/train/3848.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6.9: Flickr30K experiments with bigram model and binary inputs. R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good)." ], "reasoning": "To find the R@K: avg_txt value for k=5 in the shallow Image Annotation model, locate the column where k=5 and find the corresponding value in the R@K: avg_txt row.", "question": "What is the R@K: avg_txt value for k=5 in the shallow Image Annotation model?", "answer": "So, the answer is {\"answer\": \"30.94\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe rPrecision(5) value for the shallow Image Search model with k=1 is 12.7.\n" }, { "role": "assistant", "content": "To verify this statement, locate the column for the shallow Image Search model with k=1 and check the rPrecision(5) value. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3848.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6.9: Flickr30K experiments with bigram model and binary inputs. R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good)." ], "reasoning": "To verify this statement, locate the column for the shallow Image Search model with k=1 and check the rPrecision(5) value.", "statement": "The rPrecision(5) value for the shallow Image Search model with k=1 is 12.7.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 6.10: Flickr30K experiments with SSE textual model (window size =5). R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The model has a context window of size 5 and uses 300-dimensional embeddings for both word and phrase levels, initialized with word2vec, which significantly improves performance.However, empirical results show another story and the model performs not as well as other previously discussed models. The architecture has context window of size 5 and both word level and phrase level embeddings of 300 dimensions. This model is initialized with word2vec, and it is important to note that if we did not use word2vec initialization the performance fell staggeringly to about half the current performance." } ], "images": [ "scigen/train/3849.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6.10: Flickr30K experiments with SSE textual model (window size =5). R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good)." ], "reasoning": "The model has a context window of size 5 and uses 300-dimensional embeddings for both word and phrase levels, initialized with word2vec, which significantly improves performance.", "description": "However, empirical results show another story and the model performs not as well as other previously discussed models. The architecture has context window of size 5 and both word level and phrase level embeddings of 300 dimensions. This model is initialized with word2vec, and it is important to note that if we did not use word2vec initialization the performance fell staggeringly to about half the current performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 6.10: Flickr30K experiments with SSE textual model (window size =5). R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good).'. Solve the following question based on the table.\n\nQuestion: What is the R@K: avg_txt value for k=5 in the SSE for textual model Image Annotation?\nLet's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the R@K: avg_txt value for k=5 in the SSE for textual model Image Annotation, locate the column with k=5 under the Image Annotation section and find the corresponding R@K: avg_txt row. So, the answer is {\"answer\": \"24.54\"}." } ], "images": [ "scigen/train/3849.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6.10: Flickr30K experiments with SSE textual model (window size =5). R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good)." ], "reasoning": "To find the R@K: avg_txt value for k=5 in the SSE for textual model Image Annotation, locate the column with k=5 under the Image Annotation section and find the corresponding R@K: avg_txt row.", "question": "What is the R@K: avg_txt value for k=5 in the SSE for textual model Image Annotation?", "answer": "So, the answer is {\"answer\": \"24.54\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe rPrecision(5) value for the SSE for textual model Image Search with k=1 is 7.4.\n" }, { "role": "assistant", "content": "To verify the rPrecision(5) value for the SSE for textual model Image Search with k=1, locate the column with k=1 under the Image Search section and find the corresponding rPrecision(5) row. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3849.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6.10: Flickr30K experiments with SSE textual model (window size =5). R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good)." ], "reasoning": "To verify the rPrecision(5) value for the SSE for textual model Image Search with k=1, locate the column with k=1 under the Image Search section and find the corresponding rPrecision(5) row.", "statement": "The rPrecision(5) value for the SSE for textual model Image Search with k=1 is 7.4.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6.11: Comparison between existing image text matching systems. R@K is Recall@K (high is good). In our models, for image annotation we report R@K: any_txt and for image search we report R@K: avg_txt.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our model uses a simple textual model and achieves comparable results to more complex models, with most of its strength coming from the visual model and training methodology.When comparing the results it is interesting to note that most recent research has gone into complicated modeling on the textual domain. They also try other types of recurrent and recursive neural networks. In contrast we use simple textual model and achieve comparable results. The bulk of our model’s strength comes from the visual model and the training methodology which specializes the performance towards a particular task boosting its performance. In our experiments we notice that temporal dependency (or information about word order) modeled with simple bi-grams or tri-grams is enough to achieve good results. The simplest addition to our model would be to turn on updating for our CNN while we are training on the image-text data set. This is called finetuning, since the CNN is pretrained on image data alone. However this will come at a cost of significantly additional training time, hence it is a trade off." } ], "images": [ "scigen/train/3850.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6.11: Comparison between existing image text matching systems. R@K is Recall@K (high is good). In our models, for image annotation we report R@K: any_txt and for image search we report R@K: avg_txt." ], "reasoning": "Our model uses a simple textual model and achieves comparable results to more complex models, with most of its strength coming from the visual model and training methodology.", "description": "When comparing the results it is interesting to note that most recent research has gone into complicated modeling on the textual domain. They also try other types of recurrent and recursive neural networks. In contrast we use simple textual model and achieve comparable results. The bulk of our model’s strength comes from the visual model and the training methodology which specializes the performance towards a particular task boosting its performance. In our experiments we notice that temporal dependency (or information about word order) modeled with simple bi-grams or tri-grams is enough to achieve good results. The simplest addition to our model would be to turn on updating for our CNN while we are training on the image-text data set. This is called finetuning, since the CNN is pretrained on image data alone. However this will come at a cost of significantly additional training time, hence it is a trade off.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 6.11: Comparison between existing image text matching systems. R@K is Recall@K (high is good). In our models, for image annotation we report R@K: any_txt and for image search we report R@K: avg_txt.', solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model achieves the highest R@10 score in the Flickr30K: Comparison image annotation task?\n" }, { "role": "assistant", "content": "By examining the R@10 column under the Flickr30K: Comparison image annotation task, the highest score is 54.7, achieved by the DeFrag (*) + Finetune CNN model. So, the answer is {\"answer\": \"DeFrag (*) + Finetune CNN\"}." } ], "images": [ "scigen/train/3850.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6.11: Comparison between existing image text matching systems. R@K is Recall@K (high is good). In our models, for image annotation we report R@K: any_txt and for image search we report R@K: avg_txt." ], "reasoning": "By examining the R@10 column under the Flickr30K: Comparison image annotation task, the highest score is 54.7, achieved by the DeFrag (*) + Finetune CNN model.", "question": "Which model achieves the highest R@10 score in the Flickr30K: Comparison image annotation task?", "answer": "So, the answer is {\"answer\": \"DeFrag (*) + Finetune CNN\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nOur model: n-gram achieves a higher R@1 score in the Flickr30K: Comparison image search task than DeFrag FAO.\n" }, { "role": "assistant", "content": "In the R@1 column under the Flickr30K: Comparison image search task, our model: n-gram has a score of 10.5, while DeFrag FAO has a score of 7.6. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3850.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6.11: Comparison between existing image text matching systems. R@K is Recall@K (high is good). In our models, for image annotation we report R@K: any_txt and for image search we report R@K: avg_txt." ], "reasoning": "In the R@1 column under the Flickr30K: Comparison image search task, our model: n-gram has a score of 10.5, while DeFrag FAO has a score of 7.6.", "statement": "Our model: n-gram achieves a higher R@1 score in the Flickr30K: Comparison image search task than DeFrag FAO.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 4: Performance of syntax-transfer generation. The larger↑ (or lower↓), the better. The results of VAE are obtained by averaging interpolation. Δword-BLEU=word-BLEU(Refsem)−word-% BLEU(Refsyn). We also compute the difference as ΔTED=TED(Refsem)−TED(Refsyn) to measure if the generated sentence is syntactically similar to Refsyn but not Refsem. Due to the difference of scale between BLEU and TED, we compute the geometric mean of Δword-BLEU and ΔTED reflect the total differences.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The multi-task and adversarial losses for syntactic and semantic latent spaces increased the total difference by 12.09, indicating successful syntax-transfer sentence generation.S4SS3SSS0Px2 Results This is because Refsyn and Refsem—even if we artificially split the latent space into two parts—play the same role in the decoder. With the multi-task and adversarial losses for syntactic and semantic latent spaces, the total difference is increased by 12.09, which shows the success of syntax-transfer sentence generation. This further implies that explicitly modeling syntax is feasible in the latent space of VAE." } ], "images": [ "scigen/train/3851.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Performance of syntax-transfer generation. The larger↑ (or lower↓), the better. The results of VAE are obtained by averaging interpolation. Δword-BLEU=word-BLEU(Refsem)−word-% BLEU(Refsyn). We also compute the difference as ΔTED=TED(Refsem)−TED(Refsyn) to measure if the generated sentence is syntactically similar to Refsyn but not Refsem. Due to the difference of scale between BLEU and TED, we compute the geometric mean of Δword-BLEU and ΔTED reflect the total differences." ], "reasoning": "The multi-task and adversarial losses for syntactic and semantic latent spaces increased the total difference by 12.09, indicating successful syntax-transfer sentence generation.", "description": "S4SS3SSS0Px2 Results This is because Refsyn and Refsem—even if we artificially split the latent space into two parts—play the same role in the decoder. With the multi-task and adversarial losses for syntactic and semantic latent spaces, the total difference is increased by 12.09, which shows the success of syntax-transfer sentence generation. This further implies that explicitly modeling syntax is feasible in the latent space of VAE.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 4: Performance of syntax-transfer generation. The larger↑ (or lower↓), the better. The results of VAE are obtained by averaging interpolation. Δword-BLEU=word-BLEU(Refsem)−word-% BLEU(Refsyn). We also compute the difference as ΔTED=TED(Refsem)−TED(Refsyn) to measure if the generated sentence is syntactically similar to Refsyn but not Refsem. Due to the difference of scale between BLEU and TED, we compute the geometric mean of Δword-BLEU and ΔTED reflect the total differences.\nQuestion: Which model achieved the highest Geo Mean Δ†?\n" }, { "role": "assistant", "content": "By examining the Geo Mean Δ† column, the model '+L(adv)rec( zsyn)+L(adv)rec( zsem)' has the highest value of 15.30. So, the answer is {\"answer\": \"15.30\"}" } ], "images": [ "scigen/train/3851.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Performance of syntax-transfer generation. The larger↑ (or lower↓), the better. The results of VAE are obtained by averaging interpolation. Δword-BLEU=word-BLEU(Refsem)−word-% BLEU(Refsyn). We also compute the difference as ΔTED=TED(Refsem)−TED(Refsyn) to measure if the generated sentence is syntactically similar to Refsyn but not Refsem. Due to the difference of scale between BLEU and TED, we compute the geometric mean of Δword-BLEU and ΔTED reflect the total differences." ], "reasoning": "By examining the Geo Mean Δ† column, the model '+L(adv)rec( zsyn)+L(adv)rec( zsem)' has the highest value of 15.30.", "question": "Which model achieved the highest Geo Mean Δ†?", "answer": "So, the answer is {\"answer\": \"15.30\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The model '+L(adv)rec( zsyn)' achieved the highest word-BLEU (corpus) Refsem↑. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe model '+L(adv)rec( zsyn)' achieved the highest word-BLEU (corpus) Refsem↑.\n" }, { "role": "assistant", "content": "By looking at the word-BLEU (corpus) Refsem↑ column, the model '+L(adv)rec( zsyn)' has the highest value of 14.33. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3851.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Performance of syntax-transfer generation. The larger↑ (or lower↓), the better. The results of VAE are obtained by averaging interpolation. Δword-BLEU=word-BLEU(Refsem)−word-% BLEU(Refsyn). We also compute the difference as ΔTED=TED(Refsem)−TED(Refsyn) to measure if the generated sentence is syntactically similar to Refsyn but not Refsem. Due to the difference of scale between BLEU and TED, we compute the geometric mean of Δword-BLEU and ΔTED reflect the total differences." ], "reasoning": "By looking at the word-BLEU (corpus) Refsem↑ column, the model '+L(adv)rec( zsyn)' has the highest value of 14.33.", "statement": "The model '+L(adv)rec( zsyn)' achieved the highest word-BLEU (corpus) Refsem↑.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1: BLEU and Forward PPL of VAE with varying KL weights on the PTB test set. The larger↑ (or lower↓), the better.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "A smaller KL weight results in higher BLEU scores but also increases Forward PPL, indicating a trade-off between fluency and reconstruction quality.S4SS1SSS0Px4 Results that BLEU and PPL are more or less contradictory. Usually, a smaller KL weight makes the autoencoder less “variational” but more “deterministic,” leading to less fluent sampled sentences but better reconstruction. If the trade-off is not analyzed explicitly, the VAE variant could have arbitrary results based on KL-weight tuning, which is unfair." } ], "images": [ "scigen/train/3852.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: BLEU and Forward PPL of VAE with varying KL weights on the PTB test set. The larger↑ (or lower↓), the better." ], "reasoning": "A smaller KL weight results in higher BLEU scores but also increases Forward PPL, indicating a trade-off between fluency and reconstruction quality.", "description": "S4SS1SSS0Px4 Results that BLEU and PPL are more or less contradictory. Usually, a smaller KL weight makes the autoencoder less “variational” but more “deterministic,” leading to less fluent sampled sentences but better reconstruction. If the trade-off is not analyzed explicitly, the VAE variant could have arbitrary results based on KL-weight tuning, which is unfair.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 1: BLEU and Forward PPL of VAE with varying KL weights on the PTB test set. The larger↑ (or lower↓), the better.' and proceed to solve the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the Forward PPL when the KL-Weight is 0.5?\n" }, { "role": "assistant", "content": "To find the Forward PPL for a KL-Weight of 0.5, locate the row where KL-Weight is 0.5 and read the corresponding Forward PPL value. So, the answer is {\"answer\": \"44.11\"}." } ], "images": [ "scigen/train/3852.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: BLEU and Forward PPL of VAE with varying KL weights on the PTB test set. The larger↑ (or lower↓), the better." ], "reasoning": "To find the Forward PPL for a KL-Weight of 0.5, locate the row where KL-Weight is 0.5 and read the corresponding Forward PPL value.", "question": "What is the Forward PPL when the KL-Weight is 0.5?", "answer": "So, the answer is {\"answer\": \"44.11\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis A KL-Weight of 1.0 results in a BLEU score higher than 8.0. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nA KL-Weight of 1.0 results in a BLEU score higher than 8.0.\n" }, { "role": "assistant", "content": "Check the BLEU score corresponding to a KL-Weight of 1.0 in the table. The BLEU score is 8.19, which is higher than 8.0. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3852.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: BLEU and Forward PPL of VAE with varying KL weights on the PTB test set. The larger↑ (or lower↓), the better." ], "reasoning": "Check the BLEU score corresponding to a KL-Weight of 1.0 in the table. The BLEU score is 8.19, which is higher than 8.0.", "statement": "A KL-Weight of 1.0 results in a BLEU score higher than 8.0.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Reverse PPL reflect the diversity and fluency of sampling data, the lower↓, the better. Training on the model sampled and evaluated on the real test set. We set the same KL weight for DSS-VAE and VAE here.(KL weight=1.0)'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "DSS-VAE achieves a Reverse PPL slightly better than PRPN-LM, and both DSS-VAE and PRPN outperform VAE and LSTM-LM in sentence generation.Results show that DSS-VAE has achieved a Reverse PPL comparable to (and slightly better than) PRPN-LM. It is also seen that explicitly modeling syntactic structures does yield better generation results—DSS-VAE and PRPN consistently outperform VAE and LSTM-LM in sentence generation." } ], "images": [ "scigen/train/3853.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Reverse PPL reflect the diversity and fluency of sampling data, the lower↓, the better. Training on the model sampled and evaluated on the real test set. We set the same KL weight for DSS-VAE and VAE here.(KL weight=1.0)" ], "reasoning": "DSS-VAE achieves a Reverse PPL slightly better than PRPN-LM, and both DSS-VAE and PRPN outperform VAE and LSTM-LM in sentence generation.", "description": "Results show that DSS-VAE has achieved a Reverse PPL comparable to (and slightly better than) PRPN-LM. It is also seen that explicitly modeling syntactic structures does yield better generation results—DSS-VAE and PRPN consistently outperform VAE and LSTM-LM in sentence generation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: Reverse PPL reflect the diversity and fluency of sampling data, the lower↓, the better. Training on the model sampled and evaluated on the real test set. We set the same KL weight for DSS-VAE and VAE here.(KL weight=1.0)'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich model has the closest Reverse PPL to DSS-VAE?\n" }, { "role": "assistant", "content": "By comparing the Reverse PPL values, PRPN-LM has a Reverse PPL of 116.67, which is closest to DSS-VAE's 116.23. So, the answer is {\"answer\": \"PRPN-LM\"}." } ], "images": [ "scigen/train/3853.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Reverse PPL reflect the diversity and fluency of sampling data, the lower↓, the better. Training on the model sampled and evaluated on the real test set. We set the same KL weight for DSS-VAE and VAE here.(KL weight=1.0)" ], "reasoning": "By comparing the Reverse PPL values, PRPN-LM has a Reverse PPL of 116.67, which is closest to DSS-VAE's 116.23.", "question": "Which model has the closest Reverse PPL to DSS-VAE?", "answer": "So, the answer is {\"answer\": \"PRPN-LM\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: DSS-VAE has a better Reverse PPL than LSTM-LM., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nDSS-VAE has a better Reverse PPL than LSTM-LM.\n" }, { "role": "assistant", "content": "The table shows that DSS-VAE has a Reverse PPL of 116.23, which is better than LSTM-LM's Reverse PPL of 132.46. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3853.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Reverse PPL reflect the diversity and fluency of sampling data, the lower↓, the better. Training on the model sampled and evaluated on the real test set. We set the same KL weight for DSS-VAE and VAE here.(KL weight=1.0)" ], "reasoning": "The table shows that DSS-VAE has a Reverse PPL of 116.23, which is better than LSTM-LM's Reverse PPL of 132.46.", "statement": "DSS-VAE has a better Reverse PPL than LSTM-LM.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Performance of paraphrase generation. The larger↑ (or lower↓), the better. Some results are quoted from †MH and ‡vae_svg_eq.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "DSS-VAE outperforms CGMH and VAE in both BLEU-ref and BLEU-ori, indicating its superiority in unsupervised paraphrase generation.We see that DSS-VAE outperforms the CGMH and the original VAE in BLEU-ref. Especially, DSS-VAE achieves a closer BLEU-ref compared with supervised paraphrase methods Gupta et al. It is seen that the line of DSS-VAE is located to the upper-left of the competing methods. In other words, the plain VAE and CGMH are “inadmissible,” meaning that DSS-VAE simultaneously outperforms them in both BLEU-ori and BLEU-ref, indicating that DSS-VAE outperforms previous state-of-the-art methods in unsupervised paraphrase generation." } ], "images": [ "scigen/train/3854.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance of paraphrase generation. The larger↑ (or lower↓), the better. Some results are quoted from †MH and ‡vae_svg_eq." ], "reasoning": "DSS-VAE outperforms CGMH and VAE in both BLEU-ref and BLEU-ori, indicating its superiority in unsupervised paraphrase generation.", "description": "We see that DSS-VAE outperforms the CGMH and the original VAE in BLEU-ref. Especially, DSS-VAE achieves a closer BLEU-ref compared with supervised paraphrase methods Gupta et al. It is seen that the line of DSS-VAE is located to the upper-left of the competing methods. In other words, the plain VAE and CGMH are “inadmissible,” meaning that DSS-VAE simultaneously outperforms them in both BLEU-ori and BLEU-ref, indicating that DSS-VAE outperforms previous state-of-the-art methods in unsupervised paraphrase generation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Performance of paraphrase generation. The larger↑ (or lower↓), the better. Some results are quoted from †MH and ‡vae_svg_eq.', unfold the question presented below, detailing every step of your calculation. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model has the highest BLEU-ref score among the unsupervised methods?\n" }, { "role": "assistant", "content": "The table shows BLEU-ref scores for different models. Among the unsupervised methods, DSS-VAE has a BLEU-ref score of 20.54, which is higher than VAE's 9.25 and CGMH's 18.85. So, the answer is {\"answer\": \"DSS-VAE\"}" } ], "images": [ "scigen/train/3854.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance of paraphrase generation. The larger↑ (or lower↓), the better. Some results are quoted from †MH and ‡vae_svg_eq." ], "reasoning": "The table shows BLEU-ref scores for different models. Among the unsupervised methods, DSS-VAE has a BLEU-ref score of 20.54, which is higher than VAE's 9.25 and CGMH's 18.85.", "question": "Which model has the highest BLEU-ref score among the unsupervised methods?", "answer": "So, the answer is {\"answer\": \"DSS-VAE\"}" }, { "messages": [ { "role": "user", "content": "DSS-VAE has a higher BLEU-ori score than CGMH. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nDSS-VAE has a higher BLEU-ori score than CGMH.\n" }, { "role": "assistant", "content": "The table shows that DSS-VAE has a BLEU-ori score of 52.77, while CGMH has a BLEU-ori score of 50.18. Therefore, DSS-VAE has a higher BLEU-ori score than CGMH. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3854.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance of paraphrase generation. The larger↑ (or lower↓), the better. Some results are quoted from †MH and ‡vae_svg_eq." ], "reasoning": "The table shows that DSS-VAE has a BLEU-ori score of 52.77, while CGMH has a BLEU-ori score of 50.18. Therefore, DSS-VAE has a higher BLEU-ori score than CGMH.", "statement": "DSS-VAE has a higher BLEU-ori score than CGMH.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: The hyper-parameters we used in PTB dataset', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The model is saved every 500 batches if it achieves a lower ELBO on the validation set.Every 500 batch, we save the model if it achieves a lower evidence lower bound (ELBO) on the validation set." } ], "images": [ "scigen/train/3855.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The hyper-parameters we used in PTB dataset" ], "reasoning": "The model is saved every 500 batches if it achieves a lower ELBO on the validation set.", "description": "Every 500 batch, we save the model if it achieves a lower evidence lower bound (ELBO) on the validation set.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 5: The hyper-parameters we used in PTB dataset\nQuestion: What is the batch size used for training the model?\n" }, { "role": "assistant", "content": "The table lists various hyperparameters and their values. The batch size is specified directly in the table. So, the answer is {\"answer\": \"32\"}." } ], "images": [ "scigen/train/3855.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The hyper-parameters we used in PTB dataset" ], "reasoning": "The table lists various hyperparameters and their values. The batch size is specified directly in the table.", "question": "What is the batch size used for training the model?", "answer": "So, the answer is {\"answer\": \"32\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe GRU dropout rate is set to 0.1.\n" }, { "role": "assistant", "content": "The table lists the GRU dropout rate under the hyperparameters, and it is indeed set to 0.1. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3855.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The hyper-parameters we used in PTB dataset" ], "reasoning": "The table lists the GRU dropout rate under the hyperparameters, and it is indeed set to 0.1.", "statement": "The GRU dropout rate is set to 0.1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: The hyper-parameters we used in PTB dataset'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The model is saved every 500 batches if it achieves a lower ELBO on the validation set.Every 500 batch, we save the model if it achieves a lower evidence lower bound (ELBO) on the validation set." } ], "images": [ "scigen/train/3856.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The hyper-parameters we used in PTB dataset" ], "reasoning": "The model is saved every 500 batches if it achieves a lower ELBO on the validation set.", "description": "Every 500 batch, we save the model if it achieves a lower evidence lower bound (ELBO) on the validation set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: The hyper-parameters we used in PTB dataset'.\n\nQuestion: What is the value of the hyperparameter λrec syn?\n" }, { "role": "assistant", "content": "To find the value of the hyperparameter λrec syn, look at the corresponding row in the table under the 'Value' column. So, the answer is {\"answer\": \"0.05\"}" } ], "images": [ "scigen/train/3856.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The hyper-parameters we used in PTB dataset" ], "reasoning": "To find the value of the hyperparameter λrec syn, look at the corresponding row in the table under the 'Value' column.", "question": "What is the value of the hyperparameter λrec syn?", "answer": "So, the answer is {\"answer\": \"0.05\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The batch size used is 50. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe batch size used is 50.\n" }, { "role": "assistant", "content": "The table lists the batch size as 50 in the 'Value' column. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3856.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The hyper-parameters we used in PTB dataset" ], "reasoning": "The table lists the batch size as 50 in the 'Value' column.", "statement": "The batch size used is 50.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 12. Evaluation of the impact of entity dummification and relation encoding in relation extraction, all using PubMedBERT. With entity dummification, the entity mentions in question are anonymized using entity type tags such as $DRUG or $GENE. With entity marker, special tags marking the start and end of an entity are appended to the entity mentions in question. Relation encoding is derived from the special [CLS] token appended to the beginning of the text or the special entity start token, or by concatenating the contextual representation of the entity mentions in question.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Entity markers prevent overfitting while preserving useful entity information, leading to better results in both datasets.Simply using the original text indeed exposes the neural methods to significant overfitting risk. Using [CLS] with the original text is the worst choice, as the relation encoding has a hard time to distinguish which entities in the text span are in question. Dummification remains the most reliable method, which works for either relation encoding method. Interestingly, using entity markers leads to slightly better results in both datasets, as it appears to prevent overfitting while preserving useful entity information. We leave it to future work to study whether this would generalize to all relation extraction tasks." } ], "images": [ "scigen/train/3857.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 12. Evaluation of the impact of entity dummification and relation encoding in relation extraction, all using PubMedBERT. With entity dummification, the entity mentions in question are anonymized using entity type tags such as $DRUG or $GENE. With entity marker, special tags marking the start and end of an entity are appended to the entity mentions in question. Relation encoding is derived from the special [CLS] token appended to the beginning of the text or the special entity start token, or by concatenating the contextual representation of the entity mentions in question." ], "reasoning": "Entity markers prevent overfitting while preserving useful entity information, leading to better results in both datasets.", "description": "Simply using the original text indeed exposes the neural methods to significant overfitting risk. Using [CLS] with the original text is the worst choice, as the relation encoding has a hard time to distinguish which entities in the text span are in question. Dummification remains the most reliable method, which works for either relation encoding method. Interestingly, using entity markers leads to slightly better results in both datasets, as it appears to prevent overfitting while preserving useful entity information. We leave it to future work to study whether this would generalize to all relation extraction tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 12. Evaluation of the impact of entity dummification and relation encoding in relation extraction, all using PubMedBERT. With entity dummification, the entity mentions in question are anonymized using entity type tags such as $DRUG or $GENE. With entity marker, special tags marking the start and end of an entity are appended to the entity mentions in question. Relation encoding is derived from the special [CLS] token appended to the beginning of the text or the special entity start token, or by concatenating the contextual representation of the entity mentions in question.', solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich classification encoding method achieved the highest DDI score?\n" }, { "role": "assistant", "content": "By examining the table, the highest DDI score is 82.42, achieved by the 'ENTITY MARKERS' input text with 'MENTION' classification encoding. So, the answer is {\"answer\": \"ENTITY MARKERS with MENTION\"}" } ], "images": [ "scigen/train/3857.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 12. Evaluation of the impact of entity dummification and relation encoding in relation extraction, all using PubMedBERT. With entity dummification, the entity mentions in question are anonymized using entity type tags such as $DRUG or $GENE. With entity marker, special tags marking the start and end of an entity are appended to the entity mentions in question. Relation encoding is derived from the special [CLS] token appended to the beginning of the text or the special entity start token, or by concatenating the contextual representation of the entity mentions in question." ], "reasoning": "By examining the table, the highest DDI score is 82.42, achieved by the 'ENTITY MARKERS' input text with 'MENTION' classification encoding.", "question": "Which classification encoding method achieved the highest DDI score?", "answer": "So, the answer is {\"answer\": \"ENTITY MARKERS with MENTION\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nUsing the original text with [CLS] classification encoding achieved the lowest ChemProt score.\n" }, { "role": "assistant", "content": "The table shows that the original text with [CLS] classification encoding has a ChemProt score of 50.52, which is the lowest among all the scores listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3857.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 12. Evaluation of the impact of entity dummification and relation encoding in relation extraction, all using PubMedBERT. With entity dummification, the entity mentions in question are anonymized using entity type tags such as $DRUG or $GENE. With entity marker, special tags marking the start and end of an entity are appended to the entity mentions in question. Relation encoding is derived from the special [CLS] token appended to the beginning of the text or the special entity start token, or by concatenating the contextual representation of the entity mentions in question." ], "reasoning": "The table shows that the original text with [CLS] classification encoding has a ChemProt score of 50.52, which is the lowest among all the scores listed.", "statement": "Using the original text with [CLS] classification encoding achieved the lowest ChemProt score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3. Datasets used in the BLURB biomedical NLP benchmark. We list the numbers of instances in train, dev, and test (e.g., entity mentions in NER and PICO elements in evidence-based medical information extraction).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "BLURB includes various biomedical NLP tasks, focusing on classification tasks like yes/no question-answering, while leaving more complex question-answering for future work.BLURB is comprised of a comprehensive set of biomedical NLP tasks from publicly available datasets, including named entity recognition (NER), evidence-based medical information extraction (PICO), relation extraction, sentence similarity, document classification, and question answering. The latter types often require additional engineering effort that are not relevant to evaluating neural language models. For simplicity, we focus on the classification tasks such as yes/ no question-answering in BLURB, and leave the inclusion of more complex question-answering to future work." } ], "images": [ "scigen/train/3858.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. Datasets used in the BLURB biomedical NLP benchmark. We list the numbers of instances in train, dev, and test (e.g., entity mentions in NER and PICO elements in evidence-based medical information extraction)." ], "reasoning": "BLURB includes various biomedical NLP tasks, focusing on classification tasks like yes/no question-answering, while leaving more complex question-answering for future work.", "description": "BLURB is comprised of a comprehensive set of biomedical NLP tasks from publicly available datasets, including named entity recognition (NER), evidence-based medical information extraction (PICO), relation extraction, sentence similarity, document classification, and question answering. The latter types often require additional engineering effort that are not relevant to evaluating neural language models. For simplicity, we focus on the classification tasks such as yes/ no question-answering in BLURB, and leave the inclusion of more complex question-answering to future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3. Datasets used in the BLURB biomedical NLP benchmark. We list the numbers of instances in train, dev, and test (e.g., entity mentions in NER and PICO elements in evidence-based medical information extraction).' and methodically tackle the question that follows. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich task has the highest number of training instances?\n" }, { "role": "assistant", "content": "By examining the 'Train' column, the EBM PICO task has the highest number of training instances with 339,167. So, the answer is {\"answer\": \"EBM PICO\"}" } ], "images": [ "scigen/train/3858.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. Datasets used in the BLURB biomedical NLP benchmark. We list the numbers of instances in train, dev, and test (e.g., entity mentions in NER and PICO elements in evidence-based medical information extraction)." ], "reasoning": "By examining the 'Train' column, the EBM PICO task has the highest number of training instances with 339,167.", "question": "Which task has the highest number of training instances?", "answer": "So, the answer is {\"answer\": \"EBM PICO\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The NCBI-disease task uses the F1 entity-level evaluation metric., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe NCBI-disease task uses the F1 entity-level evaluation metric.\n" }, { "role": "assistant", "content": "The table shows that the NCBI-disease task is evaluated using the F1 entity-level metric. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3858.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. Datasets used in the BLURB biomedical NLP benchmark. We list the numbers of instances in train, dev, and test (e.g., entity mentions in NER and PICO elements in evidence-based medical information extraction)." ], "reasoning": "The table shows that the NCBI-disease task is evaluated using the F1 entity-level metric.", "statement": "The NCBI-disease task uses the F1 entity-level evaluation metric.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 7. Evaluation of the impact of vocabulary and whole word masking on the performance of PubMedBERT on BLURB.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Using an in-domain vocabulary from PubMed yields better results than using the original BERT vocabulary, and WWM consistently improves performance.To assess the impact of pretraining options on downstream applications, we conduct several ablation studies using PubMedBERT as a running example. Using the original BERT vocabulary derived from Wikipedia & BookCorpus (by continual pretraining from the original BERT), the results are significantly worse than using an in-domain vocabulary from PubMed. Additionally, WWM leads to consistent improvement across the board, regardless of the vocabulary in use." } ], "images": [ "scigen/train/3859.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7. Evaluation of the impact of vocabulary and whole word masking on the performance of PubMedBERT on BLURB." ], "reasoning": "Using an in-domain vocabulary from PubMed yields better results than using the original BERT vocabulary, and WWM consistently improves performance.", "description": "To assess the impact of pretraining options on downstream applications, we conduct several ablation studies using PubMedBERT as a running example. Using the original BERT vocabulary derived from Wikipedia & BookCorpus (by continual pretraining from the original BERT), the results are significantly worse than using an in-domain vocabulary from PubMed. Additionally, WWM leads to consistent improvement across the board, regardless of the vocabulary in use.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 7. Evaluation of the impact of vocabulary and whole word masking on the performance of PubMedBERT on BLURB.', and tackle the question below in a sequential manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich pretraining option yields the highest score for the DDI task?\n" }, { "role": "assistant", "content": "To find the highest score for the DDI task, compare the scores across all columns for the DDI row. The highest score is 84.60 in the 'PubMed Whole Word' column. So, the answer is {\"answer\": \"84.60\"}" } ], "images": [ "scigen/train/3859.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7. Evaluation of the impact of vocabulary and whole word masking on the performance of PubMedBERT on BLURB." ], "reasoning": "To find the highest score for the DDI task, compare the scores across all columns for the DDI row. The highest score is 84.60 in the 'PubMed Whole Word' column.", "question": "Which pretraining option yields the highest score for the DDI task?", "answer": "So, the answer is {\"answer\": \"84.60\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Using PubMed Whole Word pretraining yields the highest BLURB score., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nUsing PubMed Whole Word pretraining yields the highest BLURB score.\n" }, { "role": "assistant", "content": "To verify this statement, compare the BLURB scores across all columns. The highest BLURB score is 81.35 in the 'PubMed Whole Word' column. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3859.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7. Evaluation of the impact of vocabulary and whole word masking on the performance of PubMedBERT on BLURB." ], "reasoning": "To verify this statement, compare the BLURB scores across all columns. The highest BLURB score is 81.35 in the 'PubMed Whole Word' column.", "statement": "Using PubMed Whole Word pretraining yields the highest BLURB score.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 9. Comparison of PubMedBERT performance on BLURB using standard and adversarial pretraining.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Adversarial pretraining generally leads to slight performance degradation, except in tasks like sentence similarity (BIOSSES).Adversarial pretraining has been shown to be highly effective in boosting performance in general-domain applications Surprisingly, adversarial pretraining generally leads to a slight degradation in performance, with some exceptions such as sentence similarity (BIOSSES). We hypothesize that the reason may be similar to what we observe in pretraining with full texts. Namely, adversarial training is most useful if the pretraining corpus is more diverse and relatively out-domain compared to the application tasks. We leave a more thorough evaluation of adversarial pretraining to future work." } ], "images": [ "scigen/train/3860.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 9. Comparison of PubMedBERT performance on BLURB using standard and adversarial pretraining." ], "reasoning": "Adversarial pretraining generally leads to slight performance degradation, except in tasks like sentence similarity (BIOSSES).", "description": "Adversarial pretraining has been shown to be highly effective in boosting performance in general-domain applications Surprisingly, adversarial pretraining generally leads to a slight degradation in performance, with some exceptions such as sentence similarity (BIOSSES). We hypothesize that the reason may be similar to what we observe in pretraining with full texts. Namely, adversarial training is most useful if the pretraining corpus is more diverse and relatively out-domain compared to the application tasks. We leave a more thorough evaluation of adversarial pretraining to future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 9. Comparison of PubMedBERT performance on BLURB using standard and adversarial pretraining.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}. \n\nQuestion: Which task showed the highest improvement in performance with adversarial pretraining?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By comparing the scores in the '+ adversarial' column with the 'PubMedBERT' column, the task with the highest improvement is identified. So, the answer is {\"answer\": \"BIOSSES\"}." } ], "images": [ "scigen/train/3860.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 9. Comparison of PubMedBERT performance on BLURB using standard and adversarial pretraining." ], "reasoning": "By comparing the scores in the '+ adversarial' column with the 'PubMedBERT' column, the task with the highest improvement is identified.", "question": "Which task showed the highest improvement in performance with adversarial pretraining?", "answer": "So, the answer is {\"answer\": \"BIOSSES\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nAdversarial pretraining improved the performance of the DDI task.\n" }, { "role": "assistant", "content": "The table shows that the DDI task score increased from 82.36 to 83.62 with adversarial pretraining. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3860.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 9. Comparison of PubMedBERT performance on BLURB using standard and adversarial pretraining." ], "reasoning": "The table shows that the DDI task score increased from 82.36 to 83.62 with adversarial pretraining.", "statement": "Adversarial pretraining improved the performance of the DDI task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 10. Comparison of linear layers vs recurrent neural networks for task-specific fine-tuning in named entity recognition (entity-level F1) and relation extraction (micro F1), all using the standard PubMedBERT.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The BERT model's top layer captures many non-linear dependencies, making a linear layer competitive with a Bi-LSTM.With the advent of BERT models and the self-attention mechanism, the utility of explicit sequential modeling becomes questionable. The top layer in the BERT model already captures many non-linear dependencies across the entire text span. Therefore, it’s conceivable that even a linear layer on top can perform competitively. The use of a bidirectional LSTM (Bi-LSTM) does not lead to any gain compared to linear layer." } ], "images": [ "scigen/train/3861.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10. Comparison of linear layers vs recurrent neural networks for task-specific fine-tuning in named entity recognition (entity-level F1) and relation extraction (micro F1), all using the standard PubMedBERT." ], "reasoning": "The BERT model's top layer captures many non-linear dependencies, making a linear layer competitive with a Bi-LSTM.", "description": "With the advent of BERT models and the self-attention mechanism, the utility of explicit sequential modeling becomes questionable. The top layer in the BERT model already captures many non-linear dependencies across the entire text span. Therefore, it’s conceivable that even a linear layer on top can perform competitively. The use of a bidirectional LSTM (Bi-LSTM) does not lead to any gain compared to linear layer.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 10. Comparison of linear layers vs recurrent neural networks for task-specific fine-tuning in named entity recognition (entity-level F1) and relation extraction (micro F1), all using the standard PubMedBERT.'\nQuestion: Which task-specific model shows the largest difference in performance between the Linear Layer and Bi-LSTM?\n" }, { "role": "assistant", "content": "By comparing the scores for each task-specific model, the largest difference is observed in the 0.05pt1.5pt1.5pt ChemProt model, where the Linear Layer scores 77.24 and the Bi-LSTM scores 75.40. So, the answer is {\"answer\": \"0.05pt1.5pt1.5pt ChemProt\"}" } ], "images": [ "scigen/train/3861.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10. Comparison of linear layers vs recurrent neural networks for task-specific fine-tuning in named entity recognition (entity-level F1) and relation extraction (micro F1), all using the standard PubMedBERT." ], "reasoning": "By comparing the scores for each task-specific model, the largest difference is observed in the 0.05pt1.5pt1.5pt ChemProt model, where the Linear Layer scores 77.24 and the Bi-LSTM scores 75.40.", "question": "Which task-specific model shows the largest difference in performance between the Linear Layer and Bi-LSTM?", "answer": "So, the answer is {\"answer\": \"0.05pt1.5pt1.5pt ChemProt\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Linear Layer outperforms the Bi-LSTM in all task-specific models.\n" }, { "role": "assistant", "content": "By examining the table, the Linear Layer has higher scores than the Bi-LSTM in all task-specific models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3861.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10. Comparison of linear layers vs recurrent neural networks for task-specific fine-tuning in named entity recognition (entity-level F1) and relation extraction (micro F1), all using the standard PubMedBERT." ], "reasoning": "By examining the table, the Linear Layer has higher scores than the Bi-LSTM in all task-specific models listed.", "statement": "The Linear Layer outperforms the Bi-LSTM in all task-specific models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 11. Comparison of entity-level F1 for biomedical named entity recognition (NER) using different tagging schemes and the standard PubMedBERT.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The comparison of tagging schemes (BIO, BIOUL, IO) for three biomedical NER tasks (BC5-chem, BC5-disease, JNLPBA) shows that the BIO scheme performs best for BC5-chem, while the IO scheme performs best for BC5-disease and JNLPBA.We also investigate the tagging scheme used in NER. The standard tagging scheme distinguishes words by their positions within an entity. For sequential tagging methods such as CRF and LSTM, distinguishing the position within an entity is potentially advantageous compared to the minimal IO scheme that only distinguishes between inside and outside of entities. But for BERT models, once again, the utility of more complex tagging schemes is diminished. We thus conducted a head-to-head comparison of the tagging schemes using three biomedical NER tasks in BLURB." } ], "images": [ "scigen/train/3862.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11. Comparison of entity-level F1 for biomedical named entity recognition (NER) using different tagging schemes and the standard PubMedBERT." ], "reasoning": "The comparison of tagging schemes (BIO, BIOUL, IO) for three biomedical NER tasks (BC5-chem, BC5-disease, JNLPBA) shows that the BIO scheme performs best for BC5-chem, while the IO scheme performs best for BC5-disease and JNLPBA.", "description": "We also investigate the tagging scheme used in NER. The standard tagging scheme distinguishes words by their positions within an entity. For sequential tagging methods such as CRF and LSTM, distinguishing the position within an entity is potentially advantageous compared to the minimal IO scheme that only distinguishes between inside and outside of entities. But for BERT models, once again, the utility of more complex tagging schemes is diminished. We thus conducted a head-to-head comparison of the tagging schemes using three biomedical NER tasks in BLURB.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: Table 11. Comparison of entity-level F1 for biomedical named entity recognition (NER) using different tagging schemes and the standard PubMedBERT.\nQuestion: Which tagging scheme performed best for the BC5-disease task?\n" }, { "role": "assistant", "content": "The table shows the performance of three tagging schemes (BIO, BIOUL, IO) for the BC5-disease task. The highest score for BC5-disease is 86.92, which corresponds to the IO tagging scheme. So, the answer is {\"answer\": \"IO\"}." } ], "images": [ "scigen/train/3862.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11. Comparison of entity-level F1 for biomedical named entity recognition (NER) using different tagging schemes and the standard PubMedBERT." ], "reasoning": "The table shows the performance of three tagging schemes (BIO, BIOUL, IO) for the BC5-disease task. The highest score for BC5-disease is 86.92, which corresponds to the IO tagging scheme.", "question": "Which tagging scheme performed best for the BC5-disease task?", "answer": "So, the answer is {\"answer\": \"IO\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The BIO tagging scheme performed best for the JNLPBA task. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe BIO tagging scheme performed best for the JNLPBA task.\n" }, { "role": "assistant", "content": "The table shows the performance of three tagging schemes (BIO, BIOUL, IO) for the JNLPBA task. The highest score for JNLPBA is 82.23, which corresponds to the IO tagging scheme, not BIO. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3862.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11. Comparison of entity-level F1 for biomedical named entity recognition (NER) using different tagging schemes and the standard PubMedBERT." ], "reasoning": "The table shows the performance of three tagging schemes (BIO, BIOUL, IO) for the JNLPBA task. The highest score for JNLPBA is 82.23, which corresponds to the IO tagging scheme, not BIO.", "statement": "The BIO tagging scheme performed best for the JNLPBA task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 1: Data used for training the CNN model.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The neural network was trained on a large set of tweets for word embeddings and a distant supervision corpus with emoticons for weak labels, followed by supervised training on labeled data.Training. During the first distant-supervised phase, we use emoticons to infer noisy labels on tweets in the training set [ Note that the data used for this pre-training phase do not necessarily overlap with the data used to create the word embeddings. The neural network was trained on these datasets for one epoch, before finally training on the supervised data for about 20 epochs. The word embeddings X∈Rd×n are updated during both the distant- and supervised training phases by applying back-propagation through the entire network. We used a large set of 300M tweets to create the word embeddings for each language, as well as a distant supervision corpus of 40-60M tweets for each language, where each tweet contained at least one emoticon (positive or negative smiley). Smileys were used to automatically infer weak labels and subsequently removed from the tweets. The German corpus was newly created by the authors and is available at http://spinningbytes.com/resources. In our experiments, we compare the performance of the following sentiment analysis systems: Random forest (RF) as a common baseline classifier. In a set of experiments, the amount of training in the three phases is gradually reduced. The system using all available training data for one language is also referred to as ’fully-trained CNN’ Multi-language CNN (ML-CNN), where the distant-supervised phase is performed jointly for all languages at once, and the final supervised phase independently for each language. The pre-training data is the same as in ML-CNN SemEval benchmark. It then uses the SL-CNN classifier trained for the target language to classify the tweets For French, German and Italian, we created a validation set by randomly sampling 10% of the data. For English we used the test2015 set as validation set and test2016 for testing from the SemEval-2016 challenge, see Validation set in Table" } ], "images": [ "scigen/train/3863.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Data used for training the CNN model." ], "reasoning": "The neural network was trained on a large set of tweets for word embeddings and a distant supervision corpus with emoticons for weak labels, followed by supervised training on labeled data.", "description": "Training. During the first distant-supervised phase, we use emoticons to infer noisy labels on tweets in the training set [ Note that the data used for this pre-training phase do not necessarily overlap with the data used to create the word embeddings. The neural network was trained on these datasets for one epoch, before finally training on the supervised data for about 20 epochs. The word embeddings X∈Rd×n are updated during both the distant- and supervised training phases by applying back-propagation through the entire network. We used a large set of 300M tweets to create the word embeddings for each language, as well as a distant supervision corpus of 40-60M tweets for each language, where each tweet contained at least one emoticon (positive or negative smiley). Smileys were used to automatically infer weak labels and subsequently removed from the tweets. The German corpus was newly created by the authors and is available at http://spinningbytes.com/resources. In our experiments, we compare the performance of the following sentiment analysis systems: Random forest (RF) as a common baseline classifier. In a set of experiments, the amount of training in the three phases is gradually reduced. The system using all available training data for one language is also referred to as ’fully-trained CNN’ Multi-language CNN (ML-CNN), where the distant-supervised phase is performed jointly for all languages at once, and the final supervised phase independently for each language. The pre-training data is the same as in ML-CNN SemEval benchmark. It then uses the SL-CNN classifier trained for the target language to classify the tweets For French, German and Italian, we created a validation set by randomly sampling 10% of the data. For English we used the test2015 set as validation set and test2016 for testing from the SemEval-2016 challenge, see Validation set in Table", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 1: Data used for training the CNN model.'. Solve the following question based on the table.\n\nQuestion: How many positive tweets were there in the German training dataset?\nLet's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the number of positive tweets in the German training dataset, look at the 'Pos.' column for the German language and the Training dataset row. So, the answer is {\"answer\": \"2193\"}" } ], "images": [ "scigen/train/3863.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Data used for training the CNN model." ], "reasoning": "To find the number of positive tweets in the German training dataset, look at the 'Pos.' column for the German language and the Training dataset row.", "question": "How many positive tweets were there in the German training dataset?", "answer": "So, the answer is {\"answer\": \"2193\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe English validation set contains more positive tweets than the Italian test set.\n" }, { "role": "assistant", "content": "Compare the number of positive tweets in the English validation set (1038) with the number of positive tweets in the Italian test set (177). The English validation set has more positive tweets. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3863.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Data used for training the CNN model." ], "reasoning": "Compare the number of positive tweets in the English validation set (1038) with the number of positive tweets in the Italian test set (177). The English validation set has more positive tweets.", "statement": "The English validation set contains more positive tweets than the Italian test set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: F1-scores of compared methods on the test sets. The highest scores among the three proposed models are highlighted in bold face. ML-CNN and FML-CNN are two variants of the method presented in Section 5.1.'.\n" }, { "role": "assistant", "content": "The SL-CNN method achieved the highest F1-scores across all four languages, with the best performance in Italian (67.79%) and the lowest in English (62.26%).The fully-trained SL-CNNs significantly outperforms the other methods in all four languages. The best F1-score was achieved for Italian (67.79%), followed by German (65.09%) and French (64.79%), while the system for English reached only 62.26%. The proposed SL-CNNs outperform the corresponding baselines from literature and RF." } ], "images": [ "scigen/train/3864.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: F1-scores of compared methods on the test sets. The highest scores among the three proposed models are highlighted in bold face. ML-CNN and FML-CNN are two variants of the method presented in Section 5.1." ], "reasoning": "The SL-CNN method achieved the highest F1-scores across all four languages, with the best performance in Italian (67.79%) and the lowest in English (62.26%).", "description": "The fully-trained SL-CNNs significantly outperforms the other methods in all four languages. The best F1-score was achieved for Italian (67.79%), followed by German (65.09%) and French (64.79%), while the system for English reached only 62.26%. The proposed SL-CNNs outperform the corresponding baselines from literature and RF.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: F1-scores of compared methods on the test sets. The highest scores among the three proposed models are highlighted in bold face. ML-CNN and FML-CNN are two variants of the method presented in Section 5.1.', solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich method achieved the highest F1-score for the German language?\n" }, { "role": "assistant", "content": "By comparing the F1-scores for the German language across all methods, SL-CNN achieved the highest score of 65.09%. So, the answer is {\"answer\": \"SL-CNN\"}." } ], "images": [ "scigen/train/3864.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: F1-scores of compared methods on the test sets. The highest scores among the three proposed models are highlighted in bold face. ML-CNN and FML-CNN are two variants of the method presented in Section 5.1." ], "reasoning": "By comparing the F1-scores for the German language across all methods, SL-CNN achieved the highest score of 65.09%.", "question": "Which method achieved the highest F1-score for the German language?", "answer": "So, the answer is {\"answer\": \"SL-CNN\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The SL-CNN method achieved an F1-score of 63.49% for the English language. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe SL-CNN method achieved an F1-score of 63.49% for the English language.\n" }, { "role": "assistant", "content": "The table shows that the SL-CNN method achieved an F1-score of 63.49% for the English language, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3864.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: F1-scores of compared methods on the test sets. The highest scores among the three proposed models are highlighted in bold face. ML-CNN and FML-CNN are two variants of the method presented in Section 5.1." ], "reasoning": "The table shows that the SL-CNN method achieved an F1-score of 63.49% for the English language, which matches the statement.", "statement": "The SL-CNN method achieved an F1-score of 63.49% for the English language.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Summary of the network parameters used for experimental results. L2 is the default architecture used by our approach. The alternative L1 and L3 architectures are discussed in Figure 8.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The performance of neural networks generally improves with the number of layers, as shown by evaluating SL-CNN on the English set with 1 to 3 convolutional/pooling layer pairs.on the English corpus as a function of the number of network layers. Each CNN was trained for one distant epoch. Comparing Network Architectures. One common question asked by practitioners relates to the influence of the number of layers on the performance of a neural network. We thus evaluated the performance of various architectures with multiple layers. In order to reduce the number of experiments, we evaluated the performance of the single-language model SL-CNN on the English set and varied the number of convolutional/pooling layer pairs from 1 to 3. We evaluated a total of 12 networks. The network performance generally improves with the number of layers, if a sufficient amount of training data is used in the distant-supervised phase." } ], "images": [ "scigen/train/3865.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Summary of the network parameters used for experimental results. L2 is the default architecture used by our approach. The alternative L1 and L3 architectures are discussed in Figure 8." ], "reasoning": "The performance of neural networks generally improves with the number of layers, as shown by evaluating SL-CNN on the English set with 1 to 3 convolutional/pooling layer pairs.", "description": "on the English corpus as a function of the number of network layers. Each CNN was trained for one distant epoch. Comparing Network Architectures. One common question asked by practitioners relates to the influence of the number of layers on the performance of a neural network. We thus evaluated the performance of various architectures with multiple layers. In order to reduce the number of experiments, we evaluated the performance of the single-language model SL-CNN on the English set and varied the number of convolutional/pooling layer pairs from 1 to 3. We evaluated a total of 12 networks. The network performance generally improves with the number of layers, if a sufficient amount of training data is used in the distant-supervised phase.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. The question is related to the given table titled 'Table 3: Summary of the network parameters used for experimental results. L2 is the default architecture used by our approach. The alternative L1 and L3 architectures are discussed in Figure 8.'.\n\nQuestion: What is the filter window size for the second layer in the network with 3 layers?\n" }, { "role": "assistant", "content": "The table shows the filter window sizes for each layer in networks with different numbers of layers. For the network with 3 layers (L3), the second layer has a filter window size of h2=3. So, the answer is {\"answer\": \"3\"}." } ], "images": [ "scigen/train/3865.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Summary of the network parameters used for experimental results. L2 is the default architecture used by our approach. The alternative L1 and L3 architectures are discussed in Figure 8." ], "reasoning": "The table shows the filter window sizes for each layer in networks with different numbers of layers. For the network with 3 layers (L3), the second layer has a filter window size of h2=3.", "question": "What is the filter window size for the second layer in the network with 3 layers?", "answer": "So, the answer is {\"answer\": \"3\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe network with 2 layers has a max-pooling window size of w1=4 and w2=3.\n" }, { "role": "assistant", "content": "The table indicates that the network with 2 layers (L2) has a max-pooling window size of w1=4, but it does not have a second max-pooling window size (w2). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3865.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Summary of the network parameters used for experimental results. L2 is the default architecture used by our approach. The alternative L1 and L3 architectures are discussed in Figure 8." ], "reasoning": "The table indicates that the network with 2 layers (L2) has a max-pooling window size of w1=4, but it does not have a second max-pooling window size (w2).", "statement": "The network with 2 layers has a max-pooling window size of w1=4 and w2=3.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 4: Translation experiment. We translated each source language to each target language and used the model trained on the target language to predict tweets polarity.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Translating French tweets to English and using the English model yields better F1-scores, while for English, Italian, and German tweets, not translating gives higher F1-scores.Translation Approach. The F1-score is higher when not translating tweets to another language for English, Italian and German. As an exception, we obtained better results when translating French to English and using the English model to predict sentiments." } ], "images": [ "scigen/train/3866.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Translation experiment. We translated each source language to each target language and used the model trained on the target language to predict tweets polarity." ], "reasoning": "Translating French tweets to English and using the English model yields better F1-scores, while for English, Italian, and German tweets, not translating gives higher F1-scores.", "description": "Translation Approach. The F1-score is higher when not translating tweets to another language for English, Italian and German. As an exception, we obtained better results when translating French to English and using the English model to predict sentiments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Translation experiment. We translated each source language to each target language and used the model trained on the target language to predict tweets polarity.\nQuestion: Which language pair has the highest F1-score when translating tweets?\nGive a detailed response to the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, the highest F1-score when translating tweets is for French to English with a score of 65.68. So, the answer is {\"answer\": \"French to English\"}." } ], "images": [ "scigen/train/3866.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Translation experiment. We translated each source language to each target language and used the model trained on the target language to predict tweets polarity." ], "reasoning": "By examining the table, the highest F1-score when translating tweets is for French to English with a score of 65.68.", "question": "Which language pair has the highest F1-score when translating tweets?", "answer": "So, the answer is {\"answer\": \"French to English\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nTranslating Italian tweets to English results in a higher F1-score than not translating.\n" }, { "role": "assistant", "content": "The table shows that the F1-score for Italian tweets translated to English is 64.37, while the F1-score for Italian tweets not translated is 67.79. Therefore, translating Italian tweets to English does not result in a higher F1-score. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3866.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Translation experiment. We translated each source language to each target language and used the model trained on the target language to predict tweets polarity." ], "reasoning": "The table shows that the F1-score for Italian tweets translated to English is 64.37, while the F1-score for Italian tweets not translated is 67.79. Therefore, translating Italian tweets to English does not result in a higher F1-score.", "statement": "Translating Italian tweets to English results in a higher F1-score than not translating.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: Ablation test results of BAG model on the unmasked validation set of the WIKIHOP dataset.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Removing bi-directional attention or using single attention results in a performance drop, highlighting the importance of bi-directional attention and GCN for multi-hop QA.Once we remove the bi-directional attention and put the concatenation of nodes and queries directly into the output layer, it shows significant performance drop with more than 3%, proving the necessity of attention for reasoning in multi-hop QA. If we use linear-transformation-based single attention a=hnWafq given in Luong et al. instead of our bi-directional attention, the accuracy drops with 2%, which means attention bi-directionality also contributes to the performance improvement. The similar condition will appear if we remove GCN, but use raw nodes as input for the attention layer." } ], "images": [ "scigen/train/3867.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Ablation test results of BAG model on the unmasked validation set of the WIKIHOP dataset." ], "reasoning": "Removing bi-directional attention or using single attention results in a performance drop, highlighting the importance of bi-directional attention and GCN for multi-hop QA.", "description": "Once we remove the bi-directional attention and put the concatenation of nodes and queries directly into the output layer, it shows significant performance drop with more than 3%, proving the necessity of attention for reasoning in multi-hop QA. If we use linear-transformation-based single attention a=hnWafq given in Luong et al. instead of our bi-directional attention, the accuracy drops with 2%, which means attention bi-directionality also contributes to the performance improvement. The similar condition will appear if we remove GCN, but use raw nodes as input for the attention layer.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 2: Ablation test results of BAG model on the unmasked validation set of the WIKIHOP dataset.' in mind, break down and resolve the question below step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the accuracy drop when using linear-transformation-based single attention instead of bi-directional attention?\n" }, { "role": "assistant", "content": "The table shows that using single attention results in an accuracy of 64.5, while the full model has an accuracy of 66.5. The drop is 2%. So, the answer is {\"answer\": \"2%\"}." } ], "images": [ "scigen/train/3867.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Ablation test results of BAG model on the unmasked validation set of the WIKIHOP dataset." ], "reasoning": "The table shows that using single attention results in an accuracy of 64.5, while the full model has an accuracy of 66.5. The drop is 2%.", "question": "What is the accuracy drop when using linear-transformation-based single attention instead of bi-directional attention?", "answer": "So, the answer is {\"answer\": \"2%\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nUsing single attention instead of bi-directional attention results in a 2% drop in accuracy.\n" }, { "role": "assistant", "content": "The table shows that using single attention results in an accuracy of 64.5, while the full model has an accuracy of 66.5, indicating a 2% drop. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3867.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Ablation test results of BAG model on the unmasked validation set of the WIKIHOP dataset." ], "reasoning": "The table shows that using single attention results in an accuracy of 64.5, while the full model has an accuracy of 66.5, indicating a 2% drop.", "statement": "Using single attention instead of bi-directional attention results in a 2% drop in accuracy.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Evidence Sentence Retrieval Accuracy. Sentence level Precision, Recall and F1 are evaluated by official evaluation Thorne et al. (2018a).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "BERT sentence retrieval outperforms ESIM in both Dev and Test sets, leading to improved reasoning accuracy for KGAT.The BERT sentence retrieval outperforms ESIM sentence retrieval significantly, thus also helps improve KGAT’s reasoning accuracy. Nevertheless, for more fair comparisons, our following experiments are all based on ESIM sentence retrieval, which is the one used by GEAR, our main baseline Zhou et al." } ], "images": [ "scigen/train/3868.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Evidence Sentence Retrieval Accuracy. Sentence level Precision, Recall and F1 are evaluated by official evaluation Thorne et al. (2018a)." ], "reasoning": "BERT sentence retrieval outperforms ESIM in both Dev and Test sets, leading to improved reasoning accuracy for KGAT.", "description": "The BERT sentence retrieval outperforms ESIM sentence retrieval significantly, thus also helps improve KGAT’s reasoning accuracy. Nevertheless, for more fair comparisons, our following experiments are all based on ESIM sentence retrieval, which is the one used by GEAR, our main baseline Zhou et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: Evidence Sentence Retrieval Accuracy. Sentence level Precision, Recall and F1 are evaluated by official evaluation Thorne et al. (2018a).', solve the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich model has the highest Recall@5 score on the Dev set?\n" }, { "role": "assistant", "content": "By comparing the Recall@5 scores on the Dev set, BERT has a higher score (94.37) compared to ESIM (86.72). So, the answer is {\"answer\": \"BERT\"}." } ], "images": [ "scigen/train/3868.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Evidence Sentence Retrieval Accuracy. Sentence level Precision, Recall and F1 are evaluated by official evaluation Thorne et al. (2018a)." ], "reasoning": "By comparing the Recall@5 scores on the Dev set, BERT has a higher score (94.37) compared to ESIM (86.72).", "question": "Which model has the highest Recall@5 score on the Dev set?", "answer": "So, the answer is {\"answer\": \"BERT\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nBERT has a higher F1@5 score than ESIM on the Test set.\n" }, { "role": "assistant", "content": "By comparing the F1@5 scores on the Test set, BERT has a higher score (39.14) compared to ESIM (36.80). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3868.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Evidence Sentence Retrieval Accuracy. Sentence level Precision, Recall and F1 are evaluated by official evaluation Thorne et al. (2018a)." ], "reasoning": "By comparing the F1@5 scores on the Test set, BERT has a higher score (39.14) compared to ESIM (36.80).", "statement": "BERT has a higher F1@5 score than ESIM on the Test set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Fact Verification Accuracy. The performances of top models during FEVER 1.0 shared task and BERT based models with different scenarios are presented.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "KGAT (BERT Base) outperforms BERT (Base) and BERT (Large) in both Test LA and Test FEVER scenarios.Several testing scenarios are conducted to compare KGAT effectiveness to BERT based baselines: BERT (Base) Encoder with ESIM retrieved sentences, with BERT retrieved sentences, and BERT (Large) Encoder with BERT retrieved sentences." } ], "images": [ "scigen/train/3869.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Fact Verification Accuracy. The performances of top models during FEVER 1.0 shared task and BERT based models with different scenarios are presented." ], "reasoning": "KGAT (BERT Base) outperforms BERT (Base) and BERT (Large) in both Test LA and Test FEVER scenarios.", "description": "Several testing scenarios are conducted to compare KGAT effectiveness to BERT based baselines: BERT (Base) Encoder with ESIM retrieved sentences, with BERT retrieved sentences, and BERT (Large) Encoder with BERT retrieved sentences.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Fact Verification Accuracy. The performances of top models during FEVER 1.0 shared task and BERT based models with different scenarios are presented.', answer the following question in a stepwise manner. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model has the highest Test FEVER score?\n" }, { "role": "assistant", "content": "By comparing the Test FEVER scores of all models, KGAT (BERT Base) has the highest score of 69.40. So, the answer is {\"answer\": \"KGAT (BERT Base)\"}" } ], "images": [ "scigen/train/3869.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Fact Verification Accuracy. The performances of top models during FEVER 1.0 shared task and BERT based models with different scenarios are presented." ], "reasoning": "By comparing the Test FEVER scores of all models, KGAT (BERT Base) has the highest score of 69.40.", "question": "Which model has the highest Test FEVER score?", "answer": "So, the answer is {\"answer\": \"KGAT (BERT Base)\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nKGAT (BERT Base) has a higher Test LA score than BERT (Large) Soleimani et al. (2019).\n" }, { "role": "assistant", "content": "KGAT (BERT Base) has a Test LA score of 72.81, which is higher than the Test LA score of 71.86 for BERT (Large) Soleimani et al. (2019). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3869.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Fact Verification Accuracy. The performances of top models during FEVER 1.0 shared task and BERT based models with different scenarios are presented." ], "reasoning": "KGAT (BERT Base) has a Test LA score of 72.81, which is higher than the Test LA score of 71.86 for BERT (Large) Soleimani et al. (2019).", "statement": "KGAT (BERT Base) has a higher Test LA score than BERT (Large) Soleimani et al. (2019).", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 7: ‘Decorrelated’ cross-AUC results on the BeerAdvocate data, which attempt to mitigate confounding due to overall sentiment being correlated. Each cell reports metrics over subsets of reviews in which the sentiment differs between the row and column aspects. The numbers in each cell are the AUCs w.r.t. sentiment regarding the column aspect achieved using the row and column aspect representations, respectively.'.\n" }, { "role": "assistant", "content": "For each cell (k,k′), we compare the AUC similarity of reviews using k and k′ embeddings, showing that k′ embeddings yield better results for aspect k′.Specifically, for each cell (k,k′) in the table, we first retrieve the subset of reviews in which the sentiment w.r.t. k differs from the sentiment w.r.t. k′. Then we evaluate the AUC similarity of these reviews on the basis of sentiment concerning k′ using both k and k′ embeddings, yielding a pair of AUCs (listed respectively). We observe that the using k′ embeddings to evaluate aspect k′ similarity yields better results than using k embeddings." } ], "images": [ "scigen/train/3870.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: ‘Decorrelated’ cross-AUC results on the BeerAdvocate data, which attempt to mitigate confounding due to overall sentiment being correlated. Each cell reports metrics over subsets of reviews in which the sentiment differs between the row and column aspects. The numbers in each cell are the AUCs w.r.t. sentiment regarding the column aspect achieved using the row and column aspect representations, respectively." ], "reasoning": "For each cell (k,k′), we compare the AUC similarity of reviews using k and k′ embeddings, showing that k′ embeddings yield better results for aspect k′.", "description": "Specifically, for each cell (k,k′) in the table, we first retrieve the subset of reviews in which the sentiment w.r.t. k differs from the sentiment w.r.t. k′. Then we evaluate the AUC similarity of these reviews on the basis of sentiment concerning k′ using both k and k′ embeddings, yielding a pair of AUCs (listed respectively). We observe that the using k′ embeddings to evaluate aspect k′ similarity yields better results than using k embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 7: ‘Decorrelated’ cross-AUC results on the BeerAdvocate data, which attempt to mitigate confounding due to overall sentiment being correlated. Each cell reports metrics over subsets of reviews in which the sentiment differs between the row and column aspects. The numbers in each cell are the AUCs w.r.t. sentiment regarding the column aspect achieved using the row and column aspect representations, respectively.', answer the following question. Give detailed solution consisting of each step. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich aspect pair has the highest AUC similarity when using the second aspect's embeddings?\n" }, { "role": "assistant", "content": "We need to find the highest value in the table where the second aspect's embeddings are used. The highest value is 0.80 for the pair (Look, Taste). So, the answer is {\"answer\": \"Look, Taste\"}." } ], "images": [ "scigen/train/3870.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: ‘Decorrelated’ cross-AUC results on the BeerAdvocate data, which attempt to mitigate confounding due to overall sentiment being correlated. Each cell reports metrics over subsets of reviews in which the sentiment differs between the row and column aspects. The numbers in each cell are the AUCs w.r.t. sentiment regarding the column aspect achieved using the row and column aspect representations, respectively." ], "reasoning": "We need to find the highest value in the table where the second aspect's embeddings are used. The highest value is 0.80 for the pair (Look, Taste).", "question": "Which aspect pair has the highest AUC similarity when using the second aspect's embeddings?", "answer": "So, the answer is {\"answer\": \"Look, Taste\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The AUC similarity for the pair (Aroma, Taste) using Taste embeddings is higher than using Aroma embeddings., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe AUC similarity for the pair (Aroma, Taste) using Taste embeddings is higher than using Aroma embeddings.\n" }, { "role": "assistant", "content": "For the pair (Aroma, Taste), the AUC similarity using Taste embeddings is 0.60, and using Aroma embeddings is 0.41. Since 0.60 is higher than 0.41, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3870.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: ‘Decorrelated’ cross-AUC results on the BeerAdvocate data, which attempt to mitigate confounding due to overall sentiment being correlated. Each cell reports metrics over subsets of reviews in which the sentiment differs between the row and column aspects. The numbers in each cell are the AUCs w.r.t. sentiment regarding the column aspect achieved using the row and column aspect representations, respectively." ], "reasoning": "For the pair (Aroma, Taste), the AUC similarity using Taste embeddings is 0.60, and using Aroma embeddings is 0.41. Since 0.60 is higher than 0.41, the statement is supported.", "statement": "The AUC similarity for the pair (Aroma, Taste) using Taste embeddings is higher than using Aroma embeddings.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: AUCs achieved using different representations on the Cohen et al. corpus. Models to the right of the | are supervised; those to the right of || constitute the proposed disentangled embeddings.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The proposed PICO embeddings achieve higher AUC than baseline strategies in 12 out of 14 reviews, with strictly higher AUCs in 11 out of 14 reviews, outperforming both unsupervised approaches and RR-TF.Results. In brief: The proposed PICO embeddings (concatenated) obtain an equivalent or higher AUC than baseline strategies on 12/14 reviews, and strictly higher AUCs in 11/14. It is unsurprising that we outperform unsupervised approaches, but we also best RR-TF, which was trained with the same CDSR corpus Wallace et al. DSSM Shen et al. We outperform the latter by an average performance gain of 4 points AUC (significant at 95% level using independent 2-sample t-test)." } ], "images": [ "scigen/train/3871.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: AUCs achieved using different representations on the Cohen et al. corpus. Models to the right of the | are supervised; those to the right of || constitute the proposed disentangled embeddings." ], "reasoning": "The proposed PICO embeddings achieve higher AUC than baseline strategies in 12 out of 14 reviews, with strictly higher AUCs in 11 out of 14 reviews, outperforming both unsupervised approaches and RR-TF.", "description": "Results. In brief: The proposed PICO embeddings (concatenated) obtain an equivalent or higher AUC than baseline strategies on 12/14 reviews, and strictly higher AUCs in 11/14. It is unsurprising that we outperform unsupervised approaches, but we also best RR-TF, which was trained with the same CDSR corpus Wallace et al. DSSM Shen et al. We outperform the latter by an average performance gain of 4 points AUC (significant at 95% level using independent 2-sample t-test).", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 1: AUCs achieved using different representations on the Cohen et al. corpus. Models to the right of the | are supervised; those to the right of || constitute the proposed disentangled embeddings.'. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nHow many studies did the proposed PICO embeddings achieve a higher AUC than RR-TF?\n" }, { "role": "assistant", "content": "The proposed PICO embeddings (P, I, O, [P|I|O]) achieve higher AUCs than RR-TF in 11 out of 14 studies as indicated by the description. So, the answer is {\"answer\": \"11\"}." } ], "images": [ "scigen/train/3871.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: AUCs achieved using different representations on the Cohen et al. corpus. Models to the right of the | are supervised; those to the right of || constitute the proposed disentangled embeddings." ], "reasoning": "The proposed PICO embeddings (P, I, O, [P|I|O]) achieve higher AUCs than RR-TF in 11 out of 14 studies as indicated by the description.", "question": "How many studies did the proposed PICO embeddings achieve a higher AUC than RR-TF?", "answer": "So, the answer is {\"answer\": \"11\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The proposed PICO embeddings outperform RR-TF in all studies., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe proposed PICO embeddings outperform RR-TF in all studies.\n" }, { "role": "assistant", "content": "The table shows that the proposed PICO embeddings achieve higher AUCs than RR-TF in 11 out of 14 studies, not all studies. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3871.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: AUCs achieved using different representations on the Cohen et al. corpus. Models to the right of the | are supervised; those to the right of || constitute the proposed disentangled embeddings." ], "reasoning": "The table shows that the proposed PICO embeddings achieve higher AUCs than RR-TF in 11 out of 14 studies, not all studies.", "statement": "The proposed PICO embeddings outperform RR-TF in all studies.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: AUC results for different representations on the BeerAdvocate data. Models beneath the second line are supervised.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The model consistently outperforms baseline strategies, showing the highest scores in all aspects compared to both unsupervised and triplet-supervised approaches.Our model consistently outperforms baseline strategies over all aspects. Unsurprisingly, the model outperforms unsupervised approaches. We realize consistent though modest improvement over triplet-supervised approaches that use alternative encoders." } ], "images": [ "scigen/train/3872.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: AUC results for different representations on the BeerAdvocate data. Models beneath the second line are supervised." ], "reasoning": "The model consistently outperforms baseline strategies, showing the highest scores in all aspects compared to both unsupervised and triplet-supervised approaches.", "description": "Our model consistently outperforms baseline strategies over all aspects. Unsurprisingly, the model outperforms unsupervised approaches. We realize consistent though modest improvement over triplet-supervised approaches that use alternative encoders.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nTable title: Table 5: AUC results for different representations on the BeerAdvocate data. Models beneath the second line are supervised.\nQuestion: Which model has the highest score in the Taste aspect and what is the score?\n" }, { "role": "assistant", "content": "By examining the table, the CNN + Triplet model has the highest score in the Taste aspect with a score of 0.96. So, the answer is {\"answer\": \"0.96\"}." } ], "images": [ "scigen/train/3872.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: AUC results for different representations on the BeerAdvocate data. Models beneath the second line are supervised." ], "reasoning": "By examining the table, the CNN + Triplet model has the highest score in the Taste aspect with a score of 0.96.", "question": "Which model has the highest score in the Taste aspect and what is the score?", "answer": "So, the answer is {\"answer\": \"0.96\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe CNN + Triplet model outperforms the NVDM + Triplet model in all aspects.\n" }, { "role": "assistant", "content": "By comparing the scores of CNN + Triplet and NVDM + Triplet models in all aspects (Look, Aroma, Palate, Taste), CNN + Triplet has higher scores in each aspect. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3872.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: AUC results for different representations on the BeerAdvocate data. Models beneath the second line are supervised." ], "reasoning": "By comparing the scores of CNN + Triplet and NVDM + Triplet models in all aspects (Look, Aroma, Palate, Taste), CNN + Triplet has higher scores in each aspect.", "statement": "The CNN + Triplet model outperforms the NVDM + Triplet model in all aspects.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 6: Cross AUC results for different representations on the BeerAdvocate data. Row: Embedding used. Column: Aspect evaluated against.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Aspect-embeddings perform best for their respective aspects, but performance reduction is minimal when using one aspect representation for others due to high correlation among aspect ratings.Rows correspond to the embedding used and columns to the aspect evaluated against. As expected, aspect-embeddings perform better w.r.t. the aspects for which they code, suggesting some disentanglement. However, the reduction in performance when using one aspect representation to discriminate w.r.t. others is not as pronounced as above. This is because aspect ratings are highly correlated: if taste is positive, aroma is very likely to be as well. Effectively, here sentiment entangles all of these aspects." } ], "images": [ "scigen/train/3873.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Cross AUC results for different representations on the BeerAdvocate data. Row: Embedding used. Column: Aspect evaluated against." ], "reasoning": "Aspect-embeddings perform best for their respective aspects, but performance reduction is minimal when using one aspect representation for others due to high correlation among aspect ratings.", "description": "Rows correspond to the embedding used and columns to the aspect evaluated against. As expected, aspect-embeddings perform better w.r.t. the aspects for which they code, suggesting some disentanglement. However, the reduction in performance when using one aspect representation to discriminate w.r.t. others is not as pronounced as above. This is because aspect ratings are highly correlated: if taste is positive, aroma is very likely to be as well. Effectively, here sentiment entangles all of these aspects.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}. The question is related to the given table titled 'Table 6: Cross AUC results for different representations on the BeerAdvocate data. Row: Embedding used. Column: Aspect evaluated against.'.\n\nQuestion: Which aspect embedding performs the best when evaluated against the Taste aspect?\n" }, { "role": "assistant", "content": "To determine which aspect embedding performs the best for the Taste aspect, we need to look at the Taste column and find the highest value. The highest value in the Taste column is 0.96, which corresponds to the Taste embedding. So, the answer is {\"answer\": \"Taste\"}." } ], "images": [ "scigen/train/3873.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Cross AUC results for different representations on the BeerAdvocate data. Row: Embedding used. Column: Aspect evaluated against." ], "reasoning": "To determine which aspect embedding performs the best for the Taste aspect, we need to look at the Taste column and find the highest value. The highest value in the Taste column is 0.96, which corresponds to the Taste embedding.", "question": "Which aspect embedding performs the best when evaluated against the Taste aspect?", "answer": "So, the answer is {\"answer\": \"Taste\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Aroma embedding performs better for the Palate aspect than the Look embedding.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the values in the Palate column for the Aroma and Look embeddings. The value for Aroma is 0.91, and for Look, it is 0.88. Since 0.91 is greater than 0.88, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3873.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Cross AUC results for different representations on the BeerAdvocate data. Row: Embedding used. Column: Aspect evaluated against." ], "reasoning": "To verify this statement, we need to compare the values in the Palate column for the Aroma and Look embeddings. The value for Aroma is 0.91, and for Look, it is 0.88. Since 0.91 is greater than 0.88, the statement is supported.", "statement": "The Aroma embedding performs better for the Palate aspect than the Look embedding.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 6: The Monolingual and joint Multi-translation models trained on translation pairs, and the Multi-comparable trained on the downsampled comparable set with one caption per image.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Multi-translation models trained on 29K images with single captions in each language outperform Monolingual models, and the c2c objective further improves German, French, and Czech models.The Multi-translation models are trained on 29K images paired with a single caption in each language. These models perform better than their Monolingual counterparts, and the German, French, and Czech models are further improved with the c2c objective. The Multi-comparable models are trained by randomly sampling one English and one German caption from the comparable dataset, alongside the French and Czech translation pairs. These models perform as well as the Multi-translation models, and the c2c objective brings further improvements for all languages in this setting." } ], "images": [ "scigen/train/3874.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: The Monolingual and joint Multi-translation models trained on translation pairs, and the Multi-comparable trained on the downsampled comparable set with one caption per image." ], "reasoning": "Multi-translation models trained on 29K images with single captions in each language outperform Monolingual models, and the c2c objective further improves German, French, and Czech models.", "description": "The Multi-translation models are trained on 29K images paired with a single caption in each language. These models perform better than their Monolingual counterparts, and the German, French, and Czech models are further improved with the c2c objective. The Multi-comparable models are trained by randomly sampling one English and one German caption from the comparable dataset, alongside the French and Czech translation pairs. These models perform as well as the Multi-translation models, and the c2c objective brings further improvements for all languages in this setting.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 6: The Monolingual and joint Multi-translation models trained on translation pairs, and the Multi-comparable trained on the downsampled comparable set with one caption per image.', solve this question and give detail solutions. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhich model and objective combination achieved the highest score for English?\n" }, { "role": "assistant", "content": "By comparing the scores for English across all models and objectives, the highest score is found in the Multi-comparable model with the c2c objective. So, the answer is {\"answer\": \"Multi-comparable + c2c\"}." } ], "images": [ "scigen/train/3874.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: The Monolingual and joint Multi-translation models trained on translation pairs, and the Multi-comparable trained on the downsampled comparable set with one caption per image." ], "reasoning": "By comparing the scores for English across all models and objectives, the highest score is found in the Multi-comparable model with the c2c objective.", "question": "Which model and objective combination achieved the highest score for English?", "answer": "So, the answer is {\"answer\": \"Multi-comparable + c2c\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Multi-translation model with the c2c objective achieved the highest score for French.\n" }, { "role": "assistant", "content": "The table shows that the highest score for French is 59.2, achieved by the Multi-comparable model with the c2c objective, not the Multi-translation model with the c2c objective. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3874.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: The Monolingual and joint Multi-translation models trained on translation pairs, and the Multi-comparable trained on the downsampled comparable set with one caption per image." ], "reasoning": "The table shows that the highest score for French is 59.2, achieved by the Multi-comparable model with the c2c objective, not the Multi-translation model with the c2c objective.", "statement": "The Multi-translation model with the c2c objective achieved the highest score for French.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Vocabulary overlap as measured by the Jaccard coefficient between the different languages on the translation portion of the Multi30K dataset.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The Multi30K dataset contains 29K images with English captions and translations in German, French, and Czech, and the vocabulary size is reduced by replacing infrequent words with 'UNK'.We train and evaluate our models on the translation and comparable portions of the Multi30K dataset elliott2016multi30k; elliott2017findings. The translation portion (a low-resource dataset) contains 29K images, each described in one English caption with German, French, and Czech translations. The comparable portion (a higher-resource dataset) contains the same 29K images paired with five English and five German descriptions collected independently. To reduce the vocabulary size of the joint models, we replace all words occurring fewer than four times with a special “UNK” symbol. The total number of tokens across all four languages is 17,571, and taking the union of the tokens in these four languages results in vocabulary of 16,553 tokens – a 6% reduction in vocabulary size. On the comparable portion of the dataset, the total vocabulary between English and German contains 18,337 tokens, with a union of 17,667, which is a 4% reduction in vocabulary size." } ], "images": [ "scigen/train/3875.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Vocabulary overlap as measured by the Jaccard coefficient between the different languages on the translation portion of the Multi30K dataset." ], "reasoning": "The Multi30K dataset contains 29K images with English captions and translations in German, French, and Czech, and the vocabulary size is reduced by replacing infrequent words with 'UNK'.", "description": "We train and evaluate our models on the translation and comparable portions of the Multi30K dataset elliott2016multi30k; elliott2017findings. The translation portion (a low-resource dataset) contains 29K images, each described in one English caption with German, French, and Czech translations. The comparable portion (a higher-resource dataset) contains the same 29K images paired with five English and five German descriptions collected independently. To reduce the vocabulary size of the joint models, we replace all words occurring fewer than four times with a special “UNK” symbol. The total number of tokens across all four languages is 17,571, and taking the union of the tokens in these four languages results in vocabulary of 16,553 tokens – a 6% reduction in vocabulary size. On the comparable portion of the dataset, the total vocabulary between English and German contains 18,337 tokens, with a union of 17,667, which is a 4% reduction in vocabulary size.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Vocabulary overlap as measured by the Jaccard coefficient between the different languages on the translation portion of the Multi30K dataset.\nQuestion: What is the similarity score between English and French in the translation portion of the Multi30K dataset?\nGive a detailed response to the above question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the similarity scores between different language pairs. The similarity score between English (En) and French (Fr) is found in the cell where the En row intersects with the Fr column. So, the answer is {\"answer\": \"0.06\"}." } ], "images": [ "scigen/train/3875.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Vocabulary overlap as measured by the Jaccard coefficient between the different languages on the translation portion of the Multi30K dataset." ], "reasoning": "The table shows the similarity scores between different language pairs. The similarity score between English (En) and French (Fr) is found in the cell where the En row intersects with the Fr column.", "question": "What is the similarity score between English and French in the translation portion of the Multi30K dataset?", "answer": "So, the answer is {\"answer\": \"0.06\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The similarity score between German and Czech is higher than between English and Czech., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe similarity score between German and Czech is higher than between English and Czech.\n" }, { "role": "assistant", "content": "The table shows the similarity scores between different language pairs. The similarity score between German (De) and Czech (Cz) is 0.01, while the similarity score between English (En) and Czech (Cz) is 0.02. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3875.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Vocabulary overlap as measured by the Jaccard coefficient between the different languages on the translation portion of the Multi30K dataset." ], "reasoning": "The table shows the similarity scores between different language pairs. The similarity score between German (De) and Czech (Cz) is 0.01, while the similarity score between English (En) and Czech (Cz) is 0.02.", "statement": "The similarity score between German and Czech is higher than between English and Czech.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 2: English Image-to-text (I→T) and text-to-image (T→I) retrieval results on the comparable part of Multi30K, measured by Recall at 1, 5 at 10. Typewriter font shows performance of two sets of symmetric and asymmetric models from gella2017image.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Our scores are higher than \newcitegella2017image due to different image features, showing a similar trend to their symmetric cosine similarity models, with the best results achieved through bilingual joint training with the added c2c objective.Overall our scores are higher than \\newcitegella2017image, which is most likely due to the different image features (\\newcitefaghri2017vse++ also report a large performance gain when they use the ResNet instead of the VGG image features). Nevertheless, our results show a similar trend to the symmetric cosine similarity models from \\newcitegella2017image: our best results are achieved with bilingual joint training with the added c2c objective. Their models trained with an asymmetric similarity measure show a different trend: the monolingual model is stronger than the bilingual model, and the c2c loss provides no clear improvement." } ], "images": [ "scigen/train/3876.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: English Image-to-text (I→T) and text-to-image (T→I) retrieval results on the comparable part of Multi30K, measured by Recall at 1, 5 at 10. Typewriter font shows performance of two sets of symmetric and asymmetric models from gella2017image." ], "reasoning": "Our scores are higher than \newcitegella2017image due to different image features, showing a similar trend to their symmetric cosine similarity models, with the best results achieved through bilingual joint training with the added c2c objective.", "description": "Overall our scores are higher than \\newcitegella2017image, which is most likely due to the different image features (\\newcitefaghri2017vse++ also report a large performance gain when they use the ResNet instead of the VGG image features). Nevertheless, our results show a similar trend to the symmetric cosine similarity models from \\newcitegella2017image: our best results are achieved with bilingual joint training with the added c2c objective. Their models trained with an asymmetric similarity measure show a different trend: the monolingual model is stronger than the bilingual model, and the c2c loss provides no clear improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 2: English Image-to-text (I→T) and text-to-image (T→I) retrieval results on the comparable part of Multi30K, measured by Recall at 1, 5 at 10. Typewriter font shows performance of two sets of symmetric and asymmetric models from gella2017image.', unfold the question presented below, detailing every step of your calculation. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the highest R@10 score for the T→I task and which model achieves it?\n" }, { "role": "assistant", "content": "To find the highest R@10 score for the T→I task, look at the T→I R@10 column. The highest score is 70.2, achieved by the model with bilingual joint training and the added c2c objective. So, the answer is {\"answer\": \"70.2\"}." } ], "images": [ "scigen/train/3876.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: English Image-to-text (I→T) and text-to-image (T→I) retrieval results on the comparable part of Multi30K, measured by Recall at 1, 5 at 10. Typewriter font shows performance of two sets of symmetric and asymmetric models from gella2017image." ], "reasoning": "To find the highest R@10 score for the T→I task, look at the T→I R@10 column. The highest score is 70.2, achieved by the model with bilingual joint training and the added c2c objective.", "question": "What is the highest R@10 score for the T→I task and which model achieves it?", "answer": "So, the answer is {\"answer\": \"70.2\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The monolingual model achieves a higher R@1 score for the I→T task than the bilingual model. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe monolingual model achieves a higher R@1 score for the I→T task than the bilingual model.\n" }, { "role": "assistant", "content": "To verify this statement, compare the I→T R@1 scores for the monolingual and bilingual models. The monolingual model has a score of 42.4, while the bilingual model has a score of 42.7. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3876.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: English Image-to-text (I→T) and text-to-image (T→I) retrieval results on the comparable part of Multi30K, measured by Recall at 1, 5 at 10. Typewriter font shows performance of two sets of symmetric and asymmetric models from gella2017image." ], "reasoning": "To verify this statement, compare the I→T R@1 scores for the monolingual and bilingual models. The monolingual model has a score of 42.4, while the bilingual model has a score of 42.7. Therefore, the statement is refuted.", "statement": "The monolingual model achieves a higher R@1 score for the I→T task than the bilingual model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: German Image-to-text (I→T) and text-to-image (T→I) retrieval results on the comparable part of Multi30K, measured by Recall at 1, 5 at 10. Typewriter font shows performance of two sets of symmetric and asymmetric models from gella2017image.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Our implementation with bilingual training and the additional c2c objective outperforms other models, showing systematic improvements across all performance metrics for both languages.Once again, our implementation outperforms \\newcitegella2017image, and this is likely due to the different visual features and max-of-hinges loss. However, our Bilingual model with the additional c2c objective performs the best for German, whereas \\newcitegella2017image reports the overall best results for the monolingual baseline VSE. Their models that use the asymmetric similarity function are clearly better than the Monolingual OE model. In general, the results from \\newcitegella2017image indicate the benefits of bilingual joint training, however, they do not find a clear pattern between the model configurations across languages. In our implementation, we only focused on the symmetric cosine similarity function and found a systematic pattern across both languages: bilingual training improves results on all performance metrics for both languages, and the additional c2c objective always provides further improvements." } ], "images": [ "scigen/train/3877.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: German Image-to-text (I→T) and text-to-image (T→I) retrieval results on the comparable part of Multi30K, measured by Recall at 1, 5 at 10. Typewriter font shows performance of two sets of symmetric and asymmetric models from gella2017image." ], "reasoning": "Our implementation with bilingual training and the additional c2c objective outperforms other models, showing systematic improvements across all performance metrics for both languages.", "description": "Once again, our implementation outperforms \\newcitegella2017image, and this is likely due to the different visual features and max-of-hinges loss. However, our Bilingual model with the additional c2c objective performs the best for German, whereas \\newcitegella2017image reports the overall best results for the monolingual baseline VSE. Their models that use the asymmetric similarity function are clearly better than the Monolingual OE model. In general, the results from \\newcitegella2017image indicate the benefits of bilingual joint training, however, they do not find a clear pattern between the model configurations across languages. In our implementation, we only focused on the symmetric cosine similarity function and found a systematic pattern across both languages: bilingual training improves results on all performance metrics for both languages, and the additional c2c objective always provides further improvements.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: German Image-to-text (I→T) and text-to-image (T→I) retrieval results on the comparable part of Multi30K, measured by Recall at 1, 5 at 10. Typewriter font shows performance of two sets of symmetric and asymmetric models from gella2017image.' and methodically tackle the question that follows. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model shows the highest R@10 score for the I→T task?\n" }, { "role": "assistant", "content": "The table shows the R@10 scores for various models under the I→T task. The highest R@10 score for the I→T task is 76.8, achieved by the Bilingual model with the additional c2c objective. So, the answer is {\"answer\": \"Bilingual model with c2c\"}" } ], "images": [ "scigen/train/3877.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: German Image-to-text (I→T) and text-to-image (T→I) retrieval results on the comparable part of Multi30K, measured by Recall at 1, 5 at 10. Typewriter font shows performance of two sets of symmetric and asymmetric models from gella2017image." ], "reasoning": "The table shows the R@10 scores for various models under the I→T task. The highest R@10 score for the I→T task is 76.8, achieved by the Bilingual model with the additional c2c objective.", "question": "Which model shows the highest R@10 score for the I→T task?", "answer": "So, the answer is {\"answer\": \"Bilingual model with c2c\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Monolingual model has a higher R@1 score for the T→I task than the OE model.\n" }, { "role": "assistant", "content": "The table shows that the Monolingual model has an R@1 score of 23.9 for the T→I task, while the OE model has an R@1 score of 21.0. Therefore, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3877.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: German Image-to-text (I→T) and text-to-image (T→I) retrieval results on the comparable part of Multi30K, measured by Recall at 1, 5 at 10. Typewriter font shows performance of two sets of symmetric and asymmetric models from gella2017image." ], "reasoning": "The table shows that the Monolingual model has an R@1 score of 23.9 for the T→I task, while the OE model has an R@1 score of 21.0. Therefore, the statement is supported.", "statement": "The Monolingual model has a higher R@1 score for the T→I task than the OE model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: R@10 retrieval results on the comparable part of Multi30K. Bi-translation is trained on 29K translation pair data; bi-comparable is trained by downsampling the comparable data to 29K.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The Bi-comparable model with c2c loss achieves the highest scores in German T->I and English T->I tasks, indicating that shared parameters and comparable captions improve performance.We now study whether the model can be trained on either translation pairs or independently collected bilingual captions. \\newcitegella2017image only conducted experiments on independently collected captions. However, it is known that humans have equally strong preference for translated or independently collected captions of images frank_elliott_specia_2018, which has implications for the difficulty and cost of collecting training data. Our baseline is a Monolingual model trained on 29K single-captioned images in the translation portion of Multi30K. The Bi-translation model is trained on both German and English, with shared parameters. However, the additional c2c loss degrades performance here. This could be because we only have one caption per image in each language and it is easier to find a relationship between these views of the translation pairs." } ], "images": [ "scigen/train/3878.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: R@10 retrieval results on the comparable part of Multi30K. Bi-translation is trained on 29K translation pair data; bi-comparable is trained by downsampling the comparable data to 29K." ], "reasoning": "The Bi-comparable model with c2c loss achieves the highest scores in German T->I and English T->I tasks, indicating that shared parameters and comparable captions improve performance.", "description": "We now study whether the model can be trained on either translation pairs or independently collected bilingual captions. \\newcitegella2017image only conducted experiments on independently collected captions. However, it is known that humans have equally strong preference for translated or independently collected captions of images frank_elliott_specia_2018, which has implications for the difficulty and cost of collecting training data. Our baseline is a Monolingual model trained on 29K single-captioned images in the translation portion of Multi30K. The Bi-translation model is trained on both German and English, with shared parameters. However, the additional c2c loss degrades performance here. This could be because we only have one caption per image in each language and it is easier to find a relationship between these views of the translation pairs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 4: R@10 retrieval results on the comparable part of Multi30K. Bi-translation is trained on 29K translation pair data; bi-comparable is trained by downsampling the comparable data to 29K.' and proceed to solve the following question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model and configuration achieved the highest score in the German T->I task?\n" }, { "role": "assistant", "content": "The table shows that the Bi-comparable model with c2c loss achieved the highest score in the German T->I task with a score of 49.1. So, the answer is {\"answer\": \"Bi-comparable + c2c\"}." } ], "images": [ "scigen/train/3878.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: R@10 retrieval results on the comparable part of Multi30K. Bi-translation is trained on 29K translation pair data; bi-comparable is trained by downsampling the comparable data to 29K." ], "reasoning": "The table shows that the Bi-comparable model with c2c loss achieved the highest score in the German T->I task with a score of 49.1.", "question": "Which model and configuration achieved the highest score in the German T->I task?", "answer": "So, the answer is {\"answer\": \"Bi-comparable + c2c\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The Monolingual model achieved the highest score in the English I->T task. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe Monolingual model achieved the highest score in the English I->T task.\n" }, { "role": "assistant", "content": "The table shows that the highest score in the English I->T task is 67.9, achieved by the Bi-comparable model, not the Monolingual model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3878.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: R@10 retrieval results on the comparable part of Multi30K. Bi-translation is trained on 29K translation pair data; bi-comparable is trained by downsampling the comparable data to 29K." ], "reasoning": "The table shows that the highest score in the English I->T task is 67.9, achieved by the Bi-comparable model, not the Monolingual model.", "statement": "The Monolingual model achieved the highest score in the English I->T task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 5: R@10 retrieval results on the comparable part of Multi30K. Full model trained on the 29K images of the comparable part, Half model on 14.5K images using random downsampling. For Bi-overlap, both English and German captions are used for 14.5K images. For Bi-disjoint, 14.5K images are used for English and the remaining 14.5K images for German.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The Bi-overlap model with the c2c objective improves performance for both English and German compared to the Bi-overlap model without c2c.The upper-bound is to train a Monolingual model on the full comparable corpus. For the lower bound, we train Half Monolingual models by randomly sampling half of the 29K images and their associated captions, giving 72.5K captions over 14.5K images. Unsurprisingly, the Half Monolingual models perform worse than the Full Monolingual models. In the Bi-overlap experiment, the German model is improved by collecting captions for the existing images in English. The Bi-overlap model also benefits from the added c2c objective. Finally, the Bi-disjoint model performs as well as the Bi-overlap model without the c2c objective. (It was not possible to train the Bi-disjoint model with the additional c2c objective because there are no caption pairs for the same image.)" } ], "images": [ "scigen/train/3879.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: R@10 retrieval results on the comparable part of Multi30K. Full model trained on the 29K images of the comparable part, Half model on 14.5K images using random downsampling. For Bi-overlap, both English and German captions are used for 14.5K images. For Bi-disjoint, 14.5K images are used for English and the remaining 14.5K images for German." ], "reasoning": "The Bi-overlap model with the c2c objective improves performance for both English and German compared to the Bi-overlap model without c2c.", "description": "The upper-bound is to train a Monolingual model on the full comparable corpus. For the lower bound, we train Half Monolingual models by randomly sampling half of the 29K images and their associated captions, giving 72.5K captions over 14.5K images. Unsurprisingly, the Half Monolingual models perform worse than the Full Monolingual models. In the Bi-overlap experiment, the German model is improved by collecting captions for the existing images in English. The Bi-overlap model also benefits from the added c2c objective. Finally, the Bi-disjoint model performs as well as the Bi-overlap model without the c2c objective. (It was not possible to train the Bi-disjoint model with the additional c2c objective because there are no caption pairs for the same image.)", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 5: R@10 retrieval results on the comparable part of Multi30K. Full model trained on the 29K images of the comparable part, Half model on 14.5K images using random downsampling. For Bi-overlap, both English and German captions are used for 14.5K images. For Bi-disjoint, 14.5K images are used for English and the remaining 14.5K images for German.', and sequentially solve the question, writing out each step of your reasoning process. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich model has the highest score for English I→T?\n" }, { "role": "assistant", "content": "The table shows that the Full Monolingual model has the highest score for English I→T with a score of 79.8. So, the answer is {\"answer\": \"Full Monolingual\"}." } ], "images": [ "scigen/train/3879.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: R@10 retrieval results on the comparable part of Multi30K. Full model trained on the 29K images of the comparable part, Half model on 14.5K images using random downsampling. For Bi-overlap, both English and German captions are used for 14.5K images. For Bi-disjoint, 14.5K images are used for English and the remaining 14.5K images for German." ], "reasoning": "The table shows that the Full Monolingual model has the highest score for English I→T with a score of 79.8.", "question": "Which model has the highest score for English I→T?", "answer": "So, the answer is {\"answer\": \"Full Monolingual\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Bi-disjoint model performs better than the Half Monolingual model for German T→I.\n" }, { "role": "assistant", "content": "The table shows that the Bi-disjoint model has a score of 54.9 for German T→I, while the Half Monolingual model has a score of 53.9 for German T→I. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3879.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: R@10 retrieval results on the comparable part of Multi30K. Full model trained on the 29K images of the comparable part, Half model on 14.5K images using random downsampling. For Bi-overlap, both English and German captions are used for 14.5K images. For Bi-disjoint, 14.5K images are used for English and the remaining 14.5K images for German." ], "reasoning": "The table shows that the Bi-disjoint model has a score of 54.9 for German T→I, while the Half Monolingual model has a score of 53.9 for German T→I.", "statement": "The Bi-disjoint model performs better than the Half Monolingual model for German T→I.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 7: Multilingual is trained on all translation pairs, + Comparable adds the comparable data set.'.\n" }, { "role": "assistant", "content": "Training on multilingual translation pairs significantly improves the French and Czech models, with further gains from additional comparable descriptions and the c2c objective.We find that the French and Czech models improve by 8.8 and 5.5 R@10 points respectively when they are only trained on the multilingual translation pairs (compared to the monolingual version), and by another 2.2 and 2.8 points if trained on the extra 155K English and German comparable descriptions. We also find that the additional c2c objective improves the Czech model by a further 4.8 R@10 points (this improvement is likely caused by training the model on 46 possible caption pairs). Our results show the impact of jointly training with the larger English and German resources, which demonstrates the benefits of high-to-low resource transfer." } ], "images": [ "scigen/train/3880.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Multilingual is trained on all translation pairs, + Comparable adds the comparable data set." ], "reasoning": "Training on multilingual translation pairs significantly improves the French and Czech models, with further gains from additional comparable descriptions and the c2c objective.", "description": "We find that the French and Czech models improve by 8.8 and 5.5 R@10 points respectively when they are only trained on the multilingual translation pairs (compared to the monolingual version), and by another 2.2 and 2.8 points if trained on the extra 155K English and German comparable descriptions. We also find that the additional c2c objective improves the Czech model by a further 4.8 R@10 points (this improvement is likely caused by training the model on 46 possible caption pairs). Our results show the impact of jointly training with the larger English and German resources, which demonstrates the benefits of high-to-low resource transfer.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 7: Multilingual is trained on all translation pairs, + Comparable adds the comparable data set.', solve this question and give detail solutions. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nHow much does the Czech model improve in R@10 points when trained on the extra 155K English and German comparable descriptions compared to the multilingual version?\n" }, { "role": "assistant", "content": "The Czech model improves from 51.3 R@10 points in the multilingual version to 52.4 R@10 points when trained on the extra 155K English and German comparable descriptions, resulting in an improvement of 1.1 R@10 points. So, the answer is {\"answer\": \"1.1\"}" } ], "images": [ "scigen/train/3880.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Multilingual is trained on all translation pairs, + Comparable adds the comparable data set." ], "reasoning": "The Czech model improves from 51.3 R@10 points in the multilingual version to 52.4 R@10 points when trained on the extra 155K English and German comparable descriptions, resulting in an improvement of 1.1 R@10 points.", "question": "How much does the Czech model improve in R@10 points when trained on the extra 155K English and German comparable descriptions compared to the multilingual version?", "answer": "So, the answer is {\"answer\": \"1.1\"}" }, { "messages": [ { "role": "user", "content": "The French model improves by 14.6 R@10 points when trained on the extra 155K English and German comparable descriptions compared to the monolingual version. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe French model improves by 14.6 R@10 points when trained on the extra 155K English and German comparable descriptions compared to the monolingual version.\n" }, { "role": "assistant", "content": "The French model improves from 47.0 R@10 points in the monolingual version to 61.6 R@10 points when trained on the extra 155K English and German comparable descriptions, resulting in an improvement of 14.6 R@10 points. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3880.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Multilingual is trained on all translation pairs, + Comparable adds the comparable data set." ], "reasoning": "The French model improves from 47.0 R@10 points in the monolingual version to 61.6 R@10 points when trained on the extra 155K English and German comparable descriptions, resulting in an improvement of 14.6 R@10 points.", "statement": "The French model improves by 14.6 R@10 points when trained on the extra 155K English and German comparable descriptions compared to the monolingual version.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1: Test set scores for T5 variants and previous results on the open-domain Natural Questions (NQ), WebQuestions (WQ), and TriviaQA (TQA) tasks.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Performance improves with model size, with T5-11B performing best. SSM pre-training boosts performance, achieving state-of-the-art on WebQuestions and TriviaQA.Notably, performance on each dataset improves as the model size increases, with T5-11B performing best in every case. Further, we find that using Guu et al. ’s SSM pre-training produces a substantial boost in performance. T5-11B with SSM ultimately achieves state-of-the-art on both WebQuestions and TriviaQA beating all other methods except Guu et al. Importantly, all previous methods except Ling et al." } ], "images": [ "scigen/train/3881.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Test set scores for T5 variants and previous results on the open-domain Natural Questions (NQ), WebQuestions (WQ), and TriviaQA (TQA) tasks." ], "reasoning": "Performance improves with model size, with T5-11B performing best. SSM pre-training boosts performance, achieving state-of-the-art on WebQuestions and TriviaQA.", "description": "Notably, performance on each dataset improves as the model size increases, with T5-11B performing best in every case. Further, we find that using Guu et al. ’s SSM pre-training produces a substantial boost in performance. T5-11B with SSM ultimately achieves state-of-the-art on both WebQuestions and TriviaQA beating all other methods except Guu et al. Importantly, all previous methods except Ling et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nTable title: 'Table 1: Test set scores for T5 variants and previous results on the open-domain Natural Questions (NQ), WebQuestions (WQ), and TriviaQA (TQA) tasks.'\nQuestion: Which model achieves the highest score on TriviaQA?\n" }, { "role": "assistant", "content": "The table shows that T5-11B + SSM achieves the highest score on TriviaQA with a score of 60.5. So, the answer is {\"answer\": \"T5-11B + SSM\"}." } ], "images": [ "scigen/train/3881.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Test set scores for T5 variants and previous results on the open-domain Natural Questions (NQ), WebQuestions (WQ), and TriviaQA (TQA) tasks." ], "reasoning": "The table shows that T5-11B + SSM achieves the highest score on TriviaQA with a score of 60.5.", "question": "Which model achieves the highest score on TriviaQA?", "answer": "So, the answer is {\"answer\": \"T5-11B + SSM\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nT5-11B outperforms all other models on WebQuestions.\n" }, { "role": "assistant", "content": "The table shows that T5-11B + SSM achieves the highest score on WebQuestions with a score of 44.7, not T5-11B alone. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3881.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Test set scores for T5 variants and previous results on the open-domain Natural Questions (NQ), WebQuestions (WQ), and TriviaQA (TQA) tasks." ], "reasoning": "The table shows that T5-11B + SSM achieves the highest score on WebQuestions with a score of 44.7, not T5-11B alone.", "statement": "T5-11B outperforms all other models on WebQuestions.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 1: Most similar sentences (from 1000-sentence subsample) in the SICK test data according to the Tree-LSTM. Tree/M/S denote relatedness (with the sentence preceding each group) predicted by the Tree-LSTM/MaLSTM/Siam-CSTI.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Siam-CSTI outperforms TreeLSTM and TF-IDF cosine similarity in identifying similar sentences, leveraging sequential and recursive sentence aspects.it is evident that Siam-CSTI performs better (T-test with p-value=0.02) than similar state-of-the-art architectures: TreeLSTM We also experimented with basic TF-IDF cosine similarity and empirically found the superior performance of Siam-CSTI. The network seems to exploit the sequential and recursive aspects of the sentences to learn a rich set of semantics that help in identifying similar sentences." } ], "images": [ "scigen/train/3882.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Most similar sentences (from 1000-sentence subsample) in the SICK test data according to the Tree-LSTM. Tree/M/S denote relatedness (with the sentence preceding each group) predicted by the Tree-LSTM/MaLSTM/Siam-CSTI." ], "reasoning": "Siam-CSTI outperforms TreeLSTM and TF-IDF cosine similarity in identifying similar sentences, leveraging sequential and recursive sentence aspects.", "description": "it is evident that Siam-CSTI performs better (T-test with p-value=0.02) than similar state-of-the-art architectures: TreeLSTM We also experimented with basic TF-IDF cosine similarity and empirically found the superior performance of Siam-CSTI. The network seems to exploit the sequential and recursive aspects of the sentences to learn a rich set of semantics that help in identifying similar sentences.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nTable title: Table 1: Most similar sentences (from 1000-sentence subsample) in the SICK test data according to the Tree-LSTM. Tree/M/S denote relatedness (with the sentence preceding each group) predicted by the Tree-LSTM/MaLSTM/Siam-CSTI.\n\nQuestion: What is the highest score for the sentence 'a woman is cutting potatoes' in the table?\n" }, { "role": "assistant", "content": "The table shows the scores for the sentence 'a woman is cutting potatoes' under columns Tree, M, and S. The highest score among these is 4.91. So, the answer is {\"answer\": \"4.91\"}." } ], "images": [ "scigen/train/3882.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Most similar sentences (from 1000-sentence subsample) in the SICK test data according to the Tree-LSTM. Tree/M/S denote relatedness (with the sentence preceding each group) predicted by the Tree-LSTM/MaLSTM/Siam-CSTI." ], "reasoning": "The table shows the scores for the sentence 'a woman is cutting potatoes' under columns Tree, M, and S. The highest score among these is 4.91.", "question": "What is the highest score for the sentence 'a woman is cutting potatoes' in the table?", "answer": "So, the answer is {\"answer\": \"4.91\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The sentence 'a group of men is playing with a ball on the beach' has a higher score in the Tree column than in the S column. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe sentence 'a group of men is playing with a ball on the beach' has a higher score in the Tree column than in the S column.\n" }, { "role": "assistant", "content": "The table shows that the score for 'a group of men is playing with a ball on the beach' is 3.79 in the Tree column and 2.68 in the S column, confirming the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3882.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Most similar sentences (from 1000-sentence subsample) in the SICK test data according to the Tree-LSTM. Tree/M/S denote relatedness (with the sentence preceding each group) predicted by the Tree-LSTM/MaLSTM/Siam-CSTI." ], "reasoning": "The table shows that the score for 'a group of men is playing with a ball on the beach' is 3.79 in the Tree column and 2.68 in the S column, confirming the statement.", "statement": "The sentence 'a group of men is playing with a ball on the beach' has a higher score in the Tree column than in the S column.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Comparison Results (%) on DUC Datasets'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The HNet(T) system outperforms other systems across multiple years, demonstrating the effectiveness of its deep network architecture and transfer learning method.This is encouraging because despite having not so complex deep network architecture the HNet-B system is able to learn efficient document-dependent semantic features. It also outperforms the Reg_Manual baseline which uses human-compiled features for obtaining the document-independent features and the graph-based summarization system LexRank. This is due to the fact that the additional BLSTM network used in HNet-B helps in learning temporal (sequential) dependencies among variable length phrases exploiting past as well as future context. Finally, our proposed model (HNet/HNet (T)) significantly outperforms our basic systems HNet-B/HNet-B (T) which supports the fact that HNet is equipped with suitable deep network architecture for procuring latent semantic features (document-independent features) from a sentence. In the following, we analyze different aspects of the proposed system. Contribution of Document Independent Features To explore the contribution of the learned document independent features towards the saliency estimation of a sentence prior to the summary, we follow a simple approach. For each sentence, we ignore document dependent features by setting the ϕe vector to 0, and then applying the regression transform to calculate the saliency score. We refer to this model as HNet−. This setting helps us in analyzing the intuitive features latent in our heterogeneous representation of the sentence without consideration of the contextual features. The drop in points has resulted due to the absence of document dependent features. Therefore, we can conclude that document independent features have a major contribution towards saliency estimation of a sentence. Contribution of Transfer Learning Method The fact that increase in training data results in better performance as the system becomes more robust motivated us to pre-train the HNet-B and HNet systems on Daily Mail dataset first and then fine-tune the systems to multi-document summarization setting. We refer to these systems as HNet-B(T) and HNet(T). HNet(T) is the best performing system amongst the HNet variants." } ], "images": [ "scigen/train/3883.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Comparison Results (%) on DUC Datasets" ], "reasoning": "The HNet(T) system outperforms other systems across multiple years, demonstrating the effectiveness of its deep network architecture and transfer learning method.", "description": "This is encouraging because despite having not so complex deep network architecture the HNet-B system is able to learn efficient document-dependent semantic features. It also outperforms the Reg_Manual baseline which uses human-compiled features for obtaining the document-independent features and the graph-based summarization system LexRank. This is due to the fact that the additional BLSTM network used in HNet-B helps in learning temporal (sequential) dependencies among variable length phrases exploiting past as well as future context. Finally, our proposed model (HNet/HNet (T)) significantly outperforms our basic systems HNet-B/HNet-B (T) which supports the fact that HNet is equipped with suitable deep network architecture for procuring latent semantic features (document-independent features) from a sentence. In the following, we analyze different aspects of the proposed system. Contribution of Document Independent Features To explore the contribution of the learned document independent features towards the saliency estimation of a sentence prior to the summary, we follow a simple approach. For each sentence, we ignore document dependent features by setting the ϕe vector to 0, and then applying the regression transform to calculate the saliency score. We refer to this model as HNet−. This setting helps us in analyzing the intuitive features latent in our heterogeneous representation of the sentence without consideration of the contextual features. The drop in points has resulted due to the absence of document dependent features. Therefore, we can conclude that document independent features have a major contribution towards saliency estimation of a sentence. Contribution of Transfer Learning Method The fact that increase in training data results in better performance as the system becomes more robust motivated us to pre-train the HNet-B and HNet systems on Daily Mail dataset first and then fine-tune the systems to multi-document summarization setting. We refer to these systems as HNet-B(T) and HNet(T). HNet(T) is the best performing system amongst the HNet variants.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Comparison Results (%) on DUC Datasets\nQuestion: Which system had the highest ROUGE-2 score in 2004?\nGive a detailed response to the above question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the 2004 ROUGE-2 column, we see that HNet(T) has the highest score of 11.29. So, the answer is {\"answer\": \"HNet(T)\"}" } ], "images": [ "scigen/train/3883.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Comparison Results (%) on DUC Datasets" ], "reasoning": "By examining the 2004 ROUGE-2 column, we see that HNet(T) has the highest score of 11.29.", "question": "Which system had the highest ROUGE-2 score in 2004?", "answer": "So, the answer is {\"answer\": \"HNet(T)\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: HNet-B(T) had the highest ROUGE-1 score in 2002., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nHNet-B(T) had the highest ROUGE-1 score in 2002.\n" }, { "role": "assistant", "content": "By examining the 2002 ROUGE-1 column, we see that HNet(T) had the highest score of 39.94, not HNet-B(T). So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3883.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Comparison Results (%) on DUC Datasets" ], "reasoning": "By examining the 2002 ROUGE-1 column, we see that HNet(T) had the highest score of 39.94, not HNet-B(T).", "statement": "HNet-B(T) had the highest ROUGE-1 score in 2002.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: Quartet improves accuracy over SOTA bert-no-expl across question types.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Quartet significantly outperforms bert-no-expl on the most challenging out-of-para questions, indicating improved alignment involving commonsense reasoning.Both Quartet achieves large gains over bert-no-expl on the most challenging out-of-para questions. This suggests that Quartet improves the alignment of qp and xi that involves some commonsense reasoning." } ], "images": [ "scigen/train/3884.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Quartet improves accuracy over SOTA bert-no-expl across question types." ], "reasoning": "Quartet significantly outperforms bert-no-expl on the most challenging out-of-para questions, indicating improved alignment involving commonsense reasoning.", "description": "Both Quartet achieves large gains over bert-no-expl on the most challenging out-of-para questions. This suggests that Quartet improves the alignment of qp and xi that involves some commonsense reasoning.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 5: Quartet improves accuracy over SOTA bert-no-expl across question types.' and proceed to solve the following question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model achieves the highest overall score?\n" }, { "role": "assistant", "content": "By comparing the 'overall' column for all models, Quartet has the highest score of 82.07. So, the answer is {\"answer\": \"Quartet\"}" } ], "images": [ "scigen/train/3884.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Quartet improves accuracy over SOTA bert-no-expl across question types." ], "reasoning": "By comparing the 'overall' column for all models, Quartet has the highest score of 82.07.", "question": "Which model achieves the highest overall score?", "answer": "So, the answer is {\"answer\": \"Quartet\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: Quartet has the lowest score in the in-para category., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nQuartet has the lowest score in the in-para category.\n" }, { "role": "assistant", "content": "By examining the 'in-para' column, Quartet has a score of 73.49, which is not the lowest. Majority has the lowest score of 0.00. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3884.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Quartet improves accuracy over SOTA bert-no-expl across question types." ], "reasoning": "By examining the 'in-para' column, Quartet has a score of 73.49, which is not the lowest. Majority has the lowest score of 0.00.", "statement": "Quartet has the lowest score in the in-para category.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 3: Accuracy of the explanation structure (i,j,di,de). Overall explanation accuracy is accexpl.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Quartet outperforms baselines on every component of the explanation and improves both QA and explanation accuracy, showing that optimizing for explanations leads to better end-task performance.Quartet is also the best model on explanation accuracy. Quartet also outperforms baselines on every component of the explanation. Quartet performs better at predicting i than j. This trend correlates with human performance- picking on the second supporting sentence is harder because in a procedural text neighboring steps can have similar effects. Quartet not only improves QA accuracy but also the explanation accuracy. This shows that if a model is optimized for explanations, it leads to better performance on end-task. Thus, with this result we establish that (at least on our task) models can make better predictions when forced to generate a sensible explanation structure. An educational psychology study Dunlosky et al. However, their hypothesis is not conclusively validated due to lack of evidence." } ], "images": [ "scigen/train/3885.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Accuracy of the explanation structure (i,j,di,de). Overall explanation accuracy is accexpl." ], "reasoning": "Quartet outperforms baselines on every component of the explanation and improves both QA and explanation accuracy, showing that optimizing for explanations leads to better end-task performance.", "description": "Quartet is also the best model on explanation accuracy. Quartet also outperforms baselines on every component of the explanation. Quartet performs better at predicting i than j. This trend correlates with human performance- picking on the second supporting sentence is harder because in a procedural text neighboring steps can have similar effects. Quartet not only improves QA accuracy but also the explanation accuracy. This shows that if a model is optimized for explanations, it leads to better performance on end-task. Thus, with this result we establish that (at least on our task) models can make better predictions when forced to generate a sensible explanation structure. An educational psychology study Dunlosky et al. However, their hypothesis is not conclusively validated due to lack of evidence.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Accuracy of the explanation structure (i,j,di,de). Overall explanation accuracy is accexpl.\nQuestion: Which model has the highest accuracy in predicting acccde?\nSolve the above question based on the table information. Let's think step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the highest accuracy in predicting acccde, compare the acccde values for all models in the table. Human has the highest acccde value of 96.30. So, the answer is {\"answer\": \"Human\"}." } ], "images": [ "scigen/train/3885.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Accuracy of the explanation structure (i,j,di,de). Overall explanation accuracy is accexpl." ], "reasoning": "To find the model with the highest accuracy in predicting acccde, compare the acccde values for all models in the table. Human has the highest acccde value of 96.30.", "question": "Which model has the highest accuracy in predicting acccde?", "answer": "So, the answer is {\"answer\": \"Human\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nQuartet has a higher accci than acccj.\n" }, { "role": "assistant", "content": "To verify this statement, compare the accci and acccj values for Quartet in the table. Quartet's accci is 69.24 and acccj is 65.97, so accci is indeed higher than acccj. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3885.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Accuracy of the explanation structure (i,j,di,de). Overall explanation accuracy is accexpl." ], "reasoning": "To verify this statement, compare the accci and acccj values for Quartet in the table. Quartet's accci is 69.24 and acccj is 65.97, so accci is indeed higher than acccj.", "statement": "Quartet has a higher accci than acccj.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 4: Quartet improves accuracy on the QA (end task) by 7% points.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Quartet outperforms bert-no-expl in QA accuracy by solving explanations and deriving answers from them.We compare against the existing SOTA on WIQA no-explanation task. The major difference between bert-no-expl and Quartet is that bert-no-expl solves only the QA task, whereas Quartet solves explanations, and the answer to the QA task is derived from the explanation. Multi-tasking (i.e., explaining the answer) provides the gains to Quartet." } ], "images": [ "scigen/train/3886.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Quartet improves accuracy on the QA (end task) by 7% points." ], "reasoning": "Quartet outperforms bert-no-expl in QA accuracy by solving explanations and deriving answers from them.", "description": "We compare against the existing SOTA on WIQA no-explanation task. The major difference between bert-no-expl and Quartet is that bert-no-expl solves only the QA task, whereas Quartet solves explanations, and the answer to the QA task is derived from the explanation. Multi-tasking (i.e., explaining the answer) provides the gains to Quartet.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 4: Quartet improves accuracy on the QA (end task) by 7% points.', solve the following question step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the QA accuracy difference between bert-no-expl and Quartet?\n" }, { "role": "assistant", "content": "To find the QA accuracy difference, subtract the QA accuracy of bert-no-expl (75.19) from that of Quartet (82.07). So, the answer is {\"answer\": \"6.88\"}." } ], "images": [ "scigen/train/3886.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Quartet improves accuracy on the QA (end task) by 7% points." ], "reasoning": "To find the QA accuracy difference, subtract the QA accuracy of bert-no-expl (75.19) from that of Quartet (82.07).", "question": "What is the QA accuracy difference between bert-no-expl and Quartet?", "answer": "So, the answer is {\"answer\": \"6.88\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nQuartet has a higher QA accuracy than bert-w/-expl.\n" }, { "role": "assistant", "content": "The table shows that Quartet has a QA accuracy of 82.07, while bert-w/-expl has a QA accuracy of 75.06, indicating that Quartet has a higher QA accuracy. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3886.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Quartet improves accuracy on the QA (end task) by 7% points." ], "reasoning": "The table shows that Quartet has a QA accuracy of 82.07, while bert-w/-expl has a QA accuracy of 75.06, indicating that Quartet has a higher QA accuracy.", "statement": "Quartet has a higher QA accuracy than bert-w/-expl.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6: Confusion matrix for di (left) and de overall (right). (gold is on x-axis, predicted on y-axis.)', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The majority of '−' misclassifications for di should have been ' +', and a similar but less skewed trend is observed for de.di, de errors: When the model incorrectly predicts di, a major source of error is when ‘−’ is misclassified. 70% of the ‘−’ mistakes, should have been classified as ‘+’. A similar trend is observed for de but the misclassification of ‘− is less skewed." } ], "images": [ "scigen/train/3887.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Confusion matrix for di (left) and de overall (right). (gold is on x-axis, predicted on y-axis.)" ], "reasoning": "The majority of '−' misclassifications for di should have been ' +', and a similar but less skewed trend is observed for de.", "description": "di, de errors: When the model incorrectly predicts di, a major source of error is when ‘−’ is misclassified. 70% of the ‘−’ mistakes, should have been classified as ‘+’. A similar trend is observed for de but the misclassification of ‘− is less skewed.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 6: Confusion matrix for di (left) and de overall (right). (gold is on x-axis, predicted on y-axis.)'. Solve the following question based on the table.\n\nQuestion: What is the total number of '−' misclassifications for di and de combined?\nLet's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the total number of '−' misclassifications, add the '−' misclassifications for di (47) and de (639). So, the answer is {\"answer\": \"686\"}." } ], "images": [ "scigen/train/3887.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Confusion matrix for di (left) and de overall (right). (gold is on x-axis, predicted on y-axis.)" ], "reasoning": "To find the total number of '−' misclassifications, add the '−' misclassifications for di (47) and de (639).", "question": "What is the total number of '−' misclassifications for di and de combined?", "answer": "So, the answer is {\"answer\": \"686\"}." }, { "messages": [ { "role": "user", "content": "Most of the '−' misclassifications for di should have been classified as '+'. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nMost of the '−' misclassifications for di should have been classified as '+'.\n" }, { "role": "assistant", "content": "The table shows that 70% of the '−' mistakes for di should have been classified as '+', which supports the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3887.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Confusion matrix for di (left) and de overall (right). (gold is on x-axis, predicted on y-axis.)" ], "reasoning": "The table shows that 70% of the '−' mistakes for di should have been classified as '+', which supports the statement.", "statement": "Most of the '−' misclassifications for di should have been classified as '+'.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 6: Confusion matrix for di (left) and de overall (right). (gold is on x-axis, predicted on y-axis.)', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The majority of '−' misclassifications for di should have been classified as '+', while de shows a similar but less skewed trend.di, de errors: When the model incorrectly predicts di, a major source of error is when ‘−’ is misclassified. 70% of the ‘−’ mistakes, should have been classified as ‘+’. A similar trend is observed for de but the misclassification of ‘− is less skewed." } ], "images": [ "scigen/train/3888.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Confusion matrix for di (left) and de overall (right). (gold is on x-axis, predicted on y-axis.)" ], "reasoning": "The majority of '−' misclassifications for di should have been classified as '+', while de shows a similar but less skewed trend.", "description": "di, de errors: When the model incorrectly predicts di, a major source of error is when ‘−’ is misclassified. 70% of the ‘−’ mistakes, should have been classified as ‘+’. A similar trend is observed for de but the misclassification of ‘− is less skewed.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 6: Confusion matrix for di (left) and de overall (right). (gold is on x-axis, predicted on y-axis.)', answer the following question. Give detailed solution consisting of each step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat percentage of '−' misclassifications for di should have been classified as '+'?\n" }, { "role": "assistant", "content": "According to the description, 70% of the '−' mistakes for di should have been classified as '+'. So, the answer is {\"answer\": \"70%\"}" } ], "images": [ "scigen/train/3888.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Confusion matrix for di (left) and de overall (right). (gold is on x-axis, predicted on y-axis.)" ], "reasoning": "According to the description, 70% of the '−' mistakes for di should have been classified as '+'.", "question": "What percentage of '−' misclassifications for di should have been classified as '+'?", "answer": "So, the answer is {\"answer\": \"70%\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe majority of '−' misclassifications for de should have been classified as '+'.\n" }, { "role": "assistant", "content": "The description states that the misclassification of '−' for de is less skewed compared to di, but does not provide specific percentages, making it unclear if the majority should have been classified as '+'. So, the answer is {\"answer\": \"not enough info\"}" } ], "images": [ "scigen/train/3888.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Confusion matrix for di (left) and de overall (right). (gold is on x-axis, predicted on y-axis.)" ], "reasoning": "The description states that the misclassification of '−' for de is less skewed compared to di, but does not provide specific percentages, making it unclear if the majority should have been classified as '+'.", "statement": "The majority of '−' misclassifications for de should have been classified as '+'.", "answer": "So, the answer is {\"answer\": \"not enough info\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 3: Human ratings of summaries along four evaluation dimensions, averaged over three expert annotators, broken down by extractive and abstractive models. The M* codes follow the notation described in Section 3.2. The three highest-rated models in each column are in bold.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Pretrained models like Pegasus, BART, and T5 performed best on consistency and fluency but scored lower on relevance and coherence.Scores for ground truth summaries are included as a point of reference. We find that pretrained models such as Pegasus, BART, and T5 consistently performed best on most dimensions. Notably, the mentioned models scored highest on consistency and fluency while obtaining lower scores for relevance and coherence. Scores for extractive models highlight the known shortcomings of such approaches, which are lack of coherence of summaries and issues with selecting relevant content. Abstractive model ratings show an increasing trend with respect to the date of publication. This is a promising result as it suggests that the quality of models is improving with time. Worth noting is also the fact that reference summaries did not score well on consistency. Upon examination of the annotations, we found that the reference summaries often contained extraneous information, such as hyperlinks and click-bait descriptions of other articles. As this information was not present in the source document, the annotators interpreted it as a hallucination and gave such examples a low consistency score." } ], "images": [ "scigen/train/3889.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Human ratings of summaries along four evaluation dimensions, averaged over three expert annotators, broken down by extractive and abstractive models. The M* codes follow the notation described in Section 3.2. The three highest-rated models in each column are in bold." ], "reasoning": "Pretrained models like Pegasus, BART, and T5 performed best on consistency and fluency but scored lower on relevance and coherence.", "description": "Scores for ground truth summaries are included as a point of reference. We find that pretrained models such as Pegasus, BART, and T5 consistently performed best on most dimensions. Notably, the mentioned models scored highest on consistency and fluency while obtaining lower scores for relevance and coherence. Scores for extractive models highlight the known shortcomings of such approaches, which are lack of coherence of summaries and issues with selecting relevant content. Abstractive model ratings show an increasing trend with respect to the date of publication. This is a promising result as it suggests that the quality of models is improving with time. Worth noting is also the fact that reference summaries did not score well on consistency. Upon examination of the annotations, we found that the reference summaries often contained extraneous information, such as hyperlinks and click-bait descriptions of other articles. As this information was not present in the source document, the annotators interpreted it as a hallucination and gave such examples a low consistency score.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Human ratings of summaries along four evaluation dimensions, averaged over three expert annotators, broken down by extractive and abstractive models. The M* codes follow the notation described in Section 3.2. The three highest-rated models in each column are in bold.', unfold the question presented below, detailing every step of your calculation. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model scored the highest on consistency and what was the score?\n" }, { "role": "assistant", "content": "By examining the table, the highest score for consistency is 4.99, achieved by the model M2 - BanditSum. So, the answer is {\"answer\": \"M2 - BanditSum with a score of 4.99\"}." } ], "images": [ "scigen/train/3889.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Human ratings of summaries along four evaluation dimensions, averaged over three expert annotators, broken down by extractive and abstractive models. The M* codes follow the notation described in Section 3.2. The three highest-rated models in each column are in bold." ], "reasoning": "By examining the table, the highest score for consistency is 4.99, achieved by the model M2 - BanditSum.", "question": "Which model scored the highest on consistency and what was the score?", "answer": "So, the answer is {\"answer\": \"M2 - BanditSum with a score of 4.99\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe reference summaries scored the highest on fluency.\n" }, { "role": "assistant", "content": "The table shows that the reference summaries scored 4.79 on fluency, while several other models scored higher, such as M0 - LEAD-3 with 4.94. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3889.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Human ratings of summaries along four evaluation dimensions, averaged over three expert annotators, broken down by extractive and abstractive models. The M* codes follow the notation described in Section 3.2. The three highest-rated models in each column are in bold." ], "reasoning": "The table shows that the reference summaries scored 4.79 on fluency, while several other models scored higher, such as M0 - LEAD-3 with 4.94.", "statement": "The reference summaries scored the highest on fluency.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title '(a) Model scores from summarization-specific evaluation metrics.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "Large models pretrained on vast data generally received the highest scores, but metrics like S3, SummaQA, SMS, CHRF, and METEOR favored extractive models.The results align with insights coming from human evaluation of models. We found that for most metrics, the highest scores were assigned to large models pretrained on vast quantities of data. However, several metrics, such as S3, SummaQA, SMS, CHRF, and METEOR tended to favor extractive models, assigning highest scores to their outputs." } ], "images": [ "scigen/train/3890.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(a) Model scores from summarization-specific evaluation metrics." ], "reasoning": "Large models pretrained on vast data generally received the highest scores, but metrics like S3, SummaQA, SMS, CHRF, and METEOR favored extractive models.", "description": "The results align with insights coming from human evaluation of models. We found that for most metrics, the highest scores were assigned to large models pretrained on vast quantities of data. However, several metrics, such as S3, SummaQA, SMS, CHRF, and METEOR tended to favor extractive models, assigning highest scores to their outputs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of '(a) Model scores from summarization-specific evaluation metrics.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich model received the highest score for the S3 metric?\n" }, { "role": "assistant", "content": "To find the model with the highest S3 score, look at the S3 column and identify the highest value. The highest S3 score is 0.6395/0.7039, which belongs to the M4 - REFRESH model. So, the answer is {\"answer\": \"M4 - REFRESH\"}." } ], "images": [ "scigen/train/3890.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(a) Model scores from summarization-specific evaluation metrics." ], "reasoning": "To find the model with the highest S3 score, look at the S3 column and identify the highest value. The highest S3 score is 0.6395/0.7039, which belongs to the M4 - REFRESH model.", "question": "Which model received the highest score for the S3 metric?", "answer": "So, the answer is {\"answer\": \"M4 - REFRESH\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The M4 - REFRESH model received the highest score for the SummaQA metric. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe M4 - REFRESH model received the highest score for the SummaQA metric.\n" }, { "role": "assistant", "content": "To verify this statement, look at the SummaQA column and identify the highest value. The highest SummaQA score is 0.1652, which belongs to the M0 - LEAD-3 model, not the M4 - REFRESH model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3890.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(a) Model scores from summarization-specific evaluation metrics." ], "reasoning": "To verify this statement, look at the SummaQA column and identify the highest value. The highest SummaQA score is 0.1652, which belongs to the M0 - LEAD-3 model, not the M4 - REFRESH model.", "statement": "The M4 - REFRESH model received the highest score for the SummaQA metric.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled '(b) Model scores from other text generation evaluation metrics.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Large models pretrained on vast quantities of data generally received the highest scores, but metrics like S3, SummaQA, SMS, CHRF, and METEOR favored extractive models.The results align with insights coming from human evaluation of models. We found that for most metrics, the highest scores were assigned to large models pretrained on vast quantities of data. However, several metrics, such as S3, SummaQA, SMS, CHRF, and METEOR tended to favor extractive models, assigning highest scores to their outputs." } ], "images": [ "scigen/train/3891.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(b) Model scores from other text generation evaluation metrics." ], "reasoning": "Large models pretrained on vast quantities of data generally received the highest scores, but metrics like S3, SummaQA, SMS, CHRF, and METEOR favored extractive models.", "description": "The results align with insights coming from human evaluation of models. We found that for most metrics, the highest scores were assigned to large models pretrained on vast quantities of data. However, several metrics, such as S3, SummaQA, SMS, CHRF, and METEOR tended to favor extractive models, assigning highest scores to their outputs.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: (b) Model scores from other text generation evaluation metrics.\nQuestion: Which model has the highest METEOR score among extractive models?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the highest METEOR score among extractive models, look at the METEOR column under Extractive Models and identify the highest value. So, the answer is {\"answer\": \"0.2395\"}." } ], "images": [ "scigen/train/3891.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(b) Model scores from other text generation evaluation metrics." ], "reasoning": "To find the highest METEOR score among extractive models, look at the METEOR column under Extractive Models and identify the highest value.", "question": "Which model has the highest METEOR score among extractive models?", "answer": "So, the answer is {\"answer\": \"0.2395\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model M4 - REFRESH has the highest METEOR score among extractive models.\n" }, { "role": "assistant", "content": "The METEOR score for M4 - REFRESH is 0.2395, which is the highest among the extractive models listed in the table. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3891.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(b) Model scores from other text generation evaluation metrics." ], "reasoning": "The METEOR score for M4 - REFRESH is 0.2395, which is the highest among the extractive models listed in the table.", "statement": "The model M4 - REFRESH has the highest METEOR score among extractive models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 1: Image-Caption retrieval results on the Flickr8k test set. R@N is the percentage of items for which the correct image or caption was retrieved in the top N (higher is better). Med r is the median rank of the correct image or caption (lower is better). We also report the 95 percent confidence interval for the R@N scores.'.\n" }, { "role": "assistant", "content": "The MBN-GRU model significantly outperforms previous spoken caption-to-image methods, with the largest improvement being 23.2 percentage points on R@10 compared to [Chrupala2017].The caption embeddings are ranked by cosine distance to the image and vice versa where R@N is the percentage of test items for which the correct image or caption was in the top N results. We compare our models to [Harwath2015] and [Chrupala2017], and include our own character-based model for comparison. [Harwath2015] is a convolutional approach, whereas [Chrupala2017] is an approach using recurrent highway networks with scalar attention. The character-based model is similar to the model we use here and was trained on the original Flickr8k text captions (see [merkx2019] for a full description). Both our MFCC and MBN based model significantly outperform previous spoken caption-to-image methods on the Flickr8k dataset. The largest improvement is the MBN model which outperforms the results reported in [Chrupala2017] by as much as 23.2 percentage points on R@10. The MFCC model also improves on previous results but scores significantly lower than the MBN model across the board, improving as much as 12.3 percentage points over previous work. There is a large performance gap between the text-caption to image retrieval results and the spoken-caption to image results, showing there is still a lot of room for improvement." } ], "images": [ "scigen/train/3892.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Image-Caption retrieval results on the Flickr8k test set. R@N is the percentage of items for which the correct image or caption was retrieved in the top N (higher is better). Med r is the median rank of the correct image or caption (lower is better). We also report the 95 percent confidence interval for the R@N scores." ], "reasoning": "The MBN-GRU model significantly outperforms previous spoken caption-to-image methods, with the largest improvement being 23.2 percentage points on R@10 compared to [Chrupala2017].", "description": "The caption embeddings are ranked by cosine distance to the image and vice versa where R@N is the percentage of test items for which the correct image or caption was in the top N results. We compare our models to [Harwath2015] and [Chrupala2017], and include our own character-based model for comparison. [Harwath2015] is a convolutional approach, whereas [Chrupala2017] is an approach using recurrent highway networks with scalar attention. The character-based model is similar to the model we use here and was trained on the original Flickr8k text captions (see [merkx2019] for a full description). Both our MFCC and MBN based model significantly outperform previous spoken caption-to-image methods on the Flickr8k dataset. The largest improvement is the MBN model which outperforms the results reported in [Chrupala2017] by as much as 23.2 percentage points on R@10. The MFCC model also improves on previous results but scores significantly lower than the MBN model across the board, improving as much as 12.3 percentage points over previous work. There is a large performance gap between the text-caption to image retrieval results and the spoken-caption to image results, showing there is still a lot of room for improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 1: Image-Caption retrieval results on the Flickr8k test set. R@N is the percentage of items for which the correct image or caption was retrieved in the top N (higher is better). Med r is the median rank of the correct image or caption (lower is better). We also report the 95 percent confidence interval for the R@N scores.', solve this question and give detail solutions. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model shows the highest improvement in Caption to Image R@10 compared to [Chrupala2017]?\n" }, { "role": "assistant", "content": "The description states that the MBN model outperforms [Chrupala2017] by 23.2 percentage points on R@10. The table shows that [Chrupala2017] has a Caption to Image R@10 of 25.3±1.2, while the MBN-GRU model has 48.5±1.4, confirming the largest improvement. So, the answer is {\"answer\": \"MBN-GRU\"}." } ], "images": [ "scigen/train/3892.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Image-Caption retrieval results on the Flickr8k test set. R@N is the percentage of items for which the correct image or caption was retrieved in the top N (higher is better). Med r is the median rank of the correct image or caption (lower is better). We also report the 95 percent confidence interval for the R@N scores." ], "reasoning": "The description states that the MBN model outperforms [Chrupala2017] by 23.2 percentage points on R@10. The table shows that [Chrupala2017] has a Caption to Image R@10 of 25.3±1.2, while the MBN-GRU model has 48.5±1.4, confirming the largest improvement.", "question": "Which model shows the highest improvement in Caption to Image R@10 compared to [Chrupala2017]?", "answer": "So, the answer is {\"answer\": \"MBN-GRU\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The MFCC-GRU model has a higher Caption to Image R@1 score than [Chrupala2017]. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe MFCC-GRU model has a higher Caption to Image R@1 score than [Chrupala2017].\n" }, { "role": "assistant", "content": "The table shows that the MFCC-GRU model has a Caption to Image R@1 score of 8.4±0.8, while [Chrupala2017] has a score of 5.5±0.6, indicating that the MFCC-GRU model indeed has a higher score. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3892.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Image-Caption retrieval results on the Flickr8k test set. R@N is the percentage of items for which the correct image or caption was retrieved in the top N (higher is better). Med r is the median rank of the correct image or caption (lower is better). We also report the 95 percent confidence interval for the R@N scores." ], "reasoning": "The table shows that the MFCC-GRU model has a Caption to Image R@1 score of 8.4±0.8, while [Chrupala2017] has a score of 5.5±0.6, indicating that the MFCC-GRU model indeed has a higher score.", "statement": "The MFCC-GRU model has a higher Caption to Image R@1 score than [Chrupala2017].", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 2: Alignment of RST-DT relations for those labels that were identified as theoretically interesting cases of discrepancies between the three mapping proposals.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "RST-DT's Comparison label should be mapped to both PDTB Conjunction and Contrast, while Background and Circumstance labels are more general than predicted by UniDim and ISO.Regarding the first label, PDTB does not have a directly corresponding label for RST-DT’s Comparison label. Because the RST-DT annotation manual explicitly defines these relations as non-contrastive, \\namecitesanders2016 mapped this label to Conjunction while \\namecitebunt2016 mapped it to Contrast. This distribution is similar for explicit and implicit instances of Comparison. Typical markers for explicit instances are while, but and however. We therefore conclude that RST-DT ’s Comparison should be mapped to both PDTB Conjunction and Contrast. Finally, we consider the labels Background and Circumstance. UniDim mapped Background to PDTB Conjunction and Asynchronous, whereas ISO only mapped it to Conjunction. Regarding Circumstance, UniDim mapped it to PDTB Conjunction, Synchronous and Asynchronous, and ISO only mapped it to Synchronous. RST-DT’s Background and Circumstance therefore seem to be more general labels than both UniDim and ISO predicted. This could be due to the lack of corresponding label in PDTB." } ], "images": [ "scigen/train/3893.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Alignment of RST-DT relations for those labels that were identified as theoretically interesting cases of discrepancies between the three mapping proposals." ], "reasoning": "RST-DT's Comparison label should be mapped to both PDTB Conjunction and Contrast, while Background and Circumstance labels are more general than predicted by UniDim and ISO.", "description": "Regarding the first label, PDTB does not have a directly corresponding label for RST-DT’s Comparison label. Because the RST-DT annotation manual explicitly defines these relations as non-contrastive, \\namecitesanders2016 mapped this label to Conjunction while \\namecitebunt2016 mapped it to Contrast. This distribution is similar for explicit and implicit instances of Comparison. Typical markers for explicit instances are while, but and however. We therefore conclude that RST-DT ’s Comparison should be mapped to both PDTB Conjunction and Contrast. Finally, we consider the labels Background and Circumstance. UniDim mapped Background to PDTB Conjunction and Asynchronous, whereas ISO only mapped it to Conjunction. Regarding Circumstance, UniDim mapped it to PDTB Conjunction, Synchronous and Asynchronous, and ISO only mapped it to Synchronous. RST-DT’s Background and Circumstance therefore seem to be more general labels than both UniDim and ISO predicted. This could be due to the lack of corresponding label in PDTB.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 2: Alignment of RST-DT relations for those labels that were identified as theoretically interesting cases of discrepancies between the three mapping proposals.' to sequentially solve the question presented below. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich RST-DT label has the highest total count?\n" }, { "role": "assistant", "content": "By examining the 'Total' column in the table, we see that the Circumstance label has the highest total count of 294. So, the answer is {\"answer\": \"Circumstance\"}" } ], "images": [ "scigen/train/3893.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Alignment of RST-DT relations for those labels that were identified as theoretically interesting cases of discrepancies between the three mapping proposals." ], "reasoning": "By examining the 'Total' column in the table, we see that the Circumstance label has the highest total count of 294.", "question": "Which RST-DT label has the highest total count?", "answer": "So, the answer is {\"answer\": \"Circumstance\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Background label in RST-DT is mapped to PDTB Conjunction and Asynchronous by UniDim.\n" }, { "role": "assistant", "content": "According to the description, UniDim mapped the Background label to PDTB Conjunction and Asynchronous, which is supported by the table. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3893.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Alignment of RST-DT relations for those labels that were identified as theoretically interesting cases of discrepancies between the three mapping proposals." ], "reasoning": "According to the description, UniDim mapped the Background label to PDTB Conjunction and Asynchronous, which is supported by the table.", "statement": "The Background label in RST-DT is mapped to PDTB Conjunction and Asynchronous by UniDim.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: Results of the different tests. In LKit we only evaluated the positive and negative results (it also classifies sentences with no polarity).'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The baseline SL-O system achieved values between 54% (ca) and 62.95% (eu), similar to the supervised model. For Basque, the SL-O system's results were comparable to those of San Vicente and Saralegi's classifier.The baseline (SL-O) obtained values between 54% (ca) and 62.95% (eu), results that are in line to those obtained by the supervised model. As we are not aware of available sa tools for ca, we could not compare our results with other systems. For Basque, San Vicente and Saralegi They used a simple average polarity ratio classifier, which is similar to our baseline. Even if the lexica are different, their results are very similar to our SL-O system (63% vs 62,95%), and they also show that manually reviewing the lexica can boost the accuracy by up to 13%. In gl and pt the best values were obtained using individual lexica together with syntactic rules, while the Iberian system achieved the best results in the other languages." } ], "images": [ "scigen/train/3894.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results of the different tests. In LKit we only evaluated the positive and negative results (it also classifies sentences with no polarity)." ], "reasoning": "The baseline SL-O system achieved values between 54% (ca) and 62.95% (eu), similar to the supervised model. For Basque, the SL-O system's results were comparable to those of San Vicente and Saralegi's classifier.", "description": "The baseline (SL-O) obtained values between 54% (ca) and 62.95% (eu), results that are in line to those obtained by the supervised model. As we are not aware of available sa tools for ca, we could not compare our results with other systems. For Basque, San Vicente and Saralegi They used a simple average polarity ratio classifier, which is similar to our baseline. Even if the lexica are different, their results are very similar to our SL-O system (63% vs 62,95%), and they also show that manually reviewing the lexica can boost the accuracy by up to 13%. In gl and pt the best values were obtained using individual lexica together with syntactic rules, while the Iberian system achieved the best results in the other languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 4: Results of the different tests. In LKit we only evaluated the positive and negative results (it also classifies sentences with no polarity).', solve the following question. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich language had the highest score using the ML+O method, and what was the score?\n" }, { "role": "assistant", "content": "To find the highest score using the ML+O method, look at the ML+O column and identify the highest value. The highest score is 76.50 for the language es. So, the answer is {\"answer\": \"76.50\"}." } ], "images": [ "scigen/train/3894.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results of the different tests. In LKit we only evaluated the positive and negative results (it also classifies sentences with no polarity)." ], "reasoning": "To find the highest score using the ML+O method, look at the ML+O column and identify the highest value. The highest score is 76.50 for the language es.", "question": "Which language had the highest score using the ML+O method, and what was the score?", "answer": "So, the answer is {\"answer\": \"76.50\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The SL-O system achieved the highest score for the gl language. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe SL-O system achieved the highest score for the gl language.\n" }, { "role": "assistant", "content": "To verify this statement, look at the SL-O column and compare the scores for all languages. The highest score in the SL-O column is 62.95 for the eu language, not gl. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3894.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results of the different tests. In LKit we only evaluated the positive and negative results (it also classifies sentences with no polarity)." ], "reasoning": "To verify this statement, look at the SL-O column and compare the scores for all languages. The highest score in the SL-O column is 62.95 for the eu language, not gl.", "statement": "The SL-O system achieved the highest score for the gl language.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: Impact of the operations (O) with mono (sl) and multilingual lexica (ml) and of the ml with (+O) and without operations (-O).', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Using the same set of universal rules improves classifier performance in all languages and settings, with the impact varying between 3.5 and over 15 percentage points.Concerning the rules (columns 2 and 3), the results show that using the same set of universal rules improves the performance of the classifier in all the languages and settings. Their impact varies between 3.5 percentage points (ca) and more than 15 (es) and, for each language, the rules provide a similar effect in monolingual and multilingual lexica (except for ca, with much higher values in the ML scenario). On average, using multilingual lexica had a positive impact of 1.3 (-O) and 2.8 points (+O). As mentioned, ca has a different behaviour: the gain from rules when using monolingual lexica is about 3.50 points (lower than other languages), and the benefit of the ML lexicon without syntactic rules is of 4.25 points. However, when combining both the universal rules and the ML lexicon its performance increases ≈15 points, turning out that the combination of these two factors is decisive." } ], "images": [ "scigen/train/3895.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Impact of the operations (O) with mono (sl) and multilingual lexica (ml) and of the ml with (+O) and without operations (-O)." ], "reasoning": "Using the same set of universal rules improves classifier performance in all languages and settings, with the impact varying between 3.5 and over 15 percentage points.", "description": "Concerning the rules (columns 2 and 3), the results show that using the same set of universal rules improves the performance of the classifier in all the languages and settings. Their impact varies between 3.5 percentage points (ca) and more than 15 (es) and, for each language, the rules provide a similar effect in monolingual and multilingual lexica (except for ca, with much higher values in the ML scenario). On average, using multilingual lexica had a positive impact of 1.3 (-O) and 2.8 points (+O). As mentioned, ca has a different behaviour: the gain from rules when using monolingual lexica is about 3.50 points (lower than other languages), and the benefit of the ML lexicon without syntactic rules is of 4.25 points. However, when combining both the universal rules and the ML lexicon its performance increases ≈15 points, turning out that the combination of these two factors is decisive.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 5: Impact of the operations (O) with mono (sl) and multilingual lexica (ml) and of the ml with (+O) and without operations (-O).'. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: What is the average positive impact of using multilingual lexica with and without syntactic rules?\n\nYour Solution:\n" }, { "role": "assistant", "content": "The average positive impact of using multilingual lexica without syntactic rules is 1.3 points, and with syntactic rules is 2.8 points. So, the answer is {\"answer\": \"2.05\"}" } ], "images": [ "scigen/train/3895.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Impact of the operations (O) with mono (sl) and multilingual lexica (ml) and of the ml with (+O) and without operations (-O)." ], "reasoning": "The average positive impact of using multilingual lexica without syntactic rules is 1.3 points, and with syntactic rules is 2.8 points.", "question": "What is the average positive impact of using multilingual lexica with and without syntactic rules?", "answer": "So, the answer is {\"answer\": \"2.05\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe combination of universal rules and the ML lexicon increases performance by approximately 15 points for ca.\n" }, { "role": "assistant", "content": "The table shows that for ca, the combination of universal rules and the ML lexicon increases performance by 15.5 points. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3895.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Impact of the operations (O) with mono (sl) and multilingual lexica (ml) and of the ml with (+O) and without operations (-O)." ], "reasoning": "The table shows that for ca, the combination of universal rules and the ML lexicon increases performance by 15.5 points.", "statement": "The combination of universal rules and the ML lexicon increases performance by approximately 15 points for ca.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 5: The performance of our average word embeddings with our projection method in comparison to other approaches. ‡We trained CCA on word-alignments extracted from TED transcripts using fast_align (i.e., CCA uses the same data source as our method).'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Our suggested approach yields significantly better cross-lingual word embeddings and sentence embeddings compared to CCA, with even greater improvements when re-mapping AR embeddings.However, in preliminary experiments, we found that our suggested approach produces considerably better cross-lingual word embeddings in our setup. Compared to the word-level projection method CCA we obtain substantially better cross-lingual sentence embeddings, and even stronger improvements when re-mapping AR embeddings, even though these are already bilingual." } ], "images": [ "scigen/train/3896.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The performance of our average word embeddings with our projection method in comparison to other approaches. ‡We trained CCA on word-alignments extracted from TED transcripts using fast_align (i.e., CCA uses the same data source as our method)." ], "reasoning": "Our suggested approach yields significantly better cross-lingual word embeddings and sentence embeddings compared to CCA, with even greater improvements when re-mapping AR embeddings.", "description": "However, in preliminary experiments, we found that our suggested approach produces considerably better cross-lingual word embeddings in our setup. Compared to the word-level projection method CCA we obtain substantially better cross-lingual sentence embeddings, and even stronger improvements when re-mapping AR embeddings, even though these are already bilingual.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 5: The performance of our average word embeddings with our projection method in comparison to other approaches. ‡We trained CCA on word-alignments extracted from TED transcripts using fast_align (i.e., CCA uses the same data source as our method).\n\nQuestion: Which model and projection combination achieved the highest cross-lingual score?\n" }, { "role": "assistant", "content": "To find the highest cross-lingual score, we need to look at the '∑ X-Ling' column and identify the highest value. The highest value is 74.6, achieved by 'FT (our projection)'. So, the answer is {\"answer\": \"FT (our projection)\"}." } ], "images": [ "scigen/train/3896.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The performance of our average word embeddings with our projection method in comparison to other approaches. ‡We trained CCA on word-alignments extracted from TED transcripts using fast_align (i.e., CCA uses the same data source as our method)." ], "reasoning": "To find the highest cross-lingual score, we need to look at the '∑ X-Ling' column and identify the highest value. The highest value is 74.6, achieved by 'FT (our projection)'.", "question": "Which model and projection combination achieved the highest cross-lingual score?", "answer": "So, the answer is {\"answer\": \"FT (our projection)\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The original AR model achieved the highest in-language score. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe original AR model achieved the highest in-language score.\n" }, { "role": "assistant", "content": "To verify this statement, we need to look at the '∑ In-Language' column and identify the highest value. The highest value is 80.8, achieved by 'FT (monolingual)', not the original AR model. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3896.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The performance of our average word embeddings with our projection method in comparison to other approaches. ‡We trained CCA on word-alignments extracted from TED transcripts using fast_align (i.e., CCA uses the same data source as our method)." ], "reasoning": "To verify this statement, we need to look at the '∑ In-Language' column and identify the highest value. The highest value is 80.8, achieved by 'FT (monolingual)', not the original AR model.", "statement": "The original AR model achieved the highest in-language score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Monolingual results. Brackets show the different power means that were applied to all individual embeddings. † we normalized the embeddings of our full model with the z-norm as proposed by LeCun:1998.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Concatenating multiple word embedding types and incorporating power means significantly improves sentence embeddings, with the combination yielding an average improvement of 3pp over the best individual embedding.The results show that we can substantially improve sentence embeddings when concatenating multiple word embedding types. All four embedding types concatenated achieve 2pp improvement over the best individual embeddings (GV). Incorporating further power means also substantially improve performances. GV improves by 0.6pp on average, GN by 1.9pp, MS by 2.1pp and AR by 3.7pp when concatenating p=±∞ to the standard value p=1 (dimensionality increases to 900). The combination of concatenation of embedding types and power means gives an average improvement of 3pp over the individually best embedding type." } ], "images": [ "scigen/train/3897.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Monolingual results. Brackets show the different power means that were applied to all individual embeddings. † we normalized the embeddings of our full model with the z-norm as proposed by LeCun:1998." ], "reasoning": "Concatenating multiple word embedding types and incorporating power means significantly improves sentence embeddings, with the combination yielding an average improvement of 3pp over the best individual embedding.", "description": "The results show that we can substantially improve sentence embeddings when concatenating multiple word embedding types. All four embedding types concatenated achieve 2pp improvement over the best individual embeddings (GV). Incorporating further power means also substantially improve performances. GV improves by 0.6pp on average, GN by 1.9pp, MS by 2.1pp and AR by 3.7pp when concatenating p=±∞ to the standard value p=1 (dimensionality increases to 900). The combination of concatenation of embedding types and power means gives an average improvement of 3pp over the individually best embedding type.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 2: Monolingual results. Brackets show the different power means that were applied to all individual embeddings. † we normalized the embeddings of our full model with the z-norm as proposed by LeCun:1998.', and sequentially solve the question, writing out each step of your reasoning process. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhat is the average improvement in performance when concatenating GV, GN, MS, and AR embeddings with power means?\n" }, { "role": "assistant", "content": "The description states that concatenating all four embedding types (GV, GN, MS, AR) with power means gives an average improvement of 3pp over the best individual embedding type. So, the answer is {\"answer\": \"3pp\"}." } ], "images": [ "scigen/train/3897.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Monolingual results. Brackets show the different power means that were applied to all individual embeddings. † we normalized the embeddings of our full model with the z-norm as proposed by LeCun:1998." ], "reasoning": "The description states that concatenating all four embedding types (GV, GN, MS, AR) with power means gives an average improvement of 3pp over the best individual embedding type.", "question": "What is the average improvement in performance when concatenating GV, GN, MS, and AR embeddings with power means?", "answer": "So, the answer is {\"answer\": \"3pp\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim Concatenating GV, GN, MS, and AR embeddings with power means achieves an average improvement of 3pp over the best individual embedding type., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nConcatenating GV, GN, MS, and AR embeddings with power means achieves an average improvement of 3pp over the best individual embedding type.\n" }, { "role": "assistant", "content": "The description confirms that the combination of concatenating embedding types and power means gives an average improvement of 3pp over the best individual embedding type. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3897.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Monolingual results. Brackets show the different power means that were applied to all individual embeddings. † we normalized the embeddings of our full model with the z-norm as proposed by LeCun:1998." ], "reasoning": "The description confirms that the combination of concatenating embedding types and power means gives an average improvement of 3pp over the best individual embedding type.", "statement": "Concatenating GV, GN, MS, and AR embeddings with power means achieves an average improvement of 3pp over the best individual embedding type.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: Average scores (en→de and en→fr) for additional power means (based on BV ⊕ AR ⊕ FT).'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The most effective power mean is p=3, while p=-1 is the least effective, and additional means improve results with decreasing marginal returns.We chose several intuitive values for power mean in addition to the ones already tried out, namely p=−1 (harmonic mean), p=0.5, p=2 (quadratic mean), and p=3 (cubic mean). We notice that p=3 is the most effective power mean here and p=−1 is (by far) least effective. We discuss below why p=−1 hurts performances in this case. For all cases with p>0, additional means tend to further improve the results, but with decreasing marginal returns. This also means that improvements are not merely due to additional dimensions but due to addition of complementary information." } ], "images": [ "scigen/train/3898.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Average scores (en→de and en→fr) for additional power means (based on BV ⊕ AR ⊕ FT)." ], "reasoning": "The most effective power mean is p=3, while p=-1 is the least effective, and additional means improve results with decreasing marginal returns.", "description": "We chose several intuitive values for power mean in addition to the ones already tried out, namely p=−1 (harmonic mean), p=0.5, p=2 (quadratic mean), and p=3 (cubic mean). We notice that p=3 is the most effective power mean here and p=−1 is (by far) least effective. We discuss below why p=−1 hurts performances in this case. For all cases with p>0, additional means tend to further improve the results, but with decreasing marginal returns. This also means that improvements are not merely due to additional dimensions but due to addition of complementary information.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: Average scores (en→de and en→fr) for additional power means (based on BV ⊕ AR ⊕ FT).\nQuestion: What is the Σ X-Ling value for the most effective power mean?\nSolve the above question based on the table information. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The description states that p=3 is the most effective power mean. From the table, the Σ X-Ling value for p=1,±∞,3 is 73.6. So, the answer is {\"answer\": \"73.6\"}" } ], "images": [ "scigen/train/3898.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Average scores (en→de and en→fr) for additional power means (based on BV ⊕ AR ⊕ FT)." ], "reasoning": "The description states that p=3 is the most effective power mean. From the table, the Σ X-Ling value for p=1,±∞,3 is 73.6.", "question": "What is the Σ X-Ling value for the most effective power mean?", "answer": "So, the answer is {\"answer\": \"73.6\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe least effective power mean has a Σ In-Language value of 61.6.\n" }, { "role": "assistant", "content": "The description states that p=-1 is the least effective power mean. From the table, the Σ In-Language value for p=1,±∞,-1 is 61.6. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3898.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Average scores (en→de and en→fr) for additional power means (based on BV ⊕ AR ⊕ FT)." ], "reasoning": "The description states that p=-1 is the least effective power mean. From the table, the Σ In-Language value for p=1,±∞,-1 is 61.6.", "statement": "The least effective power mean has a Σ In-Language value of 61.6.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 1: Average BLEU scores and length ratios over small and large beams. ⋆ indicates our methods.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The proposed methods outperform previous methods and the hyperparameter-free baseline (length normalization) by +2.0 BLEU, with significant improvement from the optimal stopping technique by around +0.9 BLEU.Without loss of generality, we show results with both small and large beam sizes, which average over b=14,15,16 and b=39,40,41, respectively. On the other hand, results from length normalization method show that optimal stopping technique gains significant improvement by around +0.9 BLEU. While with both, our proposed methods beat all previous methods, and gain improvement over hyperparameter-free baseline (i.e. length normalization) by +2.0 BLEU." } ], "images": [ "scigen/train/3899.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Average BLEU scores and length ratios over small and large beams. ⋆ indicates our methods." ], "reasoning": "The proposed methods outperform previous methods and the hyperparameter-free baseline (length normalization) by +2.0 BLEU, with significant improvement from the optimal stopping technique by around +0.9 BLEU.", "description": "Without loss of generality, we show results with both small and large beam sizes, which average over b=14,15,16 and b=39,40,41, respectively. On the other hand, results from length normalization method show that optimal stopping technique gains significant improvement by around +0.9 BLEU. While with both, our proposed methods beat all previous methods, and gain improvement over hyperparameter-free baseline (i.e. length normalization) by +2.0 BLEU.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 1: Average BLEU scores and length ratios over small and large beams. ⋆ indicates our methods.', and sequentially solve the question, writing out each step of your reasoning process. Output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the test BLEU score for the method with the highest dev BLEU score?\n" }, { "role": "assistant", "content": "The method with the highest dev BLEU score is 'Bounded adaptive-rewards*' with a dev BLEU score of 40.14. The corresponding test BLEU score for this method is 36.23. So, the answer is {\"answer\": \"36.23\"}" } ], "images": [ "scigen/train/3899.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Average BLEU scores and length ratios over small and large beams. ⋆ indicates our methods." ], "reasoning": "The method with the highest dev BLEU score is 'Bounded adaptive-rewards*' with a dev BLEU score of 40.14. The corresponding test BLEU score for this method is 36.23.", "question": "What is the test BLEU score for the method with the highest dev BLEU score?", "answer": "So, the answer is {\"answer\": \"36.23\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe method 'BP-Norm*' has the highest test BLEU score.\n" }, { "role": "assistant", "content": "The table shows that the method 'Bounded adaptive-rewards*' has the highest test BLEU score of 36.23, while 'BP-Norm*' has a test BLEU score of 36.22, which is slightly lower. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3899.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Average BLEU scores and length ratios over small and large beams. ⋆ indicates our methods." ], "reasoning": "The table shows that the method 'Bounded adaptive-rewards*' has the highest test BLEU score of 36.23, while 'BP-Norm*' has a test BLEU score of 36.22, which is slightly lower.", "statement": "The method 'BP-Norm*' has the highest test BLEU score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 1: Average BLEU scores and length ratios over small and large beams. ⋆ indicates our methods.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The proposed methods outperform previous methods and the hyperparameter-free baseline by +2.0 BLEU, with optimal stopping technique showing a +0.9 BLEU improvement.Without loss of generality, we show results with both small and large beam sizes, which average over b=14,15,16 and b=39,40,41, respectively. On the other hand, results from length normalization method show that optimal stopping technique gains significant improvement by around +0.9 BLEU. While with both, our proposed methods beat all previous methods, and gain improvement over hyperparameter-free baseline (i.e. length normalization) by +2.0 BLEU." } ], "images": [ "scigen/train/3900.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Average BLEU scores and length ratios over small and large beams. ⋆ indicates our methods." ], "reasoning": "The proposed methods outperform previous methods and the hyperparameter-free baseline by +2.0 BLEU, with optimal stopping technique showing a +0.9 BLEU improvement.", "description": "Without loss of generality, we show results with both small and large beam sizes, which average over b=14,15,16 and b=39,40,41, respectively. On the other hand, results from length normalization method show that optimal stopping technique gains significant improvement by around +0.9 BLEU. While with both, our proposed methods beat all previous methods, and gain improvement over hyperparameter-free baseline (i.e. length normalization) by +2.0 BLEU.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhat is the test BLEU score for the method with the highest dev BLEU score?\nSolve the above question based on the table titled 'Table 1: Average BLEU scores and length ratios over small and large beams. ⋆ indicates our methods.'. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The method with the highest dev BLEU score is 'with predicted length' with a dev BLEU score of 39.53. The corresponding test BLEU score for this method is 35.81. So, the answer is {\"answer\": \"35.81\"}" } ], "images": [ "scigen/train/3900.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Average BLEU scores and length ratios over small and large beams. ⋆ indicates our methods." ], "reasoning": "The method with the highest dev BLEU score is 'with predicted length' with a dev BLEU score of 39.53. The corresponding test BLEU score for this method is 35.81.", "question": "What is the test BLEU score for the method with the highest dev BLEU score?", "answer": "So, the answer is {\"answer\": \"35.81\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe method 'with predicted length' has the highest test BLEU score.\n" }, { "role": "assistant", "content": "The table shows that the method 'with predicted length' has a test BLEU score of 35.81, which is not the highest. The highest test BLEU score is 35.84, achieved by the 'BP-Norm' method. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3900.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Average BLEU scores and length ratios over small and large beams. ⋆ indicates our methods." ], "reasoning": "The table shows that the method 'with predicted length' has a test BLEU score of 35.81, which is not the highest. The highest test BLEU score is 35.84, achieved by the 'BP-Norm' method.", "statement": "The method 'with predicted length' has the highest test BLEU score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 2: Results on unlabeled domains. Models in bold are our models while the rest is taken from DBLP:journals/corr/ZhaoZWCMG17. Highest domain performance is shown in bold.'.\n" }, { "role": "assistant", "content": "MAN achieves competitive performance compared to MDAN, with each achieving higher accuracy on two out of four target domains, and MAN can handle multiple target domains simultaneously.It shows that MAN outperforms several baseline systems, such as a MLP trained on the source-domains, as well as single-source domain adaptation methods such as mSDA Chen et al. et al. Finally, when compared to MDAN, MAN and MDAN each achieves higher accuracy on two out of the four target domains, and the average accuracy of MAN is similar to MDAN. Therefore, MAN achieves competitive performance for the domains without annotated corpus. Nevertheless, unlike MS-DA methods, MAN can handle multiple target domains at one time." } ], "images": [ "scigen/train/3901.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on unlabeled domains. Models in bold are our models while the rest is taken from DBLP:journals/corr/ZhaoZWCMG17. Highest domain performance is shown in bold." ], "reasoning": "MAN achieves competitive performance compared to MDAN, with each achieving higher accuracy on two out of four target domains, and MAN can handle multiple target domains simultaneously.", "description": "It shows that MAN outperforms several baseline systems, such as a MLP trained on the source-domains, as well as single-source domain adaptation methods such as mSDA Chen et al. et al. Finally, when compared to MDAN, MAN and MDAN each achieves higher accuracy on two out of the four target domains, and the average accuracy of MAN is similar to MDAN. Therefore, MAN achieves competitive performance for the domains without annotated corpus. Nevertheless, unlike MS-DA methods, MAN can handle multiple target domains at one time.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Results on unlabeled domains. Models in bold are our models while the rest is taken from DBLP:journals/corr/ZhaoZWCMG17. Highest domain performance is shown in bold.', answer the question below by showing each progressive step toward the answer. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich method achieves the highest average accuracy across all target domains?\n" }, { "role": "assistant", "content": "To find the method with the highest average accuracy, compare the 'Avg.' column for all methods. The highest value is 82.72, achieved by MDAN (S-MAX). So, the answer is {\"answer\": \"MDAN (S-MAX)\"}" } ], "images": [ "scigen/train/3901.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on unlabeled domains. Models in bold are our models while the rest is taken from DBLP:journals/corr/ZhaoZWCMG17. Highest domain performance is shown in bold." ], "reasoning": "To find the method with the highest average accuracy, compare the 'Avg.' column for all methods. The highest value is 82.72, achieved by MDAN (S-MAX).", "question": "Which method achieves the highest average accuracy across all target domains?", "answer": "So, the answer is {\"answer\": \"MDAN (S-MAX)\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence MDAN (S-MAX) achieves the highest accuracy on the Electronics target domain. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nMDAN (S-MAX) achieves the highest accuracy on the Electronics target domain.\n" }, { "role": "assistant", "content": "Check the 'Elec.' column for the highest value. The highest value is 85.34, which is achieved by MDAN (S-MAX). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3901.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on unlabeled domains. Models in bold are our models while the rest is taken from DBLP:journals/corr/ZhaoZWCMG17. Highest domain performance is shown in bold." ], "reasoning": "Check the 'Elec.' column for the highest value. The highest value is 85.34, which is achieved by MDAN (S-MAX).", "statement": "MDAN (S-MAX) achieves the highest accuracy on the Electronics target domain.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1: MDTC results on the Amazon dataset. Models in bold are ours while the performance of the rest is taken from wu2015. Numbers in parentheses indicate standard errors, calculated based on 5 runs. Bold numbers indicate the highest performance in each domain, and ∗ shows statistical significance (p<0.05) over CMSC under a one-sample T-Test.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "MAN models with both L2 and NLL loss functions outperform all Shared-Private baseline systems in each domain, showing stable and superior performance.Within each category, various architectures are examined, such as Least Square (LS), SVM, and Logistic Regression (LR). As explained before, we use MLP as our feature extractors for all our models (bold ones). Among our models, the ones with the MAN prefix use adversarial training, and MAN-L2 and MAN-NLL indicate the L2 loss and NLL loss MAN, respectively. This is corroborated by the fact that our SP-MLP model performs comparably to CMSC, while the latter relies on external resources such as sentiment lexica. Moreover, when our multinomial adversarial nets are introduced, further improvement is observed. With both loss functions, MAN outperforms all Shared-Private baseline systems on each domain, and achieves statistically significantly higher overall performance. For our MAN-SP models, we provide the mean accuracy as well as the standard errors over five runs, to illustrate the performance variance and conduct significance test. It can be seen that MAN’s performance is relatively stable, and consistently outperforms CMSC." } ], "images": [ "scigen/train/3902.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: MDTC results on the Amazon dataset. Models in bold are ours while the performance of the rest is taken from wu2015. Numbers in parentheses indicate standard errors, calculated based on 5 runs. Bold numbers indicate the highest performance in each domain, and ∗ shows statistical significance (p<0.05) over CMSC under a one-sample T-Test." ], "reasoning": "MAN models with both L2 and NLL loss functions outperform all Shared-Private baseline systems in each domain, showing stable and superior performance.", "description": "Within each category, various architectures are examined, such as Least Square (LS), SVM, and Logistic Regression (LR). As explained before, we use MLP as our feature extractors for all our models (bold ones). Among our models, the ones with the MAN prefix use adversarial training, and MAN-L2 and MAN-NLL indicate the L2 loss and NLL loss MAN, respectively. This is corroborated by the fact that our SP-MLP model performs comparably to CMSC, while the latter relies on external resources such as sentiment lexica. Moreover, when our multinomial adversarial nets are introduced, further improvement is observed. With both loss functions, MAN outperforms all Shared-Private baseline systems on each domain, and achieves statistically significantly higher overall performance. For our MAN-SP models, we provide the mean accuracy as well as the standard errors over five runs, to illustrate the performance variance and conduct significance test. It can be seen that MAN’s performance is relatively stable, and consistently outperforms CMSC.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 1: MDTC results on the Amazon dataset. Models in bold are ours while the performance of the rest is taken from wu2015. Numbers in parentheses indicate standard errors, calculated based on 5 runs. Bold numbers indicate the highest performance in each domain, and ∗ shows statistical significance (p<0.05) over CMSC under a one-sample T-Test.' in mind, break down and resolve the question below step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhich model achieves the highest average accuracy across all domains?\n" }, { "role": "assistant", "content": "By examining the 'Avg.' column, the model with the highest average accuracy is MAN-NLL-SP-MLP with an average accuracy of 85.66. So, the answer is {\"answer\": \"MAN-NLL-SP-MLP\"}." } ], "images": [ "scigen/train/3902.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: MDTC results on the Amazon dataset. Models in bold are ours while the performance of the rest is taken from wu2015. Numbers in parentheses indicate standard errors, calculated based on 5 runs. Bold numbers indicate the highest performance in each domain, and ∗ shows statistical significance (p<0.05) over CMSC under a one-sample T-Test." ], "reasoning": "By examining the 'Avg.' column, the model with the highest average accuracy is MAN-NLL-SP-MLP with an average accuracy of 85.66.", "question": "Which model achieves the highest average accuracy across all domains?", "answer": "So, the answer is {\"answer\": \"MAN-NLL-SP-MLP\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe SP-MLP model performs better than CMSC-SVM3 in the DVD domain.\n" }, { "role": "assistant", "content": "In the DVD domain, SP-MLP has an accuracy of 84.05, while CMSC-SVM3 has an accuracy of 83.26. Therefore, SP-MLP performs better than CMSC-SVM3 in the DVD domain. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3902.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: MDTC results on the Amazon dataset. Models in bold are ours while the performance of the rest is taken from wu2015. Numbers in parentheses indicate standard errors, calculated based on 5 runs. Bold numbers indicate the highest performance in each domain, and ∗ shows statistical significance (p<0.05) over CMSC under a one-sample T-Test." ], "reasoning": "In the DVD domain, SP-MLP has an accuracy of 84.05, while CMSC-SVM3 has an accuracy of 83.26. Therefore, SP-MLP performs better than CMSC-SVM3 in the DVD domain.", "statement": "The SP-MLP model performs better than CMSC-SVM3 in the DVD domain.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 3: Results on the FDU-MTL dataset. Bolded models are ours, while the rest is from P17-1001. Highest performance is each domain is highlighted. For our full MAN models, standard errors are shown in parenthese and statistical significance (p<0.01) over ASP-MTL is indicated by *.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "MAN achieves state-of-the-art performance with an 88.4% overall accuracy, surpassing ASP-MTL by 2.3%.When MAN is introduced, we attain the state-of-the-art performance on every domain with a 88.4% overall accuracy, surpassing ASP-MTL by a significant margin of 2.3%." } ], "images": [ "scigen/train/3903.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Results on the FDU-MTL dataset. Bolded models are ours, while the rest is from P17-1001. Highest performance is each domain is highlighted. For our full MAN models, standard errors are shown in parenthese and statistical significance (p<0.01) over ASP-MTL is indicated by *." ], "reasoning": "MAN achieves state-of-the-art performance with an 88.4% overall accuracy, surpassing ASP-MTL by 2.3%.", "description": "When MAN is introduced, we attain the state-of-the-art performance on every domain with a 88.4% overall accuracy, surpassing ASP-MTL by a significant margin of 2.3%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Results on the FDU-MTL dataset. Bolded models are ours, while the rest is from P17-1001. Highest performance is each domain is highlighted. For our full MAN models, standard errors are shown in parenthese and statistical significance (p<0.01) over ASP-MTL is indicated by *.\nQuestion: What is the overall accuracy of ASP-MTL and how much lower is it compared to MAN?\nSolve the above question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The overall accuracy of ASP-MTL is 86.1%. MAN achieves an overall accuracy of 88.4%, which is 2.3% higher than ASP-MTL. So, the answer is {\"answer\": \"86.1% and 2.3%\"}" } ], "images": [ "scigen/train/3903.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Results on the FDU-MTL dataset. Bolded models are ours, while the rest is from P17-1001. Highest performance is each domain is highlighted. For our full MAN models, standard errors are shown in parenthese and statistical significance (p<0.01) over ASP-MTL is indicated by *." ], "reasoning": "The overall accuracy of ASP-MTL is 86.1%. MAN achieves an overall accuracy of 88.4%, which is 2.3% higher than ASP-MTL.", "question": "What is the overall accuracy of ASP-MTL and how much lower is it compared to MAN?", "answer": "So, the answer is {\"answer\": \"86.1% and 2.3%\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nMAN-L2-SP-CNN has the highest accuracy in the camera domain.\n" }, { "role": "assistant", "content": "The table shows that MAN-L2-SP-CNN has an accuracy of 91.4% in the camera domain, which is the highest among all models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3903.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Results on the FDU-MTL dataset. Bolded models are ours, while the rest is from P17-1001. Highest performance is each domain is highlighted. For our full MAN models, standard errors are shown in parenthese and statistical significance (p<0.01) over ASP-MTL is indicated by *." ], "reasoning": "The table shows that MAN-L2-SP-CNN has an accuracy of 91.4% in the camera domain, which is the highest among all models listed.", "statement": "MAN-L2-SP-CNN has the highest accuracy in the camera domain.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 2: Results (Accuracy) on Validation (A).'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "RoBERTa\\scriptsize{OMCS} shows an improvement over RoBERTa\\scriptsize{Large} on the test set, and the use of prompt templates further enhances performance on both the development and test sets.Comparing with the original RoBERTa large model, the RoBERTa\\scriptsize{OMCS}, equipping with the additional commonsense textual corpus, obtains an improvement of 1.6 over RoBERTa\\scriptsize{Large} on the development dataset, which provides evidence that the additional textual corpus facilitates the PLM with commonsense knowledge to a certain degree. Comparing with the baselines, the models with reconstructed input based on prompt template obatin strong improvement over baselines on both the development set and test set. We observe that two types of prompt template, denoted by P1 and P2, show up almost the same performance on test set based on RoBERTa\\scriptsize{OMCS}. We suppose the reason for that two prompt templates cause the same effect is the same semantic of the intention behind the different forms of the surface. And both of prompt templates offer the same hints about the task to the PLM. In the final official evaluation, we submit the prediction from RoBERTa\\scriptsize{OMCS}+P2 to the leaderboard as our final result. In the group of baseline model, the RoBERTa\\scriptsize{OMCS} surprisingly gets a lower score compared with RoBERTa\\scriptsize{Large} on the development set. When reconstructing the original input with prompt template, it brings 0.8∼1.9 gain over baseline models, i.e. RoBERTa\\scriptsize{Large} and RoBERTa\\scriptsize{OMCS}, on development set. The reversal of RoBERTa\\scriptsize{OMCS}’s performance on both the development and test set proves that the prompt template achieves the role of activating the potential knowledge inner PLM. Moreover, with the additional information from true statement, the models with +P+C further get improved on the development set. Though the performance of RoBERTa\\scriptsize{OMCS} exhibits no advanced over RoBERTa\\scriptsize{Large} on the development set, its performance shows 0.2∼0.5 gains over RoBERTa\\scriptsize{Large} on test set. In the final official evaluation of Explanation Selection subtask, we commit the result of RoBERTa\\scriptsize{OMCS}+P+C to the leaderboard as our final result." } ], "images": [ "scigen/train/3904.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results (Accuracy) on Validation (A)." ], "reasoning": "RoBERTa\\scriptsize{OMCS} shows an improvement over RoBERTa\\scriptsize{Large} on the test set, and the use of prompt templates further enhances performance on both the development and test sets.", "description": "Comparing with the original RoBERTa large model, the RoBERTa\\scriptsize{OMCS}, equipping with the additional commonsense textual corpus, obtains an improvement of 1.6 over RoBERTa\\scriptsize{Large} on the development dataset, which provides evidence that the additional textual corpus facilitates the PLM with commonsense knowledge to a certain degree. Comparing with the baselines, the models with reconstructed input based on prompt template obatin strong improvement over baselines on both the development set and test set. We observe that two types of prompt template, denoted by P1 and P2, show up almost the same performance on test set based on RoBERTa\\scriptsize{OMCS}. We suppose the reason for that two prompt templates cause the same effect is the same semantic of the intention behind the different forms of the surface. And both of prompt templates offer the same hints about the task to the PLM. In the final official evaluation, we submit the prediction from RoBERTa\\scriptsize{OMCS}+P2 to the leaderboard as our final result. In the group of baseline model, the RoBERTa\\scriptsize{OMCS} surprisingly gets a lower score compared with RoBERTa\\scriptsize{Large} on the development set. When reconstructing the original input with prompt template, it brings 0.8∼1.9 gain over baseline models, i.e. RoBERTa\\scriptsize{Large} and RoBERTa\\scriptsize{OMCS}, on development set. The reversal of RoBERTa\\scriptsize{OMCS}’s performance on both the development and test set proves that the prompt template achieves the role of activating the potential knowledge inner PLM. Moreover, with the additional information from true statement, the models with +P+C further get improved on the development set. Though the performance of RoBERTa\\scriptsize{OMCS} exhibits no advanced over RoBERTa\\scriptsize{Large} on the development set, its performance shows 0.2∼0.5 gains over RoBERTa\\scriptsize{Large} on test set. In the final official evaluation of Explanation Selection subtask, we commit the result of RoBERTa\\scriptsize{OMCS}+P+C to the leaderboard as our final result.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: Results (Accuracy) on Validation (A).', answer the question below by showing each progressive step toward the answer. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich model achieved the highest score on the development set?\n" }, { "role": "assistant", "content": "By comparing the scores on the development set, RoBERTa\\scriptsize{OMCS}+P2 achieved the highest score of 96.9. So, the answer is {\"answer\": \"RoBERTa\\scriptsize{OMCS}+P2\"}" } ], "images": [ "scigen/train/3904.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results (Accuracy) on Validation (A)." ], "reasoning": "By comparing the scores on the development set, RoBERTa\\scriptsize{OMCS}+P2 achieved the highest score of 96.9.", "question": "Which model achieved the highest score on the development set?", "answer": "So, the answer is {\"answer\": \"RoBERTa\\scriptsize{OMCS}+P2\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRoBERTa\\scriptsize{OMCS} achieved a higher score than RoBERTa\\scriptsize{Large} on the test set.\n" }, { "role": "assistant", "content": "The table shows that RoBERTa\\scriptsize{OMCS} scored 95.6 on the test set, while RoBERTa\\scriptsize{Large} scored 93.2, indicating that RoBERTa\\scriptsize{OMCS} achieved a higher score. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3904.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results (Accuracy) on Validation (A)." ], "reasoning": "The table shows that RoBERTa\\scriptsize{OMCS} scored 95.6 on the test set, while RoBERTa\\scriptsize{Large} scored 93.2, indicating that RoBERTa\\scriptsize{OMCS} achieved a higher score.", "statement": "RoBERTa\\scriptsize{OMCS} achieved a higher score than RoBERTa\\scriptsize{Large} on the test set.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Results (Accuracy) on Validation (A).'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "RoBERTa\\scriptsize{OMCS} shows improvement over RoBERTa\\scriptsize{Large} on the test set, but not on the development set. Prompt templates further enhance performance, especially when combined with additional information.Comparing with the original RoBERTa large model, the RoBERTa\\scriptsize{OMCS}, equipping with the additional commonsense textual corpus, obtains an improvement of 1.6 over RoBERTa\\scriptsize{Large} on the development dataset, which provides evidence that the additional textual corpus facilitates the PLM with commonsense knowledge to a certain degree. Comparing with the baselines, the models with reconstructed input based on prompt template obatin strong improvement over baselines on both the development set and test set. We observe that two types of prompt template, denoted by P1 and P2, show up almost the same performance on test set based on RoBERTa\\scriptsize{OMCS}. We suppose the reason for that two prompt templates cause the same effect is the same semantic of the intention behind the different forms of the surface. And both of prompt templates offer the same hints about the task to the PLM. In the final official evaluation, we submit the prediction from RoBERTa\\scriptsize{OMCS}+P2 to the leaderboard as our final result. In the group of baseline model, the RoBERTa\\scriptsize{OMCS} surprisingly gets a lower score compared with RoBERTa\\scriptsize{Large} on the development set. When reconstructing the original input with prompt template, it brings 0.8∼1.9 gain over baseline models, i.e. RoBERTa\\scriptsize{Large} and RoBERTa\\scriptsize{OMCS}, on development set. The reversal of RoBERTa\\scriptsize{OMCS}’s performance on both the development and test set proves that the prompt template achieves the role of activating the potential knowledge inner PLM. Moreover, with the additional information from true statement, the models with +P+C further get improved on the development set. Though the performance of RoBERTa\\scriptsize{OMCS} exhibits no advanced over RoBERTa\\scriptsize{Large} on the development set, its performance shows 0.2∼0.5 gains over RoBERTa\\scriptsize{Large} on test set. In the final official evaluation of Explanation Selection subtask, we commit the result of RoBERTa\\scriptsize{OMCS}+P+C to the leaderboard as our final result." } ], "images": [ "scigen/train/3905.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results (Accuracy) on Validation (A)." ], "reasoning": "RoBERTa\\scriptsize{OMCS} shows improvement over RoBERTa\\scriptsize{Large} on the test set, but not on the development set. Prompt templates further enhance performance, especially when combined with additional information.", "description": "Comparing with the original RoBERTa large model, the RoBERTa\\scriptsize{OMCS}, equipping with the additional commonsense textual corpus, obtains an improvement of 1.6 over RoBERTa\\scriptsize{Large} on the development dataset, which provides evidence that the additional textual corpus facilitates the PLM with commonsense knowledge to a certain degree. Comparing with the baselines, the models with reconstructed input based on prompt template obatin strong improvement over baselines on both the development set and test set. We observe that two types of prompt template, denoted by P1 and P2, show up almost the same performance on test set based on RoBERTa\\scriptsize{OMCS}. We suppose the reason for that two prompt templates cause the same effect is the same semantic of the intention behind the different forms of the surface. And both of prompt templates offer the same hints about the task to the PLM. In the final official evaluation, we submit the prediction from RoBERTa\\scriptsize{OMCS}+P2 to the leaderboard as our final result. In the group of baseline model, the RoBERTa\\scriptsize{OMCS} surprisingly gets a lower score compared with RoBERTa\\scriptsize{Large} on the development set. When reconstructing the original input with prompt template, it brings 0.8∼1.9 gain over baseline models, i.e. RoBERTa\\scriptsize{Large} and RoBERTa\\scriptsize{OMCS}, on development set. The reversal of RoBERTa\\scriptsize{OMCS}’s performance on both the development and test set proves that the prompt template achieves the role of activating the potential knowledge inner PLM. Moreover, with the additional information from true statement, the models with +P+C further get improved on the development set. Though the performance of RoBERTa\\scriptsize{OMCS} exhibits no advanced over RoBERTa\\scriptsize{Large} on the development set, its performance shows 0.2∼0.5 gains over RoBERTa\\scriptsize{Large} on test set. In the final official evaluation of Explanation Selection subtask, we commit the result of RoBERTa\\scriptsize{OMCS}+P+C to the leaderboard as our final result.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 2: Results (Accuracy) on Validation (A).\nQuestion: Which model achieved the highest score on the development set?\n" }, { "role": "assistant", "content": "By comparing the development set scores of all models, RoBERTa\\scriptsize{OMCS}+P+C achieved the highest score of 94.5. So, the answer is {\"answer\": \"RoBERTa\\scriptsize{OMCS}+P+C\"}" } ], "images": [ "scigen/train/3905.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results (Accuracy) on Validation (A)." ], "reasoning": "By comparing the development set scores of all models, RoBERTa\\scriptsize{OMCS}+P+C achieved the highest score of 94.5.", "question": "Which model achieved the highest score on the development set?", "answer": "So, the answer is {\"answer\": \"RoBERTa\\scriptsize{OMCS}+P+C\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis RoBERTa\\scriptsize{OMCS} outperformed RoBERTa\\scriptsize{Large} on the development set. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRoBERTa\\scriptsize{OMCS} outperformed RoBERTa\\scriptsize{Large} on the development set.\n" }, { "role": "assistant", "content": "The table shows that RoBERTa\\scriptsize{OMCS} scored 92.0 on the development set, while RoBERTa\\scriptsize{Large} scored 93.1, indicating that RoBERTa\\scriptsize{OMCS} did not outperform RoBERTa\\scriptsize{Large} on the development set. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3905.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results (Accuracy) on Validation (A)." ], "reasoning": "The table shows that RoBERTa\\scriptsize{OMCS} scored 92.0 on the development set, while RoBERTa\\scriptsize{Large} scored 93.1, indicating that RoBERTa\\scriptsize{OMCS} did not outperform RoBERTa\\scriptsize{Large} on the development set.", "statement": "RoBERTa\\scriptsize{OMCS} outperformed RoBERTa\\scriptsize{Large} on the development set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: ABX scores and bitrates for various codebook sizes on the clean ZeroSpeech19 English test set, using the “∅→{2}” model.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The ABX score is best for 1024 codebook vectors, and models with smaller codebooks achieve lower bitrates.We find that the ABX score is best for 1024 codebook vectors, although the performance is quite good for all models. Unsurprisingly, models with smaller codebooks also achieve lower bitrates." } ], "images": [ "scigen/train/3906.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: ABX scores and bitrates for various codebook sizes on the clean ZeroSpeech19 English test set, using the “∅→{2}” model." ], "reasoning": "The ABX score is best for 1024 codebook vectors, and models with smaller codebooks achieve lower bitrates.", "description": "We find that the ABX score is best for 1024 codebook vectors, although the performance is quite good for all models. Unsurprisingly, models with smaller codebooks also achieve lower bitrates.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 4: ABX scores and bitrates for various codebook sizes on the clean ZeroSpeech19 English test set, using the “∅→{2}” model.' in mind, break down and resolve the question below step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich codebook size achieves the highest bitrate and what is the corresponding ABX score?\n" }, { "role": "assistant", "content": "By examining the table, the highest bitrate is 390.61, which corresponds to the codebook size of 1024. The ABX score for this codebook size is 11.79. So, the answer is {\"answer\": \"1024, 11.79\"}." } ], "images": [ "scigen/train/3906.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: ABX scores and bitrates for various codebook sizes on the clean ZeroSpeech19 English test set, using the “∅→{2}” model." ], "reasoning": "By examining the table, the highest bitrate is 390.61, which corresponds to the codebook size of 1024. The ABX score for this codebook size is 11.79.", "question": "Which codebook size achieves the highest bitrate and what is the corresponding ABX score?", "answer": "So, the answer is {\"answer\": \"1024, 11.79\"}." }, { "messages": [ { "role": "user", "content": "The model with the smallest codebook size has the lowest bitrate. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model with the smallest codebook size has the lowest bitrate.\n" }, { "role": "assistant", "content": "The table shows that the smallest codebook size is 128, and its bitrate is 295.65, which is indeed the lowest bitrate among all the models. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3906.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: ABX scores and bitrates for various codebook sizes on the clean ZeroSpeech19 English test set, using the “∅→{2}” model." ], "reasoning": "The table shows that the smallest codebook size is 128, and its bitrate is 295.65, which is indeed the lowest bitrate among all the models.", "statement": "The model with the smallest codebook size has the lowest bitrate.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: ABX scores and RLE bitrates for various SNRs on the noisy ZeroSpeech19 English test set. “R-B” stands for “RLE-Bitrate,” and (n) denotes a model trained on the noisy Places Audio dataset. For the WaveNet-VQ models, (ZS) and (PA) respectively denote training on the ZeroSpeech 19 English training set, and the clean Places Audio dataset.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The ResDAVEnet-VQ models are more robust to noise than the WaveNet-VQ models, with the best ResDAVEnet-VQ model achieving an ABX of 23.96% in high noise conditions, while WaveNet-VQ models degrade to nearly-random ABX scores.Training and testing on noisy data In Hsu et al. Here, we examine whether this robustness is also exemplified by the quantized versions of this model. For each ZeroSpeech testing waveform, we randomly sampled an AudioSet waveform of the same duration and performed linear mixing with a signal-to-noise ratio (SNR) selected randomly within a specified range. We construct low, medium, and high noise testing sets, corresponding to SNRs of 20-30 dB, 10-20 dB, and 0-10 dB. We find that for all models, a worsening SNR results in a deterioration in ABX performance. However, the ResDAVEnet-VQ models prove to be far more noise robust than the Wavenet-VQ model; even in the high noise testing set, the best ResDAVEnet-VQ model achieves an ABX of 23.96%, while the WaveNet-VQ models degrade to nearly-random ABX scores of 42.55% and 42.67%. Given that a ResDAVEnet-VQ model trained on the “clean” Places Audio captions is highly robust to additive noise on the ABX discrimination task, we investigated whether adding noise to the Places Audio captions themselves would result in an even higher degree of noise robustness. ZeroSpeech ABX tasks. In general, we observe a degradation ABX score in the clean conditions, but with a significantly higher degree of noise robustness in the noisier conditions." } ], "images": [ "scigen/train/3907.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: ABX scores and RLE bitrates for various SNRs on the noisy ZeroSpeech19 English test set. “R-B” stands for “RLE-Bitrate,” and (n) denotes a model trained on the noisy Places Audio dataset. For the WaveNet-VQ models, (ZS) and (PA) respectively denote training on the ZeroSpeech 19 English training set, and the clean Places Audio dataset." ], "reasoning": "The ResDAVEnet-VQ models are more robust to noise than the WaveNet-VQ models, with the best ResDAVEnet-VQ model achieving an ABX of 23.96% in high noise conditions, while WaveNet-VQ models degrade to nearly-random ABX scores.", "description": "Training and testing on noisy data In Hsu et al. Here, we examine whether this robustness is also exemplified by the quantized versions of this model. For each ZeroSpeech testing waveform, we randomly sampled an AudioSet waveform of the same duration and performed linear mixing with a signal-to-noise ratio (SNR) selected randomly within a specified range. We construct low, medium, and high noise testing sets, corresponding to SNRs of 20-30 dB, 10-20 dB, and 0-10 dB. We find that for all models, a worsening SNR results in a deterioration in ABX performance. However, the ResDAVEnet-VQ models prove to be far more noise robust than the Wavenet-VQ model; even in the high noise testing set, the best ResDAVEnet-VQ model achieves an ABX of 23.96%, while the WaveNet-VQ models degrade to nearly-random ABX scores of 42.55% and 42.67%. Given that a ResDAVEnet-VQ model trained on the “clean” Places Audio captions is highly robust to additive noise on the ABX discrimination task, we investigated whether adding noise to the Places Audio captions themselves would result in an even higher degree of noise robustness. ZeroSpeech ABX tasks. In general, we observe a degradation ABX score in the clean conditions, but with a significantly higher degree of noise robustness in the noisier conditions.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 2: ABX scores and RLE bitrates for various SNRs on the noisy ZeroSpeech19 English test set. “R-B” stands for “RLE-Bitrate,” and (n) denotes a model trained on the noisy Places Audio dataset. For the WaveNet-VQ models, (ZS) and (PA) respectively denote training on the ZeroSpeech 19 English training set, and the clean Places Audio dataset.' in mind, break down and resolve the question below step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the ABX score of the best ResDAVEnet-VQ model in the 0-10 dB noise condition?\n" }, { "role": "assistant", "content": "To find the ABX score of the best ResDAVEnet-VQ model in the 0-10 dB noise condition, we look at the table under the '0-10 dB ABX' column and identify the lowest ABX score among the ResDAVEnet-VQ models. So, the answer is {\"answer\": \"16.09\"}." } ], "images": [ "scigen/train/3907.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: ABX scores and RLE bitrates for various SNRs on the noisy ZeroSpeech19 English test set. “R-B” stands for “RLE-Bitrate,” and (n) denotes a model trained on the noisy Places Audio dataset. For the WaveNet-VQ models, (ZS) and (PA) respectively denote training on the ZeroSpeech 19 English training set, and the clean Places Audio dataset." ], "reasoning": "To find the ABX score of the best ResDAVEnet-VQ model in the 0-10 dB noise condition, we look at the table under the '0-10 dB ABX' column and identify the lowest ABX score among the ResDAVEnet-VQ models.", "question": "What is the ABX score of the best ResDAVEnet-VQ model in the 0-10 dB noise condition?", "answer": "So, the answer is {\"answer\": \"16.09\"}." }, { "messages": [ { "role": "user", "content": "The WaveNet-VQ (PA) model has a higher ABX score in clean conditions than the ResDAVEnet-VQ models. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe WaveNet-VQ (PA) model has a higher ABX score in clean conditions than the ResDAVEnet-VQ models.\n" }, { "role": "assistant", "content": "To verify this statement, we compare the 'Clean ABX' scores of the WaveNet-VQ (PA) model and the ResDAVEnet-VQ models. The WaveNet-VQ (PA) model has a 'Clean ABX' score of 24.87, while the ResDAVEnet-VQ models have lower scores. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3907.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: ABX scores and RLE bitrates for various SNRs on the noisy ZeroSpeech19 English test set. “R-B” stands for “RLE-Bitrate,” and (n) denotes a model trained on the noisy Places Audio dataset. For the WaveNet-VQ models, (ZS) and (PA) respectively denote training on the ZeroSpeech 19 English training set, and the clean Places Audio dataset." ], "reasoning": "To verify this statement, we compare the 'Clean ABX' scores of the WaveNet-VQ (PA) model and the ResDAVEnet-VQ models. The WaveNet-VQ (PA) model has a 'Clean ABX' score of 24.87, while the ResDAVEnet-VQ models have lower scores.", "statement": "The WaveNet-VQ (PA) model has a higher ABX score in clean conditions than the ResDAVEnet-VQ models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: ABX scores on the ZeroSpeech 19 English test set using features derived from the output of the Res3 block of the ResDAVEnet audio branch (pre-quantization).', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The ABX error rate remains relatively stable within each curriculum, indicating that the initial model determines which layer captures phonetic information most effectively.We present results from two experiments that support this view. We observe that the ABX error rate changes very little within each individual curriculum. This indicates that the initial model sets the stage for which layer learns to capture phonetic information with the highest salience, and that subsequent training steps do not tend to move this information elsewhere." } ], "images": [ "scigen/train/3908.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: ABX scores on the ZeroSpeech 19 English test set using features derived from the output of the Res3 block of the ResDAVEnet audio branch (pre-quantization)." ], "reasoning": "The ABX error rate remains relatively stable within each curriculum, indicating that the initial model determines which layer captures phonetic information most effectively.", "description": "We present results from two experiments that support this view. We observe that the ABX error rate changes very little within each individual curriculum. This indicates that the initial model sets the stage for which layer learns to capture phonetic information with the highest salience, and that subsequent training steps do not tend to move this information elsewhere.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 5: ABX scores on the ZeroSpeech 19 English test set using features derived from the output of the Res3 block of the ResDAVEnet audio branch (pre-quantization).', answer the question below by showing each progressive step toward the answer. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model ID has the highest ABX error rate?\n" }, { "role": "assistant", "content": "By examining the table, we see that the model ID {3} has the highest ABX error rate of 32.24. So, the answer is {\"answer\": \"{3}\"}." } ], "images": [ "scigen/train/3908.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: ABX scores on the ZeroSpeech 19 English test set using features derived from the output of the Res3 block of the ResDAVEnet audio branch (pre-quantization)." ], "reasoning": "By examining the table, we see that the model ID {3} has the highest ABX error rate of 32.24.", "question": "Which model ID has the highest ABX error rate?", "answer": "So, the answer is {\"answer\": \"{3}\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model ID ∅→{2,3} has the lowest ABX error rate.\n" }, { "role": "assistant", "content": "The table shows that the model ID ∅ has the lowest ABX error rate of 10.86, not ∅→{2,3}, which has an ABX error rate of 12.68. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3908.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: ABX scores on the ZeroSpeech 19 English test set using features derived from the output of the Res3 block of the ResDAVEnet audio branch (pre-quantization)." ], "reasoning": "The table shows that the model ID ∅ has the lowest ABX error rate of 10.86, not ∅→{2,3}, which has an ABX error rate of 12.68.", "statement": "The model ID ∅→{2,3} has the lowest ABX error rate.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 6: The number of codebook vectors at a particular VQ layer that learned to be a detector for any word with an F1 score greater than 0.5.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "Word detectors emerge significantly at the VQ3 layer when trained from a cold-start, and they remain at VQ3 even when a second VQ layer is added.This suggests implicit word recognition ability can be localized at different layers in the ResDAVEnet audio model, and exactly where it emerges depends upon the VQ training curriculum. We hypothesize that this is due in part to two factors: In a warm-start model, whatever type of information (subword-like, word-like) the continuous model learned to encode at a particular convolutional layer (or residual block) does not change after a quantizer is appended to that layer. In a cold-start model, each active quantization layer forms a potential bottleneck, restricting the amount of information that is able to pass through to subsequent layers. According to this hypothesis, if word-level recognition tends to emerge at a particular layer in an unconstrained network with no quantization bottleneck, it will stay there when quantization is introduced for fine-tuning. However, when a quantization bottleneck is introduced from the very beginning of training, the gradient flowing down into the lower network layers is more constrained during the initial training epochs (when the gradient tends to be the largest). This may have the effect of steering the optimizer into a different part of the parameter space, in which word recognition occurs at a different layer than it otherwise would. Here, we claim that a codebook vector belonging to a particular VQ layer has learned to be a word detector if its F1 score for any word appearing in the test set exceeds 0.5 (as measured on the test set). There are several interesting things to note here. First, we only observe a significant number of word detectors at the VQ3 layer when that layer is trained from a cold-start. Even when adding a second VQ layer as in the {3}→{2,3} model, these detectors remain at VQ3." } ], "images": [ "scigen/train/3909.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: The number of codebook vectors at a particular VQ layer that learned to be a detector for any word with an F1 score greater than 0.5." ], "reasoning": "Word detectors emerge significantly at the VQ3 layer when trained from a cold-start, and they remain at VQ3 even when a second VQ layer is added.", "description": "This suggests implicit word recognition ability can be localized at different layers in the ResDAVEnet audio model, and exactly where it emerges depends upon the VQ training curriculum. We hypothesize that this is due in part to two factors: In a warm-start model, whatever type of information (subword-like, word-like) the continuous model learned to encode at a particular convolutional layer (or residual block) does not change after a quantizer is appended to that layer. In a cold-start model, each active quantization layer forms a potential bottleneck, restricting the amount of information that is able to pass through to subsequent layers. According to this hypothesis, if word-level recognition tends to emerge at a particular layer in an unconstrained network with no quantization bottleneck, it will stay there when quantization is introduced for fine-tuning. However, when a quantization bottleneck is introduced from the very beginning of training, the gradient flowing down into the lower network layers is more constrained during the initial training epochs (when the gradient tends to be the largest). This may have the effect of steering the optimizer into a different part of the parameter space, in which word recognition occurs at a different layer than it otherwise would. Here, we claim that a codebook vector belonging to a particular VQ layer has learned to be a word detector if its F1 score for any word appearing in the test set exceeds 0.5 (as measured on the test set). There are several interesting things to note here. First, we only observe a significant number of word detectors at the VQ3 layer when that layer is trained from a cold-start. Even when adding a second VQ layer as in the {3}→{2,3} model, these detectors remain at VQ3.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 6: The number of codebook vectors at a particular VQ layer that learned to be a detector for any word with an F1 score greater than 0.5.\nQuestion: Which VQ layer has the highest number of word detectors when trained from a cold-start?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "From the table, the highest number of word detectors (279) is observed at VQ3 in the model with a cold-start training approach. So, the answer is {\"answer\": \"3\"}." } ], "images": [ "scigen/train/3909.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: The number of codebook vectors at a particular VQ layer that learned to be a detector for any word with an F1 score greater than 0.5." ], "reasoning": "From the table, the highest number of word detectors (279) is observed at VQ3 in the model with a cold-start training approach.", "question": "Which VQ layer has the highest number of word detectors when trained from a cold-start?", "answer": "So, the answer is {\"answer\": \"3\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The model with three quantized layers and VQ layer 4 has more than 250 word detectors., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe model with three quantized layers and VQ layer 4 has more than 250 word detectors.\n" }, { "role": "assistant", "content": "According to the table, the model with three quantized layers and VQ layer 4 has 253 word detectors, which is more than 250. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3909.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: The number of codebook vectors at a particular VQ layer that learned to be a detector for any word with an F1 score greater than 0.5." ], "reasoning": "According to the table, the model with three quantized layers and VQ layer 4 has 253 word detectors, which is more than 250.", "statement": "The model with three quantized layers and VQ layer 4 has more than 250 word detectors.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Result of the test set'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The SVM classifier achieved higher accuracy compared to NB, with the top 200 Info-Gain words performing better than the top 600 words.(83 percent for SVM) , we showed that the result of this research is almost comparable with the other works in this area. One important note here is that reviewed works leveraged various types of features as well sentiment. The result presented in this work is solely based on lexical features. To take a step further, we tested the trained classifier on the 10 percent test set data (as explained before). The results are slightly different from what we achieved earlier. Since we have a small data set, we decided to choose (1) top 200 words to avoid overfitting, (2) and top 600 words as one of the best features. Same as the training, SVM resulted in higher accuracy compared to NB. However, the 200 info-Gain words performed better." } ], "images": [ "scigen/train/3910.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Result of the test set" ], "reasoning": "The SVM classifier achieved higher accuracy compared to NB, with the top 200 Info-Gain words performing better than the top 600 words.", "description": "(83 percent for SVM) , we showed that the result of this research is almost comparable with the other works in this area. One important note here is that reviewed works leveraged various types of features as well sentiment. The result presented in this work is solely based on lexical features. To take a step further, we tested the trained classifier on the 10 percent test set data (as explained before). The results are slightly different from what we achieved earlier. Since we have a small data set, we decided to choose (1) top 200 words to avoid overfitting, (2) and top 600 words as one of the best features. Same as the training, SVM resulted in higher accuracy compared to NB. However, the 200 info-Gain words performed better.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 6: Result of the test set' and methodically tackle the question that follows. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the overall accuracy of the SVM classifier using the top 200 Info-Gain words?\n" }, { "role": "assistant", "content": "The table shows the overall accuracy for SVM and NB classifiers using different sets of Info-Gain words. The SVM classifier using the top 200 Info-Gain words has an accuracy of 78. So, the answer is {\"answer\": \"78\"}" } ], "images": [ "scigen/train/3910.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Result of the test set" ], "reasoning": "The table shows the overall accuracy for SVM and NB classifiers using different sets of Info-Gain words. The SVM classifier using the top 200 Info-Gain words has an accuracy of 78.", "question": "What is the overall accuracy of the SVM classifier using the top 200 Info-Gain words?", "answer": "So, the answer is {\"answer\": \"78\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence The NB classifier achieved an overall accuracy of 70 using the top 600 Info-Gain words. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe NB classifier achieved an overall accuracy of 70 using the top 600 Info-Gain words.\n" }, { "role": "assistant", "content": "The table shows the overall accuracy for the NB classifier using the top 600 Info-Gain words as 70. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3910.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Result of the test set" ], "reasoning": "The table shows the overall accuracy for the NB classifier using the top 600 Info-Gain words as 70.", "statement": "The NB classifier achieved an overall accuracy of 70 using the top 600 Info-Gain words.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 1: Number of reviews of each movie', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The dataset consists of reviews of eight films from Amazon, categorized into High (5 stars) and Low (1, 2, and 3 stars) reviews, with 4-star reviews excluded.We used the reviews of eight films from Amazon . Around 65 percent of the reviews are 5 star and 20 percent of them are 1, 2 and 3 stars. To have more distinct classes, 4-star reviews were excluded from the dataset. For preprocessing, first, all the reviews were divided in two categories/classes as High (5Stars) and Low (1, 2, and 3 stars). We then removed the stop words and tokenized the sentences. The resulted dataset consisted of around 39,802 sentences and 307,138 words. Words in a collection of documents mostly follow the zip’s law, where the rare and common words are scattered on the end of two tails of the graph. To address this problem, we removed the words with less than 10 counts. The words with high counts will be downscaled in feature selection, using TF-IDF or information gain. More details can be found in the following section." } ], "images": [ "scigen/train/3911.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Number of reviews of each movie" ], "reasoning": "The dataset consists of reviews of eight films from Amazon, categorized into High (5 stars) and Low (1, 2, and 3 stars) reviews, with 4-star reviews excluded.", "description": "We used the reviews of eight films from Amazon . Around 65 percent of the reviews are 5 star and 20 percent of them are 1, 2 and 3 stars. To have more distinct classes, 4-star reviews were excluded from the dataset. For preprocessing, first, all the reviews were divided in two categories/classes as High (5Stars) and Low (1, 2, and 3 stars). We then removed the stop words and tokenized the sentences. The resulted dataset consisted of around 39,802 sentences and 307,138 words. Words in a collection of documents mostly follow the zip’s law, where the rare and common words are scattered on the end of two tails of the graph. To address this problem, we removed the words with less than 10 counts. The words with high counts will be downscaled in feature selection, using TF-IDF or information gain. More details can be found in the following section.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 1: Number of reviews of each movie', unfold the steps to solve the question given next. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nQuestion: Which film has the highest number of 5-star reviews?\n" }, { "role": "assistant", "content": "To find the film with the highest number of 5-star reviews, we need to compare the 5-star review counts for each film. Food Inc. has the highest number of 5-star reviews with 1949. So, the answer is {\"answer\": \"Food Inc.\"}." } ], "images": [ "scigen/train/3911.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Number of reviews of each movie" ], "reasoning": "To find the film with the highest number of 5-star reviews, we need to compare the 5-star review counts for each film. Food Inc. has the highest number of 5-star reviews with 1949.", "question": "Which film has the highest number of 5-star reviews?", "answer": "So, the answer is {\"answer\": \"Food Inc.\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The film with the lowest number of total reviews is Citizenfour. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe film with the lowest number of total reviews is Citizenfour.\n" }, { "role": "assistant", "content": "To verify this statement, we need to compare the total number of reviews for each film. Citizenfour has the lowest number of total reviews with 199. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3911.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Number of reviews of each movie" ], "reasoning": "To verify this statement, we need to compare the total number of reviews for each film. Citizenfour has the lowest number of total reviews with 199.", "statement": "The film with the lowest number of total reviews is Citizenfour.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Experiment results on the MT 06/08 test sets. AVG = average BLEU scores for test sets. We highlight the best result in bold. “⇑”: significantly better than Baseline (p < 0.01); “∗∗”: significantly better than BRAE (p < 0.01); “↑”: significantly better than BCorrRAE (p < 0.05).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "BattRAE significantly improves translation quality on all test sets, achieving up to 1.63 BLEU points improvement over the Baseline and outperforming BCorrRAE by 0.48 BLEU points and BRAE by almost 1 BLEU point on average.BattRAE significantly improves translation quality on all test sets in terms of BLEU. Especially, it achieves an improvement of up to 1.63 BLEU points on average over the Baseline. Comparing with the two neural baselines, our BattRAE model obtains consistent improvements on all test sets. It significantly outperforms BCorrRAE by 0.48 BLEU points, and BRAE by almost 1 BLEU point on average. We contribute these improvements to the incorporation of clues and interactions at different levels of granularity since neither BCorrRAE nor BRAE explore them." } ], "images": [ "scigen/train/3912.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Experiment results on the MT 06/08 test sets. AVG = average BLEU scores for test sets. We highlight the best result in bold. “⇑”: significantly better than Baseline (p < 0.01); “∗∗”: significantly better than BRAE (p < 0.01); “↑”: significantly better than BCorrRAE (p < 0.05)." ], "reasoning": "BattRAE significantly improves translation quality on all test sets, achieving up to 1.63 BLEU points improvement over the Baseline and outperforming BCorrRAE by 0.48 BLEU points and BRAE by almost 1 BLEU point on average.", "description": "BattRAE significantly improves translation quality on all test sets in terms of BLEU. Especially, it achieves an improvement of up to 1.63 BLEU points on average over the Baseline. Comparing with the two neural baselines, our BattRAE model obtains consistent improvements on all test sets. It significantly outperforms BCorrRAE by 0.48 BLEU points, and BRAE by almost 1 BLEU point on average. We contribute these improvements to the incorporation of clues and interactions at different levels of granularity since neither BCorrRAE nor BRAE explore them.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Experiment results on the MT 06/08 test sets. AVG = average BLEU scores for test sets. We highlight the best result in bold. “⇑”: significantly better than Baseline (p < 0.01); “∗∗”: significantly better than BRAE (p < 0.01); “↑”: significantly better than BCorrRAE (p < 0.05).\nQuestion: How much does BattRAE improve the average BLEU score compared to the Baseline?\nSolve the above question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The average BLEU score for the Baseline is 27.61, and for BattRAE, it is 29.24. The improvement is calculated as 29.24 - 27.61. So, the answer is {\"answer\": \"1.63\"}" } ], "images": [ "scigen/train/3912.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Experiment results on the MT 06/08 test sets. AVG = average BLEU scores for test sets. We highlight the best result in bold. “⇑”: significantly better than Baseline (p < 0.01); “∗∗”: significantly better than BRAE (p < 0.01); “↑”: significantly better than BCorrRAE (p < 0.05)." ], "reasoning": "The average BLEU score for the Baseline is 27.61, and for BattRAE, it is 29.24. The improvement is calculated as 29.24 - 27.61.", "question": "How much does BattRAE improve the average BLEU score compared to the Baseline?", "answer": "So, the answer is {\"answer\": \"1.63\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: BattRAE outperforms BCorrRAE by 0.48 BLEU points on average., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nBattRAE outperforms BCorrRAE by 0.48 BLEU points on average.\n" }, { "role": "assistant", "content": "The average BLEU score for BCorrRAE is 28.76, and for BattRAE, it is 29.24. The difference is calculated as 29.24 - 28.76, which equals 0.48. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3912.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Experiment results on the MT 06/08 test sets. AVG = average BLEU scores for test sets. We highlight the best result in bold. “⇑”: significantly better than Baseline (p < 0.01); “∗∗”: significantly better than BRAE (p < 0.01); “↑”: significantly better than BCorrRAE (p < 0.05)." ], "reasoning": "The average BLEU score for BCorrRAE is 28.76, and for BattRAE, it is 29.24. The difference is calculated as 29.24 - 28.76, which equals 0.48.", "statement": "BattRAE outperforms BCorrRAE by 0.48 BLEU points on average.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Comparison different POS variants (data source/tag granularity): Baseline (BL) and the difference in performance on the +POS system when using the UD Corpus with UPOS (UD/UPOS) or with PTB tabs (UD/PTB), as well as the Wall Street Journal with PTB tags (WSJ/PTB).'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The UD/UPOS auxiliary data consistently outperforms other sources in various tasks due to its sufficient linguistic signal and smaller label inventory.Therefore we have also measured the effect of using different sources of Pos auxiliary data to give account for the possible differences in label inventory and corpus for all tasks, high and low-level, cf. We have used the PTB version of the English UD corpus (UD/PTB) as well as the training section of the Wall Street Journal (WSJ) treebank as of Pos (WSJ/PTB) auxiliary task. The former offers the opportunity to change the Pos inventory to the three times larger PTB inventory while using the same corpus. However, the characteristics of the UD/UPOS we have used as Pos throughout the article makes it a more suitable auxiliary source, in fact it systematically outperforms the other two. We argue that UD/UPOS has enough linguistic signal to be a useful auxiliary task, while still depending on a smaller label inventory. Interestingly, if we use Pos for Chunk (cf." } ], "images": [ "scigen/train/3913.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Comparison different POS variants (data source/tag granularity): Baseline (BL) and the difference in performance on the +POS system when using the UD Corpus with UPOS (UD/UPOS) or with PTB tabs (UD/PTB), as well as the Wall Street Journal with PTB tags (WSJ/PTB)." ], "reasoning": "The UD/UPOS auxiliary data consistently outperforms other sources in various tasks due to its sufficient linguistic signal and smaller label inventory.", "description": "Therefore we have also measured the effect of using different sources of Pos auxiliary data to give account for the possible differences in label inventory and corpus for all tasks, high and low-level, cf. We have used the PTB version of the English UD corpus (UD/PTB) as well as the training section of the Wall Street Journal (WSJ) treebank as of Pos (WSJ/PTB) auxiliary task. The former offers the opportunity to change the Pos inventory to the three times larger PTB inventory while using the same corpus. However, the characteristics of the UD/UPOS we have used as Pos throughout the article makes it a more suitable auxiliary source, in fact it systematically outperforms the other two. We argue that UD/UPOS has enough linguistic signal to be a useful auxiliary task, while still depending on a smaller label inventory. Interestingly, if we use Pos for Chunk (cf.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 3: Comparison different POS variants (data source/tag granularity): Baseline (BL) and the difference in performance on the +POS system when using the UD Corpus with UPOS (UD/UPOS) or with PTB tabs (UD/PTB), as well as the Wall Street Journal with PTB tags (WSJ/PTB).', answer the question below by showing each progressive step toward the answer. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhich auxiliary data source shows the highest improvement for the MPQA task?\n" }, { "role": "assistant", "content": "By comparing the Δ values for the MPQA task across different auxiliary data sources, we see that the highest improvement is with ΔUD/UPOS at 5.62. So, the answer is {\"answer\": \"UD/UPOS\"}." } ], "images": [ "scigen/train/3913.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Comparison different POS variants (data source/tag granularity): Baseline (BL) and the difference in performance on the +POS system when using the UD Corpus with UPOS (UD/UPOS) or with PTB tabs (UD/PTB), as well as the Wall Street Journal with PTB tags (WSJ/PTB)." ], "reasoning": "By comparing the Δ values for the MPQA task across different auxiliary data sources, we see that the highest improvement is with ΔUD/UPOS at 5.62.", "question": "Which auxiliary data source shows the highest improvement for the MPQA task?", "answer": "So, the answer is {\"answer\": \"UD/UPOS\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The ΔWSJ/PTB auxiliary data source shows the highest improvement for the Chunk task., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe ΔWSJ/PTB auxiliary data source shows the highest improvement for the Chunk task.\n" }, { "role": "assistant", "content": "By comparing the Δ values for the Chunk task across different auxiliary data sources, we see that ΔWSJ/PTB shows an improvement of 0.18, which is the same as ΔUD/PTB. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3913.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Comparison different POS variants (data source/tag granularity): Baseline (BL) and the difference in performance on the +POS system when using the UD Corpus with UPOS (UD/UPOS) or with PTB tabs (UD/PTB), as well as the Wall Street Journal with PTB tags (WSJ/PTB)." ], "reasoning": "By comparing the Δ values for the Chunk task across different auxiliary data sources, we see that ΔWSJ/PTB shows an improvement of 0.18, which is the same as ΔUD/PTB.", "statement": "The ΔWSJ/PTB auxiliary data source shows the highest improvement for the Chunk task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Baseline (BL) and best system performance difference (Δ) for all main tasks—improvements in bold, significant improvements underlined—plus number of systems over baseline for each main task.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The results show that increasing the size of the hidden layers reduces generalization performance, as indicated by the negative ΔBest values for Frames, Ner, and Supersenses.Given the size of the space of possible task combinations for MTL, we only report the baseline and the results of the best system. The last column indicates the amount of systems that beat the baseline for a given certain main task. Our results confirm that increasing the size of the hidden layers reduces generalization performance. This is the case for all setups. (Frames 0, Mpqa: 2, Ner: 0, SemTraits: 9, Supersenses: 0)." } ], "images": [ "scigen/train/3914.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Baseline (BL) and best system performance difference (Δ) for all main tasks—improvements in bold, significant improvements underlined—plus number of systems over baseline for each main task." ], "reasoning": "The results show that increasing the size of the hidden layers reduces generalization performance, as indicated by the negative ΔBest values for Frames, Ner, and Supersenses.", "description": "Given the size of the space of possible task combinations for MTL, we only report the baseline and the results of the best system. The last column indicates the amount of systems that beat the baseline for a given certain main task. Our results confirm that increasing the size of the hidden layers reduces generalization performance. This is the case for all setups. (Frames 0, Mpqa: 2, Ner: 0, SemTraits: 9, Supersenses: 0).", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 2: Baseline (BL) and best system performance difference (Δ) for all main tasks—improvements in bold, significant improvements underlined—plus number of systems over baseline for each main task.', answer the following question. Give detailed solution consisting of each step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich main task had the highest improvement over the baseline?\n" }, { "role": "assistant", "content": "To find the main task with the highest improvement over the baseline, look at the ΔBest column and identify the highest positive value. So, the answer is {\"answer\": \"SemTraits\"}" } ], "images": [ "scigen/train/3914.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Baseline (BL) and best system performance difference (Δ) for all main tasks—improvements in bold, significant improvements underlined—plus number of systems over baseline for each main task." ], "reasoning": "To find the main task with the highest improvement over the baseline, look at the ΔBest column and identify the highest positive value.", "question": "Which main task had the highest improvement over the baseline?", "answer": "So, the answer is {\"answer\": \"SemTraits\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The main task 'Mpqa' had a positive improvement over the baseline., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe main task 'Mpqa' had a positive improvement over the baseline.\n" }, { "role": "assistant", "content": "The ΔBest value for Mpqa is 0.96, which is positive, indicating an improvement over the baseline. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3914.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Baseline (BL) and best system performance difference (Δ) for all main tasks—improvements in bold, significant improvements underlined—plus number of systems over baseline for each main task." ], "reasoning": "The ΔBest value for Mpqa is 0.96, which is positive, indicating an improvement over the baseline.", "statement": "The main task 'Mpqa' had a positive improvement over the baseline.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: Comparison default hierarchical systems using a lower-level bi-LSTM for characters (BL w+c) versus system using only words (w).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "Disabling character features improves performance for Frames and MPQA but decreases performance for other tasks, especially Ner.Throughout the article we used the default network structure which includes a lower-level bi-LSTM at the character level. However, we hypothesize that the character features are not equally important for all tasks. In fact, if we disable the character features, making the system only depend on word information (cf. , we observe that two of the tasks (albeit the ones with the overall lowest performance) increase their performance in about 2.5 points, namely Mpqa and Frames. For the other two tasks we observe drops up to a maximum of 8-points for Ner. Character embeddings are informative for Ner, because they approximate the well-known capitalization features in traditional models. Character features are not informative for tasks that are more dependent on word identity (like Frames), but are indeed useful for tasks where parts of the word can be informative, such as Pos or Ner." } ], "images": [ "scigen/train/3915.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison default hierarchical systems using a lower-level bi-LSTM for characters (BL w+c) versus system using only words (w)." ], "reasoning": "Disabling character features improves performance for Frames and MPQA but decreases performance for other tasks, especially Ner.", "description": "Throughout the article we used the default network structure which includes a lower-level bi-LSTM at the character level. However, we hypothesize that the character features are not equally important for all tasks. In fact, if we disable the character features, making the system only depend on word information (cf. , we observe that two of the tasks (albeit the ones with the overall lowest performance) increase their performance in about 2.5 points, namely Mpqa and Frames. For the other two tasks we observe drops up to a maximum of 8-points for Ner. Character embeddings are informative for Ner, because they approximate the well-known capitalization features in traditional models. Character features are not informative for tasks that are more dependent on word identity (like Frames), but are indeed useful for tasks where parts of the word can be informative, such as Pos or Ner.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 5: Comparison default hierarchical systems using a lower-level bi-LSTM for characters (BL w+c) versus system using only words (w).', answer the question below by showing each progressive step toward the answer. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich task shows the highest performance drop when character features are disabled?\n" }, { "role": "assistant", "content": "By comparing the performance drops in the 'Δonly w' column, Ner shows the highest drop of -8.05 points. Thus, the answer is {\"answer\": \"Ner\"}." } ], "images": [ "scigen/train/3915.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison default hierarchical systems using a lower-level bi-LSTM for characters (BL w+c) versus system using only words (w)." ], "reasoning": "By comparing the performance drops in the 'Δonly w' column, Ner shows the highest drop of -8.05 points.", "question": "Which task shows the highest performance drop when character features are disabled?", "answer": "Thus, the answer is {\"answer\": \"Ner\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nDisabling character features improves the performance of MPQA by 2.91 points.\n" }, { "role": "assistant", "content": "The table shows that the performance of MPQA increases by 2.91 points when character features are disabled. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3915.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison default hierarchical systems using a lower-level bi-LSTM for characters (BL w+c) versus system using only words (w)." ], "reasoning": "The table shows that the performance of MPQA increases by 2.91 points when character features are disabled.", "statement": "Disabling character features improves the performance of MPQA by 2.91 points.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: Contribution of each features (filter size : [4,6] each with 100 filters)'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Including position features significantly improves recall and F1 score, while adding PoS and Chunk features further enhances performance.In order to investigate the contribution of each feature in final result we gradually include one feature in our model and compared the performance. First we use only random vector (RV) representation along with entity types (T) (first row in the table) as a baseline for our comparison. Adding position features (2nd row) lead to approximately 15% increase in recall, 7% in precision and 11.7% in F1 score. However including PoS and Chunk features although improved recall and F1 score by 4.3% and 1.3% but precision was decreased by 3.6%. In the second set of experiments, we first use pre-trained word vectors along with entity types (4th row) and later repeated the similar experiments as previously. Here again, inclusion of position features improved the recall by more than 14% and F1 score by around 11%. This clearly indicates word position relative to the entities of interest plays important role in deciding their influence in the context. Further including PoS and Chunk features also led to performance improvement." } ], "images": [ "scigen/train/3916.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Contribution of each features (filter size : [4,6] each with 100 filters)" ], "reasoning": "Including position features significantly improves recall and F1 score, while adding PoS and Chunk features further enhances performance.", "description": "In order to investigate the contribution of each feature in final result we gradually include one feature in our model and compared the performance. First we use only random vector (RV) representation along with entity types (T) (first row in the table) as a baseline for our comparison. Adding position features (2nd row) lead to approximately 15% increase in recall, 7% in precision and 11.7% in F1 score. However including PoS and Chunk features although improved recall and F1 score by 4.3% and 1.3% but precision was decreased by 3.6%. In the second set of experiments, we first use pre-trained word vectors along with entity types (4th row) and later repeated the similar experiments as previously. Here again, inclusion of position features improved the recall by more than 14% and F1 score by around 11%. This clearly indicates word position relative to the entities of interest plays important role in deciding their influence in the context. Further including PoS and Chunk features also led to performance improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 4: Contribution of each features (filter size : [4,6] each with 100 filters)', solve this question and give detail solutions. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the F1 score when using pre-trained word vectors along with entity types and position features?\n" }, { "role": "assistant", "content": "From the table, the F1 score for pre-trained word vectors along with entity types (WV + T) is 63.82. Adding position features (P1 + P2) increases the F1 score to 70.97. So, the answer is {\"answer\": \"70.97\"}" } ], "images": [ "scigen/train/3916.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Contribution of each features (filter size : [4,6] each with 100 filters)" ], "reasoning": "From the table, the F1 score for pre-trained word vectors along with entity types (WV + T) is 63.82. Adding position features (P1 + P2) increases the F1 score to 70.97.", "question": "What is the F1 score when using pre-trained word vectors along with entity types and position features?", "answer": "So, the answer is {\"answer\": \"70.97\"}" }, { "messages": [ { "role": "user", "content": "Begin with the sentence Including PoS and Chunk features with pre-trained word vectors and entity types results in the highest precision. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nIncluding PoS and Chunk features with pre-trained word vectors and entity types results in the highest precision.\n" }, { "role": "assistant", "content": "The table shows that the highest precision (76.34) is achieved when PoS and Chunk features are included with pre-trained word vectors and entity types. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3916.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Contribution of each features (filter size : [4,6] each with 100 filters)" ], "reasoning": "The table shows that the highest precision (76.34) is achieved when PoS and Chunk features are included with pre-trained word vectors and entity types.", "statement": "Including PoS and Chunk features with pre-trained word vectors and entity types results in the highest precision.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 5: Comparative performance of SVM and CNN with filter length [4,6] each with 100 filters', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The results shown are averages over 5-folds, and various features were used for each entity pair instance, including words, PoS tags, bigrams, and chunk tags.We could not compare our results directly with the state of the art results obtained on the i2b2 dataset as we did not have the complete dataset. The following features are used for each entity pair instance: Any word between relation arguments Any PoS tags between relation arguments. We used genia tagger for PoS Any bigram between relation arguments Word preceding first and second argument Any three words succeeding the first and second arguments Sequence of chunk tags between relation arguments. We used genia tagger for chunk tag String of words between relation arguments First and second argument type (problem, treatment and test) Order of argument type appeared in sentence Distance between two arguments in terms of number of words Presence of only punctuation sign between arguments. This way we prepared attribute-value and numerical features for each instances. Here again results shown are average over the 5-folds." } ], "images": [ "scigen/train/3917.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparative performance of SVM and CNN with filter length [4,6] each with 100 filters" ], "reasoning": "The results shown are averages over 5-folds, and various features were used for each entity pair instance, including words, PoS tags, bigrams, and chunk tags.", "description": "We could not compare our results directly with the state of the art results obtained on the i2b2 dataset as we did not have the complete dataset. The following features are used for each entity pair instance: Any word between relation arguments Any PoS tags between relation arguments. We used genia tagger for PoS Any bigram between relation arguments Word preceding first and second argument Any three words succeeding the first and second arguments Sequence of chunk tags between relation arguments. We used genia tagger for chunk tag String of words between relation arguments First and second argument type (problem, treatment and test) Order of argument type appeared in sentence Distance between two arguments in terms of number of words Presence of only punctuation sign between arguments. This way we prepared attribute-value and numerical features for each instances. Here again results shown are average over the 5-folds.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: 'Table 5: Comparative performance of SVM and CNN with filter length [4,6] each with 100 filters'\nQuestion: Which model achieved the highest F-score?\n" }, { "role": "assistant", "content": "By comparing the F-scores of all models listed in the table, the CNN model has the highest F-score of 71.16. So, the answer is {\"answer\": \"CNN\"}" } ], "images": [ "scigen/train/3917.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparative performance of SVM and CNN with filter length [4,6] each with 100 filters" ], "reasoning": "By comparing the F-scores of all models listed in the table, the CNN model has the highest F-score of 71.16.", "question": "Which model achieved the highest F-score?", "answer": "So, the answer is {\"answer\": \"CNN\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe SVM (Linear, C=0.1) model achieved a higher precision than the CNN model.\n" }, { "role": "assistant", "content": "The table shows that the precision for the SVM (Linear, C=0.1) model is 73.75, while the precision for the CNN model is 76.34. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3917.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparative performance of SVM and CNN with filter length [4,6] each with 100 filters" ], "reasoning": "The table shows that the precision for the SVM (Linear, C=0.1) model is 73.75, while the precision for the CNN model is 76.34. Therefore, the statement is refuted.", "statement": "The SVM (Linear, C=0.1) model achieved a higher precision than the CNN model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Microblog results with all features on validation and test sets.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Random Forests achieved the highest cosine similarity and lowest MAE in both training and validation sets, with an official score of 0.6948 in the test set.Results in the test set are worse than in the validation set with the exception to MLP. The official score obtained in sub-task 5.1 was 0.6948 using Random Forests (RF), which is the regressor that achieves higher cosine similarity and lower MAE in both training and validation set." } ], "images": [ "scigen/train/3918.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Microblog results with all features on validation and test sets." ], "reasoning": "Random Forests achieved the highest cosine similarity and lowest MAE in both training and validation sets, with an official score of 0.6948 in the test set.", "description": "Results in the test set are worse than in the validation set with the exception to MLP. The official score obtained in sub-task 5.1 was 0.6948 using Random Forests (RF), which is the regressor that achieves higher cosine similarity and lower MAE in both training and validation set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: Microblog results with all features on validation and test sets.'. Output the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: Which regressor achieved the highest cosine similarity in the validation set?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By comparing the cosine similarity values in the validation set for all regressors, Random Forests (RF) achieved the highest value of 0.7960. So, the answer is {\"answer\": \"RF\"}." } ], "images": [ "scigen/train/3918.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Microblog results with all features on validation and test sets." ], "reasoning": "By comparing the cosine similarity values in the validation set for all regressors, Random Forests (RF) achieved the highest value of 0.7960.", "question": "Which regressor achieved the highest cosine similarity in the validation set?", "answer": "So, the answer is {\"answer\": \"RF\"}." }, { "messages": [ { "role": "user", "content": "SVR achieved the lowest MAE in the test set. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSVR achieved the lowest MAE in the test set.\n" }, { "role": "assistant", "content": "By comparing the MAE values in the test set for all regressors, SVR has an MAE of 0.2526, which is higher than the MAE values for RF (0.1886) and MLP (0.2132). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3918.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Microblog results with all features on validation and test sets." ], "reasoning": "By comparing the MAE values in the test set for all regressors, SVR has an MAE of 0.2526, which is higher than the MAE values for RF (0.1886) and MLP (0.2132).", "statement": "SVR achieved the lowest MAE in the test set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Features performance breakdown on test set using RF.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Combining BoW and BoE features results in higher cosine similarity and lower MAE compared to using all features.Interestingly, bag-of-words (BoW) and bag-of-embeddings (BoE) complement each other, obtaining better cosine similarity than the system using all features. Financial word embeddings (BoE) capture relevant information regarding the target variables. As a single group of features it achieves a cosine similarity of 0.6118 and MAE of 0.2322. It is also able to boost the overall performance of BoW with gains of more than 0.06 in cosine similarity and reducing MAE more than 0.03." } ], "images": [ "scigen/train/3919.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Features performance breakdown on test set using RF." ], "reasoning": "Combining BoW and BoE features results in higher cosine similarity and lower MAE compared to using all features.", "description": "Interestingly, bag-of-words (BoW) and bag-of-embeddings (BoE) complement each other, obtaining better cosine similarity than the system using all features. Financial word embeddings (BoE) capture relevant information regarding the target variables. As a single group of features it achieves a cosine similarity of 0.6118 and MAE of 0.2322. It is also able to boost the overall performance of BoW with gains of more than 0.06 in cosine similarity and reducing MAE more than 0.03.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 3: Features performance breakdown on test set using RF.', unfold the question presented below, detailing every step of your calculation. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhat is the cosine similarity achieved by combining BoW and BoE features?\n" }, { "role": "assistant", "content": "The table shows that the combination of BoW and BoE features achieves a cosine similarity of 0.7023. So, the answer is {\"answer\": \"0.7023\"}." } ], "images": [ "scigen/train/3919.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Features performance breakdown on test set using RF." ], "reasoning": "The table shows that the combination of BoW and BoE features achieves a cosine similarity of 0.7023.", "question": "What is the cosine similarity achieved by combining BoW and BoE features?", "answer": "So, the answer is {\"answer\": \"0.7023\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The combination of BoW and BoE features achieves the highest cosine similarity. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe combination of BoW and BoE features achieves the highest cosine similarity.\n" }, { "role": "assistant", "content": "The table indicates that the combination of BoW and BoE features achieves a cosine similarity of 0.7023, which is higher than any other combination or single feature set. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3919.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Features performance breakdown on test set using RF." ], "reasoning": "The table indicates that the combination of BoW and BoE features achieves a cosine similarity of 0.7023, which is higher than any other combination or single feature set.", "statement": "The combination of BoW and BoE features achieves the highest cosine similarity.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 4: News Headlines results with all features on validation and test sets.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The MLP regressor achieved the highest cosine similarity of 0.68 on the test set, outperforming both RF and SVR.Results obtained in news headlines are very different from the ones of the previous sub-task, proving that predicting sentiment polarity and intensity in news headlines is a complete different problem compared to microblogs. The best regressor of sub-task 5.1, RF is outperformed by both SVR and MLP. The official result obtained at sub-task 5.2 was a cosine similarity of 0.68 using MLP." } ], "images": [ "scigen/train/3920.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: News Headlines results with all features on validation and test sets." ], "reasoning": "The MLP regressor achieved the highest cosine similarity of 0.68 on the test set, outperforming both RF and SVR.", "description": "Results obtained in news headlines are very different from the ones of the previous sub-task, proving that predicting sentiment polarity and intensity in news headlines is a complete different problem compared to microblogs. The best regressor of sub-task 5.1, RF is outperformed by both SVR and MLP. The official result obtained at sub-task 5.2 was a cosine similarity of 0.68 using MLP.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nTable title: Table 4: News Headlines results with all features on validation and test sets.\nQuestion: Which regressor achieved the highest cosine similarity on the test set?\n" }, { "role": "assistant", "content": "By comparing the cosine similarity values for the test set across all regressors, MLP has the highest value of 0.6800. So, the answer is {\"answer\": \"MLP\"}." } ], "images": [ "scigen/train/3920.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: News Headlines results with all features on validation and test sets." ], "reasoning": "By comparing the cosine similarity values for the test set across all regressors, MLP has the highest value of 0.6800.", "question": "Which regressor achieved the highest cosine similarity on the test set?", "answer": "So, the answer is {\"answer\": \"MLP\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe RF regressor achieved the highest cosine similarity on the validation set.\n" }, { "role": "assistant", "content": "By comparing the cosine similarity values for the validation set, SVR has a higher value (0.6397) than RF (0.5316). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3920.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: News Headlines results with all features on validation and test sets." ], "reasoning": "By comparing the cosine similarity values for the validation set, SVR has a higher value (0.6397) than RF (0.5316).", "statement": "The RF regressor achieved the highest cosine similarity on the validation set.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 5: Features performance breakdown on test set using MLP.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Word embeddings are not effective, while lexical features perform better in news headlines than microblogs, with the best results achieved using all features.The most evident observation is that word embeddings are not effective in this scenario. On the other hand, lexical based features have significantly better performance in news headlines than in microblogs. Despite this, the best results are obtained using all features." } ], "images": [ "scigen/train/3921.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Features performance breakdown on test set using MLP." ], "reasoning": "Word embeddings are not effective, while lexical features perform better in news headlines than microblogs, with the best results achieved using all features.", "description": "The most evident observation is that word embeddings are not effective in this scenario. On the other hand, lexical based features have significantly better performance in news headlines than in microblogs. Despite this, the best results are obtained using all features.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: Features performance breakdown on test set using MLP.'.\n\nQuestion: Which feature combination achieves the best performance in terms of Cosine similarity?\n" }, { "role": "assistant", "content": "The table shows the Cosine similarity scores for different feature combinations. The highest Cosine similarity score is 0.6800, which is achieved by using all features. So, the answer is {\"answer\": \"All\"}" } ], "images": [ "scigen/train/3921.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Features performance breakdown on test set using MLP." ], "reasoning": "The table shows the Cosine similarity scores for different feature combinations. The highest Cosine similarity score is 0.6800, which is achieved by using all features.", "question": "Which feature combination achieves the best performance in terms of Cosine similarity?", "answer": "So, the answer is {\"answer\": \"All\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nLexical based features have the lowest MAE.\n" }, { "role": "assistant", "content": "The table shows the MAE scores for different feature combinations. Lexical based features (Lex) have an MAE of 0.2788, which is not the lowest. The lowest MAE is 0.2269, achieved by the combination of BoW + Lex. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3921.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Features performance breakdown on test set using MLP." ], "reasoning": "The table shows the MAE scores for different feature combinations. Lexical based features (Lex) have an MAE of 0.2788, which is not the lowest. The lowest MAE is 0.2269, achieved by the combination of BoW + Lex.", "statement": "Lexical based features have the lowest MAE.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 4: The impact of λ on AR.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The impact of λ on AR is investigated, and λ is set to 0.001 for CatGAN in all experiments due to its good trade-off between quality and diversity.The impact of λ is investigated on AR. In the right-hand side of Eq. To balance the sample quality and diversity, λ is set to increase from 0 to 1. In practice, λ is set to 0.001 for CatGAN on all experiments, since it shows a good trade-off between quality and diversity." } ], "images": [ "scigen/train/3922.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: The impact of λ on AR." ], "reasoning": "The impact of λ on AR is investigated, and λ is set to 0.001 for CatGAN in all experiments due to its good trade-off between quality and diversity.", "description": "The impact of λ is investigated on AR. In the right-hand side of Eq. To balance the sample quality and diversity, λ is set to increase from 0 to 1. In practice, λ is set to 0.001 for CatGAN on all experiments, since it shows a good trade-off between quality and diversity.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: The impact of λ on AR.\nQuestion: What is the BLEU-3 score when λ is set to 0.001?\nSolve the above question based on the table information. Let's think step by step. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the BLEU-3 score for λ set to 0.001, locate the BLEU-3 row and the column corresponding to 0.001. So, the answer is {\"answer\": \"0.943\"}." } ], "images": [ "scigen/train/3922.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: The impact of λ on AR." ], "reasoning": "To find the BLEU-3 score for λ set to 0.001, locate the BLEU-3 row and the column corresponding to 0.001.", "question": "What is the BLEU-3 score when λ is set to 0.001?", "answer": "So, the answer is {\"answer\": \"0.943\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The BLEU-4 score decreases as λ increases from 0 to 1., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe BLEU-4 score decreases as λ increases from 0 to 1.\n" }, { "role": "assistant", "content": "To verify this statement, observe the BLEU-4 scores across the columns from 0 to 1. The scores are 0.868, 0.867, 0.841, 0.825, and 0.803, respectively, indicating a decreasing trend. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3922.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: The impact of λ on AR." ], "reasoning": "To verify this statement, observe the BLEU-4 scores across the columns from 0 to 1. The scores are 0.868, 0.867, 0.841, 0.825, and 0.803, respectively, indicating a decreasing trend.", "statement": "The BLEU-4 score decreases as λ increases from 0 to 1.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 1: The NLLoracle scores on category text generation. For the NLLoracle scores, the lower the better.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The synthetic data experiments with sequence lengths of 20 and 40 show that CatGAN has the lowest NLLoracle scores compared to SentiGAN and CSGAN.The synthetic data experiments are set with sequence length 20 and 40. NLLoracle is used to measure the sample quality, and the ground-truth scores are 5.748 and 4.015 for different sequence length, respectively." } ], "images": [ "scigen/train/3923.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The NLLoracle scores on category text generation. For the NLLoracle scores, the lower the better." ], "reasoning": "The synthetic data experiments with sequence lengths of 20 and 40 show that CatGAN has the lowest NLLoracle scores compared to SentiGAN and CSGAN.", "description": "The synthetic data experiments are set with sequence length 20 and 40. NLLoracle is used to measure the sample quality, and the ground-truth scores are 5.748 and 4.015 for different sequence length, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 1: The NLLoracle scores on category text generation. For the NLLoracle scores, the lower the better.' in mind, break down and resolve the question below step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich GAN model has the lowest NLLoracle score for sequence length 40?\n" }, { "role": "assistant", "content": "For sequence length 40, compare the NLLoracle scores of SentiGAN, CSGAN, and CatGAN. CatGAN has the lowest score of 6.498. So, the answer is {\"answer\": \"CatGAN\"}." } ], "images": [ "scigen/train/3923.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The NLLoracle scores on category text generation. For the NLLoracle scores, the lower the better." ], "reasoning": "For sequence length 40, compare the NLLoracle scores of SentiGAN, CSGAN, and CatGAN. CatGAN has the lowest score of 6.498.", "question": "Which GAN model has the lowest NLLoracle score for sequence length 40?", "answer": "So, the answer is {\"answer\": \"CatGAN\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nCSGAN has a higher NLLoracle score than SentiGAN for sequence length 20.\n" }, { "role": "assistant", "content": "For sequence length 20, CSGAN has an NLLoracle score of 8.431, which is higher than SentiGAN's score of 6.976. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3923.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The NLLoracle scores on category text generation. For the NLLoracle scores, the lower the better." ], "reasoning": "For sequence length 20, CSGAN has an NLLoracle score of 8.431, which is higher than SentiGAN's score of 6.976.", "statement": "CSGAN has a higher NLLoracle score than SentiGAN for sequence length 20.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: The performance comparison on MR. ↑ means higher is better, and ↓ means lower is better.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "CatGAN outperforms SentiGAN and CSGAN in BLEU scores on both MR and AR datasets, and maintains good diversity and quality as indicated by NLLgen and NLLdiv scores.The real data experiments are conducted on MR and AR. After the same preprocessing, MR consists of 6,216 unique words with the maximum sentence length 15, and AR contains 6,416 unique words with the maximum sentence length 40. On MR, since SentiGAN is designed to generate sentiment text, it shows better results than CSGAN on the BLEU scores. On AR, the sufficient training samples improve the performance of all methods. CSGAN heavily relies on the auxiliary classifier and the RL algorithm, and it shows a significant quality degradation than CatGAN on BLEU while generating long sentences on AR. Compared with the baselines, CatGAN is not limited by the category type of data and obtains the better BLEU scores on both MR and AR, which also shows that CatGAN can catch the dependencies in short and long sentences. Besides, on AR, CatGAN gets 3.104 on NLLgen and 1.539 on NLLdiv, which illustrates that our model can maintain good diversity while significantly improving quality. Actually, optimizing NLLdiv based score function can lead to a better NLLdiv, yet CatGAN still consistently bests NLLdiv and an existing metric, NLLgen." } ], "images": [ "scigen/train/3924.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: The performance comparison on MR. ↑ means higher is better, and ↓ means lower is better." ], "reasoning": "CatGAN outperforms SentiGAN and CSGAN in BLEU scores on both MR and AR datasets, and maintains good diversity and quality as indicated by NLLgen and NLLdiv scores.", "description": "The real data experiments are conducted on MR and AR. After the same preprocessing, MR consists of 6,216 unique words with the maximum sentence length 15, and AR contains 6,416 unique words with the maximum sentence length 40. On MR, since SentiGAN is designed to generate sentiment text, it shows better results than CSGAN on the BLEU scores. On AR, the sufficient training samples improve the performance of all methods. CSGAN heavily relies on the auxiliary classifier and the RL algorithm, and it shows a significant quality degradation than CatGAN on BLEU while generating long sentences on AR. Compared with the baselines, CatGAN is not limited by the category type of data and obtains the better BLEU scores on both MR and AR, which also shows that CatGAN can catch the dependencies in short and long sentences. Besides, on AR, CatGAN gets 3.104 on NLLgen and 1.539 on NLLdiv, which illustrates that our model can maintain good diversity while significantly improving quality. Actually, optimizing NLLdiv based score function can lead to a better NLLdiv, yet CatGAN still consistently bests NLLdiv and an existing metric, NLLgen.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 2: The performance comparison on MR. ↑ means higher is better, and ↓ means lower is better.', answer the question below by showing each progressive step toward the answer. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhich method shows the best performance in terms of BLEU-3 score?\n" }, { "role": "assistant", "content": "By comparing the BLEU-3 scores in the table, CatGAN has the highest BLEU-3 score of 0.335 ± 0.032. So, the answer is {\"answer\": \"CatGAN\"}." } ], "images": [ "scigen/train/3924.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: The performance comparison on MR. ↑ means higher is better, and ↓ means lower is better." ], "reasoning": "By comparing the BLEU-3 scores in the table, CatGAN has the highest BLEU-3 score of 0.335 ± 0.032.", "question": "Which method shows the best performance in terms of BLEU-3 score?", "answer": "So, the answer is {\"answer\": \"CatGAN\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis CSGAN has the lowest NLLgen score. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nCSGAN has the lowest NLLgen score.\n" }, { "role": "assistant", "content": "The table shows that CSGAN has an NLLgen score of 2.912, which is higher than both SentiGAN (2.436) and CatGAN (1.619 ± 0.169). Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3924.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: The performance comparison on MR. ↑ means higher is better, and ↓ means lower is better." ], "reasoning": "The table shows that CSGAN has an NLLgen score of 2.912, which is higher than both SentiGAN (2.436) and CatGAN (1.619 ± 0.169).", "statement": "CSGAN has the lowest NLLgen score.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: The performance comparison on AR.'.\n" }, { "role": "assistant", "content": "CatGAN outperforms SentiGAN and CSGAN in BLEU scores and maintains good diversity and quality in NLL metrics.The real data experiments are conducted on MR and AR. After the same preprocessing, MR consists of 6,216 unique words with the maximum sentence length 15, and AR contains 6,416 unique words with the maximum sentence length 40. On MR, since SentiGAN is designed to generate sentiment text, it shows better results than CSGAN on the BLEU scores. On AR, the sufficient training samples improve the performance of all methods. CSGAN heavily relies on the auxiliary classifier and the RL algorithm, and it shows a significant quality degradation than CatGAN on BLEU while generating long sentences on AR. Compared with the baselines, CatGAN is not limited by the category type of data and obtains the better BLEU scores on both MR and AR, which also shows that CatGAN can catch the dependencies in short and long sentences. Besides, on AR, CatGAN gets 3.104 on NLLgen and 1.539 on NLLdiv, which illustrates that our model can maintain good diversity while significantly improving quality. Actually, optimizing NLLdiv based score function can lead to a better NLLdiv, yet CatGAN still consistently bests NLLdiv and an existing metric, NLLgen." } ], "images": [ "scigen/train/3925.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The performance comparison on AR." ], "reasoning": "CatGAN outperforms SentiGAN and CSGAN in BLEU scores and maintains good diversity and quality in NLL metrics.", "description": "The real data experiments are conducted on MR and AR. After the same preprocessing, MR consists of 6,216 unique words with the maximum sentence length 15, and AR contains 6,416 unique words with the maximum sentence length 40. On MR, since SentiGAN is designed to generate sentiment text, it shows better results than CSGAN on the BLEU scores. On AR, the sufficient training samples improve the performance of all methods. CSGAN heavily relies on the auxiliary classifier and the RL algorithm, and it shows a significant quality degradation than CatGAN on BLEU while generating long sentences on AR. Compared with the baselines, CatGAN is not limited by the category type of data and obtains the better BLEU scores on both MR and AR, which also shows that CatGAN can catch the dependencies in short and long sentences. Besides, on AR, CatGAN gets 3.104 on NLLgen and 1.539 on NLLdiv, which illustrates that our model can maintain good diversity while significantly improving quality. Actually, optimizing NLLdiv based score function can lead to a better NLLdiv, yet CatGAN still consistently bests NLLdiv and an existing metric, NLLgen.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 3: The performance comparison on AR.', answer the following question in a stepwise manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich method shows the highest BLEU-4 score?\n" }, { "role": "assistant", "content": "By comparing the BLEU-4 scores of SentiGAN, CSGAN, and CatGAN, CatGAN has the highest score of 0.867. So, the answer is {\"answer\": \"CatGAN\"}" } ], "images": [ "scigen/train/3925.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The performance comparison on AR." ], "reasoning": "By comparing the BLEU-4 scores of SentiGAN, CSGAN, and CatGAN, CatGAN has the highest score of 0.867.", "question": "Which method shows the highest BLEU-4 score?", "answer": "So, the answer is {\"answer\": \"CatGAN\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nCSGAN has the lowest NLLgen score.\n" }, { "role": "assistant", "content": "The table shows that CatGAN has the lowest NLLgen score of 3.104, not CSGAN. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3925.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The performance comparison on AR." ], "reasoning": "The table shows that CatGAN has the lowest NLLgen score of 3.104, not CSGAN.", "statement": "CSGAN has the lowest NLLgen score.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 5: The ablation study on AR.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The ablation study shows that removing TMS or OMS from CatGAN results in lower BLEU scores, indicating worse quality, while CatGAN w/o H performs worse than CatGAN w/o O but still better than SentiGAN.For illustrating the effectiveness of TMS and OMS, the ablation study is conducted on AR. The whole hierarchical evolutionary learning algorithm is removed as CatGAN w/o H, TMS is removed from CatGAN as CatGAN w/o T, and OMS is replaced with lCatRaGθ to form CatGAN w/o O. Compared with CatGAN, CatGAN w/o T shows the degradation on all BLEU scores and only increases NLLdiv by 0.016, which means the worse quality and the similar diversity, respectively. Although CatGAN w/o O achieves competitive sample diversity over CatGAN, it shows a significant degradation on BLEU, which means OMS can effectively guide our model. CatGAN w/o H gets the worse sample quality than CatGAN w/o O, but it still outperforms SentiGAN. The ablation study illustrates that combining TMS and OMS facilitates generating diversified and high-quality samples on real data." } ], "images": [ "scigen/train/3926.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: The ablation study on AR." ], "reasoning": "The ablation study shows that removing TMS or OMS from CatGAN results in lower BLEU scores, indicating worse quality, while CatGAN w/o H performs worse than CatGAN w/o O but still better than SentiGAN.", "description": "For illustrating the effectiveness of TMS and OMS, the ablation study is conducted on AR. The whole hierarchical evolutionary learning algorithm is removed as CatGAN w/o H, TMS is removed from CatGAN as CatGAN w/o T, and OMS is replaced with lCatRaGθ to form CatGAN w/o O. Compared with CatGAN, CatGAN w/o T shows the degradation on all BLEU scores and only increases NLLdiv by 0.016, which means the worse quality and the similar diversity, respectively. Although CatGAN w/o O achieves competitive sample diversity over CatGAN, it shows a significant degradation on BLEU, which means OMS can effectively guide our model. CatGAN w/o H gets the worse sample quality than CatGAN w/o O, but it still outperforms SentiGAN. The ablation study illustrates that combining TMS and OMS facilitates generating diversified and high-quality samples on real data.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 5: The ablation study on AR.', solve this question and give detail solutions. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich method shows the highest NLLdiv score and what does it indicate about the sample diversity?\n" }, { "role": "assistant", "content": "The table shows that CatGAN w/o O has the highest NLLdiv score of 1.618, which indicates that it achieves competitive sample diversity over CatGAN. So, the answer is {\"answer\": \"CatGAN w/o O\"}." } ], "images": [ "scigen/train/3926.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: The ablation study on AR." ], "reasoning": "The table shows that CatGAN w/o O has the highest NLLdiv score of 1.618, which indicates that it achieves competitive sample diversity over CatGAN.", "question": "Which method shows the highest NLLdiv score and what does it indicate about the sample diversity?", "answer": "So, the answer is {\"answer\": \"CatGAN w/o O\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nCatGAN w/o T has the highest BLEU-4 score among all methods.\n" }, { "role": "assistant", "content": "The table shows that CatGAN has the highest BLEU-4 score of 0.867, while CatGAN w/o T has a BLEU-4 score of 0.836, which is lower than CatGAN. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3926.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: The ablation study on AR." ], "reasoning": "The table shows that CatGAN has the highest BLEU-4 score of 0.867, while CatGAN w/o T has a BLEU-4 score of 0.836, which is lower than CatGAN.", "statement": "CatGAN w/o T has the highest BLEU-4 score among all methods.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 6: The NLLoracle scores on general text generation.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "CatGAN outperforms all baselines in NLLoracle scores for both sequence lengths 20 and 40, demonstrating better sample quality and ability to capture long-term dependencies.The synthetic data experiments run with sequence length 20 and 40, and the ground-truth NLLoracle scores are 5.750 and 4.071, respectively. Compared with all baselines, CatGAN outperforms the best of them by 0.303 and 0.521 on NLLoracle with different sequence length, respectively, which verifies the better sample quality. Specially, with sequence length 40, CatGAN significantly improves the metric by 0.956 and 3.723 over LeakGAN and RankGAN, respectively, and it illustrates that our model is more powerful to catch long-term dependencies. With the help of the hierarchical evolutionary learning algorithm, CatGAN outperforms RelGAN which also employs the temperature and the Gumbel-Softmax relaxation." } ], "images": [ "scigen/train/3927.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: The NLLoracle scores on general text generation." ], "reasoning": "CatGAN outperforms all baselines in NLLoracle scores for both sequence lengths 20 and 40, demonstrating better sample quality and ability to capture long-term dependencies.", "description": "The synthetic data experiments run with sequence length 20 and 40, and the ground-truth NLLoracle scores are 5.750 and 4.071, respectively. Compared with all baselines, CatGAN outperforms the best of them by 0.303 and 0.521 on NLLoracle with different sequence length, respectively, which verifies the better sample quality. Specially, with sequence length 40, CatGAN significantly improves the metric by 0.956 and 3.723 over LeakGAN and RankGAN, respectively, and it illustrates that our model is more powerful to catch long-term dependencies. With the help of the hierarchical evolutionary learning algorithm, CatGAN outperforms RelGAN which also employs the temperature and the Gumbel-Softmax relaxation.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 6: The NLLoracle scores on general text generation.\nQuestion: How much does CatGAN improve over LeakGAN in NLLoracle score for sequence length 40?\n" }, { "role": "assistant", "content": "The table shows that for sequence length 40, LeakGAN has an NLLoracle score of 7.191, and CatGAN has a score of 6.235. The improvement is calculated as 7.191 - 6.235. So, the answer is {\"answer\": \"0.956\"}." } ], "images": [ "scigen/train/3927.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: The NLLoracle scores on general text generation." ], "reasoning": "The table shows that for sequence length 40, LeakGAN has an NLLoracle score of 7.191, and CatGAN has a score of 6.235. The improvement is calculated as 7.191 - 6.235.", "question": "How much does CatGAN improve over LeakGAN in NLLoracle score for sequence length 40?", "answer": "So, the answer is {\"answer\": \"0.956\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nCatGAN has the lowest NLLoracle score for sequence length 20.\n" }, { "role": "assistant", "content": "The table shows that for sequence length 20, CatGAN has an NLLoracle score of 6.377, which is lower than SeqGAN, RankGAN, LeakGAN, and RelGAN. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3927.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: The NLLoracle scores on general text generation." ], "reasoning": "The table shows that for sequence length 20, CatGAN has an NLLoracle score of 6.377, which is lower than SeqGAN, RankGAN, LeakGAN, and RelGAN.", "statement": "CatGAN has the lowest NLLoracle score for sequence length 20.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Table 7: The performance comparison on EN.'.\n" }, { "role": "assistant", "content": "The CatGAN model achieves the highest BLEU scores across all n-gram levels and improves NLLgen and NLLdiv compared to other baselines, indicating better sample quality and diversity.The real data experiments are conducted on the EN dataset. After the preprocessing, EN contains 5,255 unique words with the maximum sentence length 51. Under the premise of achieving better BLEU scores, our model improves NLLgen and NLLdiv by 0.04 and 0.021 than the best performance of the baselines, which shows CatGAN can maintain the higher sample quality and diversity simultaneously." } ], "images": [ "scigen/train/3928.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: The performance comparison on EN." ], "reasoning": "The CatGAN model achieves the highest BLEU scores across all n-gram levels and improves NLLgen and NLLdiv compared to other baselines, indicating better sample quality and diversity.", "description": "The real data experiments are conducted on the EN dataset. After the preprocessing, EN contains 5,255 unique words with the maximum sentence length 51. Under the premise of achieving better BLEU scores, our model improves NLLgen and NLLdiv by 0.04 and 0.021 than the best performance of the baselines, which shows CatGAN can maintain the higher sample quality and diversity simultaneously.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 7: The performance comparison on EN.' and proceed to solve the following question in a stepwise manner. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nWhich model has the highest BLEU-4 score and what is its value?\n" }, { "role": "assistant", "content": "By examining the table, we see that CatGAN has the highest BLEU-4 score with a value of 0.603. So, the answer is {\"answer\": \"CatGAN with 0.603\"}" } ], "images": [ "scigen/train/3928.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: The performance comparison on EN." ], "reasoning": "By examining the table, we see that CatGAN has the highest BLEU-4 score with a value of 0.603.", "question": "Which model has the highest BLEU-4 score and what is its value?", "answer": "So, the answer is {\"answer\": \"CatGAN with 0.603\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nCatGAN has the lowest NLLgen score among all models.\n" }, { "role": "assistant", "content": "The table shows that CatGAN has an NLLgen score of 2.316, which is lower than SeqGAN, RankGAN, LeakGAN, and RelGAN. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3928.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: The performance comparison on EN." ], "reasoning": "The table shows that CatGAN has an NLLgen score of 2.316, which is lower than SeqGAN, RankGAN, LeakGAN, and RelGAN.", "statement": "CatGAN has the lowest NLLgen score among all models.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Effect of sub-segmentation of negative recordings.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "Sub-segmenting negative recordings significantly improves training performance, reducing overfitting and improving alignment quality.We first show the effect of sub-segmenting negative recordings on training. We start from the training data with only speed-perturbation applied. Apparently without sub-segmentation the performance is far from satisfactory, indicating the alignments learned with the LF-MMI system is poor. We also inspected the training/validation loss in both cases (not shown here), and found that there is severe overfitting when training without sub-segmentation." } ], "images": [ "scigen/train/3929.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Effect of sub-segmentation of negative recordings." ], "reasoning": "Sub-segmenting negative recordings significantly improves training performance, reducing overfitting and improving alignment quality.", "description": "We first show the effect of sub-segmenting negative recordings on training. We start from the training data with only speed-perturbation applied. Apparently without sub-segmentation the performance is far from satisfactory, indicating the alignments learned with the LF-MMI system is poor. We also inspected the training/validation loss in both cases (not shown here), and found that there is severe overfitting when training without sub-segmentation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 2: Effect of sub-segmentation of negative recordings.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhat is the False Rejection Rate (FRR) for SNIPS with sub-segmentation?\n" }, { "role": "assistant", "content": "The table shows the FRR for SNIPS with and without sub-segmentation. The FRR for SNIPS with sub-segmentation is 0.6%. So, the answer is {\"answer\": \"0.6%\"}." } ], "images": [ "scigen/train/3929.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Effect of sub-segmentation of negative recordings." ], "reasoning": "The table shows the FRR for SNIPS with and without sub-segmentation. The FRR for SNIPS with sub-segmentation is 0.6%.", "question": "What is the False Rejection Rate (FRR) for SNIPS with sub-segmentation?", "answer": "So, the answer is {\"answer\": \"0.6%\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe FRR for Mobvoi without sub-segmentation is higher than with sub-segmentation.\n" }, { "role": "assistant", "content": "The table shows that the FRR for Mobvoi without sub-segmentation is 47%, while with sub-segmentation it is 5.6%. This indicates that the FRR is indeed higher without sub-segmentation. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3929.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Effect of sub-segmentation of negative recordings." ], "reasoning": "The table shows that the FRR for Mobvoi without sub-segmentation is 47%, while with sub-segmentation it is 5.6%. This indicates that the FRR is indeed higher without sub-segmentation.", "statement": "The FRR for Mobvoi without sub-segmentation is higher than with sub-segmentation.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Effect of data augmentation.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The data augmentation strategy, which includes additive noise and reverberation, increases the training data by a factor of 7 and significantly reduces the FRR for both SNIPS and Mobvoi datasets.We augment the training data using the MUSAN corpus in the following way: we randomly apply additive noise from the “babble”, “music” and “noise” data sets separately on each copy of the original training data once. For each training example, “babble” is added as background noises 3 to 7 times with SNRs ranging from 13 to 20; “music” is added as background noises once with SNRs ranging from 5 to 15; “noise” is added as foreground noises at the interval of 1 second with SNRs ranging from 0 to 15. Then reverberation is also separately applied to the training data using the simulated RIRs with room sizes uniformly sampled from 1 meter to 30 meters. The above procedure increase the training data by a factor of 4. We then apply 3 way speed-perturbation on top of the original training set. The augmentation strategy together results in about 7× more training data. It can be seen the augmentation strategy is highly effective, where FRR with SNIPS is even 0 at FAH=0.5." } ], "images": [ "scigen/train/3930.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Effect of data augmentation." ], "reasoning": "The data augmentation strategy, which includes additive noise and reverberation, increases the training data by a factor of 7 and significantly reduces the FRR for both SNIPS and Mobvoi datasets.", "description": "We augment the training data using the MUSAN corpus in the following way: we randomly apply additive noise from the “babble”, “music” and “noise” data sets separately on each copy of the original training data once. For each training example, “babble” is added as background noises 3 to 7 times with SNRs ranging from 13 to 20; “music” is added as background noises once with SNRs ranging from 5 to 15; “noise” is added as foreground noises at the interval of 1 second with SNRs ranging from 0 to 15. Then reverberation is also separately applied to the training data using the simulated RIRs with room sizes uniformly sampled from 1 meter to 30 meters. The above procedure increase the training data by a factor of 4. We then apply 3 way speed-perturbation on top of the original training set. The augmentation strategy together results in about 7× more training data. It can be seen the augmentation strategy is highly effective, where FRR with SNIPS is even 0 at FAH=0.5.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 3: Effect of data augmentation.' and methodically tackle the question that follows. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the FRR for the SNIPS dataset at FAH=0.5 with data augmentation?\n" }, { "role": "assistant", "content": "The table shows the FRR for the SNIPS dataset at FAH=0.5 with and without data augmentation. With data augmentation, the FRR for SNIPS is 0. So, the answer is {\"answer\": \"0\"}." } ], "images": [ "scigen/train/3930.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Effect of data augmentation." ], "reasoning": "The table shows the FRR for the SNIPS dataset at FAH=0.5 with and without data augmentation. With data augmentation, the FRR for SNIPS is 0.", "question": "What is the FRR for the SNIPS dataset at FAH=0.5 with data augmentation?", "answer": "So, the answer is {\"answer\": \"0\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nData augmentation reduces the FRR for the Mobvoi dataset at FAH=1.5.\n" }, { "role": "assistant", "content": "The table shows that the FRR for the Mobvoi dataset at FAH=1.5 is 5.6% without data augmentation and 0.4% with data augmentation, indicating a reduction. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3930.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Effect of data augmentation." ], "reasoning": "The table shows that the FRR for the Mobvoi dataset at FAH=1.5 is 5.6% without data augmentation and 0.4% with data augmentation, indicating a reduction.", "statement": "Data augmentation reduces the FRR for the Mobvoi dataset at FAH=1.5.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: Effect of alignment-free LF-MMI loss.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The alignment-free LF-MMI loss shows better performance in terms of FRR on both SNIPS and Mobvoi datasets compared to regular LF-MMI and cross-entropy losses.To compare our proposed alignment-free LF-MMI loss with regular LF-MMI and conventional cross-entropy loss for our task, we train a phoneme-based HMM-GMM system to generate the numerator lattice (for regular LF-MMI loss) or the forced alignments (for conventional cross-entropy loss) for the same sub-segmented and augmented training data. the network architectures are the same as what is used for alignment-free LF-MMI training except the final layer (depending on the loss being used). It is worth noting that we believe SNIPS results are more indicative of performance, as after manually listening to the false alarms in Mobvoi at this specific operating point, we found that all the false alarms (9 in total) are actually intentionally pronounced with a different tone on the last character “wen”, which is extremely difficult for the model to learn given the limited amount of data; some would even argue that those examples should be labeled as positive cases in order for the model to accommodate Chinese speakers with accents. The better performance of the system with alignment-free LF-MMI loss is possibly due to the capability of learning flexible alignments than GMM models." } ], "images": [ "scigen/train/3931.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Effect of alignment-free LF-MMI loss." ], "reasoning": "The alignment-free LF-MMI loss shows better performance in terms of FRR on both SNIPS and Mobvoi datasets compared to regular LF-MMI and cross-entropy losses.", "description": "To compare our proposed alignment-free LF-MMI loss with regular LF-MMI and conventional cross-entropy loss for our task, we train a phoneme-based HMM-GMM system to generate the numerator lattice (for regular LF-MMI loss) or the forced alignments (for conventional cross-entropy loss) for the same sub-segmented and augmented training data. the network architectures are the same as what is used for alignment-free LF-MMI training except the final layer (depending on the loss being used). It is worth noting that we believe SNIPS results are more indicative of performance, as after manually listening to the false alarms in Mobvoi at this specific operating point, we found that all the false alarms (9 in total) are actually intentionally pronounced with a different tone on the last character “wen”, which is extremely difficult for the model to learn given the limited amount of data; some would even argue that those examples should be labeled as positive cases in order for the model to accommodate Chinese speakers with accents. The better performance of the system with alignment-free LF-MMI loss is possibly due to the capability of learning flexible alignments than GMM models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}. The question is related to the given table titled 'Table 4: Effect of alignment-free LF-MMI loss.'.\n\nQuestion: Which loss function has the lowest FRR on the SNIPS dataset?\n" }, { "role": "assistant", "content": "By comparing the FRR values for each loss function on the SNIPS dataset, the alignment-free LF-MMI loss has the lowest FRR of 0%. So, the answer is {\"answer\": \"alignment-free LF-MMI\"}." } ], "images": [ "scigen/train/3931.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Effect of alignment-free LF-MMI loss." ], "reasoning": "By comparing the FRR values for each loss function on the SNIPS dataset, the alignment-free LF-MMI loss has the lowest FRR of 0%.", "question": "Which loss function has the lowest FRR on the SNIPS dataset?", "answer": "So, the answer is {\"answer\": \"alignment-free LF-MMI\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe cross-entropy loss has a higher FRR on the Mobvoi dataset compared to the regular LF-MMI loss.\n" }, { "role": "assistant", "content": "The table shows that the FRR for cross-entropy loss on the Mobvoi dataset is 3.5%, while the FRR for regular LF-MMI loss is 0.2%, indicating that the statement is correct. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3931.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Effect of alignment-free LF-MMI loss." ], "reasoning": "The table shows that the FRR for cross-entropy loss on the Mobvoi dataset is 3.5%, while the FRR for regular LF-MMI loss is 0.2%, indicating that the statement is correct.", "statement": "The cross-entropy loss has a higher FRR on the Mobvoi dataset compared to the regular LF-MMI loss.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 5: Effect of using alignments from Alignment-free LF-MMI for regular LF-MMI.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The experiment aims to evaluate the performance of regular LF-MMI using alignments from an alignment-free LF-MMI system and to see if further refinement with regular LF-MMI improves performance, especially at a smaller FAH for SNIPS.The experiment from the previous section motivates us to do an additional experiment investigating how the regular LF-MMI performs when it gets alignments from the alignment-free LF-MMI system instead of from a GMM model, and whether the regular LF-MMI training could further improve the performance as a refinement of our existing system. Note that as we already achieve FRR=0 at FAH=0.5 with SNIPS using our alignment-free LF-MMI system, we set the operating point at a smaller FAH (0.04) for it." } ], "images": [ "scigen/train/3932.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Effect of using alignments from Alignment-free LF-MMI for regular LF-MMI." ], "reasoning": "The experiment aims to evaluate the performance of regular LF-MMI using alignments from an alignment-free LF-MMI system and to see if further refinement with regular LF-MMI improves performance, especially at a smaller FAH for SNIPS.", "description": "The experiment from the previous section motivates us to do an additional experiment investigating how the regular LF-MMI performs when it gets alignments from the alignment-free LF-MMI system instead of from a GMM model, and whether the regular LF-MMI training could further improve the performance as a refinement of our existing system. Note that as we already achieve FRR=0 at FAH=0.5 with SNIPS using our alignment-free LF-MMI system, we set the operating point at a smaller FAH (0.04) for it.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 5: Effect of using alignments from Alignment-free LF-MMI for regular LF-MMI.\nQuestion: What is the FRR for SNIPS at FAH=0.04 using the alignment-free LF-MMI system?\nSolve the above question. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows the FRR values for different systems and datasets. For SNIPS at FAH=0.04 using the alignment-free LF-MMI system, the FRR is listed in the second row under the SNIPS column. So, the answer is {\"answer\": \"0.2\"}." } ], "images": [ "scigen/train/3932.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Effect of using alignments from Alignment-free LF-MMI for regular LF-MMI." ], "reasoning": "The table shows the FRR values for different systems and datasets. For SNIPS at FAH=0.04 using the alignment-free LF-MMI system, the FRR is listed in the second row under the SNIPS column.", "question": "What is the FRR for SNIPS at FAH=0.04 using the alignment-free LF-MMI system?", "answer": "So, the answer is {\"answer\": \"0.2\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe regular LF-MMI refinement improves the FRR for SNIPS at FAH=0.04 compared to the alignment-free LF-MMI system.\n" }, { "role": "assistant", "content": "The table shows that the FRR for SNIPS at FAH=0.04 is 0.2 for the alignment-free LF-MMI system and 0.1 for the regular LF-MMI refinement, indicating an improvement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3932.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Effect of using alignments from Alignment-free LF-MMI for regular LF-MMI." ], "reasoning": "The table shows that the FRR for SNIPS at FAH=0.04 is 0.2 for the alignment-free LF-MMI system and 0.1 for the regular LF-MMI refinement, indicating an improvement.", "statement": "The regular LF-MMI refinement improves the FRR for SNIPS at FAH=0.04 compared to the alignment-free LF-MMI system.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 3. StoryGraph: Top news stories of 2018 (Alexander Nwala, 2018a) & 2019 (Alexander Nwala, 2019)'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The attention score quantifies the attention given to news stories, allowing identification of top stories for specific periods. The top stories of 2018 and 2019 were identified using this method.Our investigation into measuring near-real time news similarity and quantifying the attention of news sources has resulted in the following contributions. First, we proposed the attention score, a transparent method for quantifying attention given to a news story by different news sources. The attention score facilitates finding the top news stories for a given day, month, or year. This enabled us to show the top stories of 2018 and 2019 Second, we introduced the StoryGraph service, which has been running for over two years (since August 8, 2017), generating news similarity graphs every 10 minutes from 17 news organizations across the left, center, and right partisanship spectrum. Stories surrounding the release of the Mueller Report (red dots in Fig. On March 22, 2019, Robert Mueller submitted his report to AG William Barr (attention score = 18.72). Two days later, AG William Barr released his summary (principal conclusions) of the report. This story received the most attention (attention score = 22.93) in 2019. AG William Barr’s principal conclusions of the Mueller report was received with skepticism by the Democrats who claimed the conclusions were highly favorable to President Trump. In contrast, the Republicans claimed the summary exonerated the President from any wrongdoing. The next top story in 2019 (blue dots in Fig. No. 2) with attention score of 18.60 was Speaker Nancy Pelosi ’s announcement of an official impeachment inquiry (September 24, 2019) four days after the whistleblower’s report. Similarly, at rank three (green dots in Fig." } ], "images": [ "scigen/train/3933.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3. StoryGraph: Top news stories of 2018 (Alexander Nwala, 2018a) & 2019 (Alexander Nwala, 2019)" ], "reasoning": "The attention score quantifies the attention given to news stories, allowing identification of top stories for specific periods. The top stories of 2018 and 2019 were identified using this method.", "description": "Our investigation into measuring near-real time news similarity and quantifying the attention of news sources has resulted in the following contributions. First, we proposed the attention score, a transparent method for quantifying attention given to a news story by different news sources. The attention score facilitates finding the top news stories for a given day, month, or year. This enabled us to show the top stories of 2018 and 2019 Second, we introduced the StoryGraph service, which has been running for over two years (since August 8, 2017), generating news similarity graphs every 10 minutes from 17 news organizations across the left, center, and right partisanship spectrum. Stories surrounding the release of the Mueller Report (red dots in Fig. On March 22, 2019, Robert Mueller submitted his report to AG William Barr (attention score = 18.72). Two days later, AG William Barr released his summary (principal conclusions) of the report. This story received the most attention (attention score = 22.93) in 2019. AG William Barr’s principal conclusions of the Mueller report was received with skepticism by the Democrats who claimed the conclusions were highly favorable to President Trump. In contrast, the Republicans claimed the summary exonerated the President from any wrongdoing. The next top story in 2019 (blue dots in Fig. No. 2) with attention score of 18.60 was Speaker Nancy Pelosi ’s announcement of an official impeachment inquiry (September 24, 2019) four days after the whistleblower’s report. Similarly, at rank three (green dots in Fig.", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nTable title: 'Table 3. StoryGraph: Top news stories of 2018 (Alexander Nwala, 2018a) & 2019 (Alexander Nwala, 2019)'\nQuestion: What was the attention score of the top news story in 2018?\n" }, { "role": "assistant", "content": "The top news story in 2018 was 'Kavanaugh and Christine Blasey Ford testify before congress' with an attention score of 25.85. So, the answer is {\"answer\": \"25.85\"}." } ], "images": [ "scigen/train/3933.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3. StoryGraph: Top news stories of 2018 (Alexander Nwala, 2018a) & 2019 (Alexander Nwala, 2019)" ], "reasoning": "The top news story in 2018 was 'Kavanaugh and Christine Blasey Ford testify before congress' with an attention score of 25.85.", "question": "What was the attention score of the top news story in 2018?", "answer": "So, the answer is {\"answer\": \"25.85\"}." }, { "messages": [ { "role": "user", "content": "The top news story in 2019 had an attention score higher than 22. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe top news story in 2019 had an attention score higher than 22.\n" }, { "role": "assistant", "content": "The top news story in 2019 was 'AG William Barr releases Mueller Report’s principal conclusions' with an attention score of 22.93, which is higher than 22. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3933.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3. StoryGraph: Top news stories of 2018 (Alexander Nwala, 2018a) & 2019 (Alexander Nwala, 2019)" ], "reasoning": "The top news story in 2019 was 'AG William Barr releases Mueller Report’s principal conclusions' with an attention score of 22.93, which is higher than 22.", "statement": "The top news story in 2019 had an attention score higher than 22.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Tabela 4: 2-classes experiments results with 4 datasets'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The performance of sentiment analysis methods varies significantly across different datasets, with methods like VADER and SentiStrength showing high accuracy but low coverage, indicating a tradeoff between prediction performance and coverage.We start the analysis of our experiments by comparing the results of all previously discussed metrics for all datasets. For simplicity, we choose to discuss results only for these datasets as they come from different sources and help us to illustrate the main findings from our analysis. Results for all the other datasets are presented in the Additional File 1. There are many interesting observations we can make from these results, summarized next. Methods prediction performance varies considerably from one dataset to another: First, we note the same social media text can be interpreted very differently depending on the choice of a sentiment method. Overall, we note that all the methods yielded with large variations across the different datasets. Although the first two datasets contain tweets, they belong to different contexts, which affects the performance of some methods like VADER. Another important aspect to be analyzed in this Table is the coverage. Although SentiStrength has presented good Macro-F1 values, its coverage is usually low as this method tends to classify a high number of instances as neutral. Another expected result is the good Macro-F1 values obtained by Emoticons, specially in the Twitter datasets. It is important to highlight that, in spite of achieving high accuracy and Macro-F1, the coverage of many methods, such as PANAS, VADER, and SentiStrength, is low (e.g. below 30%) as they only infer the polarity of part of the input sentences. Thus, the choice of a sentiment analysis is highly dependent on the data and application, suggesting that researchers and practitioners need to take into account this tradeoff between prediction performance and coverage. Existing methods let space for improvements: We can note that the performance of the evaluated methods are ok, but there is a lot of space for improvements. Considering that we are looking at the performance of the best methods out of 24 unsupervised tools, these numbers suggest that current sentence-level sentiment analysis methods still let a lot of space for improvements. Additionally, we also noted that the best method for each dataset varies considerably from one dataset to another. This might indicate that each method complements the others in different ways." } ], "images": [ "scigen/train/3934.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Tabela 4: 2-classes experiments results with 4 datasets" ], "reasoning": "The performance of sentiment analysis methods varies significantly across different datasets, with methods like VADER and SentiStrength showing high accuracy but low coverage, indicating a tradeoff between prediction performance and coverage.", "description": "We start the analysis of our experiments by comparing the results of all previously discussed metrics for all datasets. For simplicity, we choose to discuss results only for these datasets as they come from different sources and help us to illustrate the main findings from our analysis. Results for all the other datasets are presented in the Additional File 1. There are many interesting observations we can make from these results, summarized next. Methods prediction performance varies considerably from one dataset to another: First, we note the same social media text can be interpreted very differently depending on the choice of a sentiment method. Overall, we note that all the methods yielded with large variations across the different datasets. Although the first two datasets contain tweets, they belong to different contexts, which affects the performance of some methods like VADER. Another important aspect to be analyzed in this Table is the coverage. Although SentiStrength has presented good Macro-F1 values, its coverage is usually low as this method tends to classify a high number of instances as neutral. Another expected result is the good Macro-F1 values obtained by Emoticons, specially in the Twitter datasets. It is important to highlight that, in spite of achieving high accuracy and Macro-F1, the coverage of many methods, such as PANAS, VADER, and SentiStrength, is low (e.g. below 30%) as they only infer the polarity of part of the input sentences. Thus, the choice of a sentiment analysis is highly dependent on the data and application, suggesting that researchers and practitioners need to take into account this tradeoff between prediction performance and coverage. Existing methods let space for improvements: We can note that the performance of the evaluated methods are ok, but there is a lot of space for improvements. Considering that we are looking at the performance of the best methods out of 24 unsupervised tools, these numbers suggest that current sentence-level sentiment analysis methods still let a lot of space for improvements. Additionally, we also noted that the best method for each dataset varies considerably from one dataset to another. This might indicate that each method complements the others in different ways.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Tabela 4: 2-classes experiments results with 4 datasets', solve the following question. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich method has the highest Macro-F1 value for the 'Tweets_RND_II' dataset?\n" }, { "role": "assistant", "content": "To find the method with the highest Macro-F1 value for the 'Tweets_RND_II' dataset, we look at the 'MacroF1' column under the 'Tweets_RND_II' dataset. The highest value is 94.12, which corresponds to the PANAS-t method. So, the answer is {\"answer\": \"PANAS-t\"}." } ], "images": [ "scigen/train/3934.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Tabela 4: 2-classes experiments results with 4 datasets" ], "reasoning": "To find the method with the highest Macro-F1 value for the 'Tweets_RND_II' dataset, we look at the 'MacroF1' column under the 'Tweets_RND_II' dataset. The highest value is 94.12, which corresponds to the PANAS-t method.", "question": "Which method has the highest Macro-F1 value for the 'Tweets_RND_II' dataset?", "answer": "So, the answer is {\"answer\": \"PANAS-t\"}." }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nSentiStrength has the highest coverage in the 'Comments_Digg' dataset.\n" }, { "role": "assistant", "content": "To verify this statement, we check the 'Coverage' column under the 'Comments_Digg' dataset. The highest coverage value is 98.72, which corresponds to the Emoticons DS method, not SentiStrength. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3934.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Tabela 4: 2-classes experiments results with 4 datasets" ], "reasoning": "To verify this statement, we check the 'Coverage' column under the 'Comments_Digg' dataset. The highest coverage value is 98.72, which corresponds to the Emoticons DS method, not SentiStrength.", "statement": "SentiStrength has the highest coverage in the 'Comments_Digg' dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Tabela 5: 3-classes experiments results with 4 datasets'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The performance of sentiment analysis methods varies significantly across different datasets, with some methods like Umigon performing well on Twitter datasets but poorly on others like Comments_NYT. This indicates that each method has strengths and weaknesses depending on the dataset.We start the analysis of our experiments by comparing the results of all previously discussed metrics for all datasets. For simplicity, we choose to discuss results only for these datasets as they come from different sources and help us to illustrate the main findings from our analysis. Results for all the other datasets are presented in the Additional File 1. There are many interesting observations we can make from these results, summarized next. Umigon, the best method in five Twitter datasets, felt to the eighteenth place in the Comments_NYT dataset. We can also note the lower Macro-F1 values for some methods like Emoticons are due to the high number of sentences without emoticons in the datasets. Methods like Emoticons DS and PANAS tend do classify only a small part of instances as neutral and also presented a poor performance in the 3-class experiments. Methods like SenticNet and LIWC were not originally developed for detecting neutral sentences and also achieved low values of Macro-F1. However, they also do not appear among the best methods in the 2-class experiments, which is the task they were originally designed for. Existing methods let space for improvements: We can note that the performance of the evaluated methods are ok, but there is a lot of space for improvements. Considering that we are looking at the performance of the best methods out of 24 unsupervised tools, these numbers suggest that current sentence-level sentiment analysis methods still let a lot of space for improvements. Additionally, we also noted that the best method for each dataset varies considerably from one dataset to another. This might indicate that each method complements the others in different ways." } ], "images": [ "scigen/train/3935.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Tabela 5: 3-classes experiments results with 4 datasets" ], "reasoning": "The performance of sentiment analysis methods varies significantly across different datasets, with some methods like Umigon performing well on Twitter datasets but poorly on others like Comments_NYT. This indicates that each method has strengths and weaknesses depending on the dataset.", "description": "We start the analysis of our experiments by comparing the results of all previously discussed metrics for all datasets. For simplicity, we choose to discuss results only for these datasets as they come from different sources and help us to illustrate the main findings from our analysis. Results for all the other datasets are presented in the Additional File 1. There are many interesting observations we can make from these results, summarized next. Umigon, the best method in five Twitter datasets, felt to the eighteenth place in the Comments_NYT dataset. We can also note the lower Macro-F1 values for some methods like Emoticons are due to the high number of sentences without emoticons in the datasets. Methods like Emoticons DS and PANAS tend do classify only a small part of instances as neutral and also presented a poor performance in the 3-class experiments. Methods like SenticNet and LIWC were not originally developed for detecting neutral sentences and also achieved low values of Macro-F1. However, they also do not appear among the best methods in the 2-class experiments, which is the task they were originally designed for. Existing methods let space for improvements: We can note that the performance of the evaluated methods are ok, but there is a lot of space for improvements. Considering that we are looking at the performance of the best methods out of 24 unsupervised tools, these numbers suggest that current sentence-level sentiment analysis methods still let a lot of space for improvements. Additionally, we also noted that the best method for each dataset varies considerably from one dataset to another. This might indicate that each method complements the others in different ways.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Tabela 5: 3-classes experiments results with 4 datasets' and methodically tackle the question that follows. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich method had the highest Macro-F1 score in the Comments_BBC dataset?\n" }, { "role": "assistant", "content": "To find the method with the highest Macro-F1 score in the Comments_BBC dataset, look at the 'MacroF1' column for the Comments_BBC dataset. The highest value is 49.60, achieved by the SenticNet method. So, the answer is {\"answer\": \"SenticNet\"}" } ], "images": [ "scigen/train/3935.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Tabela 5: 3-classes experiments results with 4 datasets" ], "reasoning": "To find the method with the highest Macro-F1 score in the Comments_BBC dataset, look at the 'MacroF1' column for the Comments_BBC dataset. The highest value is 49.60, achieved by the SenticNet method.", "question": "Which method had the highest Macro-F1 score in the Comments_BBC dataset?", "answer": "So, the answer is {\"answer\": \"SenticNet\"}" }, { "messages": [ { "role": "user", "content": "Umigon is the best method in the Comments_NYT dataset. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nUmigon is the best method in the Comments_NYT dataset.\n" }, { "role": "assistant", "content": "The table shows that Umigon has a Macro-F1 score of 20.15 in the Comments_NYT dataset, which is not the highest. Therefore, the statement is refuted. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3935.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Tabela 5: 3-classes experiments results with 4 datasets" ], "reasoning": "The table shows that Umigon has a Macro-F1 score of 20.15 in the Comments_NYT dataset, which is not the highest. Therefore, the statement is refuted.", "statement": "Umigon is the best method in the Comments_NYT dataset.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Tabela 6: Mean Rank Table for All Datasets'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "SentiStrength and Sentiment140 have the best mean ranks for 2-class experiments but low coverage, while Semantria and OpinionLexicon have better coverage but worse mean ranks.Umigon, the best method in five Twitter datasets, felt to the eighteenth place in the Comments_NYT dataset. We can also note the lower Macro-F1 values for some methods like Emoticons are due to the high number of sentences without emoticons in the datasets. Methods like Emoticons DS and PANAS tend do classify only a small part of instances as neutral and also presented a poor performance in the 3-class experiments. Methods like SenticNet and LIWC were not originally developed for detecting neutral sentences and also achieved low values of Macro-F1. However, they also do not appear among the best methods in the 2-class experiments, which is the task they were originally designed for. The elements are sorted by the overall mean rank each method achieved based on Macro-F1 for all datasets. The top nine methods based on Macro-F1 for the 2-class experiments are: SentiStrength, Sentiment140, Semantria, OpinionLexicon, LIWC15, SO-CAL, AFINN and VADER and Umigon. With the exception of Sentistrength, replaced by Pattern.en, the other eight methods produce the best results across several datasets for both, 2 and 3-class tasks. These methods would be preferable in situations in which any sort of preliminary evaluation is not possible to be done. The mean rank for 2-class experiments is accompanied by the coverage metric, which is very important to avoid misinterpretation of the results. Observe that SentiStrength and Sentiment140 exhibited the best mean ranks for these experiments, however both present very low coverage, around 30% and 40%, a very poor result compared with Semantria and OpinionLexicon that achieved a worse mean rank (4,61 and 6,62 respectively) but an expressive better coverage, above 60%. Note also that SentiStrength and Sentiment140 present poor results in the 3-class experiments which can be explained by their bias to the neutral class as mentioned before." } ], "images": [ "scigen/train/3936.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Tabela 6: Mean Rank Table for All Datasets" ], "reasoning": "SentiStrength and Sentiment140 have the best mean ranks for 2-class experiments but low coverage, while Semantria and OpinionLexicon have better coverage but worse mean ranks.", "description": "Umigon, the best method in five Twitter datasets, felt to the eighteenth place in the Comments_NYT dataset. We can also note the lower Macro-F1 values for some methods like Emoticons are due to the high number of sentences without emoticons in the datasets. Methods like Emoticons DS and PANAS tend do classify only a small part of instances as neutral and also presented a poor performance in the 3-class experiments. Methods like SenticNet and LIWC were not originally developed for detecting neutral sentences and also achieved low values of Macro-F1. However, they also do not appear among the best methods in the 2-class experiments, which is the task they were originally designed for. The elements are sorted by the overall mean rank each method achieved based on Macro-F1 for all datasets. The top nine methods based on Macro-F1 for the 2-class experiments are: SentiStrength, Sentiment140, Semantria, OpinionLexicon, LIWC15, SO-CAL, AFINN and VADER and Umigon. With the exception of Sentistrength, replaced by Pattern.en, the other eight methods produce the best results across several datasets for both, 2 and 3-class tasks. These methods would be preferable in situations in which any sort of preliminary evaluation is not possible to be done. The mean rank for 2-class experiments is accompanied by the coverage metric, which is very important to avoid misinterpretation of the results. Observe that SentiStrength and Sentiment140 exhibited the best mean ranks for these experiments, however both present very low coverage, around 30% and 40%, a very poor result compared with Semantria and OpinionLexicon that achieved a worse mean rank (4,61 and 6,62 respectively) but an expressive better coverage, above 60%. Note also that SentiStrength and Sentiment140 present poor results in the 3-class experiments which can be explained by their bias to the neutral class as mentioned before.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nTable title: Tabela 6: Mean Rank Table for All Datasets\n\nQuestion: Which method has the highest coverage in the 2-class experiments?\n" }, { "role": "assistant", "content": "By examining the '2-Classes Coverage (%)' column, Emoticons DS has the highest coverage at 99.36%. So, the answer is {\"answer\": \"Emoticons DS\"}" } ], "images": [ "scigen/train/3936.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Tabela 6: Mean Rank Table for All Datasets" ], "reasoning": "By examining the '2-Classes Coverage (%)' column, Emoticons DS has the highest coverage at 99.36%.", "question": "Which method has the highest coverage in the 2-class experiments?", "answer": "So, the answer is {\"answer\": \"Emoticons DS\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nSentiStrength has the best mean rank in the 2-class experiments.\n" }, { "role": "assistant", "content": "The table shows that SentiStrength has the best mean rank (2.33) in the 2-class experiments. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3936.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Tabela 6: Mean Rank Table for All Datasets" ], "reasoning": "The table shows that SentiStrength has the best mean rank (2.33) in the 2-class experiments.", "statement": "SentiStrength has the best mean rank in the 2-class experiments.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Tabela 8: Best Method for each Dataset - 2-class experiments'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "VADER consistently performed well across all datasets, achieving second place five times, third place twice, and other positions less frequently.Another interesting finding is the fact that VADER, the best method in the 3-class experiments, did not achieve the first position for none of the datasets. It reachs the second place five times, the third place twice, the seventh three times, and the fourth, sixth and fifth just once. It was a special case of consistency across all datasets." } ], "images": [ "scigen/train/3937.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Tabela 8: Best Method for each Dataset - 2-class experiments" ], "reasoning": "VADER consistently performed well across all datasets, achieving second place five times, third place twice, and other positions less frequently.", "description": "Another interesting finding is the fact that VADER, the best method in the 3-class experiments, did not achieve the first position for none of the datasets. It reachs the second place five times, the third place twice, the seventh three times, and the fourth, sixth and fifth just once. It was a special case of consistency across all datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Tabela 8: Best Method for each Dataset - 2-class experiments', answer the question below by showing each progressive step toward the answer. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nHow many times did VADER achieve the second place in the 3-class experiments?\n" }, { "role": "assistant", "content": "According to the description, VADER achieved the second place five times in the 3-class experiments. So, the answer is {\"answer\": \"5\"}." } ], "images": [ "scigen/train/3937.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Tabela 8: Best Method for each Dataset - 2-class experiments" ], "reasoning": "According to the description, VADER achieved the second place five times in the 3-class experiments.", "question": "How many times did VADER achieve the second place in the 3-class experiments?", "answer": "So, the answer is {\"answer\": \"5\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nVADER achieved the first position in the 3-class experiments.\n" }, { "role": "assistant", "content": "The description states that VADER did not achieve the first position for any of the datasets in the 3-class experiments. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3937.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Tabela 8: Best Method for each Dataset - 2-class experiments" ], "reasoning": "The description states that VADER did not achieve the first position for any of the datasets in the 3-class experiments.", "statement": "VADER achieved the first position in the 3-class experiments.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Tabela 11: Mean Rank Table for Datasets of Social Networks'.\n" }, { "role": "assistant", "content": "Umigon was the best method for 3-class experiments, while SentiStrength was the best for 2-class experiments with around 30% coverage.In the context of Social Networks the best method for 3-class experiments was Umigon, followed by LIWC15 and VADER. In the case of 2-class the winner was SentiStrength with a coverage around 30% and the third and sixth place were Emoticons and Panas-t with about 18% and 6% of coverage, respectively. This highlights the importance to analyze the 2-class results together with the coverage. Overall, when there is an emoticon on the text or a word from the psychometric scale PANAS, these methods are able to tell the polarity of the sentences, but they are not able to identify the polarity of the input text for the large majority of the input text. Sentiment140, LIWC15, Semantria, OpinionLexicon and Umigon showed to be the best alternatives for detecting only positive and negative polarities in social network data due to the high coverage and prediction performance. It is important to highlight that LIWC 2007 appears on the 16th and 21th position for the 3-class and 2-class mean rank results for the social network datasets and it is a very popular method in this community. On the other side, the newest version of LIWC (2015) presented a considerable evolution obtaining the second and the fourth place in the same datasets." } ], "images": [ "scigen/train/3938.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Tabela 11: Mean Rank Table for Datasets of Social Networks" ], "reasoning": "Umigon was the best method for 3-class experiments, while SentiStrength was the best for 2-class experiments with around 30% coverage.", "description": "In the context of Social Networks the best method for 3-class experiments was Umigon, followed by LIWC15 and VADER. In the case of 2-class the winner was SentiStrength with a coverage around 30% and the third and sixth place were Emoticons and Panas-t with about 18% and 6% of coverage, respectively. This highlights the importance to analyze the 2-class results together with the coverage. Overall, when there is an emoticon on the text or a word from the psychometric scale PANAS, these methods are able to tell the polarity of the sentences, but they are not able to identify the polarity of the input text for the large majority of the input text. Sentiment140, LIWC15, Semantria, OpinionLexicon and Umigon showed to be the best alternatives for detecting only positive and negative polarities in social network data due to the high coverage and prediction performance. It is important to highlight that LIWC 2007 appears on the 16th and 21th position for the 3-class and 2-class mean rank results for the social network datasets and it is a very popular method in this community. On the other side, the newest version of LIWC (2015) presented a considerable evolution obtaining the second and the fourth place in the same datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Tabela 11: Mean Rank Table for Datasets of Social Networks', answer the question below by showing each progressive step toward the answer. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\nWhich method had the highest coverage for 2-class experiments?\n" }, { "role": "assistant", "content": "From the table, Emoticons DS had the highest coverage for 2-class experiments with 99.28%. So, the answer is {\"answer\": \"Emoticons DS\"}." } ], "images": [ "scigen/train/3938.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Tabela 11: Mean Rank Table for Datasets of Social Networks" ], "reasoning": "From the table, Emoticons DS had the highest coverage for 2-class experiments with 99.28%.", "question": "Which method had the highest coverage for 2-class experiments?", "answer": "So, the answer is {\"answer\": \"Emoticons DS\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nLIWC15 was the second-best method for 3-class experiments.\n" }, { "role": "assistant", "content": "The table shows that LIWC15 was ranked second for 3-class experiments. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3938.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Tabela 11: Mean Rank Table for Datasets of Social Networks" ], "reasoning": "The table shows that LIWC15 was ranked second for 3-class experiments.", "statement": "LIWC15 was the second-best method for 3-class experiments.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Tabela 12: Mean Rank Table for Datasets of Comments'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Umigon was the best method for 3-class experiments, while SentiStrength was the best for 2-class experiments with around 30% coverage.In the context of Social Networks the best method for 3-class experiments was Umigon, followed by LIWC15 and VADER. In the case of 2-class the winner was SentiStrength with a coverage around 30% and the third and sixth place were Emoticons and Panas-t with about 18% and 6% of coverage, respectively. This highlights the importance to analyze the 2-class results together with the coverage. Overall, when there is an emoticon on the text or a word from the psychometric scale PANAS, these methods are able to tell the polarity of the sentences, but they are not able to identify the polarity of the input text for the large majority of the input text. Sentiment140, LIWC15, Semantria, OpinionLexicon and Umigon showed to be the best alternatives for detecting only positive and negative polarities in social network data due to the high coverage and prediction performance. It is important to highlight that LIWC 2007 appears on the 16th and 21th position for the 3-class and 2-class mean rank results for the social network datasets and it is a very popular method in this community. On the other side, the newest version of LIWC (2015) presented a considerable evolution obtaining the second and the fourth place in the same datasets." } ], "images": [ "scigen/train/3939.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Tabela 12: Mean Rank Table for Datasets of Comments" ], "reasoning": "Umigon was the best method for 3-class experiments, while SentiStrength was the best for 2-class experiments with around 30% coverage.", "description": "In the context of Social Networks the best method for 3-class experiments was Umigon, followed by LIWC15 and VADER. In the case of 2-class the winner was SentiStrength with a coverage around 30% and the third and sixth place were Emoticons and Panas-t with about 18% and 6% of coverage, respectively. This highlights the importance to analyze the 2-class results together with the coverage. Overall, when there is an emoticon on the text or a word from the psychometric scale PANAS, these methods are able to tell the polarity of the sentences, but they are not able to identify the polarity of the input text for the large majority of the input text. Sentiment140, LIWC15, Semantria, OpinionLexicon and Umigon showed to be the best alternatives for detecting only positive and negative polarities in social network data due to the high coverage and prediction performance. It is important to highlight that LIWC 2007 appears on the 16th and 21th position for the 3-class and 2-class mean rank results for the social network datasets and it is a very popular method in this community. On the other side, the newest version of LIWC (2015) presented a considerable evolution obtaining the second and the fourth place in the same datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Tabela 12: Mean Rank Table for Datasets of Comments', solve the following question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich method had the highest coverage for 2-class experiments?\n" }, { "role": "assistant", "content": "The table shows the 2-Classes Coverage (%) for each method. Emoticons DS had the highest coverage at 99.31%. So, the answer is {\"answer\": \"Emoticons DS\"}." } ], "images": [ "scigen/train/3939.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Tabela 12: Mean Rank Table for Datasets of Comments" ], "reasoning": "The table shows the 2-Classes Coverage (%) for each method. Emoticons DS had the highest coverage at 99.31%.", "question": "Which method had the highest coverage for 2-class experiments?", "answer": "So, the answer is {\"answer\": \"Emoticons DS\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: LIWC15 was ranked second in 3-class experiments., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nLIWC15 was ranked second in 3-class experiments.\n" }, { "role": "assistant", "content": "The table shows that LIWC15 was ranked sixth in 3-class experiments, not second. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3939.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Tabela 12: Mean Rank Table for Datasets of Comments" ], "reasoning": "The table shows that LIWC15 was ranked sixth in 3-class experiments, not second.", "statement": "LIWC15 was ranked second in 3-class experiments.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a sentence to describe the highlighted cells in this table whose title is 'Tabela 13: Mean Rank Table for Datasets of Reviews'.\n" }, { "role": "assistant", "content": "Umigon was the best method for 3-class experiments, while SentiStrength was the best for 2-class experiments with around 30% coverage.In the context of Social Networks the best method for 3-class experiments was Umigon, followed by LIWC15 and VADER. In the case of 2-class the winner was SentiStrength with a coverage around 30% and the third and sixth place were Emoticons and Panas-t with about 18% and 6% of coverage, respectively. This highlights the importance to analyze the 2-class results together with the coverage. Overall, when there is an emoticon on the text or a word from the psychometric scale PANAS, these methods are able to tell the polarity of the sentences, but they are not able to identify the polarity of the input text for the large majority of the input text. Sentiment140, LIWC15, Semantria, OpinionLexicon and Umigon showed to be the best alternatives for detecting only positive and negative polarities in social network data due to the high coverage and prediction performance. It is important to highlight that LIWC 2007 appears on the 16th and 21th position for the 3-class and 2-class mean rank results for the social network datasets and it is a very popular method in this community. On the other side, the newest version of LIWC (2015) presented a considerable evolution obtaining the second and the fourth place in the same datasets." } ], "images": [ "scigen/train/3940.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Tabela 13: Mean Rank Table for Datasets of Reviews" ], "reasoning": "Umigon was the best method for 3-class experiments, while SentiStrength was the best for 2-class experiments with around 30% coverage.", "description": "In the context of Social Networks the best method for 3-class experiments was Umigon, followed by LIWC15 and VADER. In the case of 2-class the winner was SentiStrength with a coverage around 30% and the third and sixth place were Emoticons and Panas-t with about 18% and 6% of coverage, respectively. This highlights the importance to analyze the 2-class results together with the coverage. Overall, when there is an emoticon on the text or a word from the psychometric scale PANAS, these methods are able to tell the polarity of the sentences, but they are not able to identify the polarity of the input text for the large majority of the input text. Sentiment140, LIWC15, Semantria, OpinionLexicon and Umigon showed to be the best alternatives for detecting only positive and negative polarities in social network data due to the high coverage and prediction performance. It is important to highlight that LIWC 2007 appears on the 16th and 21th position for the 3-class and 2-class mean rank results for the social network datasets and it is a very popular method in this community. On the other side, the newest version of LIWC (2015) presented a considerable evolution obtaining the second and the fourth place in the same datasets.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}. The question is related to the given table titled 'Tabela 13: Mean Rank Table for Datasets of Reviews'.\n\nQuestion: Which method had the highest coverage for 2-class experiments?\n" }, { "role": "assistant", "content": "From the table, Emoticons DS had the highest coverage for 2-class experiments with 99.71%. So, the answer is {\"answer\": \"Emoticons DS\"}." } ], "images": [ "scigen/train/3940.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Tabela 13: Mean Rank Table for Datasets of Reviews" ], "reasoning": "From the table, Emoticons DS had the highest coverage for 2-class experiments with 99.71%.", "question": "Which method had the highest coverage for 2-class experiments?", "answer": "So, the answer is {\"answer\": \"Emoticons DS\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nLIWC15 ranked second in 3-class experiments.\n" }, { "role": "assistant", "content": "The table shows that LIWC15 does not have a ranking position for 3-class experiments, thus the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3940.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Tabela 13: Mean Rank Table for Datasets of Reviews" ], "reasoning": "The table shows that LIWC15 does not have a ranking position for 3-class experiments, thus the statement is refuted.", "statement": "LIWC15 ranked second in 3-class experiments.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table I: Number of convolutional layers for each different VDCNN depth architecture'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The network depth is calculated by summing the number of convolutions in each block, with each block containing two temporal convolutional layers, and the first convolutional layer adding one to the total depth.The following layer (3, Temp Convolution, 64) applies 64 temporal convolutions of kernel size 3, so the output tensor has size 64∗s. Its primary function is to fit the lookup table output with the modular network segment input composed by convolutional blocks. Each aforenamed block is a sequence of two temporal convolutional layers, Besides, the different network depths are obtained varying the number of convolutional blocks. As a convention, the depth of a network is given as its total number of convolutions. Considering the first convolutional layer of the network, we obtain the depth 2∗(2+2+2+2)+1=17. The following rule is employed to minimize the network’s memory footprint: Before each convolutional block doubling the number of feature maps, a pooling layer halves the temporal dimension. This strategy is inspired by the VGG and ResNets philosophy and results in three levels of feature maps: 128, 256 and 512 (see Fig. Lastly, since each standard temporal convolution turns into two (Depthwise and Pointwise), the number of convolutions per convolutional block has doubled. Nevertheless, these two convolutions work as one because it is not possible to use them separately keeping the same propose. In this way, we count them as one layer in the network depth." } ], "images": [ "scigen/train/3941.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table I: Number of convolutional layers for each different VDCNN depth architecture" ], "reasoning": "The network depth is calculated by summing the number of convolutions in each block, with each block containing two temporal convolutional layers, and the first convolutional layer adding one to the total depth.", "description": "The following layer (3, Temp Convolution, 64) applies 64 temporal convolutions of kernel size 3, so the output tensor has size 64∗s. Its primary function is to fit the lookup table output with the modular network segment input composed by convolutional blocks. Each aforenamed block is a sequence of two temporal convolutional layers, Besides, the different network depths are obtained varying the number of convolutional blocks. As a convention, the depth of a network is given as its total number of convolutions. Considering the first convolutional layer of the network, we obtain the depth 2∗(2+2+2+2)+1=17. The following rule is employed to minimize the network’s memory footprint: Before each convolutional block doubling the number of feature maps, a pooling layer halves the temporal dimension. This strategy is inspired by the VGG and ResNets philosophy and results in three levels of feature maps: 128, 256 and 512 (see Fig. Lastly, since each standard temporal convolution turns into two (Depthwise and Pointwise), the number of convolutions per convolutional block has doubled. Nevertheless, these two convolutions work as one because it is not possible to use them separately keeping the same propose. In this way, we count them as one layer in the network depth.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table I: Number of convolutional layers for each different VDCNN depth architecture' in mind, break down and resolve the question below step by step. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the total number of convolutions in the network with a depth of 29?\n" }, { "role": "assistant", "content": "To find the total number of convolutions in the network with a depth of 29, sum the convolutions in each block and the first convolutional layer: 10 (Block 512) + 10 (Block 256) + 10 (Block 128) + 10 (Block 64) + 1 (First Layer) = 41. So, the answer is {\"answer\": \"41\"}." } ], "images": [ "scigen/train/3941.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table I: Number of convolutional layers for each different VDCNN depth architecture" ], "reasoning": "To find the total number of convolutions in the network with a depth of 29, sum the convolutions in each block and the first convolutional layer: 10 (Block 512) + 10 (Block 256) + 10 (Block 128) + 10 (Block 64) + 1 (First Layer) = 41.", "question": "What is the total number of convolutions in the network with a depth of 29?", "answer": "So, the answer is {\"answer\": \"41\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe network with a depth of 49 has 16 convolutions in the Convolutional Block 128.\n" }, { "role": "assistant", "content": "According to the table, the network with a depth of 49 has 16 convolutions in the Convolutional Block 128. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3941.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table I: Number of convolutional layers for each different VDCNN depth architecture" ], "reasoning": "According to the table, the network with a depth of 49 has 16 convolutions in the Convolutional Block 128.", "statement": "The network with a depth of 49 has 16 convolutions in the Convolutional Block 128.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table IV: Number of parameters, storage and accuracy results for all evaluated CNNs'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The use of TDSCs in SVDCNN models significantly reduced convolutional parameters compared to VDCNN, with a modest performance difference of 0.4 to 1.3%, while maintaining the base property of performance improvement with depth.The use of TDSCs promoted a significant reduction in convolutional parameters compared to VDCNN. For the most in-depth network evaluated, which contains 29 convolutional layers (depth 29), the number of parameters of these convolutional layers had a reduction of 66.08%, from 4.6 to 1.56 million parameters. This quantity is slightly larger than the one obtained from the Char-CNN, 1.40 million parameters, but this network has only six convolutional layers (depth 6). Regarding accuracy results, usually, a model with such parameter reduction should present some loss of accuracy in comparison to the original model. Nevertheless, the performance difference between VDCNN and SVDCNN models varies between 0.4 and 1.3%, which is pretty modest considering the parameters and storage size reduction aforementioned. Another two fundamental results obtained are a) The base property of VDCNN model is preserved on its squeezed model: the performance still increasing up with the depth and b) The performance evaluated for the most extensive dataset, i.e., Yelp Review (62.30%), still overcomes the accuracy of the Char-CNN model (62.05%)." } ], "images": [ "scigen/train/3942.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table IV: Number of parameters, storage and accuracy results for all evaluated CNNs" ], "reasoning": "The use of TDSCs in SVDCNN models significantly reduced convolutional parameters compared to VDCNN, with a modest performance difference of 0.4 to 1.3%, while maintaining the base property of performance improvement with depth.", "description": "The use of TDSCs promoted a significant reduction in convolutional parameters compared to VDCNN. For the most in-depth network evaluated, which contains 29 convolutional layers (depth 29), the number of parameters of these convolutional layers had a reduction of 66.08%, from 4.6 to 1.56 million parameters. This quantity is slightly larger than the one obtained from the Char-CNN, 1.40 million parameters, but this network has only six convolutional layers (depth 6). Regarding accuracy results, usually, a model with such parameter reduction should present some loss of accuracy in comparison to the original model. Nevertheless, the performance difference between VDCNN and SVDCNN models varies between 0.4 and 1.3%, which is pretty modest considering the parameters and storage size reduction aforementioned. Another two fundamental results obtained are a) The base property of VDCNN model is preserved on its squeezed model: the performance still increasing up with the depth and b) The performance evaluated for the most extensive dataset, i.e., Yelp Review (62.30%), still overcomes the accuracy of the Char-CNN model (62.05%).", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table IV: Number of parameters, storage and accuracy results for all evaluated CNNs', solve the subsequent question in a stepwise manner. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the reduction in the number of convolutional parameters from VDCNN 29 to SVDCNN 29?\n" }, { "role": "assistant", "content": "The number of convolutional parameters for VDCNN 29 is 4.60 million, and for SVDCNN 29 it is 1.56 million. The reduction is calculated as 4.60 - 1.56 = 3.04 million parameters. So, the answer is {\"answer\": \"3.04 million\"}." } ], "images": [ "scigen/train/3942.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table IV: Number of parameters, storage and accuracy results for all evaluated CNNs" ], "reasoning": "The number of convolutional parameters for VDCNN 29 is 4.60 million, and for SVDCNN 29 it is 1.56 million. The reduction is calculated as 4.60 - 1.56 = 3.04 million parameters.", "question": "What is the reduction in the number of convolutional parameters from VDCNN 29 to SVDCNN 29?", "answer": "So, the answer is {\"answer\": \"3.04 million\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nSVDCNN 29 has a higher accuracy on the Yelp Full dataset compared to Char-CNN.\n" }, { "role": "assistant", "content": "The accuracy of SVDCNN 29 on the Yelp Full dataset is 63.20%, while the accuracy of Char-CNN on the same dataset is 62.05%. Therefore, SVDCNN 29 has a higher accuracy. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3942.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table IV: Number of parameters, storage and accuracy results for all evaluated CNNs" ], "reasoning": "The accuracy of SVDCNN 29 on the Yelp Full dataset is 63.20%, while the accuracy of Char-CNN on the same dataset is 62.05%. Therefore, SVDCNN 29 has a higher accuracy.", "statement": "SVDCNN 29 has a higher accuracy on the Yelp Full dataset compared to Char-CNN.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 3: Performance comparison of extractive methods under ROUGE F1 and Facet-Aware Recall (FAR).', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The best performing extractive method under FAR is UnifiedSum(E) with a score of 54.8, while the upper bound of FAR by extracting 3 sentences (Oracle) is 84.8.Results on Neural Extractive Methods. The observations on ROUGE Precision/Recall are similar. We provide them as well as more comparative analysis under facet-aware evaluation in Sec. For facet coverage, the upper bound of FAR by extracting 3 sentences (Oracle, given the ground-truth FAMs) is 84.8, much higher than all the compared methods. The best performing extractive method under FAR is UnifiedSum(E), which indicates that it covers the most facets semantically." } ], "images": [ "scigen/train/3943.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Performance comparison of extractive methods under ROUGE F1 and Facet-Aware Recall (FAR)." ], "reasoning": "The best performing extractive method under FAR is UnifiedSum(E) with a score of 54.8, while the upper bound of FAR by extracting 3 sentences (Oracle) is 84.8.", "description": "Results on Neural Extractive Methods. The observations on ROUGE Precision/Recall are similar. We provide them as well as more comparative analysis under facet-aware evaluation in Sec. For facet coverage, the upper bound of FAR by extracting 3 sentences (Oracle, given the ground-truth FAMs) is 84.8, much higher than all the compared methods. The best performing extractive method under FAR is UnifiedSum(E), which indicates that it covers the most facets semantically.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 3: Performance comparison of extractive methods under ROUGE F1 and Facet-Aware Recall (FAR).', and sequentially solve the question, writing out each step of your reasoning process. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich method has the highest ROUGE-2 score and what is its FAR score?\n" }, { "role": "assistant", "content": "The table shows that Oracle has the highest ROUGE-2 score of 32.1. The FAR score for Oracle is 84.8. So, the answer is {\"answer\": \"Oracle with FAR score of 84.8\"}." } ], "images": [ "scigen/train/3943.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Performance comparison of extractive methods under ROUGE F1 and Facet-Aware Recall (FAR)." ], "reasoning": "The table shows that Oracle has the highest ROUGE-2 score of 32.1. The FAR score for Oracle is 84.8.", "question": "Which method has the highest ROUGE-2 score and what is its FAR score?", "answer": "So, the answer is {\"answer\": \"Oracle with FAR score of 84.8\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nUnifiedSum(E) has the highest FAR score among all the methods.\n" }, { "role": "assistant", "content": "The table indicates that UnifiedSum(E) has a FAR score of 54.8, which is the highest among all the methods listed except for Oracle, which is not a method but an upper bound. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3943.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Performance comparison of extractive methods under ROUGE F1 and Facet-Aware Recall (FAR)." ], "reasoning": "The table indicates that UnifiedSum(E) has a FAR score of 54.8, which is the highest among all the methods listed except for Oracle, which is not a method but an upper bound.", "statement": "UnifiedSum(E) has the highest FAR score among all the methods.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 5: ROUGE-1 F1 of extractive and abstractive methods on noisy (N), low abstraction (L), high abstraction (H), and high quality (L + H) samples.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Abstractive methods perform well on low abstraction samples but struggle with high abstraction and noisy reference summaries, indicating reliance on extraction.Abstractive methods, however, also exhibit a huge performance gap between low and high abstraction samples, which suggests that existing abstractive methods achieve decent overall performance mainly by extraction rather than abstraction, i.e., performing well on low abstraction samples of CNN/Daily Mail. We also found that all the compared methods perform much worse on the documents with “noisy” reference summaries, implying that the randomness in the reference summaries might introduce noise to both model training and evaluation. Note that although the sample size is relatively small, we observe consistent results when analyzing different subsets of the data." } ], "images": [ "scigen/train/3944.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: ROUGE-1 F1 of extractive and abstractive methods on noisy (N), low abstraction (L), high abstraction (H), and high quality (L + H) samples." ], "reasoning": "Abstractive methods perform well on low abstraction samples but struggle with high abstraction and noisy reference summaries, indicating reliance on extraction.", "description": "Abstractive methods, however, also exhibit a huge performance gap between low and high abstraction samples, which suggests that existing abstractive methods achieve decent overall performance mainly by extraction rather than abstraction, i.e., performing well on low abstraction samples of CNN/Daily Mail. We also found that all the compared methods perform much worse on the documents with “noisy” reference summaries, implying that the randomness in the reference summaries might introduce noise to both model training and evaluation. Note that although the sample size is relatively small, we observe consistent results when analyzing different subsets of the data.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 5: ROUGE-1 F1 of extractive and abstractive methods on noisy (N), low abstraction (L), high abstraction (H), and high quality (L + H) samples.'. Solve the following question based on the table.\n\nQuestion: Which method has the highest performance on low abstraction samples?\nLet's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the method with the highest performance on low abstraction samples, look at the 'L' column and identify the highest value. The highest value in the 'L' column is 42.7, which corresponds to the BanditSum method. So, the answer is {\"answer\": \"BanditSum\"}" } ], "images": [ "scigen/train/3944.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: ROUGE-1 F1 of extractive and abstractive methods on noisy (N), low abstraction (L), high abstraction (H), and high quality (L + H) samples." ], "reasoning": "To find the method with the highest performance on low abstraction samples, look at the 'L' column and identify the highest value. The highest value in the 'L' column is 42.7, which corresponds to the BanditSum method.", "question": "Which method has the highest performance on low abstraction samples?", "answer": "So, the answer is {\"answer\": \"BanditSum\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe Refresh method has the highest performance on noisy reference summaries.\n" }, { "role": "assistant", "content": "The description mentions that methods perform worse on documents with noisy reference summaries, but the table does not provide specific data on noisy reference summaries. Therefore, there is not enough information to verify this statement. So, the answer is {\"answer\": \"not enough info\"}" } ], "images": [ "scigen/train/3944.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: ROUGE-1 F1 of extractive and abstractive methods on noisy (N), low abstraction (L), high abstraction (H), and high quality (L + H) samples." ], "reasoning": "The description mentions that methods perform worse on documents with noisy reference summaries, but the table does not provide specific data on noisy reference summaries. Therefore, there is not enough information to verify this statement.", "statement": "The Refresh method has the highest performance on noisy reference summaries.", "answer": "So, the answer is {\"answer\": \"not enough info\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 6: Performance of sentence regression approaches regarding support sentence discovery. High precision and low recall are often observed.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The table shows that while many sentence regression approaches achieve high precision, recall is relatively low, indicating that finding one support sentence per facet may miss many salient sentences.Results on Support Sentence Discovery. We first evaluate sentence regression with its original function, i.e., creating extractive labels (finding support sentences). We merge the support groups of each sample and calculate precision and recall (i.e., SAR). The relatively low recall suggests that simply finding one support sentence for each facet as most existing approaches do would miss plenty of salient sentences, which could possibly worsen the models trained on such labels since the models would treat missed support sentences as unimportant ones. On the bright side, many sentence regression approaches achieve high precision. For instance, 90.0% document sentences labeled positive by narayan-etal-2018-ranking indeed contain salient information. This is to some extent explainable as ROUGE captures lexical overlap and as we have shown, there are many copy-and-paste reference summaries in CNN/Daily Mail." } ], "images": [ "scigen/train/3945.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Performance of sentence regression approaches regarding support sentence discovery. High precision and low recall are often observed." ], "reasoning": "The table shows that while many sentence regression approaches achieve high precision, recall is relatively low, indicating that finding one support sentence per facet may miss many salient sentences.", "description": "Results on Support Sentence Discovery. We first evaluate sentence regression with its original function, i.e., creating extractive labels (finding support sentences). We merge the support groups of each sample and calculate precision and recall (i.e., SAR). The relatively low recall suggests that simply finding one support sentence for each facet as most existing approaches do would miss plenty of salient sentences, which could possibly worsen the models trained on such labels since the models would treat missed support sentences as unimportant ones. On the bright side, many sentence regression approaches achieve high precision. For instance, 90.0% document sentences labeled positive by narayan-etal-2018-ranking indeed contain salient information. This is to some extent explainable as ROUGE captures lexical overlap and as we have shown, there are many copy-and-paste reference summaries in CNN/Daily Mail.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 6: Performance of sentence regression approaches regarding support sentence discovery. High precision and low recall are often observed.\nQuestion: Which method has the highest precision and what is its recall?\n" }, { "role": "assistant", "content": "The method with the highest precision is ROUGE-AVG F1 with a precision of 90.0%. Its recall is 53.9%. So, the answer is {\"answer\": \"ROUGE-AVG F1 with recall 53.9%\"}." } ], "images": [ "scigen/train/3945.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Performance of sentence regression approaches regarding support sentence discovery. High precision and low recall are often observed." ], "reasoning": "The method with the highest precision is ROUGE-AVG F1 with a precision of 90.0%. Its recall is 53.9%.", "question": "Which method has the highest precision and what is its recall?", "answer": "So, the answer is {\"answer\": \"ROUGE-AVG F1 with recall 53.9%\"}." }, { "messages": [ { "role": "user", "content": "ROUGE-1 F1 has the highest recall among all methods. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nROUGE-1 F1 has the highest recall among all methods.\n" }, { "role": "assistant", "content": "The table shows that ROUGE-L Recall has the highest recall at 53.7%, not ROUGE-1 F1. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3945.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Performance of sentence regression approaches regarding support sentence discovery. High precision and low recall are often observed." ], "reasoning": "The table shows that ROUGE-L Recall has the highest recall at 53.7%, not ROUGE-1 F1.", "statement": "ROUGE-1 F1 has the highest recall among all methods.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 7: Correlation between ground-truth and estimated FAR scores by Pearson’s r, Spearman’s ρ, and Kendall’s τ. N denotes the number of support groups.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Creating three support groups shows the highest correlation for the same sentence regression approach, with ROUGE-1 F1 and ROUGE-AVG F1 having very high correlation with human annotation.Correlation w. Human-Annotated FAMs. We then explore the correlation between human-annotated and machine-created FAMs by evaluating extractive methods against both of them. This time we extend to find for each facet multiple support sentences and put each support sentence into a separate support group. We measure the correlation between estimated and ground-truth FAR by Pearson’s r. We measure the correlation between system rankings induced from estimated and ground-truth FAR by Spearman’s ρ and Kendall’s τ. We observe that creating three support groups consistently shows the highest correlation for the same sentence regression approach. Also, the FAMs created by ROUGE-1 F1 and ROUGE-AVG F1 have very high correlation with human annotation, indicating the usability and reliability of machine-created FAMs for system ranking." } ], "images": [ "scigen/train/3946.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Correlation between ground-truth and estimated FAR scores by Pearson’s r, Spearman’s ρ, and Kendall’s τ. N denotes the number of support groups." ], "reasoning": "Creating three support groups shows the highest correlation for the same sentence regression approach, with ROUGE-1 F1 and ROUGE-AVG F1 having very high correlation with human annotation.", "description": "Correlation w. Human-Annotated FAMs. We then explore the correlation between human-annotated and machine-created FAMs by evaluating extractive methods against both of them. This time we extend to find for each facet multiple support sentences and put each support sentence into a separate support group. We measure the correlation between estimated and ground-truth FAR by Pearson’s r. We measure the correlation between system rankings induced from estimated and ground-truth FAR by Spearman’s ρ and Kendall’s τ. We observe that creating three support groups consistently shows the highest correlation for the same sentence regression approach. Also, the FAMs created by ROUGE-1 F1 and ROUGE-AVG F1 have very high correlation with human annotation, indicating the usability and reliability of machine-created FAMs for system ranking.", "answer": "" }, { "messages": [ { "role": "user", "content": "Take a look at this table about 'Table 7: Correlation between ground-truth and estimated FAR scores by Pearson’s r, Spearman’s ρ, and Kendall’s τ. N denotes the number of support groups.', and tackle the question below in a sequential manner. The final result should be presented in the JSON format of {\"answer\": \"\"}.\nWhich method shows the highest Pearson's r correlation when N=3?\n" }, { "role": "assistant", "content": "By examining the table, the highest Pearson's r correlation when N=3 is found in the ROUGE-1 F1 method with a value of 88.4. So, the answer is {\"answer\": \"ROUGE-1 F1\"}" } ], "images": [ "scigen/train/3946.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Correlation between ground-truth and estimated FAR scores by Pearson’s r, Spearman’s ρ, and Kendall’s τ. N denotes the number of support groups." ], "reasoning": "By examining the table, the highest Pearson's r correlation when N=3 is found in the ROUGE-1 F1 method with a value of 88.4.", "question": "Which method shows the highest Pearson's r correlation when N=3?", "answer": "So, the answer is {\"answer\": \"ROUGE-1 F1\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nROUGE-2 F1 has the highest Spearman’s ρ correlation when N=3.\n" }, { "role": "assistant", "content": "The table shows that the highest Spearman’s ρ correlation when N=3 is for ROUGE-1 F1 with a value of 94.3, not ROUGE-2 F1. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3946.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Correlation between ground-truth and estimated FAR scores by Pearson’s r, Spearman’s ρ, and Kendall’s τ. N denotes the number of support groups." ], "reasoning": "The table shows that the highest Spearman’s ρ correlation when N=3 is for ROUGE-1 F1 with a value of 94.3, not ROUGE-2 F1.", "statement": "ROUGE-2 F1 has the highest Spearman’s ρ correlation when N=3.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 1: Precision (P), recall (R), F1 Score (F), and support (S) for all 637 concepts (F1 Score weighted by support) and selected individual concepts. Class membership was determined by an activation threshold of 0.5.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The model performs better on concepts with sharper boundaries like province and choreographer, but struggles with less defined concepts like location and legend.The table contains the weighted average scores accomplished for all concepts as well as the scores the model achieved for selected individual concepts, grouped semantically. The weighted classification F1 score is 0.22 which suggests that the task is complex, probably due to the highly imbalanced data set. While we observe a high score for province, our model has difficulties classifying locations, for instance. The same trend can be observed for choreographers and legends. What we see reflected in this table is the sharpness of concept boundaries. Arguably, the definition of a province is sharper than that of location. The same is true for choreographer and legend. We assume that the more precise a concept boundary, the higher the classification performance tends to be. Fig. Fig." } ], "images": [ "scigen/train/3947.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Precision (P), recall (R), F1 Score (F), and support (S) for all 637 concepts (F1 Score weighted by support) and selected individual concepts. Class membership was determined by an activation threshold of 0.5." ], "reasoning": "The model performs better on concepts with sharper boundaries like province and choreographer, but struggles with less defined concepts like location and legend.", "description": "The table contains the weighted average scores accomplished for all concepts as well as the scores the model achieved for selected individual concepts, grouped semantically. The weighted classification F1 score is 0.22 which suggests that the task is complex, probably due to the highly imbalanced data set. While we observe a high score for province, our model has difficulties classifying locations, for instance. The same trend can be observed for choreographers and legends. What we see reflected in this table is the sharpness of concept boundaries. Arguably, the definition of a province is sharper than that of location. The same is true for choreographer and legend. We assume that the more precise a concept boundary, the higher the classification performance tends to be. Fig. Fig.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 1: Precision (P), recall (R), F1 Score (F), and support (S) for all 637 concepts (F1 Score weighted by support) and selected individual concepts. Class membership was determined by an activation threshold of 0.5.\nQuestion: What is the F1 score for the concept with the highest precision?\nSolve the above question based on the table information. Let's think step by step. Output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The concept with the highest precision is island with a precision of 0.96. The F1 score for island is 0.54. So, the answer is {\"answer\": \"0.54\"}." } ], "images": [ "scigen/train/3947.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Precision (P), recall (R), F1 Score (F), and support (S) for all 637 concepts (F1 Score weighted by support) and selected individual concepts. Class membership was determined by an activation threshold of 0.5." ], "reasoning": "The concept with the highest precision is island with a precision of 0.96. The F1 score for island is 0.54.", "question": "What is the F1 score for the concept with the highest precision?", "answer": "So, the answer is {\"answer\": \"0.54\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe model has a higher F1 score for choreographer than for composer.\n" }, { "role": "assistant", "content": "The F1 score for choreographer is 0.76, while the F1 score for composer is 0.72. Therefore, the model has a higher F1 score for choreographer than for composer. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3947.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Precision (P), recall (R), F1 Score (F), and support (S) for all 637 concepts (F1 Score weighted by support) and selected individual concepts. Class membership was determined by an activation threshold of 0.5." ], "reasoning": "The F1 score for choreographer is 0.76, while the F1 score for composer is 0.72. Therefore, the model has a higher F1 score for choreographer than for composer.", "statement": "The model has a higher F1 score for choreographer than for composer.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 4: LMs performance on OBWB.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "B-NCE outperforms S-NCE in terms of training speed and simplicity, and the additional ReLu helps reduce NoPs while maintaining or improving performance.That is, B-NCE slightly outperforms S-NCE while being faster and simpler to train (training speed in 3rd column). Moreover, these results also show a much smaller difference between PPLf and PPLn compared to LTCB, which suggests that the models learned to better self-normalize due to the larger amount of training data. Similarly to LTCB, we can see that the additional ReLu helps reducing the NoPs while improving or maintaining the models performance for RNN and LSTM." } ], "images": [ "scigen/train/3948.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: LMs performance on OBWB." ], "reasoning": "B-NCE outperforms S-NCE in terms of training speed and simplicity, and the additional ReLu helps reduce NoPs while maintaining or improving performance.", "description": "That is, B-NCE slightly outperforms S-NCE while being faster and simpler to train (training speed in 3rd column). Moreover, these results also show a much smaller difference between PPLf and PPLn compared to LTCB, which suggests that the models learned to better self-normalize due to the larger amount of training data. Similarly to LTCB, we can see that the additional ReLu helps reducing the NoPs while improving or maintaining the models performance for RNN and LSTM.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 4: LMs performance on OBWB.\nQuestion: Which model has the lowest PPLn and what is its training speed?\nSolve the above question based on the table information. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "To find the model with the lowest PPLn, we look at the PPLn column and identify the lowest value, which is 48.6 for the LSTM (B-NCE) model. The training speed for this model is 13.1K. So, the answer is {\"answer\": \"13.1K\"}." } ], "images": [ "scigen/train/3948.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: LMs performance on OBWB." ], "reasoning": "To find the model with the lowest PPLn, we look at the PPLn column and identify the lowest value, which is 48.6 for the LSTM (B-NCE) model. The training speed for this model is 13.1K.", "question": "Which model has the lowest PPLn and what is its training speed?", "answer": "So, the answer is {\"answer\": \"13.1K\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe ReLu-RNN (B-NCE) model has a lower PPLf than the RNN (S-NCE) model.\n" }, { "role": "assistant", "content": "The PPLf for the ReLu-RNN (B-NCE) model is 56.6, while the PPLf for the RNN (S-NCE) model is 67.6. Therefore, the statement is supported. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3948.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: LMs performance on OBWB." ], "reasoning": "The PPLf for the ReLu-RNN (B-NCE) model is 56.6, while the PPLf for the RNN (S-NCE) model is 67.6. Therefore, the statement is supported.", "statement": "The ReLu-RNN (B-NCE) model has a lower PPLf than the RNN (S-NCE) model.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 2: LMs performance on LTCB.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "B-NCE outperforms S-NCE in terms of performance and simplicity, and increasing training data stabilizes learning and reduces performance degradation.Moreover, we can also see that B-NCE slightly outperforms S-NCE while being faster and simpler to implement. In particular, B-NCE does not require the sampling step since it uses the rest of the output words in the batch itself as noise samples to train the model on each target word. We have also observed in our experiments that the models degradation and the gap between PPLf and PPLn strongly depend on the amount of training data, the vocabulary size as well as the size of the last hidden layer. More particularly, increasing the training data leads to a more stable learning and therefore to a smaller gap between these two metrics and a much lower degradation of the models performance (see OBWB experiments below). This conclusion is valid for both, RNN and LSTM. These results confirm that adding a fully-connected bottleneck layer can significantly boost the performance of recurrent models. This idea has been previously used in computer vision tasks in combination with Convolutional Neural Networks (CNN)" } ], "images": [ "scigen/train/3949.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: LMs performance on LTCB." ], "reasoning": "B-NCE outperforms S-NCE in terms of performance and simplicity, and increasing training data stabilizes learning and reduces performance degradation.", "description": "Moreover, we can also see that B-NCE slightly outperforms S-NCE while being faster and simpler to implement. In particular, B-NCE does not require the sampling step since it uses the rest of the output words in the batch itself as noise samples to train the model on each target word. We have also observed in our experiments that the models degradation and the gap between PPLf and PPLn strongly depend on the amount of training data, the vocabulary size as well as the size of the last hidden layer. More particularly, increasing the training data leads to a more stable learning and therefore to a smaller gap between these two metrics and a much lower degradation of the models performance (see OBWB experiments below). This conclusion is valid for both, RNN and LSTM. These results confirm that adding a fully-connected bottleneck layer can significantly boost the performance of recurrent models. This idea has been previously used in computer vision tasks in combination with Convolutional Neural Networks (CNN)", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 2: LMs performance on LTCB.' in mind, break down and resolve the question below step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n\nWhich model has the lowest PPLf and what is its value?\n" }, { "role": "assistant", "content": "By examining the PPLf column, the lowest value is 62.4, which corresponds to the ReLu-LSTM (B-NCE) model. So, the answer is {\"answer\": \"ReLu-LSTM (B-NCE)\"}." } ], "images": [ "scigen/train/3949.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: LMs performance on LTCB." ], "reasoning": "By examining the PPLf column, the lowest value is 62.4, which corresponds to the ReLu-LSTM (B-NCE) model.", "question": "Which model has the lowest PPLf and what is its value?", "answer": "So, the answer is {\"answer\": \"ReLu-LSTM (B-NCE)\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe ReLu-RNN (B-NCE) model has a lower PPLf than the LSTM (softmax) model.\n" }, { "role": "assistant", "content": "The PPLf for ReLu-RNN (B-NCE) is 76.0, while for LSTM (softmax) it is 62.5. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3949.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: LMs performance on LTCB." ], "reasoning": "The PPLf for ReLu-RNN (B-NCE) is 76.0, while for LSTM (softmax) it is 62.5. Therefore, the statement is refuted.", "statement": "The ReLu-RNN (B-NCE) model has a lower PPLf than the LSTM (softmax) model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Lexicon annotation results. Fleiss’ κ and % agreement are averaged across nouns and adjectives. Lex % is agreement between annotators and the lexicon. NC indicates non-conflicting value agreement.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Annotators evaluate words based on various aspects such as Social Value, Politeness, Impact, and Factuality, and the final label is determined by the majority vote of three annotators.Annotators are given a word w, along with its definitions (for all senses) and related words, and annotate connotation independent of word sense. This setup mimics We select as the final annotator label the majority vote of three annotators." } ], "images": [ "scigen/train/3950.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Lexicon annotation results. Fleiss’ κ and % agreement are averaged across nouns and adjectives. Lex % is agreement between annotators and the lexicon. NC indicates non-conflicting value agreement." ], "reasoning": "Annotators evaluate words based on various aspects such as Social Value, Politeness, Impact, and Factuality, and the final label is determined by the majority vote of three annotators.", "description": "Annotators are given a word w, along with its definitions (for all senses) and related words, and annotate connotation independent of word sense. This setup mimics We select as the final annotator label the majority vote of three annotators.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 3: Lexicon annotation results. Fleiss’ κ and % agreement are averaged across nouns and adjectives. Lex % is agreement between annotators and the lexicon. NC indicates non-conflicting value agreement.'. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}. \n\nQuestion: Which aspect has the highest Lex % Agree?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the aspect with the highest Lex % Agree, compare the Lex % Agree values for all aspects. The highest value is 73.7% for Impact. So, the answer is {\"answer\": \"Impact\"}." } ], "images": [ "scigen/train/3950.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Lexicon annotation results. Fleiss’ κ and % agreement are averaged across nouns and adjectives. Lex % is agreement between annotators and the lexicon. NC indicates non-conflicting value agreement." ], "reasoning": "To find the aspect with the highest Lex % Agree, compare the Lex % Agree values for all aspects. The highest value is 73.7% for Impact.", "question": "Which aspect has the highest Lex % Agree?", "answer": "So, the answer is {\"answer\": \"Impact\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nPoliteness has the lowest Avg % Agree.\n" }, { "role": "assistant", "content": "To verify this statement, compare the Avg % Agree values for all aspects. Politeness has the lowest Avg % Agree at 56.6%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3950.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Lexicon annotation results. Fleiss’ κ and % agreement are averaged across nouns and adjectives. Lex % is agreement between annotators and the lexicon. NC indicates non-conflicting value agreement." ], "reasoning": "To verify this statement, compare the Avg % Agree values for all aspects. Politeness has the lowest Avg % Agree at 56.6%.", "statement": "Politeness has the lowest Avg % Agree.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Class distributions in the connotation lexicon for fully-labeled words.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The table shows uneven class distributions in various aspects, with politeness having 10.5% positive and 1% negative, and emotions calculated for 1,373 words with an average of 2 associated emotions.We find that many aspects exhibit uneven class distributions (e.g., 10.5% of words are polite and only 1% are impolite) For emotions, we calculate the class distribution using the number of fully-labeled words with at least one associated emotion (1,373 words or 18%). For these 1,373 words, the average number of associated emotions is ∼2. Our distributions are similar to previous work on verb connotations, where distributions range from 1.4% to 20.2% for the smallest class [rashkin-etal-2016-connotation]." } ], "images": [ "scigen/train/3951.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Class distributions in the connotation lexicon for fully-labeled words." ], "reasoning": "The table shows uneven class distributions in various aspects, with politeness having 10.5% positive and 1% negative, and emotions calculated for 1,373 words with an average of 2 associated emotions.", "description": "We find that many aspects exhibit uneven class distributions (e.g., 10.5% of words are polite and only 1% are impolite) For emotions, we calculate the class distribution using the number of fully-labeled words with at least one associated emotion (1,373 words or 18%). For these 1,373 words, the average number of associated emotions is ∼2. Our distributions are similar to previous work on verb connotations, where distributions range from 1.4% to 20.2% for the smallest class [rashkin-etal-2016-connotation].", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: Class distributions in the connotation lexicon for fully-labeled words.', solve this question and give detail solutions. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhat is the percentage of words with positive sentiment?\n" }, { "role": "assistant", "content": "The table lists the percentage of words with positive sentiment under the 'Sentiment' row and '% +' column. So, the answer is {\"answer\": \"56.8\"}." } ], "images": [ "scigen/train/3951.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Class distributions in the connotation lexicon for fully-labeled words." ], "reasoning": "The table lists the percentage of words with positive sentiment under the 'Sentiment' row and '% +' column.", "question": "What is the percentage of words with positive sentiment?", "answer": "So, the answer is {\"answer\": \"56.8\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe percentage of words with negative factuality is higher than the percentage of words with positive factuality.\n" }, { "role": "assistant", "content": "The table shows that the percentage of words with negative factuality is 67.2%, while the percentage of words with positive factuality is 19.0%. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3951.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Class distributions in the connotation lexicon for fully-labeled words." ], "reasoning": "The table shows that the percentage of words with negative factuality is 67.2%, while the percentage of words with positive factuality is 19.0%.", "statement": "The percentage of words with negative factuality is higher than the percentage of words with positive factuality.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 7: Cluster connotation purity ratios. Emo is based on no emotions (0) or any number (≥1).'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The connotation embedding model shows higher ratios across connotation aspects compared to pre-trained word embeddings, indicating better clustering based on connotation labels.We find that across connotation aspects, these ratios are higher for the learned connotation embeddings, compared to pre-trained word embeddings. This shows the connotation embedding model clusters words based more on connotation labels than pre-trained word embeddings." } ], "images": [ "scigen/train/3952.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Cluster connotation purity ratios. Emo is based on no emotions (0) or any number (≥1)." ], "reasoning": "The connotation embedding model shows higher ratios across connotation aspects compared to pre-trained word embeddings, indicating better clustering based on connotation labels.", "description": "We find that across connotation aspects, these ratios are higher for the learned connotation embeddings, compared to pre-trained word embeddings. This shows the connotation embedding model clusters words based more on connotation labels than pre-trained word embeddings.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 7: Cluster connotation purity ratios. Emo is based on no emotions (0) or any number (≥1).'.\n\nQuestion: What is the ratio for the 'Polite' aspect with a positive connotation in the learned connotation embeddings?\n" }, { "role": "assistant", "content": "To find the ratio for the 'Polite' aspect with a positive connotation in the learned connotation embeddings, look at the row with 'Polite' and '+' in the 'Aspect a' and 'c' columns respectively, and then find the value under the 'r a ( C ) c' column. So, the answer is {\"answer\": \"2640.14\"}" } ], "images": [ "scigen/train/3952.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Cluster connotation purity ratios. Emo is based on no emotions (0) or any number (≥1)." ], "reasoning": "To find the ratio for the 'Polite' aspect with a positive connotation in the learned connotation embeddings, look at the row with 'Polite' and '+' in the 'Aspect a' and 'c' columns respectively, and then find the value under the 'r a ( C ) c' column.", "question": "What is the ratio for the 'Polite' aspect with a positive connotation in the learned connotation embeddings?", "answer": "So, the answer is {\"answer\": \"2640.14\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe ratio for the 'Impact' aspect with a negative connotation in the pre-trained word embeddings is higher than in the learned connotation embeddings.\n" }, { "role": "assistant", "content": "To verify this statement, compare the values for the 'Impact' aspect with a negative connotation in both the 'r a ( C ) c' and 'r a ( P ) c' columns. The 'r a ( C ) c' value is 33.33 and the 'r a ( P ) c' value is 8.33, indicating that the ratio is higher in the learned connotation embeddings. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3952.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Cluster connotation purity ratios. Emo is based on no emotions (0) or any number (≥1)." ], "reasoning": "To verify this statement, compare the values for the 'Impact' aspect with a negative connotation in both the 'r a ( C ) c' and 'r a ( P ) c' columns. The 'r a ( C ) c' value is 33.33 and the 'r a ( P ) c' value is 8.33, indicating that the ratio is higher in the learned connotation embeddings.", "statement": "The ratio for the 'Impact' aspect with a negative connotation in the pre-trained word embeddings is higher than in the learned connotation embeddings.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: The number of normalized response candidates in each sub-dataset after entity replacement, both training and test data included.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "Entities in dialogues are replaced with their order of appearance to normalize utterances, improving model convergence.It is possible for the dialogue model to retrieve responses directly without any preprocessing. However, the fact that nearly all utterances contain entity information would lead to a slow model convergence. Thus, we replace all entities with the orders in which they appear in dialogues to normalize utterances. For example, if the $entity_9$ is the second distinct entity which appears in a dialogue, we rename it with $entity_order_2$ in the current episode. To better understand the behavior of our approach, we train IDS− on the SubD5 training set until 2,000 batches online updates are finished, and then freeze the model parameters and test it on the SubD5 test set. Among these responses, we pick four of them and draw their context embedding vectors. In each figure, the red dots are contexts responded by IDS− with high confidence, while the blue dots are contexts responded by human where there is low confidence." } ], "images": [ "scigen/train/3953.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: The number of normalized response candidates in each sub-dataset after entity replacement, both training and test data included." ], "reasoning": "Entities in dialogues are replaced with their order of appearance to normalize utterances, improving model convergence.", "description": "It is possible for the dialogue model to retrieve responses directly without any preprocessing. However, the fact that nearly all utterances contain entity information would lead to a slow model convergence. Thus, we replace all entities with the orders in which they appear in dialogues to normalize utterances. For example, if the $entity_9$ is the second distinct entity which appears in a dialogue, we rename it with $entity_order_2$ in the current episode. To better understand the behavior of our approach, we train IDS− on the SubD5 training set until 2,000 batches online updates are finished, and then freeze the model parameters and test it on the SubD5 test set. Among these responses, we pick four of them and draw their context embedding vectors. In each figure, the red dots are contexts responded by IDS− with high confidence, while the blue dots are contexts responded by human where there is low confidence.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: The number of normalized response candidates in each sub-dataset after entity replacement, both training and test data included.', solve the following question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nWhat is the total number of responses (RSP) across all SubD datasets?\n" }, { "role": "assistant", "content": "To find the total number of responses, sum the values for SubD1, SubD2, SubD3, SubD4, and SubD5: 41 + 41 + 66 + 72 + 137. So, the answer is {\"answer\": \"357\"}" } ], "images": [ "scigen/train/3953.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: The number of normalized response candidates in each sub-dataset after entity replacement, both training and test data included." ], "reasoning": "To find the total number of responses, sum the values for SubD1, SubD2, SubD3, SubD4, and SubD5: 41 + 41 + 66 + 72 + 137.", "question": "What is the total number of responses (RSP) across all SubD datasets?", "answer": "So, the answer is {\"answer\": \"357\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nSubD5 has the highest number of responses among all SubD datasets.\n" }, { "role": "assistant", "content": "By comparing the number of responses in each SubD dataset, SubD5 has 137 responses, which is higher than SubD1, SubD2, SubD3, and SubD4. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3953.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: The number of normalized response candidates in each sub-dataset after entity replacement, both training and test data included." ], "reasoning": "By comparing the number of responses in each SubD dataset, SubD5 has 137 responses, which is higher than SubD1, SubD2, SubD3, and SubD4.", "statement": "SubD5 has the highest number of responses among all SubD datasets.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 2: Average mean ranks of the synonym test, smaller values are better. The best result in each row is in bold. All differences are statistically significant: with p<2.2⋅10−16 for all cases.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "SOPP models rank synonym similarities higher than initial models, with statistically significant improvements and an average rank reduction of 10%.SOPP, on the other hand, produces a combined model where the synonym similarities are ranked higher than in initial models. This means that the SOPP combined models pull the synonymous words closer together than they were in the initial models. The differences in mean ranks were tested using paired Wilcoxon signed-rank test at 95% confidence level and the differences were statistically significant with p-value being less than 2.2⋅10−16 in all cases. In overall, the SOPP ranks are on average 10% lower than the mean ranks of the initial models. The absolute improvement on average between SOPP and mean of W is 3476. Although the first bin covering the rank range from 1 to 10 contains the most words for both models and the number of synonym pairs falling to further rank bins decreases the curve is not steep and close to 100 words (87 in case of SOPP and 94 in case of the initial model) belong to the last bin counting ranks higher than 100000. Looking at the farthest synonym pairs revealed that one word in these pairs is typically polysemous and its sense in the synonym pair is a relatively rarely used sense of this word, while there are other more common senses of this word with a completely different meaning. We give some examples of such synonym pairs: kaks (two) - puudulik (insufficient): the sense of this pair is the insufficient grade in high school, while the most common sense of the word kaks is the number two; ida (east) - ost (loan word from German also meaning east): the most common sense of the word ost is purchase; rubla (rouble) - kull (bank note in slang): the most common sense of the word kull is hawk." } ], "images": [ "scigen/train/3954.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Average mean ranks of the synonym test, smaller values are better. The best result in each row is in bold. All differences are statistically significant: with p<2.2⋅10−16 for all cases." ], "reasoning": "SOPP models rank synonym similarities higher than initial models, with statistically significant improvements and an average rank reduction of 10%.", "description": "SOPP, on the other hand, produces a combined model where the synonym similarities are ranked higher than in initial models. This means that the SOPP combined models pull the synonymous words closer together than they were in the initial models. The differences in mean ranks were tested using paired Wilcoxon signed-rank test at 95% confidence level and the differences were statistically significant with p-value being less than 2.2⋅10−16 in all cases. In overall, the SOPP ranks are on average 10% lower than the mean ranks of the initial models. The absolute improvement on average between SOPP and mean of W is 3476. Although the first bin covering the rank range from 1 to 10 contains the most words for both models and the number of synonym pairs falling to further rank bins decreases the curve is not steep and close to 100 words (87 in case of SOPP and 94 in case of the initial model) belong to the last bin counting ranks higher than 100000. Looking at the farthest synonym pairs revealed that one word in these pairs is typically polysemous and its sense in the synonym pair is a relatively rarely used sense of this word, while there are other more common senses of this word with a completely different meaning. We give some examples of such synonym pairs: kaks (two) - puudulik (insufficient): the sense of this pair is the insufficient grade in high school, while the most common sense of the word kaks is the number two; ida (east) - ost (loan word from German also meaning east): the most common sense of the word ost is purchase; rubla (rouble) - kull (bank note in slang): the most common sense of the word kull is hawk.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 2: Average mean ranks of the synonym test, smaller values are better. The best result in each row is in bold. All differences are statistically significant: with p<2.2⋅10−16 for all cases.' in mind, break down and resolve the question below step by step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\n\nWhat is the average rank improvement of SOPP over SOLS across all dimensions?\n" }, { "role": "assistant", "content": "To find the average rank improvement, subtract the average SOPP rank from the average SOLS rank: 73064 - 31403. So, the answer is {\"answer\": \"41661\"}" } ], "images": [ "scigen/train/3954.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Average mean ranks of the synonym test, smaller values are better. The best result in each row is in bold. All differences are statistically significant: with p<2.2⋅10−16 for all cases." ], "reasoning": "To find the average rank improvement, subtract the average SOPP rank from the average SOLS rank: 73064 - 31403.", "question": "What is the average rank improvement of SOPP over SOLS across all dimensions?", "answer": "So, the answer is {\"answer\": \"41661\"}" }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe SOPP model has a higher average rank than the Mean W model.\n" }, { "role": "assistant", "content": "The table shows that the average rank for SOPP is 31403, which is lower than the average rank for Mean W, which is 34879. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3954.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Average mean ranks of the synonym test, smaller values are better. The best result in each row is in bold. All differences are statistically significant: with p<2.2⋅10−16 for all cases." ], "reasoning": "The table shows that the average rank for SOPP is 31403, which is lower than the average rank for Mean W, which is 34879.", "statement": "The SOPP model has a higher average rank than the Mean W model.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Final errors and the number of iterations until convergence for both SOLS and SOPP. The first column shows the embedding size.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "SOPP converged in fewer iterations than SOLS due to the orthogonality constraint and variance rescaling.During training the iterative algorithm was run until the convergence threshold th=0.001 was reached. It can be seen that the convergence with SOPP took significantly fewer iterations than with SOLS. This difference is probably due to two aspects: 1) SOPP has the additional orthogonality constraint which reduces the space of feasible solutions; 2) although SOLS uses the exact analytical solutions for the least squares problem, the final solution for Y does not move directly to the direction pointed to by the analytical solutions due to the variance rescaling." } ], "images": [ "scigen/train/3955.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Final errors and the number of iterations until convergence for both SOLS and SOPP. The first column shows the embedding size." ], "reasoning": "SOPP converged in fewer iterations than SOLS due to the orthogonality constraint and variance rescaling.", "description": "During training the iterative algorithm was run until the convergence threshold th=0.001 was reached. It can be seen that the convergence with SOPP took significantly fewer iterations than with SOLS. This difference is probably due to two aspects: 1) SOPP has the additional orthogonality constraint which reduces the space of feasible solutions; 2) although SOLS uses the exact analytical solutions for the least squares problem, the final solution for Y does not move directly to the direction pointed to by the analytical solutions due to the variance rescaling.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nTable title: Table 1: Final errors and the number of iterations until convergence for both SOLS and SOPP. The first column shows the embedding size.\n\nQuestion: What is the difference in the number of iterations between SOLS and SOPP for Dim=150?\n" }, { "role": "assistant", "content": "For Dim=150, SOLS took 41 iterations while SOPP took 4 iterations. The difference is 41 - 4 = 37. So, the answer is {\"answer\": \"37\"}" } ], "images": [ "scigen/train/3955.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Final errors and the number of iterations until convergence for both SOLS and SOPP. The first column shows the embedding size." ], "reasoning": "For Dim=150, SOLS took 41 iterations while SOPP took 4 iterations. The difference is 41 - 4 = 37.", "question": "What is the difference in the number of iterations between SOLS and SOPP for Dim=150?", "answer": "So, the answer is {\"answer\": \"37\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: SOPP had a higher error than SOLS for Dim=100., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nSOPP had a higher error than SOLS for Dim=100.\n" }, { "role": "assistant", "content": "For Dim=100, the table shows that SOPP Error is 0.183933 and SOLS Error is 0.168316. Since 0.183933 is greater than 0.168316, the statement is supported. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3955.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Final errors and the number of iterations until convergence for both SOLS and SOPP. The first column shows the embedding size." ], "reasoning": "For Dim=100, the table shows that SOPP Error is 0.183933 and SOLS Error is 0.168316. Since 0.183933 is greater than 0.168316, the statement is supported.", "statement": "SOPP had a higher error than SOLS for Dim=100.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Hit@1 and Hit@10 accuracies of the analogy test. SOLS and SOPP columns show the accuracies of the combined models. Mean W, Min W and Max W show the mean, minimum and maximum accuracies of the initial models Wi, respectively. The best accuracy among the combined models and the mean of the initial models is given in bold. The last row shows the average accuracies over all embedding sizes.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The SOPP combined model outperforms the mean of the initial models in all six cases and is consistently above the maximum of the best initial models.The SOPP combined model is better than the mean of the initial models in all six cases. Furthermore, it is consistently above the maximum of the best initial models. The average accuracy of SOPP is better than the average of the mean accuracies of initial models by 41%, relatively (7.7% in absolute) in terms of Hit@1 and 27% relatively (10.5% in absolute) in terms of Hit@10." } ], "images": [ "scigen/train/3956.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Hit@1 and Hit@10 accuracies of the analogy test. SOLS and SOPP columns show the accuracies of the combined models. Mean W, Min W and Max W show the mean, minimum and maximum accuracies of the initial models Wi, respectively. The best accuracy among the combined models and the mean of the initial models is given in bold. The last row shows the average accuracies over all embedding sizes." ], "reasoning": "The SOPP combined model outperforms the mean of the initial models in all six cases and is consistently above the maximum of the best initial models.", "description": "The SOPP combined model is better than the mean of the initial models in all six cases. Furthermore, it is consistently above the maximum of the best initial models. The average accuracy of SOPP is better than the average of the mean accuracies of initial models by 41%, relatively (7.7% in absolute) in terms of Hit@1 and 27% relatively (10.5% in absolute) in terms of Hit@10.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n\nTable title: Table 3: Hit@1 and Hit@10 accuracies of the analogy test. SOLS and SOPP columns show the accuracies of the combined models. Mean W, Min W and Max W show the mean, minimum and maximum accuracies of the initial models Wi, respectively. The best accuracy among the combined models and the mean of the initial models is given in bold. The last row shows the average accuracies over all embedding sizes.\n\nQuestion: What is the relative improvement in average accuracy of SOPP over the mean accuracies of initial models in terms of Hit@10?\n" }, { "role": "assistant", "content": "The average accuracy of SOPP in terms of Hit@10 is 0.493, while the average accuracy of the mean initial models is 0.388. The relative improvement is calculated as (0.493 - 0.388) / 0.388 * 100%. So, the answer is {\"answer\": \"27%\"}." } ], "images": [ "scigen/train/3956.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Hit@1 and Hit@10 accuracies of the analogy test. SOLS and SOPP columns show the accuracies of the combined models. Mean W, Min W and Max W show the mean, minimum and maximum accuracies of the initial models Wi, respectively. The best accuracy among the combined models and the mean of the initial models is given in bold. The last row shows the average accuracies over all embedding sizes." ], "reasoning": "The average accuracy of SOPP in terms of Hit@10 is 0.493, while the average accuracy of the mean initial models is 0.388. The relative improvement is calculated as (0.493 - 0.388) / 0.388 * 100%.", "question": "What is the relative improvement in average accuracy of SOPP over the mean accuracies of initial models in terms of Hit@10?", "answer": "So, the answer is {\"answer\": \"27%\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe SOPP model has a higher Hit@1 accuracy than the maximum of the best initial models in all dimensions.\n" }, { "role": "assistant", "content": "The table shows that the Hit@1 accuracy of SOPP is higher than the Hit@1 Max W in all dimensions (50, 100, 150, 200, 250, 300). Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3956.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Hit@1 and Hit@10 accuracies of the analogy test. SOLS and SOPP columns show the accuracies of the combined models. Mean W, Min W and Max W show the mean, minimum and maximum accuracies of the initial models Wi, respectively. The best accuracy among the combined models and the mean of the initial models is given in bold. The last row shows the average accuracies over all embedding sizes." ], "reasoning": "The table shows that the Hit@1 accuracy of SOPP is higher than the Hit@1 Max W in all dimensions (50, 100, 150, 200, 250, 300).", "statement": "The SOPP model has a higher Hit@1 accuracy than the maximum of the best initial models in all dimensions.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 8: Overall results on CLOTH. Ex. denotes external data.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "Our model achieves the highest scores among models that do not use external data on CLOTH, CLOTH-H, and CLOTH-M.Our representativeness model outperforms all other models that do not use external data on CLOTH, CLOTH-H and CLOTH-M." } ], "images": [ "scigen/train/3957.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Overall results on CLOTH. Ex. denotes external data." ], "reasoning": "Our model achieves the highest scores among models that do not use external data on CLOTH, CLOTH-H, and CLOTH-M.", "description": "Our representativeness model outperforms all other models that do not use external data on CLOTH, CLOTH-H and CLOTH-M.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 8: Overall results on CLOTH. Ex. denotes external data.\nQuestion: Which model that does not use external data has the highest score on CLOTH-M?\nSolve the above question based on the table information. Let's think step by step. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "Identify the models that do not use external data (Ex. = No) and compare their scores on CLOTH-M. Our model has the highest score of 0.673. So, the answer is {\"answer\": \"Our model\"}." } ], "images": [ "scigen/train/3957.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Overall results on CLOTH. Ex. denotes external data." ], "reasoning": "Identify the models that do not use external data (Ex. = No) and compare their scores on CLOTH-M. Our model has the highest score of 0.673.", "question": "Which model that does not use external data has the highest score on CLOTH-M?", "answer": "So, the answer is {\"answer\": \"Our model\"}." }, { "messages": [ { "role": "user", "content": "Considering the claim The LSTM model has a higher score than the Stanford AR model on CLOTH., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe LSTM model has a higher score than the Stanford AR model on CLOTH.\n" }, { "role": "assistant", "content": "Compare the scores of the LSTM model and the Stanford AR model on CLOTH. The LSTM model has a score of 0.484, which is lower than the Stanford AR model's score of 0.487. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3957.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Overall results on CLOTH. Ex. denotes external data." ], "reasoning": "Compare the scores of the LSTM model and the Stanford AR model on CLOTH. The LSTM model has a score of 0.484, which is lower than the Stanford AR model's score of 0.487.", "statement": "The LSTM model has a higher score than the Stanford AR model on CLOTH.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 6: Humans’ performance compared with 1-billion-language-model. In the short context part, both 1B-LM and humans only use information of one sentence. In the long context part, humans have the whole passage as the context, while 1B-LM uses contexts of three sentences.'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "The LM performs well on short-term cloze questions but shows a significant performance gap in long-term contexts, indicating a limitation in long-term reasoning.Hence we conclude that the LM can almost perfectly solve all short-term cloze questions. However, the performance of LM is not improved significantly when a long-term context is given, indicating that the performance gap is due to the inability of long-term reasoning." } ], "images": [ "scigen/train/3958.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Humans’ performance compared with 1-billion-language-model. In the short context part, both 1B-LM and humans only use information of one sentence. In the long context part, humans have the whole passage as the context, while 1B-LM uses contexts of three sentences." ], "reasoning": "The LM performs well on short-term cloze questions but shows a significant performance gap in long-term contexts, indicating a limitation in long-term reasoning.", "description": "Hence we conclude that the LM can almost perfectly solve all short-term cloze questions. However, the performance of LM is not improved significantly when a long-term context is given, indicating that the performance gap is due to the inability of long-term reasoning.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 6: Humans’ performance compared with 1-billion-language-model. In the short context part, both 1B-LM and humans only use information of one sentence. In the long context part, humans have the whole passage as the context, while 1B-LM uses contexts of three sentences.' to sequentially solve the question presented below. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n\nWhat is the performance difference between 1B-LM and Human models on the CLOTH dataset with long context?\n" }, { "role": "assistant", "content": "To find the performance difference, subtract the 1B-LM score from the Human score for the CLOTH dataset with long context: 0.859 - 0.707. So, the answer is {\"answer\": \"0.152\"}." } ], "images": [ "scigen/train/3958.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Humans’ performance compared with 1-billion-language-model. In the short context part, both 1B-LM and humans only use information of one sentence. In the long context part, humans have the whole passage as the context, while 1B-LM uses contexts of three sentences." ], "reasoning": "To find the performance difference, subtract the 1B-LM score from the Human score for the CLOTH dataset with long context: 0.859 - 0.707.", "question": "What is the performance difference between 1B-LM and Human models on the CLOTH dataset with long context?", "answer": "So, the answer is {\"answer\": \"0.152\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe 1B-LM model performs better than the Human model on the CLOTH-M dataset with short context.\n" }, { "role": "assistant", "content": "Compare the scores of the 1B-LM and Human models on the CLOTH-M dataset with short context. The 1B-LM score is 0.723, and the Human score is 0.771. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3958.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Humans’ performance compared with 1-billion-language-model. In the short context part, both 1B-LM and humans only use information of one sentence. In the long context part, humans have the whole passage as the context, while 1B-LM uses contexts of three sentences." ], "reasoning": "Compare the scores of the 1B-LM and Human models on the CLOTH-M dataset with short context. The 1B-LM score is 0.723, and the Human score is 0.771.", "statement": "The 1B-LM model performs better than the Human model on the CLOTH-M dataset with short context.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 6: Comparison of results using large and small corpora. The small corpus is uniformly sampled from the Japanese Wikipedia (100MB). The large corpus is the entire Japanese Wikipedia (2.9GB).', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The size of the source language model affects sentiment analysis performance, especially for low-resource languages, requiring a large corpus for effective pre-training of BERT.We also investigate whether the size of the source language model affects the sentiment analysis performance on the Yahoo dataset. This is especially important for low-resource languages that do not usually have large amounts of data available for training. Thus, for effective sentiment analysis, a large corpus is required for pre-training BERT." } ], "images": [ "scigen/train/3959.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Comparison of results using large and small corpora. The small corpus is uniformly sampled from the Japanese Wikipedia (100MB). The large corpus is the entire Japanese Wikipedia (2.9GB)." ], "reasoning": "The size of the source language model affects sentiment analysis performance, especially for low-resource languages, requiring a large corpus for effective pre-training of BERT.", "description": "We also investigate whether the size of the source language model affects the sentiment analysis performance on the Yahoo dataset. This is especially important for low-resource languages that do not usually have large amounts of data available for training. Thus, for effective sentiment analysis, a large corpus is required for pre-training BERT.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 6: Comparison of results using large and small corpora. The small corpus is uniformly sampled from the Japanese Wikipedia (100MB). The large corpus is the entire Japanese Wikipedia (2.9GB).' to sequentially solve the question presented below. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nWhich model achieved the lowest error rate on the Yahoo dataset?\n" }, { "role": "assistant", "content": "By examining the table, we see that BERT BASE achieved the lowest error rate of 8.42 on the Yahoo dataset. So, the answer is {\"answer\": \"BERT BASE\"}" } ], "images": [ "scigen/train/3959.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Comparison of results using large and small corpora. The small corpus is uniformly sampled from the Japanese Wikipedia (100MB). The large corpus is the entire Japanese Wikipedia (2.9GB)." ], "reasoning": "By examining the table, we see that BERT BASE achieved the lowest error rate of 8.42 on the Yahoo dataset.", "question": "Which model achieved the lowest error rate on the Yahoo dataset?", "answer": "So, the answer is {\"answer\": \"BERT BASE\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nBERT BASE [100MB] achieved the highest error rate on the Yahoo dataset.\n" }, { "role": "assistant", "content": "The table shows that BERT BASE [100MB] has an error rate of 14.26, which is the highest among the listed models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3959.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Comparison of results using large and small corpora. The small corpus is uniformly sampled from the Japanese Wikipedia (100MB). The large corpus is the entire Japanese Wikipedia (2.9GB)." ], "reasoning": "The table shows that BERT BASE [100MB] has an error rate of 14.26, which is the highest among the listed models.", "statement": "BERT BASE [100MB] achieved the highest error rate on the Yahoo dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Rakuten test results, in error percentages. Best results from other models (GlyphNet to Super Character) obtained from Zhang and LeCun (2017) and Sun et al. (2018)'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "ELMo+BCN and ULMFiT were trained for 10 epochs each, and BERT was trained for 3 epochs, with all transfer learning methods outperforming previous methods on both Rakuten datasets.We trained ELMo+BCN and ULMFiT on the Rakuten datasets for 10 epochs each and selected the one that performed best. Since BERT fine-tunes all of its layers, we only train for 3 epochs as suggested by Devlin et al. All transfer learning-based methods outperform previous methods on both datasets, showing that these methods still work well without being fine-tuned on target corpora." } ], "images": [ "scigen/train/3960.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Rakuten test results, in error percentages. Best results from other models (GlyphNet to Super Character) obtained from Zhang and LeCun (2017) and Sun et al. (2018)" ], "reasoning": "ELMo+BCN and ULMFiT were trained for 10 epochs each, and BERT was trained for 3 epochs, with all transfer learning methods outperforming previous methods on both Rakuten datasets.", "description": "We trained ELMo+BCN and ULMFiT on the Rakuten datasets for 10 epochs each and selected the one that performed best. Since BERT fine-tunes all of its layers, we only train for 3 epochs as suggested by Devlin et al. All transfer learning-based methods outperform previous methods on both datasets, showing that these methods still work well without being fine-tuned on target corpora.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the table of 'Table 2: Rakuten test results, in error percentages. Best results from other models (GlyphNet to Super Character) obtained from Zhang and LeCun (2017) and Sun et al. (2018)', unfold the question presented below, detailing every step of your calculation. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhich model had the best performance on the Rakuten Full dataset?\n" }, { "role": "assistant", "content": "By comparing the performance of all models on the Rakuten Full dataset, BERT BASE had the lowest error rate of 40.68, indicating the best performance. So, the answer is {\"answer\": \"BERT BASE\"}" } ], "images": [ "scigen/train/3960.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Rakuten test results, in error percentages. Best results from other models (GlyphNet to Super Character) obtained from Zhang and LeCun (2017) and Sun et al. (2018)" ], "reasoning": "By comparing the performance of all models on the Rakuten Full dataset, BERT BASE had the lowest error rate of 40.68, indicating the best performance.", "question": "Which model had the best performance on the Rakuten Full dataset?", "answer": "So, the answer is {\"answer\": \"BERT BASE\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nULMFiT outperformed all other models on the Rakuten Binary dataset.\n" }, { "role": "assistant", "content": "The table shows that ULMFiT had the lowest error rate of 4.45 on the Rakuten Binary dataset, indicating it outperformed all other models. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3960.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Rakuten test results, in error percentages. Best results from other models (GlyphNet to Super Character) obtained from Zhang and LeCun (2017) and Sun et al. (2018)" ], "reasoning": "The table shows that ULMFiT had the lowest error rate of 4.45 on the Rakuten Binary dataset, indicating it outperformed all other models.", "statement": "ULMFiT outperformed all other models on the Rakuten Binary dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 3: Yahoo test results, in error percentages.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The Yahoo dataset is much smaller than the Rakuten binary dataset, and transfer-learning methods work better on it, representing real-life situations.The Yahoo dataset is approximately 112 times smaller than the Rakuten binary dataset. We believe that this dataset better represents real life/practical situations. Similar to rakuten datasets, transfer-learning based methods works better." } ], "images": [ "scigen/train/3961.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Yahoo test results, in error percentages." ], "reasoning": "The Yahoo dataset is much smaller than the Rakuten binary dataset, and transfer-learning methods work better on it, representing real-life situations.", "description": "The Yahoo dataset is approximately 112 times smaller than the Rakuten binary dataset. We believe that this dataset better represents real life/practical situations. Similar to rakuten datasets, transfer-learning based methods works better.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 3: Yahoo test results, in error percentages.\nQuestion: Which model has the lowest score on the Yahoo Binary dataset?\nGive a detailed response to the above question. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By examining the table, we can see that the BERT BASE model has the lowest score of 8.42 on the Yahoo Binary dataset. So, the answer is {\"answer\": \"BERT BASE\"}." } ], "images": [ "scigen/train/3961.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Yahoo test results, in error percentages." ], "reasoning": "By examining the table, we can see that the BERT BASE model has the lowest score of 8.42 on the Yahoo Binary dataset.", "question": "Which model has the lowest score on the Yahoo Binary dataset?", "answer": "So, the answer is {\"answer\": \"BERT BASE\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe RNN Baseline model has the highest score on the Yahoo Binary dataset.\n" }, { "role": "assistant", "content": "The table shows that the RNN Baseline model has a score of 35.29, which is the highest among all the models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3961.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Yahoo test results, in error percentages." ], "reasoning": "The table shows that the RNN Baseline model has a score of 35.29, which is the highest among all the models listed.", "statement": "The RNN Baseline model has the highest score on the Yahoo Binary dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Given a table with the title 'Table 5: Low-shot learning results for the Yahoo dataset, in error percentages. Transfer learning-based methods are trained on 13 of the total dataset, while the other models are trained on the whole dataset.', I need you to craft sentences describing the table, pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "With only 13% of the Yahoo dataset, ULMFiT and BERT outperform task-specific models, while BCN+ELMo shows comparable results, indicating significant learning during transfer learning.Low-shot learning refers to the practice of feeding a model with a small amount of training data, contrary to the normal practice of using a large amount of data. We chose the Yahoo dataset for this experiment due to its small size. that, with only 13 of the total dataset, ULMFiT and BERT perform better than task-specific models, while BCN+ELMo shows a comparable result. Clearly, this shows that the models have learned significantly during the transfer learning process." } ], "images": [ "scigen/train/3962.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Low-shot learning results for the Yahoo dataset, in error percentages. Transfer learning-based methods are trained on 13 of the total dataset, while the other models are trained on the whole dataset." ], "reasoning": "With only 13% of the Yahoo dataset, ULMFiT and BERT outperform task-specific models, while BCN+ELMo shows comparable results, indicating significant learning during transfer learning.", "description": "Low-shot learning refers to the practice of feeding a model with a small amount of training data, contrary to the normal practice of using a large amount of data. We chose the Yahoo dataset for this experiment due to its small size. that, with only 13 of the total dataset, ULMFiT and BERT perform better than task-specific models, while BCN+ELMo shows a comparable result. Clearly, this shows that the models have learned significantly during the transfer learning process.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve this question according to the provided table of 'Table 5: Low-shot learning results for the Yahoo dataset, in error percentages. Transfer learning-based methods are trained on 13 of the total dataset, while the other models are trained on the whole dataset.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieved the lowest error rate on the Yahoo Binary dataset?\n" }, { "role": "assistant", "content": "By examining the table, BERT BASE achieved the lowest error rate with a value of 10.14. So, the answer is {\"answer\": \"BERT BASE\"}" } ], "images": [ "scigen/train/3962.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Low-shot learning results for the Yahoo dataset, in error percentages. Transfer learning-based methods are trained on 13 of the total dataset, while the other models are trained on the whole dataset." ], "reasoning": "By examining the table, BERT BASE achieved the lowest error rate with a value of 10.14.", "question": "Which model achieved the lowest error rate on the Yahoo Binary dataset?", "answer": "So, the answer is {\"answer\": \"BERT BASE\"}" }, { "messages": [ { "role": "user", "content": "Regarding the sentence: BCN+ELMo performed better than KimCNN on the Yahoo Binary dataset., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nBCN+ELMo performed better than KimCNN on the Yahoo Binary dataset.\n" }, { "role": "assistant", "content": "The table shows that BCN+ELMo has an error rate of 13.51, which is lower than KimCNN's error rate of 14.25, indicating better performance. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3962.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Low-shot learning results for the Yahoo dataset, in error percentages. Transfer learning-based methods are trained on 13 of the total dataset, while the other models are trained on the whole dataset." ], "reasoning": "The table shows that BCN+ELMo has an error rate of 13.51, which is lower than KimCNN's error rate of 14.25, indicating better performance.", "statement": "BCN+ELMo performed better than KimCNN on the Yahoo Binary dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 7: Results from different parameter updating strategies. BCN+ELMo and BERTBASE are original implementations. BCN+ELMo unfreeze shows experimental results of fine-tuning both BCN and ELMo layers on target dataset while BERTBASE freeze is where BERTBASE layers are frozen and only classifier layer fine-tuned on target dataset', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Fine-tuning BERT involves training all layers, making it computationally cheaper and faster, while ELMo's layers are typically frozen, but unfreezing them can improve performance.In its original implementation, when BERT is fine-tuned, all of its layers are trained. This is quite different from fine-tuning ELMo, where its layers are frozen and only task-specific models ( BCN in our case) are updated. The hidden state associated to the first character of the input is pooled and provided to a linear layer that sits on top. This way, BERT is computationally much cheaper and faster. Result shows that using BERT as a feature extractor shows competitive performance. Unfreezing ELMo Pre-trained ELMo weights are used for initialization as well; however, weights are changed along with BCN layers. This experiment allows us to compare the performance of freezing/unfreezing ELMo layers." } ], "images": [ "scigen/train/3963.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Results from different parameter updating strategies. BCN+ELMo and BERTBASE are original implementations. BCN+ELMo unfreeze shows experimental results of fine-tuning both BCN and ELMo layers on target dataset while BERTBASE freeze is where BERTBASE layers are frozen and only classifier layer fine-tuned on target dataset" ], "reasoning": "Fine-tuning BERT involves training all layers, making it computationally cheaper and faster, while ELMo's layers are typically frozen, but unfreezing them can improve performance.", "description": "In its original implementation, when BERT is fine-tuned, all of its layers are trained. This is quite different from fine-tuning ELMo, where its layers are frozen and only task-specific models ( BCN in our case) are updated. The hidden state associated to the first character of the input is pooled and provided to a linear layer that sits on top. This way, BERT is computationally much cheaper and faster. Result shows that using BERT as a feature extractor shows competitive performance. Unfreezing ELMo Pre-trained ELMo weights are used for initialization as well; however, weights are changed along with BCN layers. This experiment allows us to compare the performance of freezing/unfreezing ELMo layers.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 7: Results from different parameter updating strategies. BCN+ELMo and BERTBASE are original implementations. BCN+ELMo unfreeze shows experimental results of fine-tuning both BCN and ELMo layers on target dataset while BERTBASE freeze is where BERTBASE layers are frozen and only classifier layer fine-tuned on target dataset', and sequentially solve the question, writing out each step of your reasoning process. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model achieved the lowest Yahoo Binary score when fine-tuned?\n" }, { "role": "assistant", "content": "The table shows the Yahoo Binary scores for different models. The lowest score is 8.42, achieved by BERT BASE. So, the answer is {\"answer\": \"BERT BASE\"}." } ], "images": [ "scigen/train/3963.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Results from different parameter updating strategies. BCN+ELMo and BERTBASE are original implementations. BCN+ELMo unfreeze shows experimental results of fine-tuning both BCN and ELMo layers on target dataset while BERTBASE freeze is where BERTBASE layers are frozen and only classifier layer fine-tuned on target dataset" ], "reasoning": "The table shows the Yahoo Binary scores for different models. The lowest score is 8.42, achieved by BERT BASE.", "question": "Which model achieved the lowest Yahoo Binary score when fine-tuned?", "answer": "So, the answer is {\"answer\": \"BERT BASE\"}." }, { "messages": [ { "role": "user", "content": "Freezing BERT BASE results in a higher Yahoo Binary score compared to unfreezing BCN+ELMo. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nFreezing BERT BASE results in a higher Yahoo Binary score compared to unfreezing BCN+ELMo.\n" }, { "role": "assistant", "content": "The table shows that BERT BASE freeze has a Yahoo Binary score of 10.68, while BCN+ELMo unfreeze has a score of 8.65, which is lower. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3963.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Results from different parameter updating strategies. BCN+ELMo and BERTBASE are original implementations. BCN+ELMo unfreeze shows experimental results of fine-tuning both BCN and ELMo layers on target dataset while BERTBASE freeze is where BERTBASE layers are frozen and only classifier layer fine-tuned on target dataset" ], "reasoning": "The table shows that BERT BASE freeze has a Yahoo Binary score of 10.68, while BCN+ELMo unfreeze has a score of 8.65, which is lower.", "statement": "Freezing BERT BASE results in a higher Yahoo Binary score compared to unfreezing BCN+ELMo.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 3: Image Description: We compare the effect of pre-training the lexical classifier and language model with different unpaired image and text data sets. As expected, we see the best result when using in domain MSCOCO data to train the lexical classifier and language model, though training with out of domain corpora is comparable. (Values in %)', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Our model achieves the best performance with in-domain data for all stages, but similar results are obtained using ImageNet for the lexical classifier and CaptionTxt or WebCorpus for the language model.Our model performs best when provided with in-domain image and text for all training stages, but performance is comparable when using ImageNet images to train the lexical classifier and CaptionTxt or WebCorpus text data to train the language model." } ], "images": [ "scigen/train/3964.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Image Description: We compare the effect of pre-training the lexical classifier and language model with different unpaired image and text data sets. As expected, we see the best result when using in domain MSCOCO data to train the lexical classifier and language model, though training with out of domain corpora is comparable. (Values in %)" ], "reasoning": "Our model achieves the best performance with in-domain data for all stages, but similar results are obtained using ImageNet for the lexical classifier and CaptionTxt or WebCorpus for the language model.", "description": "Our model performs best when provided with in-domain image and text for all training stages, but performance is comparable when using ImageNet images to train the lexical classifier and CaptionTxt or WebCorpus text data to train the language model.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 3: Image Description: We compare the effect of pre-training the lexical classifier and language model with different unpaired image and text data sets. As expected, we see the best result when using in domain MSCOCO data to train the lexical classifier and language model, though training with out of domain corpora is comparable. (Values in %)', solve this question and give detail solutions. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhat is the F1 score when using ImageNet images for the lexical classifier and CaptionTxt text data for the language model?\n" }, { "role": "assistant", "content": "To find the F1 score, locate the row where the lexical classifier is ImageNet and the language model is CaptionTxt. The F1 score in this row is 35.53. So, the answer is {\"answer\": \"35.53\"}." } ], "images": [ "scigen/train/3964.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Image Description: We compare the effect of pre-training the lexical classifier and language model with different unpaired image and text data sets. As expected, we see the best result when using in domain MSCOCO data to train the lexical classifier and language model, though training with out of domain corpora is comparable. (Values in %)" ], "reasoning": "To find the F1 score, locate the row where the lexical classifier is ImageNet and the language model is CaptionTxt. The F1 score in this row is 35.53.", "question": "What is the F1 score when using ImageNet images for the lexical classifier and CaptionTxt text data for the language model?", "answer": "So, the answer is {\"answer\": \"35.53\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Present the final answer in a JSON format {\"answer\": \"\"} like {\"answer\": \"supports\"}.\n\nThe highest METEOR score is achieved when using ImageNet images for the lexical classifier and WebCorpus text data for the language model.\n" }, { "role": "assistant", "content": "To verify this statement, compare the METEOR scores for all combinations. The highest METEOR score is 21.00, achieved with MSCOCO for both lexical classifier and language model, not with ImageNet and WebCorpus. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3964.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Image Description: We compare the effect of pre-training the lexical classifier and language model with different unpaired image and text data sets. As expected, we see the best result when using in domain MSCOCO data to train the lexical classifier and language model, though training with out of domain corpora is comparable. (Values in %)" ], "reasoning": "To verify this statement, compare the METEOR scores for all combinations. The highest METEOR score is 21.00, achieved with MSCOCO for both lexical classifier and language model, not with ImageNet and WebCorpus.", "statement": "The highest METEOR score is achieved when using ImageNet images for the lexical classifier and WebCorpus text data for the language model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 1: We compare DCC before transfer (No Transfer) to DCC with delta transfer (ΔT) and DCC with direct transfer (DT). We also compare to another competitive caption generation model (LRCN). We measure our models ability to insert new words into a generated sentence with the F1-score. We also report Bleu-1 and METEOR, which indicates overall sentence quality. DCC successfully incorporates new words and improves sentence quality. (Values in %)'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The direct transfer method outperforms the delta transfer method in F1, BLEU-1, and METEOR scores, indicating better overall performance.Direct Transfer Versus Delta Transfer. across the eight held-out training classes for the delta transfer and direct transfer methods. We also report the BLEU-1 score, which measures the overlap between generated words and words in reference sentences. By measuring the METEOR score, we ensure that our model maintains sentence fluency when inserting new objects. DCC consistently increases METEOR scores indicating that overall sentence quality improves with DCC. The direct transfer method improves F1-scores, BLEU, and METEOR scores by a larger amount than the delta transfer method and is thus used for the remainder of our experiments." } ], "images": [ "scigen/train/3965.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: We compare DCC before transfer (No Transfer) to DCC with delta transfer (ΔT) and DCC with direct transfer (DT). We also compare to another competitive caption generation model (LRCN). We measure our models ability to insert new words into a generated sentence with the F1-score. We also report Bleu-1 and METEOR, which indicates overall sentence quality. DCC successfully incorporates new words and improves sentence quality. (Values in %)" ], "reasoning": "The direct transfer method outperforms the delta transfer method in F1, BLEU-1, and METEOR scores, indicating better overall performance.", "description": "Direct Transfer Versus Delta Transfer. across the eight held-out training classes for the delta transfer and direct transfer methods. We also report the BLEU-1 score, which measures the overlap between generated words and words in reference sentences. By measuring the METEOR score, we ensure that our model maintains sentence fluency when inserting new objects. DCC consistently increases METEOR scores indicating that overall sentence quality improves with DCC. The direct transfer method improves F1-scores, BLEU, and METEOR scores by a larger amount than the delta transfer method and is thus used for the remainder of our experiments.", "answer": "" }, { "messages": [ { "role": "user", "content": "Given the table about 'Table 1: We compare DCC before transfer (No Transfer) to DCC with delta transfer (ΔT) and DCC with direct transfer (DT). We also compare to another competitive caption generation model (LRCN). We measure our models ability to insert new words into a generated sentence with the F1-score. We also report Bleu-1 and METEOR, which indicates overall sentence quality. DCC successfully incorporates new words and improves sentence quality. (Values in %)', solve the following question step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich method shows the highest improvement in METEOR score compared to No Transfer?\n" }, { "role": "assistant", "content": "By comparing the METEOR scores, the No Transfer method has a score of 19.9, while the direct transfer (DT) method has a score of 21.00. The delta transfer (ΔT) method has a score of 20.86. The DT method shows the highest improvement. So, the answer is {\"answer\": \"DT\"}." } ], "images": [ "scigen/train/3965.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: We compare DCC before transfer (No Transfer) to DCC with delta transfer (ΔT) and DCC with direct transfer (DT). We also compare to another competitive caption generation model (LRCN). We measure our models ability to insert new words into a generated sentence with the F1-score. We also report Bleu-1 and METEOR, which indicates overall sentence quality. DCC successfully incorporates new words and improves sentence quality. (Values in %)" ], "reasoning": "By comparing the METEOR scores, the No Transfer method has a score of 19.9, while the direct transfer (DT) method has a score of 21.00. The delta transfer (ΔT) method has a score of 20.86. The DT method shows the highest improvement.", "question": "Which method shows the highest improvement in METEOR score compared to No Transfer?", "answer": "So, the answer is {\"answer\": \"DT\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The delta transfer method has a higher BLEU-1 score than the direct transfer method. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe delta transfer method has a higher BLEU-1 score than the direct transfer method.\n" }, { "role": "assistant", "content": "The table shows that the BLEU-1 score for the delta transfer (ΔT) method is 64.00, while the BLEU-1 score for the direct transfer (DT) method is 64.40. Therefore, the statement is incorrect. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3965.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: We compare DCC before transfer (No Transfer) to DCC with delta transfer (ΔT) and DCC with direct transfer (DT). We also compare to another competitive caption generation model (LRCN). We measure our models ability to insert new words into a generated sentence with the F1-score. We also report Bleu-1 and METEOR, which indicates overall sentence quality. DCC successfully incorporates new words and improves sentence quality. (Values in %)" ], "reasoning": "The table shows that the BLEU-1 score for the delta transfer (ΔT) method is 64.00, while the BLEU-1 score for the direct transfer (DT) method is 64.40. Therefore, the statement is incorrect.", "statement": "The delta transfer method has a higher BLEU-1 score than the direct transfer method.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 1: L2 norms for selected concepts from the WordNet hierarchy. Input: fastText; lear: D2.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table, pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The norms after specialisation indicate a hierarchical ordering on the concepts, unlike the original SGNS model which shows no correlation with the desired hierarchical ordering.To verify that the knowledge concerning the position in the semantic hierarchy actually arises from vector norms, we also manually inspect the norms after lear specialisation. They indicate a desirable pattern in the norm values which imposes a hierarchical ordering on the concepts. Note that the original distributional SGNS model Mikolov et al. However, these norms are not at all correlated with the desired hierarchical ordering, and are therefore useless for le-related applications: the non-specialised distributional SGNS model scores 0.44, 0.48, and 0.34 on the three tasks, respectively." } ], "images": [ "scigen/train/3966.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: L2 norms for selected concepts from the WordNet hierarchy. Input: fastText; lear: D2." ], "reasoning": "The norms after specialisation indicate a hierarchical ordering on the concepts, unlike the original SGNS model which shows no correlation with the desired hierarchical ordering.", "description": "To verify that the knowledge concerning the position in the semantic hierarchy actually arises from vector norms, we also manually inspect the norms after lear specialisation. They indicate a desirable pattern in the norm values which imposes a hierarchical ordering on the concepts. Note that the original distributional SGNS model Mikolov et al. However, these norms are not at all correlated with the desired hierarchical ordering, and are therefore useless for le-related applications: the non-specialised distributional SGNS model scores 0.44, 0.48, and 0.34 on the three tasks, respectively.", "answer": "" }, { "messages": [ { "role": "user", "content": "Using the displayed table concerning the 'Table 1: L2 norms for selected concepts from the WordNet hierarchy. Input: fastText; lear: D2.', solve the subsequent question in a stepwise manner. Conclude your response with a final answer in the JSON format {\"answer\": \"\"}.\n\nWhat is the norm value for the concept 'animal' under the 'laptop' category?\n" }, { "role": "assistant", "content": "To find the norm value for 'animal' under the 'laptop' category, locate the row for 'animal' and the column for 'laptop'. The intersection gives the norm value. So, the answer is {\"answer\": \"17.71\"}." } ], "images": [ "scigen/train/3966.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: L2 norms for selected concepts from the WordNet hierarchy. Input: fastText; lear: D2." ], "reasoning": "To find the norm value for 'animal' under the 'laptop' category, locate the row for 'animal' and the column for 'laptop'. The intersection gives the norm value.", "question": "What is the norm value for the concept 'animal' under the 'laptop' category?", "answer": "So, the answer is {\"answer\": \"17.71\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe norm value for 'vertebrate' under the 'terrier' category is higher than that under the 'cabriolet' category.\n" }, { "role": "assistant", "content": "To verify this statement, compare the norm values for 'vertebrate' under the 'terrier' and 'cabriolet' categories. The norm value under 'terrier' is 10.96, and under 'cabriolet' it is 8.01. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3966.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: L2 norms for selected concepts from the WordNet hierarchy. Input: fastText; lear: D2." ], "reasoning": "To verify this statement, compare the norm values for 'vertebrate' under the 'terrier' and 'cabriolet' categories. The norm value under 'terrier' is 10.96, and under 'cabriolet' it is 8.01.", "statement": "The norm value for 'vertebrate' under the 'terrier' category is higher than that under the 'cabriolet' category.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled '(c) Summary', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Despite high human agreement on HyperLex (0.85), current state-of-the-art models achieve significantly lower scores, with the highest being 0.686.As shown by the high inter-annotator agreement on HyperLex (0.85), humans are able to consistently reason about graded le. However, current state-of-the-art representation architectures are far from this ceiling. For instance, \\newciteVulic:2017cl evaluate a plethora of architectures and report a high-score of only 0.320 Two recent representation models Nickel and Kiela (on the noun subset)." } ], "images": [ "scigen/train/3967.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "(c) Summary" ], "reasoning": "Despite high human agreement on HyperLex (0.85), current state-of-the-art models achieve significantly lower scores, with the highest being 0.686.", "description": "As shown by the high inter-annotator agreement on HyperLex (0.85), humans are able to consistently reason about graded le. However, current state-of-the-art representation architectures are far from this ceiling. For instance, \\newciteVulic:2017cl evaluate a plethora of architectures and report a high-score of only 0.320 Two recent representation models Nickel and Kiela (on the noun subset).", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to '(c) Summary', and sequentially solve the question, writing out each step of your reasoning process. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\nWhat is the highest score achieved by the representation models evaluated by Vulic:2017cl?\n" }, { "role": "assistant", "content": "The table lists various representation models and their scores. The highest score in the table is 0.686, achieved by 'Best lear'. So, the answer is {\"answer\": \"0.686\"}." } ], "images": [ "scigen/train/3967.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "(c) Summary" ], "reasoning": "The table lists various representation models and their scores. The highest score in the table is 0.686, achieved by 'Best lear'.", "question": "What is the highest score achieved by the representation models evaluated by Vulic:2017cl?", "answer": "So, the answer is {\"answer\": \"0.686\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence The highest score achieved by the representation models is 0.320. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe highest score achieved by the representation models is 0.320.\n" }, { "role": "assistant", "content": "The table shows that the highest score achieved by the representation models is 0.686, not 0.320. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3967.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "(c) Summary" ], "reasoning": "The table shows that the highest score achieved by the representation models is 0.686, not 0.320.", "statement": "The highest score achieved by the representation models is 0.320.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Analysing the importance of the synergy in the full lear model on the final performance on wbless, bless, HyperLex-All (hl-a) and HyperLex-Nouns (hl-n). Input: fastText. D2.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "The full lear model, which combines both symmetric and asymmetric terms, outperforms the individual sym-only and asym-only variants across all LE tasks.Why Symmetric + Asymmetric? We compare three variants of lear: 1) a symmetric variant which does not arrange vector norms using the LEj(BL) term (sym-only); 2) a variant which arranges norms, but does not use le constraints as additional symmetric attract constraints (asym-only); and 3) the full lear model, which uses both cost terms (full). This table shows that, while the stand-alone asym-only term seems more beneficial than the sym-only one, using the two terms jointly yields the strongest performance across all LE tasks." } ], "images": [ "scigen/train/3968.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Analysing the importance of the synergy in the full lear model on the final performance on wbless, bless, HyperLex-All (hl-a) and HyperLex-Nouns (hl-n). Input: fastText. D2." ], "reasoning": "The full lear model, which combines both symmetric and asymmetric terms, outperforms the individual sym-only and asym-only variants across all LE tasks.", "description": "Why Symmetric + Asymmetric? We compare three variants of lear: 1) a symmetric variant which does not arrange vector norms using the LEj(BL) term (sym-only); 2) a variant which arranges norms, but does not use le constraints as additional symmetric attract constraints (asym-only); and 3) the full lear model, which uses both cost terms (full). This table shows that, while the stand-alone asym-only term seems more beneficial than the sym-only one, using the two terms jointly yields the strongest performance across all LE tasks.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich lear variant achieves the highest performance in the hl-a task?\nSolve the above question based on the table titled 'Table 2: Analysing the importance of the synergy in the full lear model on the final performance on wbless, bless, HyperLex-All (hl-a) and HyperLex-Nouns (hl-n). Input: fastText. D2.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the performance of different lear variants across various tasks. The full lear model achieves the highest performance in the hl-a task with a score of 0.686. So, the answer is {\"answer\": \"full\"}." } ], "images": [ "scigen/train/3968.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Analysing the importance of the synergy in the full lear model on the final performance on wbless, bless, HyperLex-All (hl-a) and HyperLex-Nouns (hl-n). Input: fastText. D2." ], "reasoning": "The table shows the performance of different lear variants across various tasks. The full lear model achieves the highest performance in the hl-a task with a score of 0.686.", "question": "Which lear variant achieves the highest performance in the hl-a task?", "answer": "So, the answer is {\"answer\": \"full\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe sym-only variant outperforms the asym-only variant in the wbless task.\n" }, { "role": "assistant", "content": "The table shows that the sym-only variant has a score of 0.687 in the wbless task, while the asym-only variant has a higher score of 0.867. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3968.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Analysing the importance of the synergy in the full lear model on the final performance on wbless, bless, HyperLex-All (hl-a) and HyperLex-Nouns (hl-n). Input: fastText. D2." ], "reasoning": "The table shows that the sym-only variant has a score of 0.687 in the wbless task, while the asym-only variant has a higher score of 0.867. Therefore, the statement is refuted.", "statement": "The sym-only variant outperforms the asym-only variant in the wbless task.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table titled 'Table 6: Results on CNNDM summarization using ROUGE-1 (R-1), ROUGE-2 (R-2), and ROUGE-L (R-L). “36L” is short for “36-layer encoder”.'. Some table cells are highlighted with a distinct color. Please construct a concise description of the table.\n" }, { "role": "assistant", "content": "The deep Msc model outperforms both the Transformer and HierTrans models in ROUGE scores, showing significant improvements in summarization performance.Fine-tune BERT for extractive summarization. Lead3 is a baseline which simply selects the first three sentences as the summary. HibertM Zhang et al. PGNet See et al. We also implemented two baselines. One is the standard 6-layer Transformer model. We can see that the deep Msc leads to a +1.8 ROUGE improvement over Transformer. The other baseline is the hierarchical transformer summarization model (HierTrans), which involevs both a sentence-level and a document-level transformer encoders, as well as a standard transformer decoder. Note the settings for both encoders are the same (each of them have L=18, emb=512, ffn=1024, head=8). The deep Msc outperforms HierTrans by 0.5 to 0.7 ROUGE with the same depth of encoders." } ], "images": [ "scigen/train/3969.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Results on CNNDM summarization using ROUGE-1 (R-1), ROUGE-2 (R-2), and ROUGE-L (R-L). “36L” is short for “36-layer encoder”." ], "reasoning": "The deep Msc model outperforms both the Transformer and HierTrans models in ROUGE scores, showing significant improvements in summarization performance.", "description": "Fine-tune BERT for extractive summarization. Lead3 is a baseline which simply selects the first three sentences as the summary. HibertM Zhang et al. PGNet See et al. We also implemented two baselines. One is the standard 6-layer Transformer model. We can see that the deep Msc leads to a +1.8 ROUGE improvement over Transformer. The other baseline is the hierarchical transformer summarization model (HierTrans), which involevs both a sentence-level and a document-level transformer encoders, as well as a standard transformer decoder. Note the settings for both encoders are the same (each of them have L=18, emb=512, ffn=1024, head=8). The deep Msc outperforms HierTrans by 0.5 to 0.7 ROUGE with the same depth of encoders.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 6: Results on CNNDM summarization using ROUGE-1 (R-1), ROUGE-2 (R-2), and ROUGE-L (R-L). “36L” is short for “36-layer encoder”.'. Solve the following question based on the table.\n\nQuestion: Which model has the highest R-1 score in extractive summarization?\nLet's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "By comparing the R-1 scores in the extractive summarization section, Liu (2019) has the highest score of 43.25. So, the answer is {\"answer\": \"Liu (2019)\"}." } ], "images": [ "scigen/train/3969.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Results on CNNDM summarization using ROUGE-1 (R-1), ROUGE-2 (R-2), and ROUGE-L (R-L). “36L” is short for “36-layer encoder”." ], "reasoning": "By comparing the R-1 scores in the extractive summarization section, Liu (2019) has the highest score of 43.25.", "question": "Which model has the highest R-1 score in extractive summarization?", "answer": "So, the answer is {\"answer\": \"Liu (2019)\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe deep Msc model has the highest R-2 score in abstractive summarization.\n" }, { "role": "assistant", "content": "By examining the R-2 scores in the abstractive summarization section, the deep Msc model has the highest score of 19.50. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3969.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Results on CNNDM summarization using ROUGE-1 (R-1), ROUGE-2 (R-2), and ROUGE-L (R-L). “36L” is short for “36-layer encoder”." ], "reasoning": "By examining the R-2 scores in the abstractive summarization section, the deep Msc model has the highest score of 19.50.", "statement": "The deep Msc model has the highest R-2 score in abstractive summarization.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 5: Ablation study on IWSLT14 En→De task.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The ablation study shows that removing components like Cxt-Enc Attention or contextual collaboration reduces BLEU scores, and deeper models with more layers perform better than those with fewer layers.Ablation Study. We conduct ablation study to investigate the performance of each component of our model. This method achieves a 29.45 BLEU, which is lower than the best result. We additionally modify the implementation of the contextual collaboration cell Q(⋅) as \\textscFfn(⋅), which shows that the performance is reduced by 0.5 BLEU. (2) Removing Cxt-Enc Attention and/or contextual collaboration makes the BLEU score drop by ∼0.7, which suggests that multiscale collaboration helps in constructing extremely deep models. (3) Considering that the deep Msc introduces more parameters, we also train another two Msc models with about the same or double number of parameters: with 18/36 layers, embedding size 512 and Ffn layer dimension 1024. These models underperform the deeper 72-layer model, which shows that the number of parameters is not the key to the improvement." } ], "images": [ "scigen/train/3970.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Ablation study on IWSLT14 En→De task." ], "reasoning": "The ablation study shows that removing components like Cxt-Enc Attention or contextual collaboration reduces BLEU scores, and deeper models with more layers perform better than those with fewer layers.", "description": "Ablation Study. We conduct ablation study to investigate the performance of each component of our model. This method achieves a 29.45 BLEU, which is lower than the best result. We additionally modify the implementation of the contextual collaboration cell Q(⋅) as \\textscFfn(⋅), which shows that the performance is reduced by 0.5 BLEU. (2) Removing Cxt-Enc Attention and/or contextual collaboration makes the BLEU score drop by ∼0.7, which suggests that multiscale collaboration helps in constructing extremely deep models. (3) Considering that the deep Msc introduces more parameters, we also train another two Msc models with about the same or double number of parameters: with 18/36 layers, embedding size 512 and Ffn layer dimension 1024. These models underperform the deeper 72-layer model, which shows that the number of parameters is not the key to the improvement.", "answer": "" }, { "messages": [ { "role": "user", "content": "Consider the table regarding to 'Table 5: Ablation study on IWSLT14 En→De task.' to sequentially solve the question presented below. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\n\nWhat is the BLEU score when the contextual collaboration cell Q(⋅) is implemented as \textscFfn(⋅)?\n" }, { "role": "assistant", "content": "The table shows the BLEU score for different model configurations. The BLEU score when the contextual collaboration cell Q(⋅) is implemented as \textscFfn(⋅) is 29.17. So, the answer is {\"answer\": \"29.17\"}." } ], "images": [ "scigen/train/3970.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Ablation study on IWSLT14 En→De task." ], "reasoning": "The table shows the BLEU score for different model configurations. The BLEU score when the contextual collaboration cell Q(⋅) is implemented as \textscFfn(⋅) is 29.17.", "question": "What is the BLEU score when the contextual collaboration cell Q(⋅) is implemented as \textscFfn(⋅)?", "answer": "So, the answer is {\"answer\": \"29.17\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nRemoving Cxt-Enc Attention results in a BLEU score of 28.99.\n" }, { "role": "assistant", "content": "The table shows that removing Cxt-Enc Attention results in a BLEU score of 28.99. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3970.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Ablation study on IWSLT14 En→De task." ], "reasoning": "The table shows that removing Cxt-Enc Attention results in a BLEU score of 28.99.", "statement": "Removing Cxt-Enc Attention results in a BLEU score of 28.99.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 6: Validation accuracy of G-DAug with different labeling methods on WinoGrande-L and CommonsenseQA. Random relabeling assigns labels uniformly at random, and model relabeling assigns labels according to a task model. Random labels hurt accuracy, and model relabeling helps on WinoGrande but not on CommonsenseQA.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "The correctness of labels is crucial for G-DAug's performance, as shown by the significant drop in performance with random relabeling, and model relabeling improves performance in WinoGrande-L but slightly decreases it in CommonsenseQA.Even fully unsupervised language model pretraining can boost the performance of a task model, if performed on task-relevant data Gururangan et al. This raises the question of whether G-DAug boosts performance by simply exposing the model to more task-relevant text, or if the labels that G-DAug generates are in fact informative. A related question is whether G-DAug’s optional self-supervised relabeling improves performance. relabeling with a task model. When the generator labels are flipped randomly, G-DAug is unable to outperform the baselines for either dataset (and in fact dramatically underperforms the baseline on WinoGrande-L). This implies that the correctness of the labels plays an essential role in G-DAug. Relabeling with a task model provides a 1.5% absolute gain in WinoGrande-L, but a 0.4 point drop in CommonsenseQA." } ], "images": [ "scigen/train/3971.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: Validation accuracy of G-DAug with different labeling methods on WinoGrande-L and CommonsenseQA. Random relabeling assigns labels uniformly at random, and model relabeling assigns labels according to a task model. Random labels hurt accuracy, and model relabeling helps on WinoGrande but not on CommonsenseQA." ], "reasoning": "The correctness of labels is crucial for G-DAug's performance, as shown by the significant drop in performance with random relabeling, and model relabeling improves performance in WinoGrande-L but slightly decreases it in CommonsenseQA.", "description": "Even fully unsupervised language model pretraining can boost the performance of a task model, if performed on task-relevant data Gururangan et al. This raises the question of whether G-DAug boosts performance by simply exposing the model to more task-relevant text, or if the labels that G-DAug generates are in fact informative. A related question is whether G-DAug’s optional self-supervised relabeling improves performance. relabeling with a task model. When the generator labels are flipped randomly, G-DAug is unable to outperform the baselines for either dataset (and in fact dramatically underperforms the baseline on WinoGrande-L). This implies that the correctness of the labels plays an essential role in G-DAug. Relabeling with a task model provides a 1.5% absolute gain in WinoGrande-L, but a 0.4 point drop in CommonsenseQA.", "answer": "" }, { "messages": [ { "role": "user", "content": "Check the table regarding to 'Table 6: Validation accuracy of G-DAug with different labeling methods on WinoGrande-L and CommonsenseQA. Random relabeling assigns labels uniformly at random, and model relabeling assigns labels according to a task model. Random labels hurt accuracy, and model relabeling helps on WinoGrande but not on CommonsenseQA.', and sequentially solve the question, writing out each step of your reasoning process. Present the final answer in a JSON format, outlined as {\"answer\": \"\"}.\nWhat is the performance difference between the baseline and model relabeling methods for WinoGrande-L?\n" }, { "role": "assistant", "content": "The baseline performance for WinoGrande-L is 75.9, while the model relabeling performance is 77.7. The difference is 77.7 - 75.9. So, the answer is {\"answer\": \"1.8\"}." } ], "images": [ "scigen/train/3971.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: Validation accuracy of G-DAug with different labeling methods on WinoGrande-L and CommonsenseQA. Random relabeling assigns labels uniformly at random, and model relabeling assigns labels according to a task model. Random labels hurt accuracy, and model relabeling helps on WinoGrande but not on CommonsenseQA." ], "reasoning": "The baseline performance for WinoGrande-L is 75.9, while the model relabeling performance is 77.7. The difference is 77.7 - 75.9.", "question": "What is the performance difference between the baseline and model relabeling methods for WinoGrande-L?", "answer": "So, the answer is {\"answer\": \"1.8\"}." }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nRandom relabeling outperforms the baseline in CommonsenseQA.\n" }, { "role": "assistant", "content": "The table shows that the baseline performance for CommonsenseQA is 77.1, while the random relabeling performance is also 77.1. Since they are equal, random relabeling does not outperform the baseline. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3971.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: Validation accuracy of G-DAug with different labeling methods on WinoGrande-L and CommonsenseQA. Random relabeling assigns labels uniformly at random, and model relabeling assigns labels according to a task model. Random labels hurt accuracy, and model relabeling helps on WinoGrande but not on CommonsenseQA." ], "reasoning": "The table shows that the baseline performance for CommonsenseQA is 77.1, while the random relabeling performance is also 77.1. Since they are equal, random relabeling does not outperform the baseline.", "statement": "Random relabeling outperforms the baseline in CommonsenseQA.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 1: Results on the test sets of five commonsense benchmarks. RoBERTa (reported) is the result for the RoBERTa-large baseline reported in previous work. RoBERTa (ours) is our evaluation of RoBERTa-large baselines following our experimental setup. All G-DAug methods outperform the baseline methods, and G-DAug-Combo performs the best on the majority of the tasks (3/5) and achieves the highest average score.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "G-DAug-Combo achieves the highest average score of 78.7%, outperforming other methods and setting new state-of-the-art results on WinoGrande and Codah.The G-DAug methods perform better than the baselines: on average, all of the proposed selection algorithms achieve higher test performance than G-DAug-Rand. G-DAug-Combo performs the best on 3/5 tasks and obtains the highest average score 78.7%, a 1.6% absolute gain over the non-augmented baseline. Moreover, with G-DAug-Combo, we obtain a 5.0% absolute gain over previously published state of the art results on WinoGrande. , G-DAug-Combo outperforms the prior non-ensemble state-of-the-art Zhu et al. We also achieve a new state-of-the-art on Codah, where the previous best (BERT-based) score was 67.5% Chen et al. Backtranslation hurts performance: the non-augmented baseline uniformly outperforms Backtranslation, and the average score drops by 1% with Backtranslation." } ], "images": [ "scigen/train/3972.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Results on the test sets of five commonsense benchmarks. RoBERTa (reported) is the result for the RoBERTa-large baseline reported in previous work. RoBERTa (ours) is our evaluation of RoBERTa-large baselines following our experimental setup. All G-DAug methods outperform the baseline methods, and G-DAug-Combo performs the best on the majority of the tasks (3/5) and achieves the highest average score." ], "reasoning": "G-DAug-Combo achieves the highest average score of 78.7%, outperforming other methods and setting new state-of-the-art results on WinoGrande and Codah.", "description": "The G-DAug methods perform better than the baselines: on average, all of the proposed selection algorithms achieve higher test performance than G-DAug-Rand. G-DAug-Combo performs the best on 3/5 tasks and obtains the highest average score 78.7%, a 1.6% absolute gain over the non-augmented baseline. Moreover, with G-DAug-Combo, we obtain a 5.0% absolute gain over previously published state of the art results on WinoGrande. , G-DAug-Combo outperforms the prior non-ensemble state-of-the-art Zhu et al. We also achieve a new state-of-the-art on Codah, where the previous best (BERT-based) score was 67.5% Chen et al. Backtranslation hurts performance: the non-augmented baseline uniformly outperforms Backtranslation, and the average score drops by 1% with Backtranslation.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 1: Results on the test sets of five commonsense benchmarks. RoBERTa (reported) is the result for the RoBERTa-large baseline reported in previous work. RoBERTa (ours) is our evaluation of RoBERTa-large baselines following our experimental setup. All G-DAug methods outperform the baseline methods, and G-DAug-Combo performs the best on the majority of the tasks (3/5) and achieves the highest average score.', solve the following question. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich method achieves the highest average score and what is that score?\n" }, { "role": "assistant", "content": "By examining the 'Average' column in the table, G-DAug-Combo has the highest average score of 78.7%. So, the answer is {\"answer\": \"G-DAug-Combo with 78.7%\"}" } ], "images": [ "scigen/train/3972.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Results on the test sets of five commonsense benchmarks. RoBERTa (reported) is the result for the RoBERTa-large baseline reported in previous work. RoBERTa (ours) is our evaluation of RoBERTa-large baselines following our experimental setup. All G-DAug methods outperform the baseline methods, and G-DAug-Combo performs the best on the majority of the tasks (3/5) and achieves the highest average score." ], "reasoning": "By examining the 'Average' column in the table, G-DAug-Combo has the highest average score of 78.7%.", "question": "Which method achieves the highest average score and what is that score?", "answer": "So, the answer is {\"answer\": \"G-DAug-Combo with 78.7%\"}" }, { "messages": [ { "role": "user", "content": "Give you a table extracted from wikipedia infobox, determine whether the following sentence is 'supports' or 'refutes' by the table. If the table does not provide enough evidence for the sentence, then output 'not enough info' as the answer. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nBacktranslation achieves a higher average score than the non-augmented baseline.\n" }, { "role": "assistant", "content": "The table shows that Backtranslation has an average score of 76.1%, while the non-augmented baseline (RoBERTa (ours)) has an average score of 77.1%, indicating that Backtranslation performs worse. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3972.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Results on the test sets of five commonsense benchmarks. RoBERTa (reported) is the result for the RoBERTa-large baseline reported in previous work. RoBERTa (ours) is our evaluation of RoBERTa-large baselines following our experimental setup. All G-DAug methods outperform the baseline methods, and G-DAug-Combo performs the best on the majority of the tasks (3/5) and achieves the highest average score." ], "reasoning": "The table shows that Backtranslation has an average score of 76.1%, while the non-augmented baseline (RoBERTa (ours)) has an average score of 77.1%, indicating that Backtranslation performs worse.", "statement": "Backtranslation achieves a higher average score than the non-augmented baseline.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Examine the image displaying a table with the title 'Table 2: Results on WordNet-based synonym replacement sets. Metrics are the same as Table 2. G-DAug-Diversity and G-DAug-Combo achieve the highest average score.'. Notice that specific cells are emphasized with a different color. Generate a summary for the table.\n" }, { "role": "assistant", "content": "G-DAug-Combo and G-DAug-Diversity achieve the highest average performance, indicating that diverse question augmentation enhances model robustness.For Codah and HellaSwag-2K, we perturb test folds/sets, as the labels are available. Performing synonym replacement does not guarantee that the perturbed examples are still correct, due to e.g. polysemy. However, assuming the models perform similarly on invalid questions, higher accuracy on synonym-replaced questions may suggest better performance on the valid perturbations. G-DAug outperforms the baselines, with G-DAug-Combo and G-DAug-Diversity obtaining the best average performance. This suggests that augmenting with diverse questions is helpful for model robustness." } ], "images": [ "scigen/train/3973.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Results on WordNet-based synonym replacement sets. Metrics are the same as Table 2. G-DAug-Diversity and G-DAug-Combo achieve the highest average score." ], "reasoning": "G-DAug-Combo and G-DAug-Diversity achieve the highest average performance, indicating that diverse question augmentation enhances model robustness.", "description": "For Codah and HellaSwag-2K, we perturb test folds/sets, as the labels are available. Performing synonym replacement does not guarantee that the perturbed examples are still correct, due to e.g. polysemy. However, assuming the models perform similarly on invalid questions, higher accuracy on synonym-replaced questions may suggest better performance on the valid perturbations. G-DAug outperforms the baselines, with G-DAug-Combo and G-DAug-Diversity obtaining the best average performance. This suggests that augmenting with diverse questions is helpful for model robustness.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 2: Results on WordNet-based synonym replacement sets. Metrics are the same as Table 2. G-DAug-Diversity and G-DAug-Combo achieve the highest average score.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: Which method achieved the highest accuracy on the SNLI-3K dataset?\n\nYour Solution:\n" }, { "role": "assistant", "content": "By examining the SNLI-3K column, we see that Backtranslation achieved the highest accuracy with a score of 81.0. So, the answer is {\"answer\": \"Backtranslation\"}." } ], "images": [ "scigen/train/3973.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Results on WordNet-based synonym replacement sets. Metrics are the same as Table 2. G-DAug-Diversity and G-DAug-Combo achieve the highest average score." ], "reasoning": "By examining the SNLI-3K column, we see that Backtranslation achieved the highest accuracy with a score of 81.0.", "question": "Which method achieved the highest accuracy on the SNLI-3K dataset?", "answer": "So, the answer is {\"answer\": \"Backtranslation\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nG-DAug-Rand achieved the highest accuracy on the CSQA dataset.\n" }, { "role": "assistant", "content": "By looking at the CSQA column, we see that G-DAug-Rand achieved the highest accuracy with a score of 72.1. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3973.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Results on WordNet-based synonym replacement sets. Metrics are the same as Table 2. G-DAug-Diversity and G-DAug-Combo achieve the highest average score." ], "reasoning": "By looking at the CSQA column, we see that G-DAug-Rand achieved the highest accuracy with a score of 72.1.", "statement": "G-DAug-Rand achieved the highest accuracy on the CSQA dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 4: Results on ARC-Challenge Scientific QA in the closed-book setting. “Synonym” refers to the synonym replacement set, “TF:failure” refers to the TextFooler failure rate, and “TF:perturbation” refers to TextFooler average perturbation ratio. G-DAug-Influence performs the best on the validation, test and synonym replacement sets, while G-DAug-Diversity is the most robust against TextFooler.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "G-DAug-Influence achieves the highest accuracy on validation, test, and synonym sets, while G-DAug-Diversity is the most robust against adversarial attacks but has lower accuracy than G-DAug-Rand.Unlike the commonsense domain, valid science questions are harder to generate because their semantics is very precise. Despite that, G-DAug still improves on both validation and test sets by a large margin compared to the baselines. G-DAug-Influence achieves the best accuracy on the validation, test and synonym replacement sets, while G-DAug-Diversity is the most robust against the adversarial attacks but has worse accuracy than G-DAug-Rand. This suggests that optimizing for question quality is more important when the synthetic data is more noisy as it is for ARC, and that optimizing for diversity in this case can hurt performance." } ], "images": [ "scigen/train/3974.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Results on ARC-Challenge Scientific QA in the closed-book setting. “Synonym” refers to the synonym replacement set, “TF:failure” refers to the TextFooler failure rate, and “TF:perturbation” refers to TextFooler average perturbation ratio. G-DAug-Influence performs the best on the validation, test and synonym replacement sets, while G-DAug-Diversity is the most robust against TextFooler." ], "reasoning": "G-DAug-Influence achieves the highest accuracy on validation, test, and synonym sets, while G-DAug-Diversity is the most robust against adversarial attacks but has lower accuracy than G-DAug-Rand.", "description": "Unlike the commonsense domain, valid science questions are harder to generate because their semantics is very precise. Despite that, G-DAug still improves on both validation and test sets by a large margin compared to the baselines. G-DAug-Influence achieves the best accuracy on the validation, test and synonym replacement sets, while G-DAug-Diversity is the most robust against the adversarial attacks but has worse accuracy than G-DAug-Rand. This suggests that optimizing for question quality is more important when the synthetic data is more noisy as it is for ARC, and that optimizing for diversity in this case can hurt performance.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 4: Results on ARC-Challenge Scientific QA in the closed-book setting. “Synonym” refers to the synonym replacement set, “TF:failure” refers to the TextFooler failure rate, and “TF:perturbation” refers to TextFooler average perturbation ratio. G-DAug-Influence performs the best on the validation, test and synonym replacement sets, while G-DAug-Diversity is the most robust against TextFooler.', answer the question below by showing each progressive step toward the answer. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich method has the highest accuracy on the validation set and is also the most robust against adversarial attacks?\n" }, { "role": "assistant", "content": "G-DAug-Influence has the highest accuracy on the validation set with 51.5, and G-DAug-Diversity is the most robust against adversarial attacks with a TF:failure score of 13.9. So, the answer is {\"answer\": \"No single method meets both criteria.\"}" } ], "images": [ "scigen/train/3974.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Results on ARC-Challenge Scientific QA in the closed-book setting. “Synonym” refers to the synonym replacement set, “TF:failure” refers to the TextFooler failure rate, and “TF:perturbation” refers to TextFooler average perturbation ratio. G-DAug-Influence performs the best on the validation, test and synonym replacement sets, while G-DAug-Diversity is the most robust against TextFooler." ], "reasoning": "G-DAug-Influence has the highest accuracy on the validation set with 51.5, and G-DAug-Diversity is the most robust against adversarial attacks with a TF:failure score of 13.9.", "question": "Which method has the highest accuracy on the validation set and is also the most robust against adversarial attacks?", "answer": "So, the answer is {\"answer\": \"No single method meets both criteria.\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nG-DAug-Combo has the highest accuracy on the test set.\n" }, { "role": "assistant", "content": "The table shows that G-DAug-Influence has the highest accuracy on the test set with a score of 48.5, not G-DAug-Combo. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3974.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Results on ARC-Challenge Scientific QA in the closed-book setting. “Synonym” refers to the synonym replacement set, “TF:failure” refers to the TextFooler failure rate, and “TF:perturbation” refers to TextFooler average perturbation ratio. G-DAug-Influence performs the best on the validation, test and synonym replacement sets, while G-DAug-Diversity is the most robust against TextFooler." ], "reasoning": "The table shows that G-DAug-Influence has the highest accuracy on the test set with a score of 48.5, not G-DAug-Combo.", "statement": "G-DAug-Combo has the highest accuracy on the test set.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: Comparison of different training strategies on WinoGrande-L. G-DAug’s two-stage training achieves the highest accuracy.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "G-DAug's staged training approach achieves the highest accuracy compared to other methods, including mixing and importance-weighted loss.aimed at mitigating effects of noise in the generated data. Mixing refers to simply training on the union of the generated and original data, and importance-weighted loss is similar but downweights the generated examples such that their aggregate weight in the loss computation equals that of the original data. G-DAug’s staged training approach is shown to outperform the other methods on this data set." } ], "images": [ "scigen/train/3975.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Comparison of different training strategies on WinoGrande-L. G-DAug’s two-stage training achieves the highest accuracy." ], "reasoning": "G-DAug's staged training approach achieves the highest accuracy compared to other methods, including mixing and importance-weighted loss.", "description": "aimed at mitigating effects of noise in the generated data. Mixing refers to simply training on the union of the generated and original data, and importance-weighted loss is similar but downweights the generated examples such that their aggregate weight in the loss computation equals that of the original data. G-DAug’s staged training approach is shown to outperform the other methods on this data set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 5: Comparison of different training strategies on WinoGrande-L. G-DAug’s two-stage training achieves the highest accuracy.' and methodically tackle the question that follows. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich method achieves the highest accuracy according to the table?\n" }, { "role": "assistant", "content": "By comparing the accuracy values of all methods listed in the table, the Two Stage Training method has the highest accuracy of 77.7. So, the answer is {\"answer\": \"Two Stage Training\"}." } ], "images": [ "scigen/train/3975.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Comparison of different training strategies on WinoGrande-L. G-DAug’s two-stage training achieves the highest accuracy." ], "reasoning": "By comparing the accuracy values of all methods listed in the table, the Two Stage Training method has the highest accuracy of 77.7.", "question": "Which method achieves the highest accuracy according to the table?", "answer": "So, the answer is {\"answer\": \"Two Stage Training\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nMixing and No Augmentation methods have the same accuracy.\n" }, { "role": "assistant", "content": "The table shows that both Mixing and No Augmentation methods have an accuracy of 75.9. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3975.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Comparison of different training strategies on WinoGrande-L. G-DAug’s two-stage training achieves the highest accuracy." ], "reasoning": "The table shows that both Mixing and No Augmentation methods have an accuracy of 75.9.", "statement": "Mixing and No Augmentation methods have the same accuracy.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 7: Results comparing G-DAug’s filtering methods against using entire synthetic data pool for augmentation, on WinoGrande-M. The synthetic data sizes are shown in parentheses. The selection algorithms achieve comparable or better performance despite training on three times fewer synthetic examples.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "G-DAug's filtering methods achieve comparable or better accuracy with significantly less synthetic training data, reducing training costs.G-DAug ’s filtering methods are designed to identify a high-quality and diverse subset of the generated data, in order to reduce the training cost compared to training on the entire generated pool—without harming accuracy. The selection approaches provide comparable or better accuracy compared to using the whole pool, despite using 3 times less synthetic training data." } ], "images": [ "scigen/train/3976.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: Results comparing G-DAug’s filtering methods against using entire synthetic data pool for augmentation, on WinoGrande-M. The synthetic data sizes are shown in parentheses. The selection algorithms achieve comparable or better performance despite training on three times fewer synthetic examples." ], "reasoning": "G-DAug's filtering methods achieve comparable or better accuracy with significantly less synthetic training data, reducing training costs.", "description": "G-DAug ’s filtering methods are designed to identify a high-quality and diverse subset of the generated data, in order to reduce the training cost compared to training on the entire generated pool—without harming accuracy. The selection approaches provide comparable or better accuracy compared to using the whole pool, despite using 3 times less synthetic training data.", "answer": "" }, { "messages": [ { "role": "user", "content": "Give you a table image, solve this question based on the table. Let's think step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\n\nTable title: Table 7: Results comparing G-DAug’s filtering methods against using entire synthetic data pool for augmentation, on WinoGrande-M. The synthetic data sizes are shown in parentheses. The selection algorithms achieve comparable or better performance despite training on three times fewer synthetic examples.\n\nQuestion: Which filtering method achieves the highest accuracy with the least amount of synthetic training data?\n" }, { "role": "assistant", "content": "The table shows that the 'Influence' method achieves the highest accuracy of 74.4 using 127,478 synthetic data points, which is the least amount of data used among the methods listed. So, the answer is {\"answer\": \"Influence\"}." } ], "images": [ "scigen/train/3976.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: Results comparing G-DAug’s filtering methods against using entire synthetic data pool for augmentation, on WinoGrande-M. The synthetic data sizes are shown in parentheses. The selection algorithms achieve comparable or better performance despite training on three times fewer synthetic examples." ], "reasoning": "The table shows that the 'Influence' method achieves the highest accuracy of 74.4 using 127,478 synthetic data points, which is the least amount of data used among the methods listed.", "question": "Which filtering method achieves the highest accuracy with the least amount of synthetic training data?", "answer": "So, the answer is {\"answer\": \"Influence\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Diversity method achieves higher accuracy than the Whole Pool method.\n" }, { "role": "assistant", "content": "The table shows that the Diversity method has an accuracy of 73.0, while the Whole Pool method has an accuracy of 73.1. Therefore, the statement is refuted. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3976.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: Results comparing G-DAug’s filtering methods against using entire synthetic data pool for augmentation, on WinoGrande-M. The synthetic data sizes are shown in parentheses. The selection algorithms achieve comparable or better performance despite training on three times fewer synthetic examples." ], "reasoning": "The table shows that the Diversity method has an accuracy of 73.0, while the Whole Pool method has an accuracy of 73.1. Therefore, the statement is refuted.", "statement": "The Diversity method achieves higher accuracy than the Whole Pool method.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 8: Test performance of an unaugmented baseline model and ensembled with finetuned GPT-2 generator on WinoGrande. We use weighted average ensemble with weights tuned on validation data.'.\n" }, { "role": "assistant", "content": "G-DAug-Combo outperforms both the baseline and the ensemble model by 3.9 points, indicating its superior effectiveness.Generator/Task Model Ensemble. G-DAug harnesses pretrained knowledge from GPT-2 in order to improve a RoBERTa-based task model; a more standard approach to this end (albeit, with twice the computational cost at runtime) would be to ensemble the two models instead. We adopt a weighted-average ensemble method, where the weights are tuned on validation data (the tuning is important to achieve peak performance). The ensemble model performs same as the baseline model, and G-DAug-Combo outperforms both of them by 3.9 point. This suggests that G-DAug is more effective than simply ensembling the finetuned generator." } ], "images": [ "scigen/train/3977.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: Test performance of an unaugmented baseline model and ensembled with finetuned GPT-2 generator on WinoGrande. We use weighted average ensemble with weights tuned on validation data." ], "reasoning": "G-DAug-Combo outperforms both the baseline and the ensemble model by 3.9 points, indicating its superior effectiveness.", "description": "Generator/Task Model Ensemble. G-DAug harnesses pretrained knowledge from GPT-2 in order to improve a RoBERTa-based task model; a more standard approach to this end (albeit, with twice the computational cost at runtime) would be to ensemble the two models instead. We adopt a weighted-average ensemble method, where the weights are tuned on validation data (the tuning is important to achieve peak performance). The ensemble model performs same as the baseline model, and G-DAug-Combo outperforms both of them by 3.9 point. This suggests that G-DAug is more effective than simply ensembling the finetuned generator.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 8: Test performance of an unaugmented baseline model and ensembled with finetuned GPT-2 generator on WinoGrande. We use weighted average ensemble with weights tuned on validation data.\nQuestion: What is the Test AUC of the G-DAug-Combo method?\nSolve the above question. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "The table shows the Test AUC values for different methods. The G-DAug-Combo method has a Test AUC of 71.4. So, the answer is {\"answer\": \"71.4\"}." } ], "images": [ "scigen/train/3977.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: Test performance of an unaugmented baseline model and ensembled with finetuned GPT-2 generator on WinoGrande. We use weighted average ensemble with weights tuned on validation data." ], "reasoning": "The table shows the Test AUC values for different methods. The G-DAug-Combo method has a Test AUC of 71.4.", "question": "What is the Test AUC of the G-DAug-Combo method?", "answer": "So, the answer is {\"answer\": \"71.4\"}." }, { "messages": [ { "role": "user", "content": "The Baseline + Generator method has a higher Test AUC than the Baseline method. Review the table as a premise and decide if it 'supports' or 'refutes' the above hypothesis. Classify the hypothesis as 'not enough info' if the table information is not enough for a final conclusion. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe Baseline + Generator method has a higher Test AUC than the Baseline method.\n" }, { "role": "assistant", "content": "The table shows that both the Baseline and Baseline + Generator methods have the same Test AUC of 67.5. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3977.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: Test performance of an unaugmented baseline model and ensembled with finetuned GPT-2 generator on WinoGrande. We use weighted average ensemble with weights tuned on validation data." ], "reasoning": "The table shows that both the Baseline and Baseline + Generator methods have the same Test AUC of 67.5.", "statement": "The Baseline + Generator method has a higher Test AUC than the Baseline method.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 5: SParC results. For our models, we only report test set results of our best model on the dev set. ∗We improve the CD-Seq2Seq performance over yu2019sparc by separating and parsing the column names (e.g., stu_fname → student first name) and using the schema-specific output vocabulary during decoding.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The model with query attention and sequence editing achieves the highest question and interaction matching accuracy, demonstrating the effectiveness of these techniques.SParC. Similar to Spider, our model without previous query as input already outperforms SyntaxSQL-con, achieving 31.4% question matching accuracy and 14.7% interaction matching accuracy. In addition, compared with CD-Seq2Seq, our model enjoys the benefits of the table-utterance encoder, turn attention, and the joint consideration of utterances and table schemas during the decoding stage. This boosts the performance by 10% question accuracy and 6% interaction accuracy. This shows the editing approach is indeed helpful to improve the generation quality when the previous query is the oracle. This improves over our vanilla BERT model without query attention and achieves 42.7% question and 21.6% interaction matching accuracy. With query editing, our best model further improves to 47.2% question and 29.5% interaction matching accuracy. This demonstrates the effectiveness of our query attention and query editing separately, both of which are essential to make use of the previous query." } ], "images": [ "scigen/train/3978.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: SParC results. For our models, we only report test set results of our best model on the dev set. ∗We improve the CD-Seq2Seq performance over yu2019sparc by separating and parsing the column names (e.g., stu_fname → student first name) and using the schema-specific output vocabulary during decoding." ], "reasoning": "The model with query attention and sequence editing achieves the highest question and interaction matching accuracy, demonstrating the effectiveness of these techniques.", "description": "SParC. Similar to Spider, our model without previous query as input already outperforms SyntaxSQL-con, achieving 31.4% question matching accuracy and 14.7% interaction matching accuracy. In addition, compared with CD-Seq2Seq, our model enjoys the benefits of the table-utterance encoder, turn attention, and the joint consideration of utterances and table schemas during the decoding stage. This boosts the performance by 10% question accuracy and 6% interaction accuracy. This shows the editing approach is indeed helpful to improve the generation quality when the previous query is the oracle. This improves over our vanilla BERT model without query attention and achieves 42.7% question and 21.6% interaction matching accuracy. With query editing, our best model further improves to 47.2% question and 29.5% interaction matching accuracy. This demonstrates the effectiveness of our query attention and query editing separately, both of which are essential to make use of the previous query.", "answer": "" }, { "messages": [ { "role": "user", "content": "Solve the question according to the provided table image. Please provide detailed solution. Output the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 5: SParC results. For our models, we only report test set results of our best model on the dev set. ∗We improve the CD-Seq2Seq performance over yu2019sparc by separating and parsing the column names (e.g., stu_fname → student first name) and using the schema-specific output vocabulary during decoding.\nQuestion: What is the improvement in question matching accuracy from the vanilla BERT model to the model with query attention and sequence editing?\n" }, { "role": "assistant", "content": "The vanilla BERT model achieves 42.7% question matching accuracy, while the model with query attention and sequence editing achieves 47.2%. The improvement is 47.2% - 42.7% = 4.5%. So, the answer is {\"answer\": \"4.5%\"}" } ], "images": [ "scigen/train/3978.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: SParC results. For our models, we only report test set results of our best model on the dev set. ∗We improve the CD-Seq2Seq performance over yu2019sparc by separating and parsing the column names (e.g., stu_fname → student first name) and using the schema-specific output vocabulary during decoding." ], "reasoning": "The vanilla BERT model achieves 42.7% question matching accuracy, while the model with query attention and sequence editing achieves 47.2%. The improvement is 47.2% - 42.7% = 4.5%.", "question": "What is the improvement in question matching accuracy from the vanilla BERT model to the model with query attention and sequence editing?", "answer": "So, the answer is {\"answer\": \"4.5%\"}" }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe model with query attention and sequence editing achieves the highest interaction matching accuracy.\n" }, { "role": "assistant", "content": "The table shows that the model with query attention and sequence editing achieves 29.5% interaction matching accuracy, which is the highest among all models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3978.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: SParC results. For our models, we only report test set results of our best model on the dev set. ∗We improve the CD-Seq2Seq performance over yu2019sparc by separating and parsing the column names (e.g., stu_fname → student first name) and using the schema-specific output vocabulary during decoding." ], "reasoning": "The table shows that the model with query attention and sequence editing achieves 29.5% interaction matching accuracy, which is the highest among all models listed.", "statement": "The model with query attention and sequence editing achieves the highest interaction matching accuracy.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 4: Spider results on dev set and test set.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "Our method outperforms several previous models on both the dev and test sets, demonstrating the effectiveness of our utterance-table encoder and table-aware decoder.Spider. Since each question is standalone, we don’t use interaction-level decoder or query editing. Our method can achieve the performance of 36.4% on dev set and 32.9% on test set, serving as a strong model for the context-independent cross-domain text-to-SQL generation. This demonstrates the effectiveness of our utterance-table encoder and table-aware decoder to handle the semantics of user utterances and the complexity of table schemas to generate complex SQL queries in unseen domains." } ], "images": [ "scigen/train/3979.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Spider results on dev set and test set." ], "reasoning": "Our method outperforms several previous models on both the dev and test sets, demonstrating the effectiveness of our utterance-table encoder and table-aware decoder.", "description": "Spider. Since each question is standalone, we don’t use interaction-level decoder or query editing. Our method can achieve the performance of 36.4% on dev set and 32.9% on test set, serving as a strong model for the context-independent cross-domain text-to-SQL generation. This demonstrates the effectiveness of our utterance-table encoder and table-aware decoder to handle the semantics of user utterances and the complexity of table schemas to generate complex SQL queries in unseen domains.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the table titled 'Table 4: Spider results on dev set and test set.' in mind, break down and resolve the question below step by step. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n\nWhich model has the highest performance on the dev set?\n" }, { "role": "assistant", "content": "By comparing the dev set performances of all models listed in the table, the model with the highest performance is the one with the utterance-table BERT embedding, achieving 57.6%. So, the answer is {\"answer\": \"utterance-table BERT embedding\"}." } ], "images": [ "scigen/train/3979.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Spider results on dev set and test set." ], "reasoning": "By comparing the dev set performances of all models listed in the table, the model with the highest performance is the one with the utterance-table BERT embedding, achieving 57.6%.", "question": "Which model has the highest performance on the dev set?", "answer": "So, the answer is {\"answer\": \"utterance-table BERT embedding\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe IRNet (BERT) model outperforms the GNN model on the test set.\n" }, { "role": "assistant", "content": "By comparing the test set performances of the IRNet (BERT) model and the GNN model, the IRNet (BERT) model achieves 54.7% while the GNN model achieves 39.4%, indicating that the IRNet (BERT) model outperforms the GNN model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3979.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Spider results on dev set and test set." ], "reasoning": "By comparing the test set performances of the IRNet (BERT) model and the GNN model, the IRNet (BERT) model achieves 54.7% while the GNN model achieves 39.4%, indicating that the IRNet (BERT) model outperforms the GNN model.", "statement": "The IRNet (BERT) model outperforms the GNN model on the test set.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 6: ATIS results on dev set and test set.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "Our model achieves 36.2% dev and 43.9% test string accuracy on ATIS, comparable to Suhr et al. (2018).ATIS. Our model achieves 36.2% dev and 43.9% test string accuracy, comparable to \\newcitesuhr2018learning. On ATIS, we only apply our editing mechanism and reuse their utterance encoder instead of the BERT utterance-table encoder, because ATIS is single domain." } ], "images": [ "scigen/train/3980.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 6: ATIS results on dev set and test set." ], "reasoning": "Our model achieves 36.2% dev and 43.9% test string accuracy on ATIS, comparable to Suhr et al. (2018).", "description": "ATIS. Our model achieves 36.2% dev and 43.9% test string accuracy, comparable to \\newcitesuhr2018learning. On ATIS, we only apply our editing mechanism and reuse their utterance encoder instead of the BERT utterance-table encoder, because ATIS is single domain.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 6: ATIS results on dev set and test set.'. Solve the following question based on the table.\n\nQuestion: What is the difference in test set relaxed accuracy between our model and Suhr et al. (2018)?\nLet's think step by step. The final result should be presented in the JSON format of {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The test set relaxed accuracy for our model is 68.5%, and for Suhr et al. (2018) it is 69.3%. The difference is 69.3% - 68.5% = 0.8%. So, the answer is {\"answer\": \"0.8%\"}" } ], "images": [ "scigen/train/3980.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 6: ATIS results on dev set and test set." ], "reasoning": "The test set relaxed accuracy for our model is 68.5%, and for Suhr et al. (2018) it is 69.3%. The difference is 69.3% - 68.5% = 0.8%.", "question": "What is the difference in test set relaxed accuracy between our model and Suhr et al. (2018)?", "answer": "So, the answer is {\"answer\": \"0.8%\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nOur model has a higher test set strict accuracy than Suhr et al. (2018).\n" }, { "role": "assistant", "content": "The test set strict accuracy for our model is 68.1%, while for Suhr et al. (2018) it is 69.2%. Therefore, our model does not have a higher test set strict accuracy. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3980.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 6: ATIS results on dev set and test set." ], "reasoning": "The test set strict accuracy for our model is 68.1%, while for Suhr et al. (2018) it is 69.2%. Therefore, our model does not have a higher test set strict accuracy.", "statement": "Our model has a higher test set strict accuracy than Suhr et al. (2018).", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Within this image, you'll find a table bearing the title 'Table 3: Evaluation metrics on individual disease classification. The ten class labels correspond to coronary artery disease, hemorrhage, pneumonia, myocardial infarction, gastrointestinal bleeding, fracture, aortic stenos, cardiac failure, prematurity and stroke. The evaluation metrics ACC, TNR, FPR, FNR refer to accuracy, true positive rate, false positive rate, false negative rate. The mapping between label indices and diseases are: 0-coronary artery disease, 1-hemorrhage, 2-pneumonia, 3-myocardial infarction, 4-gastrointestinal bleeding, 5-fracture, 6-aortic stenosis, 7-cardiac failure, 8-prematurity, 9-stroke. The standard errors computed by 5 set of results of each model using different random splitting are appended after the metric values with a ± sign.'. Observe that certain cells in the table are marked with a distinct color. Your task is to formulate sentence descriptions for the table.\n" }, { "role": "assistant", "content": "The CNN model consistently outperforms other models in classifying both common and rare diseases, achieving high F1 scores across all categories.Furthermore, although some models have relatively high F1 score in categories with large sample size (e.g., coronary artery disease, hemorrhage, pneumonia), they performed badly on categories with less samples (e.g., myocardial infarction, aortic stenosis, prematurity). For example, the SVM achieved an F1 score value of 80.09% for the hemorrhage class, only around 4% lower than the F1 score value of the CNN model, it was only able to achieve F1 score values of 15.02% on aortic stenosis and 19.85% on prematurity. On the contrary, the CNN model performed equally well on all disease classes independently of frequently they appear and are diagnosed in the MIMIC corpus. The highest F1 score values achieved from any of the baseline models for aortic stenosis and prematurity are 42.80% and 42.85% respectively. Among all the baseline models, the logistic regression and the MLP model achieved less than 5% F1 score for the prematurity class. On the other side, the CNN model still achieved F1 score values of 61.29% and 65.04% on the same diseases. This fact further confirms that the proposed CNN model works remarkably well at classifying rare (not appearing frequently in the corpus) diseases." } ], "images": [ "scigen/train/3981.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: Evaluation metrics on individual disease classification. The ten class labels correspond to coronary artery disease, hemorrhage, pneumonia, myocardial infarction, gastrointestinal bleeding, fracture, aortic stenos, cardiac failure, prematurity and stroke. The evaluation metrics ACC, TNR, FPR, FNR refer to accuracy, true positive rate, false positive rate, false negative rate. The mapping between label indices and diseases are: 0-coronary artery disease, 1-hemorrhage, 2-pneumonia, 3-myocardial infarction, 4-gastrointestinal bleeding, 5-fracture, 6-aortic stenosis, 7-cardiac failure, 8-prematurity, 9-stroke. The standard errors computed by 5 set of results of each model using different random splitting are appended after the metric values with a ± sign." ], "reasoning": "The CNN model consistently outperforms other models in classifying both common and rare diseases, achieving high F1 scores across all categories.", "description": "Furthermore, although some models have relatively high F1 score in categories with large sample size (e.g., coronary artery disease, hemorrhage, pneumonia), they performed badly on categories with less samples (e.g., myocardial infarction, aortic stenosis, prematurity). For example, the SVM achieved an F1 score value of 80.09% for the hemorrhage class, only around 4% lower than the F1 score value of the CNN model, it was only able to achieve F1 score values of 15.02% on aortic stenosis and 19.85% on prematurity. On the contrary, the CNN model performed equally well on all disease classes independently of frequently they appear and are diagnosed in the MIMIC corpus. The highest F1 score values achieved from any of the baseline models for aortic stenosis and prematurity are 42.80% and 42.85% respectively. Among all the baseline models, the logistic regression and the MLP model achieved less than 5% F1 score for the prematurity class. On the other side, the CNN model still achieved F1 score values of 61.29% and 65.04% on the same diseases. This fact further confirms that the proposed CNN model works remarkably well at classifying rare (not appearing frequently in the corpus) diseases.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 3: Evaluation metrics on individual disease classification. The ten class labels correspond to coronary artery disease, hemorrhage, pneumonia, myocardial infarction, gastrointestinal bleeding, fracture, aortic stenos, cardiac failure, prematurity and stroke. The evaluation metrics ACC, TNR, FPR, FNR refer to accuracy, true positive rate, false positive rate, false negative rate. The mapping between label indices and diseases are: 0-coronary artery disease, 1-hemorrhage, 2-pneumonia, 3-myocardial infarction, 4-gastrointestinal bleeding, 5-fracture, 6-aortic stenosis, 7-cardiac failure, 8-prematurity, 9-stroke. The standard errors computed by 5 set of results of each model using different random splitting are appended after the metric values with a ± sign.'. Solve the following question based on the table.\n\nQuestion: What is the F1 score of the CNN model for the hemorrhage class?\nLet's think step by step. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows the F1 scores for various models and classes. For the hemorrhage class (label 0), the CNN model has an F1 score of 81.94%. So, the answer is {\"answer\": \"81.94%\"}." } ], "images": [ "scigen/train/3981.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: Evaluation metrics on individual disease classification. The ten class labels correspond to coronary artery disease, hemorrhage, pneumonia, myocardial infarction, gastrointestinal bleeding, fracture, aortic stenos, cardiac failure, prematurity and stroke. The evaluation metrics ACC, TNR, FPR, FNR refer to accuracy, true positive rate, false positive rate, false negative rate. The mapping between label indices and diseases are: 0-coronary artery disease, 1-hemorrhage, 2-pneumonia, 3-myocardial infarction, 4-gastrointestinal bleeding, 5-fracture, 6-aortic stenosis, 7-cardiac failure, 8-prematurity, 9-stroke. The standard errors computed by 5 set of results of each model using different random splitting are appended after the metric values with a ± sign." ], "reasoning": "The table shows the F1 scores for various models and classes. For the hemorrhage class (label 0), the CNN model has an F1 score of 81.94%.", "question": "What is the F1 score of the CNN model for the hemorrhage class?", "answer": "So, the answer is {\"answer\": \"81.94%\"}." }, { "messages": [ { "role": "user", "content": "Inspect the table to see whether the sentence is 'supports' or 'refutes'. If evidence is insufficient, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nThe CNN model achieved the highest F1 score for the aortic stenosis class.\n" }, { "role": "assistant", "content": "The table shows that the highest F1 score for the aortic stenosis class (label 6) is 61.29%, achieved by the CNN model. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3981.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: Evaluation metrics on individual disease classification. The ten class labels correspond to coronary artery disease, hemorrhage, pneumonia, myocardial infarction, gastrointestinal bleeding, fracture, aortic stenos, cardiac failure, prematurity and stroke. The evaluation metrics ACC, TNR, FPR, FNR refer to accuracy, true positive rate, false positive rate, false negative rate. The mapping between label indices and diseases are: 0-coronary artery disease, 1-hemorrhage, 2-pneumonia, 3-myocardial infarction, 4-gastrointestinal bleeding, 5-fracture, 6-aortic stenosis, 7-cardiac failure, 8-prematurity, 9-stroke. The standard errors computed by 5 set of results of each model using different random splitting are appended after the metric values with a ± sign." ], "reasoning": "The table shows that the highest F1 score for the aortic stenosis class (label 6) is 61.29%, achieved by the CNN model.", "statement": "The CNN model achieved the highest F1 score for the aortic stenosis class.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Review the table labeled 'Table 2: Model comparison on the CBT and CNN datasets. Results marked with 1 are from Hill et al. (2016), those marked with 2 are from Kadlec et al. (2016), those marked with 3 are from Hermann et al. (2015), and those marked with 4 are from Chen et al. (2016).'. Generate a description to describe the table, taking into account the highlighted cells.\n" }, { "role": "assistant", "content": "EpiReader outperforms other models in both validation and test scores, and its Extractor component benefits from joint training.We measure EpiReader performance at the output of both the Extractor and the Reasoner. Aside from achieving state-of-the-art results at its final output, the EpiReader framework gives a boost to its Extractor component through the joint training process. These are all higher than the analogous scores reported for the AS Reader, which we verified ourselves to within negligible difference. Based on our own work with that model, we found it to overfit the training set rapidly and significantly, achieving training accuracy scores upwards of 98% after only 2 epochs. We suspect that the Reasoner module had a regularizing effect on the Extractor, but leave the verification for future work. An analysis by \\newcitekadlec2016 indicates that the trained AS Reader includes the correct answer among its five most probable candidates on approximately 95% of test examples for both datasets. We verified that our Extractor achieved a similar rate, and of course this is vital for performance of the full system, since the Reasoner cannot recover when the correct answer is not among its inputs." } ], "images": [ "scigen/train/3982.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Model comparison on the CBT and CNN datasets. Results marked with 1 are from Hill et al. (2016), those marked with 2 are from Kadlec et al. (2016), those marked with 3 are from Hermann et al. (2015), and those marked with 4 are from Chen et al. (2016)." ], "reasoning": "EpiReader outperforms other models in both validation and test scores, and its Extractor component benefits from joint training.", "description": "We measure EpiReader performance at the output of both the Extractor and the Reasoner. Aside from achieving state-of-the-art results at its final output, the EpiReader framework gives a boost to its Extractor component through the joint training process. These are all higher than the analogous scores reported for the AS Reader, which we verified ourselves to within negligible difference. Based on our own work with that model, we found it to overfit the training set rapidly and significantly, achieving training accuracy scores upwards of 98% after only 2 epochs. We suspect that the Reasoner module had a regularizing effect on the Extractor, but leave the verification for future work. An analysis by \\newcitekadlec2016 indicates that the trained AS Reader includes the correct answer among its five most probable candidates on approximately 95% of test examples for both datasets. We verified that our Extractor achieved a similar rate, and of course this is vital for performance of the full system, since the Reasoner cannot recover when the correct answer is not among its inputs.", "answer": "" }, { "messages": [ { "role": "user", "content": "This image shows a table of 'Table 2: Model comparison on the CBT and CNN datasets. Results marked with 1 are from Hill et al. (2016), those marked with 2 are from Kadlec et al. (2016), those marked with 3 are from Hermann et al. (2015), and those marked with 4 are from Chen et al. (2016).'. Solve the following question based on the table.\n\nQuestion: What is the CNN test score of the EpiReader Extractor, and how does it compare to the AS Reader?\nLet's think step by step. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The CNN test score of the EpiReader Extractor is 72.0, which is higher than the AS Reader's score of 69.5. So, the answer is {\"answer\": \"72.0\"}." } ], "images": [ "scigen/train/3982.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Model comparison on the CBT and CNN datasets. Results marked with 1 are from Hill et al. (2016), those marked with 2 are from Kadlec et al. (2016), those marked with 3 are from Hermann et al. (2015), and those marked with 4 are from Chen et al. (2016)." ], "reasoning": "The CNN test score of the EpiReader Extractor is 72.0, which is higher than the AS Reader's score of 69.5.", "question": "What is the CNN test score of the EpiReader Extractor, and how does it compare to the AS Reader?", "answer": "So, the answer is {\"answer\": \"72.0\"}." }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The EpiReader has the highest CNN test score among all models. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe EpiReader has the highest CNN test score among all models.\n" }, { "role": "assistant", "content": "The table shows that the EpiReader has a CNN test score of 74.0, which is higher than all other models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3982.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Model comparison on the CBT and CNN datasets. Results marked with 1 are from Hill et al. (2016), those marked with 2 are from Kadlec et al. (2016), those marked with 3 are from Hermann et al. (2015), and those marked with 4 are from Chen et al. (2016)." ], "reasoning": "The table shows that the EpiReader has a CNN test score of 74.0, which is higher than all other models listed.", "statement": "The EpiReader has the highest CNN test score among all models.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 2: Model comparison on the CBT and CNN datasets. Results marked with 1 are from Hill et al. (2016), those marked with 2 are from Kadlec et al. (2016), those marked with 3 are from Hermann et al. (2015), and those marked with 4 are from Chen et al. (2016).', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "EpiReader achieves state-of-the-art results and boosts its Extractor component through joint training, outperforming AS Reader.We measure EpiReader performance at the output of both the Extractor and the Reasoner. Aside from achieving state-of-the-art results at its final output, the EpiReader framework gives a boost to its Extractor component through the joint training process. These are all higher than the analogous scores reported for the AS Reader, which we verified ourselves to within negligible difference. Based on our own work with that model, we found it to overfit the training set rapidly and significantly, achieving training accuracy scores upwards of 98% after only 2 epochs. We suspect that the Reasoner module had a regularizing effect on the Extractor, but leave the verification for future work. An analysis by \\newcitekadlec2016 indicates that the trained AS Reader includes the correct answer among its five most probable candidates on approximately 95% of test examples for both datasets. We verified that our Extractor achieved a similar rate, and of course this is vital for performance of the full system, since the Reasoner cannot recover when the correct answer is not among its inputs." } ], "images": [ "scigen/train/3983.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Model comparison on the CBT and CNN datasets. Results marked with 1 are from Hill et al. (2016), those marked with 2 are from Kadlec et al. (2016), those marked with 3 are from Hermann et al. (2015), and those marked with 4 are from Chen et al. (2016)." ], "reasoning": "EpiReader achieves state-of-the-art results and boosts its Extractor component through joint training, outperforming AS Reader.", "description": "We measure EpiReader performance at the output of both the Extractor and the Reasoner. Aside from achieving state-of-the-art results at its final output, the EpiReader framework gives a boost to its Extractor component through the joint training process. These are all higher than the analogous scores reported for the AS Reader, which we verified ourselves to within negligible difference. Based on our own work with that model, we found it to overfit the training set rapidly and significantly, achieving training accuracy scores upwards of 98% after only 2 epochs. We suspect that the Reasoner module had a regularizing effect on the Extractor, but leave the verification for future work. An analysis by \\newcitekadlec2016 indicates that the trained AS Reader includes the correct answer among its five most probable candidates on approximately 95% of test examples for both datasets. We verified that our Extractor achieved a similar rate, and of course this is vital for performance of the full system, since the Reasoner cannot recover when the correct answer is not among its inputs.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 2: Model comparison on the CBT and CNN datasets. Results marked with 1 are from Hill et al. (2016), those marked with 2 are from Kadlec et al. (2016), those marked with 3 are from Hermann et al. (2015), and those marked with 4 are from Chen et al. (2016).', solve this question and give detail solutions. In the end, output your final answer using the JSON format: {\"answer\": \"\"}.\n\nWhat is the highest score achieved by the EpiReader ensemble on the CBT-NE valid dataset?\n" }, { "role": "assistant", "content": "The table shows the performance of various models on different datasets. The highest score achieved by the EpiReader ensemble on the CBT-NE valid dataset is 76.6. So, the answer is {\"answer\": \"76.6\"}" } ], "images": [ "scigen/train/3983.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Model comparison on the CBT and CNN datasets. Results marked with 1 are from Hill et al. (2016), those marked with 2 are from Kadlec et al. (2016), those marked with 3 are from Hermann et al. (2015), and those marked with 4 are from Chen et al. (2016)." ], "reasoning": "The table shows the performance of various models on different datasets. The highest score achieved by the EpiReader ensemble on the CBT-NE valid dataset is 76.6.", "question": "What is the highest score achieved by the EpiReader ensemble on the CBT-NE valid dataset?", "answer": "So, the answer is {\"answer\": \"76.6\"}" }, { "messages": [ { "role": "user", "content": "Begin with the hypothesis The EpiReader ensemble achieved the highest score on the CBT-CN valid dataset. and analyze the table to decide whether it 'supports' or 'refutes' the above hypothesis. If only using the table information is not sufficient to give a final conclusion, label the hypothesis 'not enough info'. Format your final answer as a JSON, using the structure {\"answer\": \"\"}.\n\nThe EpiReader ensemble achieved the highest score on the CBT-CN valid dataset.\n" }, { "role": "assistant", "content": "The table shows that the EpiReader ensemble achieved a score of 73.6 on the CBT-CN valid dataset, which is the highest score among all models listed. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3983.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Model comparison on the CBT and CNN datasets. Results marked with 1 are from Hill et al. (2016), those marked with 2 are from Kadlec et al. (2016), those marked with 3 are from Hermann et al. (2015), and those marked with 4 are from Chen et al. (2016)." ], "reasoning": "The table shows that the EpiReader ensemble achieved a score of 73.6 on the CBT-CN valid dataset, which is the highest score among all models listed.", "statement": "The EpiReader ensemble achieved the highest score on the CBT-CN valid dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Providing you an table named 'Table 2: Experiment results on the Reuters-21578 corpus.'. Your challenge is to articulate sentences describing the table. Remember to consider the provided highlighted cells.\n" }, { "role": "assistant", "content": "AnchorFree consistently outperforms baselines in clustering accuracy and single-topic quality for F=3 to 25, except for overlaps in F=20 and 25.From F=3 to 25, the proposed algorithm (AnchorFree) gives very promising results: for the three considered metrics, AnchorFree consistently gives better results compared to the baselines. Particularly, the ClustAcc’s obtained by AnchorFree are at least 30% higher compared to the baselines for all cases. In addition, the single-topic quality of the topics mined by AnchorFree is the highest in terms of coherence scores; the overlaps between topics are the smallest except for F=20 and 25. In this experiment, we can see that XRAY is best in terms of single-topic quality, while AnchorFree is second best when F>6. For SimCount, AnchorFree gives the lowest values when F>6. In terms of clustering accuracy, the topics obtained by AnchorFree again lead to much higher clustering accuracies in all cases." } ], "images": [ "scigen/train/3984.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Experiment results on the Reuters-21578 corpus." ], "reasoning": "AnchorFree consistently outperforms baselines in clustering accuracy and single-topic quality for F=3 to 25, except for overlaps in F=20 and 25.", "description": "From F=3 to 25, the proposed algorithm (AnchorFree) gives very promising results: for the three considered metrics, AnchorFree consistently gives better results compared to the baselines. Particularly, the ClustAcc’s obtained by AnchorFree are at least 30% higher compared to the baselines for all cases. In addition, the single-topic quality of the topics mined by AnchorFree is the highest in terms of coherence scores; the overlaps between topics are the smallest except for F=20 and 25. In this experiment, we can see that XRAY is best in terms of single-topic quality, while AnchorFree is second best when F>6. For SimCount, AnchorFree gives the lowest values when F>6. In terms of clustering accuracy, the topics obtained by AnchorFree again lead to much higher clustering accuracies in all cases.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 2: Experiment results on the Reuters-21578 corpus.\nQuestion: What is the clustering accuracy of AnchorFree when F=10?\nSolve the above question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "To find the clustering accuracy of AnchorFree when F=10, locate the row where F=10 and then find the value under the 'ClustAc AnchorFree' column. So, the answer is {\"answer\": \"0.53\"}." } ], "images": [ "scigen/train/3984.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Experiment results on the Reuters-21578 corpus." ], "reasoning": "To find the clustering accuracy of AnchorFree when F=10, locate the row where F=10 and then find the value under the 'ClustAc AnchorFree' column.", "question": "What is the clustering accuracy of AnchorFree when F=10?", "answer": "So, the answer is {\"answer\": \"0.53\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nAnchorFree has the highest coherence score for F=6.\n" }, { "role": "assistant", "content": "To verify this statement, locate the row where F=6 and compare the coherence scores of all algorithms. AnchorFree's coherence score is -705.60, which is not the highest. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3984.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Experiment results on the Reuters-21578 corpus." ], "reasoning": "To verify this statement, locate the row where F=6 and compare the coherence scores of all algorithms. AnchorFree's coherence score is -705.60, which is not the highest.", "statement": "AnchorFree has the highest coherence score for F=6.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: Experiment results on the Reuters-21578 corpus.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "AnchorFree consistently outperforms other algorithms in clustering accuracy and single-topic quality for F=3 to 25, except for overlaps in F=20 and 25.From F=3 to 25, the proposed algorithm (AnchorFree) gives very promising results: for the three considered metrics, AnchorFree consistently gives better results compared to the baselines. Particularly, the ClustAcc’s obtained by AnchorFree are at least 30% higher compared to the baselines for all cases. In addition, the single-topic quality of the topics mined by AnchorFree is the highest in terms of coherence scores; the overlaps between topics are the smallest except for F=20 and 25. In this experiment, we can see that XRAY is best in terms of single-topic quality, while AnchorFree is second best when F>6. For SimCount, AnchorFree gives the lowest values when F>6. In terms of clustering accuracy, the topics obtained by AnchorFree again lead to much higher clustering accuracies in all cases." } ], "images": [ "scigen/train/3985.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Experiment results on the Reuters-21578 corpus." ], "reasoning": "AnchorFree consistently outperforms other algorithms in clustering accuracy and single-topic quality for F=3 to 25, except for overlaps in F=20 and 25.", "description": "From F=3 to 25, the proposed algorithm (AnchorFree) gives very promising results: for the three considered metrics, AnchorFree consistently gives better results compared to the baselines. Particularly, the ClustAcc’s obtained by AnchorFree are at least 30% higher compared to the baselines for all cases. In addition, the single-topic quality of the topics mined by AnchorFree is the highest in terms of coherence scores; the overlaps between topics are the smallest except for F=20 and 25. In this experiment, we can see that XRAY is best in terms of single-topic quality, while AnchorFree is second best when F>6. For SimCount, AnchorFree gives the lowest values when F>6. In terms of clustering accuracy, the topics obtained by AnchorFree again lead to much higher clustering accuracies in all cases.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table about 'Table 2: Experiment results on the Reuters-21578 corpus.', answer the following question in a stepwise manner. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhat is the clustering accuracy of AnchorFree when F=25?\n" }, { "role": "assistant", "content": "To find the clustering accuracy of AnchorFree when F=25, locate the row where F=25 and find the value under the ClustAcc AnchorFree column. So, the answer is {\"answer\": \"0.74\"}." } ], "images": [ "scigen/train/3985.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Experiment results on the Reuters-21578 corpus." ], "reasoning": "To find the clustering accuracy of AnchorFree when F=25, locate the row where F=25 and find the value under the ClustAcc AnchorFree column.", "question": "What is the clustering accuracy of AnchorFree when F=25?", "answer": "So, the answer is {\"answer\": \"0.74\"}." }, { "messages": [ { "role": "user", "content": "Begin with the sentence AnchorFree has the highest coherence score for F=9. and analyze the table to decide whether it 'supports' or 'refutes' the above sentence. If only using the table information is not sufficient to give a final conclusion, label the sentence 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nAnchorFree has the highest coherence score for F=9.\n" }, { "role": "assistant", "content": "To verify if AnchorFree has the highest coherence score for F=9, locate the row where F=9 and compare the Coh AnchorFree value with other coherence scores in the same row. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3985.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Experiment results on the Reuters-21578 corpus." ], "reasoning": "To verify if AnchorFree has the highest coherence score for F=9, locate the row where F=9 and compare the Coh AnchorFree value with other coherence scores in the same row.", "statement": "AnchorFree has the highest coherence score for F=9.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: Learned hyperparameters for the GP SSTK models in the fr-en dataset, with α fixed at 1. λsrc and λtgt are the hyperparameters corresponding to the kernels on the source and target parse trees, respectively. The values shown are averaged over the cross-validation results.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The model gives higher λ values to target sentence kernels compared to source sentence kernels, indicating a preference for features from target trees.We also inspect the resulting hyperparameters to obtain insights about the features used by the model. The λ values obtained are higher for the target sentence kernels than for the source sentence ones. We can interpret this as the model giving preference to features from the target trees instead of the source trees, which is what we would expect for this task." } ], "images": [ "scigen/train/3986.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Learned hyperparameters for the GP SSTK models in the fr-en dataset, with α fixed at 1. λsrc and λtgt are the hyperparameters corresponding to the kernels on the source and target parse trees, respectively. The values shown are averaged over the cross-validation results." ], "reasoning": "The model gives higher λ values to target sentence kernels compared to source sentence kernels, indicating a preference for features from target trees.", "description": "We also inspect the resulting hyperparameters to obtain insights about the features used by the model. The λ values obtained are higher for the target sentence kernels than for the source sentence ones. We can interpret this as the model giving preference to features from the target trees instead of the source trees, which is what we would expect for this task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 5: Learned hyperparameters for the GP SSTK models in the fr-en dataset, with α fixed at 1. λsrc and λtgt are the hyperparameters corresponding to the kernels on the source and target parse trees, respectively. The values shown are averaged over the cross-validation results.'.\n\nQuestion: What is the λ value for the target sentence kernel in the GP SSTK + model?\n" }, { "role": "assistant", "content": "The table shows λ values for both source and target sentence kernels in two models. For the GP SSTK + model, the λ value for the target sentence kernel is listed in the λtgt column. So, the answer is {\"answer\": \"0.3108\"}" } ], "images": [ "scigen/train/3986.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Learned hyperparameters for the GP SSTK models in the fr-en dataset, with α fixed at 1. λsrc and λtgt are the hyperparameters corresponding to the kernels on the source and target parse trees, respectively. The values shown are averaged over the cross-validation results." ], "reasoning": "The table shows λ values for both source and target sentence kernels in two models. For the GP SSTK + model, the λ value for the target sentence kernel is listed in the λtgt column.", "question": "What is the λ value for the target sentence kernel in the GP SSTK + model?", "answer": "So, the answer is {\"answer\": \"0.3108\"}" }, { "messages": [ { "role": "user", "content": "Given a table as a premise, determine if it 'supports' or 'refutes' the following hypothesis. If the table information is not sufficient to give a conclusion, the hypothesis should be considered 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe λ value for the source sentence kernel in the GP SSTK × model is higher than in the GP SSTK + model.\n" }, { "role": "assistant", "content": "The table shows that the λ value for the source sentence kernel in the GP SSTK × model is 0.1405, which is higher than the λ value of 0.1394 in the GP SSTK + model. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3986.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Learned hyperparameters for the GP SSTK models in the fr-en dataset, with α fixed at 1. λsrc and λtgt are the hyperparameters corresponding to the kernels on the source and target parse trees, respectively. The values shown are averaged over the cross-validation results." ], "reasoning": "The table shows that the λ value for the source sentence kernel in the GP SSTK × model is 0.1405, which is higher than the λ value of 0.1394 in the GP SSTK + model.", "statement": "The λ value for the source sentence kernel in the GP SSTK × model is higher than in the GP SSTK + model.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 2: Pearson’s correlation scores for the Emotion Analysis task (higher is better).'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "The GP model with tree kernels outperforms SVMs, but the SASSTKfull model overfits due to its many hyperparameters, leading to worse performance.The best GP model with tree kernels outperforms the SVMs, showing that the fine-grained model selection procedure provided by the GP models is helpful when dealing with tree kernels. However, using the SASSTK models do not help in the case of free α and the SASSTKfull actually performs worse than the original SSTK, even though the optimized marginal likelihood was higher. This is evidence that the SASSTKfull model is overfitting the training data, probably due to its large number of hyperparameters. Inspecting the free α models showed that the values found by the optimizer were very close to zero. This indicates that the model selection procedure prefer towards giving smaller weights to incomplete tree fragments. We can interpret this as the model selecting a more lexicalized feature space, which also explains why the GP RBF model on bag-of-words performed the best in this task." } ], "images": [ "scigen/train/3987.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: Pearson’s correlation scores for the Emotion Analysis task (higher is better)." ], "reasoning": "The GP model with tree kernels outperforms SVMs, but the SASSTKfull model overfits due to its many hyperparameters, leading to worse performance.", "description": "The best GP model with tree kernels outperforms the SVMs, showing that the fine-grained model selection procedure provided by the GP models is helpful when dealing with tree kernels. However, using the SASSTK models do not help in the case of free α and the SASSTKfull actually performs worse than the original SSTK, even though the optimized marginal likelihood was higher. This is evidence that the SASSTKfull model is overfitting the training data, probably due to its large number of hyperparameters. Inspecting the free α models showed that the values found by the optimizer were very close to zero. This indicates that the model selection procedure prefer towards giving smaller weights to incomplete tree fragments. We can interpret this as the model selecting a more lexicalized feature space, which also explains why the GP RBF model on bag-of-words performed the best in this task.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 2: Pearson’s correlation scores for the Emotion Analysis task (higher is better).' and proceed to solve the following question in a stepwise manner. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhich model performed the best according to the Pearson's correlation coefficient?\n" }, { "role": "assistant", "content": "The table shows the Pearson's correlation coefficients for various models. The highest value is 0.5891 for the GP BOW (free α) model. So, the answer is {\"answer\": \"GP BOW (free α)\"}." } ], "images": [ "scigen/train/3987.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: Pearson’s correlation scores for the Emotion Analysis task (higher is better)." ], "reasoning": "The table shows the Pearson's correlation coefficients for various models. The highest value is 0.5891 for the GP BOW (free α) model.", "question": "Which model performed the best according to the Pearson's correlation coefficient?", "answer": "So, the answer is {\"answer\": \"GP BOW (free α)\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe GP SASSTKfull model performs better than the GP SSTK model.\n" }, { "role": "assistant", "content": "According to the table, the Pearson's correlation coefficient for GP SASSTKfull is 0.5118, while for GP SSTK it is 0.5713. Therefore, GP SASSTKfull performs worse than GP SSTK. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3987.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: Pearson’s correlation scores for the Emotion Analysis task (higher is better)." ], "reasoning": "According to the table, the Pearson's correlation coefficient for GP SASSTKfull is 0.5118, while for GP SSTK it is 0.5713. Therefore, GP SASSTKfull performs worse than GP SSTK.", "statement": "The GP SASSTKfull model performs better than the GP SSTK model.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 4: Error scores for the Quality Estimation task (lower is better). Results are in terms of post-editing time per word. Bold scores are the best ones for each dataset.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "GP models combining tree kernels with an RBF kernel outperform all other models for the fr-en dataset, while SVMs using a combination of kernels perform worse than using the RBF kernel alone.They indicate a number of interesting findings: For the fr-en dataset, the GP models combining tree kernels with an RBF kernel outperform all other models. Results for the en-es dataset are less consistent, probably due to the small size of the dataset, but on average they are better than their SVM counterparts. The SVMs using a combination of kernels performs worse than using the RBF kernel alone. Inspecting the models, we found that grid search actually harms performance. For instance, for the fr-en dataset, MAE and RMSE for the RBF + SSTK × model before grid search are 0.4681 and 0.6016, respectively. On the other hand, for this dataset all GP models achieve better results after optimization. Unlike in the Emotion Analysis task, fixing α results in better performance, even though the resulting models have lower marginal likelihood than the ones with free α. The same effect happened when comparing the SASSTK models with the SSTK ones for the en-es dataset. Both cases are evidence of model overfitting." } ], "images": [ "scigen/train/3988.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Error scores for the Quality Estimation task (lower is better). Results are in terms of post-editing time per word. Bold scores are the best ones for each dataset." ], "reasoning": "GP models combining tree kernels with an RBF kernel outperform all other models for the fr-en dataset, while SVMs using a combination of kernels perform worse than using the RBF kernel alone.", "description": "They indicate a number of interesting findings: For the fr-en dataset, the GP models combining tree kernels with an RBF kernel outperform all other models. Results for the en-es dataset are less consistent, probably due to the small size of the dataset, but on average they are better than their SVM counterparts. The SVMs using a combination of kernels performs worse than using the RBF kernel alone. Inspecting the models, we found that grid search actually harms performance. For instance, for the fr-en dataset, MAE and RMSE for the RBF + SSTK × model before grid search are 0.4681 and 0.6016, respectively. On the other hand, for this dataset all GP models achieve better results after optimization. Unlike in the Emotion Analysis task, fixing α results in better performance, even though the resulting models have lower marginal likelihood than the ones with free α. The same effect happened when comparing the SASSTK models with the SSTK ones for the en-es dataset. Both cases are evidence of model overfitting.", "answer": "" }, { "messages": [ { "role": "user", "content": "Show the detailed solution to solve the following question. Output the final answer as JSON in the format {\"answer\": \"\"}. The question is related to the given table titled 'Table 4: Error scores for the Quality Estimation task (lower is better). Results are in terms of post-editing time per word. Bold scores are the best ones for each dataset.'.\n\nQuestion: What are the MAE and RMSE for the RBF + SSTK × model before grid search for the fr-en dataset?\n" }, { "role": "assistant", "content": "The description states that for the fr-en dataset, MAE and RMSE for the RBF + SSTK × model before grid search are 0.4681 and 0.6016, respectively. So, the answer is {\"answer\": \"MAE: 0.4681, RMSE: 0.6016\"}" } ], "images": [ "scigen/train/3988.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Error scores for the Quality Estimation task (lower is better). Results are in terms of post-editing time per word. Bold scores are the best ones for each dataset." ], "reasoning": "The description states that for the fr-en dataset, MAE and RMSE for the RBF + SSTK × model before grid search are 0.4681 and 0.6016, respectively.", "question": "What are the MAE and RMSE for the RBF + SSTK × model before grid search for the fr-en dataset?", "answer": "So, the answer is {\"answer\": \"MAE: 0.4681, RMSE: 0.6016\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim Grid search improves the performance of all GP models for the fr-en dataset., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nGrid search improves the performance of all GP models for the fr-en dataset.\n" }, { "role": "assistant", "content": "The description mentions that for the fr-en dataset, all GP models achieve better results after optimization, indicating that grid search improves their performance. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3988.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Error scores for the Quality Estimation task (lower is better). Results are in terms of post-editing time per word. Bold scores are the best ones for each dataset." ], "reasoning": "The description mentions that for the fr-en dataset, all GP models achieve better results after optimization, indicating that grid search improves their performance.", "statement": "Grid search improves the performance of all GP models for the fr-en dataset.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 1: Comparison between our model and baselines from Dodge et al. (2015) on the QA and Recs tasks evaluated according to HITS@1 and HITS@100, respectively.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "Our model outperforms QA SYSTEM, MEMN2N, and JOINT SUPERVISED EMBEDDINGS without using supplementary tricks, and further investigation is planned to improve performance on the Recs task.Despite the advantage of the QA SYSTEM, it is a carefully designed system to handle knowledge base data in the form of triples, but our model can leverage data in the form of documents, without making any assumption about the form of the input data and can be applied to different kind of tasks. Additionally, the model MEMN2N is a neural network whose weights are pre-trained on the same dataset without using the long-term memory and the models JOINT SUPERVISED EMBEDDINGS and JOINT MEMN2N are models trained across all the tasks of the dataset in order to boost performance. Despite that, our model outperforms the three above-mentioned ones without using any supplementary trick. Even though our model performance is higher than all the others on the Recs task, we believe that the obtained result may be improved and so we plan a further investigation. Moreover, the need for further investigation can be justified by the work reported in Searle and Bingham-Walker" } ], "images": [ "scigen/train/3989.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: Comparison between our model and baselines from Dodge et al. (2015) on the QA and Recs tasks evaluated according to HITS@1 and HITS@100, respectively." ], "reasoning": "Our model outperforms QA SYSTEM, MEMN2N, and JOINT SUPERVISED EMBEDDINGS without using supplementary tricks, and further investigation is planned to improve performance on the Recs task.", "description": "Despite the advantage of the QA SYSTEM, it is a carefully designed system to handle knowledge base data in the form of triples, but our model can leverage data in the form of documents, without making any assumption about the form of the input data and can be applied to different kind of tasks. Additionally, the model MEMN2N is a neural network whose weights are pre-trained on the same dataset without using the long-term memory and the models JOINT SUPERVISED EMBEDDINGS and JOINT MEMN2N are models trained across all the tasks of the dataset in order to boost performance. Despite that, our model outperforms the three above-mentioned ones without using any supplementary trick. Even though our model performance is higher than all the others on the Recs task, we believe that the obtained result may be improved and so we plan a further investigation. Moreover, the need for further investigation can be justified by the work reported in Searle and Bingham-Walker", "answer": "" }, { "messages": [ { "role": "user", "content": "Refer to the provided table image and work through the question step by step. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: 'Table 1: Comparison between our model and baselines from Dodge et al. (2015) on the QA and Recs tasks evaluated according to HITS@1 and HITS@100, respectively.'\nQuestion: Which model has the highest score in the QA task and how does it compare to OUR MODEL?\n" }, { "role": "assistant", "content": "The QA SYSTEM has the highest score in the QA task with 90.7, while OUR MODEL has a score of 86.8, making QA SYSTEM the top performer in this task. So, the answer is {\"answer\": \"QA SYSTEM\"}." } ], "images": [ "scigen/train/3989.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: Comparison between our model and baselines from Dodge et al. (2015) on the QA and Recs tasks evaluated according to HITS@1 and HITS@100, respectively." ], "reasoning": "The QA SYSTEM has the highest score in the QA task with 90.7, while OUR MODEL has a score of 86.8, making QA SYSTEM the top performer in this task.", "question": "Which model has the highest score in the QA task and how does it compare to OUR MODEL?", "answer": "So, the answer is {\"answer\": \"QA SYSTEM\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: OUR MODEL outperforms all other models in the Recs task., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"refutes\"}.\n\nOUR MODEL outperforms all other models in the Recs task.\n" }, { "role": "assistant", "content": "The table shows that OUR MODEL has the highest score in the Recs task with 30.0, which is higher than all other models listed. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3989.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: Comparison between our model and baselines from Dodge et al. (2015) on the QA and Recs tasks evaluated according to HITS@1 and HITS@100, respectively." ], "reasoning": "The table shows that OUR MODEL has the highest score in the Recs task with 30.0, which is higher than all other models listed.", "statement": "OUR MODEL outperforms all other models in the Recs task.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "This image shows a table with the title 'Table 11: Accuracy (%) with different BPE vocabulary sizes. “0” represents the character-level models.'. Some table cells are highlighted with a different color. Please generate a description for the table.\n" }, { "role": "assistant", "content": "Subword-level models outperform character-level models in German, Icelandic, and Swedish with small data sizes, but not in English and Hungarian.Hypothesis 4, finally, states that subword-level models are better than character-level models. Our experimental results of BPE-Soft models in four languages (except Swedish) show that subword-level models are superior to character-level models when the BPE vocabulary is small. In subword-level models, the vocabulary includes all the characters and learned subword units. We try different BPE vocabulary sizes. All the subword-level models are trained on LSTMs. In German, Icelandic, and Swedish where the data size is small, the subword-level models surpass character-level models. However, the subword-level models in English and Hungarian are clearly not as well as character-level models." } ], "images": [ "scigen/train/3990.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 11: Accuracy (%) with different BPE vocabulary sizes. “0” represents the character-level models." ], "reasoning": "Subword-level models outperform character-level models in German, Icelandic, and Swedish with small data sizes, but not in English and Hungarian.", "description": "Hypothesis 4, finally, states that subword-level models are better than character-level models. Our experimental results of BPE-Soft models in four languages (except Swedish) show that subword-level models are superior to character-level models when the BPE vocabulary is small. In subword-level models, the vocabulary includes all the characters and learned subword units. We try different BPE vocabulary sizes. All the subword-level models are trained on LSTMs. In German, Icelandic, and Swedish where the data size is small, the subword-level models surpass character-level models. However, the subword-level models in English and Hungarian are clearly not as well as character-level models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Please solve the question based on the given table about 'Table 11: Accuracy (%) with different BPE vocabulary sizes. “0” represents the character-level models.'. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}. \n\nQuestion: Which model and BPE size combination achieved the highest score in English?\n\nYour Solution:\n" }, { "role": "assistant", "content": "To find the highest score in English, we look at the English column and identify the highest value, which is 95.16. This score is achieved by the BPE-Transformer model with a BPE size of 0. So, the answer is {\"answer\": \"BPE-Transformer with BPE size 0\"}" } ], "images": [ "scigen/train/3990.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 11: Accuracy (%) with different BPE vocabulary sizes. “0” represents the character-level models." ], "reasoning": "To find the highest score in English, we look at the English column and identify the highest value, which is 95.16. This score is achieved by the BPE-Transformer model with a BPE size of 0.", "question": "Which model and BPE size combination achieved the highest score in English?", "answer": "So, the answer is {\"answer\": \"BPE-Transformer with BPE size 0\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe BPE-Soft model with a BPE size of 100 achieved the highest score in German.\n" }, { "role": "assistant", "content": "To verify this statement, we look at the German column and identify the highest value, which is 96.64. This score is achieved by the BPE-Soft model with a BPE size of 100. So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3990.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 11: Accuracy (%) with different BPE vocabulary sizes. “0” represents the character-level models." ], "reasoning": "To verify this statement, we look at the German column and identify the highest value, which is 96.64. This score is achieved by the BPE-Soft model with a BPE size of 100.", "statement": "The BPE-Soft model with a BPE size of 100 achieved the highest score in German.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Please succinctly describe the given table titled 'Table 5: Evaluation results in word accuracy (Acc, %) and CER. The best results in each language have background color. Many identical values in CER are different, but the difference is irrelevant in Chi-square test.', pay attention to the highlighted cells within the table.\n" }, { "role": "assistant", "content": "The Transformer model achieved the highest word accuracy in English and Hungarian, with significant improvements over the baseline, while the smallest dataset size in Swedish resulted in lower performance compared to the baseline.The baseline from \\newcitepettersson2014evaluation has very high word accuracy and low CER scores in all five languages. The results in the baseline are obtained using character-level SMT models except for Icelandic, where the combination of a Levenshtein-based method and a dictionary-based method achieved the best results. We use word accuracy and CER to evaluate the predictions. For the historical spelling normalization task, word accuracy is a very important evaluation metric. Moreover, word accuracy is the only evaluation metric in \\newcitebollmann2016improve and \\newcitebollmann2017learning. However, CER is a good supplement to word accuracy. It is more fine-grained and evaluates the character-level normalizations. In our experiments, we use Levenshtein distance to compute CER. Compared to the other four languages, we get a huge absolute improvement of 12.04% in Hungarian, improving the word accuracy from 80.1% to 92.14%. We get 0.04%, 0.86%, and 2.99% absolute improvement in German, English, and Icelandic, respectively. Our best NMT result in Swedish is still a little lower than the baseline in word accuracy. We attribute the reason to the dataset size, because Swedish has the smallest training set. Hypothesis 1 is that the performance gap between vanilla RNNs and GRUs/LSTMs will not be huge. Att-RNN even performs better than Att-GRU/LSTM in Icelandic. However, Att-RNN is clearly worse than Att-LSTM in German. These results support our Hypothesis 1 well. Hypothesis 3 is that Transformer models are better than soft-attention-based models. It is interesting that English and Hungarian have much more training data compared to the other three languages. This result reveals that Transformer models need more data to exceed RNN-based models." } ], "images": [ "scigen/train/3991.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 5: Evaluation results in word accuracy (Acc, %) and CER. The best results in each language have background color. Many identical values in CER are different, but the difference is irrelevant in Chi-square test." ], "reasoning": "The Transformer model achieved the highest word accuracy in English and Hungarian, with significant improvements over the baseline, while the smallest dataset size in Swedish resulted in lower performance compared to the baseline.", "description": "The baseline from \\newcitepettersson2014evaluation has very high word accuracy and low CER scores in all five languages. The results in the baseline are obtained using character-level SMT models except for Icelandic, where the combination of a Levenshtein-based method and a dictionary-based method achieved the best results. We use word accuracy and CER to evaluate the predictions. For the historical spelling normalization task, word accuracy is a very important evaluation metric. Moreover, word accuracy is the only evaluation metric in \\newcitebollmann2016improve and \\newcitebollmann2017learning. However, CER is a good supplement to word accuracy. It is more fine-grained and evaluates the character-level normalizations. In our experiments, we use Levenshtein distance to compute CER. Compared to the other four languages, we get a huge absolute improvement of 12.04% in Hungarian, improving the word accuracy from 80.1% to 92.14%. We get 0.04%, 0.86%, and 2.99% absolute improvement in German, English, and Icelandic, respectively. Our best NMT result in Swedish is still a little lower than the baseline in word accuracy. We attribute the reason to the dataset size, because Swedish has the smallest training set. Hypothesis 1 is that the performance gap between vanilla RNNs and GRUs/LSTMs will not be huge. Att-RNN even performs better than Att-GRU/LSTM in Icelandic. However, Att-RNN is clearly worse than Att-LSTM in German. These results support our Hypothesis 1 well. Hypothesis 3 is that Transformer models are better than soft-attention-based models. It is interesting that English and Hungarian have much more training data compared to the other three languages. This result reveals that Transformer models need more data to exceed RNN-based models.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on the table picture with the title 'Table 5: Evaluation results in word accuracy (Acc, %) and CER. The best results in each language have background color. Many identical values in CER are different, but the difference is irrelevant in Chi-square test.', unfold the steps to solve the question given next. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nQuestion: Which model achieved the highest word accuracy in German and what was the accuracy percentage?\n" }, { "role": "assistant", "content": "By examining the table, the BPE-Soft model achieved the highest word accuracy in German with a percentage of 96.64%. So, the answer is {\"answer\": \"BPE-Soft, 96.64%\"}" } ], "images": [ "scigen/train/3991.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 5: Evaluation results in word accuracy (Acc, %) and CER. The best results in each language have background color. Many identical values in CER are different, but the difference is irrelevant in Chi-square test." ], "reasoning": "By examining the table, the BPE-Soft model achieved the highest word accuracy in German with a percentage of 96.64%.", "question": "Which model achieved the highest word accuracy in German and what was the accuracy percentage?", "answer": "So, the answer is {\"answer\": \"BPE-Soft, 96.64%\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe Att-RNN model achieved the highest word accuracy in Icelandic.\n" }, { "role": "assistant", "content": "The table shows that the highest word accuracy in Icelandic was achieved by the BPE-Soft model with 87.19%, not the Att-RNN model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3991.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 5: Evaluation results in word accuracy (Acc, %) and CER. The best results in each language have background color. Many identical values in CER are different, but the difference is irrelevant in Chi-square test." ], "reasoning": "The table shows that the highest word accuracy in Icelandic was achieved by the BPE-Soft model with 87.19%, not the Att-RNN model.", "statement": "The Att-RNN model achieved the highest word accuracy in Icelandic.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image labeled 'Table 7: The results of combining the NMT-based method and the dictionary-based method. \"△\" denotes the absolute improvement on accuracy (%) compared to the NMT-based method.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The hybrid method combines NMT-based and dictionary-based methods, showing around a 5% improvement in Icelandic.Therefore, we explore a hybrid method, combining the NMT-based method and the dictionary-based method. More specifically, we first extract a list of unchanged spellings from the training set. During the evaluation, if a word is in this list, we simply copy it as its normalization. If it is not in the list, we feed it to the NMT models. In particular, the improvements on Icelandic are around 5%." } ], "images": [ "scigen/train/3992.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 7: The results of combining the NMT-based method and the dictionary-based method. \"△\" denotes the absolute improvement on accuracy (%) compared to the NMT-based method." ], "reasoning": "The hybrid method combines NMT-based and dictionary-based methods, showing around a 5% improvement in Icelandic.", "description": "Therefore, we explore a hybrid method, combining the NMT-based method and the dictionary-based method. More specifically, we first extract a list of unchanged spellings from the training set. During the evaluation, if a word is in this list, we simply copy it as its normalization. If it is not in the list, we feed it to the NMT models. In particular, the improvements on Icelandic are around 5%.", "answer": "" }, { "messages": [ { "role": "user", "content": "Table title: Table 7: The results of combining the NMT-based method and the dictionary-based method. \"△\" denotes the absolute improvement on accuracy (%) compared to the NMT-based method.\nQuestion: Which model achieved the highest accuracy for the Icelandic language?\nSolve the above question. Conclude your response with the final answer in the JSON format, structured as {\"answer\": \"\"}.\nYour detailed solution: \n" }, { "role": "assistant", "content": "By comparing the Icelandic Acc column, the Transformer model achieved the highest accuracy with a value of 91.66. So, the answer is {\"answer\": \"Transformer\"}" } ], "images": [ "scigen/train/3992.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 7: The results of combining the NMT-based method and the dictionary-based method. \"△\" denotes the absolute improvement on accuracy (%) compared to the NMT-based method." ], "reasoning": "By comparing the Icelandic Acc column, the Transformer model achieved the highest accuracy with a value of 91.66.", "question": "Which model achieved the highest accuracy for the Icelandic language?", "answer": "So, the answer is {\"answer\": \"Transformer\"}" }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe NoAtt-GRU model has the highest accuracy for the German language.\n" }, { "role": "assistant", "content": "By comparing the German Acc column, the NoAtt-GRU model has an accuracy of 95.44, which is not the highest. The highest accuracy for German is 96.42 by the NoAtt-LSTM model. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3992.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 7: The results of combining the NMT-based method and the dictionary-based method. \"△\" denotes the absolute improvement on accuracy (%) compared to the NMT-based method." ], "reasoning": "By comparing the German Acc column, the NoAtt-GRU model has an accuracy of 95.44, which is not the highest. The highest accuracy for German is 96.42 by the NoAtt-LSTM model.", "statement": "The NoAtt-GRU model has the highest accuracy for the German language.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "You are provided with a table image with caption 'Table 8: The average edit distance of the changed spellings in test set and the average edit distance of the incorrectly normalized changed spellings.'. Formulate a statement describing the table.\n" }, { "role": "assistant", "content": "The average edit distance of incorrectly normalized spellings is larger compared to the average edit distance after normalization, with Swedish having the shortest average edit distance and English the longest.For the incorrectly normalized changed spellings, the average edit distance is smaller than 2. In other words, we just need less than two edits to translate an incorrectly normalized spelling into the correct one. In the incorrect normalizations, Swedish has the shortest average edit distance 1.54, and English has the longest average edit distance 1.81. Intuitively, if a spelling has smaller edit distance, it is easier for the model to normalize this spelling correctly. That is to say, the average edit distance of incorrectly normalized spellings will be larger compared to the average edit distance before normalization. For example, the edit distance between “mōdanac” and “mondák” is 6, yet the model can normalize it correctly. Although the model normalized “mėgbètèǵeitnc̣” into “megbetegíteniúk”, which is not identical to the modern spelling “megbetegítenék”, the edit distance nevertheless decreased from 9 to 2. We hypothesize that this could be due to the fact that Hungarian belongs to a different language family than the other four languages." } ], "images": [ "scigen/train/3993.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 8: The average edit distance of the changed spellings in test set and the average edit distance of the incorrectly normalized changed spellings." ], "reasoning": "The average edit distance of incorrectly normalized spellings is larger compared to the average edit distance after normalization, with Swedish having the shortest average edit distance and English the longest.", "description": "For the incorrectly normalized changed spellings, the average edit distance is smaller than 2. In other words, we just need less than two edits to translate an incorrectly normalized spelling into the correct one. In the incorrect normalizations, Swedish has the shortest average edit distance 1.54, and English has the longest average edit distance 1.81. Intuitively, if a spelling has smaller edit distance, it is easier for the model to normalize this spelling correctly. That is to say, the average edit distance of incorrectly normalized spellings will be larger compared to the average edit distance before normalization. For example, the edit distance between “mōdanac” and “mondák” is 6, yet the model can normalize it correctly. Although the model normalized “mėgbètèǵeitnc̣” into “megbetegíteniúk”, which is not identical to the modern spelling “megbetegítenék”, the edit distance nevertheless decreased from 9 to 2. We hypothesize that this could be due to the fact that Hungarian belongs to a different language family than the other four languages.", "answer": "" }, { "messages": [ { "role": "user", "content": "With the shown table image as your reference, carefully work out a detailed solution to the following question. At the end of your output, present the final answer as JSON in the format {\"answer\": \"\"}.\nTable title: Table 8: The average edit distance of the changed spellings in test set and the average edit distance of the incorrectly normalized changed spellings.\nQuestion: Which language has the largest decrease in average edit distance after normalization?\n" }, { "role": "assistant", "content": "To find the language with the largest decrease in average edit distance, we need to subtract the 'Changed' edit distance from the 'Incorrect' edit distance for each language and compare the results. So, the answer is {\"answer\": \"English\"}." } ], "images": [ "scigen/train/3993.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 8: The average edit distance of the changed spellings in test set and the average edit distance of the incorrectly normalized changed spellings." ], "reasoning": "To find the language with the largest decrease in average edit distance, we need to subtract the 'Changed' edit distance from the 'Incorrect' edit distance for each language and compare the results.", "question": "Which language has the largest decrease in average edit distance after normalization?", "answer": "So, the answer is {\"answer\": \"English\"}." }, { "messages": [ { "role": "user", "content": "Regarding the sentence: The average edit distance for incorrectly normalized Swedish spellings is 1.54., examine the table and assess whether the sentence is 'supports' or 'refutes'. If the table information is not sufficient to give a conclusion, label it as 'not enough info'. Return the result as JSON in the format {\"answer\": \"\"}, e.g., {\"answer\": \"supports\"}.\n\nThe average edit distance for incorrectly normalized Swedish spellings is 1.54.\n" }, { "role": "assistant", "content": "The table shows that the average edit distance for incorrectly normalized Swedish spellings is indeed 1.54. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3993.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 8: The average edit distance of the changed spellings in test set and the average edit distance of the incorrectly normalized changed spellings." ], "reasoning": "The table shows that the average edit distance for incorrectly normalized Swedish spellings is indeed 1.54.", "statement": "The average edit distance for incorrectly normalized Swedish spellings is 1.54.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here's an table with the title 'Table 10: The accuracy of different models in Swedish with different dataset settings.'. Generate sentences that describes it. Pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "Moving token pairs from the test set to the training and development sets improves the performance of NMT models, as indicated by higher scores in larger training sets.As there is much more test data in Swedish compared to other languages, we test our hypothesis by moving some token pairs from test sets to training sets and development sets. More specifically, we create two new datasets, in which 27,000 and 30,000 token pairs are moved from the beginning of the test set to the training set and the development set, respectively. This result indicates that the performance of NMT models is highly related to the size of training set." } ], "images": [ "scigen/train/3994.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 10: The accuracy of different models in Swedish with different dataset settings." ], "reasoning": "Moving token pairs from the test set to the training and development sets improves the performance of NMT models, as indicated by higher scores in larger training sets.", "description": "As there is much more test data in Swedish compared to other languages, we test our hypothesis by moving some token pairs from test sets to training sets and development sets. More specifically, we create two new datasets, in which 27,000 and 30,000 token pairs are moved from the beginning of the test set to the training set and the development set, respectively. This result indicates that the performance of NMT models is highly related to the size of training set.", "answer": "" }, { "messages": [ { "role": "user", "content": "Look at the table titled 'Table 10: The accuracy of different models in Swedish with different dataset settings.' and methodically tackle the question that follows. In the last of your solution, output the final answer as JSON in the format {\"answer\": \"\"}.\nWhat is the performance score of the Transformer model when the training set size is 57,637?\n" }, { "role": "assistant", "content": "The table shows that when the training set size is 57,637, the performance score of the Transformer model is 95.77. So, the answer is {\"answer\": \"95.77\"}" } ], "images": [ "scigen/train/3994.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 10: The accuracy of different models in Swedish with different dataset settings." ], "reasoning": "The table shows that when the training set size is 57,637, the performance score of the Transformer model is 95.77.", "question": "What is the performance score of the Transformer model when the training set size is 57,637?", "answer": "So, the answer is {\"answer\": \"95.77\"}" }, { "messages": [ { "role": "user", "content": "Considering the claim The Att-GRU model performs better than the Att-RNN model when the training set size is 51,237., determine from the table whether it is 'supports' or 'refutes'. Answer 'not enough info' due to insufficient evidence. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe Att-GRU model performs better than the Att-RNN model when the training set size is 51,237.\n" }, { "role": "assistant", "content": "The table shows that when the training set size is 51,237, the Att-GRU model has a performance score of 94.79, which is lower than the Att-RNN model's score of 95.45. So, the answer is {\"answer\": \"refutes\"}" } ], "images": [ "scigen/train/3994.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 10: The accuracy of different models in Swedish with different dataset settings." ], "reasoning": "The table shows that when the training set size is 51,237, the Att-GRU model has a performance score of 94.79, which is lower than the Att-RNN model's score of 95.45.", "statement": "The Att-GRU model performs better than the Att-RNN model when the training set size is 51,237.", "answer": "So, the answer is {\"answer\": \"refutes\"}" }, { "messages": [ { "role": "user", "content": "Generate descriptive sentences to describe the table with the title 'Table 3: NLI accuracy scores with structured attention. The LP-SparseMAP models perform competitively.', pay attention to the highlighted cells.\n" }, { "role": "assistant", "content": "The relaxed LP-matching method consistently outperforms the coarse matching factor on both SNLI and MultiNLI datasets, indicating that different parametrizations can yield varying performance levels before convergence.On both datasets, the relaxed LP-matching outperforms the coarse matching factor, suggesting that, indeed, equivalent parametrizations of a model may perform differently when not run until convergence." } ], "images": [ "scigen/train/3995.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: NLI accuracy scores with structured attention. The LP-SparseMAP models perform competitively." ], "reasoning": "The relaxed LP-matching method consistently outperforms the coarse matching factor on both SNLI and MultiNLI datasets, indicating that different parametrizations can yield varying performance levels before convergence.", "description": "On both datasets, the relaxed LP-matching outperforms the coarse matching factor, suggesting that, indeed, equivalent parametrizations of a model may perform differently when not run until convergence.", "answer": "" }, { "messages": [ { "role": "user", "content": "Examine the table of 'Table 3: NLI accuracy scores with structured attention. The LP-SparseMAP models perform competitively.' and proceed to solve the following question in a stepwise manner. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\nWhich method has the highest performance on the MultiNLI test dataset?\n" }, { "role": "assistant", "content": "To find the highest performance on the MultiNLI test dataset, compare the values in the MultiNLI test column. The highest value is 71.17, achieved by the LP-sequential method. So, the answer is {\"answer\": \"LP-sequential\"}." } ], "images": [ "scigen/train/3995.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: NLI accuracy scores with structured attention. The LP-SparseMAP models perform competitively." ], "reasoning": "To find the highest performance on the MultiNLI test dataset, compare the values in the MultiNLI test column. The highest value is 71.17, achieved by the LP-sequential method.", "question": "Which method has the highest performance on the MultiNLI test dataset?", "answer": "So, the answer is {\"answer\": \"LP-sequential\"}." }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nThe LP-matching method has the highest performance on the SNLI test dataset.\n" }, { "role": "assistant", "content": "To verify this statement, compare the values in the SNLI test column. The highest value is 85.04, achieved by the LP-matching method, which supports the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3995.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: NLI accuracy scores with structured attention. The LP-SparseMAP models perform competitively." ], "reasoning": "To verify this statement, compare the values in the SNLI test column. The highest value is 85.04, achieved by the LP-matching method, which supports the statement.", "statement": "The LP-matching method has the highest performance on the SNLI test dataset.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 4: Multilabel classification test F1 scores.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "The LP-SparseMAP loss model outperforms both the unstructured and structured hinge loss models, indicating its promise for structured output learning.The structured hinge loss model is worse than the unstructured (binary logistic loss) baseline; the LP-SparseMAP loss model outperforms both. This suggests that the LP-SparseMAP loss is promising for structured output learning. We note that, in strictly-supervised setting, approaches that blend inference with learning ( e. g., Chen et al." } ], "images": [ "scigen/train/3996.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 4: Multilabel classification test F1 scores." ], "reasoning": "The LP-SparseMAP loss model outperforms both the unstructured and structured hinge loss models, indicating its promise for structured output learning.", "description": "The structured hinge loss model is worse than the unstructured (binary logistic loss) baseline; the LP-SparseMAP loss model outperforms both. This suggests that the LP-SparseMAP loss is promising for structured output learning. We note that, in strictly-supervised setting, approaches that blend inference with learning ( e. g., Chen et al.", "answer": "" }, { "messages": [ { "role": "user", "content": "Based on this table of 'Table 4: Multilabel classification test F1 scores.', answer the following question. Give detailed solution consisting of each step. Finally, your final answer should be in the JSON format of {\"answer\": \"\"}.\nWhich model has the highest score for the bibtex dataset?\n" }, { "role": "assistant", "content": "By comparing the scores for the bibtex dataset, the LP-SparseMAP loss model has the highest score of 43.43. So, the answer is {\"answer\": \"LP-SparseMAP loss\"}." } ], "images": [ "scigen/train/3996.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 4: Multilabel classification test F1 scores." ], "reasoning": "By comparing the scores for the bibtex dataset, the LP-SparseMAP loss model has the highest score of 43.43.", "question": "Which model has the highest score for the bibtex dataset?", "answer": "So, the answer is {\"answer\": \"LP-SparseMAP loss\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Provide the final answer in the JSON structure, using the format {\"answer\": \"\"}.\n\nThe structured hinge loss model has a higher score than the unstructured model for the bookmarks dataset.\n" }, { "role": "assistant", "content": "The table shows that the structured hinge loss model has a score of 33.26, which is lower than the unstructured model's score of 35.76 for the bookmarks dataset. Thus, the answer is {\"answer\": \"refutes\"}." } ], "images": [ "scigen/train/3996.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 4: Multilabel classification test F1 scores." ], "reasoning": "The table shows that the structured hinge loss model has a score of 33.26, which is lower than the unstructured model's score of 35.76 for the bookmarks dataset.", "statement": "The structured hinge loss model has a higher score than the unstructured model for the bookmarks dataset.", "answer": "Thus, the answer is {\"answer\": \"refutes\"}." }, { "messages": [ { "role": "user", "content": "Please draft sentences that offers a description of the given table titled 'Table 3: The accuracy of the classifier is reported for different set of hyperparameters (ϵ,γ). Note that the accuracy increases if γ is reduced or ϵ is increased (increase unfairness). When (ϵ,γ)=(∞,0) (most unfair) it achieves the highest accuracy.'.\n" }, { "role": "assistant", "content": "The classifier's accuracy without the unfairness measure is comparable to recent studies, but our simpler language model emphasizes reducing unfair predictions regardless of embedding technique.This accuracy is obtained by training the classifier without the unfairness measure in the loss function. The mean accuracy obtained from an unfair classifier is comparable to accuracy reported in recent studies on TED talk data [cheng2014predicting, tanveer2019causality]. However, note that our language model [le2014distributed] is much simpler when compared to methods used in the cited studies. With this simple choice we emphasize the general appeal of our approach whose goal is to reduce unfair prediction in data irrespective of the embedding technique. The hyperparameter γ in the loss function plays a critical role in determining the trade-off between fairness in prediction and its accuracy, the smaller the value of γ, the more unfair the prediction." } ], "images": [ "scigen/train/3997.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 3: The accuracy of the classifier is reported for different set of hyperparameters (ϵ,γ). Note that the accuracy increases if γ is reduced or ϵ is increased (increase unfairness). When (ϵ,γ)=(∞,0) (most unfair) it achieves the highest accuracy." ], "reasoning": "The classifier's accuracy without the unfairness measure is comparable to recent studies, but our simpler language model emphasizes reducing unfair predictions regardless of embedding technique.", "description": "This accuracy is obtained by training the classifier without the unfairness measure in the loss function. The mean accuracy obtained from an unfair classifier is comparable to accuracy reported in recent studies on TED talk data [cheng2014predicting, tanveer2019causality]. However, note that our language model [le2014distributed] is much simpler when compared to methods used in the cited studies. With this simple choice we emphasize the general appeal of our approach whose goal is to reduce unfair prediction in data irrespective of the embedding technique. The hyperparameter γ in the loss function plays a critical role in determining the trade-off between fairness in prediction and its accuracy, the smaller the value of γ, the more unfair the prediction.", "answer": "" }, { "messages": [ { "role": "user", "content": "According to the table titled 'Table 3: The accuracy of the classifier is reported for different set of hyperparameters (ϵ,γ). Note that the accuracy increases if γ is reduced or ϵ is increased (increase unfairness). When (ϵ,γ)=(∞,0) (most unfair) it achieves the highest accuracy.', solve this question and give detail solutions. The concluding answer should be in the JSON structure, formatted as {\"answer\": \"\"}.\n\nWhat is the accuracy of the classifier for the 'funny' category when γ is set to 0.2?\n" }, { "role": "assistant", "content": "To find the accuracy for the 'funny' category when γ is set to 0.2, look at the column where γ is 0.2 (i.e., (0.1,0.2) and (0.5,0.2)). The accuracy values are 0.71 and 0.67 respectively. So, the answer is {\"answer\": \"0.71 and 0.67\"}" } ], "images": [ "scigen/train/3997.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 3: The accuracy of the classifier is reported for different set of hyperparameters (ϵ,γ). Note that the accuracy increases if γ is reduced or ϵ is increased (increase unfairness). When (ϵ,γ)=(∞,0) (most unfair) it achieves the highest accuracy." ], "reasoning": "To find the accuracy for the 'funny' category when γ is set to 0.2, look at the column where γ is 0.2 (i.e., (0.1,0.2) and (0.5,0.2)). The accuracy values are 0.71 and 0.67 respectively.", "question": "What is the accuracy of the classifier for the 'funny' category when γ is set to 0.2?", "answer": "So, the answer is {\"answer\": \"0.71 and 0.67\"}" }, { "messages": [ { "role": "user", "content": "Using the given table image, determine if the claim is 'supports' or 'refutes'. If the table does not provide sufficient information, the answer should be 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe classifier's accuracy for the 'beautiful' category is highest when γ is set to 0.\n" }, { "role": "assistant", "content": "To verify this statement, look at the 'beautiful' row and compare the accuracy values across different γ settings. The highest accuracy is 0.77 when γ is set to 0 (∞,0). So, the answer is {\"answer\": \"supports\"}" } ], "images": [ "scigen/train/3997.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 3: The accuracy of the classifier is reported for different set of hyperparameters (ϵ,γ). Note that the accuracy increases if γ is reduced or ϵ is increased (increase unfairness). When (ϵ,γ)=(∞,0) (most unfair) it achieves the highest accuracy." ], "reasoning": "To verify this statement, look at the 'beautiful' row and compare the accuracy values across different γ settings. The highest accuracy is 0.77 when γ is set to 0 (∞,0).", "statement": "The classifier's accuracy for the 'beautiful' category is highest when γ is set to 0.", "answer": "So, the answer is {\"answer\": \"supports\"}" }, { "messages": [ { "role": "user", "content": "Here is an image displaying an table titled 'Table 2: NER MUC out-of-domain results for different embeddings with dimensionality = 25.', and I've highlighted certain cells with a unique color. Compose a description for this table.\n" }, { "role": "assistant", "content": "All context types improve performance by at least five points over the baseline, likely due to the robustness of continuous word embedding features.In this out-of-domain setting, all types of contexts achieve at least five points improvement over the baseline. Presumably, this is because continuous word embedding features are more robust to differences between train and test data, such as the typical vocabulary used. However, a detailed investigation of out-of-domain settings is out of scope for this paper and left for future work." } ], "images": [ "scigen/train/3998.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 2: NER MUC out-of-domain results for different embeddings with dimensionality = 25." ], "reasoning": "All context types improve performance by at least five points over the baseline, likely due to the robustness of continuous word embedding features.", "description": "In this out-of-domain setting, all types of contexts achieve at least five points improvement over the baseline. Presumably, this is because continuous word embedding features are more robust to differences between train and test data, such as the typical vocabulary used. However, a detailed investigation of out-of-domain settings is out of scope for this paper and left for future work.", "answer": "" }, { "messages": [ { "role": "user", "content": "Question: \nWhich context type achieved the highest F1 score and by how much did it improve over the baseline?\nSolve the above question based on the table titled 'Table 2: NER MUC out-of-domain results for different embeddings with dimensionality = 25.'. Provide a concluding answer in a JSON structure, using the format {\"answer\": \"\"}.\n" }, { "role": "assistant", "content": "The table shows that the DEP context type achieved the highest F1 score of 79.8. The baseline F1 score is 71.8, so the improvement is 79.8 - 71.8 = 8.0 points. So, the answer is {\"answer\": \"DEP, 8.0\"}." } ], "images": [ "scigen/train/3998.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 2: NER MUC out-of-domain results for different embeddings with dimensionality = 25." ], "reasoning": "The table shows that the DEP context type achieved the highest F1 score of 79.8. The baseline F1 score is 71.8, so the improvement is 79.8 - 71.8 = 8.0 points.", "question": "Which context type achieved the highest F1 score and by how much did it improve over the baseline?", "answer": "So, the answer is {\"answer\": \"DEP, 8.0\"}." }, { "messages": [ { "role": "user", "content": "Based on the table provided, decide whether the sentence is 'supports' or 'refutes'. If the table simply does not have enough information to make a decision, answer 'not enough info'. Show your final answer in the JSON format {\"answer\": \"\"}.\n\nThe W5 context type achieved an F1 score of 77.4.\n" }, { "role": "assistant", "content": "The table shows that the W5 context type has an F1 score of 77.4, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3998.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 2: NER MUC out-of-domain results for different embeddings with dimensionality = 25." ], "reasoning": "The table shows that the W5 context type has an F1 score of 77.4, which matches the statement.", "statement": "The W5 context type achieved an F1 score of 77.4.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." }, { "messages": [ { "role": "user", "content": "Generate a descriptive sentence about the provided table titled 'Table 1: CERs of each speaker on baseline TDNN-LSTM i-vector based acoustic model. “S”, “M” and “H” are short forms for “slight”, “medium” and “heavy” separately.', pay attention to the highlighted cells in the table.\n" }, { "role": "assistant", "content": "The baseline model has an average CER of 24.86% with a wide range from 3% to 56.62%, indicating varying performance across speakers with different accent levels.We can see that the baseline model performs differently for each speaker and the average CER is 24.86%. The CER has a wide range from 3% to 56.62%. We manually checked the recordings from different speakers and found that speaker S05 had heavy accent and speaker S01 had slight Mandarin accent. This huge difference gives the speaker adaptation methods a big challenge. It’s necessary to compare different adaptation methods in terms of accent level. We believe that this is because the baseline model is trained using data mostly from Mandarin speakers with standard accent and the baseline model itself is robust enough; in this case, direct update on the network parameters may be harmful. RSI is still not stable and over-fitting happens when a large adaptation data set is used. If memory footprint is a major consideration, we suggest to use LHUC as its has a small set of adapted parameters for each speaker; otherwise LHUC and KLD can be both considered for medium-accent speakers. We believe that KLD’s superior performance is because the posterior distribution of the heavy-accent speech is far away from that of the unaccented speech; in this case, directly updating the network parameters or dragging the two distributions closer, is the most effective means. This also clarifies why RSI is better than LHUC and why we cannot observe over-fitting in this condition." } ], "images": [ "scigen/train/3999.jpg" ], "category": [ "scigen_for_T2T" ], "table_caption": [ "Table 1: CERs of each speaker on baseline TDNN-LSTM i-vector based acoustic model. “S”, “M” and “H” are short forms for “slight”, “medium” and “heavy” separately." ], "reasoning": "The baseline model has an average CER of 24.86% with a wide range from 3% to 56.62%, indicating varying performance across speakers with different accent levels.", "description": "We can see that the baseline model performs differently for each speaker and the average CER is 24.86%. The CER has a wide range from 3% to 56.62%. We manually checked the recordings from different speakers and found that speaker S05 had heavy accent and speaker S01 had slight Mandarin accent. This huge difference gives the speaker adaptation methods a big challenge. It’s necessary to compare different adaptation methods in terms of accent level. We believe that this is because the baseline model is trained using data mostly from Mandarin speakers with standard accent and the baseline model itself is robust enough; in this case, direct update on the network parameters may be harmful. RSI is still not stable and over-fitting happens when a large adaptation data set is used. If memory footprint is a major consideration, we suggest to use LHUC as its has a small set of adapted parameters for each speaker; otherwise LHUC and KLD can be both considered for medium-accent speakers. We believe that KLD’s superior performance is because the posterior distribution of the heavy-accent speech is far away from that of the unaccented speech; in this case, directly updating the network parameters or dragging the two distributions closer, is the most effective means. This also clarifies why RSI is better than LHUC and why we cannot observe over-fitting in this condition.", "answer": "" }, { "messages": [ { "role": "user", "content": "Considering the table of 'Table 1: CERs of each speaker on baseline TDNN-LSTM i-vector based acoustic model. “S”, “M” and “H” are short forms for “slight”, “medium” and “heavy” separately.', answer the question below by showing each progressive step toward the answer. Format the ultimate answer as a JSON, using the structure {\"answer\": \"\"}.\nWhich speaker had the highest CER and what was their accent level?\n" }, { "role": "assistant", "content": "By examining the table, we see that speaker S05 had the highest CER of 56.62%. According to the table, S05 had a heavy accent. So, the answer is {\"answer\": \"S05, Heavy\"}." } ], "images": [ "scigen/train/3999.jpg" ], "category": [ "scigen_for_TQA" ], "table_caption": [ "Table 1: CERs of each speaker on baseline TDNN-LSTM i-vector based acoustic model. “S”, “M” and “H” are short forms for “slight”, “medium” and “heavy” separately." ], "reasoning": "By examining the table, we see that speaker S05 had the highest CER of 56.62%. According to the table, S05 had a heavy accent.", "question": "Which speaker had the highest CER and what was their accent level?", "answer": "So, the answer is {\"answer\": \"S05, Heavy\"}." }, { "messages": [ { "role": "user", "content": "With the table provided, ascertain whether the hypothesis is 'supports' or 'refutes' by the table, or if inconclusive, categorize it as 'not enough info' Output the final answer as JSON in the format {\"answer\": \"\"}.\n\nSpeaker S08 had a CER of 11.16% and a medium accent.\n" }, { "role": "assistant", "content": "The table shows that speaker S08 had a CER of 11.16% and a medium accent, which matches the statement. Thus, the answer is {\"answer\": \"supports\"}." } ], "images": [ "scigen/train/3999.jpg" ], "category": [ "scigen_for_TFV" ], "table_caption": [ "Table 1: CERs of each speaker on baseline TDNN-LSTM i-vector based acoustic model. “S”, “M” and “H” are short forms for “slight”, “medium” and “heavy” separately." ], "reasoning": "The table shows that speaker S08 had a CER of 11.16% and a medium accent, which matches the statement.", "statement": "Speaker S08 had a CER of 11.16% and a medium accent.", "answer": "Thus, the answer is {\"answer\": \"supports\"}." } ]